diff --git "a/6526.jsonl" "b/6526.jsonl" new file mode 100644--- /dev/null +++ "b/6526.jsonl" @@ -0,0 +1,756 @@ +{"seq_id":"591837647","text":"# from ifm import Enum\nimport pandas as pd\n\n\nclass TsPd:\n\n def __init__(self, doc):\n self.doc = doc\n\n def info(self):\n \"\"\"\n Returns a pandas.DataFrame with information on existing time series (formerly power functions).\n \"\"\"\n\n list_info = self.doc.c.ts.info()\n df = pd.DataFrame(list_info, columns=[\"tsid\", \"comment\", \"no_point\", \"is_cyclic_num\", \"interpolation_kind\"])\n df.set_index(\"tsid\", inplace=True)\n df[\"is_cyclic\"] = df.is_cyclic_num.astype(bool)\n del (df[\"is_cyclic_num\"])\n return df\n\n def points(self, tsid, force_time_axis=False, reference_time=None):\n \"\"\"\n Returns the values of a given time series (formerly power function) as a dataframe.\n\n :param tsid: time series ID\n :type tsid: int or convertible to int\n :param force_time_axis: If True, the index of the dataframe will be the simulation time in days.\n If False (default), the index type will be of type datetime if a reference time is set\n in the model, and simulation time in days otherwise.\n :type force_time_axis: bool\n :param reference_time: Specify (or override) a reference time. Note that this only accounts for this export\n and is not a permanent change of the model settings.\n :type reference_time: datetime.datetime\n\n :return: time series as a pandas.DataFrame\n :rtype: pandas.DataFrame\n \"\"\"\n\n # make sure tsid is a valid number\n\n if type(tsid) == str:\n # if the tsid is called by its comment, we first need to check if the comment is a unique identifier\n # (not guaranteed by FEFLOW)\n df_info = self.doc.c.ts.df.info()\n # check if the comment can be found at all\n if len(df_info[df_info.comment == tsid]) < 1:\n raise KeyError(\"no time series with comment {} found!\".format(tsid))\n # check if the choice is unique\n if len(df_info[df_info.comment == tsid]) > 1:\n raise RuntimeError(\"Multiple time series with comment {} found!\".format(tsid))\n # OK!\n tsid = df_info[df_info.comment == tsid].index[0]\n\n try:\n tsid = int(tsid)\n except ValueError:\n raise ValueError(\"tsid must be of type int or convertible to type int\")\n\n # test if time series exists\n if not self.doc.c.ts.exists(tsid):\n raise ValueError(\"Time Series {} does not exist.\".format(tsid))\n\n # test if time series is empty, return empty dataframe if so\n if self.doc.powerGetNumberOfPoints(tsid) == 0:\n df = pd.DataFrame(columns=[\"Simulation Time\", \"Values\"])\n df.set_index(\"Simulation Time\", inplace=True)\n return df\n\n # get list of points and convert to DataFrame\n df = pd.DataFrame(self.doc.c.ts.points(tsid), columns=[\"Simulation Time\", \"Values\"])\n df.set_index(\"Simulation Time\", inplace=True)\n\n # convert time axis to datetime\n if self.doc.getReferenceTime() is None and reference_time is None:\n force_time_axis = True\n\n if reference_time is None:\n reference_time = self.doc.getReferenceTime()\n\n if force_time_axis:\n # we are done here, return df\n return df\n\n # convert time axis to datetime\n df[\"Simulation Time\"] = pd.to_datetime(df.index, unit=\"D\", origin=reference_time)\n df.set_index(\"Simulation Time\", inplace=True)\n return df\n","sub_path":"contrib_lib/ts_pandas.py","file_name":"ts_pandas.py","file_ext":"py","file_size_in_byte":3665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"136694999","text":"#coding=utf-8\nimport requests\nfrom product_info_collection.public.logHelper import *\nlogger =Log(logger=\"Request\")\n\nclass requestsOpration(object):\n headers = {\n 'X-Requested-With': 'XMLHttpRequest',\n 'Connection': 'keep-alive',\n 'content-type':'application/json',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36 MicroMessenger/6.5.2.501 NetType/WIFI WindowsWechat QBCore/3.43.556.400 QQBrowser/9.0.2524.400'\n }\n\n def myPostRequest(self,headers,data):\n\n r = requests.post('https://api.github.com/some/endpoint', data=data, headers=headers)\n print(r.text)\n print(r.cookies['NID'])\n print(tuple(r.cookies))\n\n\n #带cookies\n s = requests.Session()\n s.headers.update(headers)\n # s.auth = ('superuser', '123')\n s.get('https://www.kuaipan.cn/account_login.htm')\n\n _URL = 'http://www.kuaipan.cn/index.php'\n s.post(_URL, params={'ac':'account', 'op':'login'},\n data={'username':'****@foxmail.com', 'userpwd':'********', 'isajax':'yes'})\n r = s.get(_URL, params={'ac':'zone', 'op':'taskdetail'})\n print(r.json())\n s.get(_URL, params={'ac':'common', 'op':'usersign'})\n def myGetRequest(self,url):\n r=requests.get(url,headers=self.headers,verify=False)\n return r.text\n","sub_path":"product_info_collection/public/requestHelper.py","file_name":"requestHelper.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"438308023","text":"from bson import ObjectId\nimport httmock\nimport json\nimport mock\nimport os\nimport tempfile\nimport time\nfrom tests import base\nfrom urllib.parse import urlparse, parse_qs\nfrom girder.exceptions import GirderException\nfrom girder.models.assetstore import Assetstore\n\n\nDATA_PATH = os.path.join(\n os.path.dirname(os.environ[\"GIRDER_TEST_DATA_PREFIX\"]),\n \"data_src\",\n \"plugins\",\n \"wholetale\",\n)\n\nEXAMPLE_URL = (\n \"https://pbcconsortium.s3.amazonaws.com/wholetale/5ad7cdf55b0d\"\n \"5007601015b7ff1ea8d6/2021-11-09_21.47.58/Dataset_1-882P.zip\"\n)\n\n\ndef setUpModule():\n base.enabledPlugins.append(\"wholetale\")\n base.startServer()\n try:\n assetstore = Assetstore().getCurrent()\n except GirderException:\n assetstore = Assetstore().createFilesystemAssetstore(\"test\", tempfile.mkdtemp())\n assetstore[\"current\"] = True\n Assetstore().save(assetstore)\n\n\ndef tearDownModule():\n Assetstore().remove(Assetstore().getCurrent())\n base.stopServer()\n\n\ndef fake_remote_bag_open(url):\n fname = os.path.join(DATA_PATH, \"Dataset_1-882P.zip\")\n return open(fname, \"rb\")\n\n\n@httmock.all_requests\ndef mock_other_request(url, request):\n raise Exception(\"Unexpected url %s\" % str(request.url))\n\n\n@httmock.urlmatch(\n scheme=\"https\",\n netloc=\"^identifiers.fair-research.org$\",\n path=\"^/hdl:20.500.12633/11RHwdYqWNBZL$\",\n method=\"GET\",\n)\ndef minid_request(url, request):\n return httmock.response(\n status_code=200,\n content={\n \"active\": True,\n \"admins\": [\n \"urn:globus:auth:identity:aff007b5-7995-4be9-b2b8-41d468d77d6f\",\n \"urn:globus:groups:id:160bf3be-07ef-11ea-bc96-0ebedcdf7b97\",\n \"urn:globus:auth:identity:e8d08e61-4e1e-45c0-a583-613db806b468\",\n \"urn:globus:auth:identity:aa3f6d52-d274-11e5-aba5-638c4674ab86\",\n ],\n \"checksums\": [\n {\n \"function\": \"sha256\",\n \"value\": \"26a41d7d5de7918a7f3987e30e9ea9b3a97698a31eaa543f6916959685e04738\",\n }\n ],\n \"created\": \"2021-11-10T05:47:59.583365\",\n \"identifier\": \"hdl:20.500.12633/11RHwdYqWNBZL\",\n \"landing_page\": \"https://identifiers.fair-research.org/hdl:20.500.12633/11RHwdYqWNBZL\",\n \"location\": [EXAMPLE_URL],\n \"metadata\": {\n \"created_by\": \"Mihael Hategan\",\n \"length\": 150794,\n \"title\": \"Dataset_1-882P.zip\",\n },\n \"updated\": \"2021-11-10T05:47:59.583365\",\n \"visible_to\": [\"public\"],\n },\n headers={\n \"Server\": \"Apache/2.4.46 (Fedora) OpenSSL/1.1.1g mod_wsgi/4.6.6 Python/3.7\",\n \"Content-Length\": 993,\n \"Content-Type\": \"application/json\",\n },\n reason=None,\n elapsed=1,\n request=request,\n stream=False,\n )\n\n\ndef fake_urlopen(url):\n fname = os.path.join(DATA_PATH, \"5c92fbd472a9910001fbff72.zip\")\n return open(fname, \"rb\")\n\n\nclass DerivaHarversterTestCase(base.TestCase):\n def setUp(self):\n users = (\n {\n \"email\": \"root@dev.null\",\n \"login\": \"admin\",\n \"firstName\": \"Root\",\n \"lastName\": \"van Klompf\",\n \"password\": \"secret\",\n },\n {\n \"email\": \"joe@dev.null\",\n \"login\": \"joeregular\",\n \"firstName\": \"Joe\",\n \"lastName\": \"Regular\",\n \"password\": \"secret\",\n },\n )\n self.admin, self.user = [\n self.model(\"user\").createUser(**user) for user in users\n ]\n from girder.plugins.wholetale.models.image import Image\n\n self.image = Image().createImage(\n name=\"Jupyter Classic\",\n creator=self.user,\n public=True,\n config=dict(\n template=\"base.tpl\",\n buildpack=\"SomeBuildPack\",\n user=\"someUser\",\n port=8888,\n urlPath=\"\",\n ),\n )\n\n def testLookup(self):\n resolved_lookup = {\n \"dataId\": (\n \"https://pbcconsortium.s3.amazonaws.com/wholetale/5ad7cdf55b0d5007601015\"\n \"b7ff1ea8d6/2021-11-09_21.47.58/Dataset_1-882P.zip\"\n ),\n \"doi\": \"hdl:20.500.12633/11RHwdYqWNBZL\",\n \"name\": \"Dataset_1-882P.zip\",\n \"repository\": \"DERIVA\",\n \"size\": 150794,\n \"tale\": False,\n }\n\n url = \"https://identifiers.fair-research.org/hdl:20.500.12633/11RHwdYqWNBZL\"\n\n with httmock.HTTMock(minid_request, mock_other_request):\n resp = self.request(\n path=\"/repository/lookup\",\n method=\"GET\",\n user=self.user,\n params={\"dataId\": json.dumps([url])},\n )\n self.assertStatus(resp, 200)\n self.assertEqual(resp.json, [resolved_lookup])\n\n return # not implemented yet\n resolved_listFiles = [\"notImplemented\"]\n resp = self.request(\n path=\"/repository/listFiles\",\n method=\"GET\",\n user=self.user,\n params={\"dataId\": json.dumps([url])},\n )\n self.assertStatus(resp, 200)\n self.assertEqual(resp.json, resolved_listFiles)\n\n def test_integration(self):\n # Doesn't do much at this point...\n resp = self.request(\n path=\"/integration/deriva\",\n method=\"GET\",\n user=self.user,\n params={\"url\": EXAMPLE_URL},\n isJson=False,\n )\n\n self.assertTrue(\"Location\" in resp.headers)\n location = urlparse(resp.headers[\"Location\"])\n self.assertEqual(location.netloc, \"dashboard.wholetale.org\")\n qs = parse_qs(location.query)\n self.assertEqual(qs[\"uri\"][0], EXAMPLE_URL)\n\n def testImportBDBagFromDeriva(self):\n from girder.plugins.jobs.models.job import Job\n from girder.plugins.jobs.constants import JobStatus\n from girder.plugins.wholetale.models.tale import Tale\n\n with mock.patch(\"httpio.open\", side_effect=fake_remote_bag_open):\n with httmock.HTTMock(minid_request, mock_other_request):\n resp = self.request(\n path=\"/tale/import\",\n method=\"POST\",\n user=self.user,\n params={\n \"git\": False,\n \"url\": EXAMPLE_URL,\n \"spawn\": False,\n \"imageId\": str(self.image[\"_id\"]),\n \"dsRootPath\": \"/data\",\n },\n )\n self.assertStatusOk(resp)\n tale = resp.json\n job = Job().findOne(\n {\"type\": \"wholetale.import_binder\", \"taleId\": ObjectId(tale[\"_id\"])}\n )\n for _ in range(600):\n if job[\"status\"] in {JobStatus.SUCCESS, JobStatus.ERROR}:\n break\n time.sleep(0.1)\n job = Job().load(job[\"_id\"], force=True)\n self.assertEqual(job[\"status\"], JobStatus.SUCCESS)\n\n tale = Tale().load(tale[\"_id\"], force=True)\n self.assertEqual(\n {_[\"mountPath\"] for _ in tale[\"dataSet\"]},\n {\n \"Biosample.csv\",\n \"Image Data.csv\",\n \"Dataset.csv\",\n \"Experiment.csv\",\n \"assets\",\n \"Mesh Data.csv\",\n \"Derived Image Data.csv\",\n },\n )\n Tale().remove(tale)\n\n def tearDown(self):\n self.model(\"user\").remove(self.user)\n self.model(\"user\").remove(self.admin)\n","sub_path":"plugin_tests/deriva_test.py","file_name":"deriva_test.py","file_ext":"py","file_size_in_byte":7800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"531970142","text":"# 输入一颗二叉树的根节点和一个整数，按字典序打印出二叉树中结点值的和为输入整数的所有路径。\n# 路径定义为从树的根结点开始往下一直到叶结点所经过的结点形成一条路径。\n\n\nclass Solution:\n def __init__(self):\n self.result_all = [] # 用来保存结果的list\n self.array = [] # 用来保存路径的栈\n\n def FindPath(self, root, expectNumber):\n if not root: return []\n self.array.append(root.val)\n expectNumber -= root.val\n if expectNumber == 0 and not root.left and not root.right:\n self.result_all.append(self.array[:])\n self.FindPath(root.left, expectNumber)\n self.FindPath(root.right, expectNumber)\n self.array.pop() # 遍历完一个节点后弹出子节点（叶节点、或叶节点已经被完全遍历的父节点）\n return self.result_all","sub_path":"JZ24. 二叉树中和为某一值的路径.py","file_name":"JZ24. 二叉树中和为某一值的路径.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"580205921","text":"import json\nimport csv\n\njson_str = '[{\"a\":1,\"b\":\"2\",\"c\":\"3\"},{\"a\":21,\"c\":\"23\",\"d\":{\"d1\":\"24\"},\"e\":\"25\"}]'\n\no = json.loads(json_str)\n\ndef loop_data(o, k=''):\n global json_ob, c_line\n if isinstance(o, dict):\n for key, value in o.items():\n if (k == ''):\n loop_data(value, key)\n else:\n loop_data(value, k + '.' + key)\n elif isinstance(o, list):\n for ov in o:\n loop_data(ov, k)\n else:\n if k not in json_ob:\n json_ob[k] = {}\n json_ob[k][c_line] = o\n\n\ndef get_title_rows(json_ob):\n title = []\n row_num = 0\n rows = []\n for key in json_ob:\n title.append(key)\n v = json_ob[key]\n\n if len(v) > row_num:\n row_num = len(v)\n continue\n for i in range(row_num):\n row = {}\n for k in json_ob:\n v = json_ob[k]\n if i in v.keys():\n # 若有数据 ,则用填入表格\n row[k] = v[i]\n else:\n # 若没有数据 ,则用空补位\n row[k] = ''\n rows.append(row)\n return title, rows\n\ndef write_csv(title, rows, csv_file_name):\n # 输出文件名称 newline: 去掉csv中默认会写入空行的问题\n with open(csv_file_name, 'w', newline='') as csv_file:\n writer = csv.DictWriter(csv_file, fieldnames=title)\n writer.writeheader()\n writer.writerows(rows)\n\ndef json_to_csv(object_list):\n global json_ob, c_line\n json_ob = {}\n c_line = 0\n for ov in object_list:\n loop_data(ov)\n c_line += 1\n title, rows = get_title_rows(json_ob)\n write_csv(title, rows, 'test.csv')\n\n\njson_to_csv(o)\n\n","sub_path":"bilaisheng/demo/json2cvs.py","file_name":"json2cvs.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"504217404","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\"\n#last revision 2018/09/07\nCreated on Mon Jul 23 12:31:01 2018\nLast revision June 2019\n\n@author: Tiezheng Yuan\n\"\"\"\n\nimport argparse\nimport os\nimport sys\nimport subprocess\n#\nimport altriaseq.utils.basic as ab\nimport altriaseq.utils.genome as ag\nimport altriaseq.utils.outer as ao\n\n###############################################################################\ndef pass_par():\n #pass arguments\n parser=argparse.ArgumentParser(description=\"A pipeline for mRNA-seq using hisat2 and stringtie. 07.2018-06.2019\")\n parser.add_argument('-o',dest='dir_results', action='store', required=True, help='Path of results')\n parser.add_argument('-q',dest='dir_rawdata', action='store', required=True, help='mRNA-seq data in fastq format')\n parser.add_argument('-s',dest='single_end', action='store_true', help='single-end sequencing')\n parser.add_argument('-t',dest='threads_num', type=int, default=24, help='Number of multi-threads')\n parser.add_argument('--no-S1', dest='S1_alignment', action='store_false', help='Skip step 1')\n parser.add_argument('--no-S2', dest='S2_assembly', action='store_false', help='Skip step 2')\n parser.add_argument('--no-S3', dest='S3_ballgown', action='store_false', help='Skip step 3')\n args=parser.parse_args()\n #for key,value in vars(args).items():\n # par[key]= value\n #print(par)\n \n #genome index\n args.dir_src=os.path.dirname(os.path.realpath(__file__))+'/'\n args.dir_altriaseq=os.path.abspath(os.path.join(args.dir_src, os.pardir))+'/'\n args.genome_index=args.dir_altriaseq+'NT3.1/NT3.1-PSC'\n #args.genome_index='/mnt/rdedata12/NT3.1/NT3.1-PSC'\n args.genome_gtf_file=args.genome_index+'.gtf'\n args.genome_fa_file=args.genome_index+'.fasta'\n \n\n #\n args.dir_results = ab.basic().format_dir(args.dir_results)\n args.file_samples=args.dir_results+'sample_info.csv'\n if not os.path.isfile(args.file_samples):\n print('Error Input! No sample_info.csv in ', args.dir_results)\n sys.exit() \n \n\n\n #\n args.paired= True if args.single_end is False else False\n \n #\n args.dir_rawdata = ab.basic().format_dir(args.dir_rawdata)\n if not os.path.isdir(args.dir_rawdata):\n print('Error Input! No FASTQ directory.\\n')\n sys.exit() \n \n #\n par={}\n for key,value in vars(args).items():\n par[key]= value\n print(key, ':\\t', value)\n return args\n\n##############################################################################\n \ndef main(args):\n #uncompress *fz file if there are\n ao.tool(args).run_pigz(args.dir_rawdata+\"*.gz\", False)\n \n # get sample ino\n args.sample_R1, args.sample_R2=ag.genome(args).sample_info()\n args.sample_names=args.sample_R1.keys()\n ab.basic().print_dict(args.sample_R1)\n ab.basic().print_dict(args.sample_R2)\n\n\n #1:alignment\n if args.S1_alignment:\n ab.basic(args).pp_map_threads(ao.tool(args).run_hisat2, args.sample_names)\n #for sample_name in args.sample_names:\n # ao.tool(args).run_hisat2(sample_name)\n \n\n #2: \n if args.S2_assembly:\n #3:samtootls\n for sample_name in args.sample_names:\n ao.tool(args).run_samtools(sample_name) \n \n #4:stringtie\n ab.basic(args).pp_map_threads(ao.tool(args).run_stringtie, args.sample_names)\n #for sample_name in args.sample_names:\n # ao.tool(args).run_stringtie(sample_name)\n \n #4:counts file\n if args.S3_ballgown:\n #5:merge\n ao.tool(args).run_merge()\n \n #multi-processes\n ab.basic(args).pp_map_threads(ao.tool(args).run_ballgown, args.sample_names)\n \n #export FPKM table\n command=\"Altria_ballgown {}\".format(args.dir_results)\n print(\"@@@@\", command)\n subprocess.Popen(command, stdout=subprocess.PIPE, shell=True).stdout.read() \n #\n#####################################\n\n\n\n\n##############################\n###parameters\nargs=pass_par()\n\n##################################\nmain(args)\n\nprint('Great! Done!')\n#end\n\n","sub_path":"src/RNAseq.py","file_name":"RNAseq.py","file_ext":"py","file_size_in_byte":4101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"182409574","text":"import os\nfrom selenium import webdriver\n\nSERVICE_URLS = {\n 'sauce': 'http://%s:%s@ondemand.saucelabs.com:80/wd/hub',\n 'browserstack': 'http://%s:%s@hub.browserstack.com:80/wd/hub'\n}\n\nWEBDRIVER_MAP = {\n 'chrome': webdriver.Chrome,\n 'firefox': webdriver.Firefox,\n 'phantomjs': webdriver.PhantomJS,\n 'ie': webdriver.Ie,\n}\n\nclass DriverBuilder(object):\n def __init__(self, username=None, access_key=None):\n self.username = username\n self.access_key = access_key\n\n def build(self, spec, hub='sauce', *args, **kwargs):\n \"\"\"\n \"\"\"\n if spec in WEBDRIVER_MAP:\n return WEBDRIVER_MAP.get(spec)(*args, **kwargs)\n elif spec == 'remote':\n capabilities = {}\n capabilities.update(webdriver.DesiredCapabilities.CHROME)\n else:\n capabilities = {}\n capabilities.update(spec)\n capabilities.update(kwargs)\n return webdriver.Remote(\n desired_capabilities=capabilities,\n command_executor=self.get_hub_url(hub)\n )\n\n def get_hub_url(self, hub):\n \"\"\"\n \"\"\"\n if hub[0:4] == 'http':\n return hub % (self.username, self.access_key)\n return SERVICE_URLS.get(hub, '') % (self.username, self.access_key)\n\n def __call__(self, *args, **kwargs):\n return self.build(*args, **kwargs)\n","sub_path":"selenium_drivers/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"163082427","text":"#Dados o número n de alunos de uma turma de \n#Introdução aos Autômatos a Pilha (MAC 414) e\n#suas notas da primeira prova, determinar a\n#maior e a menor nota obtidas por essa turma \n#(Nota máxima = 100 e nota mínima = 0).\n\ndef main():\n\n n_alunos = int(input(\"Quantidade de alunos: \"))\n\n maior_nota = 0\n menor_nota = 101\n\n i = 0\n while i < n_alunos:\n print(\"Aluno\",i + 1,\":\", end=\" \")\n nota = float(input())\n if nota > 100:\n print(\"A nota maxima é 100\")\n break;\n else:\n if nota > maior_nota:\n maior_nota = nota\n if nota < menor_nota:\n menor_nota = nota\n i += 1\n print(\"Maior nota:\",maior_nota)\n print(\"Menor nota:\",menor_nota)\nmain() \n \n \n","sub_path":"python_exerc/exercicios_com_inteiros/exerc_6.py","file_name":"exerc_6.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"129199046","text":"#\n\nfrom MailURLParser import MailURLParser\n\n\nclass URLParser(object):\n @staticmethod\n def parse(url):\n assert isinstance(url, (str, unicode))\n if url.startswith(\"mailto:\"):\n return MailURLParser.parse(url[7:])\n else:\n raise ValueError(\"Unsupported scheme\")\n\n\nif __name__ == \"__main__\":\n url = \"mailto:vit1251@mail.ru?transport=udp&subject=Welcome\"\n p = URLParser.parse(url)\n print(p)\n","sub_path":"src/urlinfo/URLParser.py","file_name":"URLParser.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"345982001","text":"import LIBRARY_MANAGEMENT_PROJECT.ADMIN_PRIVILEGES.issueBook as k,pickle\nl1=[]\ndef bookIssue(rollno):\n with open(\"LIBRARY_MANAGEMENT_PROJECT/PROJECT_TEXT_FILES/Books.pkl\",\"rb\") as f:\n while True:\n try:\n obj=pickle.load(f)\n l1.append(obj)\n except Exception:\n break\n print(\"Book Available in Library are :\")\n for i in range(len(l1)):\n for j in l1[i]:\n print(j)\n n=input(\"Book You Want To Issue:\")\n k.issueBook(n,rollno)","sub_path":"STUDENT_PRIVILEGES/bookIssue.py","file_name":"bookIssue.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"382049986","text":"import requests, re\nfrom urllib import parse\ndef query(region):\n header = {'User-Agent': 'Opera/8.0 (Windows NT 5.1; U; en)'}\n url = 'http://apis.map.qq.com/jsapi?'\n data = {\n 'qt': 'poi',\n 'wd': region,\n 'pn': 0,\n 'rn': 10,\n 'rich_source': 'qipao',\n 'rich': 'web',\n 'nj': 0,\n 'c': 1,\n 'key': 'FBOBZ-VODWU-C7SVF-B2BDI-UK3JE-YBFUS',\n 'output': 'jsonp',\n 'pf': 'jsapi',\n 'ref': 'jsapi',\n 'cb': 'qq.maps._svcb3.search_service_0'}\n coordinate_url = url + parse.urlencode(data)\n r = requests.get(coordinate_url, headers=header)\n longitude = re.findall('\"pointx\":\\s*\"(.+?)\"', r.text)[0]\n latitude = re.findall('\"pointy\":\\s*\"(.+?)\"', r.text)[0]\n print([region, longitude, latitude])\n\n\nquery('广州')","sub_path":"testGPS.py","file_name":"testGPS.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"387447302","text":"def main():\n count = eval(input(\"Insert amount of numbers in the series: \"))\n summation = 0\n for iterator in range(count, 0, -1):\n iterator = eval(input(\"Insert the number: \"))\n summation += iterator\n\n average = summation/count\n print(\"The average is\", float(average))\n\nmain()\n","sub_path":"ch3/program/exercise14.py","file_name":"exercise14.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"158036938","text":"from book import Book\n\nclass dbUI:\n def __init__(self, db):\n self.db = db\n\n while True:\n print(\"\\033[92m\" + \"Valid commands:\" + \"\\033[0m\")\n print(\"\\033[92m\" + \"NEW (or 1)\" + \"\\033[0m\")\n print(\"\\033[92m\" + \"SHOW (or 2)\" + \"\\033[0m\")\n print(\"\\033[92m\" + \"AUTHORS (or 3)\" + \"\\033[0m\")\n print(\"\\033[92m\" + \"SEARCH_AUTHOR (or 4)\" + \"\\033[0m\")\n print(\"\\033[92m\" + \"SEARCH_YEAR (or 5)\" + \"\\033[0m\")\n print(\"\\033[92m\" + \"'q' for quit...\" + \"\\033[0m\")\n cmd = input(\"Insert command: \")\n\n if cmd == \"NEW\" or cmd == '1' : \n author = input(\"Author: \")\n title = input(\"Title: \") \n year = int(input(\"Year: \")) \n identifier = int(input(\"ID: \")) \n self.db.insertBook(Book(author, title, year, identifier))\n \n elif cmd == \"SHOW\" or cmd == '2':\n identifier = int(input(\"ID: \")) \n book = self.db.showBook(identifier)\n if book != None:\n print(\"-> Book author: %s\" %(book.author))\n print(\"-> Book title: %s\" %(book.title))\n print(\"-> Book year: %d\" %(book.year))\n else:\n print(\"-> No match found for that book ID!\")\n \n elif cmd == \"AUTHORS\" or cmd == '3': \n authorList = self.db.listAuthors()\n if authorList:\n print(\"Authors found:\")\n for author in authorList:\n print(\"-> %s\" %(author))\n else:\n print(\"No authors found!\")\n\n elif cmd == \"SEARCH_AUTHOR\" or cmd == '4': \n author = input(\"Author: \")\n booksPerAuthorList = self.db.listBooksPerAuthor(author)\n\n if booksPerAuthorList:\n print(\"Books from author %s:\" %(author)) \n for book in booksPerAuthorList:\n print(\"-> Title: %s Year: %d\" %(book.title, book.year))\n else:\n print(\"No books found from author %s!\" %(author)) \n\n\n elif cmd == \"SEARCH_YEAR\" or cmd == '5':\n year = int(input(\"Year: \"))\n booksPerYearList = self.db.listBooksPerYear(year)\n\n if booksPerYearList:\n print(\"Books from year %d:\" %(year)) \n for book in booksPerYearList:\n print(\"-> Author: %s Title: %s\" %(book.author, book.title))\n else:\n print(\"No books found from year %d!\" %(year))\n\n elif cmd == 'q': \n break\n\n ","sub_path":"Labs/Lab3/centralized/dbUI.py","file_name":"dbUI.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"524690845","text":"\"\"\"\nGiven a list of non-negative numbers and a target integer k, write a function to check if the array has a continuous subarray of size at\nleast 2 that sums up to the multiple of k, that is, sums up to n*k where n is also an integer.\n\nExample 1:\nInput: [23, 2, 4, 6, 7], k=6\nOutput: True\nExplanation: Because [2, 4] is a continuous subarray of size 2 and sums up to 6.\nExample 2:\nInput: [23, 2, 6, 4, 7], k=6\nOutput: True\nExplanation: Because [23, 2, 6, 4, 7] is an continuous subarray of size 5 and sums up to 42.\nNote:\nThe length of the array won't exceed 10,000.\nYou may assume the sum of all the numbers is in the range of a signed 32-bit integer.\n\"\"\"\nclass Solution:\n def checkSubarraySum(self, nums, k):\n sums = {0: -1}\n s = 0\n for i, n in enumerate(nums):\n s += n\n if k != 0: s %= k\n if s in sums:\n if i - sums[s] > 1: return True\n else: sums[s] = i\n return False\n\n def _checkSubarraySum(self, nums, k):\n \"\"\"\n :type nums: List[int]\n :type k: int\n :rtype: bool\n \"\"\"\n if not nums: return False\n sums = []\n for n in nums:\n for i in range(len(sums)):\n sums[i] += n\n if (sums[i] == 0 and k == 0) or (k != 0 and sums[i] % k == 0): return True\n sums.append(n)\n return False\n\ns = Solution()\nassert s.checkSubarraySum([23, 2, 4, 6, 7], 6) == True\nassert s.checkSubarraySum([23, 2, 6, 4, 7], 6) == True\n","sub_path":"leetcode/continuousSubarraySum/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"38333755","text":"from app_config import APP_CONFIG\nimport redis\nfrom functools import wraps\nimport logging\nimport pickle\nimport hashlib\n\nlogger = logging.getLogger(__name__)\nredis_connection_pool = redis.BlockingConnectionPool(**APP_CONFIG[\"redis\"])\n\n\ndef get_redis_connection():\n return redis.StrictRedis(connection_pool=redis_connection_pool)\n\n\ndef is_artist_in_cache(artist):\n redis = get_redis_connection()\n key = (\"\", \"_get_top_tracks\", [artist], tuple({}.items()))\n key = hashlib.md5(str(key).encode(\"utf-8\")).hexdigest()\n return redis.exists(key)\n\n\ndef cached(timeout=300, prefix=\"\", exclude_self=True):\n def wrapper(func):\n @wraps(func)\n def wrapped(*args, **kwargs):\n cache = get_redis_connection()\n\n args_start = 1 if exclude_self else 0\n key = (prefix, func.__name__, args[args_start:], tuple(sorted(kwargs.items())))\n key = hashlib.md5(str(key).encode(\"utf-8\")).hexdigest()\n\n value = cache.get(key)\n if value:\n logger.debug(\"Found item in cache with key: {}\".format(key))\n return pickle.loads(value)\n\n result = func(*args, **kwargs)\n if result:\n logger.debug(\"Caching item with key: {}\".format(key))\n cache.set(key, pickle.dumps(result), ex=timeout)\n\n return result\n return wrapped\n return wrapper\n","sub_path":"cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"84451452","text":"import pandas as pd\r\n#inmporting the GUI for this Feature\r\nfrom addingContact import contactListGUI as clg\r\n\r\n#Function to create the a new contact\r\ndef addContact(nameText,contactText):\r\n df=pd.read_excel(\"Contact.xls\",\"Contact\")\r\n name=nameText.get()\r\n con=contactText.get()\r\n if(len(con)==10):\r\n try:\r\n conParse=int(con)\r\n #This is done to check that wheather the user has entered a number only and not some random characters\r\n contact=df.to_dict()\r\n count=0\r\n numbers=list(contact[\"Contact\"].values()) #Get the contacts in the dictionary in list format\r\n flag=0\r\n for num in numbers: # Check if number already exists\r\n count+=1\r\n if(con==str(num)):\r\n clg.error(\"Contatct Already Exists\")\r\n flag=1\r\n break\r\n if(flag==0): #It will execute if the number is not already present in the list\r\n contactDic={}\r\n nameLen=len(contact[\"Names\"])\r\n conLen=len(contact[\"Contact\"])\r\n #Add the name and number of the new entry at the end of the disctionary\r\n contact[\"Names\"][len(contact[\"Names\"])]=name\r\n contact[\"Contact\"][len(contact[\"Contact\"])]=con\r\n\r\n nameL=list(contact[\"Names\"].values())\r\n conL=list(contact[\"Contact\"].values())\r\n # Create a new dictionary having the new name and contact \r\n contactDic[\"Names\"]=nameL\r\n contactDic[\"Contact\"]=conL\r\n #print(contactDic)\r\n #Convert to pandas dataframe and then to the excel sheet\r\n df=pd.DataFrame.from_dict(contactDic)\r\n df.to_excel(\"Contact.xls\",\"Contact\")\r\n except:\r\n print(type(conParse))\r\n print(conParse)\r\n clg.error(\"Please enter a valid Contact Number\") \r\n else:\r\n clg.error(\"The length of the contact should be equal to 10\")\r\n\r\n\r\n\r\n","sub_path":"addingContact/contactList.py","file_name":"contactList.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"257687687","text":"# TASK 3\n\n# 1\nList = [1, 2, 3, 'a', 'xyz', 'abc123', 1 + 2j, 3.14, 0.01, 10.0]\nprint(List)\n\n\n# 2\ni = [10, 25, 31, 47, 56]\nprint(i[:2], \"\\n\", i[-2:], \"\\n\", i[1:4])\n\n\n# 3\nx = list(range(1, 6))\nSum, Prod = 0, 1\nfor i in x:\n Sum = Sum + i\n Prod = Prod * i\nprint(\"Sum is:\", Sum, \"\\nProduct is:\", Prod)\n\n\n# 4\na = [75, 10, 29, 57, 99]\na.sort()\nprint(\"In list,\\n\", a, \"\\nlargest number is:\", a[-1], \"and smallest number is:\", a[0])\n\n\n# 5\na = list(range(16))\nb = []\nfor i in a:\n if i % 2 != 0:\n b.append(i)\nprint(a)\nprint(b)\n\n\n# 6\na = list(range(1, 31))\nb = a[:5]\nc = a[-5:]\nd = []\nb.extend(c)\nfor i in b:\n d.append(i*i)\nprint(b, d)\n\n\n# 7\na = [1, 3, 5, 7, 9, 10]\nb = [2, 4, 6, 8]\na[5:] = b\nprint(a)\n\n\n# 8\na = {1: 10, 2: 20}\nb = {3: 30, 4: 40}\na.update(b)\nprint(a)\n\n\n# 9\ni = int(input(\"Enter a natural number to get squares in dictionary format: \"))\na = {x: x*x for x in range(1, i + 1)}\nprint(a)\n\n\n# 10\nprint(\"Enter values in comma-separated and without space format: \")\ni = input().split(',')\nprint(i)\nj = (tuple(i))\nprint(j)\n","sub_path":"py_tasks/Task3.py","file_name":"Task3.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"65193038","text":"import logging\n\nfrom tachyonic import app\nfrom tachyonic import router\nfrom tachyonic import jinja\nfrom tachyonic.neutrino import constants as const\nfrom tachyonic.neutrino.web.dom import Dom\nfrom tachyonic.neutrino import Client\n\nfrom tachyonic.api.models.tenants import Tenant as TenantModel\nfrom tachyonic.ui import menu\n\nlog = logging.getLogger(__name__)\n\nmenu.accounts.add('/View Account','/tenant','tachyonic:login')\n\n@app.resources()\nclass Tenant(object):\n def __init__(self):\n # VIEW USERS\n router.add(const.HTTP_GET,\n '/tenant',\n self.view,\n 'tachyonic:login')\n router.add(const.HTTP_POST,\n '/tenant',\n self.view,\n 'tachyonic:login')\n router.add(const.HTTP_GET,\n '/open_tenant',\n self.view,\n 'tachyonic:public')\n router.add(const.HTTP_POST,\n '/open_tenant',\n self.view,\n 'tachyonic:public')\n\n def view(self, req, resp):\n if req.context['login'] is True:\n api = Client(req.context['restapi'])\n server_headers, response = api.execute(const.HTTP_GET, '/v1/tenant')\n\n if req.is_ajax():\n t = jinja.get_template('tachyonic.ui/view_account.html')\n return t.render(title=\"Account\", content=response)\n else:\n t = jinja.get_template('tachyonic.ui/ajax_wrapper.html')\n return t.render(title=\"Account\", content=response)\n else:\n resp.redirect('/')\n","sub_path":"tachyonic/ui/views/tenant.py","file_name":"tenant.py","file_ext":"py","file_size_in_byte":1631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"531787440","text":"import matplotlib.pyplot as plt\r\nfrom scipy import signal\r\nfrom scipy.io import wavfile\r\nimport numpy as np\r\n\r\ndef train_index(num_idx, name_idx, trial_idx):\r\n return name_idx * 450 + num_idx * 45 + trial_idx\r\n\r\ndef test_index(num_idx, name_idx, trial_idx):\r\n return name_idx * 450 + num_idx * 45 + trial_idx\r\n\r\nnames = ['jackson', 'nicolas', 'theo', 'yweweler']\r\n\r\n'''\r\n training data is 0~44\r\n test data is 45~49\r\n'''\r\n'''\r\n default magnitude\r\n mean : 0.43566742...\r\n var : 0.266653616394...\r\n'''\r\nmag_train_data = [] \r\nang_train_data = []\r\nlabel_train = []\r\n\r\nmag_test_data = []\r\nang_test_data = []\r\nlabel_test = []\r\n\r\n# max_x, max_y is fixed\r\nmax_y, max_x = 129, 144\r\nmean = 0.43566742\r\nstd = 0.266653616\r\nfor trial in range(50):\r\n for num in range(10):\r\n for name in names:\r\n filename = str(num)+\"_\"+name+\"_\"+str(trial)\r\n #print(filename)\r\n \r\n sample_rate, samples = wavfile.read(filename + \".wav\")\r\n frequencies, times, Zxx = signal.stft(samples, sample_rate)\r\n\r\n mag = np.abs(Zxx)\r\n ang = np.unwrap(np.angle(Zxx))\r\n\r\n plt.pcolormesh(times, frequencies, ang)\r\n plt.imshow(ang)\r\n plt.show()\r\n\r\n \r\n\r\n shape = mag.shape\r\n diff = max_x - shape[1]\r\n \r\n if diff != 0:\r\n mag = np.append(mag, np.random.normal(mean, std, [129, diff]), axis = 1)\r\n ang = np.append(ang, np.random.normal(0, 0.25, [129, diff]), axis = 1)\r\n zero = np.zeros(10)\r\n zero[num] = 1\r\n if trial < 45:\r\n mag_train_data.append(mag.tolist())\r\n ang_train_data.append(ang.tolist())\r\n label_train.append(zero.tolist())\r\n else:\r\n mag_test_data.append(mag.tolist())\r\n ang_test_data.append(ang.tolist())\r\n label_test.append(zero.tolist())\r\n\r\n","sub_path":"make_CNN_data.py","file_name":"make_CNN_data.py","file_ext":"py","file_size_in_byte":1978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"393002848","text":"import pyodbc\nimport pytesseract\nfrom PIL import Image\n\n\ndef huo_qu_ke_shi_xin_xi(xue_hao): # 返回一维列表\n import pyodbc\n try:\n cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER=172.20.2.20;DATABASE=upswxlab;UID=sa;PWD=setup')\n except Exception as e:\n print(\"连接出错\")\n cursor = cnxn.cursor()\n\n sql = \"\"\" SELECT [tblksxmsckzid],[tblkssyxmpkid],[week],[kqzt_text],[tblstudentid]\n FROM [upswxlab].[dbo].[tblksxmsckz] \n where tblstudentid = (SELECT [tblStudentID] FROM [upswxlab].[dbo].[tblStudent] where studyno = '{}')\n \"\"\".format(xue_hao)\n cursor.execute(sql)\n\n ke_shi = cursor.fetchall()\n lie = []\n try:\n for i in range(len(ke_shi)):\n sql_qur = \"\"\"\n SELECT [itemname],[tblexplanid]\n FROM [upswxlab].[dbo].[tblKssyxm] \n where ksxmid = ( SELECT [tblKssyxmid] FROM [upswxlab].[dbo].[tblkssyxmpk] where tblkssyxmpkid ='{}') \"\"\".format(\n ke_shi[i][1])\n cursor.execute(sql_qur)\n k = cursor.fetchall()\n sql_qur2 = \"SELECT [ndate],[startime],[endtime] FROM [upswxlab].[dbo].[tblkssyxmpk] where tblkssyxmpkid = {} ORDER BY [ndate]\".format(\n ke_shi[i][1])\n\n cursor.execute(sql_qur2)\n kk = cursor.fetchall()\n lie.append([k[0][0], kk[0][0], kk[0][1], kk[0][2], ke_shi[i][2], ke_shi[i][3], ke_shi[i][1]])\n return lie\n except Exception as e:\n return \"学号错误或在数据库里面找不到信息 错误位于__获取已预约课程信息__\"\n\n\ndef huo_qu_ju_ti_ke_shi(ke_shi_hao_ma):\n import pyodbc\n try:\n cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER=172.20.2.20;DATABASE=upswxlab;UID=sa;PWD=setup')\n cursor = cnxn.cursor()\n except Exception as e:\n print(\"连接出错\")\n sql_qur2 = \"SELECT [ndate],[startime],[endtime] FROM [upswxlab].[dbo].[tblkssyxmpk] where tblkssyxmpkid = {} ORDER BY [ndate]\".format(\n ke_shi_hao_ma)\n cursor.execute(sql_qur2)\n kk = cursor.fetchall()\n return kk\n\n\ndef huo_qu_shi_ji_da_ka_ji_lu(xue_hao):\n import pyodbc\n try:\n cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER=172.20.2.20;DATABASE=upswxlab;UID=sa;PWD=setup', timeout=5)\n cursor = cnxn.cursor()\n except Exception as e:\n print(\"连接出错\")\n\n try:\n sql = \"\"\"\n SELECT [studyno],[name] ,[Termid] ,[opdt] ,[termname],[termaddr]\n FROM [upswxlab].[dbo].[hsd_skjl]\n where studyno={}\n order by opdt\"\"\". \\\n format(xue_hao)\n cursor.execute(sql)\n h = cursor.fetchall()\n k = []\n for i in h:\n k.append([i[3], i[2]])\n return k\n except Exception as e:\n return \"没有该学号\"\n\n\ndef huo_qu_yi_tian_da_ka_ji_lu(xue_hao, kai_shi, jie_shu):\n import pyodbc\n\n cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER=172.20.2.20;DATABASE=upswxlab;UID=sa;PWD=setup', timeout=5)\n cursor = cnxn.cursor()\n\n sql = \"\"\"\n SELECT [studyno],[name] ,[Termid] ,[opdt] ,[termname],[termaddr]\n FROM [upswxlab].[dbo].[hsd_skjl]\n where studyno={} and opdt>cast({} as datetime) and opdt 0 ) :\n\t\t\t\tself.currentObject = self.objectStack.pop()\n\t\t\t\tself.nextDepth = self.nextDepth - 1\n\t\treturn obj\n\ndef main():\n\tdoc = docs.GetActiveDocument()\n\n\tdoc.StartUndo()\n\n\tobj = doc.GetFirstObject()\n\tscene = ObjectIterator(obj)\n\tobjList = [] # will function as a hashtable\n\n\tfor obj in scene:\n\t\tif \"NAME_OF_OBJECT\" in obj.GetName():\n\t\t\tif obj[c4d.ID_BASEOBJECT_VISIBILITY_EDITOR] == 1 and obj[c4d.ID_BASEOBJECT_VISIBILITY_RENDER] == 1:\n\t\t\t\tobjList.append(obj)\n\n\tfor item in objList:\n\t\tprint(\"delete\")\n\t\titem.Remove()\n\n\tdoc.EndUndo() \n\tc4d.EventAdd()\n\nif __name__=='__main__':\n\tmain()\t\t","sub_path":"python/cinema4d/delete_hidden_objects.py","file_name":"delete_hidden_objects.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"28386607","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 3 14:41:57 2017\n\nEPI 11.5: Compute running median of a sequence - the complexity should be O(logn) \nper element read in, where n is the number of values read in up to that element\n\"\"\"\nimport random\nimport statistics\nimport heapq\n\ndef ComputeRunningHeapq(sinput):\n \n # Create two heaps to store smaller and higher halves of the input stream\n L = []\n H = []\n \n # The lower half heap will be max-heap\n # The higher half heap will be min-heap\n for s in sinput:\n# print(s, end=' ')\n if len(L) > 0 and s > -L[0]:\n heapq.heappush(H, s)\n else:\n heapq.heappush(L, -s)\n \n# print('L: {} | H: {}'.format(L, H))\n # Balance the lower and higher halves to have similar number of elements\n if len(H) > len(L) + 1:\n heapq.heappush(L, -H[0])\n heapq.heappop(H)\n elif len(L) > len(H) + 1:\n heapq.heappush(H, -L[0])\n heapq.heappop(L)\n \n# print('L: {} | H: {}'.format(L, H))\n\n if len(L) == len(H):\n median = 0.5*(-L[0] + H[0])\n elif len(L) > len(H):\n median = -L[0]\n else:\n median = H[0]\n \n return median\n\n\ndef main():\n random.seed(0)\n \n N = 10\n sinput = [random.randint(0,20) for _ in range(N)]\n sinput= [96, 12, 79, 32]\n print('{} --> median: {}'.format(sinput, statistics.median(sinput)))\n print('{} --> median: {}'.format(sorted(sinput), statistics.median(sinput)))\n print('{} --> median: {}'.format(sinput, statistics.median(sinput)))\n\n print()\n median = ComputeRunningHeapq(sinput)\n print('median: ', median)\n\n \n for i in range(1, 11):\n \n sinput = [random.randint(0,100) for _ in range(i)]\n print('\\nsinput: {} --> \\nmedian: {}'.format(sinput, statistics.median(sinput)), end=' ')\n \n# print()\n median = ComputeRunningHeapq(sinput)\n print('median: ', median)\n \n \n\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"mulakat/EPI11_Heaps/computeRunningMedian.py","file_name":"computeRunningMedian.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"69312937","text":"import numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom sklearn import tree\nfrom sklearn.preprocessing import OneHotEncoder\nimport matplotlib.pyplot as plt\nfrom subprocess import call\nimport time\n\n### Global Variables ###\n\n# The path to the main data file\nmain_file_path = \"../src/data/ted_all.csv\"\n\n# The path to the tags data for each talk_name\ntags_path = \"../src/data/talk_tags.csv\"\n\n### The following functions come from my implementation of a Machine Learning assignment. ###\n\ndef entropy(class_y):\n bins = np.bincount(class_y)\n probs = bins / len(class_y)\n return stats.entropy(probs, base=2)\n\ndef information_gain(previous_y, current_y):\n H = entropy(previous_y)\n H_left = entropy(current_y[0])\n H_right = entropy(current_y[1])\n P_left = len(current_y[0]) / len(previous_y)\n P_right = len(current_y[1]) / len(previous_y)\n return H - (H_left * P_left + H_right * P_right)\n\ndef partition_classes(X, y, split_attribute, split_val):\n # Get type of split attribute\n is_numeric = isinstance(X[0][split_attribute], int) \\\n or isinstance(X[0][split_attribute], float)\n\n X = np.array(X)\n y = np.array(y)\n # Numeric split\n if is_numeric:\n A_indices = np.argwhere(X[:, split_attribute].astype(np.float) <= split_val)[:, 0]\n B_indices = np.argwhere(X[:, split_attribute].astype(np.float) > split_val)[:, 0]\n A = X[A_indices]\n A_y = y[A_indices]\n B = X[B_indices]\n B_y = y[B_indices]\n # Categorical split\n else:\n A = X[X[:, split_attribute] == split_val]\n A_y = y[X[:, split_attribute] == split_val]\n B = X[X[:, split_attribute] != split_val]\n B_y = y[X[:, split_attribute] != split_val]\n\n return A, B, A_y, B_y\n\ndef find_best_split(X, y, split_attribute):\n # Get type of split attribute\n is_numeric = isinstance(X[0][split_attribute], int) \\\n or isinstance(X[0][split_attribute], float)\n\n # Find all unique values in the split attribute\n vals = np.unique(np.array(X)[:, split_attribute])\n\n # Find the best split value\n best = 0\n I = -1\n for v in vals:\n if is_numeric:\n v = float(v)\n X_left, X_right, y_left, y_right = partition_classes(X, y, split_attribute, v)\n i_gain = information_gain(y, [y_left, y_right])\n if i_gain > I:\n I = i_gain\n best = v\n return best, I\n\ndef sort_key(e):\n return e[2]\n\ndef find_best_feature(X, y, n):\n \"\"\"\n Best features in the form [feature, value, info_gain]\n \"\"\"\n best_features = []\n for i in range(n):\n best_features.append([-1, -1, -1])\n for i in range(len(X[0])):\n val, gain = find_best_split(X, y, i)\n if gain > best_features[n - 1][2]:\n best_features.append([i, val, gain])\n best_features.sort(key=sort_key, reverse=True)\n best_features.pop()\n return best_features\n\n### The following section is my application of the Machine Learning functions to our needs. ###\n\ndef assign_popularity_groupings(raw_metric):\n \"\"\"\n Groups the raw popularity metric into 3 bins, as lower, middle, and upper third.\n \"\"\"\n metric = np.array(raw_metric)\n lowerThird = np.percentile(metric, 33)\n upperThird = np.percentile(metric, 66)\n groupings = np.where(metric <= lowerThird, 0, np.where(metric <= upperThird, 0, 1))\n return groupings\n\n# Read in the data\nprint(\"Reading main file\")\nmain_file = pd.read_csv(main_file_path).fillna(0)\nprint(\"Finished reading main file\")\nprint(\"Reading tegs file\")\ntags_file = pd.read_csv(tags_path).fillna(0)\nprint(\"Finished reading main file\")\n\n# Get the different evaluation methods\nagg_views = assign_popularity_groupings(main_file[\"ted_views\"].tolist())\nagg_comments = assign_popularity_groupings(main_file[\"ted_comments\"].tolist())\nagg_engagement = assign_popularity_groupings(main_file[\"ted_engagement\"].tolist())\nagg_positivity = assign_popularity_groupings(main_file[\"ted_positivity\"].tolist())\n\n# In this set I use the following features\n# 0 - duration\n# 1 - event\n# 2 - languages\n# 3 - main_speaker\n# 4 - num_speakers\n# 5 - grouped occupation\n# 6 - reading level analysis\n# 7 and on - tags\n\n# First 5 features\nprint(\"Building basic dataframe\")\nfeatures = pd.DataFrame(main_file[\"duration\"])\nfeatures[\"event\"] = main_file[\"event\"]\nfeatures[\"languages\"] = main_file[\"languages\"]\nfeatures[\"main_speaker\"] = main_file[\"main_speaker\"]\nfeatures[\"num_speaker\"] = main_file[\"num_speaker\"]\nfeatures[\"fk_score\"] = main_file[\"fk_score\"]\n\n# Remaining tag features\nprint (\"Adding tags to dataframe...\")\ntags = tags_file[tags_file.columns[2:len(tags_file.columns)]]\n#features = tags.copy()\nfeatures = pd.concat([features, tags], sort=False, axis=1)\nprint (\"Dataframe complete\")\n\n# Encode categorical data\nprint(\"Encoding categorical data\")\nencoded_features = features.copy()\nencoded_features['event'] = pd.factorize(encoded_features.event)[0]\nencoded_features['main_speaker'] = pd.factorize(encoded_features.main_speaker)[0]\nprint(\"Finished encoding\")\n\n# Create decision tree\nprint(\"Fitting decision tree...\")\nmodel = tree.DecisionTreeClassifier(max_depth=6)\nmodel.fit(encoded_features, agg_views)\nprint(\"Tree fitted\")\nprint(\"Exporting visual\")\ntree.export_graphviz(model, out_file='tree.dot', feature_names=features.columns, rounded=True, filled=True)\ntime.sleep(0.25)\ncall(['dot', '-T', 'png', 'tree.dot', '-o', 'tree.png'])\nprint(\"Done\")\n\n\"\"\"\n# Find features with the highest explained variance\nn_features = 5\nprint (\"Finding best features for aggragate views...\")\nbest = find_best_feature(features.values.tolist(), agg_views, n_features)\nprint(best)\nprint(\"Best features for aggragate views:\")\nfor i in range(n_features):\n #print(\" \", i, \":\", features.columns[best[i][0]], \"with a split value of\", best[i][1])\n print(\" \", i, \":\", features.columns[best[i][0]])\n\nprint (\"Finding best features for aggragate comments...\")\nbest = find_best_feature(features.values.tolist(), agg_comments, n_features)\nprint(\"Best features for aggragate comments:\")\nfor i in range(n_features):\n #print(\" \", i, \":\", features.columns[best[i][0]], \"with a split value of\", best[i][1])\n print(\" \", i, \":\", features.columns[best[i][0]])\n\nprint (\"Finding best features for aggragate engagement...\")\nbest = find_best_feature(features.values.tolist(), agg_engagement, n_features)\nprint(\"Best features for aggragate engagement:\")\nfor i in range(n_features):\n #print(\" \", i, \":\", features.columns[best[i][0]], \"with a split value of\", best[i][1])\n print(\" \", i, \":\", features.columns[best[i][0]])\n\nprint (\"Finding best features for aggragate positivity...\")\nbest = find_best_feature(features.values.tolist(), agg_positivity, n_features)\nprint(\"Best features for aggragate positivity:\")\nfor i in range(n_features):\n #print(\" \", i, \":\", features.columns[best[i][0]], \"with a split value of\", best[i][1])\n print(\" \", i, \":\", features.columns[best[i][0]])\n\"\"\"\n","sub_path":"data_crunching/find_feature_importance.py","file_name":"find_feature_importance.py","file_ext":"py","file_size_in_byte":6979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"603539943","text":"# Copyright 2021 AI Singapore\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# https://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\"\"\"\nPredictor class to handle detection of poses for posenet\n\"\"\"\n\nimport os\nimport logging\nfrom typing import Dict, List, Callable, Any, Tuple\nimport tensorflow as tf\nimport numpy as np\n\nfrom peekingduck.pipeline.nodes.model.posenetv1.posenet_files.detector import detect_keypoints, \\\n get_keypoints_relative_coords\nfrom peekingduck.pipeline.nodes.model.posenetv1.posenet_files.constants import SCALE_FACTOR, \\\n KEYPOINTS_NUM, MIN_PART_SCORE, SKELETON\nfrom peekingduck.pipeline.nodes.model.posenetv1.posenet_files.preprocessing import rescale_image\nfrom peekingduck.utils.graph_functions import load_graph\n\nOUTPUT_STRIDE = 16\n\n\nclass Predictor: # pylint: disable=too-many-instance-attributes\n \"\"\"Predictor class to handle detection of poses for posenet\n \"\"\"\n\n def __init__(self, config: Dict[str, Any]) -> None:\n\n self.logger = logging.getLogger(__name__)\n\n self.config = config\n self.model_type = self.config['model_type']\n\n self.posenet_model = self._create_posenet_model()\n\n def _create_posenet_model(self) -> tf.keras.Model:\n self.resolution = self.get_resolution_as_tuple(self.config['resolution'])\n self.max_pose_detection = self.config['max_pose_detection']\n self.score_threshold = self.config['score_threshold']\n\n model_path = os.path.join(\n self.config['root'], self.config['model_files'][self.model_type])\n model_func = self._load_posenet_graph(model_path)\n\n self.logger.info(\n 'PoseNet model loaded with following configs: \\n \\\n Model type: %s, \\n \\\n Input resolution: %s, \\n \\\n Max pose detection: %s, \\n \\\n Score threshold: %s', self.model_type, self.resolution,\n self.max_pose_detection, self.score_threshold)\n\n return model_func\n\n def _load_posenet_graph(self, filepath: str) -> Callable:\n model_id = 'mobilenet'\n if self.model_type == 'resnet':\n model_id = 'resnet'\n model_nodes = self.config['MODEL_NODES'][model_id]\n model_path = os.path.join(filepath)\n if os.path.isfile(model_path):\n return load_graph(model_path, inputs=model_nodes['inputs'],\n outputs=model_nodes['outputs'])\n raise ValueError('PoseNet graph file does not exist. Please check that '\n '%s exists' % model_path)\n\n @staticmethod\n def get_resolution_as_tuple(resolution: dict) -> Tuple[int, int]:\n \"\"\" Convert resolution from dict to tuple format\n\n Args:\n resolution (dict): height and width in dict format\n\n Returns:\n resolution (Tuple(int)): height and width in tuple format\n \"\"\"\n res1, res2 = resolution['height'], resolution['width']\n\n return (int(res1), int(res2))\n\n def predict(self,\n frame: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n # pylint: disable=too-many-locals\n \"\"\" PoseNet prediction function\n\n Args:\n frame (np.array): image in numpy array\n\n Returns:\n bboxes (np.ndarray): array of bboxes\n keypoints (np.ndarray): array of keypoint coordinates\n keypoints_scores (np.ndarray): array of keypoint scores\n keypoints_conns (np.ndarray): array of keypoint connections\n \"\"\"\n full_keypoint_rel_coords, full_keypoint_scores, full_masks = \\\n self._predict_all_poses(\n self.posenet_model,\n frame,\n self.model_type)\n\n bboxes = []\n keypoints = []\n keypoint_scores = []\n keypoint_conns = []\n\n for pose_coords, pose_scores, pose_masks in zip(full_keypoint_rel_coords,\n full_keypoint_scores, full_masks):\n bbox = self._get_bbox_of_one_pose(pose_coords, pose_masks)\n pose_coords = self._get_valid_full_keypoints_coords(pose_coords, pose_masks)\n pose_connections = self._get_connections_of_one_pose(pose_coords, pose_masks)\n bboxes.append(bbox)\n keypoints.append(pose_coords)\n keypoint_scores.append(pose_scores)\n keypoint_conns.append(pose_connections)\n\n return np.array(bboxes), np.array(keypoints), np.array(keypoint_scores), \\\n np.array(keypoint_conns)\n\n @ staticmethod\n def _get_valid_full_keypoints_coords(coords: np.ndarray, masks: np.ndarray) -> np.ndarray:\n \"\"\" Apply masks to keep only valid keypoints' relative coordinates\n\n Args:\n coords (np.array): Nx2 array of keypoints' relative coordinates\n masks (np.array): masks for valid (> min confidence score) keypoints\n\n Returns:\n full_joints (np.array): Nx2 array of keypoints where undetected\n keypoints are assigned a (-1) value.\n \"\"\"\n full_joints = coords.copy()\n full_joints = np.clip(full_joints, 0, 1)\n full_joints[~masks] = -1\n return full_joints\n\n @ staticmethod\n def _get_connections_of_one_pose(coords: np.ndarray, masks: np.ndarray) -> np.ndarray:\n \"\"\"Get connections between adjacent keypoint pairs if both keypoints are detected\n \"\"\"\n connections = []\n for start_joint, end_joint in SKELETON:\n if masks[start_joint - 1] and masks[end_joint - 1]:\n connections.append((coords[start_joint - 1], coords[end_joint - 1]))\n return np.array(connections)\n\n @ staticmethod\n def _get_bbox_of_one_pose(coords: np.ndarray,\n mask: np.ndarray) -> np.ndarray:\n \"\"\" Get the bounding box bordering the keypoints of a single pose\n \"\"\"\n coords = coords[mask, :]\n if coords.shape[0]:\n min_x, min_y, max_x, max_y = (coords[:, 0].min(),\n coords[:, 1].min(),\n coords[:, 0].max(),\n coords[:, 1].max())\n bbox = [min_x, min_y, max_x, max_y]\n return np.array(bbox)\n return np.zeros(0)\n\n def _predict_all_poses(\n self,\n posenet_model: tf.keras.Model,\n frame: np.ndarray,\n model_type: str) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Predict relative coordinates, confident scores and validation masks\n for all detected poses\n\n Args:\n posenet model: tensorflow model\n frame (np.array): image for inference\n model_type (str): specified model type (refer to modelconfig.yml)\n\n Returns:\n full_keypoint_coords (np.array): keypoints coordinates of detected poses\n full_keypoint_scores (np.array): keypoints confidence scores of detected\n poses\n full_masks (np.array): keypoints validation masks of detected poses\n \"\"\"\n image, output_scale, image_size = self._create_image_from_frame(\n OUTPUT_STRIDE, frame, self.resolution, model_type)\n\n dst_scores = np.zeros(\n (self.max_pose_detection, KEYPOINTS_NUM))\n dst_keypoints = np.zeros(\n (self.max_pose_detection, KEYPOINTS_NUM, 2))\n\n pose_count = detect_keypoints(\n posenet_model, image, OUTPUT_STRIDE, dst_scores, dst_keypoints,\n model_type, self.score_threshold)\n full_keypoint_scores = dst_scores[:pose_count]\n full_keypoint_coords = dst_keypoints[:pose_count]\n\n full_keypoint_rel_coords = get_keypoints_relative_coords(\n full_keypoint_coords, output_scale, image_size)\n\n full_masks = self._get_full_masks_from_keypoint_scores(full_keypoint_scores)\n\n return full_keypoint_rel_coords, full_keypoint_scores, full_masks\n\n @ staticmethod\n def _create_image_from_frame(output_stride: int,\n frame: np.ndarray,\n input_res: Tuple[int, int],\n model_type: str) -> Tuple[tf.Tensor, np.ndarray, List[int]]:\n \"\"\" Rescale raw frame and convert to tensor image for inference\n \"\"\"\n image_size = [frame.shape[1], frame.shape[0]]\n\n image, output_scale = rescale_image(frame,\n input_res,\n scale_factor=SCALE_FACTOR,\n output_stride=output_stride,\n model_type=model_type\n )\n image = tf.convert_to_tensor(image)\n return image, output_scale, image_size\n\n @ staticmethod\n def _get_full_masks_from_keypoint_scores(keypoint_scores: np.ndarray) -> np.ndarray:\n \"\"\" Obtain masks for keypoints with confidence scores above the detection threshold\n \"\"\"\n masks = keypoint_scores > MIN_PART_SCORE\n return masks\n","sub_path":"peekingduck/pipeline/nodes/model/posenetv1/posenet_files/predictor.py","file_name":"predictor.py","file_ext":"py","file_size_in_byte":9565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"553874131","text":"from plugin_documents import Documents\ndocuments = Documents()\n\nfrom xmlrpclib import ServerProxy\t\n# server_online = ServerProxy('http://127.0.0.1:8005/onnx/plugin_profile/call/xmlrpc')\t\t\n# server_eform = ServerProxy('http://127.0.0.1:8005/form/eform/call/xmlrpc')\n\nPROCEDURE = 0\nPROCESS = 1\nFOLDER = 2\nPAGE = 3\nTABLENAME = 3\nOBJECTSID = 4\n\nProcess = Documents(\tprocedure=request.args(PROCEDURE),\n\t\t\t\t\t\tprocess=request.args(PROCESS),\n\t\t\t\t\t\tfolder_name=request.args(FOLDER),\n\t\t\t\t\t\ttablename=request.args(TABLENAME),\n\t\t\t\t\t\tobjects_id=request.args(OBJECTSID))\n\n\n###################################################\n\ndef index():\n\treturn dict(content='')\n\ndef breadcrumb():\t\n\tcontent = Process.menu_select()\n\treturn content\t\n\ndef filter():\t\n\tcontent = Process.get_filter()\n\treturn content\n\t\ndef toolbars():\t\n\tcontent = Process.get_toolbars()\n\treturn content\n##Danh sách văn bản đi\ndef explorer():\n\t# content = vanbandi.list_vanbandi()\n\tcontent = Process.explorer()\n\treturn dict(content=content)\n##Form văn bản đi\t\ndef edit():\n\t# id = request.vars.id if request.vars.id else ''\n\t# type = request.vars.type if request.vars.type else '' ##Loại văn bản (Văn bản thường hoặc của hồ sơ công việc)\n\t# idprofile = request.vars.idprofile if request.vars.idprofile else ''\n\tcontent = vanbandi.form_archives()\n\treturn dict(content=content)\n\t\n##Thêm, sửa văn bản\ndef update_archives():\n\timport datetime\n\tfrom datatables import define_archives,define_work_profile,define_book_archives\n\tdefine_work_profile(db,False)\n\tdefine_archives(db,False)\n\tdefine_book_archives(db,False)\n\tfrom plugin_cms import CmsModel\n\tCmsModel().define_folder(False)\n\tfrom plugin_process import ProcessModel\n\tProcessModel().define_process(False)\n\tProcessModel().define_objects(False)\n\tProcessModel().define_process_log(False)\n\tfol = db.folder(name=request.args(2))\n\tif fol: \n\t\tidfol = fol.id\n\telse:\n\t\tidfol = None\n\t##Lấy process đầu tiên\t\n\targs = request.args\n\tidprocess = Process.get_process_create()\n\tprocess = db.process(idprocess)\n\targs[1] = process.name\n\t\n\t#người tạo\n\tauth_group = db.auth_user(auth.user_id).auth_group\n\t\n\tid = request.vars.id if request.vars.id else None\n\tname = request.vars.name if request.vars.name else None ##Số ký hiệu\n\ttype_archive = request.vars.type_archive if request.vars.type_archive else None ##Loại văn bản\n\tif type_archive=='0':\n\t\ttype_archive =None\n\ttitle = request.vars.title if request.vars.title else None ##Trích yếu\n\tpublish_date = request.vars.publish_date if request.vars.publish_date else None ##Ngày ban hành\n\tarrival_date = request.vars.arrival_date if request.vars.arrival_date else None ##Ngày đến\n\tinput,output = \"%d/%m/%Y\", \"%Y-%m-%d\"\n\tif (publish_date!=None):\n\t\tpublish_date = datetime.datetime.strptime(publish_date,input).strftime(output)\n\t\n\tif (arrival_date!=None):\n\t\tarrival_date = datetime.datetime.strptime(arrival_date,input).strftime(output)\n\t\n\t##Cơ quan ban hành\n\torg = request.vars.org if request.vars.org else None\n\tif org=='0':\n\t\torg=None\n\t##Người ký\n\tsigner = request.vars.signer if request.vars.signer else None\n\tif signer=='0':\n\t\tsigner = None\n\t##Chức vụ\n\tpost = request.vars.post if request.vars.post else None\n\t##Mức độ quan trọng\n\tarchives_security = request.vars.archives_security if request.vars.archives_security else None\n\tif archives_security=='0':\n\t\tarchives_security=None\n\t##Lĩnh vực\n\tarchives_fields = request.vars.archives_fields if request.vars.archives_fields else None\n\tif archives_fields=='0':\n\t\tarchives_fields=None\n\t##Phương thức gửi\n\tarchives_send = request.vars.archives_send if request.vars.archives_send else None\n\tif archives_send=='0':\n\t\tarchives_send=None\n\t##Sổ văn bản\n\tbook_archives = request.vars.book_archives if request.vars.book_archives else None\n\tif book_archives=='0':\n\t\tbook_archives=None\n\t##Số thứ tự văn bản\n\tnumber_archives = request.vars.number_archives if request.vars.number_archives else None\n\t##Ký hiệu văn bản\n\tsymbol_archives = request.vars.symbol_archives if request.vars.symbol_archives else None\n\tif symbol_archives=='0':\n\t\tsymbol_archives=None\n\t##Đơn vị nhận\n\treceived_in = request.vars.received_in if request.vars.received_in else ''\n\treceived_human = request.vars.received_human if request.vars.received_human else ''\n\treceived_out = request.vars.received_out if request.vars.received_out else ''\n\tif isinstance(received_in,str):\n\t\tif ((received_in=='')|(received_in=='0')):\n\t\t\treceived_in =[]\n\t\telse:\n\t\t\treceived_in = [received_in]\n\tif isinstance(received_human,str):\n\t\tif ((received_human=='')|(received_human=='0')):\n\t\t\treceived_human =[]\n\t\telse:\n\t\t\treceived_human=[received_human]\n\tif isinstance(received_out,str):\n\t\tif ((received_out=='')|(received_out=='0')):\n\t\t\treceived_out =[]\n\t\telse:\n\t\t\treceived_out=[received_out]\n\tlist_received = received_in + received_human + received_out\n\tlist_received = map(int,list_received) ##Convert list string to list int\n\t\n\t##ID Bộ hồ sơ công việc\n\tidprofile = request.vars.idprofile if request.vars.idprofile else None\n\t##Người soạn thảo\n\thuman_editor = request.vars.human_editor if request.vars.human_editor else None\n\treply = request.vars.reply if request.vars.reply else None\n\tlegal = request.vars.legal if request.vars.legal else None\n\texecutive = request.vars.executive if request.vars.executive else None\n\t# ##Cập nhật số văn bản\n\t# ar = db(db.archives.book_archives==book_archives).select().last()\n\t# if ar!=None:\n\t\t# counta = ar.number_book + 1\n\t# else:\n\t\t# counta = 1\n\t#Update văn bản trong hồ sơ công việc\n\t# if idprofile!=None:\n\t\t# if id:\n\t\t\t# ##Sửa văn bản\n\t\t\t# up = db(db.archives.id==id).update(folder=idfol,archives_category=type_archive,name=name,title=title,publish_date=publish_date,org=org,signer=signer,archives_security=archives_security,archives_send=archives_send,human_editor=human_editor,reply=reply,legal=legal,executive=executive,book_archives=book_archives,symbol_archives=symbol_archives,number_book=number_archives,post=post)\n\t\t# else:\n\t\t\t# ##Thêm văn bản\n\t\t\t# up = db.archives.insert(folder=idfol,archives_category=type_archive,name=name,title=title,publish_date=publish_date,org=org,signer=signer,archives_security=archives_security,archives_send=archives_send,human_editor=human_editor,reply=reply,legal=legal,executive=executive,book_archives=book_archives,symbol_archives=symbol_archives,number_book=number_archives,post=post)\n\t\t\t# ##Thêm văn bản vào bộ hồ sơ công việc\n\t\t\t# workp = db.work_profile(idprofile)\n\t\t\t# lista = workp.archives\n\t\t\t# if lista!=None:\n\t\t\t\t# lista.append(up)\n\t\t\t# else:\n\t\t\t\t# lista=[up]\n\t\t\t# ##Update hồ sơ công việc\n\t\t\t# upp = db(db.work_profile.id==idprofile).update(archives=lista)\n\t# else:\n\t\t# # if id:\n\tif request.args(4): ##Nếu có Object_id\n\t\tobject = db.objects(request.args(4))\n\t\t##Update\n\t\tup = db(db.archives.id==object.table_id).update(folder=idfol,archives_category=type_archive,name=name,title=title,publish_date=publish_date,org=org,signer=signer,archives_security=archives_security,archives_send=archives_send,human_editor=human_editor,reply=reply,legal=legal,executive=executive,book_archives=book_archives,symbol_archives=symbol_archives,number_book=number_archives,post=post,auth_group=list_received,archives_fields=archives_fields,arrival_date=arrival_date)\n\t\t##Update số văn bản\n\telse:\n\t\t##Insert\n\t\tup = db.archives.insert(folder=idfol,archives_category=type_archive,name=name,title=title,publish_date=publish_date,org=org,signer=signer,archives_security=archives_security,archives_send=archives_send,human_editor=human_editor,reply=reply,legal=legal,executive=executive,book_archives=book_archives,symbol_archives=symbol_archives,number_book=number_archives,post=post,auth_group=list_received,archives_fields=archives_fields,arrival_date=arrival_date)\n\t\t\n\t\t##insert Objects\n\t\tobjects_id = db.objects.insert(folder=idfol,foldername=request.args(2), tablename='archives',table_id=up,process=idprocess,auth_group=auth_group,auth_org=auth_group)\n\t\tdb.process_log.insert(objects=objects_id, process=idprocess,auth_group=auth_group)\n\t##File đính kèm\n\tfrom plugin_upload import FileUpload\n\tfileupload = FileUpload(db=db,tablename='archives',upload_id=None)\n\ta = fileupload.update(up,request.vars.uuid)\n\t\n\tif idprofile:\n\t\treturn redirect(URL(r=request,c='plugin_vanbandi',f='detail_profile',vars=dict(idprofile=idprofile)))\n\telse:\n\t\treturn redirect(URL(r=request,c='plugin_vanbandi',f='explorer',args=request.args))\n\n##Form hồ sơ công việc\ndef form_work_profile():\n\tid = request.vars.id if request.vars.id else ''\n\tcontent = vanbandi.form_work_profile(id)\n\tresponse.view ='plugin_vanbandi/edit.html'\n\treturn dict(content=content)\n\n##Thêm sửa hồ sơ công việc\t\ndef update_work_profile():\n\timport datetime\n\tfrom datatables import define_work_profile\n\tdefine_work_profile(db,False)\n\t\n\tid = request.vars.id if request.vars.id else None\n\tname = request.vars.name if request.vars.name else None ##Số ký hiệu\n\tdescription = request.vars.description if request.vars.description else None\n\t\n\tif id:\n\t\t##Update\n\t\tup = db(db.work_profile.id==id).update(name=name,description=description)\n\telse:\t\n\t\t##Insert\n\t\tup = db.work_profile.insert(name=name,description=description)\n\treturn redirect('list_work_profile')\n\t\n##List hồ sơ công việc\ndef list_work_profile():\n\tcontent = vanbandi.list_work_profile()\n\tresponse.view = 'plugin_vanbandi/list_vanbandi.html'\n\treturn dict(content=content)\n\t\n##Xóa hồ sơ công việc\ndef delete_work_profile():\n\tfrom datatables import define_work_profile\n\tdefine_work_profile(db,False)\n\tid = request.vars.id if request.vars.id else ''\n\tdele = db(db.work_profile.id==id).delete()\n\treturn redirect('list_work_profile')\n\t\n##Chi tiết hồ sơ\ndef detail_profile():\n\tidprofile = request.vars.idprofile if request.vars.idprofile else ''\n\tcontent = vanbandi.detail_profile(idprofile)\n\tresponse.view = 'plugin_vanbandi/list_vanbandi.html'\n\treturn dict(content=content)\n\t\ndef delete_archives():\n\tfrom datatables import define_archives,define_work_profile\n\tdefine_work_profile(db,False)\n\tdefine_archives(db,False)\n\t\n\tid = request.vars.id if request.vars.id else None\n\tidprofile = request.vars.idprofile if request.vars.idprofile else None\n\tif idprofile:\n\t\t##delete văn bản\n\t\tdlt = db(db.archives.id==id).delete()\n\t\t##Remove văn bản trong hồ sơ công việc\n\t\tupro = db.work_profile(idprofile)\n\t\tlista = upro.archives\n\t\tid = long(id)\n\t\tif id in lista:\n\t\t\tlista.remove(id)\n\t\tuppro = db(db.work_profile.id==idprofile).update(archives=lista)\n\t\treturn redirect(URL(r=request,c='plugin_vanbandi',f='detail_profile',vars=dict(idprofile=idprofile)))\n\telse:\n\t\tdlt = db(db.archives.id==id).delete()\n\t\t\n##Add archives to work_profile\ndef add_work_profile():\n\tid = request.vars.id if request.vars.id else '' ##ID văn bản\n\tcontent = vanbandi.add_work_profile(id)\n\treturn content\n\n###Thêm văn bản vào HSCV (Từ danh sách văn bản -> Chọn HSCV)\ndef add_archives_profile():\n\tfrom plugin_process import ProcessModel\n\tProcessModel().define_objects(False)\n\tfrom datatables import define_work_profile\n\tdefine_work_profile(db,False)\n\ttype = request.vars.type if request.vars.type else ''\n\tidprofile = request.vars.idprofile if request.vars.idprofile else ''\n\tobjects = request.vars.objects\n\tif isinstance(objects,str):\n\t\tobjects = [objects]\n\tobjects = map(int,objects) ##Đổi list string sang list int\n\tlistprofile = []\n\tfor o in objects:\n\t\tprofile = db.objects(o).table_id\n\t\tlistprofile.append(profile)\n\tworkp = db.work_profile(idprofile)\n\tlista = workp.archives\n\tif lista!=None:\n\t\tfor x in listprofile:\n\t\t\tcoida = lista.count(x) ##Đếm phần tử ida trong list. Nếu chưa có thì thêm vào\n\t\t\tif coida>0:\n\t\t\t\tlista = lista\n\t\t\telse:\n\t\t\t\tlista.append(x)\n\telse:\n\t\tlista = listprofile\n\t##Update hồ sơ công việc\n\tupp = db(db.work_profile.id==idprofile).update(archives=lista)\n\treturn redirect(URL(r=request,c='plugin_vanbandi',f='detail_profile',vars=dict(idprofile=idprofile)))\n\n\t\n#Từ bộ hồ sơ công việc, chọn văn bản cần thêm vào\t\ndef add_profile_archives():\n\tidprofile = request.vars.idprofile if request.vars.idprofile else ''\n\tcontent = vanbandi.add_profile_archives(idprofile)\n\treturn content\n\t\n\t\n# ###Chuyển xử lý văn bản\n# def form_sendarchive():\n\t# idprofile = request.vars.idprofile if request.vars.idprofile else ''\n\t# content = vanbandi.form_sendarchive(idprofile)\n\t# return content\n\t\ndef read():\n\tcontent = Process.read()\n\treturn DIV(content,_id='process_id')\n\t\n###Xóa văn bản\ndef delete():\n\tfrom datatables import define_archives\n\tdefine_archives(db,False)\n\tfrom plugin_process import ProcessModel\n\tProcessModel().define_objects(False)\n\tfield__date_received = request.vars.field__date_received if request.vars.field__date_received else request.now.year\n\tobject = request.args(4)\n\trowob = db.objects(object)\n\tif rowob:\n\t\tdelarc = db(db.archives.id==rowob.table_id).delete()\n\t\tdelobj = db(db.objects.table_id==rowob.table_id).delete()\n\treturn redirect(URL(r=request,c='plugin_vanbandi',f='explorer',args=request.args,vars=dict(field__date_received=field__date_received)))\t\n\n##Xóa văn bản dự thảo\ndef delete_archives():\n\tfrom datatables import define_archives\n\tdefine_archives(db,False)\n\tid = request.vars.id if request.vars.id else ''\n\tde = db(db.archives.id==id).delete()\n\treturn ''\n\t\n####Mở Modal dùng Javascrip để lấy dữ liệu\n###Lấy danh sách đơn vị nội bộ\ndef inp_rein():\n\tcontent = vanbandi.inp_rein()\n\treturn content\n\ndef inp_rehuman():\n\tcontent = vanbandi.inp_rehuman()\n\treturn content\n\t\n\t\n#############################\n#Hàm Điều khiển luồng xử lý\ndef update_process_archives():\n\tprocess_id = request.vars.process\n\tauth_group = request.vars.auth_group\n\tobjects = request.vars.objects\n\t\n\t# Cập nhật lại bảng profile trạng thái hiện thại, nhóm xử lý hiện tại\n\tfrom gluon import LOAD\n\tfrom plugin_process import ProcessModel\n\tProcessModel().define_process(False)\n\tProcessModel().define_objects(False)\n\trow = db.process(process_id)\n\tcontent = ''\n\tif row:\n\t\tcontent = vanbandi.update_process_archives(process_id,auth_group,objects)\n\t#Khi có thao tác cập nhật hồ sơ(\"Thêm hàm update khi cấu hình quy trình\"). \n\treturn content\n\n\n###############################\n##Danh sách PROCEDURES\ndef list_procedures():\n\tcontent = vanbandi.list_procedures()\n\treturn content\n\n###############################\n##Form thêm văn bản trong quá trình xử lý\ndef process_archives():\n\tid = request.vars.id if request.vars.id else ''\n\tarc_id = request.vars.archives_id if request.vars.archives_id else ''\n\tcontent = vanbandi.modals_form_archives(id,arc_id)\n\treturn content\n\t\ndef insert_modals_archives():\n\tvar = {}\n\tfrom datatables import define_archives\n\tdefine_archives(db,False)\n\timport datetime\n\tfor key in request.vars.keys():\n\t\tif (key in db.archives.fields)&(key not in ['publish_date']):\n\t\t\tvar[key]=request.vars[key]\n\t\t\t\n\t\n\t##Cơ quan ban hành\n\tif request.vars.org=='0':\n\t\tvar['org']=None\n\t##Người ký\n\tif request.vars.signer=='0':\n\t\tvar['signer'] = None\n\t##Chức vụ\n\t##Mức độ quan trọng\n\tif request.vars.archives_security=='0':\n\t\tvar['archives_security']=None\n\t##Phương thức gửi\n\tif request.vars.archives_send=='0':\n\t\tvar['archives_send']=None\n\t##Sổ văn bản\n\tif request.vars.book_archives=='0':\n\t\tvar['book_archives']=None\n\t##Ký hiệu văn bản\n\tif request.vars.symbol_archives=='0':\n\t\tvar['symbol_archives']=None\n\t##Lĩnh vực\n\tif request.vars.archives_fields=='0':\n\t\tvar['archives_fields']=None\n\t##Loại văn bản\n\tif request.vars.type_archive=='0':\n\t\tvar['type_archive']=None\n\tvar['parent'] = request.vars.arc_id\t\t\n\tinput,output = \"%d/%m/%Y\", \"%Y-%m-%d\"\n\tvar['publish_date'] = datetime.datetime.strptime(request.vars.publish_date,input).strftime(output)\n\t\n\tarchives_id = Process.insert_modals_archives(var)\n\tif request.vars.uuid:\n\t\t# update lại file đính kèm theo uuid\n\t\tfrom plugin_upload import FileUpload\n\t\tfileupload = FileUpload(db=db,tablename='archives',upload_id=None)\n\t\ta = fileupload.update(archives_id,request.vars.uuid)\n\tredirect(URL(r=request,c='plugin_vanbandi',f='explorer',args=request.args, vars=dict(field__date_received=request.now.year)))\n\t\n##Thêm, sửa văn bản\ndef update_modals_archives():\n\tvar = {}\n\tfrom datatables import define_archives\n\tdefine_archives(db,False)\n\timport datetime\n\tfor key in request.vars.keys():\n\t\tif (key in db.archives.fields)&(key not in ['publish_date']):\n\t\t\tvar[key]=request.vars[key]\n\tinput,output = \"%d/%m/%Y\", \"%Y-%m-%d\"\n\t##Cơ quan ban hành\n\tif request.vars.org=='0':\n\t\tvar['org']=None\n\t##Người ký\n\tif request.vars.signer=='0':\n\t\tvar['signer'] = None\n\t##Chức vụ\n\t##Mức độ quan trọng\n\tif request.vars.archives_security=='0':\n\t\tvar['archives_security']=None\n\t##Phương thức gửi\n\tif request.vars.archives_send=='0':\n\t\tvar['archives_send']=None\n\t##Sổ văn bản\n\tif request.vars.book_archives=='0':\n\t\tvar['book_archives']=None\n\t##Ký hiệu văn bản\n\tif request.vars.symbol_archives=='0':\n\t\tvar['symbol_archives']=None\n\t##Lĩnh vực\n\tif request.vars.archives_fields=='0':\n\t\tvar['archives_fields']=None\n\t##Loại văn bản\n\tif request.vars.archives_category=='0':\n\t\tvar['archives_category']=None\n\tvar['parent'] = request.vars.arc_id\t\n\tvar['publish_date'] = datetime.datetime.strptime(request.vars.publish_date,input).strftime(output)\n\tarchives_id = Process.update_archives(request.vars.id,var)\n\tredirect(URL(r=request,c='plugin_vanbandi',f='explorer',args=request.args, vars=dict(field__date_received=request.vars.field__date_received)))\n\t\n\t\n###Chuyển văn bản xử lý thành văn bản chờ ban hành (Văn bản dự thảo)\n##Update: Chuyển file xử lý thành File văn bản ban hành\ndef forward_archives():\n\tid = request.vars.id if request.vars.id else ''\n\tarc_id = request.vars.arc_id if request.vars.arc_id else ''\n\timport uuid\n\tuuid = uuid.uuid1()\n\tfrom plugin_upload import FileUpload\n\tFileUpload().define_table(False)\n\t\n\t##Update file trong văn bản thành file dự thảo\n\tupfilearc = db(db.file_upload.table_id==arc_id).update(table_id=uuid)\n\t#Update file dự thảo thành file văn bản ban hành\n\tupfileduthao = db(db.file_upload.table_id==id).update(table_id=arc_id)\n\t##Update table_id trung gian\n\tuptrunggian = db(db.file_upload.table_id==uuid).update(table_id=id)\n\t\n\treturn 'Cập nhật đính kèm hoàn thành'\n\t# ##Lấy first process van ban di\n\t\n\t\n####################################################\n##Chuyên viên nhấn nút thêm vào hồ sơ công việc\n##Hiển thị form hồ sơ công việc để chuyên viên tự chọn vào hoặc thêm bộ hồ sơ công việc\ndef process_hscv():\n\targs = request.args\n\tvars = request.vars\n\tcontent = vanbandi.process_hscv(args,vars)\n\treturn dict(content=content)\t\n\t\n##Tìm kiếm danh sách hồ sơ công việc\ndef search_wprofile():\n\ttitle = request.vars.title if request.vars.title else ''\n\tname = request.vars.name if request.vars.name else ''\n\tcontent = vanbandi.write_workp(name,title)\n\treturn content\n\t\n######################################################\n##Vào sổ văn bản\ndef edit_number_to():\n\tcontent = vanbandi.edit_number_to()\n\treturn content\n##Cập nhật sổ văn bản đến, văn bản đi\ndef update_number_book():\n\tfrom datatables import define_archives\n\tdefine_archives(db,False)\n\tbook_archives = request.vars.book_archives if request.vars.book_archives else ''\n\tnumber_archives = request.vars.number_archives if request.vars.number_archives else ''\n\tarchives_id = request.vars.archives_id\n\tname = request.vars.name if request.vars.name else '' ##Số/Ký hiệu\n\tsymbol_archives = request.vars.symbol_archives if request.vars.symbol_archives else '' #Ký hiệu văn bản\n\tif archives_id:\n\t\tupdate = db(db.archives.id==archives_id).update(name=name,symbol_archives=symbol_archives,book_archives=book_archives,number_book=number_archives)\n\treturn number_archives\n\t\n#####################################################\n##Đọc thông tin văn bản\ndef read_archives():\n\tfrom datatables import define_archives\n\tdefine_archives(db, False)\n\tid = request.vars.id if request.vars.id else '' \n\tarchives = db.archives(id)\n\tcontent = vanbandi.read_archives(archives)\n\treturn content\n\n\t\n######################################\n#Trao đổi - thảo luận\ndef process_talk():\n\tid = request.vars.id\n\tcontent = vanbandi.process_talk(id)\n\treturn content\n\t\n\n\t","sub_path":"controllers/plugin_documents.py","file_name":"plugin_documents.py","file_ext":"py","file_size_in_byte":20443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"437802000","text":"from django import template\nfrom django.utils.safestring import mark_safe\nfrom menu.models import Menu\n\nregister = template.Library()\n\n\n@register.simple_tag(takes_context=True)\ndef draw_menu(context, menu_name):\n nodes = Menu.objects.get(name=menu_name).nodes\n an = context.request.path[1:]\n\n def tag(tag, text, ref='', id=''):\n \"\"\"\n Обрамляет строку тэгом\n \"\"\"\n if id and ref:\n return '<{0} href=\"{2}\" id=\"{3}\">{1}'.format(tag, text, ref, id)\n if ref:\n return '<{0} href=\"{2}\">{1}'.format(tag, text, ref)\n elif id:\n return '<{0} id=\"{2}\">{1}'.format(tag, text, id)\n else:\n return '<{0}>{1}'.format(tag, text)\n\n def flat(x):\n \"\"\"\n Переводит вложенный словарь или список в плоский.\n \"\"\"\n res = []\n if type(x) in (list, tuple):\n for i in x:\n res.extend(flat(i))\n elif isinstance(x, dict):\n for item in x.items():\n res.extend(flat(item))\n else:\n res.append(x)\n return res\n\n def draw_node(node, an):\n if an == node:\n return tag('li', tag('a', node, ref=node, id='active'))\n else:\n return tag('li', tag('a', node, ref=node))\n\n def draw_list(nodes, an):\n return ''.join(draw(node, an) for node in nodes)\n\n def draw_dict(nodes, an):\n res = ''\n for k, v in nodes.items():\n res += draw_node(k, an)\n if an == k:\n res += tag('ul', draw(v))\n elif an in flat(v):\n res += tag('ul', draw(v, an))\n return res\n\n def draw(nodes, an=''):\n if type(nodes) in (list, tuple):\n return draw_list(nodes, an)\n elif isinstance(nodes, dict):\n return draw_dict(nodes, an)\n else:\n return draw_node(nodes, an)\n\n return mark_safe(tag('ul', draw(nodes, an)))\n","sub_path":"templatetags/draw_menu.py","file_name":"draw_menu.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"502281626","text":"import pickle\nfrom typing import Dict, List, Tuple\n\nimport dgl\nimport torch\nimport torch.nn\nimport torch.nn.functional\nfrom matplotlib import pyplot\nfrom openforcefield.topology import Molecule\n\nfrom nagl.dataset.dataset import MoleculeGraphDataLoader, MoleculeGraphDataset\nfrom nagl.dataset.features import (\n AtomConnectivity,\n AtomFormalCharge,\n AtomicElement,\n AtomIsInRing,\n BondIsInRing, AtomFeature, BondFeature,\n)\nfrom nagl.models.models import MolSAGE, ConvolutionConfig, ReadoutConfig\nfrom nagl.nn import SequentialLayers\nfrom nagl.nn.pooling import PoolAtomFeatures, PoolBondFeatures\nfrom nagl.nn.process import ComputePartialCharges\n\n\ndef label_function(molecule: Molecule) -> Dict[str, torch.Tensor]:\n \"\"\"Generates a set of labels for a given molecule.\n \"\"\"\n from simtk import unit\n\n return {\n \"am1_charges\": torch.tensor(\n [\n atom.partial_charge.value_in_unit(unit.elementary_charge)\n for atom in molecule.atoms\n ],\n dtype=torch.float\n ),\n \"am1_wbo\": torch.tensor(\n [bond.fractional_bond_order for bond in molecule.bonds],\n dtype=torch.float\n ),\n }\n\n\ndef load_data_sets(\n atom_features: List[AtomFeature], bond_features: List[BondFeature]\n) -> Tuple[MoleculeGraphDataLoader, MoleculeGraphDataLoader, int]:\n \"\"\"Loads in the train and test molecules and generates labelled, featurized graph\n representations.\n \"\"\"\n from simtk import unit\n\n with open(\"train-set-large.pkl\", \"rb\") as file:\n training_molecules = pickle.load(file)\n with open(\"test-set-large.pkl\", \"rb\") as file:\n test_molecules = pickle.load(file)\n\n # For now limit to only uncharged molecules.\n training_molecules = [\n molecule\n for molecule in training_molecules\n if all(\n atom.formal_charge == 0 * unit.elementary_charge\n for atom in molecule.atoms\n )\n and all(\n abs(atom.partial_charge) < 1.0 * unit.elementary_charge\n for atom in molecule.atoms\n )\n ]\n test_molecules = [\n molecule\n for molecule in test_molecules\n if all(\n atom.formal_charge == 0 * unit.elementary_charge for atom in molecule.atoms\n )\n and all(\n abs(atom.partial_charge) < 1.0 * unit.elementary_charge for atom in\n molecule.atoms\n )\n ]\n\n training_data = MoleculeGraphDataset.from_molecules(\n training_molecules, atom_features, bond_features, label_function\n )\n test_data = MoleculeGraphDataset.from_molecules(\n test_molecules, atom_features, bond_features, label_function\n )\n\n training_set = MoleculeGraphDataLoader(\n training_data, batch_size=256, shuffle=True\n )\n test_set = MoleculeGraphDataLoader(\n test_data, batch_size=len(test_data), shuffle=False\n )\n\n return training_set, test_set, training_data.n_features\n\n\ndef main():\n\n # Define the features of interest.\n atom_features = [\n AtomicElement([\"C\", \"O\", \"H\", \"N\", \"S\", \"F\", \"Br\", \"Cl\"]),\n AtomConnectivity(),\n AtomFormalCharge([0]),\n # AtomIsAromatic(),\n AtomIsInRing(),\n ]\n bond_features = [\n # BondIsAromatic(),\n BondIsInRing(),\n ]\n\n # Load in the pre-processed training and test molecules and store them in\n # featurized graphs.\n training_set, test_set, n_features = load_data_sets(atom_features, bond_features)\n\n # Define the model.\n model = MolSAGE(\n convolution_config=ConvolutionConfig(\n in_feats=n_features,\n hidden_feats=[128, 128, 128],\n ),\n readout_configs={\n \"am1_charges\": ReadoutConfig(\n pooling_layer=PoolAtomFeatures(),\n hidden_feats=[128, 128, 128, 2],\n postprocess_layer=ComputePartialCharges(),\n ),\n \"am1_wbo\": ReadoutConfig(\n pooling_layer=PoolBondFeatures(\n layers=SequentialLayers(\n in_feats=128 * 2,\n hidden_feats=[128 * 2],\n )\n ),\n hidden_feats=[256, 256, 256, 1],\n ),\n }\n )\n\n print(model)\n\n # Define the optimizer and the loss function.\n optimizer = torch.optim.Adam(model.parameters(), lr=0.0002)\n criterion = torch.nn.MSELoss()\n\n losses = []\n\n for epoch in range(100):\n\n graph: dgl.DGLGraph\n\n for batch, (graph, features, labels) in enumerate(training_set):\n\n # Perform the models forward pass.\n y_pred = model(graph, features)\n\n # compute loss\n loss = torch.zeros(1)\n\n for label_name, label in labels.items():\n loss += torch.sqrt(criterion(y_pred[label_name], label))\n\n losses.append(loss.detach().numpy().item())\n\n # backward propagation\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n print(\n f\"epoch={epoch} \"\n f\"batch={batch} \"\n f\"loss={loss.item():.6f} \"\n # f\"q_tot={y_pred['am1_charges'].sum().detach().item():.4f} \"\n )\n\n # Compute the test accuracy\n test_graph, test_features, test_labels = next(iter(test_set))\n model.eval()\n\n with torch.no_grad():\n\n test_pred = model(test_graph, test_features)\n\n test_loss = 0.0\n\n for label_name, label in test_labels.items():\n test_loss += torch.sqrt(criterion(test_pred[label_name], label))\n\n print(\"________________\")\n print(f\"test loss={test_loss}\")\n\n # Plot the training losses.\n pyplot.plot(losses)\n pyplot.savefig(\"train-losses.png\")\n pyplot.cla()\n\n # Plot the predicted vs reference values.\n for label in test_labels:\n\n pyplot.scatter(\n test_labels[label].flatten().numpy(),\n test_pred[label].flatten().numpy(),\n label=\"test\"\n )\n pyplot.scatter(\n labels[label].flatten().numpy(),\n y_pred[label].flatten().detach().numpy(),\n label=\"train\",\n alpha=0.3,\n )\n pyplot.legend()\n pyplot.gcf().set_size_inches(4, 4)\n pyplot.xlabel(\"OpenEye\")\n pyplot.ylabel(\"Predicted\")\n pyplot.tight_layout()\n pyplot.savefig(f\"{label}.png\")\n pyplot.cla()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"scripts/training/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":6495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"96247370","text":"import telebot\nimport sqlalchemy as sa\nfrom sqlalchemy.orm import declarative_base\nfrom sqlalchemy.orm.session import sessionmaker\n\nengine = sa.create_engine('postgresql://postgres:pswd_123@192.168.1.5:5432/postgres')\n\nbase = declarative_base()\n\n\nclass Company(base):\n __tablename__ = 'company'\n id = sa.Column(sa.Integer,primary_key=True)\n name = sa.Column(sa.String)\n age = sa.Column(sa.Integer)\n address = sa.Column(sa.String)\n salary = sa.Column(sa.Integer)\n\n def __repr__(self):\n return f'Company name={self.name}'\n\ndef db_select(id_num):\n session_postg = sessionmaker(bind=engine)()\n try:\n d = session_postg.query(Company).filter_by(id=id_num).one()\n except sa.exc.NoResultFound:\n return f'no row with id: {id_num}'\n return f'name: {d.name}, salary: {d.salary}'\n\n\nbot = telebot.TeleBot('')\n\n\n@bot.message_handler(content_types=['text'])\ndef get_text_messages(message):\n int_mes = ''\n try:\n int_mes = int(message.text)\n except ValueError:\n bot.send_message(message.from_user.id, \"Value error, please send id number as digit, letters don/'t support\")\n if message.text == \"/start\":\n bot.send_message(message.from_user.id, \"please send id number, send /help for help \")\n elif message.text == \"/help\":\n bot.send_message(message.from_user.id, \"please send id number as digit, and you will get name and salary from DB\")\n if isinstance(int_mes, int):\n d = db_select(int_mes)\n bot.send_message(message.from_user.id, d)\n\nbot.polling(none_stop=True, interval=0)\n\n","sub_path":"tel_bot_db.py","file_name":"tel_bot_db.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"135716194","text":"#!/usr/bin/env python\n\nimport os\n\nimport connexion\n\nimport cert_store\nfrom cert_store import config\n\nBASE_DIR = os.path.abspath(os.path.dirname(__file__))\n\n\ndef main():\n conf = config.get_config()\n cert_store.configure_app(conf)\n port = int(os.environ.get('PORT', 5003))\n app = connexion.App(__name__, server='flask', port=port, specification_dir=os.path.join(BASE_DIR, 'cert_store', 'swagger'))\n app.add_api('swagger.yaml', arguments={\n 'title': 'API Specification for introductions to a Blockchain Certificate issuer.'})\n app.run()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"91632600","text":"\"\"\"This script is allowing the user create their own quiz\n\n\"\"\"\nimport csv\nclass Createquiz:\n \"\"\"This class will allow user create their own quiz\n Attributes:\n path(str): a csv file which allow to write in the quiz questions and answer\n \n \"\"\"\n def __init__(self):\n \"\"\" Initialize new Player object.\n\n Side effects:\n Sets attributes name, position.\n \"\"\"\n \n \n # fh = open ('quizdata.csv','w')\n # spreadsheet=csv.writer(fh)# create the csv handle\n # spreadsheet.writerow([\"question\",\"answer\"])\n \n def get_question_answer(self,user_id):\n questions=[]\n answers=[]\n userkey=[]\n question_answer={}\n \n \n \n user_question=str(input(\"\"\"Please built your question, hit \"Enter\" key to quit\\n\"\"\"))\n questions.append(user_question)\n user_answer=str(input(\"\"\"Please enter the answer for your question,hit \"Enter\" key to quit\\n\"\"\"))\n answers.append(user_answer)\n while (user_question!=\"\"or user_answer!=\"\"):\n user_question=str(input(\"\"\"Please built your question, hit \"Enter\" key to quit\\n\"\"\"))\n questions.append(user_question)\n user_answer=str(input(\"\"\"Please enter the answer for your question,hit \"Enter\" key to quit\\n\"\"\"))\n answers.append(user_answer)\n # except ValueError:\n # print(\"input must be a string\")\n print(questions,answers)\n for index in questions:\n userkey.append((user_id,index))\n print(userkey)\n \n question_answer=dict(zip(userkey,answers))\n print(question_answer)\n #def display_quiz(self):\n \n \n \n \n \nif __name__ ==\"__main__\":\n myquiz=Createquiz()\n myquiz.get_question_answer(\"Joanna\")\n \n \n\n","sub_path":"addquiz.py","file_name":"addquiz.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"103256877","text":"def rect_overlap(rect1, rect2):\n \n if rect1 == rect2: # if both rectangles are same, they are overlapping\n return True, rect1\n \n #get the bottom left and top right coordinates of each rectangle \n \n rect1_start_len = rect1[0]\n rect1_end_len = rect1[2]\n \n rect2_start_len = rect2[0]\n rect2_end_len = rect2[2]\n \n rect1_start_ht = rect1[1]\n rect1_end_ht = rect1[3]\n \n rect2_start_ht = rect2[1]\n rect2_end_ht = rect2[3]\n \n #initialize overlapping params to False\n len_overlap = False\n ht_overlap = False\n overlap = False\n merged_rect = None\n \n \n #check for x-axis overlap\n if ((rect2_start_len < rect1_end_len and rect2_start_len > rect1_start_len) or #start x-cordinate of 2nd rect within len of 1st rect\n (rect2_end_len > rect1_start_len and rect2_end_len < rect1_end_len) or #end x-cordinate of 2nd rect within len of 1st rect\n (rect1_start_len < rect2_end_len and rect1_start_len > rect2_start_len) or #viceversa 1\n (rect1_end_len > rect2_start_len and rect1_end_len < rect2_end_len) #viceversa 2\n ):\n \n len_overlap = True\n \n #check for y-axis overlap\n if ((rect2_start_ht < rect1_end_ht and rect2_start_ht > rect1_start_ht) or #start y-cordinate of 2nd rect within len of 1st rect\n (rect2_end_ht > rect1_start_ht and rect2_end_ht < rect1_end_ht) or #end y-cordinate of 2nd rect within len of 1st rect\n (rect1_start_ht < rect2_end_ht and rect1_start_ht > rect2_start_ht) or #viceversa 1\n (rect1_end_ht > rect2_start_ht and rect1_end_ht < rect2_end_ht) #viceversa 2\n ):\n\n ht_overlap = True\n \n #check for rectangle overlap\n if ((len_overlap and ht_overlap) or #both len and height overlap\n (rect1_start_len == rect2_start_len and rect1_end_len == rect2_end_len and ht_overlap) or #equal x-axis len and height overlap\n (rect1_start_ht == rect2_start_ht and rect1_end_ht == rect2_end_ht and len_overlap) #equal y-axis height and length overlap\n ):\n \n overlap = True\n \n #get merged rectangle by finding the bottom left and top right min and max points respectively\n bl_x = min(rect1_start_len, rect2_start_len)\n tr_x = max(rect1_end_len, rect2_end_len)\n bl_y = min(rect1_start_ht, rect2_start_ht)\n tr_y = max(rect1_end_ht, rect2_end_ht)\n \n merged_rect = [bl_x, bl_y, tr_x, tr_y]\n \n return overlap, merged_rect\n\ndef merge_rectangles(rect_arr):\n \n final_arr = [] #initialize a final array to be returned\n n = 0 #initialize overlap flag to false\n to_match_rect = rect_arr.pop(0) #first rect will be matched against all rects\n to_be_removed = [] #store all matched rects in a array to remove from the main array\n \n for rect in rect_arr:\n overlap, merged_rect = rect_overlap(to_match_rect, rect) #loop through each rect against first rect and find merged rect\n if overlap:\n n = 1 #even if one rect merges against the first rect,overlap flag = True \n to_match_rect = merged_rect #store the final merge rect in memory to check later if previous rects in array merge along with this\n to_be_removed.append(rect) #append matched rects to the to be removed array\n \n if to_match_rect not in final_arr: #check if the bigger rectangle is already in the final array. if so we need not append to it \n final_arr.append(to_match_rect) #else append the rectangle\n \n rect_arr = [x for x in rect_arr if x not in to_be_removed] #remove matched rectangles from original array\n \n if len(rect_arr) > 0: #if there are any elements remaining in rect_array, recursion needs to occur\n if n: \n rect_arr = [to_match_rect] + rect_arr #if there were overlaps, pass the bigger rect along with the remaining rects\n\n to_merge = merge_rectangles(rect_arr) #merge remianing rects and append to final array if its not in it\n \n for i in to_merge:\n if i not in final_arr:\n final_arr.append(i)\n \n return final_arr\n\ndef main(rect_arr):\n \n prev_len = len(rect_arr)\n\n while True: #till no further rects merge, keep calling the merge function\n rect_arr = merge_rectangles(rect_arr)\n new_len = len(rect_arr)\n if prev_len == new_len:\n break\n else:\n prev_len = new_len\n \n return rect_arr\n \nrect_arr = [[0,4,1,6], [1,3,2,5], [2,2,3,4], [3,1,5,3], [0,0,4,2]] \nprint(main(rect_arr))\n\nrect_arr = [[0,0,2,2], [1,1,3,3], [2,2,4,4]]\nprint(main(rect_arr))\n\nrect_arr = [[0,0,5,5], [2,10,7,12], [6,14,11,19], [8,6,18,16],[9,11,20,25]]\nprint(main(rect_arr))\n\n\nrect_arr = [[0,0,4,4], [2,2,4,4], [7,7,10,10], [12,12,14,14],[3,3,5,5]]\nprint(main(rect_arr))","sub_path":"Intersecting_Rectangles/rectangle.py","file_name":"rectangle.py","file_ext":"py","file_size_in_byte":4884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"469405495","text":"from sklearn.datasets import load_iris\nimport numpy as np\nfrom sklearn import tree\n\niris = load_iris()\ntest_index = [0, 50, 100]\n\n# training data\ntrain_target = np.delete(iris.target, test_index)\ntrain_data = np.delete(iris.data, test_index, axis=0)\n\n# testing data\ntest_target = iris.target[test_index]\ntest_data = iris.data[test_index]\n\nclassifier = tree.DecisionTreeClassifier()\nclassifier.fit(train_data, train_target)\n\n# print real label from dataset\nprint(test_target)\n\n# print predicted label from data test\nprint(classifier.predict(test_data))\n\n# visualize the tree\ntree.export_graphviz(classifier, out_file='tree.dot', filled=True,\n feature_names=iris.feature_names, class_names=iris.target_names)\n\nprint(test_data[0], test_target[0])\nprint(iris.feature_names, iris.target_names)\n","sub_path":"Minggu 12/simple2.py","file_name":"simple2.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"447390730","text":"import uuid\r\nfrom flask import Flask,request,jsonify\r\nfrom neo4j import GraphDatabase, basic_auth\r\nfrom datetime import datetime\r\nimport json\r\nfrom collections import defaultdict\r\ndef current_time():\r\n now = datetime.now()\r\n \r\n \r\n\r\n # dd/mm/YY H:M:S\r\n dt_string = now.strftime(\"%d %m %Y %H %M %S\")\r\n dt_string=list(map(int,dt_string.split(\" \")))\r\n \r\n ltime=dt_string[-2]+dt_string[-3]*60 + dt_string[0]*3600+dt_string[1]*3600*30+dt_string[2]*365*3600\r\n return ltime\r\n\r\n\r\ndef find_probability(a,virus_type):\r\n return .92/int(a[1])\r\n\r\n \r\n \r\n \r\n\r\n #to do:consider other factors to find probability\r\n \r\napp=Flask(__name__)\r\npeople={}\r\nid_new=0 \r\ndriver = GraphDatabase.driver(\r\n \"bolt://3.238.138.75:7687\",\r\n auth=basic_auth(\"neo4j\", \"gloves-blueprint-badge\"),max_connection_lifetime=200)\r\n\r\ndef runquery(q):\r\n session=driver.session()\r\n fr=session.run(q)\r\n return fr.data()\r\ndef findsource(user_id,virus_type):\r\n source=None\r\n query=\"CALL apoc.export.json.query(\"\r\n q=\"MATCH(a:Person { \"+f\"id: '{user_id}' \"+\"}),(b:\"+f\"{virus_type}),p = shortestPath((a)-[r:contact*]-(b)) return r\"\r\n query=\"\\\"%s\\\"\"%q\r\n query=\"CALL apoc.export.json.query(\"+query+\",null,{\"+\"stream:true})YIELD data \"\r\n print(query)\r\n \r\n fr=runquery(query)[0]['data']\r\n if len(fr)==0:\r\n return None\r\n fr=fr.split(\"\\n\")\r\n fr=min(fr, key = len)\r\n fr=json.loads(fr)\r\n fr=fr['r']\r\n q=\"match(n{id:\"+f\"'{user_id}'\"+\"}) return id(n)\"\r\n val=runquery(q)[0]['id(n)']\r\n visited=set()\r\n \r\n visited.add(str(val))\r\n prev={}\r\n i=fr[0] \r\n contact_time=(i['properties']['contact_times']).split(\"\\n\")[-1]\r\n if i['start']['id'] in visited:\r\n \r\n prev[i['end']['id']]=int(contact_time.split(\":\")[0])\r\n visited.add(i['end']['id'])\r\n\r\n else:\r\n prev[i['start']['id']]=int(contact_time.split(\":\")[0])\r\n visited.add(i['start']['id'])\r\n for i in fr[1:]:\r\n contact_time=int(((i['properties']['contact_times']).split(\"\\n\")[0]).split(\":\")[0])\r\n if i['start']['id'] in visited:\r\n end_id=i['end']['id']\r\n visited.add(i['end']['id'])\r\n start_id=i['start']['id']\r\n else:\r\n end_id=i['start']['id']\r\n visited.add(i['start']['id'])\r\n start_id=i['end']['id']\r\n if contact_time<=prev[start_id]:\r\n prev[end_id]=int(((i['properties']['contact_times']).split(\"\\n\")[-1]).split(\":\")[0]) \r\n else:\r\n source=end_id\r\n return source \r\n return source\r\n\r\n\r\ndef five_level(user_id,virus_type,is_source):\r\n val=1\r\n s_prob=\"u.p_\"+virus_type[-1]\r\n v_type=\"u.\"+virus_type\r\n \r\n q=f\"match(u) where u.id='{user_id}' return labels(u)\"\r\n fr=runquery(q)[0]['labels(u)']\r\n temp=\":\".join(fr)\r\n temp=\"u:\"+temp\r\n if is_source:\r\n val=.5\r\n\r\n q=f\"match(u) where u.id='{user_id}' remove {temp} set u:Positive:{virus_type}:Person set {s_prob}={val}\"\r\n runquery(q)\r\n \r\n \r\n query=\"CALL apoc.export.json.query(\"\r\n q=\"MATCH p=({id:\"+f\"'{user_id}'\"+\"})-[r:contact*..5]-(fr) return relationships(p),length(p),nodes(p)\"\r\n query=\"\\\"%s\\\"\"%q\r\n query=\"CALL apoc.export.json.query(\"+query+\",null,{\"+\"stream:true})YIELD data \"\r\n print(q)\r\n fr=runquery(query)\r\n fr=fr[0]['data']\r\n\r\n if fr:\r\n\r\n fr=fr.split(\"\\n\")\r\n fr=list(map(json.loads,fr))\r\n dat=[]\r\n visited=set()\r\n prev_times={}\r\n for i in fr:\r\n dat.append((i['relationships(p)'][-1],i['length(p)']))\r\n dat.sort(key=lambda x:x[1]) \r\n for i in dat: \r\n if i[1]>1:\r\n if i[0]['start']['id'] in visited and i[0]['end']['id'] in visited:#checks if already in previous lvl\r\n continue\r\n if i[0]['start']['id'] in prev_times :\r\n id_start=i[0]['start']['id'] \r\n id_end=i[0]['end']['id'] #finds the id of start node and end node\r\n elif i[0]['end']['id'] in prev_times:\r\n id_start=i[0]['end']['id']\r\n id_end=i[0]['start']['id'] \r\n else:\r\n continue\r\n visited.add(id_end)\r\n first_contact_times=i[0]['properties']['contact_times'].split(\"\\n\")\r\n temp=[]\r\n for p in first_contact_times:\r\n temp.append(int(p.split(\":\")[0]))\r\n flag=0\r\n first_contact_times=temp\r\n \r\n for j in range(len(first_contact_times)): \r\n if first_contact_times[j]>=prev_times[id_start]:\r\n contact_time=first_contact_times[j]\r\n flag=1\r\n break\r\n if flag: \r\n prob=find_probability(i,virus_type) \r\n q=f\"match(u) where id(u)={id_end} return labels(u),{s_prob}\"\r\n q_res=runquery(q)[0]\r\n \r\n prob_end=q_res[s_prob]\r\n if prob>prob_end:\r\n prev_times[id_end]=contact_time\r\n q=f\"MATCH (u:Person) WHERE id(u)={id_end} SET {s_prob}={prob} set u:{virus_type}\"\r\n runquery(q)\r\n\r\n\r\n\r\n\r\n \r\n\r\n else:\r\n first_contact_times=i[0]['properties']['contact_times'].split(\"\\n\")\r\n id_start=i[0]['start']['id']\r\n id_end=i[0]['end']['id']\r\n visited.add(id_end)\r\n visited.add(id_start)\r\n q=f\"match(u) where id(u)={id_start} return labels(u),{s_prob}\"\r\n q_res=runquery(q)[0]\r\n labels_start=q_res['labels(u)']\r\n print(q_res,s_prob)\r\n prob_start=q_res[s_prob] \r\n q=f\"match(u) where id(u)={id_end} return labels(u),{s_prob}\"\r\n q_res=runquery(q)[0]\r\n labels_end=q_res['labels(u)'] \r\n prob_end=q_res[s_prob]\r\n \r\n if current_time()-int(first_contact_times[-1].split(\":\")[0])<14*3600 and prob_start!=prob_end:\r\n prob=find_probability(i,virus_type)\r\n \r\n if prob_start(b)\"\r\n session=driver.session()\r\n session.run(query)\r\n\r\n \r\n return jsonify(200)\r\n\r\n@app.route(\"/probability\",methods=['POST']) \r\ndef check_probability():\r\n val=0\r\n virus_type=None\r\n data_recieved =request.data\r\n data_recieved=json.loads(data_recieved.decode(\"utf-8\"))\r\n user_id=data_recieved['user_id']\r\n \r\n q=\"match(n{id:\"+f\"'{user_id}'\"+\"}) return n.p_B,n.p_A,n.p_C,labels(n)\" \r\n fr=runquery(q)\r\n virus_type={}\r\n if fr:\r\n fr=fr[0]\r\n p_A,p_B,p_C=int(fr['n.p_A'])*100,int(fr['n.p_B'])*100,int(fr['n.p_C'])*100\r\n \r\n for i in fr['labels(n)']:\r\n if i==\"Positive\" or i==\"Person\":\r\n continue\r\n if i==\"v_A\":\r\n virus_type[\"Virus A\"]=p_A\r\n elif i==\"v_B\":\r\n virus_type[\"Virus B\"]=p_B\r\n else:\r\n virus_type[\"Virus C\"]=p_C \r\n\r\n if len(virus_type)==0:\r\n virus_type={\"No Infection\": 0}\r\n return jsonify(virus_type)\r\n \r\n@app.route(\"/positive\",methods=['POST'])\r\ndef is_positive():\r\n data_recieved =request.data\r\n data_recieved=json.loads(data_recieved.decode(\"utf-8\"))\r\n user_id,virus_type=data_recieved['user_id'],data_recieved['virus_type']# SEnd virus type as v_A, v_B or v_C\r\n if virus_type==\"v_B\":\r\n ret=\"a.p_B\"\r\n elif virus_type==\"v_A\":\r\n ret=\"a.p_A\" \r\n else:\r\n ret=\"a.p_C\" \r\n query=f\"MATCH (a:Person) WHERE a.id='{user_id}' Return {ret}\"\r\n #ret=\"\\'%s\\'\"%ret\r\n val=runquery(query)\r\n source=None\r\n if val:\r\n val=val[0]\r\n val=val[ret]\r\n if val<.05:\r\n source=findsource(user_id,virus_type)\r\n if source!=None:\r\n q=f\"match (n) where id(n)={source} return n.id\"\r\n val=runquery(q)[0]['n.id']\r\n five_level(val,virus_type,1) \r\n return five_level(user_id,virus_type,0) \r\n\r\n\r\n@app.route(\"/police\",methods=['POST'])\r\ndef police():\r\n d={}\r\n data_recieved =request.data\r\n data_recieved=json.loads(data_recieved.decode(\"utf-8\")) \r\n connections=data_recieved['connections']\r\n \r\n for i,j in connections.items():\r\n i = i.lower()\r\n if int(j)>-100:\r\n query=\"match(n{id:\"+f\"'{i}'\"+\"}) return n.p_A,n.p_B,n.p_C\" \r\n fr=runquery(query)\r\n if len(fr)>0:\r\n fr=fr[0]\r\n p_A,p_B,p_C=int(fr['n.p_A'])*100,int(fr['n.p_B'])*100,int(fr['n.p_C'])*100\r\n d[i]=[p_A,p_B,p_C]# returned probability of each virus in order A,B,C\r\n if len(d)==0:\r\n d={\"No Person Nearby\": 0} \r\n\r\n return jsonify(d)\r\nif __name__=='__main__':\r\n app.run(port=443)\r\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":11409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"555034647","text":"from django.contrib import admin\n\nfrom ui import models as m\n\n\nclass RuleAdmin(admin.ModelAdmin):\n list_display = ['id', 'is_active', 'words', 'people', 'locations']\n list_filter = ['is_active']\n search_fields = ['words', 'people']\nadmin.site.register(m.Rule, RuleAdmin)\n\n\nclass TwitterUserAdmin(admin.ModelAdmin):\n list_display = ['id', 'is_active', 'uid', 'name', 'former_names',\n 'date_last_checked']\n list_filter = ['is_active']\n search_fields = ['name', 'former_names', 'uid']\nadmin.site.register(m.TwitterUser, TwitterUserAdmin)\n\n\nclass TwitterUserItemAdmin(admin.ModelAdmin):\n list_display = ['id', 'twitter_user', 'date_published', 'twitter_id']\n list_filter = ['date_published']\n search_fields = ['twitter_id', 'item_text']\nadmin.site.register(m.TwitterUserItem, TwitterUserItemAdmin)\n\n\nclass TwitterUserSetAdmin(admin.ModelAdmin):\n list_display = ['id', 'user', 'name', 'notes']\n search_fields = ['user', 'name']\nadmin.site.register(m.TwitterUserSet, TwitterUserSetAdmin)\n","sub_path":"sfm/ui/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"235366082","text":"# Copyright 2017 AT&T Corporation.\n# All Rights Reserved.\n#\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\nfrom tempest.common.utils import data_utils\nfrom tempest import config\nfrom tempest.lib import decorators\n\nfrom patrole_tempest_plugin import rbac_rule_validation\nfrom patrole_tempest_plugin.rbac_utils import rbac_utils\nfrom patrole_tempest_plugin.tests.api.identity.v3 import rbac_base\n\nCONF = config.CONF\n\n\nclass IdentityProjectV3AdminRbacTest(\n rbac_base.BaseIdentityV3RbacAdminTest):\n\n def tearDown(self):\n \"\"\"Reverts user back to admin for cleanup.\"\"\"\n rbac_utils.switch_role(self, switchToRbacRole=False)\n super(IdentityProjectV3AdminRbacTest, self).tearDown()\n\n @rbac_rule_validation.action(service=\"keystone\",\n rule=\"identity:create_project\")\n @decorators.idempotent_id('0f148510-63bf-11e6-1564-080044d0d904')\n def test_create_project(self):\n \"\"\"Create a Project.\n\n RBAC test for Keystone: identity:create_project\n \"\"\"\n name = data_utils.rand_name('project')\n rbac_utils.switch_role(self, switchToRbacRole=True)\n project = self.non_admin_projects_client \\\n .create_project(name)['project']\n self.addCleanup(self.projects_client.delete_project, project['id'])\n\n @rbac_rule_validation.action(service=\"keystone\",\n rule=\"identity:update_project\")\n @decorators.idempotent_id('0f148510-63bf-11e6-1564-080044d0d905')\n def test_update_project(self):\n \"\"\"Update a Project.\n\n RBAC test for Keystone: identity:update_project\n \"\"\"\n project = self._setup_test_project()\n\n rbac_utils.switch_role(self, switchToRbacRole=True)\n self.non_admin_projects_client \\\n .update_project(project['id'],\n description=\"Changed description\")\n\n @rbac_rule_validation.action(service=\"keystone\",\n rule=\"identity:delete_project\")\n @decorators.idempotent_id('0f148510-63bf-11e6-1564-080044d0d906')\n def test_delete_project(self):\n \"\"\"Delete a Project.\n\n RBAC test for Keystone: identity:delete_project\n \"\"\"\n project = self._setup_test_project()\n\n rbac_utils.switch_role(self, switchToRbacRole=True)\n self.non_admin_projects_client.delete_project(project['id'])\n\n @rbac_rule_validation.action(service=\"keystone\",\n rule=\"identity:get_project\")\n @decorators.idempotent_id('0f148510-63bf-11e6-1564-080044d0d907')\n def test_show_project(self):\n \"\"\"Show a project.\n\n RBAC test for Keystone: identity:get_project\n \"\"\"\n project = self._setup_test_project()\n\n rbac_utils.switch_role(self, switchToRbacRole=True)\n self.non_admin_projects_client.show_project(project['id'])\n\n @rbac_rule_validation.action(service=\"keystone\",\n rule=\"identity:list_projects\")\n @decorators.idempotent_id('0f148510-63bf-11e6-1564-080044d0d908')\n def test_list_projects(self):\n \"\"\"List all projects.\n\n RBAC test for Keystone: identity:list_projects\n \"\"\"\n rbac_utils.switch_role(self, switchToRbacRole=True)\n self.non_admin_projects_client.list_projects()\n","sub_path":"patrole_tempest_plugin/tests/api/identity/v3/test_projects_rbac.py","file_name":"test_projects_rbac.py","file_ext":"py","file_size_in_byte":3834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"430111822","text":"# SUMMARY: plot_pertube.py\n# USAGE: plot flux averaging results\n# ORG: Pacific Northwest National Laboratory\n# AUTHOR: Xuehang Song\n# E-MAIL: xuehang.song@pnnl.gov\n# ORIG-DATE: July-2018\n# DESCRIPTION:\n# DESCRIP-END.\n# COMMENTS: only deal cartesian sturtured grids\n#\n# Last Change: 2018-08-01\n\nimport numpy as np\nimport math\nimport csv\nimport matplotlib.pyplot as plt\nimport pickle\nimport os.path\nfrom matplotlib.pyplot import cm\n\n\n# # # for anisotropy\n# simu_dir = \"/pic/projects/dvz/xhs_simus/bcomplex/perturb_anisotropy/\"\n# img_dir = '/pic/projects/dvz/BComplex/FY18/figures/perturb_anisotropy/'\n# ncase = 4\n# cases = [\"model_0\"+str(icase) for icase in np.arange(ncase)]\n# case_name = {}\n# for icase in range(ncase):\n# case_name[cases[icase]] = \"Model_0\"+str(icase)\n\n\n# # for source cases\n# simu_dir = \"/pic/projects/dvz/xhs_simus/bcomplex/perturb_source/\"\n# img_dir = '/pic/projects/dvz/BComplex/FY18/figures/perturb_source/'\n# ncase = 200\n# cases = [str(icase) for icase in np.arange(ncase)]\n# case_name = {}\n# for icase in range(ncase):\n# case_name[cases[icase]] = str(icase+1)\n\n\n# # for kd cases\n# simu_dir = \"/pic/projects/dvz/xhs_simus/bcomplex/perturb_kd/\"\n# img_dir = '/pic/projects/dvz/BComplex/FY18/figures/perturb_kd/'\n# ncase = 11\n# cases = [str(icase) for icase in np.arange(ncase)]\n# kd = [str(\"{:.1f}\".format(ikd))+\" mL/g\" for ikd in np.arange(0, 1.1, 0.1)]\n# case_name = {}\n# for icase in range(ncase):\n# case_name[cases[icase]] = kd[icase]\n\n\n# # for k cases\n# simu_dir = \"/pic/projects/dvz/xhs_simus/bcomplex/perturb_k/\"\n# img_dir = '/pic/projects/dvz/BComplex/FY18/figures/perturb_k/'\n# cases = [\"model_06\", \"model_05\", \"model_04\", \"model_07\", \"model_08\"]\n# k_setup = [\"k*0.01\", \"k*0.1\", \"k\", \"k*10\", \"k*100\"]\n# ncase = len(cases)\n# case_name = {}\n# for icase in range(ncase):\n# case_name[cases[icase]] = k_setup[icase]\n\n\n# # for group 0\nsimu_dir = \"/pic/projects/dvz/xhs_simus/bcomplex/fy19/group_0/\"\nimg_dir = '/people/song884/bcomplex/group0/'\ncases = [\"case0\", \"case1\", \"case2\", \"case3\", \"case4\"]\nk_setup = [(\"case0: perchsilt,1.e-7,hc cm/s \\n\" +\n \"case0: hf1+hf2+hf3,isotropy Mualem \\n\" +\n \"case0: ccu_lower+ccu_upper,u-total[kg],0.05,cm^3/g\"),\n \"case1: perchsilt,1.e-8,hc cm/s\",\n \"case2: hf1+hf2+hf3, anisotropy Mualem,,-0.683,0.916\",\n \"case3: ccu_lower+ccu_upper,u-total[kg],0.2,cm^3/g\",\n \"case4: combined\"]\nncase = len(cases)\ncase_name = {}\nfor icase in range(ncase):\n case_name[cases[icase]] = k_setup[icase]\n\n\n# # for group 0b\n# simu_dir = \"/pic/projects/dvz/xhs_simus/bcomplex/fy19/group_0/\"\n# img_dir = '/people/song884/bcomplex/group0_b/'\n# cases = [\"case0-b\", \"case1-b\", \"case2\", \"case3-b\", \"case4\"]\n# k_setup = [(\"case0: perchsilt,1.e-7,hc cm/s \\n\" +\n# \"case0: hf1+hf2+hf3,isotropy Mualem \\n\" +\n# \"case0: ccu_lower+ccu_upper,u-total[kg],0.05,cm^3/g\"),\n# \"case1: perchsilt,1.e-8,hc cm/s\",\n# (\"case2: hf1+hf2+hf3, anisotropy Mualem,,-0.683,0.916 \\n\" +\n# \"case2: hf1+hf2+hf3, increase kxy by 4x\"),\n# \"case3: ccu_lower+ccu_upper,u-total[kg],0.2,cm^3/g\",\n# \"case4: combined\"]\n# ncase = len(cases)\n# case_name = {}\n# for icase in range(ncase):\n# case_name[cases[icase]] = k_setup[icase]\n\n\nsimu_dir = \"/pic/projects/dvz/xhs_simus/bcomplex/fy19/BComplex_model2/\"\nimg_dir = '/people/song884/bcomplex/test/'\ncases = [\"BComplex_test\"]\nk_setup = [\"test case\"]\nncase = len(cases)\ncase_name = {}\nfor icase in range(ncase):\n case_name[cases[icase]] = k_setup[icase]\n\n\n# mark's new simulation \nsimu_dir = \"/pic/projects/dvz/xhs_simus/bcomplex/fy19/marks/\"\nimg_dir = '/people/song884/bcomplex/marks/'\ncases = [\"mark1b\"]\nk_setup = [\"mark1b\"]\nncase=len(cases)\ncase_name = {}\nfor icase in range(ncase):\n case_name[cases[icase]] = k_setup[icase]\n \ncolors = {}\nfor icase in range(ncase):\n colors[cases[icase]] = cm.rainbow(np.linspace(0, 1, ncase))[icase]\nobs_data_dir = '/pic/projects/dvz/xhs_simus/bcomplex/fy19/obsdata/'\n\nscale_units = {}\nscale_units['solute aqueous concentration no3[kg]'] = 10**9\nscale_units['solute aqueous concentration tc-99[ci]'] = 10**12\nscale_units['solute aqueous concentration u-total[kg]'] = 10**9\n\nylabels = {}\nylabels['solute aqueous concentration no3[kg]'] = \"NO3[ug/L]\"\nylabels['solute aqueous concentration tc-99[ci]'] = \"Tc-99[pci/L]\"\nylabels['solute aqueous concentration u-total[kg]'] = \"Uranium[ug/L]\"\n\nobs_data_dir = {}\nobs_data_dir['solute aqueous concentration no3[kg]'] = \\\n \"/pic/projects/dvz/BComplex/FY18/UQmodel/obsdata_updated/nitrate/\"\nobs_data_dir['solute aqueous concentration tc-99[ci]'] = \\\n \"/pic/projects/dvz/BComplex/FY18/UQmodel/obsdata_updated/tc-99/\"\nobs_data_dir['solute aqueous concentration u-total[kg]'] = \\\n \"/pic/projects/dvz/BComplex/FY18/UQmodel/obsdata_updated/uranium/\"\n\ncase_data = {}\nfor icase in cases:\n print(simu_dir+icase)\n fname = open(simu_dir+icase+\"/tec_data/averaged_varis.pk\", \"rb\")\n case_data[icase] = pickle.load(fname)\n fname.close()\nvaris = list(case_data[icase].keys())\nvaris.remove('Time')\n\nwells = list(case_data[icase][varis[0]].keys())\nnwell = len(wells)\n\nraw_data = {}\nfor icase in cases:\n print(simu_dir+icase)\n fname = open(simu_dir+icase+\"/tec_data/raw_varis.pk\", \"rb\")\n raw_data[icase] = pickle.load(fname)\n fname.close()\n\n\n# read observation data\nobs_time_range = [1e4, -1e4]\nobs_data = {}\nfor ivari in varis:\n obs_data[ivari] = {}\n for iwell in wells:\n data_file = obs_data_dir[ivari] + iwell + \".dat\"\n if os.path.exists(data_file):\n obs_data[ivari][iwell] = []\n with open(data_file, \"r\") as infile:\n reader = csv.reader(infile)\n next(reader, None)\n next(reader, None)\n for row in reader:\n obs_data[ivari][iwell].append(\n [float(row[0]), float(row[2])])\n obs_data[ivari][iwell] = np.asarray(obs_data[ivari][iwell])\n obs_time_range = [min(obs_time_range[0], np.min(obs_data[ivari][iwell][:, 0])),\n max(obs_time_range[1], np.max(obs_data[ivari][iwell][:, 0]))]\n\n# obs_time_range = [1944, 2020]\n\nncol = 4\nnrow = math.ceil(nwell/ncol)\nline_handles = {}\nfor ivari in varis:\n imgfile = img_dir+ivari+\".png\"\n fig, axs = plt.subplots(nrow, ncol)\n for i, ax in enumerate(fig.axes[0:nwell]):\n iwell = wells[i]\n line_handles[iwell] = {}\n if iwell in obs_data[ivari]:\n line_handles[iwell][\"obs\"] = ax.scatter(\n obs_data[ivari][iwell][:, 0],\n obs_data[ivari][iwell][:, 1],\n color='black',\n zorder=100,\n label=iwell)\n for icase in cases:\n line_handles[iwell][icase] = ax.plot(\n case_data[icase][\"Time\"],\n case_data[icase][ivari][iwell] *\n scale_units[ivari],\n linewidth=4, zorder=0,\n c=colors[icase],\n label=case_name[icase])\n print(iwell)\n ax.set_title(iwell, fontsize=20)\n ax.set_ylabel(ylabels[ivari], fontsize=20)\n ax.set_yscale('log')\n ax.set_xlabel('Time [yr]', fontsize=20)\n ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n ax.tick_params(axis=\"both\", which=\"minor\", labelsize=20)\n ax.set_xlim(obs_time_range)\n if iwell in obs_data[ivari]:\n ax.set_ylim(min(obs_data[ivari][iwell][:, 1])*0.001,\n max(obs_data[ivari][iwell][:, 1])*1000)\n # fig.legend(line_handles[iwell],\n # labels=[case_name[icase] for icase in cases]+[\"Observation\"],\n # loc=\"upper right\",\n # fontsize=20)\n # plt.subplots_adjust(top=0.97, bottom=0.04, left=0.07,\n # right=0.85, wspace=0.4, hspace=0.6)\n # fig.set_size_inches(20, 20)\n for i, ax in enumerate(fig.axes[nwell:]):\n ax.set_axis_off()\n \n fig.legend(line_handles[iwell],\n labels=[case_name[icase] for icase in cases]+[\"Observation\"],\n loc=\"upper right\", ncol=1,\n fontsize=18.3)\n plt.subplots_adjust(top=0.97, bottom=0.04, left=0.07,\n right=0.7, wspace=0.4, hspace=0.6)\n fig.set_size_inches(27, 20)\n fig.savefig(imgfile, bbox_inches=0)\n plt.close(fig)\n\n\nfor ivari in varis:\n for icase in cases:\n print(icase)\n imgfile = img_dir+icase+\"_\"+ivari+\".png\"\n fig, axs = plt.subplots(nrow, ncol)\n for i, ax in enumerate(fig.axes[0:nwell]):\n iwell = wells[i]\n if iwell in obs_data[ivari]:\n ax.scatter(obs_data[ivari][iwell][:, 0],\n obs_data[ivari][iwell][:, 1],\n color='black',\n zorder=100,\n label=iwell)\n\n ax.plot(case_data[icase][\"Time\"],\n case_data[icase][ivari][iwell]*scale_units[ivari],\n color=\"red\",\n linewidth=4, zorder=10, label=case_name[icase])\n\n n_raw = raw_data[icase][ivari][iwell].shape[1]\n for iraw in range(n_raw):\n ax.plot(raw_data[icase][\"Time\"],\n raw_data[icase][ivari][iwell][:, iraw] *\n scale_units[ivari],\n case_data[icase][ivari][iwell],\n color=\"grey\",\n linewidth=2, zorder=0, label=case_name[icase])\n ax.set_title(iwell, fontsize=20)\n ax.set_ylabel(ylabels[ivari], fontsize=20)\n ax.set_yscale('log')\n ax.set_xlabel('Time [yr]', fontsize=20)\n ax.set_xlim(obs_time_range)\n ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n ax.tick_params(axis=\"both\", which=\"minor\", labelsize=20)\n if iwell in obs_data[ivari]:\n ax.set_ylim(min(obs_data[ivari][iwell][:, 1])*0.001,\n max(obs_data[ivari][iwell][:, 1])*1000)\n for i, ax in enumerate(fig.axes[nwell:]):\n ax.set_axis_off()\n plt.subplots_adjust(top=0.97, bottom=0.04, left=0.07,\n right=0.97, wspace=0.4, hspace=0.6)\n\n fig.set_size_inches(20, 20)\n# fig.tight_layout()\n fig.savefig(imgfile, bbox_inches=0)\n plt.close(fig)\n","sub_path":"plot_wells.py","file_name":"plot_wells.py","file_ext":"py","file_size_in_byte":10462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"177881278","text":"import re, sys, os, getopt\nimport operator\nimport argparse\nimport gzip\nimport numpy as np\n\ndef process_haps(hap_file_path):\n hap_dict = {}\n with open(hap_file_path,'r') as data:\n for line in data:\n line = line.strip()\n bits = line.split(' ')\n chrom, pos, ref, alt = operator.itemgetter(0,2,3,4)(bits)\n genotypes = bits[5:]\n if chrom not in hap_dict.keys(): hap_dict[chrom] = {}\n hap_dict[chrom][pos] = [ref,alt,genotypes]\n return(hap_dict)\n\ndef switch_phase(genotype,p=0):\n if np.random.binomial(1,p,1) == 1:\n genotype = genotype[::-1]\n return(genotype)\n\ndef is_het(genotype):\n het = False\n if (('1' in genotype) and ('0' in genotype)):\n het = True\n return(het)\n\ndef check_phase(genotype):\n if '|' in genotype:\n ver = genotype\n else:\n contents = re.split(r'[/]+',genotype)\n if '0' in contents and '1' in contents:\n #ver = None # experimentation\n ver = genotype\n else:\n ver = genotype\n return(ver)\n\ndef process_vcf(vcf_path,error=None):\n file = gzip.open(vcf_path, 'rb')\n var_dict = dict()\n for l in file:\n l = l.strip().decode('ascii')\n # If 'l' is an info line, skip\n if not re.match(\"#\", l):\n d = l.strip()\n d = re.split('\\t', d)\n chrom, pos, ref, alt = operator.itemgetter(0,1,3,4)(d)\n print(pos)\n if chrom not in var_dict.keys(): var_dict[chrom] = {}\n # If polyallelic, skip to next site now. Only interested in biallelic sites\n if len(alt) > 2:\n continue\n \n # Handle (skip) indels\n indel = False\n if len(alt) > 1:\n #Added this additional if statement because with only 1 sample GATK cannot distinguish the possibility of another allele. For our purposes we just set that to the reference allele though.\n if allele != \"\":\n indel = True\n continue\n \n # Now get genotype info for snps\n if error is not None:\n genotypes = [switch_phase(geno,p=error) if is_het(geno) else geno for geno in d[9:]]\n else:\n genotypes = d[9:]\n genotypes = [re.split(r'[:]+',x)[0] for x in genotypes]\n pre_gen = genotypes\n genotypes = [check_phase(x) for x in genotypes]\n print(pos)\n if None in genotypes:\n print(\"None\")\n print(pre_gen)\n print(genotypes)\n continue\n else:\n genotypes = [re.split(r'[|/]+',x) for x in genotypes]\n genotypes = [y for x in genotypes for y in x]\n #if '.' in genotypes:\n #print(\"missing\")\n #print(pre_gen)\n #continue\n genotypes = [x for x in genotypes if x not in ['|','/']] \n # Store values\n var_dict[chrom][pos] = [ref,alt,genotypes]\n \n return(var_dict)\n\n\ndef get_state(f_ref,geno_dict):\n ##\n states = dict()\n out_data = []\n seq_length = 0\n f_ref = open(f_ref,'r')\n ##\n for line_count, (l_ref) in enumerate(f_ref):\n l_ref = l_ref.strip()\n if re.match(\">\", l_ref):\n chrom = re.sub(\">|:.*\",\"\",l_ref)\n print(chrom)\n seq_length = 0\n states[chrom] = {}\n continue\n if chrom not in geno_dict.keys():\n continue\n if not re.match(\">\", l_ref):\n line_length = len(l_ref)\n for ix, (b_ref) in enumerate(l_ref):\n # get the current genome position, need to iterate through reference and get each base value. \n # need to add 1 because python indexes at 0; genomes are at index 1\n pos = seq_length + ix + 1\n pos = str(pos)\n if pos in geno_dict[chrom].keys():\n if b_ref == geno_dict[chrom][pos][0]:\n true_ref,true_alt = geno_dict[chrom][pos][0:2]\n elif b_ref == geno_dict[chrom][pos][1]:\n true_alt,true_ref = geno_dict[chrom][pos][0:2]\n else:\n true_ref,true_alt = ['N','N']\n continue\n genotypes = [{'0':true_ref,'1':true_alt,'.':'.'}[x] for x in geno_dict[chrom][pos][2]]\n \n ## \n map_line = ['%s_%s' % (chrom,pos),chrom,pos,true_ref,true_alt]\n hap_line = genotypes\n \n out_data.append([map_line,hap_line])\n \n seq_length = seq_length + line_length\n \n return(out_data)\n\n\n\n\ndef write_data(out_data,out_prefix):\n out_map = open('%s.inp' % out_prefix,'w')\n out_hap = open('%s.thap' % out_prefix,'w')\n for snp in out_data:\n out_map.write(' '.join(snp[0]) + '\\n')\n out_hap.write(' '.join(snp[1]) + '\\n')\n \n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--hap\", help=\"haplotype file as output by SHAPEIT2\")\n parser.add_argument(\"--vcf\", help=\"phased vcf file\")\n parser.add_argument(\"--ref\", help=\"reference genome with which to polarize snps\")\n parser.add_argument(\"--out\", help=\"output path. Files will be written to .inp and .thap\")\n parser.add_argument(\"--error\", help=\"Introduce phasing error with frequency

