diff --git "a/6612.jsonl" "b/6612.jsonl" new file mode 100644--- /dev/null +++ "b/6612.jsonl" @@ -0,0 +1,180 @@ +{"seq_id":"165520731","text":"from django.http import HttpRequest, HttpResponse\nfrom django.shortcuts import render\n\nfrom grandchallenge.jqfileupload.forms import UploadForm\n\n\ndef uploader_widget_test(request: HttpRequest, **kwargs) -> HttpResponse:\n if request.method == \"POST\":\n test_form = UploadForm(request.POST)\n if test_form.is_valid():\n result = \"Success!!!\\n\"\n result += \"\\n\".join(\n f\" {k}: {v}\" for k, v in test_form.cleaned_data.items()\n )\n result += \"\\n\\n\"\n f1 = test_form.cleaned_data[\"upload_form\"][0]\n with f1.open() as f:\n the_bytes = f.read(16)\n result += f\"\"\"\nYou uploaded {len(test_form.cleaned_data[\"upload_form\"])} files in the first form.\n\nThe first 16 bytes of the first file were: {the_bytes}\n \"\"\"\n else:\n result = \"Validation error:\\n\"\n result += \"\\n\".join(f\" {e}\" for e in test_form.errors)\n return HttpResponse(result, content_type=\"text/plain\")\n\n else:\n test_form = UploadForm()\n return render(\n request, \"uploader_widget_test.html\", {\"testform\": test_form}\n )\n","sub_path":"app/grandchallenge/jqfileupload/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"575026180","text":"import yaml\nimport json\nimport crypt\nfrom functools import partial\nfrom config import WHEEL_CONFIG\nimport pytest\nfrom faker import Faker\nfrom factories import ContainerFactory\n\n\n@pytest.fixture(scope=\"module\")\ndef container(request, docker_client):\n obj = ContainerFactory(\n docker_client=docker_client,\n config__salt_config=None,\n config__volumes=None,\n config__host_config=None\n )\n request.addfinalizer(\n lambda: obj['docker_client'].remove_container(\n obj['config']['name'], force=True)\n )\n return obj\n\n\ndef test_master_shipped_with_sha256(container):\n \"\"\"\n Test the Master is *shipped* with hash type set to SHA256.\n \"\"\"\n master_config = container.run('cat /etc/salt/master')\n content = yaml.load(master_config)\n assert content['hash_type'] == 'sha256'\n\n\ndef test_minion_shipped_with_sha256(container):\n \"\"\"\n Test the Minion is *shipped* with hash type set to SHA256.\n \"\"\"\n minion_config = container.run('cat /etc/salt/minion')\n content = yaml.load(minion_config)\n assert content['hash_type'] == 'sha256'\n\n\n@pytest.fixture(scope=\"module\")\ndef salt_master_config(file_root, pillar_root):\n return {\n 'base_config': {\n 'hash_type': 'sha384',\n 'pillar_roots': {\n 'base': [pillar_root]\n },\n 'file_roots': {\n 'base': [file_root]\n },\n 'external_auth': {\n 'pam': {\n WHEEL_CONFIG['user']: ['@wheel']\n }\n }\n }\n }\n\n\n@pytest.fixture(scope=\"module\")\ndef master_container(request, salt_root, salt_master_config, docker_client):\n fake = Faker()\n obj = ContainerFactory(\n config__name='master_{0}_{1}'.format(fake.word(), fake.word()),\n config__salt_config__tmpdir=salt_root,\n docker_client=docker_client,\n config__salt_config__conf_type='master',\n config__salt_config__config=salt_master_config,\n config__salt_config__post__id='{0}_{1}'.format(fake.word(), fake.word()),\n config__environment=dict(PYTHONPATH='/salt-toaster')\n )\n request.addfinalizer(\n lambda: obj['docker_client'].remove_container(\n obj['config']['name'], force=True)\n )\n return obj\n\n\ndef test_hash_type_is_used(request, master, salt_master_config):\n user = WHEEL_CONFIG['user']\n password_salt = '00'\n password = crypt.crypt(WHEEL_CONFIG['password'], password_salt)\n request.addfinalizer(\n partial(master['container'].run, \"userdel {0}\".format(user)))\n master['container'].run(\"useradd {0} -p '{1}'\".format(user, password))\n raw_output = master['container'].run(\n \"python tests/scripts/wheel_config_values.py\"\n )\n output = json.loads(raw_output)\n expected = salt_master_config['base_config']['hash_type']\n assert output['data']['return']['hash_type'] == expected\n","sub_path":"tests/test_package.py","file_name":"test_package.py","file_ext":"py","file_size_in_byte":2921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"457080811","text":"import socket\nimport cv2\nimport numpy as np\nimport requests\nfrom threading import Thread\n \n# buffer return\ndef recvall(sock, count):\n \n buf = b''\n while count:\n newbuf = sock.recv(count)\n if not newbuf: return None\n buf += newbuf\n count -= len(newbuf)\n return buf\n\n# HOST = socket.gethostname() \nHOST = '192.168.255.21'\nPORT = 5000\nADDR = (HOST, PORT)\nBUFF_SIZE = 1024\n\n#TCP\ns = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\nprint('Socket created')\ns.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) \n# server IP, port\ns.bind(ADDR)\nprint('Socket bind complete')\nthreads = []\n\n \n# connection, conn: socket, addr: bind address\nconn,addr=s.accept()\n \nclass ClientTread(Thread):\n\n def __init__(self,host,port,sock):\n Thread.__init__(self)\n self.host = host\n self.port = port\n self.sock = sock\n print (\"Check the new thread \"+host+\":\"+str(port))\n \n def run(self):\n f = open(\"/home/heejunghong/BlackfencerWeb/index.html\", 'r')\n while True:\n l = f.read(BUFF_SIZE)\n while(l):\n self.sock.send(l)\n print('Sent ',repr(l))\n l = f.read(BUFF_SIZE)\n if not l:\n f.close()\n self.sock.close()\n break\n \n \nwhile True:\n\n # wait client \n s.listen(1)\n print('Socket now listening')\n (clientSocket, (host, port)) = s.accept()\n print ('Connection from ', (host, port))\n newthread = ClientThread(host, port, clientSocket)\n newthread.start()\n threads.append(newthread)\n \n for t in threads:\n t.join()\n \n # size of stringData (==(str(len(stringData))).encode().ljust(16))\n length = recvall(conn, 16)\n stringData = recvall(conn, int(length))\n data = np.fromstring(stringData, dtype = 'uint8')\n \n # decoding data\n frame = cv2.imdecode(data, cv2.IMREAD_COLOR)\n print(np.shape(frame))\n cv2.imshow('ImageWindow',frame)\n cv2.waitKey(1)\n\n\n\n","sub_path":"multi-thread/server2_2c.py","file_name":"server2_2c.py","file_ext":"py","file_size_in_byte":2037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"129510611","text":"li = [int(x) for x in list(input().split(\",\"))]\n\nfor i in range(1000):\n li.append(0) # add empty memory\n\ndef digit(number, n):\n return number // 10**n % 10\n\ndef firstsecond(li, mode, rel):\n if mode[0] == 0:\n first = li[li[i+1]]\n elif mode[0] == 1:\n first = li[i+1]\n elif mode[0] == 2:\n first = li[li[i+1] + rel]\n if mode[1] == 0:\n second = li[li[i+2]]\n elif mode[1] == 1:\n second = li[i+2]\n elif mode[1] == 2:\n second = li[li[i+2] + rel]\n \n return (first, second)\n\ndef onearg(li, mode, rel, index):\n if mode[index] == 0:\n first = li[li[i+1]]\n elif mode[index] == 1:\n first = li[i+1]\n elif mode[index] == 2:\n first = li[li[i+1] + rel]\n return first\n\nID = 1\ninput_taken = False\ni = 0\nrel = 0\nwhile i < len(li):\n mode = [0,0,0]\n for j in range(2,5):\n mode[j-2] = digit(li[i], j)\n if 10*digit(li[i], 1) + digit(li[i], 0) == 99:\n op = 99\n else:\n op = digit(li[i], 0)\n print(\"%d : %d : %d, %d\" % (i, li[i], op, rel))\n if op == 1:\n #add\n first, second = firstsecond(li, mode, rel)\n if mode[2] == 0:\n li[li[i+3]] = first + second\n elif mode[2] == 2:\n li[li[i+3]+rel] = first + second\n i += 4\n elif op == 2:\n #mul\n first, second = firstsecond(li, mode, rel)\n if mode[2] == 0:\n li[li[i+3]] = first * second\n elif mode[2] == 2:\n li[li[i+3]+rel] = first * second\n i += 4\n elif op == 3:\n # input\n if not input_taken:\n val = ID\n input_taken = True\n else:\n val = input(\"input: \")\n if mode[0] == 0:\n li[li[i+1]] = val\n elif mode[0] == 2:\n li[li[i+1]+rel] = val\n i += 2\n elif op == 4:\n # print\n first = onearg(li, mode, rel, 0)\n print(first)\n i += 2\n elif op == 5:\n # jump if true\n first, second = firstsecond(li, mode, rel)\n if first != 0:\n i = second\n else:\n i += 3\n elif op == 6:\n # jump if false\n first, second = firstsecond(li, mode, rel)\n if first == 0:\n i = second\n else:\n i += 3\n elif op == 7:\n # less than\n first, second = firstsecond(li, mode, rel)\n if mode[2] == 0:\n li[li[i+3]] = 1 if first < second else 0\n elif mode[2] == 2:\n li[li[i+3]+rel] = 1 if first < second else 0\n i += 4\n elif op == 8:\n # eq\n first, second = firstsecond(li, mode, rel)\n if mode[2] == 0:\n li[li[i+3]] = 1 if first == second else 0\n elif mode[2] == 2:\n li[li[i+3]+rel] = 1 if first == second else 0\n i += 4\n elif op == 9:\n # add to rel\n first = onearg(li, mode, rel, 0)\n rel += first\n i += 2\n elif op == 99:\n #halt\n break\n else:\n print(\"no can do\")\n break\n","sub_path":"09/9.py","file_name":"9.py","file_ext":"py","file_size_in_byte":3009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"196513153","text":"from flask import Flask, jsonify, request\nfrom flask_login import LoginManager, current_user\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_limiter import Limiter\nfrom flask_limiter.util import get_remote_address\nfrom flask_cors import CORS\nfrom config import Config\nimport time\n\nfrom flask import __version__\nfrom flask_login import __version__ as login_version\nfrom flask_sqlalchemy import __version__ as sqlalchemy_version\nfrom flask_limiter import __version__ as limiter_version\nfrom flask_cors import __version__ as cors_version\n\ndb = SQLAlchemy()\nmigrate = Migrate()\nlogin = LoginManager()\nlimiter = Limiter(key_func=get_remote_address)\n#cors = CORS(resources={r\"/api/*\": {\"origins\": \"http://127.0.0.1\"}})\n\n\ndef page_not_found(e):\n return jsonify({'error': 'data not found'}), 404\n\n\ndef unauthorized(e):\n return jsonify({'error': 'unauthorized'}), 401\n\n\ndef too_many_requests(e):\n return jsonify({'error': 'too many requests', 'details': 'this endpoint is throttled to 1 request per second'}), 429\n\n\ndef server_error(e):\n\n data = request.cookies\n if 'debug' in data and current_user.is_authenticated:\n if current_user.rank <= 3:\n\n detailed_error = {\n 'error': 'unhandled error',\n 'debugging_data': {\n 'FLAG4': 'IHACK-1a16a0273f9163a79390936d6511d9c4',\n 'SECRET_KEY': Config.SECRET_KEY,\n 'SQLALCHEMY_TRACK_MODIFICATIONS': Config.SQLALCHEMY_TRACK_MODIFICATIONS,\n 'timestamp_of_error': time.time(),\n 'flask_version': __version__,\n 'flask_modules': {\n 'flask_login': login_version,\n 'flask_sqlalchemy': sqlalchemy_version,\n 'flask_migrate': '2.4.0',\n 'flask_limiter': limiter_version,\n 'flask_cors': cors_version\n }\n }\n }\n\n return jsonify(detailed_error), 500\n\n return jsonify({'error': 'unhandled error'}), 500\n\n\ndef create_app(config_class=Config):\n app = Flask(__name__)\n app.config.from_object(config_class)\n db.init_app(app)\n migrate.init_app(app, db)\n login.init_app(app)\n limiter.init_app(app)\n #cors.init_app(app)\n\n app.register_error_handler(404, page_not_found)\n app.register_error_handler(429, too_many_requests)\n app.register_error_handler(401, unauthorized)\n app.register_error_handler(Exception, server_error)\n\n from app.apiv2 import bp as apiv2_bp\n app.register_blueprint(apiv2_bp, url_prefix='/api/v2.0')\n\n from app.apiv1 import bp as apiv1_bp\n app.register_blueprint(apiv1_bp, url_prefix='/api/v1.0')\n\n return app\n\nfrom app import models","sub_path":"server/app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"456750002","text":"from __future__ import print_function\n\nfrom args import parse_args\nimport os\nimport paddle.fluid as fluid\nimport sys\nimport time\nfrom network_conf import ctr_dnn_model_dataset\n\ndense_feature_dim = 13\n\ndef train():\n args = parse_args()\n if not os.path.isdir(args.model_output_dir):\n os.mkdir(args.model_output_dir)\n \n dense_input = fluid.layers.data(\n name=\"dense_input\", shape=[dense_feature_dim], dtype='float32')\n sparse_input_ids = [\n fluid.layers.data(name=\"C\" + str(i), shape=[1], lod_level=1, dtype=\"int64\")\n for i in range(1, 27)]\n label = fluid.layers.data(name='label', shape=[1], dtype='int64')\n\n loss, auc_var, batch_auc_var = ctr_dnn_model_dataset(dense_input, sparse_input_ids, label,\n args.embedding_size, args.sparse_feature_dim)\n\n optimizer = fluid.optimizer.Adagrad(learning_rate=1e-2)\n optimizer.minimize(loss)\n\n exe = fluid.Executor(fluid.CPUPlace())\n exe.run(fluid.default_startup_program())\n dataset = fluid.DatasetFactory().create_dataset()\n dataset.set_use_var([dense_input] + sparse_input_ids + [label])\n pipe_command = \"python criteo_reader.py %d\" % args.sparse_feature_dim\n dataset.set_pipe_command(pipe_command)\n whole_filelist = [\"raw_data/part-%d\" % x for x in range(len(os.listdir(\"raw_data\")))]\n dataset.set_filelist(whole_filelist)\n dataset.set_batch_size(args.batch_size)\n dataset.set_thread(args.thread_num)\n\n from util import run_benchmark\n duration = run_benchmark(startup_prog=fluid.default_startup_program(),\n main_prog=fluid.default_main_program(),\n batch=args.batch_size,\n thread_num=args.thread_num,\n dataset=dataset)\n print(\"total training time: %f\" % (end_time - start_time))\n\nif __name__ == '__main__':\n train()\n","sub_path":"benchmark/ps/ctr/local_train.py","file_name":"local_train.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"482204361","text":"pkgname = \"python-charset-normalizer\"\npkgver = \"3.2.0\"\npkgrel = 0\nbuild_style = \"python_module\"\nhostmakedepends = [\"python-setuptools\"]\ncheckdepends = [\"python-pytest\"]\ndepends = [\"python\"]\npkgdesc = \"Encoding and language detection\"\nmaintainer = \"q66 \"\nlicense = \"MIT\"\nurl = \"https://charset-normalizer.readthedocs.io\"\nsource = f\"https://github.com/Ousret/charset_normalizer/archive/refs/tags/{pkgver}.tar.gz\"\nsha256 = \"8f8c0a09ab745efc68ce4c1b85292ded2f06ea106f8086f614a0a9403c3dde0a\"\n# dependency of pytest\noptions = [\"!check\"]\n\n\ndef post_install(self):\n self.install_license(\"LICENSE\")\n","sub_path":"main/python-charset-normalizer/template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"296369236","text":"#!/usr/bin/env python\n\n\nimport argparse\nimport pandas as pd\nimport os\n\n \n\nparser = argparse.ArgumentParser(description='convert counts to tpm values')\nparser.add_argument('-i', '--infile', help='counts values csv file', required =True)\nparser.add_argument('-l', '--length', help='list of gene name and length', required =True)\nparser.add_argument('-o', '--outfile', help='converted values csv file', required =True)\n\nargs = parser.parse_args()\nlength = args.length\ninfile = args.infile\nout = args.outfile\n\n\n\ndf_counts = pd.read_csv(infile,sep='\\t')\n\ndf_length = pd.read_csv(length,sep='\\t')\n\ndf_col = pd.merge(df_length, df_counts, on='GeneID')\ndf_col ['length'] = df_col['length'].div(1000)\n\ndf_col.update(df_col.iloc[:,2:].div(df_col['length'], axis=\"index\"))\n\ndf_permil= df_col.iloc[:,2:].sum(axis=0)\ndf_permil = df_permil/1000000\n\ndf_col.update(df_col.iloc[:,2:]/df_permil)\ndf_col.to_csv(out,sep='\\t',index=False)","sub_path":"tpm.py","file_name":"tpm.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"224751850","text":"#A demo for how to use NLTK package to extract entities from given texts.\nfrom nltk import ne_chunk, pos_tag, word_tokenize\nfrom nltk.tree import Tree\nimport time\nfrom timeit import Timer\n\n\ndef get_continuous_chunks(text):\n chunked = ne_chunk(pos_tag(word_tokenize(text)))\n prev = None\n continuous_chunk = []\n current_chunk = []\n for i in chunked:\n if type(i) == Tree:\n current_chunk.append(\" \".join([token for token, pos in i.leaves()]))\n elif current_chunk:\n named_entity = \" \".join(current_chunk)\n if named_entity not in continuous_chunk:\n continuous_chunk.append(named_entity)\n current_chunk = []\n else:\n continue\n return continuous_chunk\nmy_sent = \"Clemson Tigers baseball represents Clemson University in college baseball at the NCAA Division I level.\"\n\nstart = time.clock()\nprint(get_continuous_chunks(my_sent))\nend = time.clock()\nprint(end - start)\n","sub_path":"nlp_test.py","file_name":"nlp_test.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"297371742","text":"from typing import List\n\nclass Solution:\n def duplicateZeros(self, arr: List[int]) -> None:\n \"\"\"\n Do not return anything, modify arr in-place instead.\n \"\"\"\n i = 0\n n = len(arr)\n\n while i < n:\n if not arr[i]:\n arr.insert(i+1, 0)\n arr.pop()\n i += 1\n i+=1\n\n \n \n\n print(arr)\n\narr = [1,0,2,3,0,4,5,0]\nprint(arr)\nsolution = Solution()\n\nsolution.duplicateZeros(arr)","sub_path":"Leetcode/Arrays_101/duplicateZeros.py","file_name":"duplicateZeros.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"408089591","text":"'''\r\nFile: part1.py\r\nAuthor: Zhengchao Yu \r\nDate: September 18, 2014 \r\nSection: 4\r\nE-Mail: zy3@umbc.edu \r\nAssignment Description: Homework 2\r\nThis homework involves a series of exercises designed to practice \r\nvariables, expressions, and if statements. \r\n\r\nAssignment Part 1:\r\nThis program will read three numbers from the user.\r\n(you may assume the user will actually enter numbers).\r\nPrint out the average of these three numbers.\r\n'''\r\n\r\nimport math\r\n\r\ndef main():\r\n\r\n print (\"\\n \")\r\n # The following line will print this program's greeting.\r\n print(\"This program will find the average of three numbers.\")\r\n print(\"----------------------------------------------------\")\r\n # The following three lines will cast the user's input into strings. \r\n number1 = input(\"Enter your first number: \")\r\n number1 = float(number1)\r\n number2 = input(\"Enter your second number: \")\r\n number2 = float(number2)\r\n number3 = input(\"Enter your third number: \")\r\n number3 = float(number3)\r\n\r\n # This function calculates the average of the three numbers. \r\n numAverage = int(( (number1) + (number2) + (number3) ) / 3) \r\n print(\"----------------------------------------------------\")\r\n \r\n # This line will print out the averaged number. \r\n print (\"Your average is: \" + str(numAverage))\r\n\r\nmain()\r\n","sub_path":"HW02/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"488865531","text":"import ntpath\nimport pandas as pd\nfrom learner.algorithms import ALGORITHM, TRAINER_CLASS, DNN_REGRESSOR, CODES\nfrom learner.model_trainers import DNNRegressor, ClassicRegressor\nfrom processor import sdf_to_csv\nfrom rdkit import Chem\nimport sklearn\n\nfilepath = 'C:\\PycharmProjects\\ml-data-qsar\\TEST\\IGC50\\IGC50_training.sdf'\nfilepath_test = 'C:\\PycharmProjects\\ml-data-qsar\\TEST\\IGC50\\IGC50_prediction.sdf'\nvaluename = 'Tox'\nclassname = 'Soluble'\n\nfptype = [{'Type': \"ENUM2ENUM\"},{'Type': \"DESC\"}]\n\ntest_set_size = 0\nmajor_subsample = 1\n\nlayers = [64,64]\n\ninput_drop_out = 0.1\ndrop_out = 0.0\nn_split = 10\noptimizer='Nadam'\nactivation='relu'\nl_rate=0.005\nbeta=0.00001\nk_constraint = 4\nmc_train_cut_off = 0.65\n\noutput_path = 'C:\\\\PycharmProjects\\\\ml.services\\\\Source\\\\callers and models'\n\ndataframe = sdf_to_csv(filepath,fptype,value_name_list=valuename)\ndataframe_test = sdf_to_csv(filepath_test,fptype,value_name_list=valuename)\n\nregressor = ALGORITHM[TRAINER_CLASS][DNN_REGRESSOR](\n ntpath.basename(filepath), valuename, dataframe,test_set_size=test_set_size,\n fptype=fptype,n_split=n_split, output_path=output_path,\n scale=\"standard\",manual_test_set=dataframe_test)\n\ndnn = regressor.train_model(CODES[DNN_REGRESSOR])\ndnn.make_plots()\nregressor.make_perfomance_csv()\n\n# dataframe = pd.read_csv(filename)\n# x = dataframe.values #returns a numpy array\n# min_max_scaler = preprocessing.MinMaxScaler()\n# x_scaled = min_max_scaler.fit_transform(x)\n# headers = [x for x in range(797)]\n# headers.append('Tox')\n# dataframe = pd.DataFrame(x_scaled,columns=headers)\n# print(dataframe)\n\n# classifier = ClassicClassifier(ntpath.basename(filepath), classname, dataframe,test_set_size=test_set_size,\n# major_subsample=major_subsample, fptype=fptype,n_split=n_split, output_path=output_path,\n# scale=\"standard\")\n#\n# NB = classifier.train_model('naivebayes')\n# NB.make_plots()\n# classifier.make_perfomance_csv()\n#\n#\n# classifier = ClassicClassifier(ntpath.basename(filepath), classname, dataframe,test_set_size=test_set_size,\n# major_subsample=major_subsample, fptype=fptype,n_split=n_split, output_path=output_path,\n# scale=\"standard\")\n#\n# DT = classifier.train_model('decisiontree')\n# DT.make_plots()\n# classifier.make_perfomance_csv()\n#\n# classifier = ClassicClassifier(ntpath.basename(filepath), classname, dataframe,test_set_size=test_set_size,\n# major_subsample=major_subsample, fptype=fptype,n_split=n_split, output_path=output_path,\n# scale=\"standard\")\n#\n# RF = classifier.train_model('randomforestclassifier')\n# RF.make_plots()\n# classifier.make_perfomance_csv()\n\n# classifier = ClassicClassifier(ntpath.basename(filepath), classname, dataframe,test_set_size=test_set_size,\n# major_subsample=major_subsample, fptype=fptype,n_split=n_split, output_path=output_path,\n# scale=\"standard\")\n# LR = classifier.train_model('linearregression')\n# LR.make_plots()\n# classifier.make_perfomance_csv()\n#\n# classifier = ClassicClassifier(ntpath.basename(filepath), classname, dataframe,test_set_size=test_set_size,\n# major_subsample=major_subsample, fptype=fptype,n_split=n_split, output_path=output_path,\n# scale=\"standard\")\n#\n# SVM = classifier.train_model('supportvectormachineclassifier')\n# SVM.make_plots()\n# classifier.make_perfomance_csv()\n# classifier.train_model(2)\n# classifier.make_plots()\n\n\n# train_dnn_valid(classifier,layers,batch_size_dnn=batch_size_dnn,k_fold=k_fold,\n# drop_out=drop_out,input_drop_out=input_drop_out,optimizer=optimizer,\n# activation=activation, l_rate=l_rate, beta=beta)\n\n\n","sub_path":"Source/callers and models/esben_project_calls.py","file_name":"esben_project_calls.py","file_ext":"py","file_size_in_byte":3878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"71062073","text":"import pandas as pd\nimport xlrd\nimport re\nimport json\n\nclass Importacao:\n def __init__(self):\n #-------------------------------------------------------\n #Pega do usuario o valor do número do fornecedor\n self.fornecedor = input(\"Qual o fornecedor que você deseja auditar? \")\n #Importações, a planilha é antiga, por isso o xlrd\n self.book = xlrd.open_workbook(\"razao.xls\", encoding_override='cp1252') \n #Transformando em DataFrame\n self.arquivo = pd.read_excel(self.book)\n self.arquivo.drop(['codi_emp', 'clasc', 'nomec', 'tipo', 'numelan',\n 'saldoant', 'contrap', 'ordem_nat_cta', 'origem', 'saldo',\n 'mascara', 'mascrel', 'zebra1', 'tipo_lan',\n 'emissao', 'codi_lote', 'nome_fantasia_incorporacao', 'nro_quebra_incorporacao',\n 'natureza', 'filial', 'codigo_scp', 'descricao_scp', 'ordem'], inplace=True, axis=1)\n #Ficam apenas as colunas \"codic\", \"datalan\", \"valdeb\", \"valcre\", \"historico\"\n #Receberá os Números das Notas fiscais\n self.historico_separado = list()\n #-------------------------------------------------------\n \n #-------------------------------------------------------\n #Excluindo colunas desnecessárias\n def arquivoDataFrame(self):\n #Faz um loop em cada relatório, para retirar os Números das nf's\n #-------------------------------------------------------\n for historico in self.arquivo['historico']:\n historicoSeparado = list()\n filtro = re.findall('([0-9]+)',historico)\n\n if (len(filtro) != 0):\n historicoSeparado.append(filtro)\n else:\n pass\n\n if (len(historicoSeparado) != 0):\n self.historico_separado.append(historicoSeparado[0])\n else:\n self.historico_separado.append(\"Lançamento sem número de NF\")\n\n #Adiciona o número separado no dataframe\n df_historicos = pd.DataFrame(self.historico_separado)\n self.arquivo['historico sep'] = df_historicos\n #-------------------------------------------------------\n print(type(self.fornecedor))\n compras = self.arquivo.loc[(self.arquivo['valcre'] != 0) & (self.arquivo['codic'] == int(self.fornecedor))]\n pagamentos = self.arquivo.loc[(self.arquivo['valdeb'] != 0) & (self.arquivo['codic'] == int(self.fornecedor))]\n saldo_anterior = self.arquivo.loc[(self.arquivo['valdeb'] == 0) & (self.arquivo['valcre'] == 0) & (self.arquivo['codic'] == int(self.fornecedor))]\n #Para teste utilize----------------------------------------------\n #compras.to_excel('compras.xlsx') \n #pagamentos.to_excel('Pagamentos.xlsx')\n #saldo_anterior.to_excel('Saldos.xlsx')\n \n \nteste = Importacao()\nteste.arquivoDataFrame()","sub_path":"importacao.py","file_name":"importacao.py","file_ext":"py","file_size_in_byte":2867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"516350708","text":"from django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.decorators.http import require_http_methods\nfrom vpitt.dbfunc import get_lesson_by_name, get_teacher_by_caf, \\\n get_korpus_by_name, get_rooms_by_korpus, print_message, \\\n get_group_by_id\nimport json\nfrom ast import literal_eval\n\n@login_required\n@require_http_methods([\"POST\"])\ndef get_teacher(request):\n name = request.POST[\"name\"]\n lesson = get_lesson_by_name(name)\n\n teachers = get_teacher_by_caf(lesson.cathedra)\n teachers_dict = {0:\"Выберете преподавателя\"}\n for item in enumerate(teachers):\n s = (item[1].teacher.last_name) + \" \" + item[1].teacher.first_name[:1] + \". \" + item[1].teacher.patronymic[:1] + \".