\\S*)\", output.getvalue())\n if tools.Version( version.group(\"version\") ) < \"3.12.0\":\n self.build_requires(\"cmake_installer/3.16.0@conan/stable\")\n\n def build(self):\n cmake = CMake(self)\n cmake.configure()\n cmake.build()\n\n def test(self):\n if platform.system() == \"Windows\":\n self.run(\".\\Debug\\example.exe\")\n else:\n self.run(\"./example\")\n","sub_path":"recipes/libField/all/test_package/conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"54981747","text":"import random\nimport csv\nimport pyexcel as pe\nfrom csv import reader\nimport os, sys\nfrom osgeo import gdal\nimport cv2\nimport numpy as np\nfrom geopy.distance import geodesic\nimport csv\nimport glob\nfrom osgeo import gdal\nfrom pyproj import Transformer\n#from yolo_object_detection import *\n#from check_sum import *\n#from slicing import *\n\ncwd = os.getcwd()\nprint(cwd + \"\\\\Object_detection\\\\yolov3_training8_last.weights\")\ndef slicing(in_file):\n\n dset = gdal.Open(in_file) \n\n\n width = dset.RasterXSize\n height = dset.RasterYSize\n tile_size_x = 5000\n tile_size_y = 5000\n\n input_filename = in_file\n output = in_file.split(\"\\\\\")\n output_filename = output[-1]\n\n out_path = cwd + \"\\\\Object_detection\\\\processes\\\\main_slices\\\\\"\n print(out_path)\n output_filename = \"5\"\n\n\n for i in range(0, width, tile_size_x):\n for j in range(0, height, tile_size_y):\n com_string = \"gdal_translate -of GTiff -srcwin \" + str(i)+ \", \" + str(j) + \", \" + str(tile_size_x) + \", \" + str(tile_size_y) + \" \" +str(input_filename) + \" \" + str(out_path) + str(output_filename) + str(i) + \"_\" + str(j) + \".tif\"\n os.system(com_string)\n\n\n\ndef check():\n \n #find if point falls in rectangle BOX\n def rectContains(rect,pt):\n logic = rect[0] < pt[0] < rect[0]+rect[2] and rect[1] < pt[1] < rect[1]+rect[3]\n return logic\n\n \n\n with open( cwd +'\\\\Object_detection\\\\processes\\\\csv_data\\\\all_objects.csv') as rbox:\n csv_reader = reader(rbox)\n header = next(csv_reader)\n if header != None:\n for row in csv_reader:\n # row variable is a list that represents a row in csv\n #print(row)\n center = row[4:]\n pt_x = int(center[0])\n pt_y = int(center[1])\n pt = [pt_x,pt_y]\n\n with open( cwd + '\\\\Object_detection\\\\processes\\\\csv_data\\\\bbox.csv') as bbox:\n csv_read = reader(bbox)\n header = next(csv_read)\n if header != None:\n for col in csv_read:\n x,y,w,h = int(col[0]),int(col[1]),int(col[2]),int(col[3])\n #print(col[0],col[1],col[2],col[3])\n rect=[x,y,w,h]\n if rectContains(rect,pt):\n with open(cwd + '\\\\Object_detection\\\\processes\\\\csv_data\\\\final_ships.csv','a',newline='') as yo:\n writer=csv.writer(yo)\n text = \"Length:\" + str(row[2])+\" & \" + \"Width:\" + str(row[3])\n writer.writerow([row[0],row[1],text])\n\ndef obj_detection(file):\n weight_path = cwd + \"\\\\Object_detection\\\\yolov3_training8_last.weights\"\n cfg_path = cwd + \"\\\\Object_detection\\\\yolov3_testing1.cfg\"\n # Load Yolo\n #net = cv2.dnn.readNet(cwd + \"\\\\Object_detection\\\\yolov3_training8_last.weights\", cwd + \"\\\\Object_detection\\\\yolov3_testing1.cfg\")\n net = cv2.dnn.readNet(weight_path, cfg_path)\n\n # Name custom object\n classes = [\"\"]\n\n # Images path\n images_path = glob.glob(file)\n #images_path = glob.glob(r\"ship.png\")\n\n\n layer_names = net.getLayerNames()\n output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n colors = np.random.uniform(0, 255, size=(len(classes), 3))\n\n # Insert here the path of your images\n random.shuffle(images_path)\n\n\n \n # loop through all the images\n for img_path in images_path:\n # Loading image\n img = cv2.imread(img_path)\n #img = cv2.resize(img, None, fx= 0.15, fy= 0.15)\n height, width, channels = img.shape\n\n # Detecting objects\n blob = cv2.dnn.blobFromImage(img, 0.00392, (512,512), (0, 0, 0), True, crop=False)\n\n net.setInput(blob)\n outs = net.forward(output_layers)\n\n # Showing informations on the screen\n class_ids = [0]\n confidences = []\n boxes = []\n for out in outs:\n for detection in out:\n scores = detection[5:]\n class_id = np.argmax(scores)\n confidence = scores[class_id]\n if confidence > 0.5:\n # Object detected\n #print(class_id)\n center_x = int(detection[0] * width)\n center_y = int(detection[1] * height)\n w = int(detection[2] * width)\n h = int(detection[3] * height)\n\n # Rectangle coordinates\n x = int(center_x - w / 2)\n y = int(center_y - h / 2)\n\n boxes.append([x, y, w, h])\n #print(boxes)\n confidences.append(float(confidence))\n class_ids.append(class_id)\n\n\n ###########Append X,Y,W,H in txt#######################\n with open(cwd + '\\\\Object_detection\\\\processes\\\\csv_data\\\\bbox.csv', mode='a') as file_:\n file_.write(\"{},{},{},{}\".format(x,y,w,h))\n file_.write(\"\\n\")\n \n indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)\n #print(indexes)\n font = cv2.FONT_HERSHEY_PLAIN\n for i in range(len(boxes)):\n if i in indexes:\n x, y, w, h = boxes[i]\n label = str(classes[class_ids[i]])\n color = colors[class_ids[i]]\n cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)\n cv2.putText(img, label, (x, y + 30), font, 3, color, 2)\n\n dst_file = img_path.split(\"\\\\\")\n dst_file = dst_file[-1]\n dst_path = cwd + \"\\\\Object_detection\\\\processes\\\\detected_ships\\\\\"+str(dst_file)\n cv2.imwrite(dst_path, img)\n key = cv2.waitKey(0)\n\n #cv2.imwrite(\"1.jpg\", img)\n\n cv2.destroyAllWindows()\n\ndef pixel2coord(file,center):\n transformer = Transformer.from_crs(\"epsg:3857\" , \"epsg:4326\")\n x,y = center[0],center[1]\n # Open tif file\n ds = gdal.Open(file)\n # GDAL affine transform parameters, According to gdal documentation xoff/yoff are image left corner, a/e are pixel wight/height and b/d is rotation and is zero if image is north up. \n xoff, a, b, yoff, d, e = ds.GetGeoTransform()\n\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n xp,yp = transformer.transform(xp , yp)\n return(yp, xp)\n\ndef main_detection(in_file):\n if in_file:\n slicing(in_file)\n #input dir path\n file = cwd + \"\\\\Object_detection\\\\processes\\\\main_slices\\\\\"\n \n #for yolo bbox\n with open(cwd + '\\\\Object_detection\\\\processes\\\\csv_data\\\\bbox.csv','w') as f:\n f.write('x,y,w,h\\n') # TRAILING NEWLINE\n f.close()\n\n #for All Ships data\n with open(cwd + '\\\\Object_detection\\\\processes\\\\csv_data\\\\final_ships.csv','w') as f:\n f.write('lan,lon,text\\n') # TRAILING NEWLINE\n f.close()\n\n\n #for contour \n with open(cwd + '\\\\Object_detection\\\\processes\\\\csv_data\\\\all_objects.csv','w') as f:\n f.write('lat,lon,length,width,center_x,center_y\\n') # TRAILING NEWLINE\n \n file_path = glob.glob(file + \"*.tif\")\n #print(file_path)\n for i in range(len(file_path)):\n file = file_path[i]\n img = cv2.imread(file,0)\n ret,thresh = cv2.threshold(img,70,255,cv2.THRESH_BINARY)\n contours,hierarchy = cv2.findContours(thresh, 1, 2)\n \n img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n lol = False\n for i in range(len(contours)):\n cnt = contours[i]\n #print(i)\n if len(cnt)>17 and len(cnt)<70:\n lol = True\n print(\".\",end=\"\")\n rect = cv2.minAreaRect(cnt)\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n image = cv2.drawContours(img,[box],0,(0,255,0),1)\n\n \n len1 = (box[0] + box[1])//2\n len2 = (box[2] + box[3])//2\n\n wid1 = (box[0] + box[3])//2\n wid2 = (box[1] + box[2])//2\n\n \n #Find Center point of the each box store its lat long\n center = int((len1[0]+len2[0])*0.5) , int((len1[1]+len2[1])*0.5)\n\n \n center_geocoord = list(pixel2coord(file,[center[0],center[1]]))\n #center_geocoord = [long,lat]\n cent_lat = center_geocoord[1]\n cent_long = center_geocoord[0]\n\n \n # length\n length_p1 = list(pixel2coord(file,[len1[0],len1[1]]))\n length_p2 = list(pixel2coord(file,[len2[0],len2[1]]))\n\n coords_1 = (length_p1[1], length_p1[0])#lat, long\n coords_2 = (length_p2[1], length_p2[0])#lat,long\n\n length = geodesic(coords_1, coords_2).meters\n\n # width\n width_p1 = list(pixel2coord(file,[wid1[0],wid1[1]]))\n width_p2 = list(pixel2coord(file,[wid2[0],wid2[1]]))\n\n coords_3 = (width_p1[1], width_p1[0])#lat, long\n coords_4 = (width_p2[1], width_p2[0])#lat,long\n\n width = geodesic(coords_3, coords_4).meters\n\n\n #image=cv2.circle(image,center, 2, (0,0,255), -1)\n #image=cv2.circle(image,(len1[0],len1[1]), 2, (0,0,255), -1)\n ################## ######################################################\n\n if width > length:\n length,width = float(\"{:.2f}\".format(width)),float(\"{:.2f}\".format(length))\n else:\n width,length = float(\"{:.2f}\".format(width)),float(\"{:.2f}\".format(length))\n \n\n\n with open(cwd + '\\\\Object_detection\\\\processes\\\\csv_data\\\\all_objects.csv','a',newline='') as f:\n writer=csv.writer(f)\n\n writer.writerow([cent_lat,cent_long,length,width,center[0],center[1]])\n\n\n \n #######################################################################\n\n ##############################Draw len1 len2 wid1 wid2 on BOX##########\n #lengh of the ship\n image=cv2.circle(img,(len1[0],len1[1]), 1, (0,0,255), 1)\n image=cv2.circle(img,(len2[0],len2[1]), 1, (0,0,255), 1)\n\n #width of the ship\n image=cv2.circle(img,(wid1[0],wid1[1]), 1, (0,0,255), 1)\n image=cv2.circle(img,(wid2[0],wid2[1]), 1, (0,0,255), 1)\n #######################################################################\n if lol:\n dst_file = file.split(\"\\\\\")\n dst_file = dst_file[-1]\n dst_path = cwd + \"\\\\Object_detection\\\\processes\\\\detected_con\\\\\"+str(dst_file)\n cv2.imwrite(dst_path, image)\n\n #Perform YOLO object detection\n obj_detection(dst_path)\n #perform check_sum\n check()\n print(\"Done\")\n\nimport sys\nfilename = str(sys.argv[1])\nmain_detection(filename)\n","sub_path":"Object_detection/main_program.py","file_name":"main_program.py","file_ext":"py","file_size_in_byte":11106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"572703944","text":"array = [5, 7, 9, 0, 3, 1, 6, 2, 4, 8]\n\n\ndef quick_sort(array, start, end):\n # start와 end값이 같다는건 배열에 남아있는 값이 1개라는 뜻 [ 정렬이 모두 끝남 ]\n if start >= end:\n return\n # pivot을 시작점으로 한다\n pivot = start\n\n # pivot보다 큰 값을 찾기위한 left\n left = start + 1\n\n # pivot보다 작은 값을 찾기위한 right\n right = end\n\n # left가 right보다 작아야 실행한다 [ right가 더 큰 경우는 left와 right가 엇갈린 경우 ]\n while left <= right:\n # 피벗이 큰 데이터를 찾을 때 까지 반복\n while left <= end and array[left] <= array[pivot]:\n left += 1\n\n # 피벗보다 작은 데이터를 찾을 때 까지 반복\n while right > start and array[right] >= array[pivot]:\n right -= 1\n\n # left와 right가 엇갈린 경우\n if left > right:\n array[right], array[pivot] = array[pivot], array[right]\n\n # 엇갈리지 않고 실행 한 경우\n else:\n array[left], array[right] = array[right], array[left]\n\n # 분할 이후, pivot을 기점으로 왼쪽과 오른쪽에서 정렬 재실행\n quick_sort(array, start, right - 1)\n quick_sort(array, right + 1, end)\n\n\nquick_sort(array, 0, len(array)-1)\n\nprint(array)","sub_path":"6. 정렬/QuickSort [퀵 정렬].py","file_name":"QuickSort [퀵 정렬].py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"81032883","text":"# -*- coding:utf-8 -*-\nfrom flask import Blueprint, render_template, request, jsonify, g\n\nfrom database.sqllite_operate import session\nfrom gz_yct_pynex import Iter_Task\n\ngengzong = Blueprint('gengzong', __name__)\n\n\n@gengzong.route('/action', methods=['GET'])\ndef gengzong_run():\n '''运行跟踪'''\n res = dict(request.args)\n Iter = Iter_Task()\n if '手动' in res:\n import time\n time.sleep(10)\n '''调用手动函数'''\n result = Iter.breadth_first(STATE='手动')\n if result:\n return jsonify({'msg': 'success_gz', 'code': 0})\n elif not result:\n return jsonify({'msg': 'session_expire', 'code': 1})\n\n\njinru = Blueprint('jinru', __name__)\n\n\n@jinru.route('/', methods=['GET'])\ndef index():\n '''进入界面'''\n if request.args.get('state') == 'finsh':\n try:\n session.execute(\n 'update yctjdugip set state=\"unuse\"')\n except Exception as e:\n session.rollback()\n raise e\n else:\n text = '\"已完成跟踪任务，成功退出，谢谢！\"'\n return render_template('unable.html', text=text)\n if g.result:\n vnc_url = 'http://116.228.76.163:3240/yct_vnc.html?path=?token=yuanqu2016'\n # vnc_url = 'http://192.168.130.27:5000/yct_vnc.html?path=?token=yuanqu2016'\n try:\n session.execute(\n 'update yctjdugip set state=\"used\"')\n except Exception as e:\n session.rollback()\n raise e\n return render_template('JsBasePage.html', vnc_url=vnc_url)\n else:\n text = '\"进度跟踪机器正在使用中，请稍后再试，谢谢！\"'\n return render_template('unable.html', text=text)\n","sub_path":"programe/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":1738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"183220728","text":"from ConfigParser import NoSectionError, NoOptionError\n\nfrom . import config\nfrom .app import app\nfrom .entrypoint import get_core_task_modules, get_plugin_task_modules\n\n\ndef main():\n try:\n include_core_tasks = config.getboolean(\n 'girder_worker', 'core_tasks')\n except (NoSectionError, NoOptionError):\n include_core_tasks = True\n\n if include_core_tasks:\n app.conf.update({\n 'CELERY_IMPORTS': get_core_task_modules()\n })\n\n app.conf.update({\n 'CELERY_INCLUDE': get_plugin_task_modules()\n })\n app.worker_main()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"girder_worker/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"605475571","text":"from django.shortcuts import render\n\nfrom dor.DTO.StuDorLog import PayLogModel\nfrom dor.models import Activity, ActvityApplyment,MeetingRoomOrderTime,MeetingRoom,MeetingRoomApplymentRecord,DorCostRecord, StuPayRecord, Student,DorBookInf\nfrom dor.DTO.MeetingLog import MeetingLogModel\nfrom dor.student_handle.device_repair_applyment import repair\nfrom dor.student_handle.resource_applyment import resource\n\ndef show_stu_activity(request):\n data=Activity.objects.filter()\n try:\n sno = request.session['userno']\n except Exception as err:\n return render(request,\"index.html\")\n sname=request.session['username']\n test = Student.objects.get(sno=sno)\n activity_log=ActvityApplyment.objects.filter(sno=sno)\n return render(request, \"student/activity.html\",\n {'sno':sno,'sname':sname,'activity':data,'sname': test.sname, 'college': test.college, 'major': test.major, 'room_no': test.room_no,\n 'stu_phone': test.stu_phone, 'email': test.email,'activity_log':activity_log})\n\n\ndef show_stu_repair(request):\n try:\n sno = request.session['userno']\n except Exception as err:\n return render(request,\"index.html\")\n sname=request.session['username']\n return repair(request)\n\ndef show_stu_pay(request):\n sname = request.session['username']\n data = DorCostRecord.objects.all()\n pay_list = []\n for i in range(0, len(data)):\n p = PayLogModel()\n test = StuPayRecord.objects.get(bill_no=data[i].cost_no)\n p.cost_no = data[i].cost_no\n p.item = data[i].item\n p.fee = data[i].fee\n p.time = data[i].time\n p.dor_no = test.dor_no\n p.status = test.check_paied\n pay_list.append(p)\n\n return render(request, \"student/payment.html\", {'paylog': pay_list,'username':sname})\n\ndef show_stu_resource(request):\n try:\n sno = request.session['userno']\n except Exception as err:\n return render(request,\"index.html\")\n sname=request.session['username']\n return resource(request)\n\ndef show_stu_meeting_room(request):\n try:\n sno = request.session['userno']\n except Exception as err:\n return render(request,\"index.html\")\n sname = request.session['username']\n # 转化成列表\n room_list = list(MeetingRoom.objects.all().values())\n for i in room_list:\n if i['include_medium_device'] == 1:\n i['include_medium_device'] = '有'\n elif i['include_medium_device'] == 0:\n i['include_medium_device'] = '无'\n\n base_stu_meeting_log = MeetingRoomApplymentRecord.objects.filter(sno=sno)\n stu_meeting_log = []\n\n for i in range(0, len(base_stu_meeting_log)):\n order_time_start = list(\n MeetingRoomOrderTime.objects.filter(meeting_room_order_time_no=base_stu_meeting_log[i].time_no_1).values())\n if base_stu_meeting_log[i].time_no_4 is not None:\n order_time_end = list(MeetingRoomOrderTime.objects.filter(\n meeting_room_order_time_no=base_stu_meeting_log[i].time_no_4).values())\n elif base_stu_meeting_log[i].time_no_3 is not None:\n order_time_end = list(MeetingRoomOrderTime.objects.filter(\n meeting_room_order_time_no=base_stu_meeting_log[i].time_no_3).values())\n elif base_stu_meeting_log[i].time_no_2 is not None:\n order_time_end = list(MeetingRoomOrderTime.objects.filter(\n meeting_room_order_time_no=base_stu_meeting_log[i].time_no_2).values())\n else:\n order_time_end = list(MeetingRoomOrderTime.objects.filter(\n meeting_room_order_time_no=base_stu_meeting_log[i].time_no_1).values())\n\n newModel = MeetingLogModel()\n newModel.sno = base_stu_meeting_log[i].sno\n newModel.meeting_room_no = base_stu_meeting_log[i].meeting_room_no\n newModel.book_date = base_stu_meeting_log[i].book_date.strftime('%Y-%m-%d')\n newModel.apply_time = base_stu_meeting_log[i].apply_time.strftime('%Y-%m-%d %H:%M:%S')\n newModel.start_time = order_time_start[0]['start_time'].strftime('%H:%M:%S')\n newModel.end_time = order_time_end[0]['end_time'].strftime('%H:%M:%S')\n if base_stu_meeting_log[i].check_apply_success == 1:\n newModel.check_apply_success = '申请已通过'\n elif base_stu_meeting_log[i].check_apply_success == 0:\n newModel.check_apply_success = '待审核'\n elif base_stu_meeting_log[i].check_apply_success == 2:\n newModel.check_apply_success = '失败'\n\n stu_meeting_log.append(newModel)\n #print(stu_meeting_log)\n\n return render(request, \"student/meeting.html\", {'username': sname, 'userno': sno, 'roomList': room_list,\n 'stuMeetingLog': stu_meeting_log})\n\n\ndef show_stu_book(request):\n try:\n sno = request.session['userno']\n except Exception as err:\n return render(request,\"index.html\")\n sname = request.session['username']\n book_sum = DorBookInf.objects.all().count()\n book_list = DorBookInf.objects.all().values()\n book_borrow_list = DorBookInf.objects.filter(book_borrowman=sname)\n book_share_list = DorBookInf.objects.filter(book_share_man=\"1\", book_borrowman=sname)\n\n return render(request, \"student/book.html\", {'username': sname, 'userno': sno,\n 'book_list': book_list,\n 'book_borrow_list': book_borrow_list,\n 'book_share_list': book_share_list,\n 'book_sum': book_sum})","sub_path":"dor/student_handle/show_stu_all_index.py","file_name":"show_stu_all_index.py","file_ext":"py","file_size_in_byte":5638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"418706410","text":"#!/usr/bin/python\nimport sys\nimport os, os.path\nimport time\nfrom datetime import datetime\n\n\nimport logging\nimport logging.config\n\n\n\nclass aLog(object):\n\n\tdef __init__(self):\n\t\t# log to the console\n\t\tself.log = logging.getLogger('reco')\n\t\tself.log.propagate = False\n\t\tself.log.setLevel(logging.DEBUG)\n\t\tformatter = logging.Formatter('[%(levelname)s]: %(message)s')\n\t\thandler_stream = logging.StreamHandler()\n\t\thandler_stream.setFormatter(formatter)\n\t\thandler_stream.setLevel(logging.DEBUG)\n\t\tself.log.addHandler(handler_stream)\n\n\t\t# log to a file\n\t\tlog_path = \"logs/\"\n\t\tflname = log_path + \"reconews-%s.log\"%datetime.now().strftime('%Y-%m-%d-%I:%M:%S')\n\t\tformatter = logging.Formatter('[LOG]: %(message)s')\n\t\thandler_file = logging.FileHandler(flname)\n\t\thandler_file.setFormatter(formatter)\n\t\thandler_file.setLevel(logging.INFO)\n\t\tself.log.addHandler(handler_file)\n\n###################################################################\n##########################MODULE TESTING###########################\n\n\tdef record(self):\n\t\tself.log.debug('something is terribly wrong')\n\t\tself.log.info('this is a log')\n\t\tself.log.warn('warning. mostly ignored.')\n\t\tself.log.error('Error happened!')\n\t\tself.log.critical('critical')\n\n###################################################################\n###################################################################\n\nalogger = aLog()\n\n\nif __name__ == \"__main__\":\n\n###################################################################\n##########################MODULE TESTING###########################\n\talogger.record()\n###################################################################\n###################################################################","sub_path":"a_logger.py","file_name":"a_logger.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"398651771","text":"\"\"\"\nModification of the docutils \".. include\" directive to make it\nfail gracefully if the included file does not exist.\nThe original code is taken from docutils source:\n\n https://sourceforge.net/p/docutils/code/HEAD/tree/trunk/docutils/docutils/parsers/rst/directives/misc.py \n\"\"\"\n\n# TODO: implement SoftInclude directive\n\nimport sys\nimport os.path\nimport re\nimport time\nfrom docutils import io, nodes, statemachine, utils\nfrom docutils.utils.error_reporting import SafeString, ErrorString\nfrom docutils.utils.error_reporting import locale_encoding\nfrom docutils.parsers.rst import Directive, convert_directive_function\nfrom docutils.parsers.rst import directives, roles, states\nfrom docutils.parsers.rst.directives.body import CodeBlock, NumberLines\nfrom docutils.parsers.rst.roles import set_classes\nfrom docutils.transforms import misc\n\n\nclass Include(Directive):\n\n \"\"\"\n Include content read from a separate source file.\n\n Content may be parsed by the parser, or included as a literal\n block. The encoding of the included file can be specified. Only\n a part of the given file argument may be included by specifying\n start and end line or text to match before and/or after the text\n to be used.\n \"\"\"\n\n required_arguments = 1\n optional_arguments = 0\n final_argument_whitespace = True\n option_spec = {'literal': directives.flag,\n 'code': directives.unchanged,\n 'encoding': directives.encoding,\n 'tab-width': int,\n 'start-line': int,\n 'end-line': int,\n 'start-after': directives.unchanged_required,\n 'end-before': directives.unchanged_required,\n # ignored except for 'literal' or 'code':\n 'number-lines': directives.unchanged, # integer or None\n 'class': directives.class_option,\n 'name': directives.unchanged}\n\n standard_include_path = os.path.join(os.path.dirname(states.__file__),\n 'include')\n\n def run(self):\n \"\"\"Include a file as part of the content of this reST file.\"\"\"\n if not self.state.document.settings.file_insertion_enabled:\n raise self.warning('\"%s\" directive disabled.' % self.name)\n source = self.state_machine.input_lines.source(\n self.lineno - self.state_machine.input_offset - 1)\n source_dir = os.path.dirname(os.path.abspath(source))\n path = directives.path(self.arguments[0])\n if path.startswith('<') and path.endswith('>'):\n path = os.path.join(self.standard_include_path, path[1:-1])\n path = os.path.normpath(os.path.join(source_dir, path))\n path = utils.relative_path(None, path)\n path = nodes.reprunicode(path)\n encoding = self.options.get(\n 'encoding', self.state.document.settings.input_encoding)\n e_handler=self.state.document.settings.input_encoding_error_handler\n tab_width = self.options.get(\n 'tab-width', self.state.document.settings.tab_width)\n try:\n self.state.document.settings.record_dependencies.add(path)\n include_file = io.FileInput(source_path=path,\n encoding=encoding,\n error_handler=e_handler)\n except UnicodeEncodeError as error:\n raise self.severe(u'Problems with \"%s\" directive path:\\n'\n 'Cannot encode input file path \"%s\" '\n '(wrong locale?).' %\n (self.name, SafeString(path)))\n except IOError as error:\n raise self.severe(u'Problems with \"%s\" directive path:\\n%s.' %\n (self.name, ErrorString(error)))\n startline = self.options.get('start-line', None)\n endline = self.options.get('end-line', None)\n try:\n if startline or (endline is not None):\n lines = include_file.readlines()\n rawtext = ''.join(lines[startline:endline])\n else:\n rawtext = include_file.read()\n except UnicodeError as error:\n raise self.severe(u'Problem with \"%s\" directive:\\n%s' %\n (self.name, ErrorString(error)))\n # start-after/end-before: no restrictions on newlines in match-text,\n # and no restrictions on matching inside lines vs. line boundaries\n after_text = self.options.get('start-after', None)\n if after_text:\n # skip content in rawtext before *and incl.* a matching text\n after_index = rawtext.find(after_text)\n if after_index < 0:\n raise self.severe('Problem with \"start-after\" option of \"%s\" '\n 'directive:\\nText not found.' % self.name)\n rawtext = rawtext[after_index + len(after_text):]\n before_text = self.options.get('end-before', None)\n if before_text:\n # skip content in rawtext after *and incl.* a matching text\n before_index = rawtext.find(before_text)\n if before_index < 0:\n raise self.severe('Problem with \"end-before\" option of \"%s\" '\n 'directive:\\nText not found.' % self.name)\n rawtext = rawtext[:before_index]\n\n include_lines = statemachine.string2lines(rawtext, tab_width,\n convert_whitespace=True)\n if 'literal' in self.options:\n # Convert tabs to spaces, if `tab_width` is positive.\n if tab_width >= 0:\n text = rawtext.expandtabs(tab_width)\n else:\n text = rawtext\n literal_block = nodes.literal_block(rawtext, source=path,\n classes=self.options.get('class', []))\n literal_block.line = 1\n self.add_name(literal_block)\n if 'number-lines' in self.options:\n try:\n startline = int(self.options['number-lines'] or 1)\n except ValueError:\n raise self.error(':number-lines: with non-integer '\n 'start value')\n endline = startline + len(include_lines)\n if text.endswith('\\n'):\n text = text[:-1]\n tokens = NumberLines([([], text)], startline, endline)\n for classes, value in tokens:\n if classes:\n literal_block += nodes.inline(value, value,\n classes=classes)\n else:\n literal_block += nodes.Text(value, value)\n else:\n literal_block += nodes.Text(text, text)\n return [literal_block]\n if 'code' in self.options:\n self.options['source'] = path\n codeblock = CodeBlock(self.name,\n [self.options.pop('code')], # arguments\n self.options,\n include_lines, # content\n self.lineno,\n self.content_offset,\n self.block_text,\n self.state,\n self.state_machine)\n return codeblock.run()\n self.state_machine.insert_input(include_lines, path)\n return []\n\n","sub_path":"sphinxutils/softinclude.py","file_name":"softinclude.py","file_ext":"py","file_size_in_byte":7586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"12979981","text":"# coding: utf-8\n\nfrom django.contrib.auth.models import User, Group\nfrom django.shortcuts import get_object_or_404, render_to_response, render\nfrom django.contrib.auth.decorators import login_required\nfrom django.template import RequestContext\nfrom django.http import HttpResponse\nfrom django.core.mail import send_mail\n\nfrom .forms import *\n\nfrom models import *\nfrom Login.models import Usuario\n\nfrom random import randint\n\nimport datetime\n\n# Create your views here.\n\ndef checkAdmin(user):\n\tif user.groups.filter(name='administradores').exists():\n\t\treturn True\n\telse:\n\t\treturn False\n\n@login_required\ndef index(request):\n\tadmin = checkAdmin(request.user)\n\n\tusuario = Usuario.objects.get(user=request.user)\n\teventos = Evento.objects.all().order_by('-created')[:20]\n\tusuarios = User.objects.all().order_by('-date_joined')[:20]\n\n\ttimeline = []\n\n\t#Cria lista de tuplas (Object, Data)\n\tfor e in eventos:\n\t\ttimeline.append((e, e.created))\n\n\tfor u in usuarios:\n\t\ttimeline.append((u, u.date_joined))\n\n\t#Faz o sort por datas e depois deixa só os Objetos na lista\n\ttimeline = sorted(timeline, key=lambda tup: tup[1], reverse=True)\n\ttimeline = [x[0] for x in timeline]\n\n\ttimeline = timeline[:15]\n\ttimeline_new = []\n\n\t#Transforma a lista novamente em tuplas do tipo (Object, 'tipo')\n\tfor item in timeline:\n\t\tif isinstance(item, User):\n\t\t\ttimeline_new.append((item, 'user'))\n\t\telif isinstance(item, Evento):\n\t\t\ttimeline_new.append((item, 'evento'))\n\n\treturn render(request, 'index.html', \n\t\t{'administrador': admin, \n\t\t'usuario': usuario,\n\t\t'timeline': timeline_new, \n\t\t'date_now': datetime.datetime.now(), })\n\n@login_required\ndef exibirEventos(request):\n\tadmin = checkAdmin(request.user)\n\n\teventos = Evento.objects.all();\n\n\tusuario = Usuario.objects.get(user=request.user)\n\n\treturn render(request, 'eventos.html',\n\t\t{'administrador': admin, \n\t\t'eventos': eventos, \n\t\t'usuario': usuario, })\n\n@login_required\ndef exibirEvento(request, pk):\n\tevento = get_object_or_404(Evento, pk=pk)\n\treturn render(request, 'evento.html', \n\t\t{'evento': evento, })\n\n@login_required\ndef deletarEvento(request, pk):\n\tadmin = checkAdmin(request.user)\n\tevento = get_object_or_404(Evento, pk=pk)\n\ttext = \"O evento \" + evento.nome + \" foi deletado com sucesso.\";\n\treturn render(request, 'index.html', \n\t\t{'modal':True, \n\t\t'modal_header': 'Evento deletado', \n\t\t'text': text })\n\n@login_required\ndef novoEvento(request):\n\tadmin = checkAdmin(request.user)\n\tif admin:\n\t\tif request.method == 'POST':\n\t\t\tform = NovoEventoForm(request.POST, request.FILES)\n\t\t\tif form.is_valid():\n\t\t\t\tnovo_evento = Evento.objects.create(nome=form.cleaned_data['nome'],\n\t\t\t\t\tdescricao=form.cleaned_data['descricao'],\n\t\t\t\t\tlocal=form.cleaned_data['local'],\n\t\t\t\t\tdata=form.cleaned_data['data'],\n\t\t\t\t\thora=form.cleaned_data['hora'], )\n\n\t\t\t\tusuario = Usuario.objects.get(user=request.user)\n\t\t\t\tnovo_evento.criado_por = usuario\n\t\t\t\tnovo_evento.save()\n\n\t\t\t\treturn render(request, 'index.html', \n\t\t\t\t\t{'administrador': admin, \n\t\t\t\t\t'modal':True, \n\t\t\t\t\t'modal_header': 'Cadastro de Evento', \n\t\t\t\t\t'text':'Evento cadastrado com sucesso!'})\n\t\t\telse:\n\t\t\t\treturn render(request, 'novoevento.html', \n\t\t\t\t\t{'administrador': admin, 'form': form, })\n\t\telse:\n\t\t\tform = NovoEventoForm()\n\t\t\treturn render(request, 'novoevento.html', \n\t\t\t\t{'administrador': admin, 'form': form, })\n\telse: # caso não seja administrador\n\t\treturn render(request, 'errorpage.html', \n\t\t\t{'error': 'Você não tem permissão para acessar essa página'})\n\n\n@login_required\ndef novaChaveUsuario(request):\n\tadmin = checkAdmin(request.user)\n\tif admin:\t\t\n\t\tif request.method == 'POST':\n\t\t\tform = NovaChaveUsuarioForm(request.POST, request.FILES)\n\t\t\tif form.is_valid():\n\t\t\t\t#Caso já exista\n\t\t\t\tif User.objects.filter(email=form.cleaned_data['email']).exists() or ChaveUsuario.objects.filter(email=form.cleaned_data['email']).exists():\n\t\t\t\t\treturn render(request, 'errorpage.html', \n\t\t\t\t\t\t{'error': 'Usuário já existe ou já tem uma chave de registro.'})\n\n\t\t\t\telse: #Gerar chave e enviar email caso não exista\n\t\t\t\t\tkey = gerarChave()\n\t\t\t\t\tnovaChave = ChaveUsuario.objects.create(email=form.cleaned_data['email'],\n\t\t\t\t\t\tchave=key)\n\t\t\t\t\tnovaChave.save()\n\n\t\t\t\t\tsend_mail('Convite SisGIM', \n\t\t\t\t\t\t'Olá, \\n' \n\t\t\t\t\t\t+ 'Você foi convidado para se cadastrar no sistema do GIM, '\n\t\t\t\t\t\t+ 'para tanto, siga para o site de registro e utilize o seguinte código: \\n' \n\t\t\t\t\t\t+ key + '\\n' \n\t\t\t\t\t\t+ 'Obrigado por fazer parte do GIM.',\n\t\t\t\t\t\t'[SisGIM] Convite', \n\t\t\t\t\t\t[form.cleaned_data['email']],\n\t\t\t\t\t\tmakeHtmlMessage(key))\n\t\t\t\t\treturn render(request, 'index.html', {'modal':True, 'text':'Chave enviada com sucesso!'})\n\n\t\telse: # caso o request.method não seja POST\n\t\t\tform = NovaChaveUsuarioForm()\n\t\t\treturn render(request, 'novachaveusuario.html', \n\t\t\t\t{'administrador': admin, 'form': form})\n\n\telse: # caso não seja administrador\n\t\treturn render(request, 'errorpage.html', \n\t\t\t{'error': 'Você não tem permissão para acessar essa página'})\n\ndef gerarChave():\n\tp1 = str(randint(0,9)) + chr(randint(ord('a'), ord('z')))\n\tp2 = str(randint(0,9)) + chr(randint(ord('A'), ord('Z')))\n\tp3 = str(randint(0,9)) + chr(randint(ord('a'), ord('z')))\n\tp4 = str(randint(0,9)) + chr(randint(ord('A'), ord('Z')))\n\treturn p1 + p2 + p3 + p4\n\ndef makeHtmlMessage(key):\n\treturn 'Olá,
