\n \n Move directories | files older than a given age [days]\n from src to dst directory\n \n Note: srs and dst paths must be absolute\"\"\")\n \n parser.add_option(\"-s\", \"--src\",\n dest=\"src\",\n default=None,\n help=\"Source, default=None\",)\n parser.add_option(\"-d\", \"--dst\",\n dest=\"dst\",\n default=None,\n help=\"Destination, default=None\",)\n parser.add_option(\"-a\", \"--age\",\n dest=\"age\",\n default=None,\n help=\"Age [days], default=None\",)\n parser.add_option(\"-i\", \"--interactive\",\n action=\"store_true\",\n dest=\"interactive\",\n default=False,\n help=\"Interactive flag, default=False \")\n (self.opts, args) = parser.parse_args()\n \n def verifyArgs(self): \n for opt, value in self.opts.__dict__.items():\n if opt == \"interactive\":\n pass\n elif value:\n pass\n #logger.info(\"%s=%s \" % (opt,value))\n else:\n logger.info(\"%s %s\" % (opt,\"missing ..\"))\n sys.exit()\n \n def move2Link(self):\n filelist = self.listContent()\n if filelist:\n for name in filelist:\n if os.path.islink(name):\n continue\n if self.ifOlder(name):\n self.moveFile(name)\n \n def listContent(self):\n if os.path.isdir(self.opts.src):\n return glob.glob(\"%s%s*\" % (self.opts.src, os.path.sep))\n elif os.path.isfile(self.opts.src):\n return self.opts.src.split()\n return None\n \n def moveFile(self,name):\n newfile = \"%s%s%s\" % (self.opts.dst,os.path.sep, os.path.basename(name))\n newfile = os.path.abspath(newfile) \n linkname = os.path.abspath(name)\n \n if self.opts.interactive:\n reply = raw_input(\"%s -> %s ? Y|n :\" % (linkname,newfile))\n if reply.lower() != \"y\":\n return\n \n shutil.move(name, self.opts.dst)\n os.symlink(newfile,linkname)\n logger.info(\"%s -> %s\" % (newfile,linkname))\n \n def ifOlder(self,p):\n mod = datetime.datetime.fromtimestamp(os.path.getmtime(p))\n if mod < self.getTimeFloor(self.opts.age): \n return True\n return False\n \n def getTimeFloor(self,days):\n return (self.now - (datetime.timedelta(days=int(days))))\n \ndef main():\n md = MoveDirs()\n md.collectArgs()\n md.verifyArgs()\n md.move2Link()\n \nif __name__ == '__main__':\n main()\n ","sub_path":"repo8/move_iso_files/move2Link.py","file_name":"move2Link.py","file_ext":"py","file_size_in_byte":3745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"376389826","text":"# coding:utf-8\nimport requests\nimport unittest\nimport json\nclass test_wuye1(unittest.TestCase):\n def setUp(self):\n print(u\"开始\")\n def test_interface1(self):\n print(u\"测试接口\")\n lp1={\"username\":\"13000000000\"}\n rd1=requests.get(\"http://172.16.4.120/wuye/public/index.php/index/index//oauthAccount?\",params=lp1)\n data=json.loads(rd1.text)\n self.assertEqual(\"success\",data[\"message\"])\n def tearDown(self):\n print(u\"结束\")\nif __name__ == '__main__':\n unittest.main","sub_path":"python_work_mei/interface/script/interface1.py","file_name":"interface1.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"335368695","text":"from .IngestorInterface import IngestorInterface\nfrom typing import List\nfrom .QuoteModel import QuoteModel\n\n\nclass TextIngestor(IngestorInterface):\n allowed_extensions = [\"txt\"]\n\n @classmethod\n def parse(cls, path: str) -> List[QuoteModel]:\n \"\"\"Return quotes from a TXT file\"\"\"\n if not cls.can_ingest(path):\n raise Exception(\"Cannot Ingest Exception\")\n quotes = []\n with open(path, \"r\") as infile:\n for line in infile.readlines():\n parsed = line.split(\" - \")\n try:\n new_quote = QuoteModel(parsed[0], parsed[1])\n quotes.append(new_quote)\n except IndexError:\n break\n return quotes\n","sub_path":"src/QuoteEngine/TextIngestor.py","file_name":"TextIngestor.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"504274294","text":"# _*_ coding:utf-8 _*_\nfrom apps.roster.models import Roster\nfrom apps.roster.serializers import RosterShortSerializer, RosterBaseSerializer, RosterSerializer, \\\n RosterDetailSerializer\nfrom rest_framework import generics\n\n\nclass RosterBaseListApi(generics.ListCreateAPIView):\n \"\"\"根据level获取\"\"\"\n\n model = Roster\n\n def get_serializer_class(self):\n level = self.kwargs.get('level')\n print(level)\n if int(level) == 0:\n return RosterShortSerializer\n elif int(level) == 1:\n return RosterBaseSerializer\n else:\n return RosterDetailSerializer\n\n def get_queryset(self):\n # 从url中获取参数\n level = self.kwargs.get('level')\n return Roster.objects.filter(level=level).order_by('id')\n\n\nclass RosterGroupListApi(generics.ListAPIView):\n \"\"\"根据parent获取组内元素\"\"\"\n\n model = Roster\n serializer_class = RosterSerializer\n\n def get_queryset(self):\n parent = self.kwargs.get('parent')\n return Roster.objects.filter(parent=parent).order_by('id')\n\n\n\nclass RosterRetrieveUpdateDestoryApi(generics.RetrieveUpdateDestroyAPIView):\n \"\"\"获取，修改，删除\"\"\"\n\n model = Roster\n serializer_class = RosterShortSerializer\n\n def get_queryset(self):\n return Roster.objects.all()","sub_path":"apps/roster/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"402627329","text":"\n\"\"\"\n UFRPE - BSI2019.1 - Matemática Discreta - Trabalho 2 - 2ª VA\n Dupla:\n Edson Kropniczki + Cristina Oliveira\n Descrição:\n classes para a implementação do algoritmo de Dijkstra,\n para cálculo da menor distância entre dois vértices de um grafo simples não-direcional\n\n Algoritmo:\n fonte: Wikipedia\n URL : https://en.wikipedia.org/wiki/Dijkstra's_algorithm\n\n Pseudo-código para determinar a menor distância entre source e target:\n\n function Dijkstra(Graph, source, target):\n\n create vertex set Q\n\n for each vertex v in Graph:\n dist[v] ← INFINITY\n prev[v] ← UNDEFINED\n add v to Q\n dist[source] ← 0\n\n while Q is not empty:\n\n u ← vertex in Q with min dist[u]\n\n remove u from Q\n if u = target:\n break\n\n for each neighbor v of u: // only v that are still in Q\n alt ← dist[u] + length(u, v)\n if alt < dist[v]:\n dist[v] ← alt\n prev[v] ← u\n\n return dist[], prev[]\n\n // pseudo-código p/ refazer o caminho reverso\n\n function reverse_path(prev, source, target):\n S ← empty sequence\n u ← target\n if prev[u] is defined or u = source: // Do something only if the vertex is reachable\n while u is defined: // Construct the shortest path with a stack S\n insert u at the beginning of S // Push the vertex onto the stack\n u ← prev[u] // Traverse from target to source\n\"\"\"\n\nimport sys\n\n\nclass Graph(list):\n\n def __init__(self):\n super(Graph, self).__init__()\n\n ##################################################################\n #\n # Implementação em Python do algoritmo de Dijkstra\n # baseada no pseudo código do Wikipedia acima\n #\n ##################################################################\n\n def dijkstra(self, source, target):\n\n # create vertex set Q\n q = []\n dist = {}\n prev = {}\n infinity = sys.maxsize # tomamos o maior inteiro disponível em Python como infinito\n\n for vertex in self:\n if vertex == source:\n dist[vertex] = 0\n else:\n dist[vertex] = infinity\n prev[vertex] = None\n q.append(vertex)\n\n while len(q) > 0:\n\n # u ← vertex in Q with min dist[u]\n u = q[0]\n ix_u = 0\n for i, v in enumerate(q):\n if dist[v] < dist[u]:\n u = v\n ix_u = i\n\n # remove u from Q\n del q[ix_u]\n\n if u == target:\n break\n\n # for each neighbor v of u:\n for edge in u.edges:\n v = edge.n2\n alt = dist[u] + edge.dist\n if alt < dist[v]:\n dist[v] = alt\n prev[v] = u\n\n return dist, prev\n\n def reverse_path(self, prev, source, target):\n s = \"\"\n distance = 0\n u = target\n if (prev[u] is not None) or (u == source):\n while u is not None:\n s = str(u.node_id) + \"->\" + s\n if prev[u] is not None:\n distance += u.get_edge(prev[u]).dist\n u = prev[u]\n s = s[:-2]\n return s, distance\n\n #################################################################################\n\n\n# Classes Node/Edge para a construção de grafos na GUI e para uso no algoritmo de Dijkstra\nclass Node:\n\n def __init__(self, node_id):\n self.node_id = node_id # save node id (label)\n self.edges = [] # create blank list of edges\n\n # check if edge exists in this node\n def _has_edge(self, edge):\n return edge in self.edges\n\n # add edge to node\n def add_edge(self, other, dist):\n edge = self.Edge(self, other, dist)\n if not self._has_edge(edge):\n self.edges.append(edge)\n other.add_edge(self, dist)\n\n # return Edge obj between this Node and @other, if any\n def get_edge(self, other):\n for edge in self.edges:\n if edge.n2 == other:\n return edge\n return None\n\n # minimum overload to make Node objects hashable, so that we can use them as dictionary keys\n # in Wikipedia Dijkstra algorithm\n def __hash__(self):\n return hash(self.node_id)\n\n def __eq__(self, other):\n return (self.node_id == other.node_id) and (self.edges == other.edges)\n\n def __ne__(self, other):\n return not(self == other)\n\n # Nested class Edge to construct tagged edge objects between Node instances\n class Edge:\n\n def __init__(self, n1, n2, dist):\n self.n1 = n1\n self.n2 = n2\n self.dist = dist\n\n def __eq__(self, other):\n return (self.n1 == other.n1) and (self.n2 == other.n2) and (self.dist == other.dist)\n\n def __lt__(self, other):\n return self.dist < other.dist\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"Graph.py","file_name":"Graph.py","file_ext":"py","file_size_in_byte":5191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"162520372","text":"#An algorithm is said to have a quadratic time complexity when it needs\n# to perform a linear time operation for each value in the input data\n\n#Bubble sort\n# Best O(n^2); Average O(n^2); Worst O(n^2)\n\ndef bubbleSort(List):\n for i in range(len(List)):\n for j in range(len(List) - 1, i, -1):\n if List[j] < List[j - 1]:\n List[j], List[j - 1] = List[j - 1], List[j]\n return List\n\nif __name__ == '__main__':\n List = [3, 4, 2, 6, 5, 7, 1, 9]\n print('Sorted List:',bubbleSort(List))","sub_path":"quadraticTime.py","file_name":"quadraticTime.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"295703063","text":"# -*- coding: utf-8 -*-\nimport string\n\n\n# the_string = \"The word the should be seen two times. It should be.\"\n\ndef get_word_list(the_string):\n # why upper? this gets rid of the fact that capitals and lowercases are different words. we don't want that\n # get rid of it, though, when we switch to hebrew. there are no captials in hebrew\n the_string = string.join(string.split(the_string, \".\"), \"\") # getting rid of the periods\n the_string = string.split(the_string.upper(), \" \")\n the_word_list = {}\n for i in range(len(the_string)):\n cur_word = the_string[i]\n\n #solving the issue of getting the last word without any out of bounds issues\n if i == len(the_string) - 1: # if we're on the last on. i.e. there is no next_word\n next_word = None # next_word is blank\n else: # or else\n next_word = the_string[i + 1] # next_word = the next word\n # print(\"cur_word = %s : next_word = %s\") %(cur_word, next_word)\n if cur_word in the_word_list:\n the_word_list[cur_word][0] += 1 # adding one to the amount of times it shows up\n if next_word in the_word_list[cur_word][1]: # if the next word is already in the the \"next word dictionary\"\n the_word_list[cur_word][1][next_word] += 1 # increment the amount of times it shows up\n else: # or else\n the_word_list[cur_word][1][next_word] = 1 # we create that new dictionary entry, and add 1 to that\n else:\n the_word_list[cur_word] = [1, {next_word : 1}] # creating a brand new entry\n\n return(the_word_list)\n","sub_path":"wordList.py","file_name":"wordList.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"239822988","text":"import sys;\nimport glob;\nimport os;\nimport re;\n\noutputfile = sys.argv[2];\nfile2 = open(outputfile, \"w\");\nfileNames = glob.glob(sys.argv[1]+'/*.txt');\n#fileNames.sort();\n\n#documents = 0;\n#term_freq = { };\n#weight_freq = { };\n#contain_dict = { };\n#vocabulary = set();\n#document_freq = { };\n\nfeature_dict = { };\nfeature_counter = 1 ;\n#print (\"filenames are\", fileNames,\"\\n\");\nfor name in fileNames:\n#\tdocuments = documents + 1;\n\n\tpath = re.search('/(.+?).[0-9]+.txt', name).group(1);\n\tindex = path.rfind('/');\n\tindex = index+1;\n\n\tfile1 = open(name, 'r', errors='ignore')\n\tlines = file1.readlines()\n\tstr_temp = ' '.join([line.strip() for line in lines]);\n\tstr_temp = re.sub(\"[^A-Za-z0-9_\\s\\'\\@\\$\\-]+\",'',str_temp);\t\n#\tstr_temp = str_temp.lower();\n\tfile1.close();\n#\tlist_array = str_temp.split(\" \");\n\tlist_array = str_temp.split();\n\n\tterm_freq = { };\n#\tfeature_dict= { };\n\tfeature_order = { };\n#\tprint(\"name is\", name);\n#\tprint (\"document is:\", list_array);\n\tfor item in list_array:\t\t\n\t\tif(item not in feature_dict):\t\t\n\t\t\tfeature_dict[item] = feature_counter;\n#\t\t\tprint(\"feature counter is :\", feature_counter);\n\t\t\tfeature_counter = feature_counter + 1;\t\t\t\n\n\t\tif(item not in feature_order):\n\t\t\tfeature_order[item] = feature_dict[item];\n\n\t\tif (item not in term_freq):\n\t\t\tterm_freq[item] = 1;\n\t\telse:\n\t\t\tterm_freq[item] = 1;\n#\t\t\tterm_freq[item] = term_freq[item] + 1;\n\n#\tprint(\"term frequency is:\", term_freq);\n\n\tstr_str = \"\";\n\tgenexp = ((k, feature_order[k]) for k in sorted(feature_order, key=feature_order.get, reverse=False))\n\tfor k,v in genexp:\n#\t\tprint (\"value is: \", v, \"term frequency s:\", term_freq[k]);\n#\t\tprint (\"str_str is:\", str_str);\n#\t\tstr_dummy = str(v);\n\t\tstr_str =str_str + str(v) + \":\" + str(term_freq[k]) + \" \";\n\t\n\tterm_freq = { };\n\tfeature_order = { };\n#\tprint (str_str);\n \n#\tfor item in list_array:\t\t\n\tif (path[index:] == \"SPAM\" or path[index:] == \"POS\"):\n\t\tfile2.write(\"1\" + \" \" + str_str+\"\\n\");\n\telif(path[index:]==\"HAM\" or path[index:] == \"NEG\"):\n\t\tfile2.write(\"-1\" + \" \" + str_str + \"\\n\");\n\telse:\n\t\tfile2.write(\"0\" + \" \" + str_str + \"\\n\");\n\tstr_str = \" \";\nfile2.close();\n","sub_path":"svmpreprocess.py","file_name":"svmpreprocess.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"264600641","text":"from datetime import datetime\nfrom marketCenter.models.base import Base, uuid\nfrom marketCenter.models.nba_stock import NBAStock as Stock\nfrom marketCenter.errors import IllegalOperation\nfrom marketCenter.db import connection\n\nclass TradeFactory(Base):\n def defaults(self):\n return {\n \"uid\": uuid(),\n \"created_at\": datetime.now()\n }\n\nclass Trade(TradeFactory):\n _collection = \"trades\"\n _keys = (\"uid\",)\n _attributes = (\"stock_id\", \"buyer_id\", \"seller_id\", \"price\", \"shares\", \"created_at\")\n\n def __init__(self, **kwargs):\n attributes = super().defaults()\n attributes.update(kwargs)\n super().__init__(**attributes)\n\n @classmethod\n async def create(cls, **attributes):\n stock = await Stock.find_one({\"uid\": attributes.get(\"stock_id\")})\n try:\n await stock.transfer(\n sender=attributes.get(\"seller_id\"), \n receiver=attributes.get(\"buyer_id\"), \n shares=int(attributes.get(\"shares\"))\n )\n except AttributeError:\n raise IllegalOperation(\"stock {} does not exist\".format(attributes.get(\"stock_id\")))\n return await super().create(**attributes)","sub_path":"marketCenter/models/trade.py","file_name":"trade.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"475216240","text":"import tkinter as tk\n\nclass APP:\n def __init__(self,master):\n frame = tk.Frame(master)\n frame.pack(side=tk.LEFT,padx=20,pady=20)\n\n self.hi_there = tk.Button(frame,command=self.sayhi,bg='black',text='打招呼',fg='blue')\n self.hi_there.pack()\n def sayhi(self):\n print('Hello World')\n \nroot=tk.Tk()\napp=APP(root)\n\nroot.mainloop()\n","sub_path":"python_learn/第一个tkinter.py","file_name":"第一个tkinter.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"445928199","text":"import spacy\n\nnlp = spacy.load('en_core_web_md') \n\nprint(\"Enter 2 Words (separated by space): \")\nwords = input()\n\ntokens = nlp(words)\n\nprint(\"Token1\\t\\tToken2\\t\\tSimilarity\")\nprint(\"-\"*60)\nfor token1 in tokens:\n\tfor token2 in tokens:\n\t\tprint(str(token1.text) + \"\\t\\t\" + str(token2.text) + \"\\t\\t\" + str(token1.similarity(token2)))","sub_path":"Assignment6/q3_c.py","file_name":"q3_c.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"525388295","text":"__author__ = 'anderson'\n\nfrom random import random\nexperimentos = 100000\ncont = 0\npPecaA = 1/1000\npPecaB = 1/500\npPecaC = 1/200\n\nfor x in range(experimentos):#Lotes\n for i in range(10):#embalagens\n if random() <= pPecaA or random() <= pPecaB or random() <= pPecaC:\n cont += 1\n break\n\nprint(cont/experimentos)\n","sub_path":"ProjetosimulacaoDiscreta/lista2/questao06b.py","file_name":"questao06b.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"225643622","text":"class Solution:\n def intersect(self, nums1: List[int], nums2: List[int]) -> List[int]:\n if len(nums2) > len(nums1):\n self.intersect(nums2, nums1)\n d = {}\n res = []\n for no in nums1:\n d[no] = d.get(no, 0) + 1\n for no in nums2:\n if no in d and d[no]:\n res.append(no)\n d[no] -= 1\n return res\n","sub_path":"350/350.intersection-of-two-arrays-ii.303730139.Accepted.leetcode.python3.py","file_name":"350.intersection-of-two-arrays-ii.303730139.Accepted.leetcode.python3.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"65750452","text":"from .common import echo\nfrom blessed import Terminal\n\n\n# @singleton\nclass Screen:\n screen = {(0, 0): \"\",}\n region = []\n disabled_region = []\n change_region_cnt: int = 0\n regions_order = []\n base_corner = {}\n\n def __init__(self):\n self.term = Terminal(force_styling=True)\n # self.log = open('log3.log', 'a')\n\n def reset(self):\n self.screen = {(0, 0): \"\",}\n\n def __render(self):\n screen2render = {}\n for ii in self.regions_order:\n region = self.region[ii]\n if not self.disabled_region[ii]:\n for c, s in region.items():\n # self.log.write(f'reg_id = {ii}, c = {c}, s = {s}\\n')\n if c in self.screen.keys():\n if c in screen2render.keys() and screen2render[c] != s:\n screen2render[c] = s\n else:\n if s != self.screen[c]:\n screen2render[c] = s\n else:\n screen2render[c] = s\n self.screen[c] = s\n\n screen2render = dict(sorted(screen2render.items(), key=lambda x:x[0][1]))\n for c,s in screen2render.items():\n echo(self.term.move(c[1], c[0]) + s)\n\n def __shift2base(self, c=None, region_id=-1):\n if c is not None and len(c) == 2:\n if region_id != -1 and region_id in self.base_corner.keys():\n base = self.base_corner[region_id]\n return c[0] + base[0], c[1] + base[1]\n return c\n\n def set_base_corner(self, region_id: int = -1, x=-1, y=-1):\n if region_id > 0 and region_id < len(self.region):\n self.base_corner[region_id] = (x, y)\n\n def bind(self) -> int:\n self.region.append({})\n self.disabled_region.append(False)\n return len(self.region) - 1\n\n def begin(self):\n self.change_region_cnt += 1\n\n def end(self):\n self.change_region_cnt -= 1\n if self.change_region_cnt == 0:\n self.__render()\n self.regions_order = []\n echo(self.term.normal)\n if self.change_region_cnt < 0:\n print(\"хуйня\")\n raise ValueError()\n\n def disable_region(self, region_id=-1):\n self.disabled_region[region_id] = True\n\n def echo(self, region_id:int=-1, x=0, y=0, string=\"\"):\n if region_id < 0 or region_id >= len(self.region):\n raise ValueError()\n\n if region_id not in self.regions_order:\n self.regions_order.append(region_id)\n\n c = self.__shift2base((x, y), region_id)\n if len(string) > 1:\n ii = 0\n style = u''\n applied_style: bool = False\n\n while True:\n if ii == len(string): break\n if string[ii] == '\\x1b' or string[ii] == '\\x0f':\n if applied_style:\n style = u''\n applied_style = False\n\n e = string[ii:].find('m')\n if e != -1:\n style += string[ii:ii + e + 1]\n ii += e + 1\n else:\n style += string[ii]\n ii += 1\n continue\n\n applied_style = True\n s = style + string[ii]\n c = self.__shift2base((x, y), region_id)\n self.region[region_id][c] = s\n x += 1\n ii += 1\n if applied_style == False and style != u'':\n try:\n self.region[region_id][c] += style\n except KeyError:\n self.region[region_id][c] = style\n else:\n self.region[region_id][c] = string\n\n self.disabled_region[region_id] = False","sub_path":"simpleinterface/screen.py","file_name":"screen.py","file_ext":"py","file_size_in_byte":3853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"249566214","text":"\n# coding: utf-8\n\n# # Forecasting Model of Monthly Settled Cases\n\n# ## Overview\n\n# In this tutorial, we will work through a time series forecasting project from end-to-end, from reviewing the dataset and defining the problem to training a final model and making predictions. This project is not exhaustive, but shows how you can get good results quickly by working through a time series forecasting problem systematically.\n# The steps of this project that we will through are as follows. \n# 1. Problem Description\n# 2. Experimental Setup\n# 3. Persistence\n# 4. Data Analysis \n# 5. ARIMA Models\n# 6. Time series evaluation\n\n# ## 1. Problem Description\n\n# The problem is to predict the Number of Settlement by month of SCA over years.\n\n# In[1]:\n\n# separate out a validation dataset\nfrom pandas import Series\nimport pandas as pd\nimport matplotlib.pyplot as plt\nget_ipython().magic(u'matplotlib inline')\nimport numpy as np\n# Import statsmodel\nimport statsmodels.api as sm\nimport statsmodels.formula.api as smf\nfrom statsmodels.tsa.stattools import adfuller\n# evaluate persistence model on time series\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\nget_ipython().magic(u'matplotlib inline')\nimport pylab\nfrom pandas import DataFrame\nfrom pandas import TimeGrouper\n# create and summarize stationary version of time series\nfrom statsmodels.tsa.stattools import adfuller\nfrom pandas import Series\nfrom statsmodels.graphics.tsaplots import plot_acf \nfrom statsmodels.graphics.tsaplots import plot_pacf\nfrom matplotlib import pyplot\nfrom statsmodels.tsa.ar_model import AR \nfrom sklearn.metrics import mean_squared_error\nfrom matplotlib import pyplot\nfrom sklearn.metrics import mean_squared_error\nimport statsmodels.tsa.api as smt\nfrom sklearn.metrics import mean_squared_error\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom math import sqrt\nimport warnings\nfrom pandas import Series\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\nimport numpy\nfrom arch import arch_model\nfrom pandas import Series\nfrom pandas import DataFrame\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom matplotlib import pyplot\nfrom pandas import Series\nfrom pandas import DataFrame\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom matplotlib import pyplot\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\nfrom pandas import Series\nfrom pandas import DataFrame\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom matplotlib import pyplot\nfrom statsmodels.graphics.tsaplots import plot_acf\nfrom statsmodels.graphics.tsaplots import plot_pacf\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom scipy.stats import boxcox\nimport numpy\nfrom statsmodels.tsa.arima_model import ARIMAResults\nfrom pandas import Series\nfrom matplotlib import pyplot\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom statsmodels.tsa.arima_model import ARIMAResults\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\nimport numpy\nfrom sklearn.ensemble import RandomForestRegressor\nfrom pandas import DataFrame, concat\n\nseries = Series.from_csv('Monthly Count of Settlement.csv',header=0)\n\n\n# ## 2. Experimental Setup\n\n# We will take the data set and split it into training and test sets for the purposes of the experiment. \n\n# ## 4. Data Analysis\n\n# Now we have a baseline prediction method and performance; now we can start digging into our data.\n# \n# We can use summary statistics and plots of the data to quickly learn more about the structure of the prediction problem. In this section, we will look at the data from five perspectives:\n# \n# 1. Summary Statistics. \n# 2. Line Plot.\n# 4. Density Plots.\n# 5. Box and Whisker Plot.\n\n# ### 4.1 Summary Statistics\n# \n# Summary statistics provide a quick look at the limits of observed values. It can help to get a quick idea of what we are working with. The example below calculates and prints summary statistics for the time series.\n\n# In[2]:\n\n# summary statistics of time series\nprint(series.describe())\n\n\n# ### 4.2 Line Plot\n\n# The first step before getting bogged down in data analysis and modeling is to establish a baseline of performance. This will provide both a template for evaluating models and a performance measure by which all more elaborate predictive models can be compared. The baseline prediction for time series forecasting is called the naive forecast (somethimes referred to as persistence).\n# \n# Here we will use walk-forward validation discussed in class. \n\n# A line plot of a time series can provide a lot of insight into the problem.\n\n# Running the naive forecast prints the prediction and observation for each iteration of the test dataset. The example ends by printing the RMSE for the model. In this case, we can see that the forecast achieved an RMSE of 0.897. \n\n# In[3]:\n\n# plot times series data for overview\npyplot.figure(1)\nseries.plot()\npylab.ylabel('Monthly Settled Cases')\npylab.xlabel('Date')\npyplot.show()\n\n\n# Some observations from the plot include:\n# - There may be an increasing trend of cash settlement amount over time.\n# - There might be systematic seasonality to the cash settlement amount for each year.\n# - The seasonal signal appears to be growing over time, suggesting a multiplicative relationship (increasing change).\n# - There do not appear to be any obvious outliers.\n# - The seasonality might suggests that the series is almost certainly non-stationary.\n# \n# There may be benefit in explicitly modeling the seasonal component and removing it. You may also explore using differencing with one or two levels in order to make the series stationary. The increasing trend or growth in the seasonal component may suggest the use of a log or other power transform.\n# \n\n# ### 4.4 Density Plot and Transformation \n\n# Reviewing plots of the density of observations can provide further insight into the structure of the data. The example below creates a histogram and density plot of the observations without any temporal structure.\n\n# In[4]:\n\npyplot.figure(3)\npyplot.subplot(211)\nseries.hist()\npyplot.subplot(212) \nseries.plot(kind='kde')\npyplot.show(3)\n\n\n# In[5]:\n\nseries=np.sqrt(series)\npyplot.figure(3)\npyplot.subplot(211)\nseries.hist()\npyplot.subplot(212) \nseries.plot(kind='kde')\npyplot.show(3)\n\n\n# It is clear a unimodel distribution with one clear peak. From the pdf graph and the histogram, we can say that the it is almost symmetric with a small long tail(the distribution have a few number of occurrences far from the central part) on the right.\n# For positive skew where tail is on the positive end, we can apply square root transformation,log transformation and inverse/reciprocal transformation. Therefore, if the log transformation is not sufficient, we can also use the next level of transformation. On the other hand, Box Cox runs all transformations automatically so that you can choose the best one.\n\n# ### 4.5 Box and Whisker Plot \n# \n# We can group the monthly data by year and get an idea of the spread of observations for each year and how this may be changing. We do expect to see some trend (increasing mean or median), but it may be interesting to see how the rest of the distribution may be changing. \n# \n# The example code below groups the observations by year and creates one box and whisker plot for each year of observations. The last year (2017) only contains 3 months and may not be a useful comparison with the 12 months of observations for other years. Therefore, only data between 1988 and 2016 was plotted.\n\n# In[6]:\n\ngroups = series['1989':'2005'].groupby(TimeGrouper('A')) \nyears = DataFrame()\nfor name, group in groups:\n years[name.year] = group.values\nyears.boxplot(return_type ='dict',figsize=(12,6))\npyplot.show(4)\n\n\n# In[7]:\n\nsplit_point = int(round(len(series)*0.8))\ntrain, holdontest = series[0:split_point], series[split_point:]\nprint('Train %d, holdonTest %d' % (len(train), len(holdontest)))\ntrain.to_csv('trainSet.csv')\nholdontest.to_csv('holdontestSet.csv')\n\n\n# In[8]:\n\nseries=Series.from_csv('trainSet.csv',header=0)\n\n\n# The plot above has 9 box and whisker plots side-by-side, one for each of the 9 years of selected data. \n# \n# Some observations from reviewing the plots include:\n# \n# - The median values for each year (red line) may show an increasing trend.\n# - There are outliers half of the time\n\n# ## 5. Time Series Models\n\n# ### 5.1 Checking stationarity \n\n# Analysis of the time series data assumes that we are working with a stationary time series. As we have seen in previous sections time series is almost certainly non-stationary. \n# \n# We can make it stationary by first differencing the series and using a statistical test to confirm that the result is stationary.\n# \n# For stationarity, the code below creates a deseasonalized version of the series and saves it to file `stationary.csv`.\n\n# The plot does not show any obvious seasonality or trend, suggesting the seasonally differenced dataset is a good starting point for modeling. \n# \n# We can use the the augmented Dickey-Fuller statistical significance test to check that the output series is stationary. \n# \n\n# In[9]:\n\nX = series.values\nX = X.astype('float32')\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n# check if stationary\nresult = adfuller(train)\nprint('ADF Statistic: %f' % result[0]) \nprint('p-value: %f' % result[1])\nprint('Critical Values:')\nfor key, value in result[4].items():\n print('\\t%s: %.3f' % (key, value)) \n\n\n# The results show that the test statistic value -4.099 is smaller than the critical value at 1% of -3.47. This suggests that we can reject the null hypothesis with a significance level of less than 1% (i.e. a low probability that the result is a statistical fluke). \n# \n# Rejecting the null hypothesis means that the time series is stationary or does not have time-dependent structure.\n# \n\n# In[10]:\n\ndef mean_absolute_percentage_error(y_true, y_pred): \n y_true, y_pred = np.array(y_true), np.array(y_pred)\n return np.mean(np.abs((y_true - y_pred) / y_true)) \n\n\n# ## 3. Naive forecast\n\n# In[11]:\n\nimport matplotlib.axes as ax\n# prepare data\nX = series.values\nX = X.astype('float32')\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n\n# walk-forward validation\nhistory = [x for x in train]\npredictions = list()\nfor i in range(len(test)):\n\t# predict\n\tyhat = history[-1]\n\tpredictions.append(yhat)\n\t# observation\n\tobs = test[i]\n\thistory.append(obs)\n#\tprint('>Predicted=%.3f, Expected=%3.f' % (yhat, obs))\n \n# report performance\nrmse = sqrt(mean_squared_error(test, predictions))\nprint('RMSE: %.3f' % rmse)\nmape= mean_absolute_percentage_error(test, predictions)\nprint('MAPE: %.3f' % mape)\n# plot\nplt.figure(figsize=(10,6))\nplt.plot(test,label='test')\nplt.plot(predictions, color='green',linestyle='--',label='predictions')\nplt.legend(fontsize=14)\nplt.show()\n\n\n# In[12]:\n\nX = series.values\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n# train autoregression\nmodel = AR(train)\nmodel_fit = model.fit()\nwindow = model_fit.k_ar\ncoef = model_fit.params\n# walk forward over time steps in test\nhistory = train[len(train)-window:]\nhistory = [history[i] for i in range(len(history))]\npredictions = list()\nfor t in range(len(test)):\n\tlength = len(history)\n\tlag = [history[i] for i in range(length-window,length)]\n\tyhat = coef[0]\n\tfor d in range(window):\n\t\tyhat += coef[d+1] * lag[window-d-1]\n\tobs = test[t]\n\tpredictions.append(yhat)\n\thistory.append(obs)\n#\tprint('predicted=%f, expected=%f' % (yhat, obs))\nprint('Lag: %s' % model_fit.k_ar)\n# report performance\nrmse = sqrt(mean_squared_error(test, predictions))\nprint('RMSE: %.3f' % rmse)\nmape= mean_absolute_percentage_error(test, predictions)\nprint('MAPE: %.3f' % mape)\n# plot\nplt.figure(figsize=(10,6))\nplt.plot(test,label='test')\nplt.plot(predictions, color='green',linestyle='--',label='predictions')\nplt.legend(fontsize=14)\nplt.show()\n\n\n# In[13]:\n\nfrom statsmodels.tsa.arima_model import ARMA\nfrom statsmodels.tsa.stattools import adfuller, arma_order_select_ic\nX = series.values\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\nres = sm.tsa.arma_order_select_ic(train, max_ar=5, max_ma=5, ic=['aic', 'bic'], trend='nc')\nres.aic_min_order\nres.bic_min_order\n\n\n# In[14]:\n\nmodel = ARMA(train, order=(1, 1)) #5,7 is decided using arma_order_select_ic method\nresults = model.fit(trend='nc', method='css-mle')\nprint(results.summary2())\n\n\n# ### 5.2 Manually Configure the ARIMA\n# \n# We will use the difference adjusted dataset as an input to the ARIMA model. \n# \n# The `ARIMA(p,d,q)` model requires three parameters and is traditionally configured manually.\n# \n# The first step is to select the lag values for the Autoregression (`AR`) and Moving Average (`MA`) parameters, `p` and `q` respectively. \n# \n# We can do this by reviewing Autocorrelation Function (`ACF`) and Partial Autocorrelation Function (`PACF`) plots. Note, we are now using the seasonally differenced `stationary.csv` as our dataset. This is because the manual seasonal differencing performed is different from the lag=1 differencing performed by the ARIMA model with the `d` parameter. (It also suggests that no further differencing may be required, and that the `d` parameter may be set to 0.) The example below creates ACF and PACF plots for the series.\n\n# In this section, we will develop an Autoregressive Integrated Moving Average, or ARIMA, model for the problem. \n# \n# An ARIMA model can be considered as a special type of regression model--in which the dependent variable has been stationarized and the independent variables are all lags of the dependent variable and/or lags of the errors. Alternatively, you can think of a hybrid ARIMA/regression model as a regression model which includes a correction for autocorrelated errors. \n# \n# We will approach modeling by both manual and automatic configuration of the ARIMA model. This will be followed by investigating the residual errors of the chosen model. As such, this section is broken down into the following steps:\n# 1. Checking stationarity\n# 2. Manually Configure the ARIMA\n# 3. Running the ARIMA model\n# 4. Review Residual Errors\n# 5. Finalize model \n# 6. Making predictions\n\n# We can make the following observations from the above plots.\n# \n# - The ACF shows a significant lag for 1 month.with perhaps some significant lag at 13 months.\n# - The PACF shows a significant lag for 1 month, with perhaps some significant lag at 13 months.\n# \n# Both the ACF and PACF show a drop-off at the same point, perhaps suggesting a mix of AR and MA.\n# \n# This quick analysis suggests an `ARIMA(1,0,1)` on the stationary data may be a good starting point. The historic observations will be seasonally differenced prior to the fitting of each ARIMA model. The differencing will be inverted for all predictions made to make them directly comparable to the expected observation in the original sale count units. Experimentation shows that this configuration of ARIMA does not converge and results in errors by the underlying\n# \n# \n\n# ### 5.31 Running the ARIMA model\n\n# The example below demonstrates the performance of the selected ARIMA model using the experiemental setup.\n\n# In[19]:\n\n# prepare data\nX = series.values\nX = X.astype('float32')\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n\n# walk-forward validation\nhistory = [x for x in train]\npredictions = list()\nfor i in range(len(test)):\n\tmodel = ARIMA(history, order=(1,0,1)) #using manually selected paratments\n\tmodel_fit = model.fit()\n\tyhat = model_fit.forecast()[0]\n\tpredictions.append(yhat)\n\t# observation\n\tobs = test[i]\n\thistory.append(obs)\n#\tprint('>Predicted=%.3f, Expected=%3.f' % (yhat, obs)) \n# report performance\nrmse = sqrt(mean_squared_error(test, predictions))\nprint('RMSE: %.3f' % rmse)\nmape= mean_absolute_percentage_error(test, predictions)\nprint('MAPE: %.3f' % mape)\n# plot\nplt.figure(figsize=(10,6))\nplt.plot(test,label='test')\nplt.plot(predictions, color='green',linestyle='--',label='predictions')\nplt.legend(fontsize=14)\nplt.show()\n\n\n# In[20]:\n\nprint(model_fit.summary().as_latex())\n\n\n# Running the ARIMA(0,1,1) model results in an RMSE of 0.844, which is better than the persistence RMSE of 0..\n# \n# This is a great start, but we may be able to get improved results with a better configured ARIMA model.\n\n# In[25]:\n\n# evaluate an ARIMA model for a given order (p,d,q) and return RMSE\ndef evaluate_arima_model(X, arima_order):\n\t# prepare training dataset\n\tX = X.astype('float32')\n\ttrain_size = int(len(X) * 0.80)\n\ttrain, test = X[0:train_size], X[train_size:]\n\thistory = [x for x in train]\n\t# make predictions\n\tpredictions = list()\n\tfor t in range(len(test)):\n\t\tmodel = ARIMA(history, order=arima_order)\n\t\tmodel_fit = model.fit(trend='nc', disp=0)\n\t\tyhat = model_fit.forecast()[0]\n\t\tpredictions.append(yhat)\n\t\thistory.append(test[t])\n\t# calculate out of sample error\n\tmse = mean_squared_error(test, predictions)\n\trmse = sqrt(mse)\n\treturn rmse\n\n# evaluate combinations of p, d and q values for an ARIMA model\ndef evaluate_models(dataset, p_values, d_values, q_values):\n\tdataset = dataset.astype('float32')\n\tbest_score, best_cfg = float(\"inf\"), None\n\tfor p in p_values:\n\t\tfor d in d_values:\n\t\t\tfor q in q_values:\n\t\t\t\torder = (p,d,q)\n\t\t\t\ttry:\n\t\t\t\t\trmse = evaluate_arima_model(dataset, order)\n\t\t\t\t\tif rmse < best_score:\n\t\t\t\t\t\tbest_score, best_cfg = rmse, order\n\t\t\t\t\tprint('ARIMA%s RMSE=%.3f' % (order,rmse))\n\t\t\t\texcept:\n\t\t\t\t\tcontinue\n\tprint('Best ARIMA%s RMSE=%.3f' % (best_cfg, best_score))\n\n# load dataset\nseries = Series.from_csv('trainSet.csv')\n# evaluate parameters\np_values = range(1, 5)\nd_values = range(0, 2)\nq_values = range(1, 5)\nwarnings.filterwarnings(\"ignore\")\nevaluate_models(series.values, p_values, d_values, q_values)\n\n\n# In[26]:\n\n# prepare data\nX = series.values\nX = X.astype('float32')\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n# walk-forward validation\nhistory = [x for x in train]\npredictions = list()\nfor i in range(len(test)):\n\tmodel = ARIMA(history, order=(4,1,1)) #using manually selected paratments\n\tmodel_fit = model.fit()\n\tyhat = model_fit.forecast()[0]\n\tpredictions.append(yhat)\n\t# observation\n\tobs = test[i]\n\thistory.append(obs)\n#\tprint('>Predicted=%.3f, Expected=%3.f' % (yhat, obs))\nprint(model_fit.summary()) \n# report performance\nrmse = sqrt(mean_squared_error(test, predictions))\nprint('RMSE: %.3f' % rmse)\nmape= mean_absolute_percentage_error(test, predictions)\nprint('MAPE: %.3f' % mape)\n# plot\nplt.figure(figsize=(10,6))\nplt.plot(test,label='test')\nplt.plot(predictions, color='green',linestyle='--',label='predictions')\nplt.legend(fontsize=14)\nplt.show()\n\n\n# In[33]:\n\n# prepare data\nX = series.values\nX = X.astype('float32')\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n# walk-forward validation\nhistory = [x for x in train]\npredictions = list()\nfor i in range(len(test)):\n\tmodel = ARIMA(history, order=(4,1,1)) #using manually selected paratments\n\tmodel_fit = model.fit(disp=False, trend='c',transparams=False)\n\tyhat = model_fit.forecast()[0]\n\tpredictions.append(yhat)\n\t# observation\n\tobs = test[i]\n\thistory.append(obs)\n#\tprint('>Predicted=%.3f, Expected=%3.f' % (yhat, obs))\nprint(model_fit.summary()) \n# report performance\nrmse = sqrt(mean_squared_error(test, predictions))\nprint('RMSE: %.3f' % rmse)\nmape= mean_absolute_percentage_error(test, predictions)\nprint('MAPE: %.3f' % mape)\n# plot\nplt.figure(figsize=(10,6))\nplt.plot(test,label='test')\nplt.plot(predictions, color='green',linestyle='--',label='predictions')\nplt.legend(fontsize=14)\nplt.show()\n\n\n# In[34]:\n\nprint(model_fit.summary().as_latex())\n\n\n# ### 5.4 Review residual errors\n\n# A good final check of a model is to review residual forecast errors. Ideally, the distribution of residual errors should be a Gaussian with a zero mean. We can check this by using summary statistics and plots to investigate the residual errors from the ARIMA(0,1,1) model. The example below calculates and summarizes the residual forecast errors.\n\n# In[36]:\n\n# summarize ARIMA forecast residuals\n# prepare data\nX = series.values\nX = X.astype('float32')\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n\n# walk-forward validation\nhistory = [x for x in train]\npredictions = list()\n\nfor i in range(len(test)):\n\t# difference data\n\t# predict\n\tmodel = ARIMA(history, order=(4,1,1))\n\tmodel_fit = model.fit(disp=False, trend='c',transparams=False)\n\tyhat = model_fit.forecast()[0]\n\tpredictions.append(yhat)\n\t# observation\n\tobs = test[i]\n\thistory.append(obs)\n\n# errors\nresiduals = [test[i]-predictions[i] for i in range(len(test))]\nresiduals = DataFrame(residuals)\nprint(residuals.describe())\n\n\n# We can see that the distribution has a right shift and that the mean is non-zero. This is perhaps a sign that the predictions are biased.\n# \n# We can examine this further by plotting the distribution of redidual errors. \n\n# In[67]:\n\nprint(residuals.describe())\nplt.figure(figsize=(8,5))\nsns.distplot(residuals, kde=False, fit=stats.gamma,color='green')\nsns.plt.title('Residual Error Distribution',fontsize=14)\n\n\n# In[69]:\n\n# prepare data\nX = series.values\nX = X.astype('float32')\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n# walk-forward validation\nhistory = [x for x in train]\npredictions = list()\nbias = -0.079681\nfor i in range(len(test)):\n\t# predict\n\tmodel = ARIMA(history, order=(4,1,1))\n\tmodel_fit = model.fit(disp=False, trend='c',transparams=False)\n\tyhat = model_fit.forecast()[0]\n\tyhat = yhat+bias\n\tpredictions.append(yhat)\n\t# observation\n\tobs = test[i]\n\thistory.append(obs)\n# report performance\nrmse = sqrt(mean_squared_error(test, predictions))\nprint('RMSE: %.3f' % rmse)\nmape= mean_absolute_percentage_error(test, predictions)\nprint('MAPE: %.3f' % mape)\n# errors\nresiduals = [test[i]-predictions[i] for i in range(len(test))]\nresiduals = DataFrame(residuals)\nprint(residuals.describe())\n# plot\npyplot.figure()\npyplot.subplot(211)\nresiduals.hist(ax=pyplot.gca())\npyplot.subplot(212)\nresiduals.plot(kind='kde', ax=pyplot.gca())\npyplot.show()\n\n\n# In[74]:\n\npyplot.figure(figsize=(11,7))\npyplot.subplot(211)\nplot_acf(residuals, ax=pyplot.gca())\nplt.title('Autocorrelation',fontsize=14)\npyplot.subplot(212)\nplot_pacf(residuals, ax=pyplot.gca())\nplt.title('Partial Autocorrelation',fontsize=14)\npyplot.show()\n\n\n# ## Model Validation\n\n# ### 5.5 Finalize model \n\n# Having manually selected the ARIMA(2,0,2) model we are in a position to finalizing the model by fitting our selected ARIMA model on the dataset, in this case on a transformed version of the entire dataset. Once fit, the model can be saved to file for later use. \n\n# In[89]:\n\nX = series.values\nX = X.astype('float32')\n# difference data\ndef __getnewargs__(self):\n\treturn ((self.endog),(self.k_lags, self.k_diff, self.k_ma))\n \nARIMA.__getnewargs__ = __getnewargs__\n\nmodel = ARIMA(X, order=(4,1,1))\nmodel_fit = model.fit(trend='nc', disp=0)\n# bias constant, could be calculated from in-sample mean residual\nbias = -0.079681\n# save model\nmodel_fit.save('model.pkl')\nnumpy.save('model_bias.npy', [bias])\n\n\n# Running the code above creates two local files:\n# \n# - `model.pkl` This is the `ARIMAResult` object from the call to `ARIMA.fit()`. This includes the coefficients and all other internal data returned when fitting the model.\n# - `model_bias.npy` This is the bias value stored as a one-row, one-column NumPy array.\n\n# ### 5.6 Making predictions\n\n# A natural case may be to load the model and make a single forecast. This is relatively straightforward and involves restoring the saved model and the bias and calling the `forecast()` function. To invert the seasonal differencing, the historical data must also be loaded. The example below loads the model, makes a prediction for the next time step, and prints the prediction.\n\n# In[90]:\n\nfrom statsmodels.tsa.arima_model import ARIMAResults\n\nseries = Series.from_csv('trainSet.csv')\nmodel_fit = ARIMAResults.load('model.pkl')\nbias = numpy.load('model_bias.npy')\nyhat = float(model_fit.forecast()[0])\nyhat = bias + yhat\nprint('Predicted: %.3f' % yhat)\n\n\n# This prediction gives the sort of result we would expect in the first instance. \n# \n# Now let us explore the model properly and use it in a simulated operational manner. In the Experimental setup section, we split the original dataset into test and training. We can load the train.csv file now and use it see how well our model really is on unseen data. There are two ways we might proceed:\n# \n# - Load the model and use it to forecast foreward in time over many months. The forecast beyond the first one or two months will quickly start to degrade in performanace as we get further away from the known data. \n# \n# - Load the model and use it in a rolling-forecast manner, updating the transform and model for each time step. This is the preferred method as it is how one would use this model in practice as it would achieve the best performance.\n# \n# As with model evaluation in previous sections, we will make predictions in a rolling-forecast manner. This means that we will step over lead times in the validation dataset and take the observations as an update to the history.\n\n# In[94]:\n\n# load and prepare datasets\ndataset = Series.from_csv('trainSet.csv')\nX = dataset.values.astype('float32')\nhistory = [x for x in X]\nvalidation = Series.from_csv('holdontestSet.csv')\ny = validation.values.astype('float32')\n# load model\nmodel_fit = ARIMAResults.load('model.pkl')\nbias = numpy.load('model_bias.npy')\n# make first prediction\npredictions = list()\nyhat = float(model_fit.forecast()[0])\nyhat = bias + yhat\npredictions.append(yhat)\nhistory.append(y[0])\nprint('>Predicted=%.3f, Expected=%3.f' % (yhat, y[0]))\n# rolling forecasts\nfor i in range(1, len(y)):\n\t# predict\n\tmodel = ARIMA(history, order=(4,1,1))\n\tmodel_fit = model.fit(trend='nc', disp=0, start_ar_lags=6)\n\tyhat = model_fit.forecast()[0]\n\tyhat = bias + yhat\n\tpredictions.append(yhat)\n\t# observation\n\tobs = y[i]\n\thistory.append(obs)\n\tprint('>Predicted=%.3f, Expected=%.3f' % (yhat, obs))\n \nrmse = sqrt(mean_squared_error(y, predictions))\nprint('RMSE: %.3f' % rmse)\nmape= mean_absolute_percentage_error(y, predictions)\nprint('MAPE: %.3f' % mape)\n# plot\nplt.figure(figsize=(10,6))\nplt.plot(y,label='holdontest')\nplt.plot(predictions, color='green',linestyle='--',label='predictions')\nplt.legend(fontsize=14)\nplt.show()\n\n\n# From the plot above we can see that the `ARIMA` model is working in the manner expected, but that our predictions are not always inline with the test data. \n# Before we look at the errors or failure modes in more detail let us now appraoch this learning problem using a diffrent set of techniques. \n# \n\n# ## 6. Supervised Learning setup\n\n# In previous sections we have trained an ARIMA model on time series data.\n# \n# Here we examin how time series forecasting can be framed as a supervised learning problem. This re-framing of your time series data allows you access to the suite of standard linear and nonlinear machine learning algorithms on your problem (for example eg. Boosted Trees). \n\n# ### 6.1 Sliding Window (univerate) \n# \n# Time series data can be reformulated as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem. We can do this by using previous time steps as input variables and use the next time step as the output variable. Let’s make this concrete with an example. Imagine we have a time series below and are trying to learn the mapping function from input to output\n# \n# $Y = f(X)$\n# \n# for input variables ($X$) and ouptut variables ($Y$). Starting with the following data:\n# \n# \n# \n# | time | \n# measure | \n#
\n# \n# | 1 | \n# 100 | \n#
\n# \n# | 2 | \n# 110 | \n#
\n# \n# | 3 | \n# 108 | \n#
\n# \n# | 4 | \n# 115 | \n#
\n# \n# | 5 | \n# 120 | \n#
\n#
\n# \n#
\n# \n# We can restructure this time series dataset as a supervised learning problem by using the value at the previous time step to predict the value at the next time-step. Re-organizing the time series dataset this way, the data would look as follows:
\n# \n#
\n# \n# | $X$ | \n# $y$ | \n#
\n# \n# | ? | \n# 100 | \n#
\n# \n# | 100 | \n# 110 | \n#
\n# \n# | 110 | \n# 108 | \n#
\n# \n# | 108 | \n# 115 | \n#
\n# \n# | 115 | \n# 120 | \n#
\n# \n# | 120 | \n# ? | \n#
\n#
\n# \n\n# Take a look at the above transformed dataset and compare it to the original time series. Here are some observations:\n# \n# - We can see that the previous time step is the input ($X$) and the next time step is the output ($y$) in our supervised learning problem.\n# - We can see that the order between the observations is preserved, and must continue to be preserved when using this dataset to train a supervised model.\n# - We can see that we have no previous value that we can use to predict the first value in the sequence. We will delete this row as we cannot use it.\n# - We can also see that we do not have a known next value to predict for the last value in the sequence. We may want to delete this value while training our supervised model also.\n# \n# The use of prior time steps to predict the next time step is called the sliding window method. For short, it may be called the window method in some literature. In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem. From this simple example, we can notice a few things:\n# \n# - We can see how this can work to turn a time series into either a regression or a classification supervised learning problem for real-valued or labeled time series values.\n# - We can see how once a time series dataset is prepared this way that any of the standard linear and nonlinear machine learning algorithms may be applied, as long as the order of the rows is preserved.\n# - We can see how the width sliding window can be increased to include more previous time steps.\n# - We can see how the sliding window approach can be used on a time series that has more than one value, or so-called multivariate time series.\n# \n\n# Let us frame the Champagne sales forecast problem as a supervised learning one (where the outputs are the differenced series values) so we can apply well-known ML regressors from `scikit`. We start with extracting features.\n\n# In[104]:\n\nseries = Series.from_csv('trainSet.csv', header=0)\nX = series.values\nX = X.astype('float32')\ntrain_size = int(len(X) * 0.80)\ntrain, test = X[0:train_size], X[train_size:]\n\n# creating lag features with pandas\ndiff = DataFrame(difference(series, months_in_year))\ndataframe = concat([diff.shift(3), diff.shift(2), diff.shift(1), diff], axis=1)\ndataframe.columns = ['t-2', 't-1', 't', 't+1']\nprint(len(dataframe))\ndataframe.head()\n\n\n# Two remarks:\n# \n# - There are 12 less rows in diff (hence in dataframe) than in series, since we are dealing with differenced series\n# - The first rows contain nans because of the lag features. Let's discard them and get the values in a form we can use within scikit.\n\n# In[133]:\n\nXX = dataframe.values[3:,0:-1]\nyy = dataframe.values[3:,-1]\n\n\n# We define the training and test sets in a way such that the 1st element in the supervised learning test set corresponds to the 1st element in the previous time series test set.\n\n# In[134]:\n\ntrain_size = int(len(series) * 0.80) - 3 - 12 # because of the lag and of the difference\nXX_train = XX[0:train_size]\nXX_test = XX[train_size:]\nyy_train = yy[0:train_size]\nyy_test = yy[train_size:]\n\n\n# We can compare the sizes of train and test sets with what we had before, and check that the size of the test set is the same.\n\n# In[135]:\n\nprint(train_size)\nprint(len(XX)-train_size)\n\n\n# ## 7. Random Forest Models\n\n# Let's train the regressor we want to work with:\n\n# In[136]:\n\nmodel = RandomForestRegressor()\nmodel.fit(XX_train, yy_train)\n\n\n# We reuse the previous evaluation code but this time we make predictions with this regressor\n\n# In[137]:\n\n# walk-forward validation\nhistory = [x for x in train]\nprediction_sl = list()\nfor i in range(len(test)):\n\tyhat = model.predict(XX_test[i,:])[0]\n\tyhat = inverse_difference(history, yhat, months_in_year)\n\tprediction_sl.append(yhat)\n\t# observation\n\tobs = test[i]\n\thistory.append(obs)\n\tprediction_sl[i]=yhat\n#\tprint('>Predicted=%.3f, Expected=%3.f' % (yhat, obs))\n\n\n# In[138]:\n\nrmse = sqrt(mean_squared_error(test, prediction_sl))\nprint('RMSE: %.3f' % rmse)\n\n\n# In[139]:\n\npyplot.plot(test, color='green')\npyplot.plot(prediction_sl, color='orange')\npyplot.show()\n\n\n# In[ ]:\n\n\n\n","sub_path":"ForecastModelforNumberofCasesSettledMonthly.py","file_name":"ForecastModelforNumberofCasesSettledMonthly.py","file_ext":"py","file_size_in_byte":33082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"181571827","text":"'''\nCreated on 2012-5-2\n\n@author: liwenjian\n'''\nfrom django.conf.urls import patterns, url\nfrom django.contrib.auth.views import logout_then_login\n\nurlpatterns = patterns('acsbbs.accounts.views',\n url(r'^register/$', 'acsbbs_register', name='acsbbs_register'),\n url(r'^login/$', 'acsbbs_login', name='acsbbs_login'),\n url(r'^logout/$', logout_then_login, name='acsbbs_logout'),\n)\n","sub_path":"acsbbs/accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"169391215","text":"from flask import Flask, jsonify, request, render_template, redirect, url_for\nfrom app import app\nimport pusher\n\n\n@app.route('/')\ndef test():\n return render_template('ajax_test.html')\n\n\n@app.route('/ajax/test', methods=['GET', 'POST'])\ndef ajax_test():\n resp = {}\n if request.method == 'POST':\n req = request.form\n\n resp['result'] = int(req['first']) + int(req['second'])\n resp['success'] = True\n return jsonify(resp)\n else:\n return redirect(url_for('test'))\n\n\n@app.route('/chat', methods=['GET'])\ndef chat():\n p = pusher.Pusher(\n app_id='86070',\n key='62270f36d7ecf7bf7ef0',\n secret='46b772c92bcc9d25fae9'\n )\n p['test_channel'].trigger('my_event', {\n 'name': request.args.get('name_data'),\n 'msg': request.args.get('msg_data')\n })\n return \"\"\n\n\n@app.route('/mini_chat')\ndef mini_chat():\n return render_template(\"mini_chat.html\")\n","sub_path":"app/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"64950356","text":"#\r\n#Question 3:\r\n#Take a list as shown below\r\n#[(1,2,3), [1,2], ['a','hit','less']]\r\n#The List contains tuple and lists. Make the elements of inner lists and tuples to outer list\r\n#\r\n\r\nlist1=[(1,2,3),[1,2],['a','hit','less']]\r\n\r\nlist2=[i for each in list1 for i in each]\r\nprint (list2)","sub_path":"Day7/D7Q3.py","file_name":"D7Q3.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"628904183","text":"import json\ndef main():\n with open(\"leagues_ids.txt\") as f:\n leagues = json.loads(f.read())\n years = []\n start = 1995\n while start <= 2019:\n\t years.append(str(start) + '-' + str(start + 1 ))\n\t start += 1\n\n for k,v in leagues.items():\n for i in range(len(years)):\n print(leagues[k], years[i], k)\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"Cloud_Hockey_Analytics/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"600047144","text":"from __future__ import absolute_import, unicode_literals\n\nfrom celery import shared_task\nfrom reddit.models import RedditPost, Pending\nfrom .redditBot import RedditBot\n\n@shared_task\ndef getHot(subreddit = 'learnpython'):\n\tRedditBot().hot(subreddit)\n\tPending.objects.filter(subreddit=subreddit).delete()\n\n@shared_task\ndef checkExistance(subreddit):\n\texists = RedditBot().checkSubreddit(subreddit)\n\tif exists:\n\t\tgetHot.delay(subreddit)\n\telse :\n\t\tPending.objects.filter(subreddit=subreddit).delete()","sub_path":"src/Alerts/reddit/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"148697754","text":"\nfrom ctypes import *\nfrom pythc import thc\nlib = cdll.LoadLibrary(\"./libTHCUNN.so\")\n\nclass _nn(object):\n def __init__(self):\n\n THNN_CudaAbs_updateOutput=lib.THNN_CudaAbs_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*\n THNN_CudaAbs_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p]\n THNN_CudaAbs_updateOutput.restype = None\n self.THNN_CudaAbs_updateOutput = THNN_CudaAbs_updateOutput\n\n THNN_CudaAbs_updateGradInput=lib.THNN_CudaAbs_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaAbs_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaAbs_updateGradInput.restype = None\n self.THNN_CudaAbs_updateGradInput = THNN_CudaAbs_updateGradInput\n\n THNN_CudaAbsCriterion_updateOutput=lib.THNN_CudaAbsCriterion_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool\n THNN_CudaAbsCriterion_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool]\n THNN_CudaAbsCriterion_updateOutput.restype = None\n self.THNN_CudaAbsCriterion_updateOutput = THNN_CudaAbsCriterion_updateOutput\n\n THNN_CudaAbsCriterion_updateGradInput=lib.THNN_CudaAbsCriterion_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool\n THNN_CudaAbsCriterion_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool]\n THNN_CudaAbsCriterion_updateGradInput.restype = None\n self.THNN_CudaAbsCriterion_updateGradInput = THNN_CudaAbsCriterion_updateGradInput\n\n THNN_CudaClassNLLCriterion_updateOutput=lib.THNN_CudaClassNLLCriterion_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool,THCudaTensor*,THCudaTensor*\n THNN_CudaClassNLLCriterion_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool,c_void_p,c_void_p]\n THNN_CudaClassNLLCriterion_updateOutput.restype = None\n self.THNN_CudaClassNLLCriterion_updateOutput = THNN_CudaClassNLLCriterion_updateOutput\n\n THNN_CudaClassNLLCriterion_updateGradInput=lib.THNN_CudaClassNLLCriterion_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool,THCudaTensor*,THCudaTensor*\n THNN_CudaClassNLLCriterion_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool,c_void_p,c_void_p]\n THNN_CudaClassNLLCriterion_updateGradInput.restype = None\n self.THNN_CudaClassNLLCriterion_updateGradInput = THNN_CudaClassNLLCriterion_updateGradInput\n\n THNN_CudaDistKLDivCriterion_updateOutput=lib.THNN_CudaDistKLDivCriterion_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool\n THNN_CudaDistKLDivCriterion_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool]\n THNN_CudaDistKLDivCriterion_updateOutput.restype = None\n self.THNN_CudaDistKLDivCriterion_updateOutput = THNN_CudaDistKLDivCriterion_updateOutput\n\n THNN_CudaDistKLDivCriterion_updateGradInput=lib.THNN_CudaDistKLDivCriterion_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool\n THNN_CudaDistKLDivCriterion_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool]\n THNN_CudaDistKLDivCriterion_updateGradInput.restype = None\n self.THNN_CudaDistKLDivCriterion_updateGradInput = THNN_CudaDistKLDivCriterion_updateGradInput\n\n THNN_CudaELU_updateOutput=lib.THNN_CudaELU_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,float\n THNN_CudaELU_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_float]\n THNN_CudaELU_updateOutput.restype = None\n self.THNN_CudaELU_updateOutput = THNN_CudaELU_updateOutput\n\n THNN_CudaELU_updateGradInput=lib.THNN_CudaELU_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,float\n THNN_CudaELU_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_float]\n THNN_CudaELU_updateGradInput.restype = None\n self.THNN_CudaELU_updateGradInput = THNN_CudaELU_updateGradInput\n\n THNN_CudaHardTanh_updateOutput=lib.THNN_CudaHardTanh_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,float,float\n THNN_CudaHardTanh_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_float,c_float]\n THNN_CudaHardTanh_updateOutput.restype = None\n self.THNN_CudaHardTanh_updateOutput = THNN_CudaHardTanh_updateOutput\n\n THNN_CudaHardTanh_updateGradInput=lib.THNN_CudaHardTanh_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,float,float\n THNN_CudaHardTanh_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_float,c_float]\n THNN_CudaHardTanh_updateGradInput.restype = None\n self.THNN_CudaHardTanh_updateGradInput = THNN_CudaHardTanh_updateGradInput\n\n THNN_CudaL1Cost_updateOutput=lib.THNN_CudaL1Cost_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*\n THNN_CudaL1Cost_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p]\n THNN_CudaL1Cost_updateOutput.restype = None\n self.THNN_CudaL1Cost_updateOutput = THNN_CudaL1Cost_updateOutput\n\n THNN_CudaL1Cost_updateGradInput=lib.THNN_CudaL1Cost_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaL1Cost_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaL1Cost_updateGradInput.restype = None\n self.THNN_CudaL1Cost_updateGradInput = THNN_CudaL1Cost_updateGradInput\n\n THNN_CudaLeakyReLU_updateOutput=lib.THNN_CudaLeakyReLU_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,double,bool\n THNN_CudaLeakyReLU_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_double,c_bool]\n THNN_CudaLeakyReLU_updateOutput.restype = None\n self.THNN_CudaLeakyReLU_updateOutput = THNN_CudaLeakyReLU_updateOutput\n\n THNN_CudaLeakyReLU_updateGradInput=lib.THNN_CudaLeakyReLU_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,double,bool\n THNN_CudaLeakyReLU_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_double,c_bool]\n THNN_CudaLeakyReLU_updateGradInput.restype = None\n self.THNN_CudaLeakyReLU_updateGradInput = THNN_CudaLeakyReLU_updateGradInput\n\n THNN_CudaLogSigmoid_updateOutput=lib.THNN_CudaLogSigmoid_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaLogSigmoid_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaLogSigmoid_updateOutput.restype = None\n self.THNN_CudaLogSigmoid_updateOutput = THNN_CudaLogSigmoid_updateOutput\n\n THNN_CudaLogSigmoid_updateGradInput=lib.THNN_CudaLogSigmoid_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaLogSigmoid_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaLogSigmoid_updateGradInput.restype = None\n self.THNN_CudaLogSigmoid_updateGradInput = THNN_CudaLogSigmoid_updateGradInput\n\n THNN_CudaLogSoftMax_updateOutput=lib.THNN_CudaLogSoftMax_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*\n THNN_CudaLogSoftMax_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p]\n THNN_CudaLogSoftMax_updateOutput.restype = None\n self.THNN_CudaLogSoftMax_updateOutput = THNN_CudaLogSoftMax_updateOutput\n\n THNN_CudaLogSoftMax_updateGradInput=lib.THNN_CudaLogSoftMax_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaLogSoftMax_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaLogSoftMax_updateGradInput.restype = None\n self.THNN_CudaLogSoftMax_updateGradInput = THNN_CudaLogSoftMax_updateGradInput\n\n THNN_CudaLookupTable_accGradParameters=lib.THNN_CudaLookupTable_accGradParameters\n # THCState*,THIndexTensor*,THCudaTensor*,THCudaTensor*,THIntegerTensor*,THCudaTensor*,THCudaTensor*,bool,int,float\n THNN_CudaLookupTable_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_bool,c_int,c_float]\n THNN_CudaLookupTable_accGradParameters.restype = None\n self.THNN_CudaLookupTable_accGradParameters = THNN_CudaLookupTable_accGradParameters\n\n THNN_CudaMarginCriterion_updateOutput=lib.THNN_CudaMarginCriterion_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool,float\n THNN_CudaMarginCriterion_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool,c_float]\n THNN_CudaMarginCriterion_updateOutput.restype = None\n self.THNN_CudaMarginCriterion_updateOutput = THNN_CudaMarginCriterion_updateOutput\n\n THNN_CudaMarginCriterion_updateGradInput=lib.THNN_CudaMarginCriterion_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool,float\n THNN_CudaMarginCriterion_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool,c_float]\n THNN_CudaMarginCriterion_updateGradInput.restype = None\n self.THNN_CudaMarginCriterion_updateGradInput = THNN_CudaMarginCriterion_updateGradInput\n\n THNN_CudaSoftMarginCriterion_updateOutput=lib.THNN_CudaSoftMarginCriterion_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int\n THNN_CudaSoftMarginCriterion_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int]\n THNN_CudaSoftMarginCriterion_updateOutput.restype = None\n self.THNN_CudaSoftMarginCriterion_updateOutput = THNN_CudaSoftMarginCriterion_updateOutput\n\n THNN_CudaSoftMarginCriterion_updateGradInput=lib.THNN_CudaSoftMarginCriterion_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int\n THNN_CudaSoftMarginCriterion_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int]\n THNN_CudaSoftMarginCriterion_updateGradInput.restype = None\n self.THNN_CudaSoftMarginCriterion_updateGradInput = THNN_CudaSoftMarginCriterion_updateGradInput\n\n THNN_CudaMSECriterion_updateOutput=lib.THNN_CudaMSECriterion_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool\n THNN_CudaMSECriterion_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool]\n THNN_CudaMSECriterion_updateOutput.restype = None\n self.THNN_CudaMSECriterion_updateOutput = THNN_CudaMSECriterion_updateOutput\n\n THNN_CudaMSECriterion_updateGradInput=lib.THNN_CudaMSECriterion_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool\n THNN_CudaMSECriterion_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool]\n THNN_CudaMSECriterion_updateGradInput.restype = None\n self.THNN_CudaMSECriterion_updateGradInput = THNN_CudaMSECriterion_updateGradInput\n\n THNN_CudaMultiMarginCriterion_updateOutput=lib.THNN_CudaMultiMarginCriterion_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool,int,THCudaTensor*\n THNN_CudaMultiMarginCriterion_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool,c_int,c_void_p]\n THNN_CudaMultiMarginCriterion_updateOutput.restype = None\n self.THNN_CudaMultiMarginCriterion_updateOutput = THNN_CudaMultiMarginCriterion_updateOutput\n\n THNN_CudaMultiMarginCriterion_updateGradInput=lib.THNN_CudaMultiMarginCriterion_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool,int,THCudaTensor*\n THNN_CudaMultiMarginCriterion_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool,c_int,c_void_p]\n THNN_CudaMultiMarginCriterion_updateGradInput.restype = None\n self.THNN_CudaMultiMarginCriterion_updateGradInput = THNN_CudaMultiMarginCriterion_updateGradInput\n\n THNN_CudaPReLU_updateOutput=lib.THNN_CudaPReLU_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,long\n THNN_CudaPReLU_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_long]\n THNN_CudaPReLU_updateOutput.restype = None\n self.THNN_CudaPReLU_updateOutput = THNN_CudaPReLU_updateOutput\n\n THNN_CudaPReLU_updateGradInput=lib.THNN_CudaPReLU_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,long\n THNN_CudaPReLU_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_long]\n THNN_CudaPReLU_updateGradInput.restype = None\n self.THNN_CudaPReLU_updateGradInput = THNN_CudaPReLU_updateGradInput\n\n THNN_CudaPReLU_accGradParameters=lib.THNN_CudaPReLU_accGradParameters\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,long,float\n THNN_CudaPReLU_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_long,c_float]\n THNN_CudaPReLU_accGradParameters.restype = None\n self.THNN_CudaPReLU_accGradParameters = THNN_CudaPReLU_accGradParameters\n\n THNN_CudaRReLU_updateOutput=lib.THNN_CudaRReLU_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,double,double,bool,bool,void*\n THNN_CudaRReLU_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_double,c_double,c_bool,c_bool,c_void_p]\n THNN_CudaRReLU_updateOutput.restype = None\n self.THNN_CudaRReLU_updateOutput = THNN_CudaRReLU_updateOutput\n\n THNN_CudaRReLU_updateGradInput=lib.THNN_CudaRReLU_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,double,double,bool,bool\n THNN_CudaRReLU_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_double,c_double,c_bool,c_bool]\n THNN_CudaRReLU_updateGradInput.restype = None\n self.THNN_CudaRReLU_updateGradInput = THNN_CudaRReLU_updateGradInput\n\n THNN_CudaSigmoid_updateOutput=lib.THNN_CudaSigmoid_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*\n THNN_CudaSigmoid_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p]\n THNN_CudaSigmoid_updateOutput.restype = None\n self.THNN_CudaSigmoid_updateOutput = THNN_CudaSigmoid_updateOutput\n\n THNN_CudaSigmoid_updateGradInput=lib.THNN_CudaSigmoid_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaSigmoid_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaSigmoid_updateGradInput.restype = None\n self.THNN_CudaSigmoid_updateGradInput = THNN_CudaSigmoid_updateGradInput\n\n THNN_CudaSmoothL1Criterion_updateOutput=lib.THNN_CudaSmoothL1Criterion_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool\n THNN_CudaSmoothL1Criterion_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool]\n THNN_CudaSmoothL1Criterion_updateOutput.restype = None\n self.THNN_CudaSmoothL1Criterion_updateOutput = THNN_CudaSmoothL1Criterion_updateOutput\n\n THNN_CudaSmoothL1Criterion_updateGradInput=lib.THNN_CudaSmoothL1Criterion_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool\n THNN_CudaSmoothL1Criterion_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_bool]\n THNN_CudaSmoothL1Criterion_updateGradInput.restype = None\n self.THNN_CudaSmoothL1Criterion_updateGradInput = THNN_CudaSmoothL1Criterion_updateGradInput\n\n THNN_CudaSoftMax_updateOutput=lib.THNN_CudaSoftMax_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*\n THNN_CudaSoftMax_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p]\n THNN_CudaSoftMax_updateOutput.restype = None\n self.THNN_CudaSoftMax_updateOutput = THNN_CudaSoftMax_updateOutput\n\n THNN_CudaSoftMax_updateGradInput=lib.THNN_CudaSoftMax_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaSoftMax_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaSoftMax_updateGradInput.restype = None\n self.THNN_CudaSoftMax_updateGradInput = THNN_CudaSoftMax_updateGradInput\n\n THNN_CudaSoftPlus_updateOutput=lib.THNN_CudaSoftPlus_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,float,float\n THNN_CudaSoftPlus_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_float,c_float]\n THNN_CudaSoftPlus_updateOutput.restype = None\n self.THNN_CudaSoftPlus_updateOutput = THNN_CudaSoftPlus_updateOutput\n\n THNN_CudaSoftPlus_updateGradInput=lib.THNN_CudaSoftPlus_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,float,float\n THNN_CudaSoftPlus_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_float,c_float]\n THNN_CudaSoftPlus_updateGradInput.restype = None\n self.THNN_CudaSoftPlus_updateGradInput = THNN_CudaSoftPlus_updateGradInput\n\n THNN_CudaSoftShrink_updateOutput=lib.THNN_CudaSoftShrink_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,double\n THNN_CudaSoftShrink_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_double]\n THNN_CudaSoftShrink_updateOutput.restype = None\n self.THNN_CudaSoftShrink_updateOutput = THNN_CudaSoftShrink_updateOutput\n\n THNN_CudaSoftShrink_updateGradInput=lib.THNN_CudaSoftShrink_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,double\n THNN_CudaSoftShrink_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_double]\n THNN_CudaSoftShrink_updateGradInput.restype = None\n self.THNN_CudaSoftShrink_updateGradInput = THNN_CudaSoftShrink_updateGradInput\n\n THNN_CudaSqrt_updateOutput=lib.THNN_CudaSqrt_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,float\n THNN_CudaSqrt_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_float]\n THNN_CudaSqrt_updateOutput.restype = None\n self.THNN_CudaSqrt_updateOutput = THNN_CudaSqrt_updateOutput\n\n THNN_CudaSqrt_updateGradInput=lib.THNN_CudaSqrt_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaSqrt_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaSqrt_updateGradInput.restype = None\n self.THNN_CudaSqrt_updateGradInput = THNN_CudaSqrt_updateGradInput\n\n THNN_CudaSquare_updateOutput=lib.THNN_CudaSquare_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*\n THNN_CudaSquare_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p]\n THNN_CudaSquare_updateOutput.restype = None\n self.THNN_CudaSquare_updateOutput = THNN_CudaSquare_updateOutput\n\n THNN_CudaSquare_updateGradInput=lib.THNN_CudaSquare_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaSquare_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaSquare_updateGradInput.restype = None\n self.THNN_CudaSquare_updateGradInput = THNN_CudaSquare_updateGradInput\n\n THNN_CudaTanh_updateOutput=lib.THNN_CudaTanh_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*\n THNN_CudaTanh_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p]\n THNN_CudaTanh_updateOutput.restype = None\n self.THNN_CudaTanh_updateOutput = THNN_CudaTanh_updateOutput\n\n THNN_CudaTanh_updateGradInput=lib.THNN_CudaTanh_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaTanh_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaTanh_updateGradInput.restype = None\n self.THNN_CudaTanh_updateGradInput = THNN_CudaTanh_updateGradInput\n\n THNN_CudaThreshold_updateOutput=lib.THNN_CudaThreshold_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,double,double,bool\n THNN_CudaThreshold_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_double,c_double,c_bool]\n THNN_CudaThreshold_updateOutput.restype = None\n self.THNN_CudaThreshold_updateOutput = THNN_CudaThreshold_updateOutput\n\n THNN_CudaThreshold_updateGradInput=lib.THNN_CudaThreshold_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,double,bool\n THNN_CudaThreshold_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_double,c_bool]\n THNN_CudaThreshold_updateGradInput.restype = None\n self.THNN_CudaThreshold_updateGradInput = THNN_CudaThreshold_updateGradInput\n\n THNN_CudaTemporalConvolution_updateOutput=lib.THNN_CudaTemporalConvolution_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int\n THNN_CudaTemporalConvolution_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int]\n THNN_CudaTemporalConvolution_updateOutput.restype = None\n self.THNN_CudaTemporalConvolution_updateOutput = THNN_CudaTemporalConvolution_updateOutput\n\n THNN_CudaTemporalConvolution_updateGradInput=lib.THNN_CudaTemporalConvolution_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int\n THNN_CudaTemporalConvolution_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int]\n THNN_CudaTemporalConvolution_updateGradInput.restype = None\n self.THNN_CudaTemporalConvolution_updateGradInput = THNN_CudaTemporalConvolution_updateGradInput\n\n THNN_CudaTemporalConvolution_accGradParameters=lib.THNN_CudaTemporalConvolution_accGradParameters\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,float\n THNN_CudaTemporalConvolution_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_float]\n THNN_CudaTemporalConvolution_accGradParameters.restype = None\n self.THNN_CudaTemporalConvolution_accGradParameters = THNN_CudaTemporalConvolution_accGradParameters\n\n THNN_CudaTemporalMaxPooling_updateOutput=lib.THNN_CudaTemporalMaxPooling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int\n THNN_CudaTemporalMaxPooling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int]\n THNN_CudaTemporalMaxPooling_updateOutput.restype = None\n self.THNN_CudaTemporalMaxPooling_updateOutput = THNN_CudaTemporalMaxPooling_updateOutput\n\n THNN_CudaTemporalMaxPooling_updateGradInput=lib.THNN_CudaTemporalMaxPooling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int\n THNN_CudaTemporalMaxPooling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int]\n THNN_CudaTemporalMaxPooling_updateGradInput.restype = None\n self.THNN_CudaTemporalMaxPooling_updateGradInput = THNN_CudaTemporalMaxPooling_updateGradInput\n\n THNN_CudaBatchNormalization_updateOutput=lib.THNN_CudaBatchNormalization_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,bool,double,double\n THNN_CudaBatchNormalization_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_bool,c_double,c_double]\n THNN_CudaBatchNormalization_updateOutput.restype = None\n self.THNN_CudaBatchNormalization_updateOutput = THNN_CudaBatchNormalization_updateOutput\n\n THNN_CudaBatchNormalization_backward=lib.THNN_CudaBatchNormalization_backward\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,float\n THNN_CudaBatchNormalization_backward.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_float]\n THNN_CudaBatchNormalization_backward.restype = None\n self.THNN_CudaBatchNormalization_backward = THNN_CudaBatchNormalization_backward\n\n THNN_CudaSpatialConvolutionMM_updateOutput=lib.THNN_CudaSpatialConvolutionMM_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int\n THNN_CudaSpatialConvolutionMM_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaSpatialConvolutionMM_updateOutput.restype = None\n self.THNN_CudaSpatialConvolutionMM_updateOutput = THNN_CudaSpatialConvolutionMM_updateOutput\n\n THNN_CudaSpatialConvolutionMM_updateGradInput=lib.THNN_CudaSpatialConvolutionMM_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int\n THNN_CudaSpatialConvolutionMM_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaSpatialConvolutionMM_updateGradInput.restype = None\n self.THNN_CudaSpatialConvolutionMM_updateGradInput = THNN_CudaSpatialConvolutionMM_updateGradInput\n\n THNN_CudaSpatialConvolutionMM_accGradParameters=lib.THNN_CudaSpatialConvolutionMM_accGradParameters\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,float\n THNN_CudaSpatialConvolutionMM_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_float]\n THNN_CudaSpatialConvolutionMM_accGradParameters.restype = None\n self.THNN_CudaSpatialConvolutionMM_accGradParameters = THNN_CudaSpatialConvolutionMM_accGradParameters\n\n THNN_CudaSpatialConvolutionLocal_updateOutput=lib.THNN_CudaSpatialConvolutionLocal_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,long,long,long,long\n THNN_CudaSpatialConvolutionLocal_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_long,c_long,c_long,c_long]\n THNN_CudaSpatialConvolutionLocal_updateOutput.restype = None\n self.THNN_CudaSpatialConvolutionLocal_updateOutput = THNN_CudaSpatialConvolutionLocal_updateOutput\n\n THNN_CudaSpatialConvolutionLocal_updateGradInput=lib.THNN_CudaSpatialConvolutionLocal_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,long,long,long,long\n THNN_CudaSpatialConvolutionLocal_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_long,c_long,c_long,c_long]\n THNN_CudaSpatialConvolutionLocal_updateGradInput.restype = None\n self.THNN_CudaSpatialConvolutionLocal_updateGradInput = THNN_CudaSpatialConvolutionLocal_updateGradInput\n\n THNN_CudaSpatialConvolutionLocal_accGradParameters=lib.THNN_CudaSpatialConvolutionLocal_accGradParameters\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,long,long,long,long,float\n THNN_CudaSpatialConvolutionLocal_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_long,c_long,c_long,c_long,c_float]\n THNN_CudaSpatialConvolutionLocal_accGradParameters.restype = None\n self.THNN_CudaSpatialConvolutionLocal_accGradParameters = THNN_CudaSpatialConvolutionLocal_accGradParameters\n\n THNN_CudaSpatialFullConvolution_updateOutput=lib.THNN_CudaSpatialFullConvolution_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,int,int\n THNN_CudaSpatialFullConvolution_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaSpatialFullConvolution_updateOutput.restype = None\n self.THNN_CudaSpatialFullConvolution_updateOutput = THNN_CudaSpatialFullConvolution_updateOutput\n\n THNN_CudaSpatialFullConvolution_updateGradInput=lib.THNN_CudaSpatialFullConvolution_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,int,int\n THNN_CudaSpatialFullConvolution_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaSpatialFullConvolution_updateGradInput.restype = None\n self.THNN_CudaSpatialFullConvolution_updateGradInput = THNN_CudaSpatialFullConvolution_updateGradInput\n\n THNN_CudaSpatialFullConvolution_accGradParameters=lib.THNN_CudaSpatialFullConvolution_accGradParameters\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,int,int,float\n THNN_CudaSpatialFullConvolution_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_float]\n THNN_CudaSpatialFullConvolution_accGradParameters.restype = None\n self.THNN_CudaSpatialFullConvolution_accGradParameters = THNN_CudaSpatialFullConvolution_accGradParameters\n\n THNN_CudaSpatialCrossMapLRN_updateOutput=lib.THNN_CudaSpatialCrossMapLRN_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,float,float,float\n THNN_CudaSpatialCrossMapLRN_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_float,c_float,c_float]\n THNN_CudaSpatialCrossMapLRN_updateOutput.restype = None\n self.THNN_CudaSpatialCrossMapLRN_updateOutput = THNN_CudaSpatialCrossMapLRN_updateOutput\n\n THNN_CudaSpatialCrossMapLRN_updateGradInput=lib.THNN_CudaSpatialCrossMapLRN_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,float,float,float\n THNN_CudaSpatialCrossMapLRN_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_float,c_float,c_float]\n THNN_CudaSpatialCrossMapLRN_updateGradInput.restype = None\n self.THNN_CudaSpatialCrossMapLRN_updateGradInput = THNN_CudaSpatialCrossMapLRN_updateGradInput\n\n THNN_CudaSpatialAdaptiveMaxPooling_updateOutput=lib.THNN_CudaSpatialAdaptiveMaxPooling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int\n THNN_CudaSpatialAdaptiveMaxPooling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int]\n THNN_CudaSpatialAdaptiveMaxPooling_updateOutput.restype = None\n self.THNN_CudaSpatialAdaptiveMaxPooling_updateOutput = THNN_CudaSpatialAdaptiveMaxPooling_updateOutput\n\n THNN_CudaSpatialAdaptiveMaxPooling_updateGradInput=lib.THNN_CudaSpatialAdaptiveMaxPooling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*\n THNN_CudaSpatialAdaptiveMaxPooling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p]\n THNN_CudaSpatialAdaptiveMaxPooling_updateGradInput.restype = None\n self.THNN_CudaSpatialAdaptiveMaxPooling_updateGradInput = THNN_CudaSpatialAdaptiveMaxPooling_updateGradInput\n\n THNN_CudaSpatialAveragePooling_updateOutput=lib.THNN_CudaSpatialAveragePooling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,bool,bool\n THNN_CudaSpatialAveragePooling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_bool,c_bool]\n THNN_CudaSpatialAveragePooling_updateOutput.restype = None\n self.THNN_CudaSpatialAveragePooling_updateOutput = THNN_CudaSpatialAveragePooling_updateOutput\n\n THNN_CudaSpatialAveragePooling_updateGradInput=lib.THNN_CudaSpatialAveragePooling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,bool,bool\n THNN_CudaSpatialAveragePooling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_bool,c_bool]\n THNN_CudaSpatialAveragePooling_updateGradInput.restype = None\n self.THNN_CudaSpatialAveragePooling_updateGradInput = THNN_CudaSpatialAveragePooling_updateGradInput\n\n THNN_CudaSpatialMaxPooling_updateOutput=lib.THNN_CudaSpatialMaxPooling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,bool\n THNN_CudaSpatialMaxPooling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_bool]\n THNN_CudaSpatialMaxPooling_updateOutput.restype = None\n self.THNN_CudaSpatialMaxPooling_updateOutput = THNN_CudaSpatialMaxPooling_updateOutput\n\n THNN_CudaSpatialMaxPooling_updateGradInput=lib.THNN_CudaSpatialMaxPooling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,bool\n THNN_CudaSpatialMaxPooling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_bool]\n THNN_CudaSpatialMaxPooling_updateGradInput.restype = None\n self.THNN_CudaSpatialMaxPooling_updateGradInput = THNN_CudaSpatialMaxPooling_updateGradInput\n\n THNN_CudaSpatialMaxUnpooling_updateOutput=lib.THNN_CudaSpatialMaxUnpooling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int\n THNN_CudaSpatialMaxUnpooling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int]\n THNN_CudaSpatialMaxUnpooling_updateOutput.restype = None\n self.THNN_CudaSpatialMaxUnpooling_updateOutput = THNN_CudaSpatialMaxUnpooling_updateOutput\n\n THNN_CudaSpatialMaxUnpooling_updateGradInput=lib.THNN_CudaSpatialMaxUnpooling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int\n THNN_CudaSpatialMaxUnpooling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int]\n THNN_CudaSpatialMaxUnpooling_updateGradInput.restype = None\n self.THNN_CudaSpatialMaxUnpooling_updateGradInput = THNN_CudaSpatialMaxUnpooling_updateGradInput\n\n THNN_CudaSpatialFractionalMaxPooling_updateOutput=lib.THNN_CudaSpatialFractionalMaxPooling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,int,int,int,int,THCudaTensor*,THCudaTensor*\n THNN_CudaSpatialFractionalMaxPooling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_void_p,c_void_p]\n THNN_CudaSpatialFractionalMaxPooling_updateOutput.restype = None\n self.THNN_CudaSpatialFractionalMaxPooling_updateOutput = THNN_CudaSpatialFractionalMaxPooling_updateOutput\n\n THNN_CudaSpatialFractionalMaxPooling_updateGradInput=lib.THNN_CudaSpatialFractionalMaxPooling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,THCudaTensor*\n THNN_CudaSpatialFractionalMaxPooling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_void_p]\n THNN_CudaSpatialFractionalMaxPooling_updateGradInput.restype = None\n self.THNN_CudaSpatialFractionalMaxPooling_updateGradInput = THNN_CudaSpatialFractionalMaxPooling_updateGradInput\n\n THNN_CudaSpatialSubSampling_updateOutput=lib.THNN_CudaSpatialSubSampling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int\n THNN_CudaSpatialSubSampling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int]\n THNN_CudaSpatialSubSampling_updateOutput.restype = None\n self.THNN_CudaSpatialSubSampling_updateOutput = THNN_CudaSpatialSubSampling_updateOutput\n\n THNN_CudaSpatialSubSampling_updateGradInput=lib.THNN_CudaSpatialSubSampling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int\n THNN_CudaSpatialSubSampling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int]\n THNN_CudaSpatialSubSampling_updateGradInput.restype = None\n self.THNN_CudaSpatialSubSampling_updateGradInput = THNN_CudaSpatialSubSampling_updateGradInput\n\n THNN_CudaSpatialSubSampling_accGradParameters=lib.THNN_CudaSpatialSubSampling_accGradParameters\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,float\n THNN_CudaSpatialSubSampling_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_float]\n THNN_CudaSpatialSubSampling_accGradParameters.restype = None\n self.THNN_CudaSpatialSubSampling_accGradParameters = THNN_CudaSpatialSubSampling_accGradParameters\n\n THNN_CudaSpatialUpSamplingNearest_updateOutput=lib.THNN_CudaSpatialUpSamplingNearest_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,int\n THNN_CudaSpatialUpSamplingNearest_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_int]\n THNN_CudaSpatialUpSamplingNearest_updateOutput.restype = None\n self.THNN_CudaSpatialUpSamplingNearest_updateOutput = THNN_CudaSpatialUpSamplingNearest_updateOutput\n\n THNN_CudaSpatialUpSamplingNearest_updateGradInput=lib.THNN_CudaSpatialUpSamplingNearest_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int\n THNN_CudaSpatialUpSamplingNearest_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int]\n THNN_CudaSpatialUpSamplingNearest_updateGradInput.restype = None\n self.THNN_CudaSpatialUpSamplingNearest_updateGradInput = THNN_CudaSpatialUpSamplingNearest_updateGradInput\n\n THNN_CudaVolumetricAveragePooling_updateOutput=lib.THNN_CudaVolumetricAveragePooling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int\n THNN_CudaVolumetricAveragePooling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaVolumetricAveragePooling_updateOutput.restype = None\n self.THNN_CudaVolumetricAveragePooling_updateOutput = THNN_CudaVolumetricAveragePooling_updateOutput\n\n THNN_CudaVolumetricAveragePooling_updateGradInput=lib.THNN_CudaVolumetricAveragePooling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int\n THNN_CudaVolumetricAveragePooling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaVolumetricAveragePooling_updateGradInput.restype = None\n self.THNN_CudaVolumetricAveragePooling_updateGradInput = THNN_CudaVolumetricAveragePooling_updateGradInput\n\n THNN_CudaVolumetricConvolution_updateOutput=lib.THNN_CudaVolumetricConvolution_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int\n THNN_CudaVolumetricConvolution_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaVolumetricConvolution_updateOutput.restype = None\n self.THNN_CudaVolumetricConvolution_updateOutput = THNN_CudaVolumetricConvolution_updateOutput\n\n THNN_CudaVolumetricConvolution_updateGradInput=lib.THNN_CudaVolumetricConvolution_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int\n THNN_CudaVolumetricConvolution_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaVolumetricConvolution_updateGradInput.restype = None\n self.THNN_CudaVolumetricConvolution_updateGradInput = THNN_CudaVolumetricConvolution_updateGradInput\n\n THNN_CudaVolumetricConvolution_accGradParameters=lib.THNN_CudaVolumetricConvolution_accGradParameters\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,float\n THNN_CudaVolumetricConvolution_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_float]\n THNN_CudaVolumetricConvolution_accGradParameters.restype = None\n self.THNN_CudaVolumetricConvolution_accGradParameters = THNN_CudaVolumetricConvolution_accGradParameters\n\n THNN_CudaVolumetricFullConvolution_updateOutput=lib.THNN_CudaVolumetricFullConvolution_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,int,int,int\n THNN_CudaVolumetricFullConvolution_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaVolumetricFullConvolution_updateOutput.restype = None\n self.THNN_CudaVolumetricFullConvolution_updateOutput = THNN_CudaVolumetricFullConvolution_updateOutput\n\n THNN_CudaVolumetricFullConvolution_updateGradInput=lib.THNN_CudaVolumetricFullConvolution_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,int,int,int\n THNN_CudaVolumetricFullConvolution_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaVolumetricFullConvolution_updateGradInput.restype = None\n self.THNN_CudaVolumetricFullConvolution_updateGradInput = THNN_CudaVolumetricFullConvolution_updateGradInput\n\n THNN_CudaVolumetricFullConvolution_accGradParameters=lib.THNN_CudaVolumetricFullConvolution_accGradParameters\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,int,int,int,float\n THNN_CudaVolumetricFullConvolution_accGradParameters.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_float]\n THNN_CudaVolumetricFullConvolution_accGradParameters.restype = None\n self.THNN_CudaVolumetricFullConvolution_accGradParameters = THNN_CudaVolumetricFullConvolution_accGradParameters\n\n THNN_CudaVolumetricMaxPooling_updateOutput=lib.THNN_CudaVolumetricMaxPooling_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int,int,int,int,bool\n THNN_CudaVolumetricMaxPooling_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_int,c_bool]\n THNN_CudaVolumetricMaxPooling_updateOutput.restype = None\n self.THNN_CudaVolumetricMaxPooling_updateOutput = THNN_CudaVolumetricMaxPooling_updateOutput\n\n THNN_CudaVolumetricMaxPooling_updateGradInput=lib.THNN_CudaVolumetricMaxPooling_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int,int,int\n THNN_CudaVolumetricMaxPooling_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int,c_int,c_int]\n THNN_CudaVolumetricMaxPooling_updateGradInput.restype = None\n self.THNN_CudaVolumetricMaxPooling_updateGradInput = THNN_CudaVolumetricMaxPooling_updateGradInput\n\n THNN_CudaSpatialReflectionPadding_updateOutput=lib.THNN_CudaSpatialReflectionPadding_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,int,int,int\n THNN_CudaSpatialReflectionPadding_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_int,c_int,c_int]\n THNN_CudaSpatialReflectionPadding_updateOutput.restype = None\n self.THNN_CudaSpatialReflectionPadding_updateOutput = THNN_CudaSpatialReflectionPadding_updateOutput\n\n THNN_CudaSpatialReflectionPadding_updateGradInput=lib.THNN_CudaSpatialReflectionPadding_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int\n THNN_CudaSpatialReflectionPadding_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int]\n THNN_CudaSpatialReflectionPadding_updateGradInput.restype = None\n self.THNN_CudaSpatialReflectionPadding_updateGradInput = THNN_CudaSpatialReflectionPadding_updateGradInput\n\n THNN_CudaSpatialReplicationPadding_updateOutput=lib.THNN_CudaSpatialReplicationPadding_updateOutput\n # THCState*,THCudaTensor*,THCudaTensor*,int,int,int\n THNN_CudaSpatialReplicationPadding_updateOutput.argtypes=[c_void_p,c_void_p,c_void_p,c_int,c_int,c_int]\n THNN_CudaSpatialReplicationPadding_updateOutput.restype = None\n self.THNN_CudaSpatialReplicationPadding_updateOutput = THNN_CudaSpatialReplicationPadding_updateOutput\n\n THNN_CudaSpatialReplicationPadding_updateGradInput=lib.THNN_CudaSpatialReplicationPadding_updateGradInput\n # THCState*,THCudaTensor*,THCudaTensor*,THCudaTensor*,int,int,int,int\n THNN_CudaSpatialReplicationPadding_updateGradInput.argtypes=[c_void_p,c_void_p,c_void_p,c_void_p,c_int,c_int,c_int,c_int]\n THNN_CudaSpatialReplicationPadding_updateGradInput.restype = None\n self.THNN_CudaSpatialReplicationPadding_updateGradInput = THNN_CudaSpatialReplicationPadding_updateGradInput\n\n\n","sub_path":"thnn.py","file_name":"thnn.py","file_ext":"py","file_size_in_byte":45545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"501231661","text":"import cv2\nfrom collections import defaultdict\nimport pickle\nimport os\nimport numpy as np\nimport itertools\nimport DeleteBackGround\nfrom sklearn.datasets import base\n\n\n\n\n\n# def actualize_dict(dict, matches, desc):\n# [dict[match]]\n\ndef getDescriptions1(path_folder1, name1, blur_mat, flag_resize = False):\n img = cv2.imread(path_folder1 + \"/\" + name1, 0)\n img = cv2.blur(img, (blur_mat,blur_mat))\n if flag_resize is not False:\n img = cv2.resize(img, (1653, 840))\n keys, des = descriptor.detectAndCompute(img, None)\n return img, keys, des\ndef getDescriptions2(path_folder1, name1, path_folder2, name2, blur):\n img1, keys1, des1 = getDescriptions1(path_folder1, name1, blur, True)\n img2, keys2, des2 = getDescriptions1(path_folder2, name2, blur, True)\n return img1, keys1, des1, img2, keys2, des2\ndef getDescriptions3(path_folder, blur):\n imageList = DeleteBackGround.loadFiles(path_folder)\n keys_list =[]\n des_list =[]\n img_list =[]\n for img_pat in imageList:\n img = cv2.imread(path_folder+\"/\"+img_pat,0)\n img=cv2.blur(img,(blur, blur))\n img = cv2.resize(img, (1653, 840))\n keys, des = descriptor.detectAndCompute(img, None)\n keys_list.append(keys)\n des_list.append(des)\n img_list.append(img)\n return img_list, keys_list, des_list\ndef bestMatches(keys_pattern, des_pattern, keys_trans, des_trans, amount=1):\n matcher = cv2.BFMatcher_create(cv2.cv2.NORM_HAMMING, crossCheck=False)\n matches_hier = matcher.knnMatch(des_pattern, des_trans, 2)\n matches = [i for (i, j) in matches_hier if i.distance < 0.8 * j.distance]\n matches = sorted(matches, key=lambda x: x.distance)\n points_patter = np.array([keys_pattern[num.queryIdx].pt for num in matches], dtype='float32').reshape(-1,1,2)\n points_trans = np.array([keys_trans[num.trainIdx].pt for num in matches], dtype='float32').reshape(-1,1,2)\n M, mask = cv2.findHomography(points_patter, points_trans, cv2.RANSAC)\n mask = mask.ravel().tolist()\n return M, mask, matches\ndef actualizeGraph(main_graph, temp_pairs):\n for pair in temp_pairs:\n key_first_node = None\n key_second_node = None\n for key in main_graph:\n if (np.isin(main_graph[key], pair[0])).all():\n key_first_node = key\n if (np.isin(main_graph[key], pair[1])).all():\n key_second_node = key\n if key_first_node is not None and key_second_node is not None:\n pass\n elif key_first_node is not None:\n main_graph[key_first_node].append(pair[1])\n elif key_second_node is not None:\n main_graph[key_second_node].append(pair[0])\n elif key_first_node is None and key_second_node is None:\n pass\n return main_graph\ndef make_part_of_dataset(des, dict, keys):\n data= []\n target = []\n target_names =[]\n #for key in keys:\n\n\n\n\ncv2.namedWindow(\"draw keypoints\", cv2.WINDOW_NORMAL)\ncv2.namedWindow(\"draw keypoints2\", cv2.WINDOW_NORMAL)\ncv2.namedWindow(\"result\", cv2.WINDOW_NORMAL)\ndescriptor = cv2.BRISK_create(thresh=30,octaves=3,patternScale=1.0)#,radiusList = np.ones((100)), numberList = np.ones((100))\npath = \"/home/krzysztof/Pulpit/banknoty/100zl_nobackground\"\npath_pattern_folder = \"/home/krzysztof/Pulpit/banknoty\"\nname_pattern_img = \"100zl_wzor.jpeg\"\n\n# images, keys, des = getDescriptions3(path, 7)\nimg_pattern, keys_pattern, des_pattern = getDescriptions1(path_pattern_folder, name_pattern_img, 7)\n\n# M, mask, matches = bestMatches(keys_pattern, des_pattern, keys, des)\n\nimageList = DeleteBackGround.loadFiles(path)\nimg_matches =None\ndict_matches = defaultdict(list)\n\n\n\nfor counter, imgPath in enumerate(imageList, start=0):\n img_trans, keys_trans, des_trans = getDescriptions1(path, imgPath, 9, True)\n M, mask, matches = bestMatches(keys_pattern, des_pattern, keys_trans, des_trans)\n matches_good = [matches[i] for i, elem in enumerate(mask) if elem is 1]\n for match in matches_good:\n dict_matches[keys_pattern[match.queryIdx].pt].append(des_trans[match.trainIdx])#\n\n # drawing keypoints\n img_matches = cv2.drawMatches(img_pattern, keys_pattern, img_trans,\n keys_trans, matches, img_matches, matchesMask=mask)#, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS\n\n cv2.imshow(\"draw keypoints\", img_matches)\n cv2.waitKey(10)\n print(len(matches_good) , len(dict_matches))\n len_dict = [len(dict_matches[key]) for key in dict_matches]\n hist = np.histogram(len_dict, bins=range(1, 24))\n print(hist[0][:])\n\ndata = np.empty((0,64),np.uint8)\ntarget = np.array([],np.uint8)\ntarget_names = np.array([])\n\nkeys_good = [key for key in dict_matches if len(dict_matches[key])>8]\nfor counter, key in enumerate(keys_good, start=0):\n target_names = np.append(target_names, str(key))\n for row in dict_matches[key]:\n data = np.append(data, np.resize(row,(1, 64)), axis=0)\n target = np.append(target, counter)\n\n\ndata_base = base.Bunch(data= data, target=target, target_names=target_names)\n\n# save database\n\nf = open(\"/home/krzysztof/Pulpit/banknoty/100zl_des/database.base\", 'wb')\npickle.dump(data_base, f)\nf.close()\n\n\n\n\n\n# combinations = itertools.combinations(imageList, 2)\n# node_pairs = list()\n# for i, comb in enumerate(combinations,start=0):\n# img1, keys1, des1, img2, keys2, des2 = getDescriptions2(path, comb[0], path, comb[1], 5)\n# M, mask, matches = bestMatches(keys1, des1, keys2, des2)\n# matches_good = [matches[i] for i, elem in enumerate(mask) if elem is 1]\n# [node_pairs.append((des1[match.queryIdx], des2[match.trainIdx])) for match in matches_good]\n# dict_matches=actualizeGraph(dict_matches, node_pairs)\n# len_dict = [len(dict_matches[key]) for key in dict_matches]\n# print(np.histogram(len_dict, bins=range(24))[0][12:])\n# img_matches = cv2.drawMatches(img1, keys1, img2,\n# keys2, matches, img_matches,\n# matchesMask=mask) # , flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS\n# cv2.imshow(\"draw keypoints\", img_matches)\n# cv2.waitKey(0)","sub_path":"OpenCVFirstProject/Create_Learning_Set.py","file_name":"Create_Learning_Set.py","file_ext":"py","file_size_in_byte":6121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"395422870","text":"import os\r\nimport sys\r\n\r\nfile_1=[]\r\nfile_2=[]\r\n\r\nf=open(\"semantic.txt\",\"r\",encoding=\"utf-8\")\r\n\r\ncount_of_sentence_1=1\r\nfor i,line in enumerate(f):\r\n line=line.rstrip()\r\n \r\n if line==\"\":\r\n count_of_sentence_1=count_of_sentence_1+1\r\n continue\r\n\r\n row=line.split('\t')\r\n file_1.append(row)\r\n\r\nf.close()\r\n\r\n\r\nf=open(\"Tests.txt\",\"r\",encoding=\"utf-8\")\r\n\r\ncount_of_sentence_2=1\r\n\r\nfor i,line in enumerate(f):\r\n line=line.rstrip()\r\n\r\n if line==\"\":\r\n count_of_sentence_2=count_of_sentence_2+1\r\n continue\r\n\r\n if count_of_sentence_2==count_of_sentence_1: \r\n break\r\n \r\n row=line.split('\t') \r\n file_2.append(row)\r\n\r\nf.close()\r\n\r\n\r\n \r\nparent_link_type_link=0\r\nparent_link = 0\r\ntype_link=0\r\nmorph_error=0\r\nline=0\r\nno_strings=0\r\n\r\n\r\nif len(file_1)!=len(file_2):\r\n no_strings=abs(len(file_2)-len(file_1))\r\n length=min(len(file_1),len(file_2))\r\nelse:\r\n length=len(file_1)\r\n\r\n\r\nf=open(\"test_result.txt\",\"w\",encoding=\"utf-8\")\r\nindex=0\r\n\r\nind=[]\r\n\r\nfor i in range(len(file_1)):\r\n index=index+1\r\n if \"SENT\" in file_1[i][3]:\r\n ind.append(index)\r\n index=0\r\n\r\n \r\nu=0\r\nall_write=0\r\nfor i in range(length):\r\n if file_1[i][3]==\"SENT\":\r\n if (all_write==0):\r\n f.write(\"Correct !\\n\")\r\n f.write(\"------------------------------\\n\")\r\n \r\n all_write=0\r\n u=u+1\r\n line=0\r\n morph_error=0\r\n parent_link_type_link=0\r\n parent_link=0\r\n type_link=0\r\n procent_1=0\r\n \r\n \r\n if (file_1[i][4]==file_2[i][4] and file_1[i][5]==file_2[i][5] and file_1[i][6]==file_2[i][6]):\r\n continue\r\n else:\r\n all_write=all_write+1\r\n if (file_1[i][5]!=file_2[i][5] or file_1[i][6]!=file_2[i][6]):\r\n parent_link_type_link = parent_link_type_link+1\r\n f.write(file_1[i][1]+\" \"+file_1[i][4]+\" \"+file_1[i][5]+\" \"+file_1[i][6]+\"\\n\")\r\n f.write(file_2[i][1]+\" \"+file_2[i][4]+\" \"+file_2[i][5]+\" \"+file_2[i][6]+\"\\n\")\r\n f.write(\"------------------------------\\n\")\r\n \r\n if (file_1[i][5]!=file_2[i][5]):\r\n parent_link = parent_link+1\r\n\r\n if (file_1[i][6]!=file_2[i][6]):\r\n type_link = type_link+1\r\n\r\n if (file_1[i][4]!=file_2[i][4]):\r\n morph_error = morph_error+1\r\n\r\n line=line+1\r\n\r\n #f.write(\"Amount_of_sent1 \"+str(count_of_sentence_1)+\"\\n\")\r\n #f.write(\"Amount_of_sent2 \"+str(count_of_sentence_2)+\"\\n\")\r\n #f.write(\"No string \"+str(no_strings)+\"\\n\")\r\n f.write(\"Line \"+str(line)+\"\\n\")\r\n f.write('Morph '+str(morph_error)+\"\\n\")\r\n procent_1=100/(int(ind[u]))\r\n f.write(\"Type+parent \"+str(100-parent_link_type_link*procent_1)+\"\\n\")\r\n f.write(\"Type \"+str(100-type_link*procent_1)+\"\\n\")\r\n f.write(\"Parent \"+str(100-parent_link*procent_1)+\"\\n\")\r\n f.write(\"------------------------------\\n\")\r\n\r\n \r\n \r\n \r\n\r\n\r\nf.close()\r\n","sub_path":"Syntax/Ver 2.0/tester.py","file_name":"tester.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"473407633","text":"from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n#from .views import *\n\nurlpatterns = [\n path('', views.index, name = 'shopme'),\n path('about', views.about, name='about'),\n path('contact', views.contact, name='contact'),\n path('search', views.search, name='search'),\n path('productview', views.productview, name='productview'),\n path('tracker', views.tracker, name='myorder'),\n path('checkout', views.checkout, name='checkout'),\n path('accounts', views.accounts, name='accounts'),\n path('help', views.help, name='help'),\n path('purchaseperuser', views.purchaseperuser, name='purchaseperuser')\n]","sub_path":"shopme/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"233934195","text":"\nimport os\nimport random\nfrom sklearn import metrics\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom options import Options\nfrom datafolder import SlideLevelDataset\nfrom models import AttnClassifier\n\n\ndef main():\n opt = Options(isTrain=False)\n opt.parse()\n\n os.makedirs(opt.test['save_dir'], exist_ok=True)\n\n opt.save_options()\n opt.print_options()\n\n mode = 'test'\n get_probs(opt, mode)\n\n # compute accuracy\n save_dir = opt.test['save_dir']\n test_prob = np.load('{:s}/{:s}_prob_results.npy'.format(save_dir, mode), allow_pickle=True).item()\n\n txt_file = open('{:s}/{:s}_results.txt'.format(save_dir, mode), 'w')\n acc, auc, auc_CIs = compute_accuracy(test_prob)\n message = '{:11s}: Acc: {:5.2f}\\tAUC: {:5.2f} ({:5.2f}, {:5.2f})'.format('All', acc * 100, auc * 100, auc_CIs[0] * 100, auc_CIs[1] * 100)\n print(message)\n txt_file.write('{:s}\\n'.format(message))\n txt_file.close()\n\n\ndef get_probs(opt, mode='test'):\n os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(x) for x in opt.test['gpus'])\n\n model_path = opt.test['model_path']\n save_dir = opt.test['save_dir']\n\n # create model\n if opt.model['name'].lower() == 'attnclassifier':\n model = AttnClassifier(2048, 2)\n else:\n raise NotImplemented\n model = model.cuda()\n\n # print(\"=> loading trained model\")\n best_checkpoint = torch.load(model_path)\n model.load_state_dict(best_checkpoint['state_dict'])\n print('Model obtained in epoch: {:d}'.format(best_checkpoint['epoch']))\n\n class_info_test = np.load('{:s}/{:s}/{:s}_class_info_{:s}.npy'.format(opt.train['data_dir'], mode, opt.data_type, opt.gene_or_pathway),\n allow_pickle=True).item()\n test_set = SlideLevelDataset('{:s}/20x_features_resnet101_hdf5/{:s}_tumor.h5'.format(opt.root_data_dir, mode), class_info_test)\n\n print(\"=> Test begins:\")\n # switch to evaluate mode\n model.eval()\n prob_results = {}\n attn_maps = {}\n slide_names = list(test_set.keys)\n for i in range(len(test_set)):\n input, target, true_indices = test_set[i]\n\n flag = '' if i < len(test_set)-1 else '\\n'\n print('\\r\\t{:d}/{:d}'.format(i+1, len(test_set)), end=flag)\n\n with torch.no_grad():\n output, attn = model(input.unsqueeze(1).cuda())\n probs = nn.functional.softmax(output, dim=1)\n probs = probs.cpu().numpy()\n\n attn_map = attn[0].detach().cpu().numpy()\n\n slide_name = slide_names[i]\n prob_results[slide_name] = {'prob': probs, 'label': target.item()}\n attn_maps[slide_name] = {'attn': attn_map, 'true_indices': np.array(true_indices)}\n\n if i % 10 == 0:\n np.save('{:s}/{:s}_attn_maps.npy'.format(save_dir, mode), attn_maps)\n np.save('{:s}/{:s}_prob_results.npy'.format(save_dir, mode), prob_results)\n\n np.save('{:s}/{:s}_prob_results.npy'.format(save_dir, mode), prob_results)\n np.save('{:s}/{:s}_attn_maps.npy'.format(save_dir, mode), attn_maps)\n\n\ndef compute_accuracy(slides_probs):\n all_probs = []\n all_labels = []\n for slide_name in slides_probs.keys():\n probs = np.array(slides_probs[slide_name]['prob'])\n label = slides_probs[slide_name]['label']\n\n all_probs.extend(probs[:, 1])\n all_labels.append(label)\n\n all_probs = np.array(all_probs)\n all_pred = (all_probs > 0.5).astype(np.float)\n\n acc = metrics.accuracy_score(all_labels, all_pred)\n if np.unique(np.array(all_labels)).size == 1:\n auc = -0.01\n auc_CIs = [-0.01, -0.01]\n else:\n auc, auc_CIs = bootstrap_AUC_CIs(all_probs, all_labels)\n return acc, auc, auc_CIs\n\n\ndef plot_roc_curve(slides_probs, save_filename):\n all_probs = []\n all_labels = []\n for slide_name in slides_probs.keys():\n probs = np.array(slides_probs[slide_name]['prob'])\n label = slides_probs[slide_name]['label']\n\n all_probs.extend(probs[:, 1])\n all_labels.append(label)\n\n all_probs = np.array(all_probs)\n\n fpr, tpr, thresholds = metrics.roc_curve(all_labels, all_probs)\n auc = metrics.auc(fpr, tpr)\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.plot(fpr, tpr)\n ax.set_xlabel('FPR')\n ax.set_ylabel('TPR')\n ax.set_aspect('equal')\n ax.set_xlim(0, 1)\n ax.set_ylim(0, 1)\n ax.set_title('ROC curve, AUC: {:.2f})'.format(auc * 100))\n fig.savefig(save_filename)\n plt.close()\n\n\ndef bootstrap_AUC_CIs(probs, labels):\n probs = np.array(probs)\n labels = np.array(labels)\n N_slide = len(probs)\n index_list = np.arange(0, N_slide)\n AUC_list = []\n i = 0\n while i < 1000:\n sampled_indices = random.choices(index_list, k=N_slide)\n sampled_probs = probs[sampled_indices]\n sampled_labels = labels[sampled_indices]\n\n if np.unique(sampled_labels).size == 1: # reject the sample if there is only one class\n continue\n\n auc_bs = metrics.roc_auc_score(sampled_labels, sampled_probs)\n AUC_list.append(auc_bs)\n i += 1\n\n assert len(AUC_list) == 1000\n AUC_list = np.array(AUC_list)\n auc_avg = np.mean(AUC_list)\n auc_CIs = [np.percentile(AUC_list, 2.5), np.percentile(AUC_list, 97.5)]\n return auc_avg, auc_CIs\n\n\nif __name__ == '__main__':\n main()","sub_path":"code_prediction_on_breast_cancer/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"487508417","text":"from Tile import Tile\n\nclass Tilemap:\n '''\n Defining the pattern of the tile map (A list of char string) 28 X 36 tiles\n '0': The tile is a blank dead space\n 'H': The tile is a dead space and has a horizontal blue line (image of map)\n 'V': The tile is a dead space and has a vertical blue line (image of map)\n '2': The tile is a dead space and has a blue bottom right corner (image of map)\n '3': The tile is a dead space and has a blue bottom left corner (image of map)\n '4': The tile is a dead space and has a blue top right corner (image of map)\n '5': The tile is a dead space and has a blue top left corner (image of map)\n 'P': The tile is a dead space and has a pink line (door of the ghosts house)\n 'G': The tile is a dead space in the ghost house, ghost can only exit the house\n 'D': The tile is a legal space, containing a dot\n 'E': The tile is a legal space, containing a energizer\n '1': The tile is a blank legal space\n\n Input Argument: gameScreen: the gameScreen window from GameStart\n '''\n\n def __init__(self, gameScreen):\n # The tile map\n tilemap = [\"2HHHHHHHHHHHH32HHHHHHHHHHHH3\",\n \"VDDDDDDDDDDDDVVDDDDDDDDDDDDV\",\n \"VD2HH3D2HHH3DVVD2HHH3D2HH3DV\",\n \"VEV00VDV000VDVVDV000VDV00VEV\",\n \"VD4HH5D4HHH5D45D4HHH5D4HH5DV\",\n \"VDDDDDDDDDDDDDDDDDDDDDDDDDDV\",\n \"VD2HH3D23D2HHHHHH3D23D2HH3DV\",\n \"VD4HH5DVVD4HH32HH5DVVD4HH5DV\",\n \"VDDDDDDVVDDDDVVDDDDVVDDDDDDV\",\n \"4HHHH3DV4HH31VV12HH5VD2HHHH5\",\n \"00000VDV2HH514514HH3VDV00000\",\n \"00000VDVV1111111111VVDV00000\",\n \"00000VDVV12HHPPHH31VVDV00000\",\n \"HHHHH5D451VGGGGGGV145D4HHHHH\",\n \"111111D111VGGGGGGV111D111111\",\n \"HHHHH3D231VGGGGGGV123D2HHHHH\",\n \"00000VDVV14HHHHHH51VVDV00000\",\n \"00000VDVV1111111111VVDV00000\",\n \"00000VDVV12HHHHHH31VVDV00000\",\n \"2HHHH5D4514HH32HH5145D4HHHH3\",\n \"VDDDDDDDDDDDDVVDDDDDDDDDDDDV\",\n \"VD2HH3D2HHH3DVVD2HHH3D2HH3DV\",\n \"VD4H3VD4HHH5D45D4HHH5DV2H5DV\",\n \"VEDDVVDDDDDDD11DDDDDDDVVDDEV\",\n \"4H3DVVD23D2HHHHHH3D23DVVD2H5\",\n \"2H5D45DVVD4HH32HH5DVVD45D4H3\",\n \"VDDDDDDVVDDDDVVDDDDVVDDDDDDV\",\n \"VD2HHHH54HH3DVVD2HH54HHHH3DV\",\n \"VD4HHHHHHHH5D45D4HHHHHHHH5DV\",\n \"VDDDDDDDDDDDDDDDDDDDDDDDDDDV\",\n \"4HHHHHHHHHHHHHHHHHHHHHHHHHH5\"]\n\n # the legal space tile dict: key: tile location, value: Tile object\n self.legaltile = dict()\n # the dead space tile dict: key: tile location, value: Tile object\n self.deadtile = dict()\n # The space tile in the ghost house dict: key: tile location, value: Tile object\n self.ghosthousetile = dict()\n\n # Initialize the whole tile map\n for i in range(len(tilemap)):\n for j in range(len(tilemap[i])):\n # When the char in map is '1', 'D', 'E', the tile is a legal space tile\n if tilemap[i][j] == '1' or tilemap[i][j] == 'D' or tilemap[i][j] == 'E':\n self.legaltile[(i,j)] = Tile(tilemap[i][j], (i,j), gameScreen)\n elif tilemap[i][j] == 'G':\n # The tile is in the ghost house, the ghost can only exit the tile\n self.ghosthousetile[(i,j)] = Tile(tilemap[i][j], (i,j), gameScreen)\n else:\n # The tile is a dead space tile\n self.deadtile[(i,j)] = Tile(tilemap[i][j], (i,j), gameScreen)\n","sub_path":"PacMan/TileMap.py","file_name":"TileMap.py","file_ext":"py","file_size_in_byte":3817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"351673893","text":"# ABC084B\nA, B = map(int, input().split())\nS = input()\nans = 'No'\nflag = True\nnum = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\nif S[A] == '-':\n aa = S[0:A]\n bb = S[A+1:A+B+1]\n for i in range(len(aa)):\n if aa[i] not in num:\n flag = False\n for k in range(len(bb)):\n if bb[k] not in num:\n flag = False\n if flag:\n ans = 'Yes'\nprint(ans)\n","sub_path":"AtCoder_Python/ABC/ABC084/ABC_084_B.py","file_name":"ABC_084_B.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"191869547","text":"##string = str(\"Hello World\")\r\n##i=0\r\n##for s in string:\r\n## if(s == ' '):\r\n## break\r\n## else:\r\n## print(string[i], end = '')\r\n## i+=1\r\n\r\n## Palindrome\r\n\r\nflag = 0\r\nx = 0\r\ny = -1\r\nfor x in string:\r\n for y in string:\r\n if (x == y):\r\n flag+=1\r\n print(y)\r\n\r\n\r\nif flag > 0:\r\n print(\"it is palindrome\")\r\n print(flag)\r\nelse:\r\n print(\"Not palindrome\")\r\n","sub_path":"string print.py","file_name":"string print.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"379942361","text":"def premik(ukaz, x, y, smer):\r\n smeri = \"NESW\"\r\n premiki = [(0, -1), (1, 0), (0, 1), (-1, 0)]\r\n ismer = smeri.index(smer)\r\n if ukaz == \"R\":\r\n smer = smeri[(ismer + 1) % 4]\r\n elif ukaz == \"L\":\r\n smer = smeri[(ismer - 1) % 4]\r\n else:\r\n dx, dy = premiki[ismer]\r\n x += dx * ukaz\r\n y += dy * ukaz\r\n return x, y, smer\r\n\r\ndef izvedi(ime_datoteke):\r\n smer = \"N\"\r\n seznam_premikov = [(0, 0, \"N\")]\r\n x = 0\r\n y = 0\r\n ukazi = open(ime_datoteke, encoding=\"utf-8\")\r\n for vrstica in ukazi:\r\n ukaz = vrstica.strip().split(\" \")\r\n if ukaz[0] == \"DESNO\":\r\n jojo = \"R\"\r\n ukaz.append(0)\r\n x += ukaz[1]\r\n if ukaz[0] == \"LEVO\":\r\n jojo = \"L\"\r\n ukaz.append(0)\r\n y += ukaz[1]\r\n if ukaz[0] == \"NAPREJ\":\r\n jojo = int(ukaz[1])\r\n premiki = premik(jojo, x, y, smer)\r\n x = premiki[0]\r\n y = premiki[1]\r\n smer = premiki[2]\r\n seznam_premikov.append(premiki)\r\n return seznam_premikov\r\n\r\ndef opisi_stanje(x, y, smer):\r\n if smer == \"N\":\r\n smer = \"^\"\r\n elif smer == \"E\":\r\n smer = \">\"\r\n elif smer == \"S\":\r\n smer = \"v\"\r\n elif smer == \"W\":\r\n smer = \"<\"\r\n return (\"{x:>3}:{y:<4}{smer}\".format(x=x, y=y, smer=smer))\r\n\r\ndef prevedi(ime_vhoda, ime_izhoda):\r\n nova_dat = open(ime_izhoda, \"w\")\r\n izveden = izvedi(ime_vhoda)\r\n for iz in izveden:\r\n x, y, smer = iz\r\n opis = opisi_stanje(x, y, smer)\r\n nova_dat.write(opis+\"\\n\")\r\n\r\ndef opisi_stanje_2(x, y, smer):\r\n x = \"(\" + str(x)\r\n if smer == \"N\":\r\n smer = \"^\"\r\n elif smer == \"E\":\r\n smer = \">\"\r\n elif smer == \"S\":\r\n smer = \"v\"\r\n elif smer == \"W\":\r\n smer = \"<\"\r\n return (\"{smer}{x:>5}:{y})\".format(x=x, y=y, smer=smer))\r\n\r\n\r\n\r\n","sub_path":"code/batch-2/vse-naloge-brez-testov/DN9-M-177.py","file_name":"DN9-M-177.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"378165803","text":"import numpy as np\nfrom numpy.random import RandomState\nfrom sklearn.base import BaseEstimator\n\nclass RandomParcels(BaseEstimator):\n \n def __init__(self, geo, n_parcels, medial_wall_inds=None,\n medial_wall_mask=None, random_state=1):\n \n # Set passed params\n self.geo = geo\n self.n_parcels = n_parcels\n self.medial_wall_inds = medial_wall_inds\n self.medial_wall_mask = medial_wall_mask\n self.random_state = random_state\n self.mask = None\n \n def get_parc(self, copy=True):\n\n if self.mask is None:\n self._generate_parc_from_params()\n\n if copy:\n return self.mask.copy()\n else:\n return self.mask\n\n def _generate_parc_from_params(self):\n\n # Proc by input args\n self._proc_geo()\n self._proc_medial_wall()\n self._proc_random_state()\n\n # Set up mask, done and flags\n self.sz = len(self._geo)\n self.reset()\n\n # Init\n self.init_parcels()\n\n # Then generate\n self.generate_parcels()\n\n def _proc_geo(self):\n self._geo = [np.array(g) for g in self.geo]\n\n def _proc_medial_wall(self):\n \n # Proc medial wall inds\n if self.medial_wall_inds is not None:\n self.m_wall = set(list(self.medial_wall_inds))\n elif self.medial_wall_mask is not None:\n self.m_wall = set(list(np.where(self.medial_wall_mask == True)[0]))\n else:\n self.m_wall = set()\n\n def _proc_random_state(self):\n \n if self.random_state is None:\n self.r_state = RandomState()\n elif isinstance(self.random_state, int):\n self.r_state = RandomState(seed = self.random_state)\n else:\n self.r_state = self.random_state\n\n def reset(self):\n '''Just reset the mask, and set w/ done info'''\n\n self.mask = np.zeros(self.sz, dtype='int16')\n self.done = self.m_wall.copy()\n self.ready, self.generated = False, False\n \n def init_parcels(self):\n\n # Generate the starting locs\n valid = np.setdiff1d(np.arange(self.sz), np.array(list(self.done)))\n self.start_locs = self.r_state.choice(valid, size=self.n_parcels,\n replace=False)\n\n # Set random probs. that each loc is chosen\n self.probs = self.r_state.random(size=self.n_parcels)\n\n def setup(self):\n '''This should be called before generating parcel,\n so after a mutation has been made, setup needs to\n be called. It also does not hurt to call setup an\n extra time, as nothing random is set.'''\n\n # Generate corresponding labels w/ each loc\n self.labels = np.arange(1, self.n_parcels+1, dtype='int16')\n\n # Mask where if == 1, then that parcel is done\n self.finished = np.zeros(self.n_parcels, dtype='bool_')\n\n # Drop the first points\n self.mask[self.start_locs] = self.labels\n\n # Set ready flag to True\n self.ready = True\n\n def get_probs(self):\n\n return self.probs / np.sum(self.probs)\n \n def choice(self):\n '''Select a valid label based on probs.'''\n \n msk = self.finished == 0\n probs = self.probs[msk] / np.sum(self.probs[msk])\n label = self.r_state.choice(self.labels[msk], p=probs)\n \n return label\n \n def get_valid_neighbors(self, loc):\n \n ns = self._geo[loc]\n valid_ns = ns[self.mask[ns] == 0]\n \n return valid_ns\n \n def generate_parcels(self):\n\n if self.ready is False:\n self.setup()\n \n # Keep looping until every spot is filled\n while (self.finished == 0).any():\n self.add_spot()\n\n # Set generated flag when done\n self.generated = True\n \n def add_spot(self):\n\n # Select which parcel to add to\n label = self.choice()\n\n # Determine valid starting locations anywhere in exisitng parcel\n current = np.where(self.mask == label)[0]\n valid = set(current) - self.done\n\n self.proc_spot(valid, label)\n\n def proc_spot(self, valid, label):\n\n # If no valid choices, then set this parcel to finished\n if len(valid) == 0:\n self.finished[label-1] = 1\n return\n\n # Select randomly from the valid starting locs\n loc = self.r_state.choice(tuple(valid))\n \n # Select a valid + open neighbor\n valid_ns = self.get_valid_neighbors(loc)\n\n if len(valid_ns) > 0:\n\n # Select a valid choice, and add it w/ the right label\n choice = self.r_state.choice(valid_ns)\n self.mask[choice] = label\n\n # If this was the only choice, mark start loc as done\n if len(valid_ns) == 1:\n self.done.add(loc)\n\n # If there are no valid choices, mark as done\n else:\n self.done.add(loc)\n\n valid.remove(loc)\n self.proc_spot(valid, label)\n","sub_path":"ABCD_ML/extensions/RandomParcels.py","file_name":"RandomParcels.py","file_ext":"py","file_size_in_byte":5073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"163294535","text":"import numpy as np\nimport pandas as pd\nfrom sympy import *\nimport json\nfrom matplotlib import pyplot as plt\nfrom matplotlib.font_manager import FontProperties\n'''\n物质平衡法预测产能\n'''\nclass Gas_prediction():\n def __init__(self,well_info):\n '''\n 初始化\n :param well_info:\n '''\n self.A = well_info['A'] # 供气面积，m^2\n self.P_L = well_info['P_L'] # Langmuir 压力系数，Mpa\n self.P_cd = well_info['P_cd'] # 临界解吸压力\n self.V_L = well_info['V_L'] # Langmuir 体积系数，m^3/t\n self.P_i = well_info['P_i'] # 初始压力，Mpa\n self.h = well_info['h'] # 煤厚,m\n self.phi_i = well_info['phi_i'] # 初始孔隙度\n self.K_i=well_info['K_i'] #初始渗透率\n self.rho_B = well_info['rho_B'] # 煤密度，t/m^3\n self.S_wi = 0.95 # 初始含水饱和度\n self.B_W = 1 # 水的地层体积系数\n self.T = 313 # 温度，K\n self.P_wf = 1 # 井底流压\n self.mu_g = 0.01 # 气体粘度，mPa/s\n self.r_e = 200 # 泄流半径，m\n self.r_w = 0.1 # 井筒半径，m\n self.s = -1 # 表皮系数\n self.mu_w = 0.6#水的粘度系数\n self.P_sc = 0.1013#标准压力，Mpa\n self.T_sc = 289#标准温度，K\n self.Z_sc = 1#标准压缩系数\n self.q_wi = 2.5#初始排水量，m^3\n self.Z_i = self.get_z(self.P_i ,self.T , 0.8)\n self.G = self.A * self.h * self.rho_B * self.V_L * (self.P_cd / (self.P_cd + self.P_L))\n self.G_f = self.A * self.h * self.phi_i * (1 - self.S_wi) * (self.P_i * self.Z_sc * self.T_sc / (self.Z_i * self.T * self.P_sc))\n\n def get_z(self, P, T, theta):\n '''\n 计算天然气压缩系数\n 算法来源：天然气压缩因子计算方法对比及应用_董萌\n :param P:当前压力，Mpa\n :param theta: 天然气相对密度\n :param T: 温度，K\n :return:\n '''\n P = P * 1000\n\n A1 = 0.31506237\n A2 = -1.046099\n A3 = -0.57832720\n A4 = 0.53530771\n A5 = -0.61232023\n A6 = -0.10488813\n A7 = 0.68127001\n A8 = 0.68446549\n P_c = 4881 - 386.11 * theta\n T_c = 92 + 116.67 * theta\n P_r = P / P_c\n T_r = T / T_c\n\n T1 = A1 + (A2 / T_r) + (A3 / T_r ** 3)\n T2 = A4 + (A5 / T_r)\n T3 = A5 * A6 / T_r\n T4 = A7 / (T_r ** 3)\n T5 = 0.27 * P_r / T_r\n\n rho = 0.27 * P_r / T_r\n rho_pass = False\n\n while rho_pass == False:\n f_rho = 1 + T1 * rho + T2 * rho ** 2 + T3 * rho ** 5 + (\n T4 * rho ** 2 * (1 + A8 * rho ** 2) * np.exp(-(A8 * rho ** 2))) - T5 / rho\n f_rho_coe = T1 + 2 * T2 * rho + 5 * T3 * rho ** 4 + 2 * T4 * rho * (\n 1 + A8 * rho ** 2 - A8 ** 2 * rho ** 4) * np.exp(-(A8 * rho ** 2)) + T5 / rho ** 2\n\n rho_old = rho\n rho_new = rho - (f_rho / f_rho_coe)\n rho = rho_new\n if rho_old - rho_new < 0.1:\n rho_pass = True\n Z = 0.27 * P_r / (rho_new * T_r)\n return Z\n\n def get_phi(self,P):\n '''\n 计算孔隙度\n 来源：一种快速准确预测煤层气井生产动态的解析模型_石军太\n :param P:当前压力\n :return:\n '''\n C_p=0.02#孔隙压缩系数\n phi=(1-C_p*(self.P_i-P))*self.phi_i\n\n return phi\n\n def get_K(self,phi):\n '''\n 计算渗透率\n 来源：一种快速准确预测煤层气井生产动态的解析模型_石军太\n :param phi:当前孔隙度\n :return:\n '''\n K=self.K_i*(phi/self.phi_i)**3\n return K\n\n def get_P_1(self, S_w, Z, phi, G_p):\n '''\n 排水降压阶段压力计算\n :param S_w: 含水饱和度\n :param Z: 气体压缩因子\n :param phi: 孔隙度\n :param G_p: 累计产气量\n :return:\n '''\n B = self.A * self.h * phi * (1 - S_w) * self.Z_sc * self.T_sc / (Z * self.T * self.P_sc)\n P = (self.G_f - G_p) / B\n return P\n\n def get_P_2(self, S_w, Z, phi, G_p):\n '''\n 煤层气解吸阶段压力计算\n :param S_w:\n :param Z:\n :param phi:\n :param G_p:\n :return:\n '''\n\n x=self.G+self.G_f-G_p\n A=self.A*self.h*self.rho_B*self.V_L\n B=self.A*self.h*phi*(1-S_w)*self.Z_sc*self.T_sc/(Z*self.T*self.P_sc)\n\n P=self.P_i\n is_pass=False\n\n while is_pass == False:\n\n f_p=B*P**2+(B*self.P_L+A-x)*P-x*self.P_L\n f_P_coe=2*B*P+B*self.P_L+A-x\n\n P_old=P\n P=P_old-(f_p/f_P_coe)\n\n if np.abs(P_old-P)<=0.0001:\n is_pass=True\n return P\n\n def get_S_w(self,W_p,phi):\n '''\n 计算含水饱和度\n :param W_p: 累计产水量\n :param phi: 孔隙度\n :return:\n '''\n S_w = self.S_wi -( self.B_W * W_p / (self.A * self.h * phi))\n return S_w\n\n def get_k_rg_k_rw(self,S_w):\n '''\n 气水相渗透率计算\n :param S_w: 含水饱和度\n :return:\n '''\n k_rg=(1-S_w)**1.1\n k_rw=S_w**2.5\n return k_rg,k_rw\n\n def get_gas_prediction(self,P,k_g,Z,P_wf,K):\n '''\n 气体产能计算\n :param P:压力\n :param k_g:气相渗透率\n :param Z:气体压缩因子\n :param P_wf:井底流压\n :param K:渗透率\n :return:q_g，日产气量，m^3\n '''\n q_g=774.6*K*k_g*self.h*(P**2-P_wf**2)/(self.T*self.mu_g*Z*( np.log(self.r_e/self.r_w)-0.75+self.s ))\n # q_g=1000*k_g*K*self.h*(P**2-P_wf**2)/(1.31*self.T*self.mu_g*Z*( np.log(self.r_e/self.r_w)-0.75+self.s ))\n\n return q_g\n\n def get_water_prediction(self,P,k_w,P_wf,K):\n '''\n 产水量计算\n :param P:\n :param k_w:\n :param P_wf:\n :param K:\n :return: q_w，日产水量，m^3\n '''\n q_w=0.543*k_w*K*self.h*(P-P_wf)/(self.B_W*self.mu_w*( np.log(self.r_e/self.r_w)-0.75+self.s ))\n return q_w\n\n def get_P_wf(self, P,k_w,K):\n '''\n 计算井底流压，根据产水量计算公式反推\n :param P:\n :param k_w:\n :param K:\n :param S_w:\n :return:\n '''\n P_wf=P-(self.q_wi*self.B_W*self.mu_w*( np.log(self.r_e/self.r_w)-0.75+self.s )/(0.543*k_w*K*self.h))\n return P_wf\n\n\n\ndef run(well_info,time):\n GP = Gas_prediction(well_info)\n\n '''\n 定义列表，存放结果\n '''\n q_g_list=[]\n q_w_list=[]\n P_list=[]\n i_list=[]\n Z_list=[]\n phi_list=[]\n S_w_list=[]\n G_P_list=[]\n W_p_list=[]\n P_wf_list=[]\n K_list=[]\n Kg_list=[]\n Kw_list=[]\n\n '''\n 定义初始参数\n '''\n P=GP.P_i\n W_p=0\n G_p=0\n Z = GP.Z_i\n phi = GP.phi_i\n K=GP.K_i\n S_w =GP.S_wi\n k_g, k_w = GP.get_k_rg_k_rw(S_w)\n P_wf = GP.get_P_wf( P,k_w,K)\n '''\n 设定排采时间，动态预测\n '''\n for i in range(time):\n # print(i+1)\n test_step=10\n if i %test_step==0:\n i_list.append(i*10)\n '''\n 排水，根据井底流压计算排水量\n '''\n if P_wf > GP.P_wf:\n q_w=GP.q_wi*10\n else:\n q_w=GP.get_water_prediction(P,k_w,P_wf,K)*10\n if i % test_step == 0:\n q_w_list.append(q_w/10)\n # print('q_w:', q_w/10)\n\n W_p = W_p + q_w\n if i % test_step == 0:\n W_p_list.append(W_p)\n '''\n 计算含水饱和度\n '''\n S_w = GP.get_S_w(W_p,phi)\n if i % test_step == 0:\n S_w_list.append(S_w)\n # print('S_w:', S_w)\n\n '''\n 计算压力\n '''\n if P > GP.P_cd:\n P=GP.get_P_1( S_w, Z, phi, G_p)\n else:\n P = GP.get_P_2(S_w, Z, phi, G_p)\n if i % test_step == 0:\n P_list.append(P)\n # print('P:',P)\n\n '''\n 计算气体压缩因子\n '''\n Z = GP.get_z(P,GP.T,0.8)\n if i % test_step == 0:\n Z_list.append(Z)\n # print('Z:',Z)\n\n '''\n 计算孔隙度\n '''\n phi=GP.get_phi(P)\n if i % test_step == 0:\n phi_list.append(phi)\n # print('phi:',phi)\n\n '''\n 计算渗透率\n '''\n K=GP.get_K(phi)\n if i % test_step == 0:\n K_list.append(K)\n\n '''\n 计算气水相渗透率\n '''\n k_g, k_w=GP.get_k_rg_k_rw(S_w)\n\n '''\n 计算井底流压，若井底流压小于设定值，则认为当前井底流压为设定值\n '''\n if P_wf > GP.P_wf:\n # P_wf = GP.P_wf\n P_wf = GP.get_P_wf(P, k_w, K)\n else:\n P_wf = GP.P_wf\n if i % test_step == 0:\n P_wf_list.append(P_wf)\n\n '''\n 根据当前压力，判断排采阶段，计算产气量\n '''\n if P > GP.P_cd:\n # q_g=GP. get_gas_prediction_level_1( P, phi, S_w, Z,G_p)\n q_g=0\n else:\n q_g = GP.get_gas_prediction(P,k_g,Z,P_wf,K)*10\n if i % test_step == 0:\n q_g_list.append(q_g/10)\n # print('q_g:',q_g/10)\n G_p = G_p + q_g\n if i % test_step == 0:\n G_P_list.append(G_p)\n # print('G_p',G_p)\n if i % test_step == 0:\n Kg_list.append(k_g)\n Kw_list.append(k_w)\n\n #\n print('采收率：',G_p/GP.G)\n\n '''\n 结果可视化\n '''\n # fig = plt.figure()\n # font = FontProperties(fname=r\"c:\\windows\\fonts\\msyh.ttc\")\n #\n # ax1 = fig.add_subplot(3, 2, 1)\n # ax1.set_title('日产水量', fontproperties=font)\n # plt.scatter(i_list, q_w_list, marker='x', color='red', s=2, label='First')\n #\n # ax2 = fig.add_subplot(3, 2, 2)\n # ax2.set_title('日产气量', fontproperties=font)\n # plt.scatter(i_list, q_g_list, marker='o', color='red', s=2, label='First')\n #\n # ax3 = fig.add_subplot(3, 2, 3)\n # ax3.set_title('压力', fontproperties=font)\n # plt.scatter(i_list, P_list, marker='x', color='blue', s=2, label='First')\n #\n # ax4 = fig.add_subplot(3, 2, 4)\n # ax4.set_title('Z', fontproperties=font)\n # plt.scatter(i_list, Z_list, marker='x', color='red', s=2, label='First')\n #\n # ax5 = fig.add_subplot(3, 2, 5)\n # ax5.set_title('井底流压', fontproperties=font)\n # plt.scatter(i_list, P_wf_list, marker='x', color='red', s=2, label='First')\n #\n # ax6 = fig.add_subplot(3, 2,6)\n # ax6.set_title('有效渗透率', fontproperties=font)\n # plt.scatter(i_list, Kg_list, marker='x', color='red', s=2, label='First')\n # plt.show()\n\n fig = plt.figure()\n font = FontProperties(fname=r\"c:\\windows\\fonts\\msyh.ttc\")\n\n ax1 = fig.add_subplot(111)\n line1=ax1.plot(i_list, Kg_list,'r',marker='o', mec='r',lw=1,ms=0,label='气相渗透率')\n\n ax2 = ax1.twinx() # this is the important function\n line2=ax2.plot(i_list, Kw_list,'b', marker='x', mec='b',lw=1,ms=0,label='水相渗透率')\n\n lns =line1+ line2\n labs = [l.get_label() for l in lns]\n plt.legend(lns, labs,prop=font,loc='center right')\n\n ax1.set_xlabel('时间（天）',fontproperties=font) # 设置x轴标题\n ax1.set_ylabel('气相渗透率', color='r',fontproperties=font) # 设置Y1轴标题\n ax2.set_ylabel('水相渗透率', color='b',fontproperties=font) # 设置Y2轴标题\n\n plt.show()\n\n return G_p\n\nif __name__==\"__main__\":\n '''\n 汇总数据至data_CBM_info.csv\n '''\n # data_P_i=np.array(pd.read_csv('data/IDW_储层压力.csv',header=None))\n # data_V_L = np.array(pd.read_csv('data/IDW_兰氏体积.csv', header=None))\n # data_P_L = np.array(pd.read_csv('data/IDW_兰氏压力.csv', header=None))\n # data_phi_i = np.array(pd.read_csv('data/IDW_孔隙度.csv', header=None))\n # data_K_i = np.array(pd.read_csv('data/IDW_渗透率.csv', header=None))\n # data_h = np.array(pd.read_csv('data/IDW_煤厚.csv', header=None))\n # data_P_cd = np.array(pd.read_csv('data/IDW_解吸压力.csv', header=None))\n #\n #\n # data_target = np.zeros([60, 60],dtype=dict)\n # for i in range(3600):\n # row=int(np.floor(i/60))\n # column=i%60\n #\n # data_target[row,column]={\n # 'V_L':data_V_L[row,column],\n # 'P_L': data_P_L[row,column],\n # 'P_cd':data_P_cd[row,column],\n # 'P_i':data_P_i[row,column],\n # 'h':data_h[row,column],\n # 'phi_i':data_phi_i[row,column],\n # 'K_i':data_K_i[row,column],\n # }\n #\n # pd.DataFrame(data_target).to_csv('data/data_CBM_info.csv',index=0,header=0)\n\n # data_CBM_info=np.array(pd.read_csv('data/data_CBM_info.csv',header=None))\n #\n # for i in range(3600):\n # row=int(np.floor(i/60))\n # column=i%60\n #\n # info_str=data_CBM_info[row,column]\n # info_dict=eval(info_str)\n #\n #\n # well_info={\n #\n # 'A':250*250,\n # 'V_L':info_dict['V_L'],\n # 'P_L': info_dict['P_L'],\n # 'P_cd':info_dict['P_cd'],\n # 'P_i':info_dict['P_i'],\n # 'h':info_dict['h'],\n # 'phi_i':info_dict['phi_i'],\n # 'K_i':info_dict['K_i'],\n # 'rho_B':1.58\n # }\n #\n # G_p=run(well_info, 180)\n # print([row,column],G_p/1000000)\n\n well_info = {\n\n 'A': 200*200,\n 'V_L':40.97,\n 'P_L': 3.69,\n 'P_cd': 6.236,\n 'P_i': 7.612,\n 'h':6.5,\n 'phi_i': 0.0498,\n 'K_i': 1.2,\n 'rho_B': 1.58\n }\n\n G_p = run(well_info, 720)\n # print([row, column], G_p / 1000000)\n\n\n","sub_path":"gas_prediction.py","file_name":"gas_prediction.py","file_ext":"py","file_size_in_byte":13784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"596526072","text":"import cv2\nimport numpy as np\nimport face_recognition\n\nimgad = face_recognition.load_image_file('ImagesBasic/Adarsh.jpg')\nimgad = cv2.cvtColor(imgad, cv2.COLOR_BGR2RGB)\nimgTest = face_recognition.load_image_file('ImagesBasic/Adarsh Test.jpg')\nimgTest = cv2.cvtColor(imgTest, cv2.COLOR_BGR2RGB)\n\nfaceloc = face_recognition.face_locations(imgad)[0]\nencodeAd = face_recognition.face_encodings(imgad)[0]\ncv2.rectangle(imgad, (faceloc[3], faceloc[0]), (faceloc[1], faceloc[2]), (255, 0, 255), 2)\n\nfacelocTest = face_recognition.face_locations(imgTest)[0]\nencodeTest = face_recognition.face_encodings(imgTest)[0]\ncv2.rectangle(imgTest, (facelocTest[3], facelocTest[0]), (facelocTest[1], facelocTest[2]), (255, 0, 255), 2)\n\nresults = face_recognition.compare_faces([encodeAd], encodeTest)\nfaceDis = face_recognition.face_distance([encodeAd], encodeTest)\nprint(results, faceDis)\ncv2.putText(imgTest, f'{results} {round(faceDis[0], 2)}', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 2)\ncv2.imshow('ADARSH', imgad)\ncv2.imshow('ADARSH TEST', imgTest)\ncv2.waitKey(0)\n","sub_path":"face.py","file_name":"face.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"338873053","text":"from flask import jsonify\r\nfrom flask_restful import request, Resource\r\n\r\nfrom .experiment.experiment_cache import ExperimentCache\r\n\r\n\r\nclass Experiments(Resource):\r\n\r\n def get(self):\r\n experiment_cache = ExperimentCache().get_experiment_cache()\r\n experiments = []\r\n for exp in experiment_cache.values():\r\n experiments.append({\r\n 'name': exp.pretty_name(),\r\n 'id': exp.__class__.__name__,\r\n 'description': exp.description(),\r\n 'domain': exp.domain().value,\r\n 'initialized': exp.status.initialized\r\n })\r\n return jsonify({'all_experiments': experiments})\r\n","sub_path":"api/experiments.py","file_name":"experiments.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"412803233","text":"import requests\nfrom UA import agents\nimport random\nimport time\nimport json\nfrom db import *\nimport multiprocessing\nfrom thread_pool import ThreadPool#自己写的线程池\nimport lxml.html as lhtml\nfrom retry import retry\nfrom proxy import get_proxies\nfrom stock_queue import get_stock\nimport re\n\nimport logging\nLOG_FORMAT = \"%(asctime)s : %(levelname)s - %(message)s\"\nlogging.basicConfig(format=LOG_FORMAT, level=logging.INFO)\n\n# helper method\ndef format_time(t):\n return '2019-'+t if len(t)==11 else t\n\ndef format_type_and_price(text):\n match = re.search(r'以([￥¥]|undefined){0,1}([0-9]{1,6}\\.{0,1}[0-9]{0,6})元{0,1}买入', text)\n if match:\n return '买入', float(match.group(2))\n match = re.search(r'委托买入价([0-9\\.]*)。', text)\n if match:\n return '买入', float(match.group(1))\n \n match = re.search(r'以([￥¥]|undefined){0,1}([0-9]{1,6}\\.{0,1}[0-9]{0,6})元{0,1}卖出', text)\n if match:\n return '卖出', float(match.group(2))\n match = re.search(r'委托卖出价([0-9\\.]*)。', text)\n if match:\n return '卖出', float(match.group(1))\n\n if '关注' in text:\n return '关注', None\n\n return '其他', None\n \n \n \n@retry(delay=10)\ndef download_transaction_from_web(symbol, num):\n real_time = str(time.time()).replace('.', '')[0:-1]#获取当前时间\n transaction_url= 'https://xueqiu.com/statuses/search.json?count=10&comment=0&symbol={symbol}&hl=0&source=trans&sort=time&page={page}&_={real_time}' # source=trans determines to get transaction\n url = transaction_url.format(symbol=symbol, page=str(num), real_time=real_time)\n session = requests.session()\n session.proxies = get_proxies() # 携带代理\n headers={\n 'User-Agent':random.choice(agents)\n }\n _first_request = session.get(url='https://xueqiu.com/', headers=headers, timeout=10)\n transactions_response = session.get(url, headers=headers, timeout=10)\n if str(transactions_response.status_code) != str(200):\n raise Exception(\"Can't download transaction from web. symbol: {}, num: {}. {}\".format(symbol, num, transactions_response.text))\n return transactions_response.text\n\ndef update_transaction_database(transactions_text, num, symbol):\n stocks_transaction = json.loads(transactions_text)['list']\n page = json.loads(transactions_text)['maxPage']#获取最大页数\n transaction_database=TransactionMongo()\n for transaction in stocks_transaction:\n try:\n text = transaction.get('text').strip()\n selector = lhtml.fromstring(text) # 里面的标签各种各样，各种嵌套，用正则调了很久，投降了，改用xpath\n transaction_text = selector.xpath('string(.)').replace(' ','')\n user_id = transaction.get('user_id') # 评论者ID\n user = transaction.get('user') # 评论者信息\n title = transaction.get('title') # 标题\n stock_code = symbol # 股票代码\n transaction_id = transaction.get('id') # 每条评论都要唯一的ID\n source = transaction.get('source')\n like_count = transaction.get('like_count')\n reply_count = transaction.get('reply_count')\n retweet_count = transaction.get('retweet_count')\n transaction_time = format_time(transaction.get('timeBefore'))\n transaction_type, price = format_type_and_price(transaction_text)\n transaction_database.push(message_id=transaction_id,message_time=transaction_time,transaction_type=transaction_type, price=price, symbol=stock_code,message=transaction_text,user_id=user_id,user=user,title=title,source=source,like_count=like_count,reply_count=reply_count,retweet_count=retweet_count,max_page=page,current_page=num)\n logging.info('{}的第{}页爬取成功, 共{}页'.format(symbol, num, page))\n except Exception as e:\n logging.warning('Json解析失败. 错误信息: {}'.format(e))\n\ndef thread_get_transaction(args):\n num, symbol, max_page = args\n logging.info('开始抓取{}的第{}页, 共{}页'.format(symbol, num, max_page))\n transactions_text = download_transaction_from_web(symbol, num)\n update_transaction_database(transactions_text, num, symbol)\n\ndef transaction_crawler(symbol):\n pool=ThreadPool(5)\n transactions_text = download_transaction_from_web(symbol, 1)\n max_page = json.loads(transactions_text)['maxPage']#获取最大页数\n for num in range(1,max_page+1):\n pool.run(func=thread_get_transaction,args=(num,symbol, max_page)) \n\ndef get_transaction():\n stock_database = StockMongo()#链接第一步抓取的股票代码数据表，根据股票代码抓取评论\n while True:\n try:\n symbol = stock_database.pop()\n transaction_crawler(symbol)\n except AttributeError:\n logging.info('All stocks are successfully scraped. ')\n break\n\ndef process_crawler():\n get_queue()\n process=[]\n num_cpus=multiprocessing.cpu_count()\n logging.info('将会启动的进程数为{}'.format(num_cpus))\n for _ in range(int(num_cpus)):\n p=multiprocessing.Process(target=get_transaction)#创建进程\n p.start()\n process.append(p)\n for p in process:\n p.join()\n \nif __name__ == '__main__':\n get_stock()\n TransactionMongo().clear()\n QueueMongo().clear()\n process_crawler()","sub_path":"deprecated/crawl_transaction.py","file_name":"crawl_transaction.py","file_ext":"py","file_size_in_byte":5440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"637780637","text":"import dialogflow\nimport os\nfrom google.api_core.exceptions import InvalidArgument\n\nDIALOGFLOW_PROJECT_ID = 'kwili-chatbot-hlonvo'\nDIALOGFLOW_LANGUAGE_CODE = 'fr-FR'\nSESSION_ID = 'current-user-id'\n\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'key.json'\n\nsession_client = dialogflow.SessionsClient()\nsession = session_client.session_path(DIALOGFLOW_PROJECT_ID, SESSION_ID)\n\ndef send_query(user_text):\n\ttext_input = dialogflow.types.TextInput(text=user_text, language_code=DIALOGFLOW_LANGUAGE_CODE)\n\tquery_input = dialogflow.types.QueryInput(text=text_input)\n\ttry:\n\t\tresponse = session_client.detect_intent(session=session, query_input=query_input)\n\t\treturn response.query_result.fulfillment_messages\n\texcept InvalidArgument:\n\t\traise","sub_path":"src/services/chat/dial.py","file_name":"dial.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"104407549","text":"# Copyright (c) 2015, Alphamonak Solutions Ltd. \n# License: GNU General Public License v3. See license.txt\n\nimport redapp\nimport redapp.permissions\n\ndef execute():\n\tredapp.reload_doc(\"core\", \"doctype\", \"block_module\")\n\tredapp.reload_doctype(\"User\")\n\tredapp.reload_doctype(\"Lead\")\n\tredapp.reload_doctype(\"Contact\")\n\n\tredapp.reload_doc('crm', 'doctype', 'newsletter_list')\n\tredapp.reload_doc('crm', 'doctype', 'newsletter_list_subscriber')\n\tredapp.reload_doc('crm', 'doctype', 'newsletter')\n\n\tredapp.permissions.reset_perms(\"Newsletter\")\n\n\tif not redapp.db.exists(\"Role\", \"Newsletter Manager\"):\n\t\tredapp.get_doc({\"doctype\": \"Role\", \"role\": \"Newsletter Manager\"}).insert()\n\n\tfor userrole in redapp.get_all(\"UserRole\", \"parent\", {\"role\": \"Sales Manager\"}):\n\t\tif redapp.db.exists(\"User\", userrole.parent):\n\t\t\tuser = redapp.get_doc(\"User\", userrole.parent)\n\t\t\tuser.append(\"user_roles\", {\n\t\t\t\t\"doctype\": \"UserRole\",\n\t\t\t\t\"role\": \"Newsletter Manager\"\n\t\t\t})\n\t\t\tuser.flags.ignore_mandatory = True\n\t\t\tuser.save()\n\n\t# create default lists\n\tgeneral = redapp.new_doc(\"Newsletter List\")\n\tgeneral.title = \"General\"\n\tgeneral.insert()\n\tgeneral.import_from(\"Lead\")\n\tgeneral.import_from(\"Contact\")\n","sub_path":"redapple/patches/v5_0/newsletter.py","file_name":"newsletter.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"609496412","text":"import configparser\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\n\nclass DbManager(object):\n \"\"\"数据连接类\"\"\"\n\n def __init__(self):\n super(DbManager, self).__init__()\n conf = configparser.ConfigParser()\n conf.read('./conf/config.ini')\n mysql_string = conf.get('db', 'mysql_string')\n self.engine = create_engine(\n mysql_string,\n connect_args={'charset': 'utf8'})\n DBSession = sessionmaker(bind=self.engine)\n self.session = DBSession()\n\n def add_data(self, data):\n try:\n self.session.add(data)\n self.session.commit()\n self.session.close()\n return True\n except Exception as e:\n print(e)\n return False\n\n def find(self, model, params, order_by=None):\n if order_by is None:\n return self.session.query(model).filter(params).first()\n else:\n return self.session.query(model).filter(params).order_by(order_by).first()\n\n def select(self, model, params, order_by=None):\n if order_by is None:\n return self.session.query(model).filter(params).all()\n else:\n return self.session.query(model).filter(params).order_by(order_by).all()\n\n def get_session(self):\n return self.session\n","sub_path":"model/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"346447273","text":"#!/usr/bin/python3\n\nimport csv\nimport os\nimport sys\nimport requests\n\n# Output filepaths and formatting\nCWD = os.getcwd()\nCSV_NAME = \"location_to_device.csv\"\nOUTPUT_PATH = CWD + \"/\" + CSV_NAME\nCOLUMN_DESCRIPTIONS = [\"Country\", \"State\", \"Device\", \"Browser\"]\n\n# Required Userstack API key:\ndef userstack_api_key() -> str:\n userstack_api_key = os.getenv('USERSTACK_KEY')\n if userstack_api_key is not None:\n return userstack_api_key\n print(\"Rquired Userstack API key not found in environment variables. Export key as USERSTACK_KEY\")\n sys.exit(os.EX_CONFIG)\n\nUSERSTACK_KEY = userstack_api_key()\nUSERSTACK_URL = \"http://api.userstack.com/detect\"\n\n# Optional IPAPI key added if present\ndef ip_api_suffix() -> str:\n ip_location_suffix = \"/json\"\n ip_api_key = os.getenv('IPAPI_KEY')\n if ip_api_key is not None:\n ip_location_suffix += \"?key=\" + ip_api_key\n return ip_location_suffix\n\nIP_API_BASE_URL = \"https://ipapi.co/\"\nIP_API_SUFFIX = ip_api_suffix()\n\n# Validate arguments and filepath\ndef argument_exists() -> bool:\n return len(sys.argv) > 1\n\ndef target_exists(path: str) -> bool:\n return os.path.exists(path)\n\ndef exit_no_input() -> None:\n print(\"Target not found at specified path. Ensure correct path to target is passed as argument. \\nExample usage: ./log_parser.py \")\n sys.exit(os.EX_NOINPUT)\n\ndef get_target_path() -> str:\n if argument_exists():\n if target_exists(sys.argv[1]):\n return sys.argv[1]\n exit_no_input()\n return \"\"\n\nTARGET_PATH = get_target_path()\n\n# Request location using IP\ndef get_location(url: str) -> {}:\n location = {}\n response = requests.get(url)\n if response.status_code == 200:\n json_response = response.json()\n country = json_response['country_name']\n region = json_response['region']\n location = {'country' : country, 'region': region}\n return location\n\n# Request device information using useragent\ndef get_device(useragent: str) -> {}:\n device_info = {}\n params = {\n 'access_key': USERSTACK_KEY,\n 'ua' : useragent\n }\n response = requests.get(USERSTACK_URL, params)\n if response.status_code == 200:\n json_response = response.json()\n device_type = json_response['device']['type']\n browser = json_response['browser']['name']\n device_info = {'type' : device_type, 'browser': browser}\n return device_info\n\ndef convert_log_to_csv() -> None:\n with open(TARGET_PATH, \"r\") as target, open(OUTPUT_PATH, \"w\", newline = \"\") as output:\n print(\"Looking up locations and devices for access log\")\n writer = csv.writer(output)\n writer.writerow(COLUMN_DESCRIPTIONS)\n\n for line in target:\n ip_addr = line.split(\" \")[0]\n print(ip_addr)\n user_agent = line.rsplit(\"\\\"\")[-2]\n\n ip_location_url = IP_API_BASE_URL + ip_addr + IP_API_SUFFIX\n location = get_location(ip_location_url)\n if location == {}:\n print(\"No location or bad response, skipping entry\")\n continue\n print(\"Location: \" + location['country'] + \" Region: \" + location['region'])\n\n device = get_device(user_agent)\n if device == {}:\n print(\"No device or bad response, skipping entry\")\n continue\n\n print(\"Device Type: \" + device['type'] + \" Browser: \" + device['browser'])\n\n writer.writerow([location['country'], location['region'], device['type'], device['browser']])\n\n print(\"Complete! CSV file written to location_device.csv\")\n\ndef main() -> None:\n convert_log_to_csv()\n sys.exit(os.EX_OK)\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"log_parser.py","file_name":"log_parser.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"461882452","text":"from __future__ import absolute_import\nimport argparse\nimport datetime\nimport textwrap\nimport logging\n\nimport boto3\n\n\nclass AwsSnapper(object):\n VERSION = '0.1'\n\n def __init__(self):\n self._loaded = False\n\n self.tag_prefix = None\n self.ec2_regions = list()\n self.sns_arn = None\n\n self.report = {\n 'started': datetime.datetime.now(),\n 'finished': None,\n 'instances_managed': 0,\n 'volumes_managed': 0,\n 'snaps_created': 0,\n 'snaps_deleted': 0,\n 'problem_volumes': list()\n }\n\n def _load_config(self):\n if self._loaded:\n return\n\n parser = argparse.ArgumentParser(description='Create and manage scheduled EBS snapshots')\n parser.add_argument('regions', metavar='region', nargs='*',\n help='EC2 Region(s) to process for snapshots',\n default=[None])\n parser.add_argument('--sns-arn', dest='sns_arn', action='store', default=None,\n help='SNS ARN for reporting results', metavar='ARN')\n parser.add_argument('--prefix', dest='tag_prefix', action='store', default='autosnap',\n help='Prefix to use for AWS tags on snapshots', metavar='PREFIX')\n parser.add_argument('--version', action='version',\n version='AwsSnapper v{}'.format(self.VERSION))\n settings = parser.parse_args()\n\n self.sns_arn = settings.sns_arn\n self.tag_prefix = settings.tag_prefix\n for region in settings.regions:\n self.ec2_regions.append(region)\n\n self._loaded = True\n\n def scan_and_snap(self, region):\n if not self._loaded:\n self._load_config()\n\n tag_interval = '{prefix}'.format(prefix=self.tag_prefix)\n tag_retain = '{prefix}_retain'.format(prefix=self.tag_prefix)\n tag_ignore = '{prefix}_ignore'.format(prefix=self.tag_prefix)\n today = datetime.date.today()\n\n if region is not None:\n ec2 = boto3.resource('ec2', region_name=region)\n else:\n ec2 = boto3.resource('ec2')\n\n instances = ec2.instances.all()\n for instance in instances:\n i_tags = instance.tags\n i_ignore = True\n i_snap_interval = 0\n i_snap_retain = 0\n i_name = instance.id\n for i_tag in i_tags:\n if i_tag['Key'] == tag_interval:\n i_ignore = False\n i_snap_interval = i_tag['Value']\n if i_tag['Key'] == tag_retain:\n i_snap_retain = i_tag['Value']\n if i_tag['Key'] == 'Name' and len(i_tag['Value']) > 2:\n i_name = '{}-({})'.format(i_tag['Value'], instance.id)\n i_name_only = '{}'.format(i_tag['Value'])\n if i_ignore:\n continue\n\n self.report['instances_managed'] += 1\n\n volumes = ec2.volumes.filter(Filters=[{'Name': 'attachment.instance-id',\n 'Values': [instance.id]}])\n for volume in volumes:\n v_snap_interval = i_snap_interval\n v_snap_retain = i_snap_retain\n v_tags = volume.tags\n v_ignore = False\n v_name = volume.id\n for v_tag in v_tags:\n if v_tag['Key'] == tag_ignore:\n v_ignore = True\n if v_tag['Key'] == tag_interval:\n v_snap_interval = v_tag['Value']\n if v_tag['Key'] == tag_retain:\n v_snap_retain = v_tag['Value']\n if v_tag['Key'] == 'Name':\n v_name = '{} ({})'.format(v_tag['Value'], volume.id)\n if v_ignore:\n continue\n\n if v_snap_interval == 0 or v_snap_retain == 0:\n self.report['problem_volumes'].append(volume.id)\n continue\n\n self.report['volumes_managed'] += 1\n\n snap_collection = ec2.snapshots.filter(Filters=[{'Name': 'volume-id',\n 'Values': [volume.id]},\n {'Name': 'tag:snapshot_tool',\n 'Values': [self.tag_prefix]}])\n snap_list = list(snap_collection)\n\n snap_needed = False\n if len(snap_list) == 0:\n snap_needed = True\n else:\n snap_list.sort(key=lambda s: s.start_time, reverse=True)\n interval = int(v_snap_interval)\n expected = snap_list[0].start_time.date() + datetime.timedelta(days=interval)\n if today >= expected:\n snap_needed = True\n\n if snap_needed:\n description = '{}: {} from {} of {}'.format(self.tag_prefix, v_name, today, i_name)\n short_description = '{}-{}-{}'.format(today, v_name, i_name_only)\n snapshot = volume.create_snapshot(Description=description)\n snapshot.create_tags(Tags=[{'Key': 'Name', 'Value': short_description},\n {'Key': 'snapshot_tool', 'Value': self.tag_prefix}])\n self.report['snaps_created'] += 1\n else:\n # too soon\n pass\n\n while len(snap_list) > int(v_snap_retain):\n snapshot_to_delete = snap_list.pop()\n snapshot_to_delete.delete()\n self.report['snaps_deleted'] += 1\n\n def generate_report(self):\n self.report['finished'] = datetime.datetime.now()\n\n report = textwrap.dedent(\"\"\"\\\n AWS Snapshot Report\n\n Run Started: {started}\n Run Finished: {finished}\n\n Snapshots created: {snaps_created}\n Snapshots deleted: {snaps_deleted}\n\n > Details:\n > Instances managed: {instances_managed}\n > Volumes managed: {volumes_managed}\n \"\"\".format(**self.report))\n\n if len(self.report['problem_volumes']) > 0:\n report += '> \\n> \\n> Volumes with tags that prevented snapshot management: \\n'\n for vol in self.report['problem_volumes']:\n report += '> * {}\\n'.format(vol)\n\n if self.sns_arn is not None:\n sns = boto3.resource('sns')\n topic = sns.Topic(self.sns_arn)\n topic.publish(Message=report, Subject='AWS Snapshot Report')\n logging.warn('Snapshot run completed at {}. Report sent via SNS.'.format(\n self.report['finished']))\n else:\n logging.warn(report)\n\n def daily_run(self):\n self._load_config()\n for region in self.ec2_regions:\n self.scan_and_snap(region)\n self.generate_report()\n\ndef main(event, context):\n snapper = AwsSnapper()\n snapper.daily_run()\n\nif __name__ == '__main__':\n main(0, 0)","sub_path":"aws-snapper.py","file_name":"aws-snapper.py","file_ext":"py","file_size_in_byte":7245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"112735508","text":"#\n# Monitor the gke pool\n# Reuse the python script name for ease of use\n#\n\nimport sys,time\nimport kubernetes\n\nkubernetes.config.load_incluster_config()\nk8s = kubernetes.client.CoreV1Api()\n\ndef get_nodes():\n cntn = 0 # number of nodes\n cntg = 0 # number of gpus\n nlist = k8s.list_node(label_selector=\"osgclass=gpu\")\n cntn = len(nlist.items)\n for i in range(cntn):\n try:\n cntg += int(nlist.items[i].status.capacity['nvidia.com/gpu'])\n except:\n pass # no gpu?\n return (cntn, cntg)\n\ndef get_pods():\n cntr = 0 # number of running\n cntp = 0 # number of pending\n plist = k8s.list_namespaced_pod(namespace=\"gke-icecube\",label_selector=\"prp-htcondor-portal=wn\")\n nels = len(plist.items)\n for i in range(nels):\n phase = plist.items[i].status.phase\n if phase == \"Pending\":\n cntp += 1\n elif phase==\"Running\":\n cntr += 1\n return (cntr, cntp)\n\nwhile True:\n try:\n (cntn, cntg) = get_nodes()\n (cntr, cntp) = get_pods()\n\n with open(\"/var/log/provisioner/logs/monitor/gke.log.\"+time.strftime(\"%Y%m%d\"),\"a\") as fd:\n fd.write(\"%s (%i) Nodes: %i (gpus: %i ) Pods running: %i pending: %i\\n\"%(time.ctime(), time.time(), cntn, cntg, cntr, cntp))\n except:\n print(\"Moonitoring excpetion!\")\n # sleep a bit\n time.sleep(60)\n\n","sub_path":"gke21-icecube/direct/provisioner-monitor/provisioner_main.py","file_name":"provisioner_main.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"2872945","text":"from rest_framework import serializers\n\nfrom .models import Category\n\nclass SubCategorySerializer(serializers.ModelSerializer):\n class Meta:\n model = Category\n fields = ('id', 'name')\n\n\nclass CreateCategorySerializer(serializers.ModelSerializer):\n parent = serializers.PrimaryKeyRelatedField(queryset=Category.objects.all(), required=False)\n\n def get_fields(self):\n fields = super(CreateCategorySerializer, self).get_fields()\n fields['children'] = CreateCategorySerializer(many=True, required=False)\n return fields\n\n\n def create(self, validated_data):\n children = validated_data.pop('children', [])\n top_cat = super().create(validated_data)\n for child in children:\n child['parent'] = top_cat\n CreateCategorySerializer().create(child)\n return top_cat\n\n\n class Meta:\n model = Category\n fields = ('name', 'parent', 'children')\n\n\nclass ReadCategorySerializer(serializers.ModelSerializer):\n parents = serializers.SerializerMethodField()\n children = serializers.SerializerMethodField()\n siblings = serializers.SerializerMethodField()\n \n def get_ancestors(self, parents, obj):\n if obj.parent:\n qs = Category.objects.filter(pk=obj.parent.pk)\n parents += qs\n if qs:\n res = self.get_parents(obj=qs[0])\n parents += res\n else:\n parents = [qs]\n return parents\n\n\n def get_parents(self, obj):\n parents = []\n ancestors = self.get_ancestors(parents, obj)\n\n serializer = SubCategorySerializer(parents, many=True)\n return serializer.data\n\n\n def get_children(self, obj):\n children = obj.children.all()\n serializer = SubCategorySerializer(children, many=True)\n return serializer.data\n\n\n def get_siblings(self, obj):\n if obj.parent:\n qs = obj.parent.children.exclude(pk=obj.pk)\n else:\n qs = Category.objects.none()\n serializer = SubCategorySerializer(qs, many=True)\n return serializer.data\n\n class Meta:\n model = Category\n fields = ('id', 'name', 'parents', 'children', 'siblings')","sub_path":"api/categories/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"597747374","text":"from time import time\nfrom src.Utils import Utils\nfrom src import Constants\nimport sqlite3\nimport os\n\n\nclass PersisterSqlite(object):\n conn = None\n insert_data_statement = None\n insert_recommendation_sql = None\n\n def __init__(self):\n #self.conn = sqlite3.connect(Constants.DB_DIR)\n self.conn = sqlite3.connect(Constants.DB_DIR)\n self.insert_data_statement = \"INSERT INTO sp500_time_series_data (symbol, company_name, trade_date, \" \\\n \"RSI_rsi, MACD_macd_signal, MACD_macd_histogram, MACD_macd,\" \\\n \"BB_real_upper_band, BB_real_middle_band, BB_real_lower_band, OBV_obv,open_price, low_price, high_price, close_price, volume) \" \\\n \"VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\"\n\n self.insert_recommendation_sql = \"INSERT INTO recommendation(symbol, model_name, recommendation)\" \\\n \"VALUES (?,?,?)\"\n\n def insert_recommendation(self, row_values):\n self.insert_row(self.insert_recommendation_sql, row_values)\n\n def insert_row(self, sql_stmt, row_values):\n try:\n db_cursor = self.conn.cursor()\n db_cursor.execute(sql_stmt, row_values)\n self.conn.commit()\n #self.conn.close()\n except sqlite3.Error as err:\n print(\"Something went wrong: {}\".format(err))\n self.conn.rollback()\n\n def insert(self, stmts):\n try:\n db_cursor = self.conn.cursor()\n for stmt in stmts:\n db_cursor.execute(stmt)\n self.conn.commit()\n self.conn.close()\n except sqlite3.Error as err:\n print(\"Something went wrong: {}\".format(err))\n self.conn.rollback()\n\n def insert_data(self, data, symbol_map):\n try:\n db_cursor = self.conn.cursor()\n for symbol in data:\n market_data = data[symbol]\n market_data = market_data.dropna()\n insert_data = []\n start_time = time()\n print(\"Persisting data for symbol: \" + symbol)\n for index, row in market_data.iterrows():\n date = index.date()\n split_factor = float(row['4. close']) / float(row['5. adjusted close'])\n insert_data.append([symbol, symbol_map[symbol], date, float(row['RSI']), float(row['MACD_Signal']), float(row['MACD_Hist']),\n float(row['MACD']), float(row['Real Upper Band']), float(row['Real Middle Band']),\n float(row['Real Lower Band']), float(row['OBV']), round(float(row['1. open']) / split_factor, 4),\n round(float(row['3. low']) / split_factor, 4), round(float(row['2. high']) / split_factor, 4), float(row['5. adjusted close']), int(row['6. volume'])])\n db_cursor.executemany(self.insert_data_statement, insert_data)\n self.conn.commit()\n end_time = time()\n print(\"Persisted data for symbol: \" + symbol + \" in ms: \" + str(end_time - start_time))\n self.conn.close()\n except sqlite3.Error as err:\n print(\"Something went wrong: {}\".format(err))\n self.conn.rollback()\n","sub_path":"PersisterSqlite.py","file_name":"PersisterSqlite.py","file_ext":"py","file_size_in_byte":3270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"260067548","text":"# -*- coding: utf-8 -*-\n\nimport sys\n\ndef creFile(rowCnt):\n\n f = open('testFile' + str(rowCnt), mode='w')\n\n for i in range(int(rowCnt)):\n f.write(str(i).rjust(20, '0') + '\\n')\n\n f.close()\n\nif __name__ == '__main__':\n \n creFile(sys.argv[1])\n\n","sub_path":"py/test1/creTestFile.py","file_name":"creTestFile.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"641445757","text":"# Import Packages\nimport os\nimport nlp\nimport torch \nfrom torch import nn\nimport logging\nimport datasets\nfrom dataclasses import dataclass, field\nfrom typing import Any, Union, Dict, List, Optional\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer\nfrom transformers import AutoModelForSeq2SeqLM\nfrom transformers import Trainer as HFTrainer\nfrom transformers import TrainingArguments\nfrom transformers import HfArgumentParser\n\n# Hyperparameters\ndevice = 'cuda' if torch.cuda.is_available else 'cpu'\nprint('* You are using device: ', device)\nlogger = logging.getLogger(__name__)\n\nmodel_checkpoint = \"facebook/bart-base\" # \"facebook/bart-base\"\nmodel_test_checkpoint = 'training_v1/'\n\nmax_source_length = 512\nmax_target_length = 32\npretrained_model = 'facebook/bart-base' # \"facebook/bart-base\"\nnum_epochs = 4\nevaluation_strategy = 'steps' # epoch/no/steps\neval_steps = 5000\nsave_steps = 5000\n\nprint('* Your hyperparameter settings:')\nprint('\\t PRETRAINED_MODEL:', pretrained_model)\nprint('\\t MAX_SOURCE_LENGTH:', max_source_length)\nprint('\\t MAX_TARGET_LENGTH:', max_target_length)\nprint('\\t NUM_EPOCHS:', num_epochs)\nprint('\\t EVALUATION_STRATEGY:', evaluation_strategy)\nprint('\\t SAVE_STEPS:', save_steps)\nprint('* Your hyperparameter settings:\\n')\nprint('\\tPRETRAINED_MODEL:', model_checkpoint)\nprint('\\tRESUME FROM: ', model_test_checkpoint)\n\n# Define Functions\ndef convert_to_features(example_batch):\n source_encoding = tokenizer.batch_encode_plus(\n example_batch['source_text'],\n max_length=max_source_length,\n padding='max_length',\n pad_to_max_length=True,\n truncation=True, \n )\n target_encoding = tokenizer.batch_encode_plus(\n example_batch['target_text'],\n max_length=max_target_length,\n padding='max_length',\n pad_to_max_length=True,\n truncation=True, \n )\n encodings = {\n 'source_ids': source_encoding['input_ids'], \n 'target_ids': target_encoding['input_ids'],\n 'attention_mask': source_encoding['attention_mask'],\n }\n\n return encodings\n\n\ndef get_correct_alignement(context, answer): \n gold_text = answer['text'][0]\n start_idx = answer['answer_start'][0]\n end_idx = start_idx + len(gold_text)\n if context[start_idx:end_idx] == gold_text:\n return start_idx, end_idx \n elif context[start_idx-1:end_idx-1] == gold_text:\n return start_idx-1, end_idx-1 \n elif context[start_idx-2:end_idx-2] == gold_text:\n return start_idx-2, end_idx-2 \n else:\n raise ValueError()\n \n \ndef process_qg_text(example):\n context = example['context']\n question = example['question']\n answer = example['answers']\n answer_text = answer['text'][0]\n start_pos, end_pos = get_correct_alignement(context, answer)\n qg_input = f\"{context[:start_pos]} {hl} {answer_text} {hl} {context[end_pos:]}\"\n qg_target = f\"{question}\"\n qg_input = qg_input + \" \"\n qg_target = qg_target + \" \"\n return {\"source_text\": qg_input, \"target_text\": qg_target}\n\n\ndef trim_batch(input_ids, pad_token_id, attention_mask=None,):\n \"\"\"Remove columns that are populated exclusively by pad_token_id\"\"\"\n keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)\n if attention_mask is None:\n return input_ids[:, keep_column_mask]\n else:\n return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])\n\ndef get_predictions(model, tokenizer, data_loader, num_beams=4, max_length=32, length_penalty=1):\n model.to(device)\n \n predictions = []\n model.eval()\n with torch.no_grad():\n for batch in tqdm(data_loader):\n outs = model.generate(\n input_ids=batch['input_ids'].to(device), \n attention_mask=batch['attention_mask'].to(device),\n num_beams=num_beams,\n max_length=max_length,\n length_penalty=length_penalty,\n )\n prediction = [tokenizer.decode(ids, skip_special_tokens=True) for ids in outs]\n predictions.extend(prediction)\n\n return predictions\n\n\nclass T2TDataCollator():\n def __init__(self, tokenizer, model_type='bart', mode='training'):\n self.tokenizer = tokenizer\n self.model_type = model_type\n self.mode = mode\n\n def __call__(self, batch: List) -> Dict[str, torch.Tensor]:\n\n input_ids = torch.stack([example['source_ids'] for example in batch])\n target_ids = torch.stack([example['target_ids'] for example in batch])\n attention_mask = torch.stack([example['attention_mask'] for example in batch])\n\n pad_token_id = self.tokenizer.pad_token_id\n \n input_ids, attention_mask = trim_batch(input_ids, pad_token_id, attention_mask=attention_mask)\n target_ids = trim_batch(target_ids, pad_token_id)\n \n if self.model_type == 'bart':\n decoder_input_ids = target_ids[:, :-1].contiguous()\n lm_labels = target_ids[:, 1:].clone()\n if self.mode == 'training':\n lm_labels[target_ids[:, 1:] == pad_token_id] = -100 \n else: # self.model_type == 't5'\n print('You are using model_type: t5.')\n lm_labels = target_ids.clone()\n decoder_input_ids = self._shift_right(lm_labels)\n if self.mode == 'training':\n lm_labels[lm_labels[:, :] == pad_token_id] = -100\n \n params = {\n \"input_ids\": input_ids, \n \"attention_mask\": attention_mask,\n \"labels\": lm_labels,\n \"decoder_input_ids\": decoder_input_ids\n }\n \n return params\n \n def _shift_right(self, input_ids):\n decoder_start_token_id = self.tokenizer.pad_token_id\n pad_token_id = self.tokenizer.pad_token_id\n\n assert (\n decoder_start_token_id is not None\n ), \"self.model.config.decoder_start_token_id has to be defined.\"\n\n # shift the inputs to the right \n shifted_input_ids = input_ids.new_zeros(input_ids.shape)\n shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()\n shifted_input_ids[..., 0] = decoder_start_token_id\n\n assert pad_token_id is not None, \"self.model.config.pad_token_id has to be defined.\"\n \n shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)\n\n assert torch.all(shifted_input_ids >= 0).item(), \"Verify that `labels` has only positive values and -100\"\n\n return shifted_input_ids\n\n\n# Load Model\nprint('* Load Model...')\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)\nprint('\\t Model structure: ', model)\n\ntokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\ntokenizer.add_tokens('','')\nprint('\\t Max length of tokenizer: ', tokenizer.model_max_length)\nhl = \"\"\nsep = \"\"\n\n# Load Valid Dataset\nprint('* Loading Datasets...')\n#train_dataset = nlp.load_dataset('squad', split=nlp.Split.TRAIN)\nvalid_dataset = nlp.load_dataset('squad', split=nlp.Split.VALIDATION)\n\nprint('* Preparing Datasets...')\nhl = \"\"\n\n# extract useful inforamtionj\n#trainset = train_data.map(process_qg_text)\nvalidset = valid_dataset.map(process_qg_text)\n#print('* The amount of train dataset:', len(trainset))\nprint('* The amount of valid dataset:', len(validset))\n\n\nprint('* Tokenizing data...')\n# tokenize data\n#train_features = trainset.map(convert_to_features, batched=True)\nvalid_features = validset.map(convert_to_features, batched=True)\n\ncolumns = [\"source_ids\", \"target_ids\", \"attention_mask\"]\n#train_features.set_format(type='torch', columns=columns)\nvalid_features.set_format(type='torch', columns=columns)\n\n#torch.save(train_features, 'data/train_v1.pt')\n#logger.info(f\"saved train dataset at data/train_v1.pt\")\n#print(\"saved train dataset at data/train_v1.pt\")\n\n#torch.save(valid_features, 'data/valid_v1.pt')\n#logger.info(f\"saved validation dataset at data/valid_v1.pt\")\n#print(\"saved validation dataset at data/valid_v1.pt\")\n\n\ndata_collator = T2TDataCollator(\n tokenizer=tokenizer,\n model_type='bart',\n mode=\"inference\"\n)\nprint('* You are using model type: ', data_collator.model_type)\n\n# Inference Model: use valid dataset to inference\nloader = torch.utils.data.DataLoader(valid_features, batch_size=32, collate_fn=data_collator)\nmodel_1 = AutoModelForSeq2SeqLM.from_pretrained(model_test_checkpoint)\npredictions = get_predictions(\n model=model_1,\n tokenizer=tokenizer,\n data_loader=loader,\n num_beams=4,\n max_length=32\n)\nwith open('inference_v1/inference_2.txt', 'w') as f:\n f.write(\"\\n\".join(predictions))\n \nwith open('inference_v1/ground_truth_2.txt', 'w') as f:\n f.write(\"\\n\".join(valid_features['question']))\n \n# with open('inference/answers.txt', 'w') as f:\n# f.write(\"\\n\".join(valid_features['answers']['text']))\n\n# Inference\n# !pip install git+https://github.com/Maluuba/nlg-eval.git@master\nfrom nlgeval import compute_metrics, compute_individual_metrics\nprint('testing on checkpoint:', model_test_checkpoint)\nmetrics_dict = compute_metrics(hypothesis='inference_v1/inference_2.txt', references=['inference_v1/ground_truth_2.txt'])\nprint('* Process Finished!')","sub_path":"bart/test_bart_v1.py","file_name":"test_bart_v1.py","file_ext":"py","file_size_in_byte":9164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"463536403","text":"\"\"\"\r\nAssignment #10\r\nJun Seob Shim\r\n8/12/2020\r\nIntro to Programming Section 012\r\nThesaurus\r\n\"\"\"\r\n#import random module\r\nimport random\r\n\r\n#define thesaurus\r\n#start counter for how many words there are\r\nwordcounter = 0\r\n#open file for reading\r\nthesaurus_file = open(\"python_asg10_Roget_Thesaurus.txt\",\"r\")\r\n\r\n\"\"\"\r\nthe thesaurus file is messed up somehow and a lot of the synonyms are just wrong (eg. synonyms for \"happy\" are agreement,\r\ncheerfulness,elegance,occasion,pleasure)which obviously doesn't make any sense. should a identically named, valid thesaurus\r\nfile be supplied, the program should work as intended\r\n\"\"\"\r\n\r\n#create empty dictionary to add to\r\nthesaurus = {}\r\n\r\n#reading and processing data from file\r\nalldata = thesaurus_file.read()\r\n\r\n#split into list by lines\r\ngiantlist = alldata.split(\"\\n\")\r\n\r\n#for each word and its synonyms\r\nfor i in range(len(giantlist)):\r\n #split each line into a list by words\r\n wordlist = giantlist[i].split(\",\")\r\n #count number of words in thesaurus\r\n wordcounter += len(wordlist)\r\n\r\n #conditions based on how many synonyms\r\n if len(wordlist) > 2:\r\n synonyms = wordlist[1:len(wordlist)-1]\r\n elif len(wordlist) == 2:\r\n synonyms = [wordlist[1]]\r\n else:\r\n synonyms = []\r\n\r\n #add to dictionary (thesaurus)\r\n thesaurus[wordlist[0]] = synonyms\r\n\r\n#close file\r\nthesaurus_file.close()\r\n\r\n#print prelim data\r\nprint(\"Total words in thesaurus: \",wordcounter)\r\nprint()\r\n\r\n#ask user for input chance\r\nchance = float(input(\"Enter a % chance to change a word: \"))\r\n\r\n#open lyrics file\r\nbaby_file = open(\"bieber_baby.txt\",\"r\")\r\n\r\n#take out into long string, and close\r\nphrase = baby_file.read()\r\nbaby_file.close()\r\n\r\n#set up new phrase to add to (remove punctuation)\r\nnewphrase = \"\"\r\n\r\n#iterate over supplied phrase, and add to empty string\r\nfor i in phrase:\r\n if i.isalpha() == True or i.isspace() == True:\r\n newphrase += i.lower()\r\n\r\n#split new string into a list (by words)\r\nphraselist = newphrase.split(\" \")\r\n\r\n#create new empty string to put final result in\r\napplied = \"\"\r\n\r\n#iterate over thesaurus\r\nfor key, value in thesaurus.items():\r\n #if the key is in the list of words\r\n if (key in phraselist) == True:\r\n #iterate over the words in the list\r\n for i in range(len(phraselist)):\r\n #if the element equals a key\r\n percentage = random.random()\r\n if phraselist[i] == key and percentage < chance:\r\n #pick a random value and replace\r\n if len(value)== 0:\r\n continue\r\n else:\r\n r = random.randint(0,len(value)-1)\r\n phraselist[i] = value[r-1].upper()\r\n\r\n#add the list back to the empty string with spaces\r\nfor i in range(len(phraselist)):\r\n applied += phraselist[i] + \" \"\r\n\r\n#print final transformed string\r\nprint(applied)\r\n","sub_path":"Intro to Programming/Assignments/Assignment 10/ShimJun Seob_assign10.py","file_name":"ShimJun Seob_assign10.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"373376674","text":"import pdb\nimport re\nimport logging\ntry:\n from encoding import encodingConstants\n from ... import constants\nexcept:\n from encoding import encodingConstants\n import constants\n\n\nfrom lark import Lark, Transformer\nsymmetric_operators = [encodingConstants.LAND, encodingConstants.LOR]\nbinary_operators = [encodingConstants.LAND, encodingConstants.LOR, encodingConstants.UNTIL,encodingConstants.IMPLIES, encodingConstants.BEFORE, encodingConstants.STRICTLY_BEFORE]\nunary_operators = [\"X\", encodingConstants.F, encodingConstants.G, encodingConstants.LNOT, encodingConstants.ENDS]\nclass SimpleTree:\n def __init__(self, label = \"dummy\"):\n self.left = None\n self.right = None\n self.label = label\n \n def __hash__(self):\n return hash((self.label, self.left, self.right))\n \n def __eq__(self, other):\n if other == None:\n return False\n else:\n return self.label == other.label and self.left == other.left and self.right == other.right\n \n def __ne__(self, other):\n return not self == other\n \n def _isLeaf(self):\n return self.right == None and self.left == None\n \n def _addLeftChild(self, child):\n if child == None:\n return\n if type(child) is str:\n child = SimpleTree(child)\n self.left = child\n \n def _addRightChild(self, child):\n if type(child) is str:\n child = SimpleTree(child)\n self.right = child\n \n def addChildren(self, leftChild = None, rightChild = None): \n self._addLeftChild(leftChild)\n self._addRightChild(rightChild)\n \n \n def addChild(self, child):\n self._addLeftChild(child)\n \n def getAllNodes(self):\n leftNodes = []\n rightNodes = []\n \n if self.left != None:\n leftNodes = self.left.getAllNodes()\n if self.right != None:\n rightNodes = self.right.getAllNodes()\n return [self] + leftNodes + rightNodes\n\n def getAllOperators(self):\n leftOperators = []\n rightOperators = []\n if self.left is None and self.right is None:\n return []\n if not self.left is None:\n leftOperators = self.left.getAllOperators()\n if not self.right is None:\n rightOperators = self.right.getAllOperators()\n return [self.label] + leftOperators + rightOperators\n\n\n def getAllLabels(self):\n if self.left != None:\n leftLabels = self.left.getAllLabels()\n else:\n leftLabels = []\n \n if self.right != None:\n rightLabels = self.right.getAllLabels()\n else:\n rightLabels = []\n return [self.label] + leftLabels + rightLabels\n\n def __repr__(self):\n if self.left == None and self.right == None:\n return self.label\n \n # the (not enforced assumption) is that if a node has only one child, that is the left one\n elif self.left != None and self.right == None:\n return self.label + '(' + self.left.__repr__() + ')'\n \n elif self.left != None and self.right != None:\n return self.label + '(' + self.left.__repr__() + ',' + self.right.__repr__() + ')'\n\n\nclass Formula(SimpleTree):\n \n def __init__(self, formulaArg = \"dummyF\"):\n \n if not isinstance(formulaArg, str):\n self.label = formulaArg[0]\n self.left = formulaArg[1]\n try:\n self.right = formulaArg[2]\n except:\n self.right = None\n else:\n super().__init__(formulaArg)\n\n def __lt__(self, other):\n\n if self.getDepth() < other.getDepth():\n return True\n elif self.getDepth() > other.getDepth():\n return False\n else:\n if self.getNumberOfSubformulas() < other.getNumberOfSubformulas():\n return True\n elif self.getNumberOfSubformulas() > other.getNumberOfSubformulas():\n return False\n if self._isLeaf() and other._isLeaf():\n return self.label < other.label\n\n if self.right is None:\n if other.right is None:\n return self.left < other.left\n else:\n return True\n\n if not self.right is None:\n if other.right is None:\n return False\n\n return self.label < other.label\n\n \"\"\"\n normalization is an incomplete method to eliminate equivalent formulas\n \"\"\"\n\n @classmethod\n def normalize(cls, f):\n\n if f is None:\n return None\n if f._isLeaf():\n return Formula([f.label, f.left, f.right])\n fLeft = Formula.normalize(f.left)\n fRight = Formula.normalize(f.right)\n\n\n if fLeft.label == \"true\":\n if f.label in [encodingConstants.LOR, encodingConstants.F, encodingConstants.G, encodingConstants.X]:\n return Formula(\"true\")\n if f.label in [encodingConstants.LAND, encodingConstants.IMPLIES]:\n return Formula.normalize(fRight)\n if f.label == encodingConstants.LNOT:\n return Formula(\"false\")\n if f.label == encodingConstants.UNTIL:\n return Formula.normalize(Formula([encodingConstants.F, fRight, None]))\n\n if fLeft.label == \"false\":\n if f.label in [encodingConstants.IMPLIES, encodingConstants.LNOT]:\n return Formula[\"true\"]\n if f.label in [encodingConstants.LAND, encodingConstants.F, encodingConstants.G, encodingConstants.X]:\n return Formula[\"false\"]\n if f.label in [encodingConstants.LOR, encodingConstants.UNTIL]:\n return Formula.normalize(fRight)\n\n if not fRight is None:\n if fRight.label == \"true\":\n if f.label in [encodingConstants.LOR, encodingConstants.IMPLIES, encodingConstants.UNTIL]:\n return Formula(\"true\")\n if f.label in [encodingConstants.LAND]:\n return Formula.normalize(fLeft)\n\n if fRight.label == \"false\":\n if f.label in []:\n return Formula[\"true\"]\n if f.label in [encodingConstants.LAND, encodingConstants.UNTIL]:\n return Formula[\"false\"]\n if f.label in [encodingConstants.LOR]:\n return Formula.normalize(fLeft)\n if f.label in [encodingConstants.IMPLIES]:\n return Formula.normalize(Formula([encodingConstants.LNOT, fRight, None]))\n\n # elimiting p&p and similar\n if fLeft == fRight:\n if f.label in [encodingConstants.LAND, encodingConstants.UNTIL, encodingConstants.LOR]:\n return Formula.normalize(fLeft)\n elif f.label in [encodingConstants.BEFORE]:\n return Formula([encodingConstants.BEFORE, fLeft, Formula(\"true\")])\n elif f.label in [encodingConstants.IMPLIES]:\n return Formula(\"true\")\n\n # eliminating Fp U p and !p U p\n if f.label == encodingConstants.UNTIL:\n if fLeft.label == encodingConstants.F or fLeft.label == encodingConstants.LNOT:\n fLeftLeft = Formula.normalize(fLeft.left)\n if fLeftLeft == fRight:\n return Formula.normalize(Formula([encodingConstants.F, fLeftLeft]))\n if fRight.label == encodingConstants.F:\n fRightLeft = Formula.normalize(fRight.left)\n if fRightLeft == fLeft:\n return fRight\n\n if f.label == encodingConstants.F and fLeft.label == encodingConstants.F:\n return fLeft\n\n # if there is p | q, don't add q | p\n if f.label in symmetric_operators and not fLeft < fRight:\n return Formula([f.label, fRight, fLeft])\n\n return Formula([f.label, fLeft, fRight])\n\n\n @classmethod\n def convertTextToFormula(cls, formulaText):\n\n f = Formula()\n try:\n formula_parser = Lark(r\"\"\"\n ?formula: _binary_expression\n |_unary_expression\n | constant\n | variable\n !constant: \"true\"\n | \"false\"\n _binary_expression: binary_operator \"(\" formula \",\" formula \")\"\n _unary_expression: unary_operator \"(\" formula \")\" \n !variable: NAME\n !binary_operator: \"and\" | \"or\" | \"->\" | \"until\" | \"B\" | \"before\"\n !unary_operator: \"eventually\" | \"G\" | \"neg\" | \"X\" | \"E\" \n %import common.SIGNED_NUMBER\n %import common.WS \n %import common.ESCAPED_STRING\n %import common.CNAME -> NAME\n %ignore WS \n \"\"\", start = 'formula')\n \n \n tree = formula_parser.parse(formulaText)\n #logging.debug(tree.pretty())\n \n except Exception as e:\n logging.error(\"can't parse formula %s\" %formulaText)\n logging.error(\"error: %s\" %e)\n \n \n f = TreeToFormula().transform(tree)\n return f\n\n # used for compatibility with DSLTL grammar\n def reFormat(self):\n lb = \"{\"\n rb = \"}\"\n if self._isLeaf():\n label_array = self.label.split(\"_\")\n if len(label_array) >= 3 and label_array[-3] == \"at\":\n # this is because of the weird thing that numbers have to be separated by comma\n formatted_label = \" \".join(label_array[:-1])\n formatted_label = formatted_label + \", \"+label_array[-1]\n return formatted_label\n if len(label_array) == 2 and label_array[0] == \"at\":\n formatted_label = \" \".join(label_array)\n return formatted_label\n else:\n return self.label\n else:\n if self.label in unary_operators:\n return lb + self.label + \" \" + self.left.reFormat() + rb\n\n if self.label in binary_operators:\n return lb + self.left.reFormat() + \" \" + self.label + \" \" + self.right.reFormat() + rb\n\n\n def prettyPrint(self, top=False):\n if top is True:\n lb = \"\"\n rb = \"\"\n else:\n lb = \"(\"\n rb = \")\"\n if self._isLeaf():\n return self.label\n if self.label in unary_operators:\n return lb + self.label +\" \"+ self.left.prettyPrint() + rb\n\n if self.label in binary_operators:\n return lb + self.left.prettyPrint() +\" \"+ self.label +\" \"+ self.right.prettyPrint() + rb\n\n \n \n def getAllVariables(self):\n allNodes = list(set(self.getAllNodes()))\n return [ node for node in allNodes if node._isLeaf() == True ]\n def getDepth(self):\n if self.left == None and self.right == None:\n return 0\n leftValue = -1\n rightValue = -1\n if self.left != None:\n leftValue = self.left.getDepth()\n if self.right != None:\n rightValue = self.right.getDepth()\n return 1 + max(leftValue, rightValue)\n \n def getNumberOfSubformulas(self):\n return len(self.getSetOfSubformulas())\n \n def getSetOfSubformulas(self):\n if self.left == None and self.right == None:\n return [self]\n leftValue = []\n rightValue = []\n if self.left != None:\n leftValue = self.left.getSetOfSubformulas()\n if self.right != None:\n rightValue = self.right.getSetOfSubformulas()\n return list(set([self] + leftValue + rightValue))\n\n \n\nclass TreeToFormula(Transformer):\n def formula(self, formulaArgs):\n return Formula(formulaArgs)\n def variable(self, varName):\n\n\n\n varStr = str(varName[0])\n varDesc = varStr.split(\"_\")\n\n # if the variables are not as expected\n\n if varDesc[0] == constants.PICK and not (varDesc[2] in constants.COLORS and varDesc[3] in constants.SHAPES):\n if varDesc[2] in constants.SHAPES or varDesc[3] in constants.COLORS:\n swap = varDesc[2]\n varDesc[2] = varDesc[3]\n varDesc[3] = swap\n\n if varDesc[2] in constants.SHAPES and varDesc[3] in constants.SHAPES:\n varDesc[2] = \"x\"\n if varDesc[2] in constants.COLORS and varDesc[3] in constants.COLORS:\n varDesc[3] = \"x\"\n varStr = \"_\".join(varDesc)\n\n return Formula([varStr, None, None])\n def constant(self, arg):\n if str(arg[0]) == \"true\":\n connector = encodingConstants.LOR\n elif str(arg[0]) == \"false\":\n connector = encodingConstants.LAND\n return Formula([connector, Formula([\"x0\", None, None]), Formula([encodingConstants.LNOT, Formula([\"x0\", None, None] ), None])])\n \n def binary_operator(self, args):\n return str(args[0])\n def unary_operator(self, args):\n return str(args[0])\n \n \n \n ","sub_path":"encoding/utils/SimpleTree.py","file_name":"SimpleTree.py","file_ext":"py","file_size_in_byte":13302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"290915370","text":"import pandas as pd\n\n\ndef find_line_index(table_title,data_list, start=0):\n for index, line in enumerate(data_list[start:]):\n if table_title in line:\n break\n return index + start\n\ndef filter_lines(line_str, data_list, start=0):\n for index, line in reversed(list(enumerate(data_list))):\n if index == start:\n break\n if line_str in line:\n del data_list[index]\n return data_list\n\ndef convert_to_rows(l,alpha_pr_col):\n number_columns = 10\n\n tags = ['f', 'F', 'h','H','k','K','n','N','R','w','s','S','t','T','r']\n\n l = l.replace('r',' ')\n for i in tags:\n l = l.replace(i, '')\n\n split_data = l.split(' ')\n\n for index, elem in reversed(list(enumerate(split_data))):\n if elem == '':\n del split_data[index]\n if elem == '-':\n split_data[index] = -1.0\n else:\n split_data[index] = float(split_data[index])\n return split_data\n\ndef take_table(txt_table):\n iter = []\n objective = []\n inf_pr = []\n inf_du = []\n lg_mu = []\n abs_d = []\n lg_rg = []\n alpha_du = []\n alpha_pr = []\n ls = []\n\n alpha_pr_ind = 8\n\n for index, r in enumerate(txt_table):\n data_row = convert_to_rows(r, alpha_pr_ind)\n if data_row:\n cols= data_row\n iter.append(cols[0])\n objective.append(cols[1])\n inf_pr.append(cols[2])\n inf_du.append(cols[3])\n lg_mu.append(cols[4])\n abs_d.append(cols[5])\n lg_rg.append(cols[6])\n alpha_du.append(cols[7])\n alpha_pr.append(cols[8])\n ls.append(cols[9])\n\n table_data = {'iter': iter, 'objective': objective,\n 'inf_pr': inf_pr, 'inf_du': inf_du,\n 'lg_mu': lg_mu, 'abs_d': abs_d,\n 'lg_rg': lg_rg, 'alpha_du': alpha_du,\n 'alpha_pr': alpha_pr, 'ls': ls}\n return table_data\n\ndef parse_IPOPT_log(file, output_file):\n content = []\n table_title = 'iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls'\n table_end = 'Number of Iterations....:'\n slack_line = 'Slack too small'\n with open(file) as f:\n content = f.readlines()\n content = [x.strip() for x in content]\n\n table_start = find_line_index(table_title, content)\n content = filter_lines(table_title,content, start=table_start+1)\n content = filter_lines(slack_line, content)\n table_data = content[table_start+1:table_end]\n table = take_table(table_data)\n\n df = pd.DataFrame(table).set_index('iter')\n\n df.to_pickle(output_file)\n return df","sub_path":"pythonProject/Parsing/IPOPT_Parse.py","file_name":"IPOPT_Parse.py","file_ext":"py","file_size_in_byte":2646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"336616541","text":"#!/usr/bin/python3\n\nimport re\nimport sys\n\ntmp = {}\n\nfor line in sys.stdin:\n line = line.strip()\n\n words = re.split(\"[ *$&#/\\t\\n\\f\\\"\\'\\\\,.:;?!\\[\\](){}<>~\\-_]\", line.lower())\n\n for word in words:\n if len(word):\n tmp[word] = tmp.get(word, 0) + 1\n\nfor k, v in tmp.items():\n print (k + '\\t' + str(v))\n\n","sub_path":"lab02/p1/mapper2.py","file_name":"mapper2.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"251055646","text":"import os\r\nimport re\r\nimport time\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n\r\nclass Samples:\r\n\tbase = \"/shares_bgfs/margres_lab/Devils/BEE_Probe_Data\"\r\n\tbatch_ids = [f\"{base}/Capture1_6-11-21/rename_key.csv\", f\"{base}/Capture2_7-29-21/rename_key.csv\", f\"{base}/Capture3/rename_key.csv\", f\"{base}/Capture4/rename_key.csv\", f\"{base}/Capture5/rename_key.csv\"]\r\n\tprelim = batch_ids[:2]\r\n\r\n\r\n\tdef __init__(self,\r\n\t\tsample_csv_path=\"/work_bgfs/d/dgallinson/data/pheno_data/master_corrections.csv\",\r\n\t\tid_paths=[]\r\n\t\t):\r\n\t\tfull_df = pd.read_csv(sample_csv_path)\r\n\t\tif id_paths:\r\n\t\t\tids = self.__L_ID(id_paths)\r\n\t\t\tself.sample_df = full_df[full_df[\"Library number\"].isin(ids)].reset_index(drop=True)\r\n\t\telse:\r\n\t\t\tself.sample_df = full_df\r\n\t\tself.factor_key = {\"pheno\": [], \"key\": [], \"val\": []}\r\n\r\n\r\n\tdef __str__(self):\r\n\t\treturn repr(self.sample_df)\r\n\r\n\r\n\tdef chip_sort(self, df, num_only=False, reset_index=True):\r\n\t\tif num_only:\r\n\t\t\tsort_df = df.sort_values(by=\"Microchip\", key=lambda x: x.str[-6:])\r\n\t\telse:\r\n\t\t\tsort_df = df.sort_values(by=\"Microchip\")\r\n\t\tif reset_index:\r\n\t\t\tsort_df.reset_index(drop=True, inplace=True)\r\n\t\treturn sort_df\r\n\r\n\r\n\tdef compare_to(self, df_path, cols, sep=\",\", id_col=\"Microchip\", inplace=True):\r\n\t\tcompare_df = pd.read_csv(df_path, sep=sep)\r\n\t\tcompare_df = compare_df[[id_col] + cols]\r\n\t\tcompare_df.rename(columns={id_col: \"Microchip\"}, inplace=True)\r\n\t\tcombined = self.sample_df.merge(compare_df, how=\"left\", on=\"Microchip\")\r\n\t\tif inplace:\r\n\t\t\tself.sample_df = combined\r\n\t\telse:\r\n\t\t\treturn combined\r\n\r\n\r\n\tdef count(self, col, pattern):\r\n\t\treturn len(self.sample_df[self.sample_df[col] == pattern])\r\n\r\n\r\n\tdef count_groups(self, col, sorted=True):\r\n\t\tgroups = self.sample_df.groupby(col)[col].count()\r\n\t\treturn groups.sort_index()\r\n\r\n\r\n\tdef count_pairs(self, pair_N):\r\n\t\tpairs = self.get_pairs()\r\n\t\treturn len(pairs[pairs == pair_N])\r\n\r\n\r\n\tdef extract_year(self, col, replace=True):\r\n\t\tdates = self.sample_df[col]\r\n\t\tyears = dates[~dates.isna()].astype(str)\r\n\t\tif (years.str.find(\".\") != -1).any():\r\n\t\t\tyears = years.str[:4]\r\n\t\telse:\r\n\t\t\tyears = years.str[-4:]\r\n\t\tnans = dates[dates.isna()]\r\n\t\tyears = pd.concat([years, nans]).sort_index()\r\n\t\tif not replace:\r\n\t\t\treturn years\r\n\t\tself.sample_df[col] = years\r\n\r\n\r\n\tdef factor_file(self, path, cols=[], silent=False):\r\n\t\tself.update_factors(cols)\r\n\t\tfactor_df = pd.DataFrame(self.factor_key)\r\n\t\tif not cols:\r\n\t\t\tsubset = factor_df\r\n\t\telse:\r\n\t\t\tsubset = factor_df[factor_df[\"pheno\"].isin(cols)]\r\n\t\tif subset.empty:\r\n\t\t\tif not silent:\r\n\t\t\t\tprint(\"No factors to print\")\r\n\t\t\treturn None\r\n\t\tprint(f\"Writing factor file to: {path}\")\r\n\t\tsubset.to_csv(path, index=False, sep=\"\\t\")\r\n\r\n\r\n\tdef fast_stats(self):\r\n\t\tprint(f\"Samples: {self.sample_df.shape[0]}\")\r\n\t\tprint(f\"Hosts: {self.sample_df[self.sample_df['Tissue'] == 'Host'].shape[0]}\")\r\n\t\tprint(f\"Tumors: {self.sample_df[self.sample_df['Tissue'] == 'Tumour'].shape[0]}\")\r\n\t\tprint(f\"Missing microchips: {self.nan_sum('Microchip', print_flag=False)}\")\r\n\t\tprint(f\"Singles: {self.count_pairs(1)}\")\r\n\t\tprint(f\"Pairs: {self.count_pairs(2)}\")\r\n\t\tprint(f\"Triplets: {self.count_pairs(3)}\")\r\n\r\n\r\n\t# col can be a string or list of strings for clustering on multiple columns\r\n\tdef get_groups(self, col):\r\n\t\treturn self.sample_df.groupby(col)\r\n\r\n\r\n\tdef get_pairs(self, pair_count=None):\r\n\t\tpairs = self.sample_df.groupby(\"Microchip\")[\"Microchip\"].count()\r\n\t\tif pair_count:\r\n\t\t\tpairs = pairs[pairs == pair_count]\r\n\t\treturn pairs\r\n\r\n\r\n\tdef nan_rows(self, col, tissue=\"both\"):\r\n\t\tif tissue == \"both\":\r\n\t\t\tsubset = self.sample_df\r\n\t\telse:\r\n\t\t\tsubset = self.sample_df[self.sample_df[\"Tissue\"] == tissue]\r\n\t\treturn subset[subset[col].isna()]\r\n\r\n\r\n\tdef nan_sum(self, col, tissue=\"both\", print_flag=True):\r\n\t\tif tissue == \"both\":\r\n\t\t\tsubset = self.sample_df\r\n\t\telse:\r\n\t\t\tsubset = self.sample_df[self.sample_df[\"Tissue\"] == tissue]\r\n\t\ttotal = len(subset)\r\n\t\tnans = subset[col].isna().sum()\r\n\t\tif print_flag and isinstance(nans, np.integer):\r\n\t\t\tprint(f\"{col}: {nans}/{total} ({nans/total*100:.2f}%) NaNs\")\r\n\t\telse:\r\n\t\t\treturn nans\r\n\r\n\r\n\t# Definition: \r\n\t# \t\t\t Functions to normalize and transform data with sample_df. These can be strung together to transform and then normalize data, such as\r\n\t# \t\t\t the log normal transform: samples.normalize(\"my_col\", method=\"log\"); samples.normalize(\"my_col\"). If the original data within a col\r\n\t# \t\t\t wish to be maintained, the new_col argument should be used, with subsequent normalizations/transformations targetting that new col.\r\n\t# \t\t\t Normalized cols are named as new cols based on the ordering of \"cols\" and \"new_cols\", e.g: cols=[\"col1\", \"col2\", \"col3\"] and\r\n\t# \t\t\t new_cols=[\"col1_log\", \"col2_norm\", \"col3_lognorm\"]\r\n\t# Arguments:\r\n\t# \t\t\t cols: \tthe columns to be normalized \r\n\t# \t\t\t method: \tmethod of normalization/transformation \r\n\t# \t\t\t inplace: \twhether or not to return data or change the column in place \r\n\t# \t\t\t new_cols: put the normalized/transformed data into new columns \r\n\t# Returns:\r\n\t# \t\t\t The normalized/transformed column(s) or None if inplace or new_cols is specified\r\n\t# Steps:\r\n\t# \t\t\t Depends on the chosen method\r\n\t# Gotchas:\r\n\t# \t\t\t inplace versus new_cols: if both are specified, new_cols will override inplace=True\r\n\t# TODO:\r\n\t# \t\t\t Test all methods by hand to ensure the norms/transforms are working.\r\n\t# \t\t\t Consider an \"undo\" method (maintaining raw values with new_cols may be sufficient)\r\n\tdef normalize(self, cols, method=\"standard\", inplace=True, new_cols=[]):\r\n\t\tmethods = [\"inv_logit\", \"log\", \"logit\", \"standard\"]\r\n\t\tcols = cols if isinstance(cols, list) else [cols]\r\n\t\tdf_cols = self.sample_df[cols]\r\n\t\tif method == \"standard\":\r\n\t\t\tmeans = df_cols.mean()\r\n\t\t\tstds = df_cols.std()\r\n\t\t\tnorms = (df_cols - means) / stds\r\n\t\telif method == \"inv_logit\":\r\n\t\t\tnorms = np.exp(df_cols) / (1 + np.exp(df_cols))\r\n\t\telif method == \"log\":\r\n\t\t\tnorms = np.log(df_cols)\r\n\t\telif method == \"logit\":\r\n\t\t\tnorms = np.log(df_cols / (1 - df_cols))\r\n\t\telse:\r\n\t\t\tprint(f\"*normalize() ERROR* Method \\\"{method}\\\" does not exist! Use: {', '.join(methods)}\")\r\n\t\t\treturn None\r\n\t\tif not inplace:\r\n\t\t\treturn norms\r\n\t\telif new_cols:\r\n\t\t\tdf_cols = new_cols\r\n\t\telse:\r\n\t\t\tdf_cols = cols\r\n\t\tfor i in range(len(df_cols)):\r\n\t\t\tself.sample_df[df_cols[i]] = norms[cols[i]]\r\n\r\n\r\n\tdef pair_rows(self, pair_count):\r\n\t\tpairs = self.get_pairs()\r\n\t\tpairs = pairs[pairs == pair_count]\r\n\t\tchips = pairs.index.values\r\n\t\treturn self.sample_df[self.sample_df[\"Microchip\"].isin(chips)]\r\n\r\n\r\n\tdef plot_col(self, col, outpath, plot=\"hist\"):\r\n\t\tplots = [\"hist\"]\r\n\t\tdata = self.sample_df[col]\r\n\t\tif plot == \"hist\":\r\n\t\t\tq25, q75 = np.percentile(data, [0.25, 0.75])\r\n\t\t\tbin_width = 2 * ((q75 - q25) / len(data)**(-1/3))\r\n\t\t\tbins = round((data.max() - data.min()) / bin_width)\r\n\t\t\tplt.hist(data, bins=bins, density=True)\r\n\t\telse:\r\n\t\t\tprint(f\"*plot_col() ERROR* Plot \\\"{plot}\\\" does not exist! Use: {', '.join(plots)}\")\r\n\t\t\treturn None\r\n\t\tplt.ylabel(col)\r\n\t\tplt.savefig(outpath)\r\n\t\tplt.close()\r\n\t\tprint(f\"Plot saved to: {outpath}\")\r\n\r\n\r\n\tdef print_col(self, col):\r\n\t\tprint(self.sample_df[col].to_list())\r\n\r\n\r\n\tdef print_cols(self, cols):\r\n\t\tprint(self.sample_df[cols])\r\n\r\n\r\n\tdef print_factor_key(self):\r\n\t\tif not self.factor_key[\"key\"]:\r\n\t\t\tprint(\"No factors have been created\")\r\n\t\t\treturn None\r\n\t\tfactor_df = pd.DataFrame(self.factor_key)\r\n\t\tfor pheno in factor_df[\"pheno\"].unique():\r\n\t\t\tcurrent = factor_df[factor_df[\"pheno\"] == pheno]\r\n\t\t\tprint(pheno)\r\n\t\t\tprint(current[[\"key\", \"val\"]].to_string(index=False))\r\n\r\n\r\n\tdef subset(self, col, pattern):\r\n\t\tself.sample_df = self.sample_df[self.sample_df[col] == pattern]\r\n\r\n\r\n\tdef subset_pairs(self):\r\n\t\tindices = self.sample_df.groupby(\"Microchip\")[\"Microchip\"].count()\r\n\t\tindices = indices[indices > 1].index\r\n\t\tself.sample_df = self.sample_df[self.sample_df[\"Microchip\"].isin(indices)]\r\n\r\n\r\n\tdef subset_non_nan(self, col):\r\n\t\tna = self.sample_df[col].isna()\r\n\t\tif isinstance(col, list):\r\n\t\t\tna = na.any(axis=1)\r\n\t\tself.sample_df = self.sample_df[~na]\r\n\r\n\r\n\tdef summarize_pairs(self):\r\n\t\tpairs = self.get_pairs()\r\n\t\tprint(\"PAIR\\tCOUNT\")\r\n\t\tfor pair in pd.unique(pairs):\r\n\t\t\tprint(f\"{pair}\\t{len(pairs[pairs == pair])}\")\r\n\r\n\r\n\tdef to_factor(self, col, inplace=True):\r\n\t\tphenotype = self.sample_df[col]\r\n\t\tunique_vals = sorted(phenotype.dropna().unique(), key=str.casefold)\r\n\t\tphenotype = phenotype.fillna(\"NA\")\r\n\t\tfactors = [i for i in range(len(unique_vals))]\r\n\t\tphenotype = phenotype.replace(unique_vals, factors)\r\n\t\tself.factor_key[\"pheno\"] += [col for i in range(len(unique_vals))]\r\n\t\tself.factor_key[\"key\"] += list(unique_vals)\r\n\t\tself.factor_key[\"val\"] += factors\r\n\t\tif inplace:\r\n\t\t\tself.sample_df[col] = phenotype\r\n\t\telse:\r\n\t\t\treturn phenotype\r\n\r\n\r\n\tdef ATOMM_pheno(self, outpath, cols):\r\n\t\ttissue = pd.unique(self.sample_df[\"Tissue\"])\r\n\t\tif len(tissue) < 2:\r\n\t\t\tprint(f\"*WARNING* the sample DF only contains a single tissue ({tissue[0]}), and thus pairs cannot be generated.\")\r\n\t\t\tprint(\"If this is a TumorSamples object, please specify the argument tissue=\\\"both\\\"\")\r\n\t\t\tprint(\"Exiting\")\r\n\t\t\treturn None\r\n\t\tself.subset_pairs()\r\n\t\tself.__handle_triplets()\r\n\t\tself.to_vcf_chip()\r\n\t\ttumor = self.chip_sort(self.sample_df[self.sample_df[\"Tissue\"] == \"Tumour\"], num_only=True)\r\n\t\tself.subset(\"Tissue\", \"Host\")\r\n\t\thost = self.chip_sort(self.sample_df, num_only=True)\r\n\t\thost.rename(columns={\"Microchip\": \"host_chip\"}, inplace=True)\r\n\t\thost[\"tumor_chip\"] = tumor[\"Microchip\"]\r\n\t\tself.sample_df = host\r\n\t\tself.to_pheno(outpath, cols, id_cols=[\"host_chip\", \"tumor_chip\"], vcf_chip=False)\r\n\r\n\r\n\tdef to_pheno(self, outpath, cols, id_cols=[\"Microchip\"], vcf_chip=True):\r\n\t\tif vcf_chip:\r\n\t\t\tself.to_vcf_chip()\r\n\t\tpheno_df = self.sample_df[id_cols + cols]\r\n\t\tpheno_df = pheno_df.fillna(\"NA\")\r\n\t\toutdir = outpath[:outpath.rfind(\".\")]\r\n\t\tfactor_out = f\"{outdir}_FACTOR_KEY.txt\"\r\n\t\tself.factor_file(factor_out, cols=cols, silent=True)\r\n\t\tprint(f\"Writing phenotype file to: {outpath}\")\r\n\t\tpheno_df.to_csv(outpath, index=False, sep=\"\\t\")\r\n\r\n\r\n\tdef to_vcf_chip(self, inplace=True):\r\n\t\tchips = self.sample_df[\"Microchip\"]\r\n\t\tchips = chips.str[-6:]\r\n\t\ttissue = self.sample_df[\"Tissue\"].str[0]\r\n\t\ttissue[tissue == \"T\"] += self.sample_df[\"TumourNumber\"]\r\n\t\tchips = tissue + \"-\" + chips\t\r\n\t\tif inplace:\r\n\t\t\tself.sample_df[\"Microchip\"] = chips\r\n\t\telse:\r\n\t\t\treturn chips\r\n\r\n\r\n\tdef undo_factor(self, pheno):\r\n\t\tfactor_df = pd.DataFrame(self.factor_key)\r\n\t\tfactor_pheno = factor_df[factor_df[\"pheno\"] == pheno]\r\n\t\tsample_pheno = self.sample_df[pheno]\r\n\t\tundone_factors = sample_pheno.replace(factor_pheno[\"val\"].to_list(), factor_pheno[\"key\"].to_list())\r\n\t\tself.sample_df[pheno] = undone_factors\r\n\t\tself.factor_key = factor_df[factor_df[\"pheno\"] != pheno].reset_index(drop=True).to_dict(orient='list')\r\n\r\n\r\n\tdef unique(self, col):\r\n\t\treturn self.sample_df[col].unique()\r\n\r\n\r\n\tdef update_factors(self, pheno_list):\r\n\t\tfor pheno in pheno_list:\r\n\t\t\tif pheno not in self.factor_key[\"pheno\"]:\r\n\t\t\t\tcontinue\r\n\t\t\tself.undo_factor(pheno)\r\n\t\t\tself.to_factor(pheno)\r\n\r\n\r\n\tdef write_pairs(self, outpath):\r\n\t\tpair_df = self.sample_df[[\"Microchip\", \"Tissue\", \"TumourNumber\"]].copy()\r\n\t\tpair_df = self.__handle_triplets(pair_df)\r\n\t\tpairs = pair_df.groupby(\"Microchip\")[\"Microchip\"].count()\r\n\t\tpair_loc = pairs[pairs > 1].index\r\n\t\tpair_df = pair_df[pair_df[\"Microchip\"].isin(pair_loc)]\r\n\t\ttumors = pair_df[pair_df[\"Tissue\"] == \"Tumour\"].sort_values(by=\"Microchip\").reset_index(drop=True)\r\n\t\ttumors = self.to_vcf_chip(tumors)\r\n\t\thosts = pair_df[pair_df[\"Tissue\"] == \"Host\"].sort_values(by=\"Microchip\").reset_index(drop=True)\r\n\t\thosts = self.to_vcf_chip(hosts)\r\n\t\ttumor_host = pd.concat([hosts, tumors], axis=1)\r\n\t\ttumor_host.columns = [\"Hosts\", \"Tumors\"]\r\n\t\ttumor_host.to_csv(outpath, index=False, sep=\"\\t\")\r\n\r\n\r\n\tdef __handle_triplets(self):\r\n\t\ttriplets = self.get_pairs(3)\r\n\t\tif triplets.empty:\r\n\t\t\treturn None\r\n\t\tprint(f\"Found {len(triplets)} triplets, please select only 1 of the duplicates to retain:\")\r\n\t\tremove_loc = []\r\n\t\tfor index in triplets.index:\r\n\t\t\tcurrent_triplet = self.sample_df[self.sample_df[\"Microchip\"] == index]\r\n\t\t\tif len(current_triplet[current_triplet[\"Tissue\"] == \"Tumour\"]) > 1:\r\n\t\t\t\tdup = current_triplet[current_triplet[\"Tissue\"] == \"Tumour\"]\r\n\t\t\telse:\r\n\t\t\t\tdup = dup = current_triplet[current_triplet[\"Tissue\"] == \"Host\"]\r\n\t\t\tdup_i = list(dup.index.values)\r\n\t\t\tprint(f'{dup[[\"Microchip\", \"Library number\", \"AnimalName\", \"Tissue\", \"YOB\", \"TrappingDate\", \"Site\", \"TumourID\", \"TumourNumber\"]]}')\r\n\t\t\tretain_loc = input(f\"Please select the index to be retained ({dup_i[0]} or {dup_i[1]}): \")\r\n\t\t\tdup_i.remove(int(retain_loc))\r\n\t\t\tremove_loc += dup_i\r\n\t\tself.sample_df = self.sample_df.loc[~self.sample_df.index.isin(remove_loc)]\r\n\r\n\r\n\tdef __isnum(self, x):\r\n\t\ttry:\r\n\t\t\tint(x)\r\n\t\texcept:\r\n\t\t\treturn False\r\n\t\treturn True\r\n\r\n\r\n\tdef __L_ID(self, paths):\r\n\t\tL_IDs = []\r\n\t\tfor path in paths:\r\n\t\t\tif path[-4:] == \".csv\":\r\n\t\t\t\tfnames = pd.read_csv(path)\r\n\t\t\t\tfnames = fnames[\"original\"].to_list()\r\n\t\t\telse:\r\n\t\t\t\tfnames = os.listdir(path)\r\n\t\t\tL_IDs += list(set([file[:5] for file in fnames]))\r\n\t\treturn L_IDs\r\n\r\n\r\n\r\n\r\nclass TumorSamples(Samples):\r\n\tdef __init__(self,\r\n\t\tsample_csv_path=\"/work_bgfs/d/dgallinson/data/pheno_data/master_corrections.csv\",\r\n\t\ttumor_csv_path=\"/work_bgfs/d/dgallinson/data/pheno_data/originals/RTumourTableRodrigo.csv\",\r\n\t\tid_paths=[],\r\n\t\ttissue=\"Host\"\r\n\t\t):\r\n\t\tsuper().__init__(sample_csv_path, id_paths)\r\n\t\ttumor_df_full = pd.read_csv(tumor_csv_path, dtype={\"TumourNumber\": str})\r\n\t\tif tissue != \"both\":\r\n\t\t\tself.sample_df = self.sample_df[self.sample_df[\"Tissue\"] == tissue]\r\n\t\tmicrochips = self.sample_df[\"Microchip\"].unique()\r\n\t\tself.tumor_df = tumor_df_full[tumor_df_full[\"Microchip\"].isin(microchips)]\r\n\t\tself.over_mmax = {\"Microchip\": [], \"volume\": []}\r\n\t\tself.failed_date_delta = []\r\n\r\n\t\r\n\t# ANALYSIS PHENOTYPE\r\n\tdef estimate_age(self):\r\n\t\ttmp_tumor_df = self.tumor_df[[\"Microchip\", \"TrapDate\", \"TumourDepth\", \"TumourLength\", \"TumourWidth\"]].copy()\r\n\t\ttmp_tumor_df[\"TrapDate\"] = pd.to_datetime(tmp_tumor_df[\"TrapDate\"])\r\n\t\tdates_df = self.__init_tumor_date(tmp_tumor_df)\r\n\t\tYOB = self.sample_df.set_index(\"Microchip\")[\"YOB\"]\r\n\t\tYOB = YOB[~YOB.index.duplicated(keep=\"first\")]\r\n\t\tYOB = pd.to_datetime(YOB)\r\n\t\tmerged = pd.concat([dates_df, YOB[dates_df.index]], axis=1)\r\n\t\tinfection_age = (merged[\"init_tumor_date\"] - merged[\"YOB\"]).dt.days\r\n\t\tinfection_age.name = \"infection_age\"\r\n\t\tinfection_age = infection_age.reset_index()\r\n\t\tself.sample_df = self.sample_df.merge(infection_age, how=\"left\", on=\"Microchip\")\r\n\r\n\r\n\tdef fast_stats_tumor(self):\r\n\t\tunique_samples = len(self.sample_df[\"Microchip\"].unique())\r\n\t\tfound_in_tumor = len(self.tumor_df[\"Microchip\"].unique())\r\n\t\tchip_date_cluster = self.tumor_df.groupby([\"Microchip\", \"TrapDate\"])[\"TrapDate\"].count()\r\n\t\ttrap_cluster = chip_date_cluster.groupby(\"Microchip\").count()\r\n\r\n\t\tprint(f\"Found in tumor DB: {(found_in_tumor/unique_samples)*100:.1f}% ({found_in_tumor}/{unique_samples})\")\r\n\t\tprint(f\"1 trap: {len(trap_cluster[trap_cluster == 1])}\")\r\n\t\tprint(f\"More than 1 trap: {len(trap_cluster[trap_cluster > 1])}\")\r\n\r\n\r\n\t# === ANALYSIS PHENOTYPE ===\r\n\t# Definition: \r\n\t# \t\t\t Obtain a proxy for devil survival similar to Margres et al. (2018; DOI: 10.1111/mec.14853) using a tumor growth back calculation\r\n\t# \t\t\t derived from Wells et al. (2017; DOI: 10.1111/ele.12776). Only devils with 2 trapping events and day_cutoff days between those\r\n\t# \t\t\t trapping events are included. After the volume of the largest tumor (from tumors recorded on the earliest trap date) is obtained,\r\n\t# \t\t\t the back calculation is used to find the number of days since the tumor was 3 mm^3. This date is then subtracted from the last\r\n\t# \t\t\t date the devil was trapped to get survival in days.\r\n\t# Arguments:\r\n\t# \t\t\t day_cutoff: \t the minimum number of days a devil must be trapped between two trapping events for inclusion in the analysis \r\n\t# \t\t\t verbose_merge: whether the intermediate calculation values should be added to the samples DF. Mostly for debugging \r\n\t# Returns:\r\n\t# \t\t\t None. Adds at least a \"devil_survival_days\" column to the samples DF\r\n\t# Steps:\r\n\t# \t\t\t 1) Obtain the difference in days from first to last trapping event\r\n\t# \t\t\t 2) Filter out devils with a single trap event or with fewer days between two events which fail the day_cutoff (these are also saved)\r\n\t# \t\t\t 3) Back calculate to find the initial tumor data (see __init_tumor_date() for more details)\r\n\t# \t\t\t 4) Obtain the date for the most recent trapping event\r\n\t# \t\t\t 5) Subtract the tumor initial date from the max trap date to obtain devil survival days\r\n\t# TODO:\r\n\t# \t\t\t The calculated dates often do not match up with Margres et al. (2018) table S1.\r\n\t# \t\t\t Attempt to change the calculation using the sum of all tumor volumes on the min trap date.\r\n\t# \t\t\t Use the Compare class to test calculation similarities\r\n\tdef survival_proxy(self, day_cutoff=40, verbose_merge=False):\r\n\t\ttmp_tumor_df = self.tumor_df[[\"Microchip\", \"TrapDate\", \"TumourDepth\", \"TumourLength\", \"TumourWidth\"]].copy()\r\n\t\ttmp_tumor_df[\"TrapDate\"] = pd.to_datetime(tmp_tumor_df[\"TrapDate\"])\r\n\t\t\r\n\t\tdate_diff = tmp_tumor_df.groupby(\"Microchip\")[\"TrapDate\"].transform(lambda x: x.max() - x.min())\r\n\t\ttmp_tumor_df[\"date_diff\"] = date_diff.dt.days\r\n\t\tself.failed_date_delta = pd.Series(tmp_tumor_df[tmp_tumor_df[\"date_diff\"] < day_cutoff][\"Microchip\"].unique())\r\n\t\ttmp_tumor_df = tmp_tumor_df[tmp_tumor_df[\"date_diff\"] >= day_cutoff]\r\n\t\t\r\n\t\tdates_df = self.__init_tumor_date(tmp_tumor_df)\r\n\t\tlast_trap = tmp_tumor_df.groupby(\"Microchip\")[\"TrapDate\"].max().loc[dates_df.index]\r\n\t\tdevil_survival_days = last_trap - dates_df[\"init_tumor_date\"]\r\n\t\t\r\n\t\tcalc_df = pd.DataFrame({\r\n\t\t\t\"Microchip\": dates_df.index.values,\r\n\t\t\t\"volume (cm^3)\": np.round(dates_df[\"tumor_volume\"].values, 2),\r\n\t\t\t\"back_calc\": dates_df[\"back_calc\"].values,\r\n\t\t\t\"first_trap\": dates_df[\"min_date\"].dt.date.values,\r\n\t\t\t\"last_trap\": last_trap.dt.date.values,\r\n\t\t\t\"init_tumor_date\": dates_df[\"init_tumor_date\"].dt.date.values,\r\n\t\t\t\"devil_survival_days\": devil_survival_days.dt.days.values})\r\n\t\tif not verbose_merge:\r\n\t\t\tcalc_df = calc_df[[\"Microchip\", \"devil_survival_days\"]]\r\n\t\tself.sample_df = self.sample_df.merge(calc_df, how=\"left\", on=\"Microchip\")\r\n\r\n\r\n\tdef get_multi_sub_40(self):\r\n\t\tif len(self.failed_date_delta) == 0:\r\n\t\t\tprint(\"Please run survival_proxy() before using this method\")\r\n\t\t\treturn None\r\n\t\tsingle_trap = self.get_trap(1)\r\n\t\treturn self.failed_date_delta[~self.failed_date_delta.isin(single_trap)]\r\n\r\n\r\n\tdef get_trap(self, trap_N):\r\n\t\ttrap_nums = self.tumor_df.groupby([\"Microchip\", \"TrapDate\"])[\"TrapDate\"].count().groupby(\"Microchip\").count()\r\n\t\treturn trap_nums[trap_nums == trap_N].index\r\n\r\n\r\n\t# This fails to get the exact tumors we sequenced but gets close. Due to mismatching info\r\n\t# between the saple CSV and tumor CSV, further resolution is impossible. Once I obtain the\r\n\t# proper phenotype data I will update this method (likely to grab based on TrapDate)\r\n\tdef get_sample_tumors(self):\r\n\t\tsample_subset = self.sample_df[self.sample_df[\"Microchip\"].isin(self.tumor_df[\"Microchip\"])]\r\n\t\tsample_tumors = sample_subset[[\"Microchip\", \"TumourNumber\"]]\r\n\t\tfound_indices = []\r\n\t\tfor i in range(sample_tumors.shape[0]):\r\n\t\t\tcurrent_sample = sample_tumors.iloc[i]\r\n\t\t\tfound_index = self.tumor_df.loc[(self.tumor_df[\"Microchip\"] == current_sample[\"Microchip\"]) & (self.tumor_df[\"TumourNumber\"] == current_sample[\"TumourNumber\"])]\r\n\t\t\tfound_index = list(found_index.index.values)\r\n\t\t\tfound_indices += found_index\r\n\t\treturn self.tumor_df.loc[found_indices]\r\n\r\n\r\n\tdef no_tumor_entry(self):\r\n\t\thosts = self.sample_df[self.sample_df[\"Tissue\"] == \"Host\"]\r\n\t\thosts_not_in = hosts[~hosts[\"Microchip\"].isin(self.tumor_df[\"Microchip\"].unique())]\r\n\t\treturn hosts_not_in\r\n\r\n\r\n\tdef print_over_mmax(self):\r\n\t\tif not self.over_mmax[\"Microchip\"]:\r\n\t\t\tprint(\"No back calculations have been done yet\")\r\n\t\t\treturn None\r\n\t\tprint(\"Microchip\\tVolume (cm^3)\")\r\n\t\tN = len(self.over_mmax[\"Microchip\"])\r\n\t\tfor i in range(N):\r\n\t\t\tprint(f\"{self.over_mmax['Microchip'][i]}\\t{self.over_mmax['volume'][i]:.3f}\")\r\n\t\tprint(f\"Length: {N}\")\r\n\r\n\r\n\t# ANALYSIS PHENOTYPE\r\n\tdef tumor_count(self):\r\n\t\tnum_tumors = self.tumor_df.groupby([\"Microchip\"])[\"TumourNumber\"].unique().str.len()\r\n\t\tnum_tumors = num_tumors.to_frame().reset_index()\r\n\t\tnum_tumors = num_tumors.rename(columns={\"TumourNumber\": \"tumor_count\"})\r\n\t\tself.sample_df = self.sample_df.merge(num_tumors, how=\"left\", on=\"Microchip\")\r\n\r\n\r\n\t# Definition: \r\n\t# \t\t\t A logistic growth back calculation derived from Wells et al. (2017; DOI: 10.1111/ele.12776) which was based on data from WPP.\r\n\t# \t\t\t Output is a negative number in days representing days from the min capture date since the tumor was 3 mm^3.\r\n\t# \t\t\t The model is not accurate enough to estimate beyond day resolution, and thus rounding to the nearest day \r\n\t# \t\t\t (default round_flag) is advised. This also stores any volumes greater than mmax.\r\n\t# Arguments:\r\n\t# \t\t\t tumor_volumes: a vector/Series of tumor volumes \r\n\t# \t\t\t mmax: \t\t max tumor size in cm^3 (from growth model) \r\n\t# \t\t\t init_size: \t initial tumor size (from growth model) \r\n\t# \t\t\t alpha: \t\t scale parameter of the logistic growth curve (from growth model) \r\n\t# \t\t\t is_cm: \t\t flag if the incoming volume is in centimeters \r\n\t# \t\t\t round_flag: \t flag if the back calculated volumes should be rounded to the nearest int \r\n\t# Returns:\r\n\t# \t\t\t numpy array containing negative numbers representing days since tumor volume = 3 mm^3\r\n\t# Steps:\r\n\t# \t\t\t 1) Convert mm to cm if is_cm=True\r\n\t# \t\t\t 2) Determine volumes >= mmax, setting those volumes to NaN and storing only those microchips and volumes not already in the over_mmax dict\r\n\t# \t\t\t 3) Perform the back calculation\r\n\t# \t\t\t 4) Round the calculations to the nearest int if round_flag=True and return\r\n\t# Gotchas:\r\n\t# \t\t\t Formula domain: (0, mmax)\r\n\t# \t\t\t Formula range: (-inf, 0); as volume -> mmax, days -> -inf\r\n\tdef __growth_back_calculation(self, tumor_volumes, mmax=202, init_size=0.0003, alpha=0.03, is_cm=False, round_flag=True):\r\n\t\ttumor_volumes = tumor_volumes.copy()\r\n\t\tif not is_cm:\r\n\t\t\ttumor_volumes /= 1000\r\n\t\tover = tumor_volumes[(tumor_volumes + 1) >= mmax]\r\n\t\tif over.size > 0:\r\n\t\t\tvstring = \"volumes\" if len(over) > 1 else \"volume\"\r\n\t\t\tprint(f\"*WARNING* found {len(over)} {vstring} greater than mmax while performing the back calculation (try print_over_mmax() for details)\")\r\n\t\t\ttumor_volumes[(tumor_volumes + 1) >= mmax] = np.nan\r\n\t\tif self.over_mmax[\"Microchip\"]:\r\n\t\t\tover = over.drop(self.over_mmax[\"Microchip\"])\r\n\t\tif len(over > 0):\r\n\t\t\tself.over_mmax[\"Microchip\"] += list(over.index.values)\r\n\t\t\tself.over_mmax[\"volume\"] += list(over.values)\r\n\t\tback_calculation = np.log((mmax / (tumor_volumes.values + 1) - 1) / (mmax - init_size)) / alpha\r\n\t\tif round_flag:\r\n\t\t\tback_calculation = np.round(back_calculation)\r\n\t\treturn back_calculation\r\n\r\n\r\n\t# Definition: \r\n\t# \t\t\t Obtain the tumor volume Series for the minimum capture date with microchips as the index.