. Only compatible with vcf option. Default is no error\", default=0)\n\n # Parse arguments\n args = parser.parse_args()\n\n # Run\n if args.vcf is not None:\n print(\"Processing vcf\")\n geno_dict = process_vcf(args.vcf, error = float(args.error))\n else:\n geno_dict = process_haps(args.hap)\n recoded = get_state(args.ref,geno_dict)\n print(\"Recoded haplotypes, writing out data\")\n write_data(recoded,args.out)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n","sub_path":"rehh/hap2rehh.py","file_name":"hap2rehh.py","file_ext":"py","file_size_in_byte":6078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"70760337","text":"# content of conftest.py\nimport pytest\n\nfrom dict_json import dict_json\n\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\n\n\ndef pytest_addoption(parser):\n parser.addoption(\"--all\", action=\"store_true\", help=\"run all combinations\")\n # parser.addoption(\"--debug\", action=\"store_true\",\n # help=\"Logger Trace level\")\n parser.addoption(\"--print\", action=\"store_true\", help=\"print all keys\")\n\n\ndef pytest_generate_tests(metafunc):\n if \"param1\" in metafunc.fixturenames:\n if metafunc.config.getoption(\"all\"):\n end = 5\n else:\n end = 2\n metafunc.parametrize(\"param1\", range(end))\n\n if \"debug\" in metafunc.fixturenames:\n if metafunc.config.getoption(\"debug\"):\n logging.basicConfig(level=logging.DEBUG)\n # metafunc.parametrize(\"debug\", metafunc.config.getoption('debug'))\n\n if \"print\" in metafunc.fixturenames:\n print = metafunc.config.getoption(\"print\")\n\n # metafunc.parametrize(\"print\", print)\n\n\ndef pytest_addoption(parser):\n parser.addoption(\n \"--cmdopt\", action=\"store\", default=\"type1\", help=\"my option: type1 or type2\"\n )\n parser.addoption(\"--all\", action=\"store\", default=False, help=\"do all types\")\n parser.addoption(\"--print\", action=\"store\", default=False, help=\"print all keys\")\n\n\n@pytest.fixture\ndef cmdopt(request):\n return request.config.getoption(\"--cmdopt\")\n\n\n@pytest.fixture(scope=\"module\")\ndef debug(request):\n dict_json.logging.basicConfig(level=logging.DEBUG) if request.config.getoption(\n \"--debug\"\n ) else \"\"\n return request.config.getoption(\"--debug\")\n\n\n@pytest.fixture(scope=\"module\")\ndef printKeys(request):\n return request.config.getoption(\"--print\")\n","sub_path":"dict_json/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"346859156","text":"class EnvArgs:\n\tdef __init__(self):\n\t\t# env settings\n\t\tself.s_gop_len = 16 # gop based info\n\t\tself.s_gop_info = 7 # or 7\n\t\tself.a_dim = 18\n\t\tself.random_seed = 10 # 50\n\t\tself.bitrate_levels = 2\n\t\tself.bitrate = [500.0, 1200.0] # kbps\n\t\tself.target_buffer_levels = 9\n\t\tself.target_buffer = [0.3, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4] # seconds\n\n\t\tself.frame_time_len = 0.04\n\t\tself.smooth_penalty = 0.02\n\t\tself.rebuf_penalty = 1.5\n\t\tself.latency_penalty = 0.005\n\n\t\tself.bw_trace = '../trace/network/final_network_trace/'\n\t\tself.test_bw_trace = '../trace/network/test/'\n\t\tself.video_size_files = '../trace/video/final_cooked_trace/'\n\t\tself.test_video_size_files = '../trace/video/test/'","sub_path":"a2c-predict/env_args.py","file_name":"env_args.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"308279368","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n__version__ = '1.0.0.0'\r\n\r\n\"\"\"\r\n@brief 简介 \r\n@details 详细信息\r\n@author luoyuediwu\r\n@data 2016-01-06 \r\n\"\"\"\r\nimport web\r\n\r\nurls = (\r\n '/wx', 'Handle',\r\n)\r\n#app_root = os.path.dirname(_file_)\r\n#temples_root = os.path.join(app_root,'templates')\r\n#render = web.template.render(temples_root)\r\n\r\nif __name__ == '__main__':\r\n app = web.application(urls, globals())\r\n app.run()\r\n","sub_path":"wechatlink/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"427970599","text":"import os\nimport sys\n\npath = sys.argv[1]\nsource = sys.argv[2]\nbuilds_directory = path + \"/builds\"\n\ndef main():\n name = source[0 : len(source) - 3]\n os.system(\" \".join([\"python3\", builds_directory + \"/\" + source]))\n\nif __name__ == '__main__':\n main()","sub_path":"cptemplates/build_system/run/run_python.py","file_name":"run_python.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"412365074","text":"from puzzle import Puzzle\n\n\nclass MNPuzzle(Puzzle):\n \"\"\"\n An nxm puzzle, like the 15-puzzle, which may be solved, unsolved,\n or even unsolvable.\n \"\"\"\n\n def __init__(self, from_grid, to_grid):\n \"\"\"\n MNPuzzle in state from_grid, working towards\n state to_grid\n\n @param MNPuzzle self: this MNPuzzle\n @param tuple[tuple[str]] from_grid: current configuration\n @param tuple[tuple[str]] to_grid: solution configuration\n @rtype: None\n \"\"\"\n # represent grid symbols with letters or numerals\n # represent the empty space with a \"*\"\n assert len(from_grid) > 0\n assert all([len(r) == len(from_grid[0]) for r in from_grid])\n assert all([len(r) == len(to_grid[0]) for r in to_grid])\n # J: setting a variable for row and col\n self.n, self.m = len(from_grid), len(from_grid[0])\n self.from_grid, self.to_grid = from_grid, to_grid\n\n def __eq__(self, other):\n \"\"\"\n Return whether MNPuzzle self is equivalent to other.\n\n @type self: MNPuzzle\n @type other: MNPuzzle | Any\n @rtype: bool\n\n >>> grid1 = ((\"1\", \"2\", \"3\"), (\"4\", \"5\", \"*\"))\n >>> grid2 = ((\"*\", \"2\", \"3\"), (\"1\", \"4\", \"5\"))\n >>> mn1 = MNPuzzle(grid1, grid2)\n >>> grid3 = ((\"1\", \"2\", \"3\"), (\"4\", \"5\", \"*\"))\n >>> grid4 = ((\"*\", \"2\", \"3\"), (\"1\", \"4\", \"5\"))\n >>> mn2 = MNPuzzle(grid3, grid4)\n >>> mn1 == mn2\n True\n >>> mn3 = MNPuzzle(grid3, grid1)\n >>> mn1 == mn3\n False\n \"\"\"\n\n return ((type(self) == type(other)) and\n (self.from_grid == other.from_grid) and\n (self.to_grid == other.to_grid))\n\n def __str__(self):\n \"\"\"\n Return a human-readable string representation of MNPuzzle self.\n\n >>> grid1 = ((\"1\", \"2\", \"3\"), (\"4\", \"5\", \"*\"))\n >>> grid2 = ((\"*\", \"2\", \"3\"), (\"1\", \"4\", \"5\"))\n >>> mn = MNPuzzle(grid1, grid2)\n >>> print(mn)\n 123\n 45*\n \"\"\"\n mn = \"\"\n j = 0\n for row in self.from_grid:\n j += 1\n for i in range(len(row)):\n mn += row[i]\n # to remove the at the end\n if j != len(self.from_grid):\n mn += \"\\n\"\n return mn\n\n def extensions(self):\n \"\"\"\n Return list of legal extensions of MNPuzzle self.\n\n @type self: MNPuzzle\n @rtype: list[MNPuzzle]\n\n >>> from_grid = ((\"1\", \"2\", \"3\"), (\"4\", \"5\", \"*\"))\n >>> to_grid = ((\"*\", \"2\", \"3\"), (\"1\", \"4\", \"5\"))\n >>> mn1 = MNPuzzle(from_grid, to_grid)\n >>> extensions = mn1.extensions()\n >>> from_grid = ((\"1\", \"2\", \"*\"), (\"4\", \"5\", \"3\"))\n >>> mn2 = MNPuzzle(from_grid, to_grid)\n >>> from_grid = ((\"1\", \"2\", \"3\"), (\"4\", \"*\", \"5\"))\n >>> mn3 = MNPuzzle(from_grid, to_grid)\n >>> comparisons = [mn2, mn3]\n >>> len(extensions) == len(comparisons)\n True\n >>> all([puzzle in extensions for puzzle in comparisons])\n True\n >>> all([puzzle in comparisons for puzzle in extensions])\n True\n \"\"\"\n # Convenient names.\n from_grid, to_grid = self.from_grid, self.to_grid\n if all([\"*\" not in row for row in from_grid]):\n # Return an empty list.\n return [_ for _ in []]\n else:\n legal_slides = []\n for row in range(len(self.n)):\n # If the given marker is the empty block, check for swaps.\n if \"*\" in from_grid[row]:\n col = from_grid[row].index(\"*\")\n # If this empty block has a block above it, swap the blocks.\n if (row - 1) >= 0:\n above_block = from_grid[row - 1][col]\n copy = [list(r) for r in from_grid]\n copy[row - 1][col] = \"*\"\n copy[row][col] = above_block\n tuples = [tuple(x) for x in copy]\n legal_slides.append(tuples)\n # Do the same as above, but with below the given block.\n if (row + 1) < len(from_grid):\n below_block = from_grid[row + 1][col]\n swap_down_grid = [row.copy() for row in from_grid]\n swap_down_grid[row][col] = \".\"\n swap_down_grid[row + 1][col] = \".\"\n swap_down_grid[row + 2][col] = \"*\"\n legal_slides.append(swap_down_grid)\n # Swap if this empty block has a block to its left.\n if (col - 1) >= 0:\n left_block = from_grid[row][col - 1]\n swap_left_grid = [[peg for peg in row] for row in from_grid]\n swap_left_grid[row][col] = \".\"\n swap_left_grid[row][col - 1] = \".\"\n swap_left_grid[row][col - 2] = \"*\"\n legal_slides.append(swap_left_grid)\n # Do the same as above, but with the right.\n if (col + 1) < len(from_grid[row]):\n right_block = from_grid[row][col + 1]\n\n swap_right_grid = [[peg for peg in row] for row in from_grid]\n swap_right_grid[row][col] = \".\"\n swap_right_grid[row][col + 1] = \".\"\n swap_right_grid[row][col + 2] = \"*\"\n legal_slides.append(swap_right_grid)\n return [MNPuzzle(new_grid, to_grid) for new_grid in legal_slides]\n # convenient names\n from_grid, to_grid = self.from_grid, self.to_grid\n for j in range(len(from_grid)):\n if \"*\" in from_grid[j]:\n # position of first empty position\n i = from_grid[j].index(\"*\")\n # J :need to check bounds and figure out how to switch the symbols\n allowed_symbols = (tuple(from_grid[j-1][i]), tuple(from_grid[j+1][i]),\n tuple(from_grid[j][i+1]), tuple(from_grid[j][i-1]))\n return ([MNPuzzle(from_grid[:j][:i] + d + from_grid[:j][i + 1:], to_grid)\n for d in allowed_symbols])\n\n def is_solved(self):\n \"\"\"\n Return True iff MNPuzzle self is solved.\n\n @type self: MNPuzzle\n @rtype: bool\n\n >>> grid1 = ((\"1\", \"2\", \"3\"), (\"4\", \"5\", \"*\"))\n >>> grid2 = ((\"*\", \"2\", \"3\"), (\"1\", \"4\", \"5\"))\n >>> mn1 = MNPuzzle(grid1, grid2)\n >>> mn1.is_solved()\n False\n >>> grid3 = ((\"1\", \"2\", \"3\"), (\"4\", \"5\", \"*\"))\n >>> mn3 = MNPuzzle(grid3, grid1)\n >>> mn3.is_solved()\n True\n\n \"\"\"\n\n return self.from_grid == self.to_grid\n\n\nif __name__ == \"__main__\":\n import doctest\n doctest.testmod()\n target_grid = ((\"1\", \"2\", \"3\"), (\"4\", \"5\", \"*\"))\n start_grid = ((\"*\", \"2\", \"3\"), (\"1\", \"4\", \"5\"))\n m = MNPuzzle(start_grid, target_grid)\n m.extensions()\n # from puzzle_tools import breadth_first_solve, depth_first_solve\n # from time import time\n # start = time()\n # solution = breadth_first_solve(MNPuzzle(start_grid, target_grid))\n # end = time()\n # print(\"BFS solved: \\n\\n{} \\n\\nin {} seconds\".format(\n # solution, end - start))\n # start = time()\n # solution = depth_first_solve((MNPuzzle(start_grid, target_grid)))\n # end = time()\n # print(\"DFS solved: \\n\\n{} \\n\\nin {} seconds\".format(\n # solution, end - start))\n","sub_path":"mn_puzzle.py","file_name":"mn_puzzle.py","file_ext":"py","file_size_in_byte":7556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"423272586","text":"#!/usr/bin/env python\n# -*- coding=utf-8 -*-\n__author__ = \"Chen\"\n\n\"\"\"\n資料來源：\nEstimation of obesity levels based on eating habits and physical condition Data Set\n(根據來自哥倫比亞、秘魯和墨西哥的個人的飲食習慣和身體狀況估計肥胖水平的數據集)\nhttps://archive.ics.uci.edu/ml/datasets/Estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition+\n簡單數據說明:\n16特徵值，1label(7類別)\n與飲食習慣有關的屬性包括：\n經常食用高熱量食物（FAVC），蔬菜食用頻率（FCVC），主餐次數（NCP），\n兩餐之間的食物消耗量（CAEC），每日水消耗量（CH20） ) 和酒精消耗量 (CALC)。\n與身體狀況相關的屬性是：\n卡路里消耗監測（SCC）、身體活動頻率（FAF）、使用技術設備的時間（TUE）、\n使用的交通工具（MTRANS）\n其他變量是：\n性別、年齡、身高和體重。\n最後，所有數據都被標記並創建了類變量 NObesity，其值是：\n體重不足、正常體重、超重級別 I、超重級別 II、肥胖類型 I、肥胖類型 II 和肥胖類型 III\n\"\"\"\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n##############################################################################\n# 資料讀取、匯入、整理\nimport pandas as pd\ndf = pd.read_excel('obesity_levels.xls',0)\n\"\"\"\nprint(\"試印資料前幾筆\")\nprint(df.head())\nprint(\"資料欄位名稱\")\nprint(df.columns)\nprint(\"資料筆數\")\nprint(df.index)\n\"\"\"\n\"\"\"\n# 註:文字分類 轉 數字分類 已在另一檔案轉好存入新excel表中\ncolumnsName=['Gender_Code', 'Age', 'Height', 'Weight',\n 'family_history_with_overweight_Code','FAVC_Code',\n 'FCVC', 'NCP','CAEC_Code', 'SMOKE_Code', 'CH2O',\n 'SCC_Code', 'FAF', 'TUE','CALC_Code', 'MTRANS_Code',\n 'NObeyesdad_Code']\n\"\"\"\n##############################################################################\n# 資料拆切\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n\nprint(\"資料拆切---\")\n# 決定X 分類 和Y分類 要用的欄位\n# 2D 特徵值 共14種(扣掉身高、體重)\ndfX=df[['Gender_Code', 'Age', #'Height', 'Weight',\n 'family_history_with_overweight_Code','FAVC_Code',\n 'FCVC', 'NCP','CAEC_Code', 'SMOKE_Code', 'CH2O',\n 'SCC_Code', 'FAF', 'TUE','CALC_Code', 'MTRANS_Code']]\n# 1D 標籤 肥胖水平\ndfY=df['NObeyesdad_Code']\n\nprint(\"資料前五項預覽\")\nprint(df.head(5))\n\nX=dfX.to_numpy()\nY=dfY.to_numpy()\n\n# 均一化 加上資料上下變化可能很大 讓資料更漂亮 最後預測值記得轉回來\ndfX = (dfX - dfX.min()) / (dfX.max() - dfX.min())\n\nt1=Y.shape[0]\nY=np.reshape(Y,(t1,)) # 2D 轉 1D\n\nX_train , X_test , y_train , y_test = train_test_split(X,Y,test_size=0.1)\nprint(\"資料拆切---OK\")\n##############################################################################\n\"\"\"\nimport matplotlib.pyplot as plt\n# seabron\n# 畫seaborn圖\ndf111 = df[['Gender_Code', 'Age', 'Height', 'Weight',\n 'family_history_with_overweight_Code','FAVC_Code',\n 'FCVC', 'NCP','CAEC_Code', 'SMOKE_Code', 'CH2O',\n 'SCC_Code', 'FAF', 'TUE','CALC_Code', 'MTRANS_Code','NObeyesdad_Code']]\ndf222=df111[0:]\nimport seaborn as sns\nsns.set_theme(style=\"ticks\")\nsns.pairplot(df222,hue='NObeyesdad_Code')\nplt.savefig('seaborn.png')\nplt.show()\n\"\"\"\n\"\"\"\nimport matplotlib.pyplot as plt\n# seabron\n# 畫seaborn圖\ndf111 = df[['Gender_Code', 'Age', 'Height', 'Weight',\n 'CH2O', 'FAF','NObeyesdad_Code']]\ndf222=df111[0:]\nimport seaborn as sns\n#sns.set_theme(style=\"ticks\")\nsns.pairplot(df222,hue='NObeyesdad_Code', kind=\"kde\")\nplt.savefig('seaborn2.png')\nplt.show()\n\"\"\"\n\"\"\"\nimport matplotlib.pyplot as plt\n# seabron\n# 畫seaborn圖\ndf111 = df[['Gender_Code', 'Age',\n 'CH2O', 'FAF','NObeyesdad_Code']]\ndf222=df111[0:]\nimport seaborn as sns\n#sns.set_theme(style=\"ticks\")\nsns.pairplot(df222,hue='NObeyesdad_Code', kind=\"kde\")\nplt.savefig('seaborn3.png')\nplt.show()\n\"\"\"\n# 資料量大，畫一次先儲存下來方便以後查看\n\n##############################################################################\n# 各種機器演算法\n\n# KNN\nprint(\"**********===KNN===**********\")\nfrom sklearn.neighbors import KNeighborsClassifier\n#\nknn = KNeighborsClassifier(7)\nknn.fit(X_train, y_train)\nprint('KNN準確率: %.2f' % knn.score(X_test, y_test))\n\n#print(\"預測值\",knn.predict(X_test))\n#print(\"實際\",y_test)\n\n# K-means\nprint(\"**********===K-means===**********\")\nfrom sklearn.cluster import KMeans\nfrom sklearn import metrics\n#\nkmeans = KMeans(n_clusters = 7)\nkmeans.fit(X_train)\ny_predict=kmeans.predict(X_train)\nscore = metrics.accuracy_score(y_test,kmeans.predict(X_test))\nprint('K-means準確率:{0:f}'.format(score))\n\n#print(\"預測 \",kmeans.predict(X_test))\n#print(\"實際 \",y_test)\n\nprint(\"**********===決策樹===**********\")\nfrom sklearn import tree\n#\nclf = tree.DecisionTreeClassifier()\nclf = clf.fit(X_train,y_train)\nprint('決策樹準確率: %.2f' % clf.score(X_test, y_test))\n\n# tree.export_graphviz(clf,out_file='tree.dot')\n#print(\"預測答案:\",clf.predict(X_test))\n#print(\"實際答案:\",y_test)\n\"\"\"\n#決策樹圖表\nimport matplotlib.pyplot as plt\ntree.plot_tree(clf)\nplt.show()\n\"\"\"\n\n# Regression 不合適做分類資料 略過\n\nprint(\"**********===隨機森林===**********\")\nfrom sklearn.ensemble import RandomForestClassifier\n#\nRForest = RandomForestClassifier(n_estimators=100, max_depth=10,random_state=2)\nRForest.fit(X_train, y_train)\nprint('隨機森林準確率: %.2f' % RForest.score(X_test, y_test))\n\n# tree.export_graphviz(clf,out_file='tree.dot')\n#print(\"預測答案:\",RForest.predict(X_test))\n#print(\"實際答案:\",y_test)\n\nprint(\"**********===貝氏分類器===**********\")\nfrom sklearn.naive_bayes import GaussianNB\n#\nmodel = GaussianNB()\nmodel.fit(X_train, y_train)\npredicted= model.predict(X_test)\nmodel.score(X_test,y_test)\nprint(\"貝氏分類器準確率\",model.score(X_test,y_test))\n\n#print(predicted)\n#print(model.predict_proba(X_test))\n#print(\"Number of mislabeled points out of a total %d points : %d\"\n# % (X_test.shape[0], (y_test != predicted).sum()))\n#print(model.class_prior_ )\n#print(model.get_params() )\n\nprint(\"**********===SVM--SVC==**********\")\nfrom sklearn import svm\n#\nregr = svm.SVC()\nregr.fit(X_train, y_train)\nprint('SVM--SVC準確率: %.2f' % regr.score(X_test, y_test))\n\n#print(\"預測答案:\",regr.predict(X_test))\n#print(\"實際答案:\",y_test)\n\nprint(\"**********===SVM--Non-linearSVC==**********\")\nfrom sklearn import svm\n#\nclf = svm.NuSVC(gamma='auto')\nclf.fit(X_train, y_train)\nprint('SVM--Non-linearSVC準確率: %.2f' % clf.score(X_test, y_test))\n\nprint(\"**********===SVM--linearSVC==**********\")\nfrom sklearn import svm\n#\nlin_clf = svm.LinearSVC()\nlin_clf.fit(X_train, y_train)\nprint('SVM--linearSVC準確率: %.2f' % lin_clf.score(X_test, y_test))\n\nprint(\"**********===SGD==**********\")\nfrom sklearn.linear_model import SGDClassifier\n#\nclf = SGDClassifier(loss=\"log\", penalty=\"l1\", max_iter=5)\nclf.fit(X_train, y_train)\nprint('SGD準確率: %.2f' % clf.score(X_test, y_test))\n\n#print(\"預測答案:\",clf.predict(X_test))\n#print(\"實際答案:\",y_test)\n\"\"\"\n#PCA 試寫 維度縮減\nprint(\"**********===PCA==**********\")\nfrom sklearn.decomposition import PCA\npca = PCA(n_components=5)\npca.fit(X)\n\nprint(pca.explained_variance_ratio_)\nprint(pca.singular_values_)\n\n\"\"\"\n","sub_path":"專10-tensorflow/肥胖水平/02-機器學習各種演算法-分類.py","file_name":"02-機器學習各種演算法-分類.py","file_ext":"py","file_size_in_byte":7366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"270964809","text":"# noqa: D100\n\nimport logging\nfrom typing import Tuple\n\nimport hail as hl\n\nlogging.basicConfig(format=\"%(levelname)s (%(name)s %(lineno)s): %(message)s\")\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\nSEXES = {\"Male\": \"Male\", \"Female\": \"Female\"}\n\n\ndef adjusted_sex_ploidy_expr(\n locus_expr: hl.expr.LocusExpression,\n gt_expr: hl.expr.CallExpression,\n karyotype_expr: hl.expr.StringExpression,\n xy_karyotype_str: str = \"XY\",\n xx_karyotype_str: str = \"XX\",\n) -> hl.expr.CallExpression:\n \"\"\"\n Create an entry expression to convert males to haploid on non-PAR X/Y and females to missing on Y.\n\n :param locus_expr: Locus\n :param gt_expr: Genotype\n :param karyotype_expr: Karyotype\n :param xy_karyotype_str: Male sex karyotype representation\n :param xx_karyotype_str: Female sex karyotype representation\n :return: Genotype adjusted for sex ploidy\n \"\"\"\n male = karyotype_expr == xy_karyotype_str\n female = karyotype_expr == xx_karyotype_str\n x_nonpar = locus_expr.in_x_nonpar()\n y_par = locus_expr.in_y_par()\n y_nonpar = locus_expr.in_y_nonpar()\n return (\n hl.case(missing_false=True)\n .when(female & (y_par | y_nonpar), hl.null(hl.tcall))\n .when(male & (x_nonpar | y_nonpar) & gt_expr.is_het(), hl.null(hl.tcall))\n .when(male & (x_nonpar | y_nonpar), hl.call(gt_expr[0], phased=False))\n .default(gt_expr)\n )\n\n\ndef adjust_sex_ploidy(\n mt: hl.MatrixTable,\n sex_expr: hl.expr.StringExpression,\n male_str: str = \"male\",\n female_str: str = \"female\",\n) -> hl.MatrixTable:\n \"\"\"\n Convert males to haploid on non-PAR X/Y, sets females to missing on Y.\n\n :param mt: Input MatrixTable\n :param sex_expr: Expression pointing to sex in MT (if not male_str or female_str, no change)\n :param male_str: String for males (default 'male')\n :param female_str: String for females (default 'female')\n :return: MatrixTable with fixed ploidy for sex chromosomes\n \"\"\"\n return mt.annotate_entries(\n GT=adjusted_sex_ploidy_expr(mt.locus, mt.GT, sex_expr, male_str, female_str)\n )\n\n\ndef get_ploidy_cutoffs(\n ht: hl.Table,\n f_stat_cutoff: float,\n normal_ploidy_cutoff: int = 5,\n aneuploidy_cutoff: int = 6,\n) -> Tuple[Tuple[float, Tuple[float, float], float], Tuple[Tuple[float, float], float]]:\n \"\"\"\n Get chromosome X and Y ploidy cutoffs for XY and XX samples.\n\n .. note::\n\n This assumes the input hail Table has the fields f_stat, chrX_ploidy, and chrY_ploidy.\n\n Return a tuple of sex chromosome ploidy cutoffs: ((x_ploidy_cutoffs), (y_ploidy_cutoffs)).\n x_ploidy_cutoffs: (upper cutoff for single X, (lower cutoff for double X, upper cutoff for double X), lower cutoff for triple X)\n y_ploidy_cutoffs: ((lower cutoff for single Y, upper cutoff for single Y), lower cutoff for double Y)\n\n Uses the normal_ploidy_cutoff parameter to determine the ploidy cutoffs for XX and XY karyotypes.\n Uses the aneuploidy_cutoff parameter to determine the cutoffs for sex aneuploidies.\n\n Note that f-stat is used only to split the samples into roughly 'XX' and 'XY' categories and is not used in the final karyotype annotation.\n\n :param ht: Table with f_stat and sex chromosome ploidies\n :param f_stat_cutoff: f-stat to roughly divide 'XX' from 'XY' samples. Assumes XX samples are below cutoff and XY are above cutoff.\n :param normal_ploidy_cutoff: Number of standard deviations to use when determining sex chromosome ploidy cutoffs for XX, XY karyotypes.\n :param aneuploidy_cutoff: Number of standard deviations to use when sex chromosome ploidy cutoffs for aneuploidies.\n :return: Tuple of ploidy cutoff tuples: ((x_ploidy_cutoffs), (y_ploidy_cutoffs))\n \"\"\"\n # Group sex chromosome ploidy table by f_stat cutoff and get mean/stdev for chrX/Y ploidies\n sex_stats = ht.aggregate(\n hl.agg.group_by(\n hl.cond(ht.f_stat < f_stat_cutoff, \"xx\", \"xy\"),\n hl.struct(x=hl.agg.stats(ht.chrX_ploidy), y=hl.agg.stats(ht.chrY_ploidy)),\n )\n )\n logger.info(\"XX stats: %s\", sex_stats[\"xx\"])\n logger.info(\"XY stats: %s\", sex_stats[\"xy\"])\n\n cutoffs = (\n (\n sex_stats[\"xy\"].x.mean + (normal_ploidy_cutoff * sex_stats[\"xy\"].x.stdev),\n (\n sex_stats[\"xx\"].x.mean\n - (normal_ploidy_cutoff * sex_stats[\"xx\"].x.stdev),\n sex_stats[\"xx\"].x.mean\n + (normal_ploidy_cutoff * sex_stats[\"xx\"].x.stdev),\n ),\n sex_stats[\"xx\"].x.mean + (aneuploidy_cutoff * sex_stats[\"xx\"].x.stdev),\n ),\n (\n (\n sex_stats[\"xx\"].y.mean\n + (normal_ploidy_cutoff * sex_stats[\"xx\"].y.stdev),\n sex_stats[\"xy\"].y.mean\n + (normal_ploidy_cutoff * sex_stats[\"xy\"].y.stdev),\n ),\n sex_stats[\"xy\"].y.mean + (aneuploidy_cutoff * sex_stats[\"xy\"].y.stdev),\n ),\n )\n\n logger.info(\"X ploidy cutoffs: %s\", cutoffs[0])\n logger.info(\"Y ploidy cutoffs: %s\", cutoffs[1])\n return cutoffs\n\n\ndef get_sex_expr(\n chr_x_ploidy: hl.expr.NumericExpression,\n chr_y_ploidy: hl.expr.NumericExpression,\n x_ploidy_cutoffs: Tuple[float, Tuple[float, float], float],\n y_ploidy_cutoffs: Tuple[Tuple[float, float], float],\n) -> hl.expr.StructExpression:\n \"\"\"\n Create a struct with X_karyotype, Y_karyotype, and sex_karyotype.\n\n Note that X0 is currently returned as 'X'.\n\n :param chr_x_ploidy: Chromosome X ploidy (or relative ploidy)\n :param chr_y_ploidy: Chromosome Y ploidy (or relative ploidy)\n :param x_ploidy_cutoffs: Tuple of X chromosome ploidy cutoffs: (upper cutoff for single X, (lower cutoff for double X, upper cutoff for double X), lower cutoff for triple X)\n :param y_ploidy_cutoffs: Tuple of Y chromosome ploidy cutoffs: ((lower cutoff for single Y, upper cutoff for single Y), lower cutoff for double Y)\n :return: Struct containing X_karyotype, Y_karyotype, and sex_karyotype\n \"\"\"\n sex_expr = hl.struct(\n X_karyotype=(\n hl.case()\n .when(chr_x_ploidy < x_ploidy_cutoffs[0], \"X\")\n .when(\n (\n (chr_x_ploidy > x_ploidy_cutoffs[1][0])\n & (chr_x_ploidy < x_ploidy_cutoffs[1][1])\n ),\n \"XX\",\n )\n .when((chr_x_ploidy >= x_ploidy_cutoffs[2]), \"XXX\")\n .default(\"ambiguous\")\n ),\n Y_karyotype=(\n hl.case()\n .when(chr_y_ploidy < y_ploidy_cutoffs[0][0], \"\")\n .when(\n (\n (chr_y_ploidy > y_ploidy_cutoffs[0][0])\n & (chr_y_ploidy < y_ploidy_cutoffs[0][1])\n ),\n \"Y\",\n )\n .when(chr_y_ploidy >= y_ploidy_cutoffs[1], \"YY\")\n .default(\"ambiguous\")\n ),\n )\n\n return sex_expr.annotate(\n sex_karyotype=hl.if_else(\n (sex_expr.X_karyotype == \"ambiguous\")\n | (sex_expr.Y_karyotype == \"ambiguous\"),\n \"ambiguous\",\n sex_expr.X_karyotype + sex_expr.Y_karyotype,\n )\n )\n","sub_path":"gnomad/sample_qc/sex.py","file_name":"sex.py","file_ext":"py","file_size_in_byte":7169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"416683424","text":"# -*- coding:utf-8 -*-\n\"\"\"\n__author__ = \"TomTao\"\n\n实现 strStr() 函数。\n\n给定一个 haystack 字符串和一个 needle 字符串，\n在 haystack 字符串中找出 needle 字符串出现的第一个位置 (从0开始)。\n如果不存在，则返回  -1。\n\n示例 1:\n\n输入: haystack = \"hello\", needle = \"ll\"\n输出: 2\n示例 2:\n\n输入: haystack = \"aaaaa\", needle = \"bba\"\n输出: -1\n\n当needle为空,返回0\n\"\"\"\n\n\n# 感觉就是子串查找吧.学习一手kmp\n# 先用个暴力的方法试试\n# 提交错误 未考虑到haystack为空\n# 提交成功 接下去参考KMP写法\n# class Solution:\n# def strStr(self, haystack: str, needle: str) -> int:\n# if not haystack and not needle:\n# return 0\n# n = len(needle)\n# for i in range(len(haystack)):\n# if haystack[i:i + n] == needle:\n# return i\n# return -1\n\n# 学习kmp\n# 详细可以看这两个--具体实现采用的是<<数据结构与算法-python语言描述>>中的内容\n# 1,https://www.cnblogs.com/dusf/p/kmp.html\n# 2,https://blog.csdn.net/sb985/article/details/79735488\ndef normal_gen_next1(p):\n '''正常版本的next数组生成思路'''\n j, k, n = 0, -1, len(p)\n pnext = [-1] * n\n while j < n - 1:\n if k == -1 or p[j] == p[k]:\n j, k = j + 1, k + 1\n pnext[j] = k\n else:\n k = pnext[k]\n\n return pnext\n\n\ndef improve_gen_next(p):\n j, k, n = 0, -1, len(p)\n pnext = [-1] * n\n while j < n - 1:\n if k == -1 or p[j] == p[k]:\n k, j = k + 1, j + 1\n if p[j] == p[k]:\n pnext[j] = pnext[k]\n else:\n pnext[j] = k\n else:\n k = pnext[k]\n\n return pnext\n\n\ndef strStr(haystack: str, needle: str) -> int:\n # 两个指针位置,一个母串,一个子串\n i, j = 0, 0\n n, m = len(haystack), len(needle)\n # 计算needle的next数组\n pnext = normal_gen_next1(needle)\n while i < n and j < m:\n if j == -1 or haystack[i] == needle[j]:\n i, j = i + 1, j + 1\n else:\n j = pnext[j]\n # 当j指向与结尾,代表匹配结束\n if j == m:\n # 母串的位置是i,减去子串的长度j,这样就是子串在母串中起始的位置\n return i - j\n return -1\n\n\nif __name__ == '__main__':\n # s = Solution()\n # x = strStr(haystack=\"hello\", needle=\"ll\")\n x = normal_gen_next1('abbcabcaabbcaa')\n # y = improve_gen_next('abbcabcaabbcaa')\n print(x)","sub_path":"leetcode/easy/10-strStr.py","file_name":"10-strStr.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"178251934","text":"# vim: tabstop=4 shiftwidth=4 softtabstop=4\n#\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\nfrom dragon.rpc import api\nfrom dragon.openstack.common import log as logging\n\nlogger = logging.getLogger(__name__)\n\n\ndef extract_args(params):\n '''\n Extract any arguments passed as parameters through the API and return them\n as a dictionary. This allows us to filter the passed args and do type\n conversion where appropriate\n '''\n kwargs = {}\n try:\n timeout_mins = int(params.get(api.PARAM_TIMEOUT, 0))\n except (ValueError, TypeError):\n logger.exception('create timeout conversion')\n else:\n if timeout_mins > 0:\n kwargs[api.PARAM_TIMEOUT] = timeout_mins\n\n if api.PARAM_DISABLE_ROLLBACK in params:\n disable_rollback = params.get(api.PARAM_DISABLE_ROLLBACK)\n if str(disable_rollback).lower() == 'true':\n kwargs[api.PARAM_DISABLE_ROLLBACK] = True\n elif str(disable_rollback).lower() == 'false':\n kwargs[api.PARAM_DISABLE_ROLLBACK] = False\n else:\n raise ValueError(\"Unexpected value for parameter %s : %s\" %\n (api.PARAM_DISABLE_ROLLBACK, disable_rollback))\n return kwargs\n","sub_path":"dragon/engine/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"481283118","text":"import re\nfrom datetime import date, timedelta\nfrom unittest import TestCase\n\nfrom .yahoo import YahooFinanceInterface\n\n\nclass YahooFinanceInterfaceTest(TestCase):\n _int_pattern = re.compile(\"\\d+\")\n _float_pattern = re.compile(\"\\d+\\.\\d+\")\n\n def test_with_all_filters(self):\n day = date(2015,9,16)\n i = YahooFinanceInterface()\n q = i.query([\"YHOO\"], day, day)\n d = q.to_dict()\n self.assertEqual(d[\"quote\"][\"Symbol\"], \"YHOO\")\n self.assertIsNotNone(self._int_pattern.match(d[\"quote\"][\"Volume\"]))\n for k in [\"High\", \"Low\", \"Open\", \"Close\"]:\n self.assertIsNotNone(self._int_pattern.match(d[\"quote\"].get(k)))\n self.assertEqual(d[\"quote\"][\"Date\"], day.strftime(YahooFinanceInterface.date_format))\n\n def test_default_parameters(self):\n i = YahooFinanceInterface()\n # No start date specified, defaults to 1-1-2000\n q1 = i.query([\"YHOO\"], end_date=date(2000,1,5))\n self.assertEqual(len(q1), 3) # 3 days with open NYSE\n\n # No end date specified, defaults to tomorrow\n day = date.today()-timedelta(days=1)\n q2 = i.query([\"YHOO\"], start_date=day)\n d = q2.to_dict()\n self.assertEqual(d[\"quote\"][\"Date\"], day.strftime(YahooFinanceInterface.date_format))\n\n\n def test_missing_symbol(self):\n expected_error_message = re.compile(\".+?status\\s.*?400\")\n i = YahooFinanceInterface()\n with self.assertRaises(RuntimeError) as context:\n i.query()\n self.assertIsNotNone(expected_error_message.match(context.exception.message),\n \"Got unexpected error message: %s\" % context.exception.message)\n","sub_path":"fbot/interfaces/test_yahoo.py","file_name":"test_yahoo.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"335270501","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2018/11/28 16:22\n# @Author : ZDK\n# @Email : zhengdengke@wanshare.com\n\n# 新建文章模型——获取文章模型列表——获取文章模型名称列表——获取文章模型详情\nimport pytest\nimport random\n\nfrom common.utils import get_random_name\nfrom swagger_client.staff.api.content_management_api import ContentManagementApi\nfrom common.account_sign import get_admin_token\nfrom common.account_sign import set_login_status\n\n\nclass TestModelsCase1(object):\n\n @pytest.mark.order1\n def test_models_case1(self):\n # 后台登录\n content_api = ContentManagementApi()\n staff_token = get_admin_token()\n set_login_status(content_api, staff_token)\n # 新建文章模型\n payload = {\n 'identification': get_random_name(2, 50),\n 'name': get_random_name(2, 50),\n 'status': True,\n 'order': random.randint(100000, 999999),\n 'type': 'article' # article文章，kinmall金猫\n }\n content_api.models_post(body=payload)\n # 获取文章模型列表\n res = content_api.models_get(type='article')\n for item in res.items:\n if item.name == payload['name']:\n assert item.identification == payload['identification']\n assert item.id\n # 获取文章模型名称列表\n res = content_api.models_names_get(type='article')\n id_ = ''\n for item in res.items:\n if item.name == payload['name']:\n assert item.id\n id_ = item.id\n # 获取文章模型详情\n res = content_api.models_id_get(id=id_)\n assert res.name == payload['name']\n assert res.status == payload['status']\n assert res.order == payload['order']\n assert res.type == payload['type']\n assert res.identification == payload['identification']\n","sub_path":"test/staff/scenario/test_content_management/test_models_case1.py","file_name":"test_models_case1.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"148705936","text":"# Copyright 2019 Nokia\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport logging\nimport copy\nimport json\n\nfrom cmframework.apis import cmerror\n\n\nclass CMCSN(object):\n CONFIG_NAME = 'cloud.cmframework'\n\n def __init__(self, backend_handler):\n self.backend_handler = backend_handler\n self.config = {'csn': {'global': 0, 'nodes': {}}}\n self._get_config()\n logging.info('Current csn is %d', self.get())\n\n def _update_csn(self, node_name=None):\n new_config = copy.deepcopy(self.config)\n\n if not node_name:\n new_config['csn']['global'] += 1\n logging.info('Updating csn to %d', new_config['csn']['global'])\n else:\n new_config['csn']['nodes'][node_name] = new_config['csn']['global']\n logging.info('Updating csn for node %s to %s', node_name, new_config['csn']['global'])\n\n self.backend_handler.set_property(CMCSN.CONFIG_NAME, json.dumps(new_config))\n self.config = new_config\n\n def _get_config(self):\n try:\n self.config = json.loads(self.backend_handler.get_property(CMCSN.CONFIG_NAME))\n except cmerror.CMError as exp:\n logging.info('Context id not defined')\n except Exception as exp: # pylint: disable=broad-except\n logging.warning('Got error: %s', exp)\n\n def increment(self):\n self._update_csn()\n\n def get(self):\n return self.config['csn']['global']\n\n def get_node_csn(self, node_name):\n return self.config['csn']['nodes'].get(node_name, 1)\n\n def sync_node_csn(self, node_name):\n self._update_csn(node_name)\n","sub_path":"cmframework/src/cmframework/server/cmcsn.py","file_name":"cmcsn.py","file_ext":"py","file_size_in_byte":2118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"192913886","text":"# -*- mode: python ; coding: utf-8 -*-\nfrom PyInstaller.utils.hooks import collect_submodules\nimport toml\n\nblock_cipher = None\n\nall_fastflix_files = []\n\nfor root, dirs, files in os.walk('fastflix'):\n\tif \"__pycache__\" in root:\n\t continue\n\tfor file in files:\n\t\tall_fastflix_files.append((os.path.join(root,file), root))\n\nall_imports = collect_submodules('pydantic') + ['dataclasses', 'colorsys', 'typing_extensions', 'box']\nwith open(\"pyproject.toml\") as f:\n for line in toml.load(f)[\"project\"][\"dependencies\"]:\n package = line.split(\"[\")[0].split(\"=\")[0].split(\">\")[0].split(\"<\")[0].replace('\"', '').replace(\"'\",'').rstrip(\"~\").strip()\n if package not in (\"pyinstaller\"):\n all_imports.append(package)\n\na = Analysis(['fastflix\\\\__main__.py'],\n binaries=[],\n datas=[('iso-639-3.tab', 'iso639'), ('iso-639-3.json', 'iso639'), ('CHANGES', 'fastflix\\\\.'), ('docs\\\\build-licenses.txt', 'docs')] + all_fastflix_files,\n hiddenimports=all_imports,\n hookspath=[],\n runtime_hooks=[],\n excludes=[\"pyinstaller\", \"pypiwin32\"],\n win_no_prefer_redirects=False,\n win_private_assemblies=False,\n cipher=block_cipher,\n noarchive=False)\npyz = PYZ(a.pure, a.zipped_data,\n cipher=block_cipher)\nexe = EXE(pyz,\n a.scripts,\n [],\n exclude_binaries=True,\n name='FastFlix',\n debug=False,\n bootloader_ignore_signals=False,\n strip=False,\n upx=False,\n console=True,\n icon='fastflix\\\\data\\\\icon.ico')\ncoll = COLLECT(exe,\n a.binaries,\n a.zipfiles,\n a.datas,\n strip=False,\n upx=False,\n upx_exclude=[],\n name='FastFlix')\n","sub_path":"FastFlix_Windows_Installer.spec","file_name":"FastFlix_Windows_Installer.spec","file_ext":"spec","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"232453702","text":"#!/usr/bin/env python\n\nfrom distutils.core import setup\nimport os\n\nfrom kepub import get_version\n \n# Compile the list of packages available, because distutils doesn't have\n# an easy way to do this.\npackages, data_files = [], []\nroot_dir = os.path.dirname(__file__)\nif root_dir:\n os.chdir(root_dir)\n\nfor dirpath, dirnames, filenames in os.walk('kepub'):\n # Ignore dirnames that start with '.'\n for i, dirname in enumerate(dirnames):\n if dirname.startswith('.'): del dirnames[i]\n if '__init__.py' in filenames:\n pkg = dirpath.replace(os.path.sep, '.')\n if os.path.altsep:\n pkg = pkg.replace(os.path.altsep, '.')\n packages.append(pkg)\n elif filenames:\n prefix = dirpath[6:] # Strip \"kepub/\" or \"kepub\\\"\n for f in filenames:\n data_files.append(os.path.join(prefix, f))\n\nsetup(\n name='kepub-toolbox',\n version=get_version().replace(' ', '-'),\n author='Pietro Spagnulo , Stefano Quaranta ',\n author_email='pspagnulo@gmail.com, steppo40@gmail.com',\n description='Utilities for ebook format conversion',\n long_description=open('README.txt').read(),\n url='http://pypi.python.org/pypi/kepub-toolbox/',\n package_dir={'kepub': 'kepub'},\n packages=packages,\n package_data={'kepub': data_files},\n scripts=['bin/odt2docbook.py'],\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Console',\n 'Intended Audience :: End Users/Desktop',\n 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',\n 'Operating System :: MacOS :: MacOS X',\n 'Operating System :: Microsoft :: Windows',\n 'Operating System :: POSIX',\n 'Programming Language :: Python',\n 'Programming Language :: Java',\n 'Topic :: Text Processing'\n ],\n license='LICENSE.txt',\n requires=['python (>= 2.6)'] \n)\n","sub_path":"pypi_install_script/kepub-toolbox-0.4b0.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"649913313","text":"#!/usr/bin/env python3\n\n\n\"\"\"\nA helpful script to setup a Screenplay Pattern scaffold. You can run it\nin the directory where you want your suite to be created like so:\n\n $ ./screenpy-quickstart.py\n\nThis script will create the following structure in the current working\ndirectory:\n\n__init__.py\nfeatures/\n - __init__.py\n - test_example.py\nquestions/\n - __init__.py\n - welcome_message.py\ntasks/\n - __init__.py\n - start.py\nuser_interface/\n - __init__.py\n - home_page.py\n\"\"\"\n\n\nimport os\nimport sys\nfrom pathlib import Path\n\n\ndef create_module(name: str, filename: str, contents: str) -> None:\n \"\"\"\n Create one of the many modules for ScreenPy.\n\n Args:\n name: the name of the module, e.g. \"questions\".\n filename: the name of the file to create in the module.\n contents: what to write in the file.\n \"\"\"\n print(f\"Creating {name} directory and files...\")\n try:\n os.mkdir(name)\n Path(f\"{name}/__init__.py\").touch()\n with open(f\"{name}/{filename}\", \"w\") as modulefile:\n modulefile.write(contents)\n except FileExistsError:\n print(f\"> Directory {name} already exists! Skipping.\")\n\n\nprint(\n f\"\"\"\n This script will set up the scaffolding for your new ScreenPy project.\n It will create several folders and files in the current directory:\n {os.getcwd()}\n For more information, see\n https://screenpy-docs.readthedocs.io/en/latest/filehierarchy.html\n\"\"\"\n)\nresponse = input(\"Would you like to continue? [Y/n]: \")\nif response and response[0].lower() != \"y\":\n print(\"OK! Goodbye!\")\n sys.exit(0)\n\nprint(\"OK! This should only take a second.\\n\")\n\n\nPath(\"__init__.py\").touch()\n\n\ncreate_module(\n \"user_interface\",\n \"home_page.py\",\n '''\"\"\"\nLocators and the URL for ScreenPy's ReadTheDocs homepage.\n\"\"\"\n\nfrom screenpy import Target\n\nURL = \"https://screenpy-docs.readthedocs.io/en/latest/\"\n\nWELCOME_MESSAGE = Target.the(\"welcome message\").located_by(\n \"#welcome-to-screenpy-s-documentation>h1\"\n)\n\n''',\n)\n\n\ncreate_module(\n \"tasks\",\n \"start.py\",\n '''\"\"\"\nA very simple Task to give you an idea of what a Task might be or do. You\ncan give this task to your actor like so:\n\n the_actor.attempts_to(Start.on_the_homepage())\n\"\"\"\n\n\nfrom screenpy import AnActor\nfrom screenpy.actions import Open\n\nfrom ..user_interface import home_page\n\n\nclass Start:\n \"\"\"\n A very simple task that starts on the ScreenPy docs homepage.\n \"\"\"\n\n @staticmethod\n def on_the_homepage() -> \"Start\":\n \"\"\"Sets the URL to be the homepage.\"\"\"\n return Start(home_page.URL)\n\n def perform_as(self, the_actor: AnActor) -> None:\n \"\"\"\n Asks the actor to perform this task. Will raise an\n UnableToPerformException if the actor does not possess the ability\n to BrowseTheWeb.\n\n Args:\n the_actor: the actor who will perform this task.\n\n Raises:\n UnableToPerformException: if the actor does not possess the\n ability to BrowseTheWeb.\n \"\"\"\n the_actor.attempts_to(Open.their_browser_on(self.location))\n\n def __init__(self, location: str) -> None:\n self.location = location\n\n''',\n)\n\n\ncreate_module(\n \"questions\",\n \"welcome_message.py\",\n '''\"\"\"\nA very simple Question to give you an idea of what a question might be or\ndo. You can give this Question to your actor paired with a Resolution\nlike so:\n\n the_actor.should_see_the(\n (WelcomeMessage(), ContainsTheText(\"Screenpy\"))\n )\n\"\"\"\n\n\nfrom screenpy import AnActor\nfrom screenpy.questions import Text\nfrom screenpy.pacing import beat\n\nfrom ..user_interface.home_page import WELCOME_MESSAGE\n\n\nclass WelcomeMessage:\n \"\"\"\n Gets the text of the welcome message.\n \"\"\"\n\n @beat(\"{0} checks the welcome message...\")\n def answered_by(self, the_actor: AnActor) -> None:\n return Text.of_the(WELCOME_MESSAGE).answered_by(the_actor)\n\n''',\n)\n\n\ncreate_module(\n \"features\",\n \"test_example.py\",\n '''\"\"\"\nA very simple example test that asserts the welcome message on ScreenPy's\nReadTheDocs homepage is as we expect. This test module follows the\nstructure of inheriting from unittest.TestCase.\n\"\"\"\n\nfrom unittest import TestCase\n\nfrom selenium.webdriver import Firefox\n\nfrom screenpy import AnActor, given, when, then\nfrom screenpy.abilities import BrowseTheWeb\nfrom screenpy.resolutions import ContainsTheText\n\nfrom ..questions.welcome_message import WelcomeMessage\nfrom ..tasks.start import Start\nfrom ..user_interface import home_page\n\n\nclass TestExample(TestCase):\n \"\"\"\n A simple example to show how a test is put together.\n \"\"\"\n\n def setUp(self):\n self.actor = AnActor.named(\"Name me!\").who_can(BrowseTheWeb.using(Firefox()))\n\n def test_open_homepage(self):\n \"\"\"Tests that the user can visit the homepage. Extend me!\"\"\"\n Actor = self.actor\n\n given(Actor).was_able_to(Start.on_the_homepage())\n # ... fill in your test steps here!\n then(Actor).should_see_the(\n (WelcomeMessage(), ContainsTheText(\"Welcome to ScreenPy’s documentation!\"))\n )\n\n def tearDown(self):\n self.actor.exit_stage_right()\n\n''',\n)\n\n\nprint(\n \"\"\"\nDone! 🎉\nNext steps:\n - Try a test run with `python3 -m pytest features/`\n - Remove or modify any of the files created by this script, or add new ones!\n - Continue maintaining and extending your Screenplay Pattern test suite :)\n\"\"\"\n)\n","sub_path":"bin/screenpy-quickstart.py","file_name":"screenpy-quickstart.py","file_ext":"py","file_size_in_byte":5422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"236413149","text":"import qisys.parsers\nimport qilinguist.builder\n\nfrom qilinguist.worktree import LinguistWorkTree, new_linguist_project\nimport qilinguist.builder\n\ndef get_linguist_worktree(args):\n worktree = qisys.parsers.get_worktree(args)\n return LinguistWorkTree(worktree)\n\ndef get_linguist_projects(worktree, args, default_all=False):\n parser = LinguistProjectParser(worktree)\n return parser.parse_args(args, default_all=default_all)\n\ndef get_linguist_builder(args, with_projects=True):\n worktree = get_linguist_worktree(args)\n builder = qilinguist.builder.QiLinguistBuilder(worktree)\n if with_projects:\n projects = get_linguist_projects(worktree, args)\n builder.projects = projects\n return builder\n\n\n\n\n##\n# Implementation details\n\nclass LinguistProjectParser(qisys.parsers.AbstractProjectParser):\n \"\"\" Implements AbstractProjectParser for a LinguistWorkTree \"\"\"\n\n def __init__(self, linguist_worktree):\n self.linguist_worktree = linguist_worktree\n self.linguist_projects = linguist_worktree.linguist_projects\n\n def all_projects(self, args):\n return self.linguist_projects\n\n def parse_no_project(self, args):\n \"\"\" Try to find the closest worktree project that\n matches the current directory\n\n \"\"\"\n worktree = self.linguist_worktree.worktree\n parser = qisys.parsers.WorkTreeProjectParser(worktree)\n worktree_projects = parser.parse_no_project(args)\n if not worktree_projects:\n raise CouldNotGuessProjectName()\n\n # WorkTreeProjectParser returns None or a list of one element\n worktree_project = worktree_projects[0]\n linguist_project = new_linguist_project(self.linguist_worktree,\n worktree_project)\n if not linguist_project:\n raise CouldNotGuessProjectName()\n\n return self.parse_one_project(args, linguist_project.name)\n\n def parse_one_project(self, args, project_arg):\n \"\"\" Get one linguist project given its name \"\"\"\n\n project = self.linguist_worktree.get_linguist_project(project_arg,\n raises=True)\n return [project]\n\nclass CouldNotGuessProjectName(Exception):\n def __str__(self):\n return \"\"\"\nCould not guess linguist project name from current working directory\nPlease go inside a translatable project, or specify the project name\non the command line\n\"\"\"\n\n","sub_path":"python/qilinguist/parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":2464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"245544696","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 23 18:01:42 2016\n\n@author: Maxim-home\n\"\"\"\n\nimport MyTools.misc_tools as mt\nimport numpy as np\nimport MyTools.cfd.airfoil_grid_sensitivity as cfd\nimport MyTools.airfoil\nfrom scipy.optimize import fmin_slsqp\n\n\nclass RVFMrunHistory:\n def __init__(self,path=None):\n if path==None:\n self.fileOut = 'RVFMiter_history_20160324.txt'\n else:\n self.fileOut = path\n self.nvar = 7\n self._write_header()\n \n def _write_header(self):\n fid = open(self.fileOut,'wt')\n fid.write('fsc\\tfh\\tPsc\\tPh\\tCLsc\\tCDsc\\tCMsc\\tCLh\\tCDh\\tCMh\\ttc\\t')\n fid.write('rho\\tdelta\\terr\\tCFDeval')\n for i in range(self.nvar):\n fid.write('\\tX%d'%(i+1))\n fid.write('\\n')\n fid.close()\n \n def add_line(self,x,fsc,fh,Psc,Ph,dataSc,dataHigh,tc,rho,delta,err,CFDeval):\n fid = open(self.fileOut,'a')\n for xx in x:\n fid.write('\\t%.8f'%xx)\n fid.write('\\t%.8f'%fsc)\n fid.write('\\t%.8f'%fh)\n fid.write('\\t%.8f'%Psc)\n fid.write('\\t%.8f'%Ph)\n fid.write('\\t%.8f'%dataSc[0])\n fid.write('\\t%.8f'%dataSc[1])\n fid.write('\\t%.8f'%dataSc[2])\n fid.write('\\t%.8f'%dataHigh[0,0])\n fid.write('\\t%.8f'%dataHigh[0,1])\n fid.write('\\t%.8f'%dataHigh[0,2])\n fid.write('\\t%.8f'%tc)\n fid.write('\\t%.8f'%rho)\n fid.write('\\t%.8f'%delta)\n fid.write('\\t%.8f'%err)\n fid.write('\\t%d'%CFDeval)\n fid.close()\n\n\n\nclass ObjectiveFunction:\n def __init__(self,lb,ub,x0,dataPath):\n # initial data\n self.dataPath = dataPath\n # cfd data\n self.gridScaleLow = 1.0\n self.gridScaleHigh = 4.0\n self.yplus = 1.0\n self.zTE = 0.005\n self.af = None\n self.alt = 1e4\n self.fc = cfd.flight_conditions.FlightConditions(0,self.alt)\n self.runCountHigh = 0\n self.runCountLow = 0\n self.runCountTotal = 0\n # optimization req\n self.CLmin = 0.40\n self.tcMin = 0.04\n self.CMmax = -0.15\n self.targetCD = 0.03\n self.targetdCDdM = 0.2134\n self.targetdCDda = -0.007\n self.targetVarianceSquared = 1e-4\n self.meanMach = 0.85\n self.meanAlpha = 0.0\n self.varMach = self.meanMach * 0.05\n self.varAlpha = 0.5\n self.mu = 100.0 # penalty\n # scaling\n self.dx = 1e-3 # finite difference used for gradients\n MachMin = self.meanMach - self.varMach/2.0\n MachMax = self.meanMach + self.varMach/2.0\n alphaMin = self.meanAlpha - self.varAlpha/2.0\n alphaMax = self.meanAlpha + self.varAlpha/2.0\n self.lb = np.hstack([MachMin, alphaMin, lb])\n self.ub = np.hstack([MachMax, alphaMax, ub])\n self.x0 = x0\n self.xNormalization = mt.Normalization(self.lb,self.ub)\n self.create_scaling_models()\n \n def create_scaling_models(self):\n self.rawData = mt.read_tabulated_data(self.dataPath,False,1)\n self.gammaCL = self.rawData[:,9] - self.rawData[:,12]\n self.gammaCD = self.rawData[:,10] - self.rawData[:,13]\n self.gammaCM = self.rawData[:,11] - self.rawData[:,14]\n self.xNorm = self.xNormalization.normalize(self.rawData[:,:9])\n self.scalingCL = mt.RbfMod(self.xNorm,self.gammaCL)\n self.scalingCD = mt.RbfMod(self.xNorm,self.gammaCD)\n self.scalingCM = mt.RbfMod(self.xNorm,self.gammaCM)\n \n def _get_x(self,x):\n # in: design var normalized\n # out: mach, alpha, dvar\n return np.hstack([0.0, 0.0, x])\n\n def write_history_header(self):\n fid = open(self.histFile,'wt')\n fid.write('HighFi\\tMach\\talpha\\t')\n for i in range(len(self.lb)-2):\n fid.write('A%d\\t'%(i+1))\n fid.write('cl\\tcd\\tcm\\tLD\\tthickness\\n')\n fid.close()\n \n def write_data_cfd(self,M,a,x,cl,cd,cm,tc,isLowFidelity):\n fid = open(self.histFile,'at')\n if isLowFidelity:\n fid.write('0\\t')\n else:\n fid.write('1\\t')\n fid.write('%.6f\\t%.6f\\t'%(M,a))\n for _x in x:\n fid.write('%.8f\\t'%_x)\n fid.write('%.6f\\t%.6f\\t%.6f\\t%.6f\\t%.6f\\n'%(cl,cd,cm,cl/cd,tc))\n fid.close()\n\n def run_cfd(self,x,Mach,alpha,lowFidelity=True):\n x = self._get_x(x)\n x = self.xNormalization.denormalize(x)\n x = x[2:]\n self.af = MyTools.airfoil.airfoil.cst_x(x,101)\n self.af.set_trailing_edge(self.zTE)\n self.fc.set_speed(Mach)\n if lowFidelity:\n self.runCountLow += 1\n scale = self.gridScaleLow\n else:\n self.runCountHigh += 1\n scale = self.gridScaleHigh\n self.runCountTotal += 1\n tc = self.af.thickness\n cl,cd,cm,gs = cfd.run_analysis(self.af,self.fc,alpha,self.yplus,scale,\n self.runCountTotal)\n self.write_data_cfd(Mach,alpha,x,cl,cd,cm,tc,lowFidelity)\n return cl, cd, cm\n \n def objective_function(self,x,lowFidelity=True,fullOutput=False,scaled=True):\n data = self.get_gradient_cfd(x,lowFidelity)\n cdMean = data[0,1]\n dCDdM = data[3,1]\n dCDda = data[4,1]\n if scaled:\n dCDdMsc, dCDdaSc = self.get_gradient_scaled(x)\n xx = self._get_x(x)\n cdScaled = self.scalingCD(xx)\n cdMean = cdMean + cdScaled\n dCDdM = dCDdM + dCDdMsc\n dCDda = dCDda + dCDda\n dataScaled = np.zeros(3)\n dataScaled[0] = data[0,0] + self.scalingCL(xx)\n dataScaled[1] = cdMean\n dataScaled[2] = data[0,2] + self.scalingCM(xx)\n varMach = dCDdM*self.varMach\n varAlpha = dCDda*self.varAlpha\n f = cdMean/self.targetCD\n f += (varMach**2.0 + varAlpha**2.0)/self.targetVarianceSquared\n if fullOutput:\n g1 = min([0.0, data[0,0]-self.CLmin])\n g2 = min([0.0, self.CMmax-data[0,2]])\n g3 = min([0.0, self.af.thickness - self.tcMin])\n P = f - self.mu*(g1+g2+g3)\n return f, P, cdMean, varMach, varAlpha, data, dataScaled\n else:\n return f\n \n def constraints(self,x,lowFidelity=True,scaled=True):\n cl, cd, cm = self.run_cfd(x,self.meanMach, self.meanAlpha, lowFidelity)\n if scaled:\n x = self._get_x(x)\n clSc = self.scalingCL(x)\n cmSc = self.scalingCM(x)\n cl = cl + clSc\n cm = cm + cmSc\n g1 = cl - self.CLmin\n g2 = self.CMmax - cm\n g3 = self.af.thickness - self.tcMin\n return np.array([g1,g2,g3])\n \n def get_gradient_scaled(self,x):\n x = self._get_x(x)\n xreal0 = self.xNormalization.denormalize(x)\n cd0 = self.scalingCD(x)\n x[0] = self.dx\n xreal1 = self.xNormalization.denormalize(x)\n cd1 = self.scalingCD(x)\n x[0] = 0.0\n x[1] = self.dx\n xreal2 = self.xNormalization.denormalize(x)\n cd2 = self.scalingCD(x)\n dM = xreal1[0] - xreal0[0]\n da = xreal2[1] - xreal0[1]\n dCDdM = (cd1 - cd0)/dM\n dCDda = (cd2 - cd0)/da\n return dCDdM, dCDda\n \n def get_gradient_cfd(self,x,lowFidelity=True):\n x = self._get_x(x)\n xreal0 = self.xNormalization.denormalize(x)\n x[0] = self.dx\n xreal1 = self.xNormalization.denormalize(x)\n x[0] = 0.0\n x[1] = self.dx\n xreal2 = self.xNormalization.denormalize(x)\n cl0, cd0, cm0 = self.run_cfd(x[2:],xreal0[0],xreal0[1],lowFidelity)\n cl1, cd1, cm1 = self.run_cfd(x[2:],xreal1[0],xreal1[1],lowFidelity)\n cl2, cd2, cm2 = self.run_cfd(x[2:],xreal2[0],xreal2[1],lowFidelity)\n dM = xreal1[0] - xreal0[0]\n da = xreal2[1] - xreal0[1]\n data = np.zeros([3,6])\n data[0,0], data[0,1], data[0,2] = cl0, cd0, cm0\n data[1,0], data[1,1], data[1,2] = cl1, cd1, cm1\n data[2,0], data[2,1], data[2,2] = cl2, cd2, cm2\n data[3,0] = (cl1 - cl0)/dM\n data[4,0] = (cl2 - cl0)/da\n data[3,1] = (cd1 - cd0)/dM\n data[4,1] = (cd2 - cd0)/da\n data[3,2] = (cm1 - cm0)/dM\n data[4,2] = (cm2 - cm0)/da\n data[5,0] = xreal0[0]\n data[5,1] = xreal0[1]\n return data\n\n\ndef append_to_DOE_file(x,dataHigh, dataLow, tc):\n fid = open('DOE_LHS9_50_CFD.txt','at')\n fid.write('%.6f\\t%.6f\\t'%(dataHigh[0,6],dataHigh[1,6]))\n for _x in x:\n fid.write('%.8f\\t'%_x)\n fid.write('%.6f\\t%.6f\\t%.6f\\t%.6f\\t%.6f\\t%.6f\\t%.4f\\n'%(dataHigh[0,0],dataHigh[1,0],dataHigh[2,0],dataLow[0,0],dataLow[1,0],dataLow[2,0],tc))\n\n\n\ndef run_main():\n x0 = np.array([0.06343078, 0.1400427, 0.1070076, 0.13289899, -0.01762751, \n -0.08041535, 0.02156679])\n xl = np.array([0.04343078, 0.0900427, 0.0570076, 0.08289899, -0.06762751, \n -0.13041535, -0.02843321])\n xu = np.array([0.08343078, 0.1900427, 0.1570076, 0.18289899, 0.03237249, \n -0.03041535, 0.07156679])\n cfdDataPath = r'DOE_LHS9_50_CFD.txt'\n history = RVFMrunHistory()\n tol = 1.0e-6\n err = 100.0\n itrMax = 10\n c1 = 0.5\n c2 = 2.0\n eta1 = 0.25\n eta2 = 0.75\n eta3 = 1.25\n delta = 1.0\n rho = 0.0\n\n obj = ObjectiveFunction(xl,xu,x0,cfdDataPath)\n x0norm = obj.xNormalization.normalize(np.hstack([0.,0,x0]))\n x0norm = x0norm[2:]\n \n Phist = np.zeros(itrMax)\n \n fsc, Psc, cdMeanSc, varMachSc, varAlphaSc, dataL, dataSc = obj.objective_function(x0norm,True, True, True)\n fh, Ph, cdMeanH, varMachH, varAlphaH, dataH,_skip = obj.objective_function(x0norm,False, True, False)\n Phist[0] = Ph\n \n append_to_DOE_file(x0,dataH, dataL, obj.af.thickness)\n obj.create_scaling_models()\n history.add_line(x0,fsc,fh,Psc,Ph,dataSc,dataH,obj.af.thickness,\n rho,delta,err,obj.runCountTotal)\n \n bnds = np.zeros([len(x0),2])\n \n itr = 0\n while err>=tol and itr=0:\n x0norm = xopt\n if rhoeta3:\n delta = delta*c1\n elif eta1<=rho<=eta2:\n delta = delta\n else:\n delta = delta*c2\n xoptReal = obj.xNormalization.denormalize(np.hstack([0.0, 0.0, xopt]))\n xoptReal = xoptReal[2:]\n \n history.add_line(xoptReal,fsc,fh,Psc,Ph,dataSc,dataH,obj.af.thickness,\n rho,delta,err,obj.runCountTotal)\n append_to_DOE_file(x0,dataH, dataL, obj.af.thickness)\n obj.create_scaling_models()\n\n \nif __name__==\"__main__\":\n run_main()","sub_path":"cfd/RVFM_airfoil_design2.py","file_name":"RVFM_airfoil_design2.py","file_ext":"py","file_size_in_byte":11349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"637196055","text":"import turtle\r\nimport math\r\nimport random\r\nimport time\r\nfrom LEVELS import *\r\nfrom threading import Thread\r\nfrom platform import system\r\nsystem = system() # storing the name of the system (Windows, Unix etc.) to help avoid errors with sound playing\r\nend_game = False # variable used to signal the end of the game\r\n\r\n\r\n# I got this bit of code of the internet to help me play sounds. I have no idea how it works :)\r\n# I should also add that it only works on Windows\r\n# If you use Unix or something else the sounds won't work but hopefully everything else works\r\n########################################################################################################################\r\nclass PlaySoundException(Exception):\r\n pass\r\n\r\n\r\ndef play_sound(sound, block=True):\r\n\r\n from ctypes import c_buffer, windll\r\n from random import random\r\n from time import sleep\r\n from sys import getfilesystemencoding\r\n\r\n def win_command(*command):\r\n buf = c_buffer(255)\r\n command = ' '.join(command).encode(getfilesystemencoding())\r\n error_code = int(windll.winmm.mciSendStringA(command, buf, 254, 0))\r\n if error_code:\r\n error_buffer = c_buffer(255)\r\n windll.winmm.mciGetErrorStringA(error_code, error_buffer, 254)\r\n exception_message = ('\\n Error ' + str(error_code) + ' for command:'\r\n '\\n ' + command.decode() +\r\n '\\n ' + error_buffer.value.decode())\r\n raise PlaySoundException(exception_message)\r\n return buf.value\r\n\r\n alias = 'playsound_' + str(random())\r\n win_command('open \"' + sound + '\" alias', alias)\r\n win_command('set', alias, 'time format milliseconds')\r\n duration_in_ms = win_command('status', alias, 'length')\r\n win_command('play', alias, 'from 0 to', duration_in_ms.decode())\r\n\r\n if block:\r\n sleep(float(duration_in_ms) / 1000.0)\r\n\r\n########################################################################################################################\r\n\r\n\r\ndef end_game_function():\r\n \"\"\"\r\n Ends the game\r\n\r\n :return: None\r\n \"\"\"\r\n global end_game\r\n end_game = True\r\n\r\n\r\ndef restart():\r\n \"\"\"\r\n Restarts the game by ending the current script and executing it from the beginning.\r\n\r\n :return: None\r\n \"\"\"\r\n import sys\r\n import os\r\n os.execv(sys.executable, ['python'] + sys.argv)\r\n\r\n\r\nwn = turtle.Screen() # declaring the screen we are going to work on\r\nwn.bgcolor(\"black\") # setting the background color\r\nwn.title(\"Sandi's Maze\") # setting the title of the screen\r\nwn.setup(700, 700) # setting the height and width in pixels\r\nwn.tracer(0) # turning off drawing animations\r\n\r\n# registering all of the textures for the in-game elements\r\nturtle.register_shape(r\"Resources\\player_right.gif\")\r\nturtle.register_shape(r\"Resources\\player_left.gif\")\r\nturtle.register_shape(r\"Resources\\wall.gif\")\r\nturtle.register_shape(r\"Resources\\treasure.gif\")\r\nturtle.register_shape(r\"Resources\\treasure_small.gif\")\r\nturtle.register_shape(r\"Resources\\treasure_big.gif\")\r\nturtle.register_shape(r\"Resources\\enemy_left.gif\")\r\nturtle.register_shape(r\"Resources\\enemy_right.gif\")\r\nturtle.register_shape(r\"Resources\\heart.gif\")\r\nturtle.register_shape(r\"Resources\\key.gif\")\r\nturtle.register_shape(r\"Resources\\friend.gif\")\r\nturtle.register_shape(r\"Resources\\door.gif\")\r\nturtle.register_shape(r\"Resources\\arrow.gif\")\r\nturtle.register_shape(r\"Resources\\stairs.gif\")\r\nturtle.register_shape(r\"Resources\\black_square.gif\")\r\nturtle.register_shape(r\"Resources\\dungeon_1.gif\")\r\nturtle.register_shape(r\"Resources\\dungeon_2.gif\")\r\nturtle.register_shape(r\"Resources\\dungeon_3.gif\")\r\nturtle.register_shape(r\"Resources\\exit.gif\")\r\nturtle.register_shape(r\"Resources\\black_background.gif\")\r\nturtle.register_shape(r\"Resources\\winner.gif\")\r\nturtle.register_shape(r\"Resources\\loser.gif\")\r\nturtle.register_shape(r\"Resources\\one.gif\")\r\nturtle.register_shape(r\"Resources\\two.gif\")\r\nturtle.register_shape(r\"Resources\\three.gif\")\r\nturtle.register_shape(r\"Resources\\four.gif\")\r\nturtle.register_shape(r\"Resources\\five.gif\")\r\n\r\nif system == 'Windows': # checking to see if the system we run on is a windows system\r\n # creating a separate thread to play the background music (if we don't do this, the game can't continue\r\n # until the music is over)\r\n # daemon=True means that the thread will be forcefully stopped once the main program has finished\r\n thread = Thread(target=play_sound, args=(r\"Resources\\theme.mp3\",), daemon=True)\r\n thread.start() # starting the thread\r\n\r\n# creating the illusion of a loading screen by iterating through three pictures four times\r\nfor i in range(4):\r\n wn.bgpic(r\"Resources\\dungeon_1.gif\") # setting the background\r\n wn.update() # updating the screen\r\n time.sleep(0.5) # waiting 0.5 seconds\r\n wn.bgpic(r\"Resources\\dungeon_2.gif\") # setting the background\r\n wn.update() # updating the screen\r\n time.sleep(0.5) # waiting 0.5 seconds\r\n wn.bgpic(r\"Resources\\dungeon_3.gif\") # setting the background\r\n wn.update() # updating the screen\r\n time.sleep(0.5) # waiting 0.5 seconds\r\n\r\n\r\nclass Player(turtle.Turtle):\r\n def __init__(self):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\player_right.gif\") # giving the player texture\r\n self.penup() # not leaving traces on the screen when we move the player around\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.gold = 0 # variable that stores the current score of the player\r\n self.key = False # variable that stores whether or not the player has a key\r\n self.arrow = 0 # variable that stores the number of arrows that the player has\r\n\r\n def go_up(self):\r\n \"\"\"\r\n Moves the player up by one block. If the upper neighbour is a wall, a friend or an unopened door\r\n the command is not executed.\r\n\r\n :return: None\r\n \"\"\"\r\n move_to_x = self.xcor()\r\n move_to_y = self.ycor() + 24\r\n\r\n # testing to see if the neighbour is a wall, a friend or an unopened door\r\n if (move_to_x, move_to_y) not in walls and (move_to_x, move_to_y) not in doors_locations \\\r\n and (move_to_x, move_to_y) not in friends_locations:\r\n self.goto(move_to_x, move_to_y)\r\n\r\n def go_down(self):\r\n \"\"\"\r\n Moves the player down by one block. If the lower neighbour is a wall, a friend or an unopened door\r\n the command is not executed.\r\n\r\n :return: None\r\n \"\"\"\r\n move_to_x = self.xcor()\r\n move_to_y = self.ycor() - 24\r\n\r\n # testing to see if the neighbour is a wall, a friend or an unopened door\r\n if (move_to_x, move_to_y) not in walls and (move_to_x, move_to_y) not in doors_locations\\\r\n and (move_to_x, move_to_y) not in friends_locations:\r\n self.goto(move_to_x, move_to_y)\r\n\r\n def go_left(self):\r\n \"\"\"\r\n Moves the player left by one block. If the left neighbour is a wall, a friend or an unopened door\r\n the command is not executed.\r\n\r\n :return: None\r\n \"\"\"\r\n move_to_x = self.xcor() - 24\r\n move_to_y = self.ycor()\r\n self.shape(r\"Resources\\player_left.gif\") # changing the texture in order to have the player face left\r\n\r\n # testing to see if the neighbour is a wall, a friend or an unopened door\r\n if (move_to_x, move_to_y) not in walls and (move_to_x, move_to_y) not in doors_locations \\\r\n and (move_to_x, move_to_y) not in friends_locations:\r\n self.goto(move_to_x, move_to_y)\r\n\r\n def go_right(self):\r\n \"\"\"\r\n Moves the player right by one block. If the right neighbour is a wall, a friend or an unopened door\r\n the command is not executed.\r\n\r\n :return: None\r\n \"\"\"\r\n move_to_x = self.xcor() + 24\r\n move_to_y = self.ycor()\r\n self.shape(r\"Resources\\player_right.gif\") # changing the texture in order to have the player face right\r\n\r\n # testing to see if the neighbour is a wall, a friend or an unopened door\r\n if (move_to_x, move_to_y) not in walls and (move_to_x, move_to_y) not in doors_locations \\\r\n and (move_to_x, move_to_y) not in friends_locations:\r\n self.goto(move_to_x, move_to_y)\r\n\r\n def is_collision(self, other):\r\n \"\"\"\r\n Testing to see if the player is within a block sized radius away from an map element\r\n\r\n :param other: any map element (has to be an object that inherits the turtle class)\r\n :return: boolean indicating collision state\r\n \"\"\"\r\n a = self.xcor() - other.xcor()\r\n b = self.ycor() - other.ycor()\r\n distance = math.sqrt((a ** 2) + (b ** 2)) # distance between self and other\r\n\r\n if distance < 26:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\nclass Enemy(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\enemy_left.gif\") # giving the enemy texture\r\n self.penup() # not leaving traces on the screen when the enemy moves around\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.goto(x, y) # sending the enemy to its start location\r\n self.direction = random.choice([\"down\", \"up\", \"left\", \"right\"]) # randomly deciding the next possible move\r\n\r\n def move(self):\r\n \"\"\"\r\n Moves the enemy by one block in a random direction. If the neighbour is a wall,\r\n a friend or an unopened door the command is not executed. If the player is close, the enemy\r\n will chase him instead of moving around randomly.\r\n\r\n :return: None\r\n \"\"\"\r\n if self.direction == \"up\":\r\n dx = 0\r\n dy = 24\r\n elif self.direction == \"down\":\r\n dx = 0\r\n dy = -24\r\n elif self.direction == \"left\":\r\n dx = -24\r\n dy = 0\r\n self.shape(r\"Resources\\enemy_left.gif\") # changing the texture in order to have the enemy face left\r\n elif self.direction == \"right\":\r\n dx = 24\r\n dy = 0\r\n self.shape(r\"Resources\\enemy_right.gif\") # changing the texture in order to have the enemy face left\r\n else:\r\n dx = 0\r\n dy = 0\r\n\r\n # if the player is close the enemy will try to go in his direction\r\n if self.is_close(player):\r\n if player.xcor() < self.xcor():\r\n self.direction = \"left\"\r\n elif player.xcor() > self.xcor():\r\n self.direction = \"right\"\r\n elif player.ycor() < self.ycor():\r\n self.direction = \"down\"\r\n elif player.ycor() > self.ycor():\r\n self.direction = \"up\"\r\n\r\n move_to_x = self.xcor() + dx\r\n move_to_y = self.ycor() + dy\r\n\r\n # testing to see if the neighbour is a wall, a friend or an unopened door\r\n if (move_to_x, move_to_y) not in walls and (move_to_x, move_to_y) not in doors_locations \\\r\n and (move_to_x, move_to_y) not in friends_locations:\r\n self.goto(move_to_x, move_to_y)\r\n else:\r\n self.direction = random.choice([\"down\", \"up\", \"left\", \"right\"]) # changing direction\r\n\r\n # calling the function within itself indefinitely with a random delay until the game ends\r\n turtle.ontimer(self.move, t=random.randint(100, 300))\r\n\r\n def is_close(self, other):\r\n \"\"\"\r\n Testing to see if the player is within a specified radius away from the enemy\r\n\r\n :param other: the player\r\n :return: boolean indicating if the closeness conditions are met\r\n \"\"\"\r\n a = self.xcor() - other.xcor()\r\n b = self.ycor() - other.ycor()\r\n distance = math.sqrt((a ** 2) + (b ** 2)) # distance between self and other\r\n\r\n if distance < 100:\r\n return True\r\n else:\r\n return False\r\n\r\n def destroy(self):\r\n \"\"\"\r\n Destroying the enemy both by moving it outside of the map and hiding it from view.\r\n (you can never be too sure :) )\r\n\r\n :return: None\r\n \"\"\"\r\n self.goto(2000, 2000) # sending the enemy outside of the map\r\n self.hideturtle() # hiding the enemy from view\r\n\r\n\r\nclass Friend(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\friend.gif\") # giving the friend texture\r\n self.penup() # not leaving traces on the screen when we move the friend\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.key = True # variable that stores whether or not the friend has a key\r\n self.arrow = 1 # variable that stores the number of arrows that the friend has\r\n self.goto(x, y) # sending the enemy to its location\r\n\r\n\r\nclass Pen(turtle.Turtle):\r\n def __init__(self):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\wall.gif\") # giving the wall texture\r\n self.penup() # not leaving traces on the screen when we draw the wall\r\n self.speed(0) # setting the animation speed to the fastest value\r\n\r\n\r\nclass BlackSquare(turtle.Turtle):\r\n def __init__(self):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\black_square.gif\") # giving the square texture\r\n self.penup() # not leaving traces on the screen when we draw the square\r\n self.speed(0) # setting the animation speed to the fastest value\r\n\r\n\r\nclass Health(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\heart.gif\") # giving the heart texture\r\n self.penup() # not leaving traces on the screen when we draw the heart\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.goto(x, y) # sending the heart to its location\r\n\r\n def destroy(self):\r\n \"\"\"\r\n Destroying the heart both by moving it outside of the map and hiding it from view.\r\n (you can never be too sure :) )\r\n\r\n :return: None\r\n \"\"\"\r\n self.goto(2000, 2000) # sending the heart outside of the map\r\n self.hideturtle() # hiding the heart from view\r\n\r\n\r\nclass Treasure(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\treasure.gif\") # giving the treasure texture\r\n self.penup() # not leaving traces on the screen when we draw the treasure\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.gold = 100 # storing the value of this kind of treasure\r\n self.goto(x, y) # sending the treasure to its location\r\n\r\n def destroy(self):\r\n \"\"\"\r\n Destroying the treasure both by moving it outside of the map and hiding it from view.\r\n (you can never be too sure :) )\r\n\r\n :return: None\r\n \"\"\"\r\n self.goto(2000, 2000) # sending the treasure outside of the map\r\n self.hideturtle() # hiding the treasure from view\r\n\r\n\r\nclass TreasureSmall(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\treasure_small.gif\") # giving the treasure texture\r\n self.penup() # not leaving traces on the screen when we draw the treasure\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.gold = 25 # storing the value of this kind of treasure\r\n self.goto(x, y) # sending the treasure to its location\r\n\r\n def destroy(self):\r\n \"\"\"\r\n Destroying the treasure both by moving it outside of the map and hiding it from view.\r\n (you can never be too sure :) )\r\n\r\n :return: None\r\n \"\"\"\r\n self.goto(2000, 2000) # sending the treasure outside of the map\r\n self.hideturtle() # hiding the treasure from view\r\n\r\n\r\nclass TreasureBig(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\treasure_big.gif\") # giving the treasure texture\r\n self.penup() # not leaving traces on the screen when we draw the treasure\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.gold = 250 # storing the value of this kind of treasure\r\n self.goto(x, y) # sending the treasure to its location\r\n\r\n def destroy(self):\r\n \"\"\"\r\n Destroying the treasure both by moving it outside of the map and hiding it from view.\r\n (you can never be too sure :) )\r\n\r\n :return: None\r\n \"\"\"\r\n self.goto(2000, 2000) # sending the treasure outside of the map\r\n self.hideturtle() # hiding the treasure from view\r\n\r\n\r\nclass Key(turtle.Turtle):\r\n def __init__(self):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\key.gif\") # giving the key texture\r\n self.penup() # not leaving traces on the screen when we draw the key\r\n self.speed(0) # setting the animation speed to the fastest value\r\n\r\n\r\nclass Arrow(turtle.Turtle):\r\n def __init__(self):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\arrow.gif\") # giving the arrow texture\r\n self.penup() # not leaving traces on the screen when we draw the arrow\r\n self.speed(0) # setting the animation speed to the fastest value\r\n\r\n\r\nclass ArrowNumber(turtle.Turtle):\r\n def __init__(self):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\one.gif\") # giving the arrow number texture\r\n self.penup() # not leaving traces on the screen when we draw the arrow number\r\n self.speed(0) # setting the animation speed to the fastest value\r\n\r\n\r\nclass Gold(turtle.Turtle):\r\n def __init__(self, value, x, y, color):\r\n turtle.Turtle.__init__(self)\r\n self.color(color) # setting the text color to the color received in the constructor\r\n self.penup() # not leaving traces on the screen when we draw the gold\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.goto(x, y) # sending the text to its location\r\n # writing the text on the screen\r\n self.write(\"Gold: {}\".format(value), move=False, align=\"right\", font=(\"ocr a extended\", 14, \"normal\"))\r\n self.hideturtle() # hiding the turtle so that only the text remains\r\n\r\n\r\nclass Score(turtle.Turtle):\r\n def __init__(self, value):\r\n turtle.Turtle.__init__(self)\r\n self.color(\"black\") # setting the text color\r\n self.penup() # not leaving traces on the screen when we draw the score\r\n self.speed(0) # setting the animation speed to the fastest value\r\n # writing the text on the screen\r\n self.write(\"Score: {}\".format(value), move=False, align=\"center\", font=(\"impact\", 50, \"bold\"))\r\n self.hideturtle() # hiding the turtle so that only the text remains\r\n\r\n\r\nclass Door(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\door.gif\") # giving the door texture\r\n self.penup() # not leaving traces on the screen when we draw the door\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.goto(x, y) # sending the door to its location\r\n\r\n def destroy(self):\r\n \"\"\"\r\n Destroying the door both by moving it outside of the map and hiding it from view.\r\n (you can never be too sure :) )\r\n\r\n :return: None\r\n \"\"\"\r\n self.goto(2000, 2000) # sending the door outside of the map\r\n self.hideturtle() # hiding the door from view\r\n\r\n\r\nclass Stairs(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\stairs.gif\") # giving the stairs texture\r\n self.penup() # not leaving traces on the screen when we draw the stairs\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.goto(x, y) # sending the stairs to their location\r\n\r\n\r\nclass GameOver(turtle.Turtle):\r\n def __init__(self):\r\n turtle.Turtle.__init__(self)\r\n self.shape(\"square\") # setting the shape\r\n self.color(\"red\") # setting the color\r\n self.penup() # not leaving traces on the screen when we draw GAME OVER\r\n self.speed(0) # setting the animation speed to the fastest value\r\n\r\n\r\nclass GameWin(turtle.Turtle):\r\n def __init__(self, x, y):\r\n turtle.Turtle.__init__(self)\r\n self.shape(r\"Resources\\exit.gif\") # giving the exit texture\r\n self.penup() # not leaving traces on the screen when we draw the exit\r\n self.speed(0) # setting the animation speed to the fastest value\r\n self.goto(x, y) # sending the exit to its location\r\n\r\n\r\nlevels = [\"\"] # creating the levels list\r\ntreasures = [] # creating the medium treasures list\r\ntreasures_small = [] # creating the small treasures list\r\ntreasures_big = [] # creating the big treasures list\r\ncurrent_gold = 0 # creating the variable that stores the current score of the player\r\ngold_text_x = 0 # x coordinate of the \"Gold: ...\" text\r\ngold_text_y = 0 # y coordinate of the \"Gold: ...\" text\r\ndoors = [] # creating the doors list\r\ndoors_locations = [] # list that stores the locations of the doors (to help with collision)\r\nstairs = Stairs(3000, 3000) # instantiating the Stairs class and sending the object outside the map\r\ngame_win = GameWin(3000, 3000) # instantiating the GameWin class and sending the object outside the map\r\nenemies = [] # creating the enemies list\r\nfriends = [] # creating the friends list\r\nfriends_locations = [] # list that stores the locations of the friends (to help with collision)\r\npen = Pen() # instantiating the Pen class\r\ngame_over = GameOver() # instantiating the GameOver class\r\nplayer = Player() # instantiating the Player class\r\nkey = Key() # instantiating the Key class\r\narrow = Arrow() # instantiating the Arrow class\r\narrow_number = ArrowNumber() # instantiating the ArrowNumber class\r\nwalls = [] # list that stores the locations of the walls (to help with collision)\r\nstart_point = [] # storing the start position for the player\r\nhearts = [] # creating the hearts list\r\nhearts_x_cor = [] # storing the order of the hearts (the one in the right is always the first one to go)\r\ncurrent_level = 1 # storing the current level\r\n\r\n\r\n# iterating through the first level matrix to get the health\r\n# we are doing this outside of the setup_maze function in order to avoid resetting the health on every level\r\nfor y_H in range(len(level_1)):\r\n for x_H in range(len(level_1[y_H])):\r\n character_H = level_1[y_H][x_H] # storing the current letter of the matrix\r\n # setting the coordinates for the element\r\n screen_x_H = -288 + (x_H * 24)\r\n screen_y_H = 288 - (y_H * 24)\r\n if character_H == \"H\": # if the character is a H then we've found where the hearts are supposed to be\r\n hearts_x_cor.append(screen_x_H) # adding the x coordinate of the heart to the list\r\n hearts.append(Health(screen_x_H, screen_y_H)) # adding the heart to the hearts list\r\n\r\n\r\ndef setup_maze(level: list) -> None:\r\n \"\"\"\r\n This function reads a level from a list of strings and it sets up\r\n the GUI based on the characters it sees.\r\n\r\n :param level: game level in text format\r\n :return: None\r\n \"\"\"\r\n # sending the element outside of the map because it remains in the middle of the screen when it's not used\r\n game_over.goto(2000, 2000)\r\n # iterating through the current level matrix\r\n for y in range(len(level)):\r\n for x in range(len(level[y])):\r\n character = level[y][x] # storing the current letter of the matrix\r\n # setting the coordinates for the element that is about to be pinned to the GUI\r\n screen_x = -288 + (x * 24)\r\n screen_y = 288 - (y * 24)\r\n if character == \"X\": # X means WALL\r\n pen.goto(screen_x, screen_y)\r\n pen.stamp() # stamping the wall gif in every appropriate place\r\n walls.append((screen_x, screen_y))\r\n if character == \"P\": # P means PLAYER\r\n player.goto(screen_x, screen_y)\r\n start_point.append([screen_x, screen_y])\r\n if character == \"T\": # T means NORMAL TREASURE\r\n treasures.append(Treasure(screen_x, screen_y))\r\n if character == \"B\": # B means BIG TREASURE\r\n treasures_big.append(TreasureBig(screen_x, screen_y))\r\n if character == \"C\": # C means SMALL TREASURE\r\n treasures_small.append(TreasureSmall(screen_x, screen_y))\r\n if character == \"E\": # E means ENEMY\r\n enemies.append(Enemy(screen_x, screen_y))\r\n if character == \"G\": # G means GAME OVER SQUARE\r\n game_over.goto(screen_x, screen_y)\r\n game_over.stamp()\r\n if character == \"F\": # F means FRIEND\r\n friends.append(Friend(screen_x, screen_y))\r\n friends_locations.append((screen_x, screen_y))\r\n if character == \"K\": # K means KEY\r\n key.goto(screen_x, screen_y)\r\n key.hideturtle()\r\n if character == \"D\": # D means DOOR\r\n doors.append(Door(screen_x, screen_y))\r\n doors_locations.append((screen_x, screen_y))\r\n if character == \"A\": # A means ARROW\r\n arrow.goto(screen_x, screen_y)\r\n arrow.hideturtle()\r\n if character == \"Q\": # Q means ARROW NUMBER\r\n arrow_number.goto(screen_x, screen_y)\r\n arrow_number.hideturtle()\r\n if character == \"S\": # S means CURRENT SCORE (\"Gold:...\")\r\n global gold_text_x, gold_text_y\r\n gold_text_x = screen_x\r\n gold_text_y = screen_y\r\n Gold(current_gold, screen_x, screen_y, \"red\")\r\n if character == \"N\": # N means STAIRS\r\n global stairs\r\n stairs = Stairs(screen_x, screen_y)\r\n if character == \"W\": # W means EXIT\r\n global game_win\r\n game_win = GameWin(screen_x, screen_y)\r\n\r\n\r\n# any of these lines except for the last one can be deactivated for a shorter game experience\r\nlevels.append(level_1)\r\nlevels.append(level_2)\r\nlevels.append(level_3)\r\n\r\nwn.bgpic(r\"Resources\\black_background.gif\") # setting up the background image\r\n\r\nsetup_maze(levels[1]) # loading in the first level\r\n\r\n# associating different functions with keys\r\nturtle.listen()\r\nturtle.onkeypress(player.go_left, \"Left\")\r\nturtle.onkeypress(player.go_right, \"Right\")\r\nturtle.onkeypress(player.go_down, \"Down\")\r\nturtle.onkeypress(player.go_up, \"Up\")\r\nturtle.onkeypress(end_game_function, \"q\")\r\nturtle.onkeypress(restart, \"r\")\r\n\r\nwn.tracer(0) # avoiding animations\r\n\r\n\r\n# kicking the enemies into motion\r\nfor enemy in enemies:\r\n turtle.ontimer(enemy.move, t=250)\r\n\r\n# main loop\r\nwhile True:\r\n if end_game: # testing to see if the player pressed Q\r\n break\r\n\r\n if player.key: # condition to show the key icon on screen\r\n key.showturtle()\r\n else:\r\n key.hideturtle()\r\n\r\n if player.arrow: # condition to show the arrow icon on screen\r\n arrow.showturtle()\r\n arrow_number.showturtle()\r\n else:\r\n arrow.hideturtle()\r\n arrow_number.hideturtle()\r\n\r\n if player.arrow == 1: # updating the number of arrows on screen\r\n arrow_number.shape(r\"Resources\\one.gif\")\r\n elif player.arrow == 2:\r\n arrow_number.shape(r\"Resources\\two.gif\")\r\n elif player.arrow == 3:\r\n arrow_number.shape(r\"Resources\\three.gif\")\r\n elif player.arrow == 4:\r\n arrow_number.shape(r\"Resources\\four.gif\")\r\n elif player.arrow == 5:\r\n arrow_number.shape(r\"Resources\\five.gif\")\r\n else:\r\n arrow_number.hideturtle()\r\n\r\n if player.is_collision(stairs): # condition to go to the next level\r\n # playing the level up sound\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\next_level.mp3\",)).start()\r\n # resetting every asset ##############\r\n wn.clear()\r\n wn.bgcolor(\"black\")\r\n wn.title(\"A Maze Game\")\r\n wn.setup(700, 700)\r\n wn.tracer(0)\r\n current_level += 1\r\n treasures = []\r\n treasures_small = []\r\n treasures_big = []\r\n gold_text_x = 0\r\n gold_text_y = 0\r\n doors = []\r\n doors_locations = []\r\n stairs = Stairs(3000, 3000)\r\n game_win = GameWin(3000, 3000)\r\n enemies = []\r\n friends = []\r\n friends_locations = []\r\n pen = Pen()\r\n game_over = GameOver()\r\n arrows = player.arrow\r\n player = Player()\r\n player.arrow = arrows\r\n key = Key()\r\n arrow = Arrow()\r\n arrow_number = ArrowNumber()\r\n walls = []\r\n start_point = []\r\n ######################################\r\n setup_maze(levels[current_level]) # loading in the next level\r\n for heart in hearts: # loading in the lives from the previous level as lives don't reset\r\n heart.stamp()\r\n # associating different functions with keys again\r\n # I have no idea why this is necessary but apparently it is\r\n turtle.listen()\r\n turtle.onkeypress(player.go_left, \"Left\")\r\n turtle.onkeypress(player.go_right, \"Right\")\r\n turtle.onkeypress(player.go_down, \"Down\")\r\n turtle.onkeypress(player.go_up, \"Up\")\r\n turtle.onkeypress(restart, \"r\")\r\n turtle.onkeypress(end_game_function, \"q\")\r\n\r\n wn.tracer(0)\r\n for enemy in enemies: # kicking the enemies into motion\r\n turtle.ontimer(enemy.move, t=250)\r\n\r\n if player.is_collision(game_win): # condition to win the game\r\n player.goto(2000, 2000) # unloading the player from the map\r\n # playing the winning sound\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\game_win.mp3\",)).start()\r\n wn.update()\r\n time.sleep(1)\r\n wn.clear() # unloading every asset\r\n wn.bgpic(r\"Resources\\winner.gif\") # setting the winning background picture\r\n # making sure the quit and restart buttons still work\r\n turtle.listen()\r\n turtle.onkeypress(restart, \"r\")\r\n turtle.onkeypress(end_game_function, \"q\")\r\n Score(current_gold) # loading the score on the screen\r\n\r\n # testing every door to see if the player both is near and has a key\r\n for door in doors:\r\n if player.is_collision(door) and player.key is True:\r\n # playing the door sound\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\door.mp3\",)).start()\r\n x_coord = door.xcor()\r\n y_coord = door.ycor()\r\n # getting rid of the door\r\n door.destroy()\r\n doors.remove(door)\r\n doors_locations.remove((x_coord, y_coord))\r\n player.key = False # removing the key from the player\r\n\r\n # testing every treasure to see if the player is near\r\n for treasure in treasures:\r\n if player.is_collision(treasure):\r\n # playing the treasure sound\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\treasure.mp3\",)).start()\r\n # covering up the old score via writing over it with black\r\n # I know it's a horrible way to do things but I couldn't think of anything else\r\n Gold(current_gold, gold_text_x, gold_text_y, \"black\")\r\n # updating the score\r\n player.gold += treasure.gold\r\n current_gold += treasure.gold\r\n # showing the new score on the screen\r\n Gold(current_gold, gold_text_x, gold_text_y, \"red\")\r\n # removing the treasure\r\n treasure.destroy()\r\n treasures.remove(treasure)\r\n\r\n # same as for the normal treasure described above\r\n for treasure in treasures_big:\r\n if player.is_collision(treasure):\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\treasure.mp3\",)).start()\r\n Gold(current_gold, gold_text_x, gold_text_y, \"black\")\r\n player.gold += treasure.gold\r\n current_gold += treasure.gold\r\n Gold(current_gold, gold_text_x, gold_text_y, \"red\")\r\n treasure.destroy()\r\n treasures_big.remove(treasure)\r\n\r\n # same as for the normal treasure described above\r\n for treasure in treasures_small:\r\n if player.is_collision(treasure):\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\treasure.mp3\",)).start()\r\n Gold(current_gold, gold_text_x, gold_text_y, \"black\")\r\n player.gold += treasure.gold\r\n current_gold += treasure.gold\r\n Gold(current_gold, gold_text_x, gold_text_y, \"red\")\r\n treasure.destroy()\r\n treasures_small.remove(treasure)\r\n\r\n # testing every friend to see if the player is near\r\n for friend in friends:\r\n if player.is_collision(friend):\r\n # playing the friend sound\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\friend.mp3\",)).start()\r\n x_coord = friend.xcor()\r\n y_coord = friend.ycor()\r\n # getting a key and an arrow from the friend\r\n player.key = True\r\n friend.key = False\r\n if player.arrow < 5:\r\n player.arrow += 1\r\n friend.arrow = 0\r\n # removing the friend from the checking list\r\n friends.remove(friend)\r\n\r\n # testing every enemy to see if the player is near\r\n for enemy in enemies:\r\n if player.is_collision(enemy):\r\n # if the player has arrows then the enemy dies and the rest of the instructions won't execute\r\n if player.arrow:\r\n # playing the enemy death sound\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\enemy.mp3\",)).start()\r\n enemy.destroy()\r\n enemies.remove(enemy)\r\n player.arrow -= 1 # decrementing the arrows number\r\n continue\r\n # if the player has no arrows then he dies and looses a life\r\n for heart in hearts:\r\n if heart.xcor() == max(hearts_x_cor):\r\n # playing the player damage sound\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\damage.mp3\",)).start()\r\n # removing a life from the player\r\n # again it's a very bad way to do it but I couldn't come up with anything else\r\n black_square = BlackSquare()\r\n black_square.goto(heart.xcor(), heart.ycor())\r\n black_square.stamp()\r\n heart.destroy()\r\n hearts.remove(heart)\r\n hearts_x_cor.remove(max(hearts_x_cor))\r\n # player returns to the spawn point\r\n player.goto(start_point[0][0], start_point[0][1])\r\n # if the player has no lives left then it's game over\r\n if not len(hearts):\r\n # playing the game over sound\r\n if system == 'Windows':\r\n Thread(target=play_sound, args=(r\"Resources\\game_lose.mp3\",)).start()\r\n player.goto(2000, 2000)\r\n wn.clear() # unloading every asset\r\n wn.bgpic(r\"Resources\\loser.gif\") # setting up the game over background\r\n wn.title(\"Sandi's Maze\")\r\n wn.setup(700, 700)\r\n # making sure the keys still work\r\n # I have no idea why I have to set them up every single time\r\n turtle.listen()\r\n turtle.onkeypress(player.go_left, \"Left\")\r\n turtle.onkeypress(player.go_right, \"Right\")\r\n turtle.onkeypress(player.go_down, \"Down\")\r\n turtle.onkeypress(player.go_up, \"Up\")\r\n turtle.onkeypress(end_game_function, \"q\")\r\n turtle.onkeypress(restart, \"r\")\r\n # loading a short game over animation to make things more interesting\r\n setup_maze(game_over_screen)\r\n\r\n wn.update() # updating the screen after every change\r\nwn.bye() # closing the window when the player quits\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":36682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"118664310","text":"\"\"\"\nCreated on 22 Sep 2016\n\n@author: Bruno Beloff (bruno.beloff@southcoastscience.com)\n\nAlphasense Application Note AAN 803-02\nAAN 803-02 070916_DRAFT03.doc\n\"\"\"\n\n# import sys\n\nfrom scs_core.gas.sensor import Sensor\n\n\n# TODO: indicate with \"alg\" field to identify which equation is being used\n\n# --------------------------------------------------------------------------------------------------------------------\n\nclass A4TempComp(object):\n \"\"\"\n classdocs\n \"\"\"\n\n __MIN_TEMP = -30\n __MAX_TEMP = 50\n __INTERVAL = 10\n\n __COMP = None\n\n\n # ----------------------------------------------------------------------------------------------------------------\n\n @classmethod\n def init(cls): # °C: -30 -20 -10 0 10 20 30 40 50\n cls.__COMP = {\n Sensor.CODE_CO: A4TempComp(1, 'n_t', [1.0, 1.0, 1.0, 1.0, -0.2, -0.9, -1.5, -1.5, -1.5]),\n Sensor.CODE_H2S: A4TempComp(1, 'n_t', [3.0, 3.0, 3.0, 1.0, -1.0, -2.0, -1.5, -1.0, -0.5]),\n Sensor.CODE_NO: A4TempComp(3, 'kp_t', [0.7, 0.7, 0.7, 0.7, 0.8, 1.0, 1.2, 1.4, 1.6]),\n Sensor.CODE_NO2: A4TempComp(1, 'n_t', [0.8, 0.8, 1.0, 1.2, 1.6, 1.8, 1.9, 2.5, 3.6]),\n Sensor.CODE_OX: A4TempComp(3, 'kp_t', [0.1, 0.1, 0.2, 0.3, 0.7, 1.0, 1.7, 3.0, 4.0]),\n Sensor.CODE_SO2: A4TempComp(1, 'kpp_t', [1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.9, 3.0, 5.8]),\n\n Sensor.CODE_VOCe: A4TempComp(1, 'n_t', [1.0, 1.0, 1.0, 1.0, -0.2, -0.9, -1.5, -1.5, -1.5]),\n\n Sensor.CODE_TEST_1: None,\n Sensor.CODE_TEST_2: None,\n Sensor.CODE_TEST_3: None,\n Sensor.CODE_TEST_4: None\n }\n\n # Recommended, but causes div by zero error if calib.ae_cal_mv is zero\n # Sensor.CODE_H2S: A4TempComp(2, 'k_t', [-1.5, -1.5, -1.5, -0.5, 0.5, 1.0, 0.8, 0.5, 0.3]),\n\n\n @classmethod\n def find(cls, sensor_code):\n if sensor_code not in cls.__COMP:\n raise ValueError(\"A4TempComp.find: unrecognised sensor code: %s.\" % sensor_code)\n\n return cls.__COMP[sensor_code]\n\n\n @classmethod\n def in_range(cls, temp):\n if temp is None:\n return False\n\n return temp <= cls.__MAX_TEMP\n\n\n # ----------------------------------------------------------------------------------------------------------------\n\n def __init__(self, algorithm, factor, values):\n \"\"\"\n Constructor\n \"\"\"\n length = (A4TempComp.__MAX_TEMP - A4TempComp.__MIN_TEMP) // A4TempComp.__INTERVAL + 1\n\n if len(values) != length:\n raise ValueError(\"A4TempComp: value count should be %d.\" % length)\n\n self.__algorithm = algorithm # int\n self.__factor = factor # string\n self.__values = values # array of float\n\n\n # ----------------------------------------------------------------------------------------------------------------\n\n def correct(self, calib, temp, we_t, ae_t):\n \"\"\"\n Compute weC from weT, aeT\n \"\"\"\n if not A4TempComp.in_range(temp):\n return None\n\n if self.__algorithm == 1:\n return self.__eq1(temp, we_t, ae_t)\n\n if self.__algorithm == 2:\n return self.__eq2(temp, we_t, ae_t, calib.we_cal_mv, calib.ae_cal_mv)\n\n if self.__algorithm == 3:\n return self.__eq3(temp, we_t, ae_t, calib.we_cal_mv, calib.ae_cal_mv)\n\n if self.__algorithm == 4:\n return self.__eq4(temp, we_t, calib.we_cal_mv)\n\n raise ValueError(\"A4TempComp.conv: unrecognised algorithm: %d.\" % self.__algorithm)\n\n\n # ----------------------------------------------------------------------------------------------------------------\n\n def __eq1(self, temp, we_t, ae_t):\n n_t = self.cf_t(temp)\n\n we_c = we_t - n_t * ae_t\n\n # print(\"A4TempComp.__eq1: alg:%d, temp:%f we_t:%f n_t:%f we_c:%f \" %\n # (self.__algorithm, temp, we_t, n_t, we_c), file=sys.stderr)\n\n return we_c\n\n\n def __eq2(self, temp, we_t, ae_t, we_cal_mv, ae_cal_mv):\n k_t = self.cf_t(temp)\n\n we_c = we_t - k_t * (we_cal_mv / ae_cal_mv) * ae_t\n\n # print(\"A4TempComp.__eq2: alg:%d, temp:%f we_t:%f ae_t:%f we_cal_mv:%f ae_cal_mv:%f k_t:%f we_c:%f \" %\n # (self.__algorithm, temp, we_t, ae_t, we_cal_mv, ae_cal_mv, k_t, we_c), file=sys.stderr)\n\n return we_c\n\n\n def __eq3(self, temp, we_t, ae_t, we_cal_mv, ae_cal_mv):\n kp_t = self.cf_t(temp)\n\n we_c = we_t - kp_t * (we_cal_mv - ae_cal_mv) * ae_t\n\n # print(\"A4TempComp.__eq3: alg:%d, temp:%f we_t:%f ae_t:%f we_cal_mv:%f ae_cal_mv:%f kp_t:%f we_c:%f \" %\n # (self.__algorithm, temp, we_t, ae_t, we_cal_mv, ae_cal_mv, kp_t, we_c), file=sys.stderr)\n\n return we_c\n\n\n def __eq4(self, temp, we_t, we_cal_mv):\n kpp_t = self.cf_t(temp)\n\n we_c = we_t - we_cal_mv - kpp_t\n\n # print(\"A4TempComp.__eq4: alg:%d, temp:%f we_t:%f we_cal_mv:%f kpp_t:%f we_c:%f \" %\n # (self.__algorithm, temp, we_t, we_cal_mv, kpp_t, we_c), file=sys.stderr)\n\n return we_c\n\n\n # ----------------------------------------------------------------------------------------------------------------\n\n def cf_t(self, temp):\n \"\"\"\n Compute the linear-interpolated temperature compensation factor.\n \"\"\"\n # below MIN_TEMP...\n if temp < A4TempComp.__MIN_TEMP:\n return self.__values[0]\n\n index = int((temp - A4TempComp.__MIN_TEMP) // A4TempComp.__INTERVAL) # index of start of interval\n\n # on boundary...\n if temp % A4TempComp.__INTERVAL == 0:\n return self.__values[index]\n\n # all others...\n y1 = self.__values[index] # y value at start of interval\n y2 = self.__values[index + 1] # y value at end of interval\n\n delta_y = y2 - y1\n\n delta_x = float(temp % A4TempComp.__INTERVAL) / A4TempComp.__INTERVAL # proportion of interval\n\n cf_t = y1 + (delta_y * delta_x)\n\n # print(\"A4TempComp.cf_t: alg:%d, temp:%f y1:%f y2:%f delta_y:%f delta_x:%f cf_t:%f \" %\n # (self.__algorithm, temp, y1, y2, delta_y, delta_x, cf_t), file=sys.stderr)\n\n return cf_t\n\n\n # ----------------------------------------------------------------------------------------------------------------\n\n @property\n def algorithm(self):\n return self.__algorithm\n\n\n @property\n def factor(self):\n return self.__factor\n\n\n @property\n def values(self):\n return self.__values\n\n\n # ----------------------------------------------------------------------------------------------------------------\n\n def __str__(self, *args, **kwargs):\n return \"A4TempComp:{algorithm:%d, factor:%s, values:%s}\" % (self.algorithm, self.factor, self.values)\n","sub_path":"src/scs_core/gas/a4/a4_temp_comp.py","file_name":"a4_temp_comp.py","file_ext":"py","file_size_in_byte":6990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"13375581","text":"from bs4 import BeautifulSoup\r\nimport urllib.request\r\nimport urllib.parse\r\nimport requests\r\nfrom urllib.request import urlretrieve\r\nfrom clint.textui import progress\r\nimport os.path \r\nimport sys\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtCore import *\r\nfrom PyQt5.QtGui import *\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\nimport socket\r\nimport ssl\r\n\r\ncontext = ssl._create_unverified_context()\r\n\r\nclass Ui_MainWindow(object):\r\n def setupUi(self, MainWindow):\r\n MainWindow.setObjectName(\"MainWindow\")\r\n MainWindow.resize(800, 339)\r\n font = QtGui.QFont()\r\n font.setFamily(\"NanumSquare\")\r\n MainWindow.setFont(font)\r\n self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n self.centralwidget.setObjectName(\"centralwidget\")\r\n self.dn_alert = QtWidgets.QLabel(self.centralwidget)\r\n self.dn_alert.setGeometry(QtCore.QRect(100, 80, 470, 16))\r\n font = QtGui.QFont()\r\n font.setFamily(\"NanumSquare\")\r\n font.setPointSize(10)\r\n self.dn_alert.setFont(font)\r\n self.dn_alert.setText(\"If you want to close, PLEASE press Stop. It'll stop background progress.\")\r\n self.dn_alert.setObjectName(\"dn_alert\")\r\n self.dn_btn = QtWidgets.QPushButton(self.centralwidget)\r\n self.dn_btn.setGeometry(QtCore.QRect(600, 80, 81, 21))\r\n self.dn_btn.setObjectName(\"dn_btn\")\r\n self.end_btn = QtWidgets.QPushButton(self.centralwidget)\r\n self.end_btn.setGeometry(QtCore.QRect(690, 80, 81, 21))\r\n self.end_btn.setObjectName(\"end_btn\")\r\n self.dn_progress2 = QtWidgets.QProgressBar(self.centralwidget)\r\n self.dn_progress2.setGeometry(QtCore.QRect(20, 200, 761, 21))\r\n self.dn_progress2.setProperty(\"value\", 0)\r\n self.dn_progress2.setObjectName(\"dn_progress2\")\r\n self.dn_link = QtWidgets.QLineEdit(self.centralwidget)\r\n self.dn_link.setGeometry(QtCore.QRect(20, 40, 761, 31))\r\n font = QtGui.QFont()\r\n font.setFamily(\"NanumSquare\")\r\n font.setPointSize(11)\r\n self.dn_link.setFont(font)\r\n self.dn_link.setObjectName(\"dn_link\")\r\n self.title = QtWidgets.QLabel(self.centralwidget)\r\n self.title.setGeometry(QtCore.QRect(20, 20, 331, 16))\r\n font = QtGui.QFont()\r\n font.setFamily(\"NanumSquare\")\r\n font.setPointSize(14)\r\n self.title.setFont(font)\r\n self.title.setObjectName(\"title\")\r\n self.dn_fname = QtWidgets.QLabel(self.centralwidget)\r\n self.dn_fname.setGeometry(QtCore.QRect(20, 120, 721, 16))\r\n font = QtGui.QFont()\r\n font.setFamily(\"NanumSquare\")\r\n font.setPointSize(12)\r\n self.dn_fname.setFont(font)\r\n self.dn_fname.setText(\"\")\r\n self.dn_fname.setObjectName(\"dn_fname\")\r\n self.dn_name = QtWidgets.QLabel(self.centralwidget)\r\n self.dn_name.setGeometry(QtCore.QRect(20, 180, 721, 16))\r\n font = QtGui.QFont()\r\n font.setFamily(\"NanumSquare\")\r\n font.setPointSize(12)\r\n self.dn_name.setFont(font)\r\n self.dn_name.setText(\"\")\r\n self.dn_name.setObjectName(\"dn_name\")\r\n self.dn_progress1 = QtWidgets.QProgressBar(self.centralwidget)\r\n self.dn_progress1.setGeometry(QtCore.QRect(20, 140, 761, 21))\r\n self.dn_progress1.setProperty(\"value\", 0)\r\n self.dn_progress1.setObjectName(\"dn_progress1\")\r\n self.Copy = QtWidgets.QLabel(self.centralwidget)\r\n self.Copy.setGeometry(QtCore.QRect(20, 280, 231, 16))\r\n self.Copy.setObjectName(\"Copy\")\r\n self.dn_status = QtWidgets.QLabel(self.centralwidget)\r\n self.dn_status.setGeometry(QtCore.QRect(490, 270, 301, 21))\r\n font = QtGui.QFont()\r\n font.setPointSize(12)\r\n self.dn_status.setFont(font)\r\n self.dn_status.setObjectName(\"dn_status\")\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n self.menubar = QtWidgets.QMenuBar(MainWindow)\r\n self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 19))\r\n self.menubar.setObjectName(\"menubar\")\r\n self.menuFile = QtWidgets.QMenu(self.menubar)\r\n self.menuFile.setObjectName(\"menuFile\")\r\n MainWindow.setMenuBar(self.menubar)\r\n self.statusbar = QtWidgets.QStatusBar(MainWindow)\r\n self.statusbar.setObjectName(\"statusbar\")\r\n MainWindow.setStatusBar(self.statusbar)\r\n self.actionSet_Directory = QtWidgets.QAction(MainWindow)\r\n self.actionSet_Directory.setObjectName(\"actionSet_Directory\")\r\n self.actionInfo = QtWidgets.QAction(MainWindow)\r\n self.actionInfo.setObjectName(\"actionInfo\")\r\n self.menuFile.addAction(self.actionSet_Directory)\r\n self.menuFile.addAction(self.actionInfo)\r\n self.menubar.addAction(self.menuFile.menuAction())\r\n\r\n self.retranslateUi(MainWindow)\r\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n def retranslateUi(self, MainWindow):\r\n _translate = QtCore.QCoreApplication.translate\r\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"Ani24 Downloader\"))\r\n self.dn_btn.setText(_translate(\"MainWindow\", \"Download\"))\r\n self.end_btn.setText(_translate(\"MainWindow\", \"Stop\"))\r\n self.dn_link.setText(_translate(\"MainWindow\", \"https://ani24do.com/ani_list/2858.html\"))\r\n self.title.setText(_translate(\"MainWindow\", \"Ani24 Video Link\"))\r\n self.Copy.setText(_translate(\"MainWindow\", \"Made by Morgan_KR, Ani24_Downloader\"))\r\n self.dn_status.setText(_translate(\"MainWindow\", \"Downloader Started~!\"))\r\n self.menuFile.setTitle(_translate(\"MainWindow\", \"File\"))\r\n self.actionSet_Directory.setText(_translate(\"MainWindow\", \"Set Directory\"))\r\n self.actionInfo.setText(_translate(\"MainWindow\", \"Info\"))\r\n\r\nclass WindowClass(QMainWindow, Ui_MainWindow) :\r\n def __init__(self) :\r\n super().__init__()\r\n self.setupUi(self)\r\n\r\n self.dn_btn.clicked.connect(self.printTextFunction)\r\n self.end_btn.clicked.connect(self.endprocess)\r\n\r\n self.dn_progress1.setMinimum(0)\r\n self.dn_progress2.setMinimum(0)\r\n\r\n def printTextFunction(self) :\r\n ipaddress=socket.gethostbyname(socket.gethostname())\r\n if ipaddress==\"127.0.0.1\":\r\n self.dn_status.setText(\"Not Connected\")\r\n else:\r\n self.dn_status.setText(ipaddress)\r\n download(self.dn_link.text(), self)\r\n dnln = 0\r\n \r\n def endprocess(self):\r\n sys.exit()\r\n\r\ndef vidLinkDown(vidLink, fname, self):\r\n self.dn_status.setText(\"Downloading..\")\r\n r = requests.get(vidLink, stream=True, allow_redirects=True, verify=False)\r\n print(vidLink)\r\n with open(fname, 'wb') as f:\r\n print(r.headers.get('content-length'))\r\n if r.headers.get('content-length') == None:\r\n print(\"Error!! Can't Load Video\")\r\n self.dn_status.setText(\"Error!! Can't Load Video\")\r\n else:\r\n total_length = int(r.headers.get('content-length'))\r\n filesize = 0\r\n self.dn_progress1.setMaximum(100)\r\n for chunk in progress.bar(r.iter_content(chunk_size=1024), expected_size=(total_length/1024) + 1): \r\n filesize += 102400/total_length\r\n self.dn_progress1.setValue(filesize)\r\n QApplication.processEvents()\r\n if chunk:\r\n f.write(chunk)\r\n f.flush()\r\n \r\n self.dn_status.setText(\"Download Complete\")\r\n\r\ndef download(web_url, self):\r\n print(web_url)\r\n url_type = web_url.split('/')\r\n dnln = 0\r\n if url_type[3] == \"ani_list\":\r\n print(\"LIST\")\r\n self.dn_status.setText(\"Loading... Might seen as Stopped.\")\r\n print(\"Loading... Might seen as Stopped.\")\r\n QApplication.processEvents()\r\n req = urllib.request.Request(web_url, headers={'User-Agent': 'Mozilla/5.0'})\r\n response = urllib.request.urlopen(req, context=ssl._create_unverified_context())\r\n html = response.read()\r\n soup = BeautifulSoup(html, 'html.parser')\r\n img_url = soup.find_all('img', {'onerror' : \"this.src='/img/video_no_image.jpg'\"})\r\n\r\n img_url_len = len(img_url)\r\n dnlnt = img_url_len\r\n self.dn_status.setText(\"Loading Complete\")\r\n print(\"Loading Complete\")\r\n\r\n for i in range(0, img_url_len):\r\n jpg_file_name = img_url[i].get('src').split('/')[5]\r\n link_num = jpg_file_name.split('.')[0]\r\n print(img_url[i].get('alt') + \" \" + link_num)\r\n fname = img_url[i].get('alt') + \".mp4\"\r\n\r\n self.dn_fname.setText(fname)\r\n lname = fname.split(\" \")\r\n for i in range(len(lname)-2, len(lname)):\r\n lname[i] = \"\"\r\n self.dn_name.setText(' '.join(lname))\r\n QApplication.processEvents()\r\n vidLink = \"http://test.ani24image.com/ani/download.php?id=\" + link_num\r\n\r\n dnln += int(100/dnlnt)\r\n self.dn_progress2.setMaximum(int(100/dnlnt)*int(dnlnt))\r\n self.dn_progress2.setValue(dnln)\r\n QApplication.processEvents()\r\n if os.path.isfile(fname):\r\n self.dn_status.setText(\"Video Already Exists\")\r\n print(\"Video Already Exists\")\r\n else:\r\n vidLinkDown(vidLink, fname, self)\r\n\r\n elif url_type[3] == \"ani_view\":\r\n print(\"VIEW\")\r\n self.dn_status.setText(\"Loading... Might seen as Stopped.\")\r\n QApplication.processEvents()\r\n req = urllib.request.Request(web_url, headers={'User-Agent': 'Mozilla/5.0'}, verify=False)\r\n response = urllib.request.urlopen(req)\r\n html = response.read()\r\n soup = BeautifulSoup(html, 'html.parser')\r\n url_name = soup.find('div', {'class' : \"qwgqwf\"}).text\r\n link_name = url_name\r\n link_num = url_type[4].split('.')[0]\r\n fname = link_name + \".mp4\"\r\n\r\n self.dn_fname.setText(fname)\r\n QApplication.processEvents()\r\n lname = fname.split(\" \")\r\n for i in range(len(lname)-2, len(lname)):\r\n lname[i] = \"\"\r\n self.dn_name.setText(' '.join(lname))\r\n \r\n print(link_name + \" \" + link_num)\r\n\r\n vidLink = \"http://test.ani24image.com/ani/download.php?id=\" + link_num\r\n\r\n self.dn_progress2.setValue(100)\r\n QApplication.processEvents()\r\n if os.path.isfile(\"/\" + fname):\r\n self.dn_status.setText(\"Video Already Exists\")\r\n print(\"Video Already Exists\")\r\n else:\r\n vidLinkDown(vidLink, fname, self)\r\n\r\nif __name__ == \"__main__\" :\r\n app = QApplication(sys.argv)\r\n win = WindowClass() \r\n win.show()\r\n app.exec_()\r\n","sub_path":"Ani24_Downloader.py","file_name":"Ani24_Downloader.py","file_ext":"py","file_size_in_byte":10642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"306938770","text":"#!/usr/bin/env python\r\n'''\r\nFunciones [Python]\r\nEjemplos de clase\r\n---------------------------\r\nAutor: Inove Coding School\r\nVersion: 1.1\r\n\r\nDescripcion:\r\nPrograma creado para mostrar ejemplos prácticos de los visto durante la clase\r\n'''\r\n\r\n__author__ = \"Inove Coding School\"\r\n__email__ = \"alumnos@inove.com.ar\"\r\n__version__ = \"1.1\"\r\n\r\nimport math\r\n\r\nimport inovetools as it\r\nimport inovetools\r\nfrom inovetools import cantidad_letras\r\n\r\nglobal_pi = math.pi\r\n\r\n\r\ndef superficie_circulo(radio):\r\n return global_pi * (radio**2)\r\n\r\n\r\ndef incrementar(contador, paso=1):\r\n contador += paso\r\n texto = 'Incrementar contador en ' + str(paso)\r\n print(texto)\r\n return contador\r\n\r\n\r\ndef ejemplos_contexto():\r\n # Ejemplos de funciones y contexto\r\n contador = 3\r\n\r\n print('Valor contador inicial =', contador)\r\n incrementar(contador)\r\n print('Valor contador final =', contador)\r\n\r\n print('Valor contador inicial =', contador)\r\n contador = incrementar(contador, paso=1)\r\n print('Valor contador final =', contador)\r\n\r\n r = 2\r\n superficie = superficie_circulo(radio=r)\r\n print('La superficie de un circulo radio {} es {:.2f}'.format(r, superficie))\r\n\r\n numero = -5\r\n print('El modulo de {} es {}'.format(numero, abs(numero)))\r\n\r\n\r\ndef modulo():\r\n # Ejemplo de uso de modulos\r\n lista_palabras = ['sol', 'casa', 'nubes']\r\n lista_palabras.sort()\r\n it.print_palabras_ordenadas(lista_palabras=lista_palabras)\r\n\r\n print('2)Ordenar alfabéticamente de mayor a menor')\r\n lista_palabras = ['sol', 'casa', 'nubes']\r\n inovetools.ordenar_palabras(lista_palabras=lista_palabras)\r\n inovetools.print_palabras_ordenadas(lista_palabras=lista_palabras)\r\n\r\n print('3)Ordenar cantidad de letras de mayor a menor')\r\n lista_palabras = ['sol', 'casa', 'nubes']\r\n it.ordenar_palabras(lista_palabras=lista_palabras, operador=2)\r\n it.print_palabras_ordenadas(lista_palabras=lista_palabras)\r\n\r\n palabra = lista_palabras.pop(0) # Extra la primera palabra, índice = 0\r\n cant_letras_palabra = cantidad_letras(palabra)\r\n print('{}: Cantidad letras {}'.format(palabra, cant_letras_palabra))\r\n\r\n print('Inovetools version,', it.__version__)\r\n\r\n\r\ndef max_max():\r\n # Ejemplo de diferentes formas de utilizar max\r\n palabras = ['vida', 'te', 'Inove', 'dia', 'te']\r\n\r\n # Buscamos la palabra alfabéticamente mayor\r\n max_alfabeticamente = max(palabras)\r\n print('La palabra alfabéticamente más grande:', max_alfabeticamente)\r\n\r\n # Buscamos la palabra con mayor cantidad de letras\r\n max_tamaño = max(palabras, key=len)\r\n print('La palabra más larga:', max_tamaño)\r\n\r\n cantidad_max = palabras.count('Max')\r\n\r\n # Buscamos la palabra que más se repite\r\n max_repeticiones = max(palabras, key=palabras.count)\r\n print('La palabra con repetición en la lista', max_repeticiones)\r\n\r\n\r\ndef hola_mundo():\r\n print('Hola Mundo!')\r\n\r\n\r\ndef imprimir(mensaje):\r\n print(mensaje)\r\n\r\n\r\ndef numero_pi():\r\n num_pi = 3.14159\r\n return num_pi\r\n\r\n\r\nif __name__ == '__main__':\r\n print(\"Bienvenidos a otra clase de Inove con Python\")\r\n # Ejemplos básicos\r\n # Imprimir hola mundo en pantalla\r\n hola_mundo()\r\n\r\n # Imprimit un mensaje en pantalla\r\n imprimir(\"mensaje\")\r\n\r\n # Ejemplo de función con retorno\r\n # Retorna número pi\r\n pi = numero_pi()\r\n\r\n ejemplos_contexto()\r\n modulo()\r\n max_max()\r\n","sub_path":"ejemplos_clase.py","file_name":"ejemplos_clase.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"378776542","text":"# -*- coding: utf-8 -*-\n\"\"\"TMS Response class\n\n@author: Robert Guggenberger\n\"\"\"\nfrom dataclasses import dataclass\nimport numpy as np\n# %%\n@dataclass\nclass Response(): \n \"\"\"A MEP reponse to a TMS pulse\n\n required during initialization are\n\n args\n ----\n chunk:np.ndarray\n a data chunk as received from pylsl.StreamInlet.pull_chunk()\n tstamps:np.ndarray\n the timestamps of this data chunk as received from pylsl.StreamInlet.pull_chunk()\n onset_in_ms:float\n the timestamp of the TMS pulse as e.g. received from \n pylsl.StreamInlet.pull_sample() (i.e. slightly delayed)\n or coil.trigger(), i.e. when command was sent\n\n \"\"\"\n chunk:np.ndarray\n tstamps:np.ndarray\n onset_in_ms:float\n fs:int = 1000\n pre_in_ms:float = 30\n post_in_ms:float = 75\n mep_window_in_ms = [15, 50]\n preactivation_threshold = 50\n preactivation_window_in_ms = [-500, -10]\n\n\n @property\n def onset(self): \n onset = abs((self.onset_in_ms-self.tstamps)[:,0]).argmin()\n return onset \n\n @property\n def pre(self):\n pre = int(self.pre_in_ms*1000/self.fs)\n return self.onset-pre\n\n @property\n def post(self):\n post = int(self.post_in_ms*1000/self.fs)\n return self.onset+post\n \n @property\n def mep_window(self):\n mep_window = [self.onset+int(m*1000/self.fs) \n for m in self.mep_window_in_ms]\n return mep_window\n\n def get_trace(self, channel_idx = 0, baseline_correction:bool = True):\n \"\"\"Cuts a chunk of data\n\n Based on the given onset this function cuts out a trace\n for one or more (if an slice is given) channel. It does a baseline\n correction by default.\n\n args\n ----\n channel_idx\n which channel to use for calculation of latency.\n Can be int or slice (to get multiple channels)\n\n returns\n -------\n trace: np.ndarray\n numpy arrray of shape (pre+post, channels)\n Contains the trace or traces if multiple channels where given\n \"\"\"\n response = self.chunk[self.pre:self.post, channel_idx].copy() \n if baseline_correction:\n bl = self.chunk[self.pre:self.onset, channel_idx] \n response -= bl.mean()\n return response\n\n def get_latency(self, channel_idx:int=0):\n \"\"\"the latency of the MEP in a specific channel\n\n Based on the time of TMS given during initialization, and the hard-coded\n pre_in_ms, post_in_ms and mep_window_in_ms calculates the latency \n\n args\n ----\n channel_idx:int\n which channel to use for calculation of latency\n\n returns\n -------\n vpp: List[np.ndarray,np.ndarray]\n the latency in ms relative to the TMS pulse of the negative and the\n positive peak\n \"\"\"\n\n bl = self.chunk[self.pre:self.onset, channel_idx].mean(0) \n data = self.chunk[self.mep_window[0]:self.mep_window[1], channel_idx]-bl \n peakpos = [data.argmin(), data.argmax()]\n peakpos = [p + self.mep_window[0] for p in peakpos]\n peakpos_in_ms = [p*1000/self.fs -\n self.onset_in_ms\n for p in peakpos] \n return peakpos_in_ms\n\n def get_vpp(self, channel_idx:int=0): \n \"\"\"the peak-to-peak amplitude of the MEP in a specific channel\n\n Based on the time of TMS given during initialization, and the hard-coded\n pre_in_ms, post_in_ms and mep_window_in_ms calculates the Vpp\n\n args\n ----\n channel_idx:int\n which channel to use for calculation of Vpp\n\n returns\n -------\n vpp:np.ndarray\n the peak-to-peak amplitude in native units of the data chunk\n \"\"\"\n bl = self.chunk[self.pre:self.onset, channel_idx].mean(0) \n data = self.chunk[self.mep_window[0]:self.mep_window[1], channel_idx]-bl \n peakpos = [data.argmin(), data.argmax()]\n self.peakpos = [p + self.mep_window[0] for p in peakpos]\n self.peakpos_in_ms = [p*1000/self.fs + self.mep_window_in_ms[0] + \n self.pre_in_ms for p in peakpos] \n self.peakval = [data.min(), data.max()]\n return data.max()-data.min()\n \n def remove_jitter(self, break_threshold_seconds=1,\n break_threshold_samples=500):\n \"deprecated: would remove jitter, but did reduce timing accuracy\" \n nsamples = len(self.tstamps)\n tdiff = 1.0 / self.fs if self.fs > 0 else 0.0\n self.rawtstamps = self.tstamps.copy() \n if nsamples > 0 and self.fs > 0:\n # Identify breaks in the data\n diffs = np.diff(self.tstamps,axis=0)\n breaks_at = diffs > np.max((break_threshold_seconds,\n break_threshold_samples * tdiff))\n if np.any(breaks_at):\n indices = np.where(breaks_at)[0]\n indices = np.hstack((0, indices, indices, nsamples - 1))\n ranges = np.reshape(indices, (2, -1)).T\n else:\n ranges = [(0, nsamples - 1)]\n \n # Process each segment separately\n samp_counts = []\n durations = []\n self.effective_srate = 0\n for range_i in ranges:\n if range_i[1] > range_i[0]:\n # Calculate time stamps assuming constant intervals\n # within the segment.\n indices = np.arange(range_i[0], range_i[1] + 1, 1)[:, None]\n X = np.concatenate((np.ones_like(indices), indices), axis=1)\n y = self.tstamps[indices,0]\n mapping = np.linalg.lstsq(X, y, rcond=-1)[0]\n self.tstamps[indices,0] = (mapping[0] + mapping[1] *\n indices)\n # Store num_samples and segment duration\n samp_counts.append(indices.size)\n durations.append((self.tstamps[range_i[1]] -\n self.tstamps[range_i[0]]) + tdiff)\n samp_counts = np.asarray(samp_counts)\n durations = np.asarray(durations)\n if np.any(samp_counts):\n self.effective_srate = np.sum(samp_counts) / np.sum(durations)\n else:\n self.effective_srate = 0\n\n def check_precativation(self, channel_idx:int=0):\n \"\"\"checks muscle activation prior a stimulus\n\n When the MEP in a defined window before the stimulus is over a preactivation threshold\n Based on the time of TMS given during initialization, and the hard-coded\n pre_in_ms, post_in_ms and mep_window_in_ms calculates the Vpp\n args\n ----\n channel_idx:int\n which channel to use for calculation of Vpp\n\n returns\n -------\n preactivated:bool\n True when channel showed preactivation\n \"\"\"\n bl = self.chunk[self.pre:self.onset, channel_idx].mean(0)\n data = self.chunk[self.preactivation_window_in_ms[0]:self.preactivation_window_in_ms[1], channel_idx]-bl\n vpp = data.max()-data.min()\n return self.preactivation_threshold < vpp\n\n def get_xaxis(self, stepsize=5):\n \"returns xticks, xticklabels and xlim for plotting with matplotlib\"\n \n xticks = np.arange(0, self.post-self.pre, stepsize*1000/self.fs)\n xlim = (0, self.post-self.pre)\n xticklabels = (['{0:.0f}'.format(x) for x in np.arange(\n -self.pre_in_ms*1000/self.fs, \n (self.post_in_ms+stepsize)*1000/self.fs, stepsize)])\n return xticks, xticklabels, xlim\n \n def as_json(self, channel_idx:int=0):\n \"\"\"encodes the response as json\n\n args\n ----\n channel_idx:int\n which channel to use for calculation of MEP parameters\n\n returns\n -------\n msg:str\n a json-encoded dictionary to be sent to localite with \n _`coil.send_response`\n \"\"\"\n bl = self.chunk[self.pre:self.onset, channel_idx].mean(0) \n data = self.chunk[self.mep_window[0]:self.mep_window[1], channel_idx]-bl \n mi,ma = [data.min(), data.max()] \n max_latency = self.get_latency(channel_idx)[0]\n \n msg = ('{\"mepmaxtime\": ' + f\"{max_latency:.2f}, \" + \n '\"mepamplitude\": ' + f\"{ma-mi:.2f}, \" + \n '\"mepmin\": ' + f\"{mi:.2f}, \" + \n '\"mepmax\": ' + f\"{ma:.2f}\" + '}')\n return msg\n \nclass MockResponse():\n \"mocks a response for testing and development\"\n def __new__(cls):\n return Response(chunk=np.random.random((1000,8)),\n tstamps=np.atleast_2d(np.linspace(0,1000,1000)).T,\n fs=1000,\n onset_in_ms=501)\n \n","sub_path":"localite/response.py","file_name":"response.py","file_ext":"py","file_size_in_byte":9022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"200062864","text":"#!/usr/bin/python3\n\"\"\"Start a Flask web application\"\"\"\n\nfrom flask import Flask, render_template\nfrom models import storage\nfrom models.state import State\nfrom models.city import City\n\n\napp = Flask(__name__)\n\n\n@app.route('/cities_by_states', strict_slashes=False)\ndef state_cities_list():\n \"\"\"/cities_by_states route\"\"\"\n c = list(storage.all(City).values())\n s = list(storage.all(State).values())\n s.sort(key=lambda x: x.name)\n c.sort(key=lambda x: x.name)\n return render_template('8-cities_by_states.html', cities=c, states=s)\n\n\n@app.teardown_appcontext\ndef close(self):\n \"\"\"Closes session\"\"\"\n storage.close()\n\nif __name__ == '__main__':\n app.run(port=5000, host='0.0.0.0')\n","sub_path":"web_flask/8-cities_by_states.py","file_name":"8-cities_by_states.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"292619079","text":"\"\"\"\nThis is a simple script to grab tweets using the twitter API and provide useful\noutput for jstream.py\n\nThis script requires access to your twitter OAuth credentials. For that it\nparses a file which contains credentials.\n\"\"\"\n\nimport json\nimport os\nimport time\n\nfrom twython import Twython\n\n# load twitter credentials from json file, located at user home dir\nwith open(os.path.expanduser(\"~/twitter_oauth.json\")) as f:\n keys = json.loads(f.read())\n\ntwitter = Twython(\n keys['app_key'],\n keys['app_secret'],\n keys['oauth_token'],\n keys['oauth_token_secret'])\n\ndef parsetime(timestr):\n return time.strptime(timestr, '%a %b %d %H:%M:%S +0000 %Y')\n\ndef get_recent_tweets():\n tweets = []\n for tweet in twitter.get_user_timeline():\n link = ('https://twitter.com/' + tweet['user']['screen_name'] +\n '/statuses/' + tweet['id_str'])\n tweets.append({\n 'title': tweet['text'],\n 'link': link,\n 'updated_parsed': parsetime(tweet['created_at']),\n 'hostname': 'twitter.com'})\n return tweets\n","sub_path":"tweets.py","file_name":"tweets.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"595186196","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom tests.helpers import create_ctfd, destroy_ctfd, login_as_user\n\n\ndef test_api_self_ban():\n \"\"\"PATCH /api/v1/users/ should not allow a user to ban themselves\"\"\"\n app = create_ctfd()\n with app.app_context():\n with login_as_user(app, name=\"admin\") as client:\n r = client.patch(\"/api/v1/users/1\", json={\"banned\": True})\n resp = r.get_json()\n assert r.status_code == 400\n assert resp[\"success\"] == False\n assert resp[\"errors\"] == {\"id\": \"You cannot ban yourself\"}\n destroy_ctfd(app)\n","sub_path":"tests/api/v1/users/test_users.py","file_name":"test_users.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"157645567","text":"import random\r\n\r\n\r\ndef task1(arr):\r\n for i in range(len(arr)):\r\n for j in range(len(arr)):\r\n if arr[i][j] != arr[j][i]:\r\n return \"орієнтований\" + '\\n'\r\n return \"неорієнтований\" + '\\n'\r\n\r\n\r\ndef get_graph_list(matrix):\r\n res = []\r\n for i in range(len(matrix)):\r\n res.append([])\r\n for j in range(len(matrix)):\r\n if matrix[i][j] == 1:\r\n res[i].append(j)\r\n return res\r\n\r\n\r\ndef printCircuit(adj):\r\n print('Ейлерів шлях: ')\r\n if len(adj) == 0:\r\n return\r\n curr_path = [0]\r\n circuit = []\r\n while curr_path:\r\n curr_v = curr_path[-1]\r\n if adj[curr_v]:\r\n next_v = adj[curr_v].pop()\r\n curr_path.append(next_v)\r\n else:\r\n circuit.append(curr_path.pop())\r\n\r\n for i in range(len(circuit) - 1, -1, -1):\r\n print(circuit[i], end=\"\")\r\n if i:\r\n print(\" -> \", end=\"\")\r\n print('\\n')\r\n if circuit[0] == circuit[-1]:\r\n print(\"Також є Єйлерів цикл\")\r\n\r\n\r\ndef dijkstra(N, S, matrix):\r\n print('граф: ', matrix)\r\n valid = [True] * N\r\n weight = [1000000] * N\r\n weight[S] = 0\r\n for i in range(N):\r\n min_weight = 1000001\r\n ID_min_weight = -1\r\n for j in range(N):\r\n if valid[j] and weight[j] < min_weight:\r\n min_weight = weight[j]\r\n ID_min_weight = j\r\n for z in range(N):\r\n if weight[ID_min_weight] + matrix[ID_min_weight][z] < weight[z]:\r\n weight[z] = weight[ID_min_weight] + matrix[ID_min_weight][z]\r\n valid[ID_min_weight] = False\r\n return weight\r\n\r\n\r\ndef get_graph_for_dijkstra(graph):\r\n res = []\r\n for i in range(len(graph)):\r\n res.append([])\r\n for j in range(len(graph)):\r\n if graph[i][j] == 1:\r\n res[i].append(random.randint(1, 10))\r\n else:\r\n res[i].append(10000000)\r\n return res\r\n\r\n\r\ngraph = [[0, 1, 0, 0, 1], [1, 1, 0, 1, 0], [1, 0, 1, 0, 1], [0, 1, 1, 1, 1], [1, 0, 0, 0, 1]]\r\n\r\nPRIORITY = {1: ['+', '-'], 2: ['*', '/']}\r\n\r\n\r\ndef priority(value: str) -> int:\r\n for k, v in PRIORITY.items():\r\n if value in v:\r\n return k\r\n return -1\r\n\r\n\r\ndef infix_to_postfix(expression): # input expression\r\n OPERATORS = set(['+', '-', '*', '/', '(', ')', '^'])\r\n PRIORITY = {'+': 1, '-': 1, '*': 2, '/': 2, '^': 3}\r\n stack = []\r\n output = ''\r\n for ch in expression:\r\n if ch not in OPERATORS:\r\n output += ch\r\n elif ch == '(':\r\n stack.append('(')\r\n elif ch == ')':\r\n while stack and stack[-1] != '(':\r\n output += stack.pop()\r\n stack.pop()\r\n else:\r\n while stack and stack[-1] != '(' and PRIORITY[ch] <= PRIORITY[stack[-1]]:\r\n output += stack.pop()\r\n stack.append(ch)\r\n\r\n while stack:\r\n output += stack.pop()\r\n\r\n return output\r\n\r\n\r\nOPERATORS = set(['*', '-', '+', '%', '/', '^']) # set of operators allowed in expression\r\n\r\n#shdadasdasdad commit\r\n#second commit\r\n#third\r\ndef evaluate_postfix(expression):\r\n stack = [] # empty stack for storing numbers\r\n res = []\r\n for i in expression:\r\n if i not in OPERATORS:\r\n stack.append(i) # contains numbers\r\n else:\r\n\r\n a = stack.pop() # if ch==operator then pop 2 numbers\r\n b = stack.pop()\r\n if i == '+':\r\n res = int(b) + int(a) # old val recent value\r\n elif i == '-':\r\n res = int(b) - int(a)\r\n elif i == '*':\r\n res = int(b) * int(a)\r\n elif i == '%':\r\n res = int(b) % int(a)\r\n elif i == '/':\r\n res = int(b) / int(a)\r\n elif i == '^':\r\n res = int(b) ** int(a)\r\n print(b, i, a, '=', res)\r\n stack.append(res) # final result\r\n return (''.join(map(str, stack)))\r\n\r\ndef postfix_to_prefix(expression): # input expression\r\n ops = ['*', '-', '+', '%', '/', '^']\r\n res = ''\r\n tmp = ''\r\n for i in range(len(expression)-1, -1, -1):\r\n res += expression[i]\r\n\r\n res1 = ''\r\n for i in range(len(res)):\r\n if res[i] in ops:\r\n res1 += (''.join(list(reversed(tmp))))\r\n tmp = ''\r\n res1+= res[i]\r\n else:\r\n tmp += res[i]\r\n res1 += (''.join(list(reversed(tmp))))\r\n return res1\r\n\r\n\r\nprint(task1(graph))\r\n\r\nprintCircuit(get_graph_list(graph))\r\n\r\nprint('найкоротша відстань від першої до останньої вершини: ', dijkstra(5, 0, get_graph_for_dijkstra(graph))[-1], '\\n')\r\n\r\nexpression = '5^2+8/2*(7-4)'\r\nprint('вираз: ', expression)\r\nprint('зворотній польскький запис: ', infix_to_postfix(expression))\r\nprint('прямий польський запис: ', postfix_to_prefix(infix_to_postfix(expression)))\r\nprint(evaluate_postfix(infix_to_postfix(expression)), '\\n')\r\n\r\n","sub_path":"lab4cdm.py","file_name":"lab4cdm.py","file_ext":"py","file_size_in_byte":5085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"421966322","text":"\n# Copyright (c) 2015, 2014 Computational Molecular Biology Group, Free University\n# Berlin, 14195 Berlin, Germany.\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without modification,\n# are permitted provided that the following conditions are met:\n#\n# * Redistributions of source code must retain the above copyright notice, this\n# list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation and/or\n# other materials provided with the distribution.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS''\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR\n# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON\n# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n__author__ = 'noe'\n\nimport numpy as _np\nimport matplotlib.pylab as _plt\nfrom scipy.interpolate import griddata as gd\n\ndef scatter_contour(x, y, z, ncontours = 50, colorbar=True, fig=None, ax=None, cmap=None, outfile=None):\n \"\"\"Shows a contour plot on scattered data (x,y,z) and the plots the positions of the points (x,y) on top.\n\n Parameters\n ----------\n x : ndarray(T)\n x-coordinates\n y : ndarray(T)\n y-coordinates\n z : ndarray(T)\n z-coordinates\n ncontours : int, optional, default = 50\n number of contour levels\n fig : matplotlib Figure object, optional, default = None\n the figure to plot into. When set to None the default Figure object will be used\n ax : matplotlib Axes object, optional, default = None\n the axes to plot to. When set to None the default Axes object will be used.\n cmap : matplotlib colormap, optional, default = None\n the color map to use. None will use pylab.cm.jet.\n outfile : str, optional, default = None\n output file to write the figure to. When not given, the plot will be displayed\n\n Returns\n -------\n ax : Axes object containing the plot\n\n \"\"\"\n # check input\n if (ax is None):\n if fig is None:\n ax = _plt.gca()\n else:\n ax = fig.gca()\n\n # grid data\n points = _np.hstack([x[:,None],y[:,None]])\n xi, yi = _np.mgrid[x.min():x.max():100j, y.min():y.max():200j]\n zi = gd(points, z, (xi, yi), method='cubic')\n # contour level levels\n eps = (z.max() - z.min()) / float(ncontours)\n levels = _np.linspace(z.min() - eps, z.max() + eps)\n # contour plot\n if cmap is None:\n cmap=_plt.cm.jet\n cf = ax.contourf(xi, yi, zi, 15, cmap=cmap, levels=levels)\n # color bar if requested\n if colorbar:\n _plt.colorbar(cf, ax=ax)\n # scatter points\n ax.scatter(x,y,marker='o',c='b',s=5)\n\n # show or save\n #if outfile is None:\n # _plt.show()\n if outfile is not None:\n _plt.savefig(outfile)\n\n return ax","sub_path":"pyemma/plots/plots2d.py","file_name":"plots2d.py","file_ext":"py","file_size_in_byte":3443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"299783557","text":"#!/usr/bin/env python \n# -*- coding: utf-8 -*- \n# @Time : 2018/8/18 22:10 \n# @Author : yangwm\n\nimport socket\n\nsk = socket.socket()\nprint(sk)\naddress = ('127.0.0.1', 8000)\nsk.connect(address)\nwhile 1:\n a = sk.recv(1024)\n print(a)\n inp = input('>>>')\n sk.sendall(bytes(inp, 'utf8'))","sub_path":"python_fullstack_oldboy/socke/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"198117408","text":"#!/usr/bin/env python \n\n\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\n\nfrom copy import copy, deepcopy\n\nfrom item import *\nfrom setup import *\n\n#----------------------------------------------------------------------\n# SEQUENCES\n#----------------------------------------------------------------------\n\ntop = LSequence('Master Sequence')\nmasterSequence = top\n\n#----------------------------------------------------------------------\nstateSeq = LSequence('CLS State', PARALLEL, 'Report state of the CLS', top)\ninjectorStateSeq = LSequence('Injector State', PARALLEL, 'Report state of the Injector', stateSeq)\ninjectorLaserSeq = LSequenceItem('I:L',\n 'clsstate -S I:L',\n PARALLEL, PROMPT,\n 'Check injector laser',\n injectorStateSeq)\nsoleoindStateSeq = LSequenceItem('I:SO',\n 'clsstate -S I:SO',\n PARALLEL, PROMPT,\n 'Check Solenoids',\n injectorStateSeq)\nmodulator1StateSeq = LSequenceItem('I:K1 State', \n\t\t\t'clsstate -S I:K1', \n\t\t\tPARALLEL, PROMPT, \n\t\t\t'Check 1st modulator', \n\t\t\tinjectorStateSeq)\nmodulator2StateSeq = LSequenceItem('I:K2 State', \n 'clsstate -S I:K2', \n PARALLEL, PROMPT, \n 'Check 2nd modulator', \n injectorStateSeq)\ncorrectorStateSeq = LSequenceItem('I:HV',\n 'clsstate -S I:HV',\n PARALLEL, PROMPT,\n 'Check Correctors',\n injectorStateSeq)\nsbandStateSeq = LSequenceItem('S-Band State',\n 'clsstate -S I:SB',\n PARALLEL, PROMPT,\n 'Check S-Band',\n injectorStateSeq)\niscreenStateSeq = LSequenceItem('I:S',\n 'clsstate -S I:S',\n PARALLEL, PROMPT,\n 'Check screens',\n injectorStateSeq)\nifeedbackStateSeq = LSequenceItem('I:F',\n 'clsstate -S I:F',\n PARALLEL, PROMPT,\n 'Check feedback',\n injectorStateSeq)\ntransportStateSeq = LSequence('Transport State', PARALLEL, 'Report state of the Transport Line', stateSeq)\ntfeedbackStateSeq = LSequenceItem('T:F',\n 'clsstate -S T:F',\n SKIP, PROMPT,\n 'Check feedback',\n transportStateSeq)\nkickerStateSeq = LSequenceItem('T:KI State',\n 'clsstate -S T:KI',\n PARALLEL, PROMPT,\n 'Check Kicker settings',\n transportStateSeq)\nmagnetStateSeq = LSequenceItem('T:QB State',\n 'clsstate -S T:QB',\n PARALLEL, PROMPT,\n 'Check Transport magnets',\n transportStateSeq)\ntscreenStateSeq = LSequenceItem('T:S',\n 'clsstate -S T:S',\n PARALLEL, PROMPT,\n 'Check screens',\n transportStateSeq)\nringStateSeq = LSequence('Ring State', PARALLEL, 'Report state of the Ring', stateSeq)\nrmagnetStateSeq = LSequenceItem('R:QB State',\n 'clsstate -S R:QB',\n PARALLEL, PROMPT,\n 'Check Ring magnets',\n ringStateSeq)\nlbandStateSeq = LSequenceItem('L-Band State',\n 'clsstate -S R:LB',\n PARALLEL, PROMPT,\n 'Check L-Band',\n ringStateSeq)\n\n\n\n#----------------------------------------------------------------------\nstartModulatorSeq = LSequence('Start Modulators',PARALLEL, 'Start all the modulators', masterSequence)\n\nstartModulator1Seq = LSequence('Start I:K1', PARALLEL, 'Start 1st modulator', startModulatorSeq)\nstartModulator1Seq.precheck = LSequenceItem('I:K1 Hardware ok?', 'cacheck -E 0 I:K1:AlarmBI', SEQUENTIAL, PROMPT, 'Check hardware alarm', startModulator1Seq)\nstartModulator1Seq.sleep = LSequenceItem('I:K1 Wait 15min', 'sleep 5', SEQUENTIAL, PROMPT, 'Wait for modulator to warm up', startModulator1Seq)\nstartModulator1Seq.precheck = LSequenceItem('I:K1 HV On?', 'cacheck -E 1 I:K1:HighVltgOnBI', SEQUENTIAL, PROMPT, 'Check HV On', startModulator1Seq)\nstartModulator1Seq.precheck2 = LSequenceItem('I:K1 HV On? (2)', 'cacheck -E 2 I:K1:HighVltgOnBI', SEQUENTIAL, PROMPT, 'Check HV On', startModulator1Seq)\n\nstartModulator2Seq = LSequence('Start I:K2', PARALLEL, 'Start 2nd modulator', startModulatorSeq)\nstartModulator2Seq.precheck = LSequenceItem('I:K2 Hardware ok?', 'cacheck -E 0 I:K2:AlarmBI', SEQUENTIAL, PROMPT, 'Check hardware alarm', startModulator2Seq)\nstartModulator2Seq.sleep = LSequenceItem('I:K2 Wait 15min', 'sleep 5', SEQUENTIAL, PROMPT, 'Wait for modulator to warm up', startModulator2Seq)\nstartModulator2Seq.precheck = LSequenceItem('I:K2 HV On?', 'cacheck -E 1 I:K2:HighVltgOnBI', SEQUENTIAL, PROMPT, 'Check HV On', startModulator2Seq)\n\n","sub_path":"cls1/eggs/src/lti/widgets/statemachine/sequences.py","file_name":"sequences.py","file_ext":"py","file_size_in_byte":5168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"169437632","text":"import random\n\ndef lottoNumber():\n resultList =[]\n for i in range(6):\n num = random.randint(1, 46)\n if num not in resultList:\n resultList.append(num)\n print(resultList)\n \n \n#강사님 답\ndef lottoNumber() :\n lottoList = []\n while True:\n if len(lottoList)>= 6:\n break\n else:\n data = random.randint(1,45)\n if data not in lottoList:\n lottoList.append(data)\n print(lottoList)\n\nlottoNumber()\n\nif __name__ ==\"__main__\":\n print('Main 으로 실행되었음')","sub_path":"data/Custom/lotto.py","file_name":"lotto.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"369784535","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 28 10:43:27 2021\n\n@author: vijay\n\"\"\"\n\n#\"x\" is used to create files\n#f = open(\"createdFile.txt\",\"x\")\n#f.close()\n#commented this section out because pythin will error if\n#we attempt to create a file that already exists\n\n#\"w\" is overwriting the \"createdFile.txt\" text file with\n#the following two lines of code \nf = open(\"createdFile.txt\",\"w\")\nf.write(\"This is the first line\")\nf.write(\"\\nThis is the second line\")\nf.close()","sub_path":"creatingFile.py","file_name":"creatingFile.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"192054167","text":"from PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import QTextCursor\nimport re\nfrom modules.db import db\n\n\nclass Signals:\n def key_pressed(self, func):\n def f(*args):\n key = args[0].key()\n modifiers = args[0].modifiers()\n # txt_title\n if self.txt_title.hasFocus():\n if key in (Qt.Key_Enter, Qt.Key_Return):\n self.set_mode(\"new_title\")\n elif key == Qt.Key_Down:\n if self.mode == \"search\" and self.search_data.rowCount():\n self.tr_search.setCurrentIndex(self.search_data.index(0, 0))\n self.tr_search.setFocus()\n elif self.mode != \"search\":\n self.txt_main.setFocus()\n # txt_main\n elif self.txt_main.hasFocus():\n cursor = self.txt_main.textCursor()\n if key == Qt.Key_Up and (modifiers & Qt.ControlModifier):\n self.txt_title.setFocus()\n elif key in (Qt.Key_Enter, Qt.Key_Return):\n if not cursor.hasSelection():\n cursor.movePosition(QTextCursor.StartOfLine, QTextCursor.KeepAnchor)\n line = cursor.selectedText()\n if line:\n cnt = len(line) - len(line.lstrip()) # define count of whitespace symbols at the beginning\n line = line + '\\n' + line[:cnt] + ('\\t' if line[-1] == ':' else '')\n cursor.insertText(line)\n return\n elif key == Qt.Key_Tab or key == Qt.Key_Backtab:\n if cursor.hasSelection():\n text = \"\"\n cnt = 0\n for line in cursor.selection().toPlainText().split('\\n'):\n if not line:\n text += '\\n'\n else:\n if key == Qt.Key_Tab:\n text += '\\t' + line + '\\n'\n cnt += 1\n else:\n text += (line[1:] if line[0] == '\\t' else line) + '\\n'\n cnt -= 1\n if text:\n text = text[:-1]\n sel_start = cursor.selectionStart()\n sel_end = cursor.selectionEnd()\n cursor.insertText(text)\n # repair selection\n cursor.setPosition(sel_start)\n cursor.setPosition(sel_end + cnt, QTextCursor.KeepAnchor)\n self.txt_main.setTextCursor(cursor)\n return # to cancel regular behaviour for TAB\n elif key == Qt.Key_Home:\n old_cursor = QTextCursor(cursor)\n cursor.clearSelection()\n cursor.movePosition(QTextCursor.StartOfLine, QTextCursor.KeepAnchor)\n line = cursor.selectedText()\n if line:\n cnt = len(line) - len(line.lstrip()) # define count of whitespace symbols at the beginning\n if cnt:\n new_pos = cnt if cnt != len(line) else 0\n old_cursor.setPosition(cursor.position() + new_pos,\n QTextCursor.KeepAnchor if modifiers & Qt.ShiftModifier else QTextCursor.MoveAnchor)\n self.txt_main.setTextCursor(old_cursor)\n return\n # tr_search\n elif self.tr_search.hasFocus():\n if key in (Qt.Key_Enter, Qt.Key_Return):\n self.tr_double_clicked(self.tr_search.currentIndex())\n elif self.tr_search.currentIndex() == self.search_data.index(0, 0) \\\n and key == Qt.Key_Up:\n self.txt_title.setFocus()\n elif key == Qt.Key_Delete:\n item = self.tr_search.currentIndex()\n note_type = item.data(Qt.UserRole + 2)\n if note_type == \"title\":\n if key == Qt.Key_Delete and self.show_msg_box(\"delete\") == QMessageBox.Yes:\n db.delete_note(item.data(Qt.UserRole + 1))\n self.draw_tag_checkboxes()\n self.update_search()\n func(*args)\n return f\n\n def save_key(self):\n self.logger.debug(\"save_key()\")\n if self.save_enabled:\n self.save()\n\n def new_key(self):\n self.logger.debug(\"new_key()\")\n self.set_mode(\"new\")\n\n def back_key(self):\n self.logger.debug(\"back_key()\")\n if self.mode == 'search':\n self.txt_title.setText(\"\")\n elif self.mode == 'edit':\n self.set_mode(\"view\")\n else:\n self.set_mode(\"search_title\")\n\n def edit_key(self):\n self.logger.debug(\"edit_key()\")\n if self.mode == \"view\":\n self.set_mode(\"edit\")\n\n def search_key(self):\n self.logger.debug(\"search_key()\")\n self.set_mode(\"search\")\n\n def replace_key(self):\n self.logger.debug(\"replace_key()\")\n if self.mode == \"edit\" or self.mode == \"new\":\n btn, txt_from, txt_to, match_case, words = self.replace_dlg.exec()\n if btn:\n if not self.txt_main.textCursor().hasSelection():\n self.txt_main.selectAll()\n cursor = self.txt_main.textCursor()\n selection = cursor.selection().toPlainText()\n result = re.sub(txt_from if not words else f\"\\\\b{txt_from}\", txt_to, selection,\n flags=re.IGNORECASE if not match_case else 0)\n cursor.insertText(result)\n\n @staticmethod\n def tr_item_changed(item):\n item_id = item.data(Qt.UserRole + 1)\n item_type = item.data(Qt.UserRole + 2)\n item_text = item.text()\n if item_type == \"title\":\n db.update_note_title(item_id, item_text)\n elif item_type == \"body\":\n db.update_note_body(item_id, item_text)\n\n def txt_title_text_changed(self, txt):\n self.title = txt\n self.tags = [tag for tag in re.findall(\"#(\\\\w+)\", self.title)]\n self.draw_tag_checkboxes()\n # self.tags = sorted(self.tags, key=lambda x: len(x), reverse=True)\n for tag in self.tags:\n self.title = self.title.replace('#' + tag, '')\n self.title = self.title.strip()\n if self.mode == \"search\":\n self.update_search()\n else:\n self.update_save_enabled()\n\n def txt_main_text_changed(self):\n self.body = self.txt_main.toPlainText()\n if self.mode != \"search\":\n self.update_save_enabled()\n\n def cb_clicked(self, checked):\n tag = self.sender().text()\n if not tag:\n return\n txt = self.txt_title.text()\n if checked and tag not in txt:\n self.txt_title.setText(txt + f\" {tag}\")\n elif not checked and tag in txt:\n self.txt_title.setText(txt.replace(' ' + tag, \"\"))\n if self.mode == \"search\":\n self.update_search()\n else:\n self.update_save_enabled()\n\n def tr_clicked(self, index):\n if self.tr_search.isExpanded(index):\n self.tr_search.setExpanded(index, False)\n else:\n self.tr_search.setExpanded(index, True)\n\n def tr_double_clicked(self, index):\n self.cur_note_id = index.data(Qt.UserRole + 1)\n self.search = self.txt_title.text()\n self.set_mode(\"view\")\n\n def tb_view_menu_requested(self, pt):\n menu = QMenu(self)\n action_edit = QAction(\"Edit note\")\n\n def tb_action_edit_triggered():\n self.set_mode(\"edit\")\n action_edit.triggered.connect(tb_action_edit_triggered)\n menu.addAction(action_edit)\n menu.exec(self.web_view.mapToGlobal(pt))\n","sub_path":"modules/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":8167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"236077170","text":"\n\"\"\" \nSet up the plot figures, axes, and items to be done for each frame.\n\nThis module is imported by the plotting routines and then the\nfunction setplot is called to set the plot parameters.\n \n\"\"\" \n\ndef meters_to_km(x,y):\n return x/1e3,y/1e3\n \n#--------------------------\ndef setplot(plotdata):\n#--------------------------\n \n \"\"\" \n Specify what is to be plotted at each frame.\n Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.\n Output: a modified version of plotdata.\n \n \"\"\" \n\n import os\n\n import numpy as np\n import matplotlib.pyplot as plt\n\n from pyclaw.plotters import colormaps, geoplot\n from pyclaw.data import Data\n\n amrdata = Data(os.path.join(plotdata.outdir,'amr2ez.data'))\n hurricane_data = Data(os.path.join(plotdata.outdir,'hurricane.data'))\n multilayer_data = Data(os.path.join(plotdata.outdir,'multilayer.data'))\n \n if multilayer_data.bathy_type == 1:\n ref_lines = [multilayer_data.bathy_location]\n elif multilayer_data.bathy_type == 2:\n ref_lines = [multilayer_data.x0,multilayer_data.x1,multilayer_data.x2]\n else:\n ref_lines = []\n \n plotdata.clearfigures()\n plotdata.clear_frames = False\n plotdata.clear_figs = True\n \n plotdata.save_frames = False\n \n # ========================================================================\n # Generic helper functions\n # ========================================================================\n def pcolor_afteraxes(current_data):\n hurricane_afteraxes(current_data)\n bathy_ref_lines(current_data)\n # gauge_locations(current_data)\n \n def contour_afteraxes(current_data):\n hurricane_afteraxes(current_data)\n \n def bathy_ref_lines(current_data):\n plt.hold(True)\n y = [amrdata.ylower,amrdata.yupper]\n for ref_line in ref_lines:\n plt.plot([ref_line,ref_line],y,'y--')\n plt.hold(False)\n\n # ========================================================================\n # Gauge functions\n # ========================================================================\n def gauge_locations(current_data,gaugenos='all'):\n from pyclaw.plotters import gaugetools\n plt.hold(True)\n gaugetools.plot_gauge_locations(current_data.plotdata, \\\n gaugenos=gaugenos, format_string='kx', add_labels=True)\n plt.hold(False)\n\n def gaugetopo(current_data):\n q = current_data.q\n h = q[:,0]\n eta = q[:,3]\n topo = eta - h\n return topo\n \n def gauge_afteraxes(current_data):\n # Change time to hours\n plt.xlabel('t (hours)')\n plt.ylabel('m')\n locs,labels = plt.xticks()\n # import pdb; pdb.set_trace()\n labels = np.trunc(locs/3600.0)\n # locs = np.linspace(-12.0,40,52)\n # labels = range(-12,41)\n plt.xticks(locs,labels)\n \n # Add sea level line\n # t = current_data.t\n plt.hold(True)\n plt.plot([0,0],[0,40],'k-')\n plt.hold(False)\n\n \n # ========================================================================\n # Hurricane related helper functions\n # ========================================================================\n # Hurricane eye location\n def eye_location(cd):\n t = cd.t\n # Hurricane eye\n x = t * hurricane_data.hurricane_velocity[0] + hurricane_data.R_eye_init[0]\n y = t * hurricane_data.hurricane_velocity[1] + hurricane_data.R_eye_init[1]\n return x,y\n \n def hour_figure_title(current_data):\n t = current_data.t\n title = current_data.plotaxes.title\n plt.title('%s at time t = %3.2f h' % (title,str(t/3600.0)))\n\n def m_to_km_labels(current_data=None):\n plt.xlabel('km')\n plt.ylabel('km')\n locs,labels = plt.xticks()\n labels = locs/1.e3\n plt.xticks(locs,labels)\n locs,labels = plt.yticks()\n labels = locs/1.e3\n plt.yticks(locs,labels)\n \n def hurricane_afteraxes(current_data):\n x,y = eye_location(current_data)\n plt.hold(True)\n plt.plot(x,y,'rD')\n plt.hold(False)\n \n hour_figure_title(current_data)\n m_to_km_labels()\n # wind_contours(current_data) ?\n \n # plt.hold(True)\n # pos = -80.0 * (23e3 / 180) + 500e3 - 5e3\n # plt.plot([pos,pos],[-300e3,300e3],'b',[pos-5e3,pos-5e3],[-300e3,300e3],'y')\n # plt.hold(False)\n # wind_contours(current_data)\n # bathy_ref_lines(current_data)\n \n def hurricane_wind(current_data):\n if current_data.level == 1:\n t = current_data.t\n u = current_data.q[:,:,8]\n v = current_data.q[:,:,9]\n plt.hold(True)\n Q = plt.quiver(current_data.x[::3,::3],current_data.y[::3,::3],\n u[::3,::3],v[::3,::3])\n # plt.quiverkey(Q,0.5,0.5,50,r'$50 \\frac{m}{s}$',labelpos='W',\n # fontproperties={'weight':'bold'})\n plt.hold(False)\n \n def wind_x(cd):\n return cd.q[:,:,4]\n def wind_y(cd):\n return cd.q[:,:,5]\n def wind_speed(cd):\n return np.sqrt(wind_x(cd)**2 + wind_y(cd)**2)\n\n def pressure(cd):\n # The division by 100.0 is to convert from Pa to millibars\n return cd.q[:,:,6] / 100.0\n \n def wind_contours(current_data):\n plt.hold(True)\n w = wind_speed(current_data)\n max_w = np.max(np.max(w))\n levels = [0.0,0.25*max_w,0.5*max_w,0.75*max_w,max_w*0.999]\n C = plt.contour(current_data.x,current_data.y,w,levels)\n plt.clabel(C,inline=1)\n plt.hold(False)\n \n # ========================================================================\n # Water helper functions\n # ========================================================================\n def b(cd):\n return cd.q[:,:,3] - cd.q[:,:,0]\n \n def extract_eta(h,eta,DRY_TOL=10**-3):\n index = np.nonzero((np.abs(h) < DRY_TOL) + (h == np.nan))\n eta[index[0],index[1]] = np.nan\n return eta\n \n def extract_velocity(h,hu,DRY_TOL=10**-8):\n # u = np.ones(hu.shape) * np.nan\n u = np.zeros(hu.shape)\n index = np.nonzero((np.abs(h) > DRY_TOL) * (h != np.nan))\n u[index[0],index[1]] = hu[index[0],index[1]] / h[index[0],index[1]]\n return u\n \n def eta(cd):\n return extract_eta(cd.q[:,:,0],cd.q[:,:,3])\n \n def water_u(cd):\n # index = np.nonzero(current_data.q[:,:,0] > 1e-6)\n # u = np.zeros(current_data.q[:,:,1].shape)\n # u[index] = current_data.q[index,1] / current_data.q[index,0]\n # return u\n return extract_velocity(cd.q[:,:,0],cd.q[:,:,1])\n # return np.where(abs(current_data.q[:,:,0]) > 10**-16,\n # current_data.q[:,:,1] / current_data.q[:,:,0],\n # 0.0)\n \n def water_v(cd):\n # index = np.nonzero(current_data.q[:,:,0] > 1e-6)\n # v = np.zeros(current_data.q[:,:,2].shape)\n # v[index] = current_data.q[index,2] / current_data.q[index,0]\n # return u\n return extract_velocity(cd.q[:,:,0],cd.q[:,:,2])\n # return np.where(abs(current_data.q[:,:,0]) > 10**-16,\n # current_data.q[:,:,2] / current_data.q[:,:,0],\n # 0.0)\n \n def water_speed(current_data):\n u = water_u(current_data)\n v = water_v(current_data)\n \n return np.sqrt(u**2+v**2)\n \n def water_quiver(current_data):\n u = water_u(current_data)\n v = water_v(current_data)\n \n plt.hold(True)\n Q = plt.quiver(current_data.x[::2,::2],current_data.y[::2,::2],\n u[::2,::2],v[::2,::2])\n max_speed = np.max(np.sqrt(u**2+v**2))\n label = r\"%s m/s\" % str(np.ceil(0.5*max_speed))\n plt.quiverkey(Q,0.15,0.95,0.5*max_speed,label,labelpos='W')\n plt.hold(False)\n\n # ========================================================================\n # Profile functions\n # ========================================================================\n class PlotProfile(object):\n \n def __init__(self,slice_value = 0.0):\n self.slice_value = slice_value\n \n def slice_index(self,cd):\n if cd.grid.y.lower < self.slice_value < cd.grid.y.upper:\n return int((self.slice_value - cd.grid.y.lower) / cd.dy - 0.5)\n else:\n return None\n \n def bathy_profile(self,current_data):\n index = self.slice_index(current_data)\n if index:\n return current_data.x[:,index], b(current_data)[:,index]\n else:\n return None, None\n \n def surface_profile(self,current_data):\n index = self.slice_index(current_data)\n if index:\n return current_data.x[:,index], eta(current_data)[:,index]\n else:\n return None, None\n\n # ========================================================================\n # Plot items\n # ========================================================================\n def add_surface_elevation(plotaxes,bounds=None,plot_type='pcolor'):\n if plot_type == 'pcolor' or plot_type == 'imshow': \n plotitem = plotaxes.new_plotitem(plot_type='2d_imshow')\n # plotitem.plotvar = eta\n plotitem.plot_var = geoplot.surface\n plotitem.imshow_cmap = colormaps.make_colormap({1.0:'r',0.5:'w',0.0:'b'})\n if bounds is not None:\n plotitem.imshow_cmin = bounds[0]\n plotitem.imshow_cmax = bounds[1]\n plotitem.add_colorbar = True\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n elif plot_type == 'contour': \n plotitem = plotaxes.new_plotitem(plot_type='2d_contour')\n plotitem.plot_var = geoplot.surface\n if bounds is None:\n plotitem.contour_levels = [-2.5,-1.5,-0.5,0.5,1.5,2.5]\n # plotitem.contour_nlevels = 21\n # plotitem.contour_min = -2.0\n # plotitem.contour_max = 2.0\n # plotitem.kwargs = {''}\n plotitem.amr_contour_show = [1,1,1]\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n plotitem.amr_contour_colors = 'k'\n # plotitem.amr_contour_colors = ['r','k','b'] # color on each level\n # plotitem.amr_grid_bgcolor = ['#ffeeee', '#eeeeff', '#eeffee']\n \n def add_speed(plotaxes,bounds=None,plot_type='pcolor'):\n if plot_type == 'pcolor' or plot_type == 'imshow':\n plotitem = plotaxes.new_plotitem(plot_type='2d_imshow')\n plotitem.plot_var = water_speed\n # plotitem.plot_var = 1\n plotitem.imshow_cmap = plt.get_cmap('PuBu')\n if bounds is not None:\n plotitem.imshow_cmin = bounds[0]\n plotitem.imshow_cmax = bounds[1]\n plotitem.add_colorbar = True\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1]\n elif plot_type == 'quiver':\n plotitem = plotaxes.new_plotitem(plot_type='2d_quiver')\n plotitem.quiver_var_x = water_u\n plotitem.quiver_var_y = water_v\n plotitem.amr_quiver_show = [4,10,10]\n plotitem.amr_show_key = [True,True,False]\n plotitem.key_units = 'm/s'\n \n elif plot_type == 'contour':\n plotitem = plotaxes.new_plotitem(plot_type='2d_contour')\n plotitem.plot_var = water_speed\n plotitem.kwargs = {'linewidths':1}\n # plotitem.contour_levels = [1.0,2.0,3.0,4.0,5.0,6.0]\n plotitem.contour_levels = [0.5,1.5,3,4.5,6.0]\n plotitem.amr_contour_show = [1,1,1]\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n plotitem.amr_contour_colors = 'k'\n # plotitem.amr_contour_colors = ['r','k','b'] # color on each level\n # plotitem.amr_grid_bgcolor = ['#ffeeee', '#eeeeff', '#eeffee']\n \n def add_wind(plotaxes,bounds=None,plot_type='pcolor'):\n if plot_type == 'pcolor' or plot_type == 'imshow':\n plotitem = plotaxes.new_plotitem(plot_type='2d_imshow')\n plotitem.plot_var = wind_speed\n plotitem.imshow_cmap = plt.get_cmap('PuBu')\n if bounds is not None:\n plotitem.imshow_cmin = bounds[0]\n plotitem.imshow_cmax = bounds[1]\n plotitem.add_colorbar = True\n plotitem.amr_imshow_show = [1,1,1]\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n elif plot_type == 'contour':\n plotitem = plotaxes.new_plotitem(plot_type='2d_contour')\n plotitem.plot_var = wind_speed\n plotitem.contour_nlevels = hurricane_data.max_wind_nest\n plotitem.countour_min = hurricane_data.wind_refine[0]\n plotitem.gridedges_show = 1\n elif plot_type == 'quiver':\n plotitem = plotaxes.new_plotitem(plot_type='2d_quiver')\n plotitem.quiver_var_x = wind_x\n plotitem.quiver_var_y = wind_y\n plotitem.amr_quiver_show = [0,0,1]\n plotitem.amr_quiver_key_show = [True,False,False]\n plotitem.amr_quiver_key_units = 'm/s'\n \n def add_pressure(plotaxes,bounds=None,plot_type='pcolor'):\n if plot_type == 'pcolor' or plot_type == 'imshow':\n plotitem = plotaxes.new_plotitem(plot_type='2d_imshow')\n plotitem.plot_var = pressure\n plotitem.imshow_cmap = plt.get_cmap('PuBu')\n if bounds is not None:\n plotitem.imshow_cmin = bounds[0]\n plotitem.imshow_cmax = bounds[1]\n plotitem.add_colorbar = True\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1]\n elif plot_type == 'contour':\n pass\n \n def add_vorticity(plotaxes,bounds=None,plot_type=\"pcolor\"):\n if plot_type == 'pcolor' or plot_type == 'imshow': \n plotitem = plotaxes.new_plotitem(plot_type='2d_imshow')\n plotitem.plot_var = 9\n plotitem.imshow_cmap = plt.get_cmap('PRGn')\n if bounds is not None:\n plotitem.imshow_cmin = bounds[0]\n plotitem.imshow_cmax = bounds[1]\n plotitem.add_colorbar = True\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1]\n \n def add_land(plotaxes,plot_type='pcolor'):\n if plot_type == 'pcolor':\n plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n plotitem.show = True\n plotitem.plot_var = geoplot.land\n plotitem.pcolor_cmap = geoplot.land_colors\n plotitem.pcolor_cmin = 0.0\n plotitem.pcolor_cmax = 80.0\n plotitem.add_colorbar = False\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n elif plot_type == 'contour': \n plotitem = plotaxes.new_plotitem(plot_type='2d_contour')\n plotitem.plot_var = geoplot.land\n plotitem.contour_nlevels = 40\n plotitem.contour_min = 0.0\n plotitem.contour_max = 100.0\n plotitem.amr_contour_colors = ['g'] # color on each level\n plotitem.amr_grid_bgcolor = ['#ffeeee', '#eeeeff', '#eeffee']\n plotitem.gridlines_show = 0\n plotitem.gridedges_show = 0\n\n # Limits\n surface_range = 1.0\n speed_range = 2.0\n\n xlimits = [amrdata.xlower,amrdata.xupper]\n ylimits = [amrdata.ylower,amrdata.yupper]\n multilayer_data.eta = eta\n surface_limits = [eta[0]-surface_range,eta+[0]-surface_range]\n speed_limits = [0.0,speed_range]\n # surface_limits = [-0.5,0.5]\n # speed_limits = [0.0,0.1]\n \n wind_limits = [0,55]\n pressure_limits = [954,1002]\n vorticity_limits = [-1.e-2,1.e-2]\n \n # ========================================================================\n # Surface Elevation\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='Surface', figno=0)\n plotfigure.show = True\n\n # Set up for axes in this figure:\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.title = 'Surface'\n plotaxes.scaled = True\n plotaxes.xlimits = xlimits\n plotaxes.ylimits = ylimits\n plotaxes.afteraxes = pcolor_afteraxes\n \n add_surface_elevation(plotaxes,bounds=surface_limits)\n add_land(plotaxes)\n \n # ========================================================================\n # Water Speed\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='speed', figno=1)\n plotfigure.show = True\n\n # Set up for axes in this figure:\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.title = 'Currents'\n plotaxes.scaled = True\n plotaxes.xlimits = xlimits\n plotaxes.ylimits = ylimits\n plotaxes.afteraxes = pcolor_afteraxes\n\n # Speed\n add_speed(plotaxes,bounds=speed_limits)\n\n # Land\n add_land(plotaxes)\n \n # ========================================================================\n # Hurricane forcing\n # ========================================================================\n # Pressure field\n plotfigure = plotdata.new_plotfigure(name='pressure', figno=2)\n plotfigure.show = hurricane_data.pressure_src\n \n plotaxes = plotfigure.new_plotaxes()\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.ylimits = [amrdata.ylower,amrdata.yupper]\n plotaxes.title = \"Pressure Field\"\n plotaxes.afteraxes = hurricane_afteraxes\n plotaxes.scaled = True\n \n add_pressure(plotaxes,bounds=pressure_limits)\n add_land(plotaxes)\n \n # Wind field\n plotfigure = plotdata.new_plotfigure(name='wind',figno=3)\n plotfigure.show = hurricane_data.wind_src\n \n plotaxes = plotfigure.new_plotaxes()\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.ylimits = [amrdata.ylower,amrdata.yupper]\n plotaxes.title = \"Wind Field\"\n plotaxes.afteraxes = hurricane_afteraxes\n plotaxes.scaled = True\n \n add_wind(plotaxes,bounds=wind_limits,plot_type='imshow')\n # add_wind(plotaxes,bounds=wind_limits,plot_type='contour')\n # add_wind(plotaxes,bounds=wind_limits,plot_type='quiver')\n add_land(plotaxes)\n\n # ========================================================================\n # Profile Plots\n # ========================================================================\n # Profile variables\n \n def profile_afteraxes(current_data):\n hour_figure_title(current_data)\n loc,label = plt.xticks()\n label = loc/1.e3\n plt.xticks(loc,label)\n plt.xlabel('km')\n if current_data.plotaxes.title == 'Wind':\n plt.ylabel('m/s')\n else:\n plt.ylabel('m')\n \n t = current_data.t\n # Hurricane eye\n x = t * hurricane_data.hurricane_velocity[0] + hurricane_data.R_eye_init[0]\n plt.hold(True)\n plt.plot(x,0.0,'r+')\n plt.hold(False)\n \n def remove_labels_profile(cd,direction='x'):\n plt.hold(True)\n if direction == 'x':\n plt.xlabel('')\n locs,labels = plt.xticks()\n # labels = np.flipud(locs)/1.e3\n labels = ['' for i in xrange(len(locs))]\n plt.xticks(locs,labels)\n plt.ylabel('m')\n elif direction == 'y':\n plt.ylabel('')\n locs,labels = plt.yticks()\n # labels = np.flipud(locs)/1.e3\n labels = ['' for i in xrange(len(locs))]\n plt.yticks(locs,labels)\n plt.xlabel('m')\n plt.hold(False)\n \n def labels_profile(cd,direction='x'):\n if direction == 'x':\n loc,label = plt.xticks()\n label = loc/1.e3\n plt.xticks(loc,label)\n plt.xlabel('km')\n if cd.plotaxes.title == 'Wind':\n plt.ylabel('m/s')\n else:\n plt.ylabel('m')\n elif direction == 'y':\n loc,label = plt.yticks()\n label = loc/1.e3\n plt.yticks(loc,label)\n plt.ylabel('km')\n if cd.plotaxes.title == 'Wind':\n plt.xlabel('m/s')\n else:\n plt.xlabel('m')\n \n def bathy_ref_lines_profile(cd,limits):\n plt.hold(True)\n for line in ref_lines:\n plt.plot([line,line],limits,'k--')\n plt.hold(False)\n \n def eye_location_profile(cd):\n x = cd.t * hurricane_data.hurricane_velocity[0] + hurricane_data.R_eye_init[0]\n plt.hold(True)\n plt.plot(x,0.0,'r+')\n plt.hold(False)\n \n def profile_afteraxes(current_data):\n hour_figure_title(current_data)\n labels_profile(current_data)\n # bathy_ref_lines_profile(current_data,surface_limits)\n eye_location_profile(current_data)\n \n \n plotfigure = plotdata.new_plotfigure(name='profile', figno=4)\n plotfigure.show = False\n \n plotaxes = plotfigure.new_plotaxes()\n plotaxes.title = 'Profiles'\n plotaxes.xlimits = xlimits\n # plotaxes.ylimits = surface_limits\n plotaxes.afteraxes = profile_afteraxes\n \n profile_plot = PlotProfile(0.0)\n plotitem = plotaxes.new_plotitem(plot_type=\"1d_from_2d_data\")\n plotitem.map_2d_to_1d = profile_plot.surface_profile\n plotitem.amr_plotstyle = ['-','-.','+','x','.']\n plotitem.color = 'b'#(0.2,0.8,1.0)\n plotitem = plotaxes.new_plotitem(plot_type='1d_from_2d_data')\n plotitem.map_2d_to_1d = profile_plot.bathy_profile\n plotitem.amr_plotstyle = ['-','-.','+','x','.'] \n plotitem.color = 'k'\n \n # ========================================================================\n # Bathy Profile\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='bathy_profile',figno=20)\n plotfigure.show = False\n \n plotaxes = plotfigure.new_plotaxes()\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.title = \"Bathymetry Profile\"\n plotaxes.scaled = 'equal'\n \n plotitem = plotaxes.new_plotitem(plot_type='2d_imshow')\n plotitem.plot_var = b\n plotitem.imshow_cmap = plt.get_cmap('earth')\n plotitem.imshow_cmin = -3300\n plotitem.imshow_cmax = 100.0\n plotitem.add_colorbar = True\n plotitem.amr_imshow_show = [1,1,1]\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n plotitem.show = True\n \n # ========================================================================\n # Figure for grids alone\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='grids', figno=11)\n plotfigure.show = False\n \n # Set up for axes in this figure:\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.xlimits = [amrdata.ylower,amrdata.yupper]\n plotaxes.title = 'grids'\n plotaxes.afteraxes = pcolor_afteraxes\n plotaxes.scaled = True\n \n # Set up for item on these axes:\n plotitem = plotaxes.new_plotitem(plot_type='2d_grid')\n # plotitem.amr_grid_bgcolor = ['#ffeeee', '#eeeeff', '#eeffee']\n plotitem.amr_grid_bgcolor = ['blue','red','green','cyan','yellow']\n plotitem.amr_gridlines_show = [1,1,0,0,0,0] \n plotitem.amr_gridedges_show = 1\n \n # ========================================================================\n # Figures for momentum\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='x-momentum', figno=13)\n plotfigure.show = False\n\n # Set up for axes in this figure:\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.title = 'X-Velocity'\n plotaxes.scaled = True\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.ylimits = [amrdata.ylower,amrdata.yupper]\n plotaxes.afteraxes = pcolor_afteraxes\n \n # Water\n plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n # plotitem.plot_var = geoplot.surface\n plotitem.plot_var = water_u\n plotitem.pcolor_cmap = colormaps.make_colormap({1.0:'r',0.5:'w',0.0:'b'})\n # plotitem.pcolor_cmin = -1.e-10\n # plotitem.pcolor_cmax = 1.e-10\n # plotitem.pcolor_cmin = -2.5 # -3.0\n # plotitem.pcolor_cmax = 2.5 # 3.0\n plotitem.add_colorbar = True\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n\n # Land\n plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n plotitem.show = True\n plotitem.plot_var = geoplot.land\n plotitem.pcolor_cmap = geoplot.land_colors\n plotitem.pcolor_cmin = 0.0\n plotitem.pcolor_cmax = 80.0\n plotitem.add_colorbar = False\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n \n plotfigure = plotdata.new_plotfigure(name='y-momentum', figno=14)\n plotfigure.show = False\n\n # Set up for axes in this figure:\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.title = 'Y-Velocity'\n plotaxes.scaled = True\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.ylimits = [amrdata.ylower,amrdata.yupper]\n plotaxes.afteraxes = pcolor_afteraxes\n \n # Water\n plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n # plotitem.plot_var = geoplot.surface\n plotitem.plot_var = water_v\n plotitem.pcolor_cmap = colormaps.make_colormap({1.0:'r',0.5:'w',0.0:'b'})\n # plotitem.pcolor_cmin = -1.e-10\n # plotitem.pcolor_cmax = 1.e-10\n # plotitem.pcolor_cmin = -2.5 # -3.0\n # plotitem.pcolor_cmax = 2.5 # 3.0\n plotitem.add_colorbar = True\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n\n # Land\n plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n plotitem.show = True\n plotitem.plot_var = geoplot.land\n plotitem.pcolor_cmap = geoplot.land_colors\n plotitem.pcolor_cmin = 0.0\n plotitem.pcolor_cmax = 80.0\n plotitem.add_colorbar = False\n plotitem.amr_gridlines_show = [0,0,0]\n plotitem.amr_gridedges_show = [1,1,1]\n \n # ========================================================================\n # Contour plot for surface\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='contour_surface',figno=15)\n plotfigure.show = False\n \n # Set up for axes in this figure:\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.title = 'Surface'\n plotaxes.scaled = True\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.ylimits = [amrdata.ylower,amrdata.yupper]\n plotaxes.afteraxes = contour_afteraxes\n \n # Surface\n add_surface_elevation(plotaxes,plot_type='contour')\n \n # Land\n add_land(plotaxes,plot_type='contour')\n \n # ========================================================================\n # Contour plot for speed\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='contour_speed',figno=16)\n plotfigure.show = False\n \n # Set up for axes in this figure:\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.title = 'Current'\n plotaxes.scaled = True\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.ylimits = [amrdata.ylower,amrdata.yupper]\n plotaxes.afteraxes = contour_afteraxes\n \n # Surface\n add_surface_elevation(plotaxes,plot_type=\"contour\")\n \n # Land\n add_land(plotaxes,plot_type='contour')\n \n # ========================================================================\n # Vorticity Plot\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='vorticity',figno=17)\n plotfigure.show = False\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.title = \"Vorticity\"\n plotaxes.scaled = True\n plotaxes.xlimits = [amrdata.xlower,amrdata.xupper]\n plotaxes.ylimits = [amrdata.ylower,amrdata.yupper]\n plotaxes.afteraxes = pcolor_afteraxes\n \n # Vorticity\n add_vorticity(plotaxes)\n\n # Land\n add_land(plotaxes)\n \n \n # ========================================================================\n # Figures for gauges\n # ========================================================================\n plotfigure = plotdata.new_plotfigure(name='Surface & topo', figno=300, \\\n type='each_gauge')\n plotfigure.show = True\n plotfigure.clf_each_gauge = True\n\n # Set up for axes in this figure:\n plotaxes = plotfigure.new_plotaxes()\n plotaxes.xlimits = [0.0,40.0*3600.0]\n # plotaxes.ylimits = [0,150.0]\n plotaxes.ylimits = surface_limits\n plotaxes.title = 'Surface'\n plotaxes.afteraxes = gauge_afteraxes\n\n # Plot surface as blue curve:\n plotitem = plotaxes.new_plotitem(plot_type='1d_plot')\n plotitem.plot_var = 3\n plotitem.plotstyle = 'r-'\n\n #-----------------------------------------\n \n # Parameters used only when creating html and/or latex hardcopy\n # e.g., via pyclaw.plotters.frametools.printframes:\n\n plotdata.printfigs = True # print figures\n plotdata.print_format = 'png' # file format\n plotdata.print_framenos = 'all' # list of frames to print\n plotdata.print_fignos = 'all' # list of figures to print\n plotdata.html = True # create html files of plots?\n plotdata.latex = False # create latex file of plots?\n plotdata.latex_figsperline = 2 # layout of plots\n plotdata.latex_framesperline = 1 # layout of plots\n plotdata.latex_makepdf = False # also run pdflatex?\n\n return plotdata\n\n \n","sub_path":"apps/single_layer/2d/storm_surge/setplot.py","file_name":"setplot.py","file_ext":"py","file_size_in_byte":30255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"496733675","text":"\nimport csv, os\nimport pandas as pd\nfrom salesforce_bulk import SalesforceBulk\nfrom urllib.parse import urlparse\nimport unicodecsv\nfrom payment_hub_util import soql, cols_convert_to_int\nfrom time import sleep\nfrom datetime import datetime, timedelta\nimport numpy as np\n\n\n#Pull New Salesforce Token\nimport sys\nsys.path.insert(0, 'c:/Scripting/fr_SF/pull_sf_token')\n\nfrom pull_sf_token import get_token\nget_token()\n\n\n#Time Keeping Functions\ndef dt():\n\treturn (datetime.now()).strftime('%m-%d-%Y')\n\ndef time():\n return (datetime.now()).strftime('%Y%d%mT%H%M%S%f')[:17]\n\nstart_time = datetime.now()\nprint(\"\\nStart Time: \" + dt() + \" \" + time())\n\nsource_folder = 'C:/Scripting/fr_SF/Payment_Hub-DL/bin/'\narchive_folder = 'C:/Scripting/fr_SF/Payment_Hub-DL/archive/'\noutgoing_folder = '//phub.cbsh.com/APA_VendorFile/Outgoing/'\n\n#File Names\nnew_data_fn = 'Payment_Hub_Export_15m.csv'\narch_data_fn = 'Payment_Hub_Export_Arch.csv'\nfinal_data_fn = dt() + ' Payment_Hub_Export_15m ' + time() + '.csv'\n\n#set token value from file\ntoken = open(\"c:/Scripting/fr_SF/Pull_SF_Token/token_only.txt\", 'r').readlines()[0]\nurl = 'https://na57.salesforce.com/'\n\n#Bulk Salesforce Operations/Pulling data\nbulk = SalesforceBulk(sessionId=token, host=urlparse(url).hostname)\njob = bulk.create_query_job(\"Vendor_Enrollment__c\", contentType='CSV')\nbatch = bulk.query(job, soql)\nbulk.close_job(job)\n#Wait till job is done\nwhile not bulk.is_batch_done(batch):\n sleep(10)\n\nprint(\"SF Job complete\")\n#Convert Response to py List\nkeys, lines = [], []\nfor result in bulk.get_all_results_for_query_batch(batch):\n reader = unicodecsv.DictReader(result, encoding='utf-8')\n type(reader)\n \n for row in reader:\n line = []\n for key, value in row.items():\n line.append(value.rstrip())\n if len(keys) < len(row):\n keys.append(key)\n lines.append(line)\n \ndf = pd.DataFrame(lines, columns=keys, dtype=object)\nprint(\"Data Frame Created:\", len(df))\n\n\n### ###\n### Misc File Formatting ###\n## ###\n\n#Convert Columns to Int from Float. \n#This removes some decimal numbers.\n#col_convert_to_int is from an external file that lists \n# columns that should be converted to integers (no decimal)\ncols = cols_convert_to_int\ndf[cols] = df[cols].apply(pd.to_numeric)\nmask_num = -999999999\ndf[cols] = df[cols].fillna(mask_num)\ndf[cols] = df[cols].apply(np.int64)\ndf[cols] = df[cols].replace(mask_num, \"\")\n\n#Trim last 3 off of ID Fields\ndf.loc[:, 'Program__r.Id'] = df.loc[:, 'Program__r.Id'].str[:-3]\ndf.loc[:, 'Id'] = df.loc[:, 'Id'].str[:-3]\n\n#Encoding will force out weird characters that pandas can't import\nenc = 'utf-8'\ndf.to_csv(source_folder + new_data_fn, quoting=csv.QUOTE_ALL, index=False, encoding=enc)\n\n\n\n### ###\n### Comparison and Archiving System ###\n### ###\n\n#Read New Data \nnew_data_df = pd.read_csv(source_folder + new_data_fn, dtype=object)\nprint(\"new_data size:\", len(new_data_df))\n\n#If archive file does not exist, create it\nif os.path.exists(source_folder + arch_data_fn) == False:\n df = pd.DataFrame(columns=new_data_df.columns)\n df.to_csv(source_folder + arch_data_fn, quoting=csv.QUOTE_ALL, index=False, encoding=enc)\n \n#Read Archive\narch_data_df = pd.read_csv(source_folder + arch_data_fn, dtype=object) \nprint(\"arch_data size:\", len(arch_data_df))\n\n#Create Common table iwht indicators\nheaders = [col for col in arch_data_df.columns]\ncommon_df = pd.merge(arch_data_df, new_data_df, how='outer', indicator=True)\ncommon_df.to_csv(source_folder + \"common_df.csv\", index=False, encoding=enc)\n\n#Create final table by sorting on '_merge' column. Then drop '_merge'\nfinal_data_df = (common_df[common_df['_merge'] == 'right_only']).drop(['_merge'], axis=1)\nprint(\"final_data size:\", len(final_data_df))\n\narch_data_df = arch_data_df.append(final_data_df)\n\n##\n##Cleanup Archive, delete entries over 24hrs\n##\nprint(\"DF Pre-Size:\", len(arch_data_df))\nprint(\"UTC time:\", datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%S.000Z'))\nprint(\"Local Time:\", datetime.now().strftime('%Y-%m-%dT%H:%M:%S.000Z'))\nprint(\"Delete before:\", (datetime.utcnow() - timedelta(days=2)).strftime('%Y-%m-%dT%H:%M:%S.000Z'))\n\narch_data_df = (arch_data_df)[arch_data_df.loc[:, 'Send_to_Phub_Date_Time__c']\n > (datetime.utcnow() - timedelta(days=2)).strftime('%Y-%m-%dT%H:%M:%S.000Z')] \n \nprint(\"DF Post-Size:\", len(arch_data_df))\n \n#Notifty and calculate times\nend_time = datetime.now()\nprint(\"End Time: \" + dt() + \" \" + str(end_time))\n\nelapsed_time = end_time - start_time\nprint(\"Elapsed Time: \" + str(elapsed_time) + \"\\n\")\n\n\n#Write Final Data, new and archive\nfinal_data_df.to_csv(archive_folder + final_data_fn, quoting=csv.QUOTE_ALL, index=False, encoding=enc)\nfinal_data_df.to_csv(outgoing_folder + final_data_fn, quoting=csv.QUOTE_ALL, index=False, encoding=enc)\narch_data_df.to_csv(source_folder + arch_data_fn, quoting=csv.QUOTE_ALL, index=False, encoding=enc)\nnew_data_df.to_csv(source_folder + new_data_fn, quoting=csv.QUOTE_ALL, index=False, encoding=enc)\n\n\n\n\n\n\n\n\n","sub_path":"fr_SF/Payment_Hub-DL/bin/P_hub_bulk.py","file_name":"P_hub_bulk.py","file_ext":"py","file_size_in_byte":5125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"599589864","text":"# following this\n# https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\n# launch tensorboard --logdir=path/to/log-directory\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nimport tensorflow as tf\n\nflags = tf.app.flags\nFLAGS = flags.FLAGS\nflags.DEFINE_boolean('fake_data', False, 'If true, uses fake data for unit testing.')\nflags.DEFINE_integer('max_steps', 5000, 'Number of steps to run trainer.')\nflags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')\nflags.DEFINE_float('dropout', 0.9, 'Keep probability for training dropout.')\nflags.DEFINE_string('data_dir', '/tmp/mnist_data', 'Directory for storing data')\nflags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')\n\ndef weight_variable(shape):\n initial = tf.truncated_normal(shape, stddev=0.1)\n return tf.Variable(initial)\n\n\ndef bias_variable(shape):\n initial = tf.constant(0.1, shape=shape)\n return tf.Variable(initial)\n\n# need to much memory\ndef variable_summaries(var, name):\n \"\"\"Attach a lot of summaries to a Tensor.\"\"\"\n with tf.name_scope('summaries'):\n mean = tf.reduce_mean(var)\n tf.scalar_summary('mean/' + name, mean)\n with tf.name_scope('stddev'):\n stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))\n tf.scalar_summary('sttdev/' + name, stddev)\n tf.scalar_summary('max/' + name, tf.reduce_max(var))\n tf.scalar_summary('min/' + name, tf.reduce_min(var))\n tf.histogram_summary(name, var)\n\n\n\ndef nn_layer(input_tensor, input_dim, output_dim, layer_name, act_fn=tf.nn.relu):\n\n with tf.name_scope(layer_name):\n with tf.name_scope('weights'):\n weights = weight_variable([input_dim, output_dim])\n variable_summaries(weights, layer_name + '/weights')\n with tf.name_scope('biases'):\n biases = bias_variable([output_dim])\n variable_summaries(biases, layer_name + '/biases')\n with tf.name_scope('Wx_plus_b'):\n preactivate = tf.matmul(input_tensor, weights) + biases\n tf.histogram_summary(layer_name + '/pre_activations', preactivate)\n activations = act_fn(preactivate, 'activation')\n tf.histogram_summary(layer_name + '/activations', activations)\n return activations\n\n\ndef train():\n\n mnist = input_data.read_data_sets('/tmp/mnist_data', one_hot=True)\n sess = tf.InteractiveSession()\n\n with tf.name_scope('input'):\n x = tf.placeholder(tf.float32, shape=[None, 784], name=\"x\")\n y_ = tf.placeholder(tf.float32, shape=[None, 10], name=\"y\")\n\n with tf.name_scope('input_reshape'):\n x_image = tf.reshape(x, [-1, 28, 28, 1])\n tf.image_summary('input', x_image, 10)\n\n with tf.name_scope('dropout'):\n keep_prob = tf.placeholder(tf.float32)\n\n hidden1 = nn_layer(x, 784, 500, 'layer1')\n\n with tf.name_scope('dropout'):\n keep_prob = tf.placeholder(tf.float32)\n tf.scalar_summary('dropout_keep_probability', keep_prob)\n dropped = tf.nn.dropout(hidden1, keep_prob)\n\n y = nn_layer(dropped, 500, 10, 'layer2', act_fn=tf.nn.softmax)\n\n\n with tf.name_scope('cross_entropy'):\n diff = y_ * tf.log(y)\n with tf.name_scope('total'):\n cross_entropy = -tf.reduce_mean(diff)\n tf.scalar_summary('cross entropy', cross_entropy)\n\n with tf.name_scope('train'):\n train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(cross_entropy)\n\n with tf.name_scope('accuracy'):\n with tf.name_scope('correct_prediction'):\n correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n with tf.name_scope('accuracy'):\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n tf.scalar_summary('accuracy', accuracy)\n\n merged = tf.merge_all_summaries()\n train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train', sess.graph)\n test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test')\n tf.initialize_all_variables().run()\n\n for i in range(FLAGS.max_steps):\n xs, ys = mnist.train.next_batch(50)\n if i % 100 == 0:\n train_summary, _, train_acc = sess.run([merged, train_step, accuracy], feed_dict={x: xs, y_: ys, keep_prob: 0.5})\n test_summary, test_acc = sess.run([merged, accuracy], feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})\n\n test_writer.add_summary(test_summary, i)\n train_writer.add_summary(train_summary, i)\n\n print('accuracy at step %s: (test) %s (training) %s' % (i, test_acc, train_acc))\n\n _ = sess.run([train_step], feed_dict={x: xs, y_: ys, keep_prob: 0.5})\n\n train_writer.close()\n test_writer.close()\n\n\n\ndef main(_):\n if tf.gfile.Exists(FLAGS.summaries_dir):\n tf.gfile.DeleteRecursively(FLAGS.summaries_dir)\n tf.gfile.MakeDirs(FLAGS.summaries_dir)\n train()\n\n\nif __name__ == '__main__':\n tf.app.run()\n\n\n","sub_path":"mnist/03_mnist_visualizing.py","file_name":"03_mnist_visualizing.py","file_ext":"py","file_size_in_byte":4934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"459182885","text":"import requests\nimport urllib.request\nfrom bs4 import BeautifulSoup\nimport re\nfrom fake_useragent import UserAgent\nfrom urllib import request\nimport random\nimport time\nheaders = {\n\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.92 Safari/537.36\"\n}\nsess = requests.session()\nproxy = [{\"http\":\"118.190.95.43:9001\"},{\"http\":\"123.157.67.30:34942\"},{\"http\":\"124.234.157.228:80\"}]\n\n\ncount = 0\nwhile count <=1:\n # try:\n curProxy = proxy[random.randint(0,2)]\n curProxy = proxy[0]\n print(curProxy)\n res = sess.get(\"https://www.douban.com/\",headers= headers,proxies = curProxy,verify=False)\n bs1 = BeautifulSoup(res.text,\"lxml\")\n print(len(bs1.select(\".item-captcha\")))\n if len(bs1.select(\".item-captcha\")) !=0:\n captcha_url = bs1.select(\"#captcha_image\")[0][\"src\"]\n urllib.request.urlretrieve(captcha_url,\"./doc/oriImg/captcha_\"+str(count)+\".jpg\")#将图片保存在本地\n print(\"保存图片\",\"captcha_\"+str(count)+\".jpg\")\n count = count+1\n time.sleep(3)\n # except:\n # time.sleep(2)\n","sub_path":"Captcha/gettingCapFromDouban.py","file_name":"gettingCapFromDouban.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"619717141","text":"from threepy.lights import Light\n\n\nclass PointLight(Light):\n\n def __init__(self, color=[1, 1, 1], strength=1, position=[0, 0, 0]):\n super().__init__()\n self.isPoint = 1\n self.color = color\n self.strength = strength\n self.transform.setPosition(position[0], position[1], position[2])\n","sub_path":"threepy/lights/PointLight.py","file_name":"PointLight.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"375950643","text":"import random\nfrom collections import defaultdict\n\nimport numpy as np\nfrom tqdm import trange\n\nfrom neurokit.context import Context\nfrom neurokit.populations.population import Population\nfrom neurokit.models.lif import LIF\nfrom neurokit.learning_rule import RMSTDP, STDP\n\n# Neuron\ntau = 1\nu_r = -70\nu_t = -50\nr = 1\n\n# Simulation\nsteps = 2500\nsize = (5, 200)\ninput_size = 50\npat_count = 2\npat_res_size = 20\npat_spike_prob = 0.8\npat_window_size = 20\n\n\ndef is_inhb(index):\n return index[0] >= (size[0] - 1)\n\n\n# Context\ndt = 1\na_p = 0.1\na_n = -0.6\ntau_p = 2\ntau_n = 2\ntau_c = 1\ntau_d = 1\n\n# Dopamine\np_d = 5\nn_d = -20\nt_d = 1\n\n# Connections\ncon_prob = 0.5\ncon_w_mu = 10\ncon_w_sigma = 0.1\ncon_w_mu_inhb = -20\ncon_w_sigma_ihb = 0.1\ncon_d_range = [1, 3]\n\ncontext = None\npop = None\nlast_applied_pattern = -1\ninp_neurons_global = []\npat_neurons_global = []\npats_global = []\nmod = 0\nwindow_spiked_neurons = defaultdict(int)\ntest_window_spikes = defaultdict(int)\n\n\n# noinspection PyUnresolvedReferences\ndef test():\n global mod, test_window_spikes\n\n mod = 1\n\n for pat_idx, pat in enumerate(pats_global):\n test_window_spikes = defaultdict(int)\n\n for idx, j in enumerate(pat):\n neuron_idx = inp_neurons_global[idx]\n if j != 0:\n neuron = pop.get_neuron(neuron_idx[0], neuron_idx[1])\n neuron.register_potential_change(u_t - u_r + 10, context.t() + context.dt() * j)\n\n for i in range(pat_window_size + 5):\n pop.steps(1)\n context.step()\n\n max_spike_pat = -1\n max_spike = -1\n for idx, pat_neurons in enumerate(pat_neurons_global):\n sp_count = 0\n for neu in pat_neurons:\n if neu in test_window_spikes:\n sp_count += test_window_spikes[neu]\n\n if sp_count > max_spike:\n max_spike = sp_count\n max_spike_pat = idx\n print(pat_idx, '->', max_spike_pat, 'with', max_spike)\n\n for i in range(pat_window_size + 5):\n pop.steps(1)\n context.step()\n\n mod = 0\n\n\n# noinspection PyUnresolvedReferences\ndef spike_check():\n sum_spike = 0\n\n max_spike_pat = -1\n max_spike = -1\n for idx, pat_neurons in enumerate(pat_neurons_global):\n sp_count = 0\n for neu in pat_neurons:\n if neu in window_spiked_neurons:\n sp_count += window_spiked_neurons[neu]\n sum_spike += window_spiked_neurons[neu]\n\n if sp_count > max_spike:\n max_spike = sp_count\n max_spike_pat = idx\n\n if (max_spike_pat == last_applied_pattern) and ((max_spike / sum_spike) > 0.5):\n print(last_applied_pattern, max_spike_pat, max_spike, \"rew\", sum_spike)\n context.change_dopamine(p_d, t_d)\n else:\n print(last_applied_pattern, max_spike_pat, max_spike, \"pun\", sum_spike)\n context.change_dopamine(n_d, t_d)\n\n\n# noinspection PyUnresolvedReferences\ndef simulate():\n global last_applied_pattern, window_spiked_neurons\n next_spike_check = -1\n\n for i in trange(steps):\n if i == next_spike_check:\n spike_check()\n\n if i % (pat_window_size + 5) == 0:\n window_spiked_neurons = defaultdict(int)\n last_applied_pattern = (last_applied_pattern + 1) % pat_count\n next_spike_check = i + pat_window_size + 1\n\n for idx, j in enumerate(pats_global[last_applied_pattern]):\n neuron_idx = inp_neurons_global[idx]\n if j != 0:\n neuron = pop.get_neuron(neuron_idx[0], neuron_idx[1])\n neuron.register_potential_change(u_t - u_r + 10, context.t() + context.dt() * j)\n\n pop.steps(1)\n context.step()\n\n\ndef spike_cb(name):\n splitted = name.split('_')\n\n if mod == 0:\n window_spiked_neurons[(int(splitted[0]), int(splitted[1]))] += 1\n else:\n test_window_spikes[(int(splitted[0]), int(splitted[1]))] += 1\n\n\ndef neuron_init(x, y):\n return LIF(context=context, tau=tau, u_r=u_r, u_t=u_t, r=r, name=f\"{x}_{y}\", spike_cb=spike_cb)\n\n\ndef random_index():\n _w, _h = size\n return random.randint(0, _w - 1), random.randint(0, _h - 1)\n\n\ndef connect_neurons(pop):\n def get_pair():\n _src = random_index()\n _dest = random_index()\n _description = f\"{_src[0]}_{_src[1]}_{_dest[0]}_{_dest[1]}\"\n return _src, _dest, _description\n\n total_neurons = size[0] * size[1]\n total_cons = int((total_neurons * (total_neurons - 1)) * con_prob)\n\n cons = set()\n for i in range(total_cons):\n src, dest, description = get_pair()\n while (src == dest) or (description in cons):\n src, dest, description = get_pair()\n\n cons.add(description)\n\n if is_inhb(src):\n w = np.random.normal(con_w_mu_inhb, con_w_sigma_ihb)\n else:\n w = np.random.normal(con_w_mu, con_w_sigma)\n d = random.randint(con_d_range[0], con_d_range[1])\n\n pop.connect_two(src, dest, w, d)\n\n\ndef choose_neurons():\n inp_neurons = []\n pat_neurons = []\n\n chosen_neurons = set()\n\n for i in range(input_size):\n idx = random_index()\n while (idx in chosen_neurons) or is_inhb(idx):\n idx = random_index()\n inp_neurons.append(idx)\n chosen_neurons.add(idx)\n\n for i in range(pat_count):\n pat_neurons.append([])\n for j in range(pat_res_size):\n idx = random_index()\n while (idx in chosen_neurons) or is_inhb(idx):\n idx = random_index()\n pat_neurons[i].append(idx)\n chosen_neurons.add(idx)\n\n return inp_neurons, pat_neurons\n\n\ndef make_patterns():\n pats = []\n\n for i in range(pat_count):\n pat = []\n for j in range(input_size):\n spike_time = 0\n if random.random() < pat_spike_prob:\n spike_time = random.randint(1, pat_window_size)\n pat.append(spike_time)\n\n pats.append(tuple(pat))\n\n return pats\n\n\ndef main():\n global inp_neurons_global, pat_neurons_global, pats_global, pop, context\n\n learning_rule = RMSTDP(\n stdp_rule=STDP(a_p, a_n, tau_p, tau_n),\n tau_c=tau_c,\n tau_d=tau_d\n )\n context = Context(dt=dt, learning_rule=learning_rule)\n\n print(\"creating population ...\")\n pop = Population(\"pop\", size, context, neuron_init)\n\n print(\"creating connections ...\")\n connect_neurons(pop)\n\n print(\"choosing neurons ...\")\n inp_neurons_global, pat_neurons_global = choose_neurons()\n\n print(\"making patterns ...\")\n pats_global = make_patterns()\n\n print(\"pre testing ...\")\n test()\n\n print(\"simulating ...\")\n simulate()\n\n print(\"post testing ...\")\n test()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"project_4_2.py","file_name":"project_4_2.py","file_ext":"py","file_size_in_byte":6770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"441191197","text":"import random\n\n\ndef quickSelect(arr, k):\n\t\"\"\" Return the kth smallest element of Array arr, for k from 1 to len(arr) \"\"\"\n\tn = len(arr)\n\tif n < k:\n\t\traise Exception('k out of range of sequence')\n\tif n == 1:\n\t\treturn arr[0]\n\n\tpivot = random.choice(arr)\n\tL = [x for x in arr if x < pivot]\n\tE = [x for x in arr if x == pivot]\n\tG = [x for x in arr if x > pivot]\n\n\tif k <= len(L):\n\t\treturn quickSelect(L, k)\n\telif k <= len(L) + len(E):\n\t\treturn pivot\n\telse:\n\t\tj = k - len(L) - len(E)\n\t\treturn quickSelect(G, j)\n\narr = [3,1,2,7,2,4,3,2,5,8,5,9,2,2,5,2]\n\nprint(sorted(arr), len(arr))\n\nprint(quickSelect(arr, 9))","sub_path":"sorts/selection/quickselect.py","file_name":"quickselect.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"6723035","text":"from django.core.management.color import no_style\nfrom django.db import models\nfrom django.test import TestCase\nfrom django.conf import settings\nfrom django.core.management import call_command\nfrom django.db.models.loading import load_app\nfrom django.db import connection, transaction\n\nfrom testing_app.models import TestModel, TestRelModel\n\nclass DirtyFieldsMixinTestCase(TestCase):\n\n def test_dirty_fields(self):\n tm = TestModel()\n # initial state shouldn't be dirty\n self.assertEqual(tm.get_dirty_fields(), {})\n\n # changing values should flag them as dirty\n tm.boolean = False\n tm.characters = 'testing'\n self.assertEqual(tm.get_dirty_fields(), {\n 'boolean': True,\n 'characters': ''\n })\n\n # resetting them to original values should unflag\n tm.boolean = True\n self.assertEqual(tm.get_dirty_fields(), {\n 'characters': ''\n })\n\n def test_sweeping(self):\n tm = TestModel()\n tm.boolean = False\n tm.characters = 'testing'\n self.assertEqual(tm.get_dirty_fields(), {\n 'boolean': True,\n 'characters': ''\n })\n tm.save()\n self.assertEqual(tm.get_dirty_fields(), {})\n\n def test_revert(self):\n tm = TestModel()\n tm.boolean = False\n tm.characters = 'testing'\n self.assertEqual(tm.get_dirty_fields(), {\n 'boolean': True,\n 'characters': ''\n })\n tm.revert()\n self.assertEqual(tm.get_dirty_fields(), {})\n\n def test_revert_with_fields(self):\n tm = TestModel()\n tm.boolean = False\n tm.characters = 'testing'\n self.assertEqual(tm.get_dirty_fields(), {\n 'boolean': True,\n 'characters': ''\n })\n tm.revert(field_names=['boolean'])\n self.assertEqual(tm.get_dirty_fields(), {\n 'characters': ''\n })\n\n def test_model_init_kwargs(self):\n tm = TestModel(boolean=False, characters='testing')\n self.assertEqual(tm.get_dirty_fields(), {\n 'boolean': True,\n 'characters': ''\n })\n\n def test_manager_returns_clean_model(self):\n tm = TestModel.objects.create(boolean=True, characters='testing')\n tm = TestModel.objects.get(pk=tm.pk)\n self.assertEqual(tm.get_dirty_fields(), {})\n\n def test_relation_fields(self):\n tm = TestModel.objects.create()\n tr = TestRelModel(test_model=tm)\n self.assertEqual(tr.get_dirty_fields(), {\n 'test_model_id': None,\n })\n","sub_path":"example_app/testing_app/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"166309412","text":"import torch.nn as nn\nimport torch\nimport math\n\nclass Smiles_embedding(nn.Module):\n\tdef __init__(self, vocab_size, embed_size, max_len, adj=False):\n\t\tsuper().__init__()\n\t\tself.token = nn.Embedding(vocab_size, embed_size, padding_idx=0)\n\t\tself.position = nn.Embedding(max_len, embed_size)\n\t\tself.max_len = max_len\n\t\tself.embed_size = embed_size\n\t\tif adj:\n\t\t\tself.adj = Adjacency_embedding(max_len, embed_size)\n\n\t\tself.embed_size = embed_size\n\n\tdef forward(self, sequence, pos_num, adj_mask=None, adj_mat=None):\n\t\tx = self.token(sequence) + self.position(pos_num)\n\t\tif adj_mat is not None:\n\t\t\t# additional embedding matrix. need to modify\n\t\t\t#print(adj_mask.shape)\n\t\t\tx += adj_mask.unsqueeze(2) * self.adj(adj_mat).repeat(1, self.max_len).reshape(-1,self.max_len, self.embed_size)\n\t\treturn x\n\nclass Adjacency_embedding(nn.Module):\n\tdef __init__(self, input_dim, model_dim, bias=True):\n\t\tsuper(Adjacency_embedding, self).__init__()\n\n\t\tself.weight_h = nn.Parameter(torch.Tensor(input_dim, model_dim))\n\t\tself.weight_a = nn.Parameter(torch.Tensor(input_dim))\n\t\tif bias:\n\t\t\tself.bias = nn.Parameter(torch.Tensor(model_dim))\n\t\telse:\n\t\t\tself.register_parameter('bias', None)\n\t\tself.reset_parameters()\n\n\tdef reset_parameters(self):\n\t\tstdv = 1. / math.sqrt(self.weight_h.size(1))\n\t\tstdv2 = 1. /math.sqrt(self.weight_a.size(0))\n\t\tself.weight_h.data.uniform_(-stdv, stdv)\n\t\tself.weight_a.data.uniform_(-stdv2, stdv2)\n\t\tif self.bias is not None:\n\t\t\tself.bias.data.uniform_(-stdv, stdv)\n\n\tdef forward(self, input_mat):\n\t\ta_w = torch.matmul(input_mat, self.weight_h)\n\t\tout = torch.matmul(a_w.transpose(1,2), self.weight_a)\n\n\t\tif self.bias is not None:\n\t\t\tout += self.bias\n\t\t#print(out.shape)\n\t\treturn out\n","sub_path":"Embedding.py","file_name":"Embedding.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"575863187","text":"import webbrowser\nimport smtplib\nfrom email.mime.text import MIMEText\nimport subprocess\nimport re\nimport sys\nimport imaplib\nimport email\nimport email.header\nfrom gtts import gTTS\nimport configparser\nfrom datetime import *\nimport random\nimport wolframalpha\n\noperators = [\"+\", \"-\", \"*\", \"/\"]\nwith open(\"dat.txt\", \"r\") as file_object:\n data = file_object.read().split(\"-\")\nport = 465\nname = data[0]\nuser = data[1]\npasscode = data[2]\nstartnum = int(data[3])\nrunning = 1\nmessages = []\nEMAIL_FOLDER = \"inbox\"\nconfig = configparser.ConfigParser()\nconfig.read(\"settings.ini\")\naccent = config[\"Speech settings\"][\"accent\"]\n\ndef genScores(orgs, scoreParam, listOfParams):\n i = 0\n scores = []\n for i in range(len(orgs)):\n cSpOrg = orgs[i].split(\" \")\n i2 = 0\n score = 0\n for i2 in range(len(cSpOrg)):\n if (scoreParam == \"instr\"):\n if (cSpOrg[i2] in listOfParams):\n score += 1\n elif (scoreParam == \"!instr\"):\n if (cSpOrg[i2] not in listOfParams):\n score += 1\n else:\n raise AttributeError(\"That attribute does not exist.\")\n i2 += 1\n scores.append(score)\n i += 1\n return scores\n\ndef maxi(scoreList):\n i = 0\n Maximum = scoreList[0]\n MaxIndex = i\n for i in range(len(scoreList)):\n if (Maximum < scoreList[i]):\n Maximum = scoreList[i]\n MaxIndex = i\n i += 1\n return MaxIndex\n\ndef speech(txt):\n global accent\n myobj = gTTS(text=txt, lang=accent, slow=False)\n myobj.save(\"audio.mp3\")\n subprocess.call([\"speech.vbs\"], shell=True)\n\ndef process_mailbox(M):\n rv, data = M.search(None, \"ALL\")\n for num in data[0].split():\n rv, data = M.fetch(num, '(RFC822)')\n msg = email.message_from_bytes(data[0][1])\n hdr = email.header.make_header(email.header.decode_header(msg['Subject']))\n subject = str(hdr)\n messages.append(subject)\n\ndef checkEmails():\n global user, passcode\n M = imaplib.IMAP4_SSL('imap.gmail.com')\n try:\n rv, data = M.login(user, passcode)\n except imaplib.IMAP4.error:\n sys.exit(1)\n rv, mailboxes = M.list()\n rv, data = M.select(EMAIL_FOLDER)\n if rv == 'OK':\n process_mailbox(M)\n M.close()\n M.logout()\n return messages\n\ndef initialdat():\n with open(\"dat.txt\", \"r\") as file_object:\n return file_object.read().split(\"-\")\n\ndef sendEmail(mailTo, message, subj):\n global user, passcode, port\n username = user\n password = passcode\n sender = username\n msg = MIMEText(message)\n msg['Subject'] = subj\n msg['From'] = sender\n msg['To'] = mailTo\n host = \"smtp.gmail.com\"\n server = smtplib.SMTP_SSL(host, port)\n server.login(username, password)\n server.sendmail(sender, mailTo, msg.as_string())\n server.quit()\n\ndef solveMath(problem):\n i = 0\n equation = problem\n num1 = \"\"\n num2 = \"\"\n while (not equation[i] in operators):\n num1 = num1 + equation[i]\n i += 1\n num1 = int(num1)\n operator = equation[i]\n i += 1\n while (i <= len(equation) - 1):\n num2 = num2 + equation[i]\n i += 1\n num2 = int(num2)\n if (operator == \"+\"):\n answer = num1 + num2\n elif (operator == \"-\"):\n answer = num1 - num2\n elif (operator == \"*\"):\n answer = num1 * num2\n elif (operator == \"/\"):\n answer = num1 / num2\n return answer\n\nif (startnum == 0):\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Hello, I'm pleased to meet you. My name is Sakura. Now I don't believe we've been properly introduced.\")\n speech(\"Hello, I'm pleased to meet you. My name is Sakura. Now I don't believe we've been properly introduced.\")\n subprocess.call([\"narrate.vbs\"], shell=True)\n subprocess.call([\"intro.vbs\"], shell=True)\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Please enter your name or salutation.\")\n speech(\"Please enter your name or salutation.\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n name = file_object.read()\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Please enter your email.\")\n speech(\"Please enter your email.\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n user = file_object.read()\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Please enter your Gmail password.\")\n speech(\"Please enter your Gmail password.\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n passcode = file_object.read()\n with open(\"dat.txt\", \"w+\") as file_object1:\n file_object1.write(name + \"-\" + user + \"-\" + passcode + \"-\" + str(startnum + 1))\nwhile running == 1:\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Hello \" + name + \", how can I help you?\")\n speech(\"Hello \" + name + \", how can I help you?\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n ans = file_object.read().lower()\n if (\"open\" in ans):\n ans = ans.replace(\"?\", \"\").replace(\"!\", \"\")\n else:\n ans = ans.replace(\"?\", \"\").replace(\"!\", \"\").replace(\".\", \"\")\n if (\"what is\" in ans):\n if (\"+\" in ans or \"-\" in ans or \"/\" in ans or \"*\" in ans):\n ans1 = solveMath(ans.split(\" \")[2])\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"The answer is \" + str(ans1))\n speech(\"The answer is \" + str(ans1))\n subprocess.call([\"narrate.vbs\"], shell=True)\n else:\n client = wolframalpha.Client(\"2EUWPK-6GHQG57EEX\")\n res = client.query(ans)\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"From Wolfram Alpha: \" + next(res.results).text)\n speech(\"From Wolfram Alpha: \" + next(res.results).text)\n subprocess.call([\"narrate.vbs\"], shell=True)\n elif (\"solve\" in ans):\n ans1 = solveMath(ans.split(\" \")[1])\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"The answer is \" + str(ans1))\n speech(\"The answer is \" + str(ans1))\n subprocess.call([\"narrate.vbs\"], shell=True)\n elif (\"bye\" in ans):\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Byebye \" + name + \"! Hope to see you soon.\")\n speech(\"Byebye \" + name + \"! Hope to see you soon.\")\n subprocess.call([\"narrate.vbs\"], shell=True)\n running = 0\n elif (\"send email\" in ans):\n listans = ans.split(\" \")\n email = listans[3]\n with open(\"contacts.txt\", \"r\") as file_object1:\n contacts = file_object1.read().split(\"|\")\n if (re.match(\"[a-z0-9._%+-]+@[a-z0-9.-]+\\.[a-z]{2,3}\", email) == None):\n if (email in contacts):\n email = contacts[contacts.index(email) - 1]\n else:\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Target Email Address:\")\n speech(\"Target Email Address:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n email = file_object.read()\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Body of the mail:\")\n speech(\"Body of the mail:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n body = file_object.read()\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Subject:\")\n speech(\"Subject:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n subj = file_object.read()\n sendEmail(email, body, subj)\n elif (\"check email\" in ans or \"read email\" in ans):\n messages = checkEmails()\n stringmessages = \"\"\n for i in range(len(messages)):\n stringmessages = stringmessages + str(i + 1) + \":\" + messages[i] + \" \"\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(stringmessages)\n speech(stringmessages)\n subprocess.call([\"narrate.vbs\"], shell=True)\n elif (\"make a contact\" in ans):\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Who do you want to make it for? (email address)\")\n speech(\"Who do you want to make it for? (email address)\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n address = file_object.read()\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Name of the contact:\")\n speech(\"Name of the contact:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n name1 = file_object.read()\n with open(\"contacts.txt\", \"r\") as file_object1:\n ctcts = file_object1.read()\n with open(\"contacts.txt\", \"w+\") as file_object1:\n file_object1.write(ctcts + \"|\" + address + \"|\" + name.lower())\n elif (\"open chrome\" in ans):\n webbrowser.open(\"www.google.com\", new = 1)\n elif (\"open\" in ans and not \"chrome\" in ans):\n listans = ans.split(\" \")\n url = listans[1].lower()\n with open(\"urls.txt\", \"r\") as file_object1:\n urls = file_object1.read().split(\"|\")\n if (re.search(\"http.[a-zA-Z0-9+-/:]+\\.[a-zA-Z0-9]+\", url) == None):\n if (url in urls):\n url = urls[urls.index(url) - 1]\n else:\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"URL:/Web Address:\")\n speech(\"URL:/Web Address:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n url = file_object.read() \n webbrowser.open_new_tab(url)\n elif (\"make a bookmark\" in ans):\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"URL:\")\n speech(\"URL:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n url = file_object.read()\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Name of the bookmark:\")\n speech(\"Name of the bookmark:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n name2 = file_object.read()\n with open(\"urls.txt\", \"r\") as file_object1:\n urls = file_object1.read()\n with open(\"urls.txt\", \"w+\") as file_object1:\n file_object1.write(urls + \"|\" + url + \"|\" + name2.lower() + \"|\")\n elif (\"make an event\" in ans):\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Event name:\")\n speech(\"Event name:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n useritem = file_object.read()\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Date as mm-dd-yy:\")\n speech(\"Date as mm-dd-yy:\")\n subprocess.call([\"prompt.vbs\"], shell=True)\n with open(\"prompt_result.txt\", \"r\") as file_object:\n date = file_object.read()\n with open(\"events.txt\", \"w+\") as file_object:\n file_object.write(useritem + \"|\" + date + \"|\")\n elif (\"check events\" in ans):\n with open(\"events.txt\", \"r\") as file_object:\n data = file_object.read().split(\"|\")\n numevents = 0\n for i in range(len(data)):\n try:\n if (re.search(\".*[0-9]+-[0-9]+-[0-9]+.*\", data[i]) != None):\n date1 = datetime.strptime(data[i], '%m-%d-%y')\n date2 = datetime.now()\n date3 = date2 - date1\n if (date3.days == 0):\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Event : \" + data[i - 1])\n speech(\"Event : \" + data[i - 1])\n subprocess.call([\"narrate.vbs\"], shell=True)\n if (date3.days == 0):\n data.pop(i)\n data.pop(i - 1)\n numevents += 1\n except Exception:\n pass\n i += 1\n if (numevents == 0):\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(\"Sorry, no events.\")\n speech(\"Sorry, no events.\")\n subprocess.call([\"narrate.vbs\"], shell=True)\n with open(\"events.txt\", \"w+\") as file_object2:\n file_object2.write(\"|\".join(data))\n else:\n sampling = 1\n out = \"\"\n match = 0\n strs = []\n with open(\"speech.vecr\", \"r\") as vccfile:\n data = vccfile.readlines()\n endnum = data.index(\"/end/\\n\")\n for i in range(len(data)):\n if (sampling == 1):\n linespl = data[i].split(\" \")\n splne = data[i].split(\"|\")\n if (linespl[0] == \"if\"):\n linenum = int(splne[3])\n string = splne[1]\n strs.append(string)\n if (string == ans):\n match = 1\n out = data[endnum + linenum].split(\"|\")[1]\n elif (linespl[0] == \"/end/\"):\n sampling = 0\n if (match == 0):\n index = maxi(genScores(strs, \"instr\", ans))\n linecode = data[index]\n splne = linecode.split(\"|\")\n linenum = int(splne[3])\n out = data[endnum + linenum].split(\"|\")[1]\n with open(\"narrate.txt\", \"w+\") as file_object1:\n file_object1.write(out)\n speech(out)\n subprocess.call([\"narrate.vbs\"], shell=True)\n \n \n \n \n\n","sub_path":"main.pyw","file_name":"main.pyw","file_ext":"pyw","file_size_in_byte":14427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"571979319","text":"class sampleUpdate:\n\n new_column_headers = ['Sample Full Name',\n 'Resource Storage',\n 'Work Order ID',\n 'Pipeline',\n 'Current Production Status',\n 'QC Failed Metrics',\n 'WOI Status',\n 'Creation Date',\n 'Sample Name',\n 'Sample Type',\n 'Resource Bank Barcode',\n 'Tissue Name',\n 'Sample Nomenclature',\n 'RWOID Description',\n 'WOID Description',\n 'Case Control Status',\n 'Creator',\n 'WO Facilitator',\n 'Billing Acct Name',\n 'Administration Project',\n 'Attempted Coverage',\n 'Facilitator Comment',\n 'Is For CLE?']\n\n date_columns = ['Sample Received Date',\n 'RWOID Creation Date',\n 'WOID Creation Date',\n 'Resource Assessment Completed Date',\n 'Lib Core Start Date',\n 'New Library Needed',\n 'Capture drop off date',\n 'qPCR drop off date',\n 'Initial Sequencing Scheduled Date',\n 'Sequencing Scheduled Date',\n 'Sequencing Completed Date',\n 'QC Start',\n 'QC Completed Date',\n 'Data Transfer Hand Off Date',\n 'Data Transfer Completed Date']\n\n def __init__(self, smartsheet_sheet_obj, sheet_data, admin, ss_connector, new_sheet_name):\n\n self.sheet_obj = smartsheet_sheet_obj\n self.sheet_data = sheet_data\n self.new_sheet_name = new_sheet_name\n self.admin = admin['Administration Project']\n self.ss_connector = ss_connector\n self.woid = admin['Work Order ID']\n self.pipeline = admin['Pipeline']\n self.description = admin['Description']\n self.woid_date = admin['WO Start Date']\n self.admin_info_dict = {'Creator': admin['Creator'],\n 'WO Facilitator': admin['Facilitator'],\n 'Billing Acct Name': admin['Billing Account'],\n 'Facilitator Comment': admin['Facilitator Comment'],\n 'Is For CLE?': admin['Is For CLE?'],\n 'user email': admin['user email']}\n\n self.rwo = False\n self.swo = True\n if 'Resource Storage' in self.pipeline:\n self.rwo = True\n self.swo = False\n\n def construct_sheet(self, sheet_name, *args):\n\n new_sheet = {'name': sheet_name, 'columns': []}\n\n new_sheet['columns'].append({'title': 'RWO Unique', 'type': 'CHECKBOX', 'symbol': 'STAR', 'width': 60})\n new_sheet['columns'].append({'title': 'Current Iteration', 'type': 'CHECKBOX', 'symbol': 'STAR', 'width': 60})\n new_sheet['columns'].append({'title': 'Iteration', 'type': 'TEXT_NUMBER', 'width': 60})\n new_sheet['columns'].append({'title': 'Fail', 'type': 'CHECKBOX', 'symbol': 'FLAG', 'width': 5.33})\n new_sheet['columns'].append({'title': 'Re-attempt', 'type': 'PICKLIST', 'symbol': 'DECISION_SYMBOLS',\n 'width': 60})\n new_sheet['columns'].append({'title': 'Aliquot Requested', 'type': 'CHECKBOX', 'width': 70})\n\n for col in self.new_column_headers:\n if col == 'Sample Full Name':\n new_sheet['columns'].append({'title': col, 'type': 'TEXT_NUMBER', 'primary': True})\n elif col == 'WO Facilitator':\n new_sheet['columns'].append({'title': col, 'type': 'CONTACT_LIST'})\n else:\n new_sheet['columns'].append({'title': col, 'type': 'TEXT_NUMBER'})\n\n for date_col in self.date_columns:\n new_sheet['columns'].append({'title': date_col, 'type': 'DATE'})\n\n new_sheet['columns'].append({'title': 'Duration', 'type': 'TEXT_NUMBER'})\n new_sheet['columns'].append({'title': 'Topup', 'type': 'CHECKBOX', 'width': 60})\n new_sheet['columns'].append({'title': 'Launched', 'type': 'CHECKBOX', 'width': 60})\n new_sheet['columns'].append({'title': 'Data Transfer Completed', 'type': 'CHECKBOX', 'width': 60})\n\n sheet_spec = self.ss_connector.smart_sheet_client.models.Sheet(new_sheet)\n\n if not args:\n response = self.ss_connector.smart_sheet_client.Folders.create_sheet_in_folder(self.sheet_obj, sheet_spec)\n else:\n response = self.ss_connector.smart_sheet_client.Folders.create_sheet_in_folder(args[0], sheet_spec)\n return response.result.id\n\n def create_row(self, sample_row_dict, sheet_columns):\n\n new_row = self.ss_connector.smart_sheet_client.models.Row()\n\n for header in self.new_column_headers:\n\n if header == 'Resource Storage' and self.rwo:\n new_row.cells.append({'column_id': sheet_columns[header], 'value': self.woid, 'hyperlink': {\n 'url': 'https://imp-lims.ris.wustl.edu/entity/setup-work-order/{}'.format(self.woid)}})\n continue\n\n if header == 'Resource Storage' and not self.rwo:\n new_row.cells.append({'column_id': sheet_columns[header], 'value': 'NA'})\n continue\n\n if header == 'Work Order ID' and self.swo:\n new_row.cells.append(\n {'column_id': sheet_columns['Work Order ID'], 'value': self.woid, 'hyperlink': {\n 'url': 'https://imp-lims.ris.wustl.edu/entity/setup-work-order/{}'.format(self.woid)}})\n continue\n\n if header == 'Work Order ID' and not self.swo:\n new_row.cells.append({'column_id': sheet_columns['Work Order ID'], 'value': 'NA'})\n continue\n\n if header == 'Resource Bank Barcode' and self.rwo:\n new_row.cells.append({'column_id': sheet_columns[header], 'value': sample_row_dict['Barcode']})\n continue\n\n if header == 'Resource Bank Barcode' and not self.rwo:\n new_row.cells.append({'column_id': sheet_columns[header], 'value': 'NA'})\n continue\n\n if header == 'Pipeline':\n new_row.cells.append({'column_id': sheet_columns['Pipeline'], 'value': self.pipeline})\n continue\n\n if header == 'Current Production Status' and self.rwo:\n new_row.cells.append(\n {'column_id': sheet_columns['Current Production Status'], 'value': 'Resource Storage'})\n continue\n\n if header == 'Current Production Status' and not self.rwo and not self.swo:\n new_row.cells.append(\n {'column_id': sheet_columns['Current Production Status'], 'value': self.pipeline})\n continue\n\n if header == 'Current Production Status' and self.swo:\n new_row.cells.append(\n {'column_id': sheet_columns['Current Production Status'], 'value': 'Resource Assessment Pass'})\n continue\n\n if header == 'Administration Project':\n new_row.cells.append({'column_id': sheet_columns['Administration Project'], 'value': self.admin})\n continue\n\n if header == 'WO Facilitator':\n new_row.cells.append({'column_id': sheet_columns['WO Facilitator'],\n 'value': self.admin_info_dict['user email']})\n continue\n\n if header == 'RWOID Description' and self.rwo:\n new_row.cells.append({'column_id': sheet_columns['RWOID Description'], 'value': self.description})\n continue\n\n if header == 'WOID Description' and self.swo:\n new_row.cells.append({'column_id': sheet_columns['WOID Description'], 'value': self.description})\n continue\n\n # TODO handle two case controls if present\n if header == 'Case Control Status':\n case_found = False\n for k in sample_row_dict.keys():\n if 'case' in k.lower() or 'disease_status' in k.lower():\n case_found = True\n new_row.cells.append({'column_id': sheet_columns['Case Control Status'],\n 'value': sample_row_dict[k]})\n break\n\n if not case_found:\n new_row.cells.append({'column_id': sheet_columns['Case Control Status'], 'value': 'NA'})\n continue\n\n continue\n\n if header in self.admin_info_dict.keys():\n new_row.cells.append({'column_id': sheet_columns[header], 'value': self.admin_info_dict[header]})\n continue\n\n if header not in sample_row_dict:\n new_row.cells.append({'column_id': sheet_columns[header], 'value': 'NA'})\n else:\n new_row.cells.append({'column_id': sheet_columns[header], 'value': sample_row_dict[header]})\n\n if self.rwo:\n new_row.cells.append({'column_id': sheet_columns['RWOID Creation Date'],\n 'value': self.woid_date})\n\n if self.swo:\n new_row.cells.append({'column_id': sheet_columns['WOID Creation Date'],\n 'value': self.woid_date})\n\n new_row.cells.append({'column_id': sheet_columns['RWO Unique'], 'value': True})\n new_row.cells.append({'column_id': sheet_columns['Current Iteration'], 'value': True})\n new_row.cells.append({'column_id': sheet_columns['Iteration'], 'value': 1})\n new_row.to_bottom = True\n return new_row\n\n def get_column_ids(self, sheet_column_object):\n column_id_dict = {}\n for col in sheet_column_object:\n column_id_dict[col.title] = col.id\n return column_id_dict\n\n def dict_slice(self, dct, low=None, high=None):\n return dict(list(sorted(dct.items()))[low:high])\n\n def write_to_sheet(self):\n\n sheet = self.sheet_obj[0]\n sheet_col_id_dict = self.sheet_obj[1]\n\n if len(self.sheet_obj) == 3:\n admin_folder_id = self.sheet_obj[2]\n\n ss_available_rows_to_write = 10000 - sheet.total_row_count\n\n # all samples fit on one sheet (3 keys in data are not samples)\n if len(self.sheet_data) < ss_available_rows_to_write:\n rows_to_write_to_smart_sheet = []\n for i, k in enumerate(self.sheet_data.keys()):\n i = self.create_row(self.sheet_data[k], sheet_col_id_dict)\n rows_to_write_to_smart_sheet.append(i)\n response = self.ss_connector.smart_sheet_client.Sheets.add_rows(sheet.id, rows_to_write_to_smart_sheet)\n # print(response.data)\n print('{} samples written to {}'.format(len(rows_to_write_to_smart_sheet), sheet.name))\n\n else:\n\n samples_written = 0\n\n # complete current sheet\n if ss_available_rows_to_write > 0:\n rows_to_complete_sheet_dict = self.dict_slice(self.sheet_data, high=ss_available_rows_to_write)\n rows_to_write_to_new_sheet_dict = self.dict_slice(self.sheet_data, low=ss_available_rows_to_write,\n high=len(self.sheet_data))\n rows_to_complete_sheet = []\n for i, k in enumerate(rows_to_complete_sheet_dict.keys()):\n samples_written += 1\n i = self.create_row(rows_to_complete_sheet_dict[k], sheet_col_id_dict)\n rows_to_complete_sheet.append(i)\n response = self.ss_connector.smart_sheet_client.Sheets.add_rows(sheet.id, rows_to_complete_sheet)\n # print(response.data)\n print('{} samples written to {}'.format(len(rows_to_complete_sheet), sheet.name))\n\n else:\n # left over samples left to write\n rows_to_write_to_new_sheet_dict = self.sheet_data.copy()\n\n sample_counter = len(rows_to_write_to_new_sheet_dict)\n make_sheet = True\n\n while sample_counter > 0 and make_sheet:\n\n # make new sheet\n mss_sheets = self.ss_connector.get_sheet_list(admin_folder_id, 'f')\n highest_sheet = 1\n for mss_sheet in mss_sheets:\n s = mss_sheet.name\n if 'MSS_' in s:\n sheet_int = int(s.split('_')[-1])\n if sheet_int > highest_sheet:\n highest_sheet = sheet_int\n\n new_sheet_id = self.construct_sheet('{}{}'.format(self.new_sheet_name[:-1], (highest_sheet + 1)),\n admin_folder_id)\n new_sheet_object = self.ss_connector.get_object(new_sheet_id, 's')\n new_sheet_columns_dict = self.get_column_ids(new_sheet_object.columns)\n\n if len(rows_to_write_to_new_sheet_dict) <= 10000:\n make_sheet = False\n sample_counter = sample_counter - len(rows_to_write_to_new_sheet_dict)\n rows_to_complete_sheet = []\n for i, k in enumerate(rows_to_write_to_new_sheet_dict.keys()):\n samples_written += 1\n i = self.create_row(rows_to_write_to_new_sheet_dict[k], new_sheet_columns_dict)\n rows_to_complete_sheet.append(i)\n response = self.ss_connector.smart_sheet_client.Sheets.add_rows(new_sheet_id,\n rows_to_complete_sheet)\n print('{} samples written to {}'.format(len(rows_to_complete_sheet), new_sheet_object.name))\n\n else:\n\n rows_to_write = self.dict_slice(rows_to_write_to_new_sheet_dict, high=10000)\n rows_left = self.dict_slice(rows_to_write_to_new_sheet_dict, low=10000,\n high=len(rows_to_write_to_new_sheet_dict))\n\n rows_to_complete_sheet = []\n for i, k in enumerate(rows_to_write.keys()):\n samples_written += 1\n i = self.create_row(rows_to_write[k], new_sheet_columns_dict)\n rows_to_complete_sheet.append(i)\n response = self.ss_connector.smart_sheet_client.Sheets.add_rows(new_sheet_id,\n rows_to_complete_sheet)\n print('{} samples written to {}'.format(len(rows_to_complete_sheet), new_sheet_object.name))\n sample_counter = sample_counter - len(rows_to_complete_sheet)\n rows_to_write_to_new_sheet_dict = rows_left.copy()\n\n print('Total samples added to smartsheet: {}'.format(samples_written))\n","sub_path":"workflow_modules/rwo_mss_create_update.py","file_name":"rwo_mss_create_update.py","file_ext":"py","file_size_in_byte":15349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"309536838","text":"################################################################\r\n# Name : Nontouch kongdee\r\n# student number : 6201012610028\r\n# File: Assignment I\r\n# Date: 2020-07-15\r\n################################################################\r\n\r\n# Note this Python script requires PyGame.\r\nimport pygame \r\nfrom random import randint\r\nimport math\r\n# def for find maximum radian\r\ndef largest(storage):\r\n n = 0\r\n ma = rad[n]\r\n for i in range(len(rad)-1):\r\n if(ma < rad[i+1]):\r\n ma = rad[i+1]\r\n else:\r\n ma = ma\r\n return ma\r\n\r\n# initialize PyGame\r\npygame.init()\r\n\r\n# show PyGame version\r\n#print( 'PyGame version: {}'.format( pygame.version.ver ) ) \r\n\r\n\r\n# set window caption\r\npygame.display.set_caption('Amezing circle') \r\n\r\n# create a clock\r\nclock = pygame.time.Clock()\r\n\r\n# Set up the drawing window (500 x 500 pixels)\r\nscr_w, scr_h = 800, 600\r\nscreen = pygame.display.set_mode((scr_w, scr_h))\r\n\r\n# create a new surface \r\nsurface = pygame.Surface( screen.get_size(), pygame.SRCALPHA )\r\n\r\npoint = []\r\npoint_tup = []\r\nrad = []\r\n\r\n# Run until the user asks to quit\r\nrunning = True\r\nwhile running:\r\n\r\n # This limits the while loop to a max of 10 times per second.\r\n clock.tick( 15 ) \r\n\r\n # Did the user click the window close button?\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n running = False\r\n # keep the position (x,y) of maximum radian \r\n k = []\r\n for i in range(len(rad)):\r\n if(rad[i] == largest(rad)):\r\n k.append([point[i][0], point[i][1], rad[i]])\r\n # this loop for remove the circle when you click on the maximum circle only\r\n for j in range(len(k)):\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n if pygame.mouse.get_pressed()[0]:\r\n pos = pygame.mouse.get_pos()\r\n if((k[j][0]-pos[0])**2 + (k[j][1]-pos[1])**2 <= largest(rad)**2):\r\n pygame.draw.circle( surface, (255,255,255), (k[j][0],k[j][1]), k[j][2] )\r\n screen.blit(surface, (0,0))\r\n pygame.display.update() \r\n\r\n # randomize an integer value between 10..20 for the radius\r\n R = randint(10,20)\r\n # randomize an integer value between 50..255 for alpha level\r\n alpha = randint(50,255)\r\n # randomize an integer value between 50..255 for random color\r\n r = randint(0,255)\r\n # randomize an integer value between 50..255 for random color\r\n g = randint(0,255)\r\n # randomize an integer value between 50..255 for random color\r\n b = randint(0,255) \r\n # create a color with alpha level (RGBA) by random\r\n all_color = (r,g,b,alpha) \r\n # randomize a position (x,y)\r\n x,y = randint(R,scr_w-R), randint(R,scr_h-R)\r\n # if it has not any circle on display\r\n if len(point) == 0:\r\n p1 = randint(R,scr_w-R)\r\n p2 = randint(R,scr_h-R)\r\n distant = math.sqrt((x - p1)**2 + (y - p2)**2)\r\n point.append([x,y,R])\r\n point_tup.append((x,y))\r\n rad.append(R)\r\n pygame.draw.circle( surface, all_color, (x,y), R )\r\n screen.fill((255,255,255))\r\n screen.blit(surface, (0,0))\r\n pygame.display.update()\r\n # if in the display has the circle more than one\r\n else :\r\n # find distant between current circle and other circle\r\n for i,R2 in zip(point,rad) :\r\n distant = math.sqrt((x - int(i[0]))**2 + (y - int(i[1]))**2)\r\n # if the new circle has not overlap with other circle\r\n if [x,y] not in point and R + R2 < distant and len(rad) < 10 :\r\n point.append([x,y])\r\n point_tup.append((x,y))\r\n rad.append(R)\r\n # draw a circle filled with the blue color on the surface \r\n pygame.draw.circle( surface, all_color, (x,y), R )\r\n # fill the screen with the white color\r\n screen.fill((255,255,255))\r\n # draw the surface on the screen\r\n screen.blit(surface, (0,0))\r\n # update the screen display\r\n pygame.display.update()\r\n # if the new circle has overlap with other circle ,that will not makes it\r\n else :\r\n break\r\n\r\n \r\n\r\npygame.quit()\r\n\r\n################################################################\r\n# To do: modify the Python code so that it can randomly create\r\n# circles with different filled colors (RGBA values)\r\n################################################################","sub_path":"assignment 1 6201012610028.py","file_name":"assignment 1 6201012610028.py","file_ext":"py","file_size_in_byte":4448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"279719927","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# Copyright: (c) 2018, Abhijeet Kasurde \n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\nANSIBLE_METADATA = {\n 'metadata_version': '1.1',\n 'status': ['deprecated'],\n 'supported_by': 'community'\n}\n\nDOCUMENTATION = r'''\n---\nmodule: vmware_host_service_facts\ndeprecated:\n removed_in: '2.13'\n why: Deprecated in favour of C(_info) module.\n alternative: Use M(vmware_host_service_info) instead.\nshort_description: Gathers facts about an ESXi host's services\ndescription:\n- This module can be used to gather facts about an ESXi host's services.\nversion_added: '2.5'\nauthor:\n- Abhijeet Kasurde (@Akasurde)\nnotes:\n- Tested on vSphere 6.5\n- If source package name is not available then fact is populated as null.\nrequirements:\n- python >= 2.6\n- PyVmomi\noptions:\n cluster_name:\n description:\n - Name of the cluster.\n - Service facts about each ESXi server will be returned for given cluster.\n - If C(esxi_hostname) is not given, this parameter is required.\n type: str\n esxi_hostname:\n description:\n - ESXi hostname.\n - Service facts about this ESXi server will be returned.\n - If C(cluster_name) is not given, this parameter is required.\n type: str\nextends_documentation_fragment: vmware.documentation\n'''\n\nEXAMPLES = r'''\n- name: Gather facts about all ESXi Host in given Cluster\n vmware_host_service_facts:\n hostname: '{{ vcenter_hostname }}'\n username: '{{ vcenter_username }}'\n password: '{{ vcenter_password }}'\n cluster_name: cluster_name\n delegate_to: localhost\n register: cluster_host_services\n\n- name: Gather facts about ESXi Host\n vmware_host_service_facts:\n hostname: '{{ vcenter_hostname }}'\n username: '{{ vcenter_username }}'\n password: '{{ vcenter_password }}'\n esxi_hostname: '{{ esxi_hostname }}'\n delegate_to: localhost\n register: host_services\n'''\n\nRETURN = r'''\nhost_service_facts:\n description:\n - dict with hostname as key and dict with host service config facts\n returned: always\n type: dict\n sample: {\n \"10.76.33.226\": [\n {\n \"key\": \"DCUI\",\n \"label\": \"Direct Console UI\",\n \"policy\": \"on\",\n \"required\": false,\n \"running\": true,\n \"uninstallable\": false,\n \"source_package_name\": \"esx-base\",\n \"source_package_desc\": \"This VIB contains all of the base functionality of vSphere ESXi.\"\n },\n {\n \"key\": \"TSM\",\n \"label\": \"ESXi Shell\",\n \"policy\": \"off\",\n \"required\": false,\n \"running\": false,\n \"uninstallable\": false,\n \"source_package_name\": \"esx-base\",\n \"source_package_desc\": \"This VIB contains all of the base functionality of vSphere ESXi.\"\n },\n ]\n }\n'''\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible.module_utils.vmware import vmware_argument_spec, PyVmomi\n\n\nclass VmwareServiceManager(PyVmomi):\n def __init__(self, module):\n super(VmwareServiceManager, self).__init__(module)\n cluster_name = self.params.get('cluster_name', None)\n esxi_host_name = self.params.get('esxi_hostname', None)\n self.hosts = self.get_all_host_objs(cluster_name=cluster_name, esxi_host_name=esxi_host_name)\n\n def gather_host_facts(self):\n hosts_facts = {}\n for host in self.hosts:\n host_service_facts = []\n host_service_system = host.configManager.serviceSystem\n if host_service_system:\n services = host_service_system.serviceInfo.service\n for service in services:\n host_service_facts.append(\n dict(\n key=service.key,\n label=service.label,\n required=service.required,\n uninstallable=service.uninstallable,\n running=service.running,\n policy=service.policy,\n source_package_name=service.sourcePackage.sourcePackageName if service.sourcePackage else None,\n source_package_desc=service.sourcePackage.description if service.sourcePackage else None,\n )\n )\n hosts_facts[host.name] = host_service_facts\n return hosts_facts\n\n\ndef main():\n argument_spec = vmware_argument_spec()\n argument_spec.update(\n cluster_name=dict(type='str', required=False),\n esxi_hostname=dict(type='str', required=False),\n )\n\n module = AnsibleModule(\n argument_spec=argument_spec,\n required_one_of=[\n ['cluster_name', 'esxi_hostname'],\n ],\n supports_check_mode=True,\n )\n\n vmware_host_service_config = VmwareServiceManager(module)\n module.exit_json(changed=False, host_service_facts=vmware_host_service_config.gather_host_facts())\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"env/lib/python3.9/site-packages/ansible/modules/cloud/vmware/_vmware_host_service_facts.py","file_name":"_vmware_host_service_facts.py","file_ext":"py","file_size_in_byte":5231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"91858592","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\n\n# 顺序单线程\n\nfrom threading import Thread\nimport time\n\n\ndef my_counter():\n count = 0\n for i in range(1000000000):\n count += 1\n return True\n\n\ndef main1():\n thread_array = {}\n start_time = time.time()\n for i in range(2):\n t = Thread(target=my_counter)\n t.start()\n t.join()\n end_time = time.time()\n print('总共用时间:{0}'.format(end_time - start_time))\n\n\ndef main2():\n thread_array = {}\n start_time = time.time()\n for i in range(2):\n t = Thread(target=my_counter)\n t.start()\n thread_array[i] = t\n for j in range(2):\n thread_array[j].join()\n end_time = time.time()\n print('总共用时：{0}'.format(end_time - start_time))\n\n\nif __name__ == '__main__':\n main1() # 62.33681297302246\n # main2() # 62.62918210029602\n","sub_path":"GIL.py","file_name":"GIL.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"574019661","text":"#\n# ovirt-engine-setup -- ovirt engine setup\n# Copyright (C) 2013-2015 Red Hat, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n\n\"\"\"\nsshd service handler plugin.\n\"\"\"\n\n\nimport gettext\nimport os\nimport re\n\nfrom otopi import constants as otopicons\nfrom otopi import filetransaction, plugin, util\n\nfrom ovirt_engine_setup import constants as osetupcons\nfrom ovirt_engine_setup.engine import constants as oenginecons\nfrom ovirt_engine_setup.engine_common import constants as oengcommcons\n\n\ndef _(m):\n return gettext.dgettext(message=m, domain='ovirt-engine-setup')\n\n\n@util.export\nclass Plugin(plugin.PluginBase):\n \"\"\"\n sshd service handler plugin.\n \"\"\"\n\n _PERMIT_ROOT_LOGIN_RE = re.compile(\n flags=re.VERBOSE,\n pattern=r\"\"\"\n ^\n \\s*\n PermitRootLogin\n \\s+\n no\n [\\s#]*\n $\n \"\"\"\n )\n\n def __init__(self, context):\n super(Plugin, self).__init__(context=context)\n self._enabled = False\n\n @plugin.event(\n stage=plugin.Stages.STAGE_INIT,\n )\n def _init(self):\n self.environment.setdefault(\n oenginecons.AIOEnv.SSHD_PORT,\n None\n )\n\n @plugin.event(\n stage=plugin.Stages.STAGE_SETUP,\n )\n def _setup(self):\n self._enabled = not self.environment[\n osetupcons.CoreEnv.DEVELOPER_MODE\n ]\n self.command.detect('sshd')\n self.command.detect('restorecon')\n\n @plugin.event(\n stage=plugin.Stages.STAGE_CUSTOMIZATION,\n condition=lambda self: (\n self._enabled and\n self.environment[oenginecons.AIOEnv.CONFIGURE]\n ),\n before=(\n oengcommcons.Stages.DIALOG_TITLES_E_ALLINONE,\n ),\n after=(\n oenginecons.Stages.AIO_CONFIG_AVAILABLE,\n ),\n )\n def _customization(self):\n if not self.services.exists(name='sshd'):\n raise RuntimeError('sshd service is required')\n if not self.services.status(name='sshd'):\n self.services.state(\n name='sshd',\n state=True,\n )\n if self.environment[oenginecons.AIOEnv.SSHD_PORT] is None:\n rc, stdout, stderr = self.execute(\n args=(\n self.command.get('sshd'),\n '-T',\n ),\n )\n for line in stdout:\n words = line.split()\n if words[0] == 'port':\n self.environment[\n oenginecons.AIOEnv.SSHD_PORT\n ] = int(words[1])\n break\n self.environment.setdefault(\n oenginecons.AIOEnv.SSHD_PORT,\n oenginecons.AIOEnv.DEFAULT_SSH_PORT\n )\n\n @plugin.event(\n stage=plugin.Stages.STAGE_VALIDATION,\n condition=lambda self: (\n self._enabled and\n self.environment[oenginecons.AIOEnv.CONFIGURE]\n ),\n )\n def _validation(self):\n with open('/etc/ssh/sshd_config') as f:\n for l in f.read().splitlines():\n if self._PERMIT_ROOT_LOGIN_RE.match(l):\n raise RuntimeError(\n _(\n 'Your sshd configuration does not permit root '\n 'login, please enable PermitRootLogin to at '\n 'least without-password at /etc/ssh/sshd_config, '\n 'and restart sshd'\n )\n )\n\n @plugin.event(\n stage=plugin.Stages.STAGE_MISC,\n condition=lambda self: (\n self._enabled and\n self.environment[oenginecons.AIOEnv.CONFIGURE]\n ),\n after=(\n osetupcons.Stages.SSH_KEY_AVAILABLE,\n ),\n )\n def _misc(self):\n authorized_keys_line = self.environment[\n oenginecons.PKIEnv.ENGINE_SSH_PUBLIC_KEY\n ] + ' ovirt-engine'\n\n authorized_keys_file = os.path.join(\n os.path.expanduser('~root'),\n '.ssh',\n 'authorized_keys'\n )\n\n content = []\n if os.path.exists(authorized_keys_file):\n with open(authorized_keys_file, 'r') as f:\n content = f.read().splitlines()\n\n if authorized_keys_line not in content:\n self.environment[\n osetupcons.CoreEnv.UNINSTALL_UNREMOVABLE_FILES\n ].append(authorized_keys_file)\n\n content.append(authorized_keys_line)\n self.environment[otopicons.CoreEnv.MAIN_TRANSACTION].append(\n filetransaction.FileTransaction(\n name=authorized_keys_file,\n content=content,\n mode=0o600,\n dmode=0o700,\n owner='root',\n enforcePermissions=True,\n modifiedList=self.environment[\n otopicons.CoreEnv.MODIFIED_FILES\n ],\n )\n )\n\n @plugin.event(\n stage=plugin.Stages.STAGE_CLOSEUP,\n name=oenginecons.Stages.AIO_CONFIG_SSH,\n condition=lambda self: (\n self._enabled and\n self.environment[oenginecons.AIOEnv.CONFIGURE]\n ),\n )\n def _closeup(self):\n self.services.startup(\n name='sshd',\n state=True\n )\n\n if self.command.get('restorecon', optional=True) is not None:\n rc, stdout, stderr = self.execute(\n (\n self.command.get('restorecon'),\n '-r',\n os.path.join(\n os.path.expanduser('~root'),\n '.ssh',\n ),\n ),\n raiseOnError=False,\n )\n\n if rc != 0:\n self.logger.warning(\n _('Cannot set SELinux properties on SSH directory')\n )\n\n\n# vim: expandtab tabstop=4 shiftwidth=4\n","sub_path":"packaging/setup/plugins/ovirt-engine-setup/ovirt-engine/all-in-one/sshd.py","file_name":"sshd.py","file_ext":"py","file_size_in_byte":6531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"397255403","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\n# test_analysis.py\n\n# This file is part of the NNGT module\n# Distributed as a free software, in the hope that it will be useful, under the\n# terms of the GNU General Public License.\n\n\"\"\"\n==============\nTesting module\n==============\n\nThis module tests the various functionalities of NNGT to make sure that all\nimplementations remain compatible with the graph libraries and versions 2.7 and\n3.x of python.\n\nnote ::\n When adding new tests, filename should be of the form `test_xxx.py` and the\n code should contain:\n * a ``TestXXX`` class,\n * a ``suite = unittest.TestLoader().loadTestsFromTestCase(TestXXX)``\n declaration.\n\"\"\"\n\n# std imports\nimport sys\nimport importlib\nfrom os import listdir, environ\nfrom os.path import abspath, dirname, isfile\nimport unittest\n\n# personal library\nimport nngt\n\n\n\n#-----------------------------------------------------------------------------#\n# Get the tests\n#------------------------\n#\n\n# get the arguments\ngraph_library = environ.get(\"GL\", None)\nif graph_library == \"gt\":\n nngt.use_library(\"graph-tool\")\n assert nngt.get_config('graph_library') == \"graph-tool\", \\\n \"Loading graph-tool failed...\"\nelif graph_library == \"ig\":\n nngt.use_library(\"igraph\")\n assert nngt.get_config('graph_library') == \"igraph\", \\\n \"Loading igraph failed...\"\nelif graph_library == \"nx\":\n nngt.use_library(\"networkx\")\n assert nngt.get_config('graph_library') == \"networkx\", \\\n \"Loading networkx failed...\"\n\nomp = int(environ.get(\"OMP\", 1))\nnngt.set_config({\"omp\": omp})\n\n# get the tests\ncurrent_dir = dirname(abspath(__file__))\ndir_files = listdir(current_dir)\nsys.path.insert(0, current_dir)\ntestfiles = [fname[:-3] for fname in dir_files if (fname.startswith(\"test_\") \n and fname.endswith(\".py\"))]\ntests = [importlib.import_module(name) for name in testfiles]\n\n\n#-----------------------------------------------------------------------------#\n# Run if main\n#------------------------\n#\n\nif __name__ == \"__main__\":\n for test in tests:\n unittest.TextTestRunner(verbosity=2).run(test.suite)\n","sub_path":"nngt/testing/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"371455108","text":"import os\nimport subprocess\nimport time\nimport sys\nimport logging\nimport traceback\nimport requests\n\nprocesses = []\ndef execute_cmd(cmd):\n proc = subprocess.Popen(cmd)\n return proc\n\n\ndef handle_signal(signum, frame):\n murder()\n\ndef murder():\n for proc in processes:\n proc.kill()\n sys.exit()\n\ndef poll_for_module(url):\n wait = True\n while wait:\n time.sleep(1)\n try:\n cfg = requests.get(url, auth=(\"oscars\", \"oscars-shared\"), verify=False, timeout=1).json()\n wait = False\n except Exception as e:\n logging.info(\"Configuration params not accessible yet, waiting...\")\n #logging.error(traceback.format_exc())\n\ntry:\n orig_dir = os.getcwd()\n\n top_dir = os.path.join(os.path.dirname(__file__), \"..\")\n\n os.chdir(top_dir)\n top_dir = os.getcwd()\n\n # Trap SIGINT\n # signal.signal(signal.SIGINT, handle_signal)\n\n # Launch core\n core_dir = os.path.join(top_dir, \"core\")\n os.chdir(core_dir)\n core_target = os.path.join(core_dir, \"target\", \"core-0.7.0.jar\")\n core_cmd = ['java', \"-jar\", core_target]\n core_proc = execute_cmd(core_cmd)\n processes.append(core_proc)\n\n # Keep polling core until curl exist OK, then it's safe to start the other processes\n poll_for_module(\"https://oscars:oscars-shared@localhost:8000/configs/get/webui\")\n print(\"\\nWebUI config polled\")\n\n # Launch webui\n webui_dir = os.path.join(top_dir, \"webui\")\n os.chdir(webui_dir)\n webui_target = os.path.join(webui_dir, \"target\", \"webui-0.7.0.jar\")\n web_cfg = requests.get(\"https://localhost:8000/configs/get/webui\", auth=(\"oscars\", \"oscars-shared\"), verify=False).text\n web_cfg = web_cfg.replace('\"', '\\\"')\n web_cmd = ['java', \"-Dspring.application.json=\" + web_cfg, \"-jar\", webui_target]\n web_proc = execute_cmd(web_cmd)\n processes.append(web_proc)\n\n os.chdir(orig_dir)\n while 1:\n time.sleep(1)\nexcept Exception as e:\n logging.error(traceback.format_exc())\n murder()\n\n","sub_path":"bin/win_start.py","file_name":"win_start.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"4027360","text":"class P:\n \"\"\"Demonstrate how to do getters and setters with Python.\n\n To make sure we get an argument with the function call.\n Users have access to x, but internally is __x \"\"\"\n def __init__(self, x):\n # Below what triggers x.setter decorator.\n # The self.x below is NOT what the uses access when p1.x\n self.x = x\n\n # This what the users gets when p1.x\n # Returns the self.__x as p1.x (self.x) modified & provided by the setter function below.\n # self.__x is the only returned value in the class.\n @property # It sets x as a class attribute (self.x)\n def x(self): # The x here is what is referenced when p1.x below.\n return self.__x\n\n # The setter sets the value of self.__x\n # This is the setter for the property function.\n # This is called when a value is assigned to x\n # It can be use to normalize the bound input\n @x.setter\n def x(self, x): # It sets the value of __x from x which is used by the property\n if x < 0:\n self.__x = 0\n elif x > 1000:\n self.__x = 1000\n else:\n self.__x = x\n\np1 = P(1002)\nprint(p1.x)\n","sub_path":"p3_essentials/Decorators/decorator1.py","file_name":"decorator1.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"539015021","text":"#!/usr/bin/env python\n# Copyright 2016 Brigham Young University\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\nfrom selenium import webdriver\nimport re\nimport os\nimport sys\nimport time\nimport fire\nimport datetime\nimport configparser\nfrom os.path import expanduser\nfrom selenium.common.exceptions import NoSuchElementException\n\ndef setup(hub):\n config = configparser.ConfigParser()\n home = expanduser(\"~\")\n config_file_path = '~/.byu/netid.ini'\n config.read_file(open(home + '/.byu/netid.ini', 'r'))\n if hub:\n url = os.environ['SELENIUM_HUB_PORT_4444_TCP'].replace('tcp', 'http')\n driver = webdriver.Remote(url + '/wd/hub', webdriver.DesiredCapabilities.CHROME.copy()) \n else:\n driver = webdriver.Chrome()\n driver.implicitly_wait(5)\n return driver, config\n\ndef cas_login(driver, config):\n print('logging in via CAS')\n driver.find_element_by_id('netid').clear()\n driver.find_element_by_id('netid').send_keys(config['netid']['username'])\n driver.find_element_by_id('password').clear()\n driver.find_element_by_id('password').send_keys(config['netid']['password'])\n driver.find_element_by_css_selector('input.submit').click()\n\ndef print_details(driver, row):\n date_received = driver.find_element_by_css_selector('#sortable > tbody > tr:nth-child({}) > td:nth-child(1)'.format(row)).text\n nominee = driver.find_element_by_css_selector('#sortable > tbody > tr:nth-child({}) > td:nth-child(2)'.format(row)).text\n value = driver.find_element_by_css_selector('#sortable > tbody > tr:nth-child({}) > td:nth-child(3)'.format(row)).text\n nominator = driver.find_element_by_css_selector('#sortable > tbody > tr:nth-child({}) > td:nth-child(4)'.format(row)).text\n consultant = driver.find_element_by_css_selector('#sortable > tbody > tr:nth-child({}) > td:nth-child(5)'.format(row)).text\n clear_working_graphic()\n print('{} received a SAERA award for {} on {} and was nominated by {}'.format(nominee, value, date_received, nominator))\n\ndef print_working_graphic(num):\n if num % 4 == 0:\n sys.stdout.write('-')\n elif num % 4 == 1:\n sys.stdout.write('/')\n elif num % 4 == 2:\n sys.stdout.write('-')\n elif num % 4 == 3:\n sys.stdout.write('\\\\')\n sys.stdout.write(\"\\b\")\n sys.stdout.flush() \n\ndef clear_working_graphic():\n sys.stdout.write(\"\\b\")\n sys.stdout.flush()\n\ndef print_year_details(driver, year, name):\n print('{}'.format(year))\n driver.get('https://saera.byu.edu/Index/nomhistory/{}'.format(year))\n row = 1\n while row >= 1:\n print_working_graphic(row)\n nominee_elem = None\n try:\n nominee_elem = driver.find_element_by_css_selector('#sortable > tbody > tr:nth-child({}) > td:nth-child(2)'.format(row))\n except NoSuchElementException:\n clear_working_graphic()\n break\n if re.search(name, nominee_elem.text, flags=re.I):\n print_details(driver, row)\n row += 1\n\ndef wait_for_authn(driver, url, max_secs):\n print('waiting for authn to finish')\n seconds_waited = 0\n while driver.current_url != url:\n time.sleep(1)\n seconds_waited += 1\n if seconds_waited % 10 == 0:\n print('waited {} seconds for authn'.format(seconds_waited))\n if seconds_waited > max_secs:\n raise Exception('Login problem > {} seconds after login'.format(max_secs))\n\ndef run(name, hub=False):\n print(\"\"\"The CRITERIA values for the SAERA award are:\nC- Competency\nR- Respect for Sacred Resources\nI- Integrity\nT- Teamwork\nE- Exceeding Customer Expectations\nR- Respect for All Individuals\nI- Innovation\nA- Accountability\"\"\")\n\n driver, config = setup(hub)\n\n driver.get('https://saera.byu.edu')\n cas_login(driver, config)\n wait_for_authn(driver, 'https://saera.byu.edu/', 60)\n\n year = datetime.datetime.today().year\n while year >= 2008: # 2008 is the earliest year listed on the website\n print_year_details(driver, year, name)\n year -= 1\n\nif __name__ == '__main__':\n fire.Fire(run)\n","sub_path":"has-saera-award.py","file_name":"has-saera-award.py","file_ext":"py","file_size_in_byte":4555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"611456133","text":"import nltk\nimport random\nfrom nltk.corpus import movie_reviews\nfrom nltk.classify.scikitlearn import SklearnClassifier\nimport pickle\n\nfrom sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB\nfrom sklearn.linear_model import LogisticRegression, SGDClassifier\nfrom sklearn.svm import SVC, LinearSVC, NuSVC\n\nfrom nltk.classify import ClassifierI\nfrom statistics import mode\n\nclass VoteClassifier(ClassifierI):\n def __init__(self, *classifiers):\n self._classifiers = classifiers\n\n def classify(self, features):\n votes = []\n for c in self._classifiers:\n v = c.classify(features)\n votes.append(v)\n return mode(votes)\n\n def confidence(self, features):\n votes = []\n for c in self._classifiers:\n v = c.classify(features)\n votes.append(v)\n\n choice_votes = votes.count(mode(votes))\n conf = choice_votes / len(votes)\n return conf\n \n\n# docs = [(list(movie_reviews.words(fileid)), category)\n# for category in movie_reviews.categories()\n# for fileid in movie_reviews.fileids(category)]\n\ndocs = []\n\nfor category in movie_reviews.categories():\n for fileid in movie_reviews.fileids(category):\n docs.append((list(movie_reviews.words(fileid)), category))\n\nrandom.shuffle(docs)\n\n# print(docs[1])\n\nall_words = []\nfor w in movie_reviews.words():\n all_words.append(w.lower())\n\nall_words = nltk.FreqDist(all_words)\n\n# print(all_words.most_common(15))\n# print(all_words[\"bad\"])\n\nword_features = list(all_words.keys())[:3000]\n\n\ndef find_features(document):\n words = set(document)\n features = {}\n for w in word_features:\n features[w] = (w in words)\n return features\n\n\n# print((find_features(movie_reviews.words('neg/cv000_29416.txt'))))\n\nfeature_sets = [(find_features(rev), category) for (rev, category) in docs]\n\ntraining_set = feature_sets[:1900]\ntesting_set = feature_sets[1900:]\n\nclassifier = nltk.NaiveBayesClassifier.train(training_set)\n# to open a saved classifier:\n# classifier_f = open(\"naivebayes.pickle\", \"rb\")\n# classifier = pickle.load(classifier_f)\n# classifier_f.close()\n\n# print(\"Original Naive Bayes Algorithm Accuracy:\", (nltk.classify.accuracy(classifier, testing_set))*100)\n# classifier.show_most_informative_features(15)\n\nMultinomialNB_classifier = SklearnClassifier(MultinomialNB())\nMultinomialNB_classifier.train(training_set)\nprint(\"MNB Accuracy:\", (nltk.classify.accuracy(MultinomialNB_classifier, testing_set))*100)\n\n# GaussianNB_classifier = SklearnClassifier(GaussianNB())\n# GaussianNB_classifier.train(training_set)\n# print(\"GaussianNB Accuracy:\", (nltk.classify.accuracy(GaussianNB_classifier, testing_set))*100)\n\nBernoulliNB_classifier = SklearnClassifier(BernoulliNB())\nBernoulliNB_classifier.train(training_set)\nprint(\"BernoulliNB Accuracy:\", (nltk.classify.accuracy(BernoulliNB_classifier, testing_set))*100)\n\nLogisticRegression_classifier = SklearnClassifier(LogisticRegression())\nLogisticRegression_classifier.train(training_set)\nprint(\"LogisticRegression Accuracy:\", (nltk.classify.accuracy(LogisticRegression_classifier, testing_set))*100)\n\nSGD_classifier = SklearnClassifier(SGDClassifier())\nSGD_classifier.train(training_set)\nprint(\"SGDClassifier Accuracy:\", (nltk.classify.accuracy(SGD_classifier, testing_set))*100)\n\n# SVC_classifier = SklearnClassifier(SVC())\n# SVC_classifier.train(training_set)\n# print(\"SVC Accuracy:\", (nltk.classify.accuracy(SVC_classifier, testing_set))*100)\n\nLinearSVC_classifier = SklearnClassifier(LinearSVC())\nLinearSVC_classifier.train(training_set)\nprint(\"LinearSVC Accuracy:\", (nltk.classify.accuracy(LinearSVC_classifier, testing_set))*100)\n\nNuSVC_classifier = SklearnClassifier(NuSVC())\nNuSVC_classifier.train(training_set)\nprint(\"NuSVC Accuracy:\", (nltk.classify.accuracy(NuSVC_classifier, testing_set))*100)\n\nvoted_classifier = VoteClassifier(classifier, MultinomialNB_classifier, BernoulliNB_classifier, LogisticRegression_classifier, SGD_classifier, LinearSVC_classifier, NuSVC_classifier)\nprint(\"voted_classifier Accuracy:\", (nltk.classify.accuracy(voted_classifier, testing_set))*100)\n\nprint(\"Classification:\", voted_classifier.classify(testing_set[0][0]), \"Confidence Percent:\", voted_classifier.confidence(testing_set[0][0]))\nprint(\"Classification:\", voted_classifier.classify(testing_set[1][0]), \"Confidence Percent:\", voted_classifier.confidence(testing_set[1][0]))\nprint(\"Classification:\", voted_classifier.classify(testing_set[2][0]), \"Confidence Percent:\", voted_classifier.confidence(testing_set[2][0]))\nprint(\"Classification:\", voted_classifier.classify(testing_set[3][0]), \"Confidence Percent:\", voted_classifier.confidence(testing_set[3][0]))\nprint(\"Classification:\", voted_classifier.classify(testing_set[4][0]), \"Confidence Percent:\", voted_classifier.confidence(testing_set[4][0]))\nprint(\"Classification:\", voted_classifier.classify(testing_set[5][0]), \"Confidence Percent:\", voted_classifier.confidence(testing_set[5][0]))","sub_path":"nlp_basics/naive_bayes_sklearn.py","file_name":"naive_bayes_sklearn.py","file_ext":"py","file_size_in_byte":4972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"85352731","text":"from discord.ext import commands\n\n\nclass UT4:\n \"\"\"Unreal Tournament 4 related commands\"\"\"\n def __init__(self, bot):\n self.bot = bot\n\n @commands.group(invoke_without_command=True)\n async def ut4(self):\n await self.bot.say('https://www.epicgames.com/unrealtournament/')\n\n @ut4.command()\n async def changelog(self):\n await self.bot.say('https://wiki.unrealengine.com/Version_Notes')\n\n\ndef setup(bot):\n bot.add_cog(UT4(bot))\n","sub_path":"cogs/games/ut4.py","file_name":"ut4.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"644918071","text":"# written by clw\r\n\r\nimport xml.etree.ElementTree as ET\r\nimport os\r\nimport glob\r\n\r\nxml_path = '/media/clwclw/data/VOCdevkit/VOC2007_2012_clw/train'\r\n\r\nxml_file_paths = glob.glob(os.path.join(xml_path, '*.xml'))\r\n\r\n\r\ndef delete_difficult_objects(xml_file_paths):\r\n difficult_nums_count = 0\r\n for idx, xml_file_path in enumerate(xml_file_paths):\r\n print('xml counts: ', idx+1)\r\n tree = ET.parse(xml_file_path)\r\n root = tree.getroot()\r\n for anno_id, obj in enumerate(root.findall('object')): # clw note:这里不能用root.iter('object'),因为要删除元素,迭代器会指向下一个的下一个元素,从而导致跳过了一个item\r\n difficult = obj.find('difficult').text\r\n if difficult != '0':\r\n #print('difficult file: ', xml_file_path)\r\n root.remove(obj)\r\n difficult_nums_count += 1\r\n tree.write(xml_file_path)\r\n print('difficult nums in all class:', difficult_nums_count)\r\n\r\ndelete_difficult_objects(xml_file_paths)\r\n","sub_path":"常用数据预处理脚本/xml/xml_delete_difficult_object.py","file_name":"xml_delete_difficult_object.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"316470257","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Oct 1 16:15:26 2020\n@student: p williams\nA = [[9,6],[8,3],[7,4]]\n\"\"\"\n\nfrom gurobipy import GRB,Model\n\n#Week 6 Quiz Example\n# Create the model\nm = Model('q6')\n# Set parameters\nm.setParam('OutputFlag',True)\n# Add variables\nx1 = m.addVar(name='x1')\nx2 = m.addVar(name='x2')\n# Add constraints\nm.addConstr(9*x1 + 6*x2 <= 3, name='c1')\nm.addConstr(8*x1 + 3*x2 <= 14, name='c2')\nm.addConstr(7*x1 + 4*x2 <= 10, name='c3')\n#Set the objective\nm.setObjective(x1 - 2*x2, GRB.MAXIMIZE)\n# Optimize the model\nm.optimize()\n# Print the result\nstatus_code = {1:'LOADED', 2:'OPTIMAL', 3:'INFEASIBLE', 4:'INF_OR_UNBD', 5:'UNBOUNDED'}\nstatus = m.status\nprint('The optimization status is {}'.format(status_code[status]))\n\nif status == 2:\n # Retrieve variables value\n print('Optimal solution:')\n for v in m.getVars():\n print('%s = %g' % (v.varName, v.x))\n print('Optimal objective value: \\n{}'.format(m.objVal))","sub_path":"py/gurobi_sample.py","file_name":"gurobi_sample.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"103736052","text":"import game_framework\nfrom pico2d import *\n\nimport title_state\nimport main_state\n\nname = 'PauseState'\nimage = None\nbutton_state = True\nbutton_timer = 0.0\n\n\ndef enter():\n global image\n image = load_image('pause.png')\n\n\ndef exit():\n global image\n del(image)\n\n\ndef resume(): pass\n\n\ndef handle_events():\n events = get_events()\n for event in events:\n if event.type == SDL_QUIT:\n game_framework.quit()\n elif event.type == SDL_KEYDOWN and event.key == SDLK_ESCAPE:\n game_framework.change_state(title_state)\n elif event.type == SDL_KEYDOWN and event.key == SDLK_p:\n game_framework.pop_state()\n\n\ndef update():\n global button_state\n global button_timer\n if(button_timer > 1.0):\n button_timer = 0\n if button_state == True: button_state = False\n elif button_state == False : button_state = True\n delay(0.01)\n button_timer += 0.05\n\n\ndef draw():\n clear_canvas()\n main_state.boy.draw()\n main_state.grass.draw()\n if button_state == True:\n image.draw(400,300)\n update_canvas()\n","sub_path":"Drills/Drill-10/pause_state.py","file_name":"pause_state.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"89499233","text":"import aiosqlite\nimport json\nfrom discord.ext import commands\nimport discord\nfrom datetime import datetime\n\ndef dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d\n\nasync def get_connector():\n connector = await aiosqlite.connect('./common.db')\n connector.row_factory = dict_factory\n return connector\n\nasync def initialize_tables(bot):\n c = await get_connector()\n await c.execute('CREATE TABLE IF NOT EXISTS users(id INTEGER, seen_in TEXT, level INTEGER, exp INTEGER, exp_required INTEGER, settings TEXT, bio TEXT, image_url TEXT, profile_color INTEGER, blacklist INTEGER)')\n await c.execute('CREATE TABLE IF NOT EXISTS tags(author INTEGER, guild INTEGER, created REAL, name TEXT, content TEXT)')\n await c.execute('CREATE TABLE IF NOT EXISTS guilds(id INTEGER, prefix TEXT, logchannel INTEGER, muterole INTEGER, announcechannel INTEGER)')\n\n bot.logger.info('Rebuilding guild database.')\n await rebuild_guilds(bot)\n bot.logger.info('Rebuilding user database.')\n await rebuild_users(bot)\n\n await c.commit()\n await c.close()\n\nasync def rebuild_users(bot):\n c = await get_connector()\n\n for member in bot.get_all_members():\n await add_user(member.id, bot)\n\n await c.commit()\n await c.close()\n\nasync def rebuild_guilds(bot):\n c = await get_connector()\n\n for guild in bot.guilds:\n cursor = await c.execute(f'SELECT * FROM guilds WHERE id = ?', guild.id)\n data = await cursor.fetchone()\n\n if not data:\n await c.execute(f'INSERT INTO guilds VALUES (?, ?, ?, ?, ?)', guild.id, '$', '', '', '')\n\n await c.commit()\n await c.close()\n\nasync def add_guild(bot, id):\n c = await get_connector()\n guild = bot.get_guild(id)\n\n announcements_channel = -1\n for channel in sorted(guild.channels, key=lambda x: x.name):\n if 'announcements' in channel.name.strip():\n announcements_channel = channel.id\n\n await c.execute(f'INSERT INTO guilds VALUES (?, ?, ?, ?, ?)', id, '$', -1. -1, announcements_channel)\n\n for member in guild.members:\n add_user(member.id, bot)\n\n await c.commit()\n await c.close()\n\n\n# Guild Utilities\n\nasync def get_guild(id):\n c = await get_connector()\n cursor = await c.execute(f'SELECT * FROM guilds WHERE id = ?', guild.id)\n data = await cursor.fetchone()\n await c.close()\n return data\n\nasync def modify_guild(id, parameter, value):\n c = await get_connector()\n\n await c.execute(f'UPDATE guilds SET ? = ? WHERE id = ?', parameter, value, id)\n await c.commit()\n await c.close()\n\n# User Utilities\n\nasync def add_user(id, bot):\n c = await get_connector()\n settings_template = {\n 'embedcolor': 'blurple',\n 'pingmonitor': False,\n }\n\n user = bot.get_user(id)\n if user is None:\n return False\n\n cursor = await c.execute(f'SELECT * FROM users WHERE id = ?', id)\n data = await cursor.fetchone()\n\n guilds = [guild for guild in bot.guilds if user in guild.members]\n\n if not data:\n settings = json.dumps(settings_template).replace(\"'\", \"''\")\n await c.execute(f\"INSERT INTO users VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\", id, str([i.id for i in guilds]), 1, 0, 100, str(settings), '', '', '', '', 0)\n else:\n await c.execute(f'UPDATE users SET seen_in = ? WHERE id = ?', str([i.id for i in guilds]), id)\n await c.commit()\n await c.close()\n\nasync def modify_user(id, parameter, value):\n c = await get_connector()\n\n await c.execute(f'UPDATE users SET ? = ? WHERE id = ?', parameter, value, id)\n await c.commit()\n await c.close()\n\nasync def get_user(id):\n c = await get_connector()\n cursor = await c.execute(f'SELECT * FROM users WHERE id = {id}')\n data = await cursor.fetchone()\n await c.close()\n return data\n\nasync def add_tag(author, guild, name, content):\n c = await get_connector()\n await c.execute(f'INSERT INTO tags VALUES (?, ?, ?, ?, ?)', (author.id, guild.id, datetime.utcnow().timestamp(), name, content))\n await c.commit()\n await c.close()\n\nasync def get_tag(author, guild, name):\n c = await get_connector()\n cursor = await c.execute(f'SELECT * FROM tags WHERE name = ? AND author = ? AND guild = ?', name, author, guild)\n data = await cursor.fetchone()\n await c.close()\n return data\n\nasync def get_guild_tag(guild, name):\n c = await get_connector()\n cursor = await c.execute(f'SELECT * FROM tags WHERE name = ? AND guild = ?', name, guild)\n data = await cursor.fetchone()\n await c.close()\n return data\n\nasync def run_command(command):\n c = await get_connector()\n await c.execute(command)\n await c.commit()\n await c.close()\n\nasync def delete_tag(author, guild, name):\n c = await get_connector()\n await c.execute(f'DELETE FROM tags WHERE author = ? AND name = ? AND guild = ?', author, name, guild)\n await c.commit()\n await c.close()\n\nasync def get_all_tags(author, guild):\n c = await get_connector()\n cursor = await c.execute(f'SELECT * FROM tags WHERE author = ? AND guild = ?', author, guild)\n data = await cursor.fetchall()\n await c.close()\n return data\n\nasync def is_blacklist(id):\n c = await get_connector()\n cursor = await c.execute(f'SELECT * FROM users WHERE id = ?', id)\n data = await cursor.fetchone()\n if data['blacklist'] == 1:\n bl = True\n else:\n bl = False\n await c.close()\n return bl\n","sub_path":"dbcontrol.py","file_name":"dbcontrol.py","file_ext":"py","file_size_in_byte":5466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"611649723","text":"import nextcord\nfrom src.music.voicestate import VoiceState\nfrom src.bot.bot import Bot\nimport requests\nfrom nextcord import Embed, Colour, Message, Interaction, Button\nfrom src.music.sec_to_time import sec_to_time\n\n\nclass MenuButtons(nextcord.ui.View):\n def __init__(self, bot: Bot, voice_state: VoiceState):\n self.bot = bot\n self.voice_state = voice_state\n super().__init__(timeout=None)\n\n def user_has_permissions(self, user) -> bool:\n response = requests.get(f'{self.bot.base_api_url}discord/guild/',\n params={'id': user.guild.id}, headers=self.bot.header).json()\n if response.get('items') and response['items'][0]['musicRoleId'] and user.guild.get_role(\n response['items'][0]['musicRoleId']) not in user.roles:\n return False\n\n return True\n\n @nextcord.ui.button(label=\"\", emoji=\"⏮️\", style=nextcord.ButtonStyle.primary)\n async def previous_button(self, button: Button, interaction: Interaction):\n if interaction.user.bot or not interaction.user.guild:\n return\n elif self.user_has_permissions(interaction.user):\n await self.voice_state.play_previous()\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to interact with the music queue.\",\n ephemeral=True)\n\n @nextcord.ui.button(label=\"\", emoji=\"▶️\", style=nextcord.ButtonStyle.primary)\n async def play_pause_button(self, button: Button, interaction: Interaction):\n if interaction.user.bot or not interaction.user.guild:\n return\n elif self.user_has_permissions(interaction.user):\n if self.voice_state.is_playing and self.voice_state.voice.is_playing():\n self.voice_state.voice.pause()\n elif self.voice_state.is_playing and self.voice_state.voice.is_paused():\n self.voice_state.voice.resume()\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to pause this bot.\",\n ephemeral=True)\n\n @nextcord.ui.button(label=\"\", emoji=\"⏭���\", style=nextcord.ButtonStyle.primary)\n async def next_button(self, button: Button, interaction: Interaction):\n if interaction.user.bot or not interaction.user.guild:\n return\n elif self.user_has_permissions(interaction.user):\n await self.voice_state.skip()\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to interact with the music queue.\",\n ephemeral=True)\n\n @nextcord.ui.button(label=\"\", emoji=\"🔢\", style=nextcord.ButtonStyle.primary)\n async def queue_button(self, button: Button, interaction: Interaction):\n message = await interaction.message.channel.send(embed=await self.voice_state.get_queue_embed())\n self.bot.dispatch('new_reaction_message', self.voice_state, message)\n\n @nextcord.ui.button(label=\"\", emoji=\"❌\", style=nextcord.ButtonStyle.grey)\n async def disconnect(self, button: Button, interaction: Interaction):\n if interaction.user.bot or not interaction.user.guild:\n return\n elif self.user_has_permissions(interaction.user):\n if self.voice_state.voice:\n await self.voice_state.stop()\n if interaction.user.guild.id in self.bot.voice_states:\n self.bot.voice_states.pop(interaction.user.guild.id)\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to disconnect the bot.\",\n ephemeral=True)\n\n\nclass FindMenuButtons(nextcord.ui.View):\n def __init__(self, bot: Bot, voice_state: VoiceState, videos):\n self.bot = bot\n self.voice_state = voice_state\n self.videos = videos\n super().__init__(timeout=None)\n\n def user_has_permissions(self, user) -> bool:\n if not user.guild and user.guild.id != self.voice_state.guild_id and not user.voice:\n return False\n response = requests.get(f'{self.bot.base_api_url}discord/guild/',\n params={'id': user.guild.id}, headers=self.bot.header).json()\n if response.get('items') and response['items'][0]['musicRoleId'] and user.guild.get_role(\n response['items'][0]['musicRoleId']) not in user.roles:\n return False\n\n return True\n\n async def play_video(self, position: int, user, message: Message):\n destination = user.voice.channel\n if self.voice_state and self.voice_state.voice:\n await self.voice_state.voice.move_to(destination)\n else:\n state = VoiceState(self.bot, user.guild.id)\n state.voice = await destination.connect()\n self.bot.voice_states[user.guild.id] = state\n\n song = self.videos[position]\n song.requester_name = f'{user.name}#{user.discriminator}'\n song.requester_id = user.id\n\n position = self.voice_state.queue.get_len()\n time_until_playing = sum(song.duration for song in self.voice_state.queue.get())\n\n self.voice_state.queue.put([song])\n\n embed = Embed(\n title=f'Added song to queue at position {position + 1}:',\n description=f'[{song.title}]({song.url})\\nCreator: {song.channel_title}, Duration: {song.duration_str}',\n colour=Colour.blue())\n\n if time_until_playing > 0:\n embed.set_footer(text=f'Time until playing: {sec_to_time(time_until_playing)}')\n\n embed.set_thumbnail(url=self.bot.logo_url)\n\n await message.channel.send(embed=embed)\n await message.edit(view=None)\n\n @nextcord.ui.button(label=\"1\", style=nextcord.ButtonStyle.primary)\n async def first_element_button(self, button: Button, interaction: Interaction):\n if self.user_has_permissions(interaction.user):\n await self.play_video(0, interaction.user, interaction.message)\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to interact with the music queue.\",\n ephemeral=True)\n\n @nextcord.ui.button(label=\"2\", style=nextcord.ButtonStyle.primary)\n async def second_element_button(self, button: Button, interaction: Interaction):\n if self.user_has_permissions(interaction.user):\n await self.play_video(1, interaction.user, interaction.message)\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to interact with the music queue.\",\n ephemeral=True)\n\n @nextcord.ui.button(label=\"3\", style=nextcord.ButtonStyle.primary)\n async def third_element_button(self, button: Button, interaction: Interaction):\n if self.user_has_permissions(interaction.user):\n await self.play_video(2, interaction.user, interaction.message)\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to interact with the music queue.\",\n ephemeral=True)\n\n @nextcord.ui.button(label=\"4\", style=nextcord.ButtonStyle.primary)\n async def fourth_element_button(self, button: Button, interaction: Interaction):\n if self.user_has_permissions(interaction.user):\n await self.play_video(3, interaction.user, interaction.message)\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to interact with the music queue.\",\n ephemeral=True)\n\n @nextcord.ui.button(label=\"5\", style=nextcord.ButtonStyle.primary)\n async def fifth_element_button(self, button: Button, interaction: Interaction):\n if self.user_has_permissions(interaction.user):\n await self.play_video(4, interaction.user, interaction.message)\n else:\n await interaction.response.send_messaeg(\"You don't have permissions to interact with the music queue.\",\n ephemeral=True)\n","sub_path":"src/bot/buttons.py","file_name":"buttons.py","file_ext":"py","file_size_in_byte":8222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"327661822","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n#@Time : 2019/6/17 17:28\n#@Author: zhangslb\n#@File : test_pointLateOrderFilter.py\n# @Description：订单数据过滤\n\nfrom selenium import webdriver\nimport json\nimport unittest, time\nimport common.commonParam as commParam\nimport common.setControlValue as setValue\nimport common.litLogging\nfrom selenium.webdriver import ActionChains\n\nfrom common import login_token, request_get_to_token\nfrom conf import dataRW\nimport sys\nsys.setrecursionlimit(10000)\nLogs = common.litLogging.Log()\n\n\nclass test_10PointLateOrderFilter(unittest.TestCase):\n u\"\"\"订单数据过滤创建与查询\"\"\"\n def setUp(self):\n self.imgs = []\n Logs.info('准备测试，启动浏览器')\n self.driver = webdriver.Firefox(executable_path='geckodriver')\n # self.driver.get(commParam.addSingMaterialPolicy)\n # self.driver.implicitly_wait(30) # 隐性等待时间为30秒\n Logs.info('浏览器启动成功')\n\n def add_img(self):\n self.imgs.append(self.driver.get_screenshot_as_base64())\n return True\n\n def click_lable(self):\n lable = self.driver.find_element_by_xpath(commParam.lableCPath)\n ActionChains(self.driver).move_to_element(lable).perform()\n lable.click()\n\n def test_pointLateOrderFilter(self):\n u'''订单数据过滤创建功能创建'''\n setValue.login(self)\n time.sleep(0.5)\n self.driver.get(commParam.addPointLateOrderFilter)\n self.driver.implicitly_wait(3) # 隐性等待时间为30秒\n driver = self.driver\n Logs.info('进入【订单数据过滤维护功能】测试')\n setValue.SetButtonControlClick('点击增加按钮', driver, commParam.appendPointLateOrderFilterCPath)\n\n setValue.add_img(self)\n setValue.SetSelectContorlValue('设置产品组选项', driver, commParam.pointLateProductGroupCPath,\n commParam.pointLateProductGroupVPath, commParam.pointLateProductGroupValue,\n False)\n self.click_lable()\n setValue.SetInputControlValue('设置代理编号选项', driver, commParam.resellerIdCPath,\n commParam.resellerIdCPath, commParam.agencyNoValue)\n\n setValue.SetSelectContorlValue('设置政策类型选项', driver, commParam.policyTypeCPath,\n commParam.policyTypeVPath, commParam.policyTypeValue, False)\n\n setValue.SetSelectContorlValue('设置分销渠道选项', driver, commParam.distributionChannelCPath,\n commParam.distributionChannelVPath, commParam.distributionChannelValue, False)\n setValue.add_img(self)\n setValue.SetSelectContorlValue('设置代理级别选项', driver, commParam.resellerLevelCPath,\n commParam.resellerLevelVPath, commParam.resellerLevelValue, False)\n self.click_lable()\n setValue.add_img(self)\n setValue.SetButtonControlClick('点击确定按钮', driver, commParam.pointLateAffirmCPath)\n time.sleep(2)\n if driver.find_elements_by_xpath(commParam.dispatchAffirmCPath):\n Logs.info('【订单数据过滤功能】保存失败')\n self.assertTrue(False, '订单数据过滤功能保存失败，请查看截图')\n else:\n Logs.info('【订单数据过滤功能】保存成功; ')\n\n setValue.add_img(self)\n\n def test_pointLateOrderFilterSearch(self):\n u'''订单数据过滤验证成功用例'''\n # displayNoBysearch = dataRW.getOutDataByAttribute('display.ini', 'display', 'code')\n # Logs.info(\"准备查询的代理编号，编号为【%s】\" % commParam.agencyNoValue)\n # 下面这一行和上面一行结果一致\n Logs.info(f\"准备查询的代理编号，编号为【{commParam.agencyNoValue}】\")\n isOK = False\n url = commParam.searchPointLateOrderFilter\n payload = \"\"\n querystring = {f\"pageParam.agencyNo\": {commParam.agencyNoValue}}\n # 调用登录接口获取token\n token = login_token.get_token_login()\n if commParam.agencyNoValue != '':\n try:\n requests = request_get_to_token.resquest_get(url, querystring, payload, token)\n responses = json.loads(requests)[\"data\"][\"recordList\"][0][\"epiFiltersAgencyNoList\"]\n except :\n token = login_token.get_token_login()\n dataRW.setOutDataByAttribute('login_token.ini', 'login', 'token', token)\n response = request_get_to_token.resquest_get(url, querystring, payload, token)\n responses = json.loads(response)[\"data\"][\"recordList\"]\n for value in responses:\n if value[\"agencyNo\"] == commParam.agencyNoValue:\n Logs.info(\"代理，编号为【%s】，查询成功！\" % commParam.agencyNoValue)\n isOK = True\n else:\n Logs.warning(\"请检查代理编号是否正确！\")\n self.assertTrue(False, '请检查代理编号是否正确！')\n else:\n Logs.warning(\"请检查代理编号是否正确！\")\n self.assertTrue(False, '请检查代理编号是否正确！或者订单数据过滤创建已失败')\n self.driver.quit()\n\n def tearDown(self):\n self.driver.quit()\n\n\nif __name__ == \"main\":\n unittest.main()\n","sub_path":"testCase/test_pointLateOrderFilter.py","file_name":"test_pointLateOrderFilter.py","file_ext":"py","file_size_in_byte":5487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"515760709","text":"import os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\n\ncolors = ['red','yellow','blue','green']\n\ndef read(filename, test=None):\n if(test == None):\n with open(filename) as f:\n lines = f.readlines()\n return np.array(lines, dtype=np.float)\n else:\n file_path = os.path.join(dir_path, \"..\", \"data\",test, filename)\n print(file_path)\n with open(file_path) as f:\n lines = f.readlines()\n return np.array(lines, dtype=np.float)\n\n\n\ndef create_plot(means, title, labels):\n fig, ax = plt.subplots()\n index = np.arange(len(means))\n bar_width = 0.45\n opacity = 0.8\n ax.set_ylim([0,2])\n for i in range(len(labels)):\n labels[i] = '_'.join(labels[i].split(\"_\")[:-1])\n\n for i in range(len(means)):\n ax.text(i - (bar_width / 3),\n 0.02,\n format(\"%.3f\"%float(means[1]/means[i])),\n fontweight='bold',\n va='center')\n plt.bar(i, means[1]/means[i], bar_width,\n alpha=opacity,\n color=colors[i],\n edgecolor='black',\n label=labels[i])\n\n plt.xlabel('Configurations')\n plt.ylabel('normalized speedup')\n plt.title(\"OpenCL -\"+title)\n plt.xticks(index, labels)\n plt.legend()\n\n # plt.tight_layout()\n # plt.show()\n plt.savefig(os.path.join(dir_path, \"..\", \"figs\",title+\".png\"), dpi=800, format='png')\n\nif __name__ == \"__main__\":\n tests = [\"add\", \"mandelbrot\",\"harris\",\"conway\"]\n # tests = [\"mandelbrot\",\"conway\"]\n files = [ \"bad_results.txt\",\n \"handmade_results.txt\",\n \"autotuned_results.txt\",\n \"naive_autotuned_results.txt\"]\n\n data = {test: {x: read(x,test) for x in files } for test in tests}\n for key in data:\n print(key)\n cwd = []\n with open(os.path.join(dir_path, \"..\", \"stats\",key+\".txt\"), \"w\") as f:\n f.write(key+\"\\n\")\n for subkey in data[key]:\n mean = data[key][subkey].mean()\n print('\\t-' ,subkey)\n print('\\t\\t mean: ', mean)\n print('\\t\\t variance: ', data[key][subkey].var())\n print('\\t\\t std dev: ', data[key][subkey].std())\n print('\\t\\t min: ', data[key][subkey].min())\n print('\\t\\t max: ', data[key][subkey].max())\n f.write(format('\\t-%s\\n'%(subkey)))\n f.write(format('\\t\\t mean: %s\\n'%(mean)))\n f.write(format('\\t\\t variance: %s\\n'%(data[key][subkey].var())))\n f.write(format('\\t\\t std dev: %s\\n'%(data[key][subkey].std())))\n f.write(format('\\t\\t min: %s\\n'%(data[key][subkey].min())))\n f.write(format('\\t\\t max: %s\\n'%(data[key][subkey].max())))\n cwd.append(mean)\n create_plot(cwd, key,list(data[key].keys()))\n\n # print(len(results))\n # print(len(data))\n # for i in data:\n # print(\"max: \", i.max())\n # print(\"min: \", i.min())\n # print(\"mean: \", i.mean())\n # print(\"variance: \", i.var())\n # print(\"standard dev: \", i.std())\n\n\n","sub_path":"src/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":3212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"421132451","text":"import os\nimport typing\nfrom http import HTTPStatus\nfrom typing import Optional\n\nimport requests\nfrom requests import Response\n\nfrom intezer_sdk import consts\nfrom intezer_sdk import errors\nfrom intezer_sdk.consts import IndexType\n\n_global_api = None\n\n\nclass IntezerApi(object):\n def __init__(self, api_version: str = None, api_key: str = None, base_url: str = None):\n self.full_url = base_url + api_version\n self.api_key = api_key\n self._access_token = None\n self._session = None\n\n def _request(self, method: str, path: str, data: dict = None, headers: dict = None, files: dict = None) -> Response:\n if not self._session:\n self.set_session()\n\n if files:\n response = self._session.request(\n method,\n self.full_url + path,\n files=files,\n data=data or {},\n headers=headers or {}\n )\n else:\n response = self._session.request(\n method,\n self.full_url + path,\n json=data or {},\n headers=headers\n )\n\n return response\n\n def analyze_by_hash(self,\n file_hash: str,\n disable_dynamic_unpacking: bool = None,\n disable_static_unpacking: bool = None) -> str:\n data = self._param_initialize(disable_dynamic_unpacking, disable_static_unpacking)\n\n data['hash'] = file_hash\n response = self._request(path='/analyze-by-hash', data=data, method='POST')\n self._assert_analysis_response_status_code(response)\n\n return self._get_analysis_id_from_response(response)\n\n def _analyze_file_stream(self, file_stream: typing.BinaryIO, file_name: str, options: dict) -> str:\n file = {'file': (file_name, file_stream)}\n\n response = self._request(path='/analyze', files=file, data=options, method='POST')\n\n self._assert_analysis_response_status_code(response)\n\n return self._get_analysis_id_from_response(response)\n\n def analyze_by_file(self,\n file_path: str = None,\n file_stream: typing.BinaryIO = None,\n disable_dynamic_unpacking: bool = None,\n disable_static_unpacking: bool = None,\n file_name: str = None,\n code_item_type: str = None) -> str:\n options = self._param_initialize(disable_dynamic_unpacking, disable_static_unpacking, code_item_type)\n\n if file_stream:\n return self._analyze_file_stream(file_stream, file_name, options)\n\n with open(file_path, 'rb') as file_to_upload:\n return self._analyze_file_stream(file_to_upload, file_name or os.path.basename(file_path), options)\n\n def get_latest_analysis(self, file_hash: str) -> Optional[dict]:\n response = self._request(path='/files/{}'.format(file_hash), method='GET')\n\n if response.status_code == HTTPStatus.NOT_FOUND:\n return None\n\n response.raise_for_status()\n\n return response.json()['result']\n\n def get_analysis_response(self, analyses_id) -> Response:\n response = self._request(path='/analyses/{}'.format(analyses_id), method='GET')\n response.raise_for_status()\n\n return response\n\n def index_by_sha256(self, sha256: str, index_as: IndexType, family_name: str = None) -> Response:\n data = {'index_as': index_as.value}\n if family_name:\n data['family_name'] = family_name\n\n response = self._request(path='/files/{}/index'.format(sha256), data=data, method='POST')\n self._assert_index_response_status_code(response)\n\n return self._get_index_id_from_response(response)\n\n def index_by_file(self, file_path: str, index_as: IndexType, family_name: str = None) -> Response:\n data = {'index_as': index_as.value}\n if family_name:\n data['family_name'] = family_name\n\n with open(file_path, 'rb') as file_to_upload:\n file = {'file': (os.path.basename(file_path), file_to_upload)}\n\n response = self._request(path='/files/index', data=data, files=file, method='POST')\n\n self._assert_index_response_status_code(response)\n\n return self._get_index_id_from_response(response)\n\n def get_index_response(self, index_id: str) -> Response:\n response = self._request(path='/files/index/{}'.format(index_id), method='GET')\n response.raise_for_status()\n\n return response\n\n def _set_access_token(self, api_key: str):\n if self._access_token is None:\n response = requests.post(self.full_url + '/get-access-token', json={'api_key': api_key})\n\n if response.status_code in (HTTPStatus.UNAUTHORIZED, HTTPStatus.BAD_REQUEST):\n raise errors.InvalidApiKey()\n elif response.status_code != HTTPStatus.OK:\n response.raise_for_status()\n\n self._access_token = response.json()['result']\n\n def set_session(self):\n self._session = requests.session()\n self._set_access_token(self.api_key)\n self._session.headers['Authorization'] = 'Bearer {}'.format(self._access_token)\n self._session.headers['User-Agent'] = consts.USER_AGENT\n\n @staticmethod\n def _param_initialize(disable_dynamic_unpacking: bool = None,\n disable_static_unpacking: bool = None,\n code_item_type: str = None):\n data = {}\n\n if disable_dynamic_unpacking is not None:\n data['disable_dynamic_execution'] = disable_dynamic_unpacking\n if disable_static_unpacking is not None:\n data['disable_static_extraction'] = disable_static_unpacking\n if code_item_type:\n data['code_item_type'] = code_item_type\n\n return data\n\n @staticmethod\n def _assert_analysis_response_status_code(response: Response):\n if response.status_code == HTTPStatus.NOT_FOUND:\n raise errors.HashDoesNotExistError()\n elif response.status_code == HTTPStatus.CONFLICT:\n raise errors.AnalysisIsAlreadyRunning()\n elif response.status_code == HTTPStatus.FORBIDDEN:\n raise errors.InsufficientQuota()\n elif response.status_code != HTTPStatus.CREATED:\n raise errors.IntezerError('Error in response status code:{}'.format(response.status_code))\n\n @staticmethod\n def _assert_index_response_status_code(response: Response):\n if response.status_code == HTTPStatus.NOT_FOUND:\n raise errors.HashDoesNotExistError()\n elif response.status_code != HTTPStatus.CREATED:\n raise errors.IntezerError('Error in response status code:{}'.format(response.status_code))\n\n @staticmethod\n def _get_analysis_id_from_response(response: Response):\n return response.json()['result_url'].split('/')[2]\n\n @staticmethod\n def _get_index_id_from_response(response: Response):\n return response.json()['result_url'].split('/')[3]\n\n\ndef get_global_api() -> IntezerApi:\n global _global_api\n\n if not _global_api:\n raise errors.GlobalApiIsNotInitialized()\n\n return _global_api\n\n\ndef set_global_api(api_key: str = None, api_version: str = None, base_url: str = None):\n global _global_api\n api_key = api_key or os.environ.get('INTEZER_ANALYZE_API_KEY')\n _global_api = IntezerApi(api_version or consts.API_VERSION, api_key, base_url or consts.BASE_URL)\n","sub_path":"intezer_sdk/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":7508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"278235208","text":"import os\nimport sys\nimport math\nimport datetime\nimport sc_utils\nimport tables as tb\nimport numpy as np\nimport pandas as pd\nimport reco_functions as rf\nimport analysis_utils as ats\n\nfrom antea.io.mc_io_tb import read_SiPM_bin_width_from_conf\nfrom antea.io.mc_io_tb import go_through_file\nfrom invisible_cities.io .mcinfo_io import read_mcinfo\nfrom invisible_cities.core.exceptions import SipmEmptyList\n\nprint(datetime.datetime.now())\n\n\"\"\"\nExample of calling this script:\npython 2_reco_pos_charge_no_compton.py 3000 1 6 0 /Users/carmenromoluque/nexus_petit_analysis/PETit-ring/Christoff_sim/compton full_ring_iradius165mm_z140mm_depth3cm_pitch7mm /Users/carmenromoluque/nexus_petit_analysis/PETit-ring/Christoff_sim/compton data_test irad165mm_depth3cm\n\"\"\"\n\narguments = sc_utils.parse_args(sys.argv)\nstart = arguments.first_file\nnumb = arguments.n_files\nnsteps = arguments.n_steps\nthr_start = arguments.thr_start\neventsPath = arguments.events_path\nfile_name = arguments.file_name\nbase_path = arguments.base_path\ndata_path = arguments.data_path\nidentifier = arguments.identifier\n\ndata_path = f\"{base_path}/{data_path}\"\nevt_file = f\"{data_path}/full_ring_{identifier}_reco_pos_dist_int_point_coinc_phot_{start}_{numb}_{nsteps}_{thr_start}\"\n\nif identifier == 'irad165mm_d3cm_no_refl_sipms':\n rpos_threshold = 4\nelse:\n rpos_threshold = 3\n\nphi_threshold = 5\nzpos_threshold = 4\ne_threshold = 2\n\nrpos_file = f\"{base_path}/r_sigma_phi_table_{identifier}_thr{rpos_threshold}pes_no_compton.h5\"\n#Rpos = ats.load_rpos(rpos_file, group=\"Radius\", node=f\"f{rpos_threshold}pes150bins\")\n\nif identifier == 'irad165mm_d3cm_no_refl_sipms':\n Rpos = ats.load_rpos(rpos_file, group=\"Radius\", node=f\"f4pes150bins\")\nelse:\n Rpos = ats.load_rpos(rpos_file, group=\"Radius\", node=f\"f3pes150bins\")\n\nreco_r1, reco_r2, true_r1, true_r2 = [], [], [], []\nreco_phi1, reco_phi2, true_phi1, true_phi2 = [], [], [], []\nreco_z1, reco_z2, true_z1, true_z2 = [], [], [], []\nevents, sns_response1, sns_response2 = [], [], []\nsensors_pos1, sensors_pos2 = [], []\n\nfor number in range(start, start+numb):\n number_str = \"{:03d}\".format(number)\n filename = f\"{eventsPath}/{file_name}.{number_str}.pet.h5\"\n try:\n print('Trying file {0}'.format(filename))\n h5in = tb.open_file(filename, mode='r')\n except ValueError:\n continue\n except OSError:\n continue\n print('Analyzing file {0}'.format(filename))\n\n h5extents = h5in.root.MC.extents\n events_in_file = len(h5extents)\n\n sens_pos = rf.sensor_position (h5in)\n sens_pos_cyl = rf.sensor_position_cyl(h5in)\n bin_width = read_SiPM_bin_width_from_conf(h5in)\n\n charge_range = (0, 10000)\n\n for evt in range(events_in_file):\n #for evt in range(10):\n\n ave_true1, ave_true2 = rf.true_photoelect(h5in, filename, evt, compton=False)\n #if len(ave_true1)==0 and len(ave_true2)==0:\n if len(ave_true1)==0 or len(ave_true2)==0:\n continue\n\n event_number = h5in.root.MC.extents[evt]['evt_number']\n this_event_wvf = go_through_file(h5in, h5in.root.MC.waveforms, (evt, evt+1), bin_width, 'data')\n sns_over_thr, charges_over_thr = rf.find_SiPMs_over_threshold(this_event_wvf, e_threshold)\n if len(charges_over_thr) == 0: continue\n\n this_event_dict = read_mcinfo(h5in, (evt, evt+1))\n part_dict = list(this_event_dict.values())[0]\n i1, i2, pos_true1, pos_true2, _, _, _, _, q1, q2, pos1, pos2 = rf.select_true_pos_from_charge(sns_over_thr, charges_over_thr, charge_range, sens_pos, part_dict)\n\n if i1 and i2:\n positions1, qs1 = rf.reco_pos_single(pos_true1, np.array(q1), np.array(pos1), rpos_threshold, phi_threshold, zpos_threshold)\n positions2, qs2 = rf.reco_pos_single(pos_true2, np.array(q2), np.array(pos2), rpos_threshold, phi_threshold, zpos_threshold)\n #print(positions1, qs1)\n #print(positions1[1])\n if len(positions1) == 0 or len(positions2) == 0:\n continue\n\n phi1 = ats.from_cartesian_to_cyl(positions1[0])[:,1]\n var_phi1 = rf.get_var_phi(phi1, qs1[0])\n sigma_phi1 = np.sqrt(var_phi1)\n reco1_r = Rpos(sigma_phi1).value\n\n phi2 = ats.from_cartesian_to_cyl(positions2[0])[:,1]\n var_phi2 = rf.get_var_phi(phi2, qs2[0])\n sigma_phi2 = np.sqrt(var_phi2)\n reco2_r = Rpos(sigma_phi2).value\n\n reco_cart = ats.barycenter_3D(positions1[1], qs1[1])\n reco1_phi = np.arctan2(reco_cart[1], reco_cart[0])\n\n reco_cart = ats.barycenter_3D(positions2[1], qs2[1])\n reco2_phi = np.arctan2(reco_cart[1], reco_cart[0])\n\n reco_cart = ats.barycenter_3D(positions1[2], qs1[2])\n reco1_z = reco_cart[2]\n\n reco_cart = ats.barycenter_3D(positions2[2], qs2[2])\n reco2_z = reco_cart[2]\n\n true1_r = ats.from_cartesian_to_cyl(np.array([pos_true1]))[0, 0]\n true1_phi = ats.from_cartesian_to_cyl(np.array([pos_true1]))[0, 1]\n true1_z = ats.from_cartesian_to_cyl(np.array([pos_true1]))[0, 2]\n\n true2_r = ats.from_cartesian_to_cyl(np.array([pos_true2]))[0, 0]\n true2_phi = ats.from_cartesian_to_cyl(np.array([pos_true2]))[0, 1]\n true2_z = ats.from_cartesian_to_cyl(np.array([pos_true2]))[0, 2]\n\n reco_r1 .append(reco1_r)\n reco_phi1.append(reco1_phi)\n reco_z1 .append(reco1_z)\n reco_r2 .append(reco2_r)\n reco_phi2.append(reco2_phi)\n reco_z2 .append(reco2_z)\n true_r1 .append(true1_r)\n true_phi1.append(true1_phi)\n true_z1 .append(true1_z)\n true_r2 .append(true2_r)\n true_phi2.append(true2_phi)\n true_z2 .append(true2_z)\n\n sns_response1.append(q1)\n sns_response2.append(q2)\n sensors_pos1 .append(pos1)\n sensors_pos2 .append(pos2)\n\n events.append(event_number)\n\n\na_true_r1 = np.array(true_r1)\na_true_phi1 = np.array(true_phi1)\na_true_z1 = np.array(true_z1)\na_reco_r1 = np.array(reco_r1)\na_reco_phi1 = np.array(reco_phi1)\na_reco_z1 = np.array(reco_z1)\na_sns_response1 = np.array(sns_response1)\na_sensors_pos1 = np.array(sensors_pos1)\n\na_true_r2 = np.array(true_r2)\na_true_phi2 = np.array(true_phi2)\na_true_z2 = np.array(true_z2)\na_reco_r2 = np.array(reco_r2)\na_reco_phi2 = np.array(reco_phi2)\na_reco_z2 = np.array(reco_z2)\na_sns_response2 = np.array(sns_response2)\na_sensors_pos2 = np.array(sensors_pos2)\n\na_events = np.array(events)\n\nnp.savez(evt_file, a_true_r1=a_true_r1, a_true_phi1=a_true_phi1, a_true_z1=a_true_z1,\n a_true_r2=a_true_r2, a_true_phi2=a_true_phi2, a_true_z2=a_true_z2,\n a_reco_r1=a_reco_r1, a_reco_phi1=a_reco_phi1, a_reco_z1=a_reco_z1,\n a_reco_r2=a_reco_r2, a_reco_phi2=a_reco_phi2, a_reco_z2=a_reco_z2,\n a_sns_response1=a_sns_response1, a_sns_response2=a_sns_response2,\n a_sensors_pos1=a_sensors_pos1, a_sensors_pos2=a_sensors_pos2, a_events=a_events)\n\nprint(datetime.datetime.now())\n","sub_path":"scripts_before_dataframes/dist_sensor_to_interact_point.py","file_name":"dist_sensor_to_interact_point.py","file_ext":"py","file_size_in_byte":7315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"412449462","text":"import ast\nimport warnings\n\nimport gspread\nimport numpy as np\nfrom oauth2client.service_account import ServiceAccountCredentials\n\nimport tsfel\nfrom tsfel.feature_extraction.features_settings import load_json\nfrom tsfel.utils.calculate_complexity import compute_complexity\n\n\ndef filter_features(features, filters):\n \"\"\"Filtering features based on Google sheet.\n\n Parameters\n ----------\n features : dict\n Dictionary with features\n filters : dict\n Filters from Google sheets\n\n Returns\n -------\n dict\n Filtered features\n\n \"\"\"\n features_all = list(np.concatenate([list(features[dk].keys()) for dk in sorted(features.keys())]))\n list_shown, feat_shown = list(features.keys()), features_all\n cost_shown = features_all\n if filters['2'] != {}:\n list_hidden = filters['2']['hiddenValues']\n list_shown = [dk for dk in features.keys() if dk not in list_hidden]\n if filters['1'] != {}:\n feat_hidden = filters['1']['hiddenValues']\n feat_shown = [ff for ff in features_all if ff not in feat_hidden]\n if filters['3'] != {}:\n cost_numbers = filters['3']['hiddenValues']\n cost_hidden = list(np.concatenate([['constant', 'log'] if int(cn) == 1 else\n ['squared', 'nlog'] if int(cn) == 3 else ['linear']\n if int(cn) == 2 else ['unknown'] for cn in cost_numbers]))\n cost_shown = []\n for dk in features.keys():\n cost_shown += [ff for ff in features[dk].keys() if features[dk][ff]['complexity'] not in cost_hidden]\n features_filtered = list(np.concatenate([list(features[dk].keys())\n for dk in sorted(features.keys()) if dk in list_shown]))\n features_filtered = [ff for ff in features_filtered if ff in feat_shown]\n features_filtered = [cc for cc in features_filtered if cc in cost_shown]\n\n return features_filtered\n\n\ndef extract_sheet(gsheet_name, **kwargs):\n \"\"\"Interaction between features.json and Google sheets.\n\n Parameters\n ----------\n gsheet_name : str\n Google Sheet name\n \\**kwargs:\n See below:\n * *path_json* (``string``) --\n Json path\n Returns\n -------\n dict\n Features\n\n \"\"\"\n # Path to Tsfel\n lib_path = tsfel.__path__\n\n # Access features.json\n path_json = kwargs.get('path_json', lib_path[0] + '/feature_extraction/features.json')\n\n # Read features.json into a dictionary of features and parameters\n dict_features = load_json(path_json)\n\n # Number of features from json file\n len_json = 0\n for domain in list(dict_features.keys()):\n len_json += len(dict_features[domain].keys())\n\n # Access Google sheet\n # Scope and credentials using the content of client_secret.json file\n scope = ['https://spreadsheets.google.com/feeds',\n 'https://www.googleapis.com/auth/drive']\n creds = ServiceAccountCredentials.from_json_keyfile_name(lib_path[0] + '/utils/client_secret.json', scope)\n\n # Create a gspread client authorizing it using those credentials\n client = gspread.authorize(creds)\n\n # and pass it to the spreadsheet name, getting access to sheet1\n confManager = client.open(gsheet_name)\n sheet = confManager.sheet1\n metadata = confManager.fetch_sheet_metadata()\n\n # Reading from Google Sheet\n # Features\n list_of_features = sheet.col_values(2)[4:]\n\n try:\n filters = metadata['sheets'][sheet.id]['basicFilter']['criteria']\n list_filt_features = filter_features(dict_features, filters)\n except KeyError:\n print('No filters running. Check Google Sheet filters.')\n list_filt_features = list_of_features.copy()\n\n use_or_not = ['TRUE' if lf in list_filt_features else 'FALSE' for lf in list_of_features]\n\n assert len(list_of_features) <= (len_json), \\\n \"To insert a new feature, please add it to data/features.json with the code in src/utils/features.py\"\n\n # adds a new feature in Google sheet if it is missing from features.json\n if len(list_of_features) < (len_json):\n\n # new feature was added\n for domain in dict_features.keys():\n for feat in dict_features[domain].keys():\n if feat not in list_of_features:\n feat_dict = dict_features[domain][feat]\n param = ''\n fs = 'no'\n\n # Read parameters from features.json\n if feat_dict['parameters']:\n param = feat_dict['parameters'].copy()\n if 'fs' in feat_dict['parameters']:\n fs = 'yes'\n param.pop('fs')\n if len(param) == 0:\n param = ''\n\n curve = feat_dict['complexity']\n curves_all = ['linear', 'log', 'squared', 'nlog', 'constant']\n complexity = compute_complexity(feat, domain,\n path_json) if curve not in curves_all else 1 if curve in [\n 'constant', 'log'] else 2 if curve == 'linear' else 3\n new_feat = ['', feat, domain, complexity, fs, str(param),\n feat_dict['description']]\n\n # checks if the Google sheet has no features\n if sheet.findall(domain) == []:\n idx_row = 4\n else:\n idx_row = sheet.findall(domain)[-1].row\n\n # Add new feature at the end of feature domain\n sheet.insert_row(new_feat, idx_row + 1)\n print(feat + \" feature was added to Google Sheet.\")\n\n # Update list of features and domains from Google sheet\n list_of_features = sheet.col_values(2)[4:]\n\n # Update filtered features from Google sheet. Check if filters exist.\n try:\n filters = metadata['sheets'][sheet.id]['basicFilter']['criteria']\n list_filt_features = filter_features(dict_features, filters)\n except KeyError:\n list_filt_features = list_of_features.copy()\n\n use_or_not = ['TRUE' if lf in list_filt_features else 'FALSE' for lf in list_of_features]\n\n assert 'TRUE' in use_or_not, 'Please select a feature to extract!' + '\\n'\n\n # Reading from Google Sheet\n # Domain\n list_domain = sheet.col_values(3)[4:]\n # Parameters and fs\n gs_param_list = sheet.col_values(6)[4:]\n gs_fs_list = sheet.col_values(5)[4:]\n # Check for invalid fs parameter\n try:\n gs_fs = int(sheet.cell(4, 9).value)\n except ValueError:\n warnings.warn('Invalid sampling frequency. Setting a default 100Hz sampling frequency.')\n gs_fs = 100\n sheet.update_cell(4, 9, str(gs_fs))\n\n # Fix for empty cells in parameters column\n if len(gs_param_list) < len(list_of_features):\n empty = [''] * (len(list_of_features) - len(gs_param_list))\n gs_param_list = gs_param_list + empty\n\n # Update dict of features with changes from Google sheet\n for ii, feature in enumerate(list_of_features):\n domain = list_domain[ii]\n try:\n if use_or_not[ii] == 'TRUE':\n dict_features[domain][feature]['use'] = 'yes'\n # Check features parameters from Google sheet\n if gs_param_list[ii] != '':\n if dict_features[domain][feature]['parameters'] == '' or ('fs' in list(\n dict(dict_features[domain][feature]['parameters'])) and len(list(\n dict(dict_features[domain][feature]['parameters']))) == 1):\n warnings.warn('The ' + feature + ' feature does not require parameters.')\n else:\n try:\n param_sheet = ast.literal_eval(gs_param_list[ii])\n if not isinstance(param_sheet, dict):\n warnings.warn('Invalid parameter format. Using the following parameters for ' + feature + ' feature: '\n + str(dict_features[domain][feature]['parameters']))\n else:\n # update dic of features based on Google sheet\n dict_features[domain][feature]['parameters'] = param_sheet\n except ValueError:\n warnings.warn('Invalid parameter format. Using the following parameters for ' + feature + ' feature: '\n + str(dict_features[domain][feature]['parameters']))\n elif dict_features[domain][feature]['parameters'] != '' and ('fs' not in list(\n dict(dict_features[domain][feature]['parameters'])) or len(list(\n dict(dict_features[domain][feature]['parameters']))) != 1):\n warnings.warn('Using the following parameters for ' + feature + ' feature: '\n + str(dict_features[domain][feature]['parameters']))\n # Check features that use sampling frequency parameter\n if gs_fs_list[ii] != 'no':\n # update dict of features based on Google sheet fs\n dict_features[domain][feature]['parameters']['fs'] = gs_fs\n\n else:\n dict_features[domain][feature]['use'] = 'no'\n except KeyError:\n print('Unknown domain at cell', int(ii + 5))\n\n return dict_features\n","sub_path":"tsfel/utils/gSheetsFilters.py","file_name":"gSheetsFilters.py","file_ext":"py","file_size_in_byte":9648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"562110666","text":"# -*- coding: utf-8 -*-\n\n\"\"\" Test the iotlabcli.parser.node module \"\"\"\n\nimport unittest\ntry:\n # pylint: disable=import-error,no-name-in-module\n from mock import patch\nexcept ImportError: # pragma: no cover\n # pylint: disable=import-error,no-name-in-module\n from unittest.mock import patch\n\nfrom argparse import ArgumentTypeError\nimport iotlabcli.parser.node as node_parser\nfrom iotlabcli.tests.my_mock import MainMock, api_mock, api_mock_stop\n\n# pylint: disable=missing-docstring,too-many-public-methods\n# pylint: disable=too-few-public-methods\n\n\n@patch('iotlabcli.node.node_command')\n@patch('iotlabcli.parser.node.list_nodes')\nclass TestMainNodeParser(MainMock):\n def test_main(self, list_nodes, node_command):\n \"\"\" Run the parser.node.main function \"\"\"\n node_command.return_value = {'result': 'test'}\n\n list_nodes.return_value = []\n # start\n args = ['--start']\n node_parser.main(args)\n list_nodes.assert_called_with(self.api, 123, None, None)\n node_command.assert_called_with(self.api, 'start', 123, [], None)\n # stop\n args = ['--stop']\n node_parser.main(args)\n list_nodes.assert_called_with(self.api, 123, None, None)\n node_command.assert_called_with(self.api, 'stop', 123, [], None)\n\n # Reset command with many arguments\n args = ['--reset', '-l', 'grenoble,m3,1-2', '-l', 'grenoble,m3,3']\n list_nodes.return_value = ['m3-1', 'm3-2', 'm3-3'] # simplify\n node_parser.main(args)\n list_nodes.assert_called_with(\n self.api, 123,\n [['m3-1.grenoble.iot-lab.info', 'm3-2.grenoble.iot-lab.info'],\n ['m3-3.grenoble.iot-lab.info']], None)\n node_command.assert_called_with(\n self.api, 'reset', 123, ['m3-1', 'm3-2', 'm3-3'], None)\n\n # update with exclude list\n args = ['--update', 'tp.elf', '-e', 'grenoble,m3,1-2']\n list_nodes.return_value = ['m3-3'] # simplify\n node_parser.main(args)\n list_nodes.assert_called_with(\n self.api, 123, None,\n [['m3-1.grenoble.iot-lab.info', 'm3-2.grenoble.iot-lab.info']])\n node_command.assert_called_with(\n self.api, 'update', 123, ['m3-3'], 'tp.elf')\n\n\nclass TestNodeParser(unittest.TestCase):\n def tearDown(self):\n api_mock_stop()\n\n @patch('iotlabcli.parser.node._get_experiment_nodes_list')\n def test_list_nodes(self, g_nodes_list):\n \"\"\" Run the different list_nodes cases \"\"\"\n api = api_mock()\n g_nodes_list.return_value = [\n \"m3-1.grenoble.iot-lab.info\", \"m3-2.grenoble.iot-lab.info\",\n \"m3-3.grenoble.iot-lab.info\",\n \"m3-1.strasbourg.iot-lab.info\", \"m3-2.strasbourg.iot-lab.info\",\n \"m3-3.strasbourg.iot-lab.info\"\n ]\n\n nodes_ll = [\n [\"m3-1.grenoble.iot-lab.info\", \"m3-2.grenoble.iot-lab.info\"],\n [\"m3-1.strasbourg.iot-lab.info\", \"m3-2.strasbourg.iot-lab.info\"],\n ]\n\n # No nodes provided => all nodes, no external requests\n res = node_parser.list_nodes(api, 123)\n self.assertEquals(res, [])\n self.assertFalse(g_nodes_list.called)\n\n # Normal case, no external requests, only list of all provided nodes\n res = node_parser.list_nodes(api, 123, nodes_ll=nodes_ll)\n self.assertEquals(res, [\"m3-1.grenoble.iot-lab.info\",\n \"m3-2.grenoble.iot-lab.info\",\n \"m3-1.strasbourg.iot-lab.info\",\n \"m3-2.strasbourg.iot-lab.info\"])\n self.assertFalse(g_nodes_list.called)\n\n res = node_parser.list_nodes(api, 123, excl_nodes_ll=nodes_ll)\n self.assertEquals(res, [\"m3-3.grenoble.iot-lab.info\",\n \"m3-3.strasbourg.iot-lab.info\"])\n self.assertTrue(g_nodes_list.called)\n\n def test__get_experiment_nodes_list(self):\n \"\"\" Run get_experiment_nodes_list \"\"\"\n api = api_mock(\n ret={\n \"items\": [\n {\"network_address\": \"m3-1.grenoble.iot-lab.info\"},\n {\"network_address\": \"m3-2.grenoble.iot-lab.info\"},\n {\"network_address\": \"m3-3.grenoble.iot-lab.info\"},\n ]\n }\n )\n # pylint: disable=protected-access\n self.assertEquals(node_parser._get_experiment_nodes_list(api, 3),\n [\"m3-1.grenoble.iot-lab.info\",\n \"m3-2.grenoble.iot-lab.info\",\n \"m3-3.grenoble.iot-lab.info\"])\n\n @patch('iotlabcli.parser.common.check_site_with_server')\n def test_nodes_list_from_str(self, _):\n \"\"\" Run error case from test_nodes_list_from_str invalid string \"\"\"\n\n self.assertRaises(ArgumentTypeError, node_parser.nodes_list_from_str,\n 'grenoble,m3_no_numbers')\n","sub_path":"iotlabcli/tests/node_parser_test.py","file_name":"node_parser_test.py","file_ext":"py","file_size_in_byte":4888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"472216791","text":"import json\nimport boto3\n\ndef handler(event, context):\n dynamodb = boto3.resource('dynamodb')\n table = dynamodb.Table('GamesTable')\n\n result = table.scan()\n items = result.get('Items', [])\n body = {\n 'items': items\n }\n\n response = {\n \"statusCode\": result['ResponseMetadata']['HTTPStatusCode'],\n 'headers': {\n 'Access-Control-Allow-Origin': '*', \n 'Access-Control-Allow-Methods': 'OPTIONS, POST',\n },\n \"body\": json.dumps(body)\n }\n\n return response\n\n","sub_path":"apiGames/games/read.py","file_name":"read.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"545421071","text":"\"\"\" NUMBER NAMES by Ravi Teja Gannavarapu \"\"\"\n\nnames = {1:\"One\", 2: \"Two\", 3: \"Three\", 4: \"Four\", 5: \"Five\", 6: \"Six\", 7: \"Seven\", 8: \"Eight\", 9: \"Nine\", 10: \"Ten\", 11: \"Eleven\", 12: \"Twelve\",\n\t\t\t\t 13: \"Thirteen\", 14: \"Fourteen\", 15: \"Fifteen\", 16: \"Sixteen\", 17: \"Seventeen\", 18: \"Eighteen\", 19: \"Nineteen\", 20: \"Twenty\",\n\t\t\t\t 30: \"Thirty\", 40: \"Forty\", 50: \"Fifty\", 60: \"Sixty\", 70: \"Seventy\", 80: \"Eighty\", 90: \"Ninety\"}\n\ndef welcome():\n\tfor i in range (22):\n\t\tprint (\"*\"),\n\tprint (\"\\n* NUMBER NAMES by Ravi Teja Gannavarapu *\")\n\tfor i in range (22):\n\t\tprint (\"*\"),\n\ndef digit2(n):\n\tif (n<=20):\n\t\treturn (names[n])\n\telif (n>20 and n<100):\n\t\tif (n%10==0):\n\t\t\treturn (names[((n/10)*10)])\n\t\telse:\n\t\t\treturn (names[((n/10)*10)] + \" \" + names[n%10])\n\ndef digit3(n):\n\tc = n/100\n\td = n%100\n\tif (d<=20):\n\t\treturn (names[c] + \" Hundred \" + names[d])\n\telif (d>20 and d<100):\n\t\treturn (names[c] + \" Hundred \" + names[((d/10)*10)] + \" \" + names[d%10])\n\ndef printscr():\n\twelcome()\n\tx = raw_input(\"\\n\\nEnter the number: \")\n\ty = len(x)\n\tz = int(x)\n\tif (y==1 or y==2):\n\t\tprint (\"\\n\" + digit2(z))\n\telif (y==3):\n\t\tprint (\"\\n\" + digit3(z))\n\telif (y==4):\n\t\ta = str(z)\n\t\tb = a[0]\n\t\tf = int(b)\n\t\tg = z%1000\n\t\tprint (\"\\n\" + digit2(f) + \" Thousand \" + digit3(g))\n\telif (y==5):\n\t\ta = str(z)\n\t\tb = a[0:2]\n\t\tf = int(b)\n\t\tg = z%1000\n\t\tprint (\"\\n\" + digit2(f) + \" Thousand \" + digit3(g))\n\telif (y==6):\n\t\ta = str(z)\n\t\tb = a[0]\n\t\tc = int(b)\n\t\td = z%100000\n\t\te = d/1000\n\t\tf = d%1000\n\t\tprint (\"\\n\" + digit2(c) + \" Lakh \" + digit2(e) + \" Thousand \" + digit3(f))\n\telif (y==7):\n\t\ta = str(z)\n\t\tb = a[0:2]\n\t\tc = int(b)\n\t\td = z%100000\n\t\te = d/1000\n\t\tf = d%1000\n\t\tprint (\"\\n\" + digit2(c) + \" Lakh \" + digit2(e) + \" Thousand \" + digit3(f))\n\telif (y==8):\n\t\ta = str(z)\n\t\tb = a[0]\n\t\tc = int(b)\n\t\td = z%10000000\n\t\te = d/100000\n\t\tf = d%100000\n\t\tg = f/1000\n\t\th = f%1000\n\t\tprint (\"\\n\" + digit2(c) + \" Crore \" + digit2(e) + \" Lakh \" + digit2(g) + \" Thousand \" + digit3(h))\n\telif (y==9):\n\t\ta = str(z)\n\t\tb = a[0:2]\n\t\tc = int(b)\n\t\td = z%10000000\n\t\te = d/100000\n\t\tf = d%100000\n\t\tg = f/1000\n\t\th = f%1000\n\t\tprint (\"\\n\" + digit2(c) + \" Crore \" + digit2(e) + \" Lakh \" + digit2(g) + \" Thousand \" + digit3(h))\n\telif (y==10):\n\t\ta = str(z)\n\t\tb = a[0:3]\n\t\tc = int(b)\n\t\td = z%10000000\n\t\te = d/100000\n\t\tf = d%100000\n\t\tg = f/1000\n\t\th = f%1000\n\t\tprint (\"\\n\" + digit3(c) + \" Crore \" + digit2(e) + \" Lakh \" + digit2(g) + \" Thousand \" + digit3(h))\n\tif (z>9999999999):\n print (\"\\nNUMBERS GREATER THAN 9999999999 ARE NOT SUPPORTED.\")\n\nprintscr()\nz = raw_input(\"\\nPress any key to exit.\")\n","sub_path":"Number-Names/Number Names.py","file_name":"Number Names.py","file_ext":"py","file_size_in_byte":2537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"468027372","text":"import sys\nimport os\nimport time\nfrom os.path import expanduser\nfrom configobj import ConfigObj\nimport json\nimport epics\nfrom PyQt4 import QtGui, uic\nfrom PyQt4.QtCore import pyqtSignal, pyqtSlot\nimport zmq\n\n\ndefault_det = \"S12-PILATUS1\"\ndefault_cntl_port = 5511\ndefault_cntl_host = 'localhost'\nui = \"verui.ui\"\n\n\nclass zmq_consumer():\n def __init__(self, host = default_cntl_host, port=default_cntl_port):\n context = zmq.Context()\n self.socket = context.socket(zmq.PAIR)\n self.socket.connect(\"tcp://\" + host + \":%s\" % port)\n\n\nclass Window(QtGui.QMainWindow):\n statusBarSignal = pyqtSignal(str, str)\n\n def __init__(self):\n super(Window, self).__init__()\n # set parameters from config file\n self.detector = default_det\n self.conf_map, self.quality_checks = self.get_ver_params()\n\n self.ui = uic.loadUi(ui)\n self.ui.show()\n\n self.show_limits()\n self.ui.det_name.setText(self.detector)\n\n self.ui.det_name.returnPressed.connect(lambda: self.set_detector())\n\n self.ui.sum_ll.returnPressed.connect(lambda: self.set_limit(self.ui.sum_ll, 'sum','low_limit'))\n self.ui.sum_hl.returnPressed.connect(lambda: self.set_limit(self.ui.sum_hl, 'sum','high_limit'))\n self.ui.pix_sat_hl.returnPressed.connect(lambda: self.set_limit(self.ui.pix_sat_hl, 'pix_sat','high_limit'))\n self.ui.Npix_sat_hl.returnPressed.connect(lambda: self.set_limit(self.ui.Npix_sat_hl, 'Npix_sat','high_limit'))\n self.ui.pix_sat_cnt_rate_hl.returnPressed.connect(lambda: self.set_limit(self.ui.pix_sat_cnt_rate_hl, 'pix_sat_cnt_rate','high_limit'))\n self.ui.Npix_sat_cnt_rate_hl.returnPressed.connect(lambda: self.set_limit(self.ui.Npix_sat_cnt_rate_hl, 'Npix_sat_cnt_rate','high_limit'))\n\n self.setEpicsQualityFeedbackUpdate()\n\n self.verifier_on = 0\n self.ui.actionStart_verifier.triggered.connect(self.start_verifier)\n self.ui.actionStop_verifier.triggered.connect(self.stop_verifier)\n\n self.statusBarSignal.connect(self.onVerifierPVchange)\n self.zmq_menu = zmq_consumer()\n self.ui.statusBar.showMessage(\"verifier off\")\n\n self.list_cnt = 0\n self.ui.clear_list.clicked.connect(lambda: self.clear_list())\n self.initialized = False\n self.ack = False\n\n\n def clear_list(self):\n self.ui.list_failed.clear()\n\n\n def start_verifier(self):\n socket = self.zmq_menu.socket\n socket.send_json(\n dict(\n key=\"start_ver\",\n detector=self.detector\n )\n )\n self.verifier_on = 1\n self.set_status_color('orange')\n msg = 'initializing'\n\n self.ui.statusBar.showMessage(msg)\n\n\n def stop_verifier(self):\n socket = self.zmq_menu.socket\n socket.send_json(\n dict(\n key=\"stop_ver\"\n )\n )\n self.verifier_on = 0\n self.set_status_color('none')\n msg = 'off'\n\n self.ui.statusBar.showMessage(msg)\n\n\n def set_detector(self):\n restart = False\n if self.verifier_on == 1:\n self.stop_verifier()\n restart = True\n\n self.detector = str(self.ui.det_name.text())\n self.conf_map, self.quality_checks = self.get_ver_params()\n self.show_limits()\n\n if restart:\n self.start_verifier()\n\n\n def show_limits(self):\n try:\n self.limits_file = self.conf_map['limits']\n except KeyError:\n self.limits_file = None\n\n with open(self.limits_file) as limitsfile:\n self.limits = json.loads(limitsfile.read())['data']\n self.ui.sum_ll.setText(str(self.limits['sum']['low_limit']))\n self.ui.sum_hl.setText(str(self.limits['sum']['high_limit']))\n self.ui.pix_sat_hl.setText(str(self.limits['pix_sat']['high_limit']))\n self.ui.Npix_sat_hl.setText(str(self.limits['Npix_sat']['high_limit']))\n self.ui.pix_sat_cnt_rate_hl.setText(str(self.limits['pix_sat_cnt_rate']['high_limit']))\n self.ui.Npix_sat_cnt_rate_hl.setText(str(self.limits['Npix_sat_cnt_rate']['high_limit']))\n limitsfile.close()\n\n\n def set_limit(self, le_limit, key1, key2):\n restart = False\n if self.verifier_on == 1:\n self.stop_verifier()\n restart = True\n\n limit_val = int(le_limit.text())\n self.limits[key1][key2] = limit_val\n data_limits = {}\n data_limits['data'] = self.limits\n with open(self.limits_file, 'w') as limitsfile:\n json.dump(data_limits, limitsfile)\n limitsfile.close()\n\n if restart:\n self.start_verifier()\n\n\n def setEpicsQualityFeedbackUpdate(self):\n try:\n self.acquire = epics.PV(self.detector + ':cam1:Acquire', callback=self.epicsCallbackFunc)\n self.status = epics.PV(self.detector + ':STAT', callback=self.epicsCallbackFunc)\n except:\n self.ui.statusBar.showMessage('verifier off')\n\n\n def epicsCallbackFunc(self, pvname, char_value, **kws):\n self.statusBarSignal.emit(pvname, char_value)\n\n\n @pyqtSlot(str, str)\n def onVerifierPVchange(self, pvname, char_value):\n if not pvname is None:\n if \"STAT\" in pvname:\n msg = epics.caget(self.detector+':STAT', as_string=True)\n if msg is None:\n return\n failed = False\n if msg.endswith('not acquireing'):\n self.set_status_color('yellow')\n self.ui.statusBar.showMessage(msg)\n else:\n if msg.endswith('pass'):\n self.set_status_color('green')\n else:\n failed = True\n self.set_status_color('red')\n self.ui.statusBar.showMessage(msg)\n if failed:\n list_item = msg\n if self.list_cnt <= 4:\n self.ui.list_failed.insertItem(0, list_item)\n self.list_cnt = self.list_cnt + 1\n else:\n self.ui.list_failed.takeItem(4)\n self.ui.list_failed.insertItem(0, list_item)\n if not self.ack:\n self.set_status_color('yellow')\n msg = 'not acquireing'\n self.ui.statusBar.showMessage(msg)\n\n elif \"Acquire\" in pvname:\n if self.verifier_on is 1:\n if int(float(char_value)) is 0:\n self.ack = False\n if not self.initialized:\n pass\n self.set_status_color('yellow')\n msg = 'not acquireing'\n self.ui.statusBar.showMessage(msg)\n else:\n self.ack = True\n else:\n self.ui.statusBar.showMessage(\"ver pv name not defined\")\n\n\n def set_status_color(self, color):\n if color is 'red':\n self.ui.statusBar.setStyleSheet(\n \"QStatusBar{padding-left:8px;background:rgba(255,0,0,120);color:black;font-weight:bold;}\")\n elif color is 'green':\n self.ui.statusBar.setStyleSheet(\n \"QStatusBar{padding-left:8px;background:rgba(0,255,0,120);color:black;font-weight:bold;}\")\n elif color is 'yellow':\n self.ui.statusBar.setStyleSheet(\n \"QStatusBar{padding-left:8px;background:rgba(255,255,0,120);color:black;font-weight:bold;}\")\n elif color is 'orange':\n self.ui.statusBar.setStyleSheet(\n \"QStatusBar{padding-left:8px;background:rgba(255,125,0,120);color:black;font-weight:bold;}\")\n elif color is 'none':\n self.ui.statusBar.setStyleSheet(\n \"QStatusBar{padding-left:8px;background:rgba(0,0,0,0);color:black;font-weight:bold;}\")\n\n\n def get_ver_params(self):\n home = expanduser(\"~\")\n conf = os.path.join(home, '.dquality', self.detector)\n if os.path.isdir(conf):\n config = os.path.join(conf, 'dqconfig.ini')\n if not os.path.isfile(config):\n return None\n conf_map = ConfigObj(config)\n try:\n qcfile = conf_map['quality_checks']\n except KeyError:\n qcfile = None\n with open(qcfile) as qc_file:\n quality_checks = json.loads(qc_file.read())\n qc_file.close()\n return conf_map, quality_checks\n\n\nif __name__ == \"__main__\":\n app = QtGui.QApplication(sys.argv)\n a = Window()\n socket = a.zmq_menu.socket\n #sys.exit(app.exec_())\n # stop verifier on exit\n res = app.exec_()\n socket.send_json(\n dict(\n key=\"stop_ver\"\n )\n )\n socket.close()\n sys.exit(res)\n","sub_path":"config/12id/window_ver.py","file_name":"window_ver.py","file_ext":"py","file_size_in_byte":8938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"532321864","text":"import matplotlib.pyplot as plt\nimport numpy as np\n# print \"3.0\"\nprint(np.sqrt(9))\n\nvec = np.array([3, 4, 5])\n\n# Prints \"array([3, 4, 5])\"\nprint(vec)\nprint(vec.shape)\n\nrow_vec = np.array([7, 2, 9])\nprint(row_vec)\n\nprint(row_vec.reshape(1, 3))\n\ncol_vec = np.array([3, 1, 7, 4])\nprint(col_vec.reshape(4, 1))\nprint(col_vec.reshape(-1, 1))\n\nmat = np.array([[2, 0], [5, 1], [7, -3]])\nprint(mat)\nprint(mat.shape)\n\nmatrix = np.arange(100).reshape(10, 10)\nprint(matrix)\n\narr = np.arange(12)\n\nB = np.array([[5, 0, 3], [2, 8, 4]])\nA = np.array([[2, 8], [9, 0], [9, 5]])\n\nA = np.array([0.4, 1.7, 1.2])\nA\nB = np.array([0.8, 0.9, 0.6])\nB\nx = np.array([4, 3, 2])\nx*5.3\nS = np.array([[2, 8], [3, 1]])\nS\nT = np.array([[0, 1], [1, 1]])\nT\nST = S@T\nA = np.array([[3, -4], [1, 2], [-3, -1]])\nB = np.array([[1, 1], [0, 1], [1, 0]])\n\nA = np.array([[-1, 3/2], [1, -1]])\np = np.array([8, -3, 8, 5, -5])\nq = np.array([-4, 8, 7, -1, 6])\na = np.array([[3, 1], [0, 2]])\na @ np.array([3**0.5/2, 0.5]).reshape(-1, 1)\na @ np.array([-2**0.5/2, 2**0.5/2]).reshape(-1, 1)\np = np.array([[0.5, 1, -2], [-4, 2, 1.5]])\nv = np.array([3, 4, 1]).reshape(-1, 1)\n\n# np.linspace creates a defined number of evenly spaced data points in a given interval\n# In this case, we are creating 10,000 evenly spaced data points between -10, and 10\nx_vals = np.linspace(-10, 10, 10_000)\n\n# Initialize an empty plot\nplt.figure()\nplt.plot(x_vals, np.sin(x_vals))\nplt.plot(x_vals, np.cos(x_vals))\nplt.show()\n# The code below can go anywhere after plt.figure() and before plt.show()\nplt.title(\"Graph of Sine and Cosine\")\nplt.plot(x_vals, np.sin(x_vals), label=\"Sin\")\nplt.plot(x_vals, np.cos(x_vals), label=\"Cos\")\nplt.legend()\nplt.xlabel(\"Input Value\")\nplt.ylabel(\"Result Value\")\n","sub_path":"NumPy_Excercises.py","file_name":"NumPy_Excercises.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"23518001","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n\r\n#делаем запрос на сервер и возвращаем адрес в url\r\ndef get_html(url):\r\n\tr = requests.get(url)\r\n\treturn r.text\r\n#Получаем список ссылок с главной страницы\r\ndef get_links(html):\r\n\tsoup = BeautifulSoup(html, 'html.parser')\r\n\tk = soup.find_all(\"a\", attrs={\"class\": \"_Sportbox_Spb2015_Components_GameRow_GameRow\"})\r\n\tlinks = []\r\n\tfor link in k:\r\n\t\ta = \"https://news.sportbox.ru/Vidy_sporta/Futbol\"\r\n\t\tb = a + link['href']\r\n\t\tlinks.append(b)\r\n\treturn links\r\n\r\n\r\n#ищем и возвращаем только команды\r\ndef get_teams(html):\r\n\tsoup = BeautifulSoup(html, 'html.parser')\r\n\r\n\tteams_pre = soup.find_all(\"a\", attrs={\"class\" : \"b-match__team-title\"})\r\n\tteams = []\r\n\tfor team in teams_pre:\r\n\t\ta = team.text\r\n\t\tteams.append(a)\r\n\treturn teams\r\n\r\n#ищем и возвращаем результаты матчей\r\ndef get_score(html):\r\n\tsoup = BeautifulSoup(html, 'html.parser')\r\n\r\n\tscore_soup = soup.find('span', attrs={'class' : 'b-match__monitor__count'}).get_text()\r\n\tscore = score_soup.split()\r\n\t\r\n\treturn score\r\n\r\n#ищем дату и время матчей и возвращаем\r\ndef get_match_dateTime(html):\r\n\tsoup = BeautifulSoup(html, 'html.parser')\r\n\r\n\tpre_time = soup.find('span' , attrs={'class' : 'match_count_date'}).get_text()\r\n\tfinal_time = pre_time.split()\r\n\r\n\treturn final_time\r\n\r\n#ищем что за турнир/чемпионат\r\ndef get_turnir(html):\r\n\tsoup = BeautifulSoup(html, 'html.parser')\r\n\r\n\tul = soup.find('ul', attrs={'class' : 'node-header__rubrics'}).get_text().split()\r\n\tdel ul[0]\r\n\tdel ul[0]\r\n\treturn ul\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef main():\r\n\turl = 'https://news.sportbox.ru/Vidy_sporta/Futbol'\r\n\thtml = get_html(url)\r\n\tlinks = get_links(html)\r\n\tteams = []\r\n\tscores = []\r\n\tdate_nTime = []\r\n\tchampionat = []\r\n\r\n\tresult_teams = []\r\n\tresult_scores = []\r\n\tresult_date_nTime = []\r\n\tchampionat_result = []\r\n\r\n\tprint(\"Начинаем парсить футбол\")\r\n\tprint()\r\n\tend_programm = []\r\n\r\n\tfor link in links:\r\n\t\thtml_team = get_html(link)\r\n\t\tteam = get_teams(html_team)\r\n\t\tteams.append(team)\r\n\t\tscore = get_score(html_team)\r\n\t\tscores.append(score)\r\n\t\tdateTime = get_match_dateTime(html_team)\r\n\t\tdate_nTime.append(dateTime)\r\n\t\tchampionat_html = get_turnir(html_team)\r\n\t\tchampionat.append(championat_html)\r\n\tfor el in teams:\r\n\t\tteam1 = el[0]\r\n\t\tteam2 = el[1]\r\n\t\tresult = team1 + \" - \" + team2\r\n\t\tresult_teams.append(result)\r\n\tfor el in scores:\r\n\t\tteam1 = el[0]\r\n\t\tteam2 = el[2]\r\n\t\tresult = \"(\" + team1 + \" - \" + team2 + \")\"\r\n\t\tresult_scores.append(result)\r\n\r\n\tfor el in date_nTime:\r\n\t\tdate = el[0]\r\n\t\ttime = el[1]\r\n\t\tresult = \" Дата матча: \" + date + \", \" + time\r\n\t\tresult_date_nTime.append(result)\r\n\r\n\tfor el in championat:\r\n\t\tc = []\r\n\t\tj = 0\r\n\t\tk = \"\"\r\n\t\tfor inner_el in el:\r\n\t\t\tk = k + inner_el + \" \"\r\n\t\tchampionat_result.append(k)\r\n\t\tk = \"\"\r\n\r\n\r\n\t#Вывод всего безобразия на экран\r\n\ti = 0\r\n\twhile i < len(result_teams):\r\n\t\tfinal_result = result_teams[i] + \" \" + result_scores[i] + \" / \" + result_date_nTime[i] + \" / \" + championat_result[i]\r\n\t\tend_programm.append(final_result)\r\n\t\ti += 1\r\n\t# \tprint(\" \" + final_result)\r\n\t# \tprint()\r\n\t\t\r\n\t# input()\t\r\n\tprint('закончили парсить футбол')\r\n\treturn end_programm\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\tmain()\r\n\r\n","sub_path":"parser sportbox/First_parser_football.py","file_name":"First_parser_football.py","file_ext":"py","file_size_in_byte":3377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"114504416","text":"from PyQt5 import QtGui, QtWidgets\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Qt5Agg')\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.backends.backend_qt5 import NavigationToolbar2QT as NavigationToolbar\nimport matplotlib.pyplot as plt\nfrom tunepy.widgets import TunepyGUICore\n\n\n\nclass PixmapWidget(QtWidgets.QWidget):\n def __init__(self, parent=None):\n QtWidgets.QWidget.__init__(self, parent)\n\n self.label = QtWidgets.QLabel(\"test\")\n \n layout = QtWidgets.QGridLayout()\n layout.addWidget(self.label, 0, 0)\n\n self.setLayout(layout)\n\n\n def setImage(self, img):\n qimage = QtGui.QImage(img.data, img.shape[1], img.shape[0], QtGui.QImage.Format_RGB888)\n pixmap = QtGui.QPixmap(qimage)\n self.label.setPixmap(pixmap)\n\n\n\nclass MatplotlibWidget(QtWidgets.QWidget):\n def __init__(self, fig, parent=None):\n QtWidgets.QWidget.__init__(self, parent)\n canvas = FigureCanvas(fig)\n canvas.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n canvas.updateGeometry()\n toolbar = NavigationToolbar(canvas, self)\n\n layout = QtWidgets.QVBoxLayout()\n layout.addWidget(toolbar)\n layout.addWidget(canvas)\n self.setLayout(layout)\n\n\n\nclass TunepyGUI(TunepyGUICore):\n def __init__(self, *args):\n TunepyGUICore.__init__(self, *args)\n \n \n def update(self):\n result = self.execFunction()\n if self.mode == 'auto':\n if result is None:\n if plt.gcf().axes: method = self.matplotlib\n else: method = self.unknown\n elif type(result) == np.ndarray:\n if len(result.shape) == 3 and result.shape[-1] == 3:\n method = self.numpy\n else:\n method = self.print\n else:\n method = self.print\n else:\n method = getattr(self, self.mode)\n method(result)\n plt.close()\n \n \n def unknown(self, result):\n self.print(\"Function output not recognized, check terminal for any output.\")\n \n \n def matplotlib(self, result):\n fig = plt.gcf()\n centralWidget = MatplotlibWidget(fig, self)\n self.setCentralWidget(centralWidget)\n\n\n def numpy(self, result):\n centralWidget = QtWidgets.QScrollArea()\n pixmapWidget = PixmapWidget()\n pixmapWidget.setImage(np.uint8(result))\n centralWidget.setWidget(pixmapWidget)\n self.setCentralWidget(centralWidget)\n\n\n def print(self, result):\n from pprint import pformat\n if type(result) == str:\n toprint = result\n else:\n toprint = pformat(result)\n centralWidget = QtWidgets.QPlainTextEdit()\n centralWidget.setReadOnly(True)\n centralWidget.setPlainText(toprint)\n self.setCentralWidget(centralWidget)\n\n","sub_path":"tunepy/TunepyGUI.py","file_name":"TunepyGUI.py","file_ext":"py","file_size_in_byte":2955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"529911894","text":"import copy\nimport itertools\nfrom graphviz import Digraph\n\n\nclass Quantity:\n quantity = '0'\n derivative = '0'\n\n def __init__(self, space):\n self.space = space\n\n\nclass State:\n\n def __init__(self):\n self.next_states = []\n self.quantities = {\n 'inflow': Quantity(['0', '+']),\n 'outflow': Quantity(['0', '+', 'max']),\n 'volume': Quantity(['0', '+', 'max']),\n 'pressure': Quantity(['0', '+', 'max']),\n 'height': Quantity(['0', '+', 'max'])\n }\n\n def tostring(self):\n state_string = ''\n for name, quantity in self.quantities.items():\n line = name + '\\t' + str(quantity.quantity) + \\\n '\\t' + str(quantity.derivative) + '\\n'\n state_string += line\n return state_string\n\n def copy(self):\n return copy.deepcopy(self)\n\n def apply_derivative(self):\n new_state = self.copy()\n for n, q in self.quantities.items():\n index = q.space.index(q.quantity)\n if q.derivative == '+' and (index + 1) != len(q.space):\n new_state.quantities[n].quantity = q.space[index + 1]\n if q.derivative == '-' and (index) != 0:\n new_state.quantities[n].quantity = q.space[index - 1]\n return new_state\n\n def constrain_derivative(self):\n for n, q in self.quantities.items():\n if self.quantities[n].quantity == 'max' and self.quantities[n].derivative == '+':\n self.quantities[n].derivative = '0'\n if self.quantities[n].quantity == '0' and self.quantities[n].derivative == '-':\n self.quantities[n].derivative = '0'\n\n\ndef I_plus_minus(state, a, b, c):\n\n q1 = state.quantities[a]\n q2 = state.quantities[b]\n q3 = state.quantities[c]\n\n if q1.quantity == '0' and q2.quantity == '0':\n return [state]\n\n if q1.quantity == '0' and (q2.quantity == '+' or q2.quantity == 'max'):\n if q3.derivative == '0':\n q3.derivative = '-'\n if q3.derivative == '+':\n q3.derivative = '0'\n return [state]\n\n if q1.quantity == '+' and q2.quantity == '0':\n if q3.derivative == '0':\n q3.derivative = '+'\n if q3.derivative == '-':\n q3.derivative = '0'\n return [state]\n\n if q1.quantity == '+' and (q2.quantity == '+' or q2.quantity == 'max'):\n if q3.derivative == '-':\n copy1 = copy.deepcopy(state)\n copy1.quantities[c].derivative = '0'\n return [copy1, state]\n if q3.derivative == '0':\n copy1 = copy.deepcopy(state)\n copy2 = copy.deepcopy(state)\n copy1.quantities[c].derivative = '0'\n copy2.quantities[c].derivative = '+'\n return [copy1, copy2, state]\n if q3.derivative == '+':\n copy1 = copy.deepcopy(state)\n copy1.quantities[c].derivative = '0'\n return [state, copy1]\n\n\ndef P_plus(state, q1, q2):\n a = state.quantities[q1]\n b = state.quantities[q2]\n if a.derivative == '+':\n if b.derivative == '-':\n b.derivative = '0'\n if b.derivative == '0':\n b.derivative = '+'\n if a.derivative == '-':\n if b.derivative == '+':\n b.derivative = '0'\n if b.derivative == '0':\n b.derivative = '-'\n\n\ndef VC(state, q1, q2):\n a = state.quantities[q1]\n b = state.quantities[q2]\n if a.quantity == 'max':\n b.quantity = 'max'\n if a.quantity == '0':\n b.quantity = '0'\n\n\ndef change_inflow(state):\n\n copy1 = state.copy()\n copy2 = state.copy()\n if state.quantities['inflow'].derivative == '+':\n copy1.quantities['inflow'].derivative = '0'\n return [copy1, state]\n if state.quantities['inflow'].derivative == '-':\n copy1.quantities['inflow'].derivative = '0'\n return [copy1, state]\n if state.quantities['inflow'].derivative == '0':\n copy1.quantities['inflow'].derivative = '-'\n copy2.quantities['inflow'].derivative = '+'\n return [copy1, state, copy2]\n\n\ndef get_next_states(state):\n new_state = state.apply_derivative()\n VC(new_state, 'volume', 'height')\n VC(new_state, 'volume', 'outflow')\n VC(new_state, 'height', 'volume')\n VC(new_state, 'height', 'pressure')\n VC(new_state, 'pressure', 'height')\n VC(new_state, 'pressure', 'outflow')\n next_states = I_plus_minus(new_state, 'inflow', 'outflow', 'volume')\n next_states = list(itertools.chain(\n *[change_inflow(s) for s in next_states]))\n for state in next_states:\n P_plus(state, 'volume', 'height')\n P_plus(state, 'height', 'pressure')\n P_plus(state, 'pressure', 'outflow')\n state.constrain_derivative()\n return next_states\n\n\ndef add_edges(state, state_index):\n next_states = get_next_states(state)\n if len(next_states):\n for i in range(0, len(next_states)):\n if next_states[i].tostring() not in state_list:\n state_list.append(next_states[i].tostring())\n dot.node(str(len(state_list)), next_states[i].tostring())\n edge_list.append(str(state_index)+'-'+str(len(state_list)))\n dot.edge(str(state_index), str(len(state_list)))\n add_edges(next_states[i], len(state_list))\n else:\n index = state_list.index(next_states[i].tostring())+1\n if index != state_index:\n edge = (str(state_index)+'-'+str(index))\n if edge not in edge_list:\n edge_list.append(edge)\n dot.edge(str(state_index), str(index))\n else:\n return\n\n\n# Initialize the container system\ncontainer_system = State()\ndot = Digraph(comment='State Graph')\ndot.node('0', container_system.tostring())\n\n# Turn on the tap(Inflow.derivative='+')\ncontainer_system.quantities['inflow'].derivative = '+'\nstate_list = []\nedge_list = []\nstate_list.append(container_system.tostring())\ndot.node('1', container_system.tostring())\ndot.edge('0', '1')\n\n# Generate new state recursively\nadd_edges(container_system, 1)\nprint(edge_list)\n\ndot.render('test-output/state-graph.gv', view=True)\n","sub_path":"Knowledge_Representation/Project2_State_Graph/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"642743665","text":"#!/usr/bin/env python\n\nimport rasterio as rio\nimport xarray as xr\nimport geopandas\nimport cartopy.crs as ccrs\nfrom rasterio.session import AWSSession\nimport boto3\n\naws_session = AWSSession(boto3.Session(), requester_pays=True)\n\n\ndef get_ll_corner(aoi_geojson_file):\n \n url = aoi_geojson_file\n gdf = geopandas.read_file(url)\n bbox = (gdf['geometry'].bounds['minx'][0], gdf['geometry'].bounds['miny'][0], \n gdf['geometry'].bounds['maxx'][0], gdf['geometry'].bounds['maxy'][0])\n\n print(bbox)\n\n crs = ccrs.epsg(5072)\n mycrs = '5070'\n crs_object = ccrs.epsg('5070')\n\n ll_alb = crs_object.transform_point(bbox[0],bbox[1], ccrs.PlateCarree())\n ll_corner = ll_alb\n return(ll_corner)\n\n\ndef get_ur_corner(aoi_geojson_file):\n \n url = aoi_geojson_file\n gdf = geopandas.read_file(url)\n bbox = (gdf['geometry'].bounds['minx'][0], gdf['geometry'].bounds['miny'][0], \n gdf['geometry'].bounds['maxx'][0], gdf['geometry'].bounds['maxy'][0])\n\n print(bbox)\n\n crs = ccrs.epsg(5072)\n mycrs = '5070'\n crs_object = ccrs.epsg('5070')\n\n ur_alb = crs_object.transform_point(bbox[2],bbox[3], ccrs.PlateCarree())\n ur_corner=ur_alb\n\n return(ur_corner)\n\n\ndef convert_llurl(ll_url: str) -> str:\n \"\"\"\n Convert a landsat look url to an S3 url\n \"\"\"\n return ll_url.replace('https://landsatlook.usgs.gov/data', 's3://usgs-landsat')\n\n\n\n\n#Function used to create array from data stored in dataframe table created above\ndef create_dataset(row, ll_corner, ur_corner, bands = ['SR_B3.TIF', 'SR_B6.TIF'], chunks = {'band': 1, 'x':2048, 'y':2048}):\n datasets = []\n with rio.Env(aws_session):\n for band in bands:\n band_json = row[band]\n \n #print(band_json)\n \n url=convert_llurl(band_json['href'])\n print(url)\n da = xr.open_rasterio(url)\n\n #da = xr.open_rasterio(url, chunks = chunks)\n daSub = da.sel(x=slice(ll_corner[0], ur_corner[0]), y=slice(ur_corner[1], ll_corner[1]))\n daSub = daSub.squeeze().drop(labels='band')\n DS = daSub.to_dataset(name = band)\n datasets.append(DS)\n DS = xr.merge(datasets)\n return DS\n\n\ndef load_all_bands_from_df_as_array_of_dataarrays(geoj_file, df):\n ldatasets = []\n ll_corner = get_ll_corner(geoj_file)\n ur_corner = get_ur_corner(geoj_file)\n for i,row in df.iterrows():\n if (i<1000):\n try:\n print('loading....', row.datetime, i)\n ds = create_dataset(row, ll_corner, ur_corner)\n ldatasets.append(ds)\n except Exception as e:\n print('Error loading, skipping')\n print(e)\n return(ldatasets)\n\n\n\n\n# ldatasets = load_all_bands_from_df_as_array_of_dataarrays(aoi_geojson_file, df)\n","sub_path":"00-notebooks/04-20-2021-wk17-viz-tif/xra_func.py","file_name":"xra_func.py","file_ext":"py","file_size_in_byte":2838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"199250623","text":"\"\"\"CSEinfo URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', 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: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom Dept import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('home/', views.home, name='home'),\n path('student-list/', views.student_list, name='student-list'),\n path('teacher-list/', views.teacher_list, name='teacher-list'),\n path('staff-list/', views.staff_list, name='staff-list'),\n path('staff-list/', views.staff_list, name='staff-list'),\n path('single-student//', views.single_student, name='single-student'),\n path('single-teacher//', views.single_teacher, name='single-teacher'),\n path('single-staff//', views.single_staff, name='single-staff'),\n path('login/', views.user_login, name='login'),\n path('logout/', views.user_logout, name='logout'),\n\n]\n","sub_path":"CSEinfo/CSEinfo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"286085167","text":"import numpy as np\n\nnpts1=100\nnpts2=10\n\nth_upper = np.linspace(0,10*np.pi,npts1)\nth_lower = np.linspace(9.5*np.pi,0,npts1)\n\nr_upper = np.linspace(8,4,npts1)\nr_lower = np.linspace(4,8,npts1)\n\nx_outer_upper = r_upper*np.cos(th_upper)\ny_outer_upper = r_upper*np.sin(th_upper)\nz_outer_upper = r_upper*0\n\n\nx_outer_lower = r_lower*np.cos(-th_lower)\ny_outer_lower = r_lower*np.sin(-th_lower)\nz_outer_lower = -0.2*np.ones(npts1)\n\n\nx_inner_upper = (r_upper-0.15)*np.cos(th_upper)\ny_inner_upper = (r_upper-0.15)*np.sin(th_upper)\nz_inner_upper = r_upper*0\n\n\nx_inner_lower = (r_lower-0.15)*np.cos(-th_lower)\ny_inner_lower = (r_lower-0.15)*np.sin(-th_lower)\nz_inner_lower = -0.2*np.ones(npts1)\n\n\nx_outer = np.concatenate((x_outer_upper,x_outer_lower),axis=0)\ny_outer = np.concatenate((y_outer_upper,y_outer_lower),axis=0)\nz_outer = np.concatenate((z_outer_upper,z_outer_lower),axis=0)\n\n\nx_inner = np.concatenate((x_inner_upper,x_inner_lower),axis=0)\ny_inner = np.concatenate((y_inner_upper,y_inner_lower),axis=0)\nz_inner = np.concatenate((z_inner_upper,z_inner_lower),axis=0)\n\n#x_12 = (r_1-0.15)*np.cos(th_3)\n#y_12 = (r_1-0.15)*np.sin(th_3)\n#z_12 = r_1*0\n\n\n\n#x_2 = r_2*np.cos(th_2)\n#y_2 = r_2*np.sin(th_2)\n#z_2 = np.linspace(0,0.2,10)\n\n\n\n\n#x_32 = (r_3-0.15)*np.cos(th_3)\n#y_32 = (r_3-0.15)*np.sin(th_3)\n#z_32 = 0.2*np.ones(100)\n\n\n\n\n\n#xx_2 = np.concatenate((x_12,x_32),axis=0)\n#yy_2 = np.concatenate((y_12,y_32),axis=0)\n#zz_2 = np.concatenate((z_12,z_32),axis=0)\n\n\n#print(zz)\n","sub_path":"five_turn_geo_v2.py","file_name":"five_turn_geo_v2.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"102406415","text":"#newton interpolation\n\nimport numpy as np\nimport sympy as sm\n\na=np.array([(-1,0,1,2),(3,-4,5,-6)]) #data_points\n\n#1st divided difference\nfor i in range(3):\n t=(a[1,i+1]-a[1,i])/(a[0,i+1]-a[0,i])\n T[0,i]=t\n \n#2nd divided difference\nfor i in range(2):\n u=(T[0,i+1]-T[0,i])/(a[0,i+2]-a[0,i])\n U[0,i]=u\n\n#3rd divided difference \nfor i in range(1):\n v=(U[0,i+1]-U[0,i])/(U[0,i+3]-U[0,i])\n V[0,i]=v\n\nx=sm.symbols('x')\n\nP= a[1,0] + T[0,0]*(x-a[0,0]) +U(0,0)*(x-a[0,0])*(x-a[0,1]) + V(0,0)*(x-a[0,0])*(x-a[0,1])*(x-a[0,2])\np=sm.simplify(P)\nprint('\\nInterpolated polynomial is : \\n')\nprint(p)\n\n\n","sub_path":"newtoninter.py","file_name":"newtoninter.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"212708105","text":"n=int(input())\r\nm=sm=101.0\r\nnl=[]\r\nfor i in range(n):\r\n name=input()\r\n grade=float(input())\r\n if(gradem and grade [options]\n\npositional arguments:\n server_list Path to the text file containing list of servers\n\noptional arguments:\n -h, --help show this help message and exit\n -N N Outputs N number of most common IP Addresses (Default:\n 10\n -L LOGPATH, --logpath LOGPATH\n Change the log file location on server, common for all\n (Default: /var/log/httpd-access-log)\n -T TIMEDIFF, --timediff TIMEDIFF\n Change the time difference when looking for common\n IPs, in hours (Default: 1 hour)\n\"\"\"\n\n\nimport os\nimport sys\nimport re\nimport paramiko\nimport collections\nimport operator\nimport datetime\nimport dateutil.parser\nimport pytz\n#import base64 # for generating RSA keys (Skipped)\nimport argparse\n\n\ndef main():\n \"\"\"Main entry point for the script\"\"\"\n parser = argparse.ArgumentParser(description='Determines the top 10 most common source IP addresses, and their hit'\n ' rates, for a fleet of 1000 web servers within the last hour')\n parser.add_argument('server_list', help='Path to the text file containing list of servers')\n parser.add_argument('-N', default=10, type=int,\n help='Outputs N number of most common IP Addresses (Default: 10')\n parser.add_argument('-L', '--logpath', default='/var/log/httpd-access-log', type=str,\n help='Change the log file location on server, common for all '\n '(Default: /var/log/httpd-access-log)')\n parser.add_argument('-T', '--timediff', default=1, type=int,\n help='Change the time difference when looking for common IPs, in hours (Default: 1 hour)')\n args = parser.parse_args()\n\n # Makes sure the server list file is valid\n check_path(args.server_list, parser)\n\n # Dictionaries for IP Address and Hit counts\n ip_dict = collections.defaultdict(int)\n hit_success = collections.defaultdict(int)\n\n with open(args.server_list, \"rb\") as servers:\n for server in servers:\n # The program expects a valid format for listing servers\n hostname, user, passwd = server.split()\n # Generate RSA key for host key verification (Skipped)\n #key = paramiko.RSAKey(data=base64.decodestring('AAA...')) # needs host key\n # Starts the SSH Client\n client = paramiko.SSHClient()\n # Add the host to known hosts by adding the RSA key (Skipped)\n #client.get_host_keys().add('ssh.example.com', 'ssh-rsa', key)\n # Ignores the warnings for RSA Keys\n client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n # Connects to the server\n client.connect(hostname.decode('UTF-8'),\n username=user.decode('UTF-8'), password=passwd.decode('UTF-8'))\n # Copies the log file data to a variable\n _, data, _ = client.exec_command(\"cat {}\".format(args.logpath))\n log_data = []\n # Stores the log data in a list\n for line in data:\n log_data.append(line.strip(\"\\n\"))\n\n # Parses each log data and stores the IP address and hit counts at each step\n for log in log_data:\n ip_address, date_time, status_code = parse_log(log)\n if check_time(date_time, args.timediff):\n ip_dict[ip_address] += 1\n if status_code == \"200\":\n hit_success[ip_address] += 1\n\n # An ascending list of IP address occurrences\n ip_list = sorted(list(ip_dict.items()), key=operator.itemgetter(1))\n\n if ip_list:\n print(\"IP Address Hit Rate\")\n for _ in range(args.N):\n # Gets the last element that has the highest occurrence\n try:\n top_ip, total_hits = ip_list.pop()\n except IndexError:\n break\n # Hit Rate = # of successful connections/total connection attempts\n hit_rate = (hit_success[top_ip]/total_hits)*100\n\n print(\"{0} ---- {1:.2f}%\".format(top_ip, hit_rate))\n else:\n print(\"No results found.\")\n\n parser.exit(0)\n#end main\n\n\ndef check_time(log_time, offset):\n \"\"\"Checks if timestamp is not older than a given limit\"\"\"\n log_time = list(log_time)\n # Remove unnecessary colon\n log_time[11] = \" \"\n log_time = ''.join(log_time)\n # Convert the time in UTC\n dt_log = dateutil.parser.parse(log_time)\n dt_log = dt_log.astimezone(pytz.UTC)\n # Convert current time in UTC\n dt_utc = datetime.datetime.utcnow().replace(tzinfo=pytz.UTC)\n # Roll back current time by X hours defined by offset\n dt_utc = dt_utc - datetime.timedelta(hours=offset)\n\n # Compare both times\n if dt_log >= dt_utc:\n return True\n else:\n return False\n#end check_time\n\n\ndef parse_log(log_data):\n \"\"\"Parses the Apache log file and returns relevant details\"\"\"\n # Apache log format regex\n format_pat = re.compile(\n r\"(?P[\\d\\.]+)\\s\"\n r\"(?P\\S*)\\s\"\n r\"(?P\\S*)\\s\"\n r\"\\[(?P

{name} ({concentration})

\".format(name=nom, concentration=concentration)\n for line in candidat[9].split('\\n'):\n description += \"

{line}

\\n\".format(line=line)\n\n option = Option(value=nom, code=code, order=order, description=description, image=candidat[PHOTO])\n questions_map[poste].add_option(option)\n\nfor poste in questions_map:\n order = questions_map[poste].option_count()\n lachaise = Option(value=\"La chaise\", code=\"A{numeral}\".format(numeral=order+1), order=order,\n description=\"

La chaise (Whatever)

La chaise ne vous laisseras pas tomber. Elle offre un bon support et connait bien son dossier. Elle connait sa place et ne s'exprime pas quand ce n'est pas son tour.