Você foi convidado para se cadastrar no sistema do GIM, para tanto, siga para o site de registro e utilize o seguinte código:
' + key + '
Obrigado por fazer parte do GIM.
'\n\n","sub_path":"Sistema/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"446750743","text":"\n\"\"\"\n# @Time : 2019-10-11 09:50\n# @Author : Function\n# @FileName : table_write.py\n# @Software: PyCharm\n\n\"\"\"\ndef get_length(ts):\n ls = []\n hs = ts['head']\n for h in hs:\n ls.append(len(h))\n bs = ts['body']\n for b in bs:\n for i in range(len(ls)):\n li = len(b[i])\n if ls[i] < li:\n ls[i] = li\n return ls\ndef out_line(ts):\n rs = '+'\n ls = get_length(ts)\n for l in ls:\n rs += '-'*(l+2)+'+'\n rs += '\\n'\n return rs\n\ndef out_head(ts):\n rs = '|'\n ls = get_length(ts)\n hs = ts['head']\n for i in range(len(ls)):\n rs += ''+hs[i]+''*(ls[i]-len(hs[i]))+' |'\n rs += '\\n'\n return rs\n\ndef out_body(ts):\n rs = ''\n ls = get_length(ts)\n bs = ts['body']\n for i in bs:\n r ='|'\n for j in range(len(ls)):\n r += ''+i[j]+''*(ls[j]-len(i[j]))+' |'\n rs += r + '\\n'\n return rs\ndef out_table(ts):\n rs = out_line(ts)\n rs += out_head(ts)\n rs += out_line(ts)\n rs += out_body(ts)\n rs += out_line(ts)\n return rs\n\nif __name__ == '__main__':\n T = {\n 'head':['用例总数','成功数','失败数','成功率','失败率'],\n 'body':[\n ['100','50', '50', '20%','80%']\n ]\n }\n print(out_table(T),end='')\n print('-------------------')\n print(out_table(T))\n","sub_path":"AutoUI/Util/table_write.py","file_name":"table_write.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"435035505","text":"import torch\nfrom torch import autograd\nimport pandas as pd\nfrom mnist_model import ConvNN\n\n\ndef main():\n test_data = pd.read_csv('./test.csv').values\n X = test_data.reshape((-1, 28, 28))\n model = ConvNN()\n model.load_state_dict(torch.load('./mnist-model.pt'))\n model.eval()\n x = autograd.Variable(torch.Tensor(X))\n max_, y_hat = torch.max(model(x), dim=1)\n output_df = pd.DataFrame(y_hat.numpy())\n output_df.index += 1\n output_df.index.name = 'ImageId'\n output_df.to_csv('./sample-output.csv', header=['Label'])\n\nif __name__ == '__main__':\n main()\n","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"47343327","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# \n# Copyright (c) 2017 Nothing, Inc. All Rights Reserved\n# \n\n\"\"\"\nFile: sum1-100.py\nAuthor: guyu(dsgdtc@163.com)\nDate: 2018/12/28 15:19\n\"\"\"\nimport sys\ndef sum_cycle(n):\n '''\n 1 to n,The sum function\n '''\n sum = 0\n for i in range(1,n + 1):\n sum += i\n return sum\n\ndef sum_recu(n):\n '''\n 1 to n,The sum function\n '''\n if n > 0:\n res = n + sum_recu(n - 1)\n input(\"etc...\")\n print(res)\n print(\"---before return do sth\")\n return res\n else:\n return 0\nprint(\"循环求和：\",sum_cycle(5))\nprint(\"递归求和：\",sum_recu(5))","sub_path":"leetcode/lc-all-solutions-master/022.generate-parentheses/sum1-100.py","file_name":"sum1-100.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"411072065","text":"def multiplicar(matrizA, matrizB):\n linhasA = len(matrizA)\n linhasB = len(matrizB)\n colunasA = len(matrizA[0])\n colunasB = len(matrizB[0])\n\n matrizC = []\n\n for linha in range(linhasA):\n matrizC.append([])\n for coluna in range(colunasB):\n matrizC[linha].append(0)\n for i in range(colunasA):\n matrizC[linha][coluna] += matrizA[linha][i] * matrizB[i][coluna]\n\n for i in range(len(matrizC)):\n for j in range(len(matrizC[i])):\n if j != len(matrizC[i])-1:\n print(matrizC[i][j], end=' ')\n else:\n print(matrizC[i][j])\n\n\ndados = input().split()\n\nquantidadeLinhasMatrizA = int(dados[0])\nquantidadeColunasMatrizA = int(dados[1])\nquantidadeLinhasMatrizB = int(dados[1])\nquantidadeColunasMatrizB = int(dados[2])\n\nmatrizA = []\nmatrizB = []\n\nfor i in range(quantidadeLinhasMatrizA):\n matrizA.append([int(x) for x in input().split()])\n\nfor i in range(quantidadeLinhasMatrizB):\n matrizB.append([int(x) for x in input().split()])\n\nmultiplicar(matrizA, matrizB)","sub_path":"Exercícios Resolvidos/The Huxley/746 - Multiplicação de Matrizes - 2.py","file_name":"746 - Multiplicação de Matrizes - 2.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"353846315","text":"# 앙상블 모델로 리뉴얼하시오.\nimport numpy as np\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, LSTM, Input\nfrom keras.callbacks import EarlyStopping\nfrom keras.layers import concatenate\nearly = EarlyStopping(monitor = 'loss', mode = 'min', patience = 10)\n\n# 1. 데이터 구성\nx1 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6],\n [5, 6, 7], [6, 7, 8], [7, 8, 9], [8, 9, 10],\n [9, 10, 11], [10, 11, 12],\n [20, 30, 40], [30, 40, 50], [40, 50, 60]])\nx2 = np.array([[10, 20, 30], [20, 30, 40], [30, 40, 50], [40, 50, 60],\n [50, 60, 70], [60, 70, 80], [70, 80, 90], [80, 90, 100],\n [90, 100, 110], [100, 110, 120],\n [2, 3, 4], [3, 4, 5], [4, 5, 6]])\n\ny = np.array([4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 50, 60, 70])\n\nx1_predict = np.array([55, 65, 75])\nx2_predict = np.array([65, 75, 85])\n\nprint(\"x1.shape : \", x1.shape) # res : (13, 3)\nprint(\"x2.shape : \", x2.shape) # res : (13, 3)\nprint(\"y.shape : \", y.shape) # res : (13, )\n\n'''\nx1 = x1.reshape(x1.shape[0], x1.shape[1], 1)\n# 13 3 1\n\nx2 = x2.reshape(x2.shape[0], x2.shape[1], 1)\n# 13 3 1\n# print(x1.shape)\n# print(x2.shape)\n\n\n# 2. 모델 구성\n# 2-1. 인풋 레이어\ninput1 = Input(shape = (3, 1))\ndense1 = LSTM(150)(input1)\ndense1_1 = Dense(81)(dense1)\ndense1_2 = Dense(88)(dense1_1)\ndense1_3 = Dense(98)(dense1_2)\ndense1_4 = Dense(48)(dense1_3)\ndense1_5 = Dense(86)(dense1_4)\n\n# 2-2 인풋 레이어\ninput2 = Input(shape = (3, 1))\ndense2 = LSTM(194)(input2)\ndense2_1 = Dense(488)(dense2)\ndense2_2 = Dense(815)(dense2_1)\ndense2_3 = Dense(128)(dense2_2)\ndense2_4 = Dense(83)(dense2_3)\ndense2_5 = Dense(88)(dense2_4)\n\n# 2-3. 레이어 병합\nmerge1 = concatenate([dense1_5, dense2_5])\nmiddle1 = Dense(51)(merge1)\nmiddle2 = Dense(95)(middle1)\nmiddle3 = Dense(58)(middle2)\nmiddle4 = Dense(54)(middle3)\n\n# 2-4. 아웃풋 레이어\noutput1 = Dense(50)(middle4)\noutput2 = Dense(148)(output1)\noutput3 = Dense(16)(output2)\noutput4 = Dense(168)(output3)\noutput5 = Dense(1)(output4)\n\n# 2-5. 모델링\nmodel = Model(inputs = [input1, input2],\n outputs = output5)\n\nmodel.summary()\n\n\n\n# 3. 실행\nmodel.compile(loss = 'mse', optimizer = 'adam', metrics = ['mse'])\nmodel.fit([x1, x2], y,\n epochs = 1000, batch_size = 32)\n # callbacks = [early])\n\nx1_predict = x1_predict.reshape(1, 3, 1)\nx2_predict = x2_predict.reshape(1, 3, 1)\n\n\n# 4. 평가 및 예측\nprint(x1_predict)\nprint(x2_predict)\n\ny_predict = model.predict([x1_predict, x2_predict])\n\nprint(y_predict)\n'''\n'''\n[[[55]\n [65]\n [75]]]\n[[[65]\n [75]\n [85]]]\n[[84.22178]]\n\n[[84.29394]]\n\n[[82.02529]]\n\n[[83.22624]]\n\n[[79.748474]]\n'''","sub_path":"keras/keras33_lstm_ensemble.py","file_name":"keras33_lstm_ensemble.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"78138192","text":"# -*- coding: utf-8 -*-\n\"\"\"\nYou are given two non-empty linked lists representing two non-negative integers. \nThe digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a linked list.\n\nYou may assume the two numbers do not contain any leading zero, except the number 0 itself.\n\"\"\"\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\nclass Solution:\n def addTwoNumbers(self, l1, l2):\n carry = 0\n head = node = ListNode('#')\n \n while l1 or l2 or carry:\n num1 = l1.val if l1 else 0\n num2 = l2.val if l2 else 0\n \n carry, newsum = divmod(carry + num1 + num2, 10)\n # divmod (13, 10) = (1, 3)\n # divmod (3, 10) = (0, 3)\n \n node.next = ListNode(newsum)\n node = node.next\n \n l1 = l1.next if l1 else None\n l2 = l2.next if l2 else None\n \n return head.next","sub_path":"Leetcode/2_Add_Two_Numbers.py","file_name":"2_Add_Two_Numbers.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"601239444","text":"from tripy.article.article import ALL_ARTICLES, SENTIMENT\r\nfrom tripy.geo.locations import INDEX_BY_NAME, NAME_BY_INDEX\r\nimport numpy as np \r\nimport json\r\nimport os\r\nimport plotly.graph_objects as go\r\n\r\nclass Sentiment_graph:\r\n def __init__(self):\r\n self.sentiment = SENTIMENT\r\n\r\n def plot_graph(self):\r\n n = len(self.sentiment)\r\n x, y = zip(*self.sentiment.items())\r\n x1 = list(x)\r\n for i in range(len(x)):\r\n x1[i] = NAME_BY_INDEX[x[i]]\r\n\r\n width=[0.35]*n\r\n\r\n fig = go.Figure(data=[\r\n go.Bar(x=x1, y=y, text=y, textposition='outside', texttemplate='%{text:.4f}',width=width)\r\n ])\r\n fig.update_layout(title_text='Sentiment Score of All Country', barmode='group', yaxis=dict(range=[min(y)-1,max(y)+1]))\r\n fig.write_html(os.getcwd()+f'{os.sep}tripy{os.sep}assets{os.sep}datas{os.sep}'+'sentiment.html')\r\n\r\n\r\n\r\nclass Probability_distribution:\r\n def __init__(self, routes, country):\r\n self._routes = routes\r\n self._country = country\r\n\r\n\r\n def plot_graph(self):\r\n\r\n n = len(self._routes)\r\n lists = sorted(self._routes.items(), reverse=True)\r\n width = [0.35]*n\r\n y, x = zip(*lists)\r\n total_score = sum(y)\r\n\r\n y = [i/total_score for i in y]\r\n\r\n x1 = list(x)\r\n for i in range(len(x1)):\r\n for j in range(len(x1[i])):\r\n for k in range(len(x1[i][j])):\r\n x1[i][j][k] = NAME_BY_INDEX[x[i][j][k]]\r\n x1[i][j] = '
'.join(x1[i][j])\r\n x1[i] = ','.join(x1[i])\r\n\r\n fig = go.Figure(data=[\r\n go.Bar(x=x1, y=y, text=y, textposition='outside', texttemplate='%{text:0.4f}',width=width)\r\n ])\r\n fig.update_layout(title_text='Probability Distribution of '+self._country, barmode='group', yaxis=dict(range=[0,1]))\r\n fig.write_html(os.getcwd()+f'{os.sep}tripy{os.sep}assets{os.sep}datas{os.sep}'+self._country+'probability.html')\r\n \r\n \r\n\r\nclass Graph:\r\n def __init__(self, country):\r\n self._articles = ALL_ARTICLES[INDEX_BY_NAME[country]]\r\n self._total_words = []\r\n self._country = country\r\n self._neg_words = []\r\n self._pos_words = []\r\n self._stop_words = []\r\n self._total_total_word = 0\r\n self._total_neg_word = 0\r\n self._total_pos_word = 0\r\n self._total_stop_word = 0\r\n for i in range(len(self._articles)):\r\n self._total_words.append(self._articles[i].get_total_word())\r\n self._total_total_word += self._total_words[i]\r\n self._stop_words.append(self._articles[i].get_stopword_freq())\r\n self._total_stop_word += self._stop_words[i]\r\n self._pos_words.append(self._articles[i].get_pos_freq())\r\n self._total_pos_word += self._pos_words[i]\r\n self._neg_words.append(self._articles[i].get_neg_freq())\r\n self._total_neg_word += self._neg_words[i]\r\n\r\n\r\n def plot_all_graph(self):\r\n x1 = [\"Article 1\",\"Article 2\",\"Article 3\",\"Article 4\",\"Article 5\"]\r\n fig = go.Figure(data=[\r\n go.Bar(x=x1, y=self._total_words, name='Total words', text=self._total_words, textposition='auto'),\r\n go.Bar(x=x1, y=self._stop_words, name='Stop words', text=self._stop_words, textposition='auto'),\r\n go.Bar(x=x1, y=self._pos_words, name='Positive words', text=self._pos_words, textposition='auto'),\r\n go.Bar(x=x1, y=self._neg_words, name='Negative words', text=self._neg_words, textposition='auto')\r\n ])\r\n fig.update_layout(title_text='Total Words in '+self._country+' articles', barmode='group')\r\n fig.write_html(os.getcwd()+f'{os.sep}tripy{os.sep}assets{os.sep}datas{os.sep}'+self._country+'graph.html')\r\n","sub_path":"tripy/article/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":3799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"92476295","text":"#! python\r\n# coding: utf-8\r\n\t\r\nimport math\t\r\n\r\ndata = list(map(float, input('Дай тр�� дійсні числа\\r\\n').split()))\r\n\r\na = data[0]\r\nb = data[1]\r\nc = data[2]\r\n\r\nif (c == 0.0):\r\n\tprint('На нуль ділиті нізя таваріщ')\r\n\t\r\nf = (1/(c*math.sqrt(2*math.pi)))*math.exp(((-1)*(a-b)**2)/(2*c**2))\r\n\r\nprint(str(f))","sub_path":"lab4_2.py","file_name":"lab4_2.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"3048832","text":"from django.shortcuts import render\n\n# Create your views here.\n\n\n\nposts = [ \t\t\t\t\t\t\t\t#gia na perasoume dedomena sthn selida dynamikos(gai thn wra)\n {\n 'author': 'CoreyMS',\n 'title': 'Blog Post 1',\n 'content': 'First post content',\n 'date_posted': 'August 27, 2018'\n },\n {\n 'author': 'Jane Doe',\n 'title': 'Blog Post 2',\n 'content': 'Second post content',\n 'date_posted': 'August 28, 2018'\n }\n]\n\n\ndef home(request):\n\tcontext = {\t\t\t\t\t\t#create dictionary that willtake posts dictionary with the parakatw tropo\n\t\t'posts' : posts \t\t\t#'key':value value=dictionary post\n\n\t}\n\treturn render(request, 'homescreen/home.html', context) #it shows the home.html render(request,path html,emfanisi dedomenwn) \n\n\ndef about(request):\n return render(request, 'homescreen/about.html', {'title':'about'}) #it shows the about.html render(request,path html)\n","sub_path":"homescreen/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"188247013","text":"#\r\n# @lc app=leetcode.cn id=3 lang=python3\r\n#\r\n# [3] 无重复字符的最长子串\r\n#\r\n# https://leetcode.cn/problems/longest-substring-without-repeating-characters/description/\r\n#\r\n# algorithms\r\n# Medium (38.99%)\r\n# Likes: 8116\r\n# Dislikes: 0\r\n# Total Accepted: 2M\r\n# Total Submissions: 5M\r\n# Testcase Example: '\"abcabcbb\"'\r\n#\r\n# 给定一个字符串 s ，请你找出其中不含有重复字符的 最长子串 的长度。\r\n# \r\n# \r\n# \r\n# 示例 1:\r\n# \r\n# \r\n# 输入: s = \"abcabcbb\"\r\n# 输出: 3 \r\n# 解释: 因为无重复字符的最长子串是 \"abc\"，所以其长度为 3。\r\n# \r\n# \r\n# 示例 2:\r\n# \r\n# \r\n# 输入: s = \"bbbbb\"\r\n# 输出: 1\r\n# 解释: 因为无重复字符的最长子串是 \"b\"，所以其长度为 1。\r\n# \r\n# \r\n# 示例 3:\r\n# \r\n# \r\n# 输入: s = \"pwwkew\"\r\n# 输出: 3\r\n# 解释: 因为无重复字符的最长子串是 \"wke\"，所以其长度为 3。\r\n# 请注意，你的答案必须是 子串 的长度，\"pwke\" 是一个子序列，不是子串。\r\n# \r\n# \r\n# \r\n# \r\n# 提示：\r\n# \r\n# \r\n# 0 <= s.length <= 5 * 10^4\r\n# s 由英文字母、数字、符号和空格组成\r\n# \r\n# \r\n#\r\n\r\n# @lc code=start\r\nclass Solution:\r\n def lengthOfLongestSubstring(self, s: str) -> int:\r\n w = {}\r\n res = l = r = 0\r\n while r < len(s):\r\n rt = s[r]\r\n r += 1\r\n w[rt] = w.get(rt, 0) + 1\r\n while w[rt] > 1:\r\n lt = s[l]\r\n l += 1\r\n w[lt] -= 1\r\n res = max(res, r - l)\r\n return res\r\n\r\n# @lc code=end\r\n\r\n","sub_path":"pkm/lbld/3.无重复字符的最长子串.py","file_name":"3.无重复字符的最长子串.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"79420153","text":"# Copyright 2019 Red Hat, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nUtility functions for blackbox testing.\n\"\"\"\n# isort: STDLIB\nimport os\nimport random\nimport string\nfrom subprocess import PIPE, Popen\nfrom tempfile import NamedTemporaryFile\n\n# isort: THIRDPARTY\nimport psutil\n\n\ndef random_string(length=4):\n \"\"\"\n Generates a random string\n :param length: Length of random string\n :return: String\n \"\"\"\n return \"{0}\".format(\n \"\".join(random.choice(string.ascii_uppercase) for _ in range(length))\n )\n\n\ndef process_exists(name):\n \"\"\"\n Look through processes, using their pids, to find one matching 'name'.\n Return None if no such process found, else return the pid.\n :param name: name of process to check\n :type name: str\n :return: pid or None\n :rtype: int or NoneType\n \"\"\"\n for proc in psutil.process_iter([\"name\"]):\n try:\n if proc.name() == name:\n return proc.pid\n except psutil.NoSuchProcess:\n pass\n\n return None\n\n\ndef exec_command(cmd):\n \"\"\"\n Executes the specified infrastructure command.\n\n :param cmd: command to execute\n :type cmd: list of str\n :returns: standard output\n :rtype: str\n :raises RuntimeError: if exit code is non-zero\n \"\"\"\n exit_code, stdout_text, stderr_text = exec_test_command(cmd)\n\n if exit_code != 0:\n raise RuntimeError(\n \"exec_command: non-zero exit code: %d\\nSTDOUT=%s\\nSTDERR=%s\"\n % (exit_code, stdout_text, stderr_text)\n )\n return stdout_text\n\n\ndef exec_test_command(cmd):\n \"\"\"\n Executes the specified test command\n :param cmd: Command and arguments as list\n :type cmd: list of str\n :returns: (exit code, std out text, std err text)\n :rtype: triple of int * str * str\n \"\"\"\n process = Popen(cmd, stdout=PIPE, stderr=PIPE, close_fds=True, env=os.environ)\n result = process.communicate()\n return (\n process.returncode,\n bytes(result[0]).decode(\"utf-8\"),\n bytes(result[1]).decode(\"utf-8\"),\n )\n\n\nclass RandomKeyTmpFile:\n \"\"\"\n Generate a random passphrase and put it in a temporary file.\n \"\"\"\n\n def __init__(self, key_bytes=32):\n \"\"\"\n Initializer\n\n :param int key_bytes: the desired length of the key in bytes\n \"\"\"\n self._tmpfile = NamedTemporaryFile(\"wb\")\n with open(\"/dev/urandom\", \"rb\") as urandom_f:\n random_bytes = urandom_f.read(key_bytes)\n self._tmpfile.write(random_bytes)\n self._tmpfile.flush()\n\n def tmpfile_name(self):\n \"\"\"\n Get the name of the temporary file.\n \"\"\"\n return self._tmpfile.name\n\n def close(self):\n \"\"\"\n Close and delete the temporary file.\n \"\"\"\n self._tmpfile.close()\n\n def __enter__(self):\n \"\"\"\n For use with the \"with\" keyword.\n\n :return str: the path of the file with the random key\n \"\"\"\n return self._tmpfile.name\n\n def __exit__(self, exc_type, exc_value, traceback):\n try:\n self._tmpfile.close()\n except Exception as error:\n if exc_value is None:\n raise error\n\n raise error from exc_value\n","sub_path":"tests/blackbox/testlib/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"534852996","text":"#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport os\nimport uuid\nfrom typing import List\n\nservice_name = os.getenv('SW_AGENT_NAME') or 'Python Service Name' # type: str\nservice_instance = os.getenv('SW_AGENT_INSTANCE') or str(uuid.uuid1()).replace('-', '') # type: str\ncollector_address = os.getenv('SW_AGENT_COLLECTOR_BACKEND_SERVICES') or '127.0.0.1:11800' # type: str\nprotocol = (os.getenv('SW_AGENT_PROTOCOL') or 'grpc').lower() # type: str\nauthentication = os.getenv('SW_AGENT_AUTHENTICATION') # type: str\nlogging_level = os.getenv('SW_AGENT_LOGGING_LEVEL') or 'INFO' # type: str\ndisable_plugins = (os.getenv('SW_AGENT_DISABLE_PLUGINS') or '').split(',') # type: List[str]\nmysql_trace_sql_parameters = True if os.getenv('SW_MYSQL_TRACE_SQL_PARAMETERS') and \\\n os.getenv('SW_MYSQL_TRACE_SQL_PARAMETERS') == 'True' else False # type: bool\nmysql_sql_parameters_max_length = int(os.getenv('SW_MYSQL_SQL_PARAMETERS_MAX_LENGTH') or '512') # type: int\nignore_suffix = os.getenv('SW_IGNORE_SUFFIX') or '.jpg,.jpeg,.js,.css,.png,.bmp,.gif,.ico,.mp3,' \\\n '.mp4,.html,.svg ' # type: str\nflask_collect_http_params = True if os.getenv('SW_FLASK_COLLECT_HTTP_PARAMS') and \\\n os.getenv('SW_FLASK_COLLECT_HTTP_PARAMS') == 'True' else False # type: bool\nhttp_params_length_threshold = int(os.getenv('SW_HTTP_PARAMS_LENGTH_THRESHOLD') or '1024') # type: int\ndjango_collect_http_params = True if os.getenv('SW_DJANGO_COLLECT_HTTP_PARAMS') and \\\n os.getenv('SW_DJANGO_COLLECT_HTTP_PARAMS') == 'True' else False # type: bool\ncorrelation_element_max_number = int(os.getenv('SW_CORRELATION_ELEMENT_MAX_NUMBER') or '3') # type: int\ncorrelation_value_max_length = int(os.getenv('SW_CORRELATION_VALUE_MAX_LENGTH') or '128') # type: int\n\n\ndef init(\n service: str = None,\n instance: str = None,\n collector: str = None,\n protocol_type: str = 'grpc',\n token: str = None,\n):\n global service_name\n service_name = service or service_name\n\n global service_instance\n service_instance = instance or service_instance\n\n global collector_address\n collector_address = collector or collector_address\n\n global protocol\n protocol = protocol_type or protocol\n\n global authentication\n authentication = token or authentication\n","sub_path":"skywalking/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"233196500","text":"# coding=utf-8\r\nimport xlrd\r\nimport xlsxwriter\r\nimport datetime\r\n\r\nWorkbook_In = xlrd.open_workbook(\"data.xlsx\") # 打开数据表格\r\nSheet_In = Workbook_In.sheet_by_name(\"Sheet1\") # 注意sheet的名称\r\nTitle = Sheet_In.row(0) # 表头\r\nOrder_Number = Sheet_In.nrows # 订单个数\r\nMachine_Number = 1 # 机器个数\r\nOrder_List = [] # 订单列表\r\nMachine_Name = [] # 机器名称列表\r\n\r\n#读取数据表\r\nfor i in range(1,Order_Number):\r\n data = Sheet_In.row(i)\r\n dic = {}\r\n for j in range(Sheet_In.ncols):\r\n cell = data[j]\r\n if j == 5 and cell.ctype == 2:\r\n val = xlrd.xldate_as_datetime(cell.value, 0)\r\n else:\r\n val = cell.value\r\n dic[Title[j].value] = val\r\n if j == 0:\r\n if i == 1:\r\n Machine_Name.append(val)\r\n elif val != Machine_Name[Machine_Number-1]:\r\n Machine_Name.append(val)\r\n Machine_Number = Machine_Number + 1\r\n # print(dic)\r\n Order_List.append(dic)\r\n\r\nOrder_Number = Order_Number - 1 # 除去表头行\r\n# print(Order_List)\r\n# print(Machine_Name)\r\n# print(Machine_Number)\r\n# print(Order_Number)\r\n\r\n#比较时间先后函数\r\ndef Compare (Date_1 , Date_2):\r\n Val_1 = Date_1.year * 13 * 32 + Date_1.month * 32 + Date_1.day\r\n Val_2 = Date_2.year * 13 * 32 + Date_2.month * 32 + Date_2.day\r\n # print(Val_1,\"+\",Val_2)\r\n return Val_1 < Val_2\r\n \r\nFind = 0\r\nCost = [] # 各订单总耗时\r\n\r\n#寻找最早开始时间\r\nfor i in range(Order_Number):\r\n Now_Order = Order_List[i]\r\n Cost.append(Now_Order[\"总量（万个）\"] / Now_Order[\"效率（万个/班）\"])\r\n if(Now_Order[\"开始日期\"] != \"\"):\r\n if(Find == 0):\r\n First_Day = Now_Order[\"开始日期\"]\r\n Find = 1\r\n elif Compare(Now_Order[\"开始日期\"] , First_Day) == 1:\r\n First_Day = Now_Order[\"开始日期\"]\r\n\r\n#时间自增函数\r\ndef Next_Day (Dat):\r\n Y = Dat.year\r\n M = Dat.month\r\n D = Dat.day\r\n R = 0\r\n if Y % 400 == 0:\r\n R = 1\r\n elif Y % 4 == 0 and Y % 100 != 0:\r\n R = 1\r\n if M==2:\r\n if R == 1 and D == 29:\r\n M = 3\r\n D = 1\r\n elif R == 0 and D == 28:\r\n M = 3\r\n D = 1\r\n else:\r\n D = D + 1\r\n elif M == 1 or M == 3 or M == 5 or M == 7 or M == 8 or M == 10 or M == 12:\r\n if D == 31:\r\n D = 1\r\n if M == 12:\r\n Y = Y + 1\r\n M = 1\r\n else:\r\n M = M + 1\r\n else:\r\n D = D + 1\r\n else:\r\n if D == 30:\r\n M = M + 1\r\n D = 1\r\n else:\r\n D = D + 1\r\n return datetime.datetime(Y,M,D)\r\n # print(New_Date,\" \",New_Date.strftime(\"%w\"))\r\n # if New_Date.strftime(\"%w\") == 0:\r\n # return Next_Day(New_Date)\r\n # else:\r\n # return New_Date\r\n \r\nNow_Date = First_Day\r\nLast_Day = First_Day\r\nSchedule_Dic = {} # 各订单的时间和机器安排\r\n\r\n# for i in range(300):\r\n# print(Now_Date)\r\n# Now_Date = Next_Day(Now_Date)\r\n\r\nfor i in range(Machine_Number):\r\n Find = 0\r\n for j in range(Order_Number):\r\n Now_Order = Order_List[j]\r\n if Now_Order[\"机器\"] == Machine_Name[i]:\r\n lst = []\r\n if Find == 0:\r\n Find = 1\r\n Left = 3\r\n if Now_Order[\"开始日期\"] == \"\":\r\n Now_Date = First_Day\r\n else:\r\n Now_Date = Now_Order[\"开始日期\"]\r\n elif Now_Order[\"开始日期\"] != \"\" and Compare(Now_Date,Now_Order[\"开始日期\"]) == 1:\r\n Now_Date = Now_Order[\"开始日期\"]\r\n Left = 3\r\n if Now_Date.strftime(\"%w\") == 0:\r\n Now_Date = Next_Day(Now_Date)\r\n Left = 3\r\n while Cost[j] != 0:\r\n if Cost[j] >= Left:\r\n Cost[j] = Cost[j] - Left\r\n lst.append({\"日期\" : Now_Date , \"机器\" : Machine_Name[i] , \"数量\" : Left * Now_Order[\"效率（万个/班）\"]})\r\n Now_Date = Next_Day(Now_Date)\r\n if Now_Date.strftime(\"%w\") == \"0\":\r\n # print(Now_Date)\r\n Now_Date = Next_Day(Now_Date)\r\n # print(Now_Date)\r\n Left = 3\r\n else:\r\n Left = Left - Cost[j]\r\n lst.append({\"日期\" : Now_Date , \"机器\" : Machine_Name[i] , \"数量\" : Cost[j] * Now_Order[\"效率（万个/班）\"]})\r\n Cost[j] = 0\r\n Schedule_Dic[j] = lst\r\n if Compare(Last_Day , Now_Date) == 1:\r\n Last_Day = Now_Date\r\n\r\n# print(First_Day)\r\n# print(Last_Day)\r\n# for i in range(Order_Number):\r\n# print(Schedule_Dic[i])\r\n# print(\"---------------------\")\r\n\r\nYea = First_Day.strftime(\"%Y年\")\r\nFir = First_Day.strftime(\"（%m.%d\")\r\nLas = Last_Day.strftime(\"~%m.%d）\")\r\nTitlename = Yea + \"生产计划预排\" + Fir + Las\r\nFilename = Titlename + \".xlsx\"\r\n\r\nWorkbook_Out = xlsxwriter.Workbook(Filename) # 建立输出表格\r\nSheet_Out = Workbook_Out.add_worksheet(\"排产\") # 建立Sheet\r\n\r\n#初始化格式\r\nTitle_Style = Workbook_Out.add_format({\r\n 'bold' : True , # 是否加粗\r\n 'font' : \"华文中宋\" , # 字体设置\r\n 'font_size' : 16 , # 文字大小设置\r\n 'align' : \"center\" , # 水平位置设置:居中\r\n\t'valign' : \"vcenter\" , # 垂直位置设置:居中\r\n 'font_color' : \"black\" , # 文字颜色设置\r\n 'fg_color' : \"white\" , # 单元格背景颜色设置\r\n 'text_wrap' : True , # 是否自动换行\r\n 'border' : 5 # 框线宽度\r\n})\r\n\r\nMachine_Style = Workbook_Out.add_format({\r\n 'bold' : True ,\r\n 'font' : \"宋体\" ,\r\n 'font_size' : 10 ,\r\n 'align' : \"center\" ,\r\n\t'valign' : \"vcenter\" ,\r\n 'font_color' : \"black\" ,\r\n 'fg_color' : \"#D8E4BC\" ,\r\n 'text_wrap' : True ,\r\n 'border' : 1\r\n})\r\n\r\nDate_Style = Workbook_Out.add_format({\r\n 'bold' : False ,\r\n 'font' : \"宋体\" ,\r\n 'font_size' : 10 ,\r\n 'align' : \"center\" ,\r\n\t'valign' : \"vcenter\" ,\r\n 'font_color' : \"black\" ,\r\n 'fg_color' : \"white\" ,\r\n 'text_wrap' : True ,\r\n 'border' : 1\r\n})\r\n\r\nDate_Style_Sunday = Workbook_Out.add_format({\r\n 'bold' : False ,\r\n 'font' : \"宋体\" ,\r\n 'font_size' : 10 ,\r\n 'align' : \"center\" ,\r\n\t'valign' : \"vcenter\" ,\r\n 'font_color' : \"black\" ,\r\n 'fg_color' : \"yellow\" ,\r\n 'text_wrap' : True ,\r\n 'border' : 1\r\n})\r\n\r\nNormal_Style = Workbook_Out.add_format({\r\n 'bold' : False ,\r\n 'font' : \"宋体\" ,\r\n 'font_size' : 10 ,\r\n\t'valign' : \"vcenter\" ,\r\n 'font_color' : \"black\" ,\r\n 'fg_color' : \"white\" ,\r\n 'text_wrap' : True ,\r\n 'border' : 1\r\n})\r\n\r\nCross_Style = Workbook_Out.add_format({\r\n 'bold' : False ,\r\n 'font' : \"宋体\" ,\r\n 'font_size' : 10 ,\r\n\t'valign' : \"vcenter\" ,\r\n 'font_color' : \"black\" ,\r\n 'fg_color' : \"#ADD8E6\" ,\r\n 'text_wrap' : True ,\r\n 'border' : 1\r\n})\r\n\r\nLight_Line = Workbook_Out.add_format({'border' : 1})\r\n\r\nHeavy_Line = Workbook_Out.add_format({'border' : 5})\r\n\r\nYellow = Workbook_Out.add_format({\r\n 'fg_color' : \"yellow\" ,\r\n 'border' : 1\r\n})\r\n\r\n#写表头\r\nSheet_Out.set_row(0 , 40)\r\nSheet_Out.merge_range(0 , 0 , 0 , 3 * Machine_Number , \"Merged Cells\")\r\nSheet_Out.write(0 , 0 , Titlename , Title_Style) # 第1行写标题\r\nSheet_Out.set_row(1 , 25)\r\nSheet_Out.set_column(0 , 0 , 8)\r\nSheet_Out.write(1 , 0 , \"机台\" , Machine_Style) # 第2行写机器名称\r\nfor i in range(Machine_Number):\r\n Sheet_Out.write(0 , 3 * i + 1 , \"\" , Heavy_Line)\r\n Sheet_Out.write(0 , 3 * i + 2 , \"\" , Heavy_Line)\r\n Sheet_Out.write(0 , 3 * i + 3 , \"\" , Heavy_Line)\r\n Sheet_Out.write(1 , 3 * i + 2 , \"\" , Light_Line)\r\n Sheet_Out.write(1 , 3 * i + 3 , \"\" , Light_Line)\r\n Sheet_Out.merge_range(1 , 3 * i + 1 , 1 , 3 * i + 3 , \"Merged Cells\")\r\n Sheet_Out.write(1 , 3 * i + 1 , Machine_Name[i] , Machine_Style)\r\n Sheet_Out.set_column(3 * i + 1 , 3 * i + 2 , 15)\r\n Sheet_Out.set_column(3 * i + 3 , 3 * i + 3 , 8)\r\n\r\nSheet_Out.write(2 , 0 , \"日期\" , Date_Style) # 表头各项信息栏\r\nfor i in range(Machine_Number):\r\n Sheet_Out.write(2 , 3 * i + 1 , \"品牌\" , Date_Style)\r\n Sheet_Out.write(2 , 3 * i + 2 , \"工单号\\n（备注）\" , Date_Style)\r\n Sheet_Out.write(2 , 3 * i + 3 , \"日产量（万印）\" , Date_Style)\r\n\r\n#写入生产计划\r\nNow_Date = First_Day\r\ncnt = 0\r\nwhile(True):\r\n cnt = cnt + 1\r\n Form_Date = Now_Date.strftime(\"%m月%d日\")\r\n if Now_Date.strftime(\"%w\") == \"0\":\r\n Sheet_Out.write(cnt + 2 , 0 , Form_Date , Date_Style_Sunday)\r\n else:\r\n Sheet_Out.write(cnt + 2 , 0 , Form_Date , Date_Style)\r\n for i in range(Machine_Number):\r\n str1 = \"\"\r\n str2 = \"\"\r\n str3 = \"\"\r\n for j in range(Order_Number):\r\n Now_Order = Order_List[j]\r\n if Now_Order[\"机器\"] == Machine_Name[i]:\r\n lst = Schedule_Dic[j]\r\n for Dic in lst:\r\n if Dic[\"日期\"] == Now_Date:\r\n if str1 == \"\":\r\n str1 = Now_Order[\"产品名称\"]\r\n else:\r\n str1 = str1 + \" +\" + Now_Order[\"产品名称\"]\r\n if Now_Order[\"工单号\"] != \"\":\r\n if str2 == \"\":\r\n str2 = Now_Order[\"工单号\"]\r\n else:\r\n str2 = str2 + \" +\" + Now_Order[\"工单号\"]\r\n if Now_Order[\"产品名称\"] != \"换牌\" and Now_Order[\"产品名称\"] != \"月保\" and Now_Order[\"产品名称\"] != \"周保\":\r\n if str3 == \"\":\r\n str3 = str(round(Dic[\"数量\"],2))\r\n else:\r\n str3 = str3 + \" +\" + str(round(Dic[\"数量\"],2))\r\n Sheet_Out.write(cnt + 2 , 3 * i + 1 , str1 , Normal_Style)\r\n Sheet_Out.write(cnt + 2 , 3 * i + 2 , str2 , Normal_Style)\r\n Sheet_Out.write(cnt + 2 , 3 * i + 3 , str3 , Normal_Style)\r\n if Now_Date.strftime(\"%w\") == \"0\":\r\n for i in range(Machine_Number):\r\n Sheet_Out.write(cnt + 2 , 3 * i + 1 , \"\" , Yellow)\r\n Sheet_Out.write(cnt + 2 , 3 * i + 2 , \"\" , Yellow)\r\n Sheet_Out.write(cnt + 2 , 3 * i + 3 , \"\" , Yellow)\r\n if Now_Date == Last_Day:\r\n break\r\n Now_Date = Next_Day(Now_Date)\r\n\r\nWorkbook_Out.close() # 保存文件\r\n\r\n# Made by 中国飞鱼","sub_path":"Transform1.1.py","file_name":"Transform1.1.py","file_ext":"py","file_size_in_byte":10562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"365814575","text":"\"\"\"\n CREATE TABLE makes (\n id INTEGER PRIMARY KEY,\n description TEXT\n )\n CREATE TABLE colors (\n id INTEGER PRIMARY KEY,\n description TEXT\n )\n CREATE TABLE models (\n id INTEGER PRIMARY KEY,\n description TEXT,\n make_id INTEGER\n )\n CREATE TABLE models-colors (\n models_id INTEGER,\n colors_id INTEGER\n )\n\"\"\"\n\nmakes = (\n (1, \"Toyota\"), (2, \"Nissan\"),\n (3, \"Ford\"), (4, \"Mini\"),\n (5, \"Honda\"), (6, \"Dodge\"),\n)\n\nmakes = {k: v for k, v in makes}\n\nmodels = (\n (1, \"Altima\", 2), (2, \"Thunderbird\", 3),\n (3, \"Dart\", 6), (4, \"Accord\", 5),\n (5, \"Prius\", 1), (6, \"Countryman\", 4),\n (7, \"Camry\", 1), (8, \"F150\", 3),\n (9, \"Civic\", 5), (10, \"Ram\", 6),\n (11, \"Cooper\", 4), (12, \"Pilot\", 5),\n (13, \"Xterra\", 2), (14, \"Sentra\", 2),\n (15, \"Charger\", 6)\n)\n\ncolors = (\n (1, \"Black\" ), (2, \"Charcoal\" ), (3, \"Red\" ), (4, \"Brick\" ),\n (5, \"Blue\" ), (6, \"Navy\" ), (7, \"White\" ), (8, \"Ivory\" )\n)\n\ncolors = {k: v for k, v in colors}\n\navailable_car_colors = (\n (1, 1), (1, 2), (1, 7), \n (2, 1), (2, 3), (2, 7), \n (3, 2), (3, 3), (3, 7), \n (4, 3), (4, 5), (4, 8),\n (5, 2), (5, 4), (5, 8), \n (6, 2), (6, 6), (6, 7), \n (7, 1), (7, 3), (7, 7), \n (8, 1), (8, 5), (8, 8),\n (9, 1), (9, 6), (9, 7), \n (10, 2), (10, 5), (10, 7), \n (11, 3), (11, 6), (11, 8), \n (12, 1), (12, 4), (12, 7),\n (13, 2), (13, 6), (13, 8), \n (14, 2), (14, 5), (14, 8), \n (15, 1), (15, 4), (15, 7)\n)\n\ncolors_available = {}\n\nfor car, color in available_car_colors:\n if car in colors_available:\n colors_available[car].append(colors[color])\n else:\n colors_available[car] = [colors[color]]\n\nmodels_by_make = {k: {} for k in makes.values()}\n\nfor car_id, name, make_id in models:\n make = makes[make_id]\n car = models_by_make[make].get(name, [])\n car.append(colors_available[car_id])\n models_by_make[make][name] = car\n\n\nif __name__ == '__main__':\n for make, models_and_colors in models_by_make.items():\n print(\"\\n{0}\\n---------------------------------------------\".format(make, models_and_colors))\n for model, colors in models_and_colors.items():\n print(\"{0} available in {1}, {2}, {3}\".format(model, *colors[0]))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"cars.py","file_name":"cars.