\r\n\t# \t\t\t When a host has multiple tumors, different grouping functions can be used (e.g., max or sum). \r\n\t# \t\t\t If this is being used with the growth back calculation, max should set for mode. Drops rows with an NA measurement.\r\n\t# Arguments:\r\n\t#\t\t\t tumor_df: a DF with at least the following cols: Microchip, TrapDate, TumourDepth, TumourLength, TumourWidth \r\n\t# \t\t\t mode: \tmethod of selecting a single volume when multiple tumors are present at the same date \r\n\t# Returns: \r\n\t# \t\t\t a DF containing Microchip as the index and cols tumor_volume (in starting units) and min_date (representing the minimum date for a sample)\r\n\t# Steps:\r\n\t#\t\t\t 1) get min date for a sample (group on Microchip)\r\n\t# \t\t\t 2) Iterate through each of these microchips, find all rows with the min date, and generate a DF from these\r\n\t# \t\t\t 3) Obrain tumor volumes via length x width x depth\r\n\t# \t\t\t 4) Group these volumes on Microchip and select a single volume for each microchip (i.e., max, mean, sum)\r\n\t# Gotchas:\r\n\t# \t\t\t Drops rows if any NA is found in TumourDepth, TumourLength, or TumourWidth\r\n\t# TODO:\r\n\t# \t\t\t The loop is an inelegant and inefficient solution, this should be vectorized\r\n\tdef __min_cap_volume(self, tumor_df, mode=\"max\"):\r\n\t\ttumor_df = tumor_df.dropna(subset=[\"TumourDepth\", \"TumourLength\", \"TumourWidth\"])\r\n\t\tmin_date_groups = tumor_df.groupby(\"Microchip\")[\"TrapDate\"].min()\r\n\t\tmin_date_groups.name = \"min_date\"\r\n\t\tmin_dates = {\"Microchip\": min_date_groups.index, \"TrapDate\": min_date_groups.values}\r\n\t\tfirst_tumor_list = []\r\n\t\t\r\n\t\tfor i in range(len(min_dates[\"Microchip\"])):\r\n\t\t\tchip = min_dates['Microchip'][i]\r\n\t\t\tdate = min_dates['TrapDate'][i]\r\n\t\t\tchip_group = tumor_df[tumor_df[\"Microchip\"] == chip]\r\n\t\t\tfirst_tumor_list.append(chip_group[chip_group[\"TrapDate\"] == date])\r\n\t\t\r\n\t\tfirst_tumor_df = pd.concat(first_tumor_list)\r\n\t\tfirst_tumor_df[\"tumor_volume\"] = first_tumor_df[\"TumourDepth\"] * first_tumor_df[\"TumourLength\"] * first_tumor_df[\"TumourWidth\"]\r\n\t\tvolume_cluster = first_tumor_df.groupby(\"Microchip\")[\"tumor_volume\"]\r\n\t\tif mode == \"mean\":\r\n\t\t\tvolume_cluster = volume_cluster.mean()\r\n\t\telif mode == \"sum\":\r\n\t\t\tvolume_cluster = volume_cluster.sum()\r\n\t\telse:\r\n\t\t\tvolume_cluster = volume_cluster.max()\r\n\t\treturn pd.concat([volume_cluster, min_date_groups], axis=1)\r\n\r\n\r\n\t# Definition:\r\n\t# \t\t\t This is a convenience function combining the functionality of __min_cap_volume()\r\n\t# \t\t\t and __growth_back_calculation() to find the initial tumor date.\r\n\t# Arguments:\r\n\t# \t\t\t tumor_df: a DF with at least the following cols: Microchip, TrapDate, TumourDepth, TumourLength, TumourWidth \r\n\t# Returns:\r\n\t# \t\t\t a DF with Microchip as the index and the following cols: tumor_volume, min_date, back_calc, init_tumor_date\r\n\t# Steps:\r\n\t# \t\t\t 1) Run __min_cap_volume() and __growth_back_calculation\r\n\t# \t\t\t 2) Get the initial tumor date by subtracting the back calc from the min date\r\n\t# \t\t\t 3) Combine everything into a DF\r\n\tdef __init_tumor_date(self, tumor_df):\r\n\t\tvolumes = self.__min_cap_volume(tumor_df)\r\n\t\tvolumes[\"tumor_volume\"] /= 1000\r\n\t\tback_calc = self.__growth_back_calculation(volumes[\"tumor_volume\"], is_cm=True)\r\n\t\tinit_tumor_dates = volumes[\"min_date\"] - pd.to_timedelta(-1 * back_calc, unit=\"d\")\r\n\t\tinit_tumor_dates.name = \"init_tumor_date\"\r\n\t\tback_calc = pd.Series(back_calc, name=\"back_calc\", index=volumes.index)\r\n\t\treturn pd.concat([volumes, back_calc, init_tumor_dates], axis=1)\r\n\r\n\r\n\r\nclass Compare:\r\n\tdef __init__(self, df, cols):\r\n\t\tself.df = df\r\n\t\tself.cols = cols\r\n\t\tself.matched = pd.DataFrame([])\r\n\t\tself.unmatched = pd.DataFrame([])\r\n\r\n\r\n\tdef compare_strict(self):\r\n\t\tself.matched = self.df[self.df[self.cols[0]] == self.df[self.cols[1]]]\r\n\t\tself.unmatched = self.df[self.df[self.cols[0]] != self.df[self.cols[1]]]\r\n\r\n\r\n\tdef diff(self, n=1, biggest=True):\r\n\t\tfirst = self.unmatched[self.cols[0]]\r\n\t\tsecond = self.unmatched[self.cols[1]]\r\n\t\tabs_diffs = sorted((first - second).abs(), reverse=biggest)\r\n\t\treturn abs_diffs[0:n]\r\n\r\n\r\n\tdef diff_stats(self):\r\n\t\tfirst = self.unmatched[self.cols[0]]\r\n\t\tsecond = self.unmatched[self.cols[1]]\r\n\t\tabs_diffs = (first - second).abs()\r\n\t\tmean = abs_diffs.mean()\r\n\t\tmedian = abs_diffs.median()\r\n\t\treturn mean, median\r\n\r\n\r\n\tdef stats(self):\r\n\t\tif self.matched.empty and self.unmatched.empty:\r\n\t\t\tprint(\"Please run a comparison method before printing stats!\")\r\n\t\t\treturn None\r\n\t\tmean, median = self.diff_stats()\r\n\t\tprint(f\"Matched: {self.matched.shape[0]}\")\r\n\t\tprint(f\"Unmatched: {self.unmatched.shape[0]}\")\r\n\t\tprint(\"----- UNMATCHED -----\")\r\n\t\tprint(f\"Biggest difference: {self.diff()[0]}\")\r\n\t\tprint(f\"Smallest difference: {self.diff(biggest=False)[0]}\")\r\n\t\tprint(f\"Mean difference: {mean:.1f}\")\r\n\t\tprint(f\"Median difference: {median}\")","sub_path":"utility/samples.py","file_name":"samples.py","file_ext":"py","file_size_in_byte":27757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"506801082","text":"\"\"\"A demo app that can be used as a skeleton template for bootstrapping the\ncreation of simple Plotly Dash apps.\n\n\"\"\"\n\nfrom collections import Counter\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport plotly.graph_objs as go\nfrom dash.dependencies import Input, State, Output, Event\n\n\napp = dash.Dash(__name__)\napp.title = 'Dash Skeleton'\n\n# If you need to run your app locally\n#app.scripts.config.serve_locally = True\n\napp.layout = html.Div([\n dcc.Markdown(\"\"\"\n# Dash Demo app\n\nThis demo app counts the number of characters in the text box and updates a bar\nchart with their frequency as you type.\"\"\"),\n html.Div(\n dcc.Textarea(\n id='text-input',\n value='Type some text into me!',\n style={'width':'40em', 'height': '5em'},\n )\n ),\n html.Div('Sort by:'),\n dcc.RadioItems(\n id='sort-type',\n options=[\n {'label': 'Frequency', 'value': 'frequency'},\n {'label': 'Character code', 'value': 'code'},\n ],\n value='frequency'\n ),\n html.Div('Normalize character case?'),\n dcc.RadioItems(\n id='normalize',\n options=[\n {'label': 'No', 'value': 'no'},\n {'label': 'Yes', 'value': 'yes'},\n ],\n value='no'\n ),\n dcc.Graph(id='graph', className='red')\n])\n\n\n@app.callback(\n Output('graph', 'figure'), # Output\n [Input('text-input', 'value'), # Inputs\n Input('sort-type', 'value'),\n Input('normalize', 'value')], \n [], # States\n [] # Events\n)\ndef callback(text, sort_type, normalize):\n if normalize == 'yes':\n text = text.lower()\n\n if sort_type == 'frequency':\n sort_func = lambda x:-x[1]\n else:\n sort_func = lambda x:ord(x[0])\n \n counts = Counter(text)\n\n if len(counts) == 0:\n x_data = []\n y_data = []\n else:\n x_data, y_data = zip(*sorted(\n counts.items(),\n key=sort_func))\n return {\n 'data': [\n {'x': x_data, 'y':y_data, 'type': 'bar', 'name': 'trace1'},\n ],\n 'layout': {\n 'title': 'Frequency of Characters',\n 'height': '600',\n 'font': {'size': 16}\n },\n }\n\n\nif __name__ == '__main__':\n # To make this app publicly available, supply the parameter host='0.0.0.0'.\n # You should also disable debug mode in production.\n app.run_server(debug=True, port=8051)\n","sub_path":"dash_skeleton.py","file_name":"dash_skeleton.py","file_ext":"py","file_size_in_byte":2513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"314435976","text":"import matplotlib.patches as patches\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nfrom matplotlib.colors import LogNorm\nfrom mpl_toolkits.mplot3d import Axes3D\n\n############################################################################################################################\n\n\"\"\" draw ellipse associated to a covariance matrix \"\"\"\ndef draw_ellipse(position, covariance, ax=None, **kwargs):\n \"\"\"Draw an ellipse with a given position and covariance\"\"\"\n ax = ax or plt.gca()\n \n # Convert covariance to principal axes\n if covariance.shape == (2, 2):\n U, s, Vt = np.linalg.svd(covariance)\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n width, height = 2 * np.sqrt(s)\n else:\n angle = 0\n width, height = 2 * np.sqrt(covariance)\n \n # Draw the Ellipse\n for nsig in range(1, 5):\n ax.add_patch(patches.Ellipse(position, nsig * width, nsig * height,\n angle, **kwargs))\n\n\"\"\" plot data and corresponding GMM prediction \"\"\"\ndef plot_gmm(means, covariances, weights, X, scale = None, labels=None, ax=None):\n \n num_features = np.shape(X)[1]\n if num_features == 2:\n\n ax = ax or plt.gca()\n\n w_factor = 0.3 / weights.max()\n for pos, covar, w in zip(means, covariances, weights):\n draw_ellipse(pos, covar, alpha=w * w_factor)\n \n if labels is not None:\n if scale is not None:\n ax.scatter(X[:, 0], X[:, 1], c=labels, s=scale, cmap='viridis', zorder=0)\n else:\n ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=0) \n else:\n if scale is not None:\n ax.scatter(X[:, 0], X[:, 1], c=labels, s=scale, cmap='viridis', zorder=0)\n else:\n ax.scatter(X[:, 0], X[:, 1], c=labels, s= 40, zorder=0)\n ax.set_xlim(min(min(X[:, 0]), min(means[:,0]) ), max(max(X[:, 0]), max(means[:,0]) ))\n ax.set_ylim(min(min(X[:, 1]), min(means[:,1]) ), max(max(X[:, 1]), max(means[:,1]) ))\n plt.ylabel(r'$z_2$',fontsize=18)\n plt.xlabel(r'$z_1$',fontsize=18)\n plt.xticks(fontsize=18)\n plt.yticks(fontsize=18)\n elif num_features ==3:\n fig = plt.figure()\n ax = ax or Axes3D(fig)\n ax.scatter3D(X[:, 0], X[:, 1], X[:,2], c=labels, s=scale, cmap='viridis')\n\n'''\n'''\ndef plot_kmeans(centers, X, labels):\n plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')\n plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);","sub_path":"clustering_plotting_tools.py","file_name":"clustering_plotting_tools.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"325168554","text":"'''\r\nChapter 1\r\n Task 2\r\n Enhanced Unit Calculator\r\n\r\n Write a program which can convert kilometers to miles, vice versa; kilograms to pounds and vice versa; Celsius to Fahrehnheit, vice versa;\r\n\r\n\r\n '''\r\ndef print_menu():\r\n print('1.Kilometers to Miles.')\r\n print('2. Miles to Kilometers.')\r\n print('3. Kilograms to Pounds.')\r\n print('4. Pounds to Kilograms.')\r\n print('5. Degrees Celsius to degrees Fahrenheit.')\r\n print('6. Degrees Fahrenheit to degrees Celsius.')\r\n\r\ndef km_miles():\r\n km=float(input('Please enter the distance in Kilometers: '))\r\n miles=km/1.609\r\n\r\n print('The above given distance is {0} Miles.'.format(miles))\r\n\r\ndef miles_km():\r\n miles=float(input('Please enter the distance in Miles: '))\r\n km=miles*1.609\r\n\r\n print('The above given distance is {0} Kilometers.'.format(km))\r\n\r\ndef kg_pounds():\r\n kg=float(input('Please enter the desired mass in Kilograms: '))\r\n pounds=kg*2.204\r\n\r\n print('The above given mass is {0} pounds.'.format(pounds))\r\n\r\ndef pounds_kg():\r\n pounds=float(input('Please enter the desired mass in Pounds: '))\r\n kg=pounds/2.204\r\n\r\n print('The above given mass is {0} Kilograms.'.format(kg))\r\n\r\ndef c_f():\r\n c=float(input('Please enter the desired temperature in degrees Celsius: '))\r\n f=c*(9/5)+32\r\n\r\n print('The above given temperature is {0} degrees Fahrenheit.'.format(f))\r\n\r\ndef f_c():\r\n f=float(input('Please enter the desired temperature in degrees Fahrenheit: '))\r\n c=(f-32)*(5/9)\r\n\r\n print('The above given temperature value is {0} fegrees Celsius.'.format(c))\r\n\r\nif __name__=='__main__':\r\n while True:\r\n print_menu()\r\n choice=input('Which conversion would you like? ')\r\n \r\n if choice=='1':\r\n km_miles()\r\n \r\n if choice=='2':\r\n miles_km()\r\n \r\n if choice=='3':\r\n kg_pounds()\r\n \r\n if choice=='4':\r\n pounds_kg()\r\n \r\n if choice=='5':\r\n c_f()\r\n \r\n if choice=='6':\r\n f_c()\r\n\r\n answer=input('Do you want to exit? (y) for yes. ')\r\n if answer=='y':\r\n break\r\n \r\n \r\n","sub_path":"Excersises/Chapter 1/5.Give Exit power to the user/Enhanced Unit Calculator.py","file_name":"Enhanced Unit Calculator.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"282075089","text":"# coding: utf-8\n\nfrom ext.db import db\n\n# '''\n# CREATE TABLE `terminal_log` (\n# `id` bigint(20) NOT NULL AUTO_INCREMENT COMMENT '自增id',\n# `no` varchar(80) NOT NULL COMMENT '日志序列号',\n# `terminal_id` bigint(20) NOT NULL COMMENT '终端id（terminal.id）',\n# `operate_time` datetime NOT NULL COMMENT '操作时间',\n# `operate_type` varchar(20) NOT NULL COMMENT '操作类���（1001：激活、1002：认证）',\n# `operate_result` varchar(20) DEFAULT NULL COMMENT '操作结果（101：、102：）',\n# `request_ip` varchar(50) DEFAULT NULL COMMENT '请求ip',\n# `request_desc` varchar(255) DEFAULT NULL COMMENT '请求描述',\n# `duration` int(20) DEFAULT NULL COMMENT '处理时长（单位：ms）',\n# `remark` varchar(255) DEFAULT NULL COMMENT '备注',\n# PRIMARY KEY (`id`)\n# ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='终端操作日志';\n# '''\n\nclass TerminalLog(db.Model):\n __tablename__ = 'terminal_log'\n\n id = db.Column(db.BIGINT(), nullable=False, autoincrement=True, primary_key=True) #, comment='自增id')\n no = db.Column(db.VARCHAR(80), nullable=False) #, comment='日志序列号')\n terminal_id = db.Column(db.BIGINT(), nullable=False,) # comment='终端id（terminal.id）')\n operate_time = db.Column(db.DATETIME, nullable=False) #, comment='操作时间')\n operate_type = db.Column(db.VARCHAR(20), nullable=False) #, comment='操作类型（1001：激活、1002：认证）')\n operate_result = db.Column(db.VARCHAR(20), default=None) #, comment='操作结果（101：、102：）')\n request_ip = db.Column(db.VARCHAR(50), default=None) #, comment='请求ip')\n request_desc = db.Column(db.VARCHAR(255), default=None) #, comment='请求描述')\n duration = db.Column(db.INT(), default=None) #, comment='处理时长（单位：ms）')\n remark = db.Column(db.VARCHAR(255), default=None) #, comment='备注')\n\n def __init__(self, no, terminal_id, operate_time, operate_type, operate_result=None, request_ip=None, request_desc=None, duration=None, remark=None):\n self.no = no\n self.terminal_id = terminal_id\n self.operate_time = operate_time\n self.operate_type = operate_type\n self.operate_result = operate_result\n self.request_ip = request_ip\n self.request_desc = request_desc\n self.duration = duration\n self.remark = remark\n\n # 映射关系\n relation = [\n ('operate_time', 'action_request_time',),\n ('operate_type', 'action_type'),\n ('operate_result', 'action_result'),\n ('request_ip', 'action_request_ip'),\n ('request_desc', 'action_request_body',),\n ('duration', 'action_process_duration',),\n ('remark', 'action_desc',),\n ]\n\n def __repr__(self):\n dct = self.__dict__\n lst = ['{}: {}'.format(k, dct[k]) for k in dct]\n return '< {}\\n{} >\\n'.format(self.__class__.__name__, '\\n'.join(lst))\n","sub_path":"sdwan/hive_bee_action_tracker(Operation Logger)/src/sql_module/terminal_log.py","file_name":"terminal_log.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"37569977","text":"from browser import document, html, ajax, window, alert\nimport urllib.request\nfrom javascript import JSON\n\nmoogleid = '904000103'\ncontainerid = '904000115'\ncontainername = {'en':'Prism Moogle', 'ko':'프리즘 모그리', 'zh':'信賴度莫古利（原晶）', 'es':'Moguri prismático', 'de':'Prismamogry', 'fr':'Prismog'}\n\ndef loadlanguage(event):\n global code\n code = event.target.value\n for b in lang_buttons:\n if 'active' in b.classList:\n b.classList.remove('active')\n event.target.classList.add('active')\n\ndef moogles(event):\n filestatus.style.display = 'inline'\n if 'ion-md-checkmark-circle' in filestatus.classList:\n filestatus.classList.remove('ion-md-checkmark-circle')\n filestatus.classList.add('ion-md-hourglass')\n elif 'ion-md-close-circle' in filestatus.classList:\n filestatus.classList.remove('ion-md-close-circle')\n filestatus.classList.add('ion-md-hourglass')\n languages = ['de', 'es', 'fr', 'ko', 'zh']\n try:\n reqw = ajax.ajax()\n requ = ajax.ajax()\n if code in languages:\n reqw.open('GET', 'https://raw.githubusercontent.com/lyrgard/ffbeEquip/master/static/GL/data_' + code + '.json', False)\n requ.open('GET', 'https://raw.githubusercontent.com/lyrgard/ffbeEquip/master/static/GL/units_' + code + '.json', False)\n else:\n reqw.open('GET', 'https://raw.githubusercontent.com/lyrgard/ffbeEquip/master/static/GL/data.json', False)\n requ.open('GET', 'https://raw.githubusercontent.com/lyrgard/ffbeEquip/master/static/GL/units.json', False)\n reqw.bind('complete', reqwComplete)\n requ.bind('complete', requComplete)\n requ.send()\n reqw.send()\n except:\n alert(\"Error: Can't connect to ffbebuilder!\")\n if 'ion-md-checkmark-circle' in filestatus.classList:\n filestatus.classList.remove('ion-md-checkmark-circle')\n filestatus.classList.add('ion-md-close-circle')\n elif 'ion-md-hourglass' in filestatus.classList:\n filestatus.classList.remove('ion-md-hourglass')\n filestatus.classList.add('ion-md-close-circle')\n reader = window.FileReader.new()\n try:\n reader.readAsText(choosefile.files[0])\n reader.bind('load', process_inventory)\n if 'ion-md-close-circle' in filestatus.classList:\n filestatus.classList.remove('ion-md-close-circle')\n filestatus.classList.add('ion-md-checkmark-circle')\n elif 'ion-md-hourglass' in filestatus.classList:\n filestatus.classList.remove('ion-md-hourglass')\n filestatus.classList.add('ion-md-checkmark-circle')\n\n except IndexError:\n alert('Error: No file was selected!')\n if 'ion-md-checkmark-circle' in filestatus.classList:\n filestatus.classList.remove('ion-md-checkmark-circle')\n filestatus.classList.add('ion-md-close-circle')\n elif 'ion-md-hourglass' in filestatus.classList:\n filestatus.classList.remove('ion-md-hourglass')\n filestatus.classList.add('ion-md-close-circle')\n except:\n alert('Error: Invalid file!')\n if 'ion-md-checkmark-circle' in filestatus.classList:\n filestatus.classList.remove('ion-md-checkmark-circle')\n filestatus.classList.add('ion-md-close-circle')\n elif 'ion-md-hourglass' in filestatus.classList:\n filestatus.classList.remove('ion-md-hourglass')\n filestatus.classList.add('ion-md-close-circle')\n \n\ndef process_inventory(event):\n global inventory\n try:\n inventory = JSON.parse(event.target.result) \n fusedict = {}\n mooglelist = [u['tmrId'] for u in inventory if u['id'] == moogleid]\n containerlist = [u['tmrId'] for u in inventory if u['id'] == containerid if u['tmr'] <1000]\n ownedunits = [u['id'][:-1] for u in inventory if u['id'][0] != '9' if u['tmr'] <1000 ]\n for tmrId in mooglelist:\n tmrName = weapondict[tmrId]['name']\n tmrUnit = weapondict[tmrId]['tmrUnit']\n unitName = unitdict[tmrUnit]['name']\n if tmrUnit[:-1] in ownedunits:\n if tmrId in fusedict.keys():\n fusedict[tmrId]['count'] += 1\n else:\n fusedict.update({tmrId:{'tmr':tmrName, 'unit':unitName, 'count':1, 'unitid':tmrUnit}})\n if tmrId in containerlist:\n if tmrId in fusedict.keys():\n fusedict[tmrId]['count'] += 1\n else:\n fusedict.update({tmrId:{'tmr':tmrName, 'unit':containername[code], 'count':1, 'unitid':containerid}}) \n fusesort = sorted(fusedict.items(), key = lambda item: item[1]['tmr'])\n document['results'].clear()\n table = html.TABLE(Class='table table-hover')\n tbody = html.TBODY()\n table <= tbody\n for l in fusesort:\n tbody <= html.TR([html.TD(l[1]['tmr']), html.TD('\\tx' + str(l[1]['count']) + '\\t'), html.TD(l[1]['unit']), html.TD(html.IMG(src='https://ffbeequip.com/img/units/unit_icon_' + l[1]['unitid'] + '.png' ) )] )\n document['results'] <= table\n except:\n alert('Error: Invalid file!')\n if 'ion-md-checkmark-circle' in filestatus.classList:\n filestatus.classList.remove('ion-md-checkmark-circle')\n filestatus.classList.add('ion-md-close-circle')\n elif 'ion-md-hourglass' in filestatus.classList:\n filestatus.classList.remove('ion-md-hourglass')\n filestatus.classList.add('ion-md-close-circle') \n\ndef reqwComplete(request):\n global weapondict\n weapondata = JSON.parse(request.responseText)\n weapondict = {w['id']:{'name':w['name'], 'tmrUnit':w['tmrUnit']} for w in weapondata if 'tmrUnit' in w.keys()}\n\ndef requComplete(request):\n global unitdict\n unitdict = JSON.parse(request.responseText)\n \n\ncode = 'en'\ndocument['privatebrowsing'].style.display = 'none'\ntoolbar = html.DIV(id='toolbar', Class='container-fluid mx-3 mb-1')\ndocument <= toolbar\nlangbar = html.DIV(id=\"langbar\", Class=\"btn-group btn-group-toggle px-3\", data_toggle=\"buttons\", role=\"group\", aria_label=\"Select language\")\nlanglabel = html.BUTTON(Class='btn-secondary rounded-left', disabled=True)\nlangtext = document.createTextNode('Select language: ')\nlanglabel <= langtext\nlangbar <= langlabel\ntoolbar <= langbar\nfilestatus = html.I(Class='icon ion-md-checkmark-circle ml-3')\nfilestatus.style.display = 'none'\ndocbar = html.DIV(id='docbar', Class='btn-group px-3')\ndoclabel = html.BUTTON(Class='btn-secondary rounded-left', disabled=True)\ndoctext = document.createTextNode('Select file: ')\ndoclabel <= doctext\ndocbar <= doclabel\ntoolbar <= docbar\nchoosefile = html.INPUT(type='file', Class='btn btn-outline-primary')\ndocbar <= choosefile\nbuttonrun = html.BUTTON(id='buttonrun', Class='btn btn-primary px-3')\nbuttontext = document.createTextNode('Show moogles!')\nbuttonrun <= buttontext\ntoolbar <= buttonrun\ntoolbar <= filestatus\n\nresults = html.DIV(id='results', Class='container-fluid')\ndocument <= results\n\nfooter = html.DIV(id='footer', Class='container-fluid')\nhomebutton = html.A(href='https://bosoneando.github.io', Class='btn btn-primary')\nhomelabel = document.createTextNode('Home')\nhomeicon = html.I(Class='icon ion-md-home pr-3')\nhomebutton <= homeicon\nhomebutton <= homelabel\nfooter <= homebutton\nhelpbutton = html.A(href='https://bosoneando.github.io/mooglefuser/help.html', Class='btn btn-primary')\nhelplabel = document.createTextNode('Help')\nhelpicon = html.I(Class='icon ion-md-help-circle pr-3')\nhelpbutton <= helpicon\nhelpbutton <= helplabel\nfooter <= helpbutton\naboutbutton = html.A(href='https://bosoneando.github.io/mooglefuser/about.html', Class='btn btn-primary')\naboutlabel = document.createTextNode('About')\nabouticon = html.I(Class='icon ion-md-information-circle pr-3')\naboutbutton <= abouticon\naboutbutton <= aboutlabel\nfooter <= aboutbutton\ngitbutton = html.A(href='https://github.com/bosoneando/mooglefuser', Class='btn btn-primary')\ngitlabel = document.createTextNode('See code on GitHub')\ngiticon = html.I(Class='icon ion-logo-github pr-3')\ngitbutton <= giticon\ngitbutton <= gitlabel\nfooter <= gitbutton\nbuilderbutton = html.A(href='https://ffbeequip.com/', Class='btn btn-primary')\nbuilderlabel = document.createTextNode('Visit ffbeequip')\nbuildericon = html.I(Class='icon ion-md-cog pr-3')\nbuilderbutton <= buildericon\nbuilderbutton <= builderlabel\nfooter <= builderbutton\ndocument <= footer\n\nunitdict = {}\nweapondict = {}\ninventory = {}\nflags = {'de':'germany', 'en':'great-britain', 'es':'spain', 'fr':'france', 'ko':'south-korea','zh':'china'}\nlang_buttons = []\nfor l, f in flags.items():\n if l == 'en':\n elt = html.BUTTON(Class='btn btn-outline-primary active', type='radio', name='lang', value=l, checked='true', data_toggle='button', title=l)\n else:\n elt = html.BUTTON(Class='btn btn-outline-primary', type='radio', name='lang', value=l, data_toggle='button', title=l)\n txt = html.IMG(src='https://img.icons8.com/color/24/000000/' + flags[l] + '.png', value=l)\n elt <= txt\n elt.bind('click', loadlanguage)\n langbar.appendChild(elt)\n lang_buttons.append(elt)\n\n\n\nbuttonrun.bind('click', moogles)\n","sub_path":"mooglefuser.py","file_name":"mooglefuser.py","file_ext":"py","file_size_in_byte":9290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"433601898","text":"import string\nfrom wordcloud import WordCloud\nfrom collections import Counter\nimport matplotlib.pyplot as plt\n\nclass EmojiCloud:\n def __init__(self,\n font_path='Symbola.ttf',\n color='yellow'):\n self.font_path = font_path\n self.color = color\n self.word_cloud = self.initialize_wordcloud()\n self.emoji_probability = None\n\n def initialize_wordcloud(self):\n word_cloud = WordCloud(font_path=self.font_path,\n width=2000,\n height=1000,\n background_color='white',\n random_state=42,\n collocations=False)\n return word_cloud\n\n def color_func(self, word, font_size, position, orientation, random_state=None,\n **kwargs):\n hue_saturation = {\n 'yellow': '42, 88%',\n 'blue': '194, 49%',\n 'green': '159, 42%',\n 'grey': '45, 2%'\n }.get(self.color)\n\n current_emoji_probability = self.emoji_probability[word]\n if current_emoji_probability >= 0.20:\n opacity = 50\n else:\n opacity = 75 - current_emoji_probability/0.2 * 5\n return f\"hsl({hue_saturation},{opacity}%)\"\n\n def generate(self, emojis):\n emoji_frequencies = Counter(emojis)\n total_count = len(emojis)\n self.emoji_probability = {emoji: count/total_count for emoji, count in emoji_frequencies.items()}\n wc = self.word_cloud.generate_from_frequencies(emoji_frequencies)\n plt.imshow(wc.recolor(color_func=self.color_func, random_state=42),\n interpolation=\"bilinear\")\n plt.axis(\"off\")","sub_path":"gists/emoji_cloud_class.py","file_name":"emoji_cloud_class.py","file_ext":"py","file_size_in_byte":1738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"630964810","text":"from sqlalchemy.types import TypeDecorator, LargeBinary\nimport msgpack\n\nfrom alpaca.common.persistence.types.mutable_dictionary import (\n MutableDictionary,\n)\n\n\nclass SerializedDictionary(TypeDecorator):\n\n impl = LargeBinary\n encoding = 'utf-8'\n\n def process_bind_param(self, value, dialect):\n if value is not None:\n value = msgpack.packb(value.data, encoding=self.encoding)\n return value\n\n def process_result_value(self, value, dialect):\n if value is not None:\n value = msgpack.unpackb(value, encoding=self.encoding)\n return value\n\n\nMutableDictionary.associate_with(SerializedDictionary)\n","sub_path":"alpaca/common/persistence/types/serialized_dictionary.py","file_name":"serialized_dictionary.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"320601344","text":"# Write your code below this line 👇\n\nimport math\n\n\ndef prime_checker(number):\n prime = True\n if number <= 1:\n prime = False\n for divisor in range(2, math.ceil(math.sqrt(number + 1))):\n if (number % divisor == 0):\n prime = False\n if prime:\n print(\"It's a prime number. \")\n else:\n print(\"It's not a prime number. \")\n\n\n# Write your code above this line 👆\n\n# Do NOT change any of the code below👇\nn = int(input(\"Check this number: \"))\nprime_checker(number=n)\n","sub_path":"Day_8_82_Interactive_Coding_Exercise_Prime_Number_Checker/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"300051830","text":"import random\r\nimport numpy as np\r\nfrom nprime.pyprime import miller_rabin\r\nimport json\r\n\r\nfirstBinary = ''\r\nsecondBinary = ''\r\nxorData = []\r\nfirstBinaryArr = []\r\nsecondBinaryArr = []\r\nproNum1Arr = []\r\n\r\nfor x in range(50000):\r\n numList = []\r\n\r\n def checkPrime():\r\n count = 0\r\n while count < 4:\r\n num = str(random.randint(1000000000, 2000000000))\r\n last = num[len(num) - 1]\r\n if int(last) != 0 and int(last) != 5 and int(last) % 2 != 0:\r\n num = int(num)\r\n\r\n prime = miller_rabin(num, 40)\r\n\r\n if prime:\r\n # print(num)\r\n # print(\"YES\")\r\n numList.append(num)\r\n count += 1\r\n # else:\r\n # print(\"NO\")\r\n\r\n # count += 1\r\n if count == 2:\r\n break\r\n\r\n def getBinary():\r\n checkPrime()\r\n # print(numList)\r\n # print()\r\n\r\n # BASE 10 Operations\r\n # print('IN BASE 10:')\r\n onebin = numList[0]\r\n twobin = numList[1]\r\n proNum = onebin * twobin\r\n\r\n results = [proNum, onebin, twobin]\r\n return results\r\n\r\n getBinaryValues = getBinary()\r\n\r\n # Collect the binary values and their product\r\n proNum1 = str(getBinaryValues[0])\r\n firstBinary = str(getBinaryValues[1])\r\n secondBinary = str(getBinaryValues[2])\r\n\r\n firstBinaryArr.append(firstBinary)\r\n secondBinaryArr.append(secondBinary)\r\n proNum1Arr.append(proNum1)\r\n\r\n# print(percentData, len(percentData))\r\n\r\nf = open(\"week4.txt\", \"w\")\r\nfor x in range(len(proNum1Arr)):\r\n f.write(firstBinaryArr[x] + ' ' + secondBinaryArr[x] + ' ' + proNum1Arr[x] + '\\n')\r\nf.close()\r\n\r\n# listFile = []\r\n# with open('myFile.txt') as f:\r\n# for line in f:\r\n# listFile.append(line.strip())\r\n \r\n# listFileline = (listFile[0]).split(' ')\r\n# print(listFileline)\r\n# f.writelines(str(percentData))\r\n# for x in range(len(percentData)):\r\n# f.write(percentData[x])\r\n\r\n# f = open(\"myFile.txt\", \"r\")\r\n# print(f.read())\r\n# f.close()","sub_path":"MyPython/nitro.py","file_name":"nitro.py","file_ext":"py","file_size_in_byte":2123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"488962413","text":"def hessian_free_newton(oracle: NewtonOracle, x: np.array, tolerance, max_iter, c1, display=True):\n benchmark = tolerance * norm(oracle.grad(x)) ** 2\n\n message = \"iterations_exceeded\"\n for i in range(max_iter + 1):\n func_val = oracle.func(x)\n grad = oracle.grad(x)\n grad_norm = norm(grad)\n\n # recorder.make_record_opt(func_val, grad_norm, x_k)\n\n if not valid_value(func_val):\n if display:\n print(\"computational error because of func_val: {}\".format(func_val))\n message = \"computational_error\"\n break\n\n if grad_norm ** 2 <= benchmark:\n message = \"success\"\n break\n\n hess = oracle.hess(x)\n\n theta = min(0.5, grad_norm ** 0.5)\n # print(\"theta = {}\".format(theta))\n direction, _ = cg(hess, -grad, -grad, theta)\n # print(\"np.dot(dir, grad) = {}\".format(np.dot(direction, grad)))\n\n while np.dot(direction, grad) >= 0:\n print(\"theta = {}\".format(theta))\n theta /= 10\n direction, _ = cg(hess, -grad, direction, theta)\n if np.invert(valid_value(direction)).any():\n if display:\n print(\"computational error because of invalid direction: {}\".format(direction))\n message = \"computational_error\"\n break\n\n if message == \"computational_error\":\n break\n\n max_alpha = get_alpha_max(x, direction)\n # print(\"alpha max = {}\".format(max_alpha))\n alpha = armijo_backtracking(oracle.func, grad, direction, x, get_alpha_max(x, direction), c1)\n # print(\"alpha = {}\".format(alpha))\n if not valid_value(alpha):\n if display:\n print(\"computational error because of alpha: {}\".format(alpha))\n message = \"computational_error\"\n break\n\n x = x + direction * alpha\n\n return x, message # , recorder.history\n\n\ndef conjugate_gradients(matvec, b, x_0, tolerance, max_iter=None):\n \"\"\"\n Solves system Ax=b using Conjugate Gradients method.\n Parameters\n ----------\n matvec : function\n Implement matrix-vector product of matrix A and arbitrary vector x\n b : 1-dimensional np.array\n Vector b for the system.\n x_0 : 1-dimensional np.array\n Starting point of the algorithm\n tolerance : float\n Epsilon value for stopping criterion.\n Stop optimization procedure and return x_k when:\n ||Ax_k - b||_2 <= tolerance * ||b||_2\n max_iter : int, or None\n Maximum number of iterations. if max_iter=None, set max_iter to n, where n is\n the dimension of the space\n Returns\n -------\n x_star : np.array\n The point found by the optimization procedure\n \"\"\"\n x = np.copy(x_0)\n\n if max_iter is None:\n max_iter = len(b)\n\n benchmark = tolerance * norm(b)\n Ax = matvec(x)\n g = Ax - b\n d = -g\n Ad = matvec(d)\n for _ in range(max_iter + 1):\n if norm(g) < benchmark:\n break\n x, Ax, g, d, Ad = _do_conjugate_grad_iter(x, Ax, g, d, Ad, b, matvec)\n\n return x\n\n\ndef _do_conjugate_grad_iter(x, Ax, g, d, Ad, b, matvec):\n \"\"\"Helper function for conjugate gradient method for solving equations\"\"\"\n alpha = np.dot(g, g) / np.dot(Ad, d)\n x_next = x + alpha * d\n Ax_next = Ax + alpha * Ad\n\n g_next = Ax_next - b\n d_next = -g_next + np.dot(g_next, g_next) / np.dot(g, g) * d\n Ad_next = matvec(d_next)\n\n return x_next, Ax_next, g_next, d_next, Ad_next\n","sub_path":"practical assignments/task 3/hessian_free_newton_stuff.py","file_name":"hessian_free_newton_stuff.py","file_ext":"py","file_size_in_byte":3536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"296057329","text":"\nclass Create_Prism(object):\n\n def __init__(self, length, width, height, ID):\n self.length = length\n self.width = width\n self.ID = ID\n self.height = height\n\n def __str__(self):\n strprint = \"\\nPrism ID: \" + str(self.ID) + \" units\\n\"\n strprint += \"Length: \"+ str(self.length) + \" units\\n\"\n strprint += \"Width: \" + str(self.width) + \" units\\n\"\n strprint += \"Surface Area: \" + str(2 * ((self.width * self.length) + (self.height * self.length) + (self.height * self.width))) + \" units\\n\"\n strprint += \"Volume: \" + str((self.height) * (self.width) * (self.length)) + \" units\"\n strprint += \"\\n\"\n return strprint\n\n\nlistofboxes = []\ntotalBoxes = 1\nmenu = \"\"\"\n 0 - Create A Prism\n 1 - Print a Prism\n 2 - Print all Prisms\n 3 - Exit\n \"\"\"\n\nprint(menu)\n\noption = input(\"\\nWhat would you like to do? (0-3): \")\n\nwhile option != \"3\":\n\n \n if option == \"0\":\n length = int(input(\"Length: \"))\n width = int(input(\"Width: \"))\n height = int(input(\"Height: \"))\n ID = totalBoxes\n\n if totalBoxes == 1:\n prism = Create_Prism(length, width, height, ID)\n listofboxes.append(prism)\n elif totalBoxes == 2:\n prism_2 = Create_Prism(length, width, height, ID)\n listofboxes.append(prism_2)\n elif totalBoxes == 3:\n prism_3 = Create_Prism(length, width, height, ID)\n listofboxes.append(prism_3)\n totalBoxes += 1\n if option == \"1\":\n thisIsTrue = 1\n while thisIsTrue:\n try:\n if listofboxes:\n print(\"There are \" + str(len(listofboxes)) + \" boxes already created.\\n\")\n else:\n print(\"You have not created any prisms yet.\")\n the_prism = int(input(\"Which one, (0-3) 0 being the first: \"))\n print(listofboxes[the_prism])\n thisIsTrue = False\n except (IndexError, ValueError):\n print(\"That prism does not exist.\")\n if option == \"2\":\n if listofboxes:\n for i in listofboxes:\n print(i)\n else:\n print(\"There are no boxes to print\")\n\n print(menu)\n option = input(\"\\nWhat would you like to do?: \")\n\n","sub_path":"Excercise 2.py","file_name":"Excercise 2.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"72039729","text":"# reference http://www.cnblogs.com/huangcong/archive/2011/08/29/2158268.html\n# https://www.codecademy.com/en/tracks/python\n# \n# http://www.linuxidc.com/Linux/2012-02/53764.htm\n# \n# http://blog.csdn.net/andy812110/article/details/43153895\n# print(\"hello world\")\n\n# a = input(\"a\")\n\n# 题目描述\n\n# 请用python编写一个计算器的控制台程序，支持加减乘除、乘方、括号、小数点，运算符优先级为括号>乘方>乘除>加减，同级别运算按照从左向右的顺序计算。\n\n# 输入描述\n\n# 数字包括\"0123456789\"，小数点为\".\"，运算符包括：加(\"+\"),减(\"-\"),乘(\"*\"),除(\"/\"),乘方(\"^\"),括号(\"()\")\n# 需要从命令行参数读入输入，例如提交文件为main.py，可以用python main.py 1+2-3+4的方式进行调用\n# 输入需要支持空格，即 python main.py 1 + 2 - 3 + 4 也需要程序能够正确给出结果\n# 输出描述\n\n# 数字需要支持小数点，输出结果取10位有效数字，有效数字位数不足时不需要补0\n# 对于格式不合法（例如括号不匹配，等等）的输入，输出 FORMAT ERROR\n# 对于不符合运算符接收的参数范围（例如）的输入，输出VALUE ERROR\n# 对于不在输入描述内的输入，输出INPUT ERROR\n# 限制\n\n# 所有测试用例中参与运算的非零数字输入的绝对值范围保证在 10^9-10^(-10) 之内, 应该输出运算结果时非零运算结果绝对值也保证在该范围内\n\n# 样例\n\n# 输入: 1 + 2 - 3 + 4\n\n# 输出: 4\n\n# 输入: 1 + 2 - 3 + 1 / 3\n\n# 输出: 0.3333333333\n\n# 输入: 1 + + 2\n\n# 输出: FORMAT ERROR\n\n# 输入: 1 / 0\n\n# 输出: VALUE ERROR\n\n# 输入: a + 1\n\n# 输出: INPUT ERROR\n\n\n# print(a)\n\ndelimiter = ''\n# mylist = ['Brazil', 'Russia', 'India', 'China']\n# print (delimiter.join(mylist))\n\nimport sys\n\nprint(__name__)\n# print (\"脚本名：\", sys.argv[0])\n# for i in range(1, len(sys.argv)):\n# print (\"参数\", i, sys.argv[i] )\n# print(delimiter.join(sys.argv[1:]))\n# print(eval(sys.argv[1]))\n# \ninput_string = delimiter.join(sys.argv[1:])\n\nprint()\n\n# print(input_string)\n\n\n\n# check parence\n\nbrackets_pairs = True\nfor x in input_string:\n\tif x == '(' :\n\t\tbrackets_pairs = False\n\t# elif brackets_pairs == False and x==')':\n\telif x==')':\n\t\tif brackets_pairs == False :\n\t\t\tbrackets_pairs = True\n\t\telse :\n\t\t\tbrackets_pairs = False\n\t\t\tbreak\n# print(brackets_pairs)\nif not brackets_pairs :\n\tprint(\"FORMAT ERROR\")\n\tsys.exit()\n\t# return\n\nprint(eval(input_string))\n","sub_path":"python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"180463711","text":"import os\n\nfolder_mama = r'C:\\Users\\Victor\\Desktop\\Tiktok' + '\\\\'\n\nfor folder, dir, files in os.walk(folder_mama):\n #print(folder.split())\n arr_folder = folder.split()\n arr_folder.pop(0)\n separator = ''\n nume_grafica = separator.join(arr_folder)\n #print(nume_grafica)\n for file in os.listdir(folder):\n #print(file)\n os.chdir(folder)\n #print(os.getcwd())\n nume_fisier_intreg = file.split('.')\n nume_fisier = nume_fisier_intreg[0]\n #print(nume_fisier)\n new_name = '{} {}.jpg'.format(nume_fisier, nume_grafica)\n print(new_name)\n #os.rename(file, new_name)\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"100187961","text":"from unittest import TestCase, main\nfrom selenium import webdriver\nfrom pyunitreport import HTMLTestRunner\nfrom google_page import GooglePage\n\nclass GoogleTest(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.driver = webdriver.Chrome(executable_path='./chromedriver.exe')\n\n def test_search(self):\n google = GooglePage(self.driver)\n google.open()\n google.search(\"Platzi\")\n\n self.assertEqual('Platzi', google.keyword)\n\n @classmethod\n def tearDownClass(cls):\n cls.driver.close()\n\n\nif __name__ == \"__main__\":\n main(verbosity=2, testRunner=HTMLTestRunner(\n output=\"google\", report_name=\"test_google\"))\n","sub_path":"pom/test_google.py","file_name":"test_google.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"331732517","text":"model = dict(\n type='FCOSMono3D',\n pretrained='open-mmlab://detectron2/resnet101_caffe',\n backbone=dict(\n type='ResNet',\n depth=101,\n num_stages=4,\n out_indices=(0, 1, 2, 3),\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=False),\n norm_eval=True,\n style='caffe'),\n neck=dict(\n type='FPN',\n in_channels=[256, 512, 1024, 2048],\n out_channels=256,\n start_level=1,\n add_extra_convs='on_output',\n num_outs=5,\n relu_before_extra_convs=True),\n bbox_head=dict(\n type='FCOSMono3DHead',\n num_classes=10,\n in_channels=256,\n stacked_convs=2,\n feat_channels=256,\n use_direction_classifier=True,\n diff_rad_by_sin=True,\n pred_attrs=True,\n pred_velo=True,\n dir_offset=0.7854, # pi/4\n strides=[8, 16, 32, 64, 128],\n group_reg_dims=(2, 1, 3, 1, 2), # offset, depth, size, rot, velo\n cls_branch=(256, ),\n reg_branch=(\n (256, ), # offset\n (256, ), # depth\n (256, ), # size\n (256, ), # rot\n () # velo\n ),\n dir_branch=(256, ),\n attr_branch=(256, ),\n loss_cls=dict(\n type='FocalLoss',\n use_sigmoid=True,\n gamma=2.0,\n alpha=0.25,\n loss_weight=1.0),\n loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),\n loss_dir=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_attr=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_centerness=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n norm_on_bbox=True,\n centerness_on_reg=True,\n center_sampling=True,\n conv_bias=True,\n dcn_on_last_conv=True),\n train_cfg=dict(\n allowed_border=0,\n code_weight=[1.0, 1.0, 0.2, 1.0, 1.0, 1.0, 1.0, 0.05, 0.05],\n pos_weight=-1,\n debug=False),\n test_cfg=dict(\n use_rotate_nms=True,\n nms_across_levels=False,\n nms_pre=1000,\n nms_thr=0.8,\n score_thr=0.05,\n min_bbox_size=0,\n max_per_img=200))\n","sub_path":"autonomous_driving/occupancy_prediction/projects/configs/_base_/models/fcos3d.py","file_name":"fcos3d.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"266931417","text":"import csv\r\nimport sys\r\nimport keras\r\nimport numpy as np\r\nfrom keras.models import Sequential, model_from_json, Model\r\nfrom keras.callbacks import EarlyStopping, Callback\r\nfrom keras.layers import Dense, Embedding, Dropout, Bidirectional, Flatten\r\nfrom keras.layers import Dot,Add,Concatenate,Input,Reshape,Merge\r\n\r\n\t\r\ndef Normalize_all(data):\r\n\tdata_return = np.zeros(shape = data.shape)\r\n\tfor i in range(data.shape[1]):\r\n\t\tmean = np.mean(data[:,i])\r\n\t\tstd = np.mean(data[:,i])\r\n\t\tdata_return[:,i] = (data[:,i]-mean)/std\r\n\treturn data_return\r\n\t\r\ndef deNormalize(y):\r\n\tnorm_y = (y*std)+mean\r\n\tnorm_y = norm_y.reshape(y.shape[0],1)\r\n\treturn norm_y\r\n\t\r\nnp.set_printoptions(suppress=True)\r\nnp.random.seed(20171209)\r\nTestFilePath = (sys.argv)[1]\r\noutFilePath = (sys.argv)[2]\r\n\r\nuser_test = []\r\nmovies_test = []\r\ndata = []\r\n\r\nbatch_size = 256\r\nmean = 0#3.58171\r\nstd = 1#1.1169\r\n\r\nprint('Parsing Data...')\r\n# reading csv to data\r\nwith open(TestFilePath, 'r') as csvfile:\r\n spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\r\n count = 0\r\n for row in spamreader:\t\r\n if(count == 0):\r\n count += 1\r\n continue\r\n data.append(row)\r\n \r\ndata = np.array(data).astype('int32')\r\n#np.random.shuffle(data)\r\n\r\nnumOfUser = 6040 #np.max(data[:,1])\r\nnumOfMovies = 3952 #np.max(data[:,2]) \r\n#print(len(userIdMap)) # 6040\r\n#print(len(movieIdMap)) # 3688\r\n\t\r\nuser_test = np.array(data[:,1]).reshape(data.shape[0],1).astype('int32')\r\nmovies_test = np.array(data[:,2]).reshape(data.shape[0],1).astype('int32')\r\n\r\nprint('user_test.shape :',user_test.shape)\r\nprint('movies_test.shape :',movies_test.shape)\r\n\r\n# load json and create model\r\njson_file = open('MF_model_frame.json', 'r')\r\nloaded_model_json = json_file.read()\r\njson_file.close()\r\nmodel = model_from_json(loaded_model_json)\r\nmodel.load_weights(\"MF_model_weight.h5\")\r\n\r\nmodel.summary()\r\n\r\nmodel.compile(loss='mse', optimizer=keras.optimizers.Adamax())#,decay=0.0001\r\n\r\n\r\ny_pred = model.predict([user_test, movies_test], verbose = 1, batch_size=batch_size)\r\ny_pred = deNormalize(y_pred)\r\n\r\nprint('y_pred.shape = ', y_pred.shape)\r\nprint(y_pred[:20,:])\r\n\r\n\r\n# writing output csv \r\nwith open(outFilePath, 'w', newline='') as csvfile:\r\n\tspamwriter = csv.writer(csvfile, delimiter=' ',quotechar='|', quoting=csv.QUOTE_MINIMAL)\r\n\tspamwriter.writerow(['TestDataID,Rating'])\r\n\tfor i in range(y_pred.shape[0]):\r\n\t\trow_list = [','.join([str(i+1),str(float(y_pred[i,0]))])]\r\n\t\tspamwriter.writerow(row_list)\r\n","sub_path":"hw5/hw5_MF_test.py","file_name":"hw5_MF_test.py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"409341691","text":"import re\n\nfrom django import template\nfrom django.utils.safestring import mark_safe\n\nregister = template.Library()\n\ndef link_seek(value):\n if not value:\n return None\n for mins, secs in re.findall(\"(\\d\\d?):(\\d\\d)\", value):\n time = int(mins)*60 + int(secs)\n link = \"\"\"%s:%s\"\"\" % (str(time), mins, secs)\n value = value.replace(\":\".join((mins,secs)), link)\n return mark_safe(value)\nregister.filter('link_seek', link_seek)\n","sub_path":"encoder/templatetags/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"34454983","text":"#!/usr/bin/env python\n#-*- coding:utf8 -*-\n\nimport os, sys, logging\n\nlogging.basicConfig(\n level=logging.DEBUG,\n format='%(asctime)s %(levelname)s %(message)s',\n filename='/var/log/teleport_restart.log'\n)\n\ndef run():\n try:\n res1 = os.popen('ps -C tp_core -o pid,cmd').readlines()\n res2 = os.popen('ps -C tp_web -o pid,cmd').readlines()\n if len(res1) < 2 or len(res2) < 2:\n logging.warning(\"teleport need to restart\")\n if os.system(\"/etc/init.d/teleport restart\") == 0: # 0表示命令执行成功，1表示失败\n logging.info(\"teleport restart successful\")\n except:\n logging.error(sys.exc_info()[1])\n\nif __name__ == '__main__':\n run()\n","sub_path":"teleport_restart(crond).py","file_name":"teleport_restart(crond).py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"172375055","text":"import bpy, json, os, os.path\n\ndef load_json():\n fr = open('test_profile.json','r')\n jsonData = json.load(fr)\n # print(json.dump(jsonData, fr, indent=2))\n # print(jsonData)\n return jsonData\n\n# レンダレイヤの設定\ndef load_render_layers(context, Rlayers):\n # print(Rlayers)\n for Rlayer in Rlayers:\n layers = Rlayers[Rlayer][\"layer\"]\n exclude = Rlayers[Rlayer][\"exclude\"]\n mask = Rlayers[Rlayer][\"mask\"]\n\n # print(\"\\n\" + Rlayer + \": \")\n # print(\"\\tlayer : \" + str(layers))\n # print(\"\\texclude : \" + str(exclude))\n # print(\"\\tmask : \" + str(mask))\n\n new_render_layer = context.scene.render.layers.new(Rlayer)\n for i in range(1,20):\n if i in layers:\n new_render_layer.layers[i] = True\n else:\n new_render_layer.layers[i] = False\n if i in exclude:\n new_render_layer.layers_exclude[i] = True\n else:\n new_render_layer.layers_exclude[i] = False\n if i in mask:\n new_render_layer.layers_zmask[i] = True\n else:\n new_render_layer.layers_zmask[i] = False\n\ndef load_objects(context, blend_file, objects):\n blend_file = blend_file + \"\\\\Object\\\\\"\n for obj_name in objects:\n bpy.ops.wm.append(filename = obj_name, directory = blend_file)\n obj = bpy.data.objects[obj_name]\n for i in range(1,20):\n if i in objects[obj_name][\"obj_layers\"]:\n obj.layers[i] = True\n else:\n obj.layers[i] = False\n\ndef load_initial_settings(context, initial_settings):\n # 解像度\n context.scene.render.resolution_x = initial_settings[\"resolution\"][\"x\"]\n context.scene.render.resolution_y = initial_settings[\"resolution\"][\"y\"]\n # カラーマネジメント\n context.scene.view_settings.view_transform = initial_settings[\"color_management\"][\"view\"]\n context.scene.view_settings.exposure = initial_settings[\"color_management\"][\"exposure\"]\n context.scene.view_settings.gamma = initial_settings[\"color_management\"][\"gamma\"]\n context.scene.view_settings.look = initial_settings[\"color_management\"][\"look\"]\n\ncontext = bpy.context\nfp = bpy.data.filepath\ndirectory = os.path.dirname(fp)\nos.chdir(directory)\nappend_file = \"G:\\\\共有ドライブ\\\\10 PJフォルダ\\\\三陽商会\\\\SnS_CD\\\\design_work\\\\2019AW\\\\MENS_SETUP\\\\10_PRODUCTION\\\\04_blender_data\\\\master_rendering.blend\"\n\njsonData = load_json()\n# load_render_layers(jsonData[\"Rlayers\"])\n# load_objects(context, append_file, jsonData[\"objects\"])\nload_initial_settings(context, jsonData[\"initial_settings\"])","sub_path":"blender_scripts/json_to_blender_settings.py","file_name":"json_to_blender_settings.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"188226025","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build\\bdist.win-amd64\\egg\\xlrd\\xlsx.py\n# Compiled at: 2013-10-17 14:03:42\nfrom __future__ import print_function, unicode_literals\nDEBUG = 0\nimport sys, re\nfrom .timemachine import *\nfrom .book import Book, Name\nfrom .biffh import error_text_from_code, XLRDError, XL_CELL_BLANK, XL_CELL_TEXT, XL_CELL_BOOLEAN, XL_CELL_ERROR\nfrom .formatting import is_date_format_string, Format, XF\nfrom .sheet import Sheet\nDLF = sys.stdout\nET = None\nET_has_iterparse = False\n\ndef ensure_elementtree_imported(verbosity, logfile):\n global ET\n global ET_has_iterparse\n if ET is not None:\n return\n else:\n if b'IronPython' in sys.version:\n import xml.etree.ElementTree as ET\n else:\n try:\n import xml.etree.cElementTree as ET\n except ImportError:\n try:\n import cElementTree as ET\n except ImportError:\n try:\n import lxml.etree as ET\n except ImportError:\n try:\n import xml.etree.ElementTree as ET\n except ImportError:\n try:\n import elementtree.ElementTree as ET\n except ImportError:\n raise Exception(b'Failed to import an ElementTree implementation')\n\n if hasattr(ET, b'iterparse'):\n _dummy_stream = BYTES_IO(b'')\n try:\n ET.iterparse(_dummy_stream)\n ET_has_iterparse = True\n except NotImplementedError:\n pass\n\n if verbosity:\n etree_version = repr([ (item, getattr(ET, item)) for item in ET.__dict__.keys() if item.lower().replace(b'_', b'') == b'version'\n ])\n print(ET.__file__, ET.__name__, etree_version, ET_has_iterparse, file=logfile)\n return\n\n\ndef split_tag(tag):\n pos = tag.rfind(b'}') + 1\n if pos >= 2:\n return (tag[:pos], tag[pos:])\n return (\n b'', tag)\n\n\ndef augment_keys(adict, uri):\n for x in list(adict.keys()):\n adict[uri + x] = adict[x]\n\n\n_UPPERCASE_1_REL_INDEX = {}\nfor _x in xrange(26):\n _UPPERCASE_1_REL_INDEX[b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'[_x]] = _x + 1\n\nfor _x in b'123456789':\n _UPPERCASE_1_REL_INDEX[_x] = 0\n\ndel _x\n\ndef cell_name_to_rowx_colx(cell_name, letter_value=_UPPERCASE_1_REL_INDEX):\n colx = 0\n charx = -1\n try:\n for c in cell_name:\n charx += 1\n lv = letter_value[c]\n if lv:\n colx = colx * 26 + lv\n else:\n colx = colx - 1\n assert 0 <= colx < X12_MAX_COLS\n break\n\n except KeyError:\n raise Exception(b'Unexpected character %r in cell name %r' % (c, cell_name))\n\n rowx = int(cell_name[charx:]) - 1\n return (rowx, colx)\n\n\nerror_code_from_text = {}\nfor _code, _text in error_text_from_code.items():\n error_code_from_text[_text] = _code\n\nU_SSML12 = b'{http://schemas.openxmlformats.org/spreadsheetml/2006/main}'\nU_ODREL = b'{http://schemas.openxmlformats.org/officeDocument/2006/relationships}'\nU_PKGREL = b'{http://schemas.openxmlformats.org/package/2006/relationships}'\nU_CP = b'{http://schemas.openxmlformats.org/package/2006/metadata/core-properties}'\nU_DC = b'{http://purl.org/dc/elements/1.1/}'\nU_DCTERMS = b'{http://purl.org/dc/terms/}'\nXML_SPACE_ATTR = b'{http://www.w3.org/XML/1998/namespace}space'\nXML_WHITESPACE = b'\\t\\n \\r'\nX12_MAX_ROWS = 1048576\nX12_MAX_COLS = 16384\nV_TAG = U_SSML12 + b'v'\nF_TAG = U_SSML12 + b'f'\nIS_TAG = U_SSML12 + b'is'\n\ndef unescape(s, subber=re.compile(b'_x[0-9A-Fa-f]{4,4}_', re.UNICODE).sub, repl=lambda mobj: unichr(int(mobj.group(0)[2:6], 16))):\n if b'_' in s:\n return subber(repl, s)\n return s\n\n\ndef cooked_text(self, elem):\n t = elem.text\n if t is None:\n return b''\n else:\n if elem.get(XML_SPACE_ATTR) != b'preserve':\n t = t.strip(XML_WHITESPACE)\n return ensure_unicode(unescape(t))\n\n\ndef get_text_from_si_or_is(self, elem, r_tag=U_SSML12 + b'r', t_tag=U_SSML12 + b't'):\n \"\"\"Returns unescaped unicode\"\"\"\n accum = []\n for child in elem:\n tag = child.tag\n if tag == t_tag:\n t = cooked_text(self, child)\n if t:\n accum.append(t)\n elif tag == r_tag:\n for tnode in child:\n if tnode.tag == t_tag:\n t = cooked_text(self, tnode)\n if t:\n accum.append(t)\n\n return (b'').join(accum)\n\n\ndef map_attributes(amap, elem, obj):\n for xml_attr, obj_attr, cnv_func_or_const in amap:\n if not xml_attr:\n setattr(obj, obj_attr, cnv_func_or_const)\n continue\n if not obj_attr:\n continue\n raw_value = elem.get(xml_attr)\n cooked_value = cnv_func_or_const(raw_value)\n setattr(obj, obj_attr, cooked_value)\n\n\ndef cnv_ST_Xstring(s):\n if s is None:\n return b''\n else:\n return ensure_unicode(s)\n\n\ndef cnv_xsd_unsignedInt(s):\n if not s:\n return None\n else:\n value = int(s)\n assert value >= 0\n return value\n\n\ndef cnv_xsd_boolean(s):\n if not s:\n return 0\n if s in ('1', 'true', 'on'):\n return 1\n if s in ('0', 'false', 'off'):\n return 0\n raise ValueError(b'unexpected xsd:boolean value: %r' % s)\n\n\n_defined_name_attribute_map = (\n (\n b'name', b'name', cnv_ST_Xstring),\n (\n b'comment', b'', cnv_ST_Xstring),\n (\n b'customMenu', b'', cnv_ST_Xstring),\n (\n b'description', b'', cnv_ST_Xstring),\n (\n b'help', b'', cnv_ST_Xstring),\n (\n b'statusBar', b'', cnv_ST_Xstring),\n (\n b'localSheetId', b'scope', cnv_xsd_unsignedInt),\n (\n b'hidden', b'hidden', cnv_xsd_boolean),\n (\n b'function', b'func', cnv_xsd_boolean),\n (\n b'vbProcedure', b'vbasic', cnv_xsd_boolean),\n (\n b'xlm', b'macro', cnv_xsd_boolean),\n (\n b'functionGroupId', b'funcgroup', cnv_xsd_unsignedInt),\n (\n b'shortcutKey', b'', cnv_ST_Xstring),\n (\n b'publishToServer', b'', cnv_xsd_boolean),\n (\n b'workbookParameter', b'', cnv_xsd_boolean),\n ('', 'any_err', 0),\n ('', 'any_external', 0),\n ('', 'any_rel', 0),\n ('', 'basic_formula_len', 0),\n ('', 'binary', 0),\n ('', 'builtin', 0),\n ('', 'complex', 0),\n ('', 'evaluated', 0),\n ('', 'excel_sheet_index', 0),\n ('', 'excel_sheet_num', 0),\n ('', 'option_flags', 0),\n ('', 'result', None),\n ('', 'stack', None))\n\ndef make_name_access_maps(bk):\n name_and_scope_map = {}\n name_map = {}\n num_names = len(bk.name_obj_list)\n for namex in xrange(num_names):\n nobj = bk.name_obj_list[namex]\n name_lcase = nobj.name.lower()\n key = (name_lcase, nobj.scope)\n if key in name_and_scope_map:\n msg = b'Duplicate entry %r in name_and_scope_map' % (key,)\n if bk.verbosity:\n print(msg, file=bk.logfile)\n name_and_scope_map[key] = nobj\n if name_lcase in name_map:\n name_map[name_lcase].append((nobj.scope, nobj))\n else:\n name_map[name_lcase] = [\n (\n nobj.scope, nobj)]\n\n for key in name_map.keys():\n alist = name_map[key]\n alist.sort()\n name_map[key] = [ x[1] for x in alist ]\n\n bk.name_and_scope_map = name_and_scope_map\n bk.name_map = name_map\n\n\nclass X12General(object):\n\n def process_stream(self, stream, heading=None):\n if self.verbosity >= 2 and heading is not None:\n fprintf(self.logfile, b'\\n=== %s ===\\n', heading)\n self.tree = ET.parse(stream)\n getmethod = self.tag2meth.get\n for elem in self.tree.getiterator():\n if self.verbosity >= 3:\n self.dump_elem(elem)\n meth = getmethod(elem.tag)\n if meth:\n meth(self, elem)\n\n self.finish_off()\n return\n\n def finish_off(self):\n pass\n\n def dump_elem(self, elem):\n fprintf(self.logfile, b'===\\ntag=%r len=%d attrib=%r text=%r tail=%r\\n', split_tag(elem.tag)[1], len(elem), elem.attrib, elem.text, elem.tail)\n\n def dumpout(self, fmt, *vargs):\n text = (b' ' + fmt + b'\\n') % vargs\n self.logfile.write(text)\n\n\nclass X12Book(X12General):\n\n def __init__(self, bk, logfile=DLF, verbosity=False):\n self.bk = bk\n self.logfile = logfile\n self.verbosity = verbosity\n self.bk.nsheets = 0\n self.bk.props = {}\n self.relid2path = {}\n self.relid2reltype = {}\n self.sheet_targets = []\n self.sheetIds = []\n\n core_props_menu = {U_CP + b'lastModifiedBy': (\n b'last_modified_by', cnv_ST_Xstring), \n U_DC + b'creator': (\n b'creator', cnv_ST_Xstring), \n U_DCTERMS + b'modified': (\n b'modified', cnv_ST_Xstring), \n U_DCTERMS + b'created': (\n b'created', cnv_ST_Xstring)}\n\n def process_coreprops(self, stream):\n if self.verbosity >= 2:\n fprintf(self.logfile, b'\\n=== coreProps ===\\n')\n self.tree = ET.parse(stream)\n getmenu = self.core_props_menu.get\n props = {}\n for elem in self.tree.getiterator():\n if self.verbosity >= 3:\n self.dump_elem(elem)\n menu = getmenu(elem.tag)\n if menu:\n attr, func = menu\n value = func(elem.text)\n props[attr] = value\n\n self.bk.user_name = props.get(b'last_modified_by') or props.get(b'creator')\n self.bk.props = props\n if self.verbosity >= 2:\n fprintf(self.logfile, b'props: %r\\n', props)\n self.finish_off()\n\n def process_rels(self, stream):\n if self.verbosity >= 2:\n fprintf(self.logfile, b'\\n=== Relationships ===\\n')\n tree = ET.parse(stream)\n r_tag = U_PKGREL + b'Relationship'\n for elem in tree.findall(r_tag):\n rid = elem.get(b'Id')\n target = elem.get(b'Target')\n reltype = elem.get(b'Type').split(b'/')[(-1)]\n if self.verbosity >= 2:\n self.dumpout(b'Id=%r Type=%r Target=%r', rid, reltype, target)\n self.relid2reltype[rid] = reltype\n if target.startswith(b'/'):\n self.relid2path[rid] = target[1:]\n else:\n self.relid2path[rid] = b'xl/' + target\n\n def do_defined_name(self, elem):\n if 0 and self.verbosity >= 3:\n self.dump_elem(elem)\n nobj = Name()\n bk = self.bk\n nobj.bk = bk\n nobj.name_index = len(bk.name_obj_list)\n bk.name_obj_list.append(nobj)\n nobj.name = elem.get(b'name')\n nobj.raw_formula = None\n nobj.formula_text = cooked_text(self, elem)\n map_attributes(_defined_name_attribute_map, elem, nobj)\n if nobj.scope is None:\n nobj.scope = -1\n if nobj.name.startswith(b'_xlnm.'):\n nobj.builtin = 1\n if self.verbosity >= 2:\n nobj.dump(header=b'=== Name object ===')\n return\n\n def do_defined_names(self, elem):\n for child in elem:\n self.do_defined_name(child)\n\n make_name_access_maps(self.bk)\n\n def do_sheet(self, elem):\n bk = self.bk\n sheetx = bk.nsheets\n rid = elem.get(U_ODREL + b'id')\n sheetId = int(elem.get(b'sheetId'))\n name = unescape(ensure_unicode(elem.get(b'name')))\n reltype = self.relid2reltype[rid]\n target = self.relid2path[rid]\n if self.verbosity >= 2:\n self.dumpout(b'sheetx=%d sheetId=%r rid=%r type=%r name=%r', sheetx, sheetId, rid, reltype, name)\n if reltype != b'worksheet':\n if self.verbosity >= 2:\n self.dumpout(b'Ignoring sheet of type %r (name=%r)', reltype, name)\n return\n state = elem.get(b'state')\n visibility_map = {None: 0, \n b'visible': 0, \n b'hidden': 1, \n b'veryHidden': 2}\n bk._sheet_visibility.append(visibility_map[state])\n sheet = Sheet(bk, position=None, name=name, number=sheetx)\n sheet.utter_max_rows = X12_MAX_ROWS\n sheet.utter_max_cols = X12_MAX_COLS\n bk._sheet_list.append(sheet)\n bk._sheet_names.append(name)\n bk.nsheets += 1\n self.sheet_targets.append(target)\n self.sheetIds.append(sheetId)\n return\n\n def do_workbookpr(self, elem):\n datemode = cnv_xsd_boolean(elem.get(b'date1904'))\n if self.verbosity >= 2:\n self.dumpout(b'datemode=%r', datemode)\n self.bk.datemode = datemode\n\n tag2meth = {b'definedNames': do_defined_names, \n b'workbookPr': do_workbookpr, \n b'sheet': do_sheet}\n augment_keys(tag2meth, U_SSML12)\n\n\nclass X12SST(X12General):\n\n def __init__(self, bk, logfile=DLF, verbosity=0):\n self.bk = bk\n self.logfile = logfile\n self.verbosity = verbosity\n if ET_has_iterparse:\n self.process_stream = self.process_stream_iterparse\n else:\n self.process_stream = self.process_stream_findall\n\n def process_stream_iterparse(self, stream, heading=None):\n if self.verbosity >= 2 and heading is not None:\n fprintf(self.logfile, b'\\n=== %s ===\\n', heading)\n si_tag = U_SSML12 + b'si'\n elemno = -1\n sst = self.bk._sharedstrings\n for event, elem in ET.iterparse(stream):\n if elem.tag != si_tag:\n continue\n elemno = elemno + 1\n if self.verbosity >= 3:\n fprintf(self.logfile, b'element #%d\\n', elemno)\n self.dump_elem(elem)\n result = get_text_from_si_or_is(self, elem)\n sst.append(result)\n elem.clear()\n\n if self.verbosity >= 2:\n self.dumpout(b'Entries in SST: %d', len(sst))\n if self.verbosity >= 3:\n for x, s in enumerate(sst):\n fprintf(self.logfile, b'SST x=%d s=%r\\n', x, s)\n\n return\n\n def process_stream_findall(self, stream, heading=None):\n if self.verbosity >= 2 and heading is not None:\n fprintf(self.logfile, b'\\n=== %s ===\\n', heading)\n self.tree = ET.parse(stream)\n si_tag = U_SSML12 + b'si'\n elemno = -1\n sst = self.bk._sharedstrings\n for elem in self.tree.findall(si_tag):\n elemno = elemno + 1\n if self.verbosity >= 3:\n fprintf(self.logfile, b'element #%d\\n', elemno)\n self.dump_elem(elem)\n result = get_text_from_si_or_is(self, elem)\n sst.append(result)\n\n if self.verbosity >= 2:\n self.dumpout(b'Entries in SST: %d', len(sst))\n return\n\n\nclass X12Styles(X12General):\n\n def __init__(self, bk, logfile=DLF, verbosity=0):\n self.bk = bk\n self.logfile = logfile\n self.verbosity = verbosity\n self.xf_counts = [0, 0]\n self.xf_type = None\n self.fmt_is_date = {}\n for x in list(range(14, 23)) + list(range(45, 48)):\n self.fmt_is_date[x] = 1\n\n self.bk._xf_index_to_xl_type_map[0] = 2\n return\n\n def do_cellstylexfs(self, elem):\n self.xf_type = 0\n\n def do_cellxfs(self, elem):\n self.xf_type = 1\n\n def do_numfmt(self, elem):\n formatCode = ensure_unicode(elem.get(b'formatCode'))\n numFmtId = int(elem.get(b'numFmtId'))\n is_date = is_date_format_string(self.bk, formatCode)\n self.fmt_is_date[numFmtId] = is_date\n fmt_obj = Format(numFmtId, is_date + 2, formatCode)\n self.bk.format_map[numFmtId] = fmt_obj\n if self.verbosity >= 3:\n self.dumpout(b'numFmtId=%d formatCode=%r is_date=%d', numFmtId, formatCode, is_date)\n\n def do_xf(self, elem):\n if self.xf_type != 1:\n return\n xfx = self.xf_counts[self.xf_type]\n self.xf_counts[self.xf_type] = xfx + 1\n xf = XF()\n self.bk.xf_list.append(xf)\n self.bk.xfcount += 1\n numFmtId = int(elem.get(b'numFmtId', b'0'))\n xf.format_key = numFmtId\n is_date = self.fmt_is_date.get(numFmtId, 0)\n self.bk._xf_index_to_xl_type_map[xfx] = is_date + 2\n if self.verbosity >= 3:\n self.dumpout(b'xfx=%d numFmtId=%d', xfx, numFmtId)\n self.dumpout(repr(self.bk._xf_index_to_xl_type_map))\n\n tag2meth = {b'cellStyleXfs': do_cellstylexfs, \n b'cellXfs': do_cellxfs, \n b'numFmt': do_numfmt, \n b'xf': do_xf}\n augment_keys(tag2meth, U_SSML12)\n\n\nclass X12Sheet(X12General):\n\n def __init__(self, sheet, logfile=DLF, verbosity=0):\n self.sheet = sheet\n self.logfile = logfile\n self.verbosity = verbosity\n self.rowx = -1\n self.bk = sheet.book\n self.sst = self.bk._sharedstrings\n self.warned_no_cell_name = 0\n self.warned_no_row_num = 0\n if ET_has_iterparse:\n self.process_stream = self.own_process_stream\n\n def own_process_stream(self, stream, heading=None):\n if self.verbosity >= 2 and heading is not None:\n fprintf(self.logfile, b'\\n=== %s ===\\n', heading)\n getmethod = self.tag2meth.get\n row_tag = U_SSML12 + b'row'\n self_do_row = self.do_row\n for event, elem in ET.iterparse(stream):\n if elem.tag == row_tag:\n self_do_row(elem)\n elem.clear()\n elif elem.tag == U_SSML12 + b'dimension':\n self.do_dimension(elem)\n\n self.finish_off()\n return\n\n def do_dimension(self, elem):\n ref = elem.get(b'ref')\n if ref:\n last_cell_ref = ref.split(b':')[(-1)]\n rowx, colx = cell_name_to_rowx_colx(last_cell_ref)\n self.sheet._dimnrows = rowx + 1\n self.sheet._dimncols = colx + 1\n\n def do_row(self, row_elem):\n\n def bad_child_tag(child_tag):\n raise Exception(b'cell type %s has unexpected child <%s> at rowx=%r colx=%r' % (cell_type, child_tag, rowx, colx))\n\n row_number = row_elem.get(b'r')\n if row_number is None:\n self.rowx += 1\n explicit_row_number = 0\n if self.verbosity and not self.warned_no_row_num:\n self.dumpout(b'no row number; assuming rowx=%d', self.rowx)\n self.warned_no_row_num = 1\n else:\n self.rowx = int(row_number) - 1\n explicit_row_number = 1\n assert 0 <= self.rowx < X12_MAX_ROWS\n rowx = self.rowx\n colx = -1\n if self.verbosity >= 3:\n self.dumpout(b' row_number=%r rowx=%d explicit=%d', row_number, self.rowx, explicit_row_number)\n letter_value = _UPPERCASE_1_REL_INDEX\n for cell_elem in row_elem:\n cell_name = cell_elem.get(b'r')\n if cell_name is None:\n colx += 1\n if self.verbosity and not self.warned_no_cell_name:\n self.dumpout(b'no cellname; assuming rowx=%d colx=%d', rowx, colx)\n self.warned_no_cell_name = 1\n else:\n colx = 0\n charx = -1\n try:\n for c in cell_name:\n charx += 1\n lv = letter_value[c]\n if lv:\n colx = colx * 26 + lv\n else:\n colx = colx - 1\n assert 0 <= colx < X12_MAX_COLS\n break\n\n except KeyError:\n raise Exception(b'Unexpected character %r in cell name %r' % (c, cell_name))\n\n if explicit_row_number and cell_name[charx:] != row_number:\n raise Exception(b'cell name %r but row number is %r' % (cell_name, row_number))\n xf_index = int(cell_elem.get(b's', b'0'))\n cell_type = cell_elem.get(b't', b'n')\n tvalue = None\n formula = None\n if cell_type == b'n':\n for child in cell_elem:\n child_tag = child.tag\n if child_tag == V_TAG:\n tvalue = child.text\n elif child_tag == F_TAG:\n formula = cooked_text(self, child)\n else:\n raise Exception(b'unexpected tag %r' % child_tag)\n\n if not tvalue:\n if self.bk.formatting_info:\n self.sheet.put_cell(rowx, colx, XL_CELL_BLANK, b'', xf_index)\n else:\n self.sheet.put_cell(rowx, colx, None, float(tvalue), xf_index)\n elif cell_type == b's':\n for child in cell_elem:\n child_tag = child.tag\n if child_tag == V_TAG:\n tvalue = child.text\n elif child_tag == F_TAG:\n formula = child.text\n else:\n bad_child_tag(child_tag)\n\n if not tvalue:\n if self.bk.formatting_info:\n self.sheet.put_cell(rowx, colx, XL_CELL_BLANK, b'', xf_index)\n else:\n value = self.sst[int(tvalue)]\n self.sheet.put_cell(rowx, colx, XL_CELL_TEXT, value, xf_index)\n elif cell_type == b'str':\n for child in cell_elem:\n child_tag = child.tag\n if child_tag == V_TAG:\n tvalue = cooked_text(self, child)\n elif child_tag == F_TAG:\n formula = cooked_text(self, child)\n else:\n bad_child_tag(child_tag)\n\n self.sheet.put_cell(rowx, colx, XL_CELL_TEXT, tvalue, xf_index)\n elif cell_type == b'b':\n for child in cell_elem:\n child_tag = child.tag\n if child_tag == V_TAG:\n tvalue = child.text\n elif child_tag == F_TAG:\n formula = cooked_text(self, child)\n else:\n bad_child_tag(child_tag)\n\n self.sheet.put_cell(rowx, colx, XL_CELL_BOOLEAN, int(tvalue), xf_index)\n elif cell_type == b'e':\n for child in cell_elem:\n child_tag = child.tag\n if child_tag == V_TAG:\n tvalue = child.text\n elif child_tag == F_TAG:\n formula = cooked_text(self, child)\n else:\n bad_child_tag(child_tag)\n\n value = error_code_from_text[tvalue]\n self.sheet.put_cell(rowx, colx, XL_CELL_ERROR, value, xf_index)\n elif cell_type == b'inlineStr':\n for child in cell_elem:\n child_tag = child.tag\n if child_tag == IS_TAG:\n tvalue = get_text_from_si_or_is(self, child)\n else:\n bad_child_tag(child_tag)\n\n assert tvalue is not None\n self.sheet.put_cell(rowx, colx, XL_CELL_TEXT, tvalue, xf_index)\n else:\n raise Exception(b'Unknown cell type %r in rowx=%d colx=%d' % (cell_type, rowx, colx))\n\n return\n\n tag2meth = {b'row': do_row}\n augment_keys(tag2meth, U_SSML12)\n\n\ndef getzflo(zipfile, member_path):\n try:\n return zipfile.open(member_path)\n except AttributeError:\n return BYTES_IO(zipfile.read(member_path))\n\n\ndef open_workbook_2007_xml(zf, component_names, logfile=sys.stdout, verbosity=0, use_mmap=0, formatting_info=0, on_demand=0, ragged_rows=0):\n ensure_elementtree_imported(verbosity, logfile)\n bk = Book()\n bk.logfile = logfile\n bk.verbosity = verbosity\n bk.formatting_info = formatting_info\n if formatting_info:\n raise NotImplementedError(b'formatting_info=True not yet implemented')\n bk.use_mmap = False\n bk.on_demand = on_demand\n if on_demand:\n if verbosity:\n print(b'WARNING *** on_demand=True not yet implemented; falling back to False', file=bk.logfile)\n bk.on_demand = False\n bk.ragged_rows = ragged_rows\n x12book = X12Book(bk, logfile, verbosity)\n zflo = getzflo(zf, b'xl/_rels/workbook.xml.rels')\n x12book.process_rels(zflo)\n del zflo\n zflo = getzflo(zf, b'xl/workbook.xml')\n x12book.process_stream(zflo, b'Workbook')\n del zflo\n props_name = b'docProps/core.xml'\n if props_name in component_names:\n zflo = getzflo(zf, props_name)\n x12book.process_coreprops(zflo)\n x12sty = X12Styles(bk, logfile, verbosity)\n if b'xl/styles.xml' in component_names:\n zflo = getzflo(zf, b'xl/styles.xml')\n x12sty.process_stream(zflo, b'styles')\n del zflo\n sst_fname = b'xl/sharedStrings.xml'\n x12sst = X12SST(bk, logfile, verbosity)\n if sst_fname in component_names:\n zflo = getzflo(zf, sst_fname)\n x12sst.process_stream(zflo, b'SST')\n del zflo\n for sheetx in range(bk.nsheets):\n fname = x12book.sheet_targets[sheetx]\n zflo = getzflo(zf, fname)\n sheet = bk._sheet_list[sheetx]\n x12sheet = X12Sheet(sheet, logfile, verbosity)\n heading = b'Sheet %r (sheetx=%d) from %r' % (sheet.name, sheetx, fname)\n x12sheet.process_stream(zflo, heading)\n del zflo\n sheet.tidy_dimensions()\n\n return bk","sub_path":"pycfiles/pythonetl_xlrd-0.9.3dev-py2.7/xlsx.py","file_name":"xlsx.py","file_ext":"py","file_size_in_byte":25737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"516562169","text":"from sklearn.ensemble import RandomForestClassifier\n\nimport pickle, json\nimport numpy as np\n\nx_train = []\ny_train = []\n\nwith open(\"data/x_train.txt\", \"r\") as file:\n x_train = np.array(json.loads(''.join(list(map(lambda x: x.strip(), file.readlines())))))\n\nwith open(\"data/y_train.txt\", \"r\") as file:\n y_train = np.array(json.loads(''.join(list(map(lambda x: x.strip(), file.readlines())))))\n\n\nmodel = RandomForestClassifier(n_estimators = 100, max_depth = 9, random_state = 42)\nmodel.fit(x_train, y_train)\n\npickle.dump(model, open(\"model.pkl\", \"wb\"))\n","sub_path":"2_stage/task1/rfc_load.py","file_name":"rfc_load.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"85940954","text":"import os\nfrom typing import Dict, List\n\nfrom PIL import Image\nimport unittest\nimport numpy as np\nimport cv2\n\nfrom mystique.utils import load_od_instance\nfrom mystique.predict_card import PredictCard\nfrom mystique.arrange_card import CardArrange\nfrom mystique.extract_properties import CollectProperties\n\ncurr_dir = os.path.dirname(__file__)\n\n\nclass TestUtil:\n\n def collect_json_objects(self, image: Image,\n model_instance: PredictCard) -> Dict:\n \"\"\"\n Returns the dict of design objects collected from the prediction\n @param image: input PIL image\n @param model_instance: model instance object\n @return: dict of design objects\n \"\"\"\n image = image.convert(\"RGB\")\n image_np = np.asarray(image)\n image_np = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)\n output_dict = model_instance.od_model.get_objects(\n image_np=image_np, image=image\n )\n return model_instance.collect_objects(output_dict=output_dict,\n pil_image=image)\n\n def collect_image_sizes(self, json_objects: Dict, image: Image) -> List:\n \"\"\"\n Returns the list of extracted image object sizes of the input image\n @param json_objects: dict of design objects\n @param image: input PIL image\n @return: list of image object sizes\n \"\"\"\n collect_properties = CollectProperties(image)\n image_objects = [image_object for image_object in\n json_objects[\"objects\"]\n if image_object[\"object\"] == \"image\"]\n for design_object in image_objects:\n property_object = getattr(collect_properties,\n design_object.get(\"object\"))\n design_object.update(property_object(design_object.get(\"coords\")))\n return [design_object.get(\"size\", \"\") for design_object in\n image_objects]\n\n\nclass TestIOU(unittest.TestCase):\n card_arrange = CardArrange()\n\n def setUp(self):\n self.image_path = os.path.join(\n curr_dir, \"../tests/test_images/test01.png\")\n self.test_util = TestUtil()\n self.model_instance = load_od_instance()\n self.model_instance = PredictCard(self.model_instance)\n\n self.image = Image.open(self.image_path)\n self.json_objects, _ = self.test_util.collect_json_objects(\n self.image, self.model_instance)\n\n def test_object_collection(self):\n \"\"\"Tests the collected object's count\"\"\"\n self.assertEqual(len(self.json_objects[\"objects\"]), 23)\n\n def test_iou(self):\n \"\"\"Tests the iou based noise removal count\"\"\"\n self.card_arrange.remove_noise_objects(self.json_objects)\n self.assertEqual(len(self.json_objects[\"objects\"]), 19)\n\n def test_image_size(self):\n \"\"\"Tests the image object sizes\"\"\"\n extracted_sizes = self.test_util.collect_image_sizes(self.json_objects,\n self.image)\n self.assertEqual(extracted_sizes, [\"Small\", \"Small\"])\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"source/pic2card/tests/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"267649879","text":"\n\nfrom xai.brain.wordbase.nouns._breakwater import _BREAKWATER\n\n#calss header\nclass _BREAKWATERS(_BREAKWATER, ):\n\tdef __init__(self,): \n\t\t_BREAKWATER.__init__(self)\n\t\tself.name = \"BREAKWATERS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"breakwater\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_breakwaters.py","file_name":"_breakwaters.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"50613329","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import Colormap\n\ndef show(X, y, clf):\n x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n\n xx, yy = np.meshgrid( np.arange(x_min, x_max, 0.1),\n np.arange(y_min, y_max, 0.1))\n\n fig, ax = plt.subplots(1, 1, sharex='col', sharey='row', figsize=(10, 8))\n\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n\n ax.contourf(xx, yy, Z, alpha=0.4, cmap=plt.cm.RdYlBu)\n ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu)\n ax.set_title(\"Test\")\n\n plt.show()","sub_path":"Day 44/show.py","file_name":"show.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"196064341","text":"while(True):\n str_=input(\"nhap day 2 \")\n \n if len(str_)<8 :\n print(\"Nhap lai pass \")\n continue\n print(str_)\n break\n\n\n\n\n\n\n\n\n\n\n\n\ndayS0=\"1234567890\"\nstr_=input(\"nhap day \")\ni=0\n\n\nv=0\nwhile(True):\n if dayS0[i]==str_[v]:\n print(\"co so\")\n break\n else:\n if len(dayS0)-1==i:\n i=0\n v+=1\n elif len(str_)-1==v:\n print(\"khong co\")\n break\n i+=1\n\n\n# n=int(input(\"nhap so\"))\n# dem=0\n# while(True):\n# print(\"n= \",n)\n# n+=1\n# if n>=1000:\n# break","sub_path":"while.py","file_name":"while.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"131215767","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nThis module provides tables for RNA secondary\nstructures and tools\n\"\"\"\n\n# import itertools\n\n__author__ = \"Bulak Arpat\"\n__copyright__ = \"Copyright 2017, Bulak Arpat\"\n__license__ = \"GPLv3\"\n__version__ = \"0.2.0\"\n__maintainer__ = \"Bulak Arpat\"\n__email__ = \"Bulak.Arpat@unil.ch\"\n__status__ = \"Development\"\n\n\n# G-quadruplex translation table\nGQUAD_LETTERS = [\"N\", \"G\"]\n# GQUAD_TABLE = {codon: GQUAD_LETTERS[\"G\" in codon] for codon in\n# [\"\".join(word) for word in itertools.product(\n# GQUAD_LETTERS, repeat=3)]}\n\n# NGN is intentially undefined. We should never come across this key\n# if we do it must be an error\nGQUAD_TABLE = {\n \"GGG\": \"G\",\n \"GGN\": \"G\",\n \"GNN\": \"G\",\n \"NNN\": \"N\",\n \"NNG\": \"G\",\n \"NGG\": \"G\",\n \"GNG\": \"G\"\n}\n","sub_path":"venv/lib/python3.4/site-packages/genomics/rna_struct.py","file_name":"rna_struct.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"127011653","text":"from bs4 import BeautifulSoup\n\nimport sites.biqu_parse as biqu\nfrom sites.site import BaseNovel\n\n__all__ = [\"Biqubook\"]\n\n\nclass Biqubook(BaseNovel):\n _encode = \"gbk\"\n\n source_site = \"http://www.biqubook.com\"\n source_title = \"笔趣阁\"\n\n def parse_base_info(self, content):\n biqu.parse_info(self, BeautifulSoup(content, \"html.parser\"), content)\n\n def parse_chapter_list(self, content):\n biqu.parse_chapters(self, BeautifulSoup(content, \"html.parser\"), False, \".listmain dd\")\n","sub_path":"sites/biqubook.py","file_name":"biqubook.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"318426770","text":"from django.shortcuts import render\nfrom forms import UserForm\nfrom django.contrib.auth import login\nfrom django.http import HttpResponseRedirect\n\ndef adduser(request):\n if request.method == \"POST\":\n form = UserForm(request.POST)\n if form.is_valid():\n new_user = User.objects.create_user(**form.cleaned_data)\n login(new_user)\n return HttpResponseRedirect('main.html')\n else:\n form = UserForm() \n\n return render(request, 'adduser.html', {'form': form}) \n","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"155411097","text":"\"\"\"config URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\n# Imports\nfrom django.conf.urls import url, include\nfrom rest_framework import routers\nfrom project.apicc import views\n\n\n#router = routers.DefaultRouter()\n#router.register(r'users', views.UserViewSet)\n\napp_name = 'apicc'\nurlpatterns = [\n# url(r'^', include(router.urls)),\n url(r'^$', views.index, name='index'),\n url(r'^updateDB/$', views.save_cryptoasset, name='updateDB'),\n url(r'^momentum_plot/(?P\\d+)/$', views.momentum_plot, name='momentum_plot'),\n url(r'^draw_mpl/$', views.draw_mpl, name='draw_mpl'),\n url(r'^momentum_plot_model/(?P\\d+)/$', views.momentum_plot, name='momentum_plot_model'),\n url(r'^draw_mpl_model/$', views.draw_mpl_model, name='draw_mpl_model'),\n]","sub_path":"project/apicc/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"332861554","text":"from rest_framework import serializers\n\nfrom .models import Product\nfrom webshop.models import ProductVariant, Brand\n\n\nclass BrandSerializer(serializers.ModelSerializer):\n class Meta:\n model = Brand\n fields = [\n 'id',\n 'name'\n ]\n\n\nclass ProductVariantSerializer(serializers.ModelSerializer):\n class Meta:\n model = ProductVariant\n fields = [\n 'id',\n 'color',\n 'price',\n 'size',\n 'image',\n 'quantity'\n ]\n\n def get_image(self, product_variant):\n return self.context['request'].build_absolute_uri(product_variant.image)\n\n\nclass ProductListSerializer(serializers.ModelSerializer):\n variants = ProductVariantSerializer(many=True)\n brand = BrandSerializer()\n\n class Meta:\n model = Product\n fields = [\n 'id',\n 'name',\n 'brand',\n 'description',\n 'variants'\n ]\n","sub_path":"webshop/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"315953696","text":"from django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.http import Http404\nfrom .models import Project, Rate, Review\nfrom django.contrib.auth.models import User\nfrom .forms import( UserRegisterForm, UserUpdateForm, ProfileUpdateForm, ProjectPostForm, RateForm, ReviewForm)\nfrom django.contrib.auth.decorators import login_required\n\n\ndef register(request):\n if request.method == 'POST':\n form = UserRegisterForm(request.POST)\n if form.is_valid():\n form.save()\n username = form.cleaned_data.get('username')\n return redirect('login')\n else:\n form = UserRegisterForm()\n return render(request, 'registration/register.html', {'form':form})\n\ndef index(request):\n return render(request, 'index.html')\n\n@login_required\ndef home(request):\n try:\n projects = Project.objects.all()\n except Exception as e:\n raise Http404()\n return render(request, 'home.html', {'projects':projects})\n\n\n@login_required\ndef profile(request):\n if request.method == 'POST':\n u_form = UserUpdateForm(request.POST, instance=request.user)\n p_form = ProfileUpdateForm(request.POST,request.FILES, instance=request.user.profile)\n\n if u_form.is_valid() and p_form.is_valid():\n u_form.save()\n p_form.save()\n messages.success(request, f'Account updated successfully!')\n return redirect('profile')\n else:\n u_form = UserUpdateForm(instance=request.user)\n p_form = ProfileUpdateForm(instance=request.user.profile)\n\n context = {\n 'u_form':u_form,\n 'p_form':p_form,\n }\n\n return render(request, 'users/profile.html', context)\n\n@login_required\ndef post_project(request):\n current_user = request.user\n if request.method=='POST':\n form = ProjectPostForm(request.POST,request.FILES)\n if form.is_valid():\n post = form.save(commit=False)\n post.user = current_user\n post.save()\n return redirect(\"home\")\n else:\n form = ProjectPostForm()\n return render(request,'project_post.html',{'form':form})\n\n@login_required\ndef project_details(request,project_id):\n projects = Project.objects.filter(id=project_id)\n all_rates = Rate.objects.filter(project=project_id)\n \n count = 0\n for i in all_rates:\n count+=i.usability\n count+=i.design\n count+=i.content\n\n if count > 0:\n av_rate = round(count/3,1) \n else:\n av_rate = 0\n\n if request.method=='POST':\n form = RateForm(request.POST)\n if form.is_valid():\n rate = form.save(commit=False)\n rate.user = request.user\n rate.project = project_id\n rate.save()\n return redirect('project_details',project_id)\n else:\n form = RateForm()\n\n votes = Rate.objects.filter(project=project_id)\n usability = []\n design = []\n content = []\n\n for i in votes:\n usability.append(i.usability)\n design.append(i.design)\n content.append(i.content)\n if len(usability) > 0 or len(design)> 0 or len(content) >0:\n av_usability = round(sum(usability)/len(usability),1)\n av_design = round(sum(design)/len(design),1)\n av_content = round(sum(content)/len(content),1)\n avRating = round((av_content + av_design + av_usability)/3,1)\n else:\n av_usability = 0.0\n av_design = 0.0\n av_content = 0.0\n avRating = 0.0\n\n '''\n Restricting user to rate only once\n '''\n arr1=[]\n for use in votes:\n arr1.append(use.user_id)\n auth = arr1\n\n if request.method=='POST':\n r_form = ReviewForm(request.POST)\n if r_form.is_valid():\n review = r_form.save(commit=False)\n review.user = request.user\n review.profile_id = project_id\n review.save()\n return redirect('project_details',project_id)\n else:\n r_form = ReviewForm()\n r_form = ReviewForm()\n user_review = Review.objects.filter(profile_id=project_id)\n\n context = {\n 'projects':projects,\n 'form':form,\n 'usability':av_usability, \n 'design':av_design,\n 'content':av_content,\n 'average':avRating,\n 'auth':auth,\n 'all_rates':all_rates,\n 'av_rate':av_rate,\n 'r_form':r_form,\n 'review':user_review\n }\n return render(request,'project_details.html', context)\n\n@login_required\ndef search(request):\n if 'search' in request.GET and request.GET['search']:\n search_term = request.GET.get('search')\n results = Project.search_project(search_term)\n return render(request,'search.html',{'projects': results})\n else:\n message=\"You have not searched any project\"\n return render(request,'search.html',{'message': message})\n\n@login_required\ndef apiView(request):\n user = request.user\n title=\"Api\"\n profile = Profile.objects.filter(user=user)[0:1]\n return render(request,'api.html',{\"title\":title,'profile':profile})\n\n","sub_path":"awwards/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"497192715","text":"#! /usr/bin/env python3\n\nimport pandas as pd\n\nd1 = pd.read_csv(\"RMS_all_patient_malignant_largeVis_embeddings_IDheader.txt\", sep=\"\\t\", header = 0, engine='python')\nd2 = pd.read_csv(\"RMS_all_patient_malignant_metadata_IDHeader.txt\", sep=\"\\t\", header = 0, engine='python')\nd3 = pd.merge(d1, d2)\n\nd3.to_csv(\"merge_RMS_data.csv\")\n\n","sub_path":"merge_rds.py","file_name":"merge_rds.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"1866798","text":"'''Train CIFAR10 with PyTorch.'''\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\nimport torchvision\nimport torchvision.transforms as transforms\nfrom tensorboard_logger import configure, log_value\n\nimport os, sys, random\nimport argparse\n\nfrom autoaugment import CIFAR10Policy\n\n# from seperable_net import *\n# from snn_graph import *\nfrom snn_graph_2 import *\n# from snn_graph_3 import *\nfrom utils import progress_bar\n\n\nparser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')\nparser.add_argument('--lr', default=0.1, type=float, help='learning rate')\nparser.add_argument('--split', default=1, type=int, help='number of devices to split the model')\nparser.add_argument('--epoch', default=200, type=int, help='epoch')\nparser.add_argument('--batch', default=128, type=int, help='batch')\nparser.add_argument('--schedule', default=50, type=int, help='schedule to decay learning rate')\nparser.add_argument('--cuda', default=-1, type=int, help='gpu index')\nparser.add_argument('--resume', '-r', default='', type=str, help='checkpoint path to resume')\nparser.add_argument('--save', default='exp', type=str, help='checkpoint path to save')\nargs = parser.parse_args()\n\nprint('===== parameter settings =====')\nprint('learining rate: %.4f'%args.lr)\nprint('split: %d'%args.split)\nprint('epoch: %d'%args.epoch)\nprint('schedule: %d'%args.schedule)\nprint('cuda: %d'%args.cuda)\nprint('resume: %s'%args.resume)\nprint('save: %s'%args.save)\nprint('===== parameter settings =====')\n\ntorch.backends.cudnn.benchmark = True\nif args.cuda==-1:\n device = 'cuda'\nelse:\n device = 'cuda:%d'%args.cuda #'cuda'#\nbest_acc = 0 # best test accuracy\nstart_epoch = 0 # start from epoch 0 or last checkpoint epoch\n\n# Data\nprint('==> Preparing data..')\ntransform_train = transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n# transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1),\n# transforms.RandomRotation(5),\n CIFAR10Policy(),\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\n\ntransform_test = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\n\ntrainset = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform_train)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch, shuffle=True, num_workers=10)\n\ntestset = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=transform_test)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=args.batch, shuffle=False, num_workers=10)\n\n\n# Model\nprint('==> Building model..')\n# net = sresnet164_cifar(num_classes=100)\nnet = resneXt_cifar(56, 4, 16, num_classes=100, is_separate=True)\n# net = densenet_BC_cifar(250, 24, num_classes=100)\nif args.cuda==-1:\n net = nn.DataParallel(net)\nnet = net.to(device)\n# net2 = resneXt_cifar(56, 1, 16, num_classes=100, is_separate=True)\n# net2 = net2.to(device)\n# net3 = resneXt_cifar(56, 1, 16, num_classes=100, is_separate=True)\n# net3 = net3.to(device)\n# net4 = resneXt_cifar(56, 1, 16, num_classes=100, is_separate=True)\n# net4 = net4.to(device)\n# net5 = resneXt_cifar(56, 1, 16, num_classes=100, is_separate=True)\n# net5 = net4.to(device)\n\n# for p in net.parameters():\n# print(p.nelement())\nprint(net)\nprint(sum(p.numel() for p in net.parameters() if p.requires_grad))\n\nif args.resume:\n\n print('==> Resuming from checkpoint..')\n assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'\n checkpoint = torch.load('./checkpoint/%s'%args.resume)\n# checkpoint = torch.load('./checkpoint/resnext_56_1_16_167.t7')\n# checkpoint2 = torch.load('./checkpoint/resnext_56_1_16_re2_198.t7')\n# checkpoint5 = torch.load('./checkpoint/resnext_56_1_16_re5_157.t7')\n# checkpoint3 = torch.load('./checkpoint/resnext_56_1_16_re3_155.t7')\n# checkpoint4 = torch.load('./checkpoint/resnext_56_1_16_re4_159.t7')\n \n# for key in checkpoint['net'].keys():\n# if 'layer' in key and 'num_batches_tracked' not in key:\n# checkpoint['net'][key] = torch.cat([checkpoint['net'][key], checkpoint2['net'][key], checkpoint3['net'][key], checkpoint4['net'][key]], 0)\n# x = checkpoint['net'][key]\n# shape = x.shape\n# shape[0] = shape[0]//4\n# r = random.randint(0, shape[0]*3)\n# checkpoint['net'][key] = torch.cat([x, x, x, x], 0)\n# tensor = torch.randn(checkpoint['net'][key].shape)/50\n# checkpoint['net'][key] = checkpoint['net'][key].to('cpu')+tensor\n# substring = key.split('device_')\n# from_key = substring[0]+'device_1'+substring[1][1:] if len(substring)>=2 else key\n# if key!=from_key:\n# checkpoint['net'][key] = checkpoint['net'][from_key]\n\n\n net.load_state_dict(checkpoint['net'])\n# net2.load_state_dict(checkpoint2['net'])\n# net3.load_state_dict(checkpoint3['net'])\n# net4.load_state_dict(checkpoint4['net'])\n# net5.load_state_dict(checkpoint5['net'])\n best_acc = checkpoint['acc']\n start_epoch = checkpoint['epoch']\n\n# print(checkpoint['net'].keys())\n# sys.exit()\n\n\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)\n\n# Training\ndef train(epoch):\n print('\\nEpoch: %d' % epoch)\n net.train()\n train_loss = 0\n correct = 0\n total = 0\n for batch_idx, (inputs, targets) in enumerate(trainloader):\n inputs, targets = inputs.to(device), targets.to(device)\n optimizer.zero_grad()\n outputs = net(inputs)\n loss = criterion(outputs, targets)\n loss.backward()\n optimizer.step()\n\n train_loss += loss.item()\n _, predicted = outputs.max(1)\n total += targets.size(0)\n correct += predicted.eq(targets).sum().item()\n\n progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))\n log_value('train_loss', train_loss/(batch_idx+1), epoch)\n log_value('train_accuracy', correct/total, epoch)\n\ndef test(epoch):\n global best_acc\n net.eval()\n# net2.eval()\n# net3.eval()\n# net4.eval()\n# net5.eval()\n test_loss = 0\n correct = 0\n total = 0\n with torch.no_grad():\n for batch_idx, (inputs, targets) in enumerate(testloader):\n inputs, targets = inputs.to(device), targets.to(device)\n outputs = net(inputs)\n# outputs = (net(inputs)+net2(inputs)+net3(inputs)+net5(inputs))\n loss = criterion(outputs, targets)\n\n test_loss += loss.item()\n _, predicted = outputs.max(1)\n total += targets.size(0)\n correct += predicted.eq(targets).sum().item()\n\n progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))\n log_value('test_loss', test_loss/(batch_idx+1), epoch)\n log_value('test_accuracy', correct/total, epoch)\n\n # Save checkpoint.\n acc = 100.*correct/total\n if acc > best_acc:\n if epoch > args.schedule*2:\n print('Saving..')\n state = {\n 'net': net.state_dict(),\n 'acc': acc,\n 'epoch': epoch,\n }\n if not os.path.isdir('checkpoint'):\n os.mkdir('checkpoint')\n net.to('cpu')\n torch.save(state, './checkpoint/%s_%d.t7'%(args.save, epoch))\n net.to(device)\n best_acc = acc\n\nconfigure('cv/'+args.save+'/log', flush_secs=5)\nfor epoch in range(args.epoch):\n \n# train(epoch)\n test(epoch)\n schedule = args.schedule\n \n if epoch in [schedule, schedule*2, schedule*3]:\n optimizer.param_groups[0]['lr'] /= 10\n\n print('now learning rate is %.4f'%optimizer.param_groups[0]['lr'])\n \n\n#python3 main.py --lr 0.1 --split 1 --epoch 200 --batch 128 --schedule 50 --cuda 1 --save resnext_56_2_16","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"205209115","text":"import torch.utils.data as data\nimport torch\nfrom torchvision import transforms\nimport torchvision.datasets as dset\nimport nltk\nimport numpy as np\nfrom torchvision import transforms\nimport torchvision.datasets as dset\nfrom PIL import Image, ImageDraw\n\nclass CocoCaptionDetection(data.Dataset):\n def __init__(self, root, captionAnnFile, instanceAnnFile, vocab, transform=None, target_transform=None):\n self.name = \"CocoCaptionDetection\"\n self.vocab = vocab\n self.captionSet = dset.CocoCaptions(root = root, annFile = captionAnnFile,\n transform=transforms.ToTensor())\n self.instanceSet = dset.CocoDetection(root = root, annFile = instanceAnnFile,\n transform=transforms.ToTensor())\n # assert len(self.captionSet) == len(self.instanceSet)\n assert self.captionSet.coco.imgs.keys() == self.instanceSet.coco.imgs.keys()\n\n # instances = json.load(open('/net/zf14/xy4cm/Projects/words2text/neuraltalk2/coco/annotations/instances_train2014.json','r'))\n # category_ids = [cat['id'] for cat in instances['categories']]\n coco_category_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27,\n 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53,\n 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80,\n 81, 82, 84, 85, 86, 87, 88, 89, 90]\n self.coco_category_id_map = {}\n for i in range(len(coco_category_ids)):\n self.coco_category_id_map[coco_category_ids[i]] = i\n\n def __getitem__(self, index, with_img=False):\n img, captions = self.captionSet[index]\n img_detection, target_detection = self.instanceSet[index]\n\n # make sure annotations refer to the same image\n if not torch.equal(img_detection, img):\n raise ValueError(\"annotations not refering to the same image\")\n\n # convert to percentage coords\n height, width = img.size()[1:]\n labels = np.array([self.coco_category_id_map[t['category_id']] for t in target_detection])\n # if there is no label put a dummy label\n if len(labels) == 0:\n labels = np.array([0])\n\n boxes = np.array([t['bbox'] for t in target_detection])\n if boxes.shape[0] == 0:\n boxes = np.array([[0.0, 0.0, width, height]])\n boxes[:, 2] += boxes[:, 0]\n boxes[:, 3] += boxes[:, 1]\n boxes[:, 0] /= width\n boxes[:, 2] /= width\n boxes[:, 1] /= height\n boxes[:, 3] /= height\n assert np.all(boxes <= 1)\n target_detection = np.hstack((boxes, np.expand_dims(labels, axis=1)))\n\n caption = captions[np.random.randint(len(captions))]\n # Convert caption (string) to word ids.\n tokens = nltk.tokenize.word_tokenize(str(caption).lower())\n caption = []\n caption.append(self.vocab(''))\n caption.extend([self.vocab(token) for token in tokens])\n caption.append(self.vocab(''))\n target_caption = torch.LongTensor(caption)\n\n if with_img:\n return img, target_caption, target_detection\n return target_caption, target_detection\n\n def __len__(self):\n return len(self.captionSet)\n\n def show_item(self, index):\n img, target_caption, target_detection = self.__getitem__(index, True)\n height, width = img.size()[1:]\n print(\"Image Size: \", img.size())\n print(target_caption)\n imgshow = transforms.ToPILImage()(img)\n draw = ImageDraw.Draw(imgshow)\n for box in target_detection[:, :4]:\n draw.rectangle([box[0]*width, box[1]*height, box[2]*width, box[3]*height])\n imgshow.show()\n\n\ndef collate_caption(data):\n \"\"\"Creates mini-batch tensors from the list of tuples (image, caption).\n\n We should build custom collate_fn rather than using default collate_fn,\n because merging caption (including padding) is not supported in default.\n Args:\n data: list of tuple (image, caption).\n - image: torch tensor of shape (3, 256, 256).\n - caption: torch tensor of shape (?); variable length.\n Returns:\n images: torch tensor of shape (batch_size, 3, 256, 256).\n targets: torch tensor of shape (batch_size, padded_length).\n lengths: list; valid length for each padded caption.\n \"\"\"\n # Sort a data list by caption length (descending order).\n data.sort(key=lambda x: len(x[0]), reverse=True)\n # images, captions, detections = zip(*data)\n captions, detections = zip(*data)\n\n detection_targets = [torch.FloatTensor(d) for d in detections]\n # Merge images (from tuple of 3D tensor to 4D tensor).\n # images = torch.stack(images, 0)\n\n # Merge captions (from tuple of 1D tensor to 2D tensor).\n lengths = [len(cap) for cap in captions]\n caption_targets = torch.LongTensor(len(captions), max(lengths)).zero_()\n for i, cap in enumerate(captions):\n end = lengths[i]\n caption_targets[i, :end] = cap[:end]\n # return images, targets, lengths, detections\n return caption_targets, lengths, detection_targets","sub_path":"coco_dataset.py","file_name":"coco_dataset.py","file_ext":"py","file_size_in_byte":5290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"55563676","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n# 运算\na = 21\nb = 23\nc = 88\n\nc = a + b\nprint (c)\n\nc = a/b\nprint (float(c))\n\nc = a*b\nd = (a + b)*c - 1 # 运算符\nprint (c)\nprint (d)\n\n\n# 比较\na = 21\nb = 23\nc = 20\n\nif a == b:\n print (\"a equle b\")\n\nelse:\n print (\"a do not equal b\")\n","sub_path":"Learn/function/Calculate/Calculate.py","file_name":"Calculate.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"439558889","text":"#===============================================================================\n# DESCRIPTION\n# Contain the statistical model to perform the downscalling\n#===============================================================================\n\n# Todo\n# -> Thinking about merging other codes function into this module\n\n#===============================================================================\n# Library\n#===============================================================================\n\n\n# from mailib.stanet.lcbstanet\n\n# from stadata.lib.LCBnet_lib import *\nfrom scipy.optimize import curve_fit\nimport statsmodels.api as sm\n\nfrom sklearn.decomposition import PCA, KernelPCA\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.preprocessing import QuantileTransformer\nfrom scipy.stats import pearsonr\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport numpy as np\n\nfrom mailib.model.nn.structures import RNN_ribeirao, DNN_ribeirao\nfrom mailib.toolbox.tools import get_df_lags\nfrom mailib.esd.quantile_maping_santander import bias_correction\n\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler\n\nfrom mailib.esd.quantile_mapping import qm\n\nclass StaMod():\n \"\"\"\n Contain function and plot to perform Empirical Orthogonal Function\n \n PARAMETERS\n df: dataframe with a variable for each stations data\n AttSta: att_sta_object with the attribut of the stations\n \"\"\"\n def __init__(self, df, AttSta):\n self.df = df\n \n self.AttSta = AttSta\n \n self.nb_PC = []\n self.eigenvalues = [] # or explained variance\n self.eigenvectors = [] # or loadings\n self.eigenpairs = [] # contain the eigen pairs of the PCA (scores and loadings)\n self.scores = pd.DataFrame([]) # contain the PC scores\n self.params_loadings =[] # contains the fit parameters for the PC loadings\n self.params_scores = [] # contains the fit parameters for the PC scores \n self.topo_index = [] # contain the topographic index at each station point\n self.standard = False # flag to see if the input data has been standardize\n self.scores_model = {} # contain the models for the scores\n self.loadings_model = {} # contain the models for the loadings\n\n def pca_transform(self, nb_PC=4,remove_mean0=False,remove_mean1=False, \n standard = False, sklearn=False,sklearn_kernel=False, cov=True):\n \"\"\"\n Perform the Principal component analysis with SKlearn\n using singular value fft\n The dataframe is standardize\n \n \n \n parameters:\n standard: default = True, standardize the dataframe\n nb_PC: default = 4, number of principal components to be used\n sklearn: if True (default=False) use svd by sklearn\n cov: if true (by default) sue the correlation matrix to perform the PCA analysis\n \n Stock in the object\n Dataframe with:\n eigenvalues\n eigenvectors\n scores\n list of vectors:\n eigenpairs\n \n NOTE:\n By default sklearn remove the mean from the dataset. So I cant use it to perform the downscalling\n \n References:\n http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html#projection-onto-the-new-feature-space\n \"\"\"\n df = self.df\n self.nb_PC = nb_PC\n\n if remove_mean0:\n print('remove_mean0')\n df = df.subtract(df.mean(axis=0), axis='columns')\n\n if remove_mean1:\n print('remove_mean1')\n df = df.subtract(df.mean(axis=1), axis='index')\n print(df)\n \n if standard:\n # standardize\n# df_std = StandardScaler().fit_transform(df)\n self.standard = True\n df = (df - df.mean(axis=0)) / df.std(axis=0) # another way to standardise\n \n \n #=======================================================================\n # Sklearn\n #=======================================================================\n if sklearn:\n print(\"o\"*80)\n print(\"SVD sklearn used\")\n print(\"o\"*80)\n\n if sklearn_kernel:\n print('sklearn_kernel')\n pca = KernelPCA(nb_PC, kernel=\"rbf\", fit_inverse_transform=True, gamma=10)\n \n #Create a PCA model with nb_PC principal components\n else:\n pca = PCA(nb_PC)\n # fit data\n pca.fit(df)\n \n #Get the components from transforming the original data.\n scores = pca.transform(df) # or PCs\n eigenvalues = pca.explained_variance_\n eigenvectors = pca.components_ # or loading \n \n # Make a list of (eigenvalue, eigenvector) tuples \n self.eigpairs = [(np.abs(self.eigenvalues[i]), self.eigenvector[i,:]) for i in range(len(self.eigenvalues))]\n\n #=======================================================================\n # Covariance Matrix\n #=======================================================================\n if cov:\n print(\"o\"*80)\n print(\"Covariance used\")\n print(\"o\"*80)\n \n X = df.values\n cov_mat = np.cov(X.T)\n eigenvalues, eigenvectors = np.linalg.eig(cov_mat)\n\n scores = X.dot(eigenvectors)\n scores = pd.DataFrame(scores, columns = np.arange(1,len(df.columns)+1), index=df.index)\n eigenvalues = pd.Series(eigenvalues, index= np.arange(1,len(df.columns)+1))\n eigenvectors = pd.DataFrame(eigenvectors.T, columns=df.columns, index=np.arange(1,len(df.columns)+1))\n\n\n self.scores = scores.iloc[:, 0:nb_PC]\n self.eigenvalues = eigenvalues#[0:nb_PC]\n self.eigenvectors = eigenvectors[0:nb_PC]\n\n\n tot = sum(eigenvalues)\n self.var_exp = [(i / tot)*100 for i in sorted(eigenvalues, reverse=True)]\n\n\n def pca_reconstruct(self, pcs = None):\n \"\"\"\n Reconstruct the original dataset with the \"nb_PC\" principal component\n Note:\n The idea is to reconstruct by hand to see if the downscalling is done correctly\n \n pcs: PCs to use to reconstruct the data\n \"\"\"\n eigenvectors = self.eigenvectors\n scores = self.scores\n \n# df = pd.DataFrame(columns=eigenvectors.columns, index=scores.index)\n\n if pcs == None:\n pcs = scores.columns\n\n dfs_pcs = {}\n\n for pc in pcs:\n print(pc)\n loads = pd.concat([eigenvectors.loc[pc,:] for i in range(len(scores.loc[:,pc]))], axis=1).T # loading matrix\n loads.index = scores.loc[:,pc].index\n \n sc = pd.concat([scores.loc[:,pc] for i in range(len(eigenvectors.loc[pc,:]))], axis=1) # scores matrix\n sc.columns = loads.columns\n df = sc.multiply(loads)\n dfs_pcs[pc] = df\n# for sta in eigenvectors.columns:\n# for i, PC in enumerate(pcs):\n# scores[PC]*eigenvectors[sta][PC]\n# else:\n# df[sta] = df[sta] + scores[PC]*eigenvectors[sta][PC]\n# print df\n return dfs_pcs\n\n# def curvfit_loadings(self, predictors=None, fit=None):\n# \"\"\"\n# DESCRIPTION\n# Fit the loadings of the principal components with input independant variable\n# (In theapplication of the LCB it is most probably the altitude or its derivative)\n# RETURN\n# The parameters of the linear regression\n# predictors: topographic parameters to fit the loadings\n# namerasters = [\n# \"8x8\",\n# \"topex\",\n# \"xx8_10000_8000____\",\n# \"xx8_20000_18000____\"\n# ]\n# params_sep: list of parameters value to perfom 2 linear regression\n# curvfit: type of fit that you want to use, linear or poly\n# \"\"\"\n#\n#\n# if not predictors:\n# params = ['Alt']* self.nb_PC\n#\n# if not fit:\n# fit = [lin]* self.nb_PC\n#\n# fit_parameters = []\n#\n# for PC, row in self.eigenvectors.iterrows():\n# X = np.array(self.AttSta.getatt(self.df.keys(), predictors[PC - 1]))\n# self.topo_index.append(X)\n#\n#\n# popt, pcov = curve_fit(fit[PC-1], X, row)\n# fit_parameters.append([x for x in popt])\n#\n# # print fit_parameters\n# # fit_parameters = np.vstack(fit_parameters)\n# # print\n# self.predictors = [pd.DataFrame(fit_parameters, index =range(1,self.nb_PC+1), columns = range(4))]\n# self.curvfit_loadings = fit\n# return self.params_loadings\n#\n# def curvfit_scores(self, predictors, fit=None):\n# \"\"\"\n# DESCRIPTION\n# Fit the Scores of the principal components with a variables\n# Input\n# A serie\n# Return\n# The parameters of the linear regression\n#\n# \"\"\"\n#\n# if not fit:\n# fit = [lin]* self.nb_PC\n#\n# scores = self.scores\n#\n# fit_parameters = []\n# for i, predictor in enumerate(predictors):\n# predictor = predictor.dropna(axis=0, how='any')\n# score = scores.iloc[:,i]\n#\n# df = pd.concat([predictor, score], axis=1, join='inner')\n#\n# popt, pcov = curve_fit(fit[i-1], df.iloc[:,0], df.iloc[:,1])\n#\n# fit_parameters.append([x for x in popt])\n#\n# fit_parameters = np.vstack(fit_parameters)\n# self.params_scores = [pd.DataFrame(fit_parameters, index =range(1,self.nb_PC+1), columns = range(len(popt)))]\n# self.curvfit_scores = fit\n# return self.params_scores\n#\n# def curvfit_predict(self, predictors_loadings, predictors_scores, params_loadings=None, params_scores=None):\n# \"\"\"\n# DESCRIPTION\n# Return an 1/2d array estimated with the previously fit's parameters and predictos\n# RETURN\n# a dictionnary of 3D numpy array containing the estimated \"loadings\", \"scores\" and \"reconstructed variable\"\n# INPUT\n#\n# predictors_loadings: 1/2d array, to be used with the loading\n# parameters to create a loading 2d array\n# predictors_scores: Serie, to be used with the scores params to reconstruct the scores\n#\n# params_loadings: (Optional) A dataframe of the parameters of the linear regression from\n# curvfit_loadings. By default, the methods will look for params_loadings in the object.\n# If it does not exist you should run curvfit_loadings\n# parms_scores: (Optional) Parameters dataframe from curvfit_scores. By default, the methods will look for params_loadings in the object.\n# If it does not exist you should run curvfit_scores\n#\n# TODO\n# I should implement a way to use a pandas serie for the input \"predictor instead of a number\n# \"\"\"\n#\n# loadings = []\n# scores = []\n# predicted = []\n#\n# curvfit_loadings = self.curvfit_loadings\n# curvfit_scores = self.curvfit_scores\n#\n#\n# if (not params_loadings or not params_scores):\n# print 'Getting parameters'\n# params_loadings, params_scores = self.get_params()\n#\n#\n# # NEED TO IMPLEMENT MATRIX MULTIPLICATION!!!!!!!!!!!!!! I use to much loop\n# for PC_nb, fit_loading, fit_score, predictor_loadings, predictor_scores in zip(range(1,self.nb_PC+1),curvfit_loadings, curvfit_scores,predictors_loadings,predictors_scores ):\n# loading_est = fit_loading(predictor_loadings, *params_loadings[0].loc[PC_nb,:])\n# score_est =fit_score(predictor_scores, *params_scores[0].loc[PC_nb,:])\n#\n# score = pd.concat([score_est]*len(loading_est), axis=1)\n# predict= score.multiply(loading_est)\n#\n# loadings.append(loading_est)\n# scores.append( score_est)\n# predicted.append( predict)\n#\n# loadings = np.array(np.dstack(loadings))\n# scores = np.array(np.dstack(scores))\n# predicted = np.array(np.dstack(predicted))\n#\n#\n# res = {'loadings':loadings, 'scores':scores, 'predicted': predicted}\n# return res\n \n def fit_curvfit(self, X, Y, fits=None):\n \"\"\"\n fit using curvfit\n \n Parameters: \n X, a list of dataframes\n Y, a dataframe (scores or loading to fit)\n fits, a list of list witht the function to fit\n \n \"\"\"\n\n res_predictors_name = []\n res_fits = []\n res_fit_parameters = []\n for i in range(len(Y.keys())):\n\n x = X[i]\n y = Y.iloc[:,i]\n fit = fits[i]\n\n popt, pcov = curve_fit(fit, x.values, y.values)\n\n try:\n res_predictors_name.append([x.columns])\n except AttributeError:\n res_predictors_name.append([x.name])\n res_fit_parameters.append([popt])\n res_fits.append(fit)\n\n# fit_parameters = np.vstack(fit_parameters)\n res_predictors_name = pd.DataFrame(res_predictors_name, index =Y.keys())\n # res_predictors_name.index = Y.keys()\n res_fit_parameters = pd.DataFrame(res_fit_parameters, index =Y.keys())\n res_fits = pd.DataFrame(res_fits, index =Y.keys())\n\n df_models = pd.concat([res_predictors_name, res_fits, res_fit_parameters],axis=1)\n df_models.columns = ['predictor', 'model','params' ]\n df_models.index = Y.keys() #range(1,len(Y.keys())+1 )\n\n return df_models \n \n# def curvfit_skill(self, df_verif, predictors_scores, metrics, params_loadings=None, params_scores=None):\n# \"\"\"\n# DESCRIPTION\n# Compute bias and RMSE to assess the model performance\n# INPUT\n# df_verif: dataframe with the observed values\n# predictors: a list of pandas series which contains the predictors for the scores SHOULD NOT BE A LIST\n# metrics: sklearn metric function to be used\n# see: http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics\n# example:\n# metrics.explained_variance_score(y_true, y_pred) Explained variance regression score function\n# metrics.mean_absolute_error(y_true, y_pred) Mean absolute error regression loss\n# metrics.mean_squared_error(y_true, y_pred[, ...]) Mean squared error regression loss\n# metrics.median_absolute_error(y_true, y_pred) Median absolute error regression loss\n# metrics.r2_score(y_true, y_pred[, ...]) R^2 (coefficient of determination) regression score function.\n# \"\"\"\n\n# if (not params_loadings or not params_scores):\n# params_loadings, params_scores = self.get_params()\n#\n# topo_index = self.topo_index\n# data = np.array([])\n# res = self.predict(topo_index, predictors_scores)\n# data = res['predicted'].sum(axis=2)\n# df_rec = pd.DataFrame(data, columns = df_verif.columns, index = predictors_scores[0].index) # should improve this\n#\n# score = pd.Series()\n#\n# for sta in df_rec:\n# df = pd.concat([df_verif[sta],df_rec[sta]], axis=1, join='inner')\n# df = df.dropna(axis=0)\n# # df.columns=['True', 'Pred']\n# df.plot()\n# plt.show()\n# score[sta] = metrics(df.iloc[:,0], df.iloc[:,1])\n# return score\n def get_curvfit_params(self):\n \"\"\"\n DESCRIPTION\n Return the params loadings and scores. and return and error if the model has not being fitted\n \"\"\"\n \n try:\n params_loadings = self.params_loadings\n params_scores = self.params_scores\n except AttributeError:\n raise AttributeError( \"The model has not been fitted, run curvfit_loadings or curvfit_scores\")\n \n return params_loadings, params_scores\n\n def plot_exp_var(self, output=None):\n \"\"\"\n DESCRIPTION\n Make a plot of the variance explaine by the principal components\n \"\"\"\n print(\"Plot explained variance\")\n var_exp = self.var_exp\n cum_var_exp = np.cumsum(var_exp)\n\n var_exp = var_exp[:self.nb_PC]\n idxs = range(1,self.nb_PC+1)\n\n\n ax = plt.figure(figsize=(6, 4)).gca()\n \n plt.bar(idxs, var_exp, alpha=0.5, align='center',\n label='individual explained variance')\n plt.step(idxs, cum_var_exp[:self.nb_PC], where='mid',\n label='cumulative explained variance')\n ax.set_xticks(idxs)\n ax.set_xticklabels(idxs)\n plt.ylabel('Explained variance (%)')\n plt.xlabel('Principal components')\n plt.legend(loc='best')\n plt.tight_layout()\n plt.grid(True, color='0.5')\n \n if output:\n plt.savefig(output)\n plt.close()\n else:\n plt.show()\n\n def plot_loading(self, params_topo=None, params_fit=None, output=False, fit=None):\n \"\"\"\n DESCRIPTION\n Plot the loadings in function of some parameters\n Parameters\n output: if given save the plot at the path indicated\n params: parameters of the linear regression beetween\n loadings and independant variables \n \"\"\"\n\n if not params_topo:\n params_topo = ['Alt']* self.nb_PC\n \n if not fit:\n fit = [None]* self.nb_PC\n \n \n for pc_nb, param_topo, func in zip(range(1,self.nb_PC+1), params_topo, fit):\n elev_real = self.AttSta.getatt(self.df.keys(),param_topo) \n fig, ax = plt.subplots()\n plt.scatter(self.eigenvectors.loc[pc_nb], elev_real, s=20)\n\n if isinstance(params_fit, pd.DataFrame):\n x = np.linspace(min(elev_real), max(elev_real),100)\n p = params_fit.loc[pc_nb,:].dropna()\n \n y = func(x, *p)\n\n plt.plot(y,x)\n plt.xlabel('PC'+str(pc_nb)+' loadings')\n plt.ylabel(\"Altitude (m)\")\n plt.grid(True, color='0.5')\n\n\n for i, txt in enumerate(self.df.columns):\n ax.annotate(txt, (self.eigenvectors.loc[pc_nb][i], elev_real[i]))\n if output:\n plt.savefig(output+str(pc_nb)+'.pdf', transparent=True)\n\n plt.show()\n \n def plot_scores(self, predictors, params_fit =None,fit=None, output=False):\n \"\"\"\n DESCRIPTION\n Make a scatter plot of the Principal component scores in function of another variables\n INPUT\n var: time serie with the same index than the dataframe sued in the pca\n \"\"\"\n \n \n scores = self.scores\n \n for i, predictor, func in zip(range(0,self.nb_PC), predictors, fit):\n predictor = predictor.dropna(axis=0, how='any')\n score = scores.iloc[:,i]\n df = pd.concat([predictor, score], axis=1, join='inner')\n\n plt.scatter(df.iloc[:,0],df.iloc[:,1] )\n \n x = np.linspace(min(predictor), max(predictor),100)\n p = params_fit.loc[i+1,:]\n y = func(x, *p)\n \n plt.figure()\n plt.plot(x,y)\n plt.grid(True)\n plt.show()\n\n if output:\n plt.savefig(output)\n else:\n plt.show()\n \n def plot_scores_ts(self, output=False):\n \"\"\"\n DESCRIPTION\n Plot scores time serie\n Parameters\n output: if given save the plot at the path indicated\n \"\"\"\n scores = self.scores\n scores.plot(subplots=True)\n plt.xlabel('Time')\n plt.ylabel(\"PCs time series\")\n plt.grid(True, color='0.5')\n if output:\n plt.savefig(output)\n else:\n plt.show()\n\n def predict_model(self, predictors_loadings, predictors_scores=None,\n model_loadings=None, model_scores=None, model_loading_curvfit=None, model_scores_curvfit=None,\n observed_scores = False, dnn_model_scores =None, rnn_model_scores =None, use_predict_func_loading = None,\n nb_PC = None, qm_maps=None,obs_scores=False, idx_test=None, idx_train=None, eqm=False):\n\n \"\"\"\n\n\n :param predictors_loadings:\n :param predictors_scores:\n :param model_loadings:\n :param model_scores:\n :param model_loading_curvfit:\n :param model_scores_curvfit:\n :param observed_scores: Use the observed scores instead of the modeled scores for the reconstruction\n :param dnn_model_scores:\n :param use_predict_func_loading:\n :param nb_PC:\n :return:\n \"\"\"\n\n\n# for PC_nb, fit_loading, predictor_loadings in zip(range(1,self.nb_PC+1),fit_loadings,predictors_loadings):\n# -\n# + print PC_nb\n# p = params_loadings[0].loc[PC_nb,:].dropna()\n# p = params_loadings[0].loc[PC_nb,:].dropna()\n# loading_est = fit_loading(predictor_loadings, *p)\n# loading_est = fit_loading(predictor_loadings, *p)\n\n loadings = []\n scores = []\n predicted = []\n\n for PC_nb in range(1,nb_PC+1):\n\n if not isinstance(obs_scores, type(False)):\n index = obs_scores.index\n y_scores = obs_scores.values\n y_scores = y_scores[:, PC_nb-1]\n else:\n\n # get the predictors\n x_scores = predictors_scores.loc[:, model_scores['predictor'][PC_nb]] # TOdo warning a bug on the selection of predicotors\n model_scores_pc = model_scores['model'][PC_nb]\n\n # get the models\n x_loadings = predictors_loadings.loc[:, model_loadings['predictor'][PC_nb]]\n model_loading_pc = model_loadings['model'][PC_nb]\n\n if model_scores_curvfit:\n pass\n elif dnn_model_scores:\n\n x_scores = pd.DataFrame(model_scores_pc.scaler_x.transform(x_scores),index=x_scores.index, columns=x_scores.columns) # scale as for training\n\n y_scores = model_scores_pc.predict(x_scores)\n y_scores = model_scores_pc.scaler_y.inverse_transform(y_scores).flatten()\n\n index = x_scores.index\n\n elif rnn_model_scores:\n\n x_scores = pd.DataFrame(model_scores_pc.scaler_x.transform(x_scores), index=x_scores.index,\n columns=x_scores.columns) # scale as for training\n # preprocessing for RNN\n X_lag = get_df_lags(x_scores, lags=range(model_scores_pc.sequence_length))\n index = X_lag.index\n # Y = Y.loc[X_lag.index, :] # get same index\n X_lag = X_lag.values.reshape(X_lag.shape[0], model_scores_pc.sequence_length, int(X_lag.shape[1] / model_scores_pc.sequence_length))\n\n y_scores = model_scores_pc.predict(X_lag)\n y_scores = model_scores_pc.scaler_y.inverse_transform(y_scores).flatten()\n\n elif not isinstance(obs_scores, pd.DataFrame):\n\n if model_scores_pc.k_constant == 1 :\n x_scores = sm.add_constant(x_scores)\n\n if isinstance(observed_scores, pd.DataFrame): # use observed score instead of predicted scores\n y_scores = observed_scores.loc[:,PC_nb].values\n else:\n y_scores = model_scores_pc.predict(x_scores)\n index = x_scores.index\n\n # Model loadings\n if model_loading_curvfit:\n y_loading = model_loadings['model'][PC_nb](x_loadings.values, *model_loadings['params'][PC_nb])\n elif use_predict_func_loading:\n pass\n else:\n if model_loading_pc.k_constant == 1 :\n x_loadings = sm.add_constant(x_loadings)\n y_loading = model_loading_pc.predict(x_loadings)\n\n if eqm:\n obs_sc= self.scores\n sim_sc = y_scores\n y_scores = bias_correction(obs_sc.loc[idx_train,PC_nb], sim_sc.loc[idx_train],\n sim_sc.loc[idx_test], method='eqm' ,extrapolate='constant', nbins=50)\n index = idx_test\n print('eqm')\n\n\n if qm_maps != None:\n\n qmap = qm_maps[PC_nb]\n old_y_scores = y_scores\n y_scores = qmap.map(old_y_scores)\n\n y_loading_test = np.array([y_loading for l in range(len(y_scores))])\n\n\n predict= y_loading_test * y_scores[:,None]\n loadings.append(y_loading)\n scores.append( y_scores)\n predicted.append( predict)\n \n loadings = np.array(np.dstack(loadings))\n scores = np.array(np.dstack(scores))\n predicted = np.array(np.dstack(predicted))\n\n # K.clear_session()\n # gc.collect()\n\n res = {'loadings':loadings, 'scores':scores, 'predicted': predicted,'index':index}\n return res\n\n def stepwise_model(self, df_PCs, predictors, lim_nb_predictors=None, constant=True, log=False, manual_selection=None):\n \"\"\"\n Find best linear predictors to fit the scores\n Linear model designed by forward selection.\n \n Parameters:\n -----------\n data : pandas DataFrame with all possible predictors and response\n \n response: string, name of response column in data\n \n log: True, print a log of the score and associated candidate\n \n Returns:\n --------\n model: an \"optimal\" fitted statsmodels linear model\n with an intercept\n selected by forward selection\n evaluated by adjusted R-squared\n \"\"\"\n models = []\n predictors_name = []\n params = []\n dic_log ={} # contain the log\n\n # remove columns not float\n predictors = predictors.select_dtypes(exclude=['object'])\n\n print(\"O\"*10)\n if lim_nb_predictors:\n print(\"Number of predictors limited to \" + str(lim_nb_predictors))\n print(\"O\"*10)\n \n for column in df_PCs:\n dic_log[column] = {}\n\n pc = df_PCs.loc[:,column]\n remaining = set(predictors.columns)\n\n selected = []\n best_score, new_best_score = 0.0, 0.0\n\n print(\"==\"*20)\n print(\"Pc\"+str(column))\n print(\"==\"*20)\n\n if not manual_selection:\n for i in range(lim_nb_predictors):\n scores_with_candidates = []\n log_pearson_with_candidates = []\n for candidate in remaining:\n x = predictors[selected + [candidate]]\n if constant:\n x = sm.add_constant(x)\n\n y = pc\n\n score = sm.OLS(y.values,x.values).fit().rsquared_adj\n log_pearson_with_candidates.append([pearsonr(x.values[:,1],y.values),candidate])\n # score = sm.OLS(y.values,x.values).fit().rsquared\n scores_with_candidates.append([score, candidate])\n\n scores_with_candidates = pd.DataFrame(scores_with_candidates, columns = ['score', \"candidate\"])\n scores_with_candidates.sort_values('score', ascending=False, inplace=True)\n\n log_pearson_with_candidates= pd.DataFrame(log_pearson_with_candidates, columns = ['score', \"candidate\"])\n log_pearson_with_candidates.sort_values('score', ascending=False, inplace=True)\n\n new_best_score, best_candidate = scores_with_candidates.iloc[0,:]\n\n if log:\n dic_log[column][str(i)] = log_pearson_with_candidates\n\n if best_score < new_best_score:\n remaining.remove(best_candidate)\n selected.append(best_candidate)\n print(selected)\n print(new_best_score)\n best_score = new_best_score\n else:\n selected = manual_selection[column]\n\n if selected == []: # if the stepwise regression was not able to select variables to create a model\n model = np.nan\n else:\n if constant:\n model = sm.OLS(pc.values, sm.add_constant(predictors[selected].values)).fit()\n print(model.rsquared_adj)\n else:\n model = sm.OLS(pc.values, predictors[selected].values).fit()\n params.append(model.params)\n models.append(model)\n predictors_name.append(selected)\n \n \n predictors_name = pd.Series(predictors_name)\n models = pd.Series(models)\n params = pd.Series(params)\n\n\n\n print(\"Saving\")\n df_models = pd.concat([predictors_name,models, params],axis=1)\n df_models.columns = ['predictor', 'model', 'params']\n df_models.index = range(1,len(df_PCs.columns)+1 )\n print(\"Done\")\n if log:\n return df_models, dic_log\n else:\n return df_models\n\n def skill_model(self, df_obs, pred, metrics, use_bias=None, hours=False, plot_summary=False, summary=None, mean_data=False, type='predicted'):\n \"\"\"\n DESCRIPTION\n Compute bias and RMSE to assess the model performance \n INPUT\n df_verif: dataframe with the observed values\n predictors: a list of pandas series which contains the predictors for the scores SHOULD NOT BE A LIST\n metrics: sklearn metric function to be used\n see: http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics\n example:\n metrics.explained_variance_score(y_true, y_pred) Explained variance regression score function\n metrics.mean_absolute_error(y_true, y_pred) Mean absolute error regression loss\n metrics.mean_squared_error(y_true, y_pred[, ...]) Mean squared error regression loss\n metrics.median_absolute_error(y_true, y_pred) Median absolute error regression loss\n metrics.r2_score(y_true, y_pred[, ...]) R^2 (coefficient of determination) regression score function.\n summary: True, print the mean statistics\n \"\"\"\n # if (not params_loadings or not params_scores):\n # params_loadings, params_scores = self.get_curvfit_params()\n\n if type == 'predicted':\n data = pred['predicted'].sum(axis=2)\n else:\n data = pred\n \n if isinstance(mean_data, pd.core.series.Series):\n df_rec = pd.DataFrame(data, columns=df_obs.columns, index=pred['index']) # should improve this\n df_rec = df_rec.add(mean_data,axis=0)\n df_obs = df_obs.add(mean_data,axis=0)\n else:\n df_rec = pd.DataFrame(data, columns = df_obs.columns, index =pred['index']) # should improve this\n \n if not hours:\n hours = df_rec.index.hour\n hours = sorted(hours)\n hours = list(set(hours))\n hours = [str(str(hour)+':00').rjust(5, '0') for hour in hours]\n\n score = pd.DataFrame(columns= df_rec.columns)\n\n # for hour in hours:\n for sta in df_rec:\n df = pd.concat([df_obs[sta], df_rec[sta]], axis=1, join='inner')\n # df = df.between_time(hour,hour)\n df = df.dropna(axis=0)\n\n if metrics == pearsonr:\n score.loc[0,sta] = metrics(df.iloc[:,0], df.iloc[:,1])[0]\n elif use_bias == True:\n bias = df.iloc[:,0] - df.iloc[:,1]\n score.loc[0, sta] = bias.mean(axis=0)\n else:\n score.loc[0, sta] = metrics(df.iloc[:, 0], df.iloc[:, 1])\n\n if summary:\n score.loc['Total_hours',:] = score.mean(axis=0)\n score.loc[:,'Total_stations'] = score.mean(axis=1)\n if plot_summary:\n plt.figure()\n c = plt.pcolor(score, cmap=\"viridis\")\n cbar = plt.colorbar()\n cbar.ax.tick_params(labelsize=14) \n# show_values(c)\n plt.title(\"Validation summary\")\n# print type(score)\n# sns.heatmap(score)\n plt.yticks(np.arange(0.5, len(score.index), 1), score.index, fontsize=14)\n plt.xticks(np.arange(0.5, len(score.columns), 1), score.columns, fontsize=14,rotation='vertical')\n plt.show()\n print(score)\n return score\n\n def hovermoller_combined(self, dfs_pcs_combined, stanames_LCB=None):\n \n for pc in dfs_pcs_combined.keys():\n \n var = dfs_pcs_combined[pc].loc[:,'T'].groupby(lambda x: x.hour).mean()\n U = dfs_pcs_combined[pc].loc[:,'U'].groupby(lambda x: x.hour).mean()\n V = dfs_pcs_combined[pc].loc[:,'V'].groupby(lambda x: x.hour).mean()\n \n AttSta = self.AttSta\n if not stanames_LCB:\n stanames_LCB = AttSta.get_sta_id_in_metadata(values = ['Head'])\n \n print(stanames_LCB)\n var.columns = stanames_LCB\n U.columns = stanames_LCB\n V.columns = stanames_LCB\n \n sort = AttSta.sortsta(stanames_LCB, 'Lon')\n sorted_sta = sort['stanames']\n sorted_lon = sort['metadata']\n position, time = np.meshgrid(sorted_lon, var.index)\n \n levels_contour=np.linspace(var.min().min(),var.max().max(),100)\n levels_colorbar=np.linspace(var.min().min(),var.max().max(),10)\n \n cmap = plt.cm.get_cmap(\"RdBu_r\")\n fig = plt.figure()\n \n cnt = plt.contourf(position, time, var.loc[:,sorted_sta].values, levels = levels_contour)\n for c in cnt.collections:\n c.set_edgecolor(\"face\")\n \n cbar = plt.colorbar(ticks=levels_colorbar)\n plt.quiver(position,time,U.values,V.values)\n # cbar.ax.tick_params()\n # qk = plt.quiverkey(a, 0.9, 1.05, 1, r'$1 \\frac{m}{s}$',\n # labelpos='E',\n # fontproperties={'weight': 'bold'})\n cbar.set_label('Some Units')\n plt.ylabel(r\"Hours\")\n plt.xlabel( r\"Longitude (Degree)\")\n plt.grid(True, color=\"0.5\")\n plt.tick_params(axis='both', which='major')\n # plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n \n plt.title(\"Principal component \" + str(pc))\n plt.show()\n\n def skill_scores(self, scores_true, scores_pred, metrics):\n\n metric_scores = {}\n for i, pc_nb in enumerate(scores_true.columns):\n metric_scores[pc_nb]= metrics( scores_true.iloc[:, i], scores_pred[:, i])\n\n return metric_scores\n\n def dnn_model(self, X, Y, epochs=None, model_path=None):\n models =[]\n\n # Scaling\n scaler_x = MinMaxScaler(feature_range=(0, 1))\n X = pd.DataFrame(scaler_x.fit_transform(X.values), columns = X.columns, index=X.index)\n\n predictor_names = []\n\n for pc_nb in Y.columns:\n scaler_y = MinMaxScaler(feature_range=(0, 1))\n\n # qt = QuantileTransformer()\n # y = qt.fit_transform(Y.loc[:, pc_nb].values.reshape(-1, 1))\n # Y_scaled = scaler_y.fit_transform(y)\n\n\n Y_scaled = scaler_y.fit_transform(Y.loc[:, pc_nb].values.reshape(-1, 1))\n\n\n print('='*100)\n model, history = DNN_ribeirao(X, Y_scaled, epochs=epochs, model_path=model_path)\n\n # save scalers\n model.scaler_x = scaler_x\n model.scaler_y = scaler_y\n\n predictor_names.append(X.columns.tolist())\n models.append(model)\n\n df_models = pd.DataFrame([predictor_names,models]).T\n df_models.columns = ['predictor', 'model']\n df_models.index = range(1,len(Y.columns)+1 )\n print(\"Done\")\n print('2')\n\n # K.clear_session()\n # gc.collect()\n return df_models\n\n def rnn_model(self, X, Y, SEQUENCE_LENGTH=8, epochs=2, model_path=None):\n models =[]\n predictor_names = []\n # Scaling\n scaler_x = MinMaxScaler(feature_range=(0, 1))\n X = pd.DataFrame(scaler_x.fit_transform(X.values), columns = X.columns, index=X.index)\n\n # preprocessing for RNN\n X_lag = get_df_lags(X,lags=range(SEQUENCE_LENGTH))\n Y = Y.loc[X_lag.index,:] # get same index\n X_lag = X_lag.values.reshape(X_lag.shape[0], SEQUENCE_LENGTH, int(X_lag.shape[1] / SEQUENCE_LENGTH))\n\n for pc_nb in Y.columns:\n scaler_y = MinMaxScaler(feature_range=(0, 1))\n Y_scaled = scaler_y.fit_transform(Y.loc[:,pc_nb].values.reshape(-1,1))\n\n model, history = RNN_ribeirao(X_lag, Y_scaled, epochs=epochs, model_path=model_path)\n\n # save scalers\n model.scaler_x = scaler_x\n model.scaler_y = scaler_y\n\n model.sequence_length = SEQUENCE_LENGTH\n predictor_names.append(X.columns.tolist())\n models.append(model)\n df_models = pd.DataFrame([predictor_names,models]).T\n df_models.columns = ['predictor', 'model']\n df_models.index = range(1,len(Y.columns)+1 )\n print(\"Done\")\n # K.clear_session()\n # gc.collect()\n return df_models\n","sub_path":"mailib/model/statmod/statmod_lib.py","file_name":"statmod_lib.py","file_ext":"py","file_size_in_byte":38200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"435111010","text":"class Producto:\n def __init__(self,id_producto,nombre,descripcion,color,material,cantidad,ancho,alto,espesor,peso,precio,divisiones,accesorios,tipo_producto_id_tipo_producto):\n self.id_producto = id_producto\n self.nombre = nombre\n self.descripcion = descripcion\n self.color = color \n self.material = material\n self.cantidad = cantidad \n self.ancho = ancho\n self.alto = alto \n self.espesor = espesor \n self.peso = peso\n self.precio = precio\n self.divisiones = divisiones\n self.accesorios = accesorios\n self.tipo_producto_id_tipo_producto = tipo_producto_id_tipo_producto \n#prueba de clase producto\n#auxProducto = Producto(0,\"puerta\",\"puerta de madera nativa\",\"rojo\",\"rauli\",2,90,210,3.5,50,80000,2,\"españoleta\",\"puerta\")\n#print(auxProducto.nombre + \" \" +str(auxProducto.espesor))","sub_path":"claseProducto.py","file_name":"claseProducto.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"165013087","text":"import sys\ndef happyNumber(input_num):\n\tsequence = []\n\toriginal_num = input_num\n\tif original_num == \"1\":\n\t\treturn (\"happy 0\")\n\t\n\tsequence.append(original_num)\n\ttemp = square_digits(original_num)\n\twhile (sequence.count(temp) == 0):\n\t\tsequence.append(temp)\n\t\ttemp = square_digits(temp)\n\t\n\ttemp = sequence.pop()\n\tif temp == 1:\n\t\treturn \"happy \"+ str(len(sequence))\n\telse:\n\t\treturn \"unhappy \" + str(len(sequence)+1)\n\ndef square_digits(number):\n\tnumber_string = str(number)\n\tresult = 0\n\tfor ch in number_string:\n\t\tresult = result + int(ch)*int(ch)\n\treturn result\n\ndef main():\n\tc = input()\n\tinput_numbers = []\n\tresult = []\n\twhile c != \"\":\n\t\tinput_numbers.append(c)\n\t\tc = input()\n\t\n\tinput_numbers.reverse()\n\twhile len(input_numbers) != 0:\n\t\ttemp1 = input_numbers.pop()\n\t\ttemp2 = happyNumber(temp1)\n\t\tresult.append(temp2)\n\t\n\tresult.reverse()\n\twhile len(result) != 0:\n\t\ttemp = result.pop()\n\t\tprint(temp)\n\t\n\tprint()\nmain()\t\n","sub_path":"algorithm/happyNumber.py","file_name":"happyNumber.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"359381533","text":"from ..database import mongo\nfrom flask_login import login_required, current_user\nfrom bson import ObjectId\nfrom pymongo import UpdateOne\nimport datetime\n\nclass AppTemplate(object):\n def __init__(self, name):\n self.name = name\n self.user = current_user.get_id()\n\n def add(self):\n return mongo.db.apptemplates.insert({\n 'name': self.name,\n 'active': 'true',\n 'user': self.user,\n 'create_date': datetime.datetime.utcnow(),\n 'update_date': datetime.datetime.utcnow()\n })\n\n @staticmethod\n def get(id):\n return mongo.db.apptemplates.find_one({\n '_id': ObjectId(id)\n })\n\n @staticmethod\n def all():\n return mongo.db.apptemplates.find({\n 'active': 'true'\n }).sort('name',1)\n\n @staticmethod\n def update(id, name):\n return mongo.db.apptemplates.update_one(\n {'_id': ObjectId(id)},\n {'$set': {\n 'name': name,\n 'update_date': datetime.datetime.utcnow()\n }\n }, upsert=False)\n\n @staticmethod\n def delete(id):\n return mongo.db.apptemplates.update_one(\n {'_id': ObjectId(id)},\n {'$set': {'active': 'false'}\n }, upsert=False)\n\n\nclass AppTemplateStep(object):\n def __init__(self, template_id, name, notes, days_before_close):\n self.template_id = template_id\n self.name = name\n self.notes = notes\n self.days_before_close = days_before_close\n\n def add(self):\n template = AppTemplate.get(self.template_id)\n next_order = template['order'] + 1 if 'order' in template else 1\n\n return mongo.db.apptemplates.update_one({\n '_id': ObjectId(self.template_id)\n },{\n '$set': { 'update_date': datetime.datetime.utcnow() },\n '$inc': {'order': 1}, #increment the project order count to keep track of # of project steps\n '$push': {\n 'steps':\n {\n '_id': ObjectId(),\n 'name': self.name,\n 'notes': self.notes,\n 'days_before_close': self.days_before_close,\n 'active': True,\n 'order': next_order, #set the new project step to the next number\n 'create_date': datetime.datetime.utcnow(),\n 'update_date': datetime.datetime.utcnow()\n }\n }\n }, upsert=False)\n\n @staticmethod\n def get(id, step_id):\n return mongo.db.apptemplates.find_one({\n '_id': ObjectId(id),\n 'steps._id': ObjectId(step_id)\n },\n {'steps.$':1})\n\n @staticmethod\n def all(id):\n return mongo.db.apptemplates.aggregate([\n { '$unwind' : '$steps' },\n { '$match' : {\n '_id' : ObjectId(id),\n 'steps.active': True,\n }\n },\n { '$sort' : { 'steps.order' : 1 } }\n ])\n\n @staticmethod\n def update(id, step_id, name, notes, days_before_close):\n return mongo.db.apptemplates.update_one({\n '_id': ObjectId(id),\n 'steps._id': ObjectId(step_id)\n },{\n '$set': {\n 'steps.$.name': name,\n 'steps.$.notes': notes,\n 'steps.$.days_before_close': days_before_close,\n 'steps.$.update_date': datetime.datetime.utcnow()\n }\n }, upsert=False)\n\n @staticmethod\n def delete(id, step_id):\n return mongo.db.apptemplates.update_one({\n '_id': ObjectId(id),\n 'steps._id': ObjectId(step_id)\n },{\n '$set': {\n 'steps.$.active': False,\n 'steps.$.update_date': datetime.datetime.utcnow()\n }\n }, upsert=False)\n\n @staticmethod\n def sort(id, step_ids):\n steps = step_ids.split(',')\n operations = []\n order = 1\n\n for step_id in steps:\n operations.append(UpdateOne({\n '_id': ObjectId(id),\n 'steps._id': ObjectId(step_id)\n },{\n '$set': {\n 'steps.$.order': order\n }\n }, upsert=False))\n\n order += 1\n\n return mongo.db.apptemplates.bulk_write(operations)\n","sub_path":"app/configuration/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"586369217","text":"import paho.mqtt.client as paho #mqtt library\nimport os\nimport json\nimport time\nfrom datetime import datetime\n\n#host name is localhost because both broker and python are Running on same \n#machine/Computer.\nbroker=\"192.168.0.38\"#\"earthron.vacustech.in\" #host name , Replace with your IP address.\nport=1883 #MQTT data listening port\nACCESS_TOKEN='M7OFDCmemyKoi461BJ4j' #not manditory\n\ndef on_publish(client,userdata,result): #create function for callback\n print(\"published data is : \")\n pass\n\nclient1= paho.Client(\"GatewayController\") #create client object\nclient1.on_publish = on_publish #assign function to callback\nclient1.username_pw_set(ACCESS_TOKEN) #access token from thingsboard device\n#client1.connect(broker,port,keepalive=60) #establishing connection\n\ndef systemcon():\n st=0\n try :\n print(\"try exception\") \n st=client1.connect(broker,port,keepalive=60) #establishing connection\n \n except:\n print(\"exception\")\n st=1;\n \n finally:\n print(\"finalyy exception\") \n if(st!=0):\n time.sleep(5)\n systemcon();\n\nsystemcon();\n\n#publishing after every 5 secs\nwhile True:\n\n payload=\"{\"\n payload+=\"\\\"status\\\":1\";\n payload+=\"}\"\n ret= client1.publish(\"Gatewaystatus\",payload) #topic name is test\n if(ret[0]!=0):\n systemcon();\n #print(payload);\n #print(\"Please check data on your Subscriber Code \\n\")\n time.sleep(30)\n \n","sub_path":"SocialDistancingGateway/general/GatewayPostman.py","file_name":"GatewayPostman.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"232911160","text":"import pickle\n\nfrom keras.models import load_model\n\nif __name__ == \"__main__\":\n with open(\"../data/netdata.pkl\", \"rb\") as f:\n tmp = pickle.load(f)\n model = load_model(\"../data/model\")\n trainx = tmp[0]\n trainy = tmp[1]\n testx = tmp[2]\n testy = tmp[3]\n\n y = model.predict(testx)\n tt = []\n for i in range(len(y)):\n s = 0\n for j in range(48):\n s += abs(y[i][j][0] - testy[i][j][0]) * 2 / (y[i][j][0] + testy[i][j][0])\n tt.append(s / 48)\n print(sum(tt) / len(tt))\n","sub_path":"src/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"235857528","text":"from threading import Thread\nfrom collections import deque\nimport logging\nfrom WarehouseServer import com, servo\nimport time\n\nimport os\n\nlogger = logging.getLogger(__name__)\n\n\nclass SinglePoint:\n def __init__(self, x, y, z, move_type):\n self.x = x\n self.y = y\n self.z = z\n self.move_type = move_type\n\n\nclass WarehousePathfinder(Thread):\n movesQueue = deque()\n\n def __init__(self, level=logging.INFO):\n Thread.__init__(self)\n logging.basicConfig()\n logger.setLevel(level)\n formatter = logging.Formatter(\"%(asctime)s %(threadName)-11s %(levelname)-10s %(message)s\")\n\n def run(self):\n while True:\n if len(self.movesQueue) > 0:\n self.go_to_point()\n time.sleep(0.1)\n\n def add_absolute_point(self, x, y, z):\n point = SinglePoint(x, y, z, \"absolute\")\n self.add_point_to_move(point)\n\n def add_relative_point(self, x, y, z):\n point = SinglePoint(x, y, z, \"relative\")\n self.add_point_to_move(point)\n\n def add_point_to_move(self, single_point):\n self.movesQueue.append(single_point)\n\n def go_to_point(self):\n point = self.movesQueue.popleft()\n\n if point.move_type == \"relative\":\n self.move_relative(point.x, point.y, point.z)\n\n if point.move_type == \"absolute\":\n self.move_absolute(point.x, point.y, point.z)\n\n @staticmethod\n def move_x_absolute(value, blocking=False):\n com.send(0, value)\n if blocking:\n while (com.x - 5 > value) or (com.x + 5 < value):\n time.sleep(0.1)\n\n @staticmethod\n def move_y_absolute(value, blocking=False):\n com.send(1, value)\n if blocking:\n while (com.y - 5 > value) or (com.y + 5 < value):\n time.sleep(0.1)\n\n @staticmethod\n def move_z_absolute(value, blocking=False):\n com.send(2, value)\n if blocking:\n while (com.z - 5 > value) or (com.z + 5 < value):\n time.sleep(0.1)\n\n @staticmethod\n def move_x_relative(value, blocking=False):\n com.send(0, com.x + value)\n value += com.x\n if blocking:\n while (com.x - 5 > value) or (com.x + 5 < value):\n time.sleep(0.1)\n\n @staticmethod\n def move_y_relative(value, blocking=False):\n com.send(1, com.y + value)\n value += com.y\n if blocking:\n while (com.y - 5 > value) or (com.y + 5 < value):\n time.sleep(0.1)\n\n @staticmethod\n def move_z_relative(value, blocking=False):\n com.send(2, com.z + value)\n value += com.z\n if blocking:\n while (com.z - 5 > value) or (com.z + 5 < value):\n time.sleep(0.1)\n\n @staticmethod\n def move_servo(value):\n servo.move_percent(value)\n\n def move_relative(self, x, y, z):\n self.move_x_relative(x)\n self.move_y_relative(y)\n self.move_z_relative(z)\n wx = x + com.x\n wy = y + com.y\n wz = z + com.z\n while (abs(com.x - wx) > 2) or (abs(com.y - wy) > 2) or (abs(com.z - wz) > 2):\n time.sleep(0.1)\n print (\"relative move done!\")\n\n def move_absolute(self, x, y, z):\n self.move_x_absolute(x)\n self.move_y_absolute(y)\n self.move_z_absolute(z)\n while (abs(com.x - x) > 2) or (abs(com.y - y) > 2) or (abs(com.z - z) > 2):\n time.sleep(0.1)\n print (\"absolute move done!\")\n\n def go_to_transition_a(self):\n self.move_absolute(1350, 8900, 3500)\n\n def sequence(self):\n self.move_absolute(2280, 2975, 5000)\n self.move_relative(0, 0, 400)\n self.move_relative(0, 370, 0)\n self.move_relative(0, 0, -2500)\n self.move_absolute(1370, 8900, 2900)\n os.system(\"echo 4=%d%% > /dev/servoblaster\" % 18)\n self.move_absolute(1972, 6500, 400)\n self.move_relative(0, -1350, 0)\n self.move_relative(0, 0, -250)\n self.move_relative(0, 2500, 0)\n self.move_relative(0, 0, 3000)\n os.system(\"echo 4=%d%% > /dev/servoblaster\" % 85.6)\n self.move_absolute(1970, 5150, 3300)\n self.move_absolute(1970, 5150, 2912)\n self.move_relative(0, 2500, 0)\n self.move_absolute(1350, 8700, 2000)\n self.move_absolute(1350, 7000, 50)\n time.sleep(5)\n self.move_absolute(1350, 8900, 3500)\n\n def sequence_backward(self):\n self.move_absolute(1350, 7000, 50)\n time.sleep(5)\n self.move_relative(0, 0, -1000)\n self.move_absolute(1978, 7000, 3000)\n\n self.move_relative(0, -1500, 0)\n self.move_relative(0, 0, -250)\n self.move_relative(0, 1500, 0)\n os.system(\"echo 4=%d%% > /dev/servoblaster\" % 18)\n\n self.move_absolute(1975, 7000, 200)\n\n\n self.move_relative(0, -2500, 0)\n # self.move_absolute(1950, 5150, 2912)\n # self.move_absolute(1950, 5150, 3300)\n # self.move_relative(0, 0, -3000)\n # self.move_relative(0, -2500, 0)\n\n\n # os.system(\"echo 4=%d%% > /dev/servoblaster\" % 85.6)\n\n\n\n # self.move_absolute(1350, 8900, 3500)\n\n\n\n @staticmethod\n def reset_stm():\n com.send(99)\n\n def home_x(self):\n com.send(4)\n\n def home_y(self):\n com.send(5)\n\n def home_z(self):\n com.send(6)\n\n def home_all(self):\n self.home_y()\n while True:\n time.sleep(0.5)\n if com.y == 0:\n time.sleep(0.5)\n if com.y == 0:\n break\n\n self.move_absolute(0, 8900, 0)\n\n self.home_z()\n while True:\n time.sleep(0.5)\n if com.z == 0:\n time.sleep(0.5)\n if com.z == 0:\n break\n\n self.home_x()\n while True:\n time.sleep(0.5)\n if com.x == 0:\n time.sleep(0.5)\n if com.x == 0:\n break\n\n time.sleep(0.5)\n\n self.go_to_transition_a()\n","sub_path":"WarehouseServer/path_thread.py","file_name":"path_thread.py","file_ext":"py","file_size_in_byte":6000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"42535817","text":"# Import required modules\nfrom datetime import date, time, datetime\nimport time\nimport RPi.GPIO as GPIO\n\n# Initialize GPIO\nGPIO.setwarnings(False)\nGPIO.setmode(GPIO.BOARD)\nGPIO.setup(8, GPIO.OUT, initial=GPIO.LOW)\n\n\n# Define current, ON, and OFF times\nnow = datetime.now()\non_time = now.replace(hour=8, minute=00, second=00)\noff_time = now.replace(hour=18, minute=00, second=00)\n\n# Loop to control GPIO output\nwhile True:\n if (now > on_time and now < off_time):\n GPIO.output(8, GPIO.HIGH)\n sleep(30)\n else:\n GPIO.output(8, GPIO.LOW)\n\n\n","sub_path":"Hydroponics.py","file_name":"Hydroponics.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"107807979","text":"\nfrom epicduels.content import game_state as CT\nfrom epicduels.content import decks\n\n# Light is_main Characters\nANAKIN_SKYWALKER = {'name': 'Anakin Skywalker',\n 'hp': 18,\n 'is_main': True,\n 'max_hp': 18,\n 'type': CT.ANAKIN_SKYWALKER,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.ANAKIN_SKYWALKER_DECK}\n\nHAN_SOLO = {'name': 'Han Solo',\n 'hp': 13,\n 'is_main': True,\n 'max_hp': 13,\n 'type': CT.HAN_SOLO,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.HAN_SOLO_DECK}\n\nLUKE_SKYWALKER = {'name': 'Luke Skywalker',\n 'hp': 17, 'is_main': True,\n 'max_hp': 17,\n 'type': CT.LUKE_SKYWALKER,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.LUKE_SKYWALKER_DECK}\n\nMACE_WINDU = {'name': 'Mace Windu',\n 'hp': 19,\n 'is_main': True,\n 'max_hp': 19,\n 'type': CT.MACE_WINDU,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.MACE_WINDU_DECK}\n\nOBI_WAN = {'name': 'Obi-Wan Kenobi',\n 'hp': 18,\n 'is_main': True,\n 'max_hp': 18,\n 'type': CT.OBI_WAN,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.OBI_WAN_KENOBI_DECK}\n\nYODA = {'name': 'Yoda',\n 'hp': 15,\n 'is_main': True,\n 'max_hp': 15,\n 'type': CT.YODA,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.YODA_DECK}\n\n# minor Characters\nCHEWBACCA = {'name': 'Chewbacca',\n 'hp': 15,\n 'is_main': False,\n 'max_hp': 15,\n 'type': CT.CHEWBACCA,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.CHEWBACCA_DECK}\n\nLEIA_SKYWALKER = {'name': 'Leia Skywalker',\n 'hp': 10,\n 'is_main': False,\n 'max_hp': 10,\n 'type': CT.LEIA_SKYWALKER,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.LEIA_SKYWALKER_DECK}\n\nPADME_AMIDALA = {'name': 'Padme Amidala',\n 'hp': 10,\n 'is_main': False,\n 'max_hp': 10,\n 'type': CT.PADME_AMIDALA,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.PADME_AMIDALA_DECK}\n\nTROOPER = {'name': 'Trooper',\n 'hp': 4,\n 'is_main': False,\n 'max_hp': 4,\n 'type': CT.TROOPER,\n 'state': CT.LIGHT,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.TROOPER_DECK}\n\n# Dark is_main Characters\nBOBA_FETT = {'name': 'Boba Fett',\n 'hp': 14,\n 'is_main': True,\n 'max_hp': 14,\n 'type': CT.BOBA_FETT,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.BOBA_FETT_DECK}\n\nCOUNT_DOOKU = {'name': 'Count Dooku',\n 'hp': 18,\n 'is_main': True,\n 'max_hp': 18,\n 'type': CT.COUNT_DOOKU,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.COUNT_DOOKU_DECK}\n\nDARTH_MAUL = {'name': 'Darth Maul',\n 'hp': 18,\n 'is_main': True,\n 'max_hp': 18,\n 'type': CT.DARTH_MAUL,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.DARTH_MAUL_DECK}\n\nDARTH_VADER = {'name': 'Darth Vader',\n 'hp': 20,\n 'is_main': True,\n 'max_hp': 20,\n 'type': CT.DARTH_VADER,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.DARTH_VADER_DECK}\n\nEMPEROR_PALPATINE = {'name': 'Emperor Palpatine',\n 'hp': 13,\n 'is_main': True,\n 'max_hp': 13,\n 'type': CT.EMPEROR_PALPATINE,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': False,\n 'deck': decks.EMPEROR_PALPATINE_DECK}\n\nJANGO_FETT = {'name': 'Jango Fett',\n 'hp': 15,\n 'is_main': True,\n 'max_hp': 15,\n 'type': CT.JANGO_FETT,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.JANGO_FETT_DECK}\n\n# minor Characters\nGREEDO = {'name': 'Greedo',\n 'hp': 7,\n 'is_main': False,\n 'max_hp': 7,\n 'type': CT.GREEDO,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.GREEDO_DECK}\n\nZAM_WESELL = {'name': 'Zam Wesell',\n 'hp': 10,\n 'is_main': False,\n 'max_hp': 10,\n 'type': CT.ZAM_WESELL,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.ZAM_WESELL_DECK}\n\nBATTLE_DROID = {'name': 'Battle Droid',\n 'hp': 3,\n 'is_main': False,\n 'max_hp': 3,\n 'type': CT.BATTLE_DROID,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.TROOPER_DECK}\n\nCRIMSON_GUARD = {'name': 'Crimson Guard',\n 'hp': 5,\n 'is_main': False,\n 'max_hp': 5,\n 'type': CT.CRIMSON_GUARD,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.CRIMSON_GUARD_DECK}\n\nSUPER_BATTLE_DROID = {'name': 'Super Battle Droid',\n 'hp': 5,\n 'is_main': False,\n 'max_hp': 5,\n 'type': CT.SUPER_BATTLE_DROID,\n 'state': CT.DARK,\n 'pos': None,\n 'is_range': True,\n 'deck': decks.SUPER_BATTLE_DROID_DECK}\n\nCHARACTERS = {\n CT.ANAKIN_SKYWALKER: ANAKIN_SKYWALKER,\n CT.HAN_SOLO: HAN_SOLO,\n CT.LUKE_SKYWALKER: LUKE_SKYWALKER,\n CT.MACE_WINDU: MACE_WINDU,\n CT.OBI_WAN: OBI_WAN,\n CT.YODA: YODA,\n CT.BOBA_FETT: BOBA_FETT,\n CT.COUNT_DOOKU: COUNT_DOOKU,\n CT.DARTH_MAUL: DARTH_MAUL,\n CT.DARTH_VADER: DARTH_VADER,\n CT.EMPEROR_PALPATINE: EMPEROR_PALPATINE,\n CT.JANGO_FETT: JANGO_FETT,\n CT.CHEWBACCA: CHEWBACCA,\n CT.TROOPER: TROOPER,\n CT.LEIA_SKYWALKER: LEIA_SKYWALKER,\n CT.PADME_AMIDALA: PADME_AMIDALA,\n CT.BATTLE_DROID: BATTLE_DROID,\n CT.CRIMSON_GUARD: CRIMSON_GUARD,\n CT.GREEDO: GREEDO,\n CT.SUPER_BATTLE_DROID: SUPER_BATTLE_DROID,\n CT.ZAM_WESELL: ZAM_WESELL\n}\n\nSQUADS = {\n CT.ANAKIN_SKYWALKER: [ANAKIN_SKYWALKER, PADME_AMIDALA],\n CT.HAN_SOLO: [HAN_SOLO, CHEWBACCA],\n CT.LUKE_SKYWALKER: [LUKE_SKYWALKER, LEIA_SKYWALKER],\n CT.MACE_WINDU: [MACE_WINDU, TROOPER, TROOPER],\n CT.OBI_WAN: [OBI_WAN, TROOPER, TROOPER],\n CT.YODA: [YODA, TROOPER, TROOPER],\n CT.BOBA_FETT: [BOBA_FETT, GREEDO],\n CT.COUNT_DOOKU: [COUNT_DOOKU, SUPER_BATTLE_DROID, SUPER_BATTLE_DROID],\n CT.DARTH_MAUL: [DARTH_MAUL, BATTLE_DROID, BATTLE_DROID],\n CT.DARTH_VADER: [DARTH_VADER, TROOPER, TROOPER],\n CT.EMPEROR_PALPATINE: [EMPEROR_PALPATINE, CRIMSON_GUARD, CRIMSON_GUARD],\n CT.JANGO_FETT: [JANGO_FETT, ZAM_WESELL]\n}\n","sub_path":"epicduels/content/characters.py","file_name":"characters.py","file_ext":"py","file_size_in_byte":7926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"490989413","text":"from collections import defaultdict\nfrom .fields import Field, Id\nfrom .db import DatabaseInterface, mongo_client, DATABASE_NAME\n\nimport json\nimport logging\nimport pymongo\n\n\nlogger = logging.getLogger(__name__)\n\n\nMAX_LIST_LENGTH = 3\n\n\nclass ModelRegistry(object):\n\n def __init__(self):\n self.models = {}\n\n def load(self, app_label, model_name):\n app_label = app_label.lower()\n model_name = model_name.lower()\n return self.models.get(app_label, {}).get(model_name)\n\n def register(self, app_label, model_name, model):\n app_label = app_label.lower()\n model_name = model_name.lower()\n if app_label in self.models:\n app_models = self.models.get(app_label)\n if model_name in app_models:\n return\n else:\n self.models[app_label][model_name] = model\n else:\n self.models[app_label] = {model_name: model}\n\n\ncache = ModelRegistry()\nload = cache.load\nregister = cache.register\n\n\ndef indexField(collection, name, field):\n if field.index or field.unique:\n kwargs = {}\n index = field.index if field.index else pymongo.ASCENDING\n kwargs['unique'] = field.unique\n kwargs['background'] = field.background\n kwargs['sparse'] = field.sparse\n if index == pymongo.GEO2D:\n kwargs['min'] = field.geo_min\n kwargs['max'] = field.geo_max\n if field.unique:\n kwargs['dropDups'] = field.drop_dups\n\n collection.create_index([(name, index), ], **kwargs)\n\n\ndef parseCompoundIndex(index):\n idx = []\n for elm in index:\n if isinstance(elm, (list, tuple)):\n idx += elm\n else:\n idx += [(elm, pymongo.ASCENDING), ]\n return idx\n\n\nclass Meta(object):\n\n def __init__(self, field_list):\n self.fields = {}\n self.fieldmap = defaultdict(list)\n for name, field in field_list:\n setattr(self, name, field)\n self.fields[name] = field\n self.fieldmap[field.__class__].append(name)\n self.fieldmap = dict(self.fieldmap)\n\n\nclass ModelBaseMeta(type):\n\n def __new__(cls, classname, bases, classDict):\n\n app_label = bases[0].__module__.split('.')[-2]\n\n # check the registry and exit if found\n model = load(app_label, classname)\n if model:\n return model\n\n # find fields from all base classes of this model\n collection_name = classDict['collection_name'] if 'collection_name' in classDict else classname.lower()\n _collection = mongo_client[DATABASE_NAME][collection_name]\n field_list = []\n for base in bases:\n # create the unique_together index\n if hasattr(base, 'unique_together'):\n idx = parseCompoundIndex(base.unique_together)\n _collection.create_index(idx, unique=True)\n if hasattr(base, 'compound_index'):\n idx = parseCompoundIndex(base.compound_index)\n _collection.create_index(idx)\n if hasattr(base, 'meta'):\n items = base.meta.fields.items()\n for name, field in items:\n # single field index creation\n indexField(_collection, name, field)\n # add the item to the list of fields to be stored in the meta\n # model\n field_list += items\n\n # clean the classDict by removing the fields from the desired\n # attributes i.e. the attributes we want to actively use on the model\n if 'unique_together' in classDict:\n idx = parseCompoundIndex(classDict['unique_together'])\n _collection.create_index(idx, unique=True)\n if 'compound_index' in classDict:\n idx = parseCompoundIndex(classDict['compound_index'])\n _collection.create_index(idx)\n for key, val in classDict.items():\n if issubclass(val.__class__, Field):\n field_list.append((key, val))\n indexField(_collection, key, val)\n del classDict[key]\n\n meta = Meta(field_list)\n\n # make the class\n new_class = type.__new__(cls, classname, bases, classDict)\n new_class.meta = meta\n\n # register the model in the ModelRegistry\n register(app_label, classname, new_class)\n\n return load(app_label, classname)\n\n\nclass Model(DatabaseInterface):\n\n __metaclass__ = ModelBaseMeta\n\n _id = Id()\n\n def __init__(self, *args, **kwargs):\n self._from_db = kwargs.get('_from_db', False)\n self._conditions = kwargs.get('_conditions')\n for fieldname, field in self.meta.fields.items():\n value = kwargs.get(fieldname, field.getDefault())\n setattr(self, fieldname, value)\n field.model = self\n field.name = fieldname\n field.value = value\n\n def __repr__(self):\n return u'<%s: %s>' % (self.__class__.__name__, self._id)\n\n def filterFields(self, field_type):\n \"\"\"\n Filters the instances within _fields for the class type specified.\n \"\"\"\n # get fieldnames\n fieldmap = self.meta.fieldmap\n if field_type in fieldmap:\n fieldnames = fieldmap[field_type]\n return [getattr(self, name) for name in fieldnames]\n return []\n\n def serialize(self, to_json=False):\n \"\"\"\n For use in model serialization. It uses the dictionary returned from\n _fields property method (below).\n \"\"\"\n data = {\n name: field.serialize(getattr(self, name), to_json=to_json)\n for name, field in self.meta.fields.items()\n if getattr(self, name) is not None\n }\n if to_json:\n return json.dumps(data)\n return data\n","sub_path":"dat/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"123485035","text":"import gi\n\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk\nfrom gi.repository import Gdk\nimport Clientes\nimport Tintas\n\n\nclass Principal(Gtk.Window):\n def __init__(self):\n Gtk.Window.__init__(self, title=\"Menu Principal\")\n self.set_default_size(400, 200)\n self.set_resizable(False)\n\n self.tintas=None\n\n caja = Gtk.Box(spacing=0, orientation=Gtk.Orientation.VERTICAL)\n\n caja.override_background_color(0, Gdk.RGBA(0.937, 0.914, 0.898, 0.5))\n\n self.add(caja)\n\n #declaro los botones\n self.botonTintas = Gtk.Button(label=\"Tintas\", margin_top=30, margin_left=50, margin_right=50)\n self.botonClientes = Gtk.Button(label=\"Clientes\", margin_top=30, margin_left=50, margin_right=50)\n self.botonSalir = Gtk.Button(label=\"Salir\", margin_top=30, margin_left=50, margin_right=50)\n\n #añado los botones a la caja\n caja.add(self.botonTintas)\n caja.add(self.botonClientes)\n caja.add(self.botonSalir)\n\n #asocio los botones a los listener del click\n self.botonTintas.connect(\"clicked\", self.on_open_clicked)\n self.botonClientes.connect(\"clicked\",self.on_open_clientes_clicked)\n self.botonSalir.connect(\"clicked\",self.on_open_salir_clicked)\n\n self.connect(\"destroy\", Gtk.main_quit)\n self.show_all()\n\n\n def on_open_clicked(self,button):\n \"\"\"Listener boton tintas.\n\n :param button:\n :return:\n \"\"\"\n self.tintas = Tintas.Tintas()\n\n\n def on_open_clientes_clicked(self,button):\n \"\"\"Listener boton clientes.\n\n :param button:\n :return:\n \"\"\"\n\n self.clientes= Clientes.Clientes()\n\n def on_open_salir_clicked(self,evt):\n \"\"\"Listener boton salir.\n\n :param evt:\n :return:\n \"\"\"\n\n Principal.destroy(self)\n\n\n\n\n","sub_path":"Principal.py","file_name":"Principal.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"541503120","text":"from __future__ import absolute_import, unicode_literals\n\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom mayan.apps.common.apps import MayanAppConfig\nfrom mayan.apps.common.classes import MissingItem\nfrom mayan.apps.common.html_widgets import TwoStateWidget\nfrom mayan.apps.common.menus import (\n menu_list_facet, menu_object, menu_secondary, menu_setup\n)\nfrom mayan.apps.common.signals import post_initial_setup, post_upgrade\nfrom mayan.apps.converter.links import link_transformation_list\nfrom mayan.apps.documents.menus import menu_documents\nfrom mayan.apps.documents.signals import post_version_upload\nfrom mayan.apps.navigation.classes import SourceColumn\n\nfrom .classes import StagingFile\nfrom .dependencies import * # NOQA\nfrom .handlers import (\n handler_copy_transformations_to_version, handler_create_default_document_source,\n handler_initialize_periodic_tasks\n)\nfrom .links import (\n link_document_create_multiple, link_setup_sources,\n link_setup_source_check_now, link_setup_source_create_imap_email,\n link_setup_source_create_pop3_email, link_setup_source_create_sane_scanner,\n link_setup_source_create_watch_folder, link_setup_source_create_webform,\n link_setup_source_create_staging_folder, link_setup_source_delete,\n link_setup_source_edit, link_setup_source_logs, link_staging_file_delete,\n link_document_version_upload\n)\nfrom .widgets import StagingFileThumbnailWidget\n\n\nclass SourcesApp(MayanAppConfig):\n app_namespace = 'sources'\n app_url = 'sources'\n has_rest_api = True\n has_tests = True\n name = 'mayan.apps.sources'\n verbose_name = _('Sources')\n\n def ready(self):\n super(SourcesApp, self).ready()\n\n POP3Email = self.get_model(model_name='POP3Email')\n IMAPEmail = self.get_model(model_name='IMAPEmail')\n Source = self.get_model(model_name='Source')\n SourceLog = self.get_model(model_name='SourceLog')\n SaneScanner = self.get_model(model_name='SaneScanner')\n StagingFolderSource = self.get_model(model_name='StagingFolderSource')\n WatchFolderSource = self.get_model(model_name='WatchFolderSource')\n WebFormSource = self.get_model(model_name='WebFormSource')\n\n MissingItem(\n label=_('Create a document source'),\n description=_(\n 'Document sources are the way in which new documents are '\n 'feed to Mayan EDMS, create at least a web form source to '\n 'be able to upload documents from a browser.'\n ),\n condition=lambda: not Source.objects.exists(),\n view='sources:setup_source_list'\n )\n\n SourceColumn(\n attribute='label', is_identifier=True, is_sortable=True,\n source=Source\n )\n SourceColumn(\n attribute='class_fullname', label=_('Type'), source=Source\n )\n SourceColumn(\n attribute='enabled', is_sortable=True, source=Source,\n widget=TwoStateWidget\n )\n\n SourceColumn(\n source=StagingFile,\n label=_('Created'),\n func=lambda context: context['object'].get_date_time_created()\n )\n\n html_widget = StagingFileThumbnailWidget()\n SourceColumn(\n source=StagingFile,\n label=_('Thumbnail'),\n func=lambda context: html_widget.render(\n instance=context['object'],\n )\n )\n\n SourceColumn(\n source=SourceLog,\n label=_('Date time'),\n func=lambda context: context['object'].datetime\n )\n SourceColumn(\n source=SourceLog,\n label=_('Message'),\n func=lambda context: context['object'].message\n )\n\n menu_documents.bind_links(links=(link_document_create_multiple,))\n\n menu_list_facet.bind_links(\n links=(\n link_setup_source_logs, link_transformation_list,\n ), sources=(\n POP3Email, IMAPEmail, SaneScanner, StagingFolderSource,\n WatchFolderSource, WebFormSource\n )\n )\n\n menu_object.bind_links(\n links=(\n link_setup_source_delete, link_setup_source_edit\n ), sources=(\n POP3Email, IMAPEmail, SaneScanner, StagingFolderSource,\n WatchFolderSource, WebFormSource\n )\n )\n menu_object.bind_links(\n links=(link_staging_file_delete,), sources=(StagingFile,)\n )\n menu_object.bind_links(\n links=(link_setup_source_check_now,),\n sources=(IMAPEmail, POP3Email, WatchFolderSource,)\n )\n menu_secondary.bind_links(\n links=(\n link_setup_sources, link_setup_source_create_webform,\n link_setup_source_create_sane_scanner,\n link_setup_source_create_staging_folder,\n link_setup_source_create_pop3_email,\n link_setup_source_create_imap_email,\n link_setup_source_create_watch_folder\n ), sources=(\n POP3Email, IMAPEmail, StagingFolderSource, WatchFolderSource,\n WebFormSource, 'sources:setup_source_list',\n 'sources:setup_source_create'\n )\n )\n menu_setup.bind_links(links=(link_setup_sources,))\n menu_secondary.bind_links(\n links=(link_document_version_upload,),\n sources=(\n 'documents:document_version_list', 'documents:upload_version',\n 'documents:document_version_revert'\n )\n )\n\n post_upgrade.connect(\n receiver=handler_initialize_periodic_tasks,\n dispatch_uid='sources_handler_initialize_periodic_tasks'\n )\n post_initial_setup.connect(\n receiver=handler_create_default_document_source,\n dispatch_uid='sources_handler_create_default_document_source'\n )\n post_version_upload.connect(\n receiver=handler_copy_transformations_to_version,\n dispatch_uid='sources_handler_copy_transformations_to_version'\n )\n","sub_path":"mayan/apps/sources/apps.py","file_name":"apps.py","file_ext":"py","file_size_in_byte":6161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"638840148","text":"\"\"\"\n[agent, opponent, ESTIMATE , COMINGOUT, VOTE, DIVINED , IDENTIFIED , AGREE,\n DISAGREE , REQUEST , INQUIRE , BECAUSE , DEAD , execute , divined , DAY]\nagent: 1 - 5,6ANY\nopponent: 1 - 5,6ANY\nestimate: 0, 1, 2, 3(werewolf, villager, seer, possessed)\ncomingout: 0, 1, 2, 3\nvote: 0, 1\ndivined: 0, 1(werewolf,villager)\nidentified: 0, 1(werewolf,villager)\nagree: 0, 1\ndisagree: 0, 1\nrequest: 0, 1\ninquire: 0, 1\nbecause: 0, 1\ndead: 0, 1\nexecute: 0, 1\ndivined(結果):0,1\nday:0,1,2\n\"\"\"\nimport numpy as np\nimport os\nfrom . import splitText\n\n# lstm\n\n\nclass preprocess2(object):\n def __init__(self):\n self.agentNum = 5\n self.content_map = {\n \"agent\": 0, \"opponent\": 1,\n \"ESTIMATE\": 2, \"COMINGOUT\": 3, \"VOTE\": 4, \"DIVINED\": 5, \"IDENTIFIED\": 6, \"AGREE\": 7, \"DISAGREE\": 8, \"REQUEST\": 9, \"INQUIRE\": 10, \"BECAUSE\": 11, \"attack\": 12, \"execute\": 13, \"divined\": 14, \"DAY\": 15\n }\n self.f_maps = []\n self.y_map = np.zeros(25)\n self.is_divine = False\n self.is_finish = False\n\n def update_result(self):\n self.y_map1 = self.y_map[:5]\n self.y_map2 = self.y_map[5:10]\n self.y_map3 = self.y_map[10:15]\n self.y_map4 = self.y_map[15:20]\n self.y_map5 = self.y_map[20:25]\n self.is_finish = True\n\n def update_status(self, contents):\n if \"Sample\" in contents[5]:\n agent = int(contents[2]) - 1\n self.y_map[agent*5+0] = 1\n\n elif \"CALM\" in contents[5]:\n agent = int(contents[2]) - 1\n self.y_map[agent*5+1] = 1\n\n elif \"Liar\" in contents[5]:\n agent = int(contents[2]) - 1\n self.y_map[agent*5+2] = 1\n\n elif \"REPEL\" in contents[5]:\n agent = int(contents[2]) - 1\n self.y_map[agent*5+3] = 1\n\n elif \"Follow\" in contents[5]:\n agent = int(contents[2]) - 1\n self.y_map[agent * 5 + 4] = 1\n\n def update_talk_content(self, agent, content):\n if len(content) == 0:\n return\n if content[0] == \"AGREE\" or content[0] == \"DISAGREE\":\n self.f_map[self.content_map[content[0]]] = 1\n self.f_map[self.content_map[\"agent\"]] = agent\n return\n if type(content[1]) == int:\n op = int(content[1])\n self.f_map[self.content_map[\"agent\"]] = agent\n self.f_map[self.content_map[\"opponent\"]] = op\n self.f_map[self.content_map[content[0]]] = 1\n elif content[1] == \"ANY\":\n self.f_map[self.content_map[\"agent\"]] = agent\n self.f_map[self.content_map[\"opponent\"]] = 5\n self.f_map[self.content_map[content[0]]] = 1\n else:\n self.update_talk_content(\n agent, splitText.splitText(content[1].replace(\"(\", \"\").replace(\")\", \"\")))\n self.update_talk_content(\n agent, splitText.splitText(content[2].replace(\"(\", \"\").replace(\")\", \"\")))\n\n def update_talk(self, contents):\n agent = int(contents[4])-1\n text = contents[5]\n texts = splitText.parse_text(text)\n for text in texts:\n content = splitText.splitText(text)\n if len(content) != 0:\n self.update_talk_content(agent, content)\n\n def update_divine(self, content):\n agent = int(content[2]) - 1\n op = int(content[3]) - 1\n self.f_map[self.content_map[\"agent\"]] = agent\n self.f_map[self.content_map[\"opponent\"]] = op\n self.f_map[self.content_map[content[1]]] = 1\n\n def update_attack(self, content):\n agent = int(content[0]) - 1\n op = int(content[2]) - 1\n self.f_map[self.content_map[\"agent\"]] = agent\n self.f_map[self.content_map[\"opponent\"]] = op\n self.f_map[self.content_map[content[1]]] = 1\n\n def update_dead(self, content):\n agent = int(content[2]) - 1\n self.f_map[self.content_map[\"agent\"]] = agent\n self.f_map[self.content_map[content[1]]] = 1\n\n def update(self, file_name):\n f = open(file_name, mode=\"r\")\n line = f.readline()\n if line[3] == \"SEER\":\n self.is_divine = True\n while line:\n self.f_map = [0] * len(self.content_map)\n # np.zeros(len(self.content_map))\n line = f.readline().rstrip(os.linesep)\n contents = line.split(\",\")\n if len(contents) == 1:\n continue\n elif contents[1] == \"talk\":\n self.update_talk(contents)\n elif contents[1] == \"execute\":\n self.update_dead(contents)\n elif contents[1] == \"attack\" and contents[3] == \"true\":\n self.update_attack(contents)\n elif contents[1] == \"divine\" and self.is_divine:\n self.update_divine(contents)\n elif contents[1] == \"status\":\n self.update_status(contents)\n elif contents[1] == \"result\":\n self.update_result()\n if any(self.f_map):\n self.f_maps.append(self.f_map)\n","sub_path":"wolf-strategy/train/utils/preprocess2.py","file_name":"preprocess2.py","file_ext":"py","file_size_in_byte":5023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"557644313","text":"import time\n\nresidence_limit = 90 # 45, 60\nschengen_constraint = 180\n\ndef visit_duration(*arguments):\n return(max(arguments) - min(arguments) + 1)\n\ndef check_input_data(visits_list, new_visit):\n days_of_visits = set()\n new_visit_set = set()\n for each in visits_list: days_of_visits.update(range(each[0], each[1] + 1))\n new_visit_set.update(range(new_visit[0], new_visit[1] + 1))\n if new_visit_set.intersection(days_of_visits):\n raise Exception('Вы уже были в это время в Европе')\n elif max(days_of_visits) >= min(new_visit_set):\n raise Exception('Ой-ей, что-то пошло не так...Вы хотите въехать в Шенген раньше последней даты выезда')\n\ndef check_date_consistency(date_start, date_end):\n if date_start >= date_end:\n print(\"Дата отъезда должна быть позднее даты начала поездки. Попробуйте еще раз\", end='\\n \\n')\n add_visit()\n\ndef visits_left(visits_list, new_visit_enter, residence_limit, schengen_constraint):\n period_start = (new_visit_enter) - (schengen_constraint)\n days_left = residence_limit\n for each_visit in visits_list:\n if each_visit[0] < period_start <= each_visit[1]:\n days_left -= visit_duration(each_visit[1], period_start)\n pass\n elif period_start <= each_visit[0]: days_left -= visit_duration(each_visit[1], each_visit[0])\n return (days_left)\n\ndef visit_add(visits_left, start, end):\n if visits_left >= (end - start + 1):\n visits.append([start, end])\n print('Ваша поездка добавлена')\n else:\n print('У вас не хватает дней для данной поездки, к этой дате въезда остаток составит всего лишь {} д'.format(visits_left))\n\ndef visit_left_print(visits_left):\n if visits_left > 0: print('Вы можете провести в путешествии {}д'.format(visits_left))\n else: print('К сожалению, Вы израсходовали все дни. Остаток: {}'.format(visits_left))\n\ndef preliminary_check():\n x = visits_left(visits, visits[-1][0], residence_limit, schengen_constraint)\n if x < 0:\n print('Что-то не в порядке с данными о поездках - вы пробыли в Европе больше дней, чем положено. Пожалуйста, проверьте информацию', visits, 'Дней израсходовано: {}'.format(residence_limit - x), sep='\\n')\n delete_visit()\n #!!!DELETE\n time.sleep(0.75)\n\ndef welcoming_msg():\n print('v - добавить визит, p - расчитать допустимую продолжительность следующей поездки,\\n r - удалить визит, e - выйти')\n time.sleep(0.25)\n print('Какое действие Вы хотите совершить?')\n\ndef delete_visit():\n print('Список поездок: {0}'.format(visits))\n print('Какую поездку Вы хотите удалить?')\n start = int(input('Дата начала поездки?'))\n end = int(input('Дата окончания поездки?'))\n time.sleep(0.75)\n counter=0\n for each in visits:\n if (start == each[0]) & (end == each[1]):\n visits.remove(each)\n counter += 1\n print('Поездка {0} была уд��лена. \\n Список поездок: {1}'.format(each, visits))\n if counter == 0: print('Мы не смогли найти Вашу поездку...')\n\ndef add_visit():\n start = int(input ('Дата начала поездки?'))\n end = int(input ('Дата окончания поездки?'))\n check_date_consistency(start, end)\n time.sleep(0.75)\n check_input_data (visits, [start, end])\n days_for_new_travel = visits_left(visits, start, residence_limit, schengen_constraint)\n visit_add(days_for_new_travel, start,end)\n print('Список поездок: {0}'.format(visits))\n\ndef exit_msg():\n print('До свидания!')\n working = False\n\ndef calculate_next_visit():\n start = int(input('Дата начала поездки?'))\n check_input_data(visits, [start,start])\n visit_left_print(\n visits_left(visits, start, residence_limit, schengen_constraint))\n\n#number of days used till 174 included - (10+15+44+5)\nvisits=[[1, 10], [25, 39], [87, 130], [150, 155]]\n#[87, 130]\n\nworking = True\nwhile working:\n preliminary_check()\n welcoming_msg()\n user_input = input()\n if user_input == 'v': add_visit()\n elif user_input == 'r': delete_visit()\n elif user_input == 'p': calculate_next_visit()\n elif user_input == 'e':\n exit_msg()\n working = False\n","sub_path":"Schengen_calc/Schengen_calculator.py","file_name":"Schengen_calculator.py","file_ext":"py","file_size_in_byte":4871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"289700919","text":"import numpy as np\nimport cv2\nimport math\n\nratio = np.load('./calibration_data/pixel2mm.npy')\n\ncamera_index = 2\n\n\ndef distance (x, y, a, b):\n return np.sqrt((x - a) ** 2 + (y-b)**2)\n\n\ndef take_pictures():\n cap = cv2.VideoCapture(camera_index)\n\n while 1:\n # Capture frame-by-frame\n ret, image = cap.read()\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n cv2.imshow('raw', gray)\n gray = cv2.GaussianBlur(gray, (5, 5), 0)\n cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY, gray)\n done = cv2.morphologyEx(gray, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_RECT,(5,5)))\n edges = cv2.Canny(gray, 100, 200)\n\n # Display the resulting frame\n contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n contours_filtered = []\n\n # Filter out large contours\n for i in contours:\n if len(i) > 20:\n contours_filtered.append(i)\n\n mu = [None]*len(contours_filtered)\n mc = [None]*len(contours_filtered)\n\n # Moments\n for i in range(len(contours_filtered)):\n mu[i] = cv2.moments(contours_filtered[i])\n\n # Get the mass centers\n for i in range(len(contours_filtered)):\n if cv2.arcLength(contours_filtered[i], True) < 20:\n continue\n # add 1e-5 to avoid division by zero\n mc[i] = (mu[i]['m10'] / (mu[i]['m00'] + 1e-5), mu[i]['m01'] / (mu[i]['m00'] + 1e-5))\n\n\n # Check duplicates\n # Filter moments and centroids\n mu_filtered = []\n mc_filtered = []\n contours_filterered = []\n\n for i in range(len(contours_filtered)):\n pushable = True\n for j in range(len(contours_filterered)):\n if distance(mc[i][0], mc[i][1], mc_filtered[j][0], mc_filtered[j][1]) < 30:\n pushable = False\n break\n if pushable:\n contours_filterered.append(contours_filtered[i])\n mu_filtered.append(mu[i])\n mc_filtered.append(mc[i])\n\n\n drawing = np.zeros((edges.shape[0],edges.shape[1], 3), dtype=np.uint8)\n principal_angle = []\n bounding_boxes = []\n actual_legnth_of_boxes = []\n for i in range(len(contours_filterered)):\n # Draw contours\n color = (np.random.randint(0,256), np.random.randint(0,256), np.random.randint(0,256))\n cv2.drawContours(drawing, contours_filterered, i, color, 2)\n bound_rect = cv2.minAreaRect(contours_filterered[i])\n \n bound_4 = cv2.boxPoints(bound_rect)\n bound_4_len = np.linalg.norm(bound_4[0] - bound_4[1]), np.linalg.norm(bound_4[1] - bound_4[2])\n bounding_boxes.append(bound_4)\n print(bound_4)\n actual_bound = bound_4_len * ratio\n actual_legnth_of_boxes.append(actual_bound)\n print(\"length of bounding box: \", bound_4_len)\n print(\"actual length: \", actual_bound)\n\n cv2.circle(drawing, (int(mc_filtered[i][0]), int(mc_filtered[i][1])), 4, color, -1)\n # Principal angle\n num = 2 * (mu_filtered[i]['m00'] * mu_filtered[i]['m11'] -mu_filtered[i]['m10'] *mu_filtered[i]['m01'])\n denom = ((mu_filtered[i]['m00'] *mu_filtered[i]['m20'] -mu_filtered[i]['m10'] *mu_filtered[i]['m10']) - (mu_filtered[i]['m00'] *mu_filtered[i]['m02'] -mu_filtered[i]['m01'] *mu_filtered[i]['m01']))\n P_angle = 0.5 * math.atan2( num, denom)\n\n if P_angle > math.pi/ 2:\n P_angle -= math.pi\n m = math.tan(P_angle)\n\n x1 = mc_filtered[i][0] + 100\n x2 = mc_filtered[i][0] - 100\n y1 = m * (x1 - mc_filtered[i][0]) + mc_filtered[i][1]\n y2 = m * (x2 - mc_filtered[i][0]) + mc_filtered[i][1]\n x1 = int(x1)\n x2 = int(x2)\n y1 = int(y1)\n y2 = int(y2)\n principal_angle.append(P_angle * 180 / math.pi )\n cv2.line(drawing, (x1, y1), (x2, y2), color)\n print(i, P_angle * 180 / math.pi , mc_filtered[i])\n \n \n cv2.imshow('Contours', drawing)\n if cv2.waitKey() == ord('q'):\n cv2.destroyWindow('raw')\n return mc_filtered, principal_angle\n \n return\n\n\ndef mapping(act, image):\n act = np.matrix(act)\n ones = np.matrix(np.ones([1, 3]))\n act = np.concatenate((act, ones.T), axis=1)\n image = np.concatenate((image, ones.T), axis=1)\n act = act.T\n image = image.T\n A = np.matmul(act, image.I)\n return A\n \n# Yellow (469.51339957488125, 131.64767848311172) top-left \n# 187.51265\n# 367.98181\n# -182.7314\n# Green (551.8404734749532, 335.5730447400397) bottom-right\n# -185.7127\n# 576.04547\n# -184.7553\n# (481.7600934857015, 97.13918453055045) top-right\n# -140.7747\n# 382.30029\n# -177.4168\n\ndef find_grabbing(p, e1, e2):\n v1 = [e1[0][0] - e1[1][0], e1[0][1] - e1[1][1]]\n v2 = [e2[0][0] - e2[1][0], e2[0][1] - e2[1][1]]\n x1, y1 = (0, 0)\n x2, y2 = (1, math.tan(p))\n p_vec = [x2 - x1, y2 - y1]\n if abs(np.dot(p_vec, v1)) > abs(np.dot(p_vec, v2)):\n return np.linalg.norm(v1)\n else:\n return np.linalg.norm(v2)\n # [(x1, y1) , (x2, y2)]\n # [(x2, y2) , (x3, y3)]","sub_path":"calib/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":5320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"649917024","text":"from ontobio.io.gpadparser import GpadParser\nfrom ontobio.io import assocparser\n\nimport yaml\n\nPOMBASE = \"tests/resources/truncated-pombase.gpad\"\n\ndef test_skim():\n p = GpadParser()\n results = p.skim(open(POMBASE,\"r\"))\n print(str(results))\n\n\ndef test_parse():\n p = GpadParser(config=assocparser.AssocParserConfig(group_metadata=yaml.load(open(\"tests/resources/mgi.dataset.yaml\"), Loader=yaml.FullLoader)))\n test_gpad_file = \"tests/resources/mgi.test.gpad\"\n results = p.parse(open(test_gpad_file, \"r\"))\n print(p.report.to_markdown())\n","sub_path":"tests/test_gpad_parser.py","file_name":"test_gpad_parser.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"157922857","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom copy import deepcopy\n\nx = np.r_[np.random.randn(100, 2) + [2, 2],\n np.random.randn(100, 2) + [-2, -2],\n np.random.randn(100, 2) + [2, -4]]\n\n[plt.scatter(e[0], e[1], c='black', s=7) for e in x]\n\nk = 3\nC_x = np.random.uniform(np.min(x[:, 0]), np.max(x[:, 0]), size=k)\nC_y = np.random.uniform(np.min(x[:, 1]), np.max(x[:, 1]), size=k)\nprint(type(C_x), C_x)\nprint(type(C_y), C_y)\nC = np.array(list(zip(C_x, C_y)), dtype=np.float32)\nprint(type(C), f'print C :{C}')\nplt.scatter(C_x, C_y, marker=\"*\", s=200, c='#005599')\nplt.show()\n\n\ndef dist(a, b, ax=1):\n return np.linalg.norm(a - b, axis=ax)\n\n\n# print(dist(np.array([[0, 0]]), np.array([[2, 2]])))\n\nC_old = np.zeros(C.shape)\nclusters = np.zeros(len(x))\ndelta = dist(C, C_old, None)\nprint(f\"delta={delta}\")\n\n\ndef plot_kmean(current_cluster, delta):\n colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k']\n fig, ax = plt.subplots()\n for i in range(k):\n points = np.array([x[j] for j in range(len(x)) if current_cluster[j] == i])\n ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])\n ax.scatter(C[:, 0], C[:, 1], marker=\"*\", s=200, c=\"#005599\")\n plt.title(f\"delta for C will be:{delta:.4f} \")\n plt.plot()\n plt.show()\n\n\nwhile delta != 0:\n print(\"start a new iteration\")\n for i in range(len(x)):\n distances = dist(x[i], C)\n cluster = np.argmin(distances)\n clusters[i] = cluster\n C_old = deepcopy(C)\n for i in range(k):\n points = [x[j] for j in range(len(x)) if clusters[j] == i]\n C[i] = np.mean(points, axis=0)\n delta = dist(C, C_old, None)\n print(f\"delta={delta}\")\n plot_kmean(clusters, delta)\n","sub_path":"demo30_kmeans2.py","file_name":"demo30_kmeans2.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"193193752","text":"def citefind(file=None, outfile='citationsfound.xml', ns='crossref'):\n root = parse(file)\n if ns == 'crossref':\n urls = root.xpath('//foo:resource/text()',\n namespaces={\"foo\": \"http://www.crossref.org/schema/4.3.7\"})\n elif ns == 'bepress':\n urls = root.xpath('//fulltext-url/text()')\n else:\n return 'Currently only support bepress schema or crossref'\n\n count = 0\n allcitations = et.Element('root')\n for url in urls:\n count += 1\n r = requests.get(url, stream=True)\n with open('metadata.pdf', 'wb') as f:\n f.write(r.content)\n f.close()\n\n raw = parser.from_file('metadata.pdf')\n f = raw['content'].encode()\n zfind = re.findall(r'(?i)doi:.+?(?= |$)', str(f))\n print(len(zfind), url)\n if len(zfind) > 0:\n dois = [y.replace('\\\\n', '').replace(' ', '').replace(\n 'doi:', '').strip('.') for y in zfind]\n dfli = []\n works = Works()\n for item in dois:\n d = works.doi(item)\n df = pd.Series(d).to_frame()\n dfli.append(df.transpose())\n\n cr = pd.concat(dfli)\n cr = cr.reset_index(drop=True)\n cr['year'] = cr.created.apply(lambda x: getyear(x))\n cr['journaltitle'] = cr['container-title'].apply(\n lambda x: gettitle(x))\n cr['surname'] = cr['author'].apply(lambda x: getsurname(x))\n try:\n cr['fpage'] = cr['page'].apply(lambda x: firstpage(x))\n except KeyError:\n cr['fpage'] = ''\n cr['issues'] = cr['journal-issue'].apply(lambda x: issuefunc(x))\n\n xml = cr[['DOI', 'ISSN', 'journaltitle', 'surname',\n 'volume', 'issues', 'fpage', 'year']]\n tree = et.SubElement(allcitations, 'citationlist')\n tree.set('file', url)\n for row in xml.iterrows():\n citation = et.SubElement(tree, 'citation')\n citation.set('key', 'key-{}'.format(str(row[1]['DOI'])))\n\n issn = et.SubElement(citation, 'issn')\n try:\n issn.text = str(row[1]['ISSN'][0])\n try:\n issn = et.SubElement(citation, 'issn')\n issn.text = str(row[1]['ISSN'][1])\n except IndexError:\n pass\n except TypeError:\n pass\n\n journal_titlex = et.SubElement(citation, 'journal_title')\n journal_titlex.text = str(row[1]['journaltitle'])\n\n authorx = et.SubElement(citation, 'author')\n authorx.text = str(row[1]['surname'])\n\n volumex = et.SubElement(citation, 'volume')\n volumex.text = str(row[1]['volume'])\n\n issuex = et.SubElement(citation, 'issue')\n issuex.text = str(row[1]['issues'])\n\n first_pagex = et.SubElement(citation, 'first_page')\n first_pagex.text = str(row[1]['fpage'])\n\n cYearx = et.SubElement(citation, 'cYear', )\n cYearx.text = str(row[1]['year'])\n\n else:\n continue\n\n with open(outfile, 'wb') as fi:\n fi.write(et.tostring(allcitations,\n pretty_print=True, xml_declaration=True))\n fi.close()\n","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":3450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"596671838","text":"\"\"\"Factories for creating new databse objects.\"\"\"\nfrom pollbot.models import User\n\n\ndef user_factory(session, user_id, name, admin=False):\n \"\"\"Create a user.\"\"\"\n user = User(user_id, name)\n session.add(user)\n session.commit()\n\n return user\n","sub_path":"tests/factories.py","file_name":"factories.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"490076460","text":"import client\r\nimport pygame\r\nimport os\r\n\r\nos.environ[\"SDL_VIDEO_CENTERED\"] = \"1\"\r\npygame.init()\r\n\r\nclient.setup()\r\n\r\npygame.mixer.music.load('sample_audio/crab_rave.mp3')\r\npygame.mixer.music.play(-1)\r\n\r\npygame.display.set_caption(\"Mind Music\")\r\nscreen = pygame.display.set_mode((360, 480))\r\n\r\nfont = pygame.font.SysFont(\"Ariel Black\", 24)\r\ntitle_font = pygame.font.SysFont(\"Ariel Black\", 48)\r\n\r\n\r\nimg1 = pygame.transform.scale(pygame.image.load(\"cover_imgs/crab_rave.jpg\"), (180, 180)).convert_alpha()\r\nimg2 = pygame.transform.scale(pygame.image.load(\"cover_imgs/megalovania.jpg\"), (180, 180)).convert_alpha()\r\n\r\ncover = img1\r\n\r\nBLACK = (0, 0, 0)\r\nWHITE = (255, 255, 255)\r\nVIOLET = (190, 80, 190)\r\nGREY = (120, 120, 120)\r\n\r\nback1 = BLACK\r\nback2 = WHITE\r\n\r\nselection = 1\r\nchange = False\r\n\r\nvol = 0\r\n\r\ndef display():\r\n screen.fill(WHITE)\r\n pygame.draw.rect(screen, VIOLET, (0, 0, 360, 80))\r\n pygame.draw.rect(screen, VIOLET, (0, 420, 360, 60))\r\n screen.blit(title_font.render(\"Mind Music\", 5, WHITE), (95, 30))\r\n screen.blit(font.render(\"Crab Rave\", 5, back2, back1), (70, 120))\r\n screen.blit(font.render(\"Megalovania\", 5, back1, back2), (190, 120))\r\n screen.blit(cover, (90, 170))\r\n screen.blit(font.render(\"Keep volume low, as sudden\", 5, BLACK), (70, 370))\r\n screen.blit(font.render(\"increases in volume may occur\", 5, BLACK), (60, 385))\r\n for v in range(1, 100, 20):\r\n if v<=vol:\r\n pygame.draw.rect(screen, WHITE, (120+v, 450-v//5, 10, (80+v)//5))\r\n else:\r\n pygame.draw.rect(screen, GREY, (120+v, 450-v//5, 10, (80+v)//5)) \r\n screen.blit(font.render(str(vol), 5, WHITE), (230, 445))\r\n \r\n\r\nexit_flag = False\r\n\r\nwhile not exit_flag:\r\n res = client.get_value(pygame.mixer.music.get_volume())\r\n pygame.mixer.music.set_volume(res[0])\r\n\r\n vol = round(pygame.mixer.music.get_volume()*100)\r\n \r\n if res[1] == 'surprised':\r\n selection *= -1\r\n change = True\r\n \r\n print('Volume:', vol)\r\n\r\n\r\n if selection == -1 and change:\r\n pygame.mixer.music.load('sample_audio/crab_rave.mp3')\r\n pygame.mixer.music.play(-1)\r\n change = False\r\n back1 = BLACK\r\n back2 = WHITE\r\n cover = img1\r\n if selection == 1 and change:\r\n pygame.mixer.music.load('sample_audio/megalovania.mp3')\r\n pygame.mixer.music.play(-1)\r\n change = False\r\n back1 = WHITE\r\n back2 = BLACK\r\n cover = img2\r\n \r\n for e in pygame.event.get():\r\n if e.type == pygame.QUIT:\r\n exit_flag = True\r\n if e.type == pygame.KEYDOWN and e.key == pygame.K_ESCAPE:\r\n exit_flag = True\r\n \r\n display() \r\n pygame.display.flip()\r\n pygame.time.delay(4)\r\n\r\npygame.quit()\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"313305680","text":"import json\nimport os\n\nimport pyseto\n\nfrom dataclasses import dataclass\nfrom glob import glob\nfrom typing import Callable, List, Any\n\nfrom eth_typing import HexStr\nfrom eth_utils import to_checksum_address\n\nfrom eth_challenge_base.config import Config\nfrom eth_challenge_base.utils import Account, Contract\n\n\n@dataclass\nclass Action:\n description: str\n handler: Callable[[], int]\n\n\nclass ActionHandler:\n def __init__(self, build_path: str, config: Config) -> None:\n with open(os.path.join(build_path, f\"{config.contract}.json\")) as fp:\n build_json = json.load(fp)\n self._contract: Contract = Contract(build_json)\n self._token_key = pyseto.Key.new(version=4, purpose=\"local\", key=os.getenv(\"TOKEN_SECRET\", \"\"))\n\n self._actions: List[Action] = [self._create_account_action(config.constructor_args),\n self._deploy_contract_action(config.constructor_value, config.constructor_args),\n self._get_flag_action(config.flag, config.solved_event)]\n if config.show_source:\n self._actions.append(self._show_source_action(build_path))\n\n def __getitem__(self, index: int) -> Action:\n return self._actions[index]\n\n def __len__(self):\n return len(self._actions)\n\n def _create_account_action(self, constructor_args: Any) -> Action:\n def action() -> int:\n account: Account = Account()\n print(f\"[+]deployer account: {account.address}\")\n token: str = pyseto.encode(self._token_key, payload=account.private_key).decode(\"utf-8\")\n print(f\"[+]token: {token}\")\n estimate_gas: int = self._contract.deploy.estimate_gas(constructor_args)\n print(f\"[+]it will cost {estimate_gas} gas to deploy, make sure that deployer account has enough ether!\")\n return 0\n\n return Action(description=\"Create an account which will be used to deploy the challenge contract\", handler=action)\n\n def _deploy_contract_action(self, constructor_value: int, constructor_args: Any) -> Action:\n def action() -> int:\n try:\n private_key: str = pyseto.decode(self._token_key, input(\"[-]input your token: \").strip()).payload.decode(\"utf-8\")\n except ValueError as e:\n print(e)\n return 1\n\n account: Account = Account(private_key)\n if account.balance() == 0:\n print(f\"[+]Don't forget to get some test ether for {account.address} first\")\n return 1\n\n contract_addr: str = account.get_deployment_address()\n try:\n tx_hash: str = self._contract.deploy(account, constructor_value, constructor_args)\n except ValueError as e:\n print(e)\n return 1\n print(f\"[+]contract address: {contract_addr}\")\n print(f\"[+]transaction hash: {tx_hash}\")\n return 0\n\n return Action(description=\"Deploy the challenge contract using your generated account\", handler=action)\n\n def _get_flag_action(self, flag: str, solved_event: str) -> Action:\n def action() -> int:\n try:\n private_key: str = pyseto.decode(self._token_key, input(\"[-]input your token: \").strip()).payload.decode(\"utf-8\")\n except ValueError as e:\n print(e)\n return 1\n\n account: Account = Account(private_key)\n nonce: int = account.nonce\n if nonce == 0:\n print(\"[+]challenge contract has not yet been deployed\")\n return 1\n\n contract_addr: str = account.get_deployment_address(nonce - 1)\n is_solved = False\n if solved_event:\n tx_hash = input(f\"[-]input tx hash that emitted {solved_event} event: \").strip()\n logs = self._contract.get_events(solved_event, HexStr(tx_hash))\n for item in logs:\n if item['address'] == contract_addr:\n is_solved = True\n else:\n is_solved = self._contract.at(to_checksum_address(contract_addr)).isSolved().call()\n\n if is_solved:\n print(f\"[+]flag: {flag}\")\n return 0\n else:\n print(\"[+]it seems that you have not solved the challenge~~~~\")\n return 1\n\n return Action(description=\"Get your flag once you meet the requirement\", handler=action)\n\n def _show_source_action(self, build_path: str) -> Action:\n def action() -> int:\n for path in glob(os.path.join(build_path, \"*.json\")):\n try:\n with open(path) as fp:\n build_json = json.load(fp)\n except json.JSONDecodeError:\n continue\n else:\n print()\n print(build_json[\"sourcePath\"])\n print(build_json[\"source\"])\n\n return 0\n\n return Action(description=\"Show the contract source code\", handler=action)\n","sub_path":"eth_challenge_base/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":5115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"583696949","text":"import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport keras\nimport multiprocessing\nimport math\nimport statistics\n\nimport sklearn\nimport sklearn.tree\nimport sklearn.ensemble\nimport sklearn.naive_bayes\nimport sklearn.svm\nfrom sklearn.feature_selection import SelectKBest, chi2\n\nfrom keras.layers import Dense, Dropout, GaussianNoise, Activation, Lambda, concatenate\nfrom keras.models import Sequential, load_model, Model\nfrom keras.regularizers import l2\nfrom keras.optimizers import Adam\n\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import classification_report, confusion_matrix, f1_score, roc_auc_score, accuracy_score, mean_squared_error\n\n#from cleverhans.attacks import SaliencyMapMethod\nfrom cleverhans.utils_keras import KerasModelWrapper\nfrom cleverhans.attacks import FastGradientMethod, DeepFool, CarliniWagnerL2\nfrom saliency_map_method import SaliencyMapMethod\n\nconfig = tf.ConfigProto(device_count={\"CPU\": multiprocessing.cpu_count()})\nkeras.backend.tensorflow_backend.set_session(tf.Session(config=config))\n\n'''\nDecision trees\n'''\ndef dtree_new(train_x, train_y, params):\n classifier = sklearn.tree.DecisionTreeClassifier(**params)\n classifier.fit(train_x, train_y)\n return classifier\n\ndef dtree_new_grid(train_x, train_y):\n classifier = sklearn.tree.DecisionTreeClassifier()\n params = {'max_depth': [3,5,7,9,11,13], 'criterion': ['gini', 'entropy']}\n search = GridSearchCV(classifier, params, scoring='roc_auc', n_jobs=8, cv=5, verbose=True)\n search.fit(train_x, train_y)\n classifier = search.best_estimator_\n print(search.best_params_)\n return search.best_params_, classifier\n\ndef dtree_test(classifier, test_x):\n pred_y = classifier.predict(test_x)\n return pred_y\n'''\nRandom Forest\n'''\ndef rforest_new(train_x, train_y, params):\n classifier = sklearn.ensemble.RandomForestClassifier(**params)\n classifier.fit(train_x, train_y)\n return classifier\n\ndef rforest_new_grid(train_x, train_y):\n classifier = sklearn.ensemble.RandomForestClassifier()\n params = {'max_depth': [5,7,9], 'criterion': ['gini', 'entropy'], 'n_estimators':[100,200,400]}\n search = GridSearchCV(classifier, params, scoring='roc_auc', n_jobs=8, cv=3, verbose=True)\n search.fit(train_x, train_y)\n classifier = search.best_estimator_\n print(search.best_params_)\n return search.best_params_, classifier\n\ndef rforest_test(classifier, test_x):\n pred_y = classifier.predict(test_x)\n return pred_y\n'''\nSVM\n'''\ndef svm_new(train_x, train_y, params):\n classifier = sklearn.svm.SVC(**params)\n classifier.fit(train_x, train_y)\n return classifier\n\ndef svm_new_grid(train_x, train_y):\n classifier = sklearn.svm.SVC()\n params = {'kernel':['rbf'], 'C':[10,100,1000], 'gamma':['auto']}\n search = GridSearchCV(classifier, params, scoring='roc_auc', n_jobs=-1, cv=3, verbose=True)\n search.fit(train_x, train_y)\n classifier = search.best_estimator_\n print(search.best_params_)\n return search.best_params_, classifier\n\ndef svm_test(classifier, test_x):\n pred_y = classifier.predict(test_x)\n return pred_y\n'''\nNaive Bayes\n'''\ndef nb_new(train_x, train_y, params):\n classifier = sklearn.naive_bayes.GaussianNB(**params)\n classifier.fit(train_x, train_y)\n return classifier\n\ndef nb_new_grid(train_x, train_y):\n classifier = sklearn.naive_bayes.GaussianNB()\n params = {'var_smoothing':[10,1e0,1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8, 1e-9]}\n search = GridSearchCV(classifier, params, scoring='roc_auc', n_jobs=8, cv=5, verbose=True)\n search.fit(train_x, train_y)\n classifier = search.best_estimator_\n print(search.best_params_)\n return search.best_params_, classifier\n\ndef nb_test(classifier, test_x):\n pred_y = classifier.predict(test_x)\n return pred_y\n'''\nNeural Network (Keras)\n'''\ndef nn_new(train_x, train_y):\n model = Sequential()\n\n model.add(Dense(30, activation='relu', input_dim=train_x.shape[1]))\n model.add(Dense(30, activation='relu'))\n model.add(Dropout(.3, noise_shape=None, seed=None))\n\n model.add(Dense(train_y.shape[1], activation='softmax'))\n\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n model.fit(train_x, train_y, epochs=5, verbose=1)\n\n return model\n\n\ndef dae_new(train_x, train_y):\n # Denoising autoencoder\n model = Sequential()\n \n model.add(GaussianNoise(1))\n model.add(Dense(train_x.shape[1]+7, activation='relu', input_dim=train_x.shape[1]))\n model.add(Dense(train_x.shape[1]+14, activation='relu'))\n model.add(Dense(train_x.shape[1]+7, activation='relu'))\n model.add(Dense(train_x.shape[1], activation='sigmoid'))\n\n #adam = Adam(lr=0.01)\n model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n model.fit(train_x, train_x, epochs=5, verbose=1, shuffle=False)\n \n #Classifier\n\n #model.pop()\n #model.pop()\n\n model.layers[0].trainable = False\n model.layers[1].trainable = False\n model.layers[2].trainable = False\n model.layers[3].trainable = False\n\n model.add(Dense(30, activation='relu'))\n model.add(Dense(30, activation='relu'))\n model.add(Dropout(.3, noise_shape=None, seed=None))\n model.add(Dense(train_y.shape[1], activation='softmax'))\n\n model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n\n model.fit(train_x, train_y, epochs=5, verbose=1)\n\n return model\n\ndef distillation_new(train_x, train_y):\n model = Sequential()\n\n model.add(Dense(30, activation='relu', input_dim=train_x.shape[1]))\n model.add(Dense(30, activation='relu'))\n model.add(Dropout(.3, noise_shape=None, seed=None))\n\n model.add(Dense(train_y.shape[1], activation='softmax'))\n\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n model.fit(train_x, train_y, epochs=5, verbose=1)\n\n new_y = []\n\n pred = model.predict(train_x)\n temperature = 100\n pred = np.array(pred)**(1/temperature)\n p_sum = pred.sum()\n sample_temp = pred/p_sum\n model2 = Sequential()\n\n model2.add(Dense(30, activation='relu', input_dim=train_x.shape[1]))\n model2.add(Dense(30, activation='relu'))\n model2.add(Dropout(.3, noise_shape=None, seed=None))\n\n model2.add(Dense(train_y.shape[1], activation='softmax'))\n\n model2.compile(optimizer=\"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n\n model2.fit(train_x, sample_temp, epochs=5, verbose=1)\n\n return model2\n\ndef binclass_new(train_x, train_y, advs, type=0, params=None):\n if type<= 3:\n lab = np.zeros((train_x.shape[0],1))\n lab[:] = 0\n\n train1 = np.concatenate((train_x, advs[0]))\n\n lab1 = np.concatenate((lab, advs[1]))\n else:\n lab = np.zeros((train_x.shape[0],2))\n lab[:,1] = 1\n\n train1 = np.concatenate((train_x, advs[0]))\n lab1 = np.concatenate((lab, advs[1]))\n\n if type==0:\n model1 = dtree_new(train_x, train_y, params)\n model2 = dtree_new(train1, lab1, params)\n elif type==1:\n model1 = rforest_new(train_x, train_y, params)\n model2 = rforest_new(train1, lab1, params)\n elif type==2:\n model1 = svm_new(train_x, train_y, params)\n model2 = svm_new(train1, lab1, params)\n elif type==3:\n model1 = nb_new(train_x, train_y, params)\n model2 = nb_new(train1, lab1, params)\n elif type==4:\n model1 = nn_new(train_x, train_y)\n model2 = nn_new(train1, lab1)\n elif type==5:\n model1 = dae_new(train_x, train_y)\n model2 = dae_new(train1, lab1)\n \n return model1, model2\n\ndef binclass_test(model1, model2, x, type=0):\n if type == 1:\n pred1 = nn_test(model1, x)\n pred2 = nn_test(model2, x)\n pred = pred1 + pred2\n pred[pred == 2] = 1\n print(pred1,pred2)\n return pred\n else:\n pred1 = dtree_test(model1, x)\n pred2 = dtree_test(model2, x)\n pred = pred1 + pred2\n pred[pred == 2] = 1\n return pred\n\ndef magnet_new(train_x, train_y, train_y2, params=None):\n model1 = Sequential()\n\n model1.add(Dense(train_x.shape[1]-7, activation='relu', input_dim=train_x.shape[1]))\n model1.add(Dense(train_x.shape[1]-14, activation='relu'))\n model1.add(Dense(train_x.shape[1]-7, activation='relu'))\n model1.add(Dense(train_x.shape[1], activation='sigmoid'))\n\n model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n model1.fit(train_x, train_x, epochs=10, verbose=1, shuffle=False)\n\n #mask = extra[0] == 1 # dos\n #mask2 = extra[0] == 0 # normal\n\n all_losses = []\n for i in range(len(train_x)):\n res = model1.evaluate(train_x[i:i+1], train_x[i:i+1], batch_size=1, verbose=0)[0]\n all_losses.append(res)\n stdev = statistics.stdev(all_losses)\n mean = statistics.mean(all_losses)\n\n stddev_multiplier = 4\n limit = mean + stddev_multiplier * stdev\n \n\n model = rforest_new(train_x, train_y2, params)\n\n return model1, model, limit\n\n\ndef nn_predicted(preds):\n argmaxed = np.argmax(preds, axis=1)\n return np.logical_not(argmaxed)\n\n\ndef nn_test(classifier, test_x):\n pred_y = classifier.predict(test_x)\n pred_y = np.argmax(pred_y, axis=1)\n pred_y[pred_y == 1] = 2\n pred_y[pred_y == 0] = 1\n pred_y[pred_y == 2] = 0\n\n return pred_y\n\ndef magnet_test(model1, model2, limit, pred):\n all_predicts = []\n for i in range(len(pred)):\n res = model1.evaluate(pred[i:i+1], pred[i:i+1], batch_size=1, verbose=0)[0]\n if res > limit:\n all_predicts.append(1)\n else:\n all_predicts.append(0)\n \n fixed_x = model1.predict(pred)\n\n predicts = rforest_test(model2, fixed_x)\n\n final_predicts = all_predicts + predicts\n final_predicts[final_predicts==2] = 1\n\n return final_predicts\n\n'''\nPerformance\n'''\ndef auc(test_y, pred_y):\n return roc_auc_score(test_y, pred_y)\n\n#dont use this, debugging only\ndef auc_safe(test_y, pred_y):\n return roc_auc_score(np.concatenate((test_y,[0,1])), np.concatenate((pred_y,[0,1])))\n\ndef f1(test_y, pred_y):\n return f1_score(test_y, pred_y)\n\ndef cmat(test_y, pred_y):\n return confusion_matrix(test_y, pred_y)\n\ndef performance(test_y, pred_y):\n print('Performance:')\n print('Auc:',auc(test_y, pred_y))\n print('F1:',f1(test_y, pred_y))\n print('Conf:\\n',cmat(test_y, pred_y))\n'''\nJSMA\n'''\ndef jsma(model, x, y, theta, gamma):\n sess = keras.backend.get_session()\n wrap = KerasModelWrapper(model)\n\n jsma = SaliencyMapMethod(wrap, sess=sess)\n\n onehottarget = np.zeros((1, y.shape[1]), dtype=np.float32)\n onehottarget[0,1] = 1\n\n jsma_params = {'theta': theta, 'gamma': gamma,\n 'clip_min': 0., 'clip_max': 1.,\n 'y_target': onehottarget}\n\n adv_x = jsma.generate_np(x, **jsma_params)\n #jsma bug fix?\n for row in range(len(adv_x)):\n for feat in range(len(adv_x[row])):\n if abs(adv_x[row][feat] - x.values[row][feat]) > abs(theta) + 0.01:\n adv_x[row][feat] = x.values[row][feat] + theta\n\n adv_x = pd.DataFrame(adv_x, columns=x.columns.values)\n \n #print(x.loc[:,'count'])\n #print(adv_x.loc[:,'count'])\n\n return adv_x\n\ndef fgsm(model, x, y, eps):\n sess = keras.backend.get_session()\n wrap = KerasModelWrapper(model)\n\n fgsm = FastGradientMethod(wrap, sess=sess)\n\n fgsm_params = {'eps': eps, 'clip_min': 0., 'clip_max': 1.}\n\n adv_x = fgsm.generate_np(x, **fgsm_params)\n\n adv_x = pd.DataFrame(adv_x, columns=x.columns.values)\n\n return adv_x\n\ndef deepfool(model, x, y, os):\n sess = keras.backend.get_session()\n wrap = KerasModelWrapper(model)\n\n deepfool = DeepFool(wrap, sess=sess)\n\n df_params = {'nb_candidate': 2, 'overshoot': os, 'max_iter': 3,'clip_min': 0., 'clip_max': 1.}\n\n adv_x = deepfool.generate_np(x, **df_params)\n\n adv_x = pd.DataFrame(adv_x, columns=x.columns.values)\n\n return adv_x\n\ndef carliniwagner(model, x, y):\n sess = keras.backend.get_session()\n wrap = KerasModelWrapper(model)\n\n carwag = CarliniWagnerL2(wrap, sess=sess)\n\n onehottarget = np.zeros((1, y.shape[1]), dtype=np.float32)\n onehottarget[0,1] = 1\n #35 for cicids\n #47 for nslkdd\n cw_params = {'confidence':0.5,\n 'batch_size':47,\n 'learning_rate': 0.2,\n 'binary_search_steps':5,\n 'max_iterations':100,\n 'abort_early':True,\n 'initial_const':0.1,\n 'clip_min':0.,\n 'clip_max':1.}\n\n adv_x = carwag.generate_np(x, **cw_params)\n\n adv_x = pd.DataFrame(adv_x, columns=x.columns.values)\n\n return adv_x\n\n\n\n\ndef attack_stats(orig, adv):\n changed_cols = {}\n changes_cols = {}\n for col in orig.columns.values:\n changed_cols[col] = 0\n changes_cols[col] = 0\n for i in range(orig.shape[0]):\n for j in range(len(orig.columns.values)):\n change = orig.values[i][j] - adv.values[i][j]\n if abs(change) >= 0.001:\n col = orig.columns.values[j]\n changed_cols[col] += 1\n changes_cols[col] = (changes_cols[col] * (changed_cols[col] - 1) + change) / changed_cols[col]\n for col in orig.columns.values:\n if changed_cols[col] > 0:\n print(col + ':')\n print(changed_cols[col])\n print(changes_cols[col])\n\ndef feature_select(data, labels, fields, ncols):\n selector = SelectKBest(chi2,k=ncols)\n selector.fit(data, labels)\n cols = selector.get_support(indices=True)\n return cols\n","sub_path":"src/classifiers.py","file_name":"classifiers.py","file_ext":"py","file_size_in_byte":13579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"394553110","text":"# -*- coding: utf-8 -*-\n\"\"\"This file is a TrabajaenMexico spider created on top of the ATSSpider\nscrapy crawl trabajaenmexico -a mining_job_id=9999 -a iteration=1 -a extract=1 -a url=\"http://www.trabajaenmexico.com/\"\n\nsample url:\n http://www.trabajaenmexico.com/\n\"\"\"\n\nfrom urlparse import urljoin\nfrom scrapy.http import Request, FormRequest\nfrom scrapy.selector import Selector\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import Prefix, ConvertDateString, HtmlFormatter\n\n\nclass TrabajaenMexico(ATSSpider):\n\n name = 'trabajaenmexico'\n download_delay = 0.3\n page = 0\n url_anchor = \"/todobuenophp.php?pagina=%s&tipo=Ver Todos&nom=&fec=Cualquier Fecha\"\n ref_re = r'iden=(\\d+)'\n details_xpath = '//td[div//*[contains(text(), \"%s\")]]/following-sibling::td//text()'\n\n def start_requests(self):\n yield self.make_request()\n\n def parse(self, response):\n sel = Selector(response)\n jobs = sel.xpath(\n '//table[@class=\"Estilo7\"]//tr/td/span/a/@href'\n ).extract()\n for job_url in jobs:\n job_url = urljoin(response.url, job_url)\n yield Request(job_url, callback=self.parse_job_callback())\n\n if jobs:\n self.page += 1\n yield self.make_request()\n\n def parse_job(self, response):\n loader = BrightcorpItemLoader(response=response)\n loader.add_value('url', response.url)\n loader.add_value(\n 'referencenumber', response.url,\n Prefix('%s-' % self.name), re=self.ref_re\n )\n loader.add_xpath(\n 'date', self.details_xpath % \"Fecha:\", ConvertDateString('%d-%m-%Y')\n )\n loader.add_xpath('title', self.details_xpath % \"Vacante:\")\n loader.add_xpath('location', self.details_xpath % \"Ciudad:\")\n loader.add_xpath('company', self.details_xpath % \"Empresa:\")\n loader.add_xpath('baseSalary', self.details_xpath % \"Salario:\")\n loader.add_xpath(\n 'description',\n '//td[div//*[contains(text(), \"%s\")]]|//td[div//*[contains(text(), \"%s\")]]/following-sibling::td' % (unicode('Descripción:', 'utf-8'), unicode('Descripción:', 'utf-8')),\n HtmlFormatter()\n )\n yield loader.load_item()\n\n def make_request(self):\n return Request(\n urljoin(self.start_urls[0], self.url_anchor % self.page),\n callback=self.parse\n )\n","sub_path":"brightcorp/brightcorp/spiders/trabajaenmexico.py","file_name":"trabajaenmexico.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"243763680","text":"import requests\n#import time\n\ndef lat_lon(*args):\n \"\"\"Funkcja jako argumenty przyjmuje poszczegolne elementy adresu\"\"\"\n adr = \"\"\n for arg in args:\n adr = adr+\"+\"+arg.replace(' ','+').replace(',','')\n adres = adr[1:]\n\n #time.sleep(1.25)\n response = requests.get(\"https://nominatim.openstreetmap.org\" \\\n \"/?format=json&addressdetails=1&q={}&format=json&limit=1\".format(adres))\n\n data = response.json()[0]\n # Zwraca szerokosc i dlugosc geograficzna\n # latwo mozna ja przerobic aby zwracala inne elementy\n return {'lat':float(data['lat']), 'lon':float(data['lon'])}\n\nif __name__ == '__main__':\n resp = lat_lon('83-323', 'Gdynia Morska', '1/3')\n print(resp['lat'])\n print(resp['lon'])\n","sub_path":"response.py","file_name":"response.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"255507467","text":"\"\"\"\n Framework Django D01 - 42\n Created on:\n Author\n\"\"\"\n\n\"\"\" \n on importe le module sys\n\"\"\"\nimport sys\n\n\"\"\" \n global variables\n\"\"\"\n\ndef my_sort(el1, el2):\n \"\"\" \n This function is my_sort() and it is doing ...\n \"\"\"\n if el1 > el2:\n return -1\n if el2 < el2:\n return 1\n return 0\n\ndef convert_list_of_tuples_into_a_dictionary(l):\n \"\"\" \n This function is convert_list_of_tuples_into_a_dictionary() and it is doing ...\n \"\"\"\n d = {}\n for key, value in l:\n d.setdefault(value, []).append(key)\n return d\n\ndef is_key_present(x, d):\n \"\"\" \n This function is is_key_present() and it is doing ...\n \"\"\"\n if x.lower() in (name.lower() for name in d):\n return True\n else:\n return False\n\ndef is_value_present(x, d):\n \"\"\" \n This function is is_value_present() and it is doing ...\n \"\"\"\n if x.lower() in (name.lower() for name in d.values()):\n return True\n else:\n return False\n\ndef get_key_from_dictionnary_using_value_search(search_value, d):\n \"\"\" \n This function is get_capital_city() and it is doing ...\n \"\"\"\n for key, value in d.items():\n #print('key:', key, \"value:\", value)\n if value.lower() == search_value.lower():\n return key\n \n return None\n\ndef get_key_from_dictionnary_using_key_search(search_key, d):\n \"\"\" \n This function is get_() and it is doing ...\n \"\"\"\n for key, value in d.items():\n #print('key:', key, \"value:\", value)\n if key.lower() == search_key.lower():\n return key\n \n return None\n\ndef main(arguments):\n \"\"\" \n This function is main() and it is doing ...\n \"\"\"\n states = {\n \"Oregon\" : \"OR\",\n \"Alabama\" : \"AL\",\n \"New Jersey\": \"NJ\",\n \"Colorado\" : \"CO\"\n }\n\n capital_cities = {\n \"OR\": \"Salem\",\n \"AL\": \"Montgomery\",\n \"NJ\": \"Trenton\",\n \"CO\": \"Denver\"\n }\n\n if len(arguments) != 2:\n sys.exit(-1)\n\n capitals_or_states = [x.strip() for x in arguments[1].split(',')]\n capitals_or_states = [x for x in capitals_or_states if len(x) > 0]\n #capitals_or_states = [x.lower() for x in capitals_or_states]\n\n for x in capitals_or_states:\n if is_value_present(x, capital_cities) == True:\n short_name_state = get_key_from_dictionnary_using_value_search(x, capital_cities)\n if short_name_state in states.values():\n full_name_state = get_key_from_dictionnary_using_value_search(short_name_state, states)\n print(capital_cities[short_name_state], 'is the capital of', full_name_state)\n else:\n print(capital_cities[short_name_state], 'is a capital ', 'can not find corresponding state')\n elif is_key_present(x, states) == True:\n full_name_state = get_key_from_dictionnary_using_key_search(x, states)\n short_name_state = states[full_name_state]\n if short_name_state in capital_cities:\n full_name_capital_city = capital_cities[short_name_state]\n print(full_name_capital_city, 'is the capital of', full_name_state)\n else:\n print(fullt_name_state, 'is a state ', 'can not find corresponding capital city')\n else:\n print(x, 'is neither a capital city nor a state')\n\n sys.exit(0)\n\nif __name__ == '__main__' :\n \"\"\" \n \"\"\"\n arguments = sys.argv\n main(arguments)\n\n","sub_path":"01-piscine_python_django.a/day01/ex05/all_in.py","file_name":"all_in.py","file_ext":"py","file_size_in_byte":3503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"624196174","text":"import os\nimport subprocess\nimport fileinput\nimport sys\nfrom random import shuffle\nimport math\n\ndef transform_ajax(v):\n for line in fileinput.input(\"ajax.obj\", inplace=True):\n if line.startswith('v '):\n newLine = line.split()\n x = float(newLine[1])\n y = float(newLine[2])\n z = float(newLine[3])\n x = x+v[0]\n y = y+v[1]\n z = z+v[2]\n line = \"v \" + str(x) + \" \" + str(y) + \" \" + str(z) + \"\\n\"\n sys.stdout.write(line)\n\nxml_file = \"cboxArea.xml\"\n# ajax center of mass = <-1.36395 17.8657 -17.3952>\n#in this transform i just took a random point on the sphere from sphere2.object\n# and a random point from ajax and created the vector from the subtraction.\n# this way ajax should have showed up in the position of the sphere (and i wanted to work from there)\n# i really didn't try a lot of things here. i decided that it's better to focus on sphere first + area light first\ntransform_ajax([-6.23+(0.44+6.32),27.31+(0.36-27.31),-21.52+(0.051+21.52)])\nsubprocess.run([\"nori.exe\", xml_file])","sub_path":"imageGeneration/ajaxCboxDraft.py","file_name":"ajaxCboxDraft.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"350607207","text":"import time\nimport pandas as pd\nfrom bs4 import BeautifulSoup as bs\n\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nimport glob\nimport os\nfrom pathlib import Path\n\n\n'''Report DB03: Dataframe with followers and theirs bios'''\n\n\nclass DB03:\n def __init__(self, username, driver, cookies):\n self.username = username\n self.driver = driver\n self.cookies = cookies\n\n def create_report(self):\n print('Creating folder')\n root_folder = Path(__file__).parents[0]\n os.chdir(root_folder)\n folder = str(self.username)\n if not os.path.exists(folder):\n os.mkdir(folder)\n print('Folder created successfully')\n\n try:\n pd.read_csv(str(self.username) + '/report_03_list_' + str(self.username) + '.csv')\n except:\n report_03 = {\n 'index': [],\n 'followers_links': [],\n 'followers_names': []\n }\n report_03 = pd.DataFrame(data=report_03)\n report_03.to_csv(str(self.username) + '/report_03_list_' + str(self.username) + '.csv', index=False)\n\n def followers_link(self):\n username = self.username\n driver = self.driver\n cookies = self.cookies\n\n driver.get('https://www.instagram.com/accounts/onetap/?next=%2F')\n for cookie in cookies:\n driver.add_cookie(cookie)\n\n print(\"Accessing instagram page to be searched\") # accessing page to be searched\n driver.get('https://www.instagram.com/' + username)\n wait = WebDriverWait(driver, 10)\n try:\n wait.until(EC.presence_of_element_located(((By.PARTIAL_LINK_TEXT, 'follower'))))\n except:\n wait.until(EC.presence_of_element_located(((By.PARTIAL_LINK_TEXT, 'seguidor'))))\n\n print(\"Accessing followers page\")\n try:\n driver.find_element_by_partial_link_text(\"follower\").click()\n except:\n driver.find_element_by_partial_link_text(\"seguidor\").click()\n print(\"Followers page accessed successfully\")\n\n try: # waiting page to be loaded\n wait.until(\n EC.presence_of_element_located(((By.XPATH, \"//*[@id='react-root']/section/main/div/ul/li[2]/a/span\"))))\n except:\n wait.until(EC.presence_of_element_located(\n ((By.XPATH, '/html/body/div[1]/section/main/div/header/section/ul/li[2]/a/span'))))\n\n try: # getting total amount of followers\n total_followers = driver.find_element_by_xpath(\n \"//*[@id='react-root']/section/main/div/ul/li[2]/a/span\").text\n total_followers = total_followers.replace('.', '')\n total_followers = total_followers.replace(',', '')\n except:\n total_followers = driver.find_element_by_xpath(\n '/html/body/div[1]/section/main/div/header/section/ul/li[2]/a/span').text\n total_followers = total_followers.replace('.', '')\n total_followers = total_followers.replace(',', '')\n\n if total_followers.find('k') == True:\n total_followers = total_followers.replace('k', '')\n total_followers = int(float(total_followers)) * 100\n print(\"Total number of followers approximately: \", total_followers)\n else:\n total_followers = int(float(total_followers))\n print(\"Total number of followers: \", total_followers)\n\n print(\"---------------------------\")\n print(\"Amount of followers: \", total_followers)\n\n fbody = driver.find_element_by_xpath(\"//div[@class='isgrP']\") # Getting screen info to scroll down\n scroll = 1\n followers_loaded = 0\n items = []\n items_names = []\n index = 0\n index_now = 0\n lista = []\n while index_now < int(float(total_followers)): # while the amount of followers scrapped is lesser than the total of followers.