' + str(userid)+'\\n\\n'\r\n msg+= '🛰 IP : ' + str(userdata['ip'])+'\\n'\r\n listado = '🗒 LISTAR : SI'\r\n if listar:listado = '🗒 LISTAR : NO'\r\n msg+= listado + '\\n'\r\n msg+= '▶️ PUERTO INICIAL : ' + str(userdata['rango_minimo'])+'\\n'\r\n msg+= '⏸ PUERTO FINAL : ' + str(userdata['rango_maximo'])+'\\n\\n'\r\n stat = '👤 ‖USER‖ 👤'\r\n if isadmin:stat = '👑 ‖OWNER‖ 👑'\r\n msg+= stat + '\\n'\r\n return msg\r\n\r\ndef porcentaje(rango_max,rango_min,port,info,bot,chat,sms,ip):\r\n maxim = int(rango_max) - int(rango_min)\r\n actual = (int(port)+1) - int(rango_min)\r\n porcent = actual / maxim\r\n porcent *= 100\r\n porcent = int(str(porcent).split('.')[0])\r\n if porcent in range(0,10):\r\n n = '🟩'*0\r\n b = '⬛️'*10\r\n elif porcent in range(10,20):\r\n n = '🟩'*1\r\n b = '⬛️'*9\r\n elif porcent in range(20,30):\r\n n = '🟩'*2\r\n b = '⬛️'*8\r\n elif porcent in range(30,40):\r\n n = '🟩'*3\r\n b = '⬛️'*7\r\n elif porcent in range(40,50):\r\n n = '🟩'*4\r\n b = '⬛️'*6\r\n elif porcent in range(50,60):\r\n n = '🟩'*5\r\n b = '⬛️'*5\r\n elif porcent in range(60,70):\r\n n = '🟩'*6\r\n b = '⬛️'*4\r\n elif porcent in range(70,80):\r\n n = '🟩'*7\r\n b = '⬛️'*3\r\n elif porcent in range(80,90):\r\n n = '🟩'*8\r\n b = '⬛️'*2\r\n elif porcent in range(90,100):\r\n n = '🟩'*9\r\n b = '⬛️'*1\r\n elif porcent == 100:\r\n n = '🟩'*10\r\n b = '⬛️'*0\r\n\r\n if porcent != 100 :porcente = '☑️'\r\n else :porcente = '✅'\r\n progress = n+b\r\n\r\n msg = '🛰 Buscando Proxy!!\\n🌐 IP : '+str(ip)+'\\n'\r\n msg+='⏯ PUERTOS : '+str(rango_min)+'-'+str(int(rango_max)-1)\r\n msg+='\\n'+progress\r\n msg+='\\n'+porcente+' PORCIENTO : '+str(porcent)+'%\\n➖➖➖➖➖➖➖\\n'\r\n msg+=info+'\\n➖➖➖➖➖➖➖'\r\n bot.editMessageText(chat_id=chat,message_id=sms.message_id,parse_mode=\"HTML\",text=msg)","repo_name":"AresDza/PR-FinderV2.3","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":5688,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"33655321949","text":"\"\"\"\nhttp://pythontutor.ru/lessons/lists/problems/same_sign_neighbours/\nДан список чисел. Если в нем есть два соседних элемента одного знака, выведите эти числа.\nЕсли соседних элементов одного знака нет — не выводите ничего. Если таких пар соседей несколько — выведите первую пару.\n\"\"\"\n\n_l = input().split()\nl = [int(i) for i in _l]\n\nfor i in range(len(l) - 1):\n if l[i] < 0 and l[i + 1] < 0 or l[i] > 0 and l[i + 1] > 0:\n print(l[i], l[i + 1])\n break\n","repo_name":"ornichola/learning-new","sub_path":"pythontutor-ru/07_lists/04_same_sign_neighbours.py","file_name":"04_same_sign_neighbours.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"ru","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"10277312125","text":"import glob\r\nimport json\r\nimport os.path\r\nimport io\r\nimport time\r\nimport pytz\r\nimport datetime\r\nimport emoji\r\nimport pickle\r\nimport configparser\r\n\r\nfrom PIL import Image, ImageTk\r\nimport PySimpleGUI as sg\r\n\r\n#interface declarations\r\nsg.theme(\"SystemDefault\")\r\n\r\n#logic declarations\r\nworkout_path = '/Workouts/'\r\n\r\nSPORTS = {\r\n 0: 'Running',\r\n 1: 'Cycling, transport',\r\n 2: 'Cycling, sport',\r\n 3: 'Mountain biking',\r\n 4: 'Skating',\r\n 5: 'Roller skiing',\r\n 6: 'Skiing, cross country',\r\n 7: 'Skiing, downhill',\r\n 8: 'Snowboarding',\r\n 9: 'Kayaking',\r\n 10: 'Kite surfing',\r\n 11: 'Rowing',\r\n 12: 'Sailing',\r\n 13: 'Windsurfing',\r\n 14: 'Fitness walking',\r\n 15: 'Golfing',\r\n 16: 'Hiking',\r\n 17: 'Orienteering',\r\n 18: 'Walking',\r\n 19: 'Riding',\r\n 20: 'Swimming',\r\n 21: 'Spinning',\r\n 22: 'Other',\r\n 23: 'Aerobics',\r\n 24: 'Badminton',\r\n 25: 'Baseball',\r\n 26: 'Basketball',\r\n 27: 'Boxing',\r\n 28: 'Climbing stairs',\r\n 29: 'Cricket',\r\n 30: 'Cross training',\r\n 31: 'Dancing',\r\n 32: 'Fencing',\r\n 33: 'Football, American',\r\n 34: 'Football, rugby',\r\n 35: 'Football, soccer',\r\n 36: 'Handball',\r\n 37: 'Hockey',\r\n 38: 'Pilates',\r\n 39: 'Polo',\r\n 40: 'Scuba diving',\r\n 41: 'Squash',\r\n 42: 'Table tennis',\r\n 43: 'Tennis',\r\n 44: 'Volleyball, beach',\r\n 45: 'Volleyball, indoor',\r\n 46: 'Weight training',\r\n 47: 'Yoga',\r\n 48: 'Martial arts',\r\n 49: 'Gymnastics',\r\n 50: 'Step counter'\r\n}\r\n\r\ndef get_img_data(f, maxsize=(500, 200), first=False):\r\n \"\"\"Generate image data using PIL\r\n \"\"\"\r\n img = Image.open(f)\r\n img.thumbnail(maxsize)\r\n if first: # tkinter is inactive the first time\r\n bio = io.BytesIO()\r\n img.save(bio, format=\"PNG\")\r\n imgsize=img.size\r\n del img\r\n return bio.getvalue(), imgsize\r\n return ImageTk.PhotoImage(img)\r\n\r\ndef FieldColumn(name, key, value=''):\r\n \"\"\"Generate a column that contains two text fields - label and value\r\n \"\"\"\r\n layout = [\r\n [sg.Text(name, size=(10,1)), sg.Text(value if value is not None else '', size=(18,1), key=key)]\r\n ]\r\n return sg.Col(layout, pad=(0,0))\r\n\r\n\r\n\r\ndef _to_python_time(endomondo_time):\r\n try:\r\n pt = datetime.datetime.strptime(endomondo_time, \"%Y-%m-%d %H:%M:%S.%f\").replace(tzinfo=pytz.utc)\r\n except ValueError:\r\n pt = datetime.datetime.strptime(endomondo_time, \"%Y-%m-%d %H:%M:%S %Z\").replace(tzinfo=pytz.utc)\r\n return pt\r\n\r\ndef normalizefield(wodict):\r\n \"\"\"Normalize dictionary of raw Endomondo data\r\n \"\"\"\r\n if 'speed_avg' in wodict.keys():\r\n speed = float(wodict['speed_avg'])\r\n if speed != 0 :\r\n pace_sec = 60*60 / speed\r\n res = time.gmtime(pace_sec)\r\n wodict['pace'] = time.strftime('%M:%S', res)\r\n wodict['speed'] = str(round(speed, 2))\r\n else:\r\n wodict['pace'] = '0'\r\n wodict['speed'] = '0'\r\n if 'speed_avg_kmh' in wodict.keys():\r\n speed = float(wodict['speed_avg_kmh'])\r\n if speed != 0 :\r\n pace_sec = 60*60 / speed\r\n res = time.gmtime(pace_sec)\r\n wodict['pace'] = time.strftime('%M:%S', res)\r\n wodict['speed'] = str(round(speed, 2))\r\n else:\r\n wodict['pace'] = '0'\r\n wodict['speed'] = '0'\r\n if 'speed_kmh_avg' in wodict.keys():\r\n speed = float(wodict['speed_kmh_avg'])\r\n if speed != 0 :\r\n pace_sec = 60*60 / speed\r\n res = time.gmtime(pace_sec)\r\n wodict['pace'] = time.strftime('%M:%S', res)\r\n wodict['speed'] = str(round(speed, 2))\r\n else:\r\n wodict['pace'] = '0'\r\n wodict['speed'] = '0'\r\n # return normalized\r\n if 'speed_max' in wodict.keys():\r\n speed = float(wodict['speed_max'])\r\n wodict['speed_max'] = str(round(speed, 2))\r\n if 'speed_max_kmh' in wodict.keys():\r\n speed = float(wodict['speed_max_kmh'])\r\n wodict['speed_max'] = str(round(speed, 2))\r\n if 'speed_kmh_max' in wodict.keys():\r\n speed = float(wodict['speed_kmh_max'])\r\n wodict['speed_max'] = str(round(speed, 2))\r\n # return normalized\r\n if 'duration' in wodict.keys():\r\n res = time.gmtime(float(wodict['duration']))\r\n dur = time.strftime('%H:%M:%S', res)\r\n wodict['duration'] = dur\r\n if 'duration_s' in wodict.keys():\r\n res = time.gmtime(float(wodict['duration_s']))\r\n dur = time.strftime('%H:%M:%S', res)\r\n wodict['duration'] = dur\r\n if 'duration_sec' in wodict.keys():\r\n res = time.gmtime(float(wodict['duration_sec']))\r\n dur = time.strftime('%H:%M:%S', res)\r\n wodict['duration'] = dur\r\n # return normalized\r\n if 'sport' in wodict.keys():\r\n sp = wodict['sport']\r\n if isinstance(sp, int):\r\n try:\r\n wodict['sport'] = SPORTS[sp]\r\n except KeyError:\r\n wodict['sport'] = SPORTS[22] #Unknown sport - 'Other'\r\n else:\r\n wodict['sport'] = sp.capitalize().replace('_', ' ')\r\n # return normalized\r\n if 'distance' in wodict.keys():\r\n wodict['distance'] = str(round(float(wodict['distance']),2))\r\n if 'distance_km' in wodict.keys():\r\n wodict['distance'] = str(round(float(wodict['distance_km']),2))\r\n # return normalized\r\n if 'start_time' in wodict.keys():\r\n tt = _to_python_time(wodict['start_time'])\r\n wodict['date'] = tt.date()\r\n wodict['time'] = tt.time()\r\n wodict['start_time'] = wodict['start_time']\r\n # return normalized\r\n if 'message' in wodict.keys():\r\n wodict['message'] = emoji.get_emoji_regexp().sub(r'', wodict['message'])\r\n if 'ascent' in wodict.keys():\r\n wodict['ascend_m'] = wodict['ascent']\r\n if 'descent' in wodict.keys():\r\n wodict['descend_m'] = wodict['descent']\r\n \r\n #HEART RATE\r\n if 'heart_rate_avg' in wodict.keys():\r\n wodict['heart_rate_avg_bpm'] = wodict['heart_rate_avg']\r\n if 'heart_rate_max' in wodict.keys():\r\n wodict['heart_rate_max_bpm'] = wodict['heart_rate_max']\r\n if 'heart_rate_bpm_avg' in wodict.keys():\r\n wodict['heart_rate_avg_bpm'] = wodict['heart_rate_bpm_avg']\r\n if 'heart_rate_bpm_max' in wodict.keys():\r\n wodict['heart_rate_max_bpm'] = wodict['heart_rate_bpm_max']\r\n\r\n if 'cadence_avg' in wodict.keys():\r\n wodict['cadence_avg_rpm'] = wodict['cadence_avg']\r\n if 'cadence_max' in wodict.keys():\r\n wodict['cadence_max_rpm'] = wodict['cadence_max']\r\n \r\n #ALTITUDE\r\n if 'altitude_min' in wodict.keys():\r\n wodict['altitude_min_m'] = wodict['altitude_min']\r\n if 'altitude_max' in wodict.keys():\r\n wodict['altitude_max_m'] = wodict['altitude_max']\r\n if 'altitude_m_min' in wodict.keys():\r\n wodict['altitude_min_m'] = wodict['altitude_m_min']\r\n if 'altitude_m_max' in wodict.keys():\r\n wodict['altitude_max_m'] = wodict['altitude_m_max']\r\n \r\n if 'calories' in wodict.keys():\r\n wodict['calories_kcal'] = wodict['calories']\r\n # return normalized\r\n\r\ndef loadfull(path):\r\n \"\"\"Load data from Endomondo backup\r\n \"\"\"\r\n dd=[]\r\n #create index to find workout by start_time (actually, date and time string)\r\n indx = {}\r\n fullpath = \"\".join([path, workout_path, '*.json']) #TODO: join paths correctly\r\n files = glob.glob(fullpath) #Load list of all JSON workout files\r\n total = len(files) #needed for progress bar\r\n for i, f in enumerate(files):\r\n with open(f, encoding='utf-8') as p:\r\n w = json.load(p)\r\n workout_dict = {}\r\n if isinstance(w, list):\r\n for dict in w:\r\n #skip GPS track part for workout\r\n if 'points' in dict.keys():\r\n continue\r\n normalizefield(dict)\r\n workout_dict.update(dict)\r\n else:\r\n #we suppose it's dict (and we're dealing with backup from endobackup.py)\r\n normalizefield(w)\r\n workout_dict.update(w)\r\n workout_dict.update({'json_file': f}) #add path of processed file for future references\r\n dd.append(workout_dict)\r\n if total > 1:\r\n if not sg.OneLineProgressMeter('Loading Endo Backup', i, total-1, 'kkk', path):\r\n break\r\n #sort before creating an index\r\n dd.sort(key=lambda a: a['date'])\r\n dlen = len(dd)\r\n #create index to find specific workout using start time of workout\r\n #will need it when we will download comments from Endomondo\r\n for i, d in enumerate(dd):\r\n indx[d['start_time'][:-2]] = i #-2 to remove milliseconds from start time\r\n if dlen>1:\r\n sg.OneLineProgressMeter('Creating index', i, dlen-1)\r\n return dd, indx\r\n\r\ndef updatetable(data, dd, window):\r\n \"\"\"Update data table of the main window with data from dd\r\n \"\"\"\r\n #data.clear()\r\n data = []\r\n for dict in dd:\r\n dict.setdefault('message', '') #avoid None in empty messages\r\n dict.setdefault('num_comments', '')\r\n piclist = dict.get('pictures')\r\n numpic = ' ' if piclist is None else len(piclist) #find out number of pictures in the workout\r\n data.append([dict.get('date'), dict.get('time'), dict.get('sport'),\r\n dict.get('distance'), dict.get('duration'), dict.get('pace'),\r\n numpic, dict.get('message'), dict.get('num_comments')])\r\n window['-DATA-'].update(data)\r\n\r\ndef updatecomments(dd, comm, indx):\r\n comm.sort(key=lambda a: a['start_time'])\r\n maxw = len(comm)\r\n for i, c in enumerate(comm):\r\n lcpc = c.get('num_comments')\r\n #TODO: notify if amount of workouts in databases are not the same\r\n if lcpc:\r\n #check if there are comments in workout\r\n if lcpc>0:\r\n try:\r\n #find corresponding workout in internal database\r\n j = indx[c['start_time'][:-4]]\r\n dd[j]['num_comments']=lcpc\r\n dd[j]['ecomments'] = c.get('comments')\r\n except:\r\n pass\r\n sg.OneLineProgressMeter('Updating workouts', i, maxw-1, 'kkk')\r\n\r\ndef main():\r\n #Layout of lower frame of main window \r\n details_frame = [\r\n [FieldColumn(\"Sport: \", '-SPORT-'), FieldColumn(\"Date: \",'-DATE-'),\r\n FieldColumn(\"Time: \", '-STARTTIME-'), FieldColumn(\"Duration: \", '-DURATION-'),\r\n FieldColumn(\"Distance: \", '-DISTANCE-')],\r\n [FieldColumn(\"Pace: \", '-PACE-'), FieldColumn(\"Ascent: \", '-ASC-'), \r\n FieldColumn(\"Descent: \", '-DESC-')],\r\n [sg.Frame('Note', [[sg.Text(key='-NOTE-', size=(180,6))]])]\r\n ]\r\n\r\n #List of labels for main table\r\n tabl_head = ['Date', 'Time', 'Type', 'Distance', 'Duration', 'Pace', 'Photos', 'Note', 'Comments']\r\n #Fill data for main table (needed as placeholder to define size for initial layout)\r\n data = [[' '*15,' '*15,' '*15,' '*10,' '*10,' '*10,' '*10,' '*45,' '*10] for row in range(16)]\r\n\r\n #Main window layout\r\n layout = [\r\n [sg.FolderBrowse(target='-FOLDER-'), sg.Input(key='-FOLDER-', enable_events=True), \r\n sg.Submit(), sg.Button('Fetch Comments', key='-FETCH-'), sg.Exit()],\r\n [sg.Table(data, headings=tabl_head, justification='center', select_mode='browse',\r\n key='-DATA-', num_rows=30, enable_events=True, bind_return_key=True, max_col_width=100)],\r\n [sg.Column(details_frame, expand_y=True, expand_x=True)]\r\n ]\r\n\r\n \r\n window = sg.Window('EndoView', layout, size=(1320,670), finalize=True)\r\n window['-DATA-'].bind('