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"557491","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@Author: tushushu\n@Date: 2021-07-18 16:46:54\n@Last Modified by: tushushu\n@Last Modified time: 2021-07-18 16:46:54\n\"\"\"\n\n\nclass Solution:\n def numDecodings(self, s: str) -> int:\n \"\"\"\n F(n) = F(n - 1) + F(n - 2) * can decode(s[n - 1], s[n])\n \"\"\"\n if s[0] == \"0\":\n return 0\n if len(s) == 1:\n return 1\n codes = set([str(x) for x in range(1, 27)])\n dp = [0] * len(s)\n dp[0] = 1\n\n if s[0:2] in codes:\n if s[1] == \"0\":\n dp[1] = 1\n else:\n dp[1] = 2\n else:\n if s[1] == \"0\":\n return 0\n dp[1] = 1\n for i in range(2, len(s)):\n if s[i - 1:i + 1] in codes:\n if s[i] == \"0\":\n dp[i] = dp[i - 2]\n else:\n dp[i] = dp[i - 2] + dp[i - 1]\n else:\n if s[i] == \"0\":\n return 0\n dp[i] = dp[i - 1]\n return dp[-1]\n","sub_path":"python/091. 解码方法.py","file_name":"091. 解码方法.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"98141118","text":"import os\nfrom unittest.mock import Mock, patch\nimport pytest\nfrom battenberg.errors import InvalidRepositoryException\nfrom battenberg.utils import open_repository, open_or_init_repository, construct_keypair\n\n\n@pytest.fixture\ndef Repository() -> Mock:\n with patch('battenberg.utils.Repository') as Repository:\n yield Repository\n\n\n@pytest.fixture\ndef discover_repository() -> Mock:\n with patch('battenberg.utils.discover_repository') as discover_repository:\n yield discover_repository\n\n\n@pytest.fixture\ndef init_repository() -> Mock:\n with patch('battenberg.utils.init_repository') as init_repository:\n yield init_repository\n\n\n@pytest.fixture\ndef Keypair() -> Mock:\n with patch('battenberg.utils.Keypair') as Keypair:\n yield Keypair\n\n\ndef test_open_repository():\n path = 'test-path'\n with pytest.assertRaises(ValueError) as e:\n open_repository(path)\n\n assert str(e.value) == f'{path} is not a valid repository path.'\n\n\ndef test_open_repository(Repository: Mock, discover_repository: Mock):\n path = 'test-path'\n assert open_repository(path) == Repository.return_value\n Repository.assert_called_once_with(discover_repository.return_value)\n discover_repository.assert_called_once_with(path)\n\n\ndef test_open_repository_raises_on_invalid_path():\n path = 'test-path'\n with pytest.raises(InvalidRepositoryException) as e:\n open_repository(path)\n\n assert str(e.value) == f'{path} is not a valid repository path.'\n\n\ndef test_open_or_init_repository_opens_repo(Repository: Mock, discover_repository: Mock):\n path = 'test-path'\n template = 'test-template'\n assert open_or_init_repository(path, template) == Repository.return_value\n Repository.assert_called_once_with(discover_repository.return_value)\n discover_repository.assert_called_once_with(path)\n\n\ndef test_open_or_init_repository_initializes_repo(init_repository: Mock, Repository: Mock,\n discover_repository: Mock):\n discover_repository.side_effect = Exception('No repo found')\n\n path = 'test-path'\n template = 'test-template'\n initial_branch = 'test-initial_branch'\n repo = init_repository.return_value\n assert open_or_init_repository(path, template, initial_branch) == repo\n init_repository.assert_called_once_with(path, initial_head=initial_branch)\n init_repository.return_value.create_commit.assert_called_once_with(\n 'HEAD',\n repo.default_signature,\n repo.default_signature,\n 'Initialized repository',\n repo.index.write_tree.return_value,\n []\n )\n\n\n@patch('battenberg.utils.subprocess')\ndef test_open_or_init_repository_initializes_repo_with_inferred_initial_branch(subprocess: Mock,\n init_repository: Mock, Repository: Mock, discover_repository: Mock):\n initial_branch = 'refs/heads/main'\n subprocess.run.return_value.stdout = f'ref: {initial_branch} HEAD'\n discover_repository.side_effect = Exception('No repo found')\n\n path = 'test-path'\n template = 'test-template'\n repo = init_repository.return_value\n assert open_or_init_repository(path, template) == repo\n repo.references['HEAD'].set_target.assert_called_once_with(initial_branch)\n\n\n@pytest.mark.parametrize('stdout', ('', 'invalid-symref'))\n@patch('battenberg.utils.subprocess')\ndef test_open_or_init_repository_initializes_repo_with_invalid_remote_branches(subprocess: Mock,\n init_repository: Mock, Repository: Mock, discover_repository: Mock, stdout: str):\n subprocess.run.return_value.stdout = stdout\n discover_repository.side_effect = Exception('No repo found')\n\n path = 'test-path'\n template = 'test-template'\n repo = init_repository.return_value\n assert open_or_init_repository(path, template) == repo\n\n\ndef test_construct_keypair_defaults(Keypair: Mock):\n construct_keypair()\n user_home = os.path.expanduser('~')\n Keypair.assert_called_once_with('git', f'{user_home}/.ssh/id_rsa.pub',\n f'{user_home}/.ssh/id_rsa', '')\n\n\ndef test_construct_keypair(Keypair: Mock):\n public_key_path = 'test-public_key_path'\n private_key_path = 'test-private_key_path'\n passphrase = 'test-passphrase'\n construct_keypair(public_key_path, private_key_path, passphrase)\n Keypair.assert_called_once_with('git', public_key_path, private_key_path, passphrase)\n","sub_path":"tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":4342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"387100528","text":"import threading\nimport queue\nimport time\nimport pyupbit\nimport datetime\nfrom collections import deque\nTICKER = \"KRW-EOS\"\n\nclass Consumer(threading.Thread):\n def __init__(self, q):\n super().__init__()\n self.q = q\n self.ticker = TICKER\n\n self.ma5 = deque(maxlen=5)\n self.ma10 = deque(maxlen=10)\n self.ma15 = deque(maxlen=15)\n self.ma50 = deque(maxlen=50)\n self.ma120 = deque(maxlen=120)\n\n df = pyupbit.get_ohlcv(self.ticker, interval=\"minute1\")\n self.ma5.extend(df['close'])\n self.ma10.extend(df['close'])\n self.ma15.extend(df['close'])\n self.ma50.extend(df['close'])\n self.ma120.extend(df['close'])\n\n\n def run(self):\n price_curr = None # 현재 가격\n hold_flag = False # 보유 여부\n wait_flag = False # 대기 여부\n\n with open(\"key/upbit_key.txt\", \"r\") as f:\n access = f.readline().strip()\n secret = f.readline().strip()\n\n upbit = pyupbit.Upbit(access, secret)\n cash = upbit.get_balance() # 2개 이상 종목 돌릴 시 모든 cash 코드 임의 설정\n print(\"보유현금:\", cash)\n\n i = 0\n\n while True:\n try:\n if not self.q.empty():\n if price_curr != None:\n self.ma5.append(price_curr)\n self.ma10.append(price_curr)\n self.ma15.append(price_curr)\n self.ma50.append(price_curr)\n self.ma120.append(price_curr)\n\n curr_ma5 = sum(self.ma5) / len(self.ma5)\n curr_ma10 = sum(self.ma10) / len(self.ma10)\n curr_ma15 = sum(self.ma15) / len(self.ma15)\n curr_ma50 = sum(self.ma50) / len(self.ma50)\n curr_ma120 = sum(self.ma120) / len(self.ma120)\n\n price_open = self.q.get()\n if hold_flag == False:\n price_buy = price_open * 1.005\n price_sell = price_open * 1.015\n wait_flag = False\n\n price_curr = pyupbit.get_current_price(self.ticker)\n if price_curr == None:\n continue\n\n if hold_flag == False and wait_flag == False and \\\n price_curr >= price_buy and curr_ma5 >= curr_ma10 and \\\n curr_ma10 >= curr_ma15 and curr_ma15 >= curr_ma50 and \\\n curr_ma50 >= curr_ma120 and curr_ma15 <= curr_ma50 * 1.03:\n # 0.05%\n while True:\n ret = upbit.buy_market_order(self.ticker, cash * 0.9995)\n if ret == None or \"error\" in ret:\n print(\"<< 매수 주문 Error >>\")\n time.sleep(0.5)\n continue\n print(\"매수 주문\", ret)\n break\n\n while True:\n order = upbit.get_order(ret['uuid'])\n if order != None and len(order['trades']) > 0:\n print(\"<< 매수 주문이 체결되었습니다 >>\\n\", order)\n break\n else:\n print(\"매수 주문 대기 중...\")\n time.sleep(0.5)\n\n while True:\n volume = upbit.get_balance(self.ticker)\n if volume != None and volume != 0:\n break\n print(\"보유량 계산중...\")\n time.sleep(0.5)\n\n while True:\n price_sell = pyupbit.get_tick_size(price_sell)\n ret = upbit.sell_limit_order(self.ticker, price_sell, volume)\n if ret == None or 'error' in ret:\n print(\"<< 지정가 매도 주문 Error >>\")\n time.sleep(0.5)\n else:\n print(\"<< 지정가 매도주문이 접수되었습니다 >>\\n\", ret)\n hold_flag = True\n break\n \n cash = upbit.get_balance()\n\n if hold_flag == True:\n uncomp = upbit.get_order(self.ticker)\n\n if (price_curr / price_buy) <= 0.98: # 2% 하락시 손절 매도\n while True:\n upbit.cancel_order(uncomp[0]['uuid'])\n if len(upbit.get_order(self.ticker)) == 0:\n print(\"<< 지정가 매도주문이 취소되었습니다 >>\\n\", ret)\n break\n\n upbit.sell_market_order(self.ticker, volume)\n while True:\n volume = upbit.get_balance(self.ticker)\n if volume == 0:\n print(\"<< 손절 주문(-2%)이 완료되었습니다 >>\")\n cash = upbit.get_balance()\n hold_flag = False\n wait_flag = True\n break\n else:\n print(\"손절 주문(-2%) 대기중...\")\n time.sleep(0.5)\n\n elif uncomp != None and len(uncomp) == 0:\n cash = upbit.get_balance()\n if cash == None:\n continue\n print(\"<< 지정가 매도가 체결되었습니다 >>\")\n hold_flag = False\n wait_flag = True\n\n # 10 seconds\n if i == (5 * 10):\n print(f\"[{datetime.datetime.now()}]\")\n print(f\"{TICKER} 보유량:{upbit.get_balance_t(self.ticker)}, 보유KRW: {cash}, hold_flag= {hold_flag}, wait_flag= {wait_flag} signal = {curr_ma5 >= curr_ma10 and curr_ma10 >= curr_ma15 and curr_ma15 >= curr_ma50 and curr_ma50 >= curr_ma120 and curr_ma15 <= curr_ma50 * 1.03}\")\n print(f\"현재: {price_curr}, 매수 목표: {int(price_buy)}, 지정 매도: {price_sell}, 손절 예상: {int(price_buy * 0.98)}\")\n i = 0\n i += 1\n except:\n print(\"error\")\n\n time.sleep(0.2)\n\nclass Producer(threading.Thread):\n def __init__(self, q):\n super().__init__()\n self.q = q\n\n def run(self):\n while True:\n price = pyupbit.get_current_price(TICKER)\n self.q.put(price)\n time.sleep(60)\n\nnow = datetime.datetime.now()\nprint(f'환영합니다 -- Upbit Auto Trading -- [{now.year}-{now.month}-{now.day} {now.hour}:{now.minute}:{now.second}]')\nprint('트레이딩 대기중...')\nwhile True:\n now = datetime.datetime.now()\n if now.second == 1: # 대기 후 1초에 시작\n q = queue.Queue()\n Producer(q).start()\n Consumer(q).start()\n break\n","sub_path":"realtime_1percent_gap/realtime_1percent.py","file_name":"realtime_1percent.py","file_ext":"py","file_size_in_byte":7211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"117136784","text":"#!/usr/bin/env python \n\n\nfrom enthought.traits.api import *\nfrom enthought.traits.ui.api import *\nfrom enthought.pyface.api import FileDialog, OK\n\nfrom .selection import RectangleSelection\nfrom numpy import indices, zeros\nfrom . import base_fit\nimport pickle\n\ndef _get(object,name):\n d = getattr(object, 'parameters')\n return d.get(name)\n \ndef _set(object,name,value):\n d = getattr(object, 'parameters')\n d[name] = value\n setattr(object,'parameters',d)\n\nResultsProperty = Property(_get, _set, trait =Float, depends_on = 'parameters')\n\n\ndef results_view_item(name):\n cname = 'is_' + name + '_constant'\n return HGroup(\n Item(\n name, \n label = name,\n springy = True,\n enabled_when = cname + '!= True'), \n Item(cname, label = 'constant'),\n springy = True)\n\ndef results_view(names):\n group = list(map(results_view_item, names))\n return View(HGroup(Group(*group, springy = True), \n \n # Group(Item('excluded_parameters', show_label = False,style = 'custom'), label = 'Excluded from fit')\n ))\n\n\n\n\n\nclass Results(HasTraits):\n \"\"\" Object used to display and store fit results.\n Dynamically filled with traits\n >>> r = Results()\n >>> r.add_parameters(a = 1., b = 3.)\n \"\"\"\n view = Instance(View)\n \n #parameters which are specified as conmstant\n constant_parameters = Property(depends_on = 'is_+')\n \n iteration_index = Int(0)\n \n parameters = List(Str)\n \n @cached_property\n def _get_constant_parameters(self):\n p = [name for name in self.parameters if getattr(self, 'is_' + name + '_constant', None) == True ]\n return tuple(p)\n\n \n def default_traits_view(self):\n return self.view\n \n def add_parameters(self, **parameters):\n for name, value in list(parameters.items()):\n self.add_trait(name,Float)\n self.add_trait('is_' + name + '_constant', Bool)\n setattr(self,name,value)\n self.add_trait('is_' + name + '_constant', Bool)\n self.parameters = list(parameters.keys())\n self.view = results_view(sorted(parameters.keys()))\n\n def get_parameters(self):\n return self.get(*self.parameters)\n\n def set_parameters(self, **parameters):\n return self.set(**parameters) \n \n def set_constant_parameters(self, *names):\n for name in self.parameters:\n if name in names:\n setattr(self,'is_' + name + '_constant', True)\n else:\n setattr(self,'is_' + name + '_constant', False)\n \nclass Statistics(HasTraits):\n \"\"\" Object used to display the statistics.\n \"\"\"\n mean = CFloat\n max = CFloat\n min = CFloat\n std = CFloat\n sum = CFloat\n view = View('min', 'max', 'sum', 'mean', 'std')\n \n \nclass Fit(HasTraits):\n results = Instance(Results,transient = True)\n \n\n \n function = Instance(base_fit.Function)\n function_str = Str('n+a/s/2/pi*exp(-((x-x0)**2+(y-y0)**2)/2/s**2)', \n enter_set = True, \n auto_set = False,\n label = 'Fit Function')\n #fit function name\n name = Trait('gauss2D', {'gauss2D': 'n+a/s/2/pi*exp(-((x-x0)**2+(y-y0)**2)/2/s**2)',\n 'custom' : ''})\n \n is_fitting = Bool(True, label = 'Fit') \n\n message = Str\n \n view = View(\n Group(\n HGroup('is_fitting',\n Item('name', show_label = False, springy = True, enabled_when = 'is_fitting')),\n Item('function_str', show_label = False, springy = True, enabled_when = 'is_fitting == True and name == \"custom\"'),\n \n Group(Item('results', style = 'custom', show_label = False),\n show_border = True, label = 'Fit parameters', enabled_when = 'is_fitting', \n ), \n\n Item('message', show_label = False, style = 'readonly')\n )\n )\n \n @on_trait_change('results.[+]')\n def update_function(self):\n self.function.SetConstant(*self.results.constant_parameters)\n self.function.SetParameters(**self.results.get(*self.results.parameters))\n \n def _function_str_changed(self, new):\n self.function = base_fit.Function(self.function_str)\n self.results = self._results_default()\n \n def _function_default(self):\n return base_fit.Function(self.function_str)\n \n def _results_default(self):\n r = Results()\n r.add_parameters(**self.function.parDict)\n return r\n \n def _name_changed(self,value):\n if value != 'custom':\n self.function_str = self.name_\n \n \n def fit(self, image, ind):\n \"\"\"\n Fit image based on indices\n \"\"\"\n try:\n results = base_fit.fit(self.function, image, ind)\n except:\n self.message = 'fit error'\n raise\n return\n if results:\n self.results.trait_set(**results)\n self.message = ''\n else:\n self.message = 'Not converged!'\n \nclass AnalysisOld(HasTraits):\n \"\"\"\n Object to display analysis settings and statistics and fit results\n \"\"\"\n selection = Instance(RectangleSelection)\n statistics = Instance(Statistics, transient = True)\n #fit_results must be transient = True, because it is only used as an interface to fit_function parameters\n # fit_results are actually stored in fit_function\n fit_results = Instance(Results,transient = True)\n \n\n \n fit_function = Instance(base_fit.Function)\n fit_function_str = Str('n+a*exp(-((x-x0)**2+(y-y0)**2)/2/s**2)', \n enter_set = True, \n auto_set = False,\n label = 'Fit Function')\n \n fit_name = Trait('gauss2D', {'gauss2D': 'n+a*exp(-((x-x0)**2+(y-y0)**2)/2/s**2)',\n 'custom' : ''})\n \n is_fitting = Bool(True, label = 'Fit')\n name = Str\n message = Str\n \n view = View(\n Group(\n Group(\n Item('selection', style = 'custom', show_label = False),\n show_border = True, label = 'Selection'\n ),\n Group(Item('statistics', style = 'custom', show_label = False),\n show_border = True, label = 'Statistics'\n ),\n\n HGroup('is_fitting',\n Item('fit_name', show_label = False, springy = True, enabled_when = 'is_fitting')),\n Item('fit_function_str', show_label = False, springy = True, enabled_when = 'is_fitting == True and fit_name == \"custom\"'),\n \n Group(Item('fit_results', style = 'custom', show_label = False),\n show_border = True, label = 'Fit parameters', enabled_when = 'is_fitting', \n ), \n\n Item('message', show_label = False, style = 'readonly')\n )\n )\n \n def _constants_default(self):\n return []\n \n def _statistics_default(self):\n return Statistics()\n \n def _selection_default(self):\n return RectangleSelection()\n \n @on_trait_change('fit_results.[+]')\n def update_fit_function(self):\n self.fit_function.SetConstant(*self.fit_results.constant_parameters)\n self.fit_function.SetParameters(**self.fit_results.get(*self.fit_results.parameters))\n \n def _fit_function_str_changed(self, new):\n self.fit_function = base_fit.Function(self.fit_function_str)\n self.fit_results = self._fit_results_default()\n \n def _fit_function_default(self):\n return base_fit.Function(self.fit_function_str)\n \n def _fit_results_default(self):\n r = Results()\n r.add_parameters(**self.fit_function.parDict)\n return r\n \n def _fit_name_changed(self,value):\n if value != 'custom':\n self.fit_function_str = self.fit_name_\n \n \n def fit(self, image):\n im = self.selection.slice_image(image)\n ind = self.selection.slice_indices(indices(image.shape))\n try:\n results = base_fit.fit(self.fit_function, im, ind)\n except:\n self.message = 'fit error'\n raise\n return\n if results:\n self.fit_results.trait_set(**results)\n self.message = ''\n else:\n self.message = 'not converged'\n \n def calc_statistics(self, image):\n \"\"\"\n Calculates image statistics on a predefined selection\n \"\"\"\n im = self.selection.slice_image(image)\n self.statistics.mean = im.mean()\n self.statistics.std = im.std()\n self.statistics.max = im.max()\n self.statistics.min = im.min()\n self.statistics.sum = im.sum()\n\nclass Analysis(HasTraits):\n \"\"\"\n Object to display analysis settings and statistics and fit results\n \"\"\"\n selection = Instance(RectangleSelection)\n statistics = Instance(Statistics, transient = True)\n fitting = Instance(Fit)\n \n name = Str\n \n view = View(\n Group(\n Group(\n Item('selection', style = 'custom', show_label = False),\n show_border = True, label = 'Selection'\n ),\n Group(Item('statistics', style = 'custom', show_label = False),\n show_border = True, label = 'Statistics'\n ),\n \n Item('fitting', style = 'custom', show_label = False)\n )\n )\n \n def _statistics_default(self):\n return Statistics()\n \n def _selection_default(self):\n return RectangleSelection()\n \n def _fitting_default(self):\n return Fit()\n\n def fit(self, image):\n im = self.selection.slice_image(image)\n ind = self.selection.slice_indices(indices(image.shape))\n self.fitting.fit(im,ind)\n \n def calc_statistics(self, image):\n \"\"\"\n Calculates image statistics on a predefined selection\n \"\"\"\n im = self.selection.slice_image(image)\n self.statistics.mean = im.mean()\n self.statistics.std = im.std()\n self.statistics.max = im.max()\n self.statistics.min = im.min()\n self.statistics.sum = im.sum()\n\n\n\nPOINTS = [Analysis(name = 'point 0')]\n\n#class ListView(HasTraits):\n# data_name = Str('data')\n# _data = Property()\n# \n# view = Instance(View)\n# add = Button()\n# load = Button()\n# save = Button()\n# \n# index_high = Property(Int,depends_on = 'points',transient = True) \n# index = Property(Int, depends_on = 'analysis,index_high',transient = True)\n# \n# def _get__data(self):\n# return getattr(self, self.data_name)\n# \n# def _set__data(self, value):\n# return setattr(self, self.data_name, value) \n# \n# def default_traits_view(self):\n# return self.view \n# \n# def __init__(self, data, **kw):\n# super(ListView, self).__init__(**kw)\n# klass = data[0].__class__\n# self.add_trait(self.data_name, List(klass))\n# self.add_trait('selected', Instance(klass, transient = True))\n# setattr(self, self.data_name, data)\n# self.view = View(\n# Item('index' ,\n# editor = RangeEditor( low = 0,\n# high_name = 'index_high',\n# is_float = False,\n# mode = 'spinner' )),\n# HGroup(\n# Item('add', show_label = False, springy = True),\n# Item('load', show_label = False, springy = True),\n# Item('save', show_label = False, springy = True),\n# ),\n# '_',\n# VGroup( \n# Item( self.data_name + '@',\n# id = 'notebook',\n# show_label = False,\n# editor = ListEditor( use_notebook = True, \n# deletable = True,\n# selected = 'selected',\n# export = 'DockWindowShell',\n# page_name = '.name' \n# )\n# ), \n# ),\n# id = 'enthought.traits.ui.demo.Traits UI Demo.Advanced.'\n# 'List_editor_notebook_selection_demo',\n# dock = 'horizontal' , resizable = True, height = -0.5) \n#\n# def _load_fired(self):\n# f = FileDialog(action = 'open', \n# title = 'Load state',\n# default_path = self.filename, \n# wildcard = '*.state')\n# if f.open() == OK:\n# self.filename = f.path\n# self.from_file()\n#\n# def _save_fired(self):\n# f = FileDialog(action = 'save as', \n# title = 'Save state',\n# default_path = self.filename, \n# wildcard = '*.state')\n# if f.open() == OK:\n# self.filename = f.path\n# self.to_file() \n#\n# \n# def _get_index_high(self):\n# return len(self._data)-1\n# \n# def _add_fired(self):\n# data = (self._data[self.index]).clone_traits()\n# data.name = self.data_name + str(len(self._data)) \n# self._data.append(data)\n# self.selected = self._data[-1]\n# \n# @on_trait_change('_data[]') \n# def _update_names(self):\n# #update point names\n# for i, d in enumerate(self._data):\n# d.name = 'data ' + str(i)\n#\n# def _get_index(self):\n# try:\n# return self._data.index(self.selected)\n# except:\n# return self.index_high\n#\n# def _set_index(self, value):\n# self.selected = self._data[value]\n# \n# def from_file(self):\n# with open(self.filename, 'rb') as f:\n# self._data = pickle.load(f) \n# self.index = 0\n# \n# def to_file(self):\n# with open(self.filename, 'wb') as f:\n# pickle.dump(self._data, f)\n\n\nclass Experiment(HasTraits):\n \"\"\" Object used to store analysis objects in List of points\n \"\"\"\n points = List(Analysis)\n analysis = Instance(Analysis,transient = True)\n \n #default filename for points load, save\n filename = File()\n \n add_point = Button()\n load = Button()\n save = Button()\n \n index_high = Property(Int,depends_on = 'points',transient = True) \n index = Property(Int, depends_on = 'analysis,index_high',transient = True)\n\n view = View(\n Item('index' ,\n editor = RangeEditor( low = 0,\n high_name = 'index_high',\n is_float = False,\n mode = 'spinner' )),\n HGroup(\n Item('add_point', show_label = False, springy = True),\n Item('load', show_label = False, springy = True),\n Item('save', show_label = False, springy = True),\n ),\n '_',\n VGroup( \n Item( 'points@',\n id = 'notebook',\n show_label = False,\n editor = ListEditor( use_notebook = True, \n deletable = True,\n selected = 'analysis',\n export = 'DockWindowShell',\n page_name = '.name' \n )\n ), \n ),\n id = 'enthought.traits.ui.demo.Traits UI Demo.Advanced.'\n 'List_editor_notebook_selection_demo',\n dock = 'horizontal' , resizable = True, height = -0.5) \n\n\n def _load_fired(self):\n f = FileDialog(action = 'open', \n title = 'Load points',\n default_path = self.filename, \n wildcard = '*.points')\n if f.open() == OK:\n self.filename = f.path\n self.from_file()\n\n def _save_fired(self):\n f = FileDialog(action = 'save as', \n title = 'Save points',\n default_path = self.filename, \n wildcard = '*.points')\n if f.open() == OK:\n self.filename = f.path\n self.to_file() \n \n def _analysis_default(self):\n return self.points[0]\n \n def _get_index_high(self):\n return len(self.points)-1\n \n def _add_point_fired(self):\n point = (self.points[self.index]).clone_traits()\n point.name = 'point' + str(len(self.points)) \n self.points.append(point)\n self.analysis = self.points[-1]\n \n def _points_default(self):\n return POINTS\n \n @on_trait_change('points[]') \n def _update_names(self):\n #update point names\n for i, point in enumerate(self.points):\n point.name = 'point ' + str(i)\n\n def _get_index(self):\n try:\n return self.points.index(self.analysis)\n except:\n return self.index_high\n\n def _set_index(self, value):\n self.analysis = self.points[value]\n \n def from_file(self):\n with open(self.filename, 'rb') as f:\n self.points = pickle.load(f) \n self.index = 0\n \n def to_file(self):\n with open(self.filename, 'wb') as f:\n pickle.dump(self.points, f)\n\nif __name__ == '__main__':\n c = Experiment()\n #c = ListView([Analysis(name = 'point 0')])\n c.configure_traits()\n","sub_path":"labtools/analysis/npimage/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":17977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"354947771","text":"n = int(input())\n\nfor i in range(2*n):\n if i < n:\n print(' ' * i , end='')\n print('* '*(n-i))\n elif i == n:\n continue\n else:\n print(' ' * (2*n-i-1) , end='')\n print('* '*((i-n+1)))","sub_path":"Python-dev/task1/08.py","file_name":"08.py","file_ext":"py","file_size_in_byte":224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"489434829","text":"# -*- coding: utf-8 -*-\n__author__= \"姜维洋\"\nimport random\nimport time\n\nfrom NingNuo.settings import User_agent\nclass RandomUserAgentMiddleware(object):\n def process_request(self,request,spider):\n useragent = random.choice(User_agent)\n request.headers[\"User-Agent\"] = useragent\n\n\nclass RandomDelayMiddleware(object):\n def __init__(self, delay):\n self.delay = delay\n\n @classmethod\n def from_crawler(cls, crawler):\n delay = crawler.spider.settings.get(\"RANDOM_DELAY\", 10)\n if not isinstance(delay, int):\n raise ValueError(\"RANDOM_DELAY need a int\")\n return cls(delay)\n\n def process_request(self, request, spider):\n delay = random.randint(0, self.delay)\n print(f\"---------延时时间：{delay}-----------\")\n time.sleep(delay)\n","sub_path":"previous_version/NingNuo/middlewares.py","file_name":"middlewares.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"621544037","text":"from torchvision import transforms\r\nimport torch.nn as nn\r\nimport torchvision.models as models\r\n\r\nclass FeatureExtractor():\r\n def __init__(self):\r\n resnet18 = models.resnet18(pretrained=True)\r\n modules = list(resnet18.children())[:-1]\r\n self.resnet18 = nn.Sequential(*modules)\r\n for p in self.resnet18.parameters():\r\n p.requires_grad = False\r\n self.transform = transforms.Compose([\r\n transforms.Resize((224,224)),\r\n transforms.ToTensor(),\r\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]\r\n )\r\n self.flatten = nn.Flatten()\r\n\r\n\r\n def extract_features(self, img):\r\n feat = self.resnet18(self.transform(img).unsqueeze(0))\r\n feat = self.flatten(feat).squeeze(0)\r\n return feat\r\n","sub_path":"PAL/resnet.py","file_name":"resnet.