\"\n teachers_dict[item[0]+1] = s\n teachers_dict = json.dumps(teachers_dict, ensure_ascii=False)\n return HttpResponse(teachers_dict)\n\n@login_required\n@require_http_methods([\"POST\"])\ndef get_room(request):\n name = request.POST[\"name\"]\n korpus = get_korpus_by_name(name)\n rooms = get_rooms_by_korpus(korpus)\n rooms_dict = {0:\"\"}\n for item in enumerate(rooms):\n s = (item[1].number)\n rooms_dict[item[0]+1] = s\n rooms_dict = json.dumps(rooms_dict, ensure_ascii=False)\n return HttpResponse(rooms_dict)\n\n@login_required\n@require_http_methods([\"POST\"])\ndef save_changes(request):\n var = literal_eval(list(request.POST.keys())[0])\n json_from_user = var[\"0\"]\n group = get_group_by_id(int(json_from_user[\"id\"]))\n group.tt_json = json_from_user\n group.save()\n return HttpResponse(200)\n","sub_path":"timetable/ajax_request.py","file_name":"ajax_request.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"603367649","text":"# Databricks notebook source\nfile_path = \"abfss://deltalake@.dfs.core.windows.net/bronze/dg-retail/orders/2022/10/2022_10_11orders.parquet\"\ntable_name = \"dead_orders\"\n\n# COMMAND ----------\n\n# DBTITLE 1,Import libraries\nimport great_expectations as ge\nimport pyspark.sql.functions as F\n\n# COMMAND ----------\n\n# DBTITLE 1,Initiate GE data context\ncontext = ge.get_context()\n\n# COMMAND ----------\n\n# DBTITLE 1,Read dataframe\ndf = spark.read.parquet(file_path)\ndf = df.select([F.col(col).alias(col.replace(' ', '_').replace('-', '_').replace('#', '').replace('&', '').replace('__', '_')) for col in df.columns])\n\n# COMMAND ----------\n\n# DBTITLE 1,Run checkpoint (execute data validation)\ncheckpoint_name=f\"validate_df_{table_name}\"\ncheckpoint_result = context.run_checkpoint(\n checkpoint_name=checkpoint_name,\n batch_request={\n \"runtime_parameters\": {\"batch_data\": df},\n \"batch_identifiers\": {\n \"pipeline_stage\": \"lab\",\n \"run_id\": \"d225c4ef-93d2-41cc-b884-128651e356ac\",\n },\n },\n)\n","sub_path":"2022/2022_10_12_dataMindsConnect2022/2022_10_12_dataMindsConnect2022_DataQuality/great_expectations/40_ge_validate_df.py","file_name":"40_ge_validate_df.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"116850583","text":"from tests import SiteTest, app\n\nclass ApiEndpoints(SiteTest):\n \"\"\" Test cases for the api subdomain \"\"\"\n def test_api_unknown_route(self):\n \"\"\" Check api unknown route \"\"\"\n response = self.client.get('/', app.config['API_SUBDOMAIN'])\n self.assertEqual(response.json, {'error_code': 0, 'error_message': 'Unknown API route'})\n self.assertEqual(response.status_code, 404)\n\n def test_api_healthcheck(self):\n \"\"\" Check healthcheck url responds \"\"\"\n response = self.client.get('/healthcheck', app.config['API_SUBDOMAIN'])\n self.assertEqual(response.json, {'status': 'ok'})\n self.assertEqual(response.status_code, 200)\n\n def test_api_route_errors(self):\n \"\"\" Check api route errors \"\"\"\n from pysite.base_route import APIView\n from pysite.constants import ErrorCodes\n\n av = APIView()\n av.error(ErrorCodes.unauthorized)\n av.error(ErrorCodes.bad_data_format)\n\n","sub_path":"tests/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"515849885","text":"import os\nimport time\n\npid = os.fork()\n\n\nif pid == 0:\n #由子进程执行\n for i in range(5):\n print('这是儿子 {}'.format(i))\n time.sleep(1)\nelse:\n #由父进程执行\n print('爸爸等你')\n #阻塞在此处，等待子进程执行完成，回收子进程资源\n ret_pid, result = os.wait()\n print('回收了子进程 {}，儿子退出时的状态为 {}'.format(ret_pid, result))\n # os.waitpid(pid,0)\n print('爸爸走了，儿子保重')\n print('父子双亡')","sub_path":"就业/linux网络编程/05/wait回收.py","file_name":"wait回收.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"172571968","text":"__author__ = 'niyatishah(nxs6032) & yashjain(yj8359)'\n\nimport turtle, random\nfrom copy import copy\n\nt = turtle.Turtle()\nl = turtle.Turtle()\ncolors = [\"#00FF00\", \"#3CB371\", \"#ADFF2F\", \"#6B8E23\", \"#32CD32\", \"#90EE90\"]\nchoice = [\"true\", \"false\"]\nWINDOW_WIDTH = 300\nWINDOW_HEIGHT = 300\n\n\ndef init():\n \"\"\"\n Initializing the drawing window.\n :return:\n \"\"\"\n turtle.title('Recursion Tree Lab 3') # Giving the window a name\n turtle.setworldcoordinates(-WINDOW_WIDTH, -WINDOW_WIDTH, # Setting the Coordinates\n WINDOW_WIDTH, WINDOW_HEIGHT)\n l.hideturtle()\n t.speed(0)\n t.left(90)\n t.penup()\n t.sety(-280)\n t.pendown()\n\n\ndef leaf(leafy, l):\n \"\"\"\n The function draws leaves on the tree depending on the leafiness factor defined by the user.\n The Leaf's turtle is unhidden while the trees turtle is hidden.\n :param leafy: States how many leaves are there on the branch (also the leafiness parameter\n :param l: Turtle of leaf\n :return:none\n \"\"\"\n t.hideturtle()\n l.showturtle()\n # no_of_leaves=3\n randomOfLeaf = random.randint(0, 10)\n leaf_count_on_branch = random.randint(0, 3)\n\n if (leafy > randomOfLeaf > 0):\n no_of_leaves = 0\n while no_of_leaves < leaf_count_on_branch:\n l.width(1)\n l.color(\"darkgreen\")\n l.fillcolor(random.choice(colors))\n l.begin_fill()\n l.forward(24.1)\n l.left(61)\n l.forward(8.5)\n l.left(88)\n l.forward(8.5)\n l.left(61)\n l.forward(24.1)\n l.left(100)\n l.end_fill()\n no_of_leaves = no_of_leaves + 1\n # leaf(leafy,l)\n l.color(\"brown\")\n l.hideturtle()\n t.showturtle()\n\n\ndef randomness(branchLen, depth, leafy):\n try:\n branchLen = random.randint(5, int(branchLen * 3 / 4))\n # depth = random.randint(0, 2 * depth)\n leafy = random.randint(1, leafy)\n except ValueError:\n branchLen = 10\n except ValueError:\n depth = 1\n except ValueError:\n leafy = 1\n else:\n return\n\n\ndef tree(branchLen, L, bushiness, depth, t, l):\n\n \"\"\"\n This function draws the tree depending on the parameters given by the user.\n Tree's turtle is unhidden while the leaf's turtle is hidden\n :param branchLen: The length of the branch\n :param depth: The number of times a branch will create a sub branch\n :param leafy: The number of leaves a branch would have.\n :param t: Turtle used to draw trunk and branch\n :param l: Turtle used to draw leaf\n :return:None\n \"\"\"\n if depth > 0:\n if branchLen > 3:\n try:\n Br = random.randint(3, int(branchLen * 3 / 4))\n except ValueError:\n Br = branchLen\n\n else:\n t.forward(branchLen)\n t.right(40)\n try:\n Br = random.randint(3, int(branchLen * 3 / 4))\n except ValueError:\n Br = branchLen\n\n else:\n tree(branchLen - Br, L,bushiness,depth - 1, t, l)\n t.left(50)\n try:\n Br = random.randint(3, int(branchLen * 3 / 4))\n except ValueError:\n Br = branchLen\n\n else:\n tree(branchLen - Br, L,bushiness,depth - 1, t, l)\n t.left(20)\n try:\n Br = random.randint(3, int(branchLen * 3 / 4))\n except ValueError:\n Br = branchLen\n\n else:\n tree(branchLen - Br, L,bushiness,depth - 1, t, l)\n t.right(30)\n l = copy(t)\n leaf(L, l)\n t.backward(branchLen)\n\n\ndef main():\n height_of_tree = input(\"Enter height of tree and it should be less then 500\")\n height_of_trunk = int(height_of_tree) / 3\n leafiness = input(\"Enter the probability of leafiness (0 - 10), 0 being th elowest probability and 10 highest\")\n bushiness = input(\"Enter the probability of bushiness (0-10), where 0 is the lowest and 10 is highest probability\")\n depth = input(\"Enter depth of tree\")\n init()\n t.color(\"brown\")\n t.width(4)\n\n tree(height_of_trunk, int(leafiness), int(bushiness), int(depth), t, l)\n\n t.hideturtle()\n input('Hit enter to close...')\n turtle.done()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"practice/Enhanced_tree.py","file_name":"Enhanced_tree.py","file_ext":"py","file_size_in_byte":4410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"66482476","text":"import numpy as np\nfrom pandas import read_csv\nfrom matplotlib import pyplot\n# load dataset\ndataset = read_csv('../../oldriemann/data/gzetaE12/percentile_calc.csv', header=0)\nvalues = dataset.values\n# specify columns to plot\ngroups = np.arange(1, dataset.shape[1], 1)\ni = 1\n# plot each column\npyplot.figure()\nfor group in groups:\n\tpyplot.subplot(len(groups), 1, i)\n\tpyplot.plot(values[:, group])\n\tpyplot.title(dataset.columns[group], y=0.5, loc='right')\n\ti += 1\npyplot.show()\n","sub_path":"python/riemann/plot_quantiles.py","file_name":"plot_quantiles.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"498751079","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport numpy as np\nimport torch\nfrom itertools import permutations\n\n\ndef loss_calc(est, ref, loss_type):\n \"\"\"\n time-domain loss: sisdr\n \"\"\"\n # time domain (wav input)\n if loss_type == \"sisdr\":\n loss = batch_SDR_torch(est, ref)\n if loss_type == \"mse\":\n loss = batch_mse_torch(est, ref)\n if loss_type == \"log_mse\":\n loss = batch_log_mse_torch(est, ref)\n\n\n return loss\n\n\ndef calc_sdr_torch(estimation, origin, mask=None):\n \"\"\"\n batch-wise SDR caculation for one audio file on pytorch Variables.\n estimation: (batch, nsample)\n origin: (batch, nsample)\n mask: optional, (batch, nsample), binary\n \"\"\"\n \n if mask is not None:\n origin = origin * mask\n estimation = estimation * mask\n \n origin_power = torch.pow(origin, 2).sum(1, keepdim=True) + 1e-8 # (batch, 1)\n \n scale = torch.sum(origin*estimation, 1, keepdim=True) / origin_power # (batch, 1)\n \n est_true = scale * origin # (batch, nsample)\n est_res = estimation - est_true # (batch, nsample)\n \n true_power = torch.pow(est_true, 2).sum(1)\n res_power = torch.pow(est_res, 2).sum(1)\n \n return 10*torch.log10(true_power) - 10*torch.log10(res_power) # (batch, 1)\n\n\ndef batch_SDR_torch(estimation, origin, mask=None):\n \"\"\"\n batch-wise SDR caculation for multiple audio files.\n estimation: (batch, nsource, nsample)\n origin: (batch, nsource, nsample)\n mask: optional, (batch, nsample), binary\n \"\"\"\n \n batch_size_est, nsource_est, nsample_est = estimation.size()\n batch_size_ori, nsource_ori, nsample_ori = origin.size()\n \n assert batch_size_est == batch_size_ori, \"Estimation and original sources should have same shape.\"\n assert nsource_est == nsource_ori, \"Estimation and original sources should have same shape.\"\n assert nsample_est == nsample_ori, \"Estimation and original sources should have same shape.\"\n \n assert nsource_est < nsample_est, \"Axis 1 should be the number of sources, and axis 2 should be the signal.\"\n \n batch_size = batch_size_est\n nsource = nsource_est\n nsample = nsample_est\n \n # zero mean signals\n estimation = estimation - torch.mean(estimation, 2, keepdim=True).expand_as(estimation)\n origin = origin - torch.mean(origin, 2, keepdim=True).expand_as(estimation)\n \n # possible permutations\n perm = list(set(permutations(np.arange(nsource))))\n \n # pair-wise SDR\n SDR = torch.zeros((batch_size, nsource, nsource)).type(estimation.type())\n for i in range(nsource):\n for j in range(nsource):\n SDR[:,i,j] = calc_sdr_torch(estimation[:,i], origin[:,j], mask)\n \n # choose the best permutation\n SDR_max = []\n SDR_perm = []\n for permute in perm:\n sdr = []\n for idx in range(len(permute)):\n sdr.append(SDR[:,idx,permute[idx]].view(batch_size,-1))\n sdr = torch.sum(torch.cat(sdr, 1), 1)\n SDR_perm.append(sdr.view(batch_size, 1))\n SDR_perm = torch.cat(SDR_perm, 1)\n SDR_max, _ = torch.max(SDR_perm, dim=1)\n \n return - SDR_max / nsource\n\n# def calc_mse_torch(estimation, origin):\n# return torch.mean(torch.pow(estimation-origin,2),1).mean(1)\n\ndef batch_mse_torch(estimation, origin):\n \"\"\"\n batch-wise mse caculation for multiple audio files.\n estimation: (batch, nsource, frames, freq_bins)\n origin: (batch, nsource, frames, freq_bins)\n nsource = 2\n \"\"\"\n mse1 = torch.sqrt(torch.pow(estimation - origin, 2).mean([3])).mean([1,2])\n mse2 = torch.sqrt(torch.pow(estimation - origin.flip([1]), 2).mean([3])).mean([1,2])\n return torch.stack((mse1, mse2),1).min(1)[0]\n\ndef batch_log_mse_torch(estimation, origin):\n \"\"\"\n batch-wise mse caculation for multiple audio files.\n estimation: (batch, nsource, frames, freq_bins)\n origin: (batch, nsource, frames, freq_bins)\n nsource = 2\n \"\"\"\n # eps = 1e-20\n # mse1 = torch.log10(torch.sqrt(torch.pow(estimation - origin, 2).mean([3])).mean([1,2])+eps)\n # mse2 = torch.log10(torch.sqrt(torch.pow(estimation - origin.flip([1]), 2).mean([3])).mean([1,2])+eps)\n mse1 = torch.log10(torch.pow(estimation - origin, 2).mean([3])).mean([1,2])\n mse2 = torch.log10(torch.pow(estimation - origin.flip([1]), 2).mean([3])).mean([1,2])\n return torch.stack((mse1, mse2),1).min(1)[0]\n\nif __name__ == \"__main__\":\n est = torch.rand(10, 2, 32, 1000)\n ref = torch.rand(10, 2, 32, 1000)\n\n out = loss_calc(est, ref, \"mse\")\n print(out.shape)\n print(out)\n ","sub_path":"nnet/losses.py","file_name":"losses.py","file_ext":"py","file_size_in_byte":4549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"400241901","text":"import argparse\n\ndef get_args():\n p = argparse.ArgumentParser(description=\"Hola Mundo\",formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n p.add_argument('nombre', metavar='nombre', help='Digite su nombre')\n return p.parse_args()\n\ndef main():\n args = get_args()\n n = args.nombre\n print(f'Hola {n}')\n\nif __name__ == '__main__':\n main()\n\n\n","sub_path":"helloWorld.py","file_name":"helloWorld.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"573947389","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\nCreated on Tue Feb 4 14:47:00 2020\n\n@author: Melisa, Morgane\n\"\"\"\n\nimport os\n\nimport datetime\nimport numpy as np\nimport pickle\nimport math\n\nfrom caiman.source_extraction.cnmf.cnmf import load_CNMF\nfrom caiman.base.rois import register_multisession\n\nfrom Database.database_connection import database\n\ncursor = database.cursor()\n\n\"\"\"\n\nMethod possibilities (model method): registration (True) or matching (False)\ncost_threshold: threshold for cost in matching with Hungarian matching algorithm.\nmax_dist : maximum distance between centroids to allow a matching.\nmax_cell_size and min_cell size should be taken from the distribution of typical sizes (use function typical size)\nparameters = { 'session_wise': False,'model_method': False, 'cost_threshold' : 0.9 , 'max_dist' : 15 ,\n 'min_cell_size' : 10, 'max_cell_size' : 25}\n\n\"\"\"\n\nclass estimates(object):\n def __init__(self , A = None, C = None):\n self.A = A\n self.C = C\n\ndef run_registration(input_file):\n\n \"\"\"\n This is the main registering function. Is is supposed to be run after trial wise component evaluation.\n Registration takes over different contours of trial wise source extracted contours and do a matching between cells.\n It can use two different methods: Hungarian matching algorithm (RegisterMulti) (as implement in Giovannucci, et al.\n 2019) or cell registration (CellReg)using centroids distance and spatial correlation (as implemented in Sheintuch, et al. 2017).\n Default method is registration with no modeling of distributions of centroids and spatial correlation.\n\n \"\"\"\n sql = \"SELECT mouse,session,trial,is_rest,decoding_v,cropping_v,motion_correction_v,alignment_v,source_extraction_v,equalization_v,component_evaluation_v,registration_v FROM Analysis WHERE component_evaluation_main=?\"\n val = [input_file, ]\n cursor.execute(sql, val)\n result = cursor.fetchall()\n data = []\n inter = []\n for x in result:\n inter = x\n for y in inter:\n data.append(y)\n\n # Update the database\n\n if data[11] == 0:\n data[11] = 1\n file_name = f\"mouse_{data[0]}_session_{data[1]}_trial_{data[2]}.{data[3]}.v{data[4]}.{data[5]}.{data[6]}.{data[7]}.{data[9]}.{data[8]}.{data[10]}.{data[11]}\"\n sql1 = \"UPDATE Analysis SET motion_correction_meta=?,motion_correction_v=? WHERE cropping_main=? \"\n val1 = [file_name, data[11], input_file]\n cursor.execute(sql1, val1)\n\n else:\n data[6] += 1\n file_name = f\"mouse_{data[0]}_session_{data[1]}_trial_{data[2]}.{data[3]}.v{data[4]}.{data[5]}.{data[6]}\"\n sql2 = \"INSERT INTO Analysis (motion_correction_meta,motion_correction_v) VALUES (?,?)\"\n val2 = [file_name, data[11]]\n cursor.execute(sql2, val2)\n database.commit()\n\n database.commit()\n\n\n if parameters['session_wise'] == False:\n data_dir = os.environ['DATA_DIR'] + 'data/interim/registration/trial_wise/main/'\n else:\n data_dir = os.environ['DATA_DIR'] + 'data/interim/registration/session_wise/main/'\n\n file_name = db.create_file_name(step_index, row_new.name)\n output_file_path = data_dir + f'{file_name}.pkl'\n\n ##create the dictionary with metadata information\n output = {\n 'main': output_file_path,\n 'meta': {\n 'analysis': {\n 'analyst': os.environ['ANALYST'],\n 'date': datetime.datetime.today().strftime(\"%m-%d-%Y\"),\n 'time': datetime.datetime.today().strftime(\"%H:%M:%S\")\n },\n 'duration': {}\n }\n }\n\n ## take alignment data for the timeline of alingment\n first_row = df.iloc[0]\n alignmnet_output = eval(first_row['alignment_output'])\n alignment_timeline_file = alignmnet_output['meta']['timeline']\n\n\n ## multiple list created to append the relevant information for the registration and creation of a unique time trace\n ## matrix (cnm.estimates.A and cnm.estimates.C ) both taken after component evaluation\n A_list = [] ## list for contour matrix on multiple trials\n #A_size = [] ## list for the size of A (just to verify it is always the same size)\n FOV_size = [] ## list for the cn filter dim (to verify it is always the same dims)\n A_number_components = [] ## list with the total number of components extracted for each trial\n C_dims = [] ## dimension of C, to keep track of timeline\n C_list = [] ## list with traces for each trial\n evaluated_trials = []\n evaluated_session = []\n typical_size = []\n for i in range(len(df)):\n row = df.iloc[i]\n component_evaluation_hdf5_file_path = eval(row['component_evaluation_output'])['main']\n corr_path = eval(row['source_extraction_output'])['meta']['corr']['main']\n cnm = load_CNMF(component_evaluation_hdf5_file_path)\n cn_filter = np.load(db.get_file(corr_path))\n\n FOV_size.append(cn_filter.shape)\n #A_size.append(cnm.estimates.A.shape[0])\n A_number_components.append(cnm.estimates.idx_components.shape[0])\n A_list.append(cnm.estimates.A[:, cnm.estimates.idx_components])\n C_dims.append(cnm.estimates.C.shape)\n size = cnm.estimates.A[:, cnm.estimates.idx_components].sum(axis=0)\n for j in range(len(cnm.estimates.idx_components)):\n typical_size.append(size[0, j])\n if cnm.estimates.bl is None:\n C_list.append(cnm.estimates.C[cnm.estimates.idx_components, :])\n else:\n C_list.append(cnm.estimates.C[cnm.estimates.idx_components, :]-cnm.estimates.bl[cnm.estimates.idx_components,np.newaxis])\n evaluated_trials.append( (df.iloc[i].name[2] -1) * 2 + df.iloc[i].name[3]) ## number that goes from 0 to 42\n evaluated_session.append(df.iloc[i].name[1])\n\n ## add a size restriction on the neurons that will further be processed. This restriction boundary\n # decision is based in the histogram of typical neuronal sizes\n min_size = parameters['min_cell_size']\n max_size = parameters['max_cell_size']\n new_A_list = []\n new_C_list = []\n A_components = []\n C_dims_new = []\n new_evaluated_trials= []\n new_evaluated_session = []\n for i in range(len(A_list)):\n accepted_size = []\n size = A_list[i].sum(axis=0)\n for j in range(size.shape[1]):\n if size[0, j] > 10 and size[0, j] < 25:\n accepted_size.append(j)\n if len(accepted_size) > 1:\n new_A_list.append(A_list[i][:, accepted_size])\n new_C_list.append(C_list[i][accepted_size, :])\n A_components.append(A_number_components[i])\n C_dims_new.append(new_C_list[-1].shape)\n new_evaluated_trials.append(evaluated_trials[i])\n new_evaluated_session.append(evaluated_session[i])\n A_list = new_A_list\n C_list = new_C_list\n\n ## run CaImAn registration rutine that use the Hungarian matching algorithm in the contours list\n spatial_union, assignments, match = register_multisession(A=A_list, dims=FOV_size[0], thresh_cost=parameters['cost_threshold'], max_dist=parameters['max_dist'])\n\n ## open the timeline and create the new traces matrix C_matrix\n with open(alignment_timeline_file, 'rb') as f:\n timeline = pickle.load(f)\n total_time = timeline[len(timeline) - 1][1] + C_list[len(C_list)-1].shape[1]\n timeline.append(['End',total_time])\n C_matrix = np.zeros((spatial_union.shape[1], total_time))\n\n new_assignments = np.zeros((spatial_union.shape[1],len(timeline)))\n for i in range(spatial_union.shape[1]):\n for j in range(assignments.shape[1]):\n trial = new_evaluated_trials[j]\n if math.isnan(assignments[i, j]) == False:\n new_assignments[i][trial] = assignments[i, j]+1\n\n unique_session = []\n for x in evaluated_session:\n if x not in unique_session:\n unique_session.append(x)\n session_vector = np.arange(0,len(unique_session))\n final_evaluated_session = []\n for i in range(assignments.shape[1]):\n for j in range(len(unique_session)):\n if new_evaluated_session[i] == unique_session[j]:\n final_evaluated_session.append(session_vector[j])\n\n\n for i in range(spatial_union.shape[1]):\n for j in range(assignments.shape[1]):\n trial = (final_evaluated_session[j]+1) * new_evaluated_trials[j]\n print(trial)\n if math.isnan(assignments[i, j]) == False:\n C_matrix[i][timeline[trial][1]:timeline[trial][1]+C_dims_new[j][1]] = (C_list[j])[int(assignments[i, j]), :]\n\n cnm_registration = estimates(A=spatial_union, C=C_matrix)\n with open(output_file_path, 'wb') as output_file:\n pickle.dump(cnm_registration, output_file, pickle.HIGHEST_PROTOCOL)\n\n\n return","sub_path":"Steps/registering.py","file_name":"registering.py","file_ext":"py","file_size_in_byte":8768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"317291880","text":"import cv2\nimport numpy as np\nimport os\nfrom os import path\n\ncap = cv2.VideoCapture('data/output.avi')\n\n\n\n\ndir = input(\"Hey, please enter the name of the person in the video. This will add the label to their face when they are deteced. \")\ndef_dir = 'images/' + dir\nfolder = os.fsencode(def_dir)\n\nif not os.path.exists(def_dir):\n os.makedirs(def_dir)\n\nret = True\ncurrentFrame = 0\nwhile(ret):\n ret, frame = cap.read()\n\n\n name = str(currentFrame) + '.jpg'\n print ('Creating...' + name)\n cv2.imwrite(os.path.join(def_dir , name), frame)\n\n currentFrame += 1\n\n\ncount = len([item for item in os.listdir(def_dir) if os.path.isfile(os.path.join(def_dir, item))])\ncount -=1\ncut_dir = 'images/' + dir + \"/\"+ str(count) + \".jpg\"\nfin_dir = str(cut_dir)\n\nfor file in os.listdir(folder):\n name = os.fsencode(file)\n\nos.remove(fin_dir)\n\n\n\n\n\ncap.release()\ncv2.destroyAllWindows()\n","sub_path":"src/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"565163386","text":"'''\n1704. Determine if String Halves Are Alike\nProblem Link : https://leetcode.com/problems/determine-if-string-halves-are-alike/\nRuntime: 36 ms\nMemory Usage: 13.6 MB\n'''\nclass Solution(object):\n def halvesAreAlike(self, s):\n s=s.lower()\n a,b=0,0\n for i in range(len(s)/2):\n if s[i]=='a' or s[i]=='e' or s[i]=='i' or s[i]=='o' or s[i]=='u':\n a+=1\n for j in range(len(s)/2,len(s)):\n if s[j]=='a' or s[j]=='e' or s[j]=='i' or s[j]=='o' or s[j]=='u':\n b+=1\n if a==b:\n return True\n else:\n return False","sub_path":"Python/1704. Determine if String Halves Are Alike.py","file_name":"1704. Determine if String Halves Are Alike.