Bienvenid@ a este bloghhhaaarsss
'\n\n mail.send(msg)\n\n","repo_name":"polifer27/miblog","sub_path":"mail.py","file_name":"mail.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29617863288","text":"'''\nGiven a number int_input, find the product of all the digits\nexample: \n\tinput: 123\n\toutput: 6\n'''\ndef main():\n s = int(input())\n sum = 1\n while s > 0:\n print(s)\n rem = s%10\n sum = sum*rem\n s = s//10\n print(sum)\nmain()\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\"\"\"\ndef main():\n '''\n Read any number from the input, store it in variable int_input.\n '''\n s = str(input())\n z = \"\"\n for n in s:\n if n ==\"!\" or n ==\"@\" or n ==\"#\" or n ==\"$\" or n ==\"%\" or n ==\"^\" or n ==\"&\" or n ==\"*\":\n z+=\" \"\n else:\n z+=n\n print(z) \nmain()\"\"\"\n","repo_name":"madhavimvh/madhavi","sub_path":"cspp1-assignments/m6/p3/digit_product.py","file_name":"digit_product.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71803831079","text":"\r\nimport csv\r\nimport datetime\r\nnow = datetime.datetime.now()\r\n\r\n#Load class information\r\nfrom classes.team import Team\r\n\r\n'''Fill in the starting bracket (list of 64 teams, with names, regions, and seeds)'''\r\n\r\n#Read through the file once, getting the first team\r\ndef load_tourny_teams(year):\r\n teams = []\r\n with open('data/Big_Dance_CSV.csv', 'r') as teams1_file:\r\n readCSV = csv.reader(teams1_file, delimiter=',')\r\n #Skip the header row\r\n header = next(readCSV)\r\n for row in readCSV:\r\n #Record region_num, region, seed, and team name for each team\r\n r_year = row[0]\r\n round = row[1]\r\n if (int(r_year) == year and int(round) == 1):\r\n region_number = row[2]\r\n region_name = row[3]\r\n seed = row[4]\r\n score = row[5]\r\n team = row[6]\r\n\r\n # https://api.sportradar.us/ncaamb/{access_level}/{version}/{language_code}/league/hierarchy.{format}?api_key={your_api_key}\r\n\r\n team = Team(r_year, round, region_number, region_name, seed, score, team)\r\n teams.append(team)\r\n\r\n #Read through the file once, getting the other, opponent team\r\n with open('data/Big_Dance_CSV.csv', 'r') as teams2_file:\r\n readCSV = csv.reader(teams2_file, delimiter=',')\r\n #Skip the header row\r\n header = next(readCSV)\r\n for row in readCSV:\r\n #Record region_num, region, seed, and team name for each team\r\n r_year = row[0]\r\n round = row[1]\r\n if (int(r_year) == year and int(round) == 1):\r\n region_number = row[2]\r\n region_name = row[3]\r\n seed = row[7]\r\n score = row[8]\r\n team = row[9]\r\n\r\n team = Team(r_year, round, region_number, region_name, seed, score, team)\r\n teams.append(team)\r\n\r\n #Confirm there are 64 teams loaded into the tournamnet\r\n assert len(teams) == 64, \"Wrong number of teams. Check team name spelling\"\r\n\r\n return teams\r\n","repo_name":"cgunther13/march-madness","sub_path":"data/load_tourny_teams.py","file_name":"load_tourny_teams.py","file_ext":"py","file_size_in_byte":2091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"16554273895","text":"from flask import Flask, render_template, url_for, request, redirect, flash, make_response\nfrom random import randint\nfrom flask_sqlalchemy import SQLAlchemy\n\n# App und Einstellungen\n\napp = Flask(__name__)\n\napp.secret_key = '6ca97bc342cdbecabbadb5c47b006ef4'\napp.config['SQLALCHEMY_DATABASE_URI']='sqlite:///site.db'\n\n# Datenbank\ndb = SQLAlchemy(app)\n\n# Klassen für die Datenbank\n\nclass User(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n user_name = db.Column(db.String(20), unique=True, nullable=False)\n secret_number = db.Column(db.Integer)\n\n # definiert was erscheint bei print bzw. wenn das Objekt angezeigt werden soll\n def __repr__(self):\n return f\"User('{self.user_name}', '{self.secret_number}')\"\n\n@app.route(\"/\", methods=[\"POST\", \"GET\"])\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/new_game\", methods=[\"POST\", \"GET\"])\ndef new_game():\n number_guess = request.form.get(\"guess\")\n user_name = request.form.get(\"user_name\")\n\n if request.method == \"POST\":\n\n user = User.query.filter_by(user_name=user_name).first()\n\n if user is None:\n new_user = User(user_name=user_name, secret_number=randint(0,30))\n db.session.add(new_user)\n db.session.commit()\n print(new_user)\n\n user = User.query.filter_by(user_name=user_name).first()\n secret = user.secret_number\n\n if int(number_guess) == secret:\n flash(\"Great. That's correct. The number was \" + str(secret) +\n \". If would like to guess another number just guess again.\", \"success\")\n user.secret_number=randint(0,30)\n db.session.commit()\n\n elif int(number_guess)>secret:\n flash(\"Sorry, that's too high.\", \"danger\")\n else:\n flash(\"Sorry, that's too low.\", \"danger\")\n\n return redirect(\"/new_game\")\n\n return render_template('new_game.html')\n\n\nif __name__ == '__main__':\n app.run(debug=True)","repo_name":"Hoch3007/WebDev01","sub_path":"18/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22834057204","text":"from PyWinRD.Props import ERR_START, OUT_START, MESSAGE_END, SOCKET_DELAY, recv_all, OPERATION_END, OPERATION_START, PORTION_END\r\nfrom subprocess import Popen, PIPE\r\nfrom datetime import datetime\r\nfrom threading import Thread\r\nfrom pathlib import Path\r\nfrom time import sleep\r\nimport traceback\r\nimport shutil\r\nimport socket\r\nimport sys\r\n\r\nnewline_used = True\r\n\r\n\r\nclass RemoteExecuter:\r\n\r\n def __init__(self, soc):\r\n self.soc = soc\r\n self.p = None\r\n self.exception = None\r\n\r\n def socket_stdout(self, s):\r\n global newline_used\r\n\r\n if s[-1] == '\\n':\r\n newline_used = True\r\n\r\n else:\r\n newline_used = False\r\n\r\n sys.stdout.write(s)\r\n\r\n self.soc.sendall(b''.join([OUT_START, bytes(s, 'utf-8'), MESSAGE_END]))\r\n sleep(SOCKET_DELAY)\r\n\r\n def socket_stderr(self, s):\r\n global newline_used\r\n\r\n if s[-1] == '\\n':\r\n newline_used = True\r\n\r\n else:\r\n newline_used = False\r\n\r\n sys.stderr.write(s)\r\n\r\n self.soc.sendall(b''.join([ERR_START, bytes(str(s), 'utf-8'), MESSAGE_END]))\r\n sleep(SOCKET_DELAY)\r\n\r\n def socket_stdin(self, s):\r\n sys.stdout.write(s.decode(\"utf-8\"))\r\n\r\n self.p.stdin.write(s)\r\n self.p.stdin.flush()\r\n\r\n def stdout_listener(self):\r\n\r\n while self.p.poll() is None:\r\n o = self.p.stdout.read1().decode('utf-8')\r\n\r\n if o:\r\n self.socket_stdout(o)\r\n\r\n def stderr_listener(self):\r\n\r\n while self.p.poll() is None:\r\n e = self.p.stderr.read1().decode('utf-8')\r\n\r\n if e:\r\n self.socket_stderr(e)\r\n\r\n def stdin_listener(self):\r\n\r\n while self.p.poll() is None:\r\n\r\n try:\r\n messages = recv_all(self.soc)\r\n if messages[0] == OPERATION_START:\r\n break\r\n\r\n for m in messages:\r\n self.socket_stdin(m)\r\n\r\n except ConnectionResetError:\r\n self.exception = ConnectionResetError\r\n self.p.kill()\r\n break\r\n\r\n def process_listener(self, p: Popen, ol: Thread, el: Thread):\r\n\r\n while (p.poll() is None) or ol.is_alive() or el.is_alive():\r\n sleep(0.2)\r\n\r\n self.soc.sendall(b''.join([OPERATION_END, MESSAGE_END]))\r\n\r\n def execute(self, command):\r\n self.p = Popen(command.split(' '), stdout=PIPE, stderr=PIPE, stdin=PIPE)\r\n\r\n ol = Thread(target=self.stdout_listener, daemon=True)\r\n ol.start()\r\n\r\n el = Thread(target=self.stderr_listener, daemon=True)\r\n el.start()\r\n\r\n il = Thread(target=self.stdin_listener, daemon=True)\r\n il.start()\r\n\r\n while self.p.poll() is None:\r\n sleep(0.2)\r\n\r\n self.soc.sendall(b''.join([OPERATION_END, MESSAGE_END]))\r\n\r\n ol.join()\r\n el.join()\r\n il.join()\r\n\r\n\r\nclass WinRDServer:\r\n\r\n def __init__(self, host='', port=2345, password=''):\r\n self.host = host\r\n self.port = port\r\n self.password = password\r\n\r\n self.address_family = socket.AF_INET\r\n self.protocol = socket.SOCK_STREAM\r\n\r\n self.soc = socket.socket(self.address_family, self.protocol)\r\n self.soc.bind((self.host, self.port))\r\n\r\n def start(self):\r\n global newline_used\r\n\r\n self.soc.listen(1)\r\n while True:\r\n client_soc, client_addr = self.soc.accept()\r\n client_soc.setblocking(False)\r\n\r\n password = recv_all(client_soc)[0].decode('utf-8')\r\n if password != self.password:\r\n client_soc.sendall(b''.join([b'0', MESSAGE_END]))\r\n continue\r\n\r\n else:\r\n client_soc.sendall(b''.join([b'1', MESSAGE_END]))\r\n\r\n if not newline_used:\r\n sys.stderr.write('\\n\\n')\r\n newline_used = True\r\n sys.stderr.write(f'{datetime.now().strftime(\"[%Y-%m-%d %H:%M:%S]\")} {client_addr[0]} Connected.\\n')\r\n\r\n remote_executer = RemoteExecuter(client_soc)\r\n Path('Temp').mkdir(exist_ok=True)\r\n\r\n while True:\r\n\r\n try:\r\n info = recv_all(client_soc)[0].split(PORTION_END)\r\n operation, data = info[0].decode('utf-8'), info[1:]\r\n\r\n if operation == 'Deploy':\r\n path = 'Temp' / Path(data[0].decode('utf-8'))\r\n\r\n if not newline_used:\r\n sys.stderr.write('\\n')\r\n newline_used = True\r\n sys.stderr.write(f'{datetime.now().strftime(\"[%Y-%m-%d %H:%M:%S]\")} {client_addr[0]} Is Deploying \"{path}\".\\n')\r\n\r\n path.parent.mkdir(parents=True, exist_ok=True)\r\n with open(path, 'wb') as f:\r\n f.write(data[1])\r\n\r\n client_soc.sendall(b''.join([OPERATION_END, MESSAGE_END]))\r\n continue\r\n\r\n elif operation == 'Terminal':\r\n command = data[0].decode('utf-8')\r\n\r\n if not newline_used:\r\n sys.stderr.write('\\n')\r\n newline_used = True\r\n sys.stderr.write(f'{datetime.now().strftime(\"[%Y-%m-%d %H:%M:%S]\")} {client_addr[0]} Is Executing \"{command}\".\\n')\r\n\r\n remote_executer.execute(command)\r\n\r\n elif operation == 'Debug':\r\n filename = data[0].decode('utf-8')\r\n\r\n if not newline_used:\r\n sys.stderr.write('\\n')\r\n newline_used = True\r\n sys.stderr.write(f'{datetime.now().strftime(\"[%Y-%m-%d %H:%M:%S]\")} {client_addr[0]} Is Debugging \"{filename}\".\\n')\r\n\r\n path = 'Temp' / Path(filename)\r\n with open(path, 'wb') as f:\r\n f.write(data[1])\r\n\r\n remote_executer.execute(f'python {path}')\r\n if remote_executer.exception:\r\n raise remote_executer.exception\r\n\r\n else:\r\n continue\r\n\r\n except ConnectionResetError:\r\n break\r\n\r\n except Exception: # noqa\r\n traceback.print_exc()\r\n\r\n if not newline_used:\r\n sys.stderr.write('\\n')\r\n newline_used = True\r\n sys.stderr.write(f'{datetime.now().strftime(\"[%Y-%m-%d %H:%M:%S]\")} {client_addr[0]} Disconnected.