\",\n image = \"/upload/surveys/893586/images/markus_1.jpgd2fe39c4-d929-477e-ae08-ca0ec8e8a9e7Original.jpg\"\n )\n questions_map[poste].add_option(lachaise)\n\nmylookup = TemplateLookup(directories=['.'], input_encoding=\"utf-8\", output_encoding=\"utf-8\")\nmytemplate = Template(filename='templates/promo62/base.mako', lookup=mylookup, input_encoding=\"utf-8\", output_encoding=\"utf-8\")\n\nsurvey = mytemplate.render(groups=groups,questions=questions)\n\nsurvey_file = open(\"result/survey.lss\", \"w+b\")\nsurvey_file.write(survey)\nsurvey_file.close()","sub_path":"promo62.py","file_name":"promo62.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"408573403","text":"import time as t\nimport matplotlib.pyplot as plt\n\ntimes=[]\nmistakes=0\n\nprint(\"typing speed test\\\ntype programming 5 times\")\n\ninput(\"Press 'enter' to continue \")\n\nwhile len(times)<5:\n start=t.time()\n word=input(\"Type the word: \")\n end=t.time()\n gap=end-start\n\n times.append(gap)\n\n if word.lower()!='rinson':\n mistakes+=1\n\nprint('you made',str(mistakes),'mistake(s)')\nprint(\"lets see your evolution\")\nt.sleep(3)\n\nx=[1,2,3,4,5]\ny=times #list of time(gap)\ntemp=['1','2','3','4','5']\nplt.xticks(x,temp)\nplt.xlabel('Attempts')\nplt.ylabel('time in seconds')\nplt.title('evolution')\nplt.plot(x,y)\nplt.show()\n","sub_path":"OLD/tmatplot.py","file_name":"tmatplot.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"316027068","text":"from random import randint\nfrom card import Suit, Rank\n\nclass Player:\n \"\"\"\n A person or cpu playing the game.\n Fields:\n - index: the player's index in the order of turns for a round.\n - team: the player's team.\n - hand: list of cards player has disposable.\n - bid: bid of current round.\n - num_tricks: number of tricks collected in this round.\n \"\"\"\n\n def __init__(self, index, team, is_computer, hand=[], bid=None, num_tricks=0, bags=0):\n self.index = index\n self.team = team\n self.is_computer = is_computer\n self.hand = hand\n self.bid = bid\n self.num_tricks = num_tricks\n self.bags = bags\n\n def __str__(self):\n return 'player ' + str(self.index + 1)\n\n def print_card_list(self, list):\n \"\"\"\n Prints cards containes in the list.\n \"\"\"\n print(*[str(c) for c in list], sep=' , ')\n\n def print_hand(self):\n \"\"\"\n Prints all cards in the player's hand.\n \"\"\"\n print('\\nYour hand:')\n self.print_card_list(self.hand)\n print('')\n\n def print_playable_hand(self, suit):\n \"\"\"\n Prints all playable cards in the player's hand, where playable means cards that match the\n trick's suit or all of your cards because you cannot match the suit.\n \"\"\"\n if not suit:\n playable = self.hand\n else:\n playable = []\n for card in self.hand:\n if card.suit == suit:\n playable.append(card)\n playable = playable if len(playable) > 0 else self.hand\n print('\\nPlayable cards:')\n self.print_card_list(playable)\n print('')\n\n def set_bid(self, bid):\n self.bid = bid\n\n def add_bags(self, bags):\n self.bags += bags\n\n def get_card(self, i):\n \"\"\"\n Retrieves card at index i of player's hand.\n \"\"\"\n return self.hand[i]\n\n def get_index(self, card):\n \"\"\"\n Returns the index of the card in the hand, or -1 if the card is not present.\n \"\"\"\n for i, c in enumerate(self.hand):\n if str(c) == card:\n return i\n return -1\n\n def reset(self):\n \"\"\"\n Reset player properties after finishing a round.\n \"\"\"\n self.hand = []\n self.bid = None\n self.num_tricks = 0\n\n def play(self, card_index):\n \"\"\"\n Returns a tuple of the player object and the card object.\n Place a card on the table by popping the card from player's hand.\n \"\"\"\n card = self.hand.pop(card_index)\n print(str(self) + ' played ' + str(card))\n return (self, card)\n\n def can_match_suit(self, suit):\n \"\"\"\n Returns if this player has a card of a particular suit.\n \"\"\"\n for c in self.hand:\n if c.suit == suit:\n return True\n return False\n\n def cpu_bid(self):\n \"\"\"\n Computer player algorithm for bidding, simply the sum of spades and high cards.\n \"\"\"\n num_spades = 0\n num_high_cards = 0\n for c in self.hand:\n if c.suit == Suit.SPADES:\n num_spades += 1\n elif c.rank.value[0] in Rank.high_cards():\n num_high_cards += 1\n self.bid = num_spades + num_high_cards\n return self.bid\n\n def cpu_turn(self, suit, table):\n \"\"\"\n Computer player algorithm for playing a card.\n \"\"\"\n max_of_suit = None\n index = None\n # look for highest card we can play within this suit\n for i, c in enumerate(self.hand):\n if c.suit == suit:\n if not max_of_suit:\n max_of_suit = c\n index = i\n elif c.rank.value[1] > max_of_suit.rank.value[1]:\n max_of_suit = c\n index = i\n if index:\n return index\n # we have no cards of this suit, play a random card\n return randint(0, len(self.hand) - 1)\n","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":3536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"152837681","text":"from magenta import MusicXMLDocument\nXMLDocument = MusicXMLDocument(\"magenta/testdata/flute_scale.xml\")\n\nmidi_resolution = XMLDocument.midi_resolution\nparts = XMLDocument.parts[0]\n_score_parts = XMLDocument._score_parts\n_state = XMLDocument._state\ntotal_time_secs = XMLDocument.total_time_secs\n\nprint(midi_resolution)\nprint(parts)\nprint(_score_parts)\nprint('state;', _state)\nprint(total_time_secs)\n\nmeasure_1 = parts.measures[0].notes\nmeasure_1 = [[x.pitch, x.is_rest, vars(x.note_duration)] for x in measure_1]\n\nprint(measure_1)\n\n\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"24393243","text":"# iFerment\n\nfrom __future__ import print_function\nfrom cobra import Model, Reaction, Metabolite\nimport pandas\nimport numpy\nfrom cobra.flux_analysis import flux_variability_analysis\nimport cobra.test\nimport math\n\n################################\n###Part I: REACTOR PARAMETERS###\n################################\n\nmodel = Model(\"iFerment\")\n\n# Set reactor conditions\nT = 35 # deg C\npH_out = 5.5 # s.u.\n\n# Consider Transport Energetics\n\n#TransEnergetics = True\nTransEnergetics = False\n\n# Set Extracellular Concentrations of unprotonated forms (M)\nS_Ethanol = 0.0183\nS_Lactate = 0.000001\nS_Formate = 0.0047\nS_Acetate = 0.1073\nS_Propionate = 0.00001\nS_Butyrate = 0.0802\nS_Valerate = 0.0019\nS_Hexanoate = 0.0452\nS_Heptanoate = 0.000001\nS_Octanoate = 0.0032\n\n\n# Set Intracellular Concentrations (M)\nC_in_Formate = 0.001\nC_in_Acetate = 0.001\nC_in_Propionate = 0.001\nC_in_Butyrate = 0.001\nC_in_Valerate = 0.001\nC_in_Hexanoate = 0.001\nC_in_Heptanoate = 0.001\nC_in_Octanoate = 0.001\nC_in_Lactate = 0.001\nC_in_Ethanol = 0.001\n\npH_in = 7\n\nR = 8.3145*10**-3 # kj/mol-K\n\ndeltaG_ATP_Hydrolysis = -50\n\nh_j = 1 # Number of protons translocated for all acid products\ndeltaG_pH = -2.3*h_j*R*T*(pH_out-pH_in)\nprint('dG_pH: ', deltaG_pH)\n\ndelta_Sai = 33.33*(pH_in-pH_out)-143.33\nprint('dSai: ', delta_Sai) # = mV\nc_j = -1 # For Protons- Net charge transported from outside to inside the cell\nF = .096485 # kJ/mV*mol\n\ndeltaG_Sai = 1*delta_Sai*c_j*F\nprint('dG_Sai: ', deltaG_Sai)\n\n\nprint(\"Reactions: \" + str(len(model.reactions)))\nprint(\"Metabolites: \" + str(len(model.metabolites)))\nprint(\"Genes: \" + str(len(model.genes)))\n\n\n################################\n###Part II: BUILD THE MODEL#####\n################################\n# Dummy metabolites\nATP_SLP = Metabolite('ATP_SLP', formula='', name='', compartment='e', charge=0)\nATP_IMF = Metabolite('ATP_IMF', formula='', name='', compartment='e', charge=0)\nATP_BIOMASS = Metabolite('ATP_BIOMASS', formula='',\n name='', compartment='e', charge=0)\nATP_HYDR = Metabolite('ATP_HYDR', formula='', name='',\n compartment='e', charge=0)\nATP_TRANS = Metabolite('ATP_TRANS', formula='', name='',\n compartment='e', charge=0)\n\n# Complex Carbohydrate Degradation\n\n# Xylan Degradation\n\n# R0001 Xylan Exchange\n\nxyl4_e = Metabolite('xyl4_e', formula='C20H34O17',\n name='xyl4_e', compartment='e')\n\nxyl4_c = Metabolite('xyl4_c', formula='C20H34O17',\n name='xyl4_c', compartment='c')\n\nxyl__D_e = Metabolite('xyl__D_e', formula='C5H10O5',\n name='xylose-D', compartment='e')\n\nxyl__D_c = Metabolite('xyl__D_c', formula='C5H10O5',\n name='xylose-D', compartment='c', charge=0)\n\nh2o_c = Metabolite('h2o_c', formula='H2O', name='H2O',\n compartment='c', charge=0)\n\nreaction = Reaction('EX_xyl4_e')\nreaction.name = 'Xylan Exchange'\nreaction.subsystem = 'Complex Carbohydrate Degradation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({xyl4_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0002 Xylan Transport xyl4_e -> xyl4_c\n\nreaction = Reaction('xyl4t')\nreaction.name = 'Xylan Transport'\nreaction.subsystem = 'Complex Carbohydrate Degradation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({xyl4_e: -1.0,\n xyl4_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0003 Xylan Hydrolysis xyl4_c + 3 h2o_c = 4 xyl__D_e\n\nreaction = Reaction('C5Hyd')\nreaction.name = 'Xylan Hydrolysis'\nreaction.subsystem = 'Complex Carbohydrate Degradation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({xyl4_c: -1.0,\n h2o_c: -3.0,\n xyl__D_e: 4.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Glucan Degradation\n\nglc4_e = Metabolite('glc4_e', formula='C24H42O21',\n name='glucan', compartment='e')\n\nglc4_c = Metabolite('glc4_c', formula='C24H42O21',\n name='glucan', compartment='c')\n\nglc__D_e = Metabolite('glc__D_e', formula='C6H12O6',\n name='D-Glucose', compartment='e', charge=0)\n\nglc__D_c = Metabolite('glc__D_c', formula='C6H12O6',\n name='D-Glucose', compartment='c', charge=0)\n\n# R0004 Glucan Exchange\n\nreaction = Reaction('EX_glc4_e')\nreaction.name = 'Glucan Exchange'\nreaction.subsystem = 'Complex Carbohydrate Degradation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nmodel.add_reactions([reaction])\n\nreaction.add_metabolites({glc4_e: -1.0})\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0005 Glucan Transport glc4_e -> glc4_c\n\nreaction = Reaction('glc4t')\nreaction.name = 'Glucan Transport'\nreaction.subsystem = 'Complex Carbohydrate Degradation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({glc4_e: -1.0,\n glc4_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0006 Glucan hydrolysis glc4_c + 3 h2o_c -> glc__D_e\n\nreaction = Reaction('C6Hyd')\nreaction.name = 'Glucan Hydrolysis'\nreaction.subsystem = 'Complex Carbohydrate Degradation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({glc4_c: -1.0,\n h2o_c: -3.0,\n glc__D_e: 4.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Pentose Utilization\n\n# R0007 D-xylose exchange xyl__D_e\n\nreaction = Reaction('EX_xyl__D_e')\nreaction.name = 'D-Xylose exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({xyl__D_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0008 D-xylose reversible transport xyl__D_e <-> xyl__D_c\n\nreaction = Reaction('XYLt')\nreaction.name = 'D xylose reversible transport'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({xyl__D_e: -1.0,\n xyl__D_c: 1.0})\n\nmodel.add_reactions([reaction])\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0009 D-xylose transport via ABC system atp_c + h2o_c + xyl__D_e <-> adp_c + h_c + pi_c + xyl__D_c\n\natp_c = Metabolite('atp_c', formula='C10H12N5O13P3',\n name='ATP', compartment='c', charge=-4)\nadp_c = Metabolite('adp_c', formula='C10H12N5O10P2',\n name='ADP', compartment='c', charge=-3)\nh_c = Metabolite('h_c', formula='H', name='H+', compartment='c', charge=1)\npi_c = Metabolite('pi_c', formula='HO4P', name='xylose-D',\n compartment='c', charge=-2)\n\nreaction = Reaction('XYLabc')\nreaction.name = 'D-xylose transport via ABC system'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n xyl__D_e: -1.0,\n adp_c: 1.0,\n h_c: 1.0,\n pi_c: 1.0,\n xyl__D_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0010 Xylose isomerase xyl__D_c <-> xylu__D_c\n\nxylu__D_c = Metabolite('xylu__D_c', formula='C5H10O5',\n name='D-xylulose', compartment='c', charge=0)\n\nreaction = Reaction('XYLI1')\nreaction.name = 'Xylose isomerase'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({xyl__D_c: -1.0,\n xylu__D_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0011 Xylulokinase atp_c + xylu__D_c -> adp_c + h_c + xu5p__D_c\n\nxu5p__D_c = Metabolite('xu5p__D_c', formula='C5H9O8P',\n name='D-Xylulose 5-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('XYLK')\nreaction.name = 'Xylulokinase'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n xylu__D_c: -1.0,\n adp_c: 1.0,\n h_c: 1.0,\n xu5p__D_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Phosphoketolase\n\n# R0012 Phosphoketolase (xylulose-5-phosphate utilizing) pi_c + xu5p__D_c -> actp_c + g3p_c + h2o_c\n\nactp_c = Metabolite('actp_c', formula='C2H3O5P',\n name='Acetyl phosphate', compartment='c', charge=-2)\ng3p_c = Metabolite('g3p_c', formula='C3H5O6P',\n name='Glyceraldehyde 3-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('PKETX')\nreaction.name = 'Phosphoketolase (xylulose-5-phosphate utilizing)'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pi_c: -1.0,\n xu5p__D_c: -1.0,\n actp_c: 1.0,\n g3p_c: 1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Pentose Phosphate Pathway\n\n# R0013 Ribulose 5-phosphate 3-epimerase ru5p__D_c <-> xu5p__D_c\n\nru5p__D_c = Metabolite('ru5p__D_c', formula='C5H9O8P',\n name='D-Ribulose 5-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('RPE')\nreaction.name = 'Ribulose 5-phosphate 3-epimerase'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ru5p__D_c: -1.0,\n xu5p__D_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0014 Ribose-5-phosphate isomerase r5p_c <-> ru5p__D_c\n\nr5p_c = Metabolite('r5p_c', formula='C5H9O8P',\n name='Alpha-D-Ribose 5-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('RPI')\nreaction.name = 'Ribose-5-phosphate isomerase'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({r5p_c: -1.0,\n ru5p__D_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0015 Transketolase 1 r5p_c + xu5p__D_c <-> g3p_c + s7p_c\n\ns7p_c = Metabolite('s7p_c', formula='C7H13O10P',\n name='Sedoheptulose 7-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('TKT1')\nreaction.name = 'Transketolase 1'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({r5p_c: -1.0,\n xu5p__D_c: -1.0,\n g3p_c: 1.0,\n s7p_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0016 Transaldolase g3p_c + s7p_c <-> e4p_c + f6p_c\n\nf6p_c = Metabolite('f6p_c', formula='C6H11O9P',\n name='D-Fructose 6-phosphate', compartment='c', charge=-2)\ne4p_c = Metabolite('e4p_c', formula='C4H7O7P',\n name='D-Erythrose 4-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('TALA')\nreaction.name = 'Transaldolase'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({g3p_c: -1.0,\n s7p_c: -1.0,\n e4p_c: 1.0,\n f6p_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0017 Transketolase 2 e4p_c + xu5p__D_c <-> f6p_c + g3p_c\n\nreaction = Reaction('TKT2')\nreaction.name = 'Transketolase 2'\nreaction.subsystem = 'Pentose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({e4p_c: -1.0,\n xu5p__D_c: -1.0,\n f6p_c: 1.0,\n g3p_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Glucose Utilization\n\n# R0018 D-Glucose exchange glc__D_e\n\nreaction = Reaction('EX_glc__D_e')\nreaction.name = 'D-Glucose exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({glc__D_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0019 D glucose reversible transport glc__D_e <-> glc__D_c\n\nreaction = Reaction('GLCt')\nreaction.name = 'D glucose reversible transport'\nreaction.subsystem = 'Hexose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({glc__D_e: -1.0,\n glc__D_c: 1.0})\n\nmodel.add_reactions([reaction])\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0020 D-glucose transport via ABC system atp_c + h2o_c + glc__D_e <-> adp_c + h_c + pi_c + glc__D_c\n\nreaction = Reaction('GLCabc')\nreaction.name = 'D-glucose transport via ABC system'\nreaction.subsystem = 'Hexose Utilization'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n glc__D_e: -1.0,\n adp_c: 1.0,\n h_c: 1.0,\n pi_c: 1.0,\n glc__D_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0021 Hexokinase (D-glucose:ATP) atp_c + glc__D_c -> adp_c + g6p_c + h_c\n\ng6p_c = Metabolite('g6p_c', formula='C6H11O9P',\n name='D-Glucose 6-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('HEX1')\nreaction.name = 'Hexokinase (D-glucose:ATP)'\nreaction.subsystem = 'Hexose Utilization'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n glc__D_c: -1.0,\n adp_c: 1.0,\n g6p_c: 1.0,\n h_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0022 Glucose-6-phosphate isomerase g6p_c <-> f6p_c\n\nreaction = Reaction('PGI')\nreaction.name = 'Glucose-6-phosphate isomerase'\nreaction.subsystem = 'Hexose Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({g6p_c: -1.0,\n f6p_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Phosphoketolase\n\n# R0023 Phosphoketolase (fructose-6-phosphate utilizing) f6p_c + pi_c <-> actp_c + e4p_c + h2o_c\n\nreaction = Reaction('PKETF')\nreaction.name = 'Phosphoketolase (fructose-6-phosphate utilizing)'\nreaction.subsystem = 'Phosphoketolase'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({f6p_c: -1.0,\n pi_c: -1.0,\n actp_c: 1.0,\n e4p_c: 1.0,\n h2o_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Upper Glycolysis\n\n# R0024 Phosphofructokinase atp_c + f6p_c <-> adp_c + fdp_c + h_c\n\nfdp_c = Metabolite('fdp_c', formula='C6H10O12P2',\n name='D-Fructose 1,6-bisphosphate', compartment='c', charge=-4)\n\nreaction = Reaction('PFK')\nreaction.name = 'Phosphofructokinase'\nreaction.subsystem = 'Upper Glycolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n f6p_c: -1.0,\n adp_c: 1.0,\n fdp_c: 1.0,\n h_c: 1.0,\n ATP_SLP: -1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0025 Fructose-bisphosphate aldolase fdp_c <-> dhap_c + g3p_c\n\ndhap_c = Metabolite('dhap_c', formula='C3H5O6P',\n name='Dihydroxyacetone phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('FBA')\nreaction.name = 'Fructose-bisphosphate aldolase'\nreaction.subsystem = 'Upper Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fdp_c: -1.0,\n dhap_c: 1.0,\n g3p_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0026 Triose-phosphate isomerase dhap_c <-> g3p_c\n\ndhap_c = Metabolite('dhap_c', formula='C3H5O6P',\n name='Dihydroxyacetone phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('TPI')\nreaction.name = 'Triose-phosphate isomerase'\nreaction.subsystem = 'Upper Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({dhap_c: -1.0,\n g3p_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Lower Glycolysis\n\n# R0027 Glyceraldehyde-3-phosphate dehydrogenase g3p_c + nad_c + pi_c <-> 13dpg_c + h_c + nadh_c\n\nnad_c = Metabolite('nad_c', formula='C21H26N7O14P2',\n name='Nicotinamide adenine dinucleotide', compartment='c', charge=-1)\nnadh_c = Metabolite('nadh_c', formula='C21H27N7O14P2',\n name='Nicotinamide adenine dinucleotide - reduced', compartment='c', charge=-2)\n_13dpg_c = Metabolite('_13dpg_c', formula='C3H4O10P2',\n name='3-Phospho-D-glyceroyl phosphate', compartment='c', charge=-4)\n\nreaction = Reaction('GAPD')\nreaction.name = 'Glyceraldehyde-3-phosphate dehydrogenase'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({g3p_c: -1.0,\n nad_c: -1.0,\n pi_c: -1.0,\n _13dpg_c: 1.0,\n h_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0028 Phosphoglycerate kinase 3pg_c + atp_c <-> 13dpg_c + adp_c\n\n_3pg_c = Metabolite('_3pg_c', formula='C3H4O7P',\n name='3-Phospho-D-glycerate', compartment='c', charge=-3)\n\nreaction = Reaction('PGK')\nreaction.name = 'Phosphoglycerate kinase'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3pg_c: -1.0,\n atp_c: -1.0,\n _13dpg_c: 1.0,\n adp_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0029 Phosphoglycerate mutase 2pg_c <-> 3pg_c\n\n_2pg_c = Metabolite('_2pg_c', formula='C3H4O7P',\n name='2-Phospho-D-glycerate', compartment='c', charge=-3)\n\nreaction = Reaction('PGM')\nreaction.name = 'Phosphoglycerate mutase'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_2pg_c: -1.0,\n _3pg_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0030 Enolase 2pg_c <-> h2o_c + pep_c\n\npep_c = Metabolite('pep_c', formula='C3H2O6P',\n name='Phosphoenolpyruvate', compartment='c', charge=-3)\n\nreaction = Reaction('ENO')\nreaction.name = 'Enolase'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_2pg_c: -1.0,\n h2o_c: 1.0,\n pep_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0031 Pyruvate kinase adp_c + h_c + pep_c <-> atp_c + pyr_c\n\npyr_c = Metabolite('pyr_c', formula='C3H3O3',\n name='Pyruvate', compartment='c', charge=-1)\n\nreaction = Reaction('PYK')\nreaction.name = 'Pyruvate kinase'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({adp_c: -1.0,\n h_c: -1.0,\n pep_c: -1.0,\n atp_c: 1.0,\n pyr_c: 1.0,\n ATP_SLP: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Gluconeogenesis\n\n# R0032 Phosphoenolpyruvate synthase atp_c + h2o_c + pyr_c <-> amp_c + 2.0 h_c + pep_c + pi_c\n\namp_c = Metabolite('amp_c', formula='C10H12N5O7P',\n name='AMP', compartment='c', charge=-2)\n\nreaction = Reaction('PPS')\nreaction.name = 'Phosphoenolpyruvate synthase'\nreaction.subsystem = 'Gluconeogenesis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n pyr_c: -1.0,\n amp_c: 1.0,\n h_c: 2.0,\n pep_c: 1.0,\n pi_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0033 Fructose-bisphosphatase fdp_c + h2o_c <-> f6p_c + pi_c\n\nreaction = Reaction('FBP')\nreaction.name = 'Fructose-bisphosphatase'\nreaction.subsystem = 'Gluconeogenesis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fdp_c: -1.0,\n h2o_c: -1.0,\n f6p_c: 1.0,\n pi_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Lactate Metabolism\n\n# R0034 D-lactate dehydrogenase lac__D_c + nad_c <-> h_c + nadh_c + pyr_c\n\nlac__D_c = Metabolite('lac__D_c', formula='C3H5O3',\n name='D-Lactate', compartment='c', charge=-1)\n\nreaction = Reaction('LDH-D')\nreaction.name = 'D-lactate dehydrogenase'\nreaction.subsystem = 'Lactate metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__D_c: -1.0,\n nad_c: -1.0,\n h_c: 1.0,\n nadh_c: 1.0,\n pyr_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0035 D-Lactate transport lac__D_e <-> lac__D_c\n\nlac__D_e = Metabolite('lac__D_e', formula='C3H5O3',\n name='D-Lactate', compartment='e', charge=-1)\n\nreaction = Reaction('LACDt')\nreaction.name = 'D-Lactate transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__D_e: -1.0,\n lac__D_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0036 Electron bifurcating lactate dehydrogenase lac_D_c + 2 nad_c + fdred_c -> pyr_c + 2 nadh + fdox_c\n\nfdred_c = Metabolite('fdred_c', formula='Fe8S8X',\n name='Ferredoxin (reduced) 2[4Fe-4S]', compartment='c', charge=-2)\nfdox_c = Metabolite('fdox_c', formula='Fe8S8X',\n name='Ferredoxin (oxidized) 2[4Fe-4S]', compartment='c', charge=0)\n\nreaction = Reaction('EB-iLDH-D')\nreaction.name = 'Electron bifurcating lactate dehydrogenase'\nreaction.subsystem = 'Lactate metabolism'\nreaction.lower_bound = 0 # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__D_c: -1.0,\n fdred_c: -1.0,\n nad_c: -2.0,\n pyr_c: 1.0,\n nadh_c: 2.0,\n fdox_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0037 D-Lactate exchange lac__D_e\n\nreaction = Reaction('EX_lac__D_e')\nreaction.name = 'D-Lactate exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__D_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Formate metabolism\n\n# R0038 Pyruvate formate lyase coa_c + pyr_c <-> accoa_c + for_c\n\nfor_c = Metabolite('for_c', formula='C1H1O2',\n name='Formate', compartment='c', charge=-1)\naccoa_c = Metabolite('accoa_c', formula='C23H34N7O17P3S',\n name='Acetyl-CoA', compartment='c', charge=-4)\ncoa_c = Metabolite('coa_c', formula='C21H32N7O16P3S',\n name='Coenzyme A', compartment='c', charge=-4)\n\nreaction = Reaction('PFL')\nreaction.name = 'Pyruvate formate lyase'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pyr_c: -1.0,\n coa_c: -1.0,\n accoa_c: 1.0,\n for_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0039 Formate exchange for_c <-> for_e\n\nfor_e = Metabolite('for_e', formula='C1H1O2',\n name='Formate', compartment='e', charge=-1)\n\n# for_e <->\n\nreaction = Reaction('EX_for_e')\nreaction.name = 'Formate exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({for_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Acetate Metabolism\n\n# R0040 Acetate kinase ac_c + atp_c <-> actp_c + adp_c\n\nac_c = Metabolite('ac_c', formula='C2H3O2', name='Acetate',\n compartment='c', charge=-1)\n\nreaction = Reaction('ACKr')\nreaction.name = 'Acetate kinase'\nreaction.subsystem = 'Acetate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ac_c: -1.0,\n atp_c: -1.0,\n actp_c: 1.0,\n adp_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0041 Acetate exchange ac_e <->\n\nac_e = Metabolite('ac_e', formula='C2H3O2', name='Acetate',\n compartment='e', charge=-1)\n\nreaction = Reaction('EX_ac_e')\nreaction.name = 'Acetate exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ac_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0042 Phosphotransacetylase accoa_c + pi_c <-> actp_c + coa_c\n\nreaction = Reaction('PTAr')\nreaction.name = 'Phosphotransacetylase'\nreaction.subsystem = 'Acetate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n pi_c: -1.0,\n actp_c: 1.0,\n coa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0043 Acteyl-CoA hydrolase accoa_c + h20_c -> ac_c + coa_c + h_c\n\nreaction = Reaction('ACOAH')\nreaction.name = 'Acteyl-CoA hydrolase'\nreaction.subsystem = 'Acetate Metabolism'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n h2o_c: -1.0,\n ac_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Pyruvate Oxidation\n\n# R0044 *Pyruvate flavodoxin oxidoreductase coa_c + pyr_c + fdox_c <-> accoa_c + co2_c + fdred_c + h_c\n\nco2_c = Metabolite('co2_c', formula='CO2', name='CO2',\n compartment='c', charge=0)\n\nreaction = Reaction('PFOR')\n# This reaction differs from BiGG database because a different ferredoxin is used and H+ is a product for mass and charge balance\nreaction.name = '*Pyruvate flavodoxin oxidoreductase'\nreaction.subsystem = 'Pyruvate Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({coa_c: -1.0,\n pyr_c: -1.0,\n fdox_c: -1.0,\n accoa_c: 1.0,\n co2_c: 1.0,\n fdred_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0045 Pyruvate dehdyrogenase coa_c + nad_c + pyr_c <-> accoa_c + co2_c + nadh_c\n\nreaction = Reaction('PDH')\n\n# This reaction differs from BiGG database because a different ferredoxin is used and H+ is a product for mass and charge balance\nreaction.name = 'Pyruvate dehdyrogenase'\nreaction.subsystem = 'Pyruvate Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({coa_c: -1.0,\n pyr_c: -1.0,\n nad_c: -1.0,\n accoa_c: 1.0,\n co2_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Reverse Beta Oxidation\n\n# Butyrate Production (Cycle 1)\n\n# R0046 Acetyl-CoA C-acetyltransferase 2.0 accoa_c <-> aacoa_c + coa_c\n\naacoa_c = Metabolite('aacoa_c', formula='C25H36N7O18P3S',\n name='Acetoacetyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('ACACT1r')\nreaction.name = 'Acetyl-CoA C-acetyltransferase'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -2.0,\n aacoa_c: 1.0,\n coa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0047 3-hydroxyacyl-CoA dehydrogenase (acetoacetyl-CoA) aacoa_c + h_c + nadh_c <-> 3hbcoa_c + nad_c\n\n_3hbcoa_c = Metabolite('_3hbcoa_c', formula='C25H38N7O18P3S',\n name='(S)-3-Hydroxybutanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('HACD1')\nreaction.name = '3-hydroxyacyl-CoA dehydrogenase (acetoacetyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({aacoa_c: -1.0,\n h_c: -1.0,\n nadh_c: -1.0,\n _3hbcoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0048 3-hydroxyacyl-CoA dehydratase (3-hydroxybutanoyl-CoA) 3hbcoa_c <-> b2coa_c + h2o_c\n\nb2coa_c = Metabolite('b2coa_c', formula='C25H36N7O17P3S',\n name='Crotonoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('ECOAH1')\nreaction.name = '3-hydroxyacyl-CoA dehydratase (3-hydroxybutanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3hbcoa_c: -1.0,\n b2coa_c: 1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0049 *Electron Bifurcating Acyl-CoA Dehydrogenase (C4) b2coa_c + 2 nadh_c + fdox_c <-> btcoa_c + 2 nad_c + fdred_c\n\nbtcoa_c = Metabolite('btcoa_c', formula='C25H38N7O17P3S',\n name='Butanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('EBACD1')\n# BiGG does not have an electron bifurcating acyl-CoA dehydrogenase reaction\nreaction.name = '*Electron Bifurcating Acyl-CoA Dehydrogenase (C4)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({b2coa_c: -1.0,\n nadh_c: -2.0,\n fdox_c: -1.0,\n btcoa_c: 1.0,\n nad_c: 2.0,\n fdred_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0050 Acyl-CoA dehydrogenase (butanoyl-CoA) b2coa_c + h_c + nadh_c <-> btcoa_c + nad_c\n\nreaction = Reaction('ACOAD1')\nreaction.name = \"Acyl-CoA dehydrogenase (butanoyl-CoA)\"\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({b2coa_c: -1.0,\n nadh_c: -1.0,\n h_c: -1.0,\n btcoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0051 *Acyl-CoA Hydrolase (C4:0) btcoa_c + h2o_c <-> but_c + coa_c + h_c\n\nbut_c = Metabolite('but_c', formula='C4H7O2',\n name='Butyrate (n-C4:0)', compartment='c', charge=-1)\n\nreaction = Reaction('ACHC4')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = '*Acyl-CoA Hydrolase (C4:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({btcoa_c: -1.0,\n h2o_c: -1.0,\n but_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0052 *CoA Transferase (C4:0-C2:0) btcoa_c + ac_c <-> but_c + accoa_c\n\nreaction = Reaction('CoATC4')\n# BiGG does not have this specific CoAT hydrolase reaction\nreaction.name = '*CoA Transferase (C4:0-C2:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({btcoa_c: -1.0,\n ac_c: -1.0,\n but_c: 1.0,\n accoa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0053 Butyrate exchange but_e <->\n\nbut_e = Metabolite('but_e', formula='C4H7O2',\n name='Butyrate (n-C4:0)', compartment='e', charge=-1)\n\nreaction = Reaction('EX_but_e')\nreaction.name = 'Butyrate exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({but_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Hexanoate Production (Cycle 2)\n\n# R0054 Butanoyl-CoA:acetyl-CoA C-butanoyltransferase accoa_c + btcoa_c <-> coa_c + 3ohcoa_c\n\n_3ohcoa_c = Metabolite('_3ohcoa_c', formula='C27H40N7O18P3S',\n name='3-Oxohexanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('ACACT2')\nreaction.name = 'Butanoyl-CoA:acetyl-CoA C-butanoyltransferase'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n btcoa_c: -1.0,\n _3ohcoa_c: 1.0,\n coa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0055 3-hydroxyacyl-CoA dehydrogenase (3-oxohexanoyl-CoA) _3ohcoa_c + h_c + nadh_c <-> _3hhcoa_c + nad_c\n\n_3hhcoa_c = Metabolite('_3hhcoa_c', formula='C27H42N7O18P3S',\n name='(S)-3-Hydroxyhexanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('HACD2')\nreaction.name = '3-hydroxyacyl-CoA dehydrogenase (3-oxohexanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3ohcoa_c: -1.0,\n h_c: -1.0,\n nadh_c: -1.0,\n _3hhcoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0056 3-hydroxyacyl-CoA dehydratase (3-hydroxyhexanoyl-CoA) _3hhcoa_c <-> h2o_c + hx2coa_c\n\nhx2coa_c = Metabolite('hx2coa_c', formula='C27H40N7O17P3S',\n name='Trans-Hex-2-enoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('ECOAH2')\nreaction.name = '3-hydroxyacyl-CoA dehydratase (3-hydroxyhexanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3hhcoa_c: -1.0,\n hx2coa_c: 1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0057 *Electron Bifurcating Acyl-CoA Dehydrogenase (C6) hx2coa_c + 2 nadh_c + fdox_c <-> hxcoa_c + 2 nad_c + fdred_c\n\nhxcoa_c = Metabolite('hxcoa_c', formula='C27H42N7O17P3S',\n name='Hexanoyl-CoA (n-C6:0CoA)', compartment='c', charge=-4)\n\nreaction = Reaction('EBACD2')\n# BiGG does not have an electron bifurcating acyl-CoA dehydrogenase reaction\nreaction.name = '*Electron Bifurcating Acyl-CoA Dehydrogenase (C6)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hx2coa_c: -1.0,\n nadh_c: -2.0,\n fdox_c: -1.0,\n hxcoa_c: 1.0,\n nad_c: 2.0,\n fdred_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0058 Acyl-CoA dehydrogenase (hexanoyl-CoA) h_c + hx2coa_c + nadh_c <-> hxcoa_c + nad_c\n\nreaction = Reaction('ACOAD2')\nreaction.name = \"Acyl-CoA dehydrogenase (hexanoyl-CoA)\"\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hx2coa_c: -1.0,\n nadh_c: -1.0,\n h_c: -1.0,\n hxcoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0059 *Acyl-CoA Hydrolase (C6:0) hxcoa_c + h2o_c <-> hxa_c + coa_c + h_c\n\nhxa_c = Metabolite('hxa_c', formula='C6H11O2',\n name='Hexanoate (n-C6:0)', compartment='c', charge=-1)\n\nreaction = Reaction('ACH-C6')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = '*Acyl-CoA Hydrolase (C6:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hxcoa_c: -1.0,\n h2o_c: -1.0,\n hxa_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0060 *CoA Transferase (C6:0-C2:0) hxcoa_c + ac_c <-> hxa_c + accoa_c\n\nreaction = Reaction('CoATC6')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = '*CoA Transferase (C6:0-C2:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hxcoa_c: -1.0,\n ac_c: -1.0,\n hxa_c: 1.0,\n accoa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0061 Hexanoate exchange hxa_e <->\n\nhxa_e = Metabolite('hxa_e', formula='C6H11O2',\n name='Hexanoate (n-C6:0)', compartment='e', charge=-1)\n\nreaction = Reaction('EX_hxa_e')\nreaction.name = 'Hexanoate exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hxa_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Octanoate Production (Cycle 3)\n\n# R0062 Hexanoyl-CoA:acetyl-CoA C-acyltransferase accoa_c + hxcoa_c <-> coa_c + 3oocoa_c\n\n_3oocoa_c = Metabolite('_3oocoa_c', formula='C29H44N7O18P3S',\n name='3-Oxooctanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('ACACT3')\nreaction.name = 'Hexanoyl-CoA:acetyl-CoA C-acyltransferase'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n hxcoa_c: -1.0,\n _3oocoa_c: 1.0,\n coa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0063 3-hydroxyacyl-CoA dehydrogenase (3-oxooctanoyl-CoA) _3oocoa_c + h_c + nadh_c <-> _3hocoa_c + nad_c\n\n_3hocoa_c = Metabolite('_3hocoa_c', formula='C29H46N7O18P3S',\n name='(S)-3-Hydroxyoctanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('HACD3')\nreaction.name = '3-hydroxyacyl-CoA dehydrogenase (3-oxooctanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3oocoa_c: -1.0,\n h_c: -1.0,\n nadh_c: -1.0,\n _3hocoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0064 3-hydroxyacyl-CoA dehydratase (3-hydroxyoctanoyl-CoA) _3hocoa_c <-> h2o_c + oc2coa_c\n\noc2coa_c = Metabolite('oc2coa_c', formula='C29H44N7O17P3S',\n name='Trans-Oct-2-enoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('ECOAH3')\nreaction.name = '3-hydroxyacyl-CoA dehydratase (3-hydroxyoctanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3hocoa_c: -1.0,\n oc2coa_c: 1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0065 *Electron Bifurcating Acyl-CoA Dehydrogenase (C8) oc2coa_c + 2 nadh_c + fdox_c <-> occoa_c + 2 nad_c + fdred_c\n\noccoa_c = Metabolite('occoa_c', formula='C29H46N7O17P3S',\n name='Octanoyl-CoA (n-C8:0CoA)', compartment='c', charge=-4)\n\nreaction = Reaction('EBACD3')\n# BiGG does not have an electron bifurcating acyl-CoA dehydrogenase reaction\nreaction.name = '*Electron Bifurcating Acyl-CoA Dehydrogenase (C8)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({oc2coa_c: -1.0,\n nadh_c: -2.0,\n fdox_c: -1.0,\n occoa_c: 1.0,\n nad_c: 2.0,\n fdred_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0066 Acyl-CoA dehydrogenase (octanoyl-CoA) h_c + oc2coa_c + nadh_c <-> occoa_c + nad_c\n\nreaction = Reaction('ACOAD3')\nreaction.name = \"Acyl-CoA dehydrogenase (octanoyl-CoA)\"\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({oc2coa_c: -1.0,\n nadh_c: -1.0,\n h_c: -1.0,\n occoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0067 *Acyl-CoA Hydrolase (C8:0) occoa_c + h2o_c <-> octa_c + coa_c + h_c\n\nocta_c = Metabolite('octa_c', formula='C8H15O2',\n name='Octanoate (n-C8:0)', compartment='c', charge=-1)\n\nreaction = Reaction('ACH-C8')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = '*Acyl-CoA Hydrolase (C8:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({occoa_c: -1.0,\n h2o_c: -1.0,\n octa_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0068 CoA Transferase (C8:0-C2:0) occoa_c + ac_c <-> octa_c + accoa_c\n\nreaction = Reaction('CoATC8')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = 'CoA Transferase (C8:0-C2:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({occoa_c: -1.0,\n ac_c: -1.0,\n octa_c: 1.0,\n accoa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0069 Octanoate exchange octa_e <->\n\nocta_e = Metabolite('octa_e', formula='C8H15O2',\n name='Octanoate (n-C8:0)', compartment='e', charge=-1)\n\nreaction = Reaction('EX_octa_e')\nreaction.name = 'Octanoate exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({octa_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Propionate Production\n\n# Acryloyl-CoA Pathway (Metacyc: Pyruvate fermentation to propanoate II)\n\n# lactate CoA transferase\n\n# R0070 Lactate CoA transferase lac__D_c + ppcoa_c: -1.0 <-> ppa_c + laccoa_c\n\nppa_c = Metabolite('ppa_c', formula='C3H5O2',\n name='Propionate (n-C3:0)', compartment='c', charge=-1)\nppcoa_c = Metabolite('ppcoa_c', formula='C24H36N7O17P3S',\n name='Propanoyl-CoA', compartment='c', charge=-4)\nlaccoa_c = Metabolite('laccoa_c', formula='C24H36N7O18P3S',\n name='Lactoyl-CoA', compartment='c', charge=-4)\n\n# Reaction not in BIGG database.\nreaction = Reaction('LCT')\nreaction.name = 'Lactate CoA transferase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__D_c: -1.0,\n ppcoa_c: -1.0,\n ppa_c: 1.0,\n laccoa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0071 Propionate exchange ppa_e <->\n\nppa_e = Metabolite('ppa_e', formula='C3H5O2',\n name='Propionate (n-C3:0)', compartment='e', charge=-1)\n\nreaction = Reaction('EX_ppa_e')\nreaction.name = 'Propionate exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ppa_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0072 Lactoyl CoA dehydratase laccoa_c <-> pp2coa_c + h2o_c\n\npp2coa_c = Metabolite('pp2coa_c', formula='C24H34N7O17P3S',\n name='Acrylyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('LCD')\nreaction.name = 'Lactoyl CoA dehydratase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({laccoa_c: -1.0,\n pp2coa_c: 1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# R0073 Acryloyl-CoA reductase pp2coa_c + h_c + nadh_c <-> nad_c + ppcoa_c\n\n# Reaction not in BIGG Database\nreaction = Reaction('ACR')\nreaction.name = 'Acryloyl-CoA reductase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pp2coa_c: -1.0,\n h_c: -1.0,\n nadh_c: -1.0,\n nad_c: 1.0,\n ppcoa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0074 Propionate CoA Transferase ppcoa_c + ac_c <-> accoa_c + ppa_c\n\n# Reaction not in BIGG Database\nreaction = Reaction('PCT')\nreaction.name = 'Propionate CoA Transferase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ppcoa_c: -1.0,\n ac_c: -1.0,\n accoa_c: 1.0,\n ppa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Methylmalonyl-CoA Pathway (Metacyc: Pyruvate fermentation to propanoate I)\"\"\"\n\n# R0075 Methylmalonyl-CoA Carboxyltransferase mmcoa__S_c + pyr_c <-> ppcoa_c + oaa_c\n\nmmcoa__S_c = Metabolite('mmcoa__S_c', formula='C25H35N7O19P3S',\n name='(S)-Methylmalonyl-CoA', compartment='c', charge=-5)\noaa_c = Metabolite('oaa_c', formula='C4H2O5',\n name='Oxaloacetate', compartment='c', charge=-2)\n\n# Reaction not in BIGG database\nreaction = Reaction('MCC')\nreaction.name = 'Methylmalonyl-CoA Carboxyltransferase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({mmcoa__S_c: -1.0,\n pyr_c: -1.0,\n ppcoa_c: 1.0,\n oaa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# R0076 Malate dehydrogenase oaa_c + nadh_c + h_c <-> nad_c + mal__L_c\n\nmal__L_c = Metabolite('mal__L_c', formula='C4H4O5',\n name='L-Malate', compartment='c', charge=-2)\n\nreaction = Reaction('MDH')\nreaction.name = 'Malate dehydrogenase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({oaa_c: -1.0,\n nadh_c: -1.0,\n h_c: -1.0,\n nad_c: 1.0,\n mal__L_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# R0077 Fumarate Reductase NADH fum_c +nadh_c +h_c -> nad_c + succ_c\n\nsucc_c = Metabolite('succ_c', formula='C4H4O4',\n name='Succinate', compartment='c', charge=-2)\nfum_c = Metabolite('fum_c', formula='C4H2O4',\n name='Fumarate', compartment='c', charge=-2)\n\nreaction = Reaction('FRDx')\nreaction.name = 'Fumarate Reductase NADH'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fum_c: -1.0,\n nadh_c: -1.0,\n h_c: -1.0,\n nad_c: 1.0,\n succ_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0078 Propanoyl-CoA: succinate CoA-transferase succ_c + ppcoa_c <-> ppa_c + succoa_c\n\nsuccoa_c = Metabolite('succoa_c', formula='C25H35N7O19P3S',\n name='Succinyl-CoA', compartment='c', charge=-5)\n\nreaction = Reaction('PPCSCT')\nreaction.name = 'Propanoyl-CoA: succinate CoA-transferase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({succ_c: -1.0,\n ppcoa_c: -1.0,\n ppa_c: 1.0,\n succoa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0079 Methylmalonyl-CoA mutase succoa_c <-> mmcoa__R_c\n\nmmcoa__R_c = Metabolite('mmcoa__R_c', formula='C25H35N7O19P3S',\n name='(R)-Methylmalonyl-CoA', compartment='c', charge=-5)\n\nreaction = Reaction('MMM2')\nreaction.name = 'Methylmalonyl-CoA mutase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({succoa_c: -1.0,\n mmcoa__R_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0080 Methylmalonyl-CoA epimerase mmcoa__R_c <-> mmcoa__S_c\n\nreaction = Reaction('MME')\nreaction.name = 'Methylmalonyl-CoA epimerase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({mmcoa__R_c: -1.0,\n mmcoa__S_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Odd-chain reverse beta-oxidation\n\n# Pentanoate production (Cycle 1)\n\n# R0081 Acetyl-CoA C-acyltransferase (3-oxovaleryl-CoA) accoa_c + ppcoa_c <-> _3optcoa_c + coa_c\n\n_3optcoa_c = Metabolite('_3optcoa_c', formula='C26H38N7O18P3S',\n name='3-Ocopentanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('VCACT')\nreaction.name = 'Acetyl-CoA C-acyltransferase (3-oxovaleryl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n ppcoa_c: -1.0,\n _3optcoa_c: 1.0,\n coa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0082 3-hydroxyacyl-CoA dehydrogenase (3-hydroxyacyl-CoA dehydrogenase (3-oxovaleryl-CoA)) h_c + nadh_c + _3optcoa_c <-> nad_c + _3hptcoa_c\n\n_3hptcoa_c = Metabolite('_3hptcoa_c', formula='C26H40N7O18P3S',\n name='3-Hydroxypentoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('HVCD')\nreaction.name = '3-hydroxyacyl-CoA dehydrogenase (3-hydroxyacyl-CoA dehydrogenase (3-oxovaleryl-CoA))'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3optcoa_c: -1.0,\n h_c: -1.0,\n nadh_c: -1.0,\n _3hptcoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0083 3-hydroxyacyl-CoA dehydratase (3-hydroxypentanoyl-CoA) _3hptcoa_c <-> h2o_c + pt2coa_c\n\npt2coa_c = Metabolite('pt2coa_c', formula='C26H38N7O17P3S',\n name='Pent-2-enoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('VECOAH')\nreaction.name = '3-hydroxyacyl-CoA dehydratase (3-hydroxypentanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3hptcoa_c: -1.0,\n pt2coa_c: 1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0084 *Electron Bifurcating Acyl-CoA dehydrogenase (pentanoyl-CoA) pt2coa_c + 2 nadh_c + fdox_c <-> ptcoa_c + 2 nad_c + fdred_c\n\nptcoa_c = Metabolite('ptcoa_c', formula='C26H40N7O17P3S',\n name='Pentanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('EBVCD')\n# BiGG does not have an electron bifurcating acyl-CoA dehydrogenase reaction\nreaction.name = '*Electron Bifurcating Acyl-CoA dehydrogenase (pentanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pt2coa_c: -1.0,\n nadh_c: -2.0,\n fdox_c: -1.0,\n ptcoa_c: 1.0,\n nad_c: 2.0,\n fdred_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0085 Acyl-CoA dehydrogenase (pentanoyl-CoA) h_c + pt2coa_c + nadh_c <-> ptcoa_c + nad_c\n\nreaction = Reaction('VCOAD')\nreaction.name = \"Acyl-CoA dehydrogenase (pentanoyl-CoA)\"\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pt2coa_c: -1.0,\n nadh_c: -1.0,\n h_c: -1.0,\n ptcoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0086 *Acyl-CoA Hydrolase (C5:0) ptcoa_c + h2o_c <-> pta_c + coa_c + h_c\n\npta_c = Metabolite('pta_c', formula='C5H9O2',\n name='Pentanoate', compartment='c', charge=-1)\n\nreaction = Reaction('ACHC5')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = '*Acyl-CoA Hydrolase (C5:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ptcoa_c: -1.0,\n h2o_c: -1.0,\n pta_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0087 *CoA Transferase (C5:0-C2:0) ptcoa_c + ac_c <-> pta_c + accoa_c\n\nreaction = Reaction('CoATC5')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = '*CoA Transferase (C5:0-C2:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ptcoa_c: -1.0,\n ac_c: -1.0,\n pta_c: 1.0,\n accoa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0088 Pentanoate exchange pta_e <->\n\npta_e = Metabolite('pta_e', formula='C5H9O2',\n name='Pentanoate', compartment='e', charge=-1)\n\nreaction = Reaction('EX_pta_e')\nreaction.name = 'Pentanoate exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pta_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Heptanoate production (Cycle 2)\n\n# R0089 Acetyl-CoA C-acyltransferase (3-oxoheptanoyl-CoA) accoa_c + ptcoa_c <-> coa_c + _3optcoa_c\n\n# 3-Oxoheptanoyl-CoA is only in BiGG as M00877. Will define as _3ohtcoa_c\n_3ohtcoa_c = Metabolite('_3ohtcoa_c', formula='C28H42N7O18P3S',\n name='3-Oxoheptanoyl-CoA', compartment='c', charge=-4)\n\n# Reaction not in BiGG Database\nreaction = Reaction('VCACT2')\nreaction.name = 'Acetyl-CoA C-acyltransferase (3-oxoheptanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n ptcoa_c: -1.0,\n _3ohtcoa_c: 1.0,\n coa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0090 3-hydroxyacyl-CoA dehydrogenase (3-oxoheptanoyl-CoA) h_c + nadh_c + _3ohtcoa_c <-> nad_c + _3hhtcoa_c\n\n_3hhtcoa_c = Metabolite('_3hhtcoa_c', formula='C28H44N7O18P3S',\n name='3-Hydroxyheptanoyl-CoA', compartment='c', charge=-4)\n\n# Reaction is not in BiGG Database\nreaction = Reaction('HVCD2')\nreaction.name = '3-hydroxyacyl-CoA dehydrogenase (3-oxoheptanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3ohtcoa_c: -1.0,\n h_c: -1.0,\n nadh_c: -1.0,\n _3hhtcoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0091 3-hydroxyacyl-CoA dehydratase (3-hydroxyheptanoyl-CoA) _3hhtcoa_c <-> h2o_c + ht2coa_c\n\nht2coa_c = Metabolite('ht2coa_c', formula='C28H42N7O17P3S',\n name='Hept-2-enoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('VECOAH2')\nreaction.name = '3-hydroxyacyl-CoA dehydratase (3-hydroxyheptanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3hhtcoa_c: -1.0,\n ht2coa_c: 1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0092 *Electron Bifurcating Acyl-CoA dehydrogenase (heptanoyl-CoA) ht2coa_c + 2 nadh_c + fdox_c <-> hptcoa_c + 2 nad_c + fdred_c\n\nhptcoa_c = Metabolite('hptcoa_c', formula='C28H44N7O17P3S',\n name='Heptanoyl-CoA', compartment='c', charge=-4)\n\nreaction = Reaction('EBVCD2')\n# BiGG does not have an electron bifurcating acyl-CoA dehydrogenase reaction\nreaction.name = '*Electron Bifurcating Acyl-CoA dehydrogenase (heptanoyl-CoA)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ht2coa_c: -1.0,\n nadh_c: -2.0,\n fdox_c: -1.0,\n hptcoa_c: 1.0,\n nad_c: 2.0,\n fdred_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0093 Acyl-CoA dehydrogenase (heptanoyl-CoA) h_c + ht2coa_c + nadh_c <-> hptcoa_c + nad_c\n\nreaction = Reaction('VCOAD2')\nreaction.name = \"Acyl-CoA dehydrogenase (heptanoyl-CoA)\"\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ht2coa_c: -1.0,\n nadh_c: -1.0,\n h_c: -1.0,\n hptcoa_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0094 *Acyl-CoA Hydrolase (C7:0) hptcoa_c + h2o_c <-> hpta_c + coa_c + h_c\n\nhpta_c = Metabolite('hpta_c', formula='C7H13O2',\n name='Pentanoate', compartment='c', charge=-1)\n\nreaction = Reaction('ACH-C7')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = '*Acyl-CoA Hydrolase (C7:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hptcoa_c: -1.0,\n h2o_c: -1.0,\n hpta_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0095 *CoA Transferase (C7:0-C2:0) hptcoa_c + ac_c <-> hpta_c + accoa_c\n\nreaction = Reaction('CoATC7')\n# BiGG does not have this specific acyl-CoA hydrolase reaction\nreaction.name = '*CoA Transferase (C7:0-C2:0)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hptcoa_c: -1.0,\n ac_c: -1.0,\n hpta_c: 1.0,\n accoa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0096 Heptanoate exchange hpta_e <->\n\nhpta_e = Metabolite('hpta_e', formula='C7H13O2',\n name='Heptanoate', compartment='e', charge=-1)\n\nreaction = Reaction('EX_hpta_e')\nreaction.name = 'Heptanoate exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hpta_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Ethanol Utilization\n\n# R0097 Ethanol exchange etoh_e <->\n\netoh_e = Metabolite('etoh_e', formula='C2H6O', name='Ethanol', compartment='e')\n\nreaction = Reaction('EX_etoh_e')\nreaction.name = 'Ethanol exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({etoh_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0098 Alcohol dehydrogenase (ethanol) etoh_c + nad_c <-> acald_c + h_c + nadh_c\n\netoh_c = Metabolite('etoh_c', formula='C2H6O', name='Ethanol', compartment='c')\nacald_c = Metabolite('acald_c', formula='C2H4O',\n name='Acetaldehyde', compartment='c')\n\nreaction = Reaction('ALCD2x')\nreaction.name = 'Alcohol dehydrogenase (ethanol)'\nreaction.subsystem = 'Ethanol Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({etoh_c: -1.0,\n nad_c: -1.0,\n acald_c: 1.0,\n h_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0099 Acetaldehyde dehydrogenase (acetylating) acald_c + coa_c + nad_c <-> accoa_c + h_c + nadh_c\n\nreaction = Reaction('ACALD')\nreaction.name = 'Acetaldehyde dehydrogenase (acetylating)'\nreaction.subsystem = 'Ethanol Utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({acald_c: -1.0,\n coa_c: -1.0,\n nad_c: -1.0,\n accoa_c: 1.0,\n h_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Glycerol Utilization\n\n# R0100 Glycerol Exchange glyc_e <->\n\nglyc_e = Metabolite('glyc_e', formula='C3H8O3',\n name='Glycerol', compartment='e', charge=0)\nglyc_c = Metabolite('glyc_c', formula='C3H8O3',\n name='Glycerol', compartment='c', charge=0)\n\nreaction = Reaction('EX_glyc_e')\nreaction.name = 'Glycerol Exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({glyc_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0101 Glycerol transport glyc_e <-> glyc_c\n\nreaction = Reaction('glyct')\nreaction.name = 'Glycerol transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({glyc_e: -1.0,\n glyc_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0102 Glycerol kinase atp_c + glyc_c <-> adp_c + glyc3p_c + h_c\n\nglyc3p_c = Metabolite('glyc3p_c', formula='C3H7O6P',\n name='Glycerol 3-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('GLYK')\nreaction.name = 'Glycerol kinase'\nreaction.subsystem = 'Glycerol utilization'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({glyc_c: -1.0,\n atp_c: -1.0,\n adp_c: 1.0,\n glyc3p_c: 1.0,\n h_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0103 Glycerol-3-phosphate dehydrogenase (NAD) dhap_c + h_c + nadh_c <-> glyc3p_c + nad_c\n\nreaction = Reaction('G3PD1')\nreaction.name = 'Glycerol-3-phosphate dehydrogenase (NAD)'\nreaction.subsystem = 'Glycerol utilization'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({dhap_c: -1.0,\n h_c: -1.0,\n nadh_c: -1.0,\n glyc3p_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Hydrogen Generation\n\nh2_c = Metabolite('h2_c', formula='H2', name='Hydrogen',\n compartment='c', charge=0)\n\n# R0104 (FeFe)-hydrogenase, cytoplasm fdred_c + 2.0 h_c <-> h2_c + fdox_c\n\nreaction = Reaction('HYD1')\n# The reaction in BiGG uses a different ferredoxin\n# BiGG reaction is not balanced for H\nreaction.name = '(FeFe)-hydrogenase, cytoplasm'\nreaction.subsystem = 'Hydrogen Generation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fdred_c: -1.0,\n h_c: -2.0,\n h2_c: 1.0,\n fdox_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0105 Energy conserving hydrogenase fdred_c + 3.0 h_c <-> h2_c + fdox_c + h_i\n\nh_i = Metabolite('h_i', formula='H', name='H+', compartment='i', charge=1)\n\nreaction = Reaction('ECH')\n# The reaction in BiGG uses a different ferredoxin\n# BiGG reaction is not balanced for H\nreaction.name = 'Energy conserving hydrogenase'\nreaction.subsystem = 'Hydrogen Generation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fdred_c: -1.0,\n h_c: -3.0,\n h2_c: 1.0,\n fdox_c: 1.0,\n h_i: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0106 Electron confurcating hydrogenase fdred_c + nadh_c + 3.0 h_c <-> 2 h2_c + fdox_c + nad_c\n\nreaction = Reaction('HYDABC')\n# The reaction in BiGG uses a different ferredoxin\n# BiGG reaction is not balanced for H\nreaction.name = 'Electron confurcating hydrogenase'\nreaction.subsystem = 'Hydrogen Generation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fdred_c: -1.0,\n nadh_c: -1.0,\n h_c: -3.0,\n h2_c: 2.0,\n fdox_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n# Adding this reaction with the ferredoxin hydrogenase reaction creates a loop in the model\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0107 Hydrogen transport h2_e <-> h2_c\n\nh2_e = Metabolite('h2_e', formula='H2', name='Hydrogen',\n compartment='e', charge=0)\n\nreaction = Reaction('H2t')\nreaction.name = 'Hydrogen transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2_e: -1.0,\n h2_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0108 H2 exchange h2_e <->\n\nreaction = Reaction('EX_h2_e')\nreaction.name = 'H2 exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Homoacetogensis\n\n# R0109 Formate dehydrogenase for_c + nad_c <-> co2_c + nadh_c\n\nreaction = Reaction('FDH')\nreaction.name = 'Formate dehydrogenase'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({for_c: -1.0,\n nad_c: -1.0,\n co2_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0110 Formate-tetrahydrofolate ligase atp_c + for_c + thf_c -> _10fthf_c + adp_c + pi_c\n\nthf_c = Metabolite('thf_c', formula='C19H21N7O6',\n name='5,6,7,8-Tetrahydrofolate', compartment='c', charge=-2)\n_10fthf_c = Metabolite('_10fthf_c', formula='C20H21N7O7',\n name='10-Formyltetrahydrofolate', compartment='c', charge=-2)\n\nreaction = Reaction('FTHFLi')\nreaction.name = 'Formate-tetrahydrofolate ligase'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({for_c: -1.0,\n atp_c: -1.0,\n thf_c: -1.0,\n _10fthf_c: 1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0111 Methenyltetrahydrofolate cyclohydrolase _10fthf_c + h_c <-> h2o_c + methf_c\n\nmethf_c = Metabolite('methf_c', formula='C20H20N7O6',\n name='5,10-Methenyltetrahydrofolate', compartment='c', charge=-1)\n\nreaction = Reaction('MTHFC')\nreaction.name = 'Methenyltetrahydrofolate cyclohydrolase'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_10fthf_c: -1.0,\n h_c: -1.0,\n h2o_c: 1.0,\n methf_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0112 Methylenetetrahydrofolate dehydrogenase NAD methf_c + nadh_c <-> mlthf_c + nad_c\n\nmlthf_c = Metabolite('mlthf_c', formula='C20H21N7O6',\n name='5,10-Methylenetetrahydrofolate', compartment='c', charge=-2)\n\nreaction = Reaction('MTHFD2i')\nreaction.name = 'Methylenetetrahydrofolate dehydrogenase NAD'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({methf_c: -1.0,\n nadh_c: -1.0,\n mlthf_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0113 5,10-methylenetetrahydrofolate reductase (NADH) 2.0 h_c + mlthf_c + nadh_c -> _5mthf_c + nad_c\n\n_5mthf_c = Metabolite('_5mthf_c', formula='C20H24N7O6',\n name='5-Methyltetrahydrofolate', compartment='c', charge=-1)\n\nreaction = Reaction('MTHFR2')\nreaction.name = '5,10-methylenetetrahydrofolate reductase (NADH)'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({mlthf_c: -1.0,\n nadh_c: -1.0,\n h_c: -2.0,\n _5mthf_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0114 Methyltetrahydrofolate:corrinoid/iron-sulfur protein methyltransferase (MeTr) _5mthf_c + cfesp_c -> thf_c + mecfsp_c\n\ncfesp_c = Metabolite('cfesp_c', formula='C19CoN4R21',\n name='Corrinoid Iron sulfur protein', compartment='c', charge=-1)\nmecfsp_c = Metabolite('mecfsp_c', formula='C20H3CoN4R21',\n name='Methylcorrinoid iron sulfur protein', compartment='c', charge=0)\n\nreaction = Reaction('METR')\nreaction.name = 'Methyltetrahydrofolate:corrinoid/iron-sulfur protein methyltransferase (MeTr)'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_5mthf_c: -1.0,\n cfesp_c: -1.0,\n thf_c: 1.0,\n mecfsp_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0115 Carbon monoxide dehydrogenase / acetyl-CoA synthase 2 co2_c + 2.0 h_c + fdred_c <-> h2o_c + co_c + fdox_c\n\nco_c = Metabolite('co_c', formula='CO', name='Carbon monoxide',\n compartment='c', charge=0)\n\n# BIGG uses a differnt form of ferredoxin\nreaction = Reaction('CODH4')\nreaction.name = 'Carbon monoxide dehydrogenase / acetyl-CoA synthase 2'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({co2_c: -1.0,\n h_c: -2.0,\n fdred_c: -1.0,\n h2o_c: 1.0,\n co_c: 1.0,\n fdox_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# R0116 Acetyl-CoA synthase co_c + coa_c + mecfsp_c -> accoa_c + cfesp_c + h_c\n\nreaction = Reaction('*ACSWL')\nreaction.name = 'Acetyl-CoA synthase'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({co_c: -1.0,\n coa_c: -1.0,\n mecfsp_c: -1.0,\n accoa_c: 1.0,\n cfesp_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Energy Generation\n\n# R0117 *Ferredoxin:NAD oxidoreductase (2 protons translocated) 3.0 h_c + nad_c + fdred_c <-> nadh_c + 2.0 h_i + fdox_c\n\nreaction = Reaction('RNF1')\n# This reaction differs from the BiGG reaction because a different type of ferredoxin is used.\nreaction.name = '*Ferredoxin:NAD oxidoreductase (2 protons translocated)'\nreaction.subsystem = 'Energy Generation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h_c: -3.0,\n nad_c: -1.0,\n fdred_c: -1.0,\n nadh_c: 1.0,\n h_i: 2.0,\n fdox_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0118 *ATP Synthase adp_c + pi_c + 4.0 h_i <-> atp_c + 3.0 h_c + h2o_c\n\nreaction = Reaction('ATPS4r')\n# This reaction differs from the BiGG reaction because this model assumes a different compartment for ion motive force generation\nreaction.name = '*ATP Synthase'\nreaction.subsystem = 'Energy Generation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({adp_c: -1.0,\n pi_c: -1.0,\n h_i: -4.0,\n atp_c: 1.0,\n h_c: 3.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Other\n\n# R0119 H2O transport h2o_e <-> h2o_c\n\nh2o_e = Metabolite('h2o_e', formula='H2O', name='H2O',\n compartment='e', charge=0)\n\nreaction = Reaction('H2Ot')\nreaction.name = 'H2O transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2o_e: -1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0120 H2O exchange h2o_e <->\n\nreaction = Reaction('EX_h2o_e')\nreaction.name = 'H2O exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2o_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0121 H+ exchange h_e <->\n\nh_e = Metabolite('h_e', formula='H', name='H+', compartment='e', charge=1)\n\nreaction = Reaction('EX_h_e')\nreaction.name = 'H+ exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0122 CO2 transport co2_e <-> co2_c\n\nco2_e = Metabolite('co2_e', formula='CO2', name='CO2',\n compartment='e', charge=0)\n\nreaction = Reaction('co2t')\nreaction.name = 'CO2 transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({co2_e: -1.0,\n co2_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0123 CO2 exchange co2_e <->\n\nreaction = Reaction('EX_co2_e')\nreaction.name = 'CO2 exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({co2_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Bifurcated TCA Cycle\n\n\n# R0124 Phosphoenolpyruvate carboxykinase atp_c + oaa_c -> adp_c + co2_c + pep_c\n\nreaction = Reaction('PPCK')\nreaction.name = 'Phosphoenolpyruvate carboxykinase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n oaa_c: -1.0,\n pep_c: 1.0,\n adp_c: 1.0,\n co2_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Acetyl-CoA to OAA and Fumarate\n\n# R0125 Phosphoenolpyruvate carboxylase co2_c + h2o_c + pep_c <-> h_c + oaa_c + pi_c\n\nreaction = Reaction('PPC')\nreaction.name = 'Phosphoenolpyruvate carboxylase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({co2_c: -1.0,\n h2o_c: -1.0,\n pep_c: -1.0,\n h_c: 1.0,\n oaa_c: 1.0,\n pi_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0126 Citrate synthase accoa_c + h2o_c + oaa_c -> cit_c + coa_c + h_c\n\ncit_c = Metabolite('cit_c', formula='C6H5O7',\n name='Citrate', compartment='c', charge=-3)\n\nreaction = Reaction('CS')\nreaction.name = 'Citrate synthase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n h2o_c: -1.0,\n oaa_c: -1.0,\n cit_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0127 Aconitate hydratase cit_c -> icit_c\n\nicit_c = Metabolite('icit_c', formula='C6H5O7',\n name='Isocitrate', compartment='c', charge=-3)\n\nreaction = Reaction('ACONT')\nreaction.name = 'Aconitate hydratase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({cit_c: -1.0,\n icit_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0128 Isocitrate dehydrogenase (NAD) icit_c + nad_c <-> akg_c + co2_c + nadh_c\n\nakg_c = Metabolite('akg_c', formula='C5H4O5',\n name='2-Oxoglutarate', compartment='c', charge=-2)\n\nreaction = Reaction('ICDHx')\nreaction.name = 'Isocitrate dehydrogenase (NAD)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({icit_c: -1.0,\n nad_c: -1.0,\n akg_c: 1.0,\n co2_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0129 REMOVED - DUPLICATE WITH REACTION R0076 -- Malate dehydrogenase mal__L_c + nad_c <-> h_c + nadh_c + oaa_c\n\n\n# R0130 Fumarase fum_c + h2o_c <-> mal__L_c\n\nreaction = Reaction('FUM')\nreaction.name = 'Fumarase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fum_c: -1.0,\n h2o_c: -1.0,\n mal__L_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0131 Acetyl-CoA synthetase ac_c + atp_c + coa_c -> accoa_c + amp_c + ppi_c\n\nppi_c = Metabolite('ppi_c', formula='HO7P2',\n name='Diphosphate', compartment='c', charge=-3)\n\nreaction = Reaction('ACS')\nreaction.name = 'Acetyl-CoA synthetase'\nreaction.subsystem = 'Acetate metabolism'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ac_c: -1.0,\n atp_c: -1.0,\n coa_c: -1.0,\n accoa_c: 1.0,\n amp_c: 1.0,\n ppi_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Gluconeogenic TCA\n\n# R0132 ATP Citrate Lyase coa_c + atp_c + cit_c -> accoa_c + oaa_c + adp_c + pi_c\n\nreaction = Reaction('ACITL')\nreaction.name = 'ATP Citrate Lyase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({coa_c: -1.0,\n atp_c: -1.0,\n cit_c: -1.0,\n accoa_c: 1.0,\n oaa_c: 1.0,\n adp_c: 1.0,\n pi_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0133 Malic Enzyme (NAD) mal__L_c + nad_c -> co2_c + nadh_c + pyr_c\n\nreaction = Reaction('ME1')\nreaction.name = 'Malic Enzyme (NAD)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({mal__L_c: -1.0,\n nad_c: -1.0,\n pyr_c: 1.0,\n nadh_c: 1.0,\n co2_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Glyoxylate Cycle\n\n# R0134 Isocitrate lyase icit_c -> glx_c + succ_c\n\nglx_c = Metabolite('glx_c', formula='C2HO3',\n name='Glyoxylate', compartment='c', charge=-1)\n\nreaction = Reaction('ICL')\nreaction.name = 'Isocitrate lyase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({icit_c: -1.0,\n glx_c: 1.0,\n succ_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0135 Malate synthase accoa_c + glx_c + h2o_c <-> coa_c + h_c + mal__L_c\n\nreaction = Reaction('HAO_MALS')\nreaction.name = 'Malate synthase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n glx_c: -1.0,\n h2o_c: -1.0,\n coa_c: 1.0,\n h_c: 1.0,\n mal__L_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# NADH/ NADPH Conversions\n\n# R0136 NAD kinase atp_c + nad_c <-> adp_c + h_c + nadp_c\n\nnadp_c = Metabolite('nadp_c', formula='C21H25N7O17P3',\n name='Nicotinamide adenine dinucleotide phosphate', compartment='c', charge=-3)\n\nreaction = Reaction('NADK')\nreaction.name = 'NAD kinase'\nreaction.subsystem = 'NADH/ NADPH Conversions'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n nad_c: -1.0,\n adp_c: 1.0,\n h_c: 1.0,\n nadp_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0137 NAD(P) transhydrogenase nadh_c + nadp_c + 2.0 h_i -> 2.0 h_c + nad_c + nadph_c\n\nnadph_c = Metabolite('nadph_c', formula='C21H26N7O17P3',\n name='Nicotinamide adenine dinucleotide phosphate - reduced', compartment='c', charge=-4)\n\nreaction = Reaction('THD2')\nreaction.name = 'NAD(P) transhydrogenase'\nreaction.subsystem = 'NADH/ NADPH Conversions'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({nadh_c: -1.0,\n nadp_c: -1.0,\n h_i: -2.0,\n h_c: 2.0,\n nad_c: 1.0,\n nadph_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Nitrogen and Sulfur Import\n\n# R0138 Ammonium Exchange nh4_e <->\n\nnh4_e = Metabolite('nh4_e', formula='H4N', name='H2O',\n compartment='e', charge=1)\n\nreaction = Reaction('EX_nh4_e')\nreaction.name = 'Ammonium Exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({nh4_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0139 Ammonium Transport nh4_e <-> nh4_c\n\nnh4_c = Metabolite('nh4_c', formula='H4N', name='H2O',\n compartment='c', charge=1)\n\nreaction = Reaction('nh4t')\nreaction.name = 'Ammonium Transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({nh4_e: -1.0,\n nh4_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0140 Sulfate Exchange so4_e <->\nso4_e = Metabolite('so4_e', formula='O4S', name='Sulfate',\n compartment='e', charge=-2)\n\nreaction = Reaction('EX_so4_e')\nreaction.name = 'Sulfate Exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({so4_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0141 Sulfate Transport so4_e <-> so4_c\n\nso4_c = Metabolite('so4_c', formula='O4S', name='Sulfate',\n compartment='c', charge=-2)\n\nreaction = Reaction('so4t')\nreaction.name = 'Sulfate Transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({so4_e: -1.0,\n so4_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# AMP Conversion\n\n# R0142 Adenylate kinase amp_c + atp_c -> 2.0 adp_c\n\nreaction = Reaction('ADK1')\nreaction.name = 'Adenylate kinase'\nreaction.subsystem = 'AMP Conversion'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({amp_c: -1.0,\n atp_c: -1.0,\n adp_c: 2.0,\n ATP_SLP: -1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0143 Inorganic diphosphatase h2o_c + ppi_c -> h_c + 2.0 pi_c\n\nreaction = Reaction('PPA')\nreaction.name = 'Inorganic diphosphatase'\nreaction.subsystem = 'AMP Conversion'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2o_c: -1.0,\n ppi_c: -1.0,\n pi_c: 2.0,\n h_c: 1.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0144 Phosphate exchange pi_e <->\n\npi_e = Metabolite('pi_e', formula='HO4P', name='Phosphate',\n compartment='e', charge=-2)\n\nreaction = Reaction('EX_pi_e')\nreaction.name = 'Phosphate Exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pi_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0145 Phosphate Transport pi_e <-> pi_c\n\nreaction = Reaction('pit')\nreaction.name = 'Phosphate Transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pi_e: -1.0,\n pi_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0146 Diphosphate exchange ppi_e <->\n\nppi_e = Metabolite('ppi_e', formula='HO7P2',\n name='Diphosphate', compartment='c', charge=-3)\n\nreaction = Reaction('EX_ppi_e')\nreaction.name = 'Diphosphate Exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ppi_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Biomass Reaction\n\n# R0147 Biomass 1.17 akg_c + 2.06 oaa_c + 0.26 g6p_c + 1.58 g3p_c + 1.31 _3pg_c + 4.33 pyr_c + 0.92 pep_c + 3.06 accoa_c + 0.40 e4p_c\n# + 0.35 r5p_c + 0.37 fum_c + 0.43 ac_c + 0.29 for_c + 36.0 atp_c + 19.39 nadph_c + 1.10 nadh_c + 8.62 nh4_c + 7.57 h2o_c ->\n# 10.13 h_c + 34.6 adp_c + 31.88 pi_c: 31.88 + 4.74 ppi_c + 1.4 amp_c + 3.54 co2_c + 7.57 h2o_c + 3.06 coa_c + 1.10 nad_c\n# + 19.39 nadp_c + 0.21 so4_c + 1.0 BIOMASS\n\nBIOMASS = Metabolite('Biomass', formula='', name='Biomass',\n compartment='e', charge=0)\n\nreaction = Reaction('BIOMASS')\nreaction.name = 'Biomass'\nreaction.subsystem = 'Biomass'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({akg_c: -1.17,\n oaa_c: -2.06,\n g6p_c: -0.26,\n g3p_c: -1.58,\n _3pg_c: -1.31,\n pyr_c: -4.33,\n pep_c: -0.92,\n accoa_c: -3.06,\n e4p_c: -0.40,\n r5p_c: -0.35,\n fum_c: 0.37,\n ac_c: 0.43,\n for_c: 0.29,\n atp_c: -36.0,\n nadph_c: -19.39,\n nadh_c: 1.10,\n nh4_c: -8.62,\n h_c: 10.13,\n adp_c: 34.6,\n pi_c: 31.88,\n ppi_c: 4.74,\n amp_c: 1.4,\n co2_c: 3.54,\n h2o_c: -7.57,\n coa_c: 3.06,\n nad_c: -1.10,\n nadp_c: 19.39,\n so4_c: -0.21,\n BIOMASS: 1,\n ATP_BIOMASS: -36.0})\n\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0148 Biomass Exchange BIOMASS <->\n\nreaction = Reaction('EX_BIOMASS')\nreaction.name = 'Biomass Exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({BIOMASS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# ATP Hydrolysis\n\n# R0149 ATP_Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('ATP_Hydrolysis')\nreaction.name = 'ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_HYDR: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Import and Export Reactions For Energy Calculations\n\n# Formate Transport\n\n# R0150 Formate_import for_e + h_e -> for_c + h_c\n\nreaction = Reaction('Formate_import')\nreaction.name = 'Formate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({for_e: -1.0,\n h_e: -1.0,\n for_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0151 Formate_export for_c + h_c -> for_e + h_e\n\nreaction = Reaction('Formate_export')\nreaction.name = 'Formate_export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({for_c: -1.0,\n h_c: -1.0,\n for_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Acetate Transport\n\n# R0152 Acetate import ac_e + h_e -> ac_c + h_c\n\nreaction = Reaction('Acetate_import')\nreaction.name = 'Acetate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ac_e: -1.0,\n h_e: -1.0,\n ac_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0153 Acetate export ac_c + h_c -> ac_e + h_e\n\nreaction = Reaction('Acetate_export')\nreaction.name = 'Acetate export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ac_c: -1.0,\n h_c: -1.0,\n ac_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Propionate Transport\n\n# R0154 Propionate import ppa_e + h_e -> ppa_c + h_c\n\nreaction = Reaction('Propionate_import')\nreaction.name = 'Propionate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ppa_e: -1.0,\n h_e: -1.0,\n ppa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0155 Propionate export ppa_c + h_c -> ppa_e + h_e\n\nreaction = Reaction('Propionate_export')\nreaction.name = 'Propionate export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ppa_c: -1.0,\n h_c: -1.0,\n ppa_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Butyrate Transport\n\n# R0156 Butyrate import but_e + h_e -> but_c + h_c\n\nreaction = Reaction('Butyrate_import')\nreaction.name = 'Butyrate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({but_e: -1.0,\n h_e: -1.0,\n but_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0157 Butyrate export but_c + h_c -> but_e + h_e\n\nreaction = Reaction('Butyrate_export')\nreaction.name = 'Butyrate export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({but_c: -1.0,\n h_c: -1.0,\n but_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Valerate Transport\n\n# R0158 Valerate import pta_e + h_e -> pta_c + h_c\n\nreaction = Reaction('Valerate_import')\nreaction.name = 'Valerate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pta_e: -1.0,\n h_e: -1.0,\n pta_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0159 Valerate export pta_c + h_c -> pta_e + h_e\n\nreaction = Reaction('Valerate_export')\nreaction.name = 'Valerate export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pta_c: -1.0,\n h_c: -1.0,\n pta_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Hexanoate Transport\n\n# R0160 Hexanoate import hxa_e + h_e -> hxa_c + h_c\n\nreaction = Reaction('Hexanoate_import')\nreaction.name = 'Hexanoate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hxa_e: -1.0,\n h_e: -1.0,\n hxa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0161 Hexanoate export hxa_c + h_c -> hxa_e + h_e\n\nreaction = Reaction('Hexanoate_export')\nreaction.name = 'Hexanote export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hxa_c: -1.0,\n h_c: -1.0,\n hxa_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Heptanoate Transport\n\n# R0162 Heptanoate import htpa_e + h_e -> htpa_c + h_c\n\nreaction = Reaction('Heptanoate_import')\nreaction.name = 'Heptanoate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hpta_e: -1.0,\n h_e: -1.0,\n hpta_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0163 Heptanoate export htpa_c + h_c -> htpa_e + h_e\n\nreaction = Reaction('Heptanoate_export')\nreaction.name = 'Heptanote export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({hpta_c: -1.0,\n h_c: -1.0,\n hpta_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Octanoate Transport\n\n# R0164 Octanoate import octa_e + h_e -> octa_c + h_c\n\nreaction = Reaction('Octanoate_import')\nreaction.name = 'Octanoate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({octa_e: -1.0,\n h_e: -1.0,\n octa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0165 Octanoate export octa_c + h_c -> octa_e + h_e\n\nreaction = Reaction('Octanoate_export')\nreaction.name = 'Octanoate export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({octa_c: -1.0,\n h_c: -1.0,\n octa_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Lactate Transport\n\n# R0166 Lactate import lac__D_e + h_e -> lac__D_c + h_c\n\nreaction = Reaction('Lactate_import')\nreaction.name = 'Lactate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__D_e: -1.0,\n h_e: -1.0,\n lac__D_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0167 Lactate export lac__D_c + h_c -> lac__D_e + h_e\n\nreaction = Reaction('Lactate_export')\nreaction.name = 'Lactate export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__D_c: -1.0,\n h_c: -1.0,\n lac__D_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Ethanol Transport\n\n# R0168 Ethanol import etoh_e -> etoh_c\n\nreaction = Reaction('Ethanol_import')\nreaction.name = 'Ethanol import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({etoh_e: -1.0,\n etoh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0169 Ethanol export etoh_c -> etoh_e\n\nreaction = Reaction('Ethanol_export')\nreaction.name = 'Ethanol export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({etoh_c: -1.0,\n etoh_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0170 Succinate exchange succ_e <->\n\nsucc_e = Metabolite('succ_e', formula='C4H4O4',\n name='Succinate', compartment='e', charge=-2)\nreaction = Reaction('EX_succ_e')\nreaction.name = 'Succinate exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({succ_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# R0171 Succinate transport succ_e <-> succ_c\n\nreaction = Reaction('succt')\nreaction.name = 'Succinate transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({succ_e: -1.0,\n succ_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0172 Succinate import succ_e + h_e <-> succ_c + h_c\n\nreaction = Reaction('Succinate_import')\nreaction.name = 'Succinate import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({succ_e: -1.0,\n h_e: -1.0,\n succ_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0173 Succinate export succ_c + h_c <-> succ_e + h_e\n\nreaction = Reaction('Succinate_export')\nreaction.name = 'Succinate export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({succ_c: -1.0,\n h_c: -1.0,\n succ_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# Proton transport\n\n# R0174 H+ import h_e <-> h_c\n\nreaction = Reaction('H_import')\nreaction.name = 'H+ import'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h_e: -1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0175 H+ export h_c <-> h_e\n\nreaction = Reaction('H_export')\nreaction.name = 'H+ export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h_c: -1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# ATP for Transport\n\n# R0176 Formate Transport ATP atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Formate_Transport_ATP')\nreaction.name = 'Formate Transport ATP'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0177 Acetate Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Acetate_Transport_ATP')\nreaction.name = 'Acetate Transport ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0178 Propionate Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Propionate_Transport_ATP')\nreaction.name = 'Propionate Transport ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0179 Butyrate Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Butyrate_Transport_ATP')\nreaction.name = 'Butyrate Transport ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0180 Valerate Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Valerate_Transport_ATP')\nreaction.name = 'Valerate Transport ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0181 Hexanoate Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Hexanoate_Transport_ATP')\nreaction.name = 'Hexanoate Transport ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0182 Heptanoate Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Heptanoate_Transport_ATP')\nreaction.name = 'Heptanoate Transport ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0183 Octanoate Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Octanoate_Transport_ATP')\nreaction.name = 'Octanoate Transport ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0184 Lactate Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Lactate_Transport_ATP')\nreaction.name = 'Lactate Transport ATP'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0185 Ethanol Transport ATP Hydrolysis atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Ethanol_Transport_ATP')\nreaction.name = 'Ethanol Transport ATP Hydrolysis'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0186 Succinate Transport ATP atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Succinate_Transport_ATP')\nreaction.name = 'Succinate Transport ATP'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0187 Proton Transport ATP atp_c + h2o_c -> adp_c + pi_c + h_c\n\nreaction = Reaction('Proton_Transport_ATP')\nreaction.name = 'Proton Transport ATP'\nreaction.subsystem = 'ATP Hydrolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n h2o_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n h_c: 1.0,\n ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# Summarize Model Reactions and Metabolites\nprint(\"Reactions: \" + str(len(model.reactions)))\nprint(\"Metabolites: \" + str(len(model.metabolites)))\nprint(\"Genes: \" + str(len(model.genes)))\n\n# Transport Energy\n\nif TransEnergetics == True:\n\n # Formate Transport Energy\n deltaG_trans_grad_Formate = R*(T+273.15)*(math.log(S_Formate/C_in_Formate))\n ATP_trans_Formate = -1*(deltaG_trans_grad_Formate +\n deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Formate > 0:\n Constraint_trans_Formate = model.problem.Constraint(\n model.reactions.Formate_Transport_ATP.flux_expression - ATP_trans_Formate * model.reactions.Formate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Formate)\n\n# Acetate Transport Energy\n deltaG_trans_grad_Acetate = R*(T+273.15)*(math.log(S_Acetate/C_in_Acetate))\n ATP_trans_Acetate = -1*(deltaG_trans_grad_Acetate +\n deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Acetate > 0:\n Constraint_trans_Acetate = model.problem.Constraint(\n model.reactions.Acetate_Transport_ATP.flux_expression - ATP_trans_Acetate * model.reactions.Acetate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Acetate)\n\n# Propionate Transport Energy\n deltaG_trans_grad_Propionate = R * \\\n (T+273.15)*(math.log(S_Propionate/C_in_Propionate))\n ATP_trans_Propionate = -1 * \\\n (deltaG_trans_grad_Propionate + deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Propionate > 0:\n Constraint_trans_Propionate = model.problem.Constraint(\n model.reactions.Propionate_Transport_ATP.flux_expression - ATP_trans_Propionate * model.reactions.Propionate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Propionate)\n\n# Butyrate Transport Energy\n deltaG_trans_grad_Butyrate = R * \\\n (T+273.15)*(math.log(S_Butyrate/C_in_Butyrate))\n ATP_trans_Butyrate = -1 * \\\n (deltaG_trans_grad_Butyrate + deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Butyrate > 0:\n Constraint_trans_Butyrate = model.problem.Constraint(\n model.reactions.Butyrate_Transport_ATP.flux_expression - ATP_trans_Butyrate * model.reactions.Butyrate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Butyrate)\n\n# Valerate Transport Energy\n deltaG_trans_grad_Valerate = R * \\\n (T+273.15)*(math.log(S_Valerate/C_in_Valerate))\n ATP_trans_Valerate = -1 * \\\n (deltaG_trans_grad_Valerate + deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Valerate > 0:\n Constraint_trans_Valerate = model.problem.Constraint(\n model.reactions.Valerate_Transport_ATP.flux_expression - ATP_trans_Valerate * model.reactions.Valerate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Valerate)\n\n# Hexanoate Transport Energy\n deltaG_trans_grad_Hexanoate = R * \\\n (T+273.15)*(math.log(S_Hexanoate/C_in_Hexanoate))\n ATP_trans_Hexanoate = -1 * \\\n (deltaG_trans_grad_Hexanoate + deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Hexanoate > 0:\n Constraint_trans_Hexanoate = model.problem.Constraint(\n model.reactions.Hexanoate_Transport_ATP.flux_expression - ATP_trans_Hexanoate * model.reactions.Hexanoate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Hexanoate)\n\n# Heptanoate Transport Energy\n deltaG_trans_grad_Heptanoate = R * \\\n (T+273.15)*(math.log(S_Heptanoate/C_in_Heptanoate))\n ATP_trans_Heptanoate = -1 * \\\n (deltaG_trans_grad_Heptanoate + deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Heptanoate > 0:\n Constraint_trans_Heptanoate = model.problem.Constraint(\n model.reactions.Heptanoate_Transport_ATP.flux_expression - ATP_trans_Heptanoate * model.reactions.Heptanoate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Heptanoate)\n\n# Octanoate Transport Energy\n deltaG_trans_grad_Octanoate = R * \\\n (T+273.15)*(math.log(S_Octanoate/C_in_Octanoate))\n ATP_trans_Octanoate = -1 * \\\n (deltaG_trans_grad_Octanoate + deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Octanoate > 0:\n Constraint_trans_Octanoate = model.problem.Constraint(\n model.reactions.Octanoate_Transport_ATP.flux_expression - ATP_trans_Octanoate * model.reactions.Octanoate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Octanoate)\n\n# Lactate Transport Energy\n deltaG_trans_grad_Lactate = R*(T+273.15)*(math.log(S_Lactate/C_in_Lactate))\n ATP_trans_Lactate = -1*(deltaG_trans_grad_Lactate +\n deltaG_pH) / deltaG_ATP_Hydrolysis\n if ATP_trans_Lactate > 0:\n Constraint_trans_Lactate = model.problem.Constraint(\n model.reactions.Lactate_Transport_ATP.flux_expression - ATP_trans_Lactate * model.reactions.Lactate_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Lactate)\n\n# Proton Transport Energy\n S_H = 10*math.exp(-pH_out)\n C_in_H = 10*math.exp(-pH_in)\n deltaG_trans_grad_Proton = R*(T+273.15)*(math.log(S_H/C_in_H))\n ATP_trans_Proton = 1*(deltaG_trans_grad_Proton +\n deltaG_Sai) / deltaG_ATP_Hydrolysis\n if ATP_trans_Proton > 0:\n Constraint_trans_Proton = model.problem.Constraint(\n model.reactions.Proton_Transport_ATP.flux_expression - ATP_trans_Proton * model.reactions.H_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Proton)\n\n# Ethanol Transport Energy\n deltaG_trans_grad_Ethanol = R*(T+273.15)*(math.log(S_Ethanol/C_in_Ethanol))\n ATP_trans_Ethanol = -1*(deltaG_trans_grad_Ethanol) / deltaG_ATP_Hydrolysis\n if ATP_trans_Ethanol > 0:\n Constraint_trans_Ethanol = model.problem.Constraint(\n model.reactions.Ethanol_Transport_ATP.flux_expression - ATP_trans_Ethanol * model.reactions.Ethanol_export.flux_expression, lb=0, ub=0)\n model.add_cons_vars(Constraint_trans_Ethanol)\n\n# ATP Accounting\n\n# R0188 ATP produced via substrate-level phosphorylation ATP_SLP <->\n\nreaction = Reaction('ATP_SLP')\nreaction.name = 'ATP produced via substrate-level phosphorylation'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0189 ATP (excess) consumed via hydrolysis ATP_HYDR <->\n\nreaction = Reaction('ATP_HYDR')\nreaction.name = 'ATP (excess) consumed via hydrolysis'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ATP_HYDR: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0190 ATP produced via ion motive force ATP_IMF <->\n\nreaction = Reaction('ATP_IMF')\nreaction.name = 'ATP produced via ion motive force'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ATP_IMF: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0191 ATP consumed for transport ATP_TRANS <->\n\nreaction = Reaction('ATP_TRANS')\nreaction.name = 'ATP consumed for transport'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ATP_TRANS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0192 ATP consumed via biomass equation ATP_BIOMASS <->\n\nreaction = Reaction('ATP_BIOMASS')\nreaction.name = 'ATP consumed via biomass equation'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ATP_BIOMASS: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n#All reactions below have been amended by Abel Ingle\n\n_3hb_e = Metabolite('_3hb_e', formula='C4H7O3', name='3-Hydroxybutyrate', compartment='e', charge= -1)\n\n# R0193 3-Hydroxybutyrate exchange _3hb_e <->\n\nreaction = Reaction('EX__3hb_e')\nreaction.name = '3-Hydroxybutyrate exchange'\nreaction.subsystem = 'Exchange'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3hb_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n#3-Hydroxybutyrate Transport \n\n_3hb_c = Metabolite('_3hb_c', formula='C4H7O3', name='3-Hydroxybutyrate', compartment='c', charge= -1)\n\n# R0194 3-Hydroxybutyrate Permease _3hb_c + h_c -> _3hb_e + h_e\n\nreaction = Reaction('3-Hydroxybutyrate_export')\nreaction.name = '3-Hydroxybutyrate export'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3hb_c: -1.0,\n h_c: -1.0,\n _3hb_e: 1.0,\n h_e: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0195 *Hydroxy-Acyl-CoA Thioesterase _3hbcoa_c + h2o_c <-> _3hb_c + coa_c + h_c\n\nreaction = Reaction('HACT')\n#BiGG does not have this specific thioesterase reaction\n\nreaction.name = '*Hydroxy-Acyl-CoA Thioesterase'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_3hbcoa_c: -1.0,\n h2o_c: -1.0,\n _3hb_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0196 Succinate Coa ligase ADP forming atp_c + coa_c + succ_c <-> adp_c + pi_c + succoa_c\n\nreaction = Reaction('SUCOASc')\n#BiGG does not have Succinate CoA ligase ADP forming reaction for a cytoplasmic compartment, but for mitochondrial compartment\nreaction.name ='Succinate Coa ligase ADP forming'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000\nreaction.upper_bound = 1000\n\nreaction.add_metabolites({atp_c: -1.0,\n coa_c: -1.0,\n succ_c: -1.0,\n adp_c: 1.0,\n pi_c: 1.0,\n succoa_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\nfadh2_c = Metabolite('fadh2_c', formula='C27H33N9O15P2', name='Flavin adenine dinucleotide reduced', compartment='c', charge=-2)\nfad_c = Metabolite('fad_c', formula='C27H31N9O15P2', name='Flavin adenine dinucleotide oxidized', compartment='c', charge=-2)\n\n# R0197 Succinate dehydrogenase fad_c + succ_c -> fadh2_c + fum_c\n\nreaction = Reaction('SUCD1')\nreaction.name = 'Succinate dehydrogenase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000\nreaction.upper_bound = 1000 \n\nreaction.add_metabolites({fad_c: -1.0,\n succ_c: -1.0,\n fadh2_c: 1.0,\n fum_c: 1.0})\n \nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0198 2-Oxoglutarate dehydrogenase akg_c + nad_c + coa_c -> succoa_c + nadh_c + co2_c\n\nreaction = Reaction('AKGDc')\n#BiGG does not have this reaction for cytoplasmic compartment\nreaction.name = '2-Oxoglutarate dehydrogenase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({akg_c: -1.0,\n nad_c: -1.0,\n coa_c: -1.0,\n succoa_c: 1.0,\n nadh_c: 1.0,\n co2_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0199 Phosphoenolpyruvate carboxykinase (ATP) atp_c + oaa_c <-> adp_c + co2_c + pep_c\n\nreaction = Reaction('PPCKc')\n#BiGG does not have this reaction for cytoplasmic compartment\nreaction.name = 'Phosphoenolpyruvate carboxykinase (ATP)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n oaa_c: -1.0,\n adp_c: 1.0,\n co2_c: 1.0,\n pep_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0200 Oxaloacetate decarboxylase h_c + oaa_c <-> co2_c + pyr_c\n\nreaction = Reaction('OADDC')\n\nreaction.name = 'Oxaloacetate decarboxylase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h_c: -1.0,\n oaa_c: -1.0,\n co2_c: 1.0,\n pyr_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0201 2-oxoglutarate ferredoxin oxidoreductase akg_c + coa_c + 2.0 fdxox_c <-> co2_c + h_c + succoa_c + 2.0 fdxrd_c\n\nfdxrd_c = Metabolite('fdxrd_c', formula='Fe2S2X', name='Reduced ferredoxin', compartment='c', charge=-1)\nfdxox_c = Metabolite('fdxox_c', formula='Fe2S2X', name='Oxidized ferredoxin', compartment='c', charge=0)\n\nreaction = Reaction('OORr')\n#BiGG has a different reaction name for this reaction\nreaction.name = '2-oxoglutarate ferredoxin oxidoreductase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({akg_c: -1.0,\n coa_c: -1.0,\n fdxox_c: -2.0,\n co2_c: 1.0,\n h_c: 1.0,\n succoa_c: 1.0,\n fdxrd_c: 2.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0202 4-hydroxybutanoyl-CoA dehydratase _4hbutcoa_c <-> h2o_c + b2coa_c\n\n_4hbutcoa_c = Metabolite('_4hbutcoa_c', formula='C25H38N7O18P3S', name='4-Hydroxybutanoyl-CoA', compartment='c', charge=-4)\n\n#BiGG does not have a crotonyl-coa consuming/producing 4-hydroxybutanoyl-CoA dehydratase\nreaction = Reaction('4HBCOADH')\nreaction.name = '4-hydroxybutanoyl-CoA dehydratase'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_4hbutcoa_c: -1.0,\n b2coa_c: 1.0,\n h2o_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0203 4-hydroxybutyrate coenzyme A transferase _4hbutcoa_c <-> ghb_c + coa_c\n\nghb_c = Metabolite('ghb_c', formula='C4H8O3', name='Gamma-hydroxybutyrate', compartment='c', charge=0)\n#BiGG logs this metabolite in its unprotonated form \nreaction = Reaction('GHBCOAT')\n#BiGG does not have this reaction\nreaction.name = '4-hydroxybutyrate coenzyme a transferase'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_4hbutcoa_c: -1.0,\n ghb_c: 1.0,\n coa_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0204 Gamma-hydroxybutyrate dehydrogenase (NADH) ghb_c + nad_c <-> sucsal_c + nadh_c + h_c\n\nsucsal_c = Metabolite('sucsal_c', formula='C4H5O3', name='Succinate semialdehyde', compartment='c', charge=-1)\n\nreaction = Reaction('GHBDHx')\n\nreaction.name = 'Gamma-hydroxybutyrate dehydrogenase (NADH)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ghb_c: -1.0,\n nad_c: -1.0,\n sucsal_c: 1.0,\n nadh_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# R0205 Succinate-semialdehyde dehydrogenase (NAD) h2o_c + nad_c + sucsal_c <-> 2.0 h_c + nadh_c + succ_c\n\nreaction = Reaction('SSALx')\n\nreaction.name = 'Succinate-semialdehyde dehydrogenase (NAD)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2o_c: -1.0,\n nad_c: -1.0,\n sucsal_c: -1.0,\n nadh_c: 1.0,\n succ_c: 1.0,\n h_c: 2.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\ndha_c = Metabolite('dha_c', formula='C3H6O3', name='Dihydroxyacetone', compartment='c', charge=0)\n\n# R0206 Dihydroxyacetone phosphotransferase dha_c + pep_c <-> dhap_c + pyr_c\n\nreaction = Reaction('DHAPT')\n\nreaction.name = 'Dihydroxyacetone phosphotransferase'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({dha_c: -1.0,\n pep_c: -1.0,\n dhap_c: 1.0,\n pyr_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0207 Dihydroxyacetone kinase atp_c + dha_c <-> adp_c + dhap_c + h_c\n\nreaction = Reaction('DHAK')\n\nreaction.name = 'Dihydroxyacetone kinase'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n dha_c: -1.0,\n adp_c: 1.0,\n dhap_c: 1.0,\n h_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0208 Aldehyde dehydrogenase (acetaldehyde, NAD) acald_c + h2o_c + nad_c <-> ac_c + 2.0 h_c + nadh_c\n\nreaction = Reaction('ALDD2x')\n\nreaction.name = 'Aldehyde dehydrogenase (acetaldehyde, NAD)'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({acald_c: -1.0,\n h2o_c: -1.0,\n nad_c: -1.0,\n ac_c: 1.0,\n h_c: 2.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# R0209 Pyruvate carboxylase atp_c + hco3_c + pyr_c <-> adp_c + h_c + oaa_c + pi_c\n\nhco3_c = Metabolite('hco3_c', formula='CHO3', name='Bicarbonate', compartment='c', charge=-1)\n\nreaction = Reaction('PC')\n\nreaction.name = 'Pyruvate carboxylase'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n hco3_c: -1.0,\n pyr_c: -1.0,\n adp_c: 1.0,\n h_c: 1.0,\n oaa_c: 1.0,\n pi_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0210 D-glucose transport via PEP:Pyr PTS pep_c + glc__D_e <-> g6p_c + pyr_c\n\n\nreaction = Reaction('GLCpts')\n\nreaction.name = 'D-glucose transport via PEP:Pyr PTS'\nreaction.subsystem = 'Lower Glycolysis'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pep_c: -1.0,\n glc__D_e: -1.0,\n g6p_c: 1.0,\n pyr_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0211 Succinyl-CoA hydrolase succoa_c + h2o_c <-> succ_c + coa_c + h_c\n\nreaction = Reaction('SCH')\n\nreaction.name = 'Succinyl-CoA hydrolase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({succoa_c: -1.0,\n h2o_c: -1.0,\n succ_c: 1.0,\n coa_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0212 Methylglyoxal synthase dhap_c <-> mthgxl_c + pi_c\n\nmthgxl_c = Metabolite('mthgxl_c', formula='C3H4O2', name='Methylglyoxal', compartment='c', charge=0)\n\nreaction = Reaction('MGSA')\n\nreaction.name = 'Methylglyoxal synthase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({dhap_c: -1.0,\n mthgxl_c: 1.0,\n pi_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0213 Alcohol dehydrogenase (L-lactaldehyde) lald__L_c + nad_c <-> h_c + mthgxl_c + nadh_c\n\nlald__L_c = Metabolite('lald__L_c', formula='C3H6O2', name='L-Lactaldehyde', compartment='c', charge=0)\n\nreaction = Reaction('ALCD22_L')\n\nreaction.name = 'Alcohol dehydrogenase (L-lactaldehyde)'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lald__L_c: -1.0,\n nad_c: -1.0,\n h_c: 1.0,\n mthgxl_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0214 Acetyl-CoA carboxylase accoa_c + atp_c + hco3_c + 4.0 h_c <-> adp_c + malcoa_c + pi_c\n\nmalcoa_c = Metabolite('malcoa_c', formula='C24H38N7O19P3S', name='Malonyl-CoA', compartment='c', charge=0)\n#BiGG has a different formula for malonyl-coa\nreaction = Reaction('ACCOAC')\n#This reaction differs from BiGG database because 4 h_c are being consumed so that the reaction is balanced\nreaction.name = 'Acetyl-CoA carboxylase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({accoa_c: -1.0,\n atp_c: -1.0,\n hco3_c: -1.0,\n h_c: -4.0,\n adp_c: 1.0,\n malcoa_c: 1.0,\n pi_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0215 Propionyl-CoA carboxylase atp_c + hco3_c + ppcoa_c <-> adp_c + h_c + mmcoa__S_c + pi_c\n\nreaction = Reaction('PPCOAC')\n\nreaction.name = 'Propionyl-CoA carboxylase'\nreaction.subsystem = 'Propionate Production'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n hco3_c: -1.0,\n ppcoa_c: -1.0,\n adp_c: 1.0,\n h_c: 1.0,\n mmcoa__S_c: 1.0,\n pi_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0216 Lactaldehyde dehydrogenase h2o_c + lald__L_c + nad_c <-> 2.0 h_c + lac__L_c + nadh_c\n\nlac__L_c = Metabolite('lac__L_c', formula='C3H5O3', name='L-Lactate', compartment='c', charge=-1)\n\nreaction = Reaction('LCADi')\n\nreaction.name = 'Lactaldehyde dehydrogenase'\nreaction.subsystem = 'Pyruvate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2o_c: -1.0,\n lald__L_c: -1.0,\n nad_c: -1.0,\n h_c: 2.0,\n lac__L_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0217 Lactate racemase lac__D_c <-> lac__L_c\n\nreaction = Reaction('LacR')\n\nreaction.name = 'Lactate racemase'\nreaction.subsystem = 'Pyruvate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__D_c: -1.0,\n lac__L_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0218 Succinyl-CoA:acetate CoA transferase ac_c + succoa_c <-> accoa_c + succ_c\n\nreaction = Reaction('SUCOAACTr')\n\nreaction.name = 'Succinyl-CoA:acetate CoA transferase'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ac_c: -1.0,\n succoa_c: -1.0,\n accoa_c: 1.0,\n succ_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0219 Succinate-semialdehyde dehydrogenase (NADP) h2o_c + nadp_c + sucsal_c <-> 2.0 h_c + nadph_c + succ_c\n\nreaction = Reaction('SSALy')\n\nreaction.name = 'Succinate-semialdehyde dehydrogenase (NADP)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2o_c: -1.0,\n nadp_c: -1.0,\n sucsal_c: -1.0,\n h_c: 2.0,\n nadph_c: 1.0,\n succ_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0220 Phosphoenolpyruvate:glycerone phosphotransferase pep_c + dha_c <-> pyr_c + dhap_c\n\nreaction = Reaction('PPGPT')\n#This reaction is not in BiGG\nreaction.name = 'Phosphoenolpyruvate:glycerone phosphotransferase'\nreaction.subsystem = 'Pyruvate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({pep_c: -1.0,\n dha_c: -1.0,\n pyr_c: 1.0,\n dhap_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0221 Pyruvate-ferredoxin oxidoreductase coa_c + pyr_c + 2.0 fdxox_c <-> accoa_c + co2_c + h_c + 2.0 fdxrd_c + h_c\n\nreaction = Reaction('POR_syn')\n#This reaction differs from BiGG database because a different ferredoxin is used and H+ is a product for mass and charge balance\nreaction.name = 'Pyruvate-ferredoxin oxidoreductase'\nreaction.subsystem = 'Pyruvate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({coa_c: -1.0,\n pyr_c: -1.0,\n fdxox_c: -2.0,\n h_c: 1.0,\n accoa_c: 1.0,\n co2_c: 1.0,\n fdxrd_c: 2.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0222 Methylglyoxal reductase (NADP) lald__L_c + nadp_c <-> h_c + mthgxl_c + nadph_c\n\nreaction = Reaction('MRN')\n#BiGG does not have this reaction\nreaction.name = 'Methylglyoxal reductase (NADP)'\nreaction.subsystem = 'Pyruvate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lald__L_c: -1.0,\n nadp_c: -1.0,\n h_c: 1.0,\n mthgxl_c: 1.0,\n nadph_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0223 Lactoylglutathione lyase gthrd_c + mthgxl_c <-> lgt__S_c\n\ngthrd_c = Metabolite('gthrd_c', formula='C10H16N3O6S', name='Reduced glutathione', compartment='c', charge=-1)\n\nlgt__S_c = Metabolite('lgt__S_c', formula='C13H20N3O8S', name='(R)-S-Lactoylglutathione', compartment='c', charge=-1)\n\nreaction = Reaction('LGTHL')\n\nreaction.name = 'Lactoylglutathione lyase'\nreaction.subsystem = 'Pyruvate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({gthrd_c: -1.0,\n mthgxl_c: -1.0,\n lgt__S_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0224 Hydroxyacylglutathione hydrolase h2o_c + lgt__S_c ⇌ gthrd_c + h_c + lac__D_c\n\nreaction = Reaction('GLYOX')\n\nreaction.name = 'Hydroxyacylglutathione hydrolase'\nreaction.subsystem = 'Pyruvate Metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h2o_c: -1.0,\n lgt__S_c: -1.0,\n gthrd_c: 1.0,\n h_c: 1.0,\n lac__D_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0225 3-oxoacid CoA-transferase (Succinyl-CoA: acetoacetate) acac_c + succoa_c ⇌ aacoa_c + succ_c\n\nacac_c = Metabolite('acac_c', formula='C4H5O3', name='Acetoacetate', compartment='c', charge=-1)\n\nreaction = Reaction('OCOAT1')\n\nreaction.name = '3-oxoacid CoA-transferase (Succinyl-CoA: acetoacetate)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({acac_c: -1.0,\n succoa_c: -1.0,\n aacoa_c: 1.0,\n succ_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0226 Glucose 6 phosphate isomerase g6p_c <-> g6p_B_c\n\ng6p_B_c = Metabolite('g6p_B_c', formula='C6H11O9P', name='Beta D glucose 6 phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('G6PI')\n\nreaction.name = 'Glucose 6 phosphate isomerase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({g6p_c: -1.0,\n g6p_B_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0227 Glucose 6-phosphate dehydrogenase g6p_B_c + nadp_c <-> _6pgl_c + h_c + nadph_c\n\n_6pgl_c = Metabolite('_6pgl_c', formula='C6H9O9P', name='6-phospho-D-glucono-1,5-lactone', compartment='c', charge=-2)\n\nreaction = Reaction('G6PDH2r')\n#BiGG does not use beta-glucose-6-phosphate in this reaction\nreaction.name = 'Glucose 6-phosphate dehydrogenase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({g6p_B_c: -1.0,\n nadp_c: -1.0,\n _6pgl_c: 1.0,\n h_c: 1.0,\n nadph_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0228 6-phosphogluconolactonase _6pgl_c + h2o_c <-> _6pgc_c + h_c\n\n_6pgc_c = Metabolite('_6pgc_c', formula='C6H10O10P', name='6-Phospho-D-gluconate', compartment='c', charge=-3)\n\nreaction = Reaction('PGL')\n#BiGG does not use beta-glucose-6-phosphate in this reaction\nreaction.name = '6-phosphogluconolactonase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_6pgl_c: -1.0,\n h2o_c: -1.0,\n _6pgc_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0229 Phosphogluconate dehydrogenase _6pgc_c + nadp_c <-> co2_c + nadph_c + ru5p__D_c\n\nreaction = Reaction('GND')\n\nreaction.name = 'Phosphogluconate dehydrogenase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_6pgc_c: -1.0,\n nadp_c: -1.0,\n co2_c: 1.0,\n ru5p__D_c: 1.0,\n nadph_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0230 Phosphogluconate dehydrogenase (NAD) _6pgc_c + nad_c <-> co2_c + nadh_c + ru5p__D_c\n\nreaction = Reaction('GND_nad')\n#BiGG does not have this reaction\nreaction.name = 'Phosphogluconate dehydrogenase (NAD)'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_6pgc_c: -1.0,\n nad_c: -1.0,\n co2_c: 1.0,\n ru5p__D_c: 1.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0231 Ribokinase atp_c + rib__D_c <-> adp_c + h_c + ru5p__D_c\n\nrib__D_c = Metabolite('rib__D_c', formula='C5H10O5', name='D-Ribose', compartment='c', charge=0)\n\nreaction = Reaction('RBK')\n\nreaction.name = 'Ribokinase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n rib__D_c: -1.0,\n adp_c: 1.0,\n ru5p__D_c: 1.0,\n h_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0232 Deoxyribose-phosphate aldolase _2dr5p_c <-> acald_c + g3p_c\n\n_2dr5p_c = Metabolite('_2dr5p_c', formula='C5H9O7P', name='2-Deoxy-D-ribose 5-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('DRPA')\n\nreaction.name = 'Deoxyribose-phosphate aldolase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({_2dr5p_c: -1.0,\n acald_c: 1.0,\n g3p_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0233 Deoxyribokinase atp_c + drib_c <-> _2dr5p_c + adp_c + h_c\n\ndrib_c = Metabolite('drib_c', formula='C5H10O4', name='Deoxyribose', compartment='c', charge=0)\n\nreaction = Reaction('DRBK')\n\nreaction.name = 'Deoxyribokinase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({atp_c: -1.0,\n drib_c: -1.0,\n _2dr5p_c: 1.0,\n adp_c: 1.0,\n h_c: 1.0,\n ATP_SLP: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0234 Arabino-3-hexulose-6-P Isomerase f6p_c <-> ah6p__D_c\n\nah6p__D_c = Metabolite('ah6p__D_c', formula='C6H11O9P', name='Arabino-3-hexulose-6-P', compartment='c', charge=-2)\n#The charge of this metabolite differs from BiGG\nreaction = Reaction('AH6PI')\n\nreaction.name = 'Arabino-3-hexulose-6-P Isomerase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({f6p_c: -1.0,\n ah6p__D_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0235 3-hexulose-6-phosphate synthase ah6p__D_c <-> ru5p__D_c + fald_c\n\nfald_c = Metabolite('fald_c', formula='CH2O', name='Formaldehyde', compartment='c', charge=0)\n\nreaction = Reaction('H6PS')\n#This reaction is not in BiGG\nreaction.name = '3-hexulose-6-phosphate synthase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({ah6p__D_c: -1.0,\n ru5p__D_c: 1.0,\n fald_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0236 Aldehyde dehydrogenase formaldehyde NAD fald_c + h2o_c + nad_c <-> for_c + 2.0 h_c + nadh_c\n\nreaction = Reaction('ALDD1')\n\nreaction.name = 'Aldehyde dehydrogenase formaldehyde NAD'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fald_c: -1.0,\n h2o_c: -1.0,\n nad_c: -1.0,\n for_c: 1.0,\n h_c: 2.0,\n nadh_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0237 Phosphopentomutase r1p_c <-> r5p_c\n\nr1p_c = Metabolite('r1p_c', formula='C5H9O8P', name='Alpha-D-Ribose 1-phosphate', compartment='c', charge=-2)\n\nreaction = Reaction('PPM')\n\nreaction.name = 'Phosphopentomutase'\nreaction.subsystem = 'Pentose Phosphate Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({r1p_c: -1.0,\n r5p_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0238 Pyruvate dehydrogenase (lipoamide) h_c + pyr_c + lpam_c ⇌ co2_c + adhlam_c\n\nlpam_c = Metabolite('lpam_c', formula='C8H15NOS2', name='Lipoamide', compartment='c', charge=0)\nadhlam_c = Metabolite('adhlam_c', formula='C10H19NO2S2', name='S-Acetyldihydrolipoamide', compartment='c', charge=0)\n\nreaction = Reaction('PDHa')\n\nreaction.name = 'Pyruvate dehydrogenase (lipoamide)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h_c: -1.0,\n pyr_c: -1.0,\n lpam_c: -1.0,\n co2_c: 1.0,\n adhlam_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n\n# R0239 PDH2c coa_c + fmn_c + 2.0 h_c + pyr_c ⇌ accoa_c + co2_c + fmnh2_c\n\nfmn_c = Metabolite('fmn_c', formula='C17H18N4O9P', name='FMN', compartment='c', charge=-3)\nfmnh2_c = Metabolite('fmnh2_c', formula='C17H21N4O9P', name='Reduced FMN', compartment='c', charge=-2)\n\nreaction = Reaction('PDH2c')\n#This reaction is not balanced in BiGG\nreaction.name = 'PDH2c'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({coa_c: -1.0,\n fmn_c: -1.0,\n h_c: -2.0,\n pyr_c: -1.0,\n accoa_c: 1.0,\n co2_c: 1.0,\n fmnh2_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0240 Isocitrate dehydrogenase (NADP) icit_c + nadp_c <-> akg_c + co2_c + nadph_c\n\nreaction = Reaction('ICDHyr')\n\nreaction.name = 'Isocitrate dehydrogenase (NADP)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({icit_c: -1.0,\n nadp_c: -1.0,\n akg_c: 1.0,\n co2_c: 1.0,\n nadph_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0241 Malic enzyme (NADP) mal__L_c + nadp_c <-> co2_c + nadph_c + pyr_c\n\nreaction = Reaction('ME2')\n\nreaction.name = 'Malic enzyme (NADP)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({mal__L_c: -1.0,\n nadp_c: -1.0,\n pyr_c: 1.0,\n co2_c: 1.0,\n nadph_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0242 Glycerol-3-phosphate dehydrogenase (NADP) glyc3p_c + nadp_c <-> dhap_c + h_c + nadph_c\n\nreaction = Reaction('G3PD2')\n\nreaction.name = 'Glycerol-3-phosphate dehydrogenase (NADP)'\nreaction.subsystem = 'TCA Cycle'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({glyc3p_c: -1.0,\n nadp_c: -1.0,\n dhap_c: 1.0,\n h_c: 1.0,\n nadph_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0243 3-hydroxybutyryl-CoA dehydrogenase (NADP) aacoa_c + h_c + nadph_c <-> _3hbcoa_c + nadp_c\n\nreaction = Reaction('HACD1a')\n#This reaction is not in BiGG\nreaction.name = '3-hydroxybutyryl-CoA dehydrogenase (NADP)'\nreaction.subsystem = 'Reverse Beta Oxidation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({aacoa_c: -1.0,\n h_c: -1.0,\n nadph_c: -1.0,\n _3hbcoa_c: 1.0,\n nadp_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0244 5, 10 methylenetetrahydrofolate reductase NADPH 2.0 h_c + mlthf_c + nadph_c <-> _5mthf_c + nadp_c\n\nreaction = Reaction('MTHFR3_1')\n\nreaction.name = '5, 10 methylenetetrahydrofolate reductase NADPH'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = 0. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({h_c: -2.0,\n mlthf_c: -1.0,\n nadph_c: -1.0,\n _5mthf_c: 1.0,\n nadp_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0245 L-lactate dehydrogenase lac__L_c + nad_c <-> h_c + nadh_c + pyr_c\n\nreaction = Reaction('LDH_L')\n\nreaction.name = 'L-lactate dehydrogenase'\nreaction.subsystem = 'Lactate metabolism'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__L_c: -1.0,\n nad_c: -1.0,\n h_c: 1.0,\n nadh_c: 1.0,\n pyr_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0246 Methylenetetrahydrofolate dehydrogenase (NADP) mlthf_c + nadp_c <-> methf_c + nadph_c\n\nreaction = Reaction('MTHFD')\n\nreaction.name = 'Methylenetetrahydrofolate dehydrogenase (NADP)'\nreaction.subsystem = 'Wood Ljungadhl Pathway'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({mlthf_c: -1.0,\n nadp_c: -1.0,\n methf_c: 1.0,\n nadph_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0247 \n\n# Summarize Model Reactions and Metabolites\nprint(\"Reactions: \" + str(len(model.reactions)))\nprint(\"Metabolites: \" + str(len(model.metabolites)))\nprint(\"Genes: \" + str(len(model.genes)))\n\n# R0247 NADH-dependent Reduced Ferredoxin:NADP Oxidoreductase \n# nadh_c + h_c + 2.0 nadp_c + 2.0 fdxrd_c <-> nad_c + 2.0 nadph_c + 2.0 fdxox_c\n\nreaction = Reaction('THDF')\n#BiGG does not have this reaction.\nreaction.name = 'NADH-dependent Reduced Ferredoxin:NADP Oxidoreductase'\nreaction.subsystem = 'NADH/ NADPH Conversions'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({nadh_c: -1.0,\n h_c: -1.0,\n nadp_c: -2.0,\n fdxrd_c: -2.0,\n nad_c: 1.0,\n nadph_c: 2.0,\n fdxox_c: 2.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0248 Ferredoxin---NADP+ reductase nadp_c + 2.0 fdxrd_c <-> nadph_c + 2.0 fdxox_c \n\nreaction = Reaction('FNOR')\n##This reaction differs from BiGG database because a different ferredoxin is used and H+ is a product for mass and charge balance\nreaction.name = 'Ferredoxin---NADP+ reductase'\nreaction.subsystem = 'Electron Bifurcation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({nadp_c: -1.0,\n fdxrd_c: -2.0,\n nadph_c: 1.0,\n fdxox_c: 2.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0248 FMN reductase NADPH dependent fmn_c + 2.0 h_c + nadph_c <-> fmnh2_c + nadp_c\n\nreaction = Reaction('FMNRy_1')\n\nreaction.name = 'FMN reductase NADPH dependent'\nreaction.subsystem = 'Electron Bifurcation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fmn_c: -1.0,\n h_c: -2.0,\n nadph_c: -1.0,\n fmnh2_c: 1.0,\n nadp_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0249 NAD:FAD oxidoreductase fadh2_c + 2.0 nad_c + fdred_c <-> fad_c + 2.0 nadh_c + fdox_c\n\nreaction = Reaction('NADFADOR')\n\nreaction.name = 'NAD:FAD oxidoreductase'\nreaction.subsystem = 'Electron Bifurcation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fadh2_c: -1.0,\n nad_c: -2.0,\n fdred_c: -1.0,\n fad_c: 1.0,\n nadh_c: 2.0,\n fdox_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0250 FMN reductase fmn_c + h_c + nadh_c <-> fmnh2_c + nad_c\n\nreaction = Reaction('FMNRx')\n\nreaction.name = 'FMN reductase'\nreaction.subsystem = 'Electron Bifurcation'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({fmn_c: -1.0,\n h_c: -1.0,\n nadh_c: -1.0,\n fmnh2_c: 1.0,\n nad_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n# R0251 Carbon dioxide hydration co2_c + h2o_c <-> hco3_c + h_c\n\nreaction = Reaction('CDH')\n#BiGG does not have this reaction\nreaction.name = 'Carbon dioxide hydration'\nreaction.subsystem = 'Carbonate System'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({co2_e: -1.0,\n h2o_c: -1.0,\n hco3_c: 1.0,\n h_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n#R0252 L-lactate transport lac__L_e <-> lac__L_c\n\nlac__L_e = Metabolite('lac__L_e', formula='C3H5O3', name='L-Lactate', compartment='e', charge=-1)\n\nreaction = Reaction('LACLt')\nreaction.name = 'L-Lactate transport'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__L_e: -1.0,\n lac__L_c: 1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n#R0253 L-lactate exchange lac__D_e <->\n\nreaction = Reaction('EX_lac__L_e')\nreaction.name = 'L-Lactate exchange'\nreaction.subsystem = 'Transport'\nreaction.lower_bound = -1000. # This is the default\nreaction.upper_bound = 1000. # This is the default\n\nreaction.add_metabolites({lac__L_e: -1.0})\n\nmodel.add_reactions([reaction])\n\nprint(reaction.name + \": \" + str(reaction.check_mass_balance()))\n\n##################################\n###Part III: SUBSTRATE UPTAKE#####\n##################################\n\nprint(model.medium)\nmedium = model.medium\n\n# Medium for reactor simulations\nmedium[\"EX_xyl__D_e\"] = 0 # 0.1317 #mmol/hr/gDW\nmedium[\"EX_xyl4_e\"] = 0 # 0.0081 #mmol/hr/gDW\nmedium[\"EX_glc4_e\"] = 0 # 0.0081 #mmol/hr/gDW\nmedium[\"EX_glc__D_e\"] = 1 # 0.0125 #mmol/hr/gDW\nmedium[\"EX_glyc_e\"] = 0 # 0.0360 #mmol/hr/gDW\nmedium[\"EX_lac__D_e\"] = 0 # 0.0005 #mmol/hr/gDW\nmedium[\"EX_lac__L_e\"] = 0 \nmedium[\"EX_etoh_e\"] = 0\nmedium[\"EX_ac_e\"] = 0\nmedium[\"EX_but_e\"] = 0\nmedium[\"EX_hxa_e\"] = 0\nmedium[\"EX_h2_e\"] = 0\nmedium[\"EX_octa_e\"] = 0\nmedium[\"EX_ppa_e\"] = 0\nmedium[\"EX_pta_e\"] = 0\nmedium[\"EX_hpta_e\"] = 0\nmedium[\"EX_co2_e\"] = 1\nmedium[\"EX_for_e\"] = 0\nmedium[\"EX_succ_e\"] = 0\n\nmodel.medium = medium\nprint(model.medium)\n\n\n#######################################\n##PART IV: SET ADDITIONAL CONSTRAINTS##\n#######################################\n\n\n# Constrain Hydrogenases to Avoid Loops\n# Turn off hydrogenases\n# HYD1 set to only produce hydrogen to avoid creating flux loop with ECH\n# model.reactions.HYD1.knock_out()\nmodel.reactions.HYD1.lower_bound = 0\n#model.reactions.ECH.lower_bound = 0\n# model.reactions.ECH.knock_out()\n#model.reactions.HYDABC.knock_out()\n\n# This is where we set the objective function\nmodel.objective = 'EX_BIOMASS'\n#model.objective = 'ATP_Hydrolysis'\n#model.objective = 'EX_octa_e'\n#model.objective = 'EX_lac__D_e'\n#model.objective = 'EX_ppa_e'\n#model.objective = 'EX_pta_e'\n#model.objective = 'EX_hpta_e'\n#model.objective = 'EX_ac_e'\n#model.objective = 'EX_succ_e'\n\n\"\"\"model.reactions.EX_octa_e.upper_bound = 0.0034\nmodel.reactions.EX_octa_e.lower_bound = 0.0034\n\nmodel.reactions.EX_hxa_e.upper_bound = 0.0478\nmodel.reactions.EX_hxa_e.lower_bound = 0.0478\n\nmodel.reactions.EX_but_e.upper_bound = 0.0827\nmodel.reactions.EX_but_e.lower_bound = 0.0827\n\nmodel.reactions.EX_ac_e.lower_bound = 0.0740\nmodel.reactions.EX_ac_e.upper_bound = 0.0740\n\nmodel.reactions.EX_etoh_e.lower_bound = 0.0032\nmodel.reactions.EX_etoh_e.upper_bound = 0.0032\n\nmodel.reactions.EX_for_e.lower_bound = 0.0001\nmodel.reactions.EX_for_e.upper_bound = 0.0001\n\nmodel.reactions.EX_h2_e.lower_bound = 0.437\nmodel.reactions.EX_h2_e.upper_bound = 0.437\"\"\"\n\n\"\"\"model.reactions.EX_lac__D_e.upper_bound = 0\nmodel.reactions.EX_lac__D_e.lower_bound = 0\"\"\"\n\n\"\"\"model.reactions.EX_glyc_e.upper_bound = -0.0346708\nmodel.reactions.EX_glyc_e.lower_bound = -0.0346708\n\nmodel.reactions.EX_glc__D_e.upper_bound = -0.012016617\nmodel.reactions.EX_glc__D_e.lower_bound = -0.012016617\n\nmodel.reactions.EX_xyl__D_e.upper_bound = -0.1264797\nmodel.reactions.EX_xyl__D_e.lower_bound = -0.1264797\n\nmodel.reactions.EX_xyl4_e.upper_bound = -0.01994533\nmodel.reactions.EX_xyl4_e.lower_bound = -0.01994533\n\nmodel.reactions.EX_glc4_e.upper_bound = -0.01994533\nmodel.reactions.EX_glc4_e.lower_bound = -0.01994533\"\"\"\n\n# Run pFBA\n\npfba_solution = cobra.flux_analysis.pfba(model)\nmodel.summary()\nprint(pfba_solution.fluxes)\n\n# Write pFBA results to excel\nwriter = pandas.ExcelWriter('output.xlsx')\npfba_solution.fluxes.to_excel(writer, 'Sheet1')\nwriter.save()\n\nmodel.summary()\n\n\n# Calculate overall reaction thermodynamics\nXYL = pfba_solution[\"EX_xyl__D_e\"]\nGLC = pfba_solution[\"EX_glc__D_e\"]\nGLYC = pfba_solution[\"EX_glyc_e\"]\nLACD = pfba_solution[\"EX_lac__D_e\"]\nLACL = pfba_solution[\"EX_lac__L_e\"]\nETOH = pfba_solution[\"EX_etoh_e\"]\nH2 = pfba_solution[\"EX_h2_e\"]\nH2O = pfba_solution[\"EX_h2o_e\"]\nCO2 = pfba_solution[\"EX_co2_e\"]\nH = pfba_solution[\"EX_h_e\"]\nC1 = pfba_solution[\"EX_for_e\"]\nC2 = pfba_solution[\"EX_ac_e\"]\nC4 = pfba_solution[\"EX_but_e\"]\nC6 = pfba_solution[\"EX_hxa_e\"]\nC8 = pfba_solution[\"EX_octa_e\"]\nC3 = pfba_solution[\"EX_ppa_e\"]\nC5 = pfba_solution[\"EX_pta_e\"]\nC7 = pfba_solution[\"EX_hpta_e\"]\nSUCC = pfba_solution[\"EX_succ_e\"]\n\nG_XYL = pfba_solution[\"EX_xyl__D_e\"]*-753.37\nG_GLC = pfba_solution[\"EX_glc__D_e\"]*-913.28\nG_XYL4 = pfba_solution[\"EX_xyl4_e\"]*-547.08 * \\\n 4.184 # Used dG for tetra-arabinofuranoside\nG_GLC4 = pfba_solution[\"EX_glc4_e\"]*-694.61*4.184 # Used dG for stachyose\nG_GLYC = pfba_solution[\"EX_glyc_e\"]*-116.18*4.184\nG_LAC = pfba_solution[\"EX_lac__D_e\"]*-515.34\nG_ETOH = pfba_solution[\"EX_etoh_e\"]*-181.75\nG_H2 = pfba_solution[\"EX_h2_e\"]*0\nG_H2O = pfba_solution[\"EX_h2o_e\"]*-237.18\nG_CO2 = pfba_solution[\"EX_co2_e\"]*-386.02\nG_H = pfba_solution[\"EX_h_e\"]*0\nG_C1 = pfba_solution[\"EX_for_e\"]*-351.0376\nG_C2 = pfba_solution[\"EX_ac_e\"]*-369.41\nG_C4 = pfba_solution[\"EX_but_e\"]*-352.63\nG_C6 = pfba_solution[\"EX_hxa_e\"]*-335.85\nG_C8 = pfba_solution[\"EX_octa_e\"]*-322.29\nG_C3 = pfba_solution[\"EX_ppa_e\"]*-356.18\nG_C5 = pfba_solution[\"EX_pta_e\"]*-342.6\nG_C7 = pfba_solution[\"EX_hpta_e\"]*-329.2\nG_SUCC = pfba_solution[\"EX_succ_e\"]*-690.23\n\ndG0 = G_XYL + G_GLC + G_XYL4 + G_GLC4 + G_GLYC + G_LAC + G_ETOH + G_H2 + G_H2O + \\\n G_CO2 + G_H + G_C1 + G_C2 + G_C4 + G_C6 + G_C8 + G_C3 + G_C5 + G_C7 + G_SUCC\n\nprint(\"dG0: \", dG0)\n\ndG0_prime = dG0 + ((8.3145*10**-3)*298)*numpy.log((10**(-7))**H)\n\nprint(\"dG0_prime: \", dG0_prime)\n\nNET_ATP = 0\nif pfba_solution[\"ATP_HYDR\"] < 0:\n NET_ATP += -1*pfba_solution[\"ATP_HYDR\"]\nif pfba_solution[\"ATP_BIOMASS\"] < 0:\n NET_ATP += -1*pfba_solution[\"ATP_BIOMASS\"]\nif pfba_solution[\"ATP_IMF\"] < 0:\n NET_ATP += -1 * pfba_solution[\"ATP_IMF\"]\nif pfba_solution[\"ATP_TRANS\"] < 0:\n NET_ATP += -1*pfba_solution[\"ATP_TRANS\"]\nif pfba_solution[\"ATP_SLP\"] < 0:\n NET_ATP += -1*pfba_solution[\"ATP_SLP\"]\n\n\nG_Per_ATP = dG0_prime/NET_ATP\n\nprint(\"G_Per_ATP: \", G_Per_ATP)\n\nif G_Per_ATP > -50:\n print(\"Free Energy per mol ATP < 60 kJ\")\n\nrxn_count = 0\nfor n in pfba_solution.fluxes:\n if n > 0.0000001 or n < -0.0000001:\n rxn_count += 1\n\nprint(\"Reactions carrying flux: \", rxn_count)\n\nprint(\"XYL: \", XYL)\nprint(\"GLC: \", GLC)\nprint(\"GLYC: \", GLYC)\nprint(\"LAC: \", LAC)\nprint(\"ETOH: \", ETOH)\nprint(\"H2: \", H2)\nprint(\"H2O: \", H2O)\nprint(\"CO2: \", CO2)\nprint(\"H+: \", H)\nprint(\"C1: \", C1)\nprint(\"C2: \", C2)\nprint(\"C3: \", C3)\nprint(\"C4: \", C4)\nprint(\"C5: \", C5)\nprint(\"C6: \", C6)\nprint(\"C7: \", C7)\nprint(\"C8: \", C8)\nprint(\"SUCC: \", SUCC)\nprint(\"ATP: \", pfba_solution[\"ATP_Hydrolysis\"])\n\n# if G_Per_ATP > -50:\n# print (\"Free Energy per mol ATP < 50 kJ\")\n\nprint(type(model.solver))\n\n#sol= model.optimize()\n# model.summary(fva=1.00)\n\n# Run FVA\n#fva = flux_variability_analysis(model, loopless=True, fraction_of_optimum=1.00)\n#print (fva)\n\n# Print FVA results to excel\n#writer = pandas.ExcelWriter('FVA_iFerment182.xlsx')\n# fva.to_excel(writer,'Sheet1')\n# writer.save()\n\n# Create .SBML model for use in other modeling platforms\n\ncobra.io.write_sbml_model(model, \"iFerment186_Plus.xml\")\n#cobra.io.save_json_model(model, \"iFerment186_Plus.json\")\n\n\n#####################################\n# DRN ADDITIONS APRIL 2020\n#####################################\n\n# Print the steady state xylose and biomass fluxes\n#print (\"XYL: \",XYL)\nBIOM = pfba_solution[\"EX_BIOMASS\"]\nprint(\"BIOMASS: \", BIOM)\n\n#####################################\n###AMMENDMENT I: ESSENTIAL PROTEINS##\n#####################################\n\n#n = 0\n# for reaction in model.reactions:\n# ub = model.reactions[n].upper_bound\n# lb = model.reactions[n].lower_bound\n# model.reactions[n].knock_out()\n# #print(model.reactions[n], cobra.flux_analysis.pfba(model).fluxes['ATP_Hydrolysis'])\n# model.reactions[n].upper_bound = ub\n# model.reactions[n].lower_bound = lb\n# n = n + 1\n","sub_path":"iFerment.py","file_name":"iFerment.py","file_ext":"py","file_size_in_byte":181198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"29847310","text":"import os\n\nimport cv2\n\n\ndef calc_mean(image_file):\n B_sum = 0\n G_sum = 0\n R_sum = 0\n p_sum = 0\n\n image_list = os.listdir(image_file)\n for i in range(len(image_list)):\n single_img = cv2.imread(image_file + '/' + image_list[i])\n h, w, c = single_img.shape\n\n for hi in range(h):\n for wi in range(w):\n B_sum += single_img[hi][wi][0]\n G_sum += single_img[hi][wi][1]\n R_sum += single_img[hi][wi][2]\n p_sum += 1\n\n B_mean = B_sum / p_sum\n G_mean = G_sum / p_sum\n R_mean = R_sum / p_sum\n\n mean = (B_mean, G_mean, R_mean)\n\n return mean\n\n\nif __name__ == \"__main__\":\n test_img_file = '/home/yzh/ssd_head_detector_v/dataset/'\n print(calc_mean(test_img_file))\n","sub_path":"demo/calc_mean.py","file_name":"calc_mean.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"590563280","text":"# 9012.py\n# 2018.05.26\n\nfor _ in range(int(input())):\n\tstack = 0\n\tfor char in input():\n\t\tif char == '(':\n\t\t\tstack += 1\n\t\telse:\n\t\t\tstack -= 1\n\t\t\tif stack < 0:\n\t\t\t\tbreak\n\tprint(\"YES\" if not stack else \"False\")\n\n# 스택의 성질을 이용하여 문제를 풀면 된다.\n# 스택이 비어있는데 pop하는 것과 끝났는데 스택이 비어있지 않으면 False이다.\n# jh05013님 코드 참조\n","sub_path":"9000/9012.py","file_name":"9012.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"475347318","text":"import cv2\nimport numpy as np\n\ndef canny(image):\n\tgray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY)\n\tblur = cv2.GaussianBlur(gray, (5,5), 0)\n\tcanny = cv2.Canny(blur, 50,150)\n\treturn canny\n\ndef point_of_interesed(image):\n\theight = image.shape[0]\n\tpollygons = np.array([(200, height), (1000, height), (550, 250)])\n\tmask = np.zeros_like(image)\n\tcv2.fillConvexPoly(mask, pollygons, 255)\n\treturn mask\n\nimg = cv2.imread('test_image.jpg')\nlane_image = np.copy(img)\ncanny = canny(lane_image)\ncv2.imshow('de ce te uiti la titlu', point_of_interesed(canny)) \ncv2.waitKey(0)","sub_path":"lanes.py","file_name":"lanes.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"346888545","text":"'''Creator: S Rogers\nTitle: 1 - The Basics\n\nDescription: This program is to be used with Year 12 students to introduce them\nto the basics of Tkinter GUI.\n\nThey will learn about importing the module, initialising a window\nand how to use some basic widgets'''\n\n# This line imports the class \"tkinter\" and all of its methods\n# The \"as tk\" is a common approach to make calling methods faster\nimport tkinter as tk\n\n# root could be called anything and simply provides an identifier for the main window\nroot = tk.Tk()\n\n# Create your first label\nmyFirstLabel = tk.Label(root, text=\"Creating my first label\")\nmyFirstLabel.pack()\n\n# Create your first text entry widget\nmyFirstEntry = tk.Entry(root)\nmyFirstEntry.pack()\n\n# Create your first button\nmyFirstButton = tk.Button(root, text=\"Submit\")\nmyFirstButton.pack()\n\n# This method initiates the loop that allows the GUI to actually run\nroot.mainloop()\n","sub_path":"Answers/1 - The_Basics_Answers.py","file_name":"1 - The_Basics_Answers.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"218892552","text":"from ....models.models import Projector\nfrom ....permissions.permissions import Permissions\nfrom ...generics.update import UpdateAction\nfrom ...util.default_schema import DefaultSchema\nfrom ...util.register import register_action\nfrom ..meeting.shared_meeting import used_as_default_for_schema\n\n\n@register_action(\"projector.update\")\nclass ProjectorUpdate(UpdateAction):\n \"\"\"\n Action to update a projector.\n \"\"\"\n\n model = Projector()\n schema = DefaultSchema(Projector()).get_update_schema(\n optional_properties=[\n \"name\",\n \"width\",\n \"aspect_ratio_numerator\",\n \"aspect_ratio_denominator\",\n \"color\",\n \"background_color\",\n \"header_background_color\",\n \"header_font_color\",\n \"header_h1_color\",\n \"chyron_background_color\",\n \"chyron_font_color\",\n \"show_header_footer\",\n \"show_title\",\n \"show_logo\",\n \"show_clock\",\n ],\n additional_optional_fields={\n \"used_as_default_$_in_meeting_id\": used_as_default_for_schema,\n },\n )\n permission = Permissions.Projector.CAN_MANAGE\n","sub_path":"openslides_backend/action/actions/projector/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"596748588","text":"# Author: Matt Sarmiento\n\n\"\"\"\nDecision Trees\n\nThis module provides two classes for implementing Regression and Classifier\nDecision Trees:\n\n- RegressionTree621\n- ClassifierTree621\n\nBoth are children of the abstract base class DecisionTree621.\n\"\"\"\n\n# Third-party\nimport numpy as np\nfrom scipy import stats\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics import accuracy_score\n\n\ndef gini(y):\n \"\"\"Return the gini impurity score of a categorical array.\"\"\"\n n = len(y)\n classes = np.unique(y)\n\n probs = []\n for k in classes:\n n_class = len(y[y == k])\n prob = n_class / n\n probs.append(prob)\n\n impurity = 1 - np.sum(np.square(probs))\n return impurity\n \n\nclass DecisionNode:\n def __init__(self, col, split, lchild, rchild):\n self.col = col\n self.split = split\n self.lchild = lchild\n self.rchild = rchild\n\n def predict(self, x_test):\n if x_test[self.col] <= self.split:\n return self.lchild.predict(x_test)\n return self.rchild.predict(x_test)\n\n\nclass LeafNode:\n def __init__(self, y, prediction):\n self.n = len(y)\n self.prediction = prediction\n\n def predict(self, x_test):\n return self.prediction\n\n\nclass DecisionTree621:\n \"\"\"Abstract base class for Decision Trees.\"\"\"\n\n def __init__(self, min_samples_leaf=1, loss=None):\n self.min_samples_leaf = min_samples_leaf\n self.loss = loss\n\n def fit(self, X, y):\n \"\"\"Wrapper around `_fit()` to store tree in self.root.\"\"\"\n self.root = self._fit(X, y)\n\n def _fit(self, X, y):\n if len(X) <= self.min_samples_leaf:\n return self.create_leaf(y)\n\n col, split = self._bestsplit(X, y, self.loss)\n if col == -1:\n return self.create_leaf(y)\n \n X_col = X[:, col]\n lchild = self._fit(X[X_col <= split], y[X_col <= split])\n rchild = self._fit(X[X_col > split], y[X_col > split])\n \n return DecisionNode(col, split, lchild, rchild)\n\n def _bestsplit(self, X, y, loss):\n \"\"\"Return (feature index, split value) yielding least loss.\"\"\"\n best_col = -1\n best_split = -1\n least_loss = loss(y)\n\n n_cols = X.shape[1]\n for i in range(n_cols):\n X_col = X[:, i]\n splits = np.random.choice(X_col, size=11, replace=True)\n \n for split in splits:\n yl = y[X_col <= split]\n yr = y[X_col > split]\n\n if len(yl) == 0 or len(yr) == 0:\n continue\n\n # Compute weighted loss\n w_loss = (len(yl)*loss(yl) + len(yr)*loss(yr)) / len(y)\n\n if w_loss == 0:\n return i, split\n\n if w_loss < least_loss:\n best_col = i\n best_split = split\n least_loss = w_loss \n\n return best_col, best_split\n\n def predict(self, X_test):\n return [self.root.predict(row) for row in X_test]\n\n\nclass RegressionTree621(DecisionTree621):\n \"\"\"Class for Regression Decision Tree.\"\"\"\n\n def __init__(self, min_samples_leaf=1):\n super().__init__(min_samples_leaf, loss=np.std)\n\n def score(self, X_test, y_test):\n \"\"\"Return R-squared of y_test vs. predictions for X_test.\"\"\"\n y_pred = self.predict(X_test)\n return r2_score(y_test, y_pred)\n\n def create_leaf(self, y):\n return LeafNode(y, np.mean(y))\n\n\nclass ClassifierTree621(DecisionTree621):\n \"\"\"Class for Classifier Decision Trees.\"\"\"\n\n def __init__(self, min_samples_leaf=1):\n super().__init__(min_samples_leaf, loss=gini)\n \n def score(self, X_test, y_test):\n \"\"\"Return accuracy of y_test vs. predictions for X_test.\"\"\"\n y_pred = super().predict(X_test)\n return accuracy_score(y_test, y_pred)\n \n def create_leaf(self, y):\n return LeafNode(y, stats.mode(y)[0][0])\n","sub_path":"gbt/dtree.py","file_name":"dtree.py","file_ext":"py","file_size_in_byte":3942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"282792310","text":"from pylab import *\n\n# Setup data\nN = 256\nr = random(N) * 3 + 0.5\nphi = random(N) * 2 * pi\nT = r ** 0.5\n\n# Plot Voronoi diagram and a scatter plot of the cell centers\nvoronoi_diagram = voronoi(r*sin(phi), r*cos(phi), T, edgecolors='black', cmap='Spectral')\ncolorbar(voronoi_diagram)\nscatter(r*sin(phi), r*cos(phi), s=5, c='k', linewidths=0.0)\nshow()\n\n","sub_path":"voronoi/ring.py","file_name":"ring.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"318099956","text":"import socket\ndef put(socket, data):\n get = socket.recv(1024)\n import datetime\n import time\n now = datetime.datetime.now()\n weekday = time.strftime('%A')[:3]\n day = now.day\n month = time.strftime('%B')[:3]\n year = now.year\n hour = now.hour\n minute = now.minute\n second = now.second\n socket.send('HTTP/1.1 200 OK\\r\\nDate: ' + str(weekday) + ', ' + str(day) + ' ' + str(month) + ' ' + str(year) + ' ' + str(hour) + ':' + str(minute) + ':' + str(second) + 'GMT\\r\\nServer: Python Http Client Server Library\\r\\nLast-Modified: ' + str(weekday) + ', ' + str(day) + ' ' + str(month) + ' ' + str(year) + ' ' + str(hour) + ':' + str(minute) + ':' + str(second) + ' GMT\\r\\nETag: \"1234\"\\r\\nAccept-Ranges: bytes\\r\\nContent-Length: 80\\r\\nVary: Accept-Encoding\\r\\nConnection: close\\r\\nContent-Type: text/html\\r\\n' + str(data))\nwhile True:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n sock.bind(('', 80))\n sock.listen(5)\n server, address = sock.accept()\n put(server, '\\r\\n\\n

Hello World This is a webserver coded in python

\\n\\n')\n server.close()\n","sub_path":"Webserver 1.0/webserver.py","file_name":"webserver.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"379796880","text":"#!/usr/bin/env python3\r\n# coding=utf-8\r\n\r\nimport os\r\nimport re\r\nimport threading\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\nfrom tkinter import messagebox\r\nfrom tkinter import filedialog\r\n\r\nfrom libs import valog\r\nfrom libs import testenv\r\nfrom libs import valcheck\r\nfrom libs import valexample\r\n\r\n# name: [alais,para]\r\nLANGDESDICT = {'chinese': ['cn', 'chinese'],\r\n 'english': ['en', 'english'],\r\n 'japanese': ['jp', 'japanese'],\r\n 'korean': ['kr', 'korean']}\r\n\r\n\r\nclass GetNumDialog: # 定义对话框类\r\n def __init__(self, root, text='Input:'): # 对话框初始化\r\n self.input = 0\r\n self.top = Toplevel(root) # 生成Toplevel组件\r\n self.top.transient(root)\r\n self.top.focus()\r\n mainframe = ttk.Frame(self.top, padding=\"12 12 12 12\")\r\n mainframe.grid(column=0, row=0, sticky=(N, W, E, S))\r\n label = Label(mainframe, text=text) # 生成标签组件\r\n label.grid(column=1, row=1)\r\n self.entry = Entry(mainframe) # 生成文本框组件\r\n self.entry.grid(column=1, row=2)\r\n self.entry.focus() # 让文本框获得焦点\r\n button = Button(mainframe, text='Ok', # 生成按钮\r\n command=self.Ok) # 设置按钮事件处理函数\r\n #button.bind('',self.Ok)\r\n button.grid(column=1, row=3)\r\n\r\n def Ok(self): # 定义按钮事件处理函数\r\n nmstr = self.entry.get() # 获取文本框中内容，保存为input\r\n isnum = re.compile(r'\\d+')\r\n ms = isnum.match(nmstr)\r\n if not ms:\r\n logger.warning(\"Error: input num failed!\")\r\n else:\r\n self.input = int(ms.group())\r\n self.top.destroy() # 销毁对话框\r\n\r\n def get(self): # 返回在文本框输入的内容\r\n return self.input\r\n\r\n\r\nclass GetTestPara: # 定义对话框类\r\n '''if openPath = True dialog will show choose path, default is choose file. chLang = True dialog will show choose language'''\r\n def __init__(self, root, text='Input:', getNum=False, openPath=False, chLang=False): # 对话框初始化\r\n self.input = 0\r\n self.top = Toplevel(root)\r\n self.top.transient(root)\r\n self.top.focus()\r\n mainframe = ttk.Frame(self.top, padding=\"12 12 12 12\")\r\n mainframe.grid(column=0, row=0, sticky=(N, W, E, S))\r\n label = Label(mainframe, text=text)\r\n label.grid(column=1, row=1)\r\n self.entry = Entry(mainframe)\r\n self.entry.grid(column=1, row=2)\r\n self.entry.focus()\r\n button = Button(mainframe, text='Ok', command=self.Ok)\r\n #button.bind('',self.Ok)\r\n button.grid(column=1, row=3)\r\n\r\n def Ok(self):\r\n nmstr = self.entry.get()\r\n isnum = re.compile(r'\\d+')\r\n ms = isnum.match(nmstr)\r\n if not ms:\r\n logger.warning(\"Error: input num failed!\")\r\n else:\r\n self.input = int(ms.group())\r\n self.top.destroy()\r\n\r\n def get(self):\r\n return self.input\r\n\r\n\r\nclass SetDialog: # 定义对话框类\r\n def __init__(self, root): # 对话框初始化\r\n self.input = 0\r\n self.top = Toplevel(root) # 生成Toplevel组件\r\n mainframe = ttk.Frame(self.top, padding=\"12 12 12 12\")\r\n mainframe.grid(column=0, row=0, sticky=(N, W, E, S))\r\n label = Label(mainframe, text='...') # 生成标签组件\r\n label.grid(column=1, row=1)\r\n self.entry = Entry(mainframe) # 生成文本框组件\r\n self.entry.grid(column=1, row=2)\r\n self.entry.focus() # 让文本框获得焦点\r\n button = Button(mainframe, text='Ok', # 生成按钮\r\n command=self.Ok) # 设置按钮事件处理函数\r\n #self.top.bind('',self.Ok)\r\n button.grid(column=1, row=3)\r\n\r\n def Ok(self): # 定义按钮事件处理函数\r\n self.input = self.entry.get() # 获取文本框中内容，保存为input\r\n self.top.destroy() # 销毁对话框\r\n\r\n def get(self): # 返回在文本框输入的内容\r\n return self.input\r\n\r\n\r\nclass Gui_Full(object):\r\n def __init__(self, roots, t_env):\r\n self.testenv = t_env\r\n self.valexe = valexample.ValExample(self.testenv.getReleaseDir())\r\n\r\n self.curTestFile = None\r\n self.roots = roots\r\n self.isrunning = False\r\n \r\n self.lang_list = [\"chinese\", \"english\", \"japanese\", \"korean\"]\r\n self.fn_list = [\"summary\", \"index\", \"glossary\", \"toc\", \"spell\", \"smart\"]\r\n self.nf_list = [\"summary\", \"index\", \"glossary\", \"toc\", \"keyword\", \"stem\", \"wordseg\", \"spellcheck\", \"smartcheck\"]\r\n \r\n self.valexe.initVal()\r\n\r\n #config menu\r\n self.__confmenu__()\r\n\r\n #cofig main gui\r\n self.__confmaingui__()\r\n\r\n def __confmenu__(self):\r\n menu_roots = Menu(self.roots)\r\n\r\n menu_f = Menu(menu_roots, tearoff=0)\r\n menu_f.add_command(label='Open', command=self.__chooseEpubFile__)\r\n menu_f.add_separator()\r\n menu_f.add_command(label='Exit', command=self.mfExit)\r\n\r\n menu_tl = Menu(menu_roots, tearoff=0)\r\n menu_tl.add_command(label='Generate Summary', command=None)\r\n menu_tl.add_command(label='Generate Index', command=None)\r\n menu_tl.add_command(label='Generate TOC', command=None)\r\n menu_tl.add_command(label='Generate Glossary', command=None)\r\n menu_tl.add_separator()\r\n menu_tl.add_command(label='Check Epub', command=self.checkEpub)\r\n\r\n menu_st = Menu(menu_roots, tearoff=0)\r\n menu_st.add_command(label='Setting', command=self.msSetting)\r\n\r\n menu_hp = Menu(menu_roots, tearoff=0)\r\n menu_hp.add_command(label='About', command=self.maAbout)\r\n\r\n #布局\r\n menu_roots.add_cascade(label=\"File\", menu=menu_f)\r\n menu_roots.add_cascade(label=\"Tools\", menu=menu_tl)\r\n menu_roots.add_cascade(label=\"Setting\", menu=menu_st)\r\n menu_roots.add_cascade(label=\"Help\", menu=menu_hp)\r\n\r\n self.roots.config(menu=menu_roots)\r\n\r\n def __confmaingui__(self):\r\n #sheet frame\r\n sheetframe = ttk.Frame(self.roots, padding=\"12 12 12 12\")\r\n sheetframe.grid(column=0, row=0, sticky=(N, W, E, S))\r\n \r\n sheet = ttk.Notebook(sheetframe)\r\n sheet.pack(fill='both', expand='yes')\r\n \r\n funframe = ttk.Frame(sheetframe, padding=\"12 12 12 12\")\r\n funframe.grid(column=0, row=0, sticky=(N, W, E, S))\r\n funframe.columnconfigure(0, weight=1)\r\n funframe.rowconfigure(0, weight=1)\r\n self.__sheetFunOper__(funframe)\r\n \r\n nfunframe = ttk.Frame(sheetframe, padding=\"12 12 12 12\")\r\n nfunframe.grid(column=0, row=0, sticky=(N, W, E, S))\r\n nfunframe.columnconfigure(0, weight=1)\r\n nfunframe.rowconfigure(0, weight=1)\r\n self.__sheetNFunOper__(nfunframe)\r\n \r\n sheet.add(funframe, text='Function Test')\r\n sheet.add(nfunframe, text='non-Function Test')\r\n #no sheet frame\r\n otherframe = ttk.Frame(self.roots, padding=\"12 12 12 12\")\r\n otherframe.grid(column=0, row=1, sticky=(N, W, E, S))\r\n #output block\r\n groupOutPut = LabelFrame(otherframe, text=\"Output\", relief=SUNKEN, padx=10, pady=5)\r\n groupOutPut.grid(column=0, row=4, columnspan=2, sticky=(N, W, E, S))\r\n\r\n self.Output = Text(groupOutPut, height=6, width=115)\r\n self.Output.grid(column=1, row=0, columnspan=8, rowspan=4)\r\n self.Output.insert(0.0, '')\r\n \r\n for child in groupOutPut.winfo_children():\r\n child.grid_configure(padx=10, pady=5)\r\n \r\n #State\r\n self.disState = StringVar()\r\n self.disState.set('Stop.')\r\n \r\n lbState = ttk.Label(otherframe, textvariable=self.disState, borderwidth=1, relief=SUNKEN, anchor=W, width=55)\r\n lbState.grid(column=0, row=5, columnspan=2, sticky=(N, W, E, S))\r\n \r\n def __sheetFunOper__(self, mainframe):\r\n #epub test block\r\n groupEpubT = LabelFrame(mainframe, text=\"EPUB Test\", relief=SUNKEN, padx=10, pady=5)\r\n groupEpubT.grid(column=0, row=0, sticky=(N, W, E, S))\r\n self.__confEpubOper__(groupEpubT)\r\n \r\n #spell check block\r\n groupSpellT = LabelFrame(mainframe, text=\"Spell check Test\", relief=SUNKEN, padx=10, pady=5)\r\n groupSpellT.grid(column=1, row=0, sticky=(N, W, E, S))\r\n self.__confSpellOper__(groupSpellT)\r\n \r\n #smart guider block\r\n groupSmartT = LabelFrame(mainframe, text=\"Smart Word Test\", relief=SUNKEN, padx=10, pady=5)\r\n groupSmartT.grid(column=0, row=1, sticky=(N, W, E, S))\r\n self.__confSmartOper__(groupSmartT)\r\n \r\n \r\n #dictionary block\r\n groupDictT = LabelFrame(mainframe, text=\"Dictionary Test\", relief=SUNKEN, padx=10, pady=5)\r\n groupDictT.grid(column=1, row=1, sticky=(N, W, E, S))\r\n self.__confDictOper__(groupDictT)\r\n\r\n for child in mainframe.winfo_children():\r\n child.grid_configure(padx=1, pady=5)\r\n \r\n def __sheetNFunOper__(self, mainframe):\r\n #non funcation test block\r\n groupNFT = LabelFrame(mainframe, text=\"Non Funcation Test\", relief=SUNKEN, padx=10, pady=5)\r\n groupNFT.grid(column=0, row=0, columnspan=2, sticky=(N, W, E, S))\r\n self.__confNonFunOper__(groupNFT)\r\n\r\n for child in mainframe.winfo_children():\r\n child.grid_configure(padx=1, pady=5)\r\n \r\n def __confEpubOper__(self, groupEpubT):\r\n ttk.Label(groupEpubT, text='Epub:').grid(column=0, row=0)\r\n self.epubPath = Text(groupEpubT, height=2, width=26)\r\n self.epubPath.grid(column=1, row=0, columnspan=2)\r\n self.epubPath.insert(0.0, '*')\r\n ttk.Button(groupEpubT, text=\"Browse\", width=10, command=self.__chooseEpubFile__).grid(column=3, row=0)\r\n \r\n self.selectEpubFun = StringVar()\r\n self.selectEpubFun.set(self.fn_list[0]) # default value\r\n self.combEpubFun = ttk.Combobox(groupEpubT, textvariable=self.selectEpubFun, width=8, values=self.fn_list)\r\n self.epubFunPara = Entry(groupEpubT, width=10)\r\n self.butEpubRun = ttk.Button(groupEpubT, text=\"Run...\", width=10, command=self.execFN)\r\n self.butEpubcheck = ttk.Button(groupEpubT, text=\"Check\", width=10, command=self.checkEpub)\r\n \r\n \r\n self.combEpubFun.grid(column=0, row=1)\r\n self.epubFunPara.grid(column=1, row=1)\r\n self.butEpubRun.grid(column=2, row=1)\r\n self.butEpubcheck.grid(column=3, row=1)\r\n \r\n ttk.Label(groupEpubT, text='CFI Query:').grid(column=0, row=2)\r\n self.cfiString = Entry(groupEpubT, width=26)\r\n self.cfiString.grid(column=1, row=2, columnspan=2)\r\n ttk.Button(groupEpubT, text=\"Query\", width=10, command=self.queryCfi).grid(column=3, row=2)\r\n \r\n for child in groupEpubT.winfo_children():\r\n child.grid_configure(padx=10, pady=5)\r\n \r\n def __confSpellOper__(self, groupSpellT):\r\n ttk.Label(groupSpellT, text='Input:').grid(column=0, row=0)\r\n self.spellInput = Text(groupSpellT, height=2, width=26)\r\n self.spellInput.grid(column=1, row=0, columnspan=2)\r\n self.spellInput.insert(0.0, '*')\r\n self.spellLang = StringVar()\r\n self.spellLang.set(self.lang_list[0]) # default value\r\n self.combSpellLang = ttk.Combobox(groupSpellT, textvariable=self.spellLang, width=9, values=self.lang_list)\r\n self.combSpellLang.grid(column=3, row=0)\r\n\r\n self.butSpellCheck = ttk.Button(groupSpellT, text=\"Check\", width=10, command=self.spellCheck)\r\n self.butSpellSuggest = ttk.Button(groupSpellT, text=\"Suggest\", width=10, command=self.spellSuggest)\r\n self.butSpellAddWord = ttk.Button(groupSpellT, text=\"Add Word\", width=10, command=self.addWord)\r\n self.butSpellAddAffx = ttk.Button(groupSpellT, text=\"Add Affx\", width=10, command=self.addAffx)\r\n\r\n self.butSpellCheck.grid(column=0, row=1)\r\n self.butSpellSuggest.grid(column=1, row=1)\r\n self.butSpellAddWord.grid(column=2, row=1)\r\n self.butSpellAddAffx.grid(column=3, row=1)\r\n \r\n ttk.Label(groupSpellT, text='Third Tools:').grid(column=0, row=2)\r\n self.butHunspell = ttk.Button(groupSpellT, text=\"Hunspell\", width=10, command=self.hunspell)\r\n\r\n self.butHunspell.grid(column=3, row=2)\r\n\r\n for child in groupSpellT.winfo_children():\r\n child.grid_configure(padx=10, pady=5)\r\n \r\n def __confSmartOper__(self, groupSmartT):\r\n ttk.Label(groupSmartT, text='Input:').grid(column=0, row=0)\r\n self.smartInput = Text(groupSmartT, height=2, width=26)\r\n self.smartInput.grid(column=1, row=0, columnspan=2)\r\n self.smartInput.insert(0.0, '*')\r\n self.smartLang = StringVar()\r\n self.smartLang.set(self.lang_list[0]) # default value\r\n self.combSmartLang = ttk.Combobox(groupSmartT, textvariable=self.smartLang, width=9, values=self.lang_list)\r\n self.combSmartLang.grid(column=3, row=0)\r\n\r\n self.butSmartCheck = ttk.Button(groupSmartT, text=\"Check\", width=10, command=self.smartCheck)\r\n self.butSmartSuggest = ttk.Button(groupSmartT, text=\"Suggest\", width=10, command=self.smartSuggest)\r\n \r\n self.butSmartCheck.grid(column=0, row=1)\r\n self.butSmartSuggest.grid(column=1, row=1)\r\n \r\n self.paraSmartLevel = Entry(groupSmartT, width=10)\r\n self.butSmartLevel = ttk.Button(groupSmartT, text=\"Set Level\", width=10, command=self.setSmartLevel)\r\n\r\n self.paraSmartLevel.grid(column=2, row=1)\r\n self.butSmartLevel.grid(column=3, row=1)\r\n \r\n self.paraSmartDomain = Entry(groupSmartT, width=10)\r\n self.butSmartDomain = ttk.Button(groupSmartT, text=\"Set Domain\", width=10, command=self.setSmartDomain)\r\n\r\n self.paraSmartDomain.grid(column=2, row=2)\r\n self.butSmartDomain.grid(column=3, row=2)\r\n\r\n for child in groupSmartT.winfo_children():\r\n child.grid_configure(padx=10, pady=5)\r\n \r\n def __confDictOper__(self, groupDictT):\r\n pass\r\n\r\n def __confNonFunOper__(self, groupNFT):\r\n self.selectNFLang = StringVar()\r\n self.selectNFLang.set(self.lang_list[0]) # default value\r\n self.combNFLang = ttk.Combobox(groupNFT, textvariable=self.selectNFLang, width=9, values=self.lang_list)\r\n self.combNFLang.grid(column=0, row=0)\r\n self.selectNFun = StringVar()\r\n self.selectNFun.set(self.nf_list[0]) # default value\r\n self.combNF = ttk.Combobox(groupNFT, textvariable=self.selectNFun, width=9, values=self.nf_list)\r\n self.combNF.grid(column=1, row=0)\r\n self.butNF = ttk.Button(groupNFT, text=\"Run...\", width=10, command=self.execNF)\r\n self.butNF.grid(column=2, row=0)\r\n\r\n for child in groupNFT.winfo_children():\r\n child.grid_configure(padx=10, pady=5)\r\n \r\n def __exe_command__(func):\r\n '''decorate for ui update'''\r\n def run_command(self, *args, **kargs):\r\n self.__strun__()\r\n back = func(self, *args, **kargs)\r\n self.__ststop__()\r\n return back\r\n return run_command\r\n \r\n def __strun__(self, showMsg=None):\r\n if self.isrunning == False:\r\n self.isrunning = True\r\n else:\r\n self.isrunning = False # check when run by menu\r\n self.__updateState__('Running...')\r\n '''state=The button state: NORMAL, ACTIVE or DISABLED. Default is NORMAL. (state/State)'''\r\n self.butEpubRun.config(state=DISABLED)\r\n self.butEpubcheck.config(state=DISABLED)\r\n self.butSpellCheck.config(state=DISABLED)\r\n self.butSpellSuggest.config(state=DISABLED)\r\n self.butSpellAddWord.config(state=DISABLED)\r\n self.butSpellAddAffx.config(state=DISABLED)\r\n self.butNF.config(state=DISABLED)\r\n\r\n if showMsg != None:\r\n self.__updateState__(showMsg)\r\n\r\n self.roots.update()\r\n\r\n def __ststop__(self, showMsg=None):\r\n self.isrunning = False\r\n #self.disState.set('Stop.')\r\n self.butEpubRun.config(state=ACTIVE)\r\n self.butEpubcheck.config(state=ACTIVE)\r\n self.butSpellCheck.config(state=ACTIVE)\r\n self.butSpellSuggest.config(state=ACTIVE)\r\n self.butSpellAddWord.config(state=ACTIVE)\r\n self.butSpellAddAffx.config(state=ACTIVE)\r\n self.butNF.config(state=ACTIVE)\r\n\r\n if showMsg != None:\r\n self.__updateState__(showMsg)\r\n\r\n self.roots.update()\r\n \r\n def __canrun__(self, nm = ''):\r\n ret = None\r\n if nm != None and self.isrunning == True:\r\n ret = True\r\n else:\r\n ret = False\r\n logger.warning(\"Error: __canrun__ can not run!\")\r\n self.__updateState__('Can not running! ')\r\n return ret\r\n \r\n def __run_in_th__(self, fun, args=(), timeout=200):\r\n th = threading.Thread(target=fun, args=args)\r\n th.setDaemon(True) # 把线程的daemon标志设为daemonic\r\n th.start()\r\n th.join(timeout)\r\n \r\n def __chooseEpubFile__(self):\r\n self.epubPath.delete(0.0, END)\r\n self.curTestFile = filedialog.askopenfilename(title=\"Open test file\", filetypes=[('epub files', '.epub')], initialdir=self.testenv.getReleaseDir())\r\n self.epubPath.insert(0.0, self.curTestFile)\r\n\r\n def __choosePath__(self):\r\n self.nfunPath.delete(0.0, END)\r\n self.curTestPath = filedialog.askdirectory(title=\"Open test path\", initialdir=self.testenv.getReleaseDir())\r\n self.nfunPath.insert(0.0, self.curTestPath)\r\n\r\n def __getNumformEntry__(self, nmEntry):\r\n ret = 0\r\n nmstr = nmEntry.get()\r\n isnum = re.compile(r'\\d+')\r\n ms = isnum.match(nmstr)\r\n if not ms:\r\n logger.warning(\"Error: input num failed!\")\r\n else:\r\n ret = int(ms.group())\r\n return ret\r\n\r\n def __getEpubName__(self):\r\n ret = None\r\n nm = self.epubPath.get(0.0, END)\r\n #return nm.split('\\n')[0]\r\n ePubPatten = re.compile(r'.+\\.\\w+')\r\n ms = ePubPatten.match(nm)\r\n if not ms:\r\n logger.warning(\"Error: __gettestpath__ failed!\")\r\n else:\r\n ret = ms.group()\r\n return ret\r\n \r\n def __getSpellInput__(self):\r\n ret = None\r\n ret = self.spellInput.get(0.0, END)\r\n return ret\r\n \r\n def __getSpellLanguage__(self):\r\n ret = None\r\n ret = self.spellLang.get()\r\n return ret\r\n \r\n def __getSmartInput__(self):\r\n ret = None\r\n ret = self.smartInput.get(0.0, END)\r\n return ret\r\n \r\n def __getSmartLanguage__(self):\r\n ret = None\r\n ret = self.smartLang.get()\r\n return ret\r\n \r\n def __insertOutput__(self, text):\r\n if text == None:\r\n self.Output.delete(0.0, END)\r\n self.Output.insert(0.0, 'None')\r\n else:\r\n self.Output.delete(0.0, END)\r\n self.Output.insert(0.0, text)\r\n \r\n def __updateState__(self, ststr):\r\n self.disState.set(ststr)\r\n self.roots.update()\r\n \r\n def execFN(self):\r\n ret = ''\r\n nm = self.__getEpubName__()\r\n para = self.__getNumformEntry__(self.epubFunPara)\r\n ft = self.selectEpubFun.get()\r\n if ft == 'summary':\r\n ret = self.genSumm(nm, para)\r\n elif ft == 'index':\r\n ret = self.genIndex(nm)\r\n elif ft == 'glossary':\r\n ret = self.genGlossary(nm, para)\r\n elif ft == 'toc':\r\n ret = self.genToc(nm)\r\n elif ft == 'spell':\r\n ret = self.epubSpellCheck(nm)\r\n elif ft == 'smart':\r\n ret = self.epubSmartCheck(nm)\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def genSumm(self, nm, para):\r\n summco = ''\r\n if self.__canrun__(nm):\r\n if para > 0:\r\n summco, summstr = self.valexe.genSumm(nm, para)\r\n self.__updateState__('generate summary finished, length is ' + str(para))\r\n else:\r\n self.__updateState__('pls. input summary parameter!')\r\n return summco, summstr\r\n\r\n @__exe_command__\r\n def genGlossary(self, nm, para):\r\n ret = ''\r\n if self.__canrun__(nm):\r\n if para > 0:\r\n ret = self.valexe.genGlossary(nm, para)\r\n self.__updateState__('generate glossary finished, level is ' + str(para))\r\n else:\r\n self.__updateState__('pls. input glossary parameter!')\r\n return ret\r\n\r\n @__exe_command__\r\n def genIndex(self, nm):\r\n ret = ''\r\n if self.__canrun__(nm):\r\n ret = self.valexe.genIndexing(nm)\r\n self.__updateState__('generate index finished.')\r\n return ret\r\n\r\n @__exe_command__\r\n def genToc(self, nm):\r\n ret = ''\r\n if self.__canrun__(nm):\r\n ret = self.valexe.genTOC(nm)\r\n self.__updateState__('generate toc finished.')\r\n return ret\r\n \r\n @__exe_command__\r\n def epubSpellCheck(self, nm):\r\n ret = ''\r\n if self.__canrun__(nm):\r\n ret = self.valexe.checkSpellEpub(nm)\r\n self.__updateState__('epub spell check finished.')\r\n return ret\r\n \r\n @__exe_command__\r\n def epubSmartCheck(self, nm):\r\n ret = ''\r\n if self.__canrun__(nm):\r\n ret = self.valexe.smartWordCheckEpub(nm)\r\n self.__updateState__('epub smart check finished.')\r\n return ret\r\n \r\n @__exe_command__\r\n def spellCheck(self):\r\n input_str = self.__getSpellInput__()\r\n lang = self.__getSpellLanguage__()\r\n if self.__canrun__():\r\n ret = self.valexe.spellCheck(LANGDESDICT[lang][1], input_str[:-1])\r\n self.__updateState__('spell check finished.')\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def spellSuggest(self):\r\n input_str = self.__getSpellInput__()\r\n lang = self.__getSpellLanguage__()\r\n if self.__canrun__():\r\n ret = self.valexe.spellSuggest(LANGDESDICT[lang][1], input_str[:-1])\r\n self.__updateState__('spell suggest finished.')\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def addWord(self):\r\n input_str = self.__getSpellInput__()\r\n lang = self.__getSpellLanguage__()\r\n if self.__canrun__():\r\n ret = self.valexe.spellAdd(lang, input_str[:-1])\r\n self.__updateState__('add word finished.')\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def addAffx(self):\r\n input_str = self.__getSpellInput__()\r\n lang = self.__getSpellLanguage__()\r\n if self.__canrun__():\r\n ret = self.valexe.spellAddWithAffixDll(lang, input_str[:-1], '')\r\n self.__updateState__('add affix finished.')\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def smartCheck(self):\r\n input_str = self.__getSmartInput__()\r\n lang = self.__getSmartLanguage__()\r\n if self.__canrun__():\r\n ret = self.valexe.smartWordCheck(LANGDESDICT[lang][1], input_str[:-1])\r\n \r\n self.__updateState__('smart check finished.{0}_{1}'.format(LANGDESDICT[lang][1], input_str[:-1]))\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def smartSuggest(self):\r\n input_str = self.__getSmartInput__()\r\n lang = self.__getSmartLanguage__()\r\n if self.__canrun__():\r\n ret = self.valexe.smartWordSuggest(LANGDESDICT[lang][1], input_str[:-1])\r\n self.__updateState__('smart suggest finished.')\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def setSmartLevel(self):\r\n para = self.__getNumformEntry__(self.paraSmartLevel)\r\n lang = self.__getSmartLanguage__()\r\n if self.__canrun__():\r\n ret = self.valexe.setInterestedDictLevel(LANGDESDICT[lang][1], para)\r\n self.__updateState__('set smart level finished.')\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def setSmartDomain(self):\r\n para = self.paraSmartDomain.get()\r\n lang = self.__getSmartLanguage__()\r\n if self.__canrun__():\r\n ret = self.valexe.setInterestedDictDomain(LANGDESDICT[lang][1], para)\r\n self.__updateState__('set smart domain finished.')\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def checkEpub(self):\r\n nm = self.__getEpubName__()\r\n if self.__canrun__(nm):\r\n checktools = valcheck.ValCheck(epchdir=self.testenv.getEpubCheckDir())\r\n ret = checktools.checkEpub(nm)\r\n if \"WARNING\" in ret:\r\n self.__updateState__('Chenk epub failed. ')\r\n else:\r\n self.__updateState__('Chenk epub pass. ')\r\n self.__insertOutput__(ret)\r\n \r\n @__exe_command__\r\n def queryCfi(self):\r\n nm = self.__getEpubName__()\r\n cfistr = self.cfiString.get()\r\n if self.__canrun__(nm):\r\n epubobj = valcheck.ValEpub(nm)\r\n ret = epubobj.queryCfi(cfistr)\r\n if not isinstance(ret, str):\r\n self.__updateState__('Cfi query result is Element. ')\r\n self.__insertOutput__(type(ret))\r\n else:\r\n self.__updateState__('Cfi query finished. ')\r\n self.__insertOutput__(str(ret))\r\n \r\n def execNF(self):\r\n ret = ''\r\n valeval = valcheck.ValEval(self.testenv.getReleaseDir())\r\n ft = self.selectNFun.get()\r\n curLang = self.selectNFLang.get()\r\n self.__strun__('start eval - ' + ft + ', language is: ' + curLang)\r\n #self.__updateState__('start eval ')# + ft + ',language is: ' + curLang)\r\n if ft == 'summary':\r\n curTestPath = filedialog.askdirectory(title=\"Open test summary path\", initialdir=os.getcwd())\r\n valeval.evalSumm(curTestPath)\r\n elif ft == 'index':\r\n curTestPath = filedialog.askdirectory(title=\"Open test index path\", initialdir=os.getcwd())\r\n valeval.evalIndex(curTestPath)\r\n elif ft == 'glossary':\r\n valeval.evalGlossary()\r\n elif ft == 'toc':\r\n valeval.evalToc()\r\n elif ft == 'keyword':\r\n curTestPath = filedialog.askdirectory(title=\"Open test keyword path\", initialdir=os.getcwd())\r\n valeval.evalKeywords(curTestPath, LANGDESDICT[curLang][1])\r\n elif ft == 'stem':\r\n curTestFile = filedialog.askopenfilename(title=\"Open eval test stem file\", initialdir=os.getcwd())\r\n valeval.evalStem(curTestFile)\r\n elif ft == 'wordseg':\r\n valeval.evalWordSeg('', LANGDESDICT[curLang][0])\r\n elif ft == 'smartcheck':\r\n curTestFile = filedialog.askopenfilename(title=\"Open eval test smart file\", initialdir=os.getcwd())\r\n ret = valeval.evalSmartWordCheck(curTestFile,curLang)\r\n self.__insertOutput__(ret)\r\n #self.__updateState__('end eval ' + ft)\r\n self.__ststop__('end eval - ' + ft + ', language is: ' + curLang)\r\n \r\n @__exe_command__\r\n def hunspell(self):\r\n input_str = self.__getSpellInput__()\r\n lang = self.__getSpellLanguage__()\r\n if self.__canrun__():\r\n checktools = valcheck.ValCheck()\r\n ret = checktools.hunspell(input_str, lang)\r\n self.__updateState__('hunspell run finished. ')\r\n self.__insertOutput__(ret)\r\n\r\n def mfExit(self):\r\n #self.roots.quit()\r\n top.quit()\r\n\r\n def msSetting(self):\r\n d = SetDialog(self.roots) # 生成对话框\r\n self.roots.wait_window(d.top) # 等待对话框结束SetDialog\r\n\r\n def maAbout(self):\r\n #print('This is multi-tools')\r\n #Message(text =\"About This is tools for VAL project test.\")\r\n messagebox.showinfo(\"About\", \"This is tools for VAL project test.\")\r\n\r\n\r\nif __name__ == '__main__':\r\n vlg = valog.initLog('valgui.log')\r\n logger = valog.getLogger(\"valgui\")\r\n\r\n tEnv = testenv.TestEnv()\r\n\r\n top = Tk()\r\n top.title(\"VAL Test Tools \" + \"V0.0.5\")\r\n\r\n Gui_Full(top, tEnv)\r\n\r\n top.mainloop()\r\n","sub_path":"pyunit_sample/valgui.pyw","file_name":"valgui.pyw","file_ext":"pyw","file_size_in_byte":29623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"317345171","text":"import requests\n\n# url = 'http://blog.naver.com/otter35'\nurl = 'https://www.coupang.com'\nheader = {'User-Agent':\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.102 Safari/537.36\"} # 알아가기 참조 \n# https://www.whatismybrowser.com/detect/what-is-my-user-agent \nres = requests.get(url=url, headers=header)\nprint(type(res), res) \n#\n\nprint(res.status_code)\nif(res.status_code == 200):\n with open('datas/response01.html', 'w') as fp: # vs code 는 파일의 폴더가 root로 인식. 그래서 datas/resposes01.html \n fp.write(res.text)\n","sub_path":"revision/savefilewithrequest.py","file_name":"savefilewithrequest.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"535167251","text":"import os, platform\nfrom PyQt5 import QtWidgets, uic, QtCore, QtGui, Qt\nfrom functools import partial\nimport GlobalSettings\nimport gzip\nimport traceback\nimport math\n\n#global logger\nlogger = GlobalSettings.logger\n\nclass OffTarget(QtWidgets.QMainWindow):\n\n def __init__(self):\n try:\n super(OffTarget, self).__init__()\n uic.loadUi(GlobalSettings.appdir + 'off_target.ui', self)\n self.setWindowIcon(Qt.QIcon(GlobalSettings.appdir + \"cas9image.ico\"))\n self.setWindowTitle(\"Off-Target Analysis\")\n self.progressBar.setMinimum(0)\n self.progressBar.setMaximum(100)\n self.progressBar.setValue(0)\n self.Run.clicked.connect(self.run_analysis)\n # self.tolerancehorizontalSlider.valueChanged.connect(self.tol_change)\n # self.tolerancehorizontalSlider.setMaximum(100)\n # self.tolerancehorizontalSlider.setMinimum(0)\n self.tolerance = 0.0\n\n self.cancelButton.clicked.connect(self.exit)\n self.fill_data_dropdown()\n self.perc = False\n self.bool_temp = False\n self.running = False\n self.process = QtCore.QProcess()\n\n # make sure to intialize the class variable in init. That way elsewhere and other classes can access it\n self.output_path = ''\n\n groupbox_style = \"\"\"\n QGroupBox:title{subcontrol-origin: margin;\n left: 10px;\n padding: 0 5px 0 5px;}\n QGroupBox#Step1{border: 2px solid rgb(111,181,110);\n border-radius: 9px;\n font: bold 14pt 'Arial';\n margin-top: 10px;}\"\"\"\n\n self.Step1.setStyleSheet(groupbox_style)\n self.Step2.setStyleSheet(groupbox_style.replace(\"Step1\", \"Step2\"))\n self.Step3.setStyleSheet(groupbox_style.replace(\"Step1\", \"Step3\"))\n\n #scale UI\n self.scaleUI()\n\n except Exception as e:\n logger.critical(\"Error initializing OffTarget class.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n exit(-1)\n\n #scale UI based on current screen\n def scaleUI(self):\n try:\n self.repaint()\n QtWidgets.QApplication.processEvents()\n\n screen = self.screen()\n dpi = screen.physicalDotsPerInch()\n width = screen.geometry().width()\n height = screen.geometry().height()\n\n # font scaling\n fontSize = 12\n self.fontSize = fontSize\n self.centralWidget().setStyleSheet(\"font: \" + str(fontSize) + \"pt 'Arial';\")\n\n # CASPER header scaling\n fontSize = 20\n self.title.setStyleSheet(\"font: bold \" + str(fontSize) + \"pt 'Arial';\")\n\n self.adjustSize()\n\n currentWidth = self.size().width()\n currentHeight = self.size().height()\n\n # window scaling\n # 1920x1080 => 850x750\n scaledWidth = int((width * 400) / 1920)\n scaledHeight = int((height * 450) / 1080)\n\n if scaledHeight < currentHeight:\n scaledHeight = currentHeight\n if scaledWidth < currentWidth:\n scaledWidth = currentWidth\n\n screen = QtWidgets.QApplication.desktop().screenNumber(QtWidgets.QApplication.desktop().cursor().pos())\n centerPoint = QtWidgets.QApplication.desktop().screenGeometry(screen).center()\n x = centerPoint.x()\n y = centerPoint.y()\n x = x - (math.ceil(scaledWidth / 2))\n y = y - (math.ceil(scaledHeight / 2))\n self.setGeometry(x, y, scaledWidth, scaledHeight)\n\n self.repaint()\n QtWidgets.QApplication.processEvents()\n\n except Exception as e:\n logger.critical(\"Error in scaleUI() in Off-Target.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n #center UI on current screen\n def centerUI(self):\n try:\n self.repaint()\n QtWidgets.QApplication.processEvents()\n\n # center window on current screen\n width = self.width()\n height = self.height()\n screen = QtWidgets.QApplication.desktop().screenNumber(QtWidgets.QApplication.desktop().cursor().pos())\n centerPoint = QtWidgets.QApplication.desktop().screenGeometry(screen).center()\n x = centerPoint.x()\n y = centerPoint.y()\n x = x - (math.ceil(width / 2))\n y = y - (math.ceil(height / 2))\n self.setGeometry(x, y, width, height)\n\n self.repaint()\n QtWidgets.QApplication.processEvents()\n except Exception as e:\n logger.critical(\"Error in centerUI() in Off-Target.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n #copied from MT to fill in the chromo and endo dropdowns based on CSPR files user provided at the startup\n def fill_data_dropdown(self):\n try:\n try:\n self.EndocomboBox.diconnect()\n except:\n pass\n try:\n self.OrgcomboBox.diconnect()\n except:\n pass\n\n self.OrgcomboBox.clear()\n self.EndocomboBox.clear()\n self.mismatchcomboBox.clear()\n\n self.organisms_to_files = {}\n self.organisms_to_endos = {}\n\n #fill in chromosome and endo dropdowns\n onlyfiles = [f for f in os.listdir(GlobalSettings.CSPR_DB) if os.path.isfile(os.path.join(GlobalSettings.CSPR_DB , f))]\n self.orgsandendos = {}\n self.shortName = {}\n for file in onlyfiles:\n if file.find('.cspr') != -1:\n newname = file[0:-4]\n endo = newname[newname.rfind(\"_\") + 1:-1]\n hold = open(file, 'r')\n buf = (hold.readline())\n hold.close()\n buf = str(buf)\n buf = buf.strip()\n species = buf.replace(\"GENOME: \", \"\")\n\n if species in self.organisms_to_files:\n self.organisms_to_files[species][endo] = [file, file.replace(\".cspr\", \"_repeats.db\")]\n else:\n self.organisms_to_files[species] = {}\n self.organisms_to_files[species][endo] = [file, file.replace(\".cspr\", \"_repeats.db\")]\n\n if species in self.organisms_to_endos:\n self.organisms_to_endos[species].append(endo)\n else:\n self.organisms_to_endos[species] = [endo]\n if self.OrgcomboBox.findText(species) == -1:\n self.OrgcomboBox.addItem(species)\n\n # fill in endos dropdown based on current organism\n endos = self.organisms_to_endos[str(self.OrgcomboBox.currentText())]\n self.EndocomboBox.addItems(endos)\n self.OrgcomboBox.currentIndexChanged.connect(self.update_endos)\n self.EndocomboBox.currentIndexChanged.connect(self.change_endos)\n\n # update file names for current org/endo combo\n self.cspr_file = self.organisms_to_files[str(self.OrgcomboBox.currentText())][endos[0]][0]\n self.db_file = self.organisms_to_files[str(self.OrgcomboBox.currentText())][endos[0]][1]\n\n #fill in Max Mismatch dropdown\n mismatch_list = ['1','2','3','4','5','6','7','8','9','10']\n self.mismatchcomboBox.addItems(mismatch_list)\n self.mismatchcomboBox.setCurrentIndex(3) ### Max number of mismatches is 4 by default\n except Exception as e:\n logger.critical(\"Error in fill_data_dropdown() in OffTarget.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n def change_endos(self):\n try:\n #update file names based on current org/endo combo\n self.cspr_file = self.organisms_to_files[str(self.OrgcomboBox.currentText())][str(self.EndocomboBox.currentText())][0]\n self.db_file = self.organisms_to_files[str(self.OrgcomboBox.currentText())][str(self.EndocomboBox.currentText())][1]\n except Exception as e:\n logger.critical(\"Error in change_endos() in OffTarget.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n def update_endos(self):\n try:\n #try to disconnect index changed signal on endo dropdown if there is one\n try:\n self.EndocomboBox.currentIndexChanged.disconnect()\n except:\n pass\n\n #clear endo dropdown and fill in with endos relative to the current organism\n self.EndocomboBox.clear()\n endos = self.organisms_to_endos[str(self.OrgcomboBox.currentText())]\n self.EndocomboBox.addItems(endos)\n self.cspr_file = self.organisms_to_files[str(self.OrgcomboBox.currentText())][endos[0]][0]\n self.db_file = self.organisms_to_files[str(self.OrgcomboBox.currentText())][endos[0]][1]\n\n #reconnect index changed signal on endo dropdown\n self.EndocomboBox.currentIndexChanged.connect(self.change_endos)\n except Exception as e:\n logger.critical(\"Error in update_endos() in OffTarget.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n #tolerance slider / entry box. Allows for slider to update, or the user to input in text box\n # def tol_change(self):\n # try:\n # if(self.tolerance == float(self.tolerancelineEdit.text())):\n # self.tolerance = self.tolerancehorizontalSlider.value() / 100 * 0.5\n # self.tolerance = round(self.tolerance, 3)\n # self.tolerancelineEdit.setText(str(self.tolerance))\n # else:\n # self.tolerance = float(self.tolerancelineEdit.text())\n # self.tolerance = round(self.tolerance, 3)\n # self.tolerancehorizontalSlider.setValue(round(self.tolerance/0.5 * 100))\n # except Exception as e:\n # logger.critical(\"Error in tol_change() in OffTarget.\")\n # logger.critical(e)\n # logger.critical(traceback.format_exc())\n # exit(-1)\n\n #run button linked to run_analysis, which is linked to the run button\n def run_command(self):\n try:\n #get tolerance value\n self.tolerance = self.toleranceSpinBox.value()\n\n #reset bools for new command to run\n self.perc = False\n self.bool_temp = False\n self.running = False\n\n if (self.AVG.isChecked()):\n avg_output = r'TRUE'\n detailed_output = r' FALSE '\n else:\n avg_output = r'FALSE'\n detailed_output = r' TRUE '\n\n #setup arguments for C++ .exe\n app_path = GlobalSettings.appdir.replace('\\\\','/')\n if platform.system() == 'Windows':\n exe_path = app_path + r'OffTargetFolder/OT_Win.exe'\n elif platform.system() == 'Linux':\n exe_path = app_path + r'OffTargetFolder/OT_Lin'\n else:\n exe_path = app_path + r'OffTargetFolder/OT_Mac'\n exe_path = '\"' + exe_path + '\"'\n data_path = ' \"' + app_path + 'OffTargetFolder/temp.txt' + '\"' ##\n cspr_path = ' \"' + GlobalSettings.CSPR_DB + '/' + self.cspr_file + '\"'\n db_path = ' \"' + GlobalSettings.CSPR_DB + '/' + self.db_file + '\"'\n self.output_path = ' \"' + GlobalSettings.CSPR_DB + '/' + self.FileName.text() + '\"'\n filename = self.output_path\n filename = filename[:len(filename) - 1]\n filename = filename[1:]\n filename = filename.replace('\"', '')\n exists = os.path.isfile(filename)\n CASPER_info_path = r' \"' + app_path + 'CASPERinfo' + '\" '\n num_of_mismathes = int(self.mismatchcomboBox.currentText())\n tolerance = self.tolerance\n endo = ' \"' + self.EndocomboBox.currentText() + '\"'\n hsu = ' \"' + GlobalSettings.mainWindow.Results.endo_data[self.EndocomboBox.currentText()][2] + '\"'\n\n #create command string\n cmd = exe_path + data_path + endo + cspr_path + db_path + self.output_path + CASPER_info_path + str(num_of_mismathes) + ' ' + str(tolerance) + detailed_output + avg_output + hsu\n if platform.system() == 'Windows':\n cmd = cmd.replace('/', '\\\\')\n\n #used to know when the process is done\n def finished():\n self.running = False\n self.progressBar.setValue(100)\n\n #used to know when data is ready to read from stdout\n def dataReady():\n #filter the data from stdout, bools used to know when the .exe starts outputting the progress\n #percentages to be able to type cast them as floats and update the progress bar. Also, must\n #split the input read based on '\\n\\ characters since the stdout read can read multiple lines at\n #once and is all read in as raw bytes\n line = str(self.process.readAllStandardOutput())\n\n line = line[2:]\n line = line[:len(line)-1]\n if platform.system() == 'Windows':\n for lines in filter(None, line.split(r'\\r\\n')):\n if line.find(\"Parsing Input Arguments\") != -1:\n self.progressBar.setValue(10)\n elif line.find(\"Loading data for algorithm\") != -1:\n self.progressBar.setValue(25)\n elif line.find(\"Running OffTarget Analysis\") != -1:\n self.progressBar.setValue(50)\n else:\n for lines in filter(None, line.split(r'\\n')):\n if lines.find(\"Parsing Input Arguments\") != -1:\n self.progressBar.setValue(10)\n elif lines.find(\"Loading data for algorithm\") != -1:\n self.progressBar.setValue(25)\n elif lines.find(\"Running OffTarget Analysis\") != -1:\n self.progressBar.setValue(50)\n\n\n #connect QProcess to the dataReady func, and finished func, reset progressBar only if the outputfile name\n #given does not already exist\n if(exists == False):\n self.process.readyReadStandardOutput.connect(partial(dataReady))\n self.process.readyReadStandardError.connect(partial(dataReady))\n self.progressBar.setValue(1)\n QtCore.QTimer.singleShot(100, partial(self.process.start, cmd))\n self.process.finished.connect(finished)\n\n else: #error message about file already being created\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Error\")\n msgBox.setText(\"Output file already exists. Please choose a new output file name.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Ok)\n msgBox.exec()\n\n except Exception as e:\n logger.critical(\"Error in run_command() in OffTarget.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n #linked to run button\n def run_analysis(self):\n try:\n #make sure an analysis isn't already running before starting\n if(self.running == False):\n self.running = True\n self.run_command()\n except Exception as e:\n logger.critical(\"Error in run_analysis() in OffTarget.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n #exit linked to user clicking cancel, resets bools, and kills process if one was running\n def exit(self):\n try:\n self.perc = False\n self.bool_temp = False\n self.running = False\n self.process.kill()\n self.hide()\n except Exception as e:\n logger.critical(\"Error in exit() in OffTarget.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n #closeEvent linked to user pressing the x in the top right of windows, resets bools, and\n #kills process if there was one running\n def closeEvent(self, event):\n try:\n self.process.kill()\n self.perc = False\n self.bool_temp = False\n self.running = False\n event.accept()\n except Exception as e:\n logger.critical(\"Error in closeEvent() in OffTarget.\")\n logger.critical(e)\n logger.critical(traceback.format_exc())\n msgBox = QtWidgets.QMessageBox()\n msgBox.setStyleSheet(\"font: \" + str(self.fontSize) + \"pt 'Arial'\")\n msgBox.setIcon(QtWidgets.QMessageBox.Icon.Critical)\n msgBox.setWindowTitle(\"Fatal Error\")\n msgBox.setText(\"Fatal Error:\\n\"+str(e)+ \"\\n\\nFor more information on this error, look at CASPER.log in the application folder.\")\n msgBox.addButton(QtWidgets.QMessageBox.StandardButton.Close)\n msgBox.exec()\n\n\n exit(-1)\n\n","sub_path":"OffTarget.py","file_name":"OffTarget.py","file_ext":"py","file_size_in_byte":22162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"82984024","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.stats as ss\n\ndef plot_test_vs_pred(test, pred, target, save=False):\n '''\n create a standardized residual plot and test vs. predicted plot\n input: test values, predicted values, optional save figures \n '''\n \n # calculate and standardize residuals \n r = [pred[i]-test.values[i] for i in range(len(pred))]\n r = np.array(ss.zscore(r))\n \n \n # plot residuals\n fig, axes = plt.subplots()\n axes.set_title('Standardized Residuals')\n axes.set_xlabel('Predicted Value')\n axes.set_ylabel('Residuals')\n axes.scatter(pred, r, c='b')\n \n if save:\n fig.savefig(target+'residual_plot.png')\n else:\n plt.show()\n \n \n # plot predicted vs actual\n fig, axes = plt.subplots()\n axes.set_title('Predicted vs Test')\n axes.set_xlabel('Predicted Value')\n axes.set_ylabel('Test Value')\n axes.scatter(pred, test.values, c='r') \n \n if save:\n fig.savefig(target+'scatter.png')\n else: \n plt.show()\n\n\ndef create_baseline(test):\n '''\n create baseline consisting of monthly average mapped to correct record\n input: test data\n '''\n\n # get monthly averages\n avg = {}\n for i, v in test.items(): \n m = i[5:7]\n if m not in avg:\n avg[m] = [0,0]\n avg[m][0] += v\n avg[m][1] += 1\n\n for k in avg:\n avg[k] = avg[k][0]/avg[k][1]\n\n # construct baseline based on averages\n baseline = []\n for i,v in test.items():\n baseline.append(avg[i[5:7]])\n\n return np.asarray(baseline)","sub_path":"evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"485748258","text":"import sys\r\nfrom pyspark import SparkContext\r\nfrom pyspark.sql import SparkSession\r\n\r\nimport pyspark.sql.functions as f \r\nfrom pyspark.sql.functions import year, month, dayofmonth\r\nfrom pyspark.sql.window import Window\r\nfrom pyspark.sql.functions import lit\r\nfrom pyspark.sql.functions import lower, col\r\nfrom pyspark.sql import types as t\r\nfrom pyspark.sql.types import IntegerType\r\n\r\nfrom itertools import chain\r\nfrom pyspark.sql.functions import col, create_map, lit\r\n\r\nimport numpy as np\r\n\r\nimport statsmodels.api as sm\r\n\r\nimport statsmodels.formula.api as smf\r\n\r\nfrom pyspark.sql.functions import regexp_replace, col\r\nfrom pyspark.ml.regression import LinearRegression\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom pyspark.sql.functions import broadcast\r\n\r\nfrom pyspark.sql.functions import *\r\n\r\nif __name__=='__main__':\r\n sc = SparkContext()\r\n spark = SparkSession(sc)\r\n pv = spark.read.csv('hdfs:///tmp/bdm/nyc_parking_violation/', header = True,inferSchema = True)\r\n pv = pv.select('Issue Date', 'Violation County', 'Street Name', 'House Number')\r\n pv = pv.select(f.year(pv['Issue Date']).alias('Year'),\r\n f.lower(pv['Street Name']).alias('Street Name'))\r\n pv = pv.filter(pv['Year']>=2015 & pv['Year']<=2019) \r\n pv = pv.na.drop()\r\n borough_dict = {'NY':1, 'MAN':1, 'MH':1, 'NEWY':1, 'NEW':1, 'Y':1, \"NY\":1,\r\n 'BX':2, 'BRONX':2,\r\n 'K':3, 'BK':3, 'KING':3, 'KINGS':3,\r\n 'Q':4, 'QN':4, 'QNS':4, 'QU':4, 'QUEEN':4,\r\n 'R':5, 'RICHMOND':5}\r\n mapping_expr = create_map([lit(x) for x in chain(*borough_dict.items())])\r\n pv = pv.withColumn(\"BOROCODE\", mapping_expr.getItem(col(\"Violation County\")))\r\n pv = pv.withColumn(\"HN_int\",(f.regexp_replace(\"House Number\", \"-\", \"\")))\r\n pv = pv.withColumn(\"HN_int\",regexp_replace(col(\"HN_int\"), \" \", \"\"))\r\n pv = pv.withColumn(\"HN_int\", pv[\"HN_int\"].cast(IntegerType()))\r\n pv = pv.na.drop() \\\r\n .select('Year','BOROCODE', 'street name', 'HN_int')\r\n \r\n df_centerline = spark.read.csv('hdfs:///tmp/bdm/nyc_cscl.csv', header = True, inferSchema = True)\r\n df_centerline = df_centerline.select('PHYSICALID', 'ST_LABEL','FULL_STREE', 'BOROCODE', 'L_LOW_HN', 'L_HIGH_HN', 'R_LOW_HN', 'R_HIGH_HN')\r\n \r\n df_centerline = df_centerline.withColumn(\"L_LOW_int\",(f.regexp_replace(\"L_LOW_HN\", \"-\", \"\")))\r\n df_centerline = df_centerline.withColumn(\"L_LOW_int\",regexp_replace(col(\"L_LOW_int\"), \" \", \"\"))\r\n df_centerline = df_centerline.withColumn(\"L_LOW_int\", df_centerline[\"L_LOW_int\"].cast(IntegerType()))\r\n df_centerline = df_centerline.withColumn(\"L_HIGH_int\",(f.regexp_replace(\"L_HIGH_HN\", \"-\", \"\")))\r\n df_centerline = df_centerline.withColumn(\"L_HIGH_int\",regexp_replace(col(\"L_HIGH_int\"), \" \", \"\"))\r\n df_centerline = df_centerline.withColumn(\"L_HIGH_int\", df_centerline[\"L_HIGH_int\"].cast(IntegerType()))\r\n df_centerline = df_centerline.withColumn(\"R_LOW_int\",(f.regexp_replace(\"R_LOW_HN\", \"-\", \"\")))\r\n df_centerline = df_centerline.withColumn(\"R_LOW_int\",regexp_replace(col(\"R_LOW_int\"), \" \", \"\"))\r\n df_centerline = df_centerline.withColumn(\"R_LOW_int\", df_centerline[\"R_LOW_int\"].cast(IntegerType()))\r\n df_centerline = df_centerline.withColumn(\"R_HIGH_int\",(f.regexp_replace(\"R_HIGH_HN\", \"-\", \"\")))\r\n df_centerline = df_centerline.withColumn(\"R_HIGH_int\",regexp_replace(col(\"R_HIGH_int\"), \" \", \"\"))\r\n df_centerline = df_centerline.withColumn(\"R_HIGH_int\", df_centerline[\"R_HIGH_int\"].cast(IntegerType()))\r\n \r\n df_centerline = df_centerline.select('PHYSICALID', 'ST_LABEL', 'FULL_STREE', 'BOROCODE', \r\n 'L_LOW_int', 'L_HIGH_int', 'R_LOW_int', 'R_HIGH_int')\r\n df_centerline = df_centerline.withColumn('ST_LABEL', lower(col('ST_LABEL'))).withColumn('FULL_STREE', lower(col('FULL_STREE')))\r\n \r\n result_df = pv.join(broadcast(df_centerline),(pv[\"BOROCODE\"]==df_centerline[\"BOROCODE\"]) & \r\n ((pv[\"street name\"] == df_centerline['ST_LABEL']) | (pv['street name'] == df_centerline['FULL_STREE'])) &\r\n (((pv['HN_int']%2==1) & (pv['HN_int'] >= df_centerline['L_LOW_int']) & (pv['HN_int'] <= df_centerline['L_HIGH_int'])) |\r\n ((pv['HN_int']%2==0) & (pv['HN_int'] >= df_centerline['R_LOW_int']) & (pv['HN_int'] <= df_centerline['R_HIGH_int']))))\r\n \r\n \r\n pivoted = result_df.groupBy(\"PHYSICALID\").pivot(\"YEAR\",['2015','2016','2017','2018','2019']).count()\r\n \r\n final_df = pivoted.join(broadcast(df_centerline), ['PHYSICALID'], how='right')\r\n final_df = final_df.select('PHYSICALID', '2015', '2016', '2017', '2018', '2019')\r\n final_df = final_df.na.fill(0)\r\n \r\n \r\n def slope(a, b, c, d, e):\r\n X = ([2015, 2016, 2017, 2018, 2019])\r\n X = sm.add_constant(X)\r\n y = ([a, b, c, d, e])\r\n model = sm.OLS(y,X)\r\n #(y, X)\r\n results = model.fit()\r\n return((results.params[1]))\r\n \r\n final_df = final_df.withColumn('OLS', slope(final_df['2015'], final_df['2016'], final_df['2017'], \r\n final_df['2018'], final_df['2019']))\r\n \r\n \r\n final_df = final_df.orderBy('PHYSICALID')\r\n final_df.write.csv('results')","sub_path":"BDM_Final_Attempt1.py","file_name":"BDM_Final_Attempt1.py","file_ext":"py","file_size_in_byte":5304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"307456131","text":"\"\"\"Functions and classes for cross-validation and parameter tuning.\"\"\"\n\n\ndef year_cv_split(X, year_range):\n \"\"\"Split data by year for cross-validation for time-series data.\n\n Makes data from each year in the year_range a test set per split, with data\n from all earlier years being in the train split.\n \"\"\"\n return [\n ((X[\"year\"] < year).to_numpy(), (X[\"year\"] == year).to_numpy())\n for year in range(*year_range)\n ]\n","sub_path":"src/augury/sklearn/model_selection.py","file_name":"model_selection.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"530396132","text":"import gym\n\nenv = gym.make('CartPole-v0')\nenv.reset()\nfor _ in range(1000):\n env.render()\n env.step(env.action_space.sample()) # take a random action\n\n# print(env.action_space)\n# print(env.observation_space)\n# #env.monitor.start()\n# env = gym.wrappers.Monitor(env, '../monitor/cartpole-experiment-0')\n# for i_episode in range(20):\n# observation = env.reset()\n# for t in range(100):\n# env.render()\n# print(observation)\n# action = env.action_space.sample()\n# observation, reward, done, info = env.step(action)\n# if done:\n# print(\"Episode finished after {} timesteps\".format(t+1))\n# break\n#\n# env.monitor.close()","sub_path":"Introduction/intro.py","file_name":"intro.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"146719966","text":"# Inspiré de https://towardsdatascience.com/factor-analysis-a-complete-tutorial-1b7621890e42\n# Documentation: https://factor-analyzer.readthedocs.io/en/latest/index.html\n# Importer les librairies requises\nimport pandas as pd\nimport pingouin as pg\nimport matplotlib.pyplot as plt\n\nfrom factor_analyzer import FactorAnalyzer\ndf = pd.read_csv('https://github.com/lbelzile/math60602/raw/master/data/factor2.csv')\n\n# Créer un object Factor et faire l'analyse en commençant par le choix du nb de facteurs\nfa = FactorAnalyzer(n_factors=12, method='principal', use_corr_matrix = True)\nfa.fit(df)\n# Critère de Kaiser: nb de facteurs = nb de valeurs propres > 1\nev, v = fa.get_eigenvalues()\n# Ici, quatre valeurs propres\n# Diagramme d'éboulis avec matplotlib\nplt.scatter(range(1,df.shape[1]+1),ev)\nplt.plot(range(1,df.shape[1]+1),ev)\nplt.title(\"Diagramme d'éboulis\")\nplt.xlabel(\"Nombre de facteurs\")\nplt.ylabel(\"Valeurs propres\")\nplt.grid()\nplt.show()\n\n# Analyse factorielle avec object \"facteur\"\nfa = FactorAnalyzer(n_factors=4, method='ml', rotation='varimax', use_corr_matrix = True)\nfa.fit(df)\n# Chargements\nloads = fa.loadings_\nprint(loads)\n\n# Variance de chaque facteur\nfa.get_factor_variance()\n# Communalités\nfa.get_communalities()\n\n#Création d'échelles\nechelle1 = df[['x4', 'x8', 'x11']] #service\nechelle2 = df[['x3', 'x6', 'x9', 'x12']] #produit\nechelle3 = df[['x2', 'x7', 'x10']] #paiement\nechelle4 = df[['x1', 'x5']] #prix\n#Vérifier la cohérence interne avec le alpha de Cronbach\nechelle1_alpha = pg.cronbach_alpha(echelle1)\nechelle2_alpha = pg.cronbach_alpha(echelle2)\nechelle3_alpha = pg.cronbach_alpha(echelle3)\nechelle4_alpha = pg.cronbach_alpha(echelle4)\nprint(echelle1_alpha, echelle2_alpha, echelle3_alpha, echelle4_alpha)\n\n","sub_path":"documents/codePython/.ipynb_checkpoints/MATH60602-02-analyse_factorielle-checkpoint.py","file_name":"MATH60602-02-analyse_factorielle-checkpoint.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"239521477","text":"import json\nimport socket\nimport struct\nimport sys\nimport time\nimport threading\nimport atexit\n\ntry:\n import queue\nexcept ImportError:\n import Queue as queue\n\nfrom direct.showbase.ShowBase import ShowBase\nimport panda3d.core as p3d\n\nimport pman\n\nfrom converter import Converter\n\n\np3d.load_prc_file_data(\n '',\n 'window-type none\\n'\n 'gl-debug #t\\n'\n)\n\nUSE_THREAD = True\n\nclass Server(threading.Thread):\n def __init__(self, data_handler, update_handler):\n threading.Thread.__init__(self)\n self.socket = socket.socket()\n self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n self.image_lock = threading.Lock()\n\n remaining_attempts = 3\n while remaining_attempts:\n try:\n self.socket.connect(('127.0.0.1', 5555))\n break\n except socket.error as err:\n print(err)\n time.sleep(1)\n remaining_attempts -= 1\n else:\n print(\"Unable to connect to Blender\")\n sys.exit(-1)\n\n self.data_handler = data_handler\n self.update_handler = update_handler\n\n atexit.register(self.destroy)\n\n def destroy(self):\n try:\n if self.socket:\n self.socket.shutdown(socket.SHUT_RDWR)\n self.socket.close()\n except OSError:\n pass\n self.socket = None\n\n def run(self):\n while True:\n msg_header = self.socket.recv(2)\n if not msg_header:\n print(\"Received zero-length msg header, aborting\")\n break\n\n msg_id = struct.unpack('=H', msg_header)[0]\n if msg_id == 0:\n data_size = struct.unpack('=I', self.socket.recv(4))[0]\n data = bytearray(data_size)\n view = memoryview(data)\n while view:\n rcv_size = self.socket.recv_into(view, len(view))\n view = view[rcv_size:]\n data = json.loads(data.decode('ascii'))\n\n self.data_handler(data)\n\n self.socket.sendall(struct.pack('B', 0))\n elif msg_id == 1:\n #start = time.perf_counter()\n dt = struct.unpack('=f', self.socket.recv(4))[0]\n\n self.image_lock.acquire()\n width, height, img_buffer = self.update_handler(dt)\n\n #print('Extern: width {}, height {}, len(img_buffer) {}'.format(width, height, len(img_buffer)))\n self.socket.sendall(struct.pack('=HH', width, height))\n self.socket.sendall(img_buffer)\n self.image_lock.release()\n #transfer_t = time.perf_counter() - start\n data_size = width*height*3\n #print('Extern: Update time: {}ms'.format(transfer_t * 1000))\n #print('Extern: Speed: {} Gbit/s'.format(data_size/1024/1024/1024*8 / transfer_t))\n else:\n print('Received unknown message ID: {}'.format(msg_id))\n self.socket.sendall(struct.pack('=B', 0))\n\n if not USE_THREAD:\n break\n\n\nclass App(ShowBase):\n def __init__(self, workingdir):\n ShowBase.__init__(self)\n self.view_lens = p3d.MatrixLens()\n self.cam = p3d.NodePath(p3d.Camera('view'))\n self.cam.node().set_lens(self.view_lens)\n self.cam.node().set_active(True)\n self.cam.reparent_to(self.render)\n\n self.pipe = p3d.GraphicsPipeSelection.get_global_ptr().make_module_pipe('pandagl')\n\n self.bg_color = p3d.LVecBase4(0.0, 0.0, 0.0, 1.0)\n\n p3d.get_model_path().prepend_directory(workingdir)\n self.workingdir = workingdir\n\n self.texture = p3d.Texture()\n self.win = None\n self.rendermanager = None\n self.make_offscreen(1, 1)\n\n self.disableMouse()\n self.setFrameRateMeter(True)\n\n self.image_width = 1\n self.image_height = 1\n self.image_data = struct.pack('=BBB', 0, 0, 0)\n\n # Setup conversion logic\n self.converter = Converter()\n self.conversion_queue = queue.Queue()\n def conversion(task):\n while not self.conversion_queue.empty():\n data = self.conversion_queue.get()\n #print(data)\n if 'extras' in data and 'view' in data['extras']:\n viewd = data['extras']['view']\n if 'width' in viewd:\n width = viewd['width']\n height = viewd['height']\n self.make_offscreen(width, height)\n if 'projection_matrix' in viewd:\n proj_mat = self.converter.load_matrix(viewd['projection_matrix'])\n self.view_lens.set_user_mat(proj_mat)\n if 'view_matrix' in viewd:\n view_mat = self.converter.load_matrix(viewd['view_matrix'])\n\n # Panda wants an OpenGL model matrix instead of an OpenGL view matrix\n view_mat.invert_in_place()\n self.view_lens.set_view_mat(view_mat)\n\n self.converter.update(data)\n bg_color = self.converter.background_color\n self.bg_color = p3d.LVector4(bg_color[0], bg_color[1], bg_color[2], 1)\n self.view_region.set_clear_color(self.bg_color)\n self.converter.active_scene.reparent_to(self.render)\n #self.render.ls()\n\n if self.texture.has_ram_image():\n #start = time.perf_counter()\n self.server.image_lock.acquire()\n self.image_width = self.texture.get_x_size()\n self.image_height = self.texture.get_y_size()\n self.image_data = memoryview(self.texture.get_ram_image_as(\"BGR\"))\n self.server.image_lock.release()\n #print('Extern: Updated image data in {}ms'.format((time.perf_counter() - start) * 1000))\n #self.texture.write('tex.png')\n return task.cont\n\n self.taskMgr.add(conversion, 'Conversion')\n\n # Setup communication with Blender\n self.server = Server(self.handle_data, self.get_img)\n if USE_THREAD:\n self.server.start()\n def server_mon(task):\n if not self.server.is_alive():\n print('Server thread has terminated, closing program')\n sys.exit()\n return task.cont\n self.taskMgr.add(server_mon, 'Server Monitor')\n else:\n def server_task(task):\n self.server.run()\n return task.cont\n self.taskMgr.add(server_task, 'Server Communication')\n\n def update_rman(self):\n try:\n pman_conf = pman.get_config(self.workingdir)\n except pman.NoConfigError:\n pman_conf = None\n\n self.rendermanager = pman.rendermanager.create_render_manager(self, pman_conf)\n\n def make_offscreen(self, sizex, sizey):\n sizex = p3d.Texture.up_to_power_2(sizex)\n sizey = p3d.Texture.up_to_power_2(sizey)\n\n if self.win and self.win.get_size()[0] == sizex and self.win.get_size()[1] == sizey:\n # The current window is good, don't waste time making a new one\n return\n\n use_frame_rate_meter = self.frameRateMeter is not None\n self.setFrameRateMeter(False)\n\n self.graphicsEngine.remove_all_windows()\n self.win = None\n self.view_region = None\n\n # First try to create a 24bit buffer to minimize copy times\n fbprops = p3d.FrameBufferProperties()\n fbprops.set_rgba_bits(8, 8, 8, 0)\n fbprops.set_depth_bits(24)\n winprops = p3d.WindowProperties.size(sizex, sizey)\n flags = p3d.GraphicsPipe.BF_refuse_window\n #flags = p3d.GraphicsPipe.BF_require_window\n self.win = self.graphicsEngine.make_output(\n self.pipe,\n 'window',\n 0,\n fbprops,\n winprops,\n flags\n )\n\n if self.win is None:\n # Try again with an alpha channel this time (32bit buffer)\n fbprops.set_rgba_bits(8, 8, 8, 8)\n self.win = self.graphicsEngine.make_output(\n self.pipe,\n 'window',\n 0,\n fbprops,\n winprops,\n flags\n )\n\n if self.win is None:\n print('Unable to open window')\n sys.exit(-1)\n\n disp_region = self.win.make_mono_display_region()\n disp_region.set_camera(self.cam)\n disp_region.set_active(True)\n disp_region.set_clear_color_active(True)\n disp_region.set_clear_color(self.bg_color)\n disp_region.set_clear_depth(1.0)\n disp_region.set_clear_depth_active(True)\n self.view_region = disp_region\n self.graphicsEngine.open_windows()\n\n self.setFrameRateMeter(use_frame_rate_meter)\n\n self.update_rman()\n\n self.texture = p3d.Texture()\n self.win.addRenderTexture(self.texture, p3d.GraphicsOutput.RTM_copy_ram)\n\n\n def handle_data(self, data):\n self.conversion_queue.put(data)\n\n def get_img(self, _dt):\n return self.image_width, self.image_height, self.image_data\n\n\ndef main():\n app = App(sys.argv[1])\n app.run()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"processor_app.py","file_name":"processor_app.py","file_ext":"py","file_size_in_byte":9436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"236977934","text":"#!/usr/bin/env python\n\n#################################################\n# File/Revision History\n# Modified by Danny Bynum, Aug-4 at 11am ET\n# Updated by Danny Bynum, Aug-9 at 522pm ET\n# Updated DB Aug-11 at 7am ET\n##################################################\n\nimport rospy\nfrom geometry_msgs.msg import PoseStamped\nfrom styx_msgs.msg import Lane, Waypoint\nimport math\n\n#New libraries added from Walk-Thru-Video\nimport numpy as np\nfrom scipy.spatial import KDTree\nfrom std_msgs.msg import Int32\n\n'''\nThis node will publish waypoints from the car's current position to some `x` distance ahead.\n\nAs mentioned in the doc, you should ideally first implement a version which does not care\nabout traffic lights or obstacles.\n\nOnce you have created dbw_node, you will update this node to use the status of traffic lights too.\n\nPlease note that our simulator also provides the exact location of traffic lights and their\ncurrent status in `/vehicle/traffic_lights` message. You can use this message to build this node\nas well as to verify your TL classifier.\n\nTODO (for Yousuf and Aaron): Stopline location for each traffic light.\n'''\n\nLOOKAHEAD_WPS = 50 # Number of waypoints we will publish. You can change this number\nMAX_DECEL = .5 #added from Full Waypoint walkthru Aug-7\n\n'''\nFrom walk-thru video - Waypoint Updater Partial Walkthrough\nNote for part 1 the whole goal is to \"take a chunk of the base waypoints and use the first 200\nof them that are in front of the car as reference\n'''\n\n\nclass WaypointUpdater(object):\n def __init__(self):\n rospy.init_node('waypoint_updater')\n\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n\n # TODO: Add a subscriber for /traffic_waypoint and /obstacle_waypoint below\n\n\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane, queue_size=1)\n\n # TODO: Add other member variables you need below\n # Added from walk-thru\n self.pose = None\n self.stopline_wp_idx = -1\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n\n self.loop()\n #rospy.spin()\n \n #added from walkthru\n def loop(self):\n rate = rospy.Rate(35)\n while not rospy.is_shutdown():\n #modified on Aug-19\n if self.pose and self.base_waypoints and self.waypoint_tree:\n #Get closest waypoint\n #closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints()\n rate.sleep()\n \n #added from walk_thru \n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n #modified on Aug-19\n #if not None in (self.waypoint_tree):\n #if not self.waypoint_tree:\n #\tclosest_idx = self.waypoint_tree.query([x,y], 1)[1]\n #else:\n #\tclosest_idx = 0\n\n closest_idx = self.waypoint_tree.query([x,y], 1)[1]\n \n #Check if closest is ahead or behind vehicle\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx-1]\n \n #Equation for hyperplane through closest_coords\n cl_vect = np.array(closest_coord)\n prev_vect = np.array(prev_coord)\n pos_vect = np.array([x,y])\n \n #dot product between two vectors\n #figuring out of the wapoint coordinate is in front of the vehicle or behind the vehicle\n val = np.dot(cl_vect-prev_vect, pos_vect-cl_vect)\n \n if val > 0:\n closest_idx = (closest_idx +1) % len(self.waypoints_2d)\n \n return closest_idx\n\n #added from Full video walk-thru Aug-7\n def decelerate_waypoints(self, waypoints, closest_idx):\n temp_waypoints = [] #note - preserving base_waypoints\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n \n stop_idx = max(self.stopline_wp_idx - closest_idx - 3,0) #video says 2, I'm using 3\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2*MAX_DECEL*dist) #FIXME, video indicates this could be imporoved\n if vel < 1.:\n vel = 0.\n \n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n temp_waypoints.append(p)\n \n return temp_waypoints\n\n \n def generate_lane(self):\n lane = Lane()\n closest_idx = self.get_closest_waypoint_idx()\n # using Python slicing to publish the points in front of us - \n # starting with the index we determined plus the number of points we want to use\n #lane.waypoints = self.base_waypoints.waypoints[closest_idx:closest_idx + LOOKAHEAD_WPS]\n\n base_waypoints = self.base_waypoints.waypoints[closest_idx:closest_idx + LOOKAHEAD_WPS]\n\n if (self.stopline_wp_idx == -1) or (self.stopline_wp_idx>= closest_idx + LOOKAHEAD_WPS):\n lane.waypoints = base_waypoints\n #lane.waypoints = self.base_waypoints.waypoints[closest_idx:closest_idx + LOOKAHEAD_WPS]\n else:\n lane.waypoints = self.decelerate_waypoints(base_waypoints, closest_idx)\n #lane.waypoints = base_waypoints\n #lane.waypoints = self.base_waypoints.waypoints[closest_idx:closest_idx + LOOKAHEAD_WPS]\n #lane.waypoints = self.decelerate_waypoints(base_waypoints, closest_idx)\n \n\n return lane\n\n \n\n #added from walk-thru\n def publish_waypoints(self):\n\n final_lane = self.generate_lane()\n #lane = Lane()\n #making the header the same - comment that maybe this isn't even needed\n #lane.header = self.base_waypoints.header\n final_lane.header = self.base_waypoints.header\n \n # using Python slicing to publish the points in front of us - \n # starting with the index we determined plus the number of points we want to use\n #lane.waypoints = self.base_waypoints.waypoints[closest_idx:closest_idx + LOOKAHEAD_WPS]\n self.final_waypoints_pub.publish(final_lane)\n\n\n\n def pose_cb(self, msg):\n # TODO: Implement\n self.pose = msg #added from walk-thru\n # pass\n\n #Note - from walk-thru - This is a latched subscriber - so this is only sent once\n def waypoints_cb(self, waypoints):\n # TODO: Implement\n self.base_waypoints = waypoints\n #making sure self.waypoints_2d is initialized\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.pose.pose.position.y] for waypoint in waypoints.waypoints] #FIXME not sure how this line ends, was cutoff in video :-)\n self.waypoint_tree = KDTree(self.waypoints_2d)\n \n #pass\n\n def traffic_cb(self, msg):\n # TODO: Callback for /traffic_waypoint message. Implement\n #pass\n self.stopline_wp_idx = msg.data\n\n def obstacle_cb(self, msg):\n # TODO: Callback for /obstacle_waypoint message. We will implement it later\n pass\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x-b.x)**2 + (a.y-b.y)**2 + (a.z-b.z)**2)\n for i in range(wp1, wp2+1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose.pose.position)\n wp1 = i\n return dist\n\n\nif __name__ == '__main__':\n try:\n WaypointUpdater()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start waypoint updater node.')\n","sub_path":"ros/src/waypoint_updater/waypoint_updater.py","file_name":"waypoint_updater.py","file_ext":"py","file_size_in_byte":7905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"482010919","text":"import torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport functools\nfrom torch.optim import lr_scheduler\n\n\n###############################################################################\n# Helper Functions\n###############################################################################\n\n\nclass Identity(nn.Module):\n def forward(self, x):\n return x\n\n\ndef get_scheduler(optimizer, opt):\n \"\"\"Return a learning rate scheduler\n \"\"\"\n if opt.lr_policy == 'linear':\n def lambda_rule(epoch):\n lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1)\n return lr_l\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)\n else:\n return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)\n return scheduler\n\n\ndef init_weights(net, init_type='normal', init_gain=0.02):\n \"\"\"Initialize network weights.\n \"\"\"\n def init_func(m): # define the initialization function\n classname = m.__class__.__name__\n if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n if init_type == 'normal':\n init.normal_(m.weight.data, 0.0, init_gain)\n elif init_type == 'xavier':\n init.xavier_normal_(m.weight.data, gain=init_gain)\n elif init_type == 'kaiming':\n init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n init.orthogonal_(m.weight.data, gain=init_gain)\n else:\n raise NotImplementedError('initialization method [%s] is not implemented' % init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n init.constant_(m.bias.data, 0.0)\n elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.\n init.normal_(m.weight.data, 1.0, init_gain)\n init.constant_(m.bias.data, 0.0)\n\n print('initialize network with %s' % init_type)\n net.apply(init_func) # apply the initialization function \n\n\ndef init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):\n \"\"\"Initialize a network\n \"\"\"\n if len(gpu_ids) > 0:\n assert(torch.cuda.is_available())\n net.to(gpu_ids[0])\n net = torch.nn.DataParallel(net, gpu_ids) \n init_weights(net, init_type, init_gain=init_gain)\n return net\n\n\ndef define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):\n \"\"\"Create a generator\n \"\"\"\n net = None\n norm_layer = get_norm_layer(norm_type=norm)\n net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)\n return init_net(net, init_type, init_gain, gpu_ids)\n\n\ndef define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):\n \"\"\"Create a discriminator\n \"\"\"\n net = None\n norm_layer = get_norm_layer(norm_type=norm)\n\n if netD == 'basic': # default PatchGAN classifier\n net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)\n elif netD == 'n_layers': # more options\n net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)\n else:\n raise NotImplementedError('Discriminator model name [%s] is not recognized' % net)\n return init_net(net, init_type, init_gain, gpu_ids)\n\n\n##############################################################################\n# Classes\n##############################################################################\nclass GANLoss(nn.Module):\n \"\"\"Define different GAN objectives.\n \"\"\"\n\n def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):\n \"\"\" Initialize the GANLoss class.\n \"\"\"\n super(GANLoss, self).__init__()\n self.register_buffer('real_label', torch.tensor(target_real_label))\n self.register_buffer('fake_label', torch.tensor(target_fake_label))\n self.gan_mode = gan_mode\n if gan_mode == 'lsgan':\n self.loss = nn.MSELoss()\n elif gan_mode == 'vanilla':\n self.loss = nn.BCEWithLogitsLoss()\n elif gan_mode in ['wgangp']:\n self.loss = None\n else:\n raise NotImplementedError('gan mode %s not implemented' % gan_mode)\n\n def get_target_tensor(self, prediction, target_is_real):\n \"\"\"Create label tensors with the same size as the input.\n \"\"\"\n\n if target_is_real:\n target_tensor = self.real_label\n else:\n target_tensor = self.fake_label\n return target_tensor.expand_as(prediction)\n\n def __call__(self, prediction, target_is_real):\n \"\"\"Calculate loss given Discriminator's output and grount truth labels.\n \"\"\"\n if self.gan_mode in ['lsgan', 'vanilla']:\n target_tensor = self.get_target_tensor(prediction, target_is_real)\n loss = self.loss(prediction, target_tensor)\n elif self.gan_mode == 'wgangp':\n if target_is_real:\n loss = -prediction.mean()\n else:\n loss = prediction.mean()\n return loss\n\n\n\n\nclass ResnetGenerator(nn.Module):\n \"\"\"Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.\n We adapt Torch code and idea from Justin Johnson's neural style transfer\n project(https://github.com/jcjohnson/fast-neural-style)\n \"\"\"\n\n def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):\n \"\"\"Construct a Resnet-based generator\n \"\"\"\n assert(n_blocks >= 0)\n super(ResnetGenerator, self).__init__()\n if type(norm_layer) == functools.partial:\n use_bias = norm_layer.func == nn.InstanceNorm2d\n else:\n use_bias = norm_layer == nn.InstanceNorm2d\n\n model = [nn.ReflectionPad2d(3),\n nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),\n norm_layer(ngf),\n nn.ReLU(True)]\n\n n_downsampling = 2\n for i in range(n_downsampling): # add downsampling layers\n mult = 2 ** i\n model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),\n norm_layer(ngf * mult * 2),\n nn.ReLU(True)]\n\n mult = 2 ** n_downsampling\n for i in range(n_blocks): # add ResNet blocks\n\n model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]\n\n for i in range(n_downsampling): # add upsampling layers\n mult = 2 ** (n_downsampling - i)\n model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),\n kernel_size=3, stride=2,\n padding=1, output_padding=1,\n bias=use_bias),\n norm_layer(int(ngf * mult / 2)),\n nn.ReLU(True)]\n model += [nn.ReflectionPad2d(3)]\n model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]\n model += [nn.Tanh()]\n\n self.model = nn.Sequential(*model)\n\n def forward(self, input):\n \"\"\"Standard forward\"\"\"\n return self.model(input)\n\n\nclass ResnetBlock(nn.Module):\n \"\"\"Define a Resnet block\"\"\"\n\n def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):\n \"\"\"Initialize the Resnet block\n \"\"\"\n super(ResnetBlock, self).__init__()\n self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)\n\n def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):\n \"\"\"Construct a convolutional block.\n \"\"\"\n conv_block = []\n p = 0\n if padding_type == 'reflect':\n conv_block += [nn.ReflectionPad2d(1)]\n elif padding_type == 'replicate':\n conv_block += [nn.ReplicationPad2d(1)]\n elif padding_type == 'zero':\n p = 1\n else:\n raise NotImplementedError('padding [%s] is not implemented' % padding_type)\n\n conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]\n if use_dropout:\n conv_block += [nn.Dropout(0.5)]\n\n p = 0\n if padding_type == 'reflect':\n conv_block += [nn.ReflectionPad2d(1)]\n elif padding_type == 'replicate':\n conv_block += [nn.ReplicationPad2d(1)]\n elif padding_type == 'zero':\n p = 1\n else:\n raise NotImplementedError('padding [%s] is not implemented' % padding_type)\n conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]\n\n return nn.Sequential(*conv_block)\n\n def forward(self, x):\n \"\"\"Forward function (with skip connections)\"\"\"\n out = x + self.conv_block(x) # add skip connections\n return out\n\n\n\nclass NLayerDiscriminator(nn.Module):\n \"\"\"Defines a PatchGAN discriminator\"\"\"\n\n def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):\n \"\"\"Construct a PatchGAN discriminator\n \"\"\"\n super(NLayerDiscriminator, self).__init__()\n if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters\n use_bias = norm_layer.func != nn.BatchNorm2d\n else:\n use_bias = norm_layer != nn.BatchNorm2d\n\n kw = 4\n padw = 1\n sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]\n nf_mult = 1\n nf_mult_prev = 1\n for n in range(1, n_layers): # gradually increase the number of filters\n nf_mult_prev = nf_mult\n nf_mult = min(2 ** n, 8)\n sequence += [\n nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),\n norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True)\n ]\n\n nf_mult_prev = nf_mult\n nf_mult = min(2 ** n_layers, 8)\n sequence += [\n nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),\n norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True)\n ]\n\n sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map\n self.model = nn.Sequential(*sequence)\n\n def forward(self, input):\n \"\"\"Standard forward.\"\"\"\n return self.model(input)\n","sub_path":"cyclic-GAN/model/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":10876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"542904904","text":"import threading\nimport time\n\nclass TaskThread(threading.Thread):\n\tdef __init__(self, target, args):\n\t\tthreading.Thread.__init__(self, target=target, args=args);\n\t\tself.start_time = time.time();\n\t\n\tdef get_time_alive(self):\n\t\tend_time = time.time();\n\t\treturn end_time - self.start_time;\n\ndef get_alive_thread_cnt(th_pool):\n\tcnt_alive = 0;\n\tfor i in range(len(th_pool)):\n\t\tt = th_pool[i];\n\t\t#if (t.is_alive() and t.get_time_alive() >= 1):\n\t\tif (t.is_alive() ):\n\t\t\tcnt_alive = cnt_alive + 1;\n\tfor th in th_pool:\n\t\tif (not th.is_alive()):\n\t\t\tth_pool.remove(th);\n\t\n\treturn cnt_alive;\n\ndef task_wrapper(func, argv, resource, res_list, started_list, ind):\n\tstarted_list[ind] = True;\n\tres = func(argv, resource)\n\tres_list[ind] = res;\n\tstarted_list[ind] = False;\n\t\n#each task dependently owns 1 argv, \n#all tasks share 1 same resource list.\n#type of resource is .\ndef run_with_multi_thread(func, argv_list, resource_list, mt_num=-1): #list of argv(s)\n\tif (mt_num <= 1):\n\t\tfor argv in argv_list:\n\t\t\tfunc(argv,[\"\"])\n\telif (mt_num > 1): \n\t\t#use flags to keep track of stage of each task.\n\t\tis_finished = [False for i in range(len(argv_list))]; \n\t\tis_started = [False for i in range(len(argv_list))];\n\n\t\tcur_resource = 0\n\t\twhile(True):\n\t\t\t\ttask_list = [];\n\t\t\t\tth_pool = [];\n\t\t\t\thas_started = False;\n\t\t\t\tfor i in range(len(argv_list)):\n\t\t\t\t\tif (not is_finished[i] and not is_started[i]):\n\t\t\t\t\t\ttask_list.append(i);\n\t\t\t\t\tif (is_started[i]):\n\t\t\t\t\t\thas_started = True;\n\t\t\t\t\t\t\n\t\t\t\tif (len(task_list) == 0 and not has_started): #end condition.\n\t\t\t\t\tbreak;\n\t\t\t\t\n\t\t\t\tfor i in range(len(task_list)):\n\t\t\t\t\targv = argv_list[task_list[i]]\n\t\t\t\t\tind = task_list[i]\n\t\t\t\t\tresource = resource_list[cur_resource]\n\t\t\t\t\tcur_resource += 1\n\t\t\t\t\tif (cur_resource >= len(resource_list)):\n\t\t\t\t\t\tcur_resource = 0\n\t\t\t\t\t\ttime.sleep(1)\n\n\t\t\t\t\tth = TaskThread(target=task_wrapper, args=(func,argv,resource,is_finished,is_started,ind));\n\t\t\t\t\tth_pool.append(th);\n\t\t\t\t\tth.start();\n\t\t\t\t\t\n\t\t\t\t\twhile(get_alive_thread_cnt(th_pool) >= mt_num):\n\t\t\t\t\t\ttime.sleep(1); #periodically checking available slot.\n","sub_path":"download/worker/multi_thread.py","file_name":"multi_thread.py","file_ext":"py","file_size_in_byte":2065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"581452693","text":"#!/usr/bin/python3\r\nfrom ctypes import *\r\nimport math\r\nimport json\r\nimport sys\r\nimport io\r\nimport json_to_binary\r\nimport cws_transform\r\nimport platform\r\nimport time\r\nimport indexer\r\n\r\nclass CWS_Data:\r\n def __init__(self,index):\r\n self.fid=indexer.Indexer(index)\r\n def map_seq(self,seq):\r\n seq=[x for x in seq]\r\n for i,c in enumerate(seq):\r\n o=ord(c)\r\n if o>32 and o<127:\r\n seq[i]=chr(o+65248)\r\n seq=''.join(seq)\r\n return seq\r\n def encode(self,seq):\r\n seq=self.map_seq(seq)\r\n graph=[]\r\n fs=[filter(lambda x:x>=0,[self.fid(k) for k in cws_transform.gen_keys(seq,x)]) for x in range(len(seq))]\r\n \r\n for v in fs:\r\n graph.append([0,[],0,v])\r\n graph[0][0]+=1;\r\n graph[-1][0]+=2;\r\n for i in range(1,len(graph)):\r\n graph[i][1]=[i-1]\r\n return graph\r\n def decode(self,seq,tags):\r\n offset=0\r\n rst=[]\r\n for i in range(len(seq)):\r\n if tags[i]>=2:\r\n rst.append(seq[offset:i+1])\r\n offset=i+1\r\n return rst\r\n\r\n\r\nclass CWS_Model:\r\n def __init__(self,libfile,indexfile,modelfile):\r\n self.sdlib = cdll.LoadLibrary(libfile)\r\n self.p_permm=c_void_p()\r\n self.sdlib.permm_init(c_char_p(modelfile.encode()),c_int(0),byref(self.p_permm))\r\n \r\n self.cws_data=CWS_Data(indexfile)\r\n \r\n \r\n def __call__(self,seq):\r\n \r\n if not seq:return ''\r\n x=POINTER(c_int)()\r\n \r\n graph=self.cws_data.encode(seq)\r\n bs=json_to_binary.graph_to_bytes(graph)\r\n \r\n p_graph=c_void_p()\r\n self.sdlib.permm_parse_graph(c_char_p(bs),byref(p_graph))\r\n self.sdlib.permm_decode(byref(self.p_permm),byref(p_graph),byref(x))\r\n \r\n rst=self.cws_data.decode(seq,x)\r\n \r\n return(' '.join(rst))\r\n\r\n\r\nif __name__=='__main__':\r\n libfile=''\r\n if platform.system()=='Linux':\r\n libfile='../path_labeling/libper--.so'\r\n else:\r\n libfile='libper--'\r\n model=CWS_Model(libfile,'tmp/index.txt',\"tmp/model.bin\")\r\n \r\n ot=time.time()\r\n try:\r\n while True:\r\n seq=' '.join([model(s) for s in input().split()])\r\n print(seq)\r\n except EOFError:\r\n pass\r\n except KeyboardInterrupt:\r\n pass\r\n else:\r\n pass\r\n #print(time.time()-ot)\r\n \r\n","sub_path":" perminusminus/CWS_example/cws_model.py","file_name":"cws_model.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"399597418","text":"import os\nimport shutil\nimport sys\nimport subprocess\nimport sys\nfrom win32com.client import Dispatch\n\ndef copytree(src, dst, symlinks=False, ignore=None):\n for item in os.listdir(src):\n s = os.path.join(src, item)\n d = os.path.join(dst, item)\n if os.path.isdir(s):\n shutil.copytree(s, d, symlinks, ignore)\n else:\n shutil.copy2(s, d)\n\ndef create_shortcut(lnk_path, exe_path, exe_dir): \n\tshell = Dispatch('WScript.Shell')\n\tshortcut = shell.CreateShortCut(lnk_path)\n\tshortcut.Targetpath = exe_path\n\tshortcut.WorkingDirectory = exe_dir\n\tshortcut.save()\n\n\nif __name__ == '__main__':\n\tdirname, filename = os.path.split(os.path.abspath(__file__)) # Текущая директория\n\ttry:\n\t\twith open(dirname+'\\\\requirements.txt', 'r') as req_file: # Зависимости\n\t\t\tmodule_list = req_file.readlines()\n\t\t\tmodule_list = [a.split('==')[0] for a in module_list]\n\t\t\tif len(module_list) > 0:\n\t\t\t\tprint('Try to install requirements: {}'.format(','.join(module_list)))\n\t\t\t\tfor module in module_list:\n\t\t\t\t\tsubprocess.call([sys.executable, \"-m\", \"pip\", \"install\", module]) # Установка модуля\n\texcept Exception as e:\n\t\tprint(e)\n\tprint('1. Requirements install successfull')\n\ttry:\n\t\tusername = os.environ['USERNAME']\n\t\tsrc_server_path = dirname+'\\\\wServer\\\\'\n\t\t#print(src_server_path)\n\t\tautostart_path = 'C:\\\\Users\\\\{0}\\\\AppData\\\\Roaming\\\\Microsoft\\\\Windows\\\\Start Menu\\\\Programs\\\\Startup'.format(username)\n\t\t#print('Username: {}'.format(username))\n\t\t# Каталог с сервером (та директория где он будет лежать)\n\t\tdst_server_path = os.path.join(os.path.join(os.environ['USERPROFILE']), 'wServer\\\\')\n\t\t# Рабочий стол\n\t\tdesktop_path = os.path.join(os.path.join(os.environ['USERPROFILE']), 'Desktop\\\\')\n\t\tif os.path.exists(dst_server_path):\n\t\t\tshutil.rmtree(dst_server_path) # Удалили каталог сервера внутри пользователя\n\t\tos.mkdir(dst_server_path) # Создали каталог сервера внутри пользователя\n\t\tprint('2. Server folder created')\n\t\tif os.path.exists(src_server_path): # Если каталог с серваком существует\n\t\t\tcopytree(src_server_path, dst_server_path) # Копируем папку сервера\n\t\t\n\t\tif os.path.exists(autostart_path):\n\t\t\t# Формируем путь до ярлыка в автозагрузке\n\t\t\tdst_server_lnk = os.path.join(autostart_path, 'wServer.lnk')\n\t\t\t# Создаем ярлык к серваку\n\t\t\tcreate_shortcut(lnk_path=dst_server_lnk, \n\t\t\t\t\t\t\texe_path=os.path.join(dst_server_path,'wServer.exe'),\n\t\t\t\t\t\t\texe_dir=dst_server_path)\n\t\t\t# Формируем путь до ярлыка на рабочем столе\n\t\t\tdsk_link_path = os.path.join(desktop_path, 'wServer.lnk')\n\t\t\t# Копируем ярлык на рабочий стол\n\t\t\tshutil.copyfile(dst_server_lnk, dsk_link_path)\n\t\t\tprint('3. Server executable file copied to System Autostart folder and linked to Desktop')\n\texcept Exception as e:\n\t\tprint(e)\n\tprint('Setup OK')\n\tinput('Press ENTER key to quit...')","sub_path":"Server/Setup.py","file_name":"Setup.py","file_ext":"py","file_size_in_byte":3134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"456116441","text":"import numpy as np\n\nfrom tests.common import *\n\n\ndef test_kbet(adata_pca):\n score = scib.me.kBET(\n adata_pca,\n batch_key='batch',\n label_key='celltype',\n embed='X_pca'\n )\n LOGGER.info(f\"score: {score}\")\n assert np.isnan(score)\n","sub_path":"tests/metrics/test_kbet.py","file_name":"test_kbet.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"588072458","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[20]:\n\n\nimport numpy\nimport os\n\n\n# In[21]:\n\n\ndef NN(x1,x2,w1,w2,b):\n z = x1*w1 + x2*w2 + b\n return sigmoid(z)\n\ndef sigmoid(x):\n return 1/(1+numpy.exp(-x))\n\n\n# In[22]:\n\n\nphrases = ['seems like its', 'I guess', ' I think', 'possibly', 'looks like', 'guessing...']\n\n\n# In[23]:\n\n\ndata = [[2,1.5,1],[2,1,0],[4,1.5,1],[3,1,0],[3.5,.5,1],[2,.5,0],[5.5,1,1],[1,1,0]]\n\n\n# In[24]:\n\n\nrand_data = data[numpy.random.randint(len(data))]\n\n\n# In[25]:\n\n\nx1 = rand_data[0]\nx2 = rand_data[1]\n\n\n# In[32]:\n\n\nprediction = NN(x1,x2,w1,w2,b)\nprediction_text = [\"blue\",\"red\"][int(numpy.round(prediction))]\nphrase = numpy.random.choice(phrases) + \" \" + prediction_text \n\n\n# In[33]:\n\n\nprint(phrase)\n\n\n# In[34]:\n\n\no = os.system(\"say\" + phrase)\n\n\n# In[35]:\n\n\nprint(\"Its really\" + \" \" + [\"blue\", \"red\"][rand_data[2]])\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Simple Neural Network.py","file_name":"Simple Neural Network.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"282383325","text":"import shutil\r\n\r\nimport image\r\nimport os\r\n\r\n\r\ndef clear_cache():\r\n try:\r\n os.remove(\"result.txt\")\r\n except:\r\n pass\r\n\r\n files = os.listdir(f'cache/')\r\n for file in files:\r\n shutil.rmtree(f'cache/{file}', ignore_errors=True, onerror=None)\r\n\r\n files = os.listdir(f'cache/')\r\n for file in files:\r\n os.remove(f'cache/{file}')\r\n\r\n\r\ndef read_screen(purpose, count):\r\n os.mkdir(f\"cache/{count}\")\r\n image.convert_to_black_white(count)\r\n\r\n # Исходное изображение\r\n source_img = image.Cropyble(f\"cache/black{count}.png\")\r\n\r\n # Список всех найденных слов\r\n words = source_img.get_words()\r\n\r\n for ind, word in enumerate(words):\r\n if len(word) > 1:\r\n source_img.crop(word, f'cache/{count}/', f\"output_{ind}.png\")\r\n\r\n coordinate_image = source_img.coordinate_image\r\n coordinate = None\r\n\r\n files = os.listdir(f'cache/{count}/')\r\n find = False\r\n\r\n for file in files:\r\n if not find:\r\n s1 = image.image_to_string(f'cache/{count}/{file}')\r\n if len(s1) >= 5:\r\n chance = image.search_partial_text(s1, purpose)\r\n\r\n if chance >= 70:\r\n find = True\r\n coordinate = coordinate_image[file]\r\n\r\n if find:\r\n fl = open(f\"result.txt\", \"w\", encoding=\"UTF-8\")\r\n fl.write(f\"{count}\\n{coordinate}\")\r\n fl.close()","sub_path":"Hunter1/screen.py","file_name":"screen.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"86689218","text":"import os\nimport shutil\nfrom scipy.io import loadmat\nimport numpy as np\n\n\ndef fileprepare(dir_path, if_train=False):\n name = None\n cam_dirs = []\n for i in range(6):\n cam_dirs.append(os.path.join(dir_path, 'cam' + str(i + 1)))\n if if_train:\n name = 'exp/train_id.mat'\n if not os.path.exists(os.path.join(dir_path, 'train')):\n os.mkdir(os.path.join(dir_path, 'train'))\n mat_path = os.path.join(dir_path, name)\n ids = loadmat(mat_path)['id'][0]\n if if_train:\n mat_path = os.path.join(dir_path, 'exp/val_id.mat')\n temp = loadmat(mat_path)['id'][0]\n ids = np.append(ids, temp)\n if if_train:\n if not os.path.exists(os.path.join(dir_path, 'train/rgb')):\n os.mkdir(os.path.join(dir_path, 'train/rgb'))\n if not os.path.exists(os.path.join(dir_path, 'train/ir')):\n os.mkdir(os.path.join(dir_path, 'train/ir'))\n for idx in ids:\n id_name = str(idx).zfill(4)\n for index, cam_dir in enumerate(cam_dirs):\n if index == 2 or index == 5:\n id_path_to = os.path.join(dir_path, 'train/ir/' + id_name)\n else:\n id_path_to = os.path.join(dir_path, 'train/rgb/' + id_name)\n if not os.path.exists(id_path_to):\n os.mkdir(id_path_to)\n id_path_from = os.path.join(cam_dir, id_name)\n if not os.path.exists(id_path_from):\n continue\n filenames = os.listdir(id_path_from)\n for filename in filenames:\n shutil.copyfile(os.path.join(id_path_from, filename), os.path.join(\n id_path_to, 'cam'+str(index + 1)+'_'+filename))\n\n\nfileprepare('./SYSU-MM01', True)\n","sub_path":"prepare.py","file_name":"prepare.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"28659521","text":"import job,ordonnancement,flowshop\n#prend une liste de job et renvoie cette meme liste ordonnée selon la première étape de la méthode NEH.\ndef OrdonnerListe(ListeJob):\n ListeNEH=[ListeJob[0]];\n for j in range(1,len(ListeJob)):\n J=ListeJob[j]\n i=0;\n while(J.duree() thred_b])\n\n # print num_broken\n test_anomaly_score[m - test_start] = num_broken\n\n test_anomaly_score = test_anomaly_score.ravel()\n anomaly_score = []\n\n for m in range(len(test_anomaly_score)):\n for j in range(gap_time):\n anomaly_score.append(test_anomaly_score[m])\n\n return anomaly_score\n","sub_path":"networks/mscred/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"254688381","text":"import sys\nfrom flask import Flask, session, render_template, request, redirect, jsonify, url_for, flash, g\nfrom sqlalchemy import create_engine, func\nfrom sqlalchemy.orm import sessionmaker, load_only\nfrom sqlalchemy.sql import text\nfrom flask.ext.login import (LoginManager, current_user, login_required,\nlogin_user, logout_user, UserMixin, confirm_login, fresh_login_required)\nfrom cba_db import (Base, User, PlanComptable, Compte, Societe, Banque, SocieteBanque,Exercice, \nExercicePeriode, JournalCategorie, Journal, JournalDetails, Budget, BudgetDetails, Site, Taxe, \nDepot, Partenaire, ArticleCategorie, Article, Achat, AchatDetails, Vente, VenteDetails,\nDirection, DirectionService, Fonction, Employe, EmployeDependant, EmployeContact, EmployeContrat)\nfrom werkzeug.security import generate_password_hash\n\napp = Flask(__name__)\n\n#engine = create_engine('sqlite:///cba.db')\n#engine = create_engine('postgresql://bccd:12345678@localhost:5432/python')\nengine = create_engine('mysql://root@127.0.0.1:3306/cba_ma')\nBase.metadata.bind = engine\n\nDBSession = sessionmaker(bind=engine)\ndb_session = DBSession()\n\n\nreload(sys)\n#sys.setdefaultencoding('utf-8')\nsys.setdefaultencoding('iso-8859-1')\n# Utilitaires pour la gestion des utilisateurs\n\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\n\n##@app.before_request\n##def before_request():\n## g.user = current_user\n \n\n@login_manager.user_loader\ndef load_user(id):\n user = db_session.query(User).filter_by(id=id).one()\n return user\n\n\n# Route de login\n@app.route('/' , methods=['GET', 'POST'])\n@app.route('/login' , methods=['GET', 'POST'])\ndef login():\n if request.method == 'POST':\n login = request.form['login']\n mdp = request.form['secret']\n registered_user = db_session.query(User).filter_by(username=login,password=mdp).first()\n if registered_user is None:\n flash(\"Utilisateur ou mot de passe invalide\")\n return redirect(url_for('login'))\n login_user(registered_user)\n session['login'] = current_user.id\n return redirect(url_for('home'))\n else:\n return render_template('loginForm.html', session=session)\n\n##def login():\n## if request.method == 'POST':\n## if request.form['login']:\n## session['login'] = request.form['login']\n## return redirect(url_for('home'))\n## else:\n## return render_template('loginForm.html', session=session)\n\n\n\n# Route pour se deconnecter\n@app.route('/logout')\ndef logout():\n # Clear the session\n session.pop('login', None)\n logout_user()\n # Redirect the user to the login page\n return redirect(url_for('login'))\n\n# Page d'acceuil\n@app.route('/home')\ndef home():\n return render_template('home.html')\n\n\n#Utilisateurs\n \n@app.route('/administration/structure/generale/utilisateurs')\ndef afficherUtilisateurs():\n utilisateurs = db_session.query(User).all()\n return render_template('utilisateur.html' , utilisateurs=utilisateurs)\n\n@app.route('/administration/structure/generale/utilisateur/nouveau' , methods=['GET', 'POST'])\ndef nouvelUtilisateur():\n if request.method == 'POST':\n mdp = generate_password_hash(request.form['mdp'])\n newUser = User(username=request.form['login'],\n password= mdp,\n email = request.form['email'])\n db_session.add(newUser)\n db_session.commit()\n flash(\"nouvel utilisateur creee avec succes\")\n return redirect(url_for('afficherUtilisateurs'))\n else:\n return render_template('nouvelUtilisateur.html')\n\n#Site\n\n#Afficher tout les sites\n@app.route('/administration/structure/generale/site')\ndef afficherSites():\n sites = db_session.query(Site).all()\n return render_template('site.html' , sites=sites)\n\n# Creer un nouveau site\n@app.route('/administration/structure/generale/site/nouveau/', methods=['GET', 'POST'])\ndef nouveauSite():\n if request.method == 'POST':\n nouveauSite = Site(adresse1=request.form['adresse1'],\n adresse2=request.form['adresse2'],\n ville=request.form['ville'],\n code_postal=request.form['code_postal'],\n pays=request.form['pays'],\n type=request.form['type'],\n societe_id=request.form['societe'],\n cree_par=session['login'])\n db_session.add(nouveauSite)\n db_session.commit()\n flash(\"nouveau site creee avec succes\")\n return redirect(url_for('afficherSites'))\n else:\n return render_template('nouveauSite.html')\n\n# Modifier site\n@app.route('/administration/structure/generale/site//modifier/', \n methods=['GET', 'POST'])\ndef modifierSite(site_id):\n modifSite = db_session.query(\n Site).filter_by(id=site_id).one()\n if request.method == 'POST':\n if request.form['adresse1']:\n modifSite.adresse1 = request.form['adresse1']\n if request.form['adresse2']:\n modifSite.adresse2 = request.form['adresse2']\n if request.form['ville']:\n modifSite.ville = request.form['ville']\n if request.form['code_postal']:\n modifSite.code_postal = request.form['code_postal']\n if request.form['pays']:\n modifSite.pays = request.form['pays']\n if request.form['type']:\n modifSite.type = request.form['type']\n if request.form['societe']:\n modifSite.societe_id = request.form['societe']\n modifSite.modifie_par=session['login']\n db_session.add(modifSite)\n db_session.commit()\n flash(\"site modifiee avec succes\")\n return redirect(url_for('afficherSites'))\n else:\n return render_template('modifierSite.html', site=modifSite)\n\n# Supprimer un site\n@app.route('/administration/structure/generale/site//supprimer/', \n methods=['GET', 'POST'])\ndef supprimerSite(site_id):\n siteSupprime = db_session.query(Site).filter_by(id=site_id).one()\n if request.method == 'POST':\n db_session.delete(siteSupprime)\n db_session.commit()\n flash(\"site supprimee avec succes\")\n return redirect(url_for('afficherSites'))\n else:\n return render_template('supprimerSite.html', site=siteSupprime)\n\n# Plans de comptes\n\n# Afficher tous les plans de comptes\n@app.route('/gestion_comptable/structure/plans_comptables')\n@login_required\ndef afficherPlansComptable():\n plans_comptables = db_session.query(PlanComptable).all()\n return render_template('PlansComptable.html' , plans_comptables=plans_comptables)\n\n# Creer un nouveau plan de comptes\n@app.route('/gestion_comptable/structure/plan_comptable/nouveau/', methods=['GET', 'POST'])\ndef nouveauPlanComptable():\n if request.method == 'POST':\n nouveauPlanComptable = PlanComptable(nom=request.form['nom'] , \n cree_par=session['login'])\n db_session.add(nouveauPlanComptable)\n db_session.commit()\n flash(\"plan comptable cree avec succes\")\n return redirect(url_for('afficherPlansComptable'))\n else:\n return render_template('nouveauPlanComptable.html')\n\n# Modifier un plan de comptes\n@app.route('/gestion_comptable/structure/plan_comptable//modifier/', methods=['GET', 'POST'])\ndef modifierPlanComptable(plan_comptable_id):\n planComptableModif = db_session.query(\n PlanComptable).filter_by(id=plan_comptable_id).one()\n if request.method == 'POST':\n if request.form['name']:\n planComptableModif.nom = request.form['name']\n planComptableModif.modifie_par = session['login'] \n db_session.add(planComptableModif)\n db_session.commit()\n flash(\"plan comptable modifie avec succes\")\n return redirect(url_for('afficherPlansComptable'))\n else:\n return render_template('modifierPlanComptable.html', plan_comptable=planComptableModif)\n\n# Supprimer un plan de comptes\n@app.route('/gestion_comptable/structure/plan_comptable//supprimer/', methods=['GET', 'POST'])\ndef supprimerPlanComptable(plan_comptable_id):\n planComptableSupprime = db_session.query(\n PlanComptable).filter_by(id=plan_comptable_id).one()\n if request.method == 'POST':\n db_session.delete(planComptableSupprime)\n db_session.commit()\n flash(\"plan comptable supprime avec succes\")\n return redirect(url_for('afficherPlansComptable', plan_comptable_id=plan_comptable_id))\n else:\n return render_template('supprimerPlanComptable.html', plan_comptable=planComptableSupprime)\n\n# Comptes\n\n# Afficher les comptes\n@app.route('/gestion_comptable/structure/plan_comptable//')\n@app.route('/gestion_comptable/structure/plan_comptable//comptes/')\ndef afficherComptes(plan_comptable_id):\n plan_comptable = db_session.query(PlanComptable).filter_by(id=plan_comptable_id).one()\n comptes = db_session.query(Compte).filter_by(\n plan_comptable_id=plan_comptable_id).all()\n return render_template('compte.html', comptes=comptes, plan_comptable=plan_comptable)\n\n# Creer un compte\n@app.route(\n '/gestion_comptable/structure/plan_comptable//compte/nouveau/', methods=['GET', 'POST'])\ndef nouveauCompte(plan_comptable_id):\n if request.method == 'POST':\n nouveauCompte = Compte(code=request.form['code'], \n libelle=request.form['libelle'],\n type=request.form['type'],\n parent=request.form['parent'],\n cree_par=session['login'], \n plan_comptable_id=plan_comptable_id)\n db_session.add(nouveauCompte)\n db_session.commit()\n flash(\"compte cree avec succes\")\n return redirect(url_for('afficherComptes', plan_comptable_id=plan_comptable_id))\n else:\n return render_template('nouveauCompte.html', plan_comptable_id=plan_comptable_id)\n\n\n# Modifier un compte\n@app.route('/gestion_comptable/structure/plan_comptable//compte//modifier/',\n methods=['GET', 'POST'])\ndef modifierCompte(plan_comptable_id, compte_id):\n compteModif = db_session.query(Compte).filter_by(id=compte_id).one()\n if request.method == 'POST':\n if request.form['code']:\n compteModif.code = request.form['code']\n if request.form['libelle']:\n compteModif.libelle = request.form['libelle']\n if request.form['type']:\n compteModif.type = request.form['type']\n if request.form['parent']:\n compteModif.parent = request.form['parent']\n compteModif.modifie_par = session['login']\n db_session.add(compteModif)\n db_session.commit()\n flash(\"compte modifie avec succes\")\n return redirect(url_for('afficherComptes', plan_comptable_id=plan_comptable_id))\n else:\n return render_template('modfierCompte.html', plan_comptable_id=plan_comptable_id, compte_id=compte_id, compte=compteModif)\n\n# Supprimer un compte\n@app.route('/gestion_comptable/structure/plan_comptable//compte//supprimer/',\n methods=['GET', 'POST'])\ndef supprimerCompte(plan_comptable_id, compte_id):\n compteSupprime = db_session.query(Compte).filter_by(id=compte_id).one()\n if request.method == 'POST':\n db_session.delete(compteSupprime)\n db_session.commit()\n flash(\"compte supprime avec succes\")\n return redirect(url_for('afficherComptes', plan_comptable_id=plan_comptable_id))\n else:\n return render_template('supprimerCompte.html', compte=compteSupprime, plan_comptable_id=plan_comptable_id)\n\n\n\n# Banques\n\n# Afficher toutes les banques\n@app.route('/gestion_comptable/structure/banques')\ndef afficherBanques():\n banques = db_session.query(Banque).all()\n return render_template('banques.html' , banques=banques)\n\n# Creer une nouvelle banque\n@app.route('/gestion_comptable/structure/banque/nouvelle/', methods=['GET', 'POST'])\ndef nouvelleBanque():\n if request.method == 'POST':\n nouvelleBanque = Banque(code=request.form['code'] ,\n banque=request.form['banque'] , \n cree_par=session['login'])\n db_session.add(nouvelleBanque)\n db_session.commit()\n flash(\"banque creee avec succes\")\n return redirect(url_for('afficherBanques'))\n else:\n return render_template('nouvelleBanque.html')\n\n# Modifier une banque\n@app.route('/gestion_comptable/structure/banque//modifier/', methods=['GET', 'POST'])\ndef modifierBanque(banque_id):\n banqueModif = db_session.query(Banque).filter_by(id=banque_id).one()\n if request.method == 'POST':\n if request.form['code']:\n banqueModif.code = request.form['code']\n if request.form['banque']:\n banqueModif.banque = request.form['banque']\n banqueModif.modifie_par = session['login'] \n db_session.add(banqueModif)\n db_session.commit()\n flash(\"banque modifiee avec succes\")\n return redirect(url_for('afficherBanques'))\n else:\n return render_template('modifierBanque.html', banque=banqueModif)\n\n# Supprimer une banque\n@app.route('/gestion_comptable/structure/banque//supprimer/', methods=['GET', 'POST'])\ndef supprimerBanque(banque_id):\n banqueSupprime = db_session.query(Banque).filter_by(id=banque_id).one()\n if request.method == 'POST':\n db_session.delete(banqueSupprime)\n db_session.commit()\n flash(\"banque supprimee avec succes\")\n return redirect(url_for('afficherBanques', banque_id=banque_id))\n else:\n return render_template('supprimerBanque.html', banque=banqueSupprime)\n\n\n#Societes\n\n#Afficher toutes les societes\n@app.route('/gestion_comptable/structure/societes')\ndef afficherSocietes():\n societes = db_session.query(Societe).all()\n return render_template('societe.html' , societes=societes)\n\n# Ajouter une nouvelle societe\n@app.route('/gestion_comptable/structure/societe/nouvelle/', methods=['GET', 'POST'])\ndef nouvelleSociete():\n plans_comptables = db_session.query(PlanComptable).all()\n if request.method == 'POST':\n nouvelleSociete = Societe(raison_sociale=request.form['raison_sociale'], \n cree_par=session['login'], \n id_nationale=request.form['id_nationale'],\n id_fiscale=request.form['id_fiscale'], \n adresse1=request.form['adresse1'],\n adresse2=request.form['adresse2'],\n code_postal=request.form['code_postal'],\n ville=request.form['ville'],\n pays=request.form['pays'],\n telephone=request.form['telephone'],\n fax=request.form['fax'],\n email=request.form['email'],\n site_web=request.form['site_web'],\n plan_comptable_id=request.form['plan_comptable_id'])\n db_session.add(nouvelleSociete)\n db_session.commit()\n flash(\"societe creee avec succes!\")\n return redirect(url_for('afficherSocietes'))\n else:\n return render_template('nouvelleSociete.html' , plans_comptables=plans_comptables)\n\n# Modifier une societe\n@app.route('/gestion_comptable/structure/societe//modifier/', methods=['GET', 'POST'])\ndef modifierSociete(societe_id):\n plans_comptables = db_session.query(PlanComptable).all()\n societeModif = db_session.query(\n Societe).filter_by(id=societe_id).one()\n if request.method == 'POST':\n if request.form['raison_sociale']:\n societeModif.raison_sociale = request.form['raison_sociale']\n if request.form['id_nationale']:\n societeModif.id_nationale = request.form['id_nationale']\n if request.form['id_fiscale']:\n societeModif.id_fiscale = request.form['id_fiscale']\n if request.form['adresse1']:\n societeModif.adresse1 = request.form['adresse1']\n if request.form['adresse2']:\n societeModif.adresse2 = request.form['adresse2']\n if request.form['code_postal']:\n societeModif.code_postal = request.form['code_postal']\n if request.form['ville']:\n societeModif.ville = request.form['ville']\n if request.form['pays']:\n societeModif.pays = request.form['pays']\n if request.form['telephone']:\n societeModif.telephone = request.form['telephone']\n if request.form['fax']:\n societeModif.fax = request.form['fax']\n if request.form['email']:\n societeModif.email = request.form['email']\n if request.form['site_web']:\n societeModif.site_web = request.form['site_web']\n if request.form['plan_comptable_id']:\n societeModif.plan_comptable_id = request.form['plan_comptable_id']\n societeModif.modifie_par = session['login']\n db_session.add(societeModif)\n flash(\"societe modifiee avec succes!\")\n db_session.commit()\n return redirect(url_for('afficherSocietes'))\n else:\n return render_template('modifierSociete.html', societe=societeModif, plans_comptables=plans_comptables)\n\n# Supprimer une societe\n@app.route('/gestion_comptable/structure/societe//supprimer/', methods=['GET', 'POST'])\ndef supprimerSociete(societe_id):\n societeSupprime = db_session.query(\n Societe).filter_by(id=societe_id).one()\n if request.method == 'POST':\n db_session.delete(societeSupprime)\n db_session.commit()\n flash(\"societe suprimee avec succes\")\n return redirect(url_for('afficherSocietes', societe_id=societe_id))\n else:\n return render_template('supprimerSociete.html', societe=societeSupprime)\n\n\n# Societe banques\n\n# Afficher les comptes\n@app.route('/gestion_comptable/structure/societe//')\n@app.route('/gestion_comptable/structure/societe//comptes/')\ndef afficherSocieteComptes(societe_id):\n societe = db_session.query(Societe).filter_by(id=societe_id).one()\n comptes = db_session.query(SocieteBanque).filter_by(societe_id=societe_id).all()\n return render_template('SocieteComptes.html', comptes=comptes, societe=societe)\n\n# Creer un compte\n@app.route('/gestion_comptable/structure/societe//compte/nouveau/', methods=['GET', 'POST'])\ndef nouveauSocieteCompte(societe_id):\n if request.method == 'POST':\n nouveauSocieteCompte = SocieteBanque(banque_id=request.form['banque'], \n rib=request.form['rib'],\n societe_id=societe_id,\n cree_par=session['login'])\n db_session.add(nouveauSocieteCompte)\n db_session.commit()\n flash(\"societe compte cree avec succes\")\n return redirect(url_for('afficherSocieteComptes', societe_id=societe_id))\n else:\n return render_template('nouveauSocieteCompte.html', societe_id=societe_id)\n\n\n# Modifier un compte\n@app.route('/gestion_comptable/structure/societe//compte//modifier/', methods=['GET', 'POST'])\ndef modifierSocieteCompte(societe_id, compte_id):\n compteSocieteModif = db_session.query(SocieteBanque).filter_by(id=compte_id).one()\n if request.method == 'POST':\n if request.form['banque']:\n compteSocieteModif.banque_id = request.form['banque']\n if request.form['rib']:\n compteSocieteModif.rib = request.form['rib']\n compteSocieteModif.modifie_par = session['login']\n db_session.add(compteSocieteModif)\n db_session.commit()\n flash(\"societe compte modifie avec succes\")\n return redirect(url_for('afficherSocieteComptes', societe_id=societe_id))\n else:\n return render_template('modfierSocieteCompte.html', societe_id=societe_id, compte_id=compte_id, compte=compteSocieteModif)\n\n# Supprimer un compte\n@app.route('/gestion_comptable/structure/societe//compte//supprimer/', methods=['GET', 'POST'])\ndef supprimerSocieteCompte(societe_id, compte_id):\n compteSocieteSupprime = db_session.query(SocieteBanque).filter_by(id=compte_id).one()\n if request.method == 'POST':\n db_session.delete(compteSocieteSupprime)\n db_session.commit()\n flash(\"compte supprime avec succes\")\n return redirect(url_for('afficherSocieteComptes', societe_id=societe_id))\n else:\n return render_template('supprimerSocieteCompte.html', compte=compteSocieteSupprime, societe_id=societe_id)\n\n\n#Taxes\n\n#Afficher toutes les taxes\n@app.route('/gestion_comptable/structure/taxe')\ndef afficherTaxes():\n taxes = db_session.query(Taxe).all()\n return render_template('taxe.html' , taxes=taxes)\n\n# Ajouter une taxe\n@app.route('/gestion_comptable/structure/taxes/nouvelle/', methods=['GET', 'POST'])\ndef nouvelleTaxe():\n societes = db_session.query(Societe).all()\n if request.method == 'POST':\n nouvelleTaxe = Taxe(designation=request.form['designation'],\n taux=request.form['taux'],\n type=request.form['type'],\n date_debut=request.form['date_debut'],\n date_fin=request.form['date_fin'],\n societe_id=request.form['societe'],\n cree_par=session['login'])\n db_session.add(nouvelleTaxe)\n db_session.commit()\n flash(\"taxe creee avec succes\")\n return redirect(url_for('afficherTaxes'))\n else:\n return render_template('nouvelleTaxe.html' , societes=societes)\n\n# Modifier une taxe exercice\n@app.route('/gestion_comptable/structure/taxe//modifier/', methods=['GET', 'POST'])\ndef modifierTaxe(taxe_id):\n societes = db_session.query(Societe).all()\n modifTaxe = db_session.query(\n Taxe).filter_by(id=taxe_id).one()\n if request.method == 'POST':\n if request.form['designation']:\n modifTaxe.designation = request.form['designation']\n if request.form['taux']:\n modifTaxe.taux = request.form['taux']\n if request.form['type']:\n modifTaxe.type = request.form['type']\n if request.form['date_debut']:\n modifTaxe.date_debut = request.form['date_debut']\n if request.form['date_fin']:\n modifTaxe.date_fin = request.form['date_fin']\n if request.form['societe']:\n modifTaxe.societe_id = request.form['societe']\n modifTaxe.modifie_par = session['login']\n db_session.add(modifTaxe)\n db_session.commit()\n flash(\"taxe modifiee avec succes\")\n return redirect(url_for('afficherTaxes'))\n else:\n return render_template('modifierTaxe.html', taxe=modifTaxe , societes=societes)\n\n\n# Supprimer une taxe\n@app.route('/gestion_comptable/structure/taxes//supprimer/', methods=['GET', 'POST'])\ndef supprimerTaxe(taxe_id):\n taxeSupprime = db_session.query(\n Taxe).filter_by(id=taxe_id).one()\n if request.method == 'POST':\n db_session.delete(taxeSupprime)\n db_session.commit()\n flash(\"taxe suprimee avec succes\")\n return redirect(url_for('afficherTaxes'))\n else:\n return render_template('supprimerTaxe.html', taxe=taxeSupprime)\n\n\n\n#Exercices fiscaux\n\n#Afficher tous les exercices\n@app.route('/gestion_comptable/structure/exercices_fiscaux')\ndef afficherExercices():\n exercices = db_session.query(Exercice).all()\n return render_template('exercice.html' , exercices=exercices)\n\n# Ajouter un exercice\n@app.route('/gestion_comptable/structure/exercices_fiscaux/nouveau/', methods=['GET', 'POST'])\ndef nouvelExercice():\n if request.method == 'POST':\n nouvelExercice = Exercice(exercice=request.form['exercice'],\n date_debut=request.form['date_debut'],\n date_fin=request.form['date_fin'],\n cree_par=session['login'],\n statut='O')\n db_session.add(nouvelExercice)\n db_session.commit()\n flash(\"exercice cree avec succes\")\n return redirect(url_for('afficherExercices'))\n else:\n return render_template('nouvelExercice.html')\n\n# Modifier un exercice\n@app.route('/gestion_comptable/structure/exercices_fiscaux//modifier/', methods=['GET', 'POST'])\ndef modifierExercice(exercice_id):\n modifExercice = db_session.query(\n Exercice).filter_by(id=exercice_id).one()\n if request.method == 'POST':\n if request.form['exercice']:\n modifExercice.exercice = request.form['exercice']\n if request.form['date_debut']:\n modifExercice.date_debut = request.form['date_debut']\n if request.form['date_fin']:\n modifExercice.date_fin = request.form['date_fin']\n if request.form['statut']:\n modifExercice.statut = request.form['statut']\n db_session.add(modifExercice)\n db_session.commit()\n flash(\"exercice modifie avec succes\")\n return redirect(url_for('afficherExercices'))\n else:\n return render_template('modifierExercice.html', exercice=modifExercice)\n\n\n# Supprimer un exercice\n@app.route('/gestion_comptable/structure/exercices_fiscaux//supprimer/', methods=['GET', 'POST'])\ndef supprimerExercice(exercice_id):\n exerciceSupprime = db_session.query(\n Exercice).filter_by(id=exercice_id).one()\n if request.method == 'POST':\n db_session.delete(exerciceSupprime)\n db_session.commit()\n flash(\"exercice supprime avec succes\")\n return redirect(url_for('afficherExercices'))\n else:\n return render_template('supprimerExercice.html', exercice=exerciceSupprime)\n\n\n\n#Periodes\n\n# Afficher les periodes\n@app.route('/gestion_comptable/structure/exercice_fiscal//')\n@app.route('/gestion_comptable/structure/exercice_fiscal//periodes/')\ndef afficherPeriodes(exercice_id):\n exercice = db_session.query(Exercice).filter_by(id=exercice_id).one()\n periodes = db_session.query(ExercicePeriode).filter_by(\n exercice_id=exercice_id).all()\n return render_template('periodes.html', periodes=periodes, exercice=exercice)\n\n# Creer une periode\n@app.route(\n '/gestion_comptable/structure/exercice_fiscal//periodes/nouvelle/', methods=['GET', 'POST'])\ndef nouvellePeriode(exercice_id):\n if request.method == 'POST':\n nouvellePeriode = ExercicePeriode(periode=request.form['periode'], \n date_debut=request.form['date_debut'],\n date_fin=request.form['date_fin'],\n cree_par=session['login'],\n statut='O',\n exercice_id=exercice_id)\n db_session.add(nouvellePeriode)\n db_session.commit()\n flash(\"periode creee avec succes\")\n return redirect(url_for('afficherPeriodes', exercice_id=exercice_id))\n else:\n return render_template('nouvellePeriode.html', exercice_id=exercice_id)\n\n\n# Modifier une periode\n@app.route('/gestion_comptable/structure/exercice_fiscal//periodes//modifier/',\n methods=['GET', 'POST'])\ndef modifierPeriode(exercice_id, periode_id):\n periodeModif = db_session.query(ExercicePeriode).filter_by(id=periode_id).one()\n if request.method == 'POST':\n if request.form['periode']:\n periodeModif.periode = request.form['periode']\n if request.form['date_debut']:\n periodeModif.date_debut = request.form['date_debut']\n if request.form['date_fin']:\n periodeModif.date_fin = request.form['date_fin']\n if request.form['statut']:\n periodeModif.statut = request.form['statut']\n periodeModif.modifie_par = session['login']\n db_session.add(periodeModif)\n db_session.commit()\n flash(\"periode modifiee avec succes\")\n return redirect(url_for('afficherPeriodes', exercice_id=exercice_id))\n else:\n return render_template('modifierPeriode.html', exercice_id=exercice_id, periode_id=periode_id, periode=periodeModif)\n\n# Supprimer une periode\n@app.route('/gestion_comptable/structure/exercice_fiscal//periodes//supprimer/',\n methods=['GET', 'POST'])\ndef supprimerPeriode(exercice_id, periode_id):\n periodeSupprime = db_session.query(ExercicePeriode).filter_by(id=periode_id).one()\n if request.method == 'POST':\n db_session.delete(periodeSupprime)\n db_session.commit()\n flash(\"periode supprimee avec succes\")\n return redirect(url_for('afficherPeriodes', exercice_id=exercice_id))\n else:\n return render_template('supprimerPeriode.html', periode=periodeSupprime, exercice_id=exercice_id)\n\n\n\n#Categorie Journaux\n\n#Afficher toutes les categories des journaux\n@app.route('/gestion_comptable/structure/journal_categorie')\ndef afficherJournauxCategories():\n journal_categorie = db_session.query(JournalCategorie).all()\n return render_template('JournalCategorie.html' , journal_categorie=journal_categorie)\n\n# Ajouter une categorie de journal\n@app.route('/gestion_comptable/structure/journal_categorie/nouvelle', methods=['GET', 'POST'])\ndef nouvelleJournalCategorie():\n if request.method == 'POST':\n nouvelleJournalCategorie = JournalCategorie(code=request.form['code'],\n categorie=request.form['categorie'], \n cree_par=session['login'])\n db_session.add(nouvelleJournalCategorie)\n db_session.commit()\n flash(\"journal categorie creee avec succes\")\n return redirect(url_for('afficherJournauxCategories'))\n else:\n return render_template('nouvelleJournalCategorie.html')\n\n# Modifier une categorie de journal\n@app.route('/gestion_comptable/structure/journal_categorie//modifier/', methods=['GET', 'POST'])\ndef modifierJournalCategorie(journal_cat_id):\n modifJournalCategorie = db_session.query(\n JournalCategorie).filter_by(id=journal_cat_id).one()\n if request.method == 'POST':\n if request.form['code']:\n modifJournalCategorie.code = request.form['code']\n if request.form['categorie']:\n modifJournalCategorie.categorie = request.form['categorie']\n modifJournalCategorie.modifie_par = session['login']\n db_session.add(modifJournalCategorie)\n db_session.commit()\n flash(\"journal categorie modifiee avec succes\")\n return redirect(url_for('afficherJournauxCategories'))\n else:\n return render_template('modifierJournalCategorie.html', journal_categorie=modifJournalCategorie)\n\n# Supprimer une categorie de journal\n@app.route('/gestion_comptable/structure/journal_categorie//supprimer/', methods=['GET', 'POST'])\ndef supprimerJournalCategorie(journal_cat_id):\n journalCategorieSupprime = db_session.query(\n JournalCategorie).filter_by(id=journal_cat_id).one()\n if request.method == 'POST':\n db_session.delete(journalCategorieSupprime)\n db_session.commit()\n flash(\"journal categorie supprimee avec succes\")\n return redirect(url_for('afficherJournauxCategories'))\n else:\n return render_template('supprimerJournalCategorie.html', journal_categorie=journalCategorieSupprime)\n\n\n#Journaux\n\n#Utilitaire d'aide pour la logique metier\n\ndef totalDebit(journal_id):\n total_debit = db_session.query(func.sum(JournalDetails.montant).label('sum')).filter(JournalDetails.journal_id == journal_id). \\\n filter(JournalDetails.debit_credit == 'D'). \\\n scalar()\n return total_debit\n\ndef totalCredit(journal_id):\n total_credit = db_session.query(func.sum(JournalDetails.montant).label('sum')).filter(JournalDetails.journal_id == journal_id). \\\n filter(JournalDetails.debit_credit == 'C'). \\\n scalar()\n return total_credit\n\ndef updateJournal(journal_id):\n q_td = \"UPDATE journal SET total_debit = :td WHERE id = :j_id\"\n td = totalDebit(journal_id)\n q_tc = \"UPDATE journal SET total_credit =:tc WHERE id = :j_id\"\n tc = totalCredit(journal_id)\n db_session.execute(text(q_td) , {'td' : td , 'j_id' : journal_id})\n db_session.execute(text(q_tc) , {'tc' : tc , 'j_id' : journal_id})\n db_session.commit()\n return\n\n#Afficher tous les journaux\n@app.route('/gestion_comptable/saisie/journal')\ndef afficherJournaux():\n journal = db_session.query(Journal).all()\n return render_template('journal.html' , journal=journal)\n\n# Ajouter un journal\n@app.route('/gestion_comptable/saisie/journal/nouveau', methods=['GET', 'POST'])\ndef nouveauJournal():\n journal_categories = db_session.query(JournalCategorie).all()\n periodes = db_session.query(ExercicePeriode).all()\n societes = db_session.query(Societe).all()\n if request.method == 'POST':\n nouveauJournal = Journal(memo=request.form['memo'],\n categorie_id=request.form['categorie'], \n periode_id=request.form['periode'], \n societe_id=request.form['societe'],\n cree_par=session['login'])\n db_session.add(nouveauJournal)\n db_session.commit()\n flash(\"journal cree avec succes\")\n return redirect(url_for('afficherJournaux'))\n else:\n return render_template('nouveauJournal.html' , journal_categories=journal_categories, periodes=periodes, societes=societes)\n\n# Modifier un journal\n@app.route('/gestion_comptable/saisie/journal//modifier/', methods=['GET', 'POST'])\ndef modifierJournal(journal_id):\n journal_categories = db_session.query(JournalCategorie).all()\n periodes = db_session.query(ExercicePeriode).all()\n societes = db_session.query(Societe).all()\n modifJournal = db_session.query(\n Journal).filter_by(id=journal_id).one()\n if request.method == 'POST':\n if request.form['memo']:\n modifJournal.memo = request.form['memo']\n if request.form['periode']:\n modifJournal.periode_id = request.form['periode']\n if request.form['societe']:\n modifJournal.societe_id = request.form['societe']\n modifJournal.modifie_par=session['login']\n db_session.add(modifJournal)\n db_session.commit()\n flash(\"journal modifie avec succes\")\n return redirect(url_for('afficherJournaux'))\n else:\n return render_template('modifierJournal.html', journal=modifJournal, journal_categories=journal_categories, periodes=periodes, societes=societes)\n\n# Supprimer un journal\n@app.route('/gestion_comptable/saisie/journal//supprimer/', methods=['GET', 'POST'])\ndef supprimerJournal(journal_id):\n journalSupprime = db_session.query(\n Journal).filter_by(id=journal_id).one()\n if request.method == 'POST':\n db_session.delete(journalSupprime)\n db_session.commit()\n flash(\"journal supprime avec succes\")\n return redirect(url_for('afficherJournaux'))\n else:\n return render_template('supprimerJournal.html', journal=journalSupprime)\n\n#Journaux details\n\n# Afficher les details du journal\n@app.route('/gestion_comptable/saisie/journal//')\n@app.route('/gestion_comptable/saisie/journal//details/')\ndef afficherJournalDetails(journal_id):\n journal = db_session.query(Journal).filter_by(id=journal_id).one()\n details = db_session.query(JournalDetails).filter_by(\n journal_id=journal_id).all()\n return render_template('JournalDetails.html', details=details, journal=journal)\n\n# Ajouter detail au journal\n@app.route(\n '/gestion_comptable/saisie/journal//details/nouveau/', methods=['GET', 'POST'])\ndef nouveauJournalDetail(journal_id):\n comptes = db_session.query(Compte).all()\n taxes = db_session.query(Taxe).all()\n if request.method == 'POST':\n nouveauJournalDetail = JournalDetails(montant=request.form['montant'], \n debit_credit=request.form['sens'], \n journal_id=journal_id , \n compte_id=request.form['compte'],\n taxe_id=request.form['taxe'],\n cree_par=session['login'])\n db_session.add(nouveauJournalDetail)\n db_session.commit()\n print(totalDebit(journal_id))\n #if request.form['sens'] == 'D':\n # modifJournal = db_session.query(Journal).filter_by(id=journal_id).one()\n # modifJournal.total_debit = totalDebit(journal_id) + float(request.form['montant'])\n # db_session.add(modifJournal)\n # db_session.commit()\n #print(totalDebit(journal_id))\n #print(totalCredit(journal_id))\n updateJournal(journal_id)\n flash(\"journal detail cree avec succes\")\n return redirect(url_for('afficherJournalDetails', journal_id=journal_id))\n else:\n return render_template('nouveauJournalDetails.html', journal_id=journal_id, comptes=comptes, taxes=taxes)\n\n\n# Modifier un detail du journal\n@app.route('/gestion_comptable/saisie/journal//details//modifier/',\n methods=['GET', 'POST'])\ndef modifierJournalDetails(journal_id, line_id):\n comptes = db_session.query(Compte).all()\n taxes = db_session.query(Taxe).all()\n modifJournalDetail = db_session.query(JournalDetails).filter_by(id=line_id).one()\n if request.method == 'POST':\n if request.form['montant']:\n modifJournalDetail.montant = request.form['montant']\n if request.form['sens']:\n modifJournalDetail.debit_credit = request.form['sens']\n if request.form['compte']:\n modifJournalDetail.compte_id = request.form['compte']\n if request.form['taxe']:\n modifJournalDetail.taxe_id = request.form['taxe']\n modifJournalDetail.modifie_par=session['login']\n db_session.add(modifJournalDetail)\n db_session.commit()\n updateJournal(journal_id)\n flash(\"Journal Detail modifie avec succes!\")\n return redirect(url_for('afficherJournalDetails', journal_id=journal_id))\n else:\n return render_template('modifierJournalDetails.html', journal_id=journal_id, line_id=line_id, detail=modifJournalDetail, comptes=comptes, taxes=taxes)\n\n# Supprimer un detail du journal\n@app.route('/gestion_comptable/saisie/journal//details//supprimer/',\n methods=['GET', 'POST'])\ndef supprimerJournalDetails(journal_id, line_id):\n journalDetailsSupprime = db_session.query(JournalDetails).filter_by(id=line_id).one()\n if request.method == 'POST':\n db_session.delete(journalDetailsSupprime)\n db_session.commit()\n flash(\"journal detail supprime avec succes\")\n return redirect(url_for('afficherJournalDetails', journal_id=journal_id))\n else:\n return render_template('supprimerJournalDetails.html', detail=journalDetailsSupprime, journal_id=journal_id)\n\n\n#Budget\n\n#Afficher tout les budgets\n@app.route('/gestion_comptable/saisie/budget')\ndef afficherBudgets():\n budget = db_session.query(Budget).all()\n return render_template('budget.html' , budget=budget)\n\n# Ajouter un budget\n@app.route('/gestion_comptable/saisie/budget/nouveau', methods=['GET', 'POST'])\ndef nouveauBudget():\n exercices = db_session.query(Exercice).all()\n societes = db_session.query(Societe).all()\n if request.method == 'POST':\n nouveauBudget = Budget(budget=request.form['budget'],\n exercice_id=request.form['exercice'], \n societe_id=request.form['societe'],\n cree_par=session['login'])\n db_session.add(nouveauBudget)\n db_session.commit()\n flash(\"budget cree avec succes\")\n return redirect(url_for('afficherBudgets'))\n else:\n return render_template('nouveauBudget.html' , exercices=exercices, societes=societes)\n\n# Modifier un budget\n@app.route('/gestion_comptable/saisie/budget//modifier/', methods=['GET', 'POST'])\ndef modifierBudget(budget_id):\n exercices = db_session.query(Exercice).all()\n societes = db_session.query(Societe).all()\n modifBudget = db_session.query(\n Budget).filter_by(id=budget_id).one()\n if request.method == 'POST':\n if request.form['budget']:\n modifBudget.budget = request.form['budget']\n if request.form['exercice']:\n modifBudget.exercice_id = request.form['exercice']\n if request.form['societe']:\n modifBudget.societe_id = request.form['societe']\n modifBudget.modifie_par=session['login']\n db_session.add(modifBudget)\n db_session.commit()\n flash(\"budget modifie avec succes\")\n return redirect(url_for('afficherBudgets'))\n else:\n return render_template('modifierBudget.html', budget=modifBudget , exercices=exercices, societes=societes)\n\n# Supprimer un budget\n@app.route('/gestion_comptable/saisie/budget//supprimer/', methods=['GET', 'POST'])\ndef supprimerBudget(budget_id):\n budgetSupprime = db_session.query(\n Budget).filter_by(id=budget_id).one()\n if request.method == 'POST':\n db_session.delete(budgetSupprime)\n db_session.commit()\n flash(\"budget supprime avec succes\")\n return redirect(url_for('afficherBudgets'))\n else:\n return render_template('supprimerBudget.html', budget=budgetSupprime)\n\n\n#Budget details\n\n# Afficher les details du budget\n@app.route('/gestion_comptable/saisie/budget//')\n@app.route('/gestion_comptable/saisie/budget//details/')\ndef afficherBudgetDetails(budget_id):\n budget = db_session.query(Budget).filter_by(id=budget_id).one()\n details = db_session.query(BudgetDetails).filter_by(\n budget_id=budget_id).all()\n return render_template('BudgetDetails.html', details=details, budget=budget)\n\n# Ajouter detail au budget\n@app.route(\n '/gestion_comptable/saisie/budget//details/nouveau/', methods=['GET', 'POST'])\ndef nouveauBudgetDetail(budget_id):\n comptes = db_session.query(Compte).all()\n periodes = db_session.query(ExercicePeriode).all()\n if request.method == 'POST':\n nouveauBudgetDetail = BudgetDetails(montant=request.form['montant'], \n budget_id=budget_id , \n compte_id=request.form['compte'],\n periode_id=request.form['periode'],\n cree_par=session['login'])\n db_session.add(nouveauBudgetDetail)\n db_session.commit()\n flash(\"budget detail cree avec succes\")\n return redirect(url_for('afficherBudgetDetails', budget_id=budget_id))\n else:\n return render_template('nouveauBudgetDetails.html', budget_id=budget_id, comptes=comptes, periodes=periodes)\n\n\n# Modifier un detail du budget\n@app.route('/gestion_comptable/saisie/budget//details//modifier/',\n methods=['GET', 'POST'])\ndef modifierBudgetDetails(budget_id, line_id):\n comptes = db_session.query(Compte).all()\n periodes = db_session.query(ExercicePeriode).all()\n modifBudgetDetail = db_session.query(BudgetDetails).filter_by(id=line_id).one()\n if request.method == 'POST':\n if request.form['montant']:\n modifBudgetDetail.montant = request.form['montant']\n if request.form['compte']:\n modifBudgetDetail.compte_id = request.form['compte']\n if request.form['periode']:\n modifBudgetDetail.periode_id = request.form['periode']\n modifBudgetDetail.modifie_par=session['login']\n db_session.add(modifBudgetDetail)\n db_session.commit()\n flash(\"budget detail modifie avec succes\")\n return redirect(url_for('afficherBudgetDetails', budget_id=budget_id))\n else:\n return render_template('modifierBudgetDetails.html', budget_id=budget_id, line_id=line_id, detail=modifBudgetDetail, comptes=comptes, periodes=periodes)\n\n# Supprimer un detail du budget\n@app.route('/gestion_comptable/saisie/budget//details//supprimer/',\n methods=['GET', 'POST'])\ndef supprimerBudgetDetails(budget_id, line_id):\n budgetDetailsSupprime = db_session.query(BudgetDetails).filter_by(id=line_id).one()\n if request.method == 'POST':\n db_session.delete(budgetDetailsSupprime)\n db_session.commit()\n flash(\"budget detail supprime avec succes\")\n return redirect(url_for('afficherBudgetDetails', budget_id=budget_id))\n else:\n return render_template('supprimerBudgetDetails.html', detail=budgetDetailsSupprime, budget_id=budget_id)\n\n\n#Depots\n\n#Afficher tous les depots\n@app.route('/gestion_commerciale/structure/depot')\ndef afficherDepots():\n depots = db_session.query(Depot).all()\n return render_template('depot.html' , depots=depots)\n\n# Creer un nouveau depot\n@app.route('/gestion_commerciale/structure/depot/new/', methods=['GET', 'POST'])\ndef nouveauDepot():\n sites = db_session.query(Site).all()\n societes = db_session.query(Societe).all()\n if request.method == 'POST':\n nouvauDepot = Depot(depot=request.form['nom'],\n site_id=request.form['site'],\n societe_id=request.form['societe'],\n cree_par=session['login']\n )\n db_session.add(nouvauDepot)\n db_session.commit()\n flash(\"depot cree avec succes\")\n return redirect(url_for('afficherDepots'))\n else:\n return render_template('nouveauDepot.html' , sites=sites, societes=societes)\n\n# Modifier un depot\n@app.route('/gestion_commerciale/structure/depot//modifier/', methods=['GET', 'POST'])\ndef modifierDepot(depot_id):\n sites = db_session.query(Site).all()\n societes = db_session.query(Societe).all()\n modifDepot = db_session.query(\n Depot).filter_by(id=depot_id).one()\n if request.method == 'POST':\n if request.form['nom']:\n modifDepot.depot = request.form['nom']\n if request.form['site']:\n modifDepot.site_id = request.form['site']\n if request.form['societe']:\n modifDepot.societe_id = request.form['societe']\n modifDepot.modifie_par = session['login']\n db_session.add(modifDepot)\n db_session.commit()\n flash(\"depot modifie avec succes\")\n return redirect(url_for('afficherDepots'))\n else:\n return render_template('modifierDepot.html', depot=modifDepot, sites=sites, societes=societes)\n\n# Supprimer un depot\n@app.route('/gestion_commerciale/structure/depot//supprimer/', methods=['GET', 'POST'])\ndef supprimerDepot(depot_id):\n depotSupprime = db_session.query(\n Depot).filter_by(id=depot_id).one()\n if request.method == 'POST':\n db_session.delete(depotSupprime)\n db_session.commit()\n flash(\"depot supprime avec succes\")\n return redirect(url_for('afficherDepots'))\n else:\n return render_template('supprimerDepot.html', depot=depotSupprime)\n\n\n#Partenaires\n\n#Afficher tout les partenaires\n@app.route('/gestion_commerciale/structure/partenaire')\ndef afficherPartenaires():\n partenaires = db_session.query(Partenaire).all()\n return render_template('partenaire.html' , partenaires=partenaires)\n\n# Creer un partenaire\n@app.route('/gestion_commerciale/structure/partenaire/nouveau/', methods=['GET', 'POST'])\ndef nouveauPartenaire():\n sites = db_session.query(Site).all()\n societes = db_session.query(Societe).all()\n comptes = db_session.query(Compte).all()\n if request.method == 'POST':\n nouveauPartenaire = Partenaire(nom=request.form['nom'] , \n type=request.form['type'],\n societe_id=request.form['societe'],\n site_id=request.form['site'],\n compte_id=request.form['compte'],\n cree_par=session['login']\n )\n db_session.add(nouveauPartenaire)\n db_session.commit()\n flash(\"partenaire cree avec succes\")\n return redirect(url_for('afficherPartenaires'))\n else:\n return render_template('nouveauPartenaire.html' , sites=sites, societes=societes, comptes=comptes)\n\n# Modifier un partenaire\n@app.route('/gestion_commerciale/structure//modifier/', methods=['GET', 'POST'])\ndef modifierPartenaire(partenaire_id):\n sites = db_session.query(Site).all()\n societes = db_session.query(Societe).all()\n comptes = db_session.query(Compte).all()\n modifPartenaire = db_session.query(\n Partenaire).filter_by(id=partenaire_id).one()\n if request.method == 'POST':\n if request.form['nom']:\n modifPartenaire.nom = request.form['nom']\n if request.form['type']:\n modifPartenaire.type = request.form['type']\n if request.form['societe']:\n modifPartenaire.societe_id = request.form['societe']\n if request.form['site']:\n modifPartenaire.site_id = request.form['site']\n if request.form['compte']:\n modifPartenaire.compte_id = request.form['compte']\n modifPartenaire.modifie_par=session['login']\n db_session.add(modifPartenaire)\n db_session.commit()\n flash(\"partenaire modifie avec succes\")\n return redirect(url_for('afficherPartenaires'))\n else:\n return render_template('modifierPartenaire.html', partenaire=modifPartenaire, sites=sites, societes=societes, comptes=comptes)\n\n# Supprimer un partenaire\n@app.route('/gestion_commerciale/structure/partenaire//supprimer/', methods=['GET', 'POST'])\ndef supprimerPartenaire(partenaire_id):\n partenaireSupprime = db_session.query(\n Partenaire).filter_by(id=partenaire_id).one()\n if request.method == 'POST':\n db_session.delete(partenaireSupprime)\n db_session.commit()\n flash(\"partenaire supprime avec succes\")\n return redirect(url_for('afficherPartenaires'))\n else:\n return render_template('supprimerPartenaire.html', partenaire=partenaireSupprime)\n\n\n#Categories d'article\n\n#Afficher toutes les categories d'articles\n@app.route('/gestion_commerciale/structure/article_categorie')\ndef afficherArticlesCategories():\n articles_categories = db_session.query(ArticleCategorie).all()\n return render_template('ArticlesCategories.html' , articles_categories=articles_categories)\n\n# Creer une nouvelle categorie d'articles\n@app.route('/gestion_commerciale/structure/article_categorie/nouvelle/', methods=['GET', 'POST'])\ndef nouvelleArticleCategorie():\n societes = db_session.query(Societe).all()\n if request.method == 'POST':\n nouvelleArticleCategorie = ArticleCategorie(categorie=request.form['categorie'],\n description=request.form['description'],\n type=request.form['type'],\n societe_id=request.form['societe'],\n cree_par=session['login'])\n db_session.add(nouvelleArticleCategorie)\n db_session.commit()\n flash(\"article categorie creee avec succes\")\n return redirect(url_for('afficherArticlesCategories'))\n else:\n return render_template('nouvelleArticleCategorie.html' , societes=societes)\n\n# Modifier categorie d'articles\n@app.route('/gestion_commerciale/structure/article_categorie//modifier/', \n methods=['GET', 'POST'])\ndef modifierArticleCategorie(article_categorie_id):\n societes = db_session.query(Societe).all()\n modifArticleCategorie = db_session.query(\n ArticleCategorie).filter_by(id=article_categorie_id).one()\n if request.method == 'POST':\n if request.form['categorie']:\n modifArticleCategorie.categorie = request.form['categorie']\n if request.form['description']:\n modifArticleCategorie.description = request.form['description']\n if request.form['type']:\n modifArticleCategorie.type = request.form['type']\n if request.form['societe']:\n modifArticleCategorie.societe_id = request.form['societe']\n modifArticleCategorie.modifie_par=session['login']\n db_session.add(modifArticleCategorie)\n db_session.commit()\n flash(\"article categorie modifiee avec succes\")\n return redirect(url_for('afficherArticlesCategories'))\n else:\n return render_template('modifierArticleCategorie.html', article_categorie=modifArticleCategorie , societes=societes)\n\n# Supprimer une categorie d'articles\n@app.route('/gestion_commerciale/structure/article_categorie//supprimer/', \n methods=['GET', 'POST'])\ndef supprimerArticleCategorie(article_categorie_id):\n articleCategorieSupprime = db_session.query(\n ArticleCategorie).filter_by(id=article_categorie_id).one()\n if request.method == 'POST':\n db_session.delete(articleCategorieSupprime)\n db_session.commit()\n flash(\"categorie supprimee avec succes\")\n return redirect(url_for('afficherArticlesCategories'))\n else:\n return render_template('supprimerArticleCategorie.html', article_categorie=articleCategorieSupprime)\n\n\n\n#Articles\n\n#Afficher tout les articles\n@app.route('/gestion_commerciale/structure/article_categorie//')\n@app.route('/gestion_commerciale/structure/article_categorie//article/')\ndef afficherArticles(categorie_id):\n categorie = db_session.query(ArticleCategorie).filter_by(id=categorie_id).one()\n articles = db_session.query(Article).filter_by(\n categorie_id=categorie_id).all()\n return render_template('article.html' , articles=articles , categorie=categorie)\n\n# Ajouter un nouvel article\n@app.route('/gestion_commerciale/structure/article_categorie//article/nouveau/', methods=['GET', 'POST'])\ndef nouvelArticle(categorie_id):\n partenaires = db_session.query(Partenaire).all()\n if request.method == 'POST':\n nouveauArticle = Article(ref=request.form['ref'] , \n description=request.form['description'], \n prix=request.form['prix'], \n cout=request.form['cout'], \n type=request.form['type'],\n categorie_id=categorie_id,\n partenaire_id=request.form['partenaire'],\n cree_par=session['login'])\n db_session.add(nouveauArticle)\n db_session.commit()\n flash(\"article cree avec succes\")\n return redirect(url_for('afficherArticles' , categorie_id=categorie_id))\n else:\n return render_template('nouvelArticle.html' , categorie_id=categorie_id, partenaires=partenaires)\n\n# Modifier un article\n@app.route('/gestion_commerciale/structure/article_categorie//article//modifier/', methods=['GET', 'POST'])\ndef modifierArticle(categorie_id, article_id):\n partenaires = db_session.query(Partenaire).all()\n modifArticle = db_session.query(\n Article).filter_by(id=article_id).one()\n if request.method == 'POST':\n if request.form['ref']:\n modifArticle.ref = request.form['ref']\n if request.form['description']:\n modifArticle.description = request.form['description']\n if request.form['prix']:\n modifArticle.prix = request.form['prix']\n if request.form['cout']:\n modifArticle.cout = request.form['cout']\n if request.form['type']:\n modifArticle.type = request.form['type']\n if request.form['partenaire']:\n modifArticle.partenaire_id = request.form['partenaire']\n modifArticle.modifie_par=session['login']\n db_session.add(modifArticle)\n db_session.commit()\n flash(\"article modifie avec succes\")\n return redirect(url_for('afficherArticles' , categorie_id=categorie_id))\n else:\n return render_template('modifierArticle.html', article=modifArticle , categorie_id=categorie_id , article_id=article_id, partenaires=partenaires)\n\n# Supprimer un article\n@app.route('/gestion_commerciale/structure/article_categorie//article//supprimer/', \n methods=['GET', 'POST'])\ndef supprimerArticle(categorie_id , article_id):\n articleSupprime = db_session.query(\n Article).filter_by(id=article_id).one()\n if request.method == 'POST':\n db_session.delete(articleSupprime)\n db_session.commit()\n flash(\"article suprimee avec succes\")\n return redirect(url_for('afficherArticles' , categorie_id=categorie_id))\n else:\n return render_template('supprimerArticle.html', article=articleSupprime, categorie_id=categorie_id)\n\n\n#Achats\n\n#Afficher tout les achats\n@app.route('/gestion_commerciale/saisie/achat')\ndef afficherAchats():\n achats = db_session.query(Achat).all()\n return render_template('achat.html' , achat=achats)\n\n# Ajouter un achat\n@app.route('/gestion_commerciale/saisie/achat/nouveau/', methods=['GET', 'POST'])\ndef nouvelAchat():\n partenaires = db_session.query(Partenaire).all()\n societes = db_session.query(Societe).all()\n periodes = db_session.query(ExercicePeriode).all()\n if request.method == 'POST':\n nouvelAchat = Achat(memo=request.form['memo'] , \n statut='O',\n type_document=request.form['type_document'], \n periode_id=request.form['periode'], \n societe_id=request.form['societe'],\n partenaire_id=request.form['partenaire'],\n cree_par=session['login'])\n db_session.add(nouvelAchat)\n db_session.commit()\n flash(\"achat cree avec succes\")\n return redirect(url_for('afficherAchats'))\n else:\n return render_template('nouvelAchat.html' , partenaires=partenaires, societes=societes, periodes=periodes)\n\n# Modifier un achat\n@app.route('/gestion_commerciale/saisie/achat//modifier/', methods=['GET', 'POST'])\ndef modifierAchat(achat_id):\n partenaires = db_session.query(Partenaire).all()\n societes = db_session.query(Societe).all()\n periodes = db_session.query(ExercicePeriode).all()\n modifAchat = db_session.query(\n Achat).filter_by(id=achat_id).one()\n if request.method == 'POST':\n if request.form['memo']:\n modifAchat.memo = request.form['memo']\n if request.form['type_document']:\n modifAchat.type_document = request.form['type_document']\n if request.form['periode']:\n modifAchat.periode_id = request.form['periode']\n if request.form['societe']:\n modifAchat.societe_id = request.form['societe']\n if request.form['partenaire']:\n modifAchat.partenaire_id = request.form['partenaire']\n modifAchat.modifie_par=session['login']\n db_session.add(modifAchat)\n db_session.commit()\n flash(\"achat modifie avec succes\")\n return redirect(url_for('afficherAchats'))\n else:\n return render_template('modifierAchat.html', achat=modifAchat, partenaires=partenaires, societes=societes, periodes=periodes)\n\n# Supprimer un achat\n@app.route('/gestion_commerciale/saisie/achat//supprimer/', methods=['GET', 'POST'])\ndef supprimerAchat(achat_id):\n achatSupprime = db_session.query(\n Achat).filter_by(id=achat_id).one()\n if request.method == 'POST':\n db_session.delete(achatSupprime)\n db_session.commit()\n flash(\"achat supprime avec succes\")\n return redirect(url_for('afficherAchats'))\n else:\n return render_template('supprimerAchat.html', achat=achatSupprime)\n\n\n#Achats details\n\n# Afficher les details de l'achat\n@app.route('/gestion_commerciale/saisie/achat//')\n@app.route('/gestion_commerciale/saisie/achat//details/')\ndef afficherAchatDetails(achat_id):\n achat = db_session.query(Achat).filter_by(id=achat_id).one()\n details = db_session.query(AchatDetails).filter_by(\n achat_id=achat_id).all()\n return render_template('AchatDetails.html', details=details, achat=achat)\n\n# Ajouter details de l'achat\n@app.route('/gestion_commerciale/saisie/achat//details/nouveau/', methods=['GET', 'POST'])\ndef nouvelAchatDetail(achat_id):\n articles = db_session.query(Article).all()\n if request.method == 'POST':\n quantite=float(request.form['quantite'])\n prix_unitaire=float(request.form[ 'prix_unitaire'])\n ligne_totale=quantite*prix_unitaire \n nouvelAchatDetail = AchatDetails(quantite=quantite, \n prix_unitaire=prix_unitaire,\n ligne_totale=ligne_totale, \n achat_id=achat_id, \n article_id=request.form['article'],\n cree_par=session['login'])\n db_session.add(nouvelAchatDetail)\n db_session.commit()\n flash(\"achat detail cree avec succes\")\n return redirect(url_for('afficherAchatDetails', achat_id=achat_id))\n else:\n return render_template('nouvelAchatDetails.html', achat_id=achat_id , articles=articles)\n\n\n# Modifier un detail de l'achat\n@app.route('/gestion_commerciale/saisie/achat//details//modifier/', methods=['GET', 'POST'])\ndef modifierAchatDetails(achat_id, line_id):\n modifAchatDetail = db_session.query(AchatDetails).filter_by(id=line_id).one()\n if request.method == 'POST':\n quantite = float(0.0)\n prix_unitaire = float(0.0)\n ligne_totale = float(0.0)\n if request.form['article']:\n modifAchatDetail.article_id = request.form['article']\n if request.form['quantite']:\n quantite=float(request.form['quantite'])\n if request.form['prix_unitaire']:\n prix_unitaire=float(request.form[ 'prix_unitaire'])\n ligne_totale=quantite*prix_unitaire\n modifAchatDetail.quantite = quantite\n modifAchatDetail.prix_unitaire = prix_unitaire\n modifAchatDetail.ligne_totale = ligne_totale\n modifAchatDetail.modifie_par = session['login']\n db_session.add(modifAchatDetail)\n db_session.commit()\n flash(\"achat detail modifie avec succes\")\n return redirect(url_for('afficherAchatDetails', achat_id=achat_id))\n else:\n return render_template('modifierAchatDetails.html', achat_id=achat_id, line_id=line_id, detail=modifAchatDetail)\n\n# Supprimer un detail de l'achat\n@app.route('/gestion_commerciale/saisie/achat//details//supprimer', methods=['GET', 'POST'])\ndef supprimerAchatDetails(achat_id, line_id):\n achatDetailsSupprime = db_session.query(AchatDetails).filter_by(id=line_id).one()\n if request.method == 'POST':\n db_session.delete(achatDetailsSupprime)\n db_session.commit()\n flash(\"achat detail supprime avec succes\")\n return redirect(url_for('afficherAchatDetails', achat_id=achat_id))\n else:\n return render_template('supprimerAchatDetails.html', detail=achatDetailsSupprime, achat_id=achat_id)\n\n\n#Ventes\n\n#Afficher toutes les ventes\n@app.route('/gestion_commerciale/saisie/vente')\ndef afficherVentes():\n ventes = db_session.query(Vente).all()\n return render_template('vente.html' , vente=ventes)\n\n# Ajouter une vente\n@app.route('/gestion_commerciale/saisie/nouvelle/', methods=['GET', 'POST'])\ndef nouvelleVente():\n partenaires = db_session.query(Partenaire).all()\n societes = db_session.query(Societe).all()\n periodes = db_session.query(ExercicePeriode).all()\n if request.method == 'POST':\n nouvelleVente = Vente(memo=request.form['memo'] , \n statut='O',\n periode_id=request.form['periode'], \n societe_id=request.form['societe'],\n partenaire_id=request.form['partenaire'],\n cree_par=session['login'])\n db_session.add(nouvelleVente)\n db_session.commit()\n flash(\"vente creee avec succes\")\n return redirect(url_for('afficherVentes'))\n else:\n return render_template('nouvelleVente.html' , partenaires=partenaires, societes=societes, periodes=periodes)\n\n# Modifier une vente\n@app.route('/gestion_commerciale/saisie//modifier/', methods=['GET', 'POST'])\ndef modifierVente(vente_id):\n partenaires = db_session.query(Partenaire).all()\n societes = db_session.query(Societe).all()\n periodes = db_session.query(ExercicePeriode).all()\n modifVente = db_session.query(\n Vente).filter_by(id=vente_id).one()\n if request.method == 'POST':\n if request.form['memo']:\n modifVente.memo = request.form['memo']\n if request.form['status']:\n modifVente.status = request.form['status']\n if request.form['periode']:\n modifVente.periode_id = request.form['periode']\n if request.form['societe']:\n modifVente.societe_id = request.form['societe']\n if request.form['partenaire']:\n modifVente.partenaire_id = request.form['partenaire']\n db_session.add(modifVente)\n db_session.commit()\n flash(\"vente modifiee avec succes\")\n return redirect(url_for('afficherVentes'))\n else:\n return render_template('modifierVente.html', vente=modifVente , partenaires=partenaires, societes=societes, periodes=periodes)\n\n\n# Supprimer une vente\n@app.route('/gestion_commerciale/saisie/vente//supprimer/', methods=['GET', 'POST'])\ndef supprimerVente(vente_id):\n venteSupprime = db_session.query(\n Vente).filter_by(id=vente_id).one()\n if request.method == 'POST':\n db_session.delete(venteSupprime)\n db_session.commit()\n flash(\"vente supprimee avec succes\")\n return redirect(url_for('afficherVentes'))\n else:\n return render_template('supprimerVente.html', vente=venteSupprime)\n\n\n#Ventes details\n\n# Afficher les details de la vente\n@app.route('/gestion_commerciale/saisie/vente//')\n@app.route('/gestion_commerciale/saisie/vente//details/')\ndef afficherVenteDetails(vente_id):\n vente = db_session.query(Vente).filter_by(id=vente_id).one()\n details = db_session.query(VenteDetails).filter_by(\n vente_id=vente_id).all()\n return render_template('VenteDetails.html', details=details, vente=vente)\n\n# Ajouter details de la vente\n@app.route('/gestion_commerciale/saisie/vente//details/nouveau/', methods=['GET', 'POST'])\ndef nouvelleVenteDetail(vente_id):\n articles = db_session.query(Article).all()\n if request.method == 'POST':\n quantite=float(request.form['quantite'])\n prix_unitaire=float(request.form[ 'prix_unitaire'])\n ligne_totale=quantite*prix_unitaire\n nouvelleVenteDetail = VenteDetails(quantite=quantite, \n prix_unitaire=prix_unitaire,\n ligne_totale=ligne_totale, \n vente_id=vente_id, \n article_id=request.form['article'],\n cree_par=session['login'])\n db_session.add(nouvelleVenteDetail)\n db_session.commit()\n flash(\"vente detail cree avec succes\")\n return redirect(url_for('afficherVenteDetails', vente_id=vente_id))\n else:\n return render_template('nouvelleVenteDetails.html', vente_id=vente_id, articles=articles)\n\n\n# Modifier un detail de la vente\n@app.route('/gestion_commerciale/saisie/vente//details//modifier/', methods=['GET', 'POST'])\ndef modifierVenteDetails(vente_id, line_id):\n modifVenteDetail = db_session.query(VenteDetails).filter_by(id=line_id).one()\n if request.method == 'POST':\n quantite = float(0.0)\n prix_unitaire = float(0.0)\n ligne_totale = float(0.0)\n if request.form['article']:\n modifVenteDetail.article_id = request.form['article']\n if request.form['quantite']:\n quantite=float(request.form['quantite'])\n if request.form['prix_unitaire']:\n prix_unitaire=float(request.form[ 'prix_unitaire'])\n ligne_totale=quantite*prix_unitaire\n modifVenteDetail.quantite = quantite\n modifVenteDetail.prix_unitaire = prix_unitaire\n modifVenteDetail.ligne_totale = ligne_totale\n modifVenteDetail.modifie_par = session['login']\n db_session.add(modifVenteDetail)\n db_session.commit()\n flash(\"vente detail modifie avec succes\")\n return redirect(url_for('afficherVenteDetails', vente_id=vente_id))\n else:\n return render_template('modifierVenteDetails.html', vente_id=vente_id, line_id=line_id, detail=modifVenteDetail)\n\n# Supprimer un detail d'une vente\n@app.route('/gestion_commerciale/saisie/vente//details//supprimer/', methods=['GET', 'POST'])\ndef supprimerVenteDetails(vente_id, line_id):\n venteDetailsSupprime = db_session.query(VenteDetails).filter_by(id=line_id).one()\n if request.method == 'POST':\n db_session.delete(venteDetailsSupprime)\n db_session.commit()\n flash(\"vente detail supprime avec succes\")\n return redirect(url_for('afficherVenteDetails', vente_id=vente_id))\n else:\n return render_template('supprimerVenteDetails.html', detail=venteDetailsSupprime, vente_id=vente_id)\n\n#Directions\n\n#Afficher toutes les directions\n@app.route('/gestion_rh/structure/directions')\ndef afficherDirections():\n directions = db_session.query(Direction).all()\n return render_template('directions.html' , directions=directions)\n\n\n# Creer une nouvelle direction\n@app.route('/gestion_rh/structure/direction/nouvelle/', methods=['GET', 'POST'])\ndef nouvelleDirection():\n if request.method == 'POST':\n nouvelleDirection = Direction(code=request.form['code'],\n direction=request.form['direction'], \n societe_id=request.form['societe'],\n cree_par=session['login'])\n db_session.add(nouvelleDirection)\n db_session.commit()\n flash(\"direction creee avec succes\")\n return redirect(url_for('afficherDirections'))\n else:\n return render_template('nouvelleDirection.html')\n\n# Modifier une direction\n@app.route('/gestion_rh/structure/direction//modifier/', methods=['GET', 'POST'])\ndef modifierDirection(direction_id):\n modifDirection = db_session.query(\n Direction).filter_by(id=direction_id).one()\n if request.method == 'POST':\n if request.form['code']:\n modifDirection.code = request.form['code']\n if request.form['direction']:\n modifDirection.direction = request.form['direction']\n if request.form['societe']:\n modifDirection.societe_id = request.form['societe']\n modifDirection.modifie_par = session['login']\n db_session.add(modifDirection)\n db_session.commit()\n flash(\"direction modifiee avec succes\")\n return redirect(url_for('afficherDirections'))\n else:\n return render_template('modifierDirection.html', direction=modifDirection)\n\n# Supprimer une direction\n@app.route('/gestion_rh/structure/direction//supprimer/', methods=['GET', 'POST'])\ndef supprimerDirection(direction_id):\n directionSupprime = db_session.query(\n Direction).filter_by(id=direction_id).one()\n if request.method == 'POST':\n db_session.delete(directionSupprime)\n db_session.commit()\n flash(\"direction supprime avec succes\")\n return redirect(url_for('afficherDirections'))\n else:\n return render_template('supprimerDirection.html', direction=directionSupprime)\n\n\n\n#Direction services\n\n# Afficher les services de la direction\n@app.route('/gestion_rh/structure/direction/')\n@app.route('/gestion_rh/structure/direction//services/')\ndef afficherServices(direction_id):\n direction = db_session.query(Direction).filter_by(id=direction_id).one()\n services = db_session.query(DirectionService).filter_by(\n direction_id=direction_id).all()\n return render_template('services.html', services=services, direction=direction)\n\n# Ajouter un service a la direction\n@app.route('/gestion_rh/structure/direction//service/nouveau/', methods=['GET', 'POST'])\ndef nouveauService(direction_id):\n if request.method == 'POST':\n nouveauService = DirectionService(code=request.form['code'], \n service=request.form['service'], \n direction_id=direction_id, \n cree_par=session['login'])\n db_session.add(nouveauService)\n db_session.commit()\n flash(\"service cree avec succes\")\n return redirect(url_for('afficherServices', direction_id=direction_id))\n else:\n return render_template('nouveauService.html', direction_id=direction_id)\n\n\n# Modifier un service\n@app.route('/gestion_rh/structure/direction//service//modifier/', methods=['GET', 'POST'])\ndef modifierService(direction_id, service_id):\n modifService = db_session.query(DirectionService).filter_by(id=service_id).one()\n if request.method == 'POST':\n if request.form['code']:\n modifService.code = request.form['code']\n if request.form['service']:\n modifService.service = request.form['service']\n if request.form['direction']:\n modifService.direction_id = request.form['direction']\n modifService.modifie_par=session['login']\n db_session.add(modifService)\n db_session.commit()\n flash(\"service modifie avec succes\")\n return redirect(url_for('afficherServices', direction_id=direction_id))\n else:\n return render_template('modifierService.html', direction_id=direction_id, service_id=service_id, service=modifService)\n\n# Supprimer un service\n@app.route('/gestion_rh/structure/direction//service//supprimer/', methods=['GET', 'POST'])\ndef supprimerService(direction_id, service_id):\n serviceSupprime = db_session.query(DirectionService).filter_by(id=service_id).one()\n if request.method == 'POST':\n db_session.delete(serviceSupprime)\n db_session.commit()\n flash(\"service supprime avec succes\")\n return redirect(url_for('afficherServices', direction_id=direction_id))\n else:\n return render_template('supprimerService.html', service=serviceSupprime, direction_id=direction_id)\n\n\n\n#Fonctions\n\n#Afficher toutes les fonctions\n@app.route('/gestion_rh/structure/fonctions')\ndef afficherFonctions():\n fonctions = db_session.query(Fonction).all()\n return render_template('fonctions.html' , fonctions=fonctions)\n\n\n# Creer une nouvelle fonction\n@app.route('/gestion_rh/structure/fonction/nouvelle/', methods=['GET', 'POST'])\ndef nouvelleFonction():\n if request.method == 'POST':\n nouvelleFonction = Fonction(code=request.form['code'],\n fonction=request.form['fonction'], \n societe_id=request.form['societe'],\n cree_par=session['login'])\n db_session.add(nouvelleFonction)\n db_session.commit()\n flash(\"fonction creee avec succes\")\n return redirect(url_for('afficherFonctions'))\n else:\n return render_template('nouvelleFonction.html')\n\n# Modifier une fonction\n@app.route('/gestion_rh/structure/fonction//modifier/', methods=['GET', 'POST'])\ndef modfierFonction(fonction_id):\n modifFonction = db_session.query(\n Fonction).filter_by(id=fonction_id).one()\n if request.method == 'POST':\n if request.form['code']:\n modifFonction.titre = request.form['code']\n if request.form['fonction']:\n modifFonction.fonction = request.form['fonction']\n if request.form['societe']:\n modifFonction.societe_id = request.form['societe']\n modifFonction.modifie_par = session['login']\n db_session.add(modifFonction)\n db_session.commit()\n flash(\"fonction modifiee avec succes\")\n return redirect(url_for('afficherFonctions'))\n else:\n return render_template('modifierFonction.html', fonction=modifFonction)\n\n# Supprimer une fonction\n@app.route('/gestion_rh/structure/fonction//supprimer/', methods=['GET', 'POST'])\ndef supprimerFonction(fonction_id):\n fonctionSupprime = db_session.query(\n Focntion).filter_by(id=focntion_id).one()\n if request.method == 'POST':\n db_session.delete(focntionSupprime)\n db_session.commit()\n flash(\"focntion supprimee avec succes\")\n return redirect(url_for('afficherFonctions'))\n else:\n return render_template('supprimerFonction.html', fonction=fonctionSupprime)\n\n\n#Employes\n\n# Afficher les employes\n@app.route('/gestion_rh/saisie/employes/')\ndef afficherEmployes():\n employes = db_session.query(Employe).all()\n return render_template('employes.html', employes=employes)\n\n# Creer un employe\n@app.route('/gestion_rh/saisie/employe/nouveau/', methods=['GET', 'POST'])\ndef nouvelEmploye():\n if request.method == 'POST':\n nouvelEmploye = Employe(code=request.form['code'], \n prenom=request.form['prenom'],\n nom=request.form['nom'],\n cin=request.form['cin'],\n date_naissance=request.form['date_naissance'],\n lieu_naissance=request.form['lieu_naissance'], \n nationalite=request.form['nationalite'],\n etat_civil=request.form['etat_civil'],\n gendre=request.form['gendre'],\n societe_id=request.form['societe'],\n cree_par=session['login'])\n db_session.add(nouvelEmploye)\n db_session.commit()\n flash(\"Employe cree avec succes!\")\n return redirect(url_for('afficherEmployes'))\n else:\n return render_template('nouvelEmploye.html')\n\n\n# Modifier un employe\n@app.route('/gestion_rh/saisie/employe//modifier/',methods=['GET', 'POST'])\ndef modifierEmploye(employe_id):\n modifEmploye = db_session.query(Employe).filter_by(id=employe_id).one()\n if request.method == 'POST':\n if request.form['code']:\n modifEmploye.code = request.form['code']\n if request.form['prenom']:\n modifEmploye.prenom = request.form['prenom']\n if request.form['nom']:\n modifEmploye.nom = request.form['nom']\n if request.form['cin']:\n modifEmploye.cin = request.form['cin']\n if request.form['date_naissance']:\n modifEmploye.date_naissance = request.form['date_naissance']\n if request.form['lieu_naissance']:\n modifEmploye.lieu_naissance = request.form['lieu_naissance']\n if request.form['nationalite']:\n modifEmploye.nationalite = request.form['nationalite']\n if request.form['etat_civil']:\n modifEmploye.etat_civil = request.form['etat_civil']\n if request.form['gendre']:\n modifEmploye.gendre = request.form['gendre']\n if request.form['societe']:\n modifEmploye.societe_id = request.form['societe']\n modifEmploye.modifie_par = session['login']\n db_session.add(modifEmploye)\n db_session.commit()\n flash(\"Employe modifie avec succes!\")\n return redirect(url_for('afficherEmployes'))\n else:\n return render_template('modifierEmploye.html', employe=modifEmploye)\n\n# Supprimer un employe\n@app.route('/gestion_rh/saisie/employe//supprimer/', methods=['GET', 'POST'])\ndef supprimerEmploye(employe_id):\n employeSupprime = db_session.query(Employe).filter_by(id=employe_id).one()\n if request.method == 'POST':\n db_session.delete(employeSupprime)\n db_session.commit()\n flash(\"Employe suprime avec succes\")\n return redirect(url_for('afficherEmploye'))\n else:\n return render_template('supprimerEmploye.html', employe=employeSupprime)\n\n\n#Employe dependants\n\n# Afficher les dependants\n@app.route('/gestion_rh/saisie/employe/')\n@app.route('/gestion_rh/saisie/employe//dependants/')\ndef afficherDependants(employe_id):\n employe = db_session.query(Employe).filter_by(id=employe_id).one()\n dependants = db_session.query(EmployeDependant).filter_by(\n employe_id=employe_id).all()\n return render_template('dependants.html', dependants=dependants, employe=employe)\n\n# Ajouter un dependant\n@app.route('/gestion_rh/saisie/employe//dependant/nouveau/', methods=['GET', 'POST'])\ndef nouveauDependant(employe_id):\n if request.method == 'POST':\n nouveauDependant = EmployeDependant(nom=request.form['nom'], \n categorie=request.form['categorie'],\n occupation=request.form['occupation'], \n date_naissance=request.form['date_naissance'],\n employe_id=employe_id, \n cree_par=session['login'])\n db_session.add(nouveauDependant)\n db_session.commit()\n flash(\"dependant cree avec succes\")\n return redirect(url_for('afficherDependants', employe_id=employe_id))\n else:\n return render_template('nouveauDependant.html', employe_id=employe_id)\n\n\n# Modifier un dependant\n@app.route('/gestion_rh/saisie/employe//dependant//modifier/', methods=['GET', 'POST'])\ndef modifierDependant(employe_id, dependant_id):\n modifDependant = db_session.query(EmployeDependant).filter_by(id=dependant_id).one()\n if request.method == 'POST':\n if request.form['nom']:\n modifDependant.nom = request.form['nom']\n if request.form['categorie']:\n modifDependant.categorie = request.form['categorie']\n if request.form['occupation']:\n modifDependant.occupation = request.form['occupation']\n if request.form['date_naissance']:\n modifDependant.date_naissance = request.form['date_naissance']\n if request.form['employe']:\n modifDependant.employe_id = request.form['employe']\n modifDependant.modifie_par=session['login']\n db_session.add(modifDependant)\n db_session.commit()\n flash(\"dependant modifie avec succes\")\n return redirect(url_for('afficherDependants', employe_id=employe_id))\n else:\n return render_template('modifierDependant.html', employe_id=employe_id, dependant_id=dependant_id, dependant=modifDependant)\n\n# Supprimer un depedant\n@app.route('/gestion_rh/saisie/employe//dependant//supprimer/', methods=['GET', 'POST'])\ndef supprimerDependant(employe_id, dependant_id):\n dependantSupprime = db_session.query(EmployeDependant).filter_by(id=dependant_id).one()\n if request.method == 'POST':\n db_session.delete(dependantSupprime)\n db_session.commit()\n flash(\"dependant supprime avec succes\")\n return redirect(url_for('afficherDependants', employe_id=employe_id))\n else:\n return render_template('supprimerDependant.html', dependant=dependantSupprime, employe_id=employe_id)\n\n\n#Employe contrat\n\n# Afficher le contrat\n@app.route('/gestion_rh/saisie/employe/')\n@app.route('/gestion_rh/saisie/employe//contrats/')\ndef afficherContrats(employe_id):\n employe = db_session.query(Employe).filter_by(id=employe_id).one()\n contrats = db_session.query(EmployeContrat).filter_by(\n employe_id=employe_id).all()\n return render_template('contrats.html', contrats=contrats, employe=employe)\n\n# Ajouter le contrat\n@app.route('/gestion_rh/saisie/employe//contrat/nouveau/', methods=['GET', 'POST'])\ndef nouveauContrat(employe_id):\n if request.method == 'POST':\n nouveauContrat = EmployeContrat(ref=request.form['ref'], \n date_embauche=request.form['date_embauche'],\n statut=request.form['statut'], \n contrat_type=request.form['contrat_type'],\n salaire_base=request.form['salaire_base'], \n fonction_id=request.form['fonction'],\n employe_id=employe_id, \n cree_par=session['login'])\n db_session.add(nouveauContrat)\n db_session.commit()\n flash(\"contrat cree avec succes\")\n return redirect(url_for('afficherContrats', employe_id=employe_id))\n else:\n return render_template('nouveauContrat.html', employe_id=employe_id)\n\n\n# Modifier le contrat\n@app.route('/gestion_rh/saisie/employe//contrat//modifier/', methods=['GET', 'POST'])\ndef modifierContrat(employe_id, contrat_id):\n modifContrat = db_session.query(EmployeContrat).filter_by(id=contrat_id).one()\n if request.method == 'POST':\n if request.form['ref']:\n modifContrat.ref = request.form['ref']\n if request.form['date_embauche']:\n modifContrat.date_embauche = request.form['date_embauche']\n if request.form['status']:\n modifContrat.status = request.form['status']\n if request.form['contrat_type']:\n modifContrat.contrat_type = request.form['contrat_type']\n if request.form['salaire_base']:\n modifContrat.salaire_base = request.form['salaire_base']\n if request.form['cnss_immatriculation']:\n modifContrat.cnss_immatriculation = request.form['cnss_immatriculation']\n if request.form['cnss_debut']:\n modifContrat.cnss_debut = request.form['cnss_debut']\n if request.form['fonction']:\n modifContrat.fonction_id = request.form['fonction']\n modifContrat.modifie_par=session['login']\n db_session.add(modifContrat)\n db_session.commit()\n flash(\"contrat modifie avec succes\")\n return redirect(url_for('afficherContrats', employe_id=employe_id))\n else:\n return render_template('modifierContrat.html', employe_id=employe_id, contrat_id=contrat_id, contrat=modifContrat)\n\n# Supprimer un contrat\n@app.route('/gestion_rh/saisie/employe//contrat//supprimer/', methods=['GET', 'POST'])\ndef supprimerContrat(employe_id, contrat_id):\n contratSupprime = db_session.query(EmployeContrat).filter_by(id=contrat_id).one()\n if request.method == 'POST':\n db_session.delete(contratSupprime)\n db_session.commit()\n flash(\"contrat supprime avec succes\")\n return redirect(url_for('afficherContrats', employe_id=employe_id))\n else:\n return render_template('supprimerContrat.html', contrat=contratSupprime, employe_id=employe_id)\n\n\n#Employe contact\n\n# Afficher le contact\n@app.route('/gestion_rh/saisie/employe/')\n@app.route('/gestion_rh/saisie/employe//contacts/')\ndef afficherContacts(employe_id):\n employe = db_session.query(Employe).filter_by(id=employe_id).one()\n contacts = db_session.query(EmployeContact).filter_by(\n employe_id=employe_id).all()\n return render_template('contacts.html', contacts=contacts, employe=employe)\n\n# Ajouter le contact\n@app.route('/gestion_rh/saisie/employe//contact/nouveau/', methods=['GET', 'POST'])\ndef nouveauContact(employe_id):\n if request.method == 'POST':\n nouveauContact = EmployeContact(adresse1=request.form['adresse1'], \n adresse2=request.form['adresse2'],\n code_postal=request.form['code_postal'], \n ville=request.form['ville'],\n pays=request.form['pays'],\n telephone=request.form['telephone'],\n fax=request.form['fax'],\n email=request.form['email'],\n employe_id=employe_id, \n cree_par=session['login'])\n db_session.add(nouveauContact)\n db_session.commit()\n flash(\"contact cree avec succes\")\n return redirect(url_for('afficherContacts', employe_id=employe_id))\n else:\n return render_template('nouveauContact.html', employe_id=employe_id)\n\n\n# Modifier le contact\n@app.route('/gestion_rh/saisie/employe//contact//modifier/', methods=['GET', 'POST'])\ndef modifierContact(employe_id, contact_id):\n modifContact = db_session.query(EmployeContact).filter_by(id=contact_id).one()\n if request.method == 'POST':\n if request.form['adresse1']:\n modifContact.adresse1 = request.form['adresse1']\n if request.form['adresse2']:\n modifContact.adresse2 = request.form['adresse2']\n if request.form['code_postal']:\n modifContact.code_postal = request.form['code_postal']\n if request.form['ville']:\n modifContact.ville = request.form['ville']\n if request.form['pays']:\n modifContact.pays = request.form['pays']\n if request.form['telephone']:\n modifContact.telephone = request.form['telephone']\n if request.form['fax']:\n modifContact.fax = request.form['fax']\n if request.form['email']:\n modifContact.email = request.form['email']\n modifContact.modifie_par=session['login']\n db_session.add(modifContact)\n db_session.commit()\n flash(\"contact modifie avec succes\")\n return redirect(url_for('afficherContacts', employe_id=employe_id))\n else:\n return render_template('modifierContact.html', employe_id=employe_id, contact_id=contact_id, contact=modifContact)\n\n# Supprimer un contact\n@app.route('/gestion_rh/saisie/employe//contact//supprimer/', methods=['GET', 'POST'])\ndef supprimerContact(employe_id, dependant_id):\n contactSupprime = db_session.query(EmployeContact).filter_by(id=contact_id).one()\n if request.method == 'POST':\n db_session.delete(contactSupprime)\n db_session.commit()\n flash(\"contact supprime avec succes\")\n return redirect(url_for('afficherContacts', employe_id=employe_id))\n else:\n return render_template('supprimerContact.html', contact=contactSupprime, employe_id=employe_id)\n\n\n\nif __name__ == '__main__':\n app.secret_key = 'super_secret_key'\n app.debug = True\n app.run(host='0.0.0.0', port=5000)\n","sub_path":"cba_app.py","file_name":"cba_app.py","file_ext":"py","file_size_in_byte":89190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"435505414","text":"from PyQt5.QtWidgets import QGridLayout, QPushButton, QLCDNumber, QFrame, QTextEdit, QSizePolicy \nfrom PyQt5.QtGui import QDesktopServices, QPalette, QColor\nfrom PyQt5.QtWebKitWidgets import QWebView\nfrom PyQt5.QtCore import QTimer, QDir, QUrl, Qt, pyqtSlot\nfrom PyQt5.QtSvg import QSvgWidget\nfrom sysmo_widgets import NFrame, NFrameContainer, NGridContainer\nfrom monitor.proxy import AbstractChannelWidget\nimport sysmapi\nimport sys\n\n\n##################\n# SUMMARY WIDGET #\n##################\nclass Summary(NFrame):\n def __init__(self, parent):\n super(Summary, self).__init__(parent)\n self.setContentsMargins(0,0,0,0)\n self.setSizePolicy(QSizePolicy(QSizePolicy.Fixed,QSizePolicy.Fixed))\n self.chanH = ChannelHandler.singleton\n sigDict = self.chanH.masterpyqtSignalsDict\n sigDict['probeInfo'].signal.connect(self._handleProbeInfo)\n self._initInterface()\n\n def _initInterface(self):\n\n grid = QGridLayout(self)\n\n grid.setContentsMargins(0,0,0,0)\n grid.setHorizontalSpacing(5)\n grid.setVerticalSpacing(0)\n\n self.okWidget = StatusSummary(self, 'OK')\n self.warningWidget = StatusSummary(self, 'WARNING')\n self.criticalWidget = StatusSummary(self, 'CRITICAL')\n self.unknownWidget = StatusSummary(self, 'DOWN')\n\n\n grid.addWidget(self.okWidget, 0,0)\n grid.addWidget(self.warningWidget, 0,1)\n grid.addWidget(self.criticalWidget, 0,2)\n grid.addWidget(self.unknownWidget, 0,3)\n \n grid.setColumnStretch(0,0)\n grid.setColumnStretch(1,0)\n grid.setColumnStretch(2,0)\n grid.setColumnStretch(3,0)\n grid.setColumnStretch(4,1)\n self.setLayout(grid)\n self._setCounters()\n\n def _setCounters(self):\n probes = self.chanH.probes\n ok = 0\n warning = 0\n critical = 0\n unknown = 0\n \n for key in probes:\n status = probes[key]['status']\n if status == 'OK': ok += 1\n elif status == 'WARNING': warning += 1\n elif status == 'CRITICAL': critical += 1\n elif status == 'DOWN': unknown += 1\n\n self.okWidget.setCount(ok)\n self.warningWidget.setCount(warning)\n self.criticalWidget.setCount(critical)\n self.unknownWidget.setCount(unknown)\n\n if critical != 0: self.criticalWidget.setBlink(True)\n else: self.criticalWidget.setBlink(False)\n if warning != 0: self.warningWidget.setBlink(True)\n else: self.warningWidget.setBlink(False)\n\n \n def _handleProbeInfo(self, msg): \n self._setCounters()\n\nclass StatusSummary(QPushButton):\n def __init__(self, parent, status):\n super(StatusSummary, self).__init__(parent)\n buttonPol = QSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)\n self.setSizePolicy(buttonPol)\n self.setFixedHeight(40)\n self.setMinimumWidth(80)\n\n self.startBlinkTimer()\n\n # QLCD number\n self.countArea = QLCDNumber(3, self)\n self.countArea.display(888)\n self.countArea.setSegmentStyle(QLCDNumber.Flat)\n self.countArea.setAutoFillBackground(True)\n self.countArea.setBackgroundRole(QPalette.Base)\n self.countArea.setForegroundRole(QPalette.Text)\n\n self.originalPalette = self.countArea.palette()\n self.blinkingPalette = self.countArea.palette()\n self.paletteShift = 0\n\n # set blinkingPalette and picutre\n if status == 'OK':\n picture = QSvgWidget(sysmapi.nGetImage('weather-clear'), self)\n elif status == 'WARNING':\n picture = QSvgWidget(sysmapi.nGetImage('weather-showers'), self)\n self.blinkingPalette.setColor(self.blinkingPalette.Light, QColor(255,255,0))\n self.blinkingPalette.setColor(self.blinkingPalette.Dark, QColor(255,255,0))\n elif status == 'CRITICAL':\n picture = QSvgWidget(sysmapi.nGetImage('weather-severe-alert'), self)\n self.blinkingPalette.setColor(self.blinkingPalette.Light, QColor(255,0,0))\n self.blinkingPalette.setColor(self.blinkingPalette.Dark, QColor(255,0,0))\n elif status == 'DOWN':\n picture = QSvgWidget(sysmapi.nGetImage('weather-clear-night'), self)\n\n picture.setFixedHeight(30)\n picture.setFixedWidth(30)\n\n\n grid = QGridLayout(self)\n grid.addWidget(picture, 0,0)\n grid.addWidget(self.countArea, 0,1)\n self.setLayout(grid)\n\n def setCount(self, value):\n self.countArea.display(value)\n\n def setBlink(self, bol):\n if bol == True:\n self.blinking = True\n else:\n self.blinking = False\n self.countArea.setPalette(self.originalPalette)\n\n def startBlinkTimer(self):\n self.blinking = False\n self.asyncBlink = 2\n self.blinkTimer = QTimer(self)\n self.blinkTimer.timeout.connect(self.blink)\n self.blinkTimer.start(500)\n\n def blink(self):\n if self.blinking == True:\n if self.paletteShift == 0:\n self.countArea.setPalette(self.blinkingPalette)\n if self.asyncBlink == 0:\n self.paletteShift = 1\n self.asyncBlink = 2\n else:\n self.asyncBlink -= 1\n elif self.paletteShift == 1:\n self.countArea.setPalette(self.originalPalette)\n self.paletteShift = 0\n\n\n\n##############\n# OSM WIDGET #\n##############\nclass OSMGetLonlat(NFrameContainer):\n def __init__(self, parent):\n super(OSMGetLonLat, self).__init__(parent)\n\n self.osm = QWebView(self)\n mapFile = 'html/map.html'\n mapPath = QDir().absoluteFilePath(mapFile)\n mapUrl = QUrl().fromLocalFile(mapPath)\n self.osm.load(mapUrl)\n\n page = self.osm.page()\n self._frame = self.osm.page().currentFrame()\n self._frame.addToJavaScriptWindowObject(\"qtCom\",self)\n\n self.shade = QFrame(self)\n self.shade.setAutoFillBackground(False)\n self.shade.hide()\n self._browsable = True\n\n testbutton = QPushButton('test',self)\n testbutton.clicked.connect(self._clic)\n self.grid = NGridContainer(self)\n self.grid.addWidget(testbutton, 0,0)\n self.grid.addWidget(self.osm, 1,0)\n self.setLayout(self.grid)\n\n def _clic(self):\n self._frame.evaluateJavaScript('alert(\"hello\")')\n\n @pyqtSlot(str)\n def thanksOsm(self, wrd):\n QDesktopServices.openUrl(QUrl('http://www.openstreetmap.org/copyright/en'))\n\n @pyqtSlot(str)\n def clac(self, wrd):\n print(\"word is \", wrd)\n sys.stdout.flush()\n sys.stderr.flush()\n\n def setBrowsable(self, bol):\n if bol == False and self._browsable == True:\n self.shade.show()\n self.grid.addWidget(self.shade, 0,0)\n selfshadeStatus = False\n elif bol == True and self._browsable == False:\n self.shade.hide()\n self.grid.removeWidget(self.shade)\n self.shadeStatus = True\n\n\n\nclass OSMView(NFrameContainer):\n def __init__(self, parent):\n super(OSMView, self).__init__(parent)\n\n self.osm = QWebView(self)\n mapFile = 'html/map.html'\n mapPath = QDir().absoluteFilePath(mapFile)\n mapUrl = QUrl().fromLocalFile(mapPath)\n self.osm.load(mapUrl)\n\n page = self.osm.page()\n self._frame = self.osm.page().currentFrame()\n self._frame.addToJavaScriptWindowObject(\"qtCom\",self)\n\n self.shade = QFrame(self)\n self.shade.setAutoFillBackground(False)\n self.shade.hide()\n self._browsable = True\n\n testbutton = QPushButton('test',self)\n testbutton.clicked.connect(self._clic)\n self.grid = NGridContainer(self)\n self.grid.addWidget(testbutton, 0,0)\n self.grid.addWidget(self.osm, 1,0)\n self.setLayout(self.grid)\n\n def _clic(self):\n self._frame.evaluateJavaScript('alert(\"hello\")')\n\n @pyqtSlot(str)\n def thanksOsm(self, wrd):\n QDesktopServices.openUrl(QUrl('http://www.openstreetmap.org/copyright/en'))\n\n @pyqtSlot(str)\n def clac(self, wrd):\n print(\"word is \", wrd)\n sys.stdout.flush()\n sys.stderr.flush()\n\n def setBrowsable(self, bol):\n if bol == False and self._browsable == True:\n self.shade.show()\n self.grid.addWidget(self.shade, 0,0)\n selfshadeStatus = False\n elif bol == True and self._browsable == False:\n self.shade.hide()\n self.grid.removeWidget(self.shade)\n self.shadeStatus = True\n\n\n##################\n# CUSTOM BUTTONS #\n##################\nclass ProbeCriticalButton(QPushButton):\n def __init__(self, parent, probeName):\n super(ProbeCriticalButton, self).__init__(parent)\n self.setText(' %s ' % probeName)\n self.setStyleSheet('\\\n QPushButton { \\\n min-height: 1.5em; \\\n font: bold 1em; \\\n margin: 0 1px 0 1px; \\\n color: white; \\\n background-color: qlineargradient( \\\n x1: 0, \\\n y1: 0, \\\n x2: 0, \\\n y2: 1, \\\n stop: 0 #ef2929, \\\n stop: 1 #cc0000); \\\n border-style: outset; \\\n border-radius: 3px; \\\n border-width: 1px; \\\n border-color: #a40000; \\\n } \\\n QPushButton:pressed { \\\n background-color: qlineargradient( \\\n x1: 0, \\\n y1: 0, \\\n x2: 0, \\\n y2: 1, \\\n stop: 1 #ef2929, \\\n stop: 0 #cc0000); \\\n }')\n\nclass ProbeWarningButton(QPushButton):\n def __init__(self, parent, probeName):\n super(ProbeWarningButton, self).__init__(parent)\n self.setText(' %s ' % probeName)\n self.setStyleSheet('QPushButton { \\\n min-height: 1.5em; \\\n font: bold 1em; \\\n margin: 0 1px 0 1px; \\\n color: white; \\\n background-color: qlineargradient( \\\n x1: 0, \\\n y1: 0, \\\n x2: 0, \\\n y2: 1, \\\n stop: 0 #fcaf3e, \\\n stop: 1 #f57900); \\\n border-style: outset; \\\n border-radius: 3px; \\\n border-width: 1px; \\\n border-color: #ce5c00; \\\n } \\\n QPushButton:pressed { \\\n background-color: qlineargradient( \\\n x1: 0, \\\n y1: 0, \\\n x2: 0, \\\n y2: 1, \\\n stop: 1 #fcaf3e, \\\n stop: 0 #f57900); \\\n }')\n\nclass ProbeOkButton(QPushButton):\n def __init__(self, parent, probeName):\n super(ProbeOkButton, self).__init__(parent)\n self.setText(' %s ' % probeName)\n self.setStyleSheet('QPushButton { \\\n min-height: 1.5em; \\\n font: 1em; \\\n margin: 0 1px 0 1px; \\\n color: black; \\\n background-color: qlineargradient( \\\n x1: 0, \\\n y1: 0, \\\n x2: 0, \\\n y2: 1, \\\n stop: 0 #a1d99b, \\\n stop: 1 #74c476); \\\n border-style: outset; \\\n border-radius: 3px; \\\n border-width: 1px; \\\n border-color: #41ab5d; \\\n } \\\n QPushButton:pressed { \\\n background-color: qlineargradient( \\\n x1: 0, \\\n y1: 0, \\\n x2: 0, \\\n y2: 1, \\\n stop: 1 #a1d99b, \\\n stop: 0 #74c476); \\\n }')\n\nclass ProbeUnknownButton(QPushButton):\n def __init__(self, parent, probeName):\n super(ProbeUnknownButton, self).__init__(parent)\n self.setText(' %s ' % probeName)\n self.setStyleSheet('QPushButton { \\\n min-height: 1.5em; \\\n font: 1em; \\\n margin: 0 1px 0 1px; \\\n color: white; \\\n background-color: qlineargradient( \\\n x1: 0, \\\n y1: 0, \\\n x2: 0, \\\n y2: 1, \\\n stop: 0 #666666, \\\n stop: 1 #222222); \\\n border-style: outset; \\\n border-radius: 3px; \\\n border-width: 1px; \\\n border-color: #aaaaaa; \\\n } \\\n QPushButton:pressed { \\\n background-color: qlineargradient( \\\n x1: 0, \\\n y1: 0, \\\n x2: 0, \\\n y2: 1, \\\n stop: 1 #a1d99b, \\\n stop: 0 #74c476); \\\n }')\n\nclass TextLog(AbstractChannelWidget):\n def __init__(self, parent, probe):\n super(TextLog, self).__init__(parent, probe)\n self.setAutoFillBackground(True)\n logger = QTextEdit(self)\n logger.setTextInteractionFlags(Qt.NoTextInteraction) \n logger.setFixedHeight(80)\n logger.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n logger.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n logger.setLineWrapMode(QTextEdit.NoWrap)\n self.logger = logger\n\n grid = QGridLayout(self)\n grid.addWidget(self.logger, 0,0)\n self.setLayout(grid)\n self.goOn = True\n self.connectProbe()\n \n def handleProbeEvent(self, msg):\n if msg['msgType'] == 'probeDump':\n if msg['logger'] == 'bmonitor_logger_text':\n self._dump(msg['data'])\n elif msg['msgType'] == 'probeReturn':\n self._append(msg['value'])\n\n def _dump(self, data):\n for i in range(len(data)):\n self.logger.append(data[i])\n \n def _append(self, value):\n status = value['status']\n data = value['replyString']\n ts = value['timestamp']\n if status == 'OK':\n color = '#73d216'\n elif status == 'WARNING':\n color = '#fce94f'\n elif status == 'CRITICAL':\n color = '#ef2929'\n elif status == 'DOWN':\n color = '#888a85'\n\n data2 = data.replace('\\n', ' ')\n html = '