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"282473645","text":"## THINGS to do::::! -- must get sleep\n## add a subnet validator == valid_ip method ... test for built-in function with pre-existing solution\n## check performance of ipaddress module vs socket module for host scanning a network\n## naming conventions???? clean up code????\n## in time clean up by adding more functionality to existing functions, using real exception handling\n## set scan timeout \n## multi thread the scanning process.... takes way too long\n## flush Mongo DB before adding entries to avoid adding to old collections\n\nimport sys\nimport nmap\nimport socket\nimport platform\nfrom multiprocessing.dummy import Pool as ThreadPool\n#import threading\nimport ipaddress\nimport subprocess\nimport menu\n#from ftplib import FTP\nfrom targetDB import Database\nfrom models.storageCom import DataManage\n\n__author__ = 'pr0c'\n\n# set userplatform to global variable to avoid repetitive checking of OS\nusersPlatform = platform.system()\n\ndef main():\n\tglobal usersPlatform\n\n\tif usersPlatform == 'Windows':\n\t\tsubprocess.call('cls', shell=True)\n\telif usersPlatform == 'Linux':\n\t\tsubprocess.call('clear', shell=True)\n\telse:\n\t\tpass\n\n\t# get ip range from user ex.(192.168.1.1/24)\n\tusersTarget = input(\"What is the ip range you want to scan?\\n(Example: 192.168.1.1/24)\\n\\n:/> \")\n\tipAddress = usersTarget.split('/')\n\t# split the input to checck and make sure the user avoid wasting time and making sure input follows \"###.###.###.##/##\"\n\tif len(ipAddress) > 2:\n\t\tprint(\"\\nToo much info; What the hell did you type?\"+\n\t\t\t\"\\nTry something like 192.168.1.0/24 || a.k.a [IP address][backslash][subnet mask]\")\n\n\telif len(ipAddress) < 2:\n\t\tprint(\"\\nNot enough info; What the hell did you type?\"+\n\t\t\t\"\\nTry something like 192.168.1.0/24 || a.k.a [IP address][backslash][subnet mask]\")\n\n\telse:\n\t\tif valid_ip(ipAddress[0]) == True:\n\t\t\tprint(\"\\nSeems to be a valid IP... let's have a crack at it, eh?!\\n\\n\")\n\t\t\tprint(\"-\"*120)\n\t\t\tprint(\"\\t\\t\\tPlease hold onto your panties because we are doing a scannies\")\n\t\t\tprint(\"-\"*120)\n\t\t\t# after successful validation of the IP range we begin the scan process joining the accepted input into cidr notation\n\t\t\tnetwork_scan('/'.join(ipAddress))\n\t\telse:\n\t\t\tsys.exit()\n\n\t# after the scan we'll pop a menu up to allow thw user to either hack, print, scan again...etc. More to come\n\tmenu.Menu.run_menu()\n\ndef valid_ip(testADDR):\n\t# validate that the IP address given is truly an IP address\n try:\n socket.inet_aton(testADDR)\n return True\n except:\n \t\tprint('\\n{} is not a valid IP address.\\n\\n*\\\\.*\\\\.*\\\\.*\\\\.Exiting./*./*./*./*'.format(testADDR))\n \t\treturn False\n\n\ndef network_scan(ipRange):\n\tpool = ThreadPool(16)\n\ttargetNet = ipaddress.ip_network(ipRange, strict=False) # strict=False will allow 192.168.1.34; ip_network doesn't like the host bit\n\tnodes = []\n\tfor node in targetNet.hosts():\n\t\tnodes.append(str(node))\n\tpool.map(host_scan, nodes)\n\tpool.close()\n\tpool.join()\n\n\ndef host_scan(host):\n\ttry:\n\t\tsock = socket.socket(2,1) # socket.AF_INET, socket.SOCK_STREAM\n\t\tsock.settimeout(0.5)\n\t\t# if connection returned value confirms port is open --> store IP address somewhere for later use\n\t\t# else continue to next node\n\t\tprint('Scanning {}...'.format(host))\n\n\t\tportState = sock.connect_ex((host, 21))\n\n\t\tif portState == 0:\n\t\t\tprint(\"\\n{} has port 21 open!\\n\".format(host))\n\t\t\tprint(\"Grabbing MAC address and hostname...\")\n\t\t\tnmap_hdwInfo(host)\n\t\telse:\n\t\t\tpass\n\n\t\tsock.close()\n\n\texcept KeyboardInterrupt:\n\t\tprint(\"You pressed Ctrl+C\")\n\t\tsys.exit()\n\n\texcept socket.gaierror:\n\t\tprint('{} could not be resolved. Exiting'.format(host))\n\t\tsys.exit()\n\n\texcept socket.error:\n\t\tprint(\"Couldn't connect to server\")\n\t\tsys.exit()\n\"\"\"\n######################################################################\n#################KEEPING THIS HERE JUST IN CASE#######################\n# perhaps python-nmap would be of greater benefit here?\n# will have to check the docs\ndef get_hdwInfo(ip):\n\tglobal usersPlatform\n\tif usersPlatform == \"Linux\":\n\t\t# ping the target host to get entry in arp table\n\t\tprobe = subprocess.Popen(['ping', ip, '-c1'], stdout=subprocess.PIPE,\n\t\t stderr=subprocess.PIPE)\n \n\t\tout, err = probe.communicate()\n \n\t\t# arp list\n\t\tprobe = subprocess.Popen(['arp', '-n'], stdout=subprocess.PIPE,\n\t\t stderr=subprocess.PIPE)\n \n\t\tout, err = probe.communicate()\n \n\t\ttry:\n\t\t\tarp = [x for x in out.split('\\n') if ip in x][0]\n\t\texcept IndexError:\n\t\t sys.exit(1) # no arp entry found\n\t\telse:\n\t\t # get the mac address from arp list\n\t\t # bug: when the IP does not exists on the local network\n\t\t # this will print out the interface name\n\t\t return ' '.join(arp.split()).split()[2]\n\n\telif usersPlatform == \"Windows\":\n\t\t# ping the target host to get entry in arp table\n\t\tprobe = subprocess.Popen(['ping', ip, '-n 1'], stdout=subprocess.PIPE,\n\t\t stderr=subprocess.PIPE)\n \n\t\tout, err = probe.communicate()\n \n\t\t# arp list\n\t\tprobe = subprocess.Popen(['arp', '-a'], stdout=subprocess.PIPE,\n\t\t stderr=subprocess.PIPE)\n \n\t\tout, err = probe.communicate()\n \n\t\ttry:\n\t\t\tarp = [x for x in out.split('\\n') if ip in x][0]\n\t\texcept IndexError:\n\t\t sys.exit(1) # no arp entry found\n\t\telse:\n\t\t # get the mac address from arp list\n\t\t # bug: when the IP does not exists on the local network\n\t\t # this will print out the interface name\n\t\t return ' '.join(arp.split()).split()[2]\n\"\"\"\n######################################################################\n# NMAP \tNMAP \tNMAP \tNMAP \tNMAP \tNMAP \tNMAP \tNMAP \tNMAP #\n######################################################################\n\ndef nmap_hdwInfo(ip):\n\tscanner = nmap.PortScanner()\n\tscanResults = scanner.scan(arguments='-sS -p 21', hosts=ip)\n\tmacAddr = scanResults['scan'][ip]['addresses']['mac']\n\thostname = scanResults['scan'][ip]['hostnames'][0]['name']\n\tpushToDB = DataManage(ip, macAddr, hostname)\n\tpushToDB.postTargets()\n\n######################################################################\n\nif __name__ == \"__main__\":\n\n\tDatabase.initialize()\n\n\tmain()","sub_path":"ftp_knocker.py","file_name":"ftp_knocker.py","file_ext":"py","file_size_in_byte":5967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"492684701","text":"import pathlib\nimport subprocess\n\nDATA = pathlib.Path('./test2/')\n\ndef jsonchecker_output(json_file):\n out = subprocess.check_output(['./bin/splc', json_file])\n return out.decode().strip()\n\ndef check_jsonchecker_error():\n casefile_list = []\n for casefile in DATA.glob('*.spl'):\n casefile_list.append(casefile)\n \n casefile_list.sort()\n for casefile in casefile_list:\n out = jsonchecker_output(casefile)\n # answer = f'duplicate key: \"{casefile.name[5:6]}\"' \n print(str(casefile) + \" splc_result:\")\n print(out)\n print(\"-\" * 100)\n # 把相应的文件读进来并且打印出来\n casefile_string = str(casefile)\n answerfile = casefile_string[:len(casefile_string)-3]\n answerfile += \"out\"\n print(answerfile + \": \")\n file = open(answerfile, \"r\")\n alllines = file.readlines()\n for aline in alllines:\n print(aline, end = \"\")\n file.close()\n print(\"=\" * 100)\n\ncheck_jsonchecker_error()\n","sub_path":"autochecker.py","file_name":"autochecker.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"184066307","text":"#!/usr/bin/env python3\nimport os\nimport multiprocessing as mp\nimport numpy as np\nimport pandas as pd\nimport time\nimport argparse\n\nimport snmcseq_utils\n\n\n################\n# CREATE THE BINNED DATA FILES FROM ALLC FILES\n################\n\n######### INPUT PARAMETERS #################\n# species = 'human' # or human\n# samples = ['Pool_2256_AD010_indexed_R1'] # LIST OF SAMPLE NAMES\n# allc_dir = '/cndd/Public_Datasets/single_cell_methylome/allc_singlecells/hs_MB_EB/' \n# # DIRECTORY WITH ALLC FILES IN IT\n\n# FILE NAME TO ALLC FILES WILL BE CONSTRUCTED AS: allc_dir + \"/allc_\" + sample[n] + \"_\" + chromosome + \".tsv\"\n# OUTPUT FILE NAME: \"binc_\" + sample[n] + \"_\" + str(bin_size) + \"_\" + chromosome + \".tsv\",\n\n# species = 'human'\n# sample = '/cndd/Public_Datasets/single_cell_methylome/allc_singlecells/hs_MB_EB/Pool_2256_AD006_indexed_R1_bismark'\n\n\ndef get_mCH_contexts():\n contexts = []\n for base1 in ['A','C','T']:\n for base2 in ['A','C','G','T']:\n contexts.append('C' + base1 + base2)\n return contexts\n\ndef get_mCG_contexts():\n return ['CGA','CGC','CGG','CGT']\n\ndef get_mouse_chromosomes(include_x=True):\n chromosomes = [str(x) for x in range(1,20)]\n if include_x:\n chromosomes.append('X')\n return chromosomes\n\ndef get_human_chromosomes(include_x=True):\n chromosomes = [str(x) for x in range(1,23)]\n if include_x:\n chromosomes.append('X')\n return chromosomes\n\ndef get_sample_from_path(path):\n sample = os.path.basename(path)\n if sample.endswith('_bismark'):\n sample = sample[:-8]\n return sample\n\ndef get_chrom_lengths_mouse():\n return {'1': 195471971, '2': 182113224, '3': 160039680, '4': 156508116, '5': 151834684, \n '6': 149736546, '7': 145441459, '8': 129401213, '9': 124595110, '10': 130694993, \n '11': 122082543, '12': 120129022, '13': 120421639, '14': 124902244, '15': 104043685, \n '16': 98207768, '17': 94987271, '18': 90702639, '19': 61431566, 'X': 171031299, 'Y': 91744698}\n\ndef get_chrom_lengths_human():\n return {'1': 248956422, '2': 242193529, '3': 198295559, '4': 190214555, '5': 181538259, '6': 170805979, \n '7': 159345973, '8': 145138636, '9': 138394717, '10': 133797422, '11': 135086622, '12': 133275309, \n '13': 114364328, '14': 107043718, '15': 101991189, '16': 90338345, '17': 83257441, '18': 80373285, \n '19': 58617616, '20': 64444167, '21': 46709983, '22': 50818468, 'X': 156040895, 'Y': 57227415}\n\n### FUNCTION FOR BINNING THE ALLC FILES\ndef bin_allc(sample, path='.', bin_size=10000, chromosomes=None, outpath = '.', species='mouse', compressed=False):\n\n if chromosomes == None:\n if species == 'human':\n chromosomes = get_human_chromosomes()\n else:\n chromosomes = get_mouse_chromosomes()\n\n for chromosome in chromosomes:\n\n fname = path + \"/allc_\" + sample + \"_\" + chromosome + \".tsv\"\n\n if compressed:\n os.system(\"bgzip -cd \" + fname + \".gz > \" + fname)\n\n if not os.path.isfile(fname):\n print(\"bin_allc: \" + fname + \" does not exist.\")\n return\n\n if not os.path.exists(outpath):\n os.makedirs(outpath)\n \n output_filename = outpath + \"/binc_\" + sample + \"_\" + str(bin_size) + \"_\" + chromosome + \".tsv\"\n if os.path.isfile(output_filename):\n print(\"File exists \"+output_filename+\", skipping...\")\n return 0\n\n df = snmcseq_utils.read_allc(fname)\n \n if compressed:\n os.remove(fname)\n\n # for bin_size in bin_sizes:\n\n if species == 'human':\n bins = np.arange(0, get_chrom_lengths_human()[chromosome], bin_size)\n else:\n bins = np.arange(0, get_chrom_lengths_mouse()[chromosome], bin_size)\n\n # mCG\n df_CG = df.loc[df.context.isin(get_mCG_contexts())]\n groups = df_CG.groupby(pd.cut(df_CG.index, bins))\n mCG = groups.sum().mc.fillna(0)\n CG = groups.sum().c.fillna(0)\n\n # mCH\n df_CH = df.loc[df.context.isin(get_mCH_contexts())]\n groups = df_CH.groupby(pd.cut(df_CH.index, bins))\n mCH = groups.sum().mc.fillna(0)\n CH = groups.sum().c.fillna(0)\n\n data = np.array([bins[:len(bins)-1], mCG.values, CG.values, mCH.values, CH.values]).astype(int)\n binned_allc = pd.DataFrame(data.transpose(), columns=['bin','mCG','CG','mCH','CH'])\n binned_allc['chr'] = chromosome\n binned_allc = binned_allc[['chr','bin','mCG','CG','mCH','CH']]\n\n binned_allc.to_csv(output_filename,\n sep=\"\\t\", header=False, index=False)\n\n\ndef create_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--input', help='input directory containing allc files', required=True)\n parser.add_argument('-o', '--output', help='directory storing output files', required=True)\n parser.add_argument('-s', '--species', help='mouse or human', default='mouse')\n parser.add_argument('-bz', '--bin_size', help='bin size', default=10000, type=int)\n parser.add_argument('-c', '--compressed', help='compressed or not', action='store_true') \n\n return parser\n\n\nif __name__ == '__main__':\n \n # if not os.path.exists(sample):\n # os.makedirs(sample)\n\n\n parser = create_parser()\n args = parser.parse_args()\n\n ti = time.time()\n bin_allc(get_sample_from_path(args.input), path=args.input, \n outpath=args.output, species=args.species, bin_size=args.bin_size, compressed=args.compressed)\n tf = time.time()\n print(\"time: %s sec\" % (tf-ti))\n\n\n### PROCESS THE WILL CALL THE BINNING FUNCTION IN A PARALLEL FASHION\n# def process(samples):\n# for sample in samples:\n# print(sample)\n# if not os.path.exists(sample):\n# os.makedirs(sample)\n# bin_allc(sample, path=allc_dir+sample, \n# outpath=sample, species=species, bin_size=100000, compressed=False)\n# return True\n\n\n# procs = min(16, len(samples))\n\n# p = mp.Pool(processes=procs)\n# split_samples = np.array_split(samples,procs)\n# pool_results = p.map(process, split_samples)\n# p.close()\n# p.join()\n\n","sub_path":"bin_allc_files.py","file_name":"bin_allc_files.py","file_ext":"py","file_size_in_byte":6142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"479243783","text":"import torch\nimport torch.nn as nn\n\nimport unittest\n\nfrom snow.baselines import Seq2Seq, MemNN, Transformer\nfrom snow.modules.transformer import PositionEmbedding\n\n\nclass TestBaselines(unittest.TestCase):\n def test_seq2seq(self):\n hidden_size = 10\n num_layers = 2\n cell_type = \"lstm\"\n bidirectional = True\n vocab_size = 100\n embed_size = 128\n batch = 3\n max_len = 3\n\n embedding = nn.Embedding(vocab_size, embed_size)\n\n seq2seq = Seq2Seq(\n output_size=vocab_size,\n hidden_size=hidden_size,\n embedding=embedding,\n num_layers=num_layers,\n cell_type=cell_type,\n attn_mode=\"mlp\",\n bidirectional=bidirectional,\n dropout_prob=0.1,\n )\n\n lengths = torch.tensor([3, 2, 1])\n enc_inputs = torch.tensor([[63, 21, 37], [32, 49, 0], [55, 0, 0]]), lengths\n dec_inputs = torch.tensor([[99, 21, 37], [98, 49, 0], [5, 0, 0]]), lengths\n\n log_probs, state = seq2seq(enc_inputs, dec_inputs)\n\n self.assertEqual(log_probs.size(0), batch)\n self.assertEqual(log_probs.size(1), max_len)\n self.assertEqual(log_probs.size(2), vocab_size)\n\n self.assertEqual(state.last_hidden.size(0), num_layers)\n self.assertEqual(state.last_hidden.size(1), batch)\n self.assertEqual(state.last_hidden.size(2), hidden_size)\n\n def test_memnn(self):\n\n batch_size = 2\n vocab_size = 100\n embed_size = 128\n max_story_len = 3\n story_seq_len = 3\n query_seq_len = 4\n\n memnn = MemNN(vocab_size, embed_size, max_story_len)\n\n memory = torch.tensor(\n [\n [[63, 21, 37], [32, 49, 0], [55, 0, 0]],\n [[63, 21, 37], [32, 49, 0], [55, 0, 0]],\n ]\n )\n query = torch.tensor([[99, 21, 37, 21], [98, 49, 5, 0]])\n\n outputs = memnn(memory, query)\n\n self.assertEqual(outputs.size(0), batch_size)\n self.assertEqual(outputs.size(-1), vocab_size)\n\n def test_trans(self):\n num_positions = 100\n embedding_dim = 128\n num_words = 1000\n position_embedding = PositionEmbedding(num_positions, embedding_dim)\n word_embedding = nn.Embedding(num_words, embedding_dim)\n\n num_heads = 8\n num_layers = 6\n inputs = torch.tensor([[1, 2, 3, 4]])\n targets = torch.tensor([[5, 6, 7, 8]])\n\n transformer = Transformer(\n num_heads, num_layers, word_embedding, position_embedding\n )\n\n log_probs, outputs = transformer(inputs, targets)\n\n self.assertEqual(log_probs.size(-1), num_words)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"tests/test_baseline.py","file_name":"test_baseline.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"365350790","text":"# 9-3. Users: Make a class called User. Create two attributes called first_name and last_name, and then create several other attributes that are typically stored in a user profile. Make a method called describe_user() that prints a summary of the user’s information. Make another method called greet_user() that prints a personalized greeting to the user. Create several instances representing different users, and call both methods for each user.\n\nclass User():\n\t\"\"\" User object model\"\"\"\n\n\tdef __init__(self, first_name, last_name, location, contact):\n\t\t\"\"\" Initialize first name, last name, location and contact preferences.\"\"\"\n\t\tself.first_name = first_name\n\t\tself.last_name = last_name\n\t\tself.location = location\n\t\tself.contact = contact\n\n\tdef describe_user(self):\n\t\tprint(str(self.first_name) + \" \" + str(self.last_name) + \" lives in \" + str(self.location) + \" and contact preferences is \" + self.contact)\n\n\tdef greet_user(self):\n\t\tprint(\"Welcome \" + str(self.first_name) + \" \" + str(self.last_name))\n\n\nfirst_user = User(\"steph\", \"curr\", \"golden state\", \"email\")\nfirst_user.describe_user()\nfirst_user.greet_user()\n\nprint(\"\\n\")\n\nsecond_user = User(\"klay\", \"thompson\", \"golden state\", \"cell phone\")\nsecond_user.describe_user()\nsecond_user.greet_user()","sub_path":"Chapter9/p9-3.py","file_name":"p9-3.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"258953158","text":"# Simulate the probability of flipping a fair coin 20 times, and getting a number greater than or equal to 15.\n# Use np.random.binomial(n, p, size) to do 10000 simulations of flipping a fair coin 20 times, \n# then see what proportion of the simulations are 15 or greater.\n\nx = np.random.binomial(20, .5, 1000)\nprint((x >= 15).mean())\n\n# or\nx = np.random.binomial(20, .5, 10000)\nresults = x >= 15 # convert to a list of booleans\nresults.mean()\n\n","sub_path":"notes_for_week4.py","file_name":"notes_for_week4.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"541345983","text":"\"\"\"Output functions for parsed zs2 chunks.\"\"\"\n\n# Author: Chris Petrich\n# Copyright: Copyright 2015, Chris Petrich\n# License: MIT\n\n \ndef chunks_to_XML(chunks, with_address=False):\n \"\"\"Produces an XML representation of the chunks.\"\"\"\n # The implementation of this function is not pretty but\n # requires no dependencies.\n\n def _xml_attr_escape(string):\n \"\"\"Escape characters in string\"\"\"\n string=string.replace('&','&')\n string=string.replace('>','>')\n string=string.replace('<','<')\n string=string.replace('\\\\\"','"')\n string=string.replace(\"\\\\'\",''')\n return string\n\n data_types = [chunk[2] for chunk in chunks]\n if data_types.count('DD') != data_types.count('end'):\n raise ValueError('Cannot generate XML file since section start and end do not balance. Output as text file instead to debug.')\n \n out=[]\n out.append('') \n section_names = []\n level = 0\n for chunk in chunks:\n address, name, data_type, data = chunk\n if data_type == 'end': level-=1\n \n _space = ' '*level\n show_address= \"address='%0.6x' \"%address if with_address else ''\n if data_type != 'end':\n # here we enclose the value in quotation marks...\n if isinstance(data,int) or isinstance(data,float) or isinstance(data,list):\n # note that 'bool' is sderived from 'int'\n display=repr(str(data))\n else:\n display = repr(data) # introduces character escapes in Python 2\n if display.startswith('u'): # only for Python 2\n display=display[1:] # granted, this is an ugly way of doing it, but it produces human readable and valid XML \n # escape XML entities in attributes\n value = _xml_attr_escape(display)\n line = \"%s<%s %stype='%s' value=%s %s>\" % (_space, name, \n show_address, \n data_type, value, \n '/' if data_type != 'DD' else '')\n else:\n line = '%s' % (_space, section_names[-1])\n \n if data_type == 'end': section_names.pop()\n elif data_type == 'DD': \n section_names.append(name)\n level+=1\n out.append(line)\n return u'\\n'.join(out)\n\n\ndef chunks_to_text_dump(chunks):\n \"\"\"Produces a string representation.