\\n\\n')\r\n\r\n shutil.rmtree('Temp', ignore_errors=True)","repo_name":"AhmedAhmedEG/PyWinRD","sub_path":"PyWinRD/Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":6726,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"11925935041","text":"import math\nimport sys\ninput = sys.stdin.readline\ndef solution(n):\n primes = [True for _ in range(4000001)];\n primes[0] = False; primes[1] = False;\n arr = [0]\n for i in range(2, 4000001):\n if primes[i]:\n arr.append(arr[-1]+i)\n for j in range(i+i, 4000001, i):\n if j > 4000000: break\n primes[j] = False\n total = 2; l = 0; r = 1; answer = 0;\n while l < r:\n if n == total:\n answer += 1\n if total < n:\n r += 1\n if r == len(arr): break\n else: l += 1\n total = arr[r] - arr[l]\n print(answer)\nif __name__ == '__main__':\n solution(int(input().strip()))","repo_name":"WonyJeong/wony-algo","sub_path":"BOJ/class/5/1644.py","file_name":"1644.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30109006259","text":"import math\n\n\ndef my_exp(x, n):\n s = 0 # начальное значение суммы раяда\n q = 1 # начальное значение добавки\n\n for k in range(n + 1):\n s += q\n q *= x / (k + 1)\n\n return s\n\n\nx = 50\n\nk_sum = 100\n\nfor n in range(k_sum):\n print('n= ', n, '->', my_exp(x, n, ))\n\n\n\n","repo_name":"AntonTroitskii/python","sub_path":"02_books_courses/02_vasilev/ch 3 functions/3_2.py","file_name":"3_2.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12209186739","text":"import time\r\n\r\nltr = [\"a\", \"b\", \"c\", \"ç\", \"d\", \"e\", \"f\", \"g\", \"ğ\", \"h\", \"ı\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"ö\", \"p\", \"q\", \"r\", \"s\", \"ş\", \"t\", \"u\", \"ü\", \"v\", \"w\", \"x\", \"y\", \"z\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"0\"]\r\nnums = [\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"0\"]\r\ndecode = []\r\nencode = []\r\n\r\nwhile True:\r\n q = input('Metninizi şifrelemek için \"ş\", çözmek için \"ç\" yazın:\\n')\r\n if q == \"ş\":\r\n x = input(\"Metninizi girin:\\n\")\r\n for l in x: \r\n if l == \"0\":\r\n encode.append(\"b\")\r\n elif l == \"9\":\r\n encode.append(\"a\")\r\n elif l in nums:\r\n encode.append(nums[nums.index(l) + 2])\r\n elif l.lower() in ltr: \r\n if l.islower():\r\n encode.append(ltr[ltr.index(l.lower()) + 2])\r\n elif l.isupper():\r\n encode.append((ltr[ltr.index(l.lower()) + 2]).upper())\r\n else:\r\n encode.append(l)\r\n for l in encode:\r\n print(l, end=\"\")\r\n print()\r\n encode = []\r\n elif q == \"ç\":\r\n y = input(\"Metninizi girin:\\n\")\r\n for l in y:\r\n if l.lower() == \"b\":\r\n decode.append(\"0\")\r\n elif l.lower() == \"a\":\r\n decode.append(\"9\")\r\n elif l in nums:\r\n if l == \"1\":\r\n decode.append(\"y\")\r\n elif l == \"2\":\r\n decode.append(\"z\")\r\n else:\r\n decode.append(nums[nums.index(l) - 2])\r\n elif l.lower() in ltr:\r\n if l.islower(): \r\n decode.append(ltr[ltr.index(l.lower()) - 2])\r\n elif l.isupper():\r\n decode.append((ltr[ltr.index(l.lower()) - 2]).upper())\r\n else:\r\n decode.append(l)\r\n for l in decode:\r\n print(l, end=\"\")\r\n print()\r\n decode = []\r\n else:\r\n print(\"Geçerli bir girdi girin\")\r\n c = input('Devam etmek için \"d\", çıkmak için \"e\" yazın:\\n')\r\n if c == \"d\":\r\n continue\r\n elif c == \"e\":\r\n break\r\n else:\r\n print(\"Geçerli bir girdi girin\")\r\n\r\nprint(\"Bizi kullandığınız için teşekkürler, şifrenizi gizli tutun\")\r\ntime.sleep(3)\r\n","repo_name":"ZilchofNowhere/little-useful-programs","sub_path":"encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":2325,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70465936361","text":"from django.shortcuts import render\nfrom django.shortcuts import redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import login\nfrom .forms import newuserForm\nfrom artigos import APIs\nfrom artigos.models import usuario\nimport pandas as pd\n\n@login_required()\ndef home(request):\n return redirect('/artigos/search')\n\n@login_required()\ndef manageusers(request):\n if request.user.is_superuser:\n if request.method == 'POST':\n pass\n else:\n users = usuario.objects.all().values()\n df = pd.DataFrame(users)\n df = df[['id','name', 'email', 'is_superuser']]\n info = df.__array__()\n return render(request, 'users.html', {\"info\": info})\n\n else:\n return redirect('/artigos/search/')\n\ndef newuser(request):\n if request.method == 'POST':\n form = newuserForm(request.POST)\n if form.is_valid():\n user = form.save()\n login(request,user)\n if request.user == user:\n APIs.sendmail(user.email)\n return redirect('/artigos/search/')\n else:\n form = newuserForm()\n return render(request, 'register.html', {'form': form})\n","repo_name":"hawkthief/SD","sub_path":"TP_SD/TP_SD/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"26294026835","text":"import gzip\n\ncode = {'AAA': 'K','AAC': 'N','AAG': 'K','AAT': 'N','ACA': 'T','ACC': 'T','ACG': 'T','ACT': 'T','AGA': 'R','AGC': 'S','AGG': 'R','AGT': 'S','ATA': 'I','ATC': 'I','ATG': 'M','ATT': 'I',\n 'CAA': 'Q','CAC': 'H','CAG': 'Q','CAT': 'H','CCA': 'P','CCC': 'P','CCG': 'P','CCT': 'P','CGA': 'R','CGC': 'R','CGG': 'R','CGT': 'R','CTA': 'L','CTC': 'L','CTG': 'L','CTT': 'L',\n 'GAA': 'E','GAC': 'D','GAG': 'E','GAT': 'D','GCA': 'A','GCC': 'A','GCG': 'A','GCT': 'A','GGA': 'G','GGC': 'G','GGG': 'G','GGT': 'G','GTA': 'V','GTC': 'V','GTG': 'V','GTT': 'V',\n 'TAA': '*','TAC': 'Y','TAG': '*','TAT': 'Y','TCA': 'S','TCC': 'S','TCG': 'S','TCT': 'S','TGA': '*','TGC': 'C','TGG': 'W','TGT': 'C','TTA': 'L','TTC': 'F','TTG': 'L','TTT': 'F',\n '---': '-'}\n\ndef translate(NT_seq):\n codons = [NT_seq[(3*i):(3*i)+3] for i in range( len(NT_seq)//3 )]\n AA_seq = ''.join(code[c] if c in code else 'X' for c in codons)\n return AA_seq\n\n\ndef chimera2sequence(block_alignment,chimera_seq):\n blocks = sorted(set([p[0] for p in block_alignment]))\n parents = range(len(block_alignment[0][1:]))\n chimera_seq = [int(i)-1 for i in chimera_seq]\n if len(blocks)!=len(chimera_seq):\n print('chimera sequence needs contain the same number of blocks as the block alignment')\n return\n if max(chimera_seq)>max(parents):\n print('too many parents - chimera blocks are not in block alignment')\n return\n sequence = ''.join([pos[chimera_seq[blocks.index(pos[0])]+1] for pos in block_alignment])\n return sequence\n\ndef generate_chimera_seqs(parents,blocks):\n parents = [str(p) for p in range(1,parents+1)]\n seqs = ['']\n for i in range(blocks):\n ns = []\n for s in seqs:\n for p in parents:\n ns.append(s+p)\n seqs = ns\n return seqs\n\n\ndef make_all_chimeras(block_alignment):\n '''given a block alignment, make a dictionary with all chimera sequences'''\n num_blocks = len(set([p[0] for p in block_alignment]))\n num_parents = len(block_alignment[0][1:])\n ch_seqs = generate_chimera_seqs(num_parents,num_blocks)\n chimeras = {}\n for ch in ch_seqs:\n chimeras[ch] = chimera2sequence(block_alignment,ch)\n return chimeras\n\n\n# a bunch of processing--sorry it's messy...\nblock_alignment = [p for p in tuple(zip(*[l for l in open('DXS_blocks_NT_alignment_and_100updown.fasta').read().strip().split('\\n') if len(l)>100])) if p[0]!='u']\nAAseqs = [translate(''.join(s)) for s in zip(*[p[1:] for p in block_alignment])]\nNT_blocks = ''.join([p[0] for p in block_alignment]) \ncodon_blocks = [NT_blocks[(3*i):(3*i)+3] for i in range(len(NT_blocks)//3 )]\nAA_blocks = ''.join([c[1] for c in codon_blocks])\nblock_alignment = tuple(zip(*[AA_blocks,]+AAseqs))\n\n\n# a dictionary that maps chimera block sequence to an amino acid sequence\nch2seq = make_all_chimeras(block_alignment) \n\n\n# load in the chimera block sequences (need to convert letters back to numbers)\nref = [s.replace('A','1').replace('B','2').replace('C','3').replace('D','4') for s in open('DXS_ref_sequences_filtered.txt').read().strip().split('\\n')]\nsel = [s.replace('A','1').replace('B','2').replace('C','3').replace('D','4') for s in open('DXS_sel_sequences_filtered.txt').read().strip().split('\\n')]\n\nrefAAs = [ch2seq[s] for s in ref]\ngzip.open('DXS_ref_AA_sequences.txt.gz','wt').write('\\n'.join(refAAs))\n\nselAAs = [ch2seq[s] for s in sel]\ngzip.open('DXS_sel_AA_sequences.txt.gz','wt').write('\\n'.join(selAAs))\n","repo_name":"RomeroLab/PU-learning-paper-analysis","sub_path":"data/DXS/chimera_blocks_to_AA_sequences.py","file_name":"chimera_blocks_to_AA_sequences.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7402503750","text":"from bs4 import BeautifulSoup\nimport requests\n\nclass ScrapEmallsSite:\n def __init__(self, values):\n self.ui_information_dict = values\n self.price_list = list()\n self.item_list = list()\n\n def set_price_list(self, soup):\n span_list = soup.find_all(\"span\", {\"class\": \"item-price\"})\n for span in span_list:\n try:\n self.price_list.append(span.contents[0])\n except:\n True\n\n def get_price_list(self):\n return self.price_list\n\n def set_item_list(self, soup):\n div_list = soup.find_all(\"div\", {\"class\": \"item-title\"})\n # print(\"div_list: \", div_list)\n for div in div_list:\n try:\n self.item_list.append(div.contents[1][\"href\"].split(\"_\")[1].split(\"-Mobile\")[0])\n except:\n True\n print(div_list)\n\n def get_item_list(self):\n return self.item_list\n\n def scrap_page(self, window):\n brand_name = self.ui_information_dict[\"company\"]\n page_number = self.ui_information_dict[\"page_number\"]\n url = f\"https://emalls.ir/%D9%85%D8%AD%D8%B5%D9%88%D9%84%D8%A7%D8%AA~Category~39~b~{brand_name}~page~{page_number}\"\n page = requests.get(url)\n soup = BeautifulSoup(page.content, 'html.parser')\n\n self.set_price_list(soup)\n self.set_item_list(soup)\n\n for (item, price) in zip(self.get_item_list(), self.get_price_list()):\n window[\"output\"].print([str(item), str(price)])\n\n window[\"url\"].update(value=url)\n print([str(self.ui_information_dict)])\n","repo_name":"ArianE102/shabakeh","sub_path":"ScrapEmallsSite.py","file_name":"ScrapEmallsSite.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"27932909242","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom tensorflow.contrib import layers\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.ops import control_flow_ops\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow.python.ops import rnn\nfrom tensorflow.python.ops import tensor_array_ops\nfrom tensorflow.python.ops import variable_scope as vs\n\n__all__ = [\"dynamic_rnn_decoder\"]\n\ndef dynamic_rnn_decoder(cell, # 多层的 RNNCell\n decoder_fn, # 对输入输出进行处理的函数\n inputs=None, # 训练时传入该参数，为response的嵌入向量拼接使用的三元组 [batch_size, decoder_len, num_embed_units+3*num_trans_units]\n sequence_length=None, # 训练时传入该参数，为response的长度向量\n parallel_iterations=None, # 没用到这个参数\n swap_memory=False, # 没用到这个参数\n time_major=False, # 表示输入的数据集是否是time-major的\n scope=None, # \"decoder_rnn\"\n name=None): # 没用到这个参数\n \"\"\"seq2seq模型的RNN动态解码器\n \"\"\"\n with ops.name_scope(name, \"dynamic_rnn_decoder\",\n [cell, decoder_fn, inputs, sequence_length,\n parallel_iterations, swap_memory, time_major, scope]):\n # 训练时对输入进行处理\n if inputs is not None:\n inputs = ops.convert_to_tensor(inputs)\n if inputs.get_shape().ndims is not None and (\n inputs.get_shape().ndims < 2):\n raise ValueError(\"Inputs must have at least two dimensions\")\n\n # 如果不是time_major就做一个转置 [batch, seq, features] -> [seq, batch, features]\n if not time_major:\n inputs = array_ops.transpose(inputs, perm=[1, 0, 2]) # [decoder_len, batch_size, num_embed_units+3*num_trans_units]\n\n dtype = inputs.dtype\n input_depth = int(inputs.get_shape()[2]) # num_embed_units+3*num_trans_units\n batch_depth = inputs.get_shape()[1].value # batch_size\n max_time = inputs.get_shape()[0].value # decoder_len\n if max_time is None:\n max_time = array_ops.shape(inputs)[0]\n\n # 将解码器的输入设置成一个TensorArray，长度为decoder_len\n inputs_ta = tensor_array_ops.TensorArray(dtype, size=max_time)\n inputs_ta = inputs_ta.unstack(inputs) # 数组的每个元素是个[batch_size, num_embed_units+3*num_trans_units]的张量\n\n########解码器复写的循环函数 ###\n def loop_fn(time, cell_output, cell_state, loop_state):\n \"\"\"loop_fn 是一个函数，这个函数在 rnn 的相邻时间步之间被调用。\n 函数的��体调用过程为：\n 1. 初始时刻，先调用一次loop_fn，获取第一个时间步的cell的输入，loop_fn 中进行读取初始时刻的输入。\n 2. 进行cell自环 (output, cell_state) = cell(next_input, state)\n 3. 在t时刻RNN计算结束时，cell有一组输出cell_output和状态cell_state，都是tensor；\n 4. 