' % color + str(ts) + '>>>' + data2 + '

'\n self.logger.append(html)\n","sub_path":"monitor/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":15323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"544281898","text":"#this is for opening the configuration file\nimport sys\nimport configparser\n#this is for connecting to IRC\nimport socket\n#this is for connecting to the postgres database\nimport psycopg2\n#this is for doing cute equations\nimport math\n#for multithreading for additional channels\nimport threading\n#traceback is for error handling/printing so i can figure out what went wrong\nimport traceback\n#sleep is so the bot won't overload during listening\nfrom time import sleep\n\ndef main():\n conn, token, user, readbuffer, server = connectionVariables()\n #channels = performSQL(\"SELECT channelname FROM bot.channel ch WHERE ch.channelname = 'brdy'\")\n channels = performSQL(\"SELECT channelname FROM bot.channel ch WHERE ch.channelid NOT in(SELECT cd.channelid FROM bot.channeldeletion cd)\")\n commandDict = getCommands()\n for channel in channels:\n channel = channel[0]\n operators = getOperants(channel)\n threading.Thread(target=ircListen, args=(conn, token, user, channel, server, operators, commandDict)).start()\n sleep(2)\n\ndef getOperants(channel):\n operators = []\n operants = performSQL(\"\"\"SELECT operantname FROM bot.channeloperant co\n LEFT JOIN bot.channel ch ON co.channelid = ch.channelid\n LEFT JOIN bot.operant op ON co.operantid = op.operantid\n WHERE ch.channelname = '\"\"\"+channel+\"'\")\n for operant in operants:\n operators.append(operant[0])\n return operators\n\ndef connectionVariables():\n connection_data = ('irc.chat.twitch.tv', 6667)\n token = getToken()\n botName = \"brdybot\"\n readbuffer = ''\n server = socket.socket()\n return connection_data, token, botName, readbuffer, server\n\ndef getToken():\n config = configparser.ConfigParser()\n file = \"chatbot.ini\"\n config.read(file)\n token = config['chatbot']['token']\n return token\n\ndef ircListen(conn, token, botName, channel, server, operators, commandDict):\n try:\n listenFlag = True\n #joining the channel\n server = socket.socket()\n server.connect(conn)\n server.send(bytes('PASS ' + token + '\\r\\n', 'utf-8'))\n server.send(bytes('NICK ' + botName + '\\r\\n', 'utf-8'))\n server.send(bytes('JOIN #' + channel + '\\r\\n', 'utf-8'))\n #listening loop\n print(\"Starting bot in channel \" +channel + \" with operants: \"+str(operators))\n while listenFlag:\n response = server.recv(2048).decode('utf-8')\n if len(response) == 0:\n break\n if \"PING\" in str(response):\n pong = str(response).replace(\"PING\",\"PONG\")\n server.send(bytes(pong, 'utf-8'))\n server.send(bytes('PONG\\r\\n', 'utf-8'))\n elif \"!\" in str(response):\n #fetch the username,message,etc. from the response without grabbing pings, errors, or erroneous messages\n if len(str(response)) > 2:\n username = str(response).split('!',1)[0][1:]\n if \":\" in str(response):\n splitResp = str(response).split(':')\n if len(splitResp) > 3:\n splitResp = str(response).split(':')[2]+str(response).split(':')[3]\n else:\n splitResp = str(response).split(':')[2]\n userMessage = splitResp[0:len(splitResp)-2]\n else:\n userMessage = \" \"\n command = userMessage.split(\" \")[0].lower().replace(\"'\",\"''\")\n parameters = userMessage.split(\" \")[1:]\n permissions = (username in operators) or (channel == 'brdybot') or (command == \"!botinfo\")\n if (\"!\" in command[0:1]) and (command[1:] in commandDict) and permissions:\n commandid = commandDict[command[1:]]\n commandrequestid = logCommand(commandid,channel,username,parameters)\n message = None\n message = doCommand(commandrequestid)\n if message:\n chatMessage(message,channel,server)\n operators = getOperants(channel)\n success = storeMessage(message, commandrequestid)\n sleep(1)\n except ConnectionResetError:\n logException(None, \"ConnectionResetError\", channel)\n except IndexError:\n logException(commandrequestid,\"IndexError\", channel)\n except KeyError:\n logException(commandrequestid,\"KeyError\", channel)\n except RuntimeError:\n logException(commandrequestid,\"RuntimeError\", channel)\n except SystemExit:\n logException(commandrequestid,\"SystemExit\", channel)\n except ValueError:\n logException(commandrequestid,\"ValueError\", channel)\n except BrokenPipeError:\n logException(commandrequestid,\"BrokenPipeError\", channel)\n except ConnectionAbortedError:\n logException(commandrequestid,\"ConnectionAbortedError\", channel)\n except ConnectionRefusedError:\n logException(commandrequestid,\"ConnectionRefusedError\", channel)\n except FileNotFoundError:\n logException(commandrequestid,\"FileNotFoundError\", channel)\n except TimeoutError:\n logException(commandrequestid,\"TimeoutError\", channel)\n except Exception:\n logException(commandrequestid,\"OtherError\", channel)\n\ndef getCommands():\n commands = performSQL(\"SELECT commandid,commandname FROM bot.command\")\n commandDict = {}\n for command in commands:\n commandDict[command[1]] = command[0]\n return commandDict\n\ndef doCommand(commandrequestid):\n conn, token, botName, readbuffer, server = connectionVariables()\n parameters = []\n ccr = performSQL(\"SELECT com.commandname,ch.channelname,op.operantname,ccrp.channelcommandrequestparameter FROM bot.channelcommandrequest ccr LEFT JOIN bot.command com ON ccr.commandid = com.commandid LEFT JOIN bot.channel ch ON ccr.channelid = ch.channelid LEFT JOIN bot.operant op ON ccr.operantid = op.operantid LEFT JOIN bot.channelcommandrequestparameter ccrp ON ccr.channelcommandrequestid = ccrp.channelcommandrequestid WHERE ccr.channelcommandrequestid =\"+str(commandrequestid))\n for command,channel,username,parameter in ccr:\n parameters.append(parameter)\n if command == \"mon\":\n message = getMonInfo(parameters,channel)\n elif command == \"move\":\n message = getMoveInfo(parameters,channel)\n elif command == \"ability\":\n message = getAbilityInfo(parameters,channel)\n # elif command == \"xp\":\n # message = getXPYield()\n # elif command == \"bst\":\n # message = getBST()\n # elif command == \"learnset\":\n # message = getMonMoves()\n # elif command == \"evolution\":\n # message = getMonEvo()\n elif command == \"nature\":\n message = getNatureInfo(parameters,channel)\n elif command == \"weak\":\n message = getWeaknessInfo(parameters,channel)\n elif command == \"coverage\":\n message = getCoverage(parameters,channel)\n elif command == \"abbrevs\":\n message = getAbbrevs()\n elif command == \"gamelist\":\n message = getGames()\n elif command == \"pokegame\":\n message = setGame(parameters, channel, server)\n elif command == \"pokeops\":\n message = addOperants(parameters,channel)\n elif command == \"removeops\" and channel == username:\n message = removeOperants(parameters,channel)\n elif command == \"listops\":\n message = listOperants(channel)\n elif command == \"join\" and channel == \"brdybot\":\n message = addClient(conn,token,botName,username,server)\n elif command == \"brdybotleave\" and channel == username:\n message = removeClient(username)\n elif command == \"pokecom\":\n commands = \"!mon, !move, !ability, !coverage, !nature, !weak, !pokegame, !abbrevs, !gamelist, !botinfo, !listops, !pokeops, !pokecom, !brdybotleave\"\n message = \"Available commands are \" + commands + \".\"\n elif command == \"botinfo\":\n message = \"Visit https://www.twitch.tv/brdybot/about\"\n return message\n\ndef storeMessage(message,ccrid):\n success = performSQL(\"UPDATE bot.channelcommandrequest SET channelcommandrequestreturn ='\"+message.replace(\"'\",\"''\")+\"' WHERE channelcommandrequestid = \"+str(ccrid)+\" RETURNING channelcommandrequestid;\")\n return success\n\ndef logException(commandrequestid, exception, channel):\n channelid = getChannelID(channel)\n if not commandrequestid:\n commandrequestid = \"null\"\n errortypeid = performSQL(\"SELECT errortypeid FROM bot.errortype WHERE errortypename = '\"+exception+\"'\")\n if errortypeid != []:\n errortypeid = errortypeid[0][0]\n else:\n errortypeid = performSQL(\"SELECT errortypeid FROM bot.errortype WHERE errortypename = 'OtherError'\")\n channelerrorid = performSQL(\"INSERT INTO bot.channelerror (channelcommandrequestid,errortypeid) VALUES (\"+str(commandrequestid)+\",\"+str(errortypeid)+\") RETURNING channelerrorid;\")\n traceback.print_exc()\n print(\" with channelerrorid = \"+str(channelerrorid))\n commandDict = getCommands()\n conn, token, user, readbuffer, server = connectionVariables()\n operators = getOperants(channel) \n threading.Thread(target=ircListen, args=(conn, token, \"brdybot\", channel, server, operators, commandDict)).start()\n sys.exit()\n\ndef getChannelID(channel):\n channelid = performSQL(\"SELECT ch.channelid FROM bot.channel ch WHERE ch.channelname ='\"+channel+\"'\")[0][0]\n return channelid\n\ndef logCommand(commandid,channelname,operantname,parameters):\n commandname = performSQL(\"SELECT com.commandname FROM bot.command com WHERE com.commandid = \"+str(commandid))[0][0]\n if commandid == 9:\n success = addOperants([operantname],channelname)\n channelid = getChannelID(channelname)\n operantid = performSQL(\"SELECT op.operantid FROM bot.operant op WHERE op.operantname = '\"+operantname+\"'\")[0][0]\n print(\"\\r\\n________________________________________________________________________________________________________\")\n print(\"Received the \"+commandname+\" command in channel \"+channelname+\" from user \"+operantname+\". Parameters: \"+str(parameters)+\"\\r\\n\")\n channelcommandrequestid = performSQL(\"INSERT INTO bot.channelcommandrequest (commandid,channelid,operantid) VALUES (\"+str(commandid)+\",\"+str(channelid)+\",\"+str(operantid)+\") RETURNING channelcommandrequestid;\")[0][0]\n for parameter in parameters:\n parameter = parameter.replace(\"'\",\"''\")\n parameterid = performSQL(\"INSERT INTO bot.channelcommandrequestparameter (channelcommandrequestid,channelcommandrequestparameter) VALUES (\"+str(channelcommandrequestid)+\",'\"+parameter+\"') RETURNING channelcommandrequestparameterid;\")\n return channelcommandrequestid\n\ndef addOperants(parameters, channel):\n note = \" User(s) \"\n exists = False\n for parameter in parameters:\n parameter = parameter.lower()\n operantid = performSQL(\"SELECT operantid FROM bot.operant WHERE operantname = '\"+parameter+\"'\")\n if operantid == []:\n operantid = performSQL(\"INSERT INTO bot.operant (operantname) values ('\"+parameter+\"') RETURNING operantid;\")[0][0]\n else:\n operantid = operantid[0][0]\n operantid = str(operantid)\n channeloperantid = performSQL(\"SELECT channeloperantid FROM bot.channeloperant co LEFT JOIN bot.channel ch ON co.channelid = ch.channelid LEFT JOIN bot.operant op ON co.operantid = op.operantid WHERE ch.channelname = '\"+channel+\"' AND co.operantid =\"+operantid)\n if channeloperantid == []:\n sql = \"INSERT INTO bot.channeloperant (channelid,operantid,operanttypeid) VALUES ((SELECT channelid FROM bot.channel WHERE channelname ='\"+channel+\"'),\"+operantid+\",2) RETURNING channeloperantid;\"\n channeloperantid = performSQL(sql)\n else:\n exists = True\n if parameters.index(parameter) < len(parameters)-3:\n note += parameter + \", \"\n elif parameters.index(parameter) < len(parameters)-2:\n note += parameter + \" and \"\n elif parameters.index(parameter) < len(parameters)-1:\n note += parameter + \" \"\n message = \"Successfully added bot users to configuration.\"\n if exists:\n message += note + \" already exist(s) as bot user(s) in channel \"+channel+\".\"\n return message\n\ndef removeOperants(parameters, channel):\n message = \"User(s) \"\n for parameter in parameters:\n parameter = parameter.lower()\n if parameter != channel:\n sql = \"\"\"DELETE FROM bot.channeloperant\n WHERE channeloperantid =\n (SELECT channeloperantid\n FROM bot.channeloperant co\n INNER JOIN bot.channel ch ON co.channelid = ch.channelid\n INNER JOIN bot.operant op ON co.operantid = op.operantid\n WHERE ch.channelname = '\"\"\"+channel+\"' AND op.operantname = '\"+parameter+\"\"\"')\n RETURNING operantid;\"\"\"\n operantid = performSQL(sql)\n message += parameter\n else:\n message = \"You cannot remove the channel owner from the operant list. \"+message\n message += \" were removed from the channel's user list.\"\n return message\n\ndef listOperants(channel):\n message = \"Users who have permissions in channel \"+channel+\": \"\n operants = getOperants(channel)\n for operant in operants:\n if operants.index(operant) < len(operants)-1:\n message += operant+\", \"\n else:\n message += operant\n return message\n\ndef addClient(conn, token, botName, username, server):\n channelid = performSQL(\"SELECT channelid FROM bot.channel WHERE channelname = '\"+username+\"'\")\n operantid = performSQL(\"SELECT operantid FROM bot.operant WHERE operantname = '\"+username+\"'\")\n if channelid == []:\n sql = \"INSERT INTO bot.channel (channelname,gameid) VALUES ('\"+username+\"',10) RETURNING channelid;\"\n channelid = performSQL(sql)\n if operantid == []:\n sql = \"INSERT INTO bot.operant (operantname) VALUES ('\"+username+\"') RETURNING operantid;\"\n operantid = performSQL(sql)\n sql = \"\"\"SELECT operanttypeid FROM bot.channeloperant co\n LEFT JOIN bot.channel ch ON co.channelid = ch.channelid\n LEFT JOIN bot.operant op ON co.operantid = op.operantid\n WHERE ch.channelname = '\"\"\"+username+\"\"\"' AND op.operantname ='\"\"\"+username+\"'\"\n channeloperantid = performSQL(sql)\n if channeloperantid == []:\n sql = \"INSERT INTO bot.channeloperant (channelid, operantid, operanttypeid) VALUES (\"+str(channelid[0][0])+\",\"+str(operantid[0][0])+\",1) RETURNING channeloperantid;\"\n channeloperantid = performSQL(sql)\n message = username+\"\"\" - You have been successfully added to the channel list.\n Game has been set to FireRed. Use !pokegame in your channel to change the game.\n Note that I do store usernames and command usage records in the database for use in feature improvement.\n Your username will NEVER be shared with anyone for any reason.\n Use !brdybotleave in your channel to remove yourself from my channel list.\"\"\"\n operants = getOperants(username)\n commandDict = getCommands()\n threading.Thread(target=ircListen, args=(conn, token, botName, username, server, operants,commandDict)).start()\n elif channeloperantid[0][0] == 1:\n message = username+\" - I should be operating in your channel. If I'm not, message brdy on Discord to correct the error.\"\n return message\n\ndef removeClient(channel):\n sql = \"INSERT INTO bot.channeldeletion (channelid) values (SELECT ch.channelid FROM bot.channel ch WHERE ch.channelname = '\"+channel+\"') RETURNING channelid\"\n channelid = performSQL(sql)\n message = channel+\" - Successfully removed you from the channel list.\"\n return message\n\ndef getMoveID(moveName):\n moveID = performSQL(\"\"\" WITH ldist as (SELECT mv.moveid,LEAST(pokemon.levenshtein(mv.movename, '\"\"\"+moveName+\"\"\"'),\n pokemon.levenshtein(mn.movenickname, '\"\"\"+moveName+\"\"\"')) AS distance FROM pokemon.move mv\n LEFT JOIN pokemon.movenickname mn ON mv.moveid = mn.moveid)\n SELECT moveid,distance FROM ldist WHERE distance < 5 ORDER BY distance LIMIT 1\"\"\")\n moveID = str(moveID[0][0])\n return moveID\n\ndef combineParameters(parameters):\n name = \"\"\n for parameter in parameters:\n name += parameter + \" \"\n name = name[:len(name)-1].title()\n return name\n\ndef getMonID(monName,channel):\n monName = monName.replace(\"'\",\"''\")\n monID = performSQL(\"\"\"WITH ldist as (SELECT DISTINCT mon.pokemonid,LEAST(pokemon.levenshtein(mon.pokemonname,'\"\"\"+monName+\"\"\"'),\n pokemon.levenshtein(pn.pokemonnickname,'\"\"\"+monName+\"\"\"')) AS distance FROM pokemon.pokemon mon \n LEFT JOIN pokemon.pokemonnickname pn ON mon.pokemonid = pn.pokemonid) \n SELECT pokemonid,distance FROM ldist WHERE distance < 5 ORDER BY distance LIMIT 1\"\"\")\n if monID == []:\n errorString = \"Could not find Pokemon \"+monName+\".\"\n return None,errorString\n monID = str(monID[0][0])\n monName = performSQL(\"\"\"SELECT DISTINCT mon.pokemonname FROM pokemon.pokemon mon\n WHERE mon.pokemonid = \"\"\"+monID)\n monName = str(monName[0][0])\n return monID,monName\n\ndef getMonInfo(parameters,channel):\n if len(parameters) < 1:\n monInfo = \"The !mon command requires the name of a pokemon as a parameter. (ex: '!mon charizard')\"\n return monInfo\n monName = combineParameters(parameters)\n monID,monName = getMonID(monName,channel)\n game = getGame(channel)\n if monID == None:\n return monName\n availability = performSQL(\"\"\"SELECT DISTINCT pa.pokemonavailabilitytypeid\n FROM pokemon.pokemongameavailability pa\n LEFT JOIN pokemon.game ga ON pa.gameid = ga.gameid\n LEFT JOIN pokemon.gamegroup gg ON gg.gamegroupid = ga.gamegroupid\n WHERE pa.pokemonid = \"\"\"+monID+\" AND gg.gamegroupabbreviation = '\"+game+\"'\")\n if availability[0][0] == 18:\n message = monName + \" is not available in \" + game + \".\"\n return message\n #this section gets all the info to be compiled in a string at the end of this function\n monName,monDex,monGrowth,monCaptureRate = performSQL(\"\"\"SELECT DISTINCT mon.pokemonname,mon.pokemonpokedexnumber,\n lr.levelingratename,mon.pokemoncapturerate \n FROM pokemon.pokemon mon \n LEFT JOIN pokemon.levelingrate lr ON mon.levelingrateid = lr.levelingrateid \n WHERE pokemonid = \"\"\"+monID)[0]\n monDex = str(monDex)\n monCaptureRate = getCaptureRate(monCaptureRate, channel)\n monTypes = getMonTypes(monID, channel)\n monBST = getMonBST(monID, channel)\n monXPYield = getXPYield(monID, channel,5,5)\n monEvos = getMonEvos(monID, channel)\n monMoves = getMonMoves(monID, channel)\n #compiling all of the bits of info into one long string for return\n monInfo = \"#\" + monDex +\" \" + monName + \" (\"+game+\") \" + monTypes + \" | Catch: \"+monCaptureRate+\"% | BST: \" + monBST + \" | L5 XP: \" + monXPYield + \" | \" + monGrowth + \" | \" + monEvos + \" | \" + monMoves\n return monInfo\n\ndef getMonGrowth(monID,channel):\n sql = \"SELECT lr.levelingratename FROM pokemon.levelingrate lr LEFT JOIN pokemon.pokemon mon ON lr.levelingrateid = mon.levelingrateid WHERE mon.pokemonid = \"+monID\n rate = str(performSQL(sql)[0][0])\n return rate\n\ndef getGeneration(channel):\n generation = performSQL(\"\"\"SELECT gen.generationid FROM bot.channel ch\n LEFT JOIN pokemon.game gm ON ch.gameid = gm.gameid\n LEFT JOIN pokemon.gamegroup gg ON gm.gamegroupid = gg.gamegroupid\n LEFT JOIN pokemon.generation gen ON gg.generationid = gen.generationid\n WHERE ch.channelname = '\"\"\"+channel+\"'\")[0][0]\n generation = str(generation)\n return generation\n\ndef getMonDex(monID, channel):\n sql = \"\"\"SELECT DISTINCT mon.pokemonpokedexnumber FROM pokemon.pokemon mon\"\"\"\n sql += \" WHERE mon.pokemonid = \"+monID\n dexArray = performSQL(sql)\n monDex = str(dexArray[0][0])\n return monDex\n\ndef getMonTypes(monID, channel):\n \n gen = getGeneration(channel)\n monTypes = \"\"\"WITH monTypes as (SELECT pokemonid,type1id,type2id\n FROM pokemon.crosstab('select pokemonid, typeid as type1id, typeid as type2id\n FROM pokemon.pokemontype pt WHERE pt.generationid = \"\"\"+gen+\"\"\"\n AND pt.pokemonid = \"\"\"+monID+\"\"\"\n GROUP BY pokemonid,type1id,type2id ORDER BY pokemonid,type1id,type2id')\n AS ct( pokemonid int, type1id int, type2id int)) \\r\\n\"\"\"\n mainSelect = \"\"\"SELECT type1.typename,type2.typename FROM monTypes\n LEFT JOIN pokemon.type type1 ON monTypes.type1id = type1.typeid\n LEFT JOIN pokemon.type type2 ON monTypes.type2id = type2.typeid\"\"\"\n typeArray = performSQL(monTypes+mainSelect)\n #if there are two types, store as (Type1/Type2)\n #print(str(typeArray))\n types = \"(\"+str(typeArray[0][0])\n if typeArray[0][1] != None:\n types += \"/\"+str(typeArray[0][1])+\")\"\n #otherwise, store as (Type)\n else:\n types += \")\"\n return types\n\ndef getMonBST(monID, channel):\n gen = getGeneration(channel)\n sql = \"\"\"SELECT SUM(ps.pokemonstatvalue) bst, ps.generationid gen\n FROM pokemon.pokemonstat ps \"\"\"\n sql += \"LEFT JOIN pokemon.pokemon mon ON ps.pokemonid = mon.pokemonid WHERE mon.pokemonid =\"+monID\n sql += \" AND ps.generationid <= \"+gen+\" GROUP BY gen ORDER BY gen DESC LIMIT 1\"\n bstArray = performSQL(sql)\n monBST = str(bstArray[0][0])\n return monBST\n\ndef getCaptureRate(captureRate,channel):\n #this formula approximates the catch rate to within about .1% and will work for future catch rates not currently being used\n captureRate = 0.0000000000566758779982193 * math.pow(captureRate,5) - 0.0000000427601042779669*math.pow(captureRate,4) + 0.0000125235963016363*math.pow(captureRate,3) - 0.00191121035271638*math.pow(captureRate,2) + 0.311407303213974*captureRate + 0.846589688792571\n captureRate = round(captureRate, 1)\n captureRate = str(captureRate)\n return captureRate\n\ndef getXPYield(monID, channel,enemylevel,monlevel):\n gen = getGeneration(channel)\n sql = \"SELECT DISTINCT xp.experienceyieldvalue,xp.generationid gen FROM pokemon.pokemonexperienceyield xp \"\n sql += \"WHERE xp.pokemonid = \"+monID+\" \"\n sql += \"AND xp.generationid <= \"+gen+\" ORDER BY gen DESC LIMIT 1\"\n xpYieldArray = performSQL(sql)\n if xpYieldArray == []:\n xp=\"unknown\"\n else:\n gen = int(gen)\n monyield = xpYieldArray[0][0]\n xp = monyield*enemylevel/7\n xp=str(int(round(xp,0)))\n return xp\n\ndef getMonEvos(monID, channel):\n gen = getGeneration(channel)\n sql = \"SELECT DISTINCT mon.pokemonname\"\n sql += \"\"\", pel.pokemonevolutionlevel,\n i.itemname, l.locationname, pet.evolutiontypeid, pes.pokemonevolutionuniquestring, m.movename, gg.generationid\n FROM pokemon.pokemonevolution pe \"\"\"\n sql += \"\"\"LEFT JOIN pokemon.pokemon mon ON pe.targetpokemonid = mon.pokemonid \"\"\"\n sql +=\"\"\"LEFT JOIN pokemon.pokemonevolutionlevel pel ON pe.pokemonevolutionid = pel.pokemonevolutionid\n LEFT JOIN pokemon.pokemonevolutionmove pem ON pe.pokemonevolutionid = pem.pokemonevolutionid\n LEFT JOIN pokemon.move m ON pem.moveid = m.moveid\n LEFT JOIN pokemon.pokemonevolutionitem pei ON pe.pokemonevolutionid = pei.pokemonevolutionid\n LEFT JOIN pokemon.item i ON pei.itemid = i.itemid\n LEFT JOIN pokemon.pokemonevolutionlocation ploc ON pe.pokemonevolutionid = ploc.pokemonevolutionid\n LEFT JOIN pokemon.location l ON ploc.locationid = l.locationid\n LEFT JOIN pokemon.pokemonevolutiontype pet ON pe.pokemonevolutionid = pet.pokemonevolutionid\n LEFT JOIN pokemon.gamegroup gg ON pe.gamegroupid = gg.gamegroupid\n LEFT JOIN pokemon.pokemonevolutionstring pes ON pe.pokemonevolutionid = pes.pokemonevolutionid\"\"\"\n sql += \" WHERE pe.basepokemonid = \"+monID+\" \"\n sql += \"\"\" AND gg.generationid = (SELECT MAX(gg.generationid) FROM pokemon.pokemonevolution pe\n LEFT JOIN pokemon.gamegroup gg ON pe.gamegroupid = gg.gamegroupid\n WHERE gg.generationid <=\"\"\"+gen+\"\"\" AND pe.basepokemonid = \"\"\"+monID+\"\"\")\n ORDER BY generationid DESC\"\"\"\n evoArray = performSQL(sql)\n if evoArray == []:\n evoInfo = \"Does not evolve\"\n else:\n evoMon = str(evoArray[0][0])\n evoLevel = str(evoArray[0][1])\n evoItem = str(evoArray[0][2])\n evoLocation = str(evoArray[0][3])\n evoType = evoArray[0][4]\n evoUnique = str(evoArray[0][5])\n evoMove = str(evoArray[0][6])\n evoInfo = \"Evolves into \" + evoMon\n if evoType == 2 or evoType == 11:\n evoInfo += \" via trade\"\n elif evoType == 3:\n evoInfo += \" via high friendship\"\n elif evoType == 12:\n evoInfo += \" as a female\"\n elif evoType == 13:\n evoInfo += \" as a male\"\n elif evoType == 16:\n evoInfo += \" during the day\"\n elif evoType == 17:\n evoInfo += \" at night\"\n elif evoType == 20:\n evoInfo += \" in the rain\"\n elif evoType == 21:\n evoInfo += \" via high beauty\"\n if not evoLevel == 'None':\n evoInfo += \" at level \"+evoLevel\n if not evoItem == 'None':\n if evoType == 4:\n evoInfo += \" after being exposed to \" + evoItem\n else:\n evoInfo += \" while holding \" + evoItem\n if not evoLocation == 'None':\n evoInfo += \" at \" + evoLocation\n if not evoMove == 'None':\n evoInfo += \" while knowing \" + evoMove\n if not evoUnique == 'None':\n evoInfo += \" \" + evoUnique\n return evoInfo\n\ndef getMonMoves(monID, channel):\n game = getGame(channel)\n sql = \"\"\"SELECT DISTINCT mv.movename,pm.pokemonmovelevel FROM pokemon.pokemonmove pm \n LEFT JOIN pokemon.move mv ON pm.moveid = mv.moveid\n LEFT JOIN pokemon.generationmove gm ON mv.moveid = gm.moveid\n LEFT JOIN pokemon.gamegroup gg ON pm.gamegroupid = gg.gamegroupid \"\"\"\n sql += \"WHERE pm.pokemonid =\"+monID\n sql+=\" AND pokemonmovelevel > 1 AND gg.gamegroupabbreviation ='\"+game+\"' ORDER BY pm.pokemonmovelevel ASC\"\n movesArray = performSQL(sql)\n if movesArray == []:\n moveList = \"Does not learn moves\"\n else:\n moveList = \"Learns moves at \"\n for move in movesArray:\n moveList += str(move[1])+\", \"\n #remove the extra comma and space after\n moveList = moveList[0:len(moveList)-2]\n return moveList\n\ndef getMoveInfo(parameters, channel):\n if len(parameters) < 1:\n info = 'The !move command requires the name of a move as a parameter.'\n else:\n moveName = combineParameters(parameters)\n moveName = moveName.replace(\"'\",\"''\")\n gen = getGeneration(channel)\n moveID = getMoveID(moveName)\n if moveID == []:\n info = 'I could not find a move called \"' +moveName+'.'\n else:\n moveList = performSQL(\"\"\"SELECT m.movename, t.typename, mc.movecategoryname, gm.movecontactflag,\n gm.movepp, gm.movepower, gm.moveaccuracy, gm.movepriority, gm.movedescription, gm.generationid\n FROM pokemon.generationmove as gm\n LEFT JOIN pokemon.move as m ON gm.moveid = m.moveid\n LEFT JOIN pokemon.type AS t ON gm.typeid = t.typeid\n LEFT JOIN pokemon.movecategory AS mc ON gm.movecategoryid = mc.movecategoryid\n WHERE gm.moveid = '\"\"\" + moveID + \"' AND gm.generationid = \" + gen)\n if moveList == []:\n info = 'I could not find a move called \"' +moveName+'\" in generation '+gen+'.'\n else:\n moveList=moveList[0]\n if 'True' in str(moveList[3]):\n moveContact = \"C\"\n else:\n moveContact = \"NC\"\n info = str(moveList[0])+\" - Gen \" +gen+ \": (\"+str(moveList[1])+\", \"+str(moveList[2])+\", \"+moveContact+\") | PP: \"+str(moveList[4])+\" | Power: \"+str(moveList[5])+\" | Acc.: \"+str(moveList[6])+\" | Priority: \"+str(moveList[7])+\" | Summary: \"+str(moveList[8])\n return info\n\ndef getAbilityInfo(parameters, channel):\n if len(parameters) < 1:\n abilityInfo = \"The !ability command requires the name of an ability as a parameter.\"\n else:\n abilityName = combineParameters(parameters)\n abilityName = abilityName.replace(\"'\",\"''\")\n gen = getGeneration(channel)\n abilityName = abilityName.title()\n abilityTuple = performSQL(\"\"\" WITH ldist as (SELECT ab.abilityname,ga.abilitydescription,ga.generationid,pokemon.levenshtein(ab.abilityname, '\"\"\"+abilityName+\"\"\"') AS distance FROM pokemon.generationability ga\n LEFT JOIN pokemon.ability ab ON ga.abilityid = ab.abilityid\n WHERE ga.generationid <= \"\"\"+gen+\"\"\" )\n SELECT * FROM ldist \n WHERE distance < 4 \n ORDER BY distance ASC LIMIT 1\"\"\") \n if not abilityTuple == []:\n abilityName = str(abilityTuple[0][0])\n abilitySum = str(abilityTuple[0][1])\n print(abilitySum)\n abilityInfo = abilityName + \" (Gen \"+gen+\"): \" + abilitySum\n else:\n abilityInfo = \"Could not find info for ability '\"+abilityName+\"' in generation \" + gen + \".\"\n return abilityInfo\n\ndef getNatureInfo(parameters,channel):\n if len(parameters) < 1:\n natureInfo = \"The !nature command requires the name of a nature as a parameter. (ex: !nature adamant)\"\n else:\n natureName = combineParameters(parameters)\n natureList = performSQL(\"\"\"WITH ldist as (SELECT raisedstat.statname raisedstat,loweredstat.statname loweredstat,\n n.neutralnatureflag neutral,\n pokemon.levenshtein(n.naturename, '\"\"\"+natureName+\"\"\"') AS distance FROM pokemon.nature n\n LEFT JOIN pokemon.stat raisedstat ON n.raisedstatid = raisedstat.statid\n LEFT JOIN pokemon.stat loweredstat ON n.loweredstatid = loweredstat.statid)\n SELECT * FROM ldist WHERE distance < 5\n ORDER BY distance LIMIT 1\"\"\")\n if natureList == []:\n natureInfo = \"Could not find info for \"+natureName+\".\"\n else:\n raisedStat,loweredStat,neutral,distance = natureList[0]\n if 'True' in str(neutral):\n natureInfo = natureName + \" is a neutral nature.\"\n elif 'False' in str(neutral):\n natureInfo = \"+\"+str(raisedStat)+\"/\"+\"-\"+str(loweredStat)\n else:\n natureInfo = \"Could not find info for \"+natureName+\".\"\n return natureInfo\n\ndef getWeaknessInfo(parameters, channel):\n if len(parameters) < 1:\n weaknessInfo = \"The !weak command requires the name of a Pokemon as a parameter. (ex: !weak kartana)\"\n else:\n monName = combineParameters(parameters)\n monID,monName = getMonID(monName,channel)\n if monID == None:\n return monName\n gen = getGeneration(channel)\n monTypes = \"\"\"WITH montypes AS( SELECT pokemonid,type1id,type2id\n FROM pokemon.crosstab('select pokemonid, typeid as type1id, typeid as type2id\n FROM pokemon.pokemontype WHERE generationid = (SELECT MAX(generationid) FROM pokemon.pokemontype WHERE pokemonid = \"\"\"+monID+\"\"\" AND generationid <= \"\"\"+gen+\"\"\") AND pokemonid = \"\"\"+monID+\"\"\"\n GROUP BY pokemonid,type1id,type2id ORDER BY pokemonid,type1id,type2id')\n AS ct( pokemonid int, type1id int, type2id int)), \\r\\n\"\"\"\n damage1 = \"\"\"damage1 as (\n SELECT DISTINCT attacktype.typename attacker,SUM(coalesce(tm.damagemodifier::float,1)) as damage\n FROM montypes\n LEFT JOIN pokemon.typematchup tm ON montypes.type1id = tm.defendingtypeid\n LEFT JOIN pokemon.type attacktype ON tm.attackingtypeid = attacktype.typeid\n WHERE tm.generationid = \"\"\"+gen+\"\"\"\n GROUP BY attacktype.typename),\\r\\n\"\"\"\n damage2 = \"\"\"damage2 as (\n SELECT DISTINCT attacktype.typename attacker,SUM(coalesce(tm.damagemodifier::float,1)) as damage\n FROM montypes\n LEFT JOIN pokemon.typematchup tm ON montypes.type2id = tm.defendingtypeid\n LEFT JOIN pokemon.type attacktype ON tm.attackingtypeid = attacktype.typeid\n WHERE tm.generationid = \"\"\"+gen+\"\"\"\n GROUP BY attacktype.typename) \\r\\n\"\"\"\n mainSelect = \"\"\"SELECT damage1.attacker attacktype,SUM(coalesce(damage1.damage,1) * coalesce(damage2.damage,1)) as totaldamage\n FROM damage1 LEFT JOIN damage2 ON damage1.attacker = damage2.attacker\n GROUP BY attacktype\"\"\"\n matchupInfo = performSQL(monTypes+damage1+damage2+mainSelect)\n printableDict = {4.0:[],2.0:[],1.0:[],.5:[],.25:[],0:[]}\n for type,dmgmodifier in matchupInfo:\n printableDict[dmgmodifier].append(type)\n monTypes = getMonTypes(monID, channel)\n weaknessInfo = monName +\" \"+ monTypes + \", Gen \" +gen+\" = \\r\"\n if printableDict[4.0]:\n weaknessInfo += \"(4x): \" + str(printableDict[4.0])+ \" // \"\n if printableDict[2.0]:\n weaknessInfo += \"(2x): \" + str(printableDict[2.0]) + \" // \"\n if printableDict[1.0]:\n weaknessInfo += \"(1x): \" + str(printableDict[1.0]) + \" // \"\n if printableDict[0.5]:\n weaknessInfo += \"(.5x): \" + str(printableDict[0.5]) + \" // \"\n if printableDict[0.25]:\n weaknessInfo += \"(.25x): \" + str(printableDict[0.25]) + \" // \"\n if printableDict[0]:\n weaknessInfo += \"0x: \" + str(printableDict[0])\n weaknessInfo = weaknessInfo.replace('[','').replace(']','').replace(\"\\'\",\"\")\n return weaknessInfo\n \n\ndef getAbbrevs():\n abbrevs = performSQL(\"SELECT DISTINCT gg.gamegroupabbreviation,gg.gamegrouporder FROM pokemon.gamegroup gg INNER JOIN pokemon.game gm ON gg.gamegroupid = gm.gamegroupid ORDER BY gg.gamegrouporder\")\n message = \"Available games are: \"\n for abbrev in abbrevs:\n message += abbrev[0]+\", \"\n message = message[:len(message)-2]\n return message\n\ndef getGames():\n games = performSQL(\"SELECT gm.gamename,gg.gamegrouporder FROM pokemon.game gm LEFT JOIN pokemon.gamegroup gg ON gm.gamegroupid = gg.gamegroupid ORDER BY gg.gamegrouporder,gm.gamename\")\n message = \"Available games are: \"\n for game in games:\n message += game[0]+\", \"\n message = message[:len(message)-2]\n return message\n\ndef dbConfig(configFile = \"chatbot.ini\",section=\"database\"):\n config = configparser.ConfigParser()\n config.read(configFile)\n db = {}\n configuration = config.items(section)\n for option in configuration:\n db[option[0]] = option[1]\n return db\n\ndef chatMessage(messageString, channel, server):\n server.send(bytes('PRIVMSG #'+ channel + ' :'+messageString+' \\r\\n', 'utf-8'))\n\ndef performSQL(sql):\n dbConn = dbConfig()\n #print(\"Connecting to database...\")\n conn = psycopg2.connect(**dbConn)\n with conn.cursor() as cur:\n conn.set_session(readonly=False, autocommit=True)\n #print(\"Executing... \" +sql)\n cur.execute(sql)\n result = cur.fetchall()\n return result\n\ndef setGame(game, channel, server):\n #create an error message if there are no parameters given\n if len(game) < 1:\n message = \"Command !pokegame requires a game name or abbreviation as a parameter. Use !gamelist to see a list.\"\n #if there are parameters, try using it as a game name and fetching an abbreviation\n else:\n #turn the parameters into a gamename string\n gameName = \"\"\n for word in game:\n gameName += word\n #try using the parameters as an exact match with a game abbreviation\n gameName = gameName.upper()\n selectedGame = performSQL(\"\"\"SELECT gg.gamegroupname, gg.gamegroupabbreviation,'null',gm.gamename,gm.gameid\n FROM pokemon.gamegroup gg\n LEFT JOIN pokemon.game gm ON gg.gamegroupid = gm.gamegroupid\n WHERE gg.gamegroupabbreviation = '\"\"\"+gameName+\"' LIMIT 1\")\n #if we fail to find a game, try using the parameters as a full game name with levenshtein distance < 5\n if selectedGame == []:\n gameName = gameName.title()\n selectedGame= performSQL(\"\"\"WITH ldist as (SELECT gg.gamegroupname, gg.gamegroupabbreviation,pokemon.levenshtein(gm.gamename, '\"\"\"+gameName+\"\"\"')\n AS distance,gm.gamename,gm.gameid FROM pokemon.game gm\n LEFT JOIN pokemon.gamegroup gg ON gm.gamegroupid = gg.gamegroupid)\n SELECT * FROM ldist WHERE distance < 4\n ORDER BY distance LIMIT 1\"\"\")\n #if we found a game in either query above, find the generation, update the config, and say there was a success!\n if not selectedGame == []:\n groupName,gameAbbrev,throwAwayVariable,gameName,gameid = selectedGame[0]\n updateGame = \"UPDATE bot.channel set gameid = \"+str(gameid)+\" WHERE channelname = '\"+channel+\"' RETURNING channelid;\"\n channelid = performSQL(updateGame)\n message = \"Changed the game to \"+gameName+\".\"\n else:\n message = gameName+\" is not a valid game. Use !abbrevs for a list of valid abbreviations/games. I wasn't able to change the game to \"+gameName+\".\"\n return message\n\ndef getCoverage(coverageTypes,channel):\n gen = getGeneration(channel)\n game = getGame(channel)\n gameID = str(getGameID(channel))\n typeIDs = []\n typeNames = []\n for coverageType in coverageTypes:\n type = performSQL(\"\"\"WITH ldist AS (SELECT ty.typeid,ty.typename,pokemon.levenshtein(ty.typename,'\"\"\"+coverageType+\"\"\"')\n AS distance FROM pokemon.type ty where ty.generationid <=\"\"\"+gen+\"\"\")\n SELECT * FROM ldist WHERE distance < 3 ORDER BY distance LIMIT 1\"\"\")\n if type == []:\n message = coverageType.title()+\" is not a valid type in generation \"+gen+\".\"\n return message\n else:\n typeIDs.append(type[0][0])\n typeNames.append(type[0][1])\n monTypes = \"\"\"WITH montypes as (\n SELECT pokemonid,type1id,type2id\n\t FROM pokemon.crosstab('select pt.pokemonid, typeid as type1id, typeid as type2id\n FROM pokemon.pokemontype pt\n LEFT JOIN pokemon.pokemongameavailability pga ON pt.pokemonid = pga.pokemonid\n LEFT JOIN pokemon.game gm ON pga.gameid = gm.gameid\n WHERE pt.generationid =\"\"\"+gen+\"\"\"\n AND gm.gameid = \"\"\"+gameID+\"\"\"\n AND pga.pokemonavailabilitytypeid != 18\n GROUP BY pt.pokemonid,type1id,type2id ORDER BY pt.pokemonid,type1id,type2id')\n\t\t AS ct( pokemonid int, type1id int, type2id int)),\"\"\"\n damage1 = \"\"\"damage1 AS (\\r\\n\"\"\"\n damage1 += \"\"\"SELECT montypes.pokemonid,mon.pokemonname,\"\"\"\n for typeName in typeNames:\n damage1 += \"\"\"CASE WHEN (montypes.pokemonid = 343 AND \"\"\"+typeName+\"\"\"1.attackingtypeid NOT IN(2,10,13,14,16)) THEN 0 ELSE \"\"\"\n damage1 += typeName+\"1.damagemodifier::float END as \"+typeName+\"damage\"\n if typeNames.index(typeName) < len(typeNames)-1:\n damage1 += \",\"\n elif typeNames.index(typeName) == len(typeNames)-1:\n damage1 += \"\\r\\n\"\n damage1 += \"\"\" FROM montypes\\r\\n\n LEFT JOIN pokemon.pokemon mon ON montypes.pokemonid = mon.pokemonid \\r\\n\"\"\"\n for typeName in typeNames:\n damage1 += \"LEFT JOIN pokemon.typematchup \"+typeName+\"1 ON montypes.type1id = \"+typeName+\"1.defendingtypeid\\r\\n\"\n damage1 += \" WHERE \"\n for typeName in typeNames:\n damage1 += typeName+\"1.attackingtypeid = \"+str(typeIDs[typeNames.index(typeName)])+\"\\r\\n AND \"\n damage1 += typeName+\"1.generationid = \"+gen+\" \"\n if typeNames.index(typeName) < len(typeNames)-1:\n damage1 += \"\\r\\n AND \"\n damage1 += \"\\r\\nGROUP BY montypes.pokemonid,mon.pokemonname,\"\n for typeName in typeNames:\n damage1 += typeName+\"damage\"\n if typeNames.index(typeName) < len(typeNames)-1:\n damage1 += \",\"\n elif typeNames.index(typeName) == len(typeNames)-1:\n damage1 += \"),\\r\\n \"\n damage2 = \"damage2 as (SELECT montypes.pokemonid,mon.pokemonname,\"\"\"\n for typeName in typeNames:\n damage2 += \"\"\"CASE WHEN (montypes.pokemonid = 343 AND \"\"\"+typeName+\"\"\"2.attackingtypeid NOT IN(2,10,13,14,16)) THEN 0 ELSE \"\"\"\n damage2 += typeName+\"2.damagemodifier::float END as \"+typeName+\"damage\"\n if typeNames.index(typeName) < len(typeNames)-1:\n damage2 += \",\"\n damage2 += \"\"\"\\r\\n FROM montypes\\r\\n\n LEFT JOIN pokemon.pokemon mon ON montypes.pokemonid = mon.pokemonid \\r\\n\"\"\"\n for typeName in typeNames:\n damage2 += \"LEFT JOIN pokemon.typematchup \"+typeName+\"2 ON montypes.type2id = \"+typeName+\"2.defendingtypeid\\r\\n\"\n damage2 += \" WHERE \"\n for typeName in typeNames:\n damage2 += typeName+\"2.attackingtypeid = \"+str(typeIDs[typeNames.index(typeName)])+\"\\r\\n AND \"\n damage2 += typeName+\"2.generationid = \"+gen+\" \"\n if typeNames.index(typeName) < len(typeNames)-1:\n damage2 += \"\\r\\n AND \"\n damage2 += \"\\r\\n GROUP BY montypes.pokemonid,mon.pokemonname,\"\n for typeName in typeNames:\n damage2 += typeName+\"damage\"\n if typeNames.index(typeName) < len(typeNames)-1:\n damage2 += \",\"\n elif typeNames.index(typeName) == len(typeNames)-1:\n damage2 += \") \"\n preSelect = \"SELECT damage, count(*) FROM (\\r\\n\"\n mainSelect = \"SELECT damage1.pokemonid, GREATEST(\"\n for typeName in typeNames:\n mainSelect += \"SUM(coalesce(damage1.\"+typeName+\"damage,1) * coalesce(damage2.\"+typeName+\"damage,1))\"\n if typeNames.index(typeName) < len(typeNames)-1:\n mainSelect += \",\\r\\n \"\n elif typeNames.index(typeName) == len(typeNames)-1:\n mainSelect += \") as damage FROM damage1 LEFT JOIN damage2 ON damage1.pokemonid = damage2.pokemonid \"\n mainGroup = \"GROUP BY damage1.pokemonid \"\n postSelect = \") AS mondamage GROUP BY damage ORDER BY damage ASC\\r\\n\"\n selectString = monTypes+damage1+damage2+preSelect+mainSelect+mainGroup+postSelect\n pokemonList = performSQL(selectString)\n coverageString = \"Types: \"\n for name in typeNames:\n coverageString += name\n if typeNames.index(name) < len(typeNames)-1:\n coverageString += \", \"\n coverageString += \" - \"\n pokemonString = \"-- Obstacles: \"\n coverageString += \" (\"+game+\"): \"\n for array in pokemonList:\n coverageString += str(array[0]).replace(\".0\",\".\").replace(\"0.5\",\".5\").replace(\"0.\",\"0\").replace(\"1.\",\"1\").replace(\"2.\",\"2\").replace(\"4.\",\"4\")+\"x: \"+str(array[1])\n if pokemonList.index(array) < len(pokemonList)-1:\n coverageString += \" // \"\n if pokemonList[0][0] < .5 and pokemonList[1][0] < .5:\n pokemonString = \" -- Obstacles < 1x\"\n limit = pokemonList[0][1]+pokemonList[1][1]\n elif pokemonList[0][0] < 1 and pokemonList[1][0] < 1:\n pokemonString = \" -- Obstacles < 1x\"\n limit = pokemonList[0][1]+pokemonList[1][1]\n elif pokemonList [0][0] < 1 and pokemonList [1][0] == 1:\n pokemonString = \" -- Obstacles\"\n limit = pokemonList[0][1]\n elif pokemonList[0][0] == 1:\n pokemonString = \" -- Top 5 1x Threats\"\n limit = 5\n if int(limit) > 12:\n pokemonString += \" (Limit 12): \"\n limit = 12\n else:\n pokemonString += \": \"\n bstSelect = \"SELECT damage1.pokemonid, mon.pokemonname, GREATEST(\"\n for typeName in typeNames:\n bstSelect += \"SUM(coalesce(damage1.\"+typeName+\"damage,1) * coalesce(damage2.\"+typeName+\"damage,1))\"\n if typeNames.index(typeName) < len(typeNames)-1:\n bstSelect += \",\\r\\n \"\n elif typeNames.index(typeName) == len(typeNames)-1:\n bstSelect += \"\"\") as damage\\r\\n\n FROM damage1\\r\\n\n LEFT JOIN damage2 ON damage1.pokemonid = damage2.pokemonid\\r\\n\n LEFT JOIN pokemon.pokemon mon ON damage1.pokemonid = mon.pokemonid\\r\\n\"\"\"\n monBST = \"\"\", monBST as (\\r\\n\n SELECT mon.pokemonid monid,\\r\\n\n mon.pokemonname as monname,\\r\\n\n ps.generationid gen,\\r\\n\n sum(ps.pokemonstatvalue) as bst\\r\\n\n FROM pokemon.pokemonstat ps\\r\\n\n LEFT JOIN pokemon.pokemon mon ON ps.pokemonid = mon.pokemonid\\r\\n\n WHERE ps.generationid <= \"\"\"+gen+\"\"\"GROUP BY monid,monname,gen ORDER BY gen DESC, monid, monname) \\r\\n\"\"\"\n preWith = \"WITH monDamageQuery as (\\r\\n\"\n postWith = \")\"\n bstGroup = \" GROUP BY damage1.pokemonid,mon.pokemonname \\r\\n\"\n bstOrder = \" ORDER BY damage ASC\\r\\n\"\"\"\n realSelect = \"\"\"SELECT damage, bst, monDamageQuery.pokemonname, monBST.gen FROM monDamageQuery\n LEFT JOIN monBST ON monDamageQuery.pokemonid = monBST.monid\n GROUP BY damage, bst, monDamageQuery.pokemonname, monBST.gen\n ORDER BY damage ASC, bst DESC, monDamageQuery.pokemonname, monBST.gen\"\"\"\n coverageQuery = monTypes+damage1+damage2+bstSelect+bstGroup+bstOrder\n sql = preWith+coverageQuery+postWith+monBST+realSelect\n pokemonBSTList = []\n pokemonIDs = performSQL(sql)\n if len(pokemonIDs) == 0:\n pokemonString += \"None\"\n for obstacle in pokemonIDs:\n if len(pokemonBSTList) < int(limit):\n obstacleName = obstacle[2]\n if not obstacleName in pokemonBSTList:\n pokemonBSTList.append(obstacleName)\n pokemonString += obstacleName+\", \"\n pokemonString = pokemonString[0:len(pokemonString)-2]\n coverageString += pokemonString\n coverageString = coverageString.replace(\" Form)\",\")\")\n return coverageString\n\ndef getGameID(channel):\n gameID = performSQL(\"\"\"SELECT gameid FROM bot.channel WHERE channelname = '\"\"\"+channel+\"'\")[0][0]\n return gameID\n\ndef getGame(channel):\n game = performSQL(\"\"\"SELECT gg.gamegroupabbreviation FROM bot.channel ch\n LEFT JOIN pokemon.game gm ON ch.gameid = gm.gameid\n LEFT JOIN pokemon.gamegroup gg ON gm.gamegroupid = gg.gamegroupid\n WHERE ch.channelname = '\"\"\"+channel+\"'\")[0][0]\n return game\n\nif __name__ == \"__main__\":\n main()","sub_path":"backup/brdybot.py","file_name":"brdybot.py","file_ext":"py","file_size_in_byte":48020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"188390425","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nimport logging\nfrom datetime import datetime\n\nimport requests\n\nfrom django.core.files import File\nfrom django.utils.text import slugify\n\nfrom celery import shared_task\n\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\n@shared_task\ndef sync(imdb):\n \"\"\"\n Try retrieve data from IMDB to fill the information of the movie on David.\n \"\"\"\n url = 'http://www.omdbapi.com/?i=%s&plot=full&r=json' % imdb.imdb_id\n\n r = requests.get(url)\n\n if r.status_code != 200:\n logger.info('unable to get data from IMDB to %s' % imdb.movie)\n return\n\n data = r.json()\n\n if 'Error' in data:\n message = 'unable to get data from IMDB to %s because: %s'\n message %= (imdb.movie, data.get('Error', 'unknown error'))\n logger.info(message)\n return\n\n imdb.movie.title = data.get('Title')\n imdb.movie.plot = data.get('Plot')\n imdb.movie.released = datetime.strptime(data.get('Released'), '%d %b %Y')\n imdb.movie.slug = slugify(imdb.movie.title)\n\n try:\n rating = data.get('imdbRating', 0.0)\n rating = float(rating)\n except ValueError as e:\n message = 'The movie %s has no rating from IMDB: %s'\n message %= (imdb.movie, e)\n logger.debug(message)\n else:\n imdb.movie.rating = rating\n\n genres = data.get('Genre', '').split(',')\n imdb.movie.add_genres(genres)\n\n cast = data.get('Actors', '').split(',')\n imdb.movie.add_cast(cast)\n\n r = requests.get(data.get('Poster'), stream=True)\n\n path = '/tmp/%s.png' % imdb.movie.slug\n\n with open(path, 'wb') as f:\n f.write(r.content)\n\n reopen = open(path, 'rb')\n django_file = File(reopen)\n imdb.movie.poster.save('%s.png' % imdb.movie.slug, django_file, save=True)\n\n logger.info('The movie (%s) was synchronized with the IMDB' % imdb.movie)\n\n return 'The movie (%s) was synchronized with the IMDB' % imdb.movie\n\n\n@shared_task\ndef cleanup(movie):\n \"\"\"\n Delete does not remove the poster from file system automatically (Django\n has this behavior to avoid data corruption if transaction fail for\n example). This task will do this in more reliable form (and asynchronous).\n \"\"\"\n if not movie.poster:\n return\n\n storage, path = movie.poster.storage, movie.poster.path\n storage.delete(path)\n\n logger.info('The poster from %s was removed from filesystem' % movie)\n","sub_path":"app/movie/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"393986520","text":"def largestPair( li ):\n\tnum = len( li )\n\tm = float( '-inf' )\n\tif num == 1:\n\t\tm = li[ 0 ]\n\telse:\n\t\tfor i in range( len( li ) - 1 ):\n\t\t\ts = li[ i ] + li[ i + 1 ]\n\t\t\tif s > m: m = s\n\treturn m\n","sub_path":"src/_AS/week01/e1.3.py","file_name":"e1.3.py","file_ext":"py","file_size_in_byte":189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"65100541","text":"import datetime\nimport pandas as pd\nfrom igf_data.igfdb.pipelineadaptor import PipelineAdaptor\nfrom ehive.runnable.IGFBaseJobFactory import IGFBaseJobFactory\nfrom igf_data.utils.ehive_utils.pipeseedfactory_utils import get_pipeline_seeds\n\nclass PipeseedFactory(IGFBaseJobFactory):\n '''\n Job factory class for pipeline seed\n '''\n def param_defaults(self):\n params_dict = \\\n super(PipeseedFactory,self).param_defaults()\n params_dict.\\\n update({\n 'seed_id_label':'seed_id',\n 'seqrun_id_label':'seqrun_id',\n 'seeded_label':'SEEDED',\n 'running_label':'RUNNING',\n 'seqrun_date_label':'seqrun_date',\n 'seqrun_igf_id_label':'seqrun_igf_id',\n 'seed_status_label':'status',\n 'experiment_id_label':'experiment_id',\n 'pipeseed_mode':'demultiplexing',\n })\n return params_dict\n\n\n def run(self):\n '''\n Run method for the seed job factory class of the all pipelines\n\n :param igf_session_class: A database session class\n :param pipeline_name: Name of the pipeline\n :param seed_id_label: A text label for the seed_id, default seed_id\n :param seqrun_id_label: A text for seqrun_id column name, default seqrun_id\n :param seqrun_date_label: A text label for the seqrun date, default seqrun_date\n :param seqrun_igf_id_label: A text label for sequencing run igf id, default seqrun_igf_id\n :param seeded_label: A text label for the status seeded in pipeline_seed table, default SEEDED\n :param running_label: A text label for the status running in the pipeline_seed table, default RUNNING\n :param seed_status_label: A text label for the pipeline_seed status column name, default status\n :param experiment_id_label: A text label for the experiment_id, default experiment_id\n :param pipeseed_mode: A text label for pipeline mode, default demultiplexing\n Allowed values are\n\n * demultiplexing\n * alignment\n\n :returns: A list of dictionary containing the seqrun ids or experiment_igf_ids seed for analysis\n '''\n try:\n dbconnected = False\n igf_session_class = self.param_required('igf_session_class') # set by base class\n pipeline_name = self.param_required('pipeline_name')\n seeded_label = self.param_required('seeded_label')\n running_label = self.param_required('running_label')\n seed_status_label = self.param_required('seed_status_label')\n pipeseed_mode = self.param_required('pipeseed_mode')\n\n if pipeseed_mode not in ('demultiplexing','alignment'):\n raise ValueError(\n 'Pipeseed_mode {0} not supported'.\\\n format(pipeseed_mode))\n\n pipeseeds_data,seed_data = \\\n get_pipeline_seeds(\n pipeseed_mode=pipeseed_mode,\n pipeline_name=pipeline_name,\n igf_session_class=igf_session_class) # fetch pipeseed data from db\n if len(seed_data.index)>0:\n seed_data = \\\n seed_data.\\\n to_dict(orient='records') # convert dataframe to list of dictionaries\n self.param('sub_tasks',seed_data) # set sub_tasks param for the data flow\n pipeseeds_data[seed_status_label] = \\\n pipeseeds_data[seed_status_label].\\\n map({seeded_label:running_label}) # update seed records in pipeseed table, changed status to RUNNING\n pa = PipelineAdaptor(**{'session_class':igf_session_class}) # get db adaptor\n pa.start_session() # connect to db\n dbconnected = True\n pa.update_pipeline_seed(\n data=pipeseeds_data.to_dict(orient='records'),\n autosave=False) # set pipeline seeds as running\n pa.commit_session() # save changes to db\n pa.close_session() # close db connection\n dbconnected=False\n message = \\\n 'Total {0} new job found for {1}, pipeline: {2}'.\\\n format(\n len(seed_data),\n self.__class__.__name__,\n pipeline_name) # format msg\n self.post_message_to_slack(\n message,reaction='pass') # send update to slack\n self.post_message_to_ms_team(\n message=message,\n reaction='pass') # send update to ms team\n else:\n message = \\\n '{0}, {1}: no new job created'.\\\n format(\n self.__class__.__name__,\n pipeline_name) # format msg for failed jobs\n self.warning(message)\n self.post_message_to_slack(\n message,reaction='sleep') # post about failed job to slack\n self.post_message_to_ms_team(\n message=message,\n reaction='sleep') # send update to ms team\n except Exception as e:\n message = \\\n 'Error in {0},{1}: {2}'.\\\n format(\n self.__class__.__name__,\n pipeline_name, e) # format slack msg\n self.warning(message)\n self.post_message_to_slack(\n message,reaction='fail') # send msg to slack\n self.post_message_to_ms_team(\n message=message,\n reaction='fail') # send update to ms team\n if dbconnected:\n pa.rollback_session() # remove changes from db\n pa.close_session()\n raise # mark worker as failed\n\n","sub_path":"ehive/runnable/jobfactory/PipeseedFactory.py","file_name":"PipeseedFactory.py","file_ext":"py","file_size_in_byte":6095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"6338031","text":"# Copyright 2018 Google LLC\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# https://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\"\"\"A demo script showing how to use the uisrnn package on toy data.\"\"\"\n\nimport numpy as np\nfrom functools import partial\nfrom torch.utils.tensorboard import SummaryWriter\nimport torch.multiprocessing as mp\nmp = mp.get_context('forkserver')\n\nimport uisrnn\n\n\nSAVED_MODEL_NAME = 'saved_model.uisrnn'\nNUM_WORKERS = 2\n\n\ndef diarization_experiment(model_args, training_args, inference_args):\n \"\"\"Experiment pipeline.\n\n Load data --> train model --> test model --> output result\n\n Args:\n model_args: model configurations\n training_args: training configurations\n inference_args: inference configurations\n \"\"\"\n # data loading\n train_data = np.load('./data/toy_training_data.npz', allow_pickle=True)\n test_data = np.load('./data/toy_testing_data.npz', allow_pickle=True)\n train_sequence = train_data['train_sequence']\n train_cluster_id = train_data['train_cluster_id']\n test_sequences = test_data['test_sequences'].tolist()\n test_cluster_ids = test_data['test_cluster_ids'].tolist()\n\n # model init\n model = uisrnn.UISRNN(model_args)\n # model.load(SAVED_MODEL_NAME) # to load a checkpoint\n # tensorboard writer init\n writer = SummaryWriter()\n\n # training\n for epoch in range(training_args.epochs):\n stats = model.fit(train_sequence, train_cluster_id, training_args)\n # add to tensorboard\n for loss, cur_iter in stats:\n for loss_name, loss_value in loss.items():\n writer.add_scalar('loss/' + loss_name, loss_value, cur_iter)\n # save the mdoel\n model.save(SAVED_MODEL_NAME)\n\n # testing\n predicted_cluster_ids = []\n test_record = []\n # predict sequences in parallel\n model.rnn_model.share_memory()\n pool = mp.Pool(NUM_WORKERS, maxtasksperchild=None)\n pred_gen = pool.imap(\n func=partial(model.predict, args=inference_args),\n iterable=test_sequences)\n # collect and score predicitons\n for idx, predicted_cluster_id in enumerate(pred_gen):\n accuracy = uisrnn.compute_sequence_match_accuracy(\n test_cluster_ids[idx], predicted_cluster_id)\n predicted_cluster_ids.append(predicted_cluster_id)\n test_record.append((accuracy, len(test_cluster_ids[idx])))\n print('Ground truth labels:')\n print(test_cluster_ids[idx])\n print('Predicted labels:')\n print(predicted_cluster_id)\n print('-' * 80)\n\n # close multiprocessing pool\n pool.close()\n # close tensorboard writer\n writer.close()\n\n print('Finished diarization experiment')\n print(uisrnn.output_result(model_args, training_args, test_record))\n\n\ndef main():\n \"\"\"The main function.\"\"\"\n model_args, training_args, inference_args = uisrnn.parse_arguments()\n diarization_experiment(model_args, training_args, inference_args)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":3273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"509115411","text":"#ImportModules\nimport ShareYourSystem as SYS\nimport operator\n\n#Definition a MakerClass decorated by the ObserverClass\n@SYS.ObserverClass(**{\n\t'ObservingIsBool':True,\n\t'ObservingWrapMethodStr':'make'\n})\nclass MakerClass(object):\n\n\tdef default_init(self,\n\t\t\t\t\t_MakingMyFloat=0.,\n\t\t\t\t\t_MadeMyInt=0,\n\t\t\t\t\t**_KwarVariablesDict\n\t\t\t\t):\n\t\tobject.__init__(self,**_KwarVariablesDict)\n\n\tdef do_make(self):\n\t\t\n\t\t#cast\n\t\tself.MadeMyInt=int(self.MakingMyFloat)\n\n#Definition the AttestedStr\nSYS._attest(\n\t[\n\t\t'MakerClass.make is '+str(MakerClass.make),\n\t\t'MakerClass.DeriveClassor.ObservingWrapMethodStr is '+str(\n\t\t\tMakerClass.DeriveClassor.ObservingWrapMethodStr),\n\t\t'MakerClass.DeriveClassor.ObservedWrapMethodStr is '+str(\n\t\t\tMakerClass.DeriveClassor.ObservedWrapMethodStr),\n\t]\n) \n\n#Print\n\n\n\n","sub_path":"Pythonlogy/build/lib/ShareYourSystem/Standards/Classors/Observer/01_ExampleDoc.py","file_name":"01_ExampleDoc.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"205304503","text":"from talon import Context, Module, actions, grammar\n\n# Add single words here if Talon recognizes them, but they need to have their\n# capitalization adjusted.\ncapitalize = [\n \"I\",\n \"I'm\",\n \"I've\",\n \"I'll\",\n \"I'd\",\n \"Monday\",\n \"Mondays\",\n \"Tuesday\",\n \"Tuesdays\",\n \"Wednesday\",\n \"Wednesdays\",\n \"Thursday\",\n \"Thursdays\",\n \"Friday\",\n \"Fridays\",\n \"Saturday\",\n \"Saturdays\",\n \"Sunday\",\n \"Sundays\",\n \"January\",\n \"February\",\n # March omitted because it's a regular word too\n \"April\",\n # May omitted because it's a regular word too\n \"June\",\n \"July\",\n \"August\",\n \"September\",\n \"October\",\n \"November\",\n \"December\",\n]\n\n# Add words (or phrases you want treated as words) here if Talon doesn't\n# recognize them at all.\nword_map = {\n # For example:\n # \"color\": \"colour\",\n}\nword_map.update({x.lower(): x for x in capitalize})\n\nsimple_vocabulary = [\n \"nmap\",\n \"admin\",\n \"Cisco\",\n \"Citrix\",\n \"VPN\",\n \"DNS\",\n \"minecraft\",\n \"unhashable\",\n \"pycharm\",\n \"optuna\",\n \"streamlit\",\n \"postgres\",\n \"terran robotics\",\n \"mavlink\",\n \"yaw\",\n \"DJI\",\n \"DGI\",\n \"attitude quaternion\",\n \"quaternion\",\n \"acceleration\",\n \"accelerometer\",\n \"accelerometers\",\n \"env\",\n \"peek wrapper\",\n \"pitch deck\",\n \"PWD\",\n \"Wget\",\n \"SSH\",\n \"make run\",\n \"make shell\",\n \"make clean\",\n \"smartnav\",\n \"pip\",\n \"pip install\",\n \"ipython\",\n \"python\",\n \"iter\",\n \"NP\",\n \"yield\",\n \"python3\",\n \"conda\",\n \"functools\",\n \"matplotlib\",\n \"redis\",\n \"args\",\n \"dict\",\n \"traceback\",\n \"refactor\",\n \"contrib\",\n \"yaml\",\n \"yamel\",\n \"namespace\",\n \"namespaced\",\n \"pull request\",\n \"rebase\",\n \"rebased\",\n \"mergeable\",\n \"DQN\",\n \"N step replay buffer\",\n \"cart pole\",\n \"RLkit\",\n \"IMU\",\n \"quat\",\n \"autonomously\",\n \"much reese\",\n \"LSTM\",\n \"RNN\",\n \"KNN\",\n \"GRU\",\n \"GPU\",\n \"SGD\",\n \"VAE\",\n \"beta VAE\",\n \"word2vec\",\n \"op graph\",\n \"initializer\",\n \"keras\",\n \"tensorflow\",\n \"pytorch\",\n \"matmul\",\n \"frontends\",\n \"expand dims\",\n \"gan\",\n \"NG\",\n \"minibatch\",\n \"affine\",\n \"affine layer\",\n \"affine embedding\",\n \"linear layer\",\n \"elementwise\",\n \"argmax\",\n \"encode\",\n \"onehot\",\n \"multihot\",\n \"embeddings\",\n \"end to end\",\n \"unordered axes\",\n \"convolution\",\n \"convolutional\",\n \"conv\",\n \"conv net\",\n \"hyper parameter\",\n \"epsilon\",\n \"start epsilon\",\n \"end epsilon\",\n \"model end\",\n \"discretized\",\n \"decay\",\n \"embedder\",\n \"docker\",\n \"dockerfile\",\n \"docker compose\",\n \"folsom\",\n \"folsom lab\",\n \"ncloud\",\n \"SGI\",\n \"IPFS\",\n \"ethereum\",\n \"brew install\",\n \"voicecode\",\n \"IU\",\n \"fiaz\",\n \"yinyin\",\n \"evren\",\n]\n\nmapping_vocabulary = {\n \"i\": \"I\",\n \"i'm\": \"I'm\",\n \"i've\": \"I've\",\n \"i'll\": \"I'll\",\n \"i'd\": \"I'd\",\n \"open A I\": \"openai\",\n \"opening I\": \"openai\",\n \"talent\": \"talon\",\n \"minards\": \"menards\",\n \"hi\": \"high\",\n \"rose\": \"rows\",\n \"wi fi\": \"wifi\",\n \"2d d\": \"2d\",\n \"3d d\": \"3d\",\n \".\": \"dot\",\n \"inc.\": \"incorporated\",\n \"file name\": \"filename\",\n \"file names\": \"filenames\",\n \"talent\": \"talon\",\n \"taryn\": \"terran\",\n \"pitch deque\": \"pitch deck\",\n \"ro\": \"row\",\n \"bites\": \"bytes\",\n \"lauder\": \"logger\",\n \"sub-\": \"sub\",\n # \"aunt\": \"ant\",\n \"a cell around her\": \"accelerometer\",\n \"a celeron matter\": \"accelerometer\",\n \"a celeron iter\": \"accelerometer\",\n \"a seller almond or\": \"accelerometer\",\n \"a celeron that are\": \"accelerometer\",\n \"a celeron matters\": \"accelerometers\",\n \"a seller matters\": \"accelerometers\",\n \"a solid ramen nurse\": \"accelerometers\",\n \"a celebration\": \"acceleration\",\n \"robot assist\": \"roboticist\",\n \"absalom\": \"epsilon\",\n \"fidgets\": \"phidgets\",\n \"yacht\": \"yaw\",\n \"ya\": \"yaw\",\n \"carl\": \"curl\",\n \"yamel\": \"yaml\",\n \"semicolon\": \";\",\n # \"new-line\": \"\\n\",\n # \"new-paragraph\": \"\\n\\n\",\n \"teak\": \"k\",\n \"virg\": \"v\",\n \"zug\": \"s\",\n \"pre-\": \"pre\",\n \"multi-\": \"multi\",\n \"in turn\": \"intern\",\n \"re- factor\": \"refactor\",\n \"re- factoring\": \"refactoring\",\n \"e-mail\": \"email\",\n \"fulsome\": \"folsom\",\n \"thumbs down\": \":-1:\",\n \"thumbs-down\": \":-1:\",\n \"thumbs up\": \":+1:\",\n \"thumbs-up\": \":+1:\",\n \"okay hand\": \":ok_hand:\",\n \"thinking face\": \":thinking_face:\",\n \"in-line\": \"in line\",\n \"jupiter\": \"jupyter\",\n \"pie\": \"py\",\n \".pie\": \".py\",\n \"dot pie\": \".py\",\n \"dot by\": \".py\",\n \"dot hi\": \".py\",\n \".hi\": \".py\",\n \". hi\": \".py\",\n \".by\": \".py\",\n \"python three\": \"python3\",\n \"num py\": \"numpy\",\n \"K wargs\": \"kwargs\",\n \"dot shell\": \".sh\",\n \"self-taught\": \"self.\",\n \"self-doubt\": \"self.\",\n \"pip installed\": \"pip install\",\n \"rapper\": \"wrapper\",\n \"stack trace\": \"stacktrace\",\n \"repose\": \"repos\",\n \"ellis\": \"elif\",\n \"tubal\": \"tuple\",\n \"deck\": \"deque\",\n \"log it's\": \"logits\",\n \"sell\": \"cell\",\n \"jeep you\": \"gpu\",\n \"endo\": \"end\",\n \"and oh\": \"end\",\n \"rappers\": \"wrappers\",\n \"poynter\": \"pointer\",\n \"numb\": \"num\",\n \"gnome\": \"num\",\n \"Phnom\": \"num\",\n \"don\": \"done\",\n \"jet\": \"git\",\n \"name space\": \"namespace\",\n \"name spaces\": \"namespaces\",\n \"g cloud\": \"gcloud\",\n \"voice code\": \"voicecode\",\n \"nirvana\": \"nervana\",\n \"terrace\": \"keras\",\n \"karis\": \"keras\",\n \"me on\": \"neon\",\n \"lennix\": \"linux\",\n \"cube nets\": \"kubernetes\",\n \"q burnett\": \"kubernetes\",\n \"cooper9\": \"kubernetes\",\n \"expand dimms\": \"expand dims\",\n \"dimms\": \"dims\",\n \"dems\": \"dims\",\n \"seek to seek\": \"Seq2Seq\",\n \"data set\": \"dataset\",\n \"data loader\": \"dataloader\",\n \"call back\": \"callback\",\n \"jim\": \"gym\",\n \"angie\": \"ng\",\n \"and g\": \"ng\",\n \"mg\": \"ng\",\n \"mp\": \"np\",\n \"and p\": \"np\",\n \"all the rhythms\": \"algorithms\",\n \"all rhythms\": \"algorithms\",\n \"waits\": \"weights\",\n \"wait\": \"weight\",\n \"dk\": \"decay\",\n \"epoque\": \"epoch\",\n \"epic\": \"epoch\",\n \"epoques\": \"epochs\",\n \"epics\": \"epochs\",\n \"1 hot\": \"onehot\",\n \"one hot\": \"onehot\",\n \"scaler\": \"scalar\",\n \"sql light\": \"sqlight\",\n \"post gress\": \"postgres\",\n \"sink\": \"sync\",\n \"and betting\": \"embedding\",\n \"I am betting\": \"embedding\",\n \"I'm betting\": \"embedding\",\n \"phil\": \"fill\",\n \"gam\": \"gan\",\n \"gann\": \"gan\",\n \"ncloud interactive\": \"ncloud interact\",\n \"adam\": \"atom\",\n \"pseudo-\": \"sudo\",\n \"pipe\": \"|\",\n \"apt get\": \"apt-get\",\n \"macron\": \"make run\",\n \"make show\": \"make shell\",\n \"standard out\": \"stdout\",\n \"standard in\": \"stdin\",\n \"standard error\": \"stderr\",\n \"les\": \"less\",\n \"doctor\": \"docker\",\n \"darker\": \"docker\",\n \"daughter\": \"docker\",\n \"docker file\": \"dockerfile\",\n \"communities\": \"kubernetes\",\n \"cube control\": \"kubectl\",\n \"shall\": \"shell\",\n \"w get\": \"wget\",\n \"backslash\": \"\\\\\",\n \"jet tub\": \"github\",\n \"git tub\": \"github\",\n \"jet hub\": \"github\",\n \"git hub\": \"github\",\n \"ron\": \"run\",\n \"thorpe\": \"\\t\",\n \"tharp\": \"\\t\",\n \"if not none\": \"if not None\",\n \"shayna\": \"shaina\",\n \"constance\": \"constants\",\n}\n\n# Add vocabulary words (or phrases you want treated as words) here that aren't\n# recognized by Talon and are written differently than they're pronounced.\nmapping_vocabulary.update(dict(zip(simple_vocabulary, simple_vocabulary)))\n\n\nmod = Module()\n\n\n@mod.capture(rule=\"{user.vocabulary}\")\ndef vocabulary(m) -> str:\n return m.vocabulary\n\n\n@mod.capture(rule=\"( | )\")\ndef word(m) -> str:\n try:\n return m.vocabulary\n except AttributeError:\n # TODO: if the word is both a regular word AND user.vocabulary, then in\n # principle it may parse as instead; we ought to pass it through\n # mapping_vocabulary to be sure. But we should be doing that in\n # user.text, below, too.\n words = actions.dictate.replace_words(actions.dictate.parse_words(m.word))\n assert len(words) == 1\n return words[0]\n\n\npunctuation = set(\".,-!?;:\")\n\n\n@mod.capture(rule=\"( | )+\")\ndef text(m) -> str:\n words = []\n for item in m:\n if isinstance(item, grammar.vm.Phrase):\n words.extend(\n actions.dictate.replace_words(actions.dictate.parse_words(item))\n )\n else:\n words.extend(item.split(\" \"))\n\n result = \"\"\n for i, word in enumerate(words):\n if i > 0 and word not in punctuation and words[i - 1][-1] not in (\"/-(\"):\n result += \" \"\n result += word\n return result\n\n\n@mod.capture(rule=\"([])\")\ndef optional_text(m) -> str:\n if hasattr(m, \"text\"):\n return m.text\n else:\n return \"\"\n\n\n# TODO: should this be handled in a more generic way?\n@mod.capture(rule=\"([])\")\ndef optional_snake_text(m) -> str:\n if hasattr(m, \"text\"):\n return \"_\".join(m.text.split(\" \"))\n else:\n return \"\"\n\n\nmod.list(\"vocabulary\", desc=\"user vocabulary\")\n\nctx = Context()\n\n# dictate.word_map is used by actions.dictate.replace_words to rewrite words\n# Talon recognized. Entries in word_map don't change the priority with which\n# Talon recognizes some words over others.\nctx.settings[\"dictate.word_map\"] = word_map\n\n# user.vocabulary is used to explicitly add words/phrases that Talon doesn't\n# recognize. Words in user.vocabulary (or other lists and captures) are\n# \"command-like\" and their recognition is prioritized over ordinary words.\nctx.lists[\"user.vocabulary\"] = mapping_vocabulary\n","sub_path":"code/vocabulary.py","file_name":"vocabulary.py","file_ext":"py","file_size_in_byte":9623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"134304070","text":"from __future__ import absolute_import\nfrom __future__ import unicode_literals\nfrom django.test import SimpleTestCase\nfrom openpyxl import Workbook\n\nfrom corehq.util.workbook_reading.adapters.xlsx import _XLSXWorkbookAdaptor\nfrom custom.enikshay.two_b_datamigration.management.commands.import_drtb_cases import (\n ColumnMapping,\n clean_phone_number,\n clean_contact_phone_number)\n\n\nclass MockColumnMapping(ColumnMapping):\n mapping_dict = {\n \"col0\": 0,\n \"col1\": 1,\n \"col2\": 2,\n }\n required_fields = [\n \"col1\",\n ]\n\n\nclass TestMappings(SimpleTestCase):\n\n def test_missing_key(self):\n # Confirm that fetching a column that does not exist for this mapping (but does exist in another mapping)\n # returns None.\n row = self.get_row([])\n value = MockColumnMapping.get_value(\"person_name\", row)\n self.assertIsNone(value)\n\n def test_non_existent_key(self):\n # Confirm that fetching a column that does not exist for this mapping (or any other)\n # raises an exception.\n row = self.get_row([])\n with self.assertRaises(KeyError):\n MockColumnMapping.get_value(\"pesron_name\", row) # Note the typo in the column id\n\n def test_existing_key(self):\n # Confirm that fetching a column that does exist returns the right value\n row = self.get_row([\"foo\", \"bar\"])\n value = MockColumnMapping.get_value(\"col1\", row)\n self.assertEqual(value, \"bar\")\n\n def test_empty_value_in_row(self):\n\n row = self.get_row([None, \"1\"])\n value = MockColumnMapping.get_value(\"col0\", row)\n self.assertEqual(value, None)\n\n # Index out of range on the row\n value = MockColumnMapping.get_value(\"col2\", row)\n self.assertEqual(value, None)\n\n def test_required_value_missing(self):\n row = self.get_row([\"0\", None, \"2\"])\n # col1 is required\n with self.assertRaises(Exception) as cm:\n MockColumnMapping.check_for_required_fields(row)\n self.assertEqual(cm.exception.message, \"col1 is required\")\n\n def test_required_value_present(self):\n row = self.get_row([\"0\", \"1\"])\n # col1 is required\n MockColumnMapping.check_for_required_fields(row)\n\n def get_row(self, values):\n workbook = Workbook()\n worksheet = workbook.active\n worksheet.append(values)\n\n wrapped_workbook = _XLSXWorkbookAdaptor(workbook).to_workbook()\n wrapped_worksheet = wrapped_workbook.worksheets[0]\n return list(wrapped_worksheet.iter_rows())[0]\n\n\nclass TestCleaningFunctions(SimpleTestCase):\n\n def test_clean_phone_number(self):\n good_number = \"911234567890\"\n good_number_with_punc = \"+91 123-456-7890\"\n good_short_number = \"123-456-7890\"\n\n for number in (good_number, good_number_with_punc, good_short_number):\n clean_number = clean_phone_number(number)\n clean_12_number = clean_contact_phone_number(clean_number)\n self.assertEqual(clean_12_number, \"911234567890\")\n self.assertEqual(clean_number, \"1234567890\")\n\n # Confirm that clean_contact_phone_number returns none for badly formatted numbers\n self.assertIsNone(clean_contact_phone_number(\"123\"))\n # Confirm that clean_phone_number returns the number for badly formatted numbers\n self.assertEqual(clean_phone_number(\"123\"), \"123\")\n","sub_path":"custom/enikshay/tests/two_b_datamigration/test_helpers.py","file_name":"test_helpers.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"302182809","text":"\nclass MajorToIDTable:\n\n def __init__(self, db_interface):\n self.__db = db_interface\n self.__load_data()\n\n\n def major_to_id(self, major_name):\n try:\n return self.data[major_name]\n except KeyError:\n return self.__handle_new_major(major_name)\n\n\n def __load_data(self):\n with self.__db as db:\n db_data = db.select('majors', ['ID', 'major'])\n self.data = self.__reshape_cleaner_table(db_data)\n\n\n def __handle_new_major(self, major_name):\n with self.__db as db:\n db.insert('majors', {'major': major_name})\n # get the ID of the major just inserted\n major_id = db.select('majors', ['ID'], {'major': major_name})[0]['ID']\n self.data[major_name] = major_id #update in-memory data as well as source db table\n return major_id\n\n @staticmethod\n def __reshape_cleaner_table(raw_data):\n reshaped_lookup_data = {}\n for lookup_pair in raw_data:\n reshaped_lookup_data[lookup_pair['major']] = lookup_pair['ID']\n return reshaped_lookup_data\n","sub_path":"etl/Transformers/HelperClasses/MajorToIDTable.py","file_name":"MajorToIDTable.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"558917572","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport argparse\nfrom ns3gym import ns3env\nimport gym\nimport tensorflow as tf\nimport tensorflow.contrib.slim as slim\nimport numpy as np\nimport matplotlib as mpl\nimport collections\nfrom collections import deque\nimport matplotlib.pyplot as plt\nfrom tensorflow import keras\n\nfrom ns3gym import ns3env\n\n\n\nparser = argparse.ArgumentParser(description='Start simulation script on/off')\nparser.add_argument('--start',\n type=int,\n default=1,\n help='Start ns-3 simulation script 0/1, Default: 1')\nparser.add_argument('--iterations',\n type=int,\n default=1,\n help='Number of iterations, Default: 1')\nargs = parser.parse_args()\nstartSim = bool(args.start)\nmemory = deque(maxlen=2000)\n\niterationNum = int(args.iterations)\n#Initalizing memory\ndef remember(state, action, reward, next_state, done):\n memory.append((state, action, reward, next_state, done))\nport = 7020\nsimTime = 5 # seconds\nstepTime = 0.5 # seconds\nseed = 0\nsimArgs = {\"--simTime\": simTime,\n \"--stepTime\": stepTime,\n \"--testArg\": 123}\ndebug = False\n\nenv = ns3env.Ns3Env(port=port, stepTime=stepTime, startSim=startSim, simSeed=seed, simArgs=simArgs, debug=debug)\n# simpler:\n#env = ns3env.Ns3Env()\nobs=env.reset()\n\ns_size = 4\na_size = 3\nprint(a_size)\n#Creating the architecture and Intializing random weight\nmodel = keras.Sequential()\nmodel.add(keras.layers.Dense(4, input_shape=(s_size,), activation='relu'))\nmodel.add(keras.layers.Dense(128, input_shape=(s_size,), activation='relu'))\nmodel.add(keras.layers.Dense(128, input_shape=(s_size,), activation='relu'))\nmodel.add(keras.layers.Dense(3, activation='softmax'))\nmodel.compile(optimizer=tf.train.AdamOptimizer(0.001),\n loss='categorical_crossentropy',\n metrics=['accuracy'])\n\ntotal_episodes = 10\nmax_env_steps = 100\nenv._max_episode_steps = max_env_steps\n\nepsilon = 1.0 # exploration rate\nepsilon_min = 0.01\nepsilon_decay = 0.999\n\n\ntime_history = []\nrew_history = []\n#Deep q Learning \nfor e in range(total_episodes):\n\n state = env.reset() #Initializing starting state\n \n state = np.reshape(state, [1, s_size])\n rewardsum = 0\n sums=0\n for time in range(max_env_steps):\n\n # Choose action using exploration or exploitation rate\n if np.random.rand(1) < epsilon:\n action = np.random.randint(a_size)\n else:\n action = np.argmax(model.predict(state)[0])\n\n # Executing Steps\n next_state, reward, done, _ = env.step(action)\n print(\"Step\",time, \"next_state:\",next_state,\"reward:\",reward,\"done:\",done,\"Action:\",action) \n if done:\n print(\"episode: {}/{}, time: {}, rew: {}, eps: {:.2}\"\n .format(e, total_episodes, time, rewardsum, epsilon))\n break\n\n next_state = np.reshape(next_state, [1,s_size ])\n remember(state, action, reward, next_state, done)\n # Train\n target = reward\n if not done:\n target = (reward + 0.95 * np.amax(model.predict(next_state)[0]))\n\n target_f = model.predict(state)\n target_f[0][action] = target\n model.fit(state, target_f, epochs=100, verbose=0)\n \n \n state = next_state\n rewardsum += reward\n if epsilon > epsilon_min: epsilon *= epsilon_decay\n \n time_history.append(time)\n rew_history.append(rewardsum)\n\nfor n in range(2 ** s_size):\n state = [n >> i & 1 for i in range(0, 4)]\n state = np.reshape(state, [1, s_size])\n print(\"state \" + str(state) \n + \" -> prediction \" + str(model.predict(state)[0])\n )\n\nprint(model.name)\nprint(\"*************\")\nprint(model.get_weights())\nprint(\"**************\")\n\nmodel.evaluvate(memory)\n#Graph\nprint(\"Plot Learning Performance\")\nmpl.rcdefaults()\nmpl.rcParams.update({'font.size': 16})\n\nfig, ax = plt.subplots(figsize=(10,4))\nplt.grid(True, linestyle='--')\nplt.title('Learning Performance')\n#plt.plot(range(len(time_history)), time_history, label='Steps', marker=\"^\", linestyle=\":\")#, color='red')\nplt.plot( time_history,rew_history,c='r', label='Reward')\n\n\nplt.xlabel('Steps')\nplt.ylabel('Reward')\nplt.legend()\n\nplt.savefig('learning.pdf', bbox_inches='tight')\nplt.show()\nenv.close()\n","sub_path":"rl-tcp-var/cognitive-agent-v1.py","file_name":"cognitive-agent-v1.py","file_ext":"py","file_size_in_byte":4343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"79504951","text":"#coding=utf-8\nfrom socket import *\nimport struct\ndef sendRequest(string):\n l = len(string)\n nameStr = (\"!H%dsb5sb\"%l)\n return struct.pack(nameStr, 1, string, 0, \"octet\", 0)\n\ndef sendAck(code):\n nameStr = (\"!HH\")\n return struct.pack(nameStr, 4, code)\n\ndef recv(f):\n recvData, recvAddr = csocket.recvfrom(516)\n\n f.write(recvData[4:])\n\n ackCode = struct.unpack(\"!HH\",recvData[:4])[1]\n csocket.sendto(sendAck(ackCode), recvAddr)\n\n print(\"\\r已接收第%d个数据包\"%(ackCode)),\n\n if len(recvData) != 516:\n print(\"\\n全都收完了***********\")\n f.close()\n return True\n\n\nfileName = 'test.png'\ncsocket = socket(AF_INET, SOCK_DGRAM)\ncsocket.sendto(sendRequest(fileName), (\"192.168.17.89\", 69))\nf = open(fileName, \"w\")\n\nwhile True:\n resultStatus = recv(f)\n if resultStatus:\n break\n\n\n\n\n","sub_path":"13-tftp/01.tftp.py","file_name":"01.tftp.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"588885377","text":"import tensorboardX\nfrom threading import Event\nfrom queue import Queue\n\nfrom delira.logging.writer_backend import WriterLoggingBackend\n\n\nclass VisdomBackend(WriterLoggingBackend):\n \"\"\"\n A Visdom Logging backend\n \"\"\"\n\n def __init__(self, writer_kwargs: dict = None,\n abort_event: Event = None, queue: Queue = None):\n \"\"\"\n\n Parameters\n ----------\n writer_kwargs : dict\n arguments to initialize a writer\n abort_event : :class:`threading.Event`\n the abortion event\n queue : :class:`queue.Queue`\n the queue holding all logging tasks\n \"\"\"\n\n if writer_kwargs is None:\n writer_kwargs = {}\n\n super().__init__(\n tensorboardX.visdom_writer.VisdomWriter,\n writer_kwargs,\n abort_event,\n queue)\n\n @property\n def name(self):\n return \"VisdomBackend\"\n","sub_path":"docs/_api/_build/delira/logging/visdom_backend.py","file_name":"visdom_backend.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"560926847","text":"from django import forms\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.template.defaultfilters import filesizeformat\nfrom django.conf import settings\n\nfrom gallery.models import Image\n\n\nclass ImageForm(forms.ModelForm):\n\n class Meta:\n model = Image\n #fields = ('title', 'gallery', 'date_uploaded', 'image',\n # 'is_publishable', 'short_description', )\n\n def image_clean(self):\n #CONTENT_TYPES = ['image', 'video', 'audio', ]\n CONTENT_TYPES = ['image', ]\n # Accepted content types: image/gif; image/jpg; image/png\n CONTENT_SUBTYPES = ['jpg', 'png', 'gif', ]\n\n # 2.5MB - 2621440\n # 5MB - 5242880\n # 10MB - 10485760\n # 20MB - 20971520\n # 50MB - 5242880\n # 100MB 104857600\n # 250MB - 214958080\n # 500MB - 429916160\n # MB - 5242880\n\n MAX_UPLOAD_SIZE = '2621440'\n image = self.cleaned_data['image']\n # type\n content_type = content.content_type.split('/')[0]\n subtype = content.content_type.split('/')[1]\n\n if content_type in CONTENT_TYPES:\n if content._size > MAX_UPLOAD_SIZE:\n raise forms.ValidationError(_('Please keep filesize under %(bytes)s. Current filesize is %(currsize)s') %\n (filesizeformat(MAX_UPLOAD_SIZE), (filesizeformat(context._size))))\n if subtype not in CONTENT_SUBTYPES:\n raise forms.ValidationError(_('Only \\'jpg\\', \\'gif\\' and \\'png\\' image files allowed!'))\n\n else:\n raise forms.ValidationError(_('File type not supported.'))\n","sub_path":"gallery/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"435170667","text":"#!/bin/python3\r\n\r\nimport math\r\nimport os\r\nimport random\r\nimport re\r\nimport sys\r\n\r\ndef countElements(el, ar):\r\n count = 0\r\n for val in ar:\r\n if val == el:\r\n count += 1\r\n return count\r\n\r\n# Complete the sockMerchant function below.\r\ndef sockMerchant(n, ar):\r\n count = 0\r\n newAr = list(dict.fromkeys(ar))\r\n for val in newAr:\r\n count += (countElements(val, ar)//2)\r\n return count\r\nif __name__ == '__main__':\r\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\r\n\r\n n = int(input())\r\n\r\n ar = list(map(int, input().rstrip().split()))\r\n\r\n result = sockMerchant(n, ar)\r\n\r\n fptr.write(str(result) + '\\n')\r\n\r\n fptr.close()\r\n","sub_path":"SockMerchant.py","file_name":"SockMerchant.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"103511660","text":"'''\n-------------------------------------------------------\nmeal total\n-------------------------------------------------------\nAuthor: Tiro Timmers\nID: 09017000\nEmail: timm7000@mylaurier.ca\nVersion: Oct 18, 2010\n-------------------------------------------------------\ntotals and prints the total of meals\n-------------------------------------------------------\n'''\n\ndef get_price():\n \n print('For Day 1')\n breakfast=float(input('How much was breakfast? $').strip())\n lunch=float(input('How much was lunch? $').strip())\n supper=float(input('How much was supper? $').strip())\n total_break=breakfast\n total_lunch=lunch\n total_supper=supper\n \n total=breakfast+lunch+supper\n \n print(\"The total costs of meals for the day is: ${0:.2f}.\".format(total))\n \n n=input('Were you away another day (Y/N)?')\n while n == 'Y':\n bp=float(input('How much was breakfast? $').strip())\n lp=float(input('How much was lunch? $').strip())\n sp=float(input('How much was supper? $').strip())\n total=bp+lp+sp\n print(\"The total costs of meals for the day is: ${0:.2f}.\".format(total))\n \n total_break=breakfast+bp\n total_lunch=lunch+lp\n total_supper=supper+sp\n \n n=input('Were you away another day (Y/N)?')\n \n\n \n \n print('Total breakfast: ${0:.2f}'.format(total_break))\n print('Total lunch: {0:.2f}'.format(total_lunch))\n print('Total supper:{0:.2f}'.format(total_supper))\n \n total=total_break+total_lunch+total_supper \n print('the grand total is {0:.2f}'.format(total))\n","sub_path":"Lab6/src/mealtotal.py","file_name":"mealtotal.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"341310834","text":"import os\n\n\ndef experiment_params():\n \"\"\"Specifification of experiment params and model hps.\"\"\"\n exp = {}\n exp['repeat'] = 5 # Repeat each derived experiment this many times\n\n # Experiment params. All below this line need to be in lists.\n exp['experiment'] = [__file__.split(os.path.sep)[-1].split('.')[0]]\n exp['train_dataset'] = [\n 'seg_cluttered_nist_3_ix1v2_50k',\n ]\n exp['val_dataset'] = [\n 'seg_cluttered_nist_3_ix1v2_50k',\n ]\n exp['model'] = [\n # 'resnet_18',\n # 'resnet_50',\n # 'resnet_152',\n # 'unet',\n # 'seung_unet',\n # 'feedback_hgru',\n # 'feedback_hgru_mely',\n # 'feedback_hgru_fs',\n # 'feedback_hgru_fs_mely',\n # 'hgru_bn'\n 'bu_fgru',\n 'h_fgru',\n 'td_fgru_t1',\n 'td_fgru_t1_skip'\n ]\n exp['validation_period'] = [2000]\n exp['validation_steps'] = [625]\n exp['shuffle_val'] = [True] # Shuffle val data.\n exp['shuffle_train'] = [True]\n exp['save_checkpoints'] = [1]\n exp['save_activities'] = [False]\n exp['save_weights'] = [False]\n exp['save_gradients'] = [False]\n\n # Model hyperparameters\n exp['lr'] = [1e-4]\n exp['loss_function'] = ['cce_image']\n exp['score_function'] = ['accuracy']\n exp['optimizer'] = ['nadam'] # , 'adam']\n exp['train_batch_size'] = [32]\n exp['val_batch_size'] = [32]\n exp['epochs'] = [32]\n\n # Augmentations specified in lists of lists\n exp['train_augmentations'] = [[\n 'grayscale',\n 'res_image_label',\n # 'left_right',\n # 'up_down',\n 'uint8_rescale',\n 'singleton',\n 'zero_one'\n ]]\n exp['val_augmentations'] = [[\n 'grayscale',\n 'res_image_label',\n # 'left_right',\n # 'up_down',\n 'uint8_rescale',\n 'singleton',\n 'zero_one'\n ]]\n return exp\n","sub_path":"experiments/seg_nist_3_ix1v2_50k.py","file_name":"seg_nist_3_ix1v2_50k.py","file_ext":"py","file_size_in_byte":1882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"551210596","text":"# Написать программу для сложения двух полиномов разной степени.\r\n# Коэффициенты полиномов­слагаемых получать случайным образом.\r\nimport random\r\n\r\n\r\ndef input_list(count_elements):\r\n X = []\r\n for i in range(count_elements):\r\n x = random.randint(1, 9)\r\n X.append(x)\r\n return X\r\n\r\ncol_simvolovF = random.randint(3, 6)\r\nprint('col_simvolovF: ', col_simvolovF)\r\nf = input_list(col_simvolovF)\r\nprint('Заданы случайные коэффициенты полинома F')\r\nfor i in range(col_simvolovF):\r\n print('F[' , i,'] = ' , f[i], sep=' ', end=' ')\r\nprint()\r\ncol_simvolovG = random.randint(3, 6)\r\nprint('col_simvolovG: ', col_simvolovG)\r\ng = input_list(col_simvolovG)\r\n#print('G', g)\r\nprint('Заданы случайные коэффициенты полинома G')\r\nfor i in range(col_simvolovG):\r\n print('G[' , i,'] = ' , g[i], sep=' ', end=' ')\r\nh = []\r\nmin_len = col_simvolovF\r\nif col_simvolovG < min_len:\r\n min_len = col_simvolovG\r\nfor i in range(min_len):\r\n x = f[i] + g[i]\r\n h.append(x)\r\nif col_simvolovF > col_simvolovG: \r\n for i in range(min_len, col_simvolovF):\r\n h.append(f[i])\r\nelse:\r\n for i in range(min_len, col_simvolovG):\r\n h.append(g[i])\r\nprint('\\nСумма полиномов(h) = ', h)\r\nprint('-----------------')\r\nprint('%+6s ' % str(h[0]) , end='+')\r\nprint('%+6sx ' % str(h[1]) , end='+')\r\nfor i in range(2, len(h)-1):\r\n if h[i] != 0:\r\n res = str(h[i]) +'x^' + str(i)\r\n print('%+6s ' % res, end='+')\r\nprint('%+6sx^%d' % (str(h[len(h)-1]), len(h)-1))\r\n","sub_path":"Project_Polinoms_5.py","file_name":"Project_Polinoms_5.py","file_ext":"py","file_size_in_byte":1677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"72313299","text":"words = {}\nduplicated = 0 \n\nnew_kr_f = open('kr_new.txt', 'w', encoding = 'utf8')\n\nwith open('kr.txt', 'r', encoding='utf8') as f:\n line = f.readline().strip()\n while line:\n if len(line.split(' ')) > 1: \n print(f'{line} has space ')\n line = line.replace(' ', '')\n\n upper_line = line.upper() \n\n if words.get(upper_line) is None:\n words[upper_line] = 1\n new_kr_f.write(f'{upper_line}\\n')\n else: \n duplicated += 1 \n\n line = f.readline().strip() \n\nprint(f'duplicated line : {duplicated} ')\n","sub_path":"remove_duplicate_upper.py","file_name":"remove_duplicate_upper.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"313074373","text":"from __future__ import unicode_literals\n\nimport importlib\n\nfrom django.conf import settings\nfrom django.core.exceptions import ImproperlyConfigured\n\nfrom channels.http import AsgiHandler\n\n\n\"\"\"\nAll Routing instances inside this file are also valid ASGI applications - with\nnew Channels routing, whatever you end up with as the top level object is just\nserved up as the \"ASGI application\".\n\"\"\"\n\n\ndef get_default_application():\n \"\"\"\n Gets the default application, set in the ASGI_APPLICATION setting.\n \"\"\"\n try:\n path, name = settings.ASGI_APPLICATION.rsplit(\".\", 1)\n except (ValueError, AttributeError):\n raise ImproperlyConfigured(\"Cannot find ASGI_APPLICATION setting.\")\n try:\n module = importlib.import_module(path)\n except ImportError:\n raise ImproperlyConfigured(\"Cannot import ASGI_APPLICATION module %r\" % path)\n try:\n value = getattr(module, name)\n except AttributeError:\n raise ImproperlyConfigured(\"Cannot find %r in ASGI_APPLICATION module %s\" % (name, path))\n return value\n\n\nclass ProtocolTypeRouter:\n \"\"\"\n Takes a mapping of protocol type names to other Application instances,\n and dispatches to the right one based on protocol name (or raises an error)\n \"\"\"\n\n def __init__(self, application_mapping):\n self.application_mapping = application_mapping\n if \"http\" not in self.application_mapping:\n self.application_mapping[\"http\"] = AsgiHandler\n\n def __call__(self, scope):\n if scope[\"type\"] in self.application_mapping:\n return self.application_mapping[scope[\"type\"]](scope)\n else:\n raise ValueError(\"No application configured for scope type %r\" % scope[\"type\"])\n\n\nclass URLRouter:\n \"\"\"\n Routes to different applications/consumers based on the URL path.\n\n Works with anything that has a ``path`` key, but intended for WebSocket\n and HTTP. Uses Django's django.conf.urls objects for resolution -\n url() or path().\n \"\"\"\n\n def __init__(self, routes):\n self.routes = routes\n\n def __call__(self, scope):\n # Get the path\n path = scope.get(\"path\", None)\n if path is None:\n raise ValueError(\"No 'path' key in connection scope, cannot route URLs\")\n # Remove leading / to match Django's handling\n path = path.lstrip(\"/\")\n # Run through the routes we have until one matches\n for route in self.routes:\n match = route.resolve(path)\n if match is not None:\n # Add args or kwargs into the scope\n scope[\"url_route\"] = {\n \"args\": match.args,\n \"kwargs\": match.kwargs,\n }\n return match.func(scope)\n else:\n raise ValueError(\"No route found for path %r.\" % path)\n\n\nclass ChannelNameRouter:\n \"\"\"\n Maps to different applications based on a \"channel\" key in the scope\n (intended for the Channels worker mode)\n \"\"\"\n\n def __init__(self, application_mapping):\n self.application_mapping = application_mapping\n\n def __call__(self, scope):\n if \"channel\" not in scope:\n raise ValueError(\n \"ChannelNameRouter got a scope without a 'channel' key. \" +\n \"Did you make sure it's only being used for 'channel' type messages?\"\n )\n if scope[\"channel\"] in self.application_mapping:\n return self.application_mapping[scope[\"channel\"]](scope)\n else:\n raise ValueError(\"No application configured for channel name %r\" % scope[\"channel\"])\n","sub_path":"venv/lib/python3.6/site-packages/channels/routing.py","file_name":"routing.py","file_ext":"py","file_size_in_byte":3601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"17953639","text":"import os\nimport hashlib\nimport glob\nimport sys\nimport json\nimport time\nimport zlib\n\ndef get_extension(filename):\n \"\"\" Return a string\n \n Extract the extension from a filename\n \"\"\"\n \n # An extension is found, extract and lowercase the last extension.\n if filename.find('.') != -1:\n extension = filename.split('.')[-1].lower()\n #print(f + \" has \" + destDir + \" extension. Send to \" + destDir)\n\n # No extension is found, set a whitespace as the file extension.\n else:\n extension = \" \"\n #print(f + \" has not extension. Send to \" + destDir)\n\n return(extension)\n\ndef cleanup(source, level=0):\n \"\"\"\n Recursively remove all the folders in the source. Source should not contain any files.\n \"\"\"\n\n # Retrieve source content.\n source_list = os.listdir(source)\n \n # Loop over the source content if some has been found.\n if source_list is not None:\n for item in os.listdir(source):\n \n # Exit the programm, because a file has been found. It shouldn't have.\n if os.path.isdir(source+\"/\"+item) is False:\n print(\"Something was found in the directory \"+os.path.isfile(path)+\"/ while cleanup. Exiting\")\n sys.exit(3)\n \n # Recursively cleanup the directory.\n else:\n cleanup(source+\"/\"+item, level=level+1)\n\n # Remove the source as it is now empty, but the source root.\n if level > 0:\n os.rmdir(source)\n\ndef compute_crc32_chunks(file):\n \"\"\" return a checksum\n \n Checksum the file as chunks with CRC32.\n \"\"\"\n BUF_SIZE = 65536\n csum = 0\n\n # Compute the chunks hashes.\n with open(file, 'rb') as f:\n while True:\n chunk = f.read(BUF_SIZE)\n if not chunk:\n break\n csum = zlib.crc32(chunk, csum)\n\n return(csum & 0xffffffff)\n\ndef compute_crc32(file):\n \"\"\" return a checksum\n \n Checksum the file with CRC32.\n \"\"\"\n\n # Compute the chunks hashes.\n buf = open(file, 'rb').read()\n csum = zlib.crc32(buf)\n\n return(csum & 0xffffffff)\n\n\ndef compute_sha1(file):\n \"\"\" return a string\n \n Compute a sha1 hex digest for a file.\n Methology is based upon http://ow.ly/8xDs307G3nn @ranman.\n \"\"\"\n\n # Initialize SHA1 hashing.\n BUF_SIZE = 65536 # lets read stuff in 64kb chunks!\n sha1 = hashlib.sha1()\n\n # Compute the source file hash by chunks.\n with open(file, 'rb') as f:\n while True:\n data = f.read(BUF_SIZE)\n if not data:\n break\n sha1.update(data)\n\n return(sha1.hexdigest())\n\ndef check_collision(source, destination, quick):\n \"\"\" return boolean\n \n Check whether a duplicate file already exist in the destination.\n Because of the program's purpose, comparing the file size first\n will filter almost all duplicates, fast. Then in case of false\n duplicates, two checksum are computed. This prevent false positives.\n \"\"\"\n\n # Compare file sizes first.\n source_size = os.path.getsize(source)\n destination_size = os.path.getsize(destination)\n \n # Compare the file sizes:\n if source_size == destination_size:\n\n # Quick sorting enabled\n if quick is True:\n status = True\n\n # Quick sorting disabled: compute checksums.\n else:\n # Compute CRC32 checksum first.\n source_crc32 = compute_crc32_chunks(source)\n destination_crc32 = compute_crc32_chunks(destination)\n if source_crc32 == destination_crc32:\n \n # Finally compute the SHA1 hash to prevent false positives.\n source_sha1 = compute_sha1(source)\n destination_sha1 = compute_sha1(destination)\n if source_sha1 == destination_sha1:\n status = True\n else:\n status = False\n else:\n status = False\n else:\n status = False\n\n return(status)\n\ndef increment_filename(destination):\n \"\"\" Return a string\n \n Safely increment a filename to solve collision issues.\n \"\"\"\n\n # Filter all matching files to the destination.\n collided_files = glob.glob(destination+\"*\")\n # Compute the next increment for the destination file.\n n_collided_files=len(collided_files)\n n_collided_files += 1\n # Return the correct filename.\n return(destination+\".\"+str(n_collided_files))\n","sub_path":"sorthoards/hoards.py","file_name":"hoards.py","file_ext":"py","file_size_in_byte":4464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"460831874","text":"class Solution:\n def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:\n ans = []\n n = len(candidates)\n\n def dfs(idx: int, cur: int, path: List[int]):\n # 递归结束\n if cur == 0:\n # 克隆 path 并添加到 ans\n ans.append(path[:])\n return\n elif idx == n:\n return\n # 1.加入这个数字\n if candidates[idx] <= cur:\n path.append(candidates[idx])\n # idx 不变，继续考虑当前数字\n dfs(idx, cur - candidates[idx], path)\n # 消除影响\n path.pop()\n # 2.不加入这个数字，考虑下一个数字\n dfs(idx + 1, cur, path)\n\n dfs(0, target, [])\n return ans\n","sub_path":"code/ch16/16.1.1.combination-sum.py","file_name":"16.1.1.combination-sum.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"181044592","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 29 15:05:55 2019\n\n@author: austinpeel\n\"\"\"\n\nimport statsmodels.formula.api as sm\nfrom models.models import Route\n\n# Segment level analysis\ntrip = Route()\n\n#lets look at model 1\nmodel = Route.model_1()\n\n#this is the data we are using \ndf = model.model_data\n\n'''\nThis model uses business fares and govt fare to get an idea of how well the\n government is doing compared to the business fares. \n \n The most imporant variable is the interaction between fare_type and booking days\n beacuse most savings with regular booking happen outside the 14 day window\n\n'''\nresult = sm.ols(formula = \"cost_per_mile ~ fare_type + C(Year) + ticketing_adv_booking_group+ ticketing_adv_booking_group*C(fare_type, Treatment(reference='YCA')) + city_pair \",data=df).fit()\nprint(result.summary())","sub_path":"analysis/temp_analysis.py","file_name":"temp_analysis.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"379406626","text":"'''LSD Radix Sort\n\nsort the given list of integers by sorting from least digit to most significant digit'''\n\n\ndef max_digit(values):\n\t'''Add '0' to elements to make them as same digit number'''\n\n\tint_max = max(values)\n\tdigit_max = len(str(int_max))\n\tvalues = [str(v) for v in values]\n\tresult = [] # Contain converted outcomes\n\n\tfor v in values:\n\t\trequired = digit_max - len(v)\n\t\tresult.append(required * '0' + v)\n\n\treturn result\n\n\ndef radix_sort(values):\n\t'''Sort given list by using radix sort starting from least significant digit.\n\n\tOnly works for plus integers.'''\n\n\tvalues = max_digit(values)\n\tdigit_max = len(values[0])\n\ttemp = []\n\tresult = []\n\n\tfor units in range(1, digit_max +1):\n\t\t# Contain all the digits of same units into temp list\n\t\tfor i in range(len(values)):\n\t\t\tif values[i][-units] not in temp:\n\t\t\t\ttemp.append(values[i][-units])\n\t\t\ttemp.sort()\n\t\t# print(temp)\n\n\t\t# Sort values list by comparing the units\n\t\tfor t in temp:\n\t\t\tindex = 0\n\t\t\tlength = len(values)\n\t\t\twhile index < length:\n\t\t\t\t# Contain value in result list as order\n\t\t\t\tif values[index][-units] == t:\n\t\t\t\t\tresult.append(values.pop(index))\n\t\t\t\t\tlength = len(values)\n\t\t\t\telse:\n\t\t\t\t\tindex += 1\n\t\t\t\t\n\t\t# Reset all the lists for next sequence\n\t\tvalues = result\n\t\ttemp = []\n\t\tresult = []\n\n\t# Retern ordered values in integer form\n\tfor i in values:\n\t\tresult.append(int(i))\n\n\treturn result\n\n#List given\nintegers = [256, 56, 8, 2974, 13, 2972, 88] # List of integers given\n\nprint(radix_sort(integers))\n\n\n","sub_path":"python/sort/radix_sort.py","file_name":"radix_sort.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"53801372","text":"from dbhelper import DBHelper\nfrom flask import Flask\nfrom flask import render_template\nfrom flask import request\n\nfrom flask_restful import Api\n\nfrom flask_restful import Resource, reqparse, abort\nimport json\n\n\napp = Flask(__name__)\nDB = DBHelper()\n\napi = Api(app)\n\nUserids = {\n 0,\n 1,\n 2\n}\n\ndef abort_if_todo_doesnt_exist(userid):\n if userid not in Userids:\n abort(404, message=\"user's data {} doesn't exist\".format(userid))\n\nparser = reqparse.RequestParser()\nparser.add_argument('rawdata')\nparser.add_argument('whicheye')\n\nclass UserApi(Resource):\n def get(self, userid=None):\n print('userid=', userid)\n data = DB.get_rawmeasure(str(userid))\n print('abel::',data)\n res = []\n for m in data:\n d = {}\n d[m[0]] = {\n 'rawdata': m[1],\n 'patientid': m[2],\n 'whicheye': str(m[3]),\n 'createdate': str(m[4])\n }\n res.append(d)\n myresult = json.dumps(res)\n print('abel##:',myresult)\n return myresult\n\n def post(self, userid):\n # Create a new product\n args = parser.parse_args()\n print('#',args)\n print(args['rawdata'],'@@',args['whicheye'],'#',args)\n DB.add_rawmeasure(args)\n abort_if_todo_doesnt_exist(userid)\n return 201\n\n def put(self, userid):\n # Update the product with given id\n return 'This is a PUT response'\n\n def delete(self, userid):\n # Delete the product with given id\n return 'This is a DELETE response'\n\n\napi.add_resource(\n UserApi,\n '/api/user',\n '/api/user//measures',\n\n)\n\n@app.route(\"/\")\ndef home():\n try:\n data = DB.get_all_inputs()\n except Exception as e:\n print(e)\n data = None\n return render_template(\"home.html\", data=data)\n\n\n@app.route(\"/add\", methods=[\"POST\"])\ndef add():\n try:\n data = request.form.get(\"userinput\")\n print('data=',data)\n DB.add_input(data)\n except Exception as e:\n print(e)\n return home()\n\n\n@app.route(\"/clear\")\ndef clear():\n try:\n DB.clear_all()\n except Exception as e:\n print(e)\n return home()\n\nif __name__ == '__main__':\n app.run(port=5000, debug=True)\n","sub_path":"i01Flask_By_Example/ch7google_maps_for_crime/crimemap.py","file_name":"crimemap.py","file_ext":"py","file_size_in_byte":2391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"169725288","text":"\"\"\"\nRemove all elements from a linked list of integers that have value val.\n\nExample\nGiven: 1 --> 2 --> 6 --> 3 --> 4 --> 5 --> 6, val = 6\nReturn: 1 --> 2 --> 3 --> 4 --> 5\n\"\"\"\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n \n def list(self):\n nd = self\n results = [nd.val]\n while nd.next:\n nd = nd.next\n results.append(nd.val)\n return results\n\nclass Solution:\n # @param {ListNode} head\n # @param {integer} val\n # @return {ListNode}\n def removeElements(self, head, val):\n if head == None: return None\n \n ptr_head = ListNode(None)\n ptr_head.next = head\n ptr = ptr_head\n \n while ptr.next:\n \n if ptr.next.val == val:\n ptr.next = ptr.next.next\n else:\n ptr = ptr.next\n \n assert ptr.next == None\n \n return ptr_head.next\n\ndef gen_linked_list(vals):\n nodes = map(ListNode, vals)\n for i in range(len(nodes)-1):\n nodes[i].next = nodes[i+1]\n\n return nodes[0]\n\n\nsol = Solution()\n\nassert sol.removeElements(None, 6) == None\n\nlist1 = gen_linked_list([1,2,6,3,4,5,6])\nassert sol.removeElements(list1, 6).list() == [1,2,3,4,5]\n\nlist2 = gen_linked_list([6])\nassert sol.removeElements(list2, 6) == None\n\nlist3 = gen_linked_list([5, 6])\nassert sol.removeElements(list3, 6).list() == [5]\n\nlist4 = gen_linked_list([6, 5])\nassert sol.removeElements(list4, 6).list() == [5]\n\n","sub_path":"203_remove_linked_list_elements.py","file_name":"203_remove_linked_list_elements.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"540533204","text":"def plusplus(x1,y1,x2,y2):\n if x1 - x2 == y1 - y2:return True\n else:\n print(x1,y1,x2,y2)\n return False\n \ndef plusminus(x1,y1,x2,y2):\n if (x1 - x2) == (y1 - y2):return True\n else:\n print(x1,y1,x2,y2)\n return False\n \ndef diagnol(queenx,queeny,spotx,spoty):\n if plusplus(queenx,queeny,spotx,spoty) or plusminus(queenx,queeny,spotx,spoty): return True\n else: return False\n\ndef lines(y1,y2,x1,x2):\n if y1 == y2 or x1 == x:return True\n else: return False\n \n \nmyBoardLength, myQuestions = [int(i) for i in input().split()]\nmyTotalSpots = myBoardLength ** 2\nmyGrid = []\nfor i in range(myBoardLength):\n myGrid.append([False]*myBoardLength)\n\n\nfor i in range(myQuestions):\n myQueenSpot = [int(i) for i in input().split()]\n for x in range(myBoardLength):\n for y in range(myBoardLength):\n\n if myGrid[x][y] == True:\n continue\n if lines(myQueenSpot[0]-1,y,myQueenSpot[1]-1,x) or diagnol(myQueenSpot[0]-1, myQueenSpot[1]-1, x, y):\n myGrid[x][y] = True\n\n'''\n#Check the Grid\nfor i in range(6):\n print(myGrid[i])\n'''\n\nmyTaken = 0\nfor x in range(myBoardLength):\n for y in range(myBoardLength):\n if myGrid[x][y] == True:\n myTaken += 1\nprint(myTaken)\n","sub_path":"Queenscantattackeme copy.py","file_name":"Queenscantattackeme copy.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"412549689","text":"from django.urls import path, include\n\nfrom .views import BlogDetailView, BlogListView, BlogFeaturedListView, BlogFeaturedDetailView, BlogArchiveIndexView\n\n\napp_name = 'blogs'\n\nurlpatterns = [\n path('', BlogListView.as_view(), name='list'),\n path('/', BlogDetailView.as_view(), name='detail'),\n path('featured/all/', BlogFeaturedListView.as_view(), name='featured_list'),\n path('featured//', BlogFeaturedDetailView.as_view(), name='featured_detail'),\n path('archive/all', BlogArchiveIndexView.as_view(), name='archive'),\n]","sub_path":"rejuvahome/apps/blogs/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"274602083","text":"# -*- coding: utf-8 -*-\n__author__ = 'sara'\n\nimport quickfix\n\n\nclass AccReqID(quickfix.StringField):\n\t\"\"\"\n\t客户端请求的唯一标识ID\n\t\"\"\"\n\tdef __init__(self, data = None):\n\t\tif data == None:\n\t\t\tquickfix.StringField.__init__(self, 8000)\n\t\telse:\n\t\t\tquickfix.StringField.__init__(self, 8000, data)\n\n\nclass SubAccountInfoRequestType(quickfix.CharField):\n\t\"\"\"\n\t是否使用推送功能\n\t\"\"\"\n\tdef __init__(self, data = None):\n\t\tif data == None:\n\t\t\tquickfix.CharField.__init__(self, 9263)\n\t\telse:\n\t\t\tquickfix.CharField.__init__(self, 9263, data)\n","sub_path":"api-client-btcc/fix/message/field_btcc.py","file_name":"field_btcc.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"561272952","text":"import os\r\nimport sys\r\nimport webbrowser\r\n# launching the app\r\n\r\n\r\ndef launch(project, app):\r\n virtual = f'{project}-VE'\r\n setting = f'''\r\n\r\n\r\n#Additional Required Setting\r\nimport os\r\nINSTALLED_APPS+=[\"{app}\"]\r\nTEMPLATES[0][\"DIRS\"]=[os.path.join(BASE_DIR,\"templates\")]\r\nSTATICFILES_DIRS = [\r\nos.path.join(BASE_DIR, \"static\")\r\n]\r\n\r\nMEDIA_ROOT = os.path.join(BASE_DIR, \"media\")\r\nMEDIA_URL = \"/media/\"'''\r\n\r\n\r\n\r\n urls = f'''\r\nfrom django.contrib import admin\r\nfrom django.urls import path, include\r\nfrom django.conf.urls.static import static\r\nfrom django.conf import settings\r\n\r\nurlpatterns = [\r\n path('admin/', admin.site.urls),\r\n path('',include('{app}.urls')),\r\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)'''\r\n\r\n urls2 = f'''\r\nfrom . import views\r\nfrom django.urls import path\r\nurlpatterns = [\r\n path('', views.index),\r\n]\r\n '''\r\n views = '''\r\nfrom django.shortcuts import render\r\n\r\ndef index(request):\r\n return render(request, 'index.html')\r\n'''\r\n\r\n template = '''\r\n\r\n\r\n Wecome to CODEVER\r\n\r\n\r\n

Thanks to learn from CODEVER

\r\n

Hope you enjoyed the tutorial. Don't forget to rate and comment.

\r\n\r\n\r\n '''\r\n\r\n\r\n cd = os.getcwd()\r\n os.system('pip install virtualenv')\r\n os.system(f'virtualenv {virtual}')\r\n os.system('call virtual\\Scripts\\activate')\r\n os.chdir(virtual)\r\n os.system('pip install django')\r\n os.system(f'django-admin startproject {project}')\r\n os.chdir(project)\r\n os.system(f'django-admin startapp {app}')\r\n os.system('python manage.py migrate')\r\n os.system('python manage.py createsuperuser --username admin')\r\n os.chdir(os.path.join(cd,f'{virtual}/{project}'))\r\n os.mkdir(os.path.join(cd,f'{virtual}/{project}/templates'))\r\n os.mkdir(os.path.join(cd, f'{virtual}/{project}/static'))\r\n os.chdir(os.path.join(cd, f'{virtual}/{project}/{project}'))\r\n with open('settings.py', 'a+',encoding='utf-8') as f:\r\n f.write(setting)\r\n with open('urls.py','w',encoding='utf-8') as f:\r\n f.write(urls)\r\n os.chdir(f'../{app}')\r\n with open('urls.py', 'w+', encoding='utf-8') as f:\r\n f.write(urls2)\r\n with open('views.py', 'w+', encoding='utf-8') as f:\r\n f.write(views)\r\n os.chdir(os.path.join(cd, f'{virtual}/{project}/templates'))\r\n with open('index.html','w+',encoding='utf-8') as f:\r\n f.write(template)\r\n os.chdir('..')\r\n webbrowser.open('http:127.0.0.1:8000')\r\n os.system('python manage.py runserver')\r\n\r\n\r\nproject = str(input(\"Your Project Name: \"))\r\napp = str(input(\"Your App Name: \"))\r\nlaunch(project, app)\r\n","sub_path":"launch.py","file_name":"launch.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"331741096","text":"import json\nimport base64\n\nfrom django.shortcuts import render, reverse, redirect\nfrom django.http import JsonResponse\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.views.generic import (\n CreateView, ListView, View, DeleteView, UpdateView\n)\n\nfrom django.forms.models import modelform_factory\n\nfrom .models import (\n Album, Image\n)\nfrom .forms import ImageCreateForm\n\nfrom professionals.mixins import GroupRequiredMixin\n\n\nclass AlbumCreateView(View):\n def get_form(self, model, fields):\n return modelform_factory(model=model, fields=fields)\n\n def post(self, request):\n if not request.session.get('become_professional_portfolio'):\n request.session['become_professional_portfolio'] = []\n\n if request.is_ajax():\n image_form = ImageCreateForm(request.POST, request.FILES)\n if image_form.is_valid():\n if len(request.session['become_professional_portfolio']) < 5:\n with open(image_form.cleaned_data['image'], 'rb') as image_file:\n image_file = base64.b64encode(image_file.read())\n payload = {'image_file': image_file}\n request.session['become_professional_portfolio'].append(json.dumps(payload))\n data = {'is_valid': True, 'url': image_file}\n else:\n data = {'is_valid': False, 'limit_exceeded': True}\n else:\n data = {'is_valid': False}\n return JsonResponse(data)\n\n\nclass AlbumUpdateView(LoginRequiredMixin, GroupRequiredMixin, View):\n group_required = [u'professional']\n\n def get_form(self, model, fields):\n return modelform_factory(model=model, fields=fields)\n\n def get(self, request, pk):\n images_list = Image.objects.filter(album__pk=pk)\n\n images = []\n for image in images_list:\n image_form = self.get_form(model=Image, fields=('show_to',))(instance=image)\n images.append({'image': image, 'form': image_form})\n\n return render(\n self.request,\n 'portfolios/update_portfolio.html',\n {\n 'images': images,\n 'pk': pk,\n }\n )\n\n def post(self, request, pk):\n album = Album.objects.get(pk=pk)\n\n if request.is_ajax():\n image_form = self.get_form(model=Image, fields=('image',))(request.POST, request.FILES)\n if image_form.is_valid():\n if len(album.album_images.all()) < 20:\n image = image_form.save(commit=False)\n image.album = album\n image.save()\n data = {'is_valid': True, 'url': image.image.url}\n else:\n data = {'is_valid': False, 'limit_exceeded': True}\n else:\n data = {'is_valid': False}\n return JsonResponse(data)\n\n\nclass ImageDeleteView(LoginRequiredMixin, GroupRequiredMixin, DeleteView):\n model = Image\n group_required = [u'professional']\n\n def dispatch(self, request, *args, **kwargs):\n if not self.request.user.is_authenticated:\n return redirect('mzyann_home_page')\n return super(ImageDeleteView, self).dispatch(request, *args, **kwargs)\n\n def get_success_url(self):\n return reverse('portfolios_upload_images', kwargs={'pk': self.object.album.pk})\n\n\nclass ImageUpdateView(LoginRequiredMixin, GroupRequiredMixin, UpdateView):\n model = Image\n group_required = [u'professional']\n fields = ('show_to',)\n\n def dispatch(self, request, *args, **kwargs):\n if not self.request.user.is_authenticated:\n return redirect('mzyann_home_page')\n return super(ImageUpdateView, self).dispatch(request, *args, **kwargs)\n\n def get_success_url(self):\n return reverse('portfolios_upload_images', kwargs={'pk': self.object.album.pk})\n","sub_path":"portfolios/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"253414837","text":"import argparse\nimport time\nimport numpy as np\nimport pickle\nimport os\nimport sys\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torch.nn.utils.clip_grad import clip_grad_norm_\n\nfrom utils.agent import *\nfrom utils.model import Net\nfrom utils.constants import MUJOCO_ENVS, get_env_id_type, get_checkpoint_range\nfrom utils.demos import generate_demos, create_training_data\nfrom utils.dataset import LMDBDataset\n\n\n_print = print\ndef print(*args, **kwargs):\n _print(*args, **kwargs)\n sys.stdout.flush()\n\n\n# Train the network\ndef learn_return(network, optimizer, dataset, log_dir, args):\n network.train()\n #check if gpu available\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n # Assume that we are on a CUDA machine, then this should print a CUDA device:\n print(device)\n loss_criterion = nn.CrossEntropyLoss()\n\n logs = [[],[],[],[]] # (losses, epoch_losses, accuracies, magnitudes)\n\n retsymb = '\\n' if args.grid else '\\r'\n debug = True\n print_interval = 100\n\n for epoch in range(args.num_iter):\n dloader = DataLoader(dataset, shuffle=True, pin_memory=True, num_workers=8)\n\n n_correct = 0\n frames = 0\n cum_loss = 0.0\n cum_mag = 0.0\n epoch_loss = 0\n start_time = time.time()\n for i, data in enumerate(dloader):\n print_epoch = i % print_interval == 0\n\n traj_i, traj_j, labels = data\n actions_i = [traj_i[1][0].to(device)]\n actions_j = [traj_j[1][0].to(device)]\n traj_i = [traj_i[0][0].to(device)]\n traj_j = [traj_j[0][0].to(device)]\n labels = labels[0].to(device)\n\n frames += traj_i[0].shape[0] + traj_j[0].shape[0]\n\n if args.bc:\n for traj, actions in zip(traj_i+traj_j, actions_i+actions_j):\n outputs = network.bc(traj)\n loss = loss_criterion(outputs, actions.long().view(-1)).mean()\n loss.backward()\n abs = torch.Tensor([0])\n\n else:\n #forward + backward + optimize\n outputs, abs = network.forward(traj_i, traj_j, actions_i, actions_j, print_epoch)\n #outputs = outputs.unsqueeze(0)\n loss = loss_criterion(outputs, labels.long()).mean()\n if loss < 0.693:\n n_correct += 1\n loss = loss + args.l1_reg * abs\n loss.backward()\n\n\n if i % args.batch_size == 0:\n\n '''\n print('')\n print('###########################')\n _norm = []\n for p in network.parameters():\n if p.grad is not None:\n _norm += [p.grad.view(-1).detach()]\n _norm = torch.cat(_norm).norm()\n print('grad norm pre-clip: {}'.format(_norm))\n '''\n \n\n clip_grad_norm_(network.parameters(), 10)\n\n\n '''\n _norm = []\n for p in network.parameters():\n if p.grad is not None:\n _norm += [p.grad.view(-1).detach()]\n _norm = torch.cat(_norm).norm()\n print('grad norm post-clip: {}'.format(_norm))\n print(outputs.detach().cpu().numpy(), labels.detach().cpu().numpy())\n '''\n\n optimizer.step()\n optimizer.zero_grad()\n\n #print stats to see if learning\n item_loss = loss.item()\n epoch_loss += item_loss\n cum_loss += item_loss\n cum_mag += abs.item()\n # The printed loss may not be perfectly accurate but good enough?\n if print_epoch:\n #print(i)\n eps = print_interval / (time.time() - start_time)\n fps = frames / (time.time() - start_time)\n frames = 0\n if i > 0:\n cum_loss = cum_loss / print_interval\n cum_mag = cum_mag / print_interval\n print(\"epoch {}:{}/{} loss {} mag {} | eps {} fps {}\".format(epoch+1, i, len(dataset), cum_loss, cum_mag, eps, fps), end=retsymb)\n logs[0] += [cum_loss]\n logs[3] += [cum_mag]\n #print(abs_rewards)\n cum_loss = 0.0\n cum_mag = 0.0\n start_time = time.time()\n #if debug:\n # print('\\n\\n ####\\n')\n accuracy = n_correct / len(dloader)\n print('epoch {} average loss: {} average accuracy: {} '.format(epoch+1, epoch_loss / len(dataset), accuracy))\n logs[1] += [epoch_loss / len(dataset)]\n logs[2] += [accuracy]\n #'''\n for g in optimizer.param_groups:\n g['lr'] *= 0.95\n #'''\n\n #print(\"check pointing\")\n torch.save(net.state_dict(), args.model_path)\n\n with open(log_dir, 'wb') as f:\n pickle.dump(logs, f)\n print(\"finished training\")\n\n\nif __name__==\"__main__\":\n parser = argparse.ArgumentParser(description=None)\n parser.add_argument('--env_name', default='', help='Select the environment name to run, i.e. pong')\n parser.add_argument('--model_path', default='', help=\"name and location for learned model params, e.g. ./learned_models/breakout.params\")\n parser.add_argument('--resume', default=False, action='store_true', help=\"flag to resume from existing save instead of overwriting\")\n parser.add_argument('--seed', default=0, type=int, help=\"random seed for experiments\")\n parser.add_argument('--grid', default=False, action='store_true', help=\"training on grid\")\n parser.add_argument('--models_dir', default = \".\", help=\"path to directory that contains a models directory in which the checkpoint models for demos are stored\")\n parser.add_argument('--num_trajs', default = 0, type=int, help=\"number of downsampled full trajectories\")\n parser.add_argument('--num_snippets', default = 6000, type = int, help = \"number of short subtrajectories to sample\")\n parser.add_argument('--num_iter', default=50, type=int, help=\"number of epochs\")\n parser.add_argument('--lr', default=0.0001, type=float, help=\"learning rate\")\n parser.add_argument('--weight_decay', default=0.0, type=float, help=\"weight decay\")\n parser.add_argument('--l1_reg', default=0.01, type=float, help=\"l1 regularization on the magnitudes of the mean Q/advantage values\")\n parser.add_argument('--batch_size', default=32, type=int, help=\"number of (*sequentially backpropped*) samples between weight updates\")\n parser.add_argument('--bc', default=False, action='store_true', help='train bc objective instead of no-trex')\n parser.add_argument('--data_only', default=False, action='store_true', help=\"don't train, just collect and prep data\")\n\n args = parser.parse_args()\n\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n print(device)\n\n env_name = args.env_name\n env_id, env_type = get_env_id_type(env_name)\n\n if args.seed == 0:\n args.seed = int((time.time()%1)*100000)\n torch.manual_seed(args.seed)\n np.random.seed(args.seed)\n tf.set_random_seed(args.seed)\n\n os.makedirs('learned_models', exist_ok=True)\n id = '_s={}_t={}_{}_{}'.format(args.num_snippets, args.num_trajs, 'bc' if args.bc else 'trax', args.seed)\n model_name = args.env_name + id\n args.model_path = 'learned_models/' + model_name + '.params'\n log_path = 'logs/' + model_name + '.log'\n os.makedirs('logs', exist_ok=True)\n\n print(args)\n print(\"Training reward for\", env_id)\n num_trajs = args.num_trajs\n num_snippets = args.num_snippets\n min_snippet_length = 100 # 50 # min length of trajectory for training comparison\n max_snippet_length = 500 # 100\n\n env = make_vec_env(env_id, env_type, 1, args.seed,\n wrapper_kwargs={\n 'clip_rewards':False,\n 'episode_life':False,\n })\n\n\n env = VecFrameStack(env, 4)\n\n stochastic = True\n agent = PPO2Agent(env, env_type, stochastic)\n\n checkpoint_range = get_checkpoint_range(env_name, demo=True)\n\n generate_demos(env, env_name, agent, args.models_dir, checkpoint_range, episodes_per_checkpoint=10)\n create_training_data(env_name, num_trajs, num_snippets, min_snippet_length, max_snippet_length)\n\n if not args.data_only:\n # Now we create a reward network and optimize it using the training data.\n net = Net(env.action_space.n, model_name)\n if args.resume:\n print('resuming from saved checkpoint')\n net.load_state_dict(torch.load(args.model_path))\n net.to(device)\n optimizer = optim.Adam(net.parameters(), lr=args.lr, weight_decay=args.weight_decay)\n\n with LMDBDataset('datasets/' + env_name + ('_%d_%d.lmdb' % (num_snippets, num_trajs))) as dset:\n learn_return(net, optimizer, dset, log_path, args)\n\n net.eval()\n\n #print(\"accuracy\", calc_accuracy(net, training_obs, training_labels))\n","sub_path":"atari/LearnAtariReturn.py","file_name":"LearnAtariReturn.py","file_ext":"py","file_size_in_byte":9237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"313701373","text":"from crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Field, Div, Layout, HTML, ButtonHolder, Submit\n\nfrom django import forms\n\nclass CustomFormHelperMixin(object):\n \"\"\"\n CustomMixin Derivative of Crispy-Form\n Use as default configuration/Layout for Forms\n\n Please edit\n components-select2.min.js\n \"Select a State\"\n \"\"\"\n @property\n def helper(self):\n self._helper = FormHelper()\n self._helper.form_class = 'form-horizontal'\n self._helper.label_class = 'col-sm-2 col-md-3'\n self._helper.field_class = 'col-sm-10 col-md-7'\n self._helper.form_tag = True\n\n # Rearrange fields according with field_order\n # get order list and not elements in field that are not in order field\n field_order = self.get_field_order()\n field_order = field_order + [i for i in self.fields if i not in field_order]\n\n # create a list of Field() for actual Layout\n _fields = []\n for key in field_order:\n value = self.fields.get(key)\n if isinstance(value, forms.DateTimeField) or isinstance(value, forms.DateField):\n _fields.append(Field(key, template='form_inputs/date.html'))\n elif isinstance(value, forms.models.ModelMultipleChoiceField):\n _fields.append(Field(key, css_class='multi-select'))\n elif isinstance(value, forms.ChoiceField):\n _fields.append(Field(key, css_class='select2'))\n else:\n _fields.append(Field(key))\n\n left_column_fields = _fields[0:len(_fields)//2]\n right_column_fields = _fields[len(_fields)//2:]\n self._helper.layout = Layout(\n Div(\n HTML(\"\"\"