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"595094965","text":"\nimport os, random\nimport numpy as np\nimport pandas as pd\nimport bloscpack as bp\nfrom sklearn.model_selection import StratifiedKFold\nfrom iterstrat.ml_stratifiers import MultilabelStratifiedKFold\n\nfrom sklearn.metrics import recall_score\n\nimport imgaug as ia\nimport imgaug.augmenters as iaa\n\nimport torch\nfrom torch.utils.data.dataloader import DataLoader\nfrom torch.nn.utils import clip_grad_value_\nimport torch.nn.functional as F\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau, CosineAnnealingLR\n\n# from apex import amp\n\n# from optim import Over9000\nfrom torch.optim import SGD, Adam\n\nfrom data import Bengaliai_DS\nfrom models_mg import mdl_sext50\nfrom mixup_pytorch_utils import *\n# from loss import CenterLoss, AngularPenaltySMLoss\nfrom senet_mod import SENetMod\n\nimport utils\n\n\n# =========================================================================================================================\n\nSEED = 19550423\n\ndef seed_everything(seed):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = True\n\nseed_everything(SEED)\n\n\n# =========================================================================================================================\n\n# augs = iaa.SomeOf(\n# (0, 2),\n# [\n# iaa.OneOf(\n# [\n# iaa.Affine(\n# scale={\"x\": (0.8, 1.1), \"y\": (0.8, 1.1)},\n# rotate=(-15, 15),\n# shear={'x': (-15, 15), 'y': (-15, 15)},\n# ),\n# iaa.PerspectiveTransform(scale=.09, keep_size=True),\n# ]\n# ),\n# ],\n# random_order=True,\n# )\n\naugs = iaa.Affine(\n scale={\"x\": (0.9, 1.1), \"y\": (0.9, 1.1)},\n rotate=(-15, 15),\n shear={'x': (-10, 10), 'y': (-10, 10)},\n)\n\n# =========================================================================================================================\n\npdf = pd.read_csv('../input/train.csv')\nunique_grapheme = pdf['grapheme'].unique()\ngrapheme_code = dict([(g, c) for g, c in zip(unique_grapheme, np.arange(unique_grapheme.shape[0]))])\npdf['grapheme_code'] = [grapheme_code[g] for g in pdf['grapheme']]\n\nskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=19550423)\nfor fold, (trn_ndx, vld_ndx) in enumerate(skf.split(pdf['image_id'].values.reshape(-1, 1), pdf.loc[:, ['grapheme_root', 'vowel_diacritic', 'consonant_diacritic']].values)):\n if fold == 0:\n break\n \nimgs = bp.unpack_ndarray_from_file('../features/train_images_size128_pad8_max_noclean.bloscpack')\nlbls = pd.read_csv('../input/train.csv').iloc[:, 1:4].values\n\ntrn_imgs = imgs[trn_ndx]\ntrn_lbls = lbls[trn_ndx]\nvld_imgs = imgs[vld_ndx]\nvld_lbls = lbls[vld_ndx]\n\n\ntraining_set = Bengaliai_DS(trn_imgs, trn_lbls, transform=augs, split_label=True, RGB=True)\nvalidation_set = Bengaliai_DS(vld_imgs, vld_lbls, split_label=True, RGB=True)\n\nbatch_size = 64\n\ntraining_loader = DataLoader(training_set, batch_size=batch_size, num_workers=4, shuffle=True)\nvalidation_loader = DataLoader(validation_set, batch_size=batch_size, num_workers=4, shuffle=False)\n\n# =========================================================================================================================\n\nN_EPOCHS = 150\n\ncheckpoint_name = 'purepytorch_wtf_sext50_lessaug_mucu_sgd_cosanneal_fld0'\n\n# =========================================================================================================================\n\nclassifier = mdl_sext50().cuda()\nclassifier.load_state_dict(torch.load('purepytorch_wtf_sext50_lessaug_mucu_sgd_cosanneal_fld0_0.pth')['model'])\noptimizer = Adam(classifier.parameters(), lr=.0005, weight_decay=0.0)\n\nlr_scheduler = CosineAnnealingLR(optimizer, T_max=N_EPOCHS, eta_min=.00001)\n\n# =========================================================================================================================\n\nlogger = utils.csv_logger(['training_loss', 'validation_loss', 'GRAPHEME_Recall', 'VOWEL_Recall', 'CONSONANT_Recall', 'Final_Recall'])\n\nfor i in range(N_EPOCHS):\n logger.new_epoch()\n # train\n classifier.train()\n \n epoch_trn_loss = []\n epoch_vld_loss = []\n epoch_vld_recall_g, epoch_vld_recall_v, epoch_vld_recall_c, epoch_vld_recall_all = [], [], [], []\n \n for j, (trn_imgs_batch, trn_lbls_batch) in enumerate(training_loader):\n \n optimizer.zero_grad()\n \n # pre-process before mixup\n trn_lbls_oh_batch = [to_onehot(l, c) for l, c in zip(trn_lbls_batch, (168, 11, 7))]\n \n # mixup\n trn_imgs_batch_mixup, trn_lbls_oh_batch_mixup, _ = Mu_InnerPeace(trn_imgs_batch, trn_lbls_oh_batch)\n \n # move to device\n trn_imgs_batch_mixup_device = trn_imgs_batch_mixup.cuda()\n trn_lbls_oh_batch_mixup_device = [l.cuda() for l in trn_lbls_oh_batch_mixup]\n \n # forward pass\n logits = classifier(trn_imgs_batch_mixup_device)\n\n losses = criterion(logits, trn_lbls_oh_batch_mixup_device)\n \n total_trn_loss = .8*losses[0] + .1*losses[1] + .1*losses[2]\n \n total_trn_loss.backward()\n optimizer.step()\n \n # record\n epoch_trn_loss.append(total_trn_loss.item())\n \n utils.display_progress(len(training_loader), j+1, {'training_loss': epoch_trn_loss[-1]})\n \n # validation\n classifier.eval()\n \n with torch.no_grad():\n for k, (vld_imgs_batch, vld_lbls_batch) in enumerate(validation_loader):\n \n # move to device\n vld_imgs_batch_device = vld_imgs_batch.cuda()\n vld_lbls_batch_device = [l.cuda() for l in vld_lbls_batch]\n vld_lbls_batch_numpy = [l.detach().cpu().numpy() for l in vld_lbls_batch]\n \n # forward pass\n logits = classifier(vld_imgs_batch_device)\n \n # loss\n vld_lbls_batch_oh = [to_onehot(l, c) for l, c in zip(vld_lbls_batch_device, (168, 11, 7))]\n losses = criterion(logits, vld_lbls_batch_oh)\n \n total_vld_loss = .8*losses[0] + .1*losses[1] + .1*losses[2]\n # record\n epoch_vld_loss.append(total_vld_loss.item())\n \n # metrics\n pred_g = logits[0].argmax(axis=1).detach().cpu().numpy()\n pred_v = logits[1].argmax(axis=1).detach().cpu().numpy()\n pred_c = logits[2].argmax(axis=1).detach().cpu().numpy()\n epoch_vld_recall_g.append(recall_score(pred_g, vld_lbls_batch_numpy[0], average='macro', zero_division=0))\n epoch_vld_recall_v.append(recall_score(pred_v, vld_lbls_batch_numpy[1], average='macro', zero_division=0))\n epoch_vld_recall_c.append(recall_score(pred_c, vld_lbls_batch_numpy[2], average='macro', zero_division=0))\n \n # display progress\n utils.display_progress(len(validation_loader), k+1, {'validation_loss': epoch_vld_loss[-1]})\n \n epoch_vld_recall_g, epoch_vld_recall_v, epoch_vld_recall_c = np.mean(epoch_vld_recall_g), np.mean(epoch_vld_recall_v), np.mean(epoch_vld_recall_c)\n final_recall = np.average([epoch_vld_recall_g, epoch_vld_recall_v, epoch_vld_recall_c], weights=[2, 1, 1])\n \n entry = {\n 'training_loss': np.mean(epoch_trn_loss),\n 'validation_loss': np.mean(epoch_vld_loss),\n 'GRAPHEME_Recall': epoch_vld_recall_g,\n 'VOWEL_Recall': epoch_vld_recall_v,\n 'CONSONANT_Recall': epoch_vld_recall_c,\n 'Final_Recall': final_recall,\n }\n \n utils.display_progress(N_EPOCHS, i+1, entry, postfix='Epochs', persist=True)\n \n # ----------------------------------\n # save model\n if entry['validation_loss'] < np.nan_to_num(logger.log['validation_loss'].min(), nan=100.):\n print('Saving new best weight.')\n torch.save(\n {\n 'epoch': i,\n 'model': classifier.state_dict(),\n 'optimizer': optimizer.state_dict(),\n }, \n os.path.join('./', checkpoint_name + '.pth'),\n )\n \n # ----------------------------------\n # log\n logger.enter(entry)\n logger.save('./{}.csv'.format(checkpoint_name))\n lr_scheduler.step()\n\n","sub_path":"code/script_purepytorch_wtf.py","file_name":"script_purepytorch_wtf.py","file_ext":"py","file_size_in_byte":8305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"303253859","text":"'''\nThis module provides some tests of the integrator to known integrals.\n\nNote, these are not the transformations, just the plain integrals, :math:`\\int_0^\\infty f(x) J_\\nu(x) dx`\n\nThere is a corresponding notebook in devel/ that runs each of these functions through a grid of N and h,\nshowing the pattern of accuracy. This could be useful for finding the correct numbers to choose for these\nfor other unknown functions.\n'''\n\nimport numpy as np\nimport inspect\nimport os\nLOCATION = \"/\".join(os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))).split(\"/\")[:-1])\nimport sys\nsys.path.insert(0, LOCATION)\n\nfrom scipy.special import k0, gamma, gammainc, gammaincc\n\ngammainc_ = lambda a,x : gamma(a)*gammainc(a,x)\ngammaincc_ = lambda a,x : gamma(a)*gammaincc(a,x)\n\nfrom hankel import HankelTransform\n\n\nclass TestAnalyticIntegrals_Order0(object):\n\n def test_f_unity(self):\n \"\"\"\n Test f(x) = 1, nu=0\n\n This test is done in the Ogata (2005) paper, section 5\"\n \"\"\"\n\n ht = HankelTransform(nu=0, N=50, h=0.1)\n ans = ht.integrate(lambda x: 1, False, False)\n print(\"Numerical Result: \", ans, \" (required %s)\"%1)\n assert np.isclose(ans,1,rtol=1e-3)\n\n def test_f_x_on_x2(self):\n \"\"\"\n Test f(x) = x/(x**2 + 1), nu=0\n\n This test is done in the Ogata (2005) paper, section 5\"\n \"\"\"\n ht = HankelTransform(nu=0, N=50, h=10**-1.5)\n\n\n ans = ht.integrate(lambda x: x/(x**2+1), False, False)\n print(\"Numerical Result: \", ans, \" (required %s)\"%k0(1))\n assert np.isclose(ans,k0(1),rtol=1e-3)\n\n # The following tests follow from the table on Wikipedia\n def test_x2(self):\n \"\"\"\n Test f(x) = x^2, nu=0\n \"\"\"\n ht = HankelTransform(nu=0, N=100, h=10**-1.5)\n\n ans = ht.integrate(lambda x: x**2, False, False)\n print(\"Numerical Result: \", ans, \" (required -1)\")\n assert np.isclose(ans,-1,rtol=1e-3)\n\n def test_x4(self):\n ht = HankelTransform(nu=0, N=150, h=10**-1.5)\n ans = ht.integrate(lambda x: x**4, False, False)\n print(\"Numerical Result: \", ans, \" (required 9)\")\n assert np.isclose(ans,9,rtol=1e-3)\n\n def test_1_on_sqrt_x(self):\n ## NOTE: this is REALLY finnicky!! (check devel/)\n ht = HankelTransform(nu=0, N=160, h=10**-3.5)\n ans = ht.integrate(lambda x: 1./np.sqrt(x), False, False)\n m = -1.5\n anl = 2**(m+1)*gamma(m/2+1)/gamma(-m/2)\n\n print(\"Numerical Result: \", ans, \" (required %s)\"%anl)\n assert np.isclose(ans,anl,rtol=1e-3)\n\n def test_x_on_sqrt_x2_pz2(self):\n # Note that the number required is highly dependent on z .... smaller z is harder.\n ht = HankelTransform(nu=0, N=50, h=10**-1.3)\n\n z = 1\n ans = ht.integrate(lambda x: x/np.sqrt(x**2+z**2), False, False)\n anl = np.exp(-z)\n print(\"Numerical Result: \", ans, \" (required %s)\"%anl)\n assert np.isclose(ans,anl,rtol=1e-3)\n\n def test_gauss(self):\n z = 2\n ht = HankelTransform(nu=0, N=50, h=0.01)\n\n ans = ht.integrate(lambda x: x*np.exp(-0.5*z**2*x**2), False, False)\n anl = 1./z**2 * np.exp(-0.5/z**2)\n print(\"Numerical Result: \", ans, \" (required %s)\"%anl)\n assert np.isclose(ans,anl,rtol=1e-3)\n\n\nclass TestAnalyticIntegrals_VaryingOrder(object):\n def powerlaw(self,s,nu,N,h):\n ht = HankelTransform(nu=nu, N=N, h=h)\n\n ans = ht.integrate(lambda x: x**(s+1), False, False)\n anl = 2**(s+1)*gamma(0.5*(2+nu+s))/gamma(0.5*(nu-s))\n\n print(\"Numerical Result: \", ans, \" (required %s)\"%anl)\n assert np.isclose(ans,anl,rtol=1e-3)\n\n def test_powerlaw(self):\n trials = [[0, 1, 50, 0.05],\n [0, 2, 50, 0.05],\n [0.5, 1, 50, 0.05],\n [-2, 2, 600, 10**-2.6], # This is pretty finnicky\n [-0.783, 1, 50, 0.05]]\n\n for s,nu,N,h in trials:\n yield self.powerlaw, s,nu,N,h\n\n def gamma_mod(self,s,nu,N,h):\n ht = HankelTransform(nu=nu, N=N, h=h)\n\n ans = ht.integrate(lambda x: x**(nu-2*s + 1)*gammainc_(s,x**2), False, False)\n anl = 0.5 ** (2*s-nu-1) * gammaincc_(1-s+nu,0.25)\n\n print(\"Numerical Result: \", ans, \" (required %s)\"%anl)\n assert np.isclose(ans,anl,rtol=1e-3)\n\n def test_gamma_mod(self):\n trials = [[0, 1, 50, 0.05],\n [0, 2, 50, 0.05],\n [0.5, 1, 50, 0.05],\n [-2, 2, 600, 10**-2.6], # This is pretty finnicky\n [-0.783, 1, 50, 0.05],\n [1.0, 0.5, 150, 0.05],\n [-1., 0.5, 50, 0.05],]\n\n for s,nu,N,h in trials:\n yield self.powerlaw, s,nu,N,h","sub_path":"tests/test_known_integrals.py","file_name":"test_known_integrals.py","file_ext":"py","file_size_in_byte":4728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"401357710","text":"import docker, pytest\n\n@pytest.yield_fixture(scope='session') \ndef redis_port():\n docker_client = docker.Client(version='auto') \n download_image_if_missing(docker_client)\n container_id, redis_port = start_redis_container(docker_client) \n yield redis_port\n docker_client.remove_container(container_id, force=True)\n\n@pytest.fixture(scope='function')\ndef our_service(our_service_session, ext_service_impostor):\n return our_service\n\n ","sub_path":"tests/unit/test_001.py","file_name":"test_001.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"179235728","text":"#coding=utf-8\nfrom sys import argv\nimport string\n\n#Strutture dati Globali\nvocali = ('a','e','i','o','u')\nmesi = ('a','b','c','d','e','h','l','m','p','r','s','t')\ncomuni = {'udine':'l483', 'verona':'l781', 'legnago': 'e512', 'negrar':'f861', 'bassano del grappa': 'a703'}\n\n#CODICI DI CONTROLLO\nregole_pari = {}\nalfabeto = string.ascii_lowercase\nfor i in xrange (0,10):\n regole_pari[str(i)] = i\nfor i in xrange (0,26):\n regole_pari[alfabeto[i]] = i\n\nregole_dispari = {}\ntemp_tuple = (1,0,5,7,9,13,15,17,19,21)\nfor i in xrange(0,10):\n regole_dispari[str(i)] = temp_tuple[i]\n regole_dispari[alfabeto[i]] = temp_tuple[i]\n\ntemp_tuple2 = (2,4,18,20,11,3,6,8,12,14,16,10,22,25,24,23)\nindex = 0\nfor i in xrange(10,26):\n regole_dispari[alfabeto[i]] = temp_tuple2[index]\n index += 1\n\nregole_resto = [alfabeto[i] for i in xrange(0,26)]\n\n#------------------------------\n\ndef estrai_cognome(aString):\n if (' ' in aString):\n aString = aString[:aString.find(' ')] + aString[aString.find(' ')+1:]\n if (\"'\" in aString):\n aString = aString[:aString.find(\"'\")] + aString[aString.find(\"'\")+1:]\n temp_string = ''\n\n temp_string = ''\n for aChar in aString:\n if not aChar in vocali:\n temp_string += aChar\n\n if len(temp_string) >= 3:\n break\n\n if (len(temp_string)<3):\n for aChar in aString:\n if aChar in vocali: #prendo le vocali\n temp_string += aChar #aggiungo la vocale\n if len(temp_string) >= 3:\n break\n if (len(temp_string) < 3): #aggiungo le x nel caso in cui la stringa non è 3\n temp_string += 'x'*(3-(len(temp_string)))\n\n return temp_string\n\ndef estrai_nome(aString):\n if (' ' in aString):\n aString = aString[:aString.find(' ')] + aString[aString.find(' ')+1:]\n temp_string = ''\n\n n_consonanti = 0\n for aChar in aString:\n if not aChar in vocali:\n n_consonanti+=1 #incremnto il numero di consonanti del nome\n\n if (n_consonanti <= 3): #quindi prendo tutti e tre le consonanti\n for aChar in aString:\n if not aChar in vocali:\n temp_string += aChar\n if len(temp_string) >= 3:\n break\n\n if (len(temp_string)<3): #prendo le vocali e le aggiungo\n for aChar in aString:\n if aChar in vocali:\n temp_string += aChar\n if (len(temp_string)>=3):\n break\n if (len(temp_string)<3):\n temp_string += 'x'*(3-(len(temp_string)))\n\n else: #caso se ho tante consonanti\n for aChar in aString:\n if not aChar in vocali:\n temp_string += aChar\n if (len(temp_string)>3):\n break\n #togliamo la consonante in posizione due\n temp_string = temp_string[:1] + temp_string[2:]\n\n return temp_string\n\ndef genera_mese(unMese):\n return mesi[int(unMese)-1]\n\n\ndef codice_comune(comune):\n return comuni[comune]\n\n\ndef genera_giorno(unGiorno, unSesso):\n if int(unGiorno) in xrange(1,31):\n if unSesso == 'm':\n return unGiorno\n elif unSesso == 'f':\n return str(int(unGiorno)+40)\n\n\ndef genera_codice_controllo(aCodiceFiscale):\n parita = 1\n temp_dispari = 0\n temp_pari = 0\n\n for aChar in aCodiceFiscale:\n if parita:\n temp_dispari += int(regole_dispari.get(aChar))\n parita = 0\n else:\n temp_pari += int(regole_pari.get(aChar))\n parita = 1\n\n return regole_resto[(temp_dispari+temp_pari) % 26]\n\ndef main(): #pragma: no cover\n nome = raw_input(\"Nome: \").lower()\n cognome = raw_input(\"Cognome: \").lower()\n data_nascita = raw_input(\"Data di nascita (gg/mm/aaaa): \").lower()\n comune = raw_input(\"Comune di nascita: \").lower()\n sesso = raw_input(\"Sesso (m/f): \").lower()\n #cognome = string.lower(argv[1])\n #nome = string.lower(argv[2])\n #data_nascita = string.lower(argv[3])\n #comune = string.lower(argv[4])\n #sesso = string.lower(argv[5])\n\n nomeCF = estrai_nome(nome)\n cognomeCF = estrai_cognome(cognome)\n\n data_nascitaCF = data_nascita.split(\"/\")\n anno_nascitaCF = data_nascitaCF[2][2:]\n\n mese_nascitaCF = genera_mese(data_nascitaCF[1])\n giorno_nascitaCF = genera_giorno(data_nascitaCF[0], sesso)\n\n codice_fiscale = cognomeCF + nomeCF + anno_nascitaCF + mese_nascitaCF + giorno_nascitaCF + codice_comune(comune.lower())\n\n codiceCF = genera_codice_controllo(codice_fiscale)\n\n codice_fiscale += codiceCF\n\n print (codice_fiscale)\n\nif __name__ == \"__main__\": #pragma: no cover\n main()\n","sub_path":"1/CodiceFiscale.py","file_name":"CodiceFiscale.py","file_ext":"py","file_size_in_byte":4716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"203505519","text":"# -*- coding: utf-8 -*-\nimport time\nimport json\nimport requests\nimport urllib3\nimport xml.etree.ElementTree as ET\nimport logging\nfrom logging.handlers import RotatingFileHandler\nimport re\nimport socket\nimport sys\nimport uuid\nimport os\nimport socketio\nfrom datetime import datetime\nimport configparser\nimport sqlite3\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom apscheduler.triggers.interval import IntervalTrigger\nfrom threading import Thread\n\n\n\n##############logging#############\n#logging.basicConfig(filename='sender.log', level=logging.DEBUG) #настраиваем логирование\nlogging.getLogger(\"urllib3\").setLevel(logging.WARNING) # Прогоняем debug сообщения от urllib\nlogger = logging.getLogger('my_logger')\nlogging.basicConfig(handlers=[RotatingFileHandler('RkeeperInfo.log', maxBytes=2000000, backupCount=10)],\n format = u'[%(asctime)s] %(levelname)s %(message)s')\n#-------------------------------------------- Config----------------------------------------------\nconfig = configparser.ConfigParser()\nconfig.read('VideoSender.cfg')\nconnected = False\ntry:\n VideoServer = config['Settings']['videoserver']\n ListenPort = config['Settings']['listenport']\n ListenIP = config['Settings']['listenip']\n XMLInterface = config['Settings']['xmlinterface']\n GUIDInterface = config['Settings']['guidinterface']\n try:\n LogLevel = int(config['Settings']['loglevel'])\n except:\n logger.critical('No loglevel in config, using CRITICAL')\n LogLevel = 0\n\nexcept Exception as e:\n logger.critical(str(e))\n logger.critical('Config file is missing or incorrect?')\n# -----------------------------Получаем GUID от сервера и сохраняем его в cfg------------------------------\n# Если не получится забрать guid - будет использоваться старое значение из cfg-----------------------------\ntry:\n guid = requests.get(GUIDInterface).text\n config['Settings']['GUID'] = guid\n with open('VideoSender.cfg', 'w') as configfile:\n config.write(configfile)\n print(guid)\nexcept:\n logger.debug('No connection to GUID server')\n\nif LogLevel == 1:\n logger.setLevel(logging.DEBUG)\nelif LogLevel == 0:\n logger.setLevel(logging.CRITICAL)\n\nProductGUID = config['Settings']['guid'] # Забираем guid из cfg\n\nsio = socketio.Client(logger=True,engineio_logger=True)\nfirstConnection = True # При первом подключении передаём authobject\nsuccess = False # Отслеживаем успешный ответ\n\n\n\n\n############Создание талиц############\ndef init(tablename, labels):\n db = sqlite3.connect('rk_item.db') # обращение к базе sqlite\n cursor = db.cursor()\n full_labels = ''\n for x in labels:\n x += ' text,'\n full_labels += x\n querytext = ('''CREATE TABLE %s (%s)''') % (tablename, full_labels[:-1])\n cursor.execute(querytext)\n\n##########указываем количество байт для чтения##########\ndef recvall():\n BUFF_SIZE = 1024 # 4 KiB\n data = b''\n sock.listen(1)\n conn, addr = sock.accept()\n while True:\n part = conn.recv(BUFF_SIZE)\n data += part\n length = len(part)\n if length < BUFF_SIZE:\n break\n try:\n logger.debug(data.decode())\n except:\n logger.debug('!!!!!!!!!!!!!!!!!!Can not wtite xml to log!!!!!!!!!!!!!!!!!!')\n return data\n\n\n\n\n\n\n\n\n# -----------------------------события для SocketIO-----------------------------------\ndef connector(limit=1):\n global connected\n if 'connected' not in globals():\n connected = False\n while connected == False:\n limit -= 1\n if limit < 0:\n logger.critical(\"Dropped packed due to server inactivity\")\n break\n try:\n sio.connect(VideoServer)\n sio.sleep(3)\n sio.emit('authentication', authObject)\n sio.sleep(3)\n\n except Exception as e:\n logger.debug('No connection to sio sever %s' % e)\n sio.disconnect()\n sio.sleep(7)\n\n\n@sio.event\ndef connect():\n global firstConnection\n global connected\n logger.debug('connection established')\n connected = True\n if firstConnection == True:\n #authObject = info_station() ####################### ToDo: COMMENT ME !!!!!!!!!!!!!!!!!!!!!!\n authObject['IsCashServer'] = True\n firstConnection = False\n else:\n authObject['IsCashServer'] = 'False'\n authObject['TerminalName'] = \"R-Keeper_pos\"\n\n@sio.event\ndef authenticated(data):\n global connected\n logger.debug('R-Keeper backup server Plugin authenticated')\n connected = True\n\n@sio.event\ndef responseOrder(data):\n global success\n logger.debug('responseOrder')\n success = True\n\n@sio.event\ndef auth_confirmation(data):\n logger.debug('auth_confirmation')\n\n@sio.event\ndef authentication_error(data):\n logger.debug('authentication_error')\n\n@sio.event\ndef disconnect():\n global connected\n connected = False\n logger.debug('R-Keeper has been disconnected')\n sio.disconnect()\n time.sleep (5)\n\n\n\n\n\n\n\n\n\n################################################################################################\n############################### Sending data scheduller ########################################\n################################################################################################\ndef sendOrder(in_contents): # Sending order every 2 seconds, while \"responceOrder\" received\n global success\n global connected\n success = False\n contents = json.loads(in_contents)\n if sio.connected ==True:\n while success == False:\n #connector()\n sio.emit('posOrder', contents)\n sio.sleep(2)\n if success == True:\n toSend.remove(in_contents)\n\n else:\n connected = False\n print ('Something wrong, reconnecting')\n trys = 5 # Количество попыток\n connector(1)\n while success == False:\n trys -=1\n sio.emit('posOrder', contents)\n sio.sleep(2)\n if trys <=0:\n break\n\ntoSend=[] # This variable stores all checks than was not sent yet\n\ndef sender():\n if len (toSend) > 0:\n toSend2 = toSend\n for i in toSend2:\n try:\n sendOrder(i)\n logger.debug('Sending')\n except Exception as e:\n logger.critical(\"!!!!!!!!!!!!!!!Failed to Send %s !!!!!!!!!!!!!!!!!!!!\" % (e))\n toSend.remove(i)\n\n\njob_defaults = {\n 'coalesce': False,\n 'max_instances': 1\n}\nsched = BackgroundScheduler(daemon=True,job_defaults=job_defaults)\nsched.add_job(sender,IntervalTrigger(seconds=30), id='autosender', replace_existing=True)\nsched.start()\n\n################################################################################################\n######################################End sending data scheduller###############################\n################################################################################################\n\n\n\n##########Читаем файл с XML запросом#########\ndef CashPlansToSql():\n try:\n headers = {'Content-Type': 'application/xml', 'Accept-Encoding': 'identity'}\n urllib3.disable_warnings() #отключаем ошибку orllib3\n xml_1 = '''\n \n \n ''' #столы\n xml_2 = '''\n \n \n ''' #перечень планов зала и столы\n xml_3 = '''\n \n \n ''' #имя кассы и план зала по умолчанию\n xml_4 = '''\n \n \n ''' #меню\n xml_5 = '''\n \n \n ''' #цены\n host = XMLInterface\n MenuItem1 = requests.post(host, verify=False, data=xml_1, headers=headers, auth=('HTTP', '1')).text #столы\n MenuItem2 = requests.post(host, verify=False, data=xml_2, headers=headers, auth=('HTTP', '1')).text #перечень планов зала и столы\n MenuItem3 = requests.post(host, verify=False, data=xml_3, headers=headers, auth=('HTTP', '1')).text #имя кассы и план зала по умолчанию\n MenuItem4 = requests.post(host, verify=False, data=xml_4, headers=headers, auth=('HTTP', '1')).text #меню\n MenuItem5 = requests.post(host, verify=False, data=xml_5, headers=headers, auth=('HTTP', '1')).text #цены\n root1 = ET.fromstring(MenuItem1) #столы\n root2 = ET.fromstring(MenuItem2) #перечень планов зала и столы\n root3 = ET.fromstring(MenuItem3) #имя кассы и план зала по умолчанию\n root4 = ET.fromstring(MenuItem4) #меню\n root5 = ET.fromstring(MenuItem5) #цены\n global cashes, all_tables, all_halls, itemsAttribs, prices\n itemsAttribs = [x.attrib for x in root4.findall('RK7Reference')[0].findall('Items')[0].findall('Item')] #элементы меню\n cashes = [x.attrib for x in root3.findall('RK7Reference')[0].findall('Items')[0].findall('Item')] #все кассы(тут берём инфу по станции, её ID и ID плана зала)\n all_tables = [x.attrib for x in root1.findall('RK7Reference')[0].findall('Items')[0].findall('Item')] #все столы(тут имя стола, его Guid и ID плана зала)\n all_halls = [x.attrib for x in root2.findall('RK7Reference')[0].findall('Items')[0].findall('Item')] #все планы залов(ID)\n prices = [x.attrib for x in root5.findall('RK7Reference')[0].findall('Items')[0].findall('Item')] #все цены\n\n #########connect_hall.db########\n if os.path.exists('rk_item.db'): # проверяем наличие rk_item.db, если нет базы, то создаём\n pass\n else:\n new_item = list([x for x in itemsAttribs[0]])\n init('menuitems', new_item)\n new_item = list([x for x in cashes[0]])\n init('hall', new_item)\n new_item = list([x for x in all_tables[0]])\n init('tables', new_item)\n new_item = list([x for x in all_halls[0]])\n init('hallplans', new_item)\n new_item = list([x for x in prices[0]])\n init('prices', new_item)\n\n db = sqlite3.connect('rk_item.db') # обращение к базе sqlite\n cursor = db.cursor()\n clear = \"DELETE FROM hall\" #очищаем таблицу с планами зала и столами\n cursor.execute(clear)\n clear2 = \"DELETE FROM hallplans\" #очищаем таблицу с названием кассы и планом зала по умолчанию\n cursor.execute(clear2)\n clear3 = \"DELETE FROM tables\" #очищаем таблицу со столами\n cursor.execute(clear3)\n clear4 = \"DELETE FROM menuitems\" #очищаем таблицу с меню\n cursor.execute(clear4)\n clear5 = \"DELETE FROM prices\" #очищаем таблицу с ценами\n cursor.execute(clear5)\n db.commit()\n for attrib in cashes:\n toSql = str(list(attrib.values()))# Преобразуем список значений словаря в строку - так надо для записи в SQL\n toSql = re.sub(r'\\[|\\]','',toSql)# Убираем все дурацкие символы, которые не любит SQL\n cursor.execute('''INSERT INTO hall VALUES ('''+ toSql + ''' )''')# Пишем в SQL\n for attrib in all_halls:\n toSql = str(list(attrib.values()))\n toSql = re.sub(r'\\[|\\]','',toSql)\n cursor.execute('''INSERT INTO hallplans VALUES ('''+ toSql + ''' )''')\n for attrib in all_tables:\n toSql = str(list(attrib.values()))\n toSql = re.sub(r'\\[|\\]','',toSql)\n cursor.execute('''INSERT INTO tables VALUES ('''+ toSql + ''' )''')\n for attrib in itemsAttribs:\n toSql = str(list(attrib.values()))\n toSql = re.sub(r'\\[|\\]', '', toSql)\n cursor.execute('''INSERT INTO menuitems VALUES (''' + toSql + ''' )''')\n for attrib in prices:\n if list(attrib.values())[6] == '1025352' and list(attrib.values())[5] == 'psDish': #price type\n toSql = str(list(attrib.values()))\n toSql = re.sub(r'\\[|\\]', '', toSql)\n cursor.execute('''INSERT INTO prices VALUES (''' + toSql + ''' )''')\n db.commit()\n except Exception as e:\n logger.critical('Failed to get cashplans to sql')\n logger.critical(e)\n\n##############получаем план зала, столы и guid#################\ndef RequestNameInHall(hall_ident):\n try:\n db = sqlite3.connect('rk_item.db') # обращение к базе sqlite\n cursor = db.cursor()\n dishRequestByCode2 = (\"\"\"SELECT t.NAME NAME, t.GUIDString GUID, hp.Name HALL_NAME \n FROM hall h\n INNER JOIN hallplans hp ON h.DEFHALLPLANID = hp.Ident\n INNER JOIN tables t ON h.DEFHALLPLANID = t.HALL\n WHERE h.DEFHALLPLANID = ?\"\"\")\n cursor.execute(dishRequestByCode2,(str(hall_ident),))\n dishdetails = cursor.fetchall()\n global plans\n plans = []\n for x in dishdetails:\n plans.append({'Number': x[0], 'Id': re.sub(r'\\{|\\}','',x[1])})\n for y in dishdetails: # ToDO Зачем здесь итерация?\n hall = {'Name': y[2]} # ToDO hall перезапишется последним значением из итерации\n hall['Tables'] = plans\n return hall\n except Exception as e:\n logger.critical('Failed to Request Name from Hall')\n logger.critical(e)\n\n\n########получаем всё меню#######\ndef RequestAllMenu():\n try:\n db = sqlite3.connect('rk_item.db') # обращение к базе sqlite\n cursor = db.cursor()\n products = []\n logger.info('Start func ' + ' RequestAllMenu ' + time.strftime('%H:%M:%S %d.%m.%y', time.localtime()) + '\\n')\n dishRequestByCode = (\"\"\"SELECT m.Ident, m.GUIDString, m.Code, m.Name, p.Value as Price\n FROM menuitems m\n INNER JOIN prices p ON m.ItemIdent = p.ObjectID\n WHERE m.Status='rsActive'\"\"\")\n cursor.execute(dishRequestByCode) # Запрашиваем все элементы блюда с определённым кодом\n logger.info('End func ' + ' RequestMenu ' + time.strftime('%H:%M:%S %d.%m.%y', time.localtime()) + '\\n')\n dishdetails = cursor.fetchall()\n for item in dishdetails:\n price = str(int(item[4]) / 100) #приводим в правильный вид цену\n products.append({'Price': price, 'Type': 'Dish', 'Name': item[3], 'Code': item[2], 'Id': re.sub(r'\\{|\\}','',item[1])})\n return products\n except Exception as e:\n logger.critical('Failed to RequestAllMenu')\n logger.critical(e)\n##############конвертируем текущий формат даты#############\ndef DtimeConvert():\n JavaDtime = datetime.now().strftime('%Y-%m-%dT%H:%M:%S.%f+03:00')\n return JavaDtime\n##############конвертируем заданны формат даты#############\ndef OrdertimeConvert(dtime):\n OrderDtime = (datetime.strptime(dtime, '%d.%m.%Y %H:%M:%S')).strftime('%Y-%m-%dT%H:%M:00.%f+03:00')\n return OrderDtime\n###########получаем информацию о планах зала, столах и меню##########\ndef info_station():\n try:\n id_hall = child.attrib['HallPlanID']\n myHallPlans = RequestNameInHall(id_hall)\n menuItems = RequestAllMenu()\n output = {\n \"IsCashServer\": True,\n \"IsBackup\": False,\n \"Password\": \"randomString\",\n \"TerminalName\": child.attrib['StationName'],\n \"TerminalGroup\": {\n \"Name\": child.attrib['StationName'],\n \"MaxCourseNumber\": 4,\n \"Id\": re.sub(r'\\{|\\}', '', child.attrib['StationGuid'])\n },\n \"Sections\": [myHallPlans]\n }\n menu = {'Groups': '', 'ProductCategories': '', 'Products': [menuItems]}\n output['Menu'] = menu\n info = output\n output = json.dumps(output, ensure_ascii=False, indent=1)\n return info\n except Exception as e:\n logger.critical('Failed to get InfoStation')\n logger.critical(e)\n\n\n\n# ----------------------------------- Main Loop --------------------------------------\ntry:\n sock = socket.socket()\n sock.bind((ListenIP, int(ListenPort))) # Слушаем нужный порт, указанный в конфиге\n logger.info('Сервер запущен')\nexcept:\n logger.debug(\"WARNING! Something already stolen this Cash_Port, choose another one. Exiting!