到t+1时刻开始进行计算之前，loop_fn被调用，调用的形式为\n loop_fn( t, cell_output, cell_state, loop_state)，而被期待的输出为：(finished, next_input, initial_state, emit_output, loop_state)；\n 5. RNN采用loop_fn返回的next_input作为输入，initial_state作为状态，计算得到新的输出。\n 在每次执行（output， cell_state） = cell(next_input, state)后，执行loop_fn()进行数据的准备和处理。\n emit_structure即上文的emit_output将会按照时间存入emit_ta中。\n loop_state记录rnn loop的变量的状态。用作记录状态\n tf.where是用来实现dynamic的。\n time: 第time个时间步之前的处理，起始为0\n cell_output: 上一个时间步的输出\n cell_state: RNNCells 的长时记忆\n loop_state: 保存了上个时间步执行后是否已经结束，如果输出 alignments，还保存了存有alignments的TensorArray\n \"\"\"\n\n # 取出循环状态\n if cell_state is None: # time=0\n if cell_output is not None:\n raise ValueError(\"Expected cell_output to be None when cell_state \" \n \"is None, but saw: %s\" % cell_output)\n if loop_state is not None:\n raise ValueError(\"Expected loop_state to be None when cell_state \" \n \"is None, but saw: %s\" % loop_state)\n context_state = None\n else: # time>=1\n if isinstance(loop_state, tuple): # 如果记录了对齐\n (done, context_state) = loop_state\n else: # 如果没有记录对齐\n done = loop_state # done: [batch_size]为bool值标识了每个batch是否已经解码结束\n context_state = None\n\n # 训练时\n if inputs is not None:\n if cell_state is None: # time=0\n next_cell_input = inputs_ta.read(0)\n else: # time>=1\n if batch_depth is not None:\n batch_size = batch_depth\n else:\n batch_size = array_ops.shape(done)[0]\n # 如果time == max_time解码结束, 则next_cell_input=[batch_size, input_depth]的全1矩阵\n # 否则，next_cell_input读取这一时间步的输入\n next_cell_input = control_flow_ops.cond(\n math_ops.equal(time, max_time),\n lambda: array_ops.zeros([batch_size, input_depth], dtype=dtype),\n lambda: inputs_ta.read(time))\n # next_done=None, emit_output=attention\n (next_done, next_cell_state, next_cell_input, emit_output, next_context_state) = \\\n decoder_fn(time, cell_state, next_cell_input, cell_output, context_state)\n # 推导时\n else:\n (next_done, next_cell_state, next_cell_input, emit_output, next_context_state) = \\\n decoder_fn(time, cell_state, None, cell_output, context_state)\n\n # 检查结束状态\n if next_done is None: # 当训练时，next_done 返回的是 None\n next_done = time >= sequence_length # 当 time >= sequence_length 时，next_done = True\n\n # 存储循环状态\n if next_context_state is None: # 如果不输出alignments\n next_loop_state = next_done\n else: # 如果输出alignments\n next_loop_state = (next_done, next_context_state)\n\n return (next_done, next_cell_input, next_cell_state,\n emit_output, next_loop_state)\n######## ###\n\n # Run raw_rnn function\n outputs_ta, final_state, final_loop_state = rnn.raw_rnn(cell,\n loop_fn,\n parallel_iterations=parallel_iterations,\n swap_memory=swap_memory,\n scope=scope)\n outputs = outputs_ta.stack()\n\n # 如果要输出alignments就获取final_context_state\n if isinstance(final_loop_state, tuple):\n final_context_state = final_loop_state[1]\n else:\n final_context_state = None\n\n # 转置回去\n if not time_major:\n # [seq, batch, features] -> [batch, seq, features]\n outputs = array_ops.transpose(outputs, perm=[1, 0, 2])\n return outputs, final_state, final_context_state\n","repo_name":"Kirito0918/improve-ccm","sub_path":"dynamic_decoder.py","file_name":"dynamic_decoder.py","file_ext":"py","file_size_in_byte":8350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29158045526","text":"# leetcode-205-同构字符串.py\n# 给定两个字符串 s 和 t，判断它们是否是同构的。\n\n# 如果 s 中的字符可以被替换得到 t ，那么这两个字符串是同构的。\n\n# 所有出现的字符都必须用另一个字符替换，同时保留字符的顺序。两个字符不能映射到同一个字符上，但字符可以映射自己本身。\n\n# 示例 1:\n\n# 输入: s = \"egg\", t = \"add\"\n# 输出: true\n# 示例 2:\n\n# 输入: s = \"foo\", t = \"bar\"\n# 输出: false\n# 示例 3:\n\n# 输入: s = \"paper\", t = \"title\"\n# 输出: true\n# 说明:\n# 你可以假设 s 和 t 具有相同的长度。\n\n\"\"\"\n思路:\ns 中的字符,替换到t上,字典啊..\n\n有点粗心啊,,只注意了一到多的情况,没注意多到一的情况,\n\n\"\"\"\n\n\nclass Solution(object):\n def isIsomorphic(self, s, t):\n \"\"\"\n :type s: str\n :type t: str\n :rtype: bool\n \"\"\"\n l = len(s)\n i = 0\n\n dic = {}\n\n while i < l:\n if s[i] in dic:\n if dic[s[i]] != t[i]:\n return False\n else:\n if t[i] in dic.values():\n return False\n dic[s[i]] = t[i]\n\n i += 1\n return True\n","repo_name":"ZX1209/gl-algorithm-practise","sub_path":"leetcode-gl-python/leetcode-205-同构字符串.py","file_name":"leetcode-205-同构字符串.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"13378777314","text":"import argparse\nimport time\nimport msgpack\nfrom enum import Enum, auto\n\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\nfrom planning_utils import a_star, heuristic, create_grid\nfrom udacidrone import Drone\nfrom udacidrone.connection import MavlinkConnection\nfrom udacidrone.messaging import MsgID\nfrom udacidrone.frame_utils import global_to_local\n\n\nclass States(Enum):\n MANUAL = auto()\n ARMING = auto()\n TAKEOFF = auto()\n WAYPOINT = auto()\n LANDING = auto()\n DISARMING = auto()\n PLANNING = auto()\n\n\nclass MotionPlanning(Drone):\n\n def __init__(self, connection):\n super().__init__(connection)\n\n self.target_position = np.array([0.0, 0.0, 0.0])\n self.waypoints = []\n self.in_mission = True\n self.check_state = {}\n\n # initial state\n self.flight_state = States.MANUAL\n\n # register all your callbacks here\n self.register_callback(MsgID.LOCAL_POSITION, self.local_position_callback)\n self.register_callback(MsgID.LOCAL_VELOCITY, self.velocity_callback)\n self.register_callback(MsgID.STATE, self.state_callback)\n\n def local_position_callback(self):\n if self.flight_state == States.TAKEOFF:\n if -1.0 * self.local_position[2] > 0.95 * self.target_position[2]:\n self.waypoint_transition()\n elif self.flight_state == States.WAYPOINT:\n if np.linalg.norm(self.target_position[0:2] - self.local_position[0:2]) < 1.0:\n if len(self.waypoints) > 0:\n self.waypoint_transition()\n else:\n if np.linalg.norm(self.local_velocity[0:2]) < 1.0:\n self.landing_transition()\n\n def velocity_callback(self):\n if self.flight_state == States.LANDING:\n if self.global_position[2] - self.global_home[2] < 0.1:\n if abs(self.local_position[2]) < 0.01:\n self.disarming_transition()\n\n def state_callback(self):\n if self.in_mission:\n if self.flight_state == States.MANUAL:\n self.arming_transition()\n elif self.flight_state == States.ARMING:\n if self.armed:\n self.plan_path()\n elif self.flight_state == States.PLANNING:\n self.takeoff_transition()\n elif self.flight_state == States.DISARMING:\n if ~self.armed & ~self.guided:\n self.manual_transition()\n\n def arming_transition(self):\n self.flight_state = States.ARMING\n print(\"arming transition\")\n self.arm()\n self.take_control()\n\n def takeoff_transition(self):\n self.flight_state = States.TAKEOFF\n print(\"takeoff transition\")\n self.takeoff(self.target_position[2])\n\n def waypoint_transition(self):\n self.flight_state = States.WAYPOINT\n print(\"waypoint transition\")\n self.target_position = self.waypoints.pop() # pop(0)\n print('target position', self.target_position)\n self.cmd_position(self.target_position[0], self.target_position[1], self.target_position[2],\n self.target_position[3])\n\n def landing_transition(self):\n self.flight_state = States.LANDING\n print(\"landing transition\")\n self.land()\n\n def disarming_transition(self):\n self.flight_state = States.DISARMING\n print(\"disarm transition\")\n self.disarm()\n self.release_control()\n\n def manual_transition(self):\n self.flight_state = States.MANUAL\n print(\"manual transition\")\n self.stop()\n self.in_mission = False\n\n def send_waypoints(self):\n print(\"Sending waypoints to simulator ...\")\n data = msgpack.dumps(self.waypoints)\n self.connection._master.write(data)\n\n def raytrace(self, p1, p2):\n cells = []\n\n x_axis = np.arange(p1[0], p2[0] + 1)\n if p1[0] != p2[0]:\n m = (p2[1] - p1[1]) / (p2[0] - p1[0])\n else: # both points are on the same vertical\n for i in np.arange(p1[1], p2[1]):\n cells.append((p1[0], i))\n return cells\n\n y0 = p1[1] - m * p1[0]\n f = np.array(list(map(lambda x: m * x + y0, x_axis)))\n\n x = 0\n y = 0\n\n while x + 1 < len(f):\n cells.append((x + p1[0], y + p1[1]))\n if f[x + 1] > y + p1[1] + 1:\n y += 1\n else:\n x += 1\n\n return cells\n\n def plan_path(self):\n self.flight_state = States.PLANNING\n print(\"Searching for a path ...\")\n TARGET_ALTITUDE = 2\n SAFETY_DISTANCE = 5\n\n self.target_position[2] = TARGET_ALTITUDE\n\n # TODO: read lat0, lon0 from colliders into floating point values\n f = open('colliders.csv', 'r+')\n l = f.readline()\n f.close()\n\n start_pos = dict((x.strip(), float(y.strip()))\n for x, y in (element.split(' ')\n for element in l.split(', ')))\n\n # TODO: set home position to (lon0, lat0, 0)\n self.set_home_position(start_pos['lon0'], start_pos['lat0'], 0)\n\n # TODO: retrieve current global position\n global_position = self.global_position\n\n # Retrieve your current position in geodetic coordinates from\n # self._latitude, self._longitude and self._altitude. Then\n # use the utility function global_to_local() to convert to local\n # position (using self.global_home as well, which you just set)\n\n # TODO: convert to current local position using global_to_local()\n local_position = global_to_local([self._longitude, self._latitude, self._altitude], self.global_home)\n\n print('global home {0}, position {1}, local position {2}'.format(self.global_home, self.global_position,\n self.local_position))\n # Read in obstacle map\n data = np.loadtxt('colliders.csv', delimiter=',', dtype='Float64', skiprows=2)\n\n # Define a grid for a particular altitude and safety margin around obstacles\n grid, north_offset, east_offset = create_grid(data, TARGET_ALTITUDE, SAFETY_DISTANCE)\n print(\"North offset = {0}, east offset = {1}\".format(north_offset, east_offset))\n # Define starting point on the grid (this is just grid center)\n grid_start = (-north_offset, -east_offset)\n # TODO: convert start position to current position rather than map center\n grid_start = (-north_offset + math.floor(self.local_position[0]),\n -east_offset + math.floor(self.local_position[1]))\n\n # Set goal as some arbitrary position on the grid\n # TODO: adapt to set goal as latitude / longitude position and convert\n\n # Test path 1: just a straight line from starting point\n goal_lat = 37.7924\n goal_lon = -122.3974\n\n # Test path 2: around the corner\n # goal_lat = 37.793933\n # goal_lon = -122.397336\n\n # Test path 3\n goal_lat = 37.793532\n goal_lon = -122.397781\n\n goal_lat = 37.797366\n goal_lon = -122.394869\n\n goal_local = global_to_local([goal_lon, goal_lat, 0], self.global_home)\n grid_goal = (-north_offset + math.floor(goal_local[0]),\n -east_offset + math.floor(goal_local[1]))\n\n # Run A* to find a path from start to goal\n # TODO: add diagonal motions with a cost of sqrt(2) to your A* implementation\n # or move to a different search space such as a graph (not done here)\n print('Local Start and Goal: ', grid_start, grid_goal)\n path, _ = a_star(grid, heuristic, grid_start, grid_goal)\n\n # TODO: prune path to minimize number of waypoints\n\n # we start trying to fly a straight line from start to goal.