\n time.sleep(1)\n followers_on_screen = driver.find_elements_by_xpath('//a[contains(@class, \"FPmhX notranslate _0imsa \")]')\n for follower_on_screen in followers_on_screen:\n items.append(follower_on_screen.get_attribute('href')) # getting followers_link\n index += 1\n\n lista = items # removing duplicates links found from list\n lista = pd.Series(lista).drop_duplicates()\n index_now = len(lista)\n print(\"Total amount of followers now: \", index_now)\n\n if index_now < int(total_followers): # if the amount of scrapped followers is lesser than the total of followers.\n index_now = 0 # resetting the amount of scrapped followers\n driver.execute_script('arguments[0].scrollTop = arguments[0].scrollTop + arguments[0].offsetHeight;',\n fbody) # does scroll down action\n time.sleep(1)\n fList = driver.find_elements_by_xpath(\"//div[@class='isgrP']//li\") # Amount of followers found\n scroll += 1\n\n print(\"---- Process Concluded ----\")\n print(\"---------------------------\")\n print(\"----- Showing results -----\")\n print(\"Amount of followers found: \", len(fList))\n print(\"Amount of ScrollDown done: \", scroll)\n print(\"---------------------------\")\n print(\"----- Processing data -----\")\n\n # scrape followers links and names from data using BS and CSV (without Selenium and Pandas)\n print(\"Scraping followers links and names from data\")\n source = driver.page_source\n data = bs(source, 'html.parser')\n index_now = 0\n index = []\n followers_links = []\n followers_names = []\n for followers in data.find_all('a', class_='FPmhX notranslate _0imsa'):\n index.append(index_now + 1)\n followers_links.append(followers['href']) # extraction of followers_links\n followers_names.append(followers.text) # extraction of followers_names\n\n report_03_list = {\n 'index': index,\n 'followers_links': followers_links,\n 'followers_names': followers_names\n }\n report_03_list = pd.DataFrame(data=report_03_list)\n report_03_list = report_03_list.drop_duplicates()\n report_03_list = report_03_list.drop_duplicates()\n report_03_list.to_csv(str(username) + '/report_03_list_' + str(username) + '.csv', index=False)\n\n print(\"---------------------------\")\n print(\"Report:\")\n print(\"Amount of followers: \", int(total_followers))\n print(\"Amount of followers loaded: \", index_now)\n print(\"---------------------------\")\n\n def followers_link_list(self):\n followers_link_list = pd.read_csv(str(self.username) + '/report_03_list_' + str(self.username) + '.csv')\n followers_link_list = followers_link_list['followers_links'].tolist()\n return followers_link_list\n\n def followers_bios(self, followers_links_list):\n username = self.username\n driver = self.driver\n \n followers_link_completed = []\n followers_name_completed = []\n followers_bio_completed = []\n followers_amount_of_posts_completed = []\n followers_amount_of_followers_completed = []\n followers_amount_of_following_completed = []\n followers_private_completed = []\n\n report_03_bio = {\n 'followers_links': followers_link_completed,\n 'followers_names': followers_name_completed,\n 'amount_of_posts': followers_amount_of_posts_completed,\n 'amount_of_followers': followers_amount_of_followers_completed,\n 'amount_of_following': followers_amount_of_following_completed,\n 'private': followers_private_completed,\n 'bio': followers_bio_completed\n }\n\n report_03_bio = pd.DataFrame(data=report_03_bio)\n report_03_bio = report_03_bio.drop_duplicates()\n\n try:\n report_03_created = pd.read_csv(str(username) + '/report_03_bio_' + str(username) + '.csv')\n report_03_bio = report_03_created.append([report_03_bio], ignore_index=True)\n report_03_bio = report_03_bio.drop_duplicates()\n report_03_bio.to_csv(str(username) + '/report_03_bio_' + str(username) + '.csv', index=False)\n except:\n report_03_bio.to_csv(str(username) + '/report_03_bio_' + str(username) + '.csv', index=False)\n\n try:\n for insta_followers_links in followers_links_list: # accessing page to be searched\n try:\n insta_followers_links = str(insta_followers_links)\n\n print(\"Accessing instagram page to be searched\")\n driver.get('https://www.instagram.com' + insta_followers_links)\n time.sleep(3)\n\n print(\"Starting scrapping\")\n source = driver.page_source\n data = bs(source, 'html.parser')\n\n # followers_link\n followers_link_completed.append(insta_followers_links)\n\n # followers_name\n try:\n name = data.find(class_='-vDIg').h1.text\n followers_name_completed.append(name)\n except:\n name = str(\"n/a\")\n followers_name_completed.append(name)\n print('name:', name)\n\n # bio\n try:\n bio = data.find(class_='-vDIg').span.text\n if bio.find(\"Followed by\") == 0:\n bio = str(\"n/a\")\n followers_bio_completed.append(bio)\n else:\n bio = data.find(class_='-vDIg').text\n followers_bio_completed.append(bio)\n except:\n bio = str(\"n/a\")\n followers_bio_completed.append(bio)\n\n # amount_of_posts\n amount_of_posts = data.findAll(class_='g47SY')[0].text\n try:\n if (amount_of_posts.find('.') or amount_of_posts.find(',')):\n amount_of_posts = amount_of_posts.replace(',', '')\n amount_of_posts = amount_of_posts.replace('.', '')\n amount_of_posts = int(amount_of_posts)\n else:\n amount_of_posts = int(amount_of_posts)\n except:\n amount_of_posts = amount_of_posts\n followers_amount_of_posts_completed.append(amount_of_posts)\n\n # amount_of_followers\n amount_of_followers = data.findAll(class_='g47SY')[1].text\n try:\n if (amount_of_followers.find('.') or amount_of_followers.find(',')):\n amount_of_followers = amount_of_followers.replace(',', '')\n amount_of_followers = amount_of_followers.replace('.', '')\n amount_of_followers = int(amount_of_followers)\n else:\n amount_of_followers = int(amount_of_followers)\n except:\n amount_of_followers = amount_of_followers\n followers_amount_of_followers_completed.append(amount_of_followers)\n\n # amount_of_following\n amount_of_following = data.findAll(class_='g47SY')[2].text\n if amount_of_following.find('.') or amount_of_following.find(','):\n amount_of_following = amount_of_following.replace('.', '')\n amount_of_following = amount_of_following.replace(',', '')\n amount_of_following = int(amount_of_following)\n else:\n amount_of_following = int(amount_of_following)\n followers_amount_of_following_completed.append(amount_of_following)\n\n # is_private\n try:\n is_private = data.find(class_='rkEop').text == 'This Account is Private'\n followers_private_completed.append(\"1\") # This Account is Private\n except:\n followers_private_completed.append(\"0\")\n\n report_03_bio = {\n 'followers_links': followers_link_completed,\n 'followers_names': followers_name_completed,\n 'amount_of_posts': followers_amount_of_posts_completed,\n 'amount_of_followers': followers_amount_of_followers_completed,\n 'amount_of_following': followers_amount_of_following_completed,\n 'private': followers_private_completed,\n 'bio': followers_bio_completed\n }\n\n report_03_bio = pd.DataFrame(data=report_03_bio)\n report_03_bio = report_03_bio.drop_duplicates()\n try:\n report_03_created = pd.read_csv(str(username) + '/report_03_bio_' + str(username) + '.csv')\n report_03_bio = report_03_created.append([report_03_bio], ignore_index=True)\n report_03_bio = report_03_bio.drop_duplicates()\n report_03_bio.to_csv(str(username) + '/report_03_bio_' + str(username) + '.csv', index=False)\n except:\n report_03_bio.to_csv(str(username) + '/report_03_bio_' + str(username) + '.csv', index=False)\n\n # deleting source of data if the post_link was processed\n report_03_link_list = pd.read_csv(str(username) + '/report_03_bio_' + str(username) + '.csv')\n report_03_link_list = report_03_link_list['followers_links'].tolist()\n\n item_processed = insta_followers_links\n try:\n if report_03_link_list.index(item_processed) >= 0:\n doc_source = pd.read_csv(str(username) + '/report_03_list_' + str(username) + '.csv') # deleting source of data\n doc_source.drop(doc_source.index[doc_source['followers_links'] == str(insta_followers_links)], inplace=True)\n doc_source.to_csv(str(username) + '/report_03_list_' + str(username) + '.csv', index=False)\n except:\n pass\n except:\n pass\n except:\n pass\n\n @staticmethod\n def delete_file_by_key_name(username, key_word_for_file):\n current_dir = os.path.dirname(os.path.realpath(__file__)) # accessing report_01_IMG (to delete processed rows)\n path = current_dir + '\\\\' + username + \"\\\\\" + '*' + key_word_for_file + '*'\n for file_to_delete in glob.glob(path):\n os.remove(file_to_delete)\n\n report_03_bio = pd.read_csv(str(username) + '/report_03_bio_' + str(username) + '.csv')\n report_03_bio.to_csv(str(username) + '/report_03_' + str(username) + '.csv', index=False) # save report_03_bio as report_03\n os.remove(str(username) + '/report_03_bio_' + str(username) + '.csv') # delete report_03_bio\n\n def run(self):\n print('Starting to create report 03')\n username = self.username\n driver = self.driver\n cookies = self.cookies\n\n start = DB03(username, driver, cookies)\n start.create_report()\n start.followers_link()\n followers_link_list = start.followers_link_list()\n start.followers_bios(followers_link_list)\n\n # deleting report 01 without after whole process:\n followers_link_list = start.followers_link_list() # create: a dataframe db02img; returns a list of img_links\n if len(followers_link_list) == 0:\n key_word_for_file = 'report_03_list'\n start.delete_file_by_key_name(username, key_word_for_file)\n\n print('Process \"Report 03\" concluded.')\n\n","sub_path":"codes/DB03.py","file_name":"DB03.py","file_ext":"py","file_size_in_byte":16362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"145145215","text":"import requests\nimport re\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport sys\nfrom PyQt4.QtGui import QApplication\nfrom PyQt4.QtCore import QUrl\nfrom PyQt4.QtWebKit import QWebPage\nimport bs4 as bs\nimport urllib.request\nimport os\nimport datetime\n\n\n###############################################################\npath_of_brandwise = 'C:\\\\LavaWebScraper\\\\BrandWiseFiles\\\\'\n###############################################################\n\nbase_url = 'http://www.allcall.hk/phones.html'\nur='http://www.allcall.hk'\ncountry = 'China'\ncompany = 'AllCall'\nmodel_list = []\nusp = []\ndisplay_list = []\nmemory_list = []\nprocessor_list = []\ncamera_list = []\nbattery_list = []\nthickness_list = []\nextras_links = []\nrecords = []\nhref = []\nst_list_heads=[]\nst_list_dets=[]\nhr=[]\nr=requests.get(base_url)\nsoup=BeautifulSoup(r.text,'html.parser')\nresults=soup.find_all('li',attrs={'class':'col-xs-6 col-sm-3 col-md-3 col-lg-3 padding-left-right-def'})\nfor i in range(len(results)):\n model_list.append(results[i].find('h4').text)\n href.append(results[i].find('a')['href'])\nHREF=[]\nfor i in range(len(href)):\n href[i]=ur + href[i]\nfor i in range(len(href)):\n r=requests.get(href[i])\n soup=BeautifulSoup(r.text,'html.parser')\n results=soup.find_all('ul',attrs={'class':'col-xs-12 col-sm-12 col-md-12 col-lg-12 padding-margin-no padding-margin-def subnavul'})\n if len(results)==1:\n sa=results[0].find_all('li')\n for a in range(len(sa)):\n sb=sa[a].text\n if sb == 'Specification':\n hr.append(ur + sa[a].find('a')['href']) \n else:\n hr.append(href[i])\n \nfor i in range(len(hr)):\n s=''\n heads=[]\n dets=[]\n r=requests.get(hr[i])\n soup=BeautifulSoup(r.text,'html.parser')\n results=soup.find_all('div',attrs={'class':'container overflow-hidden padding-tottom-5'})\n for a in range(len(results)):\n sa=results[a].find_all('ul',attrs={'class':'col-xs-12 col-sm-8 col-md-8 col-lg-8 padding-top-5 padding-margin-def top-info'})\n for b in range(len(sa)):\n sb=sa[b].find_all('li')\n for c in range(len(sb)):\n tt = sb[c].text.strip('\\n')\n tt = tt.strip('\\t')\n s=s+ tt + ' || '\n s=s.strip('\\n')\n s=s.strip('\\t').replace('\\xa0',' ')\n usp.append(s)\n for a in range(len(results)):\n sa=results[a].find_all('ul',attrs={'class':'col-xs-12 col-sm-12 col-md-12 col-lg-12 padding-margin-def content'})\n for b in range(len(sa)-1):\n sb=sa[b].find_all('li')\n for c in range(len(sb)):\n if c%2 ==0:\n heads.append(sb[c].text.strip('\\n\\t'))\n else:\n dets.append(sb[c].text.strip('\\n\\t').replace('\\xa0',' ').replace('\\n',' '))\n st_list_heads.append(heads)\n st_list_dets.append(dets)\nfor i in range(len(st_list_heads)):\n q1 = ''\n q2 = ''\n r1 = ''\n r2 = ''\n s1 = ''\n s2 = ''\n for j in range(len(st_list_heads[i])):\n if 'cpu' in st_list_heads[i][j].lower():\n processor_list.append(st_list_dets[i][j].replace(' （','(').replace('）',')'))\n if 'memory' in st_list_heads[i][j].lower():\n memory_list.append(st_list_dets[i][j])\n if 'battery' in st_list_heads[i][j].lower():\n battery_list.append(st_list_dets[i][j])\n if 'ram' in st_list_heads[i][j].lower():\n q1='RAM:- '+st_list_dets[i][j]+' || '\n if 'rom' in st_list_heads[i][j].lower():\n q2='ROM:- '+st_list_dets[i][j]+' || '\n if 'screen size' in st_list_heads[i][j].lower() or'display size' in st_list_heads[i][j].lower():\n r1 ='Size:- ' +st_list_dets[i][j]+' || '\n if 'type' in st_list_heads[i][j].lower():\n r2 ='Type:- '+ st_list_dets[i][j]+' || '\n if 'rear' in st_list_heads[i][j].lower() and 'camera' in st_list_heads[i][j].lower():\n s1='Rear camera:- '+st_list_dets[i][j]+' || '\n if 'front' in st_list_heads[i][j].lower() and 'camera' in st_list_heads[i][j].lower():\n s2='Front camera:- '+st_list_dets[i][j]+' || '\n \n \n if q1!='' or q2!='':\n memory_list.append(q1+' '+q2)\n if r1!='' or r2!='':\n display_list.append(r1+' '+r2)\n if s1!='' or s2!='':\n camera_list.append(s1 +' '+ s2)\n if len(battery_list)==i:\n battery_list.append('Not Available')\n if len(camera_list)==i:\n camera_list.append('Not Available')\n if len(display_list)==i:\n display_list.append('Not Available')\n if len(memory_list)==i:\n memory_list.append('Not Available')\n if len(processor_list)==i:\n processor_list.append('Not Available')\n if len(thickness_list)==i:\n thickness_list.append('Not Available')\n if len(usp)==i:\n usp.append('Not Available')\nprint(len(model_list))\nprint(len(usp))\nprint(len(thickness_list))\nprint(len(processor_list))\nprint(len(memory_list))\nprint(len(battery_list))\nprint(len(display_list))\nprint(len(camera_list))\nextras_links = hr\nfor i in range(len(model_list)):\n records.append((country, company, model_list[i], usp[i], display_list[i], camera_list[i], memory_list[i], battery_list[i], thickness_list[i], processor_list[i], extras_links[i]))\n\ndf = pd.DataFrame(records, columns = ['COUNTRY', 'COMPANY', 'MODEL', 'USP', 'DISPLAY', 'CAMERA', 'MEMORY', 'BATTERY', 'THICKNESS', 'PROCESSOR', 'EXTRAS/ LINKS'])\ndf.to_csv(os.path.join(path_of_brandwise, str(datetime.date.today())+ '-allcall' +'.csv'), index=False, encoding='utf-8')\n","sub_path":"allcall.py","file_name":"allcall.py","file_ext":"py","file_size_in_byte":5579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"160678129","text":"\"\"\"\nDefines the deviceCommand class\n\"\"\"\nimport threading\n\n__author__ = 'Cesar'\n\nimport time\nimport urllib2\nimport xml.etree.ElementTree as myXML\nimport config\n\n\nclass deviceCommand:\n \"\"\"\n deviceCommand class\n \"\"\"\n\n d_id = ''\n commandId = ''\n command = ''\n threadRunning = False\n\n def __init__(self, d_id, commandId, command, response, threadRunning):\n\n self.d_id = d_id\n self.commandId = commandId\n self.command = command\n self.response = response\n self.threadRunning = threadRunning\n\n # spawn a new thread for every new device\n commandFinderThread = threading.Thread(target=self.commandFinder)\n commandFinderThread.daemon = True\n commandFinderThread.start()\n\n def getCommand_via_httpPOST(self):\n \"\"\"\n Request command pending to Intelectix\n \"\"\"\n req = urllib2.Request(url='http://petrologweb.com/api/command',\n headers={'Content-Type': 'text/xml',\n 'Authorization': 'DeviceNumber={0},ApiKey=UGV0cm9sb2dDbGllbnRl'.format(self.d_id)})\n try:\n r = urllib2.urlopen(req)\n resp_xml = myXML.ElementTree(myXML.fromstring(r.read()))\n root = resp_xml.getroot()\n for resp in root.findall('{http://schemas.datacontract.org/2004/07/Ifx.Api.Model}Response'):\n if resp.find('Command') is not None and resp.find('ConsoleCommandId') is not None:\n c = resp.find('Command').text\n c_id = resp.find('ConsoleCommandId').text\n r = [c_id, c]\n return r\n return 'e:No Command found'\n except urllib2.URLError as e:\n config.logging.warning('Error on device {0} = {1}'.format(self.d_id, e.reason))\n return 'e:Connection Error'\n\n def setCommandResponse_via_httpPOST(self):\n \"\"\"\n Set the response of a command to Intelectix\n \"\"\"\n commandResponseData = myXML.parse(config.xml_root+'/setCommandResponse.xml')\n root = commandResponseData.getroot()\n root[0].text = self.commandId\n root[1].text = self.response\n req = urllib2.Request(url='http://petrologweb.com/api/command',\n data=myXML.tostring(root),\n headers={'Content-Type': 'text/xml',\n 'Authorization': 'DeviceNumber={0},ApiKey=UGV0cm9sb2dDbGllbnRl'.format(self.d_id)})\n try:\n r = urllib2.urlopen(req)\n resp_xml = myXML.ElementTree(myXML.fromstring(r.read()))\n root = resp_xml.getroot()\n resp = root.find('{http://schemas.datacontract.org/2004/07/Ifx.Api.Model}Response')\n if resp.text == 'true':\n # Response successful, get ready for next command.\n self.commandId = ''\n self.command = ''\n self.response = ''\n return True\n else:\n config.logging.warning('Error in command response!')\n return False\n\n except urllib2.URLError as e:\n config.logging.warning('Failed to open connection to server! Error = %s', e.reason)\n\n def commandFinder(self):\n \"\"\"\n Continuously looks for commands.\n \"\"\"\n self.threadRunning = True\n while True:\n try:\n if self.d_id != '':\n command = self.getCommand_via_httpPOST()\n c_id = command[0]\n c = command[1]\n if c_id != 'e':\n # If command found and commandId and command are empty (we are NOT in the middle of another\n # command) then: save command and commandId and send command to corresponding Petrolog\n if self.command == '' and self.commandId == '':\n # Process command\n if c == 'Pg38h':\n # Ignore Intelectix internal command\n config.logging.debug('Ignoring Intelectix internal command')\n self.command = c\n self.commandId = c_id\n self.response = ''\n self.setCommandResponse_via_httpPOST()\n else:\n config.logging.debug('DeviceID=[{0}] CommandID=[{1}] Command=[{2}]'\n .format(self.d_id, c_id, c))\n # Change CPU address to 0F\n c = c[2:]\n c = '0F'+c\n self.command = c\n self.commandId = c_id\n from mqttPetrolog import mqttc\n mqttc.publish(self.d_id+'/SC', self.command)\n except Exception as e:\n config.logging.error('Error in Thread! DeviceID = %s', self.d_id)\n\n time.sleep(5)\n\n# Dictionary to store Commands\nCommands = {deviceCommand}\n\n","sub_path":"deviceCommand_class.py","file_name":"deviceCommand_class.py","file_ext":"py","file_size_in_byte":5208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"525800719","text":"import importlib\nimport pytest\nfrom datetime import datetime, timedelta\nfrom unittest.mock import patch\n\nfrom snuba.clickhouse.http import JSONRowEncoder\nfrom snuba.clusters.cluster import CLUSTERS, ClickhouseClientSettings, get_cluster\nfrom snuba.clusters.storage_sets import StorageSetKey\nfrom snuba.consumer import KafkaMessageMetadata\nfrom snuba.datasets.schemas.tables import TableSchema\nfrom snuba.datasets.storages import StorageKey\nfrom snuba.datasets.storages.factory import get_storage, get_writable_storage\nfrom snuba.migrations.errors import MigrationError\nfrom snuba.migrations.groups import get_group_loader, MigrationGroup\nfrom snuba.migrations.parse_schema import get_local_schema\nfrom snuba.migrations.runner import MigrationKey, Runner\nfrom snuba.migrations.status import Status\nfrom snuba.utils.metrics.backends.dummy import DummyMetricsBackend\nfrom snuba.writer import BatchWriterEncoderWrapper\nfrom tests.fixtures import get_raw_transaction\n\n\ndef _drop_all_tables() -> None:\n for cluster in CLUSTERS:\n connection = cluster.get_query_connection(ClickhouseClientSettings.MIGRATE)\n database = cluster.get_database()\n\n data = connection.execute(\n f\"SELECT name FROM system.tables WHERE database = '{database}'\"\n )\n for (table,) in data:\n connection.execute(f\"DROP TABLE IF EXISTS {table}\")\n\n\ndef setup_function() -> None:\n _drop_all_tables()\n\n\ndef teardown_function() -> None:\n _drop_all_tables()\n\n\ndef test_get_status() -> None:\n runner = Runner()\n assert runner.get_status(\n MigrationKey(MigrationGroup.EVENTS, \"0001_events_initial\")\n ) == (Status.NOT_STARTED, None)\n runner.run_migration(MigrationKey(MigrationGroup.SYSTEM, \"0001_migrations\"))\n assert runner.get_status(\n MigrationKey(MigrationGroup.EVENTS, \"0001_events_initial\")\n ) == (Status.NOT_STARTED, None)\n runner.run_migration(MigrationKey(MigrationGroup.EVENTS, \"0001_events_initial\"))\n status = runner.get_status(\n MigrationKey(MigrationGroup.EVENTS, \"0001_events_initial\")\n )\n assert status[0] == Status.COMPLETED\n assert isinstance(status[1], datetime)\n assert status[1] > datetime.now() - timedelta(seconds=1)\n\n\ndef test_show_all() -> None:\n runner = Runner()\n assert all(\n [\n migration.status == Status.NOT_STARTED\n for (_, group_migrations) in runner.show_all()\n for migration in group_migrations\n ]\n )\n runner.run_all(force=True)\n assert all(\n [\n migration.status == Status.COMPLETED\n for (_, group_migrations) in runner.show_all()\n for migration in group_migrations\n ]\n )\n\n\ndef test_run_migration() -> None:\n runner = Runner()\n runner.run_migration(MigrationKey(MigrationGroup.SYSTEM, \"0001_migrations\"))\n\n connection = get_cluster(StorageSetKey.MIGRATIONS).get_query_connection(\n ClickhouseClientSettings.MIGRATE\n )\n assert connection.execute(\n \"SELECT group, migration_id, status, version FROM migrations_local;\"\n ) == [(\"system\", \"0001_migrations\", \"completed\", 1)]\n\n # Invalid migration ID\n with pytest.raises(MigrationError):\n runner.run_migration(MigrationKey(MigrationGroup.SYSTEM, \"xxx\"))\n\n # Run out of order\n with pytest.raises(MigrationError):\n runner.run_migration(MigrationKey(MigrationGroup.EVENTS, \"0003_errors\"))\n\n # Running with --fake\n runner.run_migration(\n MigrationKey(MigrationGroup.EVENTS, \"0001_events_initial\"), fake=True\n )\n assert connection.execute(\"SHOW TABLES LIKE 'sentry_local'\") == []\n\n\ndef test_reverse_migration() -> None:\n runner = Runner()\n runner.run_all(force=True)\n\n connection = get_cluster(StorageSetKey.MIGRATIONS).get_query_connection(\n ClickhouseClientSettings.MIGRATE\n )\n\n # Invalid migration ID\n with pytest.raises(MigrationError):\n runner.reverse_migration(MigrationKey(MigrationGroup.SYSTEM, \"xxx\"))\n\n with pytest.raises(MigrationError):\n runner.reverse_migration(MigrationKey(MigrationGroup.EVENTS, \"0003_errors\"))\n\n # Reverse with --fake\n for migration_id in reversed(\n get_group_loader(MigrationGroup.EVENTS).get_migrations()\n ):\n runner.reverse_migration(\n MigrationKey(MigrationGroup.EVENTS, migration_id), fake=True\n )\n assert (\n len(connection.execute(\"SHOW TABLES LIKE 'sentry_local'\")) == 1\n ), \"Table still exists\"\n\n\ndef test_get_pending_migrations() -> None:\n runner = Runner()\n total_migrations = get_total_migration_count()\n assert len(runner._get_pending_migrations()) == total_migrations\n runner.run_migration(MigrationKey(MigrationGroup.SYSTEM, \"0001_migrations\"))\n assert len(runner._get_pending_migrations()) == total_migrations - 1\n\n\ndef test_run_all() -> None:\n runner = Runner()\n assert len(runner._get_pending_migrations()) == get_total_migration_count()\n\n with pytest.raises(MigrationError):\n runner.run_all(force=False)\n\n runner.run_all(force=True)\n assert runner._get_pending_migrations() == []\n\n\ndef test_reverse_all() -> None:\n runner = Runner()\n all_migrations = runner._get_pending_migrations()\n runner.run_all(force=True)\n for migration in reversed(all_migrations):\n runner.reverse_migration(migration, force=True)\n\n connection = get_cluster(StorageSetKey.MIGRATIONS).get_query_connection(\n ClickhouseClientSettings.MIGRATE\n )\n assert connection.execute(\"SHOW TABLES\") == [], \"All tables should be deleted\"\n\n\ndef get_total_migration_count() -> int:\n count = 0\n for group in MigrationGroup:\n count += len(get_group_loader(group).get_migrations())\n return count\n\n\ndef test_version() -> None:\n runner = Runner()\n runner.run_migration(MigrationKey(MigrationGroup.SYSTEM, \"0001_migrations\"))\n migration_key = MigrationKey(MigrationGroup.EVENTS, \"test\")\n assert runner._get_next_version(migration_key) == 1\n runner._update_migration_status(migration_key, Status.IN_PROGRESS)\n assert runner._get_next_version(migration_key) == 2\n runner._update_migration_status(migration_key, Status.COMPLETED)\n assert runner._get_next_version(migration_key) == 3\n\n\ndef test_no_schema_differences() -> None:\n runner = Runner()\n runner.run_all(force=True)\n\n for storage_key in StorageKey:\n storage = get_storage(storage_key)\n conn = storage.get_cluster().get_query_connection(\n ClickhouseClientSettings.MIGRATE\n )\n\n schema = storage.get_schema()\n\n if not isinstance(schema, TableSchema):\n continue\n\n table_name = schema.get_local_table_name()\n local_schema = get_local_schema(conn, table_name)\n\n assert (\n schema.get_column_differences(local_schema) == []\n ), f\"Schema mismatch: {table_name} does not match schema\"\n\n\ndef test_transactions_compatibility() -> None:\n cluster = get_cluster(StorageSetKey.TRANSACTIONS)\n connection = cluster.get_query_connection(ClickhouseClientSettings.MIGRATE)\n\n def get_sampling_key() -> str:\n database = cluster.get_database()\n ((sampling_key,),) = connection.execute(\n f\"SELECT sampling_key FROM system.tables WHERE name = 'transactions_local' AND database = '{database}'\"\n )\n return sampling_key\n\n # Create old style table without sampling expression and insert data\n connection.execute(\n \"\"\"\n CREATE TABLE transactions_local (`project_id` UInt64, `event_id` UUID,\n `trace_id` UUID, `span_id` UInt64, `transaction_name` LowCardinality(String),\n `transaction_hash` UInt64 MATERIALIZED CAST(cityHash64(transaction_name), 'UInt64'),\n `transaction_op` LowCardinality(String), `transaction_status` UInt8 DEFAULT 2,\n `start_ts` DateTime, `start_ms` UInt16, `finish_ts` DateTime, `finish_ms` UInt16,\n `duration` UInt32, `platform` LowCardinality(String), `environment` LowCardinality(Nullable(String)),\n `release` LowCardinality(Nullable(String)), `dist` LowCardinality(Nullable(String)),\n `ip_address_v4` Nullable(IPv4), `ip_address_v6` Nullable(IPv6), `user` String DEFAULT '',\n `user_hash` UInt64 MATERIALIZED cityHash64(user), `user_id` Nullable(String),\n `user_name` Nullable(String), `user_email` Nullable(String),\n `sdk_name` LowCardinality(String) DEFAULT CAST('', 'LowCardinality(String)'),\n `sdk_version` LowCardinality(String) DEFAULT CAST('', 'LowCardinality(String)'),\n `http_method` LowCardinality(Nullable(String)) DEFAULT CAST('', 'LowCardinality(Nullable(String))'),\n `http_referer` Nullable(String),\n `tags.key` Array(String), `tags.value` Array(String), `_tags_flattened` String,\n `contexts.key` Array(String), `contexts.value` Array(String), `_contexts_flattened` String,\n `partition` UInt16, `offset` UInt64, `message_timestamp` DateTime, `retention_days` UInt16,\n `deleted` UInt8) ENGINE = ReplacingMergeTree(deleted) PARTITION BY (retention_days, toMonday(finish_ts))\n ORDER BY (project_id, toStartOfDay(finish_ts), transaction_name, cityHash64(span_id))\n TTL finish_ts + toIntervalDay(retention_days);\n \"\"\"\n )\n\n assert get_sampling_key() == \"\"\n generate_transactions()\n\n runner = Runner()\n runner.run_migration(MigrationKey(MigrationGroup.SYSTEM, \"0001_migrations\"))\n runner._update_migration_status(\n MigrationKey(MigrationGroup.TRANSACTIONS, \"0001_transactions\"), Status.COMPLETED\n )\n runner.run_migration(\n MigrationKey(\n MigrationGroup.TRANSACTIONS,\n \"0002_transactions_onpremise_fix_orderby_and_partitionby\",\n ),\n force=True,\n )\n\n assert get_sampling_key() == \"cityHash64(span_id)\"\n\n assert connection.execute(\"SELECT count(*) FROM transactions_local;\") == [(5,)]\n\n\ndef generate_transactions() -> None:\n from datetime import datetime\n\n table_writer = get_writable_storage(StorageKey.TRANSACTIONS).get_table_writer()\n\n rows = []\n\n for i in range(5):\n raw_transaction = get_raw_transaction()\n # Older versions of this table did not have measurements\n del raw_transaction[\"data\"][\"measurements\"]\n\n processed = (\n table_writer.get_stream_loader()\n .get_processor()\n .process_message(\n (2, \"insert\", raw_transaction),\n KafkaMessageMetadata(0, 0, datetime.utcnow()),\n )\n )\n rows.extend(processed.rows)\n\n BatchWriterEncoderWrapper(\n table_writer.get_batch_writer(metrics=DummyMetricsBackend(strict=True)),\n JSONRowEncoder(),\n ).write(rows)\n\n\ndef test_groupedmessages_compatibility() -> None:\n cluster = get_cluster(StorageSetKey.EVENTS)\n database = cluster.get_database()\n connection = cluster.get_query_connection(ClickhouseClientSettings.MIGRATE)\n\n # Create old style table witihout project ID\n connection.execute(\n \"\"\"\n CREATE TABLE groupedmessage_local (`offset` UInt64, `record_deleted` UInt8,\n `id` UInt64, `status` Nullable(UInt8), `last_seen` Nullable(DateTime),\n `first_seen` Nullable(DateTime), `active_at` Nullable(DateTime),\n `first_release_id` Nullable(UInt64)) ENGINE = ReplacingMergeTree(offset)\n ORDER BY id SAMPLE BY id SETTINGS index_granularity = 8192\n \"\"\"\n )\n\n migration_id = \"0010_groupedmessages_onpremise_compatibility\"\n\n runner = Runner()\n runner.run_migration(MigrationKey(MigrationGroup.SYSTEM, \"0001_migrations\"))\n events_migrations = get_group_loader(MigrationGroup.EVENTS).get_migrations()\n\n # Mark prior migrations complete\n for migration in events_migrations[: (events_migrations.index(migration_id))]:\n runner._update_migration_status(\n MigrationKey(MigrationGroup.EVENTS, migration), Status.COMPLETED\n )\n\n runner.run_migration(\n MigrationKey(MigrationGroup.EVENTS, migration_id), force=True,\n )\n\n assert connection.execute(\n f\"SELECT primary_key FROM system.tables WHERE name = 'groupedmessage_local' AND database = '{database}'\"\n ) == [(\"project_id, id\",)]\n\n\ndef test_settings_skipped_group() -> None:\n from snuba.migrations import groups, runner\n\n with patch(\"snuba.settings.SKIPPED_MIGRATION_GROUPS\", {\"querylog\"}):\n importlib.reload(groups)\n importlib.reload(runner)\n runner.Runner().run_all(force=True)\n\n connection = get_cluster(StorageSetKey.MIGRATIONS).get_query_connection(\n ClickhouseClientSettings.MIGRATE\n )\n assert connection.execute(\"SHOW TABLES LIKE 'querylog_local'\") == []\n","sub_path":"tests/migrations/test_runner.py","file_name":"test_runner.py","file_ext":"py","file_size_in_byte":12659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"327833071","text":"#coding:utf-8\r\n'''\r\nCreated on 2017年12月15日\r\n\r\n@author: qiujiahao\r\n\r\n@email:997018209@qq.com\r\n\r\n'''\r\n#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\nimport sys\r\nsys.path.append('..')\r\n\r\nimport os\r\nimport time\r\nfrom sklearn import metrics\r\nfrom datetime import timedelta\r\nfrom cnn.cnn_module import TextCNN\r\nfrom cnn.data import data\r\nfrom conf import *\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\n\r\ndef get_time_dif(start_time):\r\n \"\"\"获取已使用时间\"\"\"\r\n end_time = time.time()\r\n time_dif = end_time - start_time\r\n return timedelta(seconds=int(round(time_dif)))\r\n\r\ndef feed_data(x_batch, y_batch, keep_prob,model):\r\n feed_dict = {\r\n model.input_x: x_batch,\r\n model.input_y: y_batch,\r\n model.keep_prob: keep_prob\r\n }\r\n return feed_dict\r\n\r\ndef evaluate(sess, x_, y_,model):\r\n \"\"\"评估在某一数据上的准确率和损失\"\"\"\r\n feed_dict = feed_data(x_, y_, 1.0,model)\r\n loss, acc,scores = sess.run([model.loss, model.acc,model.logits], feed_dict=feed_dict)\r\n\r\n return loss, acc,scores\r\n\r\ndef train(args,data):\r\n with tf.Graph().as_default() as g:\r\n model=TextCNN(args)\r\n session = tf.Session(graph=g)\r\n with session.as_default():\r\n session.run(tf.global_variables_initializer())\r\n saver = tf.train.Saver()\r\n if not os.path.exists(args.module_path):\r\n os.makedirs(args.module_path)\r\n start_time = time.time()\r\n print('Training and evaluating...')\r\n start_time = time.time()\r\n total_batch = 0 # 总批次\r\n best_acc_val = 0.0 # 最佳验证集准确率\r\n last_improved = 0 # 记录上一次提升批次\r\n require_improvement = 1000 # 如果超过1000轮未提升，提前结束训练\r\n \r\n flag = False\r\n batches=data.get_batch_data()\r\n for index,batch in enumerate(batches):\r\n x_batch, y_batch,x_val,y_val=batch\r\n feed_dict = feed_data(x_batch, y_batch, args.dropout_keep_prob,model)\r\n if len(data.quest_label)<1000:\r\n args.print_per_batch=10\r\n if total_batch % args.print_per_batch == 0:\r\n # 每多少轮次输出在训练集和验证集上的性能\r\n feed_dict[model.keep_prob] = 1.0\r\n loss_train, acc_train= session.run([model.loss, model.acc], feed_dict=feed_dict)\r\n loss_val, acc_val,scores = evaluate(session, x_val, y_val,model) # todo\r\n \r\n if acc_val > best_acc_val:\r\n # 保存最好结果\r\n best_acc_val = acc_val\r\n last_improved = total_batch\r\n saver.save(sess=session, save_path=args.module_path)\r\n improved_str = '*'\r\n else:\r\n improved_str = ''\r\n \r\n time_dif = get_time_dif(start_time)\r\n msg = 'total_batch: {0:>6}, Train Loss: {1:>6.2}, Train Acc: {2:>7.2%},'\\\r\n + ' Val Loss: {3:>6.2}, Val Acc: {4:>7.2%}, Time: {5} {6}'\r\n print(msg.format(total_batch, loss_train, acc_train, loss_val, acc_val, time_dif, improved_str))\r\n \r\n session.run(model.optim, feed_dict=feed_dict) # 运行优化\r\n total_batch += 1\r\n \r\n if total_batch - last_improved > require_improvement:\r\n # 验证集正确率长期不提升，提前结束训练\r\n print(\"No optimization for a long time, auto-stopping...\")\r\n flag = True\r\n break # 跳出循环\r\n if flag: # 同上\r\n print('最佳准确率:',best_acc_val)\r\n\r\n\r\nif __name__ == '__main__': \r\n args=get_args()\r\n data=data(args) \r\n train(args,data)","sub_path":"11.人机对话第二次验收版本/对话_第二版/cnn/run_cnn.py","file_name":"run_cnn.py","file_ext":"py","file_size_in_byte":3974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"197207503","text":"#!/usr/bin/env python3\n# This file will automate, creation and testing of lxc containers and maas\n# long term plans, spin up maas container in LXC, a few things need to be in\n# place for this to work:\n\n #1:) LXC Containers Templates (/etc/lxc/lxc.conf) need to be setup to\n # ... include allowable loop mount perms\n #2:) LXC Containers Templates need to already include the correct Bridge\n # device to adhere to\n #3:) This is designed to run through Jenkins for automation\n #4:) Long term plans are to add systems via MaaS CLI and test\n #4a:) Commissioning / Deployment / Images\n #5:) Currently only support Trusty & Xenial\n\n\n#USAGE:\n # All user defined variables should be entered in the 'example.yaml'\n\n #1.) To Provision the Container use the '-p' flag (Useful for just building\n # A Container\n\n #2.) To Provision the Container and Configure the container use,\n #' -p -c flags\n\n #3.) To Provision & Configure the Container and Configure MAAS\n # use '-p -c -m'\n\n ####3a.) Configuring the MAAS Environment will:\n # -- Download Boot Images\n # -- Configure SSH Keys (supplied by you)\n # -- Configure Proxies (for the lab)\n\nimport subprocess\nimport os\nimport sys\nimport argparse\nimport time\nimport yaml\n\ncontainer = \"maas-qa-builder\"\n\n\nclass lxcmanager():\n\n def lxcbuilder(self, config, release):\n '''Build the lxc container, requires the location of your lxc.conf\n Useful in the event you want to have a customer config file for testing\n '''\n\n try:\n subprocess.call([\"sudo\", \"lxc-create\", \"-t\", \"ubuntu\", \"-n\",\n container, \"-f\", config, \"--\", \"-r\", release])\n except Exception as e:\n print(e)\n return False\n\n def lxcstop(self):\n ''' Stops the lxc container'''\n\n try:\n subprocess.call([\"sudo\", \"lxc-stop\", \"-n\", container, \"-f\"])\n return 1\n except Exception as e:\n print(e)\n return 0\n\n def lxcstart(self):\n ''' Start the lxc container'''\n\n try:\n subprocess.call([\"sudo\", \"lxc-start\", \"-n\", container])\n except Exception as e:\n print(e)\n return False\n\n def lxcinfo(self):\n '''Gather lxc container information'''\n\n try:\n subprocess.call([\"sudo\", \"lxc-info\", \"-n\", container])\n except Exception as e:\n print(e)\n return False\n\n def lxccheckconfig(self):\n '''Verify the template is correct, just output to console, If we find\n that the lxc.conf is present adjust the location accordingly'''\n\n loc = \"maas-qa-builder/config\"\n if os.path.isfile(\"/etc/lxc/lxc.conf\"):\n lxcconf = subprocess.check_output([\"sudo\", \"cat\",\n \"/etc/lxc/lxc.conf\"]).split(\"\\n\")\n if any('lxc.lxcpath =' in s for s in lxcconf):\n foo = filter(lambda x: 'lxc.lxcpath =' in x, lxcconf)\n bar = ''.join(foo)\n newconfig = bar.split(\"=\")[1].replace(\" \", \"\")\n subprocess.call([\"sudo\", \"cat\", newconfig + loc])\n else:\n subprocess.call([\"sudo\", \"cat\", \"/var/lib/lxc/\" + loc])\n return True\n\n def getlxcipaddress(self, interface):\n '''Get the lxc ip address '''\n\n try:\n myaddress = subprocess.getoutput([\"sudo lxc-attach -n %s -e -- \\\n ifconfig %s\" % (container, interface)]).split(\"\\n\")[1].split() \\\n [1][5:]\n return myaddress\n except Exception as e:\n print(e)\n return False\n\n def lxcruncmd(self, cmd):\n '''Take a command and run it through lxc attach'''\n p = subprocess.Popen(cmd, stderr=subprocess.PIPE,\n stdout=subprocess.PIPE, universal_newlines=True, shell=True)\n output, errors = p.communicate()\n print(output, errors, p.returncode)\n if p.returncode or errors:\n print (\"Errors Found\")\n return False\n\n\n def lxccheckdistro(self):\n '''Check the distro and do Foo if needed'''\n\n command = \"lsb_release -a\"\n commandxenial0 = \"mknod /dev/loop0 b 7 1\"\n commandxenial1 = \"mknod /dev/loop1 b 7 1\"\n commandxenial2 = \"mknod /dev/loop-control b 10 237\"\n\n try:\n foo = subprocess.getoutput([\"sudo lxc-attach -n %s -e -- %s\" %\n (container, command)]).split(\"\\t\")\n try:\n# Shell=True won't paas exceptions so i need to revisit this and improve'\n if 'xenial' in foo:\n subprocess.call([\"sudo lxc-attach -n %s -e -- %s\" %\n (container, commandxenial0)], shell=True)\n subprocess.call([\"sudo lxc-attach -n %s -e -- %s\" %\n (container, commandxenial1)], shell=True)\n subprocess.call([\"sudo lxc-attach -n %s -e -- %s\" %\n (container, commandxenial2)], shell=True)\n except Exception as e:\n print(e)\n return False\n except Exception as e:\n print(e)\n return False\n\nclass operationsmanager():\n '''Everything in here is written to pull from our yaml, if there is an\n error we will display it on the screen but not necessarily pass an\n exception'''\n\n def install_debs(self, debs):\n '''Here we install anything to make anything work'''\n\n try:\n subprocess.call([\"sudo lxc-attach -n %s -e -- apt-get install \\\n -y %s\" % (container, debs)], shell=True)\n except subprocess.CalledProcessError as e:\n print(e)\n return False\n\n def install_ppa(self, ppa):\n '''Install a PPA, for example, MAAS Devel PPA'''\n\n try:\n if ppa is \"none\":\n print(\"None\")\n subprocess.check_call([\"sudo\", \"lxc-attach\", \"-n\", container, \\\n \"-e\", \"--\", \"add-apt-repository\", \"-y\", ppa])\n self.update_pkg_index()\n except subprocess.CalledProcessError as e:\n print(e)\n return False\n\n def update_pkg_index(self):\n '''Update ubuntu pkg index'''\n\n try:\n subprocess.check_call([\"sudo\", \"lxc-attach\", \"-n\", container, \\\n \"-e\", \"--\", \"apt-get\",\"update\"])\n except subprocess.CalledProcessError as e:\n print(e)\n return False\n\n\nclass maasmanager():\n\n\n def configure_maas_admin(self, username, password, email):\n '''Configure the MAAS ADMIN; maas-region-admin to be depreciated for\n maas 2.0'''\n\n try:\n subprocess.check_call([\"sudo\", \"lxc-attach\", \"-n\", container, \\\n \"-e\", \"--\", \"sudo\", \"maas-region\", \"createadmin\", \\\n \"--username\", username, \"--password\", password, \"--email\",\\\n email])\n except subprocess.CalledProcessError as e:\n print (e)\n return False\n\n def get_maas_user_apikey(self, username):\n '''Get the maas API KEY for user; maas-region-admin to be depreciated\n for maas 2.0'''\n\n try:\n api = subprocess.getoutput([\"sudo lxc-attach -n %s -e -- \\\n maas-region apikey --username %s\" % (container, username)])\n return api\n except subprocess.CalledProcessError as e:\n print(e)\n return False\n\n def login_maas(self, profile, url, api):\n '''Set the maas boot sources'''\n\n try:\n subprocess.check_call([\"maas\", \"login\", profile, url, api])\n except subprocess.CalledProcessError as e:\n print(e)\n return False\n\n def import_boot_images(self, profile):\n ''' Import the MAAS BOOT IMAGES'''\n\n try:\n subprocess.check_call([\"maas\", profile, \"boot-resources\", \"import\"])\n except subprocess.CalledProcessError as e:\n print(e)\n return False\n\n#This does not work needs to updated, maas2.0 API and support 1.0 API\n def set_boot_sources(self, profile, url):\n ''' Configure the boot source URL prior to updating'''\n\n try:\n subprocess.call([\"maas %s boot-source update 1 \\\n url=%s\" % (profile, url)], shell=True)\n return 1\n except Exception as e:\n print(e)\n return 0\n\nclass di():\n\n def make_bootfile(self, tftploc, template, release, archive):\n ''' Create a Bootfile that lives in (tftpbootloc)\n For the host we are booting, note that the template needs to be\n present for this to happen'''\n # We will feed this via the yaml file\n\n bootfile = template + \"-\" + release + \"-\" + archive\n\n #Lets create the bootfile\n try:\n foo = subprocess.check_call([\"sudo\", \"cp\", \"-v\", tftploc + \\\n template, bootfile])\n return foo\n\n except subprocess.CalledProcessError as e:\n print(e)\n return False\n\n def configure_bootfile(self, bootfile, archive, preseed, hostname):\n ''' Configure the bootfile with the items required to PXE boot such as:\n - Preseed, Archive, Hostname'''\n\n try:\n infile = open(bootfile, \"r\")\n outfile = open(bootfile + \".mac\", \"a\")\n outfile.write(infile.read().replace(\"interface=auto\",\n preseed + \" \" + archive + \" \" + \"hostname=\" + hostname))\n infile.close()\n outfile.close()\n\n except Exception as e:\n print (e)\n return False\n\n\n\n\ndef main():\n '''Make sure that you updated your yaml template prior to running'''\n\n lxc = lxcmanager()\n ops = operationsmanager()\n maas= maasmanager()\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--provision',\n required=False,\n action='store_true',\n help=\"Provision the lxc container\")\n parser.add_argument('-c', '--configure',\n required=False,\n action='store_true',\n help=\"Configure Maas in the lxc container\")\n parser.add_argument('-i', '--ipaddress',\n required=False,\n action='store_true',\n help=\"Get Container IP\")\n parser.add_argument('-m', '--maas',\n required=False,\n action='store_true',\n help=\"Prep Maas Environment\")\n parser.add_argument('-d', '--di',\n required=False,\n action='store_true',\n help=\"Prep D-I\")\n parser.add_argument('-y', '--yaml',\n required=True,\n type=str,\n help=\"Location of Yaml File\")\n\n\n args = parser.parse_args()\n\n\n with open(args.yaml) as f:\n doc = yaml.safe_load(f)\n\n##################################################################\n## Check to see if '-p' flag is triggered, if so, Build the container,\n## check and start if flag is passed, Grab Container Info and\n## Start the Container\n## Note: We recommend using a custom lxc.conf called \"lxc.conf.lxcbot\" This file\n## Allows us to specify a hard mac address so that the IP stays the\n## same. Easy for Testing\n##################################################################\n\n if args.provision is True:\n try:\n print(\"Provisioning Container\")\n #This is for Jenkins\n sys.stdout.flush()\n #/\n lxc.lxcbuilder(doc[\"lxc_config_path\"], doc[\"lxc_release\"])\n lxc.lxcinfo()\n lxc.lxccheckconfig()\n lxc.lxcstart()\n time.sleep(10)\n #This is for Jenkins\n sys.stdout.flush()\n #/\n print (\"Container IP ADDRESS IS: \" + lxc.getlxcipaddress(\"eth0\"))\n except Exception:\n pass\n\n #This is for Jenkins\n sys.stdout.flush()\n #/\n\n##################################################################\n## Check to see if the '-c' flag is triggered, if so,\n## Configure the container and Install MAAS\n## Modify these values as you see fit..lxcmanager()\n##################################################################\n\n#We assume you want maas installed\n if args.configure is True:\n try:\n #This is for Jenkins\n sys.stdout.flush()\n #/\n print (\"Configuring the Container, please wait\")\n time.sleep(10)\n ops.update_pkg_index()\n time.sleep(5)\n ops.update_pkg_index()\n ops.install_debs(doc[\"packages_to_install\"])\n ops.install_ppa(doc[\"software_ppa\"])\n if args.maas is True:\n print(\"Going to Configure MAAS PACKAGES\")\n ops.install_debs(\"maas\")\n\n except Exception as e:\n print (e)\n\n##################################################################\n## Check to see if the '-i' flag is triggered, if so, get the\n## ip address of the container, Informational only\n##################################################################\n\n if args.ipaddress is True:\n try:\n #This is for Jenkins\n sys.stdout.flush()\n print(\"Printing Eth0 info for container \")\n print(lxc.getlxcipaddress(\"eth0\"))\n print(maas.get_maas_user_apikey(doc[\"maas-username\"]))\n except Exception as e:\n print (e)\n\n##################################################################\n## Check to see if the '-m' flag is triggered, if so, prep the MAAS\n## environment, Configure USER, APIKEY, SSHKEY, Load the Boot Images,\n## Configure Maas Proxy, etc\n##################################################################\n\n if args.maas is True:\n try:\n print(\"Starting to prep maas env\")\n #This is for Jenkins\n sys.stdout.flush()\n #/\n print (\"Configuring MAAS account\")\n maas.configure_maas_admin(doc[\"maas-username\"],\n doc[\"maas-password\"], doc[\"maas-email\"])\n time.sleep(5)\n maas.get_maas_user_apikey(doc[\"maas-username\"])\n time.sleep(2)\n maas.login_maas(doc[\"maas-profile\"], \"http://\" +\n lxc.getlxcipaddress(\"eth0\") + \"/MAAS\",\n maas.get_maas_user_apikey(doc[\"maas-username\"]))\n time.sleep(6)\n maas.import_boot_images(doc[\"maas-profile\"])\n except Exception as e:\n print (e)\n\nif __name__ == \"__main__\":\n sys.exit(main())","sub_path":"seqa.py","file_name":"seqa.py","file_ext":"py","file_size_in_byte":15037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"310187722","text":"import sys\r\nsys.path.append('C:\\\\Users\\\\Nick\\\\PycharmProjects\\\\1st-itertion\\\\1st iteration')\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datetime import datetime\r\n\r\n\r\nfrom dataUtils.dataUtils import match, dBaseAction, emailReport, writeDBv2\r\nimport AlgorithcTrading.config.crypto as cryptoConfig\r\nimport AlgorithcTrading.config.stocks as stockConfig\r\nfrom AlgorithcTrading.dataFetch import DataFetch\r\nfrom AlgorithcTrading.backtesting import Backtesting\r\nfrom AlgorithcTrading.stockUtils import getTickerYahooStats\r\n\r\n\r\nclass Trading(object):\r\n\r\n def __init__(self, asset='crypto'):\r\n\r\n if asset == 'crypto':\r\n self.dBase = cryptoConfig.dBase\r\n self.coinTable = cryptoConfig.coinTable\r\n self.saveLocation = cryptoConfig.plotSaveLocation\r\n self.title = cryptoConfig.emailTitle\r\n self.portfolio = cryptoConfig.portfolio\r\n\r\n elif asset == 'stocks':\r\n self.dBase = stockConfig.dBase\r\n self.yahooStatsTable = stockConfig.yahooStatsTable\r\n self.saveLocation = stockConfig.plotSaveLocation\r\n self.title = stockConfig.emailTitle\r\n self.portfolio = stockConfig.portfolio\r\n\r\n def runCrypto(self, runType='hourly'):\r\n\r\n # Crypto is essentially momentumless, but based purely on sentiment. It will run up and then run straight down.\r\n # Until it is more established and less correlated, momentum trading cannot work. Thus simply run very slow\r\n # averages to identify when the market might start trending upwards rather than current down trend.\r\n\r\n downloadStocks = DataFetch()\r\n downloadStocks.get_cryptoData(runList=[runType])\r\n\r\n currentPortfolio = dBaseAction(cryptoConfig.dBase, 'Select * from %s' % cryptoConfig.portfolio)\r\n if len(currentPortfolio) > 0:\r\n currentPortfolio = currentPortfolio[0].ticker.unique()\r\n else:\r\n currentPortfolio=None\r\n\r\n buysAndSells = Backtesting()\r\n buysAndSells = buysAndSells.run(runType='cryptoHourly' if runType == 'hourly' else 'cryptoDaily',\r\n returnType='BAU', method='momentum',\r\n portfolioTickers=currentPortfolio,\r\n seedLength=30,\r\n averageType='ewma',\r\n minTimeseriesVol=0.1,\r\n CTAlookback=500,\r\n minVolume=100e3,\r\n buyWindow=[400],\r\n sellWindow=[18],\r\n sellWindowB=[190],\r\n baseWindow=[500],\r\n lookback=10000,\r\n priceVolatilityPeriod=400,\r\n priceVolatilityThreshold=0.037,\r\n priceCrossMax=0.6, ####### lenient\r\n priceCrossThreshold=0.03,\r\n nPeriodsGradient=5,\r\n priceVolatilityMaxPeriod=50,\r\n priceVolatilityMaxThreshold=0.03,\r\n minObservations=10,\r\n volumeFilter=24,\r\n maxPriceBaseCross=6,\r\n maxPriceBaseCrossPeriod=100,\r\n returnAveragePeriod=50,\r\n minInterpVolatility=0.0002,\r\n minCrossGradient=-10,\r\n minCrossGradientSell=-10,\r\n sellLosers=-1,\r\n minPriceBasePercent=0.04,\r\n minVolatility=0.004,\r\n minAverageReturn=0.002,\r\n maxRollingReturn=1,\r\n maxAverageRatio=10,\r\n maxAverageRatio_returnRatio=5,\r\n minReturnRatio=0.5,\r\n type='crypto')\r\n\r\n report = pd.concat([buysAndSells['buys'], buysAndSells['sells']]).reset_index(drop=True)\r\n if len(report) > 0:\r\n coinInfo = dBaseAction(self.dBase, 'Select * from %s' % self.coinTable)[0]\r\n report = pd.merge(report, coinInfo[['ticker', 'market_cap', 'volume']], on='ticker')\r\n report['CTA'] = report['CTA'].astype(float).map('{:,.3f}'.format)\r\n report['skew'] = report['skew'].astype(float).map('{:,.5f}'.format)\r\n report['market_cap'] = report['market_cap'].astype(float).map('{:,.0f}'.format)\r\n report['volume'] = report['volume'].astype(float).map('{:,.0f}'.format)\r\n\r\n self.email_report(report, buysAndSells)\r\n\r\n report['timestamp'] = str(datetime.now().strftime('%Y-%m-%d %HH:%MM'))\r\n report['ID'] = report['ticker'] + '_' + report['timestamp']\r\n\r\n # log report of buys in historical dataBase\r\n report = report[report['action'] == 'buy'].reset_index(drop=True)\r\n if len(report)>0:\r\n writeDBv2(dBase=cryptoConfig.dBase, name=cryptoConfig.reportHistory, data=report,\r\n createArgs='(\"' + '\",\"'.join(report.columns.tolist()) + '\")',\r\n indexColName='ID',\r\n args='(\"' + '\",\"'.join(report.columns.tolist()) + '\")')\r\n\r\n def runStocks(self, runType='daily', fullUpdate=False):\r\n\r\n downloadStocks = DataFetch()\r\n downloadStocks.get_stockData(fullUpdate=fullUpdate)\r\n\r\n currentPortfolio = dBaseAction(stockConfig.dBase, 'Select * from %s' % stockConfig.portfolio)\r\n if len(currentPortfolio) > 0:\r\n currentPortfolio = currentPortfolio[0].ticker.unique()\r\n else:\r\n currentPortfolio=None\r\n\r\n buysAndSells = Backtesting()\r\n buysAndSells = buysAndSells.run(runType='stocksDaily' if runType == 'daily' else 'stocksHourly',\r\n returnType='BAU', method='momentum',\r\n sellWindow=[12], sellWindowB=[70], buyWindow=[95], baseWindow=[100],\r\n portfolioTickers=currentPortfolio,\r\n minTimeseriesVol=0.1,\r\n CTAlookback=500,\r\n minVolume=100e3,\r\n lookback=1825,\r\n priceVolatilityPeriod=100,\r\n priceVolatilityThreshold=0.037,\r\n priceCrossMax=0.35,\r\n priceCrossThreshold=0.03,\r\n nPeriodsGradient=10,\r\n priceVolatilityMaxPeriod=50,\r\n priceVolatilityMaxThreshold=0.01,\r\n minObservations=10,\r\n maxPriceBaseCross=10,\r\n maxPriceBaseCrossPeriod=50,\r\n returnAveragePeriod=50,\r\n minInterpVolatility=0.0002,\r\n minCrossGradient=-10,\r\n minCrossGradientSell=-10,\r\n sellLosers=-0.5,\r\n minPriceBasePercent=0.04,\r\n minVolatility=0.011,\r\n minAverageReturn=0.007,\r\n maxRollingReturn=1,\r\n returnRatioPeriod=50,\r\n type='stocks')\r\n\r\n report = pd.concat([buysAndSells['buys'], buysAndSells['sells']]).reset_index(drop=True)\r\n if len(report) > 0:\r\n tickerStats = getTickerYahooStats(np.sort(report.ticker.values))\r\n\r\n report = pd.merge(report, tickerStats[['ticker', 'marketCap', 'longName', 'earningsDate',\r\n 'shortRatio', 'recommendationKey', 'industry']], on='ticker')\r\n report['CTA'] = report['CTA'].astype(float).map('{:,.3f}'.format)\r\n report['skew'] = report['skew'].astype(float).map('{:,.5f}'.format)\r\n report['market_cap'] = report['marketCap'].map('{:,}'.format)\r\n report = report.drop('marketCap', axis=1)\r\n\r\n self.email_report(report, buysAndSells)\r\n\r\n report['timestamp'] = str(datetime.now().strftime('%Y-%m-%d %HH:%MM'))\r\n report['ID'] = report['ticker'] + '_' + report['timestamp']\r\n # log report of buys in historical dataBase\r\n report = report[report['action'] == 'buy'].reset_index(drop=True)\r\n if len(report)>0:\r\n writeDBv2(dBase=stockConfig.dBase, name=stockConfig.reportHistory, data=report,\r\n createArgs='(\"' + '\",\"'.join(report.columns.tolist()) + '\")',\r\n indexColName='ID',\r\n args='(\"' + '\",\"'.join(report.columns.tolist()) + '\")')\r\n\r\n def email_report(self, report, buysAndSells, maxTickers=100):\r\n\r\n # input buy into database via input script/webpage?\r\n buys = report[report.action == 'buy'].reset_index(drop=True)\r\n\r\n # emails have a maximum size, plus can only make a certain number of choices, so filter out top 100\r\n # (inclusive of crosses\r\n\r\n crosses = buys[buys.type == 'cross'].reset_index(drop=True)\r\n crosses['MC'] = crosses.market_cap.str.replace(',', '').astype(float)\r\n crosses = crosses.sort_values(by=['gradient', 'CTA', 'MC'], ascending=False)\r\n\r\n continuations = buys[buys.type == 'continuation'].reset_index(drop=True)\r\n continuations['MC'] = continuations.market_cap.str.replace(',','').astype(float)\r\n continuations = continuations.sort_values(by=['gradient', 'CTA', 'MC'], ascending=False)\r\n\r\n nContinuations = maxTickers - len(crosses)\r\n continuations = continuations.iloc[:nContinuations, :].reset_index(drop=True)\r\n buys = pd.concat([crosses, continuations]).reset_index(drop=True)\r\n buys = buys.drop('MC', axis=1)\r\n buyTickerMask = match(buys.ticker, buysAndSells['price'].columns)\r\n\r\n sells = report[report.action == 'sell'].reset_index(drop=True)\r\n # read current portfolio and check if we hold the specified sell, if so then email.\r\n # input sell into database via input script/webpage?\r\n currentPortfolio = dBaseAction(self.dBase, 'Select * from %s' % self.portfolio)\r\n if len(currentPortfolio) > 0:\r\n currentPortfolio = currentPortfolio[0]\r\n sells = sells[sells.ticker.isin(currentPortfolio.ticker.unique())].reset_index(drop=True)\r\n sellTickerMask = match(sells.ticker, buysAndSells['price'].columns)\r\n else:\r\n sells = []\r\n\r\n if len(buys) > 0:\r\n # send email\r\n emailReport(report=buys,\r\n buyCurve=buysAndSells['buyCurve'][:, buyTickerMask],\r\n sellCurve=buysAndSells['sellCurve'][:, buyTickerMask],\r\n sellCurveB=buysAndSells['sellCurveB'][:, buyTickerMask],\r\n baseCurve=buysAndSells['baseCurve'][:, buyTickerMask],\r\n price=buysAndSells['price'].iloc[:, buyTickerMask],\r\n buyLocations=buysAndSells['buyLocations'][:, buyTickerMask],\r\n sellLocations=buysAndSells['sellLocations'][:, buyTickerMask],\r\n saveLocation=self.saveLocation,\r\n chartTitleKey='BUY %s - momentum HOURLY ' % self.title)\r\n if len(sells) > 0:\r\n # send email\r\n emailReport(report=sells,\r\n buyCurve=buysAndSells['buyCurve'][:, sellTickerMask],\r\n sellCurve=buysAndSells['sellCurve'][:, sellTickerMask],\r\n sellCurveB=buysAndSells['sellCurveB'][:, sellTickerMask],\r\n baseCurve=buysAndSells['baseCurve'][:, sellTickerMask],\r\n price=buysAndSells['price'].iloc[:, sellTickerMask],\r\n buyLocations=buysAndSells['buyLocations'][:, sellTickerMask],\r\n sellLocations=buysAndSells['sellLocations'][:, sellTickerMask],\r\n saveLocation=self.saveLocation,\r\n chartTitleKey='SELL %s - momentum HOURLY ' % self.title)\r\n\r\n @staticmethod\r\n def updatePortfolio():\r\n \"\"\"\r\n Method to update portfolio and portfolio history in dataBase\r\n\r\n Expects input:\r\n assetType: str\r\n \"crypto\" or \"stocks\"\r\n action:\r\n \"buy\" or \"sell\"\r\n ticker: str\r\n what ticker is bought or sold\r\n price: float\r\n price bought at/ sold at\r\n timestamp: \"YYYY-MM-DD HH:MM\"\r\n timestamp trade was actioned\r\n\r\n\r\n \"\"\"\r\n\r\n assetCorrect = False\r\n actionCorrect=False\r\n timeCorrect=False\r\n\r\n while not assetCorrect:\r\n assetType = input('Enter Asset (crypto/stocks): ')\r\n if assetType in ['crypto', 'stocks']:\r\n assetCorrect = True\r\n\r\n while not actionCorrect:\r\n action = input('Enter Action (buy/sell): ')\r\n if action in ['buy', 'sell']:\r\n actionCorrect = True\r\n\r\n ticker = input('Enter Ticker: ')\r\n price = input('Enter Price (GBP for stocks, USD for crypto): ')\r\n quantity = input('Enter Quantity: ')\r\n\r\n while not timeCorrect:\r\n timestamp = input('Enter TimeStamp (YYYY-MM-DD HH:MM): ')\r\n try:\r\n timestamp = np.datetime64(timestamp)\r\n except Exception as e:\r\n print(str(e))\r\n else:\r\n timeCorrect = True\r\n\r\n if assetType == 'stocks':\r\n dBase = stockConfig.dBase\r\n portfolioTable = stockConfig.portfolio\r\n portfolioHistoryTable = stockConfig.portfolioHistory\r\n base = 'GBP'\r\n elif assetType == 'crypto':\r\n dBase = cryptoConfig.dBase\r\n portfolioTable = cryptoConfig.portfolio\r\n portfolioHistoryTable = cryptoConfig.portfolioHistory\r\n base = 'USD'\r\n\r\n data = pd.DataFrame({'ticker': ticker,\r\n 'price (%s)' % base: price,\r\n 'quantity': quantity,\r\n 'timestamp': timestamp}, index=[0])\r\n\r\n if action == 'buy':\r\n # add buy to portfolio\r\n writeDBv2(dBase=dBase, name=portfolioTable, data=data,\r\n createArgs='(\"' + '\",\"'.join(data.columns.tolist()) + '\")',\r\n indexColName='ticker',\r\n args='(\"' + '\",\"'.join(data.columns.tolist()) + '\")')\r\n\r\n data['ID'] = data.ticker + '_' + str(data.timestamp)\r\n data['action'] = 'buy'\r\n # log into history table\r\n writeDBv2(dBase=dBase, name=portfolioHistoryTable, data=data,\r\n createArgs='(\"' + '\",\"'.join(data.columns.tolist()) + '\", PRIMARY KEY(\"ID\"), UNIQUE(\"ID\"))',\r\n indexColName='ID',\r\n args='(\"' + '\",\"'.join(data.columns.tolist()) + '\")')\r\n print('buy made: ')\r\n print(data)\r\n\r\n elif action == 'sell':\r\n # remove ticker from portfolio\r\n assert ticker in dBaseAction(dBase, 'select distinct ticker from %s ' % portfolioTable)[0],\\\r\n '\"%s\" not in portfolio' % ticker\r\n dBaseAction(dBase, 'delete from %s where ticker = \"%s\"' % (portfolioTable, ticker), noReturn=True)\r\n # add trade to history\r\n data['ID'] = data.ticker + '_' + data.timestamp\r\n data['action'] = 'sell'\r\n # log into history table\r\n writeDBv2(dBase=dBase, name=portfolioHistoryTable, data=data,\r\n createArgs='(\"' + '\",\"'.join(data.columns.tolist()) + '\", PRIMARY KEY(\"ID\"), UNIQUE(\"ID\"))',\r\n indexColName='ID',\r\n args='(\"' + '\",\"'.join(data.columns.tolist()) + '\")')\r\n print('sell made: ')\r\n print(data)\r\n\r\n input('press enter to exit...')\r\n\r\n @staticmethod\r\n def portfolioStatus(view='current'):\r\n \"\"\"\r\n Method to view currenct portfolio and portfolio history\r\n\r\n Parameters:\r\n -----------\r\n view: str\r\n \"current\" for current portfolio\r\n \"history\" for full trade history\r\n\r\n\r\n \"\"\"\r\n\r\n assetType = input('Enter Asset (crypto/stocks): ')\r\n ticker = input('Enter Ticker: ')\r\n assert assetType in ['stocks', 'crypto'], 'dataBase must be \"crypto\" or \"stocks'\r\n\r\n if assetType == 'stocks':\r\n dBase = stockConfig.dBase\r\n portfolioTable = stockConfig.portfolio\r\n portfolioHistoryTable = stockConfig.portfolioHistory\r\n base = 'GBP'\r\n elif assetType == 'crypto':\r\n dBase = cryptoConfig.dBase\r\n portfolioTable = cryptoConfig.portfolio\r\n portfolioHistoryTable = cryptoConfig.portfolioHistory\r\n base = 'USD'\r\n\r\n if view == 'current':\r\n currentPortfolio = dBaseAction(dBase, 'Select * from %s' % portfolioTable)[0]\r\n print('###################\\n'\r\n 'Current Portfolio #\\n'\r\n '###################')\r\n\r\n print(currentPortfolio.to_string())\r\n\r\n if view == 'history':\r\n portfolioHistory = dBaseAction(dBase, 'Select * from %s' % portfolioHistoryTable)[0]\r\n print('###################\\n'\r\n 'Portfolio History #\\n'\r\n '###################')\r\n print(ticker)\r\n if ticker is not None and ticker != '':\r\n if isinstance(ticker, str):\r\n ticker = [ticker]\r\n portfolioHistory = portfolioHistory[portfolioHistory.ticker.isin(ticker)].reset_index(drop=True)\r\n print(portfolioHistory.to_string())\r\n\r\n @staticmethod\r\n def reportHistory():\r\n \"\"\"\r\n Method to view currenct portfolio and portfolio history\r\n\r\n Parameters:\r\n -----------\r\n view: str\r\n \"current\" for current portfolio\r\n \"history\" for full trade history\r\n\r\n\r\n \"\"\"\r\n\r\n assetType = input('Enter Asset (crypto/stocks): ')\r\n assert assetType in ['stocks', 'crypto'], 'dataBase must be \"crypto\" or \"stocks'\r\n\r\n if assetType == 'stocks':\r\n dBase = stockConfig.dBase\r\n table = stockConfig.reportHistory\r\n elif assetType == 'crypto':\r\n dBase = cryptoConfig.dBase\r\n table = cryptoConfig.reportHistory\r\n\r\n reportHistory = dBaseAction(dBase, 'Select * from %s' % table)[0]\r\n print('###################\\n'\r\n ' Report History #\\n'\r\n '###################')\r\n\r\n print(reportHistory.to_string())\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n if sys.argv[1] == 'cryptoHourly':\r\n trade = Trading('crypto')\r\n trade.runCrypto('hourly')\r\n elif sys.argv[1] == 'cryptoDaily':\r\n trade = Trading('crypto')\r\n trade.runCrypto('daily')\r\n elif sys.argv[1] == 'stocksDaily':\r\n trade = Trading('stocks')\r\n trade.runStocks(fullUpdate=eval(sys.argv[2]))\r\n elif sys.argv[1] == 'updatePortfolio':\r\n Trading.updatePortfolio()\r\n elif sys.argv[1] == 'portfolioStatus':\r\n Trading.portfolioStatus()\r\n","sub_path":"1st iteration/AlgorithcTrading/trading.py","file_name":"trading.py","file_ext":"py","file_size_in_byte":20257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"376217184","text":"# Python Tutorials - Internet Operations\nimport urllib.request\n\nurl = 'http://www.brainjar.com/java/host/test.html'\nstream_open = None\ntry:\n stream = urllib.request.urlopen(url)\n stream_open = True\n contents = stream.read()\n print(contents)\nfinally:\n if stream_open == True:\n stream.close()\n print('URL successfully closed')\n \n","sub_path":"internet_operations.py","file_name":"internet_operations.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"477103370","text":"'''\nAuthor: Jai Luthra\n'''\n\nimport pygame\nimport random\nimport time\n\npygame.init()\n\nwidth, height = 800, 600\ncenter = (240, 260)\nscore_loc = (10, 10)\nlife_loc = (720, 10)\nbullet_Speed = 0.4\nscore = 0\nlives = 3\nwindow = pygame.display.set_mode((width, height))\n\npygame.display.set_caption('Space Invaders')\n\nplayerImg = pygame.image.load('resources/spaceship.png')\nplayerX = (width - 64)/2\nplayerY = height - 10 - 64\nplayerX_change = 0\n\nenemyImg = pygame.image.load('resources/enemy.png')\nenemyX = random.randint(0, width - 64)\nenemyY = 0\n\nbulletImg = pygame.image.load('resources/bullet.png')\nbulletX = 0\nbulletY = playerY - 30\nbulletX_change = 0\nbulletY_change = bullet_Speed\nbullet_fired = False\n\ndef show_score():\n text = pygame.font.Font('freesansbold.ttf', 16)\n return text.render(f'Score: {score}', True, (255,255,255))\n\ndef player(playerX, playerY):\n window.blit(playerImg, (playerX, playerY))\n\ndef enemy(enemyX, enemyY):\n window.blit(enemyImg, (enemyX, enemyY))\n\ndef bullet(bulletX, bulletY):\n window.blit(bulletImg, (bulletX, bulletY))\n\n# run until a quit\nloop = True\n\nwhile(loop == True):\n window.fill((0,0,0))\n\n for event in pygame.event.get():\n # if window closed\n if event.type == pygame.QUIT:\n loop = False\n\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n playerX_change += -0.5\n if event.key == pygame.K_RIGHT:\n playerX_change += 0.5\n\n if event.key == pygame.K_SPACE:\n if not bullet_fired:\n bulletX = playerX + 16\n bullet_fired = True\n\n if event.type == pygame.KEYUP:\n if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT:\n playerX_change = 0\n\n playerX += playerX_change\n playerX = max(0, playerX)\n playerX = min(width - 64, playerX)\n player(playerX, playerY)\n\n enemyY += (score//5 + 1) * 0.1\n enemy(enemyX, enemyY)\n \n if bullet_fired:\n bullet(bulletX, bulletY)\n bulletY -= bulletY_change\n if(bulletY < -16):\n bullet_fired = False\n bulletY = playerY - 30\n\n if enemyX - 28 < bulletX < enemyX + 56 and enemyY - 56 < bulletY < enemyY + 56:\n bullet_fired = False\n bulletY = playerY - 30\n enemyX = random.randint(0, width - 64)\n enemyY = 0\n score += 1\n\n if enemyY > height - 50:\n lives-= 1\n if enemyX - 56 < playerX < enemyX + 56:\n lives = 0\n enemyX = random.randint(0, width - 64)\n enemyY = 0\n\n if lives == 0:\n for i in range(5,0,-1):\n time.sleep(1)\n window.fill((0,0,0))\n text = pygame.font.Font('freesansbold.ttf', 50)\n out = text.render('Game Over!', True, (255,255,0))\n window.blit(out, center)\n out = text.render(f'Window will exit in {i}s', True, (255,255,0))\n window.blit(out, (120,300))\n window.blit(show_score(), score_loc)\n pygame.display.update()\n loop = False\n\n else:\n text = pygame.font.Font('freesansbold.ttf', 16)\n life = text.render(f'Live(s): {lives}', True, (255,255,255))\n window.blit(life, life_loc)\n window.blit(show_score(), score_loc)\n pygame.display.update()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"393662868","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\nfrom six.moves.urllib.parse import quote\n\nfrom .exc import DownstreamError\n\n\ndef urlify(string):\n \"\"\" You might be wondering: why is this here at all, since it's basically\n doing exactly what the quote_plus function in urllib does. Well, to keep\n the 2 & 3 stuff all in one place, meaning rather than try to import the\n urllib stuff twice in each file where url-safe strings are needed, we keep\n it all in one file: here.\n\n Supporting multiple Pythons is hard.\n\n :param string: String to URLify\n :return: URLified string\n \"\"\"\n return quote(string)\n\n\ndef handle_json_response(resp):\n try:\n resp.raise_for_status()\n except Exception as ex:\n r_json = resp.json()\n raise DownstreamError(\"Error fetching downstream\"\n \"-node response: %s\" % str(ex))\n\n try:\n r_json = resp.json()\n except:\n raise DownstreamError('Invalid response from Downstream node.')\n\n return r_json\n","sub_path":"downstream_farmer/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"389542999","text":"import sys\n\nusrid = sys.argv[1]\n\nimport numpy as np\nimport MySQLdb as mdb\nfrom scipy import sparse\nfrom scipy.sparse.linalg import svds\nfrom collections import defaultdict\nfrom operator import itemgetter\n\ndef sparse_mean(mat, row = -1, column = -1):\n\t# function to take means on a sparse matrix\n\tif row != -1:\n\t\tmat = mat[row]\n\telif column != -1:\n\t\tmat = mat.transpose()[column]\n\n\tif mat.count_nonzero() != 0:\n\t\treturn mat.sum()/mat.count_nonzero()\n\telse:\n\t\treturn 0\n\ndef recommend(id):\n\tcon = mdb.connect(\"localhost\", \"root\", \"\", \"recommender\") #connection to the database\n\tdata = []\n\n\t# getting user x films ratings from the database\n\ttry:\n\t\twith con:\n\t\t\tcur = con.cursor()\n\t\t\tquery = \"SELECT * FROM user_ratings\"\n\t\t\tcur.execute(query)\n\t\t\tresult = cur.fetchall()\n\n\t\t\tfor r in result:\n\t\t\t\tdata.append(r)\n\n\t\t\tcur = con.cursor()\n\t\t\tquery = \"SELECT id FROM users ORDER BY id\"\n\t\t\tcur.execute(query)\n\n\t\t\tresult = cur.fetchall()\n\t\t\tuser_ids = [user[0] for user in result]\n\t\t\tn_users = len(user_ids)\n\n\t\t\tcur = con.cursor()\n\t\t\tquery = \"SELECT COUNT(*) FROM films\"\n\t\t\tcur.execute(query)\n\n\t\t\tn_films = cur.fetchone()[0]\n\texcept Exception:\n\t\tprint(Exception)\n\n\t# prepping the data into a user x movies rating matrix\n\tuid_matrix = user_ids.index(int(id))\n\n\tdata = np.array(data, dtype=np.float32)\n\n\tres = defaultdict(list)\n\tfor v,u,k in data: res[int(v)].append([u, k])\n\t\n\tfor i in user_ids:\n\t\tif res[i]:\n\t\t\tres[i] = np.array(res[i])\n\t\telse:\n\t\t\tres[i] = np.array([[1,4]])\n\n\tfirst_usr = int(data[:,0].min())\n\tfirst_mov = int(data[:,1].min())\n\n\tuser_ratings_mean = []\n\tfor i in res:\n\t\tuser_ratings_mean.append(sum(res[i][:,1])/len(res[i]))\n\n\turm_array = []\n\toriginal_matrix = np.zeros(shape=(n_users, n_films), dtype=np.float32)\n\n\tfor cur_rating in user_ratings_mean:\n\t\turm_array.append([cur_rating]*n_films)\n\n\ti = 0\n\tfor r in res: # users\n\t\tfor j in res[r]: #movies\n\t\t\turm_array[i][int(j[0])-1] = original_matrix[i, int(j[0])-1] = j[1]\n\t\ti += 1\n\t\n\turm = np.array(urm_array)\n\n\tcur.close()\n\tcon.close()\n\n\tdef unrated(userid, movieid):\n\t\t# function to check if user j has rated movie i\n\t\tif not original_matrix[userid,movieid]:\n\t\t\treturn 1\n\n\toriginal_matrix_sparse = sparse.csr_matrix(original_matrix)\n\tratings_mean = sparse_mean(original_matrix_sparse) # mean of all ratings in original_matrix\n\n\tfilm_ratings_mean = []\n\n\tfor i in range(n_films):\n\t\tfilm_ratings_mean.append(sparse_mean(original_matrix_sparse,-1, i))\n\n\tpredictions = []\n\n\t# naive SVD\n\tU, S, V = sparse.linalg.svds(sparse.csr_matrix(urm))\n\tP = S * V.T\n\n\tfor i in range(first_usr-1, n_users):\n\t\tfor j in range(first_mov-1, n_films):\n\t\t\tif unrated(i,j):\n\t\t\t\tp = [i,j,((ratings_mean+(film_ratings_mean[j]-ratings_mean)+(user_ratings_mean[i]-ratings_mean))+(U[i]*P[j]).sum())/2]\n\t\t\t\tpredictions.append(p)\n\n\tfor prediction in predictions:\n\t\turm[prediction[0], prediction[1]] = prediction[2]\n\n\t# prepping data to send\n\tpreds = defaultdict(list)\n\tfor a,b,c in predictions: preds[a].append([b+1,c])\n\n\tuser_preds = []\n\tfor i in preds[uid_matrix]:\n\t\tuser_preds.append(i)\n\n\tuser_preds = sorted(user_preds, key=itemgetter(1), reverse=True)\n\tprint(user_preds[:6])\n\nrecommend(usrid)","sub_path":"site/__pyscripts/ratings.py","file_name":"ratings.py","file_ext":"py","file_size_in_byte":3118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"510564761","text":"# coding:utf-8\n\n\nsalary = input(\"your salary: \")\ncommodity_list = [['iphone 7plus', '6388'], ['mac pro', '21000'], ['starbuck', '31'], ['bicycle', '1200'], ['Book', '88'], ['cup', '35']]\n\nuser = None\nhave_selected=[]\n\nif salary.isdigit():\n salary = int(salary)\n account_balaence = salary\n while user != 'q':\n\n '''\n for i in commodity_list:\n commodity_index = commodity_list.index(i)+1\n commodity_show = ' '.join(i)\n print(commodity_index, commodity_show)\n '''\n # 还有一种写法 使用enumerate()\n for index, item in enumerate(commodity_list):\n print(index+1, ' '.join(item))\n # enumerate() 取出列表的下标 和 数据\n\n commodity_num = input(\">>>: \")\n # 应该先判断 输入的是不是数字 如果是数字 先转成 int 在来判断 数字的在不在商品长度之内，在判断是不是q\n if commodity_num.isdigit():\n commodity_num = int(commodity_num)\n if commodity_num < len(commodity_list) and commodity_num >=0:\n commodity_real_price = int(commodity_list[int(commodity_num) - 1][1])\n if account_balaence < commodity_real_price:\n print('Insufficient account balance. Please refill it \\n')\n else:\n have_selected.append(commodity_list[int(commodity_num) - 1])\n account_balaence = account_balaence - commodity_real_price\n print('%s have added to shoppong cart' % commodity_list[int(commodity_num) - 1][0])\n print('your account_balaence is \\033[31;1m%s\\033[0m \\n' % account_balaence)\n else:\n print('the product %s is not exit' % commodity_num)\n elif commodity_num == 'q':\n print('-----------shopping list-------------')\n for j in have_selected:\n print(j[0])\n print('your account_balaence is \\033[31;1m%s\\033[0m \\n' % account_balaence)\n exit()\n '''\n exit('your account_balaence is \\033[31;1m%s\\033[0m \\n' % account_balaence)\n '''\n else:\n print('wrong option')\n\nelse:\n print('spelling wrong')\n\n\n\n\n\n\n\n","sub_path":"zyh_python-20170628晚改完/zyh_python/zyh_python/day2/shopping cart/shopping_cart.py","file_name":"shopping_cart.py","file_ext":"py","file_size_in_byte":2244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"56854184","text":"class Solution:\n def searchRange(self, nums, target):\n \"\"\"\n :type nums: List[int]\n :type target: int\n :rtype: List[int]\n \"\"\"\n if nums is None or len(nums) == 0:\n return -1\n res = [-1, -1]\n res[0] = self.find_first(nums, target)\n res[1] = self.find_last(nums, target)\n return res\n\n def find_first(self, nums, target):\n start, end = 0, len(nums)-1\n while start+1 < end:\n mid = (start+end)//2\n if nums[mid] == target:\n end = mid\n elif nums[mid] < target:\n start = mid\n else:\n end = mid\n\n if nums[start] == target:\n return start\n if nums[end] == target:\n return end\n return -1\n\n def find_last(self, nums, target):\n start, end = 0, len(nums)-1\n while start+1 < end:\n mid = (start+end)//2\n if nums[mid] == target:\n start = mid\n elif nums[mid] < target:\n start = mid\n else:\n end = mid\n\n if nums[end] == target:\n return end\n\n if nums[start] == target:\n return start\n return -1\n\n\ndef main():\n nums = [5, 7, 7, 8, 8, 10]\n target = 8\n sol = Solution()\n print(sol.searchRange(nums, target))\n\n\nmain()\n","sub_path":"034-find-first-and-last-position-of-element-in-sorted-array/solution1.py","file_name":"solution1.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"613956135","text":"#!/usr/bin/env python3\r\n\r\nimport json\r\nimport os\r\nimport random\r\nimport sys\r\n\r\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\r\n\r\nfrom arguments import get_arguments\r\n\r\nimport dp_parser\r\nimport bp_parser\r\nimport remove_similar_sequences as rss\r\n\r\n\r\narguments = get_arguments(path=str,\r\n data_format=str,\r\n validation_ratio=(float, 0.2),\r\n test_ratio=(float, 0.2),\r\n path_similar=(str,None))\r\n\r\npath, data_format, validation_ratio, test_ratio, path_similar = arguments\r\n\r\n# Choose correct parser\r\nif data_format == 'dp':\r\n read_data = dp_parser.read_data\r\nelif data_format == 'bp':\r\n read_data = bp_parser.read_data\r\nelse:\r\n print('Invalid format, must be dp or bp.')\r\n sys.exit()\r\n\r\n# Additional parser for the similar dataset\r\nif path_similar is not None:\r\n if data_format == 'dp':\r\n read_data = dp_parser.read_data\r\n elif data_format == 'bp':\r\n read_data = bp_parser.read_data\r\n else:\r\n print('Invalid format, must be dp or bp.')\r\n sys.exit()\r\n\r\nprint(\"Loading the dataset...\")\r\n\r\n# Load the dataset\r\ndata = list(read_data(path))\r\n\r\n# remove similar sequences if pathSimilar is set\r\nif path_similar is not None:\r\n dataSim = list(read_data(path_similar))\r\n data = rss.remove_sim_seq_list(data,dataSim)\r\n\r\nlength = len(data)\r\n\r\nprint(f\" {length} entries read\")\r\n\r\n# Shuffle the dataset in a random order\r\n# random.shuffle(data)\r\n\r\n# Split data into training, validation and test set\r\nvalidation_length = int(length * validation_ratio)\r\ntest_length = int(length * test_ratio)\r\n\r\nvalidation_data = data[:validation_length]\r\ntest_data = data[validation_length:validation_length + test_length]\r\ntraining_data = data[validation_length + test_length:]\r\n\r\nprint(\"Some statistics...\")\r\n\r\ndef avg_len(data):\r\n return sum(len(rna) for rna, db in data) / max(len(data), 1)\r\n\r\nprint(f\" validation data: {avg_len(validation_data)} // {len(validation_data)}\")\r\nprint(f\" test data: {avg_len(test_data)} // {len(test_data)}\")\r\nprint(f\" training data: {avg_len(training_data)} // {len(training_data)}\")\r\n\r\nprint(\"Writing data to files...\")\r\n\r\npath = path.rstrip('/')\r\n\r\nwith open(f'{path}-validation.json', 'w') as validation_file:\r\n json.dump(validation_data, validation_file, indent=2)\r\n\r\nwith open(f'{path}-test.json', 'w') as test_file:\r\n json.dump(test_data, test_file, indent=2)\r\n\r\nwith open(f'{path}-training.json', 'w') as training_file:\r\n json.dump(training_data, training_file, indent=2)\r\n","sub_path":"rna_prediction/src/parsing/prepare_data.py","file_name":"prepare_data.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"228095403","text":"#! /usr/bin/env python3\n\nimport argparse\n\n# VASP processing modules\nfrom vasppy.doscar import Doscar\nfrom vasppy.utils import validate_checksum\nfrom vasppy.summary import load_vasp_summary\n\n# matplotlib and figure formatting\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\nimport figure_formatting as ff\n\n# additional functions in this repository\nfrom analysis_functions import element_list_from_vasprun, read_vasprun\n\nff.set_formatting( ff.formatting )\n\ndef checksum( filename, md5, verbose=False ):\n if verbose:\n print( 'Validating checksum for {}'.format( filename ) )\n validate_checksum( '../data/{}'.format( filename ), md5 )\n if verbose:\n print( 'okay' )\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('functional', choices=['PBEsol','HSE06'],\n help='Functional to plot pDOS data for')\n parser.add_argument('-v', '--verbose', action='store_true',\n help='Verbose output')\n parser.add_argument('-f', '--fig-dir', help='Figures directory', default='.')\n args = parser.parse_args()\n return args\n\ndef main( functional, fig_dir='.', verbose=False ):\n if verbose:\n print( 'Plotting {} pDOS'.format( functional ) )\n filename = '../data/vasp_summary.yaml'\n if verbose:\n print( 'Reading VASP summary from {}'.format( filename ) )\n summary = load_vasp_summary( filename ) \n\n panel_a = { 'calc': 'FeF3 {} DOS'.format(functional),\n 'to_plot': {'Fe': 'd', 'F': 'p'},\n 'title': r'(a) FeF$_3$' }\n panel_b = { 'calc': 'FeF3 OH_F 1,2 {} DOS'.format(functional),\n 'to_plot': {'Fe': 'd', 'F': 'p', 'O': 'p'},\n 'title': r'(b) FeF$_3$ + 4(OH)$_\\mathrm{F}$' }\n panel_c = { 'calc': 'FeF3 2O_F 2V_F {} DOS'.format(functional),\n 'to_plot': {'Fe': 'd', 'F': 'p', 'O': 'p'},\n 'title': r'(c) FeF$_3$ + 2O$_\\mathrm{F}$ + 2$\\square_\\mathrm{F}$' }\n\n plotting_data = ( panel_a, panel_b, panel_c )\n fig, axes = plt.subplots(3, 1, figsize=(8,9.0))\n fig_params = {\n 'xrange': [-12, 8], \n 'title_loc': 'left', \n 'scaling': {'O': {'p': 5.0}},\n 'legend_pos': 'best'\n }\n\n title_x = 0.02\n title_y = 0.86\n\n def title( ax, text ):\n ax.text( title_x, title_y, text, horizontalalignment='left', transform=ax.transAxes )\n\n for ax, p_data in zip( axes, plotting_data ):\n calc = summary[p_data['calc']]\n tracked_files = calc['file tracking']\n for tfile in tracked_files.values():\n checksum( tfile['filename'], tfile['md5'], verbose=verbose )\n doscar_filename = tracked_files['DOSCAR']['filename']\n vasprun_filename = tracked_files['vasprun.xml']['filename']\n v = read_vasprun( '../data/{}'.format( vasprun_filename ), gzipped=True )\n\n doscar = Doscar('../data/{}'.format(doscar_filename), species=element_list_from_vasprun(v))\n doscar.energy -= v.eigenvalue_band_properties[2]\n doscar.plot_pdos(to_plot=p_data['to_plot'], ax=ax, **fig_params)\n\n ax.text(title_x,\n title_y,\n p_data['title'],\n horizontalalignment='left',\n transform=ax.transAxes)\n\n plt.tight_layout()\n plt.subplots_adjust(hspace=0)\n fig_filename = '{}/FeF3_{}_pDOS.pdf'.format(fig_dir, functional)\n if verbose:\n print('Saving figure to {}'.format(fig_filename))\n fig.savefig(fig_filename)\n\nif __name__ == '__main__':\n args = parse_args()\n main(args.functional, args.fig_dir, args.verbose)\n","sub_path":"analysis/plot_FeF3_pDOS.py","file_name":"plot_FeF3_pDOS.py","file_ext":"py","file_size_in_byte":3609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"508130703","text":"import re\nimport pandas as pd\nfrom igf_data.illumina.samplesheet import SampleSheet\nfrom igf_data.utils.sequtils import rev_comp\nfrom igf_data.process.metadata_reformat.reformat_metadata_file import Reformat_metadata_file\n\nSAMPLESHEET_COLUMNS = [\n 'Lane',\n 'Sample_ID',\n 'Sample_Name',\n 'Sample_Plate',\n 'Sample_Well',\n 'I7_Index_ID',\n 'index',\n 'I5_Index_ID',\n 'index2',\n 'Sample_Project',\n 'Description',\n 'Pool_Number'\n]\n\nclass Reformat_samplesheet_file:\n '''\n A class for reformatting samplesheet file\n\n :param infile: Input samplesheet file\n :param file_format: Input file format, default samplesheet\n List of allowed formats\n\n * samplesheet\n * csv\n\n :param samplesheet_columns: A list of expected columns in the samplesheet file\n A list of default columns\n\n * Lane\n * Sample_ID\n * Sample_Name\n * Sample_Plate\n * Sample_Well\n * I7_Index_ID\n * index\n * Sample_Project\n * Description\n * Pool_number\n\n :param remove_adapters: A toggle for removing adapters from header section ,default False\n :param revcomp_index1: A toggle for reverse complementing index1 column, default False\n :param revcomp_index2: A toggle for reverse complementing index2 column, default False\n :param tenx_label: Description label for 10x experiments, default '10X'\n :param sample_id: Sample id column name, default 'Sample_ID'\n :param sample_name: Sample name column name, default 'Sample_Name'\n :param index: I7 index column name, default 'index'\n :param index2: I5 index column name, default 'index2'\n :param sample_project: Project name column name, default 'Sample_Project'\n :param description: Description column name, default 'Description'\n :param adapter_section: Adapter section name in header, default 'Settings'\n :param adapter_keys: A list of adapter keys to be removed from samplesheet header, default ('Adapter','AdapterRead2')\n '''\n def __init__(self,infile,\n file_format='samplesheet',\n samplesheet_columns=SAMPLESHEET_COLUMNS,\n remove_adapters=False,\n revcomp_index1=False,\n revcomp_index2=False,\n tenx_label='10X',\n sample_id='Sample_ID',\n sample_name='Sample_Name',\n index='index',\n index2='index2',\n sample_project='Sample_Project',\n description='Description',\n adapter_section='Settings',\n adapter_keys=('Adapter','AdapterRead2')):\n try:\n self.infile = infile\n if file_format not in ['samplesheet','csv']:\n raise ValueError('File format {0} not supported'.format(file_format))\n\n self.file_format = file_format\n self.samplesheet_columns = samplesheet_columns\n self.tenx_label = tenx_label\n self.remove_adapters = remove_adapters\n self.revcomp_index1 = revcomp_index1\n self.revcomp_index2 = revcomp_index2\n self.sample_id = sample_id\n self.sample_name = sample_name\n self.index = index\n self.index2 = index2\n self.sample_project = sample_project\n self.description = description\n self.adapter_section = adapter_section\n self.adapter_keys = adapter_keys\n except Exception as e:\n raise ValueError('Error in initializing samplesheet reformatting, error: {0}'.\\\n format(e))\n\n @staticmethod\n def detect_tenx_barcodes(index,tenx_label='10X'):\n '''\n A static method for checking 10X I7 index barcodes\n\n :param index: I7 index string\n :param tenx_label: A string description for 10X samples, default, '10X'\n :returns: A string\n '''\n try:\n description = ''\n pattern = re.compile(r'SI-[GN]A-[A-H]\\d+',re.IGNORECASE)\n if re.match(pattern,index):\n description = tenx_label\n return description\n except Exception as e:\n raise ValueError('Failed to detect Tenx single cell barcode for index {0}, error: {1}'.\\\n format(index,e))\n\n def correct_samplesheet_data_row(self,row):\n '''\n A method for correcting samplesheet data row\n\n :param row: A Pandas Series\n :returns: A Pandas Series\n '''\n try:\n if not isinstance(row,pd.Series):\n raise TypeError('Expecting A pandas series and got {0}'.\\\n format(type(row)))\n\n if self.sample_id in row.keys():\n row[self.sample_id] = \\\n Reformat_metadata_file.\\\n sample_and_project_reformat(row[self.sample_id]) # refoemat sample id\n\n if self.sample_project in row.keys():\n row[self.sample_project] = \\\n Reformat_metadata_file.\\\n sample_and_project_reformat(row[self.sample_project]) # refoemat project name\n\n if self.sample_name in row.keys():\n row[self.sample_name] = \\\n Reformat_metadata_file.\\\n sample_name_reformat(row[self.sample_name]) # refoemat sample name\n\n if self.index in row.keys() and \\\n self.description in row.keys():\n row[self.description] = \\\n self.detect_tenx_barcodes(\\\n index=row[self.index],\n tenx_label=self.tenx_label) # add description label for 10x samples\n\n if self.index in row.keys() and \\\n self.description in row.keys() and \\\n (row[self.index]!='' or row[self.index] is not None ) and \\\n row[self.description] != self.tenx_label:\n row[self.index] = row[self.index].upper()\n if self.revcomp_index1:\n row[self.index] = rev_comp(row[self.index]) # revcomp index 1\n\n if self.index2 in row.keys() and \\\n (row[self.index2]!='' or row[self.index2] is not None ):\n row[self.index2] = row[self.index2].upper()\n if self.revcomp_index2:\n row[self.index2] = rev_comp(row[self.index2]) # revcomp index 2\n\n if self.description in row.keys() and \\\n (row[self.description] !='' or \\\n row[self.description] is not None):\n row[self.description] = row[self.description].upper() # change description to upper case letters\n return row\n except Exception as e:\n raise ValueError('Failed to correct samplesheet data row {0},error {1}'.\\\n format(row,e))\n\n def reformat_raw_samplesheet_file(self,output_file):\n '''\n A method for refoematting raw samplesheet file\n\n :param output_file: An output file path\n :returns: None\n '''\n try:\n samplesheet_data = list()\n if self.file_format == 'samplesheet':\n samplesheet = SampleSheet(infile=self.infile)\n samplesheet_data = pd.DataFrame(samplesheet._data)\n elif self.file_format == 'csv':\n samplesheet_data = pd.read_csv(self.infile,header=0,dtype=object)\n samplesheet_data.fillna('',inplace=True)\n samplesheet_data = \\\n samplesheet_data.\\\n apply(\\\n lambda row: self.correct_samplesheet_data_row(row=row),\n axis=1,\n result_type='reduce') # refoemat samplesheet data\n column_names = \\\n [column_name \\\n for column_name in samplesheet_data.columns \\\n if column_name in self.samplesheet_columns ] # filter expected column names\n if len(column_names) == 0:\n raise ValueError('No expected columns found on the samplesheet data')\n samplesheet_data = samplesheet_data[column_names] # filter samplesheet data\n if self.file_format == 'samplesheet':\n samplesheet._data = \\\n samplesheet_data.\\\n to_dict(orient='records') # update samplesheet object with new data\n if self.remove_adapters:\n for adapter_key in self.adapter_keys:\n samplesheet.\\\n modify_sample_header(\\\n section=self.adapter_section,\n type='remove',\n condition_key=adapter_key) # remove adapters from samplesheet\n samplesheet.print_sampleSheet(outfile=output_file) # print corrected samplesheet\n elif self.file_format == 'csv':\n samplesheet_data.to_csv(output_file,index=False) # dump samplesheet dat as csv file\n except Exception as e:\n raise ValueError('Failed to reformat samplesheet file {0}, error {1}'.\\\n format(self.infile,e))","sub_path":"igf_data/process/metadata_reformat/reformat_samplesheet_file.py","file_name":"reformat_samplesheet_file.py","file_ext":"py","file_size_in_byte":8902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"467603482","text":"\"\"\"downloads WDI data and saves it to disk.\n\"\"\"\n\nimport os\nimport shutil\nimport zipfile\nfrom io import BytesIO\nimport requests\nimport pandas as pd\n\nfrom worldbank_wdi import INPATH\n\nimport logging\n\nlogging.basicConfig()\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n\ndef main():\n delete_input()\n download_data()\n\n\ndef delete_input() -> None:\n \"\"\"deletes all files and folders in `{INPATH}`.\n\n WARNING: this method deletes all input data and is only intended for use\n immediately prior to `download_data()`.\n \"\"\"\n if os.path.exists(INPATH):\n shutil.rmtree(INPATH)\n logger.info(f\"Deleted all existing input files in {INPATH}\")\n\n\ndef download_data() -> None:\n \"\"\"Downloads the raw World Development Indicators data and saves it\n in csv format to `{INPATH}`.\n \"\"\"\n if not os.path.exists(INPATH):\n os.makedirs(INPATH)\n try:\n _download_data_csv()\n except: # noqa\n _download_data_excel()\n logger.info(f\"Data succcessfully downloaded to {INPATH}\")\n\n\ndef _download_data_csv() -> None:\n url = \"http://databank.worldbank.org/data/download/WDI_csv.zip\"\n logger.info(f'Downloading data from \"{url}\"...')\n res = requests.get(url)\n zf = zipfile.ZipFile(BytesIO(res.content))\n fnames = zf.namelist()\n zf.extractall(path=INPATH)\n for fname in fnames:\n fname_zip = f\"{fname}.zip\"\n pd.read_csv(os.path.join(INPATH, fname)).to_csv(\n os.path.join(INPATH, fname_zip), index=False, compression=\"gzip\"\n )\n os.remove(os.path.join(INPATH, fname))\n\n\ndef _download_data_excel() -> None:\n url = \"http://databank.worldbank.org/data/download/WDI_excel.zip\"\n logger.info(f'Downloading data from \"{url}\"...')\n res = requests.get(url)\n zf = zipfile.ZipFile(BytesIO(res.content))\n fnames = zf.namelist()\n assert len(fnames) == 1, \"Expected only one file in xlsx zip archive.\"\n sheet2df = pd.read_excel(\n BytesIO(zf.read(fnames[0])), sheet_name=None, engine=\"openpyxl\"\n )\n for sheet, df in sheet2df.items():\n fname_zip = f\"WDI{sheet}.csv.zip\"\n df.to_csv(os.path.join(INPATH, fname_zip), index=False, compression=\"gzip\")\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"worldbank_wdi/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"358368893","text":"#!/usr/bin/env python3\n\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Union\n\nfrom attrs import define, field\n\nfrom .alignment import AlignmentParameters\nfrom .reconstruction import ReconstructionParameters\nfrom .simulation import SimulationParameters, generateDirectory\n\n\nclass ExperimentType(Enum):\n LUMI = \"LUMI\"\n KOALA = \"KOALA\"\n\n\nclass ClusterEnvironment(Enum):\n HIMSTER = \"HimsterII\"\n VIRGO = \"Virgo\"\n\n\n@define\nclass Experiment:\n \"\"\"\n Dataclass for simulation, reconstruction and alignment.\n \"\"\"\n\n experimentType: ExperimentType\n cluster: ClusterEnvironment\n simParams: SimulationParameters\n recoParams: ReconstructionParameters\n alignParams: AlignmentParameters\n fitConfigPath: Path = field(default=Path(\"fitconfig-fast.json\"))\n baseDataOutputDir: Union[Path, None] = field(default=None)\n LMDdirectory: Union[Path, None] = field(default=None)\n\n def __attrs_post_init__(self) -> None:\n self.updateBaseDataDirectory()\n\n def updateBaseDataDirectory(self) -> None:\n if self.LMDdirectory is not None:\n self.baseDataOutputDir = self.LMDdirectory / Path(\n generateDirectory(self.simParams, self.alignParams)\n )\n else:\n raise AttributeError(\n \"Cannot update internal path! Please set LMD software directory first.\"\n )\n","sub_path":"python/lumifit/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"592202219","text":"n,m = map(int,input().split())\r\n\r\n#赤フラグ 玉の個数\r\narr = [1 for _ in range(n)]\r\narr1 = [False for _ in range(n)]\r\narr1[0]=True\r\nch1 = True\r\n\r\nfor i in range(m):\r\n x,y = map(int,input().split())\r\n x = x-1\r\n y = y-1\r\n if ch1 == True and x == 0:\r\n ch1 = None\r\n arr[y] +=1\r\n arr1[y] = True\r\n arr[x] -=1\r\n if arr[x] == 0:\r\n arr1[x] = False\r\n continue\r\n \r\n arr[y] +=1\r\n arr[x] -=1\r\n if ch1 == None and arr1[x] == True:\r\n \r\n arr1[y] = True\r\n \r\n if ch1 == None and arr[x] == 0:\r\n arr1[x] = False\r\n\r\ncnt=0\r\nfor i in range(n):\r\n if arr1[i] == True:\r\n cnt +=1 \r\nprint(cnt)\r\n","sub_path":"agc002/agc002_b/20191212022418.py","file_name":"20191212022418.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"588772440","text":"import numpy as np\n\ndef test():\n\n a = np.array([1,2,3])\n b = np.array([[1,2,3],[4,5,6],[7,8,9]])\n print(b)\n print(np.amin(b))\n print(np.amax(b))\n print((np.amin(b,0)))\n print(np.amin(b,1))\n print(np.median(b))\n\ndef my_type():\n\n persontype = np.dtype({\n 'names':['name', 'age', 'chinese', 'math', 'english'],\n 'formats':['S32','i', 'i', 'i', 'f']})\n\n peoples = np.array([(\"ZhangFei\",32,75,100, 90),\n (\"GuanYu\",24,85,96,88.5),\n (\"ZhaoYun\",28,85,92,96.5),\n (\"HuangZhong\",29,65,85,100)],\n dtype=persontype)\n\n ages = peoples[:]['age']\n chineses = peoples[:]['chinese']\n maths = peoples[:]['math']\n englishs = peoples[:]['english']\n print(ages,np.mean(ages))# np.mean() 取平均值\n print(chineses,np.mean(chineses))\n print(maths,np.mean(maths))\n print(englishs,np.mean(englishs))\n\ndef my_ufunc():\n\n x1 = np.arange(1,11,2) # 初始，结束，步长\n x2 = np.linspace(1,9,5) # 初始，结束，数组长度\n print(x1,x2)\n print(np.add(x1,x2))\n print(np.subtract(x1,x2))\n print(np.multiply(x1,x2))\n\ndef work():\n\n work_tpye = np.dtype({\n\n 'names':['name','yuwen','yingyu','shuxue','total'],\n 'formats':['S32','i','i','i','f']})\n\n works = np.array(\n [\n ('zhangfei',66,65,30,0),\n ('guanyu',95,85,98,0),\n ('zhaoyun',93,92,96,0),\n ('huangzhong',90,88,77,0),\n ('dianwei',80,90,90,0)\n ],\n dtype=work_tpye\n )\n\n works[:]['total'] = works[:]['yuwen'] + works[:]['yingyu']+works[:]['shuxue']\n\n print(np.amin(works[:]['yuwen']),\n np.amax(works[:]['yuwen']),\n np.mean(works[:]['yuwen']),\n np.std(works[:]['yuwen']),\n np.var(works[:]['yuwen']))\n print(np.sort(works,order='total'))\n\nwork()\n","sub_path":"np-learn.py","file_name":"np-learn.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"185900442","text":"#!/usr/bin/python3\n\nfrom NetworkManager import NetworkManager, Settings\nimport time\nfrom pprint import pprint\nimport dbus.mainloop.glib\nfrom gi.repository import GObject\n\n\ndef find_primary_connection(connection_id):\n for connection in Settings.ListConnections():\n settings = connection.GetSettings()\n if settings[\"connection\"][\"id\"] == \"Auto Ethernet\":\n return connection\n\n\ndef reset_dns_servers(connection, working_connection):\n settings = connection.GetSettings()\n settings[\"ipv4\"][\"dns\"] = [\"208.67.222.222\", \"208.67.220.220\"]\n settings[\"ipv4\"][\"ignore-auto-dns\"] = True\n connection.Update(settings)\n device_list = working_connection.Devices\n NetworkManager.DeactivateConnection(working_connection)\n NetworkManager.ActivateConnection(connection, device_list[0], \"/\")\n\n\ndef properties_changed(*args, **kwargs):\n print(\"Properties have changed\")\n working_connection = NetworkManager.PrimaryConnection\n active_connection = find_primary_connection(working_connection.Id)\n nameservers = working_connection.Ip4Config.Nameservers\n pprint(nameservers)\n if nameservers != [\"208.67.222.222\", \"208.67.220.220\"]:\n reset_dns_servers(active_connection, working_connection)\n time.sleep(1)\n working_connection = NetworkManager.PrimaryConnection\n nameservers = working_connection.Ip4Config.Nameservers\n pprint(nameservers)\n\n\ndef main():\n dbus.mainloop.glib.DBusGMainLoop(set_as_default=True)\n loop = GObject.MainLoop()\n\n properties_changed()\n NetworkManager.OnPropertiesChanged(properties_changed)\n loop.run()\n\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"dns_fixer.py","file_name":"dns_fixer.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"281514567","text":"inputfile = \"C:\\\\Users\\\\jkearns\\\\Documents\\\\GitHub\\\\advent-of-code-2017\\\\09\\\\input09.txt\"\nwith open(inputfile) as f:\n lines = f.readlines()\nstream = lines[0]\n\ntotalScore = 0\ncurrentScore = 0\nisGarbage = False\nshouldIgnore = False\n\nfor character in stream:\n if (shouldIgnore): \n shouldIgnore = False\n continue\n if (character == \"!\"):\n shouldIgnore = True\n continue\n if (isGarbage):\n if character == \">\":\n isGarbage = False\n continue\n else : continue\n if character == \"<\":\n isGarbage = True\n if character == \"{\":\n currentScore += 1\n if character == \"}\":\n totalScore += currentScore\n currentScore -= 1\n\n\n\n\nprint(totalScore)","sub_path":"09/09a.py","file_name":"09a.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"150839590","text":"from problems.old.EulerFunctions import firstNPrimes\nfrom problems.old.EulerFunctions import bSearch\nimport math\n\nallprimes = list(firstNPrimes(100000))\nprimes = []\n\nfor p in allprimes:\n tmp = []\n while p > 0:\n (p, r) = divmod(p, 10)\n tmp.append(r)\n tmp.reverse()\n primes.append(tmp)\n\n# filter\nprimes = [x for x in primes if ((not (0 in x or 2 in x or 4 in x or 6 in x or 8 in x or 5 in x))\n or x in [23, 53])\n ]\nprimes = [x for x in primes if x.count(1) + x.count(7) < 3]\nprimes = [x for x in primes if len(x) > 1]\nprimes = [x for x in primes if len(x) == 2\n or not ((x[0] in [3, 9] and x[1] in [3, 9])\n or (x[len(x) - 1] in [3, 9] and x[len(x) - 2] in [3, 9])\n )]\nprimes = [x for x in primes if not (x[0] in [1, 9] or x[len(x) - 1] in [1, 9])]\n\ntmp = primes\nprimes = []\nfor p in tmp:\n x = 0\n match = True\n for s in p:\n x = x * 10 + s\n match = match and bSearch(allprimes, x)\n xx = x\n pow = math.pow(10, math.floor(math.log(xx, 10)) + 1)\n while xx > 0 and pow >= 10:\n xx = xx % pow\n pow /= 10\n match = match and bSearch(allprimes, xx)\n if (match):\n primes.append(x)\n","sub_path":"problems/old/pb37.py","file_name":"pb37.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"67172383","text":"#coding:utf8\n\nfrom framework.data import db\nfrom bson import ObjectId,DBRef\n\ndef get_source_by_sites(sites):\n cond = map(lambda x: {'id': x}, sites)\n results = db.sites.find({'$or': cond})\n sources = []\n for result in results:\n # 是不是目录且是全选状态\n if result['isParent'] and sites[result['id']]['s']:\n _get_tree_source_by_sites([result], sources)\n else:\n sources.append(result['source'])\n\n return ';'.join(sources)\n\ndef _get_tree_source_by_sites(sites, callback):\n for site in sites:\n callback.append(site['source'])\n\n sub_sites = db.sites.find({'pId': site['id']})\n if sub_sites.count() > 0:\n _get_tree_source_by_sites(sub_sites, callback)\n\ndef execute(context):\n\n condition = context.condition\n doc = context.doc\n uid = None\n\n if doc:\n uid = doc.pop('_id', None)\n\n #import pdb;pdb.set_trace()\n \n # edit\n if doc and uid:\n sites = doc.pop('sites', None)\n\n if not uid:\n context.result = {'errno': -3, 'msg': '没有传递需要的参数：doc._id'}\n return\n\n if doc['mtype'] == '4' and sites:\n doc['source'] = get_source_by_sites(sites)\n\n db.classify.update({'_id': ObjectId(uid)}, {'$set': doc})\n\n if sites:\n db.classify_sites.update({'classify': uid}, {'classify': uid, 'sites': sites})\n\n context.result = {'errno': 2, 'msg': '修改與情工单成功'}\n return\n\n # get\n elif condition:\n uid = condition.get('_id')\n if not uid:\n context.result = {'errno': -1, 'msg': '没有传递需要的参数：doc._id'}\n return\n\n doc = db.classify.find_one({'_id': ObjectId(uid)})\n if not doc:\n context.result = {'errno': -2, 'msg': '没有找到对象'}\n return\n\n if doc.mtype == '4':\n sites = db.classify_sites.find_one({'classify': uid}) or {}\n if sites:\n doc.sites = sites.sites\n\n context.result = {'errno': 1, 'doc': doc}\n return\n\n # new\n elif doc and uid is None:\n sites = doc.pop('sites', {})\n\n if doc['mtype'] == '4' and sites:\n doc['source'] = get_source_by_sites(sites)\n\n doc['window'] = 0\n new_id = db.classify.save(doc)\n\n if doc['mtype'] == '4' and sites:\n db.classify_sites.save({'classify': str(new_id), 'sites': sites})\n\n context.result = {'errno': 0, 'msg': '新建與情工单成功', 'new_id': str(new_id)}\n return\n\n else:\n context.result = {}\n return\n\ndef __params__():\n return []\n\ndef __unnecessary_params__():\n return ['doc', 'condition']\n\ndef __result__():\n return {'result': {}}\n","sub_path":"platform/script/5604a7a3f3aa2f4442445e6b.py","file_name":"5604a7a3f3aa2f4442445e6b.py","file_ext":"py","file_size_in_byte":2763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"563759100","text":"import math\n\n\ndef karatsuba(x, y):\n str_x = str(x)\n str_y = str(y)\n\n if len(str_x) != len(str_y) or math.log(len(str_x), 2) % 1.0 != 0:\n return x * y\n\n elif len(str_x) == 1 or len(str_y) == 1:\n return x * y\n\n else:\n half_ind = max(int(len(str_x) / 2), int(len(str_y) / 2))\n a = int(str_x[:half_ind])\n b = int(str_x[half_ind:])\n c = int(str_y[:half_ind])\n d = int(str_y[half_ind:])\n\n p = a + b\n q = c + d\n\n ac = karatsuba(a, c)\n bd = karatsuba(b, d)\n pq = karatsuba(p, q)\n\n adbc = pq - ac - bd\n\n return int((math.pow(10, len(str_x)) * ac) + (math.pow(10, len(str_x) / 2) * adbc) + bd)\n\nprint(45 * 14)\nprint(karatsuba(45, 14))\n","sub_path":"karatsuba.py","file_name":"karatsuba.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"42680298","text":"\"\"\"Given: Two positive integers a and b (a a[j]:\n minimum = j\n a[i], a[minimum] = a[minimum], a[i]\n print(\"Step {} :{}\".format(i+1, a))\n print(\"정렬 후 :{}\\n\".format(a))\n for k in range(3):\n hour = a[k] // (60*60)\n min = (a[k]-hour*60*60) // 60\n sec = a[k] % 60\n print(\"{} 등 : {}시간 {}분 {}초\".format(k+1, hour, min, sec))\n\nplayer = int(input(\"마라토너의 수를 입력하시오:\"))\nrecords = [0]*player\nfor i in range(player):\n records[i] = int(input(\"마라토너의 기록을 초단위로 입력:\"))\n\nselection_sort(records)","sub_path":"Algorithm/2. Task/Sort_Selection.py","file_name":"Sort_Selection.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"1693459","text":"'''\n시작 시각 :18:20\n종료 시각 :20:05\n\n\n다음 코드에서, 출력되는 결과값이 왜 실제 출력되어야 하는 결과값보다 작은지 설명하시오.\n\n답 : 7개의 스레드가 count 변수를 100000번씩 더해서 7000000이 나와야 하는데 다른 값이 나온 이유는, 같은 변수를 동시에\n접근했기 때문이다. 실제로 count+=offset이 실제 메모리에 저장 되려면 값을 메모리에서 레지스터로 불러오고, 레스터에서 더하고,\n그 더한 값을 다시 메모리에 저장하는 과정을 거친다. 즉, 실제 값의 메모리에 더해지려면 이 모든 과정을 거쳐야 하는데\n한 스레드에서 instruction이 끝나기 전에 다른 스레드에서 덧셈을 시작하면서 실제로 더해지는 값이 더 작게 나오게 된다.\n\n해당 문제점을 해결하기 위해 사용해야 하는 해결책을 서술하고,\n\n답 : 해결법은 ��레드를 동기화 하는 것이다. 여러 방법중 Lock을 사용하는 방법이 있다. Lock은 한 스레드가 변수를 사용했으면\n다른 스레드가 사용하지 못하도록 막는 역할을 한다. 변수를 다 사용했으면 그 스레드는 변수에 대한 Lock을 풀어줘야 한다.\n이는 Release라고 부른다.\n사용법\n1. Lock.aquire()=잠금-다른 스레드의 접근을 막는다.\n2.이 안에 Code들을 무조건 한 스레드에 의해 순차적으로 실행되게 된다.\n3. Lock.release()=잠금 해제-다른 스레드들에게 접근 가능하도록 잠금을 푼다.\n\n\n\n해당 개념을 자율차 코드에 적용하였을 때\n1) 어느 부분에 적용할 수 있을지,\n\n서브 스레드를 활용해서 복잡하거나 긴 계산이 필요한 장애물, 목표와의 거리 및 차선인식등의 시스템에 적용할 수 있을 거 같다.\n\n2) 이를 통해 어떤 이점을 얻을 수 있는지\n아무래도 하나씩 값을 처리하는 것보다 동시에 여러 스레드로 계산하는 것이 처리속도가 빠르기에 자율주행 자동차 움직임이\n더 매끄러울 수 있고 이동 속도 또한 빠르게 진행시킬 수 있다.\n\n\n\n\n(Optional) 아래 코드의 실행 결과값이 실제 출력되어야 하는 결과값과 동일하게 출력되도록 변경하시오.\n\n*** Write Your Answer Below ***\n\n\n*** Your Answer Ends Here ***\n'''\n\n\nfrom threading import Thread\n\n\nclass Count:\n def __init__(self):\n self.count = 0\n\n def add_offset(self, offset):\n self.count += offset\n\n\ndef worker(idx, limit, count_obj):\n print(idx)\n for _ in range(limit):\n count_obj.add_offset(1)\n\n\ndef run_threads(func, thread_num, limit, count_obj):\n threads = []\n for i in range(thread_num):\n args = (i, limit, count_obj)\n thread = Thread(target=func, args=args)\n threads.append(thread)\n thread.start()\n\n for thread in threads:\n thread.join()\n\n\nlimit = 10 ** 6\nthread_num = 7\ncount = Count()\nrun_threads(worker, thread_num, limit, count)\nprint(f\"Result should be {thread_num * limit}, but the total count is {count.count}\")\n","sub_path":"Q2.py","file_name":"Q2.py","file_ext":"py","file_size_in_byte":3081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"437749411","text":"import json\n\nfrom flask import Blueprint\n\nfrom ...component import form\nfrom ...component import mymysql\nfrom ...exception import MyServiceException\n\napp = Blueprint('distribution_logic_directory', __name__,\n url_prefix='/distribution/logic/directory')\n\n\n@app.route('/select', methods=['POST'])\ndef select():\n return json.dumps(mymysql.execute(\"\"\"\n select id, pid, name, description\n from designer_logic_directory\n \"\"\", {\n\n }))\n\n\n@app.route('/insert', methods=['POST'])\ndef insert():\n request_data = form.check([\"pid\", \"name\"])\n pid = request_data[\"pid\"]\n name = request_data[\"name\"]\n\n insert_result = mymysql.execute(\"\"\"\n insert into designer_logic_directory(pid, name) values (%(pid)s, %(name)s)\n \"\"\", {\n \"pid\": pid,\n \"name\": name,\n })\n\n # insert logic_data\n mymysql.execute(\"\"\"\n insert into designer_logic_data(did, file) values (%(did)s, %(file)s)\n \"\"\", {\n \"did\": insert_result,\n \"file\": '',\n })\n\n return json.dumps(insert_result)\n\n\n@app.route('/update', methods=['POST'])\ndef update():\n request_data = form.check([\"id\"])\n params = {}\n update_set_sql_str = \"\"\n\n params[\"id\"] = request_data[\"id\"]\n\n if request_data.__contains__(\"name\"):\n params[\"name\"] = request_data[\"name\"]\n update_set_sql_str = \"set name=%(name)s\"\n\n if request_data.__contains__(\"pid\"):\n params[\"pid\"] = request_data[\"pid\"]\n update_set_sql_str = \"set pid=%(pid)s\"\n\n if \"\" == update_set_sql_str:\n raise MyServiceException(\"no content for update set\")\n\n return json.dumps(\n mymysql.execute(\"update designer_data_directory \" + update_set_sql_str + \" where id = %(id)s\", params))\n\n\n@app.route('/delete', methods=['POST'])\ndef delete():\n request_data = form.check([\"id\"])\n\n def get_children(_id):\n return mymysql.execute(\"\"\"\n select id\n from designer_logic_directory\n where pid = %(id)s\n \"\"\", {\"id\": _id})\n\n def delete_one_level(_id):\n # delete logic data\n mymysql.execute(\"\"\"\n delete from designer_logic_data where did = %(did)s\n \"\"\", {\"did\": _id})\n\n return mymysql.execute(\"\"\"\n delete\n from designer_logic_directory\n where id = %(id)s\n \"\"\", {\"id\": _id})\n\n def do_delete(_id):\n children = get_children(_id)\n if len(children) > 0:\n for item in children:\n do_delete(item[\"id\"])\n delete_one_level(_id)\n\n do_delete(request_data[\"id\"])\n return \"\"\n\n\n@app.route('/fork', methods=['POST'])\ndef fork():\n pass\n","sub_path":"distribution/service/logic/directory.py","file_name":"directory.py","file_ext":"py","file_size_in_byte":2678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"186423590","text":"import tkinter\nimport math\nimport cmath\n\nimport numpy\n\nimport tesla\n\n# Размер единичной окружности в пикселях\nCIRCLE_SIZE = 400\n\n\ndef formula(x, y, width, height):\n 'Формула по вещественным координатам возвращает массив'\n 'из координат на экране'\n return (math.floor(x * CIRCLE_SIZE) + width // 4,\n -math.floor(y * CIRCLE_SIZE) + height // 2)\n\n\ndef render_point(x, y, *, canvas, rad=5, fill=None, tag=None):\n # используйте render_point вместо\n # make_point, make_point_check, circle или circle_red\n canvas.create_oval(x - rad, y - rad,\n x + rad, y + rad,\n fill=fill, tag=tag)\n\n\ndef arc(rad, center, A, B, *, window_dim, canvas):\n if rad == 0:\n return\n\n A_ = formula(*A, *window_dim)\n B_ = formula(*B, *window_dim)\n center_ = formula(*center, *window_dim)\n\n def begin_end_helper(be, ab):\n for_acos_0 = (be[0] - center[0]) / rad\n\n if math.fabs(for_acos_0) <= 1:\n ret = math.acos(for_acos_0)\n else:\n ret = 0 if for_acos_0 > 0 else math.pi\n\n ret = -ret * 180 / math.pi\n\n if ab < center_[1]:\n ret = -ret\n\n return ret\n\n begin = begin_end_helper(A, A_[1])\n end = begin_end_helper(B, B_[1])\n\n if math.fabs(end - begin) >= 180:\n if end - begin > 0:\n end = 360 - end\n end = 2 * begin - end\n else:\n end += 360\n\n canvas.create_arc(\n formula(center[0] - rad, center[1] - rad, *window_dim),\n formula(center[0] + rad, center[1] + rad, *window_dim),\n start=begin, extent=end - begin, style='arc')\n\n\ndef misha_arc(a, b, *, window_dim, canvas):\n _denom = a[1] * b[0] - a[0] * b[1]\n if math.fabs(_denom) > 1e-14:\n y_O = (a[0] ** 2 * b[0] -\n a[0] * b[0] ** 2 -\n b[1] ** 2 * a[0] +\n a[1] ** 2 * b[0] -\n a[0] + b[0]) / (2 * _denom)\n else:\n canvas.create_line(*formula(*a, *window_dim),\n *formula(*b, *window_dim))\n return\n\n def helper(a):\n return (a[0] ** 2 +\n a[1] ** 2 + 1 -\n 2 * y_O * a[1]) / (2 * a[0])\n\n if math.fabs(a[0]) > 1e-14:\n x_O = helper(a)\n else:\n x_O = helper(b)\n\n c = -1 + x_O ** 2 + y_O ** 2\n R = math.sqrt(c)\n arc(R, (x_O, y_O), a, b, window_dim=window_dim, canvas=canvas)\n\n\ndef matrix_rot_otn_q(Q, alpha):\n 'матрица поворота на альфа относительно Q != центру'\n\n # TODO: понять что за _unknown_value и _tmp и придумать нормальные имена\n _unknown_value = Q[0] ** 2 + Q[1] ** 2\n\n if _unknown_value == 0:\n x, y = 1, 0\n\n else:\n _tmp = math.sqrt(_unknown_value)\n x = Q[0] / _tmp\n y = Q[1] / _tmp\n\n # А - матрица движения, что Q = [x, y, z] -> [x', 0, z], А_1 - её обратная\n angle = cmath.phase(complex(x, -y))\n A = tesla.rotation(angle)\n A_1 = numpy.linalg.inv(A)\n\n # B - переводит [x', 0, y]->[1, 0, 0]\n x = x * Q[0] + y * Q[1]\n z = Q[2]\n\n B = numpy.array(((z, 0, -x),\n (0, 1, 0),\n (-x, 0, z)))\n\n B_1 = numpy.array(((z, 0, x),\n (0, 1, 0),\n (x, 0, z)))\n\n # C - матрица поворота на альфа, а затем в нее записывается результирующая\n # матрица, которая крутит на альфа относительно Q\n C = tesla.rotation(alpha)\n\n return A_1 @ B_1 @ C @ B @ A\n\n\ndef change_to_circle(P):\n a = 1 / (1 + P[2])\n return a * P[0], a * P[1]\n\n\ndef change_to_r3(P):\n a = 1 / (1 - P[0] ** 2 - P[1] ** 2)\n return (a * 2 * P[0],\n a * 2 * P[1],\n a * (1 + P[0] ** 2 + P[1] ** 2))\n\n\ndef motif(p, q, *, window_dim, canvas):\n 'Вывод базового многоугольника.'\n _p, _q = math.pi / p, math.pi / q\n\n arr = [0, []]\n for i in range(p):\n # тут просто поворачиваем точку на единичной окружности\n angle = 2 * i * _p\n x = math.cos(angle)\n y = math.sin(angle)\n # радиус нужной окружности, можно по нему сразу будет строить сторону\n r = (math.cos(_q + _p) /\n math.sqrt((math.cos(_q) ** 2 - math.sin(_p) ** 2)))\n x *= r\n y *= r\n render_point(*formula(x, y, *window_dim),\n canvas=canvas, fill='red', tag='point')\n arr[1].append(change_to_r3([x, y]))\n\n for i in range(p):\n misha_arc(change_to_circle(arr[1][i - 1]),\n change_to_circle(arr[1][i]),\n window_dim=window_dim, canvas=canvas)\n return [arr]\n\n\ndef rotate_not_center(vert, p, q, l, k, *, window_dim, canvas):\n 'крутит массив vertex вокруг l-той вершины, k раз. Обычно q - 2 раза, но'\n 'в граничных - q - 3 раза'\n Q = vert[1][l]\n res = matrix_rot_otn_q(Q, 2 * math.pi / q)\n arr_new = []\n\n for i in range(k):\n arr_new1 = [i == 0, [vert[1][l], vert[1][l - 1]]]\n\n for j in range(p - 2):\n P = res @ numpy.array(vert[1][(l + j + 2) % p])\n arr_new1[1].append(P)\n P = change_to_circle(P)\n render_point(*formula(*P, *window_dim),\n canvas=canvas, fill='red', tag='point')\n\n arr_new.append(arr_new1)\n vert = arr_new1\n l = 0\n\n return arr_new\n\n\ndef match_vert(kol, q, p, vert, rotate_o, *, window_dim, canvas):\n 'соединяем патерн и крутит и соединяет и крутит и соединяем и кру'\n 'vert[i, 0] и vert[0, 1] - точки с предыдущего слоя. Когда кручу - не'\n 'думаю об этом вроде пока'\n for i in range(kol):\n for j in range(2, p + 1):\n misha_arc(\n change_to_circle(vert[i][1][(j - 1) % p]),\n change_to_circle(vert[i][1][j % p]),\n window_dim=window_dim, canvas=canvas)\n\n rot = rotate_o\n\n for k in range(p - 1):\n for i in range(kol):\n old = rot @ numpy.array(vert[i][1][1])\n\n for j in range(2, p):\n P = rot @ numpy.array(vert[i][1][j])\n\n render_point(*formula(*change_to_circle(P), *window_dim),\n canvas=canvas, fill='red', tag='point')\n\n misha_arc(change_to_circle(old), change_to_circle(P),\n window_dim=window_dim, canvas=canvas)\n old = P\n\n rot = rot @ rotate_o\n\n\ndef layer(arr, kol, p, q, rotate_o, n, *, window_dim, canvas):\n if n <= 0:\n return\n\n kol_new = 0\n arr_new = []\n\n for k in range(kol):\n for i in range(2 + arr[k][0], p - 1):\n arr_new1 = rotate_not_center(arr[k], p, q, i, q - 2,\n window_dim=window_dim, canvas=canvas)\n\n for j in range(q - 2):\n arr_new.append(arr_new1[j])\n\n kol_new += q - 2\n\n for i in range(p - 1, p):\n arr_new1 = rotate_not_center(arr[k], p, q, i, q - 3,\n window_dim=window_dim, canvas=canvas)\n\n for j in range(q - 3):\n arr_new.append(arr_new1[j])\n kol_new += q - 3\n\n match_vert(kol_new, q, p, arr_new, rotate_o,\n window_dim=window_dim, canvas=canvas)\n\n layer(arr_new, kol_new, p, q, rotate_o, n - 1,\n window_dim=window_dim, canvas=canvas)\n","sub_path":"tesla_old.py","file_name":"tesla_old.py","file_ext":"py","file_size_in_byte":7860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"4976357","text":"#!/usr/bin/env python3\n\ndef quick_sort(array):\n if len(array) < 2:\n return array\n else:\n pivot = array[0]\n less = [i for i in len[1:] if i <= pivot]\n greater = [i for i in len[1:] if i > pivot]\n return quick_sort(less) + pivot + quick_sort(greater)","sub_path":"quick_sort.py","file_name":"quick_sort.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"494077321","text":"from collections import deque\nimport warnings\nimport pandas as pd\nimport numpy as np\nfrom xml.etree import ElementTree as ET\n\nBIOLOGICAL_PROCESS = 'GO:0008150'\nMOLECULAR_FUNCTION = 'GO:0003674'\nCELLULAR_COMPONENT = 'GO:0005575'\nFUNC_DICT = {\n 'cc': CELLULAR_COMPONENT,\n 'mf': MOLECULAR_FUNCTION,\n 'bp': BIOLOGICAL_PROCESS}\n\nEXP_CODES = set([\n 'EXP', 'IDA', 'IPI', 'IMP', 'IGI', 'IEP', 'TAS', 'IC',\n 'HTP', 'HDA', 'HMP', 'HGI', 'HEP'])\nCAFA_TARGETS = set([\n '10090', '223283', '273057', '559292', '85962',\n '10116', '224308', '284812', '7227', '9606',\n '160488', '237561', '321314', '7955', '99287',\n '170187', '243232', '3702', '83333', '208963',\n '243273', '44689', '8355'])\n\ndef is_cafa_target(org):\n return org in CAFA_TARGETS\n\ndef is_exp_code(code):\n return code in EXP_CODES\n\n\nclass Ontology(object):\n\n def __init__(self, filename='data/go.obo', with_rels=False):\n self.ont = self.load(filename, with_rels)\n\n def has_term(self, term_id):\n return term_id in self.ont\n\n def get(self, term_id):\n if self.has_term(term_id):\n return self.ont[term_id]\n return None\n\n def load(self, filename, with_rels):\n ont = dict()\n obj = None\n with open(filename, 'r') as f:\n for line in f:\n line = line.strip()\n if not line:\n continue\n if line == '[Term]':\n if obj is not None:\n ont[obj['id']] = obj\n obj = dict()\n obj['is_a'] = list()\n obj['part_of'] = list()\n obj['regulates'] = list()\n obj['alt_ids'] = list()\n obj['is_obsolete'] = False\n continue\n elif line == '[Typedef]':\n obj = None\n else:\n if obj is None:\n continue\n l = line.split(\": \")\n if l[0] == 'id':\n obj['id'] = l[1]\n elif l[0] == 'alt_id':\n obj['alt_ids'].append(l[1])\n elif l[0] == 'is_a':\n obj['is_a'].append(l[1].split(' ! ')[0])\n elif with_rels and l[0] == 'relationship':\n it = l[1].split()\n # add all types of relationships\n obj['is_a'].append(it[1])\n elif l[0] == 'name':\n obj['name'] = l[1]\n elif l[0] == 'is_obsolete' and l[1] == 'true':\n obj['is_obsolete'] = True\n if obj is not None:\n ont[obj['id']] = obj\n for term_id in list(ont.keys()):\n for t_id in ont[term_id]['alt_ids']:\n ont[t_id] = ont[term_id]\n if ont[term_id]['is_obsolete']:\n del ont[term_id]\n for term_id, val in ont.items():\n if 'children' not in val:\n val['children'] = set()\n for p_id in val['is_a']:\n if p_id in ont:\n if 'children' not in ont[p_id]:\n ont[p_id]['children'] = set()\n ont[p_id]['children'].add(term_id)\n return ont\n\n\n def get_anchestors(self, term_id):\n if term_id not in self.ont:\n return set()\n term_set = set()\n q = deque()\n q.append(term_id)\n while(len(q) > 0):\n t_id = q.popleft()\n if t_id not in term_set:\n term_set.add(t_id)\n for parent_id in self.ont[t_id]['is_a']:\n if parent_id in self.ont:\n q.append(parent_id)\n return term_set\n\n\n def get_parents(self, term_id):\n if term_id not in self.ont:\n return set()\n term_set = set()\n for parent_id in self.ont[term_id]['is_a']:\n if parent_id in self.ont:\n term_set.add(parent_id)\n return term_set\n\n\n def get_term_set(self, term_id):\n if term_id not in self.ont:\n return set()\n term_set = set()\n q = deque()\n q.append(term_id)\n while len(q) > 0:\n t_id = q.popleft()\n if t_id not in term_set:\n term_set.add(t_id)\n for ch_id in self.ont[t_id]['children']:\n q.append(ch_id)\n return term_set\n\ndef read_fasta(lines):\n seqs = list()\n info = list()\n seq = ''\n inf = ''\n for line in lines:\n line = line.strip()\n if line.startswith('>'):\n if seq != '':\n seqs.append(seq)\n info.append(inf)\n seq = ''\n inf = line[1:]\n else:\n seq += line\n seqs.append(seq)\n info.append(inf)\n return info, seqs\n\n\nclass DataGenerator(object):\n\n def __init__(self, batch_size, is_sparse=False):\n self.batch_size = batch_size\n self.is_sparse = is_sparse\n\n def fit(self, inputs, targets=None):\n self.start = 0\n self.inputs = inputs\n self.targets = targets\n if isinstance(self.inputs, tuple) or isinstance(self.inputs, list):\n self.size = self.inputs[0].shape[0]\n else:\n self.size = self.inputs.shape[0]\n self.has_targets = targets is not None\n\n def __next__(self):\n return self.next()\n\n def reset(self):\n self.start = 0\n\n def next(self):\n if self.start < self.size:\n batch_index = np.arange(\n self.start, min(self.size, self.start + self.batch_size))\n if isinstance(self.inputs, tuple) or isinstance(self.inputs, list):\n res_inputs = []\n for inp in self.inputs:\n if self.is_sparse:\n res_inputs.append(\n inp[batch_index, :].toarray())\n else:\n res_inputs.append(inp[batch_index, :])\n else:\n if self.is_sparse:\n res_inputs = self.inputs[batch_index, :].toarray()\n else:\n res_inputs = self.inputs[batch_index, :]\n self.start += self.batch_size\n if self.has_targets:\n if self.is_sparse:\n labels = self.targets[batch_index, :].toarray()\n else:\n labels = self.targets[batch_index, :]\n return (res_inputs, labels)\n return res_inputs\n else:\n self.reset()\n return self.next()\n\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"618539065","text":"import json\n\n\ndef hello(event, context):\n body = {\n \"message\": \"Go Serverless v1.0! Your function executed successfully!\",\n \"input\": event\n }\n\n response = {\n \"statusCode\": 200,\n \"body\": json.dumps(body)\n }\n\n return response\n\n # Use this code if you don't use the http event with the LAMBDA-PROXY\n # integration\n \"\"\"\n return {\n \"message\": \"Go Serverless v1.0! Your function executed successfully!\",\n \"event\": event\n }\n \"\"\"\n\ndef NewMessage(event, context):\n statusMessage = \"Message saved successfully!!!\"\n\n #extract the message from the body of the message\n\n #store the message by making call to DAL\n\n response = {\n \"statusCode\": 200,\n \"body\": json.dumps(statusMessage)\n }\n return response\n\ndef IsNewMessage(event, context):\n retVal = false\n # Check the db for new messages\n\n statusMessage = \"Message Present: \" + str(retVal)\n response = {\n \"statusCode\":200,\n \"body\": json.dumps(statusMessage)\n }\n\ndef GetNewMessage(event, context):\n\n msg = ''\n #read if there is a new message is waiting to be read\n statusMessage = \"Message: \" + msg\n response = {\n \"statusCode\":200,\n \"body\": json.dumps(statusMessage)\n }","sub_path":"APIGW_SQS/Server/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"131682479","text":"#python #abaqus #abaqustutorial #hnrwagner \r\n\r\n#------------------------------------------------------------------------------\r\n#------------------------------------------------------------------------------\r\n\r\n\r\nfrom abaqus import *\r\nfrom abaqusConstants import *\r\nimport regionToolset\r\nimport __main__\r\nimport section\r\nimport regionToolset\r\nimport part\r\nimport material\r\nimport assembly\r\nimport step\r\nimport interaction\r\nimport load\r\nimport mesh\r\nimport job\r\nimport sketch\r\nimport visualization\r\nimport xyPlot\r\nimport connectorBehavior\r\nimport odbAccess\r\nfrom operator import add\r\nimport numpy as np\r\n\r\n\r\n#------------------------------------------------------------------------------\r\n#------------------------------------------------------------------------------\r\n\r\n# functions\r\n\r\n#------------------------------------------------------------------------------\r\n#------------------------------------------------------------------------------\r\n\r\ndef Create_Part_3D_Cylinder(radius,length,thickness,part,model):\r\n s1 = mdb.models[model].ConstrainedSketch(name='__profile__', sheetSize=200.0)\r\n g, v, d, c = s1.geometry, s1.vertices, s1.dimensions, s1.constraints\r\n s1.setPrimaryObject(option=STANDALONE)\r\n s1.CircleByCenterPerimeter(center=(0.0, 0.0), point1=(radius, 0.0))\r\n s1.CircleByCenterPerimeter(center=(0.0, 0.0), point1=(radius-thickness, 0.0))\r\n p = mdb.models[model].Part(name=part, dimensionality=THREE_D, type=DEFORMABLE_BODY)\r\n p = mdb.models[model].parts[part]\r\n p.BaseSolidExtrude(sketch=s1, depth=length)\r\n s1.unsetPrimaryObject()\r\n p = mdb.models[model].parts[part]\r\n del mdb.models[model].sketches['__profile__']\r\n \r\n\r\ndef Create_Part_2D_Cylinder(radius,length,part,model):\r\n s = mdb.models[model].ConstrainedSketch(name='__profile__', sheetSize=200.0)\r\n g, v, d, c = s.geometry, s.vertices, s.dimensions, s.constraints\r\n s.setPrimaryObject(option=STANDALONE)\r\n s.CircleByCenterPerimeter(center=(0.0, 0.0), point1=(radius, 0.0))\r\n p = mdb.models[model].Part(name=part, dimensionality=THREE_D, type=DEFORMABLE_BODY)\r\n p = mdb.models[model].parts[part]\r\n p.BaseShellExtrude(sketch=s, depth=length)\r\n s.unsetPrimaryObject()\r\n p = mdb.models[model].parts[part]\r\n del mdb.models[model].sketches['__profile__']\r\n#------------------------------------------------------------------------------\r\n\r\ndef Create_Part_2D_Cone(radiusR,height,angle,part,model):\r\n s = mdb.models[model].ConstrainedSketch(name='__profile__', sheetSize=200.0)\r\n g, v, d, c = s.geometry, s.vertices, s.dimensions, s.constraints\r\n s.setPrimaryObject(option=STANDALONE)\r\n s.ConstructionLine(point1=(0.0, -100.0), point2=(0.0, 100.0))\r\n s.FixedConstraint(entity=g[2])\r\n s.Line(point1=(radiusR, 0.0), point2=(radiusR+height*tan(angle*np.pi/180), height))\r\n p = mdb.models[model].Part(name=part, dimensionality=THREE_D, type=DEFORMABLE_BODY)\r\n p = mdb.models[model].parts[part]\r\n p.BaseShellRevolve(sketch=s, angle=360.0, flipRevolveDirection=OFF)\r\n s.unsetPrimaryObject()\r\n p = mdb.models[model].parts[part]\r\n del mdb.models[model].sketches['__profile__']\r\n\r\n#------------------------------------------------------------------------------\r\ndef Create_Part_2D_Plate(radiusR,part,model): \r\n s = mdb.models[model].ConstrainedSketch(name='__profile__', sheetSize=200.0)\r\n g, v, d, c = s.geometry, s.vertices, s.dimensions, s.constraints\r\n s.setPrimaryObject(option=STANDALONE)\r\n s.Line(point1=(-radiusR, 0.0), point2=(radiusR, 0.0))\r\n s.HorizontalConstraint(entity=g[2], addUndoState=False)\r\n p = mdb.models[model].Part(name=part, dimensionality=THREE_D, type=DEFORMABLE_BODY)\r\n p = mdb.models[model].parts[part]\r\n p.BaseShellExtrude(sketch=s, depth=radiusR*2.0)\r\n s.unsetPrimaryObject()\r\n del mdb.models[model].sketches['__profile__']\r\n\r\n#------------------------------------------------------------------------------\r\n\r\ndef Create_Part_Shim(radiusR,height,part,model,angle):\r\n s = mdb.models[model].ConstrainedSketch(name='__profile__', sheetSize=200.0)\r\n g, v, d, c = s.geometry, s.vertices, s.dimensions, s.constraints\r\n s.setPrimaryObject(option=STANDALONE)\r\n s.ConstructionLine(point1=(0.0, -100.0), point2=(0.0, 100.0))\r\n s.FixedConstraint(entity=g[2])\r\n s.Line(point1=(radiusR, 0.0), point2=(radiusR, height))\r\n p = mdb.models[model].Part(name=part, dimensionality=THREE_D, type=DEFORMABLE_BODY)\r\n p = mdb.models[model].parts[part]\r\n p.BaseShellRevolve(sketch=s, angle=angle, flipRevolveDirection=OFF)\r\n s.unsetPrimaryObject()\r\n p = mdb.models[model].parts[part]\r\n del mdb.models[model].sketches['__profile__']\r\n \r\n \r\n\r\n#------------------------------------------------------------------------------\r\n\r\ndef Create_Datum_Plane_by_Principal(type_plane,part,model,offset_plane):\r\n p = mdb.models[model].parts[part]\r\n myPlane = p.DatumPlaneByPrincipalPlane(principalPlane=type_plane, offset=offset_plane)\r\n myID = myPlane.id\r\n return myID\r\n\r\n\r\ndef Create_Set_All_Cells(model,part,set_name):\r\n p = mdb.models[model].parts[part]\r\n c = p.cells[:]\r\n p.Set(cells=c, name=set_name)\r\n\r\ndef Create_Set_All_Faces(model,part,set_name):\r\n p = mdb.models[model].parts[part]\r\n f = p.faces[:]\r\n p.Set(faces=f, name=set_name)\r\n\r\ndef Create_Material_Data(model,material_name,e11,e22,e33,nu12,nu13,nu23,g12,g13,g23,lts,lcs,tts,tcs,lss,tss):\r\n mdb.models[model].Material(name=material_name)\r\n mdb.models[model].materials[material_name].Elastic(type=ENGINEERING_CONSTANTS, table=((e11,e22,e33,nu12,nu13,nu23,g12,g13,g23), ))\r\n mdb.models[model].materials[material_name].HashinDamageInitiation(table=((lts,lcs,tts,tcs,lss,tss), ))\r\n\r\n#------------------------------------------------------------------------------\r\n\r\ndef Create_Material_Data_2D(model,material_name,e11,e22,nu12,g12,g13,g23):\r\n mdb.models[model].Material(name=material_name)\r\n mdb.models[model].materials[material_name].Elastic(type=LAMINA, table=((e11,e22,nu12,g12,g13,g23), ))\r\n#------------------------------------------------------------------------------\r\n\r\ndef Create_Set_Face(x,y,z,model,part,set_name):\r\n face = ()\r\n p = mdb.models[model].parts[part]\r\n f = p.faces\r\n myFace = f.findAt((x,y,z),)\r\n face = face + (f[myFace.index:myFace.index+1], )\r\n p.Set(faces=face, name=set_name)\r\n return myFace\r\n\r\ndef Create_Set_Edge(x,y,z,model,part,set_name):\r\n edge = ()\r\n p = mdb.models[model].parts[part]\r\n e = p.edges\r\n myEdge = e.findAt((x,y,z),)\r\n edge = edge + (e[myEdge.index:myEdge.index+1], )\r\n f = p.Set(edges=edge, name=set_name)\r\n return myEdge\r\n\r\n\r\n#-----------------------------------------------------------------------------\r\ndef Create_Set_Vertice(x,y,z,model,part,set_name):\r\n vertice = ()\r\n p = mdb.models[model].parts[part]\r\n v = p.vertices\r\n myVertice = v.findAt((x,y,z),)\r\n vertice = vertice + (v[myVertice.index:myVertice.index+1], )\r\n p.Set(vertices=vertice, name=set_name) \r\n\r\n#-----------------------------------------------------------------------------\r\ndef Create_Set_Vertice_2(x,y,z,model,part,set_name):\r\n vertice = ()\r\n a = mdb.models[model].rootAssembly\r\n v = a.instances[part].vertices\r\n myVertice = v.findAt((x,y,z),)\r\n vertice = vertice + (v[myVertice.index:myVertice.index+1], )\r\n a.Set(vertices=vertice, name=set_name)\r\n\r\n#-----------------------------------------------------------------------------\r\n\r\ndef Create_Set_Internal_Surface(x,y,z,model,part,set_name):\r\n face = ()\r\n p = mdb.models[model].parts[part]\r\n s = p.faces\r\n myFace = s.findAt((x,y,z),)\r\n face = face + (s[myFace.index:myFace.index+1], )\r\n p.Surface(side2Faces=face, name=set_name)\r\n#-----------------------------------------------------------------------------\r\n\r\ndef Create_Set_External_Surface(x,y,z,model,part,set_name):\r\n face = ()\r\n p = mdb.models[model].parts[part]\r\n s = p.faces\r\n myFace = s.findAt((x,y,z),)\r\n face = face + (s[myFace.index:myFace.index+1], )\r\n p.Surface(side1Faces=face, name=set_name)\r\n\r\n#-----------------------------------------------------------------------------\r\n\r\ndef Create_Assembly(model,part,instance_name):\r\n a = mdb.models[model].rootAssembly\r\n a.DatumCsysByDefault(CARTESIAN)\r\n p = mdb.models[model].parts[part]\r\n a.Instance(name=instance_name, part=p, dependent=ON)\r\n\r\n#-------------------------------------------------------------\r\n\r\ndef Create_Reference_Point(x,y,z,model,setname):\r\n a = mdb.models[model].rootAssembly\r\n myRP = a.ReferencePoint(point=(x, y, z))\r\n r = a.referencePoints\r\n myRP_Position = r.findAt((x, y, z),)\r\n refPoints1=(myRP_Position, )\r\n a.Set(referencePoints=refPoints1, name=setname)\r\n return myRP,myRP_Position\r\n\r\ndef Create_Constraint_Equation(model,constraint_name,set_name,set_name_rp):\r\n mdb.models[model].Equation(name=constraint_name, terms=((1.0, set_name, 2), (-1.0, set_name_rp, 2)))\r\n\r\n\r\ndef Create_Boundary_Condition_by_Instance(model,instance_name,set_name,BC_name,step_name,u1_BC,u2_BC,u3_BC,ur1_BC,ur2_BC,ur3_BC):\r\n a = mdb.models[model].rootAssembly\r\n region = a.instances[instance_name].sets[set_name]\r\n mdb.models[model].DisplacementBC(name=BC_name, createStepName=step_name, region=region, u1=u1_BC, u2=u2_BC, u3=u3_BC, ur1=ur1_BC, ur2=ur2_BC, ur3=ur3_BC, amplitude=UNSET, distributionType=UNIFORM, fieldName='', localCsys=None) \r\n\r\n\r\ndef Create_Boundary_Condition_for_Assembly(model,set_name,BC_name,step_name,u1_BC,u2_BC,u3_BC,ur1_BC,ur2_BC,ur3_BC):\r\n a = mdb.models[model].rootAssembly\r\n region = a.sets[set_name]\r\n mdb.models[model].DisplacementBC(name=BC_name, createStepName=step_name, region=region, u1=u1_BC, u2=u2_BC, u3=u3_BC, ur1=ur1_BC, ur2=ur2_BC, ur3=ur3_BC, amplitude=UNSET, distributionType=UNIFORM, fieldName='', localCsys=None) \r\n\r\ndef Create_Boundary_Condition_by_RP(model,RP_name,BC_name,step_name,u1_BC,u2_BC,u3_BC,ur1_BC,ur2_BC,ur3_BC):\r\n a = mdb.models[model].rootAssembly\r\n region = a.sets[RP_name]\r\n mdb.models[model].DisplacementBC(name=BC_name, createStepName=step_name, region=region, u1=u1_BC, u2=u2_BC, u3=u3_BC, ur1=ur1_BC, ur2=ur2_BC, ur3=ur3_BC, amplitude=UNSET, distributionType=UNIFORM, fieldName='', localCsys=None) \r\n\r\n\r\ndef Create_Analysis_Step(model,step_name,pre_step_name,Initial_inc,Max_inc,Min_inc,Inc_Number,NL_ON_OFF):\r\n a = mdb.models[model].StaticStep(name=step_name, previous=pre_step_name, initialInc=Initial_inc, maxInc=Max_inc, minInc=Min_inc)\r\n a = mdb.models[model].steps[step_name].setValues(maxNumInc=Inc_Number)\r\n a = mdb.models[model].steps[step_name].setValues(nlgeom=NL_ON_OFF)\r\n #a = mdb.models[model].steps[step_name].setValues(stabilizationMagnitude=1E-009, stabilizationMethod=DAMPING_FACTOR, continueDampingFactors=False, adaptiveDampingRatio=None)\r\n\r\ndef Create_Partion_by_Plane(model,part,id_plane):\r\n p = mdb.models[model].parts[part]\r\n c = p.cells[:]\r\n d = p.datums\r\n p.PartitionCellByDatumPlane(datumPlane=d[id_plane], cells=c)\r\n\r\n\r\ndef Create_Partion_by_Plane_2D(model,part,id_plane):\r\n p = mdb.models[model].parts[part]\r\n f = p.faces[:]\r\n d = p.datums\r\n p.PartitionFaceByDatumPlane(datumPlane=d[id_plane], faces=f)\r\n\r\n#-----------------------------------------------------------------------------\r\n\r\ndef Create_Composite_Layup(model,part,set_name,composite_name,number,material,thickness,angle):\r\n layupOrientation = None\r\n p = mdb.models[model].parts[part]\r\n region1=p.sets[set_name]\r\n normalAxisRegion = p.surfaces['Outer_Surface']\r\n primaryAxisRegion = p.sets['Set-Top-Edge']\r\n compositeLayup = mdb.models[model].parts[part].CompositeLayup(name=composite_name, description='', elementType=CONTINUUM_SHELL, symmetric=False)\r\n compositeLayup.Section(preIntegrate=OFF, integrationRule=SIMPSON, poissonDefinition=DEFAULT, thicknessModulus=None, temperature=GRADIENT, useDensity=OFF)\r\n for i in range(0,number,1):\r\n compositeLayup.CompositePly(suppressed=False, plyName='Ply-'+str(i), region=region1, material=material, thicknessType=SPECIFY_THICKNESS, thickness=thickness, orientationType=SPECIFY_ORIENT, orientationValue=angle[i], additionalRotationType=ROTATION_NONE, additionalRotationField='', axis=AXIS_3, angle=0.0, numIntPoints=3)\r\n compositeLayup.ReferenceOrientation(orientationType=DISCRETE, localCsys=None, additionalRotationType=ROTATION_ANGLE, angle=90.0, additionalRotationField='', axis=AXIS_3, stackDirection=STACK_3, normalAxisDefinition=SURFACE, normalAxisRegion=normalAxisRegion, normalAxisDirection=AXIS_3, flipNormalDirection=False, primaryAxisDefinition=EDGE, primaryAxisRegion=primaryAxisRegion, primaryAxisDirection=AXIS_2, flipPrimaryDirection=False)\r\n#-----------------------------------------------------------------------------\r\n\r\ndef Create_Composite_Layup_2D(model,part,set_name,composite_name,number,material,thickness,angle):\r\n layupOrientation = None\r\n p = mdb.models[model].parts[part]\r\n region1=p.sets[set_name]\r\n normalAxisRegion = p.surfaces['Outer_Surface']\r\n primaryAxisRegion = p.sets['Set-Top-Edge']\r\n compositeLayup = mdb.models[model].parts[part].CompositeLayup(name=composite_name, description='', elementType=SHELL, symmetric=False)\r\n compositeLayup.Section(preIntegrate=OFF, integrationRule=SIMPSON, poissonDefinition=DEFAULT, thicknessModulus=None, temperature=GRADIENT, useDensity=OFF)\r\n for i in range(0,number,1):\r\n compositeLayup.CompositePly(suppressed=False, plyName='Ply-'+str(i), region=region1, material=material, thicknessType=SPECIFY_THICKNESS, thickness=thickness, orientationType=SPECIFY_ORIENT, orientationValue=angle[i], additionalRotationType=ROTATION_NONE, additionalRotationField='', axis=AXIS_3, angle=0.0, numIntPoints=3)\r\n compositeLayup.ReferenceOrientation(orientationType=DISCRETE, localCsys=None, additionalRotationType=ROTATION_ANGLE, angle=90.0, additionalRotationField='', axis=AXIS_3, stackDirection=STACK_3, normalAxisDefinition=SURFACE, normalAxisRegion=normalAxisRegion, normalAxisDirection=AXIS_3, flipNormalDirection=False, primaryAxisDefinition=EDGE, primaryAxisRegion=primaryAxisRegion, primaryAxisDirection=AXIS_2, flipPrimaryDirection=False)\r\n \r\n#----------------------------------------------------------------------------\r\n\r\ndef Create_Mesh(model,part,size):\r\n p = mdb.models[model].parts[part]\r\n elemType1 = mesh.ElemType(elemCode=SC8R, elemLibrary=STANDARD, secondOrderAccuracy=OFF, hourglassControl=DEFAULT)\r\n elemType2 = mesh.ElemType(elemCode=SC6R, elemLibrary=STANDARD)\r\n elemType3 = mesh.ElemType(elemCode=UNKNOWN_TET, elemLibrary=STANDARD)\r\n cells = p.cells[:]\r\n pickedRegions =(cells, )\r\n p.setElementType(regions=pickedRegions, elemTypes=(elemType1, elemType2, elemType3))\r\n p.seedPart(size=size, deviationFactor=0.1, minSizeFactor=0.1)\r\n p.generateMesh()\r\n\r\ndef Create_Mesh_Solid(model,part,size):\r\n p = mdb.models[model].parts[part]\r\n elemType1 = mesh.ElemType(elemCode=C3D20R, elemLibrary=STANDARD)\r\n elemType2 = mesh.ElemType(elemCode=C3D15, elemLibrary=STANDARD)\r\n elemType3 = mesh.ElemType(elemCode=C3D10, elemLibrary=STANDARD)\r\n cells = p.cells[:]\r\n pickedRegions =(cells, )\r\n p.setElementType(regions=pickedRegions, elemTypes=(elemType1, elemType2, elemType3))\r\n p.seedPart(size=size, deviationFactor=0.1, minSizeFactor=0.1)\r\n p.generateMesh()\r\n \r\n#------------------------------------------------------------------------------\r\n\r\ndef Create_Mesh_Shell(model,part,size):\r\n p = mdb.models[model].parts[part]\r\n elemType1 = mesh.ElemType(elemCode=S4R, elemLibrary=STANDARD, secondOrderAccuracy=OFF, hourglassControl=DEFAULT)\r\n elemType2 = mesh.ElemType(elemCode=S3, elemLibrary=STANDARD)\r\n faces = p.faces[:]\r\n pickedRegions =(faces, )\r\n p.setElementType(regions=pickedRegions, elemTypes=(elemType1, elemType2))\r\n p.seedPart(size=size, deviationFactor=0.1, minSizeFactor=0.1)\r\n p.generateMesh()\r\n\r\ndef Create_SPLA(model,instance_name,set_name,load_name,step_name,load):\r\n a = mdb.models[model].rootAssembly\r\n region = a.instances[instance_name].sets[set_name]\r\n mdb.models[model].ConcentratedForce(name=load_name, createStepName=step_name, region=region, cf1=-load, distributionType=UNIFORM, field='', localCsys=None)\r\n\r\n\r\ndef Create_Pressure_Load(model,instance_name,load_name,step_name,surface,load):\r\n a = mdb.models[model].rootAssembly\r\n region = a.instances[instance_name].surfaces[surface]\r\n mdb.models[model].Pressure(name=load_name, createStepName=step_name, region=region, distributionType=UNIFORM, field='', magnitude=load, amplitude=UNSET) \r\n\r\ndef CreateCutout(model,part,radius_cutout,id_plane,edge,x,y,z):\r\n p = mdb.models[model].parts[part]\r\n e, d = p.edges, p.datums\r\n t = p.MakeSketchTransform(sketchPlane=d[id_plane], sketchUpEdge=edge, sketchPlaneSide=SIDE1, sketchOrientation=RIGHT, origin=(x, y, z))\r\n s = mdb.models[model].ConstrainedSketch(name='__profile__', sheetSize=2000.0, gridSpacing=20.0, transform=t)\r\n g, v, d1, c = s.geometry, s.vertices, s.dimensions, s.constraints\r\n s.setPrimaryObject(option=SUPERIMPOSE)\r\n p.projectReferencesOntoSketch(sketch=s, filter=COPLANAR_EDGES)\r\n s.CircleByCenterPerimeter(center=(0.0, 0.0), point1=(radius_cutout, 0.0))\r\n p.CutExtrude(sketchPlane=d[id_plane], sketchUpEdge=edge, sketchPlaneSide=SIDE1, sketchOrientation=RIGHT, sketch=s, flipExtrudeDirection=OFF)\r\n s.unsetPrimaryObject()\r\n del mdb.models[model].sketches['__profile__']\r\n\r\ndef AssignStack(model,part,face):\r\n p = mdb.models[model].parts[part]\r\n c = p.cells[:]\r\n p.assignStackDirection(referenceRegion=face, cells=c)\r\n\r\ndef CreateJob(model,job_name,cpu):\r\n a = mdb.models[model].rootAssembly\r\n mdb.Job(name=job_name, model=model, description='', type=ANALYSIS, atTime=None, waitMinutes=0, waitHours=0, queue=None, memory=90, memoryUnits=PERCENTAGE, getMemoryFromAnalysis=True, explicitPrecision=SINGLE, nodalOutputPrecision=SINGLE, echoPrint=OFF, modelPrint=OFF, contactPrint=OFF, historyPrint=OFF, userSubroutine='', scratch='', resultsFormat=ODB, multiprocessingMode=DEFAULT, numCpus=cpu, numDomains=cpu, numGPUs=0)\r\n\r\ndef SubmitJob(job_name):\r\n mdb.jobs[job_name].submit()\r\n mdb.jobs[job_name].waitForCompletion()\r\n\r\n\r\ndef Create_Material_Isotropic(model,material_name_isotropic,E,nu,Y):\r\n mdb.models[model].Material(name=material_name_isotropic)\r\n mdb.models[model].materials[material_name_isotropic].Elastic(table=((E, nu), ))\r\n #mdb.models[model].materials[material_name_isotropic].Plastic(table=((Y, 0.0), ))\r\n\r\ndef Create_Isotropic_Section(model,section_name,material_name_isotropic):\r\n mdb.models[model].HomogeneousSolidSection(name=section_name, material=material_name_isotropic, thickness=None)\r\n\r\ndef Create_Isotropic_Section_2D(model,section_name,material_name_isotropic,thickness):\r\n mdb.models[model].HomogeneousShellSection(name=section_name, preIntegrate=OFF, material=material_name_isotropic, thicknessType=UNIFORM, thickness=thickness, thicknessField='', nodalThicknessField='', idealization=NO_IDEALIZATION, poissonDefinition=DEFAULT, thicknessModulus=None, temperature=GRADIENT, useDensity=OFF, integrationRule=SIMPSON, numIntPts=5)\r\n\r\ndef Assign_Isotropic_Material(model,part,set_name,section_name):\r\n p = mdb.models[model].parts[part]\r\n region = p.sets[set_name]\r\n p.SectionAssignment(region=region, sectionName=section_name, offset=0.0, offsetType=MIDDLE_SURFACE, offsetField='', thicknessAssignment=FROM_SECTION)\r\n\r\ndef Deactivate_BC(model,BC_name,step_name):\r\n mdb.models[model].boundaryConditions[BC_name].deactivate(step_name)\r\n\r\ndef Rotate_Instance(model,instance,x,y,z,angle):\r\n a = mdb.models[model].rootAssembly\r\n a.rotate(instanceList=(instance, ), axisPoint=(0.0, 0.0, 0.0), axisDirection=(x, y, z), angle=angle)\r\n \r\n \r\n\r\n#------------------------------------------------------------------------------\r\n\r\ndef Translate_Instance(model,instance,x,y,z):\r\n a = mdb.models[model].rootAssembly\r\n a.translate(instanceList=(instance, ), vector=(x, y, z))\r\n\r\n#------------------------------------------------------------------------------\r\n\r\ndef Boolean_Merge(model,instance_merge,instance_1,instance_2):\r\n a = mdb.models[model].rootAssembly\r\n a.InstanceFromBooleanMerge(name=instance_merge, instances=(a.instances[instance_1], a.instances[instance_2], ), keepIntersections=ON, originalInstances=SUPPRESS, domain=GEOMETRY)\r\n \r\n#------------------------------------------------------------------------------\r\n\r\ndef Open_ODB_and_Write_NodeSet_data_to_text(model,step_name,variable_name,set_name,Variable_component):\r\n # open ODB file - ABAQUS Result file\r\n odb = session.openOdb(str(model)+'.odb')\r\n \r\n # list for the VARIABLE you want to evaluate\r\n Variable_v = []\r\n \r\n # analysis step for your VARIABLE\r\n lastStep=odb.steps[step_name]\r\n \r\n #loop over all increments of the analysis step and save VARIABLE information from each increment\r\n for x in range(len(lastStep.frames)):\r\n lastFrame = lastStep.frames[x]\r\n Variable = lastFrame.fieldOutputs[variable_name]\r\n center = odb.rootAssembly.nodeSets[set_name]\r\n centerRForce = Variable.getSubset(region=center)\r\n \r\n # loop over the VARIABLE and save component (x,y,z - 0,1,2) to list\r\n for i in centerRForce.values:\r\n Variable_vr = [i.data[Variable_component]]\r\n Variable_v = Variable_v + Variable_vr\r\n \r\n # Max value of Variable_v\r\n Max_Variable = [np.max(Variable_v)] \r\n Max_Variable_v = [Max_Variable]\r\n \r\n # write VARIABLE - component to text file\r\n \r\n np.savetxt(str(variable_name)+'_'+str(myString)+'.txt',Variable_v)\r\n return Max_Variable\r\n\r\n#------------------------------------------------------------------------------\r\n\r\ndef Write_Variable_to_text(variable,variable_name):\r\n \r\n # write VARIABLE - component to text file\r\n \r\n np.savetxt(str(variable_name)+'_'+str(myString)+'.txt',variable) \r\n \r\n#------------------------------------------------------------------------------\r\n#------------------------------------------------------------------------------\r\n\r\n# variables\r\n\r\n#------------------------------------------------------------------------------\r\n#------------------------------------------------------------------------------\r\n\r\n\r\n# This script can only create trunecated cones and cylinders \r\n# so check if myLength is long enough (or the absolute of mySemi_Vertex_Angle is small enough)\r\n# myRadius_r cannot be zero or negativ\r\n\r\nmyRadius = 33.0\r\nmyThickness = 0.1\r\nmyLength = 100.0\r\nmySemi_Vertex_Angle = 0.0\r\nmySlant_Length = myLength/np.cos(mySemi_Vertex_Angle*np.pi/180.0)\r\nmyRadius_r = myRadius+myLength*tan(mySemi_Vertex_Angle*np.pi/180.0)\r\n\r\n# shim parameters in degree\r\n\r\nshim_width = 2 \r\n\r\n\r\nmyPart = \"Cone\"\r\nmyPart_2 = \"Shim\"\r\nmyPart_3 = \"Cone_&_Shim\"\r\nmyPart_4 = \"Plate\"\r\n# material parameters\r\n\r\nmyE11 = 208000\r\nmyE22 = 208000\r\nmyNu12 = 0.3\r\nmyG12 = 80000\r\nmyG13 = 80000\r\nmyG23 = 80000\r\n\r\nmyE = 208000\r\nmyNu = 0.3\r\nmyY = 208\r\n\r\n\r\nmyAngle = [45,-45,0,90,90,0,-45,45]\r\n\r\nmyPlyNumber = len(myAngle)\r\n\r\n\r\nMesh_Size = 0.91\r\n\r\nN = []\r\nP = []\r\nfor ic in range(1,21,1):\r\n \r\n myString = \"GNIA_SBPA_Loop_\"+str(ic)\r\n mdb.Model(name=myString)\r\n \r\n # Imperfection Parameter\r\n myPerturbation = 0.005*ic\r\n \r\n Create_Part_2D_Cone(myRadius,myLength,mySemi_Vertex_Angle,myPart,myString)\r\n \r\n myID_1 = Create_Datum_Plane_by_Principal(XZPLANE,myPart,myString,myLength/2.0)\r\n myID_2 = Create_Datum_Plane_by_Principal(XYPLANE,myPart,myString,0.0)\r\n myID_3 = Create_Datum_Plane_by_Principal(YZPLANE,myPart,myString,0.0)\r\n \r\n Create_Set_Edge(myRadius_r,myLength,0.0,myString,myPart,\"Set-RP-2\")\r\n Create_Set_Edge(myRadius,0.0,0.0,myString,myPart,\"Set-RP-1\")\r\n Create_Set_External_Surface((myRadius+myRadius_r)/2.0,myLength/2.0,0.0,myString,myPart,\"Outer_Surface\")\r\n Create_Set_Internal_Surface((myRadius+myRadius_r)/2.0,myLength/2.0,0.0,myString,myPart,\"Internal_Surface\")\r\n Create_Set_All_Faces(myString,myPart,\"Cone_2D\")\r\n \r\n \r\n Create_Material_Data_2D(myString,\"CFRP\",myE11,myE22,myNu12,myG12,myG13,myG23)\r\n Create_Material_Isotropic(myString,\"Steel\",myE,myNu,myY)\r\n \r\n #-------------------------------------------------\r\n \r\n # SBPA modification - start\r\n \r\n #-------------------------------------------------\r\n \r\n Create_Isotropic_Section_2D(myString,\"Isotropic_section\",\"Steel\",100)\r\n \r\n \r\n Create_Part_Shim(myRadius,myPerturbation,myPart_2,myString,shim_width)\r\n Create_Assembly(myString,myPart,\"Cone\")\r\n Create_Assembly(myString,myPart_2,\"Shim\")\r\n Rotate_Instance(myString,'Shim',0,1,0,shim_width/2.0)\r\n Rotate_Instance(myString,'Cone',0,1,0,180.0)\r\n Translate_Instance(myString,'Shim',0,-myPerturbation,0)\r\n Boolean_Merge(myString,myPart_3,myPart,myPart_2)\r\n Create_Set_Edge(myRadius,0.0,0.0,myString,myPart_3,\"Set-Shim-Top-Edge\")\r\n Create_Set_Edge(myRadius,-myPerturbation,0.0,myString,myPart_3,\"Set-Shim-Bottom-Edge\")\r\n Create_Set_Edge(myRadius*np.cos(shim_width/2.0*np.pi/180.0),-myPerturbation/2.0,myRadius*np.sin(shim_width/2.0*np.pi/180.0),myString,myPart_3,\"Set-Shim-Front-Edge\")\r\n Create_Set_Edge(myRadius*np.cos(-shim_width/2.0*np.pi/180.0),-myPerturbation/2.0,myRadius*np.sin(-shim_width/2.0*np.pi/180.0),myString,myPart_3,\"Set-Shim-Back-Edge\")\r\n Create_Part_2D_Plate(myRadius,myPart_4,myString)\r\n Create_Assembly(myString,myPart_4,\"Plate\")\r\n Translate_Instance(myString,'Plate',0,-myPerturbation,-myRadius)\r\n Create_Set_All_Faces(myString,myPart_4,\"Plate_2D\")\r\n Assign_Isotropic_Material(myString,myPart_4,\"Plate_2D\",\"Isotropic_section\")\r\n Create_Set_Face(myRadius,-myPerturbation/2.0,0.0,myString,myPart_3,\"Shim_Face\")\r\n Assign_Isotropic_Material(myString,myPart_3,\"Shim_Face\",\"Isotropic_section\")\r\n Create_Set_Internal_Surface(0.0,0.0,0.0,myString,myPart_4,\"Plate_Surface\")\r\n \r\n ##------------------------------------------------\r\n \r\n myRP1,myRP_Position1 = Create_Reference_Point(0.0,0.0,0.0,myString,\"RP-1\")\r\n myRP2,myRP_Position2 = Create_Reference_Point(0.0,myLength,0.0,myString,\"RP-2\")\r\n \r\n ##------------------------------------------------\r\n \r\n # Create Sets\r\n \r\n a=mdb.models[myString].parts[myPart_3]\r\n a.SetByBoolean(name='Set-No-Shim-Top-Edge', operation=DIFFERENCE, sets=(a.sets['Set-RP-1'], a.sets['Set-Shim-Top-Edge'], ))\r\n a.SetByBoolean(name='Set-SBPA-Edge', sets=(a.sets['Set-No-Shim-Top-Edge'], a.sets['Set-Shim-Back-Edge'], a.sets['Set-Shim-Bottom-Edge'], a.sets['Set-Shim-Front-Edge'], ))\r\n \r\n # Create Interaction and contact\r\n \r\n mdb.models[myString].ContactProperty('IntProp-1')\r\n mdb.models[myString].interactionProperties['IntProp-1'].TangentialBehavior(formulation=FRICTIONLESS)\r\n mdb.models[myString].interactionProperties['IntProp-1'].NormalBehavior(pressureOverclosure=HARD, allowSeparation=ON, constraintEnforcementMethod=DEFAULT)\r\n a = mdb.models[myString].rootAssembly\r\n region1=a.instances['Plate'].surfaces['Plate_Surface']\r\n region2=a.instances['Cone_&_Shim-1'].sets['Set-SBPA-Edge']\r\n mdb.models[myString].SurfaceToSurfaceContactStd(name='Int-1', createStepName='Initial', master=region1, slave=region2, sliding=FINITE, thickness=ON, interactionProperty='IntProp-1', adjustMethod=NONE, initialClearance=OMIT, datumAxis=None, clearanceRegion=None)\r\n \r\n # Rigid Body Constraint - Tie\r\n \r\n region1=a.instances['Cone_&_Shim-1'].sets['Set-RP-2']\r\n region2=a.sets['RP-2']\r\n mdb.models[myString].RigidBody(name='Constraint-1', refPointRegion=region2, tieRegion=region1)\r\n \r\n region1=a.instances['Plate'].sets['Plate_2D']\r\n region2=a.sets['RP-1']\r\n mdb.models[myString].RigidBody(name='Constraint-2', refPointRegion=region2, tieRegion=region1)\r\n \r\n #-------------------------------------------------\r\n \r\n # SBPA modification - end\r\n \r\n #-------------------------------------------------\r\n \r\n Create_Analysis_Step(myString,\"Step-1\",\"Initial\",0.01,0.01,1E-005,300,ON)\r\n \r\n myID_1 = Create_Datum_Plane_by_Principal(XZPLANE,myPart_3,myString,myLength/2.0)\r\n myID_2 = Create_Datum_Plane_by_Principal(XYPLANE,myPart_3,myString,0.0)\r\n myID_3 = Create_Datum_Plane_by_Principal(YZPLANE,myPart_3,myString,0.0)\r\n \r\n Create_Boundary_Condition_by_Instance(myString,\"Cone_&_Shim-1\",\"Set-SBPA-Edge\",\"BC-Set-RP-1\",\"Initial\",SET,UNSET,SET,SET,SET,SET)\r\n Create_Boundary_Condition_by_Instance(myString,\"Cone_&_Shim-1\",\"Set-RP-2\",\"BC-Set-RP-2\",\"Initial\",SET,SET,SET,SET,SET,SET)\r\n Create_Boundary_Condition_by_RP(myString,\"RP-1\",\"Displacement_Load\",\"Step-1\",SET,1,SET,SET,SET,SET)\r\n \r\n Create_Partion_by_Plane_2D(myString,myPart_3,myID_2)\r\n myEdge = Create_Set_Edge((myRadius+myRadius_r)/2.0,myLength/2.0,0.0,myString,myPart_3,\"Set-Top-Edge\")\r\n Create_Partion_by_Plane_2D(myString,myPart_3,myID_3)\r\n Create_Partion_by_Plane_2D(myString,myPart_3,myID_1)\r\n \r\n #Create_Set_Vertice_2((myRadius+myRadius_r)/2.0,myLength/2.0,0.0,myString,\"Cone\",\"SPLA_Point\")\r\n #Create_Boundary_Condition_for_Assembly(myString,\"SPLA_Point\",\"SPDA-Imperfection\",\"Step-1\",-myPerturbation,UNSET,UNSET,UNSET,UNSET,UNSET)\r\n #Create_SPLA(myString,\"Cylinder-1\",\"SPLA_Point\",\"BC-Imperfection\",\"Step-1\",myPerturbation)\r\n #Create_Pressure_Load(myString,\"Cone-1-1\",\"External_Pressure\",\"Step-1\",\"Outer_Surface\",1.0)\r\n Create_Composite_Layup_2D(myString,myPart_3,\"Cone_2D\",\"Layup\",myPlyNumber,\"CFRP\",myThickness/myPlyNumber,myAngle)\r\n Create_Mesh_Shell(myString,myPart_3,Mesh_Size)\r\n Create_Mesh_Shell(myString,myPart_4,Mesh_Size)\r\n #------------------------------------------------------------------------------\r\n #------------------------------------------------------------------------------\r\n \r\n # create Job for analysis\r\n \r\n #------------------------------------------------------------------------------\r\n #------------------------------------------------------------------------------\r\n \r\n CreateJob(myString,myString,8)\r\n \r\n #SubmitJob(myString)\r\n \r\n #------------------------------------------------------------------------------\r\n #------------------------------------------------------------------------------\r\n \r\n # evaluate ABAQUS results\r\n \r\n #------------------------------------------------------------------------------\r\n #------------------------------------------------------------------------------\r\n \r\n N.append(Open_ODB_and_Write_NodeSet_data_to_text(myString,\"Step-1\",\"RF\",\"RP-1\",1))\r\n P.append(myPerturbation)\r\n \r\n Write_Variable_to_text(N,\"Buckling Load\")\r\n Write_Variable_to_text(P,\"Boundary Perturbation Height\")\r\n","sub_path":"Script_2D_SBPA_cone_001.py","file_name":"Script_2D_SBPA_cone_001.py","file_ext":"py","file_size_in_byte":30910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"70688925","text":"def main():\n import sys\n\n import argparse\n parser = argparse.ArgumentParser(\n usage=\"%(prog)s [options] [-m] SCRIPT-OR-MODULE-TO-RUN [SCRIPT_ARGS]\"\n )\n parser.add_argument(\"-s\", \"--steal-output\", action=\"store_true\"),\n\n # note: we're implementing -m as a boolean flag, mimicking pdb's behavior,\n # and makes it possible without much fuss to support cases like:\n # python -m pudb -m http.server -h\n # where the -h will be passed to the http.server module\n parser.add_argument(\"-m\", \"--module\", action=\"store_true\",\n help=\"Debug as module or package instead of as a script\")\n\n parser.add_argument(\"-le\", \"--log-errors\", nargs=1, metavar=\"FILE\",\n help=\"Log internal errors to the given file\")\n parser.add_argument(\"--pre-run\", metavar=\"COMMAND\",\n help=\"Run command before each program run\",\n default=\"\")\n parser.add_argument(\"script_args\", nargs=argparse.REMAINDER,\n help=\"Arguments to pass to script or module\")\n\n options = parser.parse_args()\n args = options.script_args\n\n if options.log_errors:\n from pudb.lowlevel import setlogfile\n setlogfile(options.log_errors[0])\n\n options_kwargs = {\n \"pre_run\": options.pre_run,\n \"steal_output\": options.steal_output,\n }\n\n if len(args) < 1:\n parser.print_help()\n sys.exit(2)\n\n mainpyfile = args[0]\n sys.argv = args\n\n if options.module:\n from pudb import runmodule\n runmodule(mainpyfile, **options_kwargs)\n else:\n from os.path import exists\n if not exists(mainpyfile):\n print(\"Error: %s does not exist\" % mainpyfile, file=sys.stderr)\n sys.exit(1)\n\n from pudb import runscript\n runscript(mainpyfile, **options_kwargs)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"pudb/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"267360552","text":"\n\nimport datetime\n\nfrom django.db import connections\nfrom django.test import TestCase\nfrom django.urls import reverse\nfrom rest_framework.authtoken.models import Token\nfrom django.contrib.auth.models import User\nfrom rest_framework.test import APIClient\n\nfrom dqa.views.api.scans import scan_post_update, scan_status, scan_order\n\n\nclass Testmetrics(TestCase):\n\n databases = ['metrics', 'metricsold', 'default']\n\n @classmethod\n def setUpClass(cls):\n super(Testmetrics, cls).setUpClass()\n\n @staticmethod\n def create_token():\n \"\"\" Generate an authorization token to test protected API paths \"\"\"\n test_user = User.objects.create_user(username='testuser1', password='123456')\n token_object = Token.objects.create(user=test_user)\n return token_object.key\n\n @staticmethod\n def clean_scan_tables():\n with connections['metricsold'].cursor() as cursor:\n sql = f\"TRUNCATE TABLE public.tblscanmessage CASCADE;\"\n cursor.execute(sql)\n sql = f\"TRUNCATE TABLE public.tblscan CASCADE;\"\n cursor.execute(sql)\n\n def test_scan_update(self):\n self.clean_scan_tables()\n output = {}\n status = scan_post_update(data=output)\n self.assertTrue(status.startswith('KeyError:'))\n output = {'start_date': datetime.date(2021, 1, 1),\n 'end_date': datetime.date(2021, 1, 15),\n 'priority': 55,\n 'network_filter': 'IU',\n 'station_filter': 'AAAA',\n 'location_filter': '00',\n 'last_updated': '2021-01-20 01:01',\n 'ordering': '4:55:2021-01-15'\n }\n status = scan_post_update(data=output)\n self.assertEqual(status, 201)\n url = reverse('scansapi')\n resp = self.client.get(url)\n self.assertEqual(resp.status_code, 200)\n resp_data = resp.data['data'][0]\n del resp_data['id']\n del resp_data['status']\n del resp_data['message']\n self.assertEqual(output, resp_data, msg=\"Scan update, return not same as saved\")\n\n def test_scans(self):\n self.clean_scan_tables()\n url = reverse('scansapi')\n output = {'start_date': datetime.date(2022, 1, 1),\n 'end_date': datetime.date(2022, 1, 15),\n 'priority': 47,\n 'network_filter': 'IU',\n 'station_filter': 'OMNA',\n 'location_filter': '00,10',\n 'last_updated': '2022-01-20 01:01',\n 'ordering': '4:47:2022-01-15'\n }\n # requires auth\n apiclient = APIClient()\n post_resp = apiclient.post(url, data=output, format='json')\n self.assertEqual(post_resp.status_code, 401)\n # POST with auth\n apiclient.credentials(HTTP_AUTHORIZATION='Token ' + self.create_token())\n post_resp = apiclient.post(url, data=output, format='json')\n self.assertEqual(post_resp.status_code, 201)\n # GET does not require auth\n resp = self.client.get(url)\n self.assertEqual(resp.status_code, 200)\n resp_data = resp.data['data'][0]\n del resp_data['id']\n del resp_data['status']\n del resp_data['message']\n self.assertEqual(output, resp_data, msg=\"Scan read, return not same as saved\")\n\n def test_scan_status(self):\n status = scan_status(False, False, \"\", None, None)\n self.assertEqual('Pending', status, msg=\"Error in Pending\")\n status = scan_status(True, False, \"\", None, None)\n self.assertEqual('Complete', status, msg=\"Error in Complete\")\n status = scan_status(False, True, \"\", None, None)\n self.assertEqual('Running', status, msg=\"Error in Running\")\n status = scan_status(False, True, \"Got lost\", None, None)\n self.assertEqual('Error', status, msg=\"Error in Error\")\n status = scan_status(False, True, \"Got lost\", 100, 50)\n self.assertEqual('Error: 50.0%', status, msg=\"Error in Error\")\n\n def test_scan_order(self):\n # order = scan_order(finished, taken, message, priority, end_date)\n order = scan_order(False, False, \"\", 99, \"2022-01-01\")\n self.assertEqual('4:99:2022-01-01', order, msg=\"Error scan order Pending\")\n order = scan_order(True, False, \"\", 98, \"2022-01-02\")\n self.assertEqual('0:98:2022-01-02', order, msg=\"Error scan order Complete\")\n order = scan_order(False, True, \"\", 97, \"2022-01-03\")\n self.assertEqual('7:97:2022-01-03', order, msg=\"Error scan order Running\")\n order = scan_order(False, True, \"Got really lost\", 96, \"2022-01-04\")\n self.assertEqual('9:96:2022-01-04', order, msg=\"Error scan order Error\")\n order = scan_order(True, True, \"\", 0, \"\")\n self.assertEqual('0:0:', order, msg=\"Error scan order scrambled\")\n","sub_path":"dqa/tests/api/test_api_scans.py","file_name":"test_api_scans.py","file_ext":"py","file_size_in_byte":4862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"262103772","text":"import matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport matplotlib.patheffects as path_effects\nimport numpy as np\nfrom PIL import Image\nfrom sklearn.metrics import precision_recall_curve\nfrom sklearn.metrics import average_precision_score\nimport math\n\nimport io\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\nfrom matplotlib.figure import Figure\n\n\n#subplot utils\ndef add_outline_path_effect(o, lw=4):\n o.set_path_effects([path_effects.Stroke(linewidth=lw, foreground='black'),\n path_effects.Normal()])\n \ndef add_text_to_subplot(ax, pos, label, size='x-large', color='white'):\n text = ax.text(pos[0], pos[1], label, size=size, weight='bold', color=color, va='top')\n add_outline_path_effect(text, 2)\n \ndef hide_subplot_axes(ax):\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n\ndef plot_image_tensor_in_subplot(ax, img_tensor):\n ima= img_tensor.cpu().numpy().transpose((1,2,0))\n ax.imshow(ima)\n\ndef show_img_in_subplot(ax, pil_image):\n im = np.array(pil_image)\n ax.imshow(im)\n \ndef draw_bbox(ax, bbox, color='white'): \n #patches expects (x,y), w, h\n rect_patch = patches.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3],fill=False, lw=2, ec=color) \n patch = ax.add_patch(rect_patch)\n add_outline_path_effect(patch, 4)\n \ndef plot_bbox_annotation(ax, bb, cat_label):\n draw_bbox(ax, bb)\n add_text_to_subplot(ax, (bb[0], bb[1]), cat_label)\n\ndef tensor_to_scalar(t):\n if t.dim()==0:\n return t.item()\n else:\n return t.numpy() \n\n#bbox utils \ndef yxyx_to_xywh(ann):\n return [ann[1], ann[0], ann[3]-ann[1], ann[2]-ann[0]]\n\n \n#plots used in largest item classifier \ndef plot_trn_image_with_annotations(im_id, jpeg_dic, JPEG_DIR, annotations_dic, category_dic, figsize=(10, 10)):\n fig, ax = plt.subplots(1, figsize=figsize)\n show_img_in_subplot(ax, Image.open(JPEG_DIR/jpeg_dic[im_id]['file_name']))\n hide_subplot_axes(ax)\n \n annotations = [annotations_dic[im_id]] if(type(annotations_dic[im_id]) == tuple) else annotations_dic[im_id]\n \n for ann in annotations:\n plot_bbox_annotation(ax, ann[0], category_dic[ann[1]])\n \n plt.show()\n\ndef plot_horizontal_bar_chart(counts, labels, title='', x_tick_step=200): \n sorted_items = sorted(zip(counts, labels), reverse=True)\n sorted_counts, sorted_labels = zip(*sorted_items)\n \n y_pos = np.arange(len(sorted_labels))\n \n fig, ax = plt.subplots(figsize=(15,8))\n ax.barh(y_pos, sorted_counts)\n ax.set_yticks(y_pos)\n ax.set_yticklabels(sorted_labels)\n ax.set_xticks(range(0, int(sorted_counts[0]) + x_tick_step, x_tick_step))\n ax.invert_yaxis() \n ax.set_facecolor('#f7f7f7')\n ax.set_title(title)\n \n for idx, val in enumerate(ax.patches):\n x_value = val.get_width() + 5\n y_value = 0.1 + val.get_y() + val.get_height()/2\n ax.text(x_value, y_value, int(sorted_counts[idx]))\n\n plt.show() \n \ndef plot_model_predictions_on_sample_batch(batch, pred_labels, actual_labels, get_label_fn, n_items=12, plot_from=0, figsize=(16,12)):\n n_rows, n_cols = (1,n_items) if n_items<=4 else (math.ceil(n_items/4), 4)\n \n fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize)\n for i,ax in enumerate(axes.flat):\n plot_idx = plot_from + i\n plot_image_tensor_in_subplot(ax, batch[plot_idx])\n\n pred_label = get_label_fn(tensor_to_scalar(pred_labels[plot_idx])) \n actual_label = get_label_fn(tensor_to_scalar(actual_labels[plot_idx])) \n\n hide_subplot_axes(ax)\n add_text_to_subplot(ax, (0,0), 'Pred: '+pred_label)\n add_text_to_subplot(ax, (0,30), 'Actual: '+actual_label, color='yellow')\n\n plt.tight_layout()\n \n \n# plots used in multi class classifier \ndef add_bar_height_labels(ax, rects):\n ha = {'center': 'center', 'right': 'left', 'left': 'right'}\n offset = {'center': 0.5, 'right': 0.57, 'left': 0.43} # x_txt = x + w*off\n\n for rect in rects:\n height = int(rect.get_height())\n ax.text(rect.get_x() + rect.get_width()*offset['center'], 1.01*height,\n '{}'.format(height), ha=ha['center'], va='bottom')\n \ndef plot_class_wise_preds_gt_true_preds(predictions, actual_instances, correct_predictions, categories): \n ind = np.arange(len(predictions)) # the x locations for the groups\n width = 0.3 # the width of the bars\n\n fig, ax = plt.subplots()\n fig.set_size_inches((25,10))\n\n rects1 = ax.bar(ind - width, predictions, width, \n label='Total class predictions')\n\n rects2 = ax.bar(ind, correct_predictions, width, \n label='Correct class predictions')\n \n rects3 = ax.bar(ind + width, actual_instances, width, \n label='Actual class instances')\n\n ax.set_xticks(ind)\n ax.set_xticklabels(categories)\n ax.legend()\n\n add_bar_height_labels(ax, rects1)\n add_bar_height_labels(ax, rects2)\n add_bar_height_labels(ax, rects3)\n plt.show()\n\ndef plot_class_precision_recall_curve(id, cats, ds_gt_label_logits, ds_pred_scores): \n sk_y_true, sk_y_pred = ds_gt_label_logits[:,id].numpy(), ds_pred_scores[:,id].numpy()\n \n average_precision = average_precision_score(sk_y_true, sk_y_pred)\n precision, recall, thresholds = precision_recall_curve(sk_y_true, sk_y_pred)\n \n plt.step(recall, precision, color='b', alpha=0.2,\n where='post')\n plt.fill_between(recall, precision, step='post', alpha=0.2,\n color='b')\n\n plt.xlabel('Recall')\n plt.ylabel('Precision')\n plt.ylim([0.0, 1.05])\n plt.xlim([0.0, 1.0])\n plt.title('Precision-Recall curve for class \"'+cats[id]+'\" : AP={0:0.2f}'.format(average_precision))\n\ndef get_graph_data_for_multi_class_pr_curves(ds_gt_label_logits, ds_pred_scores, n_classes): \n precision = dict()\n recall = dict()\n average_precision = dict()\n\n for i in range(n_classes):\n precision[i], recall[i], _ = precision_recall_curve(ds_gt_label_logits[:, i].numpy(), ds_pred_scores[:, i].numpy())\n average_precision[i] = average_precision_score(ds_gt_label_logits[:, i].numpy(), ds_pred_scores[:, i].numpy())\n\n\n # A \"micro-average\": quantifying score on all classes jointly\n precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(ds_gt_label_logits.numpy().ravel(), ds_pred_scores.numpy().ravel())\n average_precision[\"micro\"] = average_precision_score(ds_gt_label_logits.numpy(), ds_pred_scores.numpy(), average=\"micro\")\n\n print('Average precision score, micro-averaged over all classes: {0:0.2f}'\n .format(average_precision[\"micro\"]))\n \n return precision, recall, average_precision\n\ndef plot_average_precision_score_over_all_classes(precision, recall, average_precision):\n plt.figure()\n plt.step(recall['micro'], precision['micro'], color='b', alpha=0.2,\n where='post')\n plt.fill_between(recall[\"micro\"], precision[\"micro\"], step='post', alpha=0.2,\n color='b')\n\n plt.xlabel('Recall')\n plt.ylabel('Precision')\n plt.ylim([0.0, 1.05])\n plt.xlim([0.0, 1.0])\n plt.title(\n 'Average precision score, micro-averaged over all classes: AP={0:0.2f}'\n .format(average_precision[\"micro\"]))\n plt.show()\n\ndef plot_precision_recall_curves_for_multi_class_labels(precision, recall, average_precision, cats):\n n_classes = len(cats)\n \n plt.figure(figsize=(20, 20))\n lines = []\n labels = []\n \n l, = plt.plot(recall[\"micro\"], precision[\"micro\"], color='gold', lw=2)\n lines.append(l)\n labels.append('micro-average Precision-recall (area = {0:0.2f})'\n ''.format(average_precision[\"micro\"]))\n\n \n for i in range(n_classes):\n color_RGB = np.random.rand(3)\n l, = plt.plot(recall[i], precision[i], color=color_RGB, lw=2, label=cats[i])\n lines.append(l)\n labels.append('\"{0}\" (area = {1:0.2f})'\n ''.format(cats[i], average_precision[i]))\n\n fig = plt.gcf()\n fig.subplots_adjust(bottom=0.25)\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n plt.xlabel('Recall')\n plt.ylabel('Precision')\n plt.title('Precision Recall curves for all classes')\n plt.legend(lines, labels, loc=(0, -.38), prop=dict(size=15))\n\n plt.show()\n\ndef plot_precision_recall_vs_threshold_for_all_classes(n_classes, ds_gt_label_logits, ds_pred_scores, cats):\n n_rows, n_cols = (1, n_classes) if n_classes < 4 else (math.ceil(n_classes/4), 4)\n \n fig, axes = plt.subplots(n_rows, n_cols, figsize=(16, 16))\n for i,ax in enumerate(axes.flat):\n precisions, recalls, thresholds = precision_recall_curve(ds_gt_label_logits[:, i].numpy(), ds_pred_scores[:,i].numpy())\n class_label = cats[i]\n\n ax.set_title('Class: \"'+class_label+'\"')\n ax.plot(thresholds, precisions[:-1], 'b--', label='precision')\n ax.plot(thresholds, recalls[:-1], 'g--', label = 'recall')\n ax.set_ylim([0,1])\n\n if i==0:\n ax.set_xlabel('Threshold')\n ax.legend(loc='upper left')\n\n plt.tight_layout()\n plt.show() \n \n\n#plots used in largest item bbox \ndef plot_image_with_bbox(img_tensor, bbox_yxyx):\n bbox = yxyx_to_xywh(bbox_yxyx)\n \n fig, ax = plt.subplots(1)\n plot_image_tensor_in_subplot(ax, img_tensor)\n draw_bbox(ax, bbox)\n hide_subplot_axes(ax)\n plt.show() \n \ndef plot_bbox_model_predictions_on_sample_batch(batch, pred_labels, actual_labels, n_items=12, plot_from=0, figsize=(16,12)):\n n_rows, n_cols = (1,n_items) if n_items<=4 else (math.ceil(n_items/4), 4) \n fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize)\n \n for i,ax in enumerate(axes.flat):\n plot_idx = plot_from + i\n \n pred_bbox = [int(x) for x in yxyx_to_xywh(pred_labels[plot_idx])]\n actual_bbox = yxyx_to_xywh(actual_labels[plot_idx])\n \n plot_image_tensor_in_subplot(ax, batch[plot_idx])\n draw_bbox(ax, pred_bbox)\n draw_bbox(ax, actual_bbox, color='yellow')\n \n add_text_to_subplot(ax, (pred_bbox[0], pred_bbox[1]), 'Pred:')\n add_text_to_subplot(ax, (actual_bbox[0], actual_bbox[1]), 'Actual:', color='yellow')\n \n hide_subplot_axes(ax)\n \n plt.tight_layout() \n \n \n#plots used in Concat model, largest item bbox plus classifier\ndef plot_concat_model_predictions_on_sample_batch(batch, pred_labels, actual_labels, get_label_fn, n_items=12, plot_from=0, figsize=(16,12)):\n pred_bboxes, pred_cat_ids = pred_labels\n actual_bboxes, actual_cat_ids = actual_labels\n \n n_rows, n_cols = (1,n_items) if n_items<=4 else (math.ceil(n_items/4), 4) \n fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize)\n \n for i,ax in enumerate(axes.flat):\n plot_idx = plot_from + i\n \n pred_bbox = [int(x) for x in yxyx_to_xywh(pred_bboxes[plot_idx])]\n actual_bbox = yxyx_to_xywh(actual_bboxes[plot_idx])\n \n plot_image_tensor_in_subplot(ax, batch[plot_idx])\n draw_bbox(ax, pred_bbox)\n draw_bbox(ax, actual_bbox, color='yellow')\n \n add_text_to_subplot(ax, (pred_bbox[0], pred_bbox[1]), 'Pred:'+get_label_fn(tensor_to_scalar(pred_cat_ids[plot_idx])))\n add_text_to_subplot(ax, (actual_bbox[0], actual_bbox[1]), 'Actual:'+get_label_fn(tensor_to_scalar(actual_cat_ids[plot_idx])) , color='yellow')\n hide_subplot_axes(ax)\n \n plt.tight_layout()\n\ndef plot_model_result_on_test_image(pred_bbox, pred_cat_id, get_label_fn, im_path):\n im = Image.open(im_path)\n w,h = im.size\n fig, ax = plt.subplots(1, 1, dpi = 100)\n \n bbox = pred_bbox[0].clone()\n bbox = bbox/224\n bbox[0] = bbox[0]*h\n bbox[1] = bbox[1]*w\n bbox[2] = bbox[2]*h\n bbox[3] = bbox[3]*w\n \n pred_bbox = [int(x) for x in yxyx_to_xywh(bbox)]\n \n show_img_in_subplot(ax, im)\n draw_bbox(ax, pred_bbox)\n\n add_text_to_subplot(ax, (pred_bbox[0], pred_bbox[1]), 'Pred:'+get_label_fn(tensor_to_scalar(pred_cat_id[0])))\n hide_subplot_axes(ax)\n\n plt.tight_layout()\n plt.show()\n\ndef get_result_on_test_image(pred_bbox, pred_cat_id, get_label_fn, im):\n w,h = im.size\n fig, ax = plt.subplots(1, 1, dpi = 100)\n \n bbox = pred_bbox[0].clone()\n bbox = bbox/224\n bbox[0] = bbox[0]*h\n bbox[1] = bbox[1]*w\n bbox[2] = bbox[2]*h\n bbox[3] = bbox[3]*w\n \n pred_bbox = [int(x) for x in yxyx_to_xywh(bbox)]\n \n show_img_in_subplot(ax, im)\n draw_bbox(ax, pred_bbox)\n\n add_text_to_subplot(ax, (pred_bbox[0], pred_bbox[1]), 'Pred:'+get_label_fn(tensor_to_scalar(pred_cat_id[0])))\n hide_subplot_axes(ax)\n\n #canvas = FigureCanvas(fig)\n #output = io.BytesIO()\n #canvas.print_png(output)\n \n buf = io.BytesIO()\n plt.savefig(buf, format='png', bbox_inches='tight')\n buf.seek(0)\n\n #plt.tight_layout()\n #buf = io.BytesIO()\n #plt.savefig(buf, format='png', bbox_inches='tight')\n #s = buf.getvalue()\n #buf.close()\n return buf\n","sub_path":"test/libs/plot_utils.py","file_name":"plot_utils.py","file_ext":"py","file_size_in_byte":13028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"454568003","text":"\"\"\"\n\nCreated by: Nathan Starkweather\nCreated on: 04/16/2016\nCreated in: PyCharm Community Edition\n\n\n\"\"\"\nimport itertools\n\n__author__ = 'Nathan Starkweather'\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n_h = logging.StreamHandler()\n_f = logging.Formatter(\"%(created)s %(name)s %(levelname)s (%(lineno)s): %(message)s\")\n_h.setFormatter(_f)\nlogger.addHandler(_h)\nlogger.propagate = False\nlogger.setLevel(logging.DEBUG)\ndel _h, _f\n\nfrom matplotlib.backends import backend_tkagg\nfrom matplotlib.backend_bases import FigureManagerBase\nfrom matplotlib.ticker import NullFormatter, NullLocator\nfrom matplotlib.transforms import Bbox\n\nimport tkinter as tk\nfrom . import queues\n\n\nclass PlotWidgetError(Exception):\n pass\n\n\nclass _PlotWidgetFigManager(FigureManagerBase):\n pass\n\n\nclass RTPlotWidget():\n _fig_counter = itertools.count(1)\n\n __slots__ = 'frame', 'master', 'num', 'figmanager', 'figure', \\\n 'figcanvas', 'subplot', 'line', 'renderer', 'x_data', \\\n 'y_data', 'tkcanvas', 'background', 'xaxis', 'yaxis', 'all_bbox', \\\n '_setup', 'xlabel', 'ylabel', \"title_text\"\n\n def __init__(self, master, max_pts=1000):\n self.frame = tk.Frame(master)\n self.master = master\n self.num = next(self._fig_counter)\n self.figmanager = None\n self.figure = None\n self.figcanvas = None\n self.subplot = None\n self.line = None\n self.renderer = None\n self.xaxis = None\n self.yaxis = None\n self.tkcanvas = None\n self.background = None\n self.all_bbox = None\n\n if max_pts is None:\n self.x_data = queues.InfiniteBuffer()\n self.y_data = queues.InfiniteBuffer()\n else:\n self.x_data = queues.RingBuffer(max_pts)\n self.y_data = queues.RingBuffer(max_pts)\n\n self._setup = False\n\n def _cache_background(self, bbox):\n\n xaxis = self.xaxis\n yaxis = self.yaxis\n\n # cache formatters & locators\n xmjf = xaxis.get_major_formatter()\n xmjl = xaxis.get_major_locator()\n ymjf = yaxis.get_major_formatter()\n ymjl = yaxis.get_major_locator()\n\n # set null placeholders\n xaxis.set_major_formatter(NullFormatter())\n xaxis.set_major_locator(NullLocator())\n yaxis.set_major_formatter(NullFormatter())\n yaxis.set_major_locator(NullLocator())\n\n # cache background\n self.figcanvas.draw()\n background = self.figure.canvas.copy_from_bbox(bbox)\n\n # restore\n xaxis.set_major_formatter(xmjf)\n xaxis.set_major_locator(xmjl)\n yaxis.set_major_formatter(ymjf)\n yaxis.set_major_locator(ymjl)\n\n self.background = background\n\n def create_figure(self, figsize=(4, 2), dpi=None, facecolor=None,\n edgecolor=None, linewidth=0.0, frameon=None,\n subplotpars=None, tight_layout=None):\n \"\"\" Create an ordinary matplotlib figure. \"\"\"\n if self.figure:\n raise PlotWidgetError(\"Cannot override existing figure.\")\n f = backend_tkagg.Figure(figsize, dpi, facecolor, edgecolor, linewidth, frameon, subplotpars, tight_layout)\n self.figure = f\n return f\n\n def setup(self):\n if self.figure is None:\n self.create_figure()\n\n if self.subplot is None:\n self.create_subplot(1, 1, 1)\n\n # Explicitly named to avoid confusion later\n self.figcanvas = backend_tkagg.FigureCanvasTkAgg(self.figure, self.frame)\n self.tkcanvas = self.figcanvas._tkcanvas\n self.figmanager = _PlotWidgetFigManager(self.figcanvas, self.num)\n\n # initial draw to prepare plot background\n self.renderer = self.figcanvas.get_renderer()\n\n self.xaxis = xaxis = self.subplot.xaxis\n self.yaxis = yaxis = self.subplot.yaxis\n\n all_bbox = self._calculate_bbox()\n\n self.xlabel = xaxis.set_label_text(\"\")\n self.ylabel = yaxis.set_label_text(\"\")\n self.title_text = self.subplot.title\n self.title_text.set_text(\"\")\n\n self.figure.subplots_adjust(top=0.85, left=0.20, bottom=0.25)\n self._cache_background(all_bbox)\n self.all_bbox = all_bbox\n\n # this line must come after background is cached\n self.line, = self.subplot.plot(self.x_data.get(), self.y_data.get())\n self._setup = True\n\n def _calculate_bbox(self):\n r = self.renderer\n bboxes = self.xaxis.get_window_extent(r), self.yaxis.get_window_extent(r), self.subplot.bbox\n all_bbox = Bbox.union(bboxes)\n (x0, y0), (x1, y1) = all_bbox.get_points()\n w = x1 - x0\n h = y1 - y0\n all_bbox = Bbox.from_bounds(x0, y0, w * 1.02, h * 1.02)\n return all_bbox\n\n def _handle_text_update(self):\n self.all_bbox = self._calculate_bbox()\n self._cache_background(self.all_bbox)\n self._schedule_update()\n\n def set_xaxis(self, **kw):\n self.xlabel.set(**kw)\n self._handle_text_update()\n\n def set_xaxis_text(self, s):\n self.xlabel.set_text(s)\n self._handle_text_update()\n\n def set_yaxis(self, **kw):\n self.ylabel.set(**kw)\n self._handle_text_update()\n\n def set_yaxis_text(self, s):\n self.ylabel.set_text(s)\n self._handle_text_update()\n\n def set_title_text(self, s):\n self.title_text.set_text(s)\n self._handle_text_update()\n\n def create_subplot(self, *args, **kw):\n if self.subplot:\n raise PlotWidgetError(\"Cannot override existing subplot\")\n self.subplot = s = self.figure.add_subplot(*args, **kw)\n return s\n\n def grid(self, row=None, col=None):\n if not self._setup:\n self.setup()\n self.frame.grid(row=row, column=col)\n self.tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n self.frame.after(0, self._periodic_draw)\n\n def _periodic_draw(self):\n self.figure.draw(self.renderer)\n self.frame.after(5000, self._periodic_draw)\n\n def grid_forget(self):\n self.tkcanvas.grid_forget()\n self.frame.grid_forget()\n\n def set_data(self, x, y):\n self.x_data.clear()\n self.y_data.clear()\n self.x_data.extend(x)\n self.y_data.extend(y)\n self._schedule_update_data()\n\n def _schedule_update_data(self):\n self.frame.after(0, self._update_data)\n\n def extend_xy(self, x, y):\n self.x_data.extend(x)\n self.y_data.extend(y)\n self._schedule_update_data()\n\n def add_xy(self, x, y):\n self.x_data.put(x)\n self.y_data.put(y)\n self._schedule_update_data()\n\n def _update_data(self):\n self.line.set_data(self.x_data.get(), self.y_data.get())\n self.subplot.relim()\n self.subplot.autoscale_view(True, True, True)\n lower, upper = self.subplot.get_ybound()\n self.subplot.set_ylim(lower, upper + (upper - lower) * 0.02, True, None)\n self._update()\n\n def _schedule_update(self):\n self.frame.after(0, self._update)\n\n def _update(self):\n # self.figcanvas.restore_region(self.background)\n # r = self.renderer\n # self.xaxis.draw(r)\n # self.yaxis.draw(r)\n # self.line.draw(r)\n # self.figcanvas.blit(self.all_bbox)\n self.figcanvas.draw()\n","sub_path":"simplertplot/plotwidget.py","file_name":"plotwidget.py","file_ext":"py","file_size_in_byte":7314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"650196839","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nfrom numpy.testing import assert_allclose\nfrom astropy.units import Quantity\nfrom astropy.tests.helper import assert_quantity_allclose, pytest\nfrom ...utils.testing import requires_dependency, requires_data\nfrom ...spectrum import (\n LogEnergyAxis,\n integrate_spectrum,\n)\nfrom ..powerlaw import power_law_energy_flux, power_law_evaluate, power_law_flux\nfrom ..models import ExponentialCutoffPowerLaw\n\n\n@requires_dependency('scipy')\ndef test_LogEnergyAxis():\n from scipy.stats import gmean\n energy = Quantity([1, 10, 100], 'TeV')\n energy_axis = LogEnergyAxis(energy)\n\n energy = Quantity(gmean([1, 10]), 'TeV')\n pix = energy_axis.wcs_world2pix(energy.to('MeV'))\n assert_allclose(pix, 0.5)\n\n world = energy_axis.wcs_pix2world(pix)\n assert_quantity_allclose(world, energy)\n\n\ndef test_integrate_spectrum():\n \"\"\"\n Test numerical integration against analytical solution.\n \"\"\"\n e1 = Quantity(1, 'TeV')\n e2 = Quantity(10, 'TeV')\n einf = Quantity(1E10, 'TeV')\n e = Quantity(1, 'TeV')\n g = 2.3\n I = Quantity(1E-12, 'cm-2 s-1')\n\n ref = power_law_energy_flux(I=I, g=g, e=e, e1=e1, e2=e2)\n norm = power_law_flux(I=I, g=g, e=e, e1=e1, e2=einf)\n f = lambda x: x * power_law_evaluate(x, norm, g, e)\n val = integrate_spectrum(f, e1, e2)\n assert_quantity_allclose(val, ref)\n\n\n@requires_dependency('uncertainties')\ndef test_integrate_spectrum():\n \"\"\"\n Test numerical integration against analytical solution.\n \"\"\"\n from uncertainties import unumpy\n e1 = 1.\n e2 = 10.\n einf = 1E10\n e = 1.\n g = unumpy.uarray(2.3, 0.2)\n I = unumpy.uarray(1E-12, 1E-13)\n\n ref = power_law_energy_flux(I=I, g=g, e=e, e1=e1, e2=e2)\n norm = power_law_flux(I=I, g=g, e=e, e1=e1, e2=einf)\n f = lambda x: x * power_law_evaluate(x, norm, g, e)\n val = integrate_spectrum(f, e1, e2)\n\n assert_allclose(unumpy.nominal_values(val), unumpy.nominal_values(ref))\n assert_allclose(unumpy.std_devs(val), unumpy.std_devs(ref))\n\n\n@pytest.mark.xfail(reason='Spectral models cannot handle ufuncs properly')\n@requires_dependency('uncertainties')\ndef test_integrate_spectrum_ecpl():\n \"\"\"\n Test ecpl integration. Regression test for\n https://github.com/gammapy/gammapy/issues/687\n \"\"\"\n from uncertainties import unumpy\n amplitude = unumpy.uarray(1E-12, 1E-13)\n index = unumpy.uarray(2.3, 0.2)\n reference = 1\n lambda_ = 0.1\n ecpl = ExponentialCutoffPowerLaw(index, amplitude, reference, lambda_)\n emin, emax = 1, 1E10\n val = ecpl.integral(emin, emax)\n\n assert_allclose(unumpy.nominal_values(val), 5.956578235358054e-13)\n assert_allclose(unumpy.std_devs(val), 9.278302514378108e-14)\n","sub_path":"gammapy/spectrum/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"232815750","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 12 15:53:05 2017\n\n@author: Dieter Erben\n\"\"\"\n\nimport pandas as pd, numpy as np\nimport statsmodels.api as sm\n\nreturns = pd.read_csv('return.csv', na_values=\"C\").dropna()\nreturns.date = returns.date//100\n\nfama = pd.read_csv('fama.csv')\nmomentum = pd.read_csv('mom.csv')\n\nmodel = pd.merge(fama,momentum,how='inner',on='date')\nboth = pd.merge(returns,model,how='inner',on='date')\n\nhonda = both[both.PERMCO == 2172]\nford = both[both.PERMCO == 20750]\ntoyota = both[both.PERMCO == 4521]\ngm1 = both[both.PERMCO == 20799]\ngm2 = both[both.PERMCO == 53554]\n\ncompanies = [honda,ford,toyota,gm1,gm2]\n\ndef syn(company):\n company['dep'] = company.RET - company.RF\n\n x = company[['SMB','HML','Mom','Mkt-RF']]\n y = company.dep\n model = sm.OLS(y, x).fit()\n model.summary()\n company['predict'] = model.predict(x)\n company['residual'] = company.RET - company.predict - company.RF\n\n company = company[['PERMNO','date','COMNAM','PERMCO',\n 'RET','RF','predict','residual']]\n company['ret-rf'] = company.RET - company.RF\n company['residual+predict'] = company.residual + company.predict\n company['ret-expret'] = company.residual + company.RF\n company['1+(ret-exp)'] = 1 + company['ret-expret']\n company['CAR'] = np.log(company['1+(ret-exp)'])\n company['Ret'] = np.log(1+company['RET'])\n company = company[['PERMNO','date','COMNAM','PERMCO',\n 'CAR','Ret']]\n return company\n\nfinal = [syn(x) for x in companies]\n\nfinal = pd.concat(final)\nfinal.to_csv('output.csv',index=False)\n\n#sample = both[:10]\n#sample['dep'] = sample.RET - sample.RF\n#\n#sample['ind1'] = sample.SMB\n#sample['ind2'] = sample.HML\n#sample['ind3'] = sample['Mom']\n#\n#sample2 = sample[['PERMNO','dep',\n# 'ind1','ind2','ind3']]\n#\n#x = sample2[['ind1','ind2','ind3']]\n#y = sample2.dep\n","sub_path":"returns/rets.py","file_name":"rets.py","file_ext":"py","file_size_in_byte":1865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"413864993","text":"import pytest\n\n\nimport software.python_bindings as tbots\nfrom proto.validation_pb2 import *\n\n\nclass Validation(object):\n\n \"\"\"A validation function\"\"\"\n\n def get_validation_status(self, world) -> ValidationStatus:\n raise NotImplementedError(\"get_validation_status is not implemented\")\n\n def get_validation_type(self, world) -> ValidationType:\n raise NotImplementedError(\"get_validation_type is not implemented\")\n\n def get_validation_geometry(self, world) -> ValidationGeometry:\n raise NotImplementedError(\"get_validation_geometry is not implemented\")\n\n def __repr__(self):\n return \"String representation of validation not implemented\"\n\n\ndef create_validation_types(validation_class):\n \"\"\"Given a Validation implementation that returns ValidationStatus.PASSING\n when true and ValidationStatus.FAILING when false, create the 4 validation\n types with different visualization/passing/failing properties (described below)\n \n ┌───────────────────────┐ ┌─────────────────┐\n │ │──────► EventuallyTrue │\n │ │ └─────────────────┘\n │ │\n │ │ ┌─────────────────┐\n │ ├──────► EventuallyFalse │\n ┌─────────────────┐ │ │ └─────────────────┘\n │ Validation ├──────►create_validation_types│\n └─────────────────┘ │ │ ┌─────────────────┐\n │ ├──────► AlwaysTrue │\n │ │ └─────────────────┘\n │ │\n │ │ ┌─────────────────┐\n │ ├──────► AlwaysFalse │\n └───────────────────────┘ └─────────────────┘\n\n EventuallyTrue: Has to be true before the end of the test.\n EventuallyFalse: Has to be false before the end of the test. Inverts the\n validation so passing becomes failing (and vice versa)\n AlwaysTrue: Has to be true for the duration of the test.\n AlwaysFalse: Has to be false for the duration of the test.\n\n NOTE: EventuallyFalse validation is a flipped EventuallyTrue validation\n AlwaysTrue validation checks the same condition, but needs to be\n always true. AlwaysFalse is the flipped AlwaysTrue\n\n :param eventually_true: A validation function that is eventually_true\n :returns: EventuallyTrueValidation, EventuallyFalseValidation,\n AlwaysTrueValidation, AlwaysFalseValidation\n \"\"\"\n\n def constructor(self, *args, **kwargs):\n \"\"\"The 4 validation outputs will be composed of the input validation\n\n :param args/kwargs: Pass through to the validation_class\n\n \"\"\"\n self.validation = validation_class(*args, **kwargs)\n\n def flip_validation(self, world):\n \"\"\"Flip the validation status\n\n :param world: The world msg to validate on\n\n \"\"\"\n\n return {\n ValidationStatus.FAILING: ValidationStatus.PASSING,\n ValidationStatus.PASSING: ValidationStatus.FAILING,\n }[self.validation.get_validation_status(world)]\n\n # Generate the types: specifically, all Eventually validations will return\n # EVENTUALLY when get_validation_type is called, and all Always validations\n # will return ALWAYS. get_validation_status is inverted for the _False types.\n # We simply pass the validation_geometry from the validation object through.\n common = {\n \"__init__\": constructor,\n \"get_validation_geometry\": lambda self, world: self.validation.get_validation_geometry(\n world\n ),\n }\n\n eventually_true = type(\n \"EventuallyTrueValidation\",\n (Validation,),\n {\n **common,\n \"__repr__\": lambda self: \"EventuallyTrueValidation: \"\n + repr(self.validation),\n \"get_validation_type\": lambda self: ValidationType.EVENTUALLY,\n \"get_validation_status\": lambda self, world: self.validation.get_validation_status(\n world\n ),\n },\n )\n\n eventually_false = type(\n \"EventuallyFalseValidation\",\n (Validation,),\n {\n **common,\n \"__repr__\": lambda self: \"EventuallyFalseValidation: \"\n + repr(self.validation),\n \"get_validation_type\": lambda self: ValidationType.EVENTUALLY,\n \"get_validation_status\": lambda self, world: flip_validation(self, world),\n },\n )\n\n always_true = type(\n \"AlwaysTrueValidation\",\n (Validation,),\n {\n **common,\n \"__repr__\": lambda self: \"AlwaysTrueValidation: \" + repr(self.validation),\n \"get_validation_type\": lambda self: ValidationType.ALWAYS,\n \"get_validation_status\": lambda self, world: self.validation.get_validation_status(\n world\n ),\n },\n )\n\n always_false = type(\n \"AlwaysFalseValidation\",\n (Validation,),\n {\n **common,\n \"__repr__\": lambda self: \"AlwaysFalseValidation: \" + repr(self.validation),\n \"get_validation_type\": lambda self: ValidationType.ALWAYS,\n \"get_validation_status\": lambda self, world: flip_validation(self, world),\n },\n )\n\n return eventually_true, eventually_false, always_true, always_false\n\n\ndef run_validation_sequence_sets(\n world, eventually_validation_sequence_set, always_validation_sequence_set\n):\n \"\"\"Given both eventually and always validation sequence sets, (and world)\n run validation and aggregate the results in a validation proto set.\n\n :raises AssertionError: If the test fails\n :param world: World to validate with\n :param eventually_validation_sequence_set:\n A collection of sequences of eventually validations to validate.\n :param always_validation_sequence_set:\n A collection of sequences of always validations to validate.\n\n :returns: Eventually ValidationProtoSet, Always ValidationProtoSet\n\n \"\"\"\n\n # Proto that stores validation geometry and validation status of\n # all validations passed in\n always_validation_proto_set = ValidationProtoSet()\n eventually_validation_proto_set = ValidationProtoSet()\n\n def create_validation_proto_helper(validation_proto_set, validation):\n \"\"\"Helper function that computes the status and creates a\n validation_proto, and updates it in the validation_proto_set.\n\n :param validation_proto_set: The validation proto set to add to\n :param validation: The validation to put into the proto\n\n \"\"\"\n # Stores the validation result\n validation_proto = ValidationProto()\n\n # Get status\n status = validation.get_validation_status(world)\n\n # Create validation proto\n validation_proto.status = status\n validation_proto.failure_msg = str(validation) + \" failed\"\n validation_proto.validation_type = validation.get_validation_type()\n validation_proto.geometry.CopyFrom(validation.get_validation_geometry(world))\n\n validation_proto_set.validations.append(validation_proto)\n\n return status\n\n # Validate the eventually validations. Eventually valids\n for validation_sequence in list(eventually_validation_sequence_set):\n for validation in validation_sequence:\n\n # Add to validation_proto_set and get status\n status = create_validation_proto_helper(\n eventually_validation_proto_set, validation\n )\n\n # If the current validation is failing, we don't care about\n # the next one. Keep evaluating until this one passes.\n if status == ValidationStatus.FAILING:\n break\n\n # If the validation has passed, remove it from the set.\n if status == ValidationStatus.PASSING:\n validation_sequence.remove(validation)\n continue\n\n # Validate the always validations. We need to look at all of them\n for validation_sequence in always_validation_sequence_set:\n for validation in validation_sequence:\n create_validation_proto_helper(always_validation_proto_set, validation)\n\n return eventually_validation_proto_set, always_validation_proto_set\n\n\ndef check_validation(validation_proto_set):\n \"\"\"Check validation and make sure its always true\n\n :param validation_proto_set: Validation proto set\n :raises: AssertionError\n\n \"\"\"\n for validation_proto in validation_proto_set.validations:\n if validation_proto.status == ValidationStatus.FAILING:\n raise AssertionError(validation_proto.failure_msg)\n\n\ndef create_validation_geometry(geometry=[]) -> ValidationGeometry:\n \"\"\"Creates a ValidationGeometry which is a visual representation of the\n validation to be rendered as either green (PASSING) or red (FAILING)\n\n Given a list of (vectors, polygons, circles), creates a ValidationGeometry\n proto containing the protobuf representations.\n\n :param geometry: A list of geom\n :returns: ValidationGeometry\n\n \"\"\"\n\n validation_geometry = ValidationGeometry()\n\n CREATE_PROTO_DISPATCH = {\n tbots.Vector.__name__: tbots.createVectorProto,\n tbots.Polygon.__name__: tbots.createPolygonProto,\n tbots.Rectangle.__name__: tbots.createPolygonProto,\n tbots.Circle.__name__: tbots.createCircleProto,\n }\n\n ADD_TO_VALIDATION_GEOMETRY_DISPATCH = {\n tbots.Vector.__name__: validation_geometry.vectors.append,\n tbots.Polygon.__name__: validation_geometry.polygons.append,\n tbots.Rectangle.__name__: validation_geometry.polygons.append,\n tbots.Circle.__name__: validation_geometry.circles.append,\n }\n\n for geom in geometry:\n ADD_TO_VALIDATION_GEOMETRY_DISPATCH[type(geom).__name__](\n CREATE_PROTO_DISPATCH[type(geom).__name__](geom)\n )\n\n return validation_geometry\n","sub_path":"src/software/simulated_tests/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":10891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"270011894","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.home, name='main-homepage'),\n path('help', views.help_view, name='main-help'),\n path('about', views.about_view, name='main-about'),\n path('index', views.IndexView.as_view(), name='index'),\n path('api-data/', views.get_data, name='api-data'),\n path('api-params-data/', views.get_params_data, name='api-params-data'),\n]\n","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"132956876","text":"\"\"\"\nChallenge 001 - Join to Range\nAuthor: Philip Mannering\nDate: 2018-09-07\n\"\"\"\n\n\nimport pandas as pd\n\n# Read the files\nranges = pd.read_csv(\"./files/range.csv\")\ncustomer = pd.read_csv(\"./files/customer.csv\", index_col=-1)\noutput = pd.read_csv(\"../files/output.csv\")\n\n#%% Generate the rows\n\ndf = ranges\n\n# Split out start and end of range and add to the dataframe\ndf[['rng1','rng2']] = df.Range.str.extract('(\\d+)-(\\d+)').astype(int)\n\n# Transpose the new columns\ndf = df.melt(id_vars=df.columns[:-2])\n\n# Set the index to value\ndf = df.set_index('value').sort_index()\n\n# Expand range - this is the generate rows bit\ndf = df.reindex(range(df.index.min(),df.index.max()+1), method = 'ffill')\n\n# =============================================================================\n# Should really fill in the range by this group\n# dg = df.groupby('Region')\n# df = dg.apply(lambda x: x.set_index(x['value'])\n# .reindex(range(x.value.min(),x.value.max()+1)))\n# =============================================================================\n\n#df = df.reset_index().drop(columns = ['Range','Expect Revenue','variable'])\n\n#%% Join\ndf = df.join(customer)\n\n#%% Summarize\n\n# Fields to groupby \nkey = ['Region','Sales Rep', 'Responder']\n\n# Group\ndf = df.groupby(key, as_index=False)['Customer ID'].count()\ndf = df.rename(columns={'Customer ID':'Count'})\n\n\n#%% Check answer\n(df == output).all()","sub_path":"Challenge-001-Join-to-Range/P-Man/Challenge-001-Join-to-Range.py","file_name":"Challenge-001-Join-to-Range.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"638628717","text":"# -*- coding: utf-8 -*-\nimport Adafruit_DHT\nimport sys\n\n# Set sensor type : Options are DHT11,DHT22 or AM2302\nsensor=Adafruit_DHT.DHT11\n\n# Set GPIO sensor is connected to\ndht_pin=int(sys.argv[1])\n\n# Use read_retry method. This will retry up to 15 times to\n# get a sensor reading (waiting 2 seconds between each retry).\n# humidity, temperature = Adafruit_DHT.read_retry(sensor, gpio)\n\n# Reading the DHT11 is very sensitive to timings and occasionally\n# the Pi might fail to get a valid reading. So check if readings are valid.\ndef get_DHT():\n H, T = Adafruit_DHT.read_retry(Adafruit_DHT.DHT22, dht_pin)\n if H is not None and T is not None:\n return T, H\n else:\n print(\"wrong\")\n return 20, 60 # default fake value\n\nprint(get_DHT())","sub_path":"Raspi/test_DHT22.py","file_name":"test_DHT22.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"564657625","text":"# name : slip \n# date : 2019/5/17 10:36 \n# e-mail : slip1233@126.com\n\nimport socket\n\nclient = socket.socket()\n\nclient.connect(('192.168.1.124', 7788))\n\nwhile True:\n\n text = input('>>>')\n client.send(text.encode('utf-8'))\n if text == 'b':\n break\n # get = client.recv(1024)\n # print(get.decode('utf-8'))\n\nclient.close()\n\n\n","sub_path":"网络编程/day27作业/聊天工具客户端.py","file_name":"聊天工具客户端.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"500245658","text":"import os\nimport sys\nimport re\nimport pandas as pd\nimport urllib3\nimport requests\nimport mimetypes\nfrom urllib.parse import urljoin, urlparse\nfrom bs4 import BeautifulSoup\n\nfrom api_core import magic\nfrom api_core.models import FlexibleModelFactory\nfrom settings_hkstp import settings\n\nPICTURE_FOLDER = settings.BASE_DIR + os.path.join(settings.DESIGN_URL, 'media/startup_logos/')\nPICTURE_FOLDER_REL = os.path.join(settings.DESIGN_URL, 'media/startup_logos/')\nREGEX_REDIRECT = r'''.*?window\\.location\\s*=\\s*\\\"([^\"]+)\\\"'''\n\nd = r'C:\\Users\\gampe\\OneDrive\\Software\\python\\Scripts\\Log'\nos.chdir(d)\nf = open(os.path.join(d, 'HKSTP.txt'), 'w')\n\n\ndef run_crawler(base_url, relative_path_start, post_data, get_details=False):\n \"\"\"\n Run the crawler\n \"\"\"\n next_page = urljoin(base_url, relative_path_start)\n response = requests.post(next_page, json=post_data)\n if response:\n print(\"Crawling directory from\", next_page)\n else:\n sys.exit()\n soup = BeautifulSoup(response.content, \"html.parser\")\n\n company_dict_list = list()\n parent_tag = soup.find_all(\"div\", {\"class\": \"contentWrapper\"})\n counter = 0\n for link_tag in parent_tag:\n for link in link_tag.find_all(\"a\", {\"class\": \"title\"}):\n startup_name = link.text\n next_page = urljoin(base_url, link.get(\"href\"))\n counter += 1\n print(str(counter).zfill(3), startup_name, \"=>\", next_page)\n\n if startup_name is not None and get_details:\n company_dict_list.append(get_company_details(next_page, startup_name))\n\n print(company_dict_list, file=f)\n return company_dict_list\n\n\ndef get_company_details(url, name):\n \"\"\"\n Extract the company details from the HKSTP page\n \"\"\"\n\n company_dict = {\n \"name\": None,\n \"program\": None,\n \"tel\": None,\n \"fax\": None,\n \"email\": None,\n \"website\": None,\n \"address\": None,\n \"introduction\": None,\n \"product\": None,\n \"contact_person\": None,\n \"logo\": None\n }\n\n try:\n response = requests.get(url, timeout=5)\n except (\n requests.exceptions.ReadTimeout,\n requests.exceptions.ConnectionError,\n requests.exceptions.TooManyRedirects,\n urllib3.exceptions.LocationValueError\n ) as e:\n return company_dict\n\n soup = BeautifulSoup(response.content.decode('latin-1', 'ignore'), \"html.parser\")\n\n # Crawl company details\n company_dict['name'] = str(name)\n company_dict['program'] = soup.find(\"div\", {\"class\": \"content-title\"}).text.strip()\n parent_tag = soup.find_all(\"div\", {\"class\": \"info-list\"})\n for content_tag in parent_tag:\n key = magic.rewrite_with_technical_convention(content_tag.find(\"span\").text)\n if key in company_dict:\n company_dict[key] = str(content_tag.find(\"p\").text)\n else:\n print(\"key\", key)\n\n # Save company logo if possible\n if 'website' in company_dict:\n company_dict['logo'] = get_company_logo(company_dict.get('website'), name)\n\n return company_dict\n\n\ndef save_company_details_to_database(flexible_model_name, list_dict_company_details):\n \"\"\"\n Instantiates a FlexibleModel and save the company details in database\n \"\"\"\n # create the FlexibleModel startup\n model = FlexibleModelFactory.gen_flexible_model_obj(flexible_model_name, flag_df=False)\n model.content = pd.DataFrame(list_dict_company_details)\n model.content = model.content.drop_duplicates(subset='name')\n model.content = model.content.reset_index().rename(columns={'index': 'id'})\n model.has_dataframe = True\n\n # save to db\n message = model.save_to_db()\n if not(message):\n sys.exit(1)\n\n\ndef get_company_logo(url, name):\n \"\"\"\n Extract the company logo from the respective company website (if any)\n \"\"\"\n\n try:\n\n # Loop to follow redirections if any\n connection_attempts = 5\n i = 0\n for i in range(connection_attempts):\n i += i\n if i == connection_attempts-1:\n return None\n\n if '//' not in url:\n url = '%s%s' % ('http://', url)\n\n try:\n response = requests.get(url, timeout=3)\n except (\n requests.exceptions.ReadTimeout,\n requests.exceptions.ConnectionError,\n requests.exceptions.TooManyRedirects,\n urllib3.exceptions.LocationValueError\n ) as e:\n print(\"URL invalid\", url)\n continue\n\n soup = BeautifulSoup(response.content, \"html.parser\")\n redirect = re.match(REGEX_REDIRECT, str(soup), re.M|re.S)\n if redirect:\n url = urljoin(url, redirect.group(1))\n print(\"Redirection to\", url)\n else:\n break\n\n # Find logo img\n for img_tag in soup.find_all(\"img\"):\n\n picture_url = urljoin(url, img_tag[\"src\"])\n if 'logo' in picture_url.lower():\n try:\n img_response = requests.get(picture_url, timeout=3)\n except (\n requests.exceptions.ReadTimeout,\n requests.exceptions.ConnectionError,\n requests.exceptions.TooManyRedirects,\n urllib3.exceptions.LocationValueError\n ) as e:\n print(\"URL image invalid\", picture_url)\n continue\n\n if img_response:\n content_type = img_response.headers['content-type']\n extension = mimetypes.guess_extension(content_type)\n\n if extension is not None:\n picture_name = magic.rewrite_with_technical_convention(name) + extension\n\n # saved_file = open(PICTURE_FOLDER + picture_name, 'wb')\n # for chunk in img_response.iter_content(100000): # number of iterations per loop\n # saved_file.write(chunk)\n # saved_file.close()\n\n return str(PICTURE_FOLDER_REL + picture_name)\n\n except (TypeError, KeyError) as e:\n return None\n\n return None\n","sub_path":"app_crawler/crawler_hkstp.py","file_name":"crawler_hkstp.py","file_ext":"py","file_size_in_byte":6232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"403618750","text":"from typing import Callable, Generator, List, Optional\n\nimport googleapiclient.discovery\nimport googleapiclient.errors\n\nfrom data_access import DataAccess\n\nDEFAULT_MAX_RESULTS = 50\n\n\ndef gen_resources(resource: Callable, **list_params) -> Generator[List, None, None]:\n \"\"\"\n Paginates through all the data relevant to `resource`, yielding each set\n as it comes back.\n :param resource: The YouTube Data API resource function (ie: youtube.videos).\n :param list_params: Parameters to pass to the `list` call on `resource`.\n :return: Generator.\n \"\"\"\n print(\"Generating resources.\")\n if \"maxResults\" not in list_params.keys():\n list_params[\"maxResults\"] = DEFAULT_MAX_RESULTS\n\n next_page_token = None\n while True:\n if next_page_token:\n list_params[\"pageToken\"] = next_page_token\n\n request = resource().list(**list_params)\n # print(\"\\t\\tRequest made successfully.\")\n response = request.execute()\n # print(f\"\\t\\tRaw response: {response}\")\n\n data = response[\"items\"]\n print(f\"\\tRetrieved {len(data)}\")\n\n yield data\n\n if \"nextPageToken\" in response.keys():\n next_page_token = response[\"nextPageToken\"]\n else:\n print(\"\\tReached last page.\")\n break\n\n return None\n\n\ndef gen_resources_for_ids(\n resource: Callable, res_ids: List[str], **list_params\n) -> Generator[List, None, None]:\n \"\"\"\n Makes requests to retrieve all resources for `res_ids`, yielding each batch.\n :param resource: The YouTube Data API resource function (ie: youtube.videos).\n :param res_ids:\n :param list_params: Parameters to pass to the `list` call on `resource`.\n :return: Generator\n \"\"\"\n print(\"Generating resources for ids.\")\n total = len(res_ids)\n res_counter = 0\n\n if \"maxResults\" not in list_params.keys():\n list_params[\"maxResults\"] = DEFAULT_MAX_RESULTS\n max_results = DEFAULT_MAX_RESULTS\n else:\n max_results = list_params[\"maxResults\"]\n\n _res_ids = res_ids.copy()\n\n while len(_res_ids) > 0:\n request_ids = []\n for _ in range(max_results):\n request_ids.append(_res_ids.pop(0))\n\n if len(_res_ids) == 0:\n break\n\n print(\n f\"\\tRequesting {res_counter}-{res_counter + len(request_ids)} of {total}.\"\n )\n\n list_params[\"id\"] = \",\".join(request_ids)\n\n request = resource().list(**list_params)\n response = request.execute()\n yield response[\"items\"]\n\n res_counter += max_results\n\n print(\"\\tFinished requesting resources.\")\n return None\n\n\nclass YouTube:\n def __init__(self, api_key: str):\n api_service_name = \"youtube\"\n api_version = \"v3\"\n\n self.youtube = googleapiclient.discovery.build(\n api_service_name, api_version, developerKey=api_key\n )\n\n def get_pitems_for_pid(self, pid: str) -> List:\n print(f\"Requesting playlist items for {pid}.\")\n\n data = []\n\n for items in gen_resources(\n self.youtube.playlistItems, part=\"contentDetails\", playlistId=pid\n ):\n data += items\n\n return data\n\n def get_videos_for_pitems(self, pitems: List) -> List:\n print(\"Requesting videos for playlist items.\")\n\n vids: List[str] = [pitem[\"contentDetails\"][\"videoId\"] for pitem in pitems]\n data = []\n\n # Filter out videos we already have.\n da = DataAccess()\n vids = [vid for vid in vids if not da.have_video(vid)]\n\n for items in gen_resources_for_ids(\n self.youtube.videos, vids, part=\"snippet,statistics\",\n ):\n data += items\n\n return data\n\n def gen_comment_threads_for_videos(\n self, videos: List\n ) -> Generator[List, None, None]:\n \"\"\"\n Generates `commentThreads` for the `videos`, yielding on every video.\n :param videos:\n :return: Generator\n \"\"\"\n print(\"Requesting comment threads for videos.\")\n\n for video in videos:\n threads = self.get_comment_threads_for_video(video[\"id\"])\n\n yield threads\n\n return None\n\n def get_comment_threads_for_video(self, video_id: str) -> List:\n print(f\"Getting threads for {video_id}\")\n\n # Get all the threads for the video (paginated).\n threads = []\n for items in gen_resources(\n self.youtube.commentThreads,\n part=\"snippet\",\n videoId=video_id,\n textFormat=\"plainText\",\n maxResults=100,\n ): # Allows up to 100 at a time.\n threads += items\n pass\n\n for thread in threads:\n # print(thread)\n # Then get the top-level comments' replies (paginated).\n if thread[\"snippet\"][\"totalReplyCount\"] > 0:\n top_level_comment = thread[\"snippet\"][\"topLevelComment\"]\n\n print(f\"\\tGetting replies for {thread['id']}\")\n replies = []\n for items in gen_resources(\n self.youtube.comments,\n part=\"snippet\",\n parentId=top_level_comment[\"id\"],\n textFormat=\"plainText\",\n maxResults=100,\n ): # Allows up to 100 at a time.\n replies += items\n\n # And hydrate the thread with the retrieved comments.\n thread[\"replies\"] = {\"comments\": replies}\n\n return threads\n","sub_path":"youtube.py","file_name":"youtube.py","file_ext":"py","file_size_in_byte":5496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"80791820","text":"from test_env.envs.Mujoco_Maze import *\n\n\nclass Basic_Humanoid_v2(Mujoco_Maze):\n def __init__(self, paths):\n Mujoco_Maze.__init__(self, paths)\n\n def _get_obs(self, x, y):\n data = self.sim.data\n return np.concatenate([self._get_laser_dist(x, y),\n self.goal_direction(x, y),\n data.qpos.flat[2:],\n data.qvel.flat,\n data.cinert.flat,\n data.cvel.flat,\n data.qfrc_actuator.flat,\n data.cfrc_ext.flat])\n\n def step(self, a):\n if not hasattr(self, 'blocks_id'):\n self.calculate_block_pos()\n xposbefore, yposbefore = mass_center(self.model, self.sim)\n self.do_simulation(a, self.frame_skip)\n xposafter, yposafter = mass_center(self.model, self.sim)\n alive_bonus = c.survival_reward\n data = self.sim.data\n distbefore = self.distance_to_goal(xposbefore, yposbefore)\n distafter = self.distance_to_goal(xposafter, yposafter)\n lin_vel_cost = 0.25 / self.model.opt.timestep * (\n distbefore - distafter) + int(\n distafter < c.success_dist) * c.goal_r\n quad_ctrl_cost = 0.1 * np.square(data.ctrl).sum()\n quad_impact_cost = .5e-6 * np.square(data.cfrc_ext).sum()\n quad_impact_cost = min(quad_impact_cost, 10)\n reward = lin_vel_cost - quad_ctrl_cost - quad_impact_cost + alive_bonus\n qpos = self.sim.data.qpos\n done = bool(\n (qpos[2] < 1) or (qpos[2] > 2.5) or (distafter < c.success_dist))\n ob = self._get_obs(xposafter, yposafter)\n return ob, reward, done, dict(\n reward_forward=lin_vel_cost,\n reward_ctrl=-quad_ctrl_cost,\n reward_survive=alive_bonus,\n reward_contact=-quad_impact_cost,\n laserdist=ob[:4],\n dist=distafter,\n pos=[xposbefore, yposbefore],\n orientation=self.sim.data.qpos.flat[3:7],\n success=int(distafter < c.success_dist))\n\n def reset_model(self):\n const = 0.01\n qpos = self.init_qpos + self.np_random.uniform(low=-const, high=const,\n size=self.model.nq)\n qvel = self.init_qvel + self.np_random.uniform(low=-const, high=const,\n size=self.model.nv)\n self.set_state(qpos, qvel)\n\n return self._get_obs(qpos[0], qpos[1])\n","sub_path":"test_env/envs/Basic_Humanoid_v2.py","file_name":"Basic_Humanoid_v2.