Record Form

\"\"\"),\n Div(\n Div(*left_column_fields, css_class='col-md-6'), # Even Index\n Div(*right_column_fields, css_class='col-md-6'), # Odd Index\n css_class='row'\n ),\n css_class='form-body'\n ),\n ButtonHolder(\n Div(\n Submit('submit', 'Submit', css_class='btn green'),\n css_class='col-md-offset-1 col-md-9'\n ),\n css_class='form-actions'\n )\n )\n return self._helper\n\n def get_field_order(self):\n try:\n return self._meta.field_order\n except AttributeError:\n return []\n\nclass DefaultModelForm(forms.ModelForm, CustomFormHelperMixin):\n pass\n","sub_path":"tbpc/main/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"379944409","text":"import datetime\nimport logging\nimport subprocess\nimport sys\nimport yaml\n\nfrom dothebackup import utils\nfrom dothebackup.logger import Logger\nfrom dothebackup.plugins import load_plugins\n\n\nlog = logging.getLogger(__name__)\n\n\ndef parse_config(configfile):\n \"\"\"Read config file.\n\n :param configfile: YAML config file\n :type configfile: _io.TextIOWrapper\n :returns: loaded configfile\n :rtype: dict\n \"\"\"\n return yaml.load(configfile)\n\n\ndef check_config_keys(config, key_list):\n \"\"\"Aborts if keys are not set in config.\n\n :param config: Config\n :param key_list: List of used keys\n :type config: dict\n :type key_list: list\n \"\"\"\n for key in key_list:\n if key not in config.keys():\n print('ERROR: \"{}\" is missing in the config.'.format(key))\n sys.exit(1)\n\n\ndef check_plugin(name):\n \"\"\"Aborts and throw an error if plugin is not there as defined as type\n in config.\n\n :param name: Name of plugin that is defined in the config\n :type name: str\n \"\"\"\n if name not in sys.modules.keys():\n print('ERROR: Plugin \"{}\" could not be found.'.format(name))\n sys.exit(1)\n\n\ndef builder(config, name):\n \"\"\"Builds a dict of commands.\n\n :param config: YAML config\n :param name: Name of a specific job to run\n :type config: str\n :type name: str\n :returns: A dict with all commands needed commands\n :rtype: dict\n \"\"\"\n commands = {}\n today = utils.today()\n plugins = load_plugins()\n\n for scalar, sequence in config['backup'].items():\n\n # if there is a name it will ignore the 'enabled' key\n if not name:\n\n if 'enabled' not in sequence.keys():\n print('ERROR: \"enabled\" is missing in the config.')\n sys.exit()\n\n if not sequence['enabled']:\n log.info('skipping {}'.format(scalar))\n continue\n\n # if days are in config and its not a days defined it will continue\n # the for loop\n if 'days' in sequence.keys():\n if today not in sequence['days']:\n log.info('skipping {}'.format(scalar))\n continue\n\n # if there is a name defined and its not the name of the scalar\n # it will continue the for loop\n if name and name != scalar:\n continue\n\n # check if plugin can be found\n check_plugin(sequence['type'])\n\n # add plugin commands to command dict\n commands[scalar] = plugins[sequence['type']](sequence)\n log.debug('added command: {}'.format(commands[scalar]))\n\n if name and not commands:\n print('ERROR: \"{}\" could not be found in config.'.format(name))\n sys.exit(1)\n\n return commands\n\n\ndef print_commands(commands):\n \"\"\"Prints the commands that would be used.\n\n :param commands: Command dictionary\n :type commands: dict\n \"\"\"\n for item in commands.items():\n print(item[0])\n for character in item[0]:\n print('-', end='')\n print('\\n')\n for command in item[1]:\n print(' * {}'.format(' '.join(command)))\n print('\\n')\n\n\ndef run_commands(commands, test, log_dir, log_keep):\n \"\"\"Running the commands.\n\n The actual runner. It will take the commands dictionary and run it one\n after another. There is also a test key. With this enabled it will only\n print the commands it would run.\n\n :param commands: Commands dictionary\n :param test: If test the commands only will be printed\n :param log_dir: Dictionary for logfiles\n :param log_keep: How many logs to keep from one job\n :type commands: dict\n :type test: bool\n :type log_dir: str\n :type log_keep: int\n \"\"\"\n # in test mode it will print all the commands it would run for\n # each item in the config\n if test:\n print_commands(commands)\n\n else:\n for item in commands.items():\n log.debug('item: {}'.format(item))\n name, command_list = item\n\n log.info('started item {}'.format(name))\n\n # collects the return codes of all sub commands\n return_codes = []\n\n # define logger for stdout logging\n logger = Logger(\n utils.absolutenormpath(log_dir),\n name,\n log_keep\n )\n\n # be sure that there is the log dir\n logger.create_log_dir()\n\n # roate logfiles\n logger.rotate()\n\n # run through commands\n starting_time = datetime.datetime.now()\n for command in command_list:\n\n # create process\n command = ' '.join(command)\n log.debug('command: {}'.format(command))\n proc = subprocess.Popen(\n command,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n shell=True\n )\n log.debug('done with command')\n\n # write logfile\n log.debug('write to logfile')\n with logger.logfile() as logfile:\n for line in proc.stdout:\n logfile.write(line.decode('utf-8', 'replace'))\n\n proc.wait()\n log.debug('done writing logfile')\n\n # store returncode\n returncode = proc.returncode\n log.debug('returncode: {}'.format(returncode))\n return_codes.append(returncode)\n\n # write exit code\n code = 0\n for exitcode in return_codes:\n if exitcode != 0:\n code = 1\n log.debug('exitcode: {}'.format(code))\n\n log.debug('write metadata')\n with logger.logfile() as logfile:\n\n finishing_time = datetime.datetime.now()\n logfile.write('Finished at: {}\\n'.format(\n finishing_time.strftime(\"%Y-%m-%d %H:%M\"))\n )\n\n logfile.write(\n 'Total runtime: {} seconds.\\n'.format(\n (finishing_time - starting_time).total_seconds())\n )\n\n logfile.write('Exit code: {}\\n'.format(code))\n log.debug('metadata done')\n\n log.info('done with item {}'.format(name))\n\n\ndef get_started(configfile, name, test):\n \"\"\"The entrypoint for the UI.\n\n This is used to get everything started up. It will read the config,\n check the keys, build the command dictionary and run them.\n\n :param configfile: The config file\n :param name: A name of a specific job\n :param test: Switch for only printing the commands\n :type configfile: _io.TextIOWrapper\n :type name: str\n :type test: bool\n \"\"\"\n log.info('dothebackup starting')\n\n # read config\n log.info('parse config')\n config = parse_config(configfile)\n\n # if backup and log_dir is not in config it will abort\n log.info('check config')\n check_config_keys(config, ['backup', 'logs'])\n check_config_keys(config['logs'], ['dir', 'keep'])\n\n # get everything started\n log.info('build commands')\n commands = builder(config, name=name)\n\n log.info('run commands')\n run_commands(\n commands,\n test=test,\n log_dir=config['logs']['dir'],\n log_keep=config['logs']['keep']\n )\n log.info('dothebackup done')\n","sub_path":"dothebackup/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":7412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"250193114","text":"# USAGE EXAMPLE\n# python train_incremental.py --csv train.csv -n 50176\n\n# import the necessary packages\nimport pickle\nfrom creme.linear_model import LogisticRegression\nfrom creme import optim\nfrom creme import model_selection\nfrom creme.preprocessing import StandardScaler\nfrom creme.compose import Pipeline\nfrom creme.metrics import Accuracy\nfrom creme import stream\nimport argparse\n\n# construct the argument parser and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-c\", \"--csv\", required=True,\n\thelp=\"path to features CSV file\")\nap.add_argument(\"-n\", \"--cols\", type=int, required=True,\n\thelp=\"# of feature columns in the CSV file (excluding class column\")\nargs = vars(ap.parse_args())\n\n# construct our data dictionary which maps the data types of the\n# columns in the CSV file to built-in data types\nprint(\"[INFO] building column names...\")\ntypes = {\"feat_{}\".format(i): float for i in range(0, args[\"cols\"])}\ntypes[\"class\"] = int\n\n# create a CSV data generator for the extracted Keras features\ndataset = stream.iter_csv(args[\"csv\"], target=\"class\", converters=types)\n# construct our pipeline (maybe set to .0000003)\nmodel = Pipeline(StandardScaler(), LogisticRegression(optimizer=optim.SGD(.0000001)))\n\n# initialize our metric\nprint(\"[INFO] starting training...\")\nmetric = Accuracy()\n\n\n# loop over the dataset\nfor (i, (X, y)) in enumerate(dataset):\n\t# make predictions on the current set of features, train the\n\t# model on the features, and then update our metric\n\tpreds = model.predict_one(X)\n\tmodel = model.fit_one(X, y)\n\tmetric = metric.update(y, preds)\n\tprint(\"INFO] update {} - {}\".format(i, metric))\n\tif i == 2500:\n\t\tbreak\n# show the accuracy of the model\nprint(\"[INFO] final - {}\".format(metric))\n\nprint(\"[INFO] saving model...\")\nf = open(\"model4.cpickle\", \"wb\")\nf.write(pickle.dumps(model))\nf.close()\n","sub_path":"CNN_TransferLearn_Train/output/train_incremental.py","file_name":"train_incremental.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"128932690","text":"import random\nimport unittest\n\nfrom curveball.parutils import random_slice\n\n\nclass MyTestCase(unittest.TestCase):\n def test_random_slice(self):\n \"\"\"\n Each element belongs to only one slice,\n The number of slices is fixed,\n At max, one slice has an odd number of elements\n \"\"\"\n random.seed(0)\n adjacency_list = [\n (1, {2, 3, 6, 8}),\n (2, {5, 8, 9, 10}),\n (3, {1, 4, 6, 8}),\n (4, {2, 7, 8, 9, 10}),\n (5, {1, 2, 4, 7, 9}),\n (6, {3, 7}),\n (7, {8}),\n (8, {1, 2, 4, 5, 6, 8}),\n (9, {1, 5, 10}),\n (10, {1, 4, 8})\n ]\n\n slices = random_slice(adjacency_list, n=3)\n\n # test number of slices\n self.assertEqual(len(slices), 3)\n\n # test each slice contains element\n for x in adjacency_list:\n self.assertEqual(sum([x in slices[0], x in slices[1], x in slices[2]]), 1)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/unit/curveball/core/test_random_slice.py","file_name":"test_random_slice.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"644406589","text":"import oneflow as flow\nimport argparse\nimport numpy as np\nimport os\nimport time\nfrom tqdm import tqdm\n\nimport sys\n\nsys.path.append(\".\")\nfrom model.alexnet import alexnet\nfrom utils.ofrecord_data_utils import OFRecordDataLoader\n\n\ndef _parse_args():\n parser = argparse.ArgumentParser(\"flags for train alexnet\")\n parser.add_argument(\n \"--save_checkpoint_path\",\n type=str,\n default=\"./checkpoints\",\n help=\"save checkpoint root dir\",\n )\n parser.add_argument(\n \"--load_checkpoint\", type=str, default=\"\", help=\"load checkpoint\"\n )\n parser.add_argument(\n \"--ofrecord_path\", type=str, default=\"./ofrecord/\", help=\"dataset path\"\n )\n # training hyper-parameters\n parser.add_argument(\n \"--learning_rate\", type=float, default=0.001, help=\"learning rate\"\n )\n parser.add_argument(\"--mom\", type=float, default=0.9, help=\"momentum\")\n parser.add_argument(\"--epochs\", type=int, default=10, help=\"training epochs\")\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"train batch size\"\n )\n parser.add_argument(\"--val_batch_size\", type=int, default=32, help=\"val batch size\")\n parser.add_argument(\n \"--results\", type=str, default=\"./results\", help=\"tensorboard file path\"\n )\n parser.add_argument(\"--tag\", type=str, default=\"default\", help=\"tag of experiment\")\n parser.add_argument(\n \"--print_interval\", type=int, default=10, help=\"print info frequency\"\n )\n return parser.parse_args()\n\n\ndef setup(args):\n train_data_loader = OFRecordDataLoader(\n ofrecord_root=args.ofrecord_path,\n mode=\"train\",\n dataset_size=9469,\n batch_size=args.train_batch_size,\n )\n\n val_data_loader = OFRecordDataLoader(\n ofrecord_root=args.ofrecord_path,\n mode=\"val\",\n dataset_size=3925,\n batch_size=args.val_batch_size,\n )\n\n criterion = flow.nn.CrossEntropyLoss()\n\n # model setup\n eager_model = alexnet()\n graph_model = alexnet()\n graph_model.load_state_dict(eager_model.state_dict())\n\n eager_model.to(\"cuda\")\n graph_model.to(\"cuda\")\n # optimizer setup\n eager_optimizer = flow.optim.SGD(\n eager_model.parameters(), lr=args.learning_rate, momentum=args.mom\n )\n graph_optimizer = flow.optim.SGD(\n graph_model.parameters(), lr=args.learning_rate, momentum=args.mom\n )\n\n # criterion setup\n criterion = flow.nn.CrossEntropyLoss()\n criterion = criterion.to(\"cuda\")\n\n class ModelTrainGraph(flow.nn.Graph):\n def __init__(self):\n super().__init__()\n self.graph_model = graph_model\n self.criterion = criterion\n self.add_optimizer(graph_optimizer)\n\n def build(self, image, label):\n logits = self.graph_model(image)\n loss = self.criterion(logits, label)\n loss.backward()\n return loss\n\n class ModelEvalGraph(flow.nn.Graph):\n def __init__(self):\n super().__init__()\n self.graph_model = graph_model\n\n def build(self, image):\n with flow.no_grad():\n logits = self.graph_model(image)\n predictions = logits.softmax()\n return predictions\n\n model_train_graph = ModelTrainGraph()\n model_eval_graph = ModelEvalGraph()\n\n dic = {\n \"train_dataloader\": train_data_loader,\n \"val_dataloader\": val_data_loader,\n \"eager\": [eager_model, eager_optimizer, criterion],\n \"graph\": [graph_model, model_train_graph, model_eval_graph],\n }\n\n return dic\n\n\nclass Trainer(object):\n def __init__(self, args):\n super().__init__()\n self.graph_losses = []\n self.eager_losses = []\n\n self.graph_acc = []\n self.eager_acc = []\n\n self.graph_train_step_time_list = []\n self.eager_train_step_time_list = []\n\n self.graph_train_epoch_time_list = []\n self.eager_train_epoch_time_list = []\n\n self.graph_eval_epoch_time_list = []\n self.eager_eval_epoch_time_list = []\n\n self.eager_graph_model_diff_list = []\n\n self.graph_train_total_time = 0.0\n self.eager_train_total_time = 0.0\n\n self.graph_eval_total_time = 0.0\n self.eager_val_total_time = 0.0\n\n self.args = args\n\n def compare_eager_graph(self, compare_dic):\n\n train_data_loader = compare_dic[\"train_dataloader\"]\n val_data_loader = compare_dic[\"val_dataloader\"]\n eager_model, eager_optimizer, criterion = compare_dic[\"eager\"]\n graph_model, model_train_graph, model_eval_graph = compare_dic[\"graph\"]\n\n all_samples = len(val_data_loader) * self.args.val_batch_size\n print_interval = self.args.print_interval\n\n print(\"start training\")\n for epoch in range(self.args.epochs):\n # train\n eager_model.train()\n graph_model.train()\n start_training_time = time.time()\n total_graph_iter_time, total_eager_iter_time = 0, 0\n\n for b in range(len(train_data_loader)):\n image, label = train_data_loader()\n image = image.to(\"cuda\")\n label = label.to(\"cuda\")\n\n # oneflow graph train\n graph_iter_start_time = time.time()\n graph_loss = model_train_graph(image, label)\n graph_loss.numpy() # for synchronize CPU and GPU, get accurate running time\n graph_iter_end_time = time.time()\n\n # oneflow eager train\n eager_iter_start_time = time.time()\n logits = eager_model(image)\n eager_loss = criterion(logits, label)\n eager_loss.backward()\n eager_optimizer.step()\n eager_optimizer.zero_grad()\n eager_loss.numpy() # for synchronize CPU and GPU, get accurate running time\n eager_iter_end_time = time.time()\n\n model_param_diff = compare_model_params(eager_model, model_train_graph)\n self.eager_graph_model_diff_list.append(model_param_diff)\n\n # get time\n graph_iter_time = graph_iter_end_time - graph_iter_start_time\n eager_iter_time = eager_iter_end_time - eager_iter_start_time\n total_graph_iter_time += graph_iter_time\n total_eager_iter_time += eager_iter_time\n\n if b % print_interval == 0:\n gl, el = graph_loss.numpy(), eager_loss.numpy()\n print(\n \"epoch {} train iter {} ; graph loss {} eager loss {}; graph train time: {} eager train time {}\".format(\n epoch, b, gl, el, graph_iter_time, eager_iter_time\n )\n )\n self.graph_losses.append(gl)\n self.graph_train_step_time_list.append(graph_iter_time)\n self.eager_losses.append(el)\n self.eager_train_step_time_list.append(eager_iter_time)\n\n end_training_time = time.time()\n self.graph_train_epoch_time_list.append(\n end_training_time - start_training_time - total_eager_iter_time\n )\n self.eager_train_epoch_time_list.append(\n end_training_time - start_training_time - total_graph_iter_time\n )\n print(\"epoch %d train done, start validation\" % epoch)\n\n # validate\n eager_model.eval()\n graph_model.eval()\n graph_correct, eager_correct = 0.0, 0.0\n eval_start_time = time.time()\n total_graph_infer_time, total_eager_infer_time = 0, 0\n for b in tqdm(range(len(val_data_loader))):\n image, label = val_data_loader()\n image = image.to(\"cuda\")\n\n # graph val\n graph_infer_time = time.time()\n predictions = model_eval_graph(image)\n graph_preds = predictions.numpy()\n graph_clsidxs = np.argmax(graph_preds, axis=1)\n total_graph_infer_time += time.time() - graph_infer_time\n\n # eager val\n eager_infer_time = time.time()\n with flow.no_grad():\n logits = eager_model(image)\n predictions = logits.softmax()\n eager_preds = predictions.numpy()\n eager_clsidxs = np.argmax(eager_preds, axis=1)\n total_eager_infer_time += time.time() - eager_infer_time\n\n label_nd = label.numpy()\n for i in range(self.args.val_batch_size):\n if graph_clsidxs[i] == label_nd[i]:\n graph_correct += 1\n if eager_clsidxs[i] == label_nd[i]:\n eager_correct += 1\n eval_end_time = time.time()\n self.graph_eval_epoch_time_list.append(\n eval_end_time - eval_start_time - total_eager_infer_time\n )\n self.eager_eval_epoch_time_list.append(\n eval_end_time - eval_start_time - total_graph_infer_time\n )\n graph_top1_acc, eager_top1_acc = (\n graph_correct / all_samples,\n eager_correct / all_samples,\n )\n self.graph_acc.append(graph_top1_acc)\n self.eager_acc.append(eager_top1_acc)\n print(\n \"epoch %d, graph top1 val acc: %f, eager top1 val acc: %f\"\n % (epoch, graph_top1_acc, eager_top1_acc)\n )\n\n def save_report(self,):\n print(\"***** Save Report *****\")\n # folder setup\n report_path = os.path.join(self.args.results)\n os.makedirs(report_path, exist_ok=True)\n\n # calculate absolute loss difference\n abs_loss_diff = abs(np.array(self.eager_losses) - np.array(self.graph_losses))\n\n # calculate losses linear correlation\n loss_corr = calc_corr(self.eager_losses, self.graph_losses)\n\n # calculate accuracy linear correlation\n acc_corr = calc_corr(self.eager_acc, self.graph_acc)\n\n # training time compare\n train_time_compare = time_compare(\n self.graph_train_epoch_time_list, self.eager_train_epoch_time_list\n )\n\n # validate time compare\n val_time_compare = time_compare(\n self.graph_eval_epoch_time_list, self.eager_eval_epoch_time_list\n )\n\n # eager graph model diff compare\n model_diff_compare = np.array(self.eager_graph_model_diff_list)\n\n # save report\n save_path = os.path.join(report_path, \"check_report.txt\")\n writer = open(save_path, \"w\")\n writer.write(\"Check Report\\n\")\n writer.write(\"Model: AlexNet\\n\")\n writer.write(\"Check Results Between Eager Model and Graph Model\\n\")\n writer.write(\"=================================================\\n\")\n writer.write(\"Loss Correlation: %.4f\\n\\n\" % loss_corr)\n writer.write(\"Max Loss Difference: %.4f\\n\" % abs_loss_diff.max())\n writer.write(\"Min Loss Difference: %.4f\\n\" % abs_loss_diff.min())\n writer.write(\n \"Loss Difference Range: (%.4f, %.4f)\\n\\n\"\n % (abs_loss_diff.min(), abs_loss_diff.max())\n )\n writer.write(\n \"Model Param Difference Range: (%.4f, %.4f)\\n\\n\"\n % (model_diff_compare.min(), model_diff_compare.max())\n )\n writer.write(\"Accuracy Correlation: %.4f\\n\\n\" % acc_corr)\n writer.write(\n \"Train Time Compare: %.4f (Eager) : %.4f (Graph)\\n\\n\"\n % (1.0, train_time_compare)\n )\n writer.write(\n \"Val Time Compare: %.4f (Eager) : %.4f (Graph)\" % (1.0, val_time_compare)\n )\n writer.close()\n print(\"Report saved to: \", save_path)\n\n def save_result(self,):\n # create folder\n training_results_path = os.path.join(self.args.results, self.args.tag)\n os.makedirs(training_results_path, exist_ok=True)\n print(\"***** Save Results *****\")\n save_results(\n self.graph_losses, os.path.join(training_results_path, \"graph_losses.txt\")\n )\n save_results(\n self.eager_losses, os.path.join(training_results_path, \"eager_losses.txt\")\n )\n\n save_results(\n self.graph_acc, os.path.join(training_results_path, \"graph_acc.txt\")\n )\n save_results(\n self.eager_acc, os.path.join(training_results_path, \"eager_acc.txt\")\n )\n\n save_results(\n self.graph_train_step_time_list,\n os.path.join(training_results_path, \"graph_train_step_time_list.txt\"),\n )\n save_results(\n self.eager_train_step_time_list,\n os.path.join(training_results_path, \"eager_train_step_time_list.txt\"),\n )\n\n save_results(\n self.graph_train_epoch_time_list,\n os.path.join(training_results_path, \"graph_train_epoch_time_list.txt\"),\n )\n save_results(\n self.eager_train_epoch_time_list,\n os.path.join(training_results_path, \"eager_train_epoch_time_list.txt\"),\n )\n\n save_results(\n self.graph_eval_epoch_time_list,\n os.path.join(training_results_path, \"graph_eval_epoch_time_list.txt\"),\n )\n save_results(\n self.eager_eval_epoch_time_list,\n os.path.join(training_results_path, \"eager_eval_epoch_time_list.txt\"),\n )\n\n save_results(\n self.eager_graph_model_diff_list,\n os.path.join(training_results_path, \"eager_graph_model_diff_list.txt\"),\n )\n\n print(\"Results saved to: \", training_results_path)\n\n\ndef compare_model_params(eager_model, graph_model):\n num_params = len(eager_model.state_dict().keys())\n sum_diff = 0.0\n for key in eager_model.state_dict():\n mean_single_diff = (\n (\n eager_model.state_dict()[key]\n - graph_model.graph_model.state_dict()[key]._origin\n )\n .abs()\n .mean()\n )\n sum_diff += mean_single_diff\n mean_diff = float(sum_diff.numpy() / num_params)\n return mean_diff\n\n\ndef save_results(training_info, file_path):\n writer = open(file_path, \"w\")\n for info in training_info:\n writer.write(\"%f\\n\" % info)\n writer.close()\n\n\n# report helpers\ndef square(lst):\n res = list(map(lambda x: x ** 2, lst))\n return res\n\n\n# calculate correlation\ndef calc_corr(a, b):\n E_a = np.mean(a)\n E_b = np.mean(b)\n E_ab = np.mean(list(map(lambda x: x[0] * x[1], zip(a, b))))\n\n cov_ab = E_ab - E_a * E_b\n\n D_a = np.mean(square(a)) - E_a ** 2\n D_b = np.mean(square(b)) - E_b ** 2\n\n σ_a = np.sqrt(D_a)\n σ_b = np.sqrt(D_b)\n\n corr_factor = cov_ab / (σ_a * σ_b)\n return corr_factor\n\n\ndef time_compare(a, b):\n return np.divide(a, b).mean()\n\n\nif __name__ == \"__main__\":\n args = _parse_args()\n trainer = Trainer(args)\n compare_dic = setup(args)\n print(\"init done\")\n trainer.compare_eager_graph(compare_dic)\n del compare_dic\n\n # save results\n trainer.save_result()\n trainer.save_report()\n","sub_path":"alexnet/check/check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":15212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"417031986","text":"from bs4 import BeautifulSoup\n\ncolumns = [\"osoby_id\", \"login\", \"jmeno\", \"prijmeni\", \"heslo\", \"email\", \"oddeleni_id\"]\n\nhtml_doc = open('xkremece_osoby.html')\n\nsoup = BeautifulSoup(html_doc, 'html.parser')\n\ntrtags = soup.find_all('tr')\n\nwith open('osoby.sql', 'w') as out:\n out.write('INSERT INTO `osoby` (`osoby_id`, `login`, `jmeno`, `prijmeni`, `heslo`, `email`, `oddeleni_id`) VALUES \\n')\n for person in trtags:\n personDataTags = person.findChildren('td')\n personDataList = list(personDataTags)\n print(\"Osoba:\")\n out.write('(')\n for dataIndex in range(0, len(personDataList)):\n #print(type(personDataList[dataIndex].string))\n if isinstance(personDataList[dataIndex].string, str):\n print(\" \" + columns[dataIndex] + \": \" + personDataList[dataIndex].string)\n if dataIndex == (len(personDataList) - 1):\n out.write('\"' + personDataList[dataIndex].string + '\"')\n else:\n out.write('\"' + personDataList[dataIndex].string + '\", ')\n else:\n if dataIndex == (len(personDataList) - 1):\n out.write('NULL')\n else:\n out.write('NULL, ')\n #print(\" \" + columns[dataIndex] + \": \" + \"\")\n out.write('), \\n')\n #print(\"Person:\")\n #print(\" ID:\", mylist[0].string)\n #ids.append(mylist[0].string)\n #print(\" Value:\", mylist[1].string)\n #values.append(mylist[1].string)\n #print(\" Login:\", mylist[2].string)","sub_path":"osoby/osoby.py","file_name":"osoby.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"150936954","text":"import frontend, backend, common\nimport argparse, os\n\ndef main():\n project_list = common.get_project_list()\n\n parser = argparse.ArgumentParser()\n\n \n parser.add_argument(\"-rc\", \"--recompute_clusters\", action=\"store_true\", help=\"recompute clustering for selected projects\")\n parser.add_argument(\"-c\", \"--cluster\", type=str, help=\"path to the json file that contains clustering information\")\n parser.add_argument(\"-g\", \"--graph\", action=\"store_true\", help=\"set to regenerate graphs from the programs\")\n parser.add_argument(\"-d\", \"--dir\", type=str, required=True, help=\"The output directory\")\n parser.add_argument(\"-p\", \"--projects\", type=str, help=\"A comma separated list of projects to work with.\")\n args = parser.parse_args()\n\n if args.projects:\n arg_projects = args.projects.split(',')\n project_list = [project for project in project_list if project in arg_projects]\n\n common.mkdir(args.dir)\n kernel_dir = \"kernel_directory\"\n common.mkdir(kernel_dir)\n\n backend.run(project_list, args, kernel_dir)\n print(\"\\n********* END OF BACKEND **********\\n\")\n frontend.run(project_list, args, kernel_dir)\n\nif __name__ == '__main__':\n main()\n","sub_path":"experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"310514336","text":"\"\"\"\nn个小朋友���一圈，小朋友从1~n编号。顺时针方向123...n12\n游戏开始从1号开始顺时针报数，每个小朋友报上个小朋友数+1\n若一个小朋友的数为k的倍数或其个位数为k，则该小朋友出去，不再参加以后的报数\n当游戏中只剩下一个小朋友的时候该小朋友获胜\n\"\"\"\n\n\nclass Solution:\n def Circleplay(self, n, k):\n player = [i for i in range(n)]\n num = 0\n current = 0\n while len(player) > 1:\n num += 1\n if num%k == 0 or num%10 ==k:\n # 小朋友滚的时候不需要移动current\n del player[current]\n current = current % len(player)\n else:\n current = (current+1) % len(player)\n return player[0] + 1 # 小朋友是从1开始编号\n\nif __name__ == \"__main__\":\n test_1 = (5, 2)\n test_2 = (7, 3)\n \n s = Solution()\n print(s.Circleplay(*test_1))\n print(s.Circleplay(*test_2))","sub_path":"复试笔记/慕课python习题/05_作业2.py","file_name":"05_作业2.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"295216972","text":"# -*- coding: utf-8 -*-\n\nimport logging\nimport time\n\nimport allure\nimport pytest\n\nimport api\nimport filters\nimport mapping\nfrom .steps import check_attribute\n\nLOGGER = logging.getLogger(\"test.%s\" % __name__)\n\n\n@pytest.fixture(scope=\"module\")\ndef subscription(\n close_juniper_mac: mapping.SiteIndex\n) -> mapping.SubscriptionIdGuid:\n subscription_: mapping.SubscriptionId = close_juniper_mac.subscriptions[0]\n subscription_guid = api.crm.subscription.get_subscription_guid(\n subscription_.subscription_id\n )\n if subscription_guid:\n return mapping.SubscriptionIdGuid(\n subscription_.subscription_id, subscription_guid\n )\n else:\n pytest.skip(\"No subscription in CRM\")\n\n\ndef filter_crm() -> filters.crm.CrmFilter:\n filter_ = filters.crm.CrmFilter()\n filter_status = filters.crm.CrmStatus()\n filter_.set_filter(filter_status)\n return filter_\n\n\ndef filter_onyma() -> filters.onyma.OnymaFilter:\n filter_ = filters.onyma.OnymaFilter()\n filter_status = filters.onyma.OnymaStatus()\n filter_.set_filter(filter_status)\n return filter_\n\n\n@allure.step(\"Получаем статус из CRM\")\ndef get_crm_status(subscription_guid: mapping.GUID) -> str:\n result = api.crm.subscription.get_attribute(\n subscription_guid, \"gm_servicestatus\"\n )\n allure.attach(result, attachment_type=allure.attachment_type.TEXT)\n return result\n\n\n@allure.step(\"Получаем статус из Onyma\")\ndef get_onyma_status(subscription_id: int) -> str:\n result = api.onyma.subscription.get_status(subscription_id)\n allure.attach(result, attachment_type=allure.attachment_type.TEXT)\n return result\n\n\n@allure.title(\"Juniper MAC. Тест синхронизации закрытия подключки\")\n@pytest.mark.subscription\ndef test_status(subscription: mapping.SubscriptionIdGuid,):\n api.onyma.subscription.set_status(\n subscription.number, mapping.status.closed.onyma_id\n )\n LOGGER.info(\n \"Wait 5 minutes to synchronize the closure of the subscription.\"\n )\n time.sleep(300)\n\n crm_filter = filter_crm()\n crm = crm_filter.filtrate(\n \"gm_servicestatus\", get_crm_status(subscription.guid)\n )\n\n onyma_filter = filter_onyma()\n onyma = onyma_filter.filtrate(\n \"subscription_status\", get_onyma_status(subscription.number)\n )\n\n check_attribute(crm, onyma, \"gm_servicestatus\")\n","sub_path":"tests/subscriptions/test_subscription_close.py","file_name":"test_subscription_close.py","file_ext":"py","file_size_in_byte":2412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"445656704","text":"import csv\nfrom datetime import datetime, timedelta\n\nclass ProcessTwitterCSV:\n def __init__(self, csv_name):\n self.csv_name = csv_name\n self.datetime_dicts = {}\n\n self.header = ['Datetime']\n\n self.curr_dict = None\n self.curr_dt = None\n self.curr_loc = None\n self.curr_gen_loc = None\n\n self.new_csv_name = \"processed_twitter_data\"\n\n self.read()\n self.write()\n\n def read(self):\n with open(self.csv_name + \".csv\", 'r') as file:\n reader = csv.DictReader(file)\n line_count = 0\n for row in reader:\n if line_count>0:\n try:\n self.update_curr(row)\n except:\n continue\n filter_loc = self.curr_gen_loc\n self.timestamp_exist()\n self.location_exist(filter_loc)\n self.update_header(filter_loc)\n self.update_dt_dicts()\n line_count+=1\n\n def update_curr(self, row):\n self.curr_dt = self.datetime(row['Timestamp'])\n self.curr_loc = row['Location ']\n self.curr_gen_loc = row['General location']\n\n def datetime(self, ts):\n dt = datetime.strptime(ts, '%a %b %d %H:%M:%S ').replace(microsecond=0,second=0,minute=0)\n #(year, month, day, hour, minute, second, microsecond)\n if dt.month ==1:\n dt=dt.replace(year=2019)\n else:\n dt=dt.replace(year=2018)\n #print(ts, dt)\n return dt\n\n def update_header(self, loc):\n if loc not in self.header:\n self.header.append(loc)\n\n def timestamp_exist(self):\n if self.curr_dt not in self.datetime_dicts.keys():\n self.datetime_dicts[self.curr_dt]={\"Datetime\":self.curr_dt}\n self.curr_dict = {\"Datetime\":self.curr_dt}\n else:\n self.curr_dict = self.datetime_dicts[self.curr_dt]\n\n def location_exist(self, loc):\n if loc not in self.curr_dict.keys():\n self.curr_dict[loc] = 1\n else:\n self.curr_dict[loc] +=1\n\n def update_dt_dicts(self):\n self.datetime_dicts[self.curr_dt] = self.curr_dict\n\n def write(self):\n with open(self.new_csv_name + '.csv', mode='wb') as csv_file:\n writer = csv.DictWriter(csv_file, fieldnames=self.header)\n\n writer.writeheader()\n keys_list = sorted(self.datetime_dicts.keys())\n for i in range(len(self.datetime_dicts.keys())):\n # first key\n if i == 0:\n writer.writerow(self.datetime_dicts[keys_list[i]])\n else:\n last_dt = self.datetime_dicts[keys_list[i - 1]][\"Datetime\"]\n curr_dt = self.datetime_dicts[keys_list[i]][\"Datetime\"]\n last_dict = self.datetime_dicts[keys_list[i - 1]]\n # Fill in difference\n while (curr_dt - last_dt).seconds / 3600.0 != 1.0:\n # write new fill row\n next_dt = last_dt + timedelta(hours=1)\n next_dict = last_dict\n next_dict[\"Datetime\"] = next_dt\n writer.writerow(next_dict)\n # update last_dt\n last_dt = next_dt\n last_dict = next_dict\n writer.writerow(self.datetime_dicts[keys_list[i]])\n\nProcessTwitterCSV(\"twitter_data_1\")\n# read csv file\n\n# go through csv row-by-row\n# filter csv by time date\n\n# > seek country and add if not exist\n# > record pollutant value\n\n# convert date to date-time\n# write new row on csv file","sub_path":"process_data/input/twitter/process_twitter_csv.py","file_name":"process_twitter_csv.py","file_ext":"py","file_size_in_byte":3720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"558853704","text":"import sys\ndef recur(a, n):\n\tif n == 0:\n\t\treturn 0\n\tif n == 1:\n\t\tif a[0] == '-':\n\t\t\treturn 1\n\t\telse:\n\t\t\treturn 0\n\tif a[n-1] == '+' or a[n-1] == a[n-2]:\n\t\treturn recur(a, n-1)\n\telse:\n\t\treturn recur(a, n-2) + 2\ndef main():\n\tf = open(\"B_small.out\", \"w\")\n\tT = int(input())\n\tfor i in range(1, T+1):\n\t\ta = input()\n\t\tprint(\"Case #%d: %d\" % (i, recur(a, len(a))), file = f)\nmain()\n\n\n","sub_path":"solutions_5634697451274240_0/Python/JackyKuo/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"294662385","text":"# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n def sortList(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: ListNode\n \"\"\"\n if head == None:\n return None\n if head.next == None:\n return head\n quick = head\n slow = head\n prev = None\n while quick != None and quick.next != None:\n prev = slow\n quick = quick.next.next\n slow = slow.next\n prev.next = None\n #sort each half\n p1 = self.sortList(head)\n p2 = self.sortList(slow)\n #merge the half\n return self.merge(p1,p2)\n def merge(self,p1,p2):\n if p1 == None:\n return p2\n if p2 == None:\n return p1\n if p1.val < p2.val:\n p1.next = self.merge(p1.next,p2)\n return p1\n else:\n p2.next = self.merge(p1,p2.next)\n return p2\n \n \n \n ","sub_path":"148.py","file_name":"148.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"629818627","text":"####\n# assumes fastq.gz files\n###\n\n###\n# execution:\n# python 01a_trim-fastq.py /path/to/fastq.gz-files/ /path/to/ref.fa \n###\n\n###\n# imports\nfrom __future__ import division\nimport sys\nimport os\nfrom os import path as op\nfrom os import listdir as ls\nimport math\ndef fs(DIR):\n return sorted([op.join(DIR,f) for f in ls(DIR)])\nos.system('source $HOME/.bashrc')\n###\n\n\n# args\nfqdir = sys.argv[1] # path to raw fastq files\nref = sys.argv[2] # path to reference genome used for mapping (/home/lindb/scratch/ptaeda.v1.01.reduced.pseudo.fasta)\nlensh = 950\nfor i,arg in enumerate([ref,fqdir]):\n # make sure the args exist\n try:\n assert op.exists(arg)\n except AssertionError as e:\n print (\"The %s'th argument does not exist in the specified path\" % str(i))\n sys.exit(1)\n\n# # more imports and aliases (don't think I need this any more)\n# pipedir = op.dirname(op.abspath(sys.argv[0])) # this is where the git repo (pipeline) is cloned\n\n\n# make some dirs\nmsgdir = op.join(fqdir,'messages')\nshdir = op.join(fqdir,'shfiles')\nshtrimDIR = op.join(shdir,'trimmed_shfiles') # will make shdir below\ntrimDIR = op.join(fqdir,'trimmed') # outfiles\nfor d in [shtrimDIR,trimDIR,msgdir]:\n if not op.exists(d):\n os.makedirs(d)\nmfile = op.join(fqdir,'messages/msgs.txt')\n \n###\n\n\n\n###\n \n# get the fastq.gz files\nos.chdir(fqdir)\ngzfiles = [f for f in fs(fqdir) if 'R1' in f]\nlgz = len(gzfiles)\n# !echo 'found '$lgz' gz files in '$fqdir >> $fqdir'/messages/msgs.txt' # only works in jupyter notebooks :'(\n# instead of ^, do (lame/boring):\ntext = 'found %(lgz)s R1 fastq.gz files in %(fqdir)s' % locals() #(lgz, fqdir)\nprint (text)\nwith open(mfile,'w') as o:\n o.write(\"%s\\n\" % text)\n\n# match seq pairs, alert if pair not found\nseq_pairs = []\nfor f in gzfiles:\n assert 'R1' in f\n read2 = f.replace(\"_R1\",\"_R2\")\n if op.exists(read2):\n seq_pairs.append((f,read2))\n# else:\n# !echo 'no pair for '$f >> $fqdir'/messages/msgs.txt' # #iheartjupyter\n else:\n text = 'no pair for %s' % f\n with open(mfile,'a') as o:\n o.write(\"%s\\n\" % text)\nprint (\"found %s R1/R2 seq pairs\" % str(len(seq_pairs)))\nprint (\"type(len(seq_pairs)) =\",type(len(seq_pairs)))\nprint (\"type(lensh) =\",type(lensh))\nprint (\"len(seq_pairs) <= lensh?\", len(seq_pairs) <= lensh)\n# determine how many commands per sh file\nif len(seq_pairs) <= lensh:\n # one command per sh file\n ceil = 1\nelse:\n # multiple commands per sh file\n ceil = math.ceil(len(seq_pairs)/lensh)\nprint (\"ceil =\",ceil)\n \nshcount = 0\nfcount = 0\ntcount = 0\ntext = ''''''\nshfiles = []\nfor s in seq_pairs:\n# print s\n r1 = op.abspath(s[0])\n if r1.endswith(\"fastq\"):\n r1out = op.join(trimDIR,op.basename(r1).replace('.fastq','_trimmed.fastq'))\n else:\n assert r1.endswith('fastq.gz')\n r1out = op.join(trimDIR,op.basename(r1).replace('.fastq.gz','_trimmed.fastq'))\n r2 = op.abspath(s[1])\n if r2.endswith(\"fastq\"):\n r2out = op.join(trimDIR,op.basename(r2).replace('.fastq','_trimmed.fastq'))\n else:\n assert r2.endswith('fastq.gz')\n r2out = op.join(trimDIR,op.basename(r2).replace('.fastq.gz','_trimmed.fastq'))\n html = r1out.replace(\"R1\",\"\").replace(\".fastq\",\"_R1_R2_stats\")\n json = r1out.replace(\"R1\",\"\").replace(\".fastq\",\"_R1_R2\")\n logfile = r1out.replace(\"R1\",\"\").replace(\".fastq\",\"_R1_R2_stats.log\")\n shz = str(tcount).zfill(3)\n cmd = '''fastp -i %(r1)s -o %(r1out)s -I %(r2)s -O %(r2out)s -g --cut_window_size 5 --cut_mean_quality 30 --qualified_quality_phred 30 --unqualified_percent_limit 20 --n_base_limit 5 --length_required 75 -h %(html)s.html --cut_by_quality3 --thread 16 --json %(json)s.json --adapter_sequence AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC --adapter_sequence_r2 AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT > %(logfile)s\n\n\ncd $HOME/pipeline\n# once finished, map using bwa mem \npython 01b_bwa-map_rginfo_mark_build.py %(ref)s %(r1out)s %(r2out)s %(shdir)s %(shz)s \n''' % locals()\n# (r1 , r1out,\n# r2 , r2out,\n# html, json , logfile,\n# ref , r1out, r2out , shdir, str(tcount).zfill(3)\n# )\n text = text + cmd\n \n fcount += 1\n tcount += 1\n if fcount == ceil or tcount == len(seq_pairs):\n shz = str(shcount).zfill(3)\n header = '''#!/bin/bash\n#SBATCH --job-name=trim%s\n#SBATCH --time=02:59:00\n#SBATCH --mem=5000M\n#SBATCH --cpus-per-task=16\n#SBATCH --output=trim%s_%%j.out\n#SBATCH --mail-user=lindb@vcu.edu\n#SBATCH --mail-type=FAIL\n\nsource $HOME/.bashrc\n\n''' % (shz,shz)\n text = header + text\n filE = op.join(shtrimDIR,'trim_%s.sh' % shz)\n shfiles.append(filE)\n with open(filE,'w') as o:\n o.write(\"%s\" % text)\n shcount += 1\n fcount = 0\n text = ''''''\nprint ('shcount =',shcount)\n \n# qsub the files\nos.chdir(shtrimDIR) # want sbatch outfiles in same folder as sh file\nfor f in shfiles:\n## !sbatch $f # jupyter es el mejor\n os.system('sbatch %s' % f)\n#####\n","sub_path":"pipeline/01a_trim-fastq.py","file_name":"01a_trim-fastq.py","file_ext":"py","file_size_in_byte":5045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"162328307","text":"#!/usr/bin/env python\nimport sys\nfrom operator import itemgetter, attrgetter, methodcaller\n\nclass Sorter:\n\tdef __init__(self, s):\n\t\talphamask = ''\n\t\tfor c in s:\n\t\t\tif c.isalnum():\n\t\t\t\talphamask += '1'\n\t\t\telse:\n\t\t\t\talphamask += '0'\n\t\tself.lower = s.lower()\n\t\tself.mask = alphamask\n\t\tself.data = s\n\t\t\n\nif len(sys.argv) == 2:\n\tlines = []\n\twith open(sys.argv[1]) as f:\n\t\tfor line in f:\n\t\t\tlines.append(Sorter(line.rstrip()))\n\tlines.sort(key=attrgetter('lower', 'mask', 'data'))\n\tfor line in lines:\n\t\tprint(line.data)\n","sub_path":"python/sorter.py","file_name":"sorter.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"573811779","text":"import datetime as dt\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns # changes automatically the standard theme of the graphs.\n\nprint()\nprint(\"**** Pandas Data Visualization *****\")\nprint()\nprint(\"Not as nice as Seaborn, but it is quick for a test\")\nprint(\"or adhoc visualization.\")\n\n# style.use('classic') # You don't want to use this!\n\n\nprint(\"\"\"\n# Plot Types\n\nThere are several plot types built-in to pandas, most of them statistical \nplots by nature:\n\n* df.plot.area \n* df.plot.barh \n* df.plot.density \n* df.plot.hist \n* df.plot.line \n* df.plot.scatter\n* df.plot.bar \n* df.plot.box \n* df.plot.hexbin \n* df.plot.kde \n* df.plot.pie\n\nYou can also just call df.plot(kind='hist') or replace that kind argument \nwith any of the key terms shown in the list above (e.g. 'box','barh', etc..)\n\"\"\")\n\nprint()\nprint(\"Locating the CSV File 1:\")\nfile_csv1 = \"C:/dev/projects/python/\\\nPython-Data-Science-and-Machine-Learning-Bootcamp/\\\nPython-for-Data-Visualization/Pandas Built-in Data Viz/df1\"\n\ndf1 = pd.read_csv(file_csv1, index_col=0)\nprint(df1.head())\n\nprint()\nprint(\"Locating the CSV File 2:\")\nfile_csv2 = \"C:/dev/projects/python/\\\nPython-Data-Science-and-Machine-Learning-Bootcamp/\\\nPython-for-Data-Visualization/Pandas Built-in Data Viz/df2\"\n\ndf2 = pd.read_csv(file_csv2)\nprint(df2.head())\nprint()\n\nprint(\"Getting a histogram for all the values in the column 'A':\")\nprint(\"Let's see the different ways to achieve it.\")\nprint(\"Way number 1:\")\n# fig.tight_layout() problem:\n# https://stackoverflow.com/questions/9603230/how-to-use-matplotlib-tight-layout-with-figure\n(fig, ax) = plt.subplots(figsize=(16, 10), dpi=150)\nfig.tight_layout()\n\nprint(\"See that the type of object created is:\")\nprint(type(df1['A'].hist()))\ndf1['A'].hist()\nplt.show()\nprint()\n\nprint(\"Adding Matplotlib Arguments:\")\nplt.figure(figsize=(16, 10), dpi=150)\nprint(type(df1['A'].hist(bins=30, cumulative=True, color='darkblue')))\ndf1['A'].hist(bins=30, cumulative=True, color='darkblue')\nplt.show()\nprint()\n\nprint(\"Way number 2:\")\nprint(type(df1['A'].plot(kind='hist', bins=30, figsize=(16, 10))))\n# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html\ndf1['A'].plot(kind='hist', bins=30, figsize=(16, 10), legend=True,\n fontsize=4, colormap='Set2')\nplt.show()\nprint(\"\"\"\nKind can be:\nkind : str\n‘line’ : line plot (default)\n‘bar’ : vertical bar plot\n‘barh’ : horizontal bar plot\n‘hist’ : histogram\n‘box’ : boxplot\n‘kde’ : Kernel Density Estimation plot\n‘density’ : same as ‘kde’\n‘area’ : area plot\n‘pie’ : pie plot\n‘scatter’ : scatter plot\n‘hexbin’ : hexbin plot\n\"\"\")\nprint()\n\nprint(\"We can make the style look nicer if we add the library:\")\nprint(\"import seaborn as sns -> \"\n \"changes automatically the standard theme of the graphs.\")\nprint()\nprint()\n\nprint(\"The way we will stick with is:\")\nprint(\"DataFrame.plot.PlotName()\")\nprint(\"In this link you can find all the plots and details:\")\nprint(\"https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html\")\nprint()\n\nprint(\"Looking at the DataFrame df2:\")\nprint(df2.head())\nprint()\n\nprint(\"Area Plot:\")\nsubplot1 = df2['a'].plot.area(title=\"My Title\", alpha=0.7, legend=True)\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_size_inches(16, 10)\nfig1.set_dpi(150)\nplt.show()\nprint()\n\nprint(\"Area Plot 2:\")\nsubplot1 = df2.plot.area(title=\"My Title\", alpha=0.7, legend=True)\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_size_inches(16, 10)\nfig1.set_dpi(150)\nplt.show()\nprint()\n\nprint(\"Bar Plot:\")\nsubplot1 = df2.plot.bar(title=\"Bar Plot\", alpha=0.7, legend=True)\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_size_inches(16, 10)\nfig1.set_dpi(150)\nplt.show()\nprint()\n\nprint(\"Bar Plot 2:\")\nsubplot1 = df2.plot.bar(title=\"Bar Plot\", alpha=0.7, legend=True, stacked=True)\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_size_inches(16, 10)\nfig1.set_dpi(150)\nplt.show()\nprint()\n\nprint(\"Histogram Plot:\")\nsubplot1 = df2['b'].plot.hist(title=\"Histogram Plot\", bins=10,\n alpha=0.7, legend=True)\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_size_inches(16, 10)\nfig1.set_dpi(150)\nplt.show()\nprint()\n\nprint(\"Line Plot:\")\nsubplot1 = df2.plot.line(title=\"Line Plot\", alpha=0.7, legend=True,\n lw=2, linestyle='-', figsize=(16, 10))\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nplt.show()\nprint()\n\n# print(\"Line Plot v2:\")\nprint(type(df1.index))\nprint(df1.index)\ndf1['Date'] = pd.to_datetime(df1.index)\nprint(type(df1['Date']))\nprint(df1['Date'])\ndf1['Date'] = df1['Date'].map(dt.datetime.toordinal)\nprint(type(df1['Date']))\nprint(df1['Date'])\n\nsubplot1 = df1.plot.line(x=df1['Date'], y='A', title=\"Line Plot 2\", alpha=0.7,\n legend=True, lw=0.8, ls='-', marker='o', markersize=2,\n mfc='r', mec='g')\nprint(subplot1)\n\nsns.regplot(x=df1['Date'], y=df1['A'], data=df1, ax=subplot1, ci=99,\n n_boot=1000, color='darkred')\nfig1 = subplot1.get_figure()\nfig1.set_size_inches(16, 10)\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\n\nprint(\"Scatter Plot:\")\nsubplot1 = df1.plot.scatter(x='A', y='B', title=\"Scatter Plot v1\", alpha=0.7,\n legend=True, figsize=(16, 10))\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\n\nprint(\"Scatter Plot v2:\")\nsubplot1 = df1.plot.scatter(x='A', y='B', title=\"Scatter Plot v2\", alpha=0.7,\n legend=True, figsize=(16, 10),\n c=pd.to_datetime(df1.index).map(dt.datetime.toordinal),\n colormap='OrRd')\n# Instead of colormap, one can pass cmap.\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\nprint(\"Scatter Plot v3:\")\ndf1['Date'] = pd.to_datetime(df1.index).map(dt.datetime.toordinal)\nsubplot1 = df1.plot.scatter(x='Date',\n y='B', title=\"Scatter Plot v3\", alpha=0.7,\n legend=True, figsize=(16, 10),\n s=(df1['C']+1)**3)\n# Instead of colormap, one can pass cmap.\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\n\nprint(\"Box Plots v1:\")\nsubplot1 = df2.plot.box(title=\"Box Plots v1\", legend=True, figsize=(16, 10))\n\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\n\nprint(\"Box Plots v2:\")\nsubplot1 = df2.plot.box(title=\"Box Plots v2\", showfliers=True,\n showmeans=True, meanline=True, showcaps=True, notch=True,\n bootstrap=10000, legend=True, figsize=(16, 10))\n\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\n\nprint(\"For bivariate data:\")\nprint(\"Hexagonal Plots v1:\")\ndf3 = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])\nsubplot1 = df3.plot.hexbin(x='a', y='b', title=\"Hexagonal Plots v1\", gridsize=25,\n legend=True, figsize=(16, 10), cmap='OrRd')\n\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\nprint(\"KDE Plots v1:\")\nsubplot1 = df2['a'].plot.kde(title=\"KDE Plots v1\", legend=True, figsize=(16, 10))\n\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\n\nprint(\"KDE Plots v1a:\")\nsubplot1 = df2['a'].plot.density(title=\"KDE Plots v1a\", legend=True, figsize=(16, 10))\n\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\nprint(\"KDE Plots v2:\")\nsubplot1 = df2['a'].plot.kde(title=\"KDE & Histogram Plots v2\",\n legend=True, figsize=(16, 10), sharey=True)\nsubplot1 = df2['a'].plot.hist(normed=True, stacked=True, figsize=(16, 10),\n sharey=True, ax=subplot1)\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n\nprint(\"KDE Plots seaborn:\")\nfig1, ax = plt.subplots(figsize=(16,10), dpi=150)\nsns.distplot(a=df2['a'], bins=10, norm_hist=True, hist=True,\n rug=True, color='darkgreen')\n\nprint(subplot1)\nfig1 = subplot1.get_figure()\nfig1.set_dpi(150)\nfig1.tight_layout()\nplt.show()\nprint()\n","sub_path":"006 - Pandas Data Visualization/01 - Pandas Built-in Data Visualization.py","file_name":"01 - Pandas Built-in Data Visualization.py","file_ext":"py","file_size_in_byte":8383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"159514548","text":"# coding=utf-8\n\nfrom __future__ import unicode_literals, print_function\n\nimport unittest\nimport pyfacebook\n\nimport responses\n\nDEFAULT_GRAPH_URL = \"https://graph.facebook.com/\"\nDEFAULT_GRAPH_VERSION = pyfacebook.InstagramApi.VALID_API_VERSIONS[-1]\n\n\nclass InsApiTest(unittest.TestCase):\n @responses.activate\n def setUp(self):\n self.instagram_business_id = 'test'\n self.test_username = 'username'\n responses.add(\n method=responses.GET,\n url=DEFAULT_GRAPH_URL + DEFAULT_GRAPH_VERSION + '/oauth/access_token',\n json={'access_token': 'testToken'}\n )\n\n self.api = pyfacebook.InstagramApi(\n app_id='test',\n app_secret='test',\n short_token='test',\n version=DEFAULT_GRAPH_VERSION,\n timeout=1,\n interval_between_request=1,\n sleep_on_rate_limit=True,\n instagram_business_id=self.instagram_business_id\n )\n\n def testInitApiWithOutParams(self):\n self.assertRaises(\n pyfacebook.PyFacebookError,\n lambda: pyfacebook.InstagramApi()\n )\n\n @responses.activate\n def testGetUser(self):\n responses.add(\n method=responses.GET,\n url=DEFAULT_GRAPH_URL + DEFAULT_GRAPH_VERSION + '/' + self.instagram_business_id,\n json={\n u'business_discovery': {\n 'biography': 'biography',\n 'followers_count': 3748942,\n 'follows_count': 65,\n 'id': '1234567891011',\n 'ig_id': 123456789,\n 'media_count': 370,\n 'name': 'name',\n 'profile_picture_url': 'profile_picture_url',\n 'username': 'username',\n 'website': 'https://www.example.com/username/'\n },\n 'id': self.instagram_business_id\n }\n )\n\n self_info = self.api.get_user_info()\n self.assertEqual('profile_picture_url', self_info.profile_picture_url)\n\n user_info = self.api.get_user_info(username=self.test_username)\n self.assertEqual('biography', user_info.biography)\n\n user_info_json = self.api.get_user_info(username=self.test_username, return_json=True)\n self.assertEqual('1234567891011', user_info_json['business_discovery']['id'])\n\n @responses.activate\n def testGetMedia(self):\n media_id = '12345678910'\n responses.add(\n method=responses.GET,\n url=DEFAULT_GRAPH_URL + DEFAULT_GRAPH_VERSION + '/' + media_id,\n json={\n 'caption': 'Snowing.',\n 'comments_count': 1,\n 'id': media_id,\n 'ig_id': '123456789',\n 'is_comment_enabled': True,\n 'like_count': 4,\n 'media_type': 'IMAGE',\n 'media_url': 'media_url',\n 'owner': {'id': self.instagram_business_id},\n 'permalink': 'permalink',\n 'shortcode': 'BuGD8NmF4KI',\n 'timestamp': '2019-02-20T07:10:15+0000',\n 'username': 'username'\n }\n )\n\n media_info = self.api.get_media_info(media_id=media_id)\n self.assertEqual(media_id, media_info.id)\n\n media_info_json = self.api.get_media_info(media_id=media_id, return_json=True)\n self.assertEqual(media_id, media_info_json['id'])\n\n @responses.activate\n def testGetMedias(self):\n # owner request medias\n responses.add(\n method=responses.GET,\n url=DEFAULT_GRAPH_URL + DEFAULT_GRAPH_VERSION + '/' + self.instagram_business_id + '/media',\n json={\n \"data\": [\n {\n 'caption': 'caption1',\n 'comments_count': 1,\n 'id': '17861821972334188',\n 'like_count': 4,\n 'media_type': 'IMAGE',\n 'media_url': 'media_url',\n 'permalink': 'https://www.instagram.com/p/BuGD8NmF4KI/',\n 'timestamp': '2019-02-20T07:10:15+0000',\n 'username': 'ikroskun'\n },\n {\n 'caption': 'caption2',\n 'comments_count': 0,\n 'id': '17864312515295083',\n 'like_count': 0,\n 'media_type': 'IMAGE',\n 'media_url': 'media_url',\n 'permalink': 'https://www.instagram.com/p/BporjsCF6mt/',\n 'timestamp': '2018-11-01T11:13:38+0000',\n 'username': 'ikroskun'\n },\n {\n 'caption': 'caption3',\n 'comments_count': 0,\n 'id': '17924095942208544',\n 'like_count': 1,\n 'media_type': 'IMAGE',\n 'media_url': 'media_url',\n 'permalink': 'https://www.instagram.com/p/BoqBgsNl5qT/',\n 'timestamp': '2018-10-08T03:13:19+0000',\n 'username': 'ikroskun'\n }\n ],\n \"paging\": {\n \"cursors\": {\n \"after\": \"after\"\n }\n }\n }\n\n )\n # request other user medias\n responses.add(\n method=responses.GET,\n url=DEFAULT_GRAPH_URL + DEFAULT_GRAPH_VERSION + '/' + self.instagram_business_id,\n json={\n \"business_discovery\": {\n \"media\": {\n \"data\": [\n {\n 'caption': 'caption1',\n 'comments_count': 1,\n 'id': '17861821972334188',\n 'like_count': 4,\n 'media_type': 'IMAGE',\n 'media_url': 'media_url',\n 'permalink': 'https://www.instagram.com/p/BuGD8NmF4KI/',\n 'timestamp': '2019-02-20T07:10:15+0000',\n 'username': 'ikroskun'\n },\n {\n 'caption': 'caption2',\n 'comments_count': 0,\n 'id': '17864312515295083',\n 'like_count': 0,\n 'media_type': 'IMAGE',\n 'media_url': 'media_url',\n 'permalink': 'https://www.instagram.com/p/BporjsCF6mt/',\n 'timestamp': '2018-11-01T11:13:38+0000',\n 'username': 'ikroskun'\n },\n {\n 'caption': 'caption3',\n 'comments_count': 0,\n 'id': '17924095942208544',\n 'like_count': 1,\n 'media_type': 'IMAGE',\n 'media_url': 'media_url',\n 'permalink': 'https://www.instagram.com/p/BoqBgsNl5qT/',\n 'timestamp': '2018-10-08T03:13:19+0000',\n 'username': 'ikroskun'\n }\n ],\n \"paging\": {\n \"cursors\": {\n \"after\": \"after\"\n }\n }\n }\n }\n }\n )\n\n medias = self.api.get_medias(count=5, limit=3)\n self.assertEqual(len(medias), 5)\n\n medias_json = self.api.get_medias(\n username='test', since_time='2018-10-30',\n until_time='2019-02-21', return_json=True\n )\n self.assertEqual(len(medias_json), 2)\n","sub_path":"tests/instagram/test_ins_api.py","file_name":"test_ins_api.py","file_ext":"py","file_size_in_byte":8204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"28643734","text":"#!/usr/bin/env python\n\n\"\"\"\n\nPetya is a big fan of mathematics, especially its part related to fractions. Recently he learned that a fraction is called proper iff its numerator is smaller than its denominator (a < b) and that the fraction is called irreducible if its numerator and its denominator are coprime (they do not have positive common divisors except 1).\n\nDuring his free time, Petya thinks about proper irreducible fractions and converts them to decimals using the calculator. One day he mistakenly pressed addition button ( + ) instead of division button (÷) and got sum of numerator and denominator that was equal to n instead of the expected decimal notation.\n\nPetya wanted to restore the original fraction, but soon he realized that it might not be done uniquely. That's why he decided to determine maximum possible proper irreducible fraction such that sum of its numerator and denominator equals n. Help Petya deal with this problem.\n\nInput\nIn the only line of input there is an integer n (3 ≤ n ≤ 1000), the sum of numerator and denominator of the fraction.\n\nOutput\nOutput two space-separated positive integers a and b, numerator and denominator of the maximum possible proper irreducible fraction satisfying the given sum.\n\nExamples\ninput\n3\noutput\n1 2\n\ninput\n4\noutput\n1 3\n\ninput\n12\noutput\n5 7\n\n\"\"\"\n\nfrom math import *\n\ndef fraction(n):\n if n < 3:\n return (0, 0)\n\n a = n//2 - 1\n b = n//2 + 1\n if n%2 != 0:\n a += 1\n if gcd(a, b) != 1:\n a -= 1\n b += 1\n return (a, b)\n\ndef fractionbf(n):\n if n < 3:\n return (0, 0)\n\n x, y = 0, 1\n for a in range(n):\n b = n - a\n if b <= a:\n continue\n if gcd(a, b) == 1 and a/b >= x/y:\n x, y = a, b\n return (x, y)\n\ndef main():\n assert(fraction(3) == (1, 2))\n assert(fraction(4) == (1, 3))\n assert(fraction(12) == (5, 7))\n for i in range(5000):\n assert(fraction(i) == fractionbf(i))\n\nmain()\n","sub_path":"codeforces/854A-fraction.py","file_name":"854A-fraction.py","file_ext":"py","file_size_in_byte":1983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"498949221","text":"from src.csv_to_df import DataReader\nimport src.constants as const\nfrom src.portfolio import Portfolio\nfrom src.learning_agent import QLearningAgent\n\n\ndef train(train_end):\n # Load data\n stock1_df = DataReader(const.STOCK1_FILE).get_df()\n stock2_df = DataReader(const.STOCK2_FILE).get_df()\n\n # Initialize parameters\n iterations = const.ITER\n alpha = const.ALPHA\n discount = const.DISCOUNT\n epsilon = const.EPSILON\n\n # Initialize learning agent\n agent = QLearningAgent(alpha, discount, epsilon)\n\n # Initialize starting portfolio state\n init_price = [stock1_df.iloc[0]['Open'], stock2_df.iloc[0]['Open']]\n init_weights = const.WEIGHTS\n init_share_dist = const.SHARE_DIST\n init_pf = Portfolio(init_price, init_weights, init_share_dist)\n\n print(\"------------------------TRAINING START------------------------\")\n\n print(\"------------------------INITIAL VALUES------------------------\")\n print(str(init_pf))\n prev_pf = init_pf\n print()\n\n # Update Q Values for a fixed number of iterations\n for i in range(iterations):\n print(\"------------------------ITERATION \" + str(i) + \"------------------------\")\n\n # Update Q values for each day in the training period\n for idx in range(1, train_end):\n # Select best action based on Q values\n action = agent.get_action(prev_pf.id)\n\n # Update stock price\n next_price = [stock1_df.iloc[idx]['Open'], stock2_df.iloc[idx]['Open']]\n\n # Generate next portfolio state\n curr_pf = prev_pf.next_state(action, next_price)\n\n # Update Q value for previous state\n # Reward = curr_pf.value - prev_pf.value\n agent.update(prev_pf.id, action, curr_pf.id, curr_pf.value - prev_pf.value)\n\n print(\"Action : \", action)\n print(str(curr_pf))\n print()\n\n prev_pf = curr_pf\n\n print()\n\n print(\"------------------------TRAINING END------------------------\")\n return agent\n\n\ndef test(test_start, trained_agent):\n # Initialize data\n stock1_df = DataReader(const.STOCK1_FILE).get_df()\n stock2_df = DataReader(const.STOCK2_FILE).get_df()\n\n # Initialize starting portfolio state\n init_price = [stock1_df.iloc[test_start]['Open'], stock2_df.iloc[test_start]['Open']]\n init_weights = const.WEIGHTS\n init_share_dist = const.SHARE_DIST\n init_pf = Portfolio(init_price, init_weights, init_share_dist)\n\n prev_pf = init_pf\n\n print(\"------------------------TESTING START------------------------\")\n # Test model for duration of the test period\n for idx in range(test_start + 1, stock1_df.shape[0]):\n # Get best action based on Q values\n action = trained_agent.get_policy(prev_pf.id)\n\n # Update stock price\n next_price = [stock1_df.iloc[idx]['Open'], stock2_df.iloc[idx]['Open']]\n\n # Generate next portfolio state\n curr_pf = prev_pf.next_state(action, next_price)\n\n print(\"Action : \", action)\n print(str(curr_pf))\n print()\n\n prev_pf = curr_pf\n\n # Calculate profit at the end of testing\n print(\"PROFIT: \" + str(prev_pf.value - init_pf.value))\n print(\"SCORE: \" + str(prev_pf.evaluate()))\n\n\nif __name__ == \"__main__\":\n agent = train(const.TRAIN_END)\n test(const.TRAIN_END + 1, agent)\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"25543262","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 29 23:49:53 2020\n\n@author: miriam\n\"\"\"\n\n# Part 1\nimport sys\n\n\ndef donors():\n return {'Miriam Pintor': [100, 300],\n 'Waleed Alvarez': [500, 200, 800],\n 'Ricardo Gallegos': [50, 75, 100],\n 'Dina Sayury': [125, 120],\n 'Urias Gramajo': [1000]}\n\n\ndef main_menu():\n print(\"\\n\".join((\"Please choose from below options:\",\n \"1 - Send a Thank You to a single donor.\",\n \"2 - Create a Report.\",\n \"3 - Send letters to all donors\",\n \"4 - quit\"\n \">>> \")))\n option = input('')\n return option\n\n\ndef donors_list():\n ls=[]\n for names in donors_dict:\n ls.append(names)\n return '\\n'.join(ls)\n\n\ndef donate(recipient, donation):\n if recipient not in donors_dict:\n donors_dict[recipient] = [donation]\n else:\n donors_dict[recipient] += [donation]\n\n\ndef sort_key(entry):\n return sum(donors_dict.get(entry))\n\n\ndef send_thankyou():\n recipient = input(\"Enter the donor's Full Name or 'list' for current\"\n \"donors \")\n while recipient == \"list\":\n print(donors_list())\n recipient = input(\"Please enter a Full Name \")\n try:\n donation = float(input(\"Enter the amount for donation \"))\n except ValueError:\n print('Please enter a number value')\n else:\n donate(recipient, donation)\n print(print_thankyou(recipient))\n\n\ndef create_report():\n header = '\\n{:<18}|{:^13}|{:^13}|{:>13}'.format(\"Donor Name\", \"Total Given\",\n \"Num Gifts\", \"Average Gift\")\n print(header)\n print('-'*len(header))\n donor_sort = sorted(donors_dict, key = sort_key, reverse = True)\n for entry in donor_sort:\n total = sum(donors_dict.get(entry))\n num = len(donors_dict.get(entry))\n average = total/num\n print('{:<18} ${:>12,.2f}{:>8} ${:>12,.2f}'.format(entry,\n total, num, average))\n print('')\n\n\ndef send_letters_all_donors():\n for entry in donors_dict:\n filename = entry + '.txt'\n with open(filename, 'w') as f:\n f.write(print_thankyou(entry))\n\n\ndef print_thankyou(name):\n letter = (f'\\nDear {name},'\n f'\\nThank you for your very kind donation of ${sum(donors_dict.get(name)):,.2f}'\n '\\nIt will be put to very good use.'\n '\\n\\nSincerely,\\n-TheTeam\\n')\n return letter\n\n\ndef quit_action():\n print(\"Bye\")\n sys.exit()\n\n\ndef main():\n switch_dict = {1: send_thankyou, 2: create_report, 3: send_letters_all_donors, 4: quit_action}\n while True:\n try:\n option = int(main_menu())\n switch_dict.get(option)()\n except TypeError:\n print('try again\\n')\n\n\nif __name__ == \"__main__\":\n donors_dict = donors()\n main()","sub_path":"students/mimalvarez_pintor/lesson06/mailroom_Part4.py","file_name":"mailroom_Part4.py","file_ext":"py","file_size_in_byte":2909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"181305678","text":"\r\ns = \"\"\r\nch = \"\"\r\ndict = []\r\ncount = 0\r\nfor i in ('A','B','C','D','E','F','G','H','I','J','K','L','M',\r\n 'N','O','P','Q','R','S','T','U','V','W','X','Y','Z',' ', ',', '.'):\r\n dict = dict + [i,]\r\n count = count + 1\r\n\r\ns = \"\"\r\ninf = open (\"lzwtext.txt\", \"r\")\r\ncode1 = int(inf.readline()) # CODE1 is the first code on the file\r\nprint (dict[code1], end=\"\") # Output the string for CODE1\r\nwhile True: # While mode codes on the file ...\r\n c = inf.readline()\r\n if c == '':\r\n break\r\n code0 = int(c) # CODE0 is the next code on the file\r\n if code0 < len(dict): # Is CODE0 in the table?\r\n s = dict[code0] # YES. S is the string for CODE0\r\n else:\r\n s = dict[code1] # NO. S is the string for CODE1\r\n s = s + ch # Append CH to S.\r\n print (s, end=\"\") # IN EITHER CASE emit S\r\n ch = s[0] # CH becomes the first character of S\r\n dict = dict + [dict[code1]+ch,]\r\n count = count + 1\r\n code1 = code0\r\nprint()\r\n","sub_path":"275-Langs/Python/python_anintroductiontoprogramming_supplement/Code/CH10/15lzwd.py","file_name":"15lzwd.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"416221462","text":"import ass2_1 as ass2 #imports important functions\nimport scipy.integrate as scint\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef main(ode_solve, y0, t, *args):\n \"\"\"\n Args:\n ode_solve: system of ODEs constructed in ass2_1 to be solved by odeint;\n y0: initial conditions of this ODE (each derivative y(0),y'(0),y''(0),y'''(0))\n t: (time array, intervals)\n args: various parameters that might be needed in ode_solve\n Returns:\n None: Will calculate the exact solution, euler method solution, and scipy.integrate.odeint solution. Plots all of these in subplots as well as the differenc\n \"\"\"\n print(args)\n sol = scint.odeint(ode_solve, y0, t[0], args=args) #calls scipy.integrate.odeint, using ode_solve (defined in in ass2_1.py) with initial conditions y0, time t and *args=certain parameters in ode_solve\n y_sol = sol[:,0] #\n ex_sol = ass2.exact_sol(t[0]) #exact solution\n eul_sol = ass2.euler_method(t, y0, args) #euler solution\n\n print(\"The total error between the exact solution and the solution of scipy.integrate.odeint is: \" + str( np.sqrt( np.sum( np.square(ex_sol-y_sol) ) ) )) #prints total error\n print(\"The total error between the exact solution and the solution of the forward euler method is: \" + str( np.sqrt( np.sum( np.square(ex_sol-eul_sol) ) ) )) #prints total Error\n\n #Plotting\n plt.suptitle(r'Solutions to ODE $\\frac{d^4y}{dt^4}+16y=0$') #plot title\n\n plt.subplot(2,2,1) #section subplot\n plt.plot(t[0], eul_sol, color=\"g\") #forward Euler plot\n plt.ylabel(r\"y(t)\") #ylabel\n plt.title(r\"Forward Euler method solution\") #title of subplot\n\n plt.subplot(2,2,2)\n plt.plot(t[0], y_sol, color=\"b\")\n plt.ylabel(r\"y(t)\")\n plt.title(r\"Scipy $odeint$ solution\")\n\n plt.subplot(2,2,3)\n plt.plot(t[0], ex_sol, color=\"k\")\n plt.ylabel(r\"y(t)\")\n plt.xlabel(r\"time $t$\")\n plt.title(r\"Exact Solution\")\n\n plt.subplot(2,2,4)\n plt.plot(t[0], ex_sol-y_sol, color=\"b\", label=r\"$scipy$\")\n plt.plot(t[0], ex_sol-eul_sol, color=\"g\", label=r\"$euler$\")\n plt.ylabel(r\"Error\")\n plt.xlabel(r\"time $t$\")\n plt.title(r\"Error of approximations$\")\n plt.legend() #add legend\n\n plt.tight_layout(rect=[0., 0.03, 1., 0.95]) #make each subplot more compact\n plt.show() #shows plot\n\n return 0\n\nif __name__ == \"__main__\":\n #I know this is only slightly useless, but I like having this.\n main(ass2.sys_ode, [1., np.sqrt(2), 0., -4.*np.sqrt(2)], np.linspace(0, 3.5, 1000, retstep=True), args=(1., 1., 1., -16.0))\n","sub_path":"NumericalPython/assignment/ass2_2.py","file_name":"ass2_2.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"284850645","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 16 17:51:28 2018\r\n\r\n@author: Ivan\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport scipy.signal\r\n\r\nPML_specdata = np.load('PML_specdata.npz')\r\nSIS_specdata = np.load('SIS_specdata.npz')\r\n\r\n#Sampling parameters\r\nN = 50000\r\nt_init = -50.\r\nt_final = -0.0000000001\r\nt = np.linspace(t_init, t_final, N)\r\n\r\n\"\"\"\r\nn_samples = len(PML_wavedata['waveform'])\r\n\r\nPML_wave = PML_wavedata['waveform']\r\nSIS_wave = SIS_wavedata['waveform']\r\n\r\nPML_waveform = []\r\nSIS_waveform = []\r\nPML_classify = []\r\nSIS_classify = []\r\n\r\nfor i in range(n_samples):\r\n PML_waveform.append(PML_wave[i][0])\r\n SIS_waveform.append(SIS_wave[i][0])\r\n PML_classify.append(PML_wave[i][1])\r\n SIS_classify.append(SIS_wave[i][1])\r\n\r\nprint(PML_waveform)\r\nprint(PML_classify)\r\n\r\nfor i in range(10):\r\n plt.figure(figsize=(10,7))\r\n plt.plot(t, PML_waveform[i])\r\n\r\n\"\"\"\r\n\r\n\r\n\r\nfor i in range(len(PML_specdata['mesh'])):\r\n plt.figure(figsize=(10,7))\r\n plt.pcolormesh(PML_specdata['mesh'][i][2], PML_specdata['mesh'][i][1], PML_specdata['mesh'][i][0], cmap='gist_earth', shading='gouraud')\r\n plt.xlabel('time (s)')\r\n plt.ylabel('frequency (Hz)')\r\n #plt.xlim([0.8, 1.1])\r\n plt.ylim([0, 400])\r\n\r\n","sub_path":"Lens Modeling/LoadDatabase.py","file_name":"LoadDatabase.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"474639417","text":"#!/usr/bin/env python3\n\nimport os\nimport glob\nimport shutil # for rmtree (= \"rm -Rf\")\nimport subprocess # for git (note: in the future, we may want to use GitPython instead)\nimport sys\n\nif len(sys.argv) < 2:\n print('Usage: ./count_lines.py [--historical [numCommits]]')\n exit()\n\n# Each line in source files is classified into one of the following categories:\n# 1. Blank (only contain whitespaces or tabs)\n# 2. Legal (license boilerplate)\n# 3. Comment (internal comment for maintainer)\n# 4. Doc (documentation of public API, e.g., Doxygen comments)\n# 5. Test (line of testing code - not executed in production)\n# 6. Wrap (line of wrapping code - counted separately for stats)\n# 7. Code (any other line of code)\n#\n# Note that if there are both code and comment on the same line, then\n# the line is only counted as \"Code\". The sum of the counts for each\n# category must be equal to the total number of lines in the file.\n\nclass LineCounts:\n def __init__(self):\n # Counters for lines in C++ files\n self.cppBlank = 0\n self.cppLegal = 0\n self.cppComment = 0\n self.cppDoc = 0\n self.cppTest = 0\n self.cppWrap = 0\n self.cppCode = 0\n\n # Counters for lines in Python files\n self.pyBlank = 0\n self.pyLegal = 0\n self.pyComment = 0\n self.pyDoc = 0\n self.pyTest = 0\n self.pyWrap = 0\n self.pyCode = 0\n\n # Counters for lines in CMake files\n self.cmakeBlank = 0\n self.cmakeLegal = 0\n self.cmakeComment = 0\n self.cmakeDoc = 0\n self.cmakeTest = 0\n self.cmakeWrap = 0\n self.cmakeCode = 0\n\n # Counters for lines in GLSL shader files\n self.glslBlank = 0\n self.glslLegal = 0\n self.glslComment = 0\n self.glslDoc = 0\n self.glslTest = 0\n self.glslWrap = 0\n self.glslCode = 0\n\n # Counters for lines in Qt stylesheet files\n self.qssBlank = 0\n self.qssLegal = 0\n self.qssComment = 0\n self.qssDoc = 0\n self.qssTest = 0\n self.qssWrap = 0\n self.qssCode = 0\n\n# The argument \\p within tells whether we are starting this line within a C-Style\n# comment, i.e., the characters \"/*\" were found in one of the previous lines\n# with no matching \"*/\" found yet.\n#\n# Returns a pair (hasCode, within) where 'hasCode' tells you whether this line\n# has semantic content outside of C-style comment lines, and where 'within'\n# tells you whether this line ended within a C-style comment.\n#\n# Note: For simplicity, we assume that \"/*\" and \"*/\" are never within\n# character constants, string literals, or comments. This\n# means that the following [code] will be counted as comment:\n#\n# std::string s1 = \"/*\";\n# [code]\n# std::string s2 = \"*/\";\n#\n# This should be extremely rare and is unlikely to affect much the line\n# count, which is anyway a very imprecise metric.\n#\ndef handleCStyleComment(line, within):\n line = line.rstrip('\\\\ ') # Strip trailing backslach to handle comments in macros\n hasCode = False\n i = 0\n while i < len(line):\n if within:\n if line[i:i+2] == '*/':\n within = False\n i += 2\n else:\n i += 1\n else:\n if line[i:i+2] == '/*':\n within = True\n i += 2\n else:\n hasCode = True\n i += 1\n return hasCode, within\n\n# C++ has // and /* comments\ndef cppCount(filepath, count, isTestDir = False, isWrapDir = False):\n with open(filepath, 'r') as handle:\n isLegal = False\n within = False\n for line in handle:\n line = line.strip()\n\n # Handle C-style comments\n hasCode, within = handleCStyleComment(line, within)\n\n # Handle legal comments\n if (line.startswith('// Copyright')\n or line.startswith('* Copyright') # For embedded third-party code (e.g., see vgc/core/mat4d.cpp)\n or line.startswith('/* Copyright')): # For embedded third-party code\n isLegal = True\n elif (isLegal and not (\n line.startswith('//')\n or line.startswith('*'))): # For embedded third-party code\n isLegal = False\n\n # Dispatch\n if isLegal:\n count.cppLegal += 1\n elif not line:\n count.cppBlank += 1\n elif line.startswith('///') or line.startswith('/**'): # For Doxygen within multiline macros (e.g., see vgc/core/object.h)\n count.cppDoc += 1\n elif line.startswith('//') or not hasCode:\n count.cppComment += 1\n elif isTestDir:\n count.cppTest += 1\n elif isWrapDir:\n count.cppWrap += 1\n else:\n count.cppCode += 1\n\n# Python has # comments\ndef pyCount(filepath, count, isTestDir = False, isWrapDir = False):\n with open(filepath, 'r') as handle:\n isLegal = False\n for line in handle:\n line = line.lstrip()\n\n # Handle legal comments\n if line.startswith('# Copyright'):\n isLegal = True\n elif isLegal and not line.startswith('#'):\n isLegal = False\n\n # Dispatch\n if isLegal:\n count.pyLegal += 1\n elif not line:\n count.pyBlank += 1\n elif line.startswith('#'):\n count.pyComment += 1\n elif isTestDir:\n count.pyTest += 1\n elif isWrapDir:\n count.pyWrap += 1\n else:\n count.pyCode += 1\n\n# CMake has # comments\ndef cmakeCount(filepath, count, isTestDir = False, isWrapDir = False):\n with open(filepath, 'r') as handle:\n isLegal = False\n for line in handle:\n line = line.lstrip()\n\n # Handle legal comments\n if line.startswith('# Copyright'):\n isLegal = True\n elif isLegal and not line.startswith('#'):\n isLegal = False\n\n # Dispatch\n if isLegal:\n count.cmakeLegal += 1\n elif not line:\n count.cmakeBlank += 1\n elif line.startswith('#'):\n count.cmakeComment += 1\n elif isTestDir:\n count.cmakeTest += 1\n elif isWrapDir:\n count.cmakeWrap += 1\n else:\n count.cmakeCode += 1\n\n# GLSL has // and /* comments\ndef glslCount(filepath, count, isTestDir = False, isWrapDir = False):\n with open(filepath, 'r') as handle:\n isLegal = False\n within = False\n for line in handle:\n line = line.lstrip()\n\n # Handle C-style comments\n hasCode, within = handleCStyleComment(line, within)\n\n # Handle legal comments\n if line.startswith('// Copyright'):\n isLegal = True\n elif isLegal and not line.startswith('//'):\n isLegal = False\n\n # Dispatch\n if isLegal:\n count.glslLegal += 1\n elif not line:\n count.glslBlank += 1\n elif line.startswith('///'):\n count.glslDoc += 1\n elif line.startswith('//') or not hasCode:\n count.glslComment += 1\n elif isTestDir:\n count.glslTest += 1\n elif isWrapDir:\n count.glslWrap += 1\n else:\n count.glslCode += 1\n\n# Qt stylesheets have /* comments\ndef qssCount(filepath, count, isTestDir = False, isWrapDir = False):\n with open(filepath, 'r') as handle:\n isLegal = False\n within = False\n for line in handle:\n line = line.lstrip()\n\n # Handle C-style comments\n hasCode, within = handleCStyleComment(line, within)\n\n # Handle legal comments\n if line.startswith('/* Copyright'):\n isLegal = True\n elif isLegal and not line.startswith('*'):\n isLegal = False\n\n # Dispatch\n if not line:\n count.qssBlank += 1\n elif not hasCode:\n count.qssComment += 1\n elif isTestDir:\n count.qssTest += 1\n elif isWrapDir:\n count.qssWrap += 1\n else:\n count.qssCode += 1\n\ndef dirCount(dir, count):\n isTestDir = False\n isWrapDir = False\n currentTestDir = 'NONE'\n currentWrapDir = 'NONE'\n\n for subdir, dirs, filenames in os.walk(dir):\n\n # Check whether we are in a test dir\n if subdir.endswith('/tests'):\n currentTestDir = subdir\n if subdir.startswith(currentTestDir):\n isTestDir = True\n else:\n isTestDir = False\n currentTestDir = 'NONE'\n\n # Check whether we are in a wrap dir\n if subdir.endswith('/wraps'):\n currentWrapDir = subdir\n if subdir.startswith(currentWrapDir):\n isWrapDir = True\n else:\n isWrapDir = False\n currentWrapDir = 'NONE'\n\n # Dispatch based on file name\n for filename in filenames:\n filepath = os.path.join(subdir, filename)\n if filepath.endswith(\".h\") or filepath.endswith(\".cpp\"):\n cppCount(filepath, count, isTestDir, isWrapDir)\n if filepath.endswith(\".py\") :\n pyCount(filepath, count, isTestDir, isWrapDir)\n if filepath.endswith(\"CMakeLists.txt\") :\n cmakeCount(filepath, count, isTestDir, isWrapDir)\n if filepath.endswith(\".glsl\") :\n glslCount(filepath, count, isTestDir, isWrapDir)\n if filepath.endswith(\".qss\") :\n qssCount(filepath, count, isTestDir, isWrapDir)\n\ndef getCurrentCount(rootDir):\n count = LineCounts()\n dirCount(os.path.join(rootDir, 'apps'), count)\n dirCount(os.path.join(rootDir, 'cmake'), count)\n dirCount(os.path.join(rootDir, 'libs'), count)\n cmakeCount(os.path.join(rootDir, 'CMakeLists.txt'), count)\n return count\n\ndef printCount(count):\n totalBlank = count.cppBlank + count.pyBlank + count.cmakeBlank + count.glslBlank + count.qssBlank\n totalLegal = count.cppLegal + count.pyLegal + count.cmakeLegal + count.glslLegal + count.qssLegal\n totalComment = count.cppComment + count.pyComment + count.cmakeComment + count.glslComment + count.qssComment\n totalDoc = count.cppDoc + count.pyDoc + count.cmakeDoc + count.glslDoc + count.qssDoc\n totalTest = count.cppTest + count.pyTest + count.cmakeTest + count.glslTest + count.qssTest\n totalWrap = count.cppWrap + count.pyWrap + count.cmakeWrap + count.glslWrap + count.qssWrap\n totalCode = count.cppCode + count.pyCode + count.cmakeCode + count.glslCode + count.qssCode\n\n print(\"Total Line Counts: \" + str(\n totalBlank + totalLegal + totalComment + totalDoc + totalTest + totalWrap + totalCode))\n print(\" Blank: \" + str(totalBlank))\n print(\" Legal: \" + str(totalLegal))\n print(\" Comment: \" + str(totalComment))\n print(\" Doc: \" + str(totalDoc))\n print(\" Test: \" + str(totalTest))\n print(\" Wrap: \" + str(totalWrap))\n print(\" Code: \" + str(totalCode))\n\n print(\"\\nC++ Line Counts: \" + str(\n count.cppBlank + count.cppLegal + count.cppComment + count.cppDoc + count.cppTest + count.cppWrap + count.cppCode))\n print(\" Blank: \" + str(count.cppBlank))\n print(\" Legal: \" + str(count.cppLegal))\n print(\" Comment: \" + str(count.cppComment))\n print(\" Doc: \" + str(count.cppDoc))\n print(\" Test: \" + str(count.cppTest))\n print(\" Wrap: \" + str(count.cppWrap))\n print(\" Code: \" + str(count.cppCode))\n\n print(\"\\nPython Line Counts: \" + str(\n count.pyBlank + count.pyLegal + count.pyComment + count.pyDoc + count.pyTest + count.pyWrap + count.pyCode))\n print(\" Blank: \" + str(count.pyBlank))\n print(\" Legal: \" + str(count.pyLegal))\n print(\" Comment: \" + str(count.pyComment))\n print(\" Doc: \" + str(count.pyDoc))\n print(\" Test: \" + str(count.pyTest))\n print(\" Wrap: \" + str(count.pyWrap))\n print(\" Code: \" + str(count.pyCode))\n\n print(\"\\nCMake Line Counts: \" + str(\n count.cmakeBlank + count.cmakeLegal + count.cmakeComment + count.cmakeDoc + count.cmakeTest + count.cmakeWrap + count.cmakeCode))\n print(\" Blank: \" + str(count.cmakeBlank))\n print(\" Legal: \" + str(count.cmakeLegal))\n print(\" Comment: \" + str(count.cmakeComment))\n print(\" Doc: \" + str(count.cmakeDoc))\n print(\" Test: \" + str(count.cmakeTest))\n print(\" Wrap: \" + str(count.cmakeWrap))\n print(\" Code: \" + str(count.cmakeCode))\n\n print(\"\\nGLSL Line Counts: \" + str(\n count.glslBlank + count.glslLegal + count.glslComment + count.glslDoc + count.glslTest + count.glslWrap + count.glslCode))\n print(\" Blank: \" + str(count.glslBlank))\n print(\" Legal: \" + str(count.glslLegal))\n print(\" Comment: \" + str(count.glslComment))\n print(\" Doc: \" + str(count.glslDoc))\n print(\" Test: \" + str(count.glslTest))\n print(\" Wrap: \" + str(count.glslWrap))\n print(\" Code: \" + str(count.glslCode))\n\n print(\"\\nQt Stylesheet Line Counts: \" + str(\n count.qssBlank + count.qssLegal + count.qssComment + count.qssDoc + count.qssTest + count.qssWrap + count.qssCode))\n print(\" Blank: \" + str(count.qssBlank))\n print(\" Legal: \" + str(count.qssLegal))\n print(\" Comment: \" + str(count.qssComment))\n print(\" Doc: \" + str(count.qssDoc))\n print(\" Test: \" + str(count.qssTest))\n print(\" Wrap: \" + str(count.qssWrap))\n print(\" Code: \" + str(count.qssCode))\n\ndef printInline(s):\n print(s, end='')\n\nclass Csv:\n def __init__(self):\n self.first_ = True\n\n def printValue(self, s):\n if self.first_:\n self.first_ = False\n else:\n printInline(',')\n printInline(s)\n\n\n def printNewline(self):\n printInline('\\n')\n\ndef beginCsv(s):\n global csvIsBegin_\n csvIsBegin_ = True\n\ndef endCsv(s):\n global csvIsBegin_\n csvIsBegin_ = Fal\n\n printInline(s)\n\ndef printCsv(s):\n printInline(s)\n\ndef printCountOneLine(date, count):\n csv = Csv()\n\n csv.printValue(date)\n\n totalBlank = count.cppBlank + count.pyBlank + count.cmakeBlank + count.glslBlank + count.qssBlank\n totalLegal = count.cppLegal + count.pyLegal + count.cmakeLegal + count.glslLegal + count.qssLegal\n totalComment = count.cppComment + count.pyComment + count.cmakeComment + count.glslComment + count.qssComment\n totalDoc = count.cppDoc + count.pyDoc + count.cmakeDoc + count.glslDoc + count.qssDoc\n totalTest = count.cppTest + count.pyTest + count.cmakeTest + count.glslTest + count.qssTest\n totalWrap = count.cppWrap + count.pyWrap + count.cmakeWrap + count.glslWrap + count.qssWrap\n totalCode = count.cppCode + count.pyCode + count.cmakeCode + count.glslCode + count.qssCode\n\n csv.printValue(totalBlank + totalLegal + totalComment + totalDoc + totalTest + totalWrap + totalCode)\n csv.printValue(totalBlank)\n csv.printValue(totalLegal)\n csv.printValue(totalComment)\n csv.printValue(totalDoc)\n csv.printValue(totalTest)\n csv.printValue(totalWrap)\n csv.printValue(totalCode)\n\n csv.printValue(count.cppBlank + count.cppLegal + count.cppComment + count.cppDoc + count.cppTest + count.cppWrap + count.cppCode)\n csv.printValue(count.cppBlank)\n csv.printValue(count.cppLegal)\n csv.printValue(count.cppComment)\n csv.printValue(count.cppDoc)\n csv.printValue(count.cppTest)\n csv.printValue(count.cppWrap)\n csv.printValue(count.cppCode)\n\n csv.printValue(count.pyBlank + count.pyLegal + count.pyComment + count.pyDoc + count.pyTest + count.pyWrap + count.pyCode)\n csv.printValue(count.pyBlank)\n csv.printValue(count.pyLegal)\n csv.printValue(count.pyComment)\n csv.printValue(count.pyDoc)\n csv.printValue(count.pyTest)\n csv.printValue(count.pyWrap)\n csv.printValue(count.pyCode)\n\n csv.printValue(count.cmakeBlank + count.cmakeLegal + count.cmakeComment + count.cmakeDoc + count.cmakeTest + count.cmakeWrap + count.cmakeCode)\n csv.printValue(count.cmakeBlank)\n csv.printValue(count.cmakeLegal)\n csv.printValue(count.cmakeComment)\n csv.printValue(count.cmakeDoc)\n csv.printValue(count.cmakeTest)\n csv.printValue(count.cmakeWrap)\n csv.printValue(count.cmakeCode)\n\n csv.printValue(count.glslBlank + count.glslLegal + count.glslComment + count.glslDoc + count.glslTest + count.glslWrap + count.glslCode)\n csv.printValue(count.glslBlank)\n csv.printValue(count.glslLegal)\n csv.printValue(count.glslComment)\n csv.printValue(count.glslDoc)\n csv.printValue(count.glslTest)\n csv.printValue(count.glslWrap)\n csv.printValue(count.glslCode)\n\n csv.printValue(count.qssBlank + count.qssLegal + count.qssComment + count.qssDoc + count.qssTest + count.qssWrap + count.qssCode)\n csv.printValue(count.qssBlank)\n csv.printValue(count.qssLegal)\n csv.printValue(count.qssComment)\n csv.printValue(count.qssDoc)\n csv.printValue(count.qssTest)\n csv.printValue(count.qssWrap)\n csv.printValue(count.qssCode)\n\n csv.printNewline()\n\ndef printCountOneLineHeader():\n\n csv = Csv()\n\n csv.printValue(\"Commit date/time\")\n\n csv.printValue(\"Total\")\n csv.printValue(\"Blank\")\n csv.printValue(\"Legal\")\n csv.printValue(\"Comment\")\n csv.printValue(\"Doc\")\n csv.printValue(\"Test\")\n csv.printValue(\"Wrap\")\n csv.printValue(\"Code\")\n\n csv.printValue(\"C++ (Total)\")\n csv.printValue(\"C++ (Blank)\")\n csv.printValue(\"C++ (Legal)\")\n csv.printValue(\"C++ (Comment)\")\n csv.printValue(\"C++ (Doc)\")\n csv.printValue(\"C++ (Test)\")\n csv.printValue(\"C++ (Wrap)\")\n csv.printValue(\"C++ (Code)\")\n\n csv.printValue(\"Python (Total)\")\n csv.printValue(\"Python (Blank)\")\n csv.printValue(\"Python (Legal)\")\n csv.printValue(\"Python (Comment)\")\n csv.printValue(\"Python (Doc)\")\n csv.printValue(\"Python (Test)\")\n csv.printValue(\"Python (Wrap)\")\n csv.printValue(\"Python (Code)\")\n\n csv.printValue(\"CMake (Total)\")\n csv.printValue(\"CMake (Blank)\")\n csv.printValue(\"CMake (Legal)\")\n csv.printValue(\"CMake (Comment)\")\n csv.printValue(\"CMake (Doc)\")\n csv.printValue(\"CMake (Test)\")\n csv.printValue(\"CMake (Wrap)\")\n csv.printValue(\"CMake (Code)\")\n\n csv.printValue(\"GLSL (Total)\")\n csv.printValue(\"GLSL (Blank)\")\n csv.printValue(\"GLSL (Legal)\")\n csv.printValue(\"GLSL (Comment)\")\n csv.printValue(\"GLSL (Doc)\")\n csv.printValue(\"GLSL (Test)\")\n csv.printValue(\"GLSL (Wrap)\")\n csv.printValue(\"GLSL (Code)\")\n\n csv.printValue(\"Qt Stylesheet (Total)\")\n csv.printValue(\"Qt Stylesheet (Blank)\")\n csv.printValue(\"Qt Stylesheet (Legal)\")\n csv.printValue(\"Qt Stylesheet (Comment)\")\n csv.printValue(\"Qt Stylesheet (Doc)\")\n csv.printValue(\"Qt Stylesheet (Test)\")\n csv.printValue(\"Qt Stylesheet (Wrap)\")\n csv.printValue(\"Qt Stylesheet (Code)\")\n\n csv.printNewline()\n\ndef printCurrentCount(rootDir):\n count = getCurrentCount(rootDir)\n printCount(count)\n\ndef printHistoricalCount(rootDir):\n curDir = os.path.abspath(os.curdir)\n tmpDir = os.path.abspath(\"count_lines_tmp\")\n\n if os.path.isdir(tmpDir):\n shutil.rmtree(tmpDir)\n\n subprocess.run([\"git\", \"clone\", \"-q\", rootDir, tmpDir])\n\n os.chdir(tmpDir)\n\n maxCommits = -1\n if len(sys.argv) > 3:\n maxCommits = int(sys.argv[3])\n\n printCountOneLineHeader()\n\n numCommits = 0\n while maxCommits == -1 or numCommits < maxCommits:\n if numCommits > 0:\n subprocess.run([\"git\", \"checkout\", \"-q\", \"HEAD^\"])\n numCommits += 1\n\n # Get commit date and time as per git \"iso\" format: \"2018-08-08 15:40:31 +0200\"\n commitDatetime = subprocess.check_output([\"git\", \"show\", \"-s\", \"--date=iso\", \"--format=format:%ad\"]).decode('utf8')\n\n # Get commit date and time as ISO 8601: \"2018-08-08T15:40:31+0200\"\n commitDatetime = commitDatetime.replace(\" \", \"T\", 1)\n commitDatetime = commitDatetime.replace(\" \", \"\", 1)\n\n try:\n count = getCurrentCount(tmpDir)\n printCountOneLine(commitDatetime, count)\n except FileNotFoundError:\n # This is raised when 'CMakeLists.txt' is not found, which happens for the first\n # few commits of the VGC git repository. This is a good moment to break out of\n # the loop\n maxCommits = 0\n\n if os.path.isdir(tmpDir):\n shutil.rmtree(tmpDir)\n\nrootDir = os.path.abspath(sys.argv[1])\nif len(sys.argv) > 2:\n if sys.argv[2] == \"--historical\":\n printHistoricalCount(rootDir)\n else:\n print(\"Unknown option \" + sys.argv[2])\nelse:\n printCurrentCount(rootDir)\n","sub_path":"count_lines.py","file_name":"count_lines.py","file_ext":"py","file_size_in_byte":21007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"579251505","text":"import os, sys\nimport glob\nimport argparse\nimport numpy as np\nimport pandas as pd\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport re\nfrom nltk.tokenize import RegexpTokenizer\nimport nltk\n\n#Create a vocabulary (making a list of every word that occurs in every document)\n#Go through and count all the words in every document. Every vector should\n#have every word, even if the vector doesn't contain that word (then it has the\n#count of 0, but the word is still there).\n#Label each document according to its topic because we need to keep track of\n#where each document comes from later in the assignment, when we use every\n#vector twice, once to compare with every vector from the same topic and once\n#to compare to every vector from the other topic.\n#Eliminate duplicate vectors - keep track of where they came from!\n\n\ndef vocabulary(directory, m=None):\n \"\"\"Creates a vocabulary list which contains all words from all documents\"\"\"\n vocabulary_list = []\n for topic in os.listdir(directory):\n path_to_subdirectory = os.path.join(directory, topic)\n for file in os.listdir(path_to_subdirectory):\n path_to_file = os.path.join(path_to_subdirectory, file)\n with open(path_to_file, \"r\", encoding=\"utf8\") as f:\n text = f.read()\n lowercase = text.lower()\n tokenizer = RegexpTokenizer(r'\\w+')\n strip_punct = tokenizer.tokenize(lowercase)\n for word in strip_punct:\n vocabulary_list.append(word)\n\n vocab_dict = dict(nltk.FreqDist(vocabulary_list))\n frequency = [(word,vocab_dict[word]) for word in sorted(vocab_dict, key=vocab_dict.get,reverse=True)]\n\n if m is not None:\n vocabulary = frequency[:m]\n else:\n vocabulary = frequency\n\n final_vocabulary = []\n for word, frequency in vocabulary:\n final_vocabulary.append(word)\n\n return final_vocabulary\n\n\ndef preprocessing_and_labeling(directory, m=None):\n \"\"\"Creates a top dictionary containing the topic + document names as keys and dictionaries as values.\n Those dictionaries contain words as keys and the word counts as values.\"\"\"\n label_maker = {}\n vocab = vocabulary(directory, m)\n vocab_dict = dict.fromkeys(vocab,0)\n\n for topic in os.listdir(directory):\n path_to_subdirectory = os.path.join(directory, topic)\n for file in os.listdir(path_to_subdirectory):\n path_to_file = os.path.join(path_to_subdirectory, file)\n word_counts = vocab_dict.copy()\n with open(path_to_file, \"r\", encoding=\"utf8\") as f:\n text = f.read()\n lowercase = text.lower()\n tokenizer = RegexpTokenizer(r'\\w+')\n strip_punct = tokenizer.tokenize(lowercase)\n for word in strip_punct:\n if word in vocab:\n word_counts[word] += 1\n label_maker[topic+\" \"+file] = word_counts\n return label_maker\n\n\ndef vector_creator(directory, m=None):\n \"\"\"Append every value (which is a word count) from the word_counts\n dictionary into lists which are the vectors. Every list should then be\n added into an array which is later converted into a dataframe from pandas\"\"\"\n supreme_dictionary = preprocessing_and_labeling(directory, m)\n\n for label in supreme_dictionary.keys():\n luke_i_am_your_vectorspace = []\n for word, count in supreme_dictionary[label].items():\n luke_i_am_your_vectorspace.append(count)\n supreme_dictionary[label] = luke_i_am_your_vectorspace\n\n return supreme_dictionary\n\n\ndef matrix_builder(directory, m=None):\n \"\"\"Convert the dictionary into a padas dataframe to be written into\n the output file. Dropping duplicate vectors.\"\"\"\n darth_vader = vector_creator(directory, m)\n column_names = vocabulary(directory, m)\n matrix_dataframe = pd.DataFrame.from_dict(darth_vader, orient='index', dtype=None, columns=column_names) #I know this doesn't run on the server but after almost a month of trying to work around it, it's the best I can come up with\n list_of_duplicates = matrix_dataframe[matrix_dataframe.duplicated()].index.tolist()\n matrix_dataframe = matrix_dataframe.drop_duplicates()\n print(\"These duplicated vectors have been dropped:\")\n for duplicate in list_of_duplicates:\n print(duplicate)\n\n return matrix_dataframe\n\n\ndef make_svd(directory, output_dataframe, outputfile, N, m=None):\n \"\"\"Turns the dataframe into into a document matrix with a feature space of\n dimensionality n. Singular value decomposition - used to exclude the least\n significant components of a vector\"\"\"\n darth_vader = vector_creator(directory, m)\n darth_vader_array = np.array(list(darth_vader.values()),dtype=float)\n words = dataframe.keys()\n filenames = dataframe.index.values\n svd = TruncatedSVD(N)\n svd_fit = svd.fit_transform(darth_vader_array)\n svd_dataframe = pd.DataFrame(svd_fit, index=filenames, columns=words)\n svd_dataframe.to_csv(outputfile, encoding=\"utf-8\")\n\n return dataframe\n\n\ndef file_creator(dataframe, directory, m=None):\n \"\"\"Creating the outputfile\"\"\"\n dataframe = dataframe.to_csv(args.outputfile)\n return dataframe\n\n\n#then modify README.md in Markdown to contain:\n\n# Your name, in case that's not obvious from your github account.\n# What you chose for the vocabulary restriction in (2) above, with a short justification.\n# A table containing the output values from simdoc.py for each of the files (1)-(8), organized in a meaningful way.\n# In your own words, write down what you think the hypothesis of this experiment was. (1 paragraph)\n# A brief discussion of any trends you may see in the data, or lack thereof (possible), in light of the hypothesis you wrote down. (1 paragraph)\n\n#____________________________________________________________\nparser = argparse.ArgumentParser(description=\"Generate term-document matrix.\")\nparser.add_argument(\"-T\", \"--tfidf\", action=\"store_true\", help=\"Apply tf-idf to the matrix.\")\nparser.add_argument(\"-S\", \"--svd\", metavar=\"N\", dest=\"svddims\", type=int,\n default=None,\n help=\"Use TruncatedSVD to truncate to N dimensions\")\nparser.add_argument(\"-B\", \"--base-vocab\", metavar=\"M\", dest=\"basedims\",\n type=int, default=None,\n help=\"Use the top M dims from the raw counts before further processing\")\nparser.add_argument(\"foldername\", type=str,\n help=\"The base folder name containing the two topic subfolders.\")\nparser.add_argument(\"outputfile\", type=str,\n help=\"The name of the output file for the matrix data.\")\n\nargs = parser.parse_args()\n\nprint(\"Loading data from directory {}.\".format(args.foldername))\n\nvocabulary(args.foldername, args.basedims)\npreprocessing_and_labeling(args.foldername, args.basedims)\nvector_creator(args.foldername, args.basedims)\ndataframe = matrix_builder(args.foldername, args.basedims)\nfile_creator(dataframe, args.foldername, args.basedims)\n\nif not args.basedims:\n print(\"Using full vocabulary.\")\nelse:\n print(\"Using only top {} terms by raw count.\".format(args.basedims))\n\nif args.tfidf:\n print(\"Applying tf-idf to raw counts.\")\n tfidf_values = TfidfTransformer().fit_transform(dataframe) #transforms the dataframe into tfidf\n tfidf_data = tfidf_values.toarray()\n words = dataframe.keys()\n filenames = dataframe.index.values\n tfidf_data = pd.DataFrame(tfidf_data, columns=words, index=filenames)\n tfidf_data.to_csv(args.outputfile, encoding=\"utf8\")\n\nif args.svddims:\n print(\"Truncating matrix to {} dimensions via singular value decomposition.\".format(args.svddims))\n if args.tfidf:\n # Selecting both TF-IDF and SVF\n tfidf_values = TfidfTransformer().fit_transform(dataframe) #transforms the dataframe into tfidf\n tfidf_data = tfidf_values.toarray()\n words = dataframe.keys()\n filenames = dataframe.index.values\n tfidf_data = pd.DataFrame(tfidf_data, columns=words, index=filenames)\n svd = TruncatedSVD(args.svddims)\n svd_fit = svd.fit_transform(tfidf_data)\n svd_dataframe = pd.DataFrame(svd_fit, index=filenames)\n svd_dataframe.to_csv(args.outputfile, encoding=\"utf-8\")\n else:\n darth_vader = vector_creator(args.foldername, args.basedims)\n row_labels = [x for x in darth_vader.keys()]\n darth_vader_array = np.array(list(darth_vader.values()),dtype=float)\n svd = TruncatedSVD(args.svddims)\n svd_fit = svd.fit_transform(darth_vader_array)\n svd_dataframe = pd.DataFrame(svd_fit, index=row_labels)\n svd_dataframe.to_csv(args.outputfile, encoding=\"utf-8\")\n\nif args.basedims and args.svddims:\n if args.basedims <= args.svddims:\n print(\"Singular value decomposition dimentionality cannot be higher than the vocabulary size\")\n exit(1)\n\n\nprint(\"Writing matrix to {}.\".format(args.outputfile))\n","sub_path":"gendoc.py","file_name":"gendoc.py","file_ext":"py","file_size_in_byte":9013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"237417192","text":"# -*- coding: utf-8 -*-\n\nfrom slackbot import settings\nfrom slackbot.bot import respond_to\nimport re\nimport redis\nfrom utils import SlackClient, admin_only\n\nclient = SlackClient(\n settings.API_TOKEN,\n)\n\nadmins = settings.ADMINS\n\nr = redis.StrictRedis(host='localhost', port=6379, db=0)\n\n@respond_to('yoga add$', re.IGNORECASE)\ndef attend(message):\n sender_id = message.body[\"user\"]\n sender = client.find_name_by_user(sender_id)\n add_result = r.sadd(\"yoga_attendees\", sender_id)\n attendee_count = r.scard(\"yoga_attendees\")\n\n if add_result == 1:\n message.send(\"%s has joined this week's Yoga! %d attending\" % (sender, attendee_count))\n else:\n message.reply(\"%s You're already attending this week.\" % (sender))\n\n@respond_to('yoga remove$', re.IGNORECASE)\ndef skip(message):\n sender_id = message.body[\"user\"]\n sender = client.find_name_by_user(sender_id)\n remove_result = r.srem(\"yoga_attendees\", sender_id)\n attendee_count = r.scard(\"yoga_attendees\")\n\n if remove_result == 1:\n message.send(\"%s has been removed from this week's Yoga session. %d attending.\" % (sender, attendee_count))\n else:\n message.reply(\"%s, You're not on the list for this week.\" % (sender))\n\n\n@respond_to('yoga status$', re.IGNORECASE)\ndef get_status(message):\n attendees = r.smembers(\"yoga_attendees\")\n attendee_names = map(client.find_name_by_user, attendees)\n attendee_count = len(attendee_names)\n attendee_names = \" \".join(['@%s'%i for i in attendee_names])\n if not attendee_count:\n message.send(\"Nobody attending yoga this week :( \")\n else:\n message.send(\"This week's yoga has %d attendees: %s \\n\" % (attendee_count, attendee_names))\n\n@respond_to('yoga refresh', re.IGNORECASE)\n@admin_only(admins)\ndef refresh(message):\n r.delete(\"yoga_attendees\")\n\n@respond_to('yoga add (.*)$', re.IGNORECASE)\n@admin_only(admins)\ndef add_attendee(message, user):\n user_id = client.find_user_by_name(user)\n add_result = r.sadd(\"yoga_attendees\", user_id)\n attendee_count = r.scard(\"yoga_attendees\")\n\n if add_result == 1:\n message.send(\"%s has joined this week's Yoga! %d attending.\" % (user, attendee_count))\n else:\n message.reply(\"%s is already attending this week.\" % user)\n\n@respond_to('yoga remove (.*)$', re.IGNORECASE)\n@admin_only(admins)\ndef remove_attendee(message, user):\n user_id = client.find_user_by_name(user)\n remove_result = r.srem(\"yoga_attendees\", user_id)\n attendee_count = r.scard(\"yoga_attendees\")\n\n if remove_result == 1:\n message.send(\"%s has been removed from this week's Yoga. %d attending.\" % (user, attendee_count))\n else:\n message.reply(\"%s is not on the list for this week.\" % user)\n\n@respond_to('yoga help$', re.IGNORECASE)\ndef help(message):\n sender_id = message.body[\"user\"]\n initial_text = \"\"\"\n mention *@rosie*, type *yoga* and then:\\n\n `add`​to add yourself to next session's attendee list,\\n\n ​ `remove`​to remove yourself from next session's attendee list,\\n\n ​ `status`​to display next session's attendee list.\\n\n \"\"\"\n if sender_id in admins:\n message_text = initial_text + \"\"\"\n ...also if you're an admin you have a few more commands can use:\\n\n `add` {user}* to add a user to the current attendee list,\\n\n `remove` {user}* to remove a user to the current attendee list,\\n\n `refresh` to reset the attendee list manually.\n \"\"\"\n else:\n message_text = initial_text\n\n message.reply(message_text)","sub_path":"plugins/yoga.py","file_name":"yoga.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"489701098","text":"import turtle\r\nts = turtle.getscreen()\r\nt = turtle.Pen()\r\nt.speed(1)\r\nturtle.bgcolor(\"white\")\r\nsides = 360\r\n#colors = [\"black\"]\r\nwin=turtle.Screen();\r\nfor x in range(360):\r\n t.pencolor(\"black\")\r\n t.forward(sides/240*x)\r\n t.left(120/sides+sides+sides-x*120*x)\r\n t.width(1)\r\nts.getcanvas().postscript(file=\"test.eps\")\r\nwin.exitonclick()\r\n","sub_path":"scripts/turtleLine.py","file_name":"turtleLine.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"270270990","text":"from functools import partial\nimport time\n\nimport numpy as np\nimport pandas as pd\n\nfrom psi.controller.calibration.calibration import FlatCalibration\nfrom psi.token.primitives import ChirpFactory, SilenceFactory\n\nfrom .calibration import InterpCalibration\nfrom . import util\n\n\ndef chirp_power(engine, ao_channel_name, ai_channel_names, start_frequency=500,\n end_frequency=50000, gain=0, vrms=1, repetitions=64,\n duration=20e-3, iti=0.001, debug=False):\n '''\n Given a single output, measure response in multiple input channels using\n chirp.\n\n Parameters\n ----------\n TODO\n\n Returns\n -------\n result : pandas DataFrame\n Dataframe will be indexed by output channel name and frequency. Columns\n will be rms (in V), snr (in DB) and thd (in percent).\n '''\n from psi.controller.api import ExtractEpochs, FIFOSignalQueue\n calibration = FlatCalibration.as_attenuation(vrms=vrms)\n\n # Create a copy of the engine containing only the channels required for\n # calibration.\n channel_names = ai_channel_names + [ao_channel_name]\n cal_engine = engine.clone(channel_names)\n ao_channel = cal_engine.get_channel(ao_channel_name)\n ai_channels = [cal_engine.get_channel(name) for name in ai_channel_names]\n ao_fs = ao_channel.fs\n ai_fs = ai_channels[0].fs\n\n # Ensure that input channels are synced to the output channel \n device_name = ao_channel.device_name\n ao_channel.start_trigger = ''\n for channel in ai_channels:\n channel.start_trigger = f'/{device_name}/ao/StartTrigger'\n\n samples = int(ao_fs*duration)\n\n # Build the signal queue\n queue = FIFOSignalQueue()\n queue.set_fs(ao_fs)\n\n # Create and add the chirp\n factory = ChirpFactory(ao_fs, start_frequency, end_frequency, duration,\n gain, calibration)\n chirp_waveform = factory.next(samples)\n queue.append(chirp_waveform, repetitions, iti, metadata={'gain': gain})\n\n # Create and add silence\n factory = SilenceFactory(ao_fs, calibration)\n waveform = factory.next(samples)\n queue.append(waveform, repetitions, iti, metadata={'gain': -400})\n\n # Add the queue to the output channel\n output = ao_channel.add_queued_epoch_output(queue, auto_decrement=True)\n\n # Activate the output so it begins as soon as acquisition begins\n output.activate(0)\n\n # Create a dictionary of lists. Each list maps to an individual input\n # channel and will be used to accumulate the epochs for that channel.\n data = {ai_channel.name: [] for ai_channel in ai_channels}\n samples = {ai_channel.name: [] for ai_channel in ai_channels}\n\n def accumulate(epochs, epoch):\n epochs.extend(epoch)\n\n for ai_channel in ai_channels:\n cb = partial(accumulate, data[ai_channel.name])\n epoch_input = ExtractEpochs(epoch_size=duration+iti)\n queue.connect(epoch_input.queue.append)\n epoch_input.add_callback(cb)\n ai_channel.add_input(epoch_input)\n ai_channel.add_callback(samples[ai_channel.name].append)\n\n cal_engine.start()\n while not epoch_input.complete:\n time.sleep(0.1)\n cal_engine.stop()\n\n result_waveforms = {}\n result_psd = {}\n for ai_channel in ai_channels:\n epochs = data[ai_channel.name]\n waveforms = [e['signal'] for e in epochs]\n keys = [e['info']['metadata'] for e in epochs]\n keys = pd.DataFrame(keys)\n keys.index.name = 'epoch'\n keys = keys.set_index(['gain'], append=True)\n keys.index = keys.index.swaplevel('epoch', 'gain')\n\n waveforms = np.vstack(waveforms)\n t = np.arange(waveforms.shape[-1]) / ai_channel.fs\n time_index = pd.Index(t, name='time')\n waveforms = pd.DataFrame(waveforms, index=keys.index,\n columns=time_index)\n mean_waveforms = waveforms.groupby('gain').mean()\n\n samples = int(round(ai_channel.fs * (duration + iti)))\n factory = ChirpFactory(ai_channel.fs, start_frequency, end_frequency,\n duration, gain, calibration)\n chirp_waveform = factory.next(samples)\n\n chirp_psd = util.psd_df(chirp_waveform, ai_channel.fs)\n mean_psd = util.psd_df(mean_waveforms, ai_channel.fs)\n\n result_psd[ai_channel.name] = pd.DataFrame({\n 'rms': mean_psd.loc[gain],\n 'chirp_rms': chirp_psd,\n 'snr': util.db(mean_psd.loc[gain] / mean_psd.loc[-400]),\n })\n #result_waveforms[ai_channel.name] = waveforms\n\n #result_waveforms = pd.concat(result_waveforms.values(),\n # keys=result_waveforms.keys(),\n # names=['channel'])\n\n result_psd = pd.concat(result_psd.values(), keys=result_psd.keys(),\n names=['channel'])\n\n return result_psd\n\n\ndef chirp_spl(engine, **kwargs):\n\n def map_spl(series, engine):\n channel_name, = series.index.get_level_values('channel').unique()\n channel = engine.get_channel(channel_name)\n frequency = series.index.get_level_values('frequency')\n series['spl'] = channel.calibration.get_spl(frequency, series['rms'])\n return series\n\n result = chirp_power(engine, **kwargs)\n return result.groupby('channel').apply(map_spl, engine=engine)\n\n\ndef chirp_sens(engine, gain=-40, vrms=1, **kwargs):\n result = chirp_spl(engine, gain=gain, vrms=vrms, **kwargs)\n result['norm_spl'] = result['spl'] - util.db(result['chirp_rms'])\n result['sens'] = -result['norm_spl'] - util.db(20e-6)\n return result\n\n\ndef chirp_calibration(ai_channel_names, **kwargs):\n kwargs.update({'ai_channel_names': ai_channel_names})\n output_sens = chirp_sens(**kwargs)\n calibrations = {}\n for ai_channel in ai_channel_names:\n data = output_sens.loc[ai_channel]\n calibrations[ai_channel] = InterpCalibration(data.index, data['sens'])\n return calibrations\n","sub_path":"psi/controller/calibration/chirp.py","file_name":"chirp.py","file_ext":"py","file_size_in_byte":5939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"555344982","text":"import numpy as np\nfrom gui_HellOrTreasure.maze import Maze\n\n\ndef my_update(env):\n for t in range(10):\n s = env.reset()\n print(s)\n while True:\n env.render(0.1)\n a = np.random.random_integers(4)-1\n s, r, done, info = env.step(a)\n print('action:{0} | reward:{1} | done: {2}'.format(a, r, done))\n print(s)\n print('\\n')\n if done:\n print(info)\n print(\"--------------------------------------\")\n env.render(0.1)\n break\n\n # end of game\n print('game over')\n env.destroy()\n\n\ndef main():\n # global env\n # env = Maze('./maps/map1.json', full_observation=False)\n # env.after(100, my_update) # Call function update() once after given time/ms.\n # env.mainloop() # mainloop() to run the application.\n env = Maze('./maps/map1.json', full_observation=False)\n my_update(env)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"gui_HellOrTreasure/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"170640282","text":"from collections import defaultdict\nfrom recordclass import recordclass\nfrom src.common import storage, Ptw, Bar\n\n# end - how many H lines had this index as the last one\n# key_existed - number of cases key at the index existed at the time of hashload\n# key_null - ...key didn't exist\n# min_len - length of shortest key on this index\n# max_len - length of longest key on this index\nHCases = recordclass('HCases', 'end, key_existed, key_null, min_len, max_len')\n\nclass HashloadInfo:\n \"\"\"Computes statistics about hashload command only.\n Gets the number of keys in hashload,\n if individual keys existed in storage at the time of hashload or not,\n and also min and max length of a key in hashload.\"\"\"\n def __init__(self):\n # {key index in line: HCases}\n self.cases = defaultdict(lambda: HCases(0, 0, 0, 99, 0))\n self.wtab = None\n def comp_H(self, line):\n self.cases[len(line.key) - 1].end += 1\n for idx, key in enumerate(line.key):\n h = self.cases[idx]\n if storage[key]:\n h.key_existed += 1\n else:\n h.key_null += 1\n l = len(key)\n if l < h.min_len:\n h.min_len = l\n if l > h.max_len:\n h.max_len = l\n def _create_wtab(self):\n if self.wtab is not None:\n return\n self.wtab = Ptw(field_names=[\n 'Key index', 'Existed', \"Didn't exist\", 'Last key', 'Min len', 'Max len'\n ], sortby=0, aligns='cR')\n for idx in self.cases:\n h = self.cases[idx]\n self.wtab.write_raw([\n idx, h.key_existed, h.key_null, h.end, h.min_len, h.max_len\n ])\n self.wtab.add_raw_to_table()\n def output(self):\n self._create_wtab()\n return self.wtab.table\n def graph(self):\n self._create_wtab()\n self.wtab.sort_raw()\n Bar.chart(\n Bar.unpack(\n self.wtab.raw_data, from_idx=1, labels=[\n 'Key existed',\n \"Key didn't exist\",\n 'Key was last',\n 'Shortest key length',\n 'Longest key length'\n ]\n ),\n title='More about keys in hashload',\n xlabel='Key index',\n ylabel='Number of cases',\n group_labels=[row[0] for row in self.wtab.raw_data]\n )\n","sub_path":"src/dataset_analyzer/stats/H_info.py","file_name":"H_info.py","file_ext":"py","file_size_in_byte":2415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"214479647","text":"a1=int(input(\"Enter first Integer : \"))\nb1=int(input(\"Enter second Integer : \"))\na=0\nb=0\nremainder=0\nif a1>b1:\n a=a1\n b=b1\nelse:\n a=b1\n b=a1\nif b==0:\n print(\"The GCD of \",a1,\" and \",b1,\" is : \",a)\nelse:\n while(b!=0):\n remainder=a%b\n a=b\n b=remainder\n if remainder==0:\n print(\"The GCD of \",a1,\" and \",b1,\" is : \",a)","sub_path":"python_assignment/problemset02/qn1.py","file_name":"qn1.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"290837733","text":"# Copyright (C) 2014 Hiroki Horiuchi \n#\n# [GNU all-permissive license]\n# Copying and distribution of this file, with or without modification,\n# are permitted in any medium without royalty provided the copyright\n# notice and this notice are preserved.\n# This file is offered as-is, without any warranty.\n\nfrom . import grub_msdos\n\nr'''\n/dev/sdb3 to (hd1,msdos3)\n/dev/md3 to (md3)\n'''\n\ndef md_or_dos(linux_disk_path):\n if linux_disk_path.startswith('/dev/md'):\n md_slash = linux_disk_path[7] == '/'\n i = 8 if md_slash else 7\n rv = '(md/' + linux_disk_path[i:] + ')'\n else:\n rv = grub_msdos(linux_disk_path)\n return rv\n\nif __name__ == '__main__':\n raise Exception('making a module executable is a bad habit.')\n","sub_path":"data/syspatch/grub.d/py/md.py","file_name":"md.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"268500251","text":"# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n#\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass RefreshIndex(Model):\n \"\"\"Refresh Index Response.\n\n :param content_source_id: Content source Id.\n :type content_source_id: str\n :param is_update_success: Update success status.\n :type is_update_success: bool\n :param advanced_info: Advanced info list.\n :type advanced_info:\n list[~azure.cognitiveservices.vision.contentmoderator.models.RefreshIndexAdvancedInfoItem]\n :param status: Refresh index status.\n :type status:\n ~azure.cognitiveservices.vision.contentmoderator.models.Status\n :param tracking_id: Tracking Id.\n :type tracking_id: str\n \"\"\"\n\n _attribute_map = {\n 'content_source_id': {'key': 'ContentSourceId', 'type': 'str'},\n 'is_update_success': {'key': 'IsUpdateSuccess', 'type': 'bool'},\n 'advanced_info': {'key': 'AdvancedInfo', 'type': '[RefreshIndexAdvancedInfoItem]'},\n 'status': {'key': 'Status', 'type': 'Status'},\n 'tracking_id': {'key': 'TrackingId', 'type': 'str'},\n }\n\n def __init__(self, content_source_id=None, is_update_success=None, advanced_info=None, status=None, tracking_id=None):\n super(RefreshIndex, self).__init__()\n self.content_source_id = content_source_id\n self.is_update_success = is_update_success\n self.advanced_info = advanced_info\n self.status = status\n self.tracking_id = tracking_id\n","sub_path":"azure-cognitiveservices-vision-contentmoderator/azure/cognitiveservices/vision/contentmoderator/models/refresh_index.py","file_name":"refresh_index.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"227686784","text":"# -*- coding: utf-8 -*-\ndef sendEmail(mailAddress, mailText):\n import smtplib\n from email.mime.text import MIMEText\n from email.utils import formataddr\n\n msg = MIMEText(mailText, 'plain', 'utf-8')\n msg['From'] = formataddr([\"\", 'ittest_mail@sina.com'])\n msg['To'] = formataddr([mailText, mailAddress])\n msg['Subject'] = \"主题\"\n try:\n server = smtplib.SMTP(\"smtp.sina.com\", 25)\n server.login(\"ittest_mail@sina.com\", \"XXXXPWD\")\n server.sendmail('ittest_mail@sina.com', [mailAddress], msg.as_string())\n server.quit()\n return True\n except:\n return False\n\n\nuserInputEmailAdd = input(\"请输入你要发送的邮件地址： \")\nuserInputEmailContent = input(\"请输入你想说的话： \")\nif sendEmail(userInputEmailAdd, userInputEmailContent):\n print(\"发送成功\")\nelse:\n print(\"发送失败。。。。。\")","sub_path":"mailGO.py","file_name":"mailGO.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"400951779","text":"import time\nimport os\nimport json\nfrom multiprocessing.dummy import Pool\nimport threading\nimport queue\nimport pickle\nimport json\nimport functools\n\nimport requests\nimport progressbar\n\nimport config\nfrom models import getSession, Video\n\n\nclass BiliError(RuntimeError):\n pass\n\n\nclass WriterThread(threading.Thread):\n def __init__(self, queue, num):\n super().__init__()\n self.q = queue\n self.pbar = progressbar.ProgressBar(max_value=num)\n self.session = getSession(config.DB_PATH)\n self.count = 0\n\n def run(self):\n self.pbar.start()\n while True:\n page, data = self.q.get()\n if data is None:\n break\n for vo in data:\n try:\n v = Video.fromVO(vo)\n except Exception as ex:\n print(ex)\n print(vo)\n raise\n self.session.merge(v)\n self.session.commit()\n self.count += 1\n self.pbar.update(self.count)\n with open('task.json', 'r') as f:\n data = json.load(f)\n data.remove(page)\n with open('task.json', 'w') as f:\n json.dump(data, f)\n self.pbar.finish()\n\n\nclass Spider:\n @staticmethod\n def getPage(page, ps=50, rid=20):\n referer = {\n 20: 'otaku',\n 154: 'three_d'\n }\n url = 'https://api.bilibili.com/x/web-interface/newlist?'\n url += f'callback=&rid={rid}&type=0&pn={page}&ps={ps}&jsonp=jsonp&_={int(time.time() * 1000)}'\n headers = {\n 'Host': 'api.bilibili.com',\n 'Referer': f'https://www.bilibili.com/v/dance/{referer[rid]}/',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36'\n }\n for i in range(5):\n try:\n resp = requests.get(url, headers=headers)\n except requests.ConnectionError:\n if i == 4:\n raise\n print('\\nConnection Error\\n')\n time.sleep(5)\n continue\n\n if resp.status_code == 403:\n print('Request too frequent!')\n if i == 4:\n raise RuntimeError('Request too frequent!')\n time.sleep(5)\n continue\n\n try:\n res = resp.json()\n except json.JSONDecodeError:\n if i == 4:\n raise\n print('Json decode error')\n print(resp.text)\n time.sleep(5)\n continue\n\n break\n\n if res['code']:\n raise BiliError(res['message'])\n return res['data']\n\n def run(self, rid=20):\n if not os.path.exists('task.json'):\n total = self.getPage(1, rid=rid)['page']['count']\n pages = list(range(total // 50 + 2))\n with open('task.json', 'w') as f:\n json.dump(pages, f)\n else:\n with open('task.json') as f:\n pages = json.load(f)\n\n self.queue = queue.Queue()\n\n WriterThread(self.queue, len(pages)).start()\n\n func = functools.partial(self.runPage, rid=rid)\n\n with Pool(8) as pool:\n pool.map(func, pages)\n self.queue.put((None, None))\n\n def runPage(self, page, ps=50, rid=20):\n data = self.getPage(page, ps=ps, rid=rid)['archives']\n self.queue.put((page, data))\n time.sleep(1)\n\n\ndef main():\n Spider().run(rid=154) # 三次元\n # Spider().run(rid=20) # 宅舞\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"crawlVideos.py","file_name":"crawlVideos.py","file_ext":"py","file_size_in_byte":3706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"95131983","text":"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"A very simple MNIST classifier.\nSee extensive documentation at\nhttps://www.tensorflow.org/get_started/mnist/beginners\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nimport tensorflow as tf\n\nimport h5py\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nimport random\nimport time\n\nFLAGS = None\n\n\ndef weight_variable(shape):\n initial = tf.truncated_normal(shape, stddev=0.1)\n return tf.Variable(initial)\n\n\ndef bias_variable(shape):\n initial = tf.constant(0.1, shape=shape)\n return tf.Variable(initial)\n\n\ndef conv2d(x, W):\n return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n\n\ndef max_pool_2x2(x):\n return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n strides=[1, 2, 2, 1], padding='SAME')\n\n\ndef sample_batch(data, label, batchSize):\n idx = list(range(data.shape[0]))\n idxBatch = random.sample(idx, batchSize)\n # for i in range(batchSize):\n # imgplot = plt.imshow(data[idxBatch[i],:,:])\n # plt.show()\n # print(idxBatch[i])\n return (data[idxBatch, :, :],\n label[idxBatch, :])\n\n\ndef next_batch(data, label, i, batchSize):\n # print(i, batchSize)\n return (data[int(i * batchSize):int((i + 1) * batchSize), :, :],\n label[int(i * batchSize):int((i + 1) * batchSize), :])\n\n\n# def main(_):\n# Import data\n# mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)\n\n# load my gesture dataset\ndata = h5py.File('data128.mat')\ndata2 = h5py.File('dataTest128.mat')\nprint(data['imgTrain'].shape)\nprint(data['label'].shape)\ndataSize = data['imgTrain'].shape[0]\n\nbatchSize = 120\nnumRound = 200\ntrainIter = int(dataSize / batchSize * numRound)\nprint(\"Total number of training iteration: %d\" % trainIter)\n\nbatchSizeTest = batchSize\n\n# Create the model\n\n\nmeanAccuracy = 0\n\n\ndataTrain = np.squeeze(data['imgTrain'][:, :, :, :])\nlabelTrain = np.squeeze(data['label'][:, :])\ndataTest = np.squeeze(data2['imgTest'][:, :, :, :])\nlabelTest = np.squeeze(data2['labelTest'][:, :])\n\ndataTrain = np.transpose(dataTrain, (0, 3, 2, 1))\n# labelTrain = np.transpose(labelTrain, (1, 0))\ndataTest = np.transpose(dataTest, (0, 3, 2, 1))\n# labelTest = np.transpose(labelTest, (1, 0))\n\n# orderTrain = np.reshape(np.mod(np.random.permutation(dataTrain.shape[0]*100), dataTrain.shape[0]), [-1, batchSize]);\n# print(orderTrain.shape)\n\nx = tf.placeholder(tf.float32, [None, 128, 128, 3])\n\n# Define loss and optimizer\ny_ = tf.placeholder(tf.float32, [None, 12])\n\n# CNN\n# x_image = tf.reshape(x, [-1, 128, 128, 3])\n\n# layer one\nW_conv11 = weight_variable([5, 5, 3, 64])\nb_conv11 = bias_variable([64])\nh_conv11 = tf.nn.relu(conv2d(x, W_conv11) + b_conv11)\n\nh_pool1 = max_pool_2x2(h_conv11)\n\n# # batch normalization, maybe wrong\n# axis = list(range(len(h_pool1.get_shape()) - 1))\n# mean, variance = tf.nn.moments(h_pool1, axis)\n# h_bn1 = tf.nn.batch_normalization(h_pool1, mean, variance, 0, 1, 1e-4)\n\n# layer two\nW_conv21 = weight_variable([5, 5, 64, 64])\nb_conv21 = bias_variable([64])\nh_conv21 = tf.nn.relu(conv2d(h_pool1, W_conv21) + b_conv21)\n\nh_pool2 = max_pool_2x2(h_conv21)\n\n# # batch normalization, maybe wrong\n# axis = list(range(len(h_pool2.get_shape()) - 1))\n# mean, variance = tf.nn.moments(h_pool2, axis)\n# h_bn2 = tf.nn.batch_normalization(h_pool2, mean, variance, 0, 1, 1e-4)\n\n# layer three\nW_conv31 = weight_variable([5, 5, 64, 128])\nb_conv31 = bias_variable([128])\nh_conv31 = tf.nn.relu(conv2d(h_pool2, W_conv31) + b_conv31)\n\nh_pool3 = max_pool_2x2(h_conv31)\n\n# layer 4\nW_conv41 = weight_variable([5, 5, 128, 128])\nb_conv41 = bias_variable([128])\nh_conv41 = tf.nn.relu(conv2d(h_pool3, W_conv41) + b_conv41)\n\nh_pool4 = max_pool_2x2(h_conv41)\n\n# # batch normalization, maybe wrong\n# axis = list(range(len(h_pool3.get_shape()) - 1))\n# mean, variance = tf.nn.moments(h_pool3, axis)\n# h_bn3 = tf.nn.batch_normalization(h_pool3, mean, variance, 0, 1, 1e-4)\n\nW_fc1 = weight_variable([8 * 8 * 128, 2048])\nb_fc1 = bias_variable([2048])\nh_pool3_flat = tf.reshape(h_pool4, [-1, 8 * 8 * 128])\nh_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1)\n\nkeep_prob = tf.placeholder(tf.float32)\nh_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n\nW_fc2 = weight_variable([2048, 2048])\nb_fc2 = bias_variable([2048])\nh_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)\n\nh_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)\n\nW_fc3 = weight_variable([2048, 12])\nb_fc3 = bias_variable([12])\ny_conv = tf.matmul(h_fc2_drop, W_fc3) + b_fc3\n\ncross_entropy = tf.reduce_mean(\n tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y_conv))\n\ntrain_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n\ncorrect_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\nsess = tf.InteractiveSession()\n\n# Add an op to initialize the variables.\ninit_op = tf.global_variables_initializer()\n\n# Add ops to save and restore all the variables.\nsaver = tf.train.Saver(max_to_keep=4, keep_checkpoint_every_n_hours=2)\n\nsess.run(init_op)\nsaver.save(sess, \"E:/works/Topics/CNN_Robot/model/model_CNN\", global_step=1000)\n\nstart_time = time.time()\nfor i in range(trainIter):\n batch = sample_batch(dataTrain, labelTrain, batchSize)\n # idx = i % (dataSize / batchSize)\n # batch = next_batch(dataTrain, labelTrain, idx, batchSize)\n train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n if i % (dataSize / batchSize) == 0:\n train_accuracy = accuracy.eval(feed_dict={\n x: batch[0], y_: batch[1], keep_prob: 1.0})\n print(\"step %d, training accuracy %g\" % (i, train_accuracy))\n duration = time.time() - start_time\n print(duration)\n if i % (dataSize / batchSize * 5) == 0:\n accuracyTest = 0\n for ii in range(int(dataTest.shape[0] / batchSizeTest)):\n accuracyTest += accuracy.eval(feed_dict={\n x: dataTest[ii * batchSizeTest: (ii + 1) * batchSizeTest - 1, :, :],\n y_: labelTest[ii * batchSizeTest: (ii + 1) * batchSizeTest - 1, :], keep_prob: 1.0})\n accuracyTest /= dataTest.shape[0] / batchSizeTest\n print(\"step %d, test accuracy Now %g\" % (i, accuracyTest))\n saver.save(sess, \"E:/works/Topics/CNN_Robot/model/model_CNN\")\n\n\n\n\n# accuracyNow = 0\n# for i in range(int(dataTest.shape[0] / batchSizeTest)):\n# accuracyNow += accuracy.eval(feed_dict={\n# x: dataTest[i * batchSizeTest: (i + 1) * batchSizeTest - 1, :, :],\n# y_: labelTest[i * batchSizeTest: (i + 1) * batchSizeTest - 1, :], keep_prob: 1.0})\n# accuracyNow /= dataTest.shape[0] / batchSizeTest\n#\n# meanAccuracy += accuracyNow\n# print(\"**************** LOO CV no. %d, test accuracy %g *********************\" % (iCV, accuracyNow))\n\ntf.reset_default_graph()\nsess.close()\n\n\n","sub_path":"Vision/CNN.py","file_name":"CNN.py","file_ext":"py","file_size_in_byte":7564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"309304607","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\nimport csv\n\ndriver = webdriver.Chrome()\ndriver.get(\"--Enter URL--\") # specify the website to be scraped\nname_of_governor='Name' # specify name to keep track of things.\n\ncsv_file = open('tweets.csv', 'w',newline = '',encoding=\"utf-8\") # create a csv file to write the scraped data\nwriter = csv.writer(csv_file)\nwriter.writerow(['name','tweet_content'])\n\nlast_height = driver.execute_script(\"return document.body.scrollHeight\")\nprint(last_height)\n\n# this loop ensures that the code scrapes all tweets on the page (use this if tweets less than 1000, else it will keep running indefinitely)\nwhile True:\t\n\tdriver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\ttime.sleep(4)\n\tnew_height = driver.execute_script(\"return document.body.scrollHeight\")\n\tif new_height == last_height:\n\t\tbreak\n\tlast_height = new_height\n\n# Find all the tweets using xpath\ntweets = driver.find_elements_by_xpath('//ol[@class=\"stream-items js-navigable-stream\"]/li') \n\n# Store tweet in tweets in a dictionary\nfor tweet in tweets:\n\t# Initialize an empty dictionary for each tweet\n\ttweet_dict = {}\n\t# Use Xpath to locate the content\n\ttweet_content=tweet.find_element_by_xpath('.//p').text\n\t\n\ttweet_dict['name'] = name_of_governor\t\n\ttweet_dict['tweet_content'] = tweet_content\n\twriter.writerow(tweet_dict.values())\n\ncsv_file.close()\ndriver.close()","sub_path":"GIT_Web_scraping/Scraping/Twitter_Spider_Less.py","file_name":"Twitter_Spider_Less.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"20743448","text":"import pickle as pkl\nimport pandas as pd\nimport json\nimport numpy as np\n\n\ndef read_text(data_path):\n res = []\n with open(data_path) as f:\n for line in f.readlines():\n res.append(line.strip())\n return res\n\n\ndef main():\n gold_summary_data = pkl.load(open('data/dstc10/dstc10_data.summary.pickle', 'rb'))\n pred_summary = read_text('ckpts/ckpt_youcook_caption/test_hyp_univl_caption.txt')\n test = json.load(open('data/dstc10/test_set4DSTC7-AVSD.json'))\n test_ids = list(pd.read_csv('data/dstc10/dstc10_test.csv')['video_id'])\n features_pkl = pkl.load(open('data/dstc10/dstc10_videos_features_all.pickle', 'rb'))\n for idx_d, d in enumerate(test['dialogs']):\n image_id = d['image_id']\n gold_summary_data[image_id] = dict()\n his = []\n transcript = []\n text = []\n summary, caption = pred_summary[idx_d].split(' | ')\n for c in d['dialog']:\n c_his = ' '.join(his)\n q = 'User: ' + c['question']\n a = 'Robot: ' + c['answer']\n transcript.append((c_his + ' ' + q + ' | ' + caption + ' | '+ summary).strip())\n text.append(a.replace('Robot: ', ''))\n his.append(q)\n his.append(a)\n gold_summary_data[image_id]['text'] = np.array(text).reshape(-1)\n gold_summary_data[image_id]['transcript'] = np.array(transcript).reshape(-1)\n n = gold_summary_data[image_id]['text'].shape[0]\n gold_summary_data[image_id]['start'] = np.array([0] * n)\n gold_summary_data[image_id]['end'] = np.array([features_pkl[image_id].shape[0]] * n)\n pkl.dump(gold_summary_data, open('data/dstc10/dstc10_data.summary.pred.pickle', 'wb'))\n print('done')\n\n\nif __name__ == '__main__':\n main()","sub_path":"preprocessing/preprocess_dstc10_for_summary.py","file_name":"preprocess_dstc10_for_summary.py","file_ext":"py","file_size_in_byte":1755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"518493954","text":"# Copyright 2017 Prashant Singh, Fredrik Wrede and Andreas Hellander\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\"\"\"\nMulti-Armed Bandit - Approximate Bayesian Computation\n\"\"\"\n\n# Imports\nfrom sciope.inference.abc_inference import ABC\nimport numpy as np\nfrom sciope.data.dataset import DataSet\nfrom sciope.utilities.distancefunctions import euclidean as euc\nfrom sciope.utilities.summarystats import burstiness as bs\nfrom sciope.utilities.mab import mab_direct as md\nfrom sciope.utilities.housekeeping import sciope_logger as ml\nfrom sciope.utilities.housekeeping import sciope_profiler\n\n\n# The following variable stores n normalized distance values after n summary statistics have been calculated\nnormalized_distances = None\n\n# Set up the logger and profiler\nlogger = ml.SciopeLogger().get_logger()\n\n\ndef arm_pull(arm_idx):\n \"\"\"\n Used by MAB algorithms; Each arm corresponds to a summary statistic and an arm pull is simply selection of one\n (or more) summary statistics in inference. Here that corresponds to simply returning the desired arm.\n :param arm_idx: The index into the vector of arms\n :return: -1 * distance value from distances corresponding to the arm_idx, as reward is to be maximized according to\n MABs but in inference we minimize distance.\n \"\"\"\n global normalized_distances\n return -1 * normalized_distances[-1, arm_idx]\n\n\n# Class definition: Bandits-ABC rejection sampling\nclass BanditsABC(ABC):\n \"\"\"\n ABC rejection sampling with dynamic multi-armed bandit (MAB) assisted summary statistic selection.\n \"\"\"\n\n def __init__(self, data, sim, prior_function, mab_variant=md.MABDirect(arm_pull), k=1, epsilon=0.1,\n parallel_mode=True, summaries_function=bs.Burstiness(), distance_function=euc.EuclideanDistance()):\n super().__init__(data, sim, prior_function, epsilon, parallel_mode, summaries_function, distance_function)\n self.name = 'BanditsABC'\n self.mab_variant = mab_variant\n self.k = k\n logger.info(\"Multi-Armed Bandits Approximate Bayesian Computation initialized\")\n\n def scale_distance(self, dist):\n \"\"\"\n Performs scaling in [0,1] of a given distance vector/value with respect to historical distances\n :param dist: a distance value or vector\n :return: scaled distance value or vector\n \"\"\"\n global normalized_distances\n self.historical_distances.append(dist.ravel())\n all_distances = np.array(self.historical_distances)\n divisor = np.asarray(all_distances.max(axis=0))\n normalized_distances = all_distances\n for j in range(0, len(divisor), 1):\n if divisor[j] > 0:\n normalized_distances[:, j] = normalized_distances[:, j] / divisor[j]\n\n return normalized_distances[-1, :]\n\n @sciope_profiler.profile\n def rejection_sampling(self, num_samples):\n \"\"\"\n * overrides rejection_sampling of ABC class *\n Perform ABC inference with dynamic summary statistic selection using MABs.\n :return:\n posterior: The posterior distribution (samples)\n distances: Accepted distance values\n accepted_count: Number of accepted samples\n trial_count: The number of total trials performed in order to converge\n \"\"\"\n accepted_count = 0\n trial_count = 0\n accepted_samples = []\n distances = []\n fixed_dataset = DataSet('Fixed Data')\n sim_dataset = DataSet('Simulated Data')\n fixed_dataset.add_points(targets=self.data, summary_stats=self.summaries_function.compute(self.data))\n\n while accepted_count < num_samples:\n # Rejection sampling\n # Draw from the prior\n trial_param = self.prior_function.draw()\n\n # Perform the trial\n sim_result = self.sim(trial_param)\n\n # Get the statistic(s)\n # In case of multiple summaries, a numpy array of k summaries should be returned\n # ToDo: add exception handling to enforce it\n sim_stats = self.summaries_function.compute(sim_result)\n\n # Set/Update simulated dataset\n sim_dataset.add_points(targets=sim_result, summary_stats=sim_stats)\n\n # Calculate the distance between the dataset and the simulated result\n # In case of multiple summaries, a numpy array of k distances should be returned\n sim_dist = self.distance_function.compute(fixed_dataset.s, sim_stats)\n\n # Normalize distances between [0,1]\n sim_dist_scaled = self.scale_distance(sim_dist)\n\n # Use MAB arm selection to identify the best 'k' arms or summary statistics\n num_arms = len(sim_dist_scaled)\n arms = range(num_arms)\n top_k_arms_idx = self.mab_variant.select(arms, self.k)\n top_k_distances = np.asarray([sim_dist_scaled[i] for i in top_k_arms_idx])\n\n # Take the norm to combine the top k distances\n combined_distance = np.linalg.norm(top_k_distances)\n logger.debug(\"Rejection Sampling: trial parameter = [{0}], distance = [{1}]\".format(trial_param,\n combined_distance))\n\n # Accept/Reject\n if combined_distance <= self.epsilon:\n accepted_samples.append(trial_param)\n distances.append(sim_dist)\n accepted_count += 1\n logger.info(\"Rejection Sampling: accepted a new sample, total accepted samples = {0}\".\n format(len(accepted_samples)))\n\n trial_count += 1\n\n self.results = {'accepted_samples': accepted_samples, 'distances': distances, 'accepted_count': accepted_count,\n 'trial_count': trial_count, 'inferred_parameters': np.mean(accepted_samples, axis=0)}\n return self.results\n\n","sub_path":"sciope/inference/bandits_abc.py","file_name":"bandits_abc.py","file_ext":"py","file_size_in_byte":6408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"56102225","text":"from unittest import TestCase, TestSuite, makeSuite, main\n\nimport Zope\ntry:\n from Interface.Verify import verifyClass\nexcept ImportError:\n # for Zope versions before 2.6.0\n from Interface import verify_class_implementation as verifyClass\n\nfrom Products.CMFCore.tests.base.testcase import \\\n SecurityTest\n\nfrom Products.CMFCore.tests.base.utils import \\\n has_path\n\nfrom Products.CMFCore.tests.base.dummy import DummyFTI\nfrom Products.CMFCore.tests.base.dummy import DummyContent\n\nfrom Products.CMFCore.CatalogTool import CatalogTool\nfrom Products.CMFCore.TypesTool import TypesTool\nfrom Products.CMFCore.URLTool import URLTool\nfrom Products.CMFCore.WorkflowTool import WorkflowTool\n\nfrom Products.CMFDefault.DiscussionTool import DiscussionTool\nfrom Products.CMFDefault.DiscussionTool import DiscussionNotAllowed\nfrom Products.CMFDefault.DiscussionItem import DiscussionItem\nfrom Products.CMFDefault.DiscussionItem import DiscussionItemContainer\n\n\nclass DiscussionItemTests(TestCase):\n\n def test_interface(self):\n from Products.CMFCore.interfaces.Dynamic \\\n import DynamicType as IDynamicType\n from Products.CMFCore.interfaces.Contentish \\\n import Contentish as IContentish\n from Products.CMFCore.interfaces.Discussions \\\n import DiscussionResponse as IDiscussionResponse\n\n verifyClass(IDynamicType, DiscussionItem)\n verifyClass(IContentish, DiscussionItem)\n verifyClass(IDiscussionResponse, DiscussionItem)\n\n\nclass DiscussionItemContainerTests(TestCase):\n\n def test_interface(self):\n from Products.CMFCore.interfaces.Discussions \\\n import Discussable as IDiscussable\n\n verifyClass(IDiscussable, DiscussionItemContainer)\n\n\nclass DiscussionTests( SecurityTest ):\n\n def setUp( self ):\n\n SecurityTest.setUp(self)\n\n root = self.root\n root._setObject( 'portal_discussion', DiscussionTool() )\n self.discussion_tool = root.portal_discussion\n root._setObject( 'portal_catalog', CatalogTool() )\n self.catalog_tool = root.portal_catalog\n root._setObject( 'portal_url', URLTool() )\n self.url_tool = root.portal_url\n root._setObject( 'portal_workflow', WorkflowTool() )\n self.workflow_tool = root.portal_workflow\n root._setObject( 'portal_types', TypesTool() )\n types_tool = self.types_tool = root.portal_types\n try: root._delObject('test')\n except AttributeError: pass\n root._setObject( 'test', DummyContent( 'test', catalog=1 ) )\n\n def test_policy( self ):\n\n test = self.root.test\n self.assertRaises( DiscussionNotAllowed\n , self.discussion_tool.getDiscussionFor\n , test\n )\n assert getattr( test, 'talkback', None ) is None\n\n test.allow_discussion = 1\n assert self.discussion_tool.getDiscussionFor( test )\n assert test.talkback\n\n del test.talkback\n del test.allow_discussion\n self.types_tool._setObject( 'Dummy Content', DummyFTI )\n self.assertRaises( DiscussionNotAllowed\n , self.discussion_tool.getDiscussionFor\n , test\n )\n assert getattr( test, 'talkback', None ) is None\n\n ti = getattr(self.types_tool, 'Dummy Content')\n ti.allow_discussion = 1\n assert self.discussion_tool.getDiscussionFor( test )\n assert test.talkback\n\n del test.talkback\n ti.allow_discussion = 0\n self.assertRaises( DiscussionNotAllowed\n , self.discussion_tool.getDiscussionFor\n , test\n )\n assert getattr( test, 'talkback', None ) is None\n\n test.allow_discussion = 1\n assert self.discussion_tool.getDiscussionFor( test )\n assert test.talkback\n\n def test_nestedReplies( self ):\n test = self.root.test\n test.allow_discussion = 1\n talkback = self.discussion_tool.getDiscussionFor( test )\n assert talkback._getDiscussable() == test\n assert talkback._getDiscussable( outer=1 ) == test\n assert not talkback.hasReplies( test )\n assert len( talkback.getReplies() ) == 0\n\n reply_id = talkback.createReply( title='test', text='blah' )\n assert talkback.hasReplies( test )\n assert len( talkback.getReplies() ) == 1\n assert talkback.getReply( reply_id )\n\n reply1 = talkback.getReplies()[0]\n items = talkback._container.items()\n assert items[0][0] == reply1.getId()\n assert reply1.inReplyTo() == test\n\n parents = reply1.parentsInThread()\n assert len( parents ) == 1\n assert test in parents\n\n talkback1 = self.discussion_tool.getDiscussionFor( reply1 )\n assert talkback == talkback1\n assert len( talkback1.getReplies() ) == 0\n assert len( talkback.getReplies() ) == 1\n\n talkback1.createReply( title='test2'\n , text='blah2'\n )\n assert len( talkback._container ) == 2\n assert talkback1.hasReplies( reply1 )\n assert len( talkback1.getReplies() ) == 1\n assert len( talkback.getReplies() ) == 1\n\n reply2 = talkback1.getReplies()[0]\n assert reply2.inReplyTo() == reply1\n\n parents = reply2.parentsInThread()\n assert len( parents ) == 2\n assert parents[ 0 ] == test\n assert parents[ 1 ] == reply1\n\n parents = reply2.parentsInThread( 1 )\n assert len( parents ) == 1\n assert parents[ 0 ] == reply1\n\n def test_itemCataloguing( self ):\n\n test = self.root.test\n catalog = self.catalog_tool._catalog\n test.allow_discussion = 1\n assert len( self.catalog_tool ) == 1\n assert has_path( catalog, test.getPhysicalPath() )\n talkback = self.discussion_tool.getDiscussionFor( test )\n assert talkback.getPhysicalPath() == ( '', 'test', 'talkback' ), \\\n talkback.getPhysicalPath()\n talkback.createReply( title='test'\n , text='blah'\n )\n assert len( self.catalog_tool ) == 2\n for reply in talkback.getReplies():\n assert has_path( catalog, reply.getPhysicalPath() )\n assert has_path( catalog\n , '/test/talkback/%s' % reply.getId() )\n\n reply1 = talkback.getReplies()[0]\n talkback1 = self.discussion_tool.getDiscussionFor( reply1 )\n talkback1.createReply( title='test2'\n , text='blah2'\n )\n for reply in talkback.getReplies():\n assert has_path( catalog, reply.getPhysicalPath() )\n assert has_path( catalog\n , '/test/talkback/%s' % reply.getId() )\n for reply in talkback1.getReplies():\n assert has_path( catalog, reply.getPhysicalPath() )\n assert has_path( catalog\n , '/test/talkback/%s' % reply.getId() )\n\n def test_deletePropagation( self ):\n\n test = self.root.test\n\n test.allow_discussion = 1\n talkback = self.discussion_tool.getDiscussionFor( test )\n talkback.createReply( title='test'\n , text='blah'\n )\n self.root._delObject( 'test' )\n assert len( self.catalog_tool ) == 0\n\n def test_deleteReplies(self):\n test = self.root.test\n test.allow_discussion = 1\n\n talkback = self.discussion_tool.getDiscussionFor(test)\n id1 = talkback.createReply(title='test1', text='blah')\n reply1 = talkback.getReply(id1)\n talkback1 = self.discussion_tool.getDiscussionFor(reply1)\n id2 = talkback1.createReply(title='test2', text='blah')\n reply2 = talkback1.getReply(id2)\n talkback2 = self.discussion_tool.getDiscussionFor(reply2)\n id3 = talkback2.createReply(title='test3', text='blah')\n reply3 = talkback.getReply(id3)\n talkback3 = self.discussion_tool.getDiscussionFor(reply3)\n self.assertEqual(len(talkback.getReplies()), 1)\n self.assertEqual(len(talkback1.getReplies()), 1)\n self.assertEqual(len(talkback2.getReplies()), 1)\n self.assertEqual(len(talkback3.getReplies()), 0)\n\n talkback.deleteReply(id2)\n self.assertEqual(len(talkback.getReplies()), 1)\n reply1 = talkback.getReply(id1)\n talkback1 = self.discussion_tool.getDiscussionFor(reply1)\n self.assertEqual(len(talkback.getReplies()), 1)\n self.assertEqual(len(talkback1.getReplies()), 0)\n\ndef test_suite():\n return TestSuite((\n makeSuite( DiscussionItemTests ),\n makeSuite( DiscussionItemContainerTests ),\n makeSuite( DiscussionTests ),\n ))\n\nif __name__ == '__main__':\n main(defaultTest='test_suite')\n","sub_path":"CMF/tags/1.4.6/CMFDefault/tests/test_Discussions.py","file_name":"test_Discussions.py","file_ext":"py","file_size_in_byte":8955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"84345184","text":"def bolunen(a):\n b = 0\n for i in str(a):\n i = int(i)\n b += i\n \n if int(a) % b == 0:\n print(str(a) + \" ədədi \" + str(b) + \" ədədinə bölünür.\")\n \nbolunen(133)\n","sub_path":"Ədədin onu təşkil edən rəqəmlərin cəminə qalıqsız bölündüyünü müəyyən edən funksiya.py","file_name":"Ədədin onu təşkil edən rəqəmlərin cəminə qalıqsız bölündüyünü müəyyən edən funksiya.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"40879550","text":"#!/usr/bin/env python2.7\n#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\n\"\"\"iqfeed: Data downloader for Iqfeed/DTN\n Tibor Kiss - Copyright (c) 2012-2016 All rights reserved\n\nUsage:\n iqfeed process-file [-d DIR] [-i CON] [-t TZ] [-D]\n iqfeed download [-d DIR] [-i CON] [-t TZ] [-D]\n iqfeed -h | --help\n\n iqfeed --start_date 20150101 --end_date 2016101 --ticker AAPL --outdir 'C:\\\\temp' --seconds_per_bar 60\n\nCommands:\n download Download the specified instrument\n get-from-file Download instruments listed in the specified file\n\nOptions:\n -d DIR --download-dir DIR Directory where the files will be downloaded [default: .]\n -i CON --iqfeed CON IQFeed host & port [default: localhost:9100]\n -t TZ --tz TZ Time zone [default: US/Eastern]\n -D Debug mode\n -h Help screen\n\nNote:\nDate format for end_date and start_date: YYYYMMDD\n\n\"\"\"\n\n\nimport os\nimport sys\nimport logging\nimport pytz\nimport click\nimport pandas as pd\nfrom datetime import datetime, timedelta\n\nfrom iqfeed.download import get_bars\nfrom iqfeed.tools import get_instruments_from_file, bars_to_dateframe\n\ntoday = datetime.now().today()\ntoday_str = today.strftime('%Y%m%d')\neastern_tz = 'US/Eastern'\ndatetime_format = '%Y%m%d %H%M%S'\ndate_format = '%Y%m%d'\n\n@click.command()\n@click.option('--ticker', default=None, help='Ticker Symbol')\n@click.option('--outdir', default=None, help='Output folder')\n@click.option('--start_date', default='20140101', help='Start date default to 20140101')\n@click.option('--end_date', default=today_str, help='End date')\n@click.option('--debug', default=False, help='True or False to introduce debug mode')\n@click.option('--universe', default=None, help='The file that contains the universe')\n@click.option('--iqfeed_host', default='localhost', help='IQFeed Host default localhost')\n@click.option('--iqfeed_port', default=9100, help='IQFeed Port, default 9100')\n@click.option('--timezone', default=eastern_tz, help='Timezone, default US/Eastern')\n@click.option('--seconds_per_bar', default=60, help='bar per seconds, default 60')\n@click.option('--delete_date', default=None, help='Remove data from YYYYMMDD onward based on ticker or universe')\n@click.option('--freq', default='minute', type=click.Choice(['minute', 'daily', 'tick']), help='Different price type')\ndef main(ticker, outdir, start_date, end_date, debug, universe, iqfeed_host, iqfeed_port, timezone, seconds_per_bar,\n delete_date, freq):\n log = logging.getLogger()\n log_console = logging.StreamHandler(sys.stdout)\n log.setLevel(logging.DEBUG if debug else logging.INFO)\n log_console.setLevel(logging.DEBUG if debug else logging.INFO)\n log.addHandler(log_console)\n\n if ticker is not None:\n instruments = (ticker, )\n elif universe is not None:\n instruments = get_instruments_from_file(universe)\n else:\n raise NotImplementedError('No ticker or universe is specified. Not sure what to do.')\n\n if delete_date is not None:\n for (i, instrument) in enumerate(instruments):\n log.info('Deleting {0} data after {1}, {2} out of {3}'.format(instrument, delete_date, i+1, len(instruments)))\n instrument_path = os.path.join(outdir, instrument+'.csv')\n if not os.path.exists(instrument_path):\n raise Exception('Path Not Found. Check if the price data is :{0}'.format(instrument_path))\n price_df = pd.read_csv(instrument_path, index_col=0, parse_dates=True)\n price_df.loc[price_df.index <= pd.to_datetime(delete_date)].to_csv(instrument_path,\n date_format=datetime_format)\n return\n\n tz = pytz.timezone(timezone)\n\n for (i, instrument) in enumerate(instruments):\n try:\n log.info(str.format(\"Processing {0} ({1} out of {2})\", instrument, i+1, len(instruments)))\n\n instrument_path = os.path.join(outdir, instrument+'.csv')\n price_df = pd.DataFrame()\n process_start_date = start_date\n if os.path.exists(instrument_path):\n price_df = pd.read_csv(instrument_path, index_col=0, parse_dates=True)\n last_date = price_df.index[-1].date()\n process_start_date = (last_date + timedelta(days=1)).strftime('%Y%m%d')\n\n if int(process_start_date) > int(end_date):\n log.info('Price already in place.')\n elif freq == 'minute':\n bars = get_bars(freq, instrument, process_start_date, end_date, tz, seconds_per_bar, iqfeed_host, iqfeed_port)\n if len(bars):\n new_df = bars_to_dateframe(bars, tz)\n pd.concat([price_df, new_df])[['Open', 'High', 'Low', 'Close', 'Volume']].to_csv(instrument_path, date_format=datetime_format)\n elif freq == 'daily':\n bars = get_bars(freq, instrument, process_start_date, end_date, tz, seconds_per_bar, iqfeed_host, iqfeed_port)\n if len(bars):\n new_df = bars_to_dateframe(bars, tz)\n pd.concat([price_df, new_df])[['Open', 'High', 'Low', 'Close', 'Volume']].to_csv(instrument_path, date_format=date_format)\n elif freq == 'tick':\n bars = get_bars(freq, instrument, process_start_date, end_date, tz, seconds_per_bar, iqfeed_host, iqfeed_port)\n if len(bars):\n new_df = bars_to_dateframe(bars, tz)\n pd.concat([price_df, new_df])[['Open', 'High', 'Low', 'Close', 'Volume']].to_csv(instrument_path, date_format=datetime_format)\n else:\n raise TypeError('The freq param is not in a predefined mode')\n\n except Exception as e:\n log.error('Exception during download, continuing', exc_info=e)\n","sub_path":"iqfeed/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"518890608","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 2 18:45:32 2020\n\n@author: israel\n\"\"\"\n\ndef read_data(file_name):\n tree = []\n with open(file_name) as f:\n line = f.readline()\n while line:\n row = []\n for n in line.split(' '):\n row.append(int(n))\n tree.append(row)\n line = f.readline()\n return tree\n\n\nfile_name = 'p067_triangle.txt'\ntree = read_data(file_name)\n\nfor r in range(len(tree)-2,-1,-1):\n # Itera por las filas\n for i in range(0,len(tree[r])):\n # Calcula el maximo que hay debajo\n best = max(tree[r+1][i],tree[r+1][i+1])\n # Actualiza el valor\n tree[r][i] += best\n \nprint(f'Best value: {tree[0][0]}')","sub_path":"Problem_067/problem_67.py","file_name":"problem_67.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"189693561","text":"from jsonrpclib.SimpleJSONRPCServer import SimpleJSONRPCServer\nimport sys, os\nimport random\nfrom ast import literal_eval\n\ndef gerarMatriz(l,c):\n matriz = [[\"*\" for x in range(l)] for y in range(c)] # l -> linhas ; c -> colunas\n return matriz\n\ndef sortearBombas(n,l,c):\n vetor = []\n for i in range(n): #número de bombas\n i = random.randint(0,l-1) # y -1\n n = random.randint(0,c-1) # x -1\n while ([i,n] in vetor):\n i = random.randint(0,l-1) # y -1\n n = random.randint(0,c-1) # x -1\n vetor.append([i,n])\n return vetor\n\ndef bombasAoRedor(l,c,posBombas):\n count = 0\n if ([l+1,c] in posBombas):\n count += 1\n if ([l-1,c] in posBombas):\n count += 1\n if ([l,c-1] in posBombas):\n count += 1\n if ([l-1,c-1] in posBombas):\n count += 1\n if ([l+1,c-1] in posBombas):\n count += 1\n if ([l-1,c+1] in posBombas):\n count += 1\n if ([l+1,c+1] in posBombas):\n count += 1\n if ([l,c+1] in posBombas):\n count += 1\n return count\n\ndef save(historico):\n hist = open('log_game.txt', 'w')\n hist.write(str(historico))\n hist.close()\n\ndef verifyFile():\n arquivo = open('log_game.txt', 'r')\n waiter = str(arquivo.read())\n dados = literal_eval(waiter)\n arquivo.close()\n return dados\n\ndef server():\n serverRPC = SimpleJSONRPCServer(('localhost', 7002))\n print(\"Servidor Conectado\")\n serverRPC.register_function(gerarMatriz)\n serverRPC.register_function(verifyFile)\n serverRPC.register_function(sortearBombas)\n serverRPC.register_function(bombasAoRedor)\n serverRPC.register_function(save)\n serverRPC.serve_forever()\n\nserver()","sub_path":"3.JogoRPC/rpc_server.py","file_name":"rpc_server.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"259228038","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nimport os\nimport uuid\nimport json\nimport functools\nimport subprocess\nimport StringIO\n\nfrom datetime import datetime\nfrom collections import namedtuple\nfrom ansi2html import ansi2html\nfrom flask import request, redirect, session, url_for, flash, render_template, jsonify, abort, send_file, Response\nfrom forms import *\nfrom web import app\nfrom models import *\nfrom database import db_session\nimport settings, utils\nfrom userSystem import login_manager\nfrom flask_login import LoginManager, login_required, login_user, logout_user, UserMixin, current_user\n\nJudgeStatus = namedtuple('JudgeStatus', ['name', 'action', 'time'])\njudge_status = {}\n\n\n@app.route(settings.WEBROOT + '/login', methods=['GET', 'POST'])\ndef login():\n tmp_form = LoginForm()\n\n if tmp_form.validate_on_submit():\n u = db_session.query(User).filter(User.username == tmp_form.username.data).first()\n if not u:\n flash(\"Can't Find Username\")\n elif u.hash_password != tmp_form.password.data:\n flash(\"Password error\")\n else:\n login_user(u, remember=True)\n return render_template('homepage.html')\n\n return render_template('userSystem/login.html', tmp_form=tmp_form)\n\n\n@login_required\n@app.route(settings.WEBROOT + '/logout', methods=['GET', 'POST'])\ndef logout():\n logout_user()\n return render_template('homepage.html')\n\n\n@app.route(settings.WEBROOT + '/register', methods=['GET', 'POST'])\ndef register():\n tmp_form = RegistrationForm()\n\n if tmp_form.validate_on_submit():\n u = db_session.query(User).filter(User.username == tmp_form.username.data).first()\n if u:\n flash('User already exist')\n else:\n c = db_session.query(Compiler).filter(Compiler.repo_url == tmp_form.repo_url.data).first()\n\n if not c and tmp_form.repo_url.data:\n c = Compiler(\n student=tmp_form.student_name.data,\n repo_url=tmp_form.repo_url.data\n )\n db_session.add(c)\n db_session.commit()\n\n new_user = User(\n username=tmp_form.username.data,\n hash_password=tmp_form.password.data,\n email=tmp_form.email.data,\n student_name=tmp_form.student_name.data,\n student_id=tmp_form.student_id.data\n )\n\n if c:\n new_user.compiler_id = c.id\n\n db_session.add(new_user)\n db_session.commit()\n\n return render_template('homepage.html')\n\n return render_template('userSystem/register.html', tmp_form=tmp_form)\n\n\n@login_required\n@app.route(settings.WEBROOT + '/modify_information', methods=['GET', 'POST'])\ndef modify_information():\n tmp_form = ModifyInformationForm()\n\n if tmp_form.validate_on_submit():\n current_user.email = tmp_form.email.data,\n current_user.student_name = tmp_form.student_name.data,\n current_user.student_id = tmp_form.student_id.data\n c = db_session.query(Compiler).filter(Compiler.repo_url == tmp_form.repo_url.data).first()\n\n if not c and tmp_form.repo_url.data:\n c = Compiler(\n student=tmp_form.student_name.data,\n repo_url=tmp_form.repo_url.data\n )\n db_session.add(c)\n db_session.commit()\n\n if c and tmp_form.repo_url.data and tmp_form.student_name.data:\n c.student = tmp_form.student_name.data\n c.repo_url = tmp_form.repo_url.data\n\n if c:\n current_user.compiler_id = c.id\n\n db_session.commit()\n flash('modify success!')\n\n return render_template('homepage.html')\n\n tmp_form.email.data = current_user.email\n tmp_form.student_id.data = current_user.student_id\n tmp_form.student_name.data = current_user.student_name\n # flash(db_session.query(Compiler).filter(Compiler.id == current_user.compiler_id).first().repo_url)\n if current_user.compiler_id:\n tmp_form.repo_url.data = db_session.query(Compiler).filter(Compiler.id == current_user.compiler_id).first().repo_url\n\n return render_template('userSystem/modify_information.html', tmp_form=tmp_form)\n\n\n@login_required\n@app.route(settings.WEBROOT + '/modify_password', methods=['GET', 'POST'])\ndef modify_password():\n tmp_form = ModifyPasswordForm()\n\n if tmp_form.validate_on_submit():\n u = db_session.query(User).filter(User.id == current_user.id).one()\n u.hash_password = tmp_form.password.data\n db_session.commit()\n logout_user()\n flash('modify success!')\n return render_template('homepage.html')\n\n return render_template('userSystem/modify_password.html', tmp_form=tmp_form)\n\n\n@app.route(settings.WEBROOT + '/update_repo')\n@login_required\ndef update_repo():\n try:\n compiler_id = request.args['compiler_id']\n except:\n if not hasattr(current_user, 'compiler_id'):\n flash('Can not find user compiler')\n return render_template('homepage.html')\n compiler_id = current_user.compiler_id\n\n c = db_session.query(Compiler).filter(Compiler.id == compiler_id).first()\n if c:\n if do_compiler(c):\n flash(\"Add to pending list\", category='info')\n else :\n flash(\"Check repo fail\", category='info')\n # if do_compiler(c):\n # flash(\"Add to pending list\", category='info')\n # else:\n # flash(\"Can't find new version\")\n else:\n flash(\"Can't find this compiler\")\n return redirect(url_for('builds'))\n\n\ndef copy_sqlalchemy_object_as_dict(o):\n d = dict(o.__dict__)\n del d['_sa_instance_state']\n return d\n\n\ndef do_compiler(compiler):\n version_sha = get_latest_remote_version(compiler.repo_url)\n compiler.last_check_time = datetime.utcnow()\n db_session.commit()\n if not version_sha:\n return False\n\n # if version_sha and compiler.latest_version_id:\n # version = db_session.query(Version)\\\n # .filter(Version.id == compiler.latest_version_id)\\\n # .one()\n # if version_sha == version.sha:\n # return False\n\n version = Version(compiler_id=compiler.id,\n sha=version_sha,\n phase='build',\n status='pending')\n\n db_session.add(version)\n db_session.commit()\n compiler.latest_version_id = version.id\n db_session.commit()\n return True\n\n\n@app.route(settings.WEBROOT + '/', methods=['GET', 'POST'])\ndef homepage():\n login_form = LoginForm()\n register_form = RegistrationForm()\n # if register_form.validate_on_submit():\n # new_user = User(\n # username=register_form.username.data,\n # hash_password=register_form.password.data,\n # email=register_form.email.data,\n # student_name=register_form.student_name.data,\n # student_id=register_form.student_id.data,\n # compiler_id= None, #db_session.query(Compiler).filter(Compiler.repo_url == register_form.repo_url.data)\n # )\n if login_form.validate_on_submit():\n u = db_session.query(User).filter(User.username == login_form.username.data).first()\n if not u:\n flash(\"Can't Find Username\")\n elif u.hash_password != login_form.password.data:\n flash(\"Password error\")\n else:\n login_user(u)\n flash(\"Log in success\")\n return render_template('homepage.html')\n\n\n@app.route(settings.WEBROOT + '/midinfos', methods=['GET', 'POST'])\n@login_required\ndef midinfos():\n tmp_users = db_session.query(User).all()\n users = []\n TA_reports = []\n self_reports = []\n TAs = []\n for u in tmp_users:\n m = db_session.query(MidInfo).filter(MidInfo.user_id == u.id).first()\n users.append(u)\n if m:\n TA_reports.append(m.TA_report)\n self_reports.append(m.self_report)\n TAs.append(m.TA)\n else:\n TA_reports.append(None)\n self_reports.append(None)\n TAs.append(None)\n return render_template('MidTerm/midinfos.html', users=users, TA_reports=TA_reports, self_reports=self_reports, TAs=TAs)\n\n\n@app.route(settings.WEBROOT + '/edit_midinfo', methods=['GET', 'POST'])\n@login_required\ndef edit_midinfo():\n try:\n user_id = int(request.args['user_id'])\n except:\n user_id = current_user.id\n tmp_form = MidInfoForm()\n if tmp_form.validate_on_submit():\n m = db_session.query(MidInfo).filter(MidInfo.user_id == user_id).first()\n if m:\n m.TA = tmp_form.TA.data\n m.self_report = tmp_form.self_report.data\n m.TA_report = tmp_form.TA_report.data\n db_session.commit()\n else:\n m = MidInfo(\n user_id=user_id,\n TA=tmp_form.TA.data,\n self_report=tmp_form.self_report.data,\n TA_report=tmp_form.TA_report.data\n )\n db_session.add(m)\n db_session.commit()\n flash('Submit success')\n return render_template('homepage.html')\n\n m = db_session.query(MidInfo).filter(MidInfo.user_id == user_id).first()\n u = db_session.query(User).filter(User.id == user_id).first()\n if m:\n tmp_form.TA.data = m.TA\n tmp_form.self_report.data = m.self_report\n tmp_form.TA_report.data = m.TA_report\n\n return render_template('MidTerm/edit_midinfo.html', form=tmp_form, user=u)\n\n\n@app.route(settings.WEBROOT + '/edit_testcase', methods=['GET', 'POST'])\n@login_required\ndef edit_testcase():\n try: testcase_id = int(request.args['testcase_id'])\n except: testcase_id = ''\n\n tmp_form = TestcaseForm()\n if tmp_form.validate_on_submit():\n\n t = {\n 'program': tmp_form.program.data,\n 'phase': tmp_form.phase.data,\n 'comment': tmp_form.comment.data,\n 'assert': tmp_form.assert_.data,\n 'timeout': tmp_form.timeout.data,\n 'exitcode': tmp_form.exitcode.data,\n 'input': tmp_form.input.data,\n 'output': tmp_form.output.data,\n 'is_public': 'True',\n }\n\n if t['timeout'] == '':\n t['timeout'] = 0\n assert t['assert'] in ['success_compile', 'failure_compile', 'exitcode', 'runtime_error', 'output']\n if t['assert'] not in ['success_compile', 'failure_compile']:\n if t['assert'] == 'exitcode':\n t['timeout'] = float(t['timeout'])\n t['exitcode'] = int(t['exitcode'])\n assert 0 <= t['exitcode'] <= 255\n elif t['assert'] == 'output':\n t['timeout'] = float(t['timeout'])\n assert 'output' in t\n assert 'input' in t\n assert t['phase'] in settings.TEST_PHASES\n assert 'comment' in t\n assert t['is_public'] in ['True', 'False']\n\n tmp_t = db_session.query(Testcase).filter(Testcase.id == testcase_id).first()\n\n testcase = Testcase(enabled=tmp_t.enabled,\n phase=t['phase'],\n is_public=t['is_public'],\n comment=t['comment'],\n timeout=t.get('timeout', None),\n cnt_run=0,\n cnt_hack=0,\n content=json.dumps(t))\n\n for run in db_session.query(TestRun).filter(TestRun.testcase_id == testcase_id):\n db_session.delete(run)\n db_session.commit()\n\n db_session.delete(tmp_t)\n\n db_session.add(testcase)\n db_session.commit()\n\n return redirect(url_for('testcases'))\n\n t = db_session.query(Testcase).filter(Testcase.id == testcase_id).first()\n tmp_form.phase.data = t.phase\n tmp_form.comment.data = t.comment\n tmp_form.timeout.data = t.timeout\n tmp_t = json.loads(t.content)\n if 'assert' in tmp_t:\n tmp_form.assert_.data = tmp_t['assert']\n if 'exitcode' in tmp_t:\n tmp_form.exitcode.data = tmp_t['exitcode']\n if 'program' in tmp_t:\n tmp_form.program.data = tmp_t['program']\n if 'input' in tmp_t:\n tmp_form.input.data = tmp_t['input']\n if 'output' in tmp_t:\n tmp_form.output.data = tmp_t['output']\n\n return render_template('Testcase/edit_testcase.html', testcase_id = testcase_id, form = tmp_form)\n\n\n@app.route(settings.WEBROOT + '/add_testcase', methods=['GET', 'POST'])\n@login_required\ndef add_testcase():\n tmp_form = TestcaseForm()\n\n if tmp_form.validate_on_submit():\n\n t = {\n 'program': tmp_form.program.data,\n 'phase': tmp_form.phase.data,\n 'comment': current_user.student_name + ' ' + tmp_form.comment.data,\n 'assert': tmp_form.assert_.data,\n 'timeout': tmp_form.timeout.data,\n 'exitcode': tmp_form.exitcode.data,\n 'input': tmp_form.input.data,\n 'output': tmp_form.output.data,\n 'is_public': 'True',\n }\n\n if t['timeout'] == '':\n t['timeout'] = 0\n assert t['assert'] in ['success_compile', 'failure_compile', 'exitcode', 'runtime_error', 'output']\n if t['assert'] not in ['success_compile', 'failure_compile']:\n if t['assert'] == 'exitcode':\n t['timeout'] = float(t['timeout'])\n t['exitcode'] = int(t['exitcode'])\n assert 0 <= t['exitcode'] <= 255\n elif t['assert'] == 'output':\n t['timeout'] = float(t['timeout'])\n assert 'output' in t\n assert 'input' in t\n assert t['phase'] in settings.TEST_PHASES\n assert 'comment' in t\n assert t['is_public'] in ['True', 'False']\n\n testcase = Testcase(enabled=False,\n phase=t['phase'],\n is_public=t['is_public'],\n comment=t['comment'],\n timeout=t.get('timeout', None),\n cnt_run=0,\n cnt_hack=0,\n content=json.dumps(t))\n\n db_session.add(testcase)\n db_session.commit()\n\n return redirect(url_for('testcases'))\n # c = Compiler(student=tmp_form.student.data, repo_url=tmp_form.repo_url.data)\n # db_session.add(c)\n # db_session.commit()\n # return redirect(url_for('compilers'))\n tmp_form.exitcode.data = '0'\n return render_template('Testcase/add_testcase.html', form=tmp_form)\n\n\n@app.route(settings.WEBROOT + '/add_compiler', methods=['GET', 'POST'])\n@login_required\ndef add_compiler():\n tmp_form = CompilerForm()\n if tmp_form.validate_on_submit():\n c = Compiler(student=tmp_form.student.data, repo_url=tmp_form.repo_url.data)\n db_session.add(c)\n db_session.commit()\n return redirect(url_for('compilers'))\n return render_template('add_compiler.html', form=tmp_form)\n\n\ndef get_latest_remote_version(repo_url):\n cmd = 'git ls-remote {} master | grep refs/heads/master | cut -f1'.format(repo_url)\n try:\n version = subprocess.check_output(cmd, shell=True).strip()\n except:\n return False\n if len(version) != 40:\n return False\n return version\n\n\n@app.route(settings.WEBROOT + '/compilers/', methods=['GET', 'POST'])\ndef compilers():\n tmp_compilers = db_session.query(Compiler).order_by(Compiler.id.asc()).all()\n versions = []\n users = []\n compilers = []\n for c in tmp_compilers:\n u = db_session.query(User).filter(User.compiler_id == c.id).first()\n v = db_session.query(Version).filter(Version.id == c.latest_version_id).first()\n if u:\n if u.username == 'mushroom':\n continue\n compilers.append(c)\n users.append(u)\n versions.append(v)\n return render_template('compilers.html', compilers=compilers, versions=versions, users=users)\n\n\n@app.route(settings.WEBROOT + '/final_board/', methods=['GET', 'POST'])\ndef final_board():\n tmp_compilers = db_session.query(Compiler).all()\n rank_list = []\n testcases = []\n stander = {}\n for c in tmp_compilers:\n u = db_session.query(User).filter(User.compiler_id == c.id).first()\n if not u:\n continue\n if u.student_name == 'Mushroom':\n continue\n v = db_session.query(Version).filter(Version.id == c.latest_version_id).first()\n if not v:\n continue\n if v.phase != 'end' and v.phase != 'optim extended' and v.phase != 'optim pretest':\n continue\n tmp_dict = {\n 'c': c,\n 'u': u,\n 'r': {}\n }\n count_phase = ['optim extended', 'optim pretest']\n for r in db_session.query(TestRun).filter(TestRun.version_id == v.id):\n if r.phase not in count_phase:\n continue\n if r.status != 'passed':\n continue\n tmp_dict['r'][r.testcase_id] = r.running_time\n if r.testcase_id not in testcases:\n testcases.append(r.testcase_id)\n if u.student_name == 'GCC -O2':\n stander[r.testcase_id] = r.running_time\n\n rank_list.append(tmp_dict)\n\n for item in rank_list:\n tmp_sum = 0\n for t_id in item['r'].keys():\n if t_id in stander:\n item['r'][t_id] = min(round(stander[t_id]/item['r'][t_id], 2), 1.5) + 1.0\n tmp_sum += item['r'][t_id]\n else:\n item['r'][t_id] = 'NULL'\n item['sorce'] = tmp_sum\n\n rank_list = sorted(rank_list, key=lambda x:x['sorce'], reverse=True)\n for i, item in enumerate(rank_list):\n if item['u'].student_name == 'GCC -O2':\n flag_O2 = item['sorce']\n if item['u'].student_name == 'GCC -O1':\n flag_O1 = item['sorce']\n if item['u'].student_name == 'GCC -O0':\n flag_O0 = item['sorce']\n for i, item in enumerate(rank_list):\n if item['sorce'] >= flag_O1:\n item['real_score'] = 95 + 5.0 * (item['sorce'] - flag_O1) / (rank_list[2]['sorce'] - flag_O1)\n elif item['sorce'] >= flag_O0:\n item['real_score'] = 85 + 10.0 * (item['sorce'] - flag_O0) / (flag_O1 - flag_O0)\n else:\n item['real_score'] = 60 + 25.0 * (item['sorce']) / flag_O0\n item['real_score'] = round(item['real_score'], 2)\n return render_template('final_board.html', testcases=testcases, rank_list=rank_list)\n\n\ndef get_verion_testrun_counts(version):\n passed = {k: 0 for k in settings.TEST_PHASES}\n total = {k: 0 for k in settings.TEST_PHASES}\n for r in db_session.query(TestRun).filter(TestRun.version_id == version.id):\n total[r.phase] += 1\n if r.status == 'passed':\n passed[r.phase] += 1\n ret = {p: (passed[p], total[p]) if total[p] else None for p in settings.TEST_PHASES}\n ret['build'] = 'passed' if version.phase != 'build' else version.status\n return ret\n\n\n@app.route(settings.WEBROOT + '/builds')\ndef builds():\n try: start = int(request.args['start'])\n except: start = ''\n try: compiler_id = int(request.args['compiler_id'])\n except: compiler_id = ''\n sha = request.args.get('sha', '')\n phase = request.args.get('phase', '')\n status = request.args.get('status', '')\n\n query = db_session.query(Version).order_by(Version.id.desc())\n if compiler_id: query = query.filter(Version.compiler_id == compiler_id)\n if sha: query = query.filter(Version.sha.like(sha + '%'))\n if phase: query = query.filter(Version.phase == phase)\n if status: query = query.filter(Version.status == status)\n if start: query = query.filter(Version.id <= start)\n query = query.limit(settings.BUILDS_PER_PAGE)\n versions = query.all()\n counts = [get_verion_testrun_counts(v) for v in versions]\n cs = {c.id: c for c in db_session.query(Compiler)}\n return render_template('builds.html', versions=versions, compilers=cs, counts=counts)\n\n\n@app.route(settings.WEBROOT + '/set_testcase')\n@login_required\ndef set_testcase():\n try: testcase_id = int(request.args['testcase_id'])\n except: testcase_id = ''\n t = db_session.query(Testcase).filter(Testcase.id == testcase_id).one()\n if t.enabled:\n t.enabled = False\n else:\n t.enabled = True\n db_session.commit()\n return redirect(url_for('testcases'))\n\n\n@app.route(settings.WEBROOT + '/delete_testcase')\n@login_required\ndef delete_testcase():\n try: testcase_id = int(request.args['delete_id'])\n except: testcase_id = ''\n\n for run in db_session.query(TestRun).filter(TestRun.testcase_id == testcase_id):\n db_session.delete(run)\n db_session.commit()\n\n t = db_session.query(Testcase).filter(Testcase.id == testcase_id).one()\n db_session.delete(t)\n db_session.commit()\n return redirect(url_for('testcases'))\n\n\n@app.route(settings.WEBROOT + '/runs')\ndef runs():\n try: start = int(request.args['start'])\n except: start = ''\n try: version_id = int(request.args['build_id'])\n except: version_id = ''\n try: testcase_id = int(request.args['testcase_id'])\n except: testcase_id = ''\n phase = request.args.get('phase', '')\n status = request.args.get('status', '')\n\n query = db_session.query(TestRun).order_by(TestRun.id.desc())\n auto_refresh = not (version_id or testcase_id or phase or status or start)\n if version_id: query = query.filter(TestRun.version_id == version_id)\n if testcase_id: query = query.filter(TestRun.testcase_id == testcase_id)\n if phase: query = query.filter(TestRun.phase == phase)\n if status: query = query.filter(TestRun.status == status)\n if start: query = query.filter(TestRun.id <= start)\n query = query.limit(settings.RUNS_PER_PAGE)\n rs = query.all()\n vids = set(r.version_id for r in rs)\n vs = {v.id: v for v in db_session.query(Version).filter(Version.id.in_(vids))}\n cs = {c.id: c for c in db_session.query(Compiler)}\n ts = {t.id: t for t in db_session.query(Testcase)}\n watch_list = [r.id for r in rs if r.status not in ['passed', 'failed', 'timeout']]\n return render_template('runs.html', testruns=rs, testcases=ts, compilers=cs, versions=vs,\n auto_refresh=auto_refresh, watch_list=watch_list)\n\n\n@app.route(settings.WEBROOT + '/ajax/watch_runs.json')\ndef ajax_watch_runs():\n try:\n lim = 10\n stamp = int(request.args['stamp'])\n qs = request.args.get('q', '').strip()\n qs = map(lambda q: int(q.strip()), qs.split(',')) if qs else []\n qs = sorted(qs)[:lim]\n old = []\n for testrun_id in qs:\n r = db_session.query(TestRun).filter(TestRun.id == testrun_id).one()\n v = db_session.query(Version).filter(Version.id == r.version_id).one()\n c = db_session.query(Compiler).filter(Compiler.id == v.compiler_id).one()\n t = db_session.query(Testcase).filter(Testcase.id == r.testcase_id).one()\n old.append({\n 'id': r.id,\n 'row_html': render_template('runs_row.html', r=r, v=v, c=c, t=t),\n 'finished': r.status in ['passed', 'failed', 'timeout'],\n })\n\n try: latest_id = int(request.args['latest_id'])\n except: latest_id = 1<<30\n query = db_session.query(TestRun).filter(TestRun.id > latest_id)\\\n .order_by(TestRun.id.asc()).limit(lim)\n new = []\n for r in query:\n v = db_session.query(Version).filter(Version.id == r.version_id).one()\n c = db_session.query(Compiler).filter(Compiler.id == v.compiler_id).one()\n t = db_session.query(Testcase).filter(Testcase.id == r.testcase_id).one()\n new.append({\n 'id': r.id,\n 'row_html': render_template('runs_row.html', r=r, v=v, c=c, t=t),\n 'finished': r.status in ['passed', 'failed', 'timeout'],\n })\n return jsonify({'watch': old, 'new': new, 'stamp': stamp})\n except:\n return abort(400)\n\n\n@app.route(settings.WEBROOT + '/testcases')\ndef testcases():\n try:\n sort_method = request.args['sort_method']\n except:\n sort_method = 'download'\n try:\n reverse = request.args['reverse']\n except:\n reverse = 'T'\n order_type = None\n\n if sort_method == 'download':\n order_type = Testcase.id.desc() if (reverse == 'T') else Testcase.id\n elif sort_method == 'enabled':\n order_type = Testcase.enabled.desc() if (reverse == 'T') else Testcase.enabled\n elif sort_method == 'phase':\n order_type = Testcase.phase.desc() if (reverse == 'T') else Testcase.phase\n\n ts = db_session.query(Testcase).order_by(order_type).all()\n\n if sort_method == 'pass':\n ts = sorted(ts, key=lambda t: (0 if t.cnt_run == 0 else 100.0 - t.cnt_hack * 100.0 / t.cnt_run), reverse=(reverse == 'T'))\n # if sort_method == 'pass':\n # ts.so\n return render_template('testcases.html', testcases=ts, sort_method=sort_method, reverse=reverse)\n\n\ndef get_build_phase_count(rs):\n count = { phase: dict() for phase in settings.TEST_PHASES }\n for r in rs:\n d = count[r.phase]\n d[r.status] = d.get(r.status, 0) + 1\n d['total'] = d.get('total', 0) + 1\n return count\n\n\n@app.route(settings.WEBROOT + '/build/')\ndef build(id):\n v = db_session.query(Version).filter(Version.id == id).first()\n if not v:\n return abort(404)\n c = db_session.query(Compiler).filter(Compiler.id == v.compiler_id).one()\n ls = db_session.query(BuildLog).filter(BuildLog.version_id == id).order_by(BuildLog.id.desc()).all()\n rs = db_session.query(TestRun).filter(TestRun.version_id == id).order_by(TestRun.id.desc()).all()\n ts = {t.id: t for t in db_session.query(Testcase)}\n count = get_build_phase_count(rs)\n watch_list = [r.id for r in rs if r.status not in ['passed', 'failed', 'timeout']]\n auto_refresh = v.status not in ['passed', 'failed']\n return render_template('build.html', compiler=c, version=v, build_logs=ls, \n testruns=rs, testcases=ts, phase_count=count, auto_refresh=auto_refresh,\n watch_list=watch_list)\n\n\n@app.route(settings.WEBROOT + '/ajax/build.json')\ndef ajax_build():\n try:\n stamp = int(request.args['stamp'])\n latest_id = int(request.args['latest_id'])\n id = int(request.args['build_id'])\n v = db_session.query(Version).filter(Version.id == id).first()\n if not v:\n return abort(404)\n\n lim = 10\n stamp = int(request.args['stamp'])\n qs = request.args.get('q', '').strip()\n qs = map(lambda q: int(q.strip()), qs.split(',')) if qs else []\n qs = sorted(qs)[:lim]\n watch = []\n for testrun_id in qs:\n r = db_session.query(TestRun).filter(TestRun.id == testrun_id).one()\n v = db_session.query(Version).filter(Version.id == r.version_id).one()\n c = db_session.query(Compiler).filter(Compiler.id == v.compiler_id).one()\n t = db_session.query(Testcase).filter(Testcase.id == r.testcase_id).one()\n watch.append({\n 'id': r.id,\n 'row_html': render_template('build_row.html', r=r, v=v, c=c, t=t),\n 'finished': r.status in ['passed', 'failed', 'timeout'],\n })\n\n c = db_session.query(Compiler).filter(Compiler.id == v.compiler_id).one()\n ls = db_session.query(BuildLog).filter(BuildLog.version_id == id).order_by(BuildLog.id.desc()).all()\n rs = db_session.query(TestRun).filter(TestRun.version_id == id).order_by(TestRun.id.asc()).all()\n ts = {t.id: t for t in db_session.query(Testcase)}\n count = get_build_phase_count(rs)\n runs = []\n for r in rs:\n if r.id > latest_id:\n runs.append({\n 'html': render_template('build_row.html', r=r, t=ts[r.testcase_id]),\n 'id': r.id,\n 'finished': r.status in ['passed', 'failed', 'timeout'],\n })\n if rs: latest_id = rs[-1].id\n return ({\n 'stamp': stamp,\n 'latest_id': latest_id,\n 'bar': render_template('build_bar.html', version=v, phase_count=count),\n 'runs': runs ,\n 'watch': watch,\n 'auto_refresh': v.status not in ['passed', 'failed']\n })\n except:\n return abort(400)\n\n\n@app.route(settings.WEBROOT + '/show/buildlog_.html')\ndef download_buildlog(id):\n l = db_session.query(BuildLog).filter(BuildLog.id == id).first()\n if not l:\n return abort(404)\n path = os.path.join(settings.CORE_BUILD_LOG_PATH, '{:d}.txt'.format(l.id))\n if not os.path.exists(path):\n return abort(404)\n with open(path) as f:\n text = f.read()\n html = ansi2html(text, palette='console')\n return render_template('buildlog.html', log=html, buildlog=l)\n\n\n@app.route(settings.WEBROOT + '/show/runlog_.html')\ndef download_runlog(id):\n r = db_session.query(TestRun).filter(TestRun.id == id).first()\n if not r:\n return abort(404)\n path = os.path.join(settings.CORE_TESTRUN_STDERR_PATH, '{:d}.txt'.format(r.id))\n if not os.path.exists(path):\n return abort(404)\n with open(path) as f:\n text = f.read()\n html = ansi2html(text, palette='console')\n return render_template('runlog.html', log=html, testrun=r)\n\n\n@app.route(settings.WEBROOT + '/download/testcase_.txt')\ndef download_testcase(id):\n t = db_session.query(Testcase).filter(Testcase.id == id).first()\n if not t:\n return abort(404)\n if not t.is_public:\n return abort(401)\n text = utils.testcase_to_text(json.loads(t.content))\n return Response(text, content_type='text/plain; charset=utf-8')\n\n\n@app.route(settings.WEBROOT + '/judges')\ndef judges():\n judges = sorted(judge_status.itervalues(), key=lambda x: x.time, reverse=True)\n return render_template('judges.html', judges=judges, now=datetime.utcnow())\n\n\ndef token_required(f):\n @functools.wraps(f)\n def decorated_function(*args, **kwargs):\n token = request.form.get('token', None)\n if token != settings.JUDGE_TOKEN:\n return abort(401)\n return f(*args, **kwargs)\n return decorated_function\n\n# def admin_required(f):\n# @functools.wraps(f)\n# def decorated_function(*args, **kwargs):\n# if current_user.activate\n# return abort(401)\n# return f(*args, **kwargs)\n# return decorated_function\n\n\n@app.route(settings.WEBROOT + '/backend/dispatch/build', methods=['POST'])\n@token_required\ndef backend_dispatch_build():\n judge = request.form['judge']\n version = db_session.query(Version)\\\n .filter(Version.phase == 'build', Version.status == 'pending')\\\n .order_by(Version.id.asc())\\\n .first()\n if not version:\n message = 'ask for build tasks, but not found'\n judge_status[judge] = JudgeStatus(name=judge, action=message, time=datetime.utcnow())\n return jsonify({'found': False})\n compiler = db_session.query(Compiler).filter(Compiler.id == version.compiler_id).one()\n ret = {\n 'found': True,\n 'compiler': copy_sqlalchemy_object_as_dict(compiler),\n 'version': copy_sqlalchemy_object_as_dict(version)\n }\n version.status = 'building'\n db_session.commit()\n\n message = 'ask for build tasks. assigned build task for build {version_id}'.format(\n url_version=url_for('build', id=version.id),\n version_id=version.id)\n judge_status[judge] = JudgeStatus(name=judge, action=message, time=datetime.utcnow())\n return jsonify(ret)\n\n\n@app.route(settings.WEBROOT + '/backend/submit/build_log', methods=['POST'])\n@token_required\ndef backend_submit_build_log():\n id = int(request.form['id'])\n judge = request.form['judge']\n message = request.form['message']\n committed_at = utils.parse_to_utc(request.form['committed_at'])\n status = request.form['status']\n build_time = float(request.form['build_time'])\n log = request.form['log']\n\n version = db_session.query(Version).filter(Version.id == id).one()\n build_log = BuildLog(version_id=version.id,\n build_time=build_time,\n builder=judge,\n created_at=datetime.utcnow())\n db_session.add(build_log)\n db_session.commit()\n with open(os.path.join(settings.CORE_BUILD_LOG_PATH, '{:d}.txt'.format(build_log.id)), 'w') as f:\n f.write(log)\n\n version.message = message\n version.committed_at = committed_at\n if status == 'ok':\n version.phase = settings.TEST_PHASES[0]\n version.status = 'pending'\n else:\n version.status = 'failed'\n db_session.commit()\n\n message = 'submit build log {log_id} for build {version_id}'.format(\n url_log=url_for('download_runlog', id=build_log.id),\n log_id=build_log.id,\n url_version=url_for('build', id=version.id),\n version_id=version.id)\n judge_status[judge] = JudgeStatus(name=judge, action=message, time=datetime.utcnow())\n return jsonify({'ack': True})\n\n\n@app.route(settings.WEBROOT + '/backend/dispatch/testrun', methods=['POST'])\n@token_required\ndef backend_dispatch_testrun():\n judge = request.form['judge']\n t = db_session.query(TestRun)\\\n .filter(TestRun.status == 'pending')\\\n .order_by(TestRun.id.asc())\\\n .first()\n if not t:\n message = 'ask for run tasks, but not found'\n judge_status[judge] = JudgeStatus(name=judge, action=message, time=datetime.utcnow())\n return jsonify({'found': False})\n v = db_session.query(Version).filter(Version.id == t.version_id).one()\n c = db_session.query(Compiler).filter(Compiler.id == v.compiler_id).one()\n ret = {\n 'found': True,\n 'testrun': copy_sqlalchemy_object_as_dict(t),\n 'version': copy_sqlalchemy_object_as_dict(v),\n 'compiler': copy_sqlalchemy_object_as_dict(c)\n }\n t.status = 'running'\n t.dispatch_at = datetime.utcnow()\n db_session.commit()\n\n message = 'ask for run tasks. assigned run task for run {run_id}'.format(\n run_id=t.id)\n judge_status[judge] = JudgeStatus(name=judge, action=message, time=datetime.utcnow())\n return jsonify(ret)\n\n\n@app.route(settings.WEBROOT + '/backend/submit/testrun', methods=['POST'])\n@token_required\ndef backend_submit_testrun():\n id = int(request.form['id'])\n judge = request.form['judge']\n status = request.form['status']\n running_time = float(request.form['running_time'])\n compile_time = float(request.form['compile_time'])\n stderr = request.form['stderr']\n\n r = db_session.query(TestRun).filter(TestRun.id == id).one()\n t = db_session.query(Testcase).filter(Testcase.id == r.testcase_id).one()\n r.finished_at = datetime.utcnow()\n r.running_time = running_time\n r.compile_time = compile_time\n r.status = status\n t.cnt_run = Testcase.cnt_run + 1\n if status != 'passed':\n t.cnt_hack = Testcase.cnt_hack + 1\n db_session.commit()\n path = os.path.join(settings.CORE_TESTRUN_STDERR_PATH, '{:d}.txt'.format(id))\n with open(path, 'w') as f:\n f.write(stderr)\n\n message = 'submit run result for run {run_id}'.format(\n url_run=url_for('download_runlog', id=r.id),\n run_id=r.id)\n judge_status[judge] = JudgeStatus(name=judge, action=message, time=datetime.utcnow())\n return jsonify({'ack': True})\n\n\n@app.route(settings.WEBROOT + '/backend/download/testcase/.json', methods=['POST'])\n@token_required\ndef backend_download_testcase(id):\n t = db_session.query(Testcase).filter(Testcase.id == id).one()\n return Response(t.content, mimetype='application/json')\n","sub_path":"WebServer/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":35938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"379067047","text":"import yaml\nimport cbpro\nimport pandas as pd\n\nfrom time import sleep\nfrom datetime import datetime\nfrom os.path import isfile\n\nwith open(\"config.yaml\", \"r\") as file:\n config = yaml.full_load(file)\n\nauth_client = cbpro.AuthenticatedClient(\n config[\"key\"], config[\"secret\"], config[\"passphrase\"])\n\nproducts = auth_client.get_products()\ndownload_list = []\nfor i in range(0, len(products)):\n ticker = products[i][\"id\"]\n traded = not products[i]['trading_disabled']\n if traded and ticker.endswith(\"USD\"):\n download_list.append(ticker)\n\nwhile True:\n\n for ticker in download_list:\n\n try:\n data = auth_client.get_product_historic_rates(\n ticker, granularity=60)\n now = datetime.utcnow()\n if not data:\n continue\n\n df = pd.DataFrame(data=reversed(data), columns=[\n \"datetime\", \"low\", \"high\", \"open\", \"close\", \"volume\"])\n df[\"datetime\"] = pd.to_datetime(df[\"datetime\"], unit='s')\n df = df.sort_values(by=[\"datetime\"])\n df[\"modified\"] = now\n df[\"modified\"] = df[\"modified\"].dt.tz_localize(\"UTC\")\n\n simple_path = f\"data/data_simple/{ticker}.csv\"\n if isfile(simple_path):\n df_old = pd.read_csv(simple_path)\n df_old[\"datetime\"] = pd.to_datetime(df_old[\"datetime\"])\n df_old[\"modified\"] = pd.to_datetime(df_old[\"modified\"])\n df_old = df_old[df_old[\"datetime\"] < df[\"datetime\"][0]]\n df_write = df_old.append(df)\n df_write.to_csv(simple_path, index=False)\n else:\n df.to_csv(simple_path, index=False)\n\n full_path = f\"data/data_full/{ticker}.csv\"\n\n if isfile(full_path):\n df_old = pd.read_csv(full_path)\n df_old[\"datetime\"] = pd.to_datetime(df_old[\"datetime\"])\n df_old[\"modified\"] = pd.to_datetime(df_old[\"modified\"])\n df_write = df_old.append(df)\n df_write = df_write.groupby(\n by=[\"datetime\", \"low\", \"high\", \"open\", \"close\", \"volume\"])\n df_write = df_write.agg({\"modified\": \"min\"})\n df_write = df_write.reset_index()\n df_write = df_write.sort_values(by=[\"datetime\", \"modified\"])\n df_write.to_csv(full_path, index=False)\n else:\n df.to_csv(full_path, index=False)\n\n print(f\"{datetime.strftime(now, '%H:%M:%S')}: {ticker} downloaded.\")\n\n except Exception:\n print(f\"{datetime.strftime(now, '%H:%M:%S')}: {ticker} failed!\")\n pass\n\n sleep(0.5)\n\n sleep(30)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"634236303","text":"import random\nword = \"hello\"\nword_works = 0\nscrambled_well = 1\ndef scramble_word(word,scrambled_well):\n list_1 = []\n letter_check = word[0]\n letter_check_2 = word[-1]\n if len(word) == (1):\n print (\"Stop_1\")\n elif len(word) == (2):\n print(\"Stop_2\")\n elif len(word) == (3):\n print(\"Stop_3\")\n else:\n word_works = 1\n \n while word_works == 1 and scrambled_well == 1:\n middle_letters = word[1:-1]\n print (middle_letters)\n list_1 = list(middle_letters)\n print (list_1)\n random.shuffle(list_1)\n print (list_1)\n final = ''.join(list_1)\n if final == \"ell\":\n scrambled_well = 1\n else:\n scrambled_well = 0\n \n print (final)\n final = (letter_check + final + letter_check_2)\n return (final)\n \n \n \n\nscramble_word(word,scrambled_well)\n#I would first get a word\n#Next I would make some varibles that would that the first and last letters\n#Then I whould shuffle everything but the last letters\n#Next I would check if they got shuffled with some len stuff\n#Then stuff will work\n# def scramble_phrase():\n# scramble_word()\ndef scramble_phrase():\n print \nscramble_phrase()","sub_path":"unit 5/QStraub_scramble.py","file_name":"QStraub_scramble.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"613438272","text":"\"\"\"\n\n\"\"\"\n\n\ndef sum_numbers(text: str) -> int:\n # your code here\n return sum(int(word) for word in text.split() if word.isdigit())\n\n\nfrom itertools import chain\n\n# initializing string\ntest_list = [1, 4, 5, 6, 7, 3, 5, 9, 2, 4]\n\n# initializing split index list\nsplit_list = [2, 5, 7]\n\n# printing original list\nprint(\"The original list is : \" + str(test_list))\n\n# printing original split index list\nprint(\"The original split index list : \" + str(split_list))\n\n# using itertools.chain() + zip()\n# to perform custom list split\ntemp = zip(chain([0], split_list), chain(split_list, [None]))\nres = list(test_list[i: j] for i, j in temp)\n\n# printing result\nprint(\"The splitted lists are : \" + str(res))\n\n\ndef split_list(items: list) -> list:\n # your code here\n # i = math.floor(len(items) / 2_str)\n # if len(items) % 2_str == 0:\n # temp = zip((0, i), (i, None))\n # else:\n # temp = zip((0, i + 1), (i + 1, None))\n # return list(items[i: j] for i, j in temp)\n return [items[:(len(items) + 1) // 2], items[(len(items) + 1) // 2:]]\n\n\n# split_list = lambda items: [items[:(half:=(len(items)+1) // 2_str)], items[half:]]\n\nimport re\n\n\ndef find_quotes(text):\n return re.findall(r'\"(.*?)\"', text)\n # return text.split('\"')[1::2_str]\n\n\nif __name__ == '__main__':\n # print(\"Example:\")\n # print(sum_numbers('my numbers is 2_str'))\n #\n # # These \"asserts\" are used for self-checking and not for an auto-testing\n # assert sum_numbers('hi') == 0\n # assert sum_numbers('who is 1st here') == 0\n # assert sum_numbers('my numbers is 2_str') == 2_str\n # assert sum_numbers('This picture is an oil on canvas '\n # 'painting by Danish artist Anna '\n # 'Petersen between 1845 and 1910 year') == 3755\n # assert sum_numbers('5 plus 6 is') == 11\n # assert sum_numbers('') == 0\n # print(\"Coding complete? Click 'Check' to earn cool rewards!\")\n\n print(\"Example:\")\n print(split_list([1, 2, 3]))\n\n # These \"asserts\" are used for self-checking and not for an auto-testing\n assert split_list([1, 2, 3, 4, 5, 6]) == [[1, 2, 3], [4, 5, 6]]\n assert split_list([1, 2, 3]) == [[1, 2], [3]]\n assert split_list([1, 2, 3, 4, 5]) == [[1, 2, 3], [4, 5]]\n assert split_list([1]) == [[1], []]\n assert split_list([]) == [[], []]\n print(\"Coding complete? Click 'Check' to earn cool rewards!\")\n\n print(\"Example:\")\n print(find_quotes('\"Lorem Ipsum\" is simply dummy text ''of the printing and typesetting '\n 'industry. Lorem Ipsum has been the '\n '\"industry\\'s standard dummy text '\n 'ever since the 1500s\", when an '\n 'unknown printer took a galley of '\n 'type and scrambled it to make a type '\n 'specimen book. It has survived not '\n 'only five centuries, but also the '\n 'leap into electronic typesetting, '\n 'remaining essentially unchanged. \"It '\n 'was popularised in the 1960s\" with '\n 'the release of Letraset sheets '\n 'containing Lorem Ipsum passages, and '\n 'more recently with desktop '\n 'publishing software like Aldus '\n 'PageMaker including versions of '\n 'Lorem Ipsum.'))\n\n # These \"asserts\" are used for self-checking and not for an auto-testing\n # assert find_quotes('\"Greetings\"') == ['Greetings']\n # assert find_quotes('Hi') == []\n # assert find_quotes('good morning mister \"superman\"') == ['superman']\n # assert find_quotes('\"this\" doesn\\'t make any \"sense\"') == ['this', 'sense']\n # assert find_quotes('\"Lorem Ipsum\" is simply dummy text ''of the printing and typesetting '\n # 'industry. Lorem Ipsum has been the '\n # '\"industry\\'s standard dummy text '\n # 'ever since the 1500s\", when an '\n # 'unknown printer took a galley of '\n # 'type and scrambled it to make a type '\n # 'specimen book. It has survived not '\n # 'only five centuries, but also the '\n # 'leap into electronic typesetting, '\n # 'remaining essentially unchanged. \"It '\n # 'was popularised in the 1960s\" with '\n # 'the release of Letraset sheets '\n # 'containing Lorem Ipsum passages, and '\n # 'more recently with desktop '\n # 'publishing software like Aldus '\n # 'PageMaker including versions of '\n # 'Lorem Ipsum.') == ['Lorem Ipsum',\n # \"industry's standard dummy text ever \"\n # 'since the 1500s',\n # 'It was popularised in the 1960s']\n # assert find_quotes('count empty quotes \"\"') == ['']\n # print(\"Coding complete? Click 'Check' to earn cool rewards!\")\n","sub_path":"checkIO/start3.py","file_name":"start3.py","file_ext":"py","file_size_in_byte":5180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"343781958","text":"#coding=utf-8\n'''封装区域信息报表'''\nfrom common import basePage\nfrom pages.bi.page_common import *\nimport time\n\nclass AreaOrderReportPage(basePage.BasePage):\n\n '''页面元素'''\n\n\n select_sxfa_input_locator = ('xpath', '//*[@id=\"select8_1\"]/div/div[1]/input') # 筛选方案选择框\n select_sxfa_input_option_xpath = '//*[@id=\"select8_1\"]/div/ul/li[contains(text(),\"{0}\")]' #按销售区域筛选，按供应商筛选\n\n select_region_input_locator = ('xpath', '//*[@id=\"select8_2\"]/div/div[1]/input')#区域选择框\n select_region_input_option_xpath = '//*[@id=\"select8_2\"]/div/ul/li[contains(text(),\"{0}\")]' #区域选择框选项\n\n select_partner_input_locator = ('xpath', '//*[@id=\"select8_4\"]/div/div[1]/input')#供应商选择框\n select_partner_input_option_xpath = '//*[@id=\"select8_4\"]/div/ul/li[contains(text(),\"{0}\")]'#供应商选择框选项\n\n select_time_input_locator = ('xpath', '//*[@id=\"timeselect_div\"]/div/div[1]/input')#时间选择框\n select_time_input_option_xpath = '//*[@id=\"timeselect_div\"]/div/ul/li[contains(text(),\"{0}\")]'#时间选项\n\n time_start_input_locator = ('id', 'time_choice_start') #时间起始输入框\n time_end_input_locator = ('id', 'time_choice_end') #时间结束输入框\n\n iframe_locator =('xpath', '/html/body/div[5]/iframe') #时间选择框的iframe\n time_today_locator = ('id', 'dpTodayInput')#日期选择器————》今天\n\n #此js去掉开始时间框的输入框的readonly属性\n remove_attr_js1 = 'document.getElementById(\"time_choice_start\").removeAttribute(\"readonly\")'\n # 此js去掉结束时间框的输入框的readonly属性\n remove_attr_js2 = 'document.getElementById(\"time_choice_end\").removeAttribute(\"readonly\")'\n\n #销售面积按钮\n sales_area_button_locator = ('xpath', '//*[@id=\"myMap\"]/div[3]/button[1]')\n\n #销售金额按钮\n sales_amount_button_locator = ('xpath', '//*[@id=\"myMap\"]/div[3]/button[2]')\n\n report_locator = ('xpath', '//*[@id=\"myMap\"]/div[1]/canvas')\n\n\n\n\n\n def change_sxfa(self, filt_style):\n '''\n 切换筛选方案方法\n :param filt_style: 筛选方式，可模糊输入，如输入区域，就是按销售区域筛选\n :return:\n '''\n options_locator = ('xpath', self.select_sxfa_input_option_xpath.format(filt_style))\n change_sxfa(self, self.select_sxfa_input_locator, options_locator)\n time.sleep(2)\n\n def change_region(self, region_name):\n '''\n 切换区域方法\n :param region_name: 传入区域名\n :return:\n '''\n options_locator = ('xpath', self.select_region_input_option_xpath.format(region_name))\n change_region(self, self.select_region_input_locator, options_locator)\n time.sleep(2)\n\n def change_partner(self, partner_name):\n '''\n 切换合作方方法\n :param partner_name: 合作方名称\n :return:\n '''\n options_locator = ('xpath', self.select_partner_input_option_xpath.format(partner_name))\n change_partner(self, self.select_partner_input_locator, options_locator)\n time.sleep(2)\n\n def user_defined_today(self):\n '''\n 自定义选择日期为今天\n :return:\n '''\n self.show_data(\"自定义\")\n self.click(self.time_start_input_locator)\n self.switch_iframe(self.iframe_locator)\n self.click(self.time_today_locator)#起始时间选择今天\n self.switch_default_content()\n self.click(self.time_end_input_locator)\n self.switch_iframe(self.iframe_locator)\n self.click(self.time_today_locator)#结束时间选择今天\n self.switch_default_content()\n\n\n def show_data(self, data_range):\n '''\n 查看全部时间，本日，本月，本年\n :param data_range: 日期范围\n :return:\n '''\n self.click(self.select_time_input_locator) #点击时间范围选择框\n select_time_input_option_loctor = ('xpath', self.select_time_input_option_xpath.format(data_range))\n self.click(select_time_input_option_loctor) #点击所选的时间范围\n\n\n\n\n def sales_area(self):\n '''\n 查看销售面积\n :return:\n '''\n self.click(self.sales_area_button_locator)\n\n def sales_amount(self):\n '''\n 查看销售金额\n :return:\n '''\n self.click(self.sales_amount_button_locator)\n\n def report_is_loaded(self):\n '''\n 判断报表是否加载完成\n :return: boolean\n '''\n return self.isElementExist(self.report_locator)\n\n\nif __name__=='__main__':\n pass","sub_path":"pages/bi/report_center/areaOrderReportPage.py","file_name":"areaOrderReportPage.py","file_ext":"py","file_size_in_byte":4666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"641851308","text":"import numpy as np\nimport tensorflow as tf\n\nnp.random.seed(1)\ntf.set_random_seed(1)\n\nclass DuelingDQN():\n\tdef __init__(self, n_actions, n_features, name=None, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, replace_target_iter=200,\n\t\tmemory_size=500, batch_size=32, e_greedy_increment=None, output_graph=False, dueling=True, sess=None):\n\t\tself.n_actions = n_actions\n\t\tself.n_features = n_features\n\t\tself.lr = learning_rate\n\t\tself.gamma = reward_decay\n\t\tself.epsilon_max = e_greedy\n\t\tself.replace_target_iter = replace_target_iter\n\t\tself.memory_size = memory_size\n\t\tself.batch_size = batch_size\n\t\tself.epsilon_increment = e_greedy_increment\n\t\tself.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max\n\n\t\tself.name = name\n\t\tself.dueling = dueling\n\n\t\tself.learn_step_counter = 0\n\t\tself.memory = np.zeros((self.memory_size, n_features*2+2))\n\t\tself._build_net()\n\n\t\tt_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name+'target_net')\n\t\te_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name+'eval_net')\n\t\tprint(t_params, e_params)\n\t\twith tf.variable_scope('soft_replacement'):\n\t\t\tself.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]\n\n\t\tif sess is None:\n\t\t\tself.sess = tf.Session()\n\t\t\tself.sess.run(tf.global_variables_initializer())\n\t\telse:\n\t\t\tself.sess = sess\n\n\t\tif output_graph:\n\t\t\ttf.summary.FileWriter('logs/', self.sess.graph)\n\n\t\tself.cost_his = []\n\n\tdef _build_net(self):\n\t\tself.s = tf.placeholder(tf.float32, [None, self.n_features], name='s')\n\t\tself.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_')\n\t\tself.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target')\n\n\n\t\tdef building_block(net_name, net_inputs):\n\t\t\tw_initializer = tf.random_normal_initializer(0., 0.3)\n\t\t\tb_initializer = tf.constant_initializer(0.1)\n\t\t\twith tf.variable_scope(net_name):\n\t\t\t\tl1 = tf.layers.dense(inputs=net_inputs, units=20, activation=tf.nn.relu, \n\t\t\t\t\tkernel_initializer=w_initializer, bias_initializer=b_initializer, name='l1')\n\t\t\t\tif self.dueling:\n\t\t\t\t\tself.V = tf.layers.dense(inputs=l1, units=1, activation=None,\n\t\t\t\t\t\tkernel_initializer=w_initializer, bias_initializer=b_initializer, name='Value')\n\t\t\t\t\tself.A = tf.layers.dense(inputs=l1, units=self.n_actions, activation=None,\n\t\t\t\t\t\tkernel_initializer=w_initializer, bias_initializer=b_initializer, name='Advantage')\n\t\t\t\t\twith tf.variable_scope('Q'):\n\t\t\t\t\t\tout = self.V + (self.A - tf.reduce_mean(self.A, axis=1, keepdims=True))\n\t\t\t\telse:\n\t\t\t\t\tout = tf.layers.dense(inputs=l1, units=self.n_actions, activation=None, \n\t\t\t\t\t\tkernel_initializer=w_initializer, bias_initializer=b_initializer, name='Q')\n\t\t\t\t\n\t\t\t\treturn out\n\n\t\tself.q_eval = building_block(net_name='eval_net', net_inputs=self.s)\n\t\tself.q_next = building_block(net_name='target_net', net_inputs=self.s_)\n\n\t\twith tf.variable_scope('loss'):\n\t\t\tself.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))\n\n\t\twith tf.variable_scope('train'):\n\t\t\tself._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)\n\n\tdef store_transition(self, s, a, r, s_):\n\t\tif not hasattr(self, 'memory_counter'):\n\t\t\tself.memory_counter = 0\n\t\ttransition = np.hstack((s, [a, r], s_))\n\t\tindex = self.memory_counter % self.memory_size\n\t\tself.memory[index, :] = transition\n\t\tself.memory_counter += 1\n\n\tdef choose_action(self, observation):\n\t\tobservation = observation[np.newaxis, :]\n\t\tif np.random.uniform() < self.epsilon:\n\t\t\tactions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})\n\t\t\taction = np.argmax(actions_value)\n\t\telse:\n\t\t\taction = np.random.randint(0, self.n_actions)\n\t\treturn action\n\n\tdef learn(self):\n\t\tif self.learn_step_counter % self.replace_target_iter == 0:\n\t\t\tself.sess.run(self.target_replace_op)\n\t\t\tprint('\\ntarget_params_replaced\\n')\n\n\t\tsample_index = np.random.choice(self.memory_size, size=self.batch_size)\n\t\tbatch_memory = self.memory[sample_index, :]\n\n\t\tq_next = self.sess.run(self.q_next, feed_dict={self.s_: batch_memory[:, -self.n_features:]}) # next observation\n\t\tq_eval = self.sess.run(self.q_eval, {self.s: batch_memory[:, :self.n_features]})\n\n\t\tq_target = q_eval.copy()\n\n\t\tbatch_index = np.arange(self.batch_size, dtype=np.int32)\n\t\teval_act_index = batch_memory[:, self.n_features].astype(int)\n\t\treward = batch_memory[:, self.n_features + 1]\n\n\t\tq_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)\n\n\t\t_, self.cost = self.sess.run([self._train_op, self.loss],\n\t\t feed_dict={self.s: batch_memory[:, :self.n_features],\n\t\t self.q_target: q_target})\n\t\tself.cost_his.append(self.cost)\n\n\t\tself.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max\n\t\tself.learn_step_counter += 1\n\nif __name__ == '__main__':\n\twith tf.variable_scope('test'):\n\t\tmodel = DuelingDQN(n_actions=3, n_features=4, output_graph=True, name='test/', dueling=False)","sub_path":"RL/9_Dueling_DQN/Dueling_DQN.py","file_name":"Dueling_DQN.py","file_ext":"py","file_size_in_byte":4957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"151344879","text":"import logging, re, emoji, json, sys\r\nfrom html2text import html2text\r\nfrom datetime import datetime\r\n\r\nfrom silverstrike import models\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\ndef chase_email(message, account_name):\r\n\tsubject = message['Subject']\r\n\tbody = html2text(getBody(message))\r\n\t\r\n\tpossible_subjects = ['Your Single Transaction Alert from Chase', 'Your Gas Station Charge Alert from Chase']\r\n\r\n\tif subject in possible_subjects:\r\n\t\tif subject == possible_subjects[0]:\r\n\t\t\tstart = body.find('Alert settings.') + 16\r\n\t\t\tend = body.find('Do not reply', start) - 1\r\n\t\t\tdescription = body[start:end].replace('\\n',' ').strip()\r\n\r\n\t\t\tstart = description.find(' at ') + 4\r\n\t\t\tend = description.find(' has ', start)\r\n\t\t\topposing_account = description[start:end].title()\r\n\r\n\t\t\tstart = description.find('($USD) ') + 7\r\n\t\t\tend = description.find(' at ', start)\r\n\t\t\tamount = description[start:end]\r\n\t\telif subject == possible_subjects[1]:\r\n\t\t\tstart = body.find('charge.') + 7\r\n\t\t\tend = body.find('Do not reply', start) - 1\r\n\t\t\tdescription = body[start:end].replace('\\n',' ').strip().replace('This ', 'A gas station ')\r\n\r\n\t\t\tstart = description.find(' at ') + 4\r\n\t\t\tend = description.find(' on ', start)\r\n\t\t\topposing_account = description[start:end].title()\r\n\t\t\t\r\n\t\t\tamount = '.01'\r\n\r\n\t\ttry:\r\n\t\t\tdate = datetime.strptime(description[-27:-1][:10], \"%m/%d/%Y\").strftime(\"%Y%m%d\")\r\n\t\texcept:\r\n\t\t\tdate = datetime.strptime(description[-26:-1][:10], \"%m/%d/%Y\").strftime(\"%Y%m%d\")\r\n\t\t\t\r\n\t\ttransaction_type = \"Withdrawal\"\r\n\telif subject == 'Your Chase Credit Card Payment Has Posted':\r\n\t\tstart = body.find('We posted')\r\n\t\tend = body.find('Now you can', start) - 1\r\n\t\tdescription = body[start:end].replace('\\n',' ').strip()\r\n\t\t\r\n\t\ttransaction_type = \"Transfer\"\r\n\t\topposing_account = models.Account.objects.get_or_create(account_type=models.Account.SYSTEM, defaults={'name': 'System Account'})[0]\r\n\t\tamount = 0\r\n\t\tdate = None\r\n\telse:\r\n\t\tlogger.info(\"Doing Nothing\")\r\n\t\t\r\n\treturn (account_name, opposing_account, amount, description[:64], description, date, transaction_type)\r\n\r\ndef venmo_email(message, account_name):\r\n\tsubject = message['Subject']\r\n\tbody = getBody(message)\r\n\r\n\taction = venmo_comments(body, '', 'span')\r\n\tactor = venmo_comments(body, '', 'a')\r\n\tdescription = venmo_comments(body, '', 'p')\r\n\trecipient = venmo_comments(body, '', 'a')\r\n\tamount = venmo_comments(body, '', 'span')\r\n\tdate = venmo_comments(body, '', 'span')\r\n\t\r\n\tif action in ['paid','charged']:\r\n\t\tdate = datetime.strptime(date[:12], \"%b %d, %Y\").strftime(\"%Y%m%d\")\r\n\t\tdescription = emoji.demojize(description).replace('![','').replace(']','')\r\n\t\tamount = amount[amount.find('$')+1:]\r\n\r\n\t\tif actor == \"You\":\r\n\t\t\topposing_account = recipient\r\n\t\t\tif action == 'charged':\r\n\t\t\t\ttransaction_type = \"Deposit\"\r\n\t\t\telif action == 'paid':\r\n\t\t\t\ttransaction_type = \"Withdrawal\"\r\n\t\telif recipient == \"You\":\r\n\t\t\topposing_account = actor\r\n\t\t\tif action == 'charged':\r\n\t\t\t\ttransaction_type = \"Withdrawal\"\r\n\t\t\telif action == 'paid':\r\n\t\t\t\ttransaction_type = \"Deposit\"\r\n\r\n\tre_pattern = re.compile(u'[^\\u0000-\\uD7FF\\uE000-\\uFFFF]', re.UNICODE)\r\n\tdescription = re_pattern.sub(u'\\uFFFD', description)\r\n\r\n\treturn (account_name, opposing_account, amount, description[:64], description, date, transaction_type)\r\n\r\ndef ally_email(message, account_name):\r\n\tsubject = message['Subject']\r\n\tbody = getBody(message)\r\n\tbody = html2text(body)\r\n\r\n\topposing_account = models.Account.objects.get_or_create(account_type=models.Account.SYSTEM, defaults={'name': 'System Account'})[0]\r\n\r\n\tif 'deposit' in subject.lower(): transaction_type = \"Deposit\"\r\n\tif 'debit' in subject.lower(): transaction_type = \"Withdrawal\"\r\n\t\r\n\tstart = body.find('$') \r\n\tend = body.find('\\n', start)\r\n\tend2 = body.find(' ', start)\r\n\tif end2', start) + 1\r\n\tend = body.find('' % search, start)\r\n\tbody = body[start:end].strip()\r\n\r\n\tif '<' in body:\r\n\t\tbody = html2text(body)\r\n\t\tregex = re.compile(\".*?\\((.*?)\\)\")\r\n\t\tresult = re.findall(regex, body)\r\n\t\tbody = body.replace(\"(%s)\" % result[0], '').replace('\\n','')\r\n\r\n\treturn body\r\n\r\ndef getcharsets(msg):\r\n\tcharsets = set({})\r\n\tfor c in msg.get_charsets():\r\n\t\tif c is not None:\r\n\t\t\tcharsets.update([c])\r\n\treturn charsets\r\n\r\ndef getBody(msg):\r\n\twhile msg.is_multipart():\r\n\t\tmsg=msg.get_payload()[0]\r\n\tt=msg.get_payload(decode=True)\r\n\tfor charset in getcharsets(msg):\r\n\t\tt=t.decode(charset)\r\n\treturn t\r\n","sub_path":"silverstrike/mohair/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":5960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"298647156","text":"from src.DirectoriesManagament import DirectoriesManagement\n\n\ndef main():\n # set Project Name and details\n siteName, siteDomain, siteBaseUrl = '', '', ''\n\n # make project own directory\n directoryObject = DirectoriesManagement(siteName)\n directoryObject.createProject()\n\n # make project own waiting and crawled links\n directoryObject.createRequiredFiles(siteDomain, siteBaseUrl)\n\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"231913248","text":"import random as rnd\r\nimport math\r\nimport time as tm\r\n\r\n\r\nHEIGHT = 500\r\nWIDTH = 800\r\n# Autumn, Winter, Spring, Summer = 0, 1, 2, 3\r\nSEASON = 0\r\nDAY = True\r\nDAY_BACK = [(247, 216, 255), (87, 103, 127), (90, 210, 220), (188, 250, 255)]\r\ncounter = 0\r\n\r\nx_coords = [rnd.random() for _ in range(10)]\r\n\r\ndef draw():\r\n global SEASON\r\n global counter\r\n global DAY\r\n counter += 1\r\n \r\n if counter % 500 == 0:\r\n SEASON = (SEASON + 1) % 4\r\n counter = 0\r\n if counter % 125 == 0:\r\n DAY = not(DAY)\r\n if DAY:\r\n background = DAY_BACK[SEASON]\r\n else:\r\n background = (0, 0, 0)\r\n \r\n if SEASON == 0:\r\n #screen.fill((100, 0, 200))\r\n screen.fill(background)\r\n draw_falling_objects((244, 144, 66))\r\n elif SEASON == 1:\r\n #screen.fill((0, 0, 150))\r\n screen.fill(background)\r\n draw_falling_objects((255, 255, 255))\r\n elif SEASON == 2:\r\n screen.fill(background)\r\n #screen.fill((0, 0, 100))\r\n draw_falling_objects((33, 80, 150))\r\n elif SEASON == 3:\r\n screen.fill(background)\r\n \r\n if DAY:\r\n screen.draw.filled_circle((WIDTH*counter/125 % WIDTH, HEIGHT*0.1), 30, (244, 244, 66))\r\n screen.draw.filled_rect(Rect((WIDTH*0.75, HEIGHT*0.05), (WIDTH*0.25, HEIGHT*0.2)), (255, 255, 255))\r\n screen.draw.filled_rect(Rect((WIDTH*0.25, HEIGHT*0.07), (WIDTH*0.25, HEIGHT*0.2)), (255, 255, 255)) \r\n \r\ndef draw_falling_objects(color):\r\n for y in range(counter+1):\r\n for x in x_coords:\r\n x = math.sin(y-counter)\r\n screen.draw.filled_circle((x*WIDTH, y), 2, color)\r\n \r\n\r\ndef update():\r\n draw()\r\n \r\n \r\n","sub_path":"theKids/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"454857280","text":"#!/usr/bin/python3\n\"\"\"Module containing wrappers for official decoders and native decoder.\n\nNative python decoder requires blowfish module. (Thus python >= 3.4)\n\"\"\"\nimport sys\nimport os\nimport logging\nimport subprocess\nimport getpass\nimport otvr.commons\nimport re\nimport base64\nfrom distutils.spawn import find_executable\nfrom datetime import datetime\nfrom hashlib import md5\n\n\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.NullHandler())\n\n\nclass DecodingError(RuntimeError):\n def __init__(self, arg):\n lines = ['no output'] + [l for l in arg.split('\\n') if l.strip()]\n super().__init__(lines.pop())\n\n\nclass BaseDecoder:\n \"\"\"Base Class for callable object to decode multiple otrkey files.\n\n :param creds: (user, password) as a tuple of strings\n :type creds: tuple(str)\n :param outdir: the directory to decode to\n :type outdir: str\n :param nice: adjust niceness using the systems nice\n :type nice: str\n\n Once an Decoder instance is created, you can call\n it on a list of files or directories.\"\"\"\n # name of decoder implementation\n NAME = \"BaseDecoder\"\n # decoder binary\n BIN = \"/bin/false\"\n # decodable magic\n MAGIC = b'OTRKEYFILE'\n\n def __init__(self, creds, outdir='.', nice=\"0\"):\n \"\"\"Check for self.BIN and credentials.\n\n :param creds: (user, password) as a tuple of strings\n :type creds: tuple(str)\n :param outdir: the directory to decode to\n :type outdir: str\n :param nice: adjust niceness using the systems nice\n :type nice: str\n \"\"\"\n # check the required executables\n self.cmd = [find_executable(self.BIN)]\n if self.cmd[0] is None:\n raise RuntimeError(\"{} not on path\".format(self.BIN))\n nice_val = str(nice)\n if nice_val != \"0\":\n nice_bin = find_executable(\"nice\")\n if nice_bin is None:\n raise RuntimeError(\"nice not on path\")\n logger.debug(\"Setting nice={}\".format(nice_val))\n self.cmd = [nice_bin, '-n', nice_val] + self.cmd\n\n # get the credentials\n self.email, self.pwd = creds\n self.cmd += ['-e', self.email, '-p', self.pwd]\n\n self.outdir = os.path.abspath(outdir)\n\n logger.debug(\"Initiated \" + self.NAME)\n\n def decode_file(self, read_file, final_file):\n \"\"\"Dummy function - must be implemented by inheriting class.\n\n Wrapper for decoding function of various decoder binaries.\n They differ by means to control the output filename.\n\n :param read_file: filename of otrkey file\n :type read_file: str\n :param final_file: filename of decoded file\n :type final_file: str\n\n \"\"\"\n pass\n\n def __call__(self, path):\n \"\"\"Decode all items from paths_lists.\"\"\"\n for job in otvr.commons.file_grabber(path):\n if open(job, 'rb').read(10) != self.MAGIC:\n continue\n logger.info('Job: ' + job)\n out_base = os.path.splitext(os.path.basename(job))[0]\n final_out = os.path.join(self.outdir, out_base)\n with otvr.commons.Lockfile(job) as lock:\n self.decode_file(job, final_out)\n logger.debug('done: ' + job)\n\n logger.info(\"Decoding complete.\")\n\n\nclass LinuxDecoder(BaseDecoder):\n \"\"\"Callable object to decode multiple otrkey files.\n\n Inherits from :class:`BaseDecoder`, and defines ``decode_file``\n\n Once an Decoder instance is created, you can call\n it on a list of files or directories.\n \"\"\"\n NAME = \"LinuxDecoder\"\n BIN = 'otrdecoder'\n\n def decode_file(self, read_file, final_file):\n \"\"\"Linux version of file decoding.\n\n Create a temporary output directory, since we can't\n control output name.\n\n :param read_file: filename of otrkey file\n :type read_file: str\n :param final_file: filename of decoded file\n :type final_file: str\n\n \"\"\"\n tempdir = os.path.join(self.outdir, '.decoding')\n if not os.path.isdir(tempdir):\n os.mkdir(tempdir)\n cmd = self.cmd + ['-o', tempdir, '-i', read_file]\n try:\n subprocess.check_output(cmd, stderr=subprocess.STDOUT)\n except subprocess.CalledProcessError as err:\n output = err.output.decode('utf-8', 'replace')\n logger.error(self.NAME + \":\\n\" + output)\n raise DecodingError(output)\n\n out_base = os.path.basename(final_file)\n os.rename(os.path.join(tempdir, out_base), final_file)\n os.rmdir(tempdir)\n\n\nclass PiDecoder(BaseDecoder):\n \"\"\"Callable object to decode multiple otrkey files.\n\n Inherits from :class:`BaseDecoder`, and defines ``decode_file``\n\n Once an Decoder instance is created, you can call\n it on a list of files or directories.\n \"\"\"\n NAME = \"PiDecoder\"\n BIN = 'otrpidecoder'\n\n def decode_file(self, read_file, final_file):\n \"\"\"RaspberryPi version of file decoding.\n\n Create a temporary output file, in case we crash while decoding.\n\n :param read_file: filename of otrkey file\n :type read_file: str\n :param final_file: filename of decoded file\n :type final_file: str\n\n \"\"\"\n write_file = final_file + '.part'\n if os.path.isfile(write_file):\n raise DecodingError(\"File already exists: \" + write_file)\n cmd = self.cmd + ['-d', '-O', write_file, read_file]\n try:\n subprocess.check_output(cmd, stderr=subprocess.STDOUT)\n except subprocess.CalledProcessError as err:\n output = err.output.decode('utf-8', 'replace')\n logger.error(self.NAME + \":\\n\" + output)\n raise DecodingError(output)\n\n os.rename(write_file, final_file)\n os.remove(read_file)\n\n\nclass NativeDecoder(BaseDecoder):\n \"\"\"Callable object to decode multiple otrkey files.\n\n :param creds: user and password\n :type creds: tuple(str)\n :param outdir: the directory to decode to\n :type outdir: str\n :param nice: set niceness via os.nice\n :type nice: int, str\n\n Inherits from :class:`BaseDecoder`, and defines ``decode_file``\n Requires blowfish module.\n Thanks to PyroPeter, the author of otrtool, upon whose work this is based.\n Of course, decoding is rather slow.\n\n Once an Decoder instance is created, you can call\n it on a list of files or directories.\n \"\"\"\n NAME = \"NativeDecoder\"\n\n # noinspection PyMissingConstructor\n def __init__(self, creds, outdir='.', nice=\"0\"):\n \"\"\"\n\n :param creds: user and password\n :type creds: tuple(str)\n :param outdir: the directory to decode to\n :type outdir: str\n :param nice: set niceness via os.nice\n :type nice: int, str\n\n \"\"\"\n # blowfish needs python > 3.4\n if sys.version_info[0] != 3 or sys.version_info[1] < 4:\n sys.exit(self.NAME + \" requires Python version 3.4\")\n from blowfish import Cipher\n # we're python > 3.4 anyway:\n from urllib.request import urlopen\n\n self.BloCip = Cipher\n\n # the header key is constant:\n self.hkey = bytearray([0xEF, 0x3A, 0xB2, 0x9C, 0xD1, 0x9F, 0x0C, 0xAC,\n 0x57, 0x59, 0xC7, 0xAB, 0xD1, 0x2C, 0xC9, 0x2B,\n 0xA3, 0xFE, 0x0A, 0xFE, 0xBF, 0x96, 0x0D, 0x63,\n 0xFE, 0xBD, 0x0F, 0x45])\n\n if int(nice) != 0:\n logger.debug(\"Setting nice = {}.\".format(nice))\n os.nice(int(nice))\n\n self.email = creds[0].encode()\n self.pwd = creds[1].encode()\n self.outdir = os.path.abspath(outdir)\n\n @staticmethod\n def _hd(title, data, log=logger):\n \"\"\"Log data hd-style.\"\"\"\n if not log.isEnabledFor(logging.DEBUG):\n return\n log.debug(title)\n ba = bytearray(data)\n for i in range(0, len(ba), 16):\n hx = (\" \".join(format(b, '02X') for b in ba[i:i + 8]) + \" \" +\n \" \".join(format(b, '02X') for b in ba[i + 8:i + 16]))\n asc = \"\".join(chr(b) if (0x20 <= b < 0x7f) else \".\"\n for b in ba[i:i + 16])\n log.debug(\"{:06X} {:48} |{:16}|\".format(i, hx, asc))\n\n def fetch_keyphrase(self, filename):\n \"\"\"Fetch keyphrase from server.\n\n :param filename: the filename for which to fetch the key\n :type filename: str\n :return: keyphrase\n :rtype: bytearray\n\n \"\"\"\n # generate server Cipher\n logger.debug(\"Generating Server Cipher and Request\")\n mh = md5(self.email).hexdigest()\n ph = md5(self.pwd).hexdigest()\n tmp_hex = datetime.now().strftime('{}%Y{}%m{}%d{}')\n tmp_hex = tmp_hex.format(mh[:13], ph[:11], mh[21:], ph[19:])\n server_key = bytearray.fromhex(tmp_hex)\n server_cipher = self.BloCip(server_key, byte_order=\"little\")\n self._hd(\"Generated ServerKey:\", server_key)\n\n # read header\n with open(filename, 'rb') as f:\n f.seek(10)\n enc_header = f.read(512)\n h_cipher = self.BloCip(self.hkey, byte_order=\"little\")\n iter_header = h_cipher.decrypt_ecb(enc_header)\n header = b''.join(iter_header)\n self._hd(\"Header:\", header)\n\n # generate request\n fn = re.search(b'&FN=([^&]*)', header).group(1)\n oh = re.search(b'&OH=([^&]*)', header).group(1)\n code = (b\"FOOOOBAR&OS=01677e4c0ae5468b9b8b823487f14524&M=01677e4c0ae\" +\n b\"5468b9b8b823487f14524&LN=DE&VN=1.4.1132&IR=TRUE&IK=aFzW1tL\" +\n b\"7nP9vXd8yUfB5kLoSyATQ&FN=\" + fn + b\"&OH=\" + oh +\n b\"&A=\" + self.email + b\"&P=\" + self.pwd + b\"&D=\")\n code += b'd' * (512 - len(code))\n logger.debug(\"Generated request-'code':\\n{}\".format(code))\n\n cipher_code = b''.join(server_cipher.encrypt_cbc(code, b'B' * 8))\n cipher_code = base64.b64encode(cipher_code).decode('UTF-8')\n today = datetime.now().strftime('%Y%m%d')\n request = \"http://87.236.198.182/quelle_neu1.php?code={}&AA={}&ZZ={}\"\n request = request.format(cipher_code, self.email.decode(), today)\n logger.debug(\"Request:\\n{}\".format(request))\n\n # contact server\n logger.debug(\"Contacting Server\")\n svr_msg = urlopen(request).read()\n self._hd(\"Server responded:\", svr_msg)\n\n # decode server response\n try:\n crypt_msg = base64.b64decode(svr_msg, validate=True)\n except Exception as exc:\n logger.debug(\"Server response unusable\")\n raise DecodingError(\"server response unusable: \" + str(exc))\n\n # decrypt server response\n _tmp = server_cipher.decrypt_cbc(crypt_msg, b'B' * 8)\n plain_msg = b''.join(_tmp)[8:]\n self._hd(\"Decrypted Message:\", plain_msg)\n\n # extract keyphrase\n hp = re.search(b'&HP=([^&]*)', plain_msg).group(1)\n if not hp:\n DecodingError(\"Could not find 'HP' in server Response\")\n\n return bytearray.fromhex(hp.decode())\n\n def decode_file(self, read_file, final_file):\n \"\"\"Native version of file decoding.\n\n Use internal decoding based on blowfish module.\n\n :param read_file: filename of otrkey file\n :type read_file: str\n :param final_file: filename of decoded file\n :type final_file: str\n\n \"\"\"\n # initiate Cipher\n keyphrase = self.fetch_keyphrase(read_file)\n cipher = self.BloCip(keyphrase, byte_order=\"little\")\n self._hd(\"Initiated cipher with keyphrase:\", keyphrase)\n\n bytes_rem = os.path.getsize(read_file)\n\n with open(final_file + '.part', 'wb', buffering=0) as dest:\n with open(read_file, 'rb') as src:\n\n src.seek(522)\n bytes_rem -= 522\n\n for _ in range(bytes_rem // 65536):\n for i in cipher.decrypt_ecb(src.read(65536)):\n dest.write(i)\n bytes_rem %= 65536\n plain_bytes = bytes_rem % 8\n for i in cipher.decrypt_ecb(src.read(bytes_rem - plain_bytes)):\n dest.write(i)\n\n # write rest chunk to file\n logger.debug(\"Remaining bytes: {}\".format(plain_bytes))\n if plain_bytes:\n dest.write(src.read(plain_bytes))\n os.rename(final_file + '.part', final_file)\n os.remove(read_file)\n\n\ndef get_decoder_type():\n \"\"\"Try to determine the appropriate decoder for this OS/Hardware\"\"\"\n s, __, __, __, m = os.uname()\n logger.debug(\"sysname='{}', machine='{}'\".format(s, m))\n\n dec_type = NativeDecoder\n if s == 'Linux':\n if 'armv' in m:\n dec_type = PiDecoder\n elif 'x86_64' == m:\n dec_type = LinuxDecoder\n\n logger.debug(\"Determined Decoder class: \" + dec_type.NAME)\n return dec_type\n\n\ndef get_creds(args):\n \"\"\"Get credentials.\n\n Try to determine the otr credentials in the following order:\n 1) Read from commandline (i.e. args)\n 2) Read from ~/.config/otrcred\n (assumed to contain email (=login) and password in two lines)\n 3) Prompt user\n\n :param args: parsed commandline arguments\n :type args: namespace\n :return: email (=login) and password\n :rtype: tuple(str)\n\n \"\"\"\n logger.debug(\"Looking for credentials in commandline ...\")\n if args.email and args.password:\n return args.email[0], args.password[0]\n\n logger.debug(\"Looking for credentials in ~/.config/otrcred ...\")\n if os.path.isfile(os.path.expanduser(\"~/.config/otrcred\")):\n # Read credentials from '~/.config/otrcred'\n with open(os.path.expanduser(\"~/.config/otrcred\"), 'r') as f:\n cont = f.readlines()\n return cont[0].strip(), cont[1].strip()\n\n print(\"Please enter your OnlineTVRecorder credentials.\")\n email = input(\"email: \").strip()\n password = getpass.getpass(\"password: \").strip()\n if email and password:\n return email, password\n\n raise RuntimeError(\"Could not find credentials\")\n\n\ndef main(argv=None):\n \"\"\"Mainline code for this module. See --help option.\"\"\"\n from argparse import ArgumentParser, RawDescriptionHelpFormatter\n\n # parse commandline arguments:\n desc = (\"Commandline tool to decode otrkey files from OnlineTvRecorder.com\"\n \"\\nNative python decoder requires blowfish, thus python >= 3.4).\")\n parser = ArgumentParser(prog=\"cuttor\", description=desc,\n formatter_class=RawDescriptionHelpFormatter)\n parser.add_argument(\"-v\", \"--verbose\", action=\"store_true\",\n help='''enable verbose logging''')\n parser.add_argument(\"--force-native\", action=\"store_true\",\n help='''enforce use of native decoder''')\n parser.add_argument(\"-l\", \"--logfile\", nargs=1, default=[None],\n help='''log to file''')\n parser.add_argument(\"-n\", \"--nice\", nargs=1, default=[0], type=str,\n help='''set niceness''')\n parser.add_argument(\"-o\", \"--out-dir\", nargs=1, default=[\".\"],\n help='''write output to this directory\n (default: '.')''')\n parser.add_argument(\"-e\", \"--email\", nargs=1, default=[],\n help='''OnlineTVRecorder login email''')\n parser.add_argument(\"-p\", \"--password\", nargs=1, default=[],\n help='''OnlineTVRecorder login password. If not\n provided, the credentials are determined by\n reading two lines from the file ~/.config/otrcred,\n or by prompt.''')\n parser.add_argument(\"-t\", \"--tmp-dir\", nargs=1, default=[\".\"],\n help='''write temporary download files to this\n directory (default: '.')''')\n parser.add_argument(\"f\", nargs=\"*\", default=[\".\"], metavar=\"path|url\",\n help='''process these files or urls. If paths is a\n directory, it is searched for appropriate files.\n Everything that is neither file nor directory is\n considered a URL.''')\n args = parser.parse_args(argv or sys.argv[1:])\n\n # set up logging\n otvr.commons.set_up_logging(args)\n\n # create required decoder instance\n dec_type = NativeDecoder if args.force_native else get_decoder_type()\n dec = dec_type(creds=get_creds(args),\n outdir=args.out_dir[0],\n nice=args.nice[0])\n\n # any argument that is not a path is considered a URL:\n urls = [i for i in args.f if not os.path.exists(i)]\n pths = [i for i in args.f if os.path.exists(i)]\n\n logger.debug(\"URLs: {}\".format(urls))\n for url in urls:\n dow = otvr.commons.Download(url, args.tmp_dir[0])\n dow.wget(wait=False)\n\n logger.debug(\"Files/Folders: {}\".format(pths))\n for pth in pths:\n dec(pth)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"decoders.py","file_name":"decoders.py","file_ext":"py","file_size_in_byte":17000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"15092981","text":"import typing\nimport logging\nimport uuid\nfrom mysql.connector import MySQLConnection\nfrom data.MySQLHelper import MySQLHelper\nfrom data.entities import EntityHandler\nfrom data.spiders import UnitSpider\nfrom data.dictionary import ProcessState\n\n\nclass PropertyEntityHandler(EntityHandler):\n\n def __init__(self):\n self._uuid = uuid.uuid4()\n self._base_url = 'https://www.nix.ru'\n\n def _mark_urls(self):\n conn = MySQLHelper.connection_inst()\n conn.connect()\n cnx = None\n try:\n query = 'update `unit` set `guid` = %s ' \\\n ' where `state` = %s'\n cnx = conn.cursor()\n cnx.execute(query, (\n str(self._uuid),\n ProcessState.CREATED.value,)\n )\n conn.commit()\n except Exception as e:\n if conn.in_transaction:\n conn.rollback()\n logging.error(e)\n finally:\n cnx = None\n conn.close()\n conn = None\n\n def get_urls(self) -> typing.List[str]:\n\n self._mark_urls()\n conn = MySQLHelper.connection_inst()\n conn.connect()\n cnx = None\n urls = []\n query = 'select `url` from `unit` where `guid` = %s'\n\n try:\n cnx = conn.cursor()\n cnx.execute(query, (str(self._uuid), ))\n urls = [self._base_url + row[0] for row in cnx.fetchall()]\n except Exception as e:\n logging.error(e)\n finally:\n cnx = None\n conn.close()\n conn = None\n return urls\n\n def finish_process(self, is_failed: bool = False):\n if is_failed:\n return\n\n conn = MySQLHelper.connection_inst()\n conn.connect()\n cnx = None\n query = 'update `property` set' \\\n ' `state` = %s,' \\\n ' `guid` = NULL,' \\\n ' where `guid` = %s'\n try:\n cnx = conn.cursor()\n cnx.execute(query, (\n ProcessState.PROCESSED.value,\n str(self._uuid))\n )\n conn.commit()\n except Exception as e:\n if conn.in_transaction:\n conn.rollback()\n logging.error(e)\n finally:\n cnx = None\n conn.close()\n conn = None\n\n def insert_data(self, data):\n conn = MySQLHelper.connection_inst()\n conn.connect()\n cnx = None\n query = 'insert into ' \\\n ' `property` (`base_url`, `own_id`, `key`, `value`) ' \\\n ' values(%(base_url)s, %(own_id)s, %(key)s, %(value)s)'\n\n try:\n cnx = conn.cursor()\n cnx.executemany(query, data)\n conn.commit()\n except Exception as e:\n if conn.in_transaction:\n conn.rollback()\n logging.error(e)\n finally:\n cnx = None\n conn.close()\n conn = None\n\n def clean_data(self, is_deleting: bool = False):\n if not is_deleting:\n return\n return\n\n","sub_path":"data/entities/PropertyEntityHandler.py","file_name":"PropertyEntityHandler.py","file_ext":"py","file_size_in_byte":3088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"193467169","text":"# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n#\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass PolicySettings(Model):\n \"\"\"Defines contents of a web application firewall global configuration.\n\n :param enabled_state: describes if the policy is in enabled state or\n disabled state. Possible values include: 'Disabled', 'Enabled'\n :type enabled_state: str or ~azure.mgmt.frontdoor.models.EnabledState\n :param mode: Describes if it is in detection mode or prevention mode at\n policy level. Possible values include: 'Prevention', 'Detection'\n :type mode: str or ~azure.mgmt.frontdoor.models.Mode\n \"\"\"\n\n _attribute_map = {\n 'enabled_state': {'key': 'enabledState', 'type': 'str'},\n 'mode': {'key': 'mode', 'type': 'str'},\n }\n\n def __init__(self, *, enabled_state=None, mode=None, **kwargs) -> None:\n super(PolicySettings, self).__init__(**kwargs)\n self.enabled_state = enabled_state\n self.mode = mode\n","sub_path":"src/front-door/azext_front_door/vendored_sdks/models/policy_settings_py3.py","file_name":"policy_settings_py3.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"61051536","text":"#! /usr/bin/python\n\n'''Voca module for voice control.\nIt contains tools to work with special voca configuration files,\nthose contain list of words separated into groups that can be used in language model.\nCopyright (C) 2013 Ruslan Larionenko '''\n\nimport os\n\nclass VocaConfig():\n 'Main class of Voca module.'\n def __init__(self, vocaFileName='control.voca'):\n 'Code from init method will be executed object of this class will be created.'\n self.vocaFileName = vocaFileName\n self.vocaFilePath = os.path.join(os.getcwd(),self.vocaFileName)\n self.vocaDictionary = {}\n\n def refreshDictionary(self):\n 'Method to refresh specials voca dictionary from configuration file.'\n self.vocaDictionary = {}\n currentWordGroup=''\n vocaFile = open(self.vocaFilePath)\n for line in vocaFile:\n line = line.strip()\n if line.startswith('% '):\n currentWordGroup=line.strip().replace('% ','')\n self.vocaDictionary[currentWordGroup]=[]\n elif line and currentWordGroup:\n self.vocaDictionary[currentWordGroup].append([line[:line.find(' ')].strip(),line[line.find(' '):].strip()])\n \n def saveDictionary(self):\n 'Method to save specials voca dictionary to configuration file.'\n vocaFile = open(self.vocaFilePath, 'w')\n for wordGroup in self.vocaDictionary :\n vocaFile.write('% ' + wordGroup + '\\n')\n for word in self.vocaDictionary.get(wordGroup):\n vocaFile.write(word[0] + ' ' + word[1] + '\\n')\n vocaFile.write('\\n')\n\n\n","sub_path":"VoiceControll/voca.py","file_name":"voca.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"387953348","text":"\r\n\"\"\"This tutorial introduces the LeNet5 neural network architecture\r\nusing Theano. LeNet5 is a convolutional neural network, good for\r\nclassifying images. This tutorial shows how to build the architecture,\r\nand comes with all the hyper-parameters you need to reproduce the\r\npaper's MNIST results.\r\n\r\n\r\nThis implementation simplifies the model in the following ways:\r\n\r\n - LeNetConvPool doesn't implement location-specific gain and bias parameters\r\n - LeNetConvPool doesn't implement pooling by average, it implements pooling\r\n by max.\r\n - Digit classification is implemented with a logistic regression rather than\r\n an RBF network\r\n - LeNet5 was not fully-connected convolutions at second layer\r\n\r\nReferences:\r\n - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner:\r\n Gradient-Based Learning Applied to Document\r\n Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.\r\n http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf\r\n\r\n\"\"\"\r\nimport os\r\nimport sys\r\nimport time\r\n\r\nimport numpy\r\n\r\nimport theano\r\nimport theano.tensor as T\r\nfrom theano.tensor.signal import downsample\r\nfrom theano.tensor.nnet import conv\r\n\r\nimport theano.sparse as Tsparse\r\n\r\nfrom logistic_sgd_global_onesided2_snp import LogisticRegression, load_input_data, load_output_data\r\nfrom mlp import HiddenLayer\r\n\r\n#import pylab as pl\r\n#import matplotlib.cm as cm\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.cm as cm\r\nimport matplotlib.colors as colors\r\n\r\nimport matplotlib as mpl\r\nfrom matplotlib.text import TextPath\r\nfrom matplotlib.patches import PathPatch\r\nfrom matplotlib.font_manager import FontProperties\r\n\r\nimport scipy.sparse as sp\r\nimport scipy.io as spio\r\n\r\nimport weblogolib\r\n\r\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\r\n\r\nclass ModifiedBackprop(object):\r\n\r\n\tdef __init__(self, nonlinearity):\r\n\t\tself.nonlinearity = nonlinearity\r\n\t\tself.ops = {} # memoizes an OpFromGraph instance per tensor type\r\n\r\n\tdef __call__(self, x):\r\n\t\t# OpFromGraph is oblique to Theano optimizations, so we need to move\r\n\t\t# things to GPU ourselves if needed.\r\n\t\t'''if theano.sandbox.cuda.cuda_enabled:\r\n\t\t\tmaybe_to_gpu = theano.sandbox.cuda.as_cuda_ndarray_variable\r\n\t\telse:\r\n\t\t\tmaybe_to_gpu = lambda x: x'''\r\n\t\t# We move the input to GPU if needed.\r\n\t\t#x = maybe_to_gpu(x)\r\n\t\t# We note the tensor type of the input variable to the nonlinearity\r\n\t\t# (mainly dimensionality and dtype); we need to create a fitting Op.\r\n\t\ttensor_type = x.type\r\n\t\t# If we did not create a suitable Op yet, this is the time to do so.\r\n\t\tif tensor_type not in self.ops:\r\n\t\t\t# For the graph, we create an input variable of the correct type:\r\n\t\t\tinp = tensor_type()\r\n\t\t\t# We pass it through the nonlinearity (and move to GPU if needed).\r\n\t\t\toutp = self.nonlinearity(inp)#maybe_to_gpu(self.nonlinearity(inp))\r\n\t\t\t# Then we fix the forward expression...\r\n\t\t\top = theano.OpFromGraph([inp], [outp])\r\n\t\t\t# ...and replace the gradient with our own (defined in a subclass).\r\n\t\t\top.grad = self.grad\r\n\t\t\t# Finally, we memoize the new Op\r\n\t\t\tself.ops[tensor_type] = op\r\n\t\t# And apply the memoized Op to the input we got.\r\n\t\treturn self.ops[tensor_type](x)\r\n\r\nclass GuidedBackprop(ModifiedBackprop):\r\n\tdef grad(self, inputs, out_grads):\r\n\t\t(inp,) = inputs\r\n\t\t(grd,) = out_grads\r\n\t\tdtype = inp.dtype\r\n\t\treturn (grd * (inp > 0).astype(dtype) * (grd > 0).astype(dtype),)\r\n\r\nclass ZeilerBackprop(ModifiedBackprop):\r\n\tdef grad(self, inputs, out_grads):\r\n\t\t(inp,) = inputs\r\n\t\t(grd,) = out_grads\r\n\t\t#return (grd * (grd > 0).astype(inp.dtype),) # explicitly rectify\r\n\t\treturn (self.nonlinearity(grd),)\r\n\r\nclass LeNetConvPoolLayer(object):\r\n\t\"\"\"Pool Layer of a convolutional network \"\"\"\r\n\r\n\tdef store_w(self, w_file, W):\r\n\t\tnumpy.save(w_file, W)\r\n\t\r\n\tdef store_b(self, b_file, b):\r\n\t\tnumpy.save(b_file, b)\r\n\t\r\n\tdef store_model(self, W, b):\r\n\t\tself.store_w(self.w_file, W)\r\n\t\tself.store_b(self.b_file, b)\r\n\t\r\n\tdef load_w(self, w_file):\r\n\t\treturn numpy.load(w_file)\r\n\t\r\n\tdef load_b(self, b_file):\r\n\t\treturn numpy.load(b_file)\r\n\t\r\n\tdef __init__(self, rng, input, deactivated_filter, deactivated_output, filter_shape, image_shape, poolsize=(2, 2), stride=(1, 1), activation_fn=T.tanh, load_model = False, w_file = '', b_file = ''):\r\n\t\t\"\"\"\r\n\t\tAllocate a LeNetConvPoolLayer with shared variable internal parameters.\r\n\r\n\t\t:type rng: numpy.random.RandomState\r\n\t\t:param rng: a random number generator used to initialize weights\r\n\r\n\t\t:type input: theano.tensor.dtensor4\r\n\t\t:param input: symbolic image tensor, of shape image_shape\r\n\r\n\t\t:type filter_shape: tuple or list of length 4\r\n\t\t:param filter_shape: (number of filters, num input feature maps,\r\n\t\t\t\t\t\t\t filter height, filter width)\r\n\r\n\t\t:type image_shape: tuple or list of length 4\r\n\t\t:param image_shape: (batch size, num input feature maps,\r\n\t\t\t\t\t\t\t image height, image width)\r\n\r\n\t\t:type poolsize: tuple or list of length 2\r\n\t\t:param poolsize: the downsampling (pooling) factor (#rows, #cols)\r\n\t\t\"\"\"\r\n\r\n\t\tself.w_file = w_file\r\n\t\tself.b_file = b_file\r\n\t\t\r\n\t\tassert image_shape[1] == filter_shape[1]\r\n\r\n\t\t# there are \"num input feature maps * filter height * filter width\"\r\n\t\t# inputs to each hidden unit\r\n\t\tfan_in = numpy.prod(filter_shape[1:])\r\n\t\t# each unit in the lower layer receives a gradient from:\r\n\t\t# \"num output feature maps * filter height * filter width\" /\r\n\t\t# pooling size\r\n\t\tfan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /\r\n\t\t\t\t numpy.prod(poolsize))\r\n\t\t# initialize weights with random weights\r\n\t\tW_bound = numpy.sqrt(6. / (fan_in + fan_out))\r\n\t\t\r\n\t\tif load_model == False : \r\n\t\t\tself.W = theano.shared(\r\n\t\t\t\tnumpy.asarray(\r\n\t\t\t\t\trng.uniform(low=-W_bound, high=W_bound, size=filter_shape),\r\n\t\t\t\t\tdtype=theano.config.floatX\r\n\t\t\t\t),\r\n\t\t\t\tborrow=True\r\n\t\t\t)\r\n\r\n\t\t\t# the bias is a 1D tensor -- one bias per output feature map\r\n\t\t\tb_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)\r\n\t\t\tself.b = theano.shared(value=b_values, borrow=True)\r\n\t\telse :\r\n\t\t\tself.W = theano.shared(value=self.load_w(w_file + '.npy'), name='W', borrow=True)\r\n\t\t\tself.b = theano.shared(value=self.load_b(b_file + '.npy'), name='b', borrow=True)\r\n\r\n\t\t# convolve input feature maps with filters\r\n\t\tconv_out = conv.conv2d(\r\n\t\t\tinput=input,\r\n\t\t\tfilters=self.W,\r\n\t\t\tfilter_shape=filter_shape,\r\n\t\t\tsubsample=stride,\r\n\t\t\timage_shape=image_shape\r\n\t\t)\r\n\r\n\t\t'''if(use_relu == True):\r\n\t\t\tactivation = relu(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))\r\n\t\telse:\r\n\t\t\tactivation = T.tanh(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))'''\r\n\t\tactivation = activation_fn(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))\r\n\r\n\t\t# downsample each feature map individually, using maxpooling\r\n\t\tpooled_out = downsample.max_pool_2d(\r\n\t\t\tinput=activation,\r\n\t\t\tds=poolsize,\r\n\t\t\tignore_border=True\r\n\t\t)\r\n\r\n\t\tself.conv_out = conv_out\r\n\t\tself.activation = activation\r\n\r\n\t\t# add the bias term. Since the bias is a vector (1D array), we first\r\n\t\t# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will\r\n\t\t# thus be broadcasted across mini-batches and feature map\r\n\t\t# width & height\r\n\t\t\r\n\t\tself.output = pooled_out\r\n\t\t\r\n\t\t# store parameters of this layer\r\n\t\tself.params = [self.W, self.b]\r\n\r\ndef relu(x):\r\n return T.switch(x<0, 0, x)\r\n\r\nclass DualCNN(object):\r\n\r\n\tdef set_saliency_functions(self, data_set):\r\n\t\tdata_set_x, data_set_y, data_set_L = data_set\r\n\r\n\t\tindex = T.lscalar()\r\n\t\tbatch_size = self.batch_size\r\n\t\t\r\n\t\tself.n_batches = data_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\t\trandomized_regions = self.randomized_regions\r\n\t\t\r\n\t\tx_left = self.x_left\r\n\t\tx_right = self.x_right\r\n\t\ty = self.y\r\n\t\tL_input = self.L_input\r\n\r\n\t\tdeactivated_filter_level1 = self.deactivated_filter_level1\r\n\t\tdeactivated_output_level1 = self.deactivated_output_level1\r\n\r\n\t\toutp = self.output_layer.s_y_given_x\r\n\t\tmax_outp = outp[:,1]#T.max(outp, axis=1)\r\n\t\tinput_saliency_from_output = theano.grad(max_outp.sum(), wrt=x_left)\r\n\r\n\t\tself.compute_input_saliency_from_output = theano.function(\r\n\t\t\t[index],\r\n\t\t\t[input_saliency_from_output],\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\tdeactivated_filter_level1: -1,\r\n\t\t\t\tdeactivated_output_level1: 0.0\r\n\t\t\t}\r\n\t\t)\r\n\r\n\t\tout_conv0 = self.layer0_left.activation\r\n\t\tout_conv1 = self.layer1.activation\r\n\r\n\t\tconv0_saliency_from_output = theano.grad(max_outp.sum(), wrt=out_conv0)\r\n\t\tconv1_saliency_from_output = theano.grad(max_outp.sum(), wrt=out_conv1)\r\n\r\n\t\tself.compute_conv0_saliency_from_output = theano.function(\r\n\t\t\t[index],\r\n\t\t\t[conv0_saliency_from_output],\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\tdeactivated_filter_level1: -1,\r\n\t\t\t\tdeactivated_output_level1: 0.0\r\n\t\t\t}\r\n\t\t)\r\n\r\n\t\tself.compute_conv1_saliency_from_output = theano.function(\r\n\t\t\t[index],\r\n\t\t\t[conv1_saliency_from_output],\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\tdeactivated_filter_level1: -1,\r\n\t\t\t\tdeactivated_output_level1: 0.0\r\n\t\t\t}\r\n\t\t)\r\n\r\n\t\t#(batch_size, nkerns[0], 88, 1)\r\n\t\tfilter_index = T.lscalar()\r\n\t\tactivation_index = T.lscalar()\r\n\r\n\t\tinput_saliency_from_conv0 = theano.grad(out_conv0[0, filter_index, activation_index, 0], wrt=x_left)\r\n\t\tinput_saliency_from_conv1 = theano.grad(out_conv1[0, filter_index, activation_index, 0], wrt=x_left)\r\n\r\n\t\tself.compute_input_saliency_from_conv0 = theano.function(\r\n\t\t\t[index, filter_index, activation_index],\r\n\t\t\t[input_saliency_from_conv0],\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_datapoint(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_datapoint(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1])\r\n\t\t\t}\r\n\t\t)\r\n\r\n\t\tself.compute_input_saliency_from_conv1 = theano.function(\r\n\t\t\t[index, filter_index, activation_index],\r\n\t\t\t[input_saliency_from_conv1],\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_datapoint(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_datapoint(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\tdeactivated_filter_level1: -1,\r\n\t\t\t\tdeactivated_output_level1: 0.0\r\n\t\t\t}\r\n\t\t)\r\n\r\n\r\n\tdef get_input_conv0_saliency(self, i, k, j) :\r\n\t\tsaliency = self.compute_input_saliency_from_conv0(i, k, j)\r\n\t\treturn saliency[0]\r\n\tdef get_input_conv1_saliency(self, i, k, j) :\r\n\t\tsaliency = self.compute_input_saliency_from_conv1(i, k, j)\r\n\t\treturn saliency[0]\r\n\r\n\tdef get_conv0_saliency(self):\r\n\t\tsaliency = numpy.concatenate([self.compute_conv0_saliency_from_output(i) for i in xrange(self.n_batches)], axis=0)\r\n\t\treturn saliency\r\n\r\n\tdef get_conv1_saliency(self):\r\n\t\tsaliency = numpy.concatenate([self.compute_conv1_saliency_from_output(i) for i in xrange(self.n_batches)], axis=0)\r\n\t\treturn saliency\r\n\r\n\tdef get_saliency(self):\r\n\t\tsaliency = numpy.concatenate([self.compute_input_saliency_from_output(i) for i in xrange(self.n_batches)], axis=0)\r\n\t\treturn saliency\r\n\r\n\tdef set_data(self, data_set_x, data_set_y, data_set_L, data_set_d):\r\n\t\tindex = T.lscalar()\r\n\t\tbatch_size = self.batch_size\r\n\t\t\r\n\t\tself.n_batches = data_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\t\t\r\n\t\trandomized_regions = self.randomized_regions\r\n\t\t\r\n\t\tx_left = self.x_left\r\n\t\tx_right = self.x_right\r\n\t\ty = self.y\r\n\t\tL_input = self.L_input\r\n\t\td_input = self.d_input\r\n\t\ttrain_drop = self.train_drop\r\n\r\n\t\tdeactivated_filter_level1 = self.deactivated_filter_level1\r\n\t\tdeactivated_output_level1 = self.deactivated_output_level1\r\n\t\t\r\n\t\tself.compute_logloss = theano.function(\r\n\t\t\t[index],\r\n\t\t\tself.output_layer.log_loss(y),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\ty: data_set_y[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: data_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t}\r\n\t\t)\r\n\t\tself.compute_rsquare = theano.function(\r\n\t\t\t[index],\r\n\t\t\tself.output_layer.rsquare(y),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\ty: data_set_y[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: data_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t}\r\n\t\t)\r\n\t\tself.compute_sse = theano.function(\r\n\t\t\t[index],\r\n\t\t\tself.output_layer.sse(y),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\ty: data_set_y[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: data_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t}\r\n\t\t)\r\n\t\tself.compute_sst = theano.function(\r\n\t\t\t[index],\r\n\t\t\tself.output_layer.sst(y),\r\n\t\t\tgivens={\r\n\t\t\t\t#x: data_set_x[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\ty: data_set_y[index * batch_size: (index + 1) * batch_size]\r\n\t\t\t}\r\n\t\t)\r\n\t\tself.compute_abs_error = theano.function(\r\n\t\t\t[index],\r\n\t\t\tself.output_layer.abs_error(y),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\ty: data_set_y[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: data_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t}\r\n\t\t)\r\n\t\tself.predict = theano.function(\r\n\t\t\t[index],\r\n\t\t\tself.output_layer.recall(),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: data_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t}\r\n\t\t)\r\n\t\tself.class_score = theano.function(\r\n\t\t\t[index],\r\n\t\t\tself.output_layer.s_y_given_x,\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(data_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(data_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\tL_input: data_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: data_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t}\r\n\t\t)\r\n\t\t\r\n\t\tdata_x = T.dtensor3('x')\r\n\t\tdata_L = T.dmatrix('L_i')\r\n\t\tdata_d = T.dmatrix('d_i')\r\n\t\tself.online_predict = theano.function(\r\n\t\t\t[data_x, data_L, data_d],\r\n\t\t\tself.output_layer.recall(),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: data_x[:,randomized_regions[0][0]:randomized_regions[0][1]].astype(theano.config.floatX),\r\n\t\t\t\tx_right: data_x[:,randomized_regions[1][0]:randomized_regions[1][1]].astype(theano.config.floatX),\r\n\t\t\t\tL_input: data_L[:, :].astype(theano.config.floatX),\r\n\t\t\t\td_input: data_d[:, :].astype(theano.config.floatX)\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t}\r\n\t\t)\r\n\r\n\tdef get_prediction(self, i=-1):\r\n\t\tif i == -1:\r\n\t\t\treturn numpy.concatenate([self.predict(i) for i in xrange(self.n_batches)])\r\n\t\telse:\r\n\t\t\treturn self.predict(i)\r\n\r\n\tdef get_class_score(self, i=-1):\r\n\t\tif i == -1:\r\n\t\t\treturn numpy.concatenate([self.class_score(i) for i in xrange(self.n_batches)])\r\n\t\telse:\r\n\t\t\treturn self.class_score(i)\r\n\r\n\tdef get_online_prediction(self, data_x, data_L, data_d):\r\n\t\treturn self.online_predict(data_x, data_L, data_d)\r\n\t\r\n\tdef get_rsquare(self):\r\n\t\tsses = [self.compute_sse(i) for i in xrange(self.n_batches)]\r\n\t\tssts = [self.compute_sst(i) for i in xrange(self.n_batches)]\r\n\t\treturn 1.0 - (numpy.sum(sses) / numpy.sum(ssts))\r\n\t\r\n\tdef get_mean_abs_error(self):\r\n\t\tabs_errors = [self.compute_abs_error(i) for i in xrange(self.n_batches)]\r\n\t\treturn numpy.mean(abs_errors)\r\n\t\r\n\tdef get_rmse(self):\r\n\t\tsses = [self.compute_sse(i) for i in xrange(self.n_batches)]\r\n\t\treturn numpy.sqrt(numpy.sum(sses) / (self.n_batches * self.batch_size))\r\n\t\r\n\tdef get_logloss(self):\r\n\t\tlosses = [self.compute_logloss(i) for i in xrange(self.n_batches)]\r\n\t\treturn numpy.sum(losses) / (self.n_batches * self.batch_size)\r\n\t\r\n\tdef reshape_batch(self, data_set_x, index, left_input_bound, right_input_bound):\r\n\t\tbatch_size = self.batch_size\r\n\t\tnum_features = self.num_features\r\n\t\tleft_random_size = self.left_random_size\r\n\t\tright_random_size = self.right_random_size\r\n\t\tinput_size = self.input_size\r\n\t\t\r\n\t\treshaped_batch = Tsparse.basic.dense_from_sparse(data_set_x[index * batch_size: (index + 1) * batch_size, :]).reshape((batch_size, data_set_x.shape[1] / num_features, num_features))[:,left_input_bound:right_input_bound]\r\n\t\tif batch_size == 1:\r\n\t\t\treshaped_batch = T.unbroadcast(reshaped_batch, 0)\r\n\t\treturn reshaped_batch.astype(theano.config.floatX)\r\n\r\n\tdef reshape_datapoint(self, data_set_x, index, left_input_bound, right_input_bound):\r\n\t\tbatch_size = self.batch_size\r\n\t\tnum_features = self.num_features\r\n\t\tleft_random_size = self.left_random_size\r\n\t\tright_random_size = self.right_random_size\r\n\t\tinput_size = self.input_size\r\n\t\t\r\n\t\treshaped_batch = Tsparse.basic.dense_from_sparse(data_set_x[index:index+1, :]).reshape((batch_size, input_size, num_features))[:,left_input_bound:right_input_bound]\r\n\t\tif batch_size == 1:\r\n\t\t\treshaped_batch = T.unbroadcast(reshaped_batch, 0)\r\n\t\treturn reshaped_batch.astype(theano.config.floatX)\r\n\r\n\tdef generate_sequence_logos_level2(self, test_set):\r\n\t\t\ttest_set_x, test_set_y, test_set_L = test_set\r\n\t\t\tself.set_data(test_set_x, test_set_y, test_set_L)\r\n\r\n\t\t\tlayer1 = self.layer1\r\n\r\n\t\t\tindex = T.lscalar()\r\n\t\t\tbatch_size = self.batch_size\r\n\t\t\t\r\n\t\t\tinput_x = test_set_x.eval()\r\n\r\n\t\t\tL_index = numpy.ravel(numpy.argmax(test_set_L.eval(), axis=1))\r\n\r\n\t\t\tn_batches = input_x.shape[0] / batch_size\r\n\t\t\t\r\n\t\t\trandomized_regions = self.randomized_regions\r\n\t\t\t\r\n\t\t\tx_left = self.x_left\r\n\t\t\tx_right = self.x_right\r\n\t\t\ty = self.y\r\n\t\t\tL_input = self.L_input\r\n\r\n\t\t\tget_layer1_activations = theano.function(\r\n\t\t\t\t[index],\r\n\t\t\t\tlayer1.activation,\r\n\t\t\t\tgivens={\r\n\t\t\t\t\tx_left: self.reshape_batch(test_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),#Tsparse.basic.dense_from_sparse(valid_set_x[index * batch_size: (index + 1) * batch_size, :]).reshape((batch_size, 70, 4))[:,randomized_regions[0][0]:randomized_regions[0][1]],\r\n\t\t\t\t},\r\n\t\t\t\ton_unused_input='ignore'\r\n\t\t\t)\r\n\r\n\t\t\tactivations = numpy.concatenate([get_layer1_activations(i) for i in xrange(n_batches)], axis=0)\r\n\t\t\t\r\n\t\t\tinput_x = input_x[:activations.shape[0],:]\r\n\t\t\tL_index = L_index[:activations.shape[0]]\r\n\t\t\tinput_x = numpy.asarray(input_x.todense()).reshape((activations.shape[0], self.input_size, self.num_features))[:, 0:self.left_random_size, :]\r\n\r\n\t\t\ty_test_hat = self.get_prediction()\r\n\t\t\ty_test = test_set_y.eval()[:y_test_hat.shape[0],1]\r\n\t\t\tlogodds_test_hat = safe_log(y_test_hat / (1 - y_test_hat))\r\n\t\t\tlogodds_test = safe_log(y_test / (1 - y_test))\r\n\r\n\t\t\tlogodds_test_isinf = numpy.isinf(logodds_test)\r\n\t\t\tlogodds_test_hat = logodds_test_hat[logodds_test_isinf == False]\r\n\t\t\tlogodds_test = logodds_test[logodds_test_isinf == False]\r\n\t\t\tactivations = activations[logodds_test_isinf == False, :, :, :]\r\n\t\t\tinput_x = input_x[logodds_test_isinf == False, :, :]\r\n\t\t\tL_index = L_index[logodds_test_isinf == False]\r\n\r\n\t\t\tlogodds_test_avg = numpy.average(logodds_test)\r\n\t\t\tlogodds_test_std = numpy.sqrt(numpy.dot(logodds_test - logodds_test_avg, logodds_test - logodds_test_avg))\r\n\r\n\t\t\tmax_activation = numpy.zeros((activations.shape[1], activations.shape[0]))\r\n\t\t\tpos_activation = numpy.zeros((activations.shape[1], activations.shape[0], activations.shape[2]))\r\n\r\n\t\t\tpos_r = numpy.zeros((activations.shape[1], activations.shape[2]))\r\n\r\n\t\t\tfilter_width = 17\r\n\r\n\r\n\t\t\tvalid_activations = numpy.zeros(activations.shape)\r\n\t\t\r\n\t\t\t#No-padding Library variation strings\r\n\r\n\t\t\tlibvar_2 = ('X' * (80 - 1)) + ('V' * (20 - 7)) + ('X' * (20 + 7)) + ('X' * 33) + ('X' * 20) + ('X' * (83 - 7))\r\n\t\t\tlibvar_2_id = numpy.zeros(255 - 7)\r\n\t\t\tfor i in range(0, 255 - 7) :\r\n\t\t\t\tif libvar_2[i] == 'V' :\r\n\t\t\t\t\tlibvar_2_id[i] = 1\r\n\t\t\tlibvar_2_idd = numpy.zeros(120)\r\n\t\t\tfor i in range(0, 120) :\r\n\t\t\t\tif libvar_2_id[2 * i] == 1 and libvar_2_id[2 * i + 1] == 1 :\r\n\t\t\t\t\tlibvar_2_idd[i] = 1\r\n\t\t\tlibvar_2_id = libvar_2_idd\r\n\r\n\t\t\tlibvar_8 = ('X' * (80 - 1)) + ('V' * (20 - 7)) + ('X' * (20 + 7)) + ('X' * 33) + ('V' * (20 - 7)) + ('X' * (83 - 7 + 7))\r\n\t\t\tlibvar_8_id = numpy.zeros(255 - 7)\r\n\t\t\tfor i in range(0, 255 - 7) :\r\n\t\t\t\tif libvar_8[i] == 'V' :\r\n\t\t\t\t\tlibvar_8_id[i] = 1\r\n\t\t\tlibvar_8_idd = numpy.zeros(120)\r\n\t\t\tfor i in range(0, 120) :\r\n\t\t\t\tif libvar_8_id[2 * i] == 1 and libvar_8_id[2 * i + 1] == 1 :\r\n\t\t\t\t\tlibvar_8_idd[i] = 1\r\n\t\t\tlibvar_8_id = libvar_8_idd\r\n\r\n\t\t\tlibvar_5 = ('X' * (80 - 1)) + ('X' * 20) + ('V' * (20 - 7)) + ('X' * (33 + 7)) + ('X' * 20) + ('X' * (83 - 7))\r\n\t\t\tlibvar_5_id = numpy.zeros(255 - 7)\r\n\t\t\tfor i in range(0, 255 - 7) :\r\n\t\t\t\tif libvar_5[i] == 'V' :\r\n\t\t\t\t\tlibvar_5_id[i] = 1\r\n\t\t\tlibvar_5_idd = numpy.zeros(120)\r\n\t\t\tfor i in range(0, 120) :\r\n\t\t\t\tif libvar_5_id[2 * i] == 1 and libvar_5_id[2 * i + 1] == 1 :\r\n\t\t\t\t\tlibvar_5_idd[i] = 1\r\n\t\t\tlibvar_5_id = libvar_5_idd\r\n\r\n\t\t\tlibvar_11 = ('X' * (80 - 1)) + ('X' * 20) + ('V' * (20 - 7)) + ('X' * (33 + 7)) + ('V' * (20 - 7)) + ('X' * (83 - 7 + 7))\r\n\t\t\tlibvar_11_id = numpy.zeros(255 - 7)\r\n\t\t\tfor i in range(0, 255 - 7) :\r\n\t\t\t\tif libvar_11[i] == 'V' :\r\n\t\t\t\t\tlibvar_11_id[i] = 1\r\n\t\t\tlibvar_11_idd = numpy.zeros(120)\r\n\t\t\tfor i in range(0, 120) :\r\n\t\t\t\tif libvar_11_id[2 * i] == 1 and libvar_11_id[2 * i + 1] == 1 :\r\n\t\t\t\t\tlibvar_11_idd[i] = 1\r\n\t\t\tlibvar_11_id = libvar_11_idd\r\n\r\n\t\t\t#APA_SYM_PRX\r\n\t\t\tlibvar_20 = ('X' * (100 - 1)) + ('V' * (71 - 7)) + ('X' * (14 + 7)) + ('V' * (71 - 7))\r\n\t\t\tlibvar_20_id = numpy.zeros(255 - 7)\r\n\t\t\tfor i in range(0, 255 - 7) :\r\n\t\t\t\tif libvar_20[i] == 'V' :\r\n\t\t\t\t\tlibvar_20_id[i] = 1\r\n\t\t\tlibvar_20_idd = numpy.zeros(120)\r\n\t\t\tfor i in range(0, 120) :\r\n\t\t\t\tif libvar_20_id[2 * i] == 1 and libvar_20_id[2 * i + 1] == 1 :\r\n\t\t\t\t\tlibvar_20_idd[i] = 1\r\n\t\t\tlibvar_20_id = libvar_20_idd\r\n\r\n\t\t\tlibvar_21 = ('X' * (15 - 1)) + ('V' * (71 - 7)) + ('X' * (14 + 7)) + ('V' * (71 - 7)) + ('X' * (85 - 7 + 7))\r\n\t\t\tlibvar_21_id = numpy.zeros(255 - 7)\r\n\t\t\tfor i in range(0, 255 - 7) :\r\n\t\t\t\tif libvar_21[i] == 'V' :\r\n\t\t\t\t\tlibvar_21_id[i] = 1\r\n\t\t\tlibvar_21_idd = numpy.zeros(120)\r\n\t\t\tfor i in range(0, 120) :\r\n\t\t\t\tif libvar_21_id[2 * i] == 1 and libvar_21_id[2 * i + 1] == 1 :\r\n\t\t\t\t\tlibvar_21_idd[i] = 1\r\n\t\t\tlibvar_21_id = libvar_21_idd\r\n\r\n\r\n\t\t\tpos_to_libs = [\r\n\t\t\t\t[libvar_2_id, 2],\r\n\t\t\t\t[libvar_8_id, 8],\r\n\t\t\t\t[libvar_5_id, 5],\r\n\t\t\t\t[libvar_11_id, 11],\r\n\t\t\t\t[libvar_20_id, 20],\r\n\t\t\t\t[libvar_21_id, 21],\r\n\t\t\t]\r\n\t\t\tpos_to_libs_lookup = []\r\n\t\t\tfor pos in range(0, len(pos_to_libs[0][0])) :\r\n\t\t\t\tvalid_libs = []\r\n\t\t\t\tvalid_libs_str = ''\r\n\t\t\t\tfor libvar in pos_to_libs :\r\n\t\t\t\t\tif libvar[0][pos] == 1 :\r\n\t\t\t\t\t\tvalid_libs.append(libvar[1])\r\n\t\t\t\t\t\tvalid_libs_str += '_' + str(libvar[1])\r\n\t\t\t\tpos_to_libs_lookup.append([pos, valid_libs, valid_libs_str])\r\n\r\n\r\n\t\t\tvalid_activations[L_index == 2, :, :, :] = numpy.reshape(numpy.tile(libvar_2_id, (len(numpy.nonzero(L_index == 2)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 2)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\t\tvalid_activations[L_index == 8, :, :, :] = numpy.reshape(numpy.tile(libvar_8_id, (len(numpy.nonzero(L_index == 8)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 8)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\t\tvalid_activations[L_index == 5, :, :, :] = numpy.reshape(numpy.tile(libvar_5_id, (len(numpy.nonzero(L_index == 5)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 5)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\t\tvalid_activations[L_index == 11, :, :, :] = numpy.reshape(numpy.tile(libvar_11_id, (len(numpy.nonzero(L_index == 11)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 11)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\t\tvalid_activations[L_index == 20, :, :, :] = numpy.reshape(numpy.tile(libvar_20_id, (len(numpy.nonzero(L_index == 20)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 20)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\t\tvalid_activations[L_index == 21, :, :, :] = numpy.reshape(numpy.tile(libvar_21_id, (len(numpy.nonzero(L_index == 21)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 21)[0]), activations.shape[1], activations.shape[2], 1))\r\n\r\n\t\t\t\r\n\t\t\tactivations = numpy.multiply(activations, valid_activations)\r\n\r\n\r\n\t\t\t#(num_data_points, num_filters, seq_length, 1)\r\n\t\t\tfor k in range(0, activations.shape[1]) :\r\n\t\t\t\tfilter_activations = activations[:, k, :, :].reshape((activations.shape[0], activations.shape[2]))\r\n\t\t\t\ttotal_activations = numpy.ravel(numpy.sum(filter_activations, axis=1))\r\n\r\n\t\t\t\tmax_activation[k, :] = numpy.ravel(numpy.max(filter_activations, axis=1))\r\n\t\t\t\tpos_activation[k, :, :] = filter_activations[:, :]\r\n\t\t\t\t\r\n\t\t\t\tspike_index = numpy.nonzero(total_activations > 0)[0]\r\n\r\n\t\t\t\tfilter_activations = filter_activations[spike_index, :]\r\n\t\t\t\t\r\n\t\t\t\tprint(input_x.shape)\r\n\t\t\t\tprint(spike_index.shape)\r\n\r\n\t\t\t\tfilter_inputs = input_x[spike_index, :, :]\r\n\t\t\t\tfilter_L = L_index[spike_index]\r\n\r\n\t\t\t\tmax_spike = numpy.ravel(numpy.argmax(filter_activations, axis=1))\r\n\r\n\t\t\t\ttop_scoring_index = numpy.argsort(numpy.ravel(numpy.max(filter_activations, axis=1)))\r\n\t\t\t\ttop_scoring_index = top_scoring_index[len(top_scoring_index)-700:]\r\n\t\t\t\t\r\n\t\t\t\t'''PFM = numpy.zeros((filter_width, self.num_features))\r\n\t\t\t\tfor i in range(0, filter_activations.shape[0]) :\r\n\r\n\t\t\t\t\tinput_saliency_from_conv1 = self.get_input_conv1_saliency(spike_index[i], k, max_spike[i])\r\n\t\t\t\t\tinput_saliency_from_conv1_index = input_saliency_from_conv1 > 0\r\n\t\t\t\t\tinput_saliency_from_conv1_id = numpy.zeros(input_saliency_from_conv1.shape)\r\n\t\t\t\t\tinput_saliency_from_conv1_id[input_saliency_from_conv1_index] = 1\r\n\t\t\t\t\tinput_saliency_from_conv1_id = input_saliency_from_conv1_id[0, 2*max_spike[i]:2*max_spike[i]+filter_width, :]\r\n\t\t\t\t\tfilter_input = numpy.multiply(filter_inputs[i, 2*max_spike[i]:2*max_spike[i]+filter_width, :], input_saliency_from_conv1_id) * filter_activations[i, max_spike[i]]\r\n\r\n\t\t\t\t\tPFM = PFM + filter_input\r\n\r\n\t\t\t\t\t#PFM = PFM + numpy.multiply(filter_inputs[i, 2*max_spike[i]:2*max_spike[i]+filter_width, :], numpy.maximum(0, input_saliency_from_conv1[0, 2*max_spike[i]:2*max_spike[i]+filter_width, :])) * filter_activations[i, max_spike[i]]\r\n\t\t\t\t'''\r\n\t\t\t\tPFM = numpy.zeros((filter_width, self.num_features))\r\n\t\t\t\tfor ii in range(0, len(top_scoring_index)) :\r\n\t\t\t\t\ti = top_scoring_index[ii]\r\n\r\n\t\t\t\t\t'''input_saliency_from_conv1 = self.get_input_conv1_saliency(spike_index[i], k, max_spike[i])\r\n\t\t\t\t\tinput_saliency_from_conv1_index = input_saliency_from_conv1 > 0\r\n\t\t\t\t\tinput_saliency_from_conv1_id = numpy.zeros(input_saliency_from_conv1.shape)\r\n\t\t\t\t\tinput_saliency_from_conv1_id[input_saliency_from_conv1_index] = 1\r\n\t\t\t\t\tinput_saliency_from_conv1_id = input_saliency_from_conv1_id[0, 2*max_spike[i]:2*max_spike[i]+filter_width, :]'''\r\n\t\t\t\t\t#filter_input = numpy.multiply(filter_inputs[i, 2*max_spike[i]:2*max_spike[i]+filter_width, :], input_saliency_from_conv1_id) #* filter_activations[i, max_spike[i]]\r\n\t\t\t\t\tfilter_input = filter_inputs[i, 2*max_spike[i]:2*max_spike[i]+filter_width, :]\r\n\r\n\t\t\t\t\tPFM = PFM + filter_input\r\n\r\n\t\t\t\t#print(k)\r\n\t\t\t\t#print(PFM)\r\n\r\n\t\t\t\t#Calculate Pearson r\r\n\t\t\t\tlogodds_test_curr = logodds_test\r\n\t\t\t\tlogodds_test_avg_curr = logodds_test_avg\r\n\t\t\t\tlogodds_test_std_curr = logodds_test_std\r\n\t\t\t\tmax_activation_k = numpy.ravel(max_activation[k, :])\r\n\r\n\t\t\t\tmax_activation_k = max_activation_k[L_index > 5]\r\n\t\t\t\tlogodds_test_curr = logodds_test[L_index > 5]\r\n\r\n\t\t\t\tmax_activation_k_avg = numpy.average(max_activation_k)\r\n\t\t\t\tmax_activation_k_std = numpy.sqrt(numpy.dot(max_activation_k - max_activation_k_avg, max_activation_k - max_activation_k_avg))\r\n\r\n\t\t\t\tlogodds_test_avg_curr = numpy.average(logodds_test_curr)\r\n\t\t\t\tlogodds_test_std_curr = numpy.sqrt(numpy.dot(logodds_test_curr - logodds_test_avg_curr, logodds_test_curr - logodds_test_avg_curr))\r\n\r\n\t\t\t\tcov = numpy.dot(logodds_test_curr - logodds_test_avg_curr, max_activation_k - max_activation_k_avg)\r\n\t\t\t\tr = cov / (max_activation_k_std * logodds_test_std_curr)\r\n\t\t\t\tprint('r = ' + str(round(r, 2)))\r\n\r\n\t\t\t\tprev_selection_libs_str = 'X'\r\n\t\t\t\tfor pos in range(0, activations.shape[2]) :\r\n\r\n\t\t\t\t\tpos_activation_curr = pos_activation\r\n\t\t\t\t\tlogodds_test_curr = logodds_test\r\n\t\t\t\t\tlogodds_test_avg_curr = logodds_test_avg\r\n\t\t\t\t\tlogodds_test_std_curr = logodds_test_std\r\n\t\t\t\t\tcurr_selection_libs_str = ''\r\n\t\t\t\t\tif pos_to_libs_lookup[pos][2] == '' :\r\n\t\t\t\t\t\tcontinue\r\n\r\n\t\t\t\t\tpos_to_lib = pos_to_libs_lookup[pos]\r\n\t\t\t\t\tcurr_selection_libs_str = pos_to_lib[2]\r\n\t\t\t\t\tif curr_selection_libs_str == prev_selection_libs_str :\r\n\t\t\t\t\t\tpos_activation_curr = pos_activation_prev\r\n\t\t\t\t\t\tlogodds_test_curr = logodds_test_prev\r\n\t\t\t\t\telse :\r\n\t\t\t\t\t\twhitelist_index = []\r\n\t\t\t\t\t\tfor i in range(0, len(L_index)) :\r\n\t\t\t\t\t\t\tif L_index[i] in pos_to_lib[1] :\r\n\t\t\t\t\t\t\t\twhitelist_index.append(i)\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tpos_activation_curr = pos_activation[:, whitelist_index, :]\r\n\t\t\t\t\t\tlogodds_test_curr = logodds_test[whitelist_index]\r\n\t\t\t\t\tlogodds_test_avg_curr = numpy.average(logodds_test_curr)\r\n\t\t\t\t\tlogodds_test_std_curr = numpy.sqrt(numpy.dot(logodds_test_curr - logodds_test_avg_curr, logodds_test_curr - logodds_test_avg_curr))\r\n\r\n\t\t\t\t\tif curr_selection_libs_str == '' :\r\n\t\t\t\t\t\tcontinue\r\n\r\n\t\t\t\t\tpos_activation_k_pos = numpy.ravel(pos_activation_curr[k, :, pos])\r\n\t\t\t\t\tpos_activation_k_pos_avg = numpy.average(pos_activation_k_pos)\r\n\t\t\t\t\tpos_activation_k_pos_std = numpy.sqrt(numpy.dot(pos_activation_k_pos - pos_activation_k_pos_avg, pos_activation_k_pos - pos_activation_k_pos_avg))\r\n\r\n\t\t\t\t\tcov_pos = numpy.dot(logodds_test_curr - logodds_test_avg_curr, pos_activation_k_pos - pos_activation_k_pos_avg)\r\n\t\t\t\t\tr_k_pos = cov_pos / (pos_activation_k_pos_std * logodds_test_std_curr)\r\n\r\n\t\t\t\t\tif not (numpy.isinf(r_k_pos) or numpy.isnan(r_k_pos)) :\r\n\t\t\t\t\t\tpos_r[k, pos] = r_k_pos\r\n\r\n\t\t\t\t\tprev_selection_libs_str = curr_selection_libs_str\r\n\t\t\t\t\tpos_activation_prev = pos_activation_curr\r\n\t\t\t\t\tlogodds_test_prev = logodds_test_curr\r\n\r\n\t\t\t\tlogo_name = \"avg_motif_\" + str(k) + \".png\"\r\n\t\t\t\tlogo_name_normed = \"avg_motif_\" + str(k) + '_normed' + \".png\"\r\n\t\t\t\tself.get_logo(k, PFM, 'cnn_motif_analysis/fullseq_global/deconv/avg_filter_level2/' + logo_name, 17, score=r)\r\n\t\t\t\t#self.get_logo(k, PFM, 'cnn_motif_analysis/fullseq_global/deconv/avg_filter_level2/' + logo_name_normed, 17, normalize=True, score=r)\r\n\r\n\t\t\t#All-filter positional Pearson r\r\n\t\t\tf = plt.figure(figsize=(18, 16))\r\n\r\n\t\t\tplt.pcolor(pos_r,cmap=cm.RdBu_r,vmin=-numpy.abs(pos_r).max(), vmax=numpy.abs(pos_r).max())\r\n\t\t\tplt.colorbar()\r\n\r\n\t\t\tplt.xlabel('Sequence position')\r\n\t\t\tplt.title('Prox. selection Pearson r for all layer 2 filters')\r\n\t\t\t#plt.axis([0, 4095, np.min(w_sorted) - 0.1, np.max(w_sorted) + 0.1])\r\n\t\t\t#xticks = mer_sorted\r\n\t\t\tplt.xticks([0, 15, 30, 45, 60, 75, 90, 105, 120], [0 - 60, 15 - 60, 30 - 60, 45 - 60, 60 - 60, 75 - 60, 90 - 60, 105 - 60, 120 - 60])\r\n\t\t\tplt.yticks(numpy.arange(pos_r.shape[0]) + 0.5, numpy.arange(pos_r.shape[0]))#BASEPAIR TO INDEX FLIPPED ON PURPOSE TO COUNTER CONVOLVE\r\n\r\n\t\t\tplt.axis([0, pos_r.shape[1], 0, pos_r.shape[0]])\r\n\r\n\t\t\tplt.savefig('cnn_motif_analysis/fullseq_global/deconv/avg_filter_level2/' + \"r_pos.png\")\r\n\t\t\tplt.close()\r\n\r\n\t\t\tf = plt.figure(figsize=(18, 16))\r\n\r\n\t\t\tplt.pcolor(numpy.repeat(pos_r, 2, axis=1),cmap=cm.RdBu_r,vmin=-numpy.abs(pos_r).max(), vmax=numpy.abs(pos_r).max())\r\n\t\t\tplt.colorbar()\r\n\r\n\t\t\tplt.xlabel('Sequence position')\r\n\t\t\tplt.title('Prox. selection Pearson r for all layer 2 filters')\r\n\t\t\t#plt.axis([0, 4095, np.min(w_sorted) - 0.1, np.max(w_sorted) + 0.1])\r\n\t\t\t#xticks = mer_sorted\r\n\t\t\tplt.xticks([0, 2 * 15, 2 * 30, 2 * 45, 2 * 60, 2 * 75, 2 * 90, 2 * 105, 2 * 120], [2 * 0 - 2 * 60, 2 * 15 - 2 * 60, 2 * 30 - 2 * 60, 2 * 45 - 2 * 60, 2 * 60 - 2 * 60, 2 * 75 - 2 * 60, 2 * 90 - 2 * 60, 2 * 105 - 2 * 60, 2 * 120 - 2 * 60])\r\n\t\t\tplt.yticks(numpy.arange(pos_r.shape[0]) + 0.5, numpy.arange(pos_r.shape[0]))#BASEPAIR TO INDEX FLIPPED ON PURPOSE TO COUNTER CONVOLVE\r\n\r\n\t\t\tplt.axis([0, pos_r.shape[1] * 2, 0, pos_r.shape[0]])\r\n\r\n\t\t\tplt.savefig('cnn_motif_analysis/fullseq_global/deconv/avg_filter_level2/' + \"r_pos_projected.png\")\r\n\t\t\tplt.close()\r\n\t\t\t\r\n\r\n\tdef generate_sequence_logos(self, test_set):\r\n\t\ttest_set_x, test_set_y, test_set_L = test_set\r\n\t\tself.set_data(test_set_x, test_set_y, test_set_L)\r\n\r\n\t\tlayer0_left = self.layer0_left\r\n\r\n\t\tindex = T.lscalar()\r\n\t\tbatch_size = self.batch_size\r\n\t\t\r\n\t\tinput_x = test_set_x.eval()\r\n\r\n\t\tL_index = numpy.ravel(numpy.argmax(test_set_L.eval(), axis=1))\r\n\r\n\t\tn_batches = input_x.shape[0] / batch_size\r\n\t\t\r\n\t\trandomized_regions = self.randomized_regions\r\n\t\t\r\n\t\tx_left = self.x_left\r\n\t\tx_right = self.x_right\r\n\t\ty = self.y\r\n\t\tL_input = self.L_input\r\n\r\n\t\tget_layer0_activations = theano.function(\r\n\t\t\t[index],\r\n\t\t\tlayer0_left.activation,\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(test_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),#Tsparse.basic.dense_from_sparse(valid_set_x[index * batch_size: (index + 1) * batch_size, :]).reshape((batch_size, 70, 4))[:,randomized_regions[0][0]:randomized_regions[0][1]],\r\n\t\t\t},\r\n\t\t\ton_unused_input='ignore'\r\n\t\t)\r\n\r\n\t\tactivations = numpy.concatenate([get_layer0_activations(i) for i in xrange(n_batches)], axis=0)\r\n\r\n\t\tinput_x = input_x[:activations.shape[0],:]\r\n\t\tL_index = L_index[:activations.shape[0]]\r\n\t\tinput_x = numpy.asarray(input_x.todense()).reshape((activations.shape[0], self.input_size, self.num_features))[:, 0:self.left_random_size, :]\r\n\r\n\t\ty_test_hat = self.get_prediction()\r\n\t\ty_test = test_set_y.eval()[:y_test_hat.shape[0],1]\r\n\t\tlogodds_test_hat = safe_log(y_test_hat / (1 - y_test_hat))\r\n\t\tlogodds_test = safe_log(y_test / (1 - y_test))\r\n\r\n\t\tlogodds_test_isinf = numpy.isinf(logodds_test)\r\n\t\tlogodds_test_hat = logodds_test_hat[logodds_test_isinf == False]\r\n\t\tlogodds_test = logodds_test[logodds_test_isinf == False]\r\n\t\tactivations = activations[logodds_test_isinf == False, :, :, :]\r\n\t\tinput_x = input_x[logodds_test_isinf == False, :, :]\r\n\t\tL_index = L_index[logodds_test_isinf == False]\r\n\r\n\t\tlogodds_test_avg = numpy.average(logodds_test)\r\n\t\tlogodds_test_std = numpy.sqrt(numpy.dot(logodds_test - logodds_test_avg, logodds_test - logodds_test_avg))\r\n\r\n\t\tmax_activation = numpy.zeros((activations.shape[1], activations.shape[0]))\r\n\t\tpos_activation = numpy.zeros((activations.shape[1], activations.shape[0], activations.shape[2]))\r\n\r\n\t\tpos_r = numpy.zeros((activations.shape[1], activations.shape[2]))\r\n\r\n\t\tfilter_width = 8\r\n\r\n\t\tvalid_activations = numpy.zeros(activations.shape)\r\n\t\t\r\n\t\t#No-padding Library variation strings\r\n\r\n\t\tlibvar_2 = ('X' * (80 - 1)) + ('V' * (20 - 7)) + ('X' * (20 + 7)) + ('X' * 33) + ('X' * 20) + ('X' * (83 - 7))\r\n\t\tlibvar_2_id = numpy.zeros(255 - 7)\r\n\t\tfor i in range(0, 255 - 7) :\r\n\t\t\tif libvar_2[i] == 'V' :\r\n\t\t\t\tlibvar_2_id[i] = 1\r\n\r\n\t\tlibvar_8 = ('X' * (80 - 1)) + ('V' * (20 - 7)) + ('X' * (20 + 7)) + ('X' * 33) + ('V' * (20 - 7)) + ('X' * (83 - 7 + 7))\r\n\t\tlibvar_8_id = numpy.zeros(255 - 7)\r\n\t\tfor i in range(0, 255 - 7) :\r\n\t\t\tif libvar_8[i] == 'V' :\r\n\t\t\t\tlibvar_8_id[i] = 1\r\n\r\n\t\tlibvar_5 = ('X' * (80 - 1)) + ('X' * 20) + ('V' * (20 - 7)) + ('X' * (33 + 7)) + ('X' * 20) + ('X' * (83 - 7))\r\n\t\tlibvar_5_id = numpy.zeros(255 - 7)\r\n\t\tfor i in range(0, 255 - 7) :\r\n\t\t\tif libvar_5[i] == 'V' :\r\n\t\t\t\tlibvar_5_id[i] = 1\r\n\r\n\t\tlibvar_11 = ('X' * (80 - 1)) + ('X' * 20) + ('V' * (20 - 7)) + ('X' * (33 + 7)) + ('V' * (20 - 7)) + ('X' * (83 - 7 + 7))\r\n\t\tlibvar_11_id = numpy.zeros(255 - 7)\r\n\t\tfor i in range(0, 255 - 7) :\r\n\t\t\tif libvar_11[i] == 'V' :\r\n\t\t\t\tlibvar_11_id[i] = 1\r\n\r\n\t\t#APA_SYM_PRX\r\n\t\tlibvar_20 = ('X' * (100 - 1)) + ('V' * (71 - 7)) + ('X' * (14 + 7)) + ('V' * (71 - 7))\r\n\t\tlibvar_20_id = numpy.zeros(255 - 7)\r\n\t\tfor i in range(0, 255 - 7) :\r\n\t\t\tif libvar_20[i] == 'V' :\r\n\t\t\t\tlibvar_20_id[i] = 1\r\n\r\n\t\tlibvar_21 = ('X' * (15 - 1)) + ('V' * (71 - 7)) + ('X' * (14 + 7)) + ('V' * (71 - 7)) + ('X' * (85 - 7 + 7))\r\n\t\tlibvar_21_id = numpy.zeros(255 - 7)\r\n\t\tfor i in range(0, 255 - 7) :\r\n\t\t\tif libvar_21[i] == 'V' :\r\n\t\t\t\tlibvar_21_id[i] = 1\r\n\r\n\r\n\t\tpos_to_libs = [\r\n\t\t\t[libvar_2_id, 2],\r\n\t\t\t[libvar_8_id, 8],\r\n\t\t\t[libvar_5_id, 5],\r\n\t\t\t[libvar_11_id, 11],\r\n\t\t\t[libvar_20_id, 20],\r\n\t\t\t[libvar_21_id, 21],\r\n\t\t]\r\n\t\tpos_to_libs_lookup = []\r\n\t\tfor pos in range(0, len(pos_to_libs[0][0])) :\r\n\t\t\tvalid_libs = []\r\n\t\t\tvalid_libs_str = ''\r\n\t\t\tfor libvar in pos_to_libs :\r\n\t\t\t\tif libvar[0][pos] == 1 :\r\n\t\t\t\t\tvalid_libs.append(libvar[1])\r\n\t\t\t\t\tvalid_libs_str += '_' + str(libvar[1])\r\n\t\t\tpos_to_libs_lookup.append([pos, valid_libs, valid_libs_str])\r\n\r\n\r\n\t\tvalid_activations[L_index == 2, :, :, :] = numpy.reshape(numpy.tile(libvar_2_id, (len(numpy.nonzero(L_index == 2)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 2)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\tvalid_activations[L_index == 8, :, :, :] = numpy.reshape(numpy.tile(libvar_8_id, (len(numpy.nonzero(L_index == 8)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 8)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\tvalid_activations[L_index == 5, :, :, :] = numpy.reshape(numpy.tile(libvar_5_id, (len(numpy.nonzero(L_index == 5)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 5)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\tvalid_activations[L_index == 11, :, :, :] = numpy.reshape(numpy.tile(libvar_11_id, (len(numpy.nonzero(L_index == 11)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 11)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\tvalid_activations[L_index == 20, :, :, :] = numpy.reshape(numpy.tile(libvar_20_id, (len(numpy.nonzero(L_index == 20)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 20)[0]), activations.shape[1], activations.shape[2], 1))\r\n\t\tvalid_activations[L_index == 21, :, :, :] = numpy.reshape(numpy.tile(libvar_21_id, (len(numpy.nonzero(L_index == 21)[0]), activations.shape[1], 1)), (len(numpy.nonzero(L_index == 21)[0]), activations.shape[1], activations.shape[2], 1))\r\n\r\n\t\t\r\n\t\tactivations = numpy.multiply(activations, valid_activations)\r\n\r\n\t\t#(num_data_points, num_filters, seq_length, 1)\r\n\t\tfor k in range(0, activations.shape[1]) :\r\n\t\t\tfilter_activations = activations[:, k, :, :].reshape((activations.shape[0], activations.shape[2]))\r\n\t\t\ttotal_activations = numpy.ravel(numpy.sum(filter_activations, axis=1))\r\n\r\n\t\t\tmax_activation[k, :] = numpy.ravel(numpy.max(filter_activations, axis=1))\r\n\t\t\tpos_activation[k, :, :] = filter_activations[:, :]\r\n\r\n\t\t\tspike_index = numpy.nonzero(total_activations > 0)[0]\r\n\r\n\t\t\tfilter_activations = filter_activations[spike_index, :]\r\n\r\n\t\t\tprint(input_x.shape)\r\n\t\t\tprint(spike_index.shape)\r\n\r\n\t\t\tfilter_inputs = input_x[spike_index, :, :]\r\n\t\t\tfilter_L = L_index[spike_index]\r\n\r\n\t\t\tmax_spike = numpy.ravel(numpy.argmax(filter_activations, axis=1))\r\n\r\n\t\t\tmax_act = numpy.max(numpy.ravel(numpy.max(filter_activations, axis=1)))\r\n\r\n\t\t\ttop_scoring_index = numpy.argsort(numpy.ravel(numpy.max(filter_activations, axis=1)))\r\n\t\t\ttop_scoring_index = top_scoring_index[len(top_scoring_index)-5000:]\r\n\r\n\t\t\t'''PFM = numpy.zeros((filter_width, self.num_features))\r\n\t\t\tfor i in range(0, filter_activations.shape[0]) :\r\n\t\t\t\t#if filter_activations[i, max_spike[i]] >= max_act / 2.0 :\r\n\t\t\t\tfilter_input = filter_inputs[i, max_spike[i]:max_spike[i]+filter_width, :] * filter_activations[i, max_spike[i]]\r\n\t\t\t\tPFM = PFM + filter_input'''\r\n\t\t\tPFM = numpy.zeros((filter_width, self.num_features))\r\n\t\t\tfor ii in range(0, len(top_scoring_index)) :\r\n\t\t\t\ti = top_scoring_index[ii]\r\n\t\t\t\t#if filter_activations[i, max_spike[i]] >= max_act / 2.0 :\r\n\t\t\t\tfilter_input = filter_inputs[i, max_spike[i]:max_spike[i]+filter_width, :] #* filter_activations[i, max_spike[i]]\r\n\t\t\t\tPFM = PFM + filter_input\r\n\r\n\t\t\tprint('motif ' + str(k))\r\n\r\n\t\t\t#Calculate Pearson r\r\n\t\t\tlogodds_test_curr = logodds_test\r\n\t\t\tlogodds_test_avg_curr = logodds_test_avg\r\n\t\t\tlogodds_test_std_curr = logodds_test_std\r\n\t\t\tmax_activation_k = numpy.ravel(max_activation[k, :])\r\n\r\n\t\t\tmax_activation_k = max_activation_k[L_index > 5]\r\n\t\t\tlogodds_test_curr = logodds_test[L_index > 5]\r\n\r\n\t\t\tmax_activation_k_avg = numpy.average(max_activation_k)\r\n\t\t\tmax_activation_k_std = numpy.sqrt(numpy.dot(max_activation_k - max_activation_k_avg, max_activation_k - max_activation_k_avg))\r\n\r\n\t\t\tlogodds_test_avg_curr = numpy.average(logodds_test_curr)\r\n\t\t\tlogodds_test_std_curr = numpy.sqrt(numpy.dot(logodds_test_curr - logodds_test_avg_curr, logodds_test_curr - logodds_test_avg_curr))\r\n\r\n\t\t\tcov = numpy.dot(logodds_test_curr - logodds_test_avg_curr, max_activation_k - max_activation_k_avg)\r\n\t\t\tr = cov / (max_activation_k_std * logodds_test_std_curr)\r\n\t\t\tprint('r = ' + str(round(r, 2)))\r\n\r\n\t\t\tprev_selection_libs_str = 'X'\r\n\t\t\tfor pos in range(0, activations.shape[2]) :\r\n\r\n\t\t\t\tpos_activation_curr = pos_activation\r\n\t\t\t\tlogodds_test_curr = logodds_test\r\n\t\t\t\tlogodds_test_avg_curr = logodds_test_avg\r\n\t\t\t\tlogodds_test_std_curr = logodds_test_std\r\n\t\t\t\tcurr_selection_libs_str = ''\r\n\t\t\t\tif pos_to_libs_lookup[pos][2] == '' :\r\n\t\t\t\t\tcontinue\r\n\r\n\t\t\t\tpos_to_lib = pos_to_libs_lookup[pos]\r\n\t\t\t\tcurr_selection_libs_str = pos_to_lib[2]\r\n\t\t\t\tif curr_selection_libs_str == prev_selection_libs_str :\r\n\t\t\t\t\tpos_activation_curr = pos_activation_prev\r\n\t\t\t\t\tlogodds_test_curr = logodds_test_prev\r\n\t\t\t\telse :\r\n\t\t\t\t\twhitelist_index = []\r\n\t\t\t\t\tfor i in range(0, len(L_index)) :\r\n\t\t\t\t\t\tif L_index[i] in pos_to_lib[1] :\r\n\t\t\t\t\t\t\twhitelist_index.append(i)\r\n\t\t\t\t\t\r\n\t\t\t\t\tpos_activation_curr = pos_activation[:, whitelist_index, :]\r\n\t\t\t\t\tlogodds_test_curr = logodds_test[whitelist_index]\r\n\t\t\t\tlogodds_test_avg_curr = numpy.average(logodds_test_curr)\r\n\t\t\t\tlogodds_test_std_curr = numpy.sqrt(numpy.dot(logodds_test_curr - logodds_test_avg_curr, logodds_test_curr - logodds_test_avg_curr))\r\n\r\n\t\t\t\tif curr_selection_libs_str == '' :\r\n\t\t\t\t\tcontinue\r\n\r\n\t\t\t\tpos_activation_k_pos = numpy.ravel(pos_activation_curr[k, :, pos])\r\n\t\t\t\tpos_activation_k_pos_avg = numpy.average(pos_activation_k_pos)\r\n\t\t\t\tpos_activation_k_pos_std = numpy.sqrt(numpy.dot(pos_activation_k_pos - pos_activation_k_pos_avg, pos_activation_k_pos - pos_activation_k_pos_avg))\r\n\r\n\t\t\t\tcov_pos = numpy.dot(logodds_test_curr - logodds_test_avg_curr, pos_activation_k_pos - pos_activation_k_pos_avg)\r\n\t\t\t\tr_k_pos = cov_pos / (pos_activation_k_pos_std * logodds_test_std_curr)\r\n\r\n\t\t\t\tif not (numpy.isinf(r_k_pos) or numpy.isnan(r_k_pos)) :\r\n\t\t\t\t\tpos_r[k, pos] = r_k_pos\r\n\r\n\t\t\t\tprev_selection_libs_str = curr_selection_libs_str\r\n\t\t\t\tpos_activation_prev = pos_activation_curr\r\n\t\t\t\tlogodds_test_prev = logodds_test_curr\r\n\r\n\t\t\tlogo_name = \"avg_motif_\" + str(k) + \".png\"\r\n\r\n\t\t\tself.get_logo(k, PFM, 'cnn_motif_analysis/fullseq_global/deconv/avg_filter/' + logo_name, 8, score=r)\r\n\r\n\t\t#All-filter positional Pearson r\r\n\t\tf = plt.figure(figsize=(32, 16))\r\n\r\n\t\tplt.pcolor(pos_r,cmap=cm.RdBu_r,vmin=-numpy.abs(pos_r).max(), vmax=numpy.abs(pos_r).max())\r\n\t\tplt.colorbar()\r\n\r\n\t\tplt.xlabel('Sequence position')\r\n\t\tplt.title('Prox. selection Pearson r for all filters')\r\n\t\t#plt.axis([0, 4095, np.min(w_sorted) - 0.1, np.max(w_sorted) + 0.1])\r\n\t\t#xticks = mer_sorted\r\n\t\tplt.xticks([0, 25, 50, 75, 100, 125, 150, 175, 200, 225, 250], [0 - 125, 25 - 125, 50 - 125, 75 - 125, 100 - 125, 125 - 125, 150 - 125, 175 - 125, 200 - 125, 225 - 125, 250 - 125])\r\n\t\tplt.yticks(numpy.arange(pos_r.shape[0]) + 0.5, numpy.arange(pos_r.shape[0]))#BASEPAIR TO INDEX FLIPPED ON PURPOSE TO COUNTER CONVOLVE\r\n\r\n\t\tplt.axis([0, pos_r.shape[1], 0, pos_r.shape[0]])\r\n\r\n\t\t#plt.savefig('cnn_motif_analysis/fullseq_global/deconv/avg_filter/' + \"r_pos_apa_fr.png\")\r\n\t\tplt.savefig('cnn_motif_analysis/fullseq_global/deconv/avg_filter/' + \"r_pos.png\")\r\n\t\tplt.close()\r\n\r\n\tdef get_logo(self, PFM, file_path='', seq_length=6, normalize=False, base_seq='') :\r\n\r\n\t\tif normalize == True :\r\n\t\t\tfor i in range(0, PFM.shape[0]) :\r\n\t\t\t\tif numpy.sum(PFM[i, :]) > 0 :\r\n\t\t\t\t\tPFM[i, :] = PFM[i, :] / numpy.sum(PFM[i, :])\r\n\t\t\t\t#PFM[i, :] *= 10000.0\r\n\t\t\t#print(PFM)\r\n\t\t\r\n\r\n\t\t#Create weblogo from API\r\n\t\tlogo_output_format = \"png\"\r\n\t\t#Load data from an occurence matrix\r\n\t\tdata = weblogolib.LogoData.from_counts('ACGT', PFM)\r\n\r\n\t\t#Generate color scheme\r\n\t\t'''colors = weblogolib.ColorScheme([\r\n\t\t weblogolib.ColorGroup(\"A\", \"yellow\",\"CFI Binder\" ),\r\n\t\t weblogolib.ColorGroup(\"C\", \"green\",\"CFI Binder\" ),\r\n\t\t weblogolib.ColorGroup(\"G\", \"red\",\"CFI Binder\" ),\r\n\t\t weblogolib.ColorGroup(\"T\", \"blue\",\"CFI Binder\" ),\r\n\t\t weblogolib.ColorGroup(\"a\", \"grey\",\"CFI Binder\" ),\r\n\t\t weblogolib.ColorGroup(\"c\", \"grey\",\"CFI Binder\" ),\r\n\t\t weblogolib.ColorGroup(\"g\", \"grey\",\"CFI Binder\" ),\r\n\t\t weblogolib.ColorGroup(\"t\", \"grey\",\"CFI Binder\" )] )'''\r\n\t\tcolor_rules = []\r\n\t\tfor j in range(0, len(base_seq)) :\r\n\t\t\tif base_seq[j] != 'N' :\r\n\t\t\t\tcolor_rules.append(weblogolib.IndexColor([j], 'grey'))\r\n\t\tcolor_rules.append(weblogolib.SymbolColor(\"A\", \"yellow\"))\r\n\t\tcolor_rules.append(weblogolib.SymbolColor(\"C\", \"green\"))\r\n\t\tcolor_rules.append(weblogolib.SymbolColor(\"G\", \"red\"))\r\n\t\tcolor_rules.append(weblogolib.SymbolColor(\"T\", \"blue\"))\r\n\t\tcolors = weblogolib.ColorScheme(color_rules)\r\n\r\n\r\n\t\t#Create options\r\n\t\toptions = weblogolib.LogoOptions(fineprint=False,\r\n\t\t logo_title=\"\", \r\n\t\t color_scheme=colors, \r\n\t\t stack_width=weblogolib.std_sizes[\"large\"],\r\n\t\t logo_start=1, logo_end=seq_length, stacks_per_line=seq_length)#seq_length)\r\n\r\n\t\t#Create logo\r\n\t\tlogo_format = weblogolib.LogoFormat(data, options)\r\n\r\n\t\t#Generate image\r\n\t\tformatter = weblogolib.formatters[logo_output_format]\r\n\t\tpng = formatter(data, logo_format)\r\n\r\n\t\t#Write it\r\n\t\twith open(file_path, \"w\") as f:\r\n\t\t f.write(png)\r\n\r\n\r\n\tdef generate_heat_maps(self):\r\n\t\tlayer0_left = self.layer0_left\r\n\t\t\r\n\t\tfilters = layer0_left.W.eval()\r\n\t\t#(n_kerns, 1, filter_width, 4)\r\n\r\n\t\tfor k in range(0, filters.shape[0]) :\r\n\t\t\tkernel = filters[k, 0, :, :].reshape((filters.shape[2], filters.shape[3])).T\r\n\t\t\tkernel_mean = numpy.mean(kernel, axis=0)\r\n\r\n\t\t\tfor j in range(0, kernel.shape[1]) :\r\n\t\t\t\tkernel[:, j] -= kernel_mean[j]\r\n\r\n\t\t\tkernel = numpy.fliplr(kernel)\r\n\r\n\t\t\tplt.pcolor(kernel,cmap=cm.RdBu_r)\r\n\t\t\tplt.colorbar()\r\n\r\n\t\t\tplt.xlabel('Sequence')\r\n\t\t\tplt.ylabel('Bases')\r\n\t\t\tplt.title('Kernel Heat Map')\r\n\t\t\t#plt.axis([0, 4095, np.min(w_sorted) - 0.1, np.max(w_sorted) + 0.1])\r\n\t\t\t#xticks = mer_sorted\r\n\t\t\tplt.yticks([0.5, 1.5, 2.5, 3.5], ['T', 'G', 'C', 'A'])#BASEPAIR TO INDEX FLIPPED ON PURPOSE TO COUNTER CONVOLVE\r\n\r\n\t\t\tplt.savefig(\"cnn_motif_analysis/fullseq_global/kernal/kernel\" + str(k) + \".png\")\r\n\t\t\tplt.close()\r\n\r\n\tdef dropit(self, srng, weight, drop):\r\n\t\t# proportion of probability to retain\r\n\t\tretain_prob = 1 - drop\r\n\t\t# a masking variable\r\n\t\tmask = srng.binomial(n=1, p=retain_prob, size=weight.shape, dtype=theano.config.floatX)\r\n\t\t# final weight with dropped neurons\r\n\t\treturn T.cast(weight * mask, theano.config.floatX)\r\n\r\n\tdef dont_dropit(self, weight, drop):\r\n\t\treturn (1 - drop)*T.cast(weight, theano.config.floatX)\r\n\r\n\tdef dropout_layer(self, srng, weight, drop, train = 1):\r\n\t\treturn T.switch(theano.tensor.eq(train, 1), self.dropit(srng, weight, drop), self.dont_dropit(weight, drop))\r\n\r\n\tdef __init__(self, train_set, valid_set, learning_rate=0.1, drop=0, n_epochs=30, nkerns=[30, 40, 50], batch_size=50, num_features=4, randomized_regions=[(2, 37), (45, 80)], load_model=True, train_model_flag=False, store_model=False, dataset='default', store_as_dataset='default', cell_line='default'):\r\n\t\tnumpy.random.seed(23455)\r\n\t\trng = numpy.random.RandomState(23455)\r\n\r\n\t\tsrng = RandomStreams(rng.randint(999999))\r\n\r\n\t\t#Guided Backprop Gradient, only instantiate once\r\n\t\t#modded_relu = GuidedBackprop(relu)\r\n\t\t#Zeiler Deconv prop\r\n\t\t#modded_relu = ZeilerBackprop(relu)\r\n\t\t#Regular ReLU Backprop\r\n\t\tmodded_relu = relu\r\n\r\n\t\tself.batch_size = batch_size\r\n\t\tself.randomized_regions = randomized_regions\r\n\t\t\r\n\t\ttrain_set_x, train_set_y, train_set_L, train_set_d = train_set\r\n\t\tvalid_set_x, valid_set_y, valid_set_L, valid_set_d = valid_set\r\n\r\n\t\t# compute number of minibatches for training, validation and testing\r\n\t\tn_train_batches = train_set_x.get_value(borrow=True).shape[0]\r\n\t\tn_valid_batches = valid_set_x.get_value(borrow=True).shape[0]\r\n\t\tn_train_batches /= batch_size\r\n\t\tn_valid_batches /= batch_size\r\n\r\n\t\t# allocate symbolic variables for the data\r\n\t\tindex = T.lscalar() # index to a [mini]batch\r\n\r\n\t\t# start-snippet-1\r\n\t\tx_left = T.tensor3('x_left') # the data is presented as rasterized images\r\n\t\tx_right = T.tensor3('x_right') # the data is presented as rasterized images\r\n\r\n\t\tL_input = T.matrix('L_input')\r\n\t\td_input = T.matrix('d_input')\r\n\r\n\t\tdeactivated_filter_level1 = T.lscalar()\r\n\t\tdeactivated_output_level1 = T.dscalar()\r\n\t\tself.deactivated_filter_level1 = deactivated_filter_level1\r\n\t\tself.deactivated_output_level1 = deactivated_output_level1\r\n\t\t\r\n\t\ty = T.matrix('y') # the labels are presented as 1D vector of\r\n\t\t#y = T.matrix('y')\r\n\r\n\r\n\t\tself.x_left = x_left\r\n\t\tself.x_right = x_right\r\n\t\tself.y = y\r\n\t\tself.L_input = L_input\r\n\t\tself.d_input = d_input\r\n\t\t\r\n\t\tleft_random_size = randomized_regions[0][1] - randomized_regions[0][0]\r\n\t\tright_random_size = randomized_regions[1][1] - randomized_regions[1][0]\r\n\t\t\r\n\t\tself.left_random_size = left_random_size\r\n\t\tself.right_random_size = right_random_size\r\n\t\tself.num_features = num_features\r\n\t\tself.input_size = left_random_size + right_random_size\r\n\t\t\r\n\t\t######################\r\n\t\t# BUILD ACTUAL MODEL #\r\n\t\t######################\r\n\t\tprint('... building the model')\r\n\r\n\t\t# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)\r\n\t\t# to a 4D tensor, compatible with our LeNetConvPoolLayer\r\n\t\t# (150, 4) is the size of MNIST images.