\"\"\"\n out=[]\n DD_names = []\n level = 0\n \n data_types = [chunk[2] for chunk in chunks]\n if data_types.count('DD') != data_types.count('end'):\n # do not indent since there's obviously something wrong\n indent = None\n else:\n indent = ' '\n \n for chunk in chunks:\n address, name, data_type, data = chunk\n if data_type == 'end': level-=1\n \n _space = indent*level if indent is not None else ''\n \n comment = '' if data_type != 'end' else '<-'.join(DD_names[-1::-1])\n if data_type == 'end': DD_names.pop()\n \n line = u' '.join([u'%.6x:'%address, _space+name, '[%s]'%data_type, repr(data), comment])\n \n if data_type == 'DD': \n DD_names.append(name)\n level+=1\n out.append(line)\n return u'\\n'.join(out)\n","sub_path":"src/zs2decode/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"500446225","text":"\"\"\"\n下面的文件将会从csv文件中读取读取短信与电话记录，\n你将在以后的课程中了解更多有关读取文件的知识。\n\"\"\"\nimport csv\nwith open('texts.csv', 'r') as f:\n reader = csv.reader(f)\n texts = list(reader)\n\nwith open('calls.csv', 'r') as f:\n reader = csv.reader(f)\n calls = list(reader)\n\n\n\"\"\"\n任务1：\n短信和通话记录中一共有多少电话号码？每个号码只统计一次。\n输出信息：\n\"There are different telephone numbers in the records.\"\"\"\n# process texts\ntmp = list(zip(*texts))\ntexts_number_sta = set(tmp[0])\nfor item in set(tmp[1]):\n texts_number_sta.add(item)\n# process calls\ntmp = list(zip(*calls))\ncalls_number_sta = set(tmp[0])\nfor item in set(tmp[1]):\n calls_number_sta.add(item)\n# combine calls_number_sta and texts_number_sta\nfor item in texts_number_sta:\n calls_number_sta.add(item)\n\nsum_of_numbers = len(calls_number_sta)\nprint(\"There are {} different \"\n \"telephone numbers in the records.\".format(sum_of_numbers))","sub_path":"investigate texts and calls/ZH/Task1.py","file_name":"Task1.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"270947369","text":"import warnings\n\nimport pandas as pd\nimport spacy\nfrom spacy.tokens import Doc, Token, Span\n\nwarnings.filterwarnings(\"ignore\") \n\nnlp = spacy.load(\"en_core_web_sm\")\n\n# Few doc strings to experiment with\ndoc = nlp(\"Apple isn't looking at buying U.K. startup for $1 billion\")\ndoc = nlp(\"I like cats\")\ndoc = nlp(\"I like dogs more than cats\")\n\ndoc = nlp(\"This is as cold as an unused pillow\")\n\n# Ridiculously long string for speed test.\ndoc = nlp(\"\"\"The rainbow pitta (Pitta iris) is a small passerine bird in the pitta family, Pittidae, endemic to northern Australia. The species is most closely related to the superb pitta of Manus Island. A colourful bird, it has a velvet black head with chestnut stripes above the eyes, olive green upper parts, black underparts, a bright red belly and an olive green tail. The rainbow pitta lives in the monsoon forests, as well as some drier eucalypt forests. As with other pittas, it is a secretive and shy bird. The diet consists mainly of insects, arthropods and small vertebrates. Pairs defend territories and breed during the rainy season, as this time of year provides the most food for nestlings. The female lays three to four eggs with blotches inside its large domed nest. Both parents defend the nest, incubate the eggs and feed the chicks. The species is common within its range, and is not threatened. (Full article...)\"\"\")\n\n\n# Tablulating the results for better understanding\ndoc_items_df = pd.DataFrame(columns=[\"text\", \"lemma_\", \"pos_\", \"des_pos\", \"tag_\", \"des_tag\", \"dep_\", \"des_dep\", \"shape_\", \"is_alpha\", \"is_stop\"])\n\nfor token in doc:\n doc_items_df = doc_items_df.append(\n pd.DataFrame([[\n token.text\n , token.lemma_\n , token.pos_\n , spacy.explain(token.pos_)\n , token.tag_\n , spacy.explain(token.tag_)\n , token.dep_\n , spacy.explain(token.dep_)\n , token.shape_\n , token.is_alpha\n , token.is_stop\n ]\n ]\n , columns = doc_items_df.columns)\n )\ndoc_items_df.reset_index(drop = True, inplace = True)\nprint(doc_items_df)\n\n\n\"\"\"\n\n text lemma_ pos_ des_pos tag_ des_tag dep_ des_dep shape_ is_alpha is_stop\n0 Apple Apple PROPN proper noun NNP noun, proper singular nsubj nominal subject Xxxxx True False\n1 is be AUX auxiliary VBZ verb, 3rd person singular present aux auxiliary xx True True\n2 n't not PART particle RB adverb neg negation modifier x'x False True\n3 looking look VERB verb VBG verb, gerund or present participle ROOT None xxxx True False\n4 at at ADP adposition IN conjunction, subordinating or preposition prep prepositional modifier xx True True\n5 buying buy VERB verb VBG verb, gerund or present participle pcomp complement of preposition xxxx True False\n6 U.K. U.K. PROPN proper noun NNP noun, proper singular compound compound X.X. False False\n7 startup startup NOUN noun NN noun, singular or mass dobj direct object xxxx True False\n8 for for ADP adposition IN conjunction, subordinating or preposition prep prepositional modifier xxx True True\n9 $ $ SYM symbol $ symbol, currency quantmod modifier of quantifier $ False False\n10 1 1 NUM numeral CD cardinal number compound compound d False False\n11 billion billion NUM numeral CD cardinal number pobj object of preposition xxxx True False\n\n\nDescription of the columns: \n---------------------------\nText: The original word text.\nLemma: The base form of the word.\nPOS: The simple part-of-speech tag.\nTag: The detailed part-of-speech tag.\nDep: Syntactic dependency, i.e. the relation between tokens.\nShape: The word shape – capitalization, punctuation, digits.\nis alpha: Is the token an alpha character?\nis stop: Is the token part of a stop list, i.e. the most common words of the language?\n\n\"\"\"\n\nent_df = pd.DataFrame(columns=[\"Entity\", \"Start\", \"End\", \"Tag\", \"Tag_Desciption\"])\nfor ent in doc.ents:\n # print(ent.text, ent.start_char, ent.end_char, ent.label_, spacy.explain(ent.label_))\n ent_df = ent_df.append(pd.DataFrame(\n [[ent.text, ent.start_char, ent.end_char, ent.label_, spacy.explain(ent.label_)]]\n , columns = ent_df.columns))\nprint(ent_df)\n\n\"\"\"\n Entity Start End Tag Tag_Desciption\n0 Apple 0 5 ORG Companies, agencies, institutions, etc.\n0 U.K. 30 34 GPE Countries, cities, states\n0 $1 billion 47 57 MONEY Monetary values, including unit\n\nColumn Description:\n-----------------------------------\nStart = Index of the first letter in doc\nEnd = Index of the last letter in doc\n\n\"\"\"\n\n\n# Understanding relationship between words in sentences\ndoc_1 = nlp(\"I like swimming\")\ndoc_2 = nlp(\"I like cycling more than driving\")\n\nmatches_df = pd.DataFrame(columns= [\"token1\", \"token2\", \"similarity\"])\nfor token1 in doc_1:\n for token2 in doc_2:\n if token1 != token2 and not (token1.is_stop or token2.is_stop):\n matches_df = matches_df.append(\n pd.DataFrame([[token1, token2, token1.similarity(token2)]], columns = matches_df.columns)\n )\n # print(token1, token2, token1.similarity(token2))\n\nmatches_df.sort_values(\"similarity\", ascending = False).iloc[::2]\n\n\n\n\n\n\n\n\n\n","sub_path":"basic_ner_spacy.py","file_name":"basic_ner_spacy.py","file_ext":"py","file_size_in_byte":6551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"373697278","text":"import tensorflow as tf\nimport numpy as np\nimport os\n\nimport modeling\n\npathname = \"pretrained_model/cased_L-12_H-768_A-12/bert_model.ckpt\"\nbert_config = modeling.BertConfig.from_json_file(\"pretrained_model/cased_L-12_H-768_A-12/bert_config.json\")\n#configsession = tf.ConfigProto()\n#configsession.gpu_options.allow_growth = True\n#sess = tf.Session(config=configsession)\ninput_ids = tf.placeholder(shape=[64, 128], dtype=tf.int32, name=\"input_ids\")\ninput_mask = tf.placeholder(shape=[64, 128], dtype=tf.int32, name=\"input_mask\")\ntoken_type_ids = tf.placeholder(shape=[64, 128], dtype=tf.int32, name=\"token_type_ids\")\n\nwith tf.Session() as sess:\n model = modeling.BertModel(\n config=bert_config,\n is_training=True,\n input_ids=input_ids,\n input_mask=input_mask,\n token_type_ids=token_type_ids,\n use_one_hot_embeddings=False)\n label_embeddings = tf.get_variable(name=\"word_embeddings\", shape=[768, 12], initializer=tf.truncated_normal_initializer(0.02))\n pooled_output = model.get_pooled_output()\n logits = tf.matmul(pooled_output, label_embeddings)\n\n sess.run(tf.global_variables_initializer())\n print('tf-bert-transformer')\n rand_array = np.random.randint(0, 1, [64, 128])\n print(sess.run(logits, feed_dict = {input_ids:rand_array, input_mask:rand_array, token_type_ids: rand_array}))","sub_path":"tf_transformer_demo_2.py","file_name":"tf_transformer_demo_2.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"356614196","text":"# in this script we will compute the openness across 1Kb regions. \n# for each region we will take the measured openness times the number of bases overlapping the region\nfrom __future__ import print_function\n\nimport numpy\nimport pandas\nimport sys\n\n# read in data\nopenness_data = pandas.read_csv('./data/pairedData/human/Element_opn_uniq.txt', sep = '\\t', header=None, index_col=0)\nopenness_data.index.names = ['chrom_regionStart_regionEnd']\nprint(\"openness data \\n\", file = sys.stderr)\nprint(openness_data.head(), file = sys.stderr)\nprint(openness_data.shape, file = sys.stderr)\n\ngene_expression = pandas.read_csv('./data/pairedData/human/gene_ms.txt', sep = '\\t', header=None, index_col = 0)\ngene_expression.index.names = ['gene']\nprint(\"gene expression data\\n\", file = sys.stderr)\nprint(gene_expression.head(), file = sys.stderr)\nprint(gene_expression.shape, file = sys.stderr)\n\ngene2regionDistances = pandas.read_csv('./data/pairedData/human/gene2regionDistances.txt', sep = '\\t', header = 0)\nprint(\"gene to region distances\\n\", file = sys.stderr)\nprint(gene2regionDistances.head(), file = sys.stderr)\nprint(gene2regionDistances.shape, file = sys.stderr)\n\ngenes = list(gene_expression.index)\ngene2regionDistances['regionName'] = gene2regionDistances['chrom'] + '_' + gene2regionDistances['regionStart'].map(str) + '_' + gene2regionDistances['regionEnd'].map(str)\n\nimport numpy\nbinnedOpenness = numpy.zeros((gene_expression.shape[0], 2000, 201)) # 1,000,000 divided into 1Kb regions\n# I don't understand Python\n#columnNames = ('+' + range(0, 999000, 1000) + ':' + range(1000, 1000000, 1000), '-' + range(0, 999000, 1000).map(str) + ':' + range(1000, 1000000, 1000).map(str)) \n#binnedOpennes = pandas.DataFrame(binnedOpennes, index = gene_expression.index.values, columns = columnNames)\n\nfor i in range(gene_expression.shape[0]): # how to access rownames directly? \n#for i in range(10):\n print(\"i = \", i, file = sys.stderr)\n gene = genes[i]\n d = gene2regionDistances[(gene2regionDistances['gene'] == gene)] \n if d.shape[0] > 0: # make sure there's at least one region\n TSS = d['TSS'].iloc[0] # TSS is always the same, use first\n strand = 1 if d['strand'].iloc[0] == '+' else -1 # convert strand to value\n for j in range(0, 1000):\n #print(\"j = \", j, file = sys.stderr)\n # lowerLim and upperLim of bin depend on strand\n lowerLim = TSS + strand*1000*j if strand > 0 else TSS + strand*1000*(j + 1) \n upperLim = TSS + strand*1000*(j + 1) if strand > 0 else TSS + strand*1000*j\n # get regions in the bin\n regions = d[numpy.logical_and(d['regionEnd'] > lowerLim, d['regionEnd'] < upperLim)] \n # o is a place-holder for openness in region\n o = numpy.zeros((201, ))\n for r in range(regions.shape[0]): # no iteration if regions.shape[0] == 0\n o_r = openness_data.loc[regions['regionName'].iloc[r], : ]\n o_r = o_r.values\n o_r.reshape(201, )\n # normalize by length of overlap\n overlap = (min(upperLim, regions['regionEnd'].iloc[r]) - max(lowerLim, regions['regionStart'].iloc[r]))/1000.0\n o += o_r.reshape(201,)*overlap\n binnedOpenness[i, j, :] = o\n for j in range(0, 1000):\n # other direction\n #print(\"j = \", j + 1000, file = sys.stderr)\n lowerLim = TSS - strand*1000*j if strand < 0 else TSS - strand*1000*(j + 1)\n upperLim = TSS - strand*1000*(j + 1) if strand < 0 else TSS - strand*1000*j\n regions = d[numpy.logical_and(d['regionEnd'] > lowerLim, d['regionEnd'] < upperLim)]\n o = numpy.zeros(201, )\n for r in range(regions.shape[0]): # no iteration if regions.shape[0] == 0 \n o_r = openness_data.loc[regions['regionName'].iloc[r], : ]\n o_r = o_r.values\n o_r.reshape(201, )\n overlap =(min(upperLim, regions['regionEnd'].iloc[r]) - max(lowerLim, regions['regionStart'].iloc[r]))/1000.0\n o += o_r*overlap\n # 1000 for other direction\n binnedOpenness[i, j + 1000, :] = o\n\ngeneOpennnessCorrelation = numpy.zeros((binnedOpenness.shape[0], binnedOpenness.shape[1])) \nnObs = 201\nfor g in range(geneOpennnessCorrelation.shape[0]): # iterate over genes\n for r in range(geneOpennnessCorrelation.shape[1]): # iterate over regions\n x = gene_expression.iloc[g]\n y = binnedOpenness[g][r][:]\n if numpy.count_nonzero(y) > 0:\n geneOpennnessCorrelation[g][r] = numpy.corrcoef(x,y)[0][1]\n\nnumpy.savetxt('data/geneOpennnessCorrelation.txt', geneOpennnessCorrelation)\n","sub_path":"DataProcessingScripts/computeGeneOpennessCorrelation.py","file_name":"computeGeneOpennessCorrelation.py","file_ext":"py","file_size_in_byte":4454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"129924536","text":"from pylab import *\nfrom imsearch import *\nfrom imcore import CoM\nfrom fsnake import snake2im\n\nim = flood_fill(make_ball(10)).astype(int)\nstart = locate_object(im)\n\nH = contour(im,CoM(im))\nr = np.nonzero(H==H.min())[0]+ 1\nsnake_im = np.zeros(im.shape)\nsnake = init_snake(r, CoM(im), im.shape)\nsnake_im = snake2im(snake,im.shape)\n\nplot(H)\nfigure(2)\nimshow(snake_im+im)\nscatter(start[1],start[0])\nscatter(CoM(im)[1],CoM(im)[0])\nshow()","sub_path":"test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"233562637","text":"#-*- coding:utf-8 -*-\n\nimport cv2\nimport numpy as np\nfrom board import board as board\nimport yaml\nimport numpy as np\nimport cv2\nimport transforms3d\nimport dt_apriltags as apriltag\n# import cv2.aruco as aruco\nclass AprilTagBoard(board.board):\n marker_X = 7\n marker_Y = 5\n markerSeparation = 0.007776\n tag_size = 0.030385\n april_family = \"tag36h11\"\n tag_id_order = np.array([])\n boardcenter = np.array([])\n boardcorner = np.array([])\n conners_order = np.array([[-1, 1], [1, 1], [1, -1], [-1, -1]])\n # conners_order = np.array([[-1, -1], [1, -1], [1, 1], [-1, 1]])\n\n def __init__(self, configFile):\n self.getParameter(configFile)\n try:\n print(\"/auto_calibration_python/config/tagId/\" + self.tagID + \".csv\")\n self.tag_id_order = np.loadtxt(\"/auto_calibration_python/config/tagId/\" + self.tagID + \".csv\", delimiter=\",\")\n # self.tag_id_order = np.loadtxt(\"config/tagId/\" + self.tagID + \".csv\", delimiter=\",\")\n print(\"tag_path:\",self.tagID)\n except IOError:\n raise IOError(\"tagID缺失！请检查！\")\n self.get_board_points()\n # 调用apriltag库函数\n self.at_detector = apriltag.Detector(families=self.april_family)\n\n def get_board_points(self):\n self.boardcenter = np.empty([self.marker_X * self.marker_Y, 2])\n self.boardcorner = np.empty([4 * self.marker_X * self.marker_Y, 2])\n m, n = self.marker_X, self.marker_Y\n l = self.tag_size\n seq = self.markerSeparation\n for i in range(n):\n for j in range(m):\n center_x = j*(l+seq)\n center_y = i*(l+seq)\n self.boardcenter[i * m + j, 0] = center_x\n self.boardcenter[i * m + j, 1] = center_y\n for k in range(4):\n self.boardcorner[4 * (i * m + j) + k, 0] = center_x + l / 2.0 * self.conners_order[k,0]\n self.boardcorner[4 * (i * m + j) + k, 1] = center_y + l / 2.0 * self.conners_order[k,1]\n\n def getParameter(self, configfile):\n fs = cv2.FileStorage(configfile, cv2.FileStorage_READ)\n self.marker_X = int(fs.getNode(\"marker_X\").real())\n self.marker_Y = int(fs.getNode(\"marker_Y\").real())\n self.markerSeparation = fs.getNode(\"markerSeparation\").real()\n self.tag_size = fs.getNode(\"tag_size\").real()\n self.april_family = \"tag36h11\"\n # self.april_family = fs.getNode(\"april_family\").string()\n self.tagID = str(fs.getNode(\"tagID\").string())\n fs.release()\n\n def GetBoardAllPoints(self):\n return self.boardcorner\n\n def getPointsbyTagId(self, tagId):\n x, y = np.where(self.tag_id_order == tagId)\n assert len(x) != 0 and len(y) != 0, \"tagId选取错误，请检查！\"\n # print(\"lensx:\"+str(len(x)),\"lensy:\"+str(len(y)))\n # print(\"xy:\",x,y)\n # print(\"x[0]y[0]\",x[0],y[0])\n # print(\"marker config:\",self.marker_X,self.marker_Y)\n # print(self.boardcenter)\n # print(self.boardcorner)\n center = self.boardcenter[x[0] * self.marker_X + y[0], :]\n corner = self.boardcorner[4 * (x[0] * self.marker_X + y[0]):4 * (x[0] * self.marker_X + y[0]) + 4, :]\n # print(center)\n # print(corner)\n return center, corner\n\n def getObjImgPointList(self, image,verbose=0):\n tags = self.detectTags(image,verbose=verbose)\n objpoint = np.array([])\n imgpoint = np.array([])\n if len(tags) < self.marker_Y * self.marker_X / 4:\n return False, 0, 0\n print(\"board/apriltagboard len tags : \"+str(len(tags)))\n for tag in tags:\n center, conners = self.getPointsbyTagId(tag.tag_id)\n objpoint = np.append(objpoint, conners)\n imgpoint = np.append(imgpoint, tag.corners)\n objpoint = np.reshape(objpoint, [-1, 2])\n imgpoint = np.reshape(imgpoint, [-1, 2])\n return True, objpoint, imgpoint\n\n def getObjImgPointListFromCircle(self, img, row, col, verbose=0):\n blobParam = cv2.SimpleBlobDetector_Params()\n blobParam.thresholdStep = 2\n blobParam.minArea = 100.0\n blobParam.maxArea = 10000.0\n blobParam.blobColor = 0\n # Filter by Circularity\n # blobParam.filterByCircularity = True\n # blobParam.minCircularity = 0.8\n # Filter by Convexity\n # blobParam.filterByConvexity = True\n # blobParam.minConvexity = 0.8\n # Filter by Inertia\n # blobParam.filterByInertia = True\n # blobParam.minInertiaRatio = 0.01\n # cv2.imshow('img',img)\n # cv2.waitKey(0)\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n blobDetector = cv2.SimpleBlobDetector_create(blobParam)\n flags = cv2.CALIB_CB_ASYMMETRIC_GRID + cv2.CALIB_CB_CLUSTERING\n print('waiting findcirclegrid')\n found, corners = cv2.findCirclesGrid(img_gray, (col, row), flags=flags,\n blobDetector=blobDetector) # pattern_columns,pattern_rows\n # print(found)\n pattern_points = []\n for i in range(row):\n for j in range(col):\n y = i / 2\n if i % 2 == 0:\n x = j\n else:\n x = j + 0.5\n pattern_points.append((x, y))\n # pattern_points = np.hstack((pattern_points, np.zeros((col * row, 1)))).astype(np.float32) * 0.0430\n pattern_points = np.array(pattern_points).astype(np.float32) * 0.10\n # print(pattern_points)\n\n if found:\n corners2 = corners.copy()\n vis = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR)\n # print(corners2)\n if verbose:\n # cv2.drawChessboardCorners(vis, (col ,row), corners2, found)\n # cv2.imshow('result',vis)\n # cv2.waitKey(10)\n for i in range(row * col):\n print(i, row * col)\n print((corners2[i][0][0], corners2[i][0][1]))\n print(pattern_points[i])\n cv2.circle(vis, (corners2[i][0][0], corners2[i][0][1]), 2, (0, 0, 255), 1)\n cv2.imshow('result', vis)\n cv2.waitKey(0)\n return found, pattern_points, corners2\n return found, None, None\n def visible(self,campose,intrinsic,dist):\n pass\n\n\n def detectTags(self, img, cameraMatrix=None, discoff=None, verbose=0):\n \"\"\"\n 检测img中的apriltag，返回一组tag,如果不输入cameraMatrix，tags中不含位姿信息\n :param board: apiriltag.board 包含board的一些参数\n :param img: 需要检测的图片路径\n :param cameraMatrix:相机内参\n :return: tags 检测到的tags\n \"\"\"\n # img = cv2.imread(img)\n if not discoff is None:\n img = cv2.undistort(img, cameraMatrix, discoff)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n gray = cv2.convertScaleAbs(gray, alpha=1.5, beta=0)\n\n if cameraMatrix is None:\n\n tags = self.at_detector.detect(gray)\n cv2.waitKey(1000)\n\n if verbose == 1:\n img = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)\n img = self.drawTagAxis(img, tags)\n cv2.namedWindow(\"apriltag\", cv2.WINDOW_NORMAL)\n cv2.imshow(\"apriltag\", img)\n cv2.waitKey(0)\n else:\n if not discoff is None:\n gray = cv2.undistort(gray, cameraMatrix, discoff)\n camera_param = [cameraMatrix[0, 0], cameraMatrix[1, 1], cameraMatrix[0, 2], cameraMatrix[1, 2]]\n tags = self.at_detector.detect(gray, True, camera_param, self.tag_size)\n if verbose == 1:\n img = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)\n img = self.drawTagAxis(img, tags, cameraMatrix)\n cv2.namedWindow(\"apriltag\", cv2.WINDOW_NORMAL)\n cv2.imshow(\"apriltag\", cv2.resize(img,(512,512)))\n cv2.waitKey(0)\n\n return tags\n\n def drawTagAxis(self,img, tags, cameraMatrix=None, length=0.015, line_width=1):\n \"\"\"\n 在图像上画出每个tag的坐标轴，蓝色表示x轴，绿色表示y轴，红色表示z轴\n :param img: 图片\n :param tags:\n :param cameraMatrix: 相机内参\n :param length: 长度，指实际长度\n :return: img：图片\n \"\"\"\n point_x = np.array([[length, 0, 0, 1]]).T\n point_y = np.array([[0, length, 0, 1]]).T\n point_z = np.array([[0, 0, length, 1]]).T\n if cameraMatrix is None:\n for tag in tags:\n img = cv2.circle(img, (int(tag.corners[0, 0]), int(tag.corners[0, 1])), 5, (255, 0, 0), thickness=3)\n img = cv2.circle(img, (int(tag.corners[1, 0]), int(tag.corners[1, 1])), 5, (0, 255, 0), thickness=3)\n img = cv2.circle(img, (int(tag.corners[2, 0]), int(tag.corners[2, 1])), 5, (0, 0, 255), thickness=3)\n cv2.putText(img, str(tag.tag_id), (int(tag.center[0]), int(tag.center[1])), cv2.FONT_HERSHEY_SIMPLEX, 1,\n (0, 255, 0), 2)\n return img\n cameraMatrix = np.append(cameraMatrix, np.zeros([3, 1]), 1)\n for tag in tags:\n R = tag.pose_R\n T = tag.pose_t\n H = np.append(np.append(R, T, 1), np.array([[0, 0, 0, 1]]), 0)\n pro_x = np.dot(cameraMatrix, np.dot(H, point_x))\n pro_x = pro_x / pro_x[2, 0]\n pro_y = np.dot(cameraMatrix, np.dot(H, point_y))\n pro_y = pro_y / pro_y[2, 0]\n pro_z = np.dot(cameraMatrix, np.dot(H, point_z))\n pro_z = pro_z / pro_z[2, 0]\n\n cv2.putText(img, str(tag.tag_id), (int(tag.center[0]), int(tag.center[1])), cv2.FONT_HERSHEY_SIMPLEX, 1,\n (0, 255, 0), 2)\n img = cv2.line(img, (int(tag.center[0]), int(tag.center[1])), (int(pro_x[0, 0]), int(pro_x[1, 0])),\n (255, 0, 0), thickness=line_width)\n img = cv2.line(img, (int(tag.center[0]), int(tag.center[1])), (int(pro_y[0, 0]), int(pro_y[1, 0])),\n (0, 255, 0), thickness=line_width)\n img = cv2.line(img, (int(tag.center[0]), int(tag.center[1])), (int(pro_z[0, 0]), int(pro_z[1, 0])),\n (0, 0, 255), thickness=line_width)\n # img = cv2.circle(img,(int(tag.corners[0,0]),int(tag.corners[0,1])),1,(255,0,0),thickness=2)\n # img = cv2.circle(img,(int(tag.corners[1,0]),int(tag.corners[1,1])),1,(0,255,0),thickness=2)\n # img = cv2.circle(img,(int(tag.corners[2,0]),int(tag.corners[2,1])),1,(0,0,255),thickness=2)\n\n return img\n\n def extrinsic_tags(self,tags):\n \"\"\"\n 得到相机的姿态,主要是将每个tag中估计相机姿态使用4分位法进行筛选\n :param tags: 检测图片的tags\n :param board: apriltag的规格参数\n :param cameraMatrix: 相机内参\n :return: 4*4 旋转矩阵 表示相机的姿态\n \"\"\"\n q = np.array([])\n t = np.array([])\n n = len(tags)\n for tag in tags:\n q = np.append(q, transforms3d.quaternions.mat2quat(tag.pose_R))\n H = np.append(np.append(tag.pose_R, tag.pose_t, 1), np.array([[0, 0, 0, 1]]), 0)\n x, y = np.where(self.tag_id_order == tag.tag_id)\n board_center = self.boardcenter[x[0] * self.marker_X + y[0], :]\n point = np.array([[-board_center[0], -board_center[1], 0, 1]]).T\n proj = np.dot(H, point)\n orgin = proj / proj[3, 0]\n t = np.append(t, orgin[:3, 0])\n q = np.reshape(q, [-1, 4])\n t = np.reshape(t, [-1, 3])\n\n # 使用四分位法去除异常值\n if n > 4:\n q1 = q[:, 0]\n q2 = q[:, 1]\n q3 = q[:, 2]\n q4 = q[:, 3]\n Q11 = np.percentile(q1, 25)\n Q31 = np.percentile(q1, 75)\n Q12 = np.percentile(q2, 25)\n Q32 = np.percentile(q2, 75)\n Q13 = np.percentile(q3, 25)\n Q33 = np.percentile(q3, 75)\n Q14 = np.percentile(q4, 25)\n Q34 = np.percentile(q4, 75)\n IQR1 = 1.5 * (Q31 - Q11)\n IQR2 = 1.5 * (Q32 - Q12)\n IQR3 = 1.5 * (Q33 - Q13)\n IQR4 = 1.5 * (Q34 - Q14)\n for i in range(q.shape[0] - 1, -1, -1):\n flag1 = (q[i, 0] < Q11 - IQR1) | (q[i, 0] > Q31 + IQR1)\n flag2 = (q[i, 1] < Q12 - IQR2) | (q[i, 1] > Q32 + IQR2)\n flag3 = (q[i, 2] < Q13 - IQR3) | (q[i, 2] > Q33 + IQR3)\n flag4 = (q[i, 3] < Q14 - IQR4) | (q[i, 3] > Q34 + IQR4)\n if flag1 | flag2 | flag3 | flag4:\n q = np.delete(q, i, axis=0)\n mean_t = np.array([np.mean(t, axis=0)])\n std_t = np.array([np.std(t, axis=0)])\n error_t = np.empty([t.shape[0], 3], dtype=bool)\n error_t[:, :] = np.abs(t[:, :] - mean_t[0, :]) > 3 * std_t[0, :]\n index, _ = np.where(error_t)\n if np.size(index)>0:\n index_t = np.array(list(set(list(index))))\n t = np.delete(t, index_t, axis=0)\n mean_q = np.mean(q, axis=0)\n mean_t = np.mean(t, axis=0)\n pose_R = transforms3d.quaternions.quat2mat(mean_q)\n pose = np.append(np.append(pose_R, np.array([mean_t]).T, 1), np.array([[0, 0, 0, 1]]), 0)\n return pose\n\n\n\n\n\n\n","sub_path":"board/apriltagboard.py","file_name":"apriltagboard.py","file_ext":"py","file_size_in_byte":13471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"350267170","text":"\"\"\"Unit test module for user document logic\"\"\"\nfrom tempfile import NamedTemporaryFile\nfrom StringIO import StringIO\nfrom datetime import datetime\nfrom flask import current_app\nfrom flask_webtest import SessionScope\nimport os\n\nfrom tests import TestCase, TEST_USER_ID\nfrom portal.extensions import db\nfrom portal.models.auth import create_service_token\nfrom portal.models.intervention import INTERVENTION\nfrom portal.models.user_document import UserDocument\nfrom portal.models.user import get_user\n\n\nclass TestUserDocument(TestCase):\n \"\"\"User Document tests\"\"\"\n\n def test_get_user_documents(self):\n #tests whether we can successfully get the list of user documents for a user\n ud1 = UserDocument(document_type=\"TestFile\", uploaded_at=datetime.utcnow(),\n filename=\"test_file_1.txt\", filetype=\"txt\", uuid=\"012345\")\n ud2 = UserDocument(document_type=\"AlternateTestFile\", uploaded_at=datetime.utcnow(),\n filename=\"test_file_2.txt\", filetype=\"txt\", uuid=\"098765\")\n self.test_user.documents.append(ud1)\n self.test_user.documents.append(ud2)\n with SessionScope(db):\n db.session.commit()\n self.test_user = db.session.merge(self.test_user)\n self.login()\n rv = self.client.get('/api/user/{}/user_documents'.format(TEST_USER_ID))\n self.assert200(rv)\n self.assertEquals(len(rv.json['user_documents']), 2)\n # tests document_type filter\n rv = self.client.get('/api/user/{}/user_documents?document_type=TestFile'.format(TEST_USER_ID))\n self.assert200(rv)\n self.assertEquals(len(rv.json['user_documents']), 1)\n\n\n def test_post_patient_report(self):\n #tests whether we can successfully post a patient report -type user doc file\n client = self.add_client()\n client.intervention = INTERVENTION.SEXUAL_RECOVERY\n create_service_token(client=client, user=get_user(TEST_USER_ID))\n self.login()\n\n test_contents = \"This is a test.\"\n with NamedTemporaryFile(\n prefix='udoc_test_',\n suffix='.pdf',\n delete=True,\n ) as temp_pdf:\n temp_pdf.write(test_contents)\n temp_pdf.seek(0)\n tempfileIO = StringIO(temp_pdf.read())\n rv = self.client.post('/api/user/{}/patient_report'.format(TEST_USER_ID),\n content_type='multipart/form-data', \n data=dict({'file': (tempfileIO, temp_pdf.name)}))\n self.assert200(rv)\n udoc = db.session.query(UserDocument).order_by(UserDocument.id.desc()).first()\n fpath = os.path.join(current_app.root_path,\n current_app.config.get(\"FILE_UPLOAD_DIR\"),\n str(udoc.uuid))\n with open(fpath, 'r') as udoc_file:\n self.assertEqual(udoc_file.read(),test_contents)\n os.remove(fpath)\n\n self.assertEquals(udoc.user_id, TEST_USER_ID)\n self.assertEquals(udoc.intervention.description,\n INTERVENTION.SEXUAL_RECOVERY.description)\n\n\n def test_download_user_document(self):\n self.login()\n test_contents = \"This is a test.\"\n with NamedTemporaryFile(\n prefix='udoc_test_',\n suffix='.pdf',\n delete=True,\n ) as temp_pdf:\n temp_pdf.write(test_contents)\n temp_pdf.seek(0)\n tempfileIO = StringIO(temp_pdf.read())\n rv = self.client.post('/api/user/{}/patient_report'.format(TEST_USER_ID),\n content_type='multipart/form-data', \n data=dict({'file': (tempfileIO, temp_pdf.name)}))\n self.assert200(rv)\n udoc = db.session.query(UserDocument).order_by(UserDocument.id.desc()).first()\n rv = self.client.get('/api/user/{}/user_documents/{}'.format(\n TEST_USER_ID,udoc.id))\n self.assert200(rv)\n self.assertEqual(rv.data,test_contents)\n\n","sub_path":"tests/test_user_document.py","file_name":"test_user_document.py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"359933608","text":"#coding: utf-8\nimport scipy.integrate as spi\nfrom scipy import interpolate\nfrom config import Configuration\nfrom weather import Weather\nfrom bunch import Bunch\nimport numpy as np\nimport equations\nimport datetime\nimport utils\nimport rk\n\nclass Model:\n def __init__(self, configuration=Configuration('resources/otero_precipitation.cfg')):\n self.configuration=configuration\n\n self.parameters=Bunch()\n self.parameters.BS_a=configuration.getFloat('breeding_site','amount')\n self.parameters.BS_lh=configuration.getFloat('breeding_site','level_height')#in cm\n self.parameters.vBS_h=configuration.getArray('breeding_site','height')#in cm\n self.parameters.vBS_r=configuration.getArray('breeding_site','radius')#in cm\n self.parameters.vBS_s=configuration.getArray('breeding_site','surface')#in cm^2\n self.parameters.vBS_d=configuration.getArray('breeding_site','distribution')#distribution of BS. Sum must be equals to 1\n self.parameters.vBS_mf=configuration.getArray('breeding_site','manually_filled')#in cm\n self.parameters.vBS_b=configuration.getArray('breeding_site','bare')#in [0,1]\n self.parameters.vBS_ef=configuration.getArray('breeding_site','evaporation_factor')#in [0,2]\n self.parameters.n=len(self.parameters.vBS_d)\n self.parameters.m=int(np.max(np.ceil(self.parameters.vBS_h/self.parameters.BS_lh)))\n\n m,n=self.parameters.m,self.parameters.n\n self.parameters.EGG=slice(0,m*n)#in R^(mxn)\n self.parameters.LARVAE=slice(m*n,(1+m)*n)#in R^n\n self.parameters.PUPAE=slice((1+m)*n,(2+m)*n)#in R^n\n self.parameters.ADULT1=(2+m)*n#in R\n self.parameters.ADULT2=(2+m)*n+1#in R\n self.parameters.WATER=slice((2+m)*n+2,(3+m)*n+2)#in R^n\n self.parameters.OVIPOSITION=slice((3+m)*n+2,(3+2*m)*n+2)#in R^(mxn)\n self.parameters.vAlpha0=configuration.getArray('biology','alpha0')#constant to be fitted\n\n #Cordoba\n self.parameters.location={'name':configuration.getString('location','name')}\n self.start_date=configuration.getDate('simulation','start_date')\n self.end_date=configuration.getDate('simulation','end_date')\n self.time_range = np.linspace(0, (self.end_date - self.start_date).days-1, (self.end_date - self.start_date).days )\n initial_condition=configuration.getArray('simulation','initial_condition')\n self.parameters.mBS_l=np.repeat(range(0,m),n).reshape((m,n))#level helper matrix\n E0=np.zeros((m,n))\n E0[0,:]=(initial_condition[0]*self.parameters.vBS_d)\n E0=E0.transpose().reshape((1,m*n)).flatten()\n L0=initial_condition[1]*self.parameters.vBS_d\n P0=initial_condition[2]*self.parameters.vBS_d\n W0=configuration.getArray('breeding_site','initial_water')\n O0=np.zeros((m*n))\n self.parameters.initial_condition=np.concatenate( (E0,L0,P0,initial_condition[-2:],W0,O0) )\n\n WEATHER_DATA_FILENAME='data/public/'+self.parameters.location['name']+'.csv'\n self.parameters.weather=Weather(WEATHER_DATA_FILENAME,self.start_date,self.end_date)\n\n self.parameters.mf=self.parameters.weather.getAsLambdaFunction(self.parameters.weather.aps, [0,0,0,0,0,0,1.]* int( (self.end_date - self.start_date).days/7 +1) )\n\n self.validate()\n\n def validate(self):\n self.warnings=[]\n mean_temperatures=np.array([self.parameters.weather.T(t) for t in self.time_range])\n lower_bound=mean_temperatures[mean_temperatures<278.]\n upper_bound=mean_temperatures[mean_temperatures>303.]\n if(lower_bound.size>0 or upper_bound.size>0):\n self.warnings.append('Temperature out of model\\'s valid range:T<278:%s T>303:%s'%(lower_bound.size,upper_bound.size))\n\n def save(self,results_filename=None):\n #save results\n if(not results_filename):\n results_filename='data/test/previous_results/'+self.configuration.getString('location','name')+'-'+datetime.datetime.now().strftime('%Y-%m-%d__%H_%M_%S')+'.csv'\n file=open(results_filename,'w')\n daily_Y=utils.getDailyResults(self.time_range,self.Y,self.start_date,self.end_date)\n for d,daily_Y_d in enumerate(daily_Y):\n date_d=self.start_date+datetime.timedelta(days=d)\n file.write(date_d.strftime('%Y-%m-%d')+','+','.join([str(value) for value in daily_Y_d ])+ '\\n')\n #save config\n self.configuration.save(results_filename.replace('.csv','.cfg'))\n\n return results_filename\n\n\n def solveEquations(self,equations=equations.diff_eqs,method='rk'):\n time_range=self.time_range\n initial_condition=self.parameters.initial_condition\n Y=None\n\n if(method=='odeint'):\n Y = spi.odeint(equations,initial_condition,time_range,hmax=1.0,args=(self.parameters,))#the ',' in (parameters,) is very important! '(parameters)' or tuple(parameters) doesn't work#TODO: this is because it calls aps out of it's domain.Find a better way.\n elif(method=='rk'):\n Y=rk.solve(equations,initial_condition,time_range,args=(self.parameters,),steps=20)\n elif(method=='cuda_rk'):\n Y=rk.cuda_solve(equations,initial_condition,time_range,args=(self.parameters,),steps=20)\n elif(method=='rkf'):\n Y=rk.rkf_solve(equations,initial_condition,time_range,args=(self.parameters,))\n elif(method=='dopri'):\n Y=rk.scipy_solve(equations,initial_condition,time_range,'dopri',{'max_step':time_range[1]-time_range[0],'rtol':1e-3, 'atol':1e-6}, args=(self.parameters,))\n\n self.Y=Y\n return time_range,Y\n","sub_path":"src/otero_precipitation.py","file_name":"otero_precipitation.py","file_ext":"py","file_size_in_byte":5602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"481639362","text":"\"\"\"Daily Programming Challenge\n\tThe input is a single number: the number at which the game starts. \n\tWrite a program that plays the Threes game, and outputs a valid sequence of steps you need to take to get to 1. \n\tEach step should be output as the number you start at, followed by either -1 or 1 (if you are adding/subtracting 1\n\tbefore dividing), or 0 (if you are just dividing). The last line should simply be 1.\n\"\"\"\n\nuserNum = int(input(\"Input any positive number greater than 1:\"))\n\nwhile userNum <= 1:\n\tprint(\"That's not a positive number greater than 1\")\n\tuserNum = int(input(\"Input any positive number greater than 1:\"))\n\nwhile userNum!=1:\n\n remainder = userNum%3\n if remainder == 0:\n print(userNum,\" 0\")\n userNum /= 3\n\n else:\n addTest = (userNum+1)%3\n subTest = (userNum-1)%3\n if addTest == 0:\n print(userNum,\" +1\")\n userNum+=1\n userNum /= 3\n else:\n print(userNum, \" -1\")\n userNum-=1\n userNum /= 3\nprint(1.0)","sub_path":"GameOfThrees/gameOfThrees.py","file_name":"gameOfThrees.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"630005495","text":"import tkinter as tk\n\n# Create the master object\nmaster = tk.Tk()\nmaster.title('Settings')\n\n# Create the labels\ntk.Label(text = 'Set Up New Iso View').grid(row=0, column=1)\ntk.Label(text = 'Enter Scale here : ' ).grid(row=2, column=0)\ntk.Label(text = 'Plot Scale Label : ' ).grid(row=3, column=0)\n\n# Create input list\nscl = tk.StringVar(master)\nOPTIONS = [\"5:1\", \"2:1\", \"1:1\", \"1:2\", \"1:5\"] \nscl.set(OPTIONS[2]) # default value\ntk.OptionMenu(master, scl, *OPTIONS).grid(row=2, column=1)\n\nscale_label = tk.IntVar()\ncheckbox1 = tk.Checkbutton(master, variable=scale_label).grid(row=3, column=1)\n\n\ndef func_1():\n print('var:' , scale_label.get(), 'scale:' , scl.get())\n scale= scl.get()\n if scale == '1:1':\n denominator = 1.0\n numerator = 1.0\n elif scale == '1:2':\n denominator = 2.0\n numerator = 1.0\n elif scale == '1:5':\n denominator = 5.0\n numerator = 1.0\n elif scale == '2:1': \n denominator = 1.0\n numerator = 2.0\n elif scale == '5:1': \n denominator = 1.0\n numerator = 5.0\n\n print(numerator,':',denominator)\n master.destroy()\n\nbutton = tk.Button(master, text=\"Place View\", command=func_1).grid(row=4,column=1)\n\ntk.mainloop()","sub_path":"List_inputGUI.py","file_name":"List_inputGUI.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"472545412","text":"# Copyright (c) 2017-2021 Digital Asset (Switzerland) GmbH and/or its affiliates. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom dazl import async_network, frozendict\nimport pytest\n\nfrom .dars import MapSupport\n\n\n@pytest.mark.asyncio\nasync def test_map_support(sandbox):\n async with async_network(url=sandbox, dars=MapSupport) as network:\n client = network.aio_new_party()\n\n network.start()\n\n await client.ready()\n await client.create(\n \"MapSupport:Sample\",\n {\"party\": client.party, \"mappings\": {\"65\": \"A\", \"97\": \"a\"}, \"text\": None},\n )\n\n assert len(client.find_active(\"*\")) == 1\n\n\n@pytest.mark.skip(\n \"Keys with arbitrary types are no longer supported. See the comments in MapSupport.daml.\"\n)\nasync def test_complicated_map_support(sandbox):\n # This test will be re-enabled when GenMap support lands in DAML-LF 1.9\n async with async_network(url=sandbox, dars=MapSupport) as network:\n client = network.aio_new_party()\n\n await client.ready()\n await client.create(\n \"MapSupport:ComplicatedSample\",\n {\n \"party\": \"Test\",\n # Note: Python `dict`s are not hashable, so the only way to write this out\n # is to create a special dict as a key\n \"keyIsMap\": {frozendict(A=\"b\"): \"mmm\"},\n \"keyIsRecord\": {frozendict(x=2, y=4): \"rrr\"},\n \"keyIsRecordWithTypeParam\": {frozendict(x=2, y=4): \"rrr\"},\n \"keyIsVariant\": {frozendict(Apple=\"\"): \"ttt\"},\n },\n )\n\n assert len(client.find_active(\"*\")) == 1\n","sub_path":"python/tests/unit/test_map_support.py","file_name":"test_map_support.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"651317465","text":"import sys\r\nfin = open(\"inp.in\",\"r\")\r\nfout = open(\"out.txt\",\"w\")\r\nN = int(fin.readline())\r\n\r\nfor n in range(1,N+1):\r\n M = []\r\n drawPossible,wonX,wonO = True,False,False\r\n wonO=wonX=False\r\n for i in range(4):\r\n line = map(str,fin.readline().replace('\\n',''))\r\n for spot in line:\r\n if spot == \".\": drawPossible=False\r\n M.append(line)\r\n \r\n diag1 = ''.join(sorted([M[0][0],M[1][1],M[2][2],M[3][3]]))\r\n diag2 = ''.join(sorted([M[0][3],M[1][2],M[2][1],M[3][0]]))\r\n if diag1==\"XXXX\" or diag1==\"TXXX\" or diag2==\"XXXX\" or diag2==\"TXXX\": wonX = True\r\n if diag1==\"OOOO\" or diag1==\"OOOT\" or diag2==\"OOOO\" or diag2==\"OOOT\": wonO = True\r\n for i in range(4):\r\n row = ''.join(sorted(M[i]))\r\n if row==\"XXXX\" or row==\"TXXX\" or row==\"XXXX\" or row==\"TXXX\": wonX = True\r\n if row==\"OOOO\" or row==\"OOOT\" or row==\"OOOO\" or row==\"OOOT\": wonO = True\r\n col = ''.join(sorted([M[0][i],M[1][i],M[2][i],M[3][i]]))\r\n if col==\"XXXX\" or col==\"TXXX\" or col==\"XXXX\" or col==\"TXXX\": wonX = True\r\n if col==\"OOOO\" or col==\"OOOT\" or col==\"OOOO\" or col==\"OOOT\": wonO = True\r\n \r\n t = fin.readline()\r\n if drawPossible==True:\r\n if wonO==wonX==False: round = \"Draw\"\r\n else:\r\n if wonO==wonX==False: round = \"Game has not completed\"\r\n elif wonO: round= \"O won\"\r\n else: round = \"X won\"\r\n fout.write( \"Case #\" + str(n) + \": \" + round + \"\\n\")\r\n\r\nfin.close()\r\nfout.close()","sub_path":"solutions_2453486_1/Python/MarcusStuhr/goog1.py","file_name":"goog1.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"585794250","text":"import FWCore.ParameterSet.Config as cms\n\npfIsolatedMuons = cms.EDFilter(\n \"IsolatedPFCandidateSelector\",\n src = cms.InputTag(\"pfSelectedMuons\"),\n isolationValueMaps = cms.VInputTag(\n cms.InputTag(\"isoValMuonWithCharged\"),\n cms.InputTag(\"isoValMuonWithNeutral\"),\n cms.InputTag(\"isoValMuonWithPhotons\")\n ),\n ## if True isolation is relative to pT\n isRelative = cms.bool(True),\n ## if True all isoValues are combined (summed)\n isCombined = cms.bool(True),\n ## not used when isCombined=True\n # non-optimised default for loose absulute isolation\n isolationCuts = cms.vdouble( 10, \n 10,\n 10 ),\n # not used when isCombined=False\n # default value for combined relative with DR={0.4,0.4,0.4}\n # and weight={1.,1.,1.}; optimised for Z->mu,mu\n combinedIsolationCut = cms.double(0.15) \n )\n","sub_path":"PhysicsTools/PFCandProducer/python/Isolation/pfIsolatedMuons_cfi.py","file_name":"pfIsolatedMuons_cfi.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"601323140","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy.selector import Selector\nfrom maoyanspider.items import MaoyanspiderItem\nclass MaoyanSpider(scrapy.Spider):\n name = 'maoyan'\n allowed_domains = ['maoyan.com']\n start_urls = ['https://maoyan.com/films?showType=3']\n '''\n headers = {\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Accept-Language\": \"zh-CN,zh;q=0.8\",\n \"Cache-Control\": \"max-age=0\",\n \"Connection\": \"keep-alive\",\n \"Upgrade-Insecure-Requests\": \"1\",\n \"Content-Type\": \"application/x-www-form-urlencoded; charset=UTF-8\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.86 Safari/537.36\"\n } \n\n def start_requests(self):\n return [scrapy.Request(url = self.start_urls[0], headers = self.headers, callback = self.parse)]\n '''\n def parse(self, response):\n movies = Selector(response=response).xpath('//dd[position()<=10]')\n i = 1\n for movie in movies:\n url = movie.xpath('./div[1]/div[2]/a/@href').extract()[0]\n info_url = 'https://maoyan.com'+url\n #print(url)\n if i <= 10:\n yield scrapy.Request(url = info_url, callback=self.parse2)\n i = i + 1\n else:\n break\n \n def parse2(self,response):\n item = MaoyanspiderItem()\n name = response.xpath('//h1[@class=\"name\"]/text()').extract()[0]\n label = \"\".join(response.xpath('//html/body/div[3]/div/div[2]/div[1]/ul/li/a/text()').extract()).replace(' ','')\n time = response.xpath('/html/body/div[3]/div/div[2]/div[1]/ul/li[3]/text()').extract()[0]\n item['name'] = name\n item['label'] = label\n item['time'] = time\n print(name,label,time)\n yield item\n","sub_path":"week01/maoyanspider/maoyanspider/spiders/maoyan.py","file_name":"maoyan.py","file_ext":"py","file_size_in_byte":1959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"80131540","text":"import numpy as np\n\n\n# AX=B\n# A = left, B = right\ndef gaussElimin(left, right):\n n = len(right)\n for i in range(n - 1):\n for j in range(i + 1, n):\n if left[j, i]:\n lam = left[j, i] / left[i, i]\n left[j, i:n] = left[j, i:n] - lam * left[i, i:n]\n right[j] = right[j] - lam * right[i]\n for i in range(n - 1, -1, -1):\n right[i] = (right[i] - np.dot(left[i, i + 1:n], right[i + 1:n])) / left[i, i]\n return right\n\n\ndef LUdecomp(left):\n n = len(left)\n for i in range(n - 1):\n for j in range(i + 1, n):\n if left[j, i] != 0.0:\n lam = left[j, i] / left[i, i]\n left[j, i + 1:n] = left[j, i + 1:n] - lam * left[i, i + 1:n]\n left[j, i] = lam\n return left\n\n\ndef LUsolve(left, right):\n n = len(left)\n for i in range(1, n):\n right[i] = right[i] - np.dot(left[i, 0:i], right[0:i])\n right[n - 1] = right[n - 1] / left[n - 1, n - 1]\n for i in range(n - 2, -1, -1):\n right[i] = (right[i] - np.dot(left[i, i + 1:n], right[i + 1:n])) / left[i, i]\n return right\n\n\ndef findNonZeroCol(a):\n n = len(a)\n for i in range(n):\n for j in range(n):\n if a[j, i]:\n return i\n\n return -1\n\n\ndef gaussJordanElimin(left, right):\n n = len(right)\n col = findNonZeroCol(left)\n for i in range(col, n):\n for j in range(n):\n if left[j, i] and j != i:\n lam = left[j, i] / left[i, i]\n left[j, i:n] = left[j, i:n] - lam * left[i, i:n]\n right[j] = right[j] - lam * right[i]\n\n for i in range(col, n):\n if left[i, i]:\n right[i] /= left[i, i]\n left[i, i] = 1\n return right\n\n\ndef tridiagonalMatrixAlgo(under_main, above_main, main, f):\n alpha = [0]\n beta = [0]\n n = len(f)\n x = [0 for i in range(n)]\n\n for i in range(n-1):\n alpha.append(-above_main[i] / (under_main[i] * alpha[i] + main[i]))\n beta.append((f[i] - under_main[i] * beta[i]) / (under_main[i] * alpha[i] + main[i]))\n\n x[n-1] = (f[n-1] - under_main[n-2] * beta[n-1]) / (main[n-1] + under_main[n-2] * alpha[n-1])\n\n for i in reversed(range(n-1)):\n x[i] = alpha[i+1]*x[i+1] + beta[i+1]\n return x\n\n\ndef test(left, right, solve, accuracy = 0.5):\n n = len(right)\n for i in range(n):\n sum = 0\n for j in range(n):\n sum += left[i, j] * solve[j]\n if(abs(sum - right[i]) > accuracy):\n return False\n return True\n","sub_path":"eqsuove.py","file_name":"eqsuove.py","file_ext":"py","file_size_in_byte":2540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"352619034","text":"import asyncio\nimport sys\nimport threading\nimport aiohttp.web\nimport time\n\nimport static_init.tls_pattern_wrapper\n\ntry:\n from uvloop import EventLoopPolicy\nexcept ImportError:\n EventLoopPolicy = asyncio.DefaultEventLoopPolicy\n\n\nasync def call_go_spy(request: aiohttp.web.Request) -> aiohttp.web.Response:\n result = static_init.tls_pattern_wrapper.call_go_spy()\n return aiohttp.web.Response(text=str(result))\n\n\ndef make_app() -> aiohttp.web.Application:\n \"\"\"Assemble aiohttp Application.\n\n Returns:\n aiohttp.web.Application: assembled aiohttp application.\n\n \"\"\"\n app = aiohttp.web.Application()\n app.router.add_get(\"/call_go_spy\", call_go_spy)\n return app\n\n\nclass Server(threading.Thread):\n \"\"\"Manage starting and stopping the web application.\n\n This wraps some of the complexity of starting and stopping an aiohttp\n application in another thread.\n\n Note that the HTTP server takes a fraction of a second to start. Consider checking\n for availability or waiting a tenth of a second before making calls.