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"457026715","text":"'''\nSet up the version of Salt\n'''\n\n# Import python libs\nimport sys\n\n\n__version_info__ = (0, 16, 0)\n__version__ = '.'.join(map(str, __version_info__))\n\nGIT_DESCRIBE_REGEX = (\n r'(?P[\\d]{1,2})\\.(?P[\\d]{1,2})(?:\\.(?P[\\d]{0,2}))?'\n r'(?:(?:.*)-(?P[\\d]+)-(?P[a-z0-9]{8}))?'\n)\n\n\ndef __get_version(version, version_info):\n '''\n If we can get a version provided at installation time or from Git, use\n that instead, otherwise we carry on.\n '''\n try:\n # Try to import the version information provided at install time\n from salt._version import __version__, __version_info__ # pylint: disable=E0611\n return __version__, __version_info__\n except ImportError:\n pass\n\n # This might be a 'python setup.py develop' installation type. Let's\n # discover the version information at runtime.\n import os\n import re\n import warnings\n import subprocess\n\n try:\n cwd = os.path.abspath(os.path.dirname(__file__))\n except NameError:\n # We're most likely being frozen and __file__ triggered this NameError\n # Let's work around that\n import inspect\n cwd = os.path.abspath(\n os.path.dirname(inspect.getsourcefile(__get_version))\n )\n\n try:\n kwargs = dict(\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n cwd=cwd\n )\n\n if not sys.platform.startswith('win'):\n # Let's not import `salt.utils` for the above check\n kwargs['close_fds'] = True\n\n process = subprocess.Popen(['git', 'describe', '--tags'], **kwargs)\n out, err = process.communicate()\n out = out.strip()\n err = err.strip()\n\n if not out or err:\n return version, version_info\n\n match = re.search(GIT_DESCRIBE_REGEX, out)\n if not match:\n return version, version_info\n\n parsed_version = '{0}.{1}.{2}'.format(\n match.group('major'),\n match.group('minor'),\n match.group('bugfix') or '0'\n )\n\n if match.group('noc') is not None and match.group('sha') is not None:\n # This is not the exact point where a tag was created.\n # We have the extra information. Let's add it.\n parsed_version = '{0}-{1}-{2}'.format(\n parsed_version,\n match.group('noc'),\n match.group('sha')\n )\n\n parsed_version_info = tuple([\n int(g) for g in [h or '0' for h in match.groups()[:3]]\n if g.isdigit()\n ])\n\n if parsed_version_info > version_info:\n warnings.warn(\n 'The parsed version info, `{0}`, is bigger than the one '\n 'defined in the file, `{1}`. Missing version bump?'.format(\n parsed_version_info,\n version_info\n ),\n UserWarning,\n stacklevel=2\n )\n return version, version_info\n elif parsed_version_info < version_info:\n warnings.warn(\n 'The parsed version info, `{0}`, is lower than the one '\n 'defined in the file, `{1}`.'\n 'In order to get the proper salt version with the git hash '\n 'you need to update salt\\'s local git tags. Something like: '\n '\\'git fetch --tags\\' or \\'git fetch --tags upstream\\' if '\n 'you followed salt\\'s contribute documentation. The version '\n 'string WILL NOT include the git hash.'.format(\n parsed_version_info,\n version_info\n ),\n UserWarning,\n stacklevel=2\n )\n return version, version_info\n return parsed_version, parsed_version_info\n except OSError as os_err:\n if os_err.errno != 2:\n # If the errno is not 2(The system cannot find the file\n # specified), raise the exception so it can be catch by the\n # developers\n raise\n return version, version_info\n\n\n# Get additional version information if available\n__version__, __version_info__ = __get_version(__version__, __version_info__)\n# This function has executed once, we're done with it. Delete it!\ndel __get_version\n\n\ndef versions_information():\n '''\n Report on all of the versions for dependent software\n '''\n libs = (\n ('Salt', None, __version__),\n ('Python', None, sys.version.rsplit('\\n')[0].strip()),\n ('Jinja2', 'jinja2', '__version__'),\n ('M2Crypto', 'M2Crypto', 'version'),\n ('msgpack-python', 'msgpack', 'version'),\n ('msgpack-pure', 'msgpack_pure', 'version'),\n ('pycrypto', 'Crypto', '__version__'),\n ('PyYAML', 'yaml', '__version__'),\n ('PyZMQ', 'zmq', '__version__'),\n ('ZMQ', 'zmq', 'zmq_version')\n )\n for name, imp, attr in libs:\n if imp is None:\n yield name, attr\n continue\n try:\n imp = __import__(imp)\n version = getattr(imp, attr)\n if callable(version):\n version = version()\n if isinstance(version, (tuple, list)):\n version = '.'.join(map(str, version))\n yield name, version\n except ImportError:\n yield name, None\n\n\ndef versions_report():\n '''\n Yield each library properly formatted for a console clean output.\n '''\n libs = list(versions_information())\n\n padding = max(len(lib[0]) for lib in libs) + 1\n\n fmt = '{0:>{pad}}: {1}'\n\n for name, version in libs:\n yield fmt.format(name, version or 'Not Installed', pad=padding)\n\n\nif __name__ == '__main__':\n print(__version__)\n","sub_path":"salt/version.py","file_name":"version.py","file_ext":"py","file_size_in_byte":5773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"196596992","text":"#CopyRight no@none.not\nimport numpy as np\n\n \ndef format_dict(d):\n return '{' + ', '.join('%d: %f' % (a, d[a]) for a in d) + '}'\n\nclass AlphaEggGameNode():\n def __init__(self, egg_leftover, player_label, parent = None):\n self.parent = parent\n self.player_label = player_label\n self.avg_gain = 0.0\n self.n_visits = 0\n self.egg_leftover = egg_leftover\n self.children = {}\n self.play_prob = {}\n\n def __str__(self):\n return '{\"avg_gain\": %f, \"n_visits\": %d, \"prob\": %s, \"egg_leftover\": %d, \"player\": %d' % (self.avg_gain, self.n_visits, format_dict(self.play_prob), self.egg_leftover, self.player_label) + ', \"children\": {' + ', '.join(['\\n \"%d-c-%d\": %s'%(self.egg_leftover, a, self.children[a]) for a in self.children]) + '}}'\n \n def expand(self, game):\n if self.egg_leftover > 0 and self.children == {}:\n actions = game.feasible_actions(self.egg_leftover)\n self.children = {a: AlphaEggGameNode(self.egg_leftover - a, self.player_label * -1, self)\n for a in actions}\n \n def foward_select_PUCT(self, P):\n assert self.children != {}\n N = self.n_visits\n C = 1.0\n scores = {a: np.random.rand() * 0.01 + self.children[a].avg_gain * self.player_label +\n C * P[a - 1] * np.sqrt(self.n_visits) / (1.0 + self.children[a].n_visits)\n for a in self.children}\n a = max(scores, key = scores.get)\n return self.children[a]\n\n def backward_update(self, gain_new):\n assert self != None\n self.avg_gain = (self.avg_gain * self.n_visits + gain_new) / (1.0 + self.n_visits)\n self.n_visits += 1.0\n return self.parent\n\n def select_next(self):\n assert self.children != {}\n assert self.n_visits > 0\n N = sum([self.children[a].n_visits for a in self.children])\n self.play_prob = {a: 1.0 * self.children[a].n_visits / N for a in self.children} \n choice = np.random.choice(list(self.play_prob.keys()), 1, p = list(self.play_prob.values()))[0]\n self.children = {choice: self.children[choice]}\n return self.children[choice]\n\n\nclass DDPGEggGameNode():\n def __init__(self, egg_leftover, player_label):\n self.egg_leftover = egg_leftover\n self.player_label = player_label\n self.action = -1\n \n def select_next(self, game, action_posibility):\n actions = game.feasible_actions(self.egg_leftover)\n action_sum = sum([action_posibility[i - 1] for i in actions]) + len(actions)\n play_prob = {i: (action_posibility[i - 1] + 1.0) / action_sum for i in actions}\n a = np.random.choice(list(play_prob.keys()), 1, p = list(play_prob.values()))[0]\n self.action = a\n return DDPGEggGameNode(self.egg_leftover - a, self.player_label * -1)\n \n","sub_path":"game_node.py","file_name":"game_node.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"684818","text":"# evadingliability.py\n# \n# Copyright 2015 Wangolo Joel \n# \n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n# \n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n# \n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n# MA 02110-1301, USA.\n# \n# \nfrom fraudcategory.subsystems import falseid \n\n\nclass EvadingLiability(falseid.FalseID):\n def __init__(self,category,code=6, length=15):\n super(EvadingLiability, self).__init__(category, code=5,length=length)\n \n self.subcategory = [\"FALSE REPORT FINANCIAL DOCS LOST\", \"FALSE REPORT OF ATM MISUSE\",\n \"SIGNIFICANT MISUSE OF ACCOUNT\", \"GOODS RETURNED (REFUNDS)\",\n \"DEBIT/STOP ORDER\", \"MULTILE ENCASHMENT\", \"CHEQUE\",\"CREDIT CARD\",\n \"PERSONAL LOAN\", \"MORTAGE FINANCE\", \"CAR LOAN\", \"RETAIL CREDIT\",\n \"INTERNET BANKING\", \"INTERNET SHOPPING\", \"OTHERS\"\n ]\n","sub_path":"CRB/fraudcategory/subsystems/evadingliability.py","file_name":"evadingliability.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"12694150","text":"\"\"\"coyotevoicetoolkit URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.8/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import include, url\nfrom django.contrib import admin\nfrom django.contrib.auth import views as auth_views\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.voice_toolkit_home, name='home'),\n url(r'^admin/', include(admin.site.urls)),\n url(r'^efc/$', views.extension_forwarding_check, name='extension_forwarding_check'), \n url(r'^eutnc/$', views.end_user_telephone_number_check, name='end_user_telephone_number_check'),\n url(r'^aua/$', views.application_user_association, name='application_user_association'),\n url(r'^aud/$', views.application_user_disassociation, name='application_user_disassociation'),\n url(r'^uonb/$', views.user_onboarding, name='user_onboarding'),\n url(r'^uoffb/$',views.user_offboarding, name='user_offboarding'),\n url(r'^dc/$', views.device_copy, name='device_copy'),\n url(r'^dl/$', views.device_lookup, name='device_lookup'),\n url(r'^ipdl/$', views.ip_device_lookup, name='ip_device_lookup'),\n url(r'^dd/$', views.device_delete, name='device_delete'),\n url(r'^upr/$', views.user_pin_reset, name='user_pin_reset'),\n url(r'^ls/$', views.ldap_sync, name='ldap_sync'),\n url(r'^nc/$', views.name_change, name='name_change'),\n url(r'^emli/$', views.em_login, name='em_login'),\n url(r'^emlo/$', views.em_logout, name='em_logout'),\n url(r'^emuq/$', views.em_user_query, name='em_user_query'),\n url(r'^emdq/$', views.em_device_query, name='em_device_query'),\n url(r'^ec/$', views.extension_change, name='extension_change'),\n url(r'^tro_fwd/$', views.trowbridge_forwarding, name='trowbridge_forwarding'),\n url(r'^ed/$', views.enduser_devices, name='enduser_devices'),\n url(r'^ei/$', views.enduser_info, name='enduser_info'),\n url(r'^login/$', auth_views.login, name='login'),\n url(r'^logout/$', auth_views.logout_then_login, name='logout'),\n]\n","sub_path":"coyotevoicetoolkit/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"562795439","text":"#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n#\n# Copyright (c) 2015 Wind River Systems, Inc.\n#\n\nimport mock\nimport six\n\nfrom oslo_config import cfg\n\nfrom heat.common import exception\nfrom heat.common import template_format\nfrom heat.engine.clients.os import nova\nfrom heat.engine import resource\nfrom heat.engine import scheduler\nfrom heat.tests.common import HeatTestCase\nfrom heat.tests import utils\n\nfrom heat.engine.resources.wr import wr_scaling_policy as sp\n\nas_template = '''\n{\n 'heat_template_version': '2013-05-23',\n 'resources': {\n 'scale_up': {\n 'type': 'OS::WR::ScalingPolicy',\n 'properties': {\n 'ServerName': '5678',\n 'ScalingResource': 'cpu',\n 'ScalingDirection': 'up',\n 'Cooldown': '60',\n }\n }\n }\n}\n'''\n\n\nclass ScalingPolicyTest(HeatTestCase):\n def setUp(self):\n super(ScalingPolicyTest, self).setUp()\n cfg.CONF.set_default('heat_waitcondition_server_url',\n 'http://server.test:8000/v1/waitcondition')\n self.stub_keystoneclient()\n self.ctx = utils.dummy_context()\n\n # For unit testing purpose. Register resource provider\n # explicitly.\n resource._register_class(\"OS::WR::ScalingPolicy\", sp.ScalingPolicy)\n\n def _stub_nova_server_get(self, not_found=False):\n mock_server = mock.MagicMock()\n mock_server.image = {'id': 'dd619705-468a-4f7d-8a06-b84794b3561a'}\n if not_found:\n self.patchobject(nova.NovaClientPlugin, 'get_server',\n side_effect=exception.EntityNotFound(\n entity='Server',\n name='5678'))\n else:\n self.patchobject(nova.NovaClientPlugin, 'get_server',\n return_value=mock_server)\n\n def create_scaling_policy(self, t, stack, resource_name):\n rsrc = stack[resource_name]\n self.assertIsNone(rsrc.validate())\n scheduler.TaskRunner(rsrc.create)()\n self.assertEqual((rsrc.CREATE, rsrc.COMPLETE), rsrc.state)\n return rsrc\n\n def test_resource_mapping(self):\n mapping = sp.resource_mapping()\n self.assertEqual(1, len(mapping))\n self.assertEqual(sp.ScalingPolicy, mapping['OS::WR::ScalingPolicy'])\n\n def test_scaling_policy_constraint_validation(self):\n self._stub_nova_server_get(not_found=True)\n t = template_format.parse(as_template)\n stack = utils.parse_stack(t)\n exc = self.assertRaises(exception.StackValidationFailed,\n stack.validate)\n self.assertIn(\"The Server (5678) could not be found.\",\n six.text_type(exc))\n self.m.ReplayAll()\n self.m.VerifyAll()\n\n def test_scaling_policy_creation(self):\n t = template_format.parse(as_template)\n stack = utils.parse_stack(t)\n self._stub_nova_server_get()\n self.m.ReplayAll()\n self.create_scaling_policy(t, stack, 'scale_up')\n self.m.VerifyAll()\n\n def test_scaling_policy_signal(self):\n t = template_format.parse(as_template)\n stack = utils.parse_stack(t)\n self._stub_nova_server_get()\n self.m.ReplayAll()\n up_policy = self.create_scaling_policy(t, stack, 'scale_up')\n up_policy.handle_signal()\n self.m.VerifyAll()\n","sub_path":"heat/tests/wr/test_wr_scaling_policy.py","file_name":"test_wr_scaling_policy.py","file_ext":"py","file_size_in_byte":3935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"503297137","text":"#! /usr/bin/python2.7\n\n#python2.7\n\nimport socket\nimport sys\nimport time\n\nPORT = 4242\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver_address = ('127.0.0.1', PORT)\ns.bind(server_address)\n\ns.listen(1)\n\nwhile True:\n print(\"Waiting for connection\")\n connection, connection_address = s.accept()\n print(\"Connection from address: \", connection_address) \n try:\n while True:\n connection.send(\"Arbituary Data\\n\")\n time.sleep(1)\n except socket.error:\n connection.close()\n \n finally:\n connection.close()\n\ns.close()\n","sub_path":"Comms/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"526205870","text":"#================================================\n#Plots for the Value Function Iteration Lab\n#================================================\nimport matplotlib\nmatplotlib.rcParams = matplotlib.rc_params_from_file('../../matplotlibrc')\nimport numpy as np\nimport math\nimport scipy as sp\nfrom matplotlib import pyplot as plt\nfrom matplotlib import cm\nfrom mpl_toolkits . mplot3d import Axes3D\ndef eatCake(beta, N, Wmax=1., T=None, finite=True, plot=False):\n \"\"\"\n Solve the finite horizon cake-eating problem using Value Function iteration.\n Inputs:\n T -- final time period\n beta -- discount factor\n N -- number of discrete intervals to break up the cake\n size -- size of the cake to begin with\n plot -- boolean indicating whether to plot value function surface and policy function\n surface\n Returns:\n values -- numpy array of shape (N, T+2) (if finite=True) or shape (N,) (if finite=False)\n giving the value function at each time period for each state\n psi -- numpy array of shape (N, T+1) (if finite=True) or shape (N,) (if finite=False)\n giving the policy at each time period for each state.\n \"\"\"\n states = np.linspace(0,Wmax,N) #state space vector\n actions = np.tile(states, N).reshape((N,N)).T\n actions = actions - actions.T\n actions[actions<0] = 0\n rewards = np.sqrt(actions)\n rewards[np.triu_indices(N, k=1)] = -1e10 #pre-computed reward function\n n_range = np.arange(N) #this is used several times, so initialize it once\n if finite:\n values = np.zeros((N, T+2))\n psi = np.zeros((N, T+1))\n for i in xrange(T,-1,-1):\n argmaxs = np.argmax(rewards + beta*values[:,i+1].reshape(1,N), axis=1)\n values[:,i] = (rewards + beta*values[:,i+1].reshape(1,N))[n_range,argmaxs]\n psi[:,i] = states[argmaxs]\n x=np.arange(0,N)\n \n if plot:\n x=np.arange(0,N)\n y=np.arange(0,T+2)\n X,Y=np.meshgrid(x,y)\n fig1 = plt.figure()\n ax1= Axes3D(fig1)\n ax1.plot_surface(states[X],Y,sp.transpose(values), cmap=cm.coolwarm)\n plt.show ()\n \n fig2 = plt.figure() \n ax2 = Axes3D(fig2)\n y = np.arange(0,T+1)\n X,Y=np.meshgrid(x,y)\n ax2.plot_surface(states[X],Y,sp.transpose(psi), cmap = cm.coolwarm)\n plt.show()\n else:\n values = np.zeros(N)\n psi = np.zeros(N)\n delta = 1.\n while delta >= 1e-9:\n values1 = values.copy()\n argmaxs = np.argmax(rewards + beta*values1.reshape(1,N), axis=1)\n values = (rewards + beta*values.reshape(1,N))[n_range, argmaxs]\n psi = states[argmaxs]\n delta = ((values-values1)**2).sum()\n if plot:\n plt.plot(states, psi)\n plt.show()\n \n return values, psi\n\ndef plot_finite_horiz():\n #First compute solution to problem 1\n beta = 0.9;\n T = 10;\n N = 100;\n u = lambda c: sp.sqrt(c);\n W = sp.linspace(0,1,N);\n X, Y = sp.meshgrid(W,W);\n Wdiff = Y-X\n index = Wdiff <0;\n Wdiff[index] = 0;\n util_grid = u(Wdiff);\n util_grid[index] = -10**10;\n V = sp.zeros((N,T+2));\n psi = sp.zeros((N,T+1));\n \n \n for k in xrange(T,-1,-1):\n val = util_grid + beta*sp.tile(sp.transpose(V[:,k+1]),(N,1));\n vt = sp.amax(val, axis = 1);\n psi_ind = sp.argmax(val,axis = 1)\n V[:,k] = vt;\n psi[:,k] = W[psi_ind];\n \n #now create plots\n #fixed time plot\n \n plt.figure()\n plt.plot(V[:,5])\n plt.title(r'Value function for $t = 5$')\n plt.ylabel(r'$V$')\n plt.xlabel(r'$W$')\n plt.savefig('fixed_time.pdf') \n \n #fixed W plot\n plt.figure()\n plt.plot(V[50,:])\n plt.title(r'Value function for $W = 0.505$')\n plt.ylabel(r'$V$')\n plt.xlabel(r'$t$')\n plt.savefig('fixed_w.pdf')\n \n#plot delta -> 0 \ndef plot_delta(): \n beta = 0.99\n N = 1000\n u = lambda c: sp.sqrt(c)\n W = sp.linspace(0,1,N)\n X, Y = sp.meshgrid(W,W)\n Wdiff = sp.transpose(X-Y)\n index = Wdiff <0\n Wdiff[index] = 0\n util_grid = u(Wdiff)\n util_grid[index] = -10**10\n \n Vprime = sp.zeros((N,1))\n delta = sp.ones(1)\n tol = 10**-9\n it = 0\n max_iter = 500\n \n while (delta[-1] >= tol) and (it < max_iter):\n V = Vprime\n it += 1;\n print(it)\n val = util_grid + beta*sp.transpose(V)\n Vprime = sp.amax(val, axis = 1)\n Vprime = Vprime.reshape((N,1))\n delta = sp.append(delta,sp.dot(sp.transpose(Vprime - V),Vprime-V))\n \n plt.figure()\n plt.plot(delta[1:])\n plt.ylabel(r'$\\delta_k$')\n plt.xlabel('iteration')\n plt.savefig('convergence.pdf')\n\ndef infiniteHorizon():\n \"\"\"\n Plot policy function for infinite time horizon cake eating problem.\n \"\"\"\n values, psi = eatCake(.9, 100, finite=False)\n states = np.linspace(0,1,100)\n plt.figure()\n plt.title(r'Policy Function')\n plt.ylabel(r'$\\psi$')\n plt.xlabel(r'$W$')\n plt.plot(states, psi)\n plt.savefig('infiniteHorizon.pdf')\n plt.clf()\ninfiniteHorizon()\n","sub_path":"Algorithms/ValueFunctionIter/VFI_plots.py","file_name":"VFI_plots.py","file_ext":"py","file_size_in_byte":5202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"28945165","text":"import math\nclass Solution:\n def addBinary(self, a: str, b: str) -> str:\n def binaryToDecimal(a):\n su = 0\n a = [int(item) for item in a]\n a.reverse()\n for i in range(len(a)):\n su += 2**i * a[i]\n return su\n \n def decimalToBinary(a):\n if a == 0:\n return '0'\n power = int(math.log(a, 2))\n li = ['0'] * (power + 1)\n while a > 0:\n power = int(math.log(a, 2)) \n li[power] = '1'\n a -= 2**power\n li.reverse()\n return ''.join(li) \n return decimalToBinary(binaryToDecimal(a) + binaryToDecimal(b))\n","sub_path":"revamp/67_add_binary/sol2.py","file_name":"sol2.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"208375813","text":"import unittest\nfrom expr import Expr, AffExpr, QuadExpr\nfrom expr import AbsExpr, HingeExpr\nfrom expr import CompExpr, EqExpr, LEqExpr\nfrom expr import BoundExpr\n\nfrom variable import Variable\n\nimport numpy as np\n\nfrom ipdb import set_trace as st\n\nfs = [(lambda x: x, lambda x: np.array([[1]]),\n lambda x: np.array([[0]])),\n (lambda x: x**2, lambda x: 2*x,\n lambda x: np.array([[2]])),\n (lambda x: x**3, lambda x: 3*x**2,\n lambda x: 6*x)]\nxs = [1., 2., -1., 0.]\nxs = [np.array([[x]]) for x in xs]\nxs_flat = [x[0] for x in xs]\n\n# adding multi-dimensional fs and xs\nf = (lambda x: np.array([[x[0,0]**2+x[1,0]**2]]),\n lambda x: np.array([[2*x[0,0], 2*x[1,0]]]),\n lambda x: 2*np.eye(2))\nfs_multi = [f]\nxs_multi = [np.array([[0.0],[0.0]]), np.array([[2.0],[-2.0]]),\n np.array([[1.0],[0.0]]), np.array([[0.0],[1.0]]),\n np.array([[-1.0],[0.0]]), np.array([[0.0],[-1.0]])]\nN = 10\nd = 10\n\ndef test_expr_val_grad(ut, e, x, y, y_prime):\n y = np.array(y)\n y_prime = np.array(y_prime)\n y_e = np.array(e.eval(x))\n ut.assertTrue(np.allclose(y_e, y))\n y_prime_e = np.array(e.grad(x))\n ut.assertTrue(np.allclose(y_prime_e, y_prime))\n\ndef test_expr_val_grad_hess(ut, e, x, y, y_prime, y_d_prime):\n y = np.array(y)\n y_prime = np.array(y_prime)\n y_e = np.array(e.eval(x))\n ut.assertTrue(np.allclose(y_e, y))\n y_prime_e = np.array(e.grad(x))\n ut.assertTrue(np.allclose(y_prime_e, y_prime))\n y_d_prime_e = np.array(e.hess(x))\n ut.assertTrue(np.allclose(y_d_prime_e, y_d_prime))\n\ndef test_expr_val_grad_hess_with_num_check(ut, e, x, y, y_prime, y_d_prime):\n y = np.array(y)\n y_prime = np.array(y_prime)\n y_e = np.array(e.eval(x))\n ut.assertTrue(np.allclose(y_e, y))\n y_prime_e = np.array(e.grad(x, num_check=True))\n ut.assertTrue(np.allclose(y_prime_e, y_prime))\n y_d_prime_e = np.array(e.hess(x, num_check=True))\n ut.assertTrue(np.allclose(y_d_prime_e, y_d_prime))\n\nclass TestExpr(unittest.TestCase):\n\n def test_expr_eval_grad_hess(self):\n for f, fder, fhess in fs:\n e = Expr(f)\n for x in xs:\n y = f(x)\n y_prime = fder(x)\n y_d_prime = fhess(x)\n test_expr_val_grad_hess(self, e, x, y, y_prime, y_d_prime)\n\n def test_expr_eval_grad_hess_flat(self):\n for f, fder, fhess in fs:\n e = Expr(f)\n for x in xs_flat:\n with self.assertRaises(Exception) as cm:\n e.grad(np.array([[[x[0]]]]))\n self.assertTrue(\"Input shape not supported\" in cm.exception.message)\n y = f(x)\n y_prime = fder(x)\n y_d_prime = fhess(x)\n test_expr_val_grad_hess(self, e, x, y, y_prime, y_d_prime)\n\n def test_expr_eval_grad_hess_multi(self):\n for f, fder, fhess in fs_multi:\n e = Expr(f)\n for x in xs_multi:\n y = f(x)\n y_prime = fder(x)\n y_d_prime = fhess(x)\n test_expr_val_grad_hess(self, e, x, y, y_prime, y_d_prime)\n\n def test_expr_eval_grad_hess_w_fder_and_fhess(self):\n for f, fder, fhess in fs:\n e = Expr(f, fder, fhess)\n for x in xs:\n y = f(x)\n y_prime = fder(x)\n y_d_prime = fhess(x)\n test_expr_val_grad_hess_with_num_check(self, e, x, y, y_prime, y_d_prime)\n\n def test_expr_eval_grad_hess_multi_w_fder_fhess_and_num_check(self):\n for f, fder, fhess in fs_multi:\n e = Expr(f, fder, fhess)\n for x in xs_multi:\n y = f(x)\n y_prime = fder(x)\n y_d_prime = fhess(x)\n test_expr_val_grad_hess_with_num_check(self, e, x, y, y_prime, y_d_prime)\n\n def test_expr_num_check(self):\n f, fder, fhess = fs_multi[0]\n x = xs_multi[0]\n e = Expr(f)\n with self.assertRaises(AssertionError):\n e.grad(x, num_check=True)\n with self.assertRaises(AssertionError):\n e.hess(x, num_check=True)\n\n # wrong fder and fhess\n fder = lambda x: np.array([[2*x[0,0]+1, 2*x[1,0]+1]])\n fhess = lambda x: 3*np.eye(2)\n e = Expr(f, fder, fhess)\n\n with self.assertRaises(Exception) as cm:\n e.grad(x, num_check=True)\n self.assertTrue(\"Numerical and analytical gradients aren't close\"\\\n in cm.exception.message)\n with self.assertRaises(Exception) as cm:\n e.hess(x, num_check=True)\n self.assertTrue(\"Numerical and analytical hessians aren't close\"\\\n in cm.exception.message)\n\n try:\n e.grad(x, num_check=True, atol=1.)\n e.hess(x, num_check=True, atol=1.)\n except Exception:\n self.fail(\"gradient and hessian calls should not raise exception.\")\n\n def test_convexify_deg_1(self):\n for f, fder, _ in fs:\n e = Expr(f)\n for x in xs:\n y = f(x)\n y_prime = fder(x)\n\n aff_e = e.convexify(x, degree=1)\n self.assertIsInstance(aff_e, AffExpr)\n A = aff_e.A\n b = aff_e.b\n self.assertTrue(np.allclose(A, fder(x)))\n self.assertTrue(np.allclose(b, f(x) - A.dot(x)))\n self.assertTrue(np.allclose(aff_e.eval(x), f(x)))\n\n def test_convexify_deg_2_multi_dim(self):\n x0 = np.array([[5.0],[5.0]])\n f = lambda x: np.vstack((\\\n x[0,0]**2 + x[1,0]**2 - 4, \\\n -((x[0,0]-1)**2 +(x[1,0]**2-1)**2 - 0.25), \\\n -((x[0,0]+1)**2 +(x[1,0]**2-1)**2 - 0.25), \\\n -((x[0,0])**2 + 7*(x[1,0]+1-x[0,0]**2/2)**2 - 0.8)))\n e = Expr(f)\n aff_e = e.convexify(x0)\n self.assertTrue(aff_e.A.shape[0] == aff_e.b.shape[0])\n\n def test_convexify_deg_2(self):\n for f, fder, fhess in fs:\n e = Expr(f)\n for x in xs:\n y = f(x)\n y_prime = fder(x)\n y_d_prime = fhess(x)\n y_d_prime = np.maximum(y_d_prime, np.zeros((1,1)))\n\n quad_e = e.convexify(x, degree=2)\n self.assertIsInstance(quad_e, QuadExpr)\n Q = np.maximum(quad_e.Q, np.zeros((1,1)))\n A = quad_e.A\n b = quad_e.b\n self.assertTrue(np.allclose(Q, y_d_prime))\n self.assertTrue(np.allclose(A, \\\n y_prime -np.transpose(x).dot(y_d_prime)))\n self.assertTrue(np.allclose(b, \\\n np.array(0.5*np.transpose(x).dot(y_d_prime)).dot(x) \\\n - y_prime.dot(x) + y))\n self.assertTrue(np.allclose(quad_e.eval(x), y))\n\n def test_convexify_deg_2_negative_hessian(self):\n f = lambda x: -x**2\n f_hess = np.array([[-2.0]])\n e = Expr(f)\n quad_e = e.convexify(np.zeros((1,1)), degree=2)\n self.assertIsInstance(quad_e, QuadExpr)\n Q = quad_e.Q\n self.assertTrue(np.allclose(Q, np.zeros((1,1))))\n\n\nclass TestAffExpr(unittest.TestCase):\n\n def test_aff_expr_eval_grad_hess(self):\n for _ in range(N):\n A = np.random.rand(d, d)\n b = np.random.rand(d, 1)\n x = np.random.rand(d, 1)\n y = A.dot(x) + b\n y_prime = A.T\n e = AffExpr(A, b)\n test_expr_val_grad(self, e, x, y, y_prime)\n\n hess = np.zeros((d,d))\n self.assertTrue(np.allclose(e.hess(b), hess))\n self.assertTrue(np.allclose(e.hess(np.ones((d,1))), hess))\n self.assertTrue(np.allclose(e.hess(np.zeros((d,1))), hess))\n self.assertTrue(np.allclose(e.hess(x), hess))\n\nclass TestQuadExpr(unittest.TestCase):\n\n def test_quad_expr_eval_grad_hess(self):\n for _ in range(N):\n A = np.random.rand(1, d)\n b = np.random.rand(1)\n Q = np.random.rand(d, d)\n x = np.random.rand(d, 1)\n y = 0.5*x.T.dot(Q.dot(x)) + A.dot(x) + b\n y_prime = 0.5*(Q.T.dot(x) + Q.dot(x)) + A.T\n e = QuadExpr(Q, A, b)\n\n test_expr_val_grad(self, e, x, y, y_prime)\n hess = Q\n self.assertTrue(np.allclose(e.hess(b), hess))\n self.assertTrue(np.allclose(e.hess(np.ones((d,1))), hess))\n self.assertTrue(np.allclose(e.hess(np.zeros((d,1))), hess))\n self.assertTrue(np.allclose(e.hess(x), hess))\n\nclass TestAbsExpr(unittest.TestCase):\n\n def test_abs_expr_eval(self):\n for _ in range(N):\n A = np.random.rand(d, d) - 0.5*np.ones((d,d))\n b = np.random.rand(d, 1) - 0.5*np.ones((d,1))\n x = np.random.rand(d, 1) - 0.5*np.ones((d,1))\n y = A.dot(x) + b\n y_prime = A.T\n e = AffExpr(A, b)\n abs_e = AbsExpr(e)\n self.assertTrue(np.allclose(np.absolute(e.eval(x)), abs_e.eval(x)))\n\nclass TestHingeExpr(unittest.TestCase):\n\n def test_hinge_expr_eval(self):\n for _ in range(N):\n A = np.random.rand(d, d) - 0.5*np.ones((d,d))\n b = np.random.rand(d, 1) - 0.5*np.ones((d,1))\n x = np.random.rand(d, 1) - 0.5*np.ones((d,1))\n y = A.dot(x) + b\n y_prime = A.T\n e = AffExpr(A, b)\n hinge_e = HingeExpr(e)\n zeros = np.zeros((1,1))\n self.assertTrue(np.allclose(np.maximum(e.eval(x), zeros), hinge_e.eval(x)))\n\nclass TestCompExpr(unittest.TestCase):\n\n def test_comp_expr(self):\n f, fder, _ = fs[0]\n e = Expr(f)\n val = np.array([0])\n comp_e = CompExpr(e, val)\n self.assertEqual(comp_e.expr, e)\n self.assertTrue(np.allclose(comp_e.val, val))\n\n # check to ensure that modifying val won't modifying the comp expr\n val[0] = 1\n self.assertTrue(not np.allclose(comp_e.val, val))\n\n with self.assertRaises(NotImplementedError) as nie:\n comp_e.eval(0)\n with self.assertRaises(NotImplementedError) as nie:\n comp_e.convexify(0)\n with self.assertRaises(Exception) as e:\n comp_e.grad(0)\n\nclass TestEqExpr(unittest.TestCase):\n\n def test_eq_expr_eval(self):\n for _ in range(N):\n A = np.random.rand(d, d) - 0.5*np.ones((d,d))\n b = np.random.rand(d, 1) - 0.5*np.ones((d,1))\n x = np.random.rand(d, 1) - 0.5*np.ones((d,1))\n y = A.dot(x) + b\n y_prime = A.T\n e = AffExpr(A, b)\n\n val = e.eval(x)\n eq_e = EqExpr(e, val)\n self.assertTrue(eq_e.eval(x, tol=0.0))\n self.assertTrue(eq_e.eval(x, tol=0.01))\n\n val = e.eval(x) + 0.1\n eq_e = EqExpr(e, val)\n self.assertFalse(eq_e.eval(x, tol=0.01))\n self.assertTrue(eq_e.eval(x, tol=0.1))\n\n def test_eq_expr_convexify(self):\n for f, fder, _ in fs:\n e = Expr(f)\n for x in xs:\n y = f(x)\n y_prime = fder(x)\n\n eq_e = EqExpr(e, np.array([1.0]))\n abs_e = eq_e.convexify(x)\n self.assertIsInstance(abs_e, AbsExpr)\n\n aff_e = abs_e.expr\n A = aff_e.A\n b = aff_e.b\n self.assertTrue(np.allclose(A, fder(x)))\n self.assertTrue(np.allclose(b, f(x) - A.dot(x) - 1.0))\n\n self.assertTrue(np.allclose(abs_e.eval(x), np.absolute(y-1.0)))\n x2 = x+1.0\n self.assertTrue(np.allclose(abs_e.eval(x2), np.absolute(A.dot(x2)+b)))\n\nclass TestLEqExpr(unittest.TestCase):\n\n def test_leq_expr_eval(self):\n for _ in range(N):\n A = np.random.rand(d, d) - 0.5*np.ones((d,d))\n b = np.random.rand(d, 1) - 0.5*np.ones((d,1))\n x = np.random.rand(d, 1) - 0.5*np.ones((d,1))\n y = A.dot(x) + b\n y_prime = A.T\n e = AffExpr(A, b)\n\n val = e.eval(x)\n leq_e = LEqExpr(e, val)\n self.assertTrue(leq_e.eval(x, tol=0.0))\n self.assertTrue(leq_e.eval(x, tol=0.01))\n\n val = e.eval(x) + 0.1\n leq_e = LEqExpr(e, val)\n self.assertTrue(leq_e.eval(x, tol=0.01))\n self.assertTrue(leq_e.eval(x, tol=0.1))\n\n val = e.eval(x) - 0.1\n leq_e = LEqExpr(e, val)\n self.assertFalse(leq_e.eval(x, tol=0.01))\n self.assertTrue(leq_e.eval(x, tol=0.1+1e-8))\n\n def test_leq_expr_convexify(self):\n for f, fder, _ in fs:\n e = Expr(f)\n for x in xs:\n y = f(x)\n y_prime = fder(x)\n\n leq_e = LEqExpr(e, np.array([1.0]))\n hinge_e = leq_e.convexify(x)\n self.assertIsInstance(hinge_e, HingeExpr)\n\n aff_e = hinge_e.expr\n A = aff_e.A\n b = aff_e.b\n self.assertTrue(np.allclose(A, fder(x)))\n self.assertTrue(np.allclose(b, f(x) - A.dot(x) - 1.0))\n\n self.assertTrue(np.allclose(hinge_e.eval(x), np.maximum(y-1.0, np.zeros(y.shape))))\n\n x2 = x+1.0\n self.assertTrue(np.allclose(hinge_e.eval(x2), np.maximum(A.dot(x2)+b, np.zeros(y.shape))))\n\nclass TestBoundExpr(unittest.TestCase):\n\n def test_bound_expr(self):\n b_e = BoundExpr(1,2)\n self.assertEqual(b_e.expr, 1)\n self.assertEqual(b_e.var, 2)\n\n def test_bound_expr_eval_convexify(self):\n for f, fder, _ in fs:\n e = Expr(f)\n for x in xs:\n y = f(x)\n\n dummy_grb_vars = np.array([[1]])\n v = Variable(dummy_grb_vars, x)\n\n b_e = BoundExpr(e, v)\n self.assertTrue(np.allclose(b_e.eval(), e.eval(x)))\n\n cvx_b_e = b_e.convexify()\n self.assertIsInstance(cvx_b_e, BoundExpr)\n self.assertEqual(cvx_b_e.var, v)\n\n cvx_e = b_e.expr.convexify(x)\n self.assertTrue(np.allclose(cvx_e.A, cvx_b_e.expr.A))\n self.assertTrue(np.allclose(cvx_e.b, cvx_b_e.expr.b))\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"sco/test/test_expr.py","file_name":"test_expr.py","file_ext":"py","file_size_in_byte":14219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"71798386","text":"# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n def __repr__(self):\n return str(self.val)\n\n @classmethod\n def fromList(cls, nums):\n nodes = [cls(num) if num else None for num in nums]\n for i, node in enumerate(nodes):\n if node is None:\n continue\n li = 2 * i + 1\n ri = 2 * i + 2\n if li < len(nums):\n node.left = nodes[li]\n if ri < len(nums):\n node.right = nodes[ri]\n return nodes[0]\n\n\nclass Solution:\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n def helper(node, p, q):\n if not node:\n return False, False, None\n\n pf, qf = False, False\n if node == p:\n pf = True\n if node == q:\n qf = True\n\n lpf, lqf, n = helper(node.left, p, q)\n if lpf and lqf:\n return lpf, lqf, n\n rpf, rqf, n = helper(node.right, p, q)\n if rpf and rqf:\n return rpf, rqf, n\n\n pf = pf or lpf or rpf\n qf = qf or lqf or rqf\n return pf, qf, node if pf and qf else None\n\n if p == q:\n return p\n\n _, _, common = helper(root, p, q)\n return common\n\n def lowestCommonAncestor2(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n def helper(node, p, q):\n if not node:\n return None\n elif node in (p, q):\n return node\n\n l = helper(node.left, p, q)\n r = helper(node.right, p, q)\n\n return node if l and r else l or r\n return helper(root, p, q)\n\n\nif __name__ == '__main__':\n s = Solution()\n nums = [3, 5, 1, 6, 2, 0, 8, None, None, 7, 4]\n root = TreeNode.fromList(nums)\n r = s.lowestCommonAncestor(root, root.left, root.right)\n print(r)\n r = s.lowestCommonAncestor2(root, root.left, root.right)\n print(r)\n r = s.lowestCommonAncestor(root, root.left, root.left.left)\n print(r)\n r = s.lowestCommonAncestor2(root, root.left, root.left.left)\n print(r)\n \n\n ","sub_path":"leetcode/medium/lowest-common-ancestor-of-a-binary-tree.py","file_name":"lowest-common-ancestor-of-a-binary-tree.py","file_ext":"py","file_size_in_byte":2309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"365795985","text":"from urllib.parse import quote\nimport requests, json, time\n\ndef search_epmc_for_keyword(search_term, base_url='https://www.ebi.ac.uk/europepmc/webservices/rest/search'):\n try:\n if search_term is None or \\\n search_term ==\"\":\n raise ValueError('No search term found')\n encoded_search_term = \\\n quote(search_term)\n formatted_url = \\\n '{0}?query={1}&format=json&sort_date:y%20BDESC'.\\\n format(base_url, encoded_search_term)\n all_data = list()\n cursor = ''\n while True:\n data, cursor = \\\n get_pmc_data(\n url=formatted_url,\n cursor=cursor)\n if len(data) > 0 or cursor !='':\n all_data.extend(data)\n time.sleep(2)\n if cursor == '':\n break\n return all_data\n except Exception as e:\n raise ValueError('Failed to search ePMC, error: {0}'.format(e))\n\n\ndef get_pmc_data(url, cursor=''):\n '''\n A method for fetching pmc data\n\n :param orcid_id: An orcid id\n :param cursor: A cursor string, default empty string\n '''\n try:\n data = list()\n url_str = \\\n '{0}&cursorMark={1}'.\\\n format(url, cursor)\n response = requests.get(url_str)\n if response.ok:\n json_data = \\\n json.loads(response.content.decode('utf-8'))\n data = json_data['resultList']['result']\n if 'nextCursorMark' in json_data and \\\n cursor != json_data['nextCursorMark']:\n cursor = json_data['nextCursorMark']\n else:\n cursor = ''\n return data, cursor\n except Exception as e:\n raise ValueError(e)","sub_path":"igf_data/utils/epmc_utils.py","file_name":"epmc_utils.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"118697718","text":"import os\nimport numpy\nimport datetime\n\n__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\n\ndef get_microseconds(entry):\n start = entry.index(\",\")+1\n end = entry.index(\"+\")\n value = entry[start:end] \n return int(value)\n\ndef get_iso_time_str(entry):\n end = entry.index(\"+\")\n return entry[:end]\n\ndef get_data_amount_time(entry):\n values = entry.split(\" \", 1)[1].split(\",\")\n return [value.split(\":\")[1].strip() for value in values]\n\ndef create_datetime(iso_str):\n return datetime.datetime.strptime(iso_str, \"%Y-%m-%dT%H:%M:%S,%f\")\n\ndef get_duration_monotonic(filename):\n durations = []\n f = open(os.path.join(__location__, \"data\", filename), \"r\")\n content = f.readlines()\n data_provider = (content[i:i+2] for i in range(0, len(content)-1, 1))\n for entry in data_provider:\n data_amount = int(get_data_amount_time(entry[1])[0])\n t1_logtime = create_datetime(get_iso_time_str(entry[1]))\n t0_logtime = create_datetime(get_iso_time_str(entry[0]))\n t1_monotonic_time = int(get_data_amount_time(entry[1])[1])\n t0_monotonic_time = int(get_data_amount_time(entry[0])[1])\n durations.append((data_amount,\n (t1_logtime - t0_logtime).microseconds, # .total_seconds()\n t1_monotonic_time - t0_monotonic_time))\n return durations\n\ndef get_duration_system_monotonic(filepath):\n durations = []\n f = open(filepath, \"r\")\n content = f.readlines()\n data_provider = (content[i:i+2] for i in range(0, len(content)-1, 1))\n for entry in data_provider:\n data_amount = int(get_data_amount_time(entry[1])[0])\n t1_logtime = create_datetime(get_iso_time_str(entry[1]))\n t0_logtime = create_datetime(get_iso_time_str(entry[0]))\n t1_system_time = int(get_data_amount_time(entry[1])[1])\n t0_system_time = int(get_data_amount_time(entry[0])[1])\n t1_monotonic_time = int(get_data_amount_time(entry[1])[2])\n t0_monotonic_time = int(get_data_amount_time(entry[0])[2])\n durations.append((data_amount,\n (t1_logtime - t0_logtime).microseconds, # .total_seconds()\n t1_system_time - t0_system_time,\n t1_monotonic_time - t0_monotonic_time))\n return durations\n\ndef get_dummy_duration(filepath):\n durations = []\n f = open(filepath, \"r\")\n content = f.readlines()\n data_provider = (content[i:i+2] for i in range(0, len(content)-1, 1))\n for entry in data_provider:\n t1 = create_datetime(get_iso_time_str(entry[1]))\n t0 = create_datetime(get_iso_time_str(entry[0]))\n duration = t1 - t0\n durations.append(duration.microseconds)\n return durations\n\ndef log_output(header, durations):\n print(header)\n print(\"num values: \", len(durations))\n print(\"mean (us): \", numpy.mean(durations))\n print(\"median (us): \", numpy.median(durations))\n print(\"std (us): \", numpy.std(durations))\n print(\"min (us): \", numpy.min(durations))\n print(\"max (us): \", numpy.max(durations))\n print(\"range of changes (us): \", numpy.max(durations) - numpy.min(durations))\n print(\"range of changes (%): \", 100 * (numpy.max(durations) - numpy.min(durations)) / float(numpy.max(durations)))\n print(\"---------------------------------\")\n\ndef test_realtime_system(folder):\n data_entries = [(folder + \"10us\", \"10 us, 10.000 values = 100.000 us\"),\n (folder + \"20us\", \"20 us, 10.000 values = 200.000 us\")]\n for entry in data_entries:\n filename = entry[0]\n header = entry[1]\n duration = get_dummy_duration(filename)\n log_output(header, duration)\n\ndef test_different_clocks(folder):\n data_filepath = os.path.join(folder, \"time_data_speed_all_clocks\")\n durations = get_duration_system_monotonic(data_filepath)\n print(\"logging: \", numpy.mean([duration[1] for duration in durations]))\n print(\"system: \", numpy.mean([duration[2] for duration in durations]) / 1000.0) # ns to us\n print(\"monotonic: \", numpy.mean([duration[3] for duration in durations]) / 1000.0)\n\nif __name__ == '__main__':\n # dmesg --time-format=iso\n folder = \"./data/\"\n test_realtime_system(folder)\n test_different_clocks(folder)\n","sub_path":"light-communication-signaling/src/light_timing/time_analysis.py","file_name":"time_analysis.py","file_ext":"py","file_size_in_byte":4274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"31328983","text":"import cv2\nimport torch\nimport numpy as np\nimport random\n\nimport os\nimport mmap\nimport sys\nimport simplejpeg\n\n\ndef readImageWithMmap(path):\n fd = os.open(path, os.O_RDONLY | os.O_DIRECT) \n mm = mmap.mmap(fd, 0, access=mmap.ACCESS_READ) \n os.close(fd)\n img = np.fromstring(mm.read(), dtype=\"uint8\")\n\n return img\n\ndef random_crop(image, crop_height, crop_width):\n max_x = image.shape[1] - crop_width + 1\n max_y = image.shape[0] - crop_height + 1\n x = np.random.randint(0, max_x)\n y = np.random.randint(0, max_y)\n crop = image[y: y + crop_height, x: x + crop_width]\n return crop\n\n\ndef resize(img, square=224):\n height, width, _ = img.shape\n if height > width:\n height = height * square / width\n width = square\n else:\n width = width * square / height\n height = square\n dim = (int(width), int(height))\n img = cv2.resize(img, dim)\n return img\n\n\ndef transform(image):\n # image = cv2.imdecode(image, cv2.IMREAD_COLOR)\n try:\n image = simplejpeg.decode_jpeg(image, colorspace=\"bgr\")\n except:\n image = cv2.imdecode(image, cv2.IMREAD_COLOR)\n image = resize(image)\n image = random_crop(image,224,224)\n\n\n if random.random()<0.5:\n image = cv2.flip(image, 1)\n\n image = image / 255.0\n image = image - (0.485, 0.456, 0.406)\n image = image / (0.229, 0.224, 0.225)\n image = image.transpose((2, 0, 1))\n # print(image.shape)\n image = torch.from_numpy(image)\n return image","sub_path":"src/modified-pytorch/modifiedprep.py","file_name":"modifiedprep.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"564450291","text":"import pickle\n\nimport numpy as np\nimport torch\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom torch.utils.data import Dataset\n\nfrom .utils import noisify_sym\n\n\nclass CIFAR10(Dataset):\n \n train_list = [\n ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],\n ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],\n ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],\n ['data_batch_4', '634d18415352ddfa80567beed471001a'],\n ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],\n ]\n \n test_list = [\n ['test_batch', '40351d587109b95175f43aff81a1287e'],\n ]\n \n def __init__(self, train=True, transform = transforms.ToTensor(), target_transform=None, noise_rate = 0.2):\n self.train = train\n self.transform = transform\n self.train_data = np.zeros((0,3,32,32))\n self.train_labels = np.zeros((0,))\n self.noise_rate = noise_rate \n\n if self.train:\n for fentry in self.train_list:\n f = fentry[0]\n file = './data/cifar-10-batches-py/' + f\n fo = open(file, 'rb')\n entry = pickle.load(fo, encoding='latin1')\n self.train_data = np.concatenate((self.train_data, entry['data'].reshape((10000,3,32,32))))\n self.train_labels = np.concatenate((self.train_labels, np.array(entry['labels'])))\n self.train_labels = torch.from_numpy(noisify_sym(self.train_labels.astype('uint8'), noise_rate = self.noise_rate, random_state = 0)[0])\n \n \n else:\n f = self.test_list[0][0]\n file = './data/cifar-10-batches-py/' + f\n fo = open(file, 'rb')\n entry = pickle.load(fo, encoding='latin1')\n self.train_data = np.concatenate((self.train_data, entry['data'].reshape((10000,3,32,32))))\n self.train_labels = torch.from_numpy(np.concatenate((self.train_labels, np.array(entry['labels']))))\n\n self.train_data = self.train_data.transpose(0,2,3,1)\n \n\n def __getitem__(self,index):\n \n img, label = self.train_data[index], self.train_labels[index]\n img = Image.fromarray(img.astype('uint8'))\n if self.transform is not None:\n img = self.transform(img)\n return img, label, index\n \n def __len__(self):\n \n return self.train_data.shape[0]\n","sub_path":"data/cifar.py","file_name":"cifar.py","file_ext":"py","file_size_in_byte":2406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"184933376","text":"from glob import glob\nimport os\nimport shutil\n\nif __name__ == '__main__':\n label_name = list(open('label.txt'))\n for i in range(len(label_name)):\n dir_name = label_name[i].replace('\\n', '')\n os.mkdir('images/{}'.format(dir_name))\n for k in range(1, 81):\n shutil.move('jpg/image_{}.jpg'.format(str(k+i*80).zfill(4)), 'images/{}/{}.jpg'.format(dir_name, k))\n","sub_path":"flower/recognition/data/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"611734920","text":"import datetime\nDATE = datetime.datetime.now()\n\nclass Item:\n\n def __init__(self):\n self.newItem = []\n\n def addItem(self, newItem):\n Factory.addItem(self.newItem)\n\n\nclass Factory:\n\n def __init__(self):\n self.list_of_items = []\n self.items_on_the_list = 0\n\n def addItem(self, item_attribute_list):\n self.list_of_items.append(item_attribute_list)\n self.items_on_the_list = self.items_on_the_list + 1\n\n def printItemList(self):\n itemPrint = 0\n numberOfItems = (self.items_on_the_list - 1)\n while itemPrint < numberOfItems:\n print(self.list_of_items[itemPrint][0] + \", \" + self.list_of_items[itemPrint][1] + \", \" +self.list_of_items[itemPrint][2])\n itemPrint += 1\n\nitem = Item()\nnewItem = []\nnewItem = ['iPhone', '', 'X1Y']\nitem.addItem(newItem)\nnewItem = ['Samsung s5', '', 'X2A']\nitem.addItem(newItem)\nnewItem = ['Orange Juice', '2018, 6, 20', 'X1Y']\nitem.addItem(newItem)\nnewItem = ['Veggie Juice', '2019, 6, 20', 'Z3Z']\nitem.addItem(newItem)\nnewItem = ['School Bag', '', 'Y2A']\nitem.addItem(newItem)\nitem.printItemList()\n","sub_path":"old_versions/warehousev3.py","file_name":"warehousev3.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"50864555","text":"#abstract base class\nfrom abc import ABC\nfrom django.db import models\nfrom django.db.models.deletion import CASCADE\n\nfrom django.contrib.auth.models import AbstractUser\n\n#Room for scaling?\nclass Teams(models.Model):\n\n team_name = models.CharField(\n verbose_name='Name of the team', \n blank=False, null=False,\n max_length=200,\n help_text='Provide a name for your team'\n )\n\n team_description = models.TextField(\n verbose_name='Describe the team', \n blank=False, null=False,\n help_text='A short blurb about the team'\n )\n\n def __str__(self):\n return self.team_name\n\n class Meta:\n pass\n\nclass User( AbstractUser ):\n '''\n Team members model \n '''\n \n class Skill_level(ABC):\n '''\n An abstract class that helps use numberical skill level values in a more readable way.\n The vaiables in this class may be used in queries instead of numberical values\n '''\n JUNIOR = 1\n INTERMEDIATE = 2\n SENIOR = 3 \n\n #This tuple defines the choices available for the skill level field, which is a numerical value\n skillLevelChoices = (\n\n (Skill_level.JUNIOR, \"Junior\"),\n (Skill_level.INTERMEDIATE, \"Intermediate\"),\n (Skill_level.SENIOR, \"Senior\"),\n )\n\n #====== FIELDS ==========================\n\n USERNAME_FIELD = 'email'\n EMAIL_FIELD = 'email'\n REQUIRED_FIELDS = ['first_name']\n\n email = models.EmailField(verbose_name='email address', unique=True)\n username = models.CharField(max_length=1, help_text='Not used', default='', unique=False, null=True, blank=True)\n\n\n skill_level = models.PositiveIntegerField(\n choices=skillLevelChoices, \n default=Skill_level.JUNIOR\n )\n\n #Room for scaling?\n # team = models.ForeignKey(\n # Teams,\n # verbose_name=\"Name of team\",\n # help_text=\"Which team does this member belong to?\",\n # blank=False, \n # null=False,\n # on_delete=CASCADE\n # )\n\n #===== END OF FIELDS ====================\n\n def __str__(self):\n return __class__.__name__\n\n class Meta:\n app_label = 'teamaker'\n ordering = []\n verbose_name = \"Team member\"\n verbose_name_plural = \"Team members\"","sub_path":"backend/teamaker/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"582521472","text":"import socket\nimport thread\nimport time\n\ndef handle(s):\n print(repr(s.recv(4096)))\n sock.send(r'''HTTP/1.0 200 OK\n Content-Type: text/plain\n\n Hello, world!\n\n ''')\n # s.send('''\n # HTTP/1.1 101 Web Socket Protocol Handshake\\r\n # Upgrade: WebSocket\\r\n # Connection: Upgrade\\r\n # WebSocket-Origin: http://localhost:8888\\r\n # WebSocket-Location: ws://localhost:9876/\\r\n # WebSocket-Protocol: sample\n # '''.strip() + '\\r\\n\\r\\n')\n time.sleep(1)\n s.send('hello')\n time.sleep(1)\n s.send('world')\n s.close()\n\ns = socket.socket()\ns.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\ns.bind(('', 9876));\ns.listen(1);\nwhile 1:\n t,_ = s.accept();\n thread.start_new_thread(handle,(t,))\n # threading.Thread(target = handle, args = (t,)).start()\n\nt.close()\n","sub_path":"server/server_test.py","file_name":"server_test.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"324506940","text":"'''\n# Задание-1:\n# Доработайте реализацию программы из примера examples/5_with_args.py,\n# добавив реализацию следующих команд (переданных в качестве аргументов):\n# cp - создает копию указанного файла\n# rm - удаляет указанный файл (запросить подтверждение операции)\n# cd - меняет текущую директорию на указанную\n# ls - отображение полного пути текущей директории\n# путь считать абсолютным (full_path) -\n# в Linux начинается с /, в Windows с имени диска,\n# все остальные пути считать относительными.\n\n# Важно! Все операции должны выполняться в той директории, в который вы находитесь.\n# Исходной директорией считать ту, в которой был запущен скрипт.\n\n# P.S. По возможности, сделайте кросс-платформенную реализацию.\n'''\n\nimport os\nfrom os.path import splitext as split\nimport sys\nfrom shutil import copyfile as copy\n\n\nprint('sys.argv = ', sys.argv)\n\n\ndef print_help():\n print(\"help - получение справки\")\n print(\"cp - создает копию указанного файла\")\n print(\"rm - удаляет указанный файл (запросить подтверждение операции)\")\n print(\"cd - меняет текущую директорию на указанную\")\n print(\"ls - отображение полного пути текущей директории\")\n\ndef cp_def():\n print(\"cp - создает копию указанного файла\")\n try:\n copy(dir_name, split(dir_name)[0] + \"(copy)\" + split(dir_name)[1])\n print(\"Создана копия файла {} под именем {}\".format(dir_name, split(dir_name)[0] + \"(copy)\" + split(dir_name)[1]))\n except FileNotFoundError:\n print(\"Файл с таким именем не найден\")\n except TypeError:\n print(\"Не введено имя файла\")\n\ndef rm_def():\n print(\"rm - удаляет указанный файл\")\n q = input(\"Вы уверены, что хотите удалить {}? y/n:\".format(dir_name))\n while q != \"y\" and q != \"n\":\n print(\"Введено неверное значение.\")\n q = input(\"Вы уверены, что хотите удалить {}? y/n:\".format(dir_name))\n if q == \"y\":\n try:\n os.remove(dir_name)\n print(\"Файл {} удален\".format(dir_name))\n except FileNotFoundError:\n print(\"Файл с таким именем не найден\")\n else:\n print(\"Операция отменена пользователем\")\n return\n\ndef cd_def():\n print(\"cd - меняет текущую директорию на указанную\")\n try:\n os.chdir(dir_name)\n print(\"Текущей директорией стала {}:\".format(dir_name))\n print(\"os.getcwd() =\", os.getcwd())\n except FileNotFoundError:\n q = input(\"Директории {} не существует. Создать? y/n:\".format(dir_name))\n while q != \"y\" and q != \"n\":\n print(\"Введено неверное значение.\")\n q = input(\"Создать директорию {}? y/n:\".format(dir_name))\n if q == \"y\":\n os.mkdir(dir_name)\n print(\"Создана директория\", dir_name)\n os.chdir(dir_name)\n print(\"Текущая директория (os.getcwd()):\", os.getcwd())\n else:\n print(\"Операция отменена пользователем\")\n return\n\ndef ls_def():\n print(\"ls - отображение полного пути текущей директории\")\n print(\"Текущая директория:\", os.getcwd())\n\ndo = {\n \"help\": print_help,\n \"cp\": cp_def,\n \"rm\": rm_def,\n \"cd\": cd_def,\n \"ls\": ls_def,\n}\n\ntry:\n dir_name = sys.argv[2]\nexcept IndexError:\n dir_name = None\n\ntry:\n key = sys.argv[1]\nexcept IndexError:\n key = None\n\n\nif key:\n if do.get(key):\n do[key]()\n else:\n print(\"Задан неверный ключ\")\n print(\"Укажите ключ help для получения справки\")","sub_path":"les5-hard1.py","file_name":"les5-hard1.py","file_ext":"py","file_size_in_byte":4780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"151498887","text":"#!/usr/bin/python\n\ndef primeFactors(x):\n\n # define index to start at 2 to not get infinite loop in while loop\n i = 2\n # create empty list\n factors = list()\n\n # iterate index to x\n while i <= x:\n # while i is a factor of x\n while x % i == 0:\n # add it to the list\n factors.append(i)\n # and perform division\n x /= i\n # interate i\n i += 1\n # once complete return\n return factors\n","sub_path":"primefactors.py","file_name":"primefactors.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"24318288","text":"\n# This script creates the input files for a set of options\n\nfrom string import Template\nfrom shutil import copyfile\nimport numpy as np\nimport sys\n\nif len(sys.argv)<5:\n print(\"Usage: python {} tag selection_data_tag selection_random_tag apply_dataselection_to_random\")\n sys.exit(0)\n\nprint(\"tag: {}\".format(sys.argv[1]))\nprint(\"selection_data_tag: {}\".format(sys.argv[2]))\nprint(\"selection_random_tag: {}\".format(sys.argv[3]))\nprint(\"apply_dataselection_to_random: {}\".format(sys.argv[4]))\n\nf=open('Templates/input_22pin_template.py',mode='r')\nmyinput = Template( f.read() )\nf.close\n\nif sys.argv[2]=='None':\n mySDT=None\nelse:\n mySDT=\"'\"+sys.argv[2]+\"'\"\n\nif sys.argv[3]=='None':\n mySRT=None\nelse:\n mySRT=\"'\"+sys.argv[3]+\"'\"\n\nf=open('input_22pin{}.py'.format(sys.argv[1]),mode='w')\nf.write(myinput.substitute(SDT = mySDT, SRT = mySRT, ADTR = sys.argv[4]))\nf.close()\n\nprint(\"Written file input_22pin{}.py\".format(sys.argv[1]))\n","sub_path":"Pipeline/Example/createInput_22pin.py","file_name":"createInput_22pin.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"209547490","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport datetime\nimport hamster.client\nimport reports\nimport argparse\nimport pdfkit\nimport gettext\ngettext.install('brainz', '../datas/translations/')\n\n# custom settings:\n\nreportTitle = \"My Activities Report\"\nactivityFilter = \"unfiled\"\n\ndef valid_date(s):\n try:\n return datetime.datetime.strptime(s, \"%Y-%m-%d\").date()\n except ValueError:\n msg = \"Not a valid date: '{0}'.\".format(s)\n raise argparse.ArgumentTypeError(msg)\n\n# find dates:\n\ntoday = datetime.date.today()\nfirst = today.replace(day=1)\npreviousLast = first - datetime.timedelta(days=1)\npreviousFirst = previousLast.replace(day=1)\n\n# assign arguments:\n\nparser = argparse.ArgumentParser(description=\"export the hamster database to pdf\")\nparser.add_argument(\"--thismonth\", action=\"store_true\", help=\"export this month's records\")\nparser.add_argument(\"--lastmonth\", action=\"store_true\", help=\"export last month's records\")\nparser.add_argument(\"-s\", dest=\"startDate\", default=today, help=\"start date (default: today)\", type=valid_date)\nparser.add_argument(\"-e\", dest=\"endDate\", default=today, help=\"end date (default: today)\", type=valid_date)\nparser.add_argument(\"-o\", dest=\"reportFile\", default=\"report.pdf\", help=\"output file (default: report.pdf)\")\n\n# parse arguments:\n\nargs = parser.parse_args()\n\nif args.thismonth:\n\targs.startDate = first\n\targs.endDate = today\n\nif args.lastmonth:\n\targs.startDate = previousFirst\n\targs.endDate = previousLast\n\n# prepare filenames:\n\nhtmlFilename = os.path.splitext(args.reportFile)[0]+\".html\"\npdfFilename = os.path.splitext(args.reportFile)[0]+\".pdf\"\n\nstorage = hamster.client.Storage()\nfacts = storage.get_facts(args.startDate, args.endDate)\n\n# generate report\n\nreports.simple(facts, args.startDate, args.endDate, htmlFilename)\n\n# convert .html to .pdf file:\n\npdfkit.from_file(htmlFilename, pdfFilename)\n","sub_path":"hamster2pdf.py","file_name":"hamster2pdf.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"638181654","text":"############################################################\n# -*- coding: utf-8 -*-\n#\n# Python-based Tool for interaction with the 10micron mounts\n# GUI with PyQT5 for python\n# Python v3.5\n#\n# Michael Würtenberger\n# (c) 2016, 2017\n#\n# Licence APL2.0\n#\n############################################################\nimport datetime\nimport json\nimport logging\nimport logging.handlers\n# numerics\nimport math\nimport os\nimport platform\nimport sys\n\nimport numpy\n\nif platform.system() == 'Windows':\n # application handling\n from winreg import *\n# commands to threads\nfrom queue import Queue\n# import for the PyQt5 Framework\nimport PyQt5\nfrom PyQt5 import QtCore\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\n# matplotlib\nfrom matplotlib import use\nuse('Qt5Agg')\nfrom matplotlib import pyplot as plt\nfrom matplotlib import figure as figure\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n# import the UI part, which is done via QT Designer and exported\nfrom baseclasses import widget\nfrom widgets import modelplotWindow\nfrom widgets import imageWindow\nfrom widgets import analyseWindow\nfrom gui import wizzard_main_ui\n\n# modeling\nfrom modeling import modelThread\n# import mount functions classes\nfrom mount import mountThread\nfrom relays import relays\nfrom remote import remoteThread\nfrom dome import ascomDomeThread\nfrom indi import indi_client\nfrom environment import ascomEnvirThread\n\nif platform.system() == 'Windows':\n from automation import uploadThread\nfrom wakeonlan import wol\n\n\nclass ShowModel(FigureCanvas):\n\n def __init__(self, parent=None):\n self.fig = figure.Figure(dpi=75, facecolor=(25/256, 25/256, 25/256))\n FigureCanvas.__init__(self, self.fig)\n self.setParent(parent)\n FigureCanvas.updateGeometry(self)\n\n\nclass MountWizzardApp(widget.MwWidget):\n logger = logging.getLogger(__name__)\n\n def __init__(self):\n super(MountWizzardApp, self).__init__()\n self.setObjectName(\"Main\")\n # setting up communication queues for inter thread communication\n self.mountCommandQueue = Queue()\n self.modelLogQueue = Queue()\n self.modelCommandQueue = Queue()\n self.messageQueue = Queue()\n self.imageQueue = Queue()\n self.INDISendCommandQueue = Queue()\n self.INDIDataQueue = Queue()\n # loading config data\n self.config = self.loadConfigData()\n # initializing the gui\n self.ui = wizzard_main_ui.Ui_MainWindow()\n self.ui.setupUi(self)\n # special setups for gui including box for matplotlib. margins to 0\n self.initUI()\n self.setWindowTitle('MountWizzard ' + BUILD_NO)\n self.ui.le_mwWorkingDir.setText(os.getcwd())\n helper = QVBoxLayout(self.ui.model)\n helper.setContentsMargins(0, 0, 0, 0)\n self.modelWidget = ShowModel(self.ui.model)\n # noinspection PyArgumentList\n helper.addWidget(self.modelWidget)\n # instantiating all subclasses and connecting thread signals\n self.relays = relays.Relays(self)\n self.mount = mountThread.Mount(self)\n self.mount.setObjectName(\"Mount\")\n self.mount.signalMountConnected.connect(self.setMountStatus)\n self.INDIworker = indi_client.INDIClient(self)\n self.INDIthread = QThread()\n self.INDIthread.setObjectName(\"INDI\")\n self.INDIworker.moveToThread(self.INDIthread)\n # noinspection PyUnresolvedReferences\n self.INDIthread.started.connect(self.INDIworker.run)\n self.INDIworker.status.connect(self.setINDIStatus)\n # threading for ascom environment data\n if platform.system() == 'Windows':\n self.workerAscomEnvironment = ascomEnvirThread.AscomEnvironment(self)\n self.threadAscomEnvironment = PyQt5.QtCore.QThread()\n self.threadAscomEnvironment.setObjectName(\"Environ\")\n self.workerAscomEnvironment.moveToThread(self.threadAscomEnvironment)\n # noinspection PyUnresolvedReferences\n self.threadAscomEnvironment.started.connect(self.workerAscomEnvironment.run)\n self.workerAscomEnvironment.finished.connect(self.workerAscomEnvironmentStop)\n self.workerAscomEnvironment.signalAscomEnvironmentConnected.connect(self.setEnvironmentStatus)\n self.threadAscomEnvironment.start()\n # threading for ascom dome data\n if platform.system() == 'Windows':\n self.workerAscomDome = ascomDomeThread.AscomDome(self)\n self.threadAscomDome = PyQt5.QtCore.QThread()\n self.threadAscomDome.setObjectName(\"Dome\")\n self.workerAscomDome.moveToThread(self.threadAscomDome)\n # noinspection PyUnresolvedReferences\n self.threadAscomDome.started.connect(self.workerAscomDome.run)\n self.workerAscomDome.finished.connect(self.workerAscomDomeStop)\n self.workerAscomDome.signalAscomDomeConnected.connect(self.setDomeStatus)\n self.threadAscomDome.start()\n # threading for remote shutdown\n self.workerRemote = remoteThread.Remote(self)\n self.threadRemote = PyQt5.QtCore.QThread()\n self.threadRemote.setObjectName(\"Remote\")\n self.workerRemote.moveToThread(self.threadRemote)\n # noinspection PyUnresolvedReferences\n self.threadRemote.started.connect(self.workerRemote.run)\n self.workerRemote.finished.connect(self.workerRemoteStop)\n # thread start will be done when enabled\n # self.threadRemote.start()\n self.workerRemote.signalRemoteShutdown.connect(self.saveConfigQuit)\n # threading for updater automation\n if platform.system() == 'Windows':\n self.workerUpload = uploadThread.UpdaterAuto(self)\n self.threadUpload = PyQt5.QtCore.QThread()\n self.threadUpload.setObjectName(\"Upload\")\n self.workerUpload.moveToThread(self.threadUpload)\n # noinspection PyUnresolvedReferences\n self.threadUpload.started.connect(self.workerUpload.run)\n self.workerUpload.finished.connect(self.workerUploadStop)\n self.threadUpload.start()\n self.workerModeling = modelThread.Modeling(self)\n self.threadModeling = PyQt5.QtCore.QThread()\n self.threadModeling.setObjectName(\"Model\")\n self.workerModeling.moveToThread(self.threadModeling)\n # noinspection PyUnresolvedReferences\n self.threadModeling.started.connect(self.workerModeling.run)\n self.workerModeling.finished.connect(self.workerModelingStop)\n self.workerModeling.signalModelConnected.connect(self.setCameraPlateStatus)\n # thread start will be done when enabled\n self.threadModeling.start()\n self.analyseWindow = analyseWindow.AnalyseWindow(self)\n self.modelWindow = modelplotWindow.ModelPlotWindow(self)\n self.imageWindow = imageWindow.ImagesWindow(self)\n # starting the threads\n self.mount.start()\n if platform.system() == 'Windows':\n self.checkASCOM()\n self.enableDisableRemoteAccess()\n self.enableDisableINDI()\n self.initConfig()\n self.mappingFunctions()\n self.checkPlatformDependableMenus()\n # print('main app', PyQt5.QtCore.QObject.thread(self), int(PyQt5.QtCore.QThread.currentThreadId()))\n # starting loop for cyclic data to gui from threads\n self.mainLoop()\n\n def workerAscomEnvironmentStop(self):\n self.threadAscomEnvironment.quit()\n self.threadAscomEnvironment.wait()\n\n def workerAscomEnvironmentSetup(self):\n # first stopping the thread for environment, than setting up, than starting the thread\n if self.workerAscomEnvironment.isRunning:\n self.workerAscomEnvironment.stop()\n self.workerAscomEnvironment.setupDriver()\n self.ui.le_ascomEnvironmentDriverName.setText(self.workerAscomEnvironment.driverName)\n self.threadAscomEnvironment.start()\n\n def setEnvironmentStatus(self, status):\n if status == 0:\n self.ui.btn_environmentConnected.setStyleSheet('QPushButton {background-color: gray;}')\n elif status == 1:\n self.ui.btn_environmentConnected.setStyleSheet('QPushButton {background-color: red;}')\n elif status == 2:\n self.ui.btn_environmentConnected.setStyleSheet('QPushButton {background-color: yellow;}')\n elif status == 3:\n self.ui.btn_environmentConnected.setStyleSheet('QPushButton {background-color: green;}')\n else:\n self.ui.btn_environmentConnected.setStyleSheet('QPushButton {background-color: black;}')\n\n def fillEnvironmentData(self):\n for valueName in self.workerAscomEnvironment.data:\n if valueName == 'DewPoint':\n self.ui.le_dewPoint.setText('{0:4.1f}'.format(self.workerAscomEnvironment.data[valueName]))\n elif valueName == 'Temperature':\n self.ui.le_temperature.setText('{0:4.1f}'.format(self.workerAscomEnvironment.data[valueName]))\n elif valueName == 'Humidity':\n self.ui.le_humidity.setText('{0:4.1f}'.format(self.workerAscomEnvironment.data[valueName]))\n elif valueName == 'Pressure':\n self.ui.le_pressure.setText('{0:4.1f}'.format(self.workerAscomEnvironment.data[valueName]))\n elif valueName == 'CloudCover':\n self.ui.le_cloudCover.setText('{0:4.1f}'.format(self.workerAscomEnvironment.data[valueName]))\n elif valueName == 'RainRate':\n self.ui.le_rainRate.setText('{0:4.1f}'.format(self.workerAscomEnvironment.data[valueName]))\n elif valueName == 'WindSpeed':\n self.ui.le_windSpeed.setText('{0:4.1f}'.format(self.workerAscomEnvironment.data[valueName]))\n elif valueName == 'WindDirection':\n self.ui.le_windDirection.setText('{0:4.1f}'.format(self.workerAscomEnvironment.data[valueName]))\n elif valueName == 'SQR':\n self.ui.le_SQR.setText('{0:4.2f}'.format(self.workerAscomEnvironment.data[valueName]))\n self.modelWindow.ui.le_SQR.setText('{0:4.2f}'.format(self.workerAscomEnvironment.data[valueName]))\n\n def workerAscomDomeStop(self):\n self.threadAscomDome.quit()\n self.threadAscomDome.wait()\n\n def workerAscomDomeSetup(self):\n # first stopping the thread for environment, than setting up, than starting the thread\n if self.workerAscomDome.isRunning:\n self.workerAscomDome.stop()\n self.workerAscomDome.setupDriver()\n self.ui.le_ascomDomeDriverName.setText(self.workerAscomDome.driverName)\n self.threadAscomDome.start()\n\n def setDomeStatus(self, status):\n if status == 0:\n self.ui.btn_domeConnected.setStyleSheet('QPushButton {background-color: gray;}')\n elif status == 1:\n self.ui.btn_domeConnected.setStyleSheet('QPushButton {background-color: red;}')\n elif status == 2:\n self.ui.btn_domeConnected.setStyleSheet('QPushButton {background-color: yellow;}')\n elif status == 3:\n self.ui.btn_domeConnected.setStyleSheet('QPushButton {background-color: green;}')\n else:\n self.ui.btn_domeConnected.setStyleSheet('QPushButton {background-color: black;}')\n\n def workerRemoteStop(self):\n self.threadRemote.quit()\n self.threadRemote.wait()\n\n def workerUploadStop(self):\n self.threadUpload.quit()\n self.threadUpload.wait()\n\n def workerModelingStop(self):\n self.threadModeling.quit()\n self.threadModeling.wait()\n\n def enableDisableRemoteAccess(self):\n if self.ui.checkEnableRemoteAccess.isChecked():\n self.messageQueue.put('Remote Access enabled')\n self.threadRemote.start()\n else:\n self.messageQueue.put('Remote Access disabled')\n if self.workerRemote.isRunning:\n self.workerRemote.stop()\n\n # noinspection PyArgumentList\n def mappingFunctions(self):\n self.ui.btn_mountQuit.clicked.connect(self.saveConfigQuit)\n self.ui.btn_mountSave.clicked.connect(self.saveConfig)\n self.ui.btn_mountBoot.clicked.connect(self.mountBoot)\n self.ui.btn_mountShutdown.clicked.connect(self.mountShutdown)\n self.ui.btn_mountPark.clicked.connect(lambda: self.mountCommandQueue.put('hP'))\n self.ui.btn_mountUnpark.clicked.connect(lambda: self.mountCommandQueue.put('PO'))\n self.ui.btn_startTracking.clicked.connect(lambda: self.mountCommandQueue.put('AP'))\n self.ui.btn_stopTracking.clicked.connect(lambda: self.mountCommandQueue.put('RT9'))\n self.ui.btn_setTrackingLunar.clicked.connect(lambda: self.mountCommandQueue.put('RT0'))\n self.ui.btn_setTrackingSolar.clicked.connect(lambda: self.mountCommandQueue.put('RT1'))\n self.ui.btn_setTrackingSideral.clicked.connect(lambda: self.mountCommandQueue.put('RT2'))\n self.ui.btn_stop.clicked.connect(lambda: self.mountCommandQueue.put('STOP'))\n self.ui.btn_mountPos1.clicked.connect(self.mountPosition1)\n self.ui.btn_mountPos2.clicked.connect(self.mountPosition2)\n self.ui.btn_mountPos3.clicked.connect(self.mountPosition3)\n self.ui.btn_mountPos4.clicked.connect(self.mountPosition4)\n self.ui.btn_mountPos5.clicked.connect(self.mountPosition5)\n self.ui.btn_mountPos6.clicked.connect(self.mountPosition6)\n self.ui.le_parkPos1Text.textChanged.connect(lambda: self.ui.btn_mountPos1.setText(self.ui.le_parkPos1Text.text()))\n self.ui.le_parkPos2Text.textChanged.connect(lambda: self.ui.btn_mountPos2.setText(self.ui.le_parkPos2Text.text()))\n self.ui.le_parkPos3Text.textChanged.connect(lambda: self.ui.btn_mountPos3.setText(self.ui.le_parkPos3Text.text()))\n self.ui.le_parkPos4Text.textChanged.connect(lambda: self.ui.btn_mountPos4.setText(self.ui.le_parkPos4Text.text()))\n self.ui.le_parkPos5Text.textChanged.connect(lambda: self.ui.btn_mountPos5.setText(self.ui.le_parkPos5Text.text()))\n self.ui.le_parkPos6Text.textChanged.connect(lambda: self.ui.btn_mountPos6.setText(self.ui.le_parkPos6Text.text()))\n self.ui.btn_setHorizonLimitHigh.clicked.connect(self.setHorizonLimitHigh)\n self.ui.btn_setHorizonLimitLow.clicked.connect(self.setHorizonLimitLow)\n self.ui.btn_setSlewRate.clicked.connect(self.setSlewRate)\n self.ui.btn_setDualTracking.clicked.connect(self.setDualTracking)\n self.ui.btn_setUnattendedFlip.clicked.connect(self.setUnattendedFlip)\n if platform.system() == 'Windows':\n self.ui.btn_setupMountDriver.clicked.connect(self.mount.MountAscom.setupDriver)\n self.ui.btn_setupDomeDriver.clicked.connect(self.workerAscomDomeSetup)\n self.ui.btn_setupAscomEnvironmentDriver.clicked.connect(self.workerAscomEnvironmentSetup)\n self.ui.btn_setRefractionParameters.clicked.connect(lambda: self.mountCommandQueue.put('SetRefractionParameter'))\n # setting lambda make the signal / slot a dedicated call. So if you press cancel without lambda, the thread affinity is to modeling,\n # because the signal is passed to the event queue of modeling and handled there. If you press cancel with lambda, the thread\n # affinity is in main, because you don't transfer it to the other event queue, but you leave it to gui event queue.\n self.ui.btn_cancelModel1.clicked.connect(lambda: self.workerModeling.cancelModeling())\n self.ui.btn_cancelModel2.clicked.connect(lambda: self.workerModeling.cancelModeling())\n self.ui.btn_cancelAnalyseModel.clicked.connect(lambda: self.workerModeling.cancelAnalyseModeling())\n self.ui.le_horizonPointsFileName.doubleClicked.connect(self.modelWindow.selectHorizonPointsFileName)\n self.ui.le_modelPointsFileName.doubleClicked.connect(self.selectModelPointsFileName)\n self.ui.checkUseMinimumHorizonLine.stateChanged.connect(self.modelWindow.selectHorizonPointsMode)\n self.ui.checkUseFileHorizonLine.stateChanged.connect(self.modelWindow.selectHorizonPointsMode)\n self.ui.altitudeMinimumHorizon.valueChanged.connect(self.modelWindow.selectHorizonPointsMode)\n self.ui.le_analyseFileName.doubleClicked.connect(self.selectAnalyseFileName)\n self.ui.btn_showActualModel.clicked.connect(lambda: self.mountCommandQueue.put('ShowAlignmentModel'))\n self.ui.checkPolarPlot.clicked.connect(self.setShowAlignmentModelMode)\n self.ui.btn_setRefractionCorrection.clicked.connect(self.setRefractionCorrection)\n self.ui.btn_runTargetRMSAlignment.clicked.connect(lambda: self.mountCommandQueue.put('RunTargetRMSAlignment'))\n self.ui.btn_deleteWorstPoint.clicked.connect(lambda: self.mountCommandQueue.put('DeleteWorstPoint'))\n self.ui.btn_flipMount.clicked.connect(lambda: self.mountCommandQueue.put('FLIP'))\n self.ui.btn_saveBackupModel.clicked.connect(lambda: self.mountCommandQueue.put('SaveBackupModel'))\n self.ui.btn_loadBackupModel.clicked.connect(lambda: self.mountCommandQueue.put('LoadBackupModel'))\n self.ui.btn_saveSimpleModel.clicked.connect(lambda: self.mountCommandQueue.put('SaveSimpleModel'))\n self.ui.btn_loadSimpleModel.clicked.connect(lambda: self.mountCommandQueue.put('LoadSimpleModel'))\n self.ui.btn_saveRefinementModel.clicked.connect(lambda: self.mountCommandQueue.put('SaveRefinementModel'))\n self.ui.btn_loadRefinementModel.clicked.connect(lambda: self.mountCommandQueue.put('LoadRefinementModel'))\n self.ui.btn_saveBaseModel.clicked.connect(lambda: self.mountCommandQueue.put('SaveBaseModel'))\n self.ui.btn_loadBaseModel.clicked.connect(lambda: self.mountCommandQueue.put('LoadBaseModel'))\n self.ui.btn_saveDSO1Model.clicked.connect(lambda: self.mountCommandQueue.put('SaveDSO1Model'))\n self.ui.btn_loadDSO1Model.clicked.connect(lambda: self.mountCommandQueue.put('LoadDSO1Model'))\n self.ui.btn_saveDSO2Model.clicked.connect(lambda: self.mountCommandQueue.put('SaveDSO2Model'))\n self.ui.btn_loadDSO2Model.clicked.connect(lambda: self.mountCommandQueue.put('LoadDSO2Model'))\n self.ui.btn_openAnalyseWindow.clicked.connect(self.analyseWindow.showAnalyseWindow)\n self.ui.btn_openModelingPlotWindow.clicked.connect(self.modelWindow.showModelingPlotWindow)\n self.ui.btn_openImageWindow.clicked.connect(self.imageWindow.showImageWindow)\n self.ui.checkEnableRemoteAccess.stateChanged.connect(self.enableDisableRemoteAccess)\n self.ui.checkEnableINDI.stateChanged.connect(self.enableDisableINDI)\n\n def enableDisableINDI(self):\n # todo: enable INDI Subsystem as soon as INDI is tested\n if self.ui.checkEnableINDI.isChecked():\n self.INDIthread.start()\n else:\n self.INDIworker.stop()\n self.INDIthread.quit()\n self.INDIthread.wait()\n\n def mountBoot(self):\n wol.send_magic_packet(self.ui.le_mountMAC.text().strip())\n self.messageQueue.put('Send WOL and boot mount !')\n self.logger.debug('Send WOL packet and boot Mount')\n\n def mountShutdown(self):\n self.mountCommandQueue.put('Shutdown')\n\n def showModelErrorPolar(self):\n if not self.workerModeling.modelData:\n return\n data = dict()\n for i in range(0, len(self.workerModeling.modelData)):\n for (keyData, valueData) in self.workerModeling.modelData[i].items():\n if keyData == 'azimuth':\n return\n if keyData in data:\n data[keyData].append(valueData)\n else:\n data[keyData] = [valueData]\n self.modelWidget.fig.clf()\n self.modelWidget.axes = self.modelWidget.fig.add_subplot(1, 1, 1, polar=True)\n self.modelWidget.axes.grid(True, color='gray')\n self.modelWidget.fig.subplots_adjust(left=0.025, right=0.975, bottom=0.075, top=0.925)\n self.modelWidget.axes.set_facecolor((32/256, 32/256, 32/256))\n self.modelWidget.axes.tick_params(axis='x', colors='white')\n self.modelWidget.axes.tick_params(axis='y', colors='white')\n self.modelWidget.axes.set_theta_zero_location('N')\n self.modelWidget.axes.set_theta_direction(-1)\n self.modelWidget.axes.set_yticks(range(0, 90, 10))\n yLabel = ['', '80', '', '60', '', '40', '', '20', '', '0']\n self.modelWidget.axes.set_yticklabels(yLabel, color='white')\n azimuth = numpy.asarray(data['Azimuth'])\n altitude = numpy.asarray(data['Altitude'])\n # self.modelWidget.axes.plot(azimuth / 180.0 * math.pi, 90 - altitude, color='black')\n cm = plt.cm.get_cmap('RdYlGn_r')\n colors = numpy.asarray(data['ModelError'])\n # noinspection PyTypeChecker\n scaleError = int(max(colors) / 4 + 1) * 4\n area = [125 if x >= max(colors) else 50 for x in data['ModelError']]\n theta = azimuth / 180.0 * math.pi\n r = 90 - altitude\n scatter = self.modelWidget.axes.scatter(theta, r, c=colors, vmin=0, vmax=scaleError, s=area, cmap=cm)\n scatter.set_alpha(0.75)\n colorbar = self.modelWidget.fig.colorbar(scatter)\n colorbar.set_label('Error [arcsec]', color='white')\n plt.setp(plt.getp(colorbar.ax.axes, 'yticklabels'), color='white')\n self.modelWidget.axes.set_rmax(90)\n self.modelWidget.axes.set_rmin(0)\n self.modelWidget.draw()\n\n def checkASCOM(self):\n appAvailable, appName, appInstallPath = self.checkRegistrationKeys('ASCOM Platform')\n if appAvailable:\n self.messageQueue.put('Found: {0}'.format(appName))\n self.logger.info('Name: {0}, Path: {1}'.format(appName, appInstallPath))\n else:\n self.logger.warning('Application ASCOM not found on computer')\n\n def checkRegistrationKeys(self, appSearchName):\n if platform.machine().endswith('64'):\n regPath = 'SOFTWARE\\\\Wow6432Node\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\\Uninstall' # regpath for 64 bit windows\n else:\n regPath = 'SOFTWARE\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\\Uninstall' # regpath for 32 bit windows\n appInstallPath = ''\n appInstalled = False\n appName = ''\n try:\n key = OpenKey(HKEY_LOCAL_MACHINE, regPath) # open registry\n for i in range(0, QueryInfoKey(key)[0]): # run through all registry application\n name = EnumKey(key, i) # get registry names of applications\n subkey = OpenKey(key, name) # open subkeys of applications\n for j in range(0, QueryInfoKey(subkey)[1]): # run through all subkeys\n values = EnumValue(subkey, j)\n if values[0] == 'DisplayName':\n appName = values[1]\n if values[0] == 'InstallLocation':\n appInstallPath = values[1]\n if appSearchName in appName:\n appInstalled = True\n CloseKey(subkey)\n break\n else:\n CloseKey(subkey) # closing the subkey for later usage\n CloseKey(key) # closing main key for later usage\n if not appInstalled:\n appInstallPath = ''\n appName = ''\n except Exception as e:\n self.logger.debug('Name: {0}, Path: {1}, error: {2}'.format(appName, appInstallPath, e))\n finally:\n return appInstalled, appName, appInstallPath\n\n def checkPlatformDependableMenus(self):\n if platform.system() != 'Windows':\n self.ui.settingsTabWidget.removeTab(5)\n self.ui.settingsTabWidget.removeTab(2)\n\n def initConfig(self):\n try:\n if 'ParkPosText1' in self.config:\n self.ui.le_parkPos1Text.setText(self.config['ParkPosText1'])\n self.ui.btn_mountPos1.setText(self.ui.le_parkPos1Text.text())\n if 'ParkPosAlt1' in self.config:\n self.ui.le_altParkPos1.setText(self.config['ParkPosAlt1'])\n if 'ParkPosAz1' in self.config:\n self.ui.le_azParkPos1.setText(self.config['ParkPosAz1'])\n if 'ParkPosText2' in self.config:\n self.ui.le_parkPos2Text.setText(self.config['ParkPosText2'])\n self.ui.btn_mountPos2.setText(self.ui.le_parkPos2Text.text())\n if 'ParkPosAlt2' in self.config:\n self.ui.le_altParkPos2.setText(self.config['ParkPosAlt2'])\n if 'ParkPosAz2' in self.config:\n self.ui.le_azParkPos2.setText(self.config['ParkPosAz2'])\n if 'ParkPosText3' in self.config:\n self.ui.le_parkPos3Text.setText(self.config['ParkPosText3'])\n self.ui.btn_mountPos3.setText(self.ui.le_parkPos3Text.text())\n if 'ParkPosAlt3' in self.config:\n self.ui.le_altParkPos3.setText(self.config['ParkPosAlt3'])\n if 'ParkPosAz3' in self.config:\n self.ui.le_azParkPos3.setText(self.config['ParkPosAz3'])\n if 'ParkPosText4' in self.config:\n self.ui.le_parkPos4Text.setText(self.config['ParkPosText4'])\n self.ui.btn_mountPos4.setText(self.ui.le_parkPos4Text.text())\n if 'ParkPosAlt4' in self.config:\n self.ui.le_altParkPos4.setText(self.config['ParkPosAlt4'])\n if 'ParkPosAz4' in self.config:\n self.ui.le_azParkPos4.setText(self.config['ParkPosAz4'])\n if 'ParkPosText5' in self.config:\n self.ui.le_parkPos5Text.setText(self.config['ParkPosText5'])\n self.ui.btn_mountPos5.setText(self.ui.le_parkPos5Text.text())\n if 'ParkPosAlt5' in self.config:\n self.ui.le_altParkPos5.setText(self.config['ParkPosAlt5'])\n if 'ParkPosAz5' in self.config:\n self.ui.le_azParkPos5.setText(self.config['ParkPosAz5'])\n if 'ParkPosText6' in self.config:\n self.ui.le_parkPos6Text.setText(self.config['ParkPosText6'])\n self.ui.btn_mountPos6.setText(self.ui.le_parkPos6Text.text())\n if 'ParkPosAlt6' in self.config:\n self.ui.le_altParkPos6.setText(self.config['ParkPosAlt6'])\n if 'ParkPosAz6' in self.config:\n self.ui.le_azParkPos6.setText(self.config['ParkPosAz6'])\n if 'ModelPointsFileName' in self.config:\n self.ui.le_modelPointsFileName.setText(self.config['ModelPointsFileName'])\n if 'CameraBin' in self.config:\n self.ui.cameraBin.setValue(self.config['CameraBin'])\n if 'CameraExposure' in self.config:\n self.ui.cameraExposure.setValue(self.config['CameraExposure'])\n if 'ISOSetting' in self.config:\n self.ui.isoSetting.setValue(self.config['ISOSetting'])\n if 'CheckFastDownload' in self.config:\n self.ui.checkFastDownload.setChecked(self.config['CheckFastDownload'])\n if 'SettlingTime' in self.config:\n self.ui.settlingTime.setValue(self.config['SettlingTime'])\n if 'CheckUseBlindSolve' in self.config:\n self.ui.checkUseBlindSolve.setChecked(self.config['CheckUseBlindSolve'])\n if 'TargetRMS' in self.config:\n self.ui.targetRMS.setValue(self.config['TargetRMS'])\n if 'PixelSize' in self.config:\n self.ui.pixelSize.setValue(self.config['PixelSize'])\n if 'FocalLength' in self.config:\n self.ui.focalLength.setValue(self.config['FocalLength'])\n if 'ScaleSubframe' in self.config:\n self.ui.scaleSubframe.setValue(self.config['ScaleSubframe'])\n if 'CheckDoSubframe' in self.config:\n self.ui.checkDoSubframe.setChecked(self.config['CheckDoSubframe'])\n if 'CheckKeepImages' in self.config:\n self.ui.checkKeepImages.setChecked(self.config['CheckKeepImages'])\n if 'CheckRunTrackingWidget' in self.config:\n self.modelWindow.ui.checkRunTrackingWidget.setChecked(self.config['CheckRunTrackingWidget'])\n if 'CheckClearModelFirst' in self.config:\n self.ui.checkClearModelFirst.setChecked(self.config['CheckClearModelFirst'])\n if 'CheckKeepRefinement' in self.config:\n self.ui.checkKeepRefinement.setChecked(self.config['CheckKeepRefinement'])\n if 'AltitudeBase' in self.config:\n self.ui.altitudeBase.setValue(self.config['AltitudeBase'])\n if 'AzimuthBase' in self.config:\n self.ui.azimuthBase.setValue(self.config['AzimuthBase'])\n if 'NumberGridPointsCol' in self.config:\n self.ui.numberGridPointsCol.setValue(self.config['NumberGridPointsCol'])\n if 'NumberGridPointsRow' in self.config:\n self.ui.numberGridPointsRow.setValue(self.config['NumberGridPointsRow'])\n if 'AltitudeMin' in self.config:\n self.ui.altitudeMin.setValue(self.config['AltitudeMin'])\n if 'AltitudeMax' in self.config:\n self.ui.altitudeMax.setValue(self.config['AltitudeMax'])\n if 'NumberPointsDSO' in self.config:\n self.ui.numberPointsDSO.setValue(self.config['NumberPointsDSO'])\n if 'NumberHoursDSO' in self.config:\n self.ui.numberHoursDSO.setValue(self.config['NumberHoursDSO'])\n if 'AnalyseFileName' in self.config:\n self.ui.le_analyseFileName.setText(self.config['AnalyseFileName'])\n if 'AltitudeTimeChange' in self.config:\n self.ui.altitudeTimeChange.setValue(self.config['AltitudeTimeChange'])\n if 'AzimuthTimeChange' in self.config:\n self.ui.azimuthTimeChange.setValue(self.config['AzimuthTimeChange'])\n if 'NumberRunsTimeChange' in self.config:\n self.ui.numberRunsTimeChange.setValue(self.config['NumberRunsTimeChange'])\n if 'DelayTimeTimeChange' in self.config:\n self.ui.delayTimeTimeChange.setValue(self.config['DelayTimeTimeChange'])\n if 'AltitudeHysterese1' in self.config:\n self.ui.altitudeHysterese1.setValue(self.config['AltitudeHysterese1'])\n if 'AltitudeHysterese2' in self.config:\n self.ui.altitudeHysterese2.setValue(self.config['AltitudeHysterese2'])\n if 'AzimuthHysterese1' in self.config:\n self.ui.azimuthHysterese1.setValue(self.config['AzimuthHysterese1'])\n if 'AzimuthHysterese2' in self.config:\n self.ui.azimuthHysterese2.setValue(self.config['AzimuthHysterese2'])\n if 'NumberRunsHysterese' in self.config:\n self.ui.numberRunsHysterese.setValue(self.config['NumberRunsHysterese'])\n if 'DelayTimeHysterese' in self.config:\n self.ui.delayTimeHysterese.setValue(self.config['DelayTimeHysterese'])\n if 'WindowPositionX' in self.config:\n self.move(self.config['WindowPositionX'], self.config['WindowPositionY'])\n except Exception as e:\n self.logger.error('Item in config.cfg not be initialize, error:{0}'.format(e))\n finally:\n pass\n\n def storeConfig(self):\n self.config['ParkPosText1'] = self.ui.le_parkPos1Text.text()\n self.config['ParkPosAlt1'] = self.ui.le_altParkPos1.text()\n self.config['ParkPosAz1'] = self.ui.le_azParkPos1.text()\n self.config['ParkPosText2'] = self.ui.le_parkPos2Text.text()\n self.config['ParkPosAlt2'] = self.ui.le_altParkPos2.text()\n self.config['ParkPosAz2'] = self.ui.le_azParkPos2.text()\n self.config['ParkPosText3'] = self.ui.le_parkPos3Text.text()\n self.config['ParkPosAlt3'] = self.ui.le_altParkPos3.text()\n self.config['ParkPosAz3'] = self.ui.le_azParkPos3.text()\n self.config['ParkPosText4'] = self.ui.le_parkPos4Text.text()\n self.config['ParkPosAlt4'] = self.ui.le_altParkPos4.text()\n self.config['ParkPosAz4'] = self.ui.le_azParkPos4.text()\n self.config['ParkPosText5'] = self.ui.le_parkPos5Text.text()\n self.config['ParkPosAlt5'] = self.ui.le_altParkPos5.text()\n self.config['ParkPosAz5'] = self.ui.le_azParkPos5.text()\n self.config['ParkPosText6'] = self.ui.le_parkPos6Text.text()\n self.config['ParkPosAlt6'] = self.ui.le_altParkPos6.text()\n self.config['ParkPosAz6'] = self.ui.le_azParkPos6.text()\n self.config['ModelPointsFileName'] = self.ui.le_modelPointsFileName.text()\n self.config['CameraBin'] = self.ui.cameraBin.value()\n self.config['CameraExposure'] = self.ui.cameraExposure.value()\n self.config['CheckFastDownload'] = self.ui.checkFastDownload.isChecked()\n self.config['ISOSetting'] = self.ui.isoSetting.value()\n self.config['SettlingTime'] = self.ui.settlingTime.value()\n self.config['CheckUseBlindSolve'] = self.ui.checkUseBlindSolve.isChecked()\n self.config['TargetRMS'] = self.ui.targetRMS.value()\n self.config['PixelSize'] = self.ui.pixelSize.value()\n self.config['FocalLength'] = self.ui.focalLength.value()\n self.config['ScaleSubframe'] = self.ui.scaleSubframe.value()\n self.config['CheckDoSubframe'] = self.ui.checkDoSubframe.isChecked()\n self.config['CheckKeepImages'] = self.ui.checkKeepImages.isChecked()\n self.config['CheckRunTrackingWidget'] = self.modelWindow.ui.checkRunTrackingWidget.isChecked()\n self.config['AltitudeBase'] = self.ui.altitudeBase.value()\n self.config['AzimuthBase'] = self.ui.azimuthBase.value()\n self.config['NumberGridPointsRow'] = self.ui.numberGridPointsRow.value()\n self.config['NumberGridPointsCol'] = self.ui.numberGridPointsCol.value()\n self.config['AltitudeMin'] = self.ui.altitudeMin.value()\n self.config['AltitudeMax'] = self.ui.altitudeMax.value()\n self.config['NumberPointsDSO'] = self.ui.numberPointsDSO.value()\n self.config['NumberHoursDSO'] = self.ui.numberHoursDSO.value()\n self.config['WindowPositionX'] = self.pos().x()\n self.config['WindowPositionY'] = self.pos().y()\n self.config['AnalyseFileName'] = self.ui.le_analyseFileName.text()\n self.config['AltitudeTimeChange'] = self.ui.altitudeTimeChange.value()\n self.config['AzimuthTimeChange'] = self.ui.azimuthTimeChange.value()\n self.config['NumberRunsTimeChange'] = self.ui.numberRunsTimeChange.value()\n self.config['DelayTimeTimeChange'] = self.ui.delayTimeTimeChange.value()\n self.config['AltitudeHysterese1'] = self.ui.altitudeHysterese1.value()\n self.config['AltitudeHysterese2'] = self.ui.altitudeHysterese2.value()\n self.config['AzimuthHysterese1'] = self.ui.azimuthHysterese1.value()\n self.config['AzimuthHysterese2'] = self.ui.azimuthHysterese2.value()\n self.config['NumberRunsHysterese'] = self.ui.numberRunsHysterese.value()\n self.config['DelayTimeHysterese'] = self.ui.delayTimeHysterese.value()\n self.config['CheckClearModelFirst'] = self.ui.checkClearModelFirst.isChecked()\n self.config['CheckKeepRefinement'] = self.ui.checkKeepRefinement.isChecked()\n\n def loadConfigData(self):\n try:\n with open('config/config.cfg', 'r') as data_file:\n return json.load(data_file)\n except Exception as e:\n self.messageQueue.put('Config.cfg could not be loaded !')\n self.logger.error('Item in config.cfg not loaded error:{0}'.format(e))\n return {}\n\n def saveConfigData(self):\n self.storeConfig()\n self.mount.storeConfig()\n self.workerModeling.storeConfig()\n if platform.system() == 'Windows':\n self.workerAscomEnvironment.storeConfig()\n self.workerAscomDome.storeConfig()\n self.workerUpload.storeConfig()\n self.modelWindow.storeConfig()\n self.imageWindow.storeConfig()\n self.analyseWindow.storeConfig()\n self.relays.storeConfig()\n self.INDIworker.storeConfig()\n try:\n if not os.path.isdir(os.getcwd() + '/config'): # if config dir doesn't exist, make it\n os.makedirs(os.getcwd() + '/config') # if path doesn't exist, generate is\n with open('config/config.cfg', 'w') as outfile:\n json.dump(self.config, outfile)\n outfile.close()\n except Exception as e:\n self.messageQueue.put('Config.cfg could not be saved !')\n self.logger.error('Item in config.cfg not saved error {0}'.format(e))\n return\n self.mount.saveActualModel() # save current loaded modeling from mount\n\n def saveConfigQuit(self):\n self.saveConfigData()\n # noinspection PyArgumentList\n QCoreApplication.instance().quit()\n\n def saveConfig(self):\n self.saveConfigData()\n self.messageQueue.put('Configuration saved.')\n\n def selectModelPointsFileName(self):\n dlg = QFileDialog()\n dlg.setViewMode(QFileDialog.List)\n dlg.setNameFilter(\"Text files (*.txt)\")\n dlg.setFileMode(QFileDialog.ExistingFile)\n # noinspection PyArgumentList\n a = dlg.getOpenFileName(self, 'Open file', os.getcwd()+'/config', 'Text files (*.txt)')\n if a[0] != '':\n self.ui.le_modelPointsFileName.setText(os.path.basename(a[0]))\n else:\n self.logger.warning('no file selected')\n\n def selectAnalyseFileName(self):\n dlg = QFileDialog()\n dlg.setViewMode(QFileDialog.List)\n dlg.setNameFilter(\"Data Files (*.dat)\")\n dlg.setFileMode(QFileDialog.AnyFile)\n # noinspection PyArgumentList\n a = dlg.getOpenFileName(self, 'Open file', os.getcwd()+'/analysedata', 'Data Files (*.dat)')\n if a[0] != '':\n self.ui.le_analyseFileName.setText(os.path.basename(a[0]))\n else:\n self.logger.warning('no file selected')\n\n def setHorizonLimitHigh(self):\n _value = int(self.ui.le_horizonLimitHigh.text())\n if _value < 0:\n _value = 0\n elif _value > 90:\n _value = 90\n self.mountCommandQueue.put('Sh+{0:02d}'.format(_value))\n\n def setHorizonLimitLow(self):\n _value = int(self.ui.le_horizonLimitLow.text())\n if _value < 0:\n _value = 0\n elif _value > 90:\n _value = 90\n self.mountCommandQueue.put('So+{0:02d}'.format(_value))\n\n def setDualTracking(self):\n _value = self.ui.le_telescopeDualTrack.text()\n if _value == 'ON':\n _value = 0\n self.ui.le_telescopeDualTrack.setText('OFF')\n else:\n _value = 1\n self.ui.le_telescopeDualTrack.setText('ON')\n self.mountCommandQueue.put('Sdat{0:01d}'.format(_value))\n\n def setUnattendedFlip(self):\n _value = self.ui.le_telescopeUnattendedFlip.text()\n if _value == 'ON':\n _value = 0\n self.ui.le_telescopeUnattendedFlip.setText('OFF')\n else:\n _value = 1\n self.ui.le_telescopeUnattendedFlip.setText('ON')\n self.mountCommandQueue.put('Suaf{0: 01d}'.format(_value))\n\n def setSlewRate(self):\n _value = int(self.ui.le_slewRate.text())\n if _value < 1:\n _value = 1\n elif _value > 15:\n _value = 15\n self.mountCommandQueue.put('Sw{0:02d}'.format(_value))\n\n def setRefractionCorrection(self):\n _value = self.ui.le_refractionStatus.text()\n if _value == 'ON':\n _value = 0\n self.ui.le_refractionStatus.setText('OFF')\n else:\n _value = 1\n self.ui.le_refractionStatus.setText('ON')\n self.mountCommandQueue.put('SREF{0: 01d}'.format(_value))\n\n def mountPosition1(self):\n self.mountCommandQueue.put('PO') # unpark first\n self.mountCommandQueue.put('Sz{0:03d}*00'.format(int(self.ui.le_azParkPos1.text()))) # set az\n self.mountCommandQueue.put('Sa+{0:02d}*00'.format(int(self.ui.le_altParkPos1.text()))) # set alt\n self.mountCommandQueue.put('MA') # start Slewing\n\n def mountPosition2(self):\n self.mountCommandQueue.put('PO') # unpark first\n self.mountCommandQueue.put('Sz{0:03d}*00'.format(int(self.ui.le_azParkPos2.text()))) # set az\n self.mountCommandQueue.put('Sa+{0:02d}*00'.format(int(self.ui.le_altParkPos2.text()))) # set alt\n self.mountCommandQueue.put('MA') # start Slewing\n\n def mountPosition3(self):\n self.mountCommandQueue.put('PO') # unpark first\n self.mountCommandQueue.put('Sz{0:03d}*00'.format(int(self.ui.le_azParkPos3.text()))) # set az\n self.mountCommandQueue.put('Sa+{0:02d}*00'.format(int(self.ui.le_altParkPos3.text()))) # set alt\n self.mountCommandQueue.put('MA') # start Slewing\n\n def mountPosition4(self):\n self.mountCommandQueue.put('PO') # unpark first\n self.mountCommandQueue.put('Sz{0:03d}*00'.format(int(self.ui.le_azParkPos4.text()))) # set az\n self.mountCommandQueue.put('Sa+{0:02d}*00'.format(int(self.ui.le_altParkPos4.text()))) # set alt\n self.mountCommandQueue.put('MA') # start Slewing\n\n def mountPosition5(self):\n self.mountCommandQueue.put('PO') # unpark first\n self.mountCommandQueue.put('Sz{0:03d}*00'.format(int(self.ui.le_azParkPos5.text()))) # set az\n self.mountCommandQueue.put('Sa+{0:02d}*00'.format(int(self.ui.le_altParkPos5.text()))) # set alt\n self.mountCommandQueue.put('MA') # start Slewing\n\n def mountPosition6(self):\n self.mountCommandQueue.put('PO') # unpark first\n self.mountCommandQueue.put('Sz{0:03d}*00'.format(int(self.ui.le_azParkPos6.text()))) # set az\n self.mountCommandQueue.put('Sa+{0:02d}*00'.format(int(self.ui.le_altParkPos6.text()))) # set alt\n self.mountCommandQueue.put('MA') # start Slewing\n\n @QtCore.Slot(int)\n def setINDIStatus(self, status):\n if status == 0:\n self.ui.le_INDIStatus.setText('UnconnectedState')\n elif status == 1:\n self.ui.le_INDIStatus.setText('HostLookupState')\n elif status == 2:\n self.ui.le_INDIStatus.setText('ConnectingState')\n elif status == 3:\n self.ui.le_INDIStatus.setText('ConnectedState')\n elif status == 6:\n self.ui.le_INDIStatus.setText('ClosingState')\n else:\n self.ui.le_INDIStatus.setText('Error')\n\n @QtCore.Slot(dict)\n def fillINDIData(self, data):\n if data['Name'] == 'Telescope':\n self.ui.le_INDITelescope.setText(data['value'])\n elif data['Name'] == 'CCD':\n self.ui.le_INDICCD.setText(data['value'])\n elif data['Name'] == 'WEATHER':\n self.ui.le_INDIWeather.setText(data['value'])\n elif data['Name'] == 'CameraStatus':\n self.imageWindow.ui.le_INDICameraStatus.setText(data['value'])\n\n def setShowAlignmentModelMode(self):\n if self.ui.checkPolarPlot.isChecked():\n self.ui.alignErrorStars.setVisible(False)\n else:\n self.ui.alignErrorStars.setVisible(True)\n\n @QtCore.Slot(bool)\n def setMountStatus(self, status):\n if status:\n self.ui.btn_driverMountConnected.setStyleSheet('QPushButton {background-color: green;}')\n else:\n self.ui.btn_driverMountConnected.setStyleSheet('QPushButton {background-color: red;}')\n\n @QtCore.Slot(dict)\n def fillMountData(self):\n for valueName in self.mount.data:\n if valueName == 'Reply':\n pass\n if valueName == 'DualAxisTracking':\n if self.mount.data[valueName] == '1':\n self.ui.le_telescopeDualTrack.setText('ON')\n else:\n self.ui.le_telescopeDualTrack.setText('OFF')\n if valueName == 'NumberAlignmentStars':\n self.ui.le_alignNumberStars.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelRMSError':\n self.ui.le_alignErrorRMS.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelErrorPosAngle':\n self.ui.le_alignErrorPosAngle.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelPolarError':\n self.ui.le_alignErrorPolar.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelOrthoError':\n self.ui.le_alignErrorOrtho.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelTerms':\n self.ui.le_alignNumberTerms.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelKnobTurnAz':\n self.ui.le_alignKnobTurnAz.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelKnobTurnAlt':\n self.ui.le_alignKnobTurnAlt.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelErrorAz':\n self.ui.le_alignErrorAz.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelErrorAlt':\n self.ui.le_alignErrorAlt.setText(str(self.mount.data[valueName]))\n if valueName == 'ModelStarError':\n self.ui.alignErrorStars.setText(self.mount.data[valueName])\n if valueName == 'CurrentHorizonLimitLow':\n self.ui.le_horizonLimitLow.setText(str(self.mount.data[valueName]))\n if valueName == 'CurrentHorizonLimitHigh':\n self.ui.le_horizonLimitHigh.setText(str(self.mount.data[valueName]))\n if valueName == 'CurrentSiteLongitude':\n self.ui.le_siteLongitude.setText(str(self.mount.data[valueName]))\n if valueName == 'CurrentSiteLatitude':\n self.ui.le_siteLatitude.setText(str(self.mount.data[valueName]))\n if valueName == 'CurrentSiteElevation':\n self.ui.le_siteElevation.setText(str(self.mount.data[valueName]))\n if valueName == 'JulianDate':\n self.ui.le_JulianDate.setText(str(self.mount.data[valueName]))\n if valueName == 'LocalSiderealTime':\n self.ui.le_localSiderealTime.setText(str(self.mount.data[valueName]))\n if valueName == 'TelescopeTempDEC':\n self.ui.le_telescopeTempDECMotor.setText(str(self.mount.data[valueName]))\n if valueName == 'RefractionTemperature':\n self.ui.le_refractionTemperature.setText(str(self.mount.data[valueName]))\n if valueName == 'RefractionPressure':\n self.ui.le_refractionPressure.setText(str(self.mount.data[valueName]))\n if valueName == 'RefractionStatus':\n if self.mount.data[valueName] == '1':\n self.ui.le_refractionStatus.setText('ON')\n else:\n self.ui.le_refractionStatus.setText('OFF')\n if valueName == 'MountStatus':\n self.ui.le_mountStatus.setText(str(self.mount.statusReference[self.mount.data[valueName]]))\n if valueName == 'TelescopeDEC':\n self.ui.le_telescopeDEC.setText(self.mount.data[valueName])\n if valueName == 'TelescopeRA':\n self.ui.le_telescopeRA.setText(str(self.mount.data[valueName]))\n if valueName == 'TelescopeAltitude':\n self.ui.le_telescopeAltitude.setText(str(self.mount.data[valueName]))\n self.modelWindow.ui.le_telescopeAltitude.setText(str(self.mount.data[valueName]))\n if valueName == 'TelescopeAzimuth':\n self.ui.le_telescopeAzimut.setText(str(self.mount.data[valueName]))\n self.modelWindow.ui.le_telescopeAzimut.setText(str(self.mount.data[valueName]))\n if valueName == 'SlewRate':\n self.ui.le_slewRate.setText(str(self.mount.data[valueName]))\n if valueName == 'MeridianLimitTrack':\n self.ui.le_meridianLimitTrack.setText(str(self.mount.data[valueName]))\n if valueName == 'MeridianLimitSlew':\n self.ui.le_meridianLimitSlew.setText(str(self.mount.data[valueName]))\n if valueName == 'UnattendedFlip':\n if self.mount.data[valueName] == '1':\n self.ui.le_telescopeUnattendedFlip.setText('ON')\n else:\n self.ui.le_telescopeUnattendedFlip.setText('OFF')\n if valueName == 'TimeToFlip':\n self.ui.le_timeToFlip.setText(str(self.mount.data[valueName]))\n if valueName == 'TimeToMeridian':\n self.ui.le_timeToMeridian.setText(str(self.mount.data[valueName]))\n if valueName == 'FirmwareProductName':\n self.ui.le_firmwareProductName.setText(str(self.mount.data[valueName]))\n if valueName == 'FirmwareNumber':\n self.ui.le_firmwareNumber.setText(str(self.mount.data[valueName]))\n if valueName == 'FirmwareDate':\n self.ui.le_firmwareDate.setText(str(self.mount.data[valueName]))\n if valueName == 'FirmwareTime':\n self.ui.le_firmwareTime.setText(str(self.mount.data[valueName]))\n if valueName == 'HardwareVersion':\n self.ui.le_hardwareVersion.setText(str(self.mount.data[valueName]))\n if valueName == 'TelescopePierSide':\n self.ui.le_telescopePierSide.setText(str(self.mount.data[valueName]))\n if valueName == 'UTCDataValid':\n if self.mount.data[valueName] == 'V':\n self.ui.le_UTCDataValid.setText('VALID')\n elif self.mount.data[valueName] == 'E':\n self.ui.le_UTCDataValid.setText('EXPIRED')\n else:\n self.ui.le_UTCDataValid.setText('INVALID')\n if valueName == 'UTCDataExpirationDate':\n self.ui.le_UTCDataExpirationDate.setText(str(self.mount.data[valueName]))\n\n @QtCore.Slot(int)\n def setCameraPlateStatus(self, status):\n if status == 3:\n self.ui.btn_camPlateConnected.setStyleSheet('QPushButton {background-color: green;}')\n elif status == 2:\n self.ui.btn_camPlateConnected.setStyleSheet('QPushButton {background-color: yellow;}')\n elif status == 1:\n self.ui.btn_camPlateConnected.setStyleSheet('QPushButton {background-color: red;}')\n else:\n self.ui.btn_camPlateConnected.setStyleSheet('QPushButton {background-color: gray;}')\n\n def mainLoop(self):\n self.fillMountData()\n self.fillEnvironmentData()\n while not self.INDIDataQueue.empty():\n data = self.INDIDataQueue.get()\n self.fillINDIData(data)\n while not self.messageQueue.empty():\n text = self.messageQueue.get()\n self.ui.errorStatus.setText(self.ui.errorStatus.toPlainText() + text + '\\n')\n self.messageQueue.task_done()\n self.ui.errorStatus.moveCursor(QTextCursor.End)\n while not self.imageQueue.empty():\n filename = self.imageQueue.get()\n if self.imageWindow.showStatus:\n self.imageWindow.showFitsImage(filename)\n while not self.modelLogQueue.empty():\n text = self.modelLogQueue.get()\n if text == 'delete':\n self.modelWindow.ui.modellingLog.clear()\n elif text == 'backspace':\n for i in range(0, 6):\n self.modelWindow.ui.modellingLog.textCursor().deletePreviousChar()\n elif text.startswith('status'):\n self.modelWindow.ui.le_modelingStatus.setText(text[6:])\n elif text.startswith('percent'):\n self.modelWindow.ui.bar_modelingStatusPercent.setValue(int(1000 * float(text[7:])))\n elif text.startswith('timeleft'):\n self.modelWindow.ui.le_modelingStatusTime.setText(text[8:])\n elif text.startswith('#BW'):\n self.modelWindow.ui.modellingLog.setTextColor(self.COLOR_WHITE)\n self.modelWindow.ui.modellingLog.setFontWeight(QFont.Bold)\n self.modelWindow.ui.modellingLog.insertPlainText(text[3:])\n elif text.startswith('#BG'):\n self.modelWindow.ui.modellingLog.setTextColor(self.COLOR_GREEN)\n self.modelWindow.ui.modellingLog.setFontWeight(QFont.Bold)\n self.modelWindow.ui.modellingLog.insertPlainText(text[3:])\n elif text.startswith('#BY'):\n self.modelWindow.ui.modellingLog.setTextColor(self.COLOR_YELLOW)\n self.modelWindow.ui.modellingLog.setFontWeight(QFont.Bold)\n self.modelWindow.ui.modellingLog.insertPlainText(text[3:])\n else:\n self.modelWindow.ui.modellingLog.setTextColor(self.COLOR_ASTRO)\n self.modelWindow.ui.modellingLog.setFontWeight(QFont.Normal)\n self.modelWindow.ui.modellingLog.insertPlainText(text)\n self.modelWindow.ui.modellingLog.moveCursor(QTextCursor.End)\n self.modelLogQueue.task_done()\n # noinspection PyCallByClass,PyTypeChecker\n QTimer.singleShot(500, self.mainLoop)\n\n\nif __name__ == \"__main__\":\n import traceback\n import warnings\n\n def except_hook(typeException, valueException, tbackException): # manage unhandled exception here\n logging.error(traceback.format_exception(typeException, valueException, tbackException))\n sys.__excepthook__(typeException, valueException, tbackException) # then call the default handler\n\n BUILD_NO = '2.7.1 beta'\n\n warnings.filterwarnings(\"ignore\")\n name = 'mount.{0}.log'.format(datetime.datetime.now().strftime(\"%Y-%m-%d\"))\n handler = logging.handlers.RotatingFileHandler(name, backupCount=3)\n logging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s [%(levelname)7s][%(filename)20s][%(lineno)5s][%(funcName)20s][%(threadName)10s] - %(message)s',\n handlers=[handler], datefmt='%Y-%m-%d %H:%M:%S')\n\n if not os.path.isdir(os.getcwd() + '/analysedata'):\n os.makedirs(os.getcwd() + '/analysedata')\n if not os.path.isdir(os.getcwd() + '/images'):\n os.makedirs(os.getcwd() + '/images')\n if not os.path.isdir(os.getcwd() + '/config'):\n os.makedirs(os.getcwd() + '/config')\n\n logging.info('-----------------------------------------')\n logging.info('MountWizzard v ' + BUILD_NO + ' started !')\n logging.info('-----------------------------------------')\n logging.info('Platform: ' + platform.system())\n logging.info('Release: ' + platform.release())\n logging.info('Version: ' + platform.version())\n logging.info('Machine: ' + platform.machine())\n\n logging.info('working directory: {0}'.format(os.getcwd()))\n if not os.access(os.getcwd(), os.W_OK):\n logging.error('no write access to workdir')\n if not os.access(os.getcwd() + '/images', os.W_OK):\n logging.error('no write access to /images')\n if not os.access(os.getcwd() + '/config', os.W_OK):\n logging.error('no write access to /config')\n if not os.access(os.getcwd() + '/analysedata', os.W_OK):\n logging.error('no write access to /analysedata')\n\n app = QApplication(sys.argv)\n\n sys.excepthook = except_hook\n # noinspection PyCallByClass,PyTypeChecker,PyArgumentList\n app.setStyle(QStyleFactory.create('Fusion'))\n app.setWindowIcon(QIcon('mw.ico'))\n\n mountApp = MountWizzardApp()\n if mountApp.modelWindow.showStatus:\n mountApp.modelWindow.redrawModelingWindow()\n mountApp.modelWindow.showModelingPlotWindow()\n if mountApp.imageWindow.showStatus:\n mountApp.imageWindow.showImageWindow()\n if mountApp.analyseWindow.showStatus:\n mountApp.analyseWindow.showAnalyseWindow()\n mountApp.show()\n sys.exit(app.exec_())\n","sub_path":"mountwizzard/mountwizzard.py","file_name":"mountwizzard.py","file_ext":"py","file_size_in_byte":58287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"271958946","text":"import csv\nfrom datetime import datetime\n\nfrom dtk.generic.demographics import distribution_types\nfrom dtk.tools.demographics.Node import Node, nodeid_from_lat_lon\n\nfrom simtools.Utilities.General import init_logging\nlogger = init_logging('DemographicsGenerator')\n\nclass InvalidResolution(BaseException):\n pass\n\nclass DemographicsGenerator:\n \"\"\"\n Generates demographics file based on population input file.\n The population input file is csv with structure::\n\n node_label*, lat, lon, pop*\n\n *-ed columns are optional\n \"\"\"\n\n # mapping of requested arcsecond resolution -> demographic metadata arcsecond resolution.\n # All Hash values must be integers.\n CUSTOM_RESOLUTION = 'custom'\n DEFAULT_RESOLUTION = 30\n VALID_RESOLUTIONS = {\n 30: 30,\n 250: 250,\n CUSTOM_RESOLUTION: 30\n }\n\n def __init__(self, cb, nodes, demographics_type='static', res_in_arcsec=DEFAULT_RESOLUTION,\n update_demographics=None, default_pop=1000):\n \"\"\"\n Initialize the SpatialManager\n\n :param cb: config builder reference, updated after the demographics file is generated.\n :param demographics_type: could be 'static', 'growing' or a different type; currently only static is implemented in generate_nodes(self)\n :param res_in_arsec: sim grid resolution\n :param update_demographics: provide the user with a chance to update the demographics file before it's written via a user-defined function; (e.g. scale larval habitats based on initial population per node in the demographics file) see generate_demographics(self)\n \n :return:\n \"\"\"\n self.nodes = nodes\n\n self.cb = cb\n self.demographics_type = demographics_type\n self.set_resolution(res_in_arcsec)\n self.update_demographics = update_demographics\n\n # demographics data dictionary (working DTK demographics file when dumped as json)\n self.demographics = None\n self.default_pop = default_pop\n\n @staticmethod\n def arcsec_to_deg(arcsec):\n return arcsec / 3600.0\n\n @classmethod\n def from_file(cls, cb, population_input_file, demographics_type='static', res_in_arcsec=DEFAULT_RESOLUTION,\n update_demographics=None, default_pop=1000):\n nodes_list = list()\n with open(population_input_file, 'r') as pop_csv:\n reader = csv.DictReader(pop_csv)\n for row in reader:\n # Latitude\n if not 'lat' in row: raise ValueError('Column lat is required in input population file.')\n lat = float(row['lat'])\n\n # Longitude\n if not 'lon' in row: raise ValueError('Column lon is required in input population file.')\n lon = float(row['lon'])\n\n # Node label\n res_in_deg = cls.arcsec_to_deg(res_in_arcsec)\n node_label = row['node_label'] if 'node_label' in row else nodeid_from_lat_lon(lat, lon, res_in_deg)\n\n # Population\n pop = int(float(row['pop'])) if 'pop' in row else default_pop\n\n # Append the newly created node to the list\n nodes_list.append(Node(lat, lon, pop, node_label))\n\n return cls(cb, nodes_list, demographics_type, res_in_arcsec, update_demographics, default_pop)\n\n def set_demographics_type(self, demographics_type):\n self.demographics_type = demographics_type\n\n def set_update_demographics(self, update_demographics):\n self.update_demographics = update_demographics # callback function\n\n @classmethod\n def validate_res_in_arcsec(cls, res_in_arcsec):\n try:\n cls.VALID_RESOLUTIONS[res_in_arcsec]\n except KeyError:\n raise InvalidResolution(\"%s is not a valid arcsecond resolultion. Must be one of: %s\" %\n (res_in_arcsec, cls.VALID_RESOLUTIONS.keys()))\n\n def set_resolution(self, res_in_arcsec):\n \"\"\"\n The cannonical way to set arcsecond/degree resolutions on a DemographicsGenerator object. Verifies everything\n is set properly.\n\n :param res_in_arcsec: The requested resolution. e.g. 30, 250, 'custom'\n \n :return: No return value.\n \"\"\"\n self.validate_res_in_arcsec(res_in_arcsec)\n self.res_in_arcsec = self.VALID_RESOLUTIONS[res_in_arcsec]\n self.custom_resolution = True if res_in_arcsec == self.CUSTOM_RESOLUTION else False\n self.res_in_degrees = self.arcsec_to_deg(self.res_in_arcsec)\n logger.debug(\"Setting resolution to %s arcseconds (%s deg.) from selection: %s\" %\n (self.res_in_arcsec, self.res_in_degrees, res_in_arcsec))\n\n def generate_defaults(self):\n \"\"\"\n Generate the defaults section of the demographics file\n\n all of the below can be taken care of by a generic Demographics class\n (see note about refactor in dtk.generic.demographics)\n \"\"\"\n # Currently support only static population; after demographics related refactor this whole method will likely disappear anyway\n if self.demographics_type == 'static':\n self.cb.set_param(\"Birth_Rate_Dependence\", \"FIXED_BIRTH_RATE\")\n else:\n raise ValueError(\"Demographics type \" + str(self.demographics_type) + \" is not implemented!\")\n\n exponential_age_param = 0.0001068 # Corresponds to Kayin state age dist\n population_removal_rate = 23 # Based on live births & population in 2014 Kayin state census\n\n mod_mortality = {\n \"NumDistributionAxes\": 2,\n \"AxisNames\": [\"gender\", \"age\"],\n \"AxisUnits\": [\"male=0,female=1\", \"years\"],\n \"AxisScaleFactors\": [1, 365],\n \"NumPopulationGroups\": [2, 1],\n \"PopulationGroups\": [\n [0, 1],\n [0]\n ],\n \"ResultUnits\": \"annual deaths per 1000 individuals\",\n \"ResultScaleFactor\": 2.74e-06,\n \"ResultValues\": [\n [population_removal_rate],\n [population_removal_rate]\n ]\n }\n\n individual_attributes = {\n \"MortalityDistribution\": mod_mortality,\n \"AgeDistributionFlag\": distribution_types[\"EXPONENTIAL_DISTRIBUTION\"],\n \"AgeDistribution1\": exponential_age_param,\n \"RiskDistribution1\": 1,\n \"PrevalenceDistributionFlag\": 1,\n \"AgeDistribution2\": 0,\n \"PrevalenceDistribution1\": 0.13,\n \"PrevalenceDistribution2\": 0.15,\n \"RiskDistributionFlag\": 0,\n \"RiskDistribution2\": 0,\n \"MigrationHeterogeneityDistribution1\": 1,\n \"SusceptibilityDistributionFlag\": 0,\n \"MigrationHeterogeneityDistributionFlag\": 0,\n \"SusceptibilityDistribution1\": 1,\n \"MigrationHeterogeneityDistribution2\": 0,\n \"AgeDistributionFlag\": 3,\n \"SusceptibilityDistribution2\": 0\n }\n\n node_attributes = {\n \"Urban\": 0,\n \"AbovePoverty\": 0.5,\n \"Region\": 1,\n \"Seaport\": 0,\n \"Airport\": 0,\n \"Altitude\": 0\n }\n\n if self.default_pop:\n node_attributes.update({\"InitialPopulation\": self.default_pop})\n\n defaults = {\n 'IndividualAttributes': individual_attributes,\n 'NodeAttributes': node_attributes,\n }\n\n return defaults\n\n def generate_nodes(self):\n \"\"\"\n this function is currently replicated to a large extent in dtk.tools.demographics.node.nodes_for_DTK() but perhaps should not belong there\n it probably belongs to a generic Demographics class (also see one-liner note about refactor in dtk.generic.demographics)\n \"\"\"\n\n nodes = []\n for i, node in enumerate(self.nodes):\n # if res_in_degrees is custom assume node_ids are generated for a household-like setup and not based on lat/lon\n if self.custom_resolution:\n node_id = i + 1\n else:\n node_id = nodeid_from_lat_lon(float(node.lat), float(node.lon), self.res_in_degrees)\n node_attributes = node.to_dict()\n\n if self.demographics_type == 'static':\n # value correspond to a population removal rate of 45: 45/365\n birth_rate = (float(node.pop) / (1000 + 0.0)) * 0.12329\n node_attributes.update({'BirthRate': birth_rate})\n else:\n # perhaps similarly to the DTK we should have error logging modes and good generic types exception raising/handling\n # to avoid code redundancy\n print(self.demographics_type)\n raise ValueError(\"Demographics type \" + str(self.demographics_type) + \" is not implemented!\")\n\n nodes.append({'NodeID': node_id, 'NodeAttributes': node_attributes})\n\n return nodes\n\n def generate_metadata(self):\n \"\"\"\n generate demographics file metadata\n \"\"\"\n\n metadata = {\n \"Author\": \"idm\",\n \"Tool\": \"dtk-tools\",\n \"IdReference\": \"Gridded world grump%darcsec\" % self.res_in_arcsec,\n \"DateCreated\": str(datetime.now()),\n \"NodeCount\": len(self.nodes),\n \"Resolution\": int(self.res_in_arcsec)\n }\n\n return metadata\n\n def generate_demographics(self):\n \"\"\"\n return all demographics file components in a single dictionary; a valid DTK demographics file when dumped as json\n \"\"\"\n self.demographics = {'Nodes': self.generate_nodes(),\n 'Defaults': self.generate_defaults(),\n 'Metadata': self.generate_metadata()}\n\n if self.update_demographics:\n # update demographics before dict is written to file, via a user defined function and arguments\n # self.update_demographics is a partial object (see python docs functools.partial) and self.update_demographics.func references the user's function\n # the only requirement for the user defined function is that it needs to take a keyword argument demographics\n self.update_demographics(demographics=self.demographics)\n\n return self.demographics","sub_path":"dtk/tools/spatialworkflow/DemographicsGenerator.py","file_name":"DemographicsGenerator.py","file_ext":"py","file_size_in_byte":10276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"359841401","text":"\"\"\"\n공유기 설치 https://www.acmicpc.net/problem/2110\npypy3 : 292ms\npython3 : 5412ms\n\"\"\"\n\n\ndef fn(param):\n count = 1\n gong = 0\n for i in range(1, n):\n if data[i] - data[gong] >= param:\n count += 1\n gong = i\n return count >= c\n\n\ndef search():\n lo = -1\n hi = data[-1] + 1\n while lo + 1 < hi:\n mid = (lo + hi) // 2 # mid값은 data의 집의 좌표를 의미하는게 아니다.\n if fn(mid):\n lo = mid\n else:\n hi = mid\n return lo\n\n\nn, c = map(int, input().split())\ndata = [int(input()) for _ in range(n)]\ndata.sort()\n\nprint(search())\n","sub_path":"8주차 이분탐색,그래프/공유기설치/하현준.py","file_name":"하현준.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"311468748","text":"# All imports here\nimport random\n\nclass QGenerator:\n '''\n A class that assists in auto-generating queries for the template generator\n '''\n\n # Number of queries that need to be generated\n num = 0\n\n def __init__(self, num):\n '''\n Parameterized constructor for taking the number of queries to be generated as input\n :param num: Number of queries to be generated\n '''\n self.num = num\n\n # Types of queries that can be generated for the choice of products\n Queries = {\n 0: \"SELECT\",\n 1: \"INSERT\",\n # 2: \"CREATE\",\n # 2: \"DROP\",\n # 3: \"UPDATE\",\n # 4: \"DELETE\",\n # 5: \"TRUNCATE\"\n }\n\n # List of Dummy Names that can be used with their\n Names = {\n 0: \"Pavan\",\n 1: \"Yogeesh\",\n 2: \"Alankar\",\n 3: \"Tim\",\n 4: \"Andy\",\n 5: \"Robert\",\n 6: \"Anna\",\n 7: \"Cindy\",\n 8: \"Nona\",\n 9: \"Vandy\",\n 10: \"Dilan\"\n }\n\n # A list of addresses a user can belong to\n UserAddress = {\n 0: \"California\",\n 1: \"New York\",\n 2: \"Arizona\",\n 3: \"Ohio\",\n 4: \"Washington\",\n 5: \"Texas\",\n 6: \"Philadelphia\",\n 7: \"New Jersey\",\n 8: \"Georgia\",\n 9: \"Florida\",\n 10: \"Vermont\",\n 11: \"Massachusetts\"\n }\n\n # A list of categories an user can choose from\n Category = {\n 0: \"Mobile Phone\",\n 1: \"Laptop\",\n 2: \"Computer Mouse\",\n 3: \"Keyboard\",\n 4: \"Kindle\",\n 5: \"Book\",\n 6: \"CD\",\n 7: \"Headphones\",\n 8: \"Bag\",\n 9: \"Watch\",\n 10: \"Shoe\"\n }\n\n # A user's id which can be used as a foreign key\n userid = 1000\n\n def generateQueries(self):\n '''\n A method that generates queries randomly.\n :return: None\n '''\n # increase the range to generate however many queries are required to be generated\n for id in range(self.num):\n # generates a random query\n query = int(random.uniform(0, 2)) # Changes to be made here\n\n if query == 0:\n print('SELECT * from products where category = \"' + self.Category[int(random.uniform(0, 10))] \\\n + '\" and id = ' + str(self.userid) + ';', end=\" \\n\")\n self.userid += 1\n elif query == 1:\n print('INSERT INTO products(id, name, price, category) VALUES(' + str(self.userid) \\\n + \", '\" + (self.Names[int(random.uniform(0, 10))] + str(id)) \\\n + \"', \" + str(round(random.uniform(0, 1000), 2)) + ', \\'' \\\n + ', \\'' + self.Category[int(random.uniform(0, 10))] + '\\');', end=\" \\n\")\n self.userid += 1\n elif query == 2:\n print('SELECT * from products where category = \"' + self.Category[int(random.uniform(0, 10))] + '\"', end=\" \")\n elif query == 3:\n print('SELECT * from products where category = \"' + self.Category[int(random.uniform(0, 10))] + '\"', end=\" \")\n elif query == 4:\n print('SELECT * from products where category = \"' + self.Category[int(random.uniform(0, 10))] + '\"', end=\" \")\n else:\n print('SELECT * from products where category = \"' + self.Category[int(random.uniform(0, 10))] + '\"', end=\" \")\n\ndef main():\n '''\n The main program\n :return: None\n '''\n while True:\n num = input('Please enter the number of queries that need to be generated: (Eg: 10)')\n if num.isdigit():\n break\n\n num = int(num)\n # Object of class QGenerator\n qGen = QGenerator(num)\n\n # Starts the random query generation\n qGen.generateQueries()\n\n\nif __name__ == '__main__':\n main()","sub_path":"src/QGenerator.py","file_name":"QGenerator.py","file_ext":"py","file_size_in_byte":4150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"316183548","text":"#\n# Copyright (c) 2016 NORDUnet A/S\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or\n# without modification, are permitted provided that the following\n# conditions are met:\n#\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# 2. Redistributions in binary form must reproduce the above\n# copyright notice, this list of conditions and the following\n# disclaimer in the documentation and/or other materials provided\n# with the distribution.\n# 3. Neither the name of the NORDUnet nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n#\n\nimport os, binascii\nimport collections\nfrom time import time\nfrom eduid_common.session.session import SessionManager\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n\ndef manage(action):\n '''\n Decorator which causes a cookie to be set when a session method\n is called.\n\n :param action: Whether the session data has been changed or just accessed.\n When it has been changed, the call to session.commit()\n implies setting the ttl on the backend, so there is no need\n to set it explicitly.\n :type action: str ('accessed'|'changed')\n '''\n def outer(wrapped):\n def accessed(session, *arg, **kw):\n renew_backend = action == 'accessed'\n session.renew_ttl(renew_backend=renew_backend)\n return wrapped(session, *arg, **kw)\n accessed.__doc__ = wrapped.__doc__\n return accessed\n return outer\n\n\nclass SessionFactory(object):\n '''\n Session factory implementing the pyramid.interfaces.ISessionFactory\n interface.\n It uses the SessionManager defined in eduid_common.session.session\n to create sessions backed by redis.\n '''\n\n def __init__(self, settings):\n '''\n SessionFactory constructor.\n\n :param settings: the pyramid settings\n :type settings: dict\n '''\n cookie_max_age = int(settings.get('session.cookie_max_age'))\n # make sure that the data in redis outlives the session cookie\n session_ttl = 2 * cookie_max_age\n secret = settings.get('session.secret')\n self.manager = SessionManager(settings, ttl=session_ttl, secret=secret)\n\n def __call__(self, request):\n '''\n Create a session object for the given request.\n\n :param request: the request\n :type request: pyramid.request.Request\n\n :return: the session\n :rtype: Session\n '''\n raise NotImplementedError()\n\n\nclass Session(collections.MutableMapping):\n '''\n Session implementing the pyramid.interfaces.ISession interface.\n It uses the Session defined in eduid_common.session.session\n to store the session data in redis.\n '''\n\n def __init__(self, request, base_session, new=False):\n '''\n :param request: the request\n :type request: pyramid.request.Request\n :param base_session: The underlying session object\n :type base_session: eduid_common.session.session.Session\n :param new: whether the session is new or not.\n :type new: bool\n '''\n self.request = request\n self._session = base_session\n self._created = time()\n self._new = new\n self._ttl_reset = False\n\n @manage('accessed')\n def __getitem__(self, key, default=None):\n return self._session.__getitem__(key, default=None)\n\n @manage('changed')\n def __setitem__(self, key, value):\n self._session[key] = value\n self._session.commit()\n\n @manage('changed')\n def __delitem__(self, key):\n del self._session[key]\n self._session.commit()\n\n @manage('accessed')\n def __iter__(self):\n return self._session.__iter__()\n\n @manage('accessed')\n def __len__(self):\n return len(self._session)\n\n @manage('accessed')\n def __contains__(self, key):\n return self._session.__contains__(key)\n\n @property\n def created(self):\n '''\n See pyramid.interfaces.ISession\n '''\n return self._created\n\n @property\n def new(self):\n '''\n See pyramid.interfaces.ISession\n '''\n return self._new\n\n def invalidate(self):\n '''\n See pyramid.interfaces.ISession\n '''\n self._session.clear()\n name = self.request.registry.settings.get('session.key')\n domain = self.request.registry.settings.get('session.cookie_domain')\n path = self.request.registry.settings.get('session.cookie_path')\n\n def rm_cookie_callback(request, response):\n response.set_cookie(\n name=name,\n value=None,\n domain=domain,\n path=path,\n max_age=0\n )\n return True\n\n self.request.add_response_callback(rm_cookie_callback)\n\n def changed(self):\n '''\n See pyramid.interfaces.ISession\n '''\n self._session.commit()\n\n @manage('changed')\n def flash(self, msg, queue='', allow_duplicate=True):\n '''\n See pyramid.interfaces.ISession\n '''\n if not queue:\n queue = 'default'\n if 'flash_messages' not in self._session:\n self._session['flash_messages'] = {'default': []}\n if queue not in self._session['flash_messages']:\n self._session['flash_messages'][queue] = []\n if not allow_duplicate:\n if msg in self._session['flash_messages'][queue]:\n return\n self._session['flash_messages'][queue].append(msg)\n self._session.commit()\n\n @manage('changed')\n def pop_flash(self, queue=''):\n '''\n See pyramid.interfaces.ISession\n '''\n if not queue:\n queue = 'default'\n if 'flash_messages' not in self._session:\n self._session['flash_messages'] = {'default': []}\n if queue in self._session['flash_messages']:\n msgs = self._session['flash_messages'].pop(queue)\n self._session.commit()\n return msgs\n return []\n\n @manage('accessed')\n def peek_flash(self, queue=''):\n '''\n See pyramid.interfaces.ISession\n '''\n if not queue:\n queue = 'default'\n if 'flash_messages' not in self._session:\n self._session['flash_messages'] = {'default': []}\n return self._session['flash_messages'].get(queue, [])\n\n @manage('changed')\n def new_csrf_token(self):\n '''\n See pyramid.interfaces.ISession\n '''\n token = binascii.hexlify(os.urandom(20))\n self['_csrft_'] = token\n self._session.commit()\n return token\n\n @manage('accessed')\n def get_csrf_token(self):\n '''\n See pyramid.interfaces.ISession\n '''\n token = self.get('_csrft_', None)\n if token is None:\n token = self.new_csrf_token()\n return token\n\n def persist(self):\n '''\n Store the session data in the redis backend,\n and renew the ttl for it.\n '''\n self._session.commit()\n\n def renew_ttl(self, renew_backend):\n '''\n Reset the ttl for the session, both in the cookie and\n (if `renew_backend==True`) in the redis backend.\n\n :param renew_backend: whether to renew the ttl in the redis backend\n :type renew_backend: bool\n '''\n if not self._ttl_reset:\n self.set_cookie()\n if renew_backend:\n self._session.renew_ttl()\n self._ttl_reset = True\n\n def set_cookie(self):\n '''\n Set the session cookie with the token\n '''\n token = self._session.token\n settings = self.request.registry.settings\n session_name = settings.get('session.key')\n domain = settings.get('session.cookie_domain')\n path = settings.get('session.cookie_path')\n secure = settings.get('session.cookie_secure')\n httponly = settings.get('session.cookie_httponly')\n max_age = settings.get('session.cookie_max_age')\n\n def set_cookie_callback(request, response):\n response.set_cookie(\n name=session_name,\n value=token,\n domain=domain,\n path=path,\n secure=secure,\n httponly=httponly,\n max_age=max_age\n )\n return True\n\n self.request.add_response_callback(set_cookie_callback)\n\n def delete(self):\n '''\n alias for invalidate\n '''\n self.invalidate()\n","sub_path":"src/eduid_common/session/pyramid_session.py","file_name":"pyramid_session.py","file_ext":"py","file_size_in_byte":9645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"411313139","text":"from tkinter import *\n\n\ndef create_text(App, key, fontsize, background_color, given_text):\n setattr(\n App,\n key + '_text',\n Label(App.master,\n bg=background_color,\n text=given_text,\n font=(\"Fixedsys\", fontsize)\n )\n )\n\n\ndef create_variable(App, key, variable, fontsize, background_color):\n setattr(\n App,\n key + '_variable',\n IntVar()\n )\n getattr(App, key + '_variable').set(variable)\n\n setattr(\n App,\n key + '_value',\n Label(App.master,\n bg=background_color,\n textvariable=getattr(App, key + '_variable'),\n font=(\"Fixedsys\", fontsize)\n )\n )\n\n\ndef create_button(App, key):\n path = 'pictures/{}.png'.format(key)\n setattr(\n App,\n key + '_image',\n PhotoImage(file=path)\n )\n setattr(\n App,\n key + '_button',\n Label(App.master, borderwidth=0, image=getattr(App, key + '_image'))\n )\n getattr(\n App,\n key + '_button'\n ).bind('', lambda event: App.update(key))\n\n\ndef create_buttons_and_labels(App):\n \"\"\"Creation of the buttons\"\"\"\n\n # Background\n App.background_image = PhotoImage(file=\"pictures/background.png\")\n App.background_label = Label(App.master, image=App.background_image)\n App.first_background_color = '#663399'\n\n # Main info\n create_text(App, 'current_clicks', 30, App.first_background_color, \"YOUR RESPECT:\")\n create_variable(App, 'current_clicks', App.current_clicks, 30, App.first_background_color)\n App.current_clicks_value.config(width=13)\n\n # CLICKS\n create_text(App, 'click', 20, App.first_background_color, \"Power of F:\")\n create_variable(App, 'click', App.one_click, 20, App.first_background_color)\n\n # AUTO_CLICKS\n\n create_text(App, 'auto_click', 20, App.first_background_color, \"Respect per sec:\")\n create_variable(App, 'auto_click', App.auto_click, 20, App.first_background_color)\n\n # CLICK BUTTON\n App.click_button = Button(App.master,\n bg='#660066',\n activebackground='#993399',\n borderwidth=5,\n text='F',\n command=lambda: App.update(\"CLICK\"),\n font=(\"Fixedsys\", 50)\n )\n App.master.bind(\"\", lambda event: App.update(\"CLICK\"))\n\n # Auto-click buttons\n for name in App.names:\n create_button(App, name)\n create_variable(App, name + '_price', getattr(App, name + '_price'), 20, '#333399')\n\n create_button(App, 'one_click')\n create_variable(App, 'one_click_price', getattr(App, 'one_click_price'), 20, '#333399')\n","sub_path":"kayman233/review_1/src/buttons_and_labels.py","file_name":"buttons_and_labels.py","file_ext":"py","file_size_in_byte":2795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"651707450","text":"#\n#\n#\npokemon = {'Rattata': 19, 'Machop': 66, 'Seel': 86, 'Volbeat': 86, 'Solrock': 126}\np_names = []\np_number = []\nfor name, number in pokemon.items():\n p_names.append(name)\n p_number.append(number)\nprint('p_names =', p_names)\nprint('p_number =', p_number)\n\n#\n#\n#\n\ntrack_medal_counts = {'shot put': 1, 'long jump': 3, '100 meters': 2, '400 meters': 2, '100 meter hurdles': 3, 'triple jump': 3, 'steeplechase': 2, '1500 meters': 1, '5K': 0, '10K': 0, 'marathon': 0, '200 meters': 0, '400 meter hurdles': 0, 'high jump': 1}\ntrack_events = []\nfor event in track_medal_counts.items():\n track_events.append(event[0])\nprint('track_events =', track_events)\n","sub_path":"c2/tuples.py","file_name":"tuples.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"648655685","text":"import os\nimport sys\n\nfrom setuptools import setup, find_packages\n\nextra = {}\nif sys.version_info >= (3, 0):\n extra.update(\n use_2to3=True,\n )\n\nreadme = os.path.join(os.path.dirname(__file__), 'README.rst')\n\nsetup(name='nbt2yaml',\n version=\"0.3.1\",\n description=\"Read and write Minecraft NBT files using Yaml.\",\n long_description=open(readme).read(),\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'License :: OSI Approved :: BSD License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n ],\n keywords='minecraft',\n author='Mike Bayer',\n author_email='mike_mp@zzzcomputing.com',\n url='http://bitbucket.org/zzzeek/nbt2yaml',\n license='BSD',\n packages=find_packages(exclude=['ez_setup', 'tests']),\n zip_safe=False,\n install_requires=['PyYAML'],\n scripts=[\n 'scripts/nbtedit',\n 'scripts/nbt2yaml',\n 'scripts/yaml2nbt'\n ],\n test_suite='nose.collector',\n tests_require=['nose'],\n **extra\n)\n","sub_path":"pypi_install_script/nbt2yaml-0.3.1.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"256105953","text":"from django.conf.urls import url\n\nfrom . import views\n\napp_name = 'gocardless'\nurlpatterns = [\n url(\n r'^redirectflow/create/?$',\n views.RedirectFlowCreateView.as_view(),\n name='redirectflow-create'\n ),\n url(\n r'^redirectflow/success/?$',\n views.RedirectFlowSuccessView.as_view(),\n name='redirectflow-success'\n ),\n\n url(\n r'^webhook/?$',\n views.WebhookView.as_view(),\n name='webhook'\n ),\n url(\n r'^webhook/app/?$',\n views.WebhookView.as_view(),\n name='webhook-app',\n kwargs={\n 'is_app': True\n }\n ),\n\n url(\n r'^redirect/?$',\n views.OAuthRedirectView.as_view(),\n name='redirect'\n ),\n]\n","sub_path":"lunchbreak/django_gocardless/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"32389016","text":"import io\nimport json\nimport platform\nimport stat\nimport subprocess\nimport tempfile\nimport zipfile\nfrom pathlib import Path\n\nimport requests\n\nPLATFORM_TYPE = platform.system()\nBASE_DIR = Path.home() / \".antx\"\n\n\ndef get_bin_metadata():\n \"\"\"Return platfrom_type and binary_name.\"\"\"\n if \"Windows\" in PLATFORM_TYPE:\n return \"windows\", \"dmp.exe\"\n elif \"Darwin\" in PLATFORM_TYPE:\n return \"macos\", \"dmp\"\n else:\n return \"linux\", \"dmp\"\n\n\ndef get_dmp_bin_url(platform_type):\n response = requests.get(\"https://api.github.com/repos/Esukhia/node-dmp-cli/releases/latest\")\n version = response.json()[\"tag_name\"]\n return (\n f\"https://github.com/Esukhia/node-dmp-cli/releases/download/{version}/{platform_type}.zip\",\n version,\n )\n\n\ndef get_dmp_exe_path():\n out_dir = BASE_DIR / \"bin\"\n out_dir.mkdir(exist_ok=True, parents=True)\n\n platform_type, binary_name = get_bin_metadata()\n binary_path = out_dir / binary_name\n if binary_path.is_file():\n return binary_path\n\n url, version = get_dmp_bin_url(platform_type)\n print(f\"[INFO] Downloading node-dmp-cli-{version} ...\")\n r = requests.get(url, stream=True, timeout=50)\n\n # attempt 50 times to download the zip\n check = zipfile.is_zipfile(io.BytesIO(r.content))\n attempts = 0\n while not check and attempts < 50:\n r = requests.get(url, stream=True, timeout=50)\n check = zipfile.is_zipfile(io.BytesIO(r.content))\n attempts += 1\n\n if not check:\n raise IOError(\"the .zip file couldn't be downloaded.\")\n else:\n # extract the zip in the current folder\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(path=str(out_dir))\n\n print(f\"[INFO] Download completed!\")\n\n # make the binary executable\n binary_path.chmod(binary_path.stat().st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)\n return str(binary_path)\n\n\nclass optimized_diff_match_patch:\n def __init__(self):\n self.binary_path = get_dmp_exe_path()\n\n @staticmethod\n def _save_text(text1, text2):\n tmpdir = Path(tempfile.gettempdir())\n text1_path = tmpdir / \"text1.txt\"\n text2_path = tmpdir / \"text2.txt\"\n text1_path.write_text(text1, encoding=\"utf-8\")\n text2_path.write_text(text2, encoding=\"utf-8\")\n return str(text1_path), str(text2_path)\n\n @staticmethod\n def _delete_text(text1_path, text2_path):\n Path(text1_path).unlink()\n Path(text2_path).unlink()\n\n @staticmethod\n def _unescape_lr(diffs):\n \"\"\"Unescape the line-return.\"\"\"\n for diff_type, diff_text in diffs:\n if \"Windows\" in PLATFORM_TYPE:\n yield (diff_type, diff_text.replace(\"\\r\\\\n\", \"\\n\"))\n else:\n yield (diff_type, diff_text.replace(\"\\\\n\", \"\\n\"))\n\n def diff_main(self, text1, text2):\n text1_path, text2_path = self._save_text(text1, text2)\n process = subprocess.Popen(\n [str(self.binary_path), \"diff\", text1_path, text2_path], stdout=subprocess.PIPE\n )\n stdout = process.communicate()[0]\n diffs = json.loads(stdout, encoding=\"utf-8\")\n diffs = self._unescape_lr(diffs)\n self._delete_text(text1_path, text2_path)\n return diffs\n","sub_path":".env/lib/python3.8/site-packages/antx/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"44042577","text":"#!/usr/bin/env python3\n\nfrom lxml import etree\nimport sys, os\nimport shlex\nfrom multiprocessing import Pool\nimport subprocess\nimport time\nimport shutil\n\ndef main():\n\n try:\n ipi_input = sys.argv[1]\n ipi_exe = sys.argv[2]\n driver_com = sys.argv[3]\n max_beads = int(sys.argv[4])\n max_drivers = int(sys.argv[5])\n title = sys.argv[6]\n except:\n print('uffa')\n die()\n\n if not os.path.isfile(ipi_input):\n sys.stderr.write('File {} is not a valid ipi input\\n'.format(ipi_input))\n die()\n \n # if not isexe(ipi_exe):\n # die()\n results = []\n driver_com = shlex.split(driver_com)\n\n drivers_test = [1,2,4,8]\n nbeads_test = [1,2,8]\n \n for nbeads in nbeads_test:\n for drivers in drivers_test:\n\n if drivers <= nbeads:\n print('\\n\\n@@@@@@@@@@@@@ DRIVERS: {:3d} NBEADS: {:3d} @@@@@@@@@@@@@'.format(drivers, nbeads))\n last = run(drivers, nbeads, ipi_input, ipi_exe, driver_com)\n results.append([nbeads, drivers, last])\n print('#{:7s} {:7s} {:12s}\\n'.format('nbeads', 'drivers', 'time'))\n for res in results:\n print(' {:7d} {:7d} {:12.6f}\\n'.format(res[0], res[1], res[2]))\n\n msg = '#{:7s} {:7s} {:12s}\\n'.format('nbeads', 'drivers', 'time')\n for res in results:\n msg += ' {:7d} {:7d} {:12.6f}\\n'.format(res[0], res[1], res[2])\n\n print(msg)\n with open(title + '-results.dat', 'w') as f:\n f.write(msg)\n \n \ndef run(drivers, nbeads, ipi_input, ipi_exe, driver_com):\n with open('timing_ipi_input.xml', 'w') as tmp_file:\n tmp_file.write(make_input(ipi_input, nbeads))\n\n ipi_com = shlex.split(ipi_exe + ' timing_ipi_input.xml')\n\n # Start ipi\n ipi_pid = subprocess.Popen(ipi_com)\n time.sleep(2)\n\n starting_time = time.time()\n\n # Start the pool of the drivers\n pool = Pool(processes=drivers)\n \n out = [pool.apply_async(run_driver,(driver_com, 'run_'+str(idriver))) for idriver in range(drivers)]\n# out = [pool.apply_async(time.sleep,(1,)) for idriver in range(drivers)]\n # pool.terminate()\n pool.close()\n pool.join()\n os.wait()\n finished_at = time.time()\n\n return finished_at - starting_time\n\n\ndef run_driver(com,run_dir):\n if not os.path.exists(run_dir):\n os.makedirs(run_dir)\n cwd = os.getcwd()\n os.chdir(run_dir)\n shutil.copy('../dftb_in.hsd', '.')\n try:\n dr = subprocess.Popen(com, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n # dr = subprocess.check_output(com)\n except:\n pass\n os.chdir(cwd)\n\ndef make_input(ipi_input_path, nbeads):\n with open(ipi_input_path) as inf:\n ipi_input = etree.parse(inf).getroot()\n initialization = ipi_input.find('.//initialize')\n initialization.set('nbeads', str(nbeads))\n return str(etree.tostring(ipi_input, pretty_print=True).decode('ascii'))\n\n\ndef die():\n print('Usage: '+os.path.basename(sys.argv[0])+' ')\n print('Error!')\n sys.exit()\n\ndef isexe(fpath):\n return os.path.isfile(fpath) and os.access(fpath, os.X_OK)\n\nif __name__ == '__main__':\n main()\n","sub_path":"timing_ipi.py","file_name":"timing_ipi.py","file_ext":"py","file_size_in_byte":3266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"319741894","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 30 16:10:34 2020\n\n@author: lcawh\n\"\"\"\ndef clean(filename,var,varshort):\n '''Cleans the weirdly formatted climate data from Ouagadougou, return pandas\n df with just the date and variable.\n Arguments:\n ==========\n filename (str): CSV file in original-data folder\n var(str): variable in English we're looking at\n varshort (str): short version of the var in Fr which is in cell F2 (or\n whatever) to target cleaning'''\n \n import pandas as pd\n import numpy as np\n \n #load data and ditch na and extra rows\n names = ('day','01','02','03','04','05','06','07','08','09','10','11','12')\n df = pd.read_csv(f'../../02-data/original-data/climate/{filename}',names=names)\n df = df.dropna(how='all')\n df = df[(df.day!='DEC')&(df.day!='MOIS')&(df.day!='TOTAL')& \\\n (df.day!='DATE')&(df.day!='JRS')&(df['05']!=varshort)& \\\n (df['04']!='OUAG')]\n \n #add year column (all months have 31 days in this wretched sheet)\n years = []\n for i in range(1961,2005):\n yr = [i]*31\n years = years + yr\n df['year'] = years\n \n #rearrange columns\n cols = df.columns.tolist()\n cols2 = cols[-1:] + cols[:-1]\n df=df[cols2]\n \n #melt into long form and get rid of nans\n value_vars = ['01','02','03','04','05','06','07','08','09','10','11','12']\n df = pd.melt(df,id_vars=['year','day'],\n value_vars=value_vars,\n var_name='month',value_name=var)\n df=df.dropna(how='any')\n \n #add a datetime column for full date\n df['date'] = pd.to_datetime(df[['year','month','day']],dayfirst=True)\n \n #replace random nans bits and pieces\n df = df.replace(to_replace='**',value=np.nan)\n df = df.replace(to_replace='.',value=0)\n df = df.replace(to_replace='TR',value=0)\n df[var] = df[var].astype('float64')\n \n #final ouput just date and var\n final = df[['date',var]]\n \n return final\n\n#################\n# TEST FUNCTION #\n#################\n\nall_rain = clean('rainfall-19612003.csv','rainfall','PLUVI')\nall_rain","sub_path":"01-scripts/data-cleaning/clean_ouaga_data.py","file_name":"clean_ouaga_data.py","file_ext":"py","file_size_in_byte":2108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"271796033","text":"from fbchat import Client\nclass Command:\n\n def __init__(self, parameters, client:Client):\n self.user_params = parameters['user']\n self.author_id = parameters['author_id']\n self.message_object = parameters['message_object']\n self.thread_id = parameters['thread_id']\n self.thread_type = parameters['thread_type']\n self.client = client\n self.author = self.client.fetchUserInfo(self.author_id)[self.author_id]\n\n client.markAsDelivered(self.thread_id, self.message_object.uid)\n client.markAsRead(self.thread_id)\n\n self.run()\n\n def run(self):\n print(\"Running abstract command...\")\n return\n","sub_path":"command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"103855193","text":"import base64\nimport hashlib\nimport hmac\nimport logging\nimport os\nimport time\n\nfrom http.cookies import SimpleCookie\n\nfrom cryptography.exceptions import InvalidTag\nfrom cryptography.hazmat.primitives.ciphers.aead import AESGCM\n\nfrom cryptojwt import as_bytes\nfrom cryptojwt import as_unicode\nfrom cryptojwt import safe_str_cmp\nfrom cryptojwt.jwe import JWEException\nfrom cryptojwt.jwe import split_ctx_and_tag\nfrom oidcendpoint.exception import InvalidCookieSign\nfrom oidcmsg.time_util import in_a_while\n\nfrom oidcservice import rndstr\nfrom oidcmsg import time_util\n\n__author__ = 'Roland Hedberg'\n\nlogger = logging.getLogger(__name__)\n\n\nCORS_HEADERS = [\n (\"Access-Control-Allow-Origin\", \"*\"),\n (\"Access-Control-Allow-Methods\", \"GET\"),\n (\"Access-Control-Allow-Headers\", \"Authorization\")\n]\n\n\ndef _expiration(timeout, time_format=None):\n \"\"\"\n Return an expiration time\n\n :param timeout: When\n :param time_format: The format of the returned value\n :return: A timeout date\n \"\"\"\n if timeout == \"now\":\n return time_util.instant(time_format)\n else:\n # validity time should match lifetime of assertions\n return time_util.in_a_while(minutes=timeout, time_format=time_format)\n\n\ndef cookie_signature(key, *parts):\n \"\"\"Generates a cookie signature.\n\n :param key: The HMAC key to use.\n :type key: bytes\n :param parts: List of parts to include in the MAC\n :type parts: list of bytes or strings\n :returns: hexdigest of the HMAC\n \"\"\"\n\n sha1 = hmac.new(as_bytes(key), digestmod=hashlib.sha1)\n for part in parts:\n if part:\n sha1.update(as_bytes(part))\n return str(sha1.hexdigest())\n\n\ndef verify_cookie_signature(sig, key, *parts):\n \"\"\"Constant time verifier for signatures\n\n :param sig: The signature hexdigest to check\n :type sig: str\n :param key: The HMAC key to use.\n :type key: bytes\n :param parts: List of parts to include in the MAC\n :type parts: list of bytes or strings\n :raises: `InvalidCookieSign` when the signature is wrong\n \"\"\"\n return safe_str_cmp(as_unicode(sig), cookie_signature(key, *parts))\n\n\ndef _make_hashed_key(parts, hashfunc='sha256'):\n \"\"\"\n Construct a key via hashing the parts\n\n If the parts do not have enough entropy of their\n own, this doesn't help.\n\n The size of the hash digest determines the size.\n \"\"\"\n h = hashlib.new(hashfunc)\n for part in parts:\n if part:\n h.update(as_bytes(part))\n return h.digest()\n\n\ndef make_cookie(name, load, seed, domain=\"\", path=\"\", timestamp=\"\",\n enc_key=None, max_age=0):\n \"\"\"\n Create and return a cookie\n\n The cookie is secured against tampering.\n\n If you only provide a `seed`, a HMAC gets added to the cookies value\n and this is checked, when the cookie is parsed again.\n\n If you provide both `seed` and `enc_key`, the cookie gets protected\n by using AEAD encryption. This provides both a MAC over the whole cookie\n and encrypts the `load` in a single step.\n\n The `seed` and `enc_key` parameters should be byte strings of at least\n 16 bytes length each. Those are used as cryptographic keys.\n\n :param name: Cookie name\n :type name: text\n :param load: Cookie load\n :type load: text\n :param seed: A seed key for the HMAC function\n :type seed: byte string\n :param domain: The domain of the cookie\n :param path: The path specification for the cookie\n :param timestamp: A time stamp\n :type timestamp: text\n :param enc_key: The key to use for cookie encryption.\n :type enc_key: byte string\n :param max_age: The time in seconds for when a cookie will be deleted\n :type max_age: int\n :return: A SimpleCookie instance\n \"\"\"\n cookie = SimpleCookie()\n if not timestamp:\n timestamp = str(int(time.time()))\n\n bytes_load = load.encode(\"utf-8\")\n bytes_timestamp = timestamp.encode(\"utf-8\")\n\n if enc_key:\n # Make sure the key is 256-bit long, for AES-128-SIV\n #\n # This should go away once we push the keysize requirements up\n # to the top level APIs.\n key = _make_hashed_key((enc_key, seed))\n\n # key = AESGCM.generate_key(bit_length=128)\n aesgcm = AESGCM(key)\n iv = os.urandom(12)\n\n # timestamp does not need to be encrypted, just MAC'ed,\n # so we add it to 'Associated Data' only.\n ct = split_ctx_and_tag(aesgcm.encrypt(iv, bytes_load, bytes_timestamp))\n\n ciphertext, tag = ct\n cookie_payload = [bytes_timestamp,\n base64.b64encode(iv),\n base64.b64encode(ciphertext),\n base64.b64encode(tag)]\n else:\n cookie_payload = [\n bytes_load, bytes_timestamp,\n cookie_signature(seed, load, timestamp).encode('utf-8')]\n\n cookie[name] = (b\"|\".join(cookie_payload)).decode('utf-8')\n if path:\n cookie[name][\"path\"] = path\n if domain:\n cookie[name][\"domain\"] = domain\n\n if max_age:\n cookie[name][\"expires\"] = in_a_while(seconds=max_age)\n\n return cookie\n\n\ndef parse_cookie(name, seed, kaka, enc_key=None):\n \"\"\"Parses and verifies a cookie value\n\n Parses a cookie created by `make_cookie` and verifies\n it has not been tampered with.\n\n You need to provide the same `seed` and `enc_key`\n used when creating the cookie, otherwise the verification\n fails. See `make_cookie` for details about the verification.\n\n :param seed: A seed key used for the HMAC signature\n :type seed: bytes\n :param kaka: The cookie\n :param enc_key: The encryption key used.\n :type enc_key: bytes or None\n :raises InvalidCookieSign: When verification fails.\n :return: A tuple consisting of (payload, timestamp) or None if parsing fails\n \"\"\"\n if not kaka:\n return None\n\n seed = as_unicode(seed)\n\n parts = cookie_parts(name, kaka)\n if parts is None:\n return None\n elif len(parts) == 3:\n # verify the cookie signature\n clear_text, timestamp, sig = parts\n if not verify_cookie_signature(sig, seed, clear_text, timestamp):\n raise InvalidCookieSign()\n return clear_text, timestamp\n elif len(parts) == 4:\n # encrypted and signed\n timestamp = parts[0]\n iv = base64.b64decode(parts[1])\n ciphertext = base64.b64decode(parts[2])\n tag = base64.b64decode(parts[3])\n ct = ciphertext + tag\n\n # Make sure the key is 32-Bytes long\n key = _make_hashed_key((enc_key, seed))\n aesgcm = AESGCM(key)\n\n # timestamp does not need to be encrypted, just MAC'ed,\n # so we add it to 'Associated Data' only.\n aad = timestamp.encode('utf-8')\n try:\n cleartext = aesgcm.decrypt(iv, ct, aad)\n except (JWEException, InvalidTag) as err:\n raise InvalidCookieSign('{}'.format(err))\n return cleartext.decode('utf-8'), timestamp\n return None\n\n\ndef cookie_parts(name, kaka):\n \"\"\"\n Give me the parts of the cookie payload\n\n :param name: A name of a cookie object\n :param kaka: The cookie\n :return: A list of parts or None if there is no cookie object with the\n given name\n \"\"\"\n cookie_obj = SimpleCookie(as_unicode(kaka))\n morsel = cookie_obj.get(name)\n if morsel:\n return morsel.value.split(\"|\")\n else:\n return None\n\n\nclass CookieDealer(object):\n \"\"\"\n Functionality that an entity that deals with cookies need to have\n access to.\n \"\"\"\n def __init__(self, symkey='', seed_file='seed.txt', cookie=None):\n self.symkey = as_bytes(symkey)\n\n for attr, default in {'path':'', 'domain':'', 'max_age':0}.items():\n if attr not in cookie:\n cookie[attr] = default\n\n self.cookie = cookie\n\n # Need to be able to restart the OP and still use the same seed\n if os.path.isfile(seed_file):\n _seed = open(seed_file).read()\n else:\n _seed = rndstr(48)\n with open('seed.txt', \"w\") as f:\n f.write(_seed)\n\n self.seed= as_bytes(_seed)\n\n def delete_cookie(self, cookie_name=None):\n \"\"\"\n Create a cookie that will immediately expire when it hits the other\n side.\n\n :param cookie_name: Name of the cookie\n :return: A tuple to be added to headers\n \"\"\"\n if cookie_name is None:\n cookie_name = self.cookie['name']\n \n return self.create_cookie(\"\", \"\", cookie_name=cookie_name, ttl=-1,\n kill=True)\n\n def create_cookie(self, value, typ, cookie_name=None, ttl=-1, kill=False):\n \"\"\"\n\n :param value: Part of the cookie payload\n :param typ: Type of cookie\n :param cookie_name:\n :param ttl: Number of minutes before this cookie goes stale\n :param kill: Whether the the cookie should expire on arrival\n :return: A tuple to be added to headers\n \"\"\"\n if kill:\n ttl = -1\n elif ttl < 0:\n ttl = self.cookie['max_age']\n\n if cookie_name is None:\n cookie_name = self.cookie['name']\n\n try:\n srvdomain = self.cookie['domain']\n cookie_domain = \"\" if not srvdomain else srvdomain\n except AttributeError:\n cookie_domain = \"\"\n\n try:\n srvpath = self.cookie['path']\n cookie_path = \"\" if not srvpath else srvpath\n except AttributeError:\n cookie_path = \"\"\n\n # now\n timestamp = str(int(time.time()))\n\n # create cookie payload\n try:\n cookie_payload = \"::\".join([value, timestamp, typ])\n except TypeError:\n cookie_payload = \"::\".join([value[0], timestamp, typ])\n\n cookie = make_cookie(\n cookie_name, cookie_payload, self.seed,\n domain=cookie_domain, path=cookie_path, timestamp=timestamp,\n enc_key=self.symkey, max_age=ttl)\n\n return cookie\n\n def get_cookie_value(self, cookie=None, cookie_name=None):\n \"\"\"\n Return information stored in a Cookie\n\n :param cookie: A cookie instance\n :param cookie_name: The name of the cookie I'm looking for\n :return: tuple (value, timestamp, type)\n \"\"\"\n if cookie_name is None:\n cookie_name = self.cookie['name']\n\n if cookie is None or cookie_name is None:\n return None\n else:\n try:\n info, timestamp = parse_cookie(cookie_name, self.seed, cookie,\n self.symkey)\n except (TypeError, AssertionError):\n return None\n else:\n value, _ts, typ = info.split(\"::\")\n if timestamp == _ts:\n return value, _ts, typ\n return None\n","sub_path":"src/oidcop/cookie.py","file_name":"cookie.py","file_ext":"py","file_size_in_byte":10902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"521431889","text":"\n\nimport os\nimport argparse\nimport logging\n\nimport cv2\nimport numpy\n\n\ndef fix_image_size(image, expected_pixels=2E6):\n ratio = float(expected_pixels) / float(image.shape[0] * image.shape[1])\n return cv2.resize(image, (0, 0), fx=ratio, fy=ratio)\n\n\ndef estimate_blur(image, threshold=100):\n if image.ndim == 3:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n blur_map = cv2.Laplacian(image, cv2.CV_64F)\n score = numpy.var(blur_map)\n return blur_map, score, bool(score < threshold)\n\n\ndef pretty_blur_map(blur_map, sigma=5):\n abs_image = numpy.log(numpy.abs(blur_map).astype(numpy.float32))\n cv2.blur(abs_image, (sigma, sigma))\n return cv2.medianBlur(abs_image, sigma)\n\n\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='run blur detection on a single image')\n parser.add_argument('-i', '--input_image', dest=\"input_image\", type=str, required=True, help=\"directory of images\")\n # parameters\n parser.add_argument(\"-t\", \"--threshold\", dest='threshold', type=float, default=100.0, help=\"blurry threshold\")\n parser.add_argument(\"-f\", \"--fix_size\", dest=\"fix_size\", help=\"fix the image size\", action=\"store_true\")\n # options\n parser.add_argument(\"-v\", \"--verbose\", dest='verbose', help='set logging level to debug', action=\"store_true\")\n parser.add_argument(\"-d\", \"--display\", dest='display', help='display images', action=\"store_true\")\n\n args = parser.parse_args()\n\n if args.verbose:\n logging.basicConfig(level=logging.DEBUG)\n else:\n logging.basicConfig(level=logging.INFO)\n\n assert os.path.exists(args.input_image)\n\n input_image = cv2.imread(args.input_image)\n\n if args.fix_size:\n input_image = fix_image_size(input_image)\n\n blur_map, score, blurry = estimate_blur(input_image)\n\n logging.info(\"score: {0}, blurry: {1}\".format(score, blurry))\n\n if args.display:\n cv2.imshow(\"input\", input_image)\n cv2.imshow(\"result\", pretty_blur_map(blur_map))\n cv2.waitKey(0)\n","sub_path":"single.py","file_name":"single.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"374120217","text":"from django.shortcuts import render\n\n#\nfrom django.http import HttpResponse, JsonResponse, HttpResponseRedirect\nfrom .models import EdinaiaTabliza, SpisokIzmeneniiEdinaiaTabliza\nfrom .serializers import EdinaiaTablizaSerializer, SpisokIzmeneniiEdinaiaTablizaSerializer\nfrom .forms import EdinaiaTablizaFormOUF, EdinaiaTablizaFormAll, EdinaiaTablizaFormOTK, EdinaiaTablizaFormSDO, \\\n EdinaiaTablizaFormUristi, EdinaiaTablizaFormBux\n\n\n# from rest_framework.parsers import JSONParser\n# from django.views.decorators.csrf import csrf_exempt\n#\n#\n# import datetime\n# import os\n# import uuid\n# from django.db.models import Q\n#\n# import xlrd\n# from django.shortcuts import render\n#\n# from django.http import HttpResponse, JsonResponse, HttpResponseRedirect\n# from rest_framework.parsers import JSONParser\n# from django.views.decorators.csrf import csrf_exempt\n#\n# from openpyxl import load_workbook\n# import pandas as pd\n\n\n# @csrf_exempt\n# def novaia_edinaia_tabliza_list(request):\n# if request.method == 'GET':\n# content = EdinaiaTabliza.objects.all()\n#\n# serializer = EdinaiaTablizaSerializer(content, many=True)\n# return JsonResponse({'data': serializer.data}, safe=False)\n#\n#\n\n#\n#\n# @csrf_exempt\n# def novaia_edinaia_tabliza_istoria_izmenenia(request, id):\n# try:\n# content = SpisokIzmeneniiEdinaiaTabliza.objects.filter(novaia_edinaia_tabliza_id=id)\n# except SpisokIzmeneniiEdinaiaTablizaSerializer.DoesNotExist:\n# return HttpResponse(status=404)\n#\n# if request.method == 'GET':\n# serializer = SpisokIzmeneniiEdinaiaTabliza(content, many=True)\n# return JsonResponse(serializer.data, safe=False)\n#\n#\n\n\ndef render_edinaia_tabliza(request):\n forma_redaktirovania_all = EdinaiaTablizaFormAll()\n forma_redaktirovania_ouf = EdinaiaTablizaFormOUF()\n forma_redaktirovania_otk = EdinaiaTablizaFormOTK()\n forma_redaktirovania_sdo = EdinaiaTablizaFormSDO()\n forma_redaktirovania_uristi = EdinaiaTablizaFormUristi()\n forma_redaktirovania_bux = EdinaiaTablizaFormBux()\n return render(request, 'edinaia_tabliza/views/edinaia_tabliza.html', {\n 'forma_redaktirovania_all': forma_redaktirovania_all,\n 'forma_redaktirovania_ouf': forma_redaktirovania_ouf,\n 'forma_redaktirovania_otk': forma_redaktirovania_otk,\n 'forma_redaktirovania_sdo': forma_redaktirovania_sdo,\n 'forma_redaktirovania_uristi': forma_redaktirovania_uristi,\n 'forma_redaktirovania_bux': forma_redaktirovania_bux,\n })\n\n\n#####\n# API\n#####\n\ndef edinaia_tabliza_lv(request):\n if request.method == 'GET':\n content = EdinaiaTabliza.objects.all()\n serializer = EdinaiaTablizaSerializer(content, many=True)\n return JsonResponse({'data': serializer.data}, safe=False)\n\n\n\ndef edinaia_tabliza_dv(request, id):\n try:\n content = EdinaiaTabliza.objects.get(pk=id)\n except EdinaiaTabliza.DoesNotExist:\n return HttpResponse(status=404)\n\n if request.method == 'GET':\n serializer = EdinaiaTablizaSerializer(content)\n istoria_izmenenia = SpisokIzmeneniiEdinaiaTabliza.objects.filter(edinaia_tabliza_id=id).order_by('data_redaktirovania')\n istoria_izmenenia = SpisokIzmeneniiEdinaiaTablizaSerializer(istoria_izmenenia, many=True)\n\n return JsonResponse({'data': serializer.data, 'istoria_izmenenia': istoria_izmenenia.data})\n\n # elif request.method == 'PUT':\n # data = JSONParser().parse(request)\n # if data['oldvalue'] == None:\n # data['oldvalue'] = ''\n #\n # if str(data['oldvalue']) == str(data['newvalue']):\n # return HttpResponse(\"Нет изменений\")\n #\n # EdinaiaTabliza.objects.filter(id=id).update(**{data['dataField']: data[\"newvalue\"]})\n # SpisokIzmeneniiEdinaiaTabliza.objects.create(debitori_nasele_id=id, stolbez=data['dataField'],\n # staroe_znachenie=data['oldvalue'], data_redaktirovania=datetime.datetime.now(),\n # novoe_znachenie=data['newvalue'], user=request.user)\n # return HttpResponse(\"Данные обновлены\")\n #\n # elif request.method == 'DELETE':\n # content.delete()\n # return HttpResponse(status=204)","sub_path":"apps/edinaia_tabliza/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"13061496","text":"\"\"\"\n1.\tПользователь вводит данные о количестве предприятий, их наименования и прибыль\nза 4 квартала (т.е. 4 отдельных числа) для каждого предприятия.\nПрограмма должна определить среднюю прибыль (за год для всех предприятий)\nи вывести наименования предприятий, чья прибыль выше среднего и отдельно\nвывести наименования предприятий, чья прибыль ниже среднего.\n\nПодсказка:\nДля решения задачи обязательно примените какую-нибудь коллекцию из модуля collections\nДля лучшее освоения материала можете даже сделать несколько решений этого задания,\nприменив несколько коллекций из модуля collections\n\nПример:\nВведите количество предприятий для расчета прибыли: 2\nВведите название предприятия: Рога\nчерез пробел введите прибыль данного предприятия\nза каждый квартал(Всего 4 квартала): 235 345634 55 235\n\nВведите название предприятия: Копыта\nчерез пробел введите прибыль данного предприятия\nза каждый квартал(Всего 4 квартала): 345 34 543 34\n\nСредняя годовая прибыль всех предприятий: 173557.5\nПредприятия, с прибылью выше среднего значения: Рога\n\nПредприятия, с прибылью ниже среднего значения: Копыта\n\"\"\"\nfrom collections import namedtuple, defaultdict\n\n\"\"\"Решение с использованием namedtuple\"\"\"\n\nfirms = namedtuple('Firm', 'id name q_1 q_2 q_3 q_4 summ')\nfirms_list = []\n\nnum = int(input('Введите количество предприятий для расчета прибыли: '))\nfor i in range(1, num+1):\n id = i\n name = input(f'Введите название предприятия # {i}: ')\n q_1, q_2, q_3, q_4 = input('Через пробел введите прибыль данного предприятия за каждый квартал(всего 4 квартала)').split()\n summ = float(q_1) + float(q_2) + float(q_3) + float(q_4)\n firms_list.append(firms._make((id, name, q_1, q_2, q_3, q_4, summ)))\nprint(firms_list)\n\navg_profit = (sum([i.summ for i in firms_list]) / len(firms_list))\nprint(f'Средняя годовая прибыль всех предприятий: {avg_profit}')\nlist_above_avg = [i.name for i in firms_list if i.summ >= avg_profit]\nprint(f\"Предприятия, с прибылью выше или равно среднего значения: {';'.join(list_above_avg)}\")\nlist_below_avg = [i.name for i in firms_list if i.summ < avg_profit]\nprint(f\"Предприятия, с прибылью ниже среднего значения: {';'.join(list_below_avg)}\")\n\n\n\"\"\"Решение с использованием defaultdict\"\"\"\n\nfirms = defaultdict(list)\n\nnum = int(input('Введите количество предприятий для расчета прибыли: '))\nfor i in range(1, num+1):\n name = input(f'Введите название предприятия # {i}: ')\n profit = [float(i) for i in input('Через пробел введите прибыль данного предприятия за каждый квартал(всего 4 квартала): ').split()]\n firms[name] = profit\nprint(firms)\n\navg_profit = (sum([sum(i) for i in firms.values()]) / len(firms))\nprint(f'Средняя годовая прибыль всех предприятий: {avg_profit}')\nlist_above_avg = [key for key, value in firms.items() if sum(value) >= avg_profit]\nprint(f\"Предприятия, с прибылью выше или равно среднего значения: {';'.join(list_above_avg)}\")\nlist_below_avg = [key for key, value in firms.items() if sum(value) < avg_profit]\nprint(f\"Предприятия, с прибылью ниже среднего значения: {';'.join(list_below_avg)}\")\n\n","sub_path":"Урок 5. Практическое задание/task_1.py","file_name":"task_1.py","file_ext":"py","file_size_in_byte":4471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"147346973","text":"import tensorflow as tf\nimport time\nimport numpy as np\n\nimage_feature_description = {\n 'image_raw': tf.FixedLenFeature([], tf.string),\n 'label': tf.FixedLenFeature([], tf.float32),\n}\n\ndef parse_record(tfrecord):\n proto = tf.parse_single_example(tfrecord, image_feature_description)\n\n image_decoded = tf.image.decode_jpeg(proto['image_raw'], channels=3)\n image_float = tf.image.convert_image_dtype(image_decoded, tf.float32)\n\n return image_float, proto['label']\n\ndef load_dataset(filename):\n raw_dataset = tf.data.TFRecordDataset(filename)\n\n dataset = raw_dataset.map(parse_record)\n dataset = dataset.batch(10)\n return dataset\n\ntraining_dataset = load_dataset(\"D:\\\\speedchallenge\\\\temporal\\\\train.tfrecords\")\nvalidation_dataset = load_dataset(\"D:\\\\speedchallenge\\\\temporal\\\\validation.tfrecords\")\n\niterator = tf.data.Iterator.from_structure(training_dataset.output_types,\n training_dataset.output_shapes)\n\ntraining_init_op = iterator.make_initializer(training_dataset)\nvalidation_init_op = iterator.make_initializer(validation_dataset)\n\n# frame is 640 x 480 pixels, speed is a float\nframe, speed = iterator.get_next()\n\ndef residual_block(input, channels, downsample):\n shortcut = input\n strides = (1, 1)\n if downsample:\n strides = (2, 2)\n shortcut = tf.layers.conv2d(input, channels, (1, 1), strides=strides, padding='same')\n shortcut = tf.layers.batch_normalization(shortcut, training=True)\n \n conv1 = tf.layers.conv2d(input, channels, (3, 3), strides=(1, 1), padding='same')\n conv1 = tf.layers.batch_normalization(conv1, training=True)\n conv1 = tf.nn.relu(conv1)\n\n conv2 = tf.layers.conv2d(conv1, channels, (3, 3), strides=strides, padding='same')\n conv2 = tf.layers.batch_normalization(conv2, training=True)\n\n\n conv2 += shortcut\n output = tf.nn.relu(conv2)\n\n return output\n\n\nout = frame\n\n# 640x480x3 -> 20x15x64\nfor i in range(5):\n out = residual_block(out, 64, True)\n\nout = tf.reshape(out, (-1, 20*15*64))\nout = tf.layers.dense(out, 1)\n\nspeed = tf.expand_dims(speed, 1)\nloss = tf.losses.mean_squared_error(speed, out)\n\ntrain_step = tf.train.AdamOptimizer(1e-4).minimize(loss)\n\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n for epoch in range(1, 31):\n sess.run(training_init_op)\n i = 0\n previous_step_time = time.time()\n while True:\n try:\n i += 1\n l, _ = sess.run((loss, train_step))\n if i % 100 == 0:\n current_step_time = time.time()\n time_elapsed = current_step_time - previous_step_time\n print('epoch {:d} - step {:d} - time {:.2f}s : loss {:.4f}'.format(epoch, i, time_elapsed, l))\n previous_step_time = current_step_time\n except tf.errors.OutOfRangeError:\n break\n\n sess.run(validation_init_op)\n validation_losses = []\n while True:\n try:\n validation_loss = sess.run(loss)\n validation_losses.append(validation_loss)\n except tf.errors.OutOfRangeError:\n break\n print('\\n\\nmse after {} epochs: {:.4f}\\n\\n'.format(epoch, np.mean(validation_losses)))\n","sub_path":"single_frame_predictor.py","file_name":"single_frame_predictor.py","file_ext":"py","file_size_in_byte":3301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"202082306","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 27 12:03:26 2020\n\n@author: sw1906\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# ER Values Vs Control Up and down Expression\n\n\n\n# 7158 IDs (Before NAs removed)\n# 2015 Overexpressed,2478 Underexpressed\n\nER0 = pd.read_csv('GSE45584_ER0_Control.txt', sep = '\\t', header = 0)\nER0 = ER0.drop_duplicates(subset=['Gene.symbol'], keep='first')\n\nER0_OverExp = ER0.loc[ER0['logFC'] > 0]\nER0_OverExp = ER0_OverExp.dropna()\n\n\nER0_UnderExp = ER0.loc[ER0['logFC'] < 0]\nER0_UnderExp = ER0_UnderExp.dropna()\n\n\nER0_Over = ER0_OverExp['Gene.symbol'].tolist()\nER0_Under = ER0_UnderExp['Gene.symbol'].tolist()\n\n\n# 7073 IDs (Before NAs removed)\n# 2188 Overexpressed, 2207 Underexpressed\nER4 = pd.read_csv('GSE45584_ER4_Control.txt', sep = '\\t', header = 0)\nER4 = ER4.drop_duplicates(subset=['Gene.symbol'], keep='first')\n\nER4_OverExp = ER4.loc[ER4['logFC'] > 0]\nER4_OverExp = ER4_OverExp.dropna()\n\n\nER4_UnderExp = ER4.loc[ER4['logFC'] < 0]\nER4_UnderExp = ER4_UnderExp.dropna()\n\n\nER4_Over = ER4_OverExp['Gene.symbol'].tolist()\nER4_Under = ER4_UnderExp['Gene.symbol'].tolist()\n\n# 7126 IDs (Before NAs removed)\n# 2002 Overexpressed, 2309 Underexpressed\nER5 = pd.read_csv('GSE45584_ER5_Control.txt', sep = '\\t', header = 0)\nER5 = ER5.drop_duplicates(subset=['Gene.symbol'], keep='first')\n\nER5_OverExp = ER5.loc[ER5['logFC'] > 0]\nER5_OverExp = ER5_OverExp.dropna()\n\n\nER5_UnderExp = ER5.loc[ER5['logFC'] < 0]\nER5_UnderExp = ER5_UnderExp.dropna()\n\n\nER5_Over = ER5_OverExp['Gene.symbol'].tolist()\nER5_Under = ER5_UnderExp['Gene.symbol'].tolist()\n\n\n# 1041 overexpressed in all ER values\nSharedOverExp = set(ER0_Over) & set(ER4_Over) & set(ER5_Over)\n# 1090 underexpressed in all ER values\nSharedUnderExp = set(ER0_Under) & set(ER4_Under) & set(ER5_Under)\n\n#Look for genes which are underexpressed in some ER values and overexpressed in others\n\n# 19 genes under in ER0, over in ER5\nUnderER0_OverER5 = set(ER0_Under) & set(ER5_Over)\n# 14 genes over in ER5, under in ER0\nUnderER5_OverER0 = set(ER0_Over) & set(ER5_Under)\n\n# 10 genes under in ER0, over in ER4\nUnderER0_OverER4 = set(ER0_Under) & set(ER4_Over)\n# 13 genes over in ER4, under in ER0\nUnderER4_OverER0 = set(ER0_Over) & set(ER4_Under)\n\n# 11 genes under in ER5, over in ER4\nUnderER5_OverER4 = set(ER5_Under) & set(ER4_Over)\n# 8 genes over in ER4, under in ER5\nUnderER4_OverER5 = set(ER5_Over) & set(ER4_Under)\n\n# pairings of two groups showing same expression\n# 6 genes\nUnderER04_OverER5 = set(ER0_Under) & set(ER5_Over) & set(ER4_Under)\n# 6 genes\nOverER04_UnderER5 = set(ER5_Under) & set(ER4_Over) & set(ER0_Over)\n# 5 genes\nUnderER45_overER0 = set(ER5_Under) & set(ER4_Under) & set(ER0_Over)\n# 4 genes\nOverER45_underER0 = set(ER0_Under) & set(ER4_Over) & set(ER5_Over)\n# Weird middle ground jumps\n# 3 Genes\nUnderER05_OverER4 = set(ER0_Under) & set(ER4_Over) & set(ER5_Under)\n# 1 Gene\nOverER05_UnderER4 = set(ER4_Under) & set(ER5_Over) & set(ER0_Over)\n\n\n\n# 50 gene overall when comparing up/down expression between two of the three groups\nsetofall = list(set(list(UnderER0_OverER5) + list(UnderER5_OverER0) + list(UnderER0_OverER4) + list(UnderER4_OverER0) + list(UnderER5_OverER4) + list(UnderER4_OverER5)))\n\n# Take genes of interest and their logchanges into a single df\nER0.rename(columns={'logFC': 'ER0_logFC'}, inplace=True)\nER0 = ER0.dropna()\nER0 = ER0[['Gene.symbol', 'ER0_logFC']]\n\nER4.rename(columns={'logFC': 'ER4_logFC'}, inplace=True)\nER4 = ER4.dropna()\nER4 = ER4[['Gene.symbol', 'ER4_logFC']]\n\nER5.rename(columns={'logFC': 'ER5_logFC'}, inplace=True)\nER5 = ER5.dropna()\nER5 = ER5[['Gene.symbol', 'ER5_logFC']]\n\nER045 = pd.merge(ER0, ER4, on='Gene.symbol', how='inner')\nER045 = pd.merge(ER045, ER5, on='Gene.symbol', how='inner')\nER045.rename(columns={'Gene.symbol': 'Gene'}, inplace=True)\n\n\nER0ER5 = pd.merge(ER0, ER5, on='Gene.symbol', how='inner')\nER0ER5.rename(columns={'Gene.symbol': 'Gene'}, inplace=True)\nER0ER5[\"Control\"] = 0\n\n\n\n\n\n# Values for under/over ER0 and ER5\n#goi = list(set( list(UnderER0_OverER5) + list(UnderER5_OverER0) ))\n#ERgoi = ER0ER5[ER0ER5.Gene.isin(goi)]\n\n# Values if evalutating ER 4 as well\ngoi = list(setofall)\nERgoi = ER045[ER045.Gene.isin(goi)]\n\n\n# Lets visualise these genes\n\n\n# take log values and convert to array\nlogvalues = ERgoi[['ER0_logFC', 'ER4_logFC', 'ER5_logFC']]\nlogvalues = logvalues.to_numpy()\n\n\ngenes = ERgoi.Gene.tolist()\nloggroups = ['ER0_logFC', 'ER4_logFC','ER5_logFC']\n\nsns.set(rc={'figure.figsize':(11.7,8.27)})\nax = sns.heatmap(logvalues, xticklabels=loggroups, yticklabels=genes, cmap = 'PiYG')\n\n\n\n\n\n\n\n\n","sub_path":"DiffExpAnalysis.py","file_name":"DiffExpAnalysis.py","file_ext":"py","file_size_in_byte":4624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"84558249","text":"#The model on the basis of which data analysis is performed: https://uk.wikipedia.org/wiki/%D0%92%D0%B5%D0%BB%D0%B8%D0%BA%D0%B0_%D0%BF%27%D1%8F%D1%82%D1%96%D1%80%D0%BA%D0%B0_(%D0%BF%D1%81%D0%B8%D1%85%D0%BE%D0%BB%D0%BE%D0%B3%D1%96%D1%8F)\r\n#Data source: https://www.kaggle.com/tunguz/big-five-personality-test\r\n\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nimport pycountry_convert\r\n\r\ndf = pd.read_csv('Datasets/bf_data.csv', sep='\\t')\r\n\r\ncols = df.columns.values[:50]\r\ndf[cols].dropna().astype(int)\r\n\r\n#New columns are created to merge the data (according to the test rules, some values are negative)\r\ndf[\"EXT\"] = df[\"EXT1\"] - df[\"EXT2\"] + df[\"EXT3\"] - df[\"EXT4\"] + df[\"EXT5\"] - df[\"EXT6\"] + df[\"EXT7\"] - df[\"EXT8\"] + df[\"EXT9\"] - df[\"EXT10\"]\r\ndf[\"EST\"] = df[\"EST1\"] - df[\"EST2\"] + df[\"EST3\"] - df[\"EST4\"] + df[\"EST5\"] + df[\"EST6\"] + df[\"EST7\"] + df[\"EST8\"] + df[\"EST9\"] + df[\"EST10\"]\r\ndf[\"AGR\"] = - df[\"AGR1\"] + df[\"AGR2\"] - df[\"AGR3\"] + df[\"AGR4\"] - df[\"AGR5\"] + df[\"AGR6\"] - df[\"AGR7\"] + df[\"AGR8\"] + df[\"AGR9\"] + df[\"AGR10\"]\r\ndf[\"CSN\"] = df[\"CSN1\"] - df[\"CSN2\"] + df[\"CSN3\"] - df[\"CSN4\"] + df[\"CSN5\"] - df[\"CSN6\"] + df[\"CSN7\"] - df[\"CSN8\"] + df[\"CSN9\"] + df[\"CSN10\"]\r\ndf[\"OPN\"] = df[\"OPN1\"] - df[\"OPN2\"] + df[\"OPN3\"] - df[\"OPN4\"] + df[\"OPN5\"] - df[\"OPN6\"] + df[\"OPN7\"] - df[\"OPN8\"] + df[\"OPN9\"] + df[\"OPN10\"]\r\n\r\n#Correlation\r\nsns.heatmap(df[[\"EXT\", \"AGR\", \"CSN\", \"EST\", \"OPN\"]].corr(), annot=True).set_title(\"Кореляція 5 якостей людини\")\r\n\r\n#Distribution (full range of data)\r\nfig, axs = plt.subplots(ncols = 2, nrows = 3)\r\nsns.distplot(df[\"EXT\"], bins = 50, kde = False, ax = axs[0, 0]).set_title(\"Екстраверсія\")\r\nsns.distplot(df[\"EST\"], bins = 50, kde = False, ax = axs[0, 1]).set_title(\"Нейротизм\")\r\nsns.distplot(df[\"AGR\"], bins = 50, kde = False, ax = axs[1, 0]).set_title(\"Доброзичливість\")\r\nsns.distplot(df[\"CSN\"], bins = 50, kde = False, ax = axs[1, 1]).set_title(\"Сумлінність\")\r\nsns.distplot(df[\"OPN\"], bins = 50, kde = False, ax = axs[2, 0]).set_title(\"Відкритість\")\r\nfig.delaxes(axs[2, 1])\r\n\r\n#Distribution (Ukraine)\r\n\r\n#1\r\nfig, ax = plt.subplots()\r\ntraits = [\"EXT\", \"AGR\", \"CSN\", \"EST\", \"OPN\"]\r\nbf_ua = df.loc[df['country'] == 'UA', [\"EXT\", \"AGR\", \"CSN\", \"EST\", \"OPN\"]].dropna().astype(int)\r\nfor t in traits:\r\n\tsns.distplot(df[df['country'] == 'UA'][t], hist=False, label = t)\r\nplt.legend()\r\n\r\n\r\n#2\r\nfig, axs = plt.subplots(ncols = 2, nrows = 3)\r\nsns.distplot(bf_ua[\"EXT\"], bins = 50, kde = False, ax = axs[0, 0]).set_title(\"Екстраверсія\")\r\nsns.distplot(bf_ua[\"EST\"], bins = 50, kde = False, ax = axs[0, 1]).set_title(\"Нейротизм\")\r\nsns.distplot(bf_ua[\"AGR\"], bins = 50, kde = False, ax = axs[1, 0]).set_title(\"Доброзичливість\")\r\nsns.distplot(bf_ua[\"CSN\"], bins = 50, kde = False, ax = axs[1, 1]).set_title(\"Сумлінність\")\r\nsns.distplot(bf_ua[\"OPN\"], bins = 50, kde = False, ax = axs[2, 0]).set_title(\"Відкритість\")\r\nfig.delaxes(axs[2, 1])\r\n\r\n\r\nplt.show()\r\n","sub_path":"Seaborn/big_five.py","file_name":"big_five.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"325618681","text":"#coordinates in OpenCV format! => (y, x)\n\nimport draw_func\nfrom PIL import Image\n\nsize = 50\n\ndef calculate(func):\n _maxsize = (100 * 2)+ 2\n #write into file to execute string\n with open(\"./func.py\", \"w\") as f:\n f.write(\"import math\\n\\ndef f(x):\\n return {}\".format(func))\n f.close()\n\n #import and run\n try:\n import func\n except:\n print(\"Error importing custom function! Missing file or syntax error?\")\n result = []\n for x in range(-size, size+1):\n try:\n y = func.f(x)\n except :\n print(\"Syntax error! Remember to use correct Python 3 math syntax!\")\n result.append((y, x))\n if(y > _maxsize):\n _maxsize = (y*2) + 2\n \n return(result, _maxsize)\n\nresult, maxsize = calculate(input(\"f(x)=\"))\ndraw_func.draw(result, maxsize)\n\nif(True):\n Image.open(\"./func_graph.png\").show()\n","sub_path":"calc_func.py","file_name":"calc_func.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"262992033","text":"\r\n\"\"\"\r\nCreated on Sun Nov 29 16:16:28 2020\r\n\r\n@author: Guney Doruk\r\n\"\"\"\r\n\r\nimport cv2 \r\n\r\ndirectory = r'C:\\Captured images'\r\n \r\nvideo_capture = cv2.VideoCapture(0) \r\n\r\nsuccess = video_capture.isOpened()\r\n\r\nif(success == False):\r\n video_capture.open(0)\r\n success = True\r\n \r\ni = 0\r\nfps = video_capture.get(cv2.CAP_PROP_FPS)\r\nprint(fps)\r\n\r\nwhile success: \r\n \r\n ret, frame = video_capture.read() \r\n \r\n cv2.imshow('frame', frame) \r\n \r\n if cv2.waitKey(1) & 0xFF == ord('q'): \r\n break\r\n \r\n if cv2.waitKey(100) & 0xFF == ord('f'): \r\n name = \"\\deneme_\"+str(i)+ \".jpeg\"\r\n path = directory + name\r\n cv2.imwrite(path,frame)\r\n i += 1\r\n \r\nvideo_capture.release() \r\ncv2.destroyAllWindows() \r\n","sub_path":"Capture.py","file_name":"Capture.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"86141920","text":"from django.conf import settings\nfrom django.contrib.auth import get_user_model\nfrom django.http import JsonResponse, HttpResponse\nfrom django.views import generic\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.shortcuts import get_object_or_404\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nimport stripe\n\nfrom djvideomem.content.models import Pricing\n\nUser = get_user_model()\nstripe.api_key = settings.STRIPE_SECRET_KEY\n\n@csrf_exempt\ndef webhook(request):\n # You can use webhooks to receive information about asynchronous payment events.\n # For more about our webhook events check out https://stripe.com/docs/webhooks.\n webhook_secret = settings.STRIPE_WEBHOOK_SECRET\n payload = request.body\n \n # Retrieve the event by verifying the signature using the raw body and secret if webhook signing is configured.\n signature = request.META[\"HTTP_STRIPE_SIGNATURE\"]\n try:\n event = stripe.Webhook.construct_event(\n payload=payload, sig_header=signature, secret=webhook_secret)\n data = event['data']\n except Exception as e:\n return e\n # Get the type of webhook event sent - used to check the status of PaymentIntents.\n event_type = event['type']\n data_object = data['object']\n\n if event_type == 'invoice.paid':\n # Used to provision services after the trial has ended.\n # The status of the invoice will show up as paid. Store the status in your\n # database to reference when a user accesses your service to avoid hitting rate\n # limits.\n # TODO: change the users subscription and pricing\n print(data)\n\n webhook_object = data[\"object\"]\n stripe_customer_id = webhook_object[\"customer\"]\n\n stripe_sub = stripe.Subscription.retrieve(webhook_object[\"subscription\"])\n print(stripe_sub)\n stripe_price_id = stripe_sub[\"plan\"][\"id\"]\n\n pricing = Pricing.objects.get(stripe_price_id=stripe_price_id) # pylint: disable=maybe-no-member\n\n user = User.objects.get(stripe_customer_id=stripe_customer_id)\n user.subscription.status = stripe_sub[\"status\"]\n user.subscription.stripe_subscription_id = webhook_object[\"subscription\"]\n user.subscription.pricing = pricing\n user.subscription.save()\n\n if event_type == 'invoice.payment_failed':\n # If the payment fails or the customer does not have a valid payment method,\n # an invoice.payment_failed event is sent, the subscription becomes past_due.\n # Use this webhook to notify your user that their payment has\n # failed and to retrieve new card details.\n print(data)\n\n if event_type == 'invoice.finalized':\n # If you want to manually send out invoices to your customers\n # or store them locally to reference to avoid hitting Stripe rate limits.\n print(data)\n\n if event_type == 'customer.subscription.deleted':\n # handle subscription cancelled automatically based\n # upon your subscription settings. Or if the user cancels it.\n webhook_object = data[\"object\"]\n stripe_customer_id = webhook_object[\"customer\"]\n stripe_sub = stripe.Subscription.retrieve(webhook_object[\"id\"])\n user = User.objects.get(stripe_customer_id=stripe_customer_id)\n user.subscription.status = stripe_sub[\"status\"]\n user.subscription.save()\n\n if event_type == 'customer.subscription.trial_will_end':\n # Send notification to your user that the trial will end\n print(data)\n\n return HttpResponse()\n\n\nclass EnrollView(generic.TemplateView):\n template_name = \"payment/enroll.html\"\n\n\nclass PaymentView(generic.TemplateView):\n template_name = \"payment/checkout.html\"\n\n def get_context_data(self, **kwargs):\n context = super(PaymentView, self).get_context_data(**kwargs)\n pricing = get_object_or_404(Pricing, slug=kwargs[\"slug\"])\n context.update({\n \"pricing_tier\": pricing,\n \"STRIPE_PUBLIC_KEY\": settings.STRIPE_PUBLIC_KEY\n })\n return context\n\n\nclass CreateSubscriptionView(APIView):\n def post(self, request, *args, **kwargs):\n data = request.data\n print(data)\n customer_id = request.user.stripe_customer_id\n print(customer_id)\n\n try:\n # Attach the payment method to the customer\n stripe.PaymentMethod.attach(\n data['paymentMethodId'],\n customer=customer_id,\n )\n # Set the default payment method on the customer\n stripe.Customer.modify(\n customer_id,\n invoice_settings={\n 'default_payment_method': data['paymentMethodId'],\n },\n )\n\n # Create the subscription\n subscription = stripe.Subscription.create(\n customer=customer_id,\n items=[{'price': data[\"priceId\"]}],\n expand=['latest_invoice.payment_intent'],\n )\n\n data = {}\n data.update(subscription)\n\n return Response(data)\n except Exception as e:\n return Response({\n \"error\": {'message': str(e)}\n })\n\n\nclass RetryInvoiceView(APIView):\n\n def post(self, request, *args, **kwargs):\n data = request.data\n customer_id = request.user.stripe_customer_id\n try:\n\n stripe.PaymentMethod.attach(\n data['paymentMethodId'],\n customer=customer_id,\n )\n # Set the default payment method on the customer\n stripe.Customer.modify(\n customer_id,\n invoice_settings={\n 'default_payment_method': data['paymentMethodId'],\n },\n )\n\n invoice = stripe.Invoice.retrieve(\n data['invoiceId'],\n expand=['payment_intent'],\n )\n data = {}\n data.update(invoice)\n\n return Response(data)\n except Exception as e:\n return Response({\n \"error\": {'message': str(e)}\n })","sub_path":"djvideomem/payment/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"49921156","text":"from typing import List\n\nfrom view.data.event import EventData\nfrom view.enum.event_label import Label\n\n\nclass EventEditView:\n def __init__(self, event: EventData, save_url: str, label_config: List[Label]):\n self.event = event\n\n self.save_url = save_url\n self.label_config = label_config\n\n self.min_width = 400\n self.max_width = 1200\n","sub_path":"view/view/event_edit.py","file_name":"event_edit.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"422596503","text":"# -*- coding: utf8 -*-\n\nfrom utils.Utils import LogLevel\nfrom utils.Poster import post\nfrom utils.Poster import get\nfrom utils.Poster import login \nfrom utils.Poster import signup \nfrom utils.Poster import NoAuthorization\nimport utils.Poster as poster\nfrom utils.Utils import log\nfrom utils.Config import Path\nfrom utils.Utils import pcat\nfrom utils.Poster import fresh_token\nimport json\nimport os\nimport time\n\ntag_table = {\n \"C语言\":(\"Lang\", \"C语言, 一门面向过程的、抽象化的通用程序设计语言，广泛应用于底层开发。\"),\n \"Java\":(\"Lang\", \"Java是由Sun Microsystems公司于1995年 月推出的高级程序设计语言。Java可运行于多个平台，如Windows, Mac OS及其他多种UNIX版本的系统。\"),\n \"Python\":(\"Lang\", \"Python由荷兰数学和计算机科学研究学会的Guido van Rossum 于1990 年代初设计，作为一门叫做ABC语言的替代品。\"),\n \"SQL\":(\"Lang\", \"结构化查询语言（Structured Query Language）简称SQL，是一种特殊目的的编程语言，是一种数据库查询和程序设计语言，用于存取数据以及查询、更新和管理关系数据库系统。\"),\n \"环境配置\" : (\"Other\", \"环境配置相关，包括IDE安装、编译器安装。\"),\n \"工具使用\" : (\"Other\", \"开发工具使用相关，包括IDE使用、编译器使用，包括各种报错。\"),\n \"标准库\" : (\"Other\", \"标准库相关，包括标准库函数使用、理解。\"),\n \"语句\" : (\"Other\", \"语句相关，包括循环、分支等语句。\"),\n \"关键字\" : (\"Other\", \"关键字相关，包括if、while、for等。\"),\n \"代码\" : (\"Other\", \"有源代码的问题。\"),\n \"Linux\" : (\"Env\", \"由Linus Benedict Torvalds开发，并由开源社区维护的类Unix操作系统内核Linux。一般代指使用Linux内核的发行版，如Debian、Ubuntu、RHEL、Centos等。\"),\n \"Windows\" : (\"Env\", \"由微软公司（Microsoft）开发的操作系统，是目前最主流的桌面PC操作系统。\"),\n \"macOS\" : (\"Env\", \"由苹果（Apple）开发的操作系统，常用于苹果的Mac系列电脑。\"),\n \"Dev C++\" : (\"Env\", \"Windows 环境下的一个轻量级 C/C++ 集成开发环境（IDE），是一款自由软件，遵守GPL许可协议分发源代码。目前已停止维护，但仍有社区维护的版本。\"),\n \"Visual C++\" : (\"Env\", \"由微软公司（Microsoft）的免费C++集成开发环境（IDE），可提供编辑C语言。目前微软已停止维护并关闭下载渠道。\"),\n \"VS Code\" : (\"Env\", \"由微软公司（Microsoft）同社区开发的Code-OOS的构建，是一款轻量级集成开发环境（IDE），通过插件来支持多种语言的开发。\"),\n \"Visual Studio\" : (\"Env\", \"由微软公司（Microsoft）开发的重量级集成开发环境（IDE）。\"),\n \"gcc\" : (\"Env\", \"GNU Compiler Collection，GNU编译器套件，是遵循GPL协议的自由软件。支持多种语言与多种目标平台。也是GCC编译C语言所使用的的指令名。\"),\n \"clang\" : (\"Env\", \"是LLVM的C家族语言前端，是遵循BSD协议的自由软件。一般可认为是一个C语言、C++、Objective-C语言的轻量级编译器。\"),\n \"msvc\" : (\"Env\", \"由微软公司（Microsoft）开发的C++编译器，是微软旗下IDE（Visual Studio、Visual C++等）的默认C++编译器。\"),\n}\n\ntid = None\n\ndef init_tids():\n global tid\n tid = get(\"/api/questions/taglist\")\n\ndef add_tag(name: str, desc: str, category: str) -> int:\n obj = { \"name\": name, \"desc\": desc, \"category\": category}\n log(\"添加标签：\", name)\n res = post(\"/api/questions/add_tag\", obj)\n if res[\"status\"] != \"success\":\n log(\"添加标签：\", name, \"失败\", \"[{}] {}\".format(res[\"status\"], res[\"message\"]), level=LogLevel.WAR)\n return -1\n else:\n tid = res[\"tid\"]\n log(\"添加标签：\", name, \"成功\", \"tid=\", tid)\n return tid\n\ndef add_question(title: str, remarks: str, tags: list, question_jwt: str=None) -> int:\n global tid\n if tid is None:\n init_tids()\n obj = { \"title\": title, \"remarks\": remarks, \"tags\": [tid[t] for t in tags if t in tid.keys()] }\n log(\"添加问题：\", title[:10])\n res = post(\"/api/questions/add_question\", obj, jwt=question_jwt)\n if res[\"status\"] != \"success\":\n log(\"添加问题：\", title[:10], \"失败\", \"[{}] {}\".format(res[\"status\"], res[\"message\"]), level=LogLevel.WAR)\n return -1\n else:\n qid = res[\"qid\"]\n log(\"添加问题：\", title[:10], \"成功\", \"qid=\", qid)\n return qid\n\ndef add_answer(qid: int, content: str, answer_jwt: str=None) -> int:\n obj = { \"qid\": qid, \"content\": content }\n log(\"添加回答：\", content[:10])\n res = post(\"/api/questions/add_answer\", obj, jwt=answer_jwt)\n if res[\"status\"] != \"success\":\n log(\"添加回答：\", content[:10], \"失败\", \"[{}] {}\".format(res[\"status\"], res[\"message\"]), level=LogLevel.WAR)\n return -1\n else:\n aid = res[\"aid\"]\n log(\"添加回答：\", content[:10], \"成功\", \"aid=\", aid)\n return aid\n\ndef try_add_all_tags():\n for k, v in tag_table.items():\n add_tag(k, v[1], v[0])\n\ndef try_add_all_questions(question_jwt: str, answer_jwt: str):\n with open(Path.Script_CSDNData, 'r', encoding='utf8') as f:\n questions = json.load(f)\n to_add_file = pcat(Path.Script, \"questions_to_add.json\")\n to_add = None\n if os.path.exists(to_add_file):\n with open(to_add_file, 'r', encoding='utf8') as f:\n to_add = json.load(f)\n else:\n to_add = list(questions)\n added = []\n try:\n for i, question in enumerate(to_add):\n time.sleep(1)\n if i % 64 == 63:\n question_jwt = fresh_token(question_jwt)\n answer_jwt = fresh_token(answer_jwt)\n q = question[\"q\"]\n a = question[\"a\"]\n qid = add_question(q[\"title\"], q[\"remarks\"], q[\"tags\"], question_jwt)\n if qid < 0:\n continue\n aid = add_answer(qid, a[\"content\"], answer_jwt)\n if aid < 0:\n continue\n added.append(question)\n finally:\n for a in added:\n to_add.remove(a)\n with open(to_add_file, 'w', encoding='utf8') as f:\n json.dump(to_add, f)\n\n\ndef main() -> int:\n log(\"using protocol:\", poster.protocol)\n log(\"using base_url:\", poster.base_url)\n while True:\n command = input(\"signup, tag, question, or exit:\\t\")\n try:\n if command == \"signup\":\n signup()\n elif command == \"exit\":\n return\n elif command == \"tag\":\n print(\"please login with admin account\")\n login()\n try_add_all_tags()\n elif command == \"question\":\n print(\"please login with question account\")\n question_jwt = login()\n print(\"please login with answer account\")\n answer_jwt = login()\n try_add_all_questions(question_jwt, answer_jwt)\n else:\n print(\"unknow command:\", command)\n except KeyboardInterrupt:\n print(\"\")\n except NoAuthorization:\n print(\"login required!\")\n \n\nif __name__ == '__main__':\n try:\n main()\n print(\"exit with exit command\")\n except KeyboardInterrupt:\n print(\"\\nexit with keyboard interrupt\")","sub_path":"Scripts/OldDataInserter.py","file_name":"OldDataInserter.py","file_ext":"py","file_size_in_byte":7456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"593587846","text":"import os\nimport sys\n\nimport datetime\nimport numpy as np\nfrom keras.applications.densenet import DenseNet121\nfrom keras.applications.densenet import preprocess_input\n\nfrom keras.utils import to_categorical\nfrom sklearn.metrics import log_loss\nfrom collections import OrderedDict\nimport pandas as pd\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\nsys.path.append('..')\nfrom helpers.settings import arrays_folder, models_folder\n\n\nmodel = DenseNet121()\nmodel_uid = 'densenet121'\n\nPHI = (np.sqrt(5) - 1) / 2\n\nif __name__ == '__main__':\n input_folder = os.path.join(arrays_folder, 'imagenet_224')\n output_folder = os.path.join(models_folder, model_uid)\n if not os.path.isdir(output_folder):\n os.mkdir(output_folder)\n\n y_val = np.load(os.path.join(input_folder, 'y_val.npy'))\n y_val = to_categorical(y_val, 1000)\n ws = model.get_weights()\n n_batch = 50\n samples_per_batch = 50000 // n_batch\n\n n_optimization_iterations = 20\n n_selected_batch = 5\n n_ensemble = 5\n\n y_val = y_val[:n_selected_batch * samples_per_batch]\n X = np.zeros((n_selected_batch * samples_per_batch, 224, 224, 3))\n sigma_lower, sigma_upper = 5e-4, 5e-3\n\n sigmas_log = list()\n nlls_log = list()\n nlls = dict()\n for iteration in range(n_optimization_iterations):\n now = datetime.datetime.now().strftime(\"%m/%d %H:%M:%S\")\n print('=' * 100)\n print('iteration {0}... time: {1}'.format(iteration, now))\n print('=' * 100)\n sigma_1 = sigma_lower + (sigma_upper - sigma_lower) * PHI\n sigma_2 = sigma_lower + (sigma_1 - sigma_lower) * PHI\n sigmas_log.append(OrderedDict({\"iteration\": iteration, \"sigma_l\": sigma_lower,\n \"sigma_2\": sigma_2, \"sigma_1\": sigma_1,\n \"sigma_h\": sigma_upper}))\n print('sigma: {0:.6f}, {1:.6f}, {2:.6f}, {3:.6f}'.format(sigma_lower, sigma_2, sigma_1, sigma_upper))\n for sigma in [sigma_1, sigma_2]:\n if sigma not in nlls.keys():\n y_val_pred_ensemble = np.zeros((n_ensemble, *y_val.shape))\n for seed_index, seed in enumerate(range(17, 17 + n_ensemble)):\n np.random.seed(seed)\n model.set_weights(ws)\n wp = np.copy(ws)\n for index2, w in enumerate(ws):\n shape = w.shape\n if len(shape) == 4:\n noise = np.random.normal(0, sigma, (w.shape[0], w.shape[1], w.shape[2], w.shape[3]))\n wp[index2] = ws[index2] + noise\n model.set_weights(wp)\n for i in range(0, n_selected_batch):\n images_path = os.path.join(input_folder, 'x_val_' + str(i).zfill(3) + '.npy')\n array = np.load(images_path)\n X[i * samples_per_batch:(i + 1) * samples_per_batch] = preprocess_input(array)\n y_val_pred_p = model.predict(X, batch_size=64, verbose=1)\n y_val_pred_ensemble[seed_index] = y_val_pred_p\n nll_val = log_loss(y_val, y_val_pred_p)\n print('sigma: {0:.6f}, seed: {1}, nll: val {2:.4f}'.format(sigma, seed, nll_val))\n y_val_pred_ensemble_mean = np.mean(y_val_pred_ensemble, axis=0)\n nll = log_loss(y_val, y_val_pred_ensemble_mean)\n nlls[sigma] = nll\n nlls_log.append(OrderedDict({\"iteration\": iteration, \"sigma\": sigma, \"nll\": nll}))\n else:\n print('will skip for {0:6f}'.format(sigma))\n print(nlls_log)\n if nlls[sigma_1] < nlls[sigma_2]:\n sigma_lower = sigma_2\n print('updated lower side...')\n else:\n sigma_upper = sigma_1\n print('updated upper side...')\n print(sigma_lower, sigma_upper)\n pd.DataFrame(nlls_log).to_csv(os.path.join(output_folder, 'golden_optimization_nlls.csv'), index=False)\n pd.DataFrame(sigmas_log).to_csv(os.path.join(output_folder, 'golden_optimization_simgas.csv'), index=False)\n","sub_path":"ImageNet/2_optimal_sigma/densenet121.py","file_name":"densenet121.py","file_ext":"py","file_size_in_byte":4115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"243750293","text":"import os\nimport os.path\nimport webracer\nimport nose.plugins.attrib\nfrom . import utils\nfrom .apps import kitchen_sink_app\n\ntry:\n # python 3.3?\n file_not_found_exception_class = FileNotFoundError\nexcept NameError:\n file_not_found_exception_class = IOError\n\nutils.app_runner_setup(__name__, kitchen_sink_app.app, 8060)\n\nsave_dir = os.environ.get('WEBRACER_TEST_TMP') or os.path.join(os.path.dirname(__file__), 'tmp')\nnonexistent_save_dir = '/tmp/nonexistent.dee11123e367b4a7506f856cc55898fabd4caeff'\n\ndef list_save_dir():\n entries = os.listdir(save_dir)\n entries = [entry for entry in entries if entry[0] != '.']\n return entries\n\n@nose.plugins.attrib.attr('client')\n@webracer.config(host='localhost', port=8060)\nclass ResponseTest(webracer.WebTestCase):\n def setUp(self, *args, **kwargs):\n super(ResponseTest, self).setUp(*args, **kwargs)\n \n if not os.path.exists(save_dir):\n os.mkdir(save_dir)\n else:\n for entry in list_save_dir():\n os.unlink(os.path.join(save_dir, entry))\n \n @webracer.config(save_responses=True, save_dir=save_dir)\n def test_save_successful(self):\n self.assertEqual(0, len(list_save_dir()))\n \n self.get('/ok')\n self.assert_status(200)\n self.assertEqual('ok', self.response.body)\n \n entries = list_save_dir()\n # response + last symlink\n self.assertEqual(2, len(entries))\n assert 'last' in entries\n entries.remove('last')\n assert entries[0].startswith('response')\n \n @webracer.config(save_responses=True, save_dir=nonexistent_save_dir)\n def test_save_to_nonexistent_dir(self):\n assert not os.path.exists(nonexistent_save_dir)\n \n with self.assert_raises(file_not_found_exception_class) as cm:\n self.get('/ok')\n \n assert nonexistent_save_dir in str(cm.exception)\n \n assert not os.path.exists(nonexistent_save_dir)\n \n @webracer.config(save_responses=False, save_failed_responses=True,\n save_dir=save_dir)\n def test_save_failed_request(self):\n self.assertEqual(0, len(list_save_dir()))\n \n self.get('/internal_server_error')\n \n self.assertEqual(0, len(list_save_dir()))\n \n with self.assert_raises(AssertionError):\n # triggers save\n self.assert_status(200)\n \n entries = list_save_dir()\n # response + last symlink\n self.assertEqual(2, len(entries))\n assert 'last' in entries\n entries.remove('last')\n assert entries[0].startswith('response')\n \n @webracer.config(save_responses=False, save_failed_responses=True,\n save_dir=nonexistent_save_dir)\n def test_save_failed_request_to_nonexistent_dir(self):\n assert not os.path.exists(nonexistent_save_dir)\n \n self.get('/internal_server_error')\n \n with self.assert_raises(AssertionError) as cm:\n # triggers save\n self.assert_status(200)\n \n # Resulting exception should contain both assertion failure message\n # and the message involving inability to save response\n assert 'Response status 200 expected but was 500' in str(cm.exception)\n assert 'No such file or directory' in str(cm.exception)\n assert nonexistent_save_dir in str(cm.exception)\n \n assert not os.path.exists(nonexistent_save_dir)\n","sub_path":"tests/response_saving_test.py","file_name":"response_saving_test.py","file_ext":"py","file_size_in_byte":3458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"53565522","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 31 11:15:02 2019\n\n@author: russe\nNeed to convert units from ppb to some sort of mass unit but easy to do; prior to the slope determination\nCan pull the formatting aspect of this script and maybe have a function version of it \n\"\"\"\n\nimport pandas as pd\nimport glob \nimport numpy as np\nimport datetime\nfrom sklearn.linear_model import LinearRegression\nimport warnings\n\ndef MixingModel(a,b,data_gr, Consts_File):\n Consts = Consts_File\n quant = Consts['Value']['Quant']\n coff = []; coff = pd.DataFrame(coff)\n ss = a+datetime.timedelta(minutes=Consts['Value']['Chamber_Start'])\n se = b-datetime.timedelta(minutes=Consts['Value']['Chamber_End'])\n if Consts['Value']['Ambient_Start'] == 0:\n amb = a; \n else: amb = a+datetime.timedelta(minutes = Consts['Value']['Ambient_Start'])\n ambe = a+datetime.timedelta(minutes=Consts['Value']['Ambient_End'])\n # Does this get changed to [a:b] instead of [amb:ambe]? Makes a pretty big difference\n N20_amb = data_gr['N2O_ppm'][a:b].quantile([quant])\n N15Bulk_amb = data_gr['BulkAT'][amb:ambe].mean()\n x = np.linspace(0,len(data_gr[ss:se])-1,len(data_gr[ss:se]))\n Num = ((data_gr['N2O_ppm'][a:b]*data_gr['BulkAT'][a:b])-(N20_amb[quant]*N15Bulk_amb))\n Dem = (data_gr['N2O_ppm'][a:b]-N20_amb[quant])\n Bulk15Ng = Num/Dem\n Alpha_Diff = data_gr['NN15O_ppm']-data_gr['NN15O_ppm'].min()\n Beta_Diff = data_gr['N15NO_ppm']-data_gr['N15NO_ppm'].min()\n Alpha_Prop = Alpha_Diff/(Alpha_Diff+Beta_Diff); Beta_Prop = Beta_Diff/(Alpha_Diff+Beta_Diff)\n Alpha15Ng = Bulk15Ng*Alpha_Prop; Beta15Ng = Bulk15Ng*Beta_Prop # Add in the QC for this as well; again should be part of a function Not sure how much more want to do\n ok = ((data_gr['N2O_ppm'][a:b]-N20_amb[quant])*1000) > Consts['Value']['Detect']\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n preBulkNgm = np.nanmean(Bulk15Ng[amb:ambe][ok]);preBulkNgs = np.std(Bulk15Ng[amb:ambe][ok])\n BulkNgm = np.nanmean(Bulk15Ng[ss:se][ok]); BulkNgs = np.std(Bulk15Ng[ss:se][ok])\n Back = pd.DataFrame([preBulkNgm, preBulkNgs, BulkNgm, BulkNgs,N20_amb[quant],np.std(data_gr['N2O_ppm'][amb:ambe])\n ,np.nanmean(Alpha15Ng[ss:se][ok]),np.nanmean(Beta15Ng[ss:se][ok])\n ,np.nanmean(Alpha15Ng[amb:ambe][ok]),np.nanmean(Beta15Ng[amb:ambe][ok])\n ,np.nanmean(data_gr['BulkAT'][amb:ambe]),np.nanmean(data_gr['NAlphaAT'][amb:ambe]),np.nanmean(data_gr['NBetaAT'][amb:ambe])]).transpose()\n if len(x) > 1:\n model = LinearRegression().fit(x.reshape((-1, 1)),data_gr['N2O_ppm_conc'][ss:se])\n cof = pd.DataFrame([str(ss.strftime('%Y%m%d%H%M')),str(se.strftime('%Y%m%d%H%M')),model.coef_[0], kk])\n else: cof = pd.DataFrame([str(ss.strftime('%Y%m%d%H%M')),str(se.strftime('%Y%m%d%H%M')),np.NaN,kk])\n coff = pd.concat([cof.transpose(),Back],axis = 1)\n return (coff)\n\ndef Timing_Index(df):\n time = []\n for k in range (0,len(df)):\n Y = str(int(df['TIMESTAMP_START'][k]))[0:4]\n M = str(int(df['TIMESTAMP_START'][k]))[4:6] \n D = str(int(df['TIMESTAMP_START'][k]))[6:8]\n hh = str(int(df['TIMESTAMP_START'][k]))[8:10] \n mm = str(int(df['TIMESTAMP_START'][k]))[10:12]\n time.append(Y+'-'+M+'-'+D+' '+hh+':'+mm)\n time = pd.DataFrame(time); df.index = time[0]\n df.index=pd.to_datetime(df.index) # Time-based index \n return df\n\nfnames = glob.glob(r'C:\\Users\\russe\\Desktop\\LTAR\\N2O\\PCFS\\Formatted\\*.csv')\ncol = pd.read_csv(r'C:\\Users\\russe\\Desktop\\LTAR\\N2O\\PCFS\\N2O_Isotop_Columns.csv',header = 0)\nConsts_File = pd.read_csv(r'C:\\Users\\russe\\Desktop\\LTAR\\N2O\\PCFS\\LGR_Isotope_Constants.csv', header = 0, index_col = 'ID')\n\n#%%\n# Loop over the number of files within the directory (len(fnames)); fnames contains the full path for the files\nFinal = []\nFinal = pd.DataFrame(Final)\nfor k in range (10,len(fnames)):\n data = pd.read_csv(fnames[k], header = 0, index_col = 'TimeStamp') # Reads in each file in sequence; first row is the header, no index defined here\n data.index = pd.to_datetime(data.index)\n qn = (data['N2O_ppm'] > 0.3) & (data['N2O_ppm'] < 2.1) # Created a Boolean of which rows in the N2O_ppm column are below zero; because of the drops in the data, we may want to have a stricter lower bound (0.325?)\n datax = data['N2O_ppm'][qn]/1e6\n data_conc = (datax*44*(data['GasP_torr']*133.322))/(8.314*(data['GasT_C']*273.15))\n data['N2O_ppm_conc'] = data_conc\n # Here be the atom percent calculations for things\n data['BulkAT'] = (((data['NN15O_ppm']+data['N15NO_ppm'])/data['N2O_ppm'])*100)/2\n data['NAlphaAT'] = ((data['NN15O_ppm']/data['N2O_ppm'])*100)\n data['NBetaAT'] = ((data['N15NO_ppm']/data['N2O_ppm'])*100)\n test = data.groupby('MIU_DESC') \n print(fnames[k])\n for kk in test.groups.keys():\n# print(kk)\n data_gr = (test.get_group(kk))\n dd = []\n for dt in range(0,(len(data_gr)-1)):\n dd.append(data_gr.index[dt+1]-data_gr.index[dt])\n dd = pd.DataFrame(dd)\n xxn = dd >'00:00:05'\n xxn.index = data_gr.index[0:-1]\n qq = data_gr['d15NB'][0:-1][xxn[0]]\n qq = qq.index\n zn = np.where(xxn == True) \n zn = pd.DataFrame(zn).transpose()\n # This part is really crappy and needs to be cleaned up better as well\n if \"Chamber\" in kk: # Add an output for the start and end time of the chambers to be able to use with the non-iso LGR data\n for m in range (0,len(zn)):\n if m ==0: # Leave time decider but can move everything else since repeat at each time\n a = data_gr.index[0]\n b = data_gr.index[zn[0][m]]\n coff = MixingModel(a,b,data_gr, Consts_File)\n a = data_gr.index[zn[0][m]+1] # What to do with these two pieces\n b = data_gr.index[zn[0][m+1]]\n coff2 = MixingModel(a,b,data_gr, Consts_File)\n coff = pd.concat([coff, coff2])\n elif (m >0) & (m<len(zn)-1):\n a = data_gr.index[zn[0][m]+1]\n b = data_gr.index[zn[0][m+1]]\n coff = MixingModel(a,b,data_gr, Consts_File)\n elif m == (len(zn)-1):\n a = data_gr.index[zn[0][m]+1]\n b = data_gr.index[-1]\n coff = MixingModel(a,b,data_gr, Consts_File)\n Final=pd.concat([Final, coff])\n \nFinal.columns = col['Cols']\nFinal = Final.reset_index(); Final = Final.drop('index', axis = 1)\nFinal = Timing_Index(Final)\nFinal = Final.sort_index()\nFinal['N2O_Flux'] = Final['N2O_Flux']/2\nFinal['N2O_Flux_g_ha-1_d-1']=Final['N2O_Flux']*60*60*24*1000*10000\n#Final = pd.concat([Final,data]) # Maybe to get the extra data involved\nFinal.to_csv(r'C:\\Users\\russe\\Desktop\\LTAR\\N2O\\PCFS\\LGRISO_N2O_Flux_Test_MixModel_20190809.csv', index_label ='TimeStamp',index = True, na_rep = np.NaN)\n#Check = Final['2019-05-21 15:00':'2019-05-21 18:00']","sub_path":"N2O_Simple_Read_PCFS.py","file_name":"N2O_Simple_Read_PCFS.py","file_ext":"py","file_size_in_byte":7071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"373110636","text":"import pickle\nimport sys\n\nfrom heuristicSearch.utils.utils import *\nfrom heuristicSearch.envs.occupancyGrid import OccupancyGrid\n\ndef inputPoints( numPoints, occMap ):\n points = []\n for i in range(numPoints):\n print(\"Click on an point\")\n point = inputClickedPoint(occMap)\n points.append(point)\n return points\n\ndef savePoints(points, fileName):\n pickle.dump( points, open(fileName, \"wb\") )\n\ndef inputSavePoints():\n \"\"\"Function to input and pickle points.\n Takes three commands line arguments: Map to be used, file to be saved in\n and number of points.\n \"\"\"\n image = sys.argv[1]\n fileName = sys.argv[2]\n numPoints = int( sys.argv[3] )\n\n occGrid = OccupancyGrid()\n occMap = occGrid.getMapFromImage(image)\n\n points = inputPoints( numPoints, occMap )\n savePoints( points, fileName )\n\nif __name__ == \"__main__\":\n inputSavePoints()\n\n","sub_path":"save_points.py","file_name":"save_points.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"533704539","text":"\"\"\"\nKernel Functions\n================\n\nTheses kernels functions are most used for Kernel Polynomial Method\nin order to....\n\n\nAvailable methods\n-----------------\n\n - jackson\n - lorentz\n\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\n\n\ndef jackson(\n num_moments,\n precision=32,\n name_scope=None\n):\n \"\"\"\n This function generates the Jackson kernel for a given number of\n Chebyscev moments\n\n Parameters\n ----------\n num_moments: (uint)\n number of Chebyshev moments\n tf_float: (tensorflow float type)\n valids values are tf.float32, tf.float64, or tf.float128\n name_scope: (str) (default=\"get_jackson_kernel\")\n scope name for tensorflow\n\n Return\n ------\n jackson_kernel: Tensor(shape=(num_moments,), dtype=tf_float)\n\n Note\n ----\n See .. _The Kernel Polynomial Method:\n https://arxiv.org/pdf/cond-mat/0504627.pdf for more details\n \"\"\"\n tf_float = tf.float64\n if precision == 32:\n tf_float = tf.float32\n\n with tf.name_scope(name_scope, \"jackson_kernel\"):\n\n kernel_moments = tf.range(0, num_moments, dtype=tf_float)\n norm = np.pi/(num_moments+1)\n phases = kernel_moments*norm\n\n kernel = tf.math.divide(\n tf.add(\n (num_moments-kernel_moments+1)*tf.cos(phases),\n tf.sin(phases)/tf.tan(norm)\n ),\n (num_moments+1)\n )\n return kernel\n\n\ndef lorentz(\n num_moments,\n l,\n precision=32,\n name_scope=None\n):\n \"\"\"\n This function generates the Lorentz kernel for a given number of\n Chebyscev moments and a positive real number, l\n\n Parameters\n ----------\n num_moments: (int)\n positive integer, number of Chebyshev moments\n l: (float)\n positve number,\n tf_float: (tensorflow float type)\n valids values are tf.float32, tf.float64, or tf.float128\n name_scope: (str) (default=\"lorentz_kernel\")\n scope name for tensorflow\n\n Return\n ------\n kernel: Tensor(shape=(num_moments,), dtype=tf_float)\n\n Note\n ----\n See .. _The Kernel Polynomial Method:\n https://arxiv.org/pdf/cond-mat/0504627.pdf for more details\n \"\"\"\n tf_float = tf.float64\n if precision == 32:\n tf_float = tf.float32\n\n with tf.name_scope(name_scope, \"lorentz_kernel\"):\n\n kernel_moments = tf.range(0, num_moments, dtype=tf_float)\n phases = 1. - kernel_moments/num_moments\n\n kernel = tf.math.divide(\n tf.sinh(l*phases),\n tf.math.sinh(l)\n )\n return kernel\n\n\n__all__ = [\"lorentz\", \"jackson\"]\n","sub_path":"emate/utils/tfops/kernels.py","file_name":"kernels.py","file_ext":"py","file_size_in_byte":2708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"648459771","text":"# plot 集合\n\nfont_filename = 'C:/_git/vcs/_1.data/______test_files1/_font/msch.ttf'\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#設定中文字型及負號正確顯示\n#設定中文字型檔\nplt.rcParams[\"font.sans-serif\"] = \"Microsoft JhengHei\" # 將字體換成 Microsoft JhengHei\n#設定負號\nplt.rcParams[\"axes.unicode_minus\"] = False # 讓負號可正常顯示\n\ndef process_key(event):\n fig = event.canvas.figure\n ax = fig.axes[0]\n\n if event.key == \"p\":\n #previous_slice(ax) #上一張\n #draw(ax, False)\n print('你在圖上按了 p')\n elif event.key == \"n\":\n #next_slice(ax) #下一張\n #draw(ax, False)\n print('你在圖上按了 n')\n elif event.key == \"c\":\n #change_axis(ax)\n #draw(ax, True)\n print('你在圖上按了 c')\n\n fig.canvas.draw_idle()\n\n\nfig, ax = plt.subplots()\n\n#ax.volume = volume\n#ax.index = volume.shape[0] // 2\nax.axis = 0\n#ax.imshow(volume[ax.index, :, :], cMap, vmin=cMin, vmax=cMax)\nfig.canvas.mpl_connect(\"key_press_event\", process_key)\n\n\n\nplt.show()\n\n\n\n\n","sub_path":"_4.python/matplotlib/matplotlib_process_key.py","file_name":"matplotlib_process_key.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"300121823","text":"# -*- coding: utf-8 -*-\nfrom django.db import models\nfrom itertools import chain\nfrom django.template.defaultfilters import slugify\nfrom django.utils.translation import ugettext as _\nfrom pustakalaya_apps.collection.models import Collection\nfrom elasticsearch.exceptions import NotFoundError\nfrom django.contrib.auth.models import User\nfrom django.urls import reverse\nfrom .search import VideoDoc\nfrom django.core import urlresolvers\nfrom pustakalaya_apps.core.abstract_models import (\n AbstractItem,\n AbstractSeries,\n AbstractTimeStampModel,\n LinkInfo,\n EmbedVideoAudioLink\n\n)\n\nfrom pustakalaya_apps.core.models import (\n Keyword,\n Biography,\n Sponsor,\n Publisher,\n Language,\n EducationLevel,\n LicenseType,\n genre_audio_video\n)\n\n\nclass FeaturedItemManager(models.Manager):\n def get_queryset(self):\n return super(FeaturedItemManager, self).get_queryset().filter(published=\"yes\", featured=\"yes\").order_by(\"-updated_date\")[:5]\n\n\nclass Video(AbstractItem):\n \"\"\"\n Video item class\n \"\"\"\n\n collections = models.ManyToManyField(\n Collection,\n verbose_name=_(\"Add this video to these collection\"),\n blank=True,\n\n )\n\n video_original_document_authors = models.ManyToManyField(\n Biography,\n verbose_name=_(\"Original Author(s)\"),\n related_name=\"video_original_document_authors\",\n blank=True,\n\n )\n\n video_release_date = models.CharField(\n verbose_name=_(\"Release date\"),\n max_length=255,\n blank=True,\n )\n\n video_director = models.ManyToManyField(\n Biography,\n verbose_name=(\"Director\"),\n related_name=\"directors\",\n blank=True,\n\n )\n\n video_producers = models.ManyToManyField(\n Biography,\n verbose_name=_(\"Producer\"),\n related_name=\"producers\",\n blank=True,\n\n )\n\n education_levels = models.ManyToManyField(\n EducationLevel,\n verbose_name=_(\"Education Level\"),\n blank=True,\n\n )\n languages = models.ManyToManyField(\n Language,\n verbose_name=_(\"Languages\"),\n blank=True,\n\n )\n\n # Manager to return the featured objects.\n objects = models.Manager()\n featured_objects = FeaturedItemManager()\n\n video_series = models.ForeignKey(\n \"VideoSeries\",\n verbose_name=_(\"Video series\"),\n on_delete=models.CASCADE,\n blank=True,\n null=True\n )\n\n type = models.CharField(\n editable=False,\n default=\"video\",\n max_length=255\n )\n video_certificate_license = models.CharField(\n verbose_name=_(\"Certification\"),\n max_length=255,\n blank=True\n )\n age = models.CharField(\n verbose_name=_(\"Age group\"),\n max_length=255,\n blank=True,\n )\n\n sponsors = models.ManyToManyField(\n Sponsor,\n verbose_name=_(\"Sponsor\"),\n blank=True,\n\n )\n\n submitted_by = models.ForeignKey(\n User,\n on_delete=models.SET_NULL,\n editable=False,\n null=True\n )\n\n # custom video genre inherit from genre_audio_video\n\n # video_genre = models.ForeignKey(\n # \"VideoGenre\",\n # verbose_name=_(\"Video Genre\"),\n # blank=True,\n # null=True\n # )\n\n video_genre = models.ManyToManyField(\n genre_audio_video,\n verbose_name=_(\"Video Genre\"),\n blank=True,\n\n )\n # publisher = models.ForeignKey(\n # Publisher,\n # verbose_name=_(\"Publisher\"),\n # blank=True,\n # null=True\n # )\n\n publisher = models.ManyToManyField(\n Publisher,\n verbose_name=_(\"Publisher\"),\n blank=True,\n\n )\n\n keywords = models.ManyToManyField(\n Keyword,\n verbose_name=_(\"Keywords\"),\n blank=True,\n\n )\n\n license = models.ForeignKey(\n LicenseType,\n verbose_name=_(\"license\"),\n on_delete=models.SET_NULL,\n blank=True,\n null=True,\n )\n\n thumbnail = models.ImageField(\n upload_to=\"uploads/thumbnails/video/%Y/%m/%d\",\n max_length=255,\n blank=True\n )\n\n video_running_time = models.CharField(\n verbose_name=_(\"Running time in minutes\"),\n max_length=255,\n blank=True,\n\n )\n\n @property\n def getauthors(self):\n if not self.video_director:\n return [None]\n\n return [(author.getName, author.pk) for author in self.video_director.all()] or [None]\n\n\n\n def get_absolute_url(self):\n from django.urls import reverse\n return reverse(\"video:detail\", kwargs={\"title\": slugify(self.title), \"pk\": self.pk})\n\n def get_dashboard_edit_url(self):\n return reverse(\"dashboard:video_update\", kwargs={\"pk\": self.pk})\n\n\n\n def get_dashboard_delete_url(self):\n return reverse(\"dashboard:video_delete\", kwargs={\"pk\": self.pk})\n\n\n\n\n\n def get_similar_items(self):\n from pustakalaya_apps.document.models import Document\n from pustakalaya_apps.audio.models import Audio\n\n documents = Document.objects.filter(keywords__in=[keyword.id for keyword in self.keywords.all()]).distinct()[:4]\n audios = Audio.objects.filter(keywords__in=[keyword.id for keyword in self.keywords.all()]).distinct()[:4]\n videos = Video.objects.filter(keywords__in=[keyword.id for keyword in self.keywords.all()]).distinct()[:4]\n return chain(documents, audios, videos)\n\n\n\n def doc(self):\n # Parent attr\n item_attr = super(Video, self).doc()\n # Combine item attr and video attr to index in search server\n videoattr = dict(\n **item_attr,\n publisher=[publisher.publisher_name for publisher in self.publisher.all()],\n sponsors=[sponsor.name for sponsor in self.sponsors.all()], # Multi value # TODO some generators\n keywords=[keyword.keyword for keyword in self.keywords.all()],\n type=self.type,\n education_levels=[education_level.level for education_level in self.education_levels.all()],\n communities=[collection.community_name for collection in self.collections.all()],\n collections=[collection.collection_name for collection in self.collections.all()],\n collections_ids=[collection.pk for collection in self.collections.all()],\n languages=[language.language.lower() for language in self.languages.all()],\n video_running_time=self.video_running_time,\n thumbnail=self.thumbnail.name,\n # License type\n license_type=self.license.license if self.license else None,\n video_director=self.getauthors,#getattr(self.video_director, \"getname\", \"\"),\n video_series=getattr(self.video_series, \"series_name\", \"\"),\n video_certificate_license=self.video_certificate_license,\n # video_genre=getattr(self.video_genre, \"genre\", \"\"),\n video_genre=[video_genre.custom_genre for video_genre in self.video_genre.all()],\n #video_genre=self.video_genre.genre if self.video_genre else None,\n author_list=self.getauthors,\n url = self.get_absolute_url()\n\n )\n # Create a video instance\n obj = VideoDoc(**videoattr)\n return obj\n\n\n def index(self):\n \"\"\"\n Call this method to index an instance to search server\n \"\"\"\n if self.published == \"no\":\n # delete this if the published is set to no form\n self.delete_index()\n else:\n # save the doc\n self.doc().save()\n\n def get_admin_url(self):\n return urlresolvers.reverse(\"admin:%s_%s_change\" %(self._meta.app_label, self._meta.model_name), args=(self.pk,))\n\n def video_title(self):\n return self.title;\n\n def published_yes_no(self):\n return self.published\n\n def featured_yes_no(self):\n return self.featured\n\n\n def updated_date_string(self):\n return self.updated_date\n\n def bulk_index(self):\n \"\"\"\n call this method to during bulk indexing an instance to search server.