\")\n logger.critical('Не могу запустить сервер')\n sys.exit()\n\ntry:\n CashPlansToSql()\nexcept:\n logger.critical(\"No connection to R-Keeper XML\")\n sys.exit()\nstatus = False #используется для того чтобы каждый раз не переписывать order_full при login\nlogger.info('MainLoop Started.')\nwhile True:\n try:\n data = '\\n'\n dat2 = recvall()\n dat2 = dat2.decode()\n data += str(dat2)\n data += '\\n'\n\n\n except Exception as e:\n logger.critical('Can not convert RK data to string, may be wrong encoding')\n logger.debug(e)\n data =''\n try:\n root = ET.fromstring(data)\n except Exception as e:\n root = ''\n logger.debug(\"!!!!!!!!!!!!Can't parse xml!!!!!!!!!!!!!!\")\n logger.debug(e)\n for child in root:\n inside = child.attrib\n if child.tag == 'LogIn': # Login посылается всего один раз. Передаются планы залов, столы и меню\n if status == False:\n authObject = info_station()# Получаем authObject\n connector(1)\n status = True\n logger.debug('Starting connection to SIO')\n if child.tag == 'NewOrder':\n StatusOrder = \"New\"\n else:\n StatusOrder = \"Open\"\n if child.tag == 'ScreenCheck':\n try:\n all_items = root[0].findall('CheckLines')[0].findall('CheckLine')\n Waiter = {\n 'Name': child.attrib['WaiterName'],\n 'Id': re.sub(r'\\{|\\}', '', child.attrib['WaiterGuid'])\n }\n Tables = []\n try:\n Table = {\n 'Number': int(child.attrib['TableName']),\n 'Id': re.sub(r'\\{|\\}', '', child.attrib['TableGuid'])\n }\n except:\n logger.critical('Table %s is not Integer value!!' % child.attrib['TableName'])\n Table = {\n 'Number': child.attrib['TableName'],\n 'Id': re.sub(r'\\{|\\}', '', child.attrib['TableGuid'])\n }\n Tables.append(Table)\n Products = []\n for x in all_items:\n Amount = x.attrib['Qnt']\n if Amount == '0.00':\n Deleted = True\n else:\n Deleted = False\n Product = {\n 'Id': str(uuid.uuid4()),\n 'ProductId': re.sub(r'\\{|\\}', '', x.attrib['Guid']),\n 'Name': x.attrib['Name'],\n 'Price': float(x.attrib['Price']),\n 'ResultSum': float(child.attrib['FullSum']), # todo исправить\n 'Amount': float(x.attrib['Qnt']),\n \"Comment\": \"\",\n 'Deleted': Deleted,\n \"DeletionMethod\": None,\n 'PrintTime':OrdertimeConvert(x.find('Packet').attrib['WasPrinted']),\n \"ServeTime\": None,\n \"Modifiers\": []\n }\n if re.sub(r'\\{|\\}', '', x.attrib['Guid']) == ProductGUID:\n Products.append(Product)\n Guests = []\n guests = {\n 'Id': str(uuid.uuid4()),\n 'Name': child.attrib['GuestName'],\n 'Products': Products\n }\n Guests.append(guests)\n Order = {\n 'Id': re.sub(r'\\{|\\}', '', child.attrib['OrderGuid']),\n \"OrderTypeId\": None,\n \"Status\": StatusOrder,\n 'ResultSum': float(child.attrib['FullSum']),\n 'FullSum': float(child.attrib['FullSum']),\n 'OpenTime': OrdertimeConvert(child.attrib['OpenTime']),\n \"BillTime\": None,\n \"CloseTime\": None,\n 'Number': datetime.today().timestamp(),\n 'Waiter': Waiter,\n \"Cashier\": None,\n 'Tables': Tables,\n \"IsBanquetOrder\": False,\n 'Guests': Guests,\n \"PaymentItems\": []\n }\n randomOrder = {\n 'Type': \"PrintedProductsChanged\",\n 'Order': Order,\n \"Idempotency_key\": None\n }\n if Product['Deleted'] == True: #если полностью удалён\n output = json.dumps(randomOrder, ensure_ascii=False, indent=1)\n orderObject = output\n #sendOrder(orderObject)\n if randomOrder['Order']['Guests'][0]['Products']:\n toSend.append(orderObject) #\n else:\n logger.debug ('Order with other than choosen GUID')\n except Exception as e:\n logger.critical('Failed parsing ScreenCheck')\n logger.critical(e)\n\n if child.tag == 'StoreCheck': # Сохранение заказа\n try:\n output = json.dumps(randomOrder, ensure_ascii=False, indent=1)\n orderObject = output\n #sendOrder(orderObject)\n if randomOrder['Order']['Guests'][0]['Products']:\n toSend.append(orderObject)\n else:\n logger.debug('Order with other than choosen GUID')\n ##############################\n all_items = root[0].findall('CheckLines')[0].findall('CheckLine')\n Waiter = {\n 'Name': child.attrib['WaiterName'],\n 'Id': re.sub(r'\\{|\\}', '', child.attrib['WaiterGuid'])\n }\n Tables = []\n try:\n Table = {\n 'Number': int(child.attrib['TableName']),\n 'Id': re.sub(r'\\{|\\}', '', child.attrib['TableGuid'])\n }\n except:\n logger.critical('Table %s is not Integer value!!' % child.attrib['TableName'])\n Table = {\n 'Number': child.attrib['TableName'],\n 'Id': re.sub(r'\\{|\\}', '', child.attrib['TableGuid'])\n }\n Tables.append(Table)\n Products = []\n for x in all_items:\n Amount = x.attrib['Qnt']\n if Amount == '0.00':\n Deleted = True\n else:\n Deleted = False\n print_time = OrdertimeConvert(x.find('Packet').attrib['WasPrinted'])\n Product = {\n 'Id': str(uuid.uuid4()),\n 'ProductId': re.sub(r'\\{|\\}', '', x.attrib['Guid']),\n 'Name': x.attrib['Name'],\n 'Price': float(x.attrib['Price']),\n 'ResultSum': float(child.attrib['FullSum']), # todo исправить\n 'Amount': float(x.attrib['Qnt']),\n \"Comment\": \"\",\n 'Deleted': Deleted,\n \"DeletionMethod\": None,\n 'PrintTime': print_time,\n \"ServeTime\": None,\n \"Modifiers\": []\n }\n if re.sub(r'\\{|\\}', '', x.attrib['Guid']) == ProductGUID:\n Products.append(Product)\n Guests = []\n guests = {\n 'Id': str(uuid.uuid4()),\n 'Name': child.attrib['GuestName'],\n 'Products': Products\n }\n Guests.append(guests)\n Order = {\n 'Id': re.sub(r'\\{|\\}', '', child.attrib['OrderGuid']),\n \"OrderTypeId\": None,\n \"Status\": StatusOrder,\n 'ResultSum': float(child.attrib['FullSum']),\n 'FullSum': float(child.attrib['FullSum']),\n 'OpenTime': OrdertimeConvert(child.attrib['OpenTime']),\n \"BillTime\": None,\n \"CloseTime\": None,\n 'Number': datetime.today().timestamp(),\n 'Waiter': Waiter,\n \"Cashier\": None,\n 'Tables': Tables,\n \"IsBanquetOrder\": False,\n 'Guests': Guests,\n \"PaymentItems\": []\n }\n randomOrder2 = {\n 'Type': \"PrintedProductsChanged\",\n 'Order': Order,\n \"Idempotency_key\": None\n }\n if randomOrder['Order']['Id'] != randomOrder2['Order']['Id']:\n output = json.dumps(randomOrder2, ensure_ascii=False, indent=1)\n orderObject = output\n try:\n logger.debug('========================OrderObject Begin==================')\n logger.debug(str(orderObject))\n logger.debug('========================OrderObject End====================')\n except:\n logger.debug(\"Can't write orderObject\")\n #sendOrder(orderObject)\n if randomOrder['Order']['Guests'][0]['Products']:\n toSend.append(orderObject)\n else:\n logger.debug ('Order with other than choosen GUID')\n except Exception as e:\n logger.critical('Failed parsing StoreCheck')\n logger.critical(e)\n","sub_path":"RKeeperInfo0312.py","file_name":"RKeeperInfo0312.py","file_ext":"py","file_size_in_byte":27742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"622308237","text":"import argparse\nimport time\nimport math\nimport os\nimport shutil\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\n\nfrom hidden import data\nfrom hidden import utils\nfrom hidden import modelling\n\n\ndef main():\n parser = argparse.ArgumentParser(description='PyTorch Wikitext-2 RNN/LSTM Language Model')\n parser.add_argument('--split_dir', type=str, default='data/raw/ELTeC-eng',\n help='location of the data corpus')\n parser.add_argument('--model', type=str, default='LSTM',\n help='type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)')\n parser.add_argument('--min_char_freq', type=int, default=100,\n help='size of word embeddings')\n parser.add_argument('--emsize', type=int, default=64,\n help='size of embeddings')\n parser.add_argument('--nhid', type=int, default=256,\n help='number of hidden units per layer')\n parser.add_argument('--nlayers', type=int, default=1,\n help='number of layers')\n parser.add_argument('--lr', type=float, default=0.001,\n help='initial learning rate')\n parser.add_argument('--temperature', type=float, default=0.3,\n help='initial learning rate')\n parser.add_argument('--clip', type=float, default=30,\n help='gradient clipping')\n parser.add_argument('--epochs', type=int, default=10,\n help='upper epoch limit')\n parser.add_argument('--batch_size', type=int, default=24, metavar='N',\n help='batch size')\n parser.add_argument('--bptt', type=int, default=75,\n help='sequence length')\n parser.add_argument('--dropout', type=float, default=0.0,\n help='dropout applied to layers (0 = no dropout)')\n parser.add_argument('--seed', type=int, default=1111,\n help='random seed')\n parser.add_argument('--cuda', action='store_true',\n help='use CUDA')\n parser.add_argument('--reverse', default=False, action='store_true',\n help='backwards LM')\n parser.add_argument('--log-interval', type=int, default=100, metavar='N',\n help='report interval')\n parser.add_argument('--model_dir', type=str, default='data/lm_models',\n help='path to save the final model')\n\n args = parser.parse_args()\n print(args)\n\n torch.manual_seed(args.seed)\n if torch.cuda.is_available() and not args.cuda:\n print('WARNING: You have a CUDA device, so you should probably run with --cuda')\n \n if not os.path.exists(args.model_dir):\n os.mkdir(args.model_dir)\n\n args.model_prefix = f'{args.model_dir}/{os.path.basename(args.split_dir)}'\n if not args.reverse:\n try:\n shutil.rmtree(args.model_prefix)\n except FileNotFoundError:\n pass\n os.mkdir(args.model_prefix)\n\n dictionary = data.Dictionary(min_freq=args.min_char_freq)\n with open(os.sep.join((args.split_dir, 'train.txt')), 'r') as f:\n dictionary.fit(f.read())\n dictionary.dump(args.model_prefix + '/chardict.json')\n del dictionary\n\n if not args.reverse:\n model_path = args.model_prefix + '/lm_model.pt'\n else:\n model_path = args.model_prefix + '/rev_lm_model.pt'\n \n dictionary = data.Dictionary.load(args.model_prefix + '/chardict.json')\n\n print('char vocab:', dictionary.idx2char)\n\n with open(os.sep.join((args.split_dir, 'train.txt')), 'r') as f:\n train = f.read()[:100000]\n if args.reverse:\n train = train[::-1]\n train = torch.LongTensor(dictionary.transform(train))\n with open(os.sep.join((args.split_dir, 'dev.txt')), 'r') as f:\n dev = f.read()[:10000]\n if args.reverse:\n dev = dev[::-1]\n dev = torch.LongTensor(dictionary.transform(dev))\n with open(os.sep.join((args.split_dir, 'test.txt')), 'r') as f:\n test = f.read()[:10000]\n if args.reverse:\n test = test[::-1]\n test = torch.LongTensor(dictionary.transform(test))\n\n print(f'# of characters in train: {len(train)}')\n print(f'# of characters in dev: {len(dev)}')\n print(f'# of characters in test: {len(test)}')\n\n device = torch.device('cuda' if args.cuda else 'cpu')\n\n train = utils.batchify(train, args.batch_size)\n dev = utils.batchify(dev, args.batch_size)\n test = utils.batchify(test, args.batch_size)\n\n # set up model\n vocab_size = len(dictionary)\n lm = modelling.RNNModel(args.model, vocab_size, args.emsize,\n args.nhid, args.nlayers, args.dropout).to(device)\n optimizer = torch.optim.Adam(lm.parameters(), lr=args.lr, weight_decay=1e-5)\n criterion = nn.CrossEntropyLoss()\n print(lm)\n\n def epoch():\n lm.train()\n\n total_loss = 0.\n start_time = time.time()\n hidden = None\n\n for batch, i in enumerate(range(0, train.size(0) - 1, args.bptt)):\n data, targets = utils.get_batch(train, i, args.bptt)\n\n data = data.to(device)\n targets = targets.to(device)\n\n lm.zero_grad()\n output, hidden = lm(data, hidden)\n loss = criterion(output.view(-1, vocab_size), targets)\n\n loss.backward()\n torch.nn.utils.clip_grad_norm_(lm.parameters(), args.clip)\n optimizer.step()\n\n total_loss += loss.item()\n hidden = modelling.repackage_hidden(hidden)\n\n if batch % args.log_interval == 0 and batch > 0:\n cur_loss = total_loss / args.log_interval\n elapsed = time.time() - start_time\n print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.6f} | ms/batch {:5.2f} | '\n 'loss {:5.6f} | ppl {:8.2f}'.format(\n epoch_idx, batch * args.batch_size, len(train) // args.bptt * args.batch_size, lr,\n elapsed * 1000 / args.log_interval, cur_loss, math.exp(cur_loss)))\n total_loss = 0\n start_time = time.time()\n\n with torch.no_grad():\n hid_ = None\n in_ = torch.randint(vocab_size, (1, 1), dtype=torch.long).to(device)\n\n for i in range(100):\n output, hid_ = lm(in_, hid_)\n char_weights = output.squeeze().div(args.temperature).exp().cpu()\n char_idx = torch.multinomial(char_weights, 1)[0]\n in_.fill_(char_idx)\n char = dictionary.idx2char[char_idx]\n print(char, end='')\n\n print('\\n')\n\n def evaluate(data_source):\n lm.eval()\n total_loss = 0.\n hidden = None\n\n with torch.no_grad():\n for i in range(0, data_source.size(0) - 1, args.bptt):\n data, targets = utils.get_batch(data_source, i, args.bptt)\n data = data.to(device)\n targets = targets.to(device)\n output, hidden = lm(data, hidden)\n output_flat = output.view(-1, vocab_size)\n total_loss += len(data) * criterion(output_flat, targets).item()\n hidden = modelling.repackage_hidden(hidden)\n\n return total_loss / len(data_source)\n\n lr = args.lr\n best_val_loss = None\n\n try:\n for epoch_idx in range(1, args.epochs+1):\n epoch_start_time = time.time()\n epoch()\n val_loss = evaluate(dev)\n\n print('-' * 89)\n print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '\n 'valid ppl {:8.6f}'.format(epoch_idx, (time.time() - epoch_start_time),\n val_loss, math.exp(val_loss)))\n print('-' * 89)\n\n if not best_val_loss or val_loss < best_val_loss:\n with open(model_path, 'wb') as f:\n torch.save(lm, f)\n print('>>> saving model') \n best_val_loss = val_loss\n elif val_loss >= best_val_loss:\n lr *= 0.5\n print(f'>>> lowering learning rate to {lr}')\n for g in optimizer.param_groups:\n g['lr'] = lr\n\n except KeyboardInterrupt:\n print('-' * 89)\n print('Exiting from training early')\n\n with open(model_path, 'rb') as f:\n lm = torch.load(f)\n lm.rnn.flatten_parameters()\n\n test_loss = evaluate(test)\n print('=' * 89)\n print('| End of training | test loss {:5.6f} | test ppl {:8.2f}'.format(\n test_loss, math.exp(test_loss)))\n print('=' * 89)\n\nif __name__ == '__main__':\n main()","sub_path":"train_lm.py","file_name":"train_lm.py","file_ext":"py","file_size_in_byte":8798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"535356503","text":"class Solution(object):\n def isAnagram(self, s, t):\n if len(s) != len(t):\n return False\n\n originalWordCount = {}\n anagramWordCount = {}\n\n for char in set(s):\n originalWordCount[char] = s.count(char)\n for char in set(t):\n anagramWordCount[char] = t.count(char)\n\n if originalWordCount == anagramWordCount:\n return True\n else:\n return False\n","sub_path":"242-ValidAnagram.py","file_name":"242-ValidAnagram.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"477335353","text":"import streamlit as st\r\nfrom Bio.Seq import Seq\r\nfrom Bio import SeqIO\r\nimport neatbio.sequtils as utils\r\nfrom collections import Counter\r\n\r\n\r\n\r\n#new added line\r\nimport io\r\nst.set_option('deprecation.showPyplotGlobalUse', False)\r\n\r\n\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib\r\nmatplotlib.use(\"Agg\")\r\nimport numpy as np\r\n\r\n\r\n\r\ndef delta(x,y):\r\n return 0 if x == y else 1\r\n\r\n\r\ndef M(seq1,seq2,i,j,k):\r\n return sum(delta(x,y) for x,y in zip(seq1[i:i+k],seq2[j:j+k]))\r\n\r\n\r\ndef makeMatrix(seq1,seq2,k):\r\n n = len(seq1)\r\n m = len(seq2)\r\n return [[M(seq1,seq2,i,j,k) for j in range(m-k+1)] for i in range(n-k+1)]\r\n\r\n\r\ndef plotMatrix(M,t, seq1, seq2, nonblank = chr(0x25A0), blank = ' '):\r\n print(' |' + seq2)\r\n print('-'*(2 + len(seq2)))\r\n for label,row in zip(seq1,M):\r\n line = ''.join(nonblank if s < t else blank for s in row)\r\n print(label + '|' + line)\r\n\r\n\r\ndef dotplot(seq1,seq2,k = 1,t = 1):\r\n M = makeMatrix(seq1,seq2,k)\r\n plotMatrix(M, t, seq1,seq2) #experiment with character choice\r\n\r\n\r\ndef dotplotx(seq1,seq2):\r\n plt.imshow(np.array(makeMatrix(seq1,seq2,1)))\r\n # on x-axis list all sequences of seq 2\r\n xt=plt.xticks(np.arange(len(list(seq2))),list(seq2))\r\n # on y-axis list all sequences of seq 1\r\n yt=plt.yticks(np.arange(len(list(seq1))),list(seq1))\r\n plt.show()\r\n\r\n\r\n\r\ndef main():\r\n \"\"\"Bioinformatics Genome analysis web app\"\"\"\r\n\r\n\r\n st.title(\"DNA Genome analysis and Cosine Similarity Analysis web application\")\r\n menu= [\"Introduction\",\"DNA sequence Analysis\",\"Dotplot Analysis\",\"About us\"]\r\n choice= st.sidebar.selectbox(\"Select Option\",menu)\r\n\r\n if choice==\"Introduction\":\r\n st.subheader(\"Welcome to our Sequence Analysis Application :)\")\r\n elif choice==\"DNA sequence Analysis\":\r\n st.subheader(\"DNA sequence Analysis will be done here.\")\r\n seq_file=st.file_uploader(\"Upload the .FASTA file for any DNA analysis of the considered Genome.\", type=[\"fasta\",\"fa\"])\r\n\r\n if seq_file is not None:\r\n byte_str =seq_file.read()\r\n text_obj=byte_str.decode('UTF-8')\r\n dna_record= SeqIO.read(io.StringIO(text_obj),\"fasta\")\r\n #st.write(dna_record)\r\n dna_seq= dna_record.seq\r\n\r\n details= st.radio(\"Details of the DNA as provided by NCBI database:\",(\"DNA Record description\", \"Sequence\"))\r\n if details==\"DNA Record description\":\r\n st.write(dna_record.description)\r\n elif details==\"Sequence\":\r\n st.write(dna_record.seq)\r\n\r\n #Nucleotide\r\n st.subheader(\"Nucleotide Frequency :\")\r\n dna_freq=Counter(dna_seq)\r\n st.write(dna_freq)\r\n adenine_color=st.color_picker(\"Toggle the Adenine Colour \")\r\n guanine_color=st.color_picker(\"Toggle the Guanine Colour \")\r\n thymine_color=st.color_picker(\"Toggle the Thymine Colour \")\r\n cytosine_color=st.color_picker(\"Toggle the Cytosine Colour \")\r\n\r\n\r\n if st.button(\"Plot frequency\"):\r\n barlist=plt.bar(dna_freq.keys(),dna_freq.values())\r\n barlist[0].set_color(adenine_color)\r\n barlist[1].set_color(guanine_color)\r\n barlist[2].set_color(thymine_color)\r\n barlist[3].set_color(cytosine_color)\r\n st.pyplot()\r\n\r\n\r\n st.subheader(\"DNA complete Composition\")\r\n\r\n gc_score= utils.gc_content(str(dna_seq))\r\n at_score=utils.at_content(str(dna_seq))\r\n st.json({\"GC Content(for heat stability)\": gc_score,\"AT Content\":at_score })\r\n\r\n #protein synthesis\r\n st.subheader(\"Protein Synthesis operations on the DNA :\")\r\n p1=dna_seq.translate()\r\n aa_freq= Counter(str(p1))\r\n if st.checkbox(\"Transcription :\"):\r\n st.write(dna_seq.transcribe())\r\n elif st.checkbox(\"Translation :\"):\r\n st.write(dna_seq.translate())\r\n elif st.checkbox(\"Complement :\"):\r\n st.write(dna_seq.complement())\r\n elif st.checkbox(\"Amino Acid frequency :\"):\r\n st.write(aa_freq)\r\n\r\n elif st.checkbox(\"Plot the Amino Acid frequency :\"):\r\n aa_color=st.color_picker(\"Pick the Amino acid color:\")\r\n #barlist= plt.bar(aa_freq.keys(),aa_freq.values(),color=aa_color)\r\n #barlist[2].set_color(aa_color)\r\n plt.bar(aa_freq.keys(),aa_freq.values(),color=aa_color)\r\n st.pyplot()\r\n\r\n elif st.checkbox(\"The complete Amino acid name is given as\"):\r\n aa_name= str(p1).replace(\"*\",\"\")\r\n aa3= utils.convert_1to3(aa_name)\r\n st.write(aa_name)\r\n st.write(\"========================\")\r\n st.write(aa3)\r\n\r\n\r\n\r\n st.write(\"========================\")\r\n st.write(utils.get_acid_name(aa3))\r\n\r\n\r\n\r\n\r\n\r\n #Top most amino acids\r\n\r\n\r\n\r\n\r\n\r\n\r\n elif choice==\"Dotplot Analysis\":\r\n st.subheader(\"Generate Dotplot for the comparision between two DNA sequences here.\")\r\n seq_file1=st.file_uploader(\"Upload the first .FASTA file for any DNA analysis of the considered Genome.\", type=[\"fasta\",\"fa\"])\r\n seq_file2=st.file_uploader(\"Upload the second .FASTA file for any DNA analysis of the considered Genome.\", type=[\"fasta\",\"fa\"])\r\n\r\n\r\n if seq_file1 and seq_file2 is not None:\r\n byte_str1 =seq_file1.read()\r\n text_obj1=byte_str1.decode('UTF-8')\r\n dna_record1= SeqIO.read(io.StringIO(text_obj1),\"fasta\")\r\n\t\t\t\r\n byte_str2 =seq_file2.read()\r\n text_obj2=byte_str2.decode('UTF-8')\r\n dna_record2= SeqIO.read(io.StringIO(text_obj2),\"fasta\")\t\t\t\r\n\r\n #st.write(dna_record)\r\n dna_seq1= dna_record1.seq\r\n dna_seq2= dna_record2.seq\r\n\r\n details= st.radio(\"Details of the DNA as provided by NCBI database:\",(\"Record details from the NCBI database\", \"Gene Sequence\"))\r\n if details==\"Record details from the NCBI database\":\r\n st.write(dna_record1.description)\r\n st.write(\"===And the other Record is decribed as :===\")\r\n st.write(dna_record2.description)\r\n\r\n elif details==\"Gene Sequence\":\r\n st.write(dna_record1.seq)\r\n st.write(\"===And the other sequence can be given as: ===\")\r\n st.write(dna_record2.seq)\r\n\r\n\r\n display_limit=st.number_input(\"Select maximum number of Nucleotides\",10,200,50)\r\n if st.button(\"Push here for Dotplot :)\"):\r\n st.write(\"Comparing the first {} nucleotide of the two sequences\".format(display_limit))\r\n dotplotx(dna_seq1[0:display_limit],dna_seq2[0:display_limit])\r\n st.pyplot()\r\n\r\n\r\n\r\n elif choice==\"About us\":\r\n st.subheader(\"About the application and about us :)\")\r\n\r\nif __name__=='__main__':\r\n\tmain()\r\n\r\n\r\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"212428147","text":"from datetime import datetime, date, time, timedelta\nimport locale\nimport platform\nimport pymorphy2\n\nfrom bs4 import BeautifulSoup\n\nfrom webapp.db import db\nfrom webapp.news.models import News\nfrom webapp.news.parsers.utils import get_html, save_news\n\n\n# Тут зададим региональный параметр для любой ОС\nif platform.system() == 'Windows':\n locale.setlocale(locale.LC_ALL, 'russian')\nelse:\n locale.setlocale(locale.LC_TIME, 'ru_RU')\n\n\ndef parse_habr_date(date_str):\n if 'сегодня' in date_str:\n today = datetime.now()\n date_str = date_str.replace('сегдня', today.strftime('%d %B %Y'))\n elif 'вчера' in date_str:\n yesterday = datetime.now() - timedelta(days=1)\n date_str = date_str.replace('вчера', yesterday.strftime('%d %B %Y'))\n try:\n m = pymorphy2.MorphAnalyzer()\n day, month, year, qw2ewq, qwe2eewq = date_str.split(' ')\n # прео��разуем название месяца в именительный падеж с заглавной буквы\n new_month = m.parse(month)[0].inflect({'nomn'}).word.title()\n return datetime.strptime(' '.join([day, new_month, year]), '%d %B %Y')\n except ValueError:\n return datetime.now()\n\n\ndef get_news_snippets():\n html = get_html(\"https://habr.com/ru/search/?target_type=posts&q=python&order_by=date\")\n if html:\n soup = BeautifulSoup(html, 'html.parser') # Бреобразует наш HTML в дерево элементов с которым удобнее работать фунциям этой библиотеки\n all_news = soup.find(\"ul\", class_=\"content-list_posts\").findAll('li', class_='content-list__item_post') # Делаем выборку элементов страницы при помощи поиска\n #result_news = []\n for news in all_news:\n title = news.find('a', class_=\"post__title_link\").text # Выбираем текст заголовка\n url = news.find('a', class_=\"post__title_link\")[\"href\"] # Выбираем ссылку заголовка (к атрибутам обращаемся как к элементам славаря)\n published = news.find('span', class_=\"post__time\").text # Выбираем время\n published = parse_habr_date(published)\n save_news(title, url, published) # После формирования, вызываем запись в базу\n\n\ndef get_news_content():\n news_without_texh = News.query.filter(News.text.is_(None))\n for news in news_without_texh:\n html = get_html(news.url)\n if html:\n soup = BeautifulSoup(html, 'html.parser')\n # .decode_contents() позволяет получить html вместо текста\n article = soup.find('div', class_='post__text-html').decode_contents()\n if article:\n news.text = article\n db.session.add(news)\n db.session.commit()\n","sub_path":"webapp/news/parsers/habr.py","file_name":"habr.py","file_ext":"py","file_size_in_byte":3040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"60144120","text":"# -*- encoding: utf-8 -*-\nclass Solution:\n \"\"\"\n @param prices: Given an integer array\n @return: Maximum profit\n \"\"\"\n def maxProfit(self, prices):\n # write your code here\n B = []\n profit = 0\n for i in range(1,len(prices)):\n b = prices[i]-prices[i-1]\n if b > 0:\n profit += b\n return profit\n\n# 我的练习\nclass Solution2:\n \"\"\"\n @param prices: Given an integer array\n @return: Maximum profit\n \"\"\"\n def maxProfit(self, prices):\n # write your code here\n # edge case:\n if prices is None or len(prices) == 0:\n return 0\n\n le = len(prices)\n profit = []\n for i in range(1,le):\n profit.append(prices[i] - prices[i-1])\n max_profit = 0\n for i in range(len(profit)):\n if profit[i]>0:\n max_profit += profit[i]\n return max_profit\n\n\n","sub_path":"lintcode/Array/150 Best Time to Buy and Sell Stock II.py","file_name":"150 Best Time to Buy and Sell Stock II.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"285192222","text":"# !/usr/bin/env python\n# -*- coding:utf-8 -*-\n\"\"\"\nWindow's custom title bar.\n\"\"\"\n__author__ = \"jeremyjone\"\n__datetime__ = \"2019/4/30 18:08\"\n__all__ = [\"TitleWidget\"]\n\nimport os, sys\nimport PySide.QtGui as QtGui\nimport PySide.QtCore as QtCore\n\nfrom widget import UILoader\nfrom config.conModel import CONFIG\n\n\nclass TitleWidget(QtGui.QWidget):\n windowMinimumed = QtCore.Signal()\n # windowMaximumed = QtCore.Signal()\n # windowNormaled = QtCore.Signal()\n windowClosed = QtCore.Signal()\n windowMoved = QtCore.Signal(QtCore.QPoint)\n windowReleased = QtCore.Signal()\n\n def __init__(self, parent=None):\n super(TitleWidget, self).__init__(parent)\n self.mPos = None\n self.initUI()\n\n def initUI(self):\n win = UILoader(\"./Resource/ui/title.ui\", \"./Resource/css/title.css\")\n\n l = win.findChild(QtGui.QLayout, \"gridLayout\")\n self.setLayout(l)\n self.setStyleSheet(win.styleSheet())\n self.setFixedHeight(win.height())\n\n self.APPNameLabel = self.findChild(QtGui.QLabel, \"APPNameLabel\")\n self.titleTextLabel = self.findChild(QtGui.QLabel, \"titleTextLabel\")\n self.setWindowTitle()\n\n iconLabel = self.findChild(QtGui.QLabel, \"iconLabel\")\n iconLabel.setPixmap(QtGui.QPixmap(\"./Resource/icon/ffm_main.png\"))\n\n self.titleWidget = self.findChild(QtGui.QWidget, \"titleWidget\")\n # _palette = QtGui.QPalette()\n # _pixmap = QtGui.QPixmap(\"./Resource/image/background_title_2.png\").scaled(\n # self.titleWidget.width(), self.titleWidget.height(),\n # QtCore.Qt.KeepAspectRatioByExpanding, QtCore.Qt.SmoothTransformation)\n # _palette.setBrush(QtGui.QPalette.Background, QtGui.QBrush(_pixmap))\n # self.titleWidget.setPalette(_palette)\n\n self.minWinBtn = self.findChild(QtGui.QPushButton, \"minWinBtn\")\n self.minWinBtn.setToolTip(u\"最小化\")\n self.closeWinBtn = self.findChild(QtGui.QPushButton, \"closeWinBtn\")\n self.minWinBtn.clicked.connect(lambda : self.windowMinimumed.emit())\n\n self.showMinButton()\n\n if CONFIG.Window.CloseButtonExit:\n self.closeWinBtn.clicked.connect(lambda: sys.exit(0))\n self.closeWinBtn.setToolTip(u\"退出\")\n else:\n self.closeWinBtn.clicked.connect(lambda: self.windowClosed.emit())\n self.closeWinBtn.setToolTip(u\"关闭窗口\")\n\n def setWindowTitle(self, *args, **kwargs):\n if CONFIG.Window.ShowNameOnTitle:\n self.APPNameLabel.setText(CONFIG.Main.Name)\n self.titleTextLabel.setText(\" - \" + \"Jz\")\n else:\n self.APPNameLabel.setText(\"\")\n self.titleTextLabel.setText(\"\")\n\n def showMinButton(self):\n if not CONFIG.Window.ShowMinimizeButton:\n self.minWinBtn.hide()\n else:\n self.minWinBtn.show()\n\n def mousePressEvent(self, event):\n if event.button() == QtCore.Qt.LeftButton:\n self.mPos = event.pos()\n event.accept()\n # It cannot be moved when the window is maximized or full screen.\n self._canMove = not self.isMaximized() or not self.isFullScreen()\n\n def mouseReleaseEvent(self, event):\n self.mPos = None\n event.accept()\n self._canMove = False\n self.windowReleased.emit()\n\n def mouseMoveEvent(self, event):\n if event.buttons() == QtCore.Qt.LeftButton and self.mPos and self._canMove:\n self.windowMoved.emit(self.mapToGlobal(event.pos() - self.mPos))\n event.accept()\n\n def enterEvent(self, event):\n self.setCursor(QtCore.Qt.ArrowCursor)\n super(TitleWidget, self).enterEvent(event)\n\n\nif __name__ == \"__main__\":\n app = QtGui.QApplication(sys.argv)\n t = TitleWidget()\n t.show()\n sys.exit(app.exec_())\n","sub_path":"widget/titleWidget.py","file_name":"titleWidget.py","file_ext":"py","file_size_in_byte":3798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"303955038","text":"from PyQt5.QtWidgets import QApplication, QDialog\nfrom PyQt5.QtCore import Qt, pyqtSignal, QObject, QTimer\nfrom PyQt5.QtGui import QMovie\nfrom time import time\nfrom threading import Timer\nfrom datetime import datetime\n\n\n#get the UI from here\nfrom screen_saver_ui import Ui_ScreenSaverDialog\n\n\n#################################################\n# FirstDialog Class - to get the system running #\n#################################################\nclass ScreenSaverDialog(QDialog):\n\n # signal to update in 10 second\n ten_seconds_signal = pyqtSignal()\n\n def __init__(self):\n super(ScreenSaverDialog, self).__init__()\n\n # Setup the user interface from Designer.\n self.ui = Ui_ScreenSaverDialog()\n self.ui.setupUi(self)\n\n # Setup signals\n self.ui.doneButton.clicked.connect(self.accept)\n\n self.ui.doneButton.setStyleSheet(\"background-color: rgba(255, 255, 255, 0); \\\n border: 0px\");\n\n self.which_movie = 'RK'\n self.movie = QMovie(':/screen_saver/resources/Rk69.gif')\n self.ui.gifLabel.setMovie(self.movie)\n self.movie.start()\n \n # to start 10 seconds timer\n self.t10seg = QTimer()\n self.t10seg.timeout.connect(self.TimerTenSecs)\n self.t10seg.start(10000)\n\n #SIGNALS\n # connect the timer signal to the Update\n self.ten_seconds_signal.connect(self.UpdateTenSecs)\n\n\n \"\"\" QTimer callback emit a signal to not upset the timer interval \"\"\"\n def TimerTenSecs(self):\n self.ten_seconds_signal.emit()\n\n\n def UpdateTenSecs (self):\n # do a UI update if its necessary\n if self.which_movie == 'RK':\n self.movie = QMovie(':/screen_saver/resources/UIBJ.gif')\n self.ui.gifLabel.setMovie(self.movie)\n self.movie.start()\n\n self.which_movie = 'UIBJ'\n elif self.which_movie == 'UIBJ':\n self.movie = QMovie(':/screen_saver/resources/1LFE.gif')\n self.ui.gifLabel.setMovie(self.movie)\n self.movie.start()\n\n self.which_movie = '1LFE'\n elif self.which_movie == '1LFE':\n self.movie = QMovie(':/screen_saver/resources/Rk69.gif')\n self.ui.gifLabel.setMovie(self.movie)\n self.movie.start()\n\n self.which_movie = 'RK'\n\n\n \n\n\n \n### end of file ###\n","sub_path":"rpi_base/screen_saver/screen_saver_cls.py","file_name":"screen_saver_cls.py","file_ext":"py","file_size_in_byte":2383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"15449855","text":"#!