\n\n \"\"\"\n\n def __init__(self, host: str = \"127.0.0.1\", port=None) -> None:\n super().__init__()\n self.host, self.port = host, port\n asyncio.set_event_loop_policy(EventLoopPolicy())\n self.loop = asyncio.new_event_loop() # control will be passed to thread\n self.app = make_app()\n self.runner = aiohttp.web.AppRunner(self.app)\n self.loop.run_until_complete(self.runner.setup())\n self.port = 8080\n site = aiohttp.web.TCPSite(self.runner, self.host, self.port)\n self.loop.run_until_complete(site.start())\n\n def run(self) -> None:\n \"\"\"Runs in a separate thread when ``start`` is called.\"\"\"\n # This thread takes over control of the event loop.\n asyncio.set_event_loop(self.loop)\n self.loop.run_forever() # will stop when ``stop`` is called\n\n def stop(self) -> None:\n \"\"\"Arrange for the server to gracefully exit.\"\"\"\n # reminder: these functions are called from the original context\n if not self.is_alive():\n raise RuntimeError(\"httpstan Server thread is not alive.\")\n # self.loop is controlled by another thread\n # future is a concurrent.futures.Future\n future = asyncio.run_coroutine_threadsafe(self.runner.cleanup(), self.loop)\n future.result(1)\n self.loop.call_soon_threadsafe(self.loop.stop)\n self.join()\n\n\n\n\ndef main() -> None:\n server = Server()\n server.start()\n print(f\"httpstan serving on {server.host}:{server.port}\", file=sys.stderr)\n try:\n while True:\n time.sleep(0.1)\n finally:\n server.stop()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"static_init/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"510673199","text":"import sys, os\nfrom pathlib import Path\n\nif __name__ == \"__main__\":\n parentdir = str(Path(os.path.abspath(__file__)).parents[1])\n sys.path.append(parentdir)\n\n from mgmt.Workspace import svc_pr\n\n from azureml.core.compute import DatabricksCompute, ComputeTarget\n from azureml.exceptions import ComputeTargetException\n from azureml.core import Workspace\n\n access_token = os.environ.get(\"DATABRICKS_TOKEN\")\n databricks_workspace = os.environ.get(\"DATABRICKS_WORKSPACE\")\n rg = os.environ.get(\"RESOURCE_GROUP\")\n\n ws = Workspace.from_config(path = \"./script-outputs\", auth=svc_pr)\n\n try:\n print(\"Trying to create databricks compute...\")\n databricks_compute = ComputeTarget(workspace=ws, name=databricks_workspace)\n print(\"Databricks Compute Target of {} already exists\".format(databricks_workspace))\n except ComputeTargetException:\n print(\"The Compute Target of {} will be created.\".format(databricks_workspace))\n\n databricks_config = DatabricksCompute.attach_configuration(\n resource_group=rg, \n workspace_name=databricks_workspace,\n access_token=access_token\n )\n\n databricks_compute = ComputeTarget.attach(\n workspace=ws, \n name=databricks_workspace, \n attach_configuration = databricks_config\n )\n\n databricks_compute.wait_for_completion(show_output=True)\n","sub_path":"train/AddDatabricksCompute.py","file_name":"AddDatabricksCompute.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"404827362","text":"import logging\nimport os\nimport typing\n\nimport pandas\nimport pymongo\n\nfrom . import commons, db\n\nlogger = logging.getLogger()\n\ndbm: typing.Optional[db.DBManager] = None\n\n\ndef connect(url=\"mongodb://localhost:27017/mailpy-db\"):\n global dbm\n if not dbm:\n dbm = db.DBManager(url=url)\n\n\ndef disconnect():\n global dbm\n if dbm:\n dbm.disconnect()\n\n\ndef initialize_conditions():\n \"\"\"Initialize the conditions collection using the supported ones from commons.Condition\"\"\"\n global dbm\n if dbm:\n dbm.initialize_conditions()\n\n\ndef create_entry(\n pvname: str,\n emails: str,\n condition: str,\n alarm_values: str,\n unit: str,\n warning_message: str,\n subject: str,\n email_timeout: float,\n group_name: str,\n):\n global dbm\n entry: typing.Optional[commons.Entry] = None\n if not dbm:\n raise RuntimeError(\"Database not initialised\")\n\n try:\n entry = commons.Entry(\n sms_queue=None,\n pvname=pvname,\n emails=emails,\n condition=condition,\n alarm_values=alarm_values,\n unit=unit,\n warning_message=warning_message,\n subject=subject,\n email_timeout=email_timeout,\n group=commons.Group(name=group_name, enabled=True),\n dummy=True,\n )\n dbm.create_entry(entry)\n logger.info(f\"Creating entry {entry}\")\n\n except pymongo.errors.DuplicateKeyError:\n logger.warn(f\"Entry exists {entry}\")\n\n except commons.EntryException:\n logger.exception(\"Failed to create entry\")\n\n\ndef load_csv_table(table: str):\n \"\"\"Populate the database from a csv file. Initial migration.\"\"\"\n if not os.path.isfile(table):\n raise ValueError(f'Failed to load csv data. File \"{table}\" does not exist')\n\n df: typing.Optional[pandas.DataFrame] = pandas.read_csv(table)\n\n # parse other columns\n for _, row in df.iterrows():\n create_entry(\n pvname=row[\"PV\"],\n emails=row[\"emails\"],\n condition=row[\"condition\"].lower().strip(),\n alarm_values=row[\"specified value\"],\n unit=row[\"measurement unit\"],\n warning_message=row[\"warning message\"],\n subject=row[\"email subject\"],\n email_timeout=row[\"timeout\"],\n group_name=row[\"group\"],\n )\n","sub_path":"app/utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"329576348","text":"# Copyright 2019 Xanadu Quantum Technologies 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# pylint: disable=too-many-return-statements,too-many-branches,too-many-instance-attributes\n\"\"\"Strawberry Fields Blackbird parser\"\"\"\nimport sys\nimport antlr4\n\nimport numpy as np\n\nimport strawberryfields as sf\nimport strawberryfields.ops as sfo\n\nfrom blackbird import BlackbirdListener, RegRefTransform, parse\n\n\nclass StrawberryFieldsListener(BlackbirdListener):\n \"\"\"Listener to run a Blackbird program using Strawberry Fields\"\"\"\n def __init__(self):\n super().__init__()\n self.eng = None\n self.q = None\n\n self.state = None\n self.result = []\n\n def run(self):\n if self.target is None:\n raise ValueError(\"Blackbird program has no target backend\")\n\n self.eng, self.q = sf.Engine(\n len(self.active_modes),\n hbar=self.target['options'].get('hbar', 2)\n )\n\n self.target['options'].pop('hbar', None)\n\n with self.eng:\n for statement in self.queue:\n modes = statement['modes']\n\n if 'args' in statement:\n args = statement['args']\n kwargs = statement['kwargs']\n\n for idx, a in enumerate(args):\n if isinstance(a, RegRefTransform):\n regrefs = [self.q[i] for i in a.regrefs]\n args[idx] = sf.engine.RegRefTransform(regrefs, a.func, a.func_str)\n\n op = getattr(sfo, statement['op'])(*args, **kwargs)\n else:\n op = getattr(sfo, statement['op'])\n\n op | [self.q[i] for i in modes] #pylint:disable=pointless-statement\n\n shots = self.target['options'].get('shots', 1)\n self.target['options'].pop('shots', None)\n\n for _ in range(shots):\n self.eng.reset(keep_history=True)\n self.state = self.eng.run(self.target['name'], **self.target['options'])\n self.result.append([q.val for q in self.q])\n\n def print_results(self):\n \"\"\"Print the results of the blackbird program execution\"\"\"\n print('Program')\n print('-------')\n self.eng.print_applied()\n print()\n\n print('Results')\n print('-------')\n for row in self.result:\n print(row)\n\n\ndef run(file):\n \"\"\"Parse and run a blackbird program using Strawberry Fields.\n\n Args:\n file (str): location of the .xbb blackbird file to run\n Returns:\n list: list of size ``[shots, num_subsystems]``, representing\n the measured qumode values for each shot\n \"\"\"\n simulation = parse(antlr4.FileStream(file), listener=StrawberryFieldsListener)\n simulation.run()\n simulation.print_results()\n\n\nif __name__ == '__main__':\n run(sys.argv[1])\n","sub_path":"apps/strawberry_fields_listener.py","file_name":"strawberry_fields_listener.py","file_ext":"py","file_size_in_byte":3349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"24921195","text":"#################################################\n# Hw2\n# Your andrewID:tianminz\n# Your section: H\n#################################################\n\nimport cs112_s19_week2_linter\n\n### You'll need isPrime for one of the problems, so it is provided here ###\ndef isPrime(n):\n if (n < 2):\n return False\n maxFactor = round(n**0.5)\n for factor in range(2, maxFactor+1):\n if (n % factor == 0):\n return False\n return True\n\n#################################################\n# Lab2 COLLABORATIVE LAB problems \n# (Their problem descriptions will be released Friday, Jan 25)\n#################################################\n# The problems in this section are LAB PROBLEMS, which means you MUST\n# work on these with at least one collaborator. See the collaboration\n# policy in the syllabus for more details. Always list your collaborators!\n# For lab problems, YOU MUST LIST AT LEAST ONE COLLABORATOR\n\ndef isSmithNumberCollaborators():\n return \"stephan3\"\n\ndef isPrime(n):\n if (n < 2):\n return False\n maxFactor = round(n**0.5)\n for factor in range(2, maxFactor+1):\n if (n % factor == 0):\n return False\n return True\n\ndef SumDigit(n):\n sum = 0\n m = n\n while (n > 0):\n m = n % 10\n n = n //10\n sum = sum + m\n return sum\n\ndef IsPrimeFactors(n):\n x = 1\n sum = 0\n count = 0\n while (x <= n):\n if n % x == 0:\n if isPrime(x):\n sum += SumDigit(x)\n n = n/x\n count += 1\n else: \n x = x + 1\n else:\n x = x + 1\n if count == 1:\n return 0\n else:\n return sum\n\ndef isSmithNumber(n):\n if IsPrimeFactors(n) == SumDigit(n):\n return True\n return False\n\n### You can find drawFlagOfCuba in the Graphics section below ###\n\n#################################################\n# Hw2 COLLABORATIVE problem\n#################################################\n# The problems in this section are COLLABORATIVE, which means you may\n# work on them with your classmates if you wish. See the collaboration\n# policy in the syllabus for more details. Always list your collaborators!\n\n#### Debugging isMultiPowerfulNumber is a COLLABORATIVE problem ####\ndef isMultiPowerfulNumberCollaborators(n):\n return \"nobody\"\n# Bug 1: 'if n % factor = 0' should be 'if n % factor == 0'\n# Bug 2: 'for factor in range(n)' should be 'for factor in range(1,n)\n# Bug 3: We should indent 'factorCount +=1' so that 'factorCount += 1' should be in the same position with the second 'if'.\n\n#### Insert the isMultiPowerfulNumber code here ####\ndef isMultiPowerfulNumber(n):\n factorCount = 0\n for factor in range(1,n):\n if n % factor == 0 and isPrime(factor):\n if n % (factor**2) != 0:\n return False\n factorCount += 1\n return factorCount > 1\n#################################################\n# Hw2 SOLO problems\n#################################################\n\ndef isKaprekarNumber(n): \n a = n**2\n count = 0\n while a > 0:\n a = a // 10 \n count = count + 1\n a = n**2\n digit = count\n for i in range(digit):\n leftpart = a // 10**(i+1)\n rightpart = a % 10**(i+1)\n if leftpart + rightpart == n and rightpart != 0:\n return True\n return False\n\ndef nthKaprekarNumber(n):\n count = -1\n num = 0\n while(count < n):\n num += 1 #optimization\n if isKaprekarNumber(num):\n count += 1\n return num\n\ndef nearestKaprekarNumber(n):\n if n <= 0:\n return 1\n for i in range(int(n)):\n leftnew = int(n - i)\n rightnew = int(n + i)\n if isKaprekarNumber(leftnew):\n if isKaprekarNumber(rightnew):\n if n + i >= rightnew and n - i > leftnew:\n return rightnew\n return leftnew\n elif isKaprekarNumber(rightnew + 1) and ((rightnew + 1) - n) < (n - leftnew):\n return rightnew + 1\n return leftnew\n elif isKaprekarNumber(rightnew):\n if isKaprekarNumber(leftnew - 1):\n if n - (leftnew - 1) < rightnew - n:\n return leftnew - 1\n return rightnew\n\n### The three following problems are bonus problems, and therefore optional ###\n# Note: Bonus problems are solo. Do not collaborate on bonus problems. \n \ndef squaresGenerator():\n return\n\ndef nswGenerator():\n return\n\ndef nswPrimesGenerator():\n return\n\n#################################################\n# Hw2 Graphics Functions\n# All graphics must go under here to avoid angering the autograder!\n# ignore_rest\n#################################################\n\nfrom tkinter import *\n\n### Note that drawFlagOfCuba is COLLABORATIVE and a LAB problem ###\ndef drawFlagOfCubaCollaborators():\n return \"shreyas2\"\n\ndef drawFlagOfCuba(canvas, width, height):\n # draw a Belgian flag in the area bounded by (x0,y0) in\n # the top-left and (x1,y1) in the bottom-right\n stripe = height/5\n for i in range(1,6):\n if i % 2 == 0:\n canvas.create_rectangle(0, stripe* (i-1), width, stripe * i, \n fill =\"white\", width = 0)\n else:\n canvas.create_rectangle(0, stripe * (i-1), width, stripe * i,\n fill =\"darkblue\", width = 0)\n canvas.create_polygon(0,0,0,height, 2.2/5 * width, 1/2 * height,\n fill = 'red3', width = 0)\n r = height/8\n canvas.create_oval(3/10*2.2/5 * width - r, 1/2*height - r,\n 3/10*2.2/5 * width + r, 1/2*height + r, fill = 'white', width = 0)\n\n### Note that drawThreadPattern is COLLABORATIVE ###\nimport math\ndef drawThreadPatternCollaborators():\n return \"nobody\"\n\ndef drawThreadPattern(canvas, size, numSpokes, startSpoke, numSkips):\n r = size/2.2\n cx = size/2\n cy = size/2\n canvas.create_oval(cx - r, cy - r, cx + r, cy + r, width = 5) #Draw the large oval first\n #Then, draw the lines\n angle_startSpoke = 2 * math.pi * startSpoke / numSpokes\n x_startSpoke = cx - r*math.sin(angle_startSpoke)\n y_startSpoke = cy + r*math.cos(angle_startSpoke) \n angle_start = 2 * math.pi * startSpoke / numSpokes\n x_start = cx - r*math.sin(angle_start)\n y_start = cy + r*math.cos(angle_start)\n x = 0\n y = 0\n while x_startSpoke != x or y_startSpoke != y:\n startSpoke = startSpoke + numSkips\n if startSpoke >= 10:\n startSpoke = startSpoke - numSpokes\n else:\n startSpoke = startSpoke\n angle = 2 * math.pi * startSpoke / numSpokes\n x = cx - r * math.sin(angle)\n y = cy + r * math.cos(angle)\n canvas.create_line(x, y, x_start, y_start, width = 0)\n x_start = x\n y_start = y\n #Finally, draw the small ovals and change the color\n for hour in range(numSpokes):\n angle = 2 * math.pi * hour / numSpokes\n x = cx - r*math.sin(angle)\n y = cy + r*math.cos(angle)\n if hour == startSpoke:\n canvas.create_oval(x - size/40, y - size/40, x + size/40, y + size/40, fill = 'green')\n else:\n canvas.create_oval(x - size/40, y - size/40, x + size/40, y + size/40, fill = 'red')\n \n### Note that drawSteelersLogo is SOLO ###\n \ndef drawSteelersLogo(canvas, x, y, r):\n canvas.create_oval(x - r, y - r, x + r, y + r, fill = \"gray\", width = 0)\n canvas.create_oval(x - r*4/5, y - r*4/5, x + r*4/5, y + r*4/5, fill = \"white\", width = 0 )\n s = r * 1/14\n h = r * 35/54\n canvas.create_polygon(x, y - h - s, x - h*1/2, y - s - h*1/2,\n x, y - s, x + h*1/2, y - s - h*1/2, fill = \"gold\", width = 0)\n canvas.create_polygon(x + s + h*1/2, y - h*1/2, x + s, y,\n x + s + h*1/2, y + h*1/2,\n x + s + h, y, fill = \"red\", width = 0)\n canvas.create_polygon(x, y + s,\n x - h*1/2, y + s + h*1/2,\n x, y + s + h,\n x + h*1/2, y + s + h*1/2, fill = \"blue\", width = 0)\n textSize = int(r/6)\n canvas.create_text(x - r*2/5, y, text = \"Steelers\", font =\"Times \" + str(textSize))\n\n### Note that drawButtonPattern is SOLO ###\ndef drawButtonPattern(canvas, size, n):\n canvas.create_rectangle(0,0,size,size, fill = \"purple\", width = 0) #Create background\n for i in range(n): #Row \n for j in range(n): #Row 1, Column 1\n if (i + j) % 4 == 0 :\n canvas.create_oval(size/n*j, size/n*i, size/n*(j+1), size/n*(i+1), fill = \"red\", width = 1)\n r_smaller = r_larger = r_largest = size/(2*n)\n while r_smaller >= 1:\n r_smaller = 2/3*r_larger\n canvas.create_oval((r_largest - r_smaller) + (j*size/n), (r_largest - r_smaller) + (i*size/n), \n (r_largest + r_smaller) + (j*size/n), (r_largest + r_smaller) + (i*size/n) , fill = \"red\", width = 1)\n r_larger = r_smaller\n elif i % 3 == 0:\n canvas.create_oval(size/n*j, size/n*i, size/n*(j+1), size/n*(i+1), fill = \"green\", width = 1)\n r_smaller = r_larger = r_largest = size/(2*n)\n while r_smaller >= 1:\n r_smaller = 2/3*r_larger\n canvas.create_oval((r_largest - r_smaller) + (j*size/n), (r_largest - r_smaller) + (i*size/n), \n (r_largest + r_smaller) + (j*size/n), (r_largest + r_smaller) + (i*size/n) , fill = \"green\", width = 1)\n r_larger = r_smaller\n elif j % 2 != 0:\n canvas.create_oval(size/n*j, size/n*i, size/n*(j+1), size/n*(i+1), fill = \"yellow\", width = 1)\n r_smaller = r_larger = r_largest = size/(2*n)\n while r_smaller >= 1:\n r_smaller = 2/3*r_larger\n canvas.create_oval((r_largest - r_smaller) + (j*size/n), (r_largest - r_smaller) + (i*size/n), \n (r_largest + r_smaller) + (j*size/n), (r_largest + r_smaller) + (i*size/n) , fill = \"yellow\", width = 1)\n r_larger = r_smaller\n \n else:\n canvas.create_oval(size/n*j, size/n*i, size/n*(j+1), size/n*(i+1), fill = \"blue\", width = 1)\n r_smaller = r_larger = r_largest = size/(2*n)\n while r_smaller >= 1:\n r_smaller = 2/3*r_larger\n canvas.create_oval((r_largest - r_smaller) + (j*size/n), (r_largest - r_smaller) + (i*size/n), \n (r_largest + r_smaller) + (j*size/n), (r_largest + r_smaller) + (i*size/n) , fill = \"blue\", width = 1)\n r_larger = r_smaller\n\ndef testDrawButtonPattern():\n print(\"Testing drawButtonPattern()...\", end=\"\")\n runDrawButtonPattern(400, 400, 10) \n runDrawButtonPattern(300, 300, 5)\n runDrawButtonPattern(250, 250, 25)\n print(\"Done.\")\n \n#### Note that drawNiceRobot is BONUS, and therefore optional ####\ndef drawNiceRobot(canvas, width, height):\n pass\n\n#################################################\n# Hw2 Test Functions\n# ignore_rest\n#################################################\n\ndef testIsSmithNumber():\n print(\"Testing isSmithNumber()...\", end=\"\")\n assert(isSmithNumber(22) == True)\n assert(isSmithNumber(21) == False)\n assert(isSmithNumber(4) == True)\n assert(isSmithNumber(378) == True)\n assert(isSmithNumber(1) == False)\n assert(isSmithNumber(27) == True)\n assert(isSmithNumber(9) == False)\n assert(isSmithNumber(7) == False)\n print(\"Passed.\")\n\ndef runDrawFlagOfCuba(width, height):\n root = Tk()\n root.resizable(width=False, height=False) # prevents resizing window\n canvas = Canvas(root, width=width, height=height)\n canvas.configure(bd=0, highlightthickness=0)\n canvas.pack()\n # width must equal height\n assert(width == 2*height)\n drawFlagOfCuba(canvas, width, height)\n root.mainloop()\n\ndef testDrawFlagOfCuba():\n print(\"Testing drawFlagOfCuba()...\", end=\"\")\n runDrawFlagOfCuba(580, 290)\n runDrawFlagOfCuba(100, 50)\n runDrawFlagOfCuba(300, 150)\n print(\"Done.\")\n\ndef testIsMultiPowerfulNumber():\n print(\"Testing isMultiPowerfulNumber()...\", end=\"\")\n assert(isMultiPowerfulNumber(36) == True)\n assert(isMultiPowerfulNumber(72) == True)\n assert(isMultiPowerfulNumber(100) == True)\n assert(isMultiPowerfulNumber(108) == True)\n print(\"Done!\")\n\ndef runDrawThreadPattern(width, height, numSpokes, startSpoke, numSkips):\n root = Tk()\n root.resizable(width=False, height=False) # prevents resizing window\n canvas = Canvas(root, width=width, height=height)\n canvas.configure(bd=0, highlightthickness=0)\n canvas.pack()\n # width must equal height\n assert(width == height)\n drawThreadPattern(canvas, width, numSpokes, startSpoke, numSkips)\n root.mainloop()\n\ndef testDrawThreadPattern():\n print(\"Testing drawThreadPattern...\", end=\"\")\n runDrawThreadPattern(400, 400, 12, 0, 5)\n runDrawThreadPattern(200, 200, 10, 3, 4)\n runDrawThreadPattern(500, 500, 19, 8, 15)\n print(\"Done.\")\n\ndef runDrawSteelersLogo(width, height, x, y, r):\n root = Tk()\n root.resizable(width=False, height=False) # prevents resizing window\n canvas = Canvas(root, width=width, height=height)\n canvas.configure(bd=0, highlightthickness=0)\n canvas.pack()\n drawSteelersLogo(canvas, x, y, r)\n root.mainloop()\n\ndef testDrawSteelersLogo():\n print(\"Testing drawSteelersLogo...\", end=\"\")\n runDrawSteelersLogo(300, 300, 150, 150, 100)\n runDrawSteelersLogo(500, 600, 300, 200, 200)\n runDrawSteelersLogo(150, 100, 50, 60, 40)\n print(\"Done.\")\n\ndef testIsKaprekarNumber():\n print(\"Testing isKaprekarNumber()...\", end=\"\")\n assert(isKaprekarNumber(0) == False)\n assert(isKaprekarNumber(1) == True)\n assert(isKaprekarNumber(4) == False)\n assert(isKaprekarNumber(9) == True)\n assert(isKaprekarNumber(36) == False)\n assert(isKaprekarNumber(45) == True)\n assert(isKaprekarNumber(450) == False)\n print(\"Passed.\")\n\ndef testNthKaprekarNumber():\n print(\"Testing nthKaprekarNumber()...\", end=\"\")\n assert(nthKaprekarNumber(0) == 1)\n assert(nthKaprekarNumber(1) == 9)\n assert(nthKaprekarNumber(2) == 45)\n assert(nthKaprekarNumber(3) == 55)\n assert(nthKaprekarNumber(4) == 99)\n assert(nthKaprekarNumber(5) == 297)\n assert(nthKaprekarNumber(6) == 703)\n assert(nthKaprekarNumber(7) == 999)\n print('Passed.')\n\ndef testNearestKaprekarNumber():\n print(\"Testing nearestKaprekarNumber()...\", end=\"\")\n assert(nearestKaprekarNumber(1) == 1)\n assert(nearestKaprekarNumber(0) == 1)\n assert(nearestKaprekarNumber(-1) == 1)\n assert(nearestKaprekarNumber(-2) == 1)\n assert(nearestKaprekarNumber(-12345) == 1)\n assert(nearestKaprekarNumber(1.234) == 1)\n assert(nearestKaprekarNumber(4.99999999) == 1)\n assert(nearestKaprekarNumber(5) == 1)\n assert(nearestKaprekarNumber(5.00000001) == 9)\n assert(nearestKaprekarNumber(27) == 9)\n assert(nearestKaprekarNumber(28) == 45)\n assert(nearestKaprekarNumber(45) == 45)\n assert(nearestKaprekarNumber(50) == 45)\n assert(nearestKaprekarNumber(51) == 55)\n assert(nearestKaprekarNumber(1611) == 999)\n assert(nearestKaprekarNumber(1612) == 2223)\n assert(nearestKaprekarNumber(2475.4) == 2223)\n assert(nearestKaprekarNumber(2475.5) == 2223)\n assert(nearestKaprekarNumber(2475.51) == 2728)\n assert(nearestKaprekarNumber(2475.6) == 2728)\n assert(nearestKaprekarNumber(995123) == 994708)\n assert(nearestKaprekarNumber(9376543) == 9372385)\n assert(nearestKaprekarNumber(13641234) == 13641364)\n print(\"Passed.\")\n\ndef runDrawButtonPattern(width, height, n):\n root = Tk()\n root.resizable(width=False, height=False) # prevents resizing window\n canvas = Canvas(root, width=width, height=height)\n canvas.configure(bd=0, highlightthickness=0)\n canvas.pack()\n # width must equal height\n assert(width == height)\n drawButtonPattern(canvas, width, n)\n root.mainloop()\n\ndef testDrawButtonPattern():\n print(\"Testing drawButtonPattern()...\", end=\"\")\n runDrawButtonPattern(400, 400, 10) \n runDrawButtonPattern(300, 300, 5)\n runDrawButtonPattern(250, 250, 25)\n print(\"Done.