\n segments = [(0, len(path) - 1)]\n\n while segments:\n current_segment = segments.pop()\n ray_points = self.raytrace(path[current_segment[0]], path[current_segment[1]])\n if all(grid[p[0], p[1]] == 0 for p in ray_points):\n for p in [path[i] for i in range(current_segment[0] + 1, current_segment[1] - 1)]:\n path.remove(p)\n else: # if we're not successful, we halve the way into two equal segments and try again\n mid_id = current_segment[0] + math.floor((current_segment[1] - current_segment[0]) / 2)\n if current_segment[0] != mid_id:\n segments.append((current_segment[0], mid_id))\n if mid_id + 1 != current_segment[1]:\n segments.append((mid_id + 1, current_segment[1]))\n\n # Plot the execution path\n plt.imshow(grid, cmap='Greys', origin='lower')\n plt.plot(grid_start[1], grid_start[0], 'x')\n plt.plot(grid_goal[1], grid_goal[0], 'o')\n\n plt.title = \"2D path seen from above\"\n pruned_path = np.array(path)\n plt.plot(pruned_path[:, 1], pruned_path[:, 0], 'g')\n plt.scatter(pruned_path[:, 1], pruned_path[:, 0])\n plt.grid(True)\n plt.xlabel('EAST')\n plt.ylabel('NORTH')\n plt.show()\n\n # Convert path to waypoints\n waypoints = [[p[0] + north_offset, p[1] + east_offset, TARGET_ALTITUDE, 0] for p in path]\n # Set self.waypoints\n self.waypoints = waypoints\n # TODO: send waypoints to sim (this is just for visualization of waypoints)\n self.send_waypoints()\n\n def start(self):\n self.start_log(\"Logs\", \"NavLog.txt\")\n\n print(\"starting connection\")\n self.connection.start()\n\n # Only required if they do threaded\n # while self.in_mission:\n # pass\n\n self.stop_log()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('--port', type=int, default=5760, help='Port number')\n parser.add_argument('--host', type=str, default='127.0.0.1', help=\"host address, i.e. '127.0.0.1'\")\n args = parser.parse_args()\n\n conn = MavlinkConnection('tcp:{0}:{1}'.format(args.host, args.port), timeout=60)\n drone = MotionPlanning(conn)\n time.sleep(1)\n\n drone.start()\n","repo_name":"bot-motion/FCND","sub_path":"FCND-Motion-Planning/motion_planning.py","file_name":"motion_planning.py","file_ext":"py","file_size_in_byte":10222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22004337537","text":"\nfrom django.conf.urls import url\nfrom views import *\n\nurlpatterns=[\n url(r'^login/$',loginUser,name='user_login'),\n url(r'^register/$',registerUser,name='user_register'),\n url(r'^logout/$',logoutUser,name='user_logout'),\n url(r'^bootstrap/$',bootStrap,name='bootstrap_data'),\n url(r'^$',Landing.as_view(),name=\"landing\"),\n url(r'^robots\\.txt/$', TemplateView.as_view(template_name='general/robots.txt', content_type='text/plain')),\n url(r'about/^$',About.as_view(),name=\"about\"),\n\n]","repo_name":"amitsethi0843/wyrelist","sub_path":"operations/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"492845933","text":"fromYear = int(input(\"Count Leapear Min from : \"))\ntoYear = int(input(\"Count Leapear Min To : \"))\nprint(\"-------------------------------------\")\ndays = 0\nwhile fromYear <= toYear:\n if fromYear %4== 0 and fromYear %100 != 0 or fromYear %400 == 0:\n print(fromYear, \" is leapyear - 366 days\")\n days += 366\n else:\n print(fromYear, \" is not leapyear - 365 days\")\n days = days + 365\n \n fromYear +=1\n\ntotalMiniute = days * 1440\nprint(\"-----------------------------------\")\nprint(\"Total Days = \", days)\nprint(\"Total Miniutes = \", totalMiniute)","repo_name":"Islam2718/swim-with-python","sub_path":"100_days_challenges/Day11_counting_miniutes_between_two_year.py","file_name":"Day11_counting_miniutes_between_two_year.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15004225950","text":"import os\n\nimport numpy as np \nimport pandas as pd \n\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, TensorDataset, random_split\nfrom lmf.lightning_matrix_factorization import LightningMatrixFactorization\n\nimport pytorch_lightning as pl\n\nif __name__ == \"__main__\":\n\n # magic numbers\n batch_size = 262144\n learning_rate = 1e-3\n embedding_size = 32\n max_epochs = 100\n checkpoint_every = 10\n # dataloader cpu cores, based on Kaggle GPU notebooks.\n num_workers = 16\n\n file_path = os.path.abspath(__file__)\n root_path = os.path.split(os.path.split(file_path)[0])[0]\n\n print(root_path)\n my_filepath = os.path.join(root_path, \"data\", \"ratings.csv\")\n df = pd.read_csv(my_filepath)\n df = df.sample(frac=1).reset_index(drop=True)\n df = df[:1000000]\n\n print(len(df))\n\n test_split = int(0.2 * len(df))\n\n train_df = df[:-2*test_split]\n val_df = df[-2*test_split:-test_split]\n test_df = df[-test_split:]\n\n users = torch.tensor(train_df.user_id.values).long()\n books = torch.tensor(train_df.book_id.values).long()\n ratings = torch.tensor(train_df.rating.values).float()\n dataset = TensorDataset(users, books, ratings)\n\n train_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)\n\n val_users = torch.tensor(val_df.user_id.values).long()\n val_books = torch.tensor(val_df.book_id.values).long()\n val_ratings = torch.tensor(val_df.rating.values).float()\n val_dataset = TensorDataset(val_users, val_books, val_ratings)\n\n val_loader = DataLoader(val_dataset, batch_size=batch_size, num_workers=num_workers)\n\n number_of_users = np.max(df[\"user_id\"])+1\n number_of_books = np.max(df[\"book_id\"])+1\n\n lmf = LightningMatrixFactorization(number_of_books, number_of_users)\n\n if torch.cuda.is_available():\n trainer = pl.Trainer(accelerator=\"gpu\", devices=1, max_epochs=max_epochs)\n else:\n trainer = pl.Trainer(max_epochs=max_epochs)\n\n trainer.fit(model=lmf, train_dataloaders=train_loader, val_dataloaders=val_loader) \n # testing evaluation\n with torch.no_grad():\n \n test_users = torch.tensor(test_df.user_id.values).long()\n test_books = torch.tensor(test_df.book_id.values).long()\n test_ratings = torch.tensor(test_df.rating.values).float()\n \n lmf.eval()\n test_prediction = lmf(test_users, test_books)\n \n test_loss = F.mse_loss(test_prediction, test_ratings)\n \n test_msg = f\"MSE loss for test data = {test_loss:.3} \\n\"\n print(test_msg)\n\n for hh in range(10):\n # see a few examples of predictions\n lmf.eval()\n \n my_index = np.random.randint(len(test_users))\n \n my_prediction = lmf(test_users[my_index], test_books[my_index])\n \n msg = f\"Test set prediction {my_prediction}, ground truth: {test_ratings[my_index]}\"\n print(msg)\n\n","repo_name":"riveSunder/LightningMatrixFactorization","sub_path":"lmf/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72607920039","text":"#--------------------Project RFID Time-Attendance Scanner-----------------------\n# sudo raspi-config (To configure pins to read data from scanner).\n# sudo pip3 install spidev (To communicate with the scanner).\n# sudo pip3 install mfrc522 (This is for the scanner).\n# mkdir ~/pi-rfid \n# python3 ~/pi-rfid/RFID_scanner.py (Run the Script after saving)\n#\n# SDA connects to GPIO8 (Physical Pin 24) \n# SCK connects to GPIO11 (Physical Pin 23)\n# MOSI connects to GPIO10 (Physical Pin 19)\n# MISO connects to GPIO9 (Physical Pin 21)\n# GND connects to Breadboard Ground Rail.\n# RST connects to GPIO25 (Physical Pin 22)\n# 3.3v connects to 3v3 (Physical Pin 1)\n#\n\n#!/usr/bin/env python\n#********************************Libraries*************************************#\n\nimport requests # API Library\nfrom datetime import datetime\nimport RPi.GPIO as GPIO # All the functions needed to interact with GPIO Pins (pip install RPi.GPIO)\nfrom mfrc522 import SimpleMFRC522 # this is what we will use actually to talk with the RFID RC522 (https://github.com/pimylifeup/MFRC522-python) \nimport Adafruit_CharLCD as LCD # An LCD to show they have been scanned (git clone https://github.com/pimylifeup/Adafruit_Python_CharLCD.git)\nimport os as os # For File removal\nimport json as json # To store in JSon format. \n\n#******************************************************************************#\n#*******************************API Connection*********************************#\n\nurl = \"https://test.com/post\"\ntimeout = 5\nconnection = requests.post(\"https://test.com/post\", timeout=timeout)\n\n#******************************************************************************#\n\nreader = SimpleMFRC522() # Creates an object to store all our variables.\nlcd = LCD.Adafruit_CharLCD(4, 24, 23, 17, 18, 22, 16, 2, 4);\n# Raspberry Pi pin configuration:\n#lcd_rs = 4\n#lcd_en = 24\n#lcd_d4 = 23\n#lcd_d5 = 17\n#lcd_d6 = 18\n#lcd_d7 = 22\n\n#*****************************Main Body Code***********************************#\ntry:\n while True:\n # This block of code is if we want to read data to the LCD.\n LCD.clear() # Clears LCD.\n LCD.message('Place Card to\\nregister') # Prints message on LCD.\n id, text = reader.read() # Scans for Card.\n print(id) # Prints ID scanned to terminal.\n LCD.clear() # Clears LCD. \n LCD.message(\"User Scanned: \" + id) # Prints ID on LCD.\n \n if connection: # Checks if can connect with API\n \n if os.path.exists(\"card_data.json\"): # Check if file exists\n with open('card_data.json') as f:\n data = json.load(f)\n for person in data: # Loop through each card data and send to API\n response = requests.post(url, json=person)\n response.json()\n\n os.remove(\"card_data.json\") # Removes any local files of card once sent.\n\n # Gets Current Date and Time\n now = datetime.now() \n dt_string = now.strftime(\"%d/%m/%Y - %H:%M:%S\")\n\n # Sends data to API after checking files for card data\n todo = {\"card_id\": id, \"date_t\": dt_string}\n response = requests.post(url, json=todo)\n response.json()\n \n else: # Stores card data on file if no connection with API. \n#********************Stores each card scan in the json file********************* \n # THIS PREPARES THE DATA FOR STORING\n\n # Gets Current Date and Time\n now = datetime.now() \n # Changes Format of time.\n dt_string = now.strftime(\"%d/%m/%Y - %H:%M:%S\")\n # Stores data in this JSON format\n data = [{\n 'card_uid': id,\n 'date_t': dt_string,\n }] \n\n # THIS IF STATEMENT IS WHAT WRITES TO THE JSON FILE.\n\n # Check if file exists to append to the existing data in JSON format\n if os.path.exists(\"card_data.json\"): \n with open('card_data.json') as outfile:\n array = json.load(outfile)\n\n array.append(data)\n \n with open(\"card_data.json\", \"w\") as f:\n json.dump(array, f, indent=4)\n\n # If first time storing data, it will create the file.\n else: \n json_object = json.dumps(data, indent = 2)\n with open(\"card_data.json\", \"w\") as f:\n f.write(json_object) \n \nfinally:\n GPIO.cleanup() # Cleans up","repo_name":"joey101/arduino","sub_path":"RFID/RFID.py","file_name":"RFID.py","file_ext":"py","file_size_in_byte":5186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41689598827","text":"# Author: Mohamed Shammas Mohamed Ali Manaf \r\n# Date : 2018-02-24\r\n# Description : Program that calculates the smallest number divisible evenly by all numbers between 0 and 20\r\n# References : https://www.programiz.com/python-programming/break-continue\r\n#\t https://www.pythoncentral.io/pythons-range-function-explained/\r\n# Note : The program took almost 3 minutes to display the o/p because of the volume of loops involved.\r\n\r\nsmallestNum = 0 \t\t\t\t\t\t\t\t\t\t# variable to assign the smallest number\r\nintValue = 0\t\t\t\t\t\t\t\t\t\t\t# variable for iterating over the integers; starting at zero\r\nwhile smallestNum == 0: \t\t\t\t\t\t\t\t# this while loop will iterate through all integers until the smallest evenly divisble number is found\r\n\t#print(i)\r\n\tfor divisor in range(1,21):\t\t\t\t\t\t\t# the range returns the list of integers from 1 to 20\r\n\t\tif intValue % divisor == 0: \t\t\t\t\t# check if number contained in the current \"intValue\" is evenly divisble by the \"divisor\"\r\n\t\t\tif divisor == 20 and intValue % divisor == 0: # assume if the divisor is 20, at this stage the \"intValue\" should be divisible by all integers preceeding the \"divisor\" in numerical order.\r\n\t\t\t\tsmallestNum = intValue\t\t\t\t\t # assign the current value in intValue to smallestNum\r\n\t\t\t\tbreak;\r\n\t\telse:\r\n\t\t\tbreak;\t\t\t\t\t\t\t\t\t\t# break the looping if the value in intValue is not evenly divisble by any of the elements in the range list.\r\n\tintValue+=1\t\t\t\t\t\t\t\t\t\t\t# increment the value of intValue by 1\r\nprint('The smallest number evenly divisible by all number bewteen and incluing 1 and 20 is : ',smallestNum)\t\t\t\t\t\t\t\t\t\t# Display value\r\n\t\t\r\n# Output of the program is follows:\r\n# c:\\GMIT\\Scripting\\Week5 -- Smallest Number divisible>python SmallestNum.py \r\n# The smallest number evenly divisible by all number bewteen and incluing 1 and 20 is : 232792560 \r\n","repo_name":"shammas012/Python_Exercises","sub_path":"SmallestNum.py","file_name":"SmallestNum.