/bin/python\n# -*- coding: utf-8 -*-\n#\n# author : alex pokrov \n# 10/5/17 10:10 PM\n\narr = [2,3,4,5,4,3,2,3,4,2,3]\nx = 0\ni = 0\nprint(arr)\nfor element in arr:\n if arr[i] == 2 and x == 0:\n x = x+1\n elif arr[i] == 2 and x > 0:\n arr[i]+=1;\n i = i+1\n\nprint(arr)\n","sub_path":"array/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"366588646","text":"import json\nimport matplotlib.pyplot as plt\nimport requests\n\nsave = []\nmethod = 'network' # change the method where you want to get data\n\nif method == 'network':\n url = \"https://think.cs.vt.edu/corgis/json/aids/aids.json\"\n data = requests.get(url).json()\nelse:\n with open(\"aids.json\", 'r') as fr:\n data = json.load(fr)\n\nfor x in data:\n print(x['Data'].keys())\n res = x['Data']['HIV Prevalence']['Adults']\n save.append(res)\n\nplot_data = save[:100]\nprint(plot_data)\n\ny = range(len(plot_data))\n\nplt.figure()\nplt.plot(y, plot_data)\nplt.savefig(\"line plot.png\")\nplt.show()\nplt.close()\n\nplt.figure()\nplt.scatter(y, plot_data)\nplt.savefig(\"scatter plot.png\")\nplt.show()\nplt.close()\n\nplt.figure()\nplt.boxplot(y, plot_data)\nplt.savefig(\"scatter plot.png\")\nplt.show()\nplt.close()\n\n\nm = sum(plot_data) / len(plot_data)\nprint(\"mean of data:{}\".format(m))\n\nplot_data.sort()\nmedian = plot_data[len(plot_data) // 2]\nprint(\"median:{}\".format(median))\n","sub_path":"project_1028/twistedFate_500/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"338105834","text":"import os\nimport psycopg2\nfrom flask import Flask, render_template, request, session, redirect, url_for\nimport urllib.parse as urlparse\nfrom datetime import datetime\nfrom src.db import Database\nfrom src.models import Host, Application, Scan, Owner\n\napp = Flask(__name__)\napp.secret_key = 's3cret'\napp.debug = True\n\nurl = urlparse.urlparse(os.environ.get('DATABASE_URL'))\ndb = \"dbname=%s user=%s password=%s host=%s \" % (url.path[1:], url.username, url.password, url.hostname)\n\ndef exec_file(sql_file):\n conn = psycopg2.connect(db)\n cursor = conn.cursor()\n\n with open(sql_file, 'r') as sql_stm:\n statements = sql_stm.read()\n statements = statements.split('\\n\\n')\n \n for statement in statements:\n cursor.execute(statement)\n\n conn.commit()\n cursor.close()\n conn.close()\n\nexec_file(\"sql/drop_tables.sql\")\nexec_file(\"sql/create_tables.sql\")\nexec_file(\"sql/test_data.sql\")\n\n@app.route('/')\ndef index():\n return render_template('intro.html')\n\n@app.route('/hosts')\ndef hosts():\n session['logged_in'] = True\n \n conn = psycopg2.connect(db)\n cursor = conn.cursor()\n cursor.execute('SELECT * FROM host;')\n\n host_list = []\n for row in cursor.fetchall():\n host = {}\n host[\"id\"] = row[0]\n host[\"owner_id\"] = row[1]\n host[\"hostname\"] = row[2]\n host[\"ip\"] = row[3]\n host[\"platform\"] = row[4]\n host[\"date_added\"] = row[5]\n\n host_list.append(host)\n\n cursor.close()\n conn.close()\n\n return render_template('hosts.html', hosts=host_list, session=session)\n\n@app.route('/add_host', methods=['POST'])\ndef add_host():\n i = len(host_db)\n owner = Owner(i, \"owner%d\" % (i), \"type%d\" % (i))\n host = Host(i, \"owner\", request.form['ip'], request.form['hostname'], request.form['platform'], datetime.now())\n host_db.append(host)\n return redirect(url_for('hosts'))\n\n@app.route('/remove_host', methods=['POST'])\ndef remove_host():\n to_remove = None\n for host in host_db:\n if host.id == int(request.form['host_id']):\n to_remove = host\n\n if to_remove:\n host_db.remove(to_remove)\n\n return redirect(url_for('hosts'))\n\n@app.route('/database_connection')\ndef db_connect():\n try:\n conn = psycopg2.connect(db)\n cur = conn.cursor()\n db_test = DB_test(\"name\", cur)\n tables = db_test.getTables()\n\n except:\n print(\"error\")\n\n return render_template('db_test.html', tables=tables)\n\nif __name__ == '__main__':\n if os.sys.argv[1] == \"drop\":\n db.exec_file(\"sql/drop_tables.sql\")\n print(\"dropped tables\")\n\n port = int(os.environ.get('PORT', 5000))\n app.run(host='0.0.0.0', port=port, debug=True)\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"340889351","text":"import sys\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QAxContainer import *\n\nclass MyWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n\n self.kiwoom = QAxWidget(\"KHOPENAPI.KHOpenAPICtrl.1\")\n self.kiwoom.dynamicCall(\"CommConnect()\")\n\n self.kiwoom.OnReceiveTrData.connect(self.receive_trdata)\n\n self.setWindowTitle(\"Python Home Trading System by bsjeon\")\n self.setGeometry(300, 300, 300, 150)\n\n btn1 = QPushButton(\"Show\", self)\n btn1.move(190, 10)\n btn1.clicked.connect(self.btn1_clicked)\n\n self.listWidget = QListWidget(self)\n self.listWidget.setGeometry(10, 10, 170, 130)\n\n def btn1_clicked(self):\n # SetInputValue\n code = \"000030\"\n startDate = \"20160101\"\n self.kiwoom.dynamicCall(\"SetInputValue(QString, QString)\", \"종목코드\", code)\n self.kiwoom.dynamicCall(\"SetInputValue(QString, QString)\", \"시작일자\", startDate)\n\n # CommRqData\n self.kiwoom.dynamicCall(\"CommRqData(QString, QString, int, QString)\", \"opt10015_req\", \"opt10015\", 0, \"0101\")\n\n def receive_trdata(self, screen_no, rqname, trcode, recordname, prev_next, data_len, err_code, msg1, msg2):\n if rqname == \"opt10015_req\":\n name = self.kiwoom.dynamicCall(\"CommGetDataEx(QString, QString, QString, int, QString)\", trcode, \"\", rqname,\n 0, \"일자\")\n volume = self.kiwoom.dynamicCall(\"CommGetData(QString, QString, QString, int, QString)\", trcode, \"\", rqname,\n 0, \"종가\")\n print(name)\n dateClosingPriceList = []\n dateClosingPriceList.append(name.strip())\n dateClosingPriceList.append(volume.strip())\n self.listWidget.addItems(dateClosingPriceList)\n # ret = self.kiwoom.dynamicCall(\"GetCodeListByMarket(QString)\", [\"0\"])\n # kospi_code_list = ret.split(';')\n # kospi_code_name_list = []\n #\n # for x in kospi_code_list:\n # name = self.kiwoom.dynamicCall(\"GetMasterCodeName(QString)\", [x])\n # kospi_code_name_list.append(x + \" : \" + name)\n #\n # self.listWidget.addItems(kospi_code_name_list)\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n myWindow = MyWindow()\n myWindow.show()\n sys.exit(app.exec_())","sub_path":"SimpleHTS.py","file_name":"SimpleHTS.py","file_ext":"py","file_size_in_byte":2399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"248241847","text":"\"\"\" A collection of image similarity metrics. \"\"\"\n\nimport collections\n\nimport numpy as np\nfrom scipy import ndimage\n\n# Container for registration methods:\n\nMethod = collections.namedtuple('method', 'jacobian error update')\n\n\ndef gradient(image, variance=0.1):\n \"\"\" Computes the image gradient \"\"\"\n grad = np.gradient(image)\n\n dIx = ndimage.gaussian_filter(grad[1], variance).flatten()\n dIy = ndimage.gaussian_filter(grad[0], variance).flatten()\n\n return dIx, dIy\n\n# ==============================================================================\n# Forwards additive:\n# ==============================================================================\n\n\ndef forwardsAdditiveJacobian(image, template, model, p):\n \"\"\"\n Computes the jacobian dP/dE.\n\n Parameters\n ----------\n model: deformation model\n A particular deformation model.\n warpedImage: nd-array\n Input image after warping.\n p : optional list\n Current warp parameters\n\n Returns\n -------\n jacobian: nd-array\n A jacobain matrix. (m x n)\n | where: m = number of image pixels,\n | p = number of parameters.\n \"\"\"\n\n dIx, dIy = gradient(image)\n\n dPx, dPy = model.jacobian(template.coords, p)\n\n J = np.zeros_like(dPx)\n for index in range(0, dPx.shape[1]):\n J[:, index] = (dPx[:, index] * dIx) + (dPy[:, index] * dIy)\n return J\n\n\ndef forwardsAdditiveError(image, template):\n \"\"\" Compute the forwards additive error \"\"\"\n return (template - image).flatten()\n\n\ndef forwardsAdditiveUpdate(p, deltaP, model=None):\n \"\"\" Compute the forwards additive error \"\"\"\n return p + deltaP\n\n# Define the forwards additive approach:\n\nforwardsAdditive = Method(\n forwardsAdditiveJacobian,\n forwardsAdditiveError,\n forwardsAdditiveUpdate\n )\n\n# ==============================================================================\n# Forwards compositional.\n# ==============================================================================\n\n\ndef forwardsCompositionalError(image, template):\n \"\"\" Compute the forwards additive error \"\"\"\n return (template - image).flatten()\n\n\ndef forwardsCompositionalUpdate(p, deltaP, tform):\n \"\"\" Compute the forwards additive error \"\"\"\n return tform.vector(np.dot(tform.matrix(p), tform.matrix(deltaP)))\n\n\nforwardsCompositional = Method(\n forwardsAdditiveJacobian,\n forwardsCompositionalError,\n forwardsCompositionalUpdate\n )\n\n\n# ==============================================================================\n# Inverse compositional.\n# ==============================================================================\n\n\ndef inverseCompositionalJacobian(image, template, model, p):\n \"\"\"\n Computes the jacobian dP/dE.\n\n Parameters\n ----------\n model: deformation model\n A particular deformation model.\n warpedImage: nd-array\n Input image after warping.\n p : optional list\n Current warp parameters\n\n Returns\n -------\n jacobian: nd-array\n A jacobain matrix. (m x n)\n | where: m = number of image pixels,\n | p = number of parameters.\n \"\"\"\n\n dIx, dIy = gradient(template.data, variance=1.5)\n\n dPx, dPy = model.jacobian(template.coords, p)\n\n J = np.zeros_like(dPx)\n for index in range(0, dPx.shape[1]):\n J[:, index] = (dPx[:, index] * dIx) + (dPy[:, index] * dIy)\n return J\n\n\ndef inverseCompositionalError(image, template):\n \"\"\" Compute the inverse additive error \"\"\"\n return (image - template).flatten()\n\n\ndef inverseCompositionalUpdate(p, deltaP, tform):\n \"\"\" Compute the inverse compositional error \"\"\"\n\n return tform.vector(\n np.dot(tform.matrix(p), np.linalg.inv(tform.matrix(deltaP)))\n )\n\n\ninverseCompositional = Method(\n inverseCompositionalJacobian,\n inverseCompositionalError,\n inverseCompositionalUpdate\n )\n\n","sub_path":"imreg/metric.py","file_name":"metric.py","file_ext":"py","file_size_in_byte":3880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"67837775","text":"import json\r\nimport requests\r\nimport tkinter as tk\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\n\r\n# Making a list of currencies. You can add more items (currencies) to the list\r\ncurrencyList = ['AED', 'AUD', 'BHD', 'BRL', 'CAD', 'CNY', 'EUR', 'HKD', 'INR', 'USD']\r\n\r\n# Defining CreateWidgets() to create necessary widgets for the GUI\r\ndef CreateWidgets():\r\n inputAMT_Label = Label(root, text = \"AMOUNT:\", bg = \"darkolivegreen4\")\r\n inputAMT_Label.grid(row = 1, column = 0, padx = 5, pady = 5)\r\n inputAMT_Entry = Entry(root, width = 20, textvariable = getAMT)\r\n inputAMT_Entry.grid(row = 1, column = 1, padx = 5, pady = 5)\r\n\r\n fromLabel = Label(root, text = \"FROM:\", bg = \"darkolivegreen4\")\r\n fromLabel.grid(row = 2, column = 0, padx = 5, pady = 5)\r\n root.fromCombo = ttk.Combobox(root, values = currencyList)\r\n root.fromCombo.grid(row = 2, column = 1, padx = 5, pady = 5)\r\n\r\n toLabel = Label(root, text = \"TO:\", bg = \"darkolivegreen4\")\r\n toLabel.grid(row=3, column = 0, padx = 5, pady=5)\r\n root.toCombo = ttk.Combobox(root, values = currencyList)\r\n root.toCombo.grid(row = 3, column = 1, padx = 5, pady = 5)\r\n\r\n convertButton = Button(root, text = \"CONVERT\", width = 25, command = Convert)\r\n convertButton.grid(row = 4, column = 0, columnspan = 2, padx = 5, pady = 5)\r\n\r\n exrateLabel = Label(root, text = \"EXCHANGE RATE:\",bg = \"darkolivegreen4\")\r\n exrateLabel.grid(row = 5, column = 0, padx = 5, pady = 30)\r\n root.exchangerateLabel = Label(root, font = ('',20),bg = \"darkolivegreen4\")\r\n root.exchangerateLabel.grid(row = 5, column = 1, padx = 5, pady = 30)\r\n\r\n outputLabel = Label(root, text = \"CONVERTED AMOUNT:\", bg = \"darkolivegreen4\")\r\n outputLabel.grid(row = 6, column = 0,padx = 5, pady = 20)\r\n root.outputAMT_Label = Label(root, font = ('',20),bg = \"darkolivegreen4\")\r\n root.outputAMT_Label.grid(row = 6, column = 1, padx = 5, pady = 20)\r\n\r\n# Defining Convert() function for converting the curreny\r\ndef Convert():\r\n # Converting user-input amount which is a string into float type\r\n inputAmt = float(getAMT.get())\r\n fromCur = root.fromCombo.get()\r\n toCur = root.toCombo.get()\r\n apiKey = \"YOUR API KEY\"\r\n # Storing the base URL\r\n baseURL = r\"https://www.alphavantage.co/query?function=CURRENCY_EXCHANGE_RATE\"\r\n # Storing the complete URL\r\n inputURL = baseURL+\"&from_currency=\"+fromCur+\"&to_currency=\"+toCur+\"&apikey=\"+apiKey\r\n\r\n # Sending the request to URL & Fetching and Storing the response in response variable\r\n response = requests.get(inputURL)\r\n # Returning the JSON object of the response and storing it in result\r\n result = response.json()\r\n print(json.dumps(result, indent=2))\r\n\r\n # Getting the exchange rate (Required Information)\r\n exchangeRate = float(result[\"Realtime Currency Exchange Rate\"]['5. Exchange Rate'])\r\n # Displaying exchange rate in respective label after rounding to 2 decimal places\r\n root.exchangerateLabel.config(text=str(round(exchangeRate, 2)))\r\n\r\n # Calculating the converted amount and rounding the decimal to 2 places\r\n calculateAmt = round(inputAmt * exchangeRate, 2)\r\n # Displaying the converted amount in the respective label\r\n root.outputAMT_Label.config(text=str(calculateAmt))\r\n\r\n# Creating object of tk class\r\nroot = tk.Tk()\r\nroot.geometry(\"400x350\")\r\nroot.resizable(False, False)\r\nroot.title(\"PyCurrenyConverter\")\r\nroot.config(bg = \"darkolivegreen4\")\r\n\r\n# Creating tkinter variables\r\ngetAMT = StringVar()\r\n\r\n# Calling the CreateWidgets() function\r\nCreateWidgets()\r\n\r\n# Defining infinite loop to run application\r\nroot.mainloop()\r\n","sub_path":"curr.py","file_name":"curr.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"623011356","text":"# -*- coding: utf-8 -*-\n# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nevaluate wordseg for LAC and other open-source wordseg tools\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport sys\nimport os\nimport io\n\n\ndef to_unicode(string):\n \"\"\" string compatibility for python2 & python3 \"\"\"\n if sys.version_info.major == 2 and isinstance(string, str):\n return string.decode(\"utf-8\")\n else:\n return string\n\n\ndef to_set(words):\n \"\"\" cut list to set of (string, off) \"\"\"\n off = 0\n s = set()\n for w in words:\n if w:\n s.add((off, w))\n off += len(w)\n return s\n\n\ndef cal_fscore(standard, result, split_delim=\" \"):\n \"\"\" caculate fscore for wordseg\n Param: standard, list of str, ground-truth labels , e.g. [\"a b c\", \"d ef g\"]\n Param: result, list of str, predicted result, e.g. [\"ab c\", \"d e fg\"]\n \"\"\"\n assert len(standard) == len(result)\n std, rst, cor = 0, 0, 0\n for s, r in zip(standard, result):\n s = to_set(s.rstrip().split(split_delim))\n r = to_set(r.rstrip().split(split_delim))\n std += len(s)\n rst += len(r)\n cor += len(s & r)\n p = 1.0 * cor / rst\n r = 1.0 * cor / std\n f = 2 * p * r / (p + r)\n\n print(\"std, rst, cor = %d, %d, %d\" % (std, rst, cor))\n print(\"precision = %.5f, recall = %.5f, f1 = %.5f\" % (p, r, f))\n #print(\"| | %.5f | %.5f | %.5f |\" % (p, r, f))\n print(\"\")\n\n return p, r, f\n\n\ndef load_testdata(datapath=\"./data/test_data/test_part\"):\n \"\"\"none\"\"\"\n sentences = []\n sent_seg_list = []\n for line in io.open(datapath, 'r', encoding='utf8'):\n sent, label = line.strip().split(\"\\t\")\n sentences.append(sent)\n\n sent = to_unicode(sent)\n label = label.split(\" \")\n assert len(sent) == len(label)\n\n # parse segment\n words = []\n current_word = \"\"\n for w, l in zip(sent, label):\n if l.endswith(\"-B\"):\n if current_word != \"\":\n words.append(current_word)\n current_word = w\n elif l.endswith(\"-I\"):\n current_word += w\n elif l.endswith(\"-O\"):\n if current_word != \"\":\n words.append(current_word)\n words.append(w)\n current_word = \"\"\n else:\n raise ValueError(\"wrong label: \" + l)\n if current_word != \"\":\n words.append(current_word)\n sent_seg = \" \".join(words)\n sent_seg_list.append(sent_seg)\n print(\"got %d lines\" % (len(sent_seg_list)))\n return sent_seg_list, sentences\n\n\ndef get_lac_result():\n \"\"\"\n get LAC predicted result by:\n `sh run.sh | tail -n 100 > result.txt`\n \"\"\"\n sent_seg_list = []\n for line in io.open(\"./result.txt\", 'r', encoding='utf8'):\n line = line.strip().split(\" \")\n words = [pair.split(\"/\")[0] for pair in line]\n labels = [pair.split(\"/\")[1] for pair in line]\n sent_seg = \" \".join(words)\n sent_seg = to_unicode(sent_seg)\n sent_seg_list.append(sent_seg)\n return sent_seg_list\n\n\ndef get_jieba_result(sentences):\n \"\"\"\n Ref to: https://github.com/fxsjy/jieba\n Install by `pip install jieba`\n \"\"\"\n import jieba\n preds = []\n for sentence in sentences:\n sent_seg = \" \".join(jieba.lcut(sentence))\n sent_seg = to_unicode(sent_seg)\n preds.append(sent_seg)\n return preds\n\n\ndef get_thulac_result(sentences):\n \"\"\"\n Ref to: http://thulac.thunlp.org/\n Install by: `pip install thulac`\n \"\"\"\n import thulac\n preds = []\n lac = thulac.thulac(seg_only=True)\n for sentence in sentences:\n sent_seg = lac.cut(sentence, text=True)\n sent_seg = to_unicode(sent_seg)\n preds.append(sent_seg)\n return preds\n\n\ndef get_pkuseg_result(sentences):\n \"\"\"\n Ref to: https://github.com/lancopku/pkuseg-python\n Install by: `pip3 install pkuseg`\n You should noticed that pkuseg-python only support python3\n \"\"\"\n import pkuseg\n seg = pkuseg.pkuseg()\n preds = []\n for sentence in sentences:\n sent_seg = \" \".join(seg.cut(sentence))\n sent_seg = to_unicode(sent_seg)\n preds.append(sent_seg)\n return preds\n\n\ndef get_hanlp_result(sentences):\n \"\"\"\n Ref to: https://github.com/hankcs/pyhanlp\n Install by: pip install pyhanlp\n (Before using pyhanlp, you need to download the model manully.)\n \"\"\"\n from pyhanlp import HanLP\n preds = []\n for sentence in sentences:\n arraylist = HanLP.segment(sentence)\n sent_seg = \" \".join(\n [term.toString().split(\"/\")[0] for term in arraylist])\n sent_seg = to_unicode(sent_seg)\n preds.append(sent_seg)\n return preds\n\n\ndef get_nlpir_result(sentences):\n \"\"\"\n Ref to: https://github.com/tsroten/pynlpir\n Install by `pip install pynlpir`\n Run `pynlpir update` to update License\n \"\"\"\n import pynlpir\n pynlpir.open()\n preds = []\n for sentence in sentences:\n sent_seg = \" \".join(pynlpir.segment(sentence, pos_tagging=False))\n sent_seg = to_unicode(sent_seg)\n preds.append(sent_seg)\n return preds\n\n\ndef get_ltp_result(sentences):\n \"\"\"\n Ref to: https://github.com/HIT-SCIR/pyltp\n 1. Install by `pip install pyltp`\n 2. Download models from http://ltp.ai/download.html\n \"\"\"\n from pyltp import Segmentor\n segmentor = Segmentor()\n model_path = \"./ltp_data_v3.4.0/cws.model\"\n if not os.path.exists(model_path):\n raise IOError(\"LTP Model do not exist! Download it first!\")\n segmentor.load(model_path)\n preds = []\n for sentence in sentences:\n sent_seg = \" \".join(segmentor.segment(sentence))\n sent_seg = to_unicode(sent_seg)\n preds.append(sent_seg)\n segmentor.release()\n\n return preds\n\n\ndef print_array(array):\n \"\"\"print some case\"\"\"\n for i in [1, 10, 20, 30, 40]:\n print(\"case \" + str(i) + \": \\t\" + array[i])\n\n\ndef evaluate_all():\n \"\"\"none\"\"\"\n standard, sentences = load_testdata()\n print_array(standard)\n\n # evaluate lac\n preds = get_lac_result()\n print(\"lac result:\")\n print_array(preds)\n cal_fscore(standard=standard, result=preds)\n\n # evaluate jieba\n preds = get_jieba_result(sentences)\n print(\"jieba result\")\n print_array(preds)\n cal_fscore(standard=standard, result=preds)\n\n # evaluate thulac\n preds = get_thulac_result(sentences)\n print(\"thulac result\")\n print_array(preds)\n cal_fscore(standard=standard, result=preds)\n\n # evaluate pkuseg, but pyuseg only support python3\n if sys.version_info.major == 3:\n preds = get_pkuseg_result(sentences)\n print(\"pkuseg result\")\n print_array(preds)\n cal_fscore(standard=standard, result=preds)\n\n # evaluate HanLP\n preds = get_hanlp_result(sentences)\n print(\"HanLP result\")\n print_array(preds)\n cal_fscore(standard=standard, result=preds)\n\n # evaluate NLPIR\n preds = get_nlpir_result(sentences)\n print(\"NLPIR result\")\n print_array(preds)\n cal_fscore(standard=standard, result=preds)\n\n # evaluate LTP\n preds = get_ltp_result(sentences)\n print(\"LTP result\")\n print_array(preds)\n cal_fscore(standard=standard, result=preds)\n\n\nif __name__ == \"__main__\":\n evaluate_all()\n","sub_path":"PaddleNLP/lexical_analysis/compare.py","file_name":"compare.py","file_ext":"py","file_size_in_byte":7889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"115946444","text":"import pandas as pd\n\nimport datetime\nimport time\nimport math\ndf1=pd.read_csv('e://123//testgps1.csv')\ndf1['h']=pd.to_datetime(df1['b'],format='%H:%M:%S')\nfor index, row in df1.iterrows():\n #print (index)\n if index == 0 :\n df1.ix[index,'ang']=0\n oldlat=row['f']\n oldlong=row['e']\n else:\n utf1 = row['h'].strftime('%H%M%S')\n #print((df1['f'] * 100))\n curlat1=row['f']\n curlong1=row['e']\n a1 = (curlong1 - oldlong)\n b1 = (curlat1 - oldlat)\n if a1 > 0 and b1 > 0:\n slope1 = 90.0 - math.atan(abs(b1 / a1)) * 180 / 3.14\n elif a1 > 0 and b1 < 0:\n slope1 = 90.0 + math.atan(abs(b1 / a1)) * 180 / 3.14\n elif a1 < 0 and b1 > 0:\n slope1 = 270.0 + math.atan(abs(b1 / a1)) * 180 / 3.14\n elif a1 < 0 and b1 < 0:\n slope1 = 270.0 - math.atan(abs(b1 / a1)) * 180 / 3.14\n\n #print ('angle '+ str(slope1))\n lat = (row['f'] * 100)\n long = (row['e'] * 100)\n df1.ix[index, 'ang'] =10\n #print(utf1)\n str1 = '$GNRMC,' + utf1 + '.00,A,' + str(lat) + ',N,' + str(long) + ',E,35,'+ str(int(slope1)) +',180917,,,A*6E'\n print(str1)\n oldlat=row['f']\n oldlong=row['e']\n #print (df1[['e','f','h']])\n\n#print ( df1)\n#print (df1['h'][10].strftime('%M'))\n#print (df1['h'][10].strftime('%S'))\n#utf2=df1['h'][10].strftime('%Y%m%d')\n\n\n\n","sub_path":"corp_sr/line/gps_check/testgps1.py","file_name":"testgps1.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"404200472","text":"from typing import cast\n\nfrom . import base\nfrom .classification import (\n DawidSkene,\n GLAD,\n GoldMajorityVote,\n KOS,\n MACE,\n MajorityVote,\n MMSR,\n OneCoinDawidSkene,\n Wawa,\n ZeroBasedSkill\n)\nfrom .multilabel import BinaryRelevance\nfrom .embeddings import (\n ClosestToAverage,\n HRRASA,\n RASA,\n)\nfrom .image_segmentation import (\n SegmentationEM,\n SegmentationRASA,\n SegmentationMajorityVote\n)\nfrom .pairwise import (\n BradleyTerry,\n NoisyBradleyTerry\n)\nfrom .texts import (\n TextRASA,\n TextHRRASA,\n ROVER\n)\n\n__all__ = [\n 'base',\n\n 'BradleyTerry',\n 'ClosestToAverage',\n 'DawidSkene',\n 'OneCoinDawidSkene',\n 'GLAD',\n 'GoldMajorityVote',\n 'HRRASA',\n 'KOS',\n 'MACE',\n 'MMSR',\n 'MajorityVote',\n 'NoisyBradleyTerry',\n 'RASA',\n 'ROVER',\n 'SegmentationEM',\n 'SegmentationMajorityVote',\n 'SegmentationRASA',\n 'TextHRRASA',\n 'TextRASA',\n 'Wawa',\n 'ZeroBasedSkill',\n 'BinaryRelevance'\n]\n\n\ndef is_arcadia() -> bool:\n try:\n import __res\n return cast(bool, __res == __res)\n except ImportError:\n return False\n","sub_path":"crowdkit/aggregation/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"351276692","text":"import copy\nimport xml.etree.ElementTree as ET\nfrom typing import List\n\nimport openpyxl\nimport csv\nfrom qualitytools import list_of_dictionaries\n\n\nclass Section (object):\n \"\"\"An XML element.\n\n This class is the reference implementation of the Element docbook.\n # https://stackoverflow.com/questions/2358045/how-can-i-implement-a-tree-in-python-are-there-any-built-in-data-structures-in\n \"\"\"\n\n def __init__(self,title=\"\",children=None):\n self._title = title\n self._text = \"\"\n\n # a section can contain a list\n self._list=[]\n self.tables = [] # list of tables (or list of List_of_dictionaries)\n\n self._children = []\n if children is not None:\n for child in children:\n self.add_child(child)\n\n def append_text(self, new_text):\n\n # add text without leading and trailing spaces\n self._text += new_text.strip()\n\n def _set__title(self, new_title):\n self._title = new_title\n\n def _set__list(self, new_list):\n self._list.append(copy.deepcopy(new_list))\n\n def append_sub_section(self, sub_section):\n # new_section = sub section objects\n assert isinstance(sub_section, Section)\n self._children.append(sub_section)\n\n def print_list (self):\n joined_string = \"\"\n if (self._list):\n joined_string = '\\n'.join(self._list)\n return joined_string\n\n def append_table(self,new_table):\n assert isinstance(new_table, list_of_dictionaries.List_of_dictionaries)\n self.tables.append(copy.deepcopy(new_table))\n\n def print_tables (self):\n\n joined_string = \"\"\n for table in self.tables:\n joined_head = '|' + '|'.join(table._head) + '|'\n joined_body = \"\"\n for row in table._body: # table._body = list of dictionaries (row = dict)\n joined_body = joined_body + '\\n' + '|' + '|'.join(row.values()) + '|'\n joined_string = joined_string + joined_head + joined_body + '\\n'\n\n return joined_string\n\n def __str__(self, level=0):\n ret = \"\\t\"*level+repr(self._title)+\"\\n\"\n for child in self._children:\n ret += child.