\n method used by `search.py module`\n \"\"\"\n return self.doc().to_dict(include_meta=True)\n\n def delete_index(self):\n \"\"\"method to delete a video instance from search server\n This method is called by `signals.py` module\n \"\"\"\n try:\n self.doc().delete()\n except NotFoundError:\n pass\n\n def __str__(self):\n return self.title\n\n def get_admin_url(self):\n return urlresolvers.reverse(\"admin:%s_%s_change\" %(self._meta.app_label, self._meta.model_name), args=(self.pk,))\n\n\n\nclass VideoSeries(AbstractSeries):\n\n class Meta:\n verbose_name_plural = _(\"Video series\")\n ordering = [\"created_date\"]\n\n def __str__(self):\n return \"{}\".format(self.series_name)\n\n\nclass VideoFileUpload(AbstractTimeStampModel):\n \"\"\"Class to upload the multiple document objects\"\"\"\n\n file_name = models.CharField(\n _(\"File name\"),\n max_length=255,\n blank=True,\n )\n\n video = models.ForeignKey(\n Video,\n on_delete=models.CASCADE\n )\n\n upload = models.FileField(\n upload_to=\"uploads/videos/%Y/%m/\",\n max_length=255\n )\n\n\n thumbnail = models.ImageField(\n upload_to=\"uploads/thumbnails/videofile/%Y/%m/%d\",\n max_length=255,\n blank=True,\n null=True,\n help_text=_(\"maximum size of thumbnail should be 165px by 93px\")\n )\n\n # This is added to ignore the delay caused while playing video\n video_running_length = models.CharField(\n _(\"Video running length\"),\n editable=False,\n max_length=255,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.file_name\n\n class Meta:\n ordering = [\"created_date\"]\n\n\nclass VideoLinkInfo(LinkInfo):\n video = models.ForeignKey(\n Video,\n verbose_name=_(\"Link\"),\n on_delete=models.CASCADE,\n\n )\n\n def __str__(self):\n return self.video.title\n\n class Meta:\n ordering=[\"created_date\"]\n\n\nclass VideoEmbedLink(EmbedVideoAudioLink):\n video = models.ForeignKey(\n Video,\n verbose_name=_(\"Embed Link\"),\n on_delete=models.CASCADE,\n\n )\n\n def __str__(self):\n return self.video.title\n\n class Meta:\n ordering=[\"created_date\"]\n\n\n\n\nclass VideoGenre(AbstractTimeStampModel):\n genre = models.CharField(\n _(\"Genre name\"),\n max_length=255\n )\n\n genre_description = models.TextField(\n verbose_name=_(\"Genre description\"),\n blank=True\n )\n\n class Meta:\n db_table = \"video_genre\"\n\n def __str__(self):\n return self.genre\n\n","sub_path":"src/pustakalaya_apps/video/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":10688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"14923919","text":"from random import randint\nimport time\n\ndef selection_sort(lst):\n for i in range(len(lst)):\n\n # find the minimum element in remaining \n minPosition = i\n\n for j in range(i+1, len(lst)):\n if lst[minPosition] > lst[j]:\n minPosition = j\n\n # swap the found minimum element with minPosition\n temp = lst[i]\n lst[i] = lst[minPosition]\n lst[minPosition] = temp\n\n return lst\n\n\nrandList = []\nfor i in range(5):\n randList.append(randint(1,10))\n\nprint(randList)\n\nsortedList = selection_sort(randList)\n\nprint(sortedList)\n\nrand1 = []\nfor i in range(10000):\n rand1.append(randint(1,1000000))\n \nrand2 = []\nfor i in range(100000):\n rand2.append(randint(1,1000000))\n \nrand3 = []\nfor i in range(1000000):\n rand3.append(randint(1,1000000))\n\nprint(len(rand1))\nprint(len(rand2))\nprint(len(rand3))\n\n\nstart = time.time()\nsortedListA = selection_sort(rand1)\nend = time.time()\nprint(end - start)\n\nstart = time.time()\nsortedListA = selection_sort(rand2)\nend = time.time()\nprint(end - start)\n\nstart = time.time()\nsortedListA = selection_sort(rand3)\nend = time.time()\nprint(end - start)\n","sub_path":"python/data_structures_and_algorithms_BOOK/exercises/ch1/1.3.py","file_name":"1.3.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"33682227","text":"\"\"\"Module for Krake controller responsible for managing Magnum cluster resources and\ncreating their respective Kubernetes cluster. It connects to the Magnum service of the\nProject on which a MagnumCluster has been scheduled.\n\n.. code:: bash\n\n python -m krake.controller.magnum --help\n\nConfiguration is loaded from the ``controllers.scheduler`` section:\n\n.. code:: yaml\n\n api_endpoint: http://localhost:8080\n worker_count: 5\n debounce: 1.0\n poll_interval: 30\n\n tls:\n enabled: false\n client_ca: tmp/pki/ca.pem\n client_cert: tmp/pki/system:magnum.pem\n client_key: tmp/pki/system:magnum-key.pem\n\n\n log:\n ...\n\n\"\"\"\nimport logging\nimport asyncio\nimport pprint\nimport random\nimport re\nimport string\nfrom functools import partial, wraps\nfrom argparse import ArgumentParser\nfrom base64 import b64encode\n\nfrom OpenSSL import crypto\nfrom aiohttp import ClientError, ClientResponseError\n\nfrom keystoneauth1.identity.v3 import Password, ApplicationCredential\nfrom keystoneauth1.session import Session\nimport keystoneauth1.exceptions\nfrom krake.data.config import MagnumConfiguration\nfrom krake.utils import KrakeArgumentFormatter\nfrom magnumclient.v1.client import Client as MagnumV1Client\nimport magnumclient.exceptions\n\nfrom kubernetes_asyncio.config.kube_config import KubeConfigLoader\nfrom kubernetes_asyncio.client import ApiClient, CoreV1Api, Configuration\n\nfrom krake import (\n search_config,\n setup_logging,\n load_yaml_config,\n ConfigurationOptionMapper,\n)\nfrom krake.client.openstack import OpenStackApi\nfrom krake.client.kubernetes import KubernetesApi\nfrom krake.data.core import Reason, ReasonCode, Metadata, resource_ref, ResourceRef\nfrom krake.data.openstack import MagnumClusterState\nfrom krake.data.kubernetes import (\n Cluster as KubernetesCluster,\n ClusterSpec as KubernetesClusterSpec,\n ClusterStatus as KubernetesClusterStatus,\n ClusterCloudConstraints as KubernetesClusterCloudConstraints,\n CloudConstraints as KubernetesCloudConstraints,\n)\nfrom . import Controller, ControllerError, create_ssl_context, run, Reflector\n\n\nDELETION_FINALIZER = \"magnum_cluster_deletion\"\n\nlogger = logging.getLogger(\"krake.controller.openstack\")\n\n\nclass CreateFailed(ControllerError):\n \"\"\"Raised in case the creation of a Magnum cluster failed.\"\"\"\n\n code = ReasonCode.CREATE_FAILED\n\n\nclass ReconcileFailed(ControllerError):\n \"\"\"Raised in case the update of a Magnum cluster failed.\"\"\"\n\n code = ReasonCode.RECONCILE_FAILED\n\n\nclass DeleteFailed(ControllerError):\n \"\"\"Raised in case the deletion of a Magnum cluster failed.\"\"\"\n\n code = ReasonCode.DELETE_FAILED\n recoverable = False\n\n\nclass InvalidClusterTemplateType(ControllerError):\n \"\"\"Raised in case the given Magnum template is not a template for a Kubernetes\n cluster.\n \"\"\"\n\n code = ReasonCode.INVALID_CLUSTER_TEMPLATE\n\n\nOPENSTACK_ERRORS = (\n magnumclient.exceptions.ClientException,\n keystoneauth1.exceptions.ClientException,\n)\n\n\ndef format_openstack_error(error):\n \"\"\"Create a more readable error message using OpenStack specific errors.\n\n Args:\n error (BaseException): the exception whose information is used to create a\n message.\n\n Returns:\n str: the generated error message.\n\n \"\"\"\n if isinstance(error, magnumclient.exceptions.HttpError):\n return \"{message} (HTTP {status}): {method} {url}\".format(\n status=error.http_status,\n message=error.message,\n method=error.method,\n url=error.url,\n )\n elif isinstance(error, keystoneauth1.exceptions.HttpError):\n return \"{message}: {method} {url}\".format(\n message=error.message, method=error.method, url=error.url\n )\n return str(error)\n\n\nclass MagnumClusterController(Controller):\n \"\"\"The Magnum controller receives the MagnumCluster resources from the API and acts\n on it, by creating, updating or deleting their actual cluster counterparts. It uses\n the OpenStack Magnum client for this purpose.\n\n Args:\n api_endpoint (str): URL to the API\n loop (asyncio.AbstractEventLoop, optional): Event loop that should be\n used.\n ssl_context (ssl.SSLContext, optional): if given, this context will be\n used to communicate with the API endpoint.\n debounce (float, optional): value of the debounce for the\n :class:`WorkQueue`.\n worker_count (int, optional): the amount of worker function that should be\n run as background tasks.\n poll_interval (float): time in second before two attempts to modify a Magnum\n cluster (creation, deletion, update, change from FAILED state...).\n\n \"\"\"\n\n def __init__(self, *args, worker_count=5, poll_interval=30, **kwargs):\n super().__init__(*args, **kwargs)\n self.openstack_api = None\n self.kubernetes_api = None\n self.reflector = None\n self.worker_count = worker_count\n self.poll_interval = poll_interval\n\n async def prepare(self, client):\n self.client = client\n self.openstack_api = OpenStackApi(self.client)\n self.kubernetes_api = KubernetesApi(self.client)\n\n for i in range(self.worker_count):\n self.register_task(self.consume, name=f\"worker_{i}\")\n\n async def enqueue(cluster):\n # Always cleanup deleted clusters even if they are in FAILED\n # state.\n if cluster.metadata.deleted:\n if (\n cluster.metadata.finalizers\n and cluster.metadata.finalizers[-1] == DELETION_FINALIZER\n ):\n # Safe guard for infinite looping: a failed but deleted\n # application is enqueued after the poll interval to slow\n # down the infinite retry loop.\n #\n # FIXME: Should there be a retry limit?\n if cluster.status.state == MagnumClusterState.FAILED:\n logger.debug(\n \"Enqueue deleted but failed %r in %ss\",\n cluster,\n self.poll_interval,\n )\n await self.queue.put(\n cluster.metadata.uid, cluster, delay=self.poll_interval\n )\n else:\n logger.debug(\"Enqueue deleted %r\", cluster)\n await self.queue.put(cluster.metadata.uid, cluster)\n else:\n logger.debug(\"Reject deleted %r without finalizer\", cluster)\n\n # Ignore all other failed clusters\n elif cluster.status.state == MagnumClusterState.FAILED:\n logger.debug(\"Reject failed %r\", cluster)\n\n # Accept scheduled clusters\n elif cluster.status.project:\n logger.debug(\"Enqueue scheduled %r\", cluster)\n await self.queue.put(cluster.metadata.uid, cluster)\n else:\n logger.debug(\"Reject %r\", cluster)\n\n self.reflector = Reflector(\n listing=self.openstack_api.list_all_magnum_clusters,\n watching=self.openstack_api.watch_all_magnum_clusters,\n on_list=enqueue,\n on_add=enqueue,\n on_update=enqueue,\n on_delete=enqueue,\n resource_plural=\"Magnum Clusters\",\n )\n self.register_task(self.reflector, name=\"Reflector\")\n\n async def cleanup(self):\n self.openstack_api = None\n self.kubernetes_api = None\n self.reflector = None\n\n async def consume(self, run_once=False):\n \"\"\"Continuously retrieve new elements from the worker queue to be processed.\n\n Args:\n run_once (bool, optional): if True, the function only handles one resource,\n then stops. Otherwise, continue to handle each new resource on the\n queue indefinitely.\n\n \"\"\"\n while True:\n key, cluster = await self.queue.get()\n try:\n await self.process_cluster(cluster)\n finally:\n await self.queue.done(key)\n if run_once:\n break # Only used for tests\n\n async def process_cluster(self, cluster):\n \"\"\"Process a Magnum cluster: if the given cluster is marked for deletion, delete\n the actual cluster. Otherwise, start the reconciliation between a Magnum cluster\n spec and its state.\n\n Handle any :class:`ControllerError` or the supported OpenStack error that are\n raised during the processing.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster to process.\n\n \"\"\"\n try:\n logger.debug(\"Handle %r\", cluster)\n\n if cluster.metadata.deleted:\n await self.delete_magnum_cluster(cluster)\n else:\n await self.reconcile_magnum_cluster(cluster)\n\n except ControllerError as error:\n logger.error(error)\n\n reason = Reason(code=error.code, message=error.message)\n cluster.status.reason = reason\n cluster.status.state = MagnumClusterState.FAILED\n\n await self.openstack_api.update_magnum_cluster_status(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n\n except OPENSTACK_ERRORS as error:\n message = format_openstack_error(error)\n logger.error(message)\n\n cluster.status.reason = Reason(\n code=ReasonCode.OPENSTACK_ERROR, message=message\n )\n cluster.status.state = MagnumClusterState.FAILED\n\n await self.openstack_api.update_magnum_cluster_status(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n\n async def reconcile_magnum_cluster(self, cluster):\n \"\"\"Depending on the state of the given Magnum cluster, start the rapprochement\n of the wanted state of the cluster to the desired one.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the cluster whose actual state\n will be modified to match the desired one.\n\n \"\"\"\n magnum = await self.create_magnum_client(cluster)\n\n # Ensure that deletion finalizer exists\n if DELETION_FINALIZER not in cluster.metadata.finalizers:\n cluster.metadata.finalizers.append(DELETION_FINALIZER)\n await self.openstack_api.update_magnum_cluster(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n\n # Simple finite state machine (FSM). Every state is handled by an\n # action. Action are methods of the controller. An action can return\n # another action (\"transition\"). If no action is returned, the FSM\n # terminates (terminal state).\n\n # Initial actions\n actions = {\n MagnumClusterState.PENDING: self.on_pending,\n MagnumClusterState.CREATING: self.on_creating,\n MagnumClusterState.RUNNING: self.on_running,\n MagnumClusterState.RECONCILING: self.on_reconciling,\n }\n action = actions.get(cluster.status.state)\n if not action:\n logger.warning(\n \"Unknown reconciliation state %r for %r\", cluster.status.state, cluster\n )\n return\n\n while action:\n action = await action(cluster, magnum)\n\n logger.info(\"Reconciliation of %r finished\", cluster)\n\n async def on_pending(self, cluster, magnum):\n \"\"\"Called when a Magnum cluster with the PENDING state needs reconciliation.\n\n Initiate the creation of a Magnum cluster using the registered Magnum template,\n but does not ensure that the creation succeeded.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster to actually\n create on its scheduled OpenStack project.\n magnum (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service on the project.\n\n Returns:\n callable: the next function to be called, as the Magnum cluster changed its\n state. In this case, the Magnum cluster has now the CREATING state, thus\n the function returned is :meth:`on_creating`.\n\n \"\"\"\n # Transition into \"CREATING\" state\n cluster.status.state = MagnumClusterState.CREATING\n\n # Create forward reference to Kubernetes cluster resource. The\n # resource does not exist. The reconcile loop will detect this and\n # create the resource.\n if not cluster.status.cluster:\n cluster.status.cluster = ResourceRef(\n api=KubernetesCluster.api,\n kind=KubernetesCluster.kind,\n name=f\"{cluster.metadata.name}-{randstr()}\",\n namespace=cluster.metadata.namespace,\n )\n\n await self.openstack_api.update_magnum_cluster_status(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n\n cluster_template = await read_magnum_cluster_template(\n client=magnum, cluster=cluster\n )\n if cluster_template.coe != \"kubernetes\":\n raise InvalidClusterTemplateType(\n message=f\"Invalid cluster template type {cluster_template.coe!r}\"\n )\n\n # Request OpenStack to create the cluster\n response = await create_magnum_cluster(client=magnum, cluster=cluster)\n\n # Save ID of Magnum cluster resource\n cluster.status.cluster_id = response.uuid\n await self.openstack_api.update_magnum_cluster_status(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n\n return self.on_creating\n\n async def on_creating(self, cluster, magnum):\n \"\"\"Called when a Magnum cluster with the CREATING state needs reconciliation.\n\n Watch over a Magnum cluster currently being created on its scheduled OpenStack\n project, and updates the corresponding Kubernetes cluster created in the API.\n\n As the Magnum cluster is in a stable state at the end, no further processing\n method is needed to return.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster that needs\n to be processed.\n magnum (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service on the project.\n\n \"\"\"\n await self.wait_for_running(cluster, magnum)\n await self.reconcile_kubernetes_resource(cluster, magnum)\n return None\n\n async def on_running(self, cluster, magnum):\n \"\"\"Called when a Magnum cluster with the RUNNING state needs reconciliation.\n\n If the Magnum cluster needs to be resized, initiate the resizing. Otherwise,\n updates the corresponding Kubernetes cluster created in the API.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster that needs\n to be processed.\n magnum (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service on the project.\n\n Returns:\n callable: the next function to be called, as the Magnum cluster changed its\n state. In the case of resizing, the Magnum cluster has now the\n RECONCILING state, thus the function returned is :meth:`on_creating`.\n Otherwise, as the state is stable at the end, no further processing\n is needed and None is returned.\n\n \"\"\"\n if (\n cluster.spec.node_count is not None\n and cluster.status.node_count != cluster.spec.node_count\n ):\n cluster.status.state = MagnumClusterState.RECONCILING\n await self.openstack_api.update_magnum_cluster_status(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n await resize_magnum_cluster(magnum, cluster)\n return self.on_reconciling\n\n await self.reconcile_kubernetes_resource(cluster, magnum)\n return None\n\n async def on_reconciling(self, cluster, magnum):\n \"\"\"Called when a Magnum cluster with the RECONCILING state needs reconciliation.\n\n Watch over a Magnum cluster already created on its scheduled OpenStack project,\n and updates the corresponding Kubernetes cluster created in the API.\n\n As the Magnum cluster is in a stable state at the end, no further processing\n method is needed to return.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster that needs\n to be processed.\n magnum (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service on the project.\n\n \"\"\"\n await self.wait_for_running(cluster, magnum)\n await self.reconcile_kubernetes_resource(cluster, magnum)\n return None\n\n async def delete_magnum_cluster(self, cluster):\n \"\"\"Initiate the deletion of the actual given Magnum cluster, and wait for its\n deletion. The finalizer specific to the Magnum Controller is also removed from\n the Magnum cluster resource.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster that needs\n to be deleted.\n\n \"\"\"\n # FIXME: during its deletion, a MagnumCluster is updated, and thus put into the\n # queue again on the controller to be deleted. After deletion, the\n # MagnumCluster is released from the active resources of the queue, and the\n # updated resource is handled again. However, there are no actual resource\n # anymore, as it has been deleted. To prevent this, the worker verifies that\n # the MagnumCluster is not deleted yet before attempting to delete it.\n try:\n await self.openstack_api.read_magnum_cluster(\n namespace=cluster.metadata.namespace, name=cluster.metadata.name\n )\n except ClientResponseError as err:\n if err.status == 404:\n return\n raise\n\n if (\n cluster.status.project\n and cluster.status.state != MagnumClusterState.PENDING\n ):\n magnum = await self.create_magnum_client(cluster)\n\n if cluster.status.state != MagnumClusterState.DELETING:\n try:\n await delete_magnum_cluster(magnum, cluster)\n except magnumclient.exceptions.NotFound:\n pass\n\n cluster.status.state = MagnumClusterState.DELETING\n await self.openstack_api.update_magnum_cluster_status(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n\n while True:\n try:\n response = await read_magnum_cluster(magnum, cluster)\n except magnumclient.exceptions.NotFound:\n logger.info(\"The cluster has been deleted: %r\", cluster)\n break\n\n if response.status == \"DELETE_FAILED\":\n raise DeleteFailed(message=response.status_reason)\n\n await asyncio.sleep(self.poll_interval)\n\n # Remove finalizer. The owner reference does not need to be removed\n # because the API will remove the resource if there are\n # no finalizers left no matter the owners.\n cluster.metadata.finalizers.remove(DELETION_FINALIZER)\n await self.openstack_api.update_magnum_cluster(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n\n async def wait_for_running(self, cluster, magnum):\n \"\"\"Await for an actual Magnum cluster to be in a stable state, that means, when\n its creation or update is finished.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster on which\n an operation is performed that needs to be awaited.\n magnum (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service on the project.\n\n Raises:\n ControllerError: if the operation on the cluster failed, a corresponding\n error will be raised (for instance CreateFailed in case the creation of\n the cluster failed).\n\n \"\"\"\n # FIXME: This should be handled by an observer. The observer\n # periodically checks the status of the OpenStack resource and\n # if the status of the OpenStack resource is different\n # then the \".status.state\", the observer will update the Krake\n # resource state accordingly.\n while True:\n try:\n response = await read_magnum_cluster(magnum, cluster)\n except magnumclient.exceptions.NotFound:\n # Magnum cluster was deleted from OpenStack deployment. Clear\n # reference to deleted cluster and transition into PENDING\n # state again.\n #\n # Note: The referenced Kubernetes cluster resource is not\n # deleted here. The controller handling the Kubernetes\n # cluster periodically checks the status of the cluster\n # (observer). If the cluster does not exist, it will be\n # recognized by the observer.\n #\n cluster.status.cluster_id = None\n cluster.status.state = MagnumClusterState.PENDING\n\n await self.openstack_api.update_magnum_cluster_status(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n return\n\n if response.status in (\"CREATE_COMPLETE\", \"UPDATE_COMPLETE\"):\n logger.info(\"Cluster %r operation complete\", cluster)\n break\n\n if response.status == \"CREATE_FAILED\":\n raise CreateFailed(message=response.status_reason)\n\n if response.status == \"UPDATE_FAILED\":\n raise ReconcileFailed(message=response.status_reason)\n\n if response.status == \"DELETE_FAILED\":\n raise DeleteFailed(message=response.status_reason)\n\n # TODO: Handle timeout\n logger.debug(\"Operation on %r still in progress\", cluster)\n await asyncio.sleep(self.poll_interval)\n\n cluster.status.node_count = response.node_count\n cluster.status.api_address = response.api_address\n cluster.status.master_addresses = response.master_addresses\n cluster.status.node_addresses = response.node_addresses\n\n # Transition into \"RUNNING\" state\n cluster.status.state = MagnumClusterState.RUNNING\n await self.openstack_api.update_magnum_cluster_status(\n namespace=cluster.metadata.namespace,\n name=cluster.metadata.name,\n body=cluster,\n )\n\n async def reconcile_kubernetes_resource(self, cluster, magnum):\n \"\"\"Create or update the Krake resource of the Kubernetes cluster that was\n created from a given Magnum cluster.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the Kubernetes cluster will be\n created using the specifications of this Magnum cluster.\n magnum (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service on the project.\n\n Raises:\n ClientResponseError: when checking if the Kubernetes cluster resource\n already exists, raise if any HTTP error except 404 is raised.\n\n \"\"\"\n if cluster.status.state != MagnumClusterState.RUNNING:\n logger.debug(\n \"Magnum cluster %r not running. Skip Kubernetes cluster \"\n \"resource reconciliation.\",\n cluster,\n )\n return\n\n assert cluster.status.cluster, \"Cluster reference not set\"\n assert cluster.status.cluster_id, \"Magnum cluster UUID not set\"\n assert cluster.status.template, \"Cluster template UUID not set\"\n\n kube = None\n\n try:\n # Check if the Kubernetes cluster resource exists\n kube = await self.kubernetes_api.read_cluster(\n namespace=cluster.status.cluster.namespace,\n name=cluster.status.cluster.name,\n )\n except ClientResponseError as error:\n if error.status != 404:\n raise\n else:\n # Check if we can access the Kubernetes cluster with this\n # kubeconfig. If not, update kubeconfig with new certificates.\n loader = KubeConfigLoader(kube.spec.kubeconfig)\n config = Configuration()\n await loader.load_and_set(config)\n\n # If the API address is the same, check if we can access the\n # cluster. Otherwise (if the API addresses are different),\n # recreate certificates.\n if config.host == cluster.status.api_address:\n api_client = ApiClient(config)\n core_v1_api = CoreV1Api(api_client)\n try:\n await core_v1_api.list_node()\n return\n except ClientError:\n pass\n\n # Generate kubeconfig with new certificates.\n #\n # IMPORTANT: The generated certificate has admin rights on the\n # cluster.\n kubeconfig = await make_kubeconfig(magnum, cluster)\n\n if not kube:\n kube = KubernetesCluster(\n metadata=Metadata(\n namespace=cluster.status.cluster.namespace,\n name=cluster.status.cluster.name,\n uid=None,\n created=None,\n modified=None,\n owners=[resource_ref(cluster)],\n # TODO: There should be support for transitive labels and\n # metrics in the scheduler. For now, we simply copy.\n labels=cluster.metadata.labels.copy(),\n ),\n spec=KubernetesClusterSpec(\n kubeconfig=kubeconfig,\n metrics=cluster.spec.metrics.copy(),\n constraints=KubernetesClusterCloudConstraints(\n cloud=KubernetesCloudConstraints()\n ),\n ),\n status=KubernetesClusterStatus(),\n )\n await self.kubernetes_api.create_cluster(\n namespace=kube.metadata.namespace, body=kube\n )\n else:\n kube.spec.kubeconfig = kubeconfig\n await self.kubernetes_api.update_cluster(\n namespace=kube.metadata.namespace, name=kube.metadata.name, body=kube\n )\n\n async def create_magnum_client(self, cluster):\n \"\"\"Create a client to communicate with the Magnum service API for the given\n Magnum cluster. The specifications defined in the OpenStack project of the\n cluster are used to create the client.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the cluster whose project's\n specifications will be used to connect to the Magnum service.\n\n Returns:\n MagnumV1Client: the Magnum client to use to connect to the Magnum service on\n the project of the given Magnum cluster.\n\n \"\"\"\n # TODO: Handle 404 errors\n project = await self.openstack_api.read_project(\n namespace=cluster.status.project.namespace, name=cluster.status.project.name\n )\n\n try:\n return await make_magnum_client(project)\n # FIXME: \"magnum.v1.client.Client\" catches every exception\n # during endpoint discovery and transforms it into an RuntimeError\n # ... AAAAAAAAAAAAAAAAH!!!\n except RuntimeError as error:\n # Ugly workaround to get the original exception\n if isinstance(error.__context__, keystoneauth1.exceptions.ClientException):\n raise error.__context__ from None\n raise\n\n\ndef concurrent(fn):\n \"\"\"Decorator function to turn a synchronous function into an asynchronous coroutine\n that runs in another thread, that can be awaited and thus does not block the main\n asyncio loop. It is particularly useful for synchronous tasks which requires a long\n time to be run concurrently to the main asyncio loop.\n\n Example:\n\n .. code:: python\n\n @concurrent\n def my_function(args_1, arg2=value):\n # long synchronous processing...\n return result\n\n await my_function(value1, arg2=value2) # function run in another thread\n\n Args:\n fn (callable): the function to run in parallel from the main loop.\n\n Returns:\n callable: decorator around the given function. The returned callable is an\n asyncio coroutine.\n\n \"\"\"\n\n @wraps(fn)\n async def wrapper(*args, **kwargs):\n loop = asyncio.get_event_loop()\n wrapped = partial(fn, *args, **kwargs)\n return await loop.run_in_executor(None, wrapped)\n\n return wrapper\n\n\ndef randstr(length=7):\n \"\"\"Create a random string of lowercase and digit character of the given length.\n\n Args:\n length (int): specifies how many characters should be present in the returned\n string.\n\n Returns:\n str: the string randomly generated.\n\n \"\"\"\n return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n\n\ndef generate_magnum_cluster_name(cluster):\n \"\"\"Create a unique name for a Magnum cluster from its metadata. The name has the\n following structure: \"<namespace>-<name>-<random_lowercase_digit_string>\". Any\n special character that the Magnum service would see as invalid will be replaced.\n\n Args:\n cluster (krake.data.openstack.MagnumCluster): the cluster to use to create a\n name.\n\n Returns:\n str: the name generated.\n\n \"\"\"\n raw_name = f\"{cluster.metadata.namespace}-{cluster.metadata.name}-{randstr()}\"\n # Replace all special characters not accepted by the Magnum service with \"_\".\n return re.sub(\"[^A-Za-z0-9_.-]+\", \"_\", raw_name)\n\n\n@concurrent\ndef read_magnum_cluster(client, cluster):\n \"\"\"Read the actual information of the given Magnum cluster resource.\n\n Args:\n client (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service.\n cluster (krake.data.openstack.MagnumCluster): the resource whose actual\n cluster state will be read.\n\n Returns:\n magnumclient.v1.clusters.Cluster: the current information regarding the given\n Magnum cluster.\n\n \"\"\"\n return client.clusters.get(cluster.status.cluster_id)\n\n\n@concurrent\ndef create_magnum_cluster(client, cluster):\n \"\"\"Create an actual Magnum cluster by connecting to the the Magnum service.\n\n Args:\n client (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service.\n cluster (krake.data.openstack.MagnumCluster): the cluster to create.\n\n Returns:\n magnumclient.v1.clusters.Cluster: the cluster created by the Magnum service.\n\n \"\"\"\n extra = {}\n\n # None would be serialized to a string value of \"None\" which makes Magnum\n # fail the creation because \"None\" is an invalid integer value.\n if cluster.spec.master_count is not None:\n extra[\"master_count\"] = cluster.spec.master_count\n\n if cluster.spec.node_count is not None:\n extra[\"node_count\"] = cluster.spec.node_count\n\n return client.clusters.create(\n cluster_template_id=cluster.status.template,\n name=generate_magnum_cluster_name(cluster),\n create_timeout=60, # Timeout for cluster creation in minutes\n **extra,\n )\n\n\n@concurrent\ndef resize_magnum_cluster(client, cluster):\n \"\"\"Update the given Magnum cluster by changing its node count.\n\n Args:\n client (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service.\n cluster (krake.data.openstack.MagnumCluster): the cluster to resize.\n\n Returns:\n magnumclient.v1.clusters.Cluster: the cluster updated by the Magnum service.\n\n \"\"\"\n # TODO: How to handle \".node_count = None\"?\n return client.clusters.resize(\n cluster.status.cluster_id, node_count=cluster.spec.node_count\n )\n\n\n@concurrent\ndef delete_magnum_cluster(client, cluster):\n \"\"\"Delete the actual Magnum cluster that corresponds to the given resource.\n\n Args:\n client (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service.\n cluster (krake.data.openstack.MagnumCluster): the cluster to delete.\n\n Returns:\n magnumclient.v1.clusters.Cluster: the cluster deleted by the Magnum service.\n\n \"\"\"\n return client.clusters.delete(cluster.status.cluster_id)\n\n\n@concurrent\ndef read_magnum_cluster_template(client, cluster):\n \"\"\"Get the actual template associated with the one specified in the given Magnum\n cluster resource.\n\n Args:\n client (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service.\n cluster (krake.data.openstack.MagnumCluster): the template given is the one\n specified by this Magnum cluster.\n\n Returns:\n magnumclient.v1.cluster_templates.ClusterTemplate\n\n \"\"\"\n return client.cluster_templates.get(cluster.status.template)\n\n\ndef _encode_to_64(string):\n \"\"\"Compute the base 64 encoding of a string.\n\n Args:\n string (str): the string to encode.\n\n Returns:\n str: the result of the encoding.\n\n \"\"\"\n # b64encode accepts only bytes.\n return b64encode(string.encode()).decode()\n\n\nasync def make_kubeconfig(client, cluster):\n \"\"\"Create a kubeconfig for the Kubernetes cluster associated with the given Magnum\n cluster. For this process, it uses (non exhaustively) the name, address and\n certificates associated with it.\n\n Args:\n client (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service.\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster for which a\n kubeconfig will be created.\n\n Returns:\n dict: the kubeconfig created, returned as a dictionary.\n\n \"\"\"\n client_key, csr = make_csr()\n\n ca = await read_ca_certificate(client, cluster)\n client_certificate = await create_client_certificate(client, cluster, csr)\n\n return {\n \"kind\": \"Config\",\n \"apiVersion\": \"v1\",\n \"clusters\": [\n {\n \"cluster\": {\n \"certificate-authority-data\": _encode_to_64(ca),\n \"server\": cluster.status.api_address,\n },\n \"name\": cluster.status.cluster.name,\n }\n ],\n \"contexts\": [\n {\n \"context\": {\"cluster\": cluster.status.cluster.name, \"user\": \"krake\"},\n \"name\": \"admin\",\n }\n ],\n \"current-context\": \"admin\",\n \"preferences\": {},\n \"users\": [\n {\n \"name\": \"krake\",\n \"user\": {\n \"client-certificate-data\": _encode_to_64(client_certificate),\n \"client-key-data\": _encode_to_64(client_key),\n },\n }\n ],\n }\n\n\ndef make_keystone_session(project):\n \"\"\"Create an OpenStack Keystone session using the authentication information of the\n given project resource.\n\n Args:\n project (krake.data.openstack.Project): the OpenStack project to use for getting\n the credentials and endpoint.\n\n Returns:\n Session: the Keystone session created.\n\n \"\"\"\n if project.spec.auth.type == \"password\":\n auth = Password(\n auth_url=project.spec.url,\n user_id=project.spec.auth.password.user.id,\n password=project.spec.auth.password.user.password,\n project_id=project.spec.auth.password.project.id,\n )\n elif project.spec.auth.type == \"application_credential\":\n id = project.spec.auth.application_credential.id\n secret = project.spec.auth.application_credential.secret\n auth = ApplicationCredential(\n auth_url=project.spec.url,\n application_credential_id=id,\n application_credential_secret=secret,\n )\n else:\n raise NotImplementedError(\n f\"Keystone authentication '{project.spec.auth.type}' is not implemented\"\n )\n return Session(auth=auth)\n\n\n@concurrent\ndef make_magnum_client(project):\n \"\"\"Create a Magnum client to connect to the given OpenStack project.\n\n Args:\n project (krake.data.openstack.Project): the project to connect to.\n\n Returns:\n MagnumV1Client: the client to connect to the Magnum service of the given\n project.\n\n \"\"\"\n session = make_keystone_session(project)\n return MagnumV1Client(session=session)\n\n\n@concurrent\ndef read_ca_certificate(client, cluster):\n \"\"\"Get the certificate authority used by the given Magnum cluster.\n\n Args:\n client (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service.\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster for which the\n certificate authority will be retrieved.\n\n Returns:\n str: the certificate authority of the given cluster.\n\n \"\"\"\n return client.certificates.get(cluster_uuid=cluster.status.cluster_id).pem\n\n\n@concurrent\ndef create_client_certificate(client, cluster, csr):\n \"\"\"Create and get a certificate for the given Magnum cluster.\n\n Args:\n client (MagnumV1Client): the Magnum client to use to connect to the Magnum\n service.\n cluster (krake.data.openstack.MagnumCluster): the Magnum cluster for which a\n kubeconfig file will be created.\n csr (str): the certificate signing request (CSR) to use on the Magnum service\n for the creation of the certificate.\n\n Returns:\n str: the generated certificate.\n\n \"\"\"\n return client.certificates.create(\n cluster_uuid=cluster.status.cluster_id, csr=csr\n ).pem\n\n\ndef make_csr(key_size=4096):\n \"\"\"Generates a private key and corresponding certificate and certificate\n signing request.\n\n Args:\n key_size (int): Length of private key in bits\n\n Returns:\n (str, str): private key, certificate signing request (CSR)\n\n \"\"\"\n key = crypto.PKey()\n key.generate_key(crypto.TYPE_RSA, key_size)\n\n csr = crypto.X509Req()\n\n # Use \"admin\" for Common Name and \"system:masters\" for Organization to get\n # admin access to the cluster.\n #\n # @see https://docs.openstack.org/releasenotes/magnum/queens.html#relnotes-6-1-1-stable-queens-upgrade-notes # noqa\n subject = csr.get_subject()\n subject.organizationName = \"system:masters\"\n subject.commonName = \"admin\"\n\n csr.set_pubkey(key)\n csr.sign(key, \"sha256\")\n\n return (\n crypto.dump_privatekey(crypto.FILETYPE_PEM, key).decode(),\n crypto.dump_certificate_request(crypto.FILETYPE_PEM, csr).decode(),\n )\n\n\nparser = ArgumentParser(\n description=\"OpenStack Magnum controller\", formatter_class=KrakeArgumentFormatter\n)\nparser.add_argument(\"-c\", \"--config\", type=str, help=\"Path to configuration YAML file\")\n\nmapper = ConfigurationOptionMapper(MagnumConfiguration)\nmapper.add_arguments(parser)\n\n\ndef main(config):\n setup_logging(config.log)\n logger.debug(\n \"Krake Magnum Controller configuration settings:\\n %s\",\n pprint.pformat(config.serialize()),\n )\n\n tls_config = config.tls\n ssl_context = create_ssl_context(tls_config)\n logger.debug(\"TLS is %s\", \"enabled\" if ssl_context else \"disabled\")\n\n controller = MagnumClusterController(\n api_endpoint=config.api_endpoint,\n worker_count=config.worker_count,\n ssl_context=ssl_context,\n debounce=config.debounce,\n poll_interval=config.poll_interval,\n )\n run(controller)\n\n\nif __name__ == \"__main__\":\n args = vars(parser.parse_args())\n\n config = load_yaml_config(args[\"config\"] or search_config(\"magnum.yaml\"))\n kubernetes_config = mapper.merge(config, args)\n\n main(kubernetes_config)\n","sub_path":"krake/krake/controller/magnum.py","file_name":"magnum.py","file_ext":"py","file_size_in_byte":40930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"451980167","text":"from flask import Flask, request\nfrom datetime import timedelta, datetime\n\nimport rq_dashboard\n\n\nfrom config import r, q, scheduler\nfrom utils import RedisIdentifier, random_stats, Serializer\nfrom tasks import sleeper, background_task, failer, sched1\nfrom configdashboard import default_settings\n\napp = Flask(__name__)\n\n# setting up dashboard\napp.config.from_object(default_settings)\napp.register_blueprint(rq_dashboard.blueprint, url_prefix=\"/rq\")\n\n#identifier\nredis_identifier = RedisIdentifier()\n\n@app.route('/')\ndef hello():\n\n q.enqueue(sleeper)\n return f\"this is the start page{q.job_ids}, {len(q)}.\"\n\n@app.route(\"/task\")\ndef add_task():\n\n if request.args.get(\"n\"):\n\n val = request.args.get(\"n\")\n job = q.enqueue(background_task, val)\n q_len = len(q)\n\n return f\"Task {job.id} added to queue at {job.enqueued_at}. {q_len} tasks in queue\"\n\n return \"no value for n\"\n\n@app.route('/failer')\ndef failer_endpoint():\n\n if request.args.get(\"msg\"):\n val = request.args.get(\"msg\")\n\n # can be used to inspect failing jobs\n if val==\"fail\":\n job = q.enqueue(failer)\n else:\n job = q.enqueue(sleeper)\n\n return f\"failer endpoint. job_id: {job.id}, enqueued at: {job.enqueued_at}\"\n\n return \"no value for msg\"\n\n@app.route('/sched')\ndef sched():\n\n #scheduler.enqueue_in(timedelta(seconds=10), sched1)\n b = True\n sj = scheduler.enqueue_at(datetime.utcnow(), sched1, b)\n return f\"schedule: id {sj.id}. All scheduled jobs: {scheduler.get_jobs()}\"\n\n# Saving to redis manually -> pickling before saving\n@app.route('/manual/stats')\ndef stats():\n stats = random_stats()\n timestamp = datetime.utcnow()\n _id_stats = redis_identifier.create_id(typ='12weeks:stats')\n _id_meta = redis_identifier.create_id(typ='12weeks:meta')\n\n r.set(_id_stats, Serializer.to_byte(stats))\n r.hmset(_id_meta, {'timestamp': Serializer.to_byte(timestamp), 'stats_id': _id_stats})\n return f\"id: {_id_stats}, timestamp: {timestamp}\"\n\n# Loading from redis manually\n@app.route('/manual/stats/get')\ndef get_stats():\n _id_meta = redis_identifier.create_id(typ='12weeks:meta')\n _id_stats = r.hget(_id_meta, 'stats_id').decode(\"utf-8\")\n res = Serializer.from_byte(r.get(_id_stats))\n\n return f\"id: {_id_stats}, res: {res}\"\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", debug=True)\n","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"312491659","text":"\"\"\"\n绘制小猪佩奇\n参考turtle绘图介绍\nhttps://docs.python.org/3.6/library/turtle.html?highlight=turtle#turtle.penup\n\"\"\"\n# -*- coding: utf-8 -*-\nimport turtle as t\n\n\ndef draw_nose():\n\n t.up() # 提起笔\n t.goto(-100, 100) # 定位\n t.down()\n t.seth(-30) # 设置当前朝向的角度\n t.begin_fill() # 准备开始填充图形\n a = 0.4\n for i in range(120):\n if 0 <= i < 30 or 60 <= i < 90:\n a += 0.08\n t.left(3) # 向左转角度\n t.forward(a) # 向当前方向移动a的长度\n else:\n a -= 0.08\n t.left(3) # 向左转角度\n t.forward(a) # 向当前方向移动a的长度\n t.end_fill() # 填充完成\n t.up()\n t.seth(90)\n t.forward(25)\n t.seth(0)\n t.fd(10)\n t.pd()\n t.seth(10)\n t.begin_fill()\n t.circle(5)\n t.fillcolor(160, 82, 45) # 棕色，设置填充颜色\n t.end_fill()\n t.pu()\n t.seth(0)\n t.fd(20)\n t.pd()\n t.seth(10)\n t.begin_fill()\n t.circle(5)\n t.end_fill()\n\n\ndef draw_head():\n t.color((255, 155, 192), \"pink\")\n t.pu()\n t.seth(90)\n t.fd(41)\n t.seth(0)\n t.pd()\n t.begin_fill()\n t.seth(180)\n t.circle(300, -30)\n t.circle(100, -60)\n t.circle(80, -100)\n t.circle(150, -20)\n t.circle(60, -95)\n t.seth(161)\n t.circle(-300, 15)\n t.pu()\n t.goto(-100, 100)\n t.pd()\n t.seth(-30)\n a = 0.4\n for i in range(60):\n if 0 <= i < 30 or 60 <= i < 90:\n a += 0.08\n t.lt(3)\n t.fd(a)\n else:\n a -= 0.08\n t.lt(3)\n t.fd(a)\n t.end_fill()\n\n\ndef drawl_ears():\n t.pu()\n t.seth(90)\n t.fd(-7)\n t.seth(0)\n t.fd(70)\n t.begin_fill()\n t.pd()\n t.seth(100)\n t.circle(-50, 50)\n t.circle(-10, 120)\n t.circle(-50, 54)\n t.end_fill()\n t.pu()\n t.seth(-90)\n t.fd(12)\n t.seth(0)\n t.fd(30)\n t.pd()\n t.begin_fill()\n t.seth(100)\n t.circle(-50, 50)\n t.circle(-10, 120)\n t.circle(-50, 56)\n t.end_fill()\n\n\ndef draw_eyes():\n\n # 画第一只眼睛\n\n t.color((255, 155, 192), \"white\")\n t.pu()\n t.seth(90)\n t.fd(-20)\n t.seth(0)\n t.fd(-95)\n t.pd()\n t.begin_fill()\n t.circle(15, 360)\n t.end_fill()\n t.color(\"black\")\n t.pu()\n t.seth(90)\n t.fd(12)\n t.seth(0)\n t.fd(-3)\n t.pd()\n t.begin_fill()\n t.circle(3)\n t.end_fill()\n\n # 画第二只眼睛\n\n t.color((255, 155, 192), \"white\")\n t.pu()\n t.seth(90)\n t.fd(-25)\n t.seth(0)\n t.fd(40)\n t.pd()\n t.begin_fill()\n t.circle(15, 360)\n t.end_fill()\n t.color(\"black\")\n t.pu()\n t.seth(90)\n t.fd(12)\n t.seth(0)\n t.fd(-3)\n t.pd()\n t.begin_fill()\n t.circle(3)\n t.end_fill()\n\n\ndef draw_cheek():\n t.color(255, 155, 192)\n t.pu()\n t.seth(90)\n t.fd(-100)\n t.seth(0)\n t.fd(60)\n t.pd()\n t.begin_fill()\n t.circle(30)\n t.end_fill()\n\n\ndef draw_mouse():\n t.pencolor((239, 64, 64))\n t.pu()\n t.seth(90)\n t.fd(15)\n t.seth(0)\n t.fd(-100)\n t.pd()\n t.seth(-80)\n t.circle(30, 40)\n t.circle(40, 80)\n\n\ndef draw_body():\n t.color((255, 99, 71), \"red\")\n t.pu()\n t.seth(90)\n t.fd(-20)\n t.seth(0)\n t.fd(-71)\n t.pd()\n t.begin_fill()\n t.seth(-130)\n t.circle(100, 10)\n t.circle(300, 29)\n t.seth(0)\n t.fd(230)\n t.seth(90)\n t.circle(300, 30)\n t.circle(100, 5)\n t.color((255, 155, 192), (255, 100, 100))\n t.seth(-135)\n t.circle(-80, 58)\n t.circle(-180, 21)\n t.end_fill()\n\n\ndef drawl_hands():\n # 左手\n t.color((255, 155, 192))\n t.pu()\n t.seth(90)\n t.fd(-45)\n t.seth(0)\n t.fd(-25)\n t.pd()\n t.seth(-160)\n t.circle(300, 15)\n t.pu()\n t.seth(90)\n t.fd(15)\n t.pd()\n t.seth(-10)\n t.circle(-20, 90)\n\n # 右手\n t.pu()\n t.seth(90)\n t.fd(30)\n t.seth(0)\n t.fd(240)\n t.pd()\n t.seth(-20)\n t.circle(-300, 15)\n t.pu()\n t.seth(90)\n t.fd(20)\n t.pd()\n t.seth(-170)\n t.circle(20, 90)\n\n\ndef draw_legs():\n\n # 画左腿\n\n t.pensize(10)\n t.color((255, 110, 147))\n t.pu()\n t.seth(90)\n t.fd(-68)\n t.seth(0)\n t.fd(-180)\n t.pd()\n t.seth(-90)\n t.fd(40)\n t.seth(-180)\n t.color(\"black\")\n t.pensize(15)\n t.fd(20)\n\n # 画右腿\n\n t.pensize(10)\n t.color((255, 110, 147))\n t.pu()\n t.seth(90)\n t.fd(40)\n t.seth(0)\n t.fd(90)\n t.pd()\n t.seth(-90)\n t.fd(40)\n t.seth(-180)\n t.color(\"black\")\n t.pensize(15)\n t.fd(20)\n\n\ndef draw_tail():\n t.pensize(4)\n t.color((255, 155, 192))\n t.pu()\n t.seth(90)\n t.fd(70)\n t.seth(0)\n t.fd(95)\n t.pd()\n t.seth(0)\n t.circle(70, 20)\n t.circle(10, 330)\n t.circle(70, 30)\n\n\ndef main():\n \"\"\"\n 主函数\n \"\"\"\n t.pensize(4)\n t.colormode(255)\n t.color((255, 155, 192), \"pink\")\n t.setup(840, 800)\n t.speed(50)\n\n draw_nose()\n draw_head()\n drawl_ears()\n draw_eyes()\n draw_cheek()\n draw_mouse()\n draw_body()\n drawl_hands()\n draw_legs()\n draw_tail()\n\n t.exitonclick()\n\n\nif __name__ == '__main__':\n main()","sub_path":"DrawPeiqi.py","file_name":"DrawPeiqi.py","file_ext":"py","file_size_in_byte":5167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"301824592","text":"\"\"\"\r\nAssignment #4, Part 1\r\nJun Seob Shim\r\n8/10/2020\r\nIntro to Programming Section 012\r\nRoll the Dice\r\n\r\nSources:\r\nUsing multiple argument in if statement using \"or\":\r\nhttps://discuss.codecademy.com/t/can-we-compare-one-value-with-multiple-values-using-or/349398\r\n\"\"\"\r\n#import modules\r\nimport random as r\r\n\r\n#get number of dice sides from user\r\ninvalidsides = True\r\n\r\nwhile invalidsides:\r\n sides = int(input(\"How many sides on your dice (4, 6, 8, 10, 12 or 20)? \"))\r\n if sides == 4 or sides == 6 or sides == 8 or sides == 10 or sides == 12 or sides == 20:\r\n invalidsides = False\r\n print()\r\n print(\"Thanks, here we go!\")\r\n else:\r\n print(\"Invalid size, try again.\")\r\n\r\n#establish variables for dice rolling\r\nnotsnakeeyes = True\r\ndie1total = 0\r\ndie2total = 0\r\nrolls = 0\r\n\r\n#special roll counters\r\ndoubles = 0\r\nhigh = 0\r\nhighlow = 0\r\nevens = 0\r\nodds = 0\r\nsums = 0\r\n\r\nwhile notsnakeeyes:\r\n #dice values\r\n die1 = r.randint(1,sides)\r\n die2 = r.randint(1,sides)\r\n #counter values\r\n die1total += die1\r\n die2total += die2\r\n rolls += 1\r\n\r\n print(\"\\n\", rolls, \". die #1 is \",\"*\",die1,\"* \",\"and die #2 is \",\"*\",die2,\"* \",sep=\"\",end=\"\")\r\n\r\n #doubles\r\n if die1 == die2:\r\n print(\"Doubles! \", end=\"\")\r\n doubles += 1\r\n\r\n #high\r\n if die1 == die2 == sides:\r\n print(\"High! \", end=\"\")\r\n high += 1\r\n\r\n #highlow \r\n if (die1 == 1 and die2 == sides) or (die1 == sides and die2 == 1):\r\n print(\"High/Low! \", end=\"\")\r\n highlow += 1\r\n\r\n #evens \r\n if die1 % 2 == 0 and die2 % 2 == 0:\r\n print(\"Evens! \", end=\"\")\r\n evens += 1\r\n\r\n #odds \r\n if die1 % 2 != 0 and die2 % 2 != 0:\r\n print(\"Odds! \", end=\"\")\r\n odds += 1\r\n\r\n #sums \r\n if die1 + die2 == sides:\r\n print(\"Sum value is size value! \", end=\"\")\r\n sums += 1\r\n\r\n #snakeeyes \r\n if die1 == 1 and die2 == 1:\r\n print(\"Snake Eyes! \", end=\"\")\r\n notsnakeeyes = False\r\n\r\nprint()\r\nprint()\r\n\r\nprint(\"You finally got snake eyes on roll #\",rolls)\r\n\r\n#print counters for special rolls\r\nprint(\"Along the way you rolled DOUBLES \",doubles,\" time(s). (\",format((doubles/rolls)*100,\".2f\"),\"% of all rolls were doubles)\\\r\n\",sep=\"\")\r\nprint(\"Along the way you rolled TWO HIGH VALUES \",high,\" time(s). (\",format((high/rolls)*100,\".2f\"),\"% of all rolls were two \\\r\nhigh values)\",sep=\"\")\r\nprint(\"Along the way you rolled TWO EVENS \",evens,\" time(s). (\",format((evens/rolls)*100,\".2f\"),\"% of all rolls were two evens)\\\r\n\",sep=\"\")\r\nprint(\"Along the way you rolled TWO ODDS \",odds,\" time(s). (\",format((odds/rolls)*100,\".2f\"),\"% of all rolls were two odds)\",sep=\"\")\r\nprint(\"Along the way you rolled HIGH / LOW \",highlow,\" time(s). (\",format((highlow/rolls)*100,\".2f\"),\"% of all rolls were high/low)\\\r\n\",sep=\"\")\r\nprint(\"Along the way you rolled A SUM VALUE \",sums,\" time(s). (\",format((doubles/rolls)*100,\".2f\"),\"% of all rolls were \\\r\na sum value)\",sep=\"\")\r\n\r\n#print average roll\r\nprint(\"Average roll for die #1:\",format(die1total/rolls,\".2f\"))\r\nprint(\"Average roll for die #2:\",format(die2total/rolls,\".2f\"))\r\n","sub_path":"Intro to Programming/Assignments/Assignment 4/ShimJunSeob_assign4_part1.py","file_name":"ShimJunSeob_assign4_part1.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"322734074","text":"#!/usr/bin/python\nimport sys\nimport itertools;\nposcar = open(\"POSCAR\",\"r\")\nposcar_lines = poscar.readlines();\nelements = poscar_lines[0].split()\nelement_counts = poscar_lines[5].split()\n#print(elements)\n#print(element_counts)\n\n\nsorted_element_types = []\nfor i in range(0,len(element_counts)):\n sorted_element_types.extend( itertools.repeat(elements[i],int(element_counts[i])) )\nprint(sorted_element_types)\n\nspin_elements = [\"Co\",\"O\"]\nspin_ups = [\"Co\",\"O\"]\nspin_downs = [\"Cd\",\"N\"]\n\n\nincar = open(\"INCAR\",\"r\")\nmag_pars = []\nfor line in incar.readlines():\n if \"MAGMOM\" in line:\n for i in range(0,len(line)):\n if line[i] == '=':\n effect_line = line[i+1:len(line)]\n# print(effect_line);\n mag_pars = effect_line.split();\n\nprint(\"mag_parts = \" + str(mag_pars))\n\nsorted_mags = []\nfor i in mag_pars:\n if \"*\" in i:\n parts = i.split(\"*\");\n# print(\"parts = \" + str(parts))\n sorted_mags.extend( itertools.repeat(parts[1], int(parts[0]) ) )\n else:\n sorted_mags.append( i)\n\nprint(sorted_mags)\n\nif (len(sorted_element_types) != len(sorted_mags)):\n print (\"Atom count in MAGMOM mismatches count in POSCAR!\")\n exit()\n\nfor i in range(0,len(sorted_element_types)):\n \n if sorted_element_types[i] in spin_elements:\n pos = spin_elements.index(sorted_element_types[i])\n\n if float(sorted_mags[i]) > 0.0:\n sorted_element_types[i] = spin_ups[pos]\n elif float(sorted_mags[i]) < 0.0:\n sorted_element_types[i] = spin_downs[pos]\n\n#print (sorted_element_types)\n\nelements = []\nelement_counts = []\nfor i in range(0,len(sorted_element_types)):\n if (len(elements) == 0 or elements[-1] != sorted_element_types[i] ):\n elements.append (sorted_element_types[i])\n element_counts.append (1)\n else:\n element_counts[-1] = element_counts[-1] + 1\n#print(elements)\n#print(element_counts)\n\nl0 = \"\"\nl5 = \"\"\nfor i in range(0,len(elements)):\n l0 = l0 + \" \" + str(elements[i])\n l5 = l5 + \" \" + str(element_counts[i])\n \nl0 = l0 + \"\\n\"\nl5 = l5 + \"\\n\"\nposcar_lines[0] = l0\nposcar_lines[5] = l5\n\nposcar_show = open(\"POSCAR_SHOW\",\"w\")\nfor i in poscar_lines:\n poscar_show.write(i)\nposcar.close()\nposcar_show.close()\n","sub_path":"show_mag.py","file_name":"show_mag.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"438945003","text":"\"\"\"\nThis file is part of Linspector (https://linspector.org/)\nCopyright (c) 2013-2023 Johannes Findeisen <you@hanez.org>. All Rights Reserved.\nSee LICENSE.\n\"\"\"\n\nimport atexit\nimport os\nimport signal\nimport sys\nimport time\n\n\n# TODO: there is a bug when stopping the daemon. the pid_file is not being deleted. NEEDS A FIX!\nclass Linspectord:\n def __init__(self, configuration, environment, linspector, log):\n self._configuration = configuration\n self._environment = environment\n self._linspector = linspector\n self._log = log\n try:\n self._pid_file = configuration.get_option('linspector', 'pid_file')\n except Exception as err:\n log.critical('daemonize error (no pid_file set): {0}'.str(format(err)))\n\n def daemonize(self):\n # daemonize the class using the UNIX double fork mechanism.\n\n # do first fork.\n try:\n pid = os.fork()\n if pid > 0:\n # exit first parent.\n sys.exit(0)\n except OSError as err:\n self._log.critical('fork #1 failed: {0}'.str(format(err)))\n sys.exit(1)\n\n # decouple from parent environment.\n os.chdir('/')\n os.setsid()\n os.umask(0)\n\n # do second fork.\n try:\n pid = os.fork()\n if pid > 0:\n # Exit from second parent.\n sys.exit(0)\n except OSError as err:\n self._log.critical('fork #2 failed: {0}'.str(format(err)))\n sys.exit(1)\n\n # redirect standard file descriptors.\n sys.stdout.flush()\n sys.stderr.flush()\n si = open(os.devnull, 'r')\n so = open(os.devnull, 'a+')\n se = open(os.devnull, 'a+')\n\n os.dup2(si.fileno(), sys.stdin.fileno())\n os.dup2(so.fileno(), sys.stdout.fileno())\n os.dup2(se.fileno(), sys.stderr.fileno())\n\n # write pid file.\n atexit.register(self.delete_pid)\n\n pid = str(os.getpid())\n with open(self._pid_file, 'w+') as f:\n f.write(pid + '\\n')\n\n def delete_pid(self):\n os.remove(self._pid_file)\n\n def start(self):\n # start the daemon. check for a pidfile to see if the daemon already runs before.\n self._log.info('starting daemon using pid_file: ' + str(self._pid_file))\n try:\n with open(self._pid_file, 'r') as pf:\n pid = int(pf.read().strip())\n except IOError:\n pid = None\n\n if pid:\n message = 'pid_file {0} already exist. daemon already running?'\n self._log.critical(str(message.format(self._pid_file)))\n sys.exit(1)\n\n # start the daemon.\n self.daemonize()\n self.run()\n\n def stop(self):\n # stop the daemon.\n self._log.info('stopping daemon using pid_file: ' + str(self._pid_file))\n # get the pid from the pid file.\n try:\n with open(self._pid_file, 'r') as pf:\n pid = int(pf.read().strip())\n except IOError:\n pid = None\n\n if not pid:\n message = 'pid_file {0} does not exist. daemon not running?'\n self._log.error(str(message.format(self._pid_file)))\n return # not an error in a restart\n\n # try killing the daemon process.\n try:\n while 1:\n os.kill(pid, signal.SIGTERM)\n time.sleep(0.1)\n except OSError as err:\n e = str(err.args)\n if e.find('no such process') > 0:\n if os.path.exists(self._pid_file):\n os.remove(self._pid_file)\n else:\n self._log.critical(str(err.args))\n sys.exit(1)\n\n def restart(self):\n # restart the daemon.\n self._log.info('restarting daemon using pid_file: ' + str(self._pid_file))\n self.stop()\n self.start()\n\n # maybe this function can be removed in the future because Linspector should rund endless when\n # starting scheduled jobs. need to cover this in the future. for now, it is useful for testing\n # while development because the daemon even runs when internally is nothing to do.\n @staticmethod\n def run():\n signal.pause()\n","sub_path":"linspector/linspectord.py","file_name":"linspectord.py","file_ext":"py","file_size_in_byte":4246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"216973826","text":"from django.db import models\nfrom django.contrib.auth.models import User\nfrom tweets.models import Tweet\n\nclass NewsFeed(models.Model):\n user = models.ForeignKey(\n User,\n on_delete=models.SET_NULL,\n null=True,\n )\n tweet = models.ForeignKey(\n Tweet,\n on_delete=models.SET_NULL,\n null=True,\n )\n created_at = models.DateTimeField(auto_now_add=True)\n\n class Meta:\n index_together = (('user', 'created_at'),)\n unique_together = (('user', 'tweet'),)\n ordering = ('-created_at',)\n\n def __str__(self):\n # 这里是你执行 print(newsfeed instance) 的时候会显示的内容\n return f'{self.created_at} inbox of {self.user}: {self.tweet}'","sub_path":"newsfeeds/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"569133662","text":"\r\nimport logging\r\n\r\nlogger = logging.getLogger(__name__)\r\nlogger.setLevel(logging.INFO)\r\n\r\nformatter = logging.Formatter('%(levelname)s:%(message)s')\r\nfile_handler = logging.FileHandler('emp.log')\r\nfile_handler.setFormatter(formatter)\r\n\r\nlogger.addHandler(file_handler)\r\n\r\nclass Employee:\r\n\r\n def __init__(self,first,last):\r\n self.first=first\r\n self.last=last\r\n logger.info('Created Employee: {} - {}'.format(self.fullname,self.email))\r\n \r\n @property\r\n def email(self):\r\n return '{}.{}@gmail.com'.format(self.first,self.last)\r\n \r\n @property\r\n def fullname(self):\r\n return '{} {}'.format(self.first,self.last)\r\n\r\nexp_1 = Employee('payal','kutana')\r\nexp_2 = Employee('rima','jogani')","sub_path":"logger_of_employee.py","file_name":"logger_of_employee.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"421654882","text":"import socketserver, socket, subprocess, time, errno, sys\n\nhost = 'localhost'\nport = 2540\nsize = 1024\n\ndef Probe_Port(host, port):\n # AF_INET creates an IPV4 address family\n # SOCK_STREAM connections is TCP protocol\n\ttry:\n\t\ts = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\texcept socket.error as msg:\n\t\tprint (\"Failed to create socket. Error code: \", msg[0], \" , Error message : \", msg[1])\n\t\tsys.exit();\n\n\twhile True:\n\t\ttry:\n\t\t\ts.connect(( host,port))\n\t\t\tbreak\n\t\texcept OSError as e:\n\t\t\tprint(\"There was a problem connecting to device, retrying\")\n\treturn s\n\ndef write_to_reg(conn,intValue):\n# This will take an integer input and convert it to a binary string. It will also cut off the 0b at the beginning of the string.\n size = 8\n bStr_RegisterVal = bin(intValue).lstrip('0b').zfill(size) #Convert from int to binary string\n conn.send(bStr_RegisterVal.encode('utf-8') + b'\\n') #Newline is required to flush the buffer on the Tcl server\n data = conn.recv(10)\t# This will always need to have two additional bits added to the size of the string, this is for a start and stop bit.\n return data\n\n###########################\n########## Main ###########\n###########################\n\n#this will clear any previous port data\nclear_serv = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclear_serv.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n\nsubprocess.Popen('start /B start_TCL_Server.LNK', shell=True) #opens the TCL Server\ntime.sleep(1) #delays for 5 seconds\n\nconn = Probe_Port(host, port)\ntime.sleep(1) #delays for 2 seconds\n\nwhile True:\n# This is a loop that will keep asking for first the address of the register that you want to write to.\n# It will then ask for the value to write to the addressed register\n\ta =input(\"\\nPlease enter a register address : \")\n\tif a == 'end':\n\t\tbreak\n\ta_int = int(a)\n\twrite_to_reg(conn, a_int)\n\tb = input(\"\\nPlease enter the integer value to be written to the register : \")\n\tif b == 'end':\n\t\tbreak\n\tb_int = int(b)\n\twrite_to_reg(conn, b_int)\n\n# Things to do.\n\t\t# Add in an extra input for the length of the data that you want to write.\n\t\t# This will require additional vhdl\n\t\t# also need to test the length of data that can be sent. From things read it seems that it is limited by tcl (32 bits)\n\t\t# look into making a tcl function file that can have python call the individual function:\n\t\t\t#Example;\n\t\t\t\t# This is a rather old thread, but I recently stumbled on Tkinter.Tcl() which gives you direct\n\t\t\t\t# access to a Tcl interpreter in python without having to spawn a Tk GUI as Tkinter.Tk() requires.\n\n\t\t\t\t# An example... suppose you have a Tcl file (foo.tcl) with a proc called main that requires an\n\t\t\t\t# single filename as an argument... main returns a string derived from reading foo.tcl.\n\n\t\t\t\t\t\t# from Tkinter import Tcl\n\n\t\t\t\t\t\t# MYFILE = 'bar.txt'\n\t\t\t\t\t\t# tcl = Tcl()\n\t\t\t\t\t\t# # Execute proc main from foo.tcl with MYFILE as the arg\n\t\t\t\t\t\t# tcl.eval('source foo.tcl')\n\t\t\t\t\t\t# tcl_str = tcl.eval('main %s' % MYFILE)\n\t\t\t\t\t\t# # Access the contents of a Tcl variable, $tclVar from python\n\t\t\t\t\t\t# tcl.eval('set tclVar foobarme')\n\t\t\t\t\t\t# tclVar = tcl.eval('return $tclVar')\n\n\t\t\t\t# I haven't found another way to access Tcl objects from python besides through a return value, but\n\t\t\t\t# this does give you a way to interface with Tcl procs. Furthermore, you can export python functions\n\t\t\t\t# into Tcl as discussed in Using Python functions in Tkinter.Tcl()\n\t\t\t# Also refer to this site https://wiki.python.org/moin/How%20Tkinter%20can%20exploit%20Tcl/Tk%20extensions\n\n\nconn.close()\n\n\n\n\n\n\n\n\n","sub_path":"vJtag_7_29_14/input.py","file_name":"input.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"638153467","text":"import matplotlib\nimport matplotlib.pylab as plt\nimport sys\nsys.path.append('waveglow/')\nimport numpy as np\nimport torch\n\nfrom hparams import create_hparams\nfrom model import Tacotron2\nfrom layers import TacotronSTFT, STFT\nfrom audio_processing import griffin_lim\nfrom train import load_model\nfrom text import text_to_sequence\nfrom denoiser import Denoiser\nfrom scipy.io.wavfile import write\n\ndef plot_data(data, filepath, figsize=(16, 4)):\n fig, axes = plt.subplots(1, len(data), figsize=figsize)\n for i in range(len(data)):\n axes[i].imshow(data[i], aspect='auto', origin='bottom', \n interpolation='none')\n fig.savefig(filepath)\n\n# Setup hparams\n\nprint(\"Setting up hyperparams...\")\nhparams = create_hparams()\nhparams.sampling_rate = 22050\nprint(\"Done.\")\n\n# Load model from checkpoint\n#checkpoint_path = \"./models/tacotron2_statedict.pt\"\ncheckpoint_path = \"./outdir1/checkpoint_5000\"\nmodel = load_model(hparams)\nmodel.load_state_dict(torch.load(checkpoint_path)['state_dict'])\nmodel.cuda().eval()\n\n\n# Load WaveGlow for mel2audio synthesis and denoiser\n\nwaveglow_path = './models/waveglow_256channels_universal_v5.pt'\nwaveglow = torch.load(waveglow_path)['model']\nwaveglow.cuda().eval()\nfor k in waveglow.convinv:\n k.float()\ndenoiser = Denoiser(waveglow)\n\n# Prepare input text\ntext = \"आपके हिन्दी पसन्द करने पर खुशी हुई |\"\nsequence = np.array(text_to_sequence(text, ['basic_cleaners']))[None, :]\nsequence = torch.autograd.Variable(\n torch.from_numpy(sequence)).cuda().long()\n\n# Decode text input and plot results\nmel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)\nplot_data((mel_outputs.float().data.cpu().numpy()[0],\n mel_outputs_postnet.float().data.cpu().numpy()[0],\n alignments.float().data.cpu().numpy()[0].T), './melplots/plot_hindi.png')\n\n\nwith torch.no_grad():\n audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)\n\naudio_path = \"./audio/audio_hindi.wav\"\nwrite(audio_path, hparams.sampling_rate, audio[0].cpu().numpy())\n\n# Remove WaveGlow bias\n'''\naudio_denoised = denoiser(audio, strength=0.01)[:, 0]\naudio_denoised_path = \"./audio/audio_denoised.wav\"\nwrite(audio_denoised_path, hparams.sampling_rate, audio_denoised.cpu().numpy())\n'''","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"413908868","text":"# !/usr/bin/python\n# vim: set fileencoding=utf8 :\n#\n__author__ = 'keping.chu'\n\n\nfrom pyalgotrade import strategy\nfrom updaytech import UP318\nfrom basetech import LimitPrice, CloseAvg\nfrom pyalgotrade.broker.backtesting import LimitOrder, Broker\n\n\nclass LimitOrderDays(LimitOrder):\n\n def __init__(self, action, instrument, limitPrice, quantity, instrumentTraits):\n super(LimitOrderDays, self).__init__(action, instrument, limitPrice, quantity, instrumentTraits)\n self.days = 1\n\n\nclass MOrder:\n\n def __init__(self, order):\n\n self.order = order\n self.days = 1\n\n\nclass LeftBroker(Broker):\n\n def createLimitOrder(self, action, instrument, limitPrice, quantity):\n return LimitOrderDays(action, instrument, limitPrice, quantity, self.getInstrumentTraits(instrument))\n\n def commitOrderExecution(self, order, dateTime, fillInfo):\n #\n if order.isSell():\n ins = order.getInstrument()\n amt = self.getShares(ins)\n if amt <= 0:\n return\n\n super(LeftBroker, self).commitOrderExecution(order, dateTime, fillInfo)\n\n\nclass UpStrategy(strategy.BacktestingStrategy):\n\n def __init__(self, feed, instrument, days=5):\n self.broker = LeftBroker(4000000, feed)\n super(UpStrategy, self).__init__(feed, self.broker)\n self.instrument = instrument\n self.up_ = {}\n self.li_ = {}\n self.ca_ = {}\n self.days_ = days\n self.default_day = days\n self.positions = {}\n for stock in self.instrument:\n self.up_[stock] = UP318(feed.getDataSeries(stock), 5)\n self.li_[stock] = LimitPrice(feed.getDataSeries(stock), 2)\n self.ca_[stock] = CloseAvg(feed.getDataSeries(stock), 2)\n\n def onBars(self, bars):\n #订单计时\n self.sales(bars)\n self.buy(bars)\n\n def sales(self, bars):\n for ins, order in self.positions.items():\n #达到限定天数卖出\n bar = bars.getBar(order.order.getInstrument())\n if bar:\n ca = self.ca_[ins]\n if not ca[-1]:\n self.marketOrder(ins, -order.order.getQuantity())\n self.info(\"sales [%s %s %s]\" % (order.order.getInstrument(), bar.getDateTime(), order.days))\n del self.positions[ins]\n\n def buy(self, bars):\n for stock, bar in bars.items():\n up = self.up_[stock]\n #满足指标买入\n if up[-1] and (self.positions.get(stock) is None) and (self.li_[stock][-1] < 0.99) and self.ca_[stock][-1]:\n order = self.marketOrder(stock, self.round(bar.getClose()))\n self.info(\"buy [%s, %s, %s]\" % (stock, bar.getClose(), order.getQuantity()))\n self.positions[stock] = MOrder(order)\n\n def round(self, price, cash=5000):\n ratio = self.getResult() / 4000000\n amount = int(int(cash * ratio / price) / 100) * 100\n if amount == 0:\n amount = 100\n return amount\n\n","sub_path":"stratlib/updaystrategy.py","file_name":"updaystrategy.py","file_ext":"py","file_size_in_byte":3030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"460151213","text":"import numpy as np\nimport scipy.linalg as la\nimport scipy.sparse as sp\nimport scipy.sparse.linalg as spla\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.tri as mtri\nfrom matplotlib.animation import ArtistAnimation\nfrom mpl_toolkits.mplot3d import axes3d\n\nfrom itertools import product as iter_product\n\nfrom ..triangular_2d_fem import Triangular2DFEM\n\nfrom ..tools import matprint, delete_from_csr, \\\n quadrature1D, quadrature2D, BCtype\n\n# Now we inherit from a common \"Linear, Triangular Finite Element Solver\"\n# Base class contains the nodes, triangulation (triang), edge_nodes, coordinate transformations, etc. \n# As well as the definitions of the basis functions and their gradients.\n# Should refactor project_1 solver as well!\n\nclass Elasticity2DSolver(Triangular2DFEM):\n \"\"\"\n 2D-FEM solver of the static equilibrium equation:\\n\n ρuₜₜ = ∇ᵀσ(u) + f, (x, y) ∈ Ω = [-1, 1]² \\n\n Can find vibration modes/frequencies of a problem with no\n driving force term f, or solve the steady state equation\\n\n ∇ᵀσ(u) = -f, (x, y) ∈ Ω. \n\n \\nUsing Linear basis function Polynomials. \n \"\"\" \n def __init__(self, N, f, g_D, g_N, class_BC, E, nu, rho,\n quad_points=4, area=\"plate\", eps=1.0e-8):\n \"\"\"\n Initializer for 2D-FEM solver of the linear elasticity equation:\\n\n ρuₜₜ = ∇ᵀσ(u) + f, (x, y) ∈ Ω = [-1, 1]². \\n\n Can find vibration modes/frequencies of a problem with no\n driving force term f, or solve the steady state equation\n ∇ᵀσ(u) = -f, (x, y) ∈ Ω. \n\n N: Size of the mesh. (sqrt(num_nodes) for plate.) \\n\n f: Source function. ℜ² ⇒ ℜ². \\n\n E: Youngs modulus of your material. \\n\n nu: Poisson ratio of your material. \\n\n rho: Density per area of your materal. \\n\n g_D: Function g([x, y]) -> R, specifying Dirichlet boundary conditions. \\n\n g_N: Function g([x, y]) -> R, specifying Neumann boundary conditions. \\n\n class_BC: Function BC_type([x, y]) -> Bool, returning True if point [x, y] \n should be a Dirichlet BC. Assumed Neumann if not. \\n\n \"\"\"\n super(Elasticity2DSolver, self).__init__(N=N, area=area)\n\n self.edge_triangle_indexes = list(filter(lambda i: any((node in self.triang[i] for node in self.edge_nodes)), \n np.arange(len(self.triang))))\n self.edge_triangles = list(self.triang[self.edge_triangle_indexes])\n\n self.num_nodes = len(self.nodes)\n\n # Two basis functions for each node: \n # (ϕ_i_0 = [ϕ_i, 0], ϕ_i_1 = [0, ϕ_i]), i = 1, .., num_nodes.\n self.num_basis_functions = 2*self.num_nodes \n\n # Internal solver variables:\n self.quad_points = quad_points\n self.area = area # A bit superfluous.\n self.d_pairs = ((0, 0), (0, 1), (1, 0), (1, 1))\n\n # Problem source- and BC-functions:\n self.f = f\n self.g_D = g_D\n self.g_N = g_N\n \n # Material properties:\n self.E = E\n self.nu = nu\n self.rho = rho\n if callable(rho):\n self.constant_density = False\n else:\n self.constant_density = True\n\n self.class_BC = class_BC\n self.eps = eps # Big-Number Epsilon.\n \n # Store the transformation matrix C: σ_vec = C @ ε_vec\n self.C = (E/(1 - nu**2)) * np.array([[1.0, nu, 0.0],\n [ nu, 1.0, 0.0],\n [0.0, 0.0, (1 - nu)/2.0]])\n\n # Store which nodes are BCtype.Dir and their values:\n self.dirichlet_BC_mask = np.zeros(self.num_basis_functions, dtype=bool)\n self.dirichlet_BC_nodes_mask = np.zeros(self.num_nodes, dtype=bool)\n self.dirichlet_BC_basis_functions = []\n self.dirichlet_BC_values = []\n\n # Boolean indicating whether Boundary Conditions have been applied:\n self.applied_BC = False\n\n # Initialize the full Stiffness matrix to None before construction:\n self.A_h = None\n\n # Initialize the full Mass matrix to None before construction:\n self.M_h = None\n\n # Initialize the Source vector to None before construction:\n self.F_h = None\n\n # Initialize the Basis function coefficients \"u_h\" to None:\n self.u_h = None\n \n @classmethod\n def from_dict(cls, model_dict):\n return cls(**model_dict)\n \n def basis_func_eps_vec(self, i, d, J_inv_T=None, k=None):\n \"\"\"\n Calculate the epsilon-vector for a basis-function:\\n\n ε(ϕ) = [∂ϕ₁/∂x, ∂ϕ₂/∂y, ∂ϕ₁/∂y + ∂ϕ₂/∂x],\n ϕ = [ϕ₁, ϕ₂] = [(1-d)*ϕ_i, d*ϕ_i].\\n\n Based on transforming derivatives from the reference element.\\n\n i: Local basis function index. i = 0, 1, 2.\\n\n d: x- or y-component basis vector. {x: 0, y: 1}\\n\n J_inv_T: Inverse Jacobian: [∂(r,s)/∂(x, y)].T\\n\n k: Element number.\n \"\"\"\n # Slow extra checking. Might be removed. \n if J_inv_T is not None:\n pass\n elif J_inv_T is None and type(k) is int:\n J_inv_T = la.inv(self.generate_jacobian(k)).T\n else:\n raise ValueError(\"Either the inverse-Jacobian transpose 'J_int_T' or element number 'k' must be given.\")\n\n gradient = J_inv_T @ self.reference_gradients[i]\n\n eps_xx = (1 - d)*gradient[0]\n eps_yy = d*gradient[1]\n eps_xy = d*gradient[0] + (1-d)*gradient[1]\n \n return np.array([eps_xx, eps_yy, eps_xy])\n\n def sigma_vec(self, i: int, disp_vec, J_inv_T=None, k=None):\n \"\"\"\n Calculate sigma-components [σ_xx, σ_yy, σ_xy]\n at the node index. \n i: Local basis function index.\n disp_vec: Displacement of each component of the basis functions. np.array([u_1, u_2]).\n \"\"\"\n # Slow extra checking. Might be removed. \n if J_inv_T is not None:\n pass\n elif J_inv_T is None and type(k) is int:\n J_inv_T = la.inv(self.generate_jacobian(k)).T\n else:\n raise ValueError(\"Either the inverse-Jacobian transpose 'J_int_T' or element number 'k' must be given.\")\n\n eps_vec_0 = disp_vec[0]*self.basis_func_eps_vec(i, d=0, J_inv_T=J_inv_T)\n eps_vec_1 = disp_vec[1]*self.basis_func_eps_vec(i, d=1, J_inv_T=J_inv_T)\n sigma_vec = self.C @ (eps_vec_0 + eps_vec_1)\n return sigma_vec\n\n \"\"\" Stiffness matrix \"\"\"\n\n def A_i_j(self, i_loc, j_loc, d_i, d_j, A_k, J_inv_T):\n \"\"\"\n Function calculating a (Aₕ)ᵢ,ⱼ-th contribution to the \"Stiffness\"-matrix.\n i_loc: Local index of basis function. [0, 1, 2]\n j_loc: Local index of basis function. [0, 1, 2]\n d_i, d_j: Components of the vector function ϕ_i_d = [(1-d)*ϕ_i, d*ϕ_i]\n A_k: Area of the element: |Jₖ|/2\n J_inv_T: Inverse Jacobian: [∂(r,s)/∂(x, y)].T\n \"\"\"\n eps_i = self.basis_func_eps_vec(i_loc, d_i, J_inv_T) \n eps_j = self.basis_func_eps_vec(j_loc, d_j, J_inv_T) \n # Could do: eps_i.T @ self.C @ eps_j, but '.T' does nothing for 1-D array.\n return A_k * (eps_i @ self.C @ eps_j)\n\n def generate_A_h(self):\n # Hopefully updated correctly!\n \"\"\"\n Generate the Stiffness Matrix A_h, based on linear Langrange basis functions on triangles.\n \"\"\"\n self.A_h = sp.dok_matrix((self.num_basis_functions, self.num_basis_functions))\n d_pairs = ((0, 0), (0, 1), (1, 0), (1, 1))\n\n # Loop through elements (triangles): \n for k, element in enumerate(self.triang):\n # Six basis functions per element. 3 nodes, 2 functions per node.\n # (3x2)^2 = 36 interactions per element k.\n # Only 6*(6+1)/2 = 21 unique interactions due to symmetry, but\n # for simplicity in code we perform 24 calculations.\n\n J = self.generate_jacobian(k)\n J_inv_T = la.inv(J).T\n element_area = 0.5*la.det(J)\n \n # Exploit symmetry of the (A_h)_sub-matrix about i=j.\n # Only compute the upper-triangular part i <= j.\n for i_loc, node_i in enumerate(element):\n \n # Do every vector-component combination. \n for (d_i, d_j) in d_pairs:\n A_i_di_j_dj = self.A_i_j(i_loc=i_loc, j_loc=i_loc, d_i=d_i, d_j=d_j, \n A_k=element_area, J_inv_T=J_inv_T)\n self.A_h[2*node_i + d_i, 2*node_i + d_j] += A_i_di_j_dj\n\n for j_loc in range(i_loc+1, 3):\n node_j = element[j_loc]\n \n for (d_i, d_j) in d_pairs:\n A_i_di_j_dj = self.A_i_j(i_loc=i_loc, j_loc=j_loc, d_i=d_i, d_j=d_j, \n A_k=element_area, J_inv_T=J_inv_T)\n self.A_h[2*node_i + d_i, 2*node_j + d_j] += A_i_di_j_dj\n self.A_h[2*node_j + d_j, 2*node_i + d_i] += A_i_di_j_dj\n \n # Convert A_h to csr-format for ease of calculations later:\n self.A_h = self.A_h.tocsr() \n \n \"\"\" Mass matrix \"\"\"\n\n def M_i_j(self, i_loc, j_loc, d_i, d_j, det_J_k):\n \"\"\"\n Function calculating a (Mₕ)ᵢ,ⱼ-th contribution to the \"Stiffness\"-matrix. \\n\n i_loc: Local index of basis function. [0, 1, 2] \\n\n j_loc: Local index of basis function. [0, 1, 2] \\n\n d_i, d_j: Components of the vector function ϕ_i_d = [(1-d)*ϕ_i, d*ϕ_i] \\n\n det_J_k: Determinant of the jacobian for element k.\n \"\"\"\n # Only 36 interactions. 21 unique. Can calculate the 21 elements beforehand, and \n # insert the values det_J_k*M_ref for each element.\n\n # d_i, d_j = [0, 1]:\n # Dot product (ϕ_i)^T ⋅ ϕ_j = (1 - d_i)*(1 - d_j)ϕ_i*ϕ_j + (d_i)*(d_j)ϕ_i*ϕ_j.\n if d_i != d_j:\n return 0.0\n\n # As we integrate over the reference triangle, \n # we can use the basis function definitions directly:\n phi_i, phi_j = self.basis_functions[i_loc], self.basis_functions[j_loc]\n\n # Assume that this function is only used when 'self.rho' is callable. \n if self.constant_density:\n integrand = lambda p: self.rho*phi_i(p)*phi_j(p)\n else:\n integrand = lambda p: self.rho(p)*phi_i(p)*phi_j(p)\n \n p1, p2, p3 = self.reference_triangle_nodes\n I = quadrature2D(integrand, p1, p2, p3, Nq=self.quad_points)\n\n # Scale the integral up by the determinant of the Element-Jacobian:\n return I*det_J_k\n\n def generate_M_ref(self):\n # 36 interactions.\n M_ref = np.zeros((6, 6), dtype=float)\n local_indices = (0, 1, 2)\n d_pairs = ((0, 0), (0, 1), (1, 0), (1, 1))\n \n for i, j, (d_i, d_j) in iter_product(local_indices, local_indices, d_pairs):\n row, col = 2*i + d_i, 2*j + d_j\n # Calculate the mass matrix on the reference element: det_J_k = 1.0\n M_ref[row, col] = self.M_i_j(i, j, d_i, d_j, det_J_k=1.0)\n\n return M_ref\n\n def M_h_element_contribution(self, element: list, det_J_k: np.ndarray, M_ref: np.ndarray):\n \n if self.constant_density:\n # Exploit symmetry of the (A_h)_sub-matrix about i=j.\n # Only compute the upper-triangular part i <= j.\n\n for i_loc, node_i in enumerate(element):\n # All values scaled by det_J_k compared to M_ref: \n \n for (d_i, d_j) in self.d_pairs:\n i_i_value = det_J_k * M_ref[2*i_loc + d_i, 2*i_loc + d_j]\n self.M_h[2*node_i + d_i, 2*node_i + d_j] += i_i_value\n\n for j_loc in range(i_loc+1, 3):\n node_j = element[j_loc]\n \n for (d_i, d_j) in self.d_pairs:\n i_j_value = det_J_k * M_ref[2*i_loc + d_i, 2*j_loc + d_j]\n\n self.M_h[2*node_i + d_i, 2*node_j + d_j] += i_j_value\n self.M_h[2*node_j + d_j, 2*node_i + d_i] += i_j_value\n \n else:\n # Must integrate density over each element per basis function. \n for i_loc, node_i in enumerate(element):\n \n for (d_i, d_j) in self.d_pairs:\n i_i_value = self.M_i_j(i_loc, i_loc, d_i, d_j, det_J_k)\n self.M_h[2*node_i + d_i, 2*node_i + d_j] += i_i_value\n\n for j_loc in range(i_loc+1, 3):\n node_j = element[j_loc]\n \n for (d_i, d_j) in self.d_pairs:\n i_j_value = self.M_i_j(i_loc, j_loc, d_i, d_j, det_J_k)\n\n self.M_h[2*node_i + d_i, 2*node_j + d_j] += i_j_value\n self.M_h[2*node_j + d_j, 2*node_i + d_i] += i_j_value\n\n def generate_M_h(self):\n \n if self.constant_density:\n M_ref = self.generate_M_ref()\n else:\n M_ref = None\n \n self.M_h = sp.dok_matrix((self.num_basis_functions, self.num_basis_functions))\n\n # Generate the mass matrix.\n for k, element in enumerate(self.triang):\n \n # TODO: Avoid generating all Jacobians twice for A_h and M_h.\n J = self.generate_jacobian(k)\n det_J_k = la.det(J)\n self.M_h_element_contribution(element=element, det_J_k=det_J_k, M_ref=M_ref)\n\n # Convert M_h to csr-format for ease of calculations later:\n self.M_h = self.M_h.tocsr()\n\n \"\"\" Source vector \"\"\"\n\n def generate_F_h(self):\n \"\"\"\n Generate the source vector. Sum over elements and add contributions from each basis function.\n \"\"\"\n # Source function f([x, y]) must be callable.\n assert(callable(self.f))\n # Making the full Source vector:\n self.F_h = np.zeros(self.num_basis_functions)\n\n # Reference triangle nodes:\n eta1, eta2, eta3 = self.reference_triangle_nodes\n\n T = self.reference_to_global_transformation\n\n for k, element in enumerate(self.triang):\n J_k = self.generate_jacobian(k)\n det_J_k = la.det(J_k)\n\n # Loop through nodes in each element and two components per node:\n for (i, node), d in iter_product(enumerate(element), (0, 1)):\n # print(f\"i: {i}, d: {d}\\tnode: {node}\")\n \n global_index = 2*node + d\n\n # Integrand. ϕ_i_d^T⋅f([x, y]) = [(1-d)*ϕ_i, d*ϕ_i]^T⋅[f_1(x, y), f_2(x, y)] \n # = ((1 - d)*f_1(x, y) + d*f_2(x, y))*ϕ_i(x, y)\n integrand = lambda eta: self.f(T(eta, k, J_k))[d]*self.basis_functions[i](eta)\n\n # Add contribution from basis function component d. Integrate over reference triangle.\n self.F_h[global_index] += det_J_k*quadrature2D(integrand, eta1, eta2, eta3, self.quad_points)\n \n # Reshape F_h to a column vector:\n self.F_h = self.F_h.reshape(len(self.F_h), 1)\n\n \"\"\" Boundary conditions \"\"\"\n\n def apply_node_dirichlet_bc(self, node, p=None):\n if p is None:\n p = self.nodes[node]\n \n # Indicies of basis functions at 'node':\n i_0, i_1 = 2*node + 0, 2*node + 1\n\n dir_bc_value = self.g_D(p)\n u_D_x, u_D_y = dir_bc_value\n\n # Register the two basis functions at 'node'\n # as Dirichlet BC's:\n self.dirichlet_BC_mask[i_0] = True\n self.dirichlet_BC_mask[i_1] = True\n\n self.dirichlet_BC_nodes_mask[node] = True\n\n # Register basis functions as Dirichlet BC's:\n self.dirichlet_BC_basis_functions.append(i_0)\n self.dirichlet_BC_basis_functions.append(i_1)\n\n # Register their values: Sequence matters!\n self.dirichlet_BC_values.append(u_D_x)\n self.dirichlet_BC_values.append(u_D_y)\n\n # Subtract from the source vector the columns corresponding to \n # both the basis function components at 'node', times \n # their respective Dirichlet BC-value: \n self.F_h += self.A_h[:, i_0]*u_D_x\n self.F_h += self.A_h[:, i_1]*u_D_y\n\n def apply_direct_dirichlet(self):\n # Dirichlet Boundary \n for node in self.edge_nodes:\n p = self.nodes[node]\n\n if self.class_BC(p) == BCtype.Dir:\n self.apply_node_dirichlet_bc(node, p)\n \n elif self.class_BC(p) == BCtype.Neu:\n # Find which triangles the Edge node belongs to:\n raise ValueError(\"Cannot apply Neumann BC Using Direct Dirichlet function!\")\n \n # Remove redundant degrees of freedom from F_h, A_h, and M_h:\n F_mask = np.ones(len(self.F_h), dtype=bool)\n F_mask[self.dirichlet_BC_basis_functions] = False\n self.F_h = self.F_h[F_mask]\n\n self.A_h = delete_from_csr(self.A_h, row_indices=self.dirichlet_BC_basis_functions, \n col_indices=self.dirichlet_BC_basis_functions)\n if self.M_h is None:\n self.generate_M_h()\n\n self.M_h = delete_from_csr(self.M_h, row_indices=self.dirichlet_BC_basis_functions, \n col_indices=self.dirichlet_BC_basis_functions) \n self.applied_BC = True\n\n \"\"\" Solve \"\"\"\n\n def solve_direct_dirichlet(self):\n self.generate_A_h()\n self.generate_F_h()\n self.apply_direct_dirichlet()\n \n reduced_u_h = sp.linalg.spsolve(self.A_h, self.F_h)\n self.u_h = np.zeros(self.num_basis_functions)\n self.u_h[~self.dirichlet_BC_mask] = reduced_u_h\n self.u_h[self.dirichlet_BC_mask] = self.dirichlet_BC_values\n self.u_h = self.u_h.reshape((self.num_nodes, 2))\n\n def fem_solution(self, element: list = None, F_inv: callable = None, k: int = None):\n # Assume one has solved for the coefficients self.u_h:\n if F_inv is None:\n if k is not None:\n F_inv = lambda p: self.global_to_reference_transformation(p, k, J_inv=None)\n else:\n raise ValueError(\"Either the inverse coordinate transform F_inv or element index k must be given.\")\n \n if element is None and type(k) is int:\n element = self.triang[k]\n\n # Retrieve coefficients of basis function in each vector component:\n u_xs = self.u_h[[element], 0].ravel()\n u_ys = self.u_h[[element], 1].ravel()\n \n phi_0 = lambda p: self.basis_functions[0](F_inv(p))\n phi_1 = lambda p: self.basis_functions[1](F_inv(p))\n phi_2 = lambda p: self.basis_functions[2](F_inv(p))\n phi_funcs = [phi_0, phi_1, phi_2]\n\n def u_h_func(p):\n x_comp = sum([u_xs[i]*phi_funcs[i](p) for i in (0, 1, 2)])\n y_comp = sum([u_ys[i]*phi_funcs[i](p) for i in (0, 1, 2)])\n return np.array([x_comp, y_comp])\n\n return u_h_func\n\n \"\"\" Error calculation \"\"\"\n\n def find_h(self):\n h = 0\n\n for k, element in enumerate(self.triang):\n\n p1, p2, p3 = element\n x1 = self.nodes[p1]\n x2 = self.nodes[p2]\n x3 = self.nodes[p3]\n hk = max(np.linalg.norm(x2 - x1), np.linalg.norm(x3 - x1), np.linalg.norm(x3 - x2))\n\n if hk > h:\n h = hk\n\n return h\n\n def L2_norm_error(self, u_ex, quad_points=None):\n # raise NotImplementedError(\"Need to update function for ElasticitySolver2D\")\n assert isinstance(self.u_h, np.ndarray)\n\n if quad_points == None:\n quad_points = self.quad_points\n\n E = 0.0\n\n \"\"\" For each element in triangulation. \"\"\" \n for k, element in enumerate(self.triang):\n \n J_inv = la.inv(self.generate_jacobian(k))\n\n F_inv = lambda p: self.global_to_reference_transformation(p, k, J_inv=J_inv)\n\n u_h_func = self.fem_solution(element=element, F_inv=F_inv)\n\n \"\"\" err = || u_h - u_ex ||₂**2 \"\"\"\n err = lambda p: la.norm(u_h_func(p) - u_ex(p), ord=2)**2\n \n \"\"\" Gauss quadrature approximation to contribution to square error from element k \"\"\"\n x1, x2, x3 = [self.nodes[node] for node in element]\n E += quadrature2D(err, x1, x2, x3, Nq=quad_points)\n\n return np.sqrt(E)\n\n \"\"\" Generalized eigenvalue problem \"\"\"\n\n def solve_vibration_modes(self, num=20):\n \"\"\"\n Find the 'num' lowest generalized eigenvalues, along with eigenvectors. \\n\n Aₕu = ω²Mₕu \n \"\"\"\n if self.A_h is None:\n self.generate_A_h()\n if self.M_h is None:\n self.generate_M_h()\n\n eigvals_small, eigvecs_small = spla.eigsh(A=self.A_h, M=self.M_h, \n k=num, which=\"LM\", sigma=0.0)\n self.num_eigenpairs = num\n self.vibration_frequencies = eigvals_small\n\n # List of the displacement eigenvectors for each eigenvalue: \n self.vibration_eigenvectors = []\n num_dof = int(self.num_nodes - len(self.dirichlet_BC_basis_functions)//2)\n for k in range(num):\n displacement_vec = np.zeros((num_dof, 2))\n \n # Eigenvectors stored column-wise:\n vibration_eigenvec = eigvecs_small[:, k]\n \n for n, d in iter_product(range(num_dof), (0, 1)):\n displacement_vec[n, d] = vibration_eigenvec[2*n + d]\n \n self.vibration_eigenvectors.append(displacement_vec)\n \n print(\"Done solving for eigenmodes!\")\n\n def retrieve_vibration_eigenvector(self, k):\n if not self.applied_BC:\n displacement_vec = self.vibration_eigenvectors[k]\n else:\n displacement_vec = np.zeros((self.num_nodes, 2))\n num_Dir_BC_nodes = len(self.dirichlet_BC_basis_functions)//2\n Dir_BC_displacement = np.array(self.dirichlet_BC_values).reshape(num_Dir_BC_nodes, 2)\n\n displacement_vec[self.dirichlet_BC_nodes_mask] = Dir_BC_displacement\n displacement_vec[~self.dirichlet_BC_nodes_mask] = self.vibration_eigenvectors[k]\n return displacement_vec\n\n \"\"\" Visualization \"\"\"\n\n def find_max_stress(self, nodes=None, elements=None, displacement=None):\n\n if elements is None and nodes is None:\n element_triang = self.triang\n\n elif nodes is not None:\n # Find all triangles with nodes-elements as vertices.\n if type(nodes) is int:\n triangle_indices = [i for i, triangle in enumerate(self.triang) if nodes in triangle] \n else:\n # Stupidly unreadable One-line solution:\n triangle_indices = list(filter(lambda i: any((node in self.triang[i] for node in nodes)), \n np.arange(len(self.triang))))\n\n element_triang = self.triang[triangle_indices]\n\n else:\n element_triang = self.triang[elements]\n \n nodes_x = np.copy(self.nodes[:, 0])\n nodes_y = np.copy(self.nodes[:, 1])\n\n if displacement is not None:\n # Apply displacement to each node:\n assert(self.nodes.shape == displacement.shape)\n nodes_x += displacement[:, 0]\n nodes_y += displacement[:, 1]\n \n max_stress = 0\n\n for k, el in enumerate(element_triang):\n\n sigma_0 = self.sigma_vec(0, displacement[el[0],:], k=k)\n sigma_1 = self.sigma_vec(1, displacement[el[1],:], k=k)\n sigma_2 = self.sigma_vec(2, displacement[el[2],:], k=k)\n\n mts0 = 0.5 * ( sigma_0[0] + sigma_0[1])\n mts1 = 0.5 * ( sigma_1[0] + sigma_1[1])\n mts2 = 0.5 * ( sigma_2[0] + sigma_2[1])\n\n mts_el = ( mts0 + mts1 + mts2 ) / 3\n if np.abs(mts_el) > max_stress:\n max_stress = np.abs(mts_el)\n\n return max_stress\n\n def display_mesh(self, nodes=None, elements=None, displacement=None, show=True, ax=None):\n if elements is None and nodes is None:\n element_triang = self.triang\n\n elif nodes is not None:\n # Find all triangles with nodes-elements as vertices.\n if type(nodes) is int:\n triangle_indices = [i for i, triangle in enumerate(self.triang) if nodes in triangle] \n else:\n # Stupidly unreadable One-line solution:\n triangle_indices = list(filter(lambda i: any((node in self.triang[i] for node in nodes)), \n np.arange(len(self.triang))))\n\n element_triang = self.triang[triangle_indices]\n\n elif elements is not None:\n element_triang = self.triang[elements]\n \n nodes_x = np.copy(self.nodes[:, 0])\n nodes_y = np.copy(self.nodes[:, 1])\n\n if displacement is not None:\n # Apply displacement to each node:\n assert(self.nodes.shape == displacement.shape)\n nodes_x += displacement[:, 0]\n nodes_y += displacement[:, 1]\n\n if ax is not None:\n plot = ax.triplot(nodes_x, nodes_y, triangles=element_triang, color='black') \n else:\n plot = plt.triplot(nodes_x, nodes_y, triangles=element_triang)\n\n if show:\n plt.rcParams.update({\"font.size\": 20})\n plt.title(f\"{len(self.triang)} Element Triangulation\")\n axis = plt.gca()\n axis.set_xlabel(\"x\", fontsize=20)\n axis.set_ylabel(\"y\", fontsize=20)\n\n x_min, x_max = np.min(nodes_x), np.max(nodes_x)\n y_min, y_max = np.min(nodes_y), np.max(nodes_y)\n scale_x = abs(x_max - x_min)\n scale_y = abs(y_max - y_min)\n\n margin = 0.05\n plt.xlim(x_min - margin*scale_x, x_max + margin*scale_x)\n plt.ylim(y_min - margin*scale_y, y_max + margin*scale_y)\n\n plt.show()\n return \n \n else:\n return plot\n\n def display_mesh_stress(self, displacement=None, face_colors=None, norm=None, show=True, ax=None):\n\n element_triang = self.triang\n \n nodes_x = np.copy(self.nodes[:, 0])\n nodes_y = np.copy(self.nodes[:, 1])\n\n if displacement is not None:\n # Apply displacement to each node:\n assert(self.nodes.shape == displacement.shape)\n nodes_x += displacement[:, 0]\n nodes_y += displacement[:, 1]\n\n if face_colors is None:\n zcolors = np.zeros(len(element_triang), dtype=float)\n\n for k, el in enumerate(element_triang):\n J_inv_T = la.inv(self.generate_jacobian(k)).T\n\n sum_mts = 0.0\n for i, d in enumerate(el):\n\n sigma_i = self.sigma_vec(i, displacement[d, :], J_inv_T=J_inv_T)\n mts_i = (sigma_i[0] + sigma_i[1])\n\n sum_mts += mts_i\n \n zcolors[k] = sum_mts / 6.0 # Average over 2 elements in tr(σ) and 3 nodes.\n \n if ax is not None:\n plot = ax.tripcolor(nodes_x, nodes_y, triangles=element_triang, facecolors=zcolors, edgecolors='k', norm=norm)\n else:\n # Set a bigger font size for text:\n plt.rcParams.update({'font.size': 20, 'figure.figsize': (16, 9)})\n plt.suptitle(\"Displacement Stress\")\n plt.subplots_adjust(left=0.1, bottom=0.1, right=0.95, top=0.93)\n plt.xlabel(\"x [m]\")\n plt.ylabel(\"y [m]\")\n plot = plt.tripcolor(nodes_x, nodes_y, triangles=element_triang, facecolors=zcolors, edgecolors='k', norm=norm)\n cbar = plt.colorbar(plot)\n cbar.ax.get_yaxis().labelpad = 50\n cbar.set_label(r\"Mean Total Stress $\\sigma$ [Pa]\", rotation=270, fontsize=22)\n\n if show:\n\n x_min, x_max = np.min(nodes_x), np.max(nodes_x)\n y_min, y_max = np.min(nodes_y), np.max(nodes_y)\n scale_x = abs(x_max - x_min)\n scale_y = abs(y_max - y_min)\n\n margin = 0.05\n plt.xlim(x_min - margin*scale_x, x_max + margin*scale_x)\n plt.ylim(y_min - margin*scale_y, y_max + margin*scale_y)\n\n plt.show()\n\n else:\n return plot\n\n def display_vector_field(self, u=None, title=None):\n \"\"\"\n Display a vector field over the domain Ω.\\n\n u([x, y]) → [u_x(x, y), u_y(x, y)]\n \"\"\"\n # Vector X- and Y-components:\n if callable(u):\n vectors = np.array([u(p) for p in self.nodes])\n elif type(u) is np.ndarray:\n # Assume u contains displacement of each node:\n # u.shape = (num_nodes, 2)\n vectors = np.array([u_p for u_p in u])\n else:\n # Use internal solution u_h:\n vectors = np.array([u_p for u_p in self.u_h])\n\n plt.rcParams.update({'font.size': 18})\n\n fig = plt.figure(figsize=(14, 14))\n ax = fig.add_subplot(111)\n\n if title is None:\n fig.suptitle(\"Vector Field Plot\")\n else:\n fig.suptitle(title)\n # Arrow locations:\n X, Y = self.nodes[:, 0], self.nodes[:, 1]\n\n U, V = vectors[:, 0], vectors[:, 1]\n \n Q = ax.quiver(X, Y, U, V, scale=12, angles='xy', scale_units='xy')\n ax.quiverkey(Q, X=0.3, Y=0.97, U=1, coordinates='figure',\n label='Quiver key, length = 1', labelpos='W')\n \n ax.triplot(X, Y, self.triang, alpha=0.3, zorder=-10) \n ax.set_xlabel(\"X\")\n ax.set_ylabel(\"Y\", rotation=0)\n\n plt.subplots_adjust(left=0.08, bottom=0.08, right=0.97, top=0.95)\n plt.show()\n\n def display_single_element_error(self, k: int, u: callable, ax=None, show=False, quiver=False):\n # assert callable(u)\n element = self.triang[k]\n p0, p1, p2 = self.nodes[element]\n\n num = 50\n r_s = np.linspace(0, 1.0, num)\n delta_r = 1.0/num\n \n internal_nodes = []\n for r in r_s:\n s = 0.0\n while s + r <= 1.0:\n internal_nodes.append(p0 + r*(p1 - p0) + s*(p2 - p0))\n s += delta_r\n\n internal_nodes = np.array(internal_nodes)\n u_h_func = self.fem_solution(k=k)\n errors = np.array([la.norm(u(p) - u_h_func(p), ord=2)**2 for p in internal_nodes])\n X, Y = internal_nodes[:, 0], internal_nodes[:, 1]\n\n if ax is None:\n fig = plt.figure(figsize=(14, 14))\n fig.suptitle(\"Single Element Vector Field Plot\")\n ax = fig.add_subplot(111, projection='3d')\n ax.set_xlabel(\"X\")\n ax.set_ylabel(\"Y\", rotation=0)\n elif quiver == True:\n # Arrow locations:\n vectors = np.array([u(p) for p in internal_nodes])\n ax.scatter(X, Y)\n U, V = vectors[:, 0], vectors[:, 1]\n \n Q = ax.quiver(X, Y, U, V)\n ax.quiverkey(Q, X=0.3, Y=0.97, U=1, coordinates='figure',\n label='Quiver key, length = 1', labelpos='W')\n \n else:\n ax.plot_trisurf(X, Y, errors, color='black')\n \n if show:\n plt.subplots_adjust(left=0.08, bottom=0.08, right=0.97, top=0.95)\n plt.show()\n\n def display_L2_error(self, u_ex: callable):\n\n fig, ax = plt.subplots(figsize=(12, 12), subplot_kw={'projection': '3d'})\n for k, element in enumerate(self.triang):\n\n self.display_single_element_error(k, u=u_ex, ax=ax, show=False)\n\n plt.show()\n\n def display_vibration_mode(self, k):\n if k > self.num_eigenpairs - 1:\n raise ValueError(f\"Too high an eigen-number. Have only solved for {self.num_eigenpairs} eigenpairs.\")\n displacement_vec = self.retrieve_vibration_eigenvector(k)\n return self.display_mesh(displacement=displacement_vec)\n\n def vibration_stress_mosaic(self, k, alpha=1, dims=(3,3), figsize=(10,12), dpi=None, show=None,\n savename=None, title=None):\n \n if k > self.num_eigenpairs - 1:\n raise ValueError(f\"Too high an eigen-number. Have only solved for {self.num_eigenpairs} eigenpairs.\")\n\n displacement_vec = self.retrieve_vibration_eigenvector(k)\n\n max_stretch = self.find_max_stress(displacement=displacement_vec*alpha)\n\n # Set a bigger font size for text:\n fs = 36\n plt.rcParams.update({'font.size': fs})\n\n norm = mpl.colors.Normalize(vmin=-max_stretch, vmax=max_stretch)\n\n fig, axs = plt.subplots(dims[0], dims[1], figsize=figsize, sharex=True, sharey=True)\n \n if title is None:\n plt.subplots_adjust(top=0.95)\n\n fig.suptitle(title)\n\n plt.subplots_adjust(left=0.11, bottom=0.11, wspace=0, hspace=0, right=0.89)\n K = dims[0] * dims[1]\n\n for i, phi in enumerate(np.linspace(0, np.pi, K)):\n self.display_mesh_stress(displacement=alpha*np.cos(phi)*displacement_vec,\n norm=norm, show=False, ax=axs.flatten()[i])\n\n cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=None), ax=axs[:,dims[1]-1])\n cbar.ax.get_yaxis().labelpad = 30\n cbar.set_label(r\"Mean Total Stress $\\sigma$ [Pa]\", rotation=270, fontsize=fs)\n\n labax = fig.add_subplot(111, frameon=False)\n # hide tick and tick label of the big axis\n labax.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n labax.set_xlabel(\"$x \\, [m]$\")\n labax.set_ylabel(\"$y \\, [m]$\")\n labax.get_yaxis().labelpad = 20\n\n if savename is not None:\n fig.savefig(f\"root/project_2/figures/{savename}.png\", dpi=dpi)\n\n if show is None:\n if savename is None:\n show = True\n else:\n show = False\n\n if show:\n plt.show()\n\n return\n\n def show_frequencies(self, show=None, savename=None, title=None, figsize=None):\n\n if self.vibration_frequencies is None:\n raise Exception(\"Need to calculate vibration frequencies first.\")\n\n if title is None:\n title = \"Vibration-frequencies\"\n \n plt.rcParams.update({'font.size': 24})\n\n fig, axs = plt.subplots(1,2, figsize=figsize)\n\n plt.subplots_adjust(wspace=0.21, left=0.15, right=0.95)\n\n fig.suptitle(title)\n\n if np.any(np.isclose(self.vibration_frequencies, 0, atol=1e-10*np.amax(self.vibration_frequencies))):\n eps = 1.1*np.abs(np.amin(self.vibration_frequencies))\n else:\n eps = 0\n \n vibs = np.sqrt(self.vibration_frequencies + eps) / (2*np.pi)\n axs[0].plot(np.arange(vibs.shape[0]), vibs, 'k.', markersize=15)\n axs[0].set_title(\"All frequencies, $f_k$\")\n axs[0].set_ylabel(\"[Hz]\")\n axs[0].get_yaxis().labelpad = 13\n axs[0].set_xlabel(r\"$k$\")\n\n non_zero_vibs = np.sqrt(self.vibration_frequencies[3:]) / (2*np.pi)\n\n fund_freq = non_zero_vibs[0] # Fundamental frequency. First non-zero frequency.\n n_max = np.max(non_zero_vibs / fund_freq)\n\n axs[1].plot(np.arange(3, non_zero_vibs.shape[0] + 3), non_zero_vibs / fund_freq, 'k.', markersize=15)\n for i in range(1, int(n_max) + 1):\n axs[1].axhline(y=i, color='black', lw=0.4)\n\n axs[1].set_title(\"Non-zero frequencies, $f_k \\, / \\, f_3$\")\n axs[1].set_xlabel(r\"$k$\")\n\n if show is None:\n if savename is None:\n show = True\n else:\n show = False\n\n if show:\n plt.show()\n return\n\n if savename is not None:\n fig.savefig(f\"root/project_2/figures/{savename}.png\")\n\n return\n\n \"\"\" Animation \"\"\"\n\n def animate_vibration_mode(self, k, alpha=1, l=1, show=None, savename=None, playtime=5, fps=60, repeat_delay=0, title=None):\n if k > self.num_eigenpairs - 1:\n raise ValueError(f\"Too high an eigen-number. Have only solved for {self.num_eigenpairs} eigenpairs.\")\n\n displacement_vec = self.retrieve_vibration_eigenvector(k)\n\n N_frames = playtime * fps\n ts = np.linspace(0, 2*np.pi, N_frames)\n\n fig, ax = plt.subplots(figsize=(16, 16))\n if title is None:\n fig.suptitle(f\"Eigen vibration mode {k}\", fontsize=18)\n else:\n fig.suptitle(title, fontsize=18)\n\n artists = [self.display_mesh(displacement=alpha*np.sin(l*t)*displacement_vec,\n show=False, ax=ax) for t in ts]\n\n ani = ArtistAnimation(fig, artists, interval=1000//fps, repeat_delay=repeat_delay, repeat=True, blit=True)\n\n if savename is not None:\n ani.save(f\"root/project_2/animations/{savename}.mp4\")\n\n if show is None:\n if savename is None:\n show = True\n else:\n show = False\n \n if show:\n plt.show()\n return\n\n # Clean up memory\n fig.clear()\n plt.close(fig)\n del fig, ax, artists, ani\n import gc\n gc.collect()\n\n return\n\n def animate_vibration_mode_stress(self, k, alpha=1, l=1, show=None, savename=None, \n playtime=5, fps=60, repeat_delay=0, title=None):\n if k > self.num_eigenpairs - 1:\n raise ValueError(f\"Too high an eigen-number. Have only solved for {self.num_eigenpairs} eigenpairs.\")\n\n displacement_vec = self.retrieve_vibration_eigenvector(k)\n\n N_frames = int(playtime * fps)\n ts = np.linspace(0, 2*np.pi, N_frames)\n\n max_stretch = self.find_max_stress(displacement=displacement_vec*alpha)\n\n # Set a bigger font size for text:\n plt.rcParams.update({'font.size': 20})\n norm = mpl.colors.Normalize(vmin=-max_stretch, vmax=max_stretch)\n\n fig, ax = plt.subplots(figsize=(1.5*16,1.5*9))\n \n plt.subplots_adjust(left=0.05, bottom=0.05, right=0.99, top=0.94)\n ax.set_xlabel(\"x [m]\")\n ax.set_ylabel(\"y [m]\")\n\n if title is None:\n fig.suptitle(f\"Vibration mode {k} for {self.area} with {len(self.triang)} Elements\")\n elif type(title) is str:\n fig.suptitle(title)\n else:\n pass\n\n artists = [self.display_mesh_stress(displacement=alpha*np.sin(l*t)*displacement_vec,\n norm=norm, show=False, ax=ax).findobj() for t in ts]\n\n ani = ArtistAnimation(fig, artists, interval=1000//fps, repeat_delay=repeat_delay, repeat=True, blit=True)\n cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=None), ax=ax)\n cbar.ax.get_yaxis().labelpad = 50\n cbar.set_label(r\"Mean Total Stress $\\sigma$ [Pa]\", rotation=270, fontsize=24)\n\n if savename is not None:\n ani.save(f\"root/project_2/animations/{savename}.mp4\")\n\n if show is None:\n if savename is None:\n show = True\n else:\n show = False\n \n if show:\n plt.show()\n return \n \n # Clean up memory\n fig.clear()\n plt.close(fig)\n del fig, ax, artists, ani, norm, cbar\n import gc\n gc.collect()\n\n return\n","sub_path":"root/project_2/elasticity_solver.py","file_name":"elasticity_solver.py","file_ext":"py","file_size_in_byte":39682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"125782853","text":"import pytest\nimport requests\n\nfrom yippi import YippiClient\nfrom yippi.Enums import Rating\nfrom yippi.Exceptions import APIError\nfrom yippi.Exceptions import UserError\n\n\n@pytest.fixture(scope=\"module\")\ndef vcr_cassette_dir(request):\n return \"tests/cassettes/sync\"\n\n\n@pytest.fixture\ndef client():\n session = requests.Session()\n return YippiClient(\"Yippi\", \"0.1\", \"Error-\", session=session)\n\n\n@pytest.mark.vcr()\ndef test_getpost(client):\n post = client.post(1383235)\n assert post.id == 1383235\n assert post.created_at == \"2017-11-20T12:23:11.340-05:00\"\n assert post.updated_at == \"2020-04-17T20:27:20.798-04:00\"\n assert post.file == {\n \"width\": 767,\n \"height\": 1000,\n \"ext\": \"png\",\n \"size\": 489122,\n \"md5\": \"539fd6c8c9af7b79693783b995ddf640\",\n \"url\": \"https://static1.e621.net/data/53/9f/539fd6c8c9af7b79693783b995ddf640.png\",\n }\n assert post.preview == {\n \"width\": 115,\n \"height\": 150,\n \"url\": \"https://static1.e621.net/data/preview/53/9f/539fd6c8c9af7b79693783b995ddf640.jpg\",\n }\n assert post.sample == {\n \"has\": False,\n \"height\": 1000,\n \"width\": 767,\n \"url\": \"https://static1.e621.net/data/53/9f/539fd6c8c9af7b79693783b995ddf640.png\",\n }\n assert post.score == {\"up\": 126, \"down\": -1, \"total\": 125}\n assert post.tags == {\n \"general\": [\n \"5_fingers\",\n \"anthro\",\n \"bed\",\n \"bedding\",\n \"blanket\",\n \"clothed\",\n \"clothing\",\n \"door\",\n \"duo\",\n \"fingers\",\n \"fur\",\n \"furniture\",\n \"lying\",\n \"male\",\n \"male/male\",\n \"on_side\",\n \"pillow\",\n \"sleeping\",\n \"spooning\",\n ],\n \"species\": [\n \"bird_dog\",\n \"canid\",\n \"canine\",\n \"canis\",\n \"domestic_dog\",\n \"golden_retriever\",\n \"hunting_dog\",\n \"mammal\",\n \"retriever\",\n \"wolf\",\n ],\n \"character\": [\"daniel_segja\", \"joel_mustard\"],\n \"copyright\": [\"patreon\"],\n \"artist\": [\"zeta-haru\"],\n \"invalid\": [],\n \"lore\": [],\n \"meta\": [\"comic\"],\n }\n assert post.locked_tags == []\n assert post.change_seq == 23384218\n assert post.flags == {\n \"pending\": False,\n \"flagged\": False,\n \"note_locked\": False,\n \"status_locked\": False,\n \"rating_locked\": False,\n \"deleted\": False,\n }\n assert post.rating == Rating.SAFE\n assert post.fav_count == 306\n assert post.sources == [\n \"https://www.furaffinity.net/view/25500100/\",\n \"https://furaffinity.net/user/zeta-haru\",\n ]\n assert post.pools == [6527]\n assert post.relationships == {\n \"parent_id\": None,\n \"has_children\": False,\n \"has_active_children\": False,\n \"children\": [],\n }\n assert post.approver_id == 38571\n assert post.uploader_id == 269143\n assert post.description == \"\"\n assert post.comment_count == 42\n assert not post.is_favorited\n\n\n@pytest.mark.vcr()\ndef test_404(client):\n with pytest.raises(UserError):\n client.post(99999999999)\n\n\n@pytest.mark.vcr()\ndef test_post_search(client):\n assert client.posts(\"m/m\")\n assert client.posts([\"m/m\", \"rating:s\"])\n assert len(client.posts(\"m/m\", limit=1)) == 1\n assert client.posts(\"m/m\", page=1)\n\n\n@pytest.mark.vcr()\ndef test_post_search_error(client):\n with pytest.raises(UserError):\n client.posts(\"m/m\", page=1000)\n\n\n@pytest.mark.vcr()\ndef test_note(client):\n note = client.notes(post_id=2222254, creator_id=366315, limit=1)[0]\n assert note.id == 257037\n assert note.created_at == \"2020-04-19T02:58:56.716-04:00\"\n assert note.updated_at == \"2020-04-19T02:58:56.716-04:00\"\n assert note.creator_id == 366315\n assert note.x == 774\n assert note.y == 136\n assert note.width == 36\n assert note.height == 50\n assert note.version == 2\n assert note.is_active\n assert note.post_id == 2222254\n assert note.body == \"Chu~\"\n assert note.creator_name == \"Mutter\"\n\n\n@pytest.mark.vcr()\ndef test_flags(client):\n flag = client.flags(post_id=2213076, limit=1)[-1]\n assert flag.id == 368383\n assert flag.created_at == \"2020-04-19T02:50:38.030-04:00\"\n assert flag.post_id == 2213076\n assert \"Inferior version\" in flag.reason\n assert not flag.is_resolved\n assert flag.updated_at == \"2020-04-19T02:50:38.030-04:00\"\n assert not flag.is_deletion\n assert flag.category == \"normal\"\n\n\n@pytest.mark.vcr()\ndef test_pools(client):\n pool = client.pools(\"Critical Success\")[0]\n assert pool.id == 6059\n assert pool.name == \"Critical_Success\"\n assert pool.created_at == \"2015-05-12T03:12:04.070-04:00\"\n assert pool.updated_at == \"2020-01-11T07:33:28.640-05:00\"\n assert pool.creator_id == 80719\n assert \"Terry already knew DMing a roleplay game\" in pool.description\n assert not pool.is_active\n assert pool.category == \"series\"\n assert not pool.is_deleted\n assert set([653514, 653515, 653820]).issubset(pool.post_ids)\n assert pool.creator_name == \"Emserdalf\"\n assert pool.post_count == 48\n\n\n@pytest.mark.vcr()\ndef test_500(client):\n with pytest.raises(APIError):\n # We can't simulate e621 error, so we just use external help.\n client._call_api(\"GET\", \"https://httpstat.us/500\")\n","sub_path":"tests/test_yippisync.py","file_name":"test_yippisync.py","file_ext":"py","file_size_in_byte":5454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"440359836","text":"from bookshelf import app\nfrom bookshelf.models import db, Book, Author\nimport unittest\n\n\nclass BooksTests(unittest.TestCase):\n def setUp(self):\n self.app = app.test_client()\n app.config.from_object('bookshelf.config.TestingConfig')\n with app.app_context():\n db.create_all()\n author = Author(name='Test Author')\n book = Book(title='Test Book', author=[author])\n db.session.add(author)\n db.session.add(book)\n db.session.commit()\n\n def tearDown(self):\n self.app = app.test_client()\n app.config.from_object('bookshelf.config.TestingConfig')\n with app.app_context():\n db.drop_all()\n\n def test_home_status_code(self):\n result = self.app.get('/')\n self.assertEquals(result.status_code, 200)\n self.assertIn('Bookshelf test app', result.data)\n\n def test_display_books_status_code(self):\n result = self.app.get('/books/list')\n self.assertEquals(result.status_code, 200)\n self.assertIn('Test Book', result.data)\n\n def test_create_book_status_code_without_login(self):\n result = self.app.post('/books/create')\n self.assertEquals(result.status_code, 302)\n\n def test_update_book_status_code_without_login(self):\n result = self.app.post('/books/update/1')\n self.assertEquals(result.status_code, 302)\n\n def test_delete_book_status_code_without_login(self):\n result = self.app.delete('/books/delete/1')\n self.assertEquals(result.status_code, 302)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"49582424","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb 23 11:41:35 2018\n\n@author: wangg\n\"\"\"\n\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n\nX = tf.placeholder(tf.float32,[None,784])\nY = tf.placeholder(tf.float32,[None,10])\nkeep_prob = tf.placeholder(tf.float32)\n\nLR = 1e-3\ntraining_epochs = 500\ndisplay_step = 10\nbatch_size = 128\ndropout = 0.75\n\nWeights = {\n \"Conv1\": tf.Variable(tf.random_normal([5,5,1,32])),\n \"Conv2\": tf.Variable(tf.random_normal([5,5,32,64])),\n \"FC1\": tf.Variable(tf.random_normal([7*7*64,1024])),\n \"FC2\": tf.Variable(tf.random_normal([1024,10]))\n }\n\nBiases = {\n \"bc1\": tf.Variable(tf.random_normal([32])),\n \"bc2\": tf.Variable(tf.random_normal([64])),\n \"bf1\": tf.Variable(tf.random_normal([1024])),\n \"bf2\": tf.Variable(tf.random_normal([10]))\n }\n\ndef conv2d(x, W, b, strides=1):\n x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n x = tf.nn.bias_add(x, b)\n return tf.nn.relu(x)\n\n\ndef maxpool2d(x, k=2):\n return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')\n\ndef ConvMNIST(x,weights,biases,dropout):\n x = tf.reshape(x,shape=[-1,28,28,1])\n conv1 = conv2d(x,weights[\"Conv1\"],biases[\"bc1\"])\n conv1 = maxpool2d(conv1,k=2)\n conv2 = conv2d(conv1,weights[\"Conv2\"],biases[\"bc2\"])\n conv2 = maxpool2d(conv2,k=2)\n fc1 = tf.reshape(conv2, [-1, weights[\"FC1\"].get_shape().as_list()[0]])\n fc1 = tf.add(tf.matmul(fc1,weights[\"FC1\"]),biases[\"bf1\"])\n fc1 = tf.nn.relu(fc1)\n fc1 = tf.nn.dropout(fc1,dropout)\n fc2 = tf.add(tf.matmul(fc1,weights[\"FC2\"]),biases[\"bf2\"])\n return fc2\n\nlogits = ConvMNIST(X,Weights,Biases,keep_prob)\nprediction = tf.nn.softmax(logits)\n\nloss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))\ntrain = tf.train.AdamOptimizer(learning_rate=LR).minimize(loss_op)\n\ncorrect_pred = tf.equal(tf.argmax(prediction,1),tf.argmax(Y,1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))\n\ninit = tf.global_variables_initializer()\n#\nwith tf.Session() as sess:\n sess.run(init)\n for epochs in range(1,training_epochs+1):\n batch_x, batch_y = mnist.train.next_batch(batch_size)\n sess.run(train,feed_dict={X:batch_x,Y:batch_y,keep_prob: dropout})\n# print('finished')\n if epochs%display_step ==0 or epochs ==1:\n loss = sess.run(loss_op, feed_dict={X:batch_x,Y:batch_y,keep_prob: 1.0})\n acc = sess.run(accuracy, feed_dict={X:batch_x,Y:batch_y,keep_prob: 1.0})\n print(\"Step \" + str(epochs) + \", Minibatch Loss= \" +\"{:.4f}\".format(loss)+\",accuracy=\"\\\n +\"{:.3f}\".format(acc))\n# \n print('Training Finished!')\n test_acc = sess.run(accuracy, feed_dict={X: mnist.test.images,Y: mnist.test.labels,keep_prob: 1.0})\n print(\"Testing result:\"+\"{:.3f}\".format(test_acc))","sub_path":"CNN for MNIST.py","file_name":"CNN for MNIST.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"367351167","text":"from pyrogram import filters\r\nfrom pyrogram.handlers import MessageHandler\r\nimport player\r\nfrom config import SUDO_FILTER\r\nfrom strings import get_string as _\r\n\r\n\r\nasync def pause_resume(client, message):\r\n if player.pause_resume():\r\n await message.reply_text(_(\"paused\"))\r\n else:\r\n await message.reply_text(_(\"nothing_playing_pause\"))\r\n\r\n__handlers__ = [\r\n [\r\n MessageHandler(\r\n pause_resume,\r\n filters.command(\"pause\", \"/\")\r\n & SUDO_FILTER\r\n )\r\n ]\r\n]\r\n","sub_path":"handlers/pause_resume.py","file_name":"pause_resume.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"194963019","text":"from search import models\nimport json\nimport elasticsearch\n\n\n# elasticsearch --config=/usr/local/opt/elasticsearch/config/elasticsearch.yml\n\nes = elasticsearch.Elasticsearch()\n\nfor v in models.Venue.objects.all():\n v_dict = {}\n v_dict['name'] = v.name\n v_dict['location'] = json.loads(v.location)\n\n if v.menu:\n v_dict['menu'] = json.loads(v.menu)\n\n v_dict['stats'] = json.loads(v.stats)\n v_dict['categories'] = json.loads(v.categories)\n\n tips = v.tip_set.all()\n tips_dict = [t.as_dict() for t in tips]\n v_dict['tips'] = tips_dict\n\n es.index(index='4sreviews', doc_type='venues', id=v.venue_id, body=v_dict)\n","sub_path":"web/import_elasticsearch.py","file_name":"import_elasticsearch.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"347451676","text":"'''\nThis file will solve the sudoku puzzle using depth first search, The approach to solve \nthe sudoku problem is that every row, column and quadrant must have a unique value for 1-9 (no duplicates)\nin it. Thus, this means that the sum of every row, column and quadrant must be the same. for example in \na 9x9 (sum must be 45). In the file, the solver is using brute force (backtracking) to search through the board\nthus it try possible solutions to solve the grid and once it reached an invalid value, it will backtrack and \nrecursively try to solve itself. Note: a checking function is also added to ensure that the sum rule will\nwork for every case (i.e it will ensure that no duplicates are entered in the sudoku board)\n\nlink to documentation on using sum solving technique: https://en.wikipedia.org/wiki/Killer_sudoku#The_Rule_of_45\n'''\nimport time\n\nclass Sudoku(object):\n\n def __init__(self, initial):\n self.initial = initial \n self.size = len(initial) # Defines board type, ex. 9x9\n self.height = int(self.size**0.5) # Defines height of quadrant ex. 3 for 9x9\n\n def check_if_valid(self, state):\n '''\n Checks whether or not the values entered from the gui are valid i.e no duplicate values\n\n Returns: True or False \n '''\n # Check rows and columns \n for row in range(self.size):\n #elminates all the zeros in the row\n row_check = [x for x in state[row] if x is not 0]\n #check if there are duplicate values in the row \n if len(row_check)!=len(set(row_check)):\n return False \n col_checker = []\n for column in range(self.size):\n #don't want to add 0 to our list\n if state[column][row] == 0:\n pass\n #check if we already seen the value before -- indicates duplicate\n elif state[column][row] in col_checker:\n return False \n #never seen before, add to our checker \n else:\n col_checker.append(state[column][row])\n # Check quadrants\n for column in range(0,self.size,int(self.size**0.5)):\n for row in range(0,self.size,self.height):\n checker = []\n for block_row in range(0,self.height):\n for block_column in range(int(self.size**0.5)):\n #don't want to add 0 to our list\n if state[row + block_row][column + block_column] == 0:\n pass\n #check if we already seen the value before -- indicates duplicate\n elif state[row + block_row][column + block_column] in checker:\n print(\"not passing block\")\n return False\n #never seen before, add to our checker \n else: \n checker.append(state[row + block_row][column + block_column])\n return True\n\n def goal(self, state):\n '''\n The board with the solutions or the \"goal\" board, since the contraints in sudoku is that in each row, \n column and quadrant there must unique digits ex. on a 9x9 a row must contain all digits 1-9. Thus this means that\n the sum of the quadrants, row, columns must be the same ex. 9x9, sum= 45. So using this we can find whether\n a board is \"correct\" or incorrect without having to know the exact solution to the board\n\n Returns: True, if the sum is satisfied for all rows, columns and quadrants else:returns False\n '''\n # Expected sum of each row, column or quadrant.\n total = sum(range(1, self.size+1))\n\n # Check rows and columns \n for row in range(self.size):\n #sum of row is invalid or row contains empty values\n if (len(state[row]) != self.size) or (sum(state[row]) != total):\n return False\n #counter\n column_total = 0\n for column in range(self.size):\n #sums the column \n column_total += state[column][row]\n #column value is invalid\n if (column_total != total):\n return False\n\n # Check quadrants\n for column in range(0,self.size,int(self.size**0.5)):\n for row in range(0,self.size,self.height):\n #counter\n block_total = 0\n for block_row in range(0,self.height):\n for block_column in range(int(self.size**0.5)):\n #adds/sums the values in the quadrant\n block_total += state[row + block_row][column + block_column]\n #quadrant values are invalid\n if (block_total != total):\n return False\n #passed all conditions, boards is \"correct\"\n return True\n\n def get_values(self, values, used):\n '''\n Gets the set of valid numbers from values that are not currently in use\n\n Returns: set of valid numbers\n '''\n return [number for number in values if number not in used]\n\n def get_empty_spot(self, grid, state):\n '''\n Get the first empty spot on the grid that is empty (marked with 0)\n\n Returns: position of empty spot on grid \n '''\n for row in range(grid):\n for col in range(grid):\n #found an empty spot\n if state[row][col] == 0:\n return row, col\n\n def get_row_values(self, state, row):\n '''\n filter the valid value based on row \n ex. if a row was filled with values 1,2,3,4,5 then valid values would be 6,7,8,9\n\n Returns: valid values in row \n '''\n num_set = range(1, self.size+1) # Defines set of valid numbers that can be placed on board\n #finding the values in use\n in_row = [number for number in state[row] if (number != 0)] \n #get values that are not in the row\n valid_values = self.get_values(num_set, in_row)\n return valid_values\n\n def get_col_values(self, valid_values, state, column):\n '''\n filter the valid value based on column\n ex. if a column was filled with values 1,2,3,4,5 then valid values would be 6,7,8,9\n\n Returns: valid values in a column\n '''\n in_column = [] # List of valid values in spot's column\n for column_index in range(self.size):\n #get the values already in the column\n if state[column_index][column] != 0:\n in_column.append(state[column_index][column])\n #get the values that are not in the column\n valid_values = self.get_values(valid_values, in_column)\n return valid_values\n\n # Filter valid values based on quadrant\n def get_quad_values(self, valid_values, state, row, column):\n '''\n filter the valid value based on quadrants\n ex. if a column was filled with values 1,2,3,4,5 then valid values would be 6,7,8,9\n\n Returns: valid values in a quadrant\n '''\n in_quad = [] \n #row where the quadrant begins (so for ex row_start will be either 0, 3, 6 in a 9x9 grid)\n row_start = int(row/self.height)*self.height\n #column where quadrant begins (so for ex col_start will be either 0, 3, 6 in a 9x9 grid)\n col_start = int(column/int(self.size**0.5))*int(self.size**0.5)\n for quad_row in range(0, self.height):\n #get the the values in use in the quadrant\n for quad_col in range(int(self.size**0.5)):\n in_quad.append(state[row_start + quad_row][col_start + quad_col])\n #get the values that are not in the quadrant\n valid_values = self.get_values(valid_values, in_quad)\n return valid_values \n\n def get_options(self, state, row, column):\n '''\n Get the valid values that satisfy row, column and quadrant conditions\n\n Returns: valid values\n '''\n options = self.get_row_values(state, row)\n options = self.get_col_values(options, state, column)\n options = self.get_quad_values(options, state, row, column)\n\n return options \n\ndef backtrack(board):\n solution = Sudoku(board)\n #found the solution \n if solution.goal(board):\n return board\n # Get first empty spot\n row,col = solution.get_empty_spot(solution.size, board)\n # Get spot's viable options\n options = solution.get_options(board, row, col)\n\n for i in options:\n board[row][col] = i # Try option\n # Recursively fill in the board\n if backtrack(board):\n #found solution\n return board \n else:\n board[row][col] = 0 #backtracks\n\n return None\n\ndef Solve(board):\n '''\n Solves the Sudoku Board using backtracking and prints the time to solve on the terminal \n\n Returns: None\n '''\n check = Sudoku(board)\n #checks if the initial board we are starting with is valid\n if not check.check_if_valid(board):\n return False\n\n print (\"Solving using Backtracking\")\n start_time = time.time()\n solution = backtrack(board)\n elapsed_time = time.time() - start_time\n\n if solution:\n print (elapsed_time)\n return solution \n else:\n return False\n\n#########FOR THE GUI TO BE ABLE TO READ THE SOLUTION#########################\n\ndef convert(size,board):\n '''\n Converts a larger list containing the values of the sudoku board \n to a list of sublist containing the values of the sudoku board (ex. [1,2,3,4] -> [[1,2], [3,4]]) \n\n Returns: original list as a list of sublist\n '''\n return [board[x:x+size] for x in range(0, size**2, size)]\n\ndef send(size,rows,cols,grid):\n '''\n Converts the list(grid of solutions) into a readable form to be \n read by the gui so it can be displayed on the gui \n\n Returns: list will filled solutions\n '''\n #convert the board received from gui into a readble form for our DFS solver\n board = convert(size,grid)\n #find the solution\n solution = Solve(board)\n if solution == False:\n return None\n #reconvert board(with the solution) into the original form(format) from the gui\n s = {}\n #~ cols = '123456789' #the x-axis of the sudoku grid\n #~ rows = 'ABCDEFGHI' #the y-axis of the sudoku grd\n for i in range(size):\n for j in range(size):\n s[rows[i]+cols[j]] = solution[i][j]\n #return the gui readable solution\n return s\n\n","sub_path":"backtracking_solve.py","file_name":"backtracking_solve.py","file_ext":"py","file_size_in_byte":10545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"468141698","text":"from keras.models import Model\nfrom keras.layers import Dense,Input,LSTM,Bidirectional,Activation,Conv1D,GRU\nfrom keras.layers import Dropout,Embedding, MaxPooling1D, Flatten\nfrom keras.layers import GlobalAveragePooling1D, GlobalMaxPooling1D, concatenate, SpatialDropout1D\nfrom keras.optimizers import Adam\n\n\nfrom keras.preprocessing import text, sequence\nimport numpy as np\n\ndef get_model(opt): \n inp = Input(shape=(opt['maxlen'], ))\n x = Embedding(opt['vocab_size'], opt['embedding_size'],\n weights=[opt['embedding_matrix']], trainable=opt['train_embed'])(inp)\n x = SpatialDropout1D(opt['embed_dropout'])(x)\n\n x = Bidirectional(GRU(opt['hidden_size'], return_sequences=True,\n dropout=opt['dropout'],recurrent_dropout=opt['dropout']))(x)\n x = Conv1D(opt['num_filters'], kernel_size=3, padding=\"valid\", kernel_initializer=\"glorot_uniform\")(x)\n avg_pool = GlobalAveragePooling1D()(x)\n max_pool = GlobalMaxPooling1D()(x)\n x = concatenate([avg_pool, max_pool]) \n # x = Dense(128, activation='relu')(x)\n # x = Dropout(opt['dropout'])(x)\n\n outp = Dense(opt['nb_classes'], activation=\"softmax\")(x)\n \n model = Model(inputs=inp, outputs=outp)\n model.compile(loss='categorical_crossentropy',\n optimizer=Adam(lr=opt['lr']),\n metrics=['accuracy'])\n model.summary()\n return model\n\n\ndef main():\n opt = { 'maxlen': 50,\n 'vocab_size': 100000,\n 'embedding_size': 100,\n 'train_embed': True,\n 'embed_dropout': 0.1,\n 'dropout': 0.1,\n 'hidden_size': 128,\n 'num_filters': 128,\n 'nb_classes': 5,\n 'lr': 1e-3\n }\n\n opt['embedding_matrix'] = np.zeros((opt['vocab_size'], opt['embedding_size']))\n model = get_model(opt)\n\n\nif __name__ == '__main__':\n main()","sub_path":"sentiment_analysis/models/BiGruWithConv.py","file_name":"BiGruWithConv.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"594231742","text":"#!/usr/bin/env python\nfrom receptionist import Receptionist\nimport sys\nfrom RPi import ReceptionPi\nfrom UserDB import UserDB\nfrom FacialRecognition_Drive import FaceRecognition_Drive\n\n\nclass App:\n \"\"\"\n This class shows the Library Management System\n Menu options as well as ensures basic functionalities.\n\n ...\n\n Methods\n -------\n menu()\n This method shows and runs the main functionalities \n of the Library Managment System consol application.\n\n \"\"\"\n\n def menu(self):\n re = Receptionist()\n drive = FaceRecognition_Drive()\n receptionPi = ReceptionPi()\n print(\"\"\"\n =====================================================\n * * * * * * \n * * * * * * * \n * * * * * * \n * * * * * *\n * * * * * * *\n * * * * * * * * * * *\n ======================================================\n \"\"\")\n while True:\n print(\" ************MAIN MENU**************\")\n choice = input(\"\"\"\n 1: Register\n 2: Log in using username and passward\n 3: Log in using face recognation\n 4: Quit/Log Out\n\n Please enter your choice: \"\"\")\n\n if choice == '1':\n re.register()\n elif choice == '2':\n name = re.login()\n if name is not False:\n receptionPi.login(name)\n\n elif choice == '3':\n data = drive.face_Recognation()\n receptionPi.login(name)\n elif choice == '4':\n sys.exit()\n else:\n print(\"You must only select either 1,2,3 or 4.\")\n print(\"Please try again\")\n\n\nif __name__ == \"__main__\":\n App().menu()\n","sub_path":"RPi/App.py","file_name":"App.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"114540694","text":"import numpy as np\nfrom scipy.interpolate import bisplrep, bisplev\nfrom glob import glob\nfrom blobFinder import BlobFinder\n\nclass Calibrate:\n def __init__(self, fname, grid_size):\n \"\"\"\n fname: Filepath to directory with calibration pictures\n \n The pictures names need to follow the convention:\n cal*.jpg, where * is an int starting from 0. Each picture increments\n the location of the object on the x-axis by one unit until grid_size, where\n the y-axis is increased and the x-axis resets.\n\n i.e. for grid_size 3 and 6 pictures\n cal0: 0,0\n cal1: 1,0\n cal2: 2,0\n cal3: 0,1\n cal4: 1,1\n cal5: 2,1\n\n There must also be an image called bg.jpg, which is the background image that will\n be used throughout.\n \"\"\"\n imageFnames = glob(fname+'/cal*.jpg')\n tableCoords = []\n blobs = []\n for imgName in imageFnames:\n start = imgName.rfind('cal')\n number = int(imgName[start+3:-4])\n tableCoords.append((number%grid_size,number//grid_size))\n\n blob = BlobFinder(imgName,fname+'/bg.jpg').findBlob()\n blobs.append(blob)\n\n chart = Chart(tableCoords, blobs)\n\n return chart\n\nclass Chart:\n def __init__(self, tableCoords, blobs):\n \"\"\"\n tableCoords: List of 2-tuples of x,y coordinates\n blobs: Matching list of initialized blob objects\n \"\"\"\n tableXs = np.array([_[0] for _ in tableCoords])\n tableYs = np.array([_[1] for _ in tableCoords])\n\n blobXs = np.array([_.getLocation()[0] for _ in blobs])\n blobYs = np.array([_.getLocation()[1] for _ in blobs])\n\n self.tableToBlobTckX = bisplrep(tableXs,tableYs,blobXs)\n self.tableToBlobTckY = bisplrep(tableXs,tableYs,blobYs)\n\n self.blobToTableTckX = bisplrep(blobXs,blobYs,tableXs)\n self.blobToTableTckY = bisplrep(blobXs,blobYs,tableYs)\n\n def blobToTable(self, x, y):\n tableX = bisplev(x, y, self.blobToTableTckX)\n tableY = bisplev(x, y, self.blobToTableTckY)\n return tableX, tableY\n\n def tableToBlob(self, x, y):\n blobX = bisplev(x, y, self.tableToBlobTckX)\n blobY = bisplev(x, y, self.tableToBlobTckY)\n return blobX, blobY","sub_path":"calibration.py","file_name":"calibration.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"561597688","text":"import random\r\nfrom typing import List, Tuple\r\n\r\nfrom Classes.Board import Board\r\nfrom Classes.Delta import Delta\r\nfrom Classes.Pos2D import Pos2D\r\nfrom Classes.Square import Square\r\nfrom Enums.GamePhase import GamePhase\r\nfrom Enums.PlayerColor import PlayerColor\r\n\r\n\r\nclass IDSAgent:\r\n\r\n # When playing massacre, we don't want to sacrifice our own pieces to kill\r\n # enemy pieces. Therefore, weigh white pieces higher than black pieces.\r\n _WHITE_WEIGHT: float = 1.1\r\n _BLACK_WEIGHT: float = 1.0\r\n # How much to weigh the manhattan distance. We only want to weigh the\r\n # manhattan distance enough for it to play a role when there are no options\r\n # to kill an enemy piece, hence the low weight.\r\n _DIST_WEIGHT: float = 0.001\r\n # We really don't want to repeat board states. When a repeat board is\r\n _REPEAT_BOARD: int = -99999\r\n # Rating-decimal place rounding. Used to prevent floating point imprecision\r\n # from interfering with move decisions.\r\n _RATING_NUM_ROUNDING: int = 10\r\n # The length of the history of recent board states the agent can remember.\r\n # Used to avoid repeat moves. Can be quiet high, as each board (being\r\n # represented by a string) does not take much memory.\r\n _BOARD_HISTORY_MEMORY: int = 512\r\n\r\n # A reference to the current board that the agent is on.\r\n _board: Board\r\n # The depth to go in each iteration of the iterative-deepening search\r\n # algorithm i.e. number of moves to look ahead.\r\n _depth: int\r\n # A list of recent boards. Each board is stored in its string form, for\r\n # memory and efficiency purposes.\r\n # This will allow us to check if a move results in a previous board, and\r\n # therefore not perform that move, avoiding\r\n # endless loops in the process.\r\n _recent_board_history: List[str] = []\r\n\r\n def __init__(self, start_board: Board, depth: int, seed: int = None):\r\n self._board = start_board\r\n self._depth = depth\r\n if (seed is not None):\r\n random.seed(seed)\r\n\r\n def massacre(self):\r\n \"\"\"\r\n This method runs the algorithm until all enemy pieces are killed (or the\r\n game is over otherwise). This method uses a variant of iterative\r\n deepening search. All moves within '_DEPTH' + 1 levels will be explored,\r\n at which point the most promising move will be made. We then, again,\r\n evaluate all possible moves within '_DEPTH' + 1 levels, perform the\r\n best-rated move, etc etc.\r\n \"\"\"\r\n\r\n # While the game is not finished, perform the algorithm.\r\n while (self._board.phase != GamePhase.FINISHED):\r\n # Evaluate all of the possible moves from this board.\r\n deltas: List[Delta] = self._board.get_all_possible_deltas(PlayerColor.WHITE)\r\n # Shuffling the calculated deltas can assist in avoiding endless\r\n # loops.\r\n random.shuffle(deltas)\r\n\r\n # Get the best move to perform.\r\n best_delta: Delta = \\\r\n IDSAgent.get_best_delta(self._board, PlayerColor.WHITE, self._depth,\r\n self._recent_board_history)[0]\r\n\r\n # Before performing the move, save the current board into the recent\r\n # boards history list.\r\n self._recent_board_history = \\\r\n ([str(self._board)]\r\n + self._recent_board_history[:IDSAgent._BOARD_HISTORY_MEMORY])\r\n\r\n # Perform the move, replacing the reference to the old board with\r\n # the new one.\r\n self._board = self._board.get_next_board(best_delta)\r\n\r\n # Print the delta/move made.\r\n print(best_delta)\r\n\r\n @staticmethod\r\n def get_board_ratings(board: Board, depth: int,\r\n recent_board_history: List[str]) -> List[float]:\r\n \"\"\"\r\n Returns a list of ratings for the given board of size 'depth' + 1. For\r\n example, if this function returns this list: [2.2, 1.6, 1.7], that means\r\n that the given board is rated 1.7, the best board state from the given\r\n board is rated 1.6, and the best board state from that 1.6 board is\r\n rated 2.2. i.e. the list of ratings corresponds to levels as so:\r\n [depth, depth - 1, depth - 2, ... 0] where 0 is the given/current\r\n board's rating. By returning a list of ratings, we can better compare\r\n series of moves, particularly a series which results in the same state,\r\n but by different means e.g. 'kill enemy piece at (2, 2) then move to\r\n (2, 1)' vs 'move to (2, 1) then kill enemy piece at (2, 2)'. In this\r\n scenario, we can prioritize the former, thanks to the list of ratings\r\n being returned. recent_board_history is list of boards represented by\r\n strings used to avoid repeating previous board states, avoiding endless\r\n loops in the process.\r\n \"\"\"\r\n\r\n # If the current board has been explored in recent board history, return\r\n # the appropriate rating to discourage its selection in a list e.g.\r\n # [-99999].\r\n if (str(board) in recent_board_history):\r\n return [IDSAgent._REPEAT_BOARD]\r\n\r\n # If we're at the end of our search, either due to depth being equal to\r\n # 0 or the board being marked as finished, simply return the heuristic\r\n # value of the board in a list.\r\n if (depth == 0 or board.phase == GamePhase.FINISHED):\r\n return [IDSAgent.get_heuristic_value(board)]\r\n\r\n # Evaluate all possible moves from this board and keep track of the best\r\n # one.\r\n best_rating = IDSAgent.get_best_delta(board, PlayerColor.WHITE, depth,\r\n recent_board_history)[1]\r\n\r\n # Return the list of ratings from the best move and attach this given\r\n # board's rating onto the end.\r\n return best_rating + [IDSAgent.get_heuristic_value(board)]\r\n\r\n @staticmethod\r\n def get_best_delta(board: Board, player: PlayerColor, depth: int,\r\n recent_board_history: List[str]) \\\r\n -> Tuple[Delta, List[float]]:\r\n \"\"\"\r\n Returns the highest-rated (or best) move from the current board for the\r\n given player, exploring 'depth' number of levels to determine the best\r\n move. recent_board_history helps avoid repeating board states. Along\r\n with the delta object for the best move, also returns a list of floats,\r\n containing the ratings for the series of moves used to rate the returned\r\n delta. This list is more thoroughly explained in the docs for\r\n 'get_board_ratings'.\r\n \"\"\"\r\n # Evaluate all of the possible moves from this board.\r\n deltas: List[Delta] = board.get_all_possible_deltas(player)\r\n # Shuffling the calculated deltas can assist in avoiding endless loops,\r\n # particularly if board states are being repeated.\r\n random.shuffle(deltas)\r\n\r\n # Iterate through every valid move, rating them and keeping track of the\r\n # best move along the way.\r\n best_delta: Tuple[Delta, List[float]] = (None, [-999999])\r\n for delta in deltas:\r\n delta_ratings: List[float] = \\\r\n IDSAgent.get_board_ratings(board.get_next_board(delta),\r\n depth - 1, recent_board_history)\r\n\r\n # This \"max\" criteria defined by the lambda looks a bit complex, so\r\n # let's explain. Keep in mind that floats further to the left in a\r\n # given list represents the rating of a board further down the game\r\n # three. When finding the best move sequence (from the current\r\n # board's perspective), we prioritize moves that result in the best\r\n # score at its furthest down board state i.e. [5 1 1] is better than\r\n # [1 9 9], because three moves down, it will have a board rated 5 vs\r\n # the other's board which is rated 1. In the event of this first\r\n # value being equal, we prefer the shortest list i.e. [5 2] is\r\n # better than [5 3 2]. This is because the shorter list means that\r\n # the game will be over sooner (but still have a board rating as\r\n # good as the longer list). This will help the algorithm execute\r\n # killing moves in 'Massacre' and not put them off by doing an\r\n # inconsequential move first. Finally, if the lengths are the same,\r\n # we prioritize the list with the highest rating at any given index\r\n # i.e. [5 3 3] is better than [5 3 2] because it means that we're\r\n # doing the moves that will keep the board's rating as high as\r\n # possible (again, only if the comparison gets to this point).\r\n # Sorted example according to this criteria:\r\n # [4 3 2] > [3 1] > [3 2 2] > [3 2 1] > [-999].\r\n best_delta = max([best_delta, (delta, delta_ratings)],\r\n key=lambda x: (x[1][0], -len(x[1]), x[1]))\r\n\r\n return best_delta\r\n\r\n @staticmethod\r\n def get_heuristic_value(board: Board):\r\n \"\"\"\r\n Given a board, calculates and returns its rating based on heuristics.\r\n \"\"\"\r\n # Get a list of all squares with white pieces and a list of squares with\r\n # black pieces.\r\n white_squares: List[Square] = board.get_player_squares(PlayerColor.WHITE)\r\n black_squares: List[Square] = board.get_player_squares(PlayerColor.BLACK)\r\n\r\n # If there are any black pieces, calculate the sum of all white pieces'\r\n # manhattan displacement to the first black piece in the list. This\r\n # piece will remain consistent until it is dead. This fixes the issue of\r\n # white pieces, when separated from the black pieces, not being able to\r\n # find their way to the black pieces easily.\r\n manhattan_dist_sum: int = 0\r\n if (len(black_squares) > 0):\r\n black_square: Square = black_squares[0]\r\n for white_square in white_squares:\r\n displacement: Pos2D = (black_square.pos - white_square.pos)\r\n manhattan_dist_sum += abs(displacement.x) + abs(displacement.y)\r\n\r\n # Calculate the number of white and black pieces. This is a very\r\n # important heuristic that will help prioritize preserving white's own\r\n # pieces and killing the enemy's black pieces.\r\n num_white_pieces: int = len(white_squares)\r\n num_black_pieces: int = len(black_squares)\r\n\r\n # Return the heuristic rating by using the appropriate weights.\r\n return round(IDSAgent._WHITE_WEIGHT * num_white_pieces\r\n - IDSAgent._BLACK_WEIGHT * num_black_pieces\r\n - IDSAgent._DIST_WEIGHT * manhattan_dist_sum,\r\n IDSAgent._RATING_NUM_ROUNDING)\r\n","sub_path":"Part B/Classes/Agents/IDSAgent.py","file_name":"IDSAgent.py","file_ext":"py","file_size_in_byte":10921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"161608882","text":"'''\nCreated on Dec 26, 2014\n\n@author: dsingh\n'''\nfrom TreesAndGraphs.__init__ import errorCodes\n\nclass binarySearchTree():\n '''\n classdocs\n '''\n INITIALVALUE = 9999\n\n def __init__(self,value):\n '''\n Constructor\n '''\n self.value = value\n self.leftChild = None\n self.rightChild = errorCodes.NULL\n \n def searchNode(self,currentNode,value):\n if currentNode == errorCodes.NULL:\n print('No node with value', value,'is found')\n return errorCodes.NULL\n \n currentNodeValue = currentNode.value\n \n if currentNodeValue == value:\n print('Node found')\n return currentNode\n \n if value < currentNodeValue:\n currentNode = currentNode.leftChild\n elif value > currentNodeValue:\n currentNode = currentNode.rightChild\n \n self.searchNode(currentNode,value)\n \n def addNode(self,value):\n currentNode = self\n parentNode = self\n \n while not currentNode == errorCodes.NULL:\n parentNode = currentNode\n if value > parentNode.value:\n currentNode = parentNode.rightChild\n elif value < parentNode.value:\n currentNode = parentNode.leftChild\n else:\n print('Node already exists!!')\n return\n \n \n newNode = binarySearchTree(value)\n if value > parentNode.value:\n parentNode.rightChild = newNode\n else:\n parentNode.leftChild = newNode\n \n def inOrderTraversal(self,node):\n if node == errorCodes.NULL:\n return\n \n self.inOrderTraversal(node.leftChild)\n print(node.value,'<-->')\n self.inOrderTraversal(node.rightChild)\n \n \n \n \n \n \n ","sub_path":"pythonPrograms.src/TreesAndGraphs/binaryTree.py","file_name":"binaryTree.py","file_ext":"py","file_size_in_byte":1938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"352228370","text":"import time\nimport telebot as tb\nfrom lxml import etree\nfrom DataSearch import utility as ut\nfrom ChatBot import dictionary as cc\n\n\ntoken = '706415631:AAG1Y6sfLmvxU_TENOaVwGA3hzXdaGJiaWo'\npathOfPhoto = 'C:/Users\\linuk\\Downloads\\Staedel_Teilset\\Abbildungen_Teilset/'\npathOfDataset = 'C:/Users\\linuk\\Downloads\\Staedel_Teilset/Objekte.xml'\npathOfGene = 'C:/Users\\linuk\\Workspace\\HeyDr.Jo\\src/ChatBot/generatedDataSet.xml'\ntree = etree.parse(pathOfDataset)\nroot = tree.getroot()\n__currentrecord__ = None\n__knowInfo__ = 0\n__artistName__ =''\n__style__ = ''\n\ntreeGene = etree.parse(pathOfGene)\nrootGene = treeGene.getroot()\ndictionary = cc.dict\nbot = tb.TeleBot(token)\n\n\ndef get_semantic(text,dict):\n for dd in dict:\n if text in dict[dd]:\n return dd\n\n\ndef get_from_data(command,rootAll,rootGene):\n return ut.get_start_info(command,rootAll)\n\n\n@bot.message_handler(commands=['start', 'help'])\ndef send_welcome(message):\n bot.reply_to(message, u\"Dear customer, I am Dr. Jo! \"\n u\"\\nToday I will be your museum guide \"\n u\"and provide you some professional and interesting information about our art objects! \"\n u\"\\nWhich object are you currently looking at or interested in? \")\n\n\n@bot.message_handler(content_types='text')\ndef get_input(message):#List):\n try:\n global __currentrecord__, __knowInfo__, __artistName__, __style__\n chatid = message.chat.id\n #if message.text.upper() == 'YES' and __knowInfo__ == 1:\n # detail_Info, dict = ut.get_details(__currentrecord__)\n # __artistName__ = dict['artist']\n # __style__ = dict['style']\n # bot.reply_to(message, detail_Info)\n #chatid = message.chat.id\n # __knowInfo__=2\n #return\n if message.text.upper()=='YES' and __knowInfo__== 2:\n chatid = message.chat.id\n bot.send_message(chatid,u'What would you like to know, ' \\\n ' introductions about the artist or style or some related objects of this object in our museum?' \\\n '\\n(artist,style,related objects)')\n __knowInfo__=3\n return\n elif message.text.upper()==\"ARTIST\" and __knowInfo__==3:\n chatid=message.chat.id\n bot.send_message(chatid, ut.search_artist_xml(__artistName__,rootGene) )\n __knowInfo__ = 2\n bot.send_message(chatid, u'\\n\\n\\nWould you like to know about the style or '\n u'related objects of this object in our museum? ')\n return\n elif message.text.upper()==\"STYLE\" and __knowInfo__==3 :\n chatid = message.chat.id\n bot.send_message(chatid,ut.search_style_xml(__style__,rootGene))\n __knowInfo__ = 2\n bot.send_message(chatid, u'\\n\\n\\nWould you like to know about the artist or '\n u'related objects of this object in our museum? ')\n return\n\n elif message.text.upper()==\"RELATED OBJECTS\" and __knowInfo__==3:\n chatid = message.chat.id\n bot.send_message(chatid, u'still working, Coming Soon...')\n __knowInfo__= 2\n bot.send_message(chatid, u'\\n\\n\\nWould you like to know about the style or '\n u'artist of this object? ')\n return\n\n elif message.text.upper() == 'NO':\n bot.send_message(chatid, u'Please give the number or the name of your interested object!')\n __knowInfo__ = 0\n return\n else:\n print(str(__knowInfo__))\n #for message in messageList:\n title, artist, period, refnum,record = get_from_data(message.text, root, rootGene)\n __artistName__ = artist\n __currentrecord__ = record\n detail_Info, dict = ut.get_details(__currentrecord__)\n __style__ = dict['style']\n bot.reply_to(message, 'Title: \\n%s\\n\\nCreator: \\n%s\\n\\nCreated period: \\n%s\\n\\n\\n%s'%(title,artist,period,detail_Info))\n bot.send_message(chatid, u'Sending photo... Please wait')\n photo = open(pathOfPhoto+refnum+'.png','rb')\n bot.send_photo(chatid,photo)\n bot.send_message(chatid, u'Should I introduce more information about the artist or style of this object?')\n __knowInfo__ =2\n return\n except (AttributeError):\n bot.send_message(chatid,'Sorry I don\\'t understand!')\n\n\nif __name__ == '__main__':\n #bot.set_update_listener(get_input)\n while True:\n bot.polling(none_stop=True)\n time.sleep(1)\n","sub_path":"src/ChatBot/chatbot.py","file_name":"chatbot.py","file_ext":"py","file_size_in_byte":4669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"260608628","text":"#!/usr/bin/python3\n\nimport subprocess\nimport sys\nimport ipaddress\nfrom datetime import datetime\n\n# Define main \ndef main(ip_target,cdir):\n if len(cdir)<1:\n cdir=\"/29\"\n #Creating an the range for the ip\n ip_range= str(ip_target) + cdir\n net = list(ipaddress.ip_network(ip_range,False).hosts())\n \n # IP on the network to fping\n for ip in net:\n action = \"fping -a -C 5 -q \" + str(ip)\n # worked with Ricky and Alf\n try:\n results = subprocess.check_output(action,stderr=subprocess.STDOUT,shell=True)\n results_split = results.split(b\":\")\n ip = str(results_split[0].decode('UTF-8').strip())\n time = str(results_split[1].decode('UTF-8').strip())\n ip_list.append(ip)\n response = ip + \" is detected online. Response time: \" + time\n #print(response)\n except subprocess.CalledProcessError as e:\n error = e.output \n # Print Results.\n print(\"Detected Hosts:\")\n print(\"==============\")\n \n \n for ip in ip_list:\n print(ip)\n time_elapsed = datetime.now() -start_time\n print('Time Elapsed: (hh:mm:ss.ms) {}'.format(time_elapsed))\n# help from Alf \nif __name__=='__main__':\n # targets and times\n targets = sys.argv\n targets.pop(0)\n target = targets[0]\n ip_list =[]\n start_time = datetime.now()\n \n if len(targets) > 1:\n cdir = targets[1]\n main(target,cdir)\n else:\n main(target,\"/24\")","sub_path":"pysweep.py","file_name":"pysweep.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"166375520","text":"import pyttsx3\nimport datetime\nimport time\nimport speech_recognition as sr\nimport pyautogui\nimport pyaudio\nengine = pyttsx3.init('sapi5')\nvoices = engine.getProperty('voices')\nengine.setProperty('voice', voices[1].id)\n# engine.setProperty('volume',0.5)\nengine.setProperty('rate', 155)\ndef speak(audio):\n engine.say(audio)\n engine.runAndWait()\ndef downmouse():\n time.sleep(1)\n pyautogui.moveRel(0, 100, duration=0.1)\n\ndef rightmouse():\n time.sleep(1)\n pyautogui.moveRel(100, 0, duration=0.1)\ndef leftmouse():\n time.sleep(1)\n pyautogui.moveRel(-100,0,duration=0.1)\ndef upmouse():\n time.sleep(1)\n pyautogui.moveRel(0,-100,duration=0.1)\ndef rightclick():\n pyautogui.click(button='right')\ndef leftclick():\n pyautogui.click(button='left')\ndef takeCommand():\n # It takes microphone input from the usr and return string output\n\n r = sr.Recognizer()\n with sr.Microphone() as source2:\n print(\"Listening...\")\n r.adjust_for_ambient_noise(source2, duration=1)\n r.pause_threshold = 2\n\n audio = r.listen(source2)\n print(\"running\")\n\n try:\n print(\"recognising...\")\n querry = r.recognize_google(audio, language='en-in')\n print(f\"User said : {querry}\\n\")\n except Exception as e:\n print(\"Say that again please...\")\n return 'None'\n return querry\nif __name__ == '__main__':\n while True:\n querry1 = list(takeCommand().split())\n # print(querry1)\n flag=False\n for i in querry1:\n # print(i)\n if i==\"left\":\n leftmouse()\n elif i==\"right\":\n rightmouse()\n elif i==\"up\":\n upmouse()\n elif i==\"down\":\n downmouse()\n elif i==\"leftclick\":\n leftclick()\n elif i==\"rightclick\":\n rightclick()\n elif i==(\"stop\" or \"exit\" or \"quit\"):\n flag=True\n break\n if flag==True:\n break\n","sub_path":"mousefunction.py","file_name":"mousefunction.py","file_ext":"py","file_size_in_byte":2014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"619212216","text":"import os\nimport unittest\nimport vtk, qt, ctk, slicer\nimport math\nimport sys\n\n#\n# AstroMomentMapsSelfTest\n#\n\nclass AstroMomentMapsSelfTest:\n def __init__(self, parent):\n parent.title = \"Astro MomentMaps SelfTest\"\n parent.categories = [\"Testing.TestCases\"]\n parent.dependencies = [\"AstroVolume\"]\n parent.contributors = [\"\"\"\n Davide Punzo (Kapteyn Astronomical Institute) and\n Thijs van der Hulst (Kapteyn Astronomical Institute).\"\"\"]\n parent.helpText = \"\"\"\n This module was developed as a self test to perform the operations needed for generating moment maps.\n \"\"\"\n parent.acknowledgementText = \"\"\"\n\"\"\" # replace with organization, grant and thanks.\n self.parent = parent\n\n # Add this test to the SelfTest module's list for discovery when the module\n # is created. Since this module may be discovered before SelfTests itself,\n # create the list if it doesn't already exist.\n try:\n slicer.selfTests\n except AttributeError:\n slicer.selfTests = {}\n slicer.selfTests['Astro MomentMaps SelfTest'] = self.runTest\n\n def runTest(self):\n tester = AstroMomentMapsSelfTestTest()\n tester.runTest()\n\n#\n# qAstroMomentMapsSelfTestWidget\n#\n\nclass AstroMomentMapsSelfTestWidget:\n def __init__(self, parent = None):\n if not parent:\n self.parent = slicer.qMRMLWidget()\n self.parent.setLayout(qt.QVBoxLayout())\n self.parent.setMRMLScene(slicer.mrmlScene)\n else:\n self.parent = parent\n self.layout = self.parent.layout()\n if not parent:\n self.setup()\n self.parent.show()\n\n def setup(self):\n # Instantiate and connect widgets ...\n\n # reload button\n # (use this during development, but remove it when delivering\n # your module to users)\n self.reloadButton = qt.QPushButton(\"Reload\")\n self.reloadButton.toolTip = \"Reload this module.\"\n self.reloadButton.name = \"AstroMomentMapsSelfTest Reload\"\n self.layout.addWidget(self.reloadButton)\n self.reloadButton.connect('clicked()', self.onReload)\n\n # reload and test button\n # (use this during development, but remove it when delivering\n # your module to users)\n self.reloadAndTestButton = qt.QPushButton(\"Reload and Test\")\n self.reloadAndTestButton.toolTip = \"Reload this module and then run the self tests.\"\n self.layout.addWidget(self.reloadAndTestButton)\n self.reloadAndTestButton.connect('clicked()', self.onReloadAndTest)\n\n # Add vertical spacer\n self.layout.addStretch(1)\n\n def cleanup(self):\n pass\n\n def onReload(self,moduleName=\"AstroMomentMapsSelfTest\"):\n \"\"\"Generic reload method for any scripted module.\n ModuleWizard will subsitute correct default moduleName.\n \"\"\"\n globals()[moduleName] = slicer.util.reloadScriptedModule(moduleName)\n\n def onReloadAndTest(self,moduleName=\"AstroMomentMapsSelfTest\"):\n self.onReload()\n evalString = 'globals()[\"%s\"].%sTest()' % (moduleName, moduleName)\n tester = eval(evalString)\n tester.runTest()\n\n#\n# AstroMomentMapsSelfTestLogic\n#\n\nclass AstroMomentMapsSelfTestLogic:\n \"\"\"This class should implement all the actual\n computation done by your module. The interface\n should be such that other python code can import\n this class and make use of the functionality without\n requiring an instance of the Widget\n \"\"\"\n def __init__(self):\n pass\n\n def hasImageData(self,volumeNode):\n \"\"\"This is a dummy logic method that\n returns true if the passed in volume\n node has valid image data\n \"\"\"\n if not volumeNode:\n print('no volume node')\n return False\n if volumeNode.GetImageData() is None:\n print('no image data')\n return False\n return True\n\n\nclass AstroMomentMapsSelfTestTest(unittest.TestCase):\n \"\"\"\n This is the test case for your scripted module.\n \"\"\"\n\n def delayDisplay(self,message,msec=100):\n \"\"\"This utility method displays a small dialog and waits.\n This does two things: 1) it lets the event loop catch up\n to the state of the test so that rendering and widget updates\n have all taken place before the test continues and 2) it\n shows the user/developer/tester the state of the test\n so that we'll know when it breaks.\n \"\"\"\n print(message)\n self.info = qt.QDialog()\n self.infoLayout = qt.QVBoxLayout()\n self.info.setLayout(self.infoLayout)\n self.label = qt.QLabel(message,self.info)\n self.infoLayout.addWidget(self.label)\n qt.QTimer.singleShot(msec, self.info.close)\n self.info.exec_()\n\n def setUp(self):\n slicer.mrmlScene.Clear(0)\n\n def runTest(self):\n self.setUp()\n self.test_AstroMomentMapsSelfTest()\n\n def test_AstroMomentMapsSelfTest(self):\n print(\"Running AstroMomentMapsSelfTest Test case:\")\n\n self.downloadWEIN069()\n astroVolume = slicer.util.getNode(\"WEIN069\")\n rms = astroVolume.GetDisplayThreshold()\n\n mainWindow = slicer.util.mainWindow()\n mainWindow.moduleSelector().selectModule('AstroVolume')\n mainWindow.moduleSelector().selectModule('AstroMomentMaps')\n\n astroMomentMapsModule = module = slicer.modules.astromomentmaps\n astroMomentMapsModuleWidget = astroMomentMapsModule.widgetRepresentation()\n\n AstroMomentMapsParameterNode = slicer.util.getNode(\"AstroMomentMapsParameters\")\n AstroMomentMapsParameterNode.SetIntensityMin(rms * 3)\n\n QPushButtonList = astroMomentMapsModuleWidget.findChildren(qt.QPushButton)\n for QPushButton in (QPushButtonList):\n if QPushButton.name == \"ApplyButton\":\n ApplyPushButton = QPushButton\n\n self.delayDisplay('Calculating moment maps', 700)\n ApplyPushButton.click()\n\n ZeroMomentMapVolume = slicer.mrmlScene.GetNodeByID(AstroMomentMapsParameterNode.GetZeroMomentVolumeNodeID())\n pixelValue0 = ZeroMomentMapVolume.GetImageData().GetScalarComponentAsFloat(56, 68, 0, 0)\n FirstMomentMapVolume = slicer.mrmlScene.GetNodeByID(AstroMomentMapsParameterNode.GetFirstMomentVolumeNodeID())\n pixelValue1 = FirstMomentMapVolume.GetImageData().GetScalarComponentAsFloat(56, 68, 0, 0)\n SecondMomentMapVolume = slicer.mrmlScene.GetNodeByID(AstroMomentMapsParameterNode.GetSecondMomentVolumeNodeID())\n pixelValue2 = SecondMomentMapVolume.GetImageData().GetScalarComponentAsFloat(56, 68, 0, 0)\n\n if (math.fabs(pixelValue0 - 0.511788547039) < 1.e-6 and \\\n math.fabs(pixelValue1 - 5231.70947266) < 1.e-6 and \\\n math.fabs(pixelValue2 - 28.8058509827) < 1.e-6):\n self.delayDisplay('Test passed', 700)\n else:\n self.delayDisplay('Test failed', 700)\n # if run from Slicer interface remove the followinf exit\n sys.exit()\n\n\n def downloadWEIN069(self):\n import AstroSampleData\n astroSampleDataLogic = AstroSampleData.AstroSampleDataLogic()\n self.delayDisplay('Getting WEIN069 Astro Volume')\n WEIN069Volume = astroSampleDataLogic.downloadSample(\"WEIN069\")\n return WEIN069Volume\n\n\n","sub_path":"AstroMomentMaps/Testing/Python/AstroMomentMapsSelfTest.py","file_name":"AstroMomentMapsSelfTest.py","file_ext":"py","file_size_in_byte":6817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"630936741","text":"# -*- coding: utf-8 -*-\n\nfrom django import forms\nfrom django.core.exceptions import ValidationError\n\nfrom pizza_app.models import PizzaOrder, Address\n\n\nclass DeliveryForm(forms.ModelForm):\n class Meta:\n model = Address\n fields = [\n 'full',\n ]\n\n\nclass PizzaOrderForm(forms.ModelForm):\n class Meta:\n model = PizzaOrder\n exclude = [\n 'delivered',\n 'date_created',\n 'date_delivered',\n 'delivery',\n ]\n\n def clean(self):\n data = self.cleaned_data\n excluded = data['exclude']\n\n errors = []\n for item in excluded:\n if item in data['extra']:\n errors.append(str(item))\n\n if errors:\n raise ValidationError(\n 'Ingredients [{}] are in extras and excludes!'.format(\n ', '.join(errors)\n )\n )\n return data\n\n def save(self, commit=True, delivery=None):\n if delivery is None:\n raise ValueError('Delivery was not set')\n\n inst = super().save(commit=False)\n inst.delivery = delivery\n\n if commit:\n inst.save()\n\n return inst\n","sub_path":"PYTHON/course22/pizza_app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"321652922","text":"from copy import deepcopy\nimport logging\nimport time\nimport random\n\n\nclass Node(object):\n def __init__(self, state, moveMade, isMax, parent, roundNum, me, depth):\n self.state = state\n self.moveMade = moveMade\n self.isMax = isMax\n self.parent = parent\n self.roundNum = roundNum\n self.me = me\n self.depth = depth\n\n self.bestVal = None # assign to value of the state\n self.bestMove = [None, None]\n # logging.basicConfig(filename='player' + str(self.me) + '_' + str(time.time()) + '.log', level=logging.DEBUG)\n\n def setParentBest(self, value):\n if self.bestVal is None:\n self.bestVal = value\n if self.parent is not None and self.parent.bestVal is None:\n self.parent.setParentBest(value)\n\n def calc_best_move(self):\n \"\"\"\n returns a tuple (k, (x, y)) representing the value k after taking the best move (x,y)\n available from the set of valid moves\n :return:\n \"\"\"\n # if self.isMax:\n # logging.info('MAX WITH THIS STARTING STATE at depth ' + str(self.depth))\n # else:\n # logging.info('MIN WITH THIS STARTING STATE at depth ' + str(self.depth))\n\n if self.me == 1:\n my_id = 0\n their_id = 1\n else:\n my_id = 1\n their_id = 0\n\n # get possible moves\n if self.isMax:\n validMoves = self.getValidMoves(self.roundNum, my_id + 1)\n else:\n validMoves = self.getValidMoves(self.roundNum, their_id + 1)\n\n if self.roundNum < 4:\n return 0, random.choice(validMoves)\n\n # if this node is a designated leaf node\n if self.depth == 0 or len(validMoves) == 0:\n # return the value of this state\n self.bestVal = self.state_value()\n if self.parent is not None and self.parent.bestVal is None:\n self.parent.setParentBest(self.state_value())\n\n # for row in reversed(self.state):\n # row_str = []\n # for el in row:\n # if el == 0:\n # row_str.append(u'\\u2B1A')\n # elif el == 1:\n # row_str.append(u'\\u2B1C')\n # else:\n # row_str.append(u'\\u2B1B')\n # row_str.append(' ')\n # logging.info(''.join(row_str))\n # logging.info(\"Current best value: \" + str(self.bestVal))\n # if self.parent is not None:\n # logging.info(\"Parent best value: \" + str(self.parent.bestVal))\n # logging.info(' ')\n\n if self.depth == 0 or len(validMoves) == 0:\n # return the value of this state\n # logging.info(\"Leaf node returning \" + str(self.state_value()))\n return self.state_value(), None\n\n # for each possible move[row][col]\n for move in validMoves:\n # at least once - create a node and calculate it's value\n temp_state = deepcopy(self.state)\n\n # make the move onto the given state\n # change the captured stones\n if self.isMax:\n temp_state[move[0]][move[1]] = my_id\n temp_state = self.make_move(temp_state, move, my_id)\n else:\n temp_state[move[0]][move[1]] = their_id\n temp_state = self.make_move(temp_state, move, their_id)\n\n next_isMax = not self.isMax\n next_roundNum = self.roundNum + 1\n\n move_node = Node(temp_state, move, next_isMax, self, next_roundNum, self.me, self.depth - 1)\n move_res = move_node.calc_best_move()\n\n if self.parent is None:\n # Handle the decision making of the root node\n if move_res[0] >= self.bestVal:\n self.bestVal = move_res[0]\n self.bestMove = move\n else:\n # Minimize or Maximize based on parent's best value\n if self.isMax:\n # I am a max node and my parent is a min node\n if move_res[0] > self.parent.bestVal:\n # if my move is greater than my parents, no matter how high I get they will never choose me\n # I will now return the best choice out of all my turns\n # logging.error('pruning a MAX node at depth ' + str(self.depth) + \" because best value is \" + str(move_res[0]))\n return self.bestVal, None\n elif move_res[0] > self.bestVal:\n # update personal best\n # logging.info(\"Max updating best value at depth \" + str(self.depth) + \" from \" + str(self.bestVal) + \" to \" + str(move_res[0]))\n self.bestVal = move_res[0]\n else:\n # I am a min node and my parent is a max node\n if move_res[0] < self.parent.bestVal:\n # if my move is less than my parents, no matter how low I get it, they will never choose me\n # I will now return the best choice out of all my turns\n # logging.error('pruning a MIN node at depth ' + str(self.depth) + \" because best value is \" + str(move_res[0]))\n return self.bestVal, None\n elif move_res[0] < self.bestVal:\n # update personal best\n # logging.info(\"Min updating best value at depth \" + str(self.depth) + \" from \" + str(self.bestVal) + \" to \" + str(move_res[0]))\n self.bestVal = move_res[0]\n\n # if self.parent is None:\n # logging.info(\"BEST SOLUTIONS HAS BEEN FOUND\")\n # logging.info('returning move: ' + str(self.bestMove) + '+++++++++++++++++++++++++++++++++++')\n return self.bestVal, self.bestMove\n\n def make_move(self, pre_state, move, player_id):\n \"\"\"\n takes in a state and a move, returns the state after the move is made\n :return:\n \"\"\"\n pre_state[move[0]][move[1]] = player_id + 1\n return self.changeColors(pre_state, move[0], move[1], player_id)\n\n def state_value(self):\n \"\"\"\n calculates the difference between the white and black pieces and returns the \"value\" of the board\n for the player requesting the state value\n :return:\n \"\"\"\n one_count = 0\n two_count = 0\n cornerVal = 10\n sideVal = 5\n basicVal = 1\n\n # top left corner\n if self.state[0][0] == 1:\n one_count += cornerVal\n if self.state[0][0] == 2:\n two_count += cornerVal\n\n # bottom left corner\n if self.state[0][7] == 1:\n one_count += cornerVal\n if self.state[0][7] == 2:\n two_count += cornerVal\n\n # top left corner\n if self.state[7][0] == 1:\n one_count += cornerVal\n if self.state[7][0] == 2:\n two_count += cornerVal\n\n # top right corner\n if self.state[7][7] == 1:\n one_count += cornerVal\n if self.state[7][7] == 2:\n two_count += cornerVal\n\n # top row\n for cell in range(1, 6):\n if self.state[cell][0] == 1:\n one_count += sideVal\n if self.state[cell][0] == 2:\n two_count += sideVal\n\n # bottom row\n for cell in range(1, 6):\n if self.state[cell][7] == 1:\n one_count += sideVal\n if self.state[cell][7] == 2:\n two_count += sideVal\n\n # left column\n for cell in range(1, 6):\n if self.state[0][cell] == 1:\n one_count += sideVal\n if self.state[0][cell] == 2:\n two_count += sideVal\n\n # right column\n for cell in range(1, 6):\n if self.state[7][cell] == 1:\n one_count += sideVal\n if self.state[7][cell] == 2:\n two_count += sideVal\n\n for y in range(1, 6):\n for x in range(1, 6):\n if self.state[x][y] == 1:\n one_count += basicVal\n if self.state[x][y] == 2:\n two_count += basicVal\n\n if self.me == 1:\n return one_count - two_count\n elif self.me == 2:\n return two_count - one_count\n else:\n return 0\n\n \"\"\"\n OTHER\n \"\"\"\n\n def changeColors(self, start_state, row, col, turn):\n for incx in range(-1, 2):\n for incy in range(-1, 2):\n if incx == 0 and incy == 0:\n continue\n start_state = self.check_direction(start_state, row, col, incx, incy, turn)\n # for el in start_state:\n # logging.debug(str(el))\n # logging.debug(' ')\n # logging.debug(' ')\n return start_state\n\n def check_direction(self, start_state, row, col, incx, incy, turn):\n sequence = []\n end_state = deepcopy(start_state)\n for i in range(1, 8):\n r = row + incy * i\n c = col + incx * i\n\n if (r < 0) or (r > 7) or (c < 0) or (c > 7):\n break\n\n sequence.append(self.state[r][c])\n\n count = 0\n for i in range(len(sequence)):\n if turn == 0:\n if sequence[i] == 2:\n count += 1\n else:\n if sequence[i] == 1 and count > 0:\n count = 20\n break\n else:\n if sequence[i] == 1:\n count += 1\n else:\n if sequence[i] == 2 and count > 0:\n count = 20\n break\n\n if count > 10:\n if turn == 0:\n i = 1\n r = row + incy * i\n c = col + incx * i\n while (end_state[r][c] == 2):\n end_state[r][c] = 1\n i += 1\n r = row + incy * i\n c = col + incx * i\n else:\n i = 1\n r = row + incy * i\n c = col + incx * i\n while (end_state[r][c] == 1):\n end_state[r][c] = 2\n i += 1\n r = row + incy * i\n c = col + incx * i\n\n return end_state\n\n \"\"\"\n FROM AIguy\n \"\"\"\n\n def checkDirection(self, row, col, incx, incy, me):\n sequence = []\n for i in range(1, 8):\n r = row + incy * i\n c = col + incx * i\n\n if (r < 0) or (r > 7) or (c < 0) or (c > 7):\n break\n\n sequence.append(self.state[r][c])\n\n count = 0\n for i in range(len(sequence)):\n if me == 1:\n if sequence[i] == 2:\n count += 1\n else:\n if (sequence[i] == 1) and (count > 0):\n return True\n break\n else:\n if sequence[i] == 1:\n count += 1\n else:\n if (sequence[i] == 2) and (count > 0):\n return True\n break\n return False\n\n def couldBe(self, row, col, me):\n for incx in range(-1, 2):\n for incy in range(-1, 2):\n if (incx == 0) and (incy == 0):\n continue\n\n if self.checkDirection(row, col, incx, incy, me):\n return True\n\n return False\n\n # generates the set of valid moves for the player; returns a list of valid moves (validMoves)\n def getValidMoves(self, roundNum, me):\n validMoves = []\n # print \"Round: \" + str(roundNum)\n\n # for i in range(8):\n # print self.state[i]\n\n if roundNum < 4:\n if self.state[3][3] == 0:\n validMoves.append((3, 3))\n if self.state[3][4] == 0:\n validMoves.append((3, 4))\n if self.state[4][3] == 0:\n validMoves.append((4, 3))\n if self.state[4][4] == 0:\n validMoves.append((4, 4))\n else:\n for i in range(8):\n for j in range(8):\n if self.state[i][j] == 0:\n if self.couldBe(i, j, me):\n validMoves.append((i, j))\n\n return validMoves\n","sub_path":"ReversiAI_Python_v2/Node.py","file_name":"Node.py","file_ext":"py","file_size_in_byte":12483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"312043576","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport subprocess\nimport multiprocessing\nimport signal\nimport sys\nimport time\nimport paramiko\nimport traceback\nimport shutil\nimport tarfile\nimport hashlib\nfrom multiprocessing.managers import SyncManager\nfrom colors import colored\n\n\nclass deploy_environments:\n def __init__(self, logger, args, credentials, execution_name=None, ENV_NAME=None, ENV_DATA=None, uuid=None):\n self._logger = logger\n self._args = args\n self._credentials = credentials\n self._SCRIPT_PATH = os.path.dirname(os.path.realpath(__file__))\n self._EXECUTION_NAME = execution_name\n self._ENV_NAME = ENV_NAME\n self._ENV_DATA = ENV_DATA\n self._UUID = uuid\n self._COMPRESSED_FILE_NAME = 'METEOR.tar.gz'\n self._servers = '' if self._args.servers is None else '--servers \"{}\"'.format(self._args.servers)\n\n # Setup the Execution Logs Path\n self._LOGS_PATH = self._args.logs_path if self._args.logs_path.endswith('/') else self._args.logs_path + '/'\n self._LOCAL_EXECUTION_LOGS_PATH = \"{}{}/execution\".format(self._LOGS_PATH, self._EXECUTION_NAME)\n\n # Environment Variables\n if self._ENV_DATA is not None:\n # Deploy Path\n if self._ENV_DATA['ssh']['enabled'] == 'True':\n self._DEPLOY_PATH = self._ENV_DATA['ssh']['deploy_path'] + 'meteor/'\n else:\n self._DEPLOY_PATH = self._SCRIPT_PATH + '/'\n\n # Remote Execution Logs Path\n self._REMOTE_EXECUTION_LOGS_PATH = \"{}logs/{}/execution\".format(self._DEPLOY_PATH, execution_name)\n\n def validate(self, output=True, shared_array=None):\n try:\n if output:\n environment_type = '[LOCAL]' if self._ENV_DATA['ssh']['enabled'] == 'False' else '[SSH]'\n self._logger.info(colored('{} Region: {}'.format(environment_type, self._ENV_DATA['region']), attrs=['bold']))\n else:\n environment_type = '[LOCAL]' if self._ENV_DATA['ssh']['enabled'] == 'False' else '[SSH] '\n self._logger.info(colored('--> {} Region \\'{}\\' Started...'.format(environment_type, self._ENV_DATA['region']), 'yellow'))\n\n if self._ENV_DATA['ssh']['enabled'] == \"True\":\n same_version = self.check_version(output)\n if same_version:\n if output:\n self._logger.info(colored('- Region Updated.', 'green'))\n else:\n if output:\n self._logger.info(colored('- Region Outdated. Starting uploading the Meteor Engine...', 'red'))\n # Install Meteor in all SSH Regions\n self.prepare(output)\n # Setup User Execution Environment ('credentials.json', 'query_execution.py')\n self.setup(output)\n\n # Check SQL Connection of the Environment [True: All SQL Connections Succeeded | False: Some SQL Connections Failed]\n status = self.__check_sql_connection(output)\n\n if status is True:\n response = '{} Region \\'{}\\' Finished.'.format(environment_type, self._ENV_DATA['region'])\n self._logger.info(colored('--> ' + response, 'green'))\n if shared_array is not None:\n shared_array.append({\"region\": self._ENV_DATA['region'], \"success\": True, \"response\": response})\n else:\n # Handle SQL Error\n response = '{} Region \\'{}\\' Failed.'.format(environment_type, self._ENV_DATA['region'])\n self._logger.info(colored('--> ' + response, 'red'))\n if shared_array is not None:\n shared_array.append({\"region\": self._ENV_DATA['region'], \"success\": False, \"response\": response})\n\n return status\n\n except Exception as e:\n if self._ENV_DATA['ssh']['enabled'] == \"True\":\n # Handle SSH Error\n if self._credentials['execution_mode']['parallel'] == 'True':\n self._logger.info(colored(\" [{}/SSH] {} \".format(self._ENV_DATA['region'], self._ENV_DATA['ssh']['hostname']), attrs=['bold']) + str(e))\n else:\n self._logger.info(colored(\"✘\", 'red') + colored(\" [{}] \".format(self._ENV_DATA['ssh']['hostname']), attrs=['bold']) + str(e))\n response = '{} Region \\'{}\\' Failed.'.format(environment_type, self._ENV_DATA['region'])\n self._logger.info(colored('--> ' + response, 'red'))\n if shared_array is not None:\n shared_array.append({\"region\": self._ENV_DATA['region'], \"ssh\": self._ENV_DATA['ssh']['hostname'], \"success\": False, \"response\": response, \"error\": str(e)})\n raise Exception()\n except KeyboardInterrupt:\n if self._credentials['execution_mode']['parallel'] != 'True':\n raise\n\n def check_version(self, output=True): \n # Get SSH Version\n ssh_version = self.__ssh(\"cat {}version.txt\".format(self._DEPLOY_PATH))['stdout']\n if len(ssh_version) == 0:\n return False\n else:\n ssh_version = ssh_version[0].replace('\\n', '')\n\n # Get Local Version\n with open(self._SCRIPT_PATH + '/version.txt') as file_content:\n local_version = file_content.read().replace('\\n', '')\n\n # Compare Local & SSH Version\n if local_version != ssh_version:\n return False\n\n return True\n\n def generate_app_version(self):\n version = ''\n files = os.listdir(self._SCRIPT_PATH)\n for f in files:\n if not os.path.isdir(self._SCRIPT_PATH + '/' + f) and not f.endswith('.pyc') and not f.startswith('.') and not f.endswith('.gz') and f not in ['version.txt', 'query_execution.py', 'credentials.json']:\n with open(\"{0}/{1}\".format(self._SCRIPT_PATH, f), 'rb') as file_content:\n file_hash = hashlib.sha512(file_content.read()).hexdigest()\n version += file_hash\n return version\n\n def prepare(self, output=True):\n if output:\n self._logger.info('- Preparing Deploy...')\n self.__ssh(\"mkdir -p {0} && chmod 700 {0} && rm -rf {0}*\".format(self._DEPLOY_PATH))\n\n if output:\n self._logger.info('- Creating Deploy...')\n self.__local('rm -rf \"{0}\" && tar -czvf \"{0}\" . --exclude \"logs\" --exclude \"*.git*\" --exclude \"*.pyc\" --exclude \"web\" --exclude \"credentials.json\" --exclude \"query_execution.py\"'.format(self._COMPRESSED_FILE_NAME), show_output=False)\n\n if output:\n self._logger.info('- Uploading Deploy...')\n self.__put(self._SCRIPT_PATH + '/' + self._COMPRESSED_FILE_NAME, self._DEPLOY_PATH + self._COMPRESSED_FILE_NAME)\n\n if output:\n self._logger.info(\"- Uncompressing Deploy...\")\n self.__ssh(\"tar -xvzf {0}{1} -C {0} && rm -rf {0}{1}\".format(self._DEPLOY_PATH, self._COMPRESSED_FILE_NAME))\n\n if output:\n self._logger.info(\"- Installing Requirements...\")\n self.__ssh('pip install -r {}meteor/requirements.txt --user'.format(self._ENV_DATA['ssh']['deploy_path']))\n\n def setup(self, output=True):\n if output:\n self._logger.info(\"- Setting Up New Execution...\")\n\n logs_path = \"{}logs/{}/\".format(self._DEPLOY_PATH, self._EXECUTION_NAME)\n self.__ssh('mkdir -p {}'.format(logs_path))\n self.__put(self._SCRIPT_PATH + '/credentials.json', logs_path + 'credentials.json')\n self.__put(self._SCRIPT_PATH + '/query_execution.py', logs_path + 'query_execution.py')\n\n def start(self, shared_array=None, progress_array=None):\n try:\n # Parallel Execution\n if self._credentials['execution_mode']['parallel'] == \"True\":\n # SSH Execution\n if self._ENV_DATA['ssh']['enabled'] == 'True':\n # Start the Execution\n if self._args.env_start_deploy:\n deploy = self.__ssh('cd \"{0}\" && python -u meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --env_start_deploy --execution_name \"{4}\" --uuid \"{5}\"'.format(self._DEPLOY_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME, self._UUID), show_output=True, progress_array=progress_array)\n else:\n deploy = self.__ssh('cd \"{0}\" && python -u meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --execution_name \"{4}\" --uuid \"{5}\"'.format(self._DEPLOY_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME, self._UUID), show_output=True, progress_array=progress_array)\n # Local Execution\n else:\n if self._args.env_start_deploy:\n deploy = self.__local('python -u {0}/meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --env_start_deploy --execution_name \"{4}\" --logs_path \"{5}\" --uuid \"{6}\"'.format(self._SCRIPT_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME, self._args.logs_path, self._UUID), show_output=True, progress_array=progress_array)\n else:\n deploy = self.__local('python -u {0}/meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --execution_name \"{4}\" --logs_path \"{5}\" --uuid \"{6}\"'.format(self._SCRIPT_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME, self._args.logs_path, self._UUID), show_output=True, progress_array=progress_array)\n\n # Check for Execution Error\n if len(deploy['stderr']) > 0:\n # Parse stderr\n stderr_parsed = str(deploy['stderr']).split(\"Traceback (most recent call last):\\n\")\n if len(stderr_parsed) > 1:\n stderr_parsed = \"Traceback (most recent call last):\\n\" + stderr_parsed[1]\n if stderr_parsed.splitlines()[-1].startswith('Process Process-'):\n stderr_parsed = stderr_parsed.rsplit(\"\\n\",2)[0]\n else:\n stderr_parsed = deploy['stderr']\n\n shared_array.append({ \"region\": self._ENV_DATA['region'], \"success\": False, \"error\": stderr_parsed })\n else:\n shared_array.append({ \"region\": self._ENV_DATA['region'], \"success\": True })\n\n # Sequential Execution\n else:\n # SSH Execution\n if self._ENV_DATA['ssh']['enabled'] == 'True':\n # Start the Execution\n if self._args.env_start_deploy:\n stderr = self.__ssh('cd \"{0}\" && python meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --env_start_deploy --execution_name \"{4}\"'.format(self._DEPLOY_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME), show_output=True)['stderr']\n else:\n stderr = self.__ssh('cd \"{0}\" && python meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --execution_name \"{4}\"'.format(self._DEPLOY_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME), show_output=True)['stderr']\n # Local Execution\n else:\n if self._args.env_start_deploy:\n stderr = self.__local('python {0}/meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --env_start_deploy --execution_name \"{4}\" --logs_path \"{5}\"'.format(self._SCRIPT_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME, self._args.logs_path), show_output=True)['stderr']\n else:\n stderr = self.__local('python {0}/meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --execution_name \"{4}\" --logs_path \"{5}\"'.format(self._SCRIPT_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME, self._args.logs_path), show_output=True)['stderr']\n\n # Check for Execution Error\n if len(stderr) > 0:\n # Remove last '\\n' character\n raise Exception(stderr[:-1])\n\n except KeyboardInterrupt:\n if self._credentials['execution_mode']['parallel'] != 'True':\n raise\n\n def compress_logs(self, shared_array=None):\n try:\n output = self.__ssh('cd \"{0}\" && python meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --env_compress --execution_name \"{4}\"'.format(self._DEPLOY_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], self._EXECUTION_NAME))\n\n if len(output['stderr']) > 0:\n shared_array.append(output['stderr'])\n\n except (KeyboardInterrupt):\n if self._credentials['execution_mode']['parallel'] != 'True':\n raise\n\n def get_logs(self, shared_array=None):\n try:\n if self._ENV_DATA['ssh']['enabled'] == 'True':\n remote_path = \"{0}/{1}.tar.gz\".format(self._REMOTE_EXECUTION_LOGS_PATH, self._ENV_DATA['region'])\n local_path = \"{0}/{1}\".format(self._LOCAL_EXECUTION_LOGS_PATH, self._ENV_DATA['region'])\n\n # 1. Download Compressed Logs\n status = self.__get(remote_path, local_path + '.tar.gz')\n\n if status:\n # 2. Uncompress Downloaded Logs\n with tarfile.open(local_path + '.tar.gz') as tar:\n tar.extractall(path=self._LOCAL_EXECUTION_LOGS_PATH)\n\n # 3. Delete Downloaded Compressed Logs\n os.remove(local_path + '.tar.gz')\n\n except Exception:\n if self._credentials['execution_mode']['parallel'] != 'True':\n self._logger.error(colored(\"--> Error Downloading Logs:\\n{}\".format(traceback.format_exc()), 'red'))\n raise\n else:\n shared_array.append(traceback.format_exc())\n \n except KeyboardInterrupt:\n if self._credentials['execution_mode']['parallel'] != 'True':\n raise\n\n def clean_remote(self, shared_array=None):\n environment_logs = \"{}logs/{}/\".format(self._DEPLOY_PATH, self._EXECUTION_NAME)\n output = self.__ssh('rm -rf {0}'.format(environment_logs))\n\n if len(output['stderr']) > 0:\n shared_array.append(output['stderr'])\n\n def clean_local(self):\n # Delete Uncompressed Deployment Folder\n if os.path.exists(self._LOGS_PATH + self._EXECUTION_NAME):\n if os.path.isdir(self._LOGS_PATH + self._EXECUTION_NAME):\n shutil.rmtree(self._LOGS_PATH + self._EXECUTION_NAME)\n\n # Delete 'METEOR.tar.gz'\n self.__local('rm -rf {0}'.format(self._COMPRESSED_FILE_NAME), show_output=False)\n\n # Handle SIGINT from SyncManager object\n def mgr_sig_handler(self, signal, frame):\n pass\n\n # Initilizer for SyncManager\n def __mgr_init(self):\n signal.signal(signal.SIGINT, self.mgr_sig_handler)\n\n def __check_sql_connection(self, output):\n connection_succeeded = True\n\n if output:\n self._logger.info(\"- Checking SQL Connections...\")\n\n if self._credentials['execution_mode']['parallel'] == \"True\":\n # Init SyncManager\n manager = SyncManager()\n manager.start(self.__mgr_init)\n shared_array = manager.list()\n processes = []\n\n try:\n for sql in self._ENV_DATA['sql']:\n p = multiprocessing.Process(target=self.__check_sql_connection_logic, args=(sql, output, shared_array))\n p.start()\n processes.append(p)\n\n for process in processes:\n process.join()\n\n for data in shared_array:\n connection_succeeded &= data['success']\n if data['success'] is False:\n self._logger.info(colored(\" [{}/SQL] {} \".format(self._ENV_DATA['region'], data['sql']), attrs=['bold']) + data['error'])\n\n except KeyboardInterrupt:\n for process in processes:\n process.join()\n raise\n else:\n for sql in self._ENV_DATA['sql']:\n connection_succeeded &= self.__check_sql_connection_logic(sql, output)\n\n return connection_succeeded\n\n def __check_sql_connection_logic(self, sql, output, shared_array=None):\n try:\n if self._ENV_DATA['ssh']['enabled'] == 'True':\n command = 'cd \"{0}\" && python meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --env_check_sql \"{4}\" --execution_name \"{5}\"'.format(self._DEPLOY_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], sql['name'], self._EXECUTION_NAME)\n result = self.__ssh(command)['stdout']\n else:\n result = self.__local('cd \"{0}\" && python meteor.py --environment \"{1}\" {2} --env_id \"{3}\" --env_check_sql \"{4}\" --execution_name \"{5}\" --logs_path \"{6}\"'.format(self._SCRIPT_PATH, self._ENV_NAME, self._servers, self._ENV_DATA['region'], sql['name'], self._EXECUTION_NAME, self._args.logs_path), show_output=False)['stdout']\n \n if len(result) == 0:\n if output:\n self._logger.info(colored(\"✔\", 'green') + colored(\" [{}]\".format(sql['name']), attrs=['bold']) + \" Connection Succeeded\")\n if shared_array is not None:\n shared_array.append({\"region\": self._ENV_DATA['region'], \"success\": True, \"sql\": sql['name']})\n return True\n else:\n result = result[0] if type(result) is list else result\n if output:\n self._logger.error(colored(\"✘\", 'red') + colored(\" [{}] \".format(sql['name']), attrs=['bold']) + str(result.replace('\\n','')))\n if shared_array is not None:\n shared_array.append({\"region\": self._ENV_DATA['region'], \"success\": False, \"sql\": sql['name'], \"error\": result.replace('\\n','')})\n return False\n\n except KeyboardInterrupt:\n if self._credentials['execution_mode']['parallel'] != 'True':\n raise\n\n except Exception: \n if self._credentials['execution_mode']['parallel'] != 'True':\n raise\n\n def sigint(self):\n command = \"ps -U $USER -u $USER u | grep \\\"\" + str(self._UUID) + \"\\\" | grep -v grep | awk '{print $2}' | xargs kill -2\"\n\n if self._ENV_DATA['ssh']['enabled'] == 'False':\n self.__local(command)\n else:\n self.__ssh(command)\n \n def check_processes(self):\n # Check Processes Currently Executing\n attempts = 99\n\n for i in range(attempts+1):\n command = \"ps -U $USER -u $USER u | grep \\\"\" + str(self._UUID) + \"\\\" | grep -v grep | awk '{print $2}' | wc -l\"\n \n if self._ENV_DATA['ssh']['enabled'] == 'False':\n count = int(self.__local(command)['stdout'])\n print(\"-- [Attempt {}/{}] Remaining Processes: {}\".format(i+1, attempts, int(count)))\n else:\n count = int(self.__ssh(command)['stdout'][0])\n print(\"-- [Attempt {}/{}] Remaining Processes: {}\".format(i+1, attempts, int(count)))\n\n if int(count) == 0:\n break\n time.sleep(10)\n\n\n def sigkill(self):\n command = \"ps -U $USER -u $USER u | grep \\\"\" + str(self._UUID) + \"\\\" | grep -v grep | awk '{print $2}' | xargs kill -9 2> /dev/null\"\n if self._ENV_DATA['ssh']['enabled'] == 'False':\n self.__local(command)\n else:\n self.__ssh(command)\n\n ################\n # Core Methods #\n ################\n def __local(self, command, show_output=False, progress_array=None):\n # Paramiko Execute Local Command\n client = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n # Show Output \n if show_output:\n for line in client.stdout:\n if progress_array is None:\n print(line.rstrip())\n else:\n progress_array.append(line)\n\n # Return Execution Output\n return { \"stdout\": client.stdout.readlines(), \"stderr\": ''.join(client.stderr.readlines()) }\n\n def __ssh(self, command, show_output=False, progress_array=None):\n try:\n # Supress Errors Output\n sys_stderr = sys.stderr\n sys.stderr = open('/dev/null', 'w')\n\n # Init Paramiko SSH Connection\n client = paramiko.SSHClient()\n client.load_system_host_keys()\n client.set_missing_host_key_policy(paramiko.WarningPolicy())\n client.connect(self._ENV_DATA['ssh']['hostname'], port=22, username=self._ENV_DATA['ssh']['username'], password=self._ENV_DATA['ssh']['password'], key_filename=self._ENV_DATA['ssh']['key'])\n\n # Show Errors Output Again\n sys.stderr = sys_stderr\n\n # Paramiko Execute Command\n stdin, stdout, stderr = client.exec_command(command, get_pty=False)\n stdin.close()\n\n if show_output:\n for line in stdout:\n if progress_array is None:\n print(line.rstrip())\n else:\n progress_array.append(line)\n\n # Return Execution Output\n return { \"stdout\": stdout.readlines(), \"stderr\": ''.join(stderr.readlines()) }\n\n finally:\n # Paramiko Close Connection\n client.close()\n\n def __get(self, remote_path, local_path):\n try:\n # Supress Errors Output\n sys_stderr = sys.stderr\n sys.stderr = open('/dev/null', 'w')\n\n # Init Paramiko Connection\n client = paramiko.SSHClient()\n client.load_system_host_keys()\n client.set_missing_host_key_policy(paramiko.WarningPolicy())\n client.connect(self._ENV_DATA['ssh']['hostname'], port=22, username=self._ENV_DATA['ssh']['username'], password=self._ENV_DATA['ssh']['password'], key_filename=self._ENV_DATA['ssh']['key'])\n \n # Show Errors Output Again\n sys.stderr = sys_stderr\n\n # Open sftp connection\n sftp = client.open_sftp()\n\n # Check if file exists (ls)\n sftp.stat(remote_path)\n \n # Download File\n sftp.get(remote_path, local_path)\n return True\n\n except IOError:\n return False\n finally:\n sftp.close()\n\n def __put(self, local_path, remote_path):\n try:\n # Init Paramiko Connection\n client = paramiko.SSHClient()\n client.load_system_host_keys()\n client.set_missing_host_key_policy(paramiko.WarningPolicy())\n client.connect(self._ENV_DATA['ssh']['hostname'], port=22, username=self._ENV_DATA['ssh']['username'], password=self._ENV_DATA['ssh']['password'], key_filename=self._ENV_DATA['ssh']['key'])\n\n # Open sftp connection\n sftp = client.open_sftp()\n\n # Upload File\n sftp.put(local_path, remote_path)\n\n finally:\n if 'sftp' in locals():\n sftp.close()\n","sub_path":"app/deploy_environments.py","file_name":"deploy_environments.py","file_ext":"py","file_size_in_byte":23412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"413727657","text":"#!/usr/bin/env python3\n\n\nclass Node:\n def __init__(self, datum=None, next=None):\n self.datum = datum\n self.next = next\n\n\nclass SinglyLinkedList:\n def __init__(self):\n self.head = Node()\n\n def enumerate(self):\n if self.head is None:\n return []\n\n current = self.head\n while current.next is not None:\n yield current.datum\n current = current.next\n\n def add_front(self, datum):\n next = Node(datum, self.head)\n self.head = next\n\n def add_back(self, datum):\n previous = None\n current = self.head\n while current.next is not None:\n previous = current\n current = current.next\n\n added = Node(datum, current)\n if previous is None:\n self.head = added\n else:\n previous.next = added\n\n\ndef test_empty():\n empty_list = SinglyLinkedList()\n assert len(list(empty_list.enumerate())) == 0\n\n\ndef test_single_entry():\n sll = SinglyLinkedList()\n\n sll.add_front(1)\n\n enumerated = list(sll.enumerate())\n assert enumerated == [1]\n\n\ndef test_front_filled():\n sll = SinglyLinkedList()\n\n sll.add_front(1)\n sll.add_front(2)\n sll.add_front(3)\n\n enumerated = list(sll.enumerate())\n assert enumerated == [3, 2, 1]\n\n\ndef test_back_filled():\n sll = SinglyLinkedList()\n\n sll.add_back(1)\n sll.add_back(2)\n sll.add_back(3)\n\n enumerated = list(sll.enumerate())\n assert enumerated == [1, 2, 3]\n","sub_path":"Python/singly_linked_list.py","file_name":"singly_linked_list.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"291925627","text":"import configparser, re, os, gi, traceback\nfrom shutil import copyfile\nfrom pathlib import Path\nfrom functools import reduce\nfrom inspect import signature\nimport regex\nfrom ptxprint.font import TTFont\nfrom ptxprint.runner import checkoutput\nfrom ptxprint import sfm\nfrom ptxprint.sfm import usfm, style, Text\nfrom ptxprint.usfmutils import Usfm, Sheets, isScriptureText, Module\nfrom ptxprint.utils import _, universalopen, localhdrmappings, pluralstr, multstr, coltoonemax, \\\n chaps, books, bookcodes, allbooks, oneChbooks, asfloat, f2s, cachedData, pycodedir\nfrom ptxprint.dimension import Dimension\nimport ptxprint.scriptsnippets as scriptsnippets\nfrom ptxprint.interlinear import Interlinear\nfrom ptxprint.reference import Reference, RefRange, RefList, RefSeparators, AnyBooks\nfrom ptxprint.xrefs import Xrefs\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n# After universalopen to resolve circular import. Kludge\nfrom ptxprint.snippets import FancyIntro, PDFx1aOutput, Diglot, FancyBorders, ThumbTabs, Colophon, Grid\n\ndef loosint(x):\n try:\n return int(x)\n except (ValueError, TypeError):\n return 0\n\nModelMap = {\n \"L_\": (\"c_diglot\", lambda w,v: \"L\" if v else \"\"),\n \"R_\": (\"c_diglot\", lambda w,v: \"R\" if v else \"\"),\n \"date_\": (\"_date\", None),\n \"pdfdate_\": (\"_pdfdate\", None),\n \"xmpdate_\": (\"_xmpdate\", None),\n \"ifusediglotcustomsty_\": (\"_diglotcustomsty\", lambda w,v: \"%\" if not v else \"\"),\n \"ifusediglotmodsty_\": (\"_diglotmodsty\", lambda w,v: \"%\" if not v else \"\"),\n \"ifdiglotincludefootnotes_\":(\"_diglotinclfn\", lambda w,v: \"%\" if not v else \"\"),\n \"ifdiglotincludexrefs_\": (\"_diglotinclxr\", lambda w,v: \"%\" if not v else \"\"),\n \"transparency_\": (\"fcb_outputFormat\", lambda w,v: \"false\" if v in (None, \"None\", \"PDF/X-4\") else \"true\"),\n\n \"config/notes\": (\"t_configNotes\", lambda w,v: v or \"\"),\n \"config/pwd\": (\"t_invisiblePassword\", lambda w,v: v or \"\"),\n \"config/version\": (\"_version\", None),\n\n # \"project/hideadvsettings\": (\"c_showAdvancedOptions\", lambda w,v: not v),\n \"project/bookscope\": (\"r_book\", None),\n \"project/uilevel\": (\"fcb_uiLevel\", None),\n \"project/book\": (\"ecb_book\", None),\n \"project/modulefile\": (\"btn_chooseBibleModule\", lambda w,v: v.replace(\"\\\\\",\"/\") if v is not None else \"\"),\n \"project/booklist\": (\"ecb_booklist\", lambda w,v: v or \"\"),\n \"project/ifinclfrontpdf\": (\"c_inclFrontMatter\", None),\n \"project/frontincludes\": (\"btn_selectFrontPDFs\", lambda w,v: \"\\n\".join('\\\\includepdf{{{}}}'.format(s.as_posix()) \\\n for s in w.FrontPDFs) if (w.get(\"c_inclFrontMatter\") and w.FrontPDFs is not None\n and w.FrontPDFs != 'None') else \"\"),\n \"project/ifinclbackpdf\": (\"c_inclBackMatter\", None),\n \"project/backincludes\": (\"btn_selectBackPDFs\", lambda w,v: \"\\n\".join('\\\\includepdf{{{}}}'.format(s.as_posix()) \\\n for s in w.BackPDFs) if (w.get(\"c_inclBackMatter\") and w.BackPDFs is not None\n and w.BackPDFs != 'None') else \"\"),\n \"project/processscript\": (\"c_processScript\", None),\n \"project/when2processscript\": (\"r_when2processScript\", None),\n \"project/selectscript\": (\"btn_selectScript\", lambda w,v: w.customScript.as_posix() if w.customScript is not None else \"\"),\n \"project/selectxrfile\": (\"btn_selectXrFile\", None),\n \"project/usechangesfile\": (\"c_usePrintDraftChanges\", lambda w,v :\"true\" if v else \"false\"),\n \"project/ifusemodstex\": (\"c_useModsTex\", lambda w,v: \"\" if v else \"%\"),\n \"project/ifusepremodstex\": (\"c_usePreModsTex\", lambda w,v: \"\" if v else \"%\"),\n \"project/ifusecustomsty\": (\"c_useCustomSty\", lambda w,v: \"\" if v else \"%\"),\n \"project/ifusemodssty\": (\"c_useModsSty\", lambda w,v: \"\" if v else \"%\"),\n \"project/ifstarthalfpage\": (\"c_startOnHalfPage\", lambda w,v :\"true\" if v else \"false\"),\n \"project/randompicposn\": (\"c_randomPicPosn\", None),\n \"project/canonicalise\": (\"c_canonicalise\", None),\n \"project/interlinear\": (\"c_interlinear\", lambda w,v: \"\" if v else \"%\"),\n \"project/interlang\": (\"t_interlinearLang\", None),\n \"project/ruby\": (\"c_ruby\", lambda w,v : \"t\" if v else \"b\"),\n \"project/license\": (\"ecb_licenseText\", None),\n \"project/copyright\": (\"t_copyrightStatement\", lambda w,v: re.sub(r\"\\\\u([0-9a-fA-F]{4})\",\n lambda m: chr(int(m.group(1), 16)), v) if v is not None else \"\"),\n \"project/iffrontmatter\": (\"c_frontmatter\", lambda w,v: \"\" if v else \"%\"),\n \"project/periphpagebreak\": (\"c_periphPageBreak\", None),\n \"project/colophontext\": (\"txbf_colophon\", lambda w,v: re.sub(r\"\\\\u([0-9a-fA-F]{4})\",\n lambda m: chr(int(m.group(1), 16)), v) if v is not None else \"\"),\n \"project/ifcolophon\": (\"c_colophon\", lambda w,v: \"\" if v else \"%\"),\n \"project/pgbreakcolophon\": (\"c_standAloneColophon\", lambda w,v: \"\" if v else \"%\"),\n\n \"paper/height\": (\"ecb_pagesize\", lambda w,v: re.sub(r\"^.*?[,xX]\\s*(.+?)\\s*(?:\\(.*|$)\", r\"\\1\", v or \"210mm\")),\n \"paper/width\": (\"ecb_pagesize\", lambda w,v: re.sub(r\"^(.*?)\\s*[,xX].*$\", r\"\\1\", v or \"148mm\")),\n \"paper/pagesize\": (\"ecb_pagesize\", None),\n \"paper/ifwatermark\": (\"c_applyWatermark\", lambda w,v: \"\" if v else \"%\"),\n \"paper/watermarkpdf\": (\"btn_selectWatermarkPDF\", lambda w,v: w.watermarks.as_posix() \\\n if (w.get(\"c_applyWatermark\") and w.watermarks is not None and w.watermarks != 'None') else \"\"),\n \"paper/ifcropmarks\": (\"c_cropmarks\", lambda w,v :\"true\" if v else \"false\"), \n \"paper/ifgrid\": (\"c_grid\", lambda w,v :\"\" if v else \"%\"),\n \"paper/ifverticalrule\": (\"c_verticalrule\", lambda w,v :\"true\" if v else \"false\"),\n \"paper/margins\": (\"s_margins\", lambda w,v: round(float(v)) if v else \"12\"),\n \"paper/topmargin\": (\"s_topmargin\", None),\n \"paper/bottommargin\": (\"s_bottommargin\", None),\n \"paper/headerpos\": (\"s_headerposition\", None),\n \"paper/footerpos\": (\"s_footerposition\", None),\n \"paper/rulegap\": (\"s_rhruleposition\", None),\n\n \"paper/ifaddgutter\": (\"c_pagegutter\", lambda w,v :\"true\" if v else \"false\"),\n \"paper/gutter\": (\"s_pagegutter\", lambda w,v: round(float(v)) if v else \"0\"),\n \"paper/colgutteroffset\": (\"s_colgutteroffset\", lambda w,v: \"{:.1f}\".format(float(v)) if v else \"0.0\"),\n \"paper/columns\": (\"c_doublecolumn\", lambda w,v: \"2\" if v else \"1\"),\n \"paper/bottomrag\": (\"s_bottomRag\", lambda w,v: str(int(v or 0)+0.95)),\n \"paper/fontfactor\": (\"s_fontsize\", lambda w,v: f2s(float(v) / 12, dp=8) if v else \"1.000\"),\n\n \"grid/gridlines\": (\"c_gridLines\", lambda w,v: \"\\doGridLines\" if v else \"\"),\n \"grid/gridgraph\": (\"c_gridGraph\", lambda w,v: \"\\doGraphPaper\" if v else \"\"),\n \"grid/majorcolor\": (\"col_gridMajor\", None),\n \"majorcolor_\": (\"col_gridMajor\", lambda w,v: \"{:.2f} {:.2f} {:.2f}\".format(*coltoonemax(v)) if v else \"0.8 0.8 0.8\"),\n \"grid/minorcolor\": (\"col_gridMinor\", None),\n \"minorcolor_\": (\"col_gridMinor\", lambda w,v: \"{:.2f} {:.2f} {:.2f}\".format(*coltoonemax(v)) if v else \"0.8 1.0 1.0\"),\n \"grid/majorthickness\": (\"s_gridMajorThick\", None),\n \"grid/minorthickness\": (\"s_gridMinorThick\", None),\n \"grid/units\": (\"fcb_gridUnits\", None),\n \"grid/divisions\": (\"s_gridMinorDivisions\", lambda w,v: int(float(v)) if v else \"10\"),\n \"grid/xyadvance\": (\"s_gridMinorDivisions\", lambda w,v: (1 / max(asfloat(v, 4), 1)) if v else \"0.25\"),\n \"grid/xyoffset\": (\"fcb_gridOffset\", None),\n \n \"fancy/enableborders\": (\"c_borders\", lambda w,v: \"\" if v else \"%\"),\n \"fancy/pageborder\": (\"c_inclPageBorder\", lambda w,v: \"\" if v else \"%\"),\n \"fancy/pageborderfullpage\": (\"c_borderPageWide\", lambda w,v: \"\" if v else \"%\"),\n \"fancy/pagebordernfullpage_\": (\"c_borderPageWide\", lambda w,v: \"%\" if v else \"\"),\n \"fancy/pageborderpdf\": (\"btn_selectPageBorderPDF\", lambda w,v: w.pageborder.as_posix() \\\n if (w.pageborder is not None and w.pageborder != 'None') \\\n else get(\"/ptxprintlibpath\")+\"/A5 page border.pdf\"),\n \"fancy/sectionheader\": (\"c_inclSectionHeader\", lambda w,v: \"\" if v else \"%\"),\n \"fancy/sectionheaderpdf\": (\"btn_selectSectionHeaderPDF\", lambda w,v: w.sectionheader.as_posix() \\\n if (w.sectionheader is not None and w.sectionheader != 'None') \\\n else get(\"/ptxprintlibpath\")+\"/A5 section head border.pdf\"),\n \"fancy/sectionheadershift\": (\"s_inclSectionShift\", lambda w,v: float(v or \"0\")),\n \"fancy/sectionheaderscale\": (\"s_inclSectionScale\", lambda w,v: int(float(v or \"1.0\")*1000)),\n \"fancy/endofbook\": (\"c_inclEndOfBook\", lambda w,v: \"\" if v else \"%\"),\n \"fancy/endofbookpdf\": (\"btn_selectEndOfBookPDF\", lambda w,v: w.endofbook.as_posix() \\\n if (w.endofbook is not None and w.endofbook != 'None') \\\n else get(\"/ptxprintlibpath\")+\"/decoration.pdf\"),\n \"fancy/versedecorator\": (\"c_inclVerseDecorator\", lambda w,v: \"\" if v else \"%\"),\n \"fancy/versedecoratortype\": (\"r_decorator\", None),\n \"fancy/versedecoratorpdf\": (\"btn_selectVerseDecorator\", lambda w,v: w.versedecorator.as_posix() \\\n if (w.versedecorator is not None and w.versedecorator != 'None') \\\n else get(\"/ptxprintlibpath\")+\"/Verse number star.pdf\"),\n \"fancy/versedecoratorshift\": (\"s_verseDecoratorShift\", lambda w,v: float(v or \"0\")),\n \"fancy/versedecoratorscale\": (\"s_verseDecoratorScale\", lambda w,v: int(float(v or \"1.0\")*1000)),\n \"fancy/endayah\": (\"c_decorator_endayah\", lambda w,v: \"\" if v else \"%\"), # In the UI this is \"Move Ayah\"\n\n \"paragraph/linespacing\": (\"s_linespacing\", lambda w,v: f2s(float(v), dp=8) if v else \"15\"),\n \"paragraph/linespacebase\": (\"c_AdvCompatLineSpacing\", lambda w,v: 14 if v else 12),\n \"paragraph/useglyphmetrics\": (\"c_AdvCompatGlyphMetrics\", lambda w,v: \"%\" if v else \"\"),\n \"paragraph/ifjustify\": (\"c_justify\", lambda w,v: \"true\" if v else \"false\"),\n \"paragraph/ifhyphenate\": (\"c_hyphenate\", lambda w,v: \"\" if v else \"%\"),\n \"paragraph/ifomithyphen\": (\"c_omitHyphen\", lambda w,v: \"\" if v else \"%\"),\n \"paragraph/ifnothyphenate\": (\"c_hyphenate\", lambda w,v: \"%\" if v else \"\"),\n \"paragraph/ifusefallback\": (\"c_useFallbackFont\", None),\n \"paragraph/missingchars\": (\"t_missingChars\", lambda w,v: v or \"\"),\n\n \"document/sensitive\": (\"c_sensitive\", None),\n \"document/title\": (None, lambda w,v: \"[Unknown]\" if w.get(\"c_sensitive\") else w.ptsettings.get('FullName', \"[Unknown]\")),\n \"document/subject\": (\"ecb_booklist\", lambda w,v: v if w.get(\"r_book\") == \"multiple\" else w.get(\"ecb_book\")),\n \"document/author\": (None, lambda w,v: \"\" if w.get(\"c_sensitive\") else w.ptsettings.get('Copyright', \"\")),\n\n \"document/startpagenum\": (\"s_startPageNum\", lambda w,v: int(float(v)) if v else \"1\"),\n \"document/multibook\": (\"r_book_multiple\", lambda w,v: \"\" if v else \"%\"),\n \"document/toc\": (\"c_autoToC\", lambda w,v: \"\" if v else \"%\"),\n \"document/toctitle\": (\"t_tocTitle\", lambda w,v: v or \"\"),\n \"document/usetoc1\": (\"c_usetoc1\", lambda w,v: \"true\" if v else \"false\"),\n \"document/usetoc2\": (\"c_usetoc2\", lambda w,v: \"true\" if v else \"false\"),\n \"document/usetoc3\": (\"c_usetoc3\", lambda w,v: \"true\" if v else \"false\"),\n \"document/tocleaders\": (\"fcb_leaderStyle\", None),\n \"document/chapfrom\": (\"s_chapfrom\", lambda w,v: int(float(v)) if v else \"1\"),\n \"document/chapto\": (\"s_chapto\", lambda w,v: int(float(v)) if v else \"999\"),\n \"document/colgutterfactor\": (\"s_colgutterfactor\", lambda w,v: round(float(v or 4)*3)), # Hack to be fixed\n \"document/ifrtl\": (\"fcb_textDirection\", lambda w,v:\"true\" if v == \"rtl\" else \"false\"),\n \"document/toptobottom\": (\"fcb_textDirection\", lambda w,v: \"\" if v == \"ttb\" else \"%\"),\n \"document/iflinebreakon\": (\"c_linebreakon\", lambda w,v: \"\" if v else \"%\"),\n \"document/linebreaklocale\": (\"t_linebreaklocale\", lambda w,v: v or \"\"),\n \"document/script\": (\"fcb_script\", lambda w,v: \":script=\"+v.lower() if v and v != \"Zyyy\" else \"\"),\n \"document/ch1pagebreak\": (\"c_ch1pagebreak\", None),\n \"document/marginalverses\": (\"c_marginalverses\", lambda w,v: \"\" if v else \"%\"),\n \"document/columnshift\": (\"s_columnShift\", lambda w,v: v or \"16\"),\n \"document/ifshowchapternums\": (\"c_chapterNumber\", lambda w,v: \"%\" if v else \"\"),\n \"document/showxtrachapnums\": (\"c_showNonScriptureChapters\", None),\n \"document/ifshow1chbooknum\": (\"c_show1chBookNum\", None),\n \"document/ifomitverseone\": (\"c_omitverseone\", lambda w,v: \"true\" if v else \"false\"),\n \"document/ifshowversenums\": (\"c_verseNumbers\", lambda w,v: \"%\" if v else \"\"),\n \"document/ifmainbodytext\": (\"c_mainBodyText\", None),\n \"document/glueredupwords\": (\"c_glueredupwords\", None),\n \"document/ifinclfigs\": (\"c_includeillustrations\", lambda w,v: \"true\" if v else \"false\"),\n \"document/ifusepiclist\": (\"c_includeillustrations\", lambda w,v :\"\" if v else \"%\"),\n \"document/iffigexclwebapp\": (\"c_figexclwebapp\", None),\n \"document/iffigskipmissing\": (\"c_skipmissingimages\", None),\n \"document/iffigcrop\": (\"c_cropborders\", None),\n \"document/iffigplaceholders\": (\"c_figplaceholders\", lambda w,v: \"true\" if v else \"false\"),\n \"document/iffigshowcaptions\": (\"c_fighidecaptions\", lambda w,v: \"false\" if v else \"true\"),\n \"document/iffighiderefs\": (\"c_fighiderefs\", None),\n \"document/picresolution\": (\"r_pictureRes\", None),\n \"document/customfiglocn\": (\"c_useCustomFolder\", lambda w,v :\"\" if v else \"%\"),\n \"document/exclusivefolder\": (\"c_exclusiveFiguresFolder\", None),\n \"document/customfigfolder\": (\"btn_selectFigureFolder\", lambda w,v: w.customFigFolder.as_posix() \\\n if w.customFigFolder is not None else \"\"),\n \"document/imagetypepref\": (\"t_imageTypeOrder\", None),\n \"document/glossarymarkupstyle\": (\"fcb_glossaryMarkupStyle\", None),\n \"document/filterglossary\": (\"c_filterGlossary\", None),\n \"document/hangpoetry\": (\"c_hangpoetry\", lambda w,v: \"\" if v else \"%\"),\n \"document/preventorphans\": (\"c_preventorphans\", None),\n \"document/preventwidows\": (\"c_preventwidows\", None),\n \"document/sectionheads\": (\"c_sectionHeads\", None),\n \"document/parallelrefs\": (\"c_parallelRefs\", None),\n \"document/bookintro\": (\"c_bookIntro\", None),\n \"document/introoutline\": (\"c_introOutline\", None),\n \"document/indentunit\": (\"s_indentUnit\", lambda w,v: round(float(v or \"1.0\"), 1)),\n \"document/firstparaindent\": (\"c_firstParaIndent\", lambda w,v: \"true\" if v else \"false\"),\n \"document/ifhidehboxerrors\": (\"c_showHboxErrorBars\", lambda w,v :\"%\" if v else \"\"),\n \"document/hidemptyverses\": (\"c_hideEmptyVerses\", None),\n \"document/elipsizemptyvs\": (\"c_elipsizeMissingVerses\", None),\n \"document/ifspacing\": (\"c_spacing\", lambda w,v :\"\" if v else \"%\"),\n \"document/spacestretch\": (\"s_maxSpace\", lambda w,v : str((int(float(v or 150)) - 100) / 100.)),\n \"document/spaceshrink\": (\"s_minSpace\", lambda w,v : str((100 - int(float(v or 66))) / 100.)),\n \"document/ifletter\": (\"c_letterSpacing\", lambda w,v: \"\" if v else \"%\"),\n \"document/letterstretch\": (\"s_letterStretch\", lambda w,v: float(v or \"5.0\") / 100.),\n \"document/lettershrink\": (\"s_letterShrink\", lambda w,v: float(v or \"1.0\") / 100.),\n \"document/ifcolorfonts\": (\"c_colorfonts\", lambda w,v: \"%\" if v else \"\"),\n\n \"document/ifchaplabels\": (\"c_useChapterLabel\", lambda w,v: \"%\" if v else \"\"),\n \"document/clabelbooks\": (\"t_clBookList\", lambda w,v: v.upper() if v else \"\"),\n \"document/clabel\": (\"t_clHeading\", None),\n \"document/diffcolayout\": (\"c_differentColLayout\", None),\n \"document/diffcolayoutbooks\": (\"t_differentColBookList\", None),\n \"document/cloptimizepoetry\": (\"c_optimizePoetryLayout\", None),\n\n \"document/ifdiglot\": (\"c_diglot\", lambda w,v : \"\" if v else \"%\"),\n \"document/diglotprifraction\": (\"s_diglotPriFraction\", lambda w,v : round((float(v)/100), 3) if v is not None else \"0.550\"),\n \"document/diglotsecfraction\": (\"s_diglotPriFraction\", lambda w,v : round(1 - (float(v)/100), 3) if v is not None else \"0.450\"),\n \"document/diglotsecprj\": (\"fcb_diglotSecProject\", None),\n \"document/diglotpicsources\": (\"fcb_diglotPicListSources\", None),\n \"document/diglot2captions\": (\"c_diglot2captions\", None),\n \"document/diglotswapside\": (\"c_diglotSwapSide\", lambda w,v: \"true\" if v else \"false\"),\n \"document/diglotsepnotes\": (\"c_diglotSeparateNotes\", lambda w,v: \"true\" if v else \"false\"),\n \"document/diglotsecconfig\": (\"ecb_diglotSecConfig\", None),\n \"document/diglotmergemode\": (\"c_diglotMerge\", lambda w,v: \"simple\" if v else \"doc\"),\n \"document/diglotadjcenter\": (\"c_diglotAdjCenter\", None),\n \"document/diglotnotesrule\": (\"c_diglotNotesRule\", lambda w,v: \"true\" if v else \"false\"),\n \"document/diglotjoinvrule\": (\"c_diglotJoinVrule\", lambda w,v: \"true\" if v else \"false\"),\n\n \"document/hasnofront_\": (\"c_frontmatter\", lambda w,v: \"%\" if v else \"\"),\n \"document/noblankpage\": (\"c_periphSuppressPage\", None),\n\n \"header/ifshowbook\": (\"c_rangeShowBook\", lambda w,v :\"false\" if v else \"true\"),\n \"header/ifshowchapter\": (\"c_rangeShowChapter\", lambda w,v :\"false\" if v else \"true\"),\n \"header/ifshowverse\": (\"c_rangeShowVerse\", lambda w,v :\"true\" if v else \"false\"),\n # \"header/chvseparator\": (\"c_sepColon\", lambda w,v : \":\" if v else \".\"),\n \"header/chvseparator\": (\"r_CVsep\", lambda w,v : \":\" if v == \"colon\" else \".\"),\n \"header/ifrhrule\": (\"c_rhrule\", lambda w,v: \"\" if v else \"%\"),\n \"header/hdrleftside\": (\"r_hdrLeft\", None),\n \"header/hdrleft\": (\"ecb_hdrleft\", lambda w,v: v or \"-empty-\"),\n \"header/hdrcenterside\": (\"r_hdrCenter\", None),\n \"header/hdrcenter\": (\"ecb_hdrcenter\", lambda w,v: v or \"-empty-\"),\n \"header/hdrrightside\": (\"r_hdrRight\", None),\n \"header/hdrright\": (\"ecb_hdrright\", lambda w,v: v or \"-empty-\"),\n \"header/mirrorlayout\": (\"c_mirrorpages\", lambda w,v: \"true\" if v else \"false\"),\n \n \"footer/ftrcenterside\": (\"r_ftrCenter\", None),\n \"footer/ftrcenter\": (\"ecb_ftrcenter\", lambda w,v: v or \"-empty-\"),\n \"footer/ifftrtitlepagenum\": (\"c_pageNumTitlePage\", lambda w,v: \"\" if v else \"%\"),\n \"footer/ifprintconfigname\": (\"c_printConfigName\", lambda w,v: \"\" if v else \"%\"),\n # \"footer/noinkinfooter\": (\"c_noInkFooter\", None),\n\n \"notes/includefootnotes\": (\"c_includeFootnotes\", lambda w,v: \"%\" if v else \"\"),\n \"notes/fneachnewline\": (\"c_fneachnewline\", lambda w,v: \"%\" if v else \"\"),\n \"notes/fnoverride\": (\"c_fnOverride\", None),\n \"notes/iffnautocallers\": (\"c_fnautocallers\", lambda w,v :\"true\" if v else \"false\"),\n \"notes/fncallers\": (\"t_fncallers\", lambda w,v: v if w.get(\"c_fnautocallers\") else \"\"),\n \"notes/fnresetcallers\": (\"c_fnpageresetcallers\", lambda w,v: \"\" if v else \"%\"),\n \"notes/fnomitcaller\": (\"c_fnomitcaller\", lambda w,v: \"%\" if v else \"\"),\n\n \"notes/includexrefs\": (\"c_includeXrefs\", lambda w,v: \"%\" if v else \"\"),\n \"notes/xreachnewline\": (\"c_xreachnewline\", lambda w,v: \"%\" if v else \"\"),\n \"notes/xroverride\": (\"c_xrOverride\", None),\n \"notes/ifxrautocallers\": (\"c_xrautocallers\", lambda w,v :\"true\" if v else \"false\"),\n \"notes/xrcallers\": (\"t_xrcallers\", lambda w,v: v if w.get(\"c_xrautocallers\") else \"\"),\n \"notes/xrresetcallers\": (\"c_xrpageresetcallers\", lambda w,v: \"\" if v else \"%\"),\n \"notes/xromitcaller\": (\"c_xromitcaller\", lambda w,v: \"%\" if v else \"\"),\n\n \"notes/xrlocation\": (\"r_xrpos\", lambda w,v: r\"\" if v == \"centre\" else \"%\"),\n \"notes/xrpos\": (\"r_xrpos\", None),\n \"notes/xrcolside\": (\"fcb_colXRside\", None),\n \"notes/xrcentrecolwidth\": (\"s_centreColWidth\", lambda w,v: int(float(v)) if v else \"60\"),\n \"notes/xrguttermargin\": (\"s_xrGutterWidth\", lambda w,v: \"{:.1f}\".format(float(v)) if v else \"2.0\"),\n \"notes/xrcolrule\": (\"c_xrColumnRule\", lambda w,v: \"true\" if v else \"false\"),\n \"notes/xrcolbottom\": (\"c_xrColumnBottom\", lambda w,v: \"true\" if v else \"false\"),\n \"notes/ifxrexternalist\": (\"c_useXrefList\", lambda w,v: \"%\" if v else \"\"),\n \"notes/xrlistsource\": (\"r_xrSource\", None),\n \"notes/xrlistsize\": (\"s_xrSourceSize\", lambda w,v: int(float(v)) if v else \"3\"),\n \"notes/xrfilterbooks\": (\"fcb_filterXrefs\", None),\n # \"notes/xrlocalstrongs\": (\"c_strongsLocal\", None), # now added to strongsndx section below\n \"notes/addcolon\": (\"c_addColon\", None),\n \"notes/keepbookwithrefs\": (\"c_keepBookWithRefs\", None),\n \"notes/glossaryfootnotes\": (\"c_glossaryFootnotes\", None),\n \"notes/fnpos\": (\"r_fnpos\", None),\n \"notes/columnnotes_\": (\"r_fnpos\", lambda w,v: \"true\" if v == \"column\" else \"false\"),\n \"notes/endnotes_\": (\"r_fnpos\", lambda w,v: \"\" if v == \"endnotes\" else \"%\"),\n\n \"notes/iffootnoterule\": (\"c_footnoterule\", lambda w,v: \"%\" if v else \"\"),\n \"notes/ifxrefrule\": (\"c_xrefrule\", lambda w,v: \"%\" if v else \"\"),\n\n \"notes/abovenotespace\": (\"s_fnAboveSpace\", None),\n \"notes/belownoterulespace\": (\"s_fnBelowSpace\", None),\n \"notes/fnruleposn\": (\"fcb_fnHorizPosn\", None),\n \"notes/fnruleindent\": (\"s_fnIndent\", None),\n \"notes/fnrulelength\": (\"s_fnLength\", None),\n \"notes/fnrulethick\": (\"s_fnThick\", None),\n \n \"notes/abovexrefspace\": (\"s_xrAboveSpace\", None),\n \"notes/belowxrefrulespace\": (\"s_xrBelowSpace\", None),\n \"notes/xrruleposn\": (\"fcb_xrHorizPosn\", None),\n \"notes/xrruleindent\": (\"s_xrIndent\", None),\n \"notes/xrrulelength\": (\"s_xrLength\", None),\n \"notes/xrrulethick\": (\"s_xrThick\", None),\n \n \"notes/internotespace\": (\"s_internote\", lambda w,v: f2s(float(v or 3))),\n\n \"notes/horiznotespacemin\": (\"s_notespacingmin\", lambda w,v: f2s(float(v)) if v is not None else \"7\"),\n \"notes/horiznotespacemax\": (\"s_notespacingmax\", lambda w,v: f2s(float(v)) if v is not None else \"27\"),\n\n \"document/fontregular\": (\"bl_fontR\", lambda w,v,s: v.asTeXFont(s.inArchive) if v else \"\"),\n \"document/fontbold\": (\"bl_fontB\", lambda w,v,s: v.asTeXFont(s.inArchive) if v else \"\"),\n \"document/fontitalic\": (\"bl_fontI\", lambda w,v,s: v.asTeXFont(s.inArchive) if v else \"\"),\n \"document/fontbolditalic\": (\"bl_fontBI\", lambda w,v,s: v.asTeXFont(s.inArchive) if v else \"\"),\n \"document/fontextraregular\":(\"bl_fontExtraR\", lambda w,v,s: v.asTeXFont(s.inArchive) if v else \"\"),\n \"snippets/fancyintro\": (\"c_prettyIntroOutline\", None),\n \"snippets/pdfoutput\": (\"fcb_outputFormat\", None),\n \"snippets/diglot\": (\"c_diglot\", lambda w,v: True if v else False),\n \"snippets/fancyborders\": (\"c_borders\", None),\n \"document/includeimg\": (\"c_includeillustrations\", None),\n \"thumbtabs/ifthumbtabs\": (\"c_thumbtabs\", None),\n \"thumbtabs/numtabs\": (\"s_thumbtabs\", None),\n \"thumbtabs/length\": (\"s_thumblength\", None),\n \"thumbtabs/height\": (\"s_thumbheight\", None),\n \"thumbtabs/background\": (\"col_thumbback\", None),\n \"thumbtabs/rotate\": (\"c_thumbrotate\", None),\n \"thumbtabs/rotatetype\": (\"fcb_rotateTabs\", None),\n \"thumbtabs/thumbiszthumb\": (\"c_thumbIsZthumb\", None),\n \"thumbtabs/restart\": (\"c_thumbrestart\", None),\n \"thumbtabs/groups\": (\"t_thumbgroups\", None),\n\n \"scripts/mymr/syllables\": (\"c_scrmymrSyllable\", None),\n\n \"strongsndx/includenames\": (\"c_strongsInclNames\", None),\n \"strongsndx/localterms\": (\"c_strongsLocal\", None),\n \"strongsndx/showhebrew\": (\"c_strongsHeb\", None),\n \"strongsndx/showgreek\": (\"c_strongsGrk\", None),\n \"strongsndx/showindex\": (\"c_strongsNdx\", None),\n \"strongsndx/sourcelang\": (\"c_strongsSrcLg\", None),\n \"strongsndx/transliterate\": (\"c_strongsTranslit\", None),\n \"strongsndx/renderings\": (\"c_strongsRenderings\", None),\n \"strongsndx/definitions\": (\"c_strongsDefn\", None),\n \"strongsndx/keyvrsrefs\": (\"c_strongsKeyVref\", None),\n \"strongsndx/fallbackprj\": (\"fcb_strongsFallbackProj\", None),\n \"strongsndx/majorlang\": (\"fcb_strongsMajorLg\", None),\n \"strongsndx/nocomments\": (\"c_strongsNoComments\", None),\n \"strongsndx/wildcards\": (\"fcb_strongswildcards\", None),\n \"strongsndx/raglines\": (\"s_strongRag\", None),\n \"strongsndx/ndxbookid\": (\"fcb_strongsNdxBookId\", None),\n \"strongsndx/twocols\": (\"c_strongs2cols\", None),\n \"strongsndx/openineditor\": (\"c_strongsOpenIndex\", None),\n}\n\nBorders = {'c_inclPageBorder': ('pageborder', 'fancy/pageborderpdf', 'A5 page border.pdf'),\n 'c_inclSectionHeader': ('sectionheader', 'fancy/sectionheaderpdf', 'A5 section head border.pdf'),\n 'c_inclEndOfBook': ('endofbook', 'fancy/endofbookpdf', 'decoration.pdf'),\n 'c_inclVerseDecorator': ('versedecorator', 'fancy/versedecoratorpdf', 'Verse number star.pdf'),\n 'c_inclFrontMatter': ('FrontPDFs', 'project/frontincludes', '\\\\includepdf{{{}}}'),\n 'c_inclBackMatter': ('BackPDFs', 'project/backincludes', '\\\\includepdf{{{}}}'),\n 'c_applyWatermark': ('watermarks', 'paper/watermarkpdf', r'\\def\\MergePDF{{\"{}\"}}')\n}\n\n\nclass TexModel:\n _peripheralBooks = [\"FRT\", \"INT\", \"GLO\", \"TDX\", \"NDX\", \"CNC\", \"OTH\", \"BAK\", \"XXA\", \"XXB\", \"XXC\", \"XXD\", \"XXE\", \"XXF\", \"XXG\"]\n _fonts = {\n \"fontregular\": (\"bl_fontR\", None, None, None, None),\n \"fontbold\": (\"bl_fontB\", None, \"c_fakebold\", \"fontbold/embolden\", \"fontbold/slant\"),\n \"fontitalic\": (\"bl_fontI\", None, \"c_fakeitalic\", \"fontitalic/embolden\", \"fontitalic/slant\"),\n \"fontbolditalic\": (\"bl_fontBI\", None, \"c_fakebolditalic\", \"fontbolditalic/embolden\", \"fontbolditalic/slant\"),\n \"fontextraregular\": (\"bl_fontExtraR\", \"c_useFallbackFont\", None, None, None),\n }\n _mirrorRL = {r'\\lastref': r'\\firstref',\n r'\\firstref': r'\\lastref'\n }\n _swapRL = {'left': 'right',\n 'center': 'center',\n 'right': 'left'\n }\n _glossarymarkup = {\n \"no\": r\"\\1\", # \"None\": \n None: r\"\\1\", # None: \n \"bd\": r\"\\\\bd \\1\\\\bd*\", # \"format as bold\": \n \"it\": r\"\\\\it \\1\\\\it*\", # \"format as italics\": \n \"bi\": r\"\\\\bdit \\1\\\\bdit*\", # \"format as bold italics\": \n \"em\": r\"\\\\em \\1\\\\em*\", # \"format with emphasis\": \n \"ww\": r\"\\\\w \\1\\\\w*\", # \"\\w ...