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"18621116472","text":"dim=input(\"Input square matrix dimension (4 - default) \")\nprn=input(\"Print matrix in square or row(s/r, square - default) \")\nif not prn:\n prn=\"s\"\nif not dim:\n dim=4\nelse:\n dim=int(dim)\nls=[]\nls_inner=[]\ns=0\nfor i in range(dim):\n ls_inner+=([(i*dim+j+1) for j in range(dim)])\n s+=sum(ls_inner)\n ls.append(ls_inner)\n if prn.lower()==\"s\":\n print(ls_inner)\n ls_inner=[]\nif prn.lower()==\"r\":\n print(ls)\nprint(f\"Sum of all elemets of matrix {s}\")\n","repo_name":"AlexseySukhanov/HomeWork5","sub_path":"Task8.py","file_name":"Task8.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22703836996","text":"import os\r\nimport gym\r\nfrom gym.wrappers.monitoring.video_recorder import VideoRecorder\r\nimport pdb\r\nimport numpy as np\r\nimport random\r\nfrom collections import deque\r\nfrom tqdm import tqdm\r\n\r\nimport torch\r\nimport torch.nn.functional as F\r\n\r\nfrom CustomTensorBoard import ModifiedTensorBoard\r\n\r\n\r\nimport time\r\nfrom copy import deepcopy\r\n\r\n\r\nfrom normalizer import Normalizer\r\nfrom models import Actor, Critic\r\nfrom buffer import ReplayBuffer\r\nfrom arguments import GetArgs\r\nfrom mujoco_py import GlfwContext\r\nGlfwContext(offscreen=True) # Create a window to init GLFW.\r\n\r\n\r\nclass Agent:\r\n def __init__(self,env, env_params, args, models=None, record_episodes=[0,.1,.25,.5,.75,1.]):\r\n self.env= env\r\n self.env_params = env_params\r\n self.args = args\r\n\r\n\r\n # networks\r\n if models == None:\r\n self.actor = Actor(self.env_params).double()\r\n self.critic = Critic(self.env_params).double()\r\n else:\r\n self.actor , self.critic = self.LoadModels()\r\n # target networks used to predict env actions with\r\n self.actor_target = Actor(self.env_params,).double()\r\n self.critic_target = Critic(self.env_params).double()\r\n\r\n self.actor_target.load_state_dict(self.actor.state_dict())\r\n self.critic_target.load_state_dict(self.critic.state_dict())\r\n\r\n if self.args.cuda:\r\n self.actor.cuda()\r\n self.critic.cuda()\r\n self.actor_target.cuda()\r\n self.critic_target.cuda()\r\n\r\n\r\n self.actor_optim = torch.optim.Adam(self.actor.parameters(), lr=0.001)\r\n self.critic_optim = torch.optim.Adam(self.critic.parameters(), lr=0.001)\r\n\r\n self.normalize = Normalizer(env_params,self.args.gamma)\r\n self.buffer = ReplayBuffer(1_000_000, self.env_params)\r\n self.tensorboard = ModifiedTensorBoard(log_dir = f\"logs\")\r\n self.record_episodes = [int(eps * self.args.n_epochs) for eps in record_episodes]\r\n\r\n def ModelsEval(self):\r\n self.actor.eval()\r\n self.actor_target.eval()\r\n self.critic.eval()\r\n self.critic_target.eval()\r\n\r\n def ModelsTrain(self):\r\n self.actor.train()\r\n self.actor_target.train()\r\n self.critic.train()\r\n self.critic_target.train()\r\n\r\n def GreedyAction(self, state):\r\n self.ModelsEval()\r\n with torch.no_grad():\r\n state = torch.tensor(state, dtype=torch.double).unsqueeze(dim=0)\r\n if self.args.cuda:\r\n state = state.cuda()\r\n action = self.actor.forward(state).detach().cpu().numpy().squeeze()\r\n return action\r\n\r\n def NoiseAction(self, state):\r\n self.ModelsEval()\r\n with torch.no_grad():\r\n state = torch.tensor(state, dtype=torch.double).unsqueeze(dim=0)\r\n if self.args.cuda:\r\n state = state.cuda()\r\n action = self.actor.forward(state).detach().cpu().numpy()\r\n action += self.args.noise_eps * self.env_params['max_action'] * np.random.randn(*action.shape)\r\n action = np.clip(action, -self.env_params['max_action'], self.env_params['max_action'])\r\n return action.squeeze()\r\n\r\n def Update(self):\r\n self.ModelsTrain()\r\n for i in range(self.args.n_batch):\r\n state, a_batch, r_batch, nextstate, d_batch = self.buffer.SampleBuffer(self.args.batch_size)\r\n a_batch = torch.tensor(a_batch,dtype=torch.double)\r\n r_batch = torch.tensor(r_batch,dtype=torch.double)\r\n # d_batch = torch.tensor(d_batch,dtype=torch.double)\r\n state = torch.tensor(state,dtype=torch.double)\r\n nextstate = torch.tensor(nextstate,dtype=torch.double)\r\n # d_batch = 1 - d_batch\r\n\r\n if self.args.cuda:\r\n a_batch = a_batch.cuda()\r\n r_batch = r_batch.cuda()\r\n # d_batch = d_batch.cuda()\r\n state = state.cuda()\r\n nextstate = nextstate.cuda()\r\n\r\n with torch.no_grad():\r\n action_next = self.actor_target.forward(nextstate)\r\n q_next = self.critic_target.forward(nextstate,action_next)\r\n q_next = q_next.detach().squeeze()\r\n q_target = r_batch + self.args.gamma * q_next\r\n q_target = q_target.detach().squeeze()\r\n\r\n q_prime = self.critic.forward(state, a_batch).squeeze()\r\n critic_loss = F.mse_loss(q_target, q_prime)\r\n\r\n action = self.actor.forward(state)\r\n actor_loss = -self.critic.forward(state, action).mean()\r\n # params = torch.cat([x.view(-1) for x in self.actor.parameters()])\r\n # l2_reg = self.args.l2_norm *torch.norm(params,2)\r\n # actor_loss += l2_reg\r\n\r\n self.actor_optim.zero_grad()\r\n actor_loss.backward()\r\n self.actor_optim.step()\r\n\r\n self.critic_optim.zero_grad()\r\n critic_loss.backward()\r\n self.critic_optim.step()\r\n\r\n self.SoftUpdateTarget(self.critic, self.critic_target)\r\n self.SoftUpdateTarget(self.actor, self.actor_target)\r\n\r\n def Explore(self):\r\n for epoch in range(self.args.n_epochs +1):\r\n start_time = time.process_time()\r\n for cycle in range(self.args.n_cycles):\r\n for _ in range(self.args.num_rollouts_per_mpi):\r\n state = self.env.reset()\r\n for t in range(self.env_params['max_timesteps']):\r\n action = self.NoiseAction(state)\r\n nextstate, reward, done, info = self.env.step([action])\r\n nextstate = nextstate.squeeze()\r\n reward = self.normalize.normalize_reward(reward)\r\n self.buffer.StoreTransition(state, action, reward, nextstate, done)\r\n state = nextstate\r\n self.Update()\r\n avg_reward = self.Evaluate()\r\n self.tensorboard.step = epoch\r\n elapsed_time = time.process_time() - start_time\r\n print(f\"Epoch {epoch} of total of {self.args.n_epochs +1} epochs, average reward is: {avg_reward}.\\\r\n Elapsedtime: {int(elapsed_time /60)} minutes {int(elapsed_time %60)} seconds\")\r\n if epoch % 5 or epoch + 1 == self.args.n_epochs:\r\n self.SaveModels(epoch)\r\n self.record(epoch)\r\n\r\n\r\n def Evaluate(self):\r\n self.ModelsEval()\r\n total_reward = []\r\n episode_reward = 0\r\n succes_rate = []\r\n for episode in range(self.args.n_evaluate):\r\n state = self.env.reset()\r\n episode_reward = 0\r\n for t in range(self.env_params['max_timesteps']):\r\n action = self.GreedyAction(state)\r\n nextstate, reward, done, info = self.env.step([action])\r\n episode_reward += reward\r\n state = nextstate\r\n if done or t + 1 == self.env_params['max_timesteps']:\r\n total_reward.append(episode_reward)\r\n episode_reward = 0\r\n\r\n average_reward = sum(total_reward)/len(total_reward)\r\n min_reward = min(total_reward)\r\n max_reward = max(total_reward)\r\n self.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward)\r\n return average_reward\r\n\r\n def record(self, epoch):\r\n self.ModelsEval()\r\n try:\r\n if not os.path.exists(\"videos\"):\r\n os.mkdir('videos')\r\n recorder = VideoRecorder(self.env, path=f'videos/epoch-{epoch}.mp4')\r\n for _ in range(self.args.n_record):\r\n done =False\r\n state = self.env.reset()\r\n while not done:\r\n recorder.capture_frame()\r\n action = self.GreedyAction(state)\r\n nextstate,reward,done,info = self.env.step([action])\r\n state = nextstate\r\n recorder.close()\r\n except Exception as e:\r\n print(e)\r\n\r\n def SaveModels(self, ep):\r\n if not os.path.exists(\"models\"):\r\n os.mkdir('models')\r\n torch.save(self.actor.state_dict(), os.path.join('models', 'Actor.pt'))\r\n torch.save(self.critic.state_dict(), os.path.join('models', 'Critic.pt'))\r\n\r\n def LoadModels(self, actorpath, criticpath):\r\n actor = Actor(self.env_params, self.hidden_neurons)\r\n critic = Critic(self.env_params, self.hidden_neurons)\r\n actor.load_state_dict(torch.load(actorpath))\r\n critic.load_state_dict(torch.load(criticpath))\r\n return actor, critic\r\n\r\n def SoftUpdateTarget(self, source, target):\r\n for target_param, param in zip(target.parameters(), source.parameters()):\r\n target_param.data.copy_((1 - self.args.polyak) * param.data + self.args.polyak * target_param.data)\r\n\r\n","repo_name":"raphael-fortunato/Ddpg","sub_path":"ddpg_agent.py","file_name":"ddpg_agent.py","file_ext":"py","file_size_in_byte":8949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"13506143262","text":"import requests,bs4,lxml,os,json,sys,json,urllib.request\r\n\r\narray_json = json.loads(sys.argv[1])\r\n\r\npath = os.getcwd()\r\nurl = 'http://fanyi.baidu.com/v2transapi'\r\n\r\nlanguage_dict = {\"日本\":\"jp\",\"俄国\":\"ru\",\"韩国\":\"kor\",\"中国\":\"zh\",\"繁体\":\"cht\",\"英语\":\"en\"}\r\n\r\nfrom_data = language_dict[array_json['country_src']]\r\nto_data = language_dict[array_json['country_des']]\r\n\r\nconntect_data = urllib.request.quote(array_json['fanyi_src'])\r\n\r\ndata = 'from='+from_data+'&to='+to_data +'&query='+conntect_data+'&transtype=realtime&simple_means_flag=3'\r\nheaders = {\r\n 'Host': 'fanyi.baidu.com',\r\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:53.0) Gecko/20100101 Firefox/53.0',\r\n \r\n 'Accept-Language': 'zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3',\r\n 'Accept-Encoding': 'gzip, deflate',\r\n 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',\r\n 'X-Requested-With': 'XMLHttpRequest',\r\n 'Referer': 'http://fanyi.baidu.com/',\r\n\r\n 'Connection': 'keep-alive'}\r\n \r\nr = requests.post(url,headers = headers ,data = data)\r\nresponse = r.text\r\ncontent = json.loads(response)\r\ntry:\r\n \r\n print(content['trans_result']['data'][0]['dst'])\r\n \r\nexcept:\r\n print('')\r\n\r\n\r\n","repo_name":"huaSoftware/Convenience-shop","sub_path":"py/fanyi.py","file_name":"fanyi.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14050327740","text":"\nfrom typing import DefaultDict\nfrom matplotlib.pyplot import sca\nfrom mesa import Model, Agent\nfrom mesa.time import BaseScheduler, StagedActivation\nfrom mesa.datacollection import DataCollector\nfrom mesa.space import ContinuousSpace\nfrom mesa.visualization.TextVisualization import TextData\nimport math\nimport Brandon\nimport copy\nimport Constants\n\nclass ABM(Model):\n '''Creation of Agent-Based-Model Class\n Stages of Model Per Tick: \"movement\" , \"reaction_regulation\" , \"differentiation_tick\" , \"tracking_update\"\n Global Variables:\n num_stem_cells : Int\n sauce : Boolean : True if model will measure cell differentiation after a certain diff_timer elapses\n num_BMP4 : Int\n num_NOG : Int\n spawn_freq : Int : How much energy a given StemCell needs to reproduce\n diff_timer : Int\n endo_min : Int : How much concentration of BMP4 a Stem Cell needs to come into contact to at minimum differntiate into an endoderm cell\n ecto_max : Int : How little concentration of BMP4 a Stem Cell needs to come into contact to at maximum differntiate into an ectoderm cell\n one_cells_contact : Int : This attribute is used to test, it is designed to take an arbitraty Stem Cell each step and access its chemical contact\n start_diff : Boolean : When this is True, start positional differentiation if other Stem Cells have already differentiated nearby\n stem_cell_ex : StemCell : This attribute is used to latch onto a StemCell for purposes of tracking\n stem_cell_ex_diff : Boolean : This attribute indicates the example StemCell's differentiation status\n avg_x : Float : Indicates the Average X Value of all Stem Cells\n avg_y : Float : Indicates the Average Y Value of all Stem Cells\n avg_radius : Float : Indicates the Average Distance of all Stem Cells to the Centroid\n schedule : StagedActivation : Staged Activation Schedule that Follows the Stages listed above\n running : True : Batch will continually run this model's steps indefinitely'''\n\n def __init__(self, num_stem_cells: int , sauce: bool , num_BMP4: int , num_NOG: int , spawn_freq: int , diff_timer: int , endo_min: int , ecto_max: int , max_x:int=20 , max_y:int=20) -> None:\n self.num_stem_cells = num_stem_cells\n self.sauce = sauce\n self.num_BMP4 = num_BMP4\n