__str__(level+1)\n return ret\n\n def __repr__(self):\n return ''\n\n def get_leaves(self):\n leafs = []\n\n def _get_leaf_nodes(node):\n if node is not None:\n if len(node._children) == 0:\n leafs.append(node)\n for n in node._children:\n _get_leaf_nodes(n)\n\n _get_leaf_nodes(self)\n return leafs\n\n\ndef get_text_from_element_and_childs (element):\n \"\"\"Returns the text found in the current element and also in the child elements\n :element: node (elementTree)\n :return: string = text concanenated in the elment and child elements\n \"\"\"\n text=\"\"\n if len(element) == 0:\n text = element.text.replace('\\n', ' ')\n else:\n for child in element:\n text = text + ' ' + get_text_from_element_and_childs(child)\n\n strip_text=text.strip(' \\t') # remove leading and trailing text\n #strip_text = ' '.join(text.split())\n\n return strip_text\n\n\ndef get_list_from_itemizedlist (element):\n \"\"\"Returns the values of the list \"itemizedlist\"\n :element: node (elementTree)\n :return:\n list = list of the test values\n \"\"\"\n # < itemizedlist > < listitem > < para > item 1 < /listitem > < /itemizedlist >\n my_list=[]\n for item in element:\n item_value = item.find('para').text.replace('\\n', ' ')\n my_list.append(item_value)\n return my_list\n\n\ndef append_table_from_informaltable (element):\n \"\"\"Returns the header columns and the values of the table\n :element: node (elementTree)\n :return:\n table_head = list of the header columns\n table_body = 2 dimentional array = table[i][j] = value matrix element of the line i andcolumn j\n \"\"\"\n for t in element.iter('informaltable'):\n table_head = []\n for row_head in t.findall('.tgroup/thead/row/entry'):\n text=get_text_from_element_and_childs (row_head)\n table_head.append(text)\n\n table_body = []\n table_line = []\n nb_head_columns=len(table_head)\n no_column=0\n no_row=0\n for row_body in t.findall('./tgroup/tbody/row/entry'):\n text = get_text_from_element_and_childs(row_body)\n table_line.append(text)\n no_column += 1\n if no_column == nb_head_columns:\n no_row +=1\n no_column = 0\n table_body.append(copy.deepcopy(table_line))\n table_line.clear()\n if no_column != 0:\n raise ValueError (\"Error reading the table, which headers are :\", table_head)\n return table_head,table_body\n\ndef append_free_table_from_informaltable (element):\n \"\"\"\n Returns the values of the table, whatever the book xml notation (i.e. head and body data)\n :param element: node (elementTree)\n :return: able_body = 2 dimentional array = table[i][j] = value matrix element of the line i andcolumn j\n \"\"\"\n for t in element.iter('informaltable'):\n table_body = []\n for row_head in t.findall('.tgroup/thead/row/entry'):\n text=get_text_from_element_and_childs (row_head)\n table_body.append(text)\n for row_body in t.findall('.tgroup/tbody/row/entry'):\n text=get_text_from_element_and_childs (row_body)\n table_body.append(text)\n if len(table_body) == 0:\n raise ValueError (\"Error reading the table, found empty \")\n return table_body\n\ndef build_section (element,header_description):\n \"\"\"\n Returns the section with childs\n section = parent section\n element = parent element (elementTree)\n :param element:\n :param header_description: dictionary : head_key:head_value to catch in the table\n :return:\n \"\"\"\n\n section = Section()\n for child_element in element:\n if (child_element.tag == 'title'): # section title\n section._title = child_element.text.replace('\\n', ' ') if child_element.text else \"\"\n elif (child_element.tag == 'para'):\n section.append_text (child_element.text)\n elif (child_element.tag == 'itemizedlist'):\n element_list = []\n element_list = get_list_from_itemizedlist(child_element)\n section._list = copy.deepcopy(element_list)\n elif (child_element.tag == 'informaltable'):\n # add a new table_head and table_body\n\n new_table=list_of_dictionaries.List_of_dictionaries()\n if 'orientation' in header_description:\n if header_description['orientation'] == \"horizontal\":\n (element_head, element_body) = append_table_from_informaltable(child_element)\n new_table.import_table(element_head,element_body)\n elif header_description['orientation'] == \"vertical\":\n (element_head, element_body) = append_table_from_informaltable(child_element)\n new_table.import_table_with_vertical_header (element_head, element_body)\n elif header_description['orientation'] == \"free\":\n element_body = append_free_table_from_informaltable(child_element)\n new_table.import_table_with_no_header(element_body,header_description)\n section.append_table(copy.deepcopy(new_table))\n pass\n\n elif (child_element.tag == 'section'):\n child_section = build_section (child_element,header_description)\n section.append_sub_section(child_section)\n return section\n\ndef parse_section (section):\n column={}\n column.clear()\n column['name'] = section._title\n leaves = section.get_leaves()\n for leaf in leaves:\n column_name = leaf._title\n column_cell = leaf._text\n\n # add the section internal list to the section text\n if (leaf._list):\n text = leaf.print_list()\n column_cell += text\n # add the section internal list to the section text\n if (leaf.tables):\n text = leaf.print_tables()\n column_cell += text\n\n column[column_name] = column_cell\n return column\n\ndef find_section (element_parent,Xpath_pattern,header_description,sub_sections=False):\n \"\"\"\n\n :param element_parent:\n :param Xpath_pattern:\n :param header_description: dictionary : head_key:head_value to catch in the table\n :param sub_sections:\n :return:\n \"\"\"\n\n section_found=None\n element=element_parent.find (Xpath_pattern)\n if element:\n if element.tag == 'section':\n if not sub_sections:\n section_found = build_section (element,header_description)\n print (section_found)\n else:\n section_found=Section()\n for sub_section in element.findall('section'):\n child_section = build_section(sub_section,header_description)\n section_found.append_sub_section(child_section)\n else:\n raise ValueError(\"section {} not found\".format(Xpath_pattern))\n return section_found\n\ndef get_first_table_from_sections (docbook, Xpath_pattern, header_description=None, table_title_key=None):\n \"\"\"\n browse all the sub-sections and grab their table\n :param element_parent:\n :param Xpath_pattern:\n :param header_description: dictionary : head_key:head_value to catch in the table\n :param table_title_key:\n :return: table gathered all the tables of the sub-sections browsed\n \"\"\"\n\n # let's assume that the table has an horizontal header\n if header_description is None:\n header_description = {}\n header_description['orientation'] = \"horizontal\"\n\n final_table = list_of_dictionaries.List_of_dictionaries(\"Section from pattern \" + Xpath_pattern)\n tables = get_tables_from_sections(docbook.node, Xpath_pattern, header_description, table_title_key, sub_sections=True)\n for table in tables:\n final_table.body = table.body\n return final_table\n\ndef get_sections_with_one_table (docbook, Xpath_pattern,header_description,table_title_key=None, sub_sections=False):\n \"\"\"\n parse all the sections and,for each section, take its (first) table\n :param docbook: xml docbook (yet parsed with elementTree)\n :param Xpath_pattern:\n :param header_description: dictionary : head_key:head_value to catch in the table\n :param table_title_key:\n :param sub_sections: id set to True, browse sub-sections\n :return:\n \"\"\"\n\n # first_table = list_of_dictionaries.List_of_dictionaries(\"Section from pattern \" + Xpath_pattern)\n first_table = get_tables_from_sections(docbook.node, Xpath_pattern, header_description, table_title_key, sub_sections)\n return first_table\n\ndef merge_tables (section_tables, table_title_key, section_title=None):\n \"\"\"\n In a given section (or sub-section), merge all its tables\n :param section_tables: list of the tables\n :param table_title_key: add a key-value in each table recorded\n :param section_title: value of the key above (eg name of the section containing the tables)\n :return: List_of_dictionaries : merged tables\n \"\"\"\n\n\n merged_table = list_of_dictionaries.List_of_dictionaries()\n for table in section_tables:\n listofdict = list_of_dictionaries.List_of_dictionaries()\n listofdict.body = table._body\n if table_title_key is not None:\n if table_title_key == 'section_name':\n # listofdict = list with a unique line of dictionary (index 0).\n # Add a nex key:value in this line 'section_name':section_title\n listofdict._body[0]['section_name'] = section_title\n else:\n # title of the table = value of a specific key in the table\n table_title = table._body[0][table_title_key]\n listofdict.title = table_title\n # TODO: add/merge several tables (not only one !!)\n merged_table.body = listofdict._body\n merged_table.title = listofdict.title\n return merged_table\n\ndef get_tables_from_sections (element_parent, Xpath_pattern, header_description, table_title_key=None, sub_sections=False):\n \"\"\"\n find the top-level section from a Xpath pattern\n gather the tables below this section and its sub-sections\n Avoid the top-level section if if sub_sections is set\n Record the tables depending its \"orientation\" (heads horizontal or not)\n :param element_parent: top-level section\n :param Xpath_pattern: paatern to select the section above\n :param header_description: dictionary : head_key:head_value to catch in the table\n :param table_title_key: add a key-value in each table recorded\n :param sub_sections: Avoid the top-level section if if sub_sections is set\n :return: list of dictionary : all the tables merged of all the sub-sections browsed\n \"\"\"\n\n full_table = list_of_dictionaries.List_of_dictionaries()\n try:\n section = find_section(element_parent,Xpath_pattern,header_description,sub_sections)\n except ValueError as error:\n print(\"\\nWarning:\",error)\n return full_table\n if not sub_sections:\n # merge tables only in the current section\n full_table = merge_tables(section.tables, table_title_key)\n else:\n # merge tables in each sub-section browsed (section._children X)\n # i.e children[X].table[0]._body (=list of dict), children[X].table[1]._body...)\n for children in section._children:\n # get in a list of dictionary all the sub tables\n merged_tables = merge_tables(children.tables, table_title_key, children._title)\n full_table.body = merged_tables._body\n full_table.title = merged_tables.title\n\n return full_table\n\n\ndef get_chapter (docbook, Xpath_pattern,table_description):\n \"\"\"\n parse all the sections\n :param docbook: xml docbook (yet parsed with elementTree)\n :param Xpath_pattern: Xpath request\n :return: list_of_dictionaries: text contents of the chapter indexed by section name\n table[no_line]['my_section'] = text founded in the section\n \"\"\"\n\n table = list_of_dictionaries.List_of_dictionaries (\"Chapter from pattern \"+Xpath_pattern)\n try:\n chapter = find_section(docbook.node, Xpath_pattern, table_description)\n except ValueError as error:\n print(\"\\nWarning: \",error)\n return table\n\n for section_of_chapter in chapter._children:\n column_dict = parse_section(section_of_chapter)\n table.body = [ column_dict ]\n column_dict.clear()\n\n return table\n\ndef get_section_with_one_table_by_subsection (docbook, Xpath_pattern,table_description):\n \"\"\"\n configuration:\n - a chapter is composed of sub-sections.\n - each subsection is browsed and each of its vertical table is recorded in a single full table\n goal is to record a final list of dictionary, where head columns come from the heads of the tables of the subsections\n\n :param docbook: xml docbook (yet parsed with elementTree)\n :param Xpath_pattern: Xpath pattern to find the chapter\n :param table_description: dictionary : head_key:head_value to catch in the table\n :return: the final list of dictionary above\n \"\"\"\n # table_title_key='section_name' : it is assumed that each table of each subsection will contain the name of the subsection\n # horizontal_header=False : it is assumed here that the tables are \"vertical\" (heads = first column on the left)\n # sub_sections = True : brows only sub-sections of the chapter (i.e. do not take in account the chapter top section)\n table = get_tables_from_sections(docbook.node, Xpath_pattern, table_description, table_title_key='section_name', sub_sections=True)\n return table\n\ndef get_tables_from_section (docbook, Xpath_pattern,table_description):\n \"\"\"\n configuration:\n - a chapter is composed of sub-sections.\n - each subsection is browsed and each of its vertical table is recorded in a single full table\n goal is to record a final list of dictionary, where head columns come from the heads of the tables of the subsections\n\n :param docbook: xml docbook (yet parsed with elementTree)\n :param Xpath_pattern: Xpath pattern to find the chapter\n :param table_description: dictionary : head_key:head_value to catch in the table\n :return: the final list of dictionary above\n \"\"\"\n # table_title_key='section_name' : it is assumed that each table of each subsection will contain the name of the subsection\n # horizontal_header=False : it is assumed here that the tables are \"vertical\" (heads = first column on the left)\n # sub_sections = True : brows only sub-sections of the chapter (i.e. do not take in account the chapter top section)\n table = get_tables_from_sections(docbook.node, Xpath_pattern, table_description, table_title_key='section_name', sub_sections=False)\n return table\n\ndef get_section_with_one_free_table_by_subsection (docbook, Xpath_pattern, table_description):\n \"\"\"\n configuration:\n - a chapter is composed of sub-sections.\n - each subsection is browsed and each of its table without header is recorded in a single full table\n goal is to record a final list of dictionary, where head columns come from the heads of the tables of the subsections\n\n :param docbook: xml docbook (yet parsed with elementTree)\n :param Xpath_pattern: Xpath pattern to find the chapter\n :param table_description: dictionary : head_key:head_value to catch in the table\n :return: the final list of dictionary above\n \"\"\"\n\n # table_title_key='section_name' : it is assumed that each table of each subsection will contain the name of the subsection\n # horizontal_header=False : it is assumed here that the tables are \"vertical\" (heads = first column on the left)\n # sub_sections = True : brows only sub-sections of the chapter (i.e. do not take in account the chapter top section)\n table = get_tables_from_sections(docbook.node, Xpath_pattern, table_description, table_title_key='section_name', sub_sections=True)\n return table\n\ndef copy_csv_file_into_new_xl_workbook (csv_input_filename):\n \"\"\"\n read text CSV file and copy its contents into a new created workbook file\n Parameters\n ----------\n csv_input_filename: string: input text file (csv, delimiter is \";\")\n\n Returns\n -------\n (wb,ws) : openpyxl workbook and worksheet created objects\n \"\"\"\n wb = openpyxl.Workbook()\n ws = wb.active\n f = open(csv_input_filename)\n reader = csv.reader(f, delimiter=';')\n for row in reader:\n ws.append(row)\n f.close()\n return (wb,ws)\n\ndef insert_columns_and_save_xl_file (xl_filename, column_heads, workbook, sheet):\n \"\"\"\n insert empty columns in the worksheet (from column 2). First line of each column is set with the column_heads strings\n\n Parameters\n ----------\n xl_filename: string: workbook file name\n column_heads: list of strings\n workbook: openpyxl file object\n sheet: openpyxl sheet object\n \"\"\"\n for head in column_heads:\n # this statement inserts a column before column 2\n # TODO: avoid hard coded value\n sheet.insert_cols(2)\n sheet['B1'] = head\n try:\n workbook.save(xl_filename)\n except PermissionError as error:\n raise\n\n\n\ndef read_csv_file_and_create_xl_workbook(csv_filename,xl_filename,new_columns):\n \"\"\"\n create an Excel workbook from a CSV file and creating new (empty) columns with headers\n \"\"\"\n\n (wb, ws) = copy_csv_file_into_new_xl_workbook(csv_filename)\n insert_columns_and_save_xl_file(xl_filename, new_columns, wb, ws)\n\n","sub_path":"qualitytools/book_parsed.py","file_name":"book_parsed.py","file_ext":"py","file_size_in_byte":19811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"573312381","text":"def file_check(address):\n import os\n if address == \"\" or address == \" \":\n return True\n return os.path.exists(address)\n\n\ndef copy_in_str(string1, string2):\n ret_str = \"\"\n i = 0\n while i != len(string1):\n if string1[i] != string2[i]:\n ret_str += \"-\"\n if string1[i] == string2[i]:\n ret_str += string1[i]\n i += 1\n return ret_str\n\n\ndef check_exit(inp_str):\n inp_str = inp_str.lower()\n if inp_str == \"exit\":\n exit()\n\n\ndef find_protospacers(spacer, fag_seq):\n if spacer not in fag_seq:\n return False\n else:\n return fag_seq.find(spacer)\n\n\ndef read_file_with_spacers(spacers_filename):\n with open(spacers_filename, \"r\") as f:\n spacers = f.read()\n spacers = spacers.replace(\" \", \"\")\n spacers = spacers.split(\"\\n\")\n return spacers\n\n\ndef read_file_with_fag(fag_filename):\n with open(fag_filename, \"r\") as f:\n warn = f.readline()\n text = f.read()\n text = text.replace(\"\\n\", \"\")\n text = text.replace(\" \", \"\")\n return text\n\n\ndef print_pam_predictor():\n print(\"\"\"\n███████████████████████████████████████████████████████████████████████\n█____██____██_███__██████____██____██___██____███____██___██____██____█\n█_██_██_██_██__█___██████_██_██_██_██_████_██__██_██_███_███_██_██_██_█\n█____██____██_█_█__█___██____██____██___██_██__██____███_███_██_██____█\n█_█████_██_██_███__██████_█████_█_███_████_██__██_██_███_███_██_██_█_██\n█_█████_██_██_███__██████_█████_█_███___██____███_██_███_███____██_█_██\n███████████████████████████████████████████████████████████████████████\n\"\"\")\n\n\ndef main():\n right = []\n left = []\n fag_filename = []\n print_pam_predictor()\n\n spacer_filename = input(\"Enter file name with spacers : \")\n # УБРАТЬ В СЛУЧАЕ ОШИБКИ СВЯЗЯННОЙ С КОДИРОВКОЙ\n check_exit(spacer_filename)\n if file_check(spacer_filename) is False:\n print(\"\"\"\\n!!!This file is not in the directory.!!!\\n\n Please place the file being processed in a directory.\\n\"\"\")\n exit()\n\n while True:\n input_filename = input(\"Enter filename seq backteriophage or exit: \")\n check_exit(input_filename)\n if file_check(input_filename) is False:\n print(\"\"\"\\n!!!This file is not in the directory.!!!\\n\n Please place the file being processed in a directory.\\n\"\"\")\n exit()\n if input_filename == \"\":\n break\n fag_filename.append(input_filename)\n\n spacers = read_file_with_spacers(spacer_filename)\n print(\"SPACERS: \", spacers)\n for j in fag_filename:\n for i in spacers:\n text = read_file_with_fag(j)\n if find_protospacers(i, text) is not False:\n pos = find_protospacers(i, text)\n right.append(text[pos + len(i):pos + len(i) + 10])\n left.append(text[pos - 10:pos])\n try:\n save_l = left[0]\n save_r = right[0]\n for i in range(len(right)):\n save_r = copy_in_str(right[i], save_r)\n for i in range(len(left)):\n save_l = copy_in_str(left[i], save_l)\n except:\n print(\"Few spacers or bacteriophages...\")\n exit()\n print(\"LEFT : \", left)\n print(\"RIGHT : \", right)\n print(\"Possible PAM in right : \", save_r)\n print(\"Possible PAM in left : \", save_l)\n input(\"Enter...\")\nmain()\n","sub_path":"example/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"431185508","text":"import scrapy\nfrom scrapy.spiders import Rule\nfrom scrapy.http import Request\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.crawler import CrawlerProcess\nimport logging\n\n# DEFAULT OPTIONS\nDELAY = 0.125\nALLOWED_DOMAINS = ['hard.rozetka.com.ua']\nSTART_URLS = ['https://hard.rozetka.com.ua/computers/c80095/filter/producer=msi/']\nCURRENT_BRAND = 'msi'\nTABLE_NAME = 'product'\nALLOW_PAGE_RULE = (r'\\/msi-.+',)\nALLOW_ITEM_RULE = (r'\\/msi_.+',)\nDENY_PAGE_RULE = (r'comments',)\nDENY_ITEM_RULE = (r'comments',)\nDATABASE_PIPELINE = 'spider.spider.pipelines.MySqlPipeline'\n\n\nclass Product(scrapy.Item):\n title = scrapy.Field()\n code = scrapy.Field()\n price = scrapy.Field()\n images = scrapy.Field()\n attrs = scrapy.Field()\n\n\nclass RozetkaSpider(scrapy.Spider):\n name = 'rozetka'\n custom_settings = {'ITEM_PIPELINES': {DATABASE_PIPELINE: 400}}\n\n def __init__(self, options, **kwargs):\n super().__init__(name=self.name, **kwargs)\n self.allowed_domains = options.get('ALLOWED_DOMAINS') if options.get('ALLOWED_DOMAINS') else ALLOWED_DOMAINS\n self.start_urls = options.get('START_URLS') if options.get('START_URLS') else START_URLS\n self.table_name = options.get('TABLE_NAME') if options.get('TABLE_NAME') else TABLE_NAME\n\n allow_page_rule = options.get('ALLOW_PAGE_RULE') if options.get('ALLOW_PAGE_RULE') else ALLOW_PAGE_RULE\n allow_item_rule = options.get('ALLOW_ITEM_RULE') if options.get('ALLOW_ITEM_RULE') else ALLOW_ITEM_RULE\n deny_page_rule = options.get('DENY_PAGE_RULE') if options.get('DENY_PAGE_RULE') else DENY_PAGE_RULE\n deny_item_rule = options.get('DENY_ITEM_RULE') if options.get('DENY_ITEM_RULE') else DENY_ITEM_RULE\n\n self.rules = (\n Rule(LinkExtractor()),\n Rule(LinkExtractor(allow=allow_page_rule, deny=deny_page_rule), callback='parse_page'),\n Rule(LinkExtractor(allow=allow_item_rule, deny=deny_item_rule), callback='parse_item'),)\n\n def is_br(self, selector):\n if selector.css('br'):\n return True\n return False\n\n def join_br(self, selector, css_value):\n total = []\n for row in selector.css(css_value):\n total.append(self.format(row.extract()))\n return total\n\n def format(self, string):\n if string:\n return string.strip()\n\n def get_images(self, response):\n product_images = []\n for image in response.css('a img::attr(src)').extract():\n if 'goods' in image and 'images' in image:\n product_images.append(image)\n\n return product_images\n\n def parse(self, response):\n pages = response.css('#navigation_block').css('a::attr(href)').extract()\n # Parse first page(start page)\n yield Request(response.url, callback=self.parse_page)\n\n # parse other pages\n for page in pages:\n yield Request(page, callback=self.parse_page)\n\n def parse_page(self, response):\n urls = response.css('#catalog_goods_block').css('a::attr(href)').extract()\n\n for url in urls:\n # work around for deny\n if 'comments' not in url:\n yield Request(url, callback=self.parse_item, dont_filter=False)\n\n def parse_item(self, response):\n title = response.css('h1::text').extract_first()\n title = self.format(title)\n logging.warning(title)\n\n code = response.css('span.detail-code-i::text').extract_first()\n code = self.format(code)\n logging.warning(code)\n\n price = response.css('#price_container > div > div > meta::attr(content)').extract_first()\n price = self.format(price)\n logging.warning(price)\n\n names = response.css('dl').css('div').css('dt')\n values = response.css('dl').css('div').css('dd')\n\n attrs = {}\n for name, attr in zip(names, values):\n if self.is_br(attr):\n attr = self.join_br(attr, 'dd::text')\n else:\n attr = attr.css('dd::text').extract_first()\n attr = self.format(attr)\n\n name = name.css('dt::text').extract_first()\n name = self.format(name)\n logging.warning(name + ': ' + str(attr))\n attrs.update({name: attr})\n\n images = self.get_images(response)\n product = Product(title=title, code=code, price=price, images=images, attrs=attrs)\n yield product\n\n\nif __name__ == \"__main__\":\n process = CrawlerProcess()\n process.crawl(RozetkaSpider)\n process.start() # the script will block here until the crawling is finished\n pass\n","sub_path":"spider/spider/spiders/rozetka.py","file_name":"rozetka.py","file_ext":"py","file_size_in_byte":4609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"468873899","text":"# -*- coding: utf-8 -*-\nimport re\nimport Levenshtein\nimport csv\n\n# 가정: database D는 aux A에 순서에 의해 정렬되어 있으며, 정돈되어 있음.\n# 가정: 10대, 20대... 로 표시된 경우 '대'를 제거\n# 가정: 문자열에는 '~'가 없어야 한다. (있어도 되지만 결과의 정확도가 조금 떨어질 수 있음.)\n# 가정: 각 Database 공통적인 정보만을 남기고 열을 삭제한다. 이 떄, 원본 DB는 남겨놔야 이후 매칭이 가능. (이는 개선가능성이 있다.)\n\nclass Reidentify(object):\n def __init__(self):\n database = []\n aux_array = []\n # open input files. f1=releasedD. f2=auxD\n f1 = open('./넷플릭스(Real).csv', 'r')\n f2 = open('./IMDB(Real).csv', 'r')\n self.readinput(f1, database)\n self.readinput(f2, aux_array)\n f1.close()\n f2.close()\n # score array = array for N by M\n score = [[1 for col in range(len(aux_array))] for row in range(len(database))]\n self.score(database, aux_array, score)\n alpha = 0.1\n # matching set is the dictation array which will contain (candidate, possibility)\n matching = [dict() for row in range(len(database))]\n self.matching_set(score, alpha, matching)\n self.print_candidate_name(database, aux_array, matching)\n\n # print the candidate\n def print_candidate_name(self, database, aux_array, matching):\n f = open('./result.txt', 'w')\n for row in range(len(database)):\n f.write(str(database[row])+'\\n')\n for key in matching[row].keys():\n f.write('=> ' + str(aux_array[key]) + ': ' + str(matching[row][key]) + '\\n')\n f.write('\\n')\n f.close()\n\n def readinput(self, file_handler, output_matrix):\n csv_reader = csv.reader(file_handler)\n iter_csv = iter(csv_reader)\n next(iter_csv)\n for row in csv_reader:\n output_matrix.append(row)\n\n def sim(self, aux, record):\n # 속성값이 10,000~20,000 처럼 범위형으로 들어오고 , \\과 같은 특수문자가 들어온다면 제거해준다.\n aux_pure = re.sub('[\\$, ]', '', aux)\n record_pure = re.sub('[\\$, ]', '', record)\n # 10000~20000 등과 같을 때 1차원 배열 ( 10000 20000 ) 으로 문자를 치환 [~ => 띄어쓰기]\n aux_pure = re.sub('~', ' ', aux_pure)\n record_pure = re.sub('~', ' ', record_pure)\n # split by space\n aux_pure = aux_pure.split()\n record_pure = record_pure.split()\n # 애초에 space가 없었다면 배열은 원소를 하나만을 가진다. 이를 체크해서 원소가 두개이도록 모두 바꾼다.\n # (1) 입력이 (10~20)으로 들어왔다면 split 단계 이후에는 (10,20)과 같은 원소 2개의 배열 형태\n # (2) 입력이 애초에 (10~20)으로 들어온게 아니라 (10)과 같이 숫자 하나로 들어왔다면, split 단계까지 진행되었을 때 원소를 (10)만 가지게 된다.\n # 입력 인자에 통일성을 주기 위해 (2)번과 같은 경우에는 (10) => (10,10)으로 변경해준다.\n if len(aux_pure) == 1:\n aux_pure.append(aux_pure[0])\n if len(record_pure) == 1:\n record_pure.append(record_pure[0])\n\n # 배열의 원소가 숫자라면 이를 숫자로 인식하도록 int 함수를 적용.\n # 만일 두 비교대상 중 하나라도 숫자가 아니라면 문자열 vs 문자열로 비교해야하므로 is_string이라는 flag로 문자열인지 체크를 해준다.\n # 하나라도 문자열이라면 밑에 case string 을 수행하고 return\n # 모두 숫자라면 case number 를 수행\n is_string = 0\n try:\n for i in range(len(aux_pure)):\n aux_pure[i] = int(aux_pure[i])\n except ValueError:\n is_string = 1\n try:\n for i in range(len(record_pure)):\n record_pure[i] = int(record_pure[i])\n except ValueError:\n is_string = 1\n\n # case string\n # 어느 하나라도 string 이라면 Levenshtein algorithm 을 수행한다.\n # 유사도 = 1 - (다른 문자 갯수 / 긴 문자열의 길이)\n if is_string == 1:\n error = Levenshtein.distance(str(record_pure[0]), str(aux_pure[0]))\n length = len(str(record_pure[0])) if len(str(record_pure[0])) > len(str(aux_pure[0])) else len(str(aux_pure[0]))\n return 1 - (error / length)\n\n # case number\n # releaseD 의 범위 내에 aux 의 값이 포함되어있는지, 포함되어있다면 다음과 같이 유사도를 낸다.\n # 유사도 = (포함되는 수 개수 / 더 넓은 숫자범위)\n match = 0\n for i in range(record_pure[0], record_pure[1] + 1):\n if i in range(aux_pure[0], aux_pure[1] + 1):\n match += 1\n length = (record_pure[1] - record_pure[0] + 1) if (record_pure[1] - record_pure[0]) > (record_pure[1] - record_pure[0]) else (record_pure[1] - record_pure[0] + 1)\n return match / length\n\n def score(self, database, aux_array, output):\n f = open('./output.txt', 'w')\n f2 = open('./output2.txt', 'w')\n for record in range(len(database)):\n for aux in range(len(aux_array)):\n minimum = 1\n for attribute in range(len(aux_array[aux])):\n res = self.sim(aux_array[aux][attribute], database[record][attribute])\n if minimum > res:\n output[record][aux] = round(res, 4)\n minimum = round(res, 4)\n # f.writelines(str(database[record]))\n # f.writelines(str(aux_array[aux]))\n # f.writelines(str(output[record]))\n f2.write(str(record + 1) + ': ' + str(database[record]) + '\\n')\n f2.write(str(output[record]) + '\\n')\n f.close()\n f2.close()\n\n def matching_set(self, input_array, alpha, output_array):\n for row in range(len(input_array)):\n for col in range(len(input_array[row])):\n if input_array[row][col] >= alpha:\n output_array[row][col] = input_array[row][col]\n # print(row, output_array[row])\n\n def setting_alpha(self):\n pass\n\n\nif __name__ == '__main__':\n re = Reidentify()","sub_path":"src/alg_min.py","file_name":"alg_min.py","file_ext":"py","file_size_in_byte":6393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"426693847","text":"#from skimage.morphology import skeletonize\nfrom skimage import draw\nimport numpy as np\nimport Segments\nimport Quantization\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport scipy.ndimage as ndi\nimport EuclidMST\n\ndef smooth_with_function_and_mask(image, function, mask):\n bleed_over = function(mask.astype(float))\n masked_image = np.zeros(image.shape, image.dtype)\n masked_image[mask] = image[mask]\n smoothed_image = function(masked_image)\n output_image = smoothed_image / (bleed_over + np.finfo(float).eps)\n return output_image\n\ndef skeletonize(image):\n \"\"\"Return the skeleton of a binary image.\n\n Thinning is used to reduce each connected component in a binary image\n to a single-pixel wide skeleton.\n\n Parameters\n ----------\n image : numpy.ndarray\n A binary image containing the objects to be skeletonized. '1'\n represents foreground, and '0' represents background. It\n also accepts arrays of boolean values where True is foreground.\n\n Returns\n -------\n skeleton : ndarray\n A matrix containing the thinned image.\n\n See also\n --------\n medial_axis\n\n Notes\n -----\n The algorithm [1] works by making successive passes of the image,\n removing pixels on object borders. This continues until no\n more pixels can be removed. The image is correlated with a\n mask that assigns each pixel a number in the range [0...255]\n corresponding to each possible pattern of its 8 neighbouring\n pixels. A look up table is then used to assign the pixels a\n value of 0, 1, 2 or 3, which are selectively removed during\n the iterations.\n\n Note that this algorithm will give different results than a\n medial axis transform, which is also often referred to as\n \"skeletonization\".\n\n References\n ----------\n .. [1] A fast parallel algorithm for thinning digital patterns,\n T. Y. ZHANG and C. Y. SUEN, Communications of the ACM,\n March 1984, Volume 27, Number 3\n\n\n Examples\n --------\n >>> X, Y = np.ogrid[0:9, 0:9]\n >>> ellipse = (1./3 * (X - 4)**2 + (Y - 4)**2 < 3**2).astype(np.uint8)\n >>> ellipse\n array([[0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0]], dtype=uint8)\n >>> skel = skeletonize(ellipse)\n >>> skel\n array([[0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)\n\n \"\"\"\n # look up table - there is one entry for each of the 2^8=256 possible\n # combinations of 8 binary neighbours. 1's, 2's and 3's are candidates\n # for removal at each iteration of the algorithm.\n lut = [0, 0, 0, 1, 0, 0, 1, 3, 0, 0, 3, 1, 1, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 3, 0, 3, 3,\n 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 2, 2,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 2, 0,\n 0, 1, 3, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 2, 3, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 2, 3, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 1, 0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0]\n\n # convert to unsigned int (this should work for boolean values)\n skeleton = np.array(image).astype(np.uint8)\n\n # check some properties of the input image:\n # - 2D\n # - binary image with only 0's and 1's\n if skeleton.ndim != 2:\n raise ValueError('Skeletonize requires a 2D array')\n if not np.all(np.in1d(skeleton.flat, (0, 1))):\n raise ValueError('Image contains values other than 0 and 1')\n\n # create the mask that will assign a unique value based on the\n # arrangement of neighbouring pixels\n mask = np.array([[1, 2, 4],\n [128, 0, 8],\n [64, 32, 16]], np.uint8)\n\n pixelRemoved = True\n while pixelRemoved:\n pixelRemoved = False;\n\n # assign each pixel a unique value based on its foreground neighbours\n neighbours = ndi.correlate(skeleton, mask, mode='nearest')\n\n # ignore background\n neighbours *= skeleton\n\n # use LUT to categorize each foreground pixel as a 0, 1, 2 or 3\n codes = np.take(lut, neighbours)\n\n # pass 1 - remove the 1's and 3's\n code_mask = (codes == 1)\n if np.any(code_mask):\n pixelRemoved = True\n skeleton[code_mask] = 0\n code_mask = (codes == 3)\n if np.any(code_mask):\n pixelRemoved = True\n skeleton[code_mask] = 0\n\n # pass 2 - remove the 2's and 3's\n neighbours = ndi.correlate(skeleton, mask, mode='nearest')\n neighbours *= skeleton\n codes = np.take(lut, neighbours)\n code_mask = (codes == 2)\n if np.any(code_mask):\n pixelRemoved = True\n skeleton[code_mask] = 0\n code_mask = (codes == 3)\n if np.any(code_mask):\n pixelRemoved = True\n skeleton[code_mask] = 0\n\n return skeleton\n\n\nclass Skeleton:\n\n def count_neighbors(self):\n k = np.array([[1,1,1],\n [1,0,1],\n [1,1,1]])\n\n m = ndi.filters.convolve(self.skeleton,k,mode='constant',cval=0)\n nc = np.multiply(m,self.skeleton)\n self.neighbor_count = nc\n\n def euclidMstOrder(self):\n emst2 = EuclidMST.EuclidMST(self.segments.segmentList) # TODO fix\n emst2.segmentOrdering()\n self.segments.segmentList = emst2.newSegmentTree # TODO fix\n\n\n\n def __init__(self, image_matrix, sigma=0.5):\n\n if sigma >= 0.:\n mask = np.ones(image_matrix.shape, dtype=bool)\n fsmooth = lambda x: ndi.gaussian_filter(x, sigma, mode='constant')\n self.imin = smooth_with_function_and_mask(image_matrix, fsmooth, mask)\n else:\n self.imin = image_matrix\n\n self.centroids = Quantization.measCentroid(self.imin, 2)\n\n nq = np.array([[x * 255] for x in range(0, 2)])\n self.imin = Quantization.quantMatrix(self.imin, nq, self.centroids)\n plt.imshow(self.imin, cmap=cm.gray)\n plt.savefig(\"figStartOrig.png\")\n plt.clf()\n\n self.ibin = np.zeros(self.imin.shape, dtype=np.uint8)\n x0,y0 = np.where(self.imin == 0)\n self.ibin[x0,y0] = 1\n\n skel = skeletonize(self.ibin)\n self.skeleton = skel.astype(np.uint8)\n plt.imshow(self.skeleton, cmap=cm.gray)\n plt.savefig(\"figStartSkel.png\")\n plt.clf()\n\n self.segments = Segments.Segments()\n\n\n for neighbor_limit in range(1,5):\n while True:\n self.count_neighbors()\n for internal_limit in range(1,neighbor_limit+1):\n startx, starty = np.where(self.neighbor_count == internal_limit)\n if len(startx) > 0:\n break\n if len(startx) == 0:\n break\n for (x,y) in zip(startx, starty):\n if self.skeleton[x,y] == 1:\n self.follow_line(x,y)\n print(np.sum(self.skeleton))\n print(np.sum(self.neighbor_count))\n print(np.max(self.neighbor_count))\n assert (np.sum(self.neighbor_count) == 0)\n\n def follow_line(self,x,y):\n X,Y = self.skeleton.shape\n segment = [[x, y]]\n self.skeleton[x, y] = 0\n while True:\n # search for neighbor\n # decrement neighbor counts\n adjacent_counter = self.neighbor_count[x, y]\n done = adjacent_counter > 1 or adjacent_counter == 0\n self.neighbor_count[x, y] = 0\n\n for (i, j) in [(-1, 0), (0, -1), (1, 0), (0, 1), (1, 1), (1, -1), (-1, 1), (-1, -1)]:\n x_n = x + i\n y_n = y + j\n\n if (x_n < 0) or (y_n < 0) or (x_n >= X) or (y_n >= Y):\n continue\n if self.skeleton[x_n, y_n] == 1:\n self.neighbor_count[x_n, y_n] -= 1\n x_save, y_save = x_n, y_n\n\n if not done:\n segment.append([x_save, y_save])\n x, y = x_save, y_save\n self.skeleton[x, y] = 0\n else:\n break\n if len(segment) > 1:\n self.segments.append(segment)\n\n\n\n\n\n","sub_path":"Skeleton.py","file_name":"Skeleton.py","file_ext":"py","file_size_in_byte":9115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"394962232","text":"#-*- coding:utf-8 -*-\n\"\"\"\n@Author: Jeff Zhang\n@Time: 17-8-31 上午9:29\n@File: NaiveBayes.py\n\"\"\"\n\n\nimport numpy as np\n\n\nclass NaiveBayes(object):\n def __init__(self):\n pass\n\n def setOfWordsToVecTor(self, vocabularyList, smsWords):\n \"\"\"\n SMS内容匹配预料库，标记预料库的词汇出现的次数\n :param vocabularyList:\n :param smsWords:\n :return:\n \"\"\"\n vocabMarked = [0] * len(vocabularyList)\n for smsWord in smsWords:\n if smsWord in vocabularyList:\n vocabMarked[vocabularyList.index(smsWord)] += 1\n return vocabMarked\n\n def setOfWordsListToVecTor(self, vocabularyList, smsWordsList):\n \"\"\"\n 将文本数据的二维数组标记\n :param vocabularyList:\n :param smsWordsList:\n :return:\n \"\"\"\n vocabMarkedList = []\n for i in range(len(smsWordsList)):\n vocabMarked = self.setOfWordsToVecTor(vocabularyList, smsWordsList[i])\n vocabMarkedList.append(vocabMarked)\n return vocabMarkedList\n\n def fit(self, trainMarkedWords, trainCategory):\n numTrainDoc = len(trainMarkedWords)\n numWords = len(trainMarkedWords[0])\n pSpam = sum(trainCategory) / float(numTrainDoc)\n wordsInSpamNum = np.ones(numWords)\n wordsInHealthNum = np.ones(numWords)\n SpamWordsNum = 2.0\n HealthWordsNum = 2.0\n for i in range(0, numTrainDoc):\n if trainCategory[i] == 1:\n wordsInSpamNum += trainMarkedWords[i]\n SpamWordsNum += sum(trainMarkedWords[i]) # 统计Spam中语料库中词汇出现的总次数\n if trainCategory[i] == 0:\n wordsInHealthNum += trainMarkedWords[i]\n HealthWordsNum += sum(trainMarkedWords[i])\n\n pWordsSpamicity = np.log(wordsInSpamNum / SpamWordsNum)\n pWordsHealthy = np.log(wordsInHealthNum / HealthWordsNum)\n\n self.pWordsHealthy_, self.pWordsSpamicity_, self.pSpam_ = pWordsHealthy, pWordsSpamicity, pSpam\n return self\n\n\n\n def predict(self, vocabularyList, testWords):\n testWordsCount = self.setOfWordsToVecTor(vocabularyList, testWords)\n testWordsMarkedArray = np.array(testWordsCount)\n # 计算P(Ci|W)，W为向量。