\")\n\ndef runDrawNiceRobot(width, height):\n root = Tk()\n root.resizable(width=False, height=False) # prevents resizing window\n canvas = Canvas(root, width=width, height=height)\n canvas.configure(bd=0, highlightthickness=0)\n canvas.pack()\n drawNiceRobot(canvas, width, height)\n root.mainloop()\n\ndef testDrawNiceRobot():\n print(\"Testing drawNiceRobot()...\", end=\"\")\n runDrawNiceRobot(500, 500)\n runDrawNiceRobot(250, 250)\n print(\"Done.\")\n\ndef testSquaresGenerator():\n print(\"Testing squaresGenerator()...\", end=\"\")\n g = squaresGenerator()\n assert(next(g) == 1)\n assert(next(g) == 4)\n assert(next(g) == 9)\n assert(next(g) == 16)\n\n # ok, now with a for loop.\n squares = \"\"\n for square in squaresGenerator():\n if (squares != \"\"): squares += \", \"\n squares += str(square)\n if (square >= 100): break\n assert(squares == \"1, 4, 9, 16, 25, 36, 49, 64, 81, 100\")\n print(\"Passed.\")\n\ndef testNswGenerator():\n print(\"Testing nswGenerator()...\", end=\"\")\n nswNumbers = \"\"\n for nswNumber in nswGenerator():\n if (nswNumbers != \"\"): nswNumbers += \", \"\n nswNumbers += str(nswNumber)\n if (nswNumber >= 152139002499): break\n # from: http://oeis.org/A001333\n assert(nswNumbers == \"1, 1, 3, 7, 17, 41, 99, 239, 577, 1393, 3363, 8119, \"\n \"19601, 47321, 114243, 275807, 665857, 1607521, 3880899, \"\n \"9369319, 22619537, 54608393, 131836323, 318281039, \"\n \"768398401, 1855077841, 4478554083, 10812186007, \"\n \"26102926097, 63018038201, 152139002499\"\n )\n print(\"Passed.\")\n \ndef testNswPrimesGenerator():\n print(\"Testing nswPrimesGenerator()...\", end=\"\")\n nswPrimes = \"\"\n for nswPrime in nswPrimesGenerator():\n if (nswPrimes != \"\"): nswPrimes += \", \"\n nswPrimes += str(nswPrime)\n if (nswPrime >= 63018038201): break\n # from: http://oeis.org/A088165\n assert(nswPrimes == \"7, 41, 239, 9369319, 63018038201\")\n print(\"Passed.\")\n\n#################################################\n# Hw2 Main\n#################################################\n\ndef testAll():\n ### Lab problems ###\n testIsSmithNumber()\n testDrawFlagOfCuba()\n ### Collaborative problems ###\n testIsMultiPowerfulNumber()\n testDrawThreadPattern()\n ### Solo problems ###\n testDrawSteelersLogo()\n testIsKaprekarNumber()\n testNthKaprekarNumber()\n testNearestKaprekarNumber()\n testDrawButtonPattern()\n \n # Uncomment the next lines if you want to try the bonus!\n #testDrawNiceRobot()\n #testSquaresGenerator()\n #testNswGenerator()\n #testNswPrimesGenerator()\n\ndef main():\n cs112_s19_week2_linter.lint() # check for banned tokens\n testAll()\n\nif __name__ == '__main__':\n main()","sub_path":"hw2.py","file_name":"hw2.py","file_ext":"py","file_size_in_byte":19215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"64605776","text":"#!/usr/bin/python3.x\n# -*- coding=utf-8 -*-\n\"\"\"\n Time : 2021/6/3 10:41\n Author : hike\n Email : hikehaidong@gmail.com\n File Name : 01.py\n Description:\n Software : PyCharm\n\"\"\"\n\n# 判断一个字符串含有多个字符串中的任意一个\np = \"Tom is a boy,Lucy is a girl,they all like english!\"\nkeywords= 'Tom,Lucy'\nexcludes = ['english','math']\nprint (any([w in p and w for w in keywords.split(',')]))\nprint (any(e in p for e in excludes))\n\n# 判断一个字符串含有多个字符串\np = \"Tom is a boy,Lucy is a girl,they all like english!\"\nkeywords= 'Tom,Lucy'\nfilters= [\"boy\",\"like\"]\nprint(all(f in p for f in filters))\nprint(all([w in p and w for w in keywords.split(',')]))\n\n#计算一个字符串含有指定字符串的数量\np = \"Tom is a boy,Lucy is a girl,Tom like math and Lucy like english!\"\np2 = \"id\"\nkeywords= 'english,math,history,laws'\nprint(sum([1 if w in p and w else 0 for w in keywords.split(',')]))\n\n\ndef contain_keywords(keywords, *str):\n return any(k in ss for ss in str for k in keywords.split(\"|\"))\nkeywords= 'Tom|Lucy'\nprint(contain_keywords(keywords,(p,p2)))\n\n\n","sub_path":"apscheduler_yqt/MyTestFile/字符串包含/01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"5220784","text":"import time\nimport math\nimport copy\nimport logging\nimport random\nimport sys\n\nfrom pokerbot.pokerbot import PokerBot\nfrom pokerbot.card import Card, CardDeck, PENALTY_CARDS\n\nfrom .simulator import Simulator\n\nglobal logger\n\n\nclass HyperPara:\n c = 10\n n_iter = 20000\n max_run_time = 0.8 # in seconds\n\n\nclass Info:\n cur_board = []\n heart_broken = False\n is_card_exposed =False\n self_index = -1\n player_index = {}\n # {playerName: ind in players}, e.g.\n # {\"player1\": 1}\n players = []\n # list of dict, e.g.\n # [\n # {\n # \"playerName\": \"player1\"\n # \"hasCards\": [Card(), Card] #cards currently have\n # \"lackCards\": set(\"H\",\"D\")\n # \"self\": True\n # \"scoreCards\": [Card(), Card()] #the score card have\n # \"score\": 0\n # \"\"\n # },\n # ]\n picked_cards = []\n # list of tuple(runoundNamer, playerName, Card)\n picked_cards_this_round = []\n # list of tuple(playerName, Card)\n\n # card_lack_count = 0 # copy from js\n # remaining_cards = [] # copy from js\n\n def __init__(self, bot):\n self.cur_board = copy.deepcopy(bot.cur_board)\n self.heart_broken = bot.heart_broken\n self.is_card_exposed = bot.is_card_exposed\n self.self_index = bot.self_index\n self.player_index = copy.deepcopy(bot.player_index)\n self.players = copy.deepcopy(bot.players)\n self.picked_cards = copy.deepcopy(bot.picked_cards)\n self.picked_cards_this_round = copy.deepcopy(bot.picked_cards_this_round)\n\n def __str__(self):\n return \"Info(cur_board={}, heart_broken={}, is_card_exposed={},\" \\\n \"self_index={},\\n players={},\\n picked_cards={},\" \\\n \" pick_cards_this_round={})\".format(\n self.cur_board, self.heart_broken, self.is_card_exposed,\n self.self_index, self.players, self.picked_cards,\n self.picked_cards_this_round\n )\n\n @property\n def key(self):\n pc = \"@\".join([\"{}|{}|{}\".format(r, self.player_index[p], c)\n for r, p, c in self.picked_cards])\n hb = \"h\" if self.heart_broken else \"n\"\n ce = \"e\" if self.is_card_exposed else \"n\"\n pl = \"@\".join([\"{}#{}\".format(\n \"|\".join(sorted(map(str, p[\"hasCards\"]))),\n \"|\".join(sorted(map(str, p[\"lackCards\"])))) for p in self.players])\n return \"{}-{}-{}-{}\".format(pc, hb, ce, pl)\n\n def get_info_for_gen_sample(self):\n return {\"players\": self.players, \"pickedCards\": self.picked_cards}\n\n\nclass Node:\n count = 0\n value = 0\n info = None\n parent = None\n\n\nclass ActionNode(Node):\n card = None\n observations = {}\n is_root = False\n\n def __init__(self, parent_obs_node, action):\n if parent_obs_node is None:\n self.is_root = True\n self.observations = {}\n else:\n self.parent = parent_obs_node\n self.observations = {}\n assert isinstance(action, Card), \"actions is not type of Card\"\n self.card = action\n self.info = parent_obs_node.info # ?\n\n def __str__(self):\n return \"ActionNode(count={}, value={}, card={}, observation={}, is_root={})\".format(\n self.count, self.value, self.card, \",\".join(self.observations.keys()), self.is_root\n )\n\n def get_value(self):\n if self.parent.count == 0:\n return HyperPara.c\n if self.count == 0:\n return HyperPara.c * math.sqrt(math.log(self.parent.count))\n else:\n return self.value + HyperPara.c * math.sqrt(\n math.log(self.parent.count) / float(self.count))\n\n def update_value(self, reward):\n self.value = (self.value * (self.count - 1) + reward) \\\n / float(self.count)\n\n def add_observation(self, observation_buffer, state=None, info=None):\n if not self.is_root:\n assert observation_buffer[0][1] == self.card, \"obs[0][1] {} != self.card {}\".format(observation_buffer[0][1], self.card)\n obs_hash = \"|\".join([str(self.info.player_index[o[0]]) + \"-\" + str(o[1])\n for o in observation_buffer])\n if obs_hash not in self.observations:\n if info is not None:\n if self.is_root:\n logger.info(\"init from root with {}\".format(obs_hash))\n # root node no need to init observation\n self.observations[obs_hash] = ObservationNode(self, [])\n # for the root node, no need to do rollout\n # because this observation is not from root action\n self.observations[obs_hash].init_actions()\n else:\n logger.info(\"init obs with {} and info\".format(obs_hash))\n self.observations[obs_hash] = ObservationNode(self, observation_buffer)\n self.observations[obs_hash].info = info\n if state is not None:\n logger.debug(\"init obs with {} and state\".format(obs_hash))\n self.observations[obs_hash] = \\\n ObservationNode(self, observation_buffer, state)\n return obs_hash\n\n\nclass ObservationNode(Node):\n terminate = False\n actions = []\n obs = \"\"\n\n def __init__(self, parent_act_node, observation_buffer, state=None):\n if parent_act_node is not None:\n self.parent = parent_act_node\n self.obs = \"|\".join([str(parent_act_node.info.player_index[o[0]])\n + \"-\" + str(o[1]) for o in observation_buffer])\n self.actions = []\n info = copy.deepcopy(parent_act_node.info)\n if len(observation_buffer) > 0:\n assert self.parent.card == observation_buffer[0][1], \"1st obs {} != parent action card {}\".format(observation_buffer[0][1], self.parent.card)\n if state is not None:\n # update info from state: heart_broken, scoreCards, score\n info.heart_broken |= state.heart_broken\n for p, sp in zip(info.players, state.players):\n p[\"scoreCards\"] = sp[\"scoreCards\"].copy()\n p[\"score\"] = sp[\"score\"]\n\n n_cards_last_round = 4 - len(info.cur_board)\n\n # update self's hasCards,\n # the first observation should the card of current player\n info.players[info.self_index][\"hasCards\"].remove(observation_buffer[0][1])\n assert (set(info.players[info.self_index][\"hasCards\"]) == set(state.players[info.self_index][\"hasCards\"])), \"hasCards has differnt cards: {} != {}\".format(sorted(map(str,info.players[info.self_index][\"hasCards\"])), sorted(map(str,state.players[info.self_index][\"hasCards\"])))\n assert (len(info.players[info.self_index][\"hasCards\"]) == len(state.players[info.self_index][\"hasCards\"])), \"hasCards length mismatch: {} != {}\".format(sorted(map(str,info.players[info.self_index][\"hasCards\"])), sorted(map(str,state.players[info.self_index][\"hasCards\"])))\n\n # update lack cards\n last_round_cards = info.picked_cards_this_round \\\n + observation_buffer[:n_cards_last_round]\n self._update_lack_card(last_round_cards, info)\n self._update_lack_card(observation_buffer[n_cards_last_round:], info)\n\n # update board, picked_cards info\n info.cur_board = [o[1] for o in observation_buffer[n_cards_last_round:]]\n info.picked_cards_this_round = observation_buffer[n_cards_last_round:]\n if len(info.picked_cards) > 0:\n last_round_num = info.picked_cards[-1][0]\n else:\n last_round_num = 0\n if n_cards_last_round == 4:\n last_round_num += 1\n info.picked_cards.extend(\n (last_round_num, o[0], o[1])\n for o in observation_buffer[:n_cards_last_round])\n if len(observation_buffer) > n_cards_last_round:\n info.picked_cards.extend(\n (last_round_num + 1, o[0], o[1])\n for o in observation_buffer[n_cards_last_round:])\n\n self.terminate = (last_round_num == 13)\n self.info = info\n\n def __str__(self):\n return \"ObservationNode(count={}, value={}, obs={}, terminate={}, actions={}, info={})\".format(\n self.count, self.value, self.obs, self.terminate, \",\".join([str(a.card) for a in self.actions]), str(self.info)\n )\n\n def _update_lack_card(self, round_cards, info):\n if len(round_cards) > 0:\n round_suit = round_cards[0][1].suit\n for p, c in round_cards:\n if c.suit != round_suit:\n info.players[info.player_index[p]][\"lackCards\"].add(\n round_suit)\n\n def init_actions(self):\n me = self.info.players[self.info.self_index]\n if len(self.info.cur_board) > 0:\n suit = self.info.cur_board[0].suit\n suit_cards = [c for c in me[\"hasCards\"] if c.suit == suit]\n if len(suit_cards) > 0:\n actions = suit_cards\n else:\n if len(me[\"hasCards\"]) == 13:\n # in the first round, cannot picked penalty cards\n non_penalty_cards = [c for c in me[\"hasCards\"]\n if c not in PENALTY_CARDS]\n if len(non_penalty_cards) > 0:\n actions = non_penalty_cards\n else:\n # if the player has all penalty cards,\n # only hearts can picked\n actions = [c for c in me[\"hasCards\"] if c.suit == \"H\"]\n else:\n actions = me[\"hasCards\"]\n\n elif len(me[\"hasCards\"]) == 13:\n actions = [Card(\"2C\")]\n elif self.info.heart_broken:\n actions = me[\"hasCards\"]\n else:\n non_heart_cards = [c for c in me[\"hasCards\"] if c.suit != \"H\"]\n if len(non_heart_cards) > 0:\n actions = non_heart_cards\n else:\n actions = me[\"hasCards\"]\n self.actions = [ActionNode(self, a) for a in actions]\n\n\nclass State: # complete information with sampled cards\n cur_board = []\n heart_broken = False\n is_card_exposed =False\n self_index = -1\n player_index = {}\n # {playerName: ind in players}, e.g.\n # {\"player1\": 1}\n players = []\n # list of dict, e.g.\n # [\n # {\n # \"playerName\": \"player1\"\n # \"hasCards\": [Card(), Card] #cards currently have\n # \"self\": True\n # \"scoreCards\": [Card(), Card()] #the score card have\n # \"score\": 0\n # \"\"\n # },\n # ]\n picked_cards_this_round = []\n # list of tuple(playerName, Card)\n\n def __init__(self, info, samples):\n self.cur_board = copy.deepcopy(info.cur_board)\n self.heart_broken = info.heart_broken\n self.is_card_exposed = info.is_card_exposed\n self.self_index = info.self_index\n self.player_index = copy.deepcopy(info.player_index)\n self.players = copy.deepcopy(info.players)\n self.picked_cards_this_round = copy.deepcopy(info.picked_cards_this_round)\n for p, s in zip(self.players, samples):\n p[\"hasCards\"] = s\n\n def __str__(self):\n return \"State(cur_board={}, heart_broken={},is_card_exposed={},\" \\\n \"self_index={}, \\n players={}, \\n\" \\\n \"pick_cards_this_round={})\".format(\n self.cur_board, self.heart_broken, self.is_card_exposed,\n self.self_index, self.players,\n self.picked_cards_this_round\n )\n\n\nclass PomDPBot(PokerBot):\n\n def __init__(self, player_name, system_log=None):\n super(PomDPBot, self).__init__(player_name, system_log)\n self.my_hand_cards = []\n self.expose_card = False\n self.my_pass_card = []\n self.root = {}\n self.cursor = None\n self.simulator = Simulator()\n self.observation_buffer = []\n self.prev_best_action = None\n global logger\n logger = self.system_log.logger\n\n def receive_cards(self, data):\n self.my_hand_cards = self.get_cards(data)\n\n def pass_cards(self, data):\n cards = data['self']['cards']\n self.my_hand_cards = []\n for card_str in cards:\n card = Card(card_str)\n self.my_hand_cards.append(card)\n pass_cards=[]\n count=0\n for i in range(len(self.my_hand_cards)):\n card=self.my_hand_cards[len(self.my_hand_cards) - (i + 1)]\n if card==Card(\"QS\"):\n pass_cards.append(card)\n count+=1\n elif card==Card(\"TC\"):\n pass_cards.append(card)\n count += 1\n for i in range(len(self.my_hand_cards)):\n card = self.my_hand_cards[len(self.my_hand_cards) - (i + 1)]\n if card.suit_index==2:\n pass_cards.append(card)\n count += 1\n if count ==3:\n break\n if count <3:\n for i in range(len(self.my_hand_cards)):\n card = self.my_hand_cards[len(self.my_hand_cards) - (i + 1)]\n if card not in self.game_score_cards:\n pass_cards.append(card)\n count += 1\n if count ==3:\n break\n return_values=[]\n for card in pass_cards:\n return_values.append(str(card))\n message=\"Pass Cards:{}\".format(return_values)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n self.my_pass_card=pass_cards\n self.update_pass_cards(data[\"dealNumber\"], pass_cards)\n\n return return_values\n\n def new_deal(self, data):\n super(PomDPBot, self).new_deal(data)\n self.cursor = None\n self.observation_buffer = []\n self.prev_best_action = None\n\n def rollout(self, state, obs_node, depth):\n return self.simulator.run(state)\n\n def simulate(self, state, obs_node, depth):\n if obs_node.terminate:\n return 0\n if obs_node.actions is None or len(obs_node.actions) == 0:\n logger.debug(\n \"rollout: depth={}, \\n obs_node={}, \\n state={}\".format(\n depth, str(obs_node), str(state)))\n obs_node.init_actions()\n logger.debug(\n \"rollout: depth={}, \\n obs_node={}\".format(\n depth, str(obs_node), str(state)))\n return self.rollout(state, obs_node, depth)\n\n best_act_list = [obs_node.actions[0]]\n best_score = best_act_list[0].get_value()\n for a in obs_node.actions:\n score = a.get_value()\n if score > best_score + 0.000001:\n best_score = score\n best_act_list = [a]\n elif -0.000001 < score-best_score < 0.000001: # if draw\n best_act_list.append(a)\n best_action = random.choice(best_act_list)\n\n logger.debug(\n \"depth={}, \\n action_values={}\\n obs_node={}, \\n state={}, \\n best_action={}\".format(\n depth,\n \",\".join([\"({},{},{},{},{})\".format(a.card, a.value, a.count, a.parent.count, a.get_value()) for a in obs_node.actions]),\n str(obs_node), str(state), str(best_action)))\n\n simul_state, simul_obs, simul_score = self.simulator.step(state, best_action.card)\n assert best_action.card == simul_obs[0][1], \"observation {} != best_action.card{}\".format(best_action.card, simul_obs[0][1])\n\n logger.debug(\n \"depth={}, \\n simul_state={}, \\n simul_obs={}, \\n simul_score={}\\n obs_node={}, \\n state={}, \\n best_action={}\".format(\n depth, str(simul_state), str(simul_obs), simul_score, str(obs_node), str(state), str(best_action)))\n\n obs_hash = best_action.add_observation(simul_obs, state=simul_state)\n next_obs = best_action.observations[obs_hash]\n logger.debug(\n \"next_obs={}\".format(str(next_obs))\n )\n reward = simul_score \\\n + self.simulate(simul_state, next_obs, depth+1)\n\n logger.debug(\"simul_score={}, reward={}\".format(simul_score, reward))\n\n obs_node.count += 1\n best_action.count += 1\n best_action.update_value(reward)\n logger.debug(\n \"final: depth={}, \\n obs_node={}, \\n best_action={}\".format(\n depth, str(obs_node), str(best_action)))\n return reward\n\n def gen_sample(self, obs_node):\n info = obs_node.info.get_info_for_gen_sample()\n logger.debug(\"info for cardDeck = {}\".format(str(info)))\n for p in info[\"players\"]:\n for c in p[\"hasCards\"]:\n if not isinstance(c,Card):\n logger.debug(p[\"playerName\"], str(c), type(c))\n for (r,p,c) in info[\"pickedCards\"]:\n if not isinstance(c, Card):\n logger.debug(str(r),p,str(c),type(c))\n deck = CardDeck(info)\n samples = deck.gen_cards()\n state = State(obs_node.info, samples)\n return state\n\n def search(self, candidate_cards):\n\n s = time.time()\n for i in range(HyperPara.n_iter):\n state = self.gen_sample(self.cursor)\n logger.debug(\n \"i={}, \\n state={}, \\n cursor={}\".format(i, str(state), str(self.cursor)))\n self.simulate(state, self.cursor, 0)\n e = time.time() - s\n if e > HyperPara.max_run_time:\n break\n message = \"[PomDPBot] #samples: {}\".format(i)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n\n actions = self.cursor.actions\n assert set([str(a.card) for a in actions]) == set(candidate_cards), \"action, candidate cards mismatch: {}!= {}\".format(sorted([str(a.card) for a in actions]), sorted(candidate_cards))\n assert len([str(a.card) for a in actions]) == len(candidate_cards), \"action, candidate cards len mismatch: {}!= {}\".format(sorted([str(a.card) for a in actions]), sorted(candidate_cards))\n actions = [a for a in actions if str(a.card) in candidate_cards]\n\n if actions:\n best_act_list = [actions[0]]\n best_score = best_act_list[0].value\n for a in actions:\n if a.value > best_score + 0.000001:\n best_score = a.value\n best_act_list = [a]\n elif -0.000001 < a.value - best_score < 0.000001: # if draw\n best_act_list.append(a)\n best_action = random.choice(best_act_list)\n\n else:\n raise Exception(\"no actions found\")\n\n return best_action\n\n def pick_card(self, data):\n candidate_cards=data['self']['candidateCards']\n cards = data['self']['cards']\n self.my_hand_cards = []\n for card_str in cards:\n card = Card(card_str)\n self.my_hand_cards.append(card)\n message = \"My Cards:{}\".format(self.my_hand_cards)\n self.system_log.show_message(message)\n card_index=0\n message = \"Pick Card Event Content:{}\".format(data)\n self.system_log.show_message(message)\n message = \"Candidate Cards:{}\".format(candidate_cards)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n\n info = Info(self)\n if self.cursor is None:\n if info.key not in self.root:\n self.root[info.key] = ActionNode(None, None)\n self.root[info.key].info = info\n self.cursor = self.root[info.key]\n logger.debug(\"root contains: {}\".format(\",\".join(self.root.keys())))\n logger.debug(\"cursor move to root: {}\\n{}\".format(info.key, str(self.cursor)))\n\n # move cursor to real next step\n if self.prev_best_action is not None:\n if self.prev_best_action.card == self.observation_buffer[0][1]:\n self.cursor = self.prev_best_action\n else:\n logger.warning(\"last time picked action {} \"\n \"did not matched to the real take action {}\"\n \"maybe timeout, try to set less \"\n \"HyperPara.max_run_time\")\n real_next_act = [a for a in self.cursor.actions\n if a.card == self.observation_buffer[0][1]][0]\n self.cursor = real_next_act\n\n obs_hash = self.cursor.add_observation(\n self.observation_buffer, info=info)\n\n # take simulation result of from previous round's next step)\n self.cursor = self.cursor.observations[obs_hash]\n logger.debug(\n \"cursor move to observation: {}\\n{}\".format(obs_hash, str(self.cursor)))\n self.observation_buffer = []\n\n action = self.search(candidate_cards)\n picked_card = str(action.card)\n self.prev_best_action = action\n\n message = \"Pick Card:{}\".format(picked_card)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n return picked_card\n\n def expose_my_cards(self, yourcards):\n expose_card=[]\n for card in self.my_hand_cards:\n if card==Card(\"AH\"):\n expose_card.append(str(card))\n message = \"Expose Cards:{}\".format(expose_card)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n return expose_card\n\n def expose_cards_end(self,data):\n super(PomDPBot, self).expose_cards_end(data)\n players = data['players']\n expose_player=None\n expose_card=None\n for player in players:\n try:\n if player['exposedCards']!=[] and len(player['exposedCards'])>0 and player['exposedCards']!=None:\n expose_player=player['playerName']\n expose_card=player['exposedCards']\n except Exception as e:\n self.system_log.show_message(e)\n self.system_log.save_logs(e)\n if expose_player!=None and expose_card!=None:\n message=\"Player:{}, Expose card:{}\".format(expose_player,expose_card)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n self.expose_card=True\n else:\n message=\"No player expose card!