self.num_NOG = num_NOG\n self.spawn_freq = spawn_freq\n self.diff_timer = diff_timer\n self.endo_min = endo_min\n self.ecto_max = ecto_max\n self.one_cells_contact = 0\n self.start_diff = False\n self.stem_cell_ex = None\n self.stem_cell_ex_diff = None\n self.avg_x = 0\n self.avg_y = 0\n self.avg_radius = 0\n self.schedule = BaseScheduler(self)\n self.running = True\n self.space = ContinuousSpace(max_x , max_y , False , 0 , 0)\n self.center_pos = self.space.center\n self.currentIDNum = 0\n self.hasCells = True\n self.cellTouchingDict = {}\n for i in range(1 , Constants.NUM_STEM_CELLS + 1):\n self.cellTouchingDict[i] = []\n self.end_time = 0\n self.mConcX = 0\n self.mConcY = 0\n self.mR = 0\n self.cells = []\n self.NOG = []\n self.BMP4 = []\n self.BMP4vector = Brandon.unot\n self.setup()\n \n \n\n def setup(self):\n #Add StemCells to the Space\n if self.hasCells == True:\n for i in range(self.num_stem_cells):\n self.currentIDNum += 1\n c = StemCell(self.currentIDNum , self)\n self.schedule.add(c)\n r = self.random.random() * 1\n theta = self.random.random() * 2 * math.pi\n x = r * math.cos(theta) + self.center_pos[0]\n y = r * math.sin(theta) + self.center_pos[1]\n self.space.place_agent(c , (x , y))\n self.cells.append(c)\n self.stem_cell_ex = self.space._index_to_agent[self.random.randrange(0 , len(self.space._agent_points))]\n self.stem_cell_ex_diff = self.stem_cell_ex.differentiated\n\n\n\n #Add BMP4 to the Space\n for i in range(self.num_BMP4):\n self.currentIDNum += 1\n n = BMP4(self.currentIDNum, self)\n self.schedule.add(n)\n r = self.random.random()\n theta = self.random.random() * 2 * math.pi\n x = r * math.cos(theta) + self.center_pos[0]\n y = r * math.sin(theta) + self.center_pos[1]\n self.space.place_agent(n , (x , y))\n\n self.BMP4.append(n)\n\n #Add NOG to the Space\n for i in range(self.num_NOG):\n self.currentIDNum += 1\n l = NOG(self.currentIDNum, self)\n self.schedule.add(l)\n r = self.random.random()\n theta = self.random.random() * 2 * math.pi\n x = r * math.cos(theta) + self.center_pos[0]\n y = r * math.sin(theta) + self.center_pos[1]\n self.space.place_agent(l , (x , y))\n\n self.NOG.append(l)\n\n self.calcAvgs()\n \n \n\n\n\n\n def calcAvgs(self):\n x = 0\n y = 0\n r = 0\n for agent in self.space._agent_to_index:\n if type(agent) == StemCell:\n x += agent.pos[0]\n y += agent.pos[1]\n \n\n x = x / self.num_stem_cells\n y = y / self.num_stem_cells\n centroid = (x , y)\n for agent in self.space._agent_to_index:\n if type(agent) == StemCell:\n r += self.space.get_distance(centroid , agent.pos)\n r = r / self.num_stem_cells\n self.avg_x = x\n self.avg_y = y\n self.avg_radius = r\n\n\n\n \n \n\n def cascade(self):\n for agent in self.space._agent_to_index:\n if type(agent) == StemCell:\n neighbors = self.space.get_neighbors(agent.pos , agent.internalR , True)\n for neighbor in neighbors:\n if type(neighbor) == StemCell and neighbor.differentiated == \"virgin\":\n agent.differentiated = neighbor.differentiated\n agent.time_for_diff = 0\n\n\n\n def updateParams(self):\n agents = self.space._agent_to_index.keys()\n StemCells = [x for x in agents if type(x) == StemCell]\n c = StemCells[self.random.randrange(0 , len(StemCells))]\n self.one_cells_contact = c.chemical_contact\n self.stem_cell_ex_diff = self.stem_cell_ex.differentiated\n\n\n\n def updateTouchingDict(self):\n #self.cellTouchingDict\n for key in self.cellTouchingDict.keys():\n self.cellTouchingDict[key] = []\n\n for agent in self.space._agent_to_index:\n\n if type(agent) == StemCell:\n \n neighbors = self.space.get_neighbors(agent.pos , agent.internalR -.01 , include_center=False)\n for neighbor in neighbors:\n if type(neighbor) == StemCell:\n if neighbor not in self.cellTouchingDict[agent.unique_id]:\n self.cellTouchingDict[agent.unique_id].append(neighbor)\n if agent not in self.cellTouchingDict[neighbor.unique_id]:\n self.cellTouchingDict[neighbor.unique_id].append(agent)\n \n\n def updateBMP4(self):\n self.BMP4vector = Brandon.reaction(self.BMP4)\n\n\n def step(self):\n self.calcAvgs()\n if self.hasCells:\n self.updateParams()\n self.updateTouchingDict()\n self.updateBMP4()\n if self.start_diff == True:\n self.cascade()\n if self.end_time == -2:\n self.end_time = 3\n self.end_time -= 1\n self.schedule.step()\n if self.end_time == -1 and self.start_diff == True:\n self.running = False\n\n \n\n\n\n\n\n\nclass StemCell(Agent):\n '''Creation of Stem Cell Agent\n Attributes: \n Differentiated: Boolean\n Chemical Contact: Int\n Energy: Int\n Time For Diff: Int'''\n\n def __init__(self, unique_id: int, model: Model) -> None:\n super().__init__(unique_id , model)\n self.differentiated = \"virgin\"\n self.chemical_contact = 0\n self.energy = 0\n self.time_for_diff = self.random.randrange(Constants.TIME_FOR_DIFF_UPPER - 10 , Constants.TIME_FOR_DIFF_UPPER + 1)\n self.internalR = Constants.STEMCELL_R\n self.absorbedNOG = []\n\n\n def step(self):\n self.spawnCells()\n self.movement2()\n if self.model.sauce == True:\n self.differentiation_tick()\n\n def movement2(self):\n scaleFactor = 5\n self.energy += self.random.randrange(0 , 3)\n if self.differentiated == \"virgin\":\n neighbors = self.model.cellTouchingDict[self.unique_id]\n neighborPoints = []\n for neighbor in neighbors:\n points = self.intersectingPoints(neighbor)\n if points[0] != self.pos:\n neighborPoints.append(points)\n polarNeighborPoints = []\n for intersection in neighborPoints:\n newInter = self.convertIntersectingPointsPolar(intersection)\n polarNeighborPoints.append(newInter)\n nSets = []\n for intersection in polarNeighborPoints:\n nSets.append(SetRange(intersection[0][1] , intersection[1][1] , True , intersection[1]))\n neighborSet = SetRangeUnion(nSets)\n r = neighborSet.getRange()\n thetas0 = neighborSet.getPoints()\n thetas = []\n for pTuple in thetas0:\n thetas.append(pTuple[0])\n thetas.append(pTuple[1])\n centerDir = self.model.space.get_heading(self.pos , (self.model.avg_x , self.model.avg_y))\n magCenterDir = (centerDir[0] ** 2 + centerDir[1] ** 2)**0.5\n zpAngle = math.atan(centerDir[1] / centerDir[0])\n if centerDir[0] < 0 and centerDir[1] > 0:\n zpAngle += math.pi\n if centerDir[0] < 0 and centerDir[1] < 0:\n zpAngle += math.pi\n if centerDir[0] > 0 and centerDir[1] < 0:\n zpAngle += 2*math.pi\n zpAngle += self.random.random()*(math.pi)/magCenterDir\n while zpAngle > math.pi * 2:\n zpAngle -= math.pi*2\n if zpAngle < (math.pi / 2) or zpAngle > (3 * math.pi / 2):\n l = True\n else:\n l = False\n zpRange = SetRange(zpAngle + math.pi / 2 , zpAngle - math.pi / 2 , True , l)\n noThetas = True\n for theta in thetas:\n if zpRange.numInRange(theta):\n noThetas = False\n if not noThetas:\n if neighborSet.numInRange(zpAngle):\n zpAngle = self.getMinDistanceAlongCircumference(zpAngle , thetas)\n x = self.internalR * math.cos(zpAngle)\n y = self.internalR * math.sin(zpAngle)\n\n head1 = self.model.space.get_heading(self.pos , (self.pos[0] + x, self.pos[1] + y))\n head1 = (head1[0] *(((2*math.pi)-r)/(2*math.pi)) , head1[1]*(((2*math.pi)-r)/(2*math.pi)))\n h = head1[0]\n v = head1[1]\n norm = scaleFactor * (h ** 2 + v ** 2) ** (0.5)\n if h == 0 and v == 0:\n norm = 1\n newPos = (self.pos[0] + (h/norm) , self.pos[1] + (v/norm)) \n self.model.space.move_agent(self , newPos)\n\n\n\n def spawnCells(self):\n if self.energy >= self.model.spawn_freq:\n self.model.currentIDNum += 1\n newCell = StemCell(self.model.currentIDNum , self.model)\n self.model.schedule.add(newCell)\n self.model.space.place_agent(newCell , self.pos)\n self.energy = self.energy // 2\n self.model.num_stem_cells += 1\n self.model.cellTouchingDict[self.model.currentIDNum] = []\n self.model.cells.append(newCell)\n\n\n#Does not work do not use \n#(No more generic morphogens are in this model)\n def BROKEN(self):\n scaleFactor = 0.9\n self.energy += self.random.randrange(0 , 3)\n if self.differentiated == \"virgin\":\n neighbors = self.model.cellTouchingDict[self.unique_id]\n neighborPoints = []\n for neighbor in neighbors:\n points = self.intersectingPoints(neighbor)\n if points[0] != self.pos:\n neighborPoints.append(points)\n polarNeighborPoints = []\n for intersection in neighborPoints:\n newInter = self.convertIntersectingPointsPolar(intersection)\n polarNeighborPoints.append(newInter)\n nSets = []\n for intersection in polarNeighborPoints:\n nSets.append(SetRange(intersection[0][1] , intersection[1][1] , True , intersection[1]))\n neighborSet = SetRangeUnion(nSets)\n r = neighborSet.getRange()\n thetas0 = neighborSet.getPoints()\n thetas = []\n for pTuple in thetas0:\n thetas.append(pTuple[0])\n thetas.append(pTuple[1])\n hComp = 0\n vComp = 0\n for agent in self.model.morphs:\n if agent.dead == False:\n head0 = self.model.space.get_heading(self.pos , agent.pos)\n h = head0[0]\n v = head0[1]\n n = (h ** 2 + v ** 2) ** 0.5\n zpChange = (h * self.internalR / n , v * self.internalR / n)\n zeroPoint = (self.pos[0] + (zpChange[0]) , self.pos[1] + (zpChange[1]))\n zpAngle = self.determineAngle(zeroPoint)\n if zpAngle < (math.pi / 2) or zpAngle > (3 * math.pi / 2):\n l = True\n else:\n l = False\n zpRange = SetRange(zpAngle + math.pi / 2 , zpAngle - math.pi / 2 , True , l)\n x = 0\n y = 0\n noThetas = True\n for theta in thetas:\n if zpRange.numInRange(theta):\n noThetas = False\n if not noThetas:\n if neighborSet.numInRange(zpAngle):\n zpAngle = self.getMinDistanceAlongCircumference(zpAngle , thetas)\n x = self.internalR * math.cos(zpAngle)\n y = self.internalR * math.sin(zpAngle)\n head1 = self.model.space.get_heading(self.pos , (self.pos[0] + x, self.pos[1] + y))\n head1 = (head1[0] *(((2*math.pi)-r)/(2*math.pi)) , head1[1]*(((2*math.pi)-r)/(2*math.pi)))\n hComp += head1[0]\n vComp += head1[1]\n norm = scaleFactor * (hComp ** 2 + vComp ** 2) ** (0.5)\n if hComp == 0 and vComp == 0:\n norm = 1\n newPos = (self.pos[0] + (hComp/norm) , self.pos[1] + (vComp/norm)) \n self.model.space.move_agent(self , newPos)\n \n\n def isTouchingOtherCells(self , point):\n d = self.model.space.get_distance(self.pos , point)\n l = d - (2*self.internalR)\n if l <= 0:\n return True \n return False\n\n def isTouching(self , other:Agent):\n d = self.model.space.get_distance(self.pos , other.pos)\n l = d - self.internalR - other.internalR\n if l <= 0:\n return True \n return False\n\n def reaction_regulation(self):\n return\n\n def spawnBMP4(self, num):\n for i in range(num):\n self.model.currentIDNum += 1\n n = NOG(self.model.currentIDNum, self.model)\n self.model.schedule.add(n)\n r = self.internalR + 0.001\n theta = self.model.random.random() * 2 * math.pi\n x = r * math.cos(theta) + self.pos[0]\n y = r * math.sin(theta) + self.pos[1]\n self.model.space.place_agent(n , (x , y))\n self.absorbedNOG = []\n\n\n def differentiation_tick(self):\n if self.time_for_diff > 0:\n self.time_for_diff -= 1\n else:\n matrixIndex = (self.pos[0] // Brandon.dx, self.pos[1] // Brandon.dx)\n vectorIndex = points * matrixIndex[0] + matrixIndex[1]\n BMP4conc = self.model.BMP4vector[vectorIndex]\n\n\n if self.differentiated == \"virgin\":\n if self.model.start_diff == False:\n self.model.start_diff = True\n if self.BMP4conc >= self.model.endo_min:\n self.differentiated = \"endo\"\n if self.BMP4conc < self.model.endo_min and self.chemical_contact >= self.model.ecto_max:\n self.differentiated = \"meso\"\n if self.BMP4conc < self.model.ecto_max:\n self.differentiated = \"ecto\"\n \n\n\n def tracking_update(self): \n return \n\n def intersectingPoints(self , other):\n a = self.pos[0]\n b = self.pos[1]\n c = other.pos[0]\n d = other.pos[1]\n r = self.internalR\n D = ((a-c)**2 + (b-d)**2)**0.5\n if d == b:\n x1 = min(a,c) + r * ( (2)**0.5 / 2)\n x2 = x1\n y1 = ((r)**2 - (r * ( (2)**0.5 / 2))**2) ** 0.5\n y2 = -1 * y1\n else:\n if D > 2*self.internalR:\n return [(self.pos[0] , self.pos[1])]\n E = (r**2 - (D/2)**2)**0.5\n M = (c-a)/(b-d)\n B = (b**2 - d**2)/(c**2 - a**2)\n theta = math.atan2(c-a , b-d)\n midX = (a+c)/2\n midY = (b+d)/2\n dist = E * math.cos(theta)\n x2 = midX + dist\n y2 = M*x2 + B\n x1 = midX - dist\n y1 = M*x1 + B\n val = False\n head = self.model.space.get_heading(self.pos , other.pos)\n if head[0] > 0 and b > min(y2,y1) and b < max(y2,y1):\n val = True \n return [(x1,y1) , (x2,y2) , val]\n\n def convertIntersectingPointsPolar(self, points):\n polarCoords = []\n \n p = (points[0] , points[1])\n polar0 = [self.internalR]\n polar1 = [self.internalR]\n polar0.append(self.determineAngle(p[0]))\n polar1.append(self.determineAngle(p[1]))\n\n polarCoords.append(tuple(polar0))\n polarCoords.append(tuple(polar1))\n\n polarCoords.append(points[2])\n return polarCoords\n\n def determineAngle(self, point):\n radius = self.internalR\n center = self.pos\n if point == (center[0] , center[1] + radius):\n angle = math.pi / 2\n elif point == (center[0] + radius , center[1]):\n angle = 0\n elif point == (center[0] - radius , center[1]):\n angle = math.pi\n elif point == (center[0], center[1] - radius):\n angle = math.pi * 3 / 2\n\n else:\n x = point[0]\n y = point[1]\n theta = math.atan(y/x)\n if point[0] > center[0] and point[1] > center[1]:\n angle = theta\n elif point[0] < center[0] and point[1] > center[1]:\n angle = math.pi - theta\n elif point[0] > center[0] and point[1] < center[1]:\n angle = (2*math.pi) - theta\n elif point[0] < center[0] and point[1] < center[1]:\n angle = theta+(math.pi)\n return angle\n\n\n\n\n def getMinDistanceAlongCircumference(self, zeroPoint , thetas):\n distances = []\n if thetas == []:\n return zeroPoint\n else:\n for theta in thetas:\n distances.append(abs(theta - zeroPoint))\n angle = min(distances)\n corrIndex = distances.index(angle)\n return thetas[corrIndex]\n\n\n \n \n\n\n\nclass SetRange:\n\n def __init__(self , num1 , num2 , closed , lapped):\n self.lower = min(num1 , num2)\n self.upper = max(num1 , num2)\n self.closed = closed\n self.lapped = lapped\n\n def __lt__(self , other):\n return self.lower < other.lower\n\n\n def numInRange(self , num):\n if self.closed:\n if num == self.lower or num == self.upper:\n return True\n if not self.lapped:\n return (num > self.lower) and (num < self.upper)\n else:\n return (num < self.lower) or (num > self.upper)\n \n\n def getPoints(self):\n return (self.upper , self.lower)\n\n def getRange(self):\n if self.lapped:\n return (2*math.pi) - (self.upper - self.lower)\n return self.upper - self.lower\n\n def __eq__(self , other):\n return (self.upper == other.upper) and (self.lower == self.lower) and (self.closed == other.closed)\n\n def __str__(self) -> str:\n if self.closed:\n return \"[{} , {}]\".format(self.lower , self.upper)\n return \"({} , {})\".format(self.lower , self.upper)\n\n\nclass SetRangeUnion:\n \n def __init__(self , sets):\n setCopy = []\n for set in sets:\n if set.lapped == True:\n setCopy.append(SetRange(0 , set.lower , True , False))\n setCopy.append(SetRange(set.upper , math.pi*2 , True , False))\n else: \n setCopy.append(set)\n\n self.sets = sorted(setCopy)\n if len(self.sets) > 1:\n i = 1\n new = []\n currSet = self.sets[0]\n while i <= len(self.sets) - 1:\n nextSet = self.sets[i]\n if currSet.upper > nextSet.lower:\n if nextSet.upper > currSet.upper:\n currSet.upper = nextSet.upper\n else: \n new.append(currSet)\n currSet = nextSet\n i += 1\n new.append(currSet)\n self.sets = new\n\n\n def numInRange(self , num):\n for set in self.sets:\n val = set.numInRange(num)\n if val:\n return True\n return False\n\n def getRange(self):\n r = 0\n for s in self.sets:\n r += s.getRange()\n return r\n\n\n def getPoints(self):\n points = []\n for s in self.sets:\n points.append(s.getPoints())\n return points\n\n\nclass BMP4(Agent):\n '''Creation of BMP4 Agent'''\n\n def __init__(self, unique_id: int, model: Model) -> None:\n super().__init__(unique_id, model)\n self.immobilized = False\n self.immobilized_timer = 0\n self.active = True\n self.active_timer = 0\n self.internalR = Constants.BMP4_R\n\n def step(self):\n self.movement()\n self.reaction_regulation()\n\n\n def movement(self):\n if self.immobilized == False:\n heading = self.model.space.get_heading(self.pos , self.model.center_pos)\n if heading[0] > 0 and heading[1] > 0:\n if self.random.randrange(0 , 2) < 1: #Vertical Shift\n\n x = heading[0]\n y = self.random.uniform(-heading[0] , heading[1])\n\n else: #Horizontal Shift\n\n x = self.random.uniform(-heading[1] , heading[0])\n y = heading[1]\n\n elif heading[0] < 0 and heading[1] < 0:\n\n if self.random.randrange(0 , 2) < 1: #Vertical Shift\n\n x = heading[0]\n y = self.random.uniform(heading[1] , -heading[0])\n\n else: #Horizontal Shift\n\n x = self.random.uniform(heading[0] , -heading[1])\n y = heading[1]\n\n elif heading[0] < 0 and heading[1] > 0:\n\n if self.random.randrange(0 , 2) < 1: #Vertical Shift\n\n x = heading[0]\n y = self.random.uniform(heading[0] , heading[1])\n\n else: #Horizontal Shift\n\n x = self.random.uniform(heading[0] , heading[1])\n y = heading[1]\n\n elif heading[0] > 0 and heading[1] < 0:\n\n if self.random.randrange(0 , 2) < 1: #Vertical Shift\n\n x = heading[0]\n y = self.random.uniform(heading[1] , heading[0])\n\n else: #Horizontal Shift\n\n x = self.random.uniform(heading[1] , heading[0])\n y = heading[1]\n\n elif heading == (0,0):\n \n x = 1\n y = 1\n\n\n norm = (x ** 2 + y ** 2) ** 0.5\n xDisplacement = x / (norm * 3)\n yDisplacement = y / (norm * 3)\n \n self.model.space.move_agent(self , (self.pos[0] + xDisplacement , self.pos[1] + yDisplacement))\n for agent in self.model.space._agent_to_index:\n if type(agent) == StemCell:\n if self.isTouching(agent):\n xDisplacement = -xDisplacement / 2\n yDisplacement = -yDisplacement / 2\n self.model.space.move_agent(self , (self.pos[0] + xDisplacement , self.pos[1] + yDisplacement))\n if self.random.randrange(0 , 100) < 49:\n self.immobilized = True\n self.immobilized_timer = self.random.randrange(0 , 11)\n else:\n self.immobilized_timer -= 1\n if self.immobilized_timer == 0:\n self.immobilized = False\n \n \n\n\n def reaction_regulation(self):\n if self.active_timer == 0:\n for agent in self.model.space._agent_to_index:\n if type(agent) == NOG:\n if self.isTouching(agent):\n self.active = False\n self.active_timer = self.random.randrange(0 , 11)\n else:\n self.active_timer -= 1\n if self.active_timer == 0:\n self.active = True\n\n\n \n\n def isTouching(self , other:Agent):\n d = self.model.space.get_distance(self.pos , other.pos)\n l = d - self.internalR - other.internalR\n if l <= 0:\n return True \n return False\n\n\n\n\n#class NOG(Agent):\n # '''Creation of NOG Agent'''\n\n # def __init__(self, unique_id: int, model: Model) -> None:\n # super().__init__(unique_id, model)\n # self.internalR = Constants.NOG_R\n # self.absorbed = False\n\n\n #def step(self):\n # if not self.absorbed:\n # self.movement()\n\n\n #def movement(self):\n # heading = self.model.space.get_heading(self.pos , self.model.center_pos)\n # if heading[0] > 0 and heading[1] > 0:\n # if self.random.randrange(0 , 2) < 1: #Vertical Shift\n\n # x = heading[0]\n # y = self.random.uniform(-heading[0] , heading[1])\n\n # else: #Horizontal Shift\n\n #x = self.random.uniform(-heading[1] , heading[0])\n # y = heading[1]\n\n # elif heading[0] < 0 and heading[1] < 0:\n\n # if self.random.randrange(0 , 2) < 1: #Vertical Shift\n\n # x = heading[0]\n # y = self.random.uniform(heading[1] , -heading[0])\n\n # else: #Horizontal Shift\n\n # x = self.random.uniform(heading[0] , -heading[1])\n # y = heading[1]\n\n #elif heading[0] < 0 and heading[1] > 0:\n\n # if self.random.randrange(0 , 2) < 1: #Vertical Shift\n\n # x = heading[0]\n # y = self.random.uniform(heading[0] , heading[1])\n\n #else: #Horizontal Shift\n\n # x = self.random.uniform(heading[0] , heading[1])\n # y = heading[1]\n\n # elif heading[0] > 0 and heading[1] < 0:\n\n # if self.random.randrange(0 , 2) < 1: #Vertical Shift\n\n # x = heading[0]\n # y = self.random.uniform(heading[1] , heading[0])\n\n # else: #Horizontal Shift\n\n # x = self.random.uniform(heading[1] , heading[0])\n # y = heading[1]\n\n #elif heading == (0,0):\n\n # x = 1\n # y = 1\n\n\n #norm = (x ** 2 + y ** 2) ** 0.5\n # xDisplacement = x / (norm * 0.5)\n # yDisplacement = y / (norm * 0.5)\n \n #self.model.space.move_agent(self , (self.pos[0] + xDisplacement , self.pos[1] + yDisplacement))\n\n\n # def isTouching(self , other:Agent):\n # d = self.model.space.get_distance(self.pos , other.pos)\n # l = d - self.internalR - other.internalR\n # if l <= 0:\n # return True \n #return False\n\n\n\n\n\n \n\n","repo_name":"jsgreen03/StemCellABM","sub_path":"StemCellABM.py","file_name":"StemCellABM.py","file_ext":"py","file_size_in_byte":28824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"18820561059","text":"\"\"\"\nNOTES:\n- Whatever you return from an API route is what is grabbed\nfrom the Frontend response = await fetch()! That's how they link\n- Request.body() returns BYTES\n- I MUST use Request.json() to convert BYTES to JSON!\n- Request.json() returns the JSON but I can access using result['email']\n- I can access the POST Request data sent from the Frontend via Request\nbut, I can also use Pydantic models as well e.g. signup.email\n- Need to be mindful of the column names in airtable_client.create_records(data:dict)\n- NOTE 'result' depends on what I return from FastAPI endpoint\nE.g., If I just return result -> {email: \"signup@abc.com\"}\nE.g., If I return a dict { \"result\": result_json, \"a\": 1 } -> {result: {...}, a: 1}\n\"\"\"\nimport config\nimport pathlib\nimport time\nimport datetime\nimport pandas as pd\nimport ccxt\nfrom fastapi import FastAPI, Request, Response\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom pydantic import BaseModel\nfrom .airtable import Airtable\n\n# NOTE fastapi-airtable/backend/src/app.py\nBASE_DIR = pathlib.Path(__file__).parent # src\n\n\n# ===== Schemas\nclass TextArea(BaseModel):\n content: str\n\nclass User(BaseModel):\n id: int\n name: str\n\nclass Signup(BaseModel):\n email: str\n\n\n# ===== Data\nusers = [\n {\n \"id\": 1,\n \"name\": \"Gaylon\"\n },\n {\n \"id\": 2,\n \"name\": \"Ashley\"\n },\n {\n \"id\": 3,\n \"name\": \"Adrian\"\n },\n {\n \"id\": 4,\n \"name\": \"Aaron\"\n },\n]\n\napp = FastAPI()\n\n# Allow CORS for FE/BE communication\n# https://fastapi.tiangolo.com/tutorial/cors/?h=cors\norigins = [\n \"http://localhost\",\n \"http://localhost:3000\" # Vue\n]\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\n\n\n\n@app.get(\"/\")\nasync def read_root():\n return { \"message\": \"Hello, World\"}\n\n# @app.get(\"/file\")\n# def read_index():\n# return FileResponse(\"../../frontend/index.html\")\n\n@app.get(\"/vite\")\ndef read_vite(response: Response):\n # return {\"request\": request, \"message\": \"This is the return from /vite GET\"}\n # return {\"request\": request } # Error\n # return {\"message\": \"This is the return from /vite GET\"} # Works\n response.body = \"Response body content\"\n return { \"response\": response, \"fruit\": \"apple\", \"status_code\": response.status_code } # Works\n\n\n@app.get(\"/users\")\nasync def read_users():\n \"\"\"Return list of users\"\"\"\n return users # Works without any Model, Response, etc.\n\n# WORKS because it's returning the data in JSON\n@app.post('/add')\nasync def post_textarea(data: TextArea):\n # print(\"content: \", content)\n # return {\"content\": content}\n print(data.dict()) # Works\n return {**data.dict()} # Works\n\n@app.post(\"/user\", response_model=User)\nasync def create_user(request: Request, user: User):\n # Q: Need async? Don't think so for this simple task...\n # A: Nope, both work.\n # Q: Can I grab the user data from the Request?\n # NOTE Must use AWAIT with request.body()!\n # A: Yes! Here's print: request.body(): b'{\"id\":\"8\",\"name\":\"Mickey\"}'\n request_body = await request.body()\n print(\"request.body(): \", request_body)\n\n # Let's take the User object sent in the request and append to users\n # users.append(user) # Error! Need to convert to dict first!\n # NOTE Read more https://fastapi.tiangolo.com/tutorial/body/\n # print(\"type(user): \", type(user)) #