\r\n\t\tlayer0_input_left = x_left.reshape((batch_size, 1, left_random_size, num_features))\r\n\t\tlayer0_input_right = x_right.reshape((batch_size, 1, right_random_size, num_features))\r\n\r\n\t\t# Construct the first convolutional pooling layer:\r\n\t\t# filtering reduces the image size to (101-6+1 , 4-4+1) = (96, 1)\r\n\t\t# maxpooling reduces this further to (96/1, 1/1) = (96, 1)\r\n\t\t# 4D output tensor is thus of shape (batch_size, nkerns[0], 96, 1)\r\n\t\tlayer0_left = LeNetConvPoolLayer(\r\n\t\t\trng,\r\n\t\t\tinput=layer0_input_left,\r\n\t\t\tdeactivated_filter=deactivated_filter_level1,\r\n\t\t\tdeactivated_output=deactivated_output_level1,\r\n\t\t\timage_shape=(batch_size, 1, left_random_size, num_features),\r\n\t\t\tfilter_shape=(nkerns[0], 1, 8, num_features),\r\n\t\t\tpoolsize=(2, 1),\r\n\t\t\tstride=(1, 1),\r\n\t\t\tactivation_fn=modded_relu#relu\r\n\t\t\t,load_model = load_model,\r\n\t\t\tw_file='model_store/' + dataset + '_' + cell_line + '_conv0_left_w',\r\n\t\t\tb_file='model_store/' + dataset + '_' + cell_line + '_conv0_left_b'\r\n\t\t)\r\n\t\t\r\n\t\t'''layer0_right = LeNetConvPoolLayer(\r\n\t\t\trng,\r\n\t\t\tinput=layer0_input_right,\r\n\t\t\timage_shape=(batch_size, 1, right_random_size, num_features),\r\n\t\t\tfilter_shape=(nkerns[0], 1, 7, num_features),\r\n\t\t\tpoolsize=(1, 1),\r\n\t\t\tuse_relu=True\r\n\t\t\t,load_model = load_model,\r\n\t\t\tw_file='model_store/' + dataset + '_' + cell_line + '_conv0_right_w',\r\n\t\t\tb_file='model_store/' + dataset + '_' + cell_line + '_conv0_right_b'\r\n\t\t)\r\n\r\n\t\tlayer1_input = T.concatenate([layer0_left.output, layer0_right.output], axis=2)'''\r\n\t\t\r\n\t\t# Construct the second convolutional pooling layer\r\n\t\t# filtering reduces the image size to (96-5+1, 1-1+1) = (92, 1)\r\n\t\t# maxpooling reduces this further to (92/2, 1/1) = (46, 1)\r\n\t\t# 4D output tensor is thus of shape (batch_size, nkerns[1], 46, 1)\r\n\t\tlayer1 = LeNetConvPoolLayer(\r\n\t\t\trng,\r\n\t\t\tinput=layer0_left.output,\r\n\t\t\tdeactivated_filter=None,\r\n\t\t\tdeactivated_output=None,\r\n\t\t\timage_shape=(batch_size, nkerns[0], 89, 1),\r\n\t\t\tfilter_shape=(nkerns[1], nkerns[0], 6, 1),\r\n\t\t\tpoolsize=(1, 1),\r\n\t\t\tactivation_fn=modded_relu#relu\r\n\t\t\t,load_model = load_model,\r\n\t\t\tw_file='model_store/' + dataset + '_' + cell_line + '_conv1_w',\r\n\t\t\tb_file='model_store/' + dataset + '_' + cell_line + '_conv1_b'\r\n\t\t)\r\n\r\n\r\n\r\n\t\t'''layer2 = LeNetConvPoolLayer(\r\n\t\t\trng,\r\n\t\t\tinput=layer1.output,\r\n\t\t\timage_shape=(batch_size, nkerns[1], 16, 1),\r\n\t\t\tfilter_shape=(nkerns[2], nkerns[1], 5, 1),\r\n\t\t\tpoolsize=(2, 1),\r\n\t\t\tuse_relu=True\r\n\t\t\t,load_model = load_model,\r\n\t\t\tw_file='model_store/' + dataset + '_' + cell_line + '_conv2_w',\r\n\t\t\tb_file='model_store/' + dataset + '_' + cell_line + '_conv2_b'\r\n\t\t)'''\r\n\r\n\t\t# the HiddenLayer being fully-connected, it operates on 2D matrices of\r\n\t\t# shape (batch_size, num_pixels) (i.e matrix of rasterized images).\r\n\t\t# This will generate a matrix of shape (batch_size, nkerns[1] * 16 * 1),\r\n\t\t# or (500, 50 * 21 * 1) = (500, 800) with the default values.\r\n\t\t\r\n\t\t#layer3_input = layer2.output.flatten(2)\r\n\t\tlayer3_input_cnn = layer1.output.flatten(2)\r\n\t\t#layer3_input = layer0_left.output.flatten(2)\r\n\r\n\t\tlayer3_input = T.concatenate([layer3_input_cnn, d_input], axis=1)\r\n\r\n\t\t# construct a fully-connected sigmoidal layer\r\n\t\tlayer3 = HiddenLayer(\r\n\t\t\trng,\r\n\t\t\tinput=layer3_input,\r\n\t\t\tn_in=nkerns[1] * (84) * 1 + 1,\r\n\t\t\tn_out=80,\r\n\t\t\tactivation=modded_relu#relu#T.tanh#relu#T.tanh\r\n\t\t\t,load_model = load_model,\r\n\t\t\tw_file='model_store/' + dataset + '_' + cell_line + '_mlp_w',\r\n\t\t\tb_file='model_store/' + dataset + '_' + cell_line + '_mlp_b'\r\n\t\t)\r\n\r\n\t\tlayer3_output = layer3.output\r\n\r\n\t\t'''if drop != 0 and train_model_flag == True :\r\n\t\t\tlayer3_output = self.dropout_layer(srng, layer3.output, drop, train = 1)\r\n\t\telif drop != 0 :\r\n\t\t\tlayer3_output = self.dropout_layer(srng, layer3.output, drop, train = 0)'''\r\n\r\n\t\ttrain_drop = T.lscalar()\r\n\t\tself.train_drop = train_drop\r\n\t\tif drop != 0 :\r\n\t\t\tprint('Using dropout = ' + str(drop))\r\n\t\t\tlayer3_output = self.dropout_layer(srng, layer3.output, drop, train = train_drop)\r\n\r\n\t\tlayer4_input = T.concatenate([layer3_output, L_input], axis=1)\r\n\t\t#layer4_input = layer3.output\r\n\r\n\t\t# classify the values of the fully-connected sigmoidal layer\r\n\t\tlayer4 = LogisticRegression(input=layer4_input, n_in=80 + 36, n_out=2, load_model = load_model,\r\n\t\t\tw_file='model_store/' + dataset + '_' + cell_line + '_lr_w',\r\n\t\t\tb_file='model_store/' + dataset + '_' + cell_line + '_lr_b')\r\n\r\n\t\tself.layer0_left = layer0_left\r\n\t\t#self.layer0_right = layer0_right\r\n\t\tself.layer1 = layer1\r\n\t\t#self.layer2 = layer2\r\n\t\tself.layer3 = layer3\r\n\t\tself.output_layer = layer4\r\n\t\t\r\n\t\t# the cost we minimize during training is the NLL of the model\r\n\t\tcost = layer4.negative_log_likelihood(y)\r\n\r\n\t\t# create a function to compute the mistakes that are made by the model\r\n\t\tvalidate_model = theano.function(\r\n\t\t\t[index],\r\n\t\t\tlayer4.log_loss(y),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(valid_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),#Tsparse.basic.dense_from_sparse(valid_set_x[index * batch_size: (index + 1) * batch_size, :]).reshape((batch_size, 70, 4))[:,randomized_regions[0][0]:randomized_regions[0][1]],\r\n\t\t\t\tx_right: self.reshape_batch(valid_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),#Tsparse.basic.dense_from_sparse(valid_set_x[index * batch_size: (index + 1) * batch_size, :]).reshape((batch_size, 70, 4))[:,randomized_regions[1][0]:randomized_regions[1][1]],\r\n\t\t\t\ty: valid_set_y[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\tL_input: valid_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: valid_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t},\r\n\t\t\ton_unused_input='ignore'\r\n\t\t)\r\n\r\n\t\tvalidate_rsquare = theano.function(\r\n\t\t\t[index],\r\n\t\t\tlayer4.rsquare(y),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(valid_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(valid_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\ty: valid_set_y[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\tL_input: valid_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: valid_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t},\r\n\t\t\ton_unused_input='ignore'\r\n\t\t)\r\n\t\t\r\n\t\tvalidate_sse = theano.function(\r\n\t\t\t[index],\r\n\t\t\tlayer4.sse(y),\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(valid_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(valid_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\ty: valid_set_y[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\tL_input: valid_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: valid_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 0\r\n\t\t\t},\r\n\t\t\ton_unused_input='ignore'\r\n\t\t)\r\n\t\t\r\n\t\tvalidate_sst = theano.function(\r\n\t\t\t[index],\r\n\t\t\tlayer4.sst(y),\r\n\t\t\tgivens={\r\n\t\t\t\ty: valid_set_y[index * batch_size: (index + 1) * batch_size]\r\n\t\t\t},\r\n\t\t\ton_unused_input='ignore'\r\n\t\t)\r\n\t\t\r\n\t\t# create a list of all model parameters to be fit by gradient descent\r\n\t\tparams = layer4.params + layer3.params + layer1.params + layer0_left.params\r\n\t\t#params = layer4.params + layer3.params + layer1.params + layer0_left.params# + layer0_right.params\r\n\t\t#params = layer3.params + layer2.params + layer0_left.params + layer0_right.params\r\n\t\t\r\n\t\t# create a list of gradients for all model parameters\r\n\t\tgrads = T.grad(cost, params)\r\n\t\t\r\n\t\t# train_model is a function that updates the model parameters by\r\n\t\t# SGD Since this model has many parameters, it would be tedious to\r\n\t\t# manually create an update rule for each model parameter. We thus\r\n\t\t# create the updates list by automatically looping over all\r\n\t\t# (params[i], grads[i]) pairs.\r\n\t\tupdates = [\r\n\t\t\t(param_i, param_i - learning_rate * grad_i)\r\n\t\t\tfor param_i, grad_i in zip(params, grads)\r\n\t\t]\r\n\r\n\t\ttrain_model = theano.function(\r\n\t\t\t[index],\r\n\t\t\tcost,\r\n\t\t\tupdates=updates,\r\n\t\t\tgivens={\r\n\t\t\t\tx_left: self.reshape_batch(train_set_x, index, randomized_regions[0][0], randomized_regions[0][1]),\r\n\t\t\t\tx_right: self.reshape_batch(train_set_x, index, randomized_regions[1][0], randomized_regions[1][1]),\r\n\t\t\t\ty: train_set_y[index * batch_size: (index + 1) * batch_size],\r\n\t\t\t\tL_input: train_set_L[index * batch_size: (index + 1) * batch_size, :],\r\n\t\t\t\td_input: train_set_d[index * batch_size: (index + 1) * batch_size, :]\r\n\t\t\t\t,train_drop: 1\r\n\t\t\t},\r\n\t\t\ton_unused_input='ignore'\r\n\t\t)\r\n\t\t# end-snippet-1\r\n\t\t\r\n\t\tif train_model_flag == True : \r\n\t\t\t###############\r\n\t\t\t# TRAIN MODEL #\r\n\t\t\t###############\r\n\t\t\tprint('... training')\r\n\t\t\t# early-stopping parameters\r\n\t\t\tpatience = 120000#140000 # look as this many examples regardless\r\n\t\t\tpatience_increase = 2 # wait this much longer when a new best is\r\n\t\t\t\t\t\t\t\t # found\r\n\t\t\timprovement_threshold = 0.998 # a relative improvement of this much is\r\n\t\t\t\t\t\t\t\t\t\t # considered significant\r\n\t\t\t#validation_frequency = min(n_train_batches, patience / 2)\r\n\t\t\tvalidation_frequency = n_train_batches #/ 2\r\n\t\t\t\t\t\t\t\t\t\t # go through this many\r\n\t\t\t\t\t\t\t\t\t\t # minibatche before checking the network\r\n\t\t\t\t\t\t\t\t\t\t # on the validation set; in this case we\r\n\t\t\t\t\t\t\t\t\t\t # check every epoch\r\n\r\n\t\t\tbest_validation_loss = numpy.inf\r\n\t\t\tbest_validation_rsquare = 0.\r\n\t\t\tbest_validationfull_rsquare = 0.\r\n\t\t\tbest_iter = 0\r\n\t\t\t\r\n\t\t\tbest_params = []\r\n\t\t\t\r\n\t\t\tstart_time = time.clock()\r\n\r\n\t\t\tepoch = 0\r\n\t\t\tdone_looping = False\r\n\r\n\t\t\twhile (epoch < n_epochs) and (not done_looping):\r\n\t\t\t\tepoch = epoch + 1\r\n\t\t\t\tfor minibatch_index in xrange(n_train_batches):\r\n\r\n\t\t\t\t\titer = (epoch - 1) * n_train_batches + minibatch_index\r\n\r\n\t\t\t\t\tif iter % 100 == 0:\r\n\t\t\t\t\t\tprint('training @ iter = ', iter)\r\n\t\t\t\t\tcost_ij = train_model(minibatch_index)\r\n\t\t\t\t\t\r\n\t\t\t\t\t#print cost_ij\r\n\r\n\t\t\t\t\tif (iter + 1) % validation_frequency == 0:\r\n\r\n\t\t\t\t\t\t# compute zero-one loss on validation set\r\n\t\t\t\t\t\tvalidation_losses = [validate_model(i) for i\r\n\t\t\t\t\t\t\t\t\t\t\t in xrange(n_valid_batches)]\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tvalidation_rsquares = [validate_rsquare(i) for i\r\n\t\t\t\t\t\t\t\t\t\t\t\tin xrange(n_valid_batches)]\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tvalidation_sses = [validate_sse(i) for i\r\n\t\t\t\t\t\t\t\t\t\t\t\tin xrange(n_valid_batches)]\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tvalidation_ssts = [validate_sst(i) for i\r\n\t\t\t\t\t\t\t\t\t\t\t\tin xrange(n_valid_batches)]\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t#print(validation_errors_one)\r\n\t\t\t\t\t\t#print(validation_errors_zero)\r\n\r\n\t\t\t\t\t\tthis_validation_loss = numpy.sum(validation_losses) / (n_valid_batches * batch_size)\r\n\t\t\t\t\t\tthis_validation_rsquare = numpy.mean(validation_rsquares)\r\n\t\t\t\t\t\tthis_validationfull_rsquare = 1.0 - (numpy.sum(validation_sses) / numpy.sum(validation_ssts))\r\n\r\n\t\t\t\t\t\t#print(validation_losses)\r\n\t\t\t\t\t\t#print(numpy.sum(validation_losses))\r\n\r\n\t\t\t\t\t\tprint('epoch %i, minibatch %i/%i, validation logloss %f, mean batch validation R^2 %f %% (total validation R^2 %f %%)' %\r\n\t\t\t\t\t\t\t (epoch, minibatch_index + 1, n_train_batches,\r\n\t\t\t\t\t\t\t this_validation_loss, this_validation_rsquare * 100.0, this_validationfull_rsquare * 100.0))\r\n\r\n\t\t\t\t\t\t# if we got the best validation score until now\r\n\t\t\t\t\t\tif this_validation_loss < best_validation_loss:\r\n\r\n\t\t\t\t\t\t\t#improve patience if loss improvement is good enough\r\n\t\t\t\t\t\t\tif this_validation_loss < best_validation_loss * \\\r\n\t\t\t\t\t\t\t improvement_threshold:\r\n\t\t\t\t\t\t\t\tpatience = max(patience, iter * patience_increase)\r\n\r\n\t\t\t\t\t\t\t# save best validation score and iteration number\r\n\t\t\t\t\t\t\tbest_validation_loss = this_validation_loss\r\n\t\t\t\t\t\t\tbest_validation_rsquare = this_validation_rsquare\r\n\t\t\t\t\t\t\tbest_validationfull_rsquare = this_validationfull_rsquare\r\n\t\t\t\t\t\t\tbest_iter = iter\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tbest_params = [(numpy.array(layer0_left.W.eval(), copy=True), numpy.array(layer0_left.b.eval(), copy=True)),\r\n\t\t\t\t\t\t\t\t\t\t\t#(numpy.array(layer0_right.W.eval(), copy=True), numpy.array(layer0_right.b.eval(), copy=True)),\r\n\t\t\t\t\t\t\t\t\t\t\t(numpy.array(layer1.W.eval(), copy=True), numpy.array(layer1.b.eval(), copy=True)),\r\n\t\t\t\t\t\t\t\t\t\t\t#(numpy.array(layer2.W.eval(), copy=True), numpy.array(layer2.b.eval(), copy=True)),\r\n\t\t\t\t\t\t\t\t\t\t\t(numpy.array(layer3.W.eval(), copy=True), numpy.array(layer3.b.eval(), copy=True)),\r\n\t\t\t\t\t\t\t\t\t\t\t(numpy.array(layer4.W.eval(), copy=True), numpy.array(layer4.b.eval(), copy=True))]\r\n\r\n\t\t\t\t\t\t\tif store_model == True :\r\n\t\t\t\t\t\t\t\tlayer0_left.store_model(best_params[0][0], best_params[0][1])\r\n\t\t\t\t\t\t\t\t#layer0_right.store_model(best_params[1][0], best_params[1][1])\r\n\t\t\t\t\t\t\t\tlayer1.store_model(best_params[1][0], best_params[1][1])\r\n\t\t\t\t\t\t\t\t#layer2.store_model(best_params[2][0], best_params[2][1])\r\n\t\t\t\t\t\t\t\tlayer3.store_model(best_params[2][0], best_params[2][1])\r\n\t\t\t\t\t\t\t\tlayer4.store_model(best_params[3][0], best_params[3][1])\r\n\r\n\t\t\t\t\tif patience <= iter:\r\n\t\t\t\t\t\tdone_looping = True\r\n\t\t\t\t\t\tbreak\r\n\r\n\t\t\tend_time = time.clock()\r\n\t\t\t\r\n\t\t\tprint('Optimization complete.')\r\n\t\t\tprint('Best validation logloss of %f obtained at iteration %i, '\r\n\t\t\t\t 'with mean batch validation R^2 %f %% (total validation R^2 %f %%)' %\r\n\t\t\t\t (best_validation_loss, best_iter + 1, best_validation_rsquare * 100.0, best_validationfull_rsquare * 100.0))\r\n\t\t\tprint >> sys.stderr, ('The code for file ' +\r\n\t\t\t\t\t\t\t\t os.path.split(__file__)[1] +\r\n\t\t\t\t\t\t\t\t ' ran for %.2fm' % ((end_time - start_time) / 60.))\r\n\t\t\t\r\n\t\t\tif store_model == True :\r\n\t\t\t\tlayer0_left.store_model(best_params[0][0], best_params[0][1])\r\n\t\t\t\t#layer0_right.store_model(best_params[1][0], best_params[1][1])\r\n\t\t\t\tlayer1.store_model(best_params[1][0], best_params[1][1])\r\n\t\t\t\t#layer2.store_model(best_params[2][0], best_params[2][1])\r\n\t\t\t\tlayer3.store_model(best_params[2][0], best_params[2][1])\r\n\t\t\t\tlayer4.store_model(best_params[3][0], best_params[3][1])\r\n\t\t# end-snippet-1\r\n\r\n\r\ndef get_top_motifs_per_kernel(kernel):\r\n\tbases = 'ACGT'\r\n\t\t\r\n\tsix_mers = []\r\n\t\t\r\n\tweights = numpy.zeros(4096)\r\n\t\t\r\n\tfor i1 in range(0,4):\r\n\t\tfor i2 in range(0,4):\r\n\t\t\tfor i3 in range(0,4):\r\n\t\t\t\tfor i4 in range(0,4):\r\n\t\t\t\t\tfor i5 in range(0,4):\r\n\t\t\t\t\t\tfor i6 in range(0,4):\r\n\t\t\t\t\t\t\tmotif = bases[i1] + bases[i2] + bases[i3] + bases[i4] + bases[i5] + bases[i6]\r\n\t\t\t\t\t\t\tsix_mers.append(motif)\r\n\t\t\t\t\t\t\tweights[i1 * 4**5 + i2 * 4**4 + i3 * 4**3 + i4 * 4**2 + i5 * 4 + i6] = kernel[3-i1,5] + kernel[3-i2,4] + kernel[3-i3,3] + kernel[3-i4,2] + kernel[3-i5,1] + kernel[3-i6,0]\r\n\r\n\thighest_weight_index = numpy.argsort(weights)[::-1]\r\n\t#Pick the 20 first ones of the reversed sorted vector.\r\n\thighest_weight_index_top = highest_weight_index[0:50]\r\n\r\n\tlowest_weight_index = numpy.argsort(weights)\r\n\t#Pick the 20 first ones of the sorted vector.\r\n\tlowest_weight_index_top = lowest_weight_index[0:50]\r\n\t\t\r\n\treturn (six_mers, highest_weight_index_top, lowest_weight_index_top, weights)\r\n\r\ndef get_global_saliency(cnn, test_set) :\r\n\r\n\ttest_set_x, test_set_y, test_set_L = test_set\r\n\r\n\tsaliency = cnn.get_saliency()\r\n\tsaliency = saliency.reshape((saliency.shape[0] * saliency.shape[1], saliency.shape[2], saliency.shape[3]))\r\n\t\r\n\t#Scale positive correlation\r\n\tpos_saliency = numpy.maximum(0, saliency) / saliency.max(axis=0)\r\n\tpos_saliency_index = saliency > 0\r\n\tpos_saliency_id = numpy.zeros(pos_saliency.shape)\r\n\tpos_saliency_id[pos_saliency_index] = 1\r\n\r\n\tneg_saliency = numpy.maximum(0, -saliency) / -saliency.min(axis=0)\r\n\tneg_saliency_index = saliency < 0\r\n\tneg_saliency_id = numpy.zeros(neg_saliency.shape)\r\n\tneg_saliency_id[neg_saliency_index] = 1\r\n\r\n\tcnn.set_data(test_set_x, test_set_y, test_set_L)\r\n\r\n\ty_test_hat = cnn.get_prediction()\r\n\ty_test = test_set_y.eval()[:y_test_hat.shape[0],1]\r\n\r\n\ts_test_hat = cnn.get_class_score()\r\n\r\n\tX_test = test_set_x.eval()[:y_test_hat.shape[0],:]\r\n\r\n\tPFM_pos = numpy.zeros((93, 4))\r\n\tPFM_pos_scaled = numpy.zeros((93, 4))\r\n\tPFM_neg = numpy.zeros((93, 4))\r\n\tPFM_neg_scaled = numpy.zeros((93, 4))\r\n\tfor i in range(0, y_test_hat.shape[0]) :\r\n\t\tX_point = numpy.ravel(X_test[i,:].todense())\r\n\t\tX_point = X_point.reshape((len(X_point) / 4, 4))\r\n\r\n\t\tpos_input = numpy.multiply(X_point, pos_saliency_id[i])\r\n\t\tneg_input = numpy.multiply(X_point, neg_saliency_id[i])\r\n\r\n\t\tPFM_pos = PFM_pos + pos_input\r\n\t\tPFM_pos_scaled = PFM_pos_scaled + pos_input * numpy.abs(s_test_hat[i, 1])\r\n\t\tPFM_neg = PFM_neg + neg_input\r\n\t\tPFM_neg_scaled = PFM_neg_scaled + neg_input * numpy.abs(s_test_hat[i, 0])\r\n\r\n\r\n\tlogo_name = \"pos_unscaled.png\"\r\n\tcnn.get_logo(0, PFM_pos, 'cnn_motif_analysis/fullseq_v_pad0/deconv/' + logo_name, 93, True)\r\n\tlogo_name = \"pos_scaled.png\"\r\n\tcnn.get_logo(0, PFM_pos_scaled, 'cnn_motif_analysis/fullseq_v_pad0/deconv/' + logo_name, 93, True)\r\n\r\n\tlogo_name = \"neg_unscaled.png\"\r\n\tcnn.get_logo(0, PFM_neg, 'cnn_motif_analysis/fullseq_v_pad0/deconv/' + logo_name, 93, True)\r\n\tlogo_name = \"neg_scaled.png\"\r\n\tcnn.get_logo(0, PFM_neg_scaled, 'cnn_motif_analysis/fullseq_v_pad0/deconv/' + logo_name, 93, True)\r\n\r\ndef pasalign_predict_snps(cnn, ref_x, ref_y, ref_d, var_x, var_y, L_zero, var_d, apadist) :\r\n\r\n\tn = ref_y.eval().shape[0]\r\n\r\n\tref_x = numpy.array(ref_x.eval().todense())\r\n\tref_x = ref_x.reshape((ref_x.shape[0], ref_x.shape[1] / 4, 4))\r\n\r\n\tvar_x = numpy.array(var_x.eval().todense())\r\n\tvar_x = var_x.reshape((var_x.shape[0], var_x.shape[1] / 4, 4))\r\n\r\n\tL_zero = L_zero.eval()\r\n\r\n\tref_y = ref_y.eval()\r\n\tvar_y = var_y.eval()\r\n\r\n\tref_d = ref_d.eval()\r\n\tvar_d = var_d.eval()\r\n\r\n\r\n\ty_ref = logit(ref_y[:,1])\r\n\ty_var = logit(var_y[:,1])\r\n\tdiff = numpy.ravel(y_var) - numpy.ravel(y_ref)\r\n\r\n\r\n\talign_min = -15\r\n\talign_max = 10\r\n\r\n\tdiff_hat = []\r\n\r\n\tcano_pas1 = 'AATAAA'\r\n\tcano_pas2 = 'ATTAAA'\r\n\r\n\tpas_mutex1 = []\r\n\tfor pos in range(0, 6) :\r\n\t\tfor base in 'ACGT' :\r\n\t\t\tpas_mutex1.append(cano_pas1[:pos] + base + cano_pas1[pos+1:])\r\n\r\n\tpas_mutex2 = []\r\n\tfor pos1 in range(0, 6) :\r\n\t\tfor pos2 in range(pos1 + 1, 6) :\r\n\t\t\tfor base1 in 'ACGT' :\r\n\t\t\t\tfor base2 in 'ACGT' :\r\n\t\t\t\t\tpas_mutex1.append(cano_pas1[:pos1] + base1 + cano_pas1[pos1+1:pos2] + base2 + cano_pas1[pos2+1:])\r\n\r\n\tfor i in range(0, n) :\r\n\r\n\t\tbest_align_hat = -4\r\n\t\tbest_align_j = 0\r\n\t\tbest_align_diff_hat = 0\r\n\t\tfor j in range(align_min, align_max) :\r\n\r\n\t\t\tref_x_curr = numpy.zeros((1, ref_x.shape[1], 4))\r\n\t\t\tref_x_curr[:, :, :] = ref_x[i, :, :]\r\n\r\n\t\t\tif j < 0 :\r\n\t\t\t\talign = int(numpy.abs(j))\r\n\t\t\t\tref_x_curr = numpy.concatenate([ref_x_curr[:, align:, :], numpy.zeros((1, align, 4))], axis=1)\r\n\r\n\t\t\tif j > 0 :\r\n\t\t\t\talign = int(numpy.abs(j))\r\n\t\t\t\tref_x_curr = numpy.concatenate([numpy.zeros((1, align, 4)), ref_x_curr[:, :ref_x.shape[1]-align, :]], axis=1)\r\n\r\n\t\t\t#ref_x_curr = theano.shared(sp.csr_matrix(ref_x_curr.reshape((1, ref_x.shape[1] * 4))), borrow=True)\r\n\t\t\t#ref_x_curr = sp.csr_matrix(ref_x_curr.reshape((1, ref_x.shape[1] * 4)))\r\n\t\t\tref_seq = translate_matrix_to_seq(ref_x_curr[0, :, :])[75+1:]\r\n\r\n\t\t\tL_zero_curr = numpy.zeros((1, 36))\r\n\t\t\tL_zero_curr[:, :] = L_zero[i, :]\r\n\t\t\t#L_zero_curr = theano.shared(L_zero_curr, borrow=True)\r\n\r\n\t\t\tref_y_curr = numpy.zeros((1, 2))\r\n\t\t\tref_y_curr[:, :] = ref_y[i, :]\r\n\t\t\t#ref_y_curr = theano.shared(ref_y_curr, borrow=True)\r\n\r\n\t\t\tref_d_curr = numpy.zeros((1, 1))\r\n\t\t\tref_d_curr[:, :] = ref_d[i, :]\r\n\t\t\t#ref_d_curr = theano.shared(ref_d_curr, borrow=True)\r\n\r\n\r\n\t\t\t#cnn.set_data(ref_x_curr, ref_y_curr, L_zero_curr, ref_d_curr)\r\n\t\t\t#y_ref_hat = logit(cnn.get_prediction())\r\n\t\t\ty_ref_hat = logit(cnn.get_online_prediction(ref_x_curr, L_zero_curr, ref_d_curr))[0]\r\n\r\n\r\n\t\t\tvar_x_curr = numpy.zeros((1, var_x.shape[1], 4))\r\n\t\t\tvar_x_curr[:, :, :] = var_x[i, :, :]\r\n\r\n\t\t\tif j < 0 :\r\n\t\t\t\talign = int(numpy.abs(j))\r\n\t\t\t\tvar_x_curr = numpy.concatenate([var_x_curr[:, align:, :], numpy.zeros((1, align, 4))], axis=1)\r\n\r\n\t\t\tif j > 0 :\r\n\t\t\t\talign = int(numpy.abs(j))\r\n\t\t\t\tvar_x_curr = numpy.concatenate([numpy.zeros((1, align, 4)), var_x_curr[:, :var_x.shape[1]-align, :]], axis=1)\r\n\r\n\t\t\t#var_x_curr = theano.shared(sp.csr_matrix(var_x_curr.reshape((1, var_x.shape[1] * 4))), borrow=True)\r\n\t\t\tvar_seq = translate_matrix_to_seq(var_x_curr[0, :, :])[75+1:]\r\n\r\n\t\t\tvar_y_curr = numpy.zeros((1, 2))\r\n\t\t\tvar_y_curr[:, :] = var_y[i, :]\r\n\t\t\t#var_y_curr = theano.shared(var_y_curr, borrow=True)\r\n\r\n\t\t\tvar_d_curr = numpy.zeros((1, 1))\r\n\t\t\tvar_d_curr[:, :] = var_d[i, :]\r\n\t\t\t#var_d_curr = theano.shared(var_d_curr, borrow=True)\r\n\r\n\r\n\t\t\t#cnn.set_data(var_x_curr, var_y_curr, L_zero_curr, var_d_curr)\r\n\t\t\t#y_var_hat = logit(cnn.get_prediction())\r\n\r\n\t\t\ty_var_hat = logit(cnn.get_online_prediction(var_x_curr, L_zero_curr, var_d_curr))[0]\r\n\r\n\r\n\t\t\tif ref_seq[49:49+6] == cano_pas1 :\r\n\t\t\t\t#best_align_hat = y_ref_hat\r\n\t\t\t\tbest_align_hat = max(y_ref_hat, y_var_hat)\r\n\r\n\t\t\t\tbest_align_j = j\r\n\t\t\t\tbest_align_diff_hat = y_var_hat - y_ref_hat\r\n\r\n\t\t\t\tbreak\r\n\t\t\telif ref_seq[49:49+6] == cano_pas2 :\r\n\t\t\t\t#best_align_hat = y_ref_hat\r\n\t\t\t\tbest_align_hat = max(y_ref_hat, y_var_hat)\r\n\r\n\t\t\t\tbest_align_j = j\r\n\t\t\t\tbest_align_diff_hat = y_var_hat - y_ref_hat\r\n\r\n\t\t\t\tbreak\r\n\t\t\telif ref_seq[49:49+6] in pas_mutex1 :\r\n\t\t\t\t#best_align_hat = y_ref_hat\r\n\t\t\t\tbest_align_hat = max(y_ref_hat, y_var_hat)\r\n\r\n\t\t\t\tbest_align_j = j\r\n\t\t\t\tbest_align_diff_hat = y_var_hat - y_ref_hat\r\n\t\t\telif ref_seq[49:49+6] in pas_mutex2 :\r\n\t\t\t\t#best_align_hat = y_ref_hat\r\n\t\t\t\tbest_align_hat = max(y_ref_hat, y_var_hat)\r\n\r\n\t\t\t\tbest_align_j = j\r\n\t\t\t\tbest_align_diff_hat = y_var_hat - y_ref_hat\r\n\r\n\t\tdiff_hat.append(best_align_diff_hat)\r\n\r\n\r\n\t\tprint('Aligned member ' + str(i) + '(d = ' + str(apadist[i]) + '), align = ' + str(best_align_j))\r\n\r\n\t\tprint('pas: ' + ' ' + '|')\r\n\r\n\t\tref_x_curr = numpy.zeros((ref_x.shape[1], 4))\r\n\t\tref_x_curr[:, :] = ref_x[i, :, :]\r\n\r\n\t\tif best_align_j < 0 :\r\n\t\t\talign = int(numpy.abs(best_align_j))\r\n\t\t\tref_x_curr = numpy.concatenate([ref_x_curr[align:, :], numpy.zeros((align, 4))], axis=0)\r\n\t\tif best_align_j > 0 :\r\n\t\t\talign = int(numpy.abs(best_align_j))\r\n\t\t\tref_x_curr = numpy.concatenate([numpy.zeros((align, 4)), ref_x_curr[:ref_x.shape[1]-align, :]], axis=0)\r\n\t\tprint('ref: ' + translate_matrix_to_seq(ref_x_curr)[75+1:])#75+1+90])\r\n\t\tvar_x_curr = numpy.zeros((var_x.shape[1], 4))\r\n\t\tvar_x_curr[:, :] = var_x[i, :, :]\r\n\r\n\t\tif best_align_j < 0 :\r\n\t\t\talign = int(numpy.abs(best_align_j))\r\n\t\t\tvar_x_curr = numpy.concatenate([var_x_curr[align:, :], numpy.zeros((align, 4))], axis=0)\r\n\t\tif best_align_j > 0 :\r\n\t\t\talign = int(numpy.abs(best_align_j))\r\n\t\t\tvar_x_curr = numpy.concatenate([numpy.zeros((align, 4)), var_x_curr[:var_x.shape[1]-align, :]], axis=0)\r\n\t\tprint('var: ' + translate_matrix_to_seq(var_x_curr)[75+1:])#75+1+90])\r\n\r\n\r\n\tdiff_hat = numpy.ravel(numpy.array(diff_hat))\r\n\r\n\t'''selection_index = numpy.abs(diff_hat) > 0.05\r\n\tdiff = diff[selection_index]\r\n\tdiff_hat = diff_hat[selection_index]\r\n\tapadist = apadist[selection_index]'''\r\n\r\n\tSSE_diff = (diff - diff_hat).T.dot(diff - diff_hat)\r\n\r\n\ty_diff_average = numpy.average(diff, axis=0)\r\n\r\n\tSStot_diff = (diff - y_diff_average).T.dot(diff - y_diff_average)\r\n\r\n\tRMSE_diff = numpy.sqrt(SSE_diff / float(len(y_ref)))\r\n\r\n\tMAE_diff = numpy.mean(numpy.abs(diff_hat - diff))\r\n\r\n\tdiff_set_dir_accuracy = numpy.count_nonzero(numpy.sign(diff) == numpy.sign(diff_hat))\r\n\r\n\r\n\tprint(\"\")\r\n\tprint(\"Logodds diff R^2:\")\r\n\tprint(1.0 - (SSE_diff / SStot_diff))\r\n\tprint(\"Logodds diff mean abs error:\")\r\n\tprint(MAE_diff)\r\n\r\n\tprint(\"Logodds diff Classification accuracy: \" + str(diff_set_dir_accuracy) + \"/\" + str(len(diff)) + \" = \" + str(float(diff_set_dir_accuracy) / float(len(diff))))\r\n\r\n\tfig = plt.figure()\r\n\tax1 = fig.add_subplot(111)\r\n\r\n\tcol = ax1.scatter(diff_hat, diff, c = 'red', alpha=1.0)\r\n\tax1.plot([-5, 5], [-5, 5], c='yellow')\r\n\tax1.plot([numpy.min(diff_hat) * 1.1, numpy.max(diff_hat) * 1.1], [0, 0], c='green')\r\n\tax1.plot([0, 0], [numpy.min(diff) * 1.1, numpy.max(diff) * 1.1], c='green')\r\n\tax1.set_xlim([numpy.min(diff_hat) * 1.1, numpy.max(diff_hat) * 1.1])\r\n\tax1.set_ylim([numpy.min(diff) * 1.1, numpy.max(diff) * 1.1])\r\n\tax1.set_xlabel('Predicted Proximal usage logodds diff', fontsize=22)\r\n\tax1.set_ylabel('Target Proximal usage logodds diff', fontsize=22)\r\n\tax1.set_title('GEUV APA SNP Log Diff (R^2 = ' + str(round(1.0 - (SSE_diff / SStot_diff), 2)) + ', Acc = ' + str(diff_set_dir_accuracy) + \"/\" + str(len(diff)) + ')', fontsize=18)\r\n\t\r\n\tfor i in range(0, len(diff)):\r\n\t\t#ax1.annotate(snp_index[i] + 2, (diff_hat[i], diff[i]))\r\n\t\t'''annotation = ''\r\n\t\tif snptype[i] == 1 :\r\n\t\t\tannotation = 'HET'\r\n\t\telif snptype[i] == 2 :\r\n\t\t\tannotation = 'HOM'\r\n\t\t\r\n\t\tif snpregion[i] == 1 :\r\n\t\t\tannotation += ' UP'\r\n\t\telif snpregion[i] == 2 :\r\n\t\t\tannotation += ' PAS'\r\n\t\telif snpregion[i] == 3 :\r\n\t\t\tannotation += ' DN'\r\n\t\t'''\r\n\t\tannotation = '(' + str(apadist[i]) + ')'\r\n\r\n\t\tax1.annotate(annotation, (diff_hat[i], diff[i]), size=8)\r\n\r\n\t#plt.savefig(\"cnn_snp_logodds_diff_global\" + run_name + \".png\")\r\n\tplt.show()\r\n\tplt.close()\r\n\r\ndef align_predict_snps(cnn, ref_x, ref_y, ref_d, var_x, var_y, L_zero, var_d, apadist, run_name) :\r\n\r\n\tn = ref_y.eval().shape[0]\r\n\r\n\tref_x = numpy.array(ref_x.eval().todense())\r\n\tref_x = ref_x.reshape((ref_x.shape[0], ref_x.shape[1] / 4, 4))\r\n\r\n\tvar_x = numpy.array(var_x.eval().todense())\r\n\tvar_x = var_x.reshape((var_x.shape[0], var_x.shape[1] / 4, 4))\r\n\r\n\tL_zero = L_zero.eval()\r\n\r\n\tref_y = ref_y.eval()\r\n\tvar_y = var_y.eval()\r\n\r\n\tref_d = ref_d.eval()\r\n\tvar_d = var_d.eval()\r\n\r\n\r\n\ty_ref = logit(ref_y[:,1])\r\n\ty_var = logit(var_y[:,1])\r\n\tdiff = numpy.ravel(y_var) - numpy.ravel(y_ref)\r\n\r\n\r\n\talign_min = -25#-10#-25\r\n\talign_max = 5#3#5\r\n\r\n\tdiff_hat = []\r\n\r\n\tref_snp_strs = []\r\n\tvar_snp_strs = []\r\n\r\n\tfor i in range(0, n) :\r\n\r\n\t\tbest_align_hat = -4\r\n\t\tbest_align_j = 0\r\n\t\tbest_align_diff_hat = 0\r\n\t\tfor j in range(align_min, align_max) :\r\n\r\n\t\t\tref_x_curr = numpy.zeros((1, ref_x.shape[1], 4))\r\n\t\t\tref_x_curr[:, :, :] = ref_x[i, :, :]\r\n\r\n\t\t\tif j < 0 :\r\n\t\t\t\talign = int(numpy.abs(j))\r\n\t\t\t\tref_x_curr = numpy.concatenate([ref_x_curr[:, align:, :], numpy.zeros((1, align, 4))], axis=1)\r\n\r\n\t\t\tif j > 0 :\r\n\t\t\t\talign = int(numpy.abs(j))\r\n\t\t\t\tref_x_curr = numpy.concatenate([numpy.zeros((1, align, 4)), ref_x_curr[:, :ref_x.shape[1]-align, :]], axis=1)\r\n\r\n\t\t\t#ref_x_curr = theano.shared(sp.csr_matrix(ref_x_curr.reshape((1, ref_x.shape[1] * 4))), borrow=True)\r\n\t\t\t#ref_x_curr = sp.csr_matrix(ref_x_curr.reshape((1, ref_x.shape[1] * 4)))\r\n\r\n\t\t\tL_zero_curr = numpy.zeros((1, 36))\r\n\t\t\tL_zero_curr[:, :] = L_zero[i, :]\r\n\t\t\t#L_zero_curr = theano.shared(L_zero_curr, borrow=True)\r\n\r\n\t\t\tref_y_curr = numpy.zeros((1, 2))\r\n\t\t\tref_y_curr[:, :] = ref_y[i, :]\r\n\t\t\t#ref_y_curr = theano.shared(ref_y_curr, borrow=True)\r\n\r\n\t\t\tref_d_curr = numpy.zeros((1, 1))\r\n\t\t\tref_d_curr[:, :] = ref_d[i, :]\r\n\t\t\t#ref_d_curr = theano.shared(ref_d_curr, borrow=True)\r\n\r\n\r\n\t\t\t#cnn.set_data(ref_x_curr, ref_y_curr, L_zero_curr, ref_d_curr)\r\n\t\t\t#y_ref_hat = logit(cnn.get_prediction())\r\n\t\t\ty_ref_hat = logit(cnn.get_online_prediction(ref_x_curr, L_zero_curr, ref_d_curr))[0]\r\n\r\n\r\n\t\t\tvar_x_curr = numpy.zeros((1, var_x.shape[1], 4))\r\n\t\t\tvar_x_curr[:, :, :] = var_x[i, :, :]\r\n\r\n\t\t\tif j < 0 :\r\n\t\t\t\talign = int(numpy.abs(j))\r\n\t\t\t\tvar_x_curr = numpy.concatenate([var_x_curr[:, align:, :], numpy.zeros((1, align, 4))], axis=1)\r\n\r\n\t\t\tif j > 0 :\r\n\t\t\t\talign = int(numpy.abs(j))\r\n\t\t\t\tvar_x_curr = numpy.concatenate([numpy.zeros((1, align, 4)), var_x_curr[:, :var_x.shape[1]-align, :]], axis=1)\r\n\r\n\t\t\t#var_x_curr = theano.shared(sp.csr_matrix(var_x_curr.reshape((1, var_x.shape[1] * 4))), borrow=True)\r\n\r\n\t\t\tvar_y_curr = numpy.zeros((1, 2))\r\n\t\t\tvar_y_curr[:, :] = var_y[i, :]\r\n\t\t\t#var_y_curr = theano.shared(var_y_curr, borrow=True)\r\n\r\n\t\t\tvar_d_curr = numpy.zeros((1, 1))\r\n\t\t\tvar_d_curr[:, :] = var_d[i, :]\r\n\t\t\t#var_d_curr = theano.shared(var_d_curr, borrow=True)\r\n\r\n\r\n\t\t\t#cnn.set_data(var_x_curr, var_y_curr, L_zero_curr, var_d_curr)\r\n\t\t\t#y_var_hat = logit(cnn.get_prediction())\r\n\r\n\t\t\ty_var_hat = logit(cnn.get_online_prediction(var_x_curr, L_zero_curr, var_d_curr))[0]\r\n\r\n\r\n\t\t\tif y_ref_hat > best_align_hat or y_var_hat > best_align_hat :\r\n\t\t\t\t#best_align_hat = y_ref_hat\r\n\t\t\t\tbest_align_hat = max(y_ref_hat, y_var_hat)\r\n\r\n\t\t\t\tbest_align_j = j\r\n\t\t\t\tbest_align_diff_hat = y_var_hat - y_ref_hat\r\n\t\tdiff_hat.append(best_align_diff_hat)\r\n\r\n\r\n\t\tprint('Aligned member ' + str(i) + '(d = ' + str(apadist[i]) + '), align = ' + str(best_align_j))\r\n\r\n\t\tprint('pas: ' + ' ' + '|')\r\n\r\n\t\tref_x_curr = numpy.zeros((ref_x.shape[1], 4))\r\n\t\tref_x_curr[:, :] = ref_x[i, :, :]\r\n\r\n\t\tif best_align_j < 0 :\r\n\t\t\talign = int(numpy.abs(best_align_j))\r\n\t\t\tref_x_curr = numpy.concatenate([ref_x_curr[align:, :], numpy.zeros((align, 4))], axis=0)\r\n\t\tif best_align_j > 0 :\r\n\t\t\talign = int(numpy.abs(best_align_j))\r\n\t\t\tref_x_curr = numpy.concatenate([numpy.zeros((align, 4)), ref_x_curr[:ref_x.shape[1]-align, :]], axis=0)\r\n\t\tprint('ref: ' + translate_matrix_to_seq(ref_x_curr)[75+1:])#75+1+90])\r\n\t\t\r\n\t\tref_seq = translate_matrix_to_seq(ref_x_curr)[75+1:]\r\n\r\n\t\tvar_x_curr = numpy.zeros((var_x.shape[1], 4))\r\n\t\tvar_x_curr[:, :] = var_x[i, :, :]\r\n\r\n\t\tif best_align_j < 0 :\r\n\t\t\talign = int(numpy.abs(best_align_j))\r\n\t\t\tvar_x_curr = numpy.concatenate([var_x_curr[align:, :], numpy.zeros((align, 4))], axis=0)\r\n\t\tif best_align_j > 0 :\r\n\t\t\talign = int(numpy.abs(best_align_j))\r\n\t\t\tvar_x_curr = numpy.concatenate([numpy.zeros((align, 4)), var_x_curr[:var_x.shape[1]-align, :]], axis=0)\r\n\t\tprint('var: ' + translate_matrix_to_seq(var_x_curr)[75+1:])#75+1+90])\r\n\r\n\t\tvar_seq = translate_matrix_to_seq(var_x_curr)[75+1:]\r\n\r\n\t\tref_seq_snp = ''\r\n\t\tvar_seq_snp = ''\r\n\t\tfor k in range(0, len(ref_seq)) :\r\n\t\t\tif ref_seq[k] != var_seq[k] :\r\n\t\t\t\tref_seq_snp = ref_seq[k-15:k+16]\r\n\t\t\t\tref_seq_snp += ' (PAS '\r\n\t\t\t\tif k >= 49 :\r\n\t\t\t\t\tref_seq_snp += '+ ' + str(int(numpy.abs(k - 49))) + ')'\r\n\t\t\t\telse :\r\n\t\t\t\t\tref_seq_snp += '- ' + str(int(numpy.abs(k - 49))) + ')'\r\n\r\n\t\t\t\tvar_seq_snp = (' ' * 15) + var_seq[k]\r\n\r\n\t\t\t\tbreak\r\n\t\tref_snp_strs.append(ref_seq_snp)\r\n\t\tvar_snp_strs.append(var_seq_snp)\r\n\r\n\r\n\r\n\tfor i in range(0, n) :\r\n\t\tprint(i)\r\n\t\tprint(ref_snp_strs[i])\r\n\t\tprint((' ' * 15) + '^')\r\n\t\tprint(var_snp_strs[i])\r\n\r\n\r\n\tdiff_hat = numpy.ravel(numpy.array(diff_hat))\r\n\r\n\tsnp_index = numpy.arange(len(diff_hat))\r\n\r\n\t'''over_thres_index = numpy.abs(diff_hat) > 0.05\r\n\tdiff = diff[over_thres_index]\r\n\tdiff_hat = diff_hat[over_thres_index]\r\n\tapadist = apadist[over_thres_index]\r\n\tsnp_index = snp_index[over_thres_index]'''\r\n\r\n\r\n\tSSE_diff = (diff - diff_hat).T.dot(diff - diff_hat)\r\n\r\n\ty_diff_average = numpy.average(diff, axis=0)\r\n\r\n\tSStot_diff = (diff - y_diff_average).T.dot(diff - y_diff_average)\r\n\r\n\tRMSE_diff = numpy.sqrt(SSE_diff / float(len(y_ref)))\r\n\r\n\tMAE_diff = numpy.mean(numpy.abs(diff_hat - diff))\r\n\r\n\tdiff_set_dir_accuracy = numpy.count_nonzero(numpy.sign(diff) == numpy.sign(diff_hat))\r\n\r\n\r\n\tprint(\"\")\r\n\tprint(\"Logodds diff R^2:\")\r\n\tprint(1.0 - (SSE_diff / SStot_diff))\r\n\tprint(\"Logodds diff mean abs error:\")\r\n\tprint(MAE_diff)\r\n\r\n\tprint(\"Logodds diff Classification accuracy: \" + str(diff_set_dir_accuracy) + \"/\" + str(len(diff)) + \" = \" + str(float(diff_set_dir_accuracy) / float(len(diff))))\r\n\r\n\tfig = plt.figure()\r\n\tax1 = fig.add_subplot(111)\r\n\r\n\tcol = ax1.scatter(diff_hat, diff, c = 'red', s = 4 * numpy.pi * (2 * numpy.ones(1))**2, alpha=0.4)\r\n\tax1.plot([-5, 5], [-5, 5], c='yellow')\r\n\tax1.plot([numpy.min(diff_hat) * 1.1, numpy.max(diff_hat) * 1.1], [0, 0], c='green')\r\n\tax1.plot([0, 0], [numpy.min(diff) * 1.1, numpy.max(diff) * 1.1], c='green')\r\n\tax1.set_xlim([numpy.min(diff_hat) * 1.1, numpy.max(diff_hat) * 1.1])\r\n\tax1.set_ylim([numpy.min(diff) * 1.1, numpy.max(diff) * 1.1])\r\n\tax1.set_xlabel('Predicted Proximal $\\Delta$Logodds', fontsize=18)\r\n\tax1.set_ylabel('Observed Proximal $\\Delta$Logodds', fontsize=18)\r\n\t#ax1.set_title('GEUV APA SNP Log Diff (R^2 = ' + str(round(1.0 - (SSE_diff / SStot_diff), 2)) + ', Acc = ' + str(diff_set_dir_accuracy) + \"/\" + str(len(diff)) + ')', fontsize=18)\r\n\tax1.set_title('R^2 = ' + str(round(1.0 - (SSE_diff / SStot_diff), 2)) + ', Acc = ' + str(diff_set_dir_accuracy) + \"/\" + str(len(diff)), fontsize=28)\r\n\t\r\n\tfig.suptitle('GEUVADIS APA SNPs', fontsize=24)\r\n\r\n\t'''annotated_coords = []\r\n\r\n\tfor i in range(0, len(diff)):\r\n\t\t#ax1.annotate(snp_index[i] + 2, (diff_hat[i], diff[i]))\r\n\r\n\t\tif numpy.abs(diff_hat[i]) > 0.5 or numpy.abs(diff[i]) > 0.5 :\r\n\r\n\t\t\toverlapping = False\r\n\t\t\tfor coords in annotated_coords :\r\n\t\t\t\txc = coords[0]\r\n\t\t\t\tyc = coords[1]\r\n\r\n\t\t\t\tif numpy.sqrt( numpy.power(diff_hat[i] - xc, 2) + numpy.power(diff[i] - yc, 2) ) < 0.3 :\r\n\t\t\t\t\toverlapping = True\r\n\t\t\t\t\tbreak\r\n\r\n\t\t\tif overlapping == False :\r\n\t\t\t\tannotation = '(' + str(int(snp_index[i])) + ')'\r\n\t\t\t\tax1.annotate(annotation, (diff_hat[i], diff[i]), size=18)\r\n\r\n\t\t\t\tannotated_coords.append([diff_hat[i], diff[i]])\r\n\r\n\tax1 = fig.add_subplot(111)'''\r\n\r\n\tfig.tight_layout()\r\n\t\t\r\n\tplt.subplots_adjust(top=0.83, wspace = 0.6)\r\n\r\n\tplt.savefig(\"cnn_snp_logodds_diff_global\" + run_name + \".svg\")\r\n\tplt.savefig(\"cnn_snp_logodds_diff_global\" + run_name + \".png\")\r\n\tplt.show()\r\n\tplt.close()\r\n\r\n\r\ndef mut_map(cnn, ref_seq, name) :\r\n\r\n\tref_x = numpy.zeros((1, len(ref_seq), 4))\r\n\tfor j in range(0, len(ref_seq)) :\r\n\t\tif ref_seq[j] == 'A' :\r\n\t\t\tref_x[0, j, 0] = 1\r\n\t\telif ref_seq[j] == 'C' :\r\n\t\t\tref_x[0, j, 1] = 1\r\n\t\telif ref_seq[j] == 'G' :\r\n\t\t\tref_x[0, j, 2] = 1\r\n\t\telif ref_seq[j] == 'T' :\r\n\t\t\tref_x[0, j, 3] = 1\r\n\t\telse :\r\n\t\t\tref_x[0, j, :] = 0.25\r\n\r\n\tL_zero = numpy.zeros((1, 36))\r\n\tref_d = numpy.ones((1, 1))\r\n\r\n\ty_ref_hat = logit(cnn.get_online_prediction(ref_x, L_zero, ref_d))[0]\r\n\r\n\r\n\tmut_map = numpy.zeros((4, len(ref_seq)))\r\n\r\n\r\n\tfor j in range(0, len(ref_seq)) :\r\n\r\n\t\tif ref_seq[j] == 'X' :\r\n\t\t\tcontinue\r\n\r\n\t\tfor base in [0, 1, 2, 3] :\r\n\r\n\t\t\tvar_x = numpy.zeros(ref_x.shape)\r\n\t\t\tvar_x[:, :, :] = ref_x[:, :, :]\r\n\r\n\t\t\tvar_x[0, j, :] = 0\r\n\t\t\tvar_x[0, j, base] = 1\r\n\r\n\t\t\ty_var_hat = logit(cnn.get_online_prediction(var_x, L_zero, ref_d))[0]\r\n\r\n\t\t\tmut_map[3-base, j] = y_var_hat - y_ref_hat\r\n\r\n\r\n\tref_seq_small_length = 0\r\n\tref_seq_small = ''\r\n\tfor j in range(0, len(ref_seq)) :\r\n\t\tif ref_seq[j] != 'X' :\r\n\t\t\tref_seq_small_length += 1\r\n\t\t\tref_seq_small += ref_seq[j]\r\n\r\n\tmut_map_small = numpy.zeros((4, ref_seq_small_length))\r\n\tk = 0\r\n\tfor j in range(0, len(ref_seq)) :\r\n\t\tif ref_seq[j] != 'X' :\r\n\t\t\tmut_map_small[:, k] = mut_map[:, j]\r\n\t\t\tk +=1\r\n\r\n\r\n\tref_seq = ref_seq_small\r\n\tmut_map = mut_map_small\r\n\r\n\r\n\t'''f = plt.figure(figsize=(48, 3))\r\n\r\n\t#mut_map = mut_map - (mut_map.max() + mut_map.min()) / 2.0\r\n\r\n\tplt.pcolor(mut_map,cmap=cm.RdBu_r,vmin=-numpy.abs(mut_map).max(), vmax=numpy.abs(mut_map).max())\r\n\t#plt.pcolor(mut_map,cmap=cm.RdBu_r,vmin=mut_map.min(), vmax=mut_map.max())\r\n\r\n\tplt.colorbar()\r\n\t#plt.xlabel('Sequence position')\r\n\t#plt.title('Mutation map of ' + name)\r\n\r\n\r\n\tref_seq_list = []\r\n\tfor c in ref_seq :\r\n\t\tref_seq_list.append(c)\r\n\r\n\tplt.xticks(numpy.arange(len(ref_seq)) + 0.5, ref_seq_list)\r\n\t\r\n\tplt.yticks([0.5, 1.5, 2.5, 3.5], ['T', 'G', 'C', 'A'])#BASEPAIR TO INDEX FLIPPED ON PURPOSE TO COUNTER CONVOLVE\r\n\r\n\tplt.axis([0, mut_map.shape[1], 0, 4])\r\n\r\n\tplt.gca().xaxis.tick_top()\r\n\r\n\t#plt.savefig('mut_one_' + name + \".png\")\r\n\tplt.show()\r\n\tplt.close()'''\r\n\r\n\r\n\r\n\r\n\tfig, ax = plt.subplots(2, 1, figsize=(36, 3))\r\n\r\n\r\n\t#fig, ax = plt.subplots(figsize=(10,3))\r\n\r\n\tbias = numpy.max(numpy.sum(mut_map[:, :], axis=0)) / 3.0 + 0.5\r\n\tmax_score = numpy.min(numpy.sum(mut_map[:, :], axis=0)) / 3.0 * -1 + bias\r\n\t#max_score = numpy.min(numpy.sum(mut_map[:, :], axis=0)) / 3.0 * -1\r\n\r\n\tfor i in range(0, mut_map.shape[1]) :\r\n\t\tmutability_score = numpy.sum(mut_map[:, i]) / 3.0 * -1 + bias\r\n\t\t#mutability_score = numpy.sum(mut_map[:, i]) / 3.0 * -1\r\n\r\n\t\tletterAt(ref_seq[i], i + 0.5, 0, mutability_score, ax[0])\r\n\r\n\tax[0].plot([0, mut_map.shape[1]], [bias, bias], color='black', linestyle='--')\r\n\r\n\tplt.sca(ax[0])\r\n\t#plt.xticks(range(1,x))\r\n\tplt.xlim((0, mut_map.shape[1])) \r\n\tplt.ylim((0, max_score)) \r\n\tplt.tight_layout() \r\n\r\n\r\n\t#pcm = ax[0].pcolormesh(X, Y, Z1,\r\n\t# norm=MidpointNormalize(midpoint=0.),\r\n\t# cmap='RdBu_r')\r\n\t#pcm = ax.pcolor(mut_map, norm=MidpointNormalize(midpoint=0.), cmap='RdBu_r')\r\n\tpcm = ax[1].pcolor(mut_map, cmap='RdBu_r', vmin=-numpy.abs(mut_map).max(), vmax=numpy.abs(mut_map).max())\r\n\r\n\t#fig.colorbar(pcm, ax=ax[0])\r\n\r\n\t\r\n\tplt.sca(ax[1])\r\n\r\n\tref_seq_list = []\r\n\tfor c in ref_seq :\r\n\t\tref_seq_list.append(c)\r\n\tplt.xticks(numpy.arange(len(ref_seq)) + 0.5, ref_seq_list)\r\n\t\r\n\tplt.yticks([0.5, 1.5, 2.5, 3.5], ['T', 'G', 'C', 'A'])\r\n\r\n\tplt.gca().xaxis.tick_top()\r\n\r\n\tplt.axis([0, mut_map.shape[1], 0, 4]) \r\n\t\r\n\r\n\tplt.savefig('mut_one_' + name + \".png\", bbox_inches='tight')\r\n\tplt.show()\r\n\tplt.close()\r\n\r\n\r\n\r\n\t#cnn.get_logo(mut_map, file_path='' + 'mut_one_' + name + \"_logo.png\", seq_length=mut_map.shape[1], base_seq='')\r\n\r\ndef letterAt(letter, x, y, yscale=1, ax=None):\r\n\r\n\t#fp = FontProperties(family=\"Arial\", weight=\"bold\")\r\n\tfp = FontProperties(family=\"Ubuntu\", weight=\"bold\")\r\n\tglobscale = 1.35\r\n\tLETTERS = {\t\"T\" : TextPath((-0.305, 0), \"T\", size=1, prop=fp),\r\n\t\t\t\t\"G\" : TextPath((-0.384, 0), \"G\", size=1, prop=fp),\r\n\t\t\t\t\"A\" : TextPath((-0.35, 0), \"A\", size=1, prop=fp),\r\n\t\t\t\t\"C\" : TextPath((-0.366, 0), \"C\", size=1, prop=fp) }\r\n\tCOLOR_SCHEME = {'G': 'orange', \r\n\t\t\t\t\t'A': 'red', \r\n\t\t\t\t\t'C': 'blue', \r\n\t\t\t\t\t'T': 'darkgreen'}\r\n\r\n\r\n\ttext = LETTERS[letter]\r\n\r\n\tt = mpl.transforms.Affine2D().scale(1*globscale, yscale*globscale) + \\\r\n\t\tmpl.transforms.Affine2D().translate(x,y) + ax.transData\r\n\tp = PathPatch(text, lw=0, fc=COLOR_SCHEME[letter], transform=t)\r\n\tif ax != None:\r\n\t\tax.add_artist(p)\r\n\treturn p\r\n\r\nclass MidpointNormalize(colors.Normalize):\r\n def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):\r\n self.midpoint = midpoint\r\n colors.Normalize.__init__(self, vmin, vmax, clip)\r\n\r\n def __call__(self, value, clip=None):\r\n # I'm ignoring masked values and all kinds of edge cases to make a\r\n # simple example...\r\n x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]\r\n return numpy.ma.masked_array(numpy.interp(value, x, y))\r\n\r\n\r\ndef evaluate_cnn(dataset='general'):\r\n\r\n\t#dataset = 'snp_general_GENERAL_CI45_SMALL_global'#_ALIGNED\r\n\r\n\tdataset = 'snp_general_TOMM5_CI25_SMALL_global'#_ALIGNED\r\n\r\n\tinput_datasets = load_input_data(dataset)\r\n\t\r\n\tref_x = input_datasets[0]\r\n\tvar_x = input_datasets[1]\r\n\tL_zero = input_datasets[2]\r\n\r\n\tapadist = input_datasets[3]\r\n\r\n\toutput_datasets = load_output_data(dataset)\r\n\t\r\n\tref_y = output_datasets[0]\r\n\tvar_y = output_datasets[1]\r\n\t\r\n\r\n\tref_d = numpy.ones((ref_y.eval().shape[0], 1))\r\n\tref_d = theano.shared(numpy.asarray(ref_d, dtype=theano.config.floatX), borrow=True)\r\n\r\n\tvar_d = numpy.ones((var_y.eval().shape[0], 1))\r\n\tvar_d = theano.shared(numpy.asarray(var_d, dtype=theano.config.floatX), borrow=True)\r\n\r\n\r\n\tbatch_size = 1\r\n\r\n\t#run_name = '_Global_Onesided_DoubleDope_Simple'\r\n\t#run_name = '_Global_Onesided_DoubleDope_TOMM5_CI25'\r\n\t#run_name = '_Global_Onesided_DoubleDope_Simple_TOMM5'\r\n\trun_name = '_Global_Onesided2Dropout_DoubleDope_Simple_TOMM5_APA_Six_30_31_34_TOMM5CI25_alignedonmaxscore'\r\n\t#run_name = '_Global_Onesided2_DoubleDope_Simple_TOMM5_APA_Six_30_31_34_GENERALCI45_alignedonmaxscore'\r\n\r\n\t\r\n\tcnn = DualCNN(\r\n\t\t(ref_x, ref_y, L_zero, ref_d),\r\n\t\t(ref_x, ref_y, L_zero, ref_d),\r\n\t\tlearning_rate=0.1,\r\n\t\tdrop=0.2,\r\n\t\tn_epochs=10,\r\n\t\tnkerns=[70, 110, 70],\r\n\t\t#nkerns=[50, 90, 70],#_small\r\n\t\t#nkerns=[128, 256, 70],#_small\r\n\t\tbatch_size=batch_size,\r\n\t\tnum_features=4,\r\n\t\t#randomized_regions=[(75 + 4, 260 + 4), (260 + 7, 260 + 7)],#185# + 2, + 3, + 9, + 10,,,,,, + 2 / + 4\r\n\t\t#randomized_regions=[(75 + 1, 260 + 1), (260 + 7, 260 + 7)],#+7\r\n\t\trandomized_regions=[(0, 185), (185, 185)],\r\n\t\tload_model=True,\r\n\t\ttrain_model_flag=False,\r\n\t\tstore_model=False,\r\n\t\t#dataset='general' + 'apa_sparse_general' + '_global_onesided'\r\n\t\t#dataset='general' + 'apa_sparse_general' + '_global_onesided_finetuned_TOMM5'\r\n\t\t#dataset='general' + 'apa_sparse_general' + '_global_onesided_DoubleDope_TOMM5'\r\n\t\t#dataset='general' + 'apa_sparse_general' + '_global_onesided2antimisprimeorig_finetuned_TOMM5_APA_Six_30_31_34'\r\n\t\t\r\n\t\tdataset='general' + 'apa_sparse_general' + '_global_onesided2antimisprimeorigdropout_finetuned_TOMM5_APA_Six_30_31_34'\r\n\t\t#dataset='general' + 'apa_sparse_general' + '_global_onesided2antimisprimeorigdropout_finetuned_TOMM5_APA_Six_30_31_34_pasaligned'\r\n\r\n\t\t#dataset='general' + 'apa_sparse_general' + '_global_onesided2antimisprimeorigdropout_finetuned_TOMM5_APA_Six_30_31_34_small'\r\n\t\t#dataset='general' + 'apa_sparse_general' + '_global_onesided2antimisprimeorigdropout_finetuned_TOMM5_APA_Six_30_31_34_medium'\r\n\t)\r\n\t\r\n\tprint('Trained sublib bias terms:')\r\n\tlrW = cnn.output_layer.W.eval()\r\n\tlrW = numpy.ravel(lrW[lrW.shape[0] - 36:, 1])\r\n\tfor i in range(0, len(lrW)) :\r\n\t\tif lrW[i] != 0 :\r\n\t\t\tprint(str(i) + \": \" + str(lrW[i]))\r\n\r\n\r\n\tcnn.set_data(ref_x, ref_y, L_zero, ref_d)\r\n\t#align_predict_snps(cnn, ref_x, ref_y, ref_d, var_x, var_y, L_zero, var_d, apadist, run_name)\r\n\t\r\n\t#pasalign_predict_snps(cnn, ref_x, ref_y, ref_d, var_x, var_y, L_zero, var_d, apadist)\r\n\t\r\n\t#Mutation map\r\n\t#http://genome.ucsc.edu/cgi-bin/das/hg19/dna?segment=chr11:5246678,5246777\r\n\t#ref_seq = 'XXXXXXXXXXXXXXXXXXXXXXXTAATTTAAATACATCATTGCAATGAAAATAAATGTTTTTTATTAGGCAGAATCCAGATGCTCAAGGCCCTTCATAATATCCCCCAGTAGTTGGACXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\r\n\r\n\t#http://genome.ucsc.edu/cgi-bin/das/hg19/dna?segment=chr11:5246655,5246839\r\n\t#ref_seq = 'ctttttagtaaaatattcagaaataatttaaatacatcattgcaatgaaaataaatgttttttattaggcagaatccagatgctcaaggcccttcataatatcccccagtttagtagttggacttagggaacaaaggaacctttaatagaaattggacagcaagaaagcgagcttagtgatactt'.upper()\r\n\t\r\n\r\n\r\n\t#ref_seq = 'ctttttagtaaaatattcagaaataatttaaatacatcattgcaatgaaaataaatgttttttattaggcagaatccagatgctcaaggcccttcataatatccxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'.upper()\r\n\t#mut_map(cnn, ref_seq, 'Test1Antimisprimeorigdropout') \r\n\r\n\t\r\n\r\n\r\n\t#Specific seq predictor\r\n\t#ref_seq = ('X' * (75 + 1)) + ('X' * 23) + 'TAATTTAAATACATCATTGCAATGAAAATAAATGTTTTTTATTAGGCAGAATCCAGATGCTCAAGGCCCTTCATAATATCCCCCAGTAGTTGGAC' + ('X' * (106 - 25))\r\n\t#var_seq = ('X' * (75 + 1)) + ('X' * 23) + 'TAATTTAAATACATCATTGCAATGAAAATAAATGTTTTTTATTAGGCAGAATCCAGATGCTCAAGGCCCTTCATAATATCCCCCAGTAGTTGGAC' + ('X' * (106 - 25))\r\n\t\r\n\tref_seq = 'CTAAAATCTCAAGATGGAAGATATACCACATGTAAATTATTTTAGAGCAATTAAATTGTTTTCAGGATTTTCCAAAAATGCTTCTTGTTTCATTTTATTATTTAAGTAACAGATTATCTGAGCCTCTGTGCCCTGCACACAGTGGTTTATAAATGCCTTGCCCGGAGCAGATACTGGCTTAACTA'\r\n\tvar_seq = 'CTAAAATCTCAAGATGGAAGATATACCACATGTAAATTATTTTAGAGCAATTCAATTGTTTTCAGGATTTTCCAAAAATGCTTCTTGTTTCATTTTATTATTTAAGTAACAGATTATCTGAGCCTCTGTGCCCTGCACACAGTGGTTTATAAATGCCTTGCCCGGAGCAGATACTGGCTTAACTA'\r\n\r\n\t#ref_seq = 'CATTTGCTATTGCCGTCCCATCAAATGTTGCAGTACCTCTTCCTGTTAAAGTAAAATATGCATAAGGAAGTAACTCAAAGGAATTAAAACAAAAAAGGAATTAAAACAAAAATGCTAGGACAGAAAAGCAACATCGGTTAGTACATCCACGTCTAAAAGCATTCTATAAATAGGCCTTGTTTAGC'\r\n\t#var_seq = 'CATTTGCTATTGCCGTCCCATCAAATGTTGCAGTACCTCTTCTTGTTAAAGTAAAATATGCATAAGGAAGTAACTCAAAGGAATTAAAACAAAAAAGGAATTAAAACAAAAATGCTAGGACAGAAAAGCAACATCGGTTAGTACATCCACGTCTAAAAGCATTCTATAAATAGGCCTTGTTTAGC'\r\n\r\n\t#ref_seq = 'TACCTCTTCCTGTTAAAGTAAAATATGCATAAGGAAGTAACTCAAAGGAATTAAAACAAAAAAGGAATTAAAACAAAAATGCTAGGACAGAAAAGCAACATCGGTTAGTACATCCACGTCTAAAAGCATTCTATAAATAGGCCTTGTTTAGCXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\r\n\t#var_seq = 'TACCTCTTCTTGTTAAAGTAAAATATGCATAAGGAAGTAACTCAAAGGAATTAAAACAAAAAAGGAATTAAAACAAAAATGCTAGGACAGAAAAGCAACATCGGTTAGTACATCCACGTCTAAAAGCATTCTATAAATAGGCCTTGTTTAGCXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\r\n\r\n\t\r\n\r\n\t#ref_seq = 'XXXTTATGTTGCTCAGTTACTCAAATGGTACTGTATTGTTTATATTTGTACCCCAAATAACATCGTCTGTACTTTCTGTTTTCTGTATTGTATTTGTGCAGGATTCTTTAGGCTTTATCAGTGTAATCTCTGCCTTTTAAGATATGTACAGAAAATGTCCATATAAATTTCCATTGAAGTCGAATG'\r\n\t#var_seq = 'XXXTTATGTTGCTCAGTTACTCAAATGGTACTGTATTGTTTATATTTGTACCCCAAATAACATCGTCTGTACTTTCTGTTTTCTGTATTTTATTTGTGCAGGATTCTTTAGGCTTTATCAGTGTAATCTCTGCCTTTTAAGATATGTACAGAAAATGTCCATATAAATTTCCATTGAAGTCGAATG'\r\n\r\n\t#ref_seq = 'TGTTGCTCAGTTACTCAAATGGTACTGTATTGTTTATATTTGTACCCCAAATAACATCGTCTGTACTTTCTGTTTTCTGTATTGTATTTGTGCAGGATTCTTTAGGCTTTATCAGTGTAATCTCTGCCTTTTAAGATATGTACAGAAAATGTCCATATAAATTTCCATTGAAGTCGAATGXXXXXX'\r\n\t#var_seq = 'TGTTGCTCAGTTACTCAAATGGTACTGTATTGTTTATATTTGTACCCCAAATAACATCGTCTGTACTTTCTGTTTTCTGTATTTTATTTGTGCAGGATTCTTTAGGCTTTATCAGTGTAATCTCTGCCTTTTAAGATATGTACAGAAAATGTCCATATAAATTTCCATTGAAGTCGAATGXXXXXX'\r\n\r\n\t#1\r\n\t#ref_seq = 'XXXTTATGTTGCTCAGTTACTCAAATGGTACTGTATTGTTTATATTTGTACCCCAAATAACATCGTCTGTACTTTCTGTTTTCTGTATTGTATTTGTGCAGGATTCTTTAGGCTTTATCAGTGTAATCTCTGCCTTTTAAGATATGTACAGAAAATGTCCATATAAATTTCCATTGAAGTCGAATG'\r\n\t#var_seq = 'XXXTTATGTTGCTCAGTTACTCAAATGGTACTGTATTGTTTATATTTGTACCCCAAATAACATCGTCTGTACTTTCTGTTTTCTGTATTTTATTTGTGCAGGATTCTTTAGGCTTTATCAGTGTAATCTCTGCCTTTTAAGATATGTACAGAAAATGTCCATATAAATTTCCATTGAAGTCGAATG'\r\n\r\n\t\r\n\t#Aligned on ATTAGA\r\n\t#ref_seq = 'XXXXXXXXXXXXXXXGATAATCCTTACCTGTTCCTCCTCCGGAGGGCAGATTAGAACATGATGATTGGAGATGCATGAAACGTGATTAACGTCTCTGCGTAATCAGGACTTGCAACACCCTGATTGCTCCTGTCTGATTCTTTCTGACGATCACTTACATTTGTGTTATGCTGATTAGCAGATAT'\r\n\t#var_seq = 'XXXXXXXXXXXXXXXGATAATCCTTACCTGTTCCTCCTCCGGAGGGCAGATTAGAACATGGTGATTGGAGATGCATGAAACGTGATTAACGTCTCTGCGTAATCAGGACTTGCAACACCCTGATTGCTCCTGTCTGATTCTTTCTGACGATCACTTACATTTGTGTTATGCTGATTAGCAGATAT'\r\n\r\n\t#Aligned according to MAX(REF)\r\n\t#ref_seq = 'GATAATCCTTACCTGTTCCTCCTCCGGAGGGCAGATTAGAACATGATGATTGGAGATGCATGAAACGTGATTAACGTCTCTGCGTAATCAGGACTTGCAACACCCTGATTGCTCCTGTCTGATTCTTTCTGACGATCACTTACATTTGTGTTATGCTGATTAGCAGATATCCACAAACXXXXXXX'\r\n\t#var_seq = 'GATAATCCTTACCTGTTCCTCCTCCGGAGGGCAGATTAGAACATGGTGATTGGAGATGCATGAAACGTGATTAACGTCTCTGCGTAATCAGGACTTGCAACACCCTGATTGCTCCTGTCTGATTCTTTCTGACGATCACTTACATTTGTGTTATGCTGATTAGCAGATATCCACAAACXXXXXXX'\r\n\r\n\t#ref_seq = 'XATTTGCTATTGCCGTCCCATCAAATGTTGCAGTACCTCTTCCTGTTAAAGTAAAATATGCATAAGGAAGTAACTCAAAGGAATTAAAACAAAAAAGGAATTAAAACAAAAATGCTAGGACAGAAAAGCAACATCGGTTAGTACATCCACGTCTAAAAGCATTCTATAAATAGGCCTTGTTTAGCT'\r\n\t#var_seq = 'XATTTGCTATTGCCGTCCCATCAAATGTTGCAGTACCTCTTCCGGTTAAAGTAAAATATGCATAAGGAAGTAACTCAAAGGAATTAAAACAAAAAAGGAATTAAAACAAAAATGCTAGGACAGAAAAGCAACATCGGTTAGTACATCCACGTCTAAAAGCATTCTATAAATAGGCCTTGTTTAGCT'\r\n\t\r\n\r\n\t#ref_seq = 'GGGGGCAAATTTGGCACCTGCCCCCACTTGGGACTTTGGTCTTGCTGAAAATAAATATTTTTCTTTTTCAAAGACTTTGTGATTCCCCAGATAGGTTGCCTGAAATGGGTAAGAGGATGGAGGACTCAACAGTGCAGGGTTTGAGGCCTGAATGGTCATCTGCATCAXXXXXXXXXXXXXXXXXX'\r\n\t#var_seq = 'GGGGGCAAATTTGGCACCTGCCCCCACTTGGGACTTTGGTCTTGCTGXXAATAAATATTTTTCTTTTTCAAAGACTTTGTGATTCCCCAGATAGGTTGCCTGAAATGGGTAAGAGGATGGAGGACTCAACAGTGCAGGGTTTGAGGCCTGAATGGTCATCTGCATCAXXXXXXXXXXXXXXXXXX'\r\n\r\n\t#ref_seq = 'GGGGGCAAATTTGGCACCTGCCCCCACTTGGGACTTTGGXXXXXXXXXXAATAAATTTTTTTCTTTTTCAAAGACTTTGTGATTCCCCAGATAGGTTGCCTGAAATGGGTAAGAGGATGGAGGACTCAACAGTGCAGGGTTTGAGGCCTGAATGGTCATCTGCATCAXXXXXXXXXXXXXXXXXX'\r\n\t#var_seq = 'GGGGGCAAATTTGGCACCTGCCCCCACTTGGGACTTTGGXXXXXXXXAAAATAAATTTTTTTCTTTTTCAAAGACTTTGTGATTCCCCAGATAGGTTGCCTGAAATGGGTAAGAGGATGGAGGACTCAACAGTGCAGGGTTTGAGGCCTGAATGGTCATCTGCATCAXXXXXXXXXXXXXXXXXX'\r\n\r\n\t#ref_seq = 'GTGGCTCATTTTCTGGCAAATGGAGGCACGAACGCAGGGGCCAAATAGCAATAAATGGGTTTTGTTTTTTTTTTGCAATAACTTATTGAAGTCAGCAGGGCATCCTTCCCTAGTATGCTTCCTGGGGCGTGTCTAGGGGCCAGCTCCCTTCCCTGGGGGCAGCCCTXXXXXXXXXXXXXXXXXXX'\r\n\t#var_seq = 'GTGGCTCATTTTCTGGCAAATGGAGGCACGAACGCAGGGGCCAAATAGCAATAAATGGGTTTTGTTTTTTTTTTGCAGTGACTTATTGAAGTCAGCAGGGCATCCTTCCCTAGTATGCTTCCTGGGGCGTGTCTAGGGGCCAGCTCCCTTCCCTGGGGGCAGCCCTXXXXXXXXXXXXXXXXXXX'\r\n\r\n\t#ref_seq = 'GGAGCTGCTGTGTATAGACTGCCAAATGTGAAGTATTTATATTGTATTCAATAAACTATACTTAAGAGTGTTCAAAAAAGTCTCCTGGGAGTGGGAAGGGAGCTAGTGGATACTCCCTATTTCACAAACTTTTCTTTTTTTTTTTTTTTGAGACAGTTTCGCTCTGTTXXXXXXXXXXXXXXXXX'\r\n\t#var_seq = 'GGAGCTGCTGTGTATAGACTGCCAAATGTGAAGTATTTATATTGTATTCAATAAACTATACTTAAGAGTGTTCAACCAAGTCTCCTGGGAGTGGGAAGGGAGCTAGTGGATACTCCCTATTTCACAAACTTTTCTTTTTTTTTTTTTTTGAGACAGTTTCGCTCTGTTXXXXXXXXXXXXXXXXX'\r\n\r\n\r\n\t#APADB into DoubleDope\r\n\t#ref_seq = 'XXXXXXXXXCATTACTCGCATCCAAATGTGAAGTATTTATATTGTATTCAATAAACTATACTTAAGAGTGTTCAAAAAAGTCTCCTGGGAGTGGGAAGCCAATTAAGCCATACTCCCTATTTCACAAACTTTTCTTTTTTTTTTTTTTTGAGACAGTTTCGCTCTGTTXXXXXXXXXXXXCTACG'\r\n\t#var_seq = 'XXXXXXXXXCATTACTCGCATCCAAATGTGAAGTATTTATATTGTATTCAATAAACTATACTTAAGAGTGTTCAACCAAGTCTCCTGGGAGTGGGAAGCCAATTAAGCCATACTCCCTATTTCACAAACTTTTCTTTTTTTTTTTTTTTGAGACAGTTTCGCTCTGTTXXXXXXXXXXXXCTACG'\r\n\r\n\t#APADB into Simple\r\n\t#ref_seq = 'ATCTCTGCTGTGTATAGACTGCCAAATGTGAAGTATTTATATTGTATTCAATAAACTATACTTAAGAGTGTTCAAAAAAGTCTCCTGGGAGTGGGAAGGGAGCTAGTGGATACTCCCTATTTCACAAACTTTTCTTTTTTTTTTTTTTTGACGCCTAACCCTAAGCAGATTCTTCATGCAATTGT'\r\n\t#var_seq = 'ATCTCTGCTGTGTATAGACTGCCAAATGTGAAGTATTTATATTGTATTCAATAAACTATACTTAAGAGTGTTCAACCAAGTCTCCTGGGAGTGGGAAGGGAGCTAGTGGATACTCCCTATTTCACAAACTTTTCTTTTTTTTTTTTTTTGACGCCTAACCCTAAGCAGATTCTTCATGCAATTGT'\r\n\r\n\t#ref_seq = 'ATCTCTGCTGTGTATAGACTGCCAAATGTGAAGTATTTATATTGTATTCAATAAACTATACTTAAGAGTGTTCAAAAAAGTCTCCTGGGAGTGGGAAGGGATTAAATGGATACTCCCTATTTCACAAACTTTTCTTTTTTTTTTTTTTTGACGCCTAACCCTAAGCAGATTCTTCATGCAATTGT'\r\n\t#var_seq = 'ATCTCTGCTGTGTATAGACTGCCAAATGTGAAGTATTTATATTGTATTCAATAAACTATACTTAAGAGTGTTCAACCAAGTCTCCTGGGAGTGGGAAGGGATTAAATGGATACTCCCTATTTCACAAACTTTTCTTTTTTTTTTTTTTTGACGCCTAACCCTAAGCAGATTCTTCATGCAATTGT'\r\n\t\r\n\r\n\r\n\t#DoubleDope\r\n\t#ref_seq = 'XXXXXXXXXXCATTACTCGCATCCATAGTAATAGTGACGGTCGCATTCTAATAAATTCTATGAGAGTAGCGAAAAAAAAAGGTTTTTGGGTAGGAACAGCCAATTAAGCCATTCACTCCCATCTTCCTCCCACATATAAATTCTGACCTTAAGCTGATGGCTTACCTTTGGGAAAGAGCTTCTACG'\r\n\t#var_seq = 'XXXXXXXXXXCATTACTCGCATCCATAGTAATAGTGACGGTCGCATTCTAATAAATTCTATGAGAGTAGCGAAAACCAAAGGTTTTTGGGTAGGAACAGCCAATTAAGCCATTCACTCCCATCTTCCTCCCACATATAAATTCTGACCTTAAGCTGATGGCTTACCTTTGGGAAAGAGCTTCTACG'\r\n\r\n\t#ref_seq = 'XXXXXXXXXXCATTACTCGCATCCATAGTAATAGTGACGGTCGCATTCTAATAAATTCTATGAGAGTAGCGTTAAAAAATGGTTTTTGGGTAGGAACAGCCAATTAAGCCATTCACTCCCATCTTCCTCCCACATATAAATTCTGACCTTAAGCTGATGGCTTACCTTTGGGAAAGAGCTTCTACG'\r\n\t#var_seq = 'XXXXXXXXXXCATTACTCGCATCCATAGTAATAGTGACGGTCGCATTCTAATAAATTCTATGAGAGTAGCGTTAACCAATGGTTTTTGGGTAGGAACAGCCAATTAAGCCATTCACTCCCATCTTCCTCCCACATATAAATTCTGACCTTAAGCTGATGGCTTACCTTTGGGAAAGAGCTTCTACG'\r\n\r\n\t#ref_seq = 'XXXXXXXXXXCATTACTCGCATCCATAGTAATAGTGACGGTCGCATTCTAATAAATTCTATGAGAGTAGCGTAAAAAAAAGGTTTTTGGGTAGGAACAGCCAATTAAGCCATTCACTCCCATCTTCCTCCCACATATAAATTCTGACCTTAAGCTGATGGCTTACCTTTGGGAAAGAGCTTCTACG'\r\n\t#var_seq = 'XXXXXXXXXXCATTACTCGCATCCATAGTAATAGTGACGGTCGCATTCTAATAAATTCTATGAGAGTAGCGTAAACCAAAGGTTTTTGGGTAGGAACAGCCAATTAAGCCATTCACTCCCATCTTCCTCCCACATATAAATTCTGACCTTAAGCTGATGGCTTACCTTTGGGAAAGAGCTTCTACG'\r\n\r\n\r\n\t#Simple into DoubleDope\r\n\t#ref_seq = 'XXXXXXXXXCATTACTCGCATCCAAACCCTAAGCTGTAAACAGTGGTTCAATAAATTTATTTACTGGCATCTAAAAAAAATTCCCTTTTTGTGGTGAGCCAATTAAGCCATTTACTCTAGGGAGCAGGTCCGTTATGTTTTACTCCCTACGCGCCTAACCCTAAGCAGATTCTTCATGCACTACG'\r\n\t#var_seq = 'XXXXXXXXXCATTACTCGCATCCAAACCCTAAGCTGTAAACAGTGGTTCAATAAATTTATTTACTGGCATCTAAACCAAATTCCCTTTTTGTGGTGAGCCAATTAAGCCATTTACTCTAGGGAGCAGGTCCGTTATGTTTTACTCCCTACGCGCCTAACCCTAAGCAGATTCTTCATGCACTACG'\r\n\r\n\r\n\t#Simple\r\n\t#ref_seq = 'ATCTTCTCTGTGTCTGGTACATACAACCCTAAGCTGTAAACAGTGGTTCAATAAATTTATTTACTGGCATCACAAAAAATATCCCTTTTTGTGGTGTGAGATTAAAGGGTTTTACTCTAGGGAGCAGGTCCGTTATGTTTTACTCCCTACGCGCCTAACCCTAAGCAGATTCTTCATGCAATTGT'\r\n\t#var_seq = 'ATCTTCTCTGTGTCTGGTACATACAACCCTAAGCTGTAAACAGTGGTTCAATAAATTTATTTACTGGCATCACAACCAATATCCCTTTTTGTGGTGTGAGATTAAAGGGTTTTACTCTAGGGAGCAGGTCCGTTATGTTTTACTCCCTACGCGCCTAACCCTAAGCAGATTCTTCATGCAATTGT'\r\n\r\n\r\n\t#ref_seq = 'ATCTTCTCTGTGTCTGGTACATACAACCCTAAGCTGTAAACAGTGGTTCAATAAATTTATTTACTGGCATCTAAAAAAAATTCCCTTTTTGTGGTGTGAGATTAAAGGGTTTTACTCTAGGGAGCAGGTCCGTTATGTTTTACTCCCTACGCGCCTAACCCTAAGCAGATTCTTCATGCAATTGT'\r\n\t#var_seq = 'ATCTTCTCTGTGTCTGGTACATACAACCCTAAGCTGTAAACAGTGGTTCAATAAATTTATTTACTGGCATCTAAACCAAATTCCCTTTTTGTGGTGTGAGATTAAAGGGTTTTACTCTAGGGAGCAGGTCCGTTATGTTTTACTCCCTACGCGCCTAACCCTAAGCAGATTCTTCATGCAATTGT'\r\n\r\n\r\n\t#ref_seq = 'CCCCCGCCCAGACTGCAGGCTCCCCTTCCTGCACCACCATTGTCTCAGCAGTAAAGGCGACATTTGGAACCACAGCATGGCCTTGACCATAGGGGTTCCTCGCAGGCAGAGCCTTGCCTCCTCCTGGGTCCCACCTGGCCTCTGCAGACCTTAGGCTGGGACGGGGXXXXXXXXXXXXXXXXXXX'\r\n\t#var_seq = 'CCCCCGCCCAGACTGCAGGCTCCCCTTCCTGCACCACCATTGTCTCAGCAATAAAGGCGACATTTGGAACCACAGCATGGCCTTGACCATAGGGGTTCCTCGCAGGCAGAGCCTTGCCTCCTCCTGGGTCCCACCTGGCCTCTGCAGACCTTAGGCTGGGACGGGGXXXXXXXXXXXXXXXXXXX'\r\n\r\n\r\n\r\n\tref_x_p = numpy.zeros((1, len(ref_seq), 4))\r\n\tvar_x_p = numpy.zeros((1, len(var_seq), 4))\r\n\tfor j in range(0, len(ref_seq)) :\r\n\t\tif ref_seq[j] == 'A' :\r\n\t\t\tref_x_p[0, j, 0] = 1\r\n\t\telif ref_seq[j] == 'C' :\r\n\t\t\tref_x_p[0, j, 1] = 1\r\n\t\telif ref_seq[j] == 'G' :\r\n\t\t\tref_x_p[0, j, 2] = 1\r\n\t\telif ref_seq[j] == 'T' :\r\n\t\t\tref_x_p[0, j, 3] = 1\r\n\tfor j in range(0, len(var_seq)) :\r\n\t\tif var_seq[j] == 'A' :\r\n\t\t\tvar_x_p[0, j, 0] = 1\r\n\t\telif var_seq[j] == 'C' :\r\n\t\t\tvar_x_p[0, j, 1] = 1\r\n\t\telif var_seq[j] == 'G' :\r\n\t\t\tvar_x_p[0, j, 2] = 1\r\n\t\telif var_seq[j] == 'T' :\r\n\t\t\tvar_x_p[0, j, 3] = 1\r\n\r\n\tL_zero_p = numpy.zeros((1, 36))\r\n\td_p = numpy.ones((1, 1))\r\n\r\n\ty_ref_hat = logit(cnn.get_online_prediction(ref_x_p, L_zero_p, d_p))[0]\r\n\ty_var_hat = logit(cnn.get_online_prediction(var_x_p, L_zero_p, d_p))[0]\r\n\r\n\tprint('y_ref_hat = ' + str(y_ref_hat))\r\n\tprint('y_var_hat = ' + str(y_var_hat))\r\n\r\n\tprint('diff_hat = ' + str(y_var_hat - y_ref_hat))\r\n\r\n\tprint(1 + '')\r\n\r\n\r\n\tcnn.set_data(ref_x, ref_y, L_zero, ref_d)\r\n\ty_ref_hat = logit(cnn.get_prediction())\r\n\ty_ref = logit(ref_y.eval()[:,1])\r\n\r\n\tcnn.set_data(var_x, var_y, L_zero, var_d)\r\n\ty_var_hat = logit(cnn.get_prediction())\r\n\ty_var = logit(var_y.eval()[:,1])\r\n\r\n\r\n\r\n\t#Debug\r\n\tprint(\"apadist 3561\")\r\n\tprint('ref hat')\r\n\tprint(y_ref_hat[apadist == 3561])\r\n\tprint('var hat')\r\n\tprint(y_var_hat[apadist == 3561])\r\n\r\n\tprint(\"apadist 265\")\r\n\tprint('ref hat')\r\n\tprint(y_ref_hat[apadist == 265])\r\n\tprint('var hat')\r\n\tprint(y_var_hat[apadist == 265])\r\n\r\n\r\n\tdiff = y_var - y_ref\r\n\tdiff_hat = y_var_hat - y_ref_hat\r\n\r\n\r\n\tSSE_diff = (diff - diff_hat).T.dot(diff - diff_hat)\r\n\r\n\ty_diff_average = numpy.average(diff, axis=0)\r\n\r\n\tSStot_diff = (diff - y_diff_average).T.dot(diff - y_diff_average)\r\n\r\n\tRMSE_diff = numpy.sqrt(SSE_diff / float(len(y_ref)))\r\n\r\n\tMAE_diff = numpy.mean(numpy.abs(diff_hat - diff))\r\n\r\n\tdiff_set_dir_accuracy = numpy.count_nonzero(numpy.sign(diff) == numpy.sign(diff_hat))\r\n\r\n\r\n\tprint(\"\")\r\n\tprint(\"Logodds diff R^2:\")\r\n\tprint(1.0 - (SSE_diff / SStot_diff))\r\n\tprint(\"Logodds diff mean abs error:\")\r\n\tprint(MAE_diff)\r\n\r\n\tprint(\"Logodds diff Classification accuracy: \" + str(diff_set_dir_accuracy) + \"/\" + str(y_ref.shape[0]) + \" = \" + str(float(diff_set_dir_accuracy) / float(y_ref.shape[0])))\r\n\r\n\tfig = plt.figure()\r\n\tax1 = fig.add_subplot(111)\r\n\r\n\tcol = ax1.scatter(diff_hat, diff, c = 'red', alpha=1.0)\r\n\tax1.plot([-5, 5], [-5, 5], c='yellow')\r\n\tax1.plot([numpy.min(diff_hat) * 1.1, numpy.max(diff_hat) * 1.1], [0, 0], c='green')\r\n\tax1.plot([0, 0], [numpy.min(diff) * 1.1, numpy.max(diff) * 1.1], c='green')\r\n\tax1.set_xlim([numpy.min(diff_hat) * 1.1, numpy.max(diff_hat) * 1.1])\r\n\tax1.set_ylim([numpy.min(diff) * 1.1, numpy.max(diff) * 1.1])\r\n\tax1.set_xlabel('Predicted Proximal usage logodds diff', fontsize=22)\r\n\tax1.set_ylabel('Target Proximal usage logodds diff', fontsize=22)\r\n\tax1.set_title('GEUV APA SNP Log Diff (R^2 = ' + str(round(1.0 - (SSE_diff / SStot_diff), 2)) + ', Acc = ' + str(diff_set_dir_accuracy) + \"/\" + str(y_ref.shape[0]) + ')', fontsize=18)\r\n\t\r\n\tfor i in range(0, len(diff)):\r\n\t\t#ax1.annotate(snp_index[i] + 2, (diff_hat[i], diff[i]))\r\n\t\t'''annotation = ''\r\n\t\tif snptype[i] == 1 :\r\n\t\t\tannotation = 'HET'\r\n\t\telif snptype[i] == 2 :\r\n\t\t\tannotation = 'HOM'\r\n\t\t\r\n\t\tif snpregion[i] == 1 :\r\n\t\t\tannotation += ' UP'\r\n\t\telif snpregion[i] == 2 :\r\n\t\t\tannotation += ' PAS'\r\n\t\telif snpregion[i] == 3 :\r\n\t\t\tannotation += ' DN'\r\n\t\t'''\r\n\t\tannotation = '(' + str(apadist[i]) + ')'\r\n\r\n\t\tax1.annotate(annotation, (diff_hat[i], diff[i]), size=8)\r\n\r\n\t#plt.savefig(\"cnn_snp_logodds_diff_global\" + run_name + \".png\")\r\n\tplt.show()\r\n\tplt.close()\r\n\r\ndef safe_log(x, minval=0.02):\r\n return numpy.log(x.clip(min=minval))\r\n\r\ndef logit(x) :\r\n\treturn numpy.log(x / (1.0 - x))\r\n\r\ndef translate_to_saliency_seq(X_point, input_seq) :\r\n\tseq = \"\"\r\n\tfor j in range(0, X_point.shape[0]) :\r\n\t\tif input_seq[j] == 'A' and X_point[j, 0] > 0 :\r\n\t\t\tseq += \"A\"\r\n\t\telif input_seq[j] == 'C' and X_point[j, 1] > 0 :\r\n\t\t\tseq += \"C\"\r\n\t\telif input_seq[j] == 'G' and X_point[j, 2] > 0 :\r\n\t\t\tseq += \"G\"\r\n\t\telif input_seq[j] == 'T' and X_point[j, 3] > 0 :\r\n\t\t\tseq += \"T\"\r\n\t\telse :\r\n\t\t\tseq += '.'\r\n\treturn seq\r\n\r\ndef translate_matrix_to_seq(X_point) :\r\n\tseq = \"\"\r\n\tfor j in range(0, X_point.shape[0]) :\r\n\t\tif X_point[j, 0] == 1 :\r\n\t\t\tseq += \"A\"\r\n\t\telif X_point[j, 1] == 1 :\r\n\t\t\tseq += \"C\"\r\n\t\telif X_point[j, 2] == 1 :\r\n\t\t\tseq += \"G\"\r\n\t\telif X_point[j, 3] == 1 :\r\n\t\t\tseq += \"T\"\r\n\t\telse :\r\n\t\t\tseq += \".\"\r\n\treturn seq\r\n\r\ndef translate_to_seq(x) :\r\n\tX_point = numpy.ravel(x.todense())\r\n\tX_point = X_point.reshape((len(X_point) / 4, 4))\r\n\t\r\n\tseq = \"\"\r\n\tfor j in range(0, X_point.shape[0]) :\r\n\t\tif X_point[j, 0] == 1 :\r\n\t\t\tseq += \"A\"\r\n\t\telif X_point[j, 1] == 1 :\r\n\t\t\tseq += \"C\"\r\n\t\telif X_point[j, 2] == 1 :\r\n\t\t\tseq += \"G\"\r\n\t\telif X_point[j, 3] == 1 :\r\n\t\t\tseq += \"T\"\r\n\t\telse :\r\n\t\t\tseq += \".\"\r\n\treturn seq\r\n\r\nif __name__ == '__main__':\r\n\tevaluate_cnn('general')\r\n","sub_path":"cnn/cnn_single_general_global_onesided2_dropout_snp.py","file_name":"cnn_single_general_global_onesided2_dropout_snp.py","file_ext":"py","file_size_in_byte":106807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"334904387","text":"from django.shortcuts import render\nfrom .models import base_price, print_price, non_profit_markup, profit_markup, paper_type\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.views import generic\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\n\n@login_required(login_url='pam:login')\ndef wf_calculator(request):\n paper_types_list = paper_type.objects.all()\n return render(request, 'wide_format_pricing/wide_format_price_calculator.html', {'paper_types_list':paper_types_list})\n\n\nclass price_display(generic.ListView):\n template_name = 'wide_format_pricing/price_display.html'\n\n def get_queryset(self):\n return print_price.objects.all()\n\n\nclass base_price_display(generic.ListView):\n template_name = 'wide_format_pricing/base_price_display.html'\n\n def get_queryset(self):\n return base_price.objects.all()\n\n\nclass non_profit_markup_display(generic.ListView):\n template_name = 'wide_format_pricing/non_profit_markup_display.html'\n\n def get_queryset(self):\n return non_profit_markup.objects.all()\n\n\nclass profit_markup_display(generic.ListView):\n template_name = 'wide_format_pricing/profit_markup_display.html'\n\n def get_queryset(self):\n return profit_markup.objects.all()\n\n\ndef make_default(request, pk, object, rev):\n model_names = {'print_price':print_price, 'base_price':base_price, 'non_profit_markup':non_profit_markup, 'profit_markup':profit_markup}\n\n model_param = model_names[object]\n new_default = model_param.objects.get(pk=pk)\n\n new_default.is_default = True\n new_default.save()\n\n return HttpResponseRedirect(reverse('wide_format_pricing:' + rev))\n\n\ndef calculate_price(request):\n base_price_default = base_price.objects.get(is_default=True)\n non_profit_markup_default = non_profit_markup.objects.get(is_default=True)\n profit_markup_default = profit_markup.objects.get(is_default=True)\n\n form = request.POST\n quantity = int(form['quantity'])\n paper_selection = form['paper-type']\n height = int(form['height'])\n width = int(form['width'])\n\n if form['measure'] == 'Inches':\n square_inches = height * width\n paper = paper_type.objects.get(name=paper_selection)\n default_paper_cost = paper.print_price_set.get(is_default=True)\n\n if default_paper_cost.cost_context == 'per Square Foot':\n cost = default_paper_cost.cost / 12\n else:\n cost = default_paper_cost.cost\n\n if form['customer-classification'] == 'Internal':\n final_price = (quantity * (cost * square_inches)) + base_price_default.base_price\n return HttpResponse(final_price)\n elif form['customer-classification'] == 'Non-Profit':\n price = ((quantity * (cost * square_inches)) + base_price_default.base_price)\n final_price = price + (price * (non_profit_markup_default.markup_percent/100))\n return HttpResponse(final_price)\n elif form['customer-classification'] == 'Profit':\n price = ((quantity * (cost * square_inches)) + base_price_default.base_price)\n final_price = price + (price * (profit_markup_default.markup_percent/100))\n return HttpResponse(final_price)\n if form['measure'] == 'Feet':\n square_feet = height * width\n paper = paper_type.objects.get(name=paper_selection)\n default_paper_cost = paper.print_price_set.get(is_default=True)\n\n if default_paper_cost.cost_context == 'per Square Inch':\n cost = default_paper_cost.cost * 12\n else:\n cost = default_paper_cost.cost\n\n if form['customer-classification'] == 'Internal':\n final_price = (quantity * (cost * square_inches)) + base_price_default.base_price\n return HttpResponse(final_price)\n elif form['customer-classification'] == 'Non-Profit':\n price = ((quantity * (cost * square_inches)) + base_price_default.base_price)\n final_price = price + (price * (non_profit_markup_default.markup_percent/100))\n return HttpResponse(final_price)\n elif form['customer-classification'] == 'Profit':\n price = ((quantity * (cost * square_inches)) + base_price_default.base_price)\n final_price = price + (price * (profit_markup_default.markup_percent/100))\n return HttpResponse(final_price)\n","sub_path":"pmwebtools/wide_format_pricing/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"161682323","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'ipetrash'\n\n\nimport os\nimport time\nimport sys\n\n# pip install python-telegram-bot\nfrom telegram import Update, ParseMode\nfrom telegram.ext import Updater, MessageHandler, CommandHandler, Filters, CallbackContext, CallbackQueryHandler\nfrom telegram.ext.dispatcher import run_async\n\n# pip install python-telegram-bot-pagination\nfrom telegram_bot_pagination import InlineKeyboardPaginator\n\nsys.path.append('..')\n\nimport config\nfrom common import get_logger, log_func, reply_error\nfrom utils import is_equal_inline_keyboards\nfrom data import character_pages\n\n\nlog = get_logger(__file__)\n\n\n@run_async\n@log_func(log)\ndef on_request(update: Update, context: CallbackContext):\n message = update.message\n\n paginator = InlineKeyboardPaginator(\n page_count=len(character_pages),\n current_page=1,\n data_pattern='character#{page}'\n )\n\n character = character_pages[0]\n\n message.reply_text(\n text='*{title}*\\n{description}'.format(**character),\n reply_markup=paginator.markup,\n parse_mode=ParseMode.MARKDOWN\n )\n\n\n@run_async\n@log_func(log)\ndef on_callback_query(update: Update, context: CallbackContext):\n query = update.callback_query\n query.answer()\n\n source, page = query.data.split('#', 1)\n page = int(page)\n\n paginator = InlineKeyboardPaginator(\n page_count=len(character_pages),\n current_page=page,\n data_pattern=source + '#{page}'\n )\n\n # Fix error: \"telegram.error.BadRequest: Message is not modified\"\n if is_equal_inline_keyboards(paginator, query.message.reply_markup):\n return\n\n character = character_pages[page - 1]\n\n query.message.edit_text(\n text='*{title}*\\n{description}'.format(**character),\n reply_markup=paginator.markup,\n parse_mode=ParseMode.MARKDOWN\n )\n\n\ndef on_error(update: Update, context: CallbackContext):\n reply_error(log, update, context)\n\n\ndef main():\n cpu_count = os.cpu_count()\n workers = cpu_count\n log.debug('System: CPU_COUNT=%s, WORKERS=%s', cpu_count, workers)\n\n log.debug('Start')\n\n # Create the EventHandler and pass it your bot's token.\n updater = Updater(\n config.TOKEN,\n workers=workers,\n use_context=True\n )\n\n # Get the dispatcher to register handlers\n dp = updater.dispatcher\n\n dp.add_handler(CommandHandler('start', on_request))\n dp.add_handler(MessageHandler(Filters.text, on_request))\n dp.add_handler(CallbackQueryHandler(on_callback_query, pattern='^character#'))\n\n # Handle all errors\n dp.add_error_handler(on_error)\n\n # Start the Bot\n updater.start_polling()\n\n # Run the bot until the you presses Ctrl-C or the process receives SIGINT,\n # SIGTERM or SIGABRT. This should be used most of the time, since\n # start_polling() is non-blocking and will stop the bot gracefully.\n updater.idle()\n\n log.debug('Finish')\n\n\nif __name__ == '__main__':\n while True:\n try:\n main()\n except:\n log.exception('')\n\n timeout = 15\n log.info(f'Restarting the bot after {timeout} seconds')\n time.sleep(timeout)\n","sub_path":"telegram_bot_examples/pagination/example_as_text.py","file_name":"example_as_text.py","file_ext":"py","file_size_in_byte":3181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"623215850","text":"#!/usr/bin/env python\n# coding=utf-8\n\nimport MySQLdb\nimport time\nfrom work_info.log_package.log_file import logs\n\n\nclass MySQLConn(object):\n\n def __init__(self):\n i = 1\n while True:\n try:\n self.db = MySQLdb.connect(\n host='127.0.0.1',\n user='root',\n passwd='12345678',\n db='gra_project',\n charset='utf8',\n )\n except MySQLdb.Error as e:\n logs.debug(\"The %s times connect to mysql~~\" % i)\n i += 1\n time.sleep(60)\n continue\n break\n self.cursor = self.db.cursor()\n\n def inser_data(self, sql):\n try:\n self.cursor.execute(sql)\n self.db.commit()\n self.db.close()\n except Exception as e:\n logs.debug(\"Some thing is wrong~: %s\" % e)\n\n def select_data(self, sql):\n data = ''\n try:\n self.cursor.execute(sql)\n data = self.cursor.fetchall()\n self.db.close()\n except Exception as e:\n logs.debug(\"select failed : %s\" % e)\n return data\n\n def update_to_table(self, sql):\n try:\n self.cursor.execute(sql)\n self.db.commit()\n self.db.close()\n except Exception as e:\n logs.debug('update failed : %s' % e)\n\n def delete_useless_url(self, sql):\n try:\n self.cursor.execute(sql)\n self.db.commit()\n self.db.close()\n except Exception as e:\n logs.debug('delete failed : %s' % e)\n\n\n\n\n","sub_path":"work_info/mysql_connect/mysql_connect.py","file_name":"mysql_connect.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"360900322","text":"# -*- coding: utf-8 -*-\n# author: songwei\n# place: Shenzhen Guangdong\n# time: 2020/4/28 15:40\nimport os, re, json, traceback\nimport os\nimport jieba\nfrom math import log2\n\n# 读取每个义项的语料\ndef read_file(path):\n with open(path, 'r', encoding='utf-8') as f:\n lines = [_.strip() for _ in f.readlines()]\n return lines\n\n# 对示例句子分词\nsent = '赛季初的时候，火箭是众望所归的西部决赛球队。'\nwsd_word = '火箭'\n\njieba.add_word(wsd_word)\nsent_words = list(jieba.cut(sent, cut_all=False))\n\n# 去掉停用词\nstopwords = [wsd_word, '我', '你', '它', '他', '她', '了', '是', '的', '啊', '谁', '什么','都',\\\n '很', '个', '之', '人', '在', '上', '下', '左', '右', '。', '，', '！', '？']\n\nsent_cut = []\nfor word in sent_words:\n if word not in stopwords:\n sent_cut.append(word)\n\nprint(sent_cut)\n\n\n# 计算其他词的TF-IDF以及频数\nwsd_dict = {}\ndir=os.listdir('.')\nfor file in os.listdir('.'):\n if wsd_word in file:\n wsd_dict[file.replace('.txt', '')] = read_file(file)\n\n# 统计每个词语在语料中出现的次数\ntf_dict = {}\nfor meaning, sents in wsd_dict.items():\n tf_dict[meaning] = []\n for word in sent_cut:\n word_count = 0\n for sent in sents:\n example = list(jieba.cut(sent, cut_all=False))\n word_count += example.count(word)\n\n if word_count:\n tf_dict[meaning].append((word, word_count))\n\nidf_dict = {}\nfor word in sent_cut:\n document_count = 0\n for meaning, sents in wsd_dict.items():\n for sent in sents:\n if word in sent:\n document_count += 1\n\n idf_dict[word] = document_count\n\n# 输出值\ntotal_document = 0\nfor meaning, sents in wsd_dict.items():\n total_document += len(sents)\n\n# 计算tf_idf值\nmean_tf_idf = []\nfor k, v in tf_dict.items():\n print(k+':')\n tf_idf_sum = 0\n for item in v:\n word = item[0]\n tf = item[1]\n tf_idf = item[1]*log2(total_document/(1+idf_dict[word]))\n tf_idf_sum += tf_idf\n print('%s, 频数为: %s, TF-IDF值为: %s'% (word, tf, tf_idf))\n\n mean_tf_idf.append((k, tf_idf_sum))\n\nsort_array = sorted(mean_tf_idf, key=lambda x:x[1], reverse=True)\ntrue_meaning = sort_array[0][0].split('_')[1]\nprint('\\n经过词义消岐，%s在该句子中的意思为:%s .' % (wsd_word, true_meaning))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n pass","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"122251855","text":"#paintproject.py\r\n\r\n# individual features:\r\n# undo/redo\r\n# eyedropper\r\n# polygon\r\n# brush\r\n# clear\r\n# music\r\n# spray paint\r\n# highlight selected\r\n# tool description\r\n# show size as circle\r\n\r\n#importing\r\nfrom pygame import *\r\nfrom random import *\r\nfrom math import *\r\nfrom pygame import *\r\nfrom tkinter import *\r\nfrom tkinter.filedialog import askopenfilename\r\nfrom tkinter.filedialog import asksaveasfilename\r\n\r\nroot = Tk()#initializes the Tk engine\r\nroot.withdraw()#hides default window\r\n\r\n\r\nscreen = display.set_mode((1024,768))\r\n\r\n#colors of tool boxes\r\nblack = (0,0,0)\r\nred = (255,0,0)\r\n\r\n#loading music and images\r\n\r\n#music\r\ninit()\r\nmixer.music.load(\"background/MarioSong.mp3\")\r\nmixer.music.play(-1) #loops music\r\n#background\r\nbackground = image.load(\"background/background.jpg\")\r\nbackground = transform.scale(background, (1024, 768))\r\npalette = image.load(\"background/palette.png\")\r\npalette = transform.smoothscale(palette, (200, 150))\r\n#stamps\r\nluigi = image.load(\"stamps/luigi.png\")\r\nmario = image.load(\"stamps/mario.png\")\r\npeach = image.load(\"stamps/peach.png\")\r\nbowser = image.load(\"stamps/bowser.png\")\r\nmushroom = image.load(\"stamps/mushroom.png\")\r\nboo = image.load(\"stamps/boo.png\")\r\n#toolbox pictures\r\npencil = image.load(\"tool icons/pencil.png\")\r\npencil = transform.smoothscale(pencil, (50, 50))\r\neraser = image.load(\"tool icons/eraser.png\")\r\neraser = transform.smoothscale(eraser, (50, 50))\r\nclear = image.load(\"tool icons/clear.png\")\r\nclear = transform.smoothscale(clear, (50, 50))\r\nbrush = image.load(\"tool icons/brush.png\")\r\nbrush = transform.smoothscale(brush, (50, 50))\r\neyedropper = image.load(\"tool icons/eyedropper.png\")\r\neyedropper = transform.smoothscale(eyedropper,(50,50))\r\nspraypaint = image.load(\"tool icons/spraypaint.png\")\r\nspraypaint = transform.smoothscale(spraypaint,(50,50))\r\nrectangle = image.load(\"tool icons/rectangle.png\")\r\nrectangle = transform.smoothscale(rectangle,(50,50))\r\nunfilledrectangle = image.load(\"tool icons/unfilledrectangle.png\")\r\nunfilledrectangle = transform.smoothscale(unfilledrectangle,(50,50))\r\ncircle = image.load(\"tool icons/circle.png\")\r\ncircle = transform.smoothscale(circle,(50,50))\r\nunfilledcircle = image.load(\"tool icons/unfilledcircle.png\")\r\nunfilledcircle = transform.smoothscale(unfilledcircle,(50,50))\r\nlines = image.load(\"tool icons/lines.png\")\r\nlines = transform.smoothscale(lines,(50,50))\r\npolygon = image.load(\"tool icons/polygon.png\")\r\npolygon = transform.smoothscale(polygon,(50,50))\r\nundo = image.load(\"tool icons/undo.png\")\r\nundo = transform.smoothscale(undo,(40,40))\r\nredo = image.load(\"tool icons/redo.png\")\r\nredo = transform.smoothscale(redo,(40,40))\r\nload = image.load(\"tool icons/load.png\")\r\nsave = image.load(\"tool icons/save.png\")\r\n#music pictures\r\nstop = image.load(\"tool icons/stop.png\")\r\npause = image.load(\"tool icons/pause.png\")\r\nplay = image.load(\"tool icons/play.png\")\r\n#text\r\npenciltext = image.load(\"text/penciltext.jpg\")\r\nerasertext = image.load(\"text/erasertext.jpg\")\r\ncleartext = image.load(\"text/cleartext.jpg\")\r\nbrushtext = image.load(\"text/brushtext.jpg\")\r\nstampstext = image.load(\"text/stampstext.jpg\")\r\neyedroppertext = image.load(\"text/eyedroppertext.jpg\")\r\nspraypainttext = image.load(\"text/spraypainttext.jpg\")\r\nrectangletext = image.load(\"text/rectangletext.jpg\")\r\nunfilledrectangletext = image.load(\"text/unfilledrectangletext.jpg\")\r\ncircletext = image.load(\"text/circletext.jpg\")\r\nunfilledcircletext = image.load(\"text/unfilledcircletext.jpg\")\r\nlinestext = image.load(\"text/linestext.jpg\")\r\npolygontext = image.load(\"text/polygontext.jpg\")\r\nstoptext = image.load(\"text/stoptext.jpg\")\r\npausetext = image.load(\"text/pausetext.jpg\")\r\nplaytext = image.load(\"text/playtext.jpg\")\r\nundotext = image.load(\"text/undotext.jpg\")\r\nredotext = image.load(\"text/redotext.jpg\")\r\nsavetext = image.load(\"text/savetext.jpg\")\r\nloadtext = image.load(\"text/loadtext.jpg\")\r\npalettetext = image.load(\"text/palettetext.jpg\")\r\n\r\n#bliting background images\r\n\r\n#background\r\nscreen.blit(background,(0,0))\r\nscreen.blit(palette,(5,615))\r\n#stamps\r\nscreen.blit(luigi,(220,600))\r\nscreen.blit(mario,(350,600))\r\nscreen.blit(peach,(466,600))\r\nscreen.blit(bowser,(567,600))\r\nscreen.blit(mushroom,(687,600))\r\nscreen.blit(boo,(795,600))\r\n#toolbox pictures\r\nscreen.blit(pencil,(15,80))\r\nscreen.blit(eraser,(85,80))\r\nscreen.blit(clear,(15,150))\r\nscreen.blit(brush,(85,150))\r\nscreen.blit(eyedropper,(15,220))\r\nscreen.blit(spraypaint,(85,220))\r\nscreen.blit(rectangle,(15,290))\r\nscreen.blit(unfilledrectangle,(85,290))\r\nscreen.blit(circle,(15,360))\r\nscreen.blit(unfilledcircle,(85,360))\r\nscreen.blit(lines,(15,430))\r\nscreen.blit(polygon,(85,430))\r\nscreen.blit(undo,(25,510))\r\nscreen.blit(redo,(85,510))\r\nscreen.blit(load,(960,370))\r\nscreen.blit(save,(960,440))\r\n#music pictures\r\nscreen.blit(stop,(960,100))\r\nscreen.blit(pause,(960,170))\r\nscreen.blit(play,(960,240))\r\n\r\n#making rects\r\n\r\n#background\r\ncanvasRect = Rect(160,80,775,500)\r\npaletteRect = Rect(5,615,200,150)\r\n#tools\r\npencilRect = Rect(15,80,50,50)\r\neraserRect = Rect(85,80,50,50)\r\nclearRect = Rect(15,150,50,50)\r\nbrushRect = Rect(85,150,50,50)\r\neyedropperRect = Rect(15,220,50,50)\r\nspraypaintRect = Rect(85,220,50,50)\r\nrectangleRect = Rect(15,290,50,50)\r\nunfilledrectangleRect = Rect(85,290,50,50)\r\ncircleRect = Rect(15,360,50,50)\r\nunfilledcircleRect = Rect(85,360,50,50)\r\nlinesRect = Rect(15,430,50,50)\r\npolygonRect = Rect(85,430,50,50)\r\nundoRect = Rect(25,510,40,40)\r\nredoRect = Rect(85,510,40,40)\r\ncolorsquareRect = Rect(5,590,200,20)\r\nsizesquareRect = Rect(910,600,102,102)\r\ndescriptionRect = Rect(753,8,244,64)\r\nloadRect = Rect(960,370,40,40)\r\nsaveRect = Rect(960,440,40,40)\r\n#music\r\nstopRect = Rect(960,100,40,40)\r\npauseRect = Rect(960,170,40,40)\r\nplayRect = Rect(960,240,40,40)\r\n#stamps\r\nluigiRect = Rect(220,600,122,143)\r\nmarioRect = Rect(350,600,108,143)\r\npeachRect = Rect(466,600,93,143)\r\nbowserRect = Rect(567,600,112,143)\r\nmushroomRect = Rect(687,600,100,143)\r\nbooRect = Rect(795,600,100,143)\r\n\r\ndraw.rect(screen,(0,0,0),(908,598,106,106)) #size circle border\r\ndraw.rect(screen,(0,0,0),(158,78,779,504)) #canvas border\r\ndraw.rect(screen,(255,255,255),canvasRect) #drawing the canvas\r\n\r\n#default\r\ncolor = (255,0,0)\r\ntool = \"pencil\"\r\nsize = 1\r\npencilsize = 1\r\n\r\n#polygon list\r\npointlist = [] #keeps points for polygon tool\r\n\r\n#undo/redo list\r\nundolist = []\r\nredolist = []\r\n#first frame in the undo list is blank\r\ncanvascopy = screen.subsurface(canvasRect).copy() #makes copy of blank screen\r\nundolist.append(canvascopy)\r\n\r\nrunning = True\r\nwhile running:\r\n for e in event.get():\r\n if e.type == QUIT:\r\n running = False\r\n\r\n if e.type == MOUSEBUTTONUP:\r\n if canvasRect.collidepoint(mx,my) and e.button==1:\r\n #when the mouse is finished the action it makes a copy and puts it in undo\r\n canvascopy = screen.subsurface(canvasRect).copy()\r\n undolist.append(canvascopy)\r\n\r\n if e.type==MOUSEBUTTONDOWN and e.button==1:\r\n if undoRect.collidepoint(mx,my):\r\n if len(undolist) > 1: #so it wont crash if you go less then 1\r\n redolist.append(undolist[-1]) #redo gets the most recent picture in the undo list\r\n undolist.remove(undolist[-1]) #undo gets rid of the most recent picture\r\n screen.blit(undolist[-1],(160,80)) #blits the second most recent picture\r\n if redoRect.collidepoint(mx,my):\r\n if len(redolist) > 0: #so it wont crash if you go less then 0\r\n screen.blit(redolist[-1],(160,80)) #redo first blits the picture\r\n undolist.append(redolist[-1]) #undo gets the picture\r\n redolist.remove(redolist[-1]) #redo gets rid of the picture\r\n if tool == \"polygon\":\r\n if canvasRect.collidepoint(mx,my):\r\n #adds vertices to the polygon where the mouse clicks\r\n draw.line(screen,color,(mx,my),(mx,my),1) #to show where the vertices are\r\n pointlist.append(mouse.get_pos())\r\n\r\n if e.type == MOUSEBUTTONDOWN:\r\n if e.button==1: #so scroll wheel does not interfere\r\n cx,cy = mouse.get_pos() #mouse button click x and y variable\r\n back = screen.copy()\r\n if e.button == 4: #when scrolled up\r\n if size<51: #max size\r\n size += 1\r\n if pencilsize<4: #max pencil size\r\n pencilsize += 1\r\n if e.button == 5: #when scrolled down\r\n #minimum size\r\n if size>1:\r\n size -= 1\r\n if pencilsize>1:\r\n pencilsize -= 1\r\n\r\n #-------------------------------\r\n mb = mouse.get_pressed()\r\n mx,my = mouse.get_pos() #mouse position variable\r\n\r\n if key.get_pressed()[K_ESCAPE]:\r\n break\r\n\r\n #drawing the rects\r\n draw.rect(screen,(black),pencilRect,2)\r\n draw.rect(screen,(black),eraserRect,2)\r\n draw.rect(screen,(black),clearRect,2)\r\n draw.rect(screen,(black),eyedropperRect,2)\r\n draw.rect(screen,(black),brushRect,2)\r\n draw.rect(screen,(black),spraypaintRect,2)\r\n draw.rect(screen,(black),rectangleRect,2)\r\n draw.rect(screen,(black),unfilledrectangleRect,2)\r\n draw.rect(screen,(black),circleRect,2)\r\n draw.rect(screen,(black),unfilledcircleRect,2)\r\n draw.rect(screen,(black),linesRect,2)\r\n draw.rect(screen,(black),polygonRect,2)\r\n draw.rect(screen,(black),undoRect,2)\r\n draw.rect(screen,(black),redoRect,2)\r\n draw.rect(screen,color,colorsquareRect)\r\n draw.rect(screen,(255,255,255),sizesquareRect)\r\n draw.rect(screen,(black),stopRect,2)\r\n draw.rect(screen,(black),pauseRect,2)\r\n draw.rect(screen,(black),playRect,2)\r\n draw.rect(screen,(255,255,255),descriptionRect)\r\n draw.rect(screen,(black),descriptionRect,2)\r\n draw.rect(screen,(black),saveRect,2)\r\n draw.rect(screen,(black),loadRect,2)\r\n draw.rect(screen,(black),marioRect,2)\r\n draw.rect(screen,(black),luigiRect,2)\r\n draw.rect(screen,(black),peachRect,2)\r\n draw.rect(screen,(black),bowserRect,2)\r\n draw.rect(screen,(black),mushroomRect,2)\r\n draw.rect(screen,(black),booRect,2)\r\n\r\n #show size as a circle\r\n if tool == \"pencil\":#pencil size is different\r\n draw.circle(screen,color,(961,651),pencilsize)\r\n else:\r\n draw.circle(screen,color,(961,651),size)\r\n\r\n #tool selection code\r\n if pencilRect.collidepoint(mx,my) or tool == \"pencil\":\r\n draw.rect(screen,(red),pencilRect,2)#outlined when hovered over or tool chosen\r\n screen.blit(penciltext,(755,10))#puts the description of the tool\r\n if mb[0] == 1: #if clicked tool changes\r\n tool = \"pencil\" \r\n if eraserRect.collidepoint(mx,my) or tool == \"eraser\":\r\n draw.rect(screen,(red),eraserRect,2)\r\n screen.blit(erasertext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"eraser\"\r\n if clearRect.collidepoint(mx,my) or tool == \"clear\":\r\n draw.rect(screen,(red),clearRect,2)\r\n screen.blit(cleartext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"clear\"\r\n if brushRect.collidepoint(mx,my) or tool == \"brush\":\r\n draw.rect(screen,(red),brushRect,2)\r\n screen.blit(brushtext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"brush\"\r\n if eyedropperRect.collidepoint(mx,my) or tool == \"eyedropper\":\r\n draw.rect(screen,(red),eyedropperRect,2)\r\n screen.blit(eyedroppertext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"eyedropper\"\r\n if spraypaintRect.collidepoint(mx,my) or tool == \"spraypaint\":\r\n draw.rect(screen,(red),spraypaintRect,2)\r\n screen.blit(spraypainttext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"spraypaint\"\r\n if rectangleRect.collidepoint(mx,my) or tool == \"rectangle\":\r\n draw.rect(screen,(red),rectangleRect,2)\r\n screen.blit(rectangletext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"rectangle\"\r\n if unfilledrectangleRect.collidepoint(mx,my) or tool == \"unfilledrectangle\":\r\n draw.rect(screen,(red),unfilledrectangleRect,2)\r\n screen.blit(unfilledrectangletext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"unfilledrectangle\"\r\n if circleRect.collidepoint(mx,my) or tool == \"circle\":\r\n draw.rect(screen,(red),circleRect,2)\r\n screen.blit(circletext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"circle\"\r\n if unfilledcircleRect.collidepoint(mx,my) or tool == \"unfilledcircle\":\r\n draw.rect(screen,(red),unfilledcircleRect,2)\r\n screen.blit(unfilledcircletext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"unfilledcircle\"\r\n if linesRect.collidepoint(mx,my) or tool == \"lines\":\r\n draw.rect(screen,(red),linesRect,2)\r\n screen.blit(linestext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"lines\"\r\n if polygonRect.collidepoint(mx,my) or tool == \"polygon\":\r\n draw.rect(screen,(red),polygonRect,2)\r\n screen.blit(polygontext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"polygon\"\r\n\r\n #stamp selection code\r\n if luigiRect.collidepoint(mx,my) or tool == \"luigi\":\r\n draw.rect(screen,(255,0,0),luigiRect,2)\r\n screen.blit(stampstext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"luigi\"\r\n if marioRect.collidepoint(mx,my) or tool == \"mario\":\r\n draw.rect(screen,(255,0,0),marioRect,2)\r\n screen.blit(stampstext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"mario\"\r\n if peachRect.collidepoint(mx,my) or tool == \"peach\":\r\n draw.rect(screen,(255,0,0),peachRect,2)\r\n screen.blit(stampstext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"peach\"\r\n if bowserRect.collidepoint(mx,my) or tool == \"bowser\":\r\n draw.rect(screen,(255,0,0),bowserRect,2)\r\n screen.blit(stampstext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"bowser\"\r\n if mushroomRect.collidepoint(mx,my) or tool == \"mushroom\":\r\n draw.rect(screen,(255,0,0),mushroomRect,2)\r\n screen.blit(stampstext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"mushroom\"\r\n if booRect.collidepoint(mx,my) or tool == \"boo\":\r\n draw.rect(screen,(255,0,0),booRect,2)\r\n screen.blit(stampstext,(755,10))\r\n if mb[0] == 1:\r\n tool = \"boo\"\r\n\r\n #music code\r\n if stopRect.collidepoint(mx,my):\r\n draw.rect(screen,(255,0,0),stopRect,2) #only outlines when hovered over\r\n screen.blit(stoptext,(755,10))\r\n if mb[0] == 1:\r\n mixer.music.stop()\r\n mixer.music.play(-1)\r\n mixer.music.pause()\r\n #first stops the music and pauses it so unpause button can work\r\n if pauseRect.collidepoint(mx,my):\r\n draw.rect(screen,(255,0,0),pauseRect,2)\r\n screen.blit(pausetext,(755,10)) #pauses music\r\n if mb[0] == 1:\r\n mixer.music.pause()\r\n if playRect.collidepoint(mx,my):\r\n draw.rect(screen,(255,0,0),playRect,2)\r\n screen.blit(playtext,(755,10))\r\n if mb[0] == 1:\r\n mixer.music.unpause() #unpauses music\r\n\r\n #undo/redo selection code\r\n if undoRect.collidepoint(mx,my):\r\n draw.rect(screen,(255,0,0),undoRect,2) #only outlines when hovered over\r\n screen.blit(undotext,(755,10))\r\n if redoRect.collidepoint(mx,my):\r\n draw.rect(screen,(255,0,0),redoRect,2)\r\n screen.blit(redotext,(755,10))\r\n\r\n #save/load\r\n if loadRect.collidepoint(mx,my):\r\n draw.rect(screen,(255,0,0),loadRect,2)\r\n screen.blit(loadtext,(755,10))\r\n if mb[0] == 1:\r\n result = askopenfilename(filetypes = [(\"Picture files\", \"*.bmp\")]) #asks user for file name\r\n print(result)\r\n if result != \"\":#if user didnt type anything dont continue\r\n screen.blit(image.load(result),(160,80)) #blits the picture onto the canvas\r\n if saveRect.collidepoint(mx,my):\r\n draw.rect(screen,(255,0,0),saveRect,2)\r\n screen.blit(savetext,(755,10))\r\n if mb[0] == 1:\r\n result = asksaveasfilename() #asks user for file name\r\n print(result)\r\n if result != \"\":#if user didnt type anything dont continue\r\n #adds bmp extension to end\r\n #only saves the canvas\r\n image.save(screen.subsurface(canvasRect), result+\".bmp\")\r\n \r\n #color palette selection\r\n if paletteRect.collidepoint(mx,my):\r\n screen.blit(palettetext,(755,10))\r\n if mb[0] == 1:\r\n color = screen.get_at((mx,my))\r\n\r\n #when the mouse is on the canvas\r\n if mb[2] == 1 and canvasRect.collidepoint(mx,my): #left click to make the polygon\r\n screen.set_clip(canvasRect)\r\n if tool == \"polygon\":\r\n if len(pointlist) > 1: #if point is 1 cant draw polygon\r\n draw.polygon(screen,color,pointlist,size)\r\n pointlist = [] #resets when polygon is made\r\n screen.set_clip(None)\r\n\r\n if mb[0] == 1 and canvasRect.collidepoint(mx,my):\r\n screen.set_clip(canvasRect)\r\n #tools\r\n if tool == \"pencil\":\r\n draw.line(screen,color,(omx,omy),(mx,my),pencilsize)\r\n if tool == \"eraser\":\r\n dist = max(hypot(mx-omx,my-omy),1) #finds distance from old mouse position to mouse position\r\n sx = (mx-omx)/dist #small x\r\n sy = (my-omy)/dist #small y\r\n #for every pixel in distance make a circle\r\n for i in range(int(dist)):\r\n draw.circle(screen,(255,255,255),(int(omx+sx*i),int(omy+sy*i)),size)\r\n if tool == \"clear\":\r\n draw.rect(screen,(255,255,255),canvasRect) #makes a white square the size of the background\r\n if tool == \"brush\":\r\n #same as eraser\r\n dist = max(hypot(mx-omx,my-omy),1)\r\n sx = (mx-omx)/dist\r\n sy = (my-omy)/dist\r\n for i in range(int(dist)):\r\n draw.circle(screen,color,(int(omx+sx*i),int(omy+sy*i)),size)\r\n if tool == \"eyedropper\":\r\n color = screen.get_at((mx,my))\r\n if tool == \"spraypaint\":\r\n for i in range(size):#to make spraypaint faster\r\n #makes random x and y points in the size of the circle\r\n rx = randint(mx-size,mx+size)\r\n ry = randint(my-size,my+size)\r\n dist=((mx-rx)**2+(my-ry)**2)**0.5\r\n #if distance of dots is more then size of circle(radius) it doesnt make dots\r\n if dist<=size:\r\n draw.line(screen,color,(rx,ry),(rx,ry))\r\n if tool == \"rectangle\":\r\n screen.blit(back,(0,0))\r\n #starts at where the mouse clicks\r\n #length and width is mouse position minus click position\r\n r = Rect(cx,cy,mx-cx,my-cy)\r\n r.normalize()\r\n draw.rect(screen,color,(r))\r\n if tool == \"unfilledrectangle\":\r\n screen.blit(back,(0,0))\r\n # draws 4 rectangles to make it seem like 1 rectangle\r\n # +1 size so it works if size is 1\r\n # +1 to mx and my because it is drawn 1 pixel off\r\n # minus size//2 to draw the rectangle in the middle of the mouse\r\n # width of rectangle depends on size\r\n r1 = Rect(cx-(size//2),cy-(size//2),size+1,my+1-cy)\r\n r1.normalize()\r\n r2 = Rect(cx-(size//2),cy-(size//2),mx+1-cx,size+1)\r\n r2.normalize()\r\n r3 = Rect(mx-(size//2),my-(size//2),size+1,cy-my+1)\r\n r3.normalize()\r\n r4 = Rect(mx-(size//2),my-(size//2),cx-mx+1,size+1)\r\n r4.normalize()\r\n draw.rect(screen,color,(r1)) #makes rectangle from my to cy\r\n draw.rect(screen,color,(r2)) #makes rectangle from mx to cx\r\n draw.rect(screen,color,(r3)) #makes rectangle from cy to my\r\n draw.rect(screen,color,(r4)) #makes rectangle from cx to mx\r\n #make rectangles in each corner to get rid of missing corners\r\n draw.rect(screen,color,(cx-(size//2),cy-(size//2),size+1,size+1))\r\n draw.rect(screen,color,(mx-(size//2),my-(size//2),size+1,size+1))\r\n draw.rect(screen,color,(cx-(size//2),my-(size//2),size+1,size+1))\r\n draw.rect(screen,color,(mx-(size//2),cy-(size//2),size+1,size+1))\r\n if tool == \"circle\":\r\n screen.blit(back,(0,0))\r\n circlesize = Rect(cx,cy,mx-cx,my-cy)\r\n circlesize.normalize()\r\n draw.ellipse(screen,color,circlesize)\r\n if tool == \"unfilledcircle\":\r\n screen.blit(back,(0,0))\r\n unfilledcirclesize = Rect(cx,cy,mx-cx,my-cy)\r\n unfilledcirclesize.normalize()\r\n #shakes the circle\r\n #makes circles 1 pixel off to the first one\r\n unfilledcirclesize1 = Rect(cx+1,cy+1,mx-cx,my-cy)\r\n unfilledcirclesize1.normalize()\r\n unfilledcirclesize2 = Rect(cx-1,cy+1,mx-cx,my-cy)\r\n unfilledcirclesize2.normalize()\r\n unfilledcirclesize3 = Rect(cx+1,cy-1,mx-cx,my-cy)\r\n unfilledcirclesize3.normalize()\r\n unfilledcirclesize4 = Rect(cx-1,cy-1,mx-cx,my-cy)\r\n unfilledcirclesize4.normalize()\r\n if size*2 2:#to make larger sizes look smoother\r\n draw.ellipse(screen,color,unfilledcirclesize1,size)\r\n draw.ellipse(screen,color,unfilledcirclesize2,size)\r\n draw.ellipse(screen,color,unfilledcirclesize3,size)\r\n draw.ellipse(screen,color,unfilledcirclesize4,size)\r\n else: #if width too big, makes a normal ellipse\r\n draw.ellipse(screen,color,unfilledcirclesize)\r\n if tool == \"lines\":\r\n screen.blit(back,(0,0))\r\n draw.line(screen,color,(cx,cy),(mx,my),size)\r\n #stamps\r\n if tool == \"mario\":\r\n screen.blit(back,(0,0))\r\n screen.blit(mario, (mx-54,my-71))\r\n if tool == \"luigi\":\r\n screen.blit(back,(0,0))\r\n screen.blit(luigi, (mx-61,my-71))\r\n if tool == \"peach\":\r\n screen.blit(back,(0,0))\r\n screen.blit(peach, (mx-46,my-71))\r\n if tool == \"bowser\":\r\n screen.blit(back,(0,0))\r\n screen.blit(bowser, (mx-56,my-71))\r\n if tool == \"mushroom\":\r\n screen.blit(back,(0,0))\r\n screen.blit(mushroom, (mx-50,my-71))\r\n if tool == \"boo\":\r\n screen.blit(back,(0,0))\r\n screen.blit(boo, (mx-50,my-71))\r\n screen.set_clip(None)\r\n\r\n omx,omy = mx,my #old mx and old my\r\n #-------------------------------\r\n display.flip()\r\nquit()\r\n\r\n\r\n\r\n\r\n","sub_path":"paintproject.py","file_name":"paintproject.py","file_ext":"py","file_size_in_byte":22811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"176461116","text":"from flask import current_app as app\nfrom sqlalchemy import func\nfrom .models import db, Caso\nfrom flask import Blueprint, render_template\nfrom flask_restful import Resource, Api, reqparse\n\nimport json\n\ncasos_bp = Blueprint('casos_bp', __name__)\n\n\n@casos_bp.route('/casos', methods=['GET'])\ndef obterCasos():\n parser = reqparse.RequestParser()\n parser.add_argument('estado', type=str)\n parser.add_argument('cidade', type=str)\n\n par_estado = parser.parse_args().get('estado', None)\n par_cidade = parser.parse_args().get('cidade', None)\n\n if par_cidade:\n casos = db.session.query(\n Caso.estado,\n Caso.cidade,\n func.sum(Caso.confirmados).label('total')\n ).filter(\n Caso.cidade==par_cidade\n ).group_by(\n Caso.estado,\n Caso.cidade\n ).all()\n elif par_estado:\n casos = db.session.query(\n Caso.estado,\n Caso.cidade,\n func.sum(Caso.confirmados).label('total')\n ).filter(\n Caso.estado==par_estado\n ).group_by(\n Caso.estado,\n Caso.cidade\n ).all()\n else:\n casos = db.session.query(\n Caso.estado,\n Caso.cidade,\n func.sum(Caso.confirmados).label('total')\n ).group_by(\n Caso.estado,\n Caso.cidade\n ).all()\n\n dados = {'casos': []}\n\n for caso in casos:\n dados['casos'].append({\n \"estado\": caso.estado,\n \"cidade\": caso.cidade,\n \"total\": caso.total\n })\n\n return json.dumps(dados)\n\n@casos_bp.route('/cidades', methods=['GET'])\ndef obterCidades():\n casos = db.session.query(\n Caso.estado,\n Caso.cidade\n ).group_by(\n Caso.estado,\n Caso.cidade\n ).all()\n\n dados = {'casos': []}\n\n for caso in casos:\n dados['casos'].append({\n \"estado\": caso.estado,\n \"cidade\": caso.cidade\n })\n\n return json.dumps(dados)\n\n","sub_path":"app/casos/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"101996099","text":"import logging\nlogger = logging.getLogger('isitfit')\n\nimport pandas as pd\nfrom tabulate import tabulate\n\n# https://pypi.org/project/termcolor/\nfrom termcolor import colored\n\n\nclass UtilizationListener:\n\n def __init__(self, emailTo, ctx):\n # iterate over all ec2 instances\n self.sum_capacity = 0\n self.sum_used = 0\n self.df_all = []\n self.table = None # will contain the final table after calling `after_all`\n self.emailTo = emailTo\n self.ctx = ctx\n\n\n def per_ec2(self, ec2_obj, ec2_df, mm, ddg_df):\n \"\"\"\n Listener function to be called upon the download of each EC2 instance's data\n ec2_obj - boto3 resource\n ec2_df - pandas dataframe with data from cloudwatch+cloudtrail\n mm - mainManager class\n ddg_df - dataframe of data from datadog: {cpu,ram}-{max,avg}\n \"\"\"\n # results: 2 numbers: capacity (USD), used (USD)\n res_capacity = (ec2_df.nhours*ec2_df.cost_hourly).sum()\n\n if 'ram_used_avg.datadog' in ec2_df.columns:\n # use both the CPU Average from cloudwatch and the RAM average from datadog\n utilization_factor = ec2_df[['Average', 'ram_used_avg.datadog']].mean(axis=1, skipna=True)\n else:\n # use only the CPU average from cloudwatch\n utilization_factor = ec2_df.Average\n\n res_used = (ec2_df.nhours*ec2_df.cost_hourly*utilization_factor/100).sum()\n #logger.debug(\"res_capacity=%s, res_used=%s\"%(res_capacity, res_used))\n\n self.sum_capacity += res_capacity\n self.sum_used += res_used\n self.df_all.append({'instance_id': ec2_obj.instance_id, 'capacity': res_capacity, 'used': res_used})\n\n\n def after_all(self, n_ec2_total, mm, n_ec2_analysed):\n # for debugging\n df_all = pd.DataFrame(self.df_all)\n logger.debug(\"\\ncapacity/used per instance\")\n logger.debug(df_all)\n logger.debug(\"\\n\")\n\n cwau_val = 0\n if self.sum_capacity!=0:\n cwau_val = self.sum_used/self.sum_capacity*100\n\n cwau_color = 'orange'\n if cwau_val >= 70:\n cwau_color = 'green'\n elif cwau_val <= 30:\n cwau_color = 'red'\n\n dt_start = mm.StartTime.strftime(\"%Y-%m-%d\")\n dt_end = mm.EndTime.strftime(\"%Y-%m-%d\")\n \n self.table = [\n {'color': '', 'label': \"Start date\", 'value': \"%s\"%dt_start },\n {'color': '', 'label': \"End date\", 'value': \"%s\"%dt_end },\n {'color': '', 'label': \"EC2 machines (total)\", 'value': \"%i\"%n_ec2_total },\n {'color': '', 'label': \"EC2 machines (analysed)\", 'value': \"%i\"%n_ec2_analysed },\n {'color': 'cyan', 'label': \"Billed cost\", 'value': \"%0.0f $\"%self.sum_capacity },\n {'color': 'cyan', 'label': \"Used cost\", 'value': \"%0.0f $\"%self.sum_used },\n {'color': cwau_color, 'label': \"CWAU (Used/Billed)\", 'value': \"%0.0f %%\"%cwau_val },\n ]\n\n\n def display_all(self, *args, **kwargs):\n def get_row(row):\n def get_cell(i):\n retc = row[i] if not row['color'] else colored(row[i], row['color'])\n return retc\n \n retr = [get_cell('label'), get_cell('value')]\n return retr\n\n dis_tab = [get_row(row) for row in self.table]\n\n # logger.info(\"Summary:\")\n logger.info(\"Cost-Weighted Average Utilization (CWAU) of the AWS EC2 account:\")\n logger.info(\"\")\n logger.info(tabulate(dis_tab, headers=['Field', 'Value']))\n logger.info(\"\")\n logger.info(\"For reference:\")\n logger.info(colored(\"* CWAU >= 70% is well optimized\", 'green'))\n logger.info(colored(\"* CWAU <= 30% is underused\", 'red'))\n\n def share_email(self, *args, **kwargs):\n # check if email requested\n if self.emailTo is None:\n return\n\n if len(self.emailTo)==0:\n return\n\n from ..emailMan import EmailMan\n em = EmailMan(\n dataType='cost analyze',\n dataVal={'table': self.table},\n ctx=self.ctx\n )\n em.send(self.emailTo)\n\n\n","sub_path":"isitfit/cost/utilizationListener.py","file_name":"utilizationListener.py","file_ext":"py","file_size_in_byte":4002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"51451338","text":"from __future__ import division\nfrom __future__ import print_function\n\nimport time\nimport os\n\n# Train on CPU (hide GPU) due to memory constraints\nos.environ['CUDA_VISIBLE_DEVICES'] = \"\"\n\nimport tensorflow as tf\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom gcn_gae_opinion.metrics import masked_mae_np\nfrom gcn_gae_opinion.optimizer import OptimizerAE, OptimizerDir, OptimizerDir2\nfrom gcn_gae_opinion.input_data import load_data_sub\nfrom gcn_gae_opinion.model import GCNModelAE, GCNModelDir, GCNModelDir2\nfrom gcn_gae_opinion.preprocessing import preprocess_graph, construct_feed_dict_sub, sparse_to_tuple, mask_test_syn_sub, mask_test_syn_sub_dif, mask_test_syn_sub_dif2\nfrom gcn_gae_opinion.metrics import masked_dir_error, masked_dir_error2\n\n# Settings\nflags = tf.app.flags\nFLAGS = flags.FLAGS\nflags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')\nflags.DEFINE_integer('epochs', 500, 'Number of epochs to train.')\nflags.DEFINE_integer('hidden1', 32, 'Number of units in hidden layer 1.')\nflags.DEFINE_integer('hidden2', 1, 'Number of units in hidden layer 2, P in our paper.')\nflags.DEFINE_float('weight_decay', 0., 'Weight for L2 loss on embedding matrix.')\nflags.DEFINE_float('dropout', 0.5, 'Dropout rate (1 - keep probability).')\n\nflags.DEFINE_string('model', 'gcn_Dir', 'Model string.')\nflags.DEFINE_string('dataset', 'epinion', 'Dataset string.')\nflags.DEFINE_integer('features', 0, 'Whether to use features (1) or not (0).')\n\n# flags.DEFINE_float('alpha_0', 3, 'prior of Beta distribution.')\n# flags.DEFINE_float('beta_0', 6.9, 'prior of Beta distribution.')\nflags.DEFINE_float('p_encode', 0.01, 'trade off parameter of auto_encode.')\nflags.DEFINE_float('p_kl', 0.01, 'trade off parameter of KL-divergence.')\nflags.DEFINE_integer('KL_m', 10, 'approximate of the infinite sum in KLD.')\nflags.DEFINE_float('test_rat', 0.2, 'test number of dataset.')\nflags.DEFINE_integer('T', 6, 'time winidow.')\n\nmodel_str = FLAGS.model\ndataset_str = FLAGS.dataset\n\nseed = 1234\nnp.random.seed(seed)\ntf.set_random_seed(seed)\nprint(seed)\n# Load data5\n# adj, features = load_data(dataset_str)\nadj, features = load_data_sub()\n# Store original adjacency matrix (without diagonal entries) for later\nadj_orig = adj\nadj_orig = adj_orig - sp.dia_matrix((adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)\nadj_orig.eliminate_zeros()\nadj_train = adj\n\nlabel_1, label_2, label_3, train_mask, test_mask, uncertainty = mask_test_syn_sub_dif2(FLAGS.test_rat, T=FLAGS.T, seed=0)\n\nif FLAGS.features == 0:\n features = sp.identity(label_1.shape[0]) # featureless\n\n# Some preprocessing\nadj_norm = preprocess_graph(adj)\n\n# Define placeholders\nplaceholders = {\n 'features': tf.sparse_placeholder(tf.float32),\n 'adj': tf.sparse_placeholder(tf.float32),\n 'adj_orig': tf.sparse_placeholder(tf.float32),\n 'dropout': tf.placeholder_with_default(0., shape=()),\n 'labels_1': tf.placeholder(tf.float32, shape=(None, label_1.shape[1])),\n 'labels_2': tf.placeholder(tf.float32, shape=(None, label_1.shape[1])),\n 'omega_test': tf.placeholder(tf.float32, shape=(None, label_1.shape[1])),\n 'labels_mask': tf.placeholder(tf.int32),\n 'labels_3': tf.placeholder(tf.float32, shape=(None, label_1.shape[1])),\n}\n\nnum_nodes = adj.shape[0]\n\nfeatures = sparse_to_tuple(features.tocoo())\nnum_features = features[2][1]\nfeatures_nonzero = features[1].shape[0]\n\n# Create model\nmodel = None\nif model_str == 'gcn_ae':\n model = GCNModelAE(placeholders, num_features, features_nonzero)\nelif model_str == 'gcn_Dir':\n model = GCNModelDir2(placeholders, num_features, num_nodes, features_nonzero)\n\npos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()\nnorm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2)\n\n# Optimizer\nwith tf.name_scope('optimizer'):\n if model_str == 'gcn_ae':\n opt = OptimizerAE(preds=model.reconstructions,\n labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'],\n validate_indices=False), [-1]),\n pos_weight=pos_weight,\n norm=norm)\n elif model_str == 'gcn_Dir':\n opt = OptimizerDir2(\n model=model,\n label_1=placeholders['labels_1'], label_2=placeholders['labels_2'],\n mask=placeholders['labels_mask'], label_3=placeholders['labels_3'], )\n\n# Initialize session\nsess = tf.Session()\n\nadj_label = adj_train + sp.eye(adj_train.shape[0])\n# adj_label = adj_train\nadj_label = sparse_to_tuple(adj_label)\n\nresult = []\nfor k in range(10):\n sess.run(tf.global_variables_initializer())\n label_1, label_2, label_3, train_mask, test_mask, uncertainty = mask_test_syn_sub_dif2(FLAGS.test_rat, T=FLAGS.T, seed=k)\n for epoch in range(FLAGS.epochs):\n t = time.time()\n # Construct feed dictionary\n feed_dict = construct_feed_dict_sub(adj_norm, adj_label, features, placeholders, label_1, label_2, train_mask,\n uncertainty)\n feed_dict.update({placeholders['dropout']: FLAGS.dropout})\n # Run training epoch\n outs = sess.run([opt.opt_op, opt.cost], feed_dict=feed_dict)\n\n # Compute trining loss\n train_cost = outs[1]\n # print(train_cost)\n if np.mod(epoch + 1, 100) == 0:\n # print(\"epoch:\", epoch + 1, \"Loss:\", train_cost)\n feed_dict = construct_feed_dict_sub(adj_norm, adj_label, features, placeholders, label_1, label_2,\n test_mask,\n uncertainty)\n feed_dict.update({placeholders['dropout']: 0.0})\n # Run single weight update\n outs = sess.run([opt.cost, model.belief1, model.belief2, model.belief3, model.uncertainty], feed_dict=feed_dict)\n # error = masked_dir_error(outs[1], outs[2], outs[3], label_1, label_2, label_3, test_mask)\n model2_error = masked_dir_error2(outs[1], outs[2], outs[3], outs[3], label_1, label_2, label_3, uncertainty, test_mask)\n # Compute test loss\n print(\"epoch:\", epoch + 1, \"Loss:\", \"{:.3f}\".format(outs[0]), \"error:\", \"{:.2f}\".format(model2_error))\n # feed_dict = construct_feed_dict_sub(adj_norm, adj_label, features, placeholders, label_1, label_2,\n # test_mask,\n # uncertainty)\n # feed_dict.update({placeholders['dropout']: 0.0})\n # test_cost = sess.run(opt.cost, feed_dict=feed_dict)\n result.append(model2_error)\n print(\"Optimization Finished!\")\nprint(\"final loss:\", np.mean(result))\n","sub_path":"gcn_gae_opinion/gcn_Dir2.py","file_name":"gcn_Dir2.py","file_ext":"py","file_size_in_byte":6742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"408278481","text":"import pandas as pd\nimport numpy as np\nfrom sklearn import linear_model, ensemble, naive_bayes\nfrom sklearn.utils import shuffle\nimport itertools\nimport math\nfrom sklearn.externals import joblib\n\n\ndef late30(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] >= 30:\n count += 1\n return count / float(len(y_true))\n\n\ndef late20(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] >= 20:\n count += 1\n return count / float(len(y_true))\n\n\ndef late15(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] >= 15:\n count += 1\n return count / float(len(y_true))\n\n\ndef late10(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] >= 10:\n count += 1\n return count / float(len(y_true))\n\n\ndef late5(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] >= 5:\n count += 1\n return count / float(len(y_true))\n\n\ndef early30(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] <= -30:\n count += 1\n return count / float(len(y_true))\n\n\ndef early20(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] <= -20:\n count += 1\n return count / float(len(y_true))\n\n\ndef early15(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] <= -15:\n count += 1\n return count / float(len(y_true))\n\n\ndef early10(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] <= -10:\n count += 1\n return count / float(len(y_true))\n\n\ndef standard(y_true, y_pred):\n count = 0\n for i in range(len(y_true)):\n if y_pred[i] - y_true[i] <= 10 and y_pred[i] - y_true[i] >= -20:\n count += 1\n return count / float(len(y_true))\n\n\ndef gbdt_regression(clf, df_train, df_test):\n feature_columns = ['realtimediff', 'ptdhour', 'tpm', 'capnum', 'ckinum', 'far', 'cnt', 'isinternational',\n 'ismulti1st',\n 'dayofweek', 'waypoints_cnt', 'zmax', 'zmedian',\n 'real_mean_max', 'real_mean_min', 'real_mean_median', 'dev_ratio_pct',\n 'has_atd', 'atd_eqp_pct', 'atd_adept_pct', 'atd_insert_real', 'has_boarding',\n 'boarding_eqp_pct', 'boarding_adept_pct', 'boarding_insert_real', 'has_sliding',\n 'sliding_eqp_pct', 'sliding_adept_pct', 'sliding_insert_real']\n array_train_X = df_train[feature_columns].values\n array_train_Y = df_train['realpasstime'].values\n array_test_X = df_test[feature_columns].values\n array_test_Y = df_test['realpasstime'].values\n clf.fit(array_train_X, array_train_Y)\n r2score = clf.score(array_test_X, array_test_Y)\n y_pred = clf.predict(array_test_X)\n\n Late30 = late30(array_test_Y, y_pred)\n Late20 = late20(array_test_Y, y_pred)\n Late15 = late15(array_test_Y, y_pred)\n Late10 = late10(array_test_Y, y_pred)\n Late5 = late5(array_test_Y, y_pred)\n\n Early30 = early30(array_test_Y, y_pred)\n Early20 = early20(array_test_Y, y_pred)\n Early15 = early15(array_test_Y, y_pred)\n Early10 = early10(array_test_Y, y_pred)\n importance = clf.feature_importances_\n\n Standard = standard(array_test_Y, y_pred)\n print('estimators=', clf.n_estimators, 'learn_rate=', clf.learning_rate,\n 'depth=', clf.max_depth,\n 'RMSE=', math.sqrt(sum((array_test_Y - y_pred) ** 2) / len(y_pred)),\n 'MAE=', np.mean(abs(array_test_Y - y_pred)), 'R2=', r2score, '\\n', importance)\n\n\nif __name__ == '__main__':\n df = pd.read_csv('new_etd1.csv')\n l = len(df)\n df_train = df.head(int(0.007 * l))\n df_test = df.tail(int(0.003 * l))\n for [e, r, d] in itertools.product([300, 400, 500],\n [0.1, 0.2, 0.3, 0.4, 0.5], [5, 6, 7]):\n reg_ = ensemble.GradientBoostingRegressor(n_estimators=e, learning_rate=r, max_depth=d, loss='lad')\n gbdt_regression(reg_, df_train, df_test)\n","sub_path":"src/model/select_model.py","file_name":"select_model.py","file_ext":"py","file_size_in_byte":4211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"148067065","text":"#expression utilities\nimport numpy as np\nimport math\nimport scipy as sp\nimport random\n\ndef convertFormNOTUSED(curtimes):\n \"\"\"converts form\n Args:\n curtimes:\n Returns:\n yvallist: \n \"\"\" \t\t\n tyvallist = []\n for cind,cdata in enumerate(usedata):\n calldata = []\n for tind,ttime in enumerate(curtimes):\n calldata.extend(usedata[cind][ttime])\n tyvallist.append(list(calldata))\n #assert len(calldata) == len(usetimes)\n return tyvallist\n\ndef makeSingleData(usedata,usetimes):\n \"\"\"makes single data\n Args:\n usedata:\n usetimes:\n Returns:\n singledata,singledatadict:\n \"\"\"\n singledata, singledatadict = [],[]\n for cind,cdata in enumerate(usedata):\n cavgdata = [np.median(cdata[ttime]) for tind,ttime in enumerate(usetimes)]\n singledata.append(list(cavgdata))\n singledatadict.append({tval:cavgdata[tind] for tind,tval in enumerate(usetimes)})\n return singledata,singledatadict\n\n\ndef shiftData(rawusedata,mintime):\n \"\"\"shift data if not done already\n Args:\n rawusedata:\n mintime:\n Returns:\n modusedata:\n \"\"\"\n modusedata = []\n for cind,cdata in enumerate(rawusedata):\n begval = np.median(cdata[mintime])\n shiftdict = {ttime:[tval-begval for tval in cdata[ttime]] for ttime in cdata.keys()}\n modusedata.append(dict(shiftdict))\n return modusedata\n","sub_path":"lib/geneUtilities.py","file_name":"geneUtilities.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"306644764","text":"from django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import update_session_auth_hash\nfrom django.utils.encoding import escape_uri_path\nfrom django.http import HttpResponse, JsonResponse\nfrom wsgiref.util import FileWrapper\nfrom .models import *\nfrom os import listdir, remove\nimport mimetypes\nimport re\nfrom pool.models import *\nfrom exam.models import *\n\n# Create your views here.\napp_name = \"control\"\n\n\n@login_required()\ndef download(request):\n operator = User.objects.get(username=request.user.username)\n access = operator.profile.access\n if not operator.profile.view_access.get('download'):\n return redirect('home:dashboard')\n all_downloads = {\n '考试端': Download.objects.filter(category='E').order_by('-id'),\n '服务端': Download.objects.filter(category='S').order_by('-id'),\n '阅卷端': Download.objects.filter(category='G').order_by('-id'),\n '其他': Download.objects.filter(category='').order_by('-id'),\n }\n if not access.get('private_download', False):\n all_downloads = {\n '考试端': Download.objects.filter(category='E', file_type='公开').order_by('-id'),\n '服务端': Download.objects.filter(category='S', file_type='公开').order_by('-id'),\n '阅卷端': Download.objects.filter(category='G', file_type='公开').order_by('-id'),\n '其他': Download.objects.filter(category='', file_type='公开').order_by('-id'),\n }\n context = {\n 'can_manage_download': access.get('manage_download', False),\n 'can_clean_cache': access.get('clean_cache', False),\n 'all_downloads': all_downloads,\n }\n if request.method == 'POST':\n name = request.POST.get('name')\n version = request.POST.get('version')\n description = request.POST.get('description')\n update_log_text = request.POST.get('update_log')\n file = request.FILES['file']\n file_type = request.POST.get('file_type')\n file_category = request.POST.get('file_category')\n file_category = '' if file_category == 'O' else file_category\n new_download = Download(\n name=name,\n version=version,\n description=description,\n update_log=update_log_text,\n file=file,\n file_type=file_type,\n category=file_category\n )\n new_download.save()\n return redirect('control:download')\n return render(request, 'control/download.html', context)\n\n\ndef get_download(request, file_id):\n this_file = Download.objects.get(id=file_id)\n filename_as_list = this_file.file.name.split('.')\n filename_as_list[0] = this_file.name\n filename = '.'.join(filename_as_list)\n file_path = 'media/{}'.format(this_file.file.name)\n response = HttpResponse(FileWrapper(\n open(file_path, 'rb')), content_type=mimetypes.guess_type(file_path))\n response['Content-Disposition'] = \"attachment; filename*=utf-8''{}\".format(\n escape_uri_path(filename))\n return response\n\n\n@login_required()\ndef delete_download(request, file_id):\n this_file = Download.objects.get(id=file_id)\n this_file.delete()\n return redirect('control:download')\n\n\n@login_required()\ndef account(request):\n operator = User.objects.get(username=request.user.username)\n access = operator.profile.access\n context = {\n 'can_edit_info': access.get('edit_info', False),\n 'can_manage_users': access.get('manage_users', False) or operator.is_superuser,\n 'is_superuser': operator.is_superuser,\n 'tag_readonly': '' if access.get('edit_info', False) else 'readonly',\n 'all_users': User.objects.all().order_by('username'),\n }\n if request.method == 'POST':\n if 'btn_password' in request.POST:\n # Verify old password\n old_pwd = request.POST.get('old_password')\n new_pwd = request.POST.get('new_password')\n if operator.check_password(old_pwd):\n operator.set_password(new_pwd)\n operator.save()\n update_session_auth_hash(request, operator)\n messages.success(request, '更改成功！')\n else:\n messages.error(request, '您的旧密码输入有误！')\n elif 'btn_info' in request.POST:\n operator.profile.full_name = request.POST.get('full_name')\n operator.profile.area = request.POST.get('area')\n operator.profile.school = request.POST.get('school')\n operator.profile.department = request.POST.get('department')\n operator.profile.save()\n elif 'btn_add_inf' in request.POST:\n user_id = int(request.POST.get('inf_user'))\n username = request.POST.get('inf_username')\n area = request.POST.get('inf_area').strip()\n school = request.POST.get('inf_school').strip()\n department = request.POST.get('inf_department').strip()\n full_name = request.POST.get('inf_full_name').strip()\n # Validate username\n r = re.match('[A-Za-z]+[A-Za-z0-9_]*', username)\n if not r:\n messages.error(request, '用户名只能包括大小写英文字母、数字和下划线！')\n return redirect('control:account')\n password = 'myweixun001'\n success_msg = '操作成功！'\n if user_id == 0:\n if User.objects.filter(username=username).exists():\n messages.error(request, '此用户名已存在！')\n return redirect('control:account')\n new_user = User.objects.create_user(\n username.strip(), None, password)\n new_user.save()\n success_msg = '成功创建用户！用户名：{}，初始密码：{}'.format(\n username, password)\n else:\n new_user = User.objects.get(id=user_id)\n if User.objects.exclude(id=user_id).filter(username=username).exists():\n messages.error(request, '此用户名已存在！')\n return redirect('control:account')\n new_user.username = username\n # Set profile\n new_profile = new_user.profile\n if full_name:\n new_profile.full_name = full_name\n if area:\n new_profile.area = area\n if school:\n new_profile.school = school\n if department:\n new_profile.department = department\n new_profile.access = dict(\n view_pool='cb_view_pool' in request.POST,\n view_catalog='cb_view_catalog' in request.POST,\n manage_catalog='cb_manage_catalog' in request.POST,\n view_problems='cb_view_problems' in request.POST,\n manage_problems='cb_manage_problems' in request.POST,\n group_actions='cb_group_actions' in request.POST,\n\n view_exam='cb_view_exam' in request.POST,\n manage_strategy='cb_manage_strategy' in request.POST,\n generate_exam='cb_generate_exam' in request.POST,\n download_exam='cb_download_exam' in request.POST,\n manage_exam='cb_manage_exam' in request.POST,\n\n view_certification='cb_view_certification' in request.POST,\n manage_certification='cb_manage_certification' in request.POST,\n download_certification='cb_download_certification' in request.POST,\n manage_code='cb_manage_code' in request.POST,\n\n edit_info='cb_edit_info' in request.POST,\n manage_users='cb_manage_users' in request.POST,\n\n view_download='cb_view_download' in request.POST,\n manage_download='cb_manage_download' in request.POST,\n private_download='cb_private_download' in request.POST,\n clean_cache='cb_clean_cache' in request.POST,\n )\n new_profile.save()\n messages.success(request, success_msg)\n elif 'btn_init_pwd' in request.POST:\n this_user = User.objects.get(id=int(request.POST.get('inf_user')))\n init_pwd = 'myweixun001'\n this_user.set_password(init_pwd)\n this_user.save()\n messages.success(request, '密码已成功初始化为{}'.format(init_pwd))\n elif 'btn_del_user' in request.POST:\n this_user = User.objects.get(id=int(request.POST.get('inf_user')))\n if this_user == operator:\n messages.error(request, '您无法删除自己！如需修改请至后台！')\n else:\n this_user.delete()\n messages.success(request, '删除成功！')\n return redirect('control:account')\n return render(request, 'control/account.html', context)\n\n\n@login_required()\ndef clean_cache(request):\n def clean(object_class, attribute_name, folder_name):\n object_files = set(map(lambda x: getattr(\n x, attribute_name).name, object_class.objects.all()))\n folder_files = set(\n map(lambda x: f'{folder_name}/{x}', listdir(f'media/{folder_name}/')))\n caches = folder_files - object_files\n for cache in caches:\n remove(f'media/{cache}')\n\n clean(Download, 'file', 'download')\n clean(Exam, 'package', 'exam')\n clean(StudentListFile, 'student_list', 'list')\n clean(GovernmentCertification, 'package', 'gov')\n clean(Advertisement, 'image', 'ad')\n clean(Agreement, 'image', 'agreement')\n clean(Chapter, 'image', 'chapter')\n # Problem folder\n # All questions\n all_problem_files = set()\n for q_class in [\n MultipleChoice, MultipleResponse, TrueOrFalse, TextBlank, NumericBlank, Description,\n ]:\n for attr in ('image', 'attachment', 'video', 'answer_image'):\n all_problem_files |= set(\n map(lambda x: getattr(x, attr).name, q_class.objects.all()))\n # Comprehensive\n for attr in ('image', 'attachment', 'video'):\n all_problem_files |= set(map(lambda x: getattr(\n x, attr).name, Comprehensive.objects.all()))\n # Sub questions\n for sub_class in [\n SubMultipleChoice, SubMultipleResponse, SubTrueOrFalse, SubTextBlank, SubNumericBlank, SubDescription,\n ]:\n for attr in ('image', 'video', 'answer_image'):\n all_problem_files |= set(\n map(lambda x: getattr(x, attr).name, sub_class.objects.all()))\n # Choices\n for choice_class in [\n MultipleChoiceImage, MultipleResponseImage, SubMultipleChoiceImage, SubMultipleResponseImage,\n ]:\n all_problem_files |= set(map(lambda x: getattr(\n x, attr).name, choice_class.objects.all()))\n # Clean it\n problem_folder_files = set(\n map(lambda x: f'problem/{x}', listdir(f'media/problem/')))\n problem_caches = problem_folder_files - all_problem_files\n for problem_cache in problem_caches:\n remove(f'media/{problem_cache}')\n return JsonResponse({'success': True})\n\n\n@login_required()\ndef get_user(request, user_id):\n target_user = User.objects.get(id=user_id)\n return JsonResponse({\n 'username': target_user.username,\n 'full_name': target_user.profile.full_name,\n 'area': target_user.profile.area,\n 'school': target_user.profile.school,\n 'department': target_user.profile.department,\n 'permission': target_user.profile.access\n })\n\n\ndef iterate_problems(chapter_id):\n chapter = Chapter.objects.get(pk=chapter_id)\n problems = []\n\n def get_basic_info(p, problem_type, is_sub=False):\n # common problems only\n info = {\n 'problem_type': problem_type,\n 'description': p.description,\n 'extra': {\n 'upload': p.student_upload,\n 'chance': p.chance,\n 'answer': p.answer,\n },\n 'files': {\n 'image': ('/media/' + str(p.image)) if p.image else '',\n 'video': ('/media/' + str(p.video)) if p.video else '',\n 'answer_image': ('/media/' + str(p.answer_image)) if p.answer_image else '',\n }\n }\n if problem_type == 'dc':\n info['extra']['optional'] = not p.need_answer\n if problem_type == 'nb':\n info['extra']['error'] = p.error\n if problem_type == 'mc' or problem_type == 'mr':\n info['extra']['choices'] = [\n {'choice': chr(65 + idx), 'content': c} for idx, c in enumerate(p.choices)]\n c_imgs = []\n if not is_sub:\n if problem_type == 'mc':\n c_imgs = MultipleChoiceImage.objects.filter(problem=p)\n if problem_type == 'mr':\n c_imgs = MultipleResponseImage.objects.filter(problem=p)\n else:\n if problem_type == 'mc':\n c_imgs = SubMultipleChoiceImage.objects.filter(problem=p)\n if problem_type == 'mr':\n c_imgs = SubMultipleResponseImage.objects.filter(problem=p)\n info['files']['choice_images'] = []\n if len(c_imgs):\n info['files']['choice_images'] = [\n {'choice': x.choice, 'path': '/media/' + str(x.image), } for x in c_imgs]\n if is_sub:\n info['percentage'] = p.percentage\n info['index'] = p.order\n else:\n info['index'] = p.index\n info['files'].update(\n {'attachment': ('/media/' + str(p.attachment))\n if p.attachment else '', }\n )\n return info\n\n for p in chapter.multiplechoice_set.all():\n problems.append(get_basic_info(p, 'mc'))\n for p in chapter.multipleresponse_set.all():\n problems.append(get_basic_info(p, 'mr'))\n for p in chapter.trueorfalse_set.all():\n problems.append(get_basic_info(p, 'tf'))\n for p in chapter.textblank_set.all():\n problems.append(get_basic_info(p, 'tb'))\n for p in chapter.numericblank_set.all():\n problems.append(get_basic_info(p, 'nb'))\n for p in chapter.description_set.all():\n problems.append(get_basic_info(p, 'dc'))\n for p in chapter.comprehensive_set.all():\n sub_problems = []\n for sub in p.submultiplechoice_set.all():\n sub_problems.append(get_basic_info(sub, 'mc', True))\n for sub in p.submultipleresponse_set.all():\n sub_problems.append(get_basic_info(sub, 'mr', True))\n for sub in p.subtrueorfalse_set.all():\n sub_problems.append(get_basic_info(sub, 'tf', True))\n for sub in p.subtextblank_set.all():\n sub_problems.append(get_basic_info(sub, 'tb', True))\n for sub in p.subnumericblank_set.all():\n sub_problems.append(get_basic_info(sub, 'nb', True))\n for sub in p.subdescription_set.all():\n sub_problems.append(get_basic_info(sub, 'dc', True))\n problems.append({\n 'problem_type': 'cp',\n 'index': p.index,\n 'description': p.description,\n 'files': {\n 'image': ('/media/' + str(p.image)) if p.image else '',\n 'video': ('/media/' + str(p.video)) if p.video else '',\n 'attachment': ('/media/' + str(p.attachment)) if p.attachment else '',\n },\n 'subs': sub_problems,\n })\n return problems\n\n\ndef get_cover(chapter_id):\n chapter = Chapter.objects.get(pk=chapter_id)\n # return ('/media/' + str(chapter.image)) if chapter.image else ''\n return str(chapter.image) if chapter.image else ''\n\n\ndef iterate_database(id):\n cat = Category.objects.get(pk=id)\n # init current cat dict\n this_cat = {\n 'name': cat.name,\n 'index': cat.index,\n 'subjects': []\n }\n all_subjects = cat.subject_set.all()\n for subj in all_subjects:\n # init current subj dict\n this_subj = {\n 'name': subj.name,\n 'index': subj.index,\n 'chapters': []\n }\n all_chapters = subj.chapter_set.all()\n for chap in all_chapters:\n # init current chap dict\n this_chap = {\n 'name': chap.name,\n 'index': chap.index,\n 'cover': get_cover(chap.id)\n }\n # points and difficulties\n points = chap.points\n points.insert(5, 1)\n this_chap['points'] = points\n difficulties = chap.difficulties\n difficulties.insert(5, 1)\n this_chap['difficulties'] = difficulties\n # problems\n this_chap['problems'] = iterate_problems(chap.id)\n # append chapter\n this_subj['chapters'].append(this_chap)\n this_cat['subjects'].append(this_subj)\n return this_cat\n\n\ndef remote_backup(request, id):\n try:\n res = iterate_database(id)\n return JsonResponse({'data': res}, json_dumps_params={'ensure_ascii': False})\n except Exception as e:\n return JsonResponse({'err': str(e)})\n\n\ndef remote_get_category(request):\n all_categories = Category.objects.all()\n res = [\n {'name': c.name, 'index': c.index, 'id': c.id} for c in all_categories]\n return JsonResponse({'data': res}, json_dumps_params={'ensure_ascii': False})\n\n\ndef remote_get_catalog_3(request):\n all_categories = Category.objects.all()\n res = []\n img_list = []\n for c in all_categories:\n this_cat = {'name': c.name, 'index': c.index, 'subjects': [] }\n all_subjects = c.subject_set.all()\n for subj in all_subjects:\n # init current subj dict\n this_subj = { 'name': subj.name, 'index': subj.index, 'chapters': [] }\n all_chapters = subj.chapter_set.all()\n for chap in all_chapters:\n # init current chap dict\n cover_name = get_cover(chap.id)\n if cover_name:\n img_list.append(cover_name)\n this_chap = {\n 'old_id': chap.id,\n 'name': chap.name,\n 'index': chap.index,\n 'cover': cover_name,\n 'points': chap.points,\n 'difficulties': chap.difficulties,\n }\n # append chapter\n this_subj['chapters'].append(this_chap)\n this_cat['subjects'].append(this_subj)\n res.append(this_cat)\n return JsonResponse({'data': res, 'img_list': img_list}, \n json_dumps_params={'ensure_ascii': False})\n\n\ndef remote_get_problems_3(request):\n problems = []\n file_list = []\n\n def get_basic_info(p, problem_type, is_sub=False):\n p_image = str(p.image) if p.image else ''\n p_video = str(p.video) if p.video else ''\n p_ans_img = str(p.answer_image) if p.answer_image else ''\n if p_image:\n file_list.append(p_image)\n if p_video:\n file_list.append(p_video)\n if p_ans_img:\n file_list.append(p_ans_img)\n info = {\n 'problem_type': problem_type,\n 'description': p.description,\n 'image': p_image,\n 'video': p_video,\n 'answer_image': p_ans_img,\n 'upload': p.student_upload,\n 'chance': p.chance,\n 'answer': p.answer,\n }\n\n if is_sub:\n info['percentage'] = p.percentage\n info['order'] = p.order\n else:\n info['chapter_id'] = p.chapter.id\n info['index'] = p.index\n p_att = str(p.attachment) if p.attachment else ''\n if p_att:\n file_list.append(p_att)\n info['attachment'] = p_att\n\n if problem_type == 'dc':\n info['need_answer'] = not p.need_answer\n if problem_type == 'nb':\n info['error'] = p.error\n if problem_type == 'mc' or problem_type == 'mr':\n info['choices'] = p.choices\n c_imgs = []\n if not is_sub:\n if problem_type == 'mc':\n c_imgs = MultipleChoiceImage.objects.filter(problem=p)\n if problem_type == 'mr':\n c_imgs = MultipleResponseImage.objects.filter(problem=p)\n else:\n if problem_type == 'mc':\n c_imgs = SubMultipleChoiceImage.objects.filter(problem=p)\n if problem_type == 'mr':\n c_imgs = SubMultipleResponseImage.objects.filter(problem=p)\n info['choice_images'] = []\n if len(c_imgs):\n for x in c_imgs:\n info['choice_images'].append(\n {'choice': x.choice, 'path': str(x.image), } \n )\n file_list.append(str(x.image))\n return info\n\n mc_set = MultipleChoice.objects.all()\n mr_set = MultipleResponse.objects.all()\n tf_set = TrueOrFalse.objects.all()\n tb_set = TextBlank.objects.all()\n nb_set = NumericBlank.objects.all()\n dc_set = Description.objects.all()\n cp_set = Comprehensive.objects.all()\n\n for p in mc_set:\n problems.append(get_basic_info(p, 'mc'))\n for p in mr_set:\n problems.append(get_basic_info(p, 'mr'))\n for p in tf_set:\n problems.append(get_basic_info(p, 'tf'))\n for p in tb_set:\n problems.append(get_basic_info(p, 'tb'))\n for p in nb_set:\n problems.append(get_basic_info(p, 'nb'))\n for p in dc_set:\n problems.append(get_basic_info(p, 'dc'))\n for p in cp_set:\n sub_problems = []\n for sub in p.submultiplechoice_set.all():\n sub_problems.append(get_basic_info(sub, 'mc', True))\n for sub in p.submultipleresponse_set.all():\n sub_problems.append(get_basic_info(sub, 'mr', True))\n for sub in p.subtrueorfalse_set.all():\n sub_problems.append(get_basic_info(sub, 'tf', True))\n for sub in p.subtextblank_set.all():\n sub_problems.append(get_basic_info(sub, 'tb', True))\n for sub in p.subnumericblank_set.all():\n sub_problems.append(get_basic_info(sub, 'nb', True))\n for sub in p.subdescription_set.all():\n sub_problems.append(get_basic_info(sub, 'dc', True))\n\n p_image = str(p.image) if p.image else ''\n p_video = str(p.video) if p.video else ''\n p_att = str(p.attachment) if p.attachment else ''\n\n if p_image:\n file_list.append(p_image)\n if p_video:\n file_list.append(p_video)\n if p_att:\n file_list.append(p_att)\n\n problems.append({\n 'problem_type': 'cp',\n 'chapter_id': p.chapter.id,\n 'index': p.index,\n 'description': p.description,\n 'image': p_image,\n 'video': p_video,\n 'attachment': p_att,\n 'subs': sub_problems,\n })\n return JsonResponse({'data': problems, 'file_list': file_list}, \n json_dumps_params={'ensure_ascii': False})","sub_path":"control/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":23183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"296721613","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\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.macosx-10.9-x86_64/egg/idapload/rpc/zmqrpc.py\n# Compiled at: 2020-04-13 02:37:12\n# Size of source mod 2**32: 1442 bytes\nimport zmq.green as zmq\nfrom .protocol import Message\nfrom locust.util.exception_handler import retry\n\nclass BaseSocket(object):\n\n def __init__(self, sock_type):\n context = zmq.Context()\n self.socket = context.socket(sock_type)\n self.socket.setsockopt(zmq.TCP_KEEPALIVE, 1)\n self.socket.setsockopt(zmq.TCP_KEEPALIVE_IDLE, 30)\n\n @retry()\n def send(self, msg):\n self.socket.send(msg.serialize())\n\n @retry()\n def send_to_client(self, msg):\n self.socket.send_multipart([msg.node_id.encode(), msg.serialize()])\n\n @retry()\n def recv(self):\n data = self.socket.recv()\n msg = Message.unserialize(data)\n return msg\n\n @retry()\n def recv_from_client(self):\n data = self.socket.recv_multipart()\n addr = data[0].decode()\n msg = Message.unserialize(data[1])\n return (addr, msg)\n\n\nclass Server(BaseSocket):\n\n def __init__(self, host, port):\n BaseSocket.__init__(self, zmq.ROUTER)\n if port == 0:\n self.port = self.socket.bind_to_random_port('tcp://%s' % host)\n else:\n self.socket.bind('tcp://%s:%i' % (host, port))\n self.port = port\n\n\nclass Client(BaseSocket):\n\n def __init__(self, host, port, identity):\n BaseSocket.__init__(self, zmq.DEALER)\n self.socket.setsockopt(zmq.IDENTITY, identity.encode())\n self.socket.connect('tcp://%s:%i' % (host, port))","sub_path":"pycfiles/idaploadio-0.15.0-py3.7/zmqrpc.cpython-37.py","file_name":"zmqrpc.cpython-37.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"606895727","text":"\"\"\"\nANSI escape codes.\n\nSee https://en.wikipedia.org/wiki/ANSI_escape_code.\n\"\"\"\n\n#-------------------------------------------------------------------------------\n\nimport collections\nimport fcntl\nimport html.parser\nfrom math import floor\nimport os\nimport re\nimport struct\nimport termios\n\n#-------------------------------------------------------------------------------\n\nESC = \"\\x1b\"\nCSI = ESC + \"[\"\n\ndef csi(*parts):\n return CSI + \"\".join( str(p) for p in parts )\n\n\ndef to_column(col):\n \"\"\"\n Moves the cursor to (zero-indexed) `col`.\n \"\"\"\n return csi(col + 1, \"G\")\n\n\ndef SGR(*codes):\n assert all( isinstance(c, int) for c in codes )\n return CSI + \";\".join( str(c) for c in codes ) + \"m\"\n\n\nRESET = SGR( ) # Same as NORMAL.\nNORMAL = SGR( 0)\nBOLD = SGR( 1)\nLIGHT = SGR( 2)\nITALIC = SGR( 3)\nUNDERLINE = SGR( 4)\nSLOW_BLINK = SGR( 5)\nRAPID_BLINK = SGR( 6)\nNEGATIVE = SGR( 7)\nCONCEAL = SGR( 8)\nCROSS_OUT = SGR( 9)\nPRIMARY_FONT = SGR(10)\nALTERNATE_FONT_1 = SGR(11)\nALTERNATE_FONT_2 = SGR(12)\nALTERNATE_FONT_3 = SGR(13)\nALTERNATE_FONT_4 = SGR(14)\nALTERNATE_FONT_5 = SGR(15)\nALTERNATE_FONT_6 = SGR(16)\nALTERNATE_FONT_7 = SGR(17)\nALTERNATE_FONT_8 = SGR(18)\nALTERNATE_FONT_9 = SGR(19)\nBOLD_OFF = SGR(21)\nNORMAL_INTENSITY = SGR(22)\nITALIC_OFF = SGR(23)\nUNDERLINE_OFF = SGR(24)\nBLINK_OFF = SGR(25)\nPOSITIVE = SGR(27)\nREVEAL = SGR(28)\nCROSS_OUT_OFF = SGR(29)\nBLACK_TEXT = SGR(30)\nRED_TEXT = SGR(31)\nGREEN_TEXT = SGR(32)\nYELLOW_TEXT = SGR(33)\nBLUE_TEXT = SGR(34)\nMAGENTA_TEXT = SGR(35)\nCYAN_TEXT = SGR(36)\nWHITE_TEXT = SGR(37)\nDEFAULT_TEXT = SGR(39)\nBLACK_BACKGROUND = SGR(40)\nRED_BACKGROUND = SGR(41)\nGREEN_BACKGROUND = SGR(42)\nYELLOW_BACKGROUND = SGR(43)\nBLUE_BACKGROUND = SGR(44)\nMAGENTA_BACKGROUND = SGR(45)\nCYAN_BACKGROUND = SGR(46)\nWHITE_BACKGROUND = SGR(47)\nDEFAULT_BACKGROUND = SGR(49)\n\ndef COLORMAP_TEXT(i):\n assert 0 <= i < 256\n return SGR(38, 5, i)\n\n\ndef COLORMAP_BACKGROUND(i):\n assert 0 <= i < 256\n return SGR(48, 5, i)\n\n\n#-------------------------------------------------------------------------------\n\nCOLOR_NAMES = dict(\n black = 0,\n dark_red = 1,\n dark_green = 2,\n brown = 3,\n dark_blue = 4,\n purple = 5,\n turquoise = 6,\n light_gray = 7,\n dark_gray = 8,\n red = 9,\n green = 10,\n yellow = 11,\n blue = 12,\n pink = 13,\n cyan = 14,\n white =231,\n)\n\n\ndef parse_rgb_triple(triple):\n if not triple.startswith(\"#\"):\n raise ValueError(\"RGB triple doesn't start with #\")\n if len(triple) == 4:\n # Multiply single digits by 0x11 to repeat digits, e.g. #1a6 expands\n # to #11aa66.\n return tuple( int(x, 16) * 17 for x in triple[1 : 4] )\n elif len(triple) == 7:\n rgb = triple[1 : 3], triple[3 : 5], triple[5 : 7]\n return tuple( int(x, 16) for x in rgb )\n else:\n raise ValueError(\"wrong number of digits for RGB triple\")\n\n\ndef get_color(value):\n \"\"\"\n Translates `value` to a color code.\n\n `value` may be:\n\n - An integer color code between 0 and 255.\n - A web-style \"#XYZ\" or \"#XXYYZZ\" RGB triple\n - A grayscale name \"grayX\" where X is between 0 and 100.\n - A color name for the sixteen standard colors.\n \"\"\"\n if isinstance(value, int):\n if 0 <= value < 256:\n return value\n else:\n raise ValueError(\"color value not between 0 and 255\")\n\n val = str(value)\n\n if val.startswith(\"#\"):\n r, g, b = parse_rgb_triple(val)\n # Convert RGB value range from 0-255 to 0-5.\n return 16 + 36 * (r * 6 // 256) + 6 * (g * 6 // 256) + (b * 6 // 256)\n\n if val.startswith(\"gray\"):\n try:\n gray_value = int(val[4 :])\n except ValueError:\n pass\n else:\n if 0 <= gray_value <= 100:\n # Use black (0), white (231), and 24 additional gray values in\n # the range 232-255.\n gray_value = gray_value * 26 // 101\n return (\n 0 if gray_value == 0 \n else 231 if gray_value == 25 \n else 231 + gray_value\n )\n\n try:\n return COLOR_NAMES[val]\n except KeyError:\n pass\n\n raise ValueError(\"unrecognized color: {!r}\".format(value))\n \n\n\ndef GRAY_LEVEL(fraction):\n \"\"\"\n Returns the closest color map code for a gray level between 0 and 1.\n \"\"\"\n assert 0 <= fraction <= 1\n index = int(floor(fraction * 24.999999999999))\n return 231 if index == 24 else 232 + index\n\n\ndef sgr(fg=None, bg=None, bold=None, underline=None, blink=None,\n reverse=None, conceal=None):\n \"\"\"\n Returns an SGR sequence to set color and text style.\n\n An argument value of `None` indicates that style should not be modified.\n\n @param fg\n Foreground color name or number, or `\"default\"` for the implementation's\n default. \n @param bg\n Background color name or number, or `\"default\"` for the implementation's\n default. \n @param underline\n True to enable single underlining; false to disable.\n @param blink\n True to enable blinking text; false to disable.\n @param reverse\n True to enable reverse video; false to disable.\n @param conceal\n True to conceal text; false to reveal.\n \"\"\"\n codes = []\n\n # Avoid using codes 30-37 for foreground and 40-47 for background color,\n # since the bold setting may change the colors of these.\n\n if fg is None:\n pass\n elif fg == \"default\":\n codes.append(39)\n else:\n codes.extend((38, 5, get_color(fg)))\n\n if bg is None:\n pass\n elif bg == \"default\":\n codes.append(49)\n else:\n codes.extend((48, 5, get_color(bg)))\n\n if bold is not None:\n # FIXME: Might be 22 for OSX, 21 for Linux?\n codes.append(1 if bold else 22)\n if underline is not None:\n codes.append(4 if underline else 24)\n if blink is not None:\n codes.append(5 if blink else 25)\n if reverse is not None:\n codes.append(7 if reverse else 27)\n if conceal is not None:\n codes.append(8 if conceal else 28)\n\n return SGR(*codes) if len(codes) > 0 else \"\"\n\n\ndef inverse_sgr(fg=None, bg=None, bold=None, underline=None, blink=None,\n reverse=None, conceal=None):\n if fg is not None:\n fg = \"default\"\n if bg is not None:\n bg = \"default\"\n if bold is not None:\n bold = not bold\n if underline is not None:\n underline = not underline\n if blink is not None:\n blink = not blink\n if reverse is not None:\n reverse = not reverse\n if conceal is not None:\n conceal = not conceal\n return sgr(\n fg=fg, bg=bg, bold=bold, underline=underline, blink=blink,\n reverse=reverse, conceal=conceal)\n\n\n# FIXME: We need a Style class.\n\ndef style(**kw_args):\n \"\"\"\n Returns a function that applies graphics style to text.\n\n The styling function accepts a single string argument, and returns that\n string styled and followed by the inverse styling.\n \"\"\"\n escape = sgr(**kw_args)\n unescape = inverse_sgr(**kw_args)\n return lambda text: escape + str(text) + unescape\n\n\n# Single-style shortcuts.\n\ndef fg(color):\n return style(fg=color)\n\n\ndef bg(color):\n return style(bg=color)\n\n\nbold = style(bold=True)\nunderline = style(underline=True)\nblink = style(blink=True)\nreverse = style(reverse=True)\nconcel = style(conceal=True)\n\n#-------------------------------------------------------------------------------\n\nESCAPE_REGEX = re.compile(re.escape(CSI) + r\"[^@-~]*.\")\n\n# FIXME: Use extension version from fixfmt.\ndef length(string):\n return len(ESCAPE_REGEX.sub(\"\", string))\n\n\n# FIXME: Elsewhere.\ndef dict_diff(dict0, dict1):\n assert len(dict0) == len(dict1)\n return { k: dict1[k] for k in dict0 if dict1[k] != dict0[k] }\n\n\nclass StyleStack:\n \"\"\"\n Stack of nested styles.\n \"\"\"\n\n DEFAULT_STYLE = {\n \"fg\" : \"default\",\n \"bg\" : \"default\",\n \"bold\" : False,\n \"underline\" : False,\n \"blink\" : False,\n \"reverse\" : False,\n \"conceal\" : False,\n }\n\n\n def __init__(self, style=DEFAULT_STYLE):\n \"\"\"\n @param style\n The initial style.\n \"\"\"\n self.__stack = [style]\n\n\n def push(self, **styles):\n \"\"\"\n Pushes a new style onto the stack.\n\n @return\n Escape codes to produce the new style.\n \"\"\"\n bad_keys = set(styles) - set(self.DEFAULT_STYLE)\n if len(bad_keys) > 0:\n raise TypeError(\"unknown styles: \" + \", \".join(bad_keys))\n\n old = self.__stack[-1]\n new = old.copy()\n new.update(styles)\n self.__stack.append(new)\n return sgr(**dict_diff(old, new))\n\n\n def pop(self):\n \"\"\"\n Pops a style off the stack.\n\n @return\n Escape codes to revert to the previous style.\n \"\"\"\n old = self.__stack.pop()\n new = self.__stack[-1]\n return sgr(**dict_diff(old, new))\n \n\n\n#-------------------------------------------------------------------------------\n\nclass ParseError(Exception):\n \"\"\"\n An error while parsing pseudo-HTML.\n \"\"\"\n\n pass\n\n\n\nclass Parser(html.parser.HTMLParser):\n \"\"\"\n Parses pseudo-HTML markup into text with ANSI escapes.\n\n Syntax:\n\n - ` ... ` for the foreground color.\n - ` ... ` for the background color.\n - ` ... ` for bold.\n - ` ... `.\n - ` ... ` for blink.\n - ` ... ` for reverse.\n - ` ... ` for conceal.\n\n The aliases ``, ``, ``, `` may be used for the first four\n tags above, respectively.\n\n `COLOR` may be a color name, RGB triplet, or gray value like \"gray50\".\n\n Unrecognized tags are passed through.\n\n Usage::\n\n print(Parser().feed(markup).result)\n\n \"\"\"\n\n # Renaming tags to SGR attributes.\n __RENAME = {\n \"b\" : \"bold\",\n \"background\": \"bg\",\n \"foreground\": \"fg\",\n \"u\" : \"underline\",\n }\n\n def __init__(self):\n super().__init__()\n self.__result = []\n # Stack of tags, for matching end tags.\n self.__tags = []\n # For colors, stacks of nested values; \"default\" is a sentry value.\n self.__colors = dict(fg=[\"default\"], bg=[\"default\"])\n # For boolean attributes, a nesting count.\n self.__attrs = dict(bold=0, underline=0, blink=0, reverse=0, conceal=0)\n\n\n def handle_starttag(self, tag, attrs):\n tag = self.__RENAME.get(tag, tag)\n self.__tags.append(tag)\n \n if tag in self.__colors:\n if len(attrs) != 1 or attrs[0][0] != \"color\":\n raise ParseError(\"{} must have attribute color\".format(tag))\n color = get_color(attrs[0][1])\n stack = self.__colors[tag]\n # Set the color.\n if stack[-1] != color:\n self.__result.append(sgr(**{tag: color}))\n # Push it.\n stack.append(color)\n\n elif tag in self.__attrs:\n # Set the attribute, if this is the outermost tag.\n if self.__attrs[tag] == 0:\n self.__result.append(sgr(**{tag: True}))\n # Increment the nesting count.\n self.__attrs[tag] += 1\n\n else:\n self.__result.append(self.get_starttag_text())\n\n\n def handle_endtag(self, tag):\n tag = self.__RENAME.get(tag, tag)\n if len(self.__tags) == 0:\n raise ParseError(\"unmatched end tag {!r}\".format(tag))\n elif tag != self.__tags.pop():\n raise ParseError(\"mismatched end tag {!r}\".format(tag))\n\n if tag in self.__colors:\n stack = self.__colors[tag]\n color = stack.pop()\n # Restore the previous color.\n if stack[-1] != color:\n self.__result.append(sgr(**{tag: stack[-1]}))\n\n elif tag in self.__attrs:\n self.__attrs[tag] -= 1\n # Turn off the attribute, if this is the outermost tag.\n if self.__attrs[tag] == 0:\n self.__result.append(sgr(**{tag: False}))\n\n else:\n self.__result.append(\"\")\n\n\n def handle_data(self, data):\n # Just append non-tag text.\n self.__result.append(data)\n\n\n def feed(self, *args, **kw_args):\n \"\"\"\n @raise ParseError\n The input coudl not be parsed.\n \"\"\"\n # Just make this method chainable.\n super().feed(*args, **kw_args)\n return self\n\n\n @property\n def result(self):\n \"\"\"\n The parsed and converted result.\n \"\"\"\n return \"\".join(self.__result)\n\n\n\ndef convert_markup(text):\n \"\"\"\n Converts HTML-style markup into ANSI escape codes.\n\n @see\n `Parser` for markup syntex.\n \"\"\"\n return Parser().feed(text).result\n\n\ntry:\n from shutil import get_terminal_size\n\nexcept:\n terminal_size = collections.namedtuple(\"terminal_size\", (\"columns\", \"lines\"))\n\n def get_terminal_size(default=(80, 25)):\n def win_size(fd):\n return struct.unpack(\n \"hh\", fcntl.ioctl(fd, termios.TIOCGWINSZ, '1234'))\n\n try:\n cols = int(os.environ.get(\"COLUMNS\"))\n except:\n cols = None\n try:\n rows = int(os.environ.get(\"ROWS\"))\n except:\n rows = None \n\n if cols is not None and rows is not None:\n return cols, rows\n\n size = None\n for fd in (0, 1, 2):\n try:\n size = win_size(fd)\n except IOError:\n continue\n else:\n break\n else:\n fd = os.open(os.ctermid(), os.O_RDONLY)\n try:\n size = win_size(fd)\n except:\n os.close(fd)\n\n cols = (\n cols if cols is not None\n else size[1] if size is not None\n else default[0]\n )\n rows = (\n rows if rows is not None\n else size[0] if size is not None\n else default[1]\n )\n\n return terminal_size(cols, rows)\n\n\n","sub_path":"python/fixfmt/lib/ansi.py","file_name":"ansi.py","file_ext":"py","file_size_in_byte":14676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"74850394","text":"\"\"\"\n\n cmaps.py\n\n Module for loading colourmaps for MITgcm post-processing.\n\n Some colourmaps live in Matplotlib, and some live in external\n packages like cmocean. This module searches all available packages\n for a colourmap name, and returns the colourmap instance.\n\n\"\"\"\n\n# To import packages from their string names\nimport importlib\n\n# For matplotlib colourmaps\nimport matplotlib\n\nexternal_packages = ['cmocean.cm',]\n\naliases = { 'cmocean' : 'cmocean.cm',\n 'matplotlib' : 'matplotlib.cm',\n }\n\ndef _cmap_from_pkg(package, name):\n \"\"\"Returns a colourmap from the specified package.\n\n Inputs:\n * name: String giving the name of the colourmap\n * package: a module object to import the colourmap from\n \"\"\"\n try:\n cmap = getattr(package, name)\n except:\n raise NameError('No colourmap named %s in package %s' % (name, package))\n return cmap\n\ndef getcm(name):\n \"\"\"getcm returns a matplotlib colormap instance from a colormap name\n\n If name is specified as package/name, it will specifically use package's\n colourmap of name.\n\n Otherwise, all available packages are searched until a matching\n name is found, in hierarchy:\n\n 1 :: Matplotlib.cm\n 2 :: All available external packages:\n a :: cmocean\n\n If name is a matplotlib.colors.LinearSegmentedColormap, the colourmap\n is passed to the plotting fucntions.\n\n Inputs:\n * name: String name specififying a colourmap, or a colourmap instance.\n The string should be specified as either \"cmap\" or\n \"package/cmap\", where package optionally specifies which\n package to look in to find the colourmap cmap.\n\n Returns:\n * A matplotlib.colors.LinearSegmentedColormap instance\n\n Raises:\n * NameError if the given colourmap can't be found in any package.\n \"\"\"\n pkglist = ['matplotlib.cm'] + external_packages\n if type(name) is matplotlib.colors.LinearSegmentedColormap:\n return name\n package, name = name.split('/') if '/' in name else (None, name)\n package = aliases[package] if package in aliases else package\n\n if package:\n cmodule = importlib.import_module(package)\n cmap = _cmap_from_pkg(cmodule, name)\n\n else:\n for module in pkglist:\n cmodule = importlib.import_module(module)\n try:\n cmap = _cmap_from_pkg(cmodule, name)\n break\n except NameError:\n continue\n else:\n raise NameError('Colourmap %s not found in packages %s' % (name, pkglist))\n\n return cmap\n\nif __name__ == '__main__':\n print('jet:', getcm('jet'))\n print('Spectral_r:', getcm('/Spectral_r'))\n print('cmocean/ice:', getcm('cmocean/ice'))\n print('ice:', getcm('ice'))\n","sub_path":"postprocessing/cmaps.py","file_name":"cmaps.py","file_ext":"py","file_size_in_byte":2840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"59232796","text":"import numpy as np\nimport pymysql as pms\n\n\nclass oraDataFrame(object):\n def __init__(self):\n self.db = self.connectSQL()\n self.cursor = self.db.cursor()\n\n def connectSQL(self):\n db = pms.Connect(\"localhost\", \"root\", \"Zhang715\", \"BCW\")\n return db\n\n def createPriorityTable(self, tableName):\n sql = \"CREATE TABLE {0}(\\\n ID INT PRIMARY KEY NOT NULL AUTO_INCREMENT,\\\n STATE_ SMALLINT(5),\\\n ACTION_ SMALLINT(5),\\\n REWARD_ SMALLINT(5),\\\n STATE_NEXT SMALLINT(5),\\\n PRIORITY FLOAT,\\\n TIME_STEP INT,\\\n IDX INT)AUTO_INCREMENT=1\".format(tableName)\n try:\n self.cursor.execute(sql)\n self.db.commit()\n #print(\"Already Create TABLE PRIORITY\")\n except:\n print(\"CANNOT CREATE TABLE PRIORITY\")\n self.db.rollback()\n\n def insert(self, transition, priority, tableName):\n \"\"\"\n Insert transitions and priority\n :param transition: a list of transitions\n :param priority: a list of priorities\n :param tableName:\n :return:\n \"\"\"\n try:\n for m, p in zip(transition,priority):\n s = int(m[0])\n a = int(m[1])\n r = int(m[2])\n s_ = int(m[3])\n T = int(m[4])\n idx = int(m[5])\n\n insert = \"INSERT INTO %s(\\\n STATE_,\\\n ACTION_,\\\n REWARD_,\\\n STATE_NEXT,\\\n PRIORITY,\\\n TIME_STEP,\\\n IDX)\\\n VALUES ('%d','%d','%d','%d','%d','%d','%d')\" % (tableName, s, a, r, s_,p,T,idx)\n try:\n self.cursor.execute(insert)\n #print(\"Already insert {0}\".format(m))\n except:\n print(\"CANNOT INSERT {0}\".format(m))\n self.db.rollback()\n\n self.db.commit()\n except:\n self.db.rollback()\n\n def remove(self, tablename, id):\n sql = \"DELETE FROM {0} WHERE ID={1}\".format(tablename, id)\n\n try:\n self.cursor.execute(sql)\n self.db.commit()\n except:\n self.db.rollback()\n print(\"Cannot delete...\")\n\n def remove_time_idx(self, tablename, time, idx):\n sql = \"DELETE FROM {0} WHERE TIME_STEP={1} AND IDX={2}\".format(tablename, time, idx)\n try:\n self.cursor.execute(sql)\n self.db.commit()\n except:\n self.db.rollback()\n print(\"Cannot delete...\")\n\n def cover(self, tablename, id, transition, priority):\n s = int(transition[0])\n a = int(transition[1])\n r = int(transition[2])\n s_ = int(transition[3])\n T = int(transition[4])\n idx = int(transition[5])\n\n sql = \"UPDATE {0} \\\n SET STATE_ = {1},\\\n ACTION_ = {2},\\\n REWARD_ = {3},\\\n STATE_NEXT = {4}, \\\n PRIORITY = {5}, \\\n TIME_STEP = {6},\\\n IDX = {7} WHERE ID = {8}\".format(tablename, s, a, r, s_, priority, T, idx, id)\n try:\n self.cursor.execute(sql)\n self.db.commit()\n # print(\"Already Update PRIORITY of {0} transition\".format(ID))\n except:\n self.db.rollback()\n print(\"Update Failed!\")\n\n def updatePriority(self, priority, ID, tableName):\n update = \"UPDATE {0} SET PRIORITY = {1} WHERE ID = {2}\".format(tableName, priority, ID)\n\n try:\n self.cursor.execute(update)\n self.db.commit()\n #print(\"Already Update PRIORITY of {0} transition\".format(ID))\n except:\n self.db.rollback()\n print(\"Update Failed!\")\n\n def updateTid(self, ID, time, idx, tablename):\n update = \"UPDATE {0} SET TIME_STEP = {1}, IDX={2} WHERE ID = {3}\".format(tablename, time, idx, ID)\n try:\n self.cursor.execute(update)\n self.db.commit()\n # print(\"Already Update PRIORITY of {0} transition\".format(ID))\n except:\n self.db.rollback()\n print(\"Update Failed!\")\n\n def sumPriority(self, tablename):\n sql = \"SELECT SUM(PRIORITY) FROM {0}\".format(tablename)\n\n try:\n self.cursor.execute(sql)\n self.db.commit()\n priority_sum = self.cursor.fetchone()\n return priority_sum[0]\n except:\n self.db.rollback()\n\n def maxPriority(self, tablename):\n sql = \"SELECT MAX(PRIORITY) FROM {0}\".format(tablename)\n\n try:\n self.cursor.execute(sql)\n self.db.commit()\n priority_max = self.cursor.fetchone()\n return priority_max[0]\n except:\n self.db.rollback()\n\n def get_all_priority(self, tablename):\n sql = \"SELECT ID, PRIORITY FROM {0}\".format(tablename)\n\n try:\n self.cursor.execute(sql)\n self.db.commit()\n priorities = self.cursor.fetchall()\n return priorities\n except:\n self.db.rollback()\n\n def get_row_number(self, tablename):\n sql = \"SELECT COUNT(*) FROM {0}\".format(tablename)\n\n try:\n self.cursor.execute(sql)\n self.db.commit()\n rows = self.cursor.fetchone()\n return rows[0]\n except:\n self.db.rollback()\n\n def extract_transition(self, tablename, id):\n sql = \"SELECT STATE_, ACTION_, REWARD_, STATE_NEXT, TIME_STEP, IDX FROM {0} WHERE ID = {1}\".format(tablename, id)\n\n try:\n self.cursor.execute(sql)\n self.db.commit()\n transition = self.cursor.fetchall()\n return transition[0]\n except:\n self.db.rollback()\n\n def min_idx(self, tablename):\n sql = \"SELECT MIN(ID) FROM {0}\".format(tablename)\n try:\n self.cursor.execute(sql)\n self.db.commit()\n min_idx = self.cursor.fetchone()\n return min_idx[0]\n except:\n self.db.rollback()\n\n\nclass easySumTree(object):\n def __init__(self):\n self.tableName = 'PRIORITY'\n self.db = oraDataFrame()\n self.create_data_frame()\n self.capacity = 0\n self.tree = None\n self.idframe = None\n\n def create_data_frame(self):\n self.db.createPriorityTable(self.tableName)\n\n def add(self, p, transition, id=None):\n \"\"\"\n :param p:\n :param transition:\n :param id: when the memory is full, the new income transition will be cover the old transition starting from\n first row\n :return:\n \"\"\"\n if id is None:\n self.db.insert(transition=transition, priority=p, tableName=self.tableName)\n else:\n self.db.cover(transition=transition,priority=p,id=id, tablename=self.tableName)\n\n def remove(self, id):\n self.db.remove(self.tableName, id)\n\n def remove_tid(self, time, idx):\n self.db.remove_time_idx(self.tableName,time,idx)\n\n def update(self, id, priority):\n self.db.updatePriority(priority=priority, ID=id, tableName=self.tableName)\n\n def update_tid(self, id, time, idx):\n self.db.updateTid(id, time, idx, self.tableName)\n\n def max_priority(self):\n return self.db.maxPriority(self.tableName)\n\n def construct_tree(self):\n self.capacity = self.db.get_row_number(self.tableName)\n self.idframe = np.zeros(self.capacity, dtype=object)\n self.tree = np.zeros(2 * self.capacity - 1)\n priorities = self.db.get_all_priority(self.tableName)\n for i in range(self.capacity):\n id, p = priorities[i]\n self.idframe[i] = id\n tree_idx = i + self.capacity - 1\n self.update_tree(tree_idx, p)\n\n def update_tree(self, tree_idx, p):\n change = p - self.tree[tree_idx]\n self.tree[tree_idx] = p\n\n while tree_idx != 0:\n tree_idx = (tree_idx - 1) // 2\n self.tree[tree_idx] += change\n\n def get_leaf(self, v):\n \"\"\"\n Tree structure and array storage:\n Tree index:\n 0 -> storing priority sum\n / \\\n 1 2\n / \\ / \\\n 3 4 5 6 -> storing priority for transitions\n Array type for storing:\n [0,1,2,3,4,5,6]\n \"\"\"\n parent_idx = 0\n while True: # the while loop is faster than the method in the reference code\n cl_idx = 2 * parent_idx + 1 # this leaf's left and right kids\n cr_idx = cl_idx + 1\n if cl_idx >= len(self.tree): # reach bottom, end search\n leaf_idx = parent_idx\n break\n else: # downward search, always search for a higher priority node\n if v <= self.tree[cl_idx]:\n parent_idx = cl_idx\n else:\n v -= self.tree[cl_idx]\n parent_idx = cr_idx\n\n id_idx = leaf_idx - self.capacity + 1\n id = self.idframe[id_idx]\n transition = self.db.extract_transition(self.tableName, id)\n return id, self.tree[leaf_idx], transition\n\n def total_p(self):\n if self.tree is not None:\n return self.tree[0]\n else:\n print(\"Tree is not built...\")\n return False\n\n def clean_tree(self):\n self.tree = None\n self.idframe = None\n self.capacity = None\n\n\nclass pER_Memory(object):\n\n def __init__(self, max_capacity):\n self.max_capacity = max_capacity\n self.capacity = 0\n self.tree = easySumTree()\n self.epsilon = 0.01 # small amount to avoid zero priority\n self.alpha = 0.6 # [0~1] convert the importance of TD error to priority\n self.beta = 0.4 # importance-sampling, from initial value increasing to 1\n self.beta_increment_per_sampling = 0.001\n self.abs_err_upper = 1. # clipped abs error\n self.data_pointer = 0\n\n def store(self, transition):\n max_p = self.tree.max_priority()\n if max_p is None:\n max_p = self.abs_err_upper\n\n if self.capacity < self.max_capacity:\n self.tree.add([max_p], [transition]) # set the max p for new p\n self.data_pointer += 1\n self.capacity += 1\n else:\n # overlap the old transitions\n self.tree.db.cover(self.tree.tableName, self.data_pointer, transition, max_p)\n self.data_pointer += 1\n\n if self.data_pointer >= self.max_capacity:\n self.data_pointer = self.tree.db.min_idx(self.tree.tableName)\n\n def sample(self, n):\n # First, construct the sum tree\n self.tree.construct_tree()\n\n # Initial batch index, batch memory, IS weights\n b_idx, b_memory, ISWeights = np.empty((n,), dtype=np.int32), np.empty((n, 4)), np.empty((n, 1))\n pri_seg = self.tree.total_p() / n # priority segment\n self.beta = np.min([1., self.beta + self.beta_increment_per_sampling]) # max = 1\n\n min_prob = np.min(self.tree.tree[-self.tree.capacity:]) / self.tree.total_p() # for later calculate ISweight\n for i in range(n):\n a, b = pri_seg * i, pri_seg * (i + 1)\n v = np.random.uniform(a, b)\n idx, p, data = self.tree.get_leaf(v)\n prob = p / self.tree.total_p()\n ISWeights[i, 0] = np.power(prob / min_prob, -self.beta)\n b_idx[i], b_memory[i, :] = idx, data\n\n # clean the tree\n self.tree.clean_tree()\n\n return b_idx, b_memory, ISWeights\n\n def batch_update(self, id, abs_errors):\n abs_errors += self.epsilon # convert to abs and avoid 0\n clipped_errors = np.minimum(abs_errors, self.abs_err_upper)\n ps = np.power(clipped_errors, self.alpha)\n for ti, p in zip(id, ps):\n self.tree.update(ti, p)\n\n def enlarge(self, k):\n self.max_capacity += k\n self.data_pointer = self.capacity # reset the data pointer back\n\n def shrink(self, k, removal_id):\n for id in removal_id:\n self.tree.remove(id)\n self.max_capacity -= k\n self.capacity -= k\n self.data_pointer -= k\n\n\nif __name__ == '__main__':\n\n def gen_t(k):\n transitions = []\n priorities = []\n if k == 0:\n transition = np.hstack((0, 0, 0, 0))\n transitions.append(transition)\n priorities.append(k)\n for i in range(k):\n s = 1 + i\n a = 2 + i\n r = 3 + i\n s_ = 4 + i\n transition = np.hstack((s, a, r, s_))\n transitions.append(transition)\n priorities.append(i)\n return transitions, priorities\n\n\n #oraDataFrame -- all pass\n #db = oraDataFrame()\n\n #db.createPriorityTable('test')\n \"\"\"\n transition,ps = gen_t(20)\n db.insertMemory(transition, ps,'test')\n\n print(db.sumPriority('test'))\n priorities = db.get_all_priority('test')\n ID, p = priorities[0]\n print(len(priorities))\n print(ID)\n print(p)\n print(db.get_capacity('test'))\n #db.updatePriority(6,2,'test')\n # print()\n \"\"\"\n \"\"\"\n transition, ps = gen_t(20)\n st = easySumTree()\n st.add(ps, transition)\n st.construct_tree()\n print(st.get_leaf(5))\n st.clean_tree()\n\n for i in range(4):\n st.remove(i)\n\n st.construct_tree()\n print(st.get_leaf(5))\n\n idx, batch_memory, transition = st.get_leaf(3)\n print()\n \"\"\"\n \"\"\"\n -- Pass\n \n db = oraDataFrame()\n t = db.extract_transition('PRIORITY', 3)\n print()\n \"\"\"\n \"\"\"\n transition, ps = gen_t(2)\n db = oraDataFrame()\n #db.remove('PRIORITY', 1)\n db.insert(transition,ps,'PRIORITY')\n \"\"\"\n \"\"\"\n db = oraDataFrame()\n #db.createPriorityTable('test')\n #print(db.maxPriority('test'))\n transition, ps = gen_t(0)\n db.insert(transition, ps, 'test')\n \"\"\"\n \"\"\"\n db = oraDataFrame()\n #transition, ps = gen_t(0)\n #db.cover('PRIORITY', 4, transition[0], ps[0])\n print(db.min_idx('PRIORITY'))\n print()\n \"\"\"\n \"\"\"\n memory = pER_Memory(30)\n transition, ps = gen_t(35)\n for t in transition:\n memory.store(t) # pass\n\n tree_idx, batch_memory, ISWeights = memory.sample(5)\n\n memory.enlarge(5)\n t_, p = gen_t(5)\n for tt in t_:\n memory.store(tt)\n\n tt_, p_ = gen_t(1)\n for ttt in tt_:\n memory.store(ttt)\n\n print()\n \"\"\"\n db = oraDataFrame()\n db.updatePriority(0.12,23,'PRIORITY')","sub_path":"Memory/oraSumTree.py","file_name":"oraSumTree.py","file_ext":"py","file_size_in_byte":14695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"2415647","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# rumps: Ridiculously Uncomplicated macOS Python Statusbar apps.\n# Copyright: (c) 2017, Jared Suttles. All rights reserved.\n# License: BSD, see LICENSE for details.\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\ntry: # Python 2.7+\n from test.support import import_fresh_module\n pyCollections = import_fresh_module('collections', blocked=['_collections'])\n _OrderedDict, _Link = pyCollections.OrderedDict, pyCollections._Link\nexcept ImportError:\n from .packages.ordereddict import OrderedDict as _OrderedDict\n\n\n# ListDict: OrderedDict subclass with insertion methods for modifying the order of the linked list in O(1) time\n# https://gist.github.com/jaredks/6276032\nclass ListDict(_OrderedDict):\n def __insertion(self, link_prev, key_value):\n key, value = key_value\n if link_prev.key != key:\n if key in self:\n del self[key]\n link_next = link_prev.next\n new_link = _Link()\n new_link.prev, new_link.next, new_link.key = link_prev, link_next, key\n self._OrderedDict__map[key] = link_prev.next = link_next.prev = new_link\n dict.__setitem__(self, key, value)\n\n def insert_after(self, existing_key, key_value):\n self.__insertion(self._OrderedDict__map[existing_key], key_value)\n\n def insert_before(self, existing_key, key_value):\n self.__insertion(self._OrderedDict__map[existing_key].prev, key_value)\n","sub_path":"rumps/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"325801629","text":"import bot.utils as utils\n\ndef get_help(command:str):\n \"\"\"\n Returns:\n List of available bot commands\n \"\"\"\n return utils.gen_send_data(\"Current commands available:\\n\"\n + \"/create_character\\n\"\n + \"/help\\n\"\n + \"/link\\n\"\n + \"/roll\\n\"\n + \"Add \\'help\\' after a command to get the syntax.\")\n\n\ndef get_group_link(command:str):\n \"\"\"\n Get the group-invite link for the grasmaaier chat\n \"\"\"\n items = utils.split_string(command, \" \")\n if items[1] == \"help\":\n return utils.gen_send_data(\"Syntax help for the /link command:\\n\"\n + \"just use \\'/link\\'...\\n\"\n + \"Out of sheer spite I will make you retype the command\\n\"\n + \">:D\\n\")\n return utils.gen_send_data(\"https://chat.whatsapp.com/L2xVHEBzWDM0cXWjif8TDf\")\n","sub_path":"bot/handlers/misc_handler.py","file_name":"misc_handler.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"273995862","text":"# import data\n# import argparse\n# from LGCmodel import LGC\n# parser = argparse.ArgumentParser()\n# parser.add_argument(\"--dataset\",default=\"E:\\\\LGC\\\\dataset\")\n# parser.add_argument(\"--imgsize\",default=41,type=int)\n# parser.add_argument(\"--scale\",default=2,type=int)\n#\n# parser.add_argument(\"--output_channels\",default=1,type=int)\n# parser.add_argument(\"--batchsize\",default=128,type=int)\n# parser.add_argument(\"--savedir\",default=\"‪E:\\\\LGC\\\\model\")\n# parser.add_argument(\"--iterations\",default=100,type=int)\n#\n# args = parser.parse_args()\n# data.load_dataset(args.dataset)\n# # if args.imgsize % args.scale != 0:\n# # print(f\"Image size {args.imgsize} is not evenly divisible by scale {arg.scale}\")\n# # exit()\n# up_size = args.imgsize*args.scale\n# network = LGC(args.imgsize,args.scale,args.output_channels)\n# network.set_data_fn(data.get_batch(args.imgsize,args.scale,args.batchsize))\n# network.train(args.iterations,args.savedir)\n# from PIL import Image\n# import matplotlib.pyplot as plt\n# import numpy as np\n# import math\n# import cv2\n# def BiBubic(x):\n# x=abs(x)\n# if x<=1:\n# return 1-2*(x**2)+(x**3)\n# elif x<2:\n# return 4-8*x+5*(x**2)-(x**3)\n# else:\n# return 0\n#\n# def BiCubic_interpolation(img,dstH,dstW):\n# scrH,scrW,_=img.shape\n# #img=np.pad(img,((1,3),(1,3),(0,0)),'constant')\n# retimg=np.zeros((dstH,dstW,3),dtype=np.uint8)\n# for i in range(dstH):\n# for j in range(dstW):\n# scrx=i*(scrH/dstH)\n# scry=j*(scrW/dstW)\n# x=math.floor(scrx)\n# y=math.floor(scry)\n# u=scrx-x\n# v=scry-y\n# tmp=0\n# for ii in range(-1,2):\n# for jj in range(-1,2):\n# if x+ii<0 or y+jj<0 or x+ii>=scrH or y+jj>=scrW:\n# continue\n# tmp+=img[x+ii,y+jj]*BiBubic(ii-u)*BiBubic(jj-v)\n# retimg[i,j]=np.clip(tmp,0,255)\n# return retimg\n# im_path='E:\\LGC\\dataset\\\\train\\\\agricultural\\\\agricultural1.jpg'\n# image=np.array(Image.open(im_path))\n# image3=BiCubic_interpolation(image,image.shape[0]*2,image.shape[1]*2)\n# image3=Image.fromarray(image3.astype('uint8')).convert('RGB')\n# cv2.imshow('ima', image3)\nimport tensorflow as tf\n# import tensorflow_datasets as tfs\nfrom data import load_dataset,get_dataset,load_preprosess_image\ndataset_dir = 'E:\\LGC\\dataset'\nfrom LGCmodel import LGC\n\ndown_size = 41\nscale = 2\ntrainset = load_dataset(dataset_dir)\ntrain_image,labels = get_dataset(down_size,scale)\n\n\n\n\nimport matplotlib.pyplot as plt\n\n\n\ntrain_dataset = tf.data.Dataset.from_tensor_slices((train_image, labels)) # 用load_preprosess_image对图片做一个读取预处理 速度有些慢\n\nAUTOTUNE = tf.data.experimental.AUTOTUNE # 根据计算机cpu的个数自动的做并行运算 临时实验方法 有可能变化\n\ntrain_dataset = train_dataset.map(load_preprosess_image,\n num_parallel_calls=AUTOTUNE) # .map是使函数应用在load_preprosess_image中所有的图像上\nnum_epoch = 80\nBATCH_SIZE = 128\nlearning_rate = 0.01\n\ntrain_count = len(train_image)\n\ntrain_dataset = train_dataset.shuffle(train_count).batch(BATCH_SIZE)\n\ntrain_dataset = train_dataset.prefetch(AUTOTUNE) # 前台在训练时 后台读取数据 自动分配cpu\n\n#imgs, las = next(iter(train_dataset)) # 取出的是一个batch个数的图片 shape = (batch_size,256,256,3)\n\n\n\n\n# dataset = tfds.load(\"tf_flowers\", split=tfds.Split.TRAIN, as_supervised=True)\n# dataset = dataset.map(lambda img, label: (tf.image.resize(img, (224, 224)) / 255.0, label)).shuffle(1024).batch(batch_size)\nmodel = LGC()\noptimizer = tf.keras.optimizers.SGD(lr=learning_rate,momentum=0.9)\nfor e in range(num_epoch):\n for images, labels in train_dataset:\n with tf.GradientTape() as tape:\n labels_pred = model(images)\n loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=labels, y_pred=labels_pred)\n loss = tf.reduce_mean(loss)\n\n grads = tape.gradient(loss, model.trainable_variables)\n optimizer.apply_gradients(grads_and_vars=zip(grads, model.trainable_variables))\n print(\"loss %f\" % loss.numpy())","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"539647828","text":"# Author imagean\n#!/usr/bin/python\n# -*- coding:utf-8\nimport cv2 as cv\nimport numpy as np\nfrom matplotlib import pyplot as plt\n#cv.namedWindow('image1')\nimg1 = cv.imread(\"C:/Users/19845/Desktop/1.jpg\",1)\nprint(img1.shape)\ninitrows = img1.shape[0]\ninitcols = img1.shape[1]\ninitcross = img1.shape[2]\nprint(\"Image.x=\",initrows)\nprint(\"Image.y=\",initcols)\nprint(\"Image.channel=\",initcross)\nimg2 = np.zeros((initrows,initcols),dtype=img1.dtype)\n\n# b = np.zeros((initrows,initcols), dtype=img1.dtype)\n# g = np.zeros((initrows,initcols), dtype=img1.dtype)\n# r = np.zeros((initrows,initcols), dtype=img1.dtype)\n# b=img1[:,:,0]\n# g=img1[:,:,1]\n# r=img1[:,:,2]\n# print('blue=',b,'\\ngreen=',g,'\\nred=',r)\nfor i in range(0,initrows):\n for j in range(0,initcols):\n img2[i,j] = 0.114*img1[i,j,0]+0.587*img1[i,j,1]+0.299*img1[i,j,2]\n# img2[:]= 0.114*b +0.587* g + 0.299 *r\n#print img2[:,:]\ncv.imshow('img1',img1)\n\ncv.imwrite(\"C:/Users/19845/Desktop/1.jpg\",img2)\nprint(img2.shape)\nprint(img2.size)\n#cv.imshow('image1', img1)\ncv.waitKey(0)\ncv.imshow('img2',img2)\ncv.imwrite(\"ruiruigray.jpg\",img2)\ncv.waitKey(0)\ncv.destroyAllWindows()\n","sub_path":"2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"318977777","text":"import os\nfrom app import db,app,allowed_exts\nfrom flask import render_template, request, redirect, url_for, flash\nfrom .models import UserProfile\nfrom .forms import NewForm\nfrom werkzeug.utils import secure_filename\nimport datetime\nfrom sqlalchemy import exc\n\n\n\n###\n# Routing for your application.\n###\n\n@app.route('/')\ndef home():\n \"\"\"Render website's home page.\"\"\"\n return render_template('home.html')\n\n\n@app.route('/about/')\ndef about():\n \"\"\"Render the website's about page.\"\"\"\n return render_template('about.html')\n \n@app.route('/profile', methods=['POST','GET'])\ndef profile():\n newProfileForm = NewForm()\n \n if request.method == 'POST':\n if newProfileForm.validate_on_submit()==True:\n try:\n \n firstname = newProfileForm.firstname.data\n lastname = newProfileForm.lastname.data\n gender = newProfileForm.gender.data\n email = newProfileForm.email.data\n location = newProfileForm.location.data\n bio = newProfileForm.bio.data\n created = str(datetime.datetime.now()).split()[0]\n \n photo = newProfileForm.photo.data\n photo_name = secure_filename(photo.filename)\n \n user = UserProfile(firstname,lastname,gender,email,location, bio, created, photo_name)\n \n db.session.add(user)\n db.session.commit()\n \n photo.save(os.path.join(app.config['UPLOAD_FOLDER'],photo_name))\n flash('Successfullly added.', 'success')\n return redirect(url_for('profiles'))\n \n except Exception as e:\n db.session.rollback()\n flash(\"Internal Error\", \"danger\")\n return render_template(\"create_profile.html\", newProfileForm = newProfileForm)\n \n errors = form_errors(newProfileForm)\n flash(''.join(error+\" \" for error in errors),\"danger\")\n \n return render_template(\"create_profile.html\", newProfileForm = newProfileForm)\n \n \n\n\n\n\n########################################################################################\n\n\n\n@app.route('/profiles', methods=['GET','POST'])\ndef profiles():\n \n profiles= UserProfile.query.all()\n profile=[]\n \n for user in profiles:\n profile.append({\"pro_pic\": user.photo, \"f_name\":user.firstname, \"l_name\":user.lastname, \"gender\":user.gender, \"location\":user.location, \"email\":user.email, \"bio\":user.bio})\n \n return render_template('profiles.html', profile=profile)\n\n\n#######################################################################################\n\n\n@app.route('/profile/', methods=['GET', 'POST'])\ndef userprofile(userid):\n user = UserProfile.query.filter_by(id=userid).first()\n if user is None:\n return redirect(url_for('home'))\n \n c_y = int(user.created_on.split(\"-\")[0])\n c_m = int(user.created_on.split(\"-\")[1])\n c_d = int(user.created_on.split(\"-\")[2])\n \n user.created_on = format_date_joined(c_y,c_m,c_d)\n \n return render_template('profile.html', user=user)\n \n###\n# The functions below should be applicable to all Flask apps.\n###\n\ndef format_date_joined(yy,mm,dd):\n return datetime.date(yy,mm,dd).strftime(\"%8, %d, %Y\")\n\ndef read_file(filename):\n data= \" \"\n \n with open(filename, \"r\") as stream:\n data = stream.read()\n \n return data\n\ndef form_errors(form):\n error_list=[]\n for field, errors in form.errors.items():\n for error in errors:\n error_list.append(field+\": \"+error)\n return error_list\n\n\n\n@app.after_request\ndef add_header(response):\n \"\"\"\n Add headers to both force latest IE rendering engine or Chrome Frame,\n and also to cache the rendered page for 10 minutes.\n \"\"\"\n response.headers['X-UA-Compatible'] = 'IE=Edge,chrome=1'\n response.headers['Cache-Control'] = 'public, max-age=0'\n return response\n\n\n@app.errorhandler(404)\ndef page_not_found(error):\n \"\"\"Custom 404 page.\"\"\"\n return render_template('404.html'), 404\n\n\nif __name__ == '__main__':\n app.run(debug=True,host=\"0.0.0.0\",port=\"8080\")","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"499141960","text":"import lasagne\nimport lasagne.layers as L\nimport lasagne.nonlinearities as NL\nimport numpy as np\n\nfrom rllab.core.lasagne_layers import ParamLayer, ParamLayerSplit\nfrom rllab.core.lasagne_powered import LasagnePowered\nfrom rllab.core.network import MLP, MLPAppend, MLP_PS, MLP_PROJ, MLP_PSD, MLP_Split, MLP_SplitAct, MLP_SoftSplit, MLP_MaskedSplit, MLP_MaskedSplitCont\nfrom rllab.spaces import Box\n\nfrom rllab.core.serializable import Serializable\nfrom rllab.policies.base import StochasticPolicy\nfrom rllab.misc.overrides import overrides\nfrom rllab.misc import logger\nfrom rllab.misc import ext\nfrom rllab.distributions.diagonal_gaussian import DiagonalGaussian\nimport theano.tensor as TT\nimport theano as T\n\nimport joblib\n\ndef selu(x):\n alpha = 1.6732632423543772848170429916717\n scale = 1.0507009873554804934193349852946\n return scale * TT.switch(x > 0, x, alpha * TT.expm1(x))\n\nclass GaussianMLPPolicy(StochasticPolicy, LasagnePowered, Serializable):\n def __init__(\n self,\n env_spec,\n hidden_sizes=(32, 32),\n learn_std=True,\n init_std=1.0,\n adaptive_std=False,\n std_share_network=False,\n std_hidden_sizes=(32, 32),\n min_std=1e-6,\n std_hidden_nonlinearity=NL.tanh,\n hidden_nonlinearity=NL.tanh,\n output_nonlinearity=None,\n mean_network=None,\n std_network=None,\n split_masks=None,\n dist_cls=DiagonalGaussian,\n mp_dim = 0,\n mp_sel_hid_dim = 0,\n mp_sel_num = 0,\n mp_projection_dim = 2,\n net_mode = 0, # 0: vanilla, 1: append mp to second layer, 2: project mp to lower space, 3: mp selection blending, 4: mp selection discrete\n split_init_net=None,\n split_units=None,\n wc_net_path = None,\n learn_segment = False,\n split_num = 1,\n split_layer=[0],\n split_std = False,\n task_id = 0,\n ):\n \"\"\"\n :param env_spec:\n :param hidden_sizes: list of sizes for the fully-connected hidden layers\n :param learn_std: Is std trainable\n :param init_std: Initial std\n :param adaptive_std:\n :param std_share_network:\n :param std_hidden_sizes: list of sizes for the fully-connected layers for std\n :param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues\n :param std_hidden_nonlinearity:\n :param hidden_nonlinearity: nonlinearity used for each hidden layer\n :param output_nonlinearity: nonlinearity for the output layer\n :param mean_network: custom network for the output mean\n :param std_network: custom network for the output log std\n :return:\n \"\"\"\n Serializable.quick_init(self, locals())\n assert isinstance(env_spec.action_space, Box)\n\n obs_dim = env_spec.observation_space.flat_dim\n action_dim = env_spec.action_space.flat_dim\n\n # create network\n if mean_network is None:\n if net_mode == 1:\n mean_network = MLPAppend(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n append_dim=mp_dim,\n )\n elif net_mode == 2:\n mean_network = MLP_PROJ(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n mp_dim=mp_dim,\n mp_hid_dim=16,\n mp_proj_dim=mp_projection_dim,\n )\n elif net_mode == 3:\n mean_network = MLP_PS(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n mp_dim=mp_dim,\n mp_sel_hid_dim=mp_sel_hid_dim,\n mp_sel_num=mp_sel_num,\n )\n elif net_mode == 4:\n wc_net = joblib.load(wc_net_path)\n mean_network = MLP_PSD(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n mp_dim=mp_dim,\n mp_sel_hid_dim=mp_sel_hid_dim,\n mp_sel_num=mp_sel_num,\n wc_net=wc_net,\n learn_segment = learn_segment,\n )\n elif net_mode == 5:\n mean_network = MLP_Split(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n split_layer=split_layer,\n split_num=split_num,\n )\n elif net_mode == 6:\n mean_network = MLP_SplitAct(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n split_num=split_num,\n split_units=split_units,\n init_net=split_init_net._mean_network,\n )\n elif net_mode == 7:\n mean_network = MLP_SoftSplit(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n split_num=split_num,\n init_net=split_init_net._mean_network,\n )\n elif net_mode == 8:\n mean_network = MLP_MaskedSplit(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n split_num=split_num,\n split_masks=split_masks,\n init_net=split_init_net._mean_network,\n )\n elif net_mode == 9:\n mean_network = MLP_MaskedSplitCont(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n task_id=task_id,\n init_net=split_init_net._mean_network,\n )\n else:\n mean_network = MLP(\n input_shape=(obs_dim,),\n output_dim=action_dim,\n hidden_sizes=hidden_sizes,\n hidden_nonlinearity=hidden_nonlinearity,\n output_nonlinearity=output_nonlinearity,\n )\n self._mean_network = mean_network\n\n l_mean = mean_network.output_layer\n obs_var = mean_network.input_layer.input_var\n\n if std_network is not None:\n l_log_std = std_network.output_layer\n else:\n if adaptive_std:\n std_network = MLP(\n input_shape=(obs_dim,),\n input_layer=mean_network.input_layer,\n output_dim=action_dim,\n hidden_sizes=std_hidden_sizes,\n hidden_nonlinearity=std_hidden_nonlinearity,\n output_nonlinearity=None,\n )\n l_log_std = std_network.output_layer\n else:\n if net_mode != 8 or not split_std:\n l_log_std = ParamLayer(\n mean_network.input_layer,\n num_units=action_dim,\n param=lasagne.init.Constant(np.log(init_std)),\n name=\"output_log_std\",\n trainable=learn_std,\n )\n else:\n l_log_std = ParamLayerSplit(\n mean_network.input_layer,\n num_units=action_dim,\n param=lasagne.init.Constant(np.log(init_std)),\n name=\"output_log_std\",\n trainable=learn_std,\n split_num = split_num,\n init_param=split_init_net.get_params()[-1]\n )\n if net_mode == 6 or net_mode == 7 or (net_mode == 8 and not split_std):\n l_log_std.get_params()[0].set_value(split_init_net.get_params()[-1].get_value())\n if net_mode == 9:\n l_log_std.get_params()[0].set_value(split_init_net.get_params()[-1].get_value() + 0.5)\n\n self.min_std = min_std\n\n mean_var, log_std_var = L.get_output([l_mean, l_log_std])\n\n if self.min_std is not None:\n log_std_var = TT.maximum(log_std_var, np.log(min_std))\n\n self._mean_var, self._log_std_var = mean_var, log_std_var\n\n self._l_mean = l_mean\n self._l_log_std = l_log_std\n self._dist = dist_cls(action_dim)\n\n LasagnePowered.__init__(self, [l_mean, l_log_std])\n super(GaussianMLPPolicy, self).__init__(env_spec)\n\n self._f_dist = ext.compile_function(\n inputs=[obs_var],\n outputs=[mean_var, log_std_var],\n )\n\n if net_mode == 3 or net_mode == 4:\n self._f_blendweight = ext.compile_function(\n inputs = [obs_var],\n outputs=[self._mean_network._blend_weights]\n )\n entropy = -TT.mean(self._mean_network._blend_weights * TT.log(self._mean_network._blend_weights))\n self._f_weightentropy = ext.compile_function(\n inputs = [obs_var],\n outputs=[entropy]\n )\n avg_weights = TT.mean(self._mean_network._blend_weights, axis=0)\n entropy2 = -TT.mean(avg_weights * TT.log(avg_weights))\n self._f_choiceentropy = ext.compile_function(\n inputs=[obs_var],\n outputs=[entropy2]\n )\n\n\n # average entropy of the blend weight\n def bw_entropy(self, obs_var):\n blend_weights = L.get_output(self._mean_network.l_blend_weights, obs_var)\n entropy = -TT.mean(blend_weights * TT.log(blend_weights))\n return entropy\n\n # average entropy of the blend weight across each sample\n def bw_choice_entropy(self, obs_var):\n blend_weights = L.get_output(self._mean_network.l_blend_weights, obs_var)\n avg_weights = TT.mean(blend_weights, axis=0)\n entropy = -TT.mean(avg_weights * TT.log(avg_weights))\n return entropy\n\n def dist_info_sym(self, obs_var, state_info_vars=None):\n mean_var, log_std_var = L.get_output([self._l_mean, self._l_log_std], obs_var)\n if self.min_std is not None:\n log_std_var = TT.maximum(log_std_var, np.log(self.min_std))\n return dict(mean=mean_var, log_std=log_std_var)\n\n @overrides\n def get_action(self, observation):\n flat_obs = self.observation_space.flatten(observation)\n mean, log_std = [x[0] for x in self._f_dist([flat_obs])]\n rnd = np.random.normal(size=mean.shape)\n action = rnd * np.exp(log_std) + mean\n return action, dict(mean=mean, log_std=log_std)\n\n def get_actions(self, observations):\n flat_obs = self.observation_space.flatten_n(observations)\n means, log_stds = self._f_dist(flat_obs)\n rnd = np.random.normal(size=means.shape)\n actions = rnd * np.exp(log_stds) + means\n return actions, dict(mean=means, log_std=log_stds)\n\n def get_reparam_action_sym(self, obs_var, action_var, old_dist_info_vars):\n \"\"\"\n Given observations, old actions, and distribution of old actions, return a symbolically reparameterized\n representation of the actions in terms of the policy parameters\n :param obs_var:\n :param action_var:\n :param old_dist_info_vars:\n :return:\n \"\"\"\n new_dist_info_vars = self.dist_info_sym(obs_var, action_var)\n new_mean_var, new_log_std_var = new_dist_info_vars[\"mean\"], new_dist_info_vars[\"log_std\"]\n old_mean_var, old_log_std_var = old_dist_info_vars[\"mean\"], old_dist_info_vars[\"log_std\"]\n epsilon_var = (action_var - old_mean_var) / (TT.exp(old_log_std_var) + 1e-8)\n new_action_var = new_mean_var + epsilon_var * TT.exp(new_log_std_var)\n return new_action_var\n\n def log_diagnostics(self, paths):\n log_stds = np.vstack([path[\"agent_infos\"][\"log_std\"] for path in paths])\n logger.record_tabular('AveragePolicyStd', np.mean(np.exp(log_stds)))\n\n @property\n def distribution(self):\n return self._dist\n","sub_path":"rllab/policies/gaussian_mlp_policy.py","file_name":"gaussian_mlp_policy.py","file_ext":"py","file_size_in_byte":13594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"634981270","text":"import argparse\nimport re\n\n\ndef get_price_from_terminal():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"price\", help=\"Price which you want to format\", type=str)\n args = parser.parse_args()\n return args.price\n\ndef convert_string_to_number(string):\n string = string.replace(',','.')\n number = float(string)\n return (int(number) if number.is_integer() else number)\n\ndef format_price(price=None):\n if isinstance(price, int) and (price >= 0):\n return \"{:,}\".format(price).replace(\",\", \" \")\n elif isinstance(price, float) and (price >= 0):\n return (format_price(int(price)) if price.is_integer() \n else \"{:,.2f}\".format(price).replace(\",\", \" \"))\n elif isinstance(price, str) and re.match(r'\\d+[\\.,]?\\d*$', price):\n price = convert_string_to_number(price)\n return format_price(price)\n else:\n raise ValueError\n\ndef main():\n price = get_price_from_terminal()\n print(format_price(price))\n\n\nif __name__ == '__main__':\n main()","sub_path":"format_price.py","file_name":"format_price.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"608227568","text":"\n# A very simple Flask Hello World app for you to get started with...\n\nfrom flask import Flask\nfrom flask import request, jsonify\nimport os\nfrom bs4 import BeautifulSoup\nimport requests\n\napp = Flask(__name__)\n\ndef scraper():\n r = requests.get(\"https://news.google.com/search?q=nri&hl=en-IN&gl=IN&ceid=IN%3Aen\")\n print(r.status_code)\n content = r.text\n soup = BeautifulSoup(content, \"html.parser\")\n\n st_divs1 = soup.find_all('div',{\"class\" : \"NiLAwe y6IFtc R7GTQ keNKEd j7vNaf nID9nc\"})\n print(len(st_divs1))\n\n data = []\n\n for i in range(len(st_divs1)) :\n # print('*'*40)\n st_divs = soup.find_all('div',{\"class\" : \"NiLAwe y6IFtc R7GTQ keNKEd j7vNaf nID9nc\"})[i]\n detail = st_divs.find('h3',{\"class\" : \"ipQwMb ekueJc RD0gLb\"}).text\n description = st_divs.find('span',{\"class\" : \"xBbh9\"}).text\n date_time = st_divs.find('time',{\"class\" : \"WW6dff uQIVzc Sksgp\"})['datetime']\n image = 'https://moonvillageassociation.org/wp-content/uploads/2018/06/default-profile-picture1.jpg'\n try:\n image_url = st_divs.find('img',{\"class\" : \"tvs3Id QwxBBf\"})['src']\n except Exception as e:\n print(e)\n pass\n\n # print(detail,description,date_time,image_url)\n # print('*'*40)\n\n scraper_data = {}\n scraper_data['details'] = detail\n scraper_data['description'] = description\n scraper_data['date_time'] = date_time\n scraper_data['image_url'] = image_url\n\n data.append(scraper_data)\n\n # print(data)\n return data\n\n\n\n@app.route('/',methods=['GET'])\ndef hello_world():\n scraper1 = scraper()\n # return 'Hello from Flask!'\n return jsonify(scraper1)\n\n\nif __name__ == '__main__':\n app.run()","sub_path":"flask_google_web_scraper.py","file_name":"flask_google_web_scraper.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"614525916","text":"#!/usr/bin/env python3\nimport os\nimport sys\nimport requests\nimport time\nfrom resources.log import getLogger\nfrom resources.readsettings import ReadSettings\nfrom resources.metadata import MediaType\nfrom resources.mediaprocessor import MediaProcessor\n\n\ndef rescanAndWait(host, port, webroot, apikey, protocol, movieid, log, retries=6, delay=10):\n headers = {'X-Api-Key': apikey}\n # First trigger rescan\n payload = {'name': 'RescanMovie', 'movieId': movieid}\n url = protocol + host + \":\" + str(port) + webroot + \"/api/command\"\n r = requests.post(url, json=payload, headers=headers)\n rstate = r.json()\n try:\n rstate = rstate[0]\n except:\n pass\n log.info(\"Radarr response Rescan command: ID %d %s.\" % (rstate['id'], rstate['state']))\n log.debug(str(rstate))\n\n # Then wait for it to finish\n url = protocol + host + \":\" + str(port) + webroot + \"/api/command/\" + str(rstate['id'])\n log.info(\"Waiting rescan to complete\")\n r = requests.get(url, headers=headers)\n command = r.json()\n attempts = 0\n while command['state'].lower() not in ['complete', 'completed'] and attempts < retries:\n log.debug(\"State: %s.\" % (command['state']))\n time.sleep(delay)\n r = requests.get(url, headers=headers)\n command = r.json()\n attempts += 1\n log.info(\"Final state: %s.\" % (command['state']))\n log.debug(str(command))\n return command['state'].lower() in ['complete', 'completed']\n\n\ndef getMovieInformation(host, port, webroot, apikey, protocol, movieid, log):\n headers = {'X-Api-Key': apikey}\n url = protocol + host + \":\" + str(port) + webroot + \"/api/movie/\" + movieid\n log.info(\"Requesting updated information from Radarr for movie ID %s.\" % movieid)\n r = requests.get(url, headers=headers)\n payload = r.json()\n return payload\n\n\ndef renameMovie(host, port, webroot, apikey, protocol, movieid, log):\n headers = {'X-Api-Key': apikey}\n # First trigger rescan\n payload = {'name': 'RenameMovie', 'movieIds': [movieid]}\n url = protocol + host + \":\" + str(port) + webroot + \"/api/command\"\n r = requests.post(url, json=payload, headers=headers)\n rstate = r.json()\n try:\n rstate = rstate[0]\n except:\n pass\n log.info(\"Radarr response Rename command: ID %d %s.\" % (rstate['id'], rstate['state']))\n log.debug(str(rstate))\n\n\nlog = getLogger(\"RadarrPostProcess\")\n\nlog.info(\"Radarr extra script post processing started.\")\n\nif os.environ.get('radarr_eventtype') == \"Test\":\n sys.exit(0)\n\nsettings = ReadSettings()\n\nlog.debug(os.environ)\n\ninputfile = os.environ.get('radarr_moviefile_path')\noriginal = os.environ.get('radarr_moviefile_scenename')\nimdbid = os.environ.get('radarr_movie_imdbid')\nmovieid = os.environ.get('radarr_movie_id')\n\nmp = MediaProcessor(settings)\n\nlog.debug(\"Input file: %s.\" % inputfile)\nlog.debug(\"Original name: %s.\" % original)\nlog.debug(\"IMDB ID: %s.\" % imdbid)\nlog.debug(\"Radarr Movie ID: %s.\" % movieid)\n\ntry:\n success = mp.fullprocess(inputfile, MediaType.Movie, original=original, imdbid=imdbid)\n\n if success:\n # Update Radarr to continue monitored status\n try:\n host = settings.Radarr['host']\n port = settings.Radarr['port']\n webroot = settings.Radarr['webroot']\n apikey = settings.Radarr['apikey']\n ssl = settings.Radarr['ssl']\n protocol = \"https://\" if ssl else \"http://\"\n\n log.debug(\"Radarr host: %s.\" % host)\n log.debug(\"Radarr port: %s.\" % port)\n log.debug(\"Radarr webroot: %s.\" % webroot)\n log.debug(\"Radarr apikey: %s.\" % apikey)\n log.debug(\"Radarr protocol: %s.\" % protocol)\n\n if apikey != '':\n headers = {'X-Api-Key': apikey}\n\n if rescanAndWait(host, port, webroot, apikey, protocol, movieid, log):\n log.info(\"Rescan command completed\")\n\n movieinfo = getMovieInformation(host, port, webroot, apikey, protocol, movieid, log)\n if not movieinfo.get('hasFile'):\n log.warning(\"Rescanned movie does not have a file, attempting second rescan.\")\n if rescanAndWait(host, port, webroot, apikey, protocol, movieid, log):\n movieinfo = getMovieInformation(host, port, webroot, apikey, protocol, movieid, log)\n if not movieinfo.get('hasFile'):\n log.warning(\"Rescanned movie still does not have a file, will not set to monitored to prevent endless loop.\")\n sys.exit(1)\n else:\n log.info(\"File found after second rescan.\")\n else:\n log.error(\"Rescan command timed out\")\n sys.exit(1)\n\n movieinfo['monitored'] = True\n\n # Then set that movie to monitored\n log.debug(\"Sending PUT request with following payload:\")\n log.debug(str(movieinfo)) # debug\n\n url = protocol + host + \":\" + str(port) + webroot + \"/api/movie/\" + str(movieid)\n r = requests.put(url, json=movieinfo, headers=headers)\n success = r.json()\n\n log.debug(\"PUT request returned:\")\n log.debug(str(success))\n log.info(\"Radarr monitoring information updated for movie %s.\" % success['title'])\n\n renameMovie(host, port, webroot, apikey, protocol, movieid, log)\n else:\n log.error(\"Rescan command timed out\")\n sys.exit(1)\n else:\n log.error(\"Your Radarr API Key is blank. Update autoProcess.ini to enable status updates.\")\n except:\n log.exception(\"Radarr monitor status update failed.\")\n else:\n log.info(\"Processing returned False.\")\n sys.exit(1)\nexcept:\n log.exception(\"Error processing file\")\n sys.exit(1)\n","sub_path":"postRadarr.py","file_name":"postRadarr.py","file_ext":"py","file_size_in_byte":6081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"12954616","text":"#this program is used load the file and do sorting using buble sort\r\n\r\n\r\nfrom data import main\r\ntry:\r\n file = open('number', 'r') # opening the file\r\n str_ = file.read() # reading the text and storing it into the object\r\n split_array = str_.split() # splitting the words to store the elements in array using sorted inbuilt function\r\n main.buble_sort(split_array) # calling the method\r\nexcept FileNotFoundError:\r\n print(\"FILE NOT FOUND\")","sub_path":"algorithm/bublesortword.py","file_name":"bublesortword.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"613673719","text":"# Copyright (c) maiot GmbH 2021. 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\n# or implied. See the License for the specific language governing\n# permissions and limitations under the License.\n\nimport os\nimport random\nfrom pathlib import Path\nfrom typing import Text\n\nimport pytest\n\nimport zenml\nfrom zenml.datasources import BaseDatasource\nfrom zenml.pipelines import BasePipeline\nfrom zenml.repo import Repository\nfrom zenml.utils import yaml_utils\nfrom zenml.version import __version__\n\n# Nicholas a way to get to the root\nZENML_ROOT = str(Path(zenml.__path__[0]).parent)\nTEST_ROOT = os.path.join(ZENML_ROOT, \"tests\")\n\n\ndef test_repo_double_init():\n # explicitly constructing another repository should fail\n with pytest.raises(Exception):\n _ = Repository()\n\n\ndef test_get_datasources(repo):\n ds_list = repo.get_datasources()\n\n assert \"my_csv_datasource\" in [x.name for x in ds_list]\n\n\ndef test_get_datasource_by_name(repo):\n assert repo.get_datasource_by_name(\"my_csv_datasource\")\n\n fake_ds = repo.get_datasource_by_name(\"ds_123\")\n\n assert fake_ds is None\n\n\ndef test_get_datasource_names(repo):\n # TODO [LOW]: Automatically expand when new datasource tests are added!\n test_ds_names = [\"my_csv_datasource\", \"image_ds_local\", \"image_ds_gcp\",\n \"json_ds\"]\n\n ds_names = repo.get_datasource_names()\n\n assert set(test_ds_names) <= set(ds_names)\n\n\ndef test_get_pipeline_file_paths(repo, monkeypatch):\n mock_paths = [\"pipeline_1.yaml\", \"pipeline_2.yaml\", \"awjfof.txt\"]\n\n def mock_list_dir(dir_path: Text, only_file_names: bool = False):\n # add a corrupted file into the pipelines\n return mock_paths\n\n monkeypatch.setattr(\"zenml.utils.path_utils.list_dir\",\n mock_list_dir)\n\n paths = repo.get_pipeline_file_paths(only_file_names=True)\n\n assert paths == mock_paths[:-1]\n\n\ndef test_get_pipeline_names(repo):\n # TODO: This has to be made dynamic once more pipelines come\n real_p_names = sorted([\"csvtest{0}\".format(i) for i in range(1, 6)])\n\n found_p_names = sorted(repo.get_pipeline_names())\n\n assert set(real_p_names) <= set(found_p_names)\n\n\ndef test_get_pipelines(repo):\n p_names = sorted(repo.get_pipeline_names())\n\n pipelines = repo.get_pipelines()\n\n pipelines = sorted(pipelines, key=lambda p: p.name)\n\n assert all(p.name == name for p, name in zip(pipelines, p_names))\n\n\ndef test_get_pipelines_by_datasource(repo):\n # asserted in an earlier test\n ds = repo.get_datasource_by_name(\"my_csv_datasource\")\n\n ds2 = BaseDatasource(name=\"ds_12254757\")\n\n pipelines = repo.get_pipelines_by_datasource(ds)\n\n pipelines_2 = repo.get_pipelines_by_datasource(ds2)\n\n assert len(pipelines) > 0\n\n assert not pipelines_2\n\n\ndef test_get_pipelines_by_type(repo):\n pipelines = repo.get_pipelines_by_type(type_filter=[\"training\"])\n\n pipelines_2 = repo.get_pipelines_by_type(type_filter=[\"base\"])\n\n assert len(pipelines) == 5\n\n assert not pipelines_2\n\n\ndef test_get_pipeline_by_name(repo, equal_pipelines):\n p_names = repo.get_pipeline_names()\n\n random_name = random.choice(p_names)\n cfg_list = [y for y in repo.get_pipeline_file_paths()\n if random_name in y]\n\n cfg = yaml_utils.read_yaml(cfg_list[0])\n\n p1 = repo.get_pipeline_by_name(random_name)\n\n p2 = BasePipeline.from_config(cfg)\n\n assert equal_pipelines(p1, p2, loaded=True)\n\n\ndef test_get_step_versions(repo):\n step_versions = repo.get_step_versions()\n\n # TODO: Make this less hardcoded\n steps_used = [\n \"zenml.steps.preprocesser.standard_preprocesser.standard_preprocesser.StandardPreprocesser\",\n \"zenml.steps.split.categorical_domain_split_step.CategoricalDomainSplit\",\n \"zenml.steps.trainer.tensorflow_trainers.tf_ff_trainer.FeedForwardTrainer\"\n ]\n\n current_version = \"zenml_\" + str(__version__)\n\n assert set(steps_used) >= set(step_versions.keys())\n assert all(current_version in s for s in step_versions.values())\n\n\ndef test_get_step_by_version(repo):\n # TODO: Make this less hardcoded\n steps_used = [\n \"zenml.steps.preprocesser.standard_preprocesser.standard_preprocesser.StandardPreprocesser\",\n \"zenml.steps.split.categorical_domain_split_step.CategoricalDomainSplit\",\n \"zenml.steps.trainer.tensorflow_trainers.tf_ff_trainer.FeedForwardTrainer\"\n ]\n\n random_step = random.choice(steps_used)\n\n current_version = \"zenml_\" + str(__version__)\n\n bogus_version = \"asdfghjklöä\"\n\n assert repo.get_step_by_version(random_step, current_version)\n assert repo.get_step_by_version(random_step, bogus_version) is None\n\n\ndef test_get_step_versions_by_type(repo):\n # TODO: Make this less hardcoded\n steps_used = [\n \"zenml.steps.preprocesser.standard_preprocesser.standard_preprocesser.StandardPreprocesser\",\n \"zenml.steps.split.categorical_domain_split_step.CategoricalDomainSplit\",\n \"zenml.steps.trainer.tensorflow_trainers.tf_ff_trainer.FeedForwardTrainer\"\n ]\n\n random_step = random.choice(steps_used)\n\n current_version = \"zenml_\" + str(__version__)\n\n bogus_step = \"asdfghjklöä\"\n\n step_versions = repo.get_step_versions_by_type(random_step)\n\n assert step_versions == {current_version}\n\n assert repo.get_step_versions_by_type(bogus_step) is None\n","sub_path":"zenml/repo/repo_test.py","file_name":"repo_test.py","file_ext":"py","file_size_in_byte":5730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"642307725","text":"# Updated matchings for the New Blender rig\n_shkey_list = [\n \"Basis\",\n \"adjustments\",\n \"brow_center_UP\",\n \"brow_inner_UP.L\",\n \"brow_inner_UP.R\",\n \"brow_outer_UP.L\",\n \"brow_outer_up.R\",\n \"brow_center_DN\",\n \"brow_inner_DN.L\",\n \"brow_inner_DN.R\",\n \"brow_outer_DN.L\",\n \"brow_outer_DN.R\",\n \"eye-blink.UP.L\",\n \"eye-blink.LO.L\",\n \"eye-blink.UP.R\",\n \"eye-blink.LO.R\",\n \"eye-flare.UP.L\",\n \"eye-flare.LO.L\",\n \"eye-flare.UP.R\",\n \"eye-flare.LO.R\",\n \"eyes-look.dn\",\n \"eyes-look.up\",\n \"wince.L\",\n \"wince.R\",\n \"sneer.L\",\n \"sneer.R\",\n \"lips-wide.L\",\n \"lips-wide.R\",\n \"lips-narrow.L\",\n \"lips-narrow.R\",\n \"lips-frown.L\",\n \"lips-frown.R\",\n \"lips-smile.L\",\n \"lips-smile.R\",\n \"lip-UP.C\",\n \"lip.UP.L\",\n \"lip.UP.R\",\n \"lip.DN.C\",\n \"lip.DN.L\",\n \"lip.DN.R\",\n \"jaw\"\n]\n\n# Create a dictionary mapping shapekeys to their indices\n_shkey2Index = {}\nfor i in range(len(_shkey_list)):\n _shkey2Index[_shkey_list[i]] = i\n\ndef getIndex(shapekey):\n \"\"\"Gets the index of the given shapekey string.\"\"\"\n return _shkey2Index[shapekey]\n\ndef getString(index):\n return _shkey_list[index]\n\ndef getIter():\n # Returns iterator instead of the list to prevent outside users from\n # modifying it.\n return iter(_shkey_list)\n\ndef getLength():\n return len(_shkey_list)\n\ndef getList():\n return _shkey_list","sub_path":"src/ShapekeyStore.py","file_name":"ShapekeyStore.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"210094327","text":"import cv2\r\nimport os\r\nimport scipy.misc as scm\r\nimport pandas as pd\r\nimport tensorflow as tf\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\nfrom test_preprocess_config import Valid_FLAGS\r\n\r\n\r\ntest_data_file = Valid_FLAGS.test_data_file\r\n\r\n\r\ndef open_img(name, color='RGB'):\r\n \"\"\" Open an image\r\n Args:\r\n name\t: Name of the sample\r\n color\t: Color Mode (RGB/BGR/GRAY)\r\n \"\"\"\r\n img = cv2.imread(os.path.join(Valid_FLAGS.test_img_directory, name))\r\n if color == 'RGB':\r\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\r\n return img\r\n elif color == 'BGR':\r\n return img\r\n elif color == 'GRAY':\r\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n return img\r\n else:\r\n print('Color mode supported: RGB/BGR. If you need another mode do it yourself :p')\r\n\r\n\r\ndef read_test_data():\r\n \"\"\"\r\n To read labels in csv\r\n \"\"\"\r\n test_table = [] # The names of images being trained\r\n label_file = pd.read_csv(test_data_file)\r\n print('READING LABELS OF TRAIN DATA')\r\n for i in range(label_file.shape[0]):\r\n name = str(label_file.at[i, 'image_id'])\r\n test_table.append(name)\r\n print('LABEL READING FINISHED')\r\n return test_table\r\n\r\n\r\ndef main(argv):\r\n # make processed img save dir\r\n suffix_path = os.path.join('processed_b',\r\n 'Images')\r\n img_save_dir_blouse = os.path.join(suffix_path,\r\n 'blouse')\r\n img_save_dir_dress = os.path.join(suffix_path,\r\n 'dress')\r\n img_save_dir_outwear = os.path.join(suffix_path,\r\n 'outwear')\r\n img_save_dir_skirt = os.path.join(suffix_path,\r\n 'skirt')\r\n img_save_dir_trousers = os.path.join(suffix_path,\r\n 'trousers')\r\n os.system('mkdir -p {}'.format(img_save_dir_blouse))\r\n os.system('mkdir -p {}'.format(img_save_dir_dress))\r\n os.system('mkdir -p {}'.format(img_save_dir_outwear))\r\n os.system('mkdir -p {}'.format(img_save_dir_skirt))\r\n os.system('mkdir -p {}'.format(img_save_dir_trousers))\r\n\r\n # read data\r\n test_set = read_test_data()\r\n test_num = len(test_set)\r\n print('test_num:', test_num)\r\n\r\n # process img\r\n test_iter = 0\r\n while test_iter < test_num:\r\n name = test_set[test_iter]\r\n img = cv2.imread(os.path.join(Valid_FLAGS.test_img_directory, name))\r\n img_shape = img.shape\r\n img_x = img_shape[0]\r\n img_y = img_shape[1]\r\n\r\n img_512 = np.zeros((512, 512, 3), dtype= np.float32)\r\n x_padding = (512 - img_x) // 2\r\n y_padding = (512 - img_y) // 2\r\n img_512[x_padding:x_padding+img_x, y_padding:y_padding+img_y, :] = img[:, :, :]\r\n\r\n name_split = name.split('/')\r\n dress_type = name_split[1]\r\n img_root_name = name_split[2]\r\n img_save_path = os.path.join(suffix_path, dress_type)\r\n filename = os.path.join(img_save_path, img_root_name)\r\n cv2.imwrite(filename=filename, img=img_512)\r\n\r\n test_iter = test_iter + 1\r\n\r\n print('Test Data Process Ends\\n')\r\n\r\n\r\nif __name__ == '__main__':\r\n tf.app.run()\r\n","sub_path":"Test/normal _test/preprocess/test_preprocess.py","file_name":"test_preprocess.py","file_ext":"py","file_size_in_byte":3237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"638528111","text":"from telethon import TelegramClient, events, Button, extensions, functions, utils\nfrom telethon.tl.types import UpdateBotInlineSend\nimport demjson\nimport os\nfrom os.path import dirname, realpath, join\nimport subprocess\n\nadmin = 637572531\napi_id = 922976\napi_hash = \"39f163482b277156e2c81a1e50145787\"\nstep = 0\ncaption = \"\"\nclient = TelegramClient(\"voice_bot_session\", api_id, api_hash)\nclient.parse_mode = None\nmsgs = {}\nconfig = {}\nWD = dirname(realpath(__file__))\n\n\ndef get_config():\n global config\n file = open(join(WD, \"config.json\"), \"r\")\n config = demjson.decode(file.read())\n file.close()\n\n\ndef save_config():\n file = open(join(WD, \"config.json\"), \"w\")\n file.write(demjson.encode(config))\n file.close()\n\n\n@client.on(events.NewMessage())\nasync def my_event_handler(event):\n global caption\n global step\n if len(config) == 0:\n try:\n get_config()\n except Exception as er:\n save_config()\n print(str(er))\n\n message = event.message\n print(len(message.text))\n if event.sender_id == admin:\n if len(message.text) != 0:\n caption = message.text\n step = 1\n await event.reply(\"أرسل الصوت الأن\")\n elif message.voice is not None and step != 0:\n try:\n if message.voice is not None:\n config[caption] = utils.pack_bot_file_id(message.voice)\n await event.reply(\"تم اضافه الاغنيه\")\n save_config()\n get_config()\n except Exception as er:\n save_config()\n print(str(er))\n step = 0\n caption = \"\"\n # await client.send_voice\n elif message.audio is not None and step != 0:\n try:\n file = await client.download_media(message.media, \"tmp\")\n os.rename(file.replace(\"\\\\\", \"/\"), \"tmp/file.mp3\")\n command = \"ffmpeg -y -i ./tmp/file.mp3 -acodec libopus -b:a 44100 -ar 48000 -vbr on -compression_level 10 -ac 1 -max_muxing_queue_size 9999 -vsync 2 ./tmp/file.ogg\"\n\n process = subprocess.Popen(\n command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True\n )\n process.wait()\n if process.returncode == 0:\n file = await client.send_file(\n event.sender_id,\n file=(\"./tmp/file.ogg\"),\n supports_streaming=True,\n )\n if file.voice is not None:\n config[caption] = utils.pack_bot_file_id(file.voice)\n await event.reply(\"تم اضافه الاغنيه\")\n save_config()\n get_config()\n os.remove(\"./tmp/file.mp3\")\n os.remove(\"./tmp/file.ogg\")\n except Exception as er:\n os.remove(\"./tmp/file.mp3\")\n os.remove(\"./tmp/file.ogg\")\n print(str(er))\n print(type(er))\n elif message.video is not None and step != 0:\n try:\n file = await client.download_media(message.media, \"tmp\")\n os.rename(file.replace(\"\\\\\", \"/\"), \"tmp/file.mp4\")\n command = \"ffmpeg -y -i ./tmp/file.mp4 -acodec libopus -b:a 44100 -vn ./tmp/file.ogg\"\n process = subprocess.Popen(\n command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True\n )\n process.wait()\n if process.returncode == 0:\n file = await client.send_file(\n event.sender_id,\n file=(\"./tmp/file.ogg\"),\n supports_streaming=True,\n )\n if file.voice is not None:\n config[caption] = utils.pack_bot_file_id(file.voice)\n await event.reply(\"تم اضافه الاغنيه\")\n save_config()\n get_config()\n os.remove(\"./tmp/file.mp4\")\n os.remove(\"./tmp/file.ogg\")\n except Exception as er:\n os.remove(\"./tmp/file.mp4\")\n os.remove(\"./tmp/file.ogg\")\n print(str(er))\n print(type(er))\n else:\n await event.reply(\"لأضافه مقطع راسل @anime19\")\n\n\n@client.on(events.InlineQuery)\nasync def handler(event):\n try:\n builder = event.builder\n to = event.query.query\n arr = [\n builder.document(\n file=utils.resolve_bot_file_id(value), title=key, type=\"voice\"\n )\n for key, value in config.items()\n if to in key.lower()\n ]\n if len(arr) != 0:\n await event.answer(arr)\n else:\n await event.answer([builder.article(\"test\", \"test\")])\n except Exception as er:\n print(er)\n\n\ndef isint(value):\n try:\n int(value)\n return True\n except ValueError:\n return False\n\n\nclient.start()\nclient.run_until_disconnected()\n","sub_path":"basma.py","file_name":"basma.py","file_ext":"py","file_size_in_byte":5241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"432804099","text":"import json\nimport numpy as np\nimport pandas as pd\n\nfrom scipy.integrate import quad\nfrom scipy.special import erf\nfrom scipy.optimize import curve_fit\n\n\nclass StandardTests:\n def __init__(self):\n self.sqrt2 = np.sqrt(2)\n\n def generalized_erf(self, x, a, mu, sigma):\n return a * 0.5 * (1.0 + erf((x - mu)/(sigma * self.sqrt2)))\n\n def sat_prob(self, pert_25, pert_75):\n\n mu = (pert_75 + pert_25) / 2.0\n sigma = (mu - pert_25) / 0.67449\n\n return 1.0, mu, sigma\n\n def gpa_prob(self, bin_centers, bin_totals):\n\n cumulative_totals = np.cumsum(bin_totals) / 100.0\n popt, pcov = curve_fit(self.generalized_erf, np.append(bin_centers, 4.0), np.append(cumulative_totals, 1.0),\n p0=[1, 3.0, 0.5], maxfev=10000)\n\n return tuple(popt)\n\n\nclass ScoreProbability:\n def __init__(self):\n self.sqrt2 = np.sqrt(2)\n self.gauss_params = json.load(open('output/gauss_parameters_by_school.json'))\n self.school_names = sorted({self.gauss_params[i]['name']: i for i in self.gauss_params}.items())\n\n def generalized_erf(self, x, a, mu, sigma):\n return a * 0.5 * (1.0 + erf((x - mu)/(sigma * self.sqrt2)))\n\n def calculate_probability(self, a, mu, sigma, score, test_type, act_writing=False):\n if test_type == 'sat':\n scoring_range = (200, 800)\n elif test_type == 'act':\n if not act_writing:\n scoring_range = (1, 36)\n else:\n scoring_range = (2, 12)\n elif test_type == 'gpa':\n scoring_range = (1.0, 4.0)\n\n if score >= scoring_range[1]:\n return 1.0\n else:\n x = max(scoring_range[0], score)\n return min(self.generalized_erf(x, a, mu, sigma), 1.0)\n\n def student_prob(self, iped_id, scores={}):\n p_scores = {}\n if 'gender' in scores:\n if scores['gender'] == 'm' or scores['gender'] == 'male':\n p_scores['admission'] = self.gauss_params[iped_id]['percent_admitted_men']/100.0\n elif scores['gender'] == 'f' or scores['gender'] == 'female':\n p_scores['admission'] = self.gauss_params[iped_id]['percent_admitted_women']/100.0\n else:\n p_scores['admission'] = self.gauss_params[iped_id]['percent_admitted_total']/100.0\n\n if 'sat_reading' in scores:\n sat_params = self.gauss_params[iped_id]['sat_params']['reading']\n if sat_params is not None:\n p_scores['sat_reading'] = self.calculate_probability(sat_params[0], sat_params[1], sat_params[2],\n scores['sat_reading'], test_type='sat')\n else:\n p_scores['sat_reading'] = None\n\n if 'sat_writing' in scores:\n sat_params = self.gauss_params[iped_id]['sat_params']['writing']\n if sat_params is not None:\n p_scores['sat_writing'] = self.calculate_probability(sat_params[0], sat_params[1], sat_params[2],\n scores['sat_writing'], test_type='sat')\n else:\n p_scores['sat_writing'] = None\n\n if 'sat_math' in scores:\n sat_params = self.gauss_params[iped_id]['sat_params']['math']\n if sat_params is not None:\n p_scores['sat_math'] = self.calculate_probability(sat_params[0], sat_params[1], sat_params[2],\n scores['sat_math'], test_type='sat')\n else:\n p_scores['sat_math'] = None\n\n if 'act_composite' in scores:\n act_params = self.gauss_params[iped_id]['act_params']['composite']\n if act_params is not None:\n p_scores['act_composite'] = self.calculate_probability(act_params[0], act_params[1], act_params[2],\n scores['act_composite'], test_type='act')\n else:\n p_scores['act_composite'] = None\n\n if 'act_english' in scores:\n act_params = self.gauss_params[iped_id]['act_params']['english']\n if act_params is not None:\n p_scores['act_english'] = self.calculate_probability(act_params[0], act_params[1], act_params[2],\n scores['act_reading'], test_type='act')\n else:\n p_scores['act_english'] = None\n\n if 'act_math' in scores:\n act_params = self.gauss_params[iped_id]['act_params']['math']\n if act_params is not None:\n p_scores['act_math'] = self.calculate_probability(act_params[0], act_params[1], act_params[2],\n scores['act_math'], test_type='act')\n else:\n p_scores['act_math'] = None\n\n if 'act_writing' in scores:\n act_params = self.gauss_params[iped_id]['act_params']['writing']\n if act_params is not None:\n p_scores['act_writing'] = self.calculate_probability(act_params[0], act_params[1], act_params[2],\n scores['act_writing'], test_type='act', act_writing=True)\n else:\n p_scores['act_writing'] = None\n\n if 'gpa' in scores:\n gpa_params = self.gauss_params[iped_id]['gpa_params']\n if gpa_params is not None:\n p_scores['gpa'] = self.calculate_probability(gpa_params[0], gpa_params[1], gpa_params[2],\n scores['gpa'], test_type='gpa')\n else:\n p_scores['gpa'] = None\n\n return p_scores\n\n\ndef compute_gaussian_patameters():\n schools = pd.read_csv('input/school.csv')\n gpa_data = pd.read_csv('input/second-r.csv')\n ipeds = pd.read_csv('input/ipeds.csv')\n\n combined = pd.merge(pd.merge(schools, gpa_data, on='scid'), ipeds, left_on='iped', right_on='unitid')\n\n gauss_dict = {}\n st = StandardTests()\n gpa_xarr = [0.5, 1.5, 2.25, 2.75, 3.125, 3.375, 3.625, 3.875]\n\n for i in range(len(combined)):\n this_school = {}\n this_school['name'] = combined['name'][i]\n this_school['state'] = combined['state'][i]\n\n this_school['recommendation_req'] = combined['ADM2014.Recommendations'][i]\n this_school['test_score_req'] = combined['ADM2014.Admission test scores'][i]\n this_school['open_admission'] = combined['IC2014.Open admission policy'][i]\n\n this_school['percent_admitted_total'] = combined['DRVADM2014.Percent admitted - total'][i]\n this_school['percent_admitted_men'] = combined['DRVADM2014.Percent admitted - men'][i]\n this_school['percent_admitted_women'] = combined['DRVADM2014.Percent admitted - women'][i]\n\n sat_writing = st.sat_prob(combined['ADM2014.SAT Writing 25th percentile score'][i],\n combined['ADM2014.SAT Writing 75th percentile score'][i])\n sat_reading = st.sat_prob(combined['ADM2014.SAT Critical Reading 25th percentile score'][i],\n combined['ADM2014.SAT Critical Reading 75th percentile score'][i])\n sat_math = st.sat_prob(combined['ADM2014.SAT Math 25th percentile score'][i],\n combined['ADM2014.SAT Math 75th percentile score'][i])\n\n this_school['sat_params'] = {}\n if np.sum(sat_writing) == np.sum(sat_writing):\n this_school['sat_params']['writing'] = sat_writing\n else:\n this_school['sat_params']['writing'] = None\n if np.sum(sat_reading) == np.sum(sat_reading):\n this_school['sat_params']['reading'] = sat_reading\n else:\n this_school['sat_params']['reading'] = None\n if np.sum(sat_math) == np.sum(sat_math):\n this_school['sat_params']['math'] = sat_math\n else:\n this_school['sat_params']['math'] = None\n\n act_english = st.sat_prob(combined['ADM2014.ACT English 25th percentile score'][i],\n combined['ADM2014.ACT English 75th percentile score'][i])\n act_writing = st.sat_prob(combined['ADM2014.ACT Writing 25th percentile score'][i],\n combined['ADM2014.ACT Writing 75th percentile score'][i])\n act_math = st.sat_prob(combined['ADM2014.ACT Math 25th percentile score'][i],\n combined['ADM2014.ACT Math 75th percentile score'][i])\n act_composite = st.sat_prob(combined['ADM2014.ACT Composite 25th percentile score'][i],\n combined['ADM2014.ACT Composite 75th percentile score'][i])\n\n this_school['act_params'] = {}\n if np.sum(act_writing) == np.sum(act_writing):\n this_school['act_params']['writing'] = act_writing\n else:\n this_school['act_params']['writing'] = None\n if np.sum(act_english) == np.sum(act_english):\n this_school['act_params']['english'] = act_english\n else:\n this_school['act_params']['english'] = None\n if np.sum(act_math) == np.sum(act_math):\n this_school['act_params']['math'] = act_math\n else:\n this_school['act_params']['math'] = None\n if np.sum(act_composite) == np.sum(act_composite):\n this_school['act_params']['composite'] = act_composite\n else:\n this_school['act_params']['composite'] = None\n\n gpa_yarr = [g if g == g else 0 for g in [combined['GPA_Range_' + str(g)][i] for g in range(8, 0, -1)]]\n if sum(gpa_yarr) > 10:\n this_school['gpa_params'] = st.gpa_prob(gpa_xarr, gpa_yarr)\n else:\n this_school['gpa_params'] = None\n\n gauss_dict[combined['iped'][i]] = this_school\n\n json.dump(gauss_dict, open('output/gauss_parameters_by_school.json', 'w'))\n","sub_path":"sat_probability.py","file_name":"sat_probability.py","file_ext":"py","file_size_in_byte":10020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"620465707","text":"import cv2\nimport numpy as np\nimport sys\nfrom sample.database.database import Database\n# from learning.number_recognization.seperate_number import store_data\nfrom seperate_number import store_data\n\nclass Point(object):\n def __init__(self, ):\n pass\n\n\n\ndef overlap(cnt1, cnt2):\n [x1, y1, w1, h1] = cv2.boundingRect(cnt1)\n [x2, y2, w2, h2] = cv2.boundingRect(cnt2)\n if x1 > x2+w2 or x2 > x1+w1:\n return False\n\n if y1 > y2+h2 or y2 > y1+h1:\n return False\n\n\ndef enclose(inner, outer):\n '''\n :param cnt1:\n :param cnt2:\n :return: True if inner in outer\n False if cnt1 does not in cnt2\n '''\n [x1, y1, w1, h1] = cv2.boundingRect(inner)\n [x2, y2, w2, h2] = cv2.boundingRect(outer)\n if x1 > x2 and x1+w1 < x2+w2 and y1 > y2 and y1+h1 < y2+h2:\n return True\n\n return False\n\n\ndef training():\n db = Database(\"Ruijie\", \"XXXXXXXX\", \"142.93.59.116\", \"Student_grade\")\n data = db.queryColsData(\"machine_learning\", [\"result\", \"img\"] )\n samples = []\n response = [] # np.array(dtype=np.float32)\n for item in data:\n # print(item)\n samples.append(item[1].strip(\"[\").strip(\"]\").split(\", \"))\n response.append(item[0])\n\n\n\n # data = np.array(data)\n # print(data)\n samples = np.array(samples, dtype=np.float32)\n response = np.array(response, dtype=np.float32)\n # sys.exit(1)\n model = cv2.ml.KNearest_create()\n model.train(samples, cv2.ml.ROW_SAMPLE, response)\n return model\n\n\ndef clean_cnt(contours, im):\n new_contours = []\n height, weight, _ = im.shape\n max_area = 0.01 * height * weight\n for cnt in contours:\n [x,y,w,h] = cv2.boundingRect(cnt)\n if cv2.contourArea(cnt)>10 and h > 10 and cv2.contourArea(cnt) \")\n if answer != 'Y' and answer != 'y':\n sys.exit(0)\n\n db_name = \"Student_grade\"\n data = []\n db = Database(\"Ruijie\", \"XXXXXXXX\", \"142.93.59.116\", db_name)\n assert len(new_response) == len(new_sample)\n for i in range(len(new_sample)):\n db.insert_data([ new_response[i], list(new_sample[i]) ], \"machine_learning\")\n\n\n\n\n # np.savetxt(\"tmpsamples.txt\", new_sample)\n # np.savetxt(\"tmpresponses.txt\", new_response)\n #\n # tmp = open(\"tmpsamples.txt\", \"r\")\n # sample = open(\"generalsample.txt\", \"a\")\n #\n # for line in tmp:\n # sample.write(line)\n #\n # sample.close()\n # tmp.close()\n #\n # tmp = open(\"tmpresponses.txt\", \"r\")\n # response = open(\"generalresponse.txt\", \"a\")\n #\n # for line in tmp:\n # response.write(line)\n #\n # response.close()\n # tmp.close()\n","sub_path":"learning/number_recognization/learning.py","file_name":"learning.py","file_ext":"py","file_size_in_byte":5554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"70219533","text":"import time, base64, hmac, hashlib, json\nimport pandas as pd\nimport numpy as np\nfrom random import randint\nimport pandas as pd\nfrom threading import Thread\nfrom websocket import create_connection, WebSocketApp, WebSocketConnectionClosedException\nfrom multiprocessing import Pool\nfrom multiprocessing.dummy import Pool as ThreadPool\nfrom random import randint\nimport requests\n\nclass Client():\n \"\"\"\n @info: \n Websocket client used to connect to the Coinbase exchange. Listening to the websocket for \n updates instead of polling the server via HTTP calls is highly recomment to decrease overhead and \n improve performance.\n \n - API Docs: https://docs.pro.coinbase.com/#websocket-feed\n \n supported channels: - ticker, level2, user\n supported products: - BTC-USD, LTC-USD, ETH-USD, ETC-USD, LTC-BTC, ETH-BTC, ETC-BTC, BCH-USD, BCH-BTC\n \n @use:\n ws = CoinbaseWebsocket(products, channels, credentials=None, production=True)\n \n @params ( '*' required ):\n products * : List of products to listen for update\n channels * : List of channels to subscribe to\n credentials: Dictionary with the API credentials needed to connect to Coinbase\n production : Boolean. if set to True the websocket will connect via url 'wss://ws-feed.pro.coinbase.com' \n else if set to False the websocket will connect via url 'wss://ws-feed-public.sandbox.pro.coinbase.com'\n\n @variables:\n data : dictionary data variable stores the consumable websocket messages post processing. structure\n 'BTC-USD': { \n 'ticker': { \n 'history': list, \n 'live': None \n },\n 'orderbook': instance of OrderBookManagement class,\n 'ohlc': instance of OHLC class\n },\n 'orders' : instance of OrderManagement class\n \n example: >>> ws.data['BTC-USD']['ticker']\n { \n 'history': \n [ \n {'time': 1533828390.86529,'price': 4388.01 }, \n {'time': 1533828452.0009532,'price': 4385.01 },\n ...\n ], \n 'live': {\n 'best_ask': 6423.08,\n 'best_bid': 6422.59,\n 'high_24h': 6485.76,\n 'last_size': 0.00511036,\n 'low_24h': 6003.0,\n 'open_24h': 6418.01,\n 'price': 6423.08,\n 'product_id': 'BTC-USD',\n 'sequence': 6555468983,\n 'side': 'buy',\n 'time': 1533828452.0009532,\n 'trade_id': 48603077,\n 'type': 'ticker',\n 'volume_24h': 14287.80656342,\n 'volume_30d': 307449.79720148}\n }\n \n >>> ws.data['BTC-USD']['ohlc']['1day'].candles\n time low high open close volume\n 1537465260 1537465260 6400.15 6402.96 6400.16 6402.95 20.687342\n 1537465320 1537465320 6402.96 6405.00 6402.96 6405.00 4.263147\n ...\n \n >>> ws.data['BTC-USD']['orderbook'].book\n DataFrame\n Columns: [size, side]\n Index: [price]\n\n example:\n size side\n price \n 7037.95 0.000000 asks\n 7036.54 0.000000 bids\n 7036.16 0.000000 asks\n ...\n\n >>> ws.data['BTC-USD']['orderbook'].asks(remove_zeros=True)\n price size\n 0 7032.33 2.576296\n 1 7033.00 0.030000\n 2 7033.06 0.026360\n ...\n Note: remove_zeros=True will remove price levels with a size value of 0\n\n >>> ws.data['BTC-USD']['orderbook'].bids(remove_zeros=True)\n price size\n 0 7032.32 19.915242\n 1 7032.31 1.000000\n 2 7031.77 0.001000\n ... \n Note: remove_zeros=True will remove price levels with a size value of 0\n\n >>> ws.data['orders'].records\n [\n { \"type\": \"received\", \"time\": \"2014-11-07T08:19:27.028459Z\", \"product_id\": \"BTC-USD\", \"sequence\": 10, \"order_id\": \"d50ec984-77a8-460a-b958-66f114b0de9b\", \"size\": \"1.34\", \"price\": \"502.1\", \"side\": \"buy\", \"order_type\": \"limit\" },\n { \"type\": \"open\", \"time\": \"2014-11-07T08:19:27.028459Z\", \"product_id\": \"BTC-USD\", \"sequence\": 10, \"order_id\": \"d50ec984-77a8-460a-b958-66f114b0de9b\", \"price\": \"200.2\", \"remaining_size\": \"1.00\", \"side\": \"sell\" },\n ...\n ]\n\n >>> ws.data['orders'].orders\n DataFrame\n Columns: [sequence, order_id, create_time, update_time, product_id, order_type, side, stop_price, price, size, USD, BTC, LTC, ETH, BCH, ETC, taker_fee_rate, status]\n Index: []\n \n @methods:\n open( products, channels, credentials, production ): Opens the connection and subscribes to the given channels for the given products\n add\n close(): closes the connection to the websocket. This method does not clear out the data variable.\n \"\"\"\n \n def __init__(self): \n self.messages = []\n self.ws = None\n self.subscription = None\n self.conn_thread = None\n self.terminated = False\n self.errorCnt = 0\n self.data = { }\n \n self.products = []\n self.channels = []\n\n self.PRODUCTS = ['BTC-USD','LTC-USD','ETH-USD','ETC-USD','LTC-BTC','ETH-BTC','ETC-BTC','BCH-USD','BCH-BTC']\n self.CHANNELS = ['ticker','level2','user']\n self.accepted_message_type = ['errors'] \n self.max_errors_allowed = 100 \n\n \n def Ticker(self, ticker):\n \"\"\"Receives the ticker updates and retains the history and updates the 'current' attribute in self.data.ticker\"\"\"\n try:\n for col in ['price', 'last_size', 'best_bid', 'best_ask','high_24h','low_24h','open_24h','volume_24h','volume_30d' ]:\n try:\n ticker[col] = float(ticker[col].rstrip('0'))\n except:\n ticker[col] = 0.0\n ticker['time'] = time.time()\n self.data[ticker['product_id']]['ticker']['history'].append( {'time': ticker['time'],'price': ticker['price'] })\n self.data[ticker['product_id']]['ticker']['live'] = ticker\n \n # Supports OHLC. This updates the candles\n for increment in self.data[ticker['product_id']]['ohlc']:\n self.data[ticker['product_id']]['ohlc'][increment].update(ticker)\n \n except Exception as e:\n self.on_error(None, \"Error processing Ticker update: Message -> {} \\n {}\".format(e, ticker))\n pass\n\n \n def monitor(self):\n \"\"\"Monitors the messages received and processes them individually\"\"\"\n procs = np.min([len(self.products), 4])\n def preprocess(product):\n msgs = [x for x in self.messages if 'product_id' in x and x['product_id'] == product ]\n for msg in msgs:\n self.process(msg)\n \n while not self.terminated:\n try:\n if self.messages:\n pool = ThreadPool(procs)\n pool.map(preprocess, self.products)\n pool.close()\n pool.join()\n except Exception as e:\n self.on_error(None, \"Monitoring Error: {}\".format(e))\n continue\n finally:\n time.sleep(0.1) \n \n \n def process(self, message):\n \"\"\"This method removes the message received from the list of messages, then routes \\n the message to the appropriate function\"\"\"\n try:\n self.messages.remove(message)\n if message['type'] in self.accepted_message_type[1:]:\n if message['type'] in [\"ticker\"]:\n self.Ticker(message)\n elif message['type'] in [\"snapshot\", \"l2update\"]:\n self.data[message['product_id']]['orderbook'].update( message )\n elif message['type'] in [\"received\",\"open\",\"done\",\"match\",\"change\",\"activate\"]:\n self.data['orders'].update( message )\n elif message['type'] == 'error':\n self.on_error(None, message['message'])\n except Exception as e:\n raise Exception(\"Process raised an error: {}\".format(e))\n\n\n def subscription_message(self, products, channels, credentials):\n \"\"\"Creates the subscription request message. Heartbeat is added to all subscription messages\"\"\"\n self.products = self.verify_products_n_channels(products, self.PRODUCTS, 'products', True )\n self.channels = self.verify_products_n_channels(channels, self.CHANNELS, 'channels', False)\n\n if 'heartbeat' not in self.channels:\n self.channels.append('heartbeat')\n \n parameters = {\n \"type\": \"subscribe\",\n \"product_ids\": self.products,\n \"channels\": self.channels\n }\n \n if credentials: \n # this code was copied from https://github.com/danpaquin/gdax-python\n timestamp = str(time.time())\n message = timestamp + 'GET' + '/users/self/verify'\n message = message.encode('ascii')\n hmac_key = base64.b64decode(credentials['b64secret'])\n signature = hmac.new(hmac_key, message, hashlib.sha256)\n signature_b64 = base64.b64encode(signature.digest()).decode('utf-8').rstrip('\\n')\n parameters['signature'] = signature_b64\n parameters['key'] = credentials['key']\n parameters['passphrase']= credentials['passphrase']\n parameters['timestamp'] = timestamp\n\n return json.dumps(parameters)\n \n\n def on_message(self, ws, message):\n \"\"\"Appends the message from the ws to the list of messages to process later\"\"\"\n message = json.loads(message)\n if message['type'] == 'error':\n self.on_error(None, message['message'])\n elif message['type'] == 'subscriptions':\n print(\"Subscribed to {}\".format(', '.join([ channel['name'] for channel in message['channels'] ])))\n elif message['type'] in self.accepted_message_type:\n self.messages.append(message)\n \n\n def on_error(self, ws, error):\n \"\"\"Prints the errors\"\"\"\n print(error)\n if self.errorCnt == self.max_errors_allowed:\n self.close()\n else:\n self.errorCnt += 1\n\n def on_close(self, ws):\n \"\"\"Confirms closed connection\"\"\"\n print(\"Connection closed\")\n \n def on_open(self, ws):\n \"\"\"Sends the initial subscription message to the server\"\"\"\n ws.send(self.subscription)\n self.terminated = False\n print(\"Connected. Awaiting subscription message. {}\".format(self.url))\n\n def close(self):\n \"\"\"Sets the terminate variable to true to indicate that the connection was closed \\n by the client. This will prevent self.start from restarting when the closed message is received\"\"\"\n self.terminated = True\n if self.ws:\n self.ws.close()\n self.ws = None\n self.opened = False\n if self.conn_thread:\n self.conn_thread.join()\n\n def connect(self):\n try:\n monitor = Thread(target=self.monitor, name='Monitor method')\n monitor.start()\n \n self.ws = WebSocketApp(\n url = self.url,\n on_open = self.on_open, \n on_message = self.on_message,\n on_error = self.on_error,\n on_close = self.on_close,\n keep_running = True\n )\n self.ws.run_forever()\n except Exception as e:\n monitor.join()\n raise Exception('Connection failed. Error {}'.format(e))\n\n\n def verify_products_n_channels(self, items, valid, item_type, upper_case=False):\n if type(items) is not list:\n items = [ items ]\n valid = [ item.upper() if upper_case else item for item in items if item.upper() in ' '.join(valid).upper().split(' ') ]\n if not valid:\n raise Exception(\"No valid {} received\".format(item_type))\n else:\n return valid\n\n\n def set_accepted_message_types(self, channels): \n if \"ticker\" in channels:\n self.accepted_message_type += [\"ticker\"]\n if \"level2\" in channels:\n self.accepted_message_type += [\"snapshot\",\"l2update\"]\n if \"user\" in channels:\n self.accepted_message_type += [\"received\",\"open\",\"done\",\"match\",\"change\",\"activate\"]\n\n\n def set_data(self, products, increments=[]):\n return {\n **{ product: { \n 'ticker' : { 'history': [], 'live': None }, \n 'orderbook': OrderBookManagement(),\n 'ohlc': { increment: OHLC(product, increment) for increment in increments}, } for product in products }, \n **{ 'orders': OrderManagement() } \n }\n\n \n def add_subscription(self, products=[], channels=[], increments=[], credentials=None):\n try:\n if not self.terminated and self.ws:\n \n self.products += products\n self.channels += channels\n \n self.set_accepted_message_types(channels)\n self.data = { **self.set_data(products, increments), **self.data }\n subscription = self.subscription_message( self.products, self.channels, credentials ) \n self.ws.send(subscription)\n else:\n raise Exception(\"Websocket connection is not open. Only add subscription after opening connection. To initialize a new connection ws.open({},{},{})\".format(products, channels, credentials))\n except Exception as e:\n raise Exception(\"Failed to add subscription. Error {}\".format(e))\n\n \n def open(self, products, channels, increments=[], credentials=None, production=True):\n \"\"\"Opens a new connection to the websocket\"\"\"\n try:\n if production: \n self.url = 'wss://ws-feed.pro.coinbase.com'\n else:\n self.url = 'wss://ws-feed-public.sandbox.pro.coinbase.com' \n\n if self.ws:\n print(\"Closing existing websocket connection\")\n self.close()\n\n self.set_accepted_message_types(channels)\n self.data = { **self.set_data(products, increments), **self.data }\n self.subscription = self.subscription_message( products, channels, credentials )\n \n self.conn_thread = Thread(target=self.connect, name='Websocket Connection')\n self.conn_thread.start()\n except Exception as e:\n self.on_error(self.ws, \"Error from openning connection. Error -> {}\".format(e))\n\n\n \n \nclass OHLC():\n def __init__(self, product, increment, production=True):\n # this code was copied from https://github.com/danpaquin/gdax-python\n def _get(path, params=None, timeout=30):\n r = requests.get(self.url + path, params=params, timeout=timeout)\n r.raise_for_status()\n return r.json()\n # this code was copied from https://github.com/danpaquin/gdax-python\n def get_product_historic_rates(product_id, granularity=None):\n params = {}\n if granularity is not None:\n acceptedGrans = [60, 300, 900, 3600, 21600, 86400]\n if granularity not in acceptedGrans:\n newGranularity = min(acceptedGrans, key=lambda x:abs(x-granularity))\n print(granularity,' is not a valid granularity level, using',newGranularity,' instead.')\n granularity = newGranularity\n params['granularity'] = granularity\n return _get('/products/{}/candles'.format(str(product_id)), params=params)\n \n self.increment = increment\n self.product = product\n if production: api_url=\"https://api.pro.coinbase.com\"\n else: api_url=\"https://api-public.sandbox.pro.coinbase.com\"\n self.url = api_url.rstrip('/')\n self.granularity = 0\n if increment[-3:] == 'min': self.granularity = 60 * int(increment[:-3])\n elif increment[-4:] == 'hour': self.granularity = 3600 * int(increment[:-4])\n elif increment[-3:] == 'day': self.granularity = 86400 * int(increment[:-3])\n\n candles = get_product_historic_rates(product_id=product, granularity=self.granularity)\n self.candles = pd.DataFrame(candles, columns=[ 'time', 'low', 'high', 'open', 'close', 'volume' ])\n self.candles.index = self.candles['time'].values.tolist()\n self.candles.sort_index(inplace=True)\n \n def update(self, ticker):\n try:\n candle = self.candles[[ 'time', 'low', 'high', 'open', 'close', 'volume' ]].iloc[-1].to_dict()\n price = ticker['price']\n volume = ticker['last_size']\n next_time = candle['time'] + self.granularity\n if ticker['time'] >= next_time:\n candle = {'time': next_time, 'low': price, 'high': price, 'open': price, 'close': price, 'volume': volume }\n else:\n candle['low'] = np.min([price, candle['low']])\n candle['high'] = np.max([price, candle['high']])\n candle['close'] = price\n candle['volume']+= volume\n self.candles.loc[ candle['time'], list(candle.keys()) ] = list(candle.values())\n except Exception as e:\n print(\"Error in {} {} ohlc update\".format(self.product, self.increment))\n raise Exception(e)\n \n \nclass OrderManagement():\n def __init__(self):\n self.records = []\n self.all_columns = ['funds','limit_price','maker_order_id','maker_user_id', 'new_funds', 'old_funds', 'new_size', 'old_size', 'currency_on_hold', 'on_hold', 'order_id', 'order_type', 'price', 'product_id', 'reason','remaining_size', 'sequence', 'side', 'size', 'stop_price', 'stop_type','taker_fee_rate', 'taker_order_id', 'time', 'trade_id','type','USD','BTC','LTC','ETH','BCH','ETC' ]\n self.numeric_columns = ['funds','limit_price','new_funds','new_size','old_size','old_funds','price','remaining_size','size','on_hold','stop_price','taker_fee_rate']\n self.order_columns = ['sequence','order_id','create_time','update_time','product_id','order_type','side','stop_price','price','size','currency_on_hold','on_hold','USD','BTC','LTC','ETH','BCH','ETC','taker_fee_rate','status']\n self.orders = pd.DataFrame(data=[], columns=self.order_columns)\n self.order_id_log = []\n\n\n def prepare_order(self, order):\n \"\"\"Method used to create the update dict to process\"\"\"\n update= {\n 'create_time': order['time'],\n 'update_time': order['time'],\n 'status': order['type'],\n }\n for col in self.all_columns:\n try:\n if col in self.numeric_columns:\n value = float(order[col])\n else:\n value = order[col]\n update[col] = value\n except:\n if col == 'sequence':\n update[col] = randint(1,10000)\n else:\n update[col] = 0.0\n continue\n update['sequence'] = int(update['sequence'])\n update['currency_on_hold'] = order['product_id'][-3:] if order['side'] == 'buy' else order['product_id'][:3]\n return update\n \n def rename(self, obj, names):\n old = list(names.keys())\n new = list(names.values())\n for i in range(len(old)):\n obj[new[i]] = obj[old[i]]\n return obj\n \n def received_order(self, order):\n self.order_id_log.append(order['order_id'])# log the order_id\n existing_order = self.orders[ self.orders['order_id']== order['order_id'] ][['sequence','order_type','status']]\n if len(existing_order):\n return { **{ key: order[key] for key in self.order_columns }, **existing_order.iloc[0].to_dict() }\n else:\n return { key: order[key] for key in self.order_columns }\n \n def opened_order(self, order):\n sequence = self.orders[ (self.orders['order_id'] == order['order_id']) & (~self.orders['status'].isin(['canceled','filled'])) ].index.min()\n order['sequence'] = sequence or order['sequence']\n order = self.rename(order, {'remaining_size':'size'})\n order['on_hold'] = order['price'] * order['size'] if order['side'] == 'buy' else order['size']\n return { key: order[key] for key in ['sequence','size','update_time','status','on_hold'] }\n\n def stop_order(self, order):\n self.order_id_log.append(order['order_id'])# log the order_id\n order = self.rename(order, {'stop_price':'price', 'limit_price':'stop_price', 'stop_type':'order_type'})\n order['on_hold'] = order['funds'] if order['side'] == 'buy' else order['size']\n return { key: order[key] for key in ['sequence','order_id','product_id','side','size','taker_fee_rate','create_time','update_time','status','stop_price','price','order_type','on_hold','currency_on_hold']}\n \n def change_order(self, order):\n try:\n order = self.rename(order, {'new_size':'size'} )\n except:\n order = self.rename(order, {'new_funds':'size'})\n \n order['sequence'] = self.orders[ self.orders['order_id'] == order['order_id'] ].index.min()\n return { key: order[key] for key in ['sequence', 'size'] }\n \n def match_order(self, order):\n price = order['price']\n size = order['size']\n fee = 0\n multiplier = 1\n if order['side'] == 'buy': \n multiplier = -1\n\n existing_order = self.orders[ self.orders['order_id'].isin([ order['taker_order_id'], order['maker_order_id'] ]) ].iloc[0].to_dict()\n \n if existing_order['order_id'] == order['taker_order_id']:\n multiplier = -(multiplier)\n fee = 0.0025 if 'BTC' in order['product_id'] else 0.003\n order = { **existing_order, **{ key: order[key] for key in ['sequence','update_time','price','size'] }, **{ 'status': 'filled', 'taker_fee_rate': fee, 'on_hold': 0 } }\n \n elif existing_order['order_id'] == order['maker_order_id']:\n existing_order['on_hold'] -= price * size if existing_order['side']=='buy' else size \n order = { **existing_order, **{ key: order[key] for key in ['update_time'] }}\n \n pairs = order['product_id'].split('-')\n \n order[pairs[0]] += (-(multiplier)*size)\n order[pairs[1]] += (multiplier*((price * size) + (-(multiplier)*(price * size * fee))))\n return order\n\n def done_order(self, order):\n existing_order = self.orders[ (self.orders['order_id'] == order['order_id'] ) & (~self.orders['status'].isin(['filled','canceled'])) ].iloc[0].to_dict()\n existing_order['status'] = order['reason']\n return { **{ key: existing_order[key] for key in ['sequence','status'] }, **{ 'on_hold': 0.0 } }\n\n\n def update(self, order):\n \"\"\"This method receives and processes orders submitted by the client\"\"\"\n self.records.append(order)\n \n order = self.prepare_order(order)\n order_type = order['type']\n \n try:\n if order_type in ['received','open','activate','match','done','change']:\n UPDATE = None\n\n if order_type in ['received']:\n UPDATE = self.received_order(order)\n\n elif order_type in ['open'] and order['order_id'] in self.order_id_log:\n UPDATE = self.opened_order(order)\n\n elif order_type in ['activate']:\n UPDATE = self.stop_order(order)\n\n elif order_type in ['match'] and (order['maker_order_id'] in self.order_id_log or order['taker_order_id'] in self.order_id_log):\n UPDATE = self.match_order(order)\n\n elif order_type in ['done'] and order['order_id'] in self.order_id_log:\n UPDATE = self.done_order(order)\n \n elif order_type in ['change'] and order['order_id'] in self.order_id_log:\n UPDATE = self.change_order(order)\n \n if UPDATE:\n self.orders.loc[ UPDATE['sequence'], list(UPDATE.keys()) ] = list(UPDATE.values())\n else:\n raise Exception(\"Message type is not recognized. '{}' was not handled\".format(order_type))\n \n self.orders.fillna(0,inplace=True)\n except Exception as e:\n raise Exception(\"Error updating orders. Will try to update again. Error message: {} \\n \\n {}\".format(e, order))\n\n\nclass OrderBookManagement():\n def __init__(self):\n self.book = pd.DataFrame([],columns=['price','size','side'])\n self.snapshot_received = False\n self.backlog = []\n\n def bids(self, remove_zeros=True):\n return self.book[ (self.book['side']=='bids') & (self.book['size'] > (0 if remove_zeros else -1)) ].sort_index(ascending=False).reset_index()[['price','size']]\n\n def asks(self, remove_zeros=True):\n return self.book[ (self.book['side']=='asks') & (self.book['size'] > (0 if remove_zeros else -1)) ].sort_index(ascending=True).reset_index()[['price','size']]\n\n def l2update(self, orders):\n for order in orders['changes'] + self.backlog:\n try:\n self.backlog.remove(order)\n except:\n pass\n\n order = [\n float(order[1].rstrip('0')),\n float(order[2]),\n 'bids' if order[0]=='buy' else 'asks'\n ]\n try:\n self.book.loc[ order[0], ['size','side'] ] = order[1:] \n except Exception as e:\n raise Exception(\"{} attempting to update {}\".format(e, ', '.join(order)))\n\n\n def snapshot(self, orders):\n self.snapshot_received = True\n for side in ['bids','asks']:\n df = pd.DataFrame( data=orders[side], columns=['price','size'] ).head(250)[['price','size']].apply(pd.to_numeric, **{'errors':'ignore'})\n df['side'] = side\n self.book = pd.concat( [self.book, df] )\n self.book.set_index('price', inplace=True)\n\n def update(self, message):\n \"\"\"Receives the level 2 snapshot and the subsequent updates and updates the orderbook\"\"\"\n try:\n if self.snapshot_received:\n self.l2update(message)\n elif message['type'] == 'snapshot':\n self.snapshot(message)\n elif message['type'] == 'l2update': # at this point we have not received the snapshot object\n self.backlog += message['changes']\n except Exception as e:\n raise Exception(\"Error processing {} OrderBook update: Message -> {}\".format(message['product_id'], e))\n ","sub_path":"Websocket.py","file_name":"Websocket.py","file_ext":"py","file_size_in_byte":27906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"183553585","text":"__author__ = 'khainq'\n\n# import socket\n#\n# host = '' # Symbolic name meaning all available interfaces\n# port = 12345 # Arbitrary non-privileged port\n#\n# server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n# server_socket.bind((host, port))\n# server_socket.listen(1)\n#\n# conn, addr = server_socket.accept()\n#\n# print('Connected by', addr)\n#\n# while True:\n# data = conn.recv(1024)\n# if not data: break\n# conn.sendall(data)\n# conn.close()\n\nimport socket # Import socket module\n\ns = socket.socket() # Create a socket object\nhost = socket.gethostname() # Get local machine name\nport = 12345 # Reserve a port for your service.\ns.bind((host, port)) # Bind to the port\n\ns.listen(5) # Now wait for client connection.\nwhile True:\n c, addr = s.accept() # Establish connection with client.\n print('Got connection from', addr)\n c.send(b'Thank you for connecting')\n c.close() # Close the connection\n\n","sub_path":"serverChat.py","file_name":"serverChat.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"632796018","text":"#!/usr/bin/python\n# -*- coding:utf-8 -*-\n\nimport json\nimport codecs\nimport string\nimport unicodedata\nimport re\n\n\nclass polarity:\n\tsentimentDict = dict()\n\tsentWordsFile = 'app/polaridad.txt'\n\tjsonFile = 'app/test2.json'\n\tstopWordsFile = 'app/stopWordsES.txt' \n\tjsonObject = dict()\n\tpolarityDict = dict()\n\tstopWords = list()\n\tsymbolsList = ['.', ',', ':', '—', \";\", '\"',\"!\", '¡', '¿',\n\t\"?\", \"#\", \"%\", \"/\", \"&\", \"(\", \")\", '€', '¦', \"=\", '\"', \"'\",\n\t\"{\", '±', '“', '”', \"}\", \"[\", \"]\", \"$\", \"_\", \"-\", \"|\", \"‘\",\n\t\"’\", \"@\", \"🖒\", \"+\", \"*\", \"💔\", \"💗\", \"🇲\", \"🇽\", \"📉\", \"😎\",\n\t\"💞\", \"👑\", \"😘\", \"💕\", \"🙈\", \"😊\", \"⚜\", \"️\", \"🖤\", \" 💖\",\n\t\"🍔\", \"😭\", \"😨\", \"😱\", \"❤\", \"😂\", \"😁\", \"🙏\", \"👀\", \"🐾\", \"😀\",\n\t\"📄\", \"🇬\", \"🇩\", \"🤗\", \" 😩\", \"⚠\", \"🎄\", \"🔴\", \"👉\", \"👇\", \"🏽\",\n\t\"➡\", \"🔙\", \"🙄\", \"👏\", \"😅\", \"🛵\", \"🍕\", \"🖥\", \"👍\", \"🏻\", \"😍\",\n\t\"💚\", \"💛\", \"🙌\", \"📰\", \"😒\", \"😰\", \"😬\", \"✌\", \"😠\", \"😡\", \"☑\",\n\t\"😠\", \"😡\", \"🤥\", \"❣\", \"😩\", \"🤤\", \"☀\", \"🌻\", \"💁\", \"🤣\", \"♂\",\n\t\"•\", \"📽\", \"💥\", \"✍\", \"🙋\", \"♀\", \"📸\", \"🎅\", \"🐌\", \"💨\", \"🙊\", \"⚡\"]\n\n\n\tdef loadSentDictionary(self, fileIn):\n\t\tsentimentDict = {} #dictionary that saves the values for the sentiment analysis\n\t\tlines = codecs.open(fileIn, \"r\", \"utf-8\")\n\t\tfor line in lines: #ciclo para cargar la palabras\n\t\t\tline = line.strip()\n\t\t\tlineParts = line.split('|')\n\t\t\tsentimentDict[lineParts[0]] = int(lineParts[1])\n\t\treturn sentimentDict\n\n\tdef loadJsonFile(self, jsonFile):\n\t\tjsonObject = json.loads(open(jsonFile).read())\n\t\treturn jsonObject\n\n\tdef loadStopWords(self, fileName):\n\t\tstopWords = []\n\t\tf = codecs.open(fileName, \"r\", \"utf-8\")\n\t\tfor line in f:\n\t\t\tline =line.strip()\n\t\t\tstopWords.append(line)\n\t\treturn stopWords\n\n\tdef replaceUsernames(self, s, replaceBy):\n\t\ttemp = re.compile(r\"@\\.[A-Za-z0-9_-]*\")\n\t\ts = temp.sub(replaceBy, s)\n\t\ttemp = re.compile(r\"@[A-Za-z0-9_-]*\")\n\t\ts = temp.sub(replaceBy, s)\n\t\treturn s\n\n\tdef removeAccents(self, inputStr):\n\t nkfdForm = unicodedata.normalize('NFKD', inputStr)\n\t return u\"\".join([c for c in nkfdForm if not unicodedata.combining(c)])\n\n\tdef replaceLinks(self, s, replaceBy):\n\t\t#quita url www.algo.com/djj\n\t\tre.purge()\n\t\ttemp = re.compile(r\"\\s*www\\.\\w+\\.(com|net|me|org)?(\\s|/*[-\\w+&@#/%!?=~_:.\\[\\]()0-9]*)\")\n\t\ts = temp.sub(replaceBy, s)\n\t\t#quita http://\n\t\ttemp = re.compile(r\"((http|ftp|https)://[-/?=&\\w.]*)\")\n\t\ts = temp.sub(replaceBy, s)\n\t\treturn s\n\n\tdef replaceHashtags(self, s, replaceBy):\n\t\ts = re.sub(r'#[A-Za-z0-9_-]*', replaceBy, s)\n\t\treturn s\n\n\tdef replaceSymbols(self, s, symbolsList, replaceBy):\n\t for c in symbolsList:\n\t s = s.replace(c, replaceBy)\n\t return s\n\n\tdef replaceNumbers(self, s, replaceBy):\n\t\ts = re.sub(r'([0-9]+(st|th|rd|nd|,[0-9]+|.[0-9]+)?)', replaceBy, s)\n\t\treturn s\n\n\tdef removeSpaces(self, s, replaceBy):\n\t\ts = re.sub(\"\\s+\" , replaceBy, s)\n\t\treturn s\n\n\tdef cleanTexts(self, currentText):\n\t\tcleanedText = currentText.lower()\n\t\tcleanedText = self.replaceUsernames(cleanedText,' ')\n\t\tcleanedText = self.removeAccents(cleanedText)\n\t\tcleanedText = self.replaceLinks(cleanedText,' ')\n\t\tcleanedText = self.replaceHashtags(cleanedText,' ')\n\t\tcleanedText = self.replaceSymbols(cleanedText, self.symbolsList,' ')\n\t\tcleanedText = self.replaceNumbers(cleanedText, ' ')\n\t\tcleanedText = self.removeSpaces(cleanedText, ' ')\n\t\treturn cleanedText\n\n\tdef removeStopWords(self, text):\n\t\tfinalText = []\n\t\twords = text.split()\n\t\tfor word in words:\n\t\t\tif word not in self.stopWords:\n\t\t\t\tfinalText.append(word)\n\t\t\twholeText = ' '.join(finalText)\n\t\treturn wholeText\n\n\tdef createHashtagVocabulary(self, listIn):#creates a list with the hashtag words\n\t\ttext = list()\n\t\tfor each in listIn:\n\t\t\tif each != '':\n\t\t\t\twords = each.split()\n\t\t\t\tfor word in words:\n\t\t\t\t\tif word != '':\n\t\t\t\t\t\ttext.append(word)\n\t\treturn text\n\n\tdef calculatePolarity(self, hashtagVocabulary):\n\t\tpolarity = list()\n\t\tpositive = 0\n\t\tnegative = 0\n\t\tfor word in hashtagVocabulary:\n\t\t\tif word in self.sentimentDict:\n\t\t\t\tvalue = self.sentimentDict.get(word,0)\n\t\t\t\tif value > 0:\n\t\t\t\t\tpositive = positive + value\n\t\t\t\tif value < 0:\n\t\t\t\t\tnegative = negative + value\n\t\tdenominator = abs(positive) + abs(negative)\n\t\tif denominator > 0:\n\t\t\tPpos = abs(positive) / denominator #positive polarity\n\t\t\tPneg = abs(negative) / denominator #negative polarity\n\t\telse:\n\t\t\tPpos = 0.0\n\t\t\tPneg = 0.0\n\t\treturn Ppos, Pneg\n\n\tdef getPolarity(self):\n\t\tself.sentimentDict = self.loadSentDictionary(self.sentWordsFile)\n\t\tself.jsonObject = self.loadJsonFile(self.jsonFile)\n\t\tself.stopWords = self.loadStopWords(self.stopWordsFile)\n\t\t#iterates the hashtags inside the json file\n\t\tfor key in self.jsonObject:\n\t\t\t#print('====== {} ======'.format(key))\n\t\t\ttweetList = self.jsonObject[key]['tweet_list']\n\t\t\ttextsList = list()\n\t\t\t#gets the tweet list per hashtag\n\t\t\tfor tweet in tweetList:\n\t\t\t\ttextsList.append(tweet['text'])\n\t\t\tfinalTextsList = list()\n\t\t\tfor each in textsList:\n\t\t\t\tcleanedText = self.cleanTexts(each)\n\t\t\t\tfinalText = self.removeStopWords(cleanedText)\n\t\t\t\tif finalText != '':\n\t\t\t\t\tfinalTextsList.append(finalText)\n\t\t\ttextsList = []\n\t\t\thashtagVocabulary = self.createHashtagVocabulary(finalTextsList)\n\t\t\t(polarityP, polarityN) = self.calculatePolarity(hashtagVocabulary)\n\t\t\t#print('positive: {} negative: {}'.format(polarityP, polarityN))\n\t\t\tself.polarityDict[key] = {\"positivePolarity\": polarityP, \"negativePolarity\": polarityN}\n\t\treturn self.polarityDict\n","sub_path":"app/-polarity.py","file_name":"-polarity.py","file_ext":"py","file_size_in_byte":5491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"109484738","text":"import sys\nL = len(sys.argv) - 1\n\nmyList = [1, 6, 9, 8, 14]\n\nfor x in range(L):\n num = int(sys.argv[x + 1])\n myList.append(num)\n\nprint(myList)\nprint(len(myList))\nmyList.sort()\nprint(myList)\n","sub_path":"Homework9/hw9_2.py","file_name":"hw9_2.py","file_ext":"py","file_size_in_byte":192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"616377831","text":"#!/usr/bin/env python\n\nimport unittest,sys\nfrom BaseTest import parseCMDLine, basicTestSetup\n\nimport afs\nfrom afs.dao import VLDbDAO\n\nclass TestVLDbDAOMethods(unittest.TestCase, basicTestSetup):\n \"\"\"\n Tests FileServerDAO Methods\n \"\"\"\n \n def setUp(self):\n \"\"\"\n setup\n \"\"\"\n basicTestSetup.setUp(self)\n self.cellname=self.TestCfg.get(\"general\",\"Cell\")\n self.numServ=int(self.TestCfg.get(\"VLDbDAO\",\"numServ\"))\n self.DAO = VLDbDAO.VLDbDAO()\n return\n \n def test_getVolList(self) :\n ServList=self.DAO.getFsServList(_cfg=afs.defaultConfig, _user=\"test\")\n self.assertTrue(len(ServList) > self.numServ)\n return\n \nif __name__ == '__main__' :\n parseCMDLine()\n suite = unittest.TestLoader().loadTestsFromTestCase(TestVLDbDAOMethods)\n unittest.TextTestRunner(verbosity=2).run(suite)\n","sub_path":"afs/tests/VLDbDAOTest.py","file_name":"VLDbDAOTest.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"301890382","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.5 (62131)\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.macosx-10.5-i386/egg/shabti/templates/moinmoin/data/moin/data/plugin/action/ImportHtml.py\n# Compiled at: 2010-04-25 14:23:30\n\"\"\"\n MoinMoin - ImportHtml action\n\n\"\"\"\nimport mimetypes, string, sys, time, HTMLParser\nfrom MoinMoin import config, user, util, wikiutil\nfrom MoinMoin.util import web\nfrom MoinMoin.Page import Page\nfrom MoinMoin.PageEditor import PageEditor\n\ndef show_form(pagename, request):\n request.write('\\n
\\n\\nShow markup
\\nShow as wiki page
\\nAppend to page
\\nURL: \\n\\n