\\w* char style\": \n # Note that these glossary markers can be styled with \\zglm \n # But this doesn't work if fallback font is turned on for these chars\n \"fb\": r\"\\\\zglm \\u2E24\\\\zglm*\\1\\\\zglm \\u2E25\\\\zglm*\", # \"with ⸤floor⸥ brackets\": \n \"fc\": r\"\\\\zglm \\u230a\\\\zglm*\\1\\\\zglm \\u230b\\\\zglm*\", # \"with ⌊floor⌋ characters\": \n \"cc\": r\"\\\\zglm \\u231e\\\\zglm*\\1\\\\zglm \\u231f\\\\zglm*\", # \"with ⌞corner⌟ characters\":\n \"sb\": r\"*\\1\", # \"star *before word\": \n \"sa\": r\"\\1*\", # \"star after* word\": \n \"cb\": r\"^\\1\", # \"circumflex ^before word\": \n \"ca\": r\"\\1^\" # \"circumflex after^ word\": \n }\n _snippets = {\n \"snippets/fancyintro\": (\"c_prettyIntroOutline\", None, FancyIntro),\n \"snippets/pdfoutput\": (\"fcb_outputFormat\", lambda x: True, PDFx1aOutput),\n \"snippets/diglot\": (\"c_diglot\", None, Diglot),\n \"snippets/fancyborders\": (\"c_borders\", None, FancyBorders),\n \"snippets/thumbtabs\": (\"c_thumbtabs\", None, ThumbTabs),\n \"snippets/colophon\": (\"c_colophon\", None, Colophon),\n \"snippets/grid\": (\"c_grid\", None, Grid)\n }\n _settingmappings = {\n \"notes/xrcallers\": \"crossrefs\",\n \"notes/fncallers\": \"footnotes\"\n }\n _crossRefInfo = None\n\n _periphids = {\n \"title page\": \"title\",\n \"half title page\": \"halftitle\",\n \"promotional page\": \"promo\",\n \"imprimatur\": \"imprimatur\",\n \"publication data\": \"pubdata\",\n \"foreword\": \"foreword\",\n \"preface\": \"preface\",\n \"table of contents\": \"contents\",\n \"alphabetical contents\": \"alphacontents\",\n \"table of abbreviations\": \"abbreviations\",\n \"bible introduction\": \"intbible\",\n \"old testament introduction\": \"intot\",\n \"pentateuch introduction\": \"intpent\",\n \"history introduction\": \"inthistory\",\n \"poetry introduction\": \"intpoetry\",\n \"prophecy introduction\": \"intprophesy\",\n \"deuterocanon introduction\": \"intdc\",\n \"new testament introduction\": \"intnt\",\n \"gospels introduction\": \"intgospels\",\n \"epistles introduction\": \"intepistles\",\n \"letters introduction\": \"intletters\",\n \"chronology\": \"chron\",\n \"weights and measures\": \"measures\",\n \"map index\": \"maps\",\n \"lxx quotes in nt\": \"lxxquotes\",\n \"cover\": \"cover\",\n \"spine\": \"spine\"\n }\n\n _tocleaders = [\n \"\",\n r\"\\hskip .5pt .\\hskip .5pt\",\n r\"\\hskip 3pt .\\hskip 3pt\",\n r\"\\hskip 6pt \\emdash\\hskip 3pt\",\n r\"\\hrule\"\n ]\n\n _specialchars = {\n '*': 'asterisk',\n '%': 'percent',\n '#': 'hash',\n '$': 'dollar',\n '&': 'ampersand',\n '^': 'circumflex'\n }\n # '|': 'pipe'\n\n def __init__(self, printer, path, ptsettings, prjid=None, inArchive=False):\n from ptxprint.view import VersionStr\n self.VersionStr = VersionStr\n self.printer = printer\n self.ptsettings = ptsettings\n self.inArchive = inArchive\n self.changes = None\n self.localChanges = None\n self.debug = False\n self.interlinear = None\n self.imageCopyrightLangs = {}\n self.frontperiphs = None\n self.xrefs = None\n libpath = pycodedir()\n self.dict = {\"/ptxpath\": str(path).replace(\"\\\\\",\"/\"),\n \"/ptxprintlibpath\": libpath.replace(\"\\\\\",\"/\"),\n \"/iccfpath\": os.path.join(libpath, \"default_cmyk.icc\").replace(\"\\\\\",\"/\"),\n \"/ptx2pdf\": self.printer.scriptsdir.replace(\"\\\\\", \"/\")}\n self.prjid = prjid\n if self.prjid is not None:\n self.dict['project/id'] = self.prjid\n self._hdrmappings = localhdrmappings()\n if self.printer is not None:\n self.sheets = Sheets(self.printer.getStyleSheets(generated=True))\n self.update()\n\n def docdir(self):\n base = os.path.join(self.dict[\"/ptxpath\"], self.dict[\"project/id\"])\n docdir = os.path.join(base, 'local', 'ptxprint', self.printer.configName())\n return docdir, base\n\n def update(self):\n \"\"\" Update model from UI \"\"\"\n j = os.path.join\n rel = lambda x, y:os.path.relpath(x, y).replace(\"\\\\\", \"/\")\n self.printer.setDate() # Update date/time to now\n cpath = self.printer.configPath(self.printer.configName())\n rcpath = self.printer.configPath(\"\")\n self.updatefields(ModelMap.keys())\n docdir, base = self.docdir()\n self.dict[\"document/directory\"] = \".\" # os.path.abspath(docdir).replace(\"\\\\\",\"/\")\n self.dict['project/adjlists'] = rel(j(cpath, \"AdjLists\"), docdir).replace(\"\\\\\",\"/\") + \"/\"\n self.dict['project/triggers'] = rel(j(cpath, \"triggers\"), docdir).replace(\"\\\\\",\"/\") + \"/\"\n self.dict['project/piclists'] = rel(j(self.printer.working_dir, \"tmpPicLists\"), docdir).replace(\"\\\\\",\"/\") + \"/\"\n self.dict['project/id'] = self.printer.prjid\n self.dict['config/name'] = self.printer.configId\n self.dict['/ptxrpath'] = rel(self.dict['/ptxpath'], docdir)\n self.dict['/cfgrpath'] = rel(cpath, docdir)\n self.processHdrFtr(self.printer)\n # sort out caseless figures folder. This is a hack\n for p in (\"Figures\", \"figures\"):\n picdir = j(base, p)\n if os.path.exists(picdir):\n break\n self.dict[\"project/picdir\"] = rel(picdir, docdir).replace(\"\\\\\",\"/\")\n # Look in local Config folder for ptxprint-mods.tex, and drop back to shared/ptxprint if not found\n fpath = j(cpath, \"ptxprint-mods.tex\")\n if not os.path.exists(fpath):\n fpath = j(rcpath, \"ptxprint-mods.tex\")\n self.dict['/modspath'] = rel(fpath, docdir).replace(\"\\\\\",\"/\")\n fpath = j(cpath, \"ptxprint-premods.tex\")\n if not os.path.exists(fpath):\n fpath = j(rcpath, \"ptxprint-premods.tex\")\n self.dict['/premodspath'] = rel(fpath, docdir).replace(\"\\\\\",\"/\")\n if \"document/diglotcfgrpath\" not in self.dict:\n self.dict[\"document/diglotcfgrpath\"] = \"\"\n self.dict['paragraph/linespacingfactor'] = f2s(float(self.dict['paragraph/linespacing']) \\\n / self.dict[\"paragraph/linespacebase\"] / float(self.dict['paper/fontfactor']), dp=8)\n self.dict['paragraph/ifhavehyphenate'] = \"\" if os.path.exists(os.path.join(self.printer.configPath(\"\"), \\\n \"hyphen-\"+self.dict[\"project/id\"]+\".tex\")) else \"%\"\n # forward cleanup. If ask for ptxprint-mods.tex but don't have it, copy PrintDraft-mods.tex\n if self.dict[\"project/ifusemodssty\"] == \"\":\n modspath = os.path.join(cpath, \"ptxprint-mods.sty\")\n if not os.path.exists(modspath):\n spath = os.path.join(docdir, \"PrintDraft-mods.sty\")\n if os.path.exists(spath):\n copyfile(spath, modspath)\n self.dict[\"paper/pagegutter\"] = \"{:.2f}mm\".format(Dimension(self.dict[\"paper/width\"]).asunits(\"mm\") \\\n - (self.dict[\"paper/gutter\"] if self.dict[\"paper/ifaddgutter\"] == \"true\" else 0.))\n if self.dict[\"project/interlinear\"] != \"%\":\n self.interlinear = Interlinear(self.dict[\"project/interlang\"],\n os.path.join(self.dict[\"/ptxpath\"], self.dict[\"project/id\"]))\n regfont = self.printer.get(\"bl_fontR\")\n if regfont is not None:\n self.dict[\"document/spacecntxtlztn\"] = \"2\" if regfont.isCtxtSpace else \"0\"\n self.calculateMargins()\n if self.inArchive:\n for b, a in Borders.items():\n if self.dict[a[1]] is None or not self.dict[a[1]]:\n continue\n islist = a[2].startswith(\"\\\\\")\n fname = getattr(self.printer, a[0], (None if islist else a[2]))\n if fname is None:\n fname = Path(\".\" if islist else a[2])\n if islist and not isinstance(fname, (list, tuple)):\n fname = [fname]\n if islist:\n self.dict[a[1]] = \"\\n\".join(a[2].format(\"../shared/ptxprint/{}\".format(f.name)) for f in fname)\n else:\n self.dict[a[1]] = \"../shared/ptxprint/{}\".format(fname.name)\n if self.dict[\"fancy/versedecorator\"] != \"%\":\n self.dict[\"fancy/versedecoratorisfile\"] = \"\" if self.dict[\"fancy/versedecoratortype\"] == \"file\" else \"%\"\n self.dict[\"fancy/versedecoratorisayah\"] = \"\" if self.dict[\"fancy/versedecoratortype\"] == \"ayah\" else \"%\"\n else:\n self.dict[\"fancy/versedecoratorisfile\"] = \"%\"\n self.dict[\"fancy/versedecoratorisayah\"] = \"%\"\n self.dict['notes/abovenotetotal'] = f2s(float(self.dict['notes/abovenotespace'] or 0)\n + float(self.dict['notes/belownoterulespace'] or 0))\n # print(\", \".join(\"{}={}\".format(a, self.dict[\"fancy/versedecorator\"+a]) for a in (\"\", \"type\", \"isfile\", \"isayah\")))\n \n a = self.printer.get('fcb_gridOffset')\n if a == 'margin':\n vals = (self.dict[\"paper/margins\"], self.dict[\"paper/topmargin\"])\n else:\n vals = (\"0.0\", \"0.0\")\n (self.dict[\"grid/xoffset_\"], self.dict[\"grid/yoffset_\"]) = vals\n for a in ('project/frontfile', 'project/ptxprintstyfile_', 'diglot/ptxprintstyfile_'):\n if a not in self.dict:\n self.dict[a] = ''\n\n if self.dict.get('document/tocleaders', None) is None:\n self.dict['document/tocleaders'] = 0\n self.dict['document/iftocleaders'] = '' if int(self.dict['document/tocleaders'] or 0) > 0 else '%'\n self.dict['document/tocleaderstyle'] = self._tocleaders[int(self.dict['document/tocleaders'] or 0)]\n self.calcRuleParameters()\n\n def updatefields(self, a):\n global get\n def get(k): return self[k]\n for k in a:\n v = ModelMap[k]\n val = self.printer.get(v[0], skipmissing=k.startswith(\"scripts/\")) if v[0] is not None else None\n if v[1] is None:\n self.dict[k] = val\n else:\n try:\n sig = signature(v[1])\n if len(sig.parameters) == 2:\n self.dict[k] = v[1](self.printer, val)\n else:\n self.dict[k] = v[1](self.printer, val, self)\n except Exception as e:\n raise type(e)(\"In TeXModel with key {}, \".format(k) + str(e))\n\n def calcRuleParameters(self):\n notemap = {'fn': 'note', 'xr': 'xref'}\n fnrule = None\n enrule = None\n endnotes = []\n for a in ('fn', 'xr'):\n if self.dict['notes/{}pos'.format(a)] == 'endnote':\n enrule = a if enrule is None else enrule\n endnotes.append(r\"\\NoteAtEnd{{{}}}\".format(a[0]))\n elif fnrule is None:\n fnrule = a\n for a in (('Foot', fnrule), ('End', enrule)):\n dat = []\n if a[1] is not None:\n pos = int(self.dict['notes/{}ruleposn'.format(a[1])] or 0)\n left = \"\\hskip {:.2f} mm\".format(float(self.dict['notes/{}ruleindent'.format(a[1])] or 0.))\n right = r\"\\hss\"\n if pos == 2 or pos == 4: # Right or Outer\n right, left = (left, right)\n elif pos == 5:\n left = r\"\\hss\"\n if pos < 3 or pos == 5: # Left, Right or Centre\n dat.append(r\"\\def\\{}NoteRuleLeftIndent{{{}}}\".format(a[0], left))\n dat.append(r\"\\def\\{}NoteRuleRightIndent{{{}}}\".format(a[0], right))\n else:\n dat.append(r\"\\def\\{}NoteRuleLeftIndent{{\\ifodd\\pageno {}\\else {}\\fi}}\".format(a[0], left, right))\n dat.append(r\"\\def\\{}NoteRuleRightIndent{{\\ifodd\\pageno {}\\else {}\\fi}}\".format(a[0], right, left))\n dat.append(r\"\\def\\{}NoteRuleThickness{{{} pt}}\".format(a[0], self.dict['notes/{}rulethick'.format(a[1])] or \"0.4\"))\n dat.append(r\"\\def\\{}NoteRuleWidth{{{:.2f}}}\".format(a[0], float(self.dict['notes/{}rulelength'.format(a[1])] or 100.)/100))\n bspace = float(self.dict['notes/below{}rulespace'.format(notemap[a[1]])] or 0.)\n dat.append(r\"\\def\\Below{}NoteRuleSpace{{{:.1f} pt}}\".format(a[0], bspace))\n aspace = float(self.dict['notes/above{}space'.format(notemap[a[1]])] or 0.) + bspace\n dat.append(r\"\\Above{}NoteSpace={:.1f} pt\".format(a[0] if a[0] != \"Foot\" else \"\", aspace))\n self.dict['noterules/{}'.format(a[0].lower())] = \"\\n\".join(dat)\n self.dict['noterules/endnotemarkers'] = \"\\n\".join(endnotes)\n\n def __getitem__(self, key):\n return self.dict[key]\n\n def __setitem__(self, key, value):\n self.dict[key] = value\n\n def asBool(self, key, true=None, false=None):\n val = self.dict.get(key, None)\n if val is None:\n return False\n elif true is not None:\n return val == true\n elif false is not None:\n return val != false\n elif isinstance(val, bool):\n return val\n elif val == \"%\" or val == \"false\":\n return False\n else:\n return True\n\n def prePrintChecks(self):\n reasons = []\n for a in ('regular', 'bold', 'italic', 'bolditalic'):\n # print(\"Checking {}: {}\".format(a, self.dict['document/font{}'.format(a)]))\n if not self.dict['document/font{}'.format(a)]:\n reasons.append(_(\"Missing font ({})\").format(a))\n break\n return reasons\n\n def processHdrFtr(self, printer):\n \"\"\" Update model headers from model UI read values \"\"\"\n diglot = True if self.dict[\"document/ifdiglot\"] == \"\" else False\n v = self.dict[\"footer/ftrcenter\"]\n pri = self.dict[\"footer/ftrcenterside\"] == \"Pri\" \n t = self._hdrmappings.get(v, v)\n if diglot:\n t = self._addLR(t, pri)\n swap = self.dict['document/diglotswapside'] == 'true'\n ratio = float(self.dict['document/diglotprifraction'])\n # print(f\"{ratio=}\")\n if ratio > 0.5:\n lhfil = \"\\\\hskip 0pt plus {}fil\".format(f2s(ratio/(1-ratio)-1))\n rhfil = \"\"\n else:\n rhfil = \"\\\\hskip 0pt plus {}fil\".format(f2s((1-ratio)/ratio-1))\n lhfil = \"\"\n self.dict['footer/oddcenter'] = t\n self.dict['footer/evencenter'] = t\n if self.dict['footer/ifftrtitlepagenum'] == \"\":\n self.dict['footer/titleevencenter'] = self.dict['footer/titleoddcenter'] = self._addLR('\\\\pagenumber', pri)\n elif self.dict['footer/ifprintconfigname'] == \"\":\n self.dict['footer/titleevencenter'] = self.dict['footer/titleoddcenter'] = self.dict['config/name']\n else:\n self.dict['footer/titleevencenter'] = self.dict['footer/evencenter']\n self.dict['footer/titleoddcenter'] = self.dict['footer/oddcenter']\n\n mirror = self.asBool(\"header/mirrorlayout\")\n for side in ('left', 'center', 'right'):\n v = self.dict[\"header/hdr\"+side]\n pri = self.dict[\"header/hdr\"+side+\"side\"] == \"Pri\"\n t = self._hdrmappings.get(v, v)\n if diglot:\n t = self._addLR(t, pri)\n self.dict['header/odd{}'.format(side)] = t\n if mirror:\n self.dict['header/even{}'.format(self._swapRL[side])] = self.mirrorHeaders(t, diglot)\n else:\n self.dict['header/even{}'.format(side)] = t\n\n if t.startswith((r'\\first', r'\\last', r'\\range')): # ensure noVodd + noVeven is \\empty\n self.dict['header/noVodd{}'.format(side)] = r'\\empty'\n else:\n self.dict['header/noVodd{}'.format(side)] = t # copy the other header as is\n if mirror:\n if t.startswith((r'\\first', r'\\last', r'\\range')):\n self.dict['header/noVeven{}'.format(self._swapRL[side])] = r'\\empty'\n else:\n self.dict['header/noVeven{}'.format(self._swapRL[side])] = self.mirrorHeaders(t, diglot)\n else:\n if t.startswith((r'\\first', r'\\last', r'\\range')): # ensure noVodd + noVeven is \\empty\n self.dict['header/noVeven{}'.format(side)] = r'\\empty'\n else:\n self.dict['header/noVeven{}'.format(side)] = t \n\n if side == \"center\" and diglot and self.dict[\"document/diglotadjcenter\"]:\n for a in ('header/odd', 'header/even', 'footer/odd', 'footer/even',\n 'footer/titleeven', 'footer/titleodd', 'header/noVeven', 'header/noVodd'):\n b = (a+\"{}\").format(side)\n if 'even' in a:\n self.dict[b] = (rhfil if mirror ^ swap else lhfil) \\\n + self.dict[b] + (lhfil if mirror ^ swap else rhfil)\n else:\n self.dict[b] = (rhfil if swap else lhfil) + self.dict[b] + (lhfil if swap else rhfil)\n\n def _addLR(self, t, pri):\n if t in [r\"\\firstref\", r\"\\lastref\", r\"\\rangeref\", r\"\\pagenumber\", r\"\\hrsmins\", r\"\\isodate\" \\\n r\"\\book\", r\"\\bookalt\"]: \n if pri:\n t = t+'L'\n else:\n t = t+'R'\n elif t == r\"\\empty\":\n pass\n else:\n if pri:\n t = \"\\headfootL{{{}}}\".format(t)\n else:\n t = \"\\headfootR{{{}}}\".format(t)\n return t\n\n def mirrorHeaders(self, h, dig=False):\n if dig and h.endswith((\"L\", \"R\")):\n try:\n return self._mirrorRL[h[:-1]]+h[-1:]\n except KeyError:\n return h\n else:\n try:\n return self._mirrorRL[h]\n except KeyError:\n return h\n\n def calculateMargins(self):\n (marginmms, topmarginmms, bottommarginmms, headerposmms, footerposmms,\n ruleposmms, headerlabel, footerlabel) = self.printer.getMargins()\n self.dict[\"paper/topmarginfactor\"] = f2s(topmarginmms / marginmms)\n self.dict[\"paper/bottommarginfactor\"] = f2s(bottommarginmms / marginmms)\n self.dict[\"paper/headerposition\"] = f2s(headerposmms / marginmms)\n self.dict[\"paper/footerposition\"] = f2s(footerposmms / marginmms)\n self.dict[\"paper/ruleposition\"] = f2s(ruleposmms * 72.27 / 25.4)\n \n def texfix(self, path):\n return path.replace(\" \", r\"\\ \")\n\n def asTex(self, template=\"template.tex\", filedir=\".\", jobname=\"Unknown\", extra=\"\"):\n for k, v in self._settingmappings.items():\n if self.dict[k] == \"\":\n self.dict[k] = self.ptsettings.dict.get(v, \"a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z\")\n res = []\n resetPageDone = False\n docdir, docbase = self.docdir()\n self.dict['jobname'] = jobname\n self.dict['document/imageCopyrights'] = self.generateImageCopyrightText()\n # if self.dict['document/includeimg'] else self.generateEmptyImageCopyrights()\n self.dict['project/colophontext'] = re.sub(r'://', r':/ / ', self.dict['project/colophontext'])\n self.dict['project/colophontext'] = re.sub(r\"(?i)(\\\\zimagecopyrights)([A-Z]{2,3})\", \\\n lambda m:m.group(0).lower(), self.dict['project/colophontext'])\n with universalopen(os.path.join(pycodedir(), template)) as inf:\n for l in inf.readlines():\n if l.startswith(r\"%\\ptxfile\"):\n res.append(r\"\\PtxFilePath={\"+os.path.relpath(filedir, docdir).replace(\"\\\\\",\"/\")+\"/}\")\n for i, f in enumerate(self.dict['project/bookids']):\n fname = self.dict['project/books'][i]\n if extra != \"\":\n fname = re.sub(r\"^([^.]*).(.*)$\", r\"\\1\"+extra+r\".\\2\", fname)\n if i == len(self.dict['project/bookids']) - 1 and self.dict['project/ifcolophon'] == \"\":\n res.append(r\"\\lastptxfiletrue\")\n if self.dict['project/pgbreakcolophon'] != '%':\n res.append(r\"\\endbooknoejecttrue\")\n if not resetPageDone and f not in self._peripheralBooks: \n if not self.dict['document/noblankpage']:\n res.append(r\"\\ifodd\\pageno\\else\\emptyoutput \\fi\")\n res.append(r\"\\pageno={}\".format(self.dict['document/startpagenum']))\n resetPageDone = True\n if not self.asBool('document/ifshow1chbooknum') and \\\n self.asBool('document/ifshowchapternums', '%') and \\\n f in oneChbooks:\n res.append(r\"\\OmitChapterNumbertrue\")\n res.append(r\"\\ptxfile{{{}}}\".format(fname))\n res.append(r\"\\OmitChapterNumberfalse\")\n elif self.dict['document/diffcolayout'] and \\\n f in self.dict['document/diffcolayoutbooks']:\n cols = self.dict['paper/columns']\n res.append(r\"\\BodyColumns={}\".format('2' if cols == '1' else '1'))\n res.append(r\"\\ptxfile{{{}}}\".format(fname))\n res.append(r\"\\BodyColumns={}\".format(cols))\n else:\n res.append(r\"\\ptxfile{{{}}}\".format(fname))\n elif l.startswith(r\"%\\extrafont\") and self.dict[\"document/fontextraregular\"]:\n spclChars = re.sub(r\"\\[uU]([0-9a-fA-F]{4,6})\", lambda m:chr(int(m.group(1), 16)),\n self.dict[\"paragraph/missingchars\"])\n # print(spclChars.split(' '), [len(x) for x in spclChars.split(' ')])\n if self.dict[\"paragraph/ifusefallback\"] and len(spclChars):\n badlist = \"\\u2018\\u2019\\u201c\\u201d*#%\"\n specials = spclChars.replace(\" \", \"\").encode(\"raw_unicode_escape\").decode(\"raw_unicode_escape\")\n a = [\"\".join(chr(ord(c) + 16 if ord(c) < 58 else ord(c) - 23)\n for c in str(hex(ord(x)))[2:]).lower() for x in specials]\n b = [\"\".join((c) for c in str(hex(ord(x)))[2:]).lower() for x in specials]\n c = [x for x in zip(a,b) if chr(int(x[1],16)) not in badlist]\n if not len(c):\n continue\n res.append(r\"% for defname @active+ @+digit => 0->@, 1->a ... 9->i A->j B->k .. F->o\")\n res.append(r\"% 12 (size) comes from \\p size\")\n res.append(r'\\def\\extraregular{{\"{}\"}}'.format(self.dict[\"document/fontextraregular\"]))\n res.append(r\"\\catcode`\\@=11\")\n res.append(r\"\\def\\do@xtrafont{\\x@\\s@textrafont\\ifx\\thisch@rstyle\\undefined\\m@rker\\else\\thisch@rstyle\\fi}\")\n for a,b in c:\n res.append(r\"\\def\\@ctive{}{{\\leavevmode{{\\do@xtrafont {}{}}}}}\".format(a, '^'*len(b), b))\n res.append(r\"\\DefineActiveChar{{{}{}}}{{\\@ctive{}}}\".format( '^'*len(b), b, a))\n res.append(r\"\\@ctivate\")\n res.append(r\"\\catcode`\\@=12\")\n else:\n res.append(r\"% No special/missing characters specified for fallback font\")\n elif l.startswith(r\"%\\horiznotespacing\"):\n mins = float(self.dict[\"notes/horiznotespacemin\"])\n maxs = float(self.dict[\"notes/horiznotespacemax\"])\n tgts = mins + ((maxs - mins) / 3)\n minus = tgts - mins\n plus = maxs - tgts\n res.append(r\"\\internoteskip={}pt plus {}pt minus {}pt\".format(f2s(tgts), f2s(plus), f2s(minus)))\n elif l.startswith(r\"%\\optimizepoetry\"):\n bks = self.dict[\"document/clabelbooks\"]\n if self.dict[\"document/ifchaplabels\"] == \"%\" and len(bks):\n for bk in bks.split(\" \"):\n if bk in self.dict['project/bookids']:\n res.append((r\"\\setbookhook{{start}}{{{}}}{{\\gdef\\BalanceThreshold{{3}}\\clubpenalty=50\"\n + r\"\\widowpenalty=50}}\").format(bk))\n res.append((r\"\\setbookhook{{end}}{{{}}}{{\\gdef\\BalanceThreshold{{0}}\\clubpenalty=10000\"\n + r\"\\widowpenalty=10000}}\").format(bk))\n elif l.startswith(r\"%\\snippets\"):\n for k, c in sorted(self._snippets.items(), key=lambda x: x[1][2].order):\n if c[1] is None:\n v = self.asBool(k)\n else:\n v = c[1](self.dict[k])\n if v:\n fn = getattr(c[2], 'generateTex', None)\n if fn is not None:\n res.append(fn(v, self))\n elif c[2].processTex:\n res.append(c[2].texCode.format(**self.dict))\n else:\n res.append(c[2].texCode)\n script = self.dict[\"document/script\"]\n if len(script) > 0:\n sclass = getattr(scriptsnippets, script[8:].lower(), None)\n if sclass is not None:\n res.append(sclass.tex(self))\n res.append(\"\"\"\n\\\\catcode\"FDEE=1 \\\\catcode\"FDEF=2\n\\\\prepusfm\n\\\\def\\\\zcopyright\\uFDEE{project/copyright}\\uFDEF\n\\\\def\\\\zlicense\\uFDEE{project/license}\\uFDEF\n\"\"\".format(**self.dict))\n if \"diglot/copyright\" in self.dict:\n res.append(\"\\\\def\\\\zcopyrightR\\uFDEE{}\\uFDEF\".format(self.dict[\"diglot/copyright\"]))\n res.append(\"\\\\unprepusfm\")\n elif l.startswith(r\"%\\defzvar\"):\n for k in self.printer.allvars():\n res.append(r\"\\defzvar{{{}}}{{{}}}\".format(k, self.printer.getvar(k)))\n for k, e in (('toctitle', 'document/toctitle'),):\n res.append(r\"\\defzvar{{{}}}{{{}}}\".format(k, self.dict[e]))\n else:\n res.append(l.rstrip().format(**self.dict))\n return \"\\n\".join(res).replace(\"\\\\OmitChapterNumberfalse\\n\\\\OmitChapterNumbertrue\\n\",\"\")\n\n def _doperiph(self, m):\n if self.frontperiphs is None:\n frtfile = os.path.join(self.printer.settings_dir, self.printer.prjid, self.printer.getBookFilename(\"FRT\"))\n self.frontperiphs = {}\n if not os.path.exists(frtfile):\n return \"\"\n with open(frtfile, encoding=\"utf-8\") as inf:\n mode = 0 # default\n currperiphs = []\n currk = None\n for l in inf.readlines():\n ma = re.match(r'\\\\periph\\s+([^|]+)(?:\\|\\s*(?:id\\s*=\\s*\"([^\"]+)|(\\S+)))', l)\n if ma:\n if mode == 1: # already collecting so save\n self.frontperiphs[currk] = \"\\n\".join(currperiphs)\n currk = ma[2] or ma[3]\n if not currk:\n currk = self._periphids.get(m[1].lower(), m[1].lower())\n currperiphs = []\n mode = 1\n elif mode == 1:\n if r\"\\periph\" in l:\n mode = 0\n else:\n currperiphs.append(l.rstrip())\n if currk is not None:\n self.frontperiphs[currk] = \"\\n\".join(currperiphs)\n k = m[1]\n if k in self.frontperiphs:\n return self.frontperiphs[k]\n else:\n return \"\"\n\n def createFrontMatter(self, outfname):\n self.dict['project/frontfile'] = os.path.basename(outfname)\n infpath = self.printer.configFRT()\n bydir = os.path.join(pycodedir(), \"images\").replace(\"\\\\\", \"/\")\n fmt = self.dict['snippets/pdfoutput']\n cmyk = fmt in ('CMYK', 'PDF/X-1A', 'PDF/A-1')\n if os.path.exists(infpath):\n fcontent = []\n with open(infpath, encoding=\"utf-8\") as inf:\n seenperiph = False\n for l in inf.readlines():\n if l.strip().startswith(r\"\\periph\"):\n l = r\"\\pb\" if self.dict['project/periphpagebreak'] and seenperiph else \"\"\n seenperiph = True\n l = re.sub(r\"\\\\zperiphfrt\\s*\\|([^\\\\\\s]+)\\s*\\\\\\*\", self._doperiph, l)\n l = re.sub(r\"\\\\zbl\\s*\\|(\\d+)\\\\\\*\", lambda m: \"\\\\b\\n\" * int(m.group(1)), l)\n l = re.sub(r\"\\\\zccimg\\s*(.*?)(?:\\|(.*?))?\\\\\\*\",\n lambda m: r'\\fig |src=\"'+bydir+\"/\"+m.group(1)+(\"_cmyk\" if cmyk else \"\") \\\n + '.jpg\" copy=\"None\" ' + m.group(2)+ r'\\fig*', l)\n l = re.sub(r'(\\\\fig .*?src=\")(.*?)(\".*?\\\\fig\\*)', lambda m:m.group(1)+m.group(2).replace(\"\\\\\",\"/\")+m.group(3), l)\n fcontent.append(l.rstrip())\n with open(outfname, \"w\", encoding=\"utf-8\") as outf:\n outf.write(\"\\n\".join(fcontent))\n\n def flattenModule(self, infpath, outdir, usfm=None):\n outfpath = os.path.join(outdir, os.path.basename(infpath))\n doti = outfpath.rfind(\".\")\n if doti > 0:\n outfpath = outfpath[:doti] + \"-flat\" + outfpath[doti:]\n usfms = self.printer.get_usfms()\n try:\n mod = Module(infpath, usfms, usfm=usfm)\n res = mod.parse()\n except SyntaxError as e:\n return (None, e)\n if usfm is not None:\n return res\n with open(outfpath, \"w\", encoding=\"utf-8\") as outf:\n outf.write(sfm.generate(res))\n return outfpath\n\n def runConversion(self, infpath, outdir):\n outfpath = infpath\n if self.dict['project/processscript'] and self.dict['project/selectscript']:\n outfpath = os.path.join(outdir, os.path.basename(infpath))\n doti = outfpath.rfind(\".\")\n if doti > 0:\n outfpath = outfpath[:doti] + \"-conv\" + outfpath[doti:]\n cmd = [self.dict[\"project/selectscript\"], infpath, outfpath]\n checkoutput(cmd) # dont't pass cmd as list when shell=True\n return outfpath\n\n def runChanges(self, changes, bk, dat):\n for c in changes:\n logger.debug(\"Change: {}\".format(c))\n if c[0] is None:\n dat = c[1].sub(c[2], dat)\n elif isinstance(c[0], str):\n if c[0] == bk:\n dat = c[1].sub(c[2], dat)\n else:\n def simple(s):\n return c[1].sub(c[2], s)\n dat = c[0](simple, bk, dat)\n return dat\n\n def convertBook(self, bk, chaprange, outdir, prjdir, letterspace=\"\\uFDD0\"):\n printer = self.printer\n if self.changes is None:\n if self.asBool('project/usechangesfile'):\n # print(\"Applying PrntDrftChgs:\", os.path.join(prjdir, 'PrintDraftChanges.txt'))\n #cpath = self.printer.configPath(self.printer.configName())\n #self.changes = self.readChanges(os.path.join(cpath, 'changes.txt'), bk)\n self.changes = self.readChanges(os.path.join(printer.configPath(printer.configName()), 'changes.txt'), bk)\n else:\n self.changes = []\n draft = \"-\" + (printer.configName() or \"draft\")\n self.makelocalChanges(printer, bk, chaprange=chaprange)\n customsty = os.path.join(prjdir, 'custom.sty')\n if not os.path.exists(customsty):\n self.dict[\"/nocustomsty\"] = \"%\"\n else:\n self.dict[\"/nocustomsty\"] = \"\"\n fname = printer.getBookFilename(bk)\n if fname is None:\n infpath = os.path.join(prjdir, bk) # assume module\n infpath = self.flattenModule(infpath, outdir)\n if isinstance(infpath, tuple) and infpath[0] is None:\n self.printer.doError(\"Failed to flatten module text (due to a Syntax Error?):\", \n secondary=str(infpath[1]), \n title=\"PTXprint [{}] - Canonicalise Text Error!\".format(self.VersionStr),\n show=not self.printer.get(\"c_quickRun\"))\n return None\n else:\n infpath = os.path.join(prjdir, fname)\n if self.dict['project/when2processscript'] == \"before\":\n infpath = self.runConversion(infpath, outdir)\n outfname = os.path.basename(infpath)\n # outfname = fname\n doti = outfname.rfind(\".\")\n if doti > 0:\n outfname = outfname[:doti] + draft + outfname[doti:]\n outfpath = os.path.join(outdir, outfname)\n codepage = self.ptsettings.get('Encoding', 65001)\n with universalopen(infpath, cp=codepage) as inf:\n dat = inf.read()\n\n doc = None\n if self.interlinear is not None:\n doc = self._makeUSFM(dat.splitlines(True), bk)\n linelengths = [len(x) for x in dat.splitlines(True)]\n if doc is not None:\n doc.calc_PToffsets()\n self.interlinear.convertBk(bk, doc, linelengths)\n if len(self.interlinear.fails):\n printer.doError(\"The following references need to be reapproved: \" + \" \".join(self.interlinear.fails),\n show=not printer.get(\"c_quickRun\"))\n self.interlinear.fails = []\n elif bk.lower().startswith(\"xx\"):\n doc = self._makeUSFM(dat.splitlines(True), bk)\n #import pdb; pdb.set_trace()\n doc.doc = self.flattenModule(infpath, outfpath, usfm=doc)\n\n if self.changes is not None and len(self.changes):\n if doc is not None:\n dat = str(doc)\n doc = None\n dat = self.runChanges(self.changes, bk, dat)\n #self.analyzeImageCopyrights(dat)\n\n if self.dict['project/canonicalise'] \\\n or not self.asBool(\"document/bookintro\") \\\n or not self.asBool(\"document/introoutline\")\\\n or self.asBool(\"document/hidemptyverses\"):\n if doc is None:\n doc = self._makeUSFM(dat.splitlines(True), bk)\n if doc is not None:\n if not self.asBool(\"document/bookintro\") or not self.asBool(\"document/introoutline\"):\n logger.debug(\"stripIntro\")\n doc.stripIntro(not self.asBool(\"document/bookintro\"), not self.asBool(\"document/introoutline\"))\n if self.asBool(\"document/hidemptyverses\"):\n logger.debug(\"stripEmptyChVs\")\n doc.stripEmptyChVs(ellipsis=self.asBool(\"document/elipsizemptyvs\"))\n\n if self.dict['fancy/endayah'] == \"\":\n if doc is None:\n doc = self._makeUSFM(dat.splitlines(True), bk)\n logger.debug(\"versesToEnd\")\n doc.versesToEnd()\n\n if doc is not None and getattr(doc, 'doc', None) is not None:\n dat = str(doc)\n\n if self.localChanges is not None:\n dat = self.runChanges(self.localChanges, bk, dat)\n\n with open(outfpath, \"w\", encoding=\"utf-8\") as outf:\n outf.write(dat)\n if self.dict['project/when2processscript'] == \"after\":\n bn = os.path.basename(self.runConversion(outfpath, outdir))\n else:\n bn = os.path.basename(outfpath)\n\n if '-conv' in bn:\n newname = re.sub(r\"(\\{}\\-conv|\\-conv\\{}|\\-conv)\".format(draft, draft), draft, bn)\n copyfile(os.path.join(outdir, bn), os.path.join(outdir, newname))\n os.remove(os.path.join(outdir, bn))\n return newname\n else:\n return bn\n \n def _makeUSFM(self, txtlines, bk):\n # import pdb; pdb.set_trace()\n syntaxErrors = []\n try:\n doc = Usfm(txtlines, self.sheets)\n while len(doc.doc) > 1:\n if isinstance(doc.doc[0], sfm.Text):\n doc.doc.pop(0)\n else:\n break\n if len(doc.doc) != 1:\n raise ValueError(\"Badly formed USFM. Probably missing a \\\\id line\")\n doc.normalise()\n except SyntaxError as e:\n syntaxErrors.append(\"{} {} line:{}\".format(self.prjid, bk, str(e).split('line', maxsplit=1)[1]))\n except Exception as e:\n syntaxErrors.append(\"{} {} Error({}): {}\".format(self.prjid, bk, type(e), str(e)))\n traceback.print_exc()\n if len(syntaxErrors):\n dlgtitle = \"PTXprint [{}] - USFM Text Error!\".format(self.VersionStr)\n # print(syntaxErrors[0])\n # logger.info(syntaxErrors[0])\n errbits = re.match(r\"(\\S+) (...) line: (\\d+),\\d+\\s*\\[(.*?)\\]: orphan marker (\\\\.+?)\", syntaxErrors[0])\n if errbits is not None:\n self.printer.doError(\"Syntax Error warning: \", \n secondary=_(\"Examine line {} in {} on the 'Final SFM' tab of the View+Edit \" + \\\n \"page to determine the cause of this issue related to marker: {} as found in the markers: {}.\").format(\n errbits[3], errbits[2], errbits[5], errbits[4]) + \\\n \"\\n\\n\"+_(\"This warning was triggered due to 'Auto-Correct USFM' being \" + \\\n \"enabled on the Advanced tab but is due to an orphaned marker. \" + \\\n \"It means the marker does not belong in that position, or it \" + \\\n \"is missing a valid parent marker.\"), title=dlgtitle,\n show=not self.printer.get(\"c_quickRun\"))\n else:\n prtDrft = _(\"And check if a faulty rule in changes.txt has caused the error(s).\") if self.asBool(\"project/usechangesfile\") else \"\"\n self.printer.doError(_(\"Failed to normalize texts due to a Syntax Error: \"), \n secondary=\"\\n\".join(syntaxErrors)+\"\\n\\n\"+_(\"Run the Basic Checks in Paratext to ensure there are no Marker errors \"+ \\\n \"in either of the diglot projects. If this error persists, try running the Schema Check in Paratext as well.\") + \" \" + prtDrft,\n title=dlgtitle, show=not self.printer.get(\"c_quickRun\"))\n \n return None\n else:\n return doc \n\n def make_contextsfn(self, bk, *changes):\n # functional programmers eat your hearts out\n def makefn(reg, currfn):\n if currfn is not None:\n def compfn(fn, b, s):\n def domatch(m):\n return currfn(lambda x:fn(x.group(0)), m.group(0))\n return reg.sub(domatch, s) if bk is None or b == bk else s\n else:\n def compfn(fn, b, s):\n return reg.sub(lambda m:fn(m.group(0)), s) if bk is None or b == bk else s\n return compfn\n return reduce(lambda currfn, are: makefn(are, currfn), reversed([c for c in changes if c is not None]), None)\n\n def readChanges(self, fname, bk):\n changes = []\n if not os.path.exists(fname):\n return []\n qreg = r'(?:\"((?:[^\"\\\\]|\\\\.)*?)\"|' + r\"'((?:[^'\\\\]|\\\\.)*?)')\"\n with universalopen(fname) as inf:\n for i, l in enumerate(inf.readlines()):\n l = l.strip().replace(u\"\\uFEFF\", \"\")\n l = re.sub(r\"\\s*#.*$\", \"\", l)\n if not len(l):\n continue\n contexts = []\n atcontexts = []\n m = re.match(r\"^\\s*include\\s+(['\\\"])(.*?)\\1\", l)\n if m:\n changes.extend(self.readChanges(os.path.join(os.path.dirname(fname), m.group(2)), bk))\n continue\n # test for \"at\" command\n m = re.match(r\"^\\s*at\\s+(.*?)\\s+(?=in|['\\\"])\", l)\n if m:\n atref = RefList.fromStr(m.group(1), context=AnyBooks)\n for r in atref.allrefs():\n if r.chap == 0:\n atcontexts.append((r.book, None))\n elif r.verse == 0:\n atcontexts.append((r.book, regex.compile(r\"(?<=\\\\c {}(?=\\D)).*?($|\\\\[cv] )\".format(r.chap), flags=regex.S)))\n else:\n atcontexts.append((r.book, regex.compile(r\"(?<=\\\\c {}(?=\\D)(?:.(?!\\\\c))*?)\\\\v {}[ -].*?($|\\\\[cv] )\".format(r.chap, r.verse), flags=regex.S)))\n l = l[m.end():].strip()\n else:\n atcontexts = [None]\n # test for 1+ \"in\" commands\n while True:\n m = re.match(r\"^\\s*in\\s+\"+qreg+r\"\\s*:\\s*\", l)\n if not m:\n break\n try:\n contexts.append(regex.compile(m.group(1) or m.group(2), flags=regex.M))\n except re.error as e:\n self.printer.doError(\"Regular expression error: {} in changes file at line {}\".format(str(e), i+1),\n show=not self.printer.get(\"c_quickRun\"))\n break\n l = l[m.end():].strip()\n # capture the actual change\n m = re.match(r\"^\"+qreg+r\"\\s*>\\s*\"+qreg, l)\n if m:\n for at in atcontexts:\n if at is None:\n context = self.make_contextsfn(None, *contexts) if len(contexts) else None\n elif len(contexts) or at[1] is not None:\n context = self.make_contextsfn(at[0], at[1], *contexts)\n else:\n context = at[0]\n try:\n changes.append((context, regex.compile(m.group(1) or m.group(2), flags=regex.M),\n m.group(3) or m.group(4) or \"\"))\n except re.error as e:\n self.printer.doError(\"Regular expression error: {} in changes file at line {}\".format(str(e), i+1),\n show=not self.printer.get(\"c_quickRun\"))\n break\n continue\n return changes\n\n def makelocalChanges(self, printer, bk, chaprange=None):\n script = self.dict[\"document/script\"]\n if len(script):\n sscript = getattr(scriptsnippets, script[8:].lower(), None)\n if sscript is not None:\n self.changes.extend(sscript.regexes(self))\n #self.changes.append((None, regex.compile(r\"(?<=\\\\[^\\\\\\s]+)\\*(?=\\S)\", flags=regex.S), \"* \"))\n if self.printer is not None and self.printer.get(\"c_tracing\"):\n print(\"List of changes.txt:-------------------------------------------------------------\")\n report = \"\\n\".join(\"{} -> {}\".format(p[1].pattern, p[2]) for p in self.changes)\n if getattr(self.printer, \"logger\", None) is not None:\n self.printer.logger.insert_at_cursor(v)\n else:\n try:\n print(report)\n except UnicodeEncodeError:\n print(\"Unable to print details of changes.txt\")\n self.localChanges = []\n if bk == \"GLO\" and self.dict['document/filterglossary']:\n self.filterGlossary(printer)\n if chaprange is not None:\n first, last = chaprange\n elif self.dict[\"project/bookscope\"] == \"single\":\n first = int(float(self.dict[\"document/chapfrom\"]))\n last = int(float(self.dict[\"document/chapto\"]))\n else:\n first, last = (-1, -1)\n \n # Fix things that other parsers accept and we don't\n self.localChanges.append((None, regex.compile(r\"(\\\\[cv] [^ \\\\\\r\\n]+)(\\\\)\", flags=regex.S), r\"\\1 \\2\"))\n \n # Remove empty \\h markers (might need to expand this list and loop through a bunch of markers)\n self.localChanges.append((None, regex.compile(r\"(\\\\h ?\\r?\\n)\", flags=regex.S), r\"\"))\n \n # This section handles PARTIAL books (from chapter X to chapter Y)\n if self.asBool(\"document/ifchaplabels\", true=\"%\"):\n clabel = self.dict[\"document/clabel\"]\n clbooks = self.dict[\"document/clabelbooks\"].split()\n # print(\"Chapter label: '{}' for '{}' with {}\".format(clabel, \" \".join(clbooks), bk))\n if len(clabel) and (not len(clbooks) or bk in clbooks):\n #self.localChanges.append((lambda fn,b,d: d if r'\\cl ' in d else fn(d),\n # regex.compile(r\"(\\\\c 1)(?=\\s*\\r?\\n|\\s)\", flags=regex.S), \"\\\\cl {}\\n\\\\1\".format(clabel)))\n # self.localChanges.append((None,\n # regex.compile(r\"(\\\\c 1)(?=\\s*\\r?\\n|\\s)\", flags=regex.S), \"\\\\cl {}\\n\\\\1\".format(clabel)))\n self.localChanges.append((None,\n regex.compile(r\"(\\\\c )\", flags=regex.S), \"\\\\cl {}\\n\\\\1\".format(clabel)))\n \n # if self.dict[\"project/bookscope\"] == \"single\":\n if first > 1:\n self.localChanges.append((None, regex.compile(r\"\\\\c 1 ?\\r?\\n.+(?=\\\\c {} ?\\r?\\n)\".format(first), flags=regex.S), \"\"))\n if last >=0 and last < int(chaps.get(bk, 999)):\n self.localChanges.append((None, regex.compile(r\"\\\\c {} ?\\r?\\n.+\".format(last+1), flags=regex.S), \"\"))\n\n # Throw out the known \"nonpublishable\" markers and their text (if any)\n self.localChanges.append((None, regex.compile(r\"\\\\(usfm|ide|rem|sts|restore|pubinfo)( .*?)?\\n(?=\\\\)\", flags=regex.M), \"\"))\n\n # If a printout of JUST the book introductions is needed (i.e. no scripture text) then this option is very handy\n if not self.asBool(\"document/ifmainbodytext\"):\n self.localChanges.append((None, regex.compile(r\"\\\\c 1 ?\\r?\\n.+\".format(first), flags=regex.S), \"\"))\n\n # Probably need to make this more efficient for multi-book and lengthy glossaries (cache the GLO & changes reqd etc.)\n if self.asBool(\"notes/glossaryfootnotes\"):\n self.makeGlossaryFootnotes(printer, bk)\n\n # Glossary Word markup: Remove the second half of the \\w word|glossary-form\\w* and apply chosen glossary markup\n v = self.dict[\"document/glossarymarkupstyle\"]\n gloStyle = self._glossarymarkup.get(v, v)\n if v is not None: # and v != \"no\":\n if gloStyle is not None and len(v) == 2: # otherwise skip over OLD Glossary markup definitions\n self.localChanges.append((None, regex.compile(r\"\\\\\\+?w ((?:.(?!\\\\\\+w\\*))+?)(\\|[^|]+?)?\\\\\\+?w\\*\", flags=regex.M), gloStyle))\n\n if self.asBool(\"notes/includexrefs\"): # This seems back-to-front, but it is correct because of the % if v\n self.localChanges.append((None, regex.compile(r'(?i)\\\\x .+?\\\\x\\*', flags=regex.M), ''))\n \n if self.asBool(\"document/ifinclfigs\") and bk in self._peripheralBooks:\n # Remove any illustrations which don't have a |p| 'loc' field IF this setting is on\n if self.asBool(\"document/iffigexclwebapp\"):\n self.localChanges.append((None, regex.compile(r'(?i)\\\\fig ([^|]*\\|){3}([aw]+)\\|[^\\\\]*\\\\fig\\*', flags=regex.M), '')) # USFM2\n self.localChanges.append((None, regex.compile(r'(?i)\\\\fig [^\\\\]*\\bloc=\"[aw]+\"[^\\\\]*\\\\fig\\*', flags=regex.M), '')) # USFM3\n def figtozfiga(m):\n a = self.printer.picinfos.getAnchor(m.group(1), bk)\n if a is None:\n return \"\"\n ref = re.sub(r\"^\\S+\\s+\", r\"\", a)\n return \"\\\\zfiga|{}\\\\*\".format(ref)\n self.localChanges.append((None, regex.compile(r'\\\\fig.*?src=\"([^\"]+?)\".*?\\\\fig\\*', flags=regex.M), figtozfiga))\n self.localChanges.append((None, regex.compile(r'\\\\fig(?: .*?)?\\|(.*?)\\|.*?\\\\fig\\*', flags=regex.M), figtozfiga))\n\n if self.asBool(\"document/iffighiderefs\"): # del ch:vs from caption \n self.localChanges.append((None, regex.compile(r\"(\\\\fig [^\\\\]+?\\|)([0-9:.\\-,\\u2013\\u2014]+?)(\\\\fig\\*)\", \\\n flags=regex.M), r\"\\1\\3\")) # USFM2\n self.localChanges.append((None, regex.compile(r'(\\\\fig .+?)(ref=\\\"\\d+[:.]\\d+([-,\\u2013\\u2014]\\d+)?\\\")(.*?\\\\fig\\*)', \\\n flags=regex.M), r\"\\1\\4\")) # USFM3\n else:\n # Strip out all \\figs from the USFM as an internally generated temp PicList will do the same job\n self.localChanges.append((None, regex.compile(r'\\\\fig[\\s|][^\\\\]+?\\\\fig\\*', flags=regex.M), \"\"))\n\n if not self.asBool(\"document/sectionheads\"): # Drop ALL Section Headings (which also drops the Parallel passage refs now)\n self.localChanges.append((None, regex.compile(r\"\\\\[sr] .+\", flags=regex.M), \"\"))\n\n if not self.asBool(\"document/parallelrefs\"): # Drop ALL Parallel Passage References\n self.localChanges.append((None, regex.compile(r\"\\\\r .+\", flags=regex.M), \"\"))\n\n if self.asBool(\"document/preventorphans\"): # Prevent orphans at end of *any* paragraph\n self.localChanges.append((None, regex.compile(r\"(\\\\q\\d?(\\s?\\r?\\n?\\\\v)?( \\S+)+( (?!\\\\)[^\\\\\\s]{,6})) ([\\S]{,9}\\s*\\n)\", \\\n flags=regex.M), r\"\\1\\u2000\\5\"))\n self.localChanges.append((None, regex.compile(r\"(?<=\\\\[^ctm][^\\\\]+)(\\s+[^ 0-9\\\\\\n\\u2000\\u00A0]{,6}) ([^ 0-9\\\\\\n\\u2000\\u00A0]{,8}\\n(?:\\\\[pmqsc]|$))\", flags=regex.S), r\"\\1\\u2000\\2\"))\n\n if self.asBool(\"document/preventwidows\"):\n # Push the verse number onto the next line (using NBSP) if there is\n # a short widow word (3 characters or less) at the end of the line\n self.localChanges.append((None, regex.compile(r\"(\\\\v \\d+([-,]\\d+)? [\\w]{1,3}) \", flags=regex.M), r\"\\1\\u00A0\")) \n\n # By default, HIDE chapter numbers for all non-scripture (Peripheral) books (unless \"Show... is checked)\n if not self.asBool(\"document/showxtrachapnums\") and bk in TexModel._peripheralBooks:\n self.localChanges.append((None, regex.compile(r\"(\\\\c \\d+ ?\\r?\\n)\", flags=regex.M), \"\"))\n\n if self.asBool(\"document/ch1pagebreak\"):\n self.localChanges.append((None, regex.compile(r\"(\\\\c 1 ?\\r?\\n)\", flags=regex.M), r\"\\pagebreak\\r\\n\\1\"))\n\n if self.asBool(\"document/glueredupwords\"): # keep reduplicated words together\n self.localChanges.append((None, regex.compile(r\"(?<=[ ])(\\w{3,}) \\1(?=[\\s,.!?])\", flags=regex.M), r\"\\1\\u2000\\1\")) \n \n if self.asBool(\"notes/addcolon\"): # Insert a colon between \\fq (or \\xq) and following \\ft (or \\xt)\n self.localChanges.append((None, regex.compile(r\"(\\\\[fx]q .+?):* ?(\\\\[fx]t)\", flags=regex.M), r\"\\1: \\2\")) \n\n # HELP NEEDED from Martin to fix this section up again.\n # Keep book number together with book name \"1 Kings\", \"2 Samuel\" within \\xt and \\xo\n self.localChanges.append((self.make_contextsfn(None, regex.compile(r\"(\\\\[xf]t\\s[^\\\\]+)\")),\n regex.compile(r\"(\\d)\\s(\\p{L})\"), r\"\\1\\u00A0\\2\"))\n \n if self.asBool(\"notes/keepbookwithrefs\"): # keep Booknames and ch:vs nums together within \\xt and \\xo\n self.localChanges.append((self.make_contextsfn(None, regex.compile(r\"(\\\\[xf]t\\s[^\\\\]+)\")),\n regex.compile(r\"(\\d?[^\\s\\d\\-\\\\,;]{3,}[^\\\\\\s]*?)\\s(\\d+[:.]\\d+(-\\d+)?)\"), r\"\\1\\u2000\\2\"))\n self.localChanges.append((self.make_contextsfn(None, regex.compile(r\"(\\\\[xf]t\\s[^\\\\]+)\")),\n regex.compile(r\"(\\s.) \"), r\"\\1\\u2000\")) # Ensure no floating single chars in note text\n \n # keep \\xo & \\fr refs with whatever follows (i.e the bookname or footnote) so it doesn't break at end of line\n self.localChanges.append((None, regex.compile(r\"(\\\\(xo|fr) (\\d+[:.]\\d+([-,]\\d+)?)) \"), r\"\\1\\u00A0\"))\n\n for c in (\"fn\", \"xr\"):\n # Force all footnotes/x-refs to be either '+ ' or '- ' rather than '*/#'\n if self.asBool(\"notes/{}override\".format(c)):\n t = \"+\" if self.asBool(\"notes/if{}autocallers\".format(c)) else \"-\"\n self.localChanges.append((None, regex.compile(r\"\\\\{} .\".format(c[0])), r\"\\\\{} {}\".format(c[0],t)))\n # Remove the [spare] space after a note caller if the caller is omitted AND if after a digit (verse number).\n if self.asBool(\"notes/{}omitcaller\".format(c)):\n self.localChanges.append((None, regex.compile(r\"(\\d )(\\\\[{0}] - .*?\\\\[{0}]\\*)\\s+\".format(c[0])), r\"\\1\\2\"))\n\n # Paratext marks no-break space as a tilde ~\n self.localChanges.append((None, regex.compile(r\"~\", flags=regex.M), r\"\\u00A0\")) \n\n # Paratext marks forced line breaks as //\n self.localChanges.append((None, regex.compile(r\"//\", flags=regex.M), r\"\\u2028\")) \n\n # Convert hyphens from minus to hyphen\n self.localChanges.append((None, regex.compile(r\"(?<!\\\\[fx]\\s)((?<=\\s)-|-(?=\\s))\", flags=regex.M), r\"\\u2011\"))\n\n if self.asBool(\"document/toc\") and self.asBool(\"document/multibook\"):\n # Only do this IF the auto Table of Contents is enabled AND there is more than one book\n for c in range(1,4): # Remove any \\toc lines that we don't want appearing in the ToC\n if not self.asBool(\"document/usetoc{}\".format(c)):\n self.localChanges.append((None, regex.compile(r\"(\\\\toc{} .+)\".format(c), flags=regex.M), \"\"))\n\n # Add End of Book decoration PDF to Scripture books only if FancyBorders is enabled and .PDF defined\n if self.asBool(\"fancy/enableborders\") and self.asBool(\"fancy/endofbook\") and bk not in self._peripheralBooks \\\n and self.dict[\"fancy/endofbookpdf\"].lower().endswith('.pdf'):\n self.localChanges.append((None, regex.compile(r\"\\Z\", flags=regex.M), r\"\\r\\n\\z\"))\n \n # Insert a rule between end of Introduction and start of body text (didn't work earlier, but might work now)\n # self.localChanges.append((None, regex.compile(r\"(\\\\c\\s1\\s?\\r?\\n)\", flags=regex.S), r\"\\\\par\\\\vskip\\\\baselineskip\\\\hskip-\\\\columnshift\\\\hrule\\\\vskip 2\\\\baselineskip\\n\\1\"))\n\n # Apply any changes specified in snippets\n for k, c in sorted(self._snippets.items(), key=lambda x: x[1][2].order):\n if self.printer is None:\n v = self.asBool(k) if c[1] is None else c[1](self.dict[k])\n elif c[1] is None:\n v = self.printer.get(c[0])\n self.dict[k] = \"true\" if v else \"false\"\n else:\n self.dict[k] = self.printer.get(c[0])\n v = c[1](self.dict[k])\n if v: # if the c_checkbox is true then extend the list with those changes\n if k == \"snippets/fancyintro\" and bk in self._peripheralBooks: # Only allow fancyIntros for scripture books\n pass\n else:\n self.localChanges.extend(c[2].regexes)\n\n ## Final tweaks\n # Strip out any spaces either side of an en-quad \n self.localChanges.append((None, regex.compile(r\"\\s?\\u2000\\s?\", flags=regex.M), r\"\\u2000\")) \n # Change double-spaces to singles\n self.localChanges.append((None, regex.compile(r\" {2,}\", flags=regex.M), r\" \")) \n # Escape special codes % and $ that could be in the text itself\n self.localChanges.append((None, regex.compile(r\"(?<!\\\\\\S*|\\\\[fx]\\s)([{}])(\\s?)\".format(\"\".join(self._specialchars)),\n flags=regex.M), lambda m:\"\\\\\"+self._specialchars[m.group(1)]+(\"\\\\space \" if m.group(2) else \" \"))) \n\n if self.printer is not None and self.printer.get(\"c_tracing\"):\n print(\"List of Local Changes:----------------------------------------------------------\")\n report = \"\\n\".join(\"{} -> {}\".format(p[1].pattern, p[2]) for p in self.localChanges)\n if getattr(printer, \"logger\", None) is not None:\n printer.logger.insert_at_cursor(v)\n else:\n print(report)\n return self.localChanges\n\n def base(self, fpath):\n doti = fpath.rfind(\".\")\n return os.path.basename(fpath[:doti])\n\n def codeLower(self, fpath):\n cl = re.match(self.printer.getPicRe()+\"$\", self.base(fpath))\n if cl:\n return cl[0].lower()\n else:\n return \"\"\n\n def newBase(self, fpath):\n clwr = self.codeLower(fpath)\n if len(clwr):\n return clwr\n else:\n return re.sub('[()&+,.;: ]', '_', self.base(fpath).lower())\n\n def makeGlossaryFootnotes(self, printer, bk):\n # Glossary entries for the key terms appearing like footnotes\n prjid = self.dict['project/id']\n prjdir = os.path.join(self.ptsettings.basedir, prjid)\n fname = printer.getBookFilename(\"GLO\", prjdir)\n infname = os.path.join(prjdir, fname)\n if os.path.exists(infname):\n with universalopen(infname, rewrite=True) as inf:\n dat = inf.read()\n ge = re.findall(r\"\\\\p \\\\k (.+)\\\\k\\* (.+)\\r?\\n\", dat) # Finds all glossary entries in GLO book (may need to add \\ili)\n if ge is not None:\n for g in ge:\n gdefn = re.sub(r\"\\\\xt (.+)\\\\xt\\*\", r\"\\1\", g[1])\n self.localChanges.append((None, regex.compile(r\"(\\\\w (.+\\|)?{} ?\\\\w\\*)\".format(g[0]), flags=regex.M), \\\n r\"\\1\\\\f + \\\\fq {}: \\\\ft {}\\\\f* \".format(g[0],gdefn)))\n\n def filterGlossary(self, printer):\n # Only keep entries that have appeared in this collection of books\n glossentries = []\n prjid = self.dict['project/id']\n prjdir = os.path.join(self.dict[\"/ptxpath\"], prjid)\n for bk in printer.getBooks():\n if bk not in TexModel._peripheralBooks:\n fname = printer.getBookFilename(bk, prjid)\n fpath = os.path.join(prjdir, fname)\n if os.path.exists(fpath):\n with universalopen(fpath) as inf:\n sfmtxt = inf.read()\n glossentries += re.findall(r\"\\\\w .*?\\|?([^\\|]+?)\\\\w\\*\", sfmtxt)\n fname = printer.getBookFilename(\"GLO\", prjdir)\n infname = os.path.join(prjdir, fname)\n if os.path.exists(infname):\n with universalopen(infname, rewrite=True) as inf:\n dat = inf.read()\n ge = re.findall(r\"\\\\p \\\\k (.+)\\\\k\\* .+\\r?\\n\", dat) # Finds all glossary entries in GLO book\n for delGloEntry in [x for x in ge if x not in list(set(glossentries))]:\n self.localChanges.append((None, regex.compile(r\"\\\\p \\\\k {}\\\\k\\* .+\\r?\\n\".format(delGloEntry), flags=regex.M), \"\"))\n\n def analyzeImageCopyrights(self):\n if self.dict['project/iffrontmatter'] == \"\":\n try:\n with open(self.printer.configFRT(), encoding=\"utf-8\") as inf:\n txt = inf.read()\n except FileNotFoundError:\n return\n else:\n txt = self.dict['project/colophontext']\n for m in re.findall(r\"(?i)\\\\(\\S+).*?\\\\zimagecopyrights([A-Z]{2,3})?\", txt):\n self.imageCopyrightLangs[m[1].lower() if m[1] else \"en\"] = m[0]\n return\n\n def generateEmptyImageCopyrights(self):\n self.analyzeImageCopyrights()\n res = [r\"\\def\\zimagecopyrights{}\"]\n for k in self.imageCopyrightLangs.keys():\n res.append(r\"\\def\\zimagecopyrights{}{{}}\".format(k))\n return \"\\n\".join(res)\n\n def generateImageCopyrightText(self):\n artpgs = {}\n mkr='pc'\n sensitive = self['document/sensitive']\n picpagesfile = os.path.join(self.docdir()[0], self['jobname'] + \".picpages\")\n crdts = []\n cinfo = self.printer.copyrightInfo\n self.analyzeImageCopyrights()\n if os.path.exists(picpagesfile):\n with universalopen(picpagesfile) as inf:\n dat = inf.read()\n\n # \\figonpage{304}{56}{cn01617.jpg}{tl}{© David C. Cook Publishing Co, 1978.}{x170.90504pt}\n rematch = r\"\\\\figonpage\\{(\\d+)\\}\\{\\d+\\}\\{(?:\" + self.printer.getPicRe() + \"|(.*?))\\.[^}]+\\}\\{.*?\\}\\{(.*?)?\\}\\{.+?\\}\"\n m = re.findall(rematch, dat)\n msngPgs = []\n customStmt = []\n if len(m):\n for f in m:\n if not len(f) or not f[0] or f[5] == \"None\":\n continue\n a = 'co' if f[1] == 'cn' else f[1] # merge Cook's OT & NT illustrations together\n if a == '' and f[5] != '':\n artpgs.setdefault(f[5], []).append(int(f[0]))\n elif a == '':\n artpgs.setdefault('zz', []).append(int(f[0]))\n msngPgs += [f[0]] \n else:\n artpgs.setdefault(a, []).append(int(f[0]))\n artistWithMost = \"\"\n if len(artpgs):\n artpgcmp = [a for a in artpgs if a != 'zz']\n if len(artpgcmp):\n artistWithMost = max(artpgcmp, key=lambda x: len(set(artpgs[x])))\n\n langs = set(self.imageCopyrightLangs.keys())\n langs.add(\"en\")\n for lang in sorted(langs):\n crdtsstarted = False\n if os.path.exists(picpagesfile):\n hasOut = False\n mkr = self.imageCopyrightLangs.get(lang, \"pc\")\n rtl = lang in cinfo['rtl']\n if rtl == (self.dict['document/ifrtl'] == \"false\"):\n mkr += \"\\\\begin\" + (\"R\" if rtl else \"L\")\n crdts.append(\"\\\\def\\\\zimagecopyrights{}{{%\".format(lang.lower()))\n crdtsstarted = True\n plstr = cinfo[\"plurals\"].get(lang, cinfo[\"plurals\"][\"en\"])\n cpytemplate = cinfo['templates']['imageCopyright'].get(lang,\n cinfo['templates']['imageCopyright']['en'])\n for art, pgs in artpgs.items():\n if art != artistWithMost and art != 'zz':\n if len(pgs):\n pgs = sorted(set(pgs))\n plurals = pluralstr(plstr, pgs)\n artinfo = cinfo[\"copyrights\"].get(art, {'copyright': {'en': art}, 'sensitive': {'en': art}})\n if artinfo is not None and (art in cinfo['copyrights'] or len(art) > 5):\n artstr = artinfo[\"copyright\"].get(lang, artinfo[\"copyright\"][\"en\"])\n if sensitive and \"sensitive\" in artinfo:\n artstr = artinfo[\"sensitive\"].get(lang, artinfo[\"sensitive\"][\"en\"])\n cpystr = multstr(cpytemplate, lang, len(pgs), plurals, artstr.replace(\"_\", \"\\u00A0\"))\n crdts.append(\"\\\\{} {}\".format(mkr, cpystr))\n else:\n crdts.append(_(\"\\\\rem Warning: No copyright statement found for: {} on pages {}\")\\\n .format(art.upper(), pluralstr))\n hasOut = True\n if len(msngPgs):\n plurals = pluralstr(plstr, msngPgs)\n template = cinfo['templates']['imageExceptions'].get(lang,\n cinfo['templates']['imageExceptions']['en'])\n exceptPgs = \" \" + multstr(template, lang, len(msngPgs), plurals)\n else:\n exceptPgs = \"\"\n\n if len(artistWithMost):\n artinfo = cinfo[\"copyrights\"].get(artistWithMost, \n {'copyright': {'en': artistWithMost}, 'sensitive': {'en': artistWithMost}})\n if artinfo is not None and (artistWithMost in cinfo[\"copyrights\"] or len(artistWithMost) > 5):\n pgs = artpgs[artistWithMost]\n plurals = pluralstr(plstr, pgs)\n artstr = artinfo[\"copyright\"].get(lang, artinfo[\"copyright\"][\"en\"])\n if sensitive and \"sensitive\" in artinfo:\n artstr = artinfo[\"sensitive\"].get(lang, artinfo[\"sensitive\"][\"en\"])\n if not hasOut:\n template = cinfo['templates']['allIllustrations'].get(lang,\n cinfo['templates']['allIllustrations']['en'])\n else:\n template = cinfo['templates']['exceptIllustrations'].get(lang,\n cinfo['templates']['exceptIllustrations']['en'])\n cpystr = template.format(artstr.replace(\"_\", \"\\u00A0\") + exceptPgs)\n crdts.append(\"\\\\{} {}\".format(mkr, cpystr))\n if self.dict['notes/ifxrexternalist']:\n if self.dict['notes/xrlistsource'] == \"standard\":\n msg = \"\\\\{} {}\".format(mkr, cinfo['templates']['openbible.info'].get(lang,\n cinfo['templates']['openbible.info']['en']).replace(\"_\", \"\\u00A0\"))\n else:\n msg = getattr(self, 'xrefcopyright', None)\n if msg is not None:\n if not crdtsstarted:\n crdts.append(\"\\\\def\\\\zimagecopyrights{}{{%\".format(lang.lower()))\n crdtsstarted = True\n crdts.append(msg)\n if crdtsstarted:\n crdts.append(\"}\")\n if len(crdts):\n crdts.append(\"\\\\let\\\\zimagecopyrights=\\\\zimagecopyrightsen\")\n return \"\\n\".join(crdts)\n\n def createXrefTriggers(self, bk, prjdir, outpath):\n if self.xrefs is None:\n cfilter = self.dict['notes/xrfilterbooks']\n if cfilter == \"pub\":\n bl = self.printer.get(\"ecb_booklist\", \"\").split()\n filters = set(bl)\n elif cfilter == \"prj\":\n filters = set(self.printer.getAllBooks().keys())\n elif cfilter == \"all\":\n filters = None\n elif cfilter == \"ot\":\n filters = allbooks[:39]\n elif cfilter == \"nt\":\n filters = allbooks[40:67]\n if filters is not None and len(filters) == 0:\n filters = None\n localfile = None\n if self.dict['strongsndx/localterms']:\n localfile = os.path.join(self.printer.settings_dir, self.printer.prjid, \"TermRenderings.xml\")\n if not os.path.exists(localfile):\n localfile = None\n self.xrefs = Xrefs(self, filters, prjdir,\n self.dict['project/selectxrfile'] if self.dict['notes/xrlistsource'] == 'custom' else None,\n self.dict['notes/xrlistsize'], self.dict['notes/xrlistsource'], localfile)\n self.xrefs.process(bk, outpath)\n\n","sub_path":"python/lib/ptxprint/texmodel.py","file_name":"texmodel.py","file_ext":"py","file_size_in_byte":96212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"480270764","text":"#10-7. Addition Calculator: Wrap your code from Exercise 10-6 \r\n#in a while loop so the user can continue entering numbers \r\n#even if they make a mistake and enter text instead of a number.\r\nprint(\"----------Addition program for two numbers----------\")\r\nprint(\"(Enter 'quit' to exit the program.)\")\r\n\r\nwhile True:\r\n\ttry:\r\n\t\tfirstNum = input(\"\\nEnter the first number: \")\r\n\t\tif firstNum == 'quit':\tbreak\r\n\t\tfirstNum = int(firstNum)\r\n\t\tsecondNum =input(\"Enter the second number: \")\r\n\t\tif secondNum == 'quit':\tbreak\r\n\t\tsecondNum = int(secondNum)\r\n\texcept ValueError:\r\n\t\tprint(\"\\n\\tYou entered a text instead of a number, please try again!\\n\")\r\n\telse:\r\n\t\tsum = firstNum + secondNum\r\n\t\tprint(\"\\n\\tThe sum of the two numbers you have inputed is \" + str(sum) + \".\")\r\n\t\t\r\n\t\t\r\n\r\n\r\n","sub_path":"chapter_10/addition_calculator.py","file_name":"addition_calculator.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"167561358","text":"#!/usr/bin/python -u\n# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\nimport os\nimport subprocess\nimport sys\n\nif 'LDAP_PORT_389_TCP_ADDR' not in os.environ:\n print('error: contained invoked without link to an ldap contaer')\n sys.exit(1)\n\nldap_url = 'ldap://%s:%s/' % (os.environ['LDAP_PORT_389_TCP_ADDR'],\n os.environ['LDAP_PORT_389_TCP_PORT'])\n\nos.environ['DOCKER_ENTRYPOINT'] = '1'\n\nsubprocess.check_call([\n '/usr/bin/python', '-u',\n '/usr/bin/ansible-playbook', 'docker-hgrb.yml',\n '-c', 'local',\n '-t', 'docker-startup',\n '-e', 'ldap_uri=%s' % ldap_url,\n ],\n cwd='/vct/ansible')\n\ndel os.environ['DOCKER_ENTRYPOINT']\n\nsubprocess.check_call(['/sbin/service', 'rsyslog', 'start'])\n\nos.execl(sys.argv[1], *sys.argv[1:])\n","sub_path":"ansible/roles/docker-hg-reviewboard/files/entrypoint.py","file_name":"entrypoint.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"83188645","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/ben/Dropbox/studies/newpainmachine/software/FAB/fab_controller/settings.py\n# Compiled at: 2015-01-06 09:36:37\nimport os\nfrom collections import namedtuple\nimport random, pkg_resources\nFAB_VERSION = pkg_resources.require('fab_controller')[0].version\nHANDS = [\n 'left', 'right']\nPair = namedtuple('Pair', HANDS)\nBlock = namedtuple('Block', ['duration', 'grams'])\nLOG_INTERVAL = 0.5\nLOGFILE_DIR = os.path.expanduser('~/Documents/fab/logs/')\nREST_N_FROM_TOP = 500\nSTEP_DELAY = 0.0003\nTIGHT_LOOP_INTERVAL = 0.001\nALLOWABLE_DISCREPANCY = 20\nTWO_KG = Pair(0.4457, 0.4692)\nDASHBOARD_UPDATE_INTERVAL = 0.2\nSERVER_PORT = 8000\nSTEP_PIN = Pair(2, 3)\nDIRECTION_PIN = Pair(6, 7)\nHIGH_LIMIT_PIN = Pair(17, 18)\nLOW_LIMIT_PIN = Pair(15, 16)\nSENSOR_PIN = Pair(4, 5)\nUP = 0\nDOWN = 1\nMOVEMENT_LABELS = {UP: 'up', DOWN: 'down'}\nMOVEMENT = {v:k for k, v in list(MOVEMENT_LABELS.items())}\nSTEPS_PER_FULL_STEP = 8\nFULL_STEPS_PER_REV = 200\nSTEPS_PER_REV = FULL_STEPS_PER_REV * STEPS_PER_FULL_STEP\nMM_PER_REV = 5\nMM_MAX_TRAVEL = 15\nMAX_REVS = MM_MAX_TRAVEL / MM_PER_REV\nMAX_STEPS = MAX_REVS * STEPS_PER_REV\nSWITCH_CHECKING_WINDOW_LENGTH = 5\nSENSOR_MEASUREMENTS_WINDOW_LENGTH = 5","sub_path":"pycfiles/fab-controller-0.9.14.tar/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"297697645","text":"from datetime import datetime\nfrom django.db import models\nfrom django.core.exceptions import ValidationError\nfrom dynamicfleet.reservas.validators import validate_start_date\n\n\nclass Reserve(models.Model):\n STATES_CHOICES = [\n ('provisoria', 'Provisória'),\n ('confirmada', 'Confirmada'),\n ('cancelada', 'Cancelada')\n ]\n\n vehicle = models.ForeignKey(\"veiculos.Vehicle\", \n on_delete=models.CASCADE, \n related_name='reserves')\n start = models.DateTimeField('Inicio', \n default=datetime.now, \n validators=[validate_start_date])\n end = models.DateTimeField('Fim', default=datetime.now,)\n state = models.CharField('Estado da Reserva', \n max_length=10, \n choices=STATES_CHOICES, \n default='provisoria')\n\n created = models.DateTimeField('Registrado em', auto_now_add=True)\n modified = models.DateTimeField('Modificado em', auto_now=True)\n\n def clean(self):\n super(Reserve, self).clean()\n \n if self.start > self.end:\n raise ValidationError('Data de fim da reserva inválida.', 'invalid value')\n\n class Meta:\n verbose_name = 'reserva'\n verbose_name_plural = 'reservas'\n\n def __str__(self):\n return '[ veiculo: {}, start: {}, end: {}, state: {}]'.format(\n self.vehicle.model,\n self.start,\n self.end,\n self.state)\n","sub_path":"dynamicfleet/reservas/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"380817949","text":"\"\"\"\n--- Day 23: Crab Cups ---\nThe small crab challenges you to a game! The crab is going to mix up some cups, and you have to predict where they'll end up.\n\nThe cups will be arranged in a circle and labeled clockwise (your puzzle input). For example, if your labeling were 32415, \nthere would be five cups in the circle; going clockwise around the circle from the first cup, the cups would be labeled \n3, 2, 4, 1, 5, and then back to 3 again.\n\nBefore the crab starts, it will designate the first cup in your list as the current cup. The crab is then going to do 100 moves.\n\nEach move, the crab does the following actions:\n\nThe crab picks up the three cups that are immediately clockwise of the current cup. They are removed from the circle; cup spacing is adjusted as necessary to maintain the circle.\nThe crab selects a destination cup: the cup with a label equal to the current cup's label minus one. If this would select one of the cups that was just picked up, \n the crab will keep subtracting one until it finds a cup that wasn't just picked up. If at any point in this process the value goes below the lowest value on any cup's label, \n it wraps around to the highest value on any cup's label instead.\nThe crab places the cups it just picked up so that they are immediately clockwise of the destination cup. They keep the same order as when they were picked up.\nThe crab selects a new current cup: the cup which is immediately clockwise of the current cup.\n\nFor example, suppose your cup labeling were 389125467. If the crab were to do merely 10 moves, the following changes would occur:\n\n-- move 1 --\ncups: (3) 8 9 1 2 5 4 6 7 \npick up: 8, 9, 1\ndestination: 2\n\n-- move 2 --\ncups: 3 (2) 8 9 1 5 4 6 7 \npick up: 8, 9, 1\ndestination: 7\n\n-- move 3 --\ncups: 3 2 (5) 4 6 7 8 9 1 \npick up: 4, 6, 7\ndestination: 3\n\n-- move 4 --\ncups: 7 2 5 (8) 9 1 3 4 6 \npick up: 9, 1, 3\ndestination: 7\n\n-- move 5 --\ncups: 3 2 5 8 (4) 6 7 9 1 \npick up: 6, 7, 9\ndestination: 3\n\n-- move 6 --\ncups: 9 2 5 8 4 (1) 3 6 7 \npick up: 3, 6, 7\ndestination: 9\n\n-- move 7 --\ncups: 7 2 5 8 4 1 (9) 3 6 \npick up: 3, 6, 7\ndestination: 8\n\n-- move 8 --\ncups: 8 3 6 7 4 1 9 (2) 5 \npick up: 5, 8, 3\ndestination: 1\n\n-- move 9 --\ncups: 7 4 1 5 8 3 9 2 (6)\npick up: 7, 4, 1\ndestination: 5\n\n-- move 10 --\ncups: (5) 7 4 1 8 3 9 2 6 \npick up: 7, 4, 1\ndestination: 3\n\n-- final --\ncups: 5 (8) 3 7 4 1 9 2 6 \nIn the above example, the cups' values are the labels as they appear moving clockwise around the circle; the current cup is marked with ( ).\n\nAfter the crab is done, what order will the cups be in? Starting after the cup labeled 1, collect the other cups' labels clockwise into a \nsingle string with no extra characters; each number except 1 should appear exactly once. In the above example, after 10 moves, the cups \nclockwise from 1 are labeled 9, 2, 6, 5, and so on, producing 92658374. If the crab were to complete all 100 moves, \nthe order after cup 1 would be 67384529.\n\nUsing your labeling, simulate 100 moves. What are the labels on the cups after cup 1?\n\nYour puzzle input is 792845136.\n\n\n--- Part Two ---\nDue to what you can only assume is a mistranslation (you're not exactly fluent in Crab), you are quite surprised when the \ncrab starts arranging many cups in a circle on your raft - one million (1000000) in total.\n\nYour labeling is still correct for the first few cups; after that, the remaining cups are just numbered in an increasing \nfashion starting from the number after the highest number in your list and proceeding one by one until one million is reached. \n(For example, if your labeling were 54321, the cups would be numbered 5, 4, 3, 2, 1, and then start counting up from 6 until \none million is reached.) In this way, every number from one through one million is used exactly once.\n\nAfter discovering where you made the mistake in translating Crab Numbers, you realize the small crab isn't going to do \nmerely 100 moves; the crab is going to do ten million (10000000) moves!\n\nThe crab is going to hide your stars - one each - under the two cups that will end up immediately clockwise of cup 1. \nYou can have them if you predict what the labels on those cups will be when the crab is finished.\n\nIn the above example (389125467), this would be 934001 and then 159792; multiplying these together produces 149245887792.\n\nDetermine which two cups will end up immediately clockwise of cup 1. What do you get if you multiply their labels together?\n\"\"\"\n\n### IMPORTS ###\n\nimport collections\nimport cProfile\nimport datetime\nimport math\nimport numpy\nimport pickle\nimport pstats\nimport time\n\n\n### CONSTANTS ###\n\nINPUT_FILENAME = 'input0.txt'\n\ndo_profiling = False\n\n\n### FUNCTIONS ###\n\ndef parse_input( ):\n\tlines = open( INPUT_FILENAME, 'r' ).read( ).splitlines( )\n\n\treturn lines[ 0 ]\n\n\ndef main( labels ):\n\t\"\"\"\n\t\"\"\"\n\ttime_start = time.perf_counter( )\n\t\n\tcups = collections.deque( )\n\t\n\tfor l in labels:\n\t\tcups.append( int( l ) )\n\t\t\n\tmin_label = min( cups )\n\tmax_label = max( cups )\n\n\t# Add all numbers up to 1M\n\tfor i in range( max_label + 1, 1000000 + 1 ):\n\t\tcups.append( i )\n\t\n\tcur_cup = cups[ 0 ]\n\toffset = 0\n\tmove = 0\n\t\n\twhile move < 10000000:\n\t\tmove += 1\n\t\t\n\t\tif move % 1000 == 0:\n\t\t\telapsed_secs = time.perf_counter( ) - time_start\n\t\t\tsecs_per_move = elapsed_secs / move\n\t\t\thours_remaining = secs_per_move * ( 10000000 - move ) / 60.0 / 60.0\n\n\t\t\teta_time = ( datetime.datetime.now( ) + datetime.timedelta( hours = hours_remaining ) ).strftime( '%a %I:%M:%S %p' )\n\n\t\t\tprint( 'move {0} ({1:.02f}%), {2:.02f} hours remaining, ETA = {3}'.format( move, move / 10000000 * 100.0, hours_remaining, eta_time ) )\n\t\t\t\n\t\t\tif do_profiling:\n\t\t\t\tprofiler = cProfile.Profile( )\n\t\t\t\tprofiler.enable( )\n\t\t\t\n\t\t\n\t\t#print( '\\n-- move {0} --'.format( move ) )\n\t\t#print( 'cups: {0}'.format( ' '.join( [ str( c ) for c in list( cups )[ :50 ] ] ) ) )\n\t\t\t\n\t\tcups.rotate( -1 )\n\t\toffset = 999999\n\t\t\n\t\t# Remove 3 cups to right (clockwise) of current cup\n\t\tremoved = [\n\t\t cups.popleft( ),\n\t\t cups.popleft( ),\n\t\t cups.popleft( )\n\t\t]\n\t\t\n\t\t#print( 'pick up: {0}'.format( ' '.join( [ str( r ) for r in removed ] ) ) )\n\t\t\n\t\t# find destination cup\n\t\tdest_cup = None\n\t\ti = cur_cup - 1\n\t\t\n\t\twhile dest_cup is None:\n\t\t\tif i in cups:\n\t\t\t\tdest_cup = i\n\t\t\t\tcontinue\n\n\t\t\ti -= 1\n\t\t\tif i < min_label:\n\t\t\t\ti = max_label\n\t\t\n\t\t# Got our destination cup, so insert cups to right of it\n\t\tidx = cups.index( dest_cup )\n\t\t#idx = 10\n\t\t#while cups[ idx ] != dest_cup:\n\t\t\t#idx -= 1\n\t\t\t#if idx < 0:\n\t\t\t\t#idx = 999996\n\t\t\n\t\tprint( 'cur_cup =', cur_cup, 'dest_cup =', dest_cup, 'idx =', idx )\n\t\t\n\t\tif idx != len( cups ) - 1:\n\t\t\t# Have to do some rotating to get dest cup to end\n\t\t\tcups.rotate( ( idx + 1 ) * -1 )\n\t\t\t\n\t\t\t# Update cur_cup offset\n\t\t\toffset -= idx + 1\n\t\t\n\t\t# Add our removed cups\t\t\n\t\tfor r_cup in removed:\n\t\t\tcups.append( r_cup )\n\n\t\t# Update offset due to inserting those 3 again.\n\t\t# Since we added them to the end, offset drops by 3 again\n\t\toffset -= 3\n\t\t\n\t\t# Rotate to get current cup at head (first) again, then pick new current cup\n\t\tcups.rotate( len( cups ) - offset )\n\t\toffset = 0\n\n\t\tcur_cup = cups[ 1 ]\n\t\t\n\t\t# Rotate again so new cur_cup is at head (first)\n\t\tcups.rotate( -1 )\n\t\t\n\t\tif do_profiling:\n\t\t\tif move % 1000 == 0:\n\t\t\t\tprofiler.disable( )\n\t\t\t\tstats = pstats.Stats( profiler )\n\t\t\t\tstats.dump_stats( r'C:\\Users\\Home\\Dropbox (Personal)\\misc\\profile.pstats' )\t\n\n\t# Dump it out\n\tpickle.dump( cups, open( r'D:\\temp\\blah.pickle', 'wb' ) )\n\t\n\t#print( '\\n-- final --' )\n\t#print( 'cups: {0}'.format( ' '.join( [ str( c ) for c in cups ] ) ) )\n\t\t \n\t# print answer?\n\t#print( 'cups: {0}'.format( ' '.join( [ str( c ) for c in list( cups )[ :50 ] ] ) ) )\n\n\tidx = cups.index( 1 )\n\t\n\tidx1 = idx + 1\n\tif idx1 >= len( cups ):\n\t\tidx1 = idx1 - len( cups )\n\tidx2 = idx + 2\n\tif idx2 >= len( cups ):\n\t\tidx2 = idx2 - len( cups )\n\t\t\n\tval1 = cups[ idx1 ]\n\tval2 = cups[ idx2 ]\n\t#print( 'answer = {0} * {1} = {2}'.format( val1, val2, val1 * val2 ) )\n\n\t## Calculate score\n\t## Rotate so \"1\" is at end (on right)\n\t#idx = cups.index( 1 )\n\t#cups.rotate( len( cups ) - idx - 1 )\n\t\n\t#score = ''.join( [ str( c ) for c in cups ] )[ :-1 ]\n\tprint( 'values =', val1, val2 )\n\treturn val1 * val2\n\t\n# test answer = 67384529\n\n### CLASSES ###\n\n\n### MAIN ###\n\nif __name__ == \"__main__\":\n\ttime_start = time.perf_counter( )\n\n\tlabels = parse_input( )\n\t\n\tanswer = main( labels )\n\n\tprint( 'answer =', answer )\n\tprint( 'done in {0:.4f} secs'.format( time.perf_counter( ) - time_start ) )\n\n# 54 not right\n","sub_path":"2020/23/2020_day_23_2c_grow.py","file_name":"2020_day_23_2c_grow.py","file_ext":"py","file_size_in_byte":8497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"636265272","text":"#\n# Joel Labbe\n#\n# convert_v1.py\n#\n# Create a table showing Celsius to Farhenheit temperatures every 10 degrees, from 0C to 100C\n#\n# Input: Temperature in Celsius from 0C to 100c\n#\n# Processing: 1. For Celsius temperature from 0C to 100C\n# (every 10 degrees):\n# Calculate corresponding Farhenheit\n# farhenheit = 9/5*Celsius+32\n# Display Result\n#\n# Output: Table of Equivalent Temperatures in Fahrenheit \n#\n\ndef main():\n # Display Heading\n print(\"Temperature Converter ...\")\n print()\n print(\"Celsius\\t\\tFahrenheit\")\n print(\"==========================\")\n\n # For celsius from 0C to 100c\n # Calculate & display fahrenheit\n celsius = 0\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 10\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 20\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 30\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 40\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 50\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 60\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 70\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 80\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 90\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\n celsius = 100\n fahrenheit = 9/5 * celsius + 32\n print (\" \",celsius,\"\\t\\t\", fahrenheit)\n\nmain()\n \n","sub_path":"Labs/Chapter 2/convert_v1.py","file_name":"convert_v1.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"347871921","text":"\n\n#calss header\nclass _PIOUS():\n\tdef __init__(self,): \n\t\tself.name = \"PIOUS\"\n\t\tself.definitions = [u'strongly believing in religion, and living in a way that shows this belief: ', u'pretending to have sincere feelings: ', u'something that is unlikely to happen']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_pious.py","file_name":"_pious.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"442971790","text":"import sys, StringIO\n\ndef replaceAll(s, r):\n for x in r:\n s = s.replace(x, ' ', 1)\n return s\n\ndef solution(s):\n r = []\n #find \"THREE\", \"NINE\"\n for i, n in [(8, 'GEIHT'), (6, 'XSI'), (0, 'ZERO'), (4,'UFOR'), (7, 'SEVEN'),\n (5, 'VFIE'), (2, 'WTO'), (1, 'ONE'), (3, 'THREE'), (9, 'NINE')]:\n while s.find(n[0])>-1:\n r.append(i)\n s = replaceAll(s, n)\n\n return ''.join([str(x) for x in sorted(r)])\n#solution\n\n\nif __name__ == '__main__':\n if len(sys.argv)>1:\n input = file(sys.argv[1])\n else:\n input = StringIO.StringIO(\"\"\"4\nOZONETOWER\nWEIGHFOXTOURIST\nOURNEONFOE\nETHER\"\"\")\n cases = int(input.readline())\n for case in range(cases):\n s = input.readline().strip()\n print(\"Case #%d: %s\" % (case+1, solution(s)))\n","sub_path":"solutions_5648941810974720_0/Python/Wingi/pa.py","file_name":"pa.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"588147580","text":"#!/usr/bin/env python\n\nimport sys\nimport rospy\nimport moveit_commander\nimport tf\n\nfrom time import time\nfrom math import radians, degrees, cos, sin\nfrom leap_motion.msg import Human, Hand\nfrom tf.transformations import quaternion_from_euler\nfrom geometry_msgs.msg import TwistStamped, Vector3\n\nALMOST_ZERO = 0.0001\n\n# [0.7783687709988791, -0.3197063419414436, 0.6000029657171599]\n\ndef constrain(value, a, b):\n max_value = max(a, b)\n min_value = min(a, b)\n return min(max_value, max(min_value, value))\n\ndef constrain_abs(value, diviation):\n return constrain(value, -diviation, diviation)\n\ndef apply_to_vector(vector, func):\n new_vec = Vector3()\n new_vec.x = func(vector.x)\n new_vec.y = func(vector.y)\n new_vec.z = func(vector.z)\n return new_vec\n\ndef is_open(hand):\n return hand.grab_strength < 0.5\n\ndef rotate_vector(x, y):\n theta = radians(45)\n cs = cos(theta)\n sn = sin(theta)\n\n px = x * cs - y * sn\n py = x * sn + y * cs\n\n return px, py\n\ndef add_vectors(a, b):\n new_vec = Vector3()\n new_vec.x = a.x + b.x\n new_vec.y = a.y + b.y\n new_vec.z = a.z + b.z\n return new_vec\n\ndef subtract_vectors(a, b):\n new_vec = Vector3()\n new_vec.x = a.x - b.x\n new_vec.y = a.y - b.y\n new_vec.z = a.z - b.z\n return new_vec\n\ndef negate_vector(vec):\n new_vec = Vector3()\n new_vec.x = -vec.x\n new_vec.y = -vec.y\n new_vec.z = -vec.z\n return new_vec\n\n\nclass LeapListener(object):\n def __init__(self):\n super(LeapListener, self).__init__()\n moveit_commander.roscpp_initialize(sys.argv)\n rospy.init_node(\"leap_commander\")\n self.centered_position = Vector3(0.68, -0.34, 0.59)\n\n self.tf_listener = tf.TransformListener()\n self.move_group = moveit_commander.MoveGroupCommander(\"right_arm\")\n self.previous_speed = Vector3()\n self.published_zero = False\n\n self.zeroed_hand_position = Vector3(0, 0, 0.25)\n self.zeroed_in_this_loop = True\n\n self.last_timestamp = time()\n\n\n self.body_pose_publisher = rospy.Publisher(\"jog_arm_server/delta_jog_cmds\", TwistStamped, queue_size=10)\n rospy.Subscriber(\"leap_motion/leap_device\", Human, self.on_leap_msg)\n rospy.spin()\n\n def get_end_effector_position(self):\n return self.tf_listener.lookupTransform('/base_link', self.move_group.get_end_effector_link(), rospy.Time(0))\n\n def on_leap_msg(self, msg):\n if msg.right_hand is not None and msg.right_hand.is_present and is_open(msg.right_hand):\n hand = msg.right_hand\n\n hand_position = Vector3(-hand.palm_center.z, -hand.palm_center.x, hand.palm_center.y)\n # hand_position = apply_to_vector(hand_position, lambda vec: constrain_abs(vec, 0.2))\n\n current_position = self.get_end_effector_position()[0]\n current_position = Vector3(current_position[0], current_position[1], current_position[2])\n\n if not self.zeroed_in_this_loop:\n self.zeroed_hand_position = hand_position\n self.centered_position = current_position\n self.zeroed_in_this_loop = True\n relative_hand_position = subtract_vectors(hand_position, self.zeroed_hand_position)\n\n # print \"relative\"\n # print relative_hand_position\n # print \"\"\n\n desired_position = add_vectors(relative_hand_position, self.centered_position)\n\n position = Vector3(ALMOST_ZERO, ALMOST_ZERO, ALMOST_ZERO)\n rotation = Vector3(ALMOST_ZERO, ALMOST_ZERO, ALMOST_ZERO)\n \n speed = subtract_vectors(desired_position, current_position)\n\n x, z = rotate_vector(-speed.y, speed.z)\n Kp = 3\n Kd = 0\n now = time()\n time_difference = now - self.last_timestamp\n self.last_timestamp = now\n position.y = speed.x * Kp - self.previous_speed.y * time_difference * Kd\n position.x = x * Kp - self.previous_speed.x * time_difference * Kd\n position.z = z * Kp - self.previous_speed.z * time_difference * Kd\n # print position\n # print \"\"\n self.previous_speed = position\n \n \n pose = TwistStamped()\n pose.header.stamp = rospy.Time.now()\n pose.header.frame_id = \"base_link\"\n pose.twist.linear = position\n pose.twist.angular = rotation\n\n self.body_pose_publisher.publish(pose)\n self.published_zero = False\n elif not self.published_zero:\n pose = TwistStamped()\n pose.header.stamp = rospy.Time.now()\n pose.header.frame_id = \"base_link\"\n pose.twist.linear = Vector3(ALMOST_ZERO, ALMOST_ZERO, ALMOST_ZERO)\n pose.twist.angular = Vector3(ALMOST_ZERO, ALMOST_ZERO, ALMOST_ZERO)\n self.body_pose_publisher.publish(pose)\n self.published_zero = True\n self.zeroed_in_this_loop = False\n else:\n self.zeroed_in_this_loop = False\n\n\nif __name__ == \"__main__\":\n LeapListener()\n","sub_path":"husky_leap/src/leap_motion_absolute_controller.py","file_name":"leap_motion_absolute_controller.py","file_ext":"py","file_size_in_byte":5085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"443703496","text":"import xarray as xr\nimport traitlets as tl\n\nfrom podpac.core.utils import common_doc, cached_property\nfrom podpac.core.data.datasource import COMMON_DATA_DOC, DATA_DOC\nfrom podpac.core.data.file_source import BaseFileSource, FileKeysMixin, LoadFileMixin\n\n\n@common_doc(COMMON_DATA_DOC)\nclass Dataset(FileKeysMixin, LoadFileMixin, BaseFileSource):\n \"\"\"Create a DataSource node using xarray.open_dataset.\n\n Attributes\n ----------\n source : str\n Path to the dataset file.\n In addition to local paths, file://, http://, ftp://, and s3:// transport protocols are supported.\n dataset : xarray.Dataset\n Dataset object.\n coordinates : :class:`podpac.Coordinates`\n {coordinates}\n data_key : str\n data key, default 'data'\n lat_key : str\n latitude key, default 'lat'\n lon_key : str\n longitude key, default 'lon'\n time_key : str\n time key, default 'time'\n alt_key : str\n altitude key, default 'alt'\n crs : str\n Coordinate reference system of the coordinates\n extra_dim : dict\n In cases where the data contain dimensions other than ['lat', 'lon', 'time', 'alt'], these dimensions need to be selected.\n For example, if the data contains ['lat', 'lon', 'channel'], the second channel can be selected using `extra_dim=dict(channel=1)`\n \"\"\"\n\n # dataset = tl.Instance(xr.Dataset).tag(readonly=True)\n extra_dim = tl.Dict(allow_none=True).tag(attr=True)\n\n @tl.default(\"extra_dim\")\n def _default_extra_dim(self):\n return None\n\n # -------------------------------------------------------------------------\n # public api properties and methods\n # -------------------------------------------------------------------------\n\n def open_dataset(self, fp):\n return xr.open_dataset(fp)\n\n def close_dataset(self):\n super(Dataset, self).close_dataset()\n self.dataset.close()\n\n @cached_property\n def dims(self):\n \"\"\"dataset coordinate dims\"\"\"\n lookup = {self.lat_key: \"lat\", self.lon_key: \"lon\", self.alt_key: \"alt\", self.time_key: \"time\"}\n return [lookup[dim] for dim in self.dataset.dims]\n\n @cached_property\n def keys(self):\n return list(self.dataset.keys())\n\n @common_doc(COMMON_DATA_DOC)\n def get_data(self, coordinates, coordinates_index):\n \"\"\"{get_data}\"\"\"\n\n if not isinstance(self.data_key, list):\n data = self.dataset[self.data_key]\n data = data.transpose(*self.dataset.dims)\n else:\n data = self.dataset[self.data_key].to_array(dim=\"output\")\n tdims = tuple(self.dataset.dims) + (\"output\",)\n data = data.transpose(*tdims)\n\n return self.create_output_array(coordinates, data.data[coordinates_index])\n","sub_path":"podpac/core/data/dataset_source.py","file_name":"dataset_source.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"409083044","text":"'''\n最后一行是最后图片加1s,全部出车,且保留车辆pos信息\n'''\nimport cv2\nimport numpy as np\nimport os\nimport json\nfrom detect_utils import parking_line\nfrom detect_utils import show_parking_line\nfrom create import create_json_record\n\npath_img = r'E:\\home_label\\2000_02_02\\DDT2G1907ZMY00040SY' # 路径\n\nparking_space = 5 # 停车位个数 #需要手动改\n\n# #################不标车牌的IP##################\nno_plate_list = ['test_177', 'test_211', 'test_212', 'test_221', 'test_222', 'test_231',\n 'test_232', 'test_241', 'test_242', 'test_251', 'test_252', 'test_261', 'test_262']\n\n# #################创建参数和初始化##################\npath_txt = os.path.dirname(path_img) + '\\\\' + path_img.split('\\\\')[-1] + '_label.txt'\npath_json = os.path.dirname(path_img) + '\\\\' + path_img.split('\\\\')[-1] + '_label_v2.json'\nfont = cv2.FONT_HERSHEY_SIMPLEX # 使用默认字体\nchange_parking_state = 0\nshow_parking_num = 1\nchange_parking_num = 2\nframe_car_use_left_button = 3\nstate_mouse_init = 0\ndrag_start = None\nsel = (0, 0, 0, 0) # 鼠标用\nstate_num_key = change_parking_state\nstate_mouse = state_mouse_init\n\nparking_num_block = [] # 车位数据集合(选择跨车位停车时使用),是车位列表中的数字\"0,1\"\nparking_num = [] # 各停车空间的所占的停车位号,parking_num[idx]的内容是parking_num_block中的数字\nstate_loc = [] # 停车状态\npos_show = [] # 显示位置坐标\npos_show_bias = [] # 显示位置偏移后的坐标\ncolor_parking_num = [] # 停车状态颜色\ncolor_parking_space_num = []\ncar_frame_point = []\ncar_frame_point_abs = []\n\nact = 0 # 控制是否连续播放\nstate_time_label_overwrite = 0\ntime_label = ''\n\n\n# 为各项数值初始化\nfor idx, i in enumerate(range(parking_space)):\n parking_num.append([idx])\n state_loc.append(0)\n pos_show.append([0, 0])\n pos_show_bias.append([0, 0])\n color_parking_num.append((255, 0, 255))\n color_parking_space_num.append((255, 0, 0))\n car_frame_point.append([]) # 车辆框架点初始为空\n car_frame_point_abs.append([])\n\n# list_img = os.listdir(path_img)\n# for filename in list_img:\n# if not filename.endswith('jpg'):\n# list_img.remove(filename)\n# list_img.sort() # 排序\n\nfile_list = os.listdir(path_img)\nlist_img = [file for file in file_list if file.endswith('jpg')]\nlist_img.sort() # 排序\nimgs_list_only_time = ['_'.join(i.split('_')[:3]) + '.jpg' for i in list_img] # 以防文件名后为电压等\nos.chdir(path_img)\nim_data = cv2.imread(list_img[0])\n\n# ##############得到图像参数##################\nh, w = im_data.shape[0:2]\n# ##############字体大小和位置参数##################\nx_panel = int(w * 0.83)\ny_panel = int(h * 0.10)\ny_panel_bias = y_panel - int(0.05 * h)\nfont_size = w // 1000\nbias = int(w * 0.025) # 车位与状态标记在图片上的偏移\nbias_y = int(h * -0.05) # 车位idx号位置的偏移\nfont_width = int((h + w) / 1000)\n\n# ##################得到IP地址并人为规定显示位置##################\nimg_dir_name = os.path.basename(path_img)\n\nip_name = img_dir_name\n\nif ip_name in parking_line:\n parking_list_np = np.array(parking_line[ip_name]).astype(int) # 获得车位线\nelse:\n blank_list = []\n for i in range(parking_space):\n blank_list.append([[0, 0], [0, 0], [0, 0], [0, 0]]) # 待修改\n parking_list_np = np.array(blank_list)\n\nif ip_name == 'test_177':\n h_h = 600\n h_l = 1400\nelif ip_name == 'test_175':\n h_h = 700\n h_l = 1700\nelif ip_name == 'test_176':\n h_h = 500\n h_l = 1400\nelif ip_name == 'test_178':\n h_h = 250\n h_l = 1050\nelif ip_name == 'test_170':\n h_h = 250\n h_l = 1300\nelif ip_name == 'test_211':\n h_h = 350\n h_l = 850\nelif ip_name == 'test_212':\n h_h = 350\n h_l = 850\nelif ip_name == 'test_221':\n h_h = 350\n h_l = 850\nelif ip_name == 'DDZ2G1907ZMY00002SY':\n h_h = 200 # 显示的最上边界\n h_l = 1300 # 显示的最下边界\nelif ip_name == 'DDT2G1907ZMY00008SY':\n h_h = 300 # 显示的最上边界\n h_l = 1400 # 显示的最下边界\nelif ip_name == 'DDT2G1907ZMY00016SY':\n h_h = 600 # 显示的最上边界\n h_l = 1450 # 显示的最下边界\nelse:\n h_h = 1000 # 显示的最上边界\n h_l = 1800 # 显示的最下边界\n\n# 标记窗口大小设置\n# 待修改\nif ip_name == 'test_177':\n # w_w = w//3\n # w_h = (h_l - h_h)//3\n w_w = (w // 7) * 3\n w_h = ((h_l - h_h) // 7) * 3\nelif ip_name == 'test_170':\n w_w = (w // 7) * 3\n w_h = ((h_l - h_h) // 7) * 3\nelif ip_name in ['test_211', 'test_212', 'test_221']:\n w_w = (w // 3) * 3\n w_h = ((h_l - h_h) // 3) * 3\nelse:\n w_w = (w // 5) * 2\n w_h = ((h_l - h_h) // 5) * 2\n\nimg_len = len(list_img)\nidx = 0\nh_0, m_0, s_0 = os.path.splitext(list_img[0])[0].split('_')[:3] # 排序后第一个时间\nh_1, m_1, s_1 = os.path.splitext(list_img[1])[0].split('_')[:3]\n# 两幅图片时间间隔\n# 如果时间间隔不确定,这个就对程序没意义了\ngap_sec = (int(h_1) - int(h_0)) * 3600 + (int(m_1) - int(m_0)) * 60 + (int(s_1) - int(s_0))\none_min = 60 // gap_sec\nthree_mins = 180 // gap_sec\nfive_mins = 300 // gap_sec\nif one_min == 0:\n one_min = 2\nif three_mins == 0:\n three_mins = 6\nif five_mins == 0:\n five_mins = 10\n\n# 得到初始时间秒值\ninitial_time_sec = int(h_0) * 3600 + int(m_0) * 60 + int(s_0)\n\n# 全部图片时间秒\nh_f, m_f, s_f = os.path.splitext(list_img[-1])[0].split('_')[:3]\nh_sum = int(h_f) - int(h_0)\nm_sum = int(m_f) - int(m_0)\ns_sum = int(s_f) - int(s_0)\nsec_sum = h_sum * 3600 + m_sum * 60 + s_sum\n\n# 计算图片最后一张加一秒的时_分_秒表达\nsec_all = int(h_f) * 3600 + int(m_f) * 60 + int(s_f)\nsec_all_plus_one_sec = sec_all + 1\nh_last = sec_all_plus_one_sec // 3600 # 时\nm_last = sec_all_plus_one_sec % 3600 // 60 # 分\ns_last = sec_all_plus_one_sec % 60 # 秒\nlast_time = str(h_last).zfill(2) + '_' + str(m_last).zfill(2) + '_' + str(s_last).zfill(2)\nimgs_last_time_plus_one = last_time # 为了减少改动\n\n\nimg_data = cv2.imread(list_img[0])\n# global record_list\nrecord_list = []\n\nclick_time = 0\n\n\ndef onmouse(event, x, y, flags, param):\n global drag_start, sel\n global click_time\n global pos_show\n global pos_show_bias\n if state_mouse == state_mouse_init:\n if event == cv2.EVENT_LBUTTONDOWN:\n if click_time < parking_space:\n pos_show[click_time] = [x, y]\n pos_show_bias[click_time] = [x + bias, y]\n click_time += 1\n print(x, y)\n if state_mouse == frame_car_use_left_button:\n if event == cv2.EVENT_LBUTTONDOWN:\n drag_start = x, y\n sel = 0, 0, 0, 0\n elif event == cv2.EVENT_LBUTTONUP:\n drag_start = None\n car_frame_point[id_parking_num_change] = [(sel[0], sel[1]), (sel[2], sel[3])]\n car_frame_point_abs[id_parking_num_change] = [(sel[0], sel[1] + h_h), (sel[2], sel[3] + h_h)]\n print('是否保留该边框?是:点击回车确认;否:直接重画')\n # state_mouse = frame_car_use_left_button\n elif drag_start:\n if flags & cv2.EVENT_FLAG_LBUTTON:\n minpos = min(drag_start[0], x), min(drag_start[1], y)\n maxpos = max(drag_start[0], x), max(drag_start[1], y)\n sel = minpos[0], minpos[1], maxpos[0], maxpos[1]\n img = img_data.copy()\n cv2.rectangle(img, (sel[0], sel[1]), (sel[2], sel[3]), (0, 255, 255), 4)\n cv2.imshow(\"label\", img)\n\n else:\n print(\"selection is complete\")\n drag_start = None\n\n# 在数字键用来改变停车状态下的简单显示\n\n\ndef tmp_print(state_loc):\n for idx_state_loc, i in enumerate(state_loc):\n if idx_state_loc < parking_space - 1:\n print(i, end='\\t')\n else:\n print(i)\n\n# 加减一秒数值状态简单显示\n\n\ndef tmp_print_time_label(state_loc, time_label):\n for i in state_loc:\n print(i, end='\\t')\n print(time_label)\n\n# 加减一秒数值状态删除显示\n\n\ndef tmp_print_for_delete(tmp_np_array):\n for i in tmp_np_array:\n print(i, end='')\n print('<--X')\n\n# 加车牌函数\n\n\ndef add_plate_2_label_v2(data_raw): # 待修改\n parking_state_txt_record_state = []\n for i in range(parking_space):\n parking_state_txt_record_state.append('0') # 初始状态为0\n\n data_updata = [] # 第一行处理方法,见2加车牌\n for i in data_raw:\n data_updata.append(i[0])\n for idx_0 in range(parking_space):\n if (i[idx_0 + 1].split(':')[1] == '2') and (parking_state_txt_record_state[idx_0] == '0'):\n data_updata.append(i[idx_0 + 1] + ':蓝辽Axxxxx')\n else:\n data_updata.append(i[idx_0 + 1])\n if i[idx_0 + 1].split(':')[1] != '1': # 若无此判断,则012的2显示不了车牌\n parking_state_txt_record_state[idx_0] = i[idx_0 + 1].split(':')[1]\n\n data_updata = np.array(data_updata)\n data_updata = data_updata.reshape((-1, parking_space + 1))\n\n return data_updata\n\n\ndef save_json_label(record_list, parking_space, path_json, imgs_last_time_plus_one, imgs_list_only_time):\n record_for_json = create_json_record(record_list, parking_space, imgs_last_time_plus_one, imgs_list_only_time)\n file = open(path_json, 'w', encoding='utf-8')\n json.dump(record_for_json, file, ensure_ascii=False)\n file.close()\n print(f'save json at:{path_json}')\n\n\n# 补全记录(在记录的最后一条补全出车)\ndef complement_record_list(record_list, last_time, parking_space): # 这里的last_time就是最后图片加1s的时间\n record_list_tmp = np.array(record_list) # 得到记录\n record_list_tmp = record_list_tmp.reshape((-1, parking_space + 1)) # 排版\n record_list_last = record_list_tmp[-1] # 得到最后一行\n\n for i in range(parking_space + 1):\n if i == 0:\n info = last_time\n else:\n info_splits = record_list_last[i].split(':')\n if len(info_splits) == 2:\n info = info_splits[0] + ':' + '0'\n elif len(info_splits) == 3:\n info = info_splits[0] + ':' + '0' + ':' + info_splits[2]\n\n record_list.append(info)\n return record_list\n\n\ndef save_label_and_print(record_list, path_txt, parking_space):\n print('============原始数据如下===============')\n record_list = np.array(record_list)\n record_list = record_list.reshape((-1, parking_space + 1))\n for i in record_list:\n for idx_i, j in enumerate(i):\n if idx_i < parking_space:\n print(j, end=' ') # 原始数据加制表\n else:\n print(j) # 最后一个数据加回车\n # 对所有结果按时间由小到大排序\n tmp = record_list.T # np排序方法\n record_list_order = tmp[0].argsort() # 得出比较结果\n record_list_new_order = record_list[record_list_order] # 排序完毕\n # 去掉重复行\n tmp_f = record_list_new_order[0] # 为了去掉重复行\n data_raw = []\n for idx_i, j in enumerate(tmp_f): # 第一行的写法\n if idx_i < parking_space:\n data_raw.append(j)\n else:\n data_raw.append(j)\n\n for i in record_list_new_order[1:]: # 后面的写法\n if (i != tmp_f).any():\n for idx_i, j in enumerate(i):\n if idx_i < parking_space:\n data_raw.append(j)\n else:\n data_raw.append(j)\n tmp_f = i\n\n data_raw = np.array(data_raw)\n data_raw = data_raw.reshape((-1, parking_space + 1))\n\n if ip_name in no_plate_list: # ['177']\n record_list_new_order = data_raw\n else:\n record_list_new_order = add_plate_2_label_v2(data_raw)\n\n # 输出到txt\n path_txt = path_txt\n file = open(path_txt, 'a+', encoding='UTF-8')\n for i in record_list_new_order:\n for idx_i, j in enumerate(i):\n if idx_i < parking_space:\n file.write(j + ' ')\n else:\n file.write(j + '\\n')\n file.close()\n print('save txt at :{}'.format(path_txt))\n\n# 删除一行\n\n\ndef modify_record_list(record_list, list_img, idx):\n # 1.先找当前图片是否存在记录,无则显示无需修改,有则显示列表的idx\n # 2.取出相��记录,使用remove删除该图片名称下全部记录\n # if record_list is None:\n # record_list = []\n # print('记录里啥也没有,没东西可删')\n # return record_list\n # #return要返回值,否则就会把record变成none\n\n if len(record_list) == 0:\n print('记录里啥也没有,没东西可删')\n else:\n tmp_np_array = np.array(record_list)\n tmp_np_array = tmp_np_array.reshape(-1, parking_space + 1)\n delete_time = '_'.join(os.path.splitext(list_img[idx])[0].split('_')[:3])\n\n if (tmp_np_array.T[0] == delete_time).any(): # any:不加括号是bug\n idx_for_delete = [idx for idx, i in enumerate(tmp_np_array.T[0]) if i == delete_time] # 得到索引值\n idx_for_delete = idx_for_delete[::-1] # 从后往前删除,否则idx失效\n tmp_np_array_for_print = tmp_np_array[idx_for_delete] # 先取出要删除的\n for i in idx_for_delete:\n tmp_np_array = np.delete(tmp_np_array, i, 0) # 将要删除的删除\n tmp_print_for_delete(tmp_np_array_for_print) # 显示出已经删除的条目\n tmp_np_array = tmp_np_array.reshape(-1) # 恢复record\n record_list = tmp_np_array.tolist() # 恢复record\n else:\n print('记录中无当前时间条目')\n\n return record_list\n\n\ndef str_space_remove(content):\n temp = ''\n for letter in content:\n if letter != ' ':\n temp += letter\n return temp\n\n\ncv2.namedWindow('label', 0)\ncv2.namedWindow('next', 0)\ncv2.startWindowThread()\ncv2.resizeWindow('label', w_w, w_h) # 宽,高\ncv2.resizeWindow('next', w_w, w_h) # 宽,高\n\n\nwhile idx < img_len - 1:\n idx_next = idx + 1\n img_data = cv2.imread(list_img[idx])\n img_data_next = cv2.imread(list_img[idx_next])\n if img_data is None:\n idx += 1\n continue\n if img_data_next is None:\n if img_data is None:\n idx += 1\n continue\n else:\n idx_next += 1\n continue\n\n img_data = show_parking_line(img_data, parking_list_np, 4) # 画停车线\n img_data = img_data[h_h:h_l, ...]\n img_data_next = img_data_next[h_h:h_l, ...]\n\n h, m, s = os.path.splitext(list_img[idx])[0].split('_')[:3]\n h_p = int(h) - int(h_0)\n m_p = int(m) - int(m_0)\n s_p = int(s) - int(s_0)\n sec_pass = h_p * 3600 + m_p * 60 + s_p\n\n # 显示的是当前时间(使用图片文件名)\n img_data = cv2.putText(img_data, '_'.join([h.zfill(2), m.zfill(2), s.zfill(2)]), (x_panel, y_panel),\n font, font_size, (0, 0, 255), font_width) # 添加文字，字体大小，初始位置，颜色，粗细\n\n # 显示剩余时间\n img_data = cv2.putText(img_data, str(int(sec_sum - sec_pass) // 60) + 'm' + str(int(sec_sum - sec_pass) % 60) + 's', (x_panel, y_panel_bias),\n font, font_size, (0, 255, 0), font_width) # 添加文字，字体大小，初始位置，颜色，粗细\n\n if state_num_key == change_parking_num:\n if len(parking_num_block) > 1: # 车位数据集合(选择跨车位停车时使用)\n parking_num_block.sort()\n parking_num[id_parking_num_change] = parking_num_block # 写进去使命就完成了\n\n for i in range(parking_space):\n if len(car_frame_point[i]) == 0:\n pass\n else:\n # 画图时只使用当前的两点坐标,但是在记入label时,使用绝对值坐标\n img_data = cv2.rectangle(img_data, car_frame_point[i][0], car_frame_point[i][1], (0, 255, 255), 4)\n\n for i in range(parking_space):\n\n if state_num_key == show_parking_num:\n color_parking_space_num[i] = (0, 255, 255)\n elif state_num_key == change_parking_state:\n color_parking_space_num[i] = (255, 0, 0)\n elif state_num_key == change_parking_num:\n color_parking_space_num[i] = (255, 0, 0)\n\n if state_num_key == show_parking_num:\n color_parking_num[i] = (255, 0, 255)\n elif state_num_key == change_parking_state:\n color_parking_num[i] = (255, 0, 255)\n elif state_num_key == change_parking_num:\n color_parking_num[i] = (255, 0, 255)\n color_parking_num[id_parking_num_change] = (0, 255, 255)\n # 车位idx\n img_data = cv2.putText(img_data, str(i),\n (pos_show[i][0], pos_show[i][1] + bias_y), font, font_size, color_parking_space_num[i], font_width)\n\n # 车位\n img_data = cv2.putText(img_data, str(parking_num[i]),\n (pos_show[i][0], pos_show[i][1]), font, font_size, color_parking_num[i], font_width)\n\n # 状态标记\n img_data = cv2.putText(img_data, str(int(state_loc[i])),\n (pos_show_bias[i][0], pos_show_bias[i][1] + bias_y), font, font_size + 1, (0, 0, 255), font_width)\n\n # cv2.namedWindow('label',0)\n # cv2.resizeWindow('label',w_w ,w_h) #宽,高\n cv2.setMouseCallback('label', onmouse, 0) # 鼠标操作回调函数\n # cv2.startWindowThread()\n cv2.imshow('label', img_data)\n\n # cv2.namedWindow('next',0)\n # cv2.resizeWindow('next',w_w ,w_h) #宽,高\n cv2.imshow('next', img_data_next)\n\n key = cv2.waitKeyEx(1) # 等待按键\n if act == 1:\n idx += 1\n\n if key == 27: # ESC\n break\n elif key == ord(' '): # 空格控制act\n if act:\n act = 0\n else:\n act = 1\n elif key == ord('s') or key == ord('S'): # s 前进3min\n idx += three_mins\n elif key == ord('d') or key == ord('D'): # d前进5min\n idx += five_mins\n elif key == ord('a') or key == ord('A'): # a后退5min\n if idx > 0:\n idx -= five_mins\n else:\n idx = 0\n elif key == ord('w') or key == ord('W'): # w 后退3min\n if idx > 0:\n idx -= three_mins\n else:\n idx = 0\n elif key == 2490368: # 上\n if idx > 0:\n idx -= 1\n else:\n idx = 0\n\n elif key == 2621440: # 下\n idx += 1\n elif key == 2424832: # 左\n if idx > 10:\n idx -= one_min # 1min\n else:\n idx = 0\n elif key == 2555904: # 右\n idx += one_min # 1min\n elif key == 13: # p或回车\n if state_mouse == frame_car_use_left_button:\n # 待改进\n print(f'两点当前坐标为({sel[0]}, {sel[1]}), ({sel[2]}, {sel[3]})')\n print(f'两点绝对坐标为({sel[0]}, {sel[1]+h_h}), ({sel[2]}, {sel[3]+h_h})')\n state_mouse = state_mouse_init # 将状态返回为初始化\n continue\n\n if state_num_key == change_parking_num:\n if len(parking_num_block) == 0:\n parking_num_block = [] # 为其复位,归空\n print('请赋值')\n elif len(parking_num_block) == 1: # 列表里只有一个值\n state_num_key = change_parking_state\n # car_frame_point[parking_num_block[0]] = [] # 取出唯一的值作为索引,并将里面的值去掉,成为[],这样不对\n car_frame_point[id_parking_num_change] = [] # 取出唯一的值作为索引,并将里面的值去掉,成为[]\n parking_num_block = [] # 为其复位,归空\n print('赋值已完成,不需要画框了')\n elif len(parking_num_block) > 1:\n state_num_key = change_parking_state\n state_mouse = frame_car_use_left_button\n parking_num_block = [] # 为其复位,归空\n print(\"\\n赋值结束,请用鼠标左键为车画框\")\n elif state_num_key == change_parking_state:\n if state_time_label_overwrite == 0: # 如果秒数没有被改写\n # record_list.append(os.path.splitext(list_img[idx])[0]) # 先记录时间\n record_list.append('_'.join([h.zfill(2), m.zfill(2), s.zfill(2)])) # 先记录时间\n for idx_state_loc, i in enumerate(state_loc): # 再记录数据\n parking_num_str = str_space_remove(str(parking_num[idx_state_loc]))\n if len(car_frame_point[idx_state_loc]) == 0:\n record_list.append(parking_num_str + ':' + str(i))\n else:\n car_frame_point_abs_str = str_space_remove(str(car_frame_point_abs[idx_state_loc]))\n record_list.append(parking_num_str + ':' + str(i) + ':' + car_frame_point_abs_str)\n\n for i in state_loc:\n print(i, end='\\t')\n # print(os.path.splitext(list_img[idx])[0], end=' ')\n print('_'.join([h.zfill(2), m.zfill(2), s.zfill(2)]), end=' ')\n print('<---')\n\n else:\n record_list.append(time_label) # 先记录时间\n for idx_state_loc, i in enumerate(state_loc): # 再记录数据\n parking_num_str = str_space_remove(str(parking_num[idx_state_loc]))\n if len(car_frame_point[idx_state_loc]) == 0:\n record_list.append(parking_num_str + ':' + str(i))\n else:\n car_frame_point_abs_str = str_space_remove(str(car_frame_point_abs[idx_state_loc]))\n record_list.append(parking_num_str + ':' + str(i) + ':' + car_frame_point_abs_str)\n\n for i in state_loc:\n print(i, end='\\t')\n print(time_label, end=' ')\n print('<---')\n\n state_time_label_overwrite = 0 # 更改状态\n elif (key == ord('p')) or key == ord('P'): # p\n if state_num_key == change_parking_state:\n print(\"点击数字选择一个要更改的停车区域,从零开始\", end=' ')\n state_num_key = show_parking_num\n\n elif (key == ord('o')) or key == ord('O'): # o或O\n if state_num_key == show_parking_num:\n print(\"\\n状态恢复\")\n state_num_key = change_parking_state\n\n elif key == ord('f') or key == ord('F'): # f 时间标签增加1s\n tmp = sec_pass + initial_time_sec\n tmp += 1\n time_label = f'{tmp//3600:02d}_{tmp%3600//60:02d}_{tmp%60:02d}' # 上下都行\n state_time_label_overwrite = 1\n tmp_print_time_label(state_loc, time_label)\n\n elif key == ord('r') or key == ord('R'): # r 时间标签减少1s\n tmp = sec_pass + initial_time_sec\n tmp -= 1\n time_label = f'{tmp//3600:02d}_{tmp%3600//60:02d}_{tmp%60:02d}' # 上下都行\n state_time_label_overwrite = 1\n tmp_print_time_label(state_loc, time_label)\n\n elif key == ord('m') or key == ord('M'): # m 减去当前图片名下的一条数据\n record_list = modify_record_list(record_list, list_img, idx)\n\n elif key == ord('l') or key == ord('L'): # l leave 离开需要马上打印\n if len(record_list) != 0: \n record_list = complement_record_list(record_list, last_time, parking_space)\n save_label_and_print(record_list, path_txt, parking_space)\n else:\n print('there are no records to record !!!')\n # save_json_label(record_list, parking_space, path_json)\n\n elif key == 48 and parking_space > 0: # 0\n if state_num_key == change_parking_state:\n if state_loc[0] == 2:\n state_loc[0] = 0\n else:\n state_loc[0] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 0\n print('0')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(0)\n print('0', end=' ')\n\n elif key == 49 and parking_space > 1: # 1\n if state_num_key == change_parking_state: # 若数字键用来改变停车位状态\n if state_loc[1] == 2: # 修改的状态位置是0-1-2\n state_loc[1] = 0\n else:\n state_loc[1] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num: # 若数字键用来选择车位索引\n id_parking_num_change = 1 # 先确定索引的位置,若不同车在相同车位,这个索引也是不同的\n print('1')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num: # 若数字键用来标记车位\n parking_num_block.append(1)\n print('1', end=' ')\n\n elif key == 50 and parking_space > 2: # 2\n if state_num_key == change_parking_state:\n if state_loc[2] == 2:\n state_loc[2] = 0\n else:\n state_loc[2] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 2\n print('2')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(2)\n print('2', end=' ')\n\n elif key == 51 and parking_space > 3: # 3\n if state_num_key == change_parking_state:\n if state_loc[3] == 2:\n state_loc[3] = 0\n else:\n state_loc[3] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 3\n print('3')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(3)\n print('3', end=' ')\n\n elif key == 52 and parking_space > 4: # 4\n if state_num_key == change_parking_state:\n if state_loc[4] == 2:\n state_loc[4] = 0\n else:\n state_loc[4] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 4\n print('4')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(4)\n print('4', end=' ')\n\n elif key == 53 and parking_space > 5: # 5\n if state_num_key == change_parking_state:\n if state_loc[5] == 2:\n state_loc[5] = 0\n else:\n state_loc[5] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 5\n print('5')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(5)\n print('5', end=' ')\n\n elif key == 54 and parking_space > 6: # 6\n if state_num_key == change_parking_state:\n if state_loc[6] == 2:\n state_loc[6] = 0\n else:\n state_loc[6] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 6\n print('6')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(6)\n print('6', end=' ')\n\n elif key == 55 and parking_space > 7: # 7\n if state_num_key == change_parking_state:\n if state_loc[7] == 2:\n state_loc[7] = 0\n else:\n state_loc[7] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 7\n print('7')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(7)\n print('7', end=' ')\n\n elif key == 56 and parking_space > 8: # 8\n if state_num_key == change_parking_state:\n if state_loc[8] == 2:\n state_loc[8] = 0\n else:\n state_loc[8] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 8\n print('8')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(8)\n print('8', end=' ')\n\n elif key == 57 and parking_space > 9: # 9\n if state_num_key == change_parking_state:\n if state_loc[9] == 2:\n state_loc[9] = 0\n else:\n state_loc[9] += 1\n tmp_print(state_loc)\n elif state_num_key == show_parking_num:\n id_parking_num_change = 9\n print('9')\n print('按数字选择该空间所占车位,按回车确定', end=' ')\n state_num_key = change_parking_num\n elif state_num_key == change_parking_num:\n parking_num_block.append(9)\n print('9', end=' ')\n\n elif key == 57: # 9\n record_list = complement_record_list(record_list, last_time, parking_space)\n save_label_and_print(record_list, path_txt, parking_space)\n # save_json_label(record_list, parking_space, path_json, imgs_last_time_plus_one, imgs_list_only_time)\n\nif key != 27: # ESC没按输出结果到txt\n record_list = complement_record_list(record_list, last_time, parking_space)\n save_label_and_print(record_list, path_txt, parking_space)\n # save_json_label(record_list, parking_space, path_json, imgs_last_time_plus_one, imgs_list_only_time)\n\n\ncv2.destroyAllWindows()\nprint('程序结束')\n","sub_path":"label_tool_v3.py","file_name":"label_tool_v3.py","file_ext":"py","file_size_in_byte":30596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"137652206","text":"import argparse\nimport logging\n\nfrom .tag_datasource_processor import TagDatasourceProcessor\n\n\nclass TagManagerCLI:\n\n @classmethod\n def run(cls, argv):\n cls.__setup_logging()\n\n args = cls._parse_args(argv)\n args.func(args)\n\n @classmethod\n def __setup_logging(cls):\n logging.basicConfig(level=logging.INFO)\n\n @classmethod\n def _parse_args(cls, argv):\n parser = argparse.ArgumentParser(\n description=__doc__,\n formatter_class=argparse.RawDescriptionHelpFormatter\n )\n\n subparsers = parser.add_subparsers()\n\n create_tags_parser = subparsers.add_parser('create-tags', help='Create Tags')\n create_tags_parser.add_argument('--csv-file', help='CSV file with Tags information', required=True)\n create_tags_parser.set_defaults(func=cls.__create_tags)\n\n return parser.parse_args(argv)\n\n @classmethod\n def __create_tags(cls, args):\n TagDatasourceProcessor().create_tags_from_csv(file_path=args.csv_file)\n","sub_path":"src/datacatalog_tag_manager/tag_manager_cli.py","file_name":"tag_manager_cli.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"415911719","text":"# -*- coding: utf-8 -*-\r\n\r\nimport csv\r\nimport json\r\nimport logging\r\n\r\nfrom Cuisinier import Recipe, ClassifiedRecipe\r\nfrom CuisinierTFIDF import CuisinierTFIDF\r\n\r\nLOGGING_LEVEL = logging.INFO\r\nTRAINING_FILE = \"resources/train.json\"\r\nTEST_FILE = \"resources/test.json\"\r\n\r\n# Configure logging\r\nlogging.basicConfig(filename=\"log.txt\", filemode=\"w\", level=LOGGING_LEVEL)\r\n\r\n\r\ndef getClassifiedRecipes(file):\r\n f = open(file)\r\n recipes = json.loads(f.read())\r\n f.close()\r\n return [ClassifiedRecipe(recipe[\"id\"], recipe[\"cuisine\"],\r\n recipe[\"ingredients\"])\r\n for recipe in recipes]\r\n\r\n\r\ndef getRecipes(file):\r\n f = open(file)\r\n recipes = json.loads(f.read())\r\n f.close()\r\n return [Recipe(recipe[\"id\"], recipe[\"ingredients\"]) for recipe in recipes]\r\n\r\n\r\n# Writes a list of classified recipes to a given file in CSV format\r\ndef writeRecipesCSV(file, recipes):\r\n print((\"Writing recipes to \\\"\" + file + \"\\\"\"))\r\n with open(file, \"w\", newline=\"\") as fileToWrite:\r\n csv_writer = csv.writer(fileToWrite, delimiter=\",\", quotechar=\"|\",\r\n quoting=csv.QUOTE_MINIMAL)\r\n csv_writer.writerow([\"id\", \"cuisine\"])\r\n\r\n for recipe in recipes:\r\n csv_writer.writerow([recipe.id, recipe.cuisine])\r\n\r\n\r\n# Tests a Cuisinier against its own training data\r\ndef selfTest(cuisinier):\r\n # Read and parse JSON data\r\n recipes = getClassifiedRecipes(TRAINING_FILE)\r\n cuisinier.addRecipes(recipes)\r\n\r\n success = 0\r\n for recipe in recipes:\r\n result = cuisinier.classifyRecipe(Recipe(recipe.id,\r\n recipe.ingredients))\r\n if result.cuisine == recipe.cuisine:\r\n success += 1\r\n\r\n print((cuisinier.getAlgorithmType() + \" self-test accuracy: \" +\r\n str(success) + \"/\" + str(len(recipes)) +\r\n \" (\" + \"{0:.2f}\".format(success/len(recipes) * 100) + \"%)\"))\r\n\r\n\r\n# Run test data and output the results\r\ndef generateSubmission(cuisinier):\r\n # Read and parse JSON data\r\n trainingRecipes = getClassifiedRecipes(TRAINING_FILE)\r\n testRecipes = getRecipes(TEST_FILE)\r\n\r\n cuisinierType = cuisinier.getAlgorithmType()\r\n cuisinier.addRecipes(trainingRecipes)\r\n print((\"Classifying \" + str(len(testRecipes)) + \" recipes with \" +\r\n cuisinierType))\r\n writeRecipesCSV(\"results\" + cuisinierType + \".csv\",\r\n cuisinier.classifyRecipes(testRecipes))\r\n print((cuisinierType + \" classification complete\"))\r\n\r\n\r\ndef test():\r\n recipesToClassify = getRecipes(TEST_FILE)\r\n recipes = getClassifiedRecipes(TRAINING_FILE)\r\n cuisinier = Cuisinier()\r\n cuisinier.addRecipes(recipes)\r\n\r\n with open('submissionData.csv', 'wb') as fileToWrite:\r\n csv_writer = csv.writer(fileToWrite)\r\n csv_writer = csv.writer(fileToWrite, delimiter=',',\r\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\r\n csv_writer.writerow([\"id\",\"cuisine\"])\r\n\r\n success = 0\r\n for recipe in recipesToClassify:\r\n result = cuisinier.classifyRecipe(Recipe(recipe.id,\r\n recipe.ingredients))\r\n csv_writer.writerow([recipe.id, result.cuisine])\r\n\r\n\r\ndef main():\r\n cuisinier = CuisinierTFIDF()\r\n # Calculate initial accuracy\r\n selfTest(cuisinier)\r\n generateSubmission(cuisinier)\r\n\r\nmain()\r\n","sub_path":"data/external/repositories_2to3/248568/cuisinier-master/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"278696149","text":"\nimport random\nimport time\nimport pygame\npygame.init()\nrelogio = pygame.time.Clock()\npygame.display.set_caption(\"Alfabetizando\")\n\nicon = pygame.image.load(\"assets/icon.jpg\")\npygame.display.set_icon(icon)\nlargura = 800\naltura = 600\ndisplay = pygame.display.set_mode((largura, altura))\nbackground = pygame.image.load(\"assets/sky.png\")\nlapis = pygame.image.load(\"assets/lapis.png\")\nlapisLargura = 260\nlapisPosicaoX = 360\nlapisPosicaoY = 470\nlapisMovimento = 0\nlapisVelocidade = 10\nmissel = pygame.image.load(\"assets/missile.png\")\nmisselLargura = 50\nmisselAltura = 250\nmisselPosicaoX = 360\nmisselPosicaoY = 10 - misselAltura\nmisselMovimento = 0\nvelocidadeMissel = 5\npygame.mixer.music.load('assets/ironsound.mp3')\npygame.mixer.music.play(-1)\nmisselSound = pygame.mixer.Sound(\"assets/missile.wav\")\nmisselSound.set_volume(0.1)\npygame.mixer.Sound.play(misselSound)\npygame.mixer.music.set_volume(0.1)\nexplosaoSound = pygame.mixer.Sound(\"assets/explosao.wav\")\nexplosaoSound.set_volume(0.2)\n\ndef escrevendoPlacar(contador):\n font = pygame.font.SysFont(None, 25)\n texto = font.render(\"Desvios: \"+str(contador), True, (255, 255, 255))\n display.blit(texto, (10, 10))\ndef dead():\n pygame.mixer.Sound.play(explosaoSound)\n pygame.mixer.music.stop()\n font = pygame.font.SysFont(None, 150)\n texto = font.render(\"Você Morreu!\", True, (0, 0, 0))\n display.blit(texto, (100, 200))\n pygame.display.update()\n time.sleep(5)\ncontador = 0\nwhile True:\n # Trabalhar com Background\n display.fill((255, 255, 255))\n display.blit(background, (0, 0))\n # devolve uma lista de eventos da tela []\n for evento in pygame.event.get():\n if evento.type == pygame.QUIT:\n pygame.quit()\n quit()\n if evento.type == pygame.KEYDOWN:\n if evento.key == pygame.K_LEFT:\n lapisMovimento = lapisVelocidade * -1\n elif evento.key == pygame.K_RIGHT:\n lapisMovimento = lapisVelocidade\n if evento.type == pygame.KEYUP:\n lapisMovimento = 0\n lapisPosicaoX = lapisPosicaoX + lapisMovimento\n if lapisPosicaoX < 0:\n lapisPosicaoX = 0\n elif lapisPosicaoX > largura - lapisLargura:\n lapisPosicaoX = largura - lapisLargura\n display.blit(lapis, (lapisPosicaoX, lapisPosicaoY))\n misselPosicaoY = misselPosicaoY + velocidadeMissel\n escrevendoPlacar(contador)\n # controlando o míssel novo\n if misselPosicaoY > altura:\n misselPosicaoY = 10 - misselAltura\n velocidadeMissel = velocidadeMissel + 1\n pygame.mixer.Sound.play(misselSound)\n misselPosicaoX = random.randrange(0, largura)\n contador += 1 # contator = contator + 1\n display.blit(missel, (misselPosicaoX, misselPosicaoY))\n # Verificando Colisões\n if lapisPosicaoY < misselPosicaoY + misselAltura:\n if lapisPosicaoX < misselPosicaoX and lapisPosicaoX + lapisLargura > misselPosicaoX or misselPosicaoX+misselLargura > lapisPosicaoX and misselPosicaoX+misselLargura < lapisPosicaoX+lapisLargura:\n dead()\n velocidadeMissel = 5\n misselPosicaoY = 0 - misselAltura\n contador = 0\n pygame.display.update()\n relogio.tick(60)\nprint(\"Volte Sempre....\")\n","sub_path":"codigo.py","file_name":"codigo.py","file_ext":"py","file_size_in_byte":3232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"601346077","text":"#!/usr/bin/env python\n# coding=utf-8\nfrom flask import Flask\nimport os\nimport logging\nfrom logging import Formatter\nfrom logging.handlers import SMTPHandler, RotatingFileHandler\nfrom flask.ext.login import LoginManager, login_user, logout_user, current_user, login_required\nfrom newlife.view import front\nfrom newlife.view import admin\nfrom newlife.view import install\nfrom newlife.model import Admin\n\n\ndef creat_app(config):\n app = Flask(__name__)\n app.debug = True\n app.config.from_pyfile(config)\n configure_logging(app)\n temple_filter(app)\n app.register_blueprint(front.blueprint)\n app.register_blueprint(admin.blueprint)\n app.register_blueprint(install.blueprint)\n\n login_manager = LoginManager()\n login_manager.init_app(app)\n login_manager.login_view = \"admin.login\"\n\n @login_manager.user_loader\n def load_user(userid):\n admin = Admin()\n admin.get_user_id(userid)\n return admin\n return app\n\n\ndef configure_logging(app):\n mail_handler = SMTPHandler(app.config['MAIL_SERVER'], app.config[\n 'MAIL_SENDER'], app.config['MAIL_RECEIVER'], 'YourApplication Failed')\n\n mail_handler.setLevel(logging.ERROR)\n mail_handler.setFormatter(Formatter('''\n Message type: %(levelname)s\n Location: %(pathname)s:%(lineno)d\n Module: %(module)s\n Function: %(funcName)s\n Time: %(asctime)s\n\n Message:\n\n %(message)s\n '''))\n app.logger.addHandler(mail_handler)\n\n file_log = os.path.join(app.root_path,\n app.config['FILE_LOG'])\n\n file_handler = RotatingFileHandler(\n file_log, maxBytes=100000, backupCount=10)\n\n file_handler.setLevel(logging.WARNING)\n file_handler.setFormatter(Formatter(\n '%(asctime)s %(levelname)s: %(message)s '\n '[in %(pathname)s:%(lineno)d]'\n ))\n app.logger.addHandler(file_handler)\n\n\ndef temple_filter(app):\n @app.template_filter()\n def monthformat(datetime):\n return datetime.strftime(\"%b\")\n\n @app.template_filter()\n def dayformat(datetime):\n return datetime.strftime(\"%d\")\n\n @app.template_filter()\n def dateformat(datetime):\n return datetime.strftime(\"%B %d\")\n\n @app.template_filter()\n def dateformatY(datetime):\n return datetime.strftime(\"%B %d %Y\")\n\n @app.template_filter()\n def limit_file(size):\n return int(size/1024/1024)\n","sub_path":"newlife/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"255388362","text":"# # 8*8 체스판 추출하기\nm, n = map(int, input().split())\na = [input() for _ in range(m)]\nprint()\n\n# 왜틀림\n# res = None\n# for i in range(m-7): # 세로줄 8개씩 더 탐색할 반복수\n# for k in range(n -7): # 가로줄 8개씩 더 탐색할 반복수\n# # print(f'k: {a[j][k:]}', end = ' ')\n# # print(f'k: {k}')\n# pat = {0: 'W', 1:'B'} if a[i][0] == 'W' else {0: 'B', 1:'W'} # 여기가 재선언 되는 문제를 막아야함\n# # print(f'start: {k}, {i}')\n# temp = 0\n# for j in range(i, 8 + i): # 세로줄 탐색 시작수\n# # print(f'j: {j}')\n# # print(a[j])\n# t1 = []\n# cnt = 0\n# for l in range(k, 8+k): # 가로줄 탐색 시작인덱스\n# # print(f'l: {l}')\n# # print(a[j][l], end='')\n# # print(pat[l % 2], end='')\n# t1.append(pat[cnt % 2])\n# if a[j][l] != pat[cnt % 2]:\n# temp+=1\n# cnt +=1\n# # print()\n# # print(''.join(t1))\n# # print(temp)\n# pat[0], pat[1] = pat[1], pat[0]\n# # print('----------------')\n# # print(f'temp: {temp}, res: {res}')\n# if res is None : res = temp\n# elif res > temp : res = temp\n# # print('=============\\n')\n# print(res)\n\n# 결국 풀이를 봤습니다........\nz = []\nfor i in range(m-7): # 세로줄 8개씩 더 탐색할 반복수\n for k in range(n -7): # 가로줄 8개씩 더 탐색할 반복수\n w , b = 0, 0 # w로 시작할때랑 b로 시작할때 칠해야할부분 \n for j in range(i, 8 + i): # 세로줄 탐색 시작수\n for l in range(k, 8+k): # 가로줄 탐색 시작인덱스\n if (j + l) % 2 == 0 : # 시작위치 다음부터의 홀짝순서를 정할 수 있음.. 0,0 0,1 0,2 ... 1,0 1,1 1,2 각위치를 더하면 짝홀짝, 홀짝홀 이래되니\n if a[j][l] == 'W': w +=1 # 짝수가 W로 시작했다면\n else: b+=1 # B로시작했음\n else:\n if a[j][l] == 'B': w +=1 # 홀수는 B일때 세줘야함\n else: b+=1 # 홀수가 W일때 계산.\n z.append(w)\n z.append(b)\n\nprint(min(z)) # 즉, 이 배열은 w,b 중 어느 하나로 시작했을 모든 경우를 담아두는 배열임. 그러니 최소값만 구하면 되겠네\n # print('=============\\n')\n # print(z)\n# print(res)","sub_path":"step/level 11 (무차별 대입{brute force})/1018_체스판 다시 칠하기.py","file_name":"1018_체스판 다시 칠하기.py","file_ext":"py","file_size_in_byte":2577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"116387160","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 31 18:43:42 2018\n\n@author: sana\n\"\"\"\n\n'''Generating input and data file for second relax to the system '''\n'''lastData.xyz contains coordinates and atom type of the relaxed system at low temperature'''\n\n\n\nimport numpy as np\n\n\n\n\n\n\n'''conversion from real to lj unit'''\n\nsigma = 2.5 #bead size in angstrom unit\nepsilon = 0.2 # in kcal/mol\n\nepsilonH = 1.0/epsilon # strength of hydrophobic interaction \n\nmassReal = 96.0\nmassReduced = 96.0/massReal # mass is 1.0 is reduced unit. It is taken each bead is of same mass i.e. 96 Da i.e. 96 gram/mole\n\n#Bond coefficients\n\nl0 = 3.0/sigma # equilibrium bond length 3.0 angstrom unit\n#kb = round((1./2.)*100.*epsilonH/l0**2.,3) # 1/2 100 epsilonH /a^2\nkb = 171.0 * (sigma**2)/epsilon # harmonic constant for the harmonic potential. value is 171 kcal/mol/Angstrom^2 ( energy/distance^2)kb\n\n\n#Angle coefficients\n\n#kTheta = (1./2.)*20.*epsilonH # in unit (energy/radian^2)\nkTheta = 50./epsilon # in unit of energy----Our angle potential is cosine/squared K(costheta - costheta0)^2\ntheta0 = 105. # in degrees\n\n\n#Dihedral coefficients \n\n#diheA = 1.0*epsilonH\n#diheB = 1.6*epsilonH\n#diheC = 1.0*epsilonH\n\n\n#dielectric constant\ndielectric = 80.0\n\n#pair potential coordinates\n\nsigmalj = 3.0\nepsilonlj = 0.2\nrlj = 2.5 # global cutoff for the lj potential is taken as 2.5*sigma\nrlocal= ((2.0)**(1.0/6.0))*sigmalj/sigma # local cutoff for lj interaction to include only repulsive part---r at minimum well in the potential plot\n\n\nsaltConc = 0.6 # salt concentration 0.6 moles/ltr\nkappa = round(0.33*np.sqrt(saltConc)*sigma,3) # debye screening length in reduced unit---for dielectric 80 and at temp 25 degree Celsius, kappa = 0.33(sqrt(I)) in per Angstrom unit\nrcoul = 10.0/kappa # to calculate cutoff for coulomb interaction, usually, kappa*cutoff = 10.\n\n\n\n\n\n\n'''for incorporating hydrogen bonding, I tried to modify gauss potential\nof the form E = -A exp(-Br^2)\nA is in unit of energy, B in unit of per distance square and we need cutoff... I choose it to be 2 sigma\nFor hydrogen bonding, energy would be 2-12 kBT or 6-30 KJpermole\nI choose A to be 5.68kBT and B to be 0.1/sigma square '''\n\n\n\n\ngaussA = 6.612/epsilon # 1 KBT (20 degree C) is 0.58kcal/mole---For now I choose 10kBT at the point where there is cutoff for excluded volume\ngaussB = 0.1 # 0.1/sigma square in reduced unit will be 0.1\ngaussCut = (15*sigma)/sigma\n\n\n\n\n'''time conversion'''\ntimeConversion = 4200*1000/(10**(-10))**2 # conversion from kcal/Angstrom^2 to gram meter^2 /sec^2 meter^2\nljUnitTime = ((epsilon/(massReal* (sigma)**2)*timeConversion)**(1.0/2.0))/10**15 # conversion factor for time is 0.0003742 (epsilon/(mass* (sigma)^2))^(1/2) in unit per second \ntau = 10000 * ljUnitTime # tau is 10 picosecod --the value mentioned here is in femtosecond unit 1 pico second = 1000 femtosecond\ndt = 0.00005*tau # dt = 0.5 femtosecond\n\n\n\n\n'''charge conversion'''\npivalue = np.pi \npermittivity = 8.85*10**(-12) # Its unit is (C^2 sec^2)/(kg meter^3)\navogadoNum = 6.023 * 10**(23)\nconversion = 4200*10**(-10)\nljUnitcharge = np.sqrt(4*pivalue*epsilon*sigma*permittivity*conversion/avogadoNum) #charge conversion (4 pi permittivity sigma epsilon)^(1/2)\nchargeValue = round((1.6*10**(-19))/ljUnitcharge, 3) # value of 1 e charge in reduced unit \n\n\n\nkBT = 0.58 # Value in kcal/mole in 293 K i.e. 20 C temperature \ntempReduced = kBT/epsilon\n\n\n\n\n'''Pore dimensions'''\n\n\nradius = 10.0/(2.0*sigma) # Diameter of beta barrel in ahl is 1.4nm\nheight = 50.0/sigma # Height of pore is choosen to be 5nm \nzvalue = int(height/2.)+1\n\n\n\n'''I choose x and y range to go from -20 sigma unit to +20 sigma unit'''\nxStart = -20\nxEnd = 20\nyStart = -20\nyEnd = 20\n\n'''(0,0,0) lies in the Up wall'''\n\nzUp = height/2. \nzDown = -height/2.\ndz = (zUp-zDown)*sigma #in real unit\n\nresType = 26\nporeResType = resType\n\n\n\n\n\n\n\n\n\n\n'''Define parameters needed for the data file'''\n\nnumResidue = 72\nnResidue = 3*numResidue\n\n#nTail = 10 \nnMobile = nResidue\n\n\n\nnBonds = 3*numResidue-1\nnAngle = 3*numResidue -2 # Each residue have 2 backbone angles and one side angle ---first one has 2 and last one has 2\n#nDihedral= 2*(numResidue-1) -1 # Each residue have 2 backbone dihedral(Phos-Sugar-Phos-Sugar, Sugar-Phos-Sugar-Phos) last residue doesnot start with, second last has only PSPS \nnDihedral= 0 \nnImproper = 0\n\n\nnAtomType = 25+1\nnBondType = 1\nnangleType = 2\n#ndiheType = 1\nndiheType = 0\nimproType = 0\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'''Read lastData.xyz and get coordinates'''\n\n\nreaddata= open('lastData.xyz','r')\nfirstline = readdata.readline()\nsecondline = readdata.readline()\ninputdata = readdata.readlines() #coordinates data file\n\nnTotal = int(firstline) \n\n'''Generate data file for the translocation simulation'''\n\noutputatoms = open('relax.data','w')\n\noutputatoms.write(\"LAMMPS Description\\n\\n\")\n\noutputatoms.write('{0} {1}\\n'.format(nTotal, \"atoms\"))\noutputatoms.write('{0} {1}\\n'.format(int(nBonds), \"bonds\"))\noutputatoms.write('{0} {1}\\n'.format(int(nAngle), \"angles\"))\noutputatoms.write('{0} {1}\\n'.format(int(nDihedral), \"dihedrals\"))\noutputatoms.write('{0} {1}\\n\\n'.format(int(nImproper), \"impropers\"))\n\noutputatoms.write('{0} {1} {2}\\n'.format(int(nAtomType), \"atom\", \"types\"))\noutputatoms.write('{0} {1} {2}\\n'.format(int(nBondType), \"bond\", \"types\"))\noutputatoms.write('{0} {1} {2}\\n'.format(int(nangleType), \"angle\", \"types\"))\noutputatoms.write('{0} {1} {2}\\n'.format(int(ndiheType), \"dihedral\", \"types\"))\noutputatoms.write('{0} {1} {2}\\n\\n'.format(int(improType), \"improper\", \"types\"))\n\n\noutputatoms.write('{0} {1} {2} {3}\\n'.format(\"-40.0\", \"40.0\", \"xlo\", \"xhi\" ))\noutputatoms.write('{0} {1} {2} {3}\\n'.format(\"-40.0\", \"40.0\", \"ylo\", \"yhi\" ))\noutputatoms.write('{0} {1} {2} {3}\\n\\n'.format(\"-140.0\", \"140.0\", \"zlo\", \"zhi\" ))\n\n\noutputatoms.write(\"Masses\\n\\n\")\nfor j in range(nAtomType):\n outputatoms.write('{0} {1}\\n'.format(int(j+1), massReduced))\n\noutputatoms.write(\"\\nAtoms\\n\\n\")\n\n'''split contents of lines from third line of xyz file'''\n\nchargePhos = -1.*chargeValue\n\ncoordAll=np.zeros((nTotal,6))\nresidueType=np.zeros(nTotal)\ncounter = 1\n\nfor i in range(nTotal):\n if i<int(nResidue):\n residueType[i]=int(round((i)/3) +1)\n else:\n residueType[i] = int(numResidue+1)\n \n \n\nfor idx,lines in enumerate(inputdata):\n everyline = lines.split() \n if everyline[0]==\"1\":\n coordAll[idx][0]=counter\n coordAll[idx][1]=float(everyline[0])\n coordAll[idx][2]=round(chargePhos,3)\n coordAll[idx][3]=float(everyline[1])\n coordAll[idx][4]=float(everyline[2])\n coordAll[idx][5]=float(everyline[3]) \n else:\n coordAll[idx][0]=counter\n coordAll[idx][1]=float(everyline[0])\n coordAll[idx][2]=0.0\n coordAll[idx][3]=float(everyline[1])\n coordAll[idx][4]=float(everyline[2])\n coordAll[idx][5]=float(everyline[3])\n counter = counter+1\n\nfor index in range(nTotal):\n outputatoms.write(\"{} {} {} {} {} {} {}\\n\".format(int(coordAll[index][0]), int(residueType[index]), int(coordAll[index][1]), coordAll[index][2], coordAll[index][3], coordAll[index][4], coordAll[index][5])) \n \n \n'''Generate bonds and angles'''\n\noutputatoms.write('\\n Bonds \\n\\n')\n\n\n\nbondType = \"1\" \nnum = 0\nfor index in range(numResidue-1):\n num = num +1\n outputatoms.write('{0} {1} {2} {3}\\n'.format(int(num), bondType, int((index+1)*3 - 2), int(index+1)*3 -1))\n num = num +1\n outputatoms.write('{0} {1} {2} {3}\\n'.format(int(num), bondType, int((index+1)*3 - 1), int(index+1)*3 -0))\n num = num+1\n outputatoms.write('{0} {1} {2} {3}\\n'.format(int(num), bondType, int((index+1)*3 - 1), int(index+1)*3 +1))\n\n'''last bond'''\nindex = int((index+1)*3 +1)\nnum = num+1\noutputatoms.write('{0} {1} {2} {3}\\n'.format(int(num), bondType, int(index), int(index+1) ))\nnum = num +1\noutputatoms.write('{0} {1} {2} {3}\\n'.format(int(num), bondType, int(index+1), int(index+2) ))\n\n\n\n\n\n\n'''Generate list of angles'''\n\noutputatoms.write(\"\\nAngles\\n\\n\")\nnum =0\n\n'''backbone angles --Phosphate-Sugar-Phospate, Sugar-Phosphate-Sugar'''\n\nangleType = \"1\"\n\n'''first angle'''\nnum =num+1\noutputatoms.write('{} {} {} {} {}\\n'.format( int(num), angleType, \"1\", \"2\", \"4\"))\n'''in between'''\nfor index in range(numResidue-2):\n num =num+1\n outputatoms.write('{} {} {} {} {}\\n'.format( int(num), angleType, int((index+1)*3 - 1), int((index+1)*3 +1), int((index+1)*3+2) ))\n num = num+1\n outputatoms.write('{} {} {} {} {}\\n'.format( int(num), angleType, int((index+1)*3 + 1), int((index+1)*3 +2), int((index+1)*3+4) ))\n\n\n'''last angle'''\n\nindex = int((index+1)*3 +4)\nnum = num+1\noutputatoms.write('{} {} {} {} {}\\n'.format( int(num), angleType, int(index-2), int(index), int(index+1) ))\n\n\n\n'''side angles ---Phos-Sugar-Base for each nucleotide'''\n\nangleType =\"2\"\nfor index in range(numResidue):\n num = num +1\n outputatoms.write('{0} {1} {2} {3} {4}\\n'.format(int(num), angleType, int((index+1)*3 - 2), int((index+1)*3 -1), int((index+1)*3-0)))\n\n\n\n\n'''Generate inputfile for the simulation'''\nrepeatline = \"##---------------------------------\\n\"\noutInFile = open('relax.in','w')\noutInFile.write(\"################ RNA with tail ##########\\n\")\noutInFile.write(\"clear\\n\\n\")\n\noutInFile.write(\"##Initialization\\n\")\noutInFile.write(repeatline)\n\noutInFile.write('{0} {1}\\n'.format(\"units\", \"lj\"))\noutInFile.write('{0} {1}\\n'.format(\"dimension\", \"3\"))\noutInFile.write('{0} {1}\\n'.format(\"atom_style\", \"full\"))\noutInFile.write('{0} {1} {2} {3}\\n\\n'.format(\"boundary\", \"f\", \"f\", \"f\"))\n\n\noutInFile.write(\"##Atom definition\\n\")\noutInFile.write(repeatline)\noutInFile.write('{0} {1}\\n\\n'.format(\"read_data\", \"relax.data\"))\n\n\noutInFile.write(\"## Force definition\\n\")\noutInFile.write(repeatline)\n\noutInFile.write(\"## Bond definition\\n\")\noutInFile.write('{0} {1}\\n'.format(\"bond_style\", \"harmonic\"))\noutInFile.write('{0} {1} {2} {3}\\n\\n'.format(\"bond_coeff\", bondType, kb, l0))\n\noutInFile.write(\"## Angle definition\\n\")\noutInFile.write('{0} {1}\\n'.format(\"angle_style\", \"harmonic\"))\noutInFile.write('{0} {1} {2} {3}\\n'.format(\"angle_coeff\", bondType, kTheta, theta0))\noutInFile.write('{0} {1} {2} {3}\\n\\n'.format(\"angle_coeff\", angleType, kTheta, theta0))\n\noutInFile.write(\"special_bonds\tlj/coul 0 1 1 angle yes dihedral yes\\n\")\noutInFile.write('{0} {1}\\n'.format(\"dielectric\", dielectric))\n\noutInFile.write(\"## Pair definition\\n\")\noutInFile.write(repeatline)\n\noutInFile.write('{0} {1} {2} {3} {4} {5} {6:0.3f} {7} {8}\\n'.format(\"pair_style\", \"hybrid/overlay\", \"lj/cut\", rlj, \"coul/debye\", kappa, rcoul, \"gauss\", gaussCut ))\n\noutInFile.write(\"pair_modify\tshift yes\\n\")\n\n'''pair coefficient for lj'''\n\nfor i in range(nAtomType):\n for j in range(nAtomType):\n if i<j:\n break\n else:\n \n if i==j and i>=3:\n outInFile.write('{0} {1} {2} {3} {4} {5} {6:0.3f}\\n'.format(\"pair_coeff\", j+1, i+1, \"lj/cut\", float(epsilonlj/epsilon), float(sigmalj/sigma), rlocal ))\n outInFile.write(\"pair_coeff {} {} gauss {} {}\\n\".format( j+1, i+1, gaussA, gaussB))\n else:\n outInFile.write('{0} {1} {2} {3} {4} {5} {6:0.3f}\\n'.format(\"pair_coeff\", j+1, i+1, \"lj/cut\", float(epsilonlj/epsilon), float(sigmalj/sigma), rlocal ))\n\n'''pair coefficient for coulomb/debye'''\noutInFile.write('{0} {1} {2} {3}\\n'.format(\"pair_coeff\", 1, 1, \"coul/debye\"))\n\n\nrun = 3000000\n\nendpartStr =\"\\n#Group Definition \\n#---------------------------------------------------------\\n\"\nendpartStr +=\"group\t\tmobile molecule <> 1 {}\\n\".format(numResidue)\nendpartStr +=\"group\t\tpore molecule <> {} {}\\n\".format(numResidue+1, numResidue+1)\nendpartStr += \"group initial id {}\\n\\n\".format(nMobile-2)\n\nendpartStr +=\"#Neighbor Modify \\n#-------------------------------------------------------\\n\"\nendpartStr +=\"neigh_modify\texclude type {} {}\\n\\n\".format(poreResType, poreResType)\n\n\nendpartStr += \"\\n#Timestep etc\\n\"\nendpartStr += \"#-----------------------------------------------\\n\"\nendpartStr +=\"timestep {}\\n\".format(dt)\nendpartStr +=\"run_style verlet\\n\"\nendpartStr +=\"#velocity mobile create {} 5748\\n\".format(tempReduced)\nendpartStr += \"velocity \tinitial zero linear\\n\\n\"\n\n\nendpartStr+=\"#Fix\\n#------------------------------------------\\n\"\nendpartStr+=\"fix 2 mobile nve molecule\\n\"\nendpartStr+=\"fix 3 all langevin {} {} 1.0 74354\\n\".format(tempReduced, tempReduced)\nendpartStr+=\"fix\t4 initial setforce 0.0 0.0 0.0\\n\\n\"\n\n\n\n\nendpartStr+=\"#Dump\\n#----------------------------------------------\\n\"\nendpartStr+=\"thermo_style custom step temp evdwl ecoul ebond eangle pe ke etotal \\n\"\nendpartStr+=\"thermo 1000\\n\"\nendpartStr+=\"dump 1 mobile custom 5000 relax.lammpstrj id mol type x y z \\n\"\nendpartStr+=\"dump 5 all xyz {} dump.xyz\\n\".format(run)\n\nendpartStr+=\"run {} \".format(run)\nendpartStr+=\"#----------End-----------\\n\"\n\noutInFile.write(endpartStr)\n\n\n\n\n\n\n\n\n\n\nreaddata.close()\noutputatoms.close()\noutInFile.close()\n","sub_path":"buildSystem/6-16HP/relax-II.py","file_name":"relax-II.py","file_ext":"py","file_size_in_byte":13346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"115217542","text":"#!/bin/env python3\n# -*- coding: utf-8 -*-\n# version: Python3.X\n''' oxo_data is the data module for a tic - tac - toe (or OXO) game.\nIt saves and restores a game board. The functions are:\n save_game(game) -> None\n restore_game() -> game\nNote that no limits are placed on the size of data.\nThe game implementation is responsible for validating all data in and out.\n'''\n__author__ = '__L1n__w@tch'\n\nimport os.path\n\ngame_file = \".oxogame.dat\"\n\n\n# 按照惯例,不希望被模块使用者调用的函数会在名字前加一个下划线\ndef _get_path():\n \"\"\"\n Returns a valid path for data file.\n Tries to use the users home folder, defaults to cwd\n :return: string\n \"\"\"\n # 使用os模块试图得到用户的主目录,如果失败就使用当前目录\n try:\n game_path = os.environ[\"HOMEPATH\"] or os.environ[\"HOME\"]\n if not os.path.exists(game_path):\n game_path = os.getcwd()\n except (KeyError, TypeError):\n game_path = os.getcwd()\n return game_path\n\n\ndef save_game(game):\n \"\"\"\n saves a game object in the data file in the users home folder.\n No checking is done on the input, which is expected to be a list of characters\n :param game:\n :return: None\n \"\"\"\n path = os.path.join(_get_path(), game_file)\n with open(path, \"w\") as gf:\n game_str = \"\".join(game)\n gf.write(game_str)\n\n\ndef restore_game():\n \"\"\"\n Restores a game from the data file.\n The game object is a list of characters\n :return: game\n \"\"\"\n path = os.path.join(_get_path(), game_file)\n with open(path) as gf:\n game_str = gf.read()\n return list(game_str)\n\n\ndef test():\n print(\"Path = \", _get_path())\n save_game(list(\"XO XO OX\"))\n print(restore_game())\n\n\ndef main():\n test()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"Python项目开发实战/第4章 创建桌面应用/4.5 用一些对话框让命令行界面变得生动/oxo_data.py","file_name":"oxo_data.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"358867888","text":"from heapq import heappush, heappop, _heapify_max\nfrom math import ceil\nfrom operator import itemgetter\nfrom sys import argv\n\n\ndef split(spaces):\n result = {}\n # top = heappop(spaces)\n top = spaces[0][0]\n mid = ceil(top / 2.0)\n ls = mid - 1\n rs = top - ls - 1\n result['max'] = max([ls, rs])\n result['min'] = min([ls, rs])\n\n return result\n\n\ndef compute(n_stalls, n_users, spaces, start):\n result = {}\n\n for j in range(start, n_users+1):\n result = split(spaces)\n\n if spaces[0][1] == 1:\n del spaces[0]\n else:\n spaces[0][1] = spaces[0][1] - 1\n\n if result['max'] != 0:\n added = False\n for idx, v in enumerate(spaces):\n if result['max'] == v[0]:\n spaces[idx][1] = spaces[idx][1] + 1\n added = True\n break\n\n if not added:\n spaces.append([result['max'], 1])\n # heappush(spaces, result['max'])\n\n if result['min'] != 0:\n added = False\n for idx, v in enumerate(spaces):\n if result['min'] == v[0]:\n spaces[idx][1] = spaces[idx][1] + 1\n added = True\n break\n\n if not added:\n spaces.append([result['min'], 1])\n # heappush(spaces, result['min'])\n\n spaces = sorted(spaces, key=itemgetter(0), reverse=True)\n # print(spaces)\n # _heapify_max(spaces)\n\n return [result, spaces]\n\n\nif __name__ == '__main__':\n test_cases = open(argv[1])\n t = int(test_cases.readline())\n inputs = []\n outputs = []\n\n for i in range(0, t):\n input = test_cases.readline().split(' ')\n n_stalls = int(input[0])\n n_users = int(input[1].replace('\\n', ''))\n inputs.append([n_stalls, n_users, i+1])\n\n inputs = sorted(inputs, key=itemgetter(0, 1))\n heap = []\n prev = [-1, -1, -1]\n\n for x in inputs:\n if prev[0] != x[0]:\n heap = [[x[0], 1]]\n start = 1\n else:\n start = prev[1] + 1\n\n out = compute(x[0], x[1], heap, start)\n result = out[0]\n heap = out[1]\n prev = x\n outputs.append([x[2], result['max'], result['min']])\n\n outputs = sorted(outputs, key=itemgetter(0))\n\n for out in outputs:\n print(f\"Case #{out[0]}: {out[1]} {out[2]}\")\n\n test_cases.close()\n","sub_path":"solutions_python/Problem_201/2348.py","file_name":"2348.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"17099106","text":"words_set_res = ('a')\n\nprint(f'words_set_res = {words_set_res}')\n\nstr2 = '''def addition(a, b):\n for word in words_set_res:\n for i in range(len(l)):'''\n\nl = str2.split('\\n')\nfor i in range(len(l)):\n print(i, l[i])\n\nvariables = {}\nfor el in words_set_res:\n variables[el] = []\nprint(variables)\n\n# ПРОГРАМА ШУКАЄ СЛОВО В СТРОЦІ ЛИШЕ ОДИН РАЗ, А ПОТРІБНО ЗРОБИТИ ЩОБ ШУКАЛО БАГАТО РАЗ\nfor word in words_set_res:\n for i in range(len(l)):\n if word in l[i]:\n\n m = -1\n for j in range(2):\n\n m = l[i].index(word, m + 1)\n print('i', i, 'm ', m, 'word ', word)\n\n # inx_before = l[i].index(word) - 1\n # inx_after = l[i].index(word) + len(word)\n # if word == 'a':\n # print(i, inx_before, l[i][inx_before], inx_after, l[i][inx_after])\n # if not l[i][inx_before].isalpha():\n # if not l[i][inx_after].isalpha():\n # coordinates = [i, l[i].index(word)]\n # variables[word].append(coordinates)\n\n\nfor key in variables:\n print(key, variables[key])\n\n","sub_path":"lessons_with_tutor/miscellaneous/c22.py","file_name":"c22.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"178458020","text":"# coding=utf-8\n# Copyright (c) 2021, EleutherAI contributors\n# This file is based on code by the authors denoted below and has been modified from its original version.\n#\n# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# This file has been modified from its original version\n#\n\n\"\"\"Pretrain utilities.\"\"\"\nfrom datetime import datetime\nfrom functools import partial\n\nimport math\nimport sys\n\nimport torch\n\nfrom megatron.utils import Timers, init_wandb\nfrom megatron import print_rank_0\n\nfrom megatron import mpu\n\nfrom megatron.model import GPT2ModelPipe\nfrom megatron.checkpointing import load_checkpoint, save_checkpoint\nfrom megatron.data.gpt2_dataset import build_train_valid_test_datasets\n\nfrom megatron.initialize import initialize_megatron\nfrom megatron.learning_rates import AnnealingLR\nfrom megatron.model import get_params_for_weight_decay_optimization\nfrom megatron.utils import check_adlr_autoresume_termination\nfrom megatron.utils import make_data_loader\nfrom megatron.utils import report_memory\nfrom megatron.utils import tb_wandb_log\nfrom megatron.gradient_noise_scale import GradientNoiseScale\n\nfrom megatron.fp16 import fp32_to_fp16\nfrom megatron.model.gpt2_model import cross_entropy\nfrom megatron.utils import get_ltor_masks_and_position_ids\nfrom megatron.utils import reduce_losses\n\nimport deepspeed\n\n\ndef pretrain(neox_args):\n \"\"\"Main training program.\n\n This function will run the followings in the order provided:\n 1) initialize Megatron.\n 2) setup model, optimizer and lr schedule\n 3) call train_val_test_data_provider to get train/val/test datasets.\n 4) train the model.\n\n Arguments:\n neox_args: an instance of NeoXArgs containing the configuration for pretrain\n\n \"\"\"\n # setup logging and timers\n init_wandb(neox_args=neox_args)\n timers = Timers(use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n\n # Initalize and get arguments, timers, and Tensorboard writer.\n initialize_megatron(neox_args=neox_args)\n\n # Model, optimizer, and learning rate.\n timers('model and optimizer').start()\n model, optimizer, lr_scheduler = setup_model_and_optimizer(neox_args=neox_args, inference=False, get_key_value=True)\n timers('model and optimizer').stop()\n\n # Data stuff.\n timers('train/valid/test data iterators').start()\n train_data_iterator, valid_data_iterator, test_data_iterator = build_train_valid_test_data_iterators(neox_args=neox_args)\n timers('train/valid/test data iterators').stop()\n\n # Print setup timing.\n print_rank_0('done with setups ...')\n timers.log(['model and optimizer', 'train/valid/test data iterators'])\n print_rank_0('training ...')\n\n iteration = 0\n if neox_args.do_train and neox_args.train_iters > 0:\n iteration = train(\n neox_args=neox_args, \n timers=timers,\n model=model, \n optimizer=optimizer, \n lr_scheduler=lr_scheduler,\n train_data_iterator=train_data_iterator, \n valid_data_iterator=valid_data_iterator\n )\n\n if neox_args.do_valid:\n prefix = 'the end of training for val data'\n evaluate_and_print_results(\n neox_args=neox_args, \n prefix=prefix, \n forward_step_func=forward_step,\n data_iterator=valid_data_iterator, \n model=model, \n iteration=iteration, \n verbose=False\n )\n\n if neox_args.save and iteration != 0:\n save_checkpoint(neox_args=neox_args, iteration=iteration, model=model, optimizer=optimizer, lr_scheduler=lr_scheduler)\n\n if neox_args.do_test:\n # Run on test data.\n prefix = 'the end of training for test data'\n evaluate_and_print_results(\n neox_args=neox_args, \n prefix=prefix, \n forward_step_func=forward_step,\n data_iterator=test_data_iterator, \n model=model, \n iteration=0, # iteration 0 in order to always use full test data\n verbose=True\n )\n\ndef _get_batch(neox_args, tokenizer, keys, data, datatype):\n \"\"\"Support function for get_batch / get_batch pipe (to avoid code repetition)\"\"\"\n data_b = mpu.broadcast_data(keys, data, datatype)\n\n # Unpack.\n tokens_ = data_b['text'].long()\n labels = tokens_[:, 1:].contiguous()\n tokens = tokens_[:, :-1].contiguous()\n\n # Get the masks and postition ids.\n attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(\n tokens,\n tokenizer.eod,\n neox_args.reset_position_ids,\n neox_args.reset_attention_mask,\n neox_args.eod_mask_loss)\n\n return tokens, labels, loss_mask, attention_mask, position_ids\n\n\ndef get_batch(neox_args, data_iterator):\n \"\"\"Generate a batch\"\"\"\n\n # Items and their type.\n keys = ['text']\n datatype = torch.int64\n\n # Broadcast data.\n if data_iterator is not None:\n data = next(data_iterator)\n else:\n data = None\n return _get_batch(neox_args=neox_args, tokenizer=neox_args.tokenizer, keys=keys, data=data, datatype=datatype)\n\ndef get_batch_pipe(data, neox_args):\n \"\"\"A modification of get_batch() to work with the latest batch instead of an iterator. \"\"\"\n \n # Items and their type.\n keys = ['text']\n datatype = torch.int64\n\n tokens, labels, loss_mask, attention_mask, position_ids = _get_batch(neox_args, neox_args.tokenizer, keys, data, datatype)\n # unpack data\n if neox_args.precision == \"fp16\":\n # cast to fp16 because pipeline parallelism skips the FP16 wrapper.\n return fp32_to_fp16((tokens, position_ids, attention_mask)), fp32_to_fp16((labels, loss_mask))\n else:\n return (tokens, position_ids, attention_mask), (labels, loss_mask)\n\n\ndef forward_step(neox_args, timers, data_iterator, model):\n \"\"\"Forward step.\"\"\"\n\n # Get the batch.\n timers('batch generator').start()\n tokens, labels, loss_mask, attention_mask, position_ids = get_batch(neox_args=neox_args, data_iterator=data_iterator)\n timers('batch generator').stop()\n\n outputs = model((tokens, position_ids, attention_mask))\n loss = cross_entropy(outputs, (labels, loss_mask), _fp16=neox_args.fp16_lm_cross_entropy)\n\n # Reduce loss for logging.\n reduced_loss = reduce_losses([loss])\n\n return loss, {'lm loss': reduced_loss[0]}\n\ndef get_model(neox_args, inference=False, get_key_value=True):\n \"\"\"Build the model.\"\"\"\n\n print_rank_0('building GPT2 model ...')\n\n # Build model on cpu.\n model = GPT2ModelPipe(neox_args=neox_args, num_tokentypes=0, parallel_output=True, topology=mpu.get_topology(), inference=inference, get_key_value=get_key_value)\n if not neox_args.is_pipe_parallel:\n # Export PipeParallel model to nn.Sequential model to avoid the overhead of deepspeed's pipe parallel training\n model = model.to_sequential()\n else:\n # This is a hack to give us a reference to get_batch_pipe from within training.py\n # We need to call model.set_batch_fn after deepspeed.initialize\n model._megatron_batch_fn = partial(get_batch_pipe, neox_args=neox_args) \n\n if neox_args.deepspeed:\n # DeepSpeed handles CUDA, FP16, and DDP components.\n return model\n else:\n raise ValueError(\"Must be using deepspeed to run neox\")\n\n\ndef get_optimizer(model, neox_args):\n \"\"\"Set up the optimizer.\"\"\"\n if neox_args.no_load_optim:\n return None, None\n # Build parameter groups (weight decay and non-decay).\n param_groups = get_params_for_weight_decay_optimization(model, neox_args)\n print_rank_0(f'Configuring Optimizer type: {neox_args.optimizer_type} with params: {neox_args.optimizer[\"params\"]}')\n # Add model parallel attribute if it is not set.\n for param_group in param_groups:\n for param in param_group['params']:\n if not hasattr(param, 'model_parallel'):\n param.model_parallel = False\n\n if neox_args.optimizer_type.lower() in [\"cpu_adam\", \"cpu_torch_adam\"]:\n if neox_args.optimizer == \"cpu_torch_adam\":\n cpu_adam_optimizer = torch.optim.Adam\n else:\n from deepspeed.ops.adam import DeepSpeedCPUAdam\n cpu_adam_optimizer = DeepSpeedCPUAdam\n optimizer = cpu_adam_optimizer(param_groups,\n weight_decay=neox_args.weight_decay,\n **neox_args.optimizer[\"params\"])\n elif neox_args.optimizer_type.lower() == \"onebitadam\":\n assert neox_args.deepspeed\n optimizer = None\n # onebitadam needs to be instantiated within the deepspeed engine to work :|\n elif neox_args.optimizer_type.lower() == \"sm3\":\n from .optimizers import SM3\n optimizer = SM3(\n param_groups,\n **neox_args.optimizer[\"params\"])\n elif neox_args.optimizer_type.lower() == \"adam\":\n # Use Adam\n try:\n # default to apex as it's slightly faster\n from apex.optimizers import FusedAdam as Adam\n except ImportError:\n # if apex isn't installed, use deepspeed's FusedAdam\n print(\"WARNING: APEX not installed - defaulting to deepspeed's fused adam\")\n from deepspeed.ops.adam import FusedAdam as Adam\n optimizer = Adam(param_groups,\n weight_decay=neox_args.weight_decay,\n **neox_args.optimizer[\"params\"])\n else:\n raise ValueError(f\"Optimizer type {neox_args.optimizer_type} not recognized\")\n\n if neox_args.deepspeed:\n # fp16 wrapper is not required for DeepSpeed.\n return optimizer, param_groups\n else:\n raise ValueError(\"Must be using deepspeed to run neox\")\n\n\ndef get_learning_rate_scheduler(optimizer, neox_args):\n \"\"\"Build the learning rate scheduler.\"\"\"\n if neox_args.no_load_optim:\n # TODO: this should be configured as a separate arg\n return None\n if neox_args.deepspeed and neox_args.optimizer_type.lower() == \"onebitadam\":\n print_rank_0(\"WARNING: onebitadam requires the lr scheduler be built by deepspeed - \"\n \"Make sure one is added to your deepspeed config\")\n return None\n\n # Add linear learning rate scheduler.\n if neox_args.lr_decay_iters is not None:\n num_iters = neox_args.lr_decay_iters\n else:\n num_iters = neox_args.train_iters\n num_iters = max(1, num_iters)\n init_step = 0\n warmup_iter = neox_args.warmup * num_iters\n lr_scheduler = AnnealingLR(\n optimizer,\n start_lr=neox_args.lr,\n warmup_iter=warmup_iter,\n total_iters=num_iters,\n decay_style=neox_args.lr_decay_style,\n last_iter=init_step,\n min_lr=neox_args.min_lr,\n use_checkpoint_lr_scheduler=neox_args.use_checkpoint_lr_scheduler,\n override_lr_scheduler=neox_args.override_lr_scheduler)\n\n return lr_scheduler\n\n\ndef setup_model_and_optimizer(neox_args, inference=False, get_key_value=True):\n \"\"\"Setup model and optimizer.\"\"\"\n model = get_model(neox_args=neox_args, inference=inference, get_key_value=get_key_value)\n optimizer, param_groups = get_optimizer(model=model, neox_args=neox_args)\n lr_scheduler = get_learning_rate_scheduler(optimizer=optimizer, neox_args=neox_args)\n\n if neox_args.deepspeed:\n print_rank_0(\"DeepSpeed is enabled.\")\n if neox_args.no_load_optim:\n assert optimizer is None\n _model_params = None\n _lr_scheduler = None\n else:\n _model_params = param_groups if optimizer is None else None\n _lr_scheduler = lr_scheduler\n\n model, optimizer, _, lr_scheduler = deepspeed.initialize(\n model=model,\n optimizer=optimizer,\n args=neox_args,\n lr_scheduler=_lr_scheduler,\n dist_init_required=False,\n model_parameters=_model_params,\n config_params=neox_args.deepspeed_config,\n mpu=mpu if not neox_args.is_pipe_parallel else None\n )\n model.total_params = get_total_params(model.module)\n print_rank_0(f' > total params: {\"{:,}\".format(model.total_params)}')\n\n if neox_args.is_pipe_parallel:\n model.set_batch_fn(model.module._megatron_batch_fn)\n else:\n raise ValueError(\"Must be using deepspeed to run neox\")\n\n if neox_args.load is not None:\n neox_args.iteration = load_checkpoint(neox_args=neox_args, model=model, optimizer=optimizer, lr_scheduler=lr_scheduler)\n print_rank_0(f'Loading checkpoint and starting from iteration {neox_args.iteration}')\n else:\n neox_args.iteration = 0\n\n return model, optimizer, lr_scheduler\n\n\ndef backward_step(neox_args, timers, optimizer, model, loss):\n \"\"\"Backward step.\"\"\"\n\n # Backward pass.\n timers('backward-backward').start()\n if neox_args.deepspeed:\n model.backward(loss)\n else:\n raise ValueError(\"Must be using deepspeed to run neox\")\n timers('backward-backward').stop()\n\n if neox_args.deepspeed:\n # DeepSpeed backward propagation already addressed all reduce communication.\n # Reset the timer to avoid breaking timer logs below.\n timers('backward-allreduce').reset()\n else:\n raise ValueError(\"Must be using deepspeed to run neox\")\n\n\ndef train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler):\n \"\"\"Single training step.\"\"\"\n\n # Pipeline parallelism schedules forward/backward/step\n if neox_args.is_pipe_parallel:\n return train_step_pipe(neox_args=neox_args, timers=timers, model=model, data_iterator=data_iterator)\n\n # Forward model for one step.\n timers('forward').start()\n loss, loss_reduced = forward_step(neox_args=neox_args, timers=timers, data_iterator=data_iterator, model=model)\n timers('forward').stop()\n\n # Calculate gradients, reduce across processes, and clip.\n timers('backward').start()\n backward_step(neox_args=neox_args, timers=timers, optimizer=optimizer, model=model, loss=loss)\n timers('backward').stop()\n\n # Update parameters.\n skipped_iter = 0\n timers('optimizer').start()\n if neox_args.deepspeed:\n model.step()\n else:\n raise ValueError(\"Must be using deepspeed to run neox\")\n timers('optimizer').stop()\n\n return loss_reduced, skipped_iter\n\n\ndef train_step_pipe(neox_args, timers, model, data_iterator):\n \"\"\"Single training step with DeepSpeed's pipeline parallel engine. \"\"\"\n\n assert neox_args.deepspeed\n loss = model.train_batch(data_iter=data_iterator)\n loss_dict = {'lm loss': loss}\n if neox_args.precision == \"fp16\" and model.optimizer.overflow:\n skipped_iter = 1\n else:\n skipped_iter = 0\n # Don't break Megatron's timers because we changed code paths.\n for t in ['forward', 'backward', 'allreduce', 'optimizer', 'batch generator', 'data loader']:\n timers(t).reset()\n return loss_dict, skipped_iter\n\n\ndef training_log(neox_args, timers, loss_dict, total_loss_dict, learning_rate, iteration,\n loss_scale, report_memory_flag, skipped_iter, model, optimizer, noise_scale_logger):\n \"\"\"Log training information such as losses, timing, etc.\"\"\"\n\n # Update losses.\n skipped_iters_key = 'skipped iterations'\n total_loss_dict[skipped_iters_key] = total_loss_dict.get(\n skipped_iters_key, 0) + skipped_iter\n got_nan_key = 'got nan'\n\n got_nan = False\n for key in loss_dict:\n if not skipped_iter:\n total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key]\n else:\n value = loss_dict[key].float().sum().item()\n is_nan = value == float('inf') or \\\n value == -float('inf') or \\\n value != value\n got_nan = got_nan or is_nan\n\n total_loss_dict[got_nan_key] = total_loss_dict.get(\n got_nan_key, 0) + int(got_nan)\n\n # Logging.\n timers_to_log = []\n\n def add_to_logging(name):\n if name in timers.timers:\n timers_to_log.append(name)\n\n if not neox_args.is_pipe_parallel:\n add_to_logging('forward')\n add_to_logging('backward')\n add_to_logging('backward-backward')\n add_to_logging('backward-allreduce')\n add_to_logging('backward-master-grad')\n add_to_logging('backward-clip-grad')\n add_to_logging('optimizer')\n add_to_logging('batch generator')\n\n # Log timer info to tensorboard and wandb\n normalizer = iteration % neox_args.log_interval\n if normalizer == 0:\n normalizer = neox_args.log_interval\n if torch.distributed.get_rank() == 0:\n timers.write(names=timers_to_log, iteration=iteration, normalizer=normalizer)\n else:\n # with pipeline parallel, the megatron timers are overridden by the deepspeed ones.\n # Try to grab timer values from model engine. Only recently added to deeperspeed, so check that the engine\n # has that attribute first\n if hasattr(model, 'timer_values') and model.timer_values is not None:\n if model.wall_clock_breakdown() and model.global_steps % model.steps_per_print() == 0:\n timer_values = model.timer_values\n # deepspeed already logs to tensorboard / prints values, so just log to wandb\n if neox_args.use_wandb and torch.distributed.get_rank() == 0:\n for key in timer_values:\n tb_wandb_log(f\"timers/{key}\", timer_values[key], iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n\n # write losses, lr, etc. every step\n tb_wandb_log('train/learning_rate', learning_rate, iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n for key in loss_dict:\n tb_wandb_log(f'train/{key.replace(\" \", \"_\")}', loss_dict[key], iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n if neox_args.fp16:\n tb_wandb_log(f'train/loss_scale', loss_scale, iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n\n # log gradient noise scale\n if neox_args.log_gradient_noise_scale:\n if noise_scale_logger.noise_scale is not None:\n tb_wandb_log(f'train/noise_scale', noise_scale_logger.noise_scale, iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n\n # (optional) Log optimizer states to wandb / tb every step\n if neox_args.log_optimizer_states:\n for k, v in optimizer.state_dict()['optimizer_state_dict']['state'].items():\n for ki, vi in v.items(): # step, module\n if ki != 'step':\n opt_state_norm = torch.norm(vi) if hasattr(vi, 'dim') else vi\n tb_wandb_log(f'optimizer_state_norms/{k}_{ki}', opt_state_norm, iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n\n # (optional) Log grad/param norms to wandb / tb every step\n if neox_args.log_grad_norm or neox_args.log_param_norm:\n for name, param in model.module.named_parameters():\n if neox_args.log_grad_norm:\n if param.grad is not None:\n tb_wandb_log(f'gradient_norms/{name}', torch.norm(param.grad), iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n if neox_args.log_param_norm:\n tb_wandb_log(f'parameter_norms/{name}', torch.norm(param), iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n\n if iteration % neox_args.log_interval == 0:\n # log other stuff every neox_args.log_interval iters\n elapsed_time = timers('interval time').elapsed()\n iteration_time = elapsed_time / neox_args.log_interval\n samples_per_sec = get_global_batch_size(neox_args) / iteration_time\n log_string = ' samples/sec: {:.3f} |'.format(samples_per_sec)\n tb_wandb_log('runtime/samples_per_sec', samples_per_sec, iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n tb_wandb_log('runtime/iteration_time', iteration_time, iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n log_string += ' iteration {:8d}/{:8d} |'.format(iteration, neox_args.train_iters)\n log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(\n elapsed_time * 1000.0 / neox_args.log_interval)\n log_string += ' learning rate: {:.3E} |'.format(learning_rate)\n num_iterations = max(\n 1, neox_args.log_interval - total_loss_dict[skipped_iters_key])\n\n # log tflop / gpu\n flops_per_s_per_gpu = get_flops(neox_args=neox_args, model=model, iter_time_s=iteration_time)\n log_string += f' approx flops per GPU: {human_readable_flops(flops_per_s_per_gpu)} |'\n tb_wandb_log('runtime/flops_per_sec_per_gpu', flops_per_s_per_gpu, iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n\n for key in total_loss_dict:\n if key not in [skipped_iters_key, got_nan_key]:\n v = total_loss_dict[key].item() if hasattr(total_loss_dict[key], 'item') else total_loss_dict[key]\n avg = v / float(num_iterations)\n log_string += ' {}: {:.6E} |'.format(key, avg)\n total_loss_dict[key] = 0.0\n if neox_args.precision == \"fp16\":\n log_string += ' loss scale: {:.1f} |'.format(loss_scale)\n log_string += ' number of skipped iterations: {:3d} |'.format(\n total_loss_dict[skipped_iters_key])\n log_string += ' number of nan iterations: {:3d} |'.format(\n total_loss_dict[got_nan_key])\n total_loss_dict[skipped_iters_key] = 0\n total_loss_dict[got_nan_key] = 0\n print_rank_0(log_string)\n if report_memory_flag:\n report_memory('after {} iterations'.format(iteration))\n report_memory_flag = False\n\n timers.log(timers_to_log, normalizer=neox_args.log_interval)\n\n return report_memory_flag\n\n\ndef train(neox_args, timers, model, optimizer, lr_scheduler,\n train_data_iterator, valid_data_iterator):\n \"\"\"Train the model function.\"\"\"\n\n # Turn on training mode which enables dropout.\n model.train()\n\n # Tracking loss.\n total_loss_dict = {}\n\n # Iterations.\n iteration = neox_args.iteration\n\n timers('interval time').start()\n report_memory_flag = True\n\n if neox_args.log_gradient_noise_scale:\n if neox_args.zero_stage >= 1:\n raise NotImplementedError('Gradient Noise Scale logging does not work with zero stage 2+, as the '\n 'gradients are distributed across ranks.')\n noise_scale_logger = GradientNoiseScale(\n model=model,\n batch_size_small=neox_args.train_batch_size,\n n_batches=neox_args.gradient_noise_scale_n_batches,\n cpu_offload=neox_args.gradient_noise_scale_cpu_offload,\n neox_args=neox_args,\n mpu=mpu)\n else:\n noise_scale_logger = None\n\n while iteration < neox_args.train_iters:\n loss_dict, skipped_iter = train_step(\n neox_args=neox_args, \n timers=timers,\n data_iterator=train_data_iterator,\n model=model, \n optimizer=optimizer, \n lr_scheduler=lr_scheduler\n )\n iteration += 1\n if neox_args.log_gradient_noise_scale:\n noise_scale_logger.update()\n # Logging.\n loss_scale = None\n if neox_args.precision == \"fp16\":\n loss_scale = optimizer.cur_scale\n report_memory_flag = training_log(\n neox_args=neox_args, \n timers=timers, \n loss_dict=loss_dict, \n total_loss_dict=total_loss_dict, \n learning_rate=optimizer.param_groups[0]['lr'], \n iteration=iteration,\n loss_scale=loss_scale, \n report_memory_flag=report_memory_flag, \n skipped_iter=skipped_iter, \n model=model, \n optimizer=optimizer, \n noise_scale_logger=noise_scale_logger\n )\n\n # Autoresume\n if neox_args.adlr_autoresume and \\\n (iteration % neox_args.adlr_autoresume_interval == 0):\n check_adlr_autoresume_termination(neox_args=neox_args, iteration=iteration, model=model, optimizer=optimizer, lr_scheduler=lr_scheduler)\n\n # Checkpointing\n if neox_args.save and neox_args.save_interval and iteration % neox_args.save_interval == 0:\n save_checkpoint(neox_args=neox_args, iteration=iteration, model=model, optimizer=optimizer, lr_scheduler=lr_scheduler)\n\n # Evaluation\n if neox_args.eval_interval and iteration % neox_args.eval_interval == 0 and neox_args.do_valid:\n prefix = 'iteration {}'.format(iteration)\n evaluate_and_print_results(\n neox_args=neox_args, \n prefix=prefix, \n forward_step_func=forward_step,\n data_iterator=valid_data_iterator, \n model=model, \n iteration=iteration, \n verbose=False\n )\n\n if neox_args.exit_interval and iteration % neox_args.exit_interval == 0:\n torch.distributed.barrier()\n time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n rank = torch.distributed.get_rank()\n print_rank_0('rank: {} | time: {} | exiting the program at iteration {}'.format(rank, time_str, iteration))\n sys.exit()\n\n return iteration\n\n\ndef evaluate(neox_args, forward_step_fn, data_iterator, model, verbose=False):\n \"\"\"Evaluation.\"\"\"\n # Turn on evaluation mode which disables dropout.\n model.eval()\n\n total_loss_dict = {}\n\n with torch.no_grad():\n iteration = 0\n while iteration < neox_args.eval_iters:\n iteration += 1\n if verbose and iteration % neox_args.log_interval == 0:\n print_rank_0('Evaluating iter {}/{}'.format(iteration, neox_args.eval_iters))\n # Forward evaluation.\n _, loss_dict = forward_step_fn(data_iterator, model)\n\n # When contiguous memory optimizations are enabled, the buffers\n # allocated by the optimizations are deallocated during backward pass\n # in the absence of backward pass the buffers should be reset after each\n # forward pass\n if neox_args.deepspeed and neox_args.deepspeed_activation_checkpointing:\n deepspeed.checkpointing.reset()\n\n # Reduce across processes.\n for key in loss_dict:\n total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key]\n \n # Move model back to the train mode.\n model.train()\n\n for key in total_loss_dict:\n total_loss_dict[key] /= neox_args.eval_iters\n\n return total_loss_dict\n\n\ndef evaluate_and_print_results(neox_args, prefix, forward_step_func, data_iterator, model, iteration, verbose=False):\n \"\"\"Helper function to evaluate and dump results on screen.\"\"\"\n\n # Pipeline parallelism needs eval_batch() instead of a simple forward().\n if neox_args.is_pipe_parallel:\n def _eval_helper(data_iter, _):\n loss = model.eval_batch(data_iter)\n return None, {'lm loss': loss}\n forward_step_func = _eval_helper\n\n total_loss_dict = evaluate(neox_args=neox_args, forward_step_fn=forward_step_func, data_iterator=data_iterator, model=model, verbose=verbose)\n string = ' validation loss at {} | '.format(prefix)\n for key in total_loss_dict:\n string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item())\n ppl = math.exp(min(20, total_loss_dict[key].item()))\n string += '{} PPL: {:.6E} | '.format(key, ppl)\n tb_wandb_log(f\"validation/{key.replace(' ', '_')}\", total_loss_dict[key].item(), iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n tb_wandb_log(f\"validation/{key.replace(' ', '_')}_ppl\", ppl, iteration, use_wandb=neox_args.use_wandb, tensorboard_writer=neox_args.tensorboard_writer)\n\n length = len(string) + 1\n print_rank_0('-' * length)\n print_rank_0(string)\n print_rank_0('-' * length)\n\n\ndef build_train_valid_test_data_iterators(neox_args):\n \"\"\"XXX\"\"\"\n\n (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None)\n\n print_rank_0('> building train, validation, and test datasets ...')\n\n # Ensure only the first/last pipeline stages have data loaders\n if neox_args.is_pipe_parallel:\n is_first_stage = mpu.get_pipe_parallel_rank() == 0\n is_last_stage = mpu.get_pipe_parallel_rank() == mpu.get_pipe_parallel_world_size() - 1\n pipe_load = is_first_stage or is_last_stage\n else:\n pipe_load = True\n\n # Data loader only on rank 0 of each model parallel group.\n if mpu.get_model_parallel_rank() == 0 and pipe_load:\n # Rank, size, and global batch size.\n data_parallel_size = mpu.get_data_parallel_world_size()\n global_batch_size = neox_args.batch_size * data_parallel_size * neox_args.gas\n\n # Number of train/valid/test samples.\n train_iters = neox_args.train_iters\n eval_iters = (train_iters // neox_args.eval_interval + 1) * neox_args.eval_iters\n test_iters = neox_args.eval_iters\n train_val_test_num_samples = [train_iters * global_batch_size,\n eval_iters * global_batch_size,\n test_iters * global_batch_size]\n print_rank_0(' > datasets target sizes (minimum size):')\n print_rank_0(' train: {}'.format(train_val_test_num_samples[0]))\n print_rank_0(' validation: {}'.format(train_val_test_num_samples[1]))\n print_rank_0(' test: {}'.format(train_val_test_num_samples[2]))\n\n # Build the datasets.\n\n print_rank_0('> building train, validation, and test datasets for GPT2 ...')\n train_ds, valid_ds, test_ds = build_train_valid_test_datasets(\n data_prefix=neox_args.data_path,\n data_impl=neox_args.data_impl,\n splits_string=neox_args.split,\n train_valid_test_num_samples=train_val_test_num_samples,\n seq_length=neox_args.seq_length,\n seed=neox_args.seed,\n skip_warmup=(not neox_args.mmap_warmup)\n )\n print_rank_0(\"> finished creating GPT2 datasets ...\")\n\n # Build dataloders.\n train_dataloader = make_data_loader(train_ds, neox_args=neox_args)\n valid_dataloader = make_data_loader(valid_ds, neox_args=neox_args)\n test_dataloader = make_data_loader(test_ds, neox_args=neox_args)\n\n # Flags to know if we need to do training/validation/testing.\n do_train = train_dataloader is not None and neox_args.train_iters > 0\n do_valid = valid_dataloader is not None and neox_args.eval_iters > 0\n do_test = test_dataloader is not None and neox_args.eval_iters > 0\n # Need to broadcast num_tokens and num_type_tokens.\n flags = torch.cuda.LongTensor(\n [int(do_train), int(do_valid), int(do_test)])\n else:\n flags = torch.cuda.LongTensor([0, 0, 0])\n\n # Broadcast num tokens.\n if neox_args.is_pipe_parallel:\n # Only first/last pipeline stages have data loaders, so pipeline parallelism should\n # broadcast globally instead of just the model parallel group.\n torch.distributed.broadcast(flags, src=0)\n else:\n torch.distributed.broadcast(flags,\n mpu.get_model_parallel_src_rank(),\n group=mpu.get_model_parallel_group())\n neox_args.do_train = flags[0].item()\n neox_args.do_valid = flags[1].item()\n neox_args.do_test = flags[2].item()\n\n # Shift the start iterations.\n if train_dataloader is not None:\n train_dataloader.batch_sampler.start_iter = neox_args.iteration % \\\n len(train_dataloader)\n print_rank_0('setting training data start iteration to {}'.\n format(train_dataloader.batch_sampler.start_iter))\n if valid_dataloader is not None:\n start_iter_val = (neox_args.iteration // neox_args.eval_interval) * \\\n neox_args.eval_iters\n valid_dataloader.batch_sampler.start_iter = start_iter_val % \\\n len(valid_dataloader)\n print_rank_0('setting validation data start iteration to {}'.\n format(valid_dataloader.batch_sampler.start_iter))\n\n # Build iterators.\n if train_dataloader is not None:\n train_data_iterator = iter(train_dataloader)\n else:\n train_data_iterator = None\n\n if valid_dataloader is not None:\n valid_data_iterator = iter(valid_dataloader)\n else:\n valid_data_iterator = None\n\n if test_dataloader is not None:\n test_data_iterator = iter(test_dataloader)\n else:\n test_data_iterator = None\n\n return train_data_iterator, valid_data_iterator, test_data_iterator\n\n\ndef get_total_params(model):\n # Print number of parameters.\n if mpu.get_data_parallel_rank() == 0:\n params = sum([p.nelement() for p in model.parameters()])\n print(' > number of parameters on model parallel rank {}: {}'.format(\n mpu.get_model_parallel_rank(), params), flush=True)\n else:\n params = 0\n\n total_n_parameters = torch.tensor([params]).cuda(torch.cuda.current_device())\n torch.distributed.all_reduce(total_n_parameters)\n total_n_parameters = total_n_parameters.item()\n return total_n_parameters\n\n\ndef human_readable_flops(num):\n for unit in ['', 'KFLOPS', 'MFLOPS', 'GFLOPS', 'TFLOPS', 'PFLOPS', 'EFLOPS', 'ZFLOPS']:\n if abs(num) < 1000.0:\n return \"%3.1f%s\" % (num, unit)\n num /= 1000.0\n return \"%.1f%s\" % (num, 'Yi')\n\n\ndef get_global_batch_size(neox_args):\n return neox_args.batch_size * mpu.get_data_parallel_world_size() * neox_args.gas\n\n\ndef get_flops(neox_args, model, iter_time_s):\n\n world_size = torch.distributed.get_world_size()\n global_batch_size = get_global_batch_size(neox_args)\n\n ff = model.total_params * 6\n attn = neox_args.seq_length * neox_args.hidden_size * neox_args.num_layers * 60\n flops = global_batch_size * neox_args.seq_length * (ff + attn) / (iter_time_s * world_size)\n\n return flops\n","sub_path":"megatron/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":35165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"559927018","text":"class Node:\n def __init__(self, data):\n self.data = data\n self.next = None\n\nclass LinkedList:\n def __init__(self):\n self.head = None\n \n def traverse(self):\n if self.head is None:\n print(\"List has no element\")\n return\n else:\n n = self.head\n while n is not None:\n print(n.data,\" \")\n n=n.next\n \n def insert_at_start(self, data):\n new_node = Node(data)\n new_node.next = self.head\n self.head= new_node\n\n\nif __name__=='__main__':\n items =LinkedList()\n \n items.insert_at_start('40')\n items.insert_at_start('50')\n\n items.traverse()","sub_path":"data_structures/linked_lists/1_insert_start_LL/Solution/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"226104827","text":"class qryWSGiroBI:\r\n def __init__(self, formObj, parentForm) :\r\n self.app = formObj.ClientApplication\r\n self.qry = formObj.GetPanelByName('qData')\r\n self.oqlText = \\\r\n \"select from RECON_WSGiroBI \"\\\r\n \"( \"\\\r\n \" Tanggal \"\\\r\n \" , status \"\\\r\n \" , f_rtgs_out \"\\\r\n \" , f_rtgs_in \"\\\r\n \" , f_tom \"\\\r\n \" , f_bsk_debet \"\\\r\n \" , f_bsk_kredit_retur \"\\\r\n \" , f_girobi \"\\\r\n \" , j_rtgs_out \"\\\r\n \" , j_rtgs_in \"\\\r\n \" , j_tom \"\\\r\n \" , j_bsk_debet \"\\\r\n \" , j_bsk_kredit_retur \"\\\r\n \" , j_lain \"\\\r\n \" , j_total \"\\\r\n \" , ID_WSGiroBI \"\\\r\n \" , self \"\\\r\n \") then order by Tanggal;\"\r\n\r\n def setQueryText(self, oqltext):\r\n formObj = self.FormObject\r\n self.qry.OQLText = oqltext\r\n \r\n def showQuery(self):\r\n self.qry.DisplayData()\r\n\r\n def Show(self):\r\n formObj = self.FormObject\r\n uipNoData = formObj.GetUIPartByName('uipNoData')\r\n\r\n self.setQueryText(self.oqlText)\r\n self.showQuery()\r\n\r\n self.FormContainer.Show()\r\n return\r\n\r\n","sub_path":"dialogs/reconcilerak/PeragaanData/qryWSGiroBI_intr.py","file_name":"qryWSGiroBI_intr.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"536349408","text":"#!/usr/bin/python\n#coding:utf8\n#Auther: Zifan Liu\n#Date: Feb 29, 2016\n#Project interpreter: 3.x\nimport os,sys,socket\n\nbase_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))\nsys.path.append(base_dir)\nfrom day7.FTP.server.user_info.register import *\nfrom day7.FTP.client.function_list import *\n\n\n#main\nif __name__ == '__main__':\n bufsiz = 1024\n addr = ('127.0.0.1',7777)\n tcp_sock = socket.socket() #1.socket\n tcp_sock.connect(addr) #2.connect\n print(\"Try to connect server....\")\n while 1:\n #register user\n username,status = user_register()\n if status:\n print(\"OK, signup&login successful\".center(80,'#'))\n print(\"You can use these command in this terminal:\\n\\t\\t0.ls\\n\\t\\t1.cd ..\\n\\t\\t2.cd xxx\\n\\t\\t3.mkdir xxx\\n\\t\\t4.rmdir xxx\\n\\t\\t5.upload C:\\\\450.jpg\\n\\t\\t6.download C:\\\\450.jpg\".center(80,'='))\n tcp_sock.sendall(bytes(username,encoding='utf-8')) #3.send\n while 1:\n user_input_command = input(\"[%s@localhost] >> \" % username)\n tcp_sock.sendall(bytes(user_input_command,encoding='utf8'))\n server_reply = str(tcp_sock.recv(bufsiz),'utf-8')\n print(\"\\t\\t%s\" % server_reply)\n tcp_sock.close() #5.close\n else:print(\"signup&login failed\")","sub_path":"day7/FTP/client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"257917017","text":"from .data import GCP_PRIVATE_KEY\nfrom googleapiclient.http import RequestMockBuilder as GoogleApiClientRequestMockBuilder\nfrom httplib2 import Response as HttpLib2Response\nfrom journalpump.senders.google_cloud_logging import GoogleCloudLoggingSender\nfrom typing import Dict, List\nfrom unittest import mock\n\nimport json\n\n\nclass TestGoogleCloudLoggingSender:\n\n CONFIG = {\n \"google_cloud_logging_project_id\": \"project-id\",\n \"google_cloud_logging_log_id\": \"log-id\",\n \"google_cloud_logging_resource_labels\": {\n \"location\": \"us-east-1\",\n \"node_id\": \"my-test-node\",\n },\n \"google_service_account_credentials\": {\n \"type\": \"service_account\",\n \"project_id\": \"project-id\",\n \"private_key_id\": \"abcdefg\",\n \"private_key\": GCP_PRIVATE_KEY,\n \"client_email\": \"test@project-id.iam.gserviceaccount.com\",\n \"client_id\": \"123456789\",\n \"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n \"token_uri\": \"https://oauth2.googleapis.com/token\",\n \"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n \"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/test%40project-id.iam.gserviceaccount.com\", # pylint:disable=line-too-long\n },\n }\n\n def _generate_request_builder(self, entries: List[Dict[str, str]], error=None) -> GoogleApiClientRequestMockBuilder:\n \"\"\"Generate typical response body with patched `entries` key\"\"\"\n\n expected_body = {\n \"logName\": \"projects/project-id/logs/log-id\",\n \"resource\": {\n \"type\": \"generic_node\",\n \"labels\": {\"location\": \"us-east-1\", \"node_id\": \"my-test-node\"},\n },\n \"entries\": entries,\n }\n return GoogleApiClientRequestMockBuilder(\n {\n \"logging.entries.write\": (error, \"{}\", expected_body),\n },\n check_unexpected=True,\n )\n\n def test_message_string(self):\n \"\"\"Check that MESSAGE as plain text was sent\"\"\"\n\n request_builder = self._generate_request_builder([{\"jsonPayload\": {\"MESSAGE\": \"Hello\"}}])\n\n sender = GoogleCloudLoggingSender(\n name=\"googlecloudlogging\",\n reader=mock.Mock(),\n stats=mock.Mock(),\n field_filter=None,\n config=self.CONFIG,\n googleapiclient_request_builder=request_builder,\n )\n sender.send_messages(messages=[b'{\"MESSAGE\": \"Hello\"}'], cursor=None)\n assert sender._sent_count == 1 # pylint: disable=protected-access\n\n def test_missing_message(self):\n \"\"\"Check that missing `MESSAGE` key is fine\"\"\"\n\n request_builder = self._generate_request_builder([{\"jsonPayload\": {\"not_message\": \"Hello\"}}])\n\n sender = GoogleCloudLoggingSender(\n name=\"googlecloudlogging\",\n reader=mock.Mock(),\n stats=mock.Mock(),\n field_filter=None,\n config=self.CONFIG,\n googleapiclient_request_builder=request_builder,\n )\n sender.send_messages(messages=[b'{\"not_message\": \"Hello\"}'], cursor=None)\n assert sender._sent_count == 1 # pylint: disable=protected-access\n\n def test_message_json(self):\n \"\"\"Check that MESSAGE as object was decoded and sent\"\"\"\n\n expected_json_message = {\"test_key\": \"test_value\"}\n request_builder = self._generate_request_builder([{\"jsonPayload\": {\"MESSAGE\": expected_json_message}}])\n\n sender = GoogleCloudLoggingSender(\n name=\"googlecloudlogging\",\n reader=mock.Mock(),\n stats=mock.Mock(),\n field_filter=None,\n config=self.CONFIG,\n googleapiclient_request_builder=request_builder,\n )\n\n message = json.dumps({\"MESSAGE\": json.dumps(expected_json_message)})\n sender.send_messages(messages=[message.encode()], cursor=None)\n assert sender._sent_count == 1 # pylint: disable=protected-access\n\n def test_bad_request_did_not_marked_sent(self):\n \"\"\"Check that message was not marked as sent if GoogleApi returns error\"\"\"\n request_builder = self._generate_request_builder(\n [{\"jsonPayload\": {\"MESSAGE\": \"Hello\"}}],\n error=HttpLib2Response({\"status\": \"400\"}),\n )\n\n sender = GoogleCloudLoggingSender(\n name=\"googlecloudlogging\",\n reader=mock.Mock(),\n stats=mock.Mock(),\n field_filter=None,\n config=self.CONFIG,\n googleapiclient_request_builder=request_builder,\n )\n sender.send_messages(messages=[b'{\"MESSAGE\": \"Hello\"}'], cursor=None)\n assert sender._sent_count == 0 # pylint: disable=protected-access\n\n def test_correct_timestamp(self):\n \"\"\"Check severity mapping is converted correctly and timestamp is being sent.\"\"\"\n\n request_builder = self._generate_request_builder(\n [\n {\n \"timestamp\": \"2020-06-25T06:24:13.787255Z\",\n \"severity\": \"EMERGENCY\",\n \"jsonPayload\": {\"MESSAGE\": \"Hello\"},\n }\n ]\n )\n\n sender = GoogleCloudLoggingSender(\n name=\"googlecloudlogging\",\n reader=mock.Mock(),\n stats=mock.Mock(),\n field_filter=None,\n config=self.CONFIG,\n googleapiclient_request_builder=request_builder,\n )\n sender.send_messages(\n messages=[b'{\"MESSAGE\": \"Hello\", \"PRIORITY\": 0, \"timestamp\": \"2020-06-25T06:24:13.787255\"}'],\n cursor=None,\n )\n assert sender._sent_count == 1 # pylint: disable=protected-access\n","sub_path":"test/test_google_cloud_logging.py","file_name":"test_google_cloud_logging.py","file_ext":"py","file_size_in_byte":5745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"291120167","text":"class Solution(object):\n def hasPath(self, maze, start, destination):\n m,n = len(maze), len(maze[0])\n i0,j0 = start[0], start[1]\n i1,j1 = destination[0], destination[1]\n if maze[i0][j0]==1 or maze[i1][j1]==1:\n return False\n visited = set()\n def dfs(i,j):\n if i==i1 and j==j1:\n return True\n visited.add((i,j))\n for d in [1,-1]:\n k=i\n while k+d>=0 and k+d<m and maze[k+d][j]==0:\n k+=d\n \n if k!=i and (k,j) not in visited:\n if dfs(k,j):\n return True\n l=j\n while l+d>=0 and l+d<n and maze[i][l+d]==0:\n l+=d\n \n if l!=j and (i,l) not in visited:\n if dfs(i,l):\n return True\n return False\n return dfs(i0,j0)\n#Time-complexity: O(m.n)\n#spoace-complexity: O(m.n)","sub_path":"maze.py","file_name":"maze.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"37017113","text":"from math import *\nimport sys\n\nfactor = 100\n\ndef createGaussian(sigma):\n a = 1.0 / sqrt(2 * pi * sigma * sigma)\n b = 2.0 * sigma * sigma\n def gaussian(x):\n return a * exp(-(x * x)/b)\n return gaussian\n\ndef parts(li, n):\n return [li[i:i + n] for i in range(0, len(li), n)]\n\ndef generateCode(weights):\n sideLen = int(ceil(sqrt(len(weights))))\n chunks = parts(weights, sideLen)\n joinedWeights = [', '.join(row) for row in chunks]\n return \"static const float weights [{0}] = {{\\n{1}\\n}};\".format(\n len(weights), ', '.join([str(w) for w in joinedWeights])\n )\n\ndef main(n = 256, sigma = 128):\n gaussian = createGaussian(sigma)\n weights = [factor * gaussian(i) for i in range(0, n)]\n weights = [str(w) for w in weights]\n code = generateCode(weights)\n print(code)\n\n\nif __name__ == '__main__':\n args = sys.argv\n if len(args) == 3:\n main(int(args[1]), int(args[2]))\n elif len(args) == 2:\n main(int(args[1]))\n else:\n main()","sub_path":"Assets/Scripts/gaussian_weight_gen.py","file_name":"gaussian_weight_gen.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"479851623","text":"#6- Elabore um algorítmo que leia as variáveis 'c' e 'n', respectivamente código e número de horas trabalhadas de um\r\n#operário. Calcule o salário sabendo-se que ele ganha R$ 10.00 por hora. Quando o número de horas exceder a 50 calcule\r\n#o excesso de pagamento armazenando-o na variável 'e'. Caso contrário zerar tal variável. A hora excedente de trabalho\r\n#vale R$ 20.00. No final do processamento imprimir o salário total e o salário excedente.\r\n\r\nn=float(input('Insira o número de horas trabalhadas: '))\r\nc=0\r\n\r\nif (n)>50:\r\n salário_normal=float(500)\r\n salário_extra=(n-50)*20\r\n salário_total=(salário_normal+salário_extra)\r\n print(salário_normal,'mais',salário_extra)\r\n print(salário_total)\r\nelse:\r\n print('Tchuale!')\r\n","sub_path":"Seção 6 - Estruturas de decisão/Exercício 6.py","file_name":"Exercício 6.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"417694118","text":"from domain_SO.Memory import Memory\nfrom domain_SO.Disc import Disc\nfrom domain_SO.PcbTable import PcbTable\nfrom queue import Queue\nfrom domain_SO.Loader import Loader\nfrom domain_SO.CPU import CPU\nfrom domain_SO.Instruction import Instruction,InstructionType\nfrom userExceptions_SO.ProgramNotInDiscException import ProgramNotInDiscException\nfrom domain_SO.interruptions.InterruptionManager import InterruptionManager\n\nclass Kernel:\n\n def __init__(self,memory):\n self.pcbTable = PcbTable()\n self.memory = memory\n self.disc = Disc()\n self.cpu = CPU()\n self.programLoader = Loader(self.memory,self.disc)\n self.readyQueue = Queue()\n self.interruptionManager = InterruptionManager()\n\n def run(self,cmd,programName):\n try:\n #tested\n program = self.programLoader.getFromDisc(programName)\n #tested\n pcb = self.pcbTable.isPcbOf(program)\n #pass the pcb to set the basedir\n if(pcb is None):\n #new NewInterruption\n instructionNew = Instruction(InstructionType.New)\n newPcb = self.pcbTable.createPcbFor(self,program)\n self.interruptionManager.enqueueInterruption(instructionNew,newPcb)\n else:\n self.programLoader.loadToMemory(program,pcb)\n if(self.readyQueue.empty()):\n self.cpu.setPCB(pcb)\n else:\n self.readyQueue.put(pcb)\n except ProgramNotInDiscException as p:\n print(p.getMessage())\n\n\n#-pasar de running a ready -> interrupcion de timeout. Alerta va al kernel\n#-pasar de running a terminado -> disparo interrupcxion de kill, estoy en la ultima instruccion.\n#algun componente tuyo te lo expropia, limpia memoria y elimina pcb\n#y el scheduler le asigna un nuevo pcb al cpu\n#-interrupcion de termine de hacer IO\n\n\n\n#si cola de ready esta vacia va a cpu directo\n#cpu le avisa al kernel que hay interrupcion\n#reloj envia click y cpu hace fetch de instruccion\n\n#falta setear estados a los pcbs\n\n#modo usuario - cpu ejecuta instruccion\n#modo kernel - tareas de SO. el Clock no puede generar clicks\n\n#mientras se ejecuta una interrupcion no se pueden hacer clicks y por ende fetchs de instruccion\n#programar por prioridad con round robin. Cuando creas el kernel configuras con que politica de planificacion queres que trabaje\n\n\n\n\n#################\n\n#from threading import Thread\n#class Impresor(Thread)\n\n #def __init__(self,name):\n # Thread.__init__(self)\n # self.name = name\n\n #def run\n#sstart()\n\n#CPU es un thread, i/o es otro thread\n\n#cola I/O es estructura compartida entre dos Threads distintos. El CPU para grabar e I/O para acceder\n#cola de ready tambien porque es leida por el scheduler y escrita por el kernel. Manejador de interrupciones\n\n#Thread CPU\n#mientras SO este running\n#hago fetch","sub_path":"domain_SO/Kernel.py","file_name":"Kernel.py","file_ext":"py","file_size_in_byte":2901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"79378794","text":"import pygame\r\nfrom pygame.sprite import Sprite\r\n\r\nclass Enemy(Sprite):\r\n def __init__(self,screen,settings,ball):\r\n super(Enemy,self).__init__()\r\n\r\n self.screen_player = screen\r\n self.settings_enemy=settings\r\n self.ball=ball\r\n #draw center line\r\n self.midfield=pygame.Rect(0,0,settings.sidewidth,settings.screenheight)\r\n\r\n #Draw side\r\n self.main_paddle=pygame.Rect(0,0,settings.sidewidth, settings.sideheight)\r\n self.screen_rect= self.screen_player.get_rect()\r\n\r\n self.midfield.centerx=self.screen_rect.centerx\r\n\r\n #Draw bottom and top\r\n\r\n self.top_paddle=pygame.Rect(0,0,settings.bottomwidth,settings.bottomheight)\r\n self.bottom_paddle=pygame.Rect(0,0,settings.bottomwidth,settings.bottomheight)\r\n\r\n #Put paddles to the left of the screen\r\n self.main_paddle.centery=self.screen_rect.centery\r\n self.main_paddle.midright=self.screen_rect.midright\r\n\r\n\r\n self.top_paddle.centerx=self.screen_rect.centerx\r\n self.top_paddle.topright=self.screen_rect.topright\r\n\r\n self.bottom_paddle.centerx=self.screen_rect.centerx\r\n self.bottom_paddle.bottomright=self.screen_rect.bottomright\r\n\r\n\r\n self.center=float(self.main_paddle.centery)\r\n self.centertop=float(self.top_paddle.centerx)\r\n self.centerbottom=float(self.bottom_paddle.centerx)\r\n\r\n #Variables to update enemy paddles\r\n self.AI_main=float(0)\r\n self.AI_top = float(0)\r\n self.AI_bottom = float(0)\r\n\r\n self.main_paddle.centery=self.center\r\n self.top_paddle.centerx=self.centertop\r\n self.bottom_paddle.centerx=self.centerbottom\r\n\r\n self.movingright=False\r\n self.movingleft=False\r\n def main_ai(self):\r\n if self.main_paddle.centery > self.ball.rect.centery:\r\n self.AI_main=-1 # go down\r\n elif self.main_paddle.centery < self.ball.rect.centery:\r\n self.AI_main = 1#go up\r\n\r\n def top_ai(self):\r\n\r\n if self.top_paddle.centerx > self.ball.rect.centerx:\r\n self.movingleft = True\r\n #self.AI_top=-1 # go left\r\n if self.top_paddle.centerx < self.ball.rect.centerx:\r\n self.movingright = True\r\n #self.AI_top =1 # go right\r\n\r\n def bottom_ai(self):\r\n if self.bottom_paddle.centerx > self.ball.rect.centerx:\r\n self.movingleft=True\r\n # self.AI_bottom = -1 # go left\r\n if self.bottom_paddle.centerx < self.ball.rect.centerx:\r\n self.movingright = True\r\n #self.AI_bottom = 1 # go right\r\n\r\n def update(self):\r\n self.movingright = False\r\n self.movingleft = False\r\n #check positions\r\n self.main_ai()\r\n self.top_ai()\r\n self.bottom_ai()\r\n #move paddles\r\n self.main_paddle.centery += self.settings_enemy.enemyspeed * self.AI_main\r\n if self.movingleft and self.top_paddle.left > self.settings_enemy.screenwidth/2:\r\n self.top_paddle.centerx -= self.settings_enemy.enemyspeed\r\n if self.movingleft and self.bottom_paddle.left > self.settings_enemy.screenwidth / 2:\r\n self.bottom_paddle.centerx -= self.settings_enemy.enemyspeed\r\n if self.movingright:\r\n self.top_paddle.centerx += self.settings_enemy.enemyspeed\r\n if self.movingright:\r\n self.bottom_paddle.centerx += self.settings_enemy.enemyspeed\r\n\r\n def drawplayer(self):\r\n pygame.draw.rect(self.screen_player,self.settings_enemy.enemycolor,self.main_paddle)\r\n pygame.draw.rect(self.screen_player, self.settings_enemy.enemycolor, self.top_paddle)\r\n pygame.draw.rect(self.screen_player, self.settings_enemy.enemycolor, self.bottom_paddle)\r\n\r\n","sub_path":"enemy.py","file_name":"enemy.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"242795770","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom scrapy.linkextractors import LinkExtractor\nfrom doctor.items import HospitalItem,DoctorItem,DepartmentItem\nimport re\nfrom lxml import etree \nimport ast\n\nclass DoctorcastSpider(scrapy.Spider):\n\tdef __init__(self):\n\t\tsuper(DoctorcastSpider,self).__init__()\n\t\tself.remaining_links = len(self.start_urls)\n\tname = 'doctorcast'\n\n\tallowed_domains = ['haodf.com']\n\tstart_urls = (\n\t\t'https://www.haodf.com/yiyuan/beijing/list.htm',\n\t\t'https://www.haodf.com/yiyuan/shanghai/list.htm',\n\t\t'https://www.haodf.com/yiyuan/guangdong/list.htm',\n\t\t'https://www.haodf.com/yiyuan/guangxi/list.htm',\n\t\t'https://www.haodf.com/yiyuan/jiangsu/list.htm',\n\t\t'https://www.haodf.com/yiyuan/zhejiang/list.htm',\n\t\t'https://www.haodf.com/yiyuan/anhui/list.htm',\n\t\t'https://www.haodf.com/yiyuan/jiangxi/list.htm',\n\t\t'https://www.haodf.com/yiyuan/fujian/list.htm',\n\t\t'https://www.haodf.com/yiyuan/shandong/list.htm',\n\t\t'https://www.haodf.com/yiyuan/sx/list.htm',\n\t\t'https://www.haodf.com/yiyuan/hebei/list.htm',\n\t\t'https://www.haodf.com/yiyuan/henan/list.htm',\n\t\t'https://www.haodf.com/yiyuan/tianjin/list.htm',\n\t\t'https://www.haodf.com/yiyuan/liaoning/list.htm',\n\t\t'https://www.haodf.com/yiyuan/heilongjiang/list.htm',\n\t\t'https://www.haodf.com/yiyuan/jilin/list.htm',\n\t\t'https://www.haodf.com/yiyuan/hubei/list.htm',\n\t\t'https://www.haodf.com/yiyuan/hunan/list.htm',\n\t\t'https://www.haodf.com/yiyuan/sichuan/list.htm',\n\t\t'https://www.haodf.com/yiyuan/chongqing/list.htm',\n\t\t'https://www.haodf.com/yiyuan/shanxi/list.htm',\n\t\t'https://www.haodf.com/yiyuan/gansu/list.htm',\n\t\t'https://www.haodf.com/yiyuan/yunnan/list.htm',\n\t\t'https://www.haodf.com/yiyuan/xinjiang/list.htm',\n\t\t'https://www.haodf.com/yiyuan/neimenggu/list.htm',\n\t\t'https://www.haodf.com/yiyuan/hainan/list.htm',\n\t\t'https://www.haodf.com/yiyuan/guizhou/list.htm',\n\t\t'https://www.haodf.com/yiyuan/qinghai/list.htm',\n\t\t'https://www.haodf.com/yiyuan/ningxia/list.htm',\n\t\t'https://www.haodf.com/yiyuan/xizang/list.htm',\n\t\t)\n\trules = [\n\t\tRule(LinkExtractor(allow=[r'/yiyuan/.+/\\.list.htm']),callback='parse_yiyuan'),\n\t\tRule(LinkExtractor(allow=[r'/hospital/.+\\.htm']),callback='parse_hospital'),\n\t\tRule(LinkExtractor(allow=[r'/faculty/.+\\.htm']),callback='parse_faculty'),\n\t\tRule(LinkExtractor(allow=[r'/doctor/.+\\.htm']),callback='parse_doctor'),\n\t]\n\t\n\tdef parse(self, response):\n\t\tself.remaining_links -= 1\n\t\tself.logger.info('parsing hospital list page -- %s ,剩余链接数：%d' % (response.url,self.remaining_links))\n\t\thospital_links = response.xpath(\"//div[@class=\\\"m_ctt_green\\\"]/ul/li/a/@href\") \t\n\t\tfor link in hospital_links:\n\t\t\tself.remaining_links += 1\n\t\t\tyield scrapy.Request(response.urljoin(link.extract()),callback=self.parse_hospital,dont_filter=True,priority=900)\n\n\tdef parse_hospital(self, response):\n\t\tself.remaining_links -= 1\n\t\tself.logger.info('parsing hospital page -- %s ,剩余链接数：%d'%(response.url,self.remaining_links))\n\t\titem = HospitalItem()\n\t\titem['link'] = response.url\n\t\titem['name'] = response.xpath('//h1[@class=\"hospital-name\"]/text()').extract_first()\n\t\tself.logger.info('get Hospital item -- ' + item['name'])\n\t\tyield item\n\t\tfaculty_links = response.xpath(\"//a[@class=\\\"f-l-i-s-i-w-name\\\"]/@href\") \n\t\tfor link in faculty_links:\n\t\t\tself.remaining_links += 1\n\t\t\tyield scrapy.Request(response.urljoin(link.extract()),callback=self.parse_faculty,dont_filter=True,priority=800)\n\n\tdef parse_faculty(self, response):\n\t\tself.remaining_links -= 1\n\t\tself.logger.info('parsing faculty page -- %s ,剩余链接数：%d'%(response.url,self.remaining_links))\n\t\titem = DepartmentItem()\n\t\titem['link'] = response.url\n\t\titem['name'] = response.xpath('//div[@class=\"content\"]/a/text()').extract_first()\n\t\tself.logger.info('get faculty item -- ' + item['name'])\n\t\tyield item\n\t\tdoctor_links = response.xpath(\"//a[@class=\\\"name\\\"]/@href\") \n\t\tfor link in doctor_links:\n\t\t\tself.remaining_links += 1\n\t\t\tyield scrapy.Request(response.urljoin(link.extract()),callback=self.parse_doctor,dont_filter=True,priority=700)\n\t\tpnum_links = response.xpath(\"//a[@class=\\\"p_num\\\"]/@href\")\n\t\tfor link in pnum_links:\n\t\t\tself.remaining_links += 1\n\t\t\tyield scrapy.Request(response.urljoin(link.extract()),callback=self.parse_faculty,dont_filter=True,priority=800)\n\t\t\n\t\t\n\tdef parse_doctor(self, response):\n\t\tself.remaining_links -= 1\n\t\tself.logger.info('parsing doctor page -- %s ,剩余链接数：%d'%(response.url,self.remaining_links))\n\t\tdata = repr(ast.literal_eval(response.xpath('//script[1]')[1].extract()[55:-11])['content'].replace('\\n','').replace('\\t','')).replace(r'\\\\',\"\")[1:-1]\n\t\thtml = etree.HTML(data)\n\t\titem = DoctorItem()\n\t\titem['link'] = response.url\n\t\titem['name'] = html.xpath(\"//div[@class='luj']/a[6]\")[0].text\n\t\titem['hospital'] = html.xpath(\"//div[@class='luj']/a[4]\")[0].text\n\t\titem['department'] = html.xpath(\"//div[@class='luj']/a[5]\")[0].text\n\t\tyield item\n\t\t\n","sub_path":"doctor/doctor/spiders/doctorcast.py","file_name":"doctorcast.py","file_ext":"py","file_size_in_byte":4906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"286476635","text":"from django.contrib import admin\nfrom django.urls import path, include\n\n\nurlpatterns = [\n path('admin/', admin.site.urls,name='admin'),\n path('', include('cliente.urls'),name='cliente'),\n path('restaurantes/', include('restaurantes.urls'),name='restaurantes'),\n path('api/', include('api.urls'),name='api'),\n]\n\n","sub_path":"diamanfood/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"200090964","text":"import os, sys \nimport string\nimport random\nimport hashlib\n\nfrom time import gmtime, strftime\nfrom random import random\n\n\nfrom flask import Flask\nfrom flask import request\nfrom flask import redirect\nfrom flask import url_for\nfrom flask import render_template\nfrom flask import Blueprint\n\nfrom .db import db_get\nfrom .auth import login_required\n\n\nbp = Blueprint(\"peer\", __name__)\n\n################################################\n### Utility\n################################################\ndef key_generate(user):\n time_now = strftime(user + \"%a, %d %b %Y %H:%M:%S +0000\", gmtime())\n time_now = \"%s-%f\" % (time_now, random())\n md = hashlib.md5(time_now.encode())\n md_digest = md.hexdigest()\n return md_digest[-6:]\n\n################################################\n##### peer service #################\n@bp.route('/peer/register', methods = ['POST'])\ndef peer_register():\n #print (\"Register >> peer_register >>>ENTER \")\n db = db_get()\n code = key_generate('user')\n shareinfo = 'mqtt/mac012'\n db.execute(\n \"INSERT INTO invitation (code, shareinfo) VALUES (?, ?)\",\n (code, shareinfo),\n )\n db.commit()\n\n return code, 200\n\n@bp.route('/peer/get/<string:peer_id>', methods = ['GET'])\ndef peer_get(peer_id):\n db = db_get()\n share_info = db.execute(\n 'SELECT shareinfo FROM invitation WHERE code = \"%s\"' % (peer_id)\n ).fetchone()\n\n if share_info:\n return share_info['shareinfo'], 200\n else:\n return \"NONE\", 200\n \n\n################################################\n### view page \n@bp.route('/')\n@login_required\ndef index():\n db = db_get()\n invitations = db.execute(\n \"SELECT code, shareinfo, created\"\n \" FROM invitation\"\n \" ORDER BY created DESC\"\n ).fetchall()\n\n return render_template('invite/index.html', invitations=invitations)\n\n@bp.route('/peer/delete/<string:peer_id>', methods = ('POST', 'GET'))\ndef peer_delete(peer_id):\n # Get formdate\n db = db_get()\n sql_string = \"DELETE FROM invitation WHERE code = \\\"%s\\\"\" % (peer_id)\n #print ( \"peer_delete >>> \" + sql_string + \" peer_delete >>>\")\n db.execute(sql_string)\n db.commit()\n\n return redirect(url_for(\"peer.index\"))\n","sub_path":"invitation/peer.py","file_name":"peer.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"221554463","text":"#coding=utf-8\n\n# Copyright (c) 2017, Baidu.com, Inc. All Rights Reserved\n# Use of this source code is governed by a BSD-style license that can be\n# found in the LICENSE file.\n\nimport sys\nfrom familia_wrapper import InferenceEngineWrapper\n\nif sys.version_info < (3,0):\n input = raw_input\n\nif __name__ == '__main__':\n if len(sys.argv) < 4:\n sys.stderr.write(\"Usage:python {} {} {} {}.\\n\".format(\n sys.argv[0], \"model_dir\", \"conf_file\", \"emb_file\"))\n exit(-1)\n\n # 获取参数\n model_dir = sys.argv[1]\n conf_file = sys.argv[2]\n emb_file = sys.argv[3]\n # 创建InferenceEngineWrapper对象\n inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file, emb_file)\n while True:\n # 输入两个长文本\n words = input(\"Enter Keywords: \").strip()\n doc = input(\"Enter Document: \").strip()\n # 分词\n seg_list = inference_engine_wrapper.tokenize(doc)\n items = inference_engine_wrapper.cal_keywords_twe_similarity(words, \" \".join(seg_list))\n # 打印结果\n print('----------------------------')\n for item in items:\n print(item[0] + '\\t' + str(item[1]))\n","sub_path":"python/demo/document_twe_keywords_demo.py","file_name":"document_twe_keywords_demo.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"317815399","text":"# Using the workspace:\r\n\r\n# Here are a few tips orienting you to this kind of workspace.\r\n\r\n# In the top panel is a code editor where you can edit the Python file user_input_numlist.py. Scroll up and down in this panel to see all the code. You can also expand or shrink this panel by clicking and dragging its bottom border.\r\n\r\n# In the bottom panel, you can execute your Python file by clicking on New Terminal and entering python user_input_numlist.py on the command line.\r\n\r\n# The solutions have been provided on the next page, but I encourage you to try figuring out where the bug is in the code, and fixing it yourself.\r\n\r\n# Sample Output: This is what the output should look like.\r\n\r\n# >>> user_list: [23, 24, 25, 26, 27, 28, 29, 30, 31, 22]\r\n# >>> The sum of the even numbers in user_list is: 130.\r\n\r\n# initiate empty list to hold user input and sum value of zero\r\nuser_list = []\r\nlist_sum = 0\r\n\r\n# seek user input for ten numbers \r\nfor i in range(10):\r\n userInput = input(\"Enter any 2-digit number: \")\r\n \r\n# check to see if number is even and if yes, add to list_sum\r\n# print incorrect value warning when ValueError exception occurs\r\n try:\r\n number = userInput\r\n user_list.append(number)\r\n if number % 2 == 0:\r\n list_sum += number\r\n except ValueError:\r\n print(\"Incorrect value. That's not an int!\")\r\n ","sub_path":"practice_debugging.py","file_name":"practice_debugging.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"66874503","text":"from selenium import webdriver\nimport time\n\nbrowser = webdriver.Chrome()\nbrowser.get(\"http://suninjuly.github.io/wait1.html\")\n\t\ntry:\n\t# говорим WebDriver искать каждый элемент в течение 5 секунд\n\tbrowser.implicitly_wait(5)\n\tbutton = browser.find_element_by_id(\"verify\")\n\tbutton.click()\n\tmessage = browser.find_element_by_id(\"verify_message\")\n\n\tassert \"successful\" in message.text\n\tmtext = message.text\n\tprint(mtext)\n\t\nexcept Exception as error:\n print(f'{error}')\n #print(welcome_text)\n\nfinally:\n # успеваем скопировать код за 30 секунд\n #time.sleep(5)\n # закрываем браузер после всех манипуляций\n browser.quit()\n \n# не забываем оставить пустую строку в конце файла","sub_path":"lesson2.4_step3.py","file_name":"lesson2.4_step3.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"590002958","text":"from datetime import datetime\nfrom time import mktime\n\ndate = 'Today is Monday, December 23th 2013'\n\nunixTime= datetime.strptime(date, 'Today is %A, %B %dth %Y')\n\nconverted = mktime(unixTime.timetuple())\n\nprint(converted)\n\n#converte a data atual\nconverted = mktime(datetime.now().timetuple())\nprint(converted)\n\n","sub_path":"Python 101/datetimeParaUnixTimeStamp.py","file_name":"datetimeParaUnixTimeStamp.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"234895894","text":"from .canonical_linear import CanonicalLinearConstraint, canlinear_colloc_to_interpolate\nfrom ..constraint import DiscretizationType\nimport numpy as np\n\n\nclass JointAccelerationConstraint(CanonicalLinearConstraint):\n \"\"\"Joint Acceleration Constraint.\n\n A joint acceleration constraint is given by\n\n .. math ::\n\n \\ddot{\\mathbf{q}}_{min} & \\leq \\ddot{\\mathbf q} &\\leq \\ddot{\\mathbf{q}}_{max} \\\\\\\\\n \\ddot{\\mathbf{q}}_{min} & \\leq \\mathbf{q}'(s_i) u_i + \\mathbf{q}''(s_i) x_i &\\leq \\ddot{\\mathbf{q}}_{max}\n\n where :math:`u_i, x_i` are respectively the path acceleration and\n path velocity square at :math:`s_i`. For more detail see :ref:`derivationKinematics`.\n\n Rearranging the above pair of vector inequalities into the form\n required by :class:`CanonicalLinearConstraint`, we have:\n\n - :code:`a[i]` := :math:`\\mathbf q'(s_i)`\n - :code:`b[i]` := :math:`\\mathbf q''(s_i)`\n - :code:`F` := :math:`[\\mathbf{I}, -\\mathbf I]^T`\n - :code:`h` := :math:`[\\ddot{\\mathbf{q}}_{max}^T, -\\ddot{\\mathbf{q}}_{min}^T]^T`\n\n Parameters\n ----------\n alim: array\n Shape (dof, 2). The lower and upper acceleration bounds of the\n j-th joint are alim[j, 0] and alim[j, 1] respectively.\n\n discretization_scheme: :class:`.DiscretizationType`\n Can be either Collocation (0) or Interpolation\n (1). Interpolation gives more accurate results with slightly\n higher computational cost.\n\n \"\"\"\n def __init__(self, alim, discretization_scheme=DiscretizationType.Collocation):\n super(JointAccelerationConstraint, self).__init__()\n self.alim = np.array(alim, dtype=float)\n self.dof = self.alim.shape[0]\n self.set_discretization_type(discretization_scheme)\n assert self.alim.shape[1] == 2, \"Wrong input shape.\"\n self._format_string = \" Acceleration limit: \\n\"\n for i in range(self.alim.shape[0]):\n self._format_string += \" J{:d}: {:}\".format(i + 1, self.alim[i]) + \"\\n\"\n self.identical = True\n\n def compute_constraint_params(self, path, gridpoints, scaling):\n if path.get_dof() != self.get_dof():\n raise ValueError(\"Wrong dimension: constraint dof ({:d}) not equal to path dof ({:d})\".format(\n self.get_dof(), path.get_dof()\n ))\n ps = path.evald(gridpoints / scaling) / scaling\n pss = path.evaldd(gridpoints / scaling) / scaling ** 2\n N = gridpoints.shape[0] - 1\n dof = path.get_dof()\n I_dof = np.eye(dof)\n F = np.zeros((dof * 2, dof))\n g = np.zeros(dof * 2)\n ubound = np.zeros((N + 1, 2))\n g[0:dof] = self.alim[:, 1]\n g[dof:] = - self.alim[:, 0]\n F[0:dof, :] = I_dof\n F[dof:, :] = -I_dof\n if self.discretization_type == DiscretizationType.Collocation:\n return ps, pss, np.zeros_like(ps), F, g, None, None\n elif self.discretization_type == DiscretizationType.Interpolation:\n return canlinear_colloc_to_interpolate(ps, pss, np.zeros_like(ps), F, g, None, None,\n gridpoints, identical=True)\n else:\n raise NotImplementedError(\"Other form of discretization not supported!\")\n\n","sub_path":"toppra/constraint/joint_acceleration.py","file_name":"joint_acceleration.py","file_ext":"py","file_size_in_byte":3285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"23844148","text":"import tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n# This network looks much like a two-layer feedforward neural network,\n# with a few twists: first, the same weights and bias terms are shared\n# by both layers, and second, we feed inputs at each layer, and we get\n# outputs from each layer. To run the model, we need to feed it the\n# inputs at both time steps, like so:\n\nn_inputs = 3\nn_neurons = 5\n\nX0 = tf.placeholder(tf.float32, [None, n_inputs])\nX1 = tf.placeholder(tf.float32, [None, n_inputs])\n\nWx = tf.Variable(tf.random_normal(shape=[n_inputs, n_neurons],dtype=tf.float32))\nWy = tf.Variable(tf.random_normal(shape=[n_neurons,n_neurons],dtype=tf.float32))\nb = tf.Variable(tf.zeros([1, n_neurons], dtype=tf.float32))\n\nY0 = tf.tanh(tf.matmul(X0, Wx) + b)\nY1 = tf.tanh(tf.matmul(Y0, Wy) + tf.matmul(X1, Wx) + b)\n\ninit = tf.global_variables_initializer()\n\n\n# This mini-batch contains four instances, each with an input sequence\n# composed of exactly two inputs. At the end, Y0_val and Y1_val contain\n# the outputs of the network at both time steps for all neurons and all\n# instances in the mini-batch:\n# Mini-batch: instance 0,instance 1,instance 2,instance 3\nX0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\nX1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1\n\nwith tf.Session() as sess:\n init.run()\n Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})\n\n\nprint(Y0_val)\n\n# That wasn’t too hard, but of course if you want to be able to run an RNN\n# over 100 time steps, the graph is going to be pretty big. Now let’s look\n# at how to create the same model using TensorFlow’s RNN operations.\n\n","sub_path":"bin/run_13.py","file_name":"run_13.py","file_ext":"py","file_size_in_byte":1841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"273814773","text":"# ========================================================================\n# Copyright (c) 2015 The University of Washington\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# \n# http://www.apache.org/licenses/LICENSE-2.0\n# \n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ========================================================================\n#\n\n#\n# IAM messaging tools - encryption and signature methods\n#\n# \n\n# crypto class covers for openssl\nimport M2Crypto\nfrom M2Crypto import BIO, RSA, EVP, X509\n\nimport json\nimport uuid\nimport datetime\nimport dateutil.parser\nimport base64\nimport string\nimport time\nimport re\nimport os.path\nfrom sys import exit\nimport signal\nimport importlib\n\nimport urllib3\n\nimport threading\n\nfrom .exceptions import SignatureVerifyException\nfrom .exceptions import CryptKeyException\nfrom .exceptions import SigningCertException\n\n# ----- global vars (to this module) ------------------\n\n# decryption keys\n_crypt_keys = {}\n\n# public keys used for sig verify\n_public_keys = {}\n\n# private keys used for sig sign\n_private_keys = {}\n\n# ca certificate file\n_ca_file = None\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n#\n# -------------------------------------\n#\n\n#\n# accumulate header fields for signature\n#\ndef _build_sig_msg(header, txt):\n sigmsg = header['contentType'] + '\\n'\n if 'keyId' in header:\n sigmsg = sigmsg + header['iv'] + '\\n' + header['keyId'] + '\\n'\n sigmsg = sigmsg + header['messageContext'] + '\\n' + header['messageId'] + '\\n' + \\\n header['messageType'] + '\\n' + header['sender'] + '\\n' + \\\n header['signingCertUrl'] + '\\n' + header['timestamp'] + '\\n' + header['version'] + '\\n' + \\\n txt + '\\n'\n return sigmsg.encode('ascii')\n\n#\n# create a signed (and encrypted) iam message\n#\n# msg is anything\n# context is string\n\ndef encode_message(msg, context, cryptid, signid):\n \n iamHeader = {}\n iamHeader['contentType'] = 'json'\n iamHeader['version'] = 'UWIT-1'\n iamHeader['messageType'] = 'iam-test'\n u = uuid.uuid4()\n iamHeader['messageId'] = str(u)\n iamHeader['messageContext'] = base64.b64encode(context)\n iamHeader['sender'] = 'iam-msg'\n\n iamHeader['timestamp'] = datetime.datetime.utcnow().isoformat()\n if signid not in _private_keys:\n raise SigningCertException(keyid=signid, msg='not found')\n iamHeader['signingCertUrl'] = _private_keys[signid]['url']\n\n if cryptid!=None:\n if cryptid not in _crypt_keys:\n raise CryptKeyException(keyid=cryptid, msg='not found')\n iamHeader['keyId'] = cryptid\n iv = os.urandom(16)\n iamHeader['iv'] = base64.b64encode(iv)\n cipher = M2Crypto.EVP.Cipher(alg='aes_128_cbc', key=_crypt_keys[cryptid], iv=iv, op=1)\n txt = cipher.update(msg) + cipher.final()\n enctxt64 = base64.b64encode(txt)\n else:\n enctxt64 = base64.b64encode(msg)\n \n # gen the signature\n sigmsg = _build_sig_msg(iamHeader, enctxt64)\n\n key = _private_keys[signid]['key']\n key.reset_context(md='sha1')\n key.sign_init()\n key.sign_update(sigmsg)\n sig = key.sign_final()\n sig64 = base64.b64encode(sig)\n iamHeader['signature'] = sig64\n\n body = {}\n body['Message'] = enctxt64\n \n iamMessage = {}\n iamMessage['header'] = iamHeader\n iamMessage['body'] = enctxt64\n\n m64 = base64.b64encode(json.dumps(iamMessage))\n return m64\n \n#\n# receive a signed (and encrypted) iam message\n#\n\ndef decode_message(b64msg):\n global _crypt_keys \n global _public_keys \n global _ca_file \n\n # get the iam message\n msgstr = base64.b64decode(b64msg).encode('utf8','ignore')\n iam_message = json.loads(msgstr)\n\n\n if 'header' not in iam_message: \n logging.info('not an iam message')\n return None\n iamHeader = iam_message['header']\n\n try:\n # check the version\n if iamHeader['version'] != 'UWIT-1':\n logging.error('unknown version: ' + iamHeader['version'])\n return None\n\n # the signing cert should be cached most of the time\n certurl = iamHeader['signingCertUrl']\n if not certurl in _public_keys:\n logging.info('Fetching signing cert: ' + certurl)\n pem = ''\n\n if certurl.startswith('file:'):\n with open(certurl[5:], 'r') as f:\n pem = f.read()\n\n elif certurl.startswith('http'):\n if _ca_file != None:\n http = urllib3.PoolManager(\n cert_reqs='CERT_REQUIRED', # Force certificate check.