P(Ci|W)只需计算P(W|Ci)P(Ci)\n p1 = sum(testWordsMarkedArray * self.pWordsSpamicity_) + np.log(self.pSpam_)\n p0 = sum(testWordsMarkedArray * self.pWordsHealthy_) + np.log(1 - self.pSpam_)\n if p1 > p0:\n return 1\n else:\n return 0","sub_path":"SupervisedLearning/NaiveBayes.py","file_name":"NaiveBayes.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"435737426","text":"# Copyright 2019 GreenWaves Technologies, SAS\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# http://www.apache.org/licenses/LICENSE-2.0\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 sys\nfrom typing import Sequence\n\nimport numpy as np\n\nfrom graph.types import (ActivationParameters, Conv2DParameters, FcParameters,\n InputParameters, OutputParameters, PadParameters,\n PoolingParameters, SoftMaxParameters, ReshapeParameters,\n MatrixAddParameters, ConcatParameters, TransposeParameters)\nfrom quantization.quantization_record import (FilterQuantizationRecord,\n QuantizationRecord)\n\nfrom .kernels.conv2d import conv2d\nfrom .kernels.linear import linear\nfrom .kernels.misc import activation, concat, softmax, reshape, add, transpose\nfrom .kernels.pool import av_pool, max_pool\nfrom .kernels.utils import pad\n\n\nclass Executer():\n\n @classmethod\n def execute(cls, node, in_tensors, qrec=False):\n execute_switch = {\n Conv2DParameters: cls.execute_conv2d,\n FcParameters: cls.execute_linear,\n PoolingParameters: cls.execute_pooling,\n InputParameters: cls.execute_input,\n OutputParameters: cls.execute_output,\n ActivationParameters: cls.execute_activation,\n PadParameters: cls.execute_pad,\n SoftMaxParameters: cls.execute_softmax,\n ReshapeParameters: cls.execute_reshape,\n MatrixAddParameters: cls.execute_add,\n ConcatParameters: cls.execute_concat,\n TransposeParameters: cls.execute_transpose\n }\n func = execute_switch.get(node.__class__)\n if func:\n return func(node, in_tensors, qrec)\n raise NotImplementedError(\"kernel for node %s is not implemented\" % node.__class__)\n\n @staticmethod\n def execute_transpose(node: TransposeParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n\n return [transpose(node, node.in_dims, node.out_dims, in_tensors[0], qrec)], None\n\n @staticmethod\n def execute_reshape(node: ReshapeParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n\n return [reshape(node, node.in_dims, node.out_dims, in_tensors[0], qrec)], None\n\n @staticmethod\n def execute_softmax(node: SoftMaxParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n\n return [softmax(node, node.in_dims, node.out_dims, in_tensors[0], qrec)], None\n\n @staticmethod\n def execute_pad(node: PadParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n del qrec\n\n return [pad(in_tensors[0], node.in_dims[0], node.padding, \"zero\")], None\n\n @staticmethod\n def execute_concat(node: ConcatParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n # TODO - check quantization\n # This assumes that the input tensors have been transposed to the correct\n # order for the concat\n del qrec\n return [concat(node, node.in_dims, node.out_dims[0], in_tensors)], None\n\n @staticmethod\n def execute_add(node: MatrixAddParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n return [add(node, node.in_dims, node.out_dims, in_tensors, qrec)], None\n\n @staticmethod\n def execute_activation(node: ActivationParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n\n return [activation(node, node.in_dims[0], node.out_dims[0], in_tensors[0], qrec)], None\n\n @staticmethod\n def execute_input(node: InputParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n value = in_tensors[node.index]\n if qrec:\n value = qrec.out_qs[0].quantize(value)\n\n # ensure that the shape of the input is correct\n try:\n value = value.reshape(node.dims.shape)\n except ValueError as ex:\n trace_back = sys.exc_info()[2]\n raise ValueError(\n \"Input data dimensions are not compatible with graph input: {!s}\".format(ex)\n ).with_traceback(trace_back)\n if node.transpose_out:\n value = np.transpose(value, node.transpose_out)\n return [value], None\n\n @staticmethod\n def execute_output(node: OutputParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n del qrec\n result = in_tensors[0]\n if node.transpose_in:\n result = np.transpose(result, node.transpose_in)\n\n return [result], None\n\n @staticmethod\n def execute_pooling(node: PoolingParameters,\n in_tensors: Sequence[np.array],\n qrec: QuantizationRecord = None):\n if node.pool_type == \"max\":\n val = max_pool(node, node.in_dims[0], node.out_dims[0], in_tensors[0], qrec)\n elif node.pool_type == \"average\":\n val = av_pool(node, node.in_dims[0], node.out_dims[0], in_tensors[0], qrec)\n else:\n raise ValueError(\"Incorrect pooling type: %s\" % node.pool_type)\n\n return [val], None\n\n @staticmethod\n def execute_linear(node: FcParameters,\n in_tensors: Sequence[np.array],\n qrec: FilterQuantizationRecord = None):\n if qrec:\n weights = qrec.weights_q.quantize(node.weights)\n biases = qrec.biases_q.quantize(node.biases)\n else:\n weights = node.weights\n biases = node.biases\n\n details = {}\n res = linear(node,\n node.in_dims[0],\n node.out_dims[0],\n in_tensors[0],\n weights,\n biases,\n qrec=qrec,\n details=details)\n return [res], details\n\n @staticmethod\n def execute_conv2d(node: Conv2DParameters,\n in_tensors: Sequence[np.array],\n qrec: FilterQuantizationRecord = None):\n\n if qrec:\n weights = qrec.weights_q.quantize(node.weights)\n biases = qrec.biases_q.quantize(node.biases)\n else:\n weights = node.weights\n biases = node.biases\n\n details = {}\n res = conv2d(node,\n node.in_dims[0],\n node.out_dims[0],\n in_tensors[0],\n weights,\n biases,\n qrec=qrec,\n details=details)\n return [res], details\n","sub_path":"tools/nntool/execution/executer.py","file_name":"executer.py","file_ext":"py","file_size_in_byte":7428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"200346355","text":"from setuptools import setup\n\nVERSION = \"2.2.3\"\n\nsetup(\n name=\"nhltv\",\n version=VERSION,\n description=\"Download NHL games from game center\",\n url=\"https://github.com/cmaxwe/dl-nhltv\",\n license=\"None\",\n keywords=\"NHL GAMECENTER\",\n packages=[\"nhltv_lib\", \"nhltv_lib.alembic\", \"nhltv_lib.alembic.versions\"],\n data_files=[\n (\"extras\", [\"nhltv_lib/extras/black.mkv\", \"nhltv_lib/alembic.ini\"])\n ],\n include_package_data=True,\n entry_points={\"console_scripts\": [\"nhltv=nhltv_lib.main:main\"]},\n install_requires=[\n \"requests==2.22.0\",\n \"alembic==1.3.3\",\n \"SQLAlchemy==1.3.12\",\n ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"463533699","text":"import numpy as np\nimport pandas as pd\nimport bs4\nfrom urllib.request import urlopen as uReq\nfrom bs4 import BeautifulSoup as soup\n\nmy_url ='https://www.newegg.com/Product/ProductList.aspx?Submit=ENE&DEPA=0&Order=BESTMATCH&Description=graphics+card&ignorear=0&N=-1&isNodeId=1'\nuClient = uReq(my_url)\n\npage_html = uClient.read()\nuClient.close()\npage_soup = soup(page_html , \"html.parser\")\ncontainers = page_soup.findAll(\"div\", {\"class\" : \"item-container\"})\n\nfile_name = \"products.csv\"\nf = open(file_name, \"w\")\nheaders = \"brand, Product_name, shipping\\n\"\nf.write(headers)\nfor container in containers:\n brand = container.div.div.a.img[\"title\"]\n \n title_container = container.findAll(\"a\", {\"class\" : \"item-title\"})\n product_name = title_container[0].text\n \n shipping_container = container.findAll(\"li\", {\"class\" : \"price-ship\"})\n shipping = shipping_container[0].text.strip()\n \n# print(\"brand : \" + brand)\n# print(\"product_name : \" + product_name)\n# print(\"shipping : \" + shipping)\n# print(\"\")\n \n f.write(brand + \",\" + product_name.replace(\",\", \"|\") + \",\" + shipping + \"\\n\")\nf.close()\n\ntrain_df = pd.read_csv(\"products.csv\")","sub_path":"Homeworks/Homework_5/WebScraping.py","file_name":"WebScraping.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"126924685","text":"import pygame\nimport sounds\nfrom screen import screen\nfrom hindernise import feld\nfrom time import time\nfrom KlasseHarpyie import Harpyie\n\nhintergrundLevel2 = pygame.image.load(\"Bilder\\Karte\\Level2.png\")\ntor = pygame.Rect(3*feld+ feld/2, feld, 5, 5) #Ziel in Level 2\n\nmonster1 = Harpyie(200,150,3,feld,feld,2,\"Harpyie\",time())\nmonster2 = Harpyie(300,200,3,feld,feld,2,\"Harpyie\",time())\n\ndef Level2(spieler):\n screen.blit(hintergrundLevel2, (0, 0))\n spieler.steuerung()\n\n if monster1.mLaufen(spieler) or monster2.mLaufen(spieler): #!!! versuche grade das Sterben einzuführen !!!\n pygame.display.update()\n pygame.time.wait(1000)\n monster1.reset()\n monster2.reset()\n gameOver = 4\n return gameOver\n\n if spieler.obenKollision.colliderect(tor):\n spieler.x = 582\n spieler.y = 476\n pygame.mixer.Sound.play(sounds.soundTor())\n sounds.soundBoss()\n pygame.time.wait(500)\n monster1.reset()\n monster2.reset()\n level = 3\n spieler.level = 3\n return level\n else:\n level = 2\n return level","sub_path":"Pygame/Beleg/DungeonVer3/Level2.py","file_name":"Level2.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"198364546","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 20 23:45:58 2017\n\n@author: manish\n\"\"\"\nimport pandas as pd\nimport cv2\nimport numpy as np\nimport os\nfrom random import shuffle\nfrom tqdm import tqdm\n\nTRAIN_DIR = '/home/manish/Manish/Machine_Learning/Kaggle/Cifar-10/train'\nTEST_DIR = '/home/manish/Manish/Machine_Learning/Kaggle/Cifar-10/test'\nIMG_SIZE = 32\nLR = 0.001\n\nMODEL_NAME = 'cifar_10-{}-{}.model'.format(LR, '6-conv-basic')\n\nlabels_df = pd.read_csv('/home/manish/Manish/Machine_Learning/Kaggle/Cifar-10/trainLabels.csv')\nlabels_arr = []\nfor i in range(0, len(labels_df)):\n labels_arr += [[labels_df.iloc[i,0], labels_df.iloc[i,1]]]\n\ndef label_image(img):\n label_no = int(img.split('.')[0])\n if labels_arr[label_no-1][1] == 'airplane':\n return [1,0,0,0,0,0,0,0,0,0]\n elif labels_arr[label_no-1][1] == 'automobile':\n return [0,1,0,0,0,0,0,0,0,0]\n elif labels_arr[label_no-1][1] == 'bird':\n return [0,0,1,0,0,0,0,0,0,0]\n elif labels_arr[label_no-1][1] == 'cat':\n return [0,0,0,1,0,0,0,0,0,0]\n elif labels_arr[label_no-1][1] == 'deer':\n return [0,0,0,0,1,0,0,0,0,0]\n elif labels_arr[label_no-1][1] == 'dog':\n return [0,0,0,0,0,1,0,0,0,0]\n elif labels_arr[label_no-1][1] == 'frog':\n return [0,0,0,0,0,0,1,0,0,0]\n elif labels_arr[label_no-1][1] == 'horse':\n return [0,0,0,0,0,0,0,1,0,0]\n elif labels_arr[label_no-1][1] == 'ship':\n return [0,0,0,0,0,0,0,0,1,0]\n elif labels_arr[label_no-1][1] == 'truck':\n return [0,0,0,0,0,0,0,0,0,1]\n \ndef create_train_set():\n training_data = []\n for img in tqdm(os.listdir(TRAIN_DIR)):\n label = label_image(img)\n path = os.path.join(TRAIN_DIR, img)\n img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n training_data.append([np.array(img), np.array(label)])\n shuffle(training_data)\n np.save('train_data.npy', training_data)\n return training_data\n\ndef create_test_set():\n test_data = []\n for img in tqdm(os.listdir(TEST_DIR)):\n path = os.path.join(TEST_DIR, img)\n img_num = img.split('.')[0]\n img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n test_data.append([np.array(img), img_num])\n shuffle(test_data)\n np.save('test_data.npy', test_data)\n return test_data\n\n#train_data = create_train_set() #Saved the data\n\ntrain_data = np.load('train_data.npy')\n\n\nimport tflearn\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.estimator import regression\n\nconvnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\n\nconvnet = conv_2d(convnet, 32, 5 , activation = 'relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 64, 5, activation = 'relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 128, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = fully_connected(convnet, 1024, activation='relu')\nconvnet = dropout(convnet,0.8)\n\nconvnet = fully_connected(convnet, 10, activation='softmax')\nconvnet = regression(convnet, optimizer= 'adam', learning_rate = LR, loss= 'categorical_crossentropy',name='targets')\n\nmodel = tflearn.DNN(convnet, tensorboard_dir = 'log')\n\nif os.path.exists('/home/manish/Manish/Machine_Learning/Kaggle/DogvsCats/{}.meta'.format(MODEL_NAME)):\n model.load(MODEL_NAME)\n print('Model Loaded')\n \n \ntrain = train_data[:-1200]\ntest = train_data[-1200:]\n\nX = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE, IMG_SIZE, 1)\nY = [i[1] for i in train]\n\ntest_x = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\ntest_y = [i[1] for i in test]\n\nmodel.fit({'input':X}, {'targets' : Y}, n_epoch = 3, validation_set= ({'input':test_x},{'targets':test_y}), snapshot_step =500, show_metric = True, run_id= MODEL_NAME)\n\nmodel.save(MODEL_NAME)\n\n#test_data = create_test_set()\ntest_data = np.load('test_data.npy')\n\nlabels = {0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog', 6:'frog', 7:'horse', 8:'ship', 9: 'truck'}\n\n\nimport matplotlib.pyplot as plt\nfig = plt.figure()\nfor num, data in enumerate(test_data[:12]):\n img_num = data[1]\n img_data = data[0]\n \n y = fig.add_subplot(3,4,num+1)\n orig = img_data\n data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)\n \n model_out = model.predict([data])[0]\n \n str_label = labels[np.argmax(model_out)]\n \n y.imshow(orig)\n plt.title(str_label)\n y.axes.get_xaxis().set_visible(False)\n y.axes.get_yaxis().set_visible(False)\n \nplt.show()\n\n\nwith open('submission_file.csv', 'w') as f:\n f.write('id,label\\n')\n \nwith open('submission_file.csv', 'a') as f:\n for data in tqdm(test_data):\n img_num = data[1]\n img_data = data[0]\n orig = img_data\n data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)\n model_out = model.predict([data])[0]\n str_label = labels[np.argmax(model_out)]\n f.write(\"{},{}\\n\".format(img_num, str_label))\n ","sub_path":"nnmodel.py","file_name":"nnmodel.py","file_ext":"py","file_size_in_byte":5287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"538666808","text":"import boto3\nimport json\nimport pytest\nfrom moto import mock_lambda, mock_logs, mock_s3, mock_sqs\nfrom moto.core import DEFAULT_ACCOUNT_ID as ACCOUNT_ID\nfrom tests.markers import requires_docker\nfrom tests.test_awslambda.utilities import (\n get_test_zip_file_print_event,\n get_role_name,\n wait_for_log_msg,\n)\nfrom uuid import uuid4\n\n\nREGION_NAME = \"us-east-1\"\n\n\n@mock_lambda\n@mock_logs\n@mock_s3\n@pytest.mark.parametrize(\n \"match_events,actual_event\",\n [\n ([\"s3:ObjectCreated:Put\"], \"ObjectCreated:Put\"),\n ([\"s3:ObjectCreated:*\"], \"ObjectCreated:Put\"),\n ([\"s3:ObjectCreated:Post\"], None),\n ([\"s3:ObjectCreated:Post\", \"s3:ObjectCreated:*\"], \"ObjectCreated:Put\"),\n ],\n)\n@requires_docker\ndef test_objectcreated_put__invokes_lambda(match_events, actual_event):\n s3_res = boto3.resource(\"s3\", region_name=REGION_NAME)\n s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n lambda_client = boto3.client(\"lambda\", REGION_NAME)\n\n # Create S3 bucket\n bucket_name = str(uuid4())\n s3_res.create_bucket(Bucket=bucket_name)\n\n # Create AWSLambda function\n function_name = str(uuid4())[0:6]\n fn_arn = lambda_client.create_function(\n FunctionName=function_name,\n Runtime=\"python3.7\",\n Role=get_role_name(),\n Handler=\"lambda_function.lambda_handler\",\n Code={\"ZipFile\": get_test_zip_file_print_event()},\n )[\"FunctionArn\"]\n\n # Put Notification\n s3_client.put_bucket_notification_configuration(\n Bucket=bucket_name,\n NotificationConfiguration={\n \"LambdaFunctionConfigurations\": [\n {\n \"Id\": \"unrelated\",\n \"LambdaFunctionArn\": f\"arn:aws:lambda:us-east-1:{ACCOUNT_ID}:function:n/a\",\n \"Events\": [\"s3:ReducedRedundancyLostObject\"],\n },\n {\n \"Id\": \"s3eventtriggerslambda\",\n \"LambdaFunctionArn\": fn_arn,\n \"Events\": match_events,\n },\n ]\n },\n )\n\n # Put Object\n s3_client.put_object(Bucket=bucket_name, Key=\"keyname\", Body=\"bodyofnewobject\")\n\n # Find the output of AWSLambda\n expected_msg = \"FINISHED_PRINTING_EVENT\"\n log_group = f\"/aws/lambda/{function_name}\"\n msg_showed_up, all_logs = wait_for_log_msg(expected_msg, log_group, wait_time=10)\n\n if actual_event is None:\n # The event should not be fired on POST, as we've only PUT an event for now\n assert not msg_showed_up\n return\n\n # If we do have an actual event, verify the Lambda was invoked with the correct event\n assert msg_showed_up, (\n expected_msg\n + \" was not found after sending an SQS message. All logs: \"\n + str(all_logs)\n )\n\n records = [line for line in all_logs if line.startswith(\"{'Records'\")][0]\n records = json.loads(records.replace(\"'\", '\"'))[\"Records\"]\n\n records.should.have.length_of(1)\n records[0].should.have.key(\"awsRegion\").equals(REGION_NAME)\n records[0].should.have.key(\"eventName\").equals(actual_event)\n records[0].should.have.key(\"eventSource\").equals(\"aws:s3\")\n records[0].should.have.key(\"eventTime\")\n records[0].should.have.key(\"s3\")\n records[0][\"s3\"].should.have.key(\"bucket\")\n records[0][\"s3\"][\"bucket\"].should.have.key(\"arn\").equals(\n f\"arn:aws:s3:::{bucket_name}\"\n )\n records[0][\"s3\"][\"bucket\"].should.have.key(\"name\").equals(bucket_name)\n records[0][\"s3\"].should.have.key(\"configurationId\").equals(\"s3eventtriggerslambda\")\n records[0][\"s3\"].should.have.key(\"object\")\n records[0][\"s3\"][\"object\"].should.have.key(\"eTag\").equals(\n \"61ea96c3c8d2c76fc5a42bfccb6affd9\"\n )\n records[0][\"s3\"][\"object\"].should.have.key(\"key\").equals(\"keyname\")\n records[0][\"s3\"][\"object\"].should.have.key(\"size\").equals(15)\n\n\n@mock_logs\n@mock_s3\ndef test_objectcreated_put__unknown_lambda_is_handled_gracefully():\n s3_res = boto3.resource(\"s3\", region_name=REGION_NAME)\n s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n\n # Create S3 bucket\n bucket_name = str(uuid4())\n s3_res.create_bucket(Bucket=bucket_name)\n\n # Put Notification\n s3_client.put_bucket_notification_configuration(\n Bucket=bucket_name,\n NotificationConfiguration={\n \"LambdaFunctionConfigurations\": [\n {\n \"Id\": \"unrelated\",\n \"LambdaFunctionArn\": f\"arn:aws:lambda:us-east-1:{ACCOUNT_ID}:function:n/a\",\n \"Events\": [\"s3:ObjectCreated:Put\"],\n }\n ]\n },\n )\n\n # Put Object\n s3_client.put_object(Bucket=bucket_name, Key=\"keyname\", Body=\"bodyofnewobject\")\n\n # The object was persisted successfully\n resp = s3_client.get_object(Bucket=bucket_name, Key=\"keyname\")\n resp.should.have.key(\"ContentLength\").equal(15)\n resp[\"Body\"].read().should.equal(b\"bodyofnewobject\")\n\n\n@mock_s3\n@mock_sqs\ndef test_object_copy__sends_to_queue():\n s3_res = boto3.resource(\"s3\", region_name=REGION_NAME)\n s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n sqs_client = boto3.client(\"sqs\", region_name=REGION_NAME)\n\n # Create S3 bucket\n bucket_name = str(uuid4())\n s3_res.create_bucket(Bucket=bucket_name)\n\n # Create SQS queue\n queue_url = sqs_client.create_queue(QueueName=str(uuid4())[0:6])[\"QueueUrl\"]\n queue_arn = sqs_client.get_queue_attributes(\n QueueUrl=queue_url, AttributeNames=[\"QueueArn\"]\n )[\"Attributes\"][\"QueueArn\"]\n\n # Put Notification\n s3_client.put_bucket_notification_configuration(\n Bucket=bucket_name,\n NotificationConfiguration={\n \"QueueConfigurations\": [\n {\n \"Id\": \"queue_config\",\n \"QueueArn\": queue_arn,\n \"Events\": [\"s3:ObjectCreated:Copy\"],\n }\n ]\n },\n )\n\n # We should have received a test event now\n messages = sqs_client.receive_message(QueueUrl=queue_url)[\"Messages\"]\n messages.should.have.length_of(1)\n message = json.loads(messages[0][\"Body\"])\n message.should.have.key(\"Service\").equals(\"Amazon S3\")\n message.should.have.key(\"Event\").equals(\"s3:TestEvent\")\n message.should.have.key(\"Time\")\n message.should.have.key(\"Bucket\").equals(bucket_name)\n\n # Copy an Object\n s3_client.put_object(Bucket=bucket_name, Key=\"keyname\", Body=\"bodyofnewobject\")\n s3_client.copy_object(\n Bucket=bucket_name, CopySource=f\"{bucket_name}/keyname\", Key=\"key2\"\n )\n\n # Read SQS messages - we should have the Copy-event here\n resp = sqs_client.receive_message(QueueUrl=queue_url)\n resp.should.have.key(\"Messages\").length_of(1)\n records = json.loads(resp[\"Messages\"][0][\"Body\"])[\"Records\"]\n\n records.should.have.length_of(1)\n records[0].should.have.key(\"awsRegion\").equals(REGION_NAME)\n records[0].should.have.key(\"eventName\").equals(\"ObjectCreated:Copy\")\n records[0].should.have.key(\"eventSource\").equals(\"aws:s3\")\n records[0].should.have.key(\"eventTime\")\n records[0].should.have.key(\"s3\")\n records[0][\"s3\"].should.have.key(\"bucket\")\n records[0][\"s3\"][\"bucket\"].should.have.key(\"arn\").equals(\n f\"arn:aws:s3:::{bucket_name}\"\n )\n records[0][\"s3\"][\"bucket\"].should.have.key(\"name\").equals(bucket_name)\n records[0][\"s3\"].should.have.key(\"configurationId\").equals(\"queue_config\")\n records[0][\"s3\"].should.have.key(\"object\")\n records[0][\"s3\"][\"object\"].should.have.key(\"eTag\").equals(\n \"61ea96c3c8d2c76fc5a42bfccb6affd9\"\n )\n records[0][\"s3\"][\"object\"].should.have.key(\"key\").equals(\"key2\")\n records[0][\"s3\"][\"object\"].should.have.key(\"size\").equals(15)\n\n\n@mock_s3\n@mock_sqs\ndef test_object_put__sends_to_queue__using_filter():\n s3_res = boto3.resource(\"s3\", region_name=REGION_NAME)\n s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n sqs = boto3.resource(\"sqs\", region_name=REGION_NAME)\n\n # Create S3 bucket\n bucket_name = str(uuid4())\n s3_res.create_bucket(Bucket=bucket_name)\n\n # Create SQS queue\n queue = sqs.create_queue(QueueName=f\"{str(uuid4())[0:6]}\")\n queue_arn = queue.attributes[\"QueueArn\"]\n\n # Put Notification\n s3_client.put_bucket_notification_configuration(\n Bucket=bucket_name,\n NotificationConfiguration={\n \"QueueConfigurations\": [\n {\n \"Id\": \"prefixed\",\n \"QueueArn\": queue_arn,\n \"Events\": [\"s3:ObjectCreated:Put\"],\n \"Filter\": {\n \"Key\": {\"FilterRules\": [{\"Name\": \"prefix\", \"Value\": \"aa\"}]}\n },\n },\n {\n \"Id\": \"images_only\",\n \"QueueArn\": queue_arn,\n \"Events\": [\"s3:ObjectCreated:Put\"],\n \"Filter\": {\n \"Key\": {\n \"FilterRules\": [\n {\"Name\": \"prefix\", \"Value\": \"image/\"},\n {\"Name\": \"suffix\", \"Value\": \"jpg\"},\n ]\n }\n },\n },\n ]\n },\n )\n\n # Read the test-event\n resp = queue.receive_messages()\n [m.delete() for m in resp]\n\n # Create an Object that does not meet any filter\n s3_client.put_object(Bucket=bucket_name, Key=\"bb\", Body=\"sth\")\n messages = queue.receive_messages()\n messages.should.have.length_of(0)\n [m.delete() for m in messages]\n\n # Create an Object that does meet the filter - using the prefix only\n s3_client.put_object(Bucket=bucket_name, Key=\"aafilter\", Body=\"sth\")\n messages = queue.receive_messages()\n messages.should.have.length_of(1)\n [m.delete() for m in messages]\n\n # Create an Object that does meet the filter - using the prefix + suffix\n s3_client.put_object(Bucket=bucket_name, Key=\"image/yes.jpg\", Body=\"img\")\n messages = queue.receive_messages()\n messages.should.have.length_of(1)\n [m.delete() for m in messages]\n\n # Create an Object that does not meet the filter - only the prefix\n s3_client.put_object(Bucket=bucket_name, Key=\"image/no.gif\", Body=\"img\")\n messages = queue.receive_messages()\n messages.should.have.length_of(0)\n [m.delete() for m in messages]\n\n # Create an Object that does not meet the filter - only the suffix\n s3_client.put_object(Bucket=bucket_name, Key=\"nonimages/yes.jpg\", Body=\"img\")\n messages = queue.receive_messages()\n messages.should.have.length_of(0)\n [m.delete() for m in messages]\n","sub_path":"tests/test_s3/test_s3_lambda_integration.py","file_name":"test_s3_lambda_integration.py","file_ext":"py","file_size_in_byte":10568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"100061660","text":"from flask import render_template, redirect, request, session, redirect, url_for, escape\r\nfrom app import app, models, db\r\nfrom .forms import TravelerForm, TravelForm\r\nfrom .models import *\r\n# Access the models file to use SQL functions\r\n\r\n@app.route('/')\r\n@app.route('/index')\r\ndef index():\r\n username = ''\r\n if 'username' in session:\r\n username = escape(session['username'])\r\n return redirect('/travelers')\r\n else:\r\n return render_template('login.html')\r\n\r\n@app.route('/login', methods=['GET', 'POST'])\r\ndef login():\r\n form=TravelerForm\r\n if request.method=='POST':\r\n username = request.form.get(\"username\")\r\n if verify(username):\r\n session['username'] = request.form.get(\"username\")\r\n return redirect('/travelers')\r\n else:\r\n return render_template('not_authorized.html')\r\n\r\n@app.route('/signup_click', methods=['GET', 'POST'])\r\ndef signup_click():\r\n return render_template('signup.html')\r\n\r\n@app.route('/signup', methods=['GET', 'POST'])\r\ndef signup():\r\n form=TravelerForm\r\n if request.method=='POST':\r\n username = request.form.get(\"username\")\r\n if verify(username):\r\n return render_template('alreadyregistered.html')\r\n else:\r\n sign_up(username)\r\n session['username'] = request.form.get(\"username\")\r\n return redirect('/travelers')\r\n\r\n@app.route('/delete_travel/', methods=['GET', 'POST'])\r\ndef delete_travel(value):\r\n form = TravelForm(obj=value)\r\n delete_travels(value)\r\n return redirect('/travelers')\r\n\r\n@app.route('/logout')\r\ndef logout():\r\n session.pop('username', None)\r\n return redirect(url_for('index'))\r\n\r\n@app.route('/travelers')\r\ndef display_traveler():\r\n #Retreive data from database to display\r\n travels = retrieve_travels()\r\n travels2 = retrieve_travels2()\r\n username = escape(session['username'])\r\n return render_template('home.html',\r\n travels=travels, travels2=travels2, name=username)\r\n\r\n@app.route('/create_travel/', methods=['GET', 'POST'])\r\ndef create_travel(value):\r\n # Get data from the form\r\n # Send data from form to Database\r\n form = TravelForm(obj=value)\r\n username = escape(session['username'])\r\n if form.validate_on_submit():\r\n trip_name = form.trip_name.data\r\n destination = form.destination.data\r\n friend = form.friend.data\r\n insert_travels(trip_name, destination, friend, username)\r\n return redirect('/travelers')\r\n return render_template('travel.html', form=form, name=username)\r\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"}
+{"seq_id":"635953004","text":"__author__ = 'tomislav'\nfrom django.conf.urls import url\nfrom django.contrib.auth.decorators import login_required\n\nfrom . import views\n\nurlpatterns = (\n url(r\"^$\", views.LoginRegistrationView.as_view(), name='login_glarus'),\n url(r\"^validate/(?P.*)/$\", views.validate_email),\n url(r\"^membership/$\", login_required(views.MembershipView.as_view()), name='membership'),\n url(r'logout/?$',views.logout_glarus,name='logout_glarus'),\n url(r\"^buy/(?P