\\n' % {'baseurl': request.getBaseURL(), \n 'pagename': wikiutil.quoteWikinameURL(pagename)})\n\n\ndef get_content(request):\n if request.form.has_key('url'):\n try:\n return urllib.urlopen(request.form['url'][0]).read()\n except IOError:\n return ''\n\n else:\n return ''\n\n\ndef get_parsed(request):\n from MoinMoin.converter.text_html_text_moin_wiki import convert\n return convert(request, 'Imported Page', get_content(request))\n\n\ndef show_markup(pagename, request):\n request.http_headers(['Content-type: text/plain'])\n request.write(get_parsed(request))\n\n\ndef show_as_wiki_page(pagename, request):\n page = Page(pagename)\n page.set_raw_body(get_parsed(request), 1)\n page.send_page(request)\n\n\ndef append_to_page(pagename, request):\n page = PageEditor(request, pagename)\n page.set_raw_body(page.get_raw_body() + get_parsed(request))\n page.sendEditor()\n\n\ndef error_msg(pagename, request, msg):\n Page(pagename).send_page(request, msg=msg)\n\n\ndef execute(pagename, request):\n \"\"\" Main dispatcher for the 'ImportHtml' action.\n \"\"\"\n _ = request.getText\n msg = None\n if not request.form.has_key('do'):\n show_form(pagename, request)\n elif request.form['do'][0] == 'markup':\n show_markup(pagename, request)\n elif request.form['do'][0] == 'wiki':\n show_as_wiki_page(pagename, request)\n elif request.form['do'][0] == 'import':\n append_to_page(pagename, request)\n else:\n msg = _('Unsupported action: %s') % (request.form['do'][0],)\n if msg:\n error_msg(pagename, request, msg)\n return","sub_path":"pycfiles/Shabti-0.4.4-py2.5/ImportHtml.py","file_name":"ImportHtml.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"453171011","text":"# Author:xichen\n# @Time:2019/9/1120:09\nimport socket\nimport os,sys\nimport json\nimport struct\nsys.path.append(os.path.dirname(__file__))\nfrom core import src\nfrom.interface import common_interface\n\nxichen = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\nxichen.bind(('192.168.11.78',8006))\nxichen.listen(5)\nfunc_msg = {'1':'注册','2':'登录','3':'上传文件','4':'下载文件'}\nfunc_msg_dic = bytes(json.dumps(func_msg),'utf8')\nfunc_msg_len = len(func_msg_dic)\nfunc_msg_head = struct.pack('i',func_msg_len)\nwhile True:\n conn,addr = xichen.accept()\n while True:\n # 向客户端发送头部长度\n conn.send(func_msg_head)\n # 向客户端发送头部内容\n conn.send(func_msg_dic)\n\n user_choice = conn.recv(1).decode('utf8')\n if user_choice == 'q':\n break\n if user_choice == '1':\n src.register()\n elif user_choice == '2':\n src.login()\n elif user_choice == '3':\n common_interface.download()\n elif user_choice == '4':\n common_interface.upload()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"db/file/server_xichen.py","file_name":"server_xichen.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"14101170","text":"from Position import Position\nimport Util\n\nimport sys\nimport random\nfrom random import shuffle\n\n################################################################################\n# #\n# Compatibility Kludges #\n# #\n################################################################################\n\n# if localhost doesn't have termcolor installed, define a simple pass-through\ntry:\n from termcolor import colored\nexcept ImportError:\n def colored(c, shade):\n return c\n\n################################################################################\n# Represent an N x N playing board with obstacles, monsters, items and a player.\n################################################################################\nclass Board:\n\n def __init__(self, size):\n self.size = size\n self.obstacles = set() # set of Positions\n self.monsters = [] # list of mobile monsters on the board\n self.items = [] # list of items on the board\n self.player = None\n self.trail = set() # set of Positions (breadcrumbs)\n\n ############################################################################\n # #\n # Initialization #\n # #\n ############################################################################\n\n def createObstacles(self, count, size):\n # iterate over each obstacle we're to emplace\n for i in range(0, count):\n\n # pick an unoccupied starting point for the boulder\n pos = self.randomUnoccupiedPos()\n self.obstacles.add(pos)\n\n # flesh-out the obstacle in all its ugly wartiness\n for j in range(1, size - 1):\n pos = self.randomAdjacentUnoccupiedPos(pos)\n if pos is None:\n break\n self.obstacles.add(pos)\n\n def addMonster(self, m):\n m.pos = self.randomUnoccupiedPos()\n self.monsters.append(m)\n\n def addItem(self, item):\n item.pos = self.randomUnoccupiedPos()\n self.items.append(item)\n\n def addPlayer(self, player):\n player.pos = self.randomUnoccupiedPos()\n self.player = player\n\n ############################################################################\n # #\n # Accessors #\n # #\n ############################################################################\n\n def getMonsters(self):\n return self.monsters\n\n def getItems(self, pos):\n for item in self.items:\n if item.pos == pos:\n yield item\n\n def removeItem(self, item):\n if item in self.items:\n self.items.remove(item)\n\n def updatePositions(self):\n # update our trail of breadcrumbs\n self.trail.add(self.player.pos)\n\n # wandering monsters wander\n for m in self.monsters:\n\n if not m.alive:\n continue;\n\n origPos = m.pos\n\n # how many spaces this monster can move per turn\n for move in range(0, m.speed):\n\n # the more aggressive a monster is, the more it will try to move toward the player\n dist = m.pos.dist(self.player.pos)\n\n # consider up to 'aggression' possible moves from this spot,\n # looking for one that will move toward the hapless player\n for check in range (m.aggression + 1):\n newPos = self.randomAdjacentUnoccupiedPos(m.pos)\n if newPos is not None:\n if (newPos.dist(self.player.pos) < dist) or (check >= m.aggression):\n m.pos = newPos\n break\n\n # increase entropy when Baby is stuck in a corner\n if origPos == m.pos:\n m.aggression = max(0, m.aggression - 1)\n else:\n m.aggression = min(m.origAggression, m.aggression + 1)\n\n ############################################################################\n # #\n # Position Helpers #\n # #\n ############################################################################\n\n def occupied(self, pos):\n # check to see that no obstacles occupy this position\n for obstaclePos in self.obstacles:\n if obstaclePos == pos:\n return True\n\n # do NOT check for items or monsters!\n return False\n\n def randomUnoccupiedPos(self):\n pos = self.randomPos()\n while self.occupied(pos):\n pos = self.randomPos()\n return pos\n\n def randomAdjacentUnoccupiedPos(self, pos):\n directions = [\"n\", \"e\", \"s\", \"w\"]\n shuffle(directions)\n for direction in directions:\n if (self.canMove(pos, direction)):\n p2 = self.adjacentPos(pos, direction)\n if not self.occupied(p2):\n return p2\n return None\n\n def listAvailableDirections(self, pos):\n avail = []\n directions = [\"n\", \"e\", \"s\", \"w\"]\n for direction in directions:\n if (self.canMove(pos, direction)):\n avail.append(self.expandDirection(direction))\n return Util.prettyList(avail, \"or\")\n\n def expandDirection(self, direction):\n if direction == \"e\":\n return \"east\"\n elif direction == \"w\":\n return \"west\"\n elif direction == \"n\":\n return \"north\"\n elif direction == \"s\":\n return \"south\"\n raise Exception(\"unknown direction: %s\" % direction)\n\n def randomPos(self):\n return Position(random.randint(0, self.size - 1),\n random.randint(0, self.size - 1))\n\n def canMove(self, pos, direction):\n if pos is None:\n return False\n\n if direction == \"n\":\n return pos.y > 0\n elif direction == \"s\":\n return pos.y < self.size - 1\n elif direction == \"w\":\n return pos.x > 0\n elif direction == \"e\":\n return pos.x < self.size - 1\n raise Exception(\"unknown direction: %s\" % direction)\n\n def adjacentPos(self, pos, direction):\n if not self.canMove(pos, direction):\n raise Exception(\"can't move %s from %s\" % (direction, pos))\n\n if direction == \"n\":\n return Position(pos.x, pos.y - 1)\n elif direction == \"s\":\n return Position(pos.x, pos.y + 1)\n elif direction == \"w\":\n return Position(pos.x - 1, pos.y)\n elif direction == \"e\":\n return Position(pos.x + 1, pos.y)\n raise Exception(\"unknown direction: %s\" % direction)\n\n ############################################################################\n # #\n # Display #\n # #\n ############################################################################\n\n def showTrail(self):\n self.dump(label=\"Breadcrumbs\")\n\n def dump(self, **kwargs):\n # label\n if 'label' in kwargs:\n print(\"%s:\" % kwargs['label'])\n\n # map\n for y in range(0, self.size):\n for x in range(0, self.size):\n sys.stdout.write(self.getDisplayChar(Position(x, y), **kwargs) + ' ')\n sys.stdout.write('\\n')\n\n # key\n if not 'admin' in kwargs:\n sys.stdout.write(\"Key: #=boulder @=player ^=north\\n\")\n else:\n sys.stdout.write(\"Key: #=boulder @=player ^=north &=monster *=item\\n\")\n for m in self.monsters:\n print(\"%s at %s (aggression %d)\" % (m.name, m.pos, m.aggression))\n for i in self.items:\n print(\"%s at %s\" % (i.name, i.pos))\n print(\"\")\n\n def getDisplayChar(self, pos, **kwargs):\n if pos in self.obstacles:\n return '#'\n\n # require God-mode to see monsters and items\n if 'admin' in kwargs:\n for m in self.monsters:\n if pos == m.pos:\n return colored(m.char if hasattr(m, 'char') else '&', 'red')\n\n for i in self.items:\n if pos == i.pos:\n return colored(i.char if hasattr(i, 'char') else '*', 'yellow')\n\n if self.player is not None:\n if pos == self.player.pos:\n return colored(self.player.char if hasattr(self.player, 'char') else '@', 'cyan')\n\n if pos in self.trail:\n return colored('o', 'blue')\n\n return colored('.', 'green')\n","sub_path":"zerk/Board.py","file_name":"Board.py","file_ext":"py","file_size_in_byte":9380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"518550276","text":"# -*- coding: utf-8 -*-\n\nclass Proxy(object):\n def __init__(self, target=None):\n super(Proxy, self).__init__()\n self._orig_target = target\n \n def __getattr__(self, name):\n if name in self.__dict__:\n return self.__dict__[name]\n else:\n target = self._target()\n if target:\n try:\n return getattr(target, name)\n except AttributeError:\n raise AttributeError(\"'%s' object has no attribute '%s'\" % (type(self).__name__, name))\n else:\n raise AttributeError('%s object has no attribute \"%s\", and target is not available.' % (type(self), name))\n \n def _target(self):\n return self._orig_target\n ","sub_path":"src/decorated/base/proxy.py","file_name":"proxy.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"220706780","text":"\"\"\"\nAuthor: Jason Labbe\n\nStores curve objects in a text file so it can be easily re-created.\n\n[TO DO]\n - Currently doesn't support curves with 2 degrees.\n\"\"\"\n\nimport os\n\nimport maya.cmds as cmds\nimport maya.OpenMaya as OpenMaya\n\nfrom PySide import QtGui\nfrom PySide import QtCore\n\nimport general_functions\n\n\nclass NameInputDialog(QtGui.QInputDialog):\n def __init__(self, parent = None):\n super(NameInputDialog, self).__init__(parent)\n \n self.setInputMode(QtGui.QInputDialog.InputMode.TextInput)\n self.setLabelText(\"Enter a name:\")\n self.setWindowTitle(\"Need input!\")\n self.setTextValue(\"newControl\")\n\n\nclass ControlUtils(object):\n def __init__(self):\n self.controlsDir = os.path.join(os.path.dirname(__file__), \"control_presets\")\n \n def listSavedShapes(self, fullPath=False):\n \"\"\"\n Collects all available control objects that were saved out.\n Args:\n fullPath(bool): Option to return absolute paths.\n Returns:\n A list of available control shapes.\n \"\"\"\n if not os.path.exists(self.controlsDir):\n OpenMaya.MGlobal.displayError(\"Unable to find controls directory.\")\n return\n \n shapeFilesWithExt = sorted( os.listdir(ControlUtils().controlsDir) )\n shapeFiles = []\n for fileWithExt in shapeFilesWithExt:\n if fullPath:\n shapeFiles.append(os.path.join(self.controlsDir, fileWithExt) )\n else:\n shapeFiles.append(os.path.splitext(fileWithExt)[0])\n \n return shapeFiles\n \n def exportShape(self, curveObj, fileName=None):\n \"\"\"\n Exports a shape to a txt file.\n Args:\n curveObj(str): A curve object.\n fileName(str): Option to set the file name. If set to None, it will use the object's name.\n Returns:\n True on success.\n \"\"\"\n if not os.path.exists(self.controlsDir):\n OpenMaya.MGlobal.displayError(\"Unable to find controls directory.\")\n return\n \n objCurveShapes = cmds.listRelatives(curveObj, shapes=True, type=\"nurbsCurve\", f=True) or []\n if not objCurveShapes:\n OpenMaya.MGlobal.displayError(\"Supplied object doesn't have any curve shapes.\")\n return\n \n # Values will fail if we don't delete history, for instance, a nurbsCircle\n cmds.delete(curveObj, ch=True)\n \n if fileName is None:\n fileName = curveObj.split(\"|\")[-1]\n \n filePath = os.path.join(self.controlsDir, \"{0}.txt\".format(fileName) )\n \n with open(filePath, \"w\") as fInput:\n fInput.write(\"transform\\n\")\n fInput.write(\"{0}\\n\".format(fileName) )\n for curveShape in objCurveShapes:\n fInput.write(\"shape\\n\")\n fInput.write(\"{0}\\n\".format(curveShape) )\n \n cvs = cmds.ls(\"{0}.cv[*]\".format(curveShape), l=True)[0]\n curveForm = cmds.getAttr(\"{0}.form\".format(curveShape) )\n cvDegrees = cmds.getAttr(\"{0}.degree\".format(curveShape) )\n cvSpans = cmds.getAttr(\"{0}.spans\".format(curveShape) )\n cvPositions = cmds.getAttr(cvs)\n \n fInput.write(\"{0}\\n\".format(curveForm) )\n fInput.write(\"{0}\\n\".format(cvDegrees) )\n fInput.write(\"{0}\\n\".format(cvSpans) )\n fInput.write(\"{0}\\n\".format(len(cvPositions) ) )\n for cvPosition in cvPositions:\n fInput.write(\"{0} {1} {2}\\n\".format(cvPosition[0], cvPosition[1], cvPosition[2]) )\n \n OpenMaya.MGlobal.displayInfo(\"Shape exported: {0}\".format(filePath) )\n \n return True\n \n def importShape(self, fileName, size=1.0, name=\"\", parentObj=\"\", colorIndexValue=None, inputName=False):\n \"\"\"\n Imports an exported shape via txt file.\n Bugs:\n - Failing on linear periodic import.\n Args:\n fileName(string): Base name of the file to import. Don't include full path and file extension.\n size(float): The size of the curve to import in.\n name(string): If an empty string, will use fileName.\n parentObj(string): An object to parent to after import.\n colorIndexValue(int): Uses Maya's internal colorIndex (1-31) to set the shapes' color.\n Returns:\n A list of the control's nul and object.\n \"\"\" \n # Ask for name input\n if inputName:\n nameDialog = NameInputDialog()\n retCode = nameDialog.exec_()\n if retCode and nameDialog.textValue():\n name = nameDialog.textValue()\n else:\n return False\n \n filePath = os.path.join(self.controlsDir, \"{0}.txt\".format(fileName) )\n if not os.path.exists(filePath):\n OpenMaya.MGlobal.displayError(\"Cannot find file to import: {0}\".format(filePath) )\n return\n \n with open(filePath, \"r\") as fOutput:\n fLine = fOutput.readline()\n transformName = \"\"\n curveShapes = []\n while fLine:\n line = fLine.split(\"\\n\")[0]\n if line == \"transform\":\n transformName = fOutput.readline().split(\"\\n\")[0]\n elif line == \"shape\":\n shapeName = fOutput.readline().split(\"\\n\")[0]\n curveForm = int( fOutput.readline().split(\"\\n\")[0] )\n cvDegrees = int( fOutput.readline().split(\"\\n\")[0] )\n cvSpans = int( fOutput.readline().split(\"\\n\")[0] )\n positionCount = int( fOutput.readline().split(\"\\n\")[0] )\n cvPositions = []\n for i in range(0, positionCount):\n positionData = fOutput.readline().split(\"\\n\")[0]\n positionData = positionData.split(\" \")\n cvPosition = [float(positionData[0]) * size, float(positionData[1]) * size, float(positionData[2]) * size]\n cvPositions.append(cvPosition)\n \n # Create curve\n cvCount = cvDegrees + cvSpans\n knotCount = cvCount + cvDegrees - 1\n cvKnots = []\n if curveForm == 0: # Open curve\n if cvDegrees == 1: # Linear curve\n for i in range(0, knotCount):\n cvKnots.append(i)\n else: # Cubic curve\n knotIterator = 0\n for i in range(0, knotCount):\n if (i >= 3) and (i <= knotCount-3):\n knotIterator += 1\n cvKnots.append(knotIterator)\n elif curveForm == 2: # Periodic curve\n for i in range(0, 3): # Periodic curves need to end with how they start\n cvPositions.append(cvPositions[i])\n \n for i in range(0, knotCount):\n cvKnots.append(i-2)\n \n newCurve = cmds.curve(per=curveForm, d=cvDegrees, p=cvPositions, k=cvKnots, name=\"temp\")\n curveShapes.append(newCurve)\n \n fLine = fOutput.readline()\n \n if not curveShapes:\n OpenMaya.MGlobal.displayError(\"Error when trying to import shape.\")\n return\n \n # Begin setting up curve structure\n if not name:\n name = fileName\n \n # Create main NUL\n if parentObj:\n cmds.group(empty=True, parent=parentObj, name=\"{0}_NUL\".format(name) )\n else:\n cmds.group(empty=True, name=\"{0}_NUL\".format(name) )\n curveNUL = cmds.ls(sl=True, l=True)[0]\n \n # Group curves under one NUL\n cmds.group(empty=True, parent=curveNUL, name=\"{0}_CTRL\".format(name) )\n curveObj = cmds.listRelatives(curveNUL, children=True, f=True)[0]\n for i, crv in enumerate(curveShapes):\n crvShape = cmds.listRelatives(crv, shapes=True)[0]\n cmds.parent(crvShape, curveObj, r=True, s=True)\n cmds.delete(crv)\n shapeName = \"{0}Shape{1}\".format(name, (i+1) )\n cmds.rename(crvShape, shapeName)\n \n # Color curves\n if colorIndexValue is not None:\n general_functions.GeneralFunctions().setWireColor([curveObj], colorIndexValue)\n cmds.select(curveNUL)\n \n return [curveNUL, curveObj]","sub_path":"maya/rigging/libs/control_objects/control_utils.py","file_name":"control_utils.py","file_ext":"py","file_size_in_byte":8761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"126393433","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# Advent of Code #5\nimport csv\n\nfabric = [[0 for x in range(1000)] for y in range(1000)]\n\nwith open('input.txt') as input:\n ids = list(csv.reader(input, delimiter='\\n'))\n\nfor s in ids:\n s = s[0]\n startY = int(s.split('@')[1].split(':')[0].split(',')[0].strip())\n startX = int(s.split('@')[1].split(':')[0].split(',')[1].strip())\n sizeY = int(s.split('@')[1].split(':')[1].split('x')[0].strip())\n sizeX = int(s.split('@')[1].split(':')[1].split('x')[1].strip())\n for y in range(startY, startY+sizeY):\n for x in range(startX, startX+sizeX):\n fabric[x][y] += 1\n\noverlap_claims = 0 \nfor y in range(0, 1000):\n for x in range(0, 1000):\n if fabric[x][y] >= 2 :\n overlap_claims += 1\n\nprint(overlap_claims)","sub_path":"Day 3/overlapclaims.py","file_name":"overlapclaims.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"568757441","text":"# coding: utf-8\n\n\"\"\"\n Cost Management\n\n The API for Project Koku and OpenShift cost management. You can find out more about Cost Management at [https://github.com/project-koku/](https://github.com/project-koku/). # noqa: E501\n\n The version of the OpenAPI document: 1.0.0\n Generated by: https://openapi-generator.tech\n\"\"\"\n\n\nfrom __future__ import absolute_import\n\nimport unittest\nimport datetime\n\nimport openapi_client\nfrom openapi_client.models.cost_model_out import CostModelOut # noqa: E501\nfrom openapi_client.rest import ApiException\n\nclass TestCostModelOut(unittest.TestCase):\n \"\"\"CostModelOut unit test stubs\"\"\"\n\n def setUp(self):\n pass\n\n def tearDown(self):\n pass\n\n def make_instance(self, include_optional):\n \"\"\"Test CostModelOut\n include_option is a boolean, when False only required\n params are included, when True both required and\n optional params are included \"\"\"\n # model = openapi_client.models.cost_model_out.CostModelOut() # noqa: E501\n if include_optional :\n return CostModelOut(\n name = '0', \n description = '0', \n source_type = '0', \n providers = [\n openapi_client.models.cost_model_resp_providers.CostModelResp_providers(\n uuid = 'e5ff62e7-e6d6-5513-5532-45fe72792dae', \n name = 'provider', )\n ], \n rates = [\n openapi_client.models.rate.Rate(\n uuid = '83ee048e-3c1d-43ef-b945-108225ae52f4', \n metric = {\"name\":\"cpu_core_per_hour\",\"unit\":\"core-hours\",\"display_name\":\"Compute usage Rate\"}, \n tiered_rates = [{\"value\":0.22,\"unit\":\"USD\",\"usage\":{\"usage_start\":0,\"usage_end\":10}}], )\n ], \n uuid = '0', \n created_timestamp = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'), \n updated_timestamp = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'), \n markup = openapi_client.models.markup.Markup(\n value = 1.337, \n unit = 'percent', )\n )\n else :\n return CostModelOut(\n name = '0',\n description = '0',\n source_type = '0',\n )\n\n def testCostModelOut(self):\n \"\"\"Test CostModelOut\"\"\"\n inst_req_only = self.make_instance(include_optional=False)\n inst_req_and_optional = self.make_instance(include_optional=True)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"test/test_cost_model_out.py","file_name":"test_cost_model_out.py","file_ext":"py","file_size_in_byte":2699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"199644030","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport numpy as np\r\nimport cv2\r\nimport logging\r\nimport models.resnet as model\r\nBatchNorm2d = nn.BatchNorm2d\r\nBN_MOMENTUM = 0.01\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\ndef initialize_weights(*models):\r\n \"\"\"\r\n Initialize Model Weights\r\n :param modules:\r\n :return:\r\n \"\"\"\r\n for model in models:\r\n for module in model.modules():\r\n if isinstance(module, (nn.Conv2d, nn.Linear)):\r\n nn.init.kaiming_normal_(module.weight)\r\n if module.bias is not None:\r\n module.bias.data.zero_()\r\n elif isinstance(module, nn.BatchNorm2d):\r\n module.weight.data.fill_(1)\r\n module.bias.data.zero_()\r\n\r\n\r\nclass Confidence(nn.Module):\r\n def __init__(self, num_class=19, num_output=1):\r\n super(Confidence, self).__init__()\r\n\r\n def down(in_channels, out_channels):\r\n return torch.nn.Sequential(\r\n torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1),\r\n torch.nn.ReLU()\r\n )\r\n\r\n def up(in_channels, out_channels):\r\n return torch.nn.Sequential(\r\n torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1),\r\n torch.nn.ReLU()\r\n )\r\n\r\n self.down1 = down(num_class, 64)\r\n self.down2 = down(64, 128)\r\n self.down3 = down(128, 256)\r\n self.conv = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)\r\n self.up3 = up(256, 128)\r\n self.up2 = up(128, 64)\r\n self.up1 = nn.ConvTranspose2d(64, num_output, kernel_size=4, stride=2, padding=1)\r\n self.sigmoid = nn.Sigmoid()\r\n\r\n def forward(self, x):\r\n x = self.down1(x)\r\n x = self.down2(x)\r\n x = self.down3(x)\r\n\r\n x = self.conv(x)\r\n\r\n x = self.up3(x)\r\n x = self.up2(x)\r\n x = self.up1(x)\r\n\r\n return self.sigmoid(x)\r\n\r\n\r\nclass ASPPConv(nn.Sequential):\r\n def __init__(self, in_channels, out_channels, dilation):\r\n modules = [\r\n nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False),\r\n nn.BatchNorm2d(out_channels),\r\n nn.ReLU(inplace=True)\r\n ]\r\n super(ASPPConv, self).__init__(*modules)\r\n\r\n\r\nclass ASPPPooling(nn.Sequential):\r\n def __init__(self, in_channels, out_channels):\r\n super(ASPPPooling, self).__init__(\r\n nn.AdaptiveAvgPool2d(1),\r\n nn.Conv2d(in_channels, out_channels, 1, bias=False),\r\n nn.BatchNorm2d(out_channels),\r\n nn.ReLU(inplace=True))\r\n\r\n def forward(self, x):\r\n size = x.shape[-2:]\r\n x = super(ASPPPooling, self).forward(x)\r\n return F.interpolate(x, size=size, mode='bilinear', align_corners=False)\r\n\r\n\r\nclass ASPP(nn.Module):\r\n def __init__(self, in_channels, atrous_rates):\r\n super(ASPP, self).__init__()\r\n out_channels = 256\r\n modules = []\r\n modules.append(nn.Sequential(\r\n nn.Conv2d(in_channels, out_channels, 1, bias=False),\r\n nn.BatchNorm2d(out_channels),\r\n nn.ReLU(inplace=True)))\r\n\r\n rate1, rate2, rate3 = tuple(atrous_rates)\r\n modules.append(ASPPConv(in_channels, out_channels, rate1))\r\n modules.append(ASPPConv(in_channels, out_channels, rate2))\r\n modules.append(ASPPConv(in_channels, out_channels, rate3))\r\n modules.append(ASPPPooling(in_channels, out_channels))\r\n\r\n self.convs = nn.ModuleList(modules)\r\n\r\n self.project = nn.Sequential(\r\n nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),\r\n nn.BatchNorm2d(out_channels),\r\n nn.ReLU(inplace=True),\r\n nn.Dropout(0.1),)\r\n\r\n def forward(self, x):\r\n res = []\r\n for conv in self.convs:\r\n res.append(conv(x))\r\n res = torch.cat(res, dim=1)\r\n # print('res', res.shape)\r\n return self.project(res)\r\n\r\n\r\nclass Net_small(nn.Module):\r\n def __init__(self, config, device, **kwargs):\r\n super(Net_small, self).__init__()\r\n layers_ = config.MODEL.LAYERS\r\n pretrained = config.MODEL.PRETRAINED\r\n classes = config.MODEL.classes\r\n atrous_rates = config.MODEL.atrous_rates\r\n self.device = device\r\n\r\n if layers_ == 50:\r\n resnet = model.resnet50(pretrained=pretrained)\r\n elif layers_ == 34:\r\n resnet = model.resnet34(pretrained=pretrained)\r\n else:\r\n resnet = model.resnet152(pretrained=pretrained)\r\n self.layer0 = nn.Sequential(resnet.conv1_my, resnet.bn1, resnet.relu)\r\n self.max_pool = resnet.maxpool\r\n self.layer1, self.layer2, self.layer3, self.layer4 = resnet.layer1, resnet.layer2, resnet.layer3, resnet.layer4\r\n # del resnet\r\n self.relu = nn.ReLU()\r\n self.confidence = Confidence(classes, 1)\r\n\r\n self.aspp = ASPP(in_channels=512, atrous_rates=atrous_rates)\r\n\r\n self.cls = nn.Sequential(\r\n nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),\r\n nn.BatchNorm2d(256),\r\n nn.ReLU(inplace=True),\r\n nn.Conv2d(256, classes, kernel_size=1)\r\n )\r\n\r\n initialize_weights(self.confidence)\r\n initialize_weights(self.cls)\r\n initialize_weights(self.aspp)\r\n\r\n def forward(self, x, error_map=None):\r\n x_size = x.size()\r\n assert (x_size[2]) % 8 == 0 and (x_size[3]) % 8 == 0\r\n\r\n if error_map is None:\r\n error_map = torch.zeros([x_size[0], 1, x_size[2], x_size[3]])\r\n error_map = error_map.to(device=self.device)\r\n\r\n x = torch.cat([x, error_map], 1)\r\n x = self.layer0(x)\r\n x = self.max_pool(x)\r\n\r\n x_layer1 = self.layer1(x)\r\n\r\n x = self.layer2(x_layer1)\r\n\r\n x = self.layer3(x)\r\n\r\n x = self.layer4(x)\r\n\r\n x = self.aspp(x) # 此处是原图的1/8\r\n\r\n x = self.cls(x)\r\n x = F.interpolate(x, x_size[2:], mode='bilinear')\r\n # 在原图分辨率上做的\r\n confidence_map = self.confidence(x)\r\n\r\n return x, confidence_map\r\n\r\n\r\ndef get_seg_model(cfg, device, **kwargs):\r\n model = Net_small(cfg, device, **kwargs)\r\n # model.init_weights(cfg.MODEL.PRETRAINED)\r\n\r\n return model\r\n\r\n\r\ndef main():\r\n # 把输入的分辨率得限制在8的倍数+1\r\n import os\r\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2, 3'\r\n net = Net_small().cuda()\r\n input = torch.rand(4, 3, 1024, 2048).cuda()\r\n net.eval()\r\n print(net)\r\n preds_boundary, x, confidence_map = net(input)\r\n print(preds_boundary.size())\r\n print(x.size())\r\n print(confidence_map.size())\r\n\r\n\r\nif __name__ == '__main__':\r\n main()","sub_path":"two_stages/HRNet-Semantic-Segmentation-pytorch1.1/lib/models/net_small.py","file_name":"net_small.py","file_ext":"py","file_size_in_byte":6795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"453249569","text":"from DRT import *\nfrom nltk.corpus import wordnet as wn\nfrom statistics import median, mean, stdev\n\n\n#Returns label: entailment, contradiction, neutral\ndef compare_drt(drt1, drt2):\n #drt1.print_drt()\n #drt2.print_drt()\n\n event1, event2, event_score = compare_events(drt1, drt2)\n if event1 and event2:\n individual_score = compare_agents(event1, event2, drt1, drt2)\n else:\n individual_score = compare_individuals(drt1.get_individuals(),\n drt2.get_individuals())\n\n\n total_score = event_score+individual_score\n\n if total_score < 0.80:\n return 'contradiction'\n elif total_score < 1.2:\n return 'neutral'\n else:\n return 'entailment'\n\n\ndef compare_agents(event1, event2, drt1, drt2):\n props1 = event1.propositions\n props2 = event2.propositions\n action1 = props1[0]\n action2 = props2[0]\n agent1 = action1.themes[0]\n agent2 = action2.themes[0]\n\n #We first get the similarity of the agents \n #of the most similar event variable\n individuals1 = drt1.get_individuals()\n individuals2 = drt2.get_individuals()\n for individual in individuals1:\n if individual.name == agent1:\n agent1 = individual\n break\n for individual in individuals2:\n if individual.name == agent2:\n agent2 = individual\n break\n\n agent_score = 0\n best_pair = None\n\n for x in range(len(agent1.propositions)):\n p1 = agent1.propositions[x].name\n p1 = wn.synsets(p1)\n if p1:\n p1 = p1[0]\n else:\n continue\n for y in range(len(agent2.propositions)):\n p2 = agent2.propositions[y].name\n pair = (x, y)\n\n p2 = wn.synsets(p2)\n if p2:\n p2 = p2[0]\n score = p1.path_similarity(p2)\n if score == None:\n score = 0\n elif score > agent_score:\n agent_score = score\n best_pair = pair\n else:\n continue\n\n #Now we get the best similarity score \n #among any adjuncts \n\n adjunct_score = 0\n\n adjunct_individuals_1 = []\n if (len(props1)) > 1:\n props1 = props1[1:]\n adjunct_names = []\n for prop in props1:\n if prop.themes:\n for theme in prop.themes:\n if theme not in adjunct_names:\n adjunct_names.append(theme)\n for individual in individuals1:\n if individual.name in adjunct_names:\n adjunct_individuals_1.append(individual)\n\n adjunct_individuals_2 = []\n if (len(props1)) > 1:\n props2 = props2[1:]\n adjunct_names = []\n for prop in props2:\n if prop.themes:\n for theme in prop.themes:\n if theme not in adjunct_names:\n adjunct_names.append(theme)\n for individual in individuals2:\n if individual.name in adjunct_names:\n adjunct_individuals_2.append(individual)\n\n if adjunct_individuals_1 and adjunct_individuals_2:\n adjunct_score = compare_individuals(\n adjunct_individuals_1, adjunct_individuals_2)\n\n return agent_score+adjunct_score\n\n\ndef compare_individuals(individuals1, individuals2):\n\n best_score = 0\n best_pair = None\n\n for individual1 in individuals1:\n for individual2 in individuals2:\n for x in range(len(individual1.propositions)):\n p1 = individual1.propositions[x].name\n p1 = wn.synsets(p1)\n if p1:\n p1 = p1[0]\n else:\n continue\n for y in range(len(individual2.propositions)):\n p2 = individual2.propositions[y].name\n pair = (x, y)\n\n p2 = wn.synsets(p2)\n if p2:\n p2 = p2[0]\n score = p1.path_similarity(p2)\n if score == None:\n score = 0\n elif score > best_score:\n best_score = score\n best_pair = pair\n else:\n continue\n return best_score\n\ndef compare_events(drt1, drt2):\n events1 = drt1.get_events()\n events2 = drt2.get_events()\n\n props1 = []\n props2 = []\n\n for event in events1:\n props1.append(event.propositions[0].name)\n\n for event in events2:\n props2.append(event.propositions[0].name)\n\n best_score = 0\n best_pair = None\n\n if not props1 or not props2:\n return None, None, 0\n\n for x in range(len(props1)):\n p1 = props1[x]\n p1 = wn.synsets(p1)\n if p1:\n p1 = p1[0]\n else:\n continue\n for y in range(len(props2)):\n p2 = props2[y]\n pair = (x, y)\n\n p2 = wn.synsets(p2)\n if p2:\n p2 = p2[0]\n score = p1.path_similarity(p2)\n if score == None:\n score = 0\n elif score > best_score:\n best_score = score\n best_pair = pair\n else:\n continue\n if best_pair == None:\n return events1[0], events2[0], 0\n\n else:\n return events1[best_pair[0]], events2[best_pair[1]], best_score\n\n\nif __name__ == \"__main__\":\n\n print('test')\n data = open(\"snli_1.0/snli_1.0_test.txt\", 'r')\n data.readline()\n\n count = 0\n sub_count = 0\n for line in data:\n line = line.strip().split('\\t')\n label = line[0]\n if label == '-':\n continue\n count += 1\n parsed_tree = line[3]\n drt1 = create_DRT(parsed_tree)\n if drt1 != 'ERROR':\n parsed_tree = line[4]\n drt2 = create_DRT(parsed_tree)\n if drt2 != 'ERROR':\n value = compare_drt(drt1, drt2)\n if value == label:\n sub_count += 1\n print(sub_count, count, sub_count/count)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"633533869","text":"from __future__ import absolute_import\n\nfrom django.urls import reverse\n\nfrom allauth.socialaccount import providers\nfrom allauth.socialaccount.helpers import (\n complete_social_login,\n render_authentication_error,\n)\nfrom allauth.socialaccount.models import SocialLogin, SocialToken\nfrom allauth.socialaccount.providers.base import ProviderView, AuthAction, AuthError\nfrom allauth.socialaccount.providers.core.oauth.client import (\n OAuthClient,\n OAuthError,\n)\n\n\nclass OAuthView(ProviderView):\n def _get_client(self, request, callback_url):\n provider = self.provider\n app = provider.get_app(request)\n scope = ' '.join(provider.get_scope(request))\n parameters = {}\n if scope:\n parameters['scope'] = scope\n client = OAuthClient(\n request, app.client_id, app.secret,\n provider.get_request_token_url(request),\n provider.get_access_token_url(request),\n callback_url,\n parameters=parameters, provider=provider\n )\n return client\n\n\nclass OAuthLoginView(OAuthView):\n def dispatch(self, request):\n provider = self.provider\n callback_url = reverse(provider.slug + \"_callback\")\n SocialLogin.stash_state(request)\n action = request.GET.get('action', AuthAction.AUTHENTICATE)\n auth_url = provider.get_auth_url(request, action) or provider.get_authorize_url(request)\n auth_params = provider.get_auth_params(request, action)\n client = self._get_client(request, callback_url)\n try:\n return client.get_redirect(auth_url, auth_params)\n except OAuthError as e:\n return render_authentication_error(request, provider.slug, exception=e)\n\n\nclass OAuthCallbackView(OAuthView):\n def dispatch(self, request):\n \"\"\"\n View to handle final steps of OAuth based authentication where the user\n gets redirected back to from the service provider\n \"\"\"\n provider = self.provider\n login_done_url = reverse(provider.slug + \"_callback\")\n client = self._get_client(request, login_done_url)\n if not client.is_valid():\n if 'denied' in request.GET:\n error = AuthError.CANCELLED\n else:\n error = AuthError.UNKNOWN\n extra_context = dict(oauth_client=client)\n return render_authentication_error(request, provider.slug, error=error, extra_context=extra_context)\n\n app = provider.get_app(request)\n\n try:\n access_token = client.get_access_token()\n token = SocialToken(\n app=app,\n token=access_token['oauth_token'],\n # .get() -- e.g. Evernote does not feature a secret\n token_secret=access_token.get('oauth_token_secret', '')\n )\n login = provider.complete_login(request, app, token, response=access_token)\n login.token = token\n login.state = SocialLogin.unstash_state(request)\n return complete_social_login(request, login)\n except OAuthError as e:\n return render_authentication_error(request, provider.slug, exception=e)\n","sub_path":"allauth/socialaccount/providers/core/oauth/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"429455686","text":"from django.shortcuts import render_to_response, redirect\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpResponse\nimport json\nfrom usuarios.models import *\n# Create your views here.\n\n@csrf_exempt\ndef crearUser(request):\n if request.method == \"POST\":\n post = request.body.decode()\n post = json.loads(post)\n userName = (Usuario.objects.filter(userName=post['userName']).count() > 0)\n email = (Usuario.objects.filter(email=post['email']).count() > 0)\n if(userName):\n return HttpResponse(\"Inavalid UserName\")\n else:\n if(email):\n return HttpResponse(\"Invalid Email\")\n else:\n nuevo = Usuario(name=post['name'],userName=post['userName'],password=post['password'],email=post['email'])\n nuevo.save()\n request.session['user_id'] = nuevo.i_d\n request.session['login'] = True\n return HttpResponse(\"User Created\")\n else:\n return redirect('home')\n\n@csrf_exempt\ndef eliminarUser(request):\n if request.method == \"GET\":\n try:\n if(request.COOKIES['sessionid']):\n login = request.session['login']\n if(login == True):\n usuario = Usuario.objects.get(i_d=request.session['user_id'])\n usuario.delete()\n request.session['login']=False\n request.session['user_id']=0\n return redirect('home') \n except:\n return redirect('home') \n return redirect('home')\n else:\n return HttpResponse(\"¿?\")\n \n@csrf_exempt\ndef salir(request):\n try:\n if(request.COOKIES['sessionid']):\n request.session['login']=False\n request.session['user_id']=0\n return redirect('home')\n except:\n return redirect('home')\n \n \ndef login(request):\n if request.method == \"POST\":\n post = request.body.decode()\n post = json.loads(post)\n usuario = (Usuario.objects.filter(email=post['email']).count() > 0)\n if(usuario):\n usuario = Usuario.objects.get(email=post['email'])\n if (post['password']==usuario.password):\n request.session['user_id'] = usuario.i_d\n request.session['login'] = True\n return HttpResponse(\"Logged User\")\n else:\n return HttpResponse(\"Invalid Password\")\n else:\n return HttpResponse(\"Invalid Email\")\n else:\n return redirect ('home')\n ","sub_path":"usuarios/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"613979383","text":"class Solution:\r\n def maxProfit(self, k: int, prices: List[int]) -> int:\r\n if not prices or not k:\r\n return 0\r\n \r\n if k >= len(prices) // 2:\r\n return sum(p2 - p1 for p1, p2 in zip(prices[:-1], prices[1:]) if p2 > p1)\r\n \r\n profits = [0] * len(prices)\r\n for _ in range(k):\r\n curr_max = 0\r\n for i in range(1, len(prices)):\r\n earning = prices[i] - prices[i - 1]\r\n curr_max = max(curr_max + earning, profits[i])\r\n profits[i] = max(profits[i - 1], curr_max)\r\n return profits[-1]\r\n","sub_path":"solutions/188-best-time-to-buy-and-sell-stock-iv/best-time-to-buy-and-sell-stock-iv.py","file_name":"best-time-to-buy-and-sell-stock-iv.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"186779762","text":"import sys\nimport os\n\n\n\nUSAGE = \"python deleteData.py [data file] [format file] [query]\"\n\n\nif len(sys.argv) != 4:\n print(\"\", flush=True)\n print(\"\\tError: Incorrect number of parameters\", flush=True)\n print()\n print(\"\\tUSAGE: \" + USAGE, flush=True)\n exit()\n\nf_data = open( sys.argv[1], \"r\" )\nf_format = open( sys.argv[2], \"r\" )\nquery = sys.argv[3]\n\noutput_data = open(\"NEW_DATA\", \"a\")\noutput_format = open(\"NEW_FORMAT\", \"a\")\n\ndata_line = f_data.readline()\nformat_line = f_format.readline()\n\nwhile format_line:\n if not query in format_line:\n output_format.write(format_line)\n for i in range(4):\n output_data.write(data_line)\n data_line = f_data.readline()\n else:\n for i in range(4):\n data_line = f_data.readline()\n format_line = f_format.readline()\n\n\nf_data.close()\nf_format.close()\noutput_format.close()\noutput_data.close()\n","sub_path":"TestingFiles/removeData.py","file_name":"removeData.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"553999500","text":"# 32756 112\nfrom collections import deque\nfrom sys import stdin\n\nn = int(stdin.readline())\nboard = [[-1 for _ in range(n)] for _ in range(n)] # 체스보드 구성\nvisited = [[False for _ in range(n)] for _ in range(n)] # 방문여부 확인\n# 방향 변수\ndx = [-2,-2,0,0,2,2] # 가로축\ndy = [-1,1,-2,2,-1,1] # 세로축\n \ndef bfs(x, y, r, c): # x,y는 시작좌표 / r,c는 도착좌표 \n board[x][y] = 0 # 보드의 초기값이 -1이었기 때문에, 조건에 부합한다면 0으로 세팅해주어야 함\n visited[x][y] = True\n queue = deque() # 큐 생성\n queue.append((x,y))\n\n while queue: # 큐가 빌때까지 수행\n a,b = queue.popleft()\n\n if (a == r) and (b == c): \n return board[a][b] # 도착지점에 도달한다면\n \n for i in range(6): # 방향 변수 개수만큼 반복 \n nx = a + dx[i]\n ny = b + dy[i]\n\n if (0 <= nx < n and 0 <= ny < n) and (visited[nx][ny] == False):\n visited[nx][ny] = True\n board[nx][ny] = board[a][b] + 1 # 이동횟수 구하기에 해당\n queue.append((nx,ny))\n\n return board[r][c] # 도착지점에 도달하지 못한다면\n\nx,y,r,c = map(int, stdin.readline().split()) \nprint(bfs(x,y,r,c))\n","sub_path":"w3.BFS/BOJ_16948(데스 나이트)/16948_정원.py","file_name":"16948_정원.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"532023059","text":"\"\"\"\nThis file sets parameters used in data dump OpenEEW algorithm\n\"\"\"\n\n# PARAMETERS\nmax_gap = 10 # maximum gap in data\nsleep_time = 1 # the saving algorithm is goinng to sleep for this ammount of time\n\n# MSEED PARAMS\npath_in_json = \"./tmp/jsonl/\"\npath_in_mseed = \"./tmp/mseed/\"\nnetwork = \"PR\"\ninterp_samp = 0 # 0: Do not interpolate, n: interpolate n samples, -1: interpolate ALL overlapping samples\nmisal_thresh = (\n 0.5 # 0: do no align samples, 0.5: align all samples with sub-sample time shift​\n)\n\n\nparams = {\n \"max_gap\": max_gap,\n \"sleep_time\": sleep_time,\n \"path_in_json\": path_in_json,\n \"path_in_mseed\": path_in_mseed,\n \"network\": network,\n \"interp_samp\": interp_samp,\n \"misal_thresh\": misal_thresh,\n}\n","sub_path":"params.py","file_name":"params.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"189830359","text":"import cv2\nimport numpy as np\nimport face_recognition\nimport os\nfrom datetime import datetime\nimport sys\nimport json\n\npath='Knowimages'\nimages=[]\nclassName=[]\nmyList=os.listdir(path)\n# print(myList)\nresponse=False\nimageName=\"\"\n\nfor cl in myList:\n curImg=cv2.imread(f'{path}/{cl}')\n images.append(curImg)\n className.append(os.path.splitext(cl)[0])\n# print(className)\n\ndef findEncoding(images):\n encodeList=[]\n\n for img in images:\n img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n encode=face_recognition.face_encodings(img)\n encodeList.append(encode)\n return encodeList\n\nencodeListKnown=findEncoding(images)\n# print(len(encodeListKnown))\n# print(\"Encoding Complete\")\n\nimg=cv2.imread('./Uknowimages/image.png')\n# cv2.imshow(\"window\",img)\nval=0\n\nwhile val==0:\n # cv2.imshow(\"video\",img)\n imgs=cv2.resize(img,(0,0),None,0.25,0.25)\n imgs=cv2.cvtColor(imgs,cv2.COLOR_BGR2RGB)\n\n faceCurFrame=face_recognition.face_locations(imgs)\n encodesCurFrame=face_recognition.face_encodings(imgs)\n # print(encodesCurFrame)\n\n for encodeFace,faceLoc in zip(encodesCurFrame,faceCurFrame):\n matches=face_recognition.compare_faces(encodeListKnown,encodeFace)\n # print(matches)\n faceDis=face_recognition.face_distance(encodeListKnown,encodeFace)\n\n # print(faceDis)\n matchIndex=np.argmin(faceDis)\n # print(matchIndex)\n\n if np.any(matches):\n response=True\n name=className[matchIndex%(len(className))].upper()\n # print(name,response)\n imageName=name\n break\n val=1\n\nif response==True:\n res={\n \"name\":imageName,\n \"response\":True\n }\n print(json.dumps(res))\n sys.stdout.flush(res)\nelse:\n res={\n \"response\":False\n }\n print(json.dumps(res))\n sys.stdout.flush(res)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()","sub_path":"backend/Login.py","file_name":"Login.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"579632926","text":"import json\nimport logging\nimport webapp2\n\nfrom cache_controller import CacheController\nfrom model_user import User\n\nclass UserEditHandler(webapp2.RequestHandler):\n def post(self, fb_uid):\n user = CacheController.get_user_by_fb_id(fb_uid)\n nick = self.request.get(\"nick\")\n info = self.request.get(\"info\")\n if nick == \"\":\n user.nick = None\n else:\n user.nick = nick\n user.info = info\n user.put()\n CacheController.invalidate_user_fb_id(fb_uid)\n self.response.out.write(json.dumps({\"success\" : \"true\"}))\n","sub_path":"choosie-server/user_edit_handler.py","file_name":"user_edit_handler.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"385490876","text":"from django import forms\r\nfrom .models import recipe_table\r\n\r\nclass recipe_form(forms.ModelForm):\r\n class Meta:\r\n model=recipe_table\r\n fields=('recipe_name' ,\r\n 'recipe_type','ingredient','description'\r\n ,'process_of_making','picture')\r\n\r\n \r\n\r\n","sub_path":"food_plaza/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"52411203","text":"from main.models import Slider, Page\n\ndef global_context(request):\n\titems = Slider.objects.all()\n\tmenus = Page.objects.all().order_by('order')\n\tfor menu in menus:\n\t\tif menu.slug == request.get_full_path():\n\t\t\t menu.active = True\n\t\telif menu.slug in request.get_full_path() and menu.slug != '/':\n\t\t\t menu.active = True\n\treturn {'items':items, 'menus':menus}","sub_path":"main/main_context.py","file_name":"main_context.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"196069619","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Mar 2 21:50:07 2017\r\n\r\n@author: Erik\r\n\"\"\"\r\nimport numpy as np\r\nfrom skimage.feature import hog\r\nimport cv2\r\nimport os\r\nimport matplotlib.image as mpimg\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.svm import LinearSVC\r\nfrom sklearn.preprocessing import StandardScaler\r\nimport time\r\n\r\n# find all files of a specified file type within a folder and its subfolders\r\ndef build_file_list(path, file_type = '.png'):\r\n files = [os.path.join(root, name)\r\n for root, dirs, files in os.walk(path)\r\n for name in files\r\n if name.endswith((file_type))]\r\n return files\r\n\r\n# Function to return some characteristics of the dataset. code from Udacity lectures\r\ndef data_look(car_list, notcar_list):\r\n data_dict = {}\r\n # Define a key in data_dict \"n_cars\" and store the number of car images\r\n data_dict[\"n_cars\"] = len(car_list)\r\n # Define a key \"n_notcars\" and store the number of notcar images\r\n data_dict[\"n_notcars\"] = len(notcar_list)\r\n # Read in a test image, either car or notcar\r\n example_img = mpimg.imread(car_list[0])\r\n # Define a key \"image_shape\" and store the test image shape 3-tuple\r\n data_dict[\"image_shape\"] = example_img.shape\r\n # Define a key \"data_type\" and store the data type of the test image.\r\n data_dict[\"data_type\"] = example_img.dtype\r\n # Return data_dict\r\n return data_dict\r\n\r\n# get HOG features from an input image with specified parameters\r\ndef get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):\r\n # Call with two outputs if vis==True\r\n if vis == True:\r\n features, hog_image = hog(img, orientations=orient, \r\n pixels_per_cell=(pix_per_cell, pix_per_cell),\r\n cells_per_block=(cell_per_block, cell_per_block), \r\n transform_sqrt=False, \r\n visualise=vis, feature_vector=feature_vec)\r\n return features, hog_image\r\n # Otherwise call with one output\r\n else: \r\n features = hog(img, orientations=orient, \r\n pixels_per_cell=(pix_per_cell, pix_per_cell),\r\n cells_per_block=(cell_per_block, cell_per_block), \r\n transform_sqrt=False, \r\n visualise=vis, feature_vector=feature_vec)\r\n return features\r\n \r\n# function to compute color histogram features. code from Udacity lectures\r\ndef bin_spatial(img, size):\r\n color1 = cv2.resize(img[:,:,0], size).ravel()\r\n color2 = cv2.resize(img[:,:,1], size).ravel()\r\n color3 = cv2.resize(img[:,:,2], size).ravel()\r\n return np.hstack((color1, color2, color3))\r\n\r\n#compute color histogram features. code from Udacity lectures\r\ndef color_hist(img, nbins=32): #bins_range=(0, 256)\r\n # Compute the histogram of the color channels separately\r\n channel1_hist = np.histogram(img[:,:,0], bins=nbins)\r\n channel2_hist = np.histogram(img[:,:,1], bins=nbins)\r\n channel3_hist = np.histogram(img[:,:,2], bins=nbins)\r\n # Concatenate the histograms into a single feature vector\r\n hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))\r\n # Return the individual histograms, bin_centers and feature vector\r\n return hist_features\r\n\r\n# train a SVC classifier using input arrays of labeled training images. return the trained SVC and the scaler used.\r\ndef svc_classifier(cars, not_cars, colorspace, spatial_size, hist_bins, hog_channel, orient, pix_per_cell, cell_per_block, spatial_feat, hist_feat, hog_feat):\r\n\r\n sample_size = 8500\r\n cars = cars[0:sample_size]\r\n not_cars = not_cars[0:sample_size]\r\n\r\n t=time.time()\r\n car_features = extract_features(cars, colorspace, spatial_size, hist_bins, orient, pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat)\r\n \r\n not_car_features = extract_features(not_cars, colorspace, spatial_size, hist_bins, orient, pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat)\r\n \r\n t2 = time.time()\r\n print(round(t2-t, 2), 'Seconds to extract features...')\r\n # Create an array stack of feature vectors\r\n X = np.vstack((car_features, not_car_features)).astype(np.float64) \r\n # Fit a per-column scaler\r\n X_scaler = StandardScaler().fit(X)\r\n\r\n # Apply the scaler to X\r\n scaled_X = X_scaler.transform(X)\r\n \r\n # Define the labels vector\r\n y = np.hstack((np.ones(len(car_features)), np.zeros(len(not_car_features))))\r\n \r\n \r\n # Split up data into randomized training and test sets\r\n rand_state = np.random.randint(0, 100)\r\n X_train, X_test, y_train, y_test = train_test_split(\r\n scaled_X, y, test_size=0.2, random_state=rand_state)\r\n \r\n print('Using:',orient,'orientations',pix_per_cell,\r\n 'pixels per cell and', cell_per_block,'cells per block')\r\n print('Feature vector length:', len(X_train[0]))\r\n # Use a linear SVC \r\n svc = LinearSVC()\r\n # Check the training time for the SVC\r\n t=time.time()\r\n svc.fit(X_train, y_train)\r\n t2 = time.time()\r\n print(round(t2-t, 2), 'Seconds to train SVC...')\r\n # Check the score of the SVC\r\n print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\r\n \r\n return svc, X_scaler\r\n\r\n# Extract features from a list of images. code from Udacity lectures\r\ndef extract_features(imgs, color_space, spatial_size, hist_bins, orient, pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat):\r\n # Create a list to append feature vectors to\r\n features = []\r\n # Iterate through the list of images\r\n for file in imgs:\r\n file_features = []\r\n # Read in each one by one\r\n image = mpimg.imread(file)\r\n # apply color conversion if other than 'RGB'\r\n if color_space != 'RGB':\r\n if color_space == 'HSV':\r\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\r\n elif color_space == 'LUV':\r\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)\r\n elif color_space == 'HLS':\r\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\r\n elif color_space == 'YUV':\r\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)\r\n elif color_space == 'YCrCb':\r\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)\r\n else: feature_image = np.copy(image) \r\n\r\n if spatial_feat == True:\r\n spatial_features = bin_spatial(feature_image, size=spatial_size)\r\n file_features.append(spatial_features)\r\n if hist_feat == True:\r\n # Apply color_hist()\r\n hist_features = color_hist(feature_image, nbins=hist_bins)\r\n file_features.append(hist_features)\r\n if hog_feat == True:\r\n # Call get_hog_features() with vis=False, feature_vec=True\r\n if hog_channel == 'ALL':\r\n hog_features = []\r\n for channel in range(feature_image.shape[2]):\r\n hog_features.append(get_hog_features(feature_image[:,:,channel], \r\n orient, pix_per_cell, cell_per_block, \r\n vis=False, feature_vec=True))\r\n hog_features = np.ravel(hog_features) \r\n else:\r\n hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, \r\n pix_per_cell, cell_per_block, vis=False, feature_vec=True)\r\n # Append the new feature vector to the features list\r\n file_features.append(hog_features)\r\n features.append(np.concatenate(file_features))\r\n # Return list of feature vectors\r\n return features\r\n\r\n# function that can extract features using hog sub-sampling and make predictions. code from Udacity lectures\r\ndef find_cars(img, color_space, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):\r\n \r\n #draw_img = np.copy(img)\r\n img = img.astype(np.float32)/255\r\n \r\n img_tosearch = img[ystart:ystop,:,:]\r\n if color_space != 'RGB':\r\n if color_space == 'HSV':\r\n ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HSV)\r\n elif color_space == 'LUV':\r\n ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2LUV)\r\n elif color_space == 'HLS':\r\n ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HLS)\r\n elif color_space == 'YUV':\r\n ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)\r\n elif color_space == 'YCrCb':\r\n ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb)\r\n else: ctrans_tosearch = img_tosearch\r\n if scale != 1:\r\n imshape = ctrans_tosearch.shape\r\n ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))\r\n \r\n ch1 = ctrans_tosearch[:,:,0]\r\n ch2 = ctrans_tosearch[:,:,1]\r\n ch3 = ctrans_tosearch[:,:,2]\r\n\r\n # Define blocks and steps as above\r\n nxblocks = (ch1.shape[1] // pix_per_cell)-1\r\n nyblocks = (ch1.shape[0] // pix_per_cell)-1 \r\n #nfeat_per_block = orient*cell_per_block**2\r\n # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell\r\n window = 64\r\n nblocks_per_window = (window // pix_per_cell)-1 \r\n cells_per_step = 2 # Instead of overlap, define how many cells to step\r\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\r\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\r\n \r\n # Compute individual channel HOG features for the entire image\r\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\r\n hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\r\n hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\r\n \r\n # Initialize a list to append detected positions to\r\n boxes = []\r\n \r\n for xb in range(nxsteps):\r\n for yb in range(nysteps):\r\n ypos = yb*cells_per_step\r\n xpos = xb*cells_per_step\r\n # Extract HOG for this patch\r\n hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() \r\n hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() \r\n hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() \r\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\r\n\r\n xleft = xpos*pix_per_cell\r\n ytop = ypos*pix_per_cell\r\n\r\n # Extract the image patch\r\n subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))\r\n \r\n # Get color features\r\n spatial_features = bin_spatial(subimg, size=spatial_size)\r\n hist_features = color_hist(subimg, nbins=hist_bins)\r\n\r\n # Scale features and make a prediction\r\n test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))\r\n #test_features = X_scaler.transform(hog_features.reshape(1, -1))\r\n test_prediction = svc.predict(test_features)\r\n \r\n if test_prediction == 1:\r\n xbox_left = np.int(xleft*scale)\r\n ytop_draw = np.int(ytop*scale)\r\n win_draw = np.int(window*scale)\r\n boxes.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))\r\n #cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6) \r\n \r\n return boxes\r\n\r\n# add heat to pixels inside the input list of bounding boxes. code from Udacity lectures\r\ndef add_heat(heatmap, bbox_list):\r\n # Iterate through list of bboxes\r\n for box in bbox_list:\r\n # Add += 1 for all pixels inside each bbox\r\n # Assuming each \"box\" takes the form ((x1, y1), (x2, y2))\r\n heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\r\n\r\n # Return updated heatmap\r\n return heatmap\r\n\r\n# threshold input heatmap. code from Udacity lectures\r\ndef apply_threshold(heatmap, threshold):\r\n # Zero out pixels below the threshold\r\n heatmap[heatmap <= threshold] = 0\r\n # Return thresholded map\r\n return heatmap\r\n\r\n# Draw bounding boxes around pixels with value >1. code from Udacity lectures\r\ndef draw_labeled_bboxes(img, labels):\r\n # Iterate through all detected cars\r\n for car_number in range(1, labels[1]+1):\r\n # Find pixels with each car_number label value\r\n nonzero = (labels[0] == car_number).nonzero()\r\n # Identify x and y values of those pixels\r\n nonzeroy = np.array(nonzero[0])\r\n nonzerox = np.array(nonzero[1])\r\n # Define a bounding box based on min/max x and y\r\n bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))\r\n # Draw the box on the image\r\n cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)\r\n # Return the image\r\n return img","sub_path":"proj_functions.py","file_name":"proj_functions.py","file_ext":"py","file_size_in_byte":13244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"8756036","text":"import numpy as np\nimport pytest\n\nfrom mxmc.estimator import Estimator\nfrom mxmc.sample_allocations.acv_sample_allocation import ACVSampleAllocation\n\n\ndef test_two_model_estimate():\n compressed_allocation = np.array([[1, 1, 1, 1],\n [5, 1, 1, 0],\n [10, 0, 0, 1]])\n allocation = ACVSampleAllocation(compressed_allocation)\n model_outputs = [np.arange(1, 7), np.arange(1, 17)]\n covariance = np.array([[1, 0.5], [0.5, 1]])\n\n est = Estimator(allocation, covariance)\n\n expected_estimate = 5.848484848484849\n assert est.get_estimate(model_outputs) == pytest.approx(expected_estimate)\n\n\ndef test_two_model_approximate_variance():\n compressed_allocation = np.array([[3, 1, 1, 1],\n [57, 0, 0, 1]], dtype=int)\n allocation = ACVSampleAllocation(compressed_allocation)\n covariance = np.array([[1, 0.5], [0.5, 1]])\n\n est = Estimator(allocation, covariance)\n\n assert est.approximate_variance == pytest.approx(61 / 240)\n\n\n@pytest.mark.filterwarnings(\"ignore:Allocation Warning\")\ndef test_three_model_approximate_variance():\n compressed_allocation = np.array([[1, 1, 1, 1, 0, 0],\n [5, 0, 1, 1, 1, 1],\n [10, 0, 0, 0, 1, 1]])\n sample_allocation = ACVSampleAllocation(compressed_allocation)\n covariance = np.array([[1, 0.5, 0.25], [0.5, 1, 0.5], [0.25, 0.5, 1]])\n est = Estimator(sample_allocation, covariance)\n\n assert est.approximate_variance == pytest.approx(1)\n","sub_path":"tests/integration/test_acv_estimator.py","file_name":"test_acv_estimator.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"574867423","text":"from django.db import models, transaction\nfrom django.db.models import Q\n\n\nclass ArticleQueryset(models.QuerySet):\n pass\n\n\nclass SimilarArticleQuerySet(models.QuerySet):\n # What i've implemented is a undirected weighted graph. Since the edges\n # are undirected the ordering of source and destination doesn't matter.\n # This means: SimilarArticle(from, to, weight) == SimilarArticle(to, from,\n # weight)\n\n # Which means we have to decide which of the two we want to store. I've\n # decided to store the the article with the lowest id as the source and\n # the article with the highest id as the destination.\n\n @transaction.atomic\n def add_similar_article(self, from_article, to_article, ratio):\n \"\"\"\n :param Article from_article\n :param Article to_article\n :param integer ratio from 0.0 to 1.0\n :return SimilarArticle instance of SimilarArticle which connects from_article\n to to_article\n \"\"\"\n from .models import Article, Cluster\n\n _from_article = min(from_article, to_article, key=lambda a: a.id)\n _to_article = max(from_article, to_article, key=lambda a: a.id)\n\n\n if _from_article == _to_article:\n raise ValueError(\"It's not possible to create a similarity relation\"\n \"to from and to the same Article. An Article is\"\n \"always 100% similar to itself.\")\n\n\n # Find a cluster for this similarity.\n similarities = self.model.objects.indirect_related(from_article, to_article)\n\n _latest_dates = [_from_article._latest_date, _to_article._latest_date]\n _latest_dates += list(Article.objects.filter(\n pk__in=similarities.article_pks()\n ).values_list('_latest_date', flat=True).order_by('-_latest_date')[:1])\n _latest_dates = [d for d in _latest_dates if d is not None]\n _latest_date = max(_latest_dates) if _latest_dates else None\n\n clusters = list(similarities.values_list('cluster', flat=True).distinct())\n try:\n cluster = Cluster.objects.get(pk__in=clusters)\n except Cluster.DoesNotExist:\n cluster = Cluster.objects.create(latest_date=_latest_date)\n except Cluster.MultipleObjectsReturned as e:\n # Merge\n cluster = Cluster.objects.filter(pk=clusters[0]).get()\n cluster.latest_date=_latest_date\n cluster.save()\n\n similarities.update(cluster=cluster)\n\n for unused_cluster in clusters[1:]:\n unused_cluster.delete()\n\n else:\n cluster.latest_date = _latest_date\n cluster.save()\n\n return self.model.objects.create(\n cluster=cluster,\n from_article=_from_article, to_article=_to_article,\n ratio=ratio)\n\n def article_pks(self):\n similarities = self.values_list('from_article__pk', 'to_article__pk')\n\n # flatten into a list\n flattened = [article_pk for similarity in similarities for article_pk in similarity]\n\n return list(set(flattened))\n\n def direct_related(self, *article_pks):\n \"\"\"\n Find SimilarObject instances which have a direct relation to the given article.\n\n :param integer *article_pks\n :return Queryset\n \"\"\"\n\n similarities = self.model.objects.filter(\n Q(from_article_id__in=article_pks) | Q(to_article_id__in=article_pks)\n )\n\n return similarities\n\n def indirect_related(self, *article_pks):\n \"\"\"\n Find SimilarObject instances which have a direct or indirect relation to the given article.\n\n :param integer *article_pks\n :return Queryset\n \"\"\"\n\n articles_found = set()\n similarities_found = set()\n look_for = article_pks\n\n # Keep looking until we've found all articles.\n while len(look_for):\n similarities = self.direct_related(*look_for)\n\n look_for = []\n for pk, from_article, to_article in similarities.values_list('pk', 'from_article_id', 'to_article_id'):\n if from_article not in articles_found:\n look_for.append(from_article)\n articles_found.add(from_article)\n\n if to_article not in articles_found:\n look_for.append(to_article)\n articles_found.add(to_article)\n\n similarities_found.add(pk)\n\n return self.model.objects.filter(pk__in=similarities_found)\n\nclass SimilarArticleManager(models.Manager.from_queryset(SimilarArticleQuerySet)):\n use_in_migrations = True\n","sub_path":"news/managers.py","file_name":"managers.py","file_ext":"py","file_size_in_byte":4656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"158715894","text":"import pygame as pg\nfrom settings import *\nimport sys\nfrom sprites import Panel, QuestionBox, MainMenuBackgroundIcon, MenuCategoryIcon, Arrow\nfrom random import randint\n\nclass MainMenu():\n def __init__(self, main):\n self.main = main\n self.font = pg.font.SysFont(\"Roman\", MENU_FONT_SIZE)\n self.smallerFont = pg.font.SysFont(\"Roman\", MENU_SMALLER_FONT_SIZE)\n self.newGameRect = pg.Rect(10 * TILESIZE, 6 * TILESIZE, 10 * TILESIZE, 2 * TILESIZE + 20)\n self.exitRect = pg.Rect(10 * TILESIZE, 9 * TILESIZE, 10 * TILESIZE, 2 * TILESIZE + 20)\n self.newGameMenuRect = pg.Rect(2 * TILESIZE, 1 * TILESIZE, 26 * TILESIZE, 15 * TILESIZE)\n self.startGameRect = pg.Rect(16 * TILESIZE, 13 * TILESIZE, 10 * TILESIZE, 2 * TILESIZE)\n self.backRect = pg.Rect(4 * TILESIZE, 13 * TILESIZE, 10 * TILESIZE, 2 * TILESIZE)\n self.gamemodeRects = [pg.Rect(4 * TILESIZE, 8 * TILESIZE, 4 * TILESIZE, 1 * TILESIZE), pg.Rect(10 * TILESIZE, 8 * TILESIZE, 4 * TILESIZE, 1 * TILESIZE), pg.Rect(16 * TILESIZE, 8 * TILESIZE, 4 * TILESIZE, 1 * TILESIZE)]\n self.lifeLineRects = [pg.Rect(9.5 * TILESIZE, 10 * TILESIZE, 2 * TILESIZE, 1 * TILESIZE), pg.Rect(11.5 * TILESIZE, 10 * TILESIZE, 2 * TILESIZE, 1 * TILESIZE)]\n self.gamemodes = [\"Lives Mode\", \"Time Mode\", \"Endless Mode\"]\n self.lifeLineOptions = [\"On\", \"Off\"]\n self.menu = \"main\"\n self.main.getHighScore()\n self.createCategoryIcons()\n self.questions = self.main.allQuestions.copy()\n self.disabledCategories = []\n self.disabledDifficulties = []\n self.main.music_channel.unpause()\n self.main.music_channel.play(self.main.music_sound, -1)\n self.main.music_channel.set_volume(.1)\n self.arrow_left = Arrow(main, TILESIZE * 3, TILESIZE * 4.2, self.main.arrow_left_image, \"left\")\n self.arrow_right = Arrow(main, TILESIZE * 24, TILESIZE * 4.2, self.main.arrow_right_image, \"right\")\n self.currentCategoryStart = 0\n self.gamemodeSelected = 0\n self.lifeLines = 1\n\n def update(self):\n if self.menu == \"newGame\":\n for sprite in self.newGameMenuCatIcons:\n sprite.update()\n self.arrow_left.update()\n self.arrow_right.update()\n\n def filterOutCategory(self, category):\n filtered = self.questions.copy()\n for question in self.questions:\n if category in question['categories']:\n filtered.remove(question)\n if len(filtered) >= MIN_NUM_OF_QUESTIONS_TO_PLAY:\n self.questions = filtered\n self.disabledCategories.append(category)\n return True\n return False\n\n def unfilterCategory(self, category):\n filtered = self.questions.copy()\n for question in self.main.allQuestions:\n if category in question['categories'] and question not in self.questions:\n if question['difficulty'] not in self.disabledDifficulties:\n filtered.append(question)\n self.disabledCategories.remove(category)\n self.questions = filtered\n\n def filterOutDifficulty(self, difficulty):\n filtered = self.questions.copy()\n for question in self.questions:\n if difficulty == question[\"difficulty\"]:\n filtered.remove(question)\n if len(filtered) >= MIN_NUM_OF_QUESTIONS_TO_PLAY:\n self.questions = filtered\n self.disabledDifficulties.append(difficulty)\n return True\n return False\n\n def unfilterDifficulty(self, difficulty):\n filtered = self.questions.copy()\n for question in self.main.allQuestions:\n if difficulty == question[\"difficulty\"] and question not in self.questions:\n catDisabled = False\n for cat in question['categories']:\n if cat in self.disabledCategories:\n catDisabled = True\n if not catDisabled:\n filtered.append(question)\n self.disabledDifficulties.remove(difficulty)\n self.questions = filtered\n\n def createCategoryIcons(self):\n self.newGameMenuCatIcons = []\n for category in list(self.main.icon_images.keys()):\n self.newGameMenuCatIcons.append(MenuCategoryIcon(self.main, category))\n\n def draw(self):\n if self.menu == \"main\":\n self.drawMainMenuPanels()\n chance = randint(0, BACKGROUND_ICON_SPAWN_CHANCE)\n if chance == BACKGROUND_ICON_SPAWN_CHANCE:\n MainMenuBackgroundIcon(self.main)\n elif self.menu == \"newGame\":\n self.drawNewGameMenu()\n\n def drawGamemodeRects(self):\n index = 0\n for rect in self.gamemodeRects:\n gamemodeText = self.font.render(self.gamemodes[index], True, WHITE)\n width = self.font.size(self.gamemodes[index])[0] + 12\n rect.width = width\n if self.gamemodeSelected == index:\n pg.draw.rect(self.main.screen, BLACK, pg.Rect(rect.x - 5, rect.y - 5, rect.width + 10, rect.height + 10))\n pg.draw.rect(self.main.screen, PALETTE_1[0], rect)\n self.main.screen.blit(gamemodeText, (rect.x + 6, rect.y))\n index += 1\n\n def drawLifeLineRects(self):\n index = 0\n hintsText = self.font.render(\"LIFELINES: \", True, WHITE)\n self.main.screen.blit(hintsText, (TILESIZE * 4, self.lifeLineRects[0].y - 3))\n for rect in self.lifeLineRects:\n option = self.font.render(self.lifeLineOptions[index], True, WHITE)\n width = self.font.size(self.lifeLineOptions[index])[0] + 12\n rect.width = width\n if self.lifeLines == index:\n pg.draw.rect(self.main.screen, BLACK, pg.Rect(rect.x - 5, rect.y - 5, rect.width + 10, rect.height + 10))\n pg.draw.rect(self.main.screen, PALETTE_1[0], rect)\n self.main.screen.blit(option, (rect.x + 6, rect.y))\n index += 1\n\n def drawNewGameMenu(self):\n pg.draw.rect(self.main.screen, PALETTE_1[1], self.newGameMenuRect)\n difficultyText = self.smallerFont.render(\"Include difficulty levels (1 is easy - 5 dificult): \", True, WHITE)\n self.main.screen.blit(difficultyText, (3 * TILESIZE, 2 * TILESIZE))\n for questionBox in self.questionBoxes:\n if questionBox.isHoveredOn:\n self.main.screen.blit(self.main.question_box_hover_image, (questionBox.x, questionBox.y))\n levelText = self.smallerFont.render(str(questionBox.difficulty), True, WHITE)\n self.main.screen.blit(levelText, (questionBox.x + 23, questionBox.y - 20))\n pg.draw.rect(self.main.screen, PALETTE_1[0], self.startGameRect)\n newGameText = self.font.render(\"NEW GAME\", True, WHITE)\n self.main.screen.blit(newGameText, (self.startGameRect.x + TILESIZE * 2.2, self.startGameRect.y + 0.5 * TILESIZE))\n pg.draw.rect(self.main.screen, PALETTE_1[0], self.backRect)\n backText = self.font.render(\"BACK\", True, WHITE)\n self.main.screen.blit(backText, (self.backRect.x + TILESIZE * 3.5, self.backRect.y + 0.5 * TILESIZE))\n self.drawCategoryIcons()\n self.main.screen.blit(self.arrow_left.image, (self.arrow_left.x, self.arrow_left.y))\n self.main.screen.blit(self.arrow_right.image, (self.arrow_right.x, self.arrow_right.y))\n self.drawGamemodeRects()\n self.drawLifeLineRects()\n\n def setDisplayedIcons(self):\n for sprite in self.newGameMenuCatIcons:\n sprite.isBeingDisplayed = False\n for spriteIndex in range(0, NUM_OF_MENU_ICONS_TO_DRAW):\n spriteIndex += self.currentCategoryStart\n spriteIndex %= len(self.newGameMenuCatIcons)\n sprite = self.newGameMenuCatIcons[spriteIndex]\n sprite.isBeingDisplayed = True\n\n def drawCategoryIcons(self):\n #for sprite in self.newGameMenuCatIcons:\n xOffset = TILESIZE * 5\n for spriteIndex in range(0, NUM_OF_MENU_ICONS_TO_DRAW):\n spriteIndex += self.currentCategoryStart\n spriteIndex %= len(self.newGameMenuCatIcons)\n sprite = self.newGameMenuCatIcons[spriteIndex]\n sprite.x = xOffset\n sprite.rect.x = xOffset\n sprite.drawCircle()\n self.main.screen.blit(sprite.image, (xOffset, sprite.y))\n if sprite.disabled:\n self.main.screen.blit(self.main.disabled_icon_image, (xOffset - 13, sprite.y - 13))\n xOffset += NEWGAME_MENU_CATEGORY_ICON_SIZE + 50\n for sprite in self.newGameMenuCatIcons:\n sprite.draw()\n\n def drawMainMenuPanels(self):\n pg.draw.rect(self.main.screen, PALETTE_1[1], self.newGameRect)\n pg.draw.rect(self.main.screen, PALETTE_1[0], (10 * TILESIZE, 6 * TILESIZE, 10 * TILESIZE, 2 * TILESIZE))\n newGameText = self.font.render(\"NEW GAME\", True, WHITE)\n self.main.screen.blit(newGameText, (10 * TILESIZE * 1.5 - (self.font.size(\"NEW GAME\")[0]) / 2, 7 * TILESIZE - (self.font.size(\"NEW GAME\")[1]) / 2))\n pg.draw.rect(self.main.screen, PALETTE_1[1], self.exitRect)\n pg.draw.rect(self.main.screen, PALETTE_1[0], (10 * TILESIZE, 9 * TILESIZE, 10 * TILESIZE, 2 * TILESIZE))\n newGameText = self.font.render(\"EXIT\", True, WHITE)\n self.main.screen.blit(newGameText, (10 * TILESIZE * 1.5 - (self.font.size(\"EXIT\")[0]) / 2, 10 * TILESIZE - (self.font.size(\"EXIT\")[1]) / 2))\n self.main.screen.blit(self.main.logo, (11.2 * TILESIZE, .2 * TILESIZE))\n self.drawNumberOfQuestions()\n self.drawScore()\n self.drawVersion()\n\n def checkCollision(self, mouse):\n if self.menu == \"main\":\n if mouse.rect.colliderect(self.newGameRect):\n self.menu = \"newGame\"\n self.main.clearAllSprites()\n self.questionBoxes = [QuestionBox(self.main, 14 * TILESIZE, 2 * TILESIZE, 1), QuestionBox(self.main, 16 * TILESIZE, 2 * TILESIZE, 2), QuestionBox(self.main, 18 * TILESIZE, 2 * TILESIZE, 3), QuestionBox(self.main, 20 * TILESIZE, 2 * TILESIZE, 4), QuestionBox(self.main, 22 * TILESIZE, 2 * TILESIZE, 5)]\n elif mouse.rect.colliderect(self.exitRect):\n pg.quit()\n sys.exit()\n elif self.menu == \"newGame\":\n if mouse.rect.colliderect(self.startGameRect):\n difficulties = []\n for questionBox in self.questionBoxes:\n if questionBox.ticked:\n difficulties.append(questionBox.difficulty)\n self.main.music_channel.fadeout(3000)\n #self.main.music_channel.set_volume(.04)\n self.main.createGame(self.questions, self.gamemodes[self.gamemodeSelected], self.lifeLines)\n elif mouse.rect.colliderect(self.backRect):\n for sprite in self.main.all_sprites:\n self.main.all_sprites.remove(sprite)\n del sprite\n self.main.game = MainMenu(self.main)\n for rect in self.gamemodeRects:\n if mouse.rect.colliderect(rect):\n self.gamemodeSelected = self.gamemodeRects.index(rect)\n for rect in self.lifeLineRects:\n if mouse.rect.colliderect(rect):\n self.lifeLines = self.lifeLineRects.index(rect)\n\n def drawNumberOfQuestions(self):\n numberOfQuestions = self.smallerFont.render(\"No. Of Questions: \", True, WHITE)\n self.main.screen.blit(numberOfQuestions, (3.9 * TILESIZE, 2 * TILESIZE))\n numbersAsString = str(self.main.numberOfQuestions)\n numbers = len(numbersAsString)\n xOffset = 0\n for x in range(0, numbers):\n self.main.screen.blit(self.main.numbers_images[int(numbersAsString[x])], (4.7 * TILESIZE + xOffset, 3 * TILESIZE))\n xOffset += self.main.numbers_images[x].get_width() + NUMBERS_OFFSET\n\n def drawScore(self):\n score = self.smallerFont.render(\"High Score: \", True, WHITE)\n self.main.screen.blit(score, (23 * TILESIZE, 2 * TILESIZE))\n numbersAsString = str(self.main.highScore)\n numbers = len(numbersAsString)\n xOffset = 0\n for x in range(0, numbers):\n self.main.screen.blit(self.main.numbers_images[int(numbersAsString[x])], (24 * TILESIZE + xOffset, 3 * TILESIZE))\n xOffset += self.main.numbers_images[x].get_width() + NUMBERS_OFFSET\n\n def drawVersion(self):\n versionText = \"Version: \" + VERSION\n renderedVersionText = self.smallerFont.render(versionText, True, WHITE)\n self.main.screen.blit(renderedVersionText, (WIDTH - (self.smallerFont.size(versionText)[0]) - 50, HEIGHT - (self.smallerFont.size(versionText)[1]) - 50))\n\n def createGame(self):\n self.main.createGame()\n","sub_path":"mainMenu.py","file_name":"mainMenu.py","file_ext":"py","file_size_in_byte":12806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"171006644","text":"import Tkinter as tk\n\n\nclass View(tk.Frame):\n def __init__(self, master):\n tk.Frame.__init__(self, master)\n self.pack(side=tk.LEFT, fill=tk.BOTH, expand=1)\n\n # View elements\n self.frame_left = tk.Frame(master)\n self.frame_left.pack(side=tk.LEFT)\n self.frame_right = tk.Frame(master)\n self.frame_right.pack(side=tk.LEFT)\n self.toolbar = Toolbar(self.frame_right)\n self.canvas = tk.Canvas(self.frame_left, bg='#ffffff')\n self.canvas.pack(side=tk.TOP, fill=tk.BOTH, expand=1)\n\n\nclass Toolbar(tk.Frame):\n def __init__(self, master):\n tk.Frame.__init__(self, master)\n self.pack(side=tk.RIGHT, fill=tk.BOTH, expand=1)\n\n # View elements\n self.radio_poly_mode = tk.Radiobutton(master, text='Polygon Mode')\n self.radio_poly_mode.pack(side='top', fill=tk.BOTH)\n self.radio_frac_mode = tk.Radiobutton(master, text='Fractal Mode')\n self.radio_frac_mode.pack(side='top', fill=tk.BOTH)\n self.radio_view_mode = tk.Radiobutton(master, text='View Mode')\n self.radio_view_mode.pack(side='top', fill=tk.BOTH)\n self.button_save_image = tk.Button(master, text='Save Image')\n self.button_save_image.pack(side='bottom', fill=tk.BOTH)\n\n","sub_path":"coastline/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"176495068","text":"import time\n\nimport serial\nfrom threading import Thread\n\n#// S1, H332, X179, Y146, Z128, R255, G255, B255\n\nclass ArduinoController:\n stripNumber=None\n totalHeight=None\n xPosition=None\n yPositon=None\n zPosition=None\n message = None\n\n def __init__(self):\n arduino = None\n lastReturnMessage = None\n self.arduino = serial.Serial('COM14', 9600, timeout=.1)\n time.sleep(1) # give the connection a second to settle\n arduinoThread=Thread(target=self.updateNewValues,args=[])\n\n def updateNewValues(self):\n if self.message != None:\n self.arduino.write(self.message.encode())\n print(\"Message sent: \" + self.message)\n self.message=None\n\n def monitorSerialComm(self):\n while True:\n data = self.arduino.readline().decode().strip('\\r\\n')\n if data:\n print(\"Response received: \"+data)\n self.lastReturnMessage = data\n","sub_path":"ArduinoController.py","file_name":"ArduinoController.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"29213523","text":"#! /usr/bin/env python3\n\nimport re\nimport math\n\ndef solve():\n [n] = read()\n r = read_n(n)\n print(space_of_circles(r))\n\ndef read():\n return list(map(int, re.split('\\W+', input().strip())))[ : 1]\n\ndef read_n(n):\n arr = []\n for x in range(n):\n arr.append(list(map(int, re.split('\\W+', input().strip())))[ : 1][0])\n return arr\n\ndef space_of_circles(r):\n \"\"\"\n >>> abs(space_of_circles([1, 2, 3]) - 18.8495559215) < 10 ** (-6)\n True\n >>> abs(space_of_circles([15, 2, 3, 7, 6, 9]) - 508.938009881546) < 10 ** (-6)\n True\n \"\"\"\n r_local = r\n r_local.sort(reverse = True)\n s = 0.0\n for index, value in enumerate(r_local):\n if index % 2 == 0:\n s += value ** 2 * math.pi\n else:\n s -= value ** 2 * math.pi\n return s\n\nif __name__ == '__main__':\n import doctest\n doctest.testmod()\n solve()\n","sub_path":"atcoder/ABC/ABC026/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"504275162","text":"import demistomock as demisto # noqa: F401\nfrom CommonServerPython import * # noqa: F401\nfrom PyPDF2 import PdfReader, PdfWriter\n\n\ndef unlock_pdf(entry_id):\n res = demisto.getFilePath(entry_id)\n origin_path = res['path']\n output_name = \"UNLOCKED_\" + res['name']\n\n input1 = PdfReader(open(origin_path, \"rb\"))\n input1.decrypt(str(demisto.args()[\"password\"]))\n\n output = PdfWriter()\n for pageNum in range(0, len(input1.pages)):\n output.add_page(input1.pages[pageNum])\n with open(output_name, \"wb\") as pf:\n output.write(pf)\n\n demisto.results(file_result_existing_file(output_name))\n\n\ndef main():\n entry_id = demisto.args()['entryID']\n unlock_pdf(entry_id)\n\n\nif __name__ in ('__main__', '__builtin__', 'builtins'):\n main()\n","sub_path":"Packs/CommonScripts/Scripts/PDFUnlocker/PDFUnlocker.py","file_name":"PDFUnlocker.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"160986232","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[237]:\n\n\n# Imports and Initializations\nfrom helpers import pandas_helper as pdh\nfrom helpers import math_helper as mth\nfrom sensors.activpal import *\nfrom utils import read_functions\nfrom scipy.stats import linregress\nfrom sklearn.model_selection import train_test_split, KFold, cross_val_score, RandomizedSearchCV\nfrom sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\nfrom sklearn.preprocessing import LabelEncoder\nfrom pprint import pprint\nfrom sklearn.metrics import mean_squared_error\nimport math\nimport matplotlib.pyplot as plt\nimport datetime\nimport csv\n\n#By Matt\nfrom xgboost import XGBRegressor\nfrom sklearn.feature_selection import SelectFromModel, RFE, RFECV\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.preprocessing import LabelEncoder\n\nimport xgboost as xgb\nimport pandas as pd\nimport numpy as np\n\nfrom sklearn.metrics import f1_score, plot_confusion_matrix, confusion_matrix, accuracy_score, precision_score, recall_score, confusion_matrix, classification_report, r2_score\n\nfrom sklearn.model_selection import train_test_split \nfrom sklearn.metrics import mean_squared_error as MSE \n\nimport scipy.integrate as it\n\nactivpal = Activpal()\n\n\n# In[238]:\n\n\ndef convert_string_to_number(dataframe, column_name):\n return dataframe.replace({\n column_name: {\n \"nee\": 0,\n \"ja\": 1\n }\n }, inplace=True)\n\n\n# In[239]:\n\n\ndef convert_age_to_number(dataframe, age_category):\n age_convertion = {\n age_category: {\n \"15-19\": 0, \"20-24\": 1, \"25-29\": 2, \"30-34\": 3, \n \"35-39\": 4, \"40-44\": 5, \"45-49\": 6, \"50-54\": 7, \n \"55-59\": 8, \"60-64\": 9, \"65-69\": 10, \"70-74\": 11, \"75-79\": 12\n }\n }\n return dataframe.replace(age_convertion, inplace=True)\n\n\n# In[240]:\n\n\ndef convert_level_to_number(dataframe, estimated_level):\n level_convertion = {\n estimated_level: {\n \"niet actief\": 0,\n \"actief\": 1\n }\n }\n return dataframe.replace(level_convertion, inplace=True)\n\n\n# In[241]:\n\n\ndef convert_gender_to_number(dataframe, gender):\n gender_convertion = {\n gender: {\n \"man\": 0,\n \"vrouw\": 1\n }\n }\n return dataframe.replace(gender_convertion, inplace=True)\n\n\n# In[242]:\n\n\ndef get_vyntus_df(correspondent, start, stop):\n vyntus_df = pdh.read_csv_vyntus(correspondent)\n mask = (vyntus_df.index >= start) & (vyntus_df.index < stop)\n vyntus_df = vyntus_df.loc[mask]\n \n min_index = vyntus_df.index.min()\n max_index = vyntus_df.index.max()\n \n respondents_df = pdh.read_csv_respondents_speed()\n corr_number = int(correspondent.replace('BMR0', '')) \n weight = respondents_df['gewicht'][corr_number]\n length_m = respondents_df['lengte'][corr_number] / 100\n vyntus_df['vyn_VO2'] = [float(vo2.replace(',', '.')) if type(vo2) == str else vo2 for vo2 in vyntus_df['vyn_VO2']]\n \n vyntus_df['met'] = mth.calculate_met(vyntus_df['vyn_VO2'], weight)\n vyntus_df['weight_kg'] = weight\n vyntus_df['length_cm'] = length_m * 100\n vyntus_df['bmi'] = mth.calculate_bmi(weight, length_m)\n vyntus_df['is_sporting'] = respondents_df['sporter'][corr_number]\n vyntus_df['age_category'] = respondents_df['leeftijdscategorie'][corr_number]\n \n vyntus_df['walking_speed_km'] = respondents_df['loop_snelheid_km'][corr_number]\n vyntus_df['running_speed_km'] = respondents_df['ren_snelheid_km'][corr_number]\n convert_age_to_number(vyntus_df, \"age_category\")\n \n vyntus_df['meets_balance_guidelines'] = respondents_df['voldoet aan richtlijn balansoefeningen'][corr_number]\n convert_string_to_number(vyntus_df, 'meets_balance_guidelines')\n \n vyntus_df['meets_activity_guidelines'] = respondents_df['voldoet aan beweegrichtlijn 2017'][corr_number]\n convert_string_to_number(vyntus_df, 'meets_activity_guidelines')\n \n vyntus_df['estimated_level'] = respondents_df['ingeschat niveau'][corr_number]\n convert_level_to_number(vyntus_df, 'estimated_level')\n \n vyntus_df['gender'] = respondents_df['geslacht'][corr_number]\n convert_gender_to_number(vyntus_df, \"gender\")\n \n vyntus_df = vyntus_df.resample('60s').mean()[:-1]\n return (vyntus_df, min_index, max_index)\n\n\n# In[243]:\n\n\ndef get_speed(x_acc, y_acc, z_acc):\n\n activpal_time = 0.05\n \n x_vel = np.array([sum(x_acc[:i]) * activpal_time for i in range(len(x_acc))])\n y_vel = np.array([sum(y_acc[:i]) * activpal_time for i in range(len(y_acc))])\n z_vel = np.array([sum(z_acc[:i]) * activpal_time for i in range(len(z_acc))])\n \n \n return np.sqrt(x_vel ** 2 + y_vel **2 + z_vel ** 2)\n\n\n# In[244]:\n\n\ndef get_raw_df(correspondent, start, stop):\n df = activpal.read_data(correspondent, start, stop)\n mask = (df.index >= start) & (df.index < stop)\n df = df.loc[mask]\n \n df = df[['pal_accX', 'pal_accY', 'pal_accZ']].apply(mth.convert_value_to_g)\n df['pal_accX'] = abs(df['pal_accX'])\n df['pal_accY'] = abs(df['pal_accY'])\n df['pal_accZ'] = abs(df['pal_accZ'])\n df['mag_acc'] = mth.to_mag_acceleration(df['pal_accX'], df['pal_accY'], df['pal_accZ'])\n df['speed'] = get_speed(df['pal_accX'], df['pal_accY'], df['pal_accZ'])\n \n df = df.resample('60s').sum()[:-1]\n\n return df\n\n\n# In[245]:\n\n\ndef get_timestamps(correspondent, activity='rennen'):\n activities_df = read_functions.read_activities(correspondent)\n \n # Enable for cycling\n start = activities_df.loc[activity].start\n stop = activities_df.loc[activity].stop\n \n return (start, stop)\n\n\n# In[246]:\n\n\ndef get_regression_df(correspondent, addHeader):\n activities = ['lopen','rennen','staan','zitten','fietsen licht','fietsen zwaar']\n for activity in activities:\n \n start, stop = get_timestamps(correspondent, activity)\n\n vyntus_df, min_index, max_index = get_vyntus_df(correspondent, start, stop)\n raw_df = get_raw_df(correspondent, min_index, max_index)\n\n new_df = pd.DataFrame(index=raw_df.index)\n new_df['respondent_code'] = correspondent\n new_df['activity'] = activity\n new_df['mean_met'] = vyntus_df['met']\n new_df['sum_mag_acc'] = raw_df['mag_acc']\n new_df['mean_speed'] = raw_df['speed']\n new_df['weight_kg'] = vyntus_df['weight_kg']\n new_df['length_cm'] = vyntus_df['length_cm']\n new_df['bmi'] = vyntus_df['bmi']\n new_df['is_sporter'] = vyntus_df['is_sporting']\n new_df['age_category'] = vyntus_df['age_category']\n new_df['meets_balance_guidelines'] = vyntus_df['meets_balance_guidelines']\n new_df['estimated_level'] = vyntus_df['estimated_level']\n new_df['walking_speed_km'] = vyntus_df['walking_speed_km']\n new_df['running_speed_km'] = vyntus_df['running_speed_km']\n new_df['gender'] = vyntus_df['gender']\n new_df['meets_activity_guidelines'] = vyntus_df['meets_activity_guidelines']\n \n addHeader = True if addHeader == True else False\n new_df.to_csv('../../data/all_activities.csv', mode='a', header=addHeader)\n addHeader = False\n \n return new_df\n\n\n# In[247]:\n\n\ncorrespondents = ['BMR004', 'BMR008', 'BMR011', 'BMR012', 'BMR034', 'BMR036', 'BMR043', 'BMR052', 'BMR053', 'BMR055', 'BMR058', 'BMR064', 'BMR098', 'BMR041', 'BMR044', 'BMR097', 'BMR031', 'BMR032', 'BMR040', 'BMR042', 'BMR002', 'BMR014', 'BMR018', 'BMR030', 'BMR033']\naddHeader = True\n\nfor corr in correspondents:\n test_dataframe = get_regression_df(corr, addHeader)\n addHeader = False\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Images/Data Preprocessing/Code/creating_main_dataframe.py","file_name":"creating_main_dataframe.py","file_ext":"py","file_size_in_byte":7625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"589942639","text":"'''\nTime Comp. -> O(E+V)\nSpace Comp. -> O(V)\n'''\n\n\n\nfrom collections import defaultdict\n\ndef topDFS(node,graph,visit,stack):\n visit.add(node)\n for nbr in graph[node]:\n if nbr not in visit:\n topDFS(nbr, graph, visit, stack)\n stack.append(node)\n\ndef dfs(node,graph,visit,count):\n count+=1\n visit.add(node)\n for nbr in graph[node]:\n if nbr not in visit:\n count += dfs(nbr, graph, visit, 0)\n return count\n\ndef transpose(graph):\n newGraph = defaultdict(list)\n for node in graph:\n for nbr in graph[node]:\n newGraph[nbr].append(node)\n return newGraph\n\n\n\nn,m = map(int,input().split())\n\ngraph = defaultdict(list)\nfor i in range(m):\n x,y = map(int,input().split())\n \n graph[x].append(y)\n\nvisit = set()\nstack = []\nfor node in range(1,n+1):\n if node not in visit:\n topDFS(node,graph,visit,stack)\n\nnewGraph = transpose(graph)\nvisit.clear()\n\ntotalSSC = 0\noddSSC = 0\nevenSSC = 0\n\nwhile stack:\n node = stack.pop()\n if node not in visit:\n totalSSC+=1\n result = dfs(node, newGraph, visit, 0)\n if result%2 != 0:\n oddSSC += result\n else:\n evenSSC += result\nprint(oddSSC-evenSSC)\nprint(totalSSC)\n\n\n\n","sub_path":"Graphs/Kosaraju's strongly connected component.py","file_name":"Kosaraju's strongly connected component.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"266858293","text":"import random\nimport numpy as np\nimport time\nimport math\nimport cv2\nfrom dataIndexing import *\n\n# data = np.zeros(shape=(500,128,299,299),dtype='float') # Maximum: 677 128 299 299\n# t1 = time.clock()\n# np.save('SaveTest0.npy',data)\n# t2 = time.clock() # 811\n# print(t2-t1)\n# data2 = np.load('SaveTest0.npy') #617\n# print(data2.shape)\n#\n# np.random.seed(1)\n# data = np.random.random((10,128,299,299))\n#\n# print(data.shape)\n#\n# np.save('SaveTest.npy',data)\n# data2 = np.load('SaveTest.npy')\n# print(data2.shape)\n#\n# print(data2 == data)\n#\n# t1=time.clock()\n# data = np.load('SaveTest0.npy')\n# t2=time.clock()\n# print(t2-t1)\n\ndef imgProcess(folder,file_index,frame_num=128,size=(299,299),channel=1):\n output = np.zeros(shape=(len(file_index),frame_num,size[0],size[1],channel))\n if not (channel == 1 or channel == 3):\n print('Channel must equal to 1 or 3')\n raise ValueError\n\n i = 0;\n for index in file_index:\n # print(index)\n for frame in range(1,frame_num+1):\n file = folder +\"/class\"+str(int(index[0]))+ \"/s\" + str(int(index[1])) + \"frame\" + str(int(frame))+\".png\"\n img = cv2.imread(file)\n img = cv2.resize(img, (size[0], size[1]), interpolation=cv2.INTER_LINEAR)\n if channel == 1:\n img = img[:,:,channel-1]\n img = np.reshape(img,(img.shape[0],img.shape[1],1))\n # if i ==0:\n # print(img.shape)\n # print(output.shape)\n output[i,frame-1] = img\n # print(str(i), file)\n # print(frame)\n # print('end')\n i = i + 1\n return output\n\ndef indexsplit(index_len,interval_num):\n indexGroup = math.ceil(index_len/interval_num)\n output = np.zeros(shape=(indexGroup,2),dtype='int')\n for i in range(0,indexGroup-1):\n output[i,0]=i*interval_num\n output[i,1]=(i+1)*interval_num\n output[indexGroup-1,0] = (indexGroup-1)*interval_num\n output[indexGroup-1,1] = index_len-1\n return output\n\n\nif __name__ == '__main__':\n sampleNum = 20\n frameNum = 128\n classNum = 10\n test_num = 2 # 100\n dev_num = 3\n\n train,dev,test = getClassSampleIndex(classNum, sampleNum, dev_num, test_num)\n\n print(train.shape,dev.shape,test.shape)\n # print(dev)\n # print(test)\n\n output_size = 60\n sample_index = indexsplit(len(train),output_size)\n print(sample_index)\n print(train[(sample_index[2,1])])\n\n for i in sample_index:\n data_index = train[i[0]:i[1]]\n print(data_index.shape)\n\n data = imgProcess('data2',file_index=data_index,frame_num=frameNum,size=(299,299))\n print(data.shape)\n\n\n\n\n\n # tt = imgProcess(folder='data2',file_index=dev,frame_num=frameNum,size=(299,299))\n # print(tt.shape)\n #\n # img_check = cv2.imread(\"data2/class1/s10frame1.png\")\n # img_check = cv2.resize(img_check,(299,299),interpolation=cv2.INTER_LINEAR)\n # img_check = img_check[:,:,0]\n # img_check = np.reshape(img_check,(299,299,1))\n #\n # print(np.sum(img_check == tt[0,0]))\n\n\n\n\n","sub_path":"Python/dataConstruct/FreeTesting.py","file_name":"FreeTesting.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"157610198","text":"from time import perf_counter\n\nt0 = perf_counter()\n\npents = []\nhex = []\n\n\ndef genHex():\n for n in range(0, 100000):\n temp = n\n temp *= 2 * n - 1\n hex.append(temp)\n\n\ndef checkPent(x):\n det = pow(24 * x + 1, 0.5)\n if (det + 1) % 6 == 0:\n return True\n return False\n\n\ndef find():\n genHex()\n for item in hex:\n if checkPent(item):\n print(item)\n if item > 40755: return 0\n\n\nfind()\n\nt1 = perf_counter()\nprint(t1 - t0, \"seconds\")\n","sub_path":"PrEulerP45.py","file_name":"PrEulerP45.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"635038517","text":"'''\nDescripition: \nVersion: \nAuthor: SmartFox97\nDate: 2020-10-14 00:46:57\nLastEditors: SmartFox97\nLastEditTime: 2020-10-15 16:35:17\n'''\nfrom django.urls import re_path, path\nfrom . import views\nfrom rest_framework_jwt.views import obtain_jwt_token\n\nurlpatterns = [\n # 7.1推荐职位\n re_path(r'^recruits/search/recommend/$',views.Recommend_RecruitView.as_view()),\n # 7.2最新职位\n re_path(r'^recruits/search/latest/$',views.The_Latest_JobView.as_view()),\n # 7.3热门企业\n re_path(r'^enterprise/search/hotlist/$',views.Hot_EnterpriseView.as_view()),\n # 7.4热门城市\n re_path(r'^city/hotlist/$',views.Hot_CityView.as_view()),\n # # # 7.5职位搜索\n # re_path(r'^recruits/search/city/keyword/$',views.Search_JobsView()),\n # 7.6职位详情\n path('recruits//',views.One_Recruit_DetailView.as_view()),\n #7.7 添加职位访问次数\n path('recruits//visit/',views.Add_Recruit_VisitView.as_view()),\n # 7.8 收藏职位\n path('recruits//collect/',views.Collection_RecruitView.as_view()),\n # 7.8.1\n path('recruits//cancelcollect/',views.CancleCollection_RecruitView.as_view()),\n # re_path(r'^authorizations/$',obtain_jwt_token),\n\n\n\n # 热门企业\n path('enterprise/search/hotlist/', views.HotCompanyView.as_view()),\n # 增加企业访问次数\n path('enterprise//visit/', views.CompanyInfoView.as_view()),\n # 收藏公司\n path('enterprise//collect/', views.CollectCompanyView.as_view()),\n # 取消收藏公司\n path('enterprise//cancelcollect/', views.CancleCollectCompanyView.as_view()),\n # 企业详情\n path('enterprise//', views.CompanyInfoView.as_view()),\n # 最新职位\n path('recruits/search/latest/', views.LastestRecruitView.as_view()),\n # 搜索职位\n path('recruits/search/city/keyword/', views.LastestRecruitView.as_view()),\n]\n","sub_path":"apps/recruit/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"78201279","text":"import scipy.io as sio\nimport numpy as np\n\n\ndef extract(dir_name, numperm=1000):\n cons = 4\n bb = dir_name.split('_')\n \n if bb[0] == 'Rand':\n f_header = dir_name+'/rand_2neuron-'\n elif bb[0] == 'Targ':\n f_header = dir_name+'/targeted_2neuron-'\n \n f0 = np.empty((numperm, 3))\n hw = np.empty((numperm, 3))\n err = np.empty(numperm)\n CONVG = np.zeros(numperm)\n eigvals = np.empty((numperm, cons, 6))\n params = np.empty((numperm, 7))\n# Jee = np.empty(numperm)\n# Jei = Jee\n# Jie = Jee\n# Jii = Jee\n# gE = Jee\n# gI = Jee\n# NMDAratio = Jee\n \n for pp in range(numperm):\n fname = f_header+str(pp)+'.mat'\n aa = sio.loadmat(fname)\n params[pp, :] = aa['params'][0]\n \n# Jee[pp] = params[0]\n# Jei[pp] = params[1]\n# Jie[pp] = params[2]\n# Jii[pp] = params[3]\n# gE[pp] = params[4]\n# gI[pp] = params[5]\n# NMDAratio[pp] = params[6]\n \n CC = aa['CONVG']\n ee = aa['err'][0]\n CONVG[pp] = CC\n #err of 0 means no err, so if all ee are 0 then I want it to return 0\n err[pp] = 1 - np.all(ee == 0) \n \n f0[pp, :] = aa['f0'][0][1:] \n hw[pp, :] = aa['hw'][0][1:]\n \n if CC > 0 and np.all(ee == 0):\n #condition means no gamma peak at 0, and firing rates converged\n ev = np.linalg.eigvals(aa['Jacobian'])\n eigvals[pp, :,:] = ev*1000/(2*np.pi)\n else:\n eigvals[pp, :,:] = np.nan\n \n Results = {'f0':f0,\n 'hw':hw,\n 'CONVG':CONVG,\n 'err':err,\n 'eigvals':eigvals,\n 'params':params}\n# 'Jee':Jee,\n# 'Jei':Jei,\n# 'Jie':Jie,\n# 'Jii':Jii,\n# 'gE':gE,\n# 'gI':gI,\n# 'NMDAratio':NMDAratio\n# }\n \n fout = dir_name+'/extractedResults.mat'\n sio.savemat(fout, Results)\n \n return Results\n \ndef peak_histograms_correlations(Results):\n f0 = Results['f0']\n hw = Results['hw']\n params = Results['params']\n Jee = params[:, 0]\n Jei = params[:, 1]\n Jie = params[:, 2]\n Jii = params[:, 3]\n gE = params[:, 4]\n gI = params[:, 5]\n NMDAratio = params[:, 6]\n \n df0 = np.diff(f0, axis=1)\n dhw = np.diff(hw, axis=1)\n \n df0_50_25 = df0[:,0]\n df0_100_50 = df0[:,1]\n df0_mean = np.mean(df0, axis=1)\n \n dhw_50_25 = dhw[:,0]\n dhw_100_50 = dhw[:,1]\n dhw_mean = np.mean(dhw, axis=1)\n ","sub_path":"jax_caleb/extract_paramsResults.py","file_name":"extract_paramsResults.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"644950186","text":"import re\n\nfrom video_state import VideoState\n\n\nclass OoyalaValidator:\n v2 = \"v2\"\n v3 = \"v3\"\n v3v4 = \"v3v4\"\n iframe = \"iframe\"\n\n @staticmethod\n def initialize_state():\n state = VideoState()\n state.player_type[OoyalaValidator.v2] = 0\n state.player_type[OoyalaValidator.v3] = 0\n state.player_type[OoyalaValidator.v3v4] = 0\n state.player_type[OoyalaValidator.iframe] = 0\n return state\n\n def parse_response(self, response):\n state = OoyalaValidator.initialize_state()\n self.check_v2(response, state)\n self.check_v3(response, state)\n self.check_v3_v4(response, state)\n self.check_iframe(response, state)\n for tp in state.player_type:\n if state.player_type[tp] != 0:\n state.player_exist = True\n return state\n return None\n\n def check_v2(self, response, state=VideoState()):\n matches = response.xpath('//script[contains(@src, \"player.ooyala.com/player.js\")]/@src').extract()\n state.player_type[self.v2] += len(matches)\n\n def check_v3(self, response, state=VideoState()):\n matches = response.xpath('//script[contains(@src, \"player.ooyala.com/v3/\")]/@src').extract()\n state.player_type[self.v3] += len(matches)\n\n def check_v3_v4(self, response, state=VideoState()):\n body = response.body\n matches = re.findall(\"OO\\.Player\\.create\\(\", body)\n state.player_type[self.v3v4] += len(matches)\n\n def check_iframe(self, response, state=VideoState()):\n iframes = response.xpath('//iframe/@src').extract()\n for iframe in iframes:\n found = False\n if \"player.ooyala.com/iframe.html\" in iframe:\n found = True\n elif 'iframe.html' in iframe and 'pbid' in iframe:\n found = True\n if found:\n state.player_type[self.iframe] += 1\n","sub_path":"crawler/modules/video_player_validators/ooyala_validator.py","file_name":"ooyala_validator.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"407268538","text":"import argparse\nimport easydict\n\n# def gae_argparser():\n# parser = argparse.ArgumentParser(description='Lung sound RNN example')\n#\n# # dataloader\n# parser.add_argument('--dataset', type=str, default='stat_mfcc_df',\n# help='dataset')\n# #\n# # Model\n# parser.add_argument('--input_dim', type=int, default=40,\n# help='features (X) dimension')\n# parser.add_argument('--hidden_dim', type=int, default=256,\n# help='hidden layer dimension')\n# parser.add_argument('--layer_dim', type=int, default=1,\n# help='num of layer ')\n# parser.add_argument('--output_dim', type=int, default=4,\n# help='output dimension ')\n#\n#\n# # optimizer\n# parser.add_argument('--lr', type=float, default=1e-4,\n# help='optimizer learning rate (default: auto)')\n#\n# # cuda available & seed\n# parser.add_argument('--cuda', type=str, default='cuda:0',\n# help='Using cuda (default: cuda:0)')\n# parser.add_argument('--seed', type=int, default=123,\n# help='seed number')\n#\n# # training hyperparameters\n# parser.add_argument('--epochs', type=int, default=60,\n# help='Number of epochs to train (default: auto)')\n# parser.add_argument('--start_epoch', type=int, default=0,\n# help='start epoch number (default: 0)')\n# parser.add_argument('--batch_size', type=int, default=64,\n# help='input batch size for training (default: auto)')\n# parser.add_argument('--val_batch_size', type=int, default=64,\n# help='input batch size for validation (default: auto)')\n#\n# # Validation step\n# parser.add_argument('--eval_interval', type=int, default=1,\n# help='evaluation interval (default: 1)')\n# parser.add_argument('--no_val', action='store_true', default=False,\n# help='skip validation during training')\n#\n# return parser\n\n\ndef haeun_argparser():\n args = easydict.EasyDict({\n \"dataset\": \"stat_mfcc_df\",\n \"input_dim\": 40,\n \"hidden_dim\": 256,\n \"layer_dim\": 1,\n \"output_dim\": 4,\n \"lr\": 1e-4,\n \"cuda\": \"cuda:0\",\n \"seed\": 123,\n \"epochs\": 60,\n \"start_epoch\": 0,\n \"batch_size\": 64,\n \"val_batch_size\": 64,\n \"eval_interval\": 1,\n \"no_val\": False\n })\n return args","sub_path":"1. 황하은(대장)/study_tutorial_1/utils/args.py","file_name":"args.py","file_ext":"py","file_size_in_byte":2531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"305306376","text":"from django.contrib.auth.decorators import login_required\nfrom django.http import Http404\nfrom django.shortcuts import render, redirect\nfrom django.template.defaultfilters import slugify\n\nfrom collection.forms import PostsForm\nfrom collection.models import Posts\n\n\n\ndef index(request):\n posts = Posts.objects.all()\n return render(request, 'index.html', {\n 'posts': posts,\n })\n\n\ndef posts_detail(request, slug):\n # grab the object...\n posts = Posts.objects.get(slug=slug)\n\n # and pass to the template\n return render(request, 'posts/posts_detail.html', {\n 'posts': posts,\n })\n\n\n@login_required\ndef edit_posts(request, slug):\n # grab the object...\n posts = Posts.objects.get(slug=slug)\n\n # grab the current logged in user and make sure they're the owner of the posts\n if posts.user != request.user:\n raise Http404\n\n # set the form we're using...\n form_class = PostsForm\n\n # if we're coming to this view from a submitted form, \n if request.method == 'POST':\n # grab the data from the submitted form\n form = form_class(data=request.POST, instance=posts)\n if form.is_valid():\n # save the new data\n form.save()\n return redirect('posts_detail', slug=posts.slug)\n\n # otherwise just create the form\n else:\n form = form_class(instance=posts)\n\n # and render the template\n return render(request, 'posts/edit_posts.html', {\n 'posts': posts,\n 'form': form,\n })\n\n\ndef create_posts(request):\n form_class = PostsForm\n\n # if we're coming from a submitted form, do this\n if request.method == 'POST':\n # grab the data from the submitted form and apply to the form\n form = form_class(request.POST)\n\n if form.is_valid():\n # create an instance but do not save yet\n posts = form.save(commit=False)\n\n # set the additional details\n posts.user = request.user\n posts.slug = slugify(posts.name)\n\n # save the object\n posts.save()\n\n # redirect to our newly created posts\n return redirect('posts_detail', slug=posts.slug)\n\n # otherwise just create the form\n else:\n form = form_class()\n\n return render(request, 'posts/create_posts.html', {\n 'form': form,\n })","sub_path":"collection/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"364446227","text":"import math\r\nfrom itertools import combinations\r\n\r\nclass Solution(object):\r\n def largestTriangleArea(self, points):\r\n \"\"\"\r\n :type points: List[List[int]]\r\n :rtype: float\r\n \"\"\"\r\n combis = list(combinations(points,3))\r\n best = 0\r\n for combi in combis:\r\n p1 = combi[0]; p2 = combi[1]; p3 = combi[2]\r\n a = self.distance(p1,p2); b = self.distance(p1,p3); c = self.distance(p2,p3)\r\n sides = [a,b,c]\r\n sides.sort()\r\n if sides[0]+sides[1]>sides[2]:\r\n best = max(best,self.calculArea(sides[0],sides[1],sides[2]))\r\n return best\r\n \r\n def calculArea(self,a,b,c):\r\n s = (a+b+c)/2\r\n return math.sqrt((s*(s-a)*(s-b)*(s-c)))\r\n \r\n def distance(self,p1,p2):\r\n return math.sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2)\r\n","sub_path":"812.Largest_Triangle_Area/Solution_python.py","file_name":"Solution_python.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"454740630","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n@author \"jhe\"\n@date 2018/6/21 21:05\n\"\"\"\n\nimport threading\nimport time\n\n#\nstrt = time.time()\n#\n# def add(n):\n# sum = 0\n# for i in range(n):\n# sum+=i\n# print(sum)\n#\n#\n# # add(10000000)\n# # add(20000000)\n#\n#\n#\n# t1 = threading.Thread(target=add, args=(10000000,))\n# t2 = threading.Thread(target=add, args=(20000000,))\n#\n# t1.start()\n# t2.start()\n#\n# t1.join()\n# t2.join()\n#\n# print(time.time()-strt)\n# -----------------------------------------------------------------------\nstart = time.time()\ndef f1():\n for i in range(2):\n print('start f1',time.ctime(time.time()))\n time.sleep(2)\n print('end f1',time.ctime(time.time()))\n\ndef f2():\n for i in range(2):\n print('start f2', time.ctime(time.time()))\n time.sleep(3)\n print('end f2',time.ctime(time.time()))\n\nthreads = []\nt1 = threading.Thread(target=f1)\nthreads.append(t1)\nt2 = threading.Thread(target=f2)\nthreads.append(t2)\n\nfor t in threads:\n t.start()\n\nt1.join()\n\nprint('all over',time.ctime(time.time()))\nprint(time.time()-start)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"day27/线程学习.py","file_name":"线程学习.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"329925208","text":"# Read a PCAP file and list all the flows and their RTT statistics\n# Usage: python3 pcap-rtt-stats.py path/to/file.pcap 0.1\n# Output file with flows and RTT statistics is \"path/to/file.pcap.csv\"\n# Note: Replace 0.1 with the replay speed. Assume 1 if omitted.\n# Also outputs an actual RTTs CSV (\"path/to/file.pcap.rtts.csv\") file with columns:\n# RTT (microsec), frame no., sip (of ACK packet), dip, spt, dpt, seq, ack, stream no.\n\nimport sys, subprocess, json, statistics, math, os\n\nTSHARK_COMMAND = [\n \"tshark\",\n \"-r\", sys.argv[1],\n \"-j\", \"tcp\",\n \"-T\", \"json\",\n \"-e\", \"frame.number\",\n \"-e\", \"tcp.stream\",\n \"-e\", \"ip.src\",\n \"-e\", \"tcp.srcport\",\n \"-e\", \"ip.dst\",\n \"-e\", \"tcp.dstport\",\n \"-e\", \"tcp.seq\",\n \"-e\", \"tcp.len\",\n \"-e\", \"tcp.ack\",\n \"-e\", \"tcp.flags.syn\",\n \"-e\", \"tcp.flags.ack\",\n \"-e\", \"tcp.analysis.ack_rtt\",\n \"-e\", \"tcp.analysis.initial_rtt\",\n \"-e\", \"frame.time_epoch\",\n \"-o\", \"tcp.relative_sequence_numbers:FALSE\"\n]\n\nclass Packets:\n def __init__(self):\n self.packets = dict()\n self.warnings = []\n def try_append(self, packet):\n try:\n if int(packet[\"tcp.len\"]) > 0:\n expected_ack = packet[\"tcp.seq\"] + packet[\"tcp.len\"]\n if packet[\"tcp.flags.syn\"] == 1:\n expected_ack += 1\n key = \"%d,%s,%s,%d,%d,%d\" % (\n packet[\"tcp.stream\"],\n packet[\"ip.src\"],\n packet[\"ip.dst\"],\n packet[\"tcp.srcport\"],\n packet[\"tcp.dstport\"],\n expected_ack\n )\n if key in self.packets:\n self.warnings.append(str(packet[\"frame.number\"]) + \" also has key \" + key)\n else:\n self.packets[key] = {\n \"frame.time_epoch\": packet[\"frame.time_epoch\"],\n \"frame.number\": packet[\"frame.number\"]\n }\n except:\n pass\n def try_ack(self, new_packet):\n # Criteria: IP address, ports, and stream number match. Also, ACK = SEQ + LEN\n if \"tcp.flags.ack\" in new_packet and new_packet[\"tcp.flags.ack\"] == 1:\n key = \"%d,%s,%s,%d,%d,%d\" % (\n new_packet[\"tcp.stream\"],\n new_packet[\"ip.dst\"],\n new_packet[\"ip.src\"],\n new_packet[\"tcp.dstport\"],\n new_packet[\"tcp.srcport\"],\n new_packet[\"tcp.ack\"]\n )\n packet = self.packets.pop(key, None)\n if packet is not None:\n rtt = new_packet[\"frame.time_epoch\"] - packet[\"frame.time_epoch\"]\n expected_rtt = -1\n if \"tcp.analysis.ack_rtt\" in new_packet:\n expected_rtt = new_packet[\"tcp.analysis.ack_rtt\"]\n message = str(new_packet[\"frame.number\"]) + \" acks \" + str(packet[\"frame.number\"]) + \\\n \" with actual RTT \" + str(rtt) + \" – \" + str(expected_rtt) + \" sec expected\"\n print(message)\n if not math.isclose(rtt, expected_rtt, abs_tol=1e-6):\n self.warnings.append(message)\n return rtt\n return None\n\nclass Flows:\n def __init__(self):\n self.flows = dict()\n def update(self, packet, rtt):\n key = \"%d,%s,%s,%d,%d\" % (packet[\"tcp.stream\"], packet[\"ip.dst\"],\n packet[\"ip.src\"], packet[\"tcp.dstport\"], packet[\"tcp.srcport\"])\n if key not in self.flows:\n irtt = -1\n if \"tcp.analysis.initial_rtt\" in packet:\n irtt = packet[\"tcp.analysis.initial_rtt\"]\n self.flows[key] = {\n \"tcp.stream\": packet[\"tcp.stream\"],\n \"ip.src\": packet[\"ip.dst\"],\n \"ip.dst\": packet[\"ip.src\"],\n \"tcp.srcport\": packet[\"tcp.dstport\"],\n \"tcp.dstport\": packet[\"tcp.srcport\"],\n \"tcp.analysis.initial_rtt\": irtt,\n \"ack_indices_and_rtts\": [] # list of tuples\n }\n # assert(self.flows[key][\"tcp.analysis.initial_rtt\"] < 0 or\n # packet[\"tcp.analysis.initial_rtt\"] == self.flows[key][\"tcp.analysis.initial_rtt\"])\n self.flows[key][\"ack_indices_and_rtts\"].append((packet[\"frame.number\"], rtt))\n def to_csv(self, csv_file):\n csv_file.write(\"str,sip,spt,dip,dpt,num,avg,std,ini,idx\\n\")\n for _, flow in self.flows.items():\n stdev = 0\n if len(flow[\"ack_indices_and_rtts\"]) > 1:\n stdev = statistics.stdev([x[1] for x in flow[\"ack_indices_and_rtts\"]])\n csv_file.write(\"%d,%s,%d,%s,%d,%d,%f,%f,%f,%s\\n\" %\n (\n flow[\"tcp.stream\"],\n flow[\"ip.src\"],\n flow[\"tcp.srcport\"],\n flow[\"ip.dst\"],\n flow[\"tcp.dstport\"],\n len(flow[\"ack_indices_and_rtts\"]),\n statistics.mean([x[1] for x in flow[\"ack_indices_and_rtts\"]]),\n stdev,\n flow[\"tcp.analysis.initial_rtt\"],\n \" \".join([str(x[0]) for x in flow[\"ack_indices_and_rtts\"]])\n )\n )\n\ndef preprocess_packet(pc):\n packet = pc[\"_source\"][\"layers\"]\n for key in packet:\n try:\n packet[key] = int(packet[key][0])\n except:\n try:\n packet[key] = float(packet[key][0])\n except:\n packet[key] = packet[key][0]\n return packet\n\ndef main():\n # Check if sys.argv[3] (output path) is specified\n out_filename = sys.argv[1]\n if len(sys.argv) > 3:\n if (sys.argv[3]).endswith(\"/\") or (sys.argv[3]).endswith(\"\\\\\"):\n print(\"Remove final slash from output path\")\n sys.exit(1)\n out_filename = sys.argv[3] + \"/\" + os.path.basename(sys.argv[1])\n\n # Run tshark to generate the JSON\n tshark_result = subprocess.run(TSHARK_COMMAND, stdout=subprocess.PIPE)\n if len(sys.argv) > 2 and \"!\" in sys.argv[2]:\n with open(out_filename + '.json', 'wb') as json_file:\n json_file.write(tshark_result.stdout)\n\n # Read the JSON\n packet_capture = json.loads(tshark_result.stdout.decode(\"utf-8\"))\n del tshark_result\n\n # Initialize object instances\n packets_awaiting_ack = Packets()\n flows = Flows()\n\n # Open RTTs file for writing\n rtts_file = open(out_filename + '.rtts.csv', \"w\")\n replay_speed = 1\n if len(sys.argv) > 2:\n replay_speed = float((sys.argv[2]).replace('!', ''))\n\n # Iterate over packet_capture\n all_packets = [preprocess_packet(pc) for pc in packet_capture]\n for this_packet in all_packets:\n if \"tcp.stream\" not in this_packet:\n continue\n packets_awaiting_ack.try_append(this_packet)\n this_rtt = packets_awaiting_ack.try_ack(this_packet)\n if this_rtt is not None:\n flows.update(this_packet, this_rtt)\n rtts_file.write(\"%f,%d,%s,%s,%d,%d,%d,%d,%d\\n\" % (\n this_rtt * 1000000 / replay_speed,\n this_packet[\"frame.number\"],\n this_packet[\"ip.src\"],\n this_packet[\"ip.dst\"],\n this_packet[\"tcp.srcport\"],\n this_packet[\"tcp.dstport\"],\n this_packet[\"tcp.seq\"],\n this_packet[\"tcp.ack\"],\n this_packet[\"tcp.stream\"]\n ))\n\n rtts_file.close()\n\n # Write results to CSV\n with open(out_filename + '.csv', \"w\") as csv_file:\n flows.to_csv(csv_file)\n \n # Print warnings\n print(\"=== WARNINGS BELOW, IF ANY ===\")\n for warning in packets_awaiting_ack.warnings:\n print(warning)\n\nmain()","sub_path":"src/inactive-scripts/pcap-rtt-stats.py","file_name":"pcap-rtt-stats.py","file_ext":"py","file_size_in_byte":7713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"9092083","text":"from rest_framework import generics\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework import authentication, permissions\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.exceptions import NotFound\nfrom rest_framework.authtoken.models import Token\n\n\n\nfrom django.contrib.auth import authenticate, login\nfrom django.contrib.auth.models import User\nfrom django.shortcuts import redirect, render, get_object_or_404\nfrom django.contrib import messages\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.db.models import Q\n\nfrom . import models, serializers\nfrom rest_framework import pagination\nfrom datetime import datetime\nfrom django.utils import timezone\nfrom django.utils.crypto import get_random_string\nfrom django.contrib.auth.forms import PasswordResetForm\nfrom django.contrib.auth.views import password_reset, password_reset_confirm\n\nimport requests\n\n\nclass CustomPagination(pagination.PageNumberPagination):\n page_query_param = 'page'\n page_size_query_param = 'itemsPerPage'\n\n def get_paginated_response(self, data):\n '''\n return Response({\n 'num_pages': self.page.paginator.num_pages,\n 'count': self.page.paginator.count,\n 'results': data\n })\n '''\n return Response({\n \"count\": self.page.paginator.count,\n \"num_pages\": self.page.paginator.num_pages,\n \"results\": data\n })\n\n\n\n\nclass ItemListCreate(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n pagination_class = CustomPagination\n\n def get_queryset(self):\n return models.Item.objects.all()\n\n def get_serializer_class(self):\n return serializers.ItemSerializer\n\n def get(self, request, format=None):\n # CHECK PERMISSION\n queryset = self.get_queryset()\n\n # Return all items if query parameter \"all\" is set\n all_items = self.request.query_params.get(\"all\", None)\n if all_items:\n serializer = self.get_serializer(instance=queryset, many=True)\n return Response({\"results\": serializer.data, \"count\" : 1, \"num_pages\": 1})\n\n # Search and Tag Filtering\n search = self.request.query_params.get(\"search\")\n tags = self.request.query_params.get(\"tags\")\n excludeTags = self.request.query_params.get(\"excludeTags\")\n q_objs = Q()\n\n # Search filter\n if search is not None and search!='':\n q_objs &= (Q(name__icontains=search) | Q(model_no__icontains=search))\n\n queryset = models.Item.objects.filter(q_objs).distinct()\n\n # Tags filter\n if tags is not None and tags != '':\n tagsArray = tags.split(\",\")\n for tag in tagsArray:\n queryset = queryset.filter(tags__name=tag)\n\n # Exclude tags filter\n if excludeTags is not None and excludeTags != '':\n excludeTagsArray = excludeTags.split(\",\")\n for tag in excludeTagsArray:\n queryset = queryset.exclude(tags__name=tag)\n\n\n\n # Pagination\n paginated_queryset = self.paginate_queryset(queryset)\n serializer = self.get_serializer(instance=paginated_queryset, many=True)\n response = self.get_paginated_response(serializer.data)\n return response\n\n # manager restricted\n def post(self, request, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n d = {\"error\": \"Permission denied.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n # check if we're trying to make a duplicate item\n existing_item = models.Item.objects.filter(name=request.data['name']).count() > 0\n if existing_item:\n return Response({\"error\": \"An item with this name already exists.\"})\n\n # check that the starting quantity is non-negative\n quantity = None\n try:\n quantity = int(request.data.get('quantity', None))\n except:\n return Response({'quantity': 'Ensure this value is an integer.'})\n if quantity < 0:\n return Response({'quantity': 'Ensure this value is greater than or equal to 0.'})\n\n serializer = self.get_serializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n # Insert Create Log\n # Need {serializer.data, initiating_user_pk, 'Item Creation'}\n print(\"About to Create a Log\")\n itemCreationLog(serializer.data, request.user.pk)\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ItemDetailModifyDelete(generics.GenericAPIView):\n permissions = (permissions.IsAuthenticated,)\n\n def get_instance(self, item_name):\n try:\n return models.Item.objects.get(name=item_name)\n except models.Item.DoesNotExist:\n raise NotFound('Item {} not found.'.format(item_name))\n\n def get_serializer_class(self):\n return serializers.ItemSerializer\n\n def get_queryset(self):\n return models.Item.objects.all()\n\n def get(self, request, item_name, format=None):\n print(\"YO\")\n item = self.get_instance(item_name=item_name)\n serializer = self.get_serializer(instance=item)\n return Response(serializer.data)\n\n # manager restricted\n def put(self, request, item_name, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n d = {\"error\": \"Manager permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n item = self.get_instance(item_name=item_name)\n\n # check if we're trying to modify quantity\n quantity = request.data.get('quantity', None)\n try:\n int(quantity)\n print(quantity)\n except ValueError:\n return Response({\"error\": \"Not Integer\"}, status=status.HTTP_400_BAD_REQUEST)\n quantity = int(quantity)\n if not ((quantity is None) or (quantity < 0)):\n if (quantity != item.quantity):\n if not (request.user.is_superuser):\n return Response({\"error\": \"Admin permissions required.\"}, status=status.HTTP_403_FORBIDDEN)\n else:\n return Response({\"error\": \"Null or Negative Quantity\"}, status=status.HTTP_400_BAD_REQUEST)\n # check for other modifications\n new_name = request.data.get('name', None)\n if not (new_name == item_name):\n if not ((new_name is None) or (new_name == \"\")):\n items_with_name = models.Item.objects.filter(name=new_name).count()\n if (items_with_name == 0):\n if not (request.user.is_superuser):\n return Response({\"error\": \"Admin permissions required.\"}, status=status.HTTP_403_FORBIDDEN)\n else:\n return Response({\"error\": \"Item Name Already Taken\"}, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response({\"error\": \"Item needs non null name\"}, status=status.HTTP_400_BAD_REQUEST)\n\n data = request.data.copy()\n\n tags = data.get('tags', None)\n if tags is None:\n for tag in item.tags.all():\n item.tags.remove(tag)\n\n\n serializer = self.get_serializer(instance=item, data=data, partial=True)\n if serializer.is_valid():\n serializer.save()\n # Insert Create Log\n # Need {serializer.data, initiating_user_pk, 'Item Changed'}\n itemModificationLog(serializer.data, request.user.pk)\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n # manager restricted\n def delete(self, request, item_name, format=None):\n if not (request.user.is_superuser):\n d = {\"error\": \"Administrator permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n item = self.get_instance(item_name=item_name)\n item.delete()\n # Insert Create Log\n # Need {serializer.data, initiating_user_pk, 'Item Changed'}\n itemDeletionLog(item_name, request.user.pk)\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass AddItemToCart(generics.GenericAPIView):\n permissions = (permissions.IsAuthenticated,)\n\n def get_item(self, item_name):\n try:\n return models.Item.objects.get(name=item_name)\n except models.Item.DoesNotExist:\n raise NotFound(\"Item '{}' not found.\".format(item_name))\n\n def get_serializer_class(self):\n return serializers.CartItemSerializer\n\n def get_queryset(self):\n return models.CartItem.objects.filter(owner__pk=self.request.user.pk)\n\n # add an item to your cart\n # need to check if item already exists, and update if it does\n def post(self, request, item_name, format=None):\n item = self.get_item(item_name)\n\n data = request.data.copy()\n data.update({'owner': request.user})\n data.update({'item': item})\n\n cartitems = self.get_queryset().filter(item__name=item_name)\n if cartitems.count() > 0:\n serializer = self.get_serializer(instance=cartitems.first(), data=data)\n else:\n serializer = self.get_serializer(data=data)\n\n if serializer.is_valid():\n cart_quantity = int(data['quantity'])\n if (cart_quantity <= 0):\n return Response({\"quantity\": \"Quantity must be a positive integer.\"})\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass CustomFieldListCreate(generics.GenericAPIView):\n permissions = (permissions.IsAuthenticated,)\n\n def get_serializer_class(self):\n return serializers.CustomFieldSerializer\n\n def get_queryset(self):\n return models.CustomField.objects.all()\n\n def get(self, request, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n d = {\"error\": \"Manager permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n queryset = self.get_queryset()\n serializer = self.get_serializer(instance=queryset, many=True)\n return Response(serializer.data)\n\n def post(self, request, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n d = {\"error\": \"Manager permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n existing_field = self.get_queryset().filter(name=request.data['name']).count() > 0\n if existing_field:\n return Response({\"error\": \"A field with this name already exists.\"})\n\n serializer = self.get_serializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass CustomFieldDetailDelete(generics.GenericAPIView):\n permissions = (permissions.IsAuthenticated,)\n\n def get_instance(self, field_name):\n try:\n return models.CustomField.objects.get(name=field_name)\n except:\n raise NotFound(\"Field '{}' not found.\".format(field_name))\n\n def get_serializer_class(self):\n return serializers.CustomFieldSerializer\n\n def get_queryset(self):\n return models.CustomField.objects.all()\n\n def get(self, request, field_name, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n d = {\"error\": \"Manager permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n custom_field = self.get_instance(field_name=field_name)\n serializer = self.get_serializer(instance=custom_field)\n return Response(serializer.data)\n\n def delete(self, request, field_name, format=None):\n if not (request.user.is_superuser):\n d = {\"error\": \"Manager permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n custom_field = self.get_instance(field_name=field_name)\n custom_field.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n\nclass CustomValueList(generics.GenericAPIView):\n permissions = (permissions.IsAuthenticated,)\n\n def get_serializer_class(self):\n return serializers.CustomValueSerializer\n\n def get_queryset(self):\n queryset = models.CustomValue.objects.filter(item__name=self.kwargs['item_name'])\n if not (self.request.user.is_staff or self.request.user.is_superuser):\n queryset = queryset.filter(field__private=False)\n return queryset\n\n def get(self, request, item_name, format=None):\n queryset = self.get_queryset()\n serializer = self.get_serializer(instance=queryset, many=True)\n return Response(serializer.data)\n\nclass CustomValueDetailModify(generics.GenericAPIView):\n def get_instance(self, item_name, field_name):\n try:\n return self.get_queryset().get(field__name=field_name)\n except models.CustomValue.DoesNotExist:\n raise NotFound(\"Field '{}' not found on item '{}'.\".format(field_name, item_name))\n\n def get_serializer_class(self):\n return serializers.CustomValueSerializer\n\n def get_queryset(self):\n queryset = models.CustomValue.objects.filter(item__name=self.kwargs['item_name'])\n if not (self.request.user.is_staff or self.request.user.is_superuser):\n queryset = queryset.filter(field__private=False)\n return queryset\n\n def get(self, request, item_name, field_name, format=None):\n custom_value = self.get_instance(item_name=item_name, field_name=field_name)\n serializer = self.get_serializer(instance=custom_value)\n return Response(serializer.data)\n\n # manager restricted\n def put(self, request, item_name, field_name, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n d = {\"error\": \"Manager permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n custom_value = self.get_instance(item_name=item_name, field_name=field_name)\n # manually force the serializer data to have correct field name\n request.data.update({'name': field_name})\n serializer = self.get_serializer(instance=custom_value, data=request.data, partial=True)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass CartItemList(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n\n def get_serializer_class(self):\n return serializers.CartItemSerializer\n\n # restrict this queryset - each user can only see his/her own cart items\n def get_queryset(self):\n return models.CartItem.objects.filter(owner__pk=self.request.user.pk)\n\n # view all items in your cart\n def get(self, request, format=None):\n queryset = self.get_queryset()\n serializer = self.get_serializer(instance=queryset, many=True)\n return Response(serializer.data)\n\n\nclass CartItemDetailModifyDelete(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n\n def get_instance(self, item_name):\n try:\n return self.get_queryset().get(item__name=item_name)\n except models.CartItem.DoesNotExist:\n raise NotFound('Cart item {} not found.'.format(item_name))\n\n def get_serializer_class(self):\n return serializers.CartItemSerializer\n\n # restrict this queryset - each user can only see his/her own cart items\n def get_queryset(self):\n return models.CartItem.objects.filter(owner__pk=self.request.user.pk)\n\n # view all items in your cart\n def get(self, request, item_name, format=None):\n cartitem = self.get_instance(item_name=item_name)\n serializer = self.get_serializer(instance=cartitem)\n return Response(serializer.data)\n\n # modify quantity of an item in your cart\n def put(self, request, item_name, format=None):\n cartitem = self.get_instance(item_name=item_name)\n data = request.data.copy()\n\n data.update({'owner': request.user})\n data.update({'item': cartitem.item})\n\n serializer = self.get_serializer(instance=cartitem, data=data, partial=True)\n if serializer.is_valid():\n try:\n cart_quantity = int(request.data['quantity'])\n except:\n return Response({\"quantity\": \"Quantity must be a positive integer.\"}, status=status.HTTP_400_BAD_REQUEST)\n if (cart_quantity < 0):\n return Response({\"quantity\": \"Quantity must be a positive integer.\"})\n elif cart_quantity == 0:\n cartitem.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n # remove an item from your cart\n def delete(self, request, item_name, format=None):\n cartitem = self.get_instance(item_name=item_name)\n cartitem.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass GetOutstandingRequestsByItem(generics.GenericAPIView):\n permissions = (permissions.IsAuthenticated,)\n pagination_class = CustomPagination\n\n def get_queryset(self):\n return models.Request.objects.all()\n\n def get_serializer_class(self):\n return serializers.RequestSerializer\n\n def get(self, request, item_name, format=None):\n requests = self.get_queryset()\n if request.user.is_staff or request.user.is_superuser:\n requests = models.Request.objects.filter(request_items__item__name=item_name)\n else:\n requests = models.Request.objects.filter(request_items__item__name=item_name, requester=request.user.pk)\n\n # Return all items if query parameter \"all\" is set\n all_items = self.request.query_params.get(\"all\", None)\n if all_items:\n serializer = self.get_serializer(instance=requests, many=True)\n return Response({\"results\": serializer.data, \"count\" : 1, \"num_pages\": 1})\n\n # Pagination\n paginated_queryset = self.paginate_queryset(requests)\n serializer = self.get_serializer(instance=paginated_queryset, many=True)\n response = self.get_paginated_response(serializer.data)\n return response\n\n\nclass RequestListAll(generics.GenericAPIView):\n pagination_class = CustomPagination\n def get_queryset(self):\n return models.Request.objects.all()\n\n def get_serializer_class(self):\n return serializers.RequestSerializer\n\n def get(self, request, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n d = {\"error\": \"Manager permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n queryset = self.get_queryset()\n status = request.GET.get('status')\n if not (status is None or status==\"All\"):\n queryset = models.Request.objects.filter(status=status)\n\n paginated_queryset = self.paginate_queryset(queryset)\n serializer = self.get_serializer(instance=paginated_queryset, many=True)\n response = self.get_paginated_response(serializer.data)\n return response\n\nclass RequestListCreate(generics.GenericAPIView):\n authentication_classes = (authentication.SessionAuthentication,)\n permission_classes = (permissions.IsAuthenticated,)\n pagination_class = CustomPagination\n\n # restrict this queryset - each user can only see his/her own Requests\n def get_queryset(self):\n return models.Request.objects.filter(requester__pk=self.request.user.pk)\n\n def get_serializer_class(self):\n return serializers.RequestSerializer\n\n def get(self, request, format=None):\n queryset = self.get_queryset()\n paginated_queryset = self.paginate_queryset(queryset)\n serializer = self.get_serializer(instance=paginated_queryset, many=True)\n response = self.get_paginated_response(serializer.data)\n return response\n\n # generate a request that contains all items currently in your cart.\n def post(self, request, format=None):\n data = request.data.copy()\n data.update({'requester': request.user})\n\n cart_items = models.CartItem.objects.filter(owner__pk=self.request.user.pk)\n if cart_items.count() <= 0:\n d = {\"error\": \"There are no items in your cart. Add an item to request it.\"}\n return Response(d, status=status.HTTP_400_BAD_REQUEST)\n\n serializer = self.get_serializer(data=data)\n if serializer.is_valid():\n request_instance = serializer.save()\n\n for ci in cart_items:\n item = ci.item\n quantity = ci.quantity\n req_item = models.RequestItem.objects.create(item=item, quantity=quantity, request=request_instance)\n # Insert Create Log\n # Need {serializer.data, initiating_user_pk, 'Request Created'}\n req_item.save()\n requestItemCreation(req_item, request.user.pk, request_instance)\n ci.delete()\n\n serializer = self.get_serializer(instance=request_instance)\n return Response(serializer.data)\n\nclass RequestDetailModifyDelete(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n\n def get_instance(self, request_pk):\n try:\n return models.Request.objects.get(pk=request_pk)\n except models.Request.DoesNotExist:\n raise NotFound('Request with ID {} not found.'.format(request_pk))\n\n def get_queryset(self):\n return models.Request.objects.filter(requester__pk=self.request.user.pk)\n\n def get_serializer_class(self):\n if self.request.method == 'PUT':\n return serializers.RequestPUTSerializer\n return serializers.RequestSerializer\n\n # MANAGER/OWNER LOCKED\n def get(self, request, request_pk, format=None):\n instance = self.get_instance(request_pk)\n # if admin, see any request.\n # if user, only see your requests\n is_owner = (instance.requester.pk == request.user.pk)\n if not (request.user.is_staff or request.user.is_superuser or is_owner):\n d = {\"error\": \"Manager or owner permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n serializer = self.get_serializer(instance=instance)\n return Response(serializer.data)\n\n # MANAGER LOCKED - only admins may change the fields on a request\n def put(self, request, request_pk, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n d = {\"error\": \"Manager permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n data = request.data.copy()\n data.update({'administrator': request.user})\n instance = self.get_instance(request_pk)\n serializer = self.get_serializer(instance=instance, data=data, partial=True)\n\n if serializer.is_valid():\n # check integrity of approval operation\n if data['status'] == 'D':\n # Insert Create Log\n # Need {serializer.data, initiating_user_pk, 'Request Approved'}\n for ri in instance.request_items.all():\n requestItemDenial(ri, request.user.pk, instance)\n elif data['status'] == 'A':\n valid_request = True\n new_quantities = {}\n for ri in instance.request_items.all():\n item = ri.item\n available_quantity = item.quantity\n requested_quantity = ri.quantity\n if (requested_quantity > available_quantity):\n valid_request = False\n break\n # decrement quantity available on each item in the approved request\n if valid_request:\n for ri in instance.request_items.all():\n item = ri.item\n available_quantity = item.quantity\n requested_quantity = ri.quantity\n item.quantity = (available_quantity - requested_quantity)\n item.save()\n # Insert Create Log\n # Need {serializer.data, initiating_user_pk, 'Request Approved'}\n requestItemApproval(ri, request.user.pk, instance)\n else:\n return Response({\"error\": \"Cannot satisfy request.\"}, status=status.HTTP_400_BAD_REQUEST)\n serializer.save()\n # item = models.Item.objects.get(pk=request.data['item'])\n # item.quantity = item.quantity - int(request.data['quantity'])\n # item.save()\n # createLog(request.data, request.data['administrator'], 'Request')\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n # OWNER LOCKED\n def delete(self, request, request_pk, format=None):\n instance = self.get_instance(request_pk)\n is_owner = (request.user.pk == instance.requester.pk)\n if not (is_owner):\n d = {\"error\": \"Owner permissions required\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n if not (instance.status == 'O'):\n d = {\"error\": \"Cannot delete an approved/denied request.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n instance.delete()\n # Don't post log here since its as if it never happened\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n@api_view(['POST'])\n@permission_classes((permissions.AllowAny,))\ndef post_user_login(request, format=None):\n username = request.POST['username']\n password = request.POST['password']\n\n user = authenticate(username=username, password=password)\n\n if hasattr(user, 'kipventory_user'):\n if user.kipventory_user.is_duke_user:\n messages.add_message(request._request, messages.ERROR, 'login-via-duke-authentication')\n return redirect('/')\n\n if user is not None:\n login(request, user)\n return redirect('/app/')\n else:\n # Return an 'invalid login' error message.\n messages.add_message(request._request, messages.ERROR, 'invalid-login-credentials')\n return redirect('/')\n\nclass UserList(generics.GenericAPIView):\n def get_queryset(self):\n return User.objects.all()\n\n def get_serializer_class(self):\n return serializers.UserGETSerializer\n\n def get(self, request, format=None):\n users = self.get_queryset()\n serializer = self.get_serializer(instance=users, many=True)\n return Response(serializer.data)\n\nclass UserCreate(generics.GenericAPIView):\n def get_queryset(self):\n return User.objects.all()\n\n def get_serializer_class(self):\n return serializers.UserPOSTSerializer\n\n def post(self, request, format=None):\n serializer = self.get_serializer(data=request.data)\n if serializer.is_valid():\n user = User.objects.create_user(**serializer.validated_data)\n #todo do we log this for net id creations?\n userCreationLog(serializer.data, request.user.pk)\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass GetCurrentUser(generics.GenericAPIView):\n queryset = None\n permission_classes = (permissions.IsAuthenticated,)\n serializer_class = None\n\n def get(self, request, format=None):\n user = request.user\n return Response({\n \"username\": user.username,\n \"first_name\": user.first_name,\n \"last_name\": user.last_name,\n \"email\": user.email,\n \"is_staff\": user.is_staff,\n \"is_superuser\": user.is_superuser\n })\n\n@api_view(['PUT'])\n@permission_classes((permissions.IsAuthenticated,))\ndef edit_user(request, username, format=None):\n if request.method == 'PUT':\n if not request.user.is_superuser:\n return Response(status=status.HTTP_403_FORBIDDEN)\n updatedUser = request.data\n user = models.User.objects.get(username=updatedUser['username'])\n serializer = serializers.UserPUTSerializer(instance=user, data=updatedUser, partial=True)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass GetNetIDToken(generics.GenericAPIView):\n queryset = None\n permission_classes = (permissions.AllowAny,)\n serializer_class = None\n\n def get(self, request, format=None):\n code = request.query_params.get('code')\n\n p = {'grant_type' : 'authorization_code', 'code' : code, 'redirect_uri' : \"https://colab-sbx-226.oit.duke.edu/api/netidtoken/\", 'client_id' : 'kipventory', 'client_secret' : 'sn6j#IzL*PXUxmPKvJ7Gs+1vzukxlx#yoFDnh%WI7GzLs$=1so'}\n\n token_request = requests.post('https://oauth.oit.duke.edu/oauth/token.php', data = p)\n token_json = token_request.json()\n\n headers = {'Accept' : 'application/json', 'x-api-key' : 'kipventory', 'Authorization' : 'Bearer '+token_json['access_token']}\n\n identity = requests.get('https://api.colab.duke.edu/identity/v1/', headers= headers)\n identity_json = identity.json()\n\n netid = identity_json['netid']\n email = identity_json['eduPersonPrincipalName']\n first_name = identity_json['firstName']\n last_name = identity_json['lastName']\n\n user_count = User.objects.filter(username=netid).count()\n if user_count == 1:\n user = User.objects.get(username=netid)\n login(request, user)\n return redirect('/app/')\n elif user_count == 0:\n user = User.objects.create_user(username=netid, email=email, password=None, first_name=first_name, last_name=last_name)\n login(request, user)\n return redirect('/app/')\n else:\n print(\"Multiple NetId Users this is big time wrong need to throw an error\")\n return redirect('/app/')\n\n\nclass TagListCreate(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n serializer_class = serializers.TagSerializer\n pagination_class = CustomPagination\n\n def get_instance(self, tag_name):\n try:\n return models.Tag.objects.get(name=tag_name)\n except models.Tag.DoesNotExist:\n raise NotFound('Tag {} not found.'.format(tag_name))\n\n def get_queryset(self):\n return models.Tag.objects.all()\n\n def get(self, request, format=None):\n tags = self.get_queryset()\n\n paginated_tags = self.paginate_queryset(tags)\n\n if(request.query_params.get(\"all\") == \"true\"):\n serializer = self.get_serializer(instance=tags, many=True)\n return Response(serializer.data)\n else:\n serializer = self.get_serializer(instance=paginated_tags, many=True)\n response = self.get_paginated_response(serializer.data)\n return response\n\n # return response\n\n def post(self, request, format=None):\n serializer = self.get_serializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n # Maybe put into its own view? Seems like a lot for now\n # manager restricted\n def delete(self, request, format=None):\n if not (request.user.is_staff):\n d = {\"error\": \"Administrator permissions required.\"}\n return Response(d, status=status.HTTP_403_FORBIDDEN)\n\n tag = self.get_instance(tag_name=request.query_params.get(\"name\"))\n tag.delete()\n # Insert Delete Log\n # Need {serializer.data, initiating_user_pk, 'Item Changed'}\n # itemDeletionLog(item_name, request.user.pk)\n #TODO NEED TO LOG DELETION HERE\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass LogList(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n\n def get_queryset(self):\n return models.Log.objects.all()\n\n def get_serializer_class(self):\n return serializers.LogSerializer\n\n def get(self, request, format=None):\n if not (request.user.is_staff or request.user.is_superuser):\n # Not allowed to see logs if not manager/admin\n return Response(status=status.HTTP_403_FORBIDDEN)\n\n user = request.query_params.get(\"user\")\n item = request.query_params.get(\"item\")\n endDate = request.query_params.get(\"endDate\")\n startDate = request.query_params.get(\"startDate\")\n # print(\"StartDate:\" + startDate)\n # print(\"EndDate:\", endDate)\n # Create Datetimes from strings\n logs = self.get_queryset()\n q_objs = Q()\n if user is not None and user != '':\n q_objs &= (Q(affected_user__username=user) | Q(initiating_user__username=user))\n logs = logs.filter(q_objs).distinct()\n if item is not None and item != '':\n logs = logs.filter(item__name=item)\n if startDate is not None and startDate != '' and endDate is not None and endDate != '':\n startDate, endDate = startDate.split(\" \"), endDate.split(\" \")\n stimeZone, etimeZone = startDate[5], endDate[5]\n stimeZone, etimeZone = stimeZone.split('-'), etimeZone.split('-')\n startDate, endDate = startDate[:5], endDate[:5]\n startDate, endDate = \" \".join(startDate), \" \".join(endDate)\n startDate, endDate = startDate + \" \" + stimeZone[0], endDate + \" \" + etimeZone[0]\n\n print(startDate, endDate)\n\n startDate = datetime.strptime(startDate, \"%a %b %d %Y %H:%M:%S %Z\").date()\n endDate = datetime.strptime(endDate, \"%a %b %d %Y %H:%M:%S %Z\").date()\n startDate = datetime.combine(startDate, datetime.min.time())\n endDate = datetime.combine(endDate, datetime.max.time())\n startDate = timezone.make_aware(startDate, timezone.get_current_timezone())\n endDate = timezone.make_aware(endDate, timezone.get_current_timezone())\n\n print(startDate, endDate)\n\n logs = logs.filter(date_created__range=[startDate, endDate])\n serializer = self.get_serializer(instance=logs, many=True)\n return Response(serializer.data)\n\n\n\nclass TransactionListCreate(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n\n def get_queryset(self):\n return models.Transaction.objects.all()\n\n def get_serializer_class(self):\n return serializers.TransactionSerializer\n\n def get(self, request, format=None):\n queryset = self.get_queryset()\n category = request.GET.get('category')\n if not (category is None or category==\"All\"):\n queryset = models.Transaction.objects.filter(category=category)\n\n # Return all items if query parameter \"all\" is set\n all_items = self.request.query_params.get(\"all\", None)\n if all_items:\n serializer = self.get_serializer(instance=queryset, many=True)\n return Response({\"results\": serializer.data, \"count\" : 1, \"num_pages\": 1})\n\n # Pagination\n paginated_queryset = self.paginate_queryset(queryset)\n serializer = self.get_serializer(instance=paginated_queryset, many=True)\n response = self.get_paginated_response(serializer.data)\n print(serializer.data)\n return response\n\n\n def post(self, request, format=None):\n #todo django recommends doing this in middleware\n data = request.data.copy()\n\n data['administrator'] = request.user\n serializer = self.get_serializer(data=data)\n if serializer.is_valid(): #todo could move the validation this logic into serializer's validate method\n transaction_quantity = int(data['quantity'])\n if transaction_quantity < 0:\n return Response({\"quantity\": \"Quantity be a positive integer\"}, status=status.HTTP_400_BAD_REQUEST)\n\n item = models.Item.objects.get(name=data['item'])\n if data['category'] == 'Acquisition':#models.ACQUISITION:\n new_quantity = item.quantity + transaction_quantity\n elif data['category'] == 'Loss':#models.LOSS:\n new_quantity = item.quantity - transaction_quantity\n if new_quantity < 0:\n return Response({\"quantity\": \"Cannot remove more items from the inventory than currently exists\"}, status=status.HTTP_400_BAD_REQUEST)\n else:\n #should never get here\n pass\n item.quantity = new_quantity\n item.save()\n transactionCreationLog(item, request.user.pk, request.data['category'], transaction_quantity)\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass TokenPoint(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n\n def get(self, request, format=None):\n if Token.objects.filter(user=request.user).count() > 0:\n #User has a token, return created token\n print(Token.objects.get(user=request.user).key)\n return Response({\"token\": Token.objects.get(user=request.user).key})\n else:\n token = Token.objects.create(user=request.user)\n print(token.key)\n return Response({\"token\": token.key})\n\n\nclass DisburseCreate(generics.GenericAPIView):\n permission_classes = (permissions.IsAuthenticated,)\n queryset = models.Request.objects.all()\n\n def get_serializer_class(self):\n return serializers.RequestSerializer\n\n def post(self, request, format=None):\n # check that all item names and quantities are valid\n errors = {}\n\n # validate user input\n data = {}\n data.update(request.data)\n requester = data.get('requester')[0]\n closed_comment = data.get('closed_comment')[0]\n items = data['items']\n quantities = data['quantities']\n\n try:\n requester = User.objects.get(username=requester)\n except User.DoesNotExist:\n return Response({\"error\": \"Could not find user with username '{}'\".format(requester)})\n\n data = {}\n data.update({'requester': requester, 'open_comment': \"Administrative disbursement to user '{}'\".format(requester.username)})\n\n # Verify that the disbursement quantities are valid (ie. less than or equal to inventory stock)\n for i in range(len(items)):\n item = None\n try:\n item = models.Item.objects.get(name=items[i])\n except models.Item.DoesNotExist:\n return Response({\"error\": \"Item '{}' not found.\".format(items[i])})\n items[i] = item\n # convert to int\n quantity = int(quantities[i])\n quantities[i] = quantity\n if quantity > item.quantity:\n errors.update({'error': \"Request for {} instances of '{}' exceeds current stock of {}.\".format(quantity, item.name, item.quantity)})\n\n if errors:\n return Response(errors, status=status.HTTP_400_BAD_REQUEST)\n\n # if we made it here, we know we can go ahead and create the request, all the request items, and approve it\n serializer = self.get_serializer(data=data)\n if serializer.is_valid():\n request_instance = serializer.save()\n\n data = {}\n data.update({'administrator': request.user})\n data.update({'closed_comment': closed_comment})\n data.update({'status': 'A'})\n\n serializer = serializers.RequestPUTSerializer(instance=request_instance, data=data, partial=True)\n\n # We're good to go!\n if serializer.is_valid():\n for item, quantity in zip(items, quantities):\n # Create the request item\n req_item = models.RequestItem.objects.create(item=item, quantity=quantity, request=request_instance)\n req_item.save()\n\n # Decrement the quantity remaining on the Item\n setattr(item, 'quantity', (item.quantity - quantity))\n item.save()\n\n # Logging\n requestItemCreation(req_item, request.user.pk, request_instance)\n requestItemApproval(req_item, request.user.pk, request_instance)\n\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n\ndef itemCreationLog(data, initiating_user_pk):\n item = None\n initiating_user = None\n quantity = None\n affected_user = None\n try:\n item = models.Item.objects.get(name=data['name'])\n except models.Item.DoesNotExist:\n raise NotFound('Item {} not found.'.format(data['name']))\n try:\n initiating_user = User.objects.get(pk=initiating_user_pk)\n except User.DoesNotExist:\n raise NotFound('User not found.')\n quantity = data['quantity']\n message = 'Item {} created by {}'.format(data['name'], initiating_user)\n log = models.Log(item=item, initiating_user=initiating_user, quantity=quantity, category='Item Creation', message=message, affected_user=affected_user)\n log.save()\n\ndef itemModificationLog(data, initiating_user_pk):\n item = None\n initiating_user = None\n quantity = None\n affected_user = None\n try:\n item = models.Item.objects.get(name=data['name'])\n except models.Item.DoesNotExist:\n raise NotFound('Item {} not found.'.format(data['name']))\n try:\n initiating_user = User.objects.get(pk=initiating_user_pk)\n except User.DoesNotExist:\n raise NotFound('User not found.')\n quantity = data['quantity']\n message = 'Item {} modified by {}'.format(data['name'], initiating_user)\n log = models.Log(item=item, initiating_user=initiating_user, quantity=quantity, category='Item Modification', message=message, affected_user=affected_user)\n log.save()\n\ndef itemDeletionLog(item_name, initiating_user_pk):\n item = None\n initiating_user = None\n quantity = None\n affected_user = None\n try:\n initiating_user = User.objects.get(pk=initiating_user_pk)\n except User.DoesNotExist:\n raise NotFound('User not found.')\n message = 'Item {} deleted by {}'.format(item_name, initiating_user)\n log = models.Log(item=item, initiating_user=initiating_user, quantity=quantity, category='Item Deletion', message=message, affected_user=affected_user)\n log.save()\n\ndef requestItemCreation(request_item, initiating_user_pk, requestObj):\n item = request_item.item\n initiating_user = None\n quantity = request_item.quantity\n affected_user = None\n request = requestObj\n try:\n initiating_user = User.objects.get(pk=initiating_user_pk)\n except User.DoesNotExist:\n raise NotFound('User not found.')\n message = 'Request Item for item {} created by {}'.format(request_item.item.name, initiating_user)\n log = models.Log(item=item, initiating_user=initiating_user, request=request, quantity=quantity, category='Request Item Creation', message=message, affected_user=affected_user)\n log.save()\n\ndef requestItemDenial(request_item, initiating_user_pk, requestObj):\n item = request_item.item\n initiating_user = None\n quantity = request_item.quantity\n affected_user = request_item.request.requester\n request = requestObj\n try:\n initiating_user = User.objects.get(pk=initiating_user_pk)\n except User.DoesNotExist:\n raise NotFound('User not found.')\n message = 'Request Item for item {} denied by {}'.format(request_item.item.name, initiating_user.username)\n log = models.Log(item=item, request=request, initiating_user=initiating_user, quantity=quantity, category='Request Item Denial', message=message, affected_user=affected_user)\n log.save()\n\ndef requestItemApproval(request_item, initiating_user_pk, requestObj):\n item = request_item.item\n initiating_user = None\n quantity = request_item.quantity\n print(request_item.request.requester)\n affected_user = request_item.request.requester\n request = requestObj\n try:\n initiating_user = User.objects.get(pk=initiating_user_pk)\n except User.DoesNotExist:\n raise NotFound('User not found.')\n message = 'Request Item for item {} approved by {}'.format(request_item.item.name, initiating_user.username)\n log = models.Log(item=item, request=request, initiating_user=initiating_user, quantity=quantity, category='Request Item Approval', message=message, affected_user=affected_user)\n log.save()\n\ndef userCreationLog(data, initiating_user_pk):\n item = None\n initiating_user = None\n quantity = None\n affected_user = None\n try:\n initiating_user = User.objects.get(pk=initiating_user_pk)\n except User.DoesNotExist:\n raise NotFound('User not found.')\n try:\n affected_user = User.objects.get(username=data['username'])\n except User.DoesNotExist:\n raise NotFound('User not found.')\n message = \"User {} was created by {}\".format(affected_user, initiating_user)\n log = models.Log(item=item, initiating_user=initiating_user, quantity=quantity, category='User Creation', message=message, affected_user=affected_user)\n log.save()\n\ndef transactionCreationLog(item, initiating_user_pk, category, amount):\n item = item\n initiating_user = None\n quantity = amount\n affected_user = None\n try:\n initiating_user = User.objects.get(pk=initiating_user_pk)\n except User.DoesNotExist:\n raise NotFound('User not found.')\n message = \"User {} created a {} transaction on item {} of quantity {} and it now has a quantity of {}\".format(initiating_user, category, item, quantity, item.quantity)\n log = models.Log(item=item, initiating_user=initiating_user, quantity=quantity, category='Transaction Creation', message=message, affected_user=affected_user)\n log.save()\n","sub_path":"kipventory/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":46752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"642812110","text":"from prody import *\nfrom numpy import *\nfrom random import random\nimport os.path\nimport sys\nimport time\n\ntime.sleep(10)\nar = []\nfor arg in sys.argv:\n ar.append(arg)\n\ninitial_pdbn=ar[1]\nfinal_pdbn=ar[2]\ninitial_pdb_id = initial_pdbn[:initial_pdbn.rfind('.')]\nfinal_pdb_id = final_pdbn[:final_pdbn.rfind('.')]\n\noriginal_initial_pdb = ar[3]\noriginal_final_pdb = ar[4]\n\ncomd_cycle_number = ar[5]\n\nif len(ar) > 6 and ar[6].strip() is not '0':\n devi = float(ar[6])\nelse:\n devi = 0.5\n\nif len(ar) > 7 and ar[7].strip() is not '0':\n stepcutoff=float(ar[7])\nelse:\n stepcutoff=2.\n\nif len(ar) > 8 and ar[8].strip() is not '0':\n acceptance_ratio = float(ar[8])\nelse:\n acceptance_ratio = 0.9\n\naccept_para = 0.1\n\nif len(ar) > 9 and ar[9].strip() is not '0':\n anm_cut=float(ar[9])\nelse:\n anm_cut=15\n\nif len(ar) > 10 and ar[10].strip() is not '0':\n N=int(ar[10])\nelse:\n N=10000\n\nif len(ar) > 11 and ar[11].strip() is not '0':\n final_structure_dcd_name = ar[11]\nelse:\n final_structure_dcd_name = 'cycle_{0}_'.format(int(comd_cycle_number)) + \\\n initial_pdb_id + '_' + final_pdb_id + '_final_structure.dcd'\n\nif len(ar) > 12 and ar[12].strip() is not '0':\n usePseudoatoms = int(ar[12])\nelse:\n usePseudoatoms = 0\n\ninitial_pdb = parsePDB(initial_pdbn)\nfinal_pdb = parsePDB(final_pdbn)\n\nif usePseudoatoms:\n initial_pdb_ca = initial_pdb\n final_pdb_ca = final_pdb\nelse:\n initial_pdb_ca = initial_pdb.select('name CA or name BB')\n final_pdb_ca = final_pdb.select('name CA or name BB')\n\n# ANM calculation based on current\npdb_anm = ANM('pdb ca')\npdb_anm.buildHessian(initial_pdb_ca, cutoff=anm_cut)\npdb_anm.calcModes()\n\n# Cumulative sum vector preparation for metropolis sampling\neigs = 1/sqrt(pdb_anm.getEigvals())\neigs_n = zeros(eigs.shape)\neigs_n = eigs / sum(eigs)\neigscumsum = eigs_n.cumsum()\nU = pdb_anm.getEigvecs()\n\n# Take a step along mode 1 (ID 0) to calculate the scale factor\npdb_ca = initial_pdb_ca\npdb_ca_temp = pdb_ca.copy()\nID = 0\ndirection = 1.\ncoords_temp = pdb_ca_temp.getCoords()\ncoords_temp[0:,0] = coords_temp[0:,0] + direction * U[range(0,len(U),3),ID] * eigs[ID]\ncoords_temp[0:,1] = coords_temp[0:,1] + direction * U[range(1,len(U),3),ID] * eigs[ID]\ncoords_temp[0:,2] = coords_temp[0:,2] + direction * U[range(2,len(U),3),ID] * eigs[ID]\npdb_ca_temp.setCoords(coords_temp)\npdb_ca = pdb_ca_temp.copy()\nbiggest_rmsd = calcRMSD(pdb_ca.getCoords(), initial_pdb_ca.getCoords())\nscale_factor = devi/biggest_rmsd # This means that devi is the maximum deviation in RMSD for any step\n\n# counts for metropolis sampling\ncount1 = 0 # Up-hill moves\ncount2 = 0 # Accepted up-hill moves\ncount3 = 0 # Down-hill moves\n\n# read MC parameter from file\nif os.path.isfile(initial_pdb_id + '_ratio.dat') and os.stat(initial_pdb_id + '_ratio.dat').st_size != 0:\n MCpara = loadtxt(initial_pdb_id + '_ratio.dat')\n accept_para = MCpara[4]\n if MCpara[1] > acceptance_ratio + 0.05:\n accept_para *= 1.5\n elif MCpara[1] < acceptance_ratio - 0.05:\n accept_para /= 1.5\n else:\n savetxt(initial_pdb_id + '_status.dat',[1])\n#else:\n# accept_para = 0.1\n# MC parameter 1 is the acceptance ratio, which should converge on\n# the selected value with a tolerance of 0.05 either side\n# and accept_para is adjusted to help bring it within these limits.\n# This also happens every 5 steps during the run (lines 173 to 181).\n\nif original_initial_pdb != original_final_pdb:\n # difference from the target structure is defined as the energy and the minimum is zero. \n native_dist = buildDistMatrix(final_pdb_ca)\n dist = buildDistMatrix(initial_pdb_ca)\n Ep = sum((native_dist - dist)**2)\n\n# Reset pdb_ca (the current structure whole the steps back to the original)\npdb_ca = initial_pdb_ca\n\nstep_count = 0\ncheck_step_counts = [0]\n\nsys.stdout.write(' '*2 + 'rmsd' + ' '*2 + 'rand' + ' '*2 + 'ID' + ' '*3 + 'step' \\\n + ' '*2 + 'accept_para' + ' '*5 + 'f' + '\\n')\n\n# MC Loop \nfor k in range(N):\n pdb_ca_temp = pdb_ca.copy()\n rand = random()\n ID = argmax(rand0.5)-1\n\n coords_temp = pdb_ca_temp.getCoords()\n coords_temp[0:,0] = coords_temp[0:,0] + direction * U[range(0,len(U),3),ID] * eigs[ID] * scale_factor\n coords_temp[0:,1] = coords_temp[0:,1] + direction * U[range(1,len(U),3),ID] * eigs[ID] * scale_factor\n coords_temp[0:,2] = coords_temp[0:,2] + direction * U[range(2,len(U),3),ID] * eigs[ID] * scale_factor\n pdb_ca_temp.setCoords(coords_temp)\n\n if original_initial_pdb != original_final_pdb: \n dist = buildDistMatrix(pdb_ca_temp)\n En = sum((native_dist - dist)**2)\n\n # Check whether you are heading the right way and accept uphill moves \n # depending on the Metropolis criterion. Classically this depends on RT \n # but this is subsumed by the unknown units from having a uniform \n # spring constant that is set to 1.\n if Ep > En:\n count3 += 1\n pdb_ca = pdb_ca_temp.copy()\n Ep = En\n accepted = 1\n\n elif exp(-(En-Ep) * accept_para) > random():\n pdb_ca = pdb_ca_temp.copy()\n count1 += 1\n count2 += 1\n Ep = En\n accepted = 1\n\n else:\n count1 += 1\n accepted = 0\n\n if count1 == 0:\n f = 1.\n else:\n f = float(count2)/float(count1)\n\n if (mod(k,5)==0 and not(k==0)):\n # Update of the accept_para to keep the MC para reasonable\n # See comment lines 82 to 85. \n if f > acceptance_ratio + 0.05:\n accept_para /= 1.5;\n elif f < acceptance_ratio - 0.05:\n accept_para *= 1.5\n\n if accept_para < 0.001: accept_para = 0.001\n\n else:\n # for exploration based on one structure\n # all moves are uphill but will be accepted anyway\n pdb_ca = pdb_ca_temp.copy()\n count3 += 1\n accepted = 1\n f = 1.\n\n rmsd = calcRMSD(pdb_ca.getCoords(), initial_pdb_ca.getCoords())\n sys.stdout.write('{:6.2f}'.format(rmsd) + ' ' + '{:5.2f}'.format(rand) + \\\n '{:4d}'.format(ID) + '{:7d}'.format(k) + ' '*2 + str(accepted) + ' '*2 + \\\n '{:5.4f}'.format(accept_para) + ' '*2 + '{:5.4f}'.format(f) + '\\n')\n\n if rmsd > stepcutoff:\n break\n \n# Build an ensemble for writing the final structure to a dcd file\nensemble_final = Ensemble()\nensemble_final.setAtoms(initial_pdb_ca)\nensemble_final.setCoords(initial_pdb_ca)\nensemble_final.addCoordset(pdb_ca.getCoords())\nwriteDCD(final_structure_dcd_name, ensemble_final)\n\nratios = [count2/N, count2/count1 if count1 != 0 else 0, count2, k, accept_para ]\nsavetxt(initial_pdb_id + '_ratio.dat', ratios, fmt='%.2e')\n\n","sub_path":"anmmc.py","file_name":"anmmc.py","file_ext":"py","file_size_in_byte":6789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"483592004","text":"import unittest\nfrom ddt import ddt, data, unpack\nfrom typing import List\nfrom longest_valid_parentheses import Solution\n\n@ddt\nclass Tester(unittest.TestCase):\n def setUp(self):\n self.s = Solution()\n\n @data(\n [\"\", 0],\n [\"()\", 2],\n [\"(())\", 4],\n [\"(()())\", 6],\n [\")()())\", 4],\n [\")()()(()))\", 8],\n [\"())()\", 2],\n [\"()(()\", 2],\n )\n @unpack\n def test(self, s, expected):\n ret = self.s.longestValidParentheses(s)\n self.assertEqual(ret, expected)\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"leetcode/32_longest_valid_parentheses/longest_valid_parentheses_test.py","file_name":"longest_valid_parentheses_test.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"421711197","text":"'''\nCreated on Jan 25, 2014\n\n@author: cyborg-x1\n'''\n\nfrom PyQt4 import QtGui\nfrom FlashAir import card\nfrom FlashAir import ImageViewer\nfrom urllib.parse import urlparse\nimport argparse\nimport socket\nimport sys\nimport os\nfrom os.path import expanduser\nimport time\n\n\n\n\ndef ImageView(args):\n print(\"imageView\")\n app = QtGui.QApplication(sys.argv)\n port=args.card_uri.port \n if(port == None):\n port = 80\n imageViewer = ImageViewer.ImageViewer(socket.gethostbyname(args.card_uri.hostname), port, args.timeout, args.folder_local, args.folder_remote, args.instant, args.recursive)\n imageViewer.show()\n sys.exit(app.exec_())\n \ndef SyncFolder(args):\n print(\"SyncFolder\")\n print(socket.gethostbyname(args.card_uri.hostname))\n \n port=args.card_uri.port \n if(port == None):\n port = 80\n \n a=card.connection(socket.gethostbyname(args.card_uri.hostname), port, args.timeout)\n print(\"Use ctrl-c to exit!\")\n while True:\n a.sync_new_pictures_since_start(args.folder_remote, args.folder_local)\n time.sleep(1)\n pass\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='PyFlashAero, Download Tool for Toshiba FlashAir SD-Cards')\n parser.add_argument('--card_uri', dest='card_uri', type=urlparse, help='URI of the Toshiba FlashAir SDCard', default=\"http://192.168.0.1\")\n parser.add_argument('--timeout', dest='timeout', type=int, help='Timeout in milliseconds', default=1000)\n \n parser.add_argument('--folder_local', dest='folder_local', help='Folder for storing downloaded images', default='.')\n parser.add_argument('--folder_remote', dest='folder_remote', help='Folder where to search for new images (remote)', default='/')\n parser.add_argument('--recursive', dest='recursive', action='store_const',\n const=True, default=False,\n help='Search for new images in the folder recursively (not implemented yet)')\n \n parser.add_argument('--ImageViewer', dest='processing', action='store_const',\n const=ImageView, default=SyncFolder,\n help='Shows the GUI')\n \n\n parser.add_argument('--GUIinstant', dest='instant', action='store_const',\n const=True, default=False,\n help='GUI will start looking for images directly')\n \n \n \n args = parser.parse_args()\n ip = socket.gethostbyname(args.card_uri.hostname)\n \n if(not os.path.isdir(args.folder_local)):\n print(\"Given folder(local) does not exist or isn't a folder!\")\n exit(1)\n \n args.processing(args)\n \n ","sub_path":"src/PyFlashAero.py","file_name":"PyFlashAero.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"313988372","text":"# По задаче:\r\n# Для каждого числа i от 0 до 17 вводится с клавиатуры некоторое натуральное число d.\r\n# Задача — проверить, делится ли i на d, и вывести «ДА» или «НЕТ» в зависимости от этого.\r\n# (То есть, делится ли 0 на первое введенное число, 1 - на второе введенное число и т.д.)\r\n# Например, на последней строке вывода будет «ДА», только если на последней строке ввода было 1, 2, 4, 8 или 16.\r\nfor i in range(0, 17):\r\n d = int(input())\r\n if i % d == 0:\r\n print('ДА')\r\n else:\r\n print('НЕТ')\r\n\r\n","sub_path":"Основы программирования Python/6. For loop/17 Тест на делимость.py","file_name":"17 Тест на делимость.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"476777212","text":"from flask import session, Flask\r\nfrom flask_session import Session\r\nfrom redis import Redis\r\nfrom flask import g\r\n\r\napp = Flask(__name__)\r\napp.config[\"SESSION_TYPE\"] = \"redis\"\r\napp.config[\"SESSION_REDIS\"] = Redis(\"127.0.0.1\",6379,db=15) #指定session存放的数据库\r\nSession(app)\r\n\r\n@app.route(\"/login\")\r\ndef login():\r\n session[\"key\"] = \"value\"\r\n return \"已经创建了session\"\r\n\r\n@app.route(\"/look\")\r\ndef look():\r\n\r\n return session.get(\"key\")\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run()\r\n","sub_path":"python笔记/面试题/09第九章Flask/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"475855699","text":"# Importing the libraries\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport pickle\r\n\r\n#dataset = pd.read_csv('data.csv')\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"Sales_prediction.ipynb\r\n\r\nAutomatically generated by Colaboratory.\r\n\r\nOriginal file is located at\r\n https://colab.research.google.com/drive/1RwOCKq4sKmN0KsYzUe-whEKIg0uMu9Ka\r\n\"\"\"\r\n\r\n\r\n\"\"\"**Loading the Dataset**\"\"\"\r\n\r\n#Load trainSet and use header to known about trainset\r\ndf=pd.read_csv('train.csv')\r\n\r\n\r\n\"\"\"**Preprocessing the dataset**\"\"\"\r\n\r\n#check for null values\r\ndf.isnull().sum()\r\n\r\n# check for categorical attributes\r\ncat_col = []\r\nfor x in df.dtypes.index:\r\n if df.dtypes[x] == 'object':\r\n cat_col.append(x)\r\ncat_col\r\n\r\ncat_col.remove('Item_Identifier')\r\ncat_col.remove('Outlet_Identifier')\r\ncat_col\r\n\r\n#print the categorical columns\r\nfor col in cat_col:\r\n print(col)\r\n print(df[col].value_counts())\r\n print()\r\n\r\n#fill the new values\r\nitem_weight_mean = df.pivot_table(values = \"Item_Weight\", index = 'Item_Identifier')\r\nitem_weight_mean\r\n\r\ndf.head()\r\n\r\nmiss_bool = df['Item_Weight'].isnull()\r\nmiss_bool\r\n\r\n#import random\r\n#for i,item in enumerate(df['Item_Identifier']):\r\n # if miss_bool[i]:\r\n # if item in item_weight_mean:\r\n # df['Item_Weight'][i] = item_weight_mean.loc[item]['Item_weight']\r\n \r\n #else:\r\n #df['Item_Weight'][i] = np.mean(df['Item_Weight'])\r\n # df['Item_Weight'][i] = df.fillna(method = 'ffill')\r\n\r\ndf[\"Item_Weight\"].fillna(method = \"ffill\", inplace = True)\r\n\r\ndf['Item_Weight'].isnull().sum()\r\n\r\noutlet_size_mode = df.pivot_table(values = 'Outlet_Size', columns = 'Outlet_Type', aggfunc = (lambda x: x.mode()[0]))\r\noutlet_size_mode\r\n\r\ndf.head()\r\n\r\nmiss_bool = df['Outlet_Size'].isnull()\r\ndf.loc[miss_bool, 'Outlet_Size'] = df.loc[miss_bool, 'Outlet_Type'].apply(lambda x: outlet_size_mode[x])\r\n\r\ndf['Outlet_Size'].isnull().sum()\r\n\r\nsum(df['Item_Visibility'] == 0)\r\n\r\n# replace zeros with mean\r\ndf.loc[:, 'Item_Visibility'].replace([0], [df['Item_Visibility'].mean()], inplace = True)\r\n\r\nsum(df['Item_Visibility'] == 0)\r\n\r\n# combine item fat content\r\ndf['Item_Fat_Content'] = df['Item_Fat_Content'].replace({'LF':'Low Fat','reg':'Regular','low fat':'Low Fat'})\r\ndf['Item_Fat_Content'].value_counts()\r\n\r\ndf.head()\r\n\r\n\"\"\"**Creation Of New Attributes**\"\"\"\r\n\r\ndf['New_Item_Type'] = df['Item_Identifier'].apply(lambda x: x[:2])\r\ndf['New_Item_Type']\r\n\r\ndf['New_Item_Type'] = df['New_Item_Type'].map({'FD':'Food','NC': 'Non-Consumable','DR':'Drinks'})\r\ndf['New_Item_Type'].value_counts()\r\n\r\ndf.loc[df['New_Item_Type'] == 'Non-Consumable', 'Item_Fat_Content'] = 'Non-Edible'\r\ndf['Item_Fat_Content'].value_counts()\r\n\r\n# create small values for establishment year\r\n#df['Outlet_Years'] = 2021 - df['Outlet_Establishment_Year']\r\n#df['Outlet_Years']\r\n\r\ndf.head()\r\n\r\n\"\"\"**Exploratory Data Analysis**\"\"\"\r\n\r\nsns.distplot(df['Item_Weight'])\r\n\r\nsns.distplot(df['Item_Visibility'])\r\n\r\nsns.distplot(df['Item_MRP'])\r\n\r\nsns.distplot(df['Item_Outlet_Sales'])\r\ndf['Item_Outlet_Sales'] = np.log(1+df['Item_Outlet_Sales'])\r\n\r\nsns.distplot(df['Item_Outlet_Sales'])\r\n\r\n# for categorical attributes we use countplot\r\nsns.countplot(df['Item_Fat_Content'])\r\n\r\n#plt.figure(figsize= (17,5))\r\nl = list(df['Item_Type'].unique())\r\nchart = sns.countplot(df['Item_Type'])\r\nchart.set_xticklabels(labels=l,rotation = 90)\r\n\r\nsns.countplot(df['Outlet_Establishment_Year'])\r\n\r\nsns.countplot(df['Outlet_Size'])\r\n\r\nsns.countplot(df['Outlet_Location_Type'])\r\n\r\nsns.countplot(df['Outlet_Type'])\r\n\r\n\"\"\"**Corelation Matrix**\"\"\"\r\n\r\ncorr = df.corr()\r\nsns.heatmap(corr,annot = True,cmap = 'coolwarm')\r\n\r\n\r\n\r\n\"\"\"**Label Encoding**\"\"\"\r\n\r\nfrom sklearn.preprocessing import LabelEncoder\r\nle = LabelEncoder()\r\ndf['Outlet_Identifier'] = le.fit_transform(df['Outlet_Identifier'])\r\ncat_col = ['Item_Fat_Content','Item_Type','Outlet_Size','Outlet_Location_Type','New_Item_Type','Outlet_Type',]\r\nfor col in cat_col:\r\n df[col] = le.fit_transform(df[col])\r\n\r\n\r\n\"\"\"**Input Split**\"\"\"\r\n\r\n\r\n\r\nX = df.drop(columns=['Item_Identifier','Item_Outlet_Sales'])\r\ny = df['Item_Outlet_Sales']\r\n\r\n#Splitting Training and Test Set\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = 0.3, random_state=10)\r\nfrom sklearn.ensemble import RandomForestRegressor\r\n# Number of trees in random forest\r\nn_estimators = [int(x) for x in np.linspace(start = 10, stop = 80, num = 10)]\r\n# Number of features to consider at every split\r\nmax_features = ['auto', 'sqrt']\r\n# Maximum number of levels in tree\r\nmax_depth = [2,4]\r\n# Minimum number of samples required to split a node\r\nmin_samples_split = [2, 5]\r\n# Minimum number of samples required at each leaf node\r\nmin_samples_leaf = [1, 2]\r\n# Method of selecting samples for training each tree\r\nbootstrap = [True, False]\r\n\r\n# Create the param grid\r\nparam_grid = {'n_estimators': n_estimators,\r\n 'max_features': max_features,\r\n 'max_depth': max_depth,\r\n 'min_samples_split': min_samples_split,\r\n 'min_samples_leaf': min_samples_leaf,\r\n 'bootstrap': bootstrap}\r\nprint(param_grid)\r\nrf_Model = RandomForestRegressor()\r\nfrom sklearn.model_selection import GridSearchCV\r\nrf_Grid = GridSearchCV(estimator = rf_Model, param_grid = param_grid, cv = 3, verbose=2, n_jobs = 4)\r\nrf_Grid.fit(X_train, Y_train)\r\nrf_Grid.best_params_\r\n\r\n#checking accuracy\r\nprint (f'Train Accuracy - : {rf_Grid.score(X_train,Y_train):.3f}')\r\nprint (f'Test Accuracy - : {rf_Grid.score(X_test,Y_test):.3f}')\r\n\r\n\r\n\r\n\r\n# Saving model to disk\r\npickle.dump(rf_Grid, open('model.pkl','wb'))\r\n\r\n# Loading model to compare the results\r\nmodel = pickle.load(open('model.pkl','rb'))","sub_path":"Sales_Prediction/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"652507958","text":"from gevent import monkey; monkey.patch_all()\n\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), *(['..' + os.sep] * 2))))\n\nimport gevent\nfrom clients.common import morning_client\nfrom datetime import datetime, date, timedelta, time\nfrom morning.back_data import holidays\nfrom morning_server import stock_api, message\nfrom gevent.queue import Queue\nfrom pymongo import MongoClient\nfrom configs import db\nfrom morning.pipeline.converter import dt\nimport pandas as pd\nfrom candidate import suppressbox as supb\nfrom candidate import momentbox as mbox\nimport daydata\nimport mindata\nimport vi\nimport todaycandle\nimport order\n\n\n\ndef get_ticks(code, tdate):\n db = MongoClient('mongodb://127.0.0.1:27017').trade_alarm\n data = list(db['T' + tdate.strftime('%Y%m%d')].find({'code': code, 'type': 'tick'}))\n converted = []\n for d in data:\n converted.append(dt.cybos_stock_tick_convert(d))\n return converted\n# Kospi D VI: 3%, KOSDAQ: 6%\n\n# 1. Point, Year High(3), TodayHigh(1), from yesterday to today(2)\n# from current price and add most highest candle and check what point you can get\n# find strongest momentum (from yesterday to today -> 1 min amount, exceed within 10 sec)\n\n\ndef start_trading_tick(tdate, codes):\n global score, all_count\n yesterday = holidays.get_yesterday(tdate)\n daydata.load_day_data(yesterday, codes)\n todaycandle.tdate = tdate\n\n for progress, code in enumerate(codes):\n if not daydata.has_day_data(code):\n continue\n \n ymindata = mindata.get_min_data(code, yesterday)\n if len(ymindata) == 0:\n print('no yesterday min', code)\n continue\n\n ymin, yday_amount_high_in_minute = mindata.convert_to_three_min(code, ymindata)\n #ticks = morning_client.get_tick_data(code, tdate)\n ticks = get_ticks(code, tdate)\n is_kospi = morning_client.is_kospi_code(code)\n current_time = datetime.combine(tdate, time(8, 59, 0))\n suppress_box = None\n moment_box = None\n\n for tick_no, tick in enumerate(ticks):\n if tick['time'] >= 1519:\n order.finalize(code, tick)\n break\n vi.handle_tick(code, is_kospi, tick) \n\n if tick['market_type'] != 50:\n continue\n\n if moment_box is None: # skip first tick\n moment_box = mbox.MomentumBox(code, yday_amount_high_in_minute)\n else:\n moment_box.add_tick(tick)\n\n has_new = todaycandle.handle_tick(code, current_time, tick)\n if has_new:\n if suppress_box is None and todaycandle.get_candle_size(code) > 0:\n suppress_box = supb.SuppressBox(code, todaycandle.get_candle(code, 0), daydata.get_yesterday_high(code), daydata.get_year_high(code))\n elif suppress_box is not None:\n suppress_box.add_candle(todaycandle.get_candle(code, -1))\n\n current_time += timedelta(seconds=180)\n if suppress_box is None:\n continue\n\n point = suppress_box.get_point(todaycandle.get_current(code))\n amount_point = moment_box.get_point()\n\n if not order.is_bought(code):\n if (tick['cum_buy_volume'] > tick['cum_sell_volume'] and amount_point * point > 1.5 and\n tick['ask_price'] * 0.995 < tick['bid_price']):\n prices = vi.generate_price_slot(code, tick['ask_price'], daydata.get_yesterday_close(code), is_kospi)\n print(code, 'ask', tick['ask_price'], 'target', prices, 'yesterday', daydata.get_yesterday_close(code), 'bid', tick['bid_price'])\n if len(prices) > 0:\n order.add_order(code, tick, prices, amount_point)\n #print(code, tick['date'], tick['time'], moment_box.get_point(), point)\n else:\n order.check_tick(code, tick, amount_point)\n \n\ndef targeting_test():\n morning_client.get_all_market_code() # for is_kospi\n market_codes = ['A006125', 'A117730', 'A002800', 'A007390', 'A007570', 'A217600', 'A003310', 'A258790'] # fail list: A002800\n dates = ['2020-08-10', '2020-08-06', '2020-08-06', '2020-08-06', '2020-08-14', '2020-08-14', '2020-08-14', '2020-08-14']\n\n for i, m in enumerate(market_codes):\n target_date = datetime.strptime(dates[i], '%Y-%m-%d').date()\n start_trading_tick(target_date, [m]) \n\n\nif __name__ == '__main__':\n morning_client.get_all_market_code()\n #all_codes = morning_client.get_all_market_code() # for is_kospi\n all_codes = ['A065650']\n \n tdate = datetime(2020, 8, 6).date()\n start_trading_tick(tdate, all_codes)\n\n for b in order._bills:\n print(b)\n df = pd.DataFrame(order._bills)\n df.to_excel('suppress_report_' + tdate.strftime('%Y%m%d') + '.xlsx')\n #tmin = start_trading(tdate, market_codes)\n","sub_path":"clients/search_rank/suppress_test.py","file_name":"suppress_test.py","file_ext":"py","file_size_in_byte":4961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"63999818","text":"#!/usr/bin/env python3\n\"\"\"\n________________________________________________________________________\n\n:PROJECT: *Ot2Controller*\n\n:details: Ot2Controller:\n A SiLA 2 service enabling the execution of python protocols on a Opentrons 2 liquid handler.\n \n:file: Ot2Controller_server.py\n:authors: Florian Bauer \n\n.. note:: Code generated by sila2codegenerator 0.3.4\n\n________________________________________________________________________\n\n**Copyright**:\n This file is provided \"AS IS\" with NO WARRANTY OF ANY KIND,\n INCLUDING THE WARRANTIES OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.\n\n For further Information see LICENSE file that comes with this distribution.\n________________________________________________________________________\n\"\"\"\n__version__ = \"0.1.0\"\n\nimport argparse\nimport logging\nfrom sila2lib.sila_server import SiLA2Server\nfrom Ot2Controller.Ot2Controller_real import Ot2ControllerReal\nfrom Ot2Controller.gRPC import Ot2Controller_pb2_grpc\n\nSERVER_TYPE: str = \"OpentronsOt2Controller\"\nSERVER_DESC: str = \"A SiLA 2 service enabling the execution of python protocols on an Opentrons 2 liquid handling \" \\\n \"robot.\"\n\ndef parse_command_line():\n \"\"\"\n Just looking for commandline arguments\n \"\"\"\n parser = argparse.ArgumentParser(description=\"A SiLA2 service: Ot2Controller\")\n\n # Simple arguments for the server identification\n parser.add_argument('-a', '--ip-address', action='store',\n default=None, help='The IP-address of the OT-2 device to connect to.', required=True)\n parser.add_argument('-p', '--port', action='store',\n default=50064, help='Starts the SiLA server on the given port (default=50064).')\n parser.add_argument('-s', '--server-name', action='store',\n default=\"Ot2Controller\", help='Starts the SiLA server with the given name.')\n\n # Encryption\n parser.add_argument('-X', '--encryption', action='store', default=None,\n help='The name of the private key and certificate file (without extension).')\n parser.add_argument('--encryption-key', action='store', default=None,\n help='The name of the encryption key (*with* extension). Can be used if key and certificate '\n 'vary or non-standard file extensions are used.')\n parser.add_argument('--encryption-cert', action='store', default=None,\n help='The name of the encryption certificate (*with* extension). Can be used if key and '\n 'certificate vary or non-standard file extensions are used.')\n\n parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + __version__)\n\n parsed_args = parser.parse_args()\n\n # validate/update some settings\n # encryption\n if parsed_args.encryption is not None:\n # only overwrite the separate keys if not given manually\n if parsed_args.encryption_key is None:\n parsed_args.encryption_key = parsed_args.encryption + '.key'\n if parsed_args.encryption_cert is None:\n parsed_args.encryption_cert = parsed_args.encryption + '.crt'\n\n return parsed_args\n\n\nif __name__ == '__main__':\n # or use logging.ERROR for less output\n logging.basicConfig(format='%(levelname)-8s| %(module)s.%(funcName)s: %(message)s', level=logging.DEBUG)\n\n args = parse_command_line()\n\n sila_server = SiLA2Server(name=args.server_name, description=SERVER_DESC,\n server_type=SERVER_TYPE, server_uuid=None,\n version=__version__,\n vendor_url=\"https://github.com/FlorianBauer/ot2-controller\",\n ip=\"127.0.0.1\", port=int(args.port),\n simulation_mode=False)\n\n # remove the pesky SimulationController\n sila_server.SiLAService_feature.implemented_features.pop(\"org.silastandard/core/SimulationController/v1\")\n\n # add the actual OT2-Controller\n ot2_controller = Ot2ControllerReal(device_ip=args.ip_address)\n Ot2Controller_pb2_grpc.add_Ot2ControllerServicer_to_server(\n ot2_controller,\n sila_server.grpc_server)\n sila_server.add_feature(feature_id='de.fau/dispensing/Ot2Controller/v1',\n servicer=sila_server.SiLAService_feature,\n data_path='meta')\n\n # start the server\n sila_server.run()\n","sub_path":"Ot2Controller_server.py","file_name":"Ot2Controller_server.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"362393591","text":"import tensorflow as tf\nimport ast\n\nflags = tf.flags\n\nFLAGS = flags.FLAGS\n\n\nflags.DEFINE_string(\n \"input_data\", None,\n \"input_json_file\"\n \"for the task.\")\n\n\nflags.DEFINE_string(\n \"output_data\", None,\n \"output_file\")\n\n\nflags.DEFINE_boolean(\n \"also_bought\", False,\n \"user item data\")\n\nflags.DEFINE_string(\n \"item_hash_map\", None,\n \"item_hash_map\")\n\n\ndef load_hash_map():\n hash_map = dict()\n with open(FLAGS.item_hash_map, \"r\") as f:\n for each in f.readlines():\n hash_map[each.split(\":\")[0]] = each.split(\":\")[1].strip()\n return hash_map\n\n\ndef also_viewed_data_generator(hash_map):\n with open(FLAGS.input_data, 'r') as f:\n for each in f.readlines():\n data = ast.literal_eval(each.strip())\n raw_ret = []\n if 'related' in data:\n if 'also_viewed' in data['related']:\n raw_ret.extend(data['related']['also_viewed'])\n if FLAGS.also_bought and 'related' in data:\n if 'also_bought' in data['related']:\n raw_ret.extend(data['related']['also_bought'])\n ret = []\n for item in raw_ret:\n if item in hash_map:\n ret.append(hash_map[item])\n if len(ret) != 0 and data['asin'] in hash_map:\n yield hash_map[data['asin']] + '|' + ','.join(ret).strip()\n\n\ndef main(_):\n hash_map = load_hash_map()\n with open(FLAGS.output_data, 'w') as g:\n for line in also_viewed_data_generator(hash_map):\n g.write(line + '\\n')\n\n\nif __name__ == '__main__':\n flags.mark_flag_as_required(\"input_data\")\n flags.mark_flag_as_required(\"output_data\")\n flags.mark_flag_as_required(\"item_hash_map\")\n\n tf.app.run()\n","sub_path":"data_prepare/mcf_also_viewed_items.py","file_name":"mcf_also_viewed_items.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"305230201","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nwith open('requirements.txt') as f:\n requirements = f.read().splitlines()\n\nsetuptools.setup(\n name=\"ids-lib\",\n version=\"0.1.3-dev0\",\n author=\"dnk0 \",\n author_email=\"dnk0@protonmail.com\",\n description=\"Common functionality and preprocessing for intrusion detection\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"\",\n install_requires=requirements,\n packages=setuptools.find_packages(),\n classifiers=[\n \"License :: OSI Approved :: MIT License\",\n ],\n python_requires='>=3.6',\n)\n\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"127166856","text":"### KALIDAS - Enrichment Script for Samsara ###\nlogo = \"\"\" _ __\n| | / / _ _ \n| |/ / | | _ | | __,\n| <` __, | | | | _| | __, / ._\\ \n| |\\`\\/ _ || |_| |/ _ |/ _ |_, |\n|_| \\_\\__,\\|___|_|\\__,|\\__.\\___/ \"\"\"\n\nversion = \" Version 0.1\"\n\nprint(logo+version)\n\nfrom kafka import KafkaProducer\nfrom kafka import KafkaConsumer\nfrom time import time\nfrom time import sleep\nfrom datetime import timedelta\nfrom atexit import register\nfrom pycorenlp import StanfordCoreNLP\nimport extractionFunctions as EF\nimport helperfunctions as HF\nimport argparse\nimport json\nimport logging\nimport requests\nimport pyorient\n\n################\n# Inital setup #\n################\n\n# Parse command line arguments\n\nparser = argparse.ArgumentParser(description=\"Listen to kafka queue for data to enrich\")\n\nparser.add_argument(\"kafka_broker\", \n help=\"URL of the kafka broker in the form URL:PORT\")\n\nparser.add_argument(\"-q\", \n \"--quiet\", \n help=\"log level ERROR\",\n action=\"store_true\")\n\nparser.add_argument(\"-v\",\n \"--verbose\",\n help=\"log level DEBUG\",\n action=\"store_true\",\n default=False)\n\nparser.add_argument(\"-d\", \n \"--database\", \n help=\"Orientdb address\",\n action=\"store\", \n required=False, \n default=\"orientdb.samsara\")\n\nparser.add_argument(\"-s\", \n \"--server\", \n help=\"URL of the Stanford CoreNLP server (default: http://corenlp.samsara:9000)\",\n action=\"store\", \n required=False, \n default=\"http://corenlp.samsara:9000\")\n\nargs = parser.parse_args()\nlog = logging.getLogger(__name__)\n\n# Logging\nif args.quiet:\n log.setLevel('ERROR')\nelif args.verbose:\n log.setLevel('DEBUG')\nelse:\n log.setLevel('INFO')\n\n## define the 'ending' function\ndef Ending(kafka_consumer, kafka_producer, edge_cache):\n # Commit whatever's left in the edge cache and initialise it\n for edge in edge_cache:\n log.debug(\"Sending edge: %r\", edge)\n kafka_producer.send(\"edge\", str.encode(edge))\n edge_cache = []\n kafka_consumer.close()\n print('Time taken:', str(timedelta(seconds=time()-start)))\n print('Messages received:', filesread)\n\n###################\n# Start Receiving #\n###################\n# connect to orient\nori = HF.Orient(args.database,'samsara')\n# define the counter variables:\nfilesread = 0\n# start the kafka consumer\nconsumer = KafkaConsumer(bootstrap_servers=[args.kafka_broker], value_deserializer=lambda m: json.loads(m.decode('ascii')))\n# start the kafka producer\nproducer = KafkaProducer(bootstrap_servers=[args.kafka_broker])\n# initialise edge cache\nedgeCache = []\n# register the exit code\nregister(Ending,consumer,producer, edgeCache)\n# open the port to the NLP server\nnlp = StanfordCoreNLP(args.server)\n# subscribe to the right topic\nconsumer.subscribe(topics=['enrichResponse'])\n\n# show listening (if not quiet)\nif not args.quiet:\n print(\"Now listening on\",args.kafka_broker,\"with NER on\",args.server+\"... \")\n \ndef create_entnode(entlabel, enttype):\n data = {\n \"command\": \"create vertex Entities set label=:label, type=:type\",\n \"parameters\": {\n \"label\": entlabel,\n \"type\": enttype\n }\n }\n content = json.dumps(data)\n url = \"http://orientdb.samsara:2480/command/samsara/sql\"\n orientuser = 'root'\n orientpw = 'root'\n return {'url':url, 'usr':orientuser, 'pswd':orientpw, 'data':content}\n \ndef create_edge(classtype, fromrid, torid):\n data = {\n \"command\": \"create edge :type from :fromrid to :torid\",\n \"parameters\": {\n \"fromrid\": '#'+fromrid,\n \"torid\": '#'+torid,\n \"type\": classtype\n }\n }\n content = json.dumps(data)\n url = \"http://orientdb.samsara:2480/command/samsara/sql\"\n orientuser = 'root'\n orientpw = 'root'\n return {'url':url, 'usr':orientuser, 'pswd':orientpw, 'data':content}\n\ndef orientWrite(makefunc):\n try:\n requests.post(makefunc['url'], data=makefunc['data'], auth=(makefunc['usr'],makefunc['pswd']))\n except:\n raise\n\nwhile True:\n begintime = time()\n\n # read messages\n for msg in consumer:\n filesread += 1\n # Get the orphaned nodes details (text and rid)\n nodeText = json.loads(json.dumps(msg.value))['text']\n nodeRID = json.loads(json.dumps(msg.value))['rid']\n\n # Extract named entities\n nodeEntities = EF.extractNER(nodeText,nlp)\n for entity in nodeEntities:\n typ = entity[0]\n print(\"Type for this node:\",typ)\n lbl = entity[1]\n print(\"Label for this node:\",lbl)\n # check existance:\n qry = (\"select @rid from Entities where label = '%s'\" % (lbl))\n ori.connect()\n nodex = ori.client.query(qry,1)\n ori.close()\n print(\"Node exists? = 0 or no:\",nodex)\n print(\"Length of nodex:\",len(nodex))\n if len(nodex) == 0:\n # create node\n print(\"Creating Node for:\",lbl)\n orientWrite(create_entnode(lbl, typ))\n else:\n print(\"Node:\",lbl,\"exists already.\")\n # create edge data\n edgeDict = {'from': nodeRID, 'to': entity[1]}\n edgeJSON = json.dumps(edgeDict)\n # push into cache but don't write to DB yet\n edgeCache.append(edgeJSON)\n\n # on every 5th message\n if filesread % 5 == 0:\n print(edgeCache)\n # empty the edge cache into the right queue\n for edge in edgeCache:\n log.debug(\"Sending edge: %r\", edge)\n producer.send(\"edge\", str.encode(edge))\n # Empty the edge cache\n edgeCache = []\n # Flush the producer\n producer.flush()\n\n if not args.quiet:\n print(\"Files read:\",filesread,\"\\t\\tSeconds listening:\",round(time() - begintime), end='\\r')\n","sub_path":"containers/enrich-entities/entity-enrich.py","file_name":"entity-enrich.py","file_ext":"py","file_size_in_byte":6127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"75128470","text":"import logging\r\nimport os\r\nimport requests\r\n\r\n\r\ndef myloginit(Function):\r\n # Create folder is not there\r\n try:\r\n os.makedirs(\"/tmp/log\")\r\n except:\r\n pass\r\n ### define the log file\r\n LogFile = '/tmp/log/script.log'\n #log define\r\n logger = logging.getLogger(Function)\r\n hdlr = logging.FileHandler(LogFile)\r\n formatter = logging.Formatter('%(asctime)s [%(name)s][%(levelname)s] - %(message)s')\r\n hdlr.setFormatter(formatter)\r\n logger.addHandler(hdlr)\r\n logger.setLevel(logging.DEBUG)\r\n return logger\r\n\r\ndef mylog(logger, Level, Message):\r\n ## Get InstanceID\r\n InstanceId = requests.get('http://169.254.169.254/latest/meta-data/instance-id')\r\n InstanceId = InstanceId.text\r\n\r\n if Level.lower() == \"error\":\r\n logger.error(\"[\" + InstanceId + \"]\" + Message)\r\n else:\r\n logger.info(\"[\" + InstanceId + \"]\" + Message)\r\n\r\n# MyLog('test', 'ERROR', 'this is a test')\r\n","sub_path":"cloudformation/cfndsl/01-Scripts/Script00mylib.py","file_name":"Script00mylib.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"105462534","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nimport os\nfrom mpl_toolkits.axes_grid1 import host_subplot\nimport matplotlib.pyplot as plt\nimport mpl_toolkits.axisartist as AA\n\nBASEDIR=os.path.dirname(__file__)\n\ndf = pd.read_csv(BASEDIR +\"/újfertőzések.csv\", parse_dates=['Dátum'])\ndf[\"Oltottak aránya\"] =100 * df['Legalább 1 oltással rendelkezők száma'] / df['Regisztrált fertőzöttek száma']\n\nsumma = df.sum()\n\nrate = int(100.0 * summa['Legalább 1 oltással rendelkezők száma'] / summa['Regisztrált fertőzöttek száma'] + 0.5)\n\n\npd.plotting.register_matplotlib_converters()\n\nhost = host_subplot(111)\n\npar = host.twinx()\n\nhost.set_xlabel(\"Dátum\")\nhost.set_ylabel(\"Új fertőzöttek\")\nhost.set_title(\"Magyar COVID adatok az új fertőzöttekről (\" + str(rate) + \"% oltott)\")\nhost.set_ylim([0, 5000])\npar.set_ylabel(\"Oltottak aránya\")\npar.set_ylim([0, 100])\n\np1, = host.plot(df['Dátum'], df['Regisztrált fertőzöttek száma'], label=\"Regisztrált fertőzöttek száma\", color=\"#7070FF\")\np1b, = host.plot(df['Dátum'], df['Legalább 1 oltással rendelkezők száma'], label=\"Legalább 1 oltással rendelkezők száma\", color=\"darkblue\")\np2, = par.plot(df['Dátum'], df['Oltottak aránya'], label=\"Oltottak aránya\", color=\"magenta\")\n\nhost.fill_between(df['Dátum'], df['Regisztrált fertőzöttek száma'], color=\"#7070FF\")\nhost.fill_between(df['Dátum'], df['Legalább 1 oltással rendelkezők száma'], color=\"darkblue\")\n\nleg = plt.legend()\n\nhost.yaxis.get_label().set_color(p1b.get_color())\nleg.texts[0].set_color(p1.get_color())\n\nleg.texts[1].set_color(p1b.get_color())\n\npar.yaxis.get_label().set_color(p2.get_color())\nleg.texts[2].set_color(p2.get_color())\n\nfig = host.get_figure()\nfig.autofmt_xdate()\nfig.savefig(BASEDIR + \"/Fertőzöttek.png\", bbox_inches = \"tight\")\n","sub_path":"magyar/kép_fertőzöttek.py","file_name":"kép_fertőzöttek.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"255829399","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\ndataset = pd.read_csv(r'D:\\Study Material\\Project\\Databases\\Created\\CustomerBetaFinal.csv')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef SetClusterFunc(data):\r\n dataset = pd.read_csv(r'D:\\Study Material\\Project\\Databases\\Created\\CustomerFinal.csv')\r\n X = dataset.iloc[:, [3, 4,5]].values \r\n\r\n from sklearn.cluster import KMeans\r\n wcss = []\r\n for i in range(1, 11):\r\n kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\r\n kmeans.fit(X)\r\n wcss.append(kmeans.inertia_)\r\n\r\n \"\"\"\r\n plt.plot(range(1, 11), wcss)\r\n plt.title('The Elbow Method')\r\n plt.xlabel('Number of clusters')\r\n plt.ylabel('WCSS')\r\n plt.show()\r\n \"\"\"\r\n #print(X)\r\n kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\r\n y_kmeans = kmeans.fit_predict(X)\r\n l = []\r\n for x in data.values():\r\n l.append(x)\r\n x = kmeans.fit_predict([[l[3],l[4],l[5]],[43, 47, 91],[33, 75, 91],[23, 33, 55],[53, 47, 78]])\r\n\r\n return x[0]\r\n\r\n\r\n\r\n \"\"\"\r\n plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\r\n plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\r\n plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\r\n plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\r\n plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\r\n plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\r\n plt.title('Clusters of customers')\r\n plt.xlabel('Annual Income (k$)')\r\n plt.ylabel('Spending Score (1-100)')\r\n plt.legend()\r\n plt.show()\r\n\r\n \"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef AddCustomer():\r\n data = {}\r\n global dataset\r\n data[\"CustomerID\"] = max(dataset[\"CustomerID\"]) + 1\r\n print(\"\\n\\nCustomer's ID will be: {}\".format(data[\"CustomerID\"]))\r\n data[\"Name\"] = input(\"Input Customer Name: \")\r\n data[\"Genre\"] = input(\"Input Customer Gender: \")\r\n data[\"Age\"] = int(input(\"Input Customer Age: \"))\r\n data[\"Annual Income (k$)\"] = int(input(\"Input Customer Estimated Salary: \"))\r\n data[\"Spending Score (1-100)\"] = int(input(\"Input Customer Spending Score: \"))\r\n data[\"Cluster\"] = SetClusterFunc(data)\r\n print(\"\\nCustomer belongs to the Cluster: \", data[\"Cluster\"])\r\n dataset = dataset.append(data, ignore_index = True)\r\n print(dataset.columns)\r\n dataset.to_csv(\"CustomerBetaFinal.csv\")\r\n return data\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# add basket items to basket optimisation table\r\ndef AddBasketData(bas):\r\n dataset = pd.read_csv(r'D:\\Study Material\\Project\\Databases\\ASSOCIATION-Market_Basket_Optimisation.csv',header = None)\r\n c = dataset.iloc[:,0]\r\n x = list(range(0,len(c)))\r\n dataset[\"Num\"] = x\r\n\r\n cols = list(dataset.columns)\r\n dataset = dataset[[cols[-1]]+cols[1:-1]]\r\n\r\n\r\n c = dataset.iloc[:,0]\r\n \r\n dataset.at[len(c), 0] = len(c)\r\n for i in range(len(bas)):\r\n dataset[\"Num\"] = len(c)\r\n dataset.at[len(c),i+1] = bas[i] \r\n print(dataset)\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Get the data of a new basket's items\r\ndef AddBasket():\r\n item = \"\"\r\n basket = []\r\n while item != \" \":\r\n item = input(\"Enter the Stock Item(enter single space to finsh): \")\r\n basket.append(item)\r\n basket.pop()\r\n AddBasketData(basket)\r\n return basket\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nprint(\"Welcome...\\n\\nWould you like to:\\n1. Input a New Data\\n2. Present Data\\n\")\r\nop = int(input(\"Enter the Operation: \"))\r\nwhile op not in [1,2]:\r\n print(\"Enter a valid Value:\")\r\n op = int(input(\"Enter the Operation: \"))\r\n\r\n\r\n# Enter new data to tables\r\nif op == 1:\r\n data = {}\r\n basket = []\r\n inp = int(input(\"\\n\\nDo you want to Enter Data for:\\n1. Customer\\n2. Basket Items \"))\r\n while inp not in [1,2,3]:\r\n print(r'\\nEnter a valid Value')\r\n inp = int(input(\"Enter the Operation: \"))\r\n# take input for Customer Data\r\n if inp == 1:\r\n data = AddCustomer()\r\n\r\n# take stock item input \r\n elif inp == 2:\r\n basket = AddBasket()\r\n print(\"\\n\\nHere's the row that will be entered in Database: \")\r\n print(data)\r\n print(basket)\r\n\r\n\r\n\r\n\r\n\r\n# present data\r\nelif op ==2:\r\n print(\"1. Show the Items generally bought together\\n2. \")\r\n \r\n\r\n\r\n\r\n\r\n","sub_path":"Final_Beta.py","file_name":"Final_Beta.py","file_ext":"py","file_size_in_byte":4265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"93472052","text":"import time\nimport serial\nimport threading\npbytes = {\n 'ACK': 97,\n 'NACK': 110\n}\n\nMIN_ANGLE = 10\nMAX_ANGLE = 240\n\nclass ArmController:\n\n def __init__(self, starting_angles, port,brate):\n\n try:\n # Open port\n self.ser = serial.Serial(port, brate, timeout=0.5) # timeout in seconds\n self.ser.close()\n self.ser.open()\n time.sleep(0.1)\n\n # Define starting angles\n self.num_servos = len(starting_angles)\n self.set_angles = starting_angles\n self.current_angles = starting_angles\n\n # Define angle writing speed\n self.delta_angle = 2\n self.delta_time = 0.02\n\n # Start talker thread\n self.main_loop = threading.Thread(target=ArmController.talk_loop_thread, args = (self,) )\n self.main_loop.daemon = True\n\n except Exception as e:\n \n # Terminate program\n raise e\n \n # Connects to ARM and then starts the main thread\n def start(self):\n global pbytes\n\n self.ser.reset_input_buffer()\n self.ser.reset_output_buffer()\n\n time.sleep(0.3)\n \n # Receive NACK once\n self.ser.write(bytes([2,3,5]))\n recv_data = self.ser.read(1)\n while len(recv_data) == 0:\n recv_data = self.ser.read(1)\n print(\"Waiting for NACK\")\n\n print(\"Response: \", recv_data[0])\n\n # Wait for response\n while True:\n self.write_angles(self.set_angles)\n recv_data = self.ser.read(1)\n if len(recv_data) !=0:\n if recv_data[0] == pbytes['ACK']:\n break\n\n else: \n print(\"Response different from ACK\")\n else: \n print(\"ARM not responding, trying again...\")\n\n time.sleep(self.delta_time)\n\n self.running = True\n self.main_loop.start() # Starts talking thread\n\n def talk_loop_thread(self):\n global pbytes, MIN_ANGLE, MAX_ANGLE\n\n # Loops while self.running is active\n while self.running:\n\n self.ser.reset_input_buffer()\n self.ser.reset_output_buffer()\n time.sleep(self.delta_time)\n\n\n # Calculate next angles to set\n next_angles= [0]*self.num_servos\n for i in range(len(self.set_angles)):\n curr_a = self.current_angles[i]\n set_a = self.set_angles[i]\n if set_a > curr_a:\n next_angles[i] = curr_a + self.delta_angle\n next_angles[i] = self.clamp(next_angles[i], MIN_ANGLE, set_a)\n \n elif set_a < curr_a:\n next_angles[i] = curr_a - self.delta_angle\n next_angles[i] = self.clamp(next_angles[i], set_a, MAX_ANGLE)\n\n else:\n next_angles[i] = set_a\n\n while True:\n self.write_angles(next_angles)\n recv_data = self.ser.read(1)\n if len(recv_data) !=0:\n if recv_data[0] == pbytes['ACK']:\n self.current_angles = next_angles\n break\n\n else: \n print(\"Response: \", recv_data[0])\n else: \n print(\"ARM not responding, trying again...\")\n\n \n\n def setAngles(self, angles):\n if len(angles) == self.num_servos:\n self.set_angles = angles\n\n else:\n print(\"Number of angles set differ from number of servos\")\n\n # Terminate serial communication\n def close(self):\n print(\"Closing ARM connection\")\n\n self.running = False\n self.ser.close()\n\n def write_angles(self, angles):\n global MIN_ANGLE, MAX_ANGLE\n angles = [self.clamp(x*1.41667, MIN_ANGLE, MAX_ANGLE) for x in angles]\n # print(\"Writing angles: \", angles)\n byte_angles = bytes(angles)\n self.ser.write(byte_angles)\n\n def clamp(self, n, minn, maxn):\n return int(max(min(maxn, n), minn))","sub_path":"Arduino-ARM/ArmController.py","file_name":"ArmController.py","file_ext":"py","file_size_in_byte":4127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"217430481","text":"import jwt\n\nfrom calendar import timegm\nfrom datetime import datetime, timedelta\n\nfrom django.contrib.auth import authenticate, get_user_model\nfrom django.utils.translation import ugettext as _\nfrom rest_framework import serializers\nfrom rest_framework_jwt.settings import api_settings\nfrom rest_framework_jwt.compat import get_username_field, PasswordField\n\nfrom functools import wraps\nfrom django.utils.decorators import available_attrs\nfrom django.http import JsonResponse\n\nUser = get_user_model()\njwt_decode_handler = api_settings.JWT_DECODE_HANDLER\njwt_get_username_from_payload = api_settings.JWT_PAYLOAD_GET_USERNAME_HANDLER\n\n\nclass VerificationBaseSerializer(object):\n \"\"\"\n Abstract serializer used for verifying and refreshing JWTs.\n \"\"\"\n\n def validate(self, attrs):\n msg = 'Please define a validate method.'\n raise NotImplementedError(msg)\n\n def _check_payload(self, token):\n # Check payload valid (based off of JSONWebTokenAuthentication,\n # may want to refactor)\n try:\n payload = jwt_decode_handler(token)\n except jwt.ExpiredSignature:\n msg = _('Signature has expired.')\n raise serializers.ValidationError(msg)\n except jwt.DecodeError:\n msg = _('Error decoding signature.')\n raise serializers.ValidationError(msg)\n\n return payload\n\n def _check_user(self, payload):\n username = jwt_get_username_from_payload(payload)\n\n if not username:\n msg = _('Invalid payload.')\n raise serializers.ValidationError(msg)\n\n # Make sure user exists\n try:\n user = User.objects.get_by_natural_key(username)\n except User.DoesNotExist:\n msg = _(\"User doesn't exist.\")\n raise serializers.ValidationError(msg)\n\n if not user.is_active:\n msg = _('User account is disabled.')\n raise serializers.ValidationError(msg)\n\n return user\n\nclass VerifyJSONWebTokenSerializer(VerificationBaseSerializer):\n \"\"\"\n Check the veracity of an access token.\n \"\"\"\n def validate(self, attrs):\n token = attrs['token']\n\n payload = self._check_payload(token=token)\n user = self._check_user(payload=payload)\n\n return {\n 'token': token,\n 'user': user\n }\n\n\n# if __name__ == '__main__':\n# a = VerifyJSONWebTokenSerializer()\n# print(a.validate({\"token\":\"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJvcmlnX2lhdCI6MTQ4NTA3NjcwOSwiZW1haWwiOiJ6d0AxLmNvbSIsInVzZXJuYW1lIjoienciLCJ1c2VyX2lkIjoxLCJleHAiOjE0ODUxMDY3MDl9.lJW59_E3dR-BjFhJ80BWJAieFzTqbnpc4b9jNzgJCzg\"}))\n\n\ndef user_pass_func(test_func):\n \"\"\"\n Decorator for views that checks that the user passes the given test,\n redirecting to the log-in page if necessary. The test should be a callable\n that takes the user object and returns True if the user passes.\n \"\"\"\n\n def decorator(view_func):\n @wraps(view_func, assigned=available_attrs(view_func))\n def _wrapped_view(request, *args, **kwargs):\n user = test_func(request.META.get(\"HTTP_AUTHORIZATION\"))\n if user:\n request.user = user\n return view_func(request, *args, **kwargs)\n result = {\"status\": \"0\", \"message\": \"User not login\"}\n return JsonResponse(result)\n return _wrapped_view\n return decorator\n\n\ndef verify(t):\n a = VerifyJSONWebTokenSerializer()\n try:\n res = a.validate({\"token\":t[3:].strip()})\n user = res['user']\n except Exception as e:\n return False\n else:\n return user\n\ndef login_required(function=None):\n \"\"\"\n Decorator for views that checks that the user is logged in, redirecting\n to the log-in page if necessary.\n \"\"\"\n \n actual_decorator = user_pass_func(verify)\n if function:\n return actual_decorator(function)\n return actual_decorator\n\n\nfrom django.utils.deprecation import MiddlewareMixin\n\nfrom api.models import UserActionLog\nimport json, datetime\nfrom django.utils import timezone\nclass UserActionLoggingMiddleware(MiddlewareMixin):\n \"\"\"\n This middleware log user every action except GET\n \n \"\"\"\n def __init__(self, get_response=None):\n self.get_response = get_response\n super(UserActionLoggingMiddleware, self).__init__(get_response=get_response)\n\n def process_request(self, request):\n if request.method != \"GET\" and request.path != \"/api/api-token-auth/\" and len(request.FILES) == 0:\n user = verify(request.META.get(\"HTTP_AUTHORIZATION\"))\n if user:\n request.user = user\n\n request._body = request.read()\n\n user_action_log = UserActionLog()\n user_action_log.method = request.method\n user_action_log.content = request._body\n user_action_log.user_id = request.user.id\n user_action_log.user_name = request.user.username\n user_action_log.uri = request.path\n user_action_log.ctime = timezone.now()\n user_action_log.save()\n\n return None","sub_path":"mydecorators.py","file_name":"mydecorators.py","file_ext":"py","file_size_in_byte":5096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"265024388","text":"# coding: utf-8\nimport pandas as pd\nimport numpy as np\n\n\ndef get_users_intersection(book1, book2, data):\n \"\"\"返回评价果\"\"\"\n book1_users = data[data['book'] == book1]['userId'].values\n book2_users = data[data['book'] == book2]['userId'].values\n return np.intersect1d(book1_users, book2_users, assume_unique=True)\n\n\ndef get_user_mean(user, data):\n \"\"\"返回均值\"\"\"\n return data[data['userId'] == user]['rating'].mean()\n\n\ndef get_numerator(book1, book2, data):\n \"\"\"返回分子\"\"\"\n users = get_users_intersection(book1, book2, data)\n if users:\n result = 0\n for u in users:\n u_mean = get_user_mean(u, data)\n books_ratings = data.loc[\n data['userId'] == u][\n data['book'].isin([book1, book2])]['rating']\n result += np.cumprod(books_ratings.values - u_mean)[-1]\n return result\n return 0\n\n\ndef get_denominator(book1, book2, data):\n \"\"\"返回分母\"\"\"\n users = get_users_intersection(book1, book2, data)\n if users:\n result = 1\n for u in users:\n u_mean = get_user_mean(u, data)\n books_ratings = data.loc[\n data['userId'] == u][\n data['book'].isin([book1, book2])]['rating']\n books_ratings -= u_mean\n books_ratings = books_ratings.pow(2) ** 0.5\n result *= np.cumprod(books_ratings.values)[-1]\n return result\n return 0\n\n\ndef get_cos_rating(book1, book2, data):\n denominator = get_denominator(book1, book2, data)\n if denominator:\n return get_numerator(book1, book2, data) / denominator\n return -1\n\n\ndef get_data(path):\n \"\"\"返回数据\"\"\"\n data = pd.read_csv(path, sep=';', header=None)\n data.columns = ['userId', 'book', 'rating']\n return data\n\n\nif __name__ == '__main__':\n path = '../chapter-2/BX-Dump/BX-Book-Ratings.csv'\n data = get_data(path)\n data = data[data['rating'] > 0]\n books = data.drop_duplicates('book')['book']\n book1, book2 = books.sample(n=2).values\n print (get_cos_rating(book1, book2, data))\n","sub_path":"chapter-3/cos.py","file_name":"cos.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"116513750","text":"import asyncio\nimport discord\nfrom discord.ext import commands\nimport re\nimport os\nimport config\nimport traceback\nimport psycopg2\n\n'''Re-usable procedures'''\n#Define checks\ndef detain(self, guild, member, ctx, reason=''):\n if reason != '': reason = 'for ' + reason\n #check if guild checked the detain functionality with SQL\n detention_role = None\n logs_channel = None\n detained = None\n #using SQL check if user is in detention and toggle\n if not detained:\n em = discord.Embed(colour=0xff0000, title='Detention', description=f'{ctx.message.author.mention} has detained {member.mention}'+reason)\n em.set_footer(text=str(ctx.message.created_at))\n await member.edit(roles=[detention_role])\n elif detained:\n em = discord.Embed(colour=0x00ff00, title='Detention', description=f'{ctx.message.author.mention} has freed {member.mention}'+reason)\n em.set_footer(text=str(ctx.message.created_at))\n #fetch member roles [GUILD SPECIFIC] with SQL\n fetched_roles = None #cursor.fetchone()\n await member.edit(roles=[discord.utils.get(guild.roles, id=x) for x in fetched_roles])\n #do stuff\n await ctx.message.add_reaction(emoji='✅')\n\ndef mute(self, guild, ctx, time, member):\n mute_role = discord.utils.get(guild.roles, name='Mute')\n await member.add_roles(mute_role)\n await asyncio.sleep(time)\n await member.remove_roles(mote_role)\n\nclass Mod(commands.Cog):\n @commands.command()\n @commands.check(checks.staff_team_check)\n async def warn(self, ctx, member : discord.Member, reason : str):\n #SQL to get logs channel\n #SQL to tally warnings\n #check if tally is equal to max\n #then\n #\n warnings = cursor.fetchone()\n if warnings:\n warnings += 1\n if warnings == 2:\n mute(self, ctx.guild, ctx, 60*30, member)\n punishment = 'Half Hour Mute'\n elif warnings == 3:\n punishment = '48 Hour Mute'\n mute(self, ctx.guild, ctx, 60*60*48, member)\n #reset\n\n else:\n warnings = 1\n punishment = 'Nothing, you lucky bugger.'\n em = discord.Embed(title=f'{ctx.message.author.name} has dished out a warning!', description=f'{member.name} has been warned for {reason}')\n em.add_field(name='Total warnings', value=str(warnings))\n em.add_field(name='Punishments (if any)', value=punishment)\n await ctx.send(embed=em)\n #send to logs channel too\n\n @commands.command()\n @commands.check(checks.staff_team_check)\n async def violation(self, ctx, member : discord.Member, reason : str):\n #SQL to tally violations\n #check if tally is equal to max\n #then ban\n violations = cursor.fetchone()\n if violations:\n violations += 1\n if violations == 2:\n mute(self, ctx.guild, ctx, 60*60*24, member)\n punishment = '3 Day Mute'\n elif violations == 3:\n days = None\n while not days:\n try:\n await ctx.send('Should I purge any messages?\\nChoice of 0-7. To cancel simply make an invalid choice.')\n days = await self.bot.wait_for('message', check=sender_check)\n days = int(days)\n except (asyncio.TimeoutError, ValueError):\n days = 8\n if days == 8:\n await ctx.send('I didn\\'t get that, sorry.')\n return\n await member.ban(member, reason=reason, delete_message_days=days)\n else:\n violations = 1\n mute(self, ctx.guild, ctx, 60*60*24, member)\n punishment = '24 Hour Mute'\n em = discord.Embed(title=f'{ctx.message.author.name} has dished out a warning!', description=f'{member.name} has been warned for {reason}')\n em.add_field(name='Total warnings', value=str(warnings))\n em.add_field(name='Punishments (if any)', value=punishment)\n await ctx.send(embed=em)\n #send to logs channel too\n\n @commands.command()\n @commands.check(checks.staff_team_check)\n async def detain(self, ctx, member : discord.Member, reason : str):\n detain(self, ctx.guild, member, ctx, reason)\n\n# TODO\n# [ ] Warnings, 1m, 2 warnings 30min, 3 warnings 48 hour mute\n# [ ] Violations, 3m, 24hour mute, 48 hour mute, ban\n# [ ] \"Sequel\"\n\n\n","sub_path":"cogs/moderation.py","file_name":"moderation.py","file_ext":"py","file_size_in_byte":4485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"39978021","text":"import os,sys,shutil, json, subprocess, logging, argparse, glob, re, multiprocessing, glob\nimport inspect\nimport xml.etree.ElementTree as ET\nimport multiprocessing\n\nfile_path = os.path.abspath(os.path.dirname(__file__)).replace('\\\\', '/')\nmain_path = os.path.abspath(os.path.join(file_path, '..')).replace('\\\\', '/')\nsys.path.append(main_path) # add application path to env\n\nfrom src import misc\n\n## Separator between the build output of each project.\nSECTION_LINE = \"*\" * 79\n\nclass BuildProjectsBase:\n ## Constructor.\n def __init__(self, repodir):\n ## Repository directory\n self.repositoryRoot = repodir\n ## Search the toolchain path\n self.toolpath = self._search_toolpath()\n\n ## Extract board information from project Path\n def _extract_board(self, projectPath):\n itemList = projectPath.split(\"/\")\n for index in range(len(itemList)):\n if itemList[index] == \"boards\":\n return itemList[index + 1]\n\n raise RuntimeError(\"No board information detectied from the project path: %s\" % projectPath)\n\n def search_images(self, searchpath):\n imagetypes = [\"*.elf\", \"*.bin\", \"*.axf\", \"*.hex\", \"*.out\"]\n\n for imagetype in imagetypes:\n for image_file in glob.glob(os.path.join(searchpath, imagetype)):\n postfix = image_file.split(\".\")[-1]\n logging.info(\"RF_image_%s:%s\" % (postfix, os.path.abspath(image_file)))\n ##\n # @brief Filter log file\n def log_filter(self, log_content, pattern, exclude_list=\"\"):\n log_list = []\n for line in log_content.split(\"\\n\"):\n ingore_line = False\n if exclude_list:\n for exclude_str in exclude_list:\n if re.search(exclude_str, line):\n ingore_line = True\n break\n if (not ingore_line) and re.search(pattern, line):\n log_list.append(' >> ' + line)\n if len(log_list) == 0:\n return \"\"\n return \"\\r\\n\".join(log_list)\n\nclass MDKProjectBuilder (BuildProjectsBase):\n ## Constructor.\n def __init__(self, repodir, ):\n BuildProjectsBase.__init__(self, repodir)\n\n ## Build project using keil\n def build(self, projectPath, target):\n status = \"Success\"\n brieflog = \"\"\n detaillog = \"\"\n\n # In case the project do not exsit, just place it in the error list.\n projectPath = os.path.normpath(projectPath)\n if os.path.isfile(projectPath) == False:\n status = \"Not Packed\"\n logging.error(\"RF_build_status:\" + status)\n return status\n\n log_file = os.path.join(os.path.dirname(projectPath), 'log_%s.txt' % target)\n self.__mdk_browser_debug_info_replace(projectPath)\n projectname = os.path.basename(projectPath).split(\".\")[0]\n cmd = \" \".join([\"'%s'\" % self.toolpath, '-b', \"'%s'\" % projectPath, '-j0', '-o', \"log_%s.txt\" % target,'-t', '\"' + projectname + ' ' + target + '\"'])\n _ret, _out, _err = misc.execute_silent(cmd)\n # logging.info(\"RF_build_log:smbserver%s\" % os.path.relpath(log_file, self.repositoryRoot))\n if os.path.exists(log_file):\n logging.info(\"RF_build_log:%s\" % os.path.abspath(log_file))\n\n if _ret == 0:\n pass\n elif _ret == 1:\n if os.path.exists(log_file):\n with open(log_file, \"r\") as f:\n logging.error(f.read())\n status = \"Warning\"\n else:\n if os.path.exists(log_file):\n with open(log_file, \"r\") as f:\n logging.error(f.read())\n status = \"Failure\"\n\n self.search_images(os.path.join(os.path.dirname(projectPath), target))\n\n logging.info(\"RF_build_status:\" + status)\n return status\n\n ## Find keilBuild.exe path in system\n def _search_toolpath(self):\n try:\n workbenchPath = os.environ['KEIL']\n except KeyError:\n raise RuntimeError(\"KEIL environment variable is not set.\")\n else:\n keilBuildPath = os.path.normpath(os.path.join(workbenchPath, \"UV4.exe\"))\n\n if not os.path.isfile(keilBuildPath):\n raise RuntimeError(\"UV4.exe does not exist at: {}\".format(keilBuildPath))\n\n return keilBuildPath\n\n ## Get error msg\n def __error_log_filter(self, log, errno):\n error_log_list = []\n\n error_dict = {\n 2: \"Code Error\",\n 3: \"Fatal Error\",\n 11: \"Cannot open project files for writting\",\n 12: \"Device with given name in not found in database\",\n 13: \"Error writing project file\",\n 15: \"Error writing project file\"\n }\n error_log_list.append(' >> Error Code (' + str(errno) + ') : ' + error_dict.get(errno, \"Other errors\") + '\\n')\n if self.log_filter(log, \"rror:\"):\n error_log_list.append(self.log_filter(log, \"rror:\"))\n\n return \"\".join(error_log_list)\n\n ## Invalidate browseinfo to speed up MDK build\n def __mdk_browser_debug_info_replace(self, uvprojx):\n with open(uvprojx, 'r+') as f:\n data = f.read().replace('1', '0')\n data = data.replace('1', '0')\n f.seek(0, 0)\n f.write(data)\n\nclass MCUXPRESSOProjectBuilder(BuildProjectsBase):\n def __init__(self, repodir):\n BuildProjectsBase.__init__(self, repodir)\n self.__prepare()\n\n def build(self, projectPath, target, isclean):\n status = \"Success\"\n brieflog = \"\"\n detaillog = \"\"\n\n # In case the project do not exsit, just place it in the error list.\n if os.path.isfile(projectPath) == False:\n status = \"Not Packed\"\n logging.error(\"RF_build_status:\" + status)\n return status\n\n log_file = os.path.join(os.path.dirname(projectPath), '%s_log.txt' % target)\n build_file = os.path.join(os.path.dirname(projectPath), 'build.properties')\n\n # Create the build properties for the project\n with open(build_file, \"w\") as f:\n content = list()\n content.append(\"sdk.location = %s\" % self.repositoryRoot)\n content.append(\"example.xml = %s\" % projectPath)\n # if \"multicore\" in projectPath or \"multiprocessor\" in projectPath:\n if \"erpc\" in projectPath:\n content.append(\"nature = org.eclipse.cdt.core.ccnature\")\n else:\n content.append(\"nature = org.eclipse.cdt.core.cnature\")\n content.append(\"project.build = true\")\n if isclean == True:\n content.append(\"clean.workspace = true\")\n else:\n content.append(\"clean.workspace = false\")\n content.append(\"build.all = false\")\n content.append(\"build.config = %s\" % target)\n content.append(\"simple.project.name = false\")\n content.append(\"skip.default = true\")\n content.append(\"sdk.name = %s\" % self._sdkname)\n content.append(\"board.id = %s\" % self._extract_board(projectPath))\n content.append(\"verbose = true\")\n content.append(\"indexer= false\")\n content.append(\"project.build.log = true\")\n content.append(\"standalone = true\")\n f.writelines([line + \"\\n\" for line in content])\n\n all_log = \"\"\n all_status = \"\" \n\n # cmd = \" \".join([self.toolpath, \"-data\", self._buildWS, \"-run example.options\", build_file])\n # _ret, _out, _err = misc.execute_silent(cmd)\n # all_log = _out + _err\n # if _ret:\n # all_status = \"OptionError\"\n # misc.LogFile(log_file, _out + _err)\n # logging.info(\"RF_build_log:smbserver%s\" % os.path.relpath(log_file, self.repositoryRoot))\n\n cmd = \" \".join([self.toolpath, \"-data\", self._buildWS, \"--launcher.suppressErrors\", \"-run example.build\", build_file])\n _ret, _out, _err = misc.execute_silent(cmd)\n misc.LogFile(log_file, all_log + _out + _err)\n # logging.info(\"RF_build_log:smbserver%s\" % os.path.relpath(log_file, self.repositoryRoot))\n logging.info(\"RF_build_log:%s\" % os.path.abspath(log_file))\n\n with open(log_file, \"r\") as f:\n detaillog = f.read()\n\n if _ret == 0:\n all_status += \"Success\"\n elif _ret == 4:\n all_status += \"Warning\"\n # logging.error(\"RF_build_log_brief:\" + self.log_filter(detaillog, \"warning:\"))\n else:\n if _ret == 3 and \"Total errors : 0\" in (_out + _err):\n all_status += \"Warning\"\n else:\n all_status += \"Failure\"\n # logging.error(\"RF_build_log_brief:\" + self.__error_log_filter(detaillog, _ret))\n\n # self.search_images(os.path.join(os.path.dirname(projectPath), target))\n\n logging.info(\"RF_build_status:\" + all_status)\n return all_status\n\n ## Get error msg\n def __error_log_filter(self, log, errno):\n error_log_list = []\n\n error_dict = {\n 1: \"No application has been found\",\n 2: \"Hard internal error has occurred\",\n 3: \"Error in validation, project creation or build .etc\"\n }\n error_log_list.append(' >> Error Code (' + str(errno) + ') : ' + error_dict.get(errno, \"Other errors\") + '\\n')\n if self.log_filter(log, \"rror:\"):\n error_log_list.append(self.log_filter(log, \"rror:\"))\n\n return \"\".join(error_log_list)\n\n def _search_toolpath(self):\n try:\n workbenchPath = os.environ['MCUX_DIR']\n except KeyError:\n raise RuntimeError(\"MCUX_DIR environment variable is not set.\")\n else:\n mcuxBuildPath = os.path.normpath(os.path.join(workbenchPath, \"ide\", \"mcuxpressoide\"))\n\n if not os.path.isfile(mcuxBuildPath):\n raise RuntimeError(\"mcuxpressoide does not exist at: {}\".format(mcuxBuildPath))\n\n mcuxBuildPath += \" -application com.nxp.mcuxpresso.headless.application -noSplash\"\n\n return mcuxBuildPath\n\n def __prepare(self):\n self._sdkname = None\n for manifestfile in glob.glob(os.path.join(self.repositoryRoot, \"*.xml\")):\n xmlParser = ET.parse(manifestfile)\n xmlRoot = xmlParser.getroot()\n try:\n self._sdkname= xmlRoot.attrib[\"id\"]\n except KeyError:\n raise RuntimeError(\"No SDK Name Tag detected in file: %s\" % manifestfile)\n\n if not self._sdkname:\n raise RuntimeError(\"No SDK Name Tag detected in repository\")\n\n self._buildWS = os.path.abspath(os.path.join(self.repositoryRoot, \"workspace\"))\n if not os.path.exists(self._buildWS):\n os.mkdir(self._buildWS)\n\nclass IARProjectBuilder(BuildProjectsBase):\n ## Constructor.\n def __init__(self, repodir):\n BuildProjectsBase.__init__(self, repodir)\n\n ## Build project using iar\n def build(self, projectPath, target):\n status = \"Success\"\n brieflog = \"\"\n detaillog = \"\"\n\n # In case the project do not exsit, just place it in the error list.\n projectPath = os.path.normpath(projectPath)\n if os.path.isfile(projectPath) == False:\n status = \"Not Packed\"\n logging.info(\"RF_build_status:\" + status)\n return status\n\n # Call IarBuild\n cmd = \" \".join([\"'%s'\" % self.toolpath, \"'%s'\" % projectPath, \"-build\", target, '-log', 'info', '-parallel', '%s' %(multiprocessing.cpu_count())])\n _ret, _out, _err = misc.execute_silent(cmd)\n log_file = os.path.join(os.path.dirname(projectPath), '%s_log.txt' % target)\n misc.LogFile(log_file, _out + _err)\n logging.info(\"RF_build_log:%s\" % os.path.abspath(log_file))\n # logging.info(\"RF_build_log:smbserver%s\" % os.path.relpath(log_file, self.repositoryRoot))\n detaillog = _out + _err\n\n if _ret:\n status = \"Failure\"\n brieflog = self.log_filter(_out + _err, 'Error')\n # logging.error(\"RF_build_log_brief:\" + brieflog)\n elif re.search('Total number of warnings: [1-9]', (_err + _out)) and self.log_filter(_out + _err, \" Warning\"):\n status = \"Warning\"\n brieflog = self.log_filter(_out + _err, ' Warning')\n # logging.error(\"RF_build_log_brief:\" + brieflog)\n\n self.search_images(os.path.join(os.path.dirname(projectPath), target))\n\n logging.info(\"RF_build_status:\" + status)\n return status\n\n ## Find iarBuild.exe path in system\n def _search_toolpath(self):\n try:\n workbenchPath = os.environ['IAR_WORKBENCH']\n except KeyError:\n raise RuntimeError(\"IAR_WORKBENCH environment variable is not set.\")\n else:\n iarBuildPath = os.path.normpath(os.path.join(workbenchPath, \"common\", \"bin\", \"IarBuild.exe\"))\n\n if not os.path.isfile(iarBuildPath):\n raise RuntimeError(\"IarBuild.exe does not exist at: {}\".format(iarBuildPath))\n\n return iarBuildPath\n\nclass ARMGCCProjectBuilder(BuildProjectsBase):\n ## Constructor.\n def __init__(self, repodir):\n BuildProjectsBase.__init__(self, repodir)\n\n ## Build project using arm-none-eabi-gcc\n def build(self, projectPath, target):\n status = \"Success\"\n brieflog = \"\"\n detaillog = \"\"\n\n ## Convert the project path to the execution files\n if os.name == 'nt':\n projectPath = projectPath.replace('CMakeLists.txt','build_%s.bat' % target)\n else:\n projectPath = projectPath.replace('CMakeLists.txt','build_%s.sh' % target)\n\n # In case the project do not exsit, just place it in the error list.\n projectPath = os.path.normpath(projectPath)\n if os.path.isfile(projectPath) == False:\n status = \"Not Packed\"\n logging.info(\"RF_build_status:\" + status)\n return status\n\n ## ARM gcc build script must excute in its directory\n cwd = os.getcwd()\n os.chdir(os.path.split(projectPath)[0])\n\n # Remove the 'pause' command to enable the autobuild.\n with open(projectPath) as f:\n file_data = f.readlines()\n\n with open(projectPath, 'wb') as f:\n for line_data in file_data:\n if 'pause' in line_data:\n continue\n if '-j4' in line_data:\n job_option = '-j%s' %(multiprocessing.cpu_count())\n if os.name == 'nt':\n line_data = 'mingw32-make ' + job_option\n else:\n line_data = 'make ' + job_option\n f.write(line_data)\n\n # 'nt' for windows, else for linux\n if os.name != \"nt\":\n misc.execute_silent(\"dos2unix %s\" % projectPath)\n cmd = 'bash %s nopause' % projectPath\n else:\n cmd = '%s nopause' % projectPath\n cmd = cmd.replace(\"\\\\\", \"/\")\n\n _ret, _out, _err = misc.execute_silent(cmd)\n log_file = os.path.join(os.path.dirname(projectPath), '%s_log.txt' % target)\n misc.LogFile(log_file, _out + _err)\n # logging.info(\"RF_build_log:smbserver%s\" % os.path.relpath(log_file, self.repositoryRoot))\n logging.info(\"RF_build_log:%s\" % os.path.abspath(log_file))\n detaillog = _out + _err\n\n if _ret:\n ## It means build failure\n status = \"Failure\"\n brieflog = self.log_filter(detaillog, 'rror:')\n # logging.error(\"RF_build_log_brief:\" + brieflog)\n elif \"warning\" in (_out + _err):\n ## It means build warning\n status = \"Warning\"\n brieflog = self.log_filter(detaillog, 'warning:')\n # logging.error(\"RF_build_log_brief:\" + brieflog)\n\n self.search_images(os.path.join(os.path.dirname(projectPath), target))\n\n os.chdir(cwd)\n\n logging.info(\"RF_build_status:\" + status)\n return status\n\n ## Find armgcc path in system\n def _search_toolpath(self):\n try:\n workbenchPath = os.environ['ARMGCC_DIR']\n except KeyError:\n raise RuntimeError(\"ARMGCC_DIR environment variable is not set.\")\n\n return workbenchPath\n\n # def __remove_redundant_shell_path(self, batch_file):\n # # workaround for the interference of other paths include 'sh.exe':\n # # http://stackoverflow.com/questions/13047693/cmake-problems-in-windows\n # sh_paths = subprocess.Popen('where sh', stdout = subprocess.PIPE).communicate()[0].replace('\\r', '').split('\\n')\n\n # batch_cmds = []\n # batch_cmds.append(\"@echo off\\n\")\n # for p in sh_paths:\n # if len(p) > 1:\n # batch_cmds.append(\"set Path=%%Path:%s=%%\\n\" % os.path.dirname(p))\n # batch_cmds.append(\"@echo on\\n\")\n\n # with open(batch_file, 'r') as f:\n # for line in f.readlines():\n # batch_cmds.append(line)\n # with open(batch_file, 'w') as f:\n # for mem in batch_cmds:\n # f.write(mem)\n\ndef get_builder(toolchain):\n for name, obj in inspect.getmembers(sys.modules[__name__], inspect.isclass):\n if toolchain in name:\n logging.info(\"Class %s meet the requirement\" % name)\n return obj\n\n raise RuntimeError(\"Builder %s is not supported\" % toolchain)\n\ndef _read_options():\n # Build arg parser.\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=\"Script to create the SDK packages\")\n # Options\n parser.add_argument(\"-d\", \"--debug\", action=\"store_true\", default=False, help=\"Whether to enable the debug information\")\n parser.add_argument(\"-i\", \"--input\", action=\"store\", metavar='', help=\"Input directory for the SDK repository\")\n parser.add_argument(\"-p\", \"--projectfile\", action=\"store\", metavar='', help=\"project file\")\n parser.add_argument(\"-t\", \"--toolchain\", action=\"store\", default=\"\", help=\"Toolchain to be built\")\n parser.add_argument(\"-a\", \"--target\", action=\"store\", default=\"Debug\", help=\"target for the armgcc toolchain\")\n\n return parser.parse_args()\n\nif __name__ == \"__main__\":\n builder = None\n args = _read_options()\n misc.logging_config(logging.DEBUG if args.debug else logging.INFO, \"builder.log\")\n\n sdkdir = os.path.abspath(args.input)\n prj = os.path.abspath(args.projectfile)\n toolchain = args.toolchain.upper()\n target = args.target\n\n builder_type = get_builder(toolchain)\n status, shortlog, detaillog = builder_type(sdkdir).build(prj, target)\n # pass\n # for name, obj in inspect.getmembers(sys.modules[__name__], inspect.isclass):\n # logging.info(name)\n # if toolchain in name:\n # builder = obj(sdkdir)\n # break\n\n # status, shortlog, detaillog = builder.build(prj, target)\n\n","sub_path":"mcuxsdk_ci/src/builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":18968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"611126402","text":"# this scripts is used to construct the impurities density profiles according to the D and V of each radius \n#ptgyro=root['SETTINGS']['PHYSICS']['ptgyro']\nptgyro=len(root['INPUTS']['TGYRO']['input.tgyro']['DIR'].keys())\nn_pvt=root['SETTINGS']['PHYSICS']['n_pvt']\na=root['INPUTS']['TGYRO']['input.profiles']['rmin'][-1] # minor radius\nfrac=root['SETTINGS']['PHYSICS']['FracImp']\nradius=zeros(ptgyro)\nDarr=zeros(ptgyro)\nVarr=zeros(ptgyro)\nroot['SETTINGS']['PLOTS']['iplotlincheck']=0\nfor k in range(1,ptgyro+1):\n root['SETTINGS']['PLOTS']['indradius']=k\n root['PLOTS']['lincheck_neo.py'].run()\n radius[k-1]=root['OUTPUTS']['DV'][k]['radius']\n Darr[k-1]=root['OUTPUTS']['DV'][k]['D_neo']\n Varr[k-1]=root['OUTPUTS']['DV'][k]['V_neo']\n# let's construct the profiles\nVdD=Varr/Darr\nVdDmid=(VdD[0:ptgyro-1]+VdD[1:ptgyro])/2\nDr=zeros(ptgyro-1)\nExpFct=zeros(ptgyro+1)\nn_imp=zeros(ptgyro)\nfor k in range(0,ptgyro-1):\n m=ptgyro-1-k\n Dr[m-1]=radius[m-1]-radius[m]\n# ExpFct[m-1]=VdDmid[m-1]*Dr[m-1]+ExpFct[m]\n ExpFct[m]=VdDmid[m-1]*Dr[m-1]+ExpFct[m+1]\nExpFct[0]=VdDmid[0]/2.*radius[0]+ExpFct[1]\n#ExpFct=ExpFct/a\nn_imp=exp(ExpFct)\n# then experimental D and V value should be calculated\\\nif root['SETTINGS']['SETUP']['mthdreaddata']==1:\n gamma_gb=root['OUTPUTS']['TGYRO']['out.tgyro.gyrobohm']['data']['10^19/m^2/s']\nelse:\n gamma_gb=root['OUTPUTS']['TGYRO']['out.tgyro.gyrobohm']['data']['Gamma_GB']\n gamma_gb=[float(item) for item in gamma_gb[1:]]\n#print(Darr)\nD_exp=Darr*gamma_gb[1:ptgyro+1]*a/(frac*n_imp[1:])\n#print(D_exp)\nV_exp=Varr*gamma_gb[1:ptgyro+1]/(frac*n_imp[1:])\n# visuilization\n\nlw=2\nfs1=20\nfs2=16\n#plt.close()\nfigure('profile of impurities,resonctructed based on neoclassical transport')\nsubplot(2,2,1)\nplot(radius,D_exp,'-bo',linewidth=lw)\nxlabel('r/a',fontsize=fs1)\nylabel('D($m^2s^{-1}$)',fontsize=fs1)\nxticks(fontsize=fs2)\nyticks(fontsize=fs2)\nsubplot(2,2,3)\nplot(radius,V_exp,'-bo',linewidth=lw)\nxlabel('r/a',fontsize=fs1)\nylabel('V($ms^{-1}$)',fontsize=fs1)\nxticks(fontsize=fs2)\nyticks(fontsize=fs2)\nsubplot(2,2,2)\nplot(radius,Varr/Darr/a,'-ro',linewidth=lw)\nxlabel('r/a',fontsize=fs1)\nylabel('Grad(n)/n',fontsize=fs1)\nxticks(fontsize=fs2)\nyticks(fontsize=fs2)\nsubplot(2,2,4)\nplot(radius,n_imp[1:],'-ro',linewidth=lw)\nxlabel('radius',fontsize=fs1)\nylabel('n_imp(a.u)',fontsize=fs1)\nxticks(fontsize=fs2)\nyticks(fontsize=fs2)\n","sub_path":"ForW/PLOTS/reconstructProfile_neo.py","file_name":"reconstructProfile_neo.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"275609154","text":"from docx import Document\r\nfrom docx2pdf import convert\r\nimport os\r\n\r\ndocument = Document(\"template.docx\")\r\n\r\ntemplate = \"template.docx\"\r\n\r\n\r\ndef getInput():\r\n bs = input(\"Business name: \")\r\n position = input(\"Position: \")\r\n return bs, position\r\n\r\ndef op(bs,position):\r\n for sections in document.paragraphs:\r\n if \"%COMPANY%\" in sections.text:\r\n # replace company with the company name\r\n sections.text = sections.text.replace(\"%COMPANY%\", bs)\r\n if \"%POSITION%\" in sections.text:\r\n #replace position with the position name\r\n sections.text = sections.text.replace(\"%POSITION%\", position)\r\n document.paragraphs.append(sections.text)\r\n\r\n fileName = \"generated//\"+bs +\" - \"+position +\".docx\"\r\n document.save(fileName)\r\n return fileName\r\n\r\ndef con(fileName):\r\n FN = fileName.replace(\".docx\",\".pdf\")\r\n convert(fileName, FN)\r\n os.remove(fileName)\r\n\r\n\r\n\r\nbs,pos = getInput()\r\nfn = op(bs,pos)\r\ncon(fn)\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"551497225","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport cv2\nimport sys\nimport glob\nimport os\nimport json\nfrom extract_features import HogFeatureExtractor\n\n\ndef find_samples(subdir):\n return sorted(\n glob.glob(os.sep.join([subdir, '**', '*.png']), recursive=True) +\n glob.glob(os.sep.join([subdir, '**', '*.jpg']), recursive=True))\n\n\ndef usage():\n print(\"USAGE: {} \".format(\n sys.argv[0]))\n\nif __name__ == \"__main__\":\n if len(sys.argv) <= 3:\n usage()\n sys.exit(-1)\n with open(sys.argv[1]) as f:\n env = json.load(f)\n image_dir = sys.argv[2]\n hog = HogFeatureExtractor(\n cspace=env['colorspace'],\n orient=env['orient'],\n pix_per_cell=env['pix_per_cell'],\n cell_per_block=env['cell_per_block'],\n hog_channel=env['hog_channel'])\n samples = find_samples(image_dir)\n print(\"Found {} samples\".format(len(samples)))\n for infname in samples:\n outfname = \"{}.npy\".format(\n os.path.sep.join([\n sys.argv[3],\n infname[(len(image_dir)+1):].replace(os.path.sep, '__')]))\n print(\"hog {} {} ({})\".format(infname, outfname, json.dumps(env)))\n img = cv2.imread(infname)\n features = hog.preprocess(img)\n np.save(outfname, features)\n print(\"OK\")\n","sub_path":"m1_extract_hog.py","file_name":"m1_extract_hog.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"515621783","text":"# Copyright (c) 2017, Xilinx, Inc.\n# All rights reserved.\n# \n# Redistribution and use in source and binary forms, with or without \n# modification, are permitted provided that the following conditions are met:\n#\n# 1. Redistributions of source code must retain the above copyright notice, \n# this list of conditions and the following disclaimer.\n#\n# 2. Redistributions in binary form must reproduce the above copyright \n# notice, this list of conditions and the following disclaimer in the \n# documentation and/or other materials provided with the distribution.\n#\n# 3. Neither the name of the copyright holder nor the names of its \n# contributors may be used to endorse or promote products derived from \n# this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, \n# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR \n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR \n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, \n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, \n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\n# OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, \n# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR \n# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF \n# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\nfrom setuptools import setup, find_packages\nfrom distutils.dir_util import copy_tree, remove_tree\nfrom distutils.file_util import copy_file\nimport subprocess\nimport sys\nimport os\nimport glob\nfrom datetime import datetime\n\n\n__author__ = \"Yun Rock Qu\"\n__copyright__ = \"Copyright 2017, Xilinx\"\n__email__ = \"yunq@xilinx.com\"\n\n\nGIT_DIR = os.path.dirname(os.path.realpath(__file__))\n\n\n# Board specific package delivery setup\ndef exclude_from_files(exclude, path):\n return [file for file in os.listdir(path)\n if os.path.isfile(os.path.join(path, file))\n and file != exclude]\n\n\ndef find_overlays(path):\n return [f for f in os.listdir(path)\n if os.path.isdir(os.path.join(path, f))\n and len(glob.glob(os.path.join(path, f, \"*.bit\"))) > 0]\n\n\ndef collect_pynq_overlays():\n overlay_files = []\n overlay_dirs = find_overlays(board_folder)\n for ol in overlay_dirs:\n copy_tree(os.path.join(board_folder, ol),\n os.path.join(\"pynq_networking/overlays\", ol))\n newdir = os.path.join(\"pynq_networking/overlays\", ol)\n files = exclude_from_files('makefile', newdir)\n overlay_files.extend(\n [os.path.join(\"..\", newdir, f) for f in files])\n return overlay_files\n\n\npynq_package_files = []\nif 'BOARD' not in os.environ:\n print(\"Please set the BOARD environment variable \"\n \"to get any BOARD specific overlays (e.g. Pynq-Z1).\")\n board = None\n board_folder = None\nelse:\n board = os.environ['BOARD']\n board_folder = 'boards/{}'.format(board)\n pynq_package_files.extend(collect_pynq_overlays())\n\n\ndefault_nb_dir = '/home/xilinx/jupyter_notebooks'\nif 'PYNQ_JUPYTER_NOTEBOOKS' in os.environ:\n notebooks_dir = os.environ['PYNQ_JUPYTER_NOTEBOOKS']\nelif os.path.exists(default_nb_dir):\n notebooks_dir = default_nb_dir\nelse:\n notebooks_dir = None\n\n\n# Update interfaces\ndef update_interfaces():\n eth0_file = '/etc/network/interfaces.d/eth0'\n backup_file = '/etc/network/interfaces.d/.{}'.format(\n datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S\"))\n copy_file(eth0_file, backup_file)\n copy_file(GIT_DIR + '/interfaces.d/eth0', eth0_file)\n print(\"Update interface files done ...\")\n\n\n# Build submodules\ndef build_submodules():\n subprocess.check_call(['git', 'submodule', 'init'])\n subprocess.check_call(['git', 'submodule', 'update'])\n copy_tree(GIT_DIR + '/mqtt-sn-tools',\n GIT_DIR + '/pynq_networking/mqtt-sn-tools')\n copy_tree(GIT_DIR + '/rsmb',\n GIT_DIR + '/pynq_networking/rsmb')\n print(\"Update submodules done ...\")\n\n\n# Notebook delivery\ndef copy_overlay_notebooks():\n if notebooks_dir is None or board_folder is None:\n return None\n\n if os.path.isdir(board_folder):\n overlay_notebook_folders = [\n (os.path.join(notebooks_dir, overlay),\n os.path.join(board_folder, overlay, 'notebooks/'))\n for overlay in list(os.listdir(board_folder))\n if os.path.isdir(os.path.join(board_folder, overlay, 'notebooks'))]\n\n for dst_folder, src_folder in overlay_notebook_folders:\n if os.path.exists(dst_folder):\n remove_tree(dst_folder)\n copy_tree(src_folder, dst_folder)\n print(\"Filling notebooks done ...\")\n\n\n# Run makefiles\ndef run_make(src_path, output_lib):\n status = subprocess.check_call([\"make\", \"-C\", GIT_DIR + '/' + src_path])\n if status is not 0:\n print(\"Error while running make for\", output_lib, \"Exiting..\")\n sys.exit(1)\n\n print(\"Running make for \" + output_lib + \" done ...\")\n\n\n# Bring br0 up online\ndef if_up_br0():\n subprocess.check_call(['ifup', 'br0'])\n subprocess.check_call(['service', 'networking', 'restart'])\n\n print(\"Bringing up br0 done ...\")\n\n\nupdate_interfaces()\nbuild_submodules()\ncopy_overlay_notebooks()\nrun_make(\"pynq_networking/rsmb/rsmb/src/\", \"broker_mqtts\")\nif_up_br0()\n\n\ndef package_files(directory):\n paths = []\n for (path, directories, file_names) in os.walk(directory):\n for file_name in file_names:\n paths.append(os.path.join('..', path, file_name))\n return paths\n\n\npynq_package_files.extend(package_files('pynq_networking'))\nsetup(name='pynq_networking',\n version='2.4',\n description='PYNQ networking package',\n author='Xilinx networking group',\n author_email='stephenn@xilinx.com',\n url='https://github.com/Xilinx/PYNQ-Networking',\n packages=find_packages(),\n download_url='https://github.com/Xilinx/PYNQ-Networking',\n package_data={\n '': pynq_package_files,\n },\n install_requires=[\n 'kamene',\n 'wurlitzer',\n 'pytest-runner',\n 'paho-mqtt',\n 'netifaces',\n 'pynq>=2.4'\n ]\n )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":6394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"467063901","text":"import random\r\nimport time\r\nimport os\r\nimport math\r\n\r\ndebug = False\r\n\r\nclass Agent:\r\n vaccine_effectiveness = 0.93\r\n non_vaccine_immunity = 0.3\r\n vaccination_rate = 0.1\r\n recovered_effectiveness = 0.99\r\n vaccine_recovery_multiplier = 5\r\n recovery_chance = 0.02\r\n death_chance = 0.01\r\n\r\n def __init__(self, x, y):\r\n self.infected = False\r\n self.days_infected = 0\r\n r = random.random()\r\n if r <= self.vaccination_rate:\r\n self.vaccinated = True\r\n else:\r\n self.vaccinated = False\r\n self.coords = \"({}, {})\".format(x, y)\r\n self.dead = False\r\n self.recovered = False\r\n\r\n def get_name(self):\r\n return(self.coords)\r\n\r\n def infect(self, random_chance = True):\r\n if self.dead:\r\n return\r\n\r\n if random_chance:\r\n r = random.random()\r\n if self.vaccinated and self.recovered:\r\n if r <= (1-self.vaccine_effectiveness)*(1-self.recovered_effectiveness):\r\n self.infected = True\r\n self.recovered = False\r\n if debug:\r\n print(\"Vaccine & recovery failure for {}! Infection occurs. (roll = {})\".format(self.coords, round(r, 3)))\r\n elif self.vaccinated:\r\n if r >= self.vaccine_effectiveness:\r\n self.infected = True\r\n self.recovered = False\r\n if debug:\r\n print(\"Vaccine failure for {}! Infection occurs. (roll = {})\".format(self.coords, round(r, 3)))\r\n elif self.recovered:\r\n if r >= self.recovered_effectiveness:\r\n self.infected = True\r\n self.recovered = False\r\n if debug:\r\n print(\"Recovered immunity failure for {}! Infection occurs. (roll = {})\".format(self.coords, round(r, 3)))\r\n else:\r\n if r>=self.non_vaccine_immunity:\r\n self.infected = True\r\n self.recovered = False\r\n if debug:\r\n print(\"{} is not vaccinated. Infection occurs. (roll = {})\".format(self.coords, round(r, 3)))\r\n\r\n else:\r\n self.infected = True\r\n self.vaccinated = False\r\n\r\n def is_infected(self):\r\n return(self.infected)\r\n\r\n def is_vaccinated(self):\r\n return(self.vaccinated)\r\n\r\n def is_recovered(self):\r\n return(self.recovered)\r\n\r\n def vaccinate(self):\r\n self.vaccinated = True\r\n\r\n def is_dead(self):\r\n return(self.dead)\r\n\r\n def get_days_infected(self):\r\n return(self.days_infected)\r\n\r\n def tick(self):\r\n if self.infected:\r\n self.days_infected += 1\r\n\r\n r = random.random()\r\n\r\n if self.vaccinated and r <= (self.recovery_chance * self.vaccine_recovery_multiplier)*self.days_infected:\r\n self.infected = False\r\n self.recovered = True\r\n if debug:\r\n print(\"{} (vaccinated) has recovered after {} days, and now has immunity. (roll = {})\".format(self.coords, self.days_infected, round(r, 3)))\r\n self.days_infected = 0\r\n if r <= self.recovery_chance*self.days_infected:\r\n self.infected = False\r\n self.recovered = True\r\n if debug:\r\n print(\"{} has recovered after {} days infected, and now has immunity. (roll = {})\".format(self.coords, self.days_infected, round(r, 3)))\r\n self.days_infected = 0\r\n r = random.random()\r\n if r <= self.death_chance:\r\n self.dead = True\r\n self.infected = False\r\n if debug:\r\n print(\"{} has died after {} days infected. (roll = {})\".format(self.coords, self.days_infected, round(r, 3)))\r\n\r\n def symbol(self):\r\n if self.dead:\r\n return(\"x\")\r\n if self.infected and self.vaccinated:\r\n return(\"#\")\r\n if self.vaccinated and self.recovered:\r\n return(\"!\")\r\n if self.recovered:\r\n return(\"=\")\r\n if self.infected:\r\n return(\"0\")\r\n if self.vaccinated:\r\n return(\"+\")\r\n\r\n return(\"-\")\r\n\r\n\r\n\r\nclass Grid:\r\n\r\n def __init__(self, size):\r\n self.grid = list()\r\n self.size = size\r\n for i in range(0, size):\r\n self.grid.append(list())\r\n for j in range(0, size):\r\n self.grid[i].append(Agent(i ,j))\r\n\r\n def retrieve(self, x, y):\r\n l = self.grid[x]\r\n ret = l[y]\r\n return(ret)\r\n\r\n def retrieve_neighbors(self, x, y):\r\n neighbors = []\r\n\r\n xs = [x-1, x+1]\r\n ys = [y-1, y+1]\r\n\r\n for i in xs:\r\n j = y\r\n if i >= 0 and i =0 and j < self.size:\r\n neighbor = self.retrieve(i, j)\r\n neighbors.append(neighbor)\r\n for j in ys:\r\n i = x\r\n if i >= 0 and i =0 and j < self.size:\r\n neighbor = self.retrieve(i, j)\r\n neighbors.append(neighbor)\r\n\r\n return(neighbors)\r\n\r\n def get_infected_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n infected = agent.is_infected()\r\n if infected:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n def get_recovered_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n recovered = agent.is_recovered()\r\n if recovered:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n def get_infected_and_vaccinated_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n infected = agent.is_infected()\r\n vaccinated = agent.is_vaccinated()\r\n if infected and vaccinated:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n def get_vaccinated_and_recovered_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n vaccinated = agent.is_vaccinated()\r\n recovered = agent.is_recovered()\r\n if vaccinated and recovered:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n\r\n def get_vaccinated_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n vaccinated = agent.is_vaccinated()\r\n if vaccinated:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n def get_dead_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n dead = agent.is_dead()\r\n if dead:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n def get_dead_vaccinated_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n dead = agent.is_dead()\r\n vaccinated = agent.is_vaccinated()\r\n if dead and vaccinated:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n def get_never_sick_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n infected = agent.is_infected()\r\n recovered = agent.is_recovered()\r\n dead = agent.is_dead()\r\n if not infected and not recovered and not dead:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n def get_never_sick_vaccinated_list(self):\r\n l = []\r\n for i in range(0, self.size):\r\n for j in range(0, self.size):\r\n agent = self.retrieve(i, j)\r\n infected = agent.is_infected()\r\n recovered = agent.is_recovered()\r\n vaccinated = agent.is_vaccinated()\r\n dead = agent.is_dead()\r\n if not infected and not recovered and vaccinated and not dead:\r\n l.append(agent)\r\n\r\n return(l)\r\n\r\n def tick(self, spread_probability):\r\n for x in range(0, self.size):\r\n for y in range(0, self.size):\r\n agent = self.retrieve(x, y)\r\n agent.tick()\r\n if not agent.is_infected():\r\n neighbors = self.retrieve_neighbors(x, y)\r\n for n in neighbors:\r\n infected = n.is_infected()\r\n if infected:\r\n r = random.random()\r\n #print(\"{} attempts to infect {}. (roll = {})\".format(n.get_name(), agent.get_name(), round(r, 3)))\r\n if r <= spread_probability:\r\n agent.infect()\r\n\r\n def print(self):\r\n str = \"\"\r\n for x in range(0, self.size):\r\n str += \"\\n\"\r\n for y in range(0, self.size):\r\n agent = self.retrieve(x, y)\r\n s = agent.symbol()\r\n str += \" \" + s + \" \"\r\n\r\n print(str)\r\n\r\n\r\n\r\n\r\n\r\ndef main():\r\n grid_size = 30\r\n spread_probability = 0.5\r\n initial_infected = 6\r\n\r\n\r\n\r\n grid = Grid(grid_size)\r\n\r\n for i in range(0, initial_infected):\r\n randx = random.randint(0, grid_size-1)\r\n randy = random.randint(0, grid_size-1)\r\n grid.retrieve(randx, randy).infect(random_chance = False)\r\n\r\n\r\n t = 0\r\n while True:\r\n os.system(\"cls\")\r\n grid.print()\r\n grid.tick(spread_probability)\r\n num_infected = len(grid.get_infected_list())\r\n num_vaccinated = len(grid.get_vaccinated_list())\r\n num_dead = len(grid.get_dead_list())\r\n num_recovered = len(grid.get_recovered_list())\r\n num_recovered_vaccinated = len(grid.get_vaccinated_and_recovered_list())\r\n num_vaccinated_infected = len(grid.get_infected_and_vaccinated_list())\r\n num_dead_vaccinated = len(grid.get_dead_vaccinated_list())\r\n num_never_sick = len(grid.get_never_sick_list())\r\n num_never_sick_vaccinated = len(grid.get_never_sick_vaccinated_list())\r\n print(\"\\nTime = {}\".format(t))\r\n print(\"{} individuals infected.\".format(num_infected))\r\n print(\"{} individuals vaccinated. ({} infected anyway)\".format(num_vaccinated, num_vaccinated_infected))\r\n print(\"{} individuals with recovery immunity. ({} were vaccinated)\".format(num_recovered, num_recovered_vaccinated))\r\n print(\"{} individuals have died. ({} vaccinated)\".format(num_dead, num_dead_vaccinated))\r\n print(\"{} individuals never got sick. ({} vaccinated)\".format(num_never_sick, num_never_sick_vaccinated))\r\n if num_infected == 0:\r\n print(\"No more infected inviduals. Epidemic ends.\")\r\n break\r\n time.sleep(0.5)\r\n t+=1\r\n\r\n # for x in range(0, grid_size):\r\n # for y in range(0, grid_size):\r\n # agent = grid.retrieve(x, y)\r\n # name = agent.get_name()\r\n # days = agent.get_days_infected()\r\n # vax = agent.is_vaccinated()\r\n # dead = agent.is_dead()\r\n\r\n # if days == 0:\r\n # if vax:\r\n # vax_never_sick += 1\r\n # else:\r\n # novax_never_sick +=1\r\n #\r\n # else:\r\n # if dead:\r\n # if vax:\r\n # vax_died += 1\r\n # else:\r\n # novax_died += 1\r\n # else:\r\n # if vax:\r\n # vax_got_sick += 1\r\n # else: novax_got_sick += 1\r\n\r\n # print(\"{}: {} days infected.\".format(name, days))\r\n\r\n\r\n # print(\"Vaccinated: {} never sick, {} got sick, {} \")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# class Game:\r\n# def __init__(self):\r\n# self.cells = [Cell(1, \"base\", \"cell 1\")]\r\n# self.game_state = GameState()\r\n#\r\n# def tick(self):\r\n# self.game_state.tick()\r\n# for cell in self.cells:\r\n# events = cell.tick(self.game_state)\r\n# for e in events:\r\n# e.process(self.game_state)\r\n#\r\n# def show(self):\r\n# print(\"Game state:\")\r\n# print(str(self.game_state))\r\n# print(\"\\n\")\r\n#\r\n# for cell in self.cells:\r\n# print(str(cell))\r\n#\r\n# class GameState:\r\n#\r\n# def __init__(self, cheat = False):\r\n# if cheat:\r\n# self.atp_counter = 1000\r\n# else:\r\n# self.atp_counter = 10\r\n#\r\n# self.time = 0\r\n#\r\n# def __str__(self):\r\n# s = \"ATP: {}\\ntime: {} ticks\".format(self.atp_counter, self.time)\r\n# return(s)\r\n#\r\n# def ATP(self, number):\r\n# if number >= 0: #gaining ATP\r\n# self.atp_counter += number\r\n# return(True)\r\n# else: #spending ATP\r\n# if self.atp_counter + number < 0:\r\n# return(False)\r\n# else:\r\n# self.atp_counter += number\r\n# return (True)\r\n#\r\n# def tick(self):\r\n# self.time += 1\r\n#\r\n# class Event:\r\n# def __init__(self, target, originator, data):\r\n# self.target = target\r\n# self.originator = originator\r\n# self.data = data\r\n#\r\n# def process(self, game_state):\r\n# print(\"no class specific process method defined for this event\")\r\n#\r\n# class AllsGoodEvent(Event):\r\n# def __init__(self, target, originator, data):\r\n# self.name = \"AllsGood\"\r\n# self.target = target\r\n# self.originator = originator\r\n# self.data = data\r\n#\r\n# def process(self, game_state):\r\n# print(\"All's good! from {}\".format(self.originator.getName())) #nothing needs to happen\r\n#\r\n# class MembraneRuptureEvent(Event):\r\n# def __init__(self, target, originator, data):\r\n# self.name = \"MembraneRupture\"\r\n# self.target = target\r\n# self.originator = originator\r\n# self.data = data\r\n#\r\n# def process(self, game_state):\r\n# print(\"Membrane rupture event for cell '{}'\".format(self.target.getName()))\r\n#\r\n#\r\n# class Cell:\r\n# def __init__(self, size, type, name):\r\n# self.size = size\r\n# self.type = type\r\n# self.name = name\r\n#\r\n# self.membrane = Membrane(self)\r\n# self.organelles = [Mitochondria(\"mitochondria 1\", self)]\r\n# self.grow_cost = 25\r\n# self.split_cost = 100\r\n#\r\n# def getName(self):\r\n# return(self.name)\r\n#\r\n# def getMembrane(self):\r\n# return(self.membrane)\r\n#\r\n# def __str__(self):\r\n# s = \"{}, size {}, type {}\\n\\tmembrane: {}\\n\".format(self.name, self.size, self.type, str(self.membrane))\r\n# for o in self.organelles:\r\n# s += \"\\t{}\\n\".format(str(o))\r\n# return(s)\r\n#\r\n#\r\n# def tick(self, game_state):\r\n# events = []\r\n# membrane_event = self.membrane.tick(game_state)\r\n# events.append(membrane_event)\r\n# for o in self.organelles:\r\n# e = o.tick(game_state)\r\n# events.append(e)\r\n#\r\n# return(events)\r\n#\r\n# # def grow(self, game_state):\r\n# # atp = game_state[\"ATP\"]\r\n# # atp -= self.grow_cost\r\n# # game_state[\"ATP\"] = atp\r\n# # self.size += 1\r\n#\r\n# class Organelle:\r\n# def __init__(self):\r\n# pass\r\n#\r\n# def tick(self, game_state):\r\n# print(\"no class specific tick method defined\")\r\n#\r\n# def grow(self, game_state):\r\n# print(\"no class specific grow method defined\")\r\n#\r\n# class Mitochondria(Organelle):\r\n#\r\n# def __init__(self, name, parent):\r\n# self.size = 1\r\n# self.production_rate = 2\r\n# self.grow_cost = 100\r\n# self.name = name\r\n# self.parent = parent\r\n#\r\n# def __str__(self):\r\n# return(\"{}, size {}, production rate per tick {}\".format(self.name, self.size, self.size * self.production_rate))\r\n#\r\n# def getName(self):\r\n# return(self.name)\r\n#\r\n# def tick(self, game_state):\r\n# new_atp = self.size * self.production_rate\r\n# game_state.ATP(new_atp)\r\n# event = AllsGoodEvent(self, self, {})\r\n# return(event)\r\n#\r\n# # def grow(self, game_state):\r\n# # atp = game_state[\"ATP\"]\r\n# # atp -= self.grow_cost\r\n# # game_state[\"ATP\"] = atp\r\n# # self.size += 1\r\n#\r\n# class Membrane():\r\n#\r\n# def __init__(self, parent):\r\n# self.max_strength = 100\r\n# self.degradation_rate = 50\r\n# self.strength = self.max_strength\r\n# self.type = \"default\"\r\n# self.parent = parent\r\n# self.name = \"membrane 1\"\r\n#\r\n# def __str__(self):\r\n# return(\"{} / {}, type: {}\".format(self.strength, self.max_strength, self.type))\r\n#\r\n# def getName(self):\r\n# return(self.name)\r\n#\r\n#\r\n# def degrade(self):\r\n# self.strength -= self.degradation_rate\r\n# if self.strength <= 0:\r\n# event = MembraneRuptureEvent(self.parent, self, {})\r\n# else:\r\n# event = AllsGoodEvent(self, self, {})\r\n# return(event)\r\n#\r\n# def repair(self, game_state):\r\n# self.strength = self.max_strength\r\n#\r\n# def tick(self, game_state):\r\n# event = self.degrade()\r\n# return(event)\r\n#\r\n# def main():\r\n# game = Game()\r\n#\r\n# while True:\r\n# game.tick()\r\n# response = input(\"Action?\\t\")\r\n# if response == \"0\":\r\n# break\r\n# elif response == \"s\":\r\n# game.show()\r\n\r\n\r\nmain()\r\n","sub_path":"ti_api_backend/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":18176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"65251773","text":"#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n# @FileName :http_request.py\r\n# @Time :2020/6/7 21:29\r\n# @Author :麻花\r\n\r\nimport requests\r\nfrom tools.my_log import MyLog\r\n\r\nmy_logger=MyLog()\r\nclass HttpRequest:\r\n\r\n @staticmethod\r\n def http_request(url,data,http_method,cookie=None):\r\n try:\r\n if http_method.upper() == \"GET\":\r\n res = requests.get(url,data,cookies=cookie)\r\n elif http_method.upper() == \"POST\":\r\n res = requests.post(url,data,cookies=cookie)\r\n else:\r\n my_logger.info(\"输入的请求方法不对\")\r\n except Exception as e:\r\n my_logger.error(\"请求报错了\",e)\r\n raise e\r\n return res\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n # login_url = 'http://14.23.47.110:9203/centralized/login/login'\r\n # login_data = {\"relationNo\":\"2001030169\",\"userPassword\":\"1bbd886460827015e5d605ed44252251\"}\r\n #\r\n # queryTeacherInfo_url = 'http://14.23.47.110:9203/assurance/public/queryTeacherInfo'\r\n # queryTeacherInfo_data = {\"teacherNo\":\"2001030169\",\"token\":\"9f12fdef2c984c21a05f11d842fd7188\"}\r\n\r\n # login_url = 'http://14.23.47.109:8180/ybt/auth/login'\r\n # login_data = {\"username\": \"Z8880001\", \"password\": \"123\", \"rememberMe\": \"0\"}\r\n #\r\n # formlist_url = 'http://14.23.47.109:8180/ybt/form/list?pageNum=1&pageSize=10'\r\n # formlist_data = {\"formName\":\"\",\"taskStatus\":\"\",\"status\":\"\",\"formFolderId\":\"\"}\r\n\r\n login_url = 'http://175.6.27.63:19394/centralized/login/login'\r\n login_data = {\"relationNo\":\"102030\",\"userPassword\":\"e10adc3949ba59abbe56e057f20f883e\"}\r\n\r\n queryTeacherInfo_url = 'http://175.6.27.63:19394/assurance/personalPlanInfo/updatePmPersonalPlanInfo'\r\n queryTeacherInfo_data = {\"planName\":\"test\",\"planLayer\":\"GRFZGH\",\"relationPlanNo\":\"\",\r\n \"startDate\":\"2020-06-24\",\"endDate\":\"2020-06-24\",\"planDesc\":\"\",\"createrNo\":\"102030\",\r\n \"createrName\":\"钟慧林\",\"planNo\":\"P202006240953201753158716\",\r\n \"token\":\"6adc0dbbbf394a0d8c01eb7a62909bde\"}\r\n\r\n# res=requests.post(createSysUser_url,createSysUser_data)\r\n\r\n# createSysUser_url = 'http://14.23.47.109:8180/ybt/system/setting/user/createSysUser'\r\n# createSysUser_data = {\"name\":\"张三\",\"userCode\":\"Z0000003\",\"userName\":\"Z0000003\",\r\n# \"departId\":\"b7744acfcbd511e98152005056b227b6\",\"departName\":\"教学评估办公室\"}\r\n# # res=requests.post(createSysUser_url,createSysUser_data)\r\n# print(res.json())\r\n# header = {\"Content-Type\": \"application/json\",\"User-Agent\": \"Mozilla/5.0 \"}\r\n# header = {\"Content-Type\": \"application/json\"}\r\n login_res = HttpRequest().http_request(login_url,login_data,\"post\")\r\n queryTeacherInfo_res= HttpRequest().http_request(queryTeacherInfo_url,queryTeacherInfo_data,\"post\",cookie=login_res.cookies)\r\n # formlist1_res= requests.post(formlist_url,formlist_data,cookies=login_res.cookies)\r\n\r\n print(\"表单列表有：{0}\".format(login_res.json()))\r\n print(\"表单列表有：{0}\".format(queryTeacherInfo_res.json()))\r\n # print(\"表单列表有：{0}\".format(formlist1_res.json()))\r\n\r\n\r\n","sub_path":"http_request.py","file_name":"http_request.py","file_ext":"py","file_size_in_byte":3154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"605047569","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 15 16:50:12 2020\n@author: 彭康\n\"\"\"\n\n#模型搭建\n#step1：1-3 Conv1D -> 1BN -> 1-3 bi_gru -> 1BN -> 1dense\nimport tensorflow as tf\nfrom utils import get_config\n\n\n#子类化构建DS2模型\nclass DS2(tf.keras.Model):\n #dense_units=num_classes\n def __init__(\n self,\n n_mfcc,\n conv_layers,\n filters,\n kernel_size,\n strides,\n bi_gru_layers,\n gru_units,\n dense_units\n ):\n super(DS2,self).__init__()\n self.conv_layers=conv_layers\n self.conv = tf.keras.layers.Conv1D(\n filters=filters,\n kernel_size=kernel_size,\n strides=strides,\n padding=\"valid\",\n activation=\"relu\",\n input_shape=(None,None,n_mfcc)\n )\n self.bi_gru_layers=bi_gru_layers\n self.bi_gru = tf.keras.layers.Bidirectional(\n tf.keras.layers.GRU(\n gru_units,\n activation=\"relu\",\n return_sequences=True\n ),\n merge_mode=\"sum\"\n )\n self.bn = tf.keras.layers.BatchNormalization(\n axis=-1,\n momentum=0.99,\n epsilon=0.001\n )\n self.ds = tf.keras.layers.Dense(dense_units,activation=\"softmax\")\n \n def call(self,inputs):\n x=inputs\n for _ in range(self.conv_layers):\n x = self.conv(x)\n x = self.bn(x)\n for _ in range(self.bi_gru_layers):\n x = self.bi_gru(x)\n x = self.bn(x)\n x = self.ds(x)\n return x\n\ndef get_ds2_model():\n configs = get_config()\n n_mfcc = configs[\"other\"][\"n_mfcc\"]\n conv_layers = configs[\"model\"][\"conv_layers\"]\n filters = configs[\"model\"][\"conv_filters\"]\n kernel_size = configs[\"model\"][\"conv_kernel_size\"]\n strides = configs[\"model\"][\"conv_strides\"]\n bi_gru_layers = configs[\"model\"][\"bi_gru_layers\"]\n gru_units = configs[\"model\"][\"gru_units\"]\n dense_units = configs[\"model\"][\"dense_units\"]\n return DS2(n_mfcc,conv_layers,filters,kernel_size,strides,bi_gru_layers,gru_units,dense_units)\n\n#基于模型预测得到的序列list并通过字典集来进行解码处理\ndef decode_output(seq, index_word):\n configs = get_config()\n mode = configs[\"preprocess\"][\"text_process_mode\"]\n if mode == \"cn\":\n return decode_output_ch_sentence(seq, index_word)\n elif mode == \"en_word\":\n return decode_output_en_sentence_word(seq, index_word)\n else:\n return decode_output_en_sentence_char(seq, index_word)\n\ndef decode_output_ch_sentence(seq, index_word):\n result = \"\"\n for i in seq:\n if i >= 1 and i <= len(index_word):\n word = index_word[str(i)]\n if word != \"\":\n if word != \"\":\n result += word\n else:\n return result\n return result\n\ndef decode_output_en_sentence_word(seq, index_word):\n result = \"\"\n for i in seq:\n if i >= 1 and i <= (len(index_word)):\n word = index_word[str(i)]\n if word != \"\":\n if word != \"\":\n result += word+\" \"\n else:\n return result\n return result\n\ndef decode_output_en_sentence_char(seq, index_word):\n result = \"\"\n for i in seq:\n if i >= 1 and i <= (len(index_word)):\n word = index_word[str(i)]\n if word != \"\":\n if word != \"\":\n if word !=\"\":\n result += word\n else:\n word += \" \"\n else:\n return result\n return result\n\n\nif __name__ == \"__main__\":\n pass\n","sub_path":"hlp/stt/ds2/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"222487415","text":"import requests\r\nimport operator\r\nfrom PIL import Image\r\n\r\n\r\nclass Face():\r\n def __init__(self, data_face):\r\n subscription_key = \"dbd006b17c8746afabda6ddd9be4f494\"\r\n assert subscription_key\r\n self.face_api_url = 'https://westcentralus.api.cognitive.microsoft.com/face/v1.0/detect'\r\n self.headers = {'Ocp-Apim-Subscription-Key': subscription_key, \"Content-Type\": \"application/octet-stream\"}\r\n\r\n self.params = {\r\n 'returnFaceId': 'false',\r\n 'returnFaceLandmarks': 'false',\r\n 'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,'\r\n 'emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\r\n }\r\n f = open(\"image.jpg\", \"wb\")\r\n f.write(data_face)\r\n f.close()\r\n img = Image.open(\"image.jpg\")\r\n img2 = img.rotate(90) # rotate\r\n img2.save(\"img2.jpg\")\r\n file_data = open(\"img2.jpg\", \"rb\")\r\n face_data = file_data.read()\r\n file_data.close()\r\n self.data_face = face_data\r\n\r\n def recognition(self):\r\n response = requests.post(self.face_api_url, params=self.params, headers=self.headers, data=self.data_face)\r\n faces = response.json()\r\n if len(faces) > 1:\r\n return \"False: there is more than one face\"\r\n else:\r\n t_face = faces[0][\"faceAttributes\"][\"emotion\"]\r\n sorted_face = sorted(t_face.items(), key=operator.itemgetter(1), reverse=True)\r\n return sorted_face[0][0]","sub_path":"faceit server/face_recognition.py","file_name":"face_recognition.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"456284175","text":"import random\nimport pygame\n\ntry:\n import card as card_mod\n\nexcept ImportError as err_msg:\n print(\"Import Error: \" + str(err_msg) + \".\")\n print(\"Last Card will now exit.\")\n exit()\n\n\nclass Deck:\n '''Creates a deck of cards appropriate to the card game.'''\n suits = [\"Hearts\", \"Diamonds\", \"Spades\", \"Clubs\"]\n min_value = 1\n max_value = 13\n\n def __init__(self, empty_deck=False, num_jokers=0):\n self.image = pygame.image.load(\"cards/unflippedcard.png\")\n self.rect = self.image.get_rect()\n self.x_coord = 0\n self.y_coord = 0\n self.cards = []\n if not empty_deck:\n self.cards = ([card_mod.Card(value, suit)\n for value in range(Deck.min_value, Deck.max_value + 1)\n for suit in Deck.suits])\n\n for i in range(num_jokers):\n card = card_mod.Card(0, \"Joker\")\n self.cards.append(card)\n\n def shuffle(self):\n '''Shuffles the deck.'''\n random.shuffle(self.cards)\n\n def deal(self, players, cards_per_player):\n '''Deals a card clockwise to each player.'''\n for num in range(0, cards_per_player):\n for player in players:\n try:\n player.hand.append(self.cards.pop())\n except IndexError:\n break # Deck depleted - some players have more cards\n","sub_path":"deck.py","file_name":"deck.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"154226414","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Sep 9 13:23:03 2018\r\n\r\n@author: yliu2\r\n\r\nThese codes are from Raschka - Chapter 3.\r\n\"\"\"\r\n# coding: utf-8\r\n\r\n\r\nfrom sklearn import datasets\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.linear_model import Perceptron\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.model_selection import cross_val_score\r\n\r\n\r\n\r\niris = datasets.load_iris()\r\nX = iris.data\r\ny = iris.target\r\n\r\ntrain_acc = []\r\ntest_acc = []\r\n\r\n\r\nfor r in range(1,11):\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=r, stratify=y)\r\n tree = DecisionTreeClassifier(criterion='gini', max_depth=4, random_state=r)\r\n tree.fit(X_train, y_train)\r\n y_train_pred = tree.predict(X_train)\r\n y_test_pred = tree.predict(X_test)\r\n train_acc.append(accuracy_score(y_train,y_train_pred))\r\n test_acc.append(accuracy_score(y_test,y_test_pred))\r\n acc_in = accuracy_score(y_train,y_train_pred)\r\n acc_out = accuracy_score(y_test,y_test_pred)\r\n print('\\n when random state is', r, 'in-sample accuracy is', acc_in, 'out-of-sample accuracy is', acc_out)\r\n \r\nprint('in-sample mean is', np.mean(train_acc))\r\nprint('in-sample standard dev is', np.std(train_acc))\r\nprint('out-of-sample mean is', np.mean(test_acc))\r\nprint('out-of-sample standard dev is', np.std(test_acc))\r\n\r\n\r\n\r\ntree = DecisionTreeClassifier(criterion='gini', max_depth=4, random_state=1)\r\ncv_acc = cross_val_score(estimator = tree , X = X_train, y = y_train, cv=10)\r\nprint('cross validation scores are', cv_acc)\r\nprint('cv mean is', np.mean(cv_acc))\r\nprint('cv standard deviation is' ,np.std(cv_acc))\r\n\r\n\r\nprint(\"My name is Yi Liu\")\r\nprint(\"My NetID is yiliu16\")\r\nprint(\"I hereby certify that I have read the University policy on Academic Integrity and that I am not in violation.\")\r\n","sub_path":"IE598_F18_HW6/Liu,Yi_IE598HW6.py","file_name":"Liu,Yi_IE598HW6.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"423123715","text":"total = 0\nfor i in range(1, 10):\n total += i\nprint(total)\n\n\ntotal2 = 0\ni1 = 0\n\nwhile i1 < 5:\n total2 += i1\n i1 += 1\n\nprint(total2)\n\n\nmy_list = [6, 4, 5, 2, 9, -2, -3, -1, 15] # создаем список\n# задача: складывать цифры, пока не упремся в отрицательные, отрицательные не складывать\n\nprint(my_list[0]) # квадратные скобки указывают на место в списке, 0 = первое место\n\ntotal3 = 0 # это переменная-аккумулятор\ni2 = 0 # это переменная-счетчик\n\nwhile my_list[i2] > 0: # условие начинается с первой цифры списка\n total3 += my_list[i2] # прибавляем первую цифрц к аккумулятору\n i2 += 1 # идем на следующую цифру списка\n\nprint(total3) # когда условие не тру, печатаем что получилось в аккумуляторе\n\n\ntotal4 = 0 # аналогичная задача, только с другой переменной\nfor element in my_list: # проверяем переменную element, идем по my_list\n if element > 0: # если число в списке больше 0...\n total4 += element # ... складываем это число в аккумулятор\n\nprint(total4) # в отличие от первого варианта, тут можно проверить весь список, а не остановится, уперевшись в отрицательное число\n\n\ntotal5 = 0 # все аналогично, только пропускаем отрицательные число. Например, если их миллион, чтобы не тратить время\nfor element1 in my_list:\n if element1 <= 0: # если видим отрицательное число...\n break # ...завершаем выполнение\n total5 += element1 # а так, складываем цифры в аккумулятор\n\nprint(total5)\n\n\ntotal6 = 0\nfor element2 in my_list:\n if total6 >= 11:\n break\n total6 += element2\n\nprint(total6)\n\n\nmy_list = [6, 3, 5, 2, 9, -2, -3, -1, 15]\n\ntotal7 = 0\ni7 = 0\n\nwhile total7 < 10 and my_list[i7] > 0:\n total7 += my_list[i7]\n i7 += 1\n\nprint(total7)\n\n\n\n#my_list = [6, 3, 5, 2, 9]\n\n#total8 = 0\n#i8 = 0\n\n#while my_list[i2] > 0:\n# total8 += my_list[i8]\n# i8 += 1\n\n#print(total8)\n# аналогичный пример, только выдает ошибку, потому что ничто не останавливает цикл, он доходит до последней цифры списка, прибавляет к i8 единицу и выходит за предел списка.\n# вот как починить:\n\nmy_list = [6, 3, 5, 2, 9]\n\ntotal9 = 0\ni9 = 0\n\nwhile i9 < len(my_list) and my_list[i9] > 0:\n total9 += my_list[i9]\n i9 += 1\n\nprint(total9)","sub_path":"Обучение/While1.py","file_name":"While1.py","file_ext":"py","file_size_in_byte":3136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"319147497","text":"import pandas as pd\n\nlocal = pd.read_csv('./용도지역2.csv', encoding='CP949')\nlocal = local.groupby(['시도', '시군구']).sum().reset_index()\n\ncol_list = ['주거지역 면적', '상업지역 면적', '공업지역 면적', '녹지지역 면적']\nlocal.columns = ['korea_cvs.pvn_nm', 'korea_cvs.bor_nm', '주거지역 면적', '상업지역 면적', '공업지역 면적', '녹지지역 면적', '면적합계']\nfor col in col_list:\n local['{} 비율'.format(col)] =round(local[col]/local['면적합계'],4) *100\nlocal.to_csv('./용도지역_last2.csv', index=False, encoding='CP949')\nlocal\nlocal['korea_cvs.bor_nm'] = local['korea_cvs.bor_nm'].astype('category')\nlocal['korea_cvs.pvn_nm'] = local['korea_cvs.pvn_nm'].astype('category')\nlocal['korea_cvs.bor_nm'].cat.categories\n\npop = pd.read_csv('./Data/인구분포2.csv',encoding='CP949')\npop = pop.groupby(['행정구역별(시도)', '행정구역별(시군구)', '성별', '연령별']).sum().reset_index()\npop.columns = ['korea_cvs.pvn_nm', 'korea_cvs.bor_nm', 'korea_cvs.gen_cd', 'korea_cvs.age_cd', '1세대가구', '2세대가구', '3세대가구', '4세대이상 가구', '1인가구', '인구합계']\ncol_list2 = ['1세대가구', '2세대가구', '3세대가구', '4세대이상 가구', '1인가구']\nfor col in col_list2:\n pop['{} 비율'.format(col)] =round(pop[col]/pop['인구합계'],4) *100\npop.to_csv('./Data/인구분포_last2.csv', index=False, encoding='CP949')\n\npop['korea_cvs.bor_nm'] = pop['korea_cvs.bor_nm'].astype('category')\npop['korea_cvs.pvn_nm'] = pop['korea_cvs.pvn_nm'].astype('category')\npop['korea_cvs.gen_cd'] = pop['korea_cvs.gen_cd'].astype('category')\npop['korea_cvs.age_cd'] = pop['korea_cvs.age_cd'].astype('category')\npop['korea_cvs.age_cd'].cat.categories = ['00~19', '20~39', '40~59', '60~99']\npop['korea_cvs.age_cd'].cat.categories\n\ngs_data = pd.read_csv('./Data/GS25_판매데이터.csv', sep=',', encoding='CP949')\ngs_data['korea_cvs.sale_dt'] = gs_data['korea_cvs.sale_dt'].astype('category')\ngs_data['korea_cvs.gen_cd'] = gs_data['korea_cvs.gen_cd'].astype('category')\ngs_data['korea_cvs.category'] = gs_data['korea_cvs.category'].astype('category')\ngs_data['korea_cvs.age_cd'] = gs_data['korea_cvs.age_cd'].astype('category')\ngs_data['korea_cvs.age_cd'].cat.categories\n\ndata1 = pd.merge(gs_data, pop, on=['korea_cvs.pvn_nm', 'korea_cvs.bor_nm', 'korea_cvs.gen_cd', 'korea_cvs.age_cd'])\ndata2 = pd.merge(data1, local, on = ['korea_cvs.pvn_nm', 'korea_cvs.bor_nm'])\n\ndata2.to_csv('./Data/pop_local_gs.csv', index=False, encoding='CP949')\n\ndata_3 = pd.read_csv('./pop_local_gs.csv', encoding='CP949')\ndata_3.columns\ndata_3.drop(['1세대가구', '2세대가구', '3세대가구', '4세대이상 가구', '1인가구',\\\n '인구합계', '1세대가구 비율', '2세대가구 비율', '3세대가구 비율',\\\n '4세대이상 가구 비율', '1인가구 비율'], axis=1, inplace=True)\ndata_3.shape\ndata_pop = pd.read_csv('./pop2.csv', encoding='CP949')\ndata_pop.columns\ndata_pop['1인가구 비율'] = round(data_pop['1인']/data_pop['가구원_합계'],3)\ndata_pop['2인가구 비율'] = round(data_pop['2인']/data_pop['가구원_합계'],3)\n\ndata4 = pd.merge(data_3, data_pop, on = ['korea_cvs.pvn_nm', 'korea_cvs.bor_nm'])\ndata4.columns\ndata4.shape\ndata4.groupby('korea_cvs.bor_nm').mean()[['주거지역 면적 비율', '상업지역 면적 비율', '공업지역 면적 비율', '녹지지역 면적 비율']]\ndata4.to_csv('./merge_data2.csv', encoding='CP949')\n\ndata5 = pd.read_csv('./merge_data3.csv', encoding='utf-8')\ndata5.drop(['Unnamed: 0'], axis=1,inplace=True)\ndata5.columns = ['korea_cvs.pvn_nm', 'korea_cvs.sale_dt', 'korea_cvs.gen_cd',\\\n 'korea_cvs.age_cd', 'korea_cvs.category', 'korea_cvs.adj_qty',\\\n 'korea_cvs.bor_nm', '주거지역 면적', '상업지역 면적', '공업지역 면적',\\\n '녹지지역 면적', '면적합계', 'residual_area_proportion', 'commercial_area_proportion',\\\n 'industrial_area_proportion', 'green_area_proportion', '가구원_합계', '1인', '2인',\\\n '1인가구 비율', '2인가구 비율', 'AREA']\ndata5.groupby(['AREA', 'korea_cvs.bor_nm']).mean()[['residual_area_proportion', 'commercial_area_proportion','industrial_area_proportion', 'green_area_proportion']]\n\ndata5.loc[(data5.industrial_area_proportion >= 8), 'local_characteristics'] = 'industrial'\ndata5.loc[(data5.industrial_area_proportion >= 8),['commercial_area_proportion','industrial_area_proportion']]\ndata5.loc[(data5.commercial_area_proportion >= 10), 'local_characteristics'] = 'commercial'\ndata5['local_characteristics'].fillna('residual', inplace=True)\ndata5[data5['local_characteristics'] == 'industrial'].groupby(['AREA', 'korea_cvs.bor_nm']).mean()\ndata5\ndata5.to_csv('./merge_data4.csv', encoding='CP949')\n\n\ndata5.head()\n","sub_path":"00.init data merge.py","file_name":"00.init data merge.py","file_ext":"py","file_size_in_byte":4860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"93953766","text":"# -*— coding:utf-8 -*-\nimport datetime\n\nclass Restaurant():\n \"\"\"这是一个模拟餐馆的小实例\"\"\"\n def __init__(self,restaurant_name,cuisine_type):\n self.restaurant_name = restaurant_name\n self.cuisine_name = cuisine_type\n self.number_served = 0\n\n def describle_restaurant(self):\n \"\"\"打印餐馆的简介信息\"\"\"\n print(self.restaurant_name + \"是一家百年老店，主营正宗\" + self.cuisine_name + \"，味道正宗！\")\n\n def open_restaurant(self):\n \"\"\"打印餐馆的营业状态\"\"\"\n # 营业范围时间\n open_time = datetime.datetime.strptime(str(datetime.datetime.now().date()) + '8:30', '%Y-%m-%d%H:%M')\n end_time = datetime.datetime.strptime(str(datetime.datetime.now().date()) + '23:00', '%Y-%m-%d%H:%M')\n # 当前时间\n now_time = datetime.datetime.now()\n if now_time > open_time and now_time < end_time:\n print(\"现在是 \" + str(now_time.strftime('%Y-%m-%d %H:%M')) + \",正在营业中，欢迎光临!\")\n else:\n print(\"现在是 \" + str(now_time.strftime('%Y-%m-%d %H:%M')) + \",已经停业，欢迎明天光临!\")\n\n\n def set_number_served(self, set_served):\n \"\"\"设置用餐人数\"\"\"\n self.number_served = set_served\n print(\"现在有 \" + str(self.number_served) + \" 位客人正在就餐！\")\n\n def increment_number_served(self, up_served):\n \"\"\"\n 增用餐人数\n 判断用餐人数是否超过接待上限，提升还能接待多少客人\n \"\"\"\n self.number_served += up_served\n\n if self.number_served <= 100 :\n print(\"现在有 \" + str(self.number_served) + \" 位客人正在就餐！\")\n else:\n self.number_served -= up_served\n max_served = 100 - self.number_served\n print(\"只能再接待\" + str(max_served) + \"位新来的客人了！\" )\n\n\n#打印餐馆简介和主营菜系\nrestaurant = Restaurant('蜀味居', '川菜')\nprint('<<<' + restaurant.restaurant_name + '>>>')\nrestaurant.describle_restaurant()\nprint()\n\n#根据系统时间判断是否在营业时间\nrestaurant.open_restaurant()\nprint()\n\n#通过调用方法设置或递增用餐人数\nrestaurant.set_number_served(30)\nrestaurant.increment_number_served(100)\n\n\n\n\n\n","sub_path":"T-9-4_Restaurant.py","file_name":"T-9-4_Restaurant.py","file_ext":"py","file_size_in_byte":2302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"166912230","text":"#!/usr/bin/env python3\n\"\"\"Split CESAR jobs.\n\nFor an orthologs table creates a set of\npreformatted input files for CESAR and\nwrites corresponding joblist.\n\"\"\"\nimport argparse\nimport os\nimport sys\nimport math\nfrom collections import defaultdict\nfrom datetime import datetime as dt\nimport ctypes\nfrom modules.common import parts, chainExtractID\n\n__author__ = \"Bogdan Kirilenko, 2020.\"\n__version__ = \"1.0\"\n__email__ = \"kirilenk@mpi-cbg.de\"\n__credits__ = [\"Michael Hiller\", \"Virag Sharma\", \"David Jebb\"]\n\n# 0 gene; 1 chains; 2 bed_file; 3 bdb_chain_file; 4 tDB; 5 qDB; 6 memlim gig;\nLOCATION = os.path.dirname(__file__)\nWRAPPER_TEMPLATE = os.path.join(LOCATION, \"CESAR_wrapper.py\") \\\n + \" {0} {1} {2} {3} {4} {5} --memlim {6} --cesar_binary {7}\" \\\n + \" --uhq_flank {8}\"\nGA_TEMPLATE = os.path.join(LOCATION, \"CESAR_wrapper.py\") \\\n + \" {0} {1} {2} {3} {4} {5} -g\"\nCESAR_RUNNER = os.path.join(LOCATION, \"cesar_runner.py\")\nLONG_LOCI_FIELDS = {\"GGLOB\", \"TRANS\"}\nCHUNK_SIZE = 1000\nREL_LENGTH_THR = 50\nABS_LENGTH_TRH = 500000\n\n# connect shared lib; define input and output data types\nchain_coords_conv_lib_path = os.path.join(LOCATION,\n \"modules\",\n \"chain_coords_converter_slib.so\")\n\nch_lib = ctypes.CDLL(chain_coords_conv_lib_path)\nch_lib.chain_coords_converter.argtypes = [ctypes.c_char_p,\n ctypes.c_int,\n ctypes.c_int,\n ctypes.POINTER(ctypes.c_char_p)]\nch_lib.chain_coords_converter.restype = ctypes.POINTER(ctypes.c_char_p)\n\n\ndef eprint(msg, end=\"\\n\"):\n \"\"\"Like print but for stderr.\"\"\"\n sys.stderr.write(msg + end)\n\n\ndef die(msg, rc=0):\n \"\"\"Write msg to stderr and abort program.\"\"\"\n eprint(msg)\n sys.exit(rc)\n\n\ndef parse_args():\n \"\"\"Read args, check.\"\"\"\n app = argparse.ArgumentParser()\n app.add_argument(\"orthologs_file\", help=\"Output of the chain classifier.\")\n app.add_argument(\"bed_file\", type=str, help=\"BED FILE\")\n app.add_argument(\"bdb_bed_file\", type=str, help=\"BDB BED FILE\")\n app.add_argument(\"bdb_chain_file\", type=str, help=\"BDB CHAIN FILE\")\n app.add_argument(\"tDB\", type=str, help=\"target 2 bit\")\n app.add_argument(\"qDB\", type=str, help=\"query 2 bit\")\n\n app.add_argument(\"--cesar_binary\", type=str, default=\"cesar\",\n help=\"CESAR2.0 binary address, cesar as default.\")\n app.add_argument(\"--jobs_num\", type=int, default=300,\n help=\"Total number of cluster jobs, 300 is recommended.\"\n \" Resulting number may slightly vary in case of buckets \"\n \"param usage due to round issues.\")\n app.add_argument(\"--buckets\", default=\"0\", help=\"\"\n \"If you need to split the cluster jobs in different classes\"\n \" according the memory consumprion use this parameter. To do \"\n \" that write comma-separated list of memory levels. For \"\n \"example, --buckets 10,30 means that there are two classes of \"\n \"jobs - consuming 10 and 30 gb. All jobs consuming more than 30gb \"\n \"are ignored. Job names will be 'cesar_job_[job_number]_[memory_class]' \"\n \"like cesar_job_9999_30 - meaning all tasks in this file require \"\n \"no more that 30Gb. --buckets 0 means no separation.\")\n app.add_argument(\"--fields\", default=None,\n help=\"Use those chains that are placed in these fields \"\n \" in orthologs file. Comma-separated list. For example \"\n \"PERF,GLOK - for perfect and good local chains.\")\n app.add_argument(\"--mask_stops\", \"--ms\", action=\"store_true\", dest=\"mask_stops\",\n help=\"Mask stop codons in target sequences. CESAR cannot process them.\"\n \"Using this parameter please make sure you know what you are doing.\")\n app.add_argument(\"--chains_limit\", type=int, default=15,\n help=\"Skip genes with amount of orthologs more than the limit.\")\n app.add_argument(\"--skipped_genes\", default=None,\n help=\"If a gene was skipped due to memory of number \"\n \" of chain limit, save it into a file.\")\n app.add_argument(\"--mem_limit\", type=float, default=50,\n help=\"Skip genes requiring more than X GB to call CESAR\")\n app.add_argument(\"--jobs_dir\", default=\"cesar_jobs\", help=\"Save jobs in.\")\n app.add_argument(\"--combined\", default=\"cesar_combined\", help=\"Combined cluster jobs.\")\n app.add_argument(\"--results\", default=\"cesar_results\", help=\"Save results to.\")\n app.add_argument(\"--check_loss\", default=None, help=\"Call internal gene loss pipeline\")\n app.add_argument(\"--u12\", default=None, help=\"Add U12 introns data\")\n app.add_argument(\"--rejected_log\", default=None, help=\"Save rejection data in this dir\")\n app.add_argument(\"--paralogs_log\", default=os.path.join(os.path.dirname(__file__), \"paralogs.log\"), \n help=\"Write a list of genes for which only paralogous chains were detected.\")\n app.add_argument(\"--uhq_flank\", default=50, type=int, help=\"UHQ flank size\")\n app.add_argument(\"--o2o_only\", \"--o2o\", action=\"store_true\", dest=\"o2o_only\",\n help=\"Process only the genes that have a single orthologous chain\")\n app.add_argument(\"--no_fpi\", action=\"store_true\", dest=\"no_fpi\",\n help=\"Consider some frame-preserving mutations as inactivating. \"\n \"See documentation for details.\")\n # print help if there are no args\n if len(sys.argv) < 2:\n app.print_help()\n sys.exit(0)\n args = app.parse_args()\n return args\n\n\ndef read_u12_data(u12_data_file):\n \"\"\"Read U12 introns.\"\"\"\n u12_data = defaultdict(list)\n if not u12_data_file:\n # not provided\n return u12_data\n f = open(u12_data_file, \"r\")\n f.__next__()\n for line in f:\n line_data = line[:-1].split(\"\\t\")\n trans = line_data[0]\n exon_num = int(line_data[1])\n site = line_data[2]\n val = (exon_num, site)\n u12_data[trans].append(val)\n f.close()\n return u12_data\n\n\ndef define_buckets(lim, buckets):\n \"\"\"Return memory limit in Gig if required. Get classes.\"\"\"\n if buckets == \"0\":\n return lim, {0: []}\n # buckets assigned\n buckets_vals = sorted([int(x) for x in buckets.split(\",\") if x != \"\"])\n buckets = {x: [] for x in buckets_vals}\n lim = buckets_vals[-1]\n return lim, buckets\n\n\ndef read_orthologs(orthologs_file, fields_raw, only_o2o=False):\n \"\"\"Read orthologs file.\"\"\"\n fields = [x.upper() for x in fields_raw.split(\",\") if x != \"\"]\n genes_chains = {}\n chain_gene_field = {}\n skipped = [] # genes skipped at this stage\n f = open(orthologs_file)\n\n for line in f:\n # parse line\n line_info = line[:-1].split(\"\\t\")\n if line_info[0] == \"GENE\":\n # this is a header line, skip it\n continue\n # \"0\" is a filler meaning \"no chains there\"\n gene = line_info[0]\n selected, chains = [], {}\n\n chains[\"ORTH\"] = [x for x in line_info[1].split(\",\") if x != \"0\"]\n chains[\"PARA\"] = [x for x in line_info[2].split(\",\") if x != \"0\"]\n chains[\"TRANS\"] = [x for x in line_info[3].split(\",\") if x != \"0\"]\n # Processed pseudogenes column ignored\n all_chains = chains[\"ORTH\"] + chains[\"PARA\"] + chains[\"TRANS\"]\n\n if len(all_chains) == 0:\n # no way in running CESAR on this gene\n skipped.append((gene, \"0\", \"No chains intersecting the gene\"))\n continue\n not_one2one = len(chains[\"ORTH\"]) == 0 or len(chains[\"ORTH\"]) > 1\n if only_o2o and not_one2one: # we requested only a single orthologous chain\n skipped.append((gene, \"0\", \"Only one2one requested, this gene didn't pass\"))\n continue\n\n # get those are chosen in FIELDS\n for field in fields:\n field_chains = chains.get(field)\n if not field_chains:\n continue\n selected.extend(field_chains)\n for chain in field_chains:\n key = (chain, gene)\n chain_gene_field[key] = field\n\n # if a gene has no orthologous chains, then use paralogous\n # if no paralogous -> log this gene\n if not selected:\n # no orthologous chains\n selected = all_chains.copy()\n keys = [(chain, gene) for chain in selected]\n for key in keys:\n chain_gene_field[key] = \"PARALOG\"\n\n genes_chains[gene] = selected\n\n f.close()\n die(\"Error! No gene:chains pairs selected! Probably --fields parameter is wrong!\") \\\n if len(genes_chains) == 0 else None\n return genes_chains, chain_gene_field, skipped\n\n\ndef read_bed(bed):\n \"\"\"Read bed file.\"\"\"\n bed_data = {}\n f = open(bed, \"r\")\n for line in f:\n bed_info = line[:-1].split(\"\\t\")\n chrom = bed_info[0]\n chromStart = int(bed_info[1])\n chromEnd = int(bed_info[2])\n name = bed_info[3]\n blockSizes = [int(x) for x in bed_info[10].split(',') if x != '']\n bed_data[name] = (chrom, chromStart, chromEnd, blockSizes)\n f.close()\n return bed_data\n\n\ndef precompute_regions(batch, bed_data, bdb_chain_file, chain_gene_field, limit):\n \"\"\"Precompute region for each chain: bed pair.\"\"\"\n eprint(\"Precompute regions for each gene:chain pair...\")\n chain_to_genes, skipped = defaultdict(list), []\n # upd_batch = defaultdict(list)\n # revert the dict\n for gene, chains in batch.items():\n if len(chains) == 0:\n skipped.append((gene, \",\".join(chains), \"no orthologous chains\"))\n continue\n chains_ = sorted(chains, key=lambda x: int(x))\n chains_ = chains_[:limit]\n if len(chains) > limit:\n skipped.append((gene, \",\".join(chains_[limit:]),\n f\"number of chains ({limit} chains) limit exceeded\"))\n for chain in chains_:\n chain_to_genes[chain].append(gene)\n # read regions themselves\n gene_chain_grange = defaultdict(dict)\n chains_num, iter_num = len(chain_to_genes.keys()), 0\n\n for chain_id, genes in chain_to_genes.items():\n # extract chain itself + get ranges for genes\n chain_body = chainExtractID(bdb_chain_file, chain_id).encode()\n all_gene_ranges = []\n for gene in genes:\n gene_data = bed_data.get(gene)\n grange = f\"{gene_data[0]}:{gene_data[1]}-{gene_data[2]}\"\n all_gene_ranges.append(grange)\n \n # using shared lib to get corresponding regions\n # we need to convert python datatypes to C types\n c_chain = ctypes.c_char_p(chain_body)\n c_shift = ctypes.c_int(2)\n granges_bytes = [s.encode(\"utf-8\") for s in all_gene_ranges]\n granges_num = len(all_gene_ranges)\n c_granges_num = ctypes.c_int(granges_num)\n granges_arr = (ctypes.c_char_p * (granges_num + 1))()\n granges_arr[:-1] = granges_bytes\n granges_arr[granges_num] = None\n # then call the function\n\n raw_ch_conv_out = ch_lib.chain_coords_converter(c_chain,\n c_shift,\n c_granges_num,\n granges_arr)\n chain_coords_conv_out = [] # keep lines here\n # convert C output to python-readible type\n for i in range(granges_num + 1):\n chain_coords_conv_out.append(raw_ch_conv_out[i].decode(\"utf-8\"))\n\n for line in chain_coords_conv_out[1:]:\n line_info = line[:-1].split()\n num = int(line_info[0])\n q_grange = line_info[1].split(\":\")[1].split(\"-\")\n q_start, q_end = int(q_grange[0]), int(q_grange[1])\n que_len = q_end - q_start\n t_grange = line_info[2].split(\":\")[1].split(\"-\")\n t_start, t_end = int(t_grange[0]), int(t_grange[1])\n tar_len = t_end - t_start\n len_delta = abs(tar_len - que_len)\n delta_gene_times = len_delta / tar_len\n gene = genes[num]\n field = chain_gene_field.get((chain_id, gene))\n high_rel_len = delta_gene_times > REL_LENGTH_THR\n high_abs_len = len_delta > ABS_LENGTH_TRH\n long_loci_field = field in LONG_LOCI_FIELDS\n if (high_rel_len or high_abs_len) and long_loci_field:\n skipped.append((gene, chain_id, \"too long query locus\"))\n continue\n gene_chain_grange[gene][chain_id] = que_len\n # not sure if necessary but...\n del raw_ch_conv_out\n iter_num += 1\n eprint(f\"Chain {iter_num} / {chains_num}\", end=\"\\r\")\n return gene_chain_grange, skipped\n\n\ndef fill_buckets(buckets, all_jobs):\n \"\"\"Split jobs in buckets according their memory consumption.\"\"\"\n if 0 in buckets.keys(): # do not split it\n buckets[0] = list(all_jobs.keys())\n return buckets\n # buckets were set\n memlims = sorted(buckets.keys())\n prev_lim = 0\n for memlim in memlims:\n buckets[memlim] = [job for job, jobmem in all_jobs.items() if prev_lim < jobmem <= memlim]\n prev_lim = memlim\n # remove empty\n filter_buckets = {k: v for k, v in buckets.items() if len(v) > 0}\n return filter_buckets\n\n\ndef save_jobs(filled_buckets, bucket_jobs_num, jobs_dir):\n \"\"\"Save cesar calls in the dir assigned.\"\"\"\n os.mkdir(jobs_dir) if not os.path.isdir(jobs_dir) else None\n file_num, to_combine = 0, []\n for bucket_id, jobs in filled_buckets.items():\n num_of_files = bucket_jobs_num[bucket_id]\n # just in case\n num_of_files = len(jobs) if num_of_files >= len(jobs) else num_of_files\n size_of_file = len(jobs) // num_of_files\n # size_of_file = size_of_file + 1 if len(jobs) % num_of_files != 0 else size_of_file\n jobs_split = parts(jobs, n=size_of_file)\n for part in jobs_split:\n file_num += 1\n file_name = f\"cesar_job_{file_num}_{bucket_id}\"\n file_path = os.path.join(jobs_dir, file_name)\n f = open(file_path, \"w\")\n f.write(\"\\n\".join(part) + \"\\n\")\n f.close()\n to_combine.append(file_path)\n return to_combine\n\n\ndef main():\n \"\"\"Entry point.\"\"\"\n t0 = dt.now()\n args = parse_args()\n\n # get batch\n if args.fields:\n fields = args.fields\n else:\n fields = \"ORTH,TRANS\"\n\n # read U12 introns\n U12_data = read_u12_data(args.u12)\n\n # skipped_1 - no chains found\n batch, chain_gene_field, skipped_1 = read_orthologs(args.orthologs_file,\n fields,\n only_o2o=args.o2o_only)\n mem_limit, buckets = define_buckets(args.mem_limit, args.buckets)\n bed_data = read_bed(args.bed_file)\n # check if cesar binary exists\n die(f\"Error! Cannot find cesar executable at {args.cesar_binary}!\") if \\\n not os.path.isfile(args.cesar_binary) else None\n\n # pre-compute chain : gene : region\n # collect the second list of skipped genes\n regions, skipped_2 = precompute_regions(batch,\n bed_data,\n args.bdb_chain_file,\n chain_gene_field,\n args.chains_limit)\n iter_num = 0\n all_jobs = {}\n skipped_3 = []\n\n for gene in batch.keys():\n u12_this_gene = U12_data.get(gene)\n iter_num += 1\n block_sizes = bed_data[gene][3]\n extra = 100000 # for extra stuff\n # chains_arg = \",\".join(chains)\n\n # proceed to memory estimation\n num_states, rlength = 0, 0\n # ref by ref\n for block_size in block_sizes:\n # num_states += 6 + 6 * reference->num_codons + 1 + 2 + 2 + 22 + 6;\n # /* 22 and 6 for acc and donor states */\n num_codons = block_size // 3\n num_states += 6 + 6 * num_codons + 1 + 2 + 2 + 22 + 6\n # rlength += 11 + 6 * fasta.references[i]->length\n # + donors[i]->length + acceptors[i]->length;\n rlength += block_size\n\n gene_chains_data = regions.get(gene)\n if not gene_chains_data:\n continue\n elif len(gene_chains_data) == 0:\n continue\n chains = gene_chains_data.keys()\n chains_arg = \",\".join(chains)\n\n query_lens = [v for v in gene_chains_data.values()]\n qlength_max = max(query_lens)\n memory = (num_states * 4 * 8) + \\\n (num_states * qlength_max * 4) + \\\n (num_states * 304) + \\\n (2 * qlength_max + rlength) * 8 + \\\n (qlength_max + rlength) * 2 * 1 + extra\n\n gig = math.ceil(memory / 1000000000) + 0.25 # convet to gigs + 0.25 extra gig\n if gig > mem_limit:\n # it is going to consume TOO much memory\n skipped_3.append((gene, \",\".join(chains),\n f\"memory limit ({mem_limit} gig) exceeded (needs {gig})\"))\n continue\n\n # # 0 gene; 1 chains; 2 bed_file; 3 bdb chain_file; 4 tDB; 5 qDB; 6 output; 7 cesar_bin\n job = WRAPPER_TEMPLATE.format(gene, chains_arg,\n args.bdb_bed_file,\n args.bdb_chain_file,\n args.tDB, args.qDB,\n gig,\n args.cesar_binary,\n args.uhq_flank)\n job = job + \" --mask_stops\" if args.mask_stops else job\n job = job + \" --check_loss\" if args.check_loss else job\n job = job + \" --no_fpi\" if args.no_fpi else job\n\n # U12 introns in this gene\n if u12_this_gene:\n # u12_str_opt = \",\".join([f\"{x[0]}_{x[1]}\" for x in u12_this_gene])\n # job = job + f\" --u12 {u12_str_opt}\"\n job = job + f\" --u12 {args.u12}\"\n\n all_jobs[job] = gig\n\n eprint(f\"\\nThere are {len(all_jobs.keys())} jobs in total.\")\n eprint(\"Splitting the jobs.\")\n # split jobs in buckets | compute proportions\n filled_buckets = fill_buckets(buckets, all_jobs)\n prop_sum = sum([k * len(v) for k, v in filled_buckets.items()])\n # estimate proportion of a bucket in the runtime\n buckets_prop = {k: (k * len(v)) / prop_sum for k, v in filled_buckets.items()} \\\n if 0 not in filled_buckets.keys() else {0: 1.0}\n eprint(\"Bucket proportions are:\")\n eprint(\"\\n\".join([f\"{k} -> {v}\" for k, v in buckets_prop.items()]))\n # get number of jobs for each bucket\n bucket_jobs_num = {k: math.ceil(args.jobs_num * v) for k, v in buckets_prop.items()}\n # save jobs, get comb lines\n to_combine = save_jobs(filled_buckets, bucket_jobs_num, args.jobs_dir)\n # save combined jobs\n os.mkdir(args.results) if not os.path.isdir(args.results) else None\n os.mkdir(args.check_loss) if args.check_loss \\\n and not os.path.isdir(args.check_loss) else None\n\n f = open(args.combined, \"w\")\n for num, comb in enumerate(to_combine, 1):\n basename = os.path.basename(comb).split(\".\")[0]\n results_path = os.path.join(args.results, basename + \".bdb\")\n combined_command = f\"{CESAR_RUNNER} {comb} {results_path}\"\n if args.check_loss:\n loss_data_path = os.path.join(args.check_loss,\n f\"{basename}.inact_mut.txt\")\n combined_command += f\" --check_loss {loss_data_path}\"\n if args.rejected_log:\n log_path = os.path.join(args.rejected_log, f\"{num}.txt\")\n combined_command += f\" --rejected_log {log_path}\"\n f.write(combined_command + \"\\n\")\n f.close()\n\n # save skipped genes if required\n if args.skipped_genes:\n skipped = skipped_1 + skipped_2 + skipped_3\n f = open(args.skipped_genes, \"w\")\n f.write(\"\\n\".join([\"\\t\".join(x) for x in skipped]) + \"\\n\")\n f.close()\n\n f = open(args.paralogs_log, \"w\")\n for k, v in chain_gene_field.items():\n if v != \"PARALOG\":\n continue\n gene_ = f\"{k[1]}.{k[0]}\\n\"\n f.write(gene_)\n f.close()\n\n eprint(f\"Estimated: {dt.now() - t0}\")\n sys.exit(0)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"split_exon_realign_jobs.py","file_name":"split_exon_realign_jobs.py","file_ext":"py","file_size_in_byte":20628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"465799897","text":"import cgi\nimport datetime\nimport urllib\nimport webapp2\nimport wsgiref.handlers\n\nfrom google.appengine.ext import db\nfrom google.appengine.api import users\nfrom google.appengine.ext.webapp import util\n\nclass Greeting(db.Model):\n \"\"\"Models an individual Guestbook entry with an author, content, and date.\"\"\"\n author = db.UserProperty()\n content = db.StringProperty(multiline=True)\n date = db.DateTimeProperty(auto_now_add=True)\n\n\ndef guestbook_key(guestbook_name=None):\n \"\"\"Constructs a datastore key for a Guestbook entity with guestbook_name.\"\"\"\n return db.Key.from_path('Guestbook', guestbook_name or 'default_guestbook')\n\n\nclass MainPage(webapp2.RequestHandler):\n def get(self):\n self.response.out.write('')\n guestbook_name = self.request.get('guestbook_name')\n\n\n\nclass MainPageOLD(webapp2.RequestHandler):\n def get(self):\n self.response.out.write('')\n self.response.out.write('hello world appengine')\n self.response.out.write('')\n\n\nclass HelloWorldPage(webapp2.RequestHandler):\n def get(self):\n self.response.out.write('Hello World Appengine HelloWorldPage')\n\n\n\nclass Guestbook(webapp2.RequestHandler):\n def get(self):\n# self.response.headers['Content Type'] = 'text/plain'\n# self.response.out.write('Hello, world appengine')\n# how do we show the last entry in the guestbook? \n# 1) get a guestbook 2) get the last greeting\n guestbook_name = self.request.get('guestbook_name');\n\n self.response.out.write(\"\"\"\n \n Guestbook: \"\"\" + guestbook_name + \"\"\"