\n ca_certs=_ca_file, \n )\n else:\n http = urllib3.PoolManager()\n certdoc = http.request('GET', certurl)\n if certdoc.status != 200:\n logger.error('sws cert get failed: ' + certdoc.status)\n raise SigningCertException(url=certurl, status=certdoc.status)\n logger.debug('got it')\n pem = certdoc.data\n else:\n raise SigningCertException(url=certurl, status=-1)\n\n x509 = X509.load_cert_string(pem)\n key = x509.get_pubkey()\n _public_keys[certurl] = key\n\n enctxt64 = iam_message['body']\n\n # check the signature\n\n sigmsg = _build_sig_msg(iamHeader, enctxt64)\n sig = base64.b64decode(iamHeader['signature'])\n pubkey = _public_keys[certurl]\n pubkey.reset_context(md='sha1')\n pubkey.verify_init()\n pubkey.verify_update(sigmsg)\n if pubkey.verify_final(sig)!=1:\n raise SignatureVerifyException()\n\n # decrypt the message\n if 'keyId' in iamHeader:\n iv64 = iamHeader['iv']\n iv = base64.b64decode(iv64)\n keyid = iamHeader['keyId']\n if not keyid in _crypt_keys:\n logger.error('key ' + keyid + ' not found')\n raise CryptKeyException(keyid=keyid, msg='not found')\n key = _crypt_keys[keyid]\n \n enctxt = base64.b64decode(enctxt64)\n cipher = M2Crypto.EVP.Cipher(alg='aes_128_cbc', key=key, iv=iv, op=0)\n txt = cipher.update(enctxt) + cipher.final()\n else:\n txt = base64.b64decode(enctxt64)\n\n txt = [x for x in txt if x in string.printable]\n iam_message['body'] = txt\n # un-base64 the context\n try:\n iamHeader['messageContext'] = base64.b64decode(iamHeader['messageContext'])\n except TypeError:\n logger.info( 'context not base64')\n return None\n except KeyError:\n if 'AlarmName' in iam_message:\n logger.debug('alarm: ' + iam_message['AlarmName'])\n return iam_message\n\n logger.error('Unknown message key: ' )\n return None\n\n return iam_message\n\n\ndef crypt_init(cfg):\n global _crypt_keys\n global _public_keys\n global _ca_file\n\n # load the signing keys\n certs = cfg['CERTS']\n for c in certs:\n id = c['ID']\n crt = {}\n crt['url'] = c['URL']\n crt['key'] = EVP.load_key(c['KEYFILE'])\n _private_keys[id] = crt\n\n\n # load the cryption key\n keys = cfg['CRYPTS']\n for k in keys:\n id = k['ID']\n k64 = k['KEY']\n logger.debug('adding crypt key ' + id)\n kbin = base64.b64decode(k64)\n _crypt_keys[id] = kbin\n\n # are we verifying certs ( just for the signing cert )\n if 'ca_file' in cfg:\n _ca_file = cfg['CA_FILE']\n \n","sub_path":"messagetools.old/iam_message.py","file_name":"iam_message.py","file_ext":"py","file_size_in_byte":7793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"612702877","text":"from google.cloud import vision\nfrom google.cloud.vision import types\nfrom PIL import Image, ImageDraw\n\nimport dialogue\n\nformer_status = \"\"\n\ndef get_face(input_filename):\n with open(input_filename, 'rb') as image:\n faces = detect_face(image)\n if not faces:\n dialogue.play_audio(\"nobody\")\n return {'status': 'nobody', 'dialogue': dialogue.get_dialogue(\"nobody\")}\n image.seek(0)\n face_status = highlight_faces(image, faces)\n return face_status\n\ndef detect_face(face_file, max_results=4):\n client = vision.ImageAnnotatorClient()\n content = face_file.read()\n image = types.Image(content=content)\n return client.face_detection(image=image).face_annotations\n\ndef highlight_faces(image, faces):\n if len(faces) == 1:\n box = None\n left_eye = None\n right_eye = None\n nose_tip = None\n joyLikelihood = None\n for face in faces:\n left_eye = face.landmarks[0].position\n right_eye = face.landmarks[1].position\n nose_tip = face.landmarks[7].position\n joyLikelihood = face.joy_likelihood\n box = [(vertex.x, vertex.y) for vertex in face.bounding_poly.vertices]\n return check_face_loc_lonely(box,left_eye,right_eye,nose_tip,joyLikelihood)\n else:\n box = []\n left_eye = []\n right_eye = []\n nose_tip = []\n joyLikelihood = []\n for value in faces:\n left_eye.append(value.landmarks[0].position)\n right_eye.append(value.landmarks[1].position)\n nose_tip.append(value.landmarks[7].position)\n joyLikelihood.append(value.joy_likelihood)\n box.append([(vertex.x, vertex.y) for vertex in value.bounding_poly.vertices])\n return check_face_loc(box,left_eye,right_eye,nose_tip,joyLikelihood)\n\ndef check_face_loc_lonely(face_box,left_eye,right_eye,nose_tip,joyLikelihood):\n global former_status\n print(former_status)\n if (face_box[0][0]-face_box[1][0])*(face_box[1][1]-face_box[2][1]) < 150 * 150 :\n status = \"forward\"\n result = play_audio(status)\n return result #顔はもう少し上に\n if (face_box[0][0]-face_box[1][0])*(face_box[1][1]-face_box[2][1]) > 600 * 600 :\n status = \"back\"\n result = play_audio(status)\n return result\n if(face_box[0][0] > 1024*1/2) :\n status = \"right\"\n result = play_audio(status)\n return result #被写体は右に\n if(face_box[1][0] < 1024*1/2) :\n status = \"left\"\n result = play_audio(status)\n return result #被写体は左に\n if face_box[0][1] > 768*1/2 :\n status = \"forward\"\n result = play_audio(status)\n return result #顔はもう少し上に\n if face_box[3][1] < 768*1/2 :\n status = \"back\"\n result = play_audio(status)\n return result #顔はもう少し下に\n if(joyLikelihood == 1) :\n status = \"smile\"\n result = play_audio(status)\n return result #顔はもう少し下に\n result = play_audio(\"ok\")\n former_status = \"\"\n return result\n\ndef check_face_loc(face_boxes,left_eyes,right_eyes,nose_tips,joyLikelihoods):\n global former_status\n print(\"2\")\n for face_box in face_boxes:\n print(face_box)\n if(face_box[0][0] > 1024):\n status = \"center\"\n result = play_audio(status)\n return result\n if(face_box[1][0] < 0):\n status = \"center\"\n result = play_audio(status)\n return result\n if face_box[0][1] > 768:\n status = \"forwards\"\n result = play_audio(status)\n return result\n if face_box[3][1] < 0:\n status = \"backs\"\n result = play_audio(status)\n return result\n for joyLikelihood in joyLikelihoods:\n if(joyLikelihood == 1):\n status = \"smiles\"\n result = play_audio(status)\n return result\n result = play_audio(\"ok\")\n former_status = \"\"\n return result\n\ndef play_audio(status):\n global former_status\n if former_status == status:\n status += \" again\"\n else:\n former_status = status\n text = dialogue.get_dialogue(status)\n dialogue.play_audio(status)\n \n return {\"status\": status, \"dialogue\": text}\n","sub_path":"face.py","file_name":"face.py","file_ext":"py","file_size_in_byte":4350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"514068954","text":"import Tool\nimport SMO\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport KernelTransform\n'''\ncalculate w and draw the picture,\nthe variable which the α not equal zero , \nwe call support vector\n'''\ndef calculateW(alphas , data , labels):\n x = np.mat(data)\n label = np.mat(labels).transpose()\n m , n = np.shape(x)\n w = np.zeros((n , 1))\n for i in range(m):\n w += np.multiply(alphas[i] * label[i] , x[i , :].T)\n return w\n pass\n\nif __name__ == '__main__':\n data, label = Tool.loadDataSet('../Data/testSet.txt')\n b,alphas = SMO.smo(data , label , kernel=False)\n w = calculateW(alphas , data , label)\n x = np.arange(0 , 11)\n print(w)\n y = (-b - w[0]*x)/w[1]\n Tool.drawDataset(data , label , x , y.tolist()[0] , line=True , alphas=alphas)\n\n data, label = Tool.loadDataSet('../Data/testSetRBF.txt')\n b, alphas = SMO.smo(data, label,kernel=True ,maxIter=100)\n svInd = np.nonzero(alphas.A > 0)[0]\n Tool.drawDataset(data, label, line=False, alphas=alphas)\n\n\n\n\n\n\n","sub_path":"MachineLearning/Support Vector Machine/SVM_Model.py","file_name":"SVM_Model.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"406106748","text":"import webbrowser\nimport pandas as pd\nimport importlib as lib\nimport copy\nimport re\n\nofflib = lib.import_module('offlinelib')\nonlib = lib.import_module('onlinelib')\n\nratings = pd.read_csv('new_ratings.csv', index_col=0)\nbooks = pd.read_csv('BX-Books.csv', error_bad_lines=False, encoding=\"ISO-8859-1\", sep=';', warn_bad_lines=False, low_memory=False)\n\nsimil =pd.read_csv('itemsimilarity.csv', sep = '\\t', header=None)\nsimil.columns = ['ISBN1', 'ISBN2', 'Similarity']\n\nsim = offlib.ratings_matrix_user(simil)\ndic_item = offlib.ratings_matrix_item(ratings)\ndic_user = offlib.ratings_matrix_user(ratings)\n\nq = input(\"Type of Recommender System:\\n A) Item-Based \\n B) User-Based\\n\")\n\n\ndef online(sim_mat, dic, dic_user, t, df_books, q):\n \"\"\"Online procedure for recommendation. Takes in input the similarity dictionary, the Item-User dictionary, the\n User-Item dictionary, a threshold and the books' data frame and the procedure.\"\"\"\n n = int(input('How many books do you want? '))\n print('\\n')\n print(\"Loading your Recommendation...\\n\")\n rat_user = pd.read_csv('new_userXX2.csv', sep=';',dtype={'ITEM': object})\n dic_rat_user = offlib.ratings_matrix_user(rat_user)\n if q == 'A':\n dic_tot = {}\n items = list(dic_rat_user[0].keys())\n for item in items:\n ds = onlib.n_sim(sim_mat, item, t, q)\n for k, v in ds.items():\n if k not in dic_tot.keys():\n dic_tot[k] = v\n elif k in dic_tot.keys() and v < dic_tot[k]:\n pass\n else:\n dic_tot[k] = v\n nest_dic = onlib.nested_dict(dic_tot)\n new_dic_user = copy.deepcopy(dic_user)\n new_dic_user.update(dic_rat_user)\n prev = onlib.prevision(sim_mat, dic, nest_dic, new_dic_user, t, q)\n item_rec = sorted(prev.keys(), key=lambda x: prev[x][0], reverse=True)\n else:\n new_dic_user = copy.deepcopy(dic_user)\n new_dic_user.update(dic_rat_user)\n sim_nu = onlib.similarity_user_online(new_dic_user)\n if sim_nu == {}:\n print('Sorry no match found!')\n else:\n newu = 0\n ds = onlib.n_sim(sim_nu, newu, t, q)\n us_sim = ds.keys()\n l_item = []\n for u in us_sim:\n if l_item == []:\n l_item = list(new_dic_user[u].keys())\n else:\n l_item += list(new_dic_user[u].keys())\n user_zero = {}\n k = 0\n for item in l_item:\n if user_zero == {}:\n user_zero[0] = {}\n user_zero[0][item] = 0\n else:\n user_zero[0][item] = 0\n for item in list(user_zero[0].keys()):\n prev = 0\n numer = 0\n denom = 0\n for user in us_sim:\n if user in list(dic_item[item].keys()):\n numer += ds[user] * dic_item[item][user]\n denom += ds[user]\n else:\n pass\n prev = numer / denom\n user_zero[0][item] = prev\n item_rec = sorted(user_zero[0].keys(), key=lambda x: user_zero[0][x], reverse=True)\n k = 0\n print('\\n')\n for item in item_rec:\n if item in list(df_books['ISBN'].values):\n print(k + 1, ') ', df_books[df_books['ISBN'] == item]['Book-Title'].values[0], ' - ',\n df_books[df_books['ISBN'] == item]['Book-Author'].values[0], ' - ',\n item)\n else:\n print(k + 1, ') Sorry! No information for this book: ', item)\n k += 1\n if k == n:\n break\n if k < n:\n print(\"\\n\")\n print(\"Sorry! I don't have enough information to suggest you %d items.\" % n)\n print('\\n')\n print(\n \"Would you like to:\\n A) Buy a book.\\n B) Get informations on the book's author.\\n C) Get the plot.\\n D) Exit\")\n q1 = input()\n if q1 == 'A':\n q2 = input('Please insert the ISBN: ')\n webbrowser.open(\"https://www.bookgoodies.com/a/%s\" % q2)\n elif q1 == 'B':\n q2 = input('Please insert the ISBN: ')\n author = df_books[df_books['ISBN'] == q2]['Book-Author'].values[0]\n author = author.lower()\n author = author.title()\n author = author.replace(' ', '_')\n webbrowser.open(\"https://en.wikipedia.org/wiki/%s\" % author)\n elif q1 == 'C':\n q2 = input('Please insert the ISBN: ')\n name = df_books[df_books['ISBN'] == q2]['Book-Title'].values[0]\n sep1 = '('\n sep2 = ':'\n name = name.split(sep1, 1)[0]\n name = name.split(sep2, 1)[0]\n name = name.lower()\n name = name.replace('&', \"&\")\n name = re.sub(r\"\\b(?<!')[a-z]\", lambda m: m.group().upper(), name)\n name = name.replace('\\\\', '')\n name = name.replace('\"', \"\")\n name = name.replace(\" \", \"_\")\n webbrowser.open(\"https://en.wikipedia.org/wiki/%s#Plot\" % name)\n return\n\n\n\nonline(sim, dic_item, dic_user, 5, books, q)\n","sub_path":"pers.py","file_name":"pers.py","file_ext":"py","file_size_in_byte":5098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"428895281","text":"# test driver for the wiki scrape system\n\ndef test_basic():\n\timport Wiki.wiki_graph as glib\n\n\tg = glib.HashGraph()\n\tg.add(\"alpha\", [\"beta\", \"delta\", \"gamma\"])\n\tg.add(\"beta\", [\"beta\", \"delta\", \"alpha\"])\n\tg.add(\"delta\", [\"alpha\"])\n\tg.add(\"gamma\", [\"alpha\", \"beta\", \"delta\"])\n\tg.save(\"Wiki/\")\n\n\tg.print_ht()\n\tprint(\"\")\n\tg.print_g()\n\tprint(\"\")\n\n\tg0 = glib.HashGraph()\n\tg0.load(\"Wiki/\")\n\n\tg0.print_ht()\n\tprint(\"\")\n\tg0.print_g()\n\ndef test_hash():\n\timport Wiki.wiki_graph as glib\n\tg = glib.HashGraph()\n\n\tstrs = [\"math\",\n\t\t\t\"maths\",\n\t\t\t\"MATH\",\n\t\t\t\"mlth\"\n\t\t\t]\n\n\tfor s in strs:\n\t\tprint(g.hash(s))\n\ndef test_mine():\n\timport Wiki.wiki as w\n\n\tminer = w.WikiMiner()\n\n\tminer.parse(\"Mathematics\")\n\tminer.parse(\"Graph_theory\")\n\tminer.graph.print_ht()\n\tminer.graph.print_g()\n\n\tminer.save()\n\n# test the convention that nodes added to the graph with\n# also test new seed mechanic \ndef test_processed_mechanic():\n\timport Wiki.wiki as w\n\n\tminer = w.WikiMiner()\n\n\tminer.seed()\n\tminer.parse(\"Mathematics\")\n\tminer.graph.add(\"Graph_theory\", None) # over write the entry to indicate non local\n\tminer.graph.print_processed()\n\n\tminer.save()\n\n# Returns if passed or not\ndef test_rand():\n\timport Wiki.wiki as w\n\tminer = w.WikiMiner()\n\n\tfor i in range(0,100):\n\t\tprint(i)\n\t\tminer.seed()\n\n\tprint(\"Saving and Loading\")\n\tminer.save()\n\tminer0 = w.WikiMiner()\n\n\t# now check that miner and miner0 have the same dicts\n\tprint(\"Checking the Hash Table\")\n\tfor k in miner.graph.hash_table.keys():\n\t\tif k not in miner0.graph.hash_table.keys():\n\t\t\tprint(\"key not found: \" + k)\n\t\t\treturn False\n\n\t\tv1 = miner.graph.hash_table[k]\n\t\tv2 = miner0.graph.hash_table[k]\n\n\t\tif not v1 == v2:\n\t\t\tprint(\"Values do not match\")\n\t\t\treturn -1 \n\n\tprint(\"Checking the Graph\")\n\tfor k in miner.graph.graph.keys():\n\t\tif k not in miner0.graph.graph.keys():\n\t\t\tprint(\"key not found: \" + k)\n\t\t\treturn False\n\n\t\tv1 = miner.graph.graph[k]\n\t\tv2 = miner0.graph.graph[k]\n\n\t\tif not v1 == v2:\n\t\t\tprint(\"Values do not match\")\n\t\t\treturn -1 \n\n\treturn True\n\n# test the function get boundry\ndef test_bd():\n\timport Wiki.wiki_graph as glib\n\tg = glib.HashGraph()\n\n\tg.add(\"A\", [\"B\", \"C\"])\n\tg.add(\"B\", [\"A\", \"C\", \"D\"])\n\tg.add(\"C\", [\"A\", \"B\"])\n\tg.add(\"D\", None)\n\n\tprint(g.get_boundry()) # should be B\n\t","sub_path":"Wiki/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"35072025","text":"def binary_search(value_list: list, value_search: int):\n # Get list size\n list_size = len(value_list)\n # Lowest index in the list [Initial]\n index_low = 0\n # Highest index in the list [Initial]\n index_high = list_size - 1\n\n # Will be used as validator if value exists\n value_found = False\n\n # While lowest index does not exceed highest index\n while index_low <= index_high:\n # Find the middle index of the list\n # Add the lowest index and highest index, then floor divide into 2\n index_middle = (index_high + index_low) // 2\n # If value searching is greater than the middle index\n if value_search > value_list[index_middle]:\n # New lowest index is the higher half of the list\n index_low = index_middle + 1\n # If value searching is less than the middle index\n elif value_search < value_list[index_middle]:\n # New highest index us the highest half of the list\n index_high = index_middle -1\n # If value found\n elif value_search == value_list[index_middle]:\n # Change value of value_found\n value_found = f\"Value found at index {index_middle}\"\n # Stops the loop\n break\n\n # Check if value exists\n # If value exists, display the text and index\n # If value does not exists, display value not found\n print(\"Value not found\" if value_found == False else value_found)\n\n\nbinary_search([1,2,3,4,6,7,8], 8)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"31439229","text":"#!/usr/bin/env python3\n\"\"\"\nThis application is designed to decode timestamps into human-readable date/times and vice-versa\nAdditional information regarding the source of the timestamp formats and associated equations\nwill be provided in the docstrings below.\nTO DO:\n Re-evaluate error handling.\n MSDOS and FAT timestamps both need method for accepting time offset\n\nGPS Ref: http://www.leapsecond.com/java/gpsclock.htm\nLeap Seconds: https://www.nist.gov/pml/time-and-frequency-division/leap-seconds-faqs\n http://hpiers.obspm.fr/eop-pc/index.php?index=TAI-UTC_tab&lang=en\nMicrosoft DateTime: https://docs.microsoft.com/en-us/dotnet/api/system.datetime?view=netframework-4.8\nMicrosoft Time: https://docs.microsoft.com/en-ca/windows/win32/sysinfo/time\nMicrosoft 1904 Timestamp: https://docs.microsoft.com/en-us/office/troubleshoot/excel/1900-and-1904-date-system\nMicrosoft OLE Automation Date (OADate): https://docs.microsoft.com/en-us/dotnet/api/system.datetime.tooadate?view=netframework-4.8\nMSDOS wFatDate wFatTime DosDate: https://docs.microsoft.com/en-us/windows/win32/api/winbase/nf-winbase-dosdatetimetofiletime\nMicrosoft FILETIME: https://support.microsoft.com/en-ca/help/188768/info-working-with-the-filetime-structure\nMicrosoft Active Directory/LDAP Timestamp: https://docs.microsoft.com/en-us/windows/win32/adschema/a-lastlogontimestamp\nbplist timestamp: https://developer.apple.com/documentation/corefoundation/cfabsolutetime\n https://developer.apple.com/documentation/foundation/nsdate\nGSM Timestamps: https://en.wikipedia.org/wiki/GSM_03.40\n http://seven-bit-forensics.blogspot.com/2014/02/decoding-gsmsms-timestamps.html\nVMWare Snapshot timestamp: https://stuffphilwrites.com/2013/03/vmware-snapshot-forensics/\n\"\"\"\nfrom datetime import datetime as dt, timedelta\nimport struct\nfrom binascii import hexlify, unhexlify\nfrom string import hexdigits\nimport argparse\nimport sys\nfrom dateutil import parser as duparser\nimport base64\nfrom colorama import init\ninit(autoreset=True)\n\n__author__ = 'Corey Forman'\n__date__ = '7 Dec 2020'\n__version__ = '2.5'\n__description__ = 'Python 3 CLI Date Time Conversion Tool'\n\nclass TimeDecoder(object):\n \"\"\"Run the decoding class\"\"\"\n def __init__(self):\n self.epoch_1601 = dt(1601, 1, 1)\n self.epoch_1899 = dt(1899, 12, 30)\n self.epoch_1904 = dt(1904, 1, 1)\n self.epoch_1970 = dt(1970, 1, 1)\n self.epoch_1980 = dt(1980, 1, 6)\n self.epoch_2000 = dt(2000, 1, 1)\n self.epoch_2001 = dt(2001, 1, 1)\n self.hundreds_nano = 10000000\n self.nano_2001 = 1000000000\n self.epoch_active = 116444736000000000\n self.hfs_dec_subtract = 2082844800\n self.ts_funcs = [self.from_unix_sec, self.from_unix_milli, self.from_win_64_hex, self.from_win_64_hexle, self.from_chrome, self.from_ad,\n self.from_unix_hex_32be, self.from_unix_hex_32le, self.from_cookie, self.from_ole_be, self.from_ole_le, self.from_mac,\n self.from_hfs_dec, self.from_hfs_be, self.from_hfs_le, self.from_msdos, self.from_fat, self.from_systime, self.from_filetime,\n self.from_prtime, self.from_ole_auto, self.from_ms1904, self.from_ios_time, self.from_sym_time, self.from_gps_time,\n self.from_eitime, self.from_bplist, self.from_gsm, self.from_vm]\n self.date_funcs = [self.to_unix_sec, self.to_unix_milli, self.to_win_64_hex, self.to_win_64_hexle, self.to_chrome, self.to_ad, self.to_unix_hex_32be,\n self.to_unix_hex_32le, self.to_cookie, self.to_ole_be, self.to_ole_le, self.to_mac, self.to_hfs_dec, self.to_hfs_be, self.to_hfs_le,\n self.to_msdos, self.to_fat, self.to_systime, self.to_filetime, self.to_prtime, self.to_ole_auto, self.to_ms1904, self.to_ios_time,\n self.to_sym_time, self.to_gps_time, self.to_eitime, self.to_bplist, self.to_gsm, self.to_vm]\n self.in_unix_sec = self.in_unix_milli = self.in_windows_hex_64 = self.in_windows_hex_le = self.in_chrome = self.in_ad = self.in_unix_hex_32 = self.in_unix_hex_32le = self.in_cookie = self.in_ole_be = self.in_ole_le = self.in_mac = self.in_hfs_dec = self.in_hfs_be = self.in_hfs_le = self.in_fat = self.in_msdos = self.in_systemtime = self.in_filetime = self.in_prtime = self.in_ole_auto = self.in_ms1904 = self.in_iostime = self.in_symtime = self.in_gpstime = self.in_eitime = self.in_bplist = self.in_gsm = self.in_vm = None\n self.out_unix_sec = self.out_unix_milli = self.out_windows_hex_64 = self.out_windows_hex_le = self.out_chrome = self.out_adtime = self.out_unix_hex_32 = self.out_unix_hex_32le = self.out_cookie = self.out_ole_be = self.out_ole_le = self.out_mac = self.out_hfs_dec = self.out_hfs_be = self.out_hfs_le = self.out_fat = self.out_msdos = self.out_systemtime = self.out_filetime = self.out_prtime = self.out_ole_auto = self.out_ms1904 = self.out_iostime = self.out_symtime = self.out_gpstime = self.out_eitime = self.out_bplist = self.out_gsm = self.out_vm = None\n self.leapseconds = {\n 10:[dt(1972,1,1), dt(1972,7,1)],\n 11:[dt(1972,7,1), dt(1973,1,1)],\n 12:[dt(1973,1,1), dt(1974,1,1)],\n 13:[dt(1974,1,1), dt(1975,1,1)],\n 14:[dt(1975,1,1), dt(1976,1,1)],\n 15:[dt(1976,1,1), dt(1977,1,1)],\n 16:[dt(1977,1,1), dt(1978,1,1)],\n 17:[dt(1978,1,1), dt(1979,1,1)],\n 18:[dt(1979,1,1), dt(1980,1,1)],\n 19:[dt(1980,1,1), dt(1981,7,1)],\n 20:[dt(1981,7,1), dt(1982,7,1)],\n 21:[dt(1982,7,1), dt(1983,7,1)],\n 22:[dt(1983,7,1), dt(1985,7,1)],\n 23:[dt(1985,7,1), dt(1988,1,1)],\n 24:[dt(1988,1,1), dt(1990,1,1)],\n 25:[dt(1990,1,1), dt(1991,1,1)],\n 26:[dt(1991,1,1), dt(1992,7,1)],\n 27:[dt(1992,7,1), dt(1993,7,1)],\n 28:[dt(1993,7,1), dt(1994,7,1)],\n 29:[dt(1994,7,1), dt(1996,1,1)],\n 30:[dt(1996,1,1), dt(1997,7,1)],\n 31:[dt(1997,7,1), dt(1999,1,1)],\n 32:[dt(1999,1,1), dt(2006,1,1)],\n 33:[dt(2006,1,1), dt(2009,1,1)],\n 34:[dt(2009,1,1), dt(2012,7,1)],\n 35:[dt(2012,7,1), dt(2015,7,1)],\n 36:[dt(2015,7,1), dt(2017,1,1)],\n 37:[dt(2017,1,1), dt.now() - timedelta(seconds=37)]\n }\n # There have been no further leapseconds since 2017,1,1 at the __date__ of this script\n # which is why the leapseconds end with a dt.now object to valid/relevant timestamp output.\n self.left_color = \"\\033[1;31m\"\n self.right_color = \"\\033[1;m\"\n\n def run(self):\n \"\"\"Process arguments and errors\"\"\"\n if len(sys.argv[1:]) == 0:\n arg_parse.print_usage()\n arg_parse.exit()\n try:\n if args.unix:\n result, indiv_output, combined_output, reason = self.from_unix_sec()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.umil:\n result, indiv_output, combined_output, reason = self.from_unix_milli()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.wh:\n result, indiv_output, combined_output, reason = self.from_win_64_hex()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.whle:\n result, indiv_output, combined_output, reason = self.from_win_64_hexle()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.chrome:\n result, indiv_output, combined_output, reason = self.from_chrome()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.active:\n result, indiv_output, combined_output, reason = self.from_ad()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.uhbe:\n result, indiv_output, combined_output, reason = self.from_unix_hex_32be()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.uhle:\n result, indiv_output, combined_output, reason = self.from_unix_hex_32le()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.cookie:\n result, indiv_output, combined_output, reason = self.from_cookie()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.oleb:\n result, indiv_output, combined_output, reason = self.from_ole_be()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.olel:\n result, indiv_output, combined_output, reason = self.from_ole_le()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.mac:\n result, indiv_output, combined_output, reason = self.from_mac()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.hfsdec:\n result, indiv_output, combined_output, reason = self.from_hfs_dec()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.hfsbe:\n result, indiv_output, combined_output, reason = self.from_hfs_be()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.hfsle:\n result, indiv_output, combined_output, reason = self.from_hfs_le()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.fat:\n result, indiv_output, combined_output, reason = self.from_fat()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.msdos:\n result, indiv_output, combined_output, reason = self.from_msdos()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.sys:\n result, indiv_output, combined_output, reason = self.from_systime()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.ft:\n result, indiv_output, combined_output, reason = self.from_filetime()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.pr:\n result, indiv_output, combined_output, reason = self.from_prtime()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.auto:\n result, indiv_output, combined_output, reason = self.from_ole_auto()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.ms1904:\n result, indiv_output, combined_output, reason = self.from_ms1904()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.ios:\n result, indiv_output, combined_output, reason = self.from_ios_time()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.sym:\n result, indiv_output, combined_output, reason = self.from_sym_time()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.gps:\n result, indiv_output, combined_output, reason = self.from_gps_time()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.eitime:\n result, indiv_output, combined_output, reason = self.from_eitime()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.bplist:\n result, indiv_output, combined_output, reason = self.from_bplist()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.gsm:\n result, indiv_output, combined_output, reason = self.from_gsm()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.vm:\n result, indiv_output, combined_output, reason = self.from_vm()\n if indiv_output == False:\n print(reason)\n else:\n print(indiv_output)\n elif args.timestamp:\n self.to_timestamps()\n elif args.guess:\n self.from_all()\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n\n def to_timestamps(self):\n \"\"\"Convert provided date to all timestamps\"\"\"\n print ('\\nConverting Date: ' + timestamp + '\\n')\n for func in self.date_funcs:\n func()\n self.timestamp_output()\n\n def from_unix_sec(self):\n \"\"\"Convert Unix Seconds value to a date\"\"\"\n reason = \"[!] Unix seconds timestamp is 10 digits in length\"\n try:\n if not (len(unix) == 10) or not (unix.isdigit()):\n self.in_unix_sec = indiv_output = combined_output = False\n pass\n else:\n self.in_unix_sec = dt.utcfromtimestamp(float(unix)).strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Unix Seconds: \" + self.in_unix_sec + \" UTC\")\n combined_output = str(\"\\033[1;31mUnix Seconds:\\t\\t\\t\" + self.in_unix_sec + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_unix_sec = indiv_output = combined_output = False\n return self.in_unix_sec, indiv_output, combined_output, reason\n\n def to_unix_sec(self):\n \"\"\"Convert date to a Unix Seconds value\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_unix_sec = str(int((dt_obj - self.epoch_1970).total_seconds()) - int(dt_tz))\n ts_output = str(\"Unix Seconds:\\t\\t\\t\" + self.out_unix_sec)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_unix_sec = ts_output = False\n return self.out_unix_sec, ts_output\n\n def from_unix_milli(self):\n \"\"\"Convert Unix Millisecond value to a date\"\"\"\n reason = \"[!] Unix milliseconds timestamp is 13 digits in length\"\n try:\n if not (len(umil) == 13) or not (umil.isdigit()):\n self.in_unix_milli = indiv_output = combined_output = False\n pass\n else:\n self.in_unix_milli = dt.utcfromtimestamp(float(umil) / 1000.0).strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Unix Milliseconds: \" + self.in_unix_milli + \" UTC\")\n combined_output = str(\"\\033[1;31mUnix Milliseconds:\\t\\t\" + self.in_unix_milli + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_unix_milli = indiv_output = combined_output = False\n return self.in_unix_milli, indiv_output, combined_output, reason\n\n def to_unix_milli(self):\n \"\"\"Convert date to a Unix Millisecond value\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_unix_milli = str((int((dt_obj - self.epoch_1970).total_seconds() - int(dt_tz))*1000))\n ts_output = str(\"Unix Milliseconds:\\t\\t\" + self.out_unix_milli)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_unix_milli = ts_output = False\n return self.out_unix_milli, ts_output\n\n def from_win_64_hex(self):\n \"\"\"Convert a Windows 64 Hex Big-Endian value to a date\"\"\"\n reason = \"[!] Windows 64-bit Hex Big-Endian timestamp is 16 hex characters (8 bytes)\"\n try:\n if not (len(wh) == 16) or not (all(char in hexdigits for char in wh)):\n self.in_windows_hex_64 = indiv_output = combined_output = False\n pass\n else:\n base10_microseconds = int(wh, 16) / 10\n dt_obj = self.epoch_1601 + timedelta(microseconds=base10_microseconds)\n self.in_windows_hex_64 = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Windows 64-bit Hex BE: \" + self.in_windows_hex_64 + \" UTC\")\n combined_output = str(\"\\033[1;31mWindows 64-bit Hex BE:\\t\\t\" + self.in_windows_hex_64 + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_windows_hex_64 = indiv_output = combined_output = False\n return self.in_windows_hex_64, indiv_output, combined_output, reason\n\n def to_win_64_hex(self):\n \"\"\"Convert a date to a Windows 64 Hex Big-Endian value\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n minus_epoch = dt_obj - self.epoch_1601\n calculated_time = minus_epoch.microseconds + ((minus_epoch.seconds - int(dt_tz)) * 1000000) + (minus_epoch.days * 86400000000)\n self.out_windows_hex_64 = str(hex(int(calculated_time)*10))[2:].zfill(16)\n ts_output = str(\"Windows 64-bit Hex BE:\\t\\t\" + self.out_windows_hex_64)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_windows_hex_64 = ts_output = False\n return self.out_windows_hex_64, ts_output\n\n def from_win_64_hexle(self):\n \"\"\"Convert a Windows 64 Hex Little-Endian value to a date\"\"\"\n reason = \"[!] Windows 64-bit Hex Little-Endian timestamp is 16 hex characters (8 bytes)\"\n try:\n if not (len(whle) == 16) or not (all(char in hexdigits for char in whle)):\n self.in_windows_hex_le = indiv_output = combined_output = False\n pass\n else:\n indiv_output = combined_output = False\n endianness_change, = struct.unpack(\"<Q\", unhexlify(whle))\n converted_time = endianness_change / 10\n try:\n dt_obj = self.epoch_1601 + timedelta(microseconds=converted_time)\n self.in_windows_hex_le = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Windows 64-bit Hex LE: \" + self.in_windows_hex_le + \" UTC\")\n combined_output = str(\"\\033[1;31mWindows 64-bit Hex LE:\\t\\t\" + self.in_windows_hex_le + \" UTC\\033[1;m\".format())\n except OverflowError:\n pass\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_windows_hex_le = indiv_output = combined_output = False\n return self.in_windows_hex_le, indiv_output, combined_output, reason\n\n def to_win_64_hexle(self):\n \"\"\"Convert a date to a Windows 64 Hex Little-Endian value\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n minus_epoch = dt_obj - self.epoch_1601\n calculated_time = minus_epoch.microseconds + ((minus_epoch.seconds - int(dt_tz)) * 1000000) + (minus_epoch.days * 86400000000)\n self.out_windows_hex_le = str(struct.pack(\"<Q\", int(calculated_time*10)).hex())[2:].zfill(16)\n ts_output = str(\"Windows 64-bit Hex LE:\\t\\t\" + self.out_windows_hex_le)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_windows_hex_le = ts_output = False\n return self.out_windows_hex_le, ts_output\n\n def from_chrome(self):\n \"\"\"Convert a Chrome Timestamp/Webkit Value to a date\"\"\"\n reason = \"[!] Chrome/Webkit timestamp is 17 digits\"\n try:\n if not (len(chrome) == 17) or not (chrome.isdigit()):\n self.in_chrome = indiv_output = combined_output = False\n pass\n else:\n delta = timedelta(microseconds=int(chrome))\n converted_time = self.epoch_1601 + delta\n self.in_chrome = converted_time.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Google Chrome Time: \" + self.in_chrome + \" UTC\")\n combined_output = str(\"\\033[1;31mGoogle Chrome:\\t\\t\\t\" + self.in_chrome + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_chrome = indiv_output = combined_output = False\n return self.in_chrome, indiv_output, combined_output, reason\n\n def to_chrome(self):\n \"\"\"Convert a date to a Chrome Timestamp/Webkit value\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n chrome_time = ((dt_obj - self.epoch_1601).total_seconds() - int(dt_tz))* 1000000\n self.out_chrome = str(int(chrome_time))\n ts_output = str(\"Google Chrome:\\t\\t\\t\" + self.out_chrome)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_chrome = ts_output = False\n return self.out_chrome, ts_output\n\n def from_ad(self):\n \"\"\"Convert an Active Directory/LDAP timestamp to a date\"\"\"\n reason = \"[!] Active Directory/LDAP timestamps are 18 digits\"\n try:\n if not (len(active) == 18) or not (active.isdigit()):\n self.in_ad = indiv_output = combined_output = False\n pass\n else:\n dt_obj = dt.utcfromtimestamp((float(active) - self.epoch_active) / self.hundreds_nano)\n self.in_ad = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Active Directory Timestamp: \" + self.in_ad + \" UTC\")\n combined_output = str(\"\\033[1;31mActive Directory/LDAP dt:\\t\" + self.in_ad + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_ad = indiv_output = combined_output = False\n return self.in_ad, indiv_output, combined_output, reason\n\n def to_ad(self):\n \"\"\"Convert a date to an Active Directory/LDAP timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n tz_shift = int((dt_obj - self.epoch_1970).total_seconds() - int(dt_tz))\n self.out_adtime = str(int(tz_shift * self.hundreds_nano + self.epoch_active))\n ts_output = str(\"Active Directory/LDAP dt:\\t\" + self.out_adtime)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_adtime = ts_output = False\n return self.out_adtime, ts_output\n\n def from_unix_hex_32be(self):\n \"\"\"Convert a Unix Hex 32-bit Big-Endian timestamp to a date\"\"\"\n reason = \"[!] Unix Hex 32-bit Big-Endian timestamps are 8 hex characters (4 bytes)\"\n try:\n if not (len(uhbe) == 8) or not (all(char in hexdigits for char in uhbe)):\n self.in_unix_hex_32 = indiv_output = combined_output = False\n pass\n else:\n to_dec = int(uhbe, 16)\n self.in_unix_hex_32 = dt.utcfromtimestamp(float(to_dec)).strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Unix Hex 32-bit BE: \" + self.in_unix_hex_32 + \" UTC\")\n combined_output = str(\"\\033[1;31mUnix Hex 32-bit BE:\\t\\t\" + self.in_unix_hex_32 + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_unix_hex_32 = indiv_output = combined_output = False\n return self.in_unix_hex_32, indiv_output, combined_output, reason\n\n def to_unix_hex_32be(self):\n \"\"\"Convert a date to a Unix Hex 32-bit Big-Endian timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n unix_time = int((dt_obj - self.epoch_1970).total_seconds() - int(dt_tz))\n self.out_unix_hex_32 = str(struct.pack(\">L\", unix_time).hex())\n ts_output = str(\"Unix Hex 32-bit BE:\\t\\t\" + self.out_unix_hex_32)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_unix_hex_32 = ts_output = False\n return self.out_unix_hex_32, ts_output\n\n def from_unix_hex_32le(self):\n \"\"\"Convert a Unix Hex 32-bit Little-Endian timestamp to a date\"\"\"\n reason = \"[!] Unix Hex 32-bit Little-Endian timestamps are 8 hex characters (4 bytes)\"\n try:\n if not (len(uhle) == 8) or not (all(char in hexdigits for char in uhle)):\n self.in_unix_hex_32le = indiv_output = combined_output = False\n pass\n else:\n to_dec = struct.unpack(\"<L\", unhexlify(uhle))[0]\n self.in_unix_hex_32le = dt.utcfromtimestamp(float(to_dec)).strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Unix Hex 32-bit LE: \" + self.in_unix_hex_32le + \" UTC\")\n combined_output = str(\"\\033[1;31mUnix Hex 32-bit LE:\\t\\t\" + self.in_unix_hex_32le + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_unix_hex_32le = indiv_output = combined_output = False\n return self.in_unix_hex_32le, indiv_output, combined_output, reason\n\n def to_unix_hex_32le(self):\n \"\"\"Convert a date to a Unix Hex 32-bit Little-Endian timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n unix_time = int((dt_obj - self.epoch_1970).total_seconds() - int(dt_tz))\n self.out_unix_hex_32le = str(struct.pack(\"<L\", unix_time).hex())\n ts_output = str(\"Unix Hex 32-bit LE:\\t\\t\" + self.out_unix_hex_32le)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_unix_hex_32le = ts_output = False\n return self.out_unix_hex_32le, ts_output\n\n def from_cookie(self):\n \"\"\"Convert an Internet Explorer timestamp to a date\"\"\"\n reason = \"[!] Internet Explorer Cookie timestamps (txt cookies) consist of 2 integers values. Must be input with a comma between them.\"\n try:\n if not (\",\" in cookie) or not (cookie.split(\",\")[0].isdigit() and cookie.split(\",\")[1].isdigit()):\n self.in_cookie = indiv_output = combined_output = False\n pass\n else:\n low, high = [int(h, base=10) for h in cookie.split(',')]\n calc = 10**-7 * (high * 2**32 + low) - 11644473600\n dt_obj = dt.utcfromtimestamp(calc)\n self.in_cookie = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Windows Cookie Date: \" + self.in_cookie + \" UTC\")\n combined_output = str(\"\\033[1;31mWindows Cookie Date:\\t\\t\" + self.in_cookie + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_cookie = indiv_output = combined_output = False\n return self.in_cookie, indiv_output, combined_output, reason\n\n def to_cookie(self):\n \"\"\"Convert a date to Internet Explorer timestamp values\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n unix = int((dt_obj - self.epoch_1970).total_seconds() - int(dt_tz))\n high = int(((unix + 11644473600) * 10**7) / 2**32)\n low = int((unix + 11644473600) * 10**7) - (high * 2**32)\n self.out_cookie = str(low) + ',' + str(high)\n ts_output = str(\"Windows Cookie Date:\\t\\t\" + self.out_cookie)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_cookie = ts_output = False\n return self.out_cookie, ts_output\n\n def from_ole_be(self):\n \"\"\"Convert an OLE Big-Endian timestamp to a date\"\"\"\n reason = \"[!] OLE Big-Endian timestamps are 16 hex characters (8 bytes)\"\n try:\n if not (len(oleb) == 16) or not (all(char in hexdigits for char in oleb)):\n self.in_ole_be = indiv_output = combined_output = False\n pass\n else:\n delta = struct.unpack('>d', struct.pack('>Q', int(oleb, 16)))[0]\n dt_obj = self.epoch_1899 + timedelta(days=delta)\n self.in_ole_be = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Windows OLE 64-bit double BE: \" + self.in_ole_be + \" UTC\")\n combined_output = str(\"\\033[1;31mWindows OLE 64-bit double BE:\\t\" + self.in_ole_be + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_ole_be = indiv_output = combined_output = False\n return self.in_ole_be, indiv_output, combined_output, reason\n\n def to_ole_be(self):\n \"\"\"Convert a date to an OLE Big-Endian timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n delta = ((dt_obj - self.epoch_1899).total_seconds() - int(dt_tz)) / 86400\n conv = struct.unpack('<Q', struct.pack('<d', delta))[0]\n self.out_ole_be = str(struct.pack('>Q', conv).hex())\n ts_output = str(\"Windows OLE 64-bit double BE:\\t\" + self.out_ole_be)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_ole_be = ts_output = False\n return self.out_ole_be, ts_output\n\n def from_ole_le(self):\n \"\"\"Convert an OLE Little-Endian timestamp to a date\"\"\"\n reason = \"[!] OLE Little-Endian timestamps are 16 hex characters (8 bytes)\"\n try:\n if not (len(olel) == 16) or not (all(char in hexdigits for char in olel)):\n self.in_ole_le = indiv_output = combined_output = False\n pass\n else:\n to_le = hexlify(struct.pack('<Q', int(olel, 16)))\n delta = struct.unpack('>d', struct.pack('>Q', int(to_le, 16)))[0]\n dt_obj = self.epoch_1899 + timedelta(days=delta)\n self.in_ole_le = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Windows OLE 64-bit double LE: \" + self.in_ole_le + \" UTC\")\n combined_output = str(\"\\033[1;31mWindows OLE 64-bit double LE:\\t\" + self.in_ole_le + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_ole_le = indiv_output = combined_output = False\n return self.in_ole_le, indiv_output, combined_output, reason\n\n def to_ole_le(self):\n \"\"\"Convert a date to an OLE Little-Endian timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n delta = ((dt_obj - self.epoch_1899).total_seconds() - int(dt_tz)) / 86400\n conv = struct.unpack('<Q', struct.pack('<d', delta))[0]\n self.out_ole_le = str(struct.pack('<Q', conv).hex())\n ts_output = str(\"Windows OLE 64-bit double LE:\\t\" + self.out_ole_le)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_ole_le = ts_output = False\n return self.out_ole_le, ts_output\n\n def from_mac(self):\n \"\"\"Convert a Mac Absolute timestamp to a date - Also used for Safari plist timestamps\"\"\"\n reason = \"[!] Mac Absolute timestamps are 9 digits, commonly followed by a decimal and up to 6 digits for milliseconds\"\n try:\n if not (\".\" in mac) or not ((len(mac.split(\".\")[0]) == 9) and (len(mac.split(\".\")[1]) in range(0,7))) or not (''.join(mac.split(\".\")).isdigit()):\n self.in_mac = indiv_output = combined_output = False\n pass\n else:\n dt_obj = self.epoch_2001 + timedelta(seconds=float(mac))\n self.in_mac = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Mac Absolute Time: \" + self.in_mac + \" UTC\")\n combined_output = str(\"\\033[1;31mMac Absolute Time:\\t\\t\" + self.in_mac + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_mac = indiv_output = combined_output = False\n return self.in_mac, indiv_output, combined_output, reason\n\n def to_mac(self):\n \"\"\"Convert a date to a Mac Absolute timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_mac = str(int((dt_obj - self.epoch_2001).total_seconds() - int(dt_tz)))\n ts_output = str(\"Mac Absolute Time:\\t\\t\" + self.out_mac)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_mac = ts_output = False\n return self.out_mac, ts_output\n\n def from_hfs_dec(self):\n \"\"\"Convert a Mac OS/HFS+ Decimal Timestamp to a date\"\"\"\n reason = \"[!] Mac OS/HFS+ Decimal timestamps are 10 digits\"\n try:\n if not (len(hfsdec) == 10) or not (hfsdec.isdigit()) or not (int(hfsdec) >= 2082844800):\n self.in_hfs_dec = indiv_output = combined_output = False\n pass\n else:\n self.in_hfs_dec = dt.utcfromtimestamp(float(int(hfsdec) - self.hfs_dec_subtract)).strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Mac OS/HFS+ Decimal Date: \" + self.in_hfs_dec + \" UTC\")\n combined_output = str(\"\\033[1;31mMac OS/HFS+ Decimal Time:\\t\" + self.in_hfs_dec + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_hfs_dec = indiv_output = combined_output = False\n return self.in_hfs_dec, indiv_output, combined_output, reason\n\n def to_hfs_dec(self):\n \"\"\"Convert a date to a Mac OS/HFS+ Decimal Timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_hfs_dec = str(int((dt_obj - self.epoch_1904).total_seconds() - int(dt_tz)))\n ts_output = str(\"Mac OS/HFS+ Decimal Time:\\t\" + self.out_hfs_dec)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_hfs_dec = ts_output = False\n return self.out_hfs_dec, ts_output\n\n def from_hfs_be(self):\n \"\"\"Convert an HFS/HFS+ Big-Endian timestamp to a date (HFS+ is in UTC)\"\"\"\n reason = \"[!] HFS/HFS+ Big-Endian timestamps are 8 hex characters (4 bytes)\"\n try:\n if not (len(hfsbe) == 8) or not (all(char in hexdigits for char in hfsbe)):\n self.in_hfs_be = indiv_output = combined_output = False\n pass\n else:\n dt_obj = self.epoch_1904 + timedelta(seconds=int(hfsbe, 16))\n self.in_hfs_be = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"HFS/HFS+ 32-bit Hex BE: \" + self.in_hfs_be + \" HFS Local / HFS+ UTC\")\n combined_output = str(\"\\033[1;31mHFS/HFS+ 32-bit Hex BE:\\t\\t\" + self.in_hfs_be + \" HFS Local / HFS+ UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_hfs_be = indiv_output = combined_output = False\n return self.in_hfs_be, indiv_output, combined_output, reason\n\n def to_hfs_be(self):\n \"\"\"Convert a date to an HFS/HFS+ Big-Endian timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n conv = int((dt_obj - self.epoch_1904).total_seconds() - int(dt_tz))\n self.out_hfs_be = '{0:08x}'.format(conv)\n ts_output = str(\"HFS/HFS+ 32-bit Hex BE:\\t\\t\" + self.out_hfs_be)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_hfs_be = ts_output = False\n return self.out_hfs_be, ts_output\n\n def from_hfs_le(self):\n \"\"\"Convert an HFS/HFS+ Little-Endian timestamp to a date (HFS+ is in UTC)\"\"\"\n reason = \"[!] HFS/HFS+ Little-Endian timestamps are 8 hex characters (4 bytes)\"\n try:\n if not (len(hfsle) == 8) or not (all(char in hexdigits for char in hfsle)):\n self.in_hfs_le = indiv_output = combined_output = False\n pass\n else:\n to_le = struct.unpack('>I', struct.pack('<I', int(hfsle, 16)))[0]\n dt_obj = self.epoch_1904 + timedelta(seconds=to_le)\n self.in_hfs_le = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"HFS/HFS+ 32 big Hex LE: \" + self.in_hfs_le + \" HFS Local / HFS+ UTC\")\n combined_output = str(\"\\033[1;31mHFS/HFS+ 32-bit Hex LE:\\t\\t\" + self.in_hfs_le + \" HFS Local / HFS+ UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_hfs_le = indiv_output = combined_output = False\n return self.in_hfs_le, indiv_output, combined_output, reason\n\n def to_hfs_le(self):\n \"\"\"Convert a date to an HFS/HFS+ Little-Endian timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n conv = int((dt_obj - self.epoch_1904).total_seconds() - int(dt_tz))\n self.out_hfs_le = str(struct.pack('<I', conv).hex())\n ts_output = str(\"HFS/HFS+ 32-bit Hex LE:\\t\\t\" + self.out_hfs_le)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_hfs_le = ts_output = False\n return self.out_hfs_le, ts_output\n\n def from_fat(self):\n \"\"\"Convert an MS-DOS wFatDate wFatTime timestamp to a date\"\"\"\n reason = \"[!] MS-DOS wFatDate wFatTime timestamps are 8 hex characters (4 bytes)\"\n try:\n if not (len(fat) == 8) or not (all(char in hexdigits for char in fat)):\n self.in_fat = indiv_output = combined_output = False\n pass\n else:\n byte_swap = [fat[i:i+2] for i in range(0, len(fat), 2)]\n to_le = byte_swap[1]+byte_swap[0]+byte_swap[3]+byte_swap[2]\n binary = '{0:032b}'.format(int(to_le, 16))\n stamp = [binary[:7], binary[7:11], binary[11:16], binary[16:21], binary[21:27], binary[27:32]]\n for binary in stamp[:]:\n dec = int(binary, 2)\n stamp.remove(binary)\n stamp.append(dec)\n fat_year = stamp[0] + 1980\n fat_month = stamp[1]\n fat_day = stamp[2]\n fat_hour = stamp[3]\n fat_min = stamp[4]\n fat_sec = stamp[5] * 2\n if not (fat_year in range(1970,2100)) or not (fat_month in range(1,13)) or not (fat_day in range(1,32)) or not (fat_hour in range(0,24)) or not (fat_min in range(0,60)) or not (fat_sec in range(0,60)):\n self.in_fat = indiv_output = combined_output = False\n else:\n dt_obj = dt(fat_year, fat_month, fat_day, fat_hour, fat_min, fat_sec)\n self.in_fat = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"FAT Date + Time: \" + self.in_fat + \" Local\")\n combined_output = str(\"\\033[1;31mFAT Date + Time:\\t\\t\" + self.in_fat + \" Local\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_fat = indiv_output = combined_output = False\n return self.in_fat, indiv_output, combined_output, reason\n\n def to_fat(self):\n \"\"\"Convert a date to an MS-DOS wFatDate wFatTime timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n year = '{0:07b}'.format(dt_obj.year - 1980)\n month = '{0:04b}'.format(dt_obj.month)\n day = '{0:05b}'.format(dt_obj.day)\n hour = '{0:05b}'.format(dt_obj.hour)\n minute = '{0:06b}'.format(dt_obj.minute)\n seconds = '{0:05b}'.format(int(dt_obj.second / 2))\n to_hex = str(struct.pack('>I', int(year + month + day + hour + minute + seconds, 2)).hex())\n byte_swap = ''.join([to_hex[i:i+2] for i in range(0, len(to_hex), 2)][::-1])\n self.out_fat = ''.join([byte_swap[i:i+4] for i in range(0, len(byte_swap), 4)][::-1])\n ts_output = str(\"FAT Date + Time:\\t\\t\" + self.out_fat)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_fat = ts_output = False\n return self.out_fat, ts_output\n\n def from_msdos(self):\n \"\"\"Convert an MS-DOS timestamp to a date\"\"\"\n reason = \"[!] MS-DOS 32-bit timestamps are 8 hex characters (4 bytes)\"\n try:\n if not (len(msdos) == 8) or not (all(char in hexdigits for char in msdos)):\n self.in_msdos = indiv_output = combined_output = False\n pass\n else:\n swap = ''.join([msdos[i:i+2] for i in range(0, len(msdos), 2)][::-1])\n binary = '{0:032b}'.format(int(swap, 16))\n stamp = [binary[:7], binary[7:11], binary[11:16], binary[16:21], binary[21:27], binary[27:32]]\n for val in stamp[:]:\n dec = int(val, 2)\n stamp.remove(val)\n stamp.append(dec)\n dos_year = stamp[0] + 1980\n dos_month = stamp[1]\n dos_day = stamp[2]\n dos_hour = stamp[3]\n dos_min = stamp[4]\n dos_sec = stamp[5] * 2\n if not (dos_year in range(1970,2100)) or not (dos_month in range(1,13)) or not (dos_day in range(1,32)) or not (dos_hour in range(0,24)) or not (dos_min in range(0,60)) or not (dos_sec in range(0,60)):\n self.in_msdos = indiv_output = combined_output = False\n else:\n dt_obj = dt(dos_year, dos_month, dos_day, dos_hour, dos_min, dos_sec)\n self.in_msdos = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"MS-DOS 32-bit Hex Value: \" + self.in_msdos + \" Local\")\n combined_output = str(\"\\033[1;31mMS-DOS 32-bit Hex Value:\\t\" + self.in_msdos + \" Local\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_msdos = indiv_output = combined_output = False\n return self.in_msdos, indiv_output, combined_output, reason\n\n def to_msdos(self):\n \"\"\"Convert a date to an MS-DOS timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n year = '{0:07b}'.format(dt_obj.year - 1980)\n month = '{0:04b}'.format(dt_obj.month)\n day = '{0:05b}'.format(dt_obj.day)\n hour = '{0:05b}'.format(dt_obj.hour)\n minute = '{0:06b}'.format(dt_obj.minute)\n seconds = '{0:05b}'.format(int(dt_obj.second / 2))\n hexval = str(struct.pack('>I', int(year + month + day + hour + minute + seconds, 2)).hex())\n self.out_msdos = ''.join([hexval[i:i+2] for i in range(0, len(hexval), 2)][::-1])\n ts_output = str(\"MS-DOS 32-bit Hex Value:\\t\" + self.out_msdos)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_msdos = ts_output = False\n return self.out_msdos, ts_output\n\n def from_systime(self):\n \"\"\"Convert a Microsoft 128-bit SYSTEMTIME timestamp to a date\"\"\"\n reason = \"[!] Microsoft 128-bit SYSTEMTIME timestamps are 32 hex characters (16 bytes)\"\n try:\n if not (len(systime) == 32) or not (all(char in hexdigits for char in systime)):\n self.in_systemtime = indiv_output = combined_output = False\n pass\n else:\n to_le = ''.join([systime[i:i+2] for i in range(0, len(systime), 2)][::-1])\n converted = [to_le[i:i + 4] for i in range(0, len(to_le), 4)][::-1]\n stamp = []\n for i in converted:\n dec = int(i, 16)\n stamp.append(dec)\n dt_obj = dt(stamp[0], stamp[1], stamp[3], stamp[4], stamp[5], stamp[6], stamp[7]*1000)\n self.in_systemtime = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Microsoft 128-bit SYSTEMTIME: \" + self.in_systemtime + \" UTC\")\n combined_output = str(\"\\033[1;31mMicrosoft 128-bit SYSTEMTIME:\\t\" + self.in_systemtime + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_systemtime = indiv_output = combined_output = False\n return self.in_systemtime, indiv_output, combined_output, reason\n\n def to_systime(self):\n \"\"\"Convert a date to a Microsoft 128-bit SYSTEMTIME timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n micro = int(dt_obj.microsecond / 1000)\n tz_shift = dt_obj.timestamp() - int(dt_tz)\n add_micro = (tz_shift * 1000) + micro\n convert_to_seconds = add_micro / 1000\n new_dt_obj = dt.fromtimestamp(convert_to_seconds)\n full_date = new_dt_obj.strftime('%Y, %m, %w, %d, %H, %M, %S, ' + str(micro))\n stamp = []\n \"\"\" Will leave the following here for temporary Python 2 compatibility \"\"\"\n if sys.version_info >= (3, 0):\n for value in full_date.split(','):\n stamp.append(hexlify(struct.pack('<H', int(value))).decode('utf8'))\n elif sys.version_info < (3, 0):\n for value in full_date.split(','):\n stamp.append(hexlify(struct.pack('<H', int(value))))\n self.out_systemtime = ''.join(stamp)\n ts_output = str(\"Microsoft 128-bit SYSTEMTIME:\\t\" + self.out_systemtime)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_systemtime = ts_output = False\n return self.out_systemtime, ts_output\n\n def from_filetime(self):\n \"\"\"Convert a Microsoft FILETIME timestamp to a date\"\"\"\n reason = \"[!] Microsoft FILETIME timestamps are 2 sets of 8 hex characters (4 bytes), separated by a colon\"\n try:\n if not (\":\" in ft) or not (all(char in hexdigits for char in ft[0:8]) and all(char in hexdigits for char in ft[9:])):\n self.in_filetime = indiv_output = combined_output = False\n pass\n else:\n part2, part1 = [int(h, base=16) for h in ft.split(':')]\n converted_time = struct.unpack('>Q', struct.pack('>LL', part1, part2))[0]\n dt_obj = dt.utcfromtimestamp(float(converted_time - self.epoch_active) / self.hundreds_nano)\n self.in_filetime = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Microsoft FILETIME time: \" + self.in_filetime + \" UTC\")\n combined_output = str(\"\\033[1;31mMicrosoft FILETIME time:\\t\" + self.in_filetime + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_filetime = indiv_output = combined_output = False\n return self.in_filetime, indiv_output, combined_output, reason\n\n def to_filetime(self):\n \"\"\"Convert a date to a Microsoft FILETIME timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n minus_epoch = dt_obj - self.epoch_1601\n calculated_time = minus_epoch.microseconds + ((minus_epoch.seconds - int(dt_tz)) * 1000000) + (minus_epoch.days * 86400000000)\n indiv_output = str(struct.pack(\">Q\", int(calculated_time*10)).hex())\n self.out_filetime = str(indiv_output[8:]) + \":\" + str(indiv_output[:8])\n ts_output = str(\"Microsoft FILETIME time:\\t\" + self.out_filetime)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_filetime = ts_output = False\n return self.out_filetime, ts_output\n\n def from_prtime(self):\n \"\"\"Convert a Mozilla PRTime timestamp to a date\"\"\"\n reason = \"[!] Mozilla PRTime timestamps are 16 digits\"\n try:\n if not (len(pr) == 16) or not (pr.isdigit()):\n self.in_prtime = indiv_output = combined_output = False\n pass\n else:\n dt_obj = self.epoch_1970 + timedelta(microseconds=int(pr))\n self.in_prtime = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Mozilla PRTime: \" + self.in_prtime + \" UTC\")\n combined_output = str(\"\\033[1;31mMozilla PRTime:\\t\\t\\t\" + self.in_prtime + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_prtime = indiv_output = combined_output = False\n return self.in_prtime, indiv_output, combined_output, reason\n\n def to_prtime(self):\n \"\"\"Convert a date to Mozilla's PRTime timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_prtime = str(int(((dt_obj - self.epoch_1970).total_seconds() - int(dt_tz)) * 1000000))\n ts_output = str(\"Mozilla PRTime:\\t\\t\\t\" + self.out_prtime)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_prtime = ts_output = False\n return self.out_prtime, ts_output\n\n def from_ole_auto(self):\n \"\"\"Convert an OLE Automation timestamp to a date\"\"\"\n reason = \"[!] OLE Automation timestamps are 2 integers, separated by a dot. The left is 5 digits, the right is between 9-12 digits\"\n try:\n if not (\".\" in auto) or not ((len(auto.split(\".\")[0]) == 5) and (len(auto.split(\".\")[1]) in range(9,13))) or not (''.join(auto.split(\".\")).isdigit()):\n self.in_ole_auto = indiv_output = combined_output = False\n pass\n else:\n dt_obj = self.epoch_1899 + timedelta(days=float(auto))\n self.in_ole_auto = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"OLE Automation Date: \" + self.in_ole_auto + \" UTC\")\n combined_output = str(\"\\033[1;31mOLE Automation Date:\\t\\t\" + self.in_ole_auto + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_ole_auto = indiv_output = combined_output = False\n return self.in_ole_auto, indiv_output, combined_output, reason\n\n def to_ole_auto(self):\n \"\"\"Convert a date to an OLE Automation timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_ole_auto = \"{0:.12f}\".format(((dt_obj - self.epoch_1899).total_seconds() - int(dt_tz)) / 86400)\n ts_output = str(\"OLE Automation Date:\\t\\t\" + self.out_ole_auto)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_ole_auto = ts_output = False\n return self.out_ole_auto, ts_output\n\n def from_ms1904(self):\n \"\"\"Convert a Microsoft Excel 1904 timestamp to a date\"\"\"\n reason = \"[!] Microsoft Excel 1904 timestamps are 2 integers, separated by a dot. The left is 5 digits, the right is between 9-12 digits\"\n try:\n if not (\".\" in ms1904) or not ((len(ms1904.split(\".\")[0]) == 5) and (len(ms1904.split(\".\")[1]) in range(9,13))) or not (''.join(ms1904.split(\".\")).isdigit()):\n self.in_ms1904 = indiv_output = combined_output = False\n pass\n else:\n dt_obj = self.epoch_1904 + timedelta(days=float(ms1904))\n self.in_ms1904 = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"MS Excel 1904 Date: \" + self.in_ms1904 + \" UTC\")\n combined_output = str(\"\\033[1;31mMS Excel 1904 Date:\\t\\t\" + self.in_ms1904 + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_ms1904 = indiv_output = combined_output = False\n return self.in_ms1904, indiv_output, combined_output, reason\n\n def to_ms1904(self):\n \"\"\"Convert a date to a Microsoft Excel 1904 timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_ms1904 = \"{0:.12f}\".format(((dt_obj - self.epoch_1904).total_seconds() - int(dt_tz)) / 86400)\n ts_output = str(\"MS Excel 1904 Date:\\t\\t\" + self.out_ms1904)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_ms1904 = ts_output = False\n return self.out_ms1904, ts_output\n\n def from_ios_time(self):\n \"\"\"Convert an iOS 11 timestamp to a date\"\"\"\n reason = \"[!] iOS 11 timestamps are typically 15-18 digits\"\n try:\n if not (len(ios) in range(15,19)) or not (ios.isdigit()):\n self.in_iostime = indiv_output = combined_output = False\n pass\n else:\n dt_obj = (int(ios) / int(self.nano_2001)) + 978307200\n self.in_iostime = dt.utcfromtimestamp(dt_obj).strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"iOS 11 Date: \" + self.in_iostime + \" UTC\")\n combined_output = str(\"\\033[1;31miOS 11 Date:\\t\\t\\t\" + self.in_iostime + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_iostime = indiv_output = combined_output = False\n return self.in_iostime, indiv_output, combined_output, reason\n\n def to_ios_time(self):\n \"\"\"Convert a date to an iOS 11 timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_iostime = str(int(((dt_obj - self.epoch_2001).total_seconds() - int(dt_tz)) * self.nano_2001))\n ts_output = str(\"iOS 11 Date:\\t\\t\\t\" + self.out_iostime)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_iostime = ts_output = False\n return self.out_iostime, ts_output\n\n def from_sym_time(self):\n \"\"\"Convert a Symantec 6-byte hex timestamp to a date\"\"\"\n reason = \"[!] Symantec 6-byte hex timestamps are 12 hex characters\"\n try:\n if not (len(sym) == 12) or not (all(char in hexdigits for char in sym)):\n self.in_symtime = indiv_output = combined_output = False\n pass\n else:\n hex_to_dec = [int(sym[i:i+2], 16) for i in range(0, len(sym), 2)]\n hex_to_dec[0] = hex_to_dec[0] + 1970\n hex_to_dec[1] = hex_to_dec[1] + 1\n dt_obj = dt(hex_to_dec[0], hex_to_dec[1], hex_to_dec[2], hex_to_dec[3], hex_to_dec[4], hex_to_dec[5])\n self.in_symtime = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Symantec AV Timestamp: \" + self.in_symtime)\n combined_output = str(\"\\033[1;31mSymantec AV timestamp:\\t\\t\" + self.in_symtime + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_symtime = indiv_output = combined_output = False\n return self.in_symtime, indiv_output, combined_output, reason\n\n def to_sym_time(self):\n \"\"\"Convert a date to Symantec's 6-byte hex timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n sym_year = '{0:x}'.format(dt_obj.year - 1970).zfill(2)\n sym_month = '{0:x}'.format(dt_obj.month - 1).zfill(2)\n sym_day = '{0:x}'.format(dt_obj.day).zfill(2)\n sym_hour = '{0:x}'.format(dt_obj.hour).zfill(2)\n sym_minute = '{0:x}'.format(dt_obj.minute).zfill(2)\n sym_second = '{0:x}'.format(dt_obj.second).zfill(2)\n self.out_symtime = sym_year + sym_month + sym_day + sym_hour + sym_minute + sym_second\n ts_output = str(\"Symantec AV time:\\t\\t\" + self.out_symtime)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_symtime = ts_output = False\n return self.out_symtime, ts_output\n\n def date_range(self, start, end, check_date):\n \"\"\"Check if date is in range of start and end, return True if it is\"\"\"\n if start <= end:\n return start <= check_date <= end\n else:\n return start <= check_date or check_date <= end\n\n def from_gps_time(self):\n \"\"\"Convert a GPS timestamp to a date (involves leap seconds)\"\"\"\n reason = \"[!] GPS timestamps are 10 digits\"\n try:\n if not (len(gps) == 10) or not (gps.isdigit()):\n self.in_gpstime = indiv_output = combined_output = False\n pass\n else:\n leapseconds = self.leapseconds\n gps_stamp = self.epoch_1980 + timedelta(seconds=(float(gps)))\n tai_convert = gps_stamp + timedelta(seconds=19)\n epoch_convert = (tai_convert - self.epoch_1970).total_seconds()\n check_date = dt.utcfromtimestamp(epoch_convert)\n for entry in leapseconds:\n check = self.date_range(leapseconds.get(entry)[0], leapseconds.get(entry)[1], check_date)\n if check == True:\n variance = entry\n else:\n variance = 0\n gps_out = check_date - timedelta(seconds=variance)\n self.in_gpstime = gps_out.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"GPS Timestamp: \" + self.in_gpstime)\n combined_output = str(\"\\033[1;31mGPS timestamp:\\t\\t\\t\" + self.in_gpstime + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_gpstime = indiv_output = combined_output = False\n return self.in_gpstime, indiv_output, combined_output, reason\n\n def to_gps_time(self):\n \"\"\"Convert a date to a GPS timestamp (involves leap seconds)\"\"\"\n try:\n leapseconds = self.leapseconds\n check_date = duparser.parse(timestamp)\n if hasattr(check_date.tzinfo, '_offset'):\n dt_tz = check_date.tzinfo._offset.total_seconds()\n check_date = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n for entry in leapseconds:\n check = self.date_range(leapseconds.get(entry)[0], leapseconds.get(entry)[1], check_date)\n if check == True:\n variance = entry\n else:\n variance = 0\n leap_correction = check_date + timedelta(seconds=variance)\n epoch_shift = leap_correction - self.epoch_1970\n gps_stamp = (dt.utcfromtimestamp(epoch_shift.total_seconds()) - self.epoch_1980).total_seconds() - 19\n gps_stamp = int(gps_stamp) - int(dt_tz)\n self.out_gpstime = str(gps_stamp)\n ts_output = str(\"GPS time:\\t\\t\\t\" + self.out_gpstime)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_gpstime = ts_output = False\n return self.out_gpstime, ts_output\n\n def from_eitime(self):\n \"\"\"Convert a Google ei URL timestamp\"\"\"\n reason = \"[!] Google ei URL timestamps contain only URL-safe base64 characters: [A-Z][a-z][0-9][=-_]\"\n try:\n urlsafe_chars='ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz1234567890=-_'\n if not (all(char in urlsafe_chars for char in eitime)):\n self.in_eitime = indiv_output = combined_output = False\n pass\n else:\n padding_check = (len(eitime)%4)\n if padding_check != 0:\n padding_reqd = (4 - padding_check)\n result_eitime = eitime + (padding_reqd * '=')\n else:\n result_eitime = eitime\n try:\n decoded_eitime = base64.urlsafe_b64decode(result_eitime).hex()[:8]\n unix_timestamp, = struct.unpack(\"<L\", unhexlify(decoded_eitime))\n self.in_eitime = dt.utcfromtimestamp(unix_timestamp).strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"Google URL EI Timestamp: \" + self.in_eitime)\n combined_output = str(\"\\033[1;31mGoogle EI URL timestamp:\\t\" + self.in_eitime + \" UTC\\033[1;m\".format())\n except base64.binascii.Error as e:\n self.in_eitime = indiv_output = combined_output = False\n pass\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_eitime = indiv_output = combined_output = False\n return self.in_eitime, indiv_output, combined_output, reason\n\n def to_eitime(self):\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n unix_time = int((dt_obj - self.epoch_1970).total_seconds() + int(dt_tz))\n unix_hex = struct.pack(\"<L\", unix_time)\n urlsafe_encode = base64.urlsafe_b64encode(unix_hex)\n self.out_eitime = urlsafe_encode.decode(encoding=\"UTF-8\").strip(\"=\")\n ts_output = str(\"Google EI time:\\t\\t\\t\" + self.out_eitime)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_eitime = ts_output = False\n return self.out_eitime, ts_output\n\n def from_bplist(self):\n \"\"\"Convert a Binary Plist timestamp to a date\"\"\"\n reason = \"[!] Binary Plist timestamps are 9 digits\"\n try:\n if not (len(bplist) == 9) or not (bplist.isdigit()):\n self.in_bplist = indiv_output = combined_output = False\n pass\n else:\n dt_obj = self.epoch_2001 + timedelta(seconds=float(bplist))\n self.in_bplist = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"iOS Binary Plist Timestamp: \" + self.in_bplist)\n combined_output = str(\"\\033[1;31miOS Binary Plist timestamp:\\t\" + self.in_bplist + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_bplist = indiv_output = combined_output = False\n return self.in_bplist, indiv_output, combined_output, reason\n\n def to_bplist(self):\n \"\"\"Convert a date to a Binary Plist timestamp\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_bplist = str(int((dt_obj - self.epoch_2001).total_seconds()) - int(dt_tz))\n ts_output = str(\"iOS Binary Plist time:\\t\\t\" + self.out_bplist)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_bplist = ts_output = False\n return self.out_bplist, ts_output\n\n def from_gsm(self):\n \"\"\"Convert a GSM timestamp to a date\"\"\"\n reason = \"[!] GSM timestamps are 14 hex characters (7 bytes)\"\n try:\n # The last byte of the GSM timestamp is a hex representation of the timezone.\n # If the timezone bitwise operation on this byte results in a timezone offset\n # of less than -12 or greater than 12, then the value is incorrect.\n # The values in tz_in_range are hex bytes which return proper timezones.\n tz_in_range = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '0a', '0b', '0c', '0d', '0e', '0f', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '8a', '8b', '8c', '8d', '8e', '8f', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', 'a0', 'a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9', 'b0', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7', 'b8', 'b9', 'c0', 'c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8']\n tz_check = gsm[12:14][::-1].lower()\n if not (len(gsm) == 14) or not (all(char in hexdigits for char in gsm)) or not (tz_check in tz_in_range):\n self.in_gsm = indiv_output = combined_output = False\n pass\n else:\n swap = [gsm[i:i+2] for i in range(0, len(gsm), 2)]\n for value in swap[:]:\n le = value[::-1]\n swap.remove(value)\n swap.append(le)\n ts_tz = '{0:08b}'.format(int(swap[6], 16))\n if int(ts_tz[0]) == 1:\n utc_offset = -int(str(int(ts_tz[1:4], 2)) + str(int(ts_tz[4:8], 2))) * 0.25\n elif int(ts_tz[0]) == 0:\n utc_offset = int(str(int(ts_tz[0:4], 2)) + str(int(ts_tz[4:8], 2))) * 0.25\n swap[6] = utc_offset\n for string in swap[:]:\n swap.remove(string)\n swap.append(int(string))\n dt_year, dt_month, dt_day, dt_hour, dt_min, dt_sec, dt_tz = swap\n if dt_year in range(0, 20):\n dt_year = dt_year + 2000\n if dt_tz == 0:\n dt_tz = \" UTC\"\n elif dt_tz > 0:\n dt_tz = \" UTC+\" + str(dt_tz)\n else:\n dt_tz = \" UTC\" + str(dt_tz)\n self.in_gsm = str((dt(dt_year, dt_month, dt_day, dt_hour, dt_min, dt_sec).strftime('%Y-%m-%d %H:%M:%S.%f')) + dt_tz)\n indiv_output = str(\"GSM Timestamp: \" + self.in_gsm)\n combined_output = str(\"\\033[1;31mGSM Timestamp:\\t\\t\\t\" + self.in_gsm + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_gsm = indiv_output = combined_output = False\n return self.in_gsm, indiv_output, combined_output, reason\n\n def to_gsm(self):\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n else:\n dt_tz = 0\n if dt_tz == 0:\n hex_tz = '{:02d}'.format(0)\n elif dt_tz < 0:\n dt_tz = dt_tz / 3600\n conversion = str('{:02d}'.format(int(abs(dt_tz)) * 4))\n conversion_list = []\n for char in range(len(conversion)):\n conversion_list.append(conversion[char])\n high_order = '{0:04b}'.format(int(conversion_list[0]))\n low_order = '{0:04b}'.format(int(conversion_list[1]))\n high_order = '{0:04b}'.format(int(high_order, 2) + 8)\n hex_tz = hex(int((high_order + low_order),2)).lstrip('0x').upper()\n else:\n dt_tz = dt_tz / 3600\n conversion = str(int(dt_tz) *4)\n conversion_list = []\n for char in range(len(conversion)):\n conversion_list.append(conversion[char])\n high_order = '{0:04b}'.format(int(conversion_list[0]))\n low_order = '{0:04b}'.format(int(conversion_list[1]))\n hex_tz = hex(int((high_order + low_order),2)).lstrip('0x').upper()\n date_list = [str(dt_obj.year - 2000), '{:02d}'.format(dt_obj.month), '{:02d}'.format(dt_obj.day), '{:02d}'.format(dt_obj.hour), '{:02d}'.format(dt_obj.minute), '{:02d}'.format(dt_obj.second), hex_tz]\n date_value_swap = []\n for value in date_list[:]:\n be = value[::-1]\n date_value_swap.append(be)\n self.out_gsm = ''.join(date_value_swap)\n ts_output = str(\"GSM time:\\t\\t\\t\" + self.out_gsm)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_gsm = ts_output = False\n return self.out_gsm, ts_output\n\n def from_vm(self):\n \"\"\"Convert from a .vmsd createTimeHigh/createTimeLow timestamp\"\"\"\n reason = \"[!] VMSD timestamps are a 6-digit \\'High\\' value followed by a signed/unsigned integer at least 9 digits\"\n try:\n if ((\",\" not in vm) and (type(vm.split(\",\")[0])) != int) or (type(vm.split(\",\")[1]) != int):\n self.in_vm = indiv_output = combined_output = False\n pass\n else:\n cTimeHigh = int(vm.split(',')[0])\n cTimeLow = int(vm.split(',')[1])\n dt_obj = float((cTimeHigh * 2**32) + struct.unpack('I', struct.pack('i', cTimeLow))[0]) / 1000000\n self.in_vm = dt.utcfromtimestamp(dt_obj).strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"VMSD Timestamp: \" + self.in_vm)\n combined_output = str(\"\\033[1;31mVMSD Timestamp:\\t\\t\\t\" + self.in_vm + \" UTC\\033[1;m\".format())\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_vm = indiv_output = combined_output = False\n return self.in_vm, indiv_output, combined_output, reason\n\n def to_vm(self):\n \"\"\"Convert date to a .vmsd createTime* value\"\"\"\n try:\n dt_obj = duparser.parse(timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(timestamp, ignoretz=True)\n else:\n dt_tz = 0\n unix_seconds = (int((dt_obj - self.epoch_1970).total_seconds() - int(dt_tz))*1000000)\n createTimeHigh = int(float(unix_seconds) / 2**32)\n unpacked_int = unix_seconds - (createTimeHigh * 2**32)\n createTimeLow = struct.unpack('i', struct.pack('I', unpacked_int))[0]\n self.out_vm = str(createTimeHigh) + ',' + str(createTimeLow)\n ts_output = str(\"VMSD time:\\t\\t\\t\" + self.out_vm)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_vm = ts_output = False\n return self.out_vm, ts_output\n\n def from_all(self):\n \"\"\"Output all processed timestamp values\"\"\"\n \"\"\"Find date from provided timestamp\"\"\"\n this_year = int(dt.now().strftime('%Y'))\n states = []\n inputs = (self.in_unix_sec, self.in_unix_milli, self.in_windows_hex_64, self.in_windows_hex_le, self.in_chrome, self.in_ad, self.in_unix_hex_32, self.in_unix_hex_32le, self.in_cookie, self.in_ole_be, self.in_ole_le, self.in_mac, self.in_hfs_dec, self.in_hfs_be, self.in_hfs_le, self.in_msdos, self.in_fat, self.in_systemtime, self.in_filetime, self.in_prtime, self.in_ole_auto, self.in_ms1904, self.in_iostime, self.in_symtime, self.in_gpstime, self.in_eitime, self.in_bplist, self.in_gsm)\n print ('\\nGuessing Date from Timestamp: ' + sys.argv[2] + '\\r')\n print ('Outputs which do not result in a date/time value are not displayed.\\r')\n print ('\\033[1;31mMost likely results (results within +/- 5 years) are highlighted.\\n\\033[1;m'.format())\n for func in self.ts_funcs:\n result, indiv_output, combined_output, reason = func()\n states.append(result)\n if isinstance(result, str):\n if int(duparser.parse(result).strftime('%Y')) in range(this_year -5, this_year +5):\n print(combined_output)\n else:\n print(combined_output.strip(self.left_color).strip(self.right_color))\n if all([ state == False for state in states ]) :\n print ('No valid dates found. Check your input and try again.')\n print ('\\r')\n\n def timestamp_output(self):\n \"\"\"Output all processed dates from timestamp values\"\"\"\n for func in self.date_funcs:\n result, ts_output = func()\n if isinstance(result, str):\n print(ts_output)\n print('\\r')\n\nif __name__ == '__main__':\n now = dt.now().strftime('%Y-%m-%d %H:%M:%S.%f')\n arg_parse = argparse.ArgumentParser(description='Time Decoder and Converter v' +str(__version__))\n arg_parse.add_argument('--unix', metavar='<value>', help='convert from Unix Seconds')\n arg_parse.add_argument('--umil', metavar='<value>', help='convert from Unix Milliseconds')\n arg_parse.add_argument('--wh', metavar='<value>', help='convert from Windows 64-bit Hex BE')\n arg_parse.add_argument('--whle', metavar='<value>', help='convert from Windows 64-bit Hex LE')\n arg_parse.add_argument('--chrome', metavar='<value>', help='convert from Google Chrome time')\n arg_parse.add_argument('--active', metavar='<value>', help='convert from Active Directory value')\n arg_parse.add_argument('--uhbe', metavar='<value>', help='convert from Unix Hex 32-bit BE')\n arg_parse.add_argument('--uhle', metavar='<value>', help='convert from Unix Hex 32-bit LE')\n arg_parse.add_argument('--cookie', metavar='<value>', help='convert from Windows Cookie Date (Low Value,High Value)')\n arg_parse.add_argument('--oleb', metavar='<value>', help='convert from Windows OLE 64-bit BE - remove 0x and spaces! Example from SRUM: 0x40e33f5d 0x97dfe8fb should be 40e33f5d97dfe8fb')\n arg_parse.add_argument('--olel', metavar='<value>', help='convert from Windows OLE 64-bit LE')\n arg_parse.add_argument('--mac', metavar='<value>', help='convert from Mac Absolute Time')\n arg_parse.add_argument('--hfsdec', metavar='<value>', help='convert from Mac OS/HFS+ Decimal Time')\n arg_parse.add_argument('--hfsbe', metavar='<value>', help='convert from HFS(+) BE times (HFS = Local, HFS+ = UTC)')\n arg_parse.add_argument('--hfsle', metavar='<value>', help='convert from HFS(+) LE times (HFS = Local, HFS+ = UTC)')\n arg_parse.add_argument('--fat', metavar='<value>', help='convert from FAT Date + Time (wFat)')\n arg_parse.add_argument('--msdos', metavar='<value>', help='convert from 32-bit MS-DOS time - result is Local Time')\n arg_parse.add_argument('--sys', metavar='<value>', help='convert from 128-bit SYSTEMTIME')\n arg_parse.add_argument('--ft', metavar='<value>', help='convert from FILETIME timestamp')\n arg_parse.add_argument('--pr', metavar='<value>', help='convert from Mozilla\\'s PRTime')\n arg_parse.add_argument('--auto', metavar='<value>', help='convert from OLE Automation Date format')\n arg_parse.add_argument('--ms1904', metavar='<value>', help='convert from MS Excel 1904 Date format')\n arg_parse.add_argument('--ios', metavar='<value>', help='convert from iOS 11 Timestamp')\n arg_parse.add_argument('--sym', metavar='<value>', help='convert Symantec\\'s 12-byte AV Timestamp')\n arg_parse.add_argument('--gps', metavar='<value>', help='convert from a GPS Timestamp')\n arg_parse.add_argument('--eitime', metavar='<value>', help='convert from a Google EI URL Timestamp')\n arg_parse.add_argument('--bplist', metavar='<value>', help='convert from an iOS Binary Plist Timestamp')\n arg_parse.add_argument('--gsm', metavar='<value>', help='convert from a GSM Timestamp')\n arg_parse.add_argument('--vm', metavar='<value>', help='convert from a VMWare Snapshot (.vmsd) timestamp - enter as \"high value,low value\"')\n arg_parse.add_argument('--guess', metavar='<value>', help='guess timestamp and output all reasonable possibilities')\n arg_parse.add_argument('--timestamp', metavar='DATE', help='convert date to every timestamp - enter date as \\\"Y-M-D HH:MM:SS.m\\\" in 24h fmt - without argument gives current date/time', nargs='?', const=now)\n arg_parse.add_argument('--version', '-v', action='version', version='%(prog)s ' +str(__version__))\n args = arg_parse.parse_args()\n guess = args.guess; unix = args.unix; umil = args.umil; wh = args.wh; whle = args.whle; chrome = args.chrome; active = args.active; uhbe = args.uhbe; uhle = args.uhle; cookie = args.cookie; oleb = args.oleb; olel = args.olel; mac = args.mac; hfsdec = args.hfsdec; hfsbe = args.hfsbe; hfsle = args.hfsle; fat = args.fat; msdos = args.msdos; systime = args.sys; ft = args.ft; pr = args.pr; auto = args.auto; ms1904 = args.ms1904; ios = args.ios; sym = args.sym; gps = args.gps; timestamp = args.timestamp; eitime = args.eitime; bplist = args.bplist; gsm = args.gsm; vm = args.vm\n if args.guess:\n unix = umil = wh = whle = chrome = active = uhbe = uhle = cookie = oleb = olel = mac = hfsdec = hfsbe = hfsle = fat = msdos = systime = ft = pr = auto = ms1904 = ios = sym = gps = eitime = bplist = gsm = vm = guess\n\n td = TimeDecoder()\n td.run()\n","sub_path":"time_decode.py","file_name":"time_decode.py","file_ext":"py","file_size_in_byte":88368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"216418620","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nDumps all data images to the specified directory.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport cPickle\n\nfrom os import makedirs, path\n\n\nimport cv2\nimport numpy as np\n\n\n\n\nFLOAT_DTYPE = \"float32\"\n\n\n\n\ndef save_image(img, out_file):\n \"\"\"Writes the specified image to the output file.\"\"\"\n\n\n num_channels = img.shape[0]\n img_height = img.shape[1]\n img_width = img.shape[2]\n\n\n if num_channels == 1:\n out_img = img[0] * 255\n\n else:\n out_img = np.zeros((img_height, img_width, 3), dtype=FLOAT_DTYPE)\n\n out_img[:, :, 0] = img[2, :, :] * 255\n out_img[:, :, 1] = img[1, :, :] * 255\n out_img[:, :, 2] = img[0, :, :] * 255\n\n out_img[out_img > 255] = 255\n out_img[out_img < 0] = 0\n\n\n cv2.imwrite(out_file, out_img)\n\n\n\n\n\n\n\ndef main(in_data_file, out_dir):\n \"\"\"Main method called with command line arguments.\"\"\"\n\n with open(in_data_file, \"rb\") as f_in:\n images = cPickle.load(f_in)\n\n\n try:\n makedirs(out_dir)\n except OSError:\n pass\n\n\n\n for ex_ind in range(images.shape[0]):\n img = images[ex_ind]\n out_filename = path.join(out_dir, \"%08d.jpg\" % ex_ind)\n save_image(img, out_filename)\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == \"__main__\":\n import argparse\n\n parser = argparse.ArgumentParser(description=__doc__)\n\n parser.add_argument(\"in_data_file\", help=\"File storing pickled image data\")\n parser.add_argument(\"out_dir\", help=\"Directory to dump images into\")\n\n\n args = parser.parse_args()\n main(path.abspath(args.in_data_file), path.abspath(args.out_dir))\n\n\n","sub_path":"dump_data_images.py","file_name":"dump_data_images.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"576808577","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\ndef pre_processing(x_data):\n x_max = x_data.max()\n x_min = x_data.min()\n for _ in range(x_data.size):\n _temp = (x_data[_] - x_min) / (x_max - x_min)\n x_data[_] = _temp\n return x_data\n\n\ndef gradient_update(_X_k_i, w_t, training_values, prices_values, _size):\n _sum_gd = 0\n for _i in range(_size):\n wX = np.dot(w_t, training_values[_i])\n _sum_gd += ((_X_k_i * (wX - prices_values[_i])) / (2 * np.sqrt((wX - prices_values[_i]) ** 2 + 1)))\n return _sum_gd / _size\n\n\ndef loss_achieved(w_t, training_values, prices_values, _size, hyper):\n _sum_loss = 0\n for _i in range(_size):\n wX = np.dot(w_t.T, training_values[_i])\n _sum_loss += (np.sqrt(1 / (hyper ** 2) * (prices_values[_i] - wX) ** 2 + 1) - 1)\n return _sum_loss / _size\n\n\nif __name__ == '__main__':\n np.set_printoptions(suppress=True)\n df = pd.read_csv(\"./real_estate.csv\")\n\n # 1(a)\n null_index = df[df.isnull().any(axis=1) == True].index\n df.dropna(axis=0, inplace=True)\n prices = df['price']\n df.drop(columns=['transactiondate', 'latitude', 'longitude', 'price'], inplace=True)\n print(\"null indices: \", null_index.values)\n\n # 1(b)\n x_new_age = pre_processing(np.array(df['age']))\n x_new_mrt = pre_processing(np.array(df['nearestMRT']))\n x_new_nCon = pre_processing(np.array(df['nConvenience']))\n\n x_new_age_mean = x_new_age.mean()\n x_new_mrt_mean = x_new_mrt.mean()\n x_new_nCon_mean = x_new_nCon.mean()\n print(\"x_new_age_mean: \", x_new_age_mean)\n print(\"x_new_nearestMRT: \", x_new_mrt_mean)\n print(\"x_new_nConvenience: \", x_new_nCon_mean)\n\n # 2\n x_new = pd.DataFrame(columns=['age', 'nearestMRT', 'nConvenience'])\n x_new['age'] = x_new_age\n x_new['nearestMRT'] = x_new_mrt\n x_new['nConvenience'] = x_new_nCon\n size = x_new.index.size\n training_price = prices.values[:int(size / 2)]\n test_price = prices.values[int(size / 2):]\n training_set = x_new.values[:int(size / 2)]\n test_set = x_new.values[int(size / 2):]\n\n x_print = pd.DataFrame(columns=['age', 'nearestMRT', 'nConvenience', 'price'])\n x_print['age'] = x_new_age\n x_print['nearestMRT'] = x_new_mrt\n x_print['nConvenience'] = x_new_nCon\n x_print['price'] = prices.values\n\n training_print = x_print.values[:int(size / 2)]\n test_print = x_print.values[int(size / 2):]\n\n first_training_row = training_print[0]\n last_training_row = training_print[-1]\n first_test_row = test_print[0]\n last_test_row = test_print[-1]\n\n print(\"first training row: \", first_training_row)\n print(\"last training row: \", last_training_row)\n print(\"first test row: \", first_test_row)\n print(\"last test row: \", last_test_row)\n\n # 5\n fig, ax = plt.subplots(3, 3, figsize=(10, 10))\n nIter = 400\n alphas = [10, 5, 2, 1, 0.5, 0.25, 0.1, 0.05, 0.01]\n losses = []\n training_size = training_set.shape[0]\n training_gd_set = np.insert(training_set, 0, 1, axis=1)\n c = 2\n loss = []\n w_plot = []\n for i, ax in enumerate(ax.flat):\n w = np.ones(4)\n for index in range(nIter):\n temp = index\n if temp >= training_size:\n temp -= training_size\n w = w - gradient_update(training_gd_set[temp], w, training_gd_set, training_price, training_size) * alphas[\n i]\n loss_mean = loss_achieved(w, training_gd_set, training_price, training_size, c)\n loss.append(loss_mean)\n # print(w)\n losses.append(loss)\n ax.plot(losses[i])\n loss.clear()\n ax.set_title(f\"step size: {alphas[i]}\")\n\n plt.tight_layout()\n plt.show()\n\n # 5 c\n w_plot.clear()\n fig, ax = plt.subplots(figsize=(10, 10))\n w = np.ones(4)\n w_plot.append(w)\n for index in range(nIter):\n temp = index\n if temp >= training_size:\n temp -= training_size\n Xki = np.array([1, training_gd_set[temp][0], training_gd_set[temp][1], training_gd_set[temp][2]])\n w = w - gradient_update(Xki, w, training_gd_set, training_price, training_size) * 0.3\n w_plot.append(w)\n ax.plot(w_plot)\n plt.show()\n\n print(\"w0: \", w[0])\n print(\"w1: \", w[1])\n print(\"w2: \", w[2])\n print(\"w3: \", w[3])\n\n # y-model\n test_size = test_set.shape[0]\n test_set_gd_set = np.insert(test_set, 0, 1, axis=1)\n test_loss = loss_achieved(w, test_set_gd_set, test_price, test_size, c)\n train_loss = loss_achieved(w, training_gd_set, training_price, training_size, c)\n print(\"batch training loss: \", train_loss)\n print(\"batch test_loss: \", test_loss)\n\n # 6\n fig, ax = plt.subplots(3, 3, figsize=(10, 10))\n losses.clear()\n loss.clear()\n epoch_times = 6\n for i, ax in enumerate(ax.flat):\n w = np.ones(4)\n for _ in range(epoch_times):\n # for index in range(training_size):\n for index in range(training_size):\n # Xki = np.array([1, training_gd_set[index][0], training_gd_set[index][1], training_gd_set[index][2]])\n Xki = np.array(training_gd_set[index])\n wX = np.dot(w, training_gd_set[index].T)\n derivative_loss = (wX - training_price[index]) / (\n 2 * np.sqrt((wX - training_price[index]) ** 2 + 4))\n w = w - alphas[i] * derivative_loss * Xki\n loss_mean = loss_achieved(w, training_gd_set, training_price, training_size, c)\n loss.append(loss_mean)\n\n # print(w)\n losses.append(loss)\n ax.plot(losses[i])\n loss.clear()\n ax.set_title(f\"step size: {alphas[i]}\")\n\n plt.tight_layout()\n plt.show()\n\n helper = []\n w = np.ones(4)\n helper.append(w)\n for _ in range(epoch_times):\n for index in range(training_size):\n # Xki = np.array([1, training_gd_set[index][0], training_gd_set[index][1], training_gd_set[index][2]])\n Xki = np.array(training_gd_set[index])\n wX = np.dot(w, training_gd_set[index].T)\n derivative_loss = (wX - training_price[index]) / (\n 2 * np.sqrt((wX - training_price[index]) ** 2 + 4))\n w = w - 0.4 * derivative_loss * Xki\n helper.append(w)\n # loss_mean = loss_achieved(w, training_gd_set, training_price, training_size, c)\n # loss.append(loss_mean)\n plt.plot(helper)\n plt.show()\n\n print(\"w0: \", w[0])\n print(\"w1: \", w[1])\n print(\"w2: \", w[2])\n print(\"w3: \", w[3])\n\n # y-model\n test_loss = loss_achieved(w, test_set_gd_set, test_price, test_size, c)\n train_loss = loss_achieved(w, training_gd_set, training_price, training_size, c)\n print(\"SGD train_loss: \", train_loss)\n print(\"SGD test_loss: \", test_loss)\n","sub_path":"Homeworks/Homework1/Homework1 (1)/Homework1Code-1.py","file_name":"Homework1Code-1.py","file_ext":"py","file_size_in_byte":6801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"641746366","text":"from keras import Model\nfrom keras.layers import Flatten, Dense, Input, Convolution2D, MaxPooling2D, BatchNormalization, Activation, \\\n GlobalAveragePooling2D, AveragePooling2D, Reshape\nfrom keras import regularizers\nimport numpy as np\n\n# size of pooling area for max pooling\n_pool_size = (2, 2)\n_stride = (2, 2)\n# convolution kernel size\n_kernel_size = (3, 3)\nBATCH_NUM_PER_CLASS = 15\nFEATURE_DIM = 128\n\n\ndef _conv_act_bn(inputs, filters, name, kernel_size=(3, 3), norm_rate=0.0, padding='same'):\n x = Convolution2D(filters, kernel_size=kernel_size, padding=padding, name=name,\n kernel_regularizer=regularizers.l2(norm_rate))(inputs)\n\n x = Activation('relu')(x)\n x = BatchNormalization()(x)\n return x\n\n\ndef _block(layer_num, inputs, filters, name, kernel_size=(3, 3), norm_rate=0.0, padding='same'):\n x = inputs\n\n for i in range(layer_num):\n x = _conv_act_bn(x, filters, name=name + '_{}'.format(i + 1), kernel_size=kernel_size, norm_rate=norm_rate,\n padding=padding)\n x = MaxPooling2D(pool_size=_pool_size, strides=(2, 2))(x)\n return x\n\n\n# def LTC_BN(num_class, filters, layer_num, input_shape=(256, 256, 1,), norm_rate=0.0, name=None):\n# \"\"\"\n# VGG model with batch-normalize on each block.\n# :param num_class: the number of your DataSet classes\n# :param filters: a list of filters\n# :param layer_num: a list of the number of each block\n# :param name: default None. it can be given list to name the block\n# :param input_shape: the shape of input\n# :param norm_rate: l2 norm rate\n# :return: vgg batch-normalize model\n# \"\"\"\n# if len(filters) < len(layer_num):\n# print('ERROR:filters length must be equal or longer than layer_num.')\n# if name is None:\n# name = ['Conv{}'.format(i + 1) for i in range(len(layer_num))]\n# inputs = Input(shape=input_shape, name='input')\n# x = inputs\n# for i in range(len(layer_num)):\n# x = _block(layer_num=layer_num[i], inputs=x, filters=filters[i], name=name[i])\n# x = AveragePooling2D()(x)\n# x = Flatten()(x)\n# x = Dense(1024, kernel_regularizer=regularizers.l2(norm_rate))(x)\n# x = Activation('relu')(x)\n# x = BatchNormalization()(x)\n# x = Dense(512, kernel_regularizer=regularizers.l2(norm_rate))(x)\n# x = Activation('relu')(x)\n# x = BatchNormalization()(x)\n# predictions = Dense(num_class, activation='softmax', name='prediction',\n# kernel_regularizer=regularizers.l2(norm_rate))(x)\n# model = Model(inputs=inputs, outputs=predictions)\n# return model\n#\n#\n# def CNNEncoder(input_shape, norm_rate=0.0):\n# \"\"\"docstring for ClassName\"\"\"\n# inputs = Input(shape=input_shape, name='input')\n# x = Dense(1024, kernel_regularizer=regularizers.l2(norm_rate))(inputs)\n# x = Activation('relu')(x)\n# x = BatchNormalization()(x)\n# x = Dense(512, kernel_regularizer=regularizers.l2(norm_rate))(x)\n# x = Activation('relu')(x)\n# x = BatchNormalization()(x)\n# return x\ndef get_concentrate_rp(base_model, train_data, sample_data, train_label):\n \"\"\"\n 提取sample集和train集特征并结合\n :param base_model:\n :param sample_data: [-1,n,n,f] 要求每类的包含的图片数相同，且同类紧挨着,从第一类到第五类\n :param train_data: 训练集数据\n :param train_label:训练集标签\n :return: 联合特征及训练标签\n \"\"\"\n sample_features = base_model.predict(sample_data, verbose=1)\n sample_data = None\n train_features = base_model.predict(train_data, verbose=1)\n train_data = None\n # print(sample_features.shape) # (100, 8, 8, 512)\n # print(train_features.shape) # (220, 8, 8, 512)\n sample_features = np.reshape(sample_features, newshape=(\n 5, 20, base_model.output.shape[1], base_model.output.shape[2], base_model.output.shape[3]))\n # print(sample_features.shape) # (5, 20, 8, 8, 512)\n sample_features = np.sum(sample_features, 1)\n # print(sample_features.shape) # (5, 8, 8, 512)\n sample_features = np.expand_dims(sample_features, axis=0).repeat(train_features.shape[0], axis=0)\n # print(sample_features.shape) # (220, 5, 8, 8, 512)\n train_features = np.expand_dims(train_features, axis=0).repeat(5, axis=0)\n # print(train_features.shape) # (5, 220, 8, 8, 512)\n train_features = np.transpose(train_features, (1, 0, 2, 3, 4))\n # print(train_features.shape) # (220, 5, 8, 8, 512)\n relation_pairs = np.concatenate((sample_features, train_features), axis=4).reshape(train_features.shape[0], -1,\n train_features.shape[2],\n train_features.shape[2])\n # print(relation_pairs.shape) # (220*5, 512*2, 5, 5)==(1100, 1024, 8, 8)\n ralation_label = train_label\n # print(relation_label.shape) # (220, 5)\n return relation_pairs, ralation_label\n\n\ndef LTC_BN(num_class, input_shape, norm_rate=0.0):\n \"\"\"docstring for RelationNetwork\"\"\"\n\n inputs = Input(shape=input_shape, name='input')\n\n x = _conv_act_bn(inputs, filters=16, name='RN_Conv1')\n x = _conv_act_bn(x, filters=32, name='RN_Conv2')\n x = Flatten()(x)\n x = Dense(1024, kernel_regularizer=regularizers.l2(norm_rate))(x)\n x = Activation('relu')(x)\n x = BatchNormalization()(x)\n # x = Reshape(target_shape=(-1,5))(x)\n x = Dense(num_class, kernel_regularizer=regularizers.l2(norm_rate))(x)\n predictions = Activation('sigmoid')(x)\n # = Reshape(target_shape=(220, -1))(x)\n # predictions =\n\n # predictions = Dense(num_class, activation='sigmoid', name='prediction',\n # kernel_regularizer=regularizers.l2(norm_rate))(x)\n model = Model(inputs=inputs, outputs=predictions)\n return model\n\n\nif __name__ == '__main__':\n from keras.optimizers import Adam\n\n model = LTC_BN(5, input_shape=(5, 5, 128), norm_rate=0.0)\n optimizer = Adam(1e-4)\n model.compile(optimizer=optimizer, loss='mse', metrics=['accuracy']) #\n model.summary()\n","sub_path":"pano/LTC.py","file_name":"LTC.py","file_ext":"py","file_size_in_byte":6123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"194847015","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect,HttpResponse\nfrom django.template import RequestContext, loader\nfrom django.utils import timezone\n\nfrom .models import Image,Detail\n\n\ndef index(request):\n from random import randint\n \n if request.user.is_superuser:\n import_directory()\n \n i = []\n detail = []\n bgimage = Image.objects.all()\n \n if len(bgimage) > 1:\n i = bgimage[randint(0,(len(bgimage)-1))]\n if len(i.detail_set.all()) > 0: \n detail = i.detail_set.all()[0]\n \n return render(request, 'bgrunner/index.html', {'i': i, 'd': detail})\n \n\ndef detail(request,image_id):\n \n i = Image.objects.get(pk=image_id)\n detail = []\n if len(i.detail_set.all()) > 0: \n detail = i.detail_set.all()[0]\n \n return render(request, 'bgrunner/detail.html', { 'i': i ,'d' : detail })\n \n\n# SHOULD HAVE DIFFERENT VIEWS FOR EDIT AND FOR ADD - MAYBE ALLOW COMMENT-LIKE STORIES INSTEAD OF JUST OVERVIEW? START WITH JUST OVERVIEW?\n\ndef add_detail(request,image_id):\n from django.core.urlresolvers import reverse\n \n if request.user.is_superuser:\n i = Image.objects.get(pk=image_id)\n \n if len(i.detail_set.all()) > 0:\n d = i.detail_set.all()[0]\n d.title = request.POST['title']\n d.description = request.POST['description']\n d.pub_date=i.pub_date\n d.save()\n \n else:\n i.detail_set.create(title=request.POST['title'],description=request.POST['description'],pub_date=i.pub_date)\n \n \n # return HttpResponseRedirect(reverse('bgrunner:detail', args=(image_id)))\n return render(request, 'bgrunner/detail.html', { 'i': i ,'d' : i.detail_set.all()[0] })\n \n else:\n \n return HttpResponse(\"User not authorized to edit entry: %s. Visit <a href=\\\"/admin\\\">/admin</a> to login\" % image_id)\n\ndef rotate(request,image_id):\n if request.user.is_superuser:\n from PIL import Image as pilimg\n \n amt = 270\n if request.POST['rotate_amount']:\n amt = request.POST['rotate_amount']\n\n i = Image.objects.get(pk=image_id)\n \n im = pilimg.open(i.location)\n imclone = im.rotate(float(amt))\n width, height = imclone.size\n \n newname = i.name + \"_rotate_\" + str(amt) + \"deg.jpg\"\n i.location = i.location.rsplit('/',1)[0] + newname\n i.shortcut = i.shortcut.rsplit('/',1)[0] + newname\n i.width = width\n i.height = height\n i.save()\n \n imclone.save(i.location)\n \n detail = []\n if len(i.detail_set.all()) > 0: \n detail = i.detail_set.all()[0]\n \n return render(request, 'bgrunner/detail.html', { 'i': i ,'d' : detail })\n \n \n else:\n \n return HttpResponse(\"User not authorized to edit entry: %s. Visit <a href=\\\"/admin\\\">/admin</a> to login\" % image_id)\n \n\n# THIS COULD TAKE A WHILE\ndef image_fun(request,image_id):\n if request.user.is_superuser:\n \n from PIL import Image as pilimg\n\n img = Image.objects.get(pk=image_id)\n \n i = pilimg.open(img.location)\n i = i.convert(\"RGBA\")\n \n i.convert(\"RGBA\")\n datas = i.getdata()\n\n newData = []\n for item in datas:\n if item[0] < 25 and item[1] < 20 and item[2] < 25:\n newData.append((0, 0, 0, 0))\n else:\n newData.append(item)\n\n i.putdata(newData)\n \n i.save(img.location + \"__trans.png\", \"PNG\")\n \n img.shortcut += \"__trans.png\"\n detail = []\n if len(img.detail_set.all()) > 0: \n detail = img.detail_set.all()[0]\n \n return render(request, 'bgrunner/detail.html', { 'i': img ,'d' : detail })\n\n else:\n \n return HttpResponse(\"User not authorized to edit entry: %s. Visit <a href=\\\"/admin\\\">/admin</a> to login\" % image_id)\n \n\n\n\n\n \n \n \ndef save_image(name,location,height,width,shortcut):\n i = Image(name=name, location=location, pub_date=timezone.now(), height=height, width=width, shortcut=shortcut)\n i.save()\n\ndef import_directory():\n from os import listdir\n from os.path import isfile, join\n from PIL import Image as pilimg\n path = \"/Users/geoffbooth/dev/python/django/dad_slider/bgrunner/static/bgrunner/images/\"\n images = [ f for f in listdir(path) if isfile(join(path,f)) ]\n for i in images:\n file = path + i\n im = pilimg.open(file)\n width, height = im.size\n shortcut = \"bgrunner/images/\" + i\n\n save_image(name=i,location=file,height=height,width=width,shortcut=shortcut )\n ","sub_path":"bgrunner/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"351007183","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom .models import Board, Topic, User, Post\nfrom django.http import HttpResponse\nfrom django.http import Http404\nfrom .form import NewTopicForm, NewBoardForm, PostForm\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Count\nfrom django.views.generic import UpdateView, ListView\nfrom django.utils import timezone\nfrom django.utils.decorators import method_decorator\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.urls import reverse\n\n# Create your views here.\n\n#######################\n### Contoh ListView ###\n#######################\n\n### Tanpa argument / parameter ###\nclass BoardListView(ListView) :\n model = Board\n context_object_name = 'boards'\n template_name = 'home.html'\n paginate_by = 5\n\n### dengan 1 argument / parameter ###\nclass TopicListView(ListView) :\n model = Topic\n context_object_name = 'topics'\n template_name = 'topics.html'\n paginate_by = 5\n\n def get_context_data(self, **kwargs) :\n kwargs['board'] = self.board\n return super().get_context_data(**kwargs)\n\n def get_queryset(self) :\n self.board = get_object_or_404(Board, pk=self.kwargs.get('pk'))\n queryset = self.board.topics.all()\n return queryset\n\nclass PostListView(ListView) :\n model = Post\n context_object_name = 'posts'\n template_name = 'topic_posts.html'\n paginate_by = 5\n\n def get_context_data(self, **kwargs) :\n session_key = 'viewed_topic_{}'.format(self.topic.pk)\n if not self.request.session.get(session_key, False) :\n self.topic.views += 1\n self.topic.save()\n self.request.session[session_key] = True\n\n kwargs['topic'] = self.topic\n return super().get_context_data(**kwargs)\n\n def get_queryset(self) :\n self.topic = get_object_or_404(Topic, board__pk=self.kwargs.get('pk'), pk=self.kwargs.get('t_pk'))\n queryset = self.topic.posts.order_by('created_at')\n return queryset\n\n# Contoh UpdateView #\n@method_decorator(login_required, name='dispatch')\nclass PostUpdateView(UpdateView) :\n model = Post\n fields = ['message']\n template_name = 'edit_post.html'\n pk_url_kwarg = 'p_pk'\n context_object_name = 'post'\n\n def get_queryset(self):\n queryset = super().get_queryset()\n return queryset.filter(created_by=self.request.user)\n\n def form_valid(self, form) :\n post = form.save(commit = False)\n post.updated_by = self.request.user\n post.updated_at = timezone.now()\n post.save()\n\n return redirect('topic_posts', pk=post.topic.board.pk, t_pk=post.topic.pk)\n\n@login_required\ndef topic_reply(request, pk, t_pk):\n topic = get_object_or_404(Topic, board__pk = pk, pk = t_pk)\n\n if request.method == 'POST' :\n form = PostForm(request.POST)\n if form.is_valid() :\n post = form.save(commit=False)\n post.topic = topic\n post.created_by = request.user\n post.save()\n\n topic.last_updated = timezone.now()\n topic.save()\n\n topic_url = reverse('topic_posts', kwargs={'pk': pk, 't_pk': t_pk})\n topic_post_url = '{url}?page={page}#{id}'.format(\n url=topic_url,\n id=post.pk,\n page=topic.get_page_count()\n )\n\n return redirect(topic_post_url)\n #return redirect('topic_posts', pk=pk, t_pk = t_pk)\n else :\n form = PostForm()\n\n return render(request, 'topic_reply.html', {'form': form, 'topic': topic})\n\n@login_required\ndef new_topic(request, pk):\n board = get_object_or_404(Board,pk=pk)\n\n if request.method == 'POST' :\n form = NewTopicForm(request.POST)\n if form.is_valid() :\n topic = form.save(commit=False)\n topic.board = board\n topic.starter = request.user\n topic.save()\n post = Post.objects.create(\n message=form.cleaned_data.get('message'),\n topic=topic,\n created_by=request.user\n )\n return redirect('board_topics', pk=board.pk)\n else :\n form = NewTopicForm()\n\n return render(request, 'new_topic.html', {'form': form, 'board': board})\n\n@login_required\ndef new_board(request):\n\n if request.method == 'POST' :\n form = NewBoardForm(request.POST)\n if form.is_valid() :\n board = form.save(commit=False)\n board.save()\n return redirect('home')\n else:\n form = NewBoardForm()\n\n return render(request, 'new_board.html', {'form':form})\n\ndef about(request):\n return render(request, 'about.html')\n\ndef about_company(request):\n return render(request, 'about_company.html', {'company_name': 'Simple Complex'})\n\n# obsolete views\n\"\"\"\n###############################\n## Sudah diganti ke ListView ##\n###############################\n\ndef home(request):\n boards = Board.objects.all()\n return render(request, 'home.html', {'boards': boards} )\n\ndef board_topics(request, pk):\n board = get_object_or_404(Board,pk=pk)\n queryset = board.topics.all()\n page = request.GET.get('page',1)\n paginator = Paginator(queryset,5)\n topics = paginator.page(1)\n\n try :\n topics = paginator.page(page)\n except PageNotAnInteger :\n topics = paginator.page(1)\n except EmptyPage :\n topics = paginator.page(paginator.num_pages)\n\n return render(request, 'topics.html', {'board': board, 'topics': topics})\n\ndef topic_posts(request, pk, t_pk):\n topic = get_object_or_404(Topic, board__pk=pk , pk=t_pk)\n topic.views += 1\n topic.save()\n queryset = topic.posts.all()\n\n page = request.GET.get('page',1)\n paginator = Paginator(queryset,3)\n posts = paginator.page(1)\n\n try :\n posts = paginator.page(page)\n except PageNotAnInteger :\n posts = paginator.page(1)\n except EmptyPage :\n posts = paginator.page(paginator.num_pages)\n\n return render(request, 'topic_posts.html', {'topic': topic, 'posts': posts})\n\n\"\"\"\n","sub_path":"boards/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"278339598","text":"# -*- coding: utf-8 -*-\n# author: jinwilliam\n\nfrom taskqueue import tqueue as wq\nimport time\n\nclass worker(object):\n \"\"\"worker\n \n \"\"\"\n def __init__(self):\n print('worker loaded.')\n\n def init(self):\n print('worker init.')\n wq.init()\n wq.subscribe_channel()\n return True\n \n def work(self):\n while True:\n time.sleep(0.1)\n msg = wq.pull()\n msg_channel = str(msg[1], encoding='utf-8')\n msg_content = str(msg[2], encoding='utf-8')\n if msg_channel == 'pypipe_task':\n print(msg_content)\n else:\n print(msg_channel)\n \n\n\nworker = worker()\n\nif __name__ == '__main__':\n worker.init()\n worker.work()\n","sub_path":"core/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"195071383","text":"# -*- coding: utf-8 -*-\n\nfrom benedict import benedict\n\nimport pickle\nimport unittest\n\n\nclass pickle_test_case(unittest.TestCase):\n\n def test_pickle(self):\n\n d = {\n 'a': {},\n 'b': { 'x': 1 },\n 'c': [],\n 'd': [0, 1],\n 'e': 0.0,\n 'f': '',\n 'g': None,\n 'h': '0'\n }\n\n b = benedict(d, keypath_separator='/')\n b_encoded = pickle.dumps(b)\n # print(b_encoded)\n\n b_decoded = pickle.loads(b_encoded)\n # print(b_decoded)\n # print(b_decoded.keypath_separator)\n self.assertTrue(isinstance(b_decoded, benedict))\n self.assertEqual(b_decoded.keypath_separator, b.keypath_separator)\n self.assertEqual(b_decoded, b)\n","sub_path":"tests/dicts/io/test_pickle.py","file_name":"test_pickle.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"507586148","text":"import torch\nimport argparse\nfrom apex.fp16_utils import FP16_Optimizer\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--local_rank\", default=0, type=int)\nargs = parser.parse_args()\n\ntorch.cuda.set_device(args.local_rank)\ntorch.distributed.init_process_group(backend='nccl',\n init_method='env://')\n\ntorch.backends.cudnn.benchmark = True\n\nN, D_in, D_out = 64, 1024, 16\n\nx = torch.randn(N, D_in, device='cuda', dtype=torch.half)\ny = torch.randn(N, D_out, device='cuda', dtype=torch.half)\n\nmodel = torch.nn.Linear(D_in, D_out).cuda().half()\nmodel = torch.nn.parallel.DistributedDataParallel(model,\n device_ids=[args.local_rank],\n output_device=args.local_rank)\n\noptimizer = torch.optim.SGD(model.parameters(), lr=1e-3)\n### Construct FP16_Optimizer ###\noptimizer = FP16_Optimizer(optimizer)\n###\n\nloss_fn = torch.nn.MSELoss()\n\nfor t in range(500):\n optimizer.zero_grad()\n y_pred = model(x)\n loss = loss_fn(y_pred.float(), y.float())\n ### Change loss.backward() to: ###\n optimizer.backward(loss)\n ###\n optimizer.step()\n\nprint(\"final loss = \", loss)\n\n","sub_path":"examples/deprecated_api/FP16_Optimizer_simple/distributed_pytorch/distributed_data_parallel.py","file_name":"distributed_data_parallel.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"386181366","text":"# -*- coding UTF-8 -*-\n\nimport urllib.request\n\nmmurl = \"http://mm.taobao.com/json/request_top_list.htm?type=0&page=\"\n\nSUCC = 0\nFAIL = 0\n\ndef getPicUrl(htmlurl,num):\n global SUCC , FAIL\n i = 0\n flg = 1\n tmpa = 0\n tmpb = 0\n while flg == 1:\n try:\n percontHTML = urllib.request.urlopen(htmlurl).read().decode('GBK')\n pica = '''src=\"http://img0'''\n picb = \".jpg\"\n tmpa = percontHTML.find(pica,tmpb)\n tmpb = percontHTML.find(picb,tmpa)\n imgurl = percontHTML[tmpa + 5:tmpb + 4]\n #print(imgurl)\n if imgurl == \"\":\n flg = 0\n print(\"Photo\" , num , \"Down_Done\")\n else:\n i += 1\n print(\"Downloading\" , num , \"Girl\" , i , \"Photo\")\n urllib.request.urlretrieve(imgurl,\"beautiful\\\\\" + str(num) + \"-\" + str(i) + \".jpg\")\n SUCC += 1\n except:\n print(\"Fail，Server Stop!\")\n FAIL += 1\n pass\n\n\ndef getPageUrl(mmurl):\n i = 1\n j = 1\n pa = 0\n pb = 0\n hreflist = []\n while i < 3:\n url = mmurl + str(i)\n #print(url)\n cont = urllib.request.urlopen(url).read().decode('GBK')\n diva = '''<div class=\"pic s60\">'''\n divb = '</div>'\n while j<11:\n pa = cont.find(diva, pb)#div截取\n pb = cont.find(divb, pa)\n divcont = cont[pa:pb]\n #print(divcont)\n hrefa = \"<a href=\"\n hrefb = \".htm\"\n aa = divcont.find(hrefa)#url截取\n ab = divcont.find(hrefb)\n acont = divcont[aa + 9:ab + 4]\n hreflist.append(acont)\n #print(acont)\n j += 1\n j = 1\n pa = 0\n pb = 0\n print(\"Downloading\" + str(i) + \"Photo_url\")\n i += 1\n return hreflist\n\nif __name__ == \"__main__\":\n global SUCC , FAIL\n hreflist = getPageUrl(mmurl)\n print(\"Total\", len(hreflist))\n for i in range(0,len(hreflist)):\n num = i + 1\n print(\"Start Download\" , num , \"Girl\")\n test = hreflist[i]\n getPicUrl(test,num)\n print(\"Success：\" , SUCC , \"****Fail：\" , FAIL)\n","sub_path":"0830/taobao.url.py","file_name":"taobao.url.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"534307760","text":"from rethinkdb import RethinkDB\nfrom time import time as epoch\n\n\nclass DataBaseManager:\n\tdef __init__(self, client):\n\t\tself.r = RethinkDB()\n\t\tself.connection = self.r.connect(\n\t\t\tport=28015,\n\t\t\thost=\"localhost\"\n\t\t)\n\t\t# Initiate database connection\n\t\t\n\t\tself.client = client\n\t\t# Connection to discord\n\t\n\tdef get_table(self, table):\n\t\t# Returns the request table as a Cursor object, creating if not\n\t\t# exists\n\t\t\n\t\tt_list = self.r.db(\"tfr\").table_list().run(self.connection)\n\t\t# A list of all the table names\n\t\t\n\t\tif table in t_list:\n\t\t\t# Table exists, return that\n\t\t\treturn self.r.db(\"tfr\").table(table)\n\t\t\n\t\t# Create the table, it doesn't exist\n\t\tself.r.db(\"tfr\").table_create(table).run(self.connection)\n\t\t\n\t\t# Return the newly created table\n\t\treturn self.r.db(\"tfr\").table(table)\n\t\n\tdef get_prefix(self, guild):\n\t\t# Returns the prefix for the guild with given id\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure the guild (id) is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\tguild_conf = list(\n\t\t\tself.get_table(\"guilds\").filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t)\n\t\t# Return all objects in \"guilds\" table with matching guild id\n\t\t# Utilising __iter__ function to convert the result into a list\n\t\t\n\t\tif guild_conf:\n\t\t\t# An object matching the guild id was found\n\t\t\tguild_conf = guild_conf[0]\n\t\t# Select the first match\n\t\t\n\t\tif guild_conf and \"prefix\" in guild_conf.keys():\n\t\t\t# Object found and it contains a prefix, return that\n\t\t\treturn guild_conf[\"prefix\"]\n\t\t\n\t\tif not guild_conf:\n\t\t\t# Object not found, create an entry for this guild\n\t\t\tself.get_table(\"guilds\").insert(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"prefix\": self.client.config.default_prefix\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t\n\t\telse:\n\t\t\t# Object found but doesn't contain a prefix, update it to\n\t\t\t# contain the default prefix\n\t\t\tself.get_table(\"guilds\").get(guild).update(\n\t\t\t\t{\n\t\t\t\t\t\"prefix\": self.client.config.default_prefix\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t\n\t\t# Return default prefix\n\t\treturn self.client.config.default_prefix\n\t\n\tdef update_prefix(self, guild, new_prefix):\n\t\t# Updates prefix for guild with given id to new_prefix\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\t(\n\t\t\tself.get_table(\"guilds\") # Connect to guilds table\n\t\t\t.filter(\n\t\t\t\t# Filter to return objects only matching guild (id)\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t)\n\t\t\t.update( # Update all matches to the new prefix\n\t\t\t\t{\n\t\t\t\t\t\"prefix\": new_prefix\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\n\tdef add_xp(self, guild, member, amount):\n\t\t# Adds {amount} xp to {member} in {guild}\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\tif not isinstance(member, str):\n\t\t\t# Ensure member is a string\n\t\t\tmember = str(member)\n\t\t\n\t\ttable = self.get_table(\"xp\")\n\t\t\n\t\tuser_xp = list(\n\t\t\ttable.filter(\n\t\t\t\t# Filter all entries in the \"xp\" table to return only\n\t\t\t\t# those with matching guild (id) and member (id) as\n\t\t\t\t# below\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"user\": member\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t)\n\t\t# Utilise __iter__ function to convert the result into a list\n\t\t\n\t\tif user_xp:\n\t\t\t# Some match was found\n\t\t\t\n\t\t\tuser_xp = user_xp[0]\n\t\t\t# Select first match only\n\t\t\t\n\t\t\tprevious = user_xp[\"xp\"]\n\t\t\t# The XP of the user before adding the new xp\n\t\t\t\n\t\t\tself.get_table(\"xp\").get(user_xp[\"id\"]).update(\n\t\t\t\t# Update user in the database, using the id of the\n\t\t\t\t# object returned to update only the correct user\n\t\t\t\t{\n\t\t\t\t\t\"xp\": previous + amount, # Add XP\n\t\t\t\t\t\"cooldown\": (\n\t\t\t\t\t\tepoch() + self.client.config.xp_cooldown\n\t\t\t\t\t) * 1000 # Update cooldown\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t\t\n\t\t\treturn previous + amount # Return new XP total\n\t\t\n\t\telse:\n\t\t\t# No entry found in XP table matching user and guild,\n\t\t\t# create one\n\t\t\ttable.insert(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild, # Add the guild id\n\t\t\t\t\t\"user\": member, # Add the user id\n\t\t\t\t\t\"xp\": amount, # Add the total xp\n\t\t\t\t\t\"cooldown\": (\n\t\t\t\t\t\tepoch() + self.client.config.xp_cooldown\n\t\t\t\t\t) * 1000 # Add the cooldown\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t\t\n\t\t\treturn amount\n\t\n\tdef xp_level(self, guild, member):\n\t\t# Returns the xp level (as an int) of the given member\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\tif not isinstance(member, str):\n\t\t\t# Ensure member is a string\n\t\t\tmember = str(member)\n\t\t\n\t\tlevel = 1\n\t\t# Start at level 1\n\t\t\n\t\tresult = list(\n\t\t\tself.get_table(\"xp\").filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"user\": member\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t) # Attempt to find the users xp in the database\n\t\t\n\t\tif result:\n\t\t\t# If some user(s) were found, select the first only\n\t\t\tresult = result[0]\n\t\telse:\n\t\t\t# If no database entry with their xp was found, return\n\t\t\t# the starting level (1)\n\t\t\treturn level\n\t\t\n\t\txp = result[\"xp\"]\n\t\t\n\t\t# Logic adapted from Nadeko source levelling code for\n\t\t# consistency\n\t\t\n\t\tbase_xp = 36\n\t\ttotal_xp = 0\n\t\tlvl = 1\n\t\t\n\t\twhile True:\n\t\t\trequired = (base_xp + base_xp / 4.0 * (lvl - 1))\n\t\t\t\n\t\t\tif required + total_xp > xp:\n\t\t\t\tbreak\n\t\t\t\n\t\t\ttotal_xp += required\n\t\t\tlvl += 1\n\t\t\n\t\treturn lvl - 1\n\t\n\tdef in_xp_cooldown(self, member, guild):\n\t\t# Returns whether or not the member is currently on cooldown\n\t\t# to earn more xp\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\tif not isinstance(member, str):\n\t\t\t# Ensure member is a string\n\t\t\tmember = str(member)\n\t\t\n\t\t# Attempt to find user in the database and then turn the\n\t\t# result into a python list\n\t\tresult = list(\n\t\t\tself.get_table(\"xp\").filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"user\": member\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\t\n\t\tif not result:\n\t\t\t# User not found in db\n\t\t\treturn False\n\t\t\n\t\tif \"cooldown\" not in result[0]:\n\t\t\t# Somehow not in their db entry, returning False will\n\t\t\t# cause them to gain xp, which will add a cooldown entry\n\t\t\t# if not one exists\n\t\t\treturn False\n\t\t\n\t\treturn result[0][\"cooldown\"] >= epoch() * 1000\n\t\n\tdef get_xp(self, member, guild):\n\t\t# Returns the total xp for {member} in {guild} as an int\n\t\t\n\t\txp_db = list(\n\t\t\tself.get_table(\"xp\").filter(\n\t\t\t\t{\n\t\t\t\t\t# Filter all entries in the \"xp\" table to only\n\t\t\t\t\t# those which match the guild and member id\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"member\": member\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t)\n\t\t# Convert to a python list so its easy to select the first\n\t\t# result\n\t\t\n\t\treturn {\n\t\t\t\"xp\": xp_db[0][\"xp\"] if xp_db else 0,\n\t\t\t# If a db entry exists, return the xp that contains,\n\t\t\t# if not, return 0\n\t\t\t\"level\": self.xp_level(\n\t\t\t\txp_db[0][\"xp\"] if xp_db else 0, member\n\t\t\t)\n\t\t\t# Automatically generate the level using the same method\n\t\t\t# and return that also\n\t\t}\n\t\n\tdef add_qotd(self, question, thought, fact, guild, author):\n\t\t# Adds the information for a QOTD to the database\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\tif not isinstance(author, str):\n\t\t\t# Ensure author is a string\n\t\t\tauthor = str(author)\n\t\t\n\t\t(\n\t\t\tself.get_table(\"qotd\")\n\t\t\t.insert(\n\t\t\t\t# Insert QOTD dictionary into \"qotd\" table\n\t\t\t\t{\n\t\t\t\t\t\"question\": question,\n\t\t\t\t\t\"thought\": thought,\n\t\t\t\t\t\"fact\": fact,\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"author\": author\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t)\n\t\n\tdef get_qotd(self, guild):\n\t\t# Returns all qotd stored for the given guild\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\treturn list(\n\t\t\tself.get_table(\"qotd\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t)\n\t\t\t# Return all entries in \"qotd\" table matching the guild id\n\t\t\t.run(self.connection)\n\t\t) # Convert to a list for ease of use\n\t\n\tdef remove_qotd(self, qotd_id):\n\t\t# Removes the QOTD of given id from the database\n\t\t\n\t\t(\n\t\t\tself.get_table(\"qotd\")\n\t\t\t.get(qotd_id)\n\t\t\t.delete()\n\t\t\t.run(self.connection)\n\t\t)\n\t\n\tdef get_qotd_channel(self, guild):\n\t\t# Returns the id of the QOTD output channel for the given\n\t\t# guild, or None if not found\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\n\t\tconfig = list(\n\t\t\tself.get_table(\"guilds\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\t\n\t\tif len(config) == 0:\n\t\t\treturn None\n\t\t# Not really sure how this could happen,\n\t\t# A config entry should be created on the message event\n\t\t# before a command is ran if no prefix entry exists\n\t\t\n\t\tconfig = config[0]\n\t\t\n\t\tif \"qotd_channel\" in config.keys():\n\t\t\treturn config[\"qotd_channel\"]\n\t\t\n\t\treturn None\n\t\n\tdef set_qotd_channel(self, guild, channel):\n\t\t# Sets the QOTD output channel for the given guild\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\t\n\t\tif not isinstance(channel, str):\n\t\t\t# Ensure channel is a string\n\t\t\tchannel = str(channel)\n\t\t\n\t\t(\n\t\t\tself.get_table(\"guilds\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": str(guild)\n\t\t\t\t}\n\t\t\t)\n\t\t\t.update(\n\t\t\t\t{\n\t\t\t\t\t\"qotd_channel\": str(channel)\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\n\tdef set_qotd_role(self, guild, role):\n\t\t# Sets the role which will be mentioned when a QOTD is sent\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\t\n\t\tif not isinstance(role, str):\n\t\t\t# Ensure role is a string:\n\t\t\trole = str(role)\n\t\t\t\n\t\t(\n\t\t\tself.get_table(\"guilds\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": str(guild)\n\t\t\t\t}\n\t\t\t)\n\t\t\t.update(\n\t\t\t\t{\n\t\t\t\t\t\"qotd_role\": str(role)\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\n\tdef get_qotd_role(self, guild):\n\t\t# Returns the role to be mentioned when a QOTD is sent,\n\t\t# should it exist else None\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\t\n\t\tconfig = list(\n\t\t\tself.get_table(\"guilds\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\t\n\t\tif len(config) == 0:\n\t\t\treturn None\n\t\t# Not really sure how this could happen,\n\t\t# A config entry should be created on the message event\n\t\t# before a command is ran if no prefix entry exists\n\t\t\n\t\tconfig = config[0]\n\t\t\n\t\tif \"qotd_role\" in config.keys():\n\t\t\treturn config[\"qotd_role\"]\n\t\t\n\t\treturn None\n\t\n\tdef get_all_qotd(self, guild):\n\t\t# Returns a list of all the QOTD for the guild in dictionary\n\t\t# form\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\treturn list(\n\t\t\tself.get_table(\"qotd\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\n\tdef qotd_exists(self, qotd, guild):\n\t\t# Checks a QOTD entry exists with the id {qotd} and it is\n\t\t# \"owned\" by the correct guild\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\tres = (\n\t\t\tself.get_table(\"qotd\")\n\t\t\t.get(qotd)\n\t\t\t.run(self.connection)\n\t\t)\n\t\t\n\t\treturn res and res[\"guild\"] == guild\n\t\n\tdef get_filters(self, guild, channel=\"\"):\n\t\t# Returns all filters for the given guild, and channel if\n\t\t# specified\n\t\t\n\t\tmatch = {\n\t\t\t\"guild\": str(guild),\n\t\t\t\"type\": \"text\"\n\t\t}\n\t\t\n\t\tif channel:\n\t\t\tmatch[\"channel\"] = str(channel)\n\t\t\n\t\treturn list(\n\t\t\tself.get_table(\"filters\")\n\t\t\t.filter(\n\t\t\t\tmatch\n\t\t\t)\n\t\t\t.run(\n\t\t\t\tself.connection\n\t\t\t)\n\t\t)\n\t\n\tdef insert_punishment(self, **options):\n\t\t# Creates a new punishment entry\n\t\t\n\t\tself.get_table(\"punishments\").insert(\n\t\t\toptions\n\t\t).run(self.connection)\n\t\n\tdef get_img_filters(self, guild):\n\t\t# Returns all image filters stored for the given guild as a list\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\treturn list(\n\t\t\tself.get_table(\"filters\").filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"type\": \"image\"\n\t\t\t\t}\n\t\t\t).run(\n\t\t\t\tself.connection\n\t\t\t)\n\t\t)\n\t\n\tdef add_text_filter(self, punish, regex, channel, reason, guild):\n\t\t# Adds a text filter (Regular Expression) to the given guild\n\t\t# and channel\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\tif not isinstance(channel, str):\n\t\t\t# Ensure channel is a string\n\t\t\tchannel = str(channel)\n\t\t\n\t\t(\n\t\t\tself.get_table(\"filters\")\n\t\t\t.insert(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"action\": punish,\n\t\t\t\t\t\"filter\": regex,\n\t\t\t\t\t\"channel\": channel,\n\t\t\t\t\t\"reason\": reason,\n\t\t\t\t\t\"type\": \"text\"\n\t\t\t\t}\n\t\t\t).run(self.connection)\n\t\t)\n\t\n\tdef filter_exists(self, guild, f_id, f_type):\n\t\t# Returns True if the filter being searched for exists,\n\t\t# is of the correct type and for the correct guild else False\n\t\t\n\t\treturn len(\n\t\t\tlist(\n\t\t\t\tself.get_table(\"filters\")\n\t\t\t\t.filter(\n\t\t\t\t\t{\n\t\t\t\t\t\t\"guild\": (\n\t\t\t\t\t\t\tguild if isinstance(guild, str) else\n\t\t\t\t\t\t\tstr(guild)\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\"id\": f_id,\n\t\t\t\t\t\t\"type\": f_type\n\t\t\t\t\t}\n\t\t\t\t)\n\t\t\t\t.run(self.connection)\n\t\t\t)\n\t\t) > 0\n\t\n\tdef insert_img_filter(\n\t\t\tself, guild, img, ignore_colour, reason,\n\t\t\tname, punishment\n\t\t):\n\t\t# Inserts an image filter to the database (b64 encoded\n\t\t# representation of image)\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\t(\n\t\t\tself.get_table(\"filters\")\n\t\t\t.insert(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"type\": \"image\",\n\t\t\t\t\t\"img\": img,\n\t\t\t\t\t\"ignore_colour\": ignore_colour,\n\t\t\t\t\t\"reason\": reason,\n\t\t\t\t\t\"name\": name,\n\t\t\t\t\t\"action\": punishment\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\n\tdef img_filter_exists(self, guild, name):\n\t\t# Return True if there is one or more entry matching this\n\t\t# name and guild\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\t\t\n\t\treturn len(\n\t\t\tlist(\n\t\t\t\tself.get_table(\"filters\")\n\t\t\t\t.filter(\n\t\t\t\t\t{\n\t\t\t\t\t\t\"guild\": str(guild),\n\t\t\t\t\t\t\"type\": \"image\",\n\t\t\t\t\t\t\"name\": name\n\t\t\t\t\t}\n\t\t\t\t)\n\t\t\t\t.run(self.connection)\n\t\t\t)\n\t\t) > 0\n\t\n\tdef get_img_filter(self, guild, f_id):\n\t\t# Returns the image filter matching guild and id\n\t\t\n\t\tif not isinstance(guild, str):\n\t\t\t# Ensure guild is a string\n\t\t\tguild = str(guild)\n\n\t\treturn list(\n\t\t\tself.get_table(\"filters\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": str(guild),\n\t\t\t\t\t\"type\": \"image\",\n\t\t\t\t\t\"id\": f_id\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\n\tdef add_custom_command(self, guild, name, description, body):\n\t\tif not isinstance(guild, str):\n\t\t\tguild = str(guild)\n\t\t\n\t\t(\n\t\t\tself.get_table(\"customs\")\n\t\t\t.insert(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"name\": name.lower(),\n\t\t\t\t\t\"description\": description,\n\t\t\t\t\t\"body\": body\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\n\tdef get_customs(self, guild):\n\t\tif not isinstance(guild, str):\n\t\t\tguild = str(guild)\n\t\t\t\n\t\treturn list(\n\t\t\tself.get_table(\"customs\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\n\tdef remove_custom(self, name, guild):\n\t\tif not isinstance(guild, str):\n\t\t\tguild = str(guild)\n\t\t\n\t\t(\n\t\t\tself.get_table(\"customs\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"name\": name.lower(),\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t)\n\t\t\t.delete()\n\t\t\t.run(self.connection)\n\t\t)\n\n\tdef custom_command_exists(self, name, guild):\n\t\tif not isinstance(guild, str):\n\t\t\tguild = str(guild)\n\t\t\n\t\treturn len(\n\t\t\tlist(\n\t\t\t\tself.get_table(\"customs\")\n\t\t\t\t.filter(\n\t\t\t\t\t{\n\t\t\t\t\t\t\"name\": name.lower(),\n\t\t\t\t\t\t\"guild\": guild\n\t\t\t\t\t}\n\t\t\t\t)\n\t\t\t\t.run(self.connection)\n\t\t\t)\n\t\t) > 0\n\n\t@staticmethod\n\tdef get(iterable, key):\n\t\tfor i in iterable:\n\t\t\tif key(i):\n\t\t\t\treturn i\n\t\treturn None\n\n\tdef get_guild_currency(self, guild):\n\t\tif not isinstance(guild, str):\n\t\t\tguild = str(guild)\n\n\t\treturn list(\n\t\t\tself.get_table(\"currency\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\n\tdef get_currency(self, guild, member):\n\t\tif not isinstance(guild, str):\n\t\t\tguild = str(guild)\n\t\t\t\n\t\tif not isinstance(member, str):\n\t\t\tmember = str(member)\n\t\t\t\n\t\tguild_cur = self.get_guild_currency(guild)\n\t\t\n\t\tmember_cur = self.get(\n\t\t\tguild_cur,\n\t\t\tlambda m: m[\"member\"] == member\n\t\t)\n\t\t\n\t\tif not guild_cur or not member_cur:\n\t\t\tblank = {\n\t\t\t\t\"guild\": guild,\n\t\t\t\t\"member\": member,\n\t\t\t\t\"cur\": 0,\n\t\t\t\t\"gamblingSuspended\": False\n\t\t\t}\n\t\t\t\n\t\t\treturn (\n\t\t\t\tself.get_table(\"currency\")\n\t\t\t\t.insert(\n\t\t\t\t\tblank,\n\t\t\t\t\treturn_changes=True\n\t\t\t\t)\n\t\t\t\t.run(self.connection)\n\t\t\t)[\"changes\"][0][\"new_val\"]\n\t\t\t\n\t\treturn member_cur\n\n\tdef update_member_cur(self, guild, member, new_amount):\n\t\told_value = self.get_currency(guild, member)\n\t\t\n\t\t(\n\t\t\tself.get_table(\"currency\")\n\t\t\t.get(old_value[\"id\"])\n\t\t\t.update(\n\t\t\t\t{\n\t\t\t\t\t\"cur\": new_amount\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n\t\t\n\tdef is_gambling_suspended(self, guild, member):\n\t\treturn (\n\t\t\tself.get_currency(guild, member)\n\t\t\t.get(\"gamblingSuspended\", False)\n\t\t)\n\n\tdef get_shop_items(self, guild, item_type):\n\t\tif not isinstance(guild, str):\n\t\t\tguild = str(guild)\n\t\t\t\n\t\treturn list(\n\t\t\tself.get_table(\"shop\")\n\t\t\t.filter(\n\t\t\t\t{\n\t\t\t\t\t\"guild\": guild,\n\t\t\t\t\t\"type\": item_type.lower()\n\t\t\t\t}\n\t\t\t)\n\t\t\t.run(self.connection)\n\t\t)\n","sub_path":"Utils/DataBaseManager.py","file_name":"DataBaseManager.py","file_ext":"py","file_size_in_byte":16341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"312382296","text":"import unittest\nimport logging\nfrom nba_percolator import Percolator\n\nclass CreateTestCase(unittest.TestCase):\n\n source = 'specimen'\n config = {\n 'elastic': {\n 'host': 'elasticsearch'\n },\n 'paths': {\n 'incoming': '/shared-data/incoming',\n 'processed': '/shared-data/processed',\n 'jobs': '/shared-data/jobs',\n 'failed': '/shared-data/failed',\n 'done': '/shared-data/done',\n 'delta': '/shared-data/incremental'\n },\n 'sources':\n {\n 'specimen':\n {\n 'table': 'testspecimen',\n 'id': 'id',\n 'enrich': True,\n 'code': 'XC',\n 'incremental': False,\n 'path': '/shared-data/test'\n }\n },\n 'postgres':\n {\n 'host': 'postgres',\n 'user': 'postgres',\n 'pass': 'postgres',\n 'db': 'ppdb'\n }\n }\n\n def __init__(self, *args, **kwargs):\n super(CreateTestCase, self).__init__(*args, **kwargs)\n logger = logging.getLogger('percolator')\n logger.setLevel(logging.ERROR)\n self.pp = Percolator(config=self.config)\n self.pp.set_source(self.source)\n try:\n self.pp.generate_mapping(create_tables=True)\n except:\n pass\n\n def test_create_delete_record(self):\n recordID = \"test123\"\n status = 'REJECTED'\n deleteRecord = self.pp.create_delete_record(self.source, recordID, status)\n\n self.assertIsInstance(deleteRecord, dict)\n self.assertIsNotNone(deleteRecord.get('sourceSystemCode'))\n self.assertEqual(deleteRecord.get('unitID'), recordID)\n self.assertEqual(deleteRecord.get('status'), status)\n\n def test_create_name_summary(self):\n vernacularName = {\n 'test': False,\n 'name': 'vernacularName',\n 'language': 'NL',\n 'other': 'not important',\n 'forgetit': 'removed'\n }\n\n nameSummary = self.pp.create_name_summary(vernacularName)\n self.assertIsInstance(nameSummary, dict)\n self.assertEqual(nameSummary.get('name'), vernacularName.get('name'))\n self.assertEqual(nameSummary.get('language'), vernacularName.get('language'))\n self.assertIsNone(nameSummary.get('other'))\n\n def test_create_scientific_summary(self):\n scientificName = {\n 'test': False,\n 'other': 'not important',\n 'forgetit': 'removed',\n 'fullScientificName': 'fullScientificName',\n 'taxonomicStatus': 'taxonomicStatus',\n 'genusOrMonomial': 'genusOrMonomial',\n 'subgenus': 'subgenus',\n 'specificEpithet': 'specificEpithet',\n 'infraspecificEpithet': 'infraspecificEpithet',\n 'authorshipVerbatim': 'authorshipVerbatim'\n }\n\n scientificSummary = self.pp.create_scientific_summary(scientificName)\n self.assertIsInstance(scientificSummary, dict)\n self.assertEqual(scientificSummary.get('fullScientificName'), scientificName.get('fullScientificName'))\n self.assertEqual(scientificSummary.get('subgenus'), scientificName.get('subgenus'))\n self.assertIsNone(scientificSummary.get('other'))\n\n def test_create_enrichment(self):\n vernacularName = {\n 'test': False,\n 'name': 'vernacularName',\n 'language': 'NL',\n 'other': 'not important',\n 'forgetit': 'removed'\n }\n scientificName = {\n 'test': False,\n 'other': 'not important',\n 'forgetit': 'removed',\n 'fullScientificName': 'fullScientificName',\n 'taxonomicStatus': 'taxonomicStatus',\n 'genusOrMonomial': 'genusOrMonomial',\n 'subgenus': 'subgenus',\n 'specificEpithet': 'specificEpithet',\n 'infraspecificEpithet': 'infraspecificEpithet',\n 'authorshipVerbatim': 'authorshipVerbatim'\n }\n rec = {\n 'id': 'TEST123',\n 'sourceSystem': {\n 'code': 'TEST'\n },\n 'acceptedName': {\n 'scientificNameGroup': 'scientificNameGroup'\n },\n 'defaultClassification': 'test',\n 'vernacularNames': [vernacularName],\n 'synonyms': [scientificName]\n }\n enrichment = self.pp.create_enrichment(rec, 'test')\n\n self.assertIsInstance(enrichment, dict)\n self.assertIsNotNone(enrichment.get('taxonId'))\n self.assertIsNotNone(enrichment.get('synonyms'))\n self.assertIsNotNone(enrichment.get('sourceSystem'))\n self.assertIsNotNone(enrichment.get('sourceSystem').get('code'))\n self.assertIsNone(enrichment.get('defaultClassification'))\n\n def test_create_col_enrichment(self):\n vernacularName = {\n 'test': False,\n 'name': 'vernacularName',\n 'language': 'NL',\n 'other': 'not important',\n 'forgetit': 'removed'\n }\n scientificName = {\n 'test': False,\n 'other': 'not important',\n 'forgetit': 'removed',\n 'fullScientificName': 'fullScientificName',\n 'taxonomicStatus': 'taxonomicStatus',\n 'genusOrMonomial': 'genusOrMonomial',\n 'subgenus': 'subgenus',\n 'specificEpithet': 'specificEpithet',\n 'infraspecificEpithet': 'infraspecificEpithet',\n 'authorshipVerbatim': 'authorshipVerbatim'\n }\n rec = {\n 'id': 'TEST123',\n 'sourceSystem': {\n 'code': 'COL'\n },\n 'acceptedName': {\n 'scientificNameGroup': 'scientificNameGroup'\n },\n 'defaultClassification': 'test',\n 'vernacularNames': [vernacularName],\n 'synonyms': [scientificName]\n }\n enrichment = self.pp.create_enrichment(rec, 'test')\n self.assertIsNotNone(enrichment.get('defaultClassification'))\n\n def test_cache_taxon(self):\n systemCode = 'XC'\n rec = [{\n 'acceptedName': {\n 'scientificNameGroup': 'TEST'\n },\n 'id': 'test123'\n }]\n\n self.pp.cache_taxon_record(rec, systemCode)\n\n taxon = self.pp.get_taxon('TEST', self.source)\n self.assertIsNotNone(taxon)\n self.assertIsInstance(taxon, dict)\n self.assertEqual(taxon.get('id'), rec.get('id'))\n\n","sub_path":"tests/create_test.py","file_name":"create_test.py","file_ext":"py","file_size_in_byte":6644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"571792920","text":"#!/usr/bin/env python\n\"\"\"Example: 2-4\nContrast with listcomp_tshirts Ex 2-4\"\"\"\n\ncolors = ['black', 'white']\nsizes = ['S', 'M', 'L']\n\n# listcomp:\n# tshirts = [(color, size) for color in colors for size in sizes]\n\nfor tshirt in ('%s %s' % (c, s) for c in colors for s in sizes):\n print(tshirt)\n","sub_path":"02_04_genexps_tshirts.py","file_name":"02_04_genexps_tshirts.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"374244418","text":"# Python 3.7.4 64-bit | Qt 5.9.6 | PyQt5 5.9.2 | Windows 10\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: Kanak Choudhury\r\n\"\"\"\r\n\r\npath = 'Kanak_lab1\\\\';\r\n\r\nimport numpy as np\r\nimport re\r\nfrom sklearn.metrics import confusion_matrix\r\n\r\n# function to classify new data\r\n\r\ndef classify(indx, total_class, vocab_len, dt, prob, condi_prob):\r\n a1 = np.zeros([1,vocab_len]);\r\n a2 = np.where(dt[:,0]==indx+1)[0];\r\n a1[0,dt[a2,1]-1] = dt[a2,2];\r\n res = np.argmax(np.transpose(np.log(prob)) + sum(\r\n np.transpose(a1*np.log(np.transpose(condi_prob)))))+1;\r\n return int(res);\r\n\r\n# data reading\r\n\r\nprint(\" Reading Data...\")\r\ntr = open(path+'train_data.csv', 'r').read().splitlines();\r\ndim_tr = np.shape(tr)[0];\r\n\r\nts = open(path+'test_data.csv', 'r').read().splitlines();\r\ndim_ts = np.shape(ts)[0];\r\n\r\ntr_lb = open(path+'train_label.csv', 'r').read().splitlines();\r\ndim_tr_lb = np.shape(tr_lb)[0];\r\n\r\nts_lb = open(path+'test_label.csv', 'r').read().splitlines();\r\ndim_ts_lb = np.shape(ts_lb)[0];\r\n\r\nvocab = open(path+'vocabulary.txt', 'r').read().splitlines();\r\ndim_vocab = np.shape(vocab)[0];\r\n\r\nmaxdim = max(dim_tr, dim_ts, dim_tr_lb, dim_ts_lb)\r\n\r\n\r\ntr_dt=np.zeros([dim_tr,3]);\r\nts_dt=np.zeros([dim_ts,3]);\r\ntr_dt_lb=np.zeros([dim_tr_lb]);\r\nts_dt_lb=np.zeros([dim_ts_lb]);\r\n\r\nfor i in range(0,dim_tr):\r\n if i < dim_tr:\r\n vec_tr = re.split(r'\\W+', tr[i])\r\n tr_dt[i,:]=vec_tr;\r\n\r\n if i < dim_ts:\r\n vec_ts = re.split(r'\\W+', ts[i])\r\n ts_dt[i,:]=vec_ts;\r\n\r\n if i < dim_tr_lb:\r\n vec_tr_lb = re.split(r'\\W+', tr_lb[i])\r\n tr_dt_lb[i]=vec_tr_lb[0];\r\n\r\n if i < dim_ts_lb:\r\n vec_ts_lb = re.split(r'\\W+', ts_lb[i])\r\n ts_dt_lb[i]=vec_ts_lb[0];\r\ntr_dt = tr_dt.astype(int)\r\nts_dt = ts_dt.astype(int)\r\ntr_dt_lb = tr_dt_lb.astype(int)\r\nts_dt_lb = ts_dt_lb.astype(int)\r\n\r\n\r\ndel i, tr, vec_tr\r\ndel ts, vec_ts\r\ndel tr_lb, vec_tr_lb\r\ndel ts_lb, vec_ts_lb\r\n\r\n\r\n# Prior Probability\r\nomega = open(path+'map.csv', 'r').read().splitlines();\r\ndim_omega = np.shape(omega)[0]\r\n\r\nnclass_tr=[tr_dt_lb.tolist().count(i+1) for i in range(0,dim_omega)]\r\n\r\npriorl_prob = [nclass_tr[i]/dim_tr_lb for i in range(0,dim_omega)];\r\n\r\nnclass_ts=[ts_dt_lb.tolist().count(i+1) for i in range(0,dim_omega)]\r\n\r\nfor i in range(0,dim_omega):\r\n print(\" P(omega = %2d) = %.4f\" %(i+1,priorl_prob[i]))\r\n\r\ncond_prob = np.zeros([dim_vocab,dim_omega]);\r\ncont_vec = np.zeros([dim_vocab,dim_omega]);\r\n\r\n\r\n# Conditional Probability\r\nprint(\"Conditional Probablities...\")\r\nfor j in range(0,dim_omega):\r\n b1 = np.where(tr_dt_lb == j+1)[0];\r\n for i in range(0,dim_tr):\r\n if tr_dt[i,0] in b1:\r\n cont_vec[tr_dt[i,1]-1,j] = cont_vec[tr_dt[i,1]-1,j] + tr_dt[i,2];\r\n total_count = sum(cont_vec[:,j]);\r\n cond_prob[:,j] = (cont_vec[:,j] + 1)/(total_count + dim_vocab);\r\n\r\ndel b1, total_count\r\n\r\n# traing Data Classification\r\nprint(\"\\n Classifying Train Data...\")\r\ntr_pred = np.zeros(dim_tr_lb);\r\nfor k in range(0,dim_tr_lb):\r\n tr_pred[k] = classify(k, dim_omega, dim_vocab, tr_dt, priorl_prob, cond_prob)\r\n\r\n\r\ntr_conf_mat = confusion_matrix(tr_dt_lb, tr_pred)\r\nprint(\" Confusion matrix for train data-\")\r\nprint(tr_conf_mat)\r\nprint(\"\\n Overall accuracy for train data = %.4f\" %(np.trace(tr_conf_mat)/dim_tr_lb))\r\nfor i in range(0,dim_omega):\r\n print(\" Class accuracy for group %.2d = %.4f\" %(i+1,tr_conf_mat[i,i]/nclass_tr[i]))\r\n\r\n\r\n# Test Data Classification\r\nprint(\"\\n Classifying Test Data...\")\r\nts_pred = np.zeros(dim_ts_lb);\r\nfor k in range(0,dim_ts_lb):\r\n ts_pred[k] = classify(k, dim_omega, dim_vocab, ts_dt, priorl_prob, cond_prob)\r\n\r\nts_conf_mat = confusion_matrix(ts_dt_lb, ts_pred)\r\nprint(\" Confusion matrix for test data-\")\r\nprint(ts_conf_mat)\r\nprint(\"\\n Overall accuracy for test data = %.4f\" %(np.trace(ts_conf_mat)/dim_ts_lb))\r\nfor i in range(0,dim_omega):\r\n print(\" Class accuracy for group %.2d = %.4f\" %(i+1,ts_conf_mat[i,i]/nclass_ts[i]))","sub_path":"Lab1/Kanak_NaiveBayes.py","file_name":"Kanak_NaiveBayes.py","file_ext":"py","file_size_in_byte":3912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"303869505","text":"from __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.utils.data\nimport torch.nn.functional as F\nimport math\nfrom utils.model_utils import *\nfrom utils.ri_utils import *\nfrom models.vrcnet import Linear_ResBlock\n\nclass local_decoder(nn.Module):\n def __init__(self, knn=32, dilation=2, mlp=[64, 128, 128], mlp_merge=[512, 1024, 1024], input_channel=3):\n super(local_decoder, self).__init__()\n self.knn = knn\n self.dilation = dilation\n \n self.mlp1 = nn.Sequential(nn.Conv2d(input_channel, mlp[0], kernel_size=1),\n nn.ReLU(),\n nn.Conv2d(mlp[0], mlp[1], kernel_size=1),\n nn.ReLU(),\n nn.Conv2d(mlp[1], mlp[2], kernel_size=1))\n self.mlp2 = nn.Sequential(nn.Conv2d(input_channel, mlp[0], kernel_size=1),\n nn.ReLU(),\n nn.Conv2d(mlp[0], mlp[1], kernel_size=1),\n nn.ReLU(),\n nn.Conv2d(mlp[1], mlp[2], kernel_size=1))\n self.mlp_merge = nn.Sequential(nn.Conv1d(2*mlp[2], mlp_merge[0], kernel_size=1),\n nn.ReLU(),\n nn.Conv1d(mlp_merge[0], mlp_merge[1], kernel_size=1),\n nn.ReLU(),\n nn.Conv1d(mlp_merge[1], mlp_merge[2], kernel_size=1))\n\n def forward(self, points, features=None):\n batch_size, _, num_points = points.size()\n \n if features is None:\n points_knn = get_edge_features(points, knn(points, self.knn))\n points_knn = points_knn - points.unsqueeze(2).repeat(1,1,self.knn,1)\n \n points_knn_d = get_edge_features(points, dilated_knn(points, self.knn, self.dilation))\n points_knn_d = points_knn_d - points.unsqueeze(2).repeat(1,1,self.knn,1)\n \n a1, a2, a3, points_knn_d = acenn_rir_feature(points_knn_d.transpose(1,3).contiguous(),\n points.transpose(1,2).contiguous())\n _, _, _, points_knn = point_projection_feature(points_knn.transpose(1,3).contiguous(), a1, a2, a3)\n \n points_knn = points_knn.transpose(1,3).contiguous()\n points_knn_d = points_knn_d.transpose(1,3).contiguous()\n else:\n points_knn = get_edge_features(features, knn(points, self.knn))\n points_knn_d = get_edge_features(features, dilated_knn(points, self.knn, self.dilation))\n \n points_knn = self.mlp1(points_knn)\n points_knn_d = self.mlp2(points_knn_d)\n \n local_feature_knn, _ = torch.max(points_knn, 2)\n local_feature_knn_d, _ = torch.max(points_knn_d, 2)\n\n local_feature = torch.cat((local_feature_knn, local_feature_knn_d), 1)\n local_feature = self.mlp_merge(local_feature)\n return local_feature\n \n \nclass RIEncoder(nn.Module):\n def __init__(self, input_size=3, d_model=1024, num_coarse=1024, n_head=1, dropout=0.0):\n super(RIEncoder, self).__init__()\n self.num_coarse = num_coarse\n self.d_model = d_model\n self.n_head = n_head\n self.dropout = dropout\n self.conv1 = nn.Conv1d(input_size, 128, 1)\n self.conv2 = nn.Conv1d(128, 256, 1)\n self.conv3 = nn.Conv1d(512, 512, 1)\n self.conv4 = nn.Conv1d(512, d_model, 1)\n \n self.fc_qkv = nn.Linear(d_model, d_model * 3)\n self.attn = nn.MultiheadAttention(self.d_model, self.n_head, self.dropout)\n self.ln = nn.LayerNorm(d_model)\n self.fcs1 = nn.Linear(d_model, d_model)\n self.fcs2 = nn.Linear(d_model, 1)\n \n self.fc1 = nn.Linear(d_model, d_model)\n self.fc2 = nn.Linear(d_model, d_model)\n self.fc3 = nn.Linear(d_model, self.num_coarse * 3)\n\n def forward(self, points):\n batch_size, _, num_points = points.size()\n div_batch_size = batch_size // 8\n x = F.relu(self.conv1(points))\n x = self.conv2(x)\n global_feature, _ = torch.max(x, 2)\n \n x = torch.cat((x, global_feature.view(batch_size, -1, 1).repeat(1, 1, num_points).contiguous()), 1)\n x = F.relu(self.conv3(x))\n x = self.conv4(x)\n global_feature, _ = torch.max(x, 2)\n global_feature = global_feature.view(batch_size, -1)\n global_feature_tup = global_feature.chunk(8, dim=0)\n global_feature = torch.stack(global_feature_tup)\n \n qkv = self.fc_qkv(global_feature)\n q, k, v = torch.split(qkv, self.d_model, dim=2)\n multi_attn, _ = self.attn(q, k, v)\n sel = self.ln(multi_attn + global_feature)\n sel = F.softmax(self.fcs2(self.fcs1(sel)))\n global_feature = (sel * global_feature).sum(dim=0, keepdim=False)\n _, inx = sel.squeeze().max(dim=0, keepdim=False)\n \n coarse = F.relu(self.fc1(global_feature))\n coarse = F.relu(self.fc2(coarse))\n coarse = self.fc3(coarse).view(-1, 3, self.num_coarse)\n \n return global_feature, coarse, inx\n\n\nclass RIDecoder(nn.Module):\n def __init__(self, num_coarse, num_fine, scale, global_feature_size, local_feature_size, k=32, d=2):\n super(RIDecoder, self).__init__()\n self.num_coarse = num_coarse\n self.num_fine = num_fine\n self.local_feature_size = local_feature_size\n self.cat_feature_num = 2 + 3 + global_feature_size + local_feature_size\n\n self.scale = scale\n self.grid = gen_grid_up(2 ** (int(math.log2(scale))), 0.05).cuda().contiguous()\n self.conv1 = nn.Conv1d(self.cat_feature_num, 512, 1)\n self.conv2 = nn.Conv1d(512, 512, 1)\n self.conv3 = nn.Conv1d(512, 3, 1)\n \n self.decoder = local_decoder(k, d)\n\n def forward(self, global_feat, sampled_points):\n batch_size = sampled_points.size()[0]\n local_feat = self.decoder(sampled_points)\n\n grid = self.grid.clone().detach()\n grid_feat = grid.unsqueeze(0).repeat(batch_size, 1, self.num_coarse).contiguous().cuda()\n \n local_feat = torch.cat((sampled_points, local_feat), 1)\n\n point_feat = ((local_feat.transpose(1, 2).contiguous()).unsqueeze(2).repeat(1, 1, self.scale, 1).view(-1, self.num_fine, 3+self.local_feature_size)).transpose(1, 2).contiguous()\n\n global_feat = global_feat.unsqueeze(2).repeat(1, 1, self.num_fine)\n\n feat = torch.cat((grid_feat, point_feat, global_feat), 1)\n\n center = ((sampled_points.transpose(1, 2).contiguous()).unsqueeze(2).repeat(1, 1, self.scale, 1).view(-1, self.num_fine, 3)).transpose(1, 2).contiguous()\n\n fine = self.conv3(F.relu(self.conv2(F.relu(self.conv1(feat))))) + center\n return fine\n \n\nclass Model(nn.Module):\n def __init__(self, args, global_feature_size=1024, local_feature_size=1024, k=32, d=2):\n super(Model, self).__init__()\n\n self.input_size = args.input_size\n self.num_coarse = args.num_coarse\n self.num_points = args.num_points\n self.train_loss = args.loss\n self.scale = self.num_points // self.num_coarse\n\n self.encoder = RIEncoder(3, global_feature_size, self.num_coarse)\n self.decoder = RIDecoder(self.num_coarse, self.num_points, self.scale, global_feature_size, local_feature_size, k, d)\n\n def forward(self, x, gt, is_training=True, mean_feature=None, alpha=None):\n batch_size = x.size()[0]\n a1, a2, a3, x = point_projection_feature(x.transpose(1, 2).contiguous(), method='pca')\n x = x.transpose(1, 2).contiguous()\n org_point_input = x\n \n x0, x1, x2 = x.chunk(3, dim=1)\n cloud_1 = x\n #cloud_1 = torch.cat([x0, x1, x2], dim=1)\n cloud_2 = torch.cat([x0, x1, -x2], dim=1)\n cloud_3 = torch.cat([x0, -x1, x2], dim=1)\n cloud_4 = torch.cat([x0, -x1, -x2], dim=1)\n cloud_5 = torch.cat([-x0, x1, x2], dim=1)\n cloud_6 = torch.cat([-x0, x1, -x2], dim=1)\n cloud_7 = torch.cat([-x0, -x1, x2], dim=1)\n cloud_8 = torch.cat([-x0, -x1, -x2], dim=1)\n points = torch.cat([cloud_1, cloud_2, cloud_3, cloud_4,\n cloud_5, cloud_6, cloud_7, cloud_8], dim=0)\n \n feat, out1, inx = self.encoder(points)\n \n points = torch.cat((out1, org_point_input), 2)\n _, sampled_points = furthest_point_sampling(points.transpose(1,2).contiguous(), \n self.num_coarse)\n sampled_points = sampled_points.transpose(1,2).contiguous()\n \n out2 = self.decoder(feat, sampled_points)\n \n for i in range(batch_size):\n if inx[i] >= 4:\n a1[i] *= -1\n if inx[i] % 4 >= 2:\n a2[i] *= -1\n if inx[i] % 2 == 1:\n a3[i] *= -1\n \n out1 = inverse_point_projection_feature(a1, a2, a3, out1.transpose(1,2).contiguous())\n out2 = inverse_point_projection_feature(a1, a2, a3, out2.transpose(1,2).contiguous())\n\n if is_training:\n if self.train_loss == 'emd':\n loss1 = calc_emd(out1, gt)\n loss2 = calc_emd(out2, gt)\n elif self.train_loss == 'cd':\n loss1, _ = calc_cd(out1, gt)\n loss2, _ = calc_cd(out2, gt)\n else:\n raise NotImplementedError('Train loss is either CD or EMD!')\n\n total_train_loss = loss1.mean() + loss2.mean() * alpha\n return out2, loss2, total_train_loss\n else:\n #emd = calc_emd(out2, gt, eps=0.004, iterations=3000)\n cd_p, cd_t, f1 = calc_cd(out2, gt, calc_f1=True)\n return {'out1': out1, 'out2': out2, 'cd_p': cd_p, 'cd_t': cd_t, 'f1': f1}\n \n ","sub_path":"models/rinet_v2.py","file_name":"rinet_v2.py","file_ext":"py","file_size_in_byte":9904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"224097146","text":"# Copyright 2016 ClusterHQ Inc. See LICENSE file for details.\n\"\"\"\nOperation to create a container.\n\"\"\"\n\nfrom functools import partial\nfrom datetime import timedelta\nfrom uuid import UUID, uuid4\n\nfrom pyrsistent import PClass, field\nfrom zope.interface import implementer\n\nfrom flocker.apiclient import MountedDataset\nfrom flocker.common import gather_deferreds, loop_until, timeout as _timeout\nfrom flocker.control import DockerImage\n\nfrom benchmark._interfaces import IProbe, IOperation\nfrom benchmark.operations._common import select_node\n\n\nDEFAULT_TIMEOUT = timedelta(minutes=10)\n\n\ndef loop_until_state_found(reactor, get_states, state_matches, timeout):\n \"\"\"\n Loop until a state has been reached.\n\n :param get_states: Callable returning a Deferred firing with a list\n of states.\n :param state_matches: Callable that accepts a state parameter, and\n returns a boolean indicating whether the state matches.\n :param timedelta timeout: Maximum time to wait for state to be found.\n :return Deferred[Any]: The matching state.\n \"\"\"\n def state_reached():\n d = get_states()\n\n def find_match(states):\n for state in states:\n if state_matches(state):\n return state\n return None\n d.addCallback(find_match)\n\n return d\n\n d = loop_until(reactor, state_reached)\n _timeout(reactor, d, timeout.total_seconds())\n return d\n\n\ndef create_dataset(\n reactor, control_service, node_uuid, dataset_id, volume_size,\n timeout=DEFAULT_TIMEOUT\n):\n \"\"\"\n Create a dataset, then wait for it to be mounted.\n\n :param IReactorTime reactor: Twisted Reactor.\n :param IFlockerAPIV1Client control_service: Benchmark control\n service.\n :param UUID node_uuid: Node on which to create dataset.\n :param UUID dataset_id: ID for created dataset.\n :param int volume_size: Size of volume in bytes.\n :param timedelta timeout: Maximum time to wait for dataset to be\n mounted.\n :return Deferred[DatasetState]: The state of the created dataset.\n \"\"\"\n\n d = control_service.create_dataset(\n primary=node_uuid,\n maximum_size=volume_size,\n dataset_id=dataset_id,\n )\n\n def dataset_matches(dataset, state):\n return (\n state.dataset_id == dataset.dataset_id and\n state.primary == dataset.primary and\n state.path is not None\n )\n\n d.addCallback(\n lambda dataset: loop_until_state_found(\n reactor, control_service.list_datasets_state,\n partial(dataset_matches, dataset), timeout\n )\n )\n\n return d\n\n\ndef create_container(\n reactor, control_service, node_uuid, name, image, volumes=None,\n timeout=DEFAULT_TIMEOUT\n):\n \"\"\"\n Create a container, then wait for it to be running.\n\n :param IReactorTime reactor: Twisted Reactor.\n :param IFlockerAPIV1Client control_service: Benchmark control\n service.\n :param UUID node_uuid: Node on which to start the container.\n :param unicode name: Name of the container.\n :param DockerImage image: Docker image for the container.\n :param Optional[Sequence[MountedDataset]] volumes: Volumes to attach\n to the container.\n :param timedelta timeout: Maximum time to wait for container to be\n created.\n :return Deferred[ContainerState]: The state of the created container.\n \"\"\"\n\n d = control_service.create_container(node_uuid, name, image, volumes)\n\n def container_matches(container, state):\n return (\n container.name == state.name and\n container.node_uuid == state.node_uuid and\n state.running\n )\n\n d.addCallback(\n lambda container: loop_until_state_found(\n reactor, control_service.list_containers_state,\n partial(container_matches, container), timeout\n )\n )\n\n return d\n\n\ndef delete_container(reactor, control_service, container):\n \"\"\"\n Delete a container, then wait for it to be removed.\n\n :param IReactorTime reactor: Twisted Reactor.\n :param IFlockerAPIV1Client control_service: Benchmark control\n service.\n :param ContainerState container: Container to be removed.\n :return Deferred[ContainerState]: The state before removal.\n \"\"\"\n\n def container_removed(expected):\n \"\"\"\n Check whether a container has been removed (deleted and stopped).\n\n :param ContainerState expected: A container state to match against the\n results of ``list_containers_state``.\n :return Deferred[Optional[ContainerState]]: ``None`` if the\n ``expected`` container is found, or ``expected`` if it is not\n found.\n \"\"\"\n d = control_service.list_containers_state()\n\n def container_matches(inspecting, expected):\n return (\n expected.name == inspecting.name and\n expected.node_uuid == inspecting.node_uuid and\n inspecting.running\n )\n\n def no_running_match(existing_state):\n for state in existing_state:\n if container_matches(state, expected):\n return None\n return expected\n d.addCallback(no_running_match)\n return d\n\n d = control_service.delete_container(container.name)\n\n def loop_until_container_removed(_ignore):\n return loop_until(reactor, partial(container_removed, container))\n d.addCallback(loop_until_container_removed)\n\n return d\n\n\n@implementer(IProbe)\nclass CreateContainerProbe(PClass):\n \"\"\"\n Probe to create a container and wait for cluster to converge.\n \"\"\"\n\n reactor = field(mandatory=True)\n control_service = field(mandatory=True)\n node_uuid = field(type=UUID, mandatory=True)\n name = field(type=unicode, mandatory=True)\n image = field(mandatory=True)\n dataset_id = field(type=UUID, mandatory=True)\n mountpoint = field(type=unicode, mandatory=True)\n\n @classmethod\n def setup(\n cls, reactor, control_service, name, image, volume_size, mountpoint\n ):\n \"\"\"\n Create a probe.\n\n :param IReactorTime reactor: Twisted Reactor.\n :param IFlockerAPIV1Client control_service: Benchmark control service.\n :param unicode name: Name for created container.\n :param DockerImage image: Docker image for the container.\n :param int volume_size: Size of created volume, in bytes.\n :param unicode mountpoint: Mountpoint for created volume.\n :return: Deferred firing with a new probe.\n \"\"\"\n # Select an arbitrary node on which to create the container.\n d = control_service.list_nodes().addCallback(select_node)\n\n def parallel_setup(node):\n # Ensure the Docker image is cached by starting and stopping a\n # container.\n name = unicode(uuid4())\n container_setup = create_container(\n reactor, control_service, node.uuid, name, image\n )\n container_setup.addCallback(\n partial(delete_container, reactor, control_service)\n )\n\n # Create the dataset\n dataset_id = uuid4()\n dataset_setup = create_dataset(\n reactor, control_service, node.uuid, dataset_id, volume_size\n )\n\n d = gather_deferreds((container_setup, dataset_setup))\n\n # Return only the dataset state\n d.addCallback(lambda results: results[1])\n\n return d\n d.addCallback(parallel_setup)\n\n # Create the CreateContainerProbe instance.\n def create_probe(dataset_state):\n return cls(\n reactor=reactor,\n control_service=control_service,\n node_uuid=dataset_state.primary,\n name=name,\n image=image,\n dataset_id=dataset_state.dataset_id,\n mountpoint=mountpoint,\n )\n d.addCallback(create_probe)\n\n return d\n\n def run(self):\n \"\"\"\n Create a stateful container, and wait for it to be running.\n \"\"\"\n volumes = [\n MountedDataset(\n dataset_id=self.dataset_id, mountpoint=self.mountpoint\n )\n ]\n\n d = create_container(\n self.reactor, self.control_service, self.node_uuid, self.name,\n self.image, volumes\n )\n\n return d\n\n def cleanup(self):\n \"\"\"\n Delete the container and dataset created by the probe.\n \"\"\"\n d = self.control_service.delete_container(self.name)\n\n d.addCallback(\n lambda _ignore: self.control_service.delete_dataset(\n self.dataset_id\n )\n )\n\n return d\n\n\n@implementer(IOperation)\nclass CreateContainer(object):\n\n def __init__(\n self, reactor, cluster, image=u'clusterhq/mongodb', volume_size=None,\n mountpoint=u'/data'\n ):\n self.reactor = reactor\n self.control_service = cluster.get_control_service(reactor)\n self.image = DockerImage(repository=image)\n if volume_size is None:\n self.volume_size = cluster.default_volume_size()\n else:\n self.volume_size = volume_size\n self.mountpoint = mountpoint\n\n def get_probe(self):\n return CreateContainerProbe.setup(\n self.reactor,\n self.control_service,\n unicode(uuid4()),\n self.image,\n self.volume_size,\n self.mountpoint,\n )\n","sub_path":"benchmark/operations/create_container.py","file_name":"create_container.py","file_ext":"py","file_size_in_byte":9555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"226197807","text":"#================================Params.py=====================================#\n# Created by Ciaran O'Hare 2019\n\n# Description:\n# This file just sets up some of the parameters that are used throughout the\n# project. and some classes that link things together.\n\n#==============================================================================#\n\nfrom __future__ import print_function\nfrom numpy import array, sqrt, pi, exp, interp, loadtxt, zeros, shape, ones\nfrom numpy import logspace, linspace, log10\nfrom scipy.special import erf, erfi\n\n\n# Constants\nm_p = 0.9315*1e6\nm_p_keV = 0.9315*1e6\nm_e = 511.0 # keV\nc_m = 2.99792458e8 # speed of light in m/s\nc_cm = c_m*100.0 # speed of light in cm/s\nc_km = c_m/1000.0 # speed of light in km/s\nGeV_2_kg = 1.0e6*1.783e-33 # convert GeV to kg\nalph = 1.0/137.0 # fine structure constant\nm_p_kg = 1.660538782e-27 # amu in kg\na0 = 0.268173 # Bohr radius keV^-1\nN_A = 6.02214e23 # Avocado's constant\nsinTheta_Wsq = 0.2387e0 # sin^2(Theta_W) weinberg angle\nG_F_GeV = 1.16637e-5 # GeV**-2 ! Fermi constan in GeV\nJan1 = 2458849.5 # January 1st 2020\nseconds2year = 365.25*3600*24\n\n\n#==============================================================================#\n# Set Nucleus params\nclass Atom:\n def __init__(self,xi,N,Z,J,Sp,Sn,fion,E_B_vals,E_gap, eps, Vfactor):\n self.IsotopicFraction = xi\n self.NumberOfNeutrons = N\n self.NumberOfProtons = Z\n self.MassNumber = N+Z\n self.NuclearSpin = J\n self.ExpProtonSpin = Sp\n self.ExpNeutronSpin = Sp\n if J>0.0:\n self.SDEnhancement = (4.0/3.0)*((J+1.0)/J)*(Sp-Sn)**2.0\n self.IonisationFormFactor = fion\n self.BindingEnergies = E_B_vals\n self.BandGapEnergy = E_gap\n self.ElectronHoleMeanEnergy = eps\n self.VCellFactor = Vfactor\n#==============================================================================#\n\n\n\n\n\n\n\n\n#==============================================================================#\n# Set parameters of halo models and streams\nclass Halo:\n def __init__(self,rho_0,v_LSR,sig_v,v_esc,v_pec,beta,eta):\n self.LocalDensity = rho_0\n self.RotationSpeed = v_LSR\n self.Dispersion = sig_v\n self.EscapeSpeed = v_esc\n self.PeculiarVelocity = v_pec\n self.Normalisation = erf(v_esc/(sqrt(2.0)*sig_v))-\\\n sqrt(2.0/pi)*(v_esc/sig_v)*\\\n exp(-v_esc**2.0/(2.0*sig_v**2.0))\n\n self.SausageEta = eta\n if eta>0.0:\n self.SausageBeta = beta\n sigr=sqrt(3*v_LSR**2.0/(2.0*(3-2.0*beta)))\n sigphi=sqrt(3*v_LSR**2.0*(1-beta)/(2.0*(3-2.0*beta)))\n sigz=sqrt(3*v_LSR**2.0*(1-beta)/(2.0*(3-2.0*beta)))\n self.SausageDispersionTensor = array([sigr,sigphi,sigz])\n self.Normalisation = erf(v_esc/(sqrt(2.0)*sigr)) \\\n - sqrt((1.0-beta)/beta)\\\n *exp(-v_esc**2.0/(2.0*sigphi**2.0))\\\n *erfi(v_esc/(sqrt(2)*sigr)*sqrt(beta/(1-beta)))\n\n# Standard Halo Model (old parameters)\nSHM = Halo(0.3,\n 220.0,\n 156.0,\n 544.0,\n array([11.1,12.2,7.3]),\n 0.0,\n 0.0)\n\n# Standard Halo Model++\nSHMpp = Halo(0.55,\n 233.0,\n 164.8,\n 528.0,\n array([11.1,12.2,7.3]),\n 0.9,\n 0.2)\n\n####\n\nclass Stream:\n def __init__(self,v1,v2,v3,sig1,sig2,sig3):\n self.Velocity = array([v1,v2,v2])\n self.Dispersion = array([sig1,sig2,sig3])\n\nS1stream = Stream(-29.6,-297.4,-72.8,82.6, 26.9, 58.5)\nS2stream = Stream(6.0, 166.7, -242.8,48.6, 13.5, 26.0)\n#S2stream_b = Stream(-70.9, 153.3, 161.5, 83.9, 29.6, 71.5)\n#==============================================================================#\n\n\n\n\n\n\n\n\n\n\n#==============================================================================#\n# Current number of neutrinos sources:\nn_nu_tot = 15\n# Neutrino files names:\nnufile_root = \".txt\"\nnufile_dir = \"../data/neutrinos/\"\nnuname = [\"\" for x in range(0,n_nu_tot)]\nnuname[0] = \"pp\"\nnuname[1] = \"pep\"\nnuname[2] = \"hep\"\nnuname[3] = \"7Be1\"\nnuname[4] = \"7Be2\"\nnuname[5] = \"8B\"\nnuname[6] = \"13N\"\nnuname[7] = \"15O\"\nnuname[8] = \"17F\"\nnuname[9] = \"DSNB\"\nnuname[10] = \"Atm\"\nnuname[11] = \"GeoU\"\nnuname[12] = \"GeoTh\"\nnuname[13] = \"GeoK\"\nnuname[14] = \"Reactor\"\nn_Enu_vals = 1000\n# Mark which neutrinos are monochromatic\nmono = zeros(n_nu_tot,dtype=bool)\nmono[[1,3,4]] = True\n\n# Set which neutrinos are Solar\nwhichsolar = zeros(n_nu_tot,dtype=bool)\nwhichsolar[0:8] = True\n\n# Neutrino max energies (MeV):\nNuMaxEnergy = array([0.42341,1.44,18.765,0.3843,0.8613,16.34,1.193,\\\n 1.7285,1.7365,91.201,981.75\n ,4.54,2.33,1.3572,\\\n 1.1418e1])\n\n# Neutrino fluxes (cm-2 s-1 MeV-1) and uncertainties (%):\n# (from Vinyoles et al (2017) Barcelona GS98 SSM)\nNuFlux = array([5.98e10,1.44e8,7.98e3,4.93e8,4.50e9,5.16e6,\\\n 2.78e8,2.05e8,5.29e6,85.7,10.54,\\\n 3808776.91874,3352686.94783,21639789.2056,\\\n 208537.673299])\nNuUnc = array([0.006, 0.01, 0.3,0.06, 0.06, 0.02, 0.15 ,\\\n 0.17 ,0.2 ,0.5, 0.25,\\\n 0.2,0.257,0.168,\\\n 0.08])\n\n# Collect neutrino parameters:\nclass Neutrinos:\n def __init__(self,n_nu,solar_label,energies,fluxes,\\\n normlisations,uncertainties):\n self.Flux = fluxes\n self.Energy = energies\n self.Uncertainties = uncertainties*normlisations\n self.Normalisations = normlisations\n self.NumberOfNeutrinos = n_nu\n self.SolarLabel = solar_label\n\n def RecoilDistribution(self,RD):\n self.RD = RD\n#==============================================================================#\n\n\n\n#==============================================================================#\n# Location class only has latitude and longitude at the moment\nclass Location:\n def __init__(self,lat,lon):\n self.Latitude = lat\n self.Longitude = lon\n\nBoulby = Location(54.5591,0.8310)\nGranSasso = Location(42.4691, 13.5654)\nKamioka = Location(36.2381, 137.1863)\nSNOlab = Location(46.4719, -81.1868)\nStawell = Location(-37.0576, 142.7754)\nOahu = Location(21.4389, -158.0001)\nGuantanamoBay = Location(20.0117, -75.1216)\nPyongyang = Location(39.0392, 125.7625)\n#------------------------------------------------------------------------------#\n","sub_path":"erec/Params.py","file_name":"Params.py","file_ext":"py","file_size_in_byte":6404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"390697828","text":"from flask import redirect, render_template, url_for, abort, request\nfrom flask.views import MethodView\nfrom flask_login import current_user, login_required\nfrom flask_wtf import FlaskForm\nfrom wtforms.validators import InputRequired, Length, ValidationError\n\nfrom wtforms import SubmitField, HiddenField\nfrom app.models.information import Information\nfrom app.models.coupon import Coupon, COUPON_STATUS\nfrom app.models.order import Order, ORDER_STATUS\nfrom app.models.user import User\n\nfrom bson.objectid import ObjectId\nimport datetime\n\nclass CouponView(MethodView):\n def get(self):\n form = CouponForm()\n if request.args.get('status') == str(COUPON_STATUS[\"ALL\"]):\n coupons = list(Information.objects(user_id=current_user.id).first().coupon)\n for i in range(len(coupons) - 1, -1, -1):\n if coupons[i].status == COUPON_STATUS[\"EXPIRED\"] or Order.objects(buyer_id=current_user.id, coupon_id=coupons[i].id, status__ne=ORDER_STATUS[\"CANCEL\"]).first() != None:\n del coupons[i]\n status = COUPON_STATUS[\"ALL\"]\n else:\n coupons = Coupon.objects(id__nin=[coupon.id for coupon in Information.objects(user_id=current_user.id).first().coupon],\n begin_time__lte=datetime.datetime.utcnow()+datetime.timedelta(hours=8), status=COUPON_STATUS[\"ACTIVE\"])\n status = None\n\n return render_template('user/coupon/list.html', status=status, coupons=coupons, form=form, COUPON_STATUS=COUPON_STATUS)\n def post(self):\n form = CouponForm()\n\n if form.validate_on_submit():\n information = Information.objects(user_id=current_user.id).first()\n coupon = Coupon.objects(id=form.coupon_id.data).first()\n information.coupon.append(coupon)\n information.save()\n\n return redirect(url_for('user.coupon', status=COUPON_STATUS[\"ALL\"]))\n\n return self.get()\n\ndef validate_coupon(form, coupon_id):\n coupon = Coupon.objects(id=coupon_id.data, begin_time__lte=datetime.datetime.utcnow()+datetime.timedelta(hours=8), status=COUPON_STATUS[\"ACTIVE\"]).first()\n\n if coupon == None:\n raise ValidationError('此優惠券不存在')\n elif Information.objects(user_id=current_user.id, coupon__contain=coupon).first() != None:\n raise ValidationError('此優惠券已兌換')\n\nclass CouponForm(FlaskForm):\n coupon_id = HiddenField(\"\",validators=[InputRequired(), validate_coupon])\n submit = SubmitField('提交')","sub_path":"app/views/user/coupon/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":2498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"76982970","text":"from collections import defaultdict\n\nwith open(\"input\") as fp:\n lines = [line for line in fp]\n\n\ndef fIndex():\n index = {}\n for line in lines:\n parts = line.split()\n outer = \" \".join(parts[:2])\n i = 2\n inner = {}\n while i < len(parts):\n if parts[i].isdigit():\n inner[\" \".join(parts[i+1:i+3])] = int(parts[i])\n i += 3\n else:\n i += 1\n index[outer] = inner\n return index\n\n\ndef rIndex():\n forwardIndex = fIndex()\n reverseIndex = defaultdict(set)\n for k, v in forwardIndex.items():\n for bag in v.keys():\n reverseIndex[bag].add(k)\n\n return reverseIndex\n\n\ndef part1():\n output = set()\n reverseIndex = rIndex()\n\n def count(bag):\n bags = reverseIndex.get(bag)\n if not bags:\n return\n for b in bags:\n output.add(b)\n count(b)\n\n count(\"shiny gold\")\n print(len(output))\n\n\ndef part2():\n output = 0\n forwardIndex = fIndex()\n\n def count(bag):\n if not forwardIndex[bag]:\n return 0\n c = 0\n for k, v in forwardIndex.get(bag).items():\n c += v + (count(k) * v)\n return c\n\n for bag, c in forwardIndex.get(\"shiny gold\").items():\n output += c + (count(bag) * c)\n\n print(output)\n\n\npart1()\npart2()\n","sub_path":"2020/day07/day07.py","file_name":"day07.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"245763864","text":"#!/usr/bin/env python\n\"\"\"\nThis is a simple script to download an appropriate updated copy of\nFabber to use in the pipeline, prior to the release of FSL 6.0.4\n\"\"\"\nimport os\nimport sys\nimport stat\n\nimport requests\n\ndef main():\n if len(sys.argv) != 2:\n print(\"Usage: get_updated_fabber <destination dir>\")\n sys.exit(1)\n\n destdir = sys.argv[1]\n if not destdir.rstrip(\"/\").endswith(\"bin\"):\n destdir = os.path.join(destdir, \"bin\")\n\n if sys.platform == \"linux\":\n print(\"Using Linux executable built under Ubuntu 18.04.\")\n print(\"This may work for other binary compatible Linux distros\")\n print(\"including recent Centos\")\n url = \"https://github.com/ibme-qubic/fabber_models_asl/releases/download/v2.0.3/fabber_asl_ubuntu18\"\n elif sys.platform == \"darwin\":\n print(\"Using Linux executable built for Mac OSX\")\n url = \"https://github.com/ibme-qubic/fabber_models_asl/releases/download/v2.0.3/fabber_asl_mac\"\n else:\n print(\"Unsupported platform: %s - cannot download Fabber\" % sys.platform)\n sys.exit(1)\n\n print(\"Downloading %s\" % url)\n os.makedirs(destdir, exist_ok=True)\n src = requests.get(url, allow_redirects=True)\n dest = os.path.join(destdir, \"fabber_asl\")\n with open(dest, \"wb\") as dest_file:\n dest_file.write(src.content)\n os.chmod(dest, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)\n print(\"Downloaded fabber to %s\" % dest)\n\n # Check the executable actually works\n retcode=os.system(\"%s --version\" % dest)\n if retcode != 0:\n print(\"ERROR: downloaded executable did not run correctly - check if your platform is compatible\")\n sys.exit(1)\n print(\"Executable ran successfully\")\n fabberdir = os.path.abspath(destdir.rstrip(\"/\").rstrip(\"bin\").rstrip(\"/\"))\n print(\"To use in the HCP-ASL pipeline add the option --fabberdir=%s\" % fabberdir)\n\nif __name__ == '__main__':\n main()","sub_path":"scripts/get_updated_fabber.py","file_name":"get_updated_fabber.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"555995944","text":"__author__ = \"Markus Pichler\"\n__credits__ = [\"Markus Pichler\"]\n__maintainer__ = \"Markus Pichler\"\n__email__ = \"markus.pichler@tugraz.at\"\n__version__ = \"0.1\"\n__license__ = \"MIT\"\n\nimport warnings\nfrom math import floor\nfrom os import path, mkdir\nfrom webbrowser import open as show_file\nfrom scipy.optimize import newton\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\nfrom .arg_parser import heavy_rain_parser\nfrom .calculation_methods import get_u_w, get_parameters, calculate_u_w, depth_of_rainfall\nfrom .little_helpers import minutes_readable, height2rate, delta2min, rate2height\nfrom .definitions import *\nfrom .in_out import import_series, write_yaml, read_yaml\nfrom .sww_utils import (remove_timezone, guess_freq, rain_events, agg_events, event_duration,\n resample_rain_series, rain_bar_plot, IdfError, )\nfrom .plot_helpers import idf_bar_axes\nfrom .additional_scripts import measured_points\n\n\n########################################################################################################################\nclass IntensityDurationFrequencyAnalyse:\n \"\"\"\n heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012)\n\n This program reads the measurement data of the rainfall\n and calculates the distribution of the rainfall as a function of the return period and the duration\n \n for duration steps up to 12 hours (and more) and return period in a range of '0.5a <= T_n <= 100a'\n \"\"\"\n\n def __init__(self, series_kind=PARTIAL, worksheet=DWA, extended_durations=False):\n \"\"\"\n heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012)\n\n This program reads the measurement data of the rainfall\n and calculates the distribution of the rainfall as a function of the return period and the duration\n\n for duration steps up to 12 hours (and more) and return period in a range of '0.5a <= T_n <= 100a'\n\n Args:\n series_kind (str): ['partial', 'annual']\n worksheet (str): ['DWA-A_531', 'ATV-A_121', 'DWA-A_531_advektiv']\n extended_durations (bool): add [720, 1080, 1440, 2880, 4320, 5760, 7200, 8640] minutes to the calculation\n \"\"\"\n self.series_kind = series_kind\n self.worksheet = worksheet\n\n self._series = None # type: pd.Series # rain time-series\n self._freq = None # frequency of the rain series\n\n self._parameter = None # how to calculate the idf curves\n self._return_periods_frame = None # type: pd.DataFrame # with return periods of all given durations\n self._rain_events = None\n\n # sampling points of the duration steps in minutes\n self._duration_steps = [5, 10, 15, 20, 30, 45, 60]\n self._duration_steps += [i * 60 for i in [1.5, 3, 4.5, 6, 7.5, 10, 12]] # duration steps in hours\n if extended_durations:\n self._duration_steps += [i * 60 * 24 for i in\n [0.75, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0]] # duration steps in days\n\n # __________________________________________________________________________________________________________________\n @property\n def series(self):\n if self._series is None:\n raise IdfError('No Series defined for IDF-Analysis!')\n return self._series\n\n @series.setter\n def series(self, series):\n self._series = series\n\n def set_series(self, series):\n \"\"\"\n set the series for the analysis\n\n Args:\n series (pandas.Series): precipitation time-series\n \"\"\"\n if not isinstance(series, pd.Series):\n raise IdfError('The series has to be a pandas Series.')\n\n if not isinstance(series.index, pd.DatetimeIndex):\n raise IdfError('The series has to have a DatetimeIndex.')\n\n if series.index.tz is not None:\n series = remove_timezone(series)\n\n series = series.replace(0, np.NaN).dropna()\n self._freq = guess_freq(series.index)\n freq_minutes = delta2min(self._freq)\n self.duration_steps = list(filter(lambda d: d >= freq_minutes, self.duration_steps))\n self.series = series\n\n # __________________________________________________________________________________________________________________\n @property\n def duration_steps(self):\n \"\"\"\n get duration steps (in minutes) for the parameter calculation and basic evaluations\n Returns:\n list | numpy.ndarray: duration steps in minutes\n \"\"\"\n if self._duration_steps is None:\n raise IdfError('No Series defined for IDF-Analysis!')\n return self._duration_steps\n\n @duration_steps.setter\n def duration_steps(self, durations):\n \"\"\"\n set duration steps (in minutes) for the parameter calculation and basic evaluations\n Args:\n durations (list | numpy.ndarray): duration steps in minutes\n \"\"\"\n if not isinstance(durations, (list, np.ndarray)):\n raise IdfError('Duration steps have to be {} got \"{}\"'.format((list, np.ndarray), type(durations)))\n self._duration_steps = durations\n\n # __________________________________________________________________________________________________________________\n @property\n def parameters(self):\n \"\"\"\n get the calculation parameters\n\n calculation method depending on the used worksheet and on the duration\n also the parameters for each method\n\n to save some time and save the parameters with\n :func:`IntensityDurationFrequencyAnalyse.write_parameters`\n and read them later with :func:`IntensityDurationFrequencyAnalyse.read_parameters`\n\n Returns:\n list[dict]: calculation parameters\n \"\"\"\n if self._parameter is None:\n interim_results = calculate_u_w(self.series, self.duration_steps, self.series_kind)\n self._parameter = get_parameters(interim_results, self.worksheet)\n return self._parameter\n\n def write_parameters(self, filename):\n \"\"\"\n save parameters as yaml-file to save computation time.\n\n Args:\n filename (str): filename for the parameters yaml-file\n \"\"\"\n write_yaml(self.parameters, filename)\n\n def read_parameters(self, filename):\n \"\"\"\n read parameters from a .yaml-file to save computation time.\n extract interim results from parameters\n\n Args:\n filename (str): filename of the parameters yaml-file\n \"\"\"\n self._parameter = read_yaml(filename)\n\n def auto_save_parameters(self, filename):\n \"\"\"auto-save the parameters as a yaml-file to save computation time.\"\"\"\n if path.isfile(filename):\n self.read_parameters(filename)\n else:\n self.write_parameters(filename)\n\n # __________________________________________________________________________________________________________________\n def get_u_w(self, duration):\n \"\"\"\n calculate the u and w parameters depending on the durations\n\n Args:\n duration (int | float | list | numpy.ndarray | pandas.Series): in minutes\n\n Returns:\n (numpy.ndarray, numpy.ndarray) | (float, float): u and w\n \"\"\"\n return get_u_w(duration, self.parameters)\n\n # __________________________________________________________________________________________________________________\n def depth_of_rainfall(self, duration, return_period):\n \"\"\"\n calculate the height of the rainfall h in L/m² = mm\n\n Args:\n duration (int | float | list | numpy.ndarray | pandas.Series): duration: in minutes\n return_period (float): in years\n\n Returns:\n int | float | list | numpy.ndarray | pandas.Series: height of the rainfall h in L/m² = mm\n \"\"\"\n u, w = self.get_u_w(duration)\n return depth_of_rainfall(u, w, return_period, series_kind=self.series_kind)\n\n # __________________________________________________________________________________________________________________\n def rain_flow_rate(self, duration, return_period):\n \"\"\"\n convert the height of rainfall to the specific rain flow rate in [l/(s*ha)]\n if 2 array-like parameters are give, a element-wise calculation will be made.\n So the length of the array must be the same.\n\n Args:\n duration (int | float | list | numpy.ndarray | pandas.Series): in minutes\n return_period (float): in years\n\n Returns:\n int | float | list | numpy.ndarray | pandas.Series: specific rain flow rate in [l/(s*ha)]\n \"\"\"\n return height2rate(height_of_rainfall=self.depth_of_rainfall(duration=duration, return_period=return_period),\n duration=duration)\n\n # __________________________________________________________________________________________________________________\n def r_720_1(self):\n \"\"\"\n rain flow rate in [l/(s*ha)] for a duration of 12h and a return period of 1 year\n\n Returns:\n float: rain flow rate in [l/(s*ha)]\n \"\"\"\n return self.rain_flow_rate(duration=720, return_period=1)\n\n # __________________________________________________________________________________________________________________\n def get_return_period(self, height_of_rainfall, duration):\n \"\"\"\n calculate the return period, when the height of rainfall and the duration are given\n\n Args:\n height_of_rainfall (float): in [mm]\n duration (int | float | list | numpy.ndarray | pandas.Series): in minutes\n\n Returns:\n int | float | list | numpy.ndarray | pandas.Series: return period in years\n \"\"\"\n u, w = self.get_u_w(duration)\n return np.exp((height_of_rainfall - u) / w)\n\n # __________________________________________________________________________________________________________________\n def get_duration(self, height_of_rainfall, return_period):\n \"\"\"\n calculate the duration, when the height of rainfall and the return period are given\n\n Args:\n height_of_rainfall (float): in [mm]\n return_period (float): in years\n\n Returns:\n float: duration in minutes\n \"\"\"\n return newton(lambda d: self.depth_of_rainfall(d, return_period) - height_of_rainfall, x0=1)\n\n # __________________________________________________________________________________________________________________\n def result_table(self, durations=None, return_periods=None, add_names=False):\n \"\"\"\n get a standard idf table of rainfall depth with return periods as columns and durations as rows\n\n Args:\n durations (list | numpy.ndarray): list of durations in minutes for the table\n return_periods (list): list of return periods in years for the table\n add_names (bool): weather to use expressive names as index-&column-label\n\n Returns:\n pandas.DataFrame: idf table\n \"\"\"\n if durations is None:\n durations = self.duration_steps\n\n if return_periods is None:\n return_periods = [1, 2, 3, 5, 10, 20, 25, 30, 50, 75, 100]\n\n result_table = dict()\n for t in return_periods:\n result_table[t] = self.depth_of_rainfall(durations, t)\n\n result_table = pd.DataFrame(result_table, index=durations)\n\n if add_names:\n result_table.index.name = 'duration (min)'\n result_table.columns = pd.MultiIndex.from_tuples([(rp, round(1 / rp, 3)) for rp in result_table.columns])\n result_table.columns.names = ['return period (a)', 'frequency (1/a)']\n return result_table\n\n ####################################################################################################################\n def result_figure(self, min_duration=5.0, max_duration=720.0, logx=False, return_periods=None, color=False):\n duration_steps = np.arange(min_duration, max_duration + 1, 1)\n plt.style.use('bmh')\n\n if return_periods is None:\n return_periods = [1, 2, 5, 10, 50, 100]\n\n table = self.result_table(durations=duration_steps, return_periods=return_periods)\n if color:\n table.columns.name = 'T$\\\\mathsf{_N}$ in (a)'\n ax = table.plot(color=(None if color else 'black'), logx=logx, legend=color)\n\n for _, return_time in enumerate(return_periods):\n p = measured_points(self, return_time, max_duration=max_duration)\n ax.plot(p, 'k' + 'x')\n\n if not color:\n x, y = list(p.tail(1).items())[0]\n ax.text(x + 10, y, '{} a'.format(return_time), verticalalignment='center', horizontalalignment='left',\n # bbox=dict(facecolor='white', alpha=1.0, lw=1)\n )\n\n ax.tick_params(axis='both', which='both', direction='out')\n ax.set_xlabel('Duration D in (min)')\n ax.set_ylabel('Rainfall h$\\\\mathsf{_N}$ in (mm)')\n ax.set_title('IDF curves')\n\n fig = ax.get_figure()\n\n cm_to_inch = 2.54\n fig.set_size_inches(h=21 / cm_to_inch, w=29.7 / cm_to_inch) # (11.69, 8.27)\n fig.tight_layout()\n return fig, ax\n\n ####################################################################################################################\n def get_return_periods_frame(self, series, durations=None):\n \"\"\"\n\n Args:\n series (pandas.Series):\n durations (list): list of durations in minutes which are of interest (default: pre defined durations)\n\n Returns:\n pandas.DataFrame: return periods depending of the duration per datetimeindex\n \"\"\"\n if durations is None:\n durations = self.duration_steps\n\n df = pd.DataFrame(index=series.index)\n\n freq = delta2min(guess_freq(series.index))\n for d in durations:\n if d % freq != 0:\n warnings.warn('Using durations (= {} minutes), '\n 'which are not a multiple of the base frequency (= {} minutes) of the series, '\n 'will lead to misinterpretations.'.format(d, freq))\n ts_sum = series.rolling(pd.Timedelta(minutes=d)).sum()\n df[d] = self.get_return_period(height_of_rainfall=ts_sum, duration=d)\n\n # printable_names (bool): if durations should be as readable in dataframe, else in minutes\n # df = df.rename(minutes_readable, axis=0)\n\n return df.round(1)\n\n @property\n def return_periods_frame(self):\n \"\"\"\n get the return periods over the whole time-series for the default duration steps.\n\n Returns:\n pandas.DataFrame: data-frame of return periods where the columns are the duration steps\n \"\"\"\n if self._return_periods_frame is None:\n self._return_periods_frame = self.get_return_periods_frame(self.series)\n return self._return_periods_frame\n\n def write_return_periods_frame(self, filename, **kwargs):\n \"\"\"save the return-periods dataframe as a parquet-file to save computation time.\"\"\"\n df = self.return_periods_frame.copy()\n df.columns = df.columns.to_series().astype(str)\n df.to_parquet(filename, **kwargs)\n\n def read_return_periods_frame(self, filename, **kwargs):\n \"\"\"read the return-periods dataframe as a parquet-file to save computation time.\"\"\"\n df = pd.read_parquet(filename, **kwargs)\n df.columns = df.columns.to_series().astype(int)\n self._return_periods_frame = df\n\n def auto_save_return_periods_frame(self, filename):\n \"\"\"auto-save the return-periods dataframe as a parquet-file to save computation time.\"\"\"\n if path.isfile(filename):\n self.read_return_periods_frame(filename)\n else:\n self.write_return_periods_frame(filename)\n\n ####################################################################################################################\n @classmethod\n def command_line_tool(cls):\n user = heavy_rain_parser()\n\n # --------------------------------------------------\n # use the same directory as the input file and make as subdir with the name of the input_file + \"_idf_data\"\n out = '{label}_idf_data'.format(label='.'.join(user.input.split('.')[:-1]))\n\n if not path.isdir(out):\n mkdir(out)\n action = 'Creating'\n else:\n action = 'Using'\n\n print('{} the subfolder \"{}\" for the interim- and final-results.'.format(action, out))\n\n fn_pattern = path.join(out, 'idf_{}')\n\n # --------------------------------------------------\n idf = cls(series_kind=user.series_kind, worksheet=user.worksheet, extended_durations=True)\n\n # --------------------------------------------------\n parameters_fn = fn_pattern.format('parameters.yaml')\n\n if path.isfile(parameters_fn):\n print('Found existing interim-results in \"{}\" and using them for calculations.'.format(parameters_fn))\n else:\n print('Start reading the time-series {} for the analysis.'.format(user.input))\n ts = import_series(user.input).replace(0, np.NaN).dropna()\n # --------------------------------------------------\n idf.set_series(ts)\n print('Finished reading.')\n\n # --------------------------------------------------\n idf.auto_save_parameters(parameters_fn)\n\n # --------------------------------------------------\n h = user.height_of_rainfall\n r = user.flow_rate_of_rainfall\n d = user.duration\n t = user.return_period\n\n if r is not None:\n if h is None and d is not None:\n h = rate2height(rain_flow_rate=r, duration=d)\n\n elif d is None and h is not None:\n d = h/r * 1000/6\n\n if user.r_720_1:\n d = 720\n t = 1\n\n if any((h, d, t)):\n if all((d, t)):\n pass\n\n elif all((d, h)):\n t = idf.get_return_period(h, d)\n print('The return period is {:0.1f} years.'.format(t))\n\n elif all((h, t)):\n d = idf.get_duration(h, t)\n print('The duration is {:0.1f} minutes.'.format(d))\n\n print('Resultierende Regenhöhe h_N(T_n={t:0.1f}a, D={d:0.1f}min) = {h:0.2f} mm'\n ''.format(t=t, d=d, h=idf.depth_of_rainfall(d, t)))\n print('Resultierende Regenspende r_N(T_n={t:0.1f}a, D={d:0.1f}min) = {r:0.2f} L/(s*ha)'\n ''.format(t=t, d=d, r=idf.rain_flow_rate(d, t)))\n\n # --------------------------------------------------\n if user.plot:\n fig, ax = idf.result_figure()\n plot_fn = fn_pattern.format('_curves_plot.png')\n fig.savefig(plot_fn, dpi=260)\n plt.close(fig)\n show_file(plot_fn)\n print('Created the IDF-curves-plot and saved the file as \"{}\".'.format(plot_fn))\n\n # --------------------------------------------------\n if user.export_table:\n table = idf.result_table(add_names=True)\n print(table.round(2).to_string())\n table_fn = fn_pattern.format('table.csv')\n table.to_csv(table_fn, sep=';', decimal=',', float_format='%0.2f')\n print('Created the IDF-curves-plot and saved the file as \"{}\".'.format(table_fn))\n\n ####################################################################################################################\n @property\n def rain_events(self):\n \"\"\"\n get the all the rain events of the time-series\n\n Returns:\n pandas.DataFrame: data-frame of events with start-, end-time and duration\n \"\"\"\n if self._rain_events is None:\n events = rain_events(self.series)\n events[COL.DUR] = event_duration(events)\n events[COL.LP] = agg_events(events, self.series, 'sum').round(1)\n # events = events.sort_values(by=COL.LP, ascending=False)\n self._rain_events = events\n\n return self._rain_events\n\n def write_rain_events(self, filename, sep=';', decimal='.'):\n \"\"\"save the rain-events dataframe as a csv-file for external use or to save computation time.\"\"\"\n self.rain_events.to_csv(filename, index=False, sep=sep, decimal=decimal)\n\n def read_rain_events(self, filename, sep=';', decimal='.'):\n \"\"\"read the rain-events dataframe as a csv-file to save computation time.\"\"\"\n events = pd.read_csv(filename, skipinitialspace=True, sep=sep, decimal=decimal)\n events[COL.START] = pd.to_datetime(events[COL.START])\n events[COL.END] = pd.to_datetime(events[COL.END])\n events[COL.DUR] = pd.to_timedelta(events[COL.DUR])\n self._rain_events = events\n\n def auto_save_rain_events(self, filename, sep=';', decimal='.'):\n \"\"\"auto-save the rain-events dataframe as a csv-file to save computation time.\"\"\"\n if path.isfile(filename):\n self.read_rain_events(filename, sep=sep, decimal=decimal)\n else:\n self.write_rain_events(filename, sep=sep, decimal=decimal)\n\n ####################################################################################################################\n def event_report(self, filename, min_event_rain_sum=25, min_return_period=0.5, durations=None):\n \"\"\"\n create pdf file with the biggest rain events\n for each event is represented by a plot of the rain series\n and a IDF analysis where the return periods are calculated\n\n Args:\n filename (str): path (directory + filename) for the created pdf-report\n min_event_rain_sum (float): only events with a bigger rain sum will be created\n min_return_period (float): only events with a bigger return period will be analysed\n (the plot will be created anyway)\n out_path (str): path and filename of the final report\n durations (list[int]): analysed durations\n (default: [5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 360, 540, 720, 1080, 1440, 2880, 4320])\n \"\"\"\n if durations is None:\n durations = [5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 360, 540, 720, 1080, 1440, 2880, 4320]\n\n events = self.rain_events\n events[COL.LP] = agg_events(events, self.series, 'sum')\n\n main_events = events[events[COL.LP] > min_event_rain_sum].to_dict(orient='index')\n main_events = main_events.sort_values(by=COL.LP, ascending=False)\n\n unit = 'mm'\n column_name = 'Precipitation'\n\n pdf = PdfPages(filename)\n\n for _, event in tqdm(main_events.items()):\n fig, caption = self.event_plot(event, durations=durations, min_return_period=min_return_period,\n unit=unit, column_name=column_name)\n\n # -------------------------------------\n fig.get_axes()[0].set_title(caption + '\\n\\n\\n')\n\n # DIN A4\n fig.set_size_inches(w=8.27, h=11.69)\n fig.tight_layout()\n pdf.savefig(fig)\n plt.close(fig)\n\n pdf.close()\n\n def event_plot(self, event, durations=None, unit='mm', column_name='Precipitation', min_return_period=0.5):\n start = event[COL.START]\n end = event[COL.END]\n\n if durations is None:\n durations = [5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 360, 540, 720, 1080, 1440, 2880, 4320]\n\n caption = 'rain event\\nbetween {} and {}\\nwith a total sum of {:0.1f} {}\\nand a duration of {}'.format(\n start.strftime('%Y-%m-%d %H:%M'),\n end.strftime('%Y-%m-%d %H:%M'),\n event[COL.LP],\n unit,\n end - start)\n\n freq = guess_freq(self.series.index)\n pstart = start - pd.Timedelta(freq)\n pend = end + pd.Timedelta(freq)\n ts = self.series[pstart:pend].resample(freq).sum().fillna(0).copy()\n\n fig = plt.figure()\n\n # -------------------------------------\n idf_table = self.return_periods_frame[pstart:pend]\n idf_table = idf_table.rename(minutes_readable, axis=0)\n\n # print(idf_table > min_return_period)\n\n max_period, duration = idf_table.max().max(), idf_table.max().idxmax()\n caption += '\\nThe maximum return period was {:0.2f}a\\nat a duration of {}.'.format(max_period, duration)\n\n if not (idf_table > min_return_period).any().any():\n # max_period, duration = idf_table.max().max(), idf_table.max().idxmax()\n # caption += '\\nThe maximum return period was {:0.2f}a\\nat a duration of {}.'.format(max_period, duration)\n rain_ax = fig.add_subplot(111)\n\n else:\n idf_bar_ax = fig.add_subplot(211)\n idf_bar_ax = idf_bar_axes(idf_bar_ax, idf_table, durations)\n rain_ax = fig.add_subplot(212, sharex=idf_bar_ax)\n\n # -------------------------------------\n ts_sum, minutes = resample_rain_series(ts)\n rain_ax = rain_bar_plot(ts_sum, rain_ax)\n rain_ax.set_ylabel('{} in [{}/{}min]'.format(column_name, unit, minutes if minutes != 1 else ''))\n rain_ax.set_xlim(ts.index[0], ts.index[-1])\n\n return fig, caption\n","sub_path":"idf_analysis/idf_class.py","file_name":"idf_class.py","file_ext":"py","file_size_in_byte":25568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"369334705","text":"import torch\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\nfrom matplotlib import pyplot as plt\n\n\nn_data=torch.ones(100,2)\nx0=torch.normal(2*n_data,1)# normal(means,std)\ny0=torch.zeros(100)\nx1=torch.normal(-2*n_data,1)\ny1=torch.ones(100)\nx=torch.cat((x0,x1),0).type(torch.FloatTensor)\ny=torch.cat((y0,y1),).type(torch.LongTensor)\n\n\nx.y=Variable(x),Variable(y)\n# plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c=y.data.numpy(),s=100,lw=0,cmap=)\n# plt.show()\n\nclass Net(torch.nn.Module):\n def __init__(self,n_features,n_hidden,n_output=1):\n super(Net,self).__init__()\n self.hidden=torch.nn.Linear(n_features,n_hidden)\n self.predict=torch.nn.Linear(n_hidden,n_output)\n\n def forward(self,x):\n x=F.relu(self.hidden(x))\n x=self.predict(x)\n return x\n\nnet=Net(2,10,2)# input is 2d, output is 2d\nplt.ion() # print on time\nplt.show()\noptimizer=torch.optim.SGD(net.parameters(),lr=0.02)\nloss_func=torch.nn.CrossEntropyLoss()\nfor t in range(100):\n out=net(x)\n loss=loss_func(out,y)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if t%2==0:\n plt.cla()\n prediction=torch.max(F.softmax(out),1)[1] # index=0 is value ,index =1 is the position\n pred_y=prediction.data.numpy().squeeze()\n traget_y=y.data.numpy()\n plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c=pred_y,s=100,lw=0)\n accuracy=sum(pred_y==traget_y)/200.0\n plt.text(1.5,-4,'Accuracy=%0.2f'%accuracy,fontdict={'size':20,'color':'red'})\n plt.pause(0.1)\n\nplt.ioff()\nplt.show()","sub_path":"Classification.py","file_name":"Classification.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"132516961","text":"class Solution:\n def divide(self, dividend: int, divisor: int) -> int:\n ans = 0\n sign = 1 if (dividend > 0 and divisor > 0) or (dividend < 0 and divisor < 0) else -1\n dividend, divisor = abs(dividend), abs(divisor)\n times = [(divisor, 1), ]\n mask = 2\n for i in range(1, 31):\n mask += mask\n times.append((times[i - 1][0] + times[i - 1][0], times[i - 1][1] + times[i - 1][1]))\n divisor, pos = 0, 30\n while True:\n if divisor + times[pos][0] <= dividend:\n ans += times[pos][1]\n divisor += times[pos][0]\n else:\n if pos == 0:\n ans *= sign\n if ans > mask or ans <= -mask:\n return mask-1\n else:\n return ans\n pos -= 1\n\n\n\nprint(Solution().divide(10, 3))\n","sub_path":"29.py","file_name":"29.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"266499508","text":"import tensorflow as tf\n# +\nimport tensorflow as tf\nfrom docutils.nodes import target\n\nfrom keras_transformer.extras import ReusableEmbedding, TiedOutputEmbedding\nfrom keras_transformer.position import TransformerCoordinateEmbedding\n\nfrom bert_concept_embeddings.custom_layers import EncoderLayer, TimeSelfAttention, TimeAttention, Encoder\n\n\ndef time_attention_cbow_negative_sampling_model(max_seq_length: int,\n vocabulary_size: int,\n concept_embedding_size: int,\n time_window_size: int):\n \"\"\"\n\n :param max_seq_length:\n :param vocabulary_size:\n :param concept_embedding_size:\n :param time_window_size:\n :return:\n \"\"\"\n target_concepts = tf.keras.layers.Input(shape=(1,), dtype='int32', name='target_concepts')\n\n target_time_stamps = tf.keras.layers.Input(shape=(1,), dtype='int32', name='target_time_stamps')\n\n context_concepts = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='context_concepts')\n\n context_time_stamps = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='context_time_stamps')\n\n mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='mask')\n\n embedding_layer = tf.keras.layers.Embedding(vocabulary_size, concept_embedding_size, name='embedding_layer',\n mask_zero=True)\n\n time_attention_layer = TimeAttention(vocab_size=vocabulary_size,\n target_seq_len=1,\n context_seq_len=max_seq_length,\n time_window_size=time_window_size)\n\n dot_layer = tf.keras.layers.Dot(axes=2)\n\n sigmoid_layer = tf.keras.layers.Dense(1, activation='sigmoid')\n\n # shape = (batch_size, 1, embedding_size)\n target_concept_embeddings = embedding_layer(target_concepts)\n\n # shape = (batch_size, seq_len, embedding_size)\n context_concept_embeddings = embedding_layer(context_concepts)\n\n # shape = (batch_size, 1, seq_len)\n time_attentions = time_attention_layer([target_concepts,\n target_time_stamps,\n context_time_stamps,\n mask])\n\n # shape = (batch_size, 1, embedding_size)\n combined_embeddings = tf.matmul(time_attentions, context_concept_embeddings)\n\n # shape = (batch_size, 1, 1)\n concept_predictions = sigmoid_layer(dot_layer([target_concept_embeddings, combined_embeddings]))\n\n model = tf.keras.Model(\n inputs=[target_concepts, target_time_stamps, context_concepts, context_time_stamps, mask],\n outputs=[concept_predictions])\n\n return model\n\n\n# -\n\ndef time_attention_cbow_model(max_seq_length: int,\n vocabulary_size: int,\n concept_embedding_size: int,\n time_window_size: int):\n \"\"\"\n\n :param max_seq_length:\n :param vocabulary_size:\n :param concept_embedding_size:\n :param time_window_size:\n :return:\n \"\"\"\n target_concepts = tf.keras.layers.Input(shape=(1,), dtype='int32', name='target_concepts')\n\n target_time_stamps = tf.keras.layers.Input(shape=(1,), dtype='int32', name='target_time_stamps')\n\n context_concepts = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='context_concepts')\n\n context_time_stamps = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='context_time_stamps')\n\n mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='mask')\n\n embedding_layer = tf.keras.layers.Embedding(vocabulary_size, concept_embedding_size, name='embedding_layer',\n mask_zero=True)\n\n time_embedding_layer = TimeAttention(vocab_size=vocabulary_size,\n target_seq_len=1,\n context_seq_len=max_seq_length,\n time_window_size=time_window_size)\n\n dense_layer = tf.keras.layers.Dense(vocabulary_size)\n\n softmax_layer = tf.keras.layers.Softmax()\n\n # shape = (batch_size, seq_len, embedding_size)\n concept_embeddings = embedding_layer(context_concepts)\n\n if mask is not None:\n concept_embeddings = concept_embeddings * tf.cast(tf.expand_dims(mask == 0, axis=-1), dtype=tf.float32)\n\n # shape = (batch_size, 1, seq_len)\n time_embeddings = time_embedding_layer([target_concepts,\n target_time_stamps,\n context_time_stamps,\n mask])\n\n # shape = (batch_size, 1, embedding_size)\n combined_embeddings = tf.matmul(time_embeddings, concept_embeddings)\n\n # shape = (batch_size, 1, vocab_size)\n concept_predictions = softmax_layer(dense_layer(combined_embeddings))\n\n model = tf.keras.Model(\n inputs=[target_concepts, target_time_stamps, context_concepts, context_time_stamps, mask],\n outputs=[concept_predictions])\n\n return model\n\n\n# -\ndef transformer_bert_model(\n max_seq_length: int,\n time_window_size: int,\n vocabulary_size: int,\n concept_embedding_size: int,\n depth: int,\n num_heads: int,\n transformer_dropout: float = 0.1,\n embedding_dropout: float = 0.6,\n l2_reg_penalty: float = 1e-4,\n time_attention_trainable=True):\n \"\"\"\n Builds a BERT-based model (Bidirectional Encoder Representations\n from Transformers) following paper \"BERT: Pre-training of Deep\n Bidirectional Transformers for Language Understanding\"\n (https://arxiv.org/abs/1810.04805)\n\n Depending on the value passed with `use_universal_transformer` argument,\n this function applies either an Adaptive Universal Transformer (2018)\n or a vanilla Transformer (2017) to do the job (the original paper uses\n vanilla Transformer).\n \"\"\"\n masked_concept_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='masked_concept_ids')\n\n concept_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='concept_ids')\n\n time_stamps = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='time_stamps')\n\n mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name='mask')\n\n concept_mask = tf.expand_dims(tf.expand_dims(mask, axis=1), axis=1)\n\n l2_regularizer = (tf.keras.regularizers.l2(l2_reg_penalty) if l2_reg_penalty else None)\n\n embedding_layer = ReusableEmbedding(\n vocabulary_size, concept_embedding_size,\n input_length=max_seq_length,\n name='bpe_embeddings',\n # Regularization is based on paper \"A Comparative Study on\n # Regularization Strategies for Embedding-based Neural Networks\"\n # https://arxiv.org/pdf/1508.03721.pdf\n embeddings_regularizer=l2_regularizer)\n\n time_attention_layer = TimeSelfAttention(vocab_size=vocabulary_size,\n target_seq_len=max_seq_length,\n context_seq_len=max_seq_length,\n time_window_size=time_window_size,\n return_logits=True,\n self_attention_return_logits=True,\n trainable=time_attention_trainable)\n\n encoder = Encoder(name='encoder',\n num_layers=depth,\n d_model=concept_embedding_size,\n num_heads=num_heads,\n dropout_rate=transformer_dropout)\n\n output_layer = TiedOutputEmbedding(\n projection_regularizer=l2_regularizer,\n projection_dropout=embedding_dropout,\n name='concept_prediction_logits')\n\n softmax_layer = tf.keras.layers.Softmax(name='concept_predictions')\n\n coordinate_embedding_layer = TransformerCoordinateEmbedding(1, name='coordinate_embedding')\n\n next_step_input, embedding_matrix = embedding_layer(masked_concept_ids)\n\n # Building a Vanilla Transformer (described in\n # \"Attention is all you need\", 2017)\n next_step_input = coordinate_embedding_layer(next_step_input, step=0)\n # shape = (batch_size, seq_len, seq_len)\n time_attention = time_attention_layer([concept_ids, time_stamps, mask])\n # pad a dimension to accommodate the head split\n time_attention = tf.expand_dims(time_attention, axis=1)\n\n next_step_input, _ = encoder(next_step_input, concept_mask, time_attention)\n\n concept_predictions = softmax_layer(\n output_layer([next_step_input, embedding_matrix]))\n\n model = tf.keras.Model(\n inputs=[masked_concept_ids, concept_ids, time_stamps, mask],\n outputs=[concept_predictions])\n\n return model\n","sub_path":"bert_concept_embeddings/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":8974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"434022851","text":"import html\n\nimport nltk\nimport pandas as pd\nimport preprocessor as tweet_preprocessor\nfrom nltk.classify import NaiveBayesClassifier\nfrom nltk.corpus import twitter_samples\nfrom nltk.sentiment import SentimentAnalyzer\nfrom nltk.sentiment.util import extract_unigram_feats\n\ntweet_preprocessor.set_options(tweet_preprocessor.OPT.URL,\n tweet_preprocessor.OPT.MENTION,\n tweet_preprocessor.OPT.HASHTAG,\n tweet_preprocessor.OPT.RESERVED,\n tweet_preprocessor.OPT.NUMBER)\n\npositive_tweets = twitter_samples.strings(\"positive_tweets.json\")\nnegative_tweets = twitter_samples.strings(\"negative_tweets.json\")\n\ncleaned_postive_tweets = [tweet_preprocessor.clean(html.unescape(x)) for x in positive_tweets]\ncleaned_negative_tweets = [tweet_preprocessor.clean(html.unescape(x)) for x in negative_tweets]\n\nlabeled_positive_tweets = [(nltk.word_tokenize(x), 1) for x in cleaned_postive_tweets]\nlabeled_negative_tweets = [(nltk.word_tokenize(x), 0) for x in cleaned_negative_tweets]\n\ntraining_samples = labeled_positive_tweets[0:4000] + labeled_negative_tweets[0:4000]\n\nsentim_analyzer = SentimentAnalyzer()\nall_words = []\nfor tweet, sentiment_score in training_samples:\n for word in tweet:\n all_words.append(word)\n\nfeature_words = sentim_analyzer.unigram_word_feats(all_words, min_freq=10)\nsentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=feature_words)\n\ntraining_set = sentim_analyzer.apply_features(training_samples)\n\ntrainer = NaiveBayesClassifier.train\nclassifier = sentim_analyzer.train(trainer, training_set)\n\n### Case Study\n\nresults_dict = dict(\n {\n \"Airline\": list(),\n \"Positive Sentiment Count\": list(),\n \"Negative Sentiment Count\": list()\n }\n)\n\nairline_friendly_name_map = {\n \"americanair\": \"American Airlines\",\n \"united\": \"United Airlines\",\n \"southwestair\": \"Southwest Airlines\",\n \"delta\": \"Delta\"\n}\n\nfor airline_name in [\"americanair\", \"united\", \"southwestair\", \"delta\"]:\n with open(\"case_study_dataset_{}.csv\".format(airline_name), \"r\") as file_handle:\n next(file_handle) # Skip the header\n dataset = list()\n for line in file_handle.readlines():\n dataset.append(nltk.word_tokenize(tweet_preprocessor.clean(html.unescape(line))))\n\n positive_sentiment_count = 0\n negative_sentiment_count = 0\n\n for tweet in dataset:\n sentiment_score = sentim_analyzer.classify(tweet)\n if sentiment_score == 0:\n negative_sentiment_count = negative_sentiment_count + 1\n else:\n positive_sentiment_count = positive_sentiment_count + 1\n\n results_dict[\"Airline\"].append(airline_friendly_name_map[airline_name])\n results_dict[\"Positive Sentiment Count\"].append(positive_sentiment_count)\n results_dict[\"Negative Sentiment Count\"].append(negative_sentiment_count)\n\npd.DataFrame(results_dict).to_csv(\"case_study_naive_bayes_classifier_with_emojis.csv\")\n","sub_path":"nltk_sentiment_analysis/case_study/5_case_study_naive_bayes_classifier_with_emojis.py","file_name":"5_case_study_naive_bayes_classifier_with_emojis.py","file_ext":"py","file_size_in_byte":3005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"360109519","text":"class PartyAnimal:\n\n def __init__(self, name):\n self.name = name\n self.x = 0\n print(self.name, \"constructed\")\n\n def party(self):\n self.x += 1\n print(self.name, \"party count\", self.x)\n\n def __del__(self):\n print(\"I'm destructed\", self.x)\n\n\nan1 = PartyAnimal('an1')\nan2 = PartyAnimal('an2')\n\nan1.party()\nan1.party()\nan1.party()\nan2.party()\n","sub_path":"week_1/tmp_class.py","file_name":"tmp_class.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"302832119","text":"import sys\nclass Solution:\n # @param {int[][]} matrix an integer array of n * m matrix\n # @param {int} k an integer\n # @return {int} the maximum number\n def maxSlidingWindow2(self, matrix, k):\n # Write your code here\n rows = len(matrix)\n cols = len(matrix[0])\n \n sums = [[0 for j in range(cols+1)] for i in range(rows+1)]\n\n for i in range(1, rows+1):\n for j in range(1, cols+1):\n sums[i][j] = matrix[i-1][j-1] + sums[i][j-1] + sums[i-1][j] - sums[i-1][j-1]\n \n maximum = -sys.maxsize - 1\n for i in range(k, rows+1):\n for j in range(k, cols+1):\n s = sums[i][j] - sums[i][j-k] - sums[i-k][j] + sums[i-k][j-k]\n \n if s < maximum:\n maximum = s\n return s","sub_path":"code/558-Sliding_Window_Matrix_Maximum.py","file_name":"558-Sliding_Window_Matrix_Maximum.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"365486401","text":"from tkinter import *\nfrom tkinter.filedialog import *\nimport os\nfrom PIL import Image\nimport urllib.request\nimport re\nfrom threading import Thread\n\n\nclass ImageResizer:\n def __init__(self,root):\n self.root = root\n \n self.root.title('URL ImageResizer')\n self.root.grid_rowconfigure(0,minsize=30)\n self.lab = Label(root,text='Input image url:').grid(row=0,columnspan=2,padx=10)\n self.image_url = Entry(root,width=80)\n self.image_url.grid(row=0,column=2,columnspan=3,padx=10)\n\n self.root.grid_rowconfigure(1,minsize=30)\n self.lab = Label(root,text='OR').grid(row=1,column=2,columnspan=2,padx=10)\n\n self.root.grid_rowconfigure(2,minsize=30)\n self.button_resize = Button(root, text='Select .txt file with IMAGES URLS list', font='Ariel 10 bold', command=self.open_urls_list_file)\n self.button_resize.grid(row=2,column=1,columnspan=4,sticky=W+E,padx=(10,10))\n\n self.root.grid_rowconfigure(3,minsize=40)\n self.lab = Label(root,text='Set image WIDTH (px):').grid(row=3,column=1,padx=(10,0),sticky=E+W)\n self.image_width = Entry(root,width=10)\n self.image_width.grid(row=3,column=2,sticky=W+E,padx=(5,10))\n\n self.lab = Label(root,text='Set image HEIGHT (px):').grid(row=3,column=3,sticky=E)\n self.image_height = Entry(root,width=10)\n self.image_height.grid(row=3,column=4,sticky=W+E,padx=(5,10))\n \n \n self.root.grid_rowconfigure(4,minsize=40)\n self.scrollbar = Scrollbar(root)\n self.scrollbar.grid(row=4, column=5, sticky=N+S+E, padx=(0,10))\n self.textfield = Text(root) \n self.textfield.grid(row=4,column=1,columnspan=4, padx=(10,0))\n self.textfield.config(yscrollcommand=self.scrollbar.set)\n self.scrollbar.config(command=self.textfield.yview)\n \n self.root.grid_rowconfigure(5,minsize=40)\n self.button_resize = Button(root, text='RESIZE', font='Ariel 11 bold', command=self.run_resize)\n self.button_resize.grid(row=5,column=2,columnspan=2,sticky=W+E,padx=(10,10))\n\n self.root.grid_rowconfigure(6,minsize=40)\n self.button_resize = Button(root, text='ABOUT', font='Ariel 11 bold', command=self.about)\n self.button_resize.grid(row=6,column=1,sticky=W+E,padx=(10,10))\n\n self.root.grid_rowconfigure(6,minsize=40)\n self.button_exit = Button(root, text='EXIT', fg='red', font='Ariel 11 bold', command=self.exit)\n self.button_exit.grid(row=6,column=4,sticky=W+E,padx=(10,10))\n\n if not os.path.exists('Original_Images'):\n os.mkdir('Original_Images')\n \n if not os.path.exists('Resized_Images'):\n os.mkdir('Resized_Images')\n\n self.resized_images_dir_list = os.listdir(os.path.join(os.getcwd(),'Resized_Images'))\n\n # Part of code for natural sorting. Credits goes to Jeff Atwood and Mark Byers on Stackoverflow\n # https://stackoverflow.com/questions/2669059/how-to-sort-alpha-numeric-set-in-python\n convert = lambda text: int(text) if text.isdigit() else text\n alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]\n self.resized_images_dir_list_sorted = sorted(self.resized_images_dir_list, key = alphanum_key)\n \n self.textfield.insert(END,'List of files in Resized_Images directory:\\n\\n')\n \n for self.resized_image in self.resized_images_dir_list_sorted:\n self.textfield.insert(END,self.resized_image+'\\n')\n \n self.root.mainloop()\n\n def run_resize(self):\n\n t1 = Thread(target=self.textfield.insert(END,'\\nRESIZING IN PROGRESS...\\nIMAGES ASPECT RATIO WILL BE MAINTAIN...\\nALTERATIONS OF ENTERED WIDTH OR HEIGHT VALUES ARE POSSIBLE...\\n\\n'))\n t2 = Thread(target=self.resize_engine)\n t1.start()\n t2.start()\n \n \n def resize_engine(self):\n \n try: \n if not hasattr(self,'urls_list_split'): # if variable 'urls_list' not exists\n self.image_url_resize = [self.image_url.get()] # make an array of one item from main window\n else:\n self.image_url_resize = self.urls_list_split # already return as array\n\n \n self.image_W = int(self.image_width.get())\n self.image_H = int(self.image_height.get())\n \n for self.image_in_url in self.image_url_resize:\n \n try:\n \n self.image_number = os.listdir(os.path.join(os.getcwd(),'Resized_Images'))\n self.i = len(self.image_number)\n\n urllib.request.urlretrieve(self.image_in_url, os.path.join(os.getcwd(),'Original_Images', str(self.i)+'_Image.jpg'))\n self.img = Image.open(os.path.join(os.getcwd(),'Original_Images', str(self.i)+'_Image.jpg'))\n self.img.thumbnail((self.image_W,self.image_H))\n self.image_W_resized, self.image_H_resized = self.img.size\n self.img.save(os.path.join(os.getcwd(),'Resized_Images', str(self.i)+'_ResizedImage_'+str(self.image_W_resized)+'x'+str(self.image_H_resized)+'.jpg'))\n\n self.textfield.insert(END,os.path.join('Resized_Images',str(self.i)+'_ResizedImage_'+str(self.image_W_resized)+'x'+str(self.image_H_resized)+'.jpg','\\n'))\n \n\n except Exception as e:\n self.textfield.insert(END,'\\nLink '+ self.image_in_url + ' is corrupted..