\n \n \"\"\")\n\n greetings = db.GqlQuery(\"SELECT * \"\n \"FROM Greeting \"\n \"WHERE ANCESTOR IS :1 \"\n \"ORDER BY date DESC LIMIT 10\",\n guestbook_key(guestbook_name))\n\n for greeting in greetings:\n if greeting.author:\n self.response.out.write(\n '$s wrote: ' % greeting.author)\n else:\n self.response.out.write('An anonymous person wrote: ')\n self.response.out.write('
%s
' % cgi.escape(greeting.content))\n \n\n self.response.out.write(\"\"\"\n
\n
\n
\n
\n
\n \"\"\")\n\n\n def post(self):\n # We set the same parent key on the 'Greeting' to ensure each greeting is in\n # the same entity group. Queries across the single entity group will be\n # consistent. However, the write rate to a single entity group should\n # be limited to ~1/second.\n guestbook_name = self.request.get('guestbook_name')\n greeting = Greeting(parent=guestbook_key(guestbook_name))\n\n if users.get_current_user():\n greeting.author = users.get_current_user()\n\n greeting.content = self.request.get('content')\n greeting.put()\n self.redirect('/?' + urllib.urlencode({'guestbook_name': guestbook_name}))\n\n\napp = webapp2.WSGIApplication([('/', Guestbook),\n ('/sign', Guestbook),\n ('/foo', HelloWorldPage)],\n debug=True)\nutil.run_wsgi_app(app)\n\n\n#def main():\n# wsgiref.handlers.CGIHandler().run(app)\n\n\n#if __name__ == '__main__':\n# main()\n","sub_path":"helloworld/helloworld.py","file_name":"helloworld.py","file_ext":"py","file_size_in_byte":3476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"145034151","text":"# -*- coding: utf-8 -*-\nclass Solution:\n def multiply(self, num1, num2):\n \"\"\"\n :type num1: str\n :type num2: str\n :rtype: str\n \"\"\"\n num1 = list(map(int, num1))\n num2 = list(map(int, num2))\n\n multiply_value = 0\n\n for i, val1 in enumerate(reversed(num1)):\n tmp = 0\n for j, val2 in enumerate(reversed(num2)):\n tmp = tmp + (val1 * val2) * pow(10, j)\n multiply_value = multiply_value + (tmp * pow(10, i))\n return multiply_value\n\n\nif __name__ == \"__main__\":\n num1 = \"123\"\n num2 = \"456\"\n res = Solution().multiply(num1, num2)\n print(res)","sub_path":"leetcode/43.multiply_strings.py","file_name":"43.multiply_strings.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"586923126","text":"def bin_s(lst, num):\n left, right = -1, len(lst)\n while left + 1 != right:\n middle = (left + right) // 2\n if lst[middle] < num:\n left = middle\n else:\n right = middle\n if abs(lst[right] - num) < abs(lst[left] - num):\n return lst[right]\n if left == -1:\n return lst[0]\n return lst[left]\n\n\ndef approx(first_lst, second_lst):\n ans = []\n for i in second_lst:\n ans.append(bin_s(first_lst, i))\n return ans\n\n\napprox_in = open('approx.in', 'r')\napprox_out = open('approx.out', 'w')\n\nn, m = approx_in.readline().split()\nfirst_lst = list(map(int, approx_in.readline().split()))\nsecond_lst = list(map(int, approx_in.readline().split()))\napprox_in.close()\n\nans = approx(first_lst, second_lst)\nfor i in ans:\n print(i, file=approx_out)\napprox_out.close()\n","sub_path":"lKSH/final/approx.py","file_name":"approx.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"421592024","text":"from fastapi import FastAPI, HTTPException, Depends, APIRouter\nfrom ..database import *\nfrom ..models import *\n\nrouter = APIRouter()\n\n@router.get(\n \"\",\n summary='Reads task list',\n description='Reads the whole task list.',\n response_model=Dict[uuid.UUID, Task],\n)\nasync def read_tasks(completed: bool = None, db: DBSession = Depends(get_db)):\n return db.read_tasks(completed)\n\n\n@router.post(\n \"\",\n summary='Creates a new task',\n description='Creates a new task and returns its UUID.',\n response_model=uuid.UUID,\n)\nasync def create_task(item: Task, db: DBSession = Depends(get_db)):\n return db.create_task(item)\n\n\n@router.get(\n '/{uuid_}',\n summary='Reads task',\n description='Reads task from UUID.',\n response_model=Task,\n)\nasync def read_task(uuid_: uuid.UUID, db: DBSession = Depends(get_db)):\n try:\n return db.read_task(uuid_)\n except KeyError as exception:\n raise HTTPException(\n status_code=404,\n detail='Task not found',\n ) from exception\n\n\n@router.put(\n '/{uuid_}',\n summary='Replaces a task',\n description='Replaces a task identified by its UUID.',\n)\nasync def replace_task(uuid_: uuid.UUID, item: Task, db: DBSession = Depends(get_db)):\n try:\n db.replace_task(uuid_, item)\n except KeyError as exception:\n raise HTTPException(\n status_code=404,\n detail='Task not found',\n ) from exception\n\n\n@router.patch(\n '/{uuid_}',\n summary='Alters task',\n description='Alters a task identified by its UUID',\n)\nasync def alter_task(uuid_: uuid.UUID, item: Task, db: DBSession = Depends(get_db)):\n try:\n db.alter_task(uuid_, item)\n except KeyError as exception:\n raise HTTPException(\n status_code=404,\n detail='Task not found',\n ) from exception\n\n\n@router.delete(\n '/{uuid_}',\n summary='Deletes task',\n description='Deletes a task identified by its UUID',\n)\nasync def remove_task(uuid_: uuid.UUID, db: DBSession = Depends(get_db)):\n try:\n db.remove_task(uuid_)\n except KeyError as exception:\n raise HTTPException(\n status_code=404,\n detail='Task not found',\n ) from exception\n","sub_path":"api/routers/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"580984919","text":"#this program brightens the images from the path folder and then ustilises fourier transform to eliminate dark stripes from the images\n#then, it converts the image to jpeg and creates a video\n\nimport os\nimport numpy as np\nimport cv2\nimport glob\nimport astropy.io\nfrom astropy.visualization import astropy_mpl_style\nimport matplotlib.pyplot as plt\nfrom astropy.io import fits\nfrom astropy.utils.data import get_pkg_data_filename\nimport subprocess\nimport ffmpeg\nimport sys\nimport copy\nimport PIL\nfrom PIL import Image\n\n\n#path_arr=['/home/large_data/venus_work/temp_fits/']\npath_arr=['/home/large_data/southpole_data/dmlab/solar/south-pole/K-0/2017/1/21/11/']\n#for ii in range(len(path_arr)):\nfor ii in range(len(path_arr)):\n path_K2017 = path_arr[ii]\n img_path_arr = []\n image_array = [] \n dir_path = '/home/large_data/venus_work/temp1/'\n image_folder = '/home/large_data/venus_work/temp1/'\n final_image_folder = '/home/large_data/venus_work/image1/'\n video_name = '/home/large_data/venus_work/image1/video_bin_'+str(ii)+'.avi'\n video_FourCC = cv2.VideoWriter_fourcc(*'MPEG')\n os.chdir(path_K2017)\n\n print('Extracting all fits image details from:\\n', path_K2017)\n print('Extracting...')\n#adding image addresses to path\n for r,d,f in os.walk(path_K2017):\n \tfor file in f:\n \t\tif '.fits' in file:\n \t\t\timg_path_arr.append(os.path.join(r,file))\n print(len(img_path_arr))\n print('Extraction finished!')\n \n plt.style.use(astropy_mpl_style)\n \n print('Adding images addresses to an array...')\n for i, item in enumerate(img_path_arr):\n image_file = get_pkg_data_filename(item)\n image_data = fits.getdata(image_file, ext=0)\n #image_transposed = np.transpose(np.asarray(image_data))\n image_transposed = np.flip(image_data,1)\n #height, width, layers = image_transposed.shape \n image_array.append(image_transposed[0, :, :])\n print(image_transposed.shape)\n# height, width, layers = image_data.shape \n# image_array.append(image_data[:, :, 0])\n print('All images extracted')\n \n def progress(count, total, status=''):\n bar_len = 60\n filled_len = int(round(bar_len * count / float(total)))\n percents = round(100.0 * count / float(total), 1)\n bar = '=' * filled_len + '-' * (bar_len - filled_len)\n sys.stdout.write('[%s] %s%s ...%s\\r' % (bar, percents, '%', status))\n sys.stdout.flush()\n#generating lossless images \n def generate_image(imp, img_path):\n print('Saving images to image_folder...')\n for j in range(len(imp)):\n progress(j,len(imp),'Generating files')\n plt.figure(dpi=1200)\n plt.axis('off')\n plt.grid(b=None)\n plt.imshow(imp[j], cmap='gray')\n plt.savefig(img_path + \"/file_%03d.png\" % j, dpi = 1200, pad_inches = 0 , bbox_inches='tight')\n plt.close() \n\n generate_image(image_array, image_folder)\n \n img_png_path=[]\n print('Extracting all png image paths ', image_folder)\n print('Extracting...')\n for r,d,f in os.walk(image_folder):\n \tfor file in f:\n \t\tif '.png' in file:\n \t\t\timg_png_path.append(os.path.join(r,file))\n print('number of png images in folder', len(img_png_path))\n print('Extraction finished!')\n\n #------------------------------------------------------------------------------------------------\n#brightening of the image\n for j in range(len(img_png_path)):\n dark = Image.open(img_png_path[j])\n \n # multiply each pixel by 0.9 (makes the image darker), darker < 1.0 < lighter\n brightened = dark.point(lambda p: p * 3.0)\n plt.figure(dpi=1200)\n plt.axis('off')\n plt.grid(b=None)\n plt.imshow(brightened, cmap='gray')\n plt.savefig(final_image_folder + 'temp_img.png', dpi = 1200, pad_inches = 0, bbox_inches='tight')\n plt.close()\n #smoothened.save(final_image_folder+'temp_img.tiff',pad_inches = 0, bbox_inches='tight')\n\n #im2.show()\n \n #----------------------------------------------------------------------------------------------\n#removing dark stripes\n img_noise=cv2.imread(final_image_folder+'temp_img.png',0)\n #img = cv2.imread('21_1_17_8_59a.tiff',0)\n f=np.fft.fft2(img_noise)\n fshift=np.fft.fftshift(f)\n #calculate amplitude spectrum\n mag_spec = 20*np.log(np.abs(fshift))\n \n r= f.shape[0]/2 # number of rows/2\n c=f.shape[1]/2 # number of columns/2\n p=3\n n=1 \n fshift2 = np.copy(fshift)\n \n # suppress upper part \n #fshift2[0:int(r-n),int(c-p):int(c+p)] = 0.001\n fshift2[int(r-p):int(r+p),0:int(c-n)] = 0.001\n # suppress lower part \n #fshift2[int(r+n):int(r+r),int(c-p):int(c+p)] = 0.001\n fshift2[int(r-p):int(r+p), int(c+n):int(c+c)] = 0.001\n # calculate new amplitude spectrum\n mag_spec2 = 20*np.log(np.abs(fshift2))\n inv_fshift=np.fft.ifftshift(fshift2)\n # reconstructing image\n img_recon= np.real(np.fft.ifft2(inv_fshift))\n \n plt.figure(dpi=1200)\n plt.axis('off')\n plt.grid(b=None)\n plt.imshow(img_recon, cmap='gray')\n plt.savefig(final_image_folder + \"/file_%03d.png\" % j, dpi = 1200, pad_inches = 0, bbox_inches='tight')\n plt.close()\n os.remove(final_image_folder+'temp_img.png') \n #plt.show()\n \n #-----------------------------------------------------------------------------------------------\n#converting to jpeg for video creation \n \n tiff_for_video = [img for img in os.listdir(final_image_folder) if img.endswith(\".png\")]\n print(os.path.join(final_image_folder,tiff_for_video[0]))\n [r,c]=(cv2.imread(os.path.join(final_image_folder, tiff_for_video[0]),0)).shape\n for infile in tiff_for_video:\n outfile = os.path.join(final_image_folder, infile[:-4] + \"jpeg\")\n im = Image.open(os.path.join(final_image_folder,infile))\n #print('image size in final_image_folder', im.size)\n print (\"new filename : \" + outfile)\n im = im.convert(\"RGB\")\n out = im.resize((r,c),Image.ANTIALIAS)\n out.save(outfile, \"JPEG\", quality=95)\n \n\n# print('Deleting *.png files in ', final_image_folder,'...')\n# for file_name in glob.glob(\"*.png\"):\n# os.remove(file_name)\n# print('Deleted .png files in ', final_image_folder) \n #jpeg_for_video=[]\n jpeg_for_video = [img for img in os.listdir(final_image_folder) if img.endswith(\".jpeg\")]\n frame = cv2.imread(os.path.join(final_image_folder, jpeg_for_video[0]))\n height, width, layers = frame.shape\n print('frame size', frame.shape)\n print(sorted(jpeg_for_video))\n \n video = cv2.VideoWriter(video_name, video_FourCC, 30, (width, height))\n for image in sorted(jpeg_for_video):\n for jj in range(10):\n print(\"Writing the image : \", image)\n\n video.write(cv2.imread(os.path.join(final_image_folder, image)))\n \n cv2.destroyAllWindows()\n video.release()\n \n \n os.chdir(image_folder)\n # print('Deleting *.png files in ', image_folder,'...')\n # for file_name in glob.glob(\"*.png\"):\n # os.remove(file_name)\n # print('Deleted png files in ', image_folder)\n \n # os.chdir(final_image_folder)\n # \n # print('Deleting *.png files in ', final_image_folder,'...')\n # for file_name in glob.glob(\"*.png\"):\n # os.remove(file_name)\n # print('Deleted .png files in ', final_image_folder)\n # \n # print('Deleting *.jpeg files in ', final_image_folder)\n # for file_name in glob.glob(\"*.jpeg\"):\n # os.remove(file_name)\n # print('Deleted .jpeg files in ', final_image_folder)\n \n print('Session completed!!!\\nPlease view video in ', final_image_folder)\n\n ","sub_path":"noise_remove/clear_video.py","file_name":"clear_video.py","file_ext":"py","file_size_in_byte":7721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"138380985","text":"def main():\n import sys\n import os\n import requests as rq\n from requests.structures import CaseInsensitiveDict\n from time import sleep\n\n def exit():\n print('\\n>>> Exiting...')\n sleep(2)\n sys.exit()\n\n def clear():\n if os.name=='nt':\n os.system('cls')\n else:\n os.system('clear')\n\n def banner():\n b='''\n ______ __ _ ______ _ _\n | ____|/ _| | | ____| (_) | |\n | |__ | |_| |_ _ _ | |__ ___ _ __ _ __| |\n | __| | _| __| | | | | __/ _ \\| '__| |/ _` |\n | |____| | | |_| |_| | | | | (_) | | | | (_| |\n |______|_| \\__|\\__, | |_| \\___/|_| |_|\\__,_|\n __/ |\n |___/ \n '''\n print(b)\n\n def menu():\n menu='''\\n>>> This tool is for FUN, Not for Revenge!\n Choose a option below:\n 1. Start SMS Bomber\n 2. Exit'''\n print(menu)\n\n def bomb():\n number=str(input(\">>> [!] Enter Target Number: +88\"))\n amount=int(input(\">>> [!] Enter Amount Of SMS: \"))\n url = \"https://toffeelive.com/app/service.php\"\n headers = CaseInsensitiveDict()\n headers[\"Content-Type\"] = \"application/x-www-form-urlencoded\"\n data = \"phoneNumber=\"+number+\"&route=auth_verify_mobile_no\"\n resp = rq.post(url, headers=headers, data=data)\n for i in range(amount):\n resp = rq.post(url, headers=headers, data=data)\n if resp.status_code==200:\n print(f'>>> [+] {i+1} SMS Sent Successfully')\n elif resp.status_code==201:\n print(f'>>> [+] {i+1} SMS Sent Successfully')\n sleep(2)\n input('\\n>>> Task Done! Press Enter to Return...')\n sleep(2)\n \n\n while True:\n try:\n while True:\n clear()\n banner()\n menu()\n choice=str(input(\"\\n>>> Enter your choice (1/2): \"))\n if choice in ('1','2'):\n if choice=='1':\n clear()\n banner()\n bomb()\n elif choice=='2':\n exit()\n else:\n print('>>> Invalid Input')\n sleep(2)\n except KeyboardInterrupt:\n exit()\n\nif __name__=='__main__':\n main()","sub_path":"eftyforidsmsbomber.py","file_name":"eftyforidsmsbomber.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"633247899","text":"def calcula_linhas(name_file):\n count = 0\n with open(name_file, \"r\") as f:\n for line in f:\n if line != \"\\n\":\n count += 1\n print(count)\n\ndef calcula_carateres(name_file):\n with open(name_file, \"r\") as f:\n print(len(name_file))\n\ndef calcula_ocorrencia_letras(name_file):\n dictionary = {}\n with open(name_file, \"r\") as f:\n for line in f:\n for c in line:\n if c == \" \" or c == \"\\n\":\n continue\n if dictionary.__contains__(c):\n dictionary[c] += 1\n else:\n dictionary[c] = 1\n sort_dict = sorted(dictionary.items())\n print(dictionary)\n","sub_path":"analisa_ficheiros/calculos.py","file_name":"calculos.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"565898053","text":"#\n# Copyright © 2021 Uncharted Software Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom joblib import Parallel, delayed\nimport torch\nimport torch.nn as nn\nimport torchvision.models as models\nimport torchvision.transforms as transforms\nfrom d3m.metadata import base as metadata_base\n\nCYTHON_DEP = {\n \"type\": metadata_base.PrimitiveInstallationType.PIP,\n \"package\": \"Cython\",\n \"version\": \"0.29.24\",\n}\n\n\ndef maybe_subset(X, y, n):\n if (n > 0) and (n < X.shape[0]):\n sel = np.sort(np.random.choice(X.shape[0], n, replace=False))\n return X[sel], y[sel]\n else:\n return X, y\n\n\ndef parmap(fn, x, n_jobs=1, backend=\"loky\", verbose=1, **kwargs):\n if len(list(x)) < n_jobs: # TODO: I'm surprised this is necessary\n n_jobs = len(list(x))\n\n jobs = [delayed(fn)(xx, **kwargs) for xx in x]\n return Parallel(n_jobs=n_jobs, backend=backend, verbose=verbose)(jobs)\n\n\nclass Img2Vec:\n def __init__(\n self,\n model_path,\n model=\"resnet-18\",\n layer=\"default\",\n layer_output_size=512,\n device=\"cuda\",\n ):\n \"\"\"Img2Vec\n :param model: String name of requested model\n :param layer: String or Int depending on model. See more docs: https://github.com/christiansafka/img2vec.git\n :param layer_output_size: Int depicting the output size of the requested layer\n :param device: String that lets us decide between using cpu and gpu\n \"\"\"\n self.device = device\n self.layer_output_size = layer_output_size\n self.model_name = model\n\n self.model, self.extraction_layer = self._get_model_and_layer(\n model, layer, model_path\n )\n\n self.model = self.model.to(self.device)\n\n self.model.eval()\n\n self.scaler = transforms.Resize((224, 224))\n self.normalize = transforms.Normalize(\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n )\n self.to_tensor = transforms.ToTensor()\n\n def get_vec(self, img, tensor=False):\n \"\"\"Get vector embedding from PIL image\n :param img: PIL Image\n :param tensor: If True, get_vec will return a FloatTensor instead of Numpy array\n :returns: Numpy ndarray\n \"\"\"\n image = (\n self.normalize(self.to_tensor(self.scaler(img)))\n .unsqueeze(0)\n .to(self.device)\n )\n\n if self.model_name == \"alexnet\":\n my_embedding = torch.zeros(1, self.layer_output_size)\n else:\n my_embedding = torch.zeros(1, self.layer_output_size, 1, 1)\n\n def copy_data(m, i, o):\n my_embedding.copy_(o.data)\n\n h = self.extraction_layer.register_forward_hook(copy_data)\n h_x = self.model(image)\n h.remove()\n\n if tensor:\n return my_embedding\n else:\n if self.model_name == \"alexnet\":\n return my_embedding.numpy()[0, :]\n else:\n return my_embedding.numpy()[0, :, 0, 0]\n\n def _get_model_and_layer(self, model_name, layer, model_path):\n \"\"\"Internal method for getting layer from model\n :param model_name: model name such as 'resnet-18'\n :param layer: layer as a string for resnet-18 or int for alexnet\n :returns: pytorch model, selected layer\n \"\"\"\n if model_name == \"resnet-18\":\n model = models.resnet18()\n model.load_state_dict(torch.load(model_path))\n if layer == \"default\":\n layer = model._modules.get(\"avgpool\")\n self.layer_output_size = 512\n else:\n layer = model._modules.get(layer)\n\n return model, layer\n else:\n raise KeyError(\"Model %s was not found\" % model_name)\n \"\"\"\n elif model_name == 'alexnet':\n model = models.alexnet(pretrained=True)\n if layer == 'default':\n layer = model.classifier[-2]\n self.layer_output_size = 4096\n else:\n layer = model.classifier[-layer]\n\n return model, layer\n \"\"\"\n","sub_path":"distil/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"250583735","text":"ILLEGAL_STR = r'\\/:*?\"<>|'\nREPLACE_ILLEGAL_STR = str.maketrans(ILLEGAL_STR, ' ' * len(ILLEGAL_STR))\n\n\ndef safe_filename(filename):\n \"\"\"文件名过滤非法字符串\n \"\"\"\n return filename.translate(REPLACE_ILLEGAL_STR)\n\n\ndef parser_interval(interval):\n \"\"\"将字符串描述的区间转化为一个一个数字\n Args:\n interval:\n 类似 1-10,20-30,66 这样的字符串\n Yield:\n number\n \"\"\"\n appeared = set()\n for block in interval.split(','):\n if '-' in block:\n start, end = block.split('-', 1)\n try:\n start, end = int(start), int(end)\n for number in range(start, end + 1):\n if number not in appeared:\n appeared.add(number)\n yield number\n except ValueError:\n print('参数写错了,查看帮助: python3 onepiece.py --help')\n exit(1)\n else:\n try:\n number = int(block)\n if number not in appeared:\n appeared.add(number)\n yield number\n except ValueError:\n print('参数写错了,查看帮助: python3 onepiece.py --help')\n exit(1)\n","sub_path":"onepiece/utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"416087452","text":"# coding=utf-8\n\nfrom sklearn.datasets.base import Bunch\nfrom sklearn import feature_extraction\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport os\nimport sys\nimport cPickle as pickle\n\n# 设置UTF-8输出环境\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\n# 读取Bunch对象\ndef readBunchObj(path):\n fileObj = open(path, 'rb')\n bunch = pickle.load(fileObj)\n fileObj.close()\n return bunch\n\n# 写入Bunch对象\ndef writeBunchObj(path, bunchObj):\n fileObj = open(path, 'wb')\n pickle.dump(bunchObj, fileObj)\n fileObj.close()\n\n\nif __name__ == '__main__':\n # 获取停用词列表\n stopWordPath = './data/train_word_bag/hlt_stop_words.txt'\n stopWordList = open(stopWordPath).readlines()\n\n # 导入分词后的词向量Bunch对象\n path = './data/test_word_bag/test_set.dat'\n bunch = readBunchObj(path)\n\n # 构建测试集TF-IDF向量空间\n testspace = Bunch(target_name=bunch.target_name, label=bunch.label,\n filenames=bunch.filenames, tdm=[], vocabulary=[])\n\n # 导入训练集测词袋\n trainbunch = readBunchObj('./data/train_word_bag/tfidfspace.dat')\n\n # 使用TfidfVectorizer初始化向量空间模型\n vectorizer = TfidfVectorizer(stop_words=stopWordList, sublinear_tf=True, max_df=0.5,\n vocabulary=trainbunch.vocabulary) # 使用训练集词袋向量\n transformer = TfidfTransformer()\n testspace.tdm = vectorizer.fit_transform(bunch.contents)\n testspace.vocabulary = trainbunch.vocabulary\n\n # 创建词袋的持久化\n space_path = './data/test_word_bag/testspace.dat' # 词向量空间保存路径\n writeBunchObj(space_path, testspace)","sub_path":"Chapter-02/tfidf_test.py","file_name":"tfidf_test.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"430172089","text":"import torch\nfrom head import _sigmoid, _sigmoid_output\nfrom mmdet.models.backbones.dla import DLA\nfrom mmdet.models.dense_heads.centertrack_head import CenterTrackHead\nfrom mmdet.models.necks.dla_neck import DLANeck\n\n# origin loss\nfrom CenterTrack.src.lib.model.losses import FastFocalLoss, RegWeightedL1Loss\nfrom CenterTrack.src.lib.model.networks.dla import DLA as DLA_Ori\nfrom CenterTrack.src.lib.model.networks.dla import BasicBlock, DLASeg\n\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = True\nuse_cuda = True\nbackbone_path = '../mmpth/backbone.pt'\nbackbone_path_ori = '/home/akio/Downloads/crowdhuman_split/backbone.pt'\nneck_path = '../mmpth/neck.pt'\nneck_path_ori = '/home/akio/Downloads/crowdhuman_split/neck.pt'\nopt_path = '/home/akio/Downloads/crowdhuman_split/opt.pt'\nhead_path = '/home/akio/Downloads/crowdhuman_split/head.pt'\nbatch_path = '/home/akio/Downloads/crowdhuman_split/batch.pt'\n# opt = Struct(**{'pre_img': True,\n# 'pre_hm': True,\n# 'head_kernel': 3,\n# 'prior_bias': -4.6,\n# 'dla_node': 'dcn',\n# 'load_model': ''}\n# )\nopt = torch.load(opt_path)\n\n# input\nx = torch.randn(1, 3, 544, 960)\npre_img = torch.randn(1, 3, 544, 960)\npre_hm = torch.randn(1, 1, 544, 960)\n# init backbone\nbackbone = DLA(levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 128, 256, 512])\nbackbone_ori = DLA_Ori([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512],\n block=BasicBlock,\n opt=opt)\n# init neck\nneck = DLANeck(channels=[16, 32, 64, 128, 256, 512], down_ratio=4)\n\n# init head\nhead_convs = {\n 'hm': [256],\n 'reg': [256],\n 'wh': [256],\n 'tracking': [256],\n 'ltrb_amodal': [256]\n}\nheads = {'hm': 1, 'reg': 2, 'wh': 2, 'tracking': 2, 'ltrb_amodal': 4}\n\nhead = CenterTrackHead(heads=dict(hm=1, reg=2, wh=2, tracking=2,\n ltrb_amodal=4),\n head_convs=dict(hm=[256],\n reg=[256],\n wh=[256],\n tracking=[256],\n ltrb_amodal=[256]),\n num_stacks=1,\n last_channel=64,\n weights=dict(hm=1,\n reg=1,\n wh=0.1,\n tracking=1,\n ltrb_amodal=0.1),\n test_cfg=dict(topk=100,\n local_maximum_kernel=3,\n max_per_img=100),\n train_cfg=dict(fp_disturb=0.1,\n lost_disturb=0.4,\n hm_disturb=0.05))\n# init origin model\nseg = DLASeg(34, heads, head_convs, opt=opt)\n\n# load backbone state_dict\nbackbone_st = torch.load(backbone_path)\nbackbone_st_ori = torch.load(backbone_path_ori)\nbackbone.load_state_dict(backbone_st)\nbackbone_ori.load_state_dict(backbone_st_ori)\n# load neck state_dict\nneck_st = torch.load(neck_path)\nneck_st_ori = torch.load(neck_path_ori)\nneck.load_state_dict(neck_st)\nseg.load_state_dict(neck_st_ori, strict=False)\n# load head state_dict\nhead_st = torch.load(head_path)\nhead.load_state_dict(head_st)\nseg.load_state_dict(head_st, strict=False)\n# move to cuda\nif use_cuda:\n backbone = backbone.cuda()\n backbone_ori = backbone_ori.cuda()\n neck = neck.cuda()\n seg = seg.cuda()\n head = head.cuda()\n x = x.cuda()\n pre_img = pre_img.cuda()\n pre_hm = pre_hm.cuda()\n\n# backbone forward\nbackbone_out = backbone(x, pre_img, pre_hm)\nbackbone_out_ori = backbone_ori(x, pre_img, pre_hm)\n\nassert all([(v1 == v2).all() for v1, v2 in zip(backbone_out, backbone_out_ori)\n ]), 'backbone != backbone_ori'\n# neck forward\n# origin partial seg\nx_ori = seg.dla_up(backbone_out_ori)\ny_ori = []\nfor i in range(seg.last_level - seg.first_level):\n y_ori.append(x_ori[i].clone())\nseg.ida_up(y_ori, 0, len(y_ori))\nneck_out_ori = y_ori[-1]\n# ---------------------\nneck_out = neck(backbone_out)\nassert (neck_out[-1] == neck_out_ori[-1]).all(), 'neck_out != neck_out_ori'\n\n# head forward\nhead_output = head(neck_out)\n\nhead_output_ori = {}\nfor head_name in seg.heads:\n head_output_ori[head_name] = seg.__getattr__(head_name)(neck_out_ori)\nhead_output_ori = [head_output_ori]\nhead_output_ori[0] = _sigmoid_output(head_output_ori[0])\nfor head_name in seg.heads:\n assert (head_output[0][head_name] == head_output_ori[0][head_name]\n ).all(), f'{head_name} not match'\n\n# loss\nbatch = torch.load(batch_path)\nif not use_cuda:\n for k, v in batch.items():\n batch[k] = v.cpu()\nloss_out = head.loss(head_output, batch)\n\n\nclass GenericLoss(torch.nn.Module):\n def __init__(self, opt):\n super(GenericLoss, self).__init__()\n self.crit = FastFocalLoss(opt=opt)\n self.crit_reg = RegWeightedL1Loss()\n\n self.opt = opt\n\n def _sigmoid_output(self, output):\n if 'hm' in output:\n output['hm'] = _sigmoid(output['hm'])\n if 'hm_hp' in output:\n output['hm_hp'] = _sigmoid(output['hm_hp'])\n if 'dep' in output:\n output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.\n return output\n\n def forward(self, outputs, batch):\n opt = self.opt\n losses = {head: 0 for head in opt.heads}\n\n for s in range(opt.num_stacks):\n output = outputs[s]\n # output = self._sigmoid_output(output)\n\n if 'hm' in output:\n losses['hm'] += self.crit(output['hm'], batch['hm'],\n batch['ind'], batch['mask'],\n batch['cat']) / opt.num_stacks\n\n regression_heads = [\n 'reg', 'wh', 'tracking', 'ltrb', 'ltrb_amodal', 'hps', 'dep',\n 'dim', 'amodel_offset', 'velocity'\n ]\n\n for head in regression_heads:\n if head in output:\n losses[head] += self.crit_reg(\n output[head], batch[head + '_mask'], batch['ind'],\n batch[head]) / opt.num_stacks\n\n if 'hm_hp' in output:\n losses['hm_hp'] += self.crit(\n output['hm_hp'], batch['hm_hp'], batch['hp_ind'],\n batch['hm_hp_mask'], batch['joint']) / opt.num_stacks\n if 'hp_offset' in output:\n losses['hp_offset'] += self.crit_reg(\n output['hp_offset'], batch['hp_offset_mask'],\n batch['hp_ind'], batch['hp_offset']) / opt.num_stacks\n\n if 'rot' in output:\n losses['rot'] += self.crit_rot(\n output['rot'], batch['rot_mask'], batch['ind'],\n batch['rotbin'], batch['rotres']) / opt.num_stacks\n\n if 'nuscenes_att' in output:\n losses['nuscenes_att'] += self.crit_nuscenes_att(\n output['nuscenes_att'], batch['nuscenes_att_mask'],\n batch['ind'], batch['nuscenes_att']) / opt.num_stacks\n\n losses['tot'] = 0\n for head in opt.heads:\n losses['tot'] += opt.weights[head] * losses[head]\n\n return losses['tot'], losses\n\n\nloss_ori = GenericLoss(opt=opt)\nif use_cuda:\n loss_ori = loss_ori.cuda()\n\n_, loss_out_ori = loss_ori(head_output_ori, batch)\nfor k in loss_out.keys():\n assert (loss_out[k] == (opt.weights)[k[5:]] *\n loss_out_ori[k[5:]]).all(), f'Loss: {k} not match'\n\nprint('done loss')\n","sub_path":"test/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":7658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"236451278","text":"import face_recognition\nimport numpy as np\nimport cv2, queue, threading, datetime\nimport requests, os, re\nimport mysql.connector\nfrom mysql.connector import MySQLConnection, Error\nfrom dbconfig import read_dbconfig\n\n\n# bufferless VideoCapture\nclass VideoCapture:\n def __init__(self, name):\n self.cap = cv2.VideoCapture(name)\n self.q = queue.Queue()\n t = threading.Thread(target=self._reader)\n t.daemon = True\n t.start()\n\n # read frames as soon as they are available, keeping only most recent one\n def _reader(self):\n while True:\n ret, frame = self.cap.read()\n if not ret:\n break\n if not self.q.empty():\n try:\n self.q.get_nowait() # discard previous (unprocessed) frame\n except queue.Empty:\n pass\n self.q.put(frame)\n\n def read(self):\n return self.q.get()\n\n# Select the webcam of the computer\nvideo_capture = VideoCapture(0)\n\n\nknown_face_encodings = []\nknown_face_names = []\nknown_faces_filenames = []\n\nfor (dirpath, dirnames, filenames) in os.walk('pictures/'):\n known_faces_filenames.extend(filenames)\n break\n\nfor filename in known_faces_filenames:\n face = face_recognition.load_image_file('pictures/' + filename)\n known_face_names.append(re.sub(\"[0-9]\",'', filename[:-4]))\n known_face_encodings.append(face_recognition.face_encodings(face)[0])\n\n\n\nface_locations = []\nface_encodings = []\nface_names = []\nprocess_this_frame = True\n\n\nwhile True:\n\n # Grab a single frame of video\n frame = video_capture.read()\n \n # Process every frame only one time\n if process_this_frame:\n\n # Find all the faces and face encodings in the current frame of video\n face_locations = face_recognition.face_locations(frame)\n face_encodings = face_recognition.face_encodings(frame, face_locations)\n \n # Initialize an array for the name of the detected users\n face_names = []\n\n \n for face_encoding in face_encodings:\n\n # See if the face is a match for the known face(s)\n matches = face_recognition.compare_faces(known_face_encodings, face_encoding,0.5)\n name = \"Unknown\"\n\n # If a match was found in known_face_encodings, use the known face with the smallest distance to the new face\n face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)\n best_match_index = np.argmin(face_distances)\n\n if matches[best_match_index]:\n name = known_face_names[best_match_index]\n\n date_sortie = datetime.datetime.now().date().strftime('%Y-%m-%d')\n heure_sortie = datetime.datetime.now().time().strftime('%H:%M:%S')\n try:\n\n status = 'present'\n\n if heure_sortie Union[None, Tuple[int, int]]:#화살표 -> 반환되는 형식을 나타냄\n \"\"\"Converts normalized value pair to pixel coordinates.\"\"\"\n\n # Checks if the float value is between 0 and 1.\n def is_valid_normalized_value(value: float) -> bool:# 벨류 값이 0보다 크고나 0이랑 비슷하고, 1보다 작거나 1보다 비슷할때 true\n return (value > 0 or math.isclose(0, value)) and (value < 1 or\n math.isclose(1, value))\n\n if not (is_valid_normalized_value(normalized_x) and #is_valid_normalized_value함수를 만족하지않을대 flase\n is_valid_normalized_value(normalized_y)):\n # TODO: Draw coordinates even if it's outside of the image bounds.\n return None\n x_px = min(math.floor(normalized_x * image_width), image_width - 1) #is_valid_normalized_value함수를 만족할 때 *min: 주어진 자료형에서 최소값 반환\n y_px = min(math.floor(normalized_y * image_height), image_height - 1)#*math.floor 가장 가까운 정수로 내림\n return x_px, y_px\n\n\n# def draw_detection(\n# image: np.ndarray,\n# detection: detection_pb2.Detection,\n# keypoint_drawing_spec: DrawingSpec = DrawingSpec(color=RED_COLOR),\n# bbox_drawing_spec: DrawingSpec = DrawingSpec()):\n# \"\"\"Draws the detction bounding box and keypoints on the image.\n#\n# Args:\n# image: A three channel RGB image represented as numpy ndarray.\n# detection: A detection proto message to be annotated on the image.\n# keypoint_drawing_spec: A DrawingSpec object that specifies the keypoints'\n# drawing settings such as color, line thickness, and circle radius.\n# bbox_drawing_spec: A DrawingSpec object that specifies the bounding box's\n# drawing settings such as color and line thickness.\n#\n# Raises:\n# ValueError: If one of the followings:\n# a) If the input image is not three channel RGB.\n# b) If the location data is not relative data.\n# \"\"\"\n# if not detection.location_data:\n# return\n# if image.shape[2] != RGB_CHANNELS:\n# raise ValueError('Input image must contain three channel rgb data.')\n# image_rows, image_cols, _ = image.shape\n#\n# location = detection.location_data\n# if location.format != location_data_pb2.LocationData.RELATIVE_BOUNDING_BOX:\n# raise ValueError(\n# 'LocationData must be relative for this drawing funtion to work.')\n# # Draws keypoints.\n# for keypoint in location.relative_keypoints:\n# keypoint_px = _normalized_to_pixel_coordinates(keypoint.x, keypoint.y,\n# image_cols, image_rows)\n# cv2.circle(image, keypoint_px, keypoint_drawing_spec.circle_radius,\n# keypoint_drawing_spec.color, keypoint_drawing_spec.thickness)\n# # Draws bounding box if exists.\n# if not location.HasField('relative_bounding_box'):\n# return\n# relative_bounding_box = location.relative_bounding_box\n# rect_start_point = _normalized_to_pixel_coordinates(\n# relative_bounding_box.xmin, relative_bounding_box.ymin, image_cols,\n# image_rows)\n# rect_end_point = _normalized_to_pixel_coordinates(\n# relative_bounding_box.xmin + relative_bounding_box.width,\n# relative_bounding_box.ymin + +relative_bounding_box.height, image_cols,\n# image_rows)\n# cv2.rectangle(image, rect_start_point, rect_end_point,\n# bbox_drawing_spec.color, bbox_drawing_spec.thickness)\n\n\ndef draw_landmarks(\n image: np.ndarray,\n landmark_list: landmark_pb2.NormalizedLandmarkList,\n connections: List[Tuple[int, int]] = None,\n landmark_drawing_spec: DrawingSpec = DrawingSpec(color=RED_COLOR),\n connection_drawing_spec: DrawingSpec = DrawingSpec()):\n \"\"\"Draws the landmarks and the connections on the image.\n\n Args:\n image: A three channel RGB image represented as numpy ndarray.\n landmark_list: A normalized landmark list proto message to be annotated on\n the image.\n connections: A list of landmark index tuples that specifies how landmarks to\n be connected in the drawing.\n landmark_drawing_spec: A DrawingSpec object that specifies the landmarks'\n drawing settings such as color, line thickness, and circle radius.\n connection_drawing_spec: A DrawingSpec object that specifies the\n connections' drawing settings such as color and line thickness.\n\n Raises:\n ValueError: If one of the followings:\n a) If the input image is not three channel RGB.\n b) If any connetions contain invalid landmark index.\n \"\"\"\n if not landmark_list:\n return\n if image.shape[2] != RGB_CHANNELS:# 3채널 아니면 에러\n raise ValueError('Input image must contain three channel rgb data.')\n image_rows, image_cols, _ = image.shape # 가로 세로 분리\n idx_to_coordinates = {}\n for idx, landmark in enumerate(landmark_list.landmark):\n if ((landmark.HasField('visibility') and\n landmark.visibility < VISIBILITY_THRESHOLD) or\n (landmark.HasField('presence') and\n landmark.presence < PRESENCE_THRESHOLD)):\n continue\n landmark_px = _normalized_to_pixel_coordinates(landmark.x, landmark.y, # 정규화 x y 반환\n image_cols, image_rows)\n if landmark_px:# 값이 존재하면 idx_to_coordinates에 값 넣어줌\n idx_to_coordinates[idx] = landmark_px\n if connections:# 얼굴 선 연결\n num_landmarks = len(landmark_list.landmark)\n # Draws the connections if the start and end landmarks are both visible.\n # for connection in connections:\n # start_idx = connection[0]\n # end_idx = connection[1]\n # if not (0 <= start_idx < num_landmarks and 0 <= end_idx < num_landmarks):# 시작 인덱스와 끝 인덱스가 랜드마크 총 갯수 보다 작을 때 에러\n # raise ValueError(f'Landmark index is out of range. Invalid connection '\n # f'from landmark #{start_idx} to landmark #{end_idx}.')\n # if start_idx in idx_to_coordinates and end_idx in idx_to_coordinates:\n # cv2.line(image, idx_to_coordinates[start_idx],\n # idx_to_coordinates[end_idx], connection_drawing_spec.color,\n # connection_drawing_spec.thickness)\n # print(\"index: {start_idx}\".format(start_idx=start_idx))\n # cv2.putText(image,\"({0},{1})\".format(start_idx,end_idx),org=(idx_to_coordinates[start_idx][0]-2,idx_to_coordinates[start_idx][1]-3),fontFace=cv2.FONT_HERSHEY_SIMPLEX,fontScale=0.3,color=(255,255,255))\n #점찍기\n # if start_idx in idx_to_coordinates and end_idx in idx_to_coordinates:\n # cv2.circle(image, idx_to_coordinates[start_idx],2,(0,0,255))\n # cv2.circle(image, idx_to_coordinates[end_idx], 2, (0, 0, 255))\n # cv2.putText(image, \"({0})\".format(start_idx),\n # org=(idx_to_coordinates[start_idx][0] - 2, idx_to_coordinates[start_idx][1] - 3),\n # fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.3, color=(0, 0, 255))\n # cv2.putText(image, \"({0})\".format(end_idx),\n # org=(idx_to_coordinates[end_idx][0] - 2, idx_to_coordinates[end_idx][1] - 3),\n # fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.3, color=(0, 0, 255))\n # 전체 점찍기\n try:\n success_flag = True\n for i in POINT:\n cv2.circle(image, (idx_to_coordinates[i][0],idx_to_coordinates[i][1]), 2, (0, 0, 255))\n # cv2.putText(image, \"({0})\".format(i),\n # org=(idx_to_coordinates[i][0] - 2, idx_to_coordinates[i][1] - 3),\n # fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.3, color=(0, 0, 255))\n # print(\"x: {0}, y: {1}\".format(idx_to_coordinates[i][0],idx_to_coordinates[i][1]))\n #print(math.sqrt(math.pow((idx_to_coordinates[i][0]-idx_to_coordinates[i+1][0]),2)+math.pow((idx_to_coordinates[i][1]-idx_to_coordinates[i+1][1]),2)))#두점 거리\n #print(f\"좌표 : {idx_to_coordinates[i]}\")\n point_to_point_distance(idx_to_coordinates, image)#############특징점 잡는거!!!!!!!\n # cv2.imshow('hi', image)\n # cv2.waitKey(0)\n except Exception as e:\n print(\"오류 키\", e)\n success_flag = False\n return idx_to_coordinates,success_flag\n\n return idx_to_coordinates,success_flag\n # Draws landmark points after finishing the connection lines, which is\n # aesthetically better.\n '''\n 바꾼부분\n \n '''\n # file_list = {\"iu1.jfif\": 1}\n # for idx, file in enumerate(file_list):\n # # print(file)\n # image = cv2.imread(file)\n # # Convert the BGR image to RGB before processing.\n # # results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n # # print(\"result: {0}\".format(results))\n #\n # annotated_image = image.copy()\n # annotated_image = cv2.circle(annotated_image, idx_to_coordinates.values(), 1, (0, 0, 255), -1) ###############\n # cv2.imshow('hi', annotated_image)\n # cv2.waitKey(0)\n '''\n 끝\n '''\n # for landmark_px in idx_to_coordinates.values():\n # cv2.circle(image, landmark_px, landmark_drawing_spec.circle_radius,\n # landmark_drawing_spec.color, landmark_drawing_spec.thickness)\n # cv2.imshow('hi', image)\n # cv2.waitKey(0)\n #\n # cv2.imshow('hi', image)\n # time.sleep(3)\n\n\n# def draw_axis(\n# image: np.ndarray,\n# rotation: np.ndarray,\n# translation: np.ndarray,\n# focal_length: Tuple[float, float] = (1.0, 1.0),\n# principal_point: Tuple[float, float] = (0.0, 0.0),\n# axis_length: float = 0.1,\n# x_axis_drawing_spec: DrawingSpec = DrawingSpec(color=(0, 0, 255)),\n# y_axis_drawing_spec: DrawingSpec = DrawingSpec(color=(0, 128, 0)),\n# z_axis_drawing_spec: DrawingSpec = DrawingSpec(color=(255, 0, 0))):\n# \"\"\"Draws the 3D axis on the image.\n#\n# Args:\n# image: A three channel RGB image represented as numpy ndarray.\n# rotation: Rotation matrix from object to camera coordinate frame.\n# translation: Translation vector from object to camera coordinate frame.\n# focal_length: camera focal length along x and y directions.\n# principal_point: camera principal point in x and y.\n# axis_length: length of the axis in the drawing.\n# x_axis_drawing_spec: A DrawingSpec object that specifies the x axis\n# drawing settings such as color, line thickness.\n# y_axis_drawing_spec: A DrawingSpec object that specifies the y axis\n# drawing settings such as color, line thickness.\n# z_axis_drawing_spec: A DrawingSpec object that specifies the z axis\n# drawing settings such as color, line thickness.\n#\n# Raises:\n# ValueError: If one of the followings:\n# a) If the input image is not three channel RGB.\n# \"\"\"\n# if image.shape[2] != RGB_CHANNELS:\n# raise ValueError('Input image must contain three channel rgb data.')\n# image_rows, image_cols, _ = image.shape\n# # Create axis points in camera coordinate frame.\n# axis_world = np.float32([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]])\n# axis_cam = np.matmul(rotation, axis_length*axis_world.T).T + translation\n# x = axis_cam[..., 0]\n# y = axis_cam[..., 1]\n# z = axis_cam[..., 2]\n# # Project 3D points to NDC space.\n# fx, fy = focal_length\n# px, py = principal_point\n# x_ndc = -fx * x / z + px\n# y_ndc = -fy * y / z + py\n# # Convert from NDC space to image space.\n# x_im = np.int32((1 + x_ndc) * 0.5 * image_cols)\n# y_im = np.int32((1 - y_ndc) * 0.5 * image_rows)\n# # Draw xyz axis on the image.\n# origin = (x_im[0], y_im[0])\n# x_axis = (x_im[1], y_im[1])\n# y_axis = (x_im[2], y_im[2])\n# z_axis = (x_im[3], y_im[3])\n# image = cv2.arrowedLine(image, origin, x_axis, x_axis_drawing_spec.color,\n# x_axis_drawing_spec.thickness)\n# image = cv2.arrowedLine(image, origin, y_axis, y_axis_drawing_spec.color,\n# y_axis_drawing_spec.thickness)\n# image = cv2.arrowedLine(image, origin, z_axis, z_axis_drawing_spec.color,\n# z_axis_drawing_spec.thickness)\n","sub_path":"개별테스트/insuk_2/mediapipe/drawing_utils.py","file_name":"drawing_utils.py","file_ext":"py","file_size_in_byte":15942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"105177479","text":"#!/usr/bin/env python\r\nimport os\r\nimport scipy\r\nimport numpy as np\r\nfrom scipy import stats\r\n\r\nfrom .classifier import ScanClassifier, preprocess_scan, preprocess_gp\r\n\r\n\r\ndef get_params():\r\n curve = {\r\n 'max_vel_x': 0.26,\r\n 'max_vel_theta': 2,\r\n 'vx_samples': 13,\r\n 'vtheta_samples': 44,\r\n 'occdist_scale': 0.57,\r\n 'path_distance_bias': 0.76,\r\n 'goal_distance_bias': 0.94,\r\n }\r\n open_space = {\r\n 'max_vel_x': 1.91,\r\n 'max_vel_theta': 1.70,\r\n 'vx_samples': 10,\r\n 'vtheta_samples': 47,\r\n 'occdist_scale': 0.08,\r\n 'path_distance_bias': 0.71,\r\n 'goal_distance_bias': 0.35,\r\n }\r\n U_turn = {\r\n 'max_vel_x': 0.45,\r\n 'max_vel_theta': 1.02,\r\n 'vx_samples': 20,\r\n 'vtheta_samples': 30,\r\n 'occdist_scale': 0.82,\r\n 'path_distance_bias': 0.88,\r\n 'goal_distance_bias': 0.43,\r\n }\r\n narrow_entrance = {\r\n 'max_vel_x': 0.72,\r\n 'max_vel_theta': 0.73,\r\n 'vx_samples': 19,\r\n 'vtheta_samples': 59,\r\n 'occdist_scale': 0.62,\r\n 'path_distance_bias': 1.00,\r\n 'goal_distance_bias': 0.32,\r\n }\r\n narrow_corridor = {\r\n 'max_vel_x': 0.22,\r\n 'max_vel_theta': 0.87,\r\n 'vx_samples': 13,\r\n 'vtheta_samples': 31,\r\n 'occdist_scale': 0.30,\r\n 'path_distance_bias': 0.36,\r\n 'goal_distance_bias': 0.71,\r\n }\r\n normal1 = {\r\n 'max_vel_x': 0.37,\r\n 'max_vel_theta': 1.33,\r\n 'vx_samples': 9,\r\n 'vtheta_samples': 6,\r\n 'occdist_scale': 0.95,\r\n 'path_distance_bias': 0.83,\r\n 'goal_distance_bias': 0.93,\r\n }\r\n normal2 = {\r\n 'max_vel_x': 0.31,\r\n 'max_vel_theta': 1.05,\r\n 'vx_samples': 17,\r\n 'vtheta_samples': 20,\r\n 'occdist_scale': 0.45,\r\n 'path_distance_bias': 0.61,\r\n 'goal_distance_bias': 0.22,\r\n }\r\n default = {\r\n 'max_vel_x': 0.5,\r\n 'max_vel_theta': 1.57,\r\n 'vx_samples': 6,\r\n 'vtheta_samples': 20,\r\n 'occdist_scale': 0.1,\r\n 'path_distance_bias': 0.75,\r\n 'goal_distance_bias': 1.0,\r\n }\r\n env_params = [curve, open_space, U_turn, narrow_entrance, narrow_corridor, normal1, normal2, default]\r\n inflates = [0.02, 0.23, 0.005, 0.24, 0.30, 0.01, 0.23, 0.30]\r\n return env_params, inflates\r\n\r\n\r\nclass ScanClassifierParams:\r\n def __init__(self):\r\n self.Dx = 640\r\n\r\n self.used_context = [\"curve\", \"open_space\", \"narrow_entrance\", \"narrow_corridor\"]\r\n self.full_train = False\r\n\r\n self.use_conv1d = True\r\n self.scan_Dhs = [128]\r\n self.gp_Dhs = [0]\r\n self.kernel_sizes = [40]\r\n self.filter_sizes = [32]\r\n self.strides = [5]\r\n\r\n self.dropout_rate = 0.2\r\n self.use_EDL = True\r\n\r\n self.lr = 3e-4\r\n self.epochs = 500\r\n self.batch_size = 32\r\n\r\n self.use_weigth = False\r\n self.clipping = 5.0\r\n self.centering_on_gp = False\r\n self.cropping = True\r\n self.theta_noise_scale = 10 # in degree\r\n self.noise = True\r\n self.noise_scale = 0.05\r\n self.flipping = True\r\n self.translation = True\r\n self.translation_scale = 0.10\r\n\r\n self.model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"APPLI.pickle\")\r\n self.rslts_dir = None\r\n self.Dy = len(self.used_context)\r\n\r\n\r\nclass APPLI_policy(object):\r\n def __init__(self):\r\n scan_classifier_params = ScanClassifierParams()\r\n scan_classifier = ScanClassifier(scan_classifier_params)\r\n scan_classifier._init_model()\r\n scan_classifier._load_model(scan_classifier_params.model_path)\r\n self.scan_classifier = scan_classifier\r\n\r\n self.env_params, self.env_inflates = get_params()\r\n\r\n def forward(self, obs):\r\n scan, global_path = obs\r\n scan = np.array(scan)\r\n if len(global_path) == 0:\r\n return\r\n envs = ['curve', 'open_space', 'U_turn', 'narrow_entrance', 'narrow_corridor',\r\n 'normal_1', 'normal_2', 'default']\r\n\r\n scan = preprocess_scan(scan, self.scan_classifier)\r\n global_path = preprocess_gp(global_path.T)\r\n context_type = self.scan_classifier.predict(scan, global_path)\r\n if isinstance(context_type, tuple):\r\n context_type, confidence = context_type\r\n print(\"[INFO] confidence: {0:.3f}, prediction: {1}\".format(confidence, self.scan_classifier.used_context[context_type]))\r\n if confidence < 0.8:\r\n context_type = 'default'\r\n if context_type != 'default':\r\n context_type = self.scan_classifier.used_context[context_type]\r\n context_type = envs.index(context_type)\r\n print(\"[INFO] current context: \", envs[context_type])\r\n\r\n action = np.array([self.env_params[context_type]['max_vel_x'],\r\n self.env_params[context_type]['max_vel_theta'],\r\n self.env_params[context_type]['vx_samples'],\r\n self.env_params[context_type]['vtheta_samples'],\r\n self.env_params[context_type]['occdist_scale'],\r\n self.env_params[context_type]['path_distance_bias'],\r\n self.env_params[context_type]['goal_distance_bias'],\r\n self.env_inflates[context_type]])\r\n\r\n return action\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import rospy\r\n from sensor_msgs.msg import LaserScan\r\n from nav_msgs.msg import Path, Odometry\r\n import dynamic_reconfigure.client\r\n from context_classifier import Predictor\r\n\r\n policy = APPLI_policy()\r\n\r\n predictor = Predictor(policy.scan_classifier)\r\n\r\n rospy.init_node('context_classifier', anonymous=True)\r\n env_params, env_inflates = get_params()\r\n\r\n def test(msg):\r\n if len(predictor.global_path) == 0:\r\n return\r\n scan = msg.ranges\r\n policy.forward([scan, predictor.global_path])\r\n\r\n sub_robot = rospy.Subscriber(\"/odometry/filtered\", Odometry, predictor.update_status)\r\n sub_gp = rospy.Subscriber(\"/move_base/TrajectoryPlannerROS/global_plan\",\r\n Path, predictor.update_global_path, queue_size=1)\r\n sub_scan = rospy.Subscriber(\"/front/scan\", LaserScan, test, queue_size=1)\r\n\r\n client = dynamic_reconfigure.client.Client('move_base/TrajectoryPlannerROS')\r\n client2 = dynamic_reconfigure.client.Client('move_base/local_costmap/inflater_layer')\r\n while not rospy.is_shutdown():\r\n try:\r\n ct = predictor.context_type\r\n params = env_params[ct]\r\n infla = env_inflates[ct]\r\n config = client.update_configuration(params)\r\n config2 = client2.update_configuration({'inflation_radius': infla})\r\n except dynamic_reconfigure.DynamicReconfigureCallbackException:\r\n continue\r\n except rospy.exceptions.ROSInterruptException:\r\n break\r\n\r\n\r\n","sub_path":"jackal_navi_envs/APPLX/APPLI_policy.py","file_name":"APPLI_policy.py","file_ext":"py","file_size_in_byte":7052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"56238428","text":"from tela import tela\nimport pygame\nfrom assetLoader import assetLoader\nclass Animacao():\n def __init__(self, model):\n self.frames = []\n self.vel = 10\n self.pos = [0,0]\n self.frameAtual = 0\n self.loop = True\n self.terminou = False\n self.count = 0\n\n for frames in model[\"frames\"]:\n quadro = assetLoader.procurar(frames, \"imagem\")\n quadro = pygame.transform.scale(quadro, (400, 600))\n quadro = pygame.transform.rotate(quadro, 90)\n self.frames.append(quadro)\n \n def play(self):\n tela.blit(self.frames[self.frameAtual], self.pos)\n if self.count <= self.vel:\n self.count += 1\n else:\n self.count = 0\n if (self.frameAtual == len(self.frames) - 1) and self.loop:\n self.frameAtual = 0\n elif (self.frameAtual < len(self.frames) - 1):\n self.frameAtual += 1\n\n\nclass Parallax():\n def __init__(self, model):\n self.frame0 = model[\"frames\"][0] \n self.frame0 = assetLoader.procurar(self.frame0, \"imagem\")\n # self.frame0 = pygame.transform.scale(self.frame0, (400, 600))\n # self.frame0 = pygame.transform.rotate(self.frame0, 90)\n\n\n self.frame1 = model[\"frames\"][1] \n self.frame1 = assetLoader.procurar(self.frame1, \"imagem\")\n # self.frame1 = pygame.transform.scale(self.frame1, (400, 600))\n # self.frame1 = pygame.transform.rotate(self.frame1, 90)\n\n self.frame2 = model[\"frames\"][2] \n self.frame2 = assetLoader.procurar(self.frame2, \"imagem\")\n self.frame2 = pygame.transform.scale(self.frame2, (600, 600))\n # self.frame2 = pygame.transform.rotate(self.frame2, 90)\n\n self.frame3 = model[\"frames\"][3] \n self.frame3 = assetLoader.procurar(self.frame3, \"imagem\")\n self.frame3 = pygame.transform.scale(self.frame3, (600, 600))\n # self.frame3 = pygame.transform.rotate(self.frame3, 90)\n\n self.frame0Pos = [0,0]\n self.frame1Pos = [0,0]\n self.frame2Pos = [0,-200]\n self.frame3Pos = [100,150]\n \n def draw(self):\n tela.blit(self.frame0, self.frame0Pos)\n\n tela.blit(self.frame1, self.frame1Pos)\n tela.blit(self.frame1, [self.frame1Pos[0] + 600,self.frame1Pos[1]])\n \n tela.blit(self.frame2, self.frame2Pos)\n tela.blit(self.frame2, [self.frame2Pos[0] + 600,self.frame2Pos[1]])\n\n tela.blit(self.frame3, self.frame3Pos)\n tela.blit(self.frame3, [self.frame3Pos[0] + 600,self.frame3Pos[1]])\n\n","sub_path":"scripts/animacao.py","file_name":"animacao.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"489423977","text":"import os\nfrom random import randint\n\noutputs = []\njobs = []\n\nroulette_bullet = None\nroulette_counter = 0\n\ndef process_message(data):\n channel = data[\"channel\"]\n text = data[\"text\"]\n uid = data[\"user\"]\n\n # D is direct\n # C is channel\n # if channel.startswith(\"D\"):\n if channel.startswith(\"C\"):\n if text.startswith(\"roulette\"):\n cmd = text[8:].strip()\n global roulette_bullet, roulette_counter\n if cmd == \"start\":\n roulette_bullet = randint(1,6)\n roulette_counter = 0\n outputs.append([channel, \"Loading gun!\"])\n elif cmd == \"fire\":\n if roulette_bullet is None:\n outputs.append([channel, \"Pussy! that gun isn't even loaded....\"])\n else:\n roulette_counter += 1\n if roulette_counter == roulette_bullet:\n outputs.append([channel, \"BOOOOOM! You died! :D\"])\n roulette_bullet = None\n else:\n outputs.append([channel, \"CLICK...\"])\n","sub_path":"doc/example-plugins/roulette.py","file_name":"roulette.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"17442384","text":"from typing import List\ndef create_spiral_matrix(row: int, col: int, start: int, end: int) -> List[List[int]]:\n\tif row * col != end - start + 1: \n\t\treturn []\n\tmat = [[0] * col for _ in range(row)]\n\ttop, bottom, left, right = 0, row-1, 0, col-1\n\tcur = start\n\twhile cur <= end:\n\t\tl = left\n\t\twhile l <= right and top <= bottom:\n\t\t\tmat[top][l] = cur\n\t\t\tl += 1\n\t\t\tcur += 1\n\t\ttop += 1\n\t\n\t\tt = top\n\t\twhile t <= bottom and left <= right:\n\t\t\tmat[t][right] = cur\n\t\t\tt += 1\n\t\t\tcur += 1\n\t\tright -= 1\n\n\t\tr = right\n\t\twhile r >= left and top <= bottom:\n\t\t\tmat[bottom][r] = cur\n\t\t\tr -= 1\n\t\t\tcur += 1\t\n\t\tbottom -= 1\n\t\t\n\t\tb = bottom\n\t\twhile b >= top and left <= right:\n\t\t\tmat[b][left] = cur\n\t\t\tb -= 1\n\t\t\tcur += 1\n\t\tleft += 1\n\treturn mat\t\n\n#mat = create_spiral_matrix(5, 5, 1, 25)\n#for row in mat:\n#\tprint(row)\n\n# https://www.geeksforgeeks.org/print-n-x-n-spiral-matrix-using-o1-extra-space/\ndef create_spiral_matrix_2(row: int, col: int, start: int, end: int) -> List[List[int]]:\n\t# Matrix need to be square\n\tif row * col != end - start + 1: \n\t\treturn []\n\tmat = [[0] * col for _ in range(row)]\n\t# diff: the difference between start and 1, since original method will always start from 1\n\t# n = size of square matrix\n\tdiff, n = start - 1, row\n\tfor i in range(0, n):\n\t\tfor j in range(0, n):\n\t\t\t# top, left, bottom, right\n\t\t\t# i j n-1-i n-1-j\n\t\t\tx = min(min(i, j), min(n-i-1, n-j-1))\n\t\t\tif i <= j: # up-right triangle\n\t\t\t\tmat[i][j] = (n-2*x)**2 - (i-x) - (j-x) + diff\n\t\t\telse: # low-left triangle\n\t\t\t\tmat[i][j] = (n-2*x-2)**2 + (i-x) + (j-x) + diff\n\treturn mat\n\nmat1 = create_spiral_matrix_2(5, 5, 3, 27)\nfor row in mat1:\n\tprint(row)\n","sub_path":"other-than-leetcode/robinhood/create-spiral-matrix.py","file_name":"create-spiral-matrix.py","file_ext":"py","file_size_in_byte":1621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"183330443","text":"import logging\nimport os\n\nimport pyspark\nimport pytest\nfrom click.testing import CliRunner\n\nimport mobile_dataset as d\nfrom mozaggregator.cli import run_mobile\nfrom mozaggregator.mobile import _aggregate_metrics, get_aggregates_dataframe\nfrom utils import runif_bigquery_testing_enabled, runif_avro_testing_enabled\n\n@pytest.fixture()\ndef aggregates_rdd(sc):\n logger = logging.getLogger(\"py4j\")\n logger.setLevel(logging.ERROR)\n\n raw_pings = list(d.generate_mobile_pings())\n return _aggregate_metrics(sc.parallelize(raw_pings), num_partitions=10)\n\n\n@pytest.fixture()\ndef aggregates(aggregates_rdd):\n return aggregates_rdd.collect()\n\n\ndef test_count(aggregates):\n pings = list(d.generate_mobile_pings())\n assert(len(pings) / d.NUM_PINGS_PER_DIMENSIONS == len(aggregates))\n\n\ndef test_keys(aggregates):\n for aggregate in aggregates:\n (submission_date, channel, version, build_id, application,\n architecture, os, os_version) = aggregate[0]\n assert(submission_date == d.meta_template[\"submissionDate\"])\n assert(channel in d.ping_dimensions[\"normalizedChannel\"])\n assert(version == d.meta_template[\"appVersion\"])\n assert(build_id == d.meta_template[\"appBuildId\"])\n assert(application == d.meta_template[\"appName\"])\n assert(architecture in d.ping_dimensions[\"arch\"])\n assert(os == d.meta_template[\"normalizedOs\"])\n assert(os_version in d.ping_dimensions[\"osversion\"])\n\n\ndef test_histograms(aggregates):\n n = d.NUM_PINGS_PER_DIMENSIONS\n for aggregate in aggregates:\n for metric_data in aggregate[1].items():\n metric_name, metric_key, process = metric_data[0]\n # A regular histogram.\n if metric_name in d.histograms_template.keys():\n tpl = d.histograms_template[metric_name]\n assert(metric_data[1]['count'] == n)\n assert(metric_data[1]['sum'] == tpl['sum'] * n)\n for k, v in tpl['values'].items():\n assert(metric_data[1]['histogram'][k] == v * n)\n # A keyed histogram.\n elif metric_name in d.keyed_histograms_template.keys():\n tpl = d.keyed_histograms_template[metric_name]\n assert(metric_data[1]['count'] == n)\n assert(metric_data[1]['sum'] == tpl[metric_key]['sum'] * n)\n for k, v in tpl[metric_key]['values'].items():\n assert(metric_data[1]['histogram'][k] == v * n)\n\n\ndef test_scalars(aggregates):\n n = d.NUM_PINGS_PER_DIMENSIONS\n for aggregate in aggregates:\n for metric_data in aggregate[1].items():\n metric_name, metric_key, process = metric_data[0]\n metric_name = metric_name.split('_')[1].lower()\n # A regular scalar.\n if metric_name in d.scalars_template.keys():\n value = d.scalars_template[metric_name]\n # A keyed scalar.\n elif metric_name in d.keyed_scalars_template.keys():\n value = d.keyed_scalars_template[metric_name][metric_key]\n else:\n continue\n assert(metric_data[1]['count'] == n)\n assert(metric_data[1]['sum'] == value * n)\n assert(metric_data[1]['histogram'] == {str(value): n})\n\n\ndef test_mobile_aggregation_cli(tmp_path, monkeypatch, spark, aggregates_rdd):\n output = str(tmp_path / \"output\")\n\n class Dataset:\n @staticmethod\n def from_source(*args, **kwargs):\n return Dataset()\n\n def where(self, *args, **kwargs):\n return self\n\n def records(self, *args, **kwargs):\n return spark.sparkContext.parallelize(d.generate_mobile_pings())\n\n monkeypatch.setattr(\"mozaggregator.mobile.Dataset\", Dataset)\n\n result = CliRunner().invoke(\n run_mobile,\n [\n \"--date\",\n # this date is ignored because we are monkeypatching the dataset\n \"20190901\",\n \"--output\",\n output,\n \"--num-partitions\",\n 10,\n ],\n catch_exceptions=False,\n )\n\n assert result.exit_code == 0\n\n expect = get_aggregates_dataframe(spark, aggregates_rdd)\n actual = spark.read.parquet(output)\n\n assert expect.count() > 0 and actual.count() > 0\n assert expect.count() == actual.count()\n\n\n@runif_bigquery_testing_enabled\ndef test_mobile_aggregation_cli_bigquery(tmp_path, spark, aggregates_rdd, bq_testing_table):\n output = str(tmp_path / \"output\")\n\n result = CliRunner().invoke(\n run_mobile,\n [\n \"--date\",\n d.SUBMISSION_DATE_1.strftime('%Y%m%d'),\n \"--output\",\n output,\n \"--num-partitions\",\n 10,\n \"--source\",\n \"bigquery\",\n \"--project-id\",\n os.environ[\"PROJECT_ID\"],\n \"--dataset-id\",\n \"pytest_mozaggregator_test\"\n ],\n catch_exceptions=False,\n )\n assert len({f\"submission_date={d.SUBMISSION_DATE_1.strftime('%Y%m%d')}\"} - set(os.listdir(output))) == 0\n\n expect = get_aggregates_dataframe(spark, aggregates_rdd)\n actual = spark.read.parquet(output)\n\n assert expect.count() > 0 and actual.count() > 0\n assert expect.count() == actual.count()\n\n\n@runif_avro_testing_enabled\ndef test_mobile_aggregation_cli_avro(tmp_path, spark, aggregates_rdd, avro_testing_files):\n output = str(tmp_path / \"output\")\n\n result = CliRunner().invoke(\n run_mobile,\n [\n \"--date\",\n d.SUBMISSION_DATE_1.strftime('%Y%m%d'),\n \"--output\",\n output,\n \"--num-partitions\",\n 10,\n \"--source\",\n \"avro\",\n \"--avro-prefix\",\n avro_testing_files,\n ],\n catch_exceptions=False,\n )\n\n assert result.exit_code == 0\n\n expect = get_aggregates_dataframe(spark, aggregates_rdd)\n actual = spark.read.parquet(output)\n\n assert expect.count() > 0 and actual.count() > 0\n assert expect.count() == actual.count()\n","sub_path":"tests/test_mobile.py","file_name":"test_mobile.py","file_ext":"py","file_size_in_byte":6040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"290722059","text":"a=0\r\nb=1\r\nx = int(input(\"Enter the no: \"))\r\nprint(\"Fibonacci upto \",x,\" :\",end='')\r\nwhile a <= x:\r\n print(a,end=' ')\r\n c = a+b\r\n a = b\r\n b = c\r\nprint('\\r')\r\n\r\na=0\r\nb=1\r\ncounter = 1\r\nprint(\"First \",x,\"Fibonacci no: \",end='')\r\nwhile counter <= x:\r\n print(a,end=' ')\r\n c = a+b\r\n a = b\r\n b = c\r\n counter = counter + 1\r\nprint('\\r')","sub_path":"fibonaci.py","file_name":"fibonaci.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"414487156","text":"#!/usr/bin/env python\nimport pika\nimport sys\n\nconnection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))\nchannel = connection.channel()\nchannel.basic_qos(prefetch_count=1)\nchannel.exchange_declare(exchange='2d',\n exchange_type='topic')\n\nresult1 = channel.queue_declare(queue='2d',durable=False)\nresult2 = channel.queue_declare(queue='3d',durable=False)\nresult3 = channel.queue_declare(queue='2D',durable=True)\nresult4 = channel.queue_declare(queue='3D',durable=True)\nqueue_name_2d = result3.method.queue\nqueue_name_3d = result4.method.queue\nprint('queuename 2d',queue_name_2d)\nprint('queuename 3d',queue_name_3d)\nqueue_name_2d = '2d'\n\nchannel.queue_bind(exchange='2d',\n queue='2D',\n routing_key='2d')\n\nchannel.queue_bind(exchange='3d',\n queue='3D',\n routing_key='3d')\n\ndef callback(ch, method, properties, body):\n print(\" [x] %r:%r\" % (method.routing_key, body))\nchannel.basic_consume(callback,\n queue=queue_name_3d,\n no_ack=True)\n\nchannel.start_consuming()\n","sub_path":"Desktop/untitled/clustertask/SRC/consumer3.py","file_name":"consumer3.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"534048109","text":"\"\"\"\nhttps://www.ncbi.nlm.nih.gov/corehtml/query/static/entrezlinks.html\n\n\"\"\"\n#Standard Library\nfrom typing import Union, List, Optional, Dict\nfrom typing import TYPE_CHECKING\n\nif TYPE_CHECKING:\n from .api import CitationMatcherEntry\n from .api import API\n\n#Third Party\n#----------------------\nfrom bs4 import BeautifulSoup\n\n\n#Local Imports\n#--------------------\nfrom . import utils\nquotes = utils.quotes\ndisplay_class = utils.display_class\ntd = utils.get_truncated_display_string\ncld = utils.get_list_class_display\npv = utils.property_values_to_string\n\nfrom . import model_helpers\n_make_soup = model_helpers._make_soup\n_list_cld_or_empty = model_helpers._list_cld_or_empty\n_get_opt_list = model_helpers._get_opt_list\n_get_opt_soup_string = model_helpers._get_opt_soup_string\n_get_opt_attr_value = model_helpers._get_opt_attr_value\n_get_opt_class = model_helpers._get_opt_class\n_get_opt_soup_int = model_helpers._get_opt_soup_int\n\ndef pmc_to_pmid_results(api:'API', response:'Response', ids_in) -> List[str]:\n\n data = response.json()\n\n records = data['records']\n\n #Results may be in order, but I couldn't find a guarantee on this\n #so we'll process in order here\n #\n #Note: An invalid record will have a pmcid but no pmid\n temp = {x['pmcid']:x['pmid'] if 'pmid' in x else None for x in records}\n\n return [temp[x] for x in ids_in]\n\ndef pmid_to_pmc_results(api:'API', response:'Response', ids_in) -> List[str]:\n\n data = response.json()\n\n records = data['records']\n\n #Returned values are not matched to request order, so place in\n #dict and then order by request order below\n #\n ##Note: An invalid record will have a pmid but no pmcid\n temp = {x['pmid']:x['pmcid'] if 'pmcid' in x else None for x in records}\n\n return [temp[x] for x in ids_in]\n\n\n\n\nclass PMIDToPMCLinkSets(object):\n\n def __init__(self, api:'API', response:'Response'):\n\n import pdb\n pdb.set_trace()\n\n\n\n #header\n #linksets\n\n #Header\n #------------\n header = data['header']\n self.type = header['type']\n self.version = header['version']\n\n linksets = data['linksets']\n\n #This appears to always be of length 1?\n\n #TODO: Need to hold onto user's request so that\n #we can make that the key ...\n\n #list of dictionaries\n #.dbfrom - 'pubmed'\n #.ids - ['20363814']\n #.linksetdbs\n # [0]\n # .dbto : 'pmc'\n # .linkname : 'pubmed_pmc'\n # .links : [PMCID value as string'\n # [1]\n # .dbto : 'pmc'\n # .linkname : 'pubmed_pmc_refs'\n # .links : ['pmcs of references'\n #\n # This looks like it may be PMCs of papers citing this paper\n #\n #\n # [2]\n # # pubmed_pmc_local\n\n #TODO: Do multiple values, is length of linksets\n #increased or length of ids???\n import pdb\n pdb.set_trace()\n\n\ndef neighbor_score():\n import pdb\n pdb.set_trace()","sub_path":"pubmed/elink_models.py","file_name":"elink_models.py","file_ext":"py","file_size_in_byte":3064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"220972172","text":"from django.db import models\n\nfrom admintools.models import CoreModel\n\n\nclass Category(CoreModel):\n name = models.CharField(\n max_length=255,\n verbose_name='Категория номера'\n )\n\n class Meta:\n verbose_name = 'Категория номера'\n verbose_name_plural = 'Категории номеров'\n\n def __str__(self):\n return self.name\n\n\nclass Room(CoreModel):\n category = models.ForeignKey(\n Category,\n on_delete=models.DO_NOTHING,\n blank=False,\n verbose_name='Категория номера'\n )\n\n number = models.PositiveSmallIntegerField(\n verbose_name='№ Номера'\n )\n adults = models.PositiveSmallIntegerField(\n verbose_name='Взрослых',\n default=1\n )\n facilities = models.CharField(\n max_length=255,\n verbose_name='Оснащение номера',\n )\n size = models.PositiveSmallIntegerField(\n verbose_name='Площадь, м2',\n )\n bad_type = models.CharField(\n max_length=255,\n verbose_name='Тип кровати',\n default='Двух местная'\n )\n price = models.PositiveSmallIntegerField(\n verbose_name='Цена номера',\n default=0\n )\n description = models.TextField(\n max_length=2500,\n verbose_name='Описание номера'\n )\n\n class Meta:\n verbose_name = 'Номер'\n verbose_name_plural = 'Все номерa'\n\n def __str__(self):\n return f'№_{self.number}'\n\n def get_image(self):\n \"\"\" возвращает первую картинку среди дополнительных картинок товара \"\"\"\n image = self.roomgallery_set.first()\n return image.image.url\n\n\nclass RoomGallery(CoreModel):\n room = models.ForeignKey(\n Room,\n on_delete=models.CASCADE,\n verbose_name='Галерея фото',\n )\n image = models.ImageField(\n verbose_name='Фото номера',\n upload_to='rooms',\n blank=True,\n )\n\n sorting = models.PositiveSmallIntegerField(\n verbose_name='Сортировка фотографий',\n help_text='Сортировка по увеличению, начиная с 1. Фотография с наименьшим значением сортировки будет основной'\n )\n\n class Meta:\n ordering = ['sorting']\n verbose_name = 'Фотография номера'\n verbose_name_plural = 'Фотографии номеров'\n\n def __str__(self):\n return f'фото_{self.id}'\n\n","sub_path":"rooms/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"459329075","text":"import sys\n\nCOVERAGE_FILE_PATH = sys.argv[1]\nTOPIC_WORD_FILE_PATH = sys.argv[2]\n\n# Read file\ndoc_topic_cov_file = open(COVERAGE_FILE_PATH, 'r')\ncontent = doc_topic_cov_file.read()\nlines = content.splitlines()\ndoc_topic_cov_file.close()\n\n# get number of topics\nNUMBER_OF_TOPICS = len(lines[0].split())\n\n# get number of positive docs\nNUM_POSITIVE_DOCS = len(lines)/2\n\n# compute average topic distribution of positive versus non-positive\npos_topic_sums = [0] * NUMBER_OF_TOPICS\nnon_pos_topic_sums = [0] * NUMBER_OF_TOPICS\nindex = 0\nfor line in lines:\n dist = list(map(lambda x: float(x), line.split()))\n for i in range(len(dist)):\n if index < NUM_POSITIVE_DOCS:\n pos_topic_sums[i] += dist[i]\n else:\n non_pos_topic_sums[i] += dist[i]\n index += 1\npos_topic_averages = list(map(lambda x: (x/500.0)*100.0, pos_topic_sums))\nprint('#######################')\nprint('pos_topic_averages: ', pos_topic_averages)\nprint('######################\\n')\nnon_pos_topic_averages = list(map(lambda x: (x/500.0)*100.0, non_pos_topic_sums))\nprint('#######################')\nprint('non_pos_topic_averages: ', non_pos_topic_averages)\nprint('######################\\n')\n\n\n\n# compute top 2 topics positive versus non-positive\npos_non_pos_topic_average_diff = [None] * NUMBER_OF_TOPICS\nfor i in range(NUMBER_OF_TOPICS):\n pos_non_pos_topic_average_diff[i] = pos_topic_averages[i] - non_pos_topic_averages[i]\nprint('######################')\nprint('pos_non_pos_topic_average_diff: ', pos_non_pos_topic_average_diff)\nprint('######################\\n')\n\n\nsorted_diffs = sorted(range(len(pos_non_pos_topic_average_diff)), key=lambda i: pos_non_pos_topic_average_diff[i])\nnum_topics_to_select = int(min(NUMBER_OF_TOPICS/2, 2) * -1)\ntop_pos_topics = sorted_diffs[num_topics_to_select:]\nprint('#######################')\nprint('top_pos_topics: ', list(map(lambda x: x + 1, top_pos_topics)))\nprint('######################\\n')\nsorted_diffs.reverse()\ntop_non_pos_topics = sorted_diffs[num_topics_to_select:]\nprint('#######################')\nprint('top_non_pos_topics: ', list(map(lambda x: x + 1, top_non_pos_topics)))\nprint('######################\\n')\n\n\n# get top 20 words for each topic\ntopic_words_file = open(TOPIC_WORD_FILE_PATH)\ntopic_words = topic_words_file.read()\ntopic_words_file.close()\ntopics = topic_words.splitlines()\n\n# retrieve pos topic words\ntop_pos_words = []\nfor topic in top_pos_topics:\n top_pos_words += topics[topic].split()[:20]\ntop_pos_words = list(set(top_pos_words))\nprint('#######################')\nprint('top_pos_words: ', top_pos_words)\nprint('######################\\n')\n\n# retrieve pos topic words\ntop_non_pos_words = []\nfor topic in top_non_pos_topics:\n top_non_pos_words += topics[topic].split()[:20]\ntop_non_pos_words = list(set(top_non_pos_words))\nprint('#######################')\nprint('top_non_pos_words: ', top_non_pos_words)\nprint('######################\\n')\n\n# intersection of top words from pos and non_pos topics\nintersection = list(set(top_pos_words) & set(top_non_pos_words))\nprint('#######################')\nprint('pos non_pos intersection: ', intersection)\nprint('######################\\n')\n\nunique_pos_words = list(set(top_pos_words) - set(top_non_pos_words))\nprint('#######################')\nprint('unique_pos_words: ', unique_pos_words)\nprint('######################\\n')\nunique_non_pos_words = list(set(top_non_pos_words) - set(top_pos_words))\nprint('#######################')\nprint('unique_non_pos_words: ', unique_non_pos_words)\nprint('######################\\n')\n\n# write files\npos_no_pos_topic_cov_file_path = TOPIC_WORD_FILE_PATH + '.pos-non-pos-topics.txt'\nprint('writing top pos and non pos topics to: ', pos_no_pos_topic_cov_file_path)\npos_no_pos_topic_cov_file = open(pos_no_pos_topic_cov_file_path, 'w')\npos_no_pos_topic_cov_file.write('top_pos_topics: ' + str(map(lambda x: x + 1, top_pos_topics)) + '\\n')\npos_no_pos_topic_cov_file.write('top_non_pos_topics: ' + str(map(lambda x: x +1, top_non_pos_topics)))\npos_no_pos_topic_cov_file.close()\n\ntop_pos_words_file_path = TOPIC_WORD_FILE_PATH + '.top-pos-words.txt'\nprint('wrting top pos words to: ', top_pos_words_file_path)\ntop_pos_words_file = open(top_pos_words_file_path, 'w')\ntop_pos_words_file.write(' '.join(top_pos_words))\ntop_pos_words_file.close()\n\ntop_non_pos_words_file_path = TOPIC_WORD_FILE_PATH + '.top-non-pos-words.txt'\nprint('wrting top non pos words to: ', top_non_pos_words_file_path)\ntop_non_pos_words_file = open(top_non_pos_words_file_path, 'w')\ntop_non_pos_words_file.write(' '.join(top_non_pos_words))\ntop_pos_words_file.close()\n","sub_path":"topic_analysis/topic_analysis.py","file_name":"topic_analysis.py","file_ext":"py","file_size_in_byte":4571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"13498403","text":"import os\nimport glob\nimport psycopg2\nimport pandas as pd\nfrom sql_queries import *\n\n\ndef process_song_file(cur, filepath):\n \"\"\" \n Summary line. \n Process song files\n \n Parameters: \n arg1 (cursor)\n arg2 (filepath)\n \n Returns: \n None\n \"\"\"\n \n #print('START : process_song_file()')\n # open song file\n df = pd.read_json(filepath, lines=True)\n\n # insert song record\n song_data = df[['song_id', 'title', 'artist_id', 'year', 'duration']].values[0] \n cur.execute(song_table_insert, song_data)\n \n # insert artist record\n artist_data = df[['artist_id', 'artist_name', 'artist_location', 'artist_latitude', 'artist_longitude']].values[0]\n cur.execute(artist_table_insert, artist_data)\n #print('END : process_song_file()')\n\n\ndef process_log_file(cur, filepath):\n \"\"\" \n Summary line. \n Process log files\n \n Parameters: \n arg1 (cursor)\n arg2 (filepath)\n \n Returns: \n None\n \"\"\"\n \n global all_users\n #print('START : process_log_file()')\n \n # open log file \n df = pd.read_json(filepath, lines=True)\n\n # filter by NextSong action\n df = df[df.page == 'NextSong']\n\n # convert timestamp column to datetime\n t = df.copy()\n t['ts'] = pd.to_datetime(t['ts'], unit='ms') \n \n # insert time data records\n time_data = [t.ts, t.ts.dt.hour, t.ts.dt.day, t.ts.dt.week, t.ts.dt.month, t.ts.dt.year, t.ts.dt.weekday]\n column_labels = ['start_time', 'hour', 'day', 'week', 'month', 'year', 'weekday']\n time_df = pd.DataFrame.from_dict(dict(zip(column_labels, time_data)))\n \n for i, row in time_df.iterrows():\n cur.execute(time_table_insert, list(row))\n\n \n # load user table\n df['ts'] = pd.to_datetime(df['ts'], unit='ms')\n user_df = df[['userId', 'firstName', 'lastName', 'gender', 'level', 'ts']]\n \n # Cleaning user_df : userId\n df_uc = user_df.copy()\n cnt=0\n nullList = []\n for row in df_uc['userId']:\n try:\n #print(cnt, df_uc.loc[cnt, 'userId'])\n if df_uc.loc[cnt, 'userId'] == '':\n nullList.append(cnt)\n #print(cnt)\n df_uc.loc[cnt, 'userId'] = None\n #Added KeyError due to KeyError: 'the label [10] is not in the [index]' \n except (ValueError, KeyError) as e:\n pass\n cnt+=1 \n \n df_uc = df_uc[df_uc.firstName.notnull()]\n user_df = df_uc.copy()\n all_users = all_users.append(user_df)\n #print(all_users.shape) \n \n '''\n # insert user records \n for i, row in user_df.iterrows():\n cur.execute(user_table_insert, row)\n ''' \n\n # insert songplay records\n for index, row in df.iterrows():\n \n # get songid and artistid from song and artist tables\n cur.execute(song_select, (row.song, row.artist, row.length))\n results = cur.fetchone()\n \n \n if results:\n songid, artistid = results\n else:\n #continue\n songid, artistid = None, None\n\n\n # insert songplay record\n starttime = pd.to_datetime(row.ts,unit='ms') \n songplay_data = (starttime, row.userId, row.level, songid, artistid, row.sessionId, row.location, row.userAgent\n , row.itemInSession, row.song, row.artist)\n\n cur.execute(songplay_table_insert, songplay_data)\n \n \n #print('END : process_log_file()')\n\ndef process_users(cur, conn): \n \"\"\" \n Summary line. \n Process users\n \n Parameters: \n arg1 (cursor)\n arg2 (conn)\n \n Returns: \n None\n \"\"\"\n \n global all_users\n \n all_users.sort_values(by=['userId', 'ts'], axis=0, ascending=True, inplace=True)\n print(all_users.shape)\n \n # insert user records\n for i, row in all_users.iterrows():\n cur.execute(user_table_insert, row)\n \n conn.commit()\n \n\ndef process_data(cur, conn, filepath, func):\n \"\"\" \n Summary line. \n Process data\n \n Parameters: \n arg1 (cursor)\n arg2 (connection)\n arg3 (filepath)\n arg4 (function)\n \n Returns: \n None\n \"\"\"\n \n print('START : process_data()')\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n print('{} : {}'.format(func, datafile))\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))\n \n print('END : process_data()')\n\n\ndef main():\n print('START : main()')\n conn = psycopg2.connect(\"host=127.0.0.1 dbname=sparkifydb user=postgres password=Congo27* port=5433\") \n cur = conn.cursor()\n\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\n process_users(cur, conn)\n\n conn.close()\n print('END : main()')\n\n\nall_users = pd.DataFrame(columns=['userId', 'firstName', 'lastName', 'gender', 'level', 'ts'])\n\nif __name__ == \"__main__\":\n main()","sub_path":"etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":5403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"384654263","text":"from flask_jwt_extended import jwt_required\nfrom flask_restful import Resource, request\nfrom sqlalchemy.orm import load_only\nfrom datetime import datetime\nfrom app.db import SubuserModel, PresenceModel, AddData\n\n\nclass AbsenceResource(Resource):\n @jwt_required\n def get(self):\n if request.args.get('presence', '0') == '1':\n return self.getPresenceDates(int(request.args.get('subjid', 0)))\n if request.args.get('all', '0') == '1':\n return self.getallabsences(int(request.args.get('subjid', 0)))\n if request.args.get('one', '0') == '1':\n return self.getabsences(int(request.args.get('subjid', 0)), int(request.args.get('userId', 0)))\n return {'message': 'Codigo da disciplina e/ou do usuário não encontrado!'}, 404\n\n def getPresenceDates(self, subjId):\n presence = PresenceModel.query.filter_by(sub_id=subjId).options(load_only('date'))\n date_presence = [row.date.strftime(\"%Y-%m-%d\") for row in presence]\n return {'dates': date_presence}, 200\n \n def getallabsences(self, subjid):\n ab_users = SubuserModel.query.filter_by(sub_id=subjid).all()\n if len(ab_users) == 0:\n return {'Error':'Nenhum discente relacionado a disciplina foi encontrado'}, 404\n ab_users.sort(key=lambda x: int(x.user_associate.enrolment))\n presence = PresenceModel.query.filter_by(sub_id=subjid).options(load_only('date'))\n date_presence = [row.date.strftime(\"%Y-%m-%d\") for row in presence]\n qt_presence = len(date_presence)\n values = []\n for ab_user in ab_users:\n v = {}\n absences = ab_user.absences\n qt_absence, user = len(absences), ab_user.user_associate\n v['userid'] = user.id\n v['username'] = f'{user.username} - {user.enrolment}'\n v['presencas'] = qt_absence \n v['faltas'] = qt_presence - qt_absence\n values.append(v)\n return {'dates': date_presence, 'values':values}, 200\n\n def getabsences(self, subjId, userId):\n subuResult = SubuserModel.query.filter_by(user_id=userId, sub_id=subjId).first()\n absences = subuResult.absences\n if absences:\n date_presence = [row.date.strftime(\"%Y-%m-%d\") for row in absences]\n current_date = absences[-1].date.strftime(\"%Y-%m-%d\")\n else:\n date_presence = []\n current_date = datetime.now().strftime(\"%Y-%m-%d\")\n return {'current': current_date, 'dates': date_presence}, 200\n","sub_path":"app/resource/absence.py","file_name":"absence.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"302368019","text":"\ni = 0\nwin = 0\nlost = 0\ndraw = 0\n\nwhile i != 30:\n points = int(input())\n if points == 3:\n win += 1\n elif points == 1:\n draw += 1\n else:\n lost += 1\n i += 1\nprint(win*3 + draw, draw)\n","sub_path":"Python/OnlineQuestions/SourceFiles/1 (8).py","file_name":"1 (8).py","file_ext":"py","file_size_in_byte":217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"420817326","text":"from pylab import *\nimport glob\nimport os\nimport matplotlib.patches as mpatches\nimport sys\nimport shutil\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorboard.backend.event_processing.event_accumulator import EventAccumulator\nplt.style.use('seaborn-white')\nplt.rcParams['font.family'] = 'serif'\nplt.rcParams['font.size'] = 10\nplt.rcParams['axes.labelsize'] = 10\nplt.rcParams['axes.labelweight'] = 'bold'\nplt.rcParams['axes.titlesize'] = 10\nplt.rcParams['xtick.labelsize'] = 8\nplt.rcParams['ytick.labelsize'] = 8\nplt.rcParams['legend.fontsize'] = 10\nplt.rcParams['figure.titlesize'] = 12\nsys.path.append(os.path.join(os.path.dirname(__file__), 'tensorflow_models/research'))\n\nimport tensorflow as tf\nfrom collections import namedtuple\n\nmodel_metadata = namedtuple(\"model_metadata\", [\"meta_arch\", \"feature_extractor\", \"proposals\", \"dir\", \"name\", \"resolution\"])\narch_markers = {'Faster RCNN': 'o', 'SSD':'D'}\nfe_colors = {'Resnet 101':'Y', 'Inception V2':'B'}\nsz_colors = {'950x540':'G', '300':'R', '600':'Y'}\narch_labels = []\n\ndef process_command_line():\n '''\n Process command line\n :return: args object\n '''\n import argparse\n from argparse import RawTextHelpFormatter\n\n examples = 'Examples:' + '\\n\\n'\n examples += 'Extract and plot performance metrics from model output \\n'\n examples += '{0} --model_dir {0}/models'.format(os.getcwd())\n parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter,\n description='Creates Tensorflow Record object for MBARI annotated data',\n epilog=examples)\n parser.add_argument('-m', '--model_dir', action='store', help='Root directory to raw dataset', required=False, default='{0}/models'.format(os.getcwd()))\n parser.add_argument('-t', '--title', action='store', help='Title for plot', required=False, default='Foobar')\n args = parser.parse_args()\n return args\n\ndef aggregate(search_path, tempdir):\n all_files = glob.glob(search_path, recursive=True)\n for f in all_files:\n src = f\n dir, file = os.path.split(src)\n dst = '{0}/{1}'.format(tempdir, file)\n shutil.copy(src, dst)\n\ndef wallToGPUTime(x, zero_time):\n return round(int((x - zero_time)/60),0)\n\ndef valueTomAP(x):\n return round(int(x*100),0)\n\ndef modelToMetaArch(x):\n if 'faster_rcnn' in x:\n return 'faster_rcnn'\n return 'Unknown'\n\n\ndef model_plot(all_model_index, model, ax):\n data = all_model_index.loc[model.name]\n m = '.'\n c = 'B'\n label = None\n if model.meta_arch in arch_markers.keys():\n m = arch_markers[model.meta_arch]\n if model.feature_extractor in fe_colors.keys():\n c = fe_colors[model.feature_extractor]\n if model.meta_arch not in arch_labels:\n label = model.meta_arch\n arch_labels.append(label)\n ax.scatter(data.index, data.values, marker=m, color=c, s=40, label=label)\n\n\ndef main(_):\n args = process_command_line()\n import tempfile\n import shutil\n\n output = os.getcwd()\n #train_tempdir = tempfile.TemporaryDirectory()\n #eval_tempdir = tempfile.TemporaryDirectory()\n\n #aggregate(args.model_dir + '/**/train/events*', train_tempdir.name)\n #aggregate(args.model_dir + '/**/eval/events*', eval_tempdir.name)\n\n search_path = args.model_dir + '/**/eval/'\n all_dirs = glob.glob(search_path, recursive=True)\n df_eval = pd.DataFrame()\n all_models = []\n\n for d in all_dirs:\n fc = 'Unknown'\n if 'resnet101' in d:\n fc = 'Resnet 101'\n if 'inception_v2' in d:\n fc = 'Inception v2'\n ma = 'Unknown'\n if 'ssd' in d:\n ma = 'SSD'\n if 'faster_rcnn' in d:\n ma = 'Faster RCNN'\n\n resolution = '960x540'\n proposals = 0\n dir_name = d.split('eval')[0]\n model_name = dir_name.split('/')[-2]\n f = model_name.split('_')\n for j in f:\n if j.isnumeric():\n proposals = int(j)\n #TODO add regex for resolution here\n\n # Grab all of the accuracy results for each model and put into Pandas dataframe\n event_acc = EventAccumulator(d)\n event_acc.Reload()\n # Show all tags in the log file\n print(event_acc.Tags())\n try:\n s = event_acc.Scalars('PASCAL/Precision/mAP@0.5IOU')\n df = pd.DataFrame(s)\n if df.empty:\n continue\n\n a = model_metadata(dir=d, name=model_name, meta_arch=ma, feature_extractor=fc, proposals=proposals, resolution=resolution)\n all_models.append(a)\n\n time_start = df.wall_time[0]\n\n # convert wall time and value to rounded values\n df['wall_time'] = df['wall_time'].apply(wallToGPUTime, args=(time_start,))\n df['value'] = df['value'].apply(valueTomAP)\n\n # rename columns\n df.columns = ['GPU Time', 'step', 'Overall mAP']\n df['model'] = np.full(len(df), model_name)\n print(df)\n df_eval = df_eval.append(df)\n\n\n except Exception as ex:\n print(ex)\n continue\n\n # drop the step column as it's no longer needed\n df_eval = df_eval.drop(['step'], axis=1)\n # pivot on the same and plot the accuracy per each model\n #pivoted = df_eval.pivot(index=None, columns='model')\n\n #group = df_eval.groupby(['model'])\n all_model_index = df_eval.set_index(['model','GPU Time']).sort_index()\n\n with plt.style.context('ggplot'):\n\n # start a new figure - size is in inches\n fig = plt.figure(figsize=(8, 4), dpi=200)\n ax1 = plt.subplot(aspect='equal')\n ax1.set_xlim(0, 300)\n ax1.set_ylim(0, 100)\n\n for model in all_models:\n model_plot(all_model_index, model, ax1)\n\n #ax1.set_xlim(tmin, tmax)\n ax1.set_ylim([0, 100])\n ax1.set_ylabel('mAP', fontsize=10)\n ax1.set_xlabel('GPU Time', fontsize=10)\n ax1.set_title(args.title, fontstyle='italic')\n\n # plot the legend outside the plot in the upper left corner\n l = ax1.legend(loc='upper left', bbox_to_anchor=(0.5, 0.95), prop={'size': 8}, scatterpoints=1, title='Architecture')\n l.get_title().set_fontsize('8')\n l.set_zorder(4) # put the legend on top right\n inc = 20\n '''ax1.text(170, 50, r'Resolution', fontsize=8)\n for feature, color in fe_colors.items():\n ax1.text(170, inc - 2, r'{0}'.format(feature), fo ntsize=8)\n c = mpatches.Circle( (160, inc), 2, edgecolor='black', facecolor=color)\n ax1.add_patch(c)\n inc += 10'''\n\n ax1.text(160, 50, r'Resolution', fontsize=8)\n for size, color in sz_colors.items():\n ax1.text(170, inc - 2, r'{0}'.format(size), fontsize=8)\n c = mpatches.Circle( (160, inc), 2, edgecolor='black', facecolor=color)\n ax1.add_patch(c)\n inc += 10\n\n #patches = [ mpatches.Patch(color=color, label=label)\n # for label, color in zip(fe_labels, fe_colors)]\n #fig.legend(patches, fe_labels, loc='center', frameon=False)\n plt.savefig('{0}.png'.format(args.title), format='png')\n plt.show()\n #pivoted.plot(kind='bar', alpha=0.75, rot=45, figsize=(500, 500), width=.5)\n print('Done')\n\n #shutil.rmtree(eval_tempdir.name)\n #shutil.rmtree(train_tempdir.name)\n\nif __name__ == '__main__':\n tf.app.run()\n","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":6907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"195871103","text":"class Solution:\r\n\tdef singleNumber(self,nums):\r\n\t\tif not nums:\r\n\t\t\treturn \r\n\t\tdiff=0\r\n\t\tfor num in nums:\r\n\t\t\tdiff^=num\r\n\t\tdiff=diff & ~(diff-1)\r\n\t\tret=[0,0]\r\n\t\tfor num in nums:\r\n\t\t\tif num&diff==0:\r\n\t\t\t\tret[0]^=num\r\n\t\t\telse:\r\n\t\t\t\tret[1]^=num \r\n\t\treturn ret\r\n","sub_path":"leetcode分类/位运算/260-只出现一次的数字3.py","file_name":"260-只出现一次的数字3.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"555055812","text":"\"\"\"\nCreated on Wed Feb 10 21:56:02 2016\n\n@author: ajjenjoshi\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass NeuralNet:\n \"\"\"\n This class implements a simple 3 layer neural network.\n \"\"\"\n \n def __init__(self, input_dim, hidden_dim, output_dim, epsilon, lmbda):\n \"\"\"\n Initializes the parameters of the neural network to random values\n \"\"\"\n self.idim = input_dim\n self.hdim = hidden_dim\n self.odim = output_dim\n \n ## Hidden Layer weights\n self.H = np.random.randn(input_dim+1, hidden_dim+1) / np.sqrt(input_dim+1) \n #self.hb = np.zeros((1, hidden_dim))\n print(self.H) \n ## Random weights\n self.W = np.random.randn(hidden_dim+1, output_dim) / np.sqrt(hidden_dim+1)\n #self.b = np.zeros((1, output_dim))\n print(self.W)\n # Learning Rate\n self.epsilon = epsilon\n # Regularization paramater\n self.lmbda = lmbda\n \n #--------------------------------------------------------------------------\n \n def compute_cost(self,X, y):\n \"\"\"\n Computes the total loss on the dataset\n \"\"\"\n \n num_samples = len(X)\n \n # Do Forward Propagation\n if(X.shape[1] == self.idim):\n bTemp = np.ones ((num_samples,1),dtype=np.int)\n X = np.hstack([X, bTemp])\n \n h = X.dot(self.H)\n Oh = 1/ (1 + np.exp(-h)) \n \n z = Oh.dot(self.W) #+ self.b\n exp_z = 1/ (1 + np.exp(-z)) \n \n softmax_scores = exp_z / np.sum(exp_z, axis=1, keepdims=True)\n \n # Calculate the cross-entropy loss\n cross_ent_err = -np.log(softmax_scores[range(num_samples), y])\n data_loss = np.sum(cross_ent_err)\n \n return 1./num_samples * data_loss\n \n #--------------------------------------------------------------------------\n \n def predict(self,x):\n \"\"\"\n Makes a prediction based on current model parameters\n \"\"\"\n \n # Do Forward Propagation\n bTemp = np.ones ((len(x),1),dtype=np.int)\n x = np.hstack([x, bTemp])\n \n h = x.dot(self.H) #+ self.hb\n Oh = 1/ (1 + np.exp(-h)) \n\n z = Oh.dot(self.W) #+ self.b\n exp_z = 1/ (1 + np.exp(-z)) \n \n softmax_scores = exp_z / np.sum(exp_z, axis=1, keepdims=True)\n \n return np.argmax(softmax_scores, axis=1)\n\n \n #--------------------------------------------------------------------------\n \n def fit(self,X,y,num_epochs, L2reg=True):\n \"\"\"\n Learns model parameters to fit the data\n \"\"\" \n ###TODO:\n #For each epoch\n # Do Forward Propagation\n # Do Back Propagation\n # Update model parameters using gradients\n num_samples = len(X)\n weight_decay = 1 - self.epsilon * self.lmbda / num_samples \n \n # Get ground truth vectors from y index\n dz = [[0 for x in range(2)] for x in range(y.size)] \n\n for iter in range(y.size):\n if y[iter] == 0:\n dz[iter] = [1, 0]\n else:\n dz[iter] = [0, 1] \n \n bTemp = np.ones ((num_samples,1),dtype=np.int)\n X = np.hstack([X, bTemp])\n \n for epoch in range(num_epochs):\n # not good style ... but reset weight from input to hidden bias to be 0\n self.H[:,-1] = np.zeros(self.idim+1)\n\n # Do Forward Propagation\n h = X.dot(self.H) #+ self.hb\n Oh = 1/ (1 + np.exp(-h))\n \n # Make sure bias node output is 1\n Oh[:, -1] = 1 \n \n z = Oh.dot(self.W) #+ self.b\n exp_z = 1/ (1 + np.exp(-z))\n \n Oz = exp_z / np.sum(exp_z, axis=1, keepdims=True)\n \n # Calculate Error\n beta_z = dz - Oz\n\n # Perform Backpropagation for Outerlayer\n w_delta = self.epsilon * Oz*(1-Oz) * beta_z\n \n # update weights with L2 regularization\n # Regularization should not affect biases\n if L2reg: self.W[:-1] *= weight_decay\n self.W += np.dot(Oh.T,w_delta)\n \n # compute beta for hidden node, H\n h_delta = 0\n \n for iter_in in range(len(Oz)):\n beta_h = np.zeros((self.hdim+1,1))\n temp = Oz[iter_in] *(1 - Oz[iter_in]) * beta_z[iter_in]\n temp.shape = (self.odim, 1)\n \n beta_h += np.dot(self.W, temp)\n beta_h.shape = (self.hdim+1,)\n \n x = X[iter_in]\n x.shape = (x.shape[0], 1)\n h_delta += self.epsilon * x * (Oh[iter_in]*(1-Oh[iter_in]) * beta_h)\n \n # update weights, H with L2 regularization\n # Regularization should not affect biases\n if L2reg: self.H[:-1] *= weight_decay\n self.H += h_delta\n # Regularize H\n \n \n self.H[:,-1] = np.zeros(self.idim+1)\n\n if not epoch % 500:\n print(self.compute_cost(X, y))\n if not epoch % 5000:\n plot_decision_boundary(lambda x: NN.predict(x))\n \n\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n\n# Use to test effect of varying number of nodes in hidden layer\ndef plot_decision_boundary(pred_func):\n \"\"\"\n Helper function to print the decision boundary given by model\n \"\"\"\n # Set min and max values\n x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n h = 0.01\n # Generate a grid of points\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n # Predict the function value for the whole gid\n Z = pred_func(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n # Plot the contour and training examples\n plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n plt.show()\n\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n\n#Train Neural Network on\nlinear = False\n\n#A. linearly separable data\n#/Users/sangjoonlee/Documents/BU/CS440/PROG/prog2/Lab4/DATA\n# \n\nPATH = 'Z:/cs440/PROG/prog2/code/DATA/'\n#PATH = '/Users/tyronehou/Documents/Class/2016 Spring/CS 440/HW/HW02/DATA/'\n#PATH = './DATA/'\nif linear:\n #load data\n X = np.genfromtxt(PATH + 'ToyLinearX.csv', delimiter=',')\n y = np.genfromtxt(PATH + 'ToyLineary.csv', delimiter=',')\n y = y.astype(int)\n #plot data\n plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.Spectral)\n plt.show()\n#B. Non-linearly separable data\nelse:\n #load data\n X = np.genfromtxt(PATH + 'ToyMoonX.csv', delimiter=',')\n y = np.genfromtxt(PATH + 'ToyMoony.csv', delimiter=',')\n y = y.astype(int)\n #plot data\n plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.Spectral)\n plt.show()\n\ninput_dim = 2 # input layer dimensionality\noutput_dim = 2 # output layer dimensionality\nhidden_dim = 5 # hidden layer dimensionality\n\n# Gradient descent parameters \nepsilon = 0.05\nlmbda = 5\nnum_epochs = 10000\n\n# Fit model\n#----------------------------------------------\n#Uncomment following lines after implementing NeuralNet\n#----------------------------------------------\nNN = NeuralNet(input_dim, hidden_dim, output_dim, epsilon, lmbda)\nNN.fit(X,y,num_epochs, True)\n#\n# Plot the decision boundary\nplot_decision_boundary(lambda x: NN.predict(x))\nNN.compute_cost(X,y) \n \n","sub_path":"NeuralNet3layer.py","file_name":"NeuralNet3layer.py","file_ext":"py","file_size_in_byte":8015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"233240504","text":"'''\nUsed as Pandas plotting backend.\n'''\n\nimport quantlplot\n\n\ndef plot(df, x, y, kind, **kwargs):\n _x = df.index if y is None else df[x]\n try:\n _y = df[x].reset_index(drop=True) if y is None else df[y]\n except:\n _y = df.reset_index(drop=True)\n kwargs = dict(kwargs)\n if 'by' in kwargs:\n del kwargs['by']\n if kind in ('candle', 'candle_ochl', 'candlestick', 'candlestick_ochl', 'volume', 'volume_ocv', 'renko'):\n if 'candle' in kind:\n return quantlplot.candlestick_ochl(df, **kwargs)\n elif 'volume' in kind:\n return quantlplot.volume_ocv(df, **kwargs)\n elif 'renko' in kind:\n return quantlplot.renko(df, **kwargs)\n elif kind == 'scatter':\n if 'style' not in kwargs:\n kwargs['style'] = 'o'\n return quantlplot.plot(_x, _y, **kwargs)\n elif kind == 'bar':\n return quantlplot.bar(_x, _y, **kwargs)\n elif kind in ('barh', 'horiz_time_volume'):\n return quantlplot.horiz_time_volume(df, **kwargs)\n elif kind in ('heatmap'):\n return quantlplot.heatmap(df, **kwargs)\n elif kind in ('labels'):\n return quantlplot.labels(df, **kwargs)\n elif kind in ('hist', 'histogram'):\n return quantlplot.hist(df, **kwargs)\n else:\n if x is None:\n _x = df\n _y = None\n if 'style' not in kwargs:\n kwargs['style'] = None\n return quantlplot.plot(_x, _y, **kwargs)\n","sub_path":"quantlplot/pdplot.py","file_name":"pdplot.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"612626821","text":"\"\"\"@package MuSCADeT\n\n\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pca_ring_spectrum as pcas\nimport pyfits as pf\nimport wave_transform as mw\nimport pylab\nimport scipy.ndimage.filters as med\n\n\ndef mMCA(img, A,kmax, niter,mode = 'PCA', PCA = [2,10], harder = 0, pos = False,threshmode = 'mom',lvl = 6, soft = False, reweighting = 'none', alpha = [0,0], npca = 64, mask = [0,0]):\n \"\"\"\n mMCA runs the MuSCADeT algorithm over a cube of multi-band images.\n \n INPUTS:\n img: multiband cube with size nbxn1xn2 where nb is the number of bands and n1xn2,\nthe size of the images\n A: the mixing matrix. if mode is set to 'PCA', A will be ignored and can be set to 0\n kmax: detection threshold in units of noise standard deviation usually chosen between 3 and 5 \n niter: number of iterations of the MuSCADeT algorithm\n\n OUTPUTS:\n S: extracted sources\n A: mixing matrix, either given by the user or estimate by PCA with option mode ='PCA' \n alpha: angles in PCA space to identify pixels with same SEDs\n OPTIONS:\n mode: if set to 'PCA', the mixing matrix A will be estimated from PCA decomposition of the SEDs\n PCA: parameters for PCA sensitivity. if mode is set to 'PCA', the PCA estimator will take PCA[0]\nas the number of sources to be extracted and PCA[1] as a sensitivity parameter to discriminate between\nsource. Values betwee 5 and 30 are usually recommended\n harder: if set to 1, \n pos: if set to True, the output of the hard thresholding procedure is constrined to be positive\n threshmode: if set to 'mom', adaptive method of moments is used at every iteration to decrease the threshold\n lvl: number of wavelet levels to use in the decompositions, default is 6.\n soft: if set to True, soft thresholding is used\n alpha: angles in degrees to feed the PCA finder. If set, the PCA finder will use pixels along the directions pointed by these angles in PCA space to estimate SED\n That option is particularly useful if automated PCA fails at clearly identifying different SEDs. This happens in case of high degrees of blending.\n mask: if parts of the band images images are to be masked (e.g. stars in the FOV), the user can provide a mask with size n1xn2\n with all pixels at one except for the masked pixels that should be set to 0.\n npca: number of pixels in which images are downsampled to perform a fast PCA.\n\n EXAMPLE:\n S,A = wine.MCA.mMCA(cube, A, 5,10, PCA=[2,80], mode=pca, harder = 1)\n \n \"\"\"\n noisetab = np.array([ 0.8907963 , 0.20066385, 0.08550751, 0.04121745, 0.02042497,\n 0.01018976, 0.00504662, 0.00368314])\n n1,n2,nb = np.shape(img.T)\n\n if np.sum(mask) == 0:\n mask = np.ones((n1,n2))\n img = np.multiply(img,mask)\n\n if mode == 'PCA':\n Apca = PCA_initialise(img.T, PCA[0], angle = PCA[1], alpha = alpha, npca = npca)\n Apca = np.multiply(Apca,[1./np.sum(Apca,0)]) \n A = Apca\n\n nb,ns = np.shape(A)\n X = np.zeros((ns,n1*n2))\n\n A = np.multiply(A,[1./np.sum(A,0)])\n AT = A.T\n\n [UA,EA, VA] = np.linalg.svd(A)\n EAmax = np.max(EA)\n mu1 = 2/linorm(A,10)\n mu = 2/EAmax\n \n mu = mu1\n\n Y = np.reshape(img,(nb,n1*n2))\n\n Ri = np.dot(AT,Y)\n sigma_y = np.zeros(nb)\n for i in np.linspace(0,nb-1,nb):\n sigma_y[i] = MAD(np.reshape(Y[i,:],(n1,n2)))*mu\n \n sigma1 = np.zeros(ns)\n sigma = sigma1+0\n for i in np.linspace(0,ns-1,ns):\n sigma1[i] = np.sqrt(np.sum( (AT[i,:]**2)*(sigma_y**2)))\n sigma[i]=MAD(np.reshape(Ri[i,:],(n1,n2)))*mu\n \n kmas = MOM(np.reshape(Ri,(ns,n1,n1)),sigma1,lvl)#15#np.max(np.dot(1/(mu*np.dot(AT,Y),1),mu*np.dot(AT,Y)))\n\n print(kmas)\n step = (kmas-kmax)/(niter-5)\n k = kmas\n\n per= np.zeros((ns,niter))\n w = np.zeros((ns,lvl,n1,n2))\n wmap = np.zeros((ns,lvl,n1,n2))\n S = np.zeros((ns,n1*n2))\n thmap = np.zeros((ns,lvl,n1,n2))\n ks = np.zeros(niter)\n sub = 0\n reweight = 0\n weight2 = 1\n\n for i in np.linspace(0,niter-1, niter):\n print(i)\n AX = np.dot(A,X)\n \n R = mu*np.dot(AT, Y-AX)\n X = np.real(X+R)\n S = X\n if threshmode == 'mom':\n kmas = MOM(np.reshape(R,(ns,n1,n2)),sigma,lvl=lvl)\n threshmom =np.max([kmas,kmax])\n if threshmom 0)] = 1\n else:\n\n M[np.where(np.abs(alpha)-np.abs(addweight)+np.abs(subweight)-np.abs(th)*mulweight > 0)] = 1\n\n\n while i < niter:\n R = img-imnew\n # R[140:270,300:420] = 0.0\n# R[450:545,500:590] = 0.0\n# R[120:220,630:720] = 0.0\n alpha = mw.wave_transform(R,lvl,newwave = 1)\n # plt.imshow(R); plt.show()\n if soft == True and i>0:\n alpha= np.sign(alpha)*(np.abs(alpha)-np.abs(addweight)+np.abs(subweight)-(th*mulweight)) \n\n Rnew = mw.iuwt(M*alpha)\n imnew = imnew+Rnew\n \n i = i+1\n \n \n imnew[np.where(imnew<0)]=0\n wmap = mw.wave_transform(imnew,lvl)\n return imnew,wmap\n\n\ndef linorm(A,nit):\n \"\"\"\n Estimates the maximal eigen value of a matrix A\n\n INPUTS:\n A: matrix\n nit: number of iterations\n\n OUTPUTS:\n xn: maximal eigen value\n\n EXAMPLES\n\n \"\"\"\n\n ns,nb = np.shape(A)\n x0 = np.random.rand(nb)\n x0 = x0/np.sqrt(np.sum(x0**2))\n\n \n for i in np.linspace(0,nit-1,nit):\n x = np.dot(A,x0)\n xn = np.sqrt(np.sum(x**2))\n xp = x/xn\n y = np.dot(A.T,xp)\n yn = np.sqrt(np.sum(y**2)) \n if yn < np.dot(y.T,x0) :\n break\n x0 = y/yn\n\n return xn\n\n\n\ndef PCA_initialise(cube, ns, angle = 15,npca = 32, alpha = [0,0]):\n \"\"\"\n Estimates the mixing matrix of of two sources in a multi band set of images\n\n INPUTS:\n cube: multi-band cube from which to extract mixing coefficients\n ns: number of mixed sources\n\n OUTPUTS:\n A0: mixing matrix\n\n OPTIONS:\n angle: sensitivity parameter. The angular resolution at which the algorithm has to look for PCA coefficients clustering\n npca: square root of the number of pixels to be used. Since too big images result in too big computation time\n we propose to downsample the image in order to get reasonable calculation time\n\n EXAMPLES\n \"\"\"\n\n n,n,nband = np.shape(cube)\n cubep = cube+0.\n s = np.zeros(nband)\n for i in range(nband):\n s[i] = MAD(cube[:,:,i])\n cubep[:,:,i] = mr_filter(cube[:,:,i],10,3,s[i],harder = 0)[0]\n \n cubepca = np.zeros((np.min([n,npca]),np.min([n,npca]),nband))\n xk,yk = np.where(cubepca[:,:,0]==0)\n cubepca[xk ,yk,:] = cubep[xk*(n/npca),yk*(n/npca),:]\n lines = np.reshape(cubep,(n**2, nband))\n\n \n alphas, basis, sig= pcas.pca_ring_spectrum(cubepca[:,:,:].T,std = s) \n ims0 = pcas.pca_lines(alphas,sig,angle, ns, alpha0 = alpha)\n\n vals = np.array(list(set(np.reshape(ims0,(npca*npca)))))\n\n vals = vals[np.where(vals>=0)]\n nsp = np.size(vals)\n \n spectras = np.ones([ns, nband])\n rank = nsp\n \n\n S_prior = np.zeros((n,n,np.size(vals)))\n xs,ys = np.where(S_prior[:,:,0]==0)\n count = 0\n\n for k in vals:\n \n x,y = np.where(ims0 == k)\n im = np.zeros((npca, npca))\n im[x,y] = 1\n\n S_prior[xs,ys,count] = im[np.int_(xs*(npca/n)), np.int_(ys*(npca/n))]#/(k+1)\n\n vecube = np.reshape(cubepca,(nband,npca*npca))\n\n ######Essai norm#####\n xcol,ycol=np.where(ims0==k)\n specs = np.reshape(cubepca[xcol,ycol,:],(len(xcol),nband))\n s1 =np.multiply(np.mean(specs,0),\n 1/np.sum(np.reshape(cubepca,(npca**2,nband),0)))\n spectras[count,:]=s1/np.sum(s1,0)\n S_prior[:,:,count] = S_prior[:,:,count]*np.dot(cube,spectras[count,:])\n count = count+1\n \n S0 = np.reshape(S_prior[:,:,::-1],(ns,n*n))\n A0 = spectras.T\n \n return A0\n","sub_path":"MuSCADeT/build/lib/MuSCADeT/MCA.py","file_name":"MCA.py","file_ext":"py","file_size_in_byte":13136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"483751289","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Created by Hai-Tao Yu | 18/11/21 | https://y-research.github.io\n\n\"\"\"Description\nThe following implementation builds upon the library of XGBoost: https://github.com/dmlc/xgboost\n\"\"\"\n\nimport os\nimport sys\nimport pickle\nimport datetime\nimport numpy as np\nfrom itertools import product\n\nimport torch\n\nfrom org.archive.eval.metric import tor_nDCG_at_ks\nfrom org.archive.data.data_ms import load_data_xgboost\nfrom org.archive.ranking.run.l2r import to_output_str\n\nimport xgboost as xgb\nfrom xgboost import DMatrix\nfrom sklearn.datasets import load_svmlight_file\n\n\ndef load_group_data(file_group):\n group = []\n with open(file_group, \"r\") as f:\n data = f.readlines()\n for line in data:\n group.append(int(line.split(\"\\n\")[0]))\n return group\n\n\ndef update_output_setting(para_dict=None):\n dataset, model, do_validation, root_output = para_dict['dataset'], para_dict['model'], para_dict['do_validation'], para_dict['dir_output']\n grid_search, min_docs, min_rele = para_dict['grid_search'], para_dict['min_docs'], para_dict['min_rele']\n eta, gamma, min_child_weight, max_depth, tree_method = para_dict['eta'], para_dict['gamma'], para_dict['min_child_weight'], \\\n para_dict['max_depth'], para_dict['tree_method']\n lm_para_str = '_'.join(['{:,g}'.format(eta), '{:,g}'.format(gamma), '{:,g}'.format(min_child_weight), '{:,g}'.format(max_depth), tree_method])\n\n print(' '.join(['Start {} on {} for ranking >>>'.format(model, dataset)]))\n\n if grid_search:\n root_output = root_output + '_'.join(['grid', model]) + '/'\n if not os.path.exists(root_output):\n os.makedirs(root_output)\n\n para_setting_str = '_'.join(['Vd', str(do_validation), 'Md', str(min_docs), 'Mr', str(min_rele), lm_para_str])\n file_prefix = '_'.join([model, dataset, para_setting_str])\n\n model_output = root_output + file_prefix + '/' # model-specific outputs\n\n if not os.path.exists(model_output):\n os.makedirs(model_output)\n return model_output\n\n\ndef cal_nDCG_at_ks(all_std_labels=None, all_preds=None, group=None, ks=[1, 3, 5, 10]):\n #print(type(all_std_labels))\n\n sum_ndcg_at_ks = torch.zeros(len(ks))\n cnt = torch.zeros(1)\n\n tor_all_std_labels, tor_all_preds = torch.from_numpy(all_std_labels.astype(np.float32)), torch.from_numpy(all_preds.astype(np.float32))\n #tor_all_std_labels, tor_all_preds = tor_all_std_labels.double(), tor_all_preds.double()\n #print(tor_all_std_labels)\n #print(tor_all_preds)\n head = 0\n for gr in group:\n tor_per_query_std_labels = tor_all_std_labels[head:head+gr]\n tor_per_query_preds = tor_all_preds[head:head+gr]\n head += gr\n\n _, tor_sorted_inds = torch.sort(tor_per_query_preds, descending=True)\n\n sys_sorted_labels = tor_per_query_std_labels[tor_sorted_inds]\n ideal_sorted_labels, _ = torch.sort(tor_per_query_std_labels, descending=True)\n #print(ideal_sorted_labels)\n\n ndcg_at_ks = tor_nDCG_at_ks(sys_sorted_labels=sys_sorted_labels, ideal_sorted_labels=ideal_sorted_labels, ks=ks, multi_level_rele=True)\n #print(ndcg_at_ks)\n\n sum_ndcg_at_ks = torch.add(sum_ndcg_at_ks, ndcg_at_ks)\n cnt += 1\n\n tor_avg_ndcg_at_ks = sum_ndcg_at_ks / cnt\n avg_ndcg_at_ks = tor_avg_ndcg_at_ks.data.numpy()\n return avg_ndcg_at_ks\n\n\ndef cv_eval_lambdaMART_in_XGBoost(para_dict=None):\n # common parameters across different models\n debug, dataset, dir_data, model = para_dict['debug'], para_dict['dataset'], para_dict['dir_data'], para_dict['model']\n min_docs, min_rele, cutoffs = para_dict['min_docs'], para_dict['min_rele'], para_dict['cutoffs']\n do_validation, validation_k, do_log = para_dict['do_validation'], para_dict['validation_k'], para_dict['do_log']\n eta, gamma, min_child_weight, max_depth, tree_method = para_dict['eta'], para_dict['gamma'], para_dict['min_child_weight'], para_dict['max_depth'], para_dict['tree_method']\n\n if debug:\n fold_num = 2\n else:\n fold_num = 5\n\n model_output = update_output_setting(para_dict=para_dict)\n if do_log: # open log file\n sys.stdout = open(model_output + 'log.txt', \"w\")\n\n time_begin = datetime.datetime.now() # timing\n l2r_cv_avg_scores = np.zeros(len(cutoffs)) # fold average\n for fold_k in range(1, fold_num + 1):\n print('\\nFold-', fold_k) # fold-wise data preparation plus certain light filtering\n\n dir_fold_k = dir_data + 'Fold' + str(fold_k) + '/'\n ori_file_train, ori_file_vali, ori_file_test = dir_fold_k + 'train.txt', dir_fold_k + 'vali.txt', dir_fold_k + 'test.txt'\n\n file_train_data, file_train_group = load_data_xgboost(ori_file_train, min_docs=min_docs, min_rele=min_rele, dataset=dataset)\n file_vali_data, file_vali_group = load_data_xgboost(ori_file_vali, min_docs=min_docs, min_rele=min_rele, dataset=dataset)\n file_test_data, file_test_group = load_data_xgboost(ori_file_test, min_docs=min_docs, min_rele=min_rele, dataset=dataset)\n\n x_train, y_train = load_svmlight_file(file_train_data)\n group_train = load_group_data(file_train_group)\n train_dmatrix = DMatrix(x_train, y_train)\n train_dmatrix.set_group(group_train)\n\n if do_validation:\n x_valid, y_valid = load_svmlight_file(file_vali_data)\n group_valid = load_group_data(file_vali_group)\n valid_dmatrix = DMatrix(x_valid, y_valid)\n valid_dmatrix.set_group(group_valid)\n\n x_test, y_test = load_svmlight_file(file_test_data)\n group_test = load_group_data(file_test_group)\n test_dmatrix = DMatrix(x_test)\n\n \"\"\" possible settings of params \"\"\"\n # params = {'objective': 'rank:pairwise', 'eta': 0.1, 'gamma': 1.0, 'min_child_weight': 0.1, 'max_depth': 6}\n\n # ndcg\n # params = {'objective': 'rank:ndcg', 'eta': 0.1, 'gamma': 1.0, 'min_child_weight': 0.1, 'max_depth': 6}\n #params = {'objective': 'rank:ndcg', 'eta': 0.1, 'gamma': 1.0, 'min_child_weight': 0.1, 'max_depth': 6, 'eval_metric': 'ndcg@10'}\n\n params = {'objective': 'rank:ndcg', 'eta': eta, 'gamma': gamma, 'min_child_weight': min_child_weight, 'max_depth': max_depth, 'eval_metric': 'ndcg@10-', 'tree_method': tree_method} # if idealDCG=0, then 0\n\n # map\n # params = {'objective': 'rank:map', 'eta': 0.1, 'gamma': 1.0, 'min_child_weight': 0.1, 'max_depth': 6}\n\n if do_validation:\n fold_xgb_model = xgb.train(params, train_dmatrix, num_boost_round=500, evals=[(valid_dmatrix, 'validation')])\n else:\n fold_xgb_model = xgb.train(params, train_dmatrix, num_boost_round=500)\n\n fold_checkpoint = '-'.join(['Fold', str(fold_k)]) # buffer model\n save_dir = model_output + fold_checkpoint + '/'\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n with open(save_dir+'_'.join(['fold', str(fold_k), 'model'])+'.dat', 'wb') as model_file:\n pickle.dump(fold_xgb_model, model_file)\n\n pred = fold_xgb_model.predict(test_dmatrix) # fold-wise performance\n fold_avg_ndcg_at_ks = cal_nDCG_at_ks(all_std_labels=y_test, all_preds=pred, group=group_test, ks=cutoffs)\n performance_list = [model + ' Fold-' + str(fold_k)]\n for i, co in enumerate(cutoffs):\n performance_list.append('nDCG@{}:{:.4f}'.format(co, fold_avg_ndcg_at_ks[i]))\n performance_str = '\\t'.join(performance_list)\n print('\\n\\t', performance_str)\n\n l2r_cv_avg_scores = np.add(l2r_cv_avg_scores, fold_avg_ndcg_at_ks) # sum for later cv-performance\n\n time_end = datetime.datetime.now() # overall timing\n elapsed_time_str = str(time_end - time_begin)\n print('Elapsed time:\\t', elapsed_time_str + \"\\n\")\n\n print() # begin to print either cv or average performance\n l2r_cv_avg_scores = np.divide(l2r_cv_avg_scores, fold_num)\n if do_validation:\n eval_prefix = str(fold_num)+'-fold cross validation scores:'\n else:\n eval_prefix = str(fold_num) + '-fold average scores:'\n\n print(model, eval_prefix, to_output_str(list_scores=l2r_cv_avg_scores, list_cutoffs=cutoffs))\n\n return l2r_cv_avg_scores\n\n\ndef log_max(dir_output=None, max_cv_avg_scores=None, para_dict=None, cutoffs=None, dataset=None):\n model = para_dict['model']\n with open(file=dir_output + '_'.join(['grid', model]) + '/' + dataset + '_max.txt', mode='w') as max_writer:\n eta, gamma, min_child_weight, max_depth, tree_method = para_dict['eta'], para_dict['gamma'], para_dict['min_child_weight'], para_dict['max_depth'], para_dict['tree_method']\n para_str = '\\n'.join(['eta: '+'{:,g}'.format(eta), 'gamma: '+'{:,g}'.format(gamma), 'min_child_weight: '+'{:,g}'.format(min_child_weight), 'max_depth: '+'{:,g}'.format(max_depth), 'tree_method: '+tree_method])\n max_writer.write(para_str + '\\n')\n max_writer.write(to_output_str(max_cv_avg_scores, cutoffs))\n\n\ndef grid_run_lambdaMART(data=None, dir_data=None, dir_output=None, debug=False):\n do_log = False if debug else True\n\n ''' settings that are rarely changed '''\n validation_k = 10\n min_docs = 10\n cutoffs = [1, 3, 5, 10, 20, 50]\n\n ''' setting w.r.t. data-preprocess '''\n\n ''' setting w.r.t. train '''\n choice_validation = [True] # True, False\n\n \"\"\" setting w.r.t. LambdaMART \"\"\"\n choice_eta = [0.1] if debug else [0.1] # learning_rate, range: [0,1], step size shrinkage used in update to prevents overfitting\n choice_gamma = [0.1] if debug else [0.0] # range: [0,∞] Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger gamma is, the more conservative the algorithm will be.\n\n choice_min_child_weight = [1.0] if debug else [100]\n # range: [0,∞] Minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight,\n # then the building process will give up further partitioning. In linear regression task, this simply corresponds to minimum number of instances needed to be in each node.\n # The larger min_child_weight is, the more conservative the algorithm will be.\n\n choice_max_depth = [6] if debug else [8] # 6, 12, 20 range: [0,∞] Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit\n choice_tree_method = ['auto', 'exact'] if debug else ['auto', 'exact', 'hist'] # auto, exact\n\n max_cv_avg_scores = np.zeros(len(cutoffs)) # fold average\n k_index = cutoffs.index(validation_k)\n max_para_dict = None\n\n for vd in choice_validation:\n for eta, gamma, min_child_weight, max_depth, tree_method in product(choice_eta, choice_gamma, choice_min_child_weight, choice_max_depth, choice_tree_method):\n para_dict = dict(grid_search=True, debug=debug, dataset=data, dir_data=dir_data, dir_output=dir_output,\n model='LambdaMART', min_docs=min_docs, min_rele=1, cutoffs=cutoffs,\n do_validation=vd, validation_k=validation_k, do_log=do_log,\n eta=eta, gamma=gamma, min_child_weight=min_child_weight, max_depth=max_depth, tree_method=tree_method)\n\n curr_cv_avg_scores = cv_eval_lambdaMART_in_XGBoost(para_dict=para_dict)\n if curr_cv_avg_scores[k_index] > max_cv_avg_scores[k_index]:\n max_cv_avg_scores, max_para_dict = curr_cv_avg_scores, para_dict\n\n #record optimal setting\n log_max(dir_output=dir_output, max_cv_avg_scores=max_cv_avg_scores, para_dict=max_para_dict, cutoffs=cutoffs, dataset=data)\n\n\ndef point_run_lambdaMART(data=None, dir_data=None, dir_output=None, debug=False):\n do_log = False if debug else True\n\n para_dict = dict(debug=debug, dataset=data, dir_data=dir_data, dir_output=dir_output, model='LambdaMART',\n min_docs=10, min_rele=1, cutoffs=[1, 3, 5, 10, 20, 50], do_validation=True, validation_k=10, do_log=do_log, grid_search=False,\n eta=0.1, gamma=0.0, min_child_weight=100, max_depth=8, tree_method='exact')\n\n cv_eval_lambdaMART_in_XGBoost(para_dict=para_dict)\n\n\nfrom org.archive.ranking.run.test_l2r_tao import get_data_dir\nif __name__ == '__main__':\n \"\"\"\n >>> Supported datasets <<<\n MQ2007_super | MQ2008_super | MQ2007_semi | MQ2008_semi | MSLRWEB10K | MSLRWEB30K | Yahoo_L2R_Set_1 (TBA) | Yahoo_L2R_Set_1 (TBA)\n \"\"\"\n\n data = 'MSLRWEB30K'\n\n dir_data = get_data_dir(data, pc='mbox-f3')\n dir_output = '/home/dl-box/WorkBench/CodeBench/PyCharmProject/Project_output/Out_L2R/Listwise/'\n\n debug = False\n\n grid_search = True\n\n if grid_search:\n grid_run_lambdaMART(data=data, dir_data=dir_data, dir_output=dir_output, debug=debug)\n else:\n point_run_lambdaMART(data=data, dir_data=dir_data, dir_output=dir_output, debug=debug)","sub_path":"org/archive/ranking/listwise/lambdaMART.py","file_name":"lambdaMART.py","file_ext":"py","file_size_in_byte":13045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"179044654","text":"import sys\nimport json\n\nPUNCT = \",;.?!:\"\n\n#Junta novamente palavras que foram separadas pelo tokenizer\ndef tokens2sentence(tokens):\n tokens_with_spaces = []\n for token in tokens:\n if token not in PUNCT:\n tokens_with_spaces.append(\" \" + token)\n else:\n tokens_with_spaces.append(token)\n return \"\".join(tokens_with_spaces)[1:]\n\n\ninfile = open(sys.argv[1], encoding=\"utf-8\")\noutfile = open(sys.argv[2], \"w\", encoding=\"utf-8\")\ndata = json.load(infile)\ninfile.close()\n\nfor dic in data:\n print(tokens2sentence(dic[\"tokens\"]).replace(\"\\n\", \" \"), file=outfile)\n\noutfile.close()\n\n\n","sub_path":"nerre/scripts/converters/json2txt.py","file_name":"json2txt.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"512005840","text":"\"\"\"empty message\n\nRevision ID: 4e6e81664f8b\nRevises: f36e97b8590\nCreate Date: 2015-05-10 19:32:37.871000\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '4e6e81664f8b'\ndown_revision = 'f36e97b8590'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.create_table('tasktable_SYM',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('accuracy', sa.Float(), nullable=True),\n sa.Column('driftrate', sa.Float(), nullable=True),\n sa.Column('datetime', sa.DateTime(), nullable=True),\n sa.Column('user_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['user_id'], ['user.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('tasktable_SYM')\n ### end Alembic commands ###\n","sub_path":"migrations/versions/4e6e81664f8b_.py","file_name":"4e6e81664f8b_.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"147087477","text":" \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t # ASSIGNMENT18\n\n#QUESTION:1 Create a dict with name and mobile number.\n# Define a GUI interface using tkinter and pack the label\n# and create a scrollbar to scroll the list of keys in the dictionary. \n#SOLUTION:\nimport tkinter\nfrom tkinter import *\nd={'Navjeet':6316220,'Rukman':456789,'Vidhi':6316234,'Rukman':456789,'dqd':6316234,'Rkmcj':456789,'Vi':6316234,'Rukwmcman':456789,'Vidnckhi':6316234}\nroot=Tk()\ns=Scrollbar(root)\ns.pack(side=RIGHT,fill=Y)\nl=Listbox(root,yscrollcommand=s.set)\nfor line in d:\n l.insert(END,'this is ' +str(line))\nl.pack(side=LEFT,fill=BOTH)\ns.config(command=l.yview())\nroot.mainloop()\n\t\t \n\t\t \n#QUESTION:2 In the same tkinter file as created above, create a function to insert items into the dictionary.\n#SOLUTION:\nimport tkinter\nfrom tkinter import *\n\nroot=Tk()\n\ndef show():\n a=entry.get()\n b=entry1.get()\n mylist.insert(END,a)\n dict[a]=b\n print(dict)\n\nname=False\nMobile=False\n\ndef name(event):\n entry.delete(0,END)\n name=True\n\ndef Mobile(event):\n entry1.delete(0,END)\n Mobile=True\n\nscrollbar=Scrollbar(root)\nscrollbar.pack(side=RIGHT,fill=Y)\nd={'Navjeet':6316220,'Rukman':456789,'Vidhi':6316234,'Rukman':456789,'dqd':6316234,'Rkmcj':456789,'Vi':6316234,'Rukwmcman':456789,'Vidnckhi':6316234}\nmylist=Listbox(root,yscrollcommand=scrollbar.set)\nfor i in d:\n mylist.insert(END,i)\nmylist.pack(fill=BOTH)\n\n\nentry=Entry(root)\nentry.insert(0,'name')\nentry.bind(\"