\\n' + str(e) +'\\n\\n')\n \n\n self.textfield.insert(END,'IMAGE RESIZING FINISHED\\n')\n self.textfield.see(END)\n\n del self.urls_list_split\n\n except AttributeError: # if self.urls_list_split has already been deleted\n pass\n\n except ValueError:\n self.textfield.insert(END,'NO URL || HEIGHT AND WIDTH ARE NOT ENTERED OR VALUES ARE NOT AN INTEGER\\n')\n\n\n def open_urls_list_file(self):\n \n self.open_url_address_file = askopenfilename(title = \"Select file with image url addresses\",filetypes = [(\"url txt file\",\"*.txt\")])\n\n with open(self.open_url_address_file,'r') as file:\n self.urls_list = file.read()\n\n if ('http://' in self.urls_list) or ('https://' in self.urls_list):\n self.textfield.delete('1.0',END)\n self.urls_list_split = self.urls_list.split()\n self.textfield.insert(END, self.open_url_address_file+ ' file with urls list loaded... \\nSet image dimensions and click RESIZE.')\n return self.urls_list_split\n\n else:\n self.textfield.delete('1.0',END)\n self.textfield.insert(END,\"CAN'T FIND PROPER URL ADRESS IN SELECTED FILE. CHOOSE ANOTHER FILE\\n\")\n \n def about(self):\n self.about = Toplevel(self.root)\n self.about.title('About')\n self.lab_name = Label(self.about,text='Author: Aleksandar Kurjakov', font='Ariel 12 bold').grid(row=1,column=1,padx=20,pady=15)\n self.lab_contact = Label(self.about,text='Contact: kurjak021@gmail.com', font='Ariel 12 bold').grid(row=2,column=1,padx=20,pady=15)\n self.lab_version = Label(self.about,text='URL ImageResizer V1.0', font='Ariel 10 bold').grid(row=3,column=1,padx=20,pady=15)\n self.about.focus_set() \n self.about.grab_set()\n\n def exit(self):\n self.root.destroy()\n \nroot = Tk()\nApp = ImageResizer(root)\n","sub_path":"ImageResizer_from_url_list.py","file_name":"ImageResizer_from_url_list.py","file_ext":"py","file_size_in_byte":7574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"351933196","text":"from flask import Flask, render_template, request, redirect, jsonify\nfrom json import dump\nfrom Gameboard import Gameboard\nimport db\n\n\napp = Flask(__name__)\n\nimport logging\nlog = logging.getLogger('werkzeug')\nlog.setLevel(logging.ERROR)\n\ngame = None\n\n'''\nImplement '/' endpoint\nMethod Type: GET\nreturn: template player1_connect.html and status = \"Pick a Color.\"\nInitial Webpage where gameboard is initialized\n'''\n\n\n@app.route('/', methods=['GET'])\ndef player1_connect():\n global game\n game = Gameboard()\n return render_template(\"player1_connect.html\", status = 'Pick a Color.')\n\n\n'''\nHelper function that sends to all boards don't modify\n'''\n\n\n@app.route('/autoUpdate', methods=['GET'])\ndef updateAllBoards():\n try:\n return jsonify(move=game.board, winner=game.game_result,\n color=game.player1)\n except Exception:\n return jsonify(move=\"\")\n\n\n'''\nImplement '/p1Color' endpoint\nMethod Type: GET\nreturn: template player1_connect.html and status = <Color picked>\nAssign player1 their color\n'''\n\n\n@app.route('/p1Color', methods=['GET'])\ndef player1_config():\n if(request.args.get('color') == 'red'):\n game.player1 = \"red\"\n elif(request.args.get('color') == 'yellow'):\n game.player1 = \"yellow\"\n\n return render_template(\"player1_connect.html\", status = game.player1)\n\n\n\n'''\nImplement '/p2Join' endpoint\nMethod Type: GET\nreturn: template p2Join.html and status = <Color picked> or Error\nif P1 didn't pick color first\n\nAssign player2 their color \n'''\n\n\n@app.route('/p2Join', methods=['GET'])\ndef p2Join():\n if(game.player1 == \"red\"):\n game.player2 = \"yellow\"\n elif(game.player1 == \"yellow\"):\n game.player2 = \"red\"\n else:\n return \"Error\"\n\n return render_template(\"p2Join.html\", status = game.player2)\n\n'''\nImplement '/move1' endpoint\nMethod Type: POST\nreturn: jsonify (move=<CurrentBoard>,\ninvalid=True or False, winner = <currWinner>)\nIf move is valid --> invalid = False else invalid = True\nIf invalid == True, also return reason= <Why Move is Invalid>\n\nProcess Player 1's move\n'''\n\n\n@app.route('/move1', methods=['POST'])\ndef p1_move():\n col = int(request.json['column'][3]) - 1\n ret = game.validate_move('p1', col)\n\n if (ret == \"Valid\"):\n game.add_chip(game.player1, col)\n return jsonify(move=game.board, invalid=False, winner=game.game_result)\n else:\n return jsonify(move=game.board, invalid=True, reason=ret, winner=game.game_result)\n\n'''\nSame as '/move1' but instead proccess Player 2\n'''\n\n\n@app.route('/move2', methods=['POST'])\ndef p2_move():\n col = int(request.json['column'][3]) - 1\n ret = game.validate_move('p2', col)\n\n if (ret == \"Valid\"):\n game.add_chip(game.player2, col)\n return jsonify(move=game.board, invalid=False, winner=game.game_result)\n else:\n return jsonify(move=game.board, invalid=True, reason=ret, winner=game.game_result)\n \n\n\n\nif __name__ == '__main__':\n app.run(debug=True, host='127.0.0.1')\n","sub_path":"Skeleton/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"403311116","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport annoying.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Author',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('nickname', models.CharField(unique=True, max_length=64)),\n ('email', models.EmailField(max_length=75)),\n ('created_on', models.DateTimeField(auto_now_add=True)),\n ('updated_on', models.DateTimeField(auto_now=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Post',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=200)),\n ('pub_date', models.DateTimeField(verbose_name=b'date published')),\n ('content', models.TextField(max_length=20000)),\n ('keywords', annoying.fields.JSONField(null=True, blank=True)),\n ('author', models.ForeignKey(to='blog.Author')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n ]\n","sub_path":"blog/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"612618020","text":"import base64\nimport string\nimport random\nimport hashlib\nimport time\nimport struct\nfrom Crypto.Cipher import AES\nimport socket\nimport importlib\nimport config.cypher as config\nimport ast\n\nblock_size = 32\naes_key = base64.b64decode(config.aes_key)\n\ndef encrypt(data, userid):\n if not isinstance(data, dict):\n raise RuntimeError('Invalid data type')\n data = str(data)\n sample = string.digits + string.ascii_letters\n rand_str = ''.join(random.sample(sample, 16))\n data = data.encode('utf-8')\n data = bytes(rand_str, 'utf-8') + struct.pack('I', socket.htonl(len(data))) + data + bytes(userid, 'utf-8')\n data_length = len(data)\n pad_length = block_size - (data_length % block_size)\n pad = bytes(chr(pad_length), 'utf-8')\n data = data + pad * pad_length\n ase_cryptor = AES.new(aes_key, AES.MODE_CBC, aes_key[:16])\n cipher = ase_cryptor.encrypt(data)\n return base64.b64encode(cipher).decode('ascii')\n\ndef decrypt(data):\n try:\n ase_cryptor = AES.new(aes_key, AES.MODE_CBC, aes_key[:16])\n plain_text = ase_cryptor.decrypt(base64.b64decode(data))\n pad_length = plain_text[-1]\n content = plain_text[16:-pad_length]\n data_length = socket.ntohl(struct.unpack('I', content[:4])[0])\n return ast.literal_eval(content[4:4+data_length].decode('utf-8')), content[4+data_length:].decode('utf-8')\n except Exception as err:\n return {}, ''\n\ndef get_signature(*arg):\n signature_param = list(arg)\n signature_param.sort()\n return hashlib.sha1(''.join(signature_param).encode('utf-8')).hexdigest()\n\nif __name__ == '__main__':\n encrypt_data = encrypt('fuck you', 'admin')\n decrypt_data = decrypt(encrypt_data)\n print(encrypt_data, decrypt_data)\n","sub_path":"locode/cypher.py","file_name":"cypher.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"158228244","text":"from utils import read_csv, to_csv\nfrom utils import sort_by_key\nfrom utils import read_group\n\nfrom random import shuffle, choice\nimport os\nimport sys\nimport glob\n\ndef read_input(run, experiment_name):\n all_input_dicts = []\n input_dicts_batch = []\n f_paths_all = glob.glob(f'../prolific_input/run*-group_*/*.csv')\n f_paths_run_name = glob.glob(f'../prolific_input/run{run}-group_{experiment_name}/*.csv')\n header_path = f'../prolific_input/run{run}-group_{experiment_name}/header.txt'\n batch_numbers = []\n # Get info current batch\n if os.path.isfile(header_path):\n with open(header_path) as infile:\n header = infile.read().split(',')\n for f in f_paths_run_name:\n input_dicts = read_csv(f, header = header)\n input_dicts_batch.extend(input_dicts)\n if 'TEST' not in f:\n f_name = f.split('/')[-1]\n batch_n = int(f_name.split('-')[2].split('.')[0][len('batch'):])\n ##../prolific_input/run3-group_experiment1/qu70-s_qu70-batch11.csv\n batch_numbers.append(batch_n)\n # get all input dicts:\n for f in f_paths_all:\n run = f.replace('../prolific_input/', '').split('-')[0]\n #../prolific_input/run3-group_experiment1/qu70-s_qu70-batch11.csv\n input_dicts = read_csv(f, header = header)\n all_input_dicts.extend(input_dicts)\n return all_input_dicts, input_dicts_batch, batch_numbers\n\n\ndef collect_not_annotated(input_dicts, question_dicts):\n\n questions_not_annotated = []\n input_by_quid = sort_by_key(input_dicts, ['quid'])\n for d in question_dicts:\n quid = d['quid']\n if quid not in input_by_quid:\n questions_not_annotated.append(d)\n return questions_not_annotated\n\n\ndef get_annotated_questions(input_dicts, question_dicts):\n\n input_by_quid = sort_by_key(input_dicts, ['quid'])\n questions_by_quid = sort_by_key(question_dicts, ['quid'])\n questions_annotated = []\n for quid in input_by_quid:\n if quid in questions_by_quid:\n question = questions_by_quid[quid][0]\n questions_annotated.append(question)\n return questions_annotated\n\n\ndef collect_invalid(input_dicts, question_dicts):\n\n questions_by_pair = sort_by_key(question_dicts, ['property', 'concept'])\n questions_annotated = get_annotated_questions(input_dicts, question_dicts)\n questions_anntotated_by_pair = sort_by_key(questions_annotated, ['property', 'concept'])\n invalid_annotations = []\n\n for pair, questions_annotated in questions_anntotated_by_pair.items():\n questions = questions_by_pair[pair]\n if len(questions) != len(questions_annotated):\n #print('missing annotations for pair:', pair, len(questions), len(questions_annotated))\n invalid_annotations.extend(questions)\n return invalid_annotations\n\ndef get_available_questions(input_dicts, question_dicts):\n\n questions_for_annotation = []\n questions_not_annotated = collect_not_annotated(input_dicts, question_dicts)\n print('not annotated yet:', len(questions_not_annotated))\n invalid_annotations = collect_invalid(input_dicts, question_dicts)\n print('not valid', len(invalid_annotations))\n\n not_annotated_pair = sort_by_key(questions_not_annotated, ['property', 'concept'])\n invalid_pair = sort_by_key(invalid_annotations, ['property', 'concept'])\n\n for pair, questions in not_annotated_pair.items():\n if pair in invalid_pair:\n questions_for_annotation.extend(invalid_pair[pair])\n else:\n questions_for_annotation.extend(questions)\n test_for_wrong_questions(questions_for_annotation)\n return questions_for_annotation, invalid_annotations\n\ndef test_for_wrong_questions(questions_for_annotation):\n wrong_n_questions = []\n for_annotation_by_pair = sort_by_key(questions_for_annotation, ['property', 'concept'])\n for pair, questions in for_annotation_by_pair.items():\n if len(questions) > 10 or len(questions) < 3:\n wrong_n_questions.append((n, pair))\n assert len(wrong_n_questions) == 0, 'Number of questions per pair not correct.'\n\n\n\ndef get_check_and_test():\n checks = read_csv('../questions/checks.csv')\n tests = read_csv('../questions/tests.csv')\n\n rand_check = choice(checks)\n rand_test = choice(tests)\n tests_checks = [rand_check, rand_test]\n for d in tests_checks:\n if '' in d:\n d.pop('')\n return tests_checks\n\ndef get_batch(questions_to_annotate, n_qu = 70):\n batch = []\n properties = set()\n # shuffle questions:\n shuffle(questions_to_annotate)\n questions_by_pair = sort_by_key(questions_to_annotate, ['property', 'concept'])\n available_properties = set([p.split('-')[0] for p in questions_by_pair.keys()])\n\n if n_qu > len(questions_to_annotate):\n print(f'only {len(questions_to_annotate)} left - adding all to batch.')\n batch.extend(questions_to_annotate)\n else:\n print(f'still more than {n_qu} questions available.')\n for pair, questions in questions_by_pair.items():\n prop = pair.split('-')[0]\n if len(batch) < n_qu:\n if prop not in properties:\n #print('found a new one:', prop, len(batch))\n batch.extend(questions)\n properties.add(prop)\n else:\n props_not_used = available_properties.difference(properties)\n #print('properties not used:', len(props_not_used), len(batch))\n if len(props_not_used) > 0:\n continue\n else:\n batch.extend(questions)\n properties.add(prop)\n #print('no more properties, adding quetions:', len(questions))\n else:\n print('found enough questions', len(batch))\n break\n\n return batch\n\n\ndef batch_to_file(batch, url, experiment_name, run, n_qu, batch_n):\n\n # header = ['quid', 'question', 'example_pos', 'example_neg']\n header_new = ['quid','listNr', 'description', 'exampleTrue', 'exampleFalse',\\\n 'triple', 'completionUrl', 'name']\n dirpath = f'../prolific_input/run{run}-group_{experiment_name}/'\n batch_name = f'qu{n_qu}-s_qu{n_qu}-batch{batch_n}'\n filepath = f'{dirpath}{batch_name}.csv'\n pl_name = f'Agree or disagree (run{run}-{experiment_name}-batch{batch_n}-{n_qu}-{n_qu})'\n\n ### write header###\n if not os.path.isdir(dirpath):\n os.mkdir(dirpath)\n header_path = f'{dirpath}header.txt'\n if not os.path.isfile(header_path):\n with open(header_path, 'w') as outfile:\n outfile.write(','.join(header_new))\n ###\n new_dicts = []\n for d in batch:\n triple = f\"{d['relation']}-{d['property']}-{d['concept']}\"\n new_d = dict()\n new_d['quid'] = d['quid']\n new_d['listNr'] = d['listNr']\n new_d['description'] = d['question']\n new_d['exampleTrue'] = d['example_pos']\n new_d['exampleFalse'] = d['example_neg']\n new_d['run'] = run\n new_d['subList'] = 1\n new_d['completionUrl'] = url\n new_d['triple'] = triple\n new_d['name'] = pl_name\n new_dicts.append(new_d)\n to_csv(filepath, new_dicts, header=True)\n return filepath\n\n\ndef test_duplicates(input_dicts, batch_dicts, invalid_annotations):\n\n quids_batch = set([d['quid'] for d in batch_dicts])\n quids_input = set([d['quid'] for d in input_dicts])\n quids_invalid = set([d['quid'] for d in invalid_annotations])\n overlap = quids_batch.intersection(quids_input)\n valid_overlap = overlap.difference(quids_invalid)\n #print(f'Overlap between annotated and current batch: {len(valid_overlap)}')\n #print(valid_overlap)\n problematic_overlap = []\n for quid in valid_overlap:\n if quid.startswith('test') or quid.startswith('check'):\n continue\n else:\n problematic_overlap.append(quid)\n assert len(problematic_overlap) == 0, 'Already annotated questions in batch!'\n\ndef suggest_cost(n_questions):\n # UK min wage = 8.21\n # divided by 60 gives min wage per minute\n per_minute = 0.13\n time_per_question_seconds = 8\n price_per_question = (per_minute/60) *7\n final = n_questions * price_per_question\n estimated_time = (n_questions * time_per_question_seconds) / 60\n print(f'estimated time: {estimated_time} minutes')\n print(f'suggested price: {final}')\n return final, estimated_time\n\n\ndef print_task_intro(run):\n\n # load description:\n\n with open(f'../task_set_up/description_run{run}.txt') as infile:\n text_description = infile.read()\n\n with open(f'../task_set_up/instructions_run{run}.txt') as infile:\n text_instructions = infile.read()\n\n print('-------------------------------------\\n')\n print_des = input('Print description? (y/n)')\n if print_des == 'y':\n print('\\n----- Task description ------\\n')\n print(text_description)\n print_instructions = input('Print instructions? (y/n)')\n if print_instructions == 'y':\n print('\\n----- Instructions ------\\n')\n print(text_instructions)\n print_whitelist = input(\"Print whitelist (y/n)?\")\n if print_whitelist == 'y':\n with open('../task_set_up/whitelist.txt') as infile:\n whitelist = infile.read()\n print('------- Whitelist--------')\n print(whitelist)\n return print_whitelist\n\n\ndef update_log(new_log_dict):\n path = '../task_set_up/experiment_log.csv'\n log_dicts = read_csv(path)\n log_dicts.append(new_log_dict)\n to_csv(path, log_dicts)\n print(f'updated log: {path}')\n\ndef create_new_batch(run, experiment_name, url, n_participants, n_lists, n_qu=70, test=False):\n exp_dict = dict()\n all_input_dicts, input_dicts_batch, batch_numbers = read_input(run, experiment_name)\n print(batch_numbers)\n question_path = f'../questions/run{run}-all-restricted_True.csv'\n question_dicts = read_csv(question_path)\n selected_properties = read_group(experiment_name)\n\n\n print('available for batch:')\n questions_to_annotate_batch, invalid_annotations = get_available_questions(input_dicts_batch,\\\n question_dicts)\n print('availabel in total:')\n questions_to_annotate_total, invalid_annotations = get_available_questions(all_input_dicts,\\\n question_dicts)\n questions_in_selection = [d for d in questions_to_annotate_batch \\\n if d['property'] in selected_properties]\n questions_in_selection_total = [d for d in question_dicts \\\n if d['property'] in selected_properties]\n\n ### Get counts ###\n # Total dataset\n n_total = len(question_dicts)\n n_not_annotated = len(questions_to_annotate_total)\n n_annotated = n_total - n_not_annotated\n percent_total = round(n_annotated/n_total, 3) * 100\n ###\n\n # Experiment group\n n_experiment_group = len(questions_in_selection_total)\n n_not_annotated_experiment_group = len(questions_in_selection)\n n_annotated_experiment_group = n_experiment_group - n_not_annotated_experiment_group\n percent_experiment_group = round(n_annotated_experiment_group/n_experiment_group, 3) * 100\n ###\n ###########\n\n\n # Create new batch\n if batch_numbers:\n highest_batch_number = max(batch_numbers)\n else:\n highest_batch_number = 0\n if test == False:\n current_batch_n = highest_batch_number + 1\n else:\n current_batch_n = 'TEST'\n\n full_batch = []\n for n in range(n_lists):\n test_check_questions = get_check_and_test()\n new_batch = get_batch(questions_in_selection, n_qu = n_qu)\n # test for wrong number of questions\n test_for_wrong_questions(new_batch)\n # Add tests and checks (one randomly picked one each)\n new_batch.extend(test_check_questions)\n # Test if there are not duplicates\n test_duplicates(input_dicts_batch, new_batch, invalid_annotations)\n # Add listnr\n [d.update({'listNr' : str(n+1)}) for d in new_batch]\n print('Listnr', n+1, len(new_batch))\n full_batch.extend(new_batch)\n # Write batch to file\n batch_path = batch_to_file(full_batch, url, experiment_name, run, n_qu, current_batch_n)\n\n print(f'Number of questions in the total dataset: {n_total}')\n print(f'Number of questions in {experiment_name}: {n_experiment_group}')\n print(f'Number of annotated questions: {n_annotated}\\\n (of which in {experiment_name}: {n_annotated_experiment_group})')\n print(f'Percentage of annotated questions of the total: {percent_total}%')\n print(f'Percentage of annotated questions of {experiment_name}: {percent_experiment_group}%')\n print(f'Annotated {highest_batch_number} batches so far.')\n print(f'Created batch {current_batch_n} with {len(new_batch)} questions.')\n print(f'New batch written to: {batch_path}')\n pl_n = f'Agree or disagree (run{run}-{experiment_name}-batch{current_batch_n}-{n_qu}-{n_qu})'\n print(pl_n)\n p_whitelist = print_task_intro(run)\n print('\\n------ Cost ---------')\n print(f'\\nCost suggestion for {len(new_batch)} questions:\\n')\n sug_cost, t = suggest_cost(len(new_batch))\n\n total_cost_no_fee = sug_cost * n_participants\n # fill log dict:\n exp_dict['name_lingoturk'] = pl_n\n exp_dict['name_prolific'] = pl_n\n exp_dict['group'] = experiment_name\n exp_dict['batch'] = current_batch_n\n exp_dict['run'] = run\n exp_dict['n_questions'] = n_qu\n exp_dict['n_questions_batch'] = len(new_batch)\n exp_dict['n_participants'] = n_participants\n exp_dict['minutes_planned'] = t\n exp_dict['reward (pounds)'] = sug_cost\n total_cost = float(input('Enter total cost shown on Prolific: '))\n exp_dict['total_cost (pounds)'] = total_cost\n print(f'Prolific charges {float(total_cost) - total_cost_no_fee} pounds fees.')\n exp_dict['posted'] = 'yes'\n exp_dict['results_downloaded'] = ''\n exp_dict['summary downloaded'] = ''\n exp_dict['approved'] = ''\n exp_dict['comment'] = ''\n exp_dict['Code'] = url\n exp_dict['whitelist'] = p_whitelist\n if test == False:\n update_log(exp_dict)\n\n\ndef main():\n run = sys.argv[1]\n experiment_name = 'experiment3'\n url = sys.argv[2]\n # number of lists within batch\n n_lists = int(sys.argv[3])\n n_participants_per_batch = int(sys.argv[4])\n n_participants = n_lists * n_participants_per_batch\n\n #url = 'test'\n #purpose = 'test'\n if url == 'TEST':\n test = True\n url = 'https://piasommerauer.github.io/annotation'\n elif url != 'TEST':\n test = False\n\n #create_new_batch(run, experiment_name, url, n_participants, n_qu=3, test=test)\n create_new_batch(run, experiment_name, url, n_participants, n_lists, n_qu=3, test=test)\n\nif __name__ == '__main__':\n main()\n","sub_path":"scripts/.ipynb_checkpoints/create_multibatches-checkpoint.py","file_name":"create_multibatches-checkpoint.py","file_ext":"py","file_size_in_byte":15008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"119066767","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef getTargetColor(target):\n return 'b.' if target == 1 else 'r.'\n\n\ndef plot2dData(inData, targets):\n classA = inData[np.where(targets > 0)[0]]\n classB = inData[np.where(targets < 0)[0]]\n\n for c in [(classA, 'b.'), (classB, 'r.')]:\n plt.plot([p[0] for p in c[0]],\n [p[1] for p in c[0]],\n c[1]\n )\n\n plt.axis('equal')\n plt.show()\n\n\ndef plotDecBoundary(inData, targets, indicatorFunc):\n xGrid = np.linspace(-4, 8)\n yGrid = np.linspace(-4, 8)\n\n grid = np.array([[indicatorFunc(x, y)\n for x in xGrid]\n for y in yGrid])\n\n plt.contour(xGrid, yGrid, grid,\n (-1.0, 0.0, 1.0),\n colors=('red', 'black', 'blue'),\n linewidths=(1, 3, 1)\n )\n\n classA = inData[np.where(targets > 0)[0]]\n classB = inData[np.where(targets < 0)[0]]\n\n for c in [(classA, 'b.'), (classB, 'r.')]:\n plt.plot([p[0] for p in c[0]],\n [p[1] for p in c[0]],\n c[1]\n )\n\n # plt.xlim(-10, 10)\n # plt.ylim(-10, 10)\n plt.axis('equal')\n plt.show()\n","sub_path":"Lab2/PlotGenerator.py","file_name":"PlotGenerator.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"202994508","text":"import json\n\n\nclass Env(object):\n env = None\n\n def get(self, key: str) -> str:\n if self.env is None:\n self._readEnv()\n return self.env[key]\n\n def _readEnv(self):\n with open(\"env.json\") as json_data_file:\n self.env = json.load(json_data_file)\n","sub_path":"Bot/Env.py","file_name":"Env.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"369036822","text":"#----------|PROYECTO FINAL| ROAD FIGHTER | JULIAN ANDRÉS LOAIZA OSPINA |--------------------------------#\n\nfrom tkinter import *\nfrom tkinter import ttk, font\nimport time\nimport sys\nimport threading\nimport random \n\n#|Sonidos|---------------------------------------------------\n\nimport pygame\nfrom pygame.locals import *\n\npygame.mixer.init()\n\nsoundMenu = pygame.mixer.Sound(\"menu_theme.wav\")\nsoundBoton = pygame.mixer.Sound(\"menu.wav\")\nsoundOver = pygame.mixer.Sound(\"gameOver.wav\")\nsoundExplosion = pygame.mixer.Sound(\"carExplosion.wav\")\nsoundPass = pygame.mixer.Sound(\"carPass.wav\")\nsoundCar = pygame.mixer.Sound(\"car.wav\")\nsoundSticker = pygame.mixer.Sound(\"sticker.wav\")\nsoundBrake = pygame.mixer.Sound(\"carBrake.wav\")\nsoundInit1 = pygame.mixer.Sound(\"init1.wav\")\nsoundInit2 = pygame.mixer.Sound(\"init2.wav\")\n\n\n#|Menu principal|--------------------------------------------------------------\n\ndef main_menu():\n global raiz, fontMenu, butStart, butContinue, butSinglePlayer, butMultiPlayer, menu, difficulty, varContinue, sound\n \n #|Ventana|--------------------------------------------\n\n\n raiz = Tk()\n raiz.title(\"Road Fighter\")\n raiz.geometry(\"800x600\")\n raiz.resizable(width=False, height=False)\n bit = raiz.iconbitmap('RoadFighter.ico')\n \n #|Fuente|---------------------------------------------------\n\n fontMenu = font.Font(family='Haettenschweiler', size= 25)\n\n #|musica|-------------------------------------------\n\n soundMenu.play()\n\n\n #|Fondo|--------------------------------------------\n\n mainMenu = PhotoImage(file=\"mainMenu1.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0) \n raiz.update()\n time.sleep(1)\n\n mainMenu = PhotoImage(file=\"mainMenu2.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0) \n raiz.update()\n time.sleep(0.01)\n \n mainMenu = PhotoImage(file=\"mainMenu3.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n time.sleep(0.01)\n \n mainMenu = PhotoImage(file=\"mainMenu4.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n time.sleep(0.01)\n \n mainMenu = PhotoImage(file=\"mainMenu.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0) \n \n #|Botones| ---------------------------------------------------------\n\n butMultiPlayer = Button(raiz, text=\"MULTI PLAYER\",bg = \"black\", fg = \"white\",\n font= fontMenu, command= multi_player).place(x= 304, y = 303, height= 56, width= 225)\n\n butContinue = Button(raiz, text=\"CONTINUE\",bg = \"black\", fg = \"white\", \n font= fontMenu, command = _continue_).place(x= 291, y = 426, height= 56, width= 225)\n \n #-----------------------------------------------------------------------\n varContinue = False\n\n #--------------------------------------------------------------\n\n raiz.mainloop()\n \n\n#|Menu multijugador|------------------------------------------------\n\ndef multi_player():\n global mainMenu, fondoMenu, archivo, raiz, difficulty,labelGasolina1, labelGasolina2, fontSubMenu, player1, player2, varContinue, movimiento\n \n movimiento = False\n soundBoton.play()\n\n #|Fondo|----------------------------------------\n \n mainMenu = PhotoImage(file=\"subMenu.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n \n #|fuente| \n \n fontSubMenu = font.Font(family='Agency FB', size= 18) \n\n #|Nombre Jugadores Entradas|----------------------------------------\n \n player1 = StringVar()\n player1.set(\"PLAYER 1\")\n\n \n\n player2 = StringVar()\n player2.set(\"PLAYER 2\")\n\n \n\n BoxPlayer1 = Entry(raiz, textvariable = player1, font= fontSubMenu, \n justify=\"center\").place(x= 50, y = 360,height= 30, width= 200)\n\n BoxPlayer2 = Entry(raiz, textvariable = player2, font= fontSubMenu, \n justify=\"center\").place(x= 528, y = 360,height= 30, width= 200)\n\n #|Dificultades RadioButtons|-------------------------------------------\n\n difficulty= IntVar()\n\n difficulty.set(2)\n \n RadButDtifficulty1= Radiobutton(raiz, value= 1, variable= difficulty,\n bg = \"black\").place(x= 320, y = 328)\n \n RadButDifficulty2= Radiobutton(raiz, value= 2, variable= difficulty, \n bg = \"black\", font= fontSubMenu).place(x= 390, y = 320)\n \n RadButDifficulty3= Radiobutton(raiz, value= 3, variable= difficulty,\n bg = \"black\").place(x= 350, y = 385)\n \n RadDifficulty4= Radiobutton(raiz, value= 4, variable= difficulty,\n bg = \"black\").place(x= 325, y = 440)\n\n RadDifficulty5= Radiobutton(raiz, value= 5, variable= difficulty,\n bg = \"black\").place(x= 390, y = 440)\n\n butStart = Button(raiz, text=\"Start\",bg = \"black\", fg = \"white\", \n font= fontMenu, command = play_multijugador).place(x= 278, y = 515, height= 56, width= 225)\n\n if varContinue == True:\n play_multijugador()\n \n #----------------------------------------------------------------------\n\n#Continue|-------------------------\n\ndef _continue_():\n global varContinue\n\n soundBoton.play()\n\n varContinue = True\n multi_player()\n return\n\ndef play_multijugador():\n global iniciar, mainMenu, fondoMenu, archivo, movimiento, sticker1, sticker2, stickerImage, dicc, movimiento ,varContinue, butSave, minimapY, raiz,canvasIzq, labelGasolina1, labelGasolina2, playerMinimap, canvasDer, dificultad, fontSubMenu, efecto, efectoManchaP1, efectoManchaP2, GameOverP1, GameOverP2, posicionXP1, posicionYP1, roadIzq, posicionXP2, posicionYP2, movimientoP1, movimientoP2, movimiento, posicionMinivanY, posicionMinivanX, iniciar, minivan1, minivan2, velocidadPlayer, imagePlayer1, cavasImagePlayer1, imagePlayer2, cavasImagePlayer2, posicionManchaY, posicionManchaX, posicionStickerY, posicionStickerX, posicionMinivanX_2, posicionMinivanY_2, posicionRunnerX, posicionRunnerY, posicionFighterX, posicionFighterX2, posicionFighterY, coords, coords2, contadorGasolina, contadorGasolina2, cont\n\n\n archivo = {}\n\n if varContinue == True:\n partida=open(\"Archivo.py\",\"r\")\n dicc= partida.readline()\n partida.close()\n\n posicionXP1 = (100) \n\n archivo[\"posicionXP1\"] = posicionXP1\n\n\n posicionYP1 = (550)\n\n archivo[\"posicionYP1\"] = posicionYP1\n\n posicionXP2 = (100)\n\n archivo[\"posicionXP2\"] = posicionXP2\n\n posicionYP2 = (550)\n\n archivo[\"posicionYP2\"] = posicionYP2\n\n velocidadPlayer = 1\n\n movimiento = True\n\n contadorGasolina = 200\n\n archivo[\"contadorGasolina\"] = contadorGasolina\n\n contadorGasolina2 = 200\n\n archivo[\"contadorGasolina2\"] = contadorGasolina2\n\n\n#--------Movimiento Player1 ------------------#\n \n\n movimientoP1 = \"none\"\n\n def aKey(event):\n global movimientoP1\n movimientoP1 = \"izq\"\n\n def dKey(event):\n global movimientoP1\n movimientoP1 = \"der\"\n\n def sKey(event):\n global movimientoP1\n movimientoP1 = \"none\"\n \n \n#----------Movimiento player2---------------------#\n \n movimientoP2 = \"none\"\n\n def jKey(event):\n global movimientoP2\n movimientoP2 = \"izq\"\n\n def lKey(event):\n global movimientoP2\n movimientoP2 = \"der\"\n\n def kKey(event):\n global movimientoP2\n movimientoP2 = \"none\"\n \n #|save|-------------------------------------------\n\n def _save_():\n global raiz, movimiento, archivo\n\n movimiento = False\n soundCar.stop()\n\n archivoTxt = open(\"Archivo.py\", \"w\")\n archivoTxt.write(str(archivo))\n archivoTxt.close\n\n raiz.bind('<space>', start)\n \n \n #|stop|-------------------------------------------#\n\n def stop(event):\n global raiz, movimiento, butSave\n movimiento = False\n soundCar.stop()\n\n raiz.bind('<space>', start)\n\n\n #|Interacciones|------------------------------------------------------------------------------\n \n GameOverP1 = False\n GameOverP2 = False\n efectoManchaP1 = False\n efectoManchaP2 = False\n\n def GameOver():\n global GameOverP1, GameOverP2, canvasIzq, mainMenu, fondoMenu, canvasDer, movimiento, imagePlayer1, imagePlayer2, cavasImagePlayer1, cavasImagePlayer2, velocidad, raiz, varContinue\n movimiento = False\n varContinue = False\n soundCar.stop()\n \n if GameOverP1 == \"Empate\":\n soundExplosion.play()\n\n ExplosionP1Image = PhotoImage(file= \"explosion1.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n\n ExplosionP2Image = PhotoImage(file= \"explosion1.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n \n time.sleep(0.1)\n\n ExplosionP1Image = PhotoImage(file= \"explosion2.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n\n ExplosionP2Image = PhotoImage(file= \"explosion2.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n\n time.sleep(0.1)\n\n ExplosionP1Image = PhotoImage(file= \"explosion3.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n \n ExplosionP2Image = PhotoImage(file= \"explosion3.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n\n time.sleep(0.1)\n\n imagePlayer1 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n raiz.update()\n\n imagePlayer2 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n raiz.update()\n\n ExplosionP1Image = PhotoImage(file= \"explosion4.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n \n ExplosionP2Image = PhotoImage(file= \"explosion4.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n\n time.sleep(0.1)\n\n ExplosionP1Image = PhotoImage(file= \"explosion5.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n \n ExplosionP2Image = PhotoImage(file= \"explosion5.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n\n time.sleep(0.1)\n\n ExplosionP1Image = PhotoImage(file= \"explosion6.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n \n ExplosionP2Image = PhotoImage(file= \"explosion6.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n\n imagePlayer2 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n raiz.update()\n imagePlayer1 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n raiz.update()\n\n mainMenu = PhotoImage(file=\"winP1P2.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n \n \n elif GameOverP1 == True:\n soundExplosion.play()\n\n ExplosionP1Image = PhotoImage(file= \"explosion1.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n time.sleep(0.1)\n\n ExplosionP1Image = PhotoImage(file= \"explosion2.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n time.sleep(0.1)\n\n ExplosionP1Image = PhotoImage(file= \"explosion3.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n time.sleep(0.1)\n\n imagePlayer1 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n raiz.update()\n\n ExplosionP1Image = PhotoImage(file= \"explosion4.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n time.sleep(0.1)\n\n ExplosionP1Image = PhotoImage(file= \"explosion5.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n time.sleep(0.1)\n\n ExplosionP1Image = PhotoImage(file= \"explosion6.png\")\n ExplosionP1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = ExplosionP1Image)\n raiz.update()\n imagePlayer1 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n raiz.update()\n\n soundPass.play()\n\n for i in range(150):\n canvasDer.move(cavasImagePlayer2, 0, -6)\n raiz.update()\n time.sleep(0.005)\n\n mainMenu = PhotoImage(file=\"winP2.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n \n elif GameOverP2 == True: \n soundExplosion.play()\n ExplosionP2Image = PhotoImage(file= \"explosion1.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n time.sleep(0.1)\n\n ExplosionP2Image = PhotoImage(file= \"explosion2.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n time.sleep(0.1)\n\n ExplosionP2Image = PhotoImage(file= \"explosion3.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n time.sleep(0.1)\n\n imagePlayer2 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n raiz.update()\n\n ExplosionP2Image = PhotoImage(file= \"explosion2.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n time.sleep(0.1)\n\n ExplosionP2Image = PhotoImage(file= \"explosion5.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n time.sleep(0.1)\n\n ExplosionP2Image = PhotoImage(file= \"explosion6.png\")\n ExplosionP2 = canvasDer.create_image(posicionXP2, posicionYP2, image = ExplosionP2Image)\n raiz.update()\n imagePlayer2 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n raiz.update()\n soundPass.play()\n for i in range(150):\n canvasIzq.move(cavasImagePlayer1, 0, -6)\n raiz.update()\n time.sleep(0.005)\n \n mainMenu = PhotoImage(file=\"winP1.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n\n\n \n elif GameOverP2 == \"fin\":\n soundPass.play()\n for i in range(150):\n canvasIzq.move(cavasImagePlayer1, 0, -6)\n canvasDer.move(cavasImagePlayer2, 0, -6)\n raiz.update()\n time.sleep(0.005)\n\n mainMenu = PhotoImage(file=\"winP1P2.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n \n elif GameOverP1 == \"Fuel\":\n \n for i in range(3):\n canvasIzq.move(cavasImagePlayer1, 0, -1 + i)\n canvasDer.move(cavasImagePlayer2, 0, -1 + i)\n raiz.update()\n\n mainMenu = PhotoImage(file=\"winP1P2.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n\n elif GameOverP1 == \"Fuel1\":\n soundPass.play()\n \n for i in range(150):\n canvasDer.move(cavasImagePlayer2, 0, -6)\n raiz.update()\n time.sleep(0.005)\n\n mainMenu = PhotoImage(file=\"winP2.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n\n elif GameOverP2 == \"Fuel2\":\n soundPass.play()\n\n for i in range(150):\n canvasIzq.move(cavasImagePlayer1, 0, -6)\n raiz.update()\n time.sleep(0.005)\n\n mainMenu = PhotoImage(file=\"winP1.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n raiz.update()\n\n soundMenu.stop()\n soundOver.play()\n\n GameOverP1 = False \n GameOverP2 = False\n\n \n butRestart = Button(raiz, text=\"RESTART\",bg = \"black\", fg = \"white\",\n font= fontMenu, command= play_multijugador1).place(x= 291, y = 263, height= 56, width= 225)\n\n butContinue = Button(raiz, text=\"CONTINUE\",bg = \"black\", fg = \"white\", \n font= fontMenu, command = _continue_).place(x= 291, y = 334, height= 56, width= 225)\n\n butMenu = Button(raiz, text=\"BACK\",bg = \"black\", fg = \"white\", \n font= fontMenu, command = multi_player1).place(x= 291, y = 408, height= 56, width= 225)\n\n raiz.mainloop()\n\n\n def deslizamientoP1Der():\n global imagePlayer1, cavasImagePlayer1\n soundBrake.play()\n raiz.update()\n imagePlayer1 = PhotoImage(file= \"+player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.2)\n imagePlayer1 = PhotoImage(file= \"-player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n imagePlayer1 = PhotoImage(file= \"player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n return\n\n\n\n def deslizamientoP1Izq():\n global imagePlayer1, cavasImagePlayer1\n soundBrake.play()\n imagePlayer1 = PhotoImage(file= \"-player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.2)\n imagePlayer1 = PhotoImage(file= \"+player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n imagePlayer1 = PhotoImage(file= \"player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n return\n \n\n def deslizamientoP2Der():\n soundBrake.play()\n global imagePlayer2, cavasImagePlayer2 \n imagePlayer2 = PhotoImage(file= \"+player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.2)\n imagePlayer2 = PhotoImage(file= \"-player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n imagePlayer2 = PhotoImage(file= \"player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n return\n \n\n def deslizamientoP2Izq():\n soundBrake.play()\n global imagePlayer2, cavasImagePlayer2 \n imagePlayer2 = PhotoImage(file= \"-player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.2)\n imagePlayer2 = PhotoImage(file= \"+player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n imagePlayer2 = PhotoImage(file= \"player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n return\n\n def efecto_ManchaP1():\n global efectoManchaP1, imagePlayer1, cavasImagePlayer1\n efectoManchaP1 = True\n imagePlayer1, cavasImagePlayer1\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"+player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"2+player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"3+player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"4+player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"5+player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"+-player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"5-player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"4-player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"3-player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"2-player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"-player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP1 == False:\n return\n imagePlayer1 = PhotoImage(file= \"player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n if movimiento == False and efectoManchaP1 == False:\n return\n\n efectoManchaP1 = True\n \n def efecto_ManchaP2():\n global efectoManchaP2, imagePlayer2, cavasImagePlayer2\n efectoManchaP2 = True\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2, cavasImagePlayer2\n imagePlayer2 = PhotoImage(file= \"+player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"2+player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"3+player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"4+player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"5+player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"+-player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"5-player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"4-player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"3-player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"2-player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"-player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n time.sleep(0.1)\n if movimiento == False and efectoManchaP2 == False:\n return\n imagePlayer2 = PhotoImage(file= \"player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n if movimiento == False and efectoManchaP2 == False:\n return\n efectoManchaP2 = False\n\n\n\n #|Reanudar, start|---------------------------------------------------------------------------------------------------------------------------------------\n\n iniciar = True\n\n \n def start(event):\n global raiz, movimiento, iniciar, butSave, dicc, sticker1, sticker2, stickerImage, archivo, minimapY, varContinue, coords, coords2, canvasIzq, canvasDer, playerMinimap, labelGasolina1, velocidad, posicionMinivanX, posicionMinivanY, labelGasolina2, GameOverP1, GameOverP2, efecto, efectoManchaP1, efectoManchaP2, posicionXP2, posicionXP1, posicionYP1, posicionYP2, movimientoP1, movimientoP2, velocidadPlayer, imagePlayer1, cavasImagePlayer1, imagePlayer2, cavasImagePlayer2, posicionManchaY, posicionManchaX, posicionStickerY, posicionStickerX, posicionMinivanX_2, posicionMinivanY_2, posicionRunnerX, posicionRunnerY, posicionFighterX, posicionFighterX2, posicionFighterY, coords, coords2, contadorGasolina, contadorGasolina2, cont\n\n raiz.bind('<space>', stop)\n\n movimiento = True\n\n archivo[\"player1\"] = player1.get()\n\n archivo[\"player2\"] = player2.get()\n\n if iniciar == True:\n raiz.bind('<a>', aKey)\n raiz.bind('<d>', dKey)\n raiz.bind('<s>', sKey)\n raiz.bind('<j>', jKey)\n raiz.bind('<l>', lKey)\n raiz.bind('<k>', kKey)\n\n\n posicionManchaY = random.randint(-1000,-500)\n posicionManchaX = random.randint(30,165)\n\n archivo[\"posicionManchaY\"] = posicionManchaY\n archivo[\"posicionManchaX\"] = posicionManchaX\n\n\n posicionStickerY = random.randint(-7000,-4000)\n posicionStickerX = random.randint(30,165)\n\n archivo[\"posicionStickerY\"] = posicionStickerY\n archivo[\"posicionStickerX\"] = posicionStickerX\n\n posicionMinivanY = random.randint(-2000,-100)\n posicionMinivanX = random.randint(30,165)\n\n archivo[\"posicionMinivanY\"] = posicionMinivanY\n archivo[\"posicionMinivanX\"] = posicionMinivanX \n\n posicionMinivanY_2 = random.randint(-6000,-3000)\n posicionMinivanX_2 = random.randint(30,165)\n\n archivo[\"posicionMinivanY_2\"] = posicionMinivanY_2\n archivo[\"posicionMinivanX_2\"] = posicionMinivanX_2 \n\n posicionRunnerY = random.randint(-3000,-2000)\n posicionRunnerX = random.randint(30,165)\n\n archivo[\"posicionRunnerY\"] = posicionRunnerY\n archivo[\"posicionRunnerX\"] = posicionRunnerX\n\n posicionFighterY = random.randint(-7000,-4000)\n posicionFighterX = random.randint(30,165)\n posicionFighterX2 = posicionFighterX\n\n archivo[\"posicionFighterY\"] = posicionFighterY\n archivo[\"posicionFighterX\"] = posicionFighterX\n archivo[\"posicionFighterX2\"] = posicionFighterX2\n\n if varContinue == True:\n\n posicionManchaY = eval(dicc)[\"posicionManchaY\"]\n posicionManchaX = eval(dicc)[\"posicionManchaX\"]\n\n posicionStickerY = eval(dicc)[\"posicionStickerY\"]\n posicionStickerX = eval(dicc)[\"posicionStickerX\"]\n\n posicionMinivanY = eval(dicc)[\"posicionMinivanY\"]\n posicionManchaX = eval(dicc)[\"posicionMinivanX\"]\n\n posicionMinivanY_2 = eval(dicc)[\"posicionMinivanY_2\"]\n posicionMinivanX_2 = eval(dicc)[\"posicionMinivanX_2\"]\n\n posicionRunnerY= eval(dicc)[\"posicionRunnerY\"]\n posicionRunnerX = eval(dicc)[\"posicionRunnerX\"]\n\n posicionFighterY = eval(dicc)[\"posicionFighterY\"] \n posicionFighterX = eval(dicc)[\"posicionFighterX\"]\n posicionFighterX2 = eval(dicc)[\"posicionFighterX2\"]\n\n manchaImage = PhotoImage(file=\"mancha.png\") \n mancha1 = canvasDer.create_image(posicionManchaX, posicionManchaY, image = manchaImage)\n mancha2 = canvasIzq.create_image(posicionManchaX, posicionManchaY, image = manchaImage)\n\n\n\n stickerImage = PhotoImage(file=\"sticker.png\") \n sticker1 = canvasDer.create_image(posicionStickerX, posicionStickerY, image = stickerImage)\n sticker2 = canvasIzq.create_image(posicionStickerX, posicionStickerY, image = stickerImage)\n\n\n minivanImage = PhotoImage(file=\"minivan.png\") \n minivan1 = canvasDer.create_image(posicionMinivanX, posicionMinivanY, image = minivanImage)\n minivan2 = canvasIzq.create_image(posicionMinivanX, posicionMinivanY, image = minivanImage)\n\n minivan1_2 = canvasDer.create_image(posicionMinivanX_2, posicionMinivanY_2, image = minivanImage)\n minivan2_2 = canvasIzq.create_image(posicionMinivanX_2, posicionMinivanY_2, image = minivanImage)\n\n runnerImage = PhotoImage(file= \"runner.png\")\n runner1 = canvasDer.create_image(posicionRunnerX, posicionRunnerY, image = runnerImage)\n runner2 = canvasIzq.create_image(posicionRunnerX, posicionRunnerY, image = runnerImage)\n\n movimientoRunner = random.randint(0,1)\n\n velocidadRunner = 0.5\n\n fighterImage = PhotoImage(file= \"fighter.png\")\n fighter1 = canvasDer.create_image(posicionFighterX, posicionFighterY, image = fighterImage)\n fighter2 = canvasIzq.create_image(posicionFighterX2, posicionFighterY, image = fighterImage)\n\n velocidadFighter = 0.25\n\n #|Animación Init|----------------------------------\n\n soundCar.stop()\n \n for i in range(100):\n canvasIzq.move(cavasImagePlayer1, 0, -1)\n canvasDer.move(cavasImagePlayer2, 0, -1)\n time.sleep(0.01)\n raiz.update()\n \n initImage = PhotoImage(file=\"init1.png\")\n initMinimap = canvasMinimap.create_image(85, 215, image = initImage)\n raiz.update()\n soundInit1.play()\n\n initImage = PhotoImage(file=\"init2.png\")\n initMinimap = canvasMinimap.create_image(85, 215, image = initImage)\n time.sleep(1)\n raiz.update()\n soundInit1.play()\n\n initImage = PhotoImage(file=\"init3.png\")\n \n initMinimap = canvasMinimap.create_image(85, 215, image = initImage)\n time.sleep(1)\n raiz.update()\n soundInit2.play()\n\n\n #--------------------------------------------------\n\n \n movimiento = True\n\n \n#------------------------------------------------------------- \n iniciar = False\n\n velocidad = difficulty.get()\n\n archivo[\"velocidad\"] = velocidad\n\n\n coords = canvasDer.coords(roadDer)\n\n archivo[\"posicionRoadDer\"] = coords[1]\n\n coords2 = canvasIzq.coords(roadIzq)\n\n archivo[\"posicionRoadIzq\"] = coords2[1]\n\n cont = 0\n archivo[\"cont\"] = cont\n\n carSound = True\n\n if varContinue == True:\n\n velocidad = eval(dicc)[\"velocidad\"]\n contadorGasolina = eval(dicc)[\"contadorGasolina\"]\n contadorGasolina2 = eval(dicc)[\"contadorGasolina2\"]\n cont = eval(dicc)[\"cont\"]\n\n\n \n while True:\n if movimiento == True:\n if carSound == True:\n soundMenu.stop()\n soundOver.stop()\n soundCar.play()\n carSound = False\n \n canvasDer.move(roadDer, 0, velocidad)\n\n canvasIzq.move(roadIzq, 0, velocidad)\n\n coords = canvasDer.coords(roadDer)\n archivo[\"posicionRoadDer\"] = coords[1]\n\n coords2 = canvasIzq.coords(roadIzq)\n archivo[\"posicionRoadIzq\"] = coords2[1]\n\n #|gasolina y minimapa|--------------------------------------------\n\n\n cont += 1\n archivo[\"cont\"] = cont\n \n\n if cont % 100 == 0:\n\n \n contadorGasolina -= velocidad\n\n contadorGasolina2 -= velocidad\n\n\n if velocidad == 1:\n canvasMinimap.move(playerMinimap, 0, (-velocidad - 0.49)) \n minimapY -= (velocidad + 0.49) \n \n elif velocidad == 2:\n canvasMinimap.move(playerMinimap, 0, (-velocidad - 0.975)) \n minimapY -= velocidad + 0.975\n\n elif velocidad == 3:\n canvasMinimap.move(playerMinimap, 0, (-velocidad - 1.46))\n minimapY -= velocidad + 1.46\n\n elif velocidad == 4:\n canvasMinimap.move(playerMinimap, 0, (-velocidad - 1.95))\n minimapY -= velocidad + 1.95\n\n elif velocidad == 5:\n canvasMinimap.move(playerMinimap, 0, (-velocidad - 2.45)) \n minimapY -= velocidad + 2.45\n\n gasolina1.set(contadorGasolina)\n gasolina2.set(contadorGasolina2)\n\n\n\n raiz.update()\n \n #|Jugador1 y Jugador2 Efecto Explosión|------------------\n \n if ((posicionXP1 > 171.5) and (posicionXP2 > 171.5)) or ((posicionXP1 < 26) and (posicionXP2 < 26)) or ((posicionXP1 > 171.5) and (posicionXP2 < 26)) or ((posicionXP1 < 26) and (posicionXP2 > 171.5)):\n if efectoManchaP1 == True and efectoManchaP2 == True:\n efectoManchaP1 = False\n efectoManchaP2 = False\n GameOverP1 = \"Empate\"\n imagePlayer1 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n imagePlayer2 = PhotoImage(file=\"transparent.png\")\n cavasImagePlayer2 = canvasIzq.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n raiz.update()\n GameOver()\n\n \n #|MOVE JUGADOR1 |--------------------------------\n\n if movimientoP1 == \"der\" and posicionXP1 < 172:\n canvasIzq.move(cavasImagePlayer1, velocidadPlayer, 0)\n raiz.update()\n posicionXP1 += velocidadPlayer\n \n if (posicionXP1 > 171.5):\n canvasIzq.move(cavasImagePlayer1, -velocidadPlayer, 0)\n raiz.update()\n posicionXP1 -= velocidadPlayer\n\n deslizarP1Der = threading.Thread(target=deslizamientoP1Der)\n\n deslizarP1Der.start()\n\n movimientoP1 = \"izq\"\n\n contadorGasolina -= velocidad\n\n raiz.update()\n\n if efectoManchaP1 == True:\n GameOverP1 = True\n efectoManchaP1 = False\n raiz.update()\n GameOver()\n\n \n elif movimientoP1 == \"izq\" and posicionXP1 > 25:\n canvasIzq.move(cavasImagePlayer1, -velocidadPlayer, 0)\n raiz.update()\n posicionXP1 -= velocidadPlayer\n \n if (posicionXP1 < 26):\n canvasIzq.move(cavasImagePlayer1, velocidadPlayer, 0)\n\n deslizarP1Izq = threading.Thread(target=deslizamientoP1Izq)\n\n deslizarP1Izq.start()\n\n\n posicionXP1 += velocidadPlayer\n\n movimientoP1 = \"der\"\n\n contadorGasolina -= velocidad\n\n raiz.update()\n\n if efectoManchaP1 == True:\n efectoManchaP1 = False\n raiz.update()\n GameOverP1 = True\n GameOver()\n\n \n \n elif movimientoP1 == \"none\":\n canvasIzq.move(cavasImagePlayer1, 0, 0)\n raiz.update()\n\n #|MOVE JUGADOR2| -------------------------------\n\n if movimientoP2 == \"der\" and posicionXP2 < 172:\n canvasDer.move(cavasImagePlayer2, velocidadPlayer, 0)\n raiz.update()\n posicionXP2 += velocidadPlayer\n \n if (posicionXP2 > 171.5):\n canvasDer.move(cavasImagePlayer2, -velocidadPlayer, 0)\n\n deslizarP2Der = threading.Thread(target=deslizamientoP2Der)\n deslizarP2Der.start()\n\n posicionXP2 -= velocidadPlayer\n\n contadorGasolina2 -= velocidad\n \n raiz.update()\n \n movimientoP2 = \"izq\"\n\n if efectoManchaP2 == True:\n efectoManchaP2 = False\n raiz.update()\n GameOverP2 = True\n GameOver()\n \n \n elif movimientoP2 == \"izq\" and posicionXP2 > 25:\n canvasDer.move(cavasImagePlayer2, -velocidadPlayer, 0)\n raiz.update()\n posicionXP2 -= velocidadPlayer\n \n if (posicionXP2 < 26):\n canvasDer.move(cavasImagePlayer2, velocidadPlayer, 0)\n\n deslizarP2Izq = threading.Thread(target=deslizamientoP2Izq)\n\n deslizarP2Izq.start()\n raiz.update()\n\n posicionXP2 += velocidadPlayer\n\n contadorGasolina2 -= velocidad\n\n movimientoP2 = \"der\"\n\n if efectoManchaP2 == True:\n efectoManchaP2 = False\n raiz.update()\n GameOverP2 = True\n GameOver()\n \n \n elif movimientoP2 == \"none\":\n canvasDer.move(cavasImagePlayer2, 0, 0)\n raiz.update()\n\n #|Game Over|---------\n \n if (posicionXP1 in list(range(posicionMinivanX - 20, posicionMinivanX + 20))) and (posicionYP1 in list(range(posicionMinivanY - 50, posicionMinivanY + 50))) and (posicionXP2 in list(range(posicionMinivanX - 20, posicionMinivanX + 20))) and (posicionYP2 in list(range(posicionMinivanY - 50, posicionMinivanY + 50))):\n GameOverP1 = \"Empate\" \n GameOver()\n\n elif (posicionXP1 in list(range(posicionMinivanX - 20, posicionMinivanX + 20))) and (posicionYP1 in list(range(posicionMinivanY - 50, posicionMinivanY + 50))):\n \n GameOverP1 = True\n GameOver()\n\n\n elif (posicionXP2 in list(range(posicionMinivanX - 20, posicionMinivanX + 20))) and (posicionYP2 in list(range(posicionMinivanY - 50, posicionMinivanY + 50))):\n\n GameOverP2= True\n GameOver()\n\n if (posicionXP1 in list(range(posicionMinivanX_2 - 20, posicionMinivanX_2 + 20))) and (posicionYP1 in list(range(posicionMinivanY_2 - 50, posicionMinivanY_2 + 50))) and (posicionXP2 in list(range(posicionMinivanX_2 - 20, posicionMinivanX_2 + 20))) and (posicionYP2 in list(range(posicionMinivanY_2 - 50, posicionMinivanY_2 + 50))):\n GameOverP1 = \"Empate\" \n GameOver()\n\n elif (posicionXP1 in list(range(posicionMinivanX_2 - 20, posicionMinivanX_2 + 20))) and (posicionYP1 in list(range(posicionMinivanY_2 - 50, posicionMinivanY_2 + 50))):\n \n GameOverP1 = True\n GameOver()\n\n\n elif (posicionXP2 in list(range(posicionMinivanX_2 - 20, posicionMinivanX_2 + 20))) and (posicionYP2 in list(range(posicionMinivanY_2 - 50, posicionMinivanY_2 + 50))):\n\n GameOverP2= True\n GameOver()\n\n if (posicionXP1 in list(range(int(posicionRunnerX) - 20, int(posicionRunnerX) + 20))) and (posicionYP1 in list(range(posicionRunnerY - 50, posicionRunnerY + 50))) and (posicionXP2 in list(range(int(posicionRunnerX) - 20, int(posicionRunnerX) + 20))) and (posicionYP2 in list(range(posicionRunnerY - 50, posicionRunnerY + 50))):\n GameOverP1 = \"Empate\"\n GameOver()\n\n elif (posicionXP1 in list(range(int(posicionRunnerX) - 20, int(posicionRunnerX) + 20))) and (posicionYP1 in list(range(posicionRunnerY - 50, posicionRunnerY + 50))):\n \n GameOverP1 = True\n GameOver()\n\n\n elif (posicionXP2 in list(range(int(posicionRunnerX) - 20, int(posicionRunnerX) + 20))) and (posicionYP2 in list(range(posicionRunnerY - 50, posicionRunnerY + 50))):\n\n GameOverP2= True\n GameOver()\n\n if (posicionXP1 in list(range(int(posicionFighterX) - 20, int(posicionFighterX) + 20))) and (posicionYP1 in list(range(int(posicionFighterY) - 50, int(posicionFighterY) + 50))) and (posicionXP2 in list(range(int(posicionFighterX2) - 20, int(posicionFighterX2) + 20))) and (posicionYP2 in list(range(int(posicionFighterY) - 50, int(posicionFighterY) + 50))):\n GameOverP1 = \"Empate\"\n GameOver()\n\n elif (posicionXP1 in list(range(int(posicionFighterX) - 20, int(posicionFighterX) + 20))) and (posicionYP1 in list(range(int(posicionFighterY) - 50, int(posicionFighterY) + 50))):\n \n GameOverP1 = True\n GameOver()\n\n\n elif (posicionXP2 in list(range(int(posicionFighterX2) - 20, int(posicionFighterX2) + 20))) and (posicionYP2 in list(range(int(posicionFighterY) - 50, int(posicionFighterY) + 50))):\n\n GameOverP2= True\n GameOver()\n\n if (posicionXP1 in list(range(posicionManchaX - 20, posicionManchaX + 20))) and (posicionYP1 in list(range(posicionManchaY - 20, posicionManchaY + 20))) and (posicionXP2 in list(range(posicionManchaX - 20, posicionManchaX + 20))) and (posicionYP2 in list(range(posicionManchaY - 20, posicionManchaY + 20))):\n contadorGasolina -= velocidad\n contadorGasolina2 -= velocidad\n\n if efectoManchaP1 == False and efectoManchaP2 == False:\n ManchaP1 = threading.Thread(target=efecto_ManchaP1)\n soundBrake.play()\n\n ManchaP1.start()\n\n ManchaP2 = threading.Thread(target=efecto_ManchaP2)\n\n ManchaP2.start()\n\n if movimientoP1 == \"der\":\n movimientoP1 = \"izq\"\n elif movimientoP1 == \"izq\":\n movimientoP1 = \"der\"\n elif movimientoP1 == \"none\":\n movimientoP1 = random.choice([\"der\", \"izq\"])\n\n if movimientoP2 == \"der\":\n movimientoP2 = \"izq\"\n elif movimientoP2 == \"izq\":\n movimientoP2 = \"der\"\n elif movimientoP2 == \"none\":\n movimientoP2 = random.choice([\"der\", \"izq\"])\n \n \n\n elif (posicionXP1 in list(range(posicionManchaX - 20, posicionManchaX + 20))) and (posicionYP1 in list(range(posicionManchaY - 20, posicionManchaY + 20))):\n contadorGasolina -= velocidad\n\n if efectoManchaP1 == False:\n soundBrake.play()\n \n ManchaP1 = threading.Thread(target=efecto_ManchaP1)\n\n ManchaP1.start()\n\n if movimientoP1 == \"der\":\n movimientoP1 = \"izq\"\n elif movimientoP1 == \"izq\":\n movimientoP1 = \"der\"\n elif movimientoP1 == \"none\":\n movimientoP1 = random.choice([\"der\", \"izq\"])\n\n elif (posicionXP2 in list(range(posicionManchaX - 20, posicionManchaX + 20))) and (posicionYP2 in list(range(posicionManchaY - 20, posicionManchaY + 20))):\n contadorGasolina2 -= velocidad\n \n if efectoManchaP2 == False:\n soundBrake.play()\n\n ManchaP2 = threading.Thread(target=efecto_ManchaP2)\n\n ManchaP2.start()\n\n if movimientoP2 == \"der\":\n movimientoP2 = \"izq\"\n elif movimientoP2 == \"izq\":\n movimientoP2 = \"der\"\n elif movimientoP2 == \"none\":\n movimientoP2 = random.choice([\"der\", \"izq\"])\n\n\n #|Gasolina|----------------------------------------- \n\n\n raiz.update()\n \n if (posicionXP1 in list(range(posicionStickerX - 15, posicionStickerX + 15))) and (posicionYP1 in list(range(posicionStickerY - 15, posicionStickerY + 15))) and (posicionXP2 in list(range(posicionStickerX - 15, posicionStickerX + 15))) and (posicionYP2 in list(range(posicionStickerY - 15, posicionStickerY + 15))):\n contadorGasolina += 2\n contadorGasolina2 += 2\n\n soundSticker.play()\n\n stickerImage = PhotoImage(file=\"transparent.png\") \n sticker1 = canvasDer.create_image(posicionStickerX, posicionStickerY, image = stickerImage)\n raiz.update()\n stickerImage = PhotoImage(file=\"transparent.png\") \n sticker2 = canvasIzq.create_image(posicionStickerX, posicionStickerY, image = stickerImage)\n raiz.update()\n\n elif (posicionXP1 in list(range(posicionStickerX - 15, posicionStickerX + 15))) and (posicionYP1 in list(range(posicionStickerY - 15, posicionStickerY + 15))):\n \n contadorGasolina += 2\n soundSticker.play()\n\n stickerImage = PhotoImage(file=\"transparent.png\") \n sticker1 = canvasDer.create_image(posicionStickerX, posicionStickerY, image = stickerImage)\n raiz.update()\n\n elif (posicionXP2 in list(range(posicionStickerX - 15, posicionStickerX + 15))) and (posicionYP2 in list(range(posicionStickerY - 15, posicionStickerY + 15))):\n\n contadorGasolina2 += 2\n soundSticker.play()\n stickerImage = PhotoImage(file=\"transparent.png\") \n sticker2 = canvasIzq.create_image(posicionStickerX, posicionStickerY, image = stickerImage)\n raiz.update()\n \n #|Movimiento Objetos|--------------------------\n \n #|Sticker|---------------------------\n\n canvasIzq.move(sticker1, 0, velocidad)\n canvasDer.move(sticker2, 0, velocidad)\n posicionStickerY += velocidad\n raiz.update()\n\n\n if posicionStickerY > 810:\n posicionRandomY = random.randint(-8000,-7000)\n if posicionStickerX < 75:\n posicionRandomX = random.randint(-1,100)\n elif posicionStickerX > 75 and posicionStickerX < 120:\n posicionRandomX = random.randint(-75,75)\n elif posicionStickerX > 120:\n posicionRandomX = random.randint(-100,1)\n\n \n canvasIzq.move(sticker1, posicionRandomX, posicionRandomY)\n canvasDer.move(sticker2, posicionRandomX, posicionRandomY)\n posicionStickerY += posicionRandomY\n\n posicionManchaX += posicionRandomX\n\n stickerImage = PhotoImage(file=\"sticker.png\") \n sticker1 = canvasDer.create_image(posicionStickerX, posicionStickerY, image = stickerImage)\n \n sticker2 = canvasIzq.create_image(posicionStickerX, posicionStickerY, image = stickerImage)\n\n \n raiz.update()\n \n #|Manchas|--------------------------\n\n\n canvasIzq.move(mancha1, 0, velocidad)\n canvasDer.move(mancha2, 0, velocidad)\n posicionManchaY += velocidad\n raiz.update()\n\n\n if posicionManchaY > 810:\n posicionRandomY = random.randint(-6000,-4000)\n if posicionManchaX < 75:\n posicionRandomX = random.randint(-1,100)\n elif posicionManchaX > 75 and posicionManchaX < 120:\n posicionRandomX = random.randint(-75,75)\n elif posicionManchaX > 120:\n posicionRandomX = random.randint(-100,1)\n\n \n canvasIzq.move(mancha1, posicionRandomX, posicionRandomY)\n canvasDer.move(mancha2, posicionRandomX, posicionRandomY)\n posicionManchaY += posicionRandomY\n\n posicionManchaX += posicionRandomX\n\n \n raiz.update()\n\n\n \n #|minivan|-----------------------------\n\n canvasIzq.move(minivan1, 0, velocidad)\n canvasDer.move(minivan2, 0, velocidad)\n posicionMinivanY += velocidad\n raiz.update()\n\n\n if posicionMinivanY > 810:\n posicionRandomY = random.randint(-1000,-820)\n if posicionMinivanX < 75:\n posicionRandomX = random.randint(-1,100)\n elif posicionMinivanX > 75 and posicionMinivanX < 120:\n posicionRandomX = random.randint(-75,75)\n elif posicionMinivanX > 120:\n posicionRandomX = random.randint(-100,1)\n\n \n canvasIzq.move(minivan1, posicionRandomX, posicionRandomY)\n canvasDer.move(minivan2, posicionRandomX, posicionRandomY)\n posicionMinivanY += posicionRandomY\n\n posicionMinivanX += posicionRandomX\n\n \n raiz.update()\n\n\n canvasIzq.move(minivan1_2, 0, velocidad)\n canvasDer.move(minivan2_2, 0, velocidad)\n posicionMinivanY_2 += velocidad\n raiz.update()\n\n\n if posicionMinivanY_2 > 810:\n posicionRandomY = random.randint(-10000,-8000)\n if posicionMinivanX_2 < 75:\n posicionRandomX = random.randint(-1,100)\n elif posicionMinivanX_2 > 75 and posicionMinivanX_2 < 120:\n posicionRandomX = random.randint(-75,75)\n elif posicionMinivanX_2 > 120:\n posicionRandomX = random.randint(-100,1)\n\n \n canvasIzq.move(minivan1_2, posicionRandomX, posicionRandomY)\n canvasDer.move(minivan2_2, posicionRandomX, posicionRandomY)\n posicionMinivanY_2 += posicionRandomY\n\n posicionMinivanX_2 += posicionRandomX\n\n \n raiz.update()\n\n #|runner|-------------------------------\n\n if movimientoRunner == 1:\n canvasIzq.move(runner1, velocidadRunner, velocidad)\n canvasDer.move(runner2, velocidadRunner, velocidad)\n posicionRunnerY += velocidad\n posicionRunnerX += velocidadRunner\n if posicionRunnerX > 160:\n movimientoRunner = 0\n \n elif movimientoRunner == 0: \n canvasIzq.move(runner1, -velocidadRunner, velocidad)\n canvasDer.move(runner2, -velocidadRunner, velocidad)\n posicionRunnerY += velocidad\n posicionRunnerX -= velocidadRunner\n if posicionRunnerX < 40:\n movimientoRunner = 1\n\n raiz.update()\n\n\n if posicionRunnerY > 810:\n posicionRandomY = random.randint(-3000,-2000)\n if posicionRunnerX < 75:\n posicionRandomX = random.randint(-1,50)\n elif posicionRunnerX > 75 and posicionRunnerX < 120:\n posicionRandomX = random.randint(-50,50)\n elif posicionRunnerX > 120:\n posicionRandomX = random.randint(-50,1)\n\n \n canvasIzq.move(runner1, posicionRandomX, posicionRandomY)\n canvasDer.move(runner2, posicionRandomX, posicionRandomY)\n posicionRunnerY += posicionRandomY\n\n posicionRunnerX += posicionRandomX\n\n \n raiz.update()\n\n #|Fighter|--------------------------------------\n \n \n if posicionFighterX < posicionXP1:\n canvasIzq.move(fighter1, velocidadFighter, velocidad)\n posicionFighterX += velocidadFighter\n\n elif posicionFighterX > posicionXP1:\n canvasIzq.move(fighter1, -velocidadFighter, velocidad)\n posicionFighterX += -velocidadFighter\n\n elif posicionFighterX == posicionXP1:\n canvasIzq.move(fighter1, 0, velocidad)\n posicionFighterX += 0\n\n if posicionFighterX2 < posicionXP2:\n canvasDer.move(fighter2, velocidadFighter, velocidad)\n posicionFighterX2 += velocidadFighter\n \n elif posicionFighterX2 > posicionXP2:\n canvasDer.move(fighter2, -velocidadFighter, velocidad)\n posicionFighterX2 += -velocidadFighter\n \n\n elif posicionFighterX2 == posicionXP2: \n canvasDer.move(fighter2, 0, velocidad)\n posicionFighterX2 += 0\n \n posicionFighterY += velocidad\n\n\n\n if posicionFighterY > 810:\n posicionRandomY = random.randint(-5000,-4000)\n\n posicionFighterY += posicionRandomY\n \n if posicionFighterX < 75:\n posicionRandomX = random.randint(-1,50)\n elif posicionFighterX > 75 and posicionFighterX < 130:\n posicionRandomX = random.randint(-25,25)\n elif posicionFighterX > 130:\n posicionRandomX = random.randint(-50,1)\n raiz.update()\n \n canvasIzq.move(fighter1, posicionRandomX, posicionRandomY)\n posicionFighterX += posicionRandomX\n\n if posicionFighterX2 < 75:\n posicionRandomX = random.randint(-1,50)\n elif posicionFighterX2 > 75 and posicionFighterX2 < 130:\n posicionRandomX = random.randint(-25,25)\n elif posicionFighterX2 > 130:\n posicionRandomX = random.randint(-50,1)\n raiz.update()\n\n canvasDer.move(fighter2, posicionRandomX, posicionRandomY)\n posicionFighterX2 += posicionRandomX\n \n\n\n # |Fuel|---------------\n\n if (int(gasolina1.get()) <= 0) and (int(gasolina2.get()) <= 0):\n\n GameOverP1 = \"Fuel\"\n GameOver()\n\n elif int(gasolina1.get()) <= 0:\n GameOverP1 = \"Fuel1\"\n GameOver()\n elif int(gasolina2.get()) <= 0:\n GameOverP2 = \"Fuel2\"\n GameOver()\n \n\n #|Final|---------------------------------------\n\n if (coords[1]) >= 9230.0 :\n GameOverP2 = \"fin\"\n GameOver()\n \n elif movimiento == False:\n canvasDer.move(roadDer, 0, 0)\n \n canvasIzq.move(roadIzq, 0, 0)\n raiz.update()\n\n archivo[\"posicionManchaY\"] = posicionManchaY\n archivo[\"posicionManchaX\"] = posicionManchaX\n\n archivo[\"posicionStickerY\"] = posicionStickerY\n archivo[\"posicionStickerX\"] = posicionStickerX\n\n archivo[\"posicionMinivanY\"] = posicionMinivanY\n archivo[\"posicionMinivanX\"] = posicionMinivanX \n\n archivo[\"posicionMinivanY_2\"] = posicionMinivanY_2\n archivo[\"posicionMinivanX_2\"] = posicionMinivanX_2 \n\n archivo[\"posicionRunnerY\"] = posicionRunnerY\n archivo[\"posicionRunnerX\"] = posicionRunnerX\n\n archivo[\"posicionFighterY\"] = posicionFighterY\n archivo[\"posicionFighterX\"] = posicionFighterX\n archivo[\"posicionFighterX2\"] = posicionFighterX2\n\n archivo[\"posicionRoadDer\"] = coords[1]\n\n archivo[\"posicionRoadIzq\"] = coords2[1]\n\n archivo[\"contadorGasolina\"] = contadorGasolina\n\n archivo[\"contadorGasolina2\"] = contadorGasolina2\n\n archivo[\"cont\"] = cont\n\n archivo[\"posicionXP1\"] = posicionXP1\n\n archivo[\"posicionYP1\"] = posicionYP1\n\n archivo[\"posicionXP2\"] = posicionXP2\n\n archivo[\"posicionYP2\"] = posicionYP2\n\n archivo[\"minimapY\"] = minimapY\n\n\n #|Fondo|-------------------------------------------\n \n mainMenu = PhotoImage(file=\"playMultiPlayer.png\")\n fondoMenu= Label(raiz, image = mainMenu).place(x=0, y=0)\n\n fontSubMenu = font.Font(family='Harlow Solid Semiexpandida Negrita Cursiva', size= 10) \n fontSubMenu2 = font.Font(family='Harlow Solid Semiexpandida Negrita Cursiva', size= 12) \n\n \n\n #|Carretera derecha|-------------------------------------------------------\n \n roadImageDer = PhotoImage(file=\"derecha.png\")\n\n posicionRoadDer = -8850\n\n if varContinue == True:\n\n posicionRoadDer = eval(dicc)[\"posicionRoadDer\"]\n\n canvasDer = Canvas(raiz, width = 192, height = 600, borderwidth = 0)\n canvasDer.place(x= 555, y=0)\n\n roadDer = canvasDer.create_image(98, posicionRoadDer, image = roadImageDer)\n\n #|carretera izquierda|-------------------------------------------------------\n \n roadImageIzq = PhotoImage(file=\"izquierda.png\")\n\n canvasIzq = Canvas(raiz, width = 192, height = 600)\n canvasIzq.place(x= 47, y=0)\n\n posicionRoadIzq = -8850\n\n if varContinue == True:\n\n posicionRoadIzq = eval(dicc)[\"posicionRoadIzq\"]\n\n roadIzq = canvasIzq.create_image(98, posicionRoadIzq, image = roadImageIzq)\n\n #|jugadores|----------------------------------------------------------------------\n\n if varContinue == True:\n posicionXP1 = eval(dicc)[\"posicionXP1\"] \n\n posicionYP1 = eval(dicc)[\"posicionYP1\"] \n\n posicionXP2 = eval(dicc)[\"posicionXP2\"] \n\n posicionYP2 = eval(dicc)[\"posicionYP2\"] \n\n imagePlayer1 = PhotoImage(file=\"player1.png\")\n cavasImagePlayer1 = canvasIzq.create_image(posicionXP1, posicionYP1, image = imagePlayer1)\n\n imagePlayer2 = PhotoImage(file=\"player2.png\")\n cavasImagePlayer2 = canvasDer.create_image(posicionXP2, posicionYP2, image = imagePlayer2)\n\n if iniciar == True:\n canvasIzq.move(cavasImagePlayer1, 0, 100)\n canvasDer.move(cavasImagePlayer2, 0, 100)\n\n\n #|Gasolina|--------------------------------\n\n gasolina1 = StringVar()\n\n gasolina2 = StringVar()\n \n gasolina1.set(200)\n\n\n gasolina2.set(200)\n\n if varContinue == True:\n gasolina1.set(eval(dicc)[\"contadorGasolina\"])\n gasolina2.set(eval(dicc)[\"contadorGasolina\"])\n\n\n labelGasolina1 = Label(raiz, textvariable = gasolina1, fg = \"white\", bg = \"black\", font = fontSubMenu2).place(x=340, y=185)\n\n labelGasolina2 = Label(raiz, textvariable = gasolina2, fg = \"white\", bg = \"black\", font = fontSubMenu2).place(x=425, y=185)\n\n #|Minimap|-------------------------------------------------------------------\n \n minimapImage = PhotoImage(file=\"Minimap.png\")\n\n playerMinimapImage = PhotoImage(file=\"playersMinimap.png\")\n\n\n canvasMinimap = Canvas(raiz, width = 160, height = 390, borderwidth = -3, background= \"black\")\n \n\n canvasMinimap.place(x= 315, y=230)\n\n minimap = canvasMinimap.create_image(85, 180, image = minimapImage)\n\n minimapY = 180\n\n archivo[\"minimapY\"] = minimapY\n\n if varContinue == True:\n minimapY = eval(dicc)[\"minimapY\"]\n\n playerMinimap = canvasMinimap.create_image(84, minimapY, image = playerMinimapImage)\n\n #|Init|----------------------------------------------\n\n initImage = PhotoImage(file=\"init.png\")\n\n\n initMinimap = canvasMinimap.create_image(85, 215, image = initImage)\n\n #|Kilometraje|----------------------------------\n\n km = StringVar()\n variableKm = str((difficulty.get())*25) + \" km/h\"\n km.set(variableKm)\n archivo[\"km\"] = (km.get())\n if varContinue == True:\n km.set(eval(dicc)[\"km\"])\n kilometraje = Label(canvasMinimap, textvariable = km , fg = \"white\", bg = \"black\", font = fontSubMenu2).place(x=55, y=320)\n\n #|Botones|--------------------------------------------------------------------------\n fontSave = font.Font(family='Haettenschweiler', size= 13)\n\n butSave = Button(raiz, text=\"Save\",bg = \"black\", fg = \"white\", \n font= fontSave, command= _save_).place(x= 377, y = 241, height= 32, width= 48)\n\n\n butContinue = Button(raiz, text=\"Continue\",bg = \"black\", fg = \"white\", \n font= fontSave, command= _continue_).place(x= 377, y = 271, height= 32, width= 48)\n\n\n butRestart = Button(raiz, text=\"Restart\",bg = \"black\", fg = \"white\", \n font= fontSave, command= play_multijugador1).place(x= 377, y = 301, height= 32, width= 48)\n\n\n butBack = Button(raiz, text=\"Back\",bg = \"black\", fg = \"white\", \n font= fontSave, command= multi_player1).place(x= 377, y = 331, height= 32, width= 48)\n\n\n\n #----------------------------------------------------------------------------------\n\n if varContinue == True:\n\n player1.set(eval(dicc)[\"player1\"])\n player2.set(eval(dicc)[\"player2\"])\n \n\n #|Labels players|------------------------------------------------------------------------\n\n\n labelPlayer1 = Label(raiz, text = player1.get(), fg = \"red\", bg = \"black\", font = fontSubMenu).place(x=315, y=104)\n\n labelPlayer2 = Label(raiz, text = player2.get(), fg = \"cyan\", bg = \"black\", font = fontSubMenu).place(x=400, y=104)\n\n\n start(\"True\")\n \n\n raiz.mainloop()\n\n#|Funciones De Pausa| -------------------------------------------------------------------------------\n\ndef play_multijugador1():\n global varContinue\n varContinue = False\n play_multijugador()\ndef multi_player1():\n global varContinue\n varContinue = False\n multi_player()\n\n#---------------------------------------------------------------------------------------------------\n\nmain_menu()\n\n#Hola Maestra Ya hice mi primer Juego! xD","sub_path":"Road Fighter Project - Julián Loaiza/Road Fighter/Road Fighter.py","file_name":"Road Fighter.py","file_ext":"py","file_size_in_byte":67533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"432036536","text":"import pyglet.app\n\nfrom pycraft.window import Window\nfrom pycraft.world import World\nfrom pycraft.objects.player import Player\n\nWINDOW_DIMENSIONS = (800, 600)\nWINDOW_CAPTION = 'PyCraft'\nWINDOW_RESIZEABLE = True\nWINDOW_EXCLUSIVE_MOUSE = True\n\n\ndef main():\n window = Window(\n width=WINDOW_DIMENSIONS[0],\n height=WINDOW_DIMENSIONS[1],\n caption=WINDOW_CAPTION,\n resizable=WINDOW_RESIZEABLE\n )\n # Hide the mouse cursor and prevent the mouse from leaving the window.\n window.set_exclusive_mouse(WINDOW_EXCLUSIVE_MOUSE)\n world = World()\n window.set_world(world)\n player = Player()\n window.set_player(player)\n pyglet.app.run()\n","sub_path":"pycraft/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"568684137","text":"#-------------------------------------------------------------------------------\n# Name: module1\n# Purpose:\n#\n# Author: callum\n#\n# Created: 24,02,2016\n# Copyright: (c) callum 2016\n# Licence: <your licence>\n#-------------------------------------------------------------------------------\n\n# Order for Arrays: Type, Attack,defence, Duribility, mana cost, name, effect or not\n\n\n#attack left\n#durability right\n#mana midel\n\n\n#Smith:\n\nBroken_sword = [0,1,4,1,\"Broken sword\",0]\nGlass_blade = [0,5,1,3,\"Glass Blade\",0]\nSmithing_hammer = [0,1,5,4, \"Smithing Hammer\",1]\nLarge_hammer = [0,6,4,6, \"Large Hammer\",0]\n\nGloves = [1,2,2,2,\"Gloves\",0]\nSmithing_hands = [1,2,3,3,\"Smith's Hands\",1]\nForge_gloves = [1,1,4,6,\"Forge Gloves\",1]\n\nLeg_brace = [2,2,2,2,\"Leg brace\",0]\nSmiths_leg_guard = [2,3,4,3,\"Smith's Leg guards\",0]\nPeg_leg = [2,1,4,4,\"Peg Leg\",1]\n\nFace_mask =[3,2,2,2,\"Face Mask\",0]\nGoggles = [3,0,5,3,\"Goggles\",1]\nForge_guard = [3,4,5,4,\"Forge Guard\",0]\n\nSmiths_apron = [4,2,2,2,\"Smith's Apron\",0]\nAnvil = [4,3,3,4,\"Anvil\",1]\nForged_armour = [4,5,5,5,\"Forged Armour\",0]\n\n\n#Paladin\n\nHammer_of_dawn = [0,2,3,1,\"Hammer of Dawn\",0]\nLance = [0,2,5,3,\"Lance\",0]\nHoly_blade = [0,2,6,4,\"Holy Blade\",1]\nBlessed_lance = [0,7,4,6,\"Blessed Lance\",1]\n\nSteel_gauntlets = [1,1,3,2,\"steel Gauntlets\",0]\nValkyrie_gloves = [1,3,3,3,\"Valkyrie Gloves\",1]\nGigaSteel_gauntlets = [1,4,6,5,\"GigaSteel Gauntlets\",0]\n\nSteel_greaves = [2,3,2,2, \"Steel Greaves\",0]\nValkyrie_sandles = [2,3,3,3,\"Valkyrie Sandles\",0]\nGigaSteel_greaves = [2,4,6,5,\"GigaSteel Greaves\",0]\n\nSteel_helm= [3,3,2,2,\"Steel Helm\",0]\nValkyrie_helm = [3,3,3,3,\"Valkyrie helm\",1]\nGigaSteel_helm = [3,4,6,5, \"GigaSteel Helm\",1]\n\nSteel_mail = [4,3,2,2,\"Steel Mail\",0]\nValkyrie_armour = [4,4,3,4,\"Valkyrie Armour\",0]\nGigaSteel_platemail = [4,4,6,5, \"Gigasteel Plate-mail\",0]\n\n#Man at arms\n\nMace = [0,5,1,2, \"Mace\",0]\nHeavy_hammer = [0,6,2,4, \"Heavy hammer\",0]\nClaymore = [0,8,2,6,\"Claymore\",0]\nReckless_sword = [0,10,1,8, \"Reckless_sword\",0]\n\nBandages =[1,3,1,2,\"Bandages\",0]\nHand_guard = [1,2,4,4,\"Hand Guard\"]\nSpiked_shield = [1,4,4,6,\"Spiked Shield\",1]\n\nBoots = [2,3,1,2,\"Boots\",0]\nKilt = [2,2,4,4,\"Kilt\",0]\nSwift_boots = [2,4,3,6,\"Swift Boots\",1]\n\nEye_patch = [3,3,1,2,\"Eye patch\",0]\nSteel_mask = [3,2,4,4, \"Steel Mask\",0]\nConqurers_helm = [3,4,5,6,\"Conqurers Helm\",0]\n\nBlood_stained_wraps = [4,3,1,2,\"Blood Stained wraps\",0]\nWoad = [4,2,4,4,\"Woad\",0]\nConqurers_armour = [4,4,5,6,\"Conqurers armour\",0]\n\n#General\n\nStandard_sword=[0,2,3,2,\"Standard sword\",0]\nBuckler = [0,0,4,2,\"Buckler\",0]\n\n\n\nLeather_gloves\t=[1,1,2,1,\"Leather gloves\",0]\nIron_gauntlents\t=[1,3,5,4,\"Iron gauntlents\",0]\n\n\nLeather_greeves = [2,3,3,3,\"Leather greeves\",0]\nIron_greeves = [2,3,5,4, \"Iron greeves\",0]\n\n\nHeros_cap = [3,1,3,1,\"Hero's cap\",0]\nHeros_helm = [3,3,3,3,\"Hero's helm\",0]\n\n\nIron_armour = [4,3,5,4,\"Iron armour\",0]\nSteel_armour = [4,3,6,5,\"Steel armour\",0]\n\n\n","sub_path":"equipment list.py","file_name":"equipment list.py","file_ext":"py","file_size_in_byte":2902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"286250837","text":"\"\"\"empty message\n\nRevision ID: 84ffaad552c0\nRevises: f77d1aa64bbc\nCreate Date: 2020-10-25 15:06:53.765097\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '84ffaad552c0'\ndown_revision = 'f77d1aa64bbc'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('reminders', sa.Column('full_date', sa.Integer(), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('reminders', 'full_date')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/84ffaad552c0_.py","file_name":"84ffaad552c0_.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"479886416","text":"from keras.models import Model\nfrom keras.layers import Input, Dense, Conv1D, MaxPooling1D, Flatten\nfrom keras.layers.normalization import BatchNormalization\nfrom keras import backend as K\n\nimport random\nimport numpy as np\nfrom collections import defaultdict\n\nimport read\nimport tensorflow as tf\n\nnp.random.seed(1)\ntf.set_random_seed(2)\n\nnum_test_classes = 2\nmini_batch_size = 200\nbatch_size = 60\nsteps_per_epoch = mini_batch_size\nfeature_length = read.dct_length * 3 * len(read.imus)\nepochs = 10\nk = 3\nk_shot = 5\n\n\ndef get_neighbours(instance, dataset, n):\n return np.argsort(np.linalg.norm(dataset - instance, axis=1))[:n]\n\n\ndef get_triples_minibatch_indices_me(dictionary, _labels):\n triples_indices = []\n for k in dictionary.keys():\n for value in dictionary[k]:\n anchor = value\n positive = random.choice(dictionary[k])\n negative_labels = [l for l in _labels if l != k ]\n negative_label = random.choice(negative_labels)\n negative = random.choice(dictionary[negative_label])\n triples_indices.append([anchor, positive, negative])\n\n return np.asarray(triples_indices)\n\n\ndef get_triples_minibatch_data_u(x, dictionary, _labels):\n indices = get_triples_minibatch_indices_me(dictionary, _labels)\n return x[indices[:, 0]], x[indices[:, 1]], x[indices[:, 2]]\n\n\ndef triplet_generator_minibatch(x, y, no_minibatch, _labels):\n grouped = defaultdict(list)\n dict_list = []\n\n for i, label in enumerate(y):\n grouped[label].append(i)\n\n for k in list(grouped.keys()):\n random.shuffle(grouped[k])\n\n for j in range(no_minibatch):\n dictionary = {}\n\n for k in list(grouped.keys()):\n ran_sam = random.sample(grouped[k], 3)\n dictionary[k] = ran_sam\n\n dict_list.append(dictionary)\n\n i = 0\n\n while 1:\n x_anchor, x_positive, x_negative = get_triples_minibatch_data_u(x, dict_list[i], _labels)\n\n if i == (no_minibatch - 1):\n i = 0\n else:\n i += 1\n\n yield ({'anchor_input': x_anchor,\n 'positive_input': x_positive,\n 'negative_input': x_negative},\n None)\n\n\ndef triplet_loss(inputs, dist='sqeuclidean', margin='maxplus'):\n anchor, positive, negative = inputs\n positive_distance = K.square(anchor - positive)\n negative_distance = K.square(anchor - negative)\n if dist == 'euclidean':\n positive_distance = K.sqrt(K.sum(positive_distance, axis=-1, keepdims=True))\n negative_distance = K.sqrt(K.sum(negative_distance, axis=-1, keepdims=True))\n elif dist == 'sqeuclidean':\n positive_distance = K.mean(positive_distance, axis=-1, keepdims=True)\n negative_distance = K.mean(negative_distance, axis=-1, keepdims=True)\n loss = positive_distance - negative_distance\n if margin == 'maxplus':\n loss = K.maximum(0.0, 1 + loss)\n elif margin == 'softplus':\n loss = K.log(1 + K.exp(loss))\n return K.mean(loss)\n\n\ndef build_conv_model(input_shape):\n base_input = Input(input_shape)\n x = Conv1D(12, kernel_size=3, activation='relu')(base_input)\n x = MaxPooling1D(pool_size=2)(x)\n x = BatchNormalization()(x)\n x = Flatten()(x)\n x = Dense(1200, activation='relu')(x)\n embedding_model = Model(base_input, x, name='embedding')\n\n anchor_input = Input(input_shape, name='anchor_input')\n positive_input = Input(input_shape, name='positive_input')\n negative_input = Input(input_shape, name='negative_input')\n\n anchor_embedding = embedding_model(anchor_input)\n positive_embedding = embedding_model(positive_input)\n negative_embedding = embedding_model(negative_input)\n\n inputs = [anchor_input, positive_input, negative_input]\n outputs = [anchor_embedding, positive_embedding, negative_embedding]\n\n triplet_model = Model(inputs, outputs)\n triplet_model.add_loss(K.mean(triplet_loss(outputs)))\n triplet_model.compile(loss=None, optimizer='adam') # loss should be None\n\n return embedding_model, triplet_model\n\n\nfeature_data = read.read()\n\ntest_ids = list(feature_data.keys())\nall_labels = list(feature_data[test_ids[0]].keys())\n\nfor test_id in test_ids:\n # for a_label in all_labels:\n for _int in range(5):\n test_labels_indices = np.random.choice(len(all_labels), num_test_classes, False)\n test_labels = [a for ii, a in enumerate(all_labels) if ii in test_labels_indices]\n print(test_labels)\n train_labels = [a for ii, a in enumerate(all_labels) if ii not in test_labels_indices]\n print(train_labels)\n _train_data, _test_data = read.split(feature_data, test_id)\n _train_data = read.remove_class(_train_data, test_labels)\n\n _support_data, _test_data = read.support_set_split(_test_data, k_shot)\n\n _train_data, _train_labels = read.flatten(_train_data)\n _support_data, _support_labels = read.flatten(_support_data)\n\n _train_data = np.array(_train_data)\n _train_data = np.expand_dims(_train_data, 3)\n\n _support_data = np.array(_support_data)\n _support_data = np.expand_dims(_support_data, 3)\n\n _embedding_model, _triplet_model = build_conv_model((feature_length,1))\n\n _triplet_model.fit_generator(triplet_generator_minibatch(_train_data, _train_labels, mini_batch_size\n , train_labels),\n steps_per_epoch=steps_per_epoch, epochs=epochs, verbose=1)\n\n _support_preds = _embedding_model.predict(_support_data)\n\n for _l in list(_test_data[test_id].keys()):\n _test_label_data = _test_data[test_id][_l]\n _test_labels = [_l for i in range(len(_test_label_data))]\n _test_label_data = np.array(_test_label_data)\n _test_label_data = np.expand_dims(_test_label_data, 3)\n _test_preds = _embedding_model.predict(_test_label_data)\n\n acc = read.cos_knn(k, _test_preds, _test_labels, _support_preds, _support_labels)\n result = 'tn_conv, 3nn,' + str(num_test_classes) + ',' + str(test_id) + ',' + ','.join([str(t) for t in test_labels]) + ',' + str(_l) + ',' + str(acc)\n read.write_data('tn_conv_oe_n.csv', result)\n\n\n","sub_path":"selfback/tn/tn_conv_oe_n.py","file_name":"tn_conv_oe_n.py","file_ext":"py","file_size_in_byte":6246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"204530324","text":"import requests\nimport pandas as pd \nimport io\nfrom linearRegression import LinearRegression\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# The dataset that is going to train the ai\ndf = pd.read_csv(\"https://raw.githubusercontent.com/Baakchsu/LinearRegression/master/weight-height.csv\")\n#print(df.head())\nreg = LinearRegression()\n\ndef preprocess():\n x = (df['Weight']-df['Weight'].mean())/df['Weight'].std() #standardization of the dataset\n y = (df['Height']-df['Height'].mean())/df['Height'].std() #standardization of the dataset\n return(x, y)\n\ndef train():\n (x, y) = preprocess()\n (weights,intercept,parameter_cache) = reg.train(x[:-180],y[:-180], 500)\n print(parameter_cache)\n\ndef prediction():\n (x, y) = preprocess()\n pred = reg.predict(np.array(x[-180:]))\n plt.scatter(x[-180:],y[-180:])\n plt.plot(x[-180:],pred)\n plt.show()\n\ntrain()\nprediction()\n","sub_path":"runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"303582753","text":"from django.urls import path\nfrom . import views\n\n# 定义项目和开票url\napp_name = 'projects'\n\nurlpatterns = [\n\n path('index/', views.index, name='index'),\n # 项目管理\n path('explist/', views.explist, name='pro_list'),\n path('add_exp/', views.add_exp, name='add_exp'),\n path('pro/<int:pro_id>/', views.pro_detail),\n path('del_pro/<int:pro_id>/', views.pro_del),\n\n # 开票回款\n path('invoice_payment/', views.invoice_payment, name='invoice_list'),\n path('invoice/<int:inv_id>/', views.invoice_detail),\n path('del_inv/<int:inv_id>/', views.inv_del),\n\n # 开票申请记录\n path('applylist/', views.applylist, name='apply_list'), # 定义开票申请列表链接\n path('apply/<int:apply_id>/', views.apply_detail, name='apply_detail'),\n path('del_apply/<int:apply_id>/', views.apply_del),\n # 审批开票申请\n path('approve_apply/<int:apply_id>/', views.approve_apply, name='approve_apply'),\n\n # 开票申请\n path('applyinvoice/', views.applyinvoice, name='apply_inv'), # 定义开票申请链接\n\n # 导出数据\n # path('export_pro/', views.export_pro_csv),\n # path('export_inv/', views.export_inv_csv),\n\n # 暂定为单位自动导入链接\n path('test/', views.test,),\n # 定义手动添加单位链接\n path('unit_list/', views.unit_list, name='unit_list'),\n path('add_unit/', views.add_unit, name='add_unit'),\n path('unit/<int:unit_id>/', views.unit_detail, name='unit_detail'),\n path('del_unit/<int:unit_id>/', views.unit_del),\n # 定义ajax测试链接\n path('ajax/', views.get),\n path('ajax_main/', views.ajax_main),\n\n]\n","sub_path":"projects/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"441002467","text":"import sys\n\ndef write_col(source_lines, colunm_number,filename):\n\tcol=[]\n\tfor line in source_lines:\n\t\tcol.append(line.split()[colunm_number]+\"\\n\")\n\twith open(filename,\"w\")as writer:\n\t\twriter.writelines(col)\n\nf=open(\"chapter2.txt\")\nlines=f.readlines()\n\nwrite_col(lines,0,\"col1.txt\")\nwrite_col(lines,1,\"col2.txt\")\n","sub_path":"12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"624364705","text":"import threading\nimport numpy as np\nimport math\n#FOR TESTING---------\nimport time\n#END TESTING---------\n\nEPS = 1e-8\n\n#This class handles doing a single search among every (MCTS_object, States_list) pair in the list MCTS_States_list\nclass Threading_MCTS():\n \n def __init__(self, args, nnet):\n self.nnet = nnet #needed for prediction\n self.args = args\n \n #exit_flag is an event object which will control when the threaded_search function STOPS.\n #When each threaded_search stops, we access the MCTS_object for the appropriate neural network inputs\n \n def getActionProbs (self, MCTS_States_list):\n # calls parallel_search numMCTS times and outputs prob. dist. for each (MCTS object, [States list]) pair in MCTSandStates \n # method is called by method Coach.advanceEpisodes\n \n #parallel search numMCTSsims times\n for i in range(self.args['numMCTSSims']):\n #TESTING-------------------------------\n #print('')\n #print('CURRENT BATCH SIMULATION:', i)\n #print('')\n #END TESTING---------------------------\n self.parallel_search(MCTS_States_list)\n \n #Once numMCTSsims parallel_searches have been done, all edge weights in each MCTS_obj have been updated, so we retrieve the probabilities\n #given by N(s,a). Put these new probabilities into a list.\n \n actionProbs = []\n \n for MCTS_object, State_list in MCTS_States_list:\n \n s = MCTS_object.game.keyRepresentation(State_list[-1])\n \n temp_counts = [MCTS_object.Nsa[(s,a)] if (s,a) in MCTS_object.Nsa else 0 for a in range(MCTS_object.game.getActionSize(self.args))]\n total_sum = float(sum(temp_counts))\n probs = [x/total_sum for x in temp_counts]\n actionProbs.append(probs)\n \n return actionProbs \n \n def parallel_search (self, MCTS_States_list):\n #1)For each MCTS_object in MCTS_States_list, traverse down from the current root to a leaf\n #2)For each MCTS_object, States_list in MCTS_States_list, update the leaf\n #3)For each MCTS_object, update the edges which were traversed in step 1. \n \n #FOR TESTING-----------------\n #print('------------------------------------------------------------')\n #print('BEGINNING SINGLE PARALLEL_SEARCH.....')\n #print('')\n #END TESTING-----------------\n \n #Conduct a search on every (MCTS_object, States_list) pair\n \n #FOR TESTING------------------\n #print('')\n #print('BEGINNING TRAVERSING DOWN TO LEAF FOR EACH MCTS_object, States_list pair...')\n #END TESTING------------------\n \n for MCTS_object, States_list in MCTS_States_list:\n \n current_root = States_list[-1]\n \n #reinitialize search_path to be empty(since this is a new search from root to leaf)\n MCTS_object.search_path = []\n \n #start recursive search to leaf. After traversetoLeaf is called, the search path traveled\n #for MCTS_object is saved in MCTS_object.search_path.\n self.search_traversetoLeaf(MCTS_object, current_root)\n \n #After all MCTS_object.search_path have been computed, make a batch prediction by \n #compiling each MCTS_object.search_path[-1] into a single query.\n \n pas_matrix, v_matrix = self.nnet.batch_predict(MCTS_States_list)\n \n #FOR TESTING-------------------------\n #print('')\n #print('The batch prediction returned the following:')\n #print(\"pas_matrix: \", pas_matrix)\n #print(\"v_matrix: \", v_matrix)\n #print('')\n #END TESTING--------------------------\n \n #Save the batch predictions into each MCTS_object and continue with MCTS search by updating the leaf node\n #of each MCTS_object. pas_matrix and v_matrix saves the predictions from searches which end\n #on a leaf and NOT a terminal node. Hence, as we loop through MCTS_States_list, we should skip\n #pairs in which the search ended on a terminal state. Hence, the if check in the loop below.\n \n i = 0\n\n for MCTS_object, States_list in MCTS_States_list:\n last_state = MCTS_object.search_path[-1]\n\n if MCTS_object.Es[last_state.keyRep] == 0:\n MCTS_object.batchquery_prediction = [pas_matrix[i,:], v_matrix[i]]\n \n #We only need to update the last state in search path if the search \n #ended on a leaf(and not a terminal node) \n \n self.search_updateLeaf(MCTS_object)\n i += 1\n \n #Note that for each MCTS_object, we need to update edge weights of \n #search path no matter if we ended on a terminal node or not during search\n self.search_updateTraversedEdges(MCTS_object)\n \n \n def search_traversetoLeaf(self, MCTS_object, State):\n #traverse from root to leaf and store the search path into\n #MCTS_object.search_path. MCTS_object.search_path is in the form of \n #[(state, a), (state2, a2), ..., leaf or terminal state]\n\n s = MCTS_object.game.keyRepresentation(State)\n \n #BASE CASES FOR TRAVERSE TO LEAF RECURSIVE SEARCH\n \n #1)Compute the terminal reward for state if not computed before\n if s not in MCTS_object.Es: # Note that MCTS_object.Es[s] not defined is NOT THE SAME as MCTS_object.Es[s] = 0\n MCTS_object.Es[s] = MCTS_object.game.getGameEnded(State, self.args, MCTS_object.game_args)\n \n #2)Check if the current state we are on is terminal or not. If terminal, \n #attach the node to our search path and return. \n if MCTS_object.Es[s] != 0:\n MCTS_object.search_path.append(State)\n \n return\n \n #3)Check if we are at leaf by checking if MCTS_object.Ps[s] is well defined\n if s not in MCTS_object.Ps:\n #Compute the features of the leaf \n State.compute_x_S_and_res(self.args, MCTS_object.game_args)\n #Save the features of this leaf to the MCTS_object for batch prediction later. Note that\n #from compute_x_S_and_res, we first save features in state object, and then we assign it in MCTS_object.features_s[s]\n MCTS_object.features_s[s] = State.feature_dic\n #\n MCTS_object.search_path.append(State)\n return \n \n #RECURSIVE CASE. If MCTS_object.Es[s] == 0 and MCTS_object.Ps[s] is well defined, then we are not yet at a leaf, so\n #we continue the search. \n valids = MCTS_object.Vs[s] #retrieve numpy vector of valid moves\n \n cur_best = -float('inf') #temp variable which holds the current highest UCB value\n best_act = -1 #temp variable which holds the current best action with largest UCB. Initialized to -1.\n \n for a in range(MCTS_object.game.getActionSize(self.args)): #iterate over all possible actions. \n if valids[a]:\n if (s,a) in MCTS_object.Qsa:\n u = MCTS_object.Qsa[(s,a)] + self.args['cpuct']*MCTS_object.Ps[s][a]*math.sqrt(MCTS_object.Ns[s])/(1+MCTS_object.Nsa[(s,a)]) #note here that MCTS_object.Ns[s] is number of times s \n #was visited. Note that MCTS_object.Ns[s] = sum over b of MCTS_object.Nsa[(s,b)], so the equation above is equal to surag nair's notes.\n else:\n #This line occurs if (s,a) is not in MCTS_object.Qsa, which means that if we take action a, then the next node next_s must be a leaf. This (s,a) will be added to MCTS_object.Qsa below and be assigned value of v.\n u = self.args['cpuct']*MCTS_object.Ps[s][a]*math.sqrt(MCTS_object.Ns[s] + EPS) \n \n #find the largest uct value\n if u > cur_best: \n cur_best = u\n best_act = a\n\n #append the (state,action) tuple to the search path\n MCTS_object.search_path.append((State,best_act))\n #get the next state and continue recursive search by calling traversetoLeaf\n next_s = MCTS_object.game.getNextState(State, best_act, MCTS_object.game_args, 1)\n self.search_traversetoLeaf(MCTS_object, next_s) #traverse from root to a leaf or terminal node using recursive search. \n \n\n def search_updateLeaf(self, MCTS_object):\n #This should be run after search_traversetoLeaf and batch_predict has been run on ALL MCTS_objects.\n #Note that the leaf only needs to be updated assuming traversetoLeaf landed on a leaf and NOT a terminal node.\n #From MCTS_object.search_path, we need to update the following variables in MCTS_object:\n #1)Compute MCTS_object.Ps[leaf] using batch neural network prediction.\n #2)Store valid moves for leaf in MCTS_object.Vs[leaf]\n #3)Initialize visit count to 0 in MCTS_object.Ns[leaf]\n \n leaf_state = MCTS_object.search_path[-1]\n leaf_key = leaf_state.keyRep\n \n #retrieve the computed MCTS_object.Ps[leaf_key]\n MCTS_object.Ps[leaf_key] = MCTS_object.batchquery_prediction[0]\n \n valids = MCTS_object.game.getValidMoves(leaf_state) #returns a numpy vector of 0 and 1's which indicate valid moves from the set of all actions\n MCTS_object.Ps[leaf_key] = MCTS_object.Ps[leaf_key]*valids # masking(hiding) invalid moves(this element wise product between two equally sized vectors creates a vector of probabilities of valid moves) the neural network may predict. \n sum_Ps_leaf = np.sum(MCTS_object.Ps[leaf_key]) \n \n #final assignment for MCTS_object.Ps[leaf_key]\n if sum_Ps_leaf > 0:\n MCTS_object.Ps[leaf_key] /= sum_Ps_leaf # renormalize\n else:\n # if all valid moves were masked make all valid moves equally probable\n \n # NB! All valid moves may be masked if either your NNet architecture is insufficient or you have overfitting or something else.\n # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process. \n print(\"All valid moves were masked, do workaround.\")\n \n MCTS_object.Ps[leaf_key] = MCTS_object.Ps[leaf_key] + valids #These two lines makes all valid moves equally probable. \n MCTS_object.Ps[leaf_key] /= np.sum(MCTS_object.Ps[leaf_key])\n \n #augment MCTS_object.Ps[leaf_key] with prior knowledge of the true solution x. EQUATE BETA TO 1 FOR TESTING!!!\n #------------------------------------------------------------------------------------------------\n x_I = np.ceil(abs(MCTS_object.game_args.sparse_vector)) #Since x is always between -1 to 1, x_I is the indicator vector corresponding to x\n x_I = np.append(x_I, 0) #append the stopping action to x_I, so x_I is the indicator for the support of x and includes a 1 for the stopping action. \n valid_xI = x_I*valids #component-wise multiplication of indicator x_I and valids\n MCTS_object.Ps[leaf_key] = MCTS_object.args['beta']*MCTS_object.Ps[leaf_key] + (1 - self.args['beta']) * (1/np.sum(valid_xI)) * valid_xI\n #------------------------------------------------------------------------------------------------\n \n MCTS_object.Vs[leaf_key] = valids #Store the valids for leaf\n MCTS_object.Ns[leaf_key] = 0 #Initialize visit count of leaf to 0. We will update this in search_updateTraversedEdges instead.\n\n \n def search_updateTraversedEdges(self, MCTS_object):\n #For each traversed edge (s,a) in the search, we update the following:\n #MCTS_object.Qsa[(s,a)]\n #MCTS_object.Nsa[(s,a)]\n \n #Propagate the true reward up search path if search path ended on a terminal node. Otherwise, propagate up the output of the neural network\n #Note that v is a 1 by 1 np array, so hence the [0] at the end.\n if MCTS_object.Es[MCTS_object.search_path[-1].keyRep] == 0:\n v = MCTS_object.batchquery_prediction[1][0]\n else: #if last state visited in the MCTS simulation is a terminal node. \n v = MCTS_object.Es[MCTS_object.search_path[-1].keyRep]\n \n \n #Update weights of all edges in the search_path. Also increment node values. Note that the loop omits the last element because the last element is a state and not a pair.\n for (State, a) in MCTS_object.search_path[:-1]:\n #Note that State.keyRep should be well defined since every State in each (State, a) pair have had search_traversetoLeaf called on it,\n #which calls game.keyRepresentation\n s = State.keyRep\n if (s,a) in MCTS_object.Qsa:\n #FOR TESTING----------------------\n #print(\"\")\n #print('(s,a) IS IN MCTS_object.Qsa !!!!!!!!')\n #print(\"MCTS_object.identifier:\", MCTS_object.identifier)\n #print(\"current_root:\", MCTS_object.search_path[0][0].col_indices)\n #print(\"(s, a):\", State.col_indices, a)\n #print(\"(s, a): \", s, a)\n #print(\"State.inverse: \", State.inverse)\n #print(\"State.ATy: \", State.ATy)\n #Check that the State.inverse*State.ATy is indeed the solution matching np.linalg.lstsq\n #if State.col_indices != []:\n # print(\"regression solution from product of inverse and ATy: \", np.matmul(State.inverse, State.ATy))\n # x = np.linalg.lstsq(MCTS_object.game_args.sensing_matrix[:, State.col_indices], MCTS_object.game_args.obs_vector)\n # print(\"regression solution and residual from np.linalg.lstsq: \", x[0], x[1])\n #print(\"BEFORE updating Qsa, Nsa for (s,a) %%%%%%%%%\")\n #print(\"v:\", v)\n #print(\"MCTS_object.Qsa[(s,a)]:\", MCTS_object.Qsa[(s,a)])\n #print(\"MCTS_object.Nsa[(s,a)]:\", MCTS_object.Nsa[(s,a)])\n #END TESTING----------------------\n \n MCTS_object.Qsa[(s,a)] = (MCTS_object.Nsa[(s,a)]*MCTS_object.Qsa[(s,a)] + v)/(MCTS_object.Nsa[(s,a)]+1) #The v in this equation could be the true terminal reward OR the predicted reward from NN, depending on whether the search ended on a leaf which is also a terminal node. \n MCTS_object.Nsa[(s,a)] += 1\n \n #FOR TESTING----------------------\n #print(\"AFTER updating Qsa, Nsa for (s,a) %%%%%%%%%\")\n #print(\"v:\", v)\n #print(\"MCTS_object.Qsa[(s,a)]:\", MCTS_object.Qsa[(s,a)])\n #print(\"MCTS_object.Nsa[(s,a)]:\", MCTS_object.Nsa[(s,a)])\n #print(\"Other Statistics.....\")\n #print(\"MCTS_object.Ps[s]:\", MCTS_object.Ps[s])\n #print(\"\")\n #END TESTING----------------------\n \n\n else: #if (s,a) is not in dictionary MCTS_object.Qsa, that means (s,a) has never been visited before. These are edges connected to leaves!! IOW N(s,a) = 0. Hence, by the formula 3 lines above, MCTS_object.Qsa[(s,a)] = v.\n #FOR TESTING----------------------\n #print(\"\")\n #print('(s,a) NOT IN MCTS_object.Qsa !!!!!!!!')\n #print(\"MCTS_object.identifier:\", MCTS_object.identifier)\n #print(\"current_root:\", MCTS_object.search_path[0][0].col_indices)\n #print(\"(s, a): \", State.col_indices, a)\n #print(\"(s, a): \", s, a)\n #print(\"State.inverse: \", State.inverse)\n #print(\"State.ATy: \", State.ATy)\n #Check that the State.inverse*State.ATy is indeed the solution matching np.linalg.lstsq\n #if State.col_indices != []:\n # print(\"regression solution from product of inverse and ATy: \", np.matmul(State.inverse, State.ATy))\n # x = np.linalg.lstsq(MCTS_object.game_args.sensing_matrix[:, State.col_indices], MCTS_object.game_args.obs_vector)\n # print(\"regression solution and residual from np.linalg.lstsq: \", x[0], x[1])\n #END TESTING----------------------\n \n MCTS_object.Qsa[(s,a)] = v \n MCTS_object.Nsa[(s,a)] = 1\n \n #FOR TESTING----------------------\n #print(\"AFTER updating Qsa, Nsa for (s,a) %%%%%%%%%\")\n #print(\"v:\", v)\n #print(\"MCTS_object.Qsa[(s,a)]:\", MCTS_object.Qsa[(s,a)])\n #print(\"MCTS_object.Nsa[(s,a)]:\", MCTS_object.Nsa[(s,a)])\n #print(\"Other Statistics.....\")\n #print(\"MCTS_object.Ps[s]:\", MCTS_object.Ps[s])\n #END TESTING----------------------\n \n MCTS_object.Ns[s] += 1\n \n #FOR TESTING--------------------------\n #last_state = MCTS_object.search_path[-1]\n #print('')\n #print('last state col indices: ', last_state.col_indices)\n #print('last state action indices: ', last_state.action_indices)\n #last_state_key = last_state.keyRep\n #print('last state key rep: ', last_state_key)\n #print(\"State.inverse: \", last_state.inverse)\n #print(\"State.ATy: \", last_state.ATy)\n #Check that the last_state.inverse*last_state.ATy is indeed the solution matching np.linalg.lstsq\n #if last_state.col_indices != []:\n # print(\"regression solution from product of inverse and ATy: \", np.matmul(last_state.inverse, last_state.ATy))\n # x = np.linalg.lstsq(MCTS_object.game_args.sensing_matrix[:, last_state.col_indices], MCTS_object.game_args.obs_vector)\n # print(\"regression solution and residual from np.linalg.lstsq: \", x[0], x[1])\n #print('The termreward currently stored for the last state is: ', last_state.termreward)\n #last_state.computeTermReward(MCTS_object.args, MCTS_object.game_args)\n #if last_state_key in MCTS_object.Ps:\n # print('last state updated Ps[s]: ', MCTS_object.Ps[last_state_key])\n #print('')\n #print('Generated vector y is: ', MCTS_object.game_args.obs_vector)\n #print('')\n #print('------------------------------------------------------------')\n #END TESTING--------------------------\n \n \n \n \n \n \n \n \n \n","sub_path":"prev_versions/batch+matrix_inversion/alphazero_compressedsensing_nonoise_hierarchical_v2/Threading_MCTS.py","file_name":"Threading_MCTS.py","file_ext":"py","file_size_in_byte":18569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"563509970","text":"from pyexpat import model\nfrom urllib import quote_plus\n\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage\nfrom django.db.models.query_utils import Q\nfrom django.http.response import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render, get_object_or_404, redirect\n\n# Create your views here.\nfrom notions.forms import UserRegisterForm\nfrom .models import Post\nfrom .forms import PostForm\n\n\n@login_required(login_url='/login/')\ndef post_create(request):\n form = PostForm(request.POST or None,request.FILES or None)\n if form.is_valid():\n instance = form.save(commit=False)\n form.instance.user = request.user\n instance.save()\n messages.success(request, \"Successfully Created\")\n return HttpResponseRedirect(instance.get_absolute_url())\n context = {\n \"form\": form,\n }\n return render(request, \"post_form.html\", context)\n\ndef post_detail(request,slug=None):\n instance = get_object_or_404(Post,slug=slug)\n share_string = quote_plus(instance.content)\n context = {\n \"title\": instance.title,\n \"instance\": instance,\n \"share_string\":share_string,\n }\n return render(request,\"post_detail.html\",context)\n\n\ndef post_list(request):\n queryset_list = Post.objects.order_by(\"-timestamp\")\n if request.user.is_staff or request.user.is_superuser:\n queryset_list = Post.objects.all()\n\n query = request.GET.get(\"q\")\n if query:\n queryset_list = queryset_list.filter(\n Q(title__icontains=query) |\n Q(content__icontains=query) |\n Q(user__first_name__icontains=query) |\n Q(user__last_name__icontains=query)\n ).distinct()\n paginator = Paginator(queryset_list, 9) # Show 9 contacts per page\n page_request_var = \"page\"\n page = request.GET.get(page_request_var)\n try:\n queryset = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n queryset = paginator.page(1)\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n queryset = paginator.page(paginator.num_pages)\n\n context = {\n \"object_list\": queryset,\n \"title\": \"Shared Notions\",\n \"page_request_var\": page_request_var,\n }\n return render(request, \"post_list.html\", context)\n\n@login_required(login_url='/login/')\ndef post_update(request,slug=None):\n instance = get_object_or_404(Post,slug=slug)\n form = PostForm(request.POST or None,request.FILES or None,instance=instance,)\n if form.instance.user != request.user:\n response = HttpResponse(\"Oops! Only Owner of the post has this privilege\")\n response.status_code = 403\n return response\n if form.is_valid():\n instance = form.save(commit=False)\n instance.save()\n messages.success(request, \"Successfully Updated\")\n return HttpResponseRedirect(instance.get_absolute_url())\n context = {\n \"title\": instance.title,\n \"instance\": instance,\n \"form\":form,\n }\n return render(request,\"post_form.html\",context)\n@login_required(login_url='/login/')\ndef post_delete(request,slug=None):\n\n instance = get_object_or_404(Post,slug=slug)\n if instance.user != request.user:\n response = HttpResponse(\"Oops! Only Owner of the post has this privilege\")\n response.status_code = 403\n return response\n instance.delete()\n messages.success(request,\"Successfully deleted\")\n return redirect(\"posts:list\")\n\nfrom django.contrib.auth import *\nfrom .forms import UserLoginForm\n\ndef login_view(request):\n title = \"Login\"\n form = UserLoginForm(request.POST or None)\n if form.is_valid():\n username = form.cleaned_data.get(\"username\")\n password = form.cleaned_data.get('password')\n user = authenticate(username=username, password=password)\n login(request, user)\n print(request.user.is_authenticated())\n return redirect(\"posts:list\")\n return render(request, \"form.html\", {\"form\": form, \"title\": title})\n\n\ndef register_view(request):\n title = \"Register\"\n form = UserRegisterForm(request.POST or None)\n if form.is_valid():\n user = form.save(commit=False)\n password = form.cleaned_data.get('password')\n user.set_password(password)\n user.save()\n new_user = authenticate(username=user.username, password=password)\n login(request, new_user)\n return redirect(\"posts:list\")\n\n context = {\n \"form\": form,\n \"title\": title\n }\n return render(request, \"form.html\", context)\n\n\ndef logout_view(request):\n logout(request)\n return redirect(\"posts:list\")\n\n\n\n\n","sub_path":"notions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"43977160","text":"#!/usr/bin/env python2\r\n# -*- coding: UTF-8 -*-\r\n# ---------------------------------------------------------------------------\r\n# ___ __ __ __ ___\r\n# / | \\ | \\ | \\ / Automatic\r\n# \\__ |__/ |__/ |___| \\__ Annotation\r\n# \\ | | | | \\ of\r\n# ___/ | | | | ___/ Speech\r\n# =============================\r\n#\r\n# http://sldr.org/sldr000800/preview/\r\n#\r\n# ---------------------------------------------------------------------------\r\n# developed at:\r\n#\r\n# Laboratoire Parole et Langage\r\n#\r\n# Copyright (C) 2011-2015 Brigitte Bigi\r\n#\r\n# Use of this software is governed by the GPL, v3\r\n# This banner notice must not be removed\r\n# ---------------------------------------------------------------------------\r\n#\r\n# SPPAS is free software: you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation, either version 3 of the License, or\r\n# (at your option) any later version.\r\n#\r\n# SPPAS is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU General Public License for more details.\r\n#\r\n# You should have received a copy of the GNU General Public License\r\n# along with SPPAS. If not, see <http://www.gnu.org/licenses/>.\r\n#\r\n# ----------------------------------------------------------------------------\r\n# File: sndplayer.py\r\n# ----------------------------------------------------------------------------\r\n\r\n__docformat__ = \"\"\"epytext\"\"\"\r\n__authors__ = \"\"\"Brigitte Bigi\"\"\"\r\n__copyright__ = \"\"\"Copyright (C) 2011-2015 Brigitte Bigi\"\"\"\r\n\r\n\r\n# ----------------------------------------------------------------------------\r\n# Imports\r\n# ----------------------------------------------------------------------------\r\n\r\nimport wx\r\nimport logging\r\nimport wx.media\r\n\r\nfrom wxgui.sp_images import PLAYER_BACKGROUND\r\n\r\nfrom wxgui.sp_icons import PLAYER_INFO\r\nfrom wxgui.sp_icons import PLAYER_INFO_DISABLED\r\nfrom wxgui.sp_icons import PLAYER_EJECT\r\nfrom wxgui.sp_icons import PLAYER_EJECT_DISABLED\r\nfrom wxgui.sp_icons import PLAYER_NEXT\r\nfrom wxgui.sp_icons import PLAYER_NEXT_DISABLED\r\nfrom wxgui.sp_icons import PLAYER_REWIND\r\nfrom wxgui.sp_icons import PLAYER_REWIND_DISABLED\r\nfrom wxgui.sp_icons import PLAYER_PLAY\r\nfrom wxgui.sp_icons import PLAYER_PLAY_DISABLED\r\nfrom wxgui.sp_icons import PLAYER_PAUSE\r\nfrom wxgui.sp_icons import PLAYER_PAUSE_DISABLED\r\nfrom wxgui.sp_icons import PLAYER_STOP\r\nfrom wxgui.sp_icons import PLAYER_STOP_DISABLED\r\n\r\nfrom wxgui.sp_consts import TB_ICONSIZE\r\n\r\nfrom wxgui.ui.CustomEvents import FileWanderEvent\r\nimport wxgui.ui.KnobCtrl as KC\r\nfrom wxgui.structs.prefs import Preferences\r\nfrom wxgui.structs.themes import BaseTheme\r\n\r\nfrom wxgui.cutils.ctrlutils import CreateButton\r\nfrom wxgui.cutils.imageutils import spBitmap\r\n\r\nfrom wxgui.dialogs.sndinfodialog import SndInfoDialog\r\n\r\n\r\n# ---------------------------------------------------------------------------\r\n# Constants\r\n# ---------------------------------------------------------------------------\r\n\r\nTIMER_STEP = 10 # timer step event (in milliseconds)\r\nFORWARD_STEP = 1000 # forward step (in milliseconds)\r\nBACKWARD_STEP = 1000 # backward step (in milliseconds)\r\n\r\n# ---------------------------------------------------------------------------\r\n\r\nclass SndPlayer( wx.Panel ):\r\n \"\"\"\r\n Sound Player.\r\n \"\"\"\r\n\r\n def __init__(self, parent, orient=wx.VERTICAL, refreshtimer=TIMER_STEP, prefsIO=None):\r\n \"\"\" Create a new WavProperty instance. \"\"\"\r\n\r\n wx.Panel.__init__(self, parent)\r\n\r\n # members\r\n self._prefs = self._check_prefs(prefsIO)\r\n self._filename = None\r\n self._mediaplayer = None\r\n self._buttons = {}\r\n self._showpanel = None # panel to show information (clock, peakmeter, signal, ...)\r\n self._playbackSlider = None # slider (to change the position with the mouse)\r\n self._knob = None # volume control\r\n self._offsets = (0,0) # from/to offsets\r\n\r\n self.BMP_PLAYER_INFO = spBitmap( PLAYER_INFO, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_INFO_DISABLED = spBitmap( PLAYER_INFO_DISABLED, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_EJECT = spBitmap( PLAYER_EJECT, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_EJECT_DISABLED = spBitmap( PLAYER_EJECT_DISABLED, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_NEXT = spBitmap( PLAYER_NEXT, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_NEXT_DISABLED = spBitmap( PLAYER_NEXT_DISABLED, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_REWIND = spBitmap( PLAYER_REWIND, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_REWIND_DISABLED = spBitmap( PLAYER_REWIND_DISABLED, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_PLAY = spBitmap( PLAYER_PLAY, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_PLAY_DISABLED = spBitmap( PLAYER_PLAY_DISABLED, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_PAUSE = spBitmap( PLAYER_PAUSE, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_PAUSE_DISABLED = spBitmap( PLAYER_PAUSE_DISABLED, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_STOP = spBitmap( PLAYER_STOP, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n self.BMP_PLAYER_STOP_DISABLED = spBitmap( PLAYER_STOP_DISABLED, TB_ICONSIZE, theme=self._prefs.GetValue('M_ICON_THEME') )\r\n\r\n # create the audio bar\r\n if orient == wx.VERTICAL:\r\n sizer = self._build_audioadvanced()\r\n else:\r\n sizer = self._build_audiosimple()\r\n\r\n # events\r\n self.Bind(wx.EVT_SLIDER, self.onSeek)\r\n\r\n # timer, used to update the playing state\r\n self._timer = wx.Timer(self)\r\n self.Bind(wx.EVT_TIMER, self.onTimer)\r\n self._refreshTimer = refreshtimer\r\n\r\n self.SetBackgroundColour( self._prefs.GetValue(\"M_BG_COLOUR\") )\r\n self.SetForegroundColour( self._prefs.GetValue(\"M_FG_COLOUR\") )\r\n self.SetFont( self._prefs.GetValue(\"M_FONT\") )\r\n\r\n self.SetSizer( sizer )\r\n self.SetAutoLayout( True )\r\n self.Layout()\r\n\r\n # End __init__\r\n # -----------------------------------------------------------------------\r\n\r\n\r\n def _build_showpanel(self, wave):\r\n \"\"\" Build or change the show panel. \"\"\"\r\n\r\n # a showpanel is already existing\r\n if self._showpanel is not None:\r\n self._showpanel.Destroy()\r\n\r\n # no wav: show a nice picture\r\n if wave is None:\r\n self._showpanel = wx.Panel(self, size=(320,120))\r\n img = wx.Image(PLAYER_BACKGROUND, wx.BITMAP_TYPE_ANY)\r\n img2 = wx.StaticBitmap(self._showpanel, wx.ID_ANY, wx.BitmapFromImage(img))\r\n else:\r\n # a wave is given:\r\n # show dynamic information while playing (clock, peakmeter, ...)\r\n # TO DO\r\n self._showpanel = wx.Panel(self, size=(320,120))\r\n img = wx.Image(PLAYER_BACKGROUND, wx.BITMAP_TYPE_ANY)\r\n img2 = wx.StaticBitmap(self._showpanel, wx.ID_ANY, wx.BitmapFromImage(img))\r\n\r\n\r\n def _build_audioadvanced(self):\r\n \"\"\" Build the audio controls. \"\"\"\r\n\r\n # create the main sizer.\r\n sizer = wx.GridBagSizer(4, 4)\r\n bgcolour = self._prefs.GetValue('M_BG_COLOUR')\r\n\r\n # 1st column\r\n self._buttons['eject'] = CreateButton(self, self.BMP_PLAYER_EJECT_DISABLED, self.onEject, sizer, colour=bgcolour)\r\n self._buttons['next'] = CreateButton(self, self.BMP_PLAYER_NEXT_DISABLED, self.onNext, sizer, colour=bgcolour)\r\n self._buttons['previous'] = CreateButton(self, self.BMP_PLAYER_REWIND_DISABLED,self.onRewind, sizer, colour=bgcolour)\r\n\r\n # 2nd column\r\n self._build_showpanel( None )\r\n\r\n # 3rd column\r\n self._buttons['play'] = CreateButton(self, self.BMP_PLAYER_PLAY_DISABLED, self.onPlay, sizer, colour=bgcolour)\r\n self._buttons['stop'] = CreateButton(self, self.BMP_PLAYER_STOP_DISABLED, self.onStop, sizer, colour=bgcolour)\r\n self._buttons['pause'] = CreateButton(self, self.BMP_PLAYER_PAUSE_DISABLED, self.onPause, sizer, colour=bgcolour)\r\n\r\n # 4th column\r\n minvalue = 0\r\n maxvalue = 101\r\n therange = 5\r\n self._knob = KC.KnobCtrl(self, -1, size=(80, 80))\r\n self._knob.SetTags(range(minvalue, maxvalue+1, therange))\r\n self._knob.SetAngularRange(-45, 225)\r\n self._knob.SetValue( int((minvalue+maxvalue+1)/2) )\r\n tickrange = range(minvalue, maxvalue+1, therange)\r\n self._knob.SetTags(tickrange)\r\n self.Bind(KC.KC_EVENT_ANGLE_CHANGED, self.onAngleChanged, self._knob)\r\n self._knobtracker = wx.StaticText(self, -1, \"Volume = %d\" % int((minvalue+maxvalue)/2))\r\n\r\n # sizer\r\n sizer.Add(self._buttons['eject'], (0,0), flag=wx.ALL, border=4)\r\n sizer.Add(self._buttons['next'], (1,0), flag=wx.ALL, border=4)\r\n sizer.Add(self._buttons['previous'],(2,0), flag=wx.ALL, border=4)\r\n sizer.Add(self._showpanel, (0,1),(3,1), flag=wx.EXPAND|wx.ALL, border=4)\r\n sizer.Add(self._buttons['play'], (0,2), flag=wx.ALL, border=4)\r\n sizer.Add(self._buttons['stop'], (1,2), flag=wx.ALL, border=4)\r\n sizer.Add(self._buttons['pause'], (2,2), flag=wx.ALL, border=4)\r\n sizer.Add(self._knob, (0,3), (2,1), flag=wx.EXPAND|wx.TOP, border=4)\r\n sizer.Add(self._knobtracker, (2,3), flag=wx.TOP, border=4)\r\n\r\n # create playback slider\r\n self._playbackSlider = wx.Slider(self, wx.ID_ANY, size=wx.DefaultSize, style=wx.SL_HORIZONTAL|wx.SL_AUTOTICKS)\r\n sizer.Add(self._playbackSlider, (3,0), (1,4), wx.ALL|wx.EXPAND, border=4)\r\n\r\n return sizer\r\n\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def _build_audiosimple(self):\r\n \"\"\" Build the audio controls. \"\"\"\r\n\r\n self._showpanel = None\r\n self._playbackSlider = None\r\n self._knob = None\r\n self._knobtracker = None\r\n\r\n # create the main sizer.\r\n sizer = wx.BoxSizer( wx.HORIZONTAL )\r\n\r\n # create the audio bar\r\n self._buttons['info'] = CreateButton(self, self.BMP_PLAYER_INFO_DISABLED, self.onInfo, sizer)\r\n self._buttons['play'] = CreateButton(self, self.BMP_PLAYER_PLAY_DISABLED, self.onPlay, sizer)\r\n self._buttons['pause'] = CreateButton(self, self.BMP_PLAYER_PAUSE_DISABLED, self.onPause, sizer)\r\n\r\n #self._buttons['next'] = CreateButton(self, self.BMP_PLAYER_NEXT_DISABLED, self.onNext, sizer)\r\n #self._buttons['previous'] = CreateButton(self, self.BMP_PLAYER_REWIND_DISABLED,self.onRewind,sizer)\r\n #self._buttons['stop'] = CreateButton(self, self.BMP_PLAYER_STOP_DISABLED, self.onStop, sizer)\r\n\r\n # sizer\r\n sizer.Add(self._buttons['info'], 1, flag=wx.ALL, border=2)\r\n sizer.Add(self._buttons['play'], 1, flag=wx.ALL, border=2)\r\n sizer.Add(self._buttons['pause'], 1, flag=wx.ALL, border=2)\r\n\r\n return sizer\r\n\r\n # End _build_audiosimple\r\n #-------------------------------------------------------------------------\r\n\r\n\r\n def _check_prefs(self, prefs):\r\n \"\"\"\r\n Check if preferences are set properly. Set new ones if required.\r\n Return the new version.\r\n \"\"\"\r\n if prefs is None:\r\n prefs = Preferences( BaseTheme() )\r\n\r\n else:\r\n try:\r\n bg = prefs.GetValue( 'M_BG_COLOUR' )\r\n fg = prefs.GetValue( 'M_FG_COLOUR' )\r\n font = prefs.GetValue( 'M_FONT' )\r\n icons = prefs.GetValue( 'M_ICON_THEME' )\r\n except Exception:\r\n self._prefsIO.SetTheme( BaseTheme() )\r\n return prefs\r\n\r\n #-------------------------------------------------------------------------\r\n\r\n\r\n\r\n #----------------------------------------------------------------------\r\n # Methods\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def FileSelected(self, filename):\r\n \"\"\"\r\n Set a sound file.\r\n \"\"\"\r\n logging.debug(' ... sndplayer file selected ...')\r\n # do not assign the same file!!!\r\n if filename == self._filename and self._mediaplayer is not None:\r\n logging.debug(' same file name/ Return!!!')\r\n return\r\n\r\n try:\r\n m = wx.media.MediaCtrl(self, style=wx.NO_BORDER)\r\n m.Load( filename )\r\n self._length = m.Length()\r\n if self._length == 0: # **** BUG of the MediaPlayer! ****\r\n import wave\r\n w = wave.Wave_read(filename)\r\n self._length = int( 1000 * float(w.getnframes())/float(w.getframerate()) )\r\n except Exception as e:\r\n logging.info(\" ... Error loading: %s\" % filename)\r\n wx.MessageBox('Error loading: '+filename+' '+str(e), 'Info', wx.OK | wx.ICON_INFORMATION)\r\n return False\r\n\r\n # set mediaplayer with the new one\r\n self._filename = filename\r\n self._mediaplayer = m\r\n\r\n #self._mediaplayer.SetInitialSize()\r\n self.ActivateButtons(True)\r\n self._offsets = (0,self._length)\r\n if self._playbackSlider is not None:\r\n self._playbackSlider.SetRange(0, self._length)\r\n self._playbackSlider.SetTickFreq(int(self._length/10), 1)\r\n\r\n self._timer.Start( self._refreshTimer )\r\n\r\n self.Refresh()\r\n\r\n # End FileSelected\r\n #------------------------------------------------------------------------\r\n\r\n\r\n def FileDeSelected(self):\r\n \"\"\"\r\n Reset information.\r\n \"\"\"\r\n # take care... the current mediaplayer can be playing. Unset properly!!\r\n if self._mediaplayer is not None and self._mediaplayer.GetState() != wx.media.MEDIASTATE_STOPPED :\r\n self.onStop(None)\r\n\r\n if self._showpanel is not None:\r\n self._build_showpanel( None )\r\n if self._mediaplayer is not None:\r\n self._mediaplayer.Destroy()\r\n\r\n self._filename = None\r\n self._mediaplayer = None\r\n self._offsets = (0,0)\r\n if self._playbackSlider is not None:\r\n self._playbackSlider.SetRange(0, 0)\r\n\r\n self.ActivateButtons(False)\r\n self.EnableButtons(False)\r\n\r\n self._timer.Stop()\r\n\r\n self.Layout()\r\n self.Refresh()\r\n\r\n # End FileDeSelected\r\n # -----------------------------------------------------------------------\r\n\r\n\r\n def SetOffsetPeriod(self, start, end):\r\n \"\"\"\r\n Fix a start position and a end position to play the sound.\r\n \"\"\"\r\n if self._mediaplayer is not None and self._mediaplayer.GetState() == wx.media.MEDIASTATE_PLAYING:\r\n self.onStop(None)\r\n\r\n if self._mediaplayer is not None and end > self._length:\r\n end = self._length\r\n\r\n self._offsets = (start,end)\r\n if self._playbackSlider is not None:\r\n self._playbackSlider.SetRange(start,end)\r\n\r\n # End SetOffsetPeriod\r\n #----------------------------------------------------------------------\r\n\r\n\r\n #----------------------------------------------------------------------\r\n # Callbacks\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onInfo(self, event):\r\n \"\"\" Display information about the selected Wave. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n return\r\n pass\r\n try:\r\n dlg = SndInfoDialog( self, self._prefs, self._filename )\r\n except Exception as e:\r\n wx.MessageBox('No information available. %s'%str(e), 'Info', wx.OK | wx.ICON_INFORMATION)\r\n\r\n # End onInfo\r\n #-------------------------------------------------------------------------\r\n\r\n\r\n def onSeek(self,event):\r\n \"\"\" Seeks the media file according to the amount the slider has been adjusted. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n return\r\n\r\n if self._playbackSlider is not None:\r\n offset = self._playbackSlider.GetValue()\r\n else:\r\n offset = self._offsets[0]\r\n\r\n self._mediaplayer.Seek( offset, mode=wx.FromStart )\r\n\r\n # End onSeek\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onEject(self, event):\r\n \"\"\" Eject the music. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n return\r\n\r\n evt = FileWanderEvent()\r\n evt.SetEventObject(self)\r\n wx.PostEvent(self.GetParent(), evt)\r\n\r\n #self.FileDeSelected()\r\n\r\n # End onEject\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onNext(self, event):\r\n \"\"\" Go forward in the music. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n return\r\n\r\n offset = self._mediaplayer.Tell()\r\n forward = offset + FORWARD_STEP\r\n (omin,omax) = self._offsets\r\n if forward > omax:\r\n forward = omin # come back at the beginning!\r\n\r\n if self._playbackSlider is not None:\r\n self._playbackSlider.SetValue( forward )\r\n\r\n self._mediaplayer.Seek( forward, mode=wx.FromStart )\r\n\r\n # End onNext\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onRewind(self, event):\r\n \"\"\" Go backward in the music. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n return\r\n\r\n offset = self._mediaplayer.Tell()\r\n backward = offset - BACKWARD_STEP\r\n (omin,omax) = self._offsets\r\n if backward < omin:\r\n backward = omax # loop\r\n\r\n if self._playbackSlider is not None:\r\n self._playbackSlider.SetValue( backward )\r\n\r\n self._mediaplayer.Seek( backward, mode=wx.FromStart )\r\n\r\n # End onRewind\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onPause(self, event):\r\n \"\"\" Pauses the music. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n return\r\n\r\n logging.debug(' PAUSE EVENT RECEIVED ')\r\n\r\n state = self._mediaplayer.GetState()\r\n\r\n if state == wx.media.MEDIASTATE_PLAYING:\r\n self._mediaplayer.Pause()\r\n self._buttons['pause'].SetBitmapLabel( self.BMP_PLAYER_PAUSE_DISABLED )\r\n\r\n elif state == wx.media.MEDIASTATE_PAUSED:\r\n self.onPlay(event)\r\n self._buttons['play'].SetBitmapLabel( self.BMP_PLAYER_PLAY )\r\n self._buttons['pause'].SetBitmapLabel( self.BMP_PLAYER_PAUSE )\r\n\r\n # End onPause\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onPlay(self, event):\r\n \"\"\" Plays the music. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n logging.debug('onPlay. Unable to play: No media player.')\r\n return\r\n if self._mediaplayer.GetState() == wx.media.MEDIASTATE_PLAYING:\r\n logging.debug('onPlay. Unable to play: already playing!')\r\n return\r\n\r\n # save current position\r\n offset = self._mediaplayer.Tell()\r\n omin,omax = self._offsets\r\n if self._playbackSlider is not None:\r\n offset = self._playbackSlider.GetValue()\r\n elif (offset < omin or offset > omax):\r\n offset = omin\r\n\r\n if not self._mediaplayer.Play():\r\n logging.debug('onPlay. Unable to play. offset=%d'%offset)\r\n wx.MessageBox(\"Unable to Play. Offset=%d\"%offset,\r\n \"ERROR\",\r\n wx.ICON_ERROR | wx.OK)\r\n return\r\n\r\n # force to play at the good position\r\n self._mediaplayer.Seek( offset, mode=wx.FromStart ) # required!\r\n\r\n if self._knob is not None:\r\n self._mediaplayer.SetVolume( float(self._knob.GetValue())/100.0 )\r\n\r\n self._buttons['play'].SetBitmapLabel( self.BMP_PLAYER_PLAY )\r\n self._buttons['pause'].SetBitmapLabel( self.BMP_PLAYER_PAUSE )\r\n\r\n self.Refresh()\r\n\r\n # End onPlay\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onStop(self, event):\r\n \"\"\" Stops the music and resets the play button. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n return\r\n\r\n try:\r\n self._mediaplayer.Stop()\r\n s,e = self._offsets\r\n self._mediaplayer.Seek( s )\r\n if self._playbackSlider is not None:\r\n self._playbackSlider.SetValue( s )\r\n except Exception:\r\n # provide errors like:\"ressource temporairement indisponible\"\r\n pass\r\n\r\n self._buttons['play'].SetBitmapLabel( self.BMP_PLAYER_PLAY )\r\n self._buttons['pause'].SetBitmapLabel( self.BMP_PLAYER_PAUSE )\r\n\r\n # End onStop\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onAngleChanged(self, event):\r\n \"\"\" Change the volume value. \"\"\"\r\n\r\n value = event.GetValue()\r\n self._knobtracker.SetLabel(\"Volume = \" + str(value))\r\n if self._mediaplayer:\r\n self._mediaplayer.SetVolume( float(value)/100.0 )\r\n\r\n # End onAngleChanged\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onTimer(self, event):\r\n \"\"\" Keeps the player slider updated. \"\"\"\r\n\r\n if self._mediaplayer is None:\r\n return\r\n\r\n offset = self._mediaplayer.Tell()\r\n # On MacOS, it seems that offset is not so precise we could expect...\r\n # It can be + or - 2 compared to the expected value!\r\n\r\n if self._mediaplayer.GetState() == wx.media.MEDIASTATE_PLAYING and self._playbackSlider is not None:\r\n self._playbackSlider.SetValue( offset )\r\n\r\n omin,omax = self._offsets\r\n if self._mediaplayer.GetState() == wx.media.MEDIASTATE_PLAYING and (offset < omin-3 or offset > omax+3):\r\n self.onStop(event)\r\n\r\n # End onTimer\r\n #----------------------------------------------------------------------\r\n\r\n\r\n def onClose(self, event):\r\n \"\"\"\r\n Close (destructor).\r\n \"\"\"\r\n self._timer.Stop()\r\n self.Destroy()\r\n\r\n # End Close\r\n # ------------------------------------------------------------------------\r\n\r\n\r\n # -----------------------------------------------------------------------\r\n # GUI\r\n # -----------------------------------------------------------------------\r\n\r\n\r\n def SetPreferences(self, prefs):\r\n \"\"\" Set new preferences. \"\"\"\r\n\r\n self._prefs = prefs\r\n self.SetBackgroundColour( self._prefs.GetValue(\"M_BG_COLOUR\") )\r\n self.SetForegroundColour( self._prefs.GetValue(\"M_FG_COLOUR\") )\r\n self.SetFont( self._prefs.GetValue(\"M_FONT\") )\r\n # apply bg on all buttons...\r\n for b in self._buttons.keys():\r\n self._buttons[b].SetBackgroundColour( self._prefs.GetValue(\"M_BG_COLOUR\") )\r\n\r\n #-------------------------------------------------------------------------\r\n\r\n\r\n def SetFont(self, font):\r\n \"\"\" Change font of all texts. \"\"\"\r\n\r\n wx.Window.SetFont( self,font )\r\n if self._knobtracker is not None:\r\n self._knobtracker.SetFont( font )\r\n\r\n # End SetFont\r\n # -----------------------------------------------------------------------\r\n\r\n\r\n def SetBackgroundColour(self, colour):\r\n \"\"\" Change the background color of all objects. \"\"\"\r\n\r\n wx.Window.SetBackgroundColour( self,colour )\r\n\r\n for b in self._buttons:\r\n self._buttons[b].SetBackgroundColour( colour )\r\n\r\n if self._showpanel is not None:\r\n self._showpanel.SetBackgroundColour( colour )\r\n if self._knobtracker is not None:\r\n self._knobtracker.SetBackgroundColour( colour )\r\n if self._playbackSlider is not None:\r\n self._playbackSlider.SetBackgroundColour( colour )\r\n\r\n self.Refresh()\r\n\r\n # End SetForegroundColour\r\n # -----------------------------------------------------------------------\r\n\r\n\r\n def SetForegroundColour(self, colour):\r\n \"\"\" Change the foreground color of all objects. \"\"\"\r\n\r\n wx.Window.SetForegroundColour( self,colour )\r\n\r\n for b in self._buttons:\r\n self._buttons[b].SetForegroundColour( colour )\r\n\r\n if self._showpanel is not None:\r\n self._showpanel.SetForegroundColour( colour )\r\n if self._knobtracker is not None:\r\n self._knobtracker.SetForegroundColour( colour )\r\n if self._playbackSlider is not None:\r\n self._playbackSlider.SetForegroundColour( colour )\r\n\r\n self.Refresh()\r\n\r\n # End SetForegroundColour\r\n # -----------------------------------------------------------------------\r\n\r\n\r\n # ------------------------------------------------------------------------\r\n\r\n def ActivateButtons(self, value=True):\r\n self.EnableButtons(False)\r\n if value is True:\r\n self._buttons['play'].SetBitmapLabel( self.BMP_PLAYER_PLAY )\r\n self._buttons['pause'].SetBitmapLabel( self.BMP_PLAYER_PAUSE )\r\n try:\r\n self._buttons['eject'].SetBitmapLabel( self.BMP_PLAYER_EJECT )\r\n except Exception:\r\n pass\r\n try:\r\n self._buttons['info'].SetBitmapLabel( self.BMP_PLAYER_INFO )\r\n except Exception:\r\n pass\r\n try:\r\n self._buttons['next'].SetBitmapLabel( self.BMP_PLAYER_NEXT )\r\n self._buttons['previous'].SetBitmapLabel( self.BMP_PLAYER_REWIND )\r\n except Exception:\r\n pass\r\n try:\r\n self._buttons['stop'].SetBitmapLabel( self.BMP_PLAYER_STOP )\r\n except Exception:\r\n pass\r\n else:\r\n self._buttons['play'].SetBitmapLabel( self.BMP_PLAYER_PLAY_DISABLED )\r\n self._buttons['pause'].SetBitmapLabel( self.BMP_PLAYER_PAUSE_DISABLED )\r\n try:\r\n self._buttons['eject'].SetBitmapLabel( self.BMP_PLAYER_EJECT_DISABLED )\r\n except Exception:\r\n pass\r\n try:\r\n self._buttons['info'].SetBitmapLabel( self.BMP_PLAYER_INFO_DISABLED )\r\n except Exception:\r\n pass\r\n try:\r\n self._buttons['stop'].SetBitmapLabel( self.BMP_PLAYER_STOP_DISABLED )\r\n except Exception:\r\n pass\r\n try:\r\n self._buttons['previous'].SetBitmapLabel( self.BMP_PLAYER_REWIND_DISABLED )\r\n self._buttons['next'].SetBitmapLabel( self.BMP_PLAYER_NEXT_DISABLED )\r\n except Exception:\r\n pass\r\n\r\n def EnableButtons(self, value=True):\r\n for b in self._buttons:\r\n self._buttons[b].Enable( not value )\r\n\r\n# ----------------------------------------------------------------------------\r\n","sub_path":"sppas/src/wxgui/panels/sndplayer.py","file_name":"sndplayer.py","file_ext":"py","file_size_in_byte":27657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"529793734","text":"from django.db import models\nfrom eCube_UI_2.core.Add_Request.models import (DomainMasterBase, RequestModeBase, CountryMasterBase,\n FieldGroupMasterBase)\nfrom hotel.user_management.models import UserMaster, DomainMaster, CountryMaster\n\n\nclass RequestModeMaster(RequestModeBase):\n pass\n\n\nclass FieldGroupMaster(FieldGroupMasterBase):\n id = models.AutoField(db_column='GroupID', primary_key=True)\n name = models.CharField(db_column='GroupName', max_length=50)\n description = models.CharField(db_column='GroupDesc', max_length=50)\n active = models.BooleanField(db_column='Active')\n user_id = models.ForeignKey(UserMaster, db_column=\"FGM_UserId\", on_delete=models.DO_NOTHING)\n created_date = models.DateTimeField(auto_now=True, db_column='CreatedDate')\n bli_id = models.IntegerField(db_column='Bli_ID', default=0)\n\nclass temphotelmaster(models.Model):\n id = models.AutoField(db_column='index', primary_key=True)\n class Meta:\n managed = False\n db_table = 'temphotelmaster_excel'\n\n\n\nclass CityMaster(models.Model):\n id = models.AutoField(db_column='CityId', primary_key=True)\n code = models.CharField(db_column='CityCode', max_length=10)\n name = models.CharField(db_column='CityName',max_length=100)\n country = models.ForeignKey(CountryMaster, db_column=\"CountryId\", on_delete=models.CASCADE)\n _active = models.PositiveSmallIntegerField(db_column='Active', default=0)\n created_by = models.ForeignKey(UserMaster,db_column='CreatedBy', related_name='citymaster_createdby',on_delete = models.DO_NOTHING)\n created_date = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modified_by = models.ForeignKey(UserMaster, db_column='ModifiedBy', related_name='citymaster_modifiedby' ,on_delete = models.DO_NOTHING)\n modified_date = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n class Meta:\n managed = False\n db_table = 'Cities'\n\n @property\n def active(self):\n return bool(self._active)\n\n\nclass AirportCodeMaster(models.Model):\n id = models.AutoField(db_column='AirportCodeId', primary_key=True)\n code = models.CharField(db_column='AirportCode', max_length=10)\n name = models.CharField(db_column='AirportName', max_length=100)\n country = models.ForeignKey(CountryMaster, db_column=\"CountryId\", on_delete=models.CASCADE)\n city = models.ForeignKey(CityMaster, db_column=\"CityId\", on_delete=models.CASCADE)\n _active = models.PositiveSmallIntegerField(db_column='Active', default=0)\n created_by = models.ForeignKey(UserMaster,db_column='CreatedBy', related_name='airportcodemaster_created_by' ,on_delete = models.DO_NOTHING)\n created_date = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modified_by = models.ForeignKey(UserMaster, db_column='ModifiedBy', related_name='airportmaster_modified_by' , on_delete = models.DO_NOTHING)\n modified_date = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n class Meta:\n managed = False\n db_table = 'AirportCodes'\n\n @property\n def active(self):\n return bool(self._active)\n\n\nclass HotelGroupMaster(models.Model):\n id = models.AutoField(db_column='HotelGroupId', primary_key=True)\n group = models.CharField(db_column='HotelGroup', max_length=200)\n _active = models.PositiveSmallIntegerField(db_column='Active', default=0)\n created_by = models.ForeignKey(UserMaster,db_column='CreatedBy', related_name='hotelgroupmaster_createdby' ,on_delete = models.DO_NOTHING)\n created_date = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modified_by = models.ForeignKey(UserMaster, db_column='ModifiedBy', related_name='hotelgroupmaster_modifiedby', on_delete = models.DO_NOTHING)\n modified_date = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n class Meta:\n managed = False\n db_table = 'HotelGroups'\n\n @property\n def active(self):\n return bool(self._active)\n\n\nclass PointOfSaleMaster(models.Model):\n id = models.AutoField(db_column='PointOfSaleId', primary_key=True)\n point_of_sale = models.CharField(db_column='PointOfSale', max_length=100)\n code = models.CharField(db_column='PointOfSaleCode', max_length=10)\n #country = models.ForeignKey(CountryMaster, db_column=\"CountryId\", on_delete=models.CASCADE)\n _active = models.PositiveSmallIntegerField(db_column='Active', default=0)\n created_by = models.ForeignKey(UserMaster,db_column='CreatedBy', related_name='pos_createdby', on_delete = models.DO_NOTHING)\n created_date = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modified_by = models.ForeignKey(UserMaster, db_column='ModifiedBy', related_name='pos_modifiedby', on_delete = models.DO_NOTHING)\n modified_date = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n class Meta:\n managed = False\n db_table = 'HotelPOS'\n\n @property\n def active(self):\n return bool(self._active)\n\n\nclass CompetitorMaster(models.Model):\n id = models.AutoField(db_column='CompetitorId', primary_key=True)\n name = models.CharField(db_column='CompetitorName',max_length=256)\n _active = models.BooleanField(db_column='Active',default=True)\n createdDate = models.DateTimeField(db_column='CreatedDate',auto_now=True)\n updatedDate = models.DateTimeField(db_column='ModifiedDate', auto_now_add=True)\n #countries = models.ForeignKey(CountryMaster, db_column='Fk_CountryId', on_delete=models.DO_NOTHING)\n\n class Meta:\n managed = False\n db_table = 'vw_competitor'\n\n\nclass StarRatingMaster(models.Model):\n id = models.AutoField(db_column='StarRatingId', primary_key=True)\n starrating = models.CharField(db_column='StarRating', max_length=50)\n starratingcode = models.CharField(db_column='StarRatingCode', max_length=50)\n _active = models.BooleanField(db_column='Active')\n createdby = models.ForeignKey(UserMaster, db_column='CreatedBy', related_name='starrratings_created_user',\n on_delete=models.DO_NOTHING)\n createddate = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modifiedby = models.ForeignKey(UserMaster, db_column='ModifiedBy', related_name='starratings_modified_user',\n on_delete=models.DO_NOTHING)\n modifieddate = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n class Meta:\n managed = False\n db_table = 'StarRatings'\n\n\nclass HotelMaster(models.Model):\n id = models.AutoField(db_column='HotelId', primary_key=True)\n website_hotel_id = models.CharField(db_column='WebSiteHotelId', max_length=255)\n name = models.CharField(db_column='HotelName', max_length=512)\n address1 = models.CharField(db_column='HotelAddress1', max_length=255)\n address2 = models.CharField(db_column='HotelAddress2', max_length=255)\n city = models.ForeignKey(CityMaster, db_column='CityId', on_delete=models.CASCADE)\n brand = models.CharField(db_column='HotelBrandName', max_length=50)\n competitorId = models.IntegerField(db_column='CompetitorId')\n star_rating = models.ForeignKey(StarRatingMaster, db_column='StarRatingId', on_delete=models.DO_NOTHING) #to do foriegn key\n post_code = models.CharField(db_column='HotelPostCode', max_length=255)\n match_status = models.PositiveSmallIntegerField(db_column='HotelMatchStatus')\n description = models.CharField(db_column='HotelDescription', max_length=100)\n is_processed = models.PositiveSmallIntegerField(db_column='isProceesed')\n match_hotel_name = models.CharField(db_column='matchhotelname', max_length=100)\n dipbag_sync_id = models.PositiveIntegerField(db_column='DipBagSyncId') #to do foriegn key\n is_mailed = models.PositiveSmallIntegerField(db_column='IsMailed')\n is_mailed1 = models.PositiveSmallIntegerField(db_column='IsMailed1')\n _active = models.PositiveSmallIntegerField(db_column='Active', default=0)\n created_by = models.ForeignKey(UserMaster,db_column='CreatedBy', related_name='hotelmaster_created_by' ,on_delete = models.DO_NOTHING)\n created_date = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modified_by = models.ForeignKey(UserMaster, db_column='ModifiedBy',related_name='hotelmaster_modified_by', on_delete = models.DO_NOTHING)\n modified_date = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n Longitude = models.CharField(db_column='Longitude', max_length=255)\n Latitude = models.CharField(db_column='Latitude', max_length=255)\n ContractManager = models.CharField(db_column='ContractManager', max_length=255)\n DemandGroup = models.CharField(db_column='DemandGroup', max_length=255)\n YieldManager = models.CharField(db_column='YieldManager', max_length=255)\n HotelStatusId = models.IntegerField(db_column='HotelStatusId')\n\n class Meta:\n managed = False\n db_table = 'Hotels'\n\n @property\n def active(self):\n return bool(self._active)\n\n\nclass BoardTypeMaster(models.Model):\n id = models.AutoField(db_column='BoardTypeId', primary_key=True)\n boardtypecode = models.CharField(db_column='BoardTypeCode', max_length=50)\n boardtypedescription = models.CharField(db_column='BoardTypeDescription', max_length=50)\n _active = models.BooleanField(db_column='Active')\n createdby = models.ForeignKey(UserMaster, db_column='CreatedBy', related_name='boardtypes_created_user',\n on_delete=models.DO_NOTHING)\n createddate = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modifiedby = models.ForeignKey(UserMaster, db_column='ModifiedBy', related_name='boardtypes_modified_user',\n on_delete=models.DO_NOTHING)\n modifieddate = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n class Meta:\n managed = False\n db_table = 'BoardTypes'\n\n @property\n def active(self):\n return bool(self._active)\n\n\nclass RoomTypeMaster(models.Model):\n id = models.AutoField(db_column='RoomTypeId', primary_key=True)\n roomtype = models.CharField(db_column='RoomType', max_length=50)\n roomtypecode = models.CharField(db_column='RoomTypeCode', max_length=50)\n _active = models.BooleanField(db_column='Active')\n createdby = models.ForeignKey(UserMaster, db_column='CreatedBy', related_name='roomtypes_created_user',\n on_delete=models.DO_NOTHING)\n createddate = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modifiedby = models.ForeignKey(UserMaster, db_column='ModifiedBy', related_name='roomtypes_modified_user',\n on_delete=models.DO_NOTHING)\n modifieddate = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n class Meta:\n managed = False\n db_table = 'RoomTypes'\n\n\nclass BookingPeriodMaster(models.Model):\n id = models.AutoField(db_column='BookingPeriodID', primary_key=True)\n bookingperiod = models.CharField(db_column='BookingPeriod', max_length=256)\n _active = models.BooleanField(db_column='Active')\n createdby = models.ForeignKey(UserMaster, db_column='CreatedBy', related_name='bookingperiod_created_user',\n on_delete=models.DO_NOTHING)\n createddate = models.DateTimeField(db_column='CreatedDate', auto_now_add=True)\n modifiedby = models.ForeignKey(UserMaster, db_column='ModifiedBy', related_name='bookingperiod_modified_user',\n on_delete=models.DO_NOTHING)\n modifieddate = models.DateTimeField(db_column='ModifiedDatetime', auto_now=True)\n\n class Meta:\n managed = False\n db_table = 'BookingPeriod'","sub_path":"eCube_Hotel_2/eCube_UI_2/hotel/master/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":11793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"129413649","text":"\nimport time\nimport threading\n\nfrom devicecontroller.common import *\nfrom devicecontroller.MessageUtils import *\nfrom devicecontroller.config.DeviceControllerConfig import SystemId\n\n\"\"\"\nThis job collects important system information and sends back report.\nThis job is also an example of simple job, which returns just one value.\n\"\"\"\nclass JobGetSystemInfo(threading.Thread):\n requestData = None\n jobId = None\n jobManager = None\n isFinished = False\n stopped = False\n name = JOB_TYPE_SYSTEM_INFO\n startTime = None \n \n def setContext(self, jobManager, jobId, requestData):\n self.jobManager = jobManager\n self.jobId = jobId\n self.requestData = requestData\n self.isFinished = False\n self.stopped = False \n \n def run(self):\n self.startTime = int(time.time()*1000) \n self.stopped = False \n responsePayload = { \n \"hostName\": getSystemHostName(),\n \"systemId\": SystemId,\n \"softwareVersion\": SOFTWARE_VERSION,\n \"softwareName\": SOFTWARE_NAME,\n \"comProtocolVersion\": COM_PROTOCOL_VERSION,\n \"systemUptime\": getSystemUptime(), \n \"systemTime\": getSystemTime(), \n \"systemTimeZone\": getSystemTimeZone(), \n \"serviceUptime\": getServiceUptime(),\n \"jobBufferLenght\": len(self.jobManager.jobBuffer),\n \"jobsServed\": self.jobManager.index \n }\n responsePayload[\"duration\"] = int(time.time()*1000 - self.startTime);\n self.isFinished = True\n self.jobManager.dispatchResponse(getJobFinishedResponse(self.jobId, self.name, self.requestData[\"requestId\"], responsePayload))\n\n def onJobNotifyEvent(self, requestData):\n log.debug(\"onDataEvent:\" + str(requestData))\n \n def getJobTag(self):\n return self.requestData[\"jobTag\"] \n\n def stop(self):\n self.isFinished = True\n self.stopped = True \n\n\"\"\"\nThis job is used for test purposes only. It is an example of long running job\nwhich supports interaction with client. It is able to receive data during it's \nlife time, and send back messages as processing goes on. \nThis job may be aborted prematurely as well.\n\"\"\" \nclass JobLongRunningTest(threading.Thread):\n requestData = None\n jobId = None\n jobManager = None\n isFinished = False\n stopped = False\n name = JOB_TYPE_LONG_RUNNING_TEST\n notifyData = None\n startTime = None\n \n def setContext(self, jobManager, jobId, requestData):\n self.jobManager = jobManager\n self.jobId = jobId\n self.requestData = requestData\n self.isFinished = False\n self.stopped = False\n self.notifyData = \"\"\n \n def run(self):\n self.startTime = int(time.time()*1000) \n self.stopped = False\n log.debug(JOB_TYPE_LONG_RUNNING_TEST + \"job started ...\")\n maxCycles = int(self.requestData[\"requestPayload\"][\"maxCycles\"])\n log.debug(\"max cycles set to \" + str(maxCycles))\n for x in range(maxCycles):\n if (self.stopped == True):\n responseData = getJobAbortedResponse(self.jobId, self.name, self.requestData[\"requestId\"], { \"resultType\": \"intermediateResult\", \"calculatedValue\": x, \"notifyData\": self.notifyData });\n self.jobManager.dispatchResponse(responseData)\n self.isFinished = True\n return\n responseData = getJobEventResponse(self.jobId, self.name, self.requestData[\"requestId\"], { \"resultType\": \"intermediateResult\", \"calculatedValue\": x, \"notifyData\": self.notifyData });\n self.jobManager.dispatchResponse(responseData)\n time.sleep(1)\n self.isFinished = True\n responseData = getJobFinishedResponse(self.jobId, self.name, self.requestData[\"requestId\"], { \"resultType\": \"finalResult\", \"calculatedValue\": x, \"notifyData\": self.notifyData });\n self.jobManager.dispatchResponse(responseData)\n \n def onJobNotifyEvent(self, requestData):\n log.debug(\"onDataEvent:\" + str(requestData))\n self.notifyData = requestData[\"requestPayload\"][\"notifyData\"]\n\n def getJobTag(self):\n return self.requestData[\"jobTag\"] \n \n def stop(self):\n self.isFinished = True\n self.stopped = True \n \n","sub_path":"DeviceControllerPy/devicecontroller/Job.py","file_name":"Job.py","file_ext":"py","file_size_in_byte":4514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"412373899","text":"import uuid\nimport pandas as pd\nfrom typing import *\n\nimport torch\nfrom torch import nn\n\nfrom fastai.vision.all import *\n\n\nidx2lbl = {0:\"Cassava Bacterial Blight (CBB)\",\n 1:\"Cassava Brown Streak Disease (CBSD)\",\n 2:\"Cassava Green Mottle (CGM)\",\n 3:\"Cassava Mosaic Disease (CMD)\",\n 4:\"Healthy\"}\n\n# ========================================================\n# Data / Augmentation Utilities\n# ========================================================\ndef get_x(data): return data[\"filePath\"]\ndef get_y(data): return data[\"label\"]\n\n@delegates(DataBlock.dataloaders)\ndef get_dataloaders(data, item_tfms:list=None, batch_tfms:list=None, **kwargs):\n dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),\n splitter=ColSplitter(),\n get_x=get_x,\n get_y=get_y,\n item_tfms=item_tfms,\n batch_tfms=batch_tfms)\n \n dls = dblock.dataloaders(data, **kwargs)\n return dls\n \ndef get_dataset(pth:str, im_dir:str, curr_fold:int, shuffle:bool=True):\n 'loads the dataframe and formats it'\n assert curr_fold < 5\n data = pd.read_csv(pth)\n data[\"filePath\"] = [os.path.join(im_dir, data[\"image_id\"][idx]) for idx in range(len(data))]\n data[\"is_valid\"] = [data.kfold[n] == curr_fold for n in range(len(data))]\n data['label'].replace(idx2lbl, inplace=True)\n \n if shuffle: data = data.sample(frac=1).reset_index(drop=True, inplace=False)\n else : data = data.reset_index(drop=True, inplace=False)\n \n return data\n\nclass AlbumentationsTransform(RandTransform):\n 'fast.ai type transformations using albumentation transform functions'\n split_idx,order=None,2\n def __init__(self, train_aug, valid_aug): store_attr()\n \n def before_call(self, b, split_idx): self.idx = split_idx\n \n def encodes(self, img: PILImage):\n if self.idx == 0 : aug_img = self.train_aug(image=np.array(img))['image']\n else : aug_img = self.valid_aug(image=np.array(img))['image']\n return PILImage.create(aug_img)\n\n \n# ========================================================\n# General Utilities\n# ========================================================\ndef generate_random_id() -> str:\n 'returns a random id for the experiment'\n idx = uuid.uuid1()\n idx = str(idx).split(\"-\")[0]\n return idx\n\ndef cut_model(model: nn.Module, upto: int = -2) -> nn.Module:\n ls = list(model.children())[:upto]\n encoder = nn.Sequential(*ls)\n return encoder\n\ndef create_head(nf: int, n_out: int, lin_ftrs: int = 512, act: nn.Module = nn.ReLU(inplace=True)):\n 'create a custom head for a classifier'\n lin_ftrs = [nf, lin_ftrs, n_out]\n \n pool = AdaptiveConcatPool2d()\n\n layers = [pool, nn.Flatten()]\n\n layers += [\n nn.BatchNorm1d(lin_ftrs[0]),\n nn.Dropout(0.25),\n act,\n nn.Linear(lin_ftrs[0], lin_ftrs[1], bias=False),\n nn.BatchNorm1d(lin_ftrs[1]),\n nn.Dropout(0.5),\n act,\n nn.Linear(lin_ftrs[1], lin_ftrs[2], bias=False),\n ]\n return nn.Sequential(*layers)\n\ndef custom_splitter(net): return [params(net.encoder), params(net.decoder)]\n\n\n# ========================================================\n# Layers / Modules \n# ========================================================\nclass AdaptiveConcatPool2d(nn.Module):\n \"Layer that concats `AdaptiveAvgPool2d` and `AdaptiveMaxPool2d`\"\n def __init__(self, size=None):\n super(AdaptiveConcatPool2d, self).__init__()\n self.size = size or 1\n self.ap = nn.AdaptiveAvgPool2d(self.size)\n self.mp = nn.AdaptiveMaxPool2d(self.size)\n\n def forward(self, x): return torch.cat([self.mp(x), self.ap(x)], 1)\n \n@delegates(create_head)\nclass TransferLearningModel(nn.Module):\n \"Transfer Learning with pre-trained encoder.\"\n def __init__(self, encoder, num_classes, cut=-2, init=True, **kwargs):\n super(TransferLearningModel, self).__init__()\n self.encoder = cut_model(encoder, cut)\n \n ftrs = num_features_model(self.encoder) * 2\n self.decoder = create_head(nf=ftrs, n_out=num_classes, **kwargs)\n \n if init: apply_init(self.decoder, nn.init.kaiming_normal_)\n \n def forward(self, xb):\n feats = self.encoder(xb)\n logits = self.decoder(feats)\n return logits","sub_path":"experiments/cassava_utils.py","file_name":"cassava_utils.py","file_ext":"py","file_size_in_byte":4379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"292376195","text":"def permutations(lst, low, high):\n if low==high:\n return [lst]\n else:\n ls = []\n for i in permutations(lst[:-1],0,len(lst)-1):\n for j in range(len(lst)):\n ls.append(i[j:] + [lst[-1]] + i[:j])\n return ls\n \n","sub_path":"assignment/4/sl6728_hw4_q9.py","file_name":"sl6728_hw4_q9.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"70418196","text":"import smtplib\nimport sys\nimport os\nfrom email.mime.application import MIMEApplication\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email.utils import COMMASPACE, formatdate\nfrom email import encoders\n\n# python3 smtp.py mail.to.send@gmail.com filename message\nif __name__ == \"__main__\":\n gmail_user = ''\n gmail_password = ''\n send_from = gmail_user \n send_to = [sys.argv[1]]\n file_name = sys.argv[2]\n message_body = sys.argv[3]\n\n os.chdir('/Users/koi/Desktop/ComputerNetwork/lab05')\n\n files = os.listdir()\n\n part = MIMEBase('application', \"octet-stream\")\n if(files.count(file_name) == 0):\n print('File not found')\n else:\n part.set_payload(open(file_name, \"rb\").read())\n encoders.encode_base64(part)\n part.add_header('Content-Disposition', 'attachment; filename=\"{}\"'.format(file_name))\n\n msg = MIMEMultipart()\n msg['From'] = send_from\n msg['To'] = COMMASPACE.join(send_to)\n msg['Date'] = formatdate(localtime=True)\n msg['Subject'] = 'Message'\n msg.attach(MIMEText(message_body, 'plain'))\n msg.attach(part)\n \n try:\n server_ssl = smtplib.SMTP_SSL('smtp.mail.ru', 465)\n server_ssl.ehlo()\n server_ssl.login(gmail_user, gmail_password)\n server_ssl.ehlo()\n server_ssl.sendmail(send_from, send_to, msg.as_string())\n server_ssl.close()\n\n print('Email sent!')\n except Exception:\n print(\"Something went wrong!\")\n","sub_path":"lab05/smtp.py","file_name":"smtp.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"303283735","text":"import time\nimport matplotlib.pyplot as plt\n\nfrom EasyMCDM.models.Electre import Electre\nfrom EasyMCDM.models.Promethee import Promethee\nfrom EasyMCDM.models.Pareto import Pareto\nfrom EasyMCDM.models.WeightedSum import WeightedSum\nfrom EasyMCDM.models.Irmo import Irmo\n\ndata = {}\nindexes = [ 0, 1, 2, 3, 4, 5, 6 ]\nweights = [0.14,0.14,0.14,0.14,0.14,0.14,0.14]\nprefs = [\"min\",\"max\",\"min\",\"min\",\"min\",\"max\",\"min\"]\nvetoes = [45, 29, 550, 6, 4.5, 4.5, 4.5]\nindifference_threshold = 0.6\npreference_thresholds = [20, 10, 200, 4, 2, 2, 7]\n\npaddings = []\npromethee_values = []\nelectre_values = []\nweighted_values = []\npareto_values = []\n\ndata = {}\nnbindiv=200\nnbcrit=2000\n\npaddings = []\npromethee_values = []\nelectre_values = []\nweighted_values = []\npareto_values = []\n\nfor i in range(2,nbcrit,100):\n \n for j in range(nbindiv):\n data[str(j)] = [ 5 for j in range(i) ]\n \n print(i)\n indexes = [ j for j in range(i) ]\n weights = [ 1/i for j in range(i)]\n prefs = [\"min\" for j in range(i)]\n vetoes = [5 for j in range(i)]\n indifference_threshold = 0.6\n preference_thresholds = [20 for j in range(i)]\n paddings.append(i)\n\n print(len(indexes))\n print(len(weights))\n print(len(prefs))\n print(len(vetoes))\n print(len(preference_thresholds))\n print(len(data[\"0\"]))\n print(len(data))\n print()\n\n w = WeightedSum(data=data, verbose=False)\n pa = Pareto(data=data, verbose=False)\n pr = Promethee(data=data, verbose=False)\n e = Electre(data=data, verbose=False)\n\n start = time.time()\n re = e.solve(weights, prefs, vetoes, indifference_threshold, preference_thresholds)\n electre_values.append(time.time() - start)\n\n start = time.time()\n rpr = pr.solve(weights=weights, prefs=prefs)\n promethee_values.append(time.time() - start)\n\n start = time.time()\n rpa = pa.solve(indexes=indexes, prefs=prefs)\n pareto_values.append(time.time() - start)\n\n start = time.time()\n rw = w.solve(pref_indexes=indexes, prefs=prefs, weights=weights, target='min')\n weighted_values.append(time.time() - start)\n\nplt.plot(paddings, promethee_values, label=\"promethee\")\nplt.plot(paddings, electre_values, label=\"electre\")\nplt.plot(paddings, weighted_values, label=\"weighted\")\nplt.plot(paddings, pareto_values, label=\"pareto\")\nplt.legend()\nplt.savefig(\"benchmark_nb_crit.png\")","sub_path":"tests/benchmarks/benchmark_crit.py","file_name":"benchmark_crit.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"2502322","text":"## import statements\nimport requests_oauthlib\nimport webbrowser\nimport json\nimport secret_data\nfrom datetime import datetime\nimport csv\n\n## CACHING SETUP\n\nDATETIME_FORMAT = \"%Y-%m-%d %H:%M:%S.%f\"\nDEBUG = True\nCACHE_FNAME = \"cache_contents.json\"\nCREDS_CACHE_FILE = \"creds.json\"\n\ntry:\n\twith open(CACHE_FNAME,'r') as cache_file:\n\t\tcache_json = cache_file.read()\n\t\tCACHE_DICTION = json.loads(cache_json)\nexcept:\n\tCACHE_DICTION = {}\n\ntry:\n\twith open(CREDS_CACHE_FILE,'r') as creds_file:\n\t\tcache_creds = creds_file.read()\n\t\tCREDS_DICTION = json.loads(cache_creds)\nexcept:\n\tCREDS_DICTION = {}\n\ndef has_cache_expired(timestamp_str, expire_in_days):\n\tnow = datetime.now()\n\tcache_timestamp = datetime.strptime(timestamp_str,DATETIME_FORMAT)\n\n\tdelta = now - cache_timestamp\n\tdelta_in_days = delta.days\n\n\tif delta_in_days > expire_in_days:\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef get_from_cache(identifier, dictionary):\n\tidentifier = identifier.upper()\n\tif identifier in dictionary:\n\t\tdata_assoc_dict = dictionary[identifier]\n\t\tif has_cache_expired(data_assoc_dict['timestamp'],data_assoc_dict['expire_in_days']):\n\t\t\tif DEBUG:\n\t\t\t\tprint(\"Cache has expired for {}\".format(identifier))\n\t\t\tdel dictionary[identifier]\n\t\t\tdata = None\n\t\telse:\n\t\t\tdata = dictionary[identifier]['values']\n\telse:\n\t\tdata = None\n\treturn data\n\ndef set_in_data_cache(identifier,data,expire_in_days):\n\tidentifier = identifier.upper()\n\tCACHE_DICTION[identifier] = {\n\t\t'values':data,\n\t\t'timestamp':datetime.now().strftime(DATETIME_FORMAT),\n\t\t'expire_in_days': expire_in_days\n\t}\n\n\twith open(CACHE_FNAME, 'w') as cache_file:\n\t\tcache_json = json.dumps(CACHE_DICTION)\n\t\tcache_file.write(cache_json)\n\ndef set_in_creds_cache(identifier, data, expire_in_days):\n\tidentifier = identifier.upper()\n\tCREDS_DICTION[identifier] = {\n\t\t'values' : data,\n\t\t'timestamp' : datetime.now().strftime(DATETIME_FORMAT),\n\t\t'expire_in_days' : expire_in_days\n\t}\n\n\twith open(CREDS_CACHE_FILE, 'w') as cache_file:\n\t\tcache_json = json.dumps(CREDS_DICTION)\n\t\tcache_file.write(cache_json)\n\n## ADDITIONAL CODE for program should go here...\n## Perhaps authentication setup, functions to get and process data, a class definition... etc.\n'''\ndef get_data_from_api(request_url,service_ident,params_diction, expire_in_days=7):\n\tident = create_request_identifier(request_url,params_diction)\n\tdata = get_from_cache(ident,CACHE_DICTION)\n\tif data:\n\t\tif DEBUG:\n\t\t\tprint(\"Loading from data cache: {}... data\".format(ident))\n\telse:\n\t\tif DEBUG:\n\t\t\tprint(\"Fetching new data from {}\".format(request_url))\n'''\n\nCLIENT_KEY = secret_data.client_key\nCLIENT_SECRET = secret_data.client_secret\n\nREQUEST_TOKEN_URL = \"https://www.tumblr.com/oauth/request_token\" \nBASE_AUTH_URL = \"https://www.tumblr.com/oauth/authorize\"\nACCESS_TOKEN_URL = \"https://www.tumblr.com/oauth/access_token\"\n\ndef get_tokens(client_key=CLIENT_KEY,client_secret=CLIENT_SECRET,request_token_url=REQUEST_TOKEN_URL,base_authorization_url=BASE_AUTH_URL,access_token_url=ACCESS_TOKEN_URL,verifier_auto=True):\n\toauth_inst = requests_oauthlib.OAuth1Session(client_key,client_secret=client_secret)\n\tfetch_response = oauth_inst.fetch_request_token(request_token_url)\n\n\tresource_owner_key = fetch_response.get('oauth_token')\n\tresource_owner_secret = fetch_response.get('oauth_token_secret')\n\n\tauth_url = oauth_inst.authorization_url(base_authorization_url)\n\twebbrowser.open(auth_url)\n\n\tif verifier_auto:\n\t\tverifier = input(\"Please input the verifier: \")\n\telse:\n\t\tredirect_result = input(\"Paste the full redirect URL here: \")\n\t\toauth_resp = oauth_inst.parse_authorization_response(redirect_result)\n\t\tverifier = oauth_resp.get('oauth_verifier')\n\n\toauth_inst = requests_oauthlib.OAuth1Session(client_key,client_secret=client_secret,resource_owner_key=resource_owner_key,resource_owner_secret=resource_owner_secret, verifier=verifier)\n\toauth_tokens = oauth_inst.fetch_access_token(access_token_url)\n\n\tresource_owner_key, resource_owner_secret = oauth_tokens.get('oauth_token'), oauth_tokens.get('oauth_token_secret')\n\treturn client_key, client_secret, resource_owner_key, resource_owner_secret, verifier\n\ndef get_tokens_from_service(service_name_ident, expire_in_days = 7):\n\tcreds_data = get_from_cache(service_name_ident, CREDS_DICTION)\n\tif creds_data:\n\t\tif DEBUG:\n\t\t\tprint(\"Loading creds from cache...\")\n\t\t\tprint()\n\n\telse:\n\t\tif DEBUG:\n\t\t\tprint(\"Fetching fresh credentials...\")\n\t\t\tprint(\"Prepare to log in via browser.\")\n\t\t\tprint()\n\t\tcreds_data = get_tokens()\n\t\tset_in_creds_cache(service_name_ident, creds_data, expire_in_days=expire_in_days)\n\treturn creds_data\n\ndef create_request_identifier(url, params_diction):\n\ttotal_ident = url + \"?api_key=\" + params_diction\n\treturn total_ident.upper()\n\ndef get_data_from_api(request_url, service_ident, params_diction, expire_in_days=7):\n\tident = create_request_identifier(request_url, params_diction)\n\tprint(ident)\n\tdata = get_from_cache(ident, CACHE_DICTION)\n\tif data :\n\t\tif DEBUG:\n\t\t\tprint(\"Loading from data cache: {}... data\".format(ident))\n\telse:\n\t\tif DEBUG:\n\t\t\tprint(\"Fetching new data from {}\".format(request_url))\n\t\tclient_key, client_secret, resource_owner_key, resource_owner_secret,verifier = get_tokens_from_service(service_ident)\n\n\t\toauth_inst = requests_oauthlib.OAuth1Session(client_key, client_secret=client_secret, resource_owner_key=resource_owner_key,resource_owner_secret=resource_owner_secret)\n\t\tresp = oauth_inst.get(request_url,params = params_diction)\n\t\tdata_str = resp.text\n\t\tdata = json.loads(data_str)\n\t\tset_in_data_cache(ident, data, expire_in_days)\n\treturn data\n\nif __name__ == \"__main__\":\n\tif not CLIENT_KEY or not CLIENT_SECRET:\n\t\tprint(\"You need to fill in client_key and client_secret in the secret_data.py file\")\n\t\texit()\n\tif not REQUEST_TOKEN_URL or not BASE_AUTH_URL:\n\t\tprint(\"You need to fill in this API's specific OAuth2 URLs in this file.\")\n\t\texit()\n\ntumblr_photo_search_baseurl = \"https://api.tumblr.com/v2/blog/newsweek.tumblr.com/posts/photo\"\ntumblr_text_search_baseurl = \"https://api.tumblr.com/v2/blog/newsweek.tumblr.com/posts/text\"\n\ntumblr_photo_result = get_data_from_api(tumblr_photo_search_baseurl,\"api_key\",CLIENT_KEY)\nphoto_dict = tumblr_photo_result\nphoto_id_list = []\nphoto_timestamp_list = []\nphoto_tags_list = []\nphoto_url_list = []\nphoto_width_list = []\nphoto_height_list = []\nphoto_caption_list = []\nphoto_note_count = []\nfor i in photo_dict['response']['posts']:\n\tphoto_id_list.append(i['id'])\n\tphoto_timestamp_list.append(i['timestamp'])\n\tphoto_tags_list.append(i['tags'])\n\tphoto_caption_list.append(i['caption'])\n\tphoto_note_count.append(i['note_count'])\n\tfor j in i['photos']:\n\t\tphoto_url_list.append(j['original_size']['url'])\n\t\tphoto_width_list.append(j['original_size']['width'])\n\t\tphoto_height_list.append(j['original_size']['height'])\nphoto_dimensions_list = []\nfor i in range(len(photo_width_list)):\n\tphoto_dimensions_list.append(str(photo_width_list[i]) + \" x \" + str(photo_height_list[i]))\n\ntumblr_text_result = get_data_from_api(tumblr_text_search_baseurl,\"api_key\",CLIENT_KEY)\ntext_dict = tumblr_text_result['response']['posts']\ntext_id_list = []\ntext_date_list = []\ntext_timestamp_list = []\ntext_title_list = []\ntext_content_list = []\ntext_tags_list = []\ntext_note_count = []\nfor i in text_dict:\n\ttext_id_list.append(i['id'])\n\ttext_date_list.append(i['date'])\n\ttext_timestamp_list.append(i['timestamp'])\n\ttext_title_list.append(i['title'])\n\ttext_tags_list.append(i['tags'])\n\ttext_note_count.append(i['note_count'])\n\tfor j in i['trail']:\n\t\ttext_content_list.append((j['content']).strip('/n'))\n\n## Make sure to run your code and write CSV files by the end of the program.\n\noutfile_photo = open(\"tumblr_photo.csv\",\"w\")\nwith open('tumblr_photo.csv',\"w\") as outfile_photo:\n\twriter = csv.writer(outfile_photo)\n\toutfile_photo.write('\"id\",\"timestamp\",\"captions\",\"tags\",\"url\",\"dimensions\",\"note count\"\\n')\n\tfor i in range(len(photo_dict['response']['posts'])):\n\t\twriter.writerow([photo_id_list[i],photo_timestamp_list[i],photo_caption_list[i],photo_tags_list[i],photo_url_list[i],photo_dimensions_list[i],photo_note_count[i]])\noutfile_photo.close()\n\noutfile_text = open(\"tumblr_text.csv\",\"w\")\nwith open('tumblr_text.csv','w') as outfile_text:\n\twriter = csv.writer(outfile_text)\n\toutfile_text.write('\"id\",\"date\",\"timestamp\",\"title\",\"tags\",\"content\",\"note count\"\\n')\n\tfor i in range(len(text_dict)):\n\t\twriter.writerow([text_id_list[i],text_date_list[i],text_timestamp_list[i],text_title_list[i],text_tags_list[i],text_content_list[i],text_note_count[i]])\noutfile_text.close()\n","sub_path":"SI507project5_code.py","file_name":"SI507project5_code.py","file_ext":"py","file_size_in_byte":8442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"56"}
+{"seq_id":"468304642","text":"from pico2d import *\n\nimport game_framework\nimport game_world\nimport main_state\n\nfrom slave import Slave\nfrom cutyRab import CutyRab\nfrom defender import Defender\nfrom bokgy import Bokgy\nfrom meteo import Meteo\n\nclass UI:\n font = None\n\n def __init__(self):\n self.image = load_image('./resource/UI/UI.png')\n self.getSlave = load_image('./resource/UI/getSlave.png')\n self.getRecovery = load_image('./resource/UI/Recovery.png')\n self.mineral = load_image('./resource/UI/mineral.png')\n self.spirit = load_image('./resource/UI/spirit_v.png')\n self.selectBar = load_image('./resource/UI/selectBar.png')\n self.getCutyRab = load_image('./resource/UI/getCutyRab.png')\n self.getMeteo = load_image('./resource/UI/getMeteo.png')\n self.getDefender = load_image('./resource/UI/getDefender.png')\n self.getUpgrade = load_image('./resource/UI/getUpgrade.png')\n self.spirits = 0\n self.coin = 0\n\n if UI.font is None:\n UI.font = load_font('./ENCR10B.TTF', 25)\n\n def draw(self):\n self.image.draw(600, 500)\n self.selectBar.draw(245, 92.5)\n self.selectBar.draw(960, 92.5)\n self.mineral.draw(500, 95)\n self.spirit.draw(500, 135)\n\n self.getSlave.draw(105, 115)\n self.spirit.draw(80, 45)\n self.font.draw(110, 43, '15', (0, 0, 0)) # getSlave Cost\n\n self.getCutyRab.draw(199, 115)\n self.spirit.draw(177, 45)\n self.font.draw(207, 43, '70', (0, 0, 0)) # getCutyRab Cost\n\n self.getMeteo.draw(292, 115)\n self.spirit.draw(271, 45)\n self.font.draw(301, 43, '50', (0, 0, 0)) # getMeteo Cost\n\n\n self.getRecovery.draw(820, 115)\n self.mineral.draw(795, 45)\n self.font.draw(818, 43, '100', (0, 0, 0)) # getRecovery Cost\n\n self.getDefender.draw(914, 115)\n self.mineral.draw(888, 45)\n self.font.draw(912, 43, '200', (0, 0, 0)) # getDefender Cost\n\n self.getUpgrade.draw(1007, 115)\n self.mineral.draw(981, 45)\n self.font.draw(995, 43, '1500', (0, 0, 0)) # getUpgrade Cost\n\n self.font.draw(530, 135, 'Spirit: %d' % self.spirits, (0, 0, 0))\n self.font.draw(530, 95, 'Coin: %d' % self.coin, (0, 0, 0))\n\n #draw_rectangle(775,70,865,160)\n\n\n\n def update(self):\n pass\n\n def call(self, mosX, mosY):\n if 60 < mosX < 150 and 70 < mosY < 160 and self.spirits >= 15:\n slave = Slave()\n self.spirits -= 15\n game_world.add_object(slave, 1)\n\n elif 155 < mosX < 245 and 70 < mosY < 160 and self.spirits >= 70:\n cutyRab = CutyRab()\n cutyRabs = main_state.get_cutyRabs()\n self.spirits -= 70\n game_world.add_object(cutyRab, 1)\n cutyRabs.append(cutyRab)\n\n elif 250 < mosX < 340 and 70 < mosY < 160 and self.spirits >= 50:\n meteo = Meteo()\n self.spirits -= 50\n game_world.add_object(meteo, 1)\n\n elif 775 < mosX < 865 and 70 < mosY < 160 and self.coin >= 100:\n castle = main_state.get_castle()\n self.coin -= 100\n castle.hp += 100\n if castle.hp > castle.maxHP:\n castle.maxHP = castle.hp\n\n elif 870 < mosX < 960 and 70 < mosY < 160 and self.coin >= 200:\n defender = Defender()\n self.coin -= 200\n game_world.add_object(defender, 0)\n\n elif 965 < mosX < 1055 and 70 < mosY < 160 and self.coin >= 1500:\n bokgy = main_state.get_bokgy()\n if not bokgy.enhance:\n self.coin -= 1500\n bokgy.upgrade()\n\n\n\n def handle_event(self, event):\n pass\n\n","sub_path":"userInterface.py","file_name":"userInterface.py","file_ext":"py","file_size_in_byte":3694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"421073036","text":"from django import forms\nfrom django.contrib.auth.models import Group\nfrom django.views.generic.edit import CreateView\nfrom django.views.generic.edit import DeleteView\nfrom django.views.generic.edit import UpdateView\nfrom django.views.generic import DetailView\nfrom django.views.generic import ListView\nfrom django.http import HttpResponseRedirect\nfrom django.contrib import messages\nfrom django.db.models import Count\n\nfrom general.views import BaseView\nfrom general.models import Task\nfrom django.core.urlresolvers import reverse_lazy\n\nfrom userprofile.models import UserProfile\n\nimport constants as co\n\n\nclass ProfileForm(forms.ModelForm):\n def __init__(self, group_name=None, user_id=None, request=None, *args, **kwargs):\n super(ProfileForm, self).__init__(*args, **kwargs)\n self.group_name = group_name\n self.user_id = user_id\n self.request = request\n if user_id:\n self.fields['password'].required = False\n self.fields['username'].required = False\n\n class Meta:\n model = UserProfile\n fields = ['username', 'password', 'first_name', 'last_name', 'email', 'gender',\n 'country', 'phone', 'site']\n\n def clean_site(self):\n \"\"\"Specifies default Host parameter.\"\"\"\n return self.request.get_host()\n \n def save(self, commit=True):\n if self.user_id:\n user = UserProfile.objects.get(pk=self.user_id)\n user.first_name=self.cleaned_data['first_name']\n user.last_name=self.cleaned_data['last_name']\n user.email=self.cleaned_data['email']\n user.gender=self.cleaned_data['gender']\n user.country=self.cleaned_data['country']\n user.phone=self.cleaned_data['phone']\n else:\n user = UserProfile.objects.create_user(**self.cleaned_data)\n user.groups.add(Group.objects.get(name=self.group_name))\n user.save()\n return user\n\n\nclass CreateProfileView(BaseView, CreateView):\n module_name = ''\n form_class = ProfileForm\n queryset = UserProfile.objects.all()\n template_name = 'userprofile/edit.html'\n group_name = ''\n\n def get_form_kwargs(self):\n kwargs = super(CreateProfileView, self).get_form_kwargs()\n kwargs['group_name'] = self.group_name\n kwargs['user_id'] = None\n kwargs['request'] = self.request\n return kwargs\n\n\nclass ListProfileView(BaseView, ListView):\n queryset = UserProfile.objects.all()\n template_name = 'userprofile/index.html'\n\n def get_context_data(self, **kwargs):\n context = super(ListProfileView, self).get_context_data(**kwargs)\n tasks_per_user = dict(Task.objects.all().values('owner').annotate(\n tasks=Count('owner')).values_list('owner', 'tasks'))\n # Count of user's tasks.\n context['user_tasks'] = tasks_per_user\n return context\n\n\nclass DetailProfileView(BaseView, DetailView):\n template_name = 'userprofile/detail.html' \n queryset = UserProfile.objects.all()\n\n def user_id(self):\n return self.get_object().pk\n\n\nclass UpdateProfileView(BaseView, UpdateView):\n template_name = 'userprofile/edit.html'\n form_class = ProfileForm\n queryset = UserProfile.objects.all()\n group_name = ''\n\n def get_form_kwargs(self):\n kwargs = super(UpdateProfileView, self).get_form_kwargs()\n kwargs['group_name'] = self.group_name\n kwargs['user_id'] = self.user_id()\n kwargs['request'] = self.request\n return kwargs\n\n def user_id(self):\n return self.get_object().pk\n\n\nclass RemoveProfileView(BaseView, DeleteView):\n queryset = UserProfile.objects.all()\n template_name = 'userprofile/delete.html'\n\n def get_success_url(self):\n group = self.object.get_group()\n if group == co.ADMIN_GROUP:\n return reverse_lazy('admins')\n elif group == co.EDITOR_GROUP:\n return reverse_lazy('editors')\n elif group == co.WRITER_GROUP:\n return reverse_lazy('writers')\n else:\n return reverse_lazy('customers')\n\n def form_invalid(self, form):\n messages.add_message(self.request, messages.ERROR, str(form.errors))\n return HttpResponseRedirect(self.get_success_url())\n\n def user_id(self):\n return self.get_object().pk\n\n","sub_path":"userprofile/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"556467596","text":"import urllib2\nimport json\nimport logging\nimport operator\n\nfrom mycls.ntcls import User\nfrom mycls import dbcls\n\nfrom google.appengine.api import memcache\n\n\ndef call_api(id, api, ext):\n api_dict = {'LOL': [\"http://api.captainteemo.com/\", {'USER': 'player/na/%s', 'LEAGUES': 'player/na/%s/leagues', 'STATUS': 'player/na/%s/ingame', 'HONOR': 'player/na/%s/honor', 'IP': 'player/na/%s/influence_points'}],\n 'TWITCH': [\"https://api.twitch.tv/kraken/\", {'ACTIVE': 'streams/%s', 'CHANNEL': 'channels/%s'}]}\n url = api_dict[api][0] + api_dict[api][1][ext] % id\n try:\n r = urllib2.urlopen(url)\n except:\n logging.error(('FAILED API CALL FOR ' + api + ' API - EXT. ' + ext + 'USER ' + id).upper())\n return None\n return json.loads(r.read())\n\n\ndef update_results(key, sort, func):\n userList, results = get_user_list(), []\n if func == 'HONOR':\n results = memcache.get('Honor')\n else:\n for user in userList:\n results.append(func(user))\n results = sorted(results, key=operator.attrgetter(sort), reverse=True)\n if results is not None:\n memcache.set(key, results)\n logging.info(key + ' Updated')\n\n\ndef get_user_list(update=False):\n userList = memcache.get(\"userList\")\n if userList is None or update:\n cachedUsers = []\n userList = dbcls.User.all()\n logging.info('USERS HAVE BEEN LOADED FROM DATABASE!')\n for user in userList:\n cachedUsers.append(User(user.internalName, user.displayName, user.iconID))\n memcache.set(\"userList\", cachedUsers)\n return cachedUsers\n return userList\n\n\ndef rawify(text):\n return text.replace(\" \", \"\").lower()\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"411425624","text":"from tkinter import *\nfrom tkinter import messagebox\nimport tkinter.ttk as ttk\nimport subprocess\nimport sys\nimport time\nimport io\n\ntry:\n p=subprocess.Popen([r'C:\\WINDOWS\\system32\\WindowsPowerShell\\v1.0\\powershell.exe', '-ExecutionPolicy', 'Unrestricted', '-windowstyle', 'hidden', r'C:\\Users\\odss9\\Desktop\\test.ps1'])\n p.wait()\nexcept:\n pass\n\n\n\nmaster=Tk()\nmaster.title(\"THE AD\")\nmaster.geometry(\"500x500\")\nmaster.resizable(1,0)\n\n\nLabel(master, text=\"First Name\").grid(row=0, column=0,sticky=W)\nLabel(master, text=\"Last Name\").grid(row=0, column=3, padx=10)\nLabel(master, text=\"SamAccountName\").grid(row=2, column=0, pady=10,sticky=W)\nLabel(master, text=\"Comapny Name\").grid(row=3, column=0, pady=10,sticky=W)\nLabel(master, text=\"Email\").grid(row=2, column=3, padx=10)\nLabel(master, text=\"Groups\").grid(row=3, column=3, pady=10)\n\ne1 = Entry(master)\ne2 = Entry(master)\ne3 = Entry(master)\ne4 = Entry(master)\n\n\ne1.grid(row=0, column=1)\ne2.grid(row=0, column=4)\ne3.grid(row=2, column=1)\ne4.grid(row=2, column=4)\n\n\ntkvar = StringVar(master)\ntkvar1= StringVar(master)\n\n##Changes to Tuple collection from Dicitonary in order to maaintain sequence order\n\nlist1=[]\n\nwith io.open(r'C:\\Users\\odss9\\Desktop\\cc.txt', 'rb') as f:\n myNames = [line.strip() for line in f]\n\np1=subprocess.Popen([r'C:\\WINDOWS\\system32\\WindowsPowerShell\\v1.0\\powershell.exe', '-ExecutionPolicy', 'Unrestricted', '-windowstyle', 'hidden', r'C:\\Users\\odss9\\Desktop\\test1.ps1 $x'])\np1.wait()\n\nwith io.open(r'C:\\Users\\odss9\\Desktop\\cc1.txt', 'rb') as f:\n myNames1 = [line.strip() for line in f]\n\n\ntkvar.set(myNames[0])\ntkvar1.set(myNames1[0])\n\ndef create():\n print(x)\n messagebox.showinfo(title='Status', message=\"User created Successfully.\")\n \n\n\nb1 = Button(master, text=\"Create User\", command=create).grid(row=5,column=0,sticky=W,pady=50)\n\nb2 = ttk.OptionMenu(master, tkvar, myNames[0], *myNames).grid(row=3,column=1,padx=30)\n\nb3 = ttk.OptionMenu(master, tkvar1, myNames1[0], *myNames1).grid(row=3, column=4,padx=10)\n \nx=\"\"\n\ndef change_dropdown(*args):\n print(tkvar.get())\n global x\n x=tkvar.get()\n return x \n \n\n\n# link function to change dropdown\n\ntkvar.trace('w', change_dropdown)\n\nmaster.mainloop()\n","sub_path":"Nov3/template1.py","file_name":"template1.py","file_ext":"py","file_size_in_byte":2197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"144772748","text":"# Задание-1: уравнение прямой вида y = kx + b задано в виде строки.\n# Определить координату y точки с заданной координатой x.\n\nequation = 'y = -12x + 11111140.2121'\nx = 2.5\n# вычислите и выведите y\n\n\n# Задание-2: Дата задана в виде строки формата 'dd.mm.yyyy'.\n# Проверить, корректно ли введена дата.\n# Условия корректности:\n# 1. День должен приводиться к целому числу в диапазоне от 1 до 30(31)\n# (в зависимости от месяца, февраль не учитываем)\n# 2. Месяц должен приводиться к целому числу в диапазоне от 1 до 12\n# 3. Год должен приводиться к целому положительному числу в диапазоне от 1 до 9999\n# 4. Длина исходной строки для частей должна быть в соответствии с форматом \n# (т.е. 2 символа для дня, 2 - для месяца, 4 - для года)\n\n# Пример корректной даты\ndate = '01.11.1985'\n\n# Примеры некорректных дат\ndate = '01.22.1001'\ndate = '1.12.1001'\ndate = '-2.10.3001'\n\n\n# Задание-3: \"Перевёрнутая башня\" (Задача олимпиадного уровня)\n#\n# Вавилонцы решили построить удивительную башню —\n# расширяющуюся к верху и содержащую бесконечное число этажей и комнат.\n# Она устроена следующим образом — на первом этаже одна комната,\n# затем идет два этажа, на каждом из которых по две комнаты, \n# затем идёт три этажа, на каждом из которых по три комнаты и так далее:\n# ...\n# 12 13 14\n# 9 10 11\n# 6 7 8\n# 4 5\n# 2 3\n# 1\n#\n# Эту башню решили оборудовать лифтом --- и вот задача:\n# нужно научиться по номеру комнаты определять,\n# на каком этаже она находится и какая она по счету слева на этом этаже.\n#\n# Входные данные: В первой строчке задан номер комнаты N, 1 ≤ N ≤ 2 000 000 000.\n#\n# Выходные данные: Два целых числа — номер этажа и порядковый номер слева на этаже.\n#\n# Пример:\n# Вход: 13\n# Выход: 6 2\n#\n# Вход: 11\n# Выход: 5 3\n\n#1\n\nequation = 'y = -12x + 11111140.2121'\nx = 2.5\nb = 1\n\nequation = equation.split(' ')\nprint(equation)\n\nfor num in equation:\n if num == 'y':\n continue\n elif num == '=':\n continue\n elif num == '+':\n operation = True\n continue\n elif num == \"-\":\n operation = False\n continue\n elif num.find('x') >= 0:\n k = num.split('x')\n k = k[0]\n k = float(k)\n else:\n b = num\n b = float(b)\n\n\nif operation:\n function = k*x + b\n print('{} = {} * {} + {}'.format(function, k, x, b))\nelse:\n function = k*x - b\n print('{} = {} * {} - {}'.format(function, k, x, b))\n\n\n\n#2\n\ndate = '01.11.1985'\n\nmonth_max_len = 0\nmonth_name = ''\nerror = 0\nerror_d = 0\nerror_y = 0\n\nmonths = [['01','Январь',31],['02','Февраль',28],['03','Март',31],['04','Апрель',30],['05','Май',31],['06','Июнь',30],['07','Июль',31],['08','Август',31],['09','Сентябрь',30],['10','Октябрь',31],['11','Ноябрь',30],['12','Декабрь',31]]\n\ndate = date.split('.')\n\nfor month in months:\n if month[0] == date[1]:\n A = True\n month_max_len = month[2]\n month_name = month[1]\n break\n else:\n error = error +1\nif error == 12:\n print('Ошибка ввода месяца')\n\nif int(date[0]) < 1 | int(date[0]) > month_max_len | len(date[0]) != 2:\n print('Ошибка ввода дня')\n error_d = 1\nelif int(date[2]) < 1 | int(date[2]) > 9999 | len(date[2]) != 4:\n print('Ошибка ввода года')\n error_y = 1\nelif (error != 12) | (error_y != 1) | (error_d != 1):\n print('GOOD')\n\n\n#3\n\nimport math\n\nmax_flat = 2 * 10 ** 9\n\nN_Y = True\ndata = []\nstart_floor_of_cell = 1\nsum_flats = 0\ncell = 1\nposition = 0\nremains = 0\nfloor = 0\nmax_flat_in_cell = 0\ncount = 2\n\nwhile sum_flats <= max_flat:\n sum_flats = sum_flats + cell ** 2\n start_floor_of_cell = start_floor_of_cell + cell - 1\n data[len(data):] = [cell, start_floor_of_cell, sum_flats]\n cell = cell + 1\n\nwhile N_Y:\n number = int(input('Введите номер квартиры от 1 до 2*10^9 : '))\n\n while ((number <= 1) | (number >= 2 * 10 ** 9)):\n print('Ошибка')\n number = int(input('Введите номер квартиры от 1 до 2*10^9 : '))\n\n while number > max_flat_in_cell:\n max_flat_in_cell = data[count]\n count = count + 3\n\n cell_of_number = data[count - 5]\n start_floor_of_number = data[count - 4]\n\n floor = start_floor_of_number + (cell_of_number - 1 - ((max_flat_in_cell - number) // cell_of_number))\n remains = ((max_flat_in_cell - number) % cell_of_number)\n\n if remains > 0:\n position = cell_of_number - remains\n\n else:\n position = cell_of_number\n\n print('На {} этаже {} по счету слева'.format(floor, position))\n while True:\n answer = input('Повторим N \\ Y ? : ')\n if (answer == 'N') | (answer == 'n'):\n N_Y = False\n break\n elif (answer == 'y') | (answer == 'Y'):\n N_Y = True\n break\n else:\n print('Ошибка')\n\n \n","sub_path":"lesson02/home_work/hw02_hard.py","file_name":"hw02_hard.py","file_ext":"py","file_size_in_byte":6036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"200080309","text":"from torch_geometric.data import InMemoryDataset\nfrom torch_geometric.utils import to_undirected\nimport torch_geometric.transforms as T\nimport torch_geometric\nimport pandas as pd\nimport shutil, os\nimport os.path as osp\nimport torch\nimport numpy as np\nimport networkx as nx\nimport random\nfrom copy import deepcopy\n\nclass EmailDataset(InMemoryDataset):\n def __init__(self, root = 'dataset', transform=None, pre_transform=None):\n '''\n - name (str): name of the dataset\n - root (str): root directory to store the dataset folder\n ''' \n self.root = root\n self.edge_file = \"email-Eu-core-temporal.txt\"\n self._num_nodes = 986\n self._num_static_edges = 24929\n self._num_temporal_edges = 332334\n \n super(EmailDataset, self).__init__(self.root, transform, pre_transform)\n self.data, self.slices = torch.load(self.processed_paths[0])\n self.edge_set = torch.load(self.processed_paths[1])\n self.edge_split = torch.load(self.processed_paths[2])\n\n @property\n def raw_file_names(self):\n return [self.edge_file]\n\n @property\n def processed_file_names(self):\n return ['email_geometric_data_processed.pt', 'email_edge_set.pt', 'email_edge_split.pt']\n\n def process(self):\n np.random.seed(42)\n edge_df = pd.read_csv(self.edge_file, sep=' ', header=None, names=['src', 'dst', 'unixts']).sort_values('unixts')\n uniq_edge_df = edge_df[~edge_df.duplicated(subset=['src', 'dst'], keep='last')]\n \n G = nx.OrderedDiGraph()\n for idx, row in uniq_edge_df.iterrows():\n src, dst, timestamp = row.loc['src'], row.loc['dst'], row['unixts']\n G.add_edge(src, dst, time=timestamp)\n \n nodes = np.unique(np.concatenate([edge_df['src'], edge_df['dst']]))\n for node in nodes:\n src_times = np.array(edge_df[edge_df['src'] == node]['unixts'])\n dst_times = np.array(edge_df[edge_df['dst'] == node]['unixts'])\n all_times = np.concatenate([src_times, dst_times])\n # randomly select 256 or length of total times\n num_times = min(len(all_times), 256)\n feature = np.concatenate([np.random.permutation(all_times)[:num_times], np.zeros(256-num_times)])\n mask = np.array([True] * num_times + [False] * (256-num_times))\n \n G.nodes[node][\"feature\"] = feature\n G.nodes[node][\"mask\"] = mask\n \n G = nx.convert_node_labels_to_integers(G, label_attribute='original')\n \n data_edges = list(G.edges(data=True))\n data_edges = [(u, v, t['time']) for u, v, t in data_edges]\n data_edges = sorted(data_edges, key=lambda x: x[2])\n edges = np.array([(u, v) for u, v, t in data_edges])\n \n self.edge_set = set(tuple(e) for e in edges)\n assert len(self.edge_set) == self._num_static_edges\n torch.save(self.edge_set, self.processed_paths[1])\n \n edge_split = self.make_edge_split(self.edge_set, edges)\n torch.save(edge_split, self.processed_paths[2])\n \n edge_index = edge_split['train']['edge'].t()\n data = {}\n \n for i, (_, feat_dict) in enumerate(G.nodes(data=True)):\n for key, value in feat_dict.items():\n data[key] = [value] if i == 0 else data[key] + [value]\n \n for key, item in data.items():\n item = np.array(item).astype(np.float32)\n data[key] = torch.tensor(item)\n\n data['edge_index'] = edge_index.view(2, -1)\n data['num_nodes'] = G.number_of_nodes()\n data['x'] = data['feature']\n del data['feature']\n assert data['num_nodes'] == self._num_nodes\n data = torch_geometric.data.Data.from_dict(data)\n \n data = data if self.pre_transform is None else self.pre_transform(data)\n\n print('Saving...')\n torch.save(self.collate([data]), self.processed_paths[0])\n \n def get_edge_split(self):\n return self.edge_split\n \n def make_edge_split(self, edge_set, edges):\n np.random.seed(42)\n random.seed(42)\n n = self._num_static_edges\n edges = torch.tensor(edges)\n \n train = edges[:int(0.8*n)]\n valid = edges[int(0.8*n):int(0.9*n)]\n test = edges[int(0.9*n):]\n \n exist = deepcopy(self.edge_set)\n \n valid_neg = []\n for i, p in enumerate(pair_generator(range(self._num_nodes), exist)):\n if i == 2500:\n break\n valid_neg.append(p)\n \n exist |= set(valid_neg)\n \n valid_neg = torch.from_numpy(np.array(valid_neg))\n \n test_neg = []\n for i, p in enumerate(pair_generator(range(self._num_nodes), exist)):\n if i == 2500:\n break\n test_neg.append(p)\n \n test_neg = torch.from_numpy(np.array(test_neg))\n \n return {\"train\": {\"edge\": train}, \n \"valid\": {\"edge\": valid, \"edge_neg\": valid_neg}, \n \"test\": {\"edge\": test, \"edge_neg\": test_neg}}\n\n def __repr__(self):\n return '{}()'.format(self.__class__.__name__)\n\ndef pair_generator(numbers, existing_pairs=set()): \n \"\"\"Return an iterator of random pairs from a list of numbers.\"\"\" \n # Keep track of already generated pairs \n used_pairs = existing_pairs\n\n while True: \n pair = tuple(random.sample(numbers, 2))\n if pair not in used_pairs:\n used_pairs.add(pair)\n yield pair\n\nif __name__ == \"__main__\":\n dataset = EmailDataset()\n print(dataset.get_edge_split())\n data = dataset[0]\n print(data)\n print(len(dataset.edge_set))\n breakpoint()","sub_path":"email_data/.ipynb_checkpoints/data-checkpoint.py","file_name":"data-checkpoint.py","file_ext":"py","file_size_in_byte":5773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"531923217","text":"import os\n\nimport django\nimport pytest\n\n\ndef pytest_configure():\n os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'tests.settings')\n django.setup()\n\n\n@pytest.fixture\ndef json_error():\n data = {'success': False, 'error': {'info': 'Invalid json'}}\n return data\n\n\n@pytest.fixture\ndef json_success():\n data = {\n \"summary_rates\": [\n {\n \"country_code\": \"US\",\n \"country\": \"United States\",\n \"region_code\": \"CA\",\n \"region\": \"California\",\n \"minimum_rate\": {\n \"label\": \"State Tax\",\n \"rate\": 0.065\n },\n \"average_rate\": {\n \"label\": \"Tax\",\n \"rate\": 0.0827\n }\n },\n {\n \"country_code\": \"CA\",\n \"country\": \"Canada\",\n \"region_code\": \"BC\",\n \"region\": \"British Columbia\",\n \"minimum_rate\": {\n \"label\": \"GST\",\n \"rate\": 0.05\n },\n \"average_rate\": {\n \"label\": \"PST\",\n \"rate\": 0.12\n }\n },\n {\n \"country_code\": \"UK\",\n \"country\": \"United Kingdom\",\n \"region_code\": None,\n \"region\": None,\n \"minimum_rate\": {\n \"label\": \"VAT\",\n \"rate\": 0.2\n },\n \"average_rate\": {\n \"label\": \"VAT\",\n \"rate\": 0.2\n }\n }\n ]\n }\n return data\n\n\n@pytest.fixture\ndef json_types_success():\n data = {\n \"categories\": [\n {\n \"name\": \"Clothing\",\n \"product_tax_code\": \"20010\",\n \"description\": \" All human wearing apparel suitable for general use\"\n },\n {\n \"name\": \"Software as a Service\",\n \"product_tax_code\": \"30070\",\n \"description\": \"Pre-written software, delivered electronically, but access remotely.\"\n },\n {\n \"name\": \"Digital Goods\",\n \"product_tax_code\": \"31000\",\n \"description\": \"Digital products transferred electronically, meaning obtained by the purchaser by means other than tangible storage media.\"\n },\n {\n \"name\": \"Candy\",\n \"product_tax_code\": \"40010\",\n \"description\": \"Candy and similar items\"\n },\n {\n \"name\": \"Supplements\",\n \"product_tax_code\": \"40020\",\n \"description\": \"Non-food dietary supplements\"\n },\n {\n \"name\": \"Food & Groceries\",\n \"product_tax_code\": \"40030\",\n \"description\": \"Food for humans consumption, unprepared\"\n },\n {\n \"name\": \"Soft Drinks\",\n \"product_tax_code\": \"40050\",\n \"description\": \"Soft drinks, soda, and other similar beverages. Does not include fruit juices and water.\"\n },\n {\n \"name\": \"Bottled Water\",\n \"product_tax_code\": \"40060\",\n \"description\": \"Bottled, drinkable water for human consumption.\"\n },\n {\n \"name\": \"Prepared Foods\",\n \"product_tax_code\": \"41000\",\n \"description\": \"Foods intended for on-site consumption. Ex. Restaurant meals.\"\n },\n {\n \"name\": \"Non-Prescription\",\n \"product_tax_code\": \"51010\",\n \"description\": \"Drugs for human use without a prescription\"\n },\n {\n \"name\": \"Prescription\",\n \"product_tax_code\": \"51020\",\n \"description\": \"Drugs for human use with a prescription\"\n },\n {\n \"name\": \"Books\",\n \"product_tax_code\": \"81100\",\n \"description\": \"Books, printed\"\n },\n {\n \"name\": \"Textbook\",\n \"product_tax_code\": \"81110\",\n \"description\": \"Textbooks, printed\"\n },\n {\n \"name\": \"Religious Books\",\n \"product_tax_code\": \"81120\",\n \"description\": \"Religious books and manuals, printed\"\n },\n {\n \"name\": \"Magazines & Subscriptions\",\n \"product_tax_code\": \"81300\",\n \"description\": \"Periodicals, printed, sold by subscription\"\n },\n {\n \"name\": \"Magazine\",\n \"product_tax_code\": \"81310\",\n \"description\": \"Periodicals, printed, sold individually\"\n },\n {\n \"name\": \"Other Exempt\",\n \"product_tax_code\": \"99999\",\n \"description\": \"Item is exempt\"\n }\n ]\n }\n return data\n\n\n@pytest.fixture\ndef json_success_for_address():\n data = {\n \"rate\": {\n \"zip\": \"05495-2086\",\n \"country\": \"US\",\n \"country_rate\": \"0.0\",\n \"state\": \"VT\",\n \"state_rate\": \"0.06\",\n \"county\": \"CHITTENDEN\",\n \"county_rate\": \"0.0\",\n \"city\": \"WILLISTON\",\n \"city_rate\": \"0.0\",\n \"combined_district_rate\": \"0.01\",\n \"combined_rate\": \"0.07\",\n \"freight_taxable\": True\n }\n }\n return data\n\n\n@pytest.fixture\ndef json_success_for_order():\n data = {\n \"tax\": {\n \"order_total_amount\": 16.5,\n \"shipping\": 1.5,\n \"taxable_amount\": 15,\n \"amount_to_collect\": 1.35,\n \"rate\": 0.09,\n \"has_nexus\": True,\n \"freight_taxable\": False,\n \"tax_source\": \"destination\",\n \"breakdown\": {\n \"taxable_amount\": 15,\n \"tax_collectable\": 1.35,\n \"combined_tax_rate\": 0.09,\n \"state_taxable_amount\": 15,\n \"state_tax_rate\": 0.0625,\n \"state_tax_collectable\": 0.94,\n \"county_taxable_amount\": 15,\n \"county_tax_rate\": 0.0025,\n \"county_tax_collectable\": 0.04,\n \"city_taxable_amount\": 0,\n \"city_tax_rate\": 0,\n \"city_tax_collectable\": 0,\n \"special_district_taxable_amount\": 15,\n \"special_tax_rate\": 0.025,\n \"special_district_tax_collectable\": 0.38,\n \"line_items\": [\n {\n \"id\": \"1\",\n \"taxable_amount\": 15,\n \"tax_collectable\": 1.35,\n \"combined_tax_rate\": 0.09,\n \"state_taxable_amount\": 15,\n \"state_sales_tax_rate\": 0.0625,\n \"state_amount\": 0.94,\n \"county_taxable_amount\": 15,\n \"county_tax_rate\": 0.0025,\n \"county_amount\": 0.04,\n \"city_taxable_amount\": 0,\n \"city_tax_rate\": 0,\n \"city_amount\": 0,\n \"special_district_taxable_amount\": 15,\n \"special_tax_rate\": 0.025,\n \"special_district_amount\": 0.38\n }\n ]\n }\n }\n }\n return data\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":7588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"146645417","text":"import os\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\nfrom urllib.parse import urlparse\nimport re\nimport csv\nfrom pathlib import Path\nimport traceback\nimport sys\n\nproxies = {'http': 'http://localhost:1080'}\nBS_PARSER = 'html.parser'\nheaders = {\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',\n 'Accept-Encoding': 'gzip, deflate',\n 'Accept-Language': 'en-US,en;q=0.9,zh-CN;q=0.8,zh;q=0.7,ja;q=0.6',\n 'Cache-Control': 'max-age=0',\n 'Host': 'bj.ganji.com',\n 'Proxy-Connection': 'keep-alive',\n 'Upgrade-Insecure-Requests': '1',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.97 Safari/537.36'\n}\ncommunity_attr_key_list = ['小区名称', '房价', '区域商圈：', '详细地址：', '建筑类型：', '物业费用：',\n '产权类别：', '容积率：', '总户数：', '绿化率：', '建��年代：', '停车位：', '开发商：', '物业公司：',\n '经度', '纬度']\n# 更改当前目录为文件锁在目录\ncurrent_path = os.path.abspath(__file__)\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nos.chdir(current_dir)\n\n\ndef fetch(url, **kwargs):\n url = str(url)\n if url.startswith('//'):\n url = url.replace('//', 'http://', 1)\n while True:\n try:\n # r = requests.get(url, timeout=10, proxies=proxies, **kwargs)\n r = requests.get(url, timeout=10, **kwargs)\n while r.text.strip() == '':\n print('请求内容为空，重新请求')\n r = requests.get(url, timeout=10, **kwargs)\n while '进行验证码校验' in r.text:\n print(url)\n print('程序暂停，请访问链接进行人机验证！')\n os.system(\"pause\")\n r = requests.get(url, timeout=10, **kwargs)\n return r\n except TimeoutError as e:\n print(url)\n print(e)\n print('访问超时')\n except Exception as e:\n print(url)\n print(e)\n print('获取页面内容时出错了')\n\n\ndef geocode(geo_name, city=''):\n url = (\n f'http://api.map.baidu.com/geocoding/v3/?address={geo_name}'\n f'&city={city}&output=json&ak=edUmWCLHPjvuY0OXdRrNna38b4GX30VV'\n )\n r = requests.get(url)\n r = r.json()\n if r['status'] != 0:\n print(r)\n raise Exception('这里有个异常，看看吧')\n return [r['result']['location']['lng'], r['result']['location']['lat']]\n\n\ndef main(city):\n community_list = []\n city_file_path = Path(city+'.csv')\n is_exist = False\n if city_file_path.exists():\n is_exist = True\n with open(str(city_file_path), 'r') as f:\n reader = csv.reader(f)\n next(reader)\n for row in reader:\n community_list.append(row[0])\n\n city_file = open(str(city_file_path), 'a+', newline='')\n writer = csv.writer(city_file)\n if not is_exist:\n writer.writerow(community_attr_key_list)\n\n # 赶集网城市选择首页\n index_r = fetch('http://www.ganji.com/index.htm')\n\n # html.parser、lxml HTML、lxml XML、html5lib解析器区别\n index_bs = BeautifulSoup(index_r.text, BS_PARSER)\n index_a = index_bs.find('a', text=city)\n\n # 对应城市的子域名\n index_href = index_a['href']\n\n\n page = 0\n # TODO:在这里改变链接，来爬取不同区域的房租列表\n list_href = index_href + 'chuzu/pn1'\n\n # --------\n list_r = requests.get(index_href + 'chuzu/pn1')\n list_bs = BeautifulSoup(list_r.text, BS_PARSER)\n subarea_list = [[x['href'] + 'pn1',x.get_text(strip=True)] for x in list_bs.select('.thr-list a')][1:]\n for subarea in subarea_list:\n list_href = subarea[0]\n while True:\n # 出租页面列表\n page += 1\n # 不能直接访问chuzu，学校IP访问次数限制。浏览器代理访问所以不受影响\n # 翻页的时候停一下\n time.sleep(0.5)\n\n list_r = fetch(list_href)\n list_bs = BeautifulSoup(list_r.text, BS_PARSER)\n\n time.sleep(1)\n\n # ...\n \n # 链接列表\n list_a_list = list_bs.select('.ershoufang-list dd.dd-item.title a')\n\n # print(list_bs.select('.ershoufang-list'))\n\n # .address-eara:last-child\n list_list = [[l.select_one('dd.dd-item.title a'),\n l.select_one('.address-eara').get_text(strip=True).replace('...', '')]\n for l in list_bs.select('.ershoufang-list')]\n for [list_a, list_community] in list_list:\n # 暂停进行人机验证，不挂起\n # time.sleep(0.5)\n if list_community in community_list:\n print(f'{list_community}已在列表中，跳过')\n continue\n try:\n # &key=¶ms=ra 这里乱码\n item_href = list_a['href']\n # item_href = re.compile(r'entinfo=(\\d+)_0').findall(item_href)[0]\n item_href = re.compile('key=.*&').sub('', item_href)\n\n # item_r = fetch('https://jxjump.58.com/service?target=FCADV8oV3os7xtAj_6pMK7rUlrztK2I2XgGp_ir8Za1X4Ap_Zpk1zEffDjvZrKof8T9H1Etf3gzeNV-h7xP23s1aDYDtaGQMt0V1-x_7k2_EqtKo7wITX0Rw7HMRl9KE7zsZb7bPyKFQTpiGJTuRsjMqTHZzOMkNr3kS6ZtAUEnEhkw5aqSx4Uvrv63dFoqNyNhGQgafNqWfm3ZKqr6XLq3dz06ApDR5hWRpH8YZE29olTwTVbjAbE8amfg&pubid=0&apptype=10&psid=150065938206327100784721924&entinfo=40209270904961_0&cookie=|||9094ac41d9b2fdad39356072c0cc78a8&fzbref=0&key=¶ms=rankbusitime0099^desc&gjcity=bj')\n # item_r = fetch('https://jxjump.58.com/service?target=FCADV8oV3os7xtAj_6pMK7rUlrztK2I2XgGp_ir8Za1X4Ap_Zpk1zEffDjvZrKof8T9H1Etf3gzeNV-h7xP23s1aDYDtaGQMt0V1-x_7k2_EqtKo7wITX0Rw7HMRl9KE7zsZb7bPyKFQTpiGJTuRsjMqTHZzOMkNr3kS6ZtAUEnEhkw5aqSx4Uvrv63dFoqNyNhGQgafNqWfm3ZKqr6XLq3dz06ApDR5hWRpH8YZE29olTwTVbjAbE8amfg&pubid=0&apptype=10&psid=150065938206327100784721924&entinfo=40209270904961_0&gjcity=bj')\n item_r = fetch(item_href)\n item_bs = BeautifulSoup(item_r.text, BS_PARSER)\n\n # 小区链接\n item_a = item_bs.select_one(\n 'ul.er-list-two.f-clear li.er-item.f-fl .content a')\n if item_a is None:\n print(item_href)\n # 在自己封装的fetch函数中处理验证问题\n # if '验证' in item_r.text:\n # print('程序暂停，请访问链接进行人机验证！')\n # os.system(\"pause\")\n # # 这个小区也不放过\n # item_r = fetch(item_href)\n # item_bs = BeautifulSoup(item_r.text, BS_PARSER)\n # item_a = item_bs.select_one(\n # 'ul.er-list-two.f-clear li.er-item.f-fl .content a')\n \n print('此房的小区无信息')\n continue\n community_r = fetch(item_a['href'])\n community_bs = BeautifulSoup(community_r.text, BS_PARSER)\n if community_bs.select_one('.card-top .card-title') is None:\n print('小区页面错误：')\n print(community_r.text)\n print(item_a['href'])\n\n community_title = community_bs.select_one(\n '.card-top .card-title')['title']\n\n print(\n f'区域：{subarea[1]}，页数：{page}，已搜索{len(community_list)}个小区，找到小区：{community_title}')\n if community_title in community_list:\n continue\n # 字符串也是子节点\n community_price = community_bs.select_one(\n 'span.price').contents[0]\n # 是L不是1\n # 可以使用双层列表生成式\n # community_attr_list = [s for attr in community_bs.select('li.item.f-fl .content') for s in attr.stripped_strings]\n # 属性值\n community_attr_value_list = [re.sub(r'\\s+', ' ', attr.get_text(strip=True))\n for attr in community_bs.select('li.item.f-fl .content')]\n # 属性键\n # community_attr_key_list = [re.sub(r'\\s+', '', attr.get_text(strip=True))\n # for attr in community_bs.select('li.item.f-fl :first-child')]\n # print(community_attr_key_list)\n # ['区域商圈：', '详细地址：', '建筑类型：', '物业费用：', '产权类别：', '容积率：', '总户数：', '绿化率：', '建筑年代：', '停车位：', '开发商：', '物业公司：']\n\n # 根据地址获取经纬度信息\n address = city + community_attr_value_list[1] + community_title\n\n writer.writerow([community_title, community_price,\n *community_attr_value_list, *geocode(address, city)])\n community_list.append(community_title)\n except Exception as e:\n print(e)\n ex_type, ex_val, ex_stack = sys.exc_info()\n print(ex_type)\n print(ex_val)\n for stack in traceback.extract_tb(ex_stack):\n print(stack)\n continue\n # raise Exception('停停停！')\n # 下一页的链接\n list_href = list_bs.find('a', text='下一页')\n print(list_href)\n if list_href is None:\n break\n list_href = list_href['href']\n # --------\n\n\n\n city_file.close()\n\n\nif __name__ == \"__main__\":\n # city_list = [x.name for x in Path('D:\\Document\\HousePricing\\公司').iterdir()]\n # ['上海', '东莞', '北京', '南京', '厦门', '合肥', '大连', '天津', '广州', '成都', '杭州', '武汉', '沈阳', '济南', '深圳', '烟台', '苏州', '重庆', '长沙', '青岛']\n city_list = ['上海', '东莞', '北京', '南京', '厦门', '合肥', '大连', '天津', '广州', '成都', '杭州', '武汉', '沈阳', '济南', '深圳', '烟台', '苏州', '重庆', '长沙', '青岛']\n print(city_list)\n for city in city_list:\n main(city)\n","sub_path":"demo_subarea.py","file_name":"demo_subarea.py","file_ext":"py","file_size_in_byte":10813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"202584088","text":"# -*- coding: UTF-8 -*-\n# 文件名：Server.py\n\nimport time\nimport queue\nimport socket\nimport sqlite3\nimport threading\n\nclass initial_server(object):\n def __init__(self, addr=\"localhost\", port=12345):\n self.addr = addr\n self.port = port\n\n self.connections = []#已建立的连接\n self.name = {}#已建立连接的客户端套接字，ip:port字符串\n self.nametoconn = {}\n self.userlist = []#已登录用户\n self.queue = queue.Queue()\n\n self.dbconn = sqlite3.connect('UserInfo.db')#用户信息数据库\n self.dbcursor = self.dbconn.cursor()#创建数据库连接的游标\n #游标.执行sql语句\n self.dbcursor.execute('''CREATE TABLE IF NOT EXISTS USERINFO\n (USERNAME VARCHAR(20) PRIMARY KEY NOT NULL,\n PASSWORD VARCHAR(20) NOT NULL,\n LASTLOGIN VARCHAR(50) NOT NULL,\n STATUS INT(1) NOT NULL\n );''')\n self.dbcursor.execute(\"UPDATE USERINFO set STATUS = 0\")\n self.dbconn.commit()\n\n # 服务器TCP套接字\n self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \n self.s.bind((self.addr, self.port)) \n print(\"Server is listening on \" + str(port) + \"...\")\n\n def listen_to_port(self):\n # 建立的连接s监听TCP传入连接\n self.s.listen(10) \n while True:\n # 新连接是真正的连接，s还在监听\n connection_new, address = self.s.accept() \n connection_new.settimeout(0.000001) #建立的这个链接的超时时间\n add = address[0] + \":\" + str(address[1]) #IP套接字，地址信息是(ip, port)\n # 已建立的连接\n self.connections.append(connection_new) \n # key=客户端套接字，value=用户名\n self.name[add] = add\n\n def msg_queue(self):\n while True:\n # 遍历已建立的连接，\n for c in self.connections:\n try:\n # 从这个连接接受msg\n msg_recv = eval(c.recv(1024))\n # 客户端接受数据产生超时异常\n except socket.timeout: \n continue\n except SyntaxError:\n pass\n except socket.error as err:\n # 软件导致连接退出/连接被重置\n if err.errno == 10053 or err.errno == 10054:\n self.remove_connection(c) \n except ValueError:\n pass\n # 正常收到信息\n else:\n # 这个连接的(ip,port)\n addr = c.getpeername() \n # 消息入队：(发方套接字,消息,发方的连接)\n self.queue.put((addr, msg_recv, c)) \n if msg_recv[\"type\"] == \"msglen\":\n length = msg_recv[\"len\"]\n time.sleep(length * 0.0000001) \n received_length = 0\n while msg_recv[\"type\"] != \"usermsg\":\n try:\n msg_recv = \"\".encode()\n # 直至收到事先给予的长度\n while received_length < length: \n try:\n msg_recv_ = c.recv(length) #收给定长度的\n msg_recv = msg_recv + msg_recv_ #收到的总消息\n received_length = received_length + len(msg_recv_) #已收到的长度\n msg_recv = eval(msg_recv) #收到的总消息\n time.sleep(length * 0.00000001)\n #超时，继续收\n except socket.timeout:\n continue\n except SyntaxError:\n continue\n # 收到给定长度\n else: \n break\n except socket.timeout:\n continue\n except socket.error as err:\n if err.errno == 10053 or err.errno == 10054:\n self.remove_connection(c)\n except ValueError:\n pass\n # length长的消息是用户消息，入队\n self.queue.put((addr, msg_recv, c))\n\n def login_request(self, msg_recv, socket_tuple):\n username = msg_recv[\"name\"]\n self.dbcursor.execute(\"SELECT * from USERINFO where USERNAME = \\\"{Uname}\\\"\".format(Uname = username)) #通过用户名检索出对应的用户实体\n # 查询结果的下一整行\n Userinfo = self.dbcursor.fetchone()\n \n # 判断是否登录成功，给客户端反馈\n # 用户不存在/密码错误\n if Userinfo == None or Userinfo[1] != msg_recv[\"password\"]: \n flag = False\n back = {\"type\": \"loginBack\",\n \"name\":\"Server\",\n \"info\": \"loginFail\"}\n # 用户状态=1\n elif Userinfo[3] == 1:\n flag = False\n back = {\"type\": \"loginBack\",\n \"name\":\"Server\",\n \"info\": \"loginAlready\"}\n # 登录成功\n else:\n flag = True\n address = socket_tuple[0] + \":\" + str(socket_tuple[1])\n # key=登录用户的套接字，value=用户名\n self.name[address] = username\n self.userlist.append(username)\n # 这个用户的LASTLOGIN字段(数据库里)\n self.lastlogintime = Userinfo[2]\n # 更新LASTLOGIN=这次登录的时间\n self.dbcursor.execute(\"UPDATE USERINFO set LASTLOGIN = {logintime}, STATUS = 1 where USERNAME=\\\"{Uname}\\\"\".format(\n logintime = time.time(), #time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(msg_recv[\"time\"])),\n Uname = username))\n self.dbconn.commit()\n # 成功登录,反馈给客户端\n back = {\"type\": \"loginBack\",\n \"name\":\"Server\",\n \"info\": \"loginSucc\",\n \"userlist\": self.userlist}\n forward = {\"type\": \"sysmsg\",\n \"info\": \"userlogin\",\n \"name\": username,\n \"time\": time.time(),\n \"msg\": \">> {name} enters Chatting Room\".format(name=username)}\n\n for connection in self.connections:\n # 这个连接的客户端套接字\n connection_socket = connection.getpeername()\n # 这次登录的用户的套接字，和已连接的某个套接字一样\n if connection_socket == socket_tuple:\n # 登录成功\n if flag:\n # dict:用户名——>连接\n self.nametoconn[self.name[address]] = connection #key=用户名，value=连接\n connection.send(str(back).encode())\n # 这次登录的用户的套接字是新的\n elif flag:\n connection.send(str(forward).encode())\n\n def register_request(self, msg_recv, register_socket):\n # 要注册的用户名\n username = msg_recv[\"name\"]\n # 查已有用户名\n self.dbcursor.execute(\"SELECT * from USERINFO where USERNAME=\\\"{Uname}\\\"\".format(Uname = username)) # 通过用户名检索出对应的用户实体\n Userinfo = self.dbcursor.fetchone()\n\n # 用户名不存在\n if Userinfo == None: \n # 用户信息存储\n self.dbcursor.execute(\"INSERT INTO USERINFO (USERNAME, PASSWORD, LASTLOGIN, STATUS) \\\n VALUES (\\\"{Uname}\\\", \\\"{Passwd}\\\", \\\"Never\\\", 0)\".format(Uname = username, Passwd = msg_recv[\"password\"]))\n self.dbconn.commit()\n # key=套接字，value=用户名\n self.name[register_socket] = username\n self.lastlogintime = \"Never\"\n back = {\"type\": \"rgtrBack\",\n \"name\":\"Server\",\n \"info\": \"rgtrSucc\"}\n # 用户已存在\n else:\n back = {\"type\": \"rgtrBack\",\n \"name\":\"Server\",\n \"info\": \"rgtrFail\"}\n \n # 查找转发对象连接\n for connection in self.connections:\n connection_socket = connection.getpeername()\n if connection_socket == register_socket:\n connection.send(str(back).encode())\n\n # 删除连接、用户名-套接字列表name、已登录用户名列表userlist、修改数据库的用户状态、通知其他连接的套接字\n def remove_connection(self, removed_connection):\n try:\n self.connections.remove(removed_connection) \n except ValueError:\n pass\n address = removed_connection.getpeername() \n socket_str = address[0] + \":\" + str(address[1]) \n username = self.name[socket_str]\n self.name.pop(socket_str)\n # 若用户已登录\n if username in self.userlist:\n self.userlist.remove(username)\n # 用户信息数据库，修改用户状态\n dbconn1 = sqlite3.connect('userinfo.db') \n dbcursor1 = dbconn1.cursor()\n dbcursor1.execute(\"UPDATE USERINFO set STATUS=0 where USERNAME=\\\"{Uname}\\\"\".format(Uname=username))\n dbconn1.commit()\n back = {\"type\": \"sysmsg\",\n \"info\": \"userexit\",\n \"name\": username,\n \"time\": time.time(),\n \"msg\": \"<< {name} exits Chatting Room.\".format(name=username)}\n # 通知其他用户\n for c in self.connections:\n c.send(str(back).encode())\n\n def msg_forward(self, msg_forward, addr):\n socket_str = addr[0] + \":\" + str(addr[1]) \n\n if msg_forward[\"destname\"] == \"all\":\n #给所有连接了的客户端转发msg\n for c in self.connections:\n print(\"forward \")\n c.send(str(msg_forward).encode())\n else:\n #要转发的用户名的连接\n self.nametoconn[msg_forward[\"destname\"]].send(str(msg_forward).encode())\n self.nametoconn[msg_forward[\"name\"]].send(str(msg_forward).encode())\n print(\"forward \")\n\n # 监听、消息处理线程\n def run(self):\n func1 = threading.Thread(target=self.listen_to_port)\n func2 = threading.Thread(target=self.msg_queue)\n func1.start()\n func2.start()\n while True:\n # 消息队列空\n if self.queue.empty():\n continue\n # 非空，取一条\n addr, msg, conn = self.queue.get()\n\n # 3种消息类型，使用3种处理函数\n # 登录：账号密码\n if msg[\"type\"] == \"login\":\n self.login_request(msg, addr)\n # 转发信息\n elif msg[\"type\"] in (\"usermsg\", \"msglen\"):\n self.msg_forward(msg, addr)\n # 注册：用户名、密码\n elif msg[\"type\"] == \"register\":\n self.register_request(msg, addr)\n\nif __name__ == '__main__':\n server = initial_server(addr=\"127.0.0.1\", port=15000)\n server.run()\n\n","sub_path":"chatroom/Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":11781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}
+{"seq_id":"548263314","text":"# Software Engineer Intern Interview\n# \n# You are climbing a stair case. Each time you can either make 1 step or 2 \n# steps. The staircase has n steps. In how many distinct ways can you climb \n# the staircase ?\n\ndef calc(steps):\n\tprint(steps)\n\tif steps == 0:\n\t\tprint('a')\n\t\treturn 1\n\telif steps < 0:\n\t\tprint('b')\n\t\treturn 0\n\telse:\n\t\ttotal = 0\n\t\ttotal += calc(steps - 1)\n\t\ttotal += calc(steps - 2)\n\t\tprint('c')\n\t\treturn total\n\nprint(calc(4))\n","sub_path":"HackerRank/staircase .py","file_name":"staircase .py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"53"}