\"\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n self.expose_card=False\n\n def receive_opponent_cards(self,data):\n super(PomDPBot, self).receive_opponent_cards(data)\n self.my_hand_cards = self.get_cards(data)\n players = data['players']\n for player in players:\n player_name = player['playerName']\n if player_name == self.player_name:\n # picked_cards = player['pickedCards']\n receive_cards = player['receivedCards']\n # message = \"User Name:{}, Picked Cards:{}, Receive Cards:{}\".format(player_name, picked_cards,receive_cards)\n message = \"User Name:{}, Receive Cards:{}\".format(player_name, receive_cards)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n\n def turn_end(self, data):\n super(PomDPBot, self).turn_end(data)\n turnCard=data['turnCard']\n turnPlayer=data['turnPlayer']\n self.observation_buffer.append((turnPlayer, Card(turnCard)))\n\n def round_end(self,data):\n try:\n self.round_end_update(data)\n round_scores=self.get_round_scores(self.expose_card, data)\n for key in round_scores.keys():\n message = \"Player name:{}, Round score:{}\".format(key, round_scores.get(key))\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n except Exception as e:\n self.system_log.show_message(e)\n\n def deal_end(self,data):\n self.my_hand_cards=[]\n self.expose_card = False\n deal_scores,initial_cards,receive_cards,picked_cards=self.get_deal_scores(data)\n message = \"Player name:{}, Pass Cards:{}\".format(self.player_name, self.my_pass_card)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n for key in deal_scores.keys():\n message = \"Player name:{}, Deal score:{}\".format(key,deal_scores.get(key))\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n for key in initial_cards.keys():\n message = \"Player name:{}, Initial cards:{}, Receive cards:{}, Picked cards:{}\".format(key, initial_cards.get(key),receive_cards.get(key),picked_cards.get(key))\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n\n def game_over(self,data):\n game_scores = self.get_game_scores(data)\n for key in game_scores.keys():\n message = \"Player name:{}, Game score:{}\".format(key, game_scores.get(key))\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n\n def pick_history(self,data,is_timeout,pick_his):\n for key in pick_his.keys():\n message = \"Player name:{}, Pick card:{}, Is timeout:{}\".format(key,pick_his.get(key),is_timeout)\n self.system_log.show_message(message)\n self.system_log.save_logs(message)\n","sub_path":"EliteHearts-master/pomdp/pokerbot/pomdp/pomdpbot.py","file_name":"pomdpbot.py","file_ext":"py","file_size_in_byte":25739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"67664660","text":"from __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\nimport djmoney.models.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('lending', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Recommendation',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('similarity', models.FloatField()),\n ],\n ),\n migrations.AlterField(\n model_name='moneyproposalad',\n name='max_amount_currency',\n field=djmoney.models.fields.CurrencyField(choices=[('BYN', 'Belarussian Ruble')], default='BYN', editable=False, max_length=3),\n ),\n migrations.AddField(\n model_name='recommendation',\n name='proposal',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='lending.MoneyProposalAd'),\n ),\n migrations.AddField(\n model_name='recommendation',\n name='request',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='lending.MoneyRequestAd'),\n ),\n ]\n","sub_path":"9term/fipt/P2PLending/lending/migrations/0002_auto_20161203_0618.py","file_name":"0002_auto_20161203_0618.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"297741806","text":"#\n# @lc app=leetcode id=206 lang=python3\n#\n# [206] Reverse Linked List\n#\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n def reverseList(self, head: ListNode) -> ListNode:\n tmp = head\n new_head = None\n while tmp is not None:\n old_head = new_head\n new_head = ListNode(tmp.val)\n new_head.next = old_head\n tmp = tmp.next\n\n return new_head\n\n \n\n","sub_path":"python3/206.reverse-linked-list.py","file_name":"206.reverse-linked-list.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"172107967","text":"# Message class Version 0.12\n# Handy class for handling messages sent via the broker.\n# \n# Can convert received messages into an instance of this class\n# Can turn instances of this class into strings to send to the broker via MQTT\nclass Message:\n __DELIMITER = ';' # delimiter used in the message string\n __PARAM_AMT = 5 # amount of fields/parameters in the message\n\n # error messages:\n __ERROR_WRONG_FIELD_AMT = \"Wrong amount of fields supplied to the from_string method in the Message class: 5 expected, %d given\"\n __ERROR_TYPE_CONFLICT = \"Could not convert message string to a message object because of a type conflict: \\\"%s\\\". Please refer to the documentation.\"\n\n # constructor function\n # id (int): message ID\n # patient_id (int): id of the patient who sent the message\n # severity (int): how serious is the message\n # location (string): where is the message sent from\n # message (string): actual message\n def __init__(self, id:int, patient_id:int, severity:int, location:str, message:str):\n strings_concatenated = location + message\n if self.__DELIMITER in strings_concatenated:\n raise ValueError(\"No '%s' allowed in messages\" % self.__DELIMITER)\n \n self.id = id\n self.patient_id = patient_id\n self.severity = severity\n self.location = location\n self.message = message\n \n # constructor function to generate a Message from a string\n # message (string): string containing:\n # id (int): message ID\n # patient_id (int): id of the patient who sent the message\n # severity (int): how serious is the message\n # location (string): where is the message sent from\n # message (string): actual message\n # seperated by the delimiter configured in the __DELIMITER constant\n #\n # format:\n # [id];[patient_id];[severity];[location];[message]\n # where ';' is the delimiter configured in the __DELIMITER constant\n @classmethod\n def from_string(self, message:str) -> \"Message\":\n list = message.split(self.__DELIMITER)\n list_length = len(list)\n if list_length != self.__PARAM_AMT:\n raise Exception(self.__ERROR_WRONG_FIELD_AMT % list_length)\n \n try:\n id = int(list[0])\n patient_id = int(list[1])\n severity = int(list[2])\n location = list[3]\n msg = list[4]\n except ValueError as e:\n raise Exception(self.__ERROR_TYPE_CONFLICT % message) from e\n else:\n return Message(id, patient_id, severity, location, msg)\n\n @classmethod\n def is_str_message(self, string:str) -> bool:\n try:\n Message.from_string(string)\n except:\n return False\n else:\n return True\n\n # function to express this object as a string. Return format:\n # [id];[patient_id];[severity];[location];[message]\n # where ';' is the delimiter configured in the __DELIMITER constant\n def __str__(self) -> str:\n string = \"\"\n add_to_string = [self.id, self.patient_id, self.severity, self.location, self.message]\n for i in range(len(add_to_string)):\n string = string + str(add_to_string[i]) + self.__DELIMITER\n \n return string[:-1] # return all but last character","sub_path":"Message.py","file_name":"Message.py","file_ext":"py","file_size_in_byte":3492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"551841691","text":"from django.urls import path\nfrom image import views\n\napp_name = \"image\"\n\nurlpatterns = [\n path('handler/', views.handler, name='get'),\n path('files//', views.folder_detail, name=\"folders\"),\n path('image//', views.image_detail, name=\"image_detail\"),\n path('index/', views.index, name=\"index\")\n]\n","sub_path":"server/mzitu_web/image/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"429873610","text":"# -*- coding: utf-8 -*-\nimport re\nimport scrapy\nimport datetime\nimport json,time\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom ArticleSpider.items import LaGouItem,ArticleItemLoader\nfrom ArticleSpider.utils.common import get_md5\nfrom ArticleSpider.settings import SQL_DATETIME_FORMAT,SQL_DATE_FORMAT\nfrom scrapy.xlib.pydispatch import dispatcher\nfrom scrapy import signals\nfrom selenium import webdriver\nclass LagouSpider(CrawlSpider):\n name = 'lagou_login'\n allowed_domains = ['www.lagou.com']\n # start_urls = ['https://www.lagou.com/']\n start_urls = ['https://www.lagou.com']\n header = {\n \"HOST\": \"passport.lagou.com\",\n \"Referer\": \"https://www.lagou.com\",\n # 'User-Agent': agent\n }\n # 设置自己的settings\n custom_settings = {\n \"COOKIES_ENABLED\": True,\n }\n rules = (\n #如果url匹配到这些结果，follow是深度查询\n Rule(LinkExtractor(allow=r'gongsi/j\\d+.html'), follow=True),\n Rule(LinkExtractor(allow=r'zhaopin/.*'), follow=True),\n Rule(LinkExtractor(allow=r'jobs/\\d+.html.*'), callback='parse_job', follow=True),\n )\n\n def __init__(self):\n self.browser = webdriver.Chrome(executable_path=\"G:/python/scrapy/tools/chromedriver_win32/chromedriver.exe\")\n super(LagouSpider, self).__init__()\n dispatcher.connect(self.spider_closed, signals.spider_closed)\n\n def spider_closed(self, spider):\n # 当爬虫退出的时候关闭chrome\n print(\"spider closed\")\n self.browser.quit()\n\n def parse_job(self, response):\n LaGouArticleItem = ArticleItemLoader(item=LaGouItem(), response=response)\n LaGouArticleItem.add_css(\"job_name\", '.job-name::attr(title)')\n LaGouArticleItem.add_css(\"salary\", \".salary::text\")\n LaGouArticleItem.add_xpath(\"job_exp\", \"//dd[@class='job_request']/p/span[3]/text()\")\n LaGouArticleItem.add_xpath(\"edu\", \"//dd[@class='job_request']/p/span[4]/text()\")\n LaGouArticleItem.add_xpath(\"job_type\", \"//dd[@class='job_request']/p/span[5]/text()\")\n LaGouArticleItem.add_xpath(\"work_city\",\"//dd[@class='job_request']/p/span[2]/text()\")\n LaGouArticleItem.add_css(\"company_name\",\"#job_company .b2::attr(alt)\")\n LaGouArticleItem.add_css(\"company_url\",\".job_company dt a::attr(href)\")\n LaGouArticleItem.add_css(\"work_addr\",\".work_addr\")\n #LaGouArticleItem.add_xpath(\"feedback\",\"//div[@class='publisher_data']/div[2]/span[@class='tip']/i/text()\")\n LaGouArticleItem.add_css(\"create_date\",\".publish_time::text\")\n LaGouArticleItem.add_value(\"job_url\", response.url)\n LaGouArticleItem.add_value(\"job_url_id\",get_md5(response.url))\n LaGouArticleItem.add_css(\"job_advantage\", \".job-advantage p::text\")\n LaGouArticleItem.add_css(\"job_desc\",\".job_bt div\")\n LaGouArticleItem.add_css(\"tag\",\".position-label li\")\n ArticleItemLoder = LaGouArticleItem.load_item()\n return ArticleItemLoder\n #\n # def start_requests(self):\n # # return [scrapy.Request(\"https://www.zhihu.com/#signin\",headers=self.header,callback=self.login)]\n # Url = \"https://passport.lagou.com/login/login.html\"\n # return [scrapy.Request(Url, headers=self.header, callback=self.login,meta={'cookiejar':1})]\n #\n # # def get_captcha(self):\n # # import time\n # # t = str(int(time.time()*1000))\n # # captcha_url = \"https://www.zhihu.com/captcha.gif?r={0}&type=login\".format(t)\n #\n # # 以下为登录操作\n # def login(self, response):\n # Url = \"https://passport.lagou.com/login/login.html\"\n # post_data = {\n # \"phone_num\": \"18518734899@163.com\",\n # \"password\": \"727585266!@#\",\n # }\n # print(\"登录成功\")\n # return [scrapy.FormRequest(\n # url=Url,\n # headers = self.header,\n # formdata=post_data,\n # dont_filter=True,\n # callback=self.after_login\n # )]\n # def after_login(self,response):\n # print(response.status)\n # for url in self.start_urls:\n # yield self.make_requests_from_url(url)\n #\n\n\n\n\n","sub_path":"ArticleSpider/spiders/lagou_login.py","file_name":"lagou_login.py","file_ext":"py","file_size_in_byte":4197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"183039529","text":"from django import forms\nimport time\n\nfrom project.models import Project\n\nclass CreateProjectForm(forms.ModelForm):\n\tname = forms.CharField(label='Project Name',max_length=256)\n\tsummary = forms.CharField(label='Summary',max_length=500)\n\tstart_date = forms.DateField(label='Start Date')\n\tcreated_on = forms.HiddenInput()\n\tcreated_by = forms.HiddenInput()\n\n\n\tclass Meta:\n\t\tmodel = Project","sub_path":"project/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"420942613","text":"import copy\r\nfrom typing import List\r\n\r\nimport mxklabs.dimacs\r\n\r\n\r\ndef simplify(_clause_list: List[List[int]], _known_numbers: List[List[int]]):\r\n for number in _known_numbers:\r\n for clause in _clause_list[:]:\r\n try:\r\n negative_number = -number[0]\r\n except TypeError:\r\n print('error')\r\n if number[0] in clause:\r\n _clause_list.remove(clause)\r\n elif negative_number in clause:\r\n clause.remove(negative_number)\r\n return _clause_list\r\n\r\n\r\ndef remove_unit_clause(_clause_list, _known_numbers):\r\n unit_clause_list = []\r\n for clause in _clause_list:\r\n if len(clause) == 1:\r\n unit_clause_list.append(clause)\r\n for unit_clause in unit_clause_list:\r\n minus_unit_clause = [-unit_clause[0]]\r\n if unit_clause in unit_clause_list and minus_unit_clause in unit_clause_list:\r\n print('cont')\r\n exit()\r\n # clause_list, known_numbers = backtracking(clause_list, known_numbers)\r\n else:\r\n _known_numbers.append(unit_clause)\r\n\r\n\r\ndef split(_clause_list, _known_numbers):\r\n remaining_literals = set()\r\n for clause in _clause_list:\r\n for literal in clause:\r\n remaining_literals.add(abs(literal))\r\n print('split')\r\n for chosen_number in remaining_literals:\r\n copy_clause_list = copy.deepcopy(_clause_list)\r\n copy_known_numbers = copy.deepcopy(_known_numbers)\r\n copy_known_numbers.append([chosen_number])\r\n print(chosen_number)\r\n _success, _result = dp(copy_clause_list, copy_known_numbers)\r\n if _success:\r\n return _success, _result\r\n print('contradiction')\r\n return False, known_numbers\r\n\r\n\r\n# def backtracking(_clause_list, _known_numbers):\r\n# global deepcopy_list\r\n# global split_value_list\r\n# # returns clause_list to earlier state\r\n# _clause_list = deepcopy_list[-1]\r\n# # determines the last chosen (splitted value)\r\n# value_to_flip = split_value_list[-1]\r\n# # returns known_numbers to earlier state\r\n# target_index = _known_numbers.index(value_to_flip)\r\n# _known_numbers = _known_numbers[:target_index]\r\n# # changes last flipped value in know_numbers\r\n# _known_numbers.append(-value_to_flip)\r\n#\r\n# return _clause_list, _known_numbers\r\n\r\n\r\nsudoku_rules = \"sudoku-rules.txt\"\r\nsudoku_game = \"sudoku-example.txt\"\r\n\r\nclause_list = mxklabs.dimacs.read(sudoku_rules).clauses\r\nknown_numbers = mxklabs.dimacs.read(sudoku_game).clauses\r\ndeepcopy_list = []\r\nsplit_value_list = []\r\n\r\n\r\ndef dp(_clause_list: List[List[int]], _known_numbers: List[List[int]]) -> (bool, List[List[int]]):\r\n while True:\r\n clause_len = len(_clause_list)\r\n simplify(_clause_list, _known_numbers)\r\n remove_unit_clause(_clause_list, _known_numbers)\r\n\r\n if clause_len == len(_clause_list):\r\n break\r\n if len(_clause_list) != 0:\r\n return split(_clause_list, _known_numbers)\r\n return True, _known_numbers\r\n\r\n\r\nsuccess, result = dp(clause_list, known_numbers)\r\nprint(result)\r\n","sub_path":"rikursive.py","file_name":"rikursive.py","file_ext":"py","file_size_in_byte":3115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"220610627","text":"'''\n\nTo run:\n 1. open 4 terminal windows\n 2. run ResourceServer.py, AuthServer1.py, AuthServer2.py, Client.py in the seperate terminal windows\n -example command: Python3 ResourceServer.py\n 3. Make sure the last file ran is always Client.py since it will initiate the first message (if the other files are not running the message sent won't reach them)\n\n'''\nimport json\nimport time\nfrom Message import *\nimport socket\nimport multiprocessing\nimport threading\nfrom collections import deque\n#dictionary of port locations so servers can find eachother\nresourceLookup = dict()\nresourceLookup['C1'] = 6869\nresourceLookup['C2'] = 6870\nresourceLookup['C3'] = 6871\nresourceLookup['C4'] = 6868\n##############################################################################\nclass Peer():\n def __init__(self, name, policyFileName, resourceFileName, port):\n #set a name to peer\n self.name = name\n\n #get policies from json\n with open(policyFileName) as json_file:\n self.policies = json.load(json_file)\n \n #get resources from json\n with open(resourceFileName) as json_file:\n self.resources = json.load(json_file)\n\n #set up client/server props\n try:\n self.fd = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n except socket.error as err:\n 'unable to create socket: {}'.format(err)\n\n self.udp_ip = '127.0.0.1'\n self.udp_port = port\n self.fd.bind((self.udp_ip,self.udp_port))\n \n #incoming message queue\n self.messageQueue = deque()\n\n #messages sent\n self.MSent = deque()\n\n #messages recieved\n self.MRecieved = deque()\n\n self.printPolicies()\n self.printResources()\n\n #begin receiving messages in seperate thread\n recvThread = threading.Thread(target=self.recieveMessage, args=[])\n recvThread.start()\n\n #begin participateInOAuth in seperate thread\n OAuthThread = threading.Thread(target=self.participateInOAuth, args=[])\n OAuthThread.start()\n\n def recieveMessage(self):\n while True:\n r = self.fd.recvfrom(1000)\n recievedMsg = str(r[0], \"utf-8\")\n\n #convert the received msg from a str to a Message obj\n recievedMsg = json.loads(recievedMsg)\n recievedMsg = Message(**recievedMsg)\n print('{} recieved: Message[messageType:{}, resource:{}, issuer:{}, subject:{}]'.format(self.name, recievedMsg.messageType, recievedMsg.resource, recievedMsg.issuer, recievedMsg.subject))\n #add the message to the incoming message queue\n self.messageQueue.appendleft(recievedMsg)\n\n def sendMessage(self, message, port):\n #convert the recieved Message obj to a json string\n jsonMsg = json.dumps(message.__dict__) \n\n #send the json string\n self.fd.sendto(bytearray(jsonMsg, \"utf-8\"), (self.udp_ip, port))\n print('{} sent: Message[messageType:{}, resource:{}, issuer:{}, subject:{}]'.format(self.name, message.messageType, message.resource, message.issuer, message.subject))\n\n def participateInOAuth(self):\n while True:\n if self.messageQueue:\n #queue not empty, do work\n msg = self.messageQueue.pop()\n self.processMessage(msg)\n else:\n #empty queue, sleep for a little\n time.sleep(10)\n\n def processMessage(self, m):\n self.MRecieved.appendleft(m)\n if m.messageType == 'offer':\n self.resources[m.resource] = True\n msgs = self.resolutionResolver(self.MRecieved, self.MSent)\n for msg in msgs:\n self.MSent.appendleft(msg)\n self.sendMessage(msg, msg.subject)\n\n def resolutionResolver(self, MReceived, MSent):\n #latest message received\n m = MReceived[0]\n\n #set of credentials pthis requested from others\n Qsent = set()\n for msg in MSent:\n if msg.messageType == 'request':\n Qsent.add(msg.resource)\n\n #set of credentials others requested from pthis\n Qrecieved = set()\n for msg in MReceived:\n if msg.messageType == 'request':\n Qrecieved.add(msg.resource)\n\n #set of credentials pthis sent to others\n Dsent = set()\n for msg in MSent:\n if msg.messageType == 'offer':\n Dsent.add(msg.resource)\n\n #set of credentials pthis recieved from others\n Drecieved = set()\n for msg in MReceived:\n if msg.messageType == 'offer':\n Drecieved.add(msg.resource)\n\n #if incoming msg resource not in self.policies and isnt an offer\n #check if resource is in resourceLookup\n #if it is send message to where resource is located\n #else send error and quit\n #if incoming msg resource not in self.policies and is an offer and is not originalRequester\n #then find message that originally requested m.resource and use it's originalRequester to send offer to originalRequester\n if m.resource not in self.policies and m.messageType != 'offer':\n if m.resource in resourceLookup:\n m.issuer = self.udp_port\n m.subject = resourceLookup[m.resource]\n return [m]\n else:\n print(\"Resource {} can't be found\".format(m.resource))\n return\n elif m.resource not in self.policies and m.messageType == 'offer' and self.udp_port != m.originalRequester:\n for msg in MReceived:\n if msg.resource == m.resource and msg.messageType == 'request':\n m.issuer = self.udp_port\n m.subject = msg.originalRequester\n return [m]\n\n #isUnlocked is True if all credentials required for a resource have been received \n isUnlocked = True\n if m.resource in self.policies:\n for policy in self.policies[m.resource]:\n #if the policy is true, resource is available\n if policy == 'True':\n break\n #if this peer hasn't recieved any credentials, then resource not available\n if Drecieved == set():\n isUnlocked = False\n break\n #if every credential in policies has been received, then resource is unlocked, otherwise keep resource unavailable\n if policy not in Drecieved:\n isUnlocked = False\n else:\n for policy in self.policies:\n if not all(item in Drecieved for item in self.policies[policy]):\n isUnlocked = False\n\n #credentials to offer \n Dnew = set()\n\n #credentials to request\n Qnew = set()\n\n #Calculate new credentials Dnew that Pthis will send to other parties \n if m.messageType == 'offer':\n Dunlocked = Drecieved\n if isUnlocked:\n for resource in self.policies:\n if m.resource in self.policies[resource]:\n Dunlocked.add(resource)\n break\n Dnew = Dunlocked & (Qrecieved - Dsent)\n elif m.messageType == 'request' and isUnlocked:\n Dnew.add(m.resource)\n else:\n Drelevant = set(self.policies[m.resource])\n Qnew = Drelevant - Drecieved - Qsent\n\n messages = list()\n for credential in Dnew:\n sendTo = m.issuer\n messages.append(Message('offer', credential, self.udp_port, sendTo, m.originalRequester))\n \n for credential in Qnew:\n sendTo = 6868 if self.name != 'client' else resourceLookup[m.resource]\n messages.append(Message('request', credential, self.udp_port, sendTo, self.udp_port))\n\n return messages\n\n #print loaded policies\n def printPolicies(self):\n print(\"Policies:\")\n for policy in self.policies:\n print(\" {}: {}\".format(policy, self.policies[policy]))\n\n #print loaded resources\n def printResources(self):\n print(\"Resources:\")\n for resource in self.resources:\n print(\" {}: {}\".format(resource, self.resources[resource]))\n\n","sub_path":"Peer.py","file_name":"Peer.py","file_ext":"py","file_size_in_byte":7872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"12193251","text":"\"\"\"\nCurrently acting as archive.\n\"\"\"\n\nimport numpy as np\ndef Rmat(x):\n c,s = np.cos(x), np.sin(x)\n R = np.float32([c,-s,s,c]).reshape(2,2)\n return R\n\ndef linear_LS_triangulation(P1, P2, u1, u2):\n \"\"\"\n Linear Least Squares based triangulation.\n Relative speed: 0.1\n \n (u1, P1) is the reference pair containing normalized image coordinates (x, y) and the corresponding camera matrix.\n (u2, P2) is the second pair.\n \n u1 and u2 are matrices: amount of points equals #rows and should be equal for u1 and u2.\n \n The status-vector will be True for all points.\n\n from https://github.com/Eliasvan/Multiple-Quadrotor-SLAM/blob/master/Work/python_libs/triangulation.py\n \"\"\"\n A = np.zeros((4, 3))\n b = np.zeros((4, 1))\n \n # Create array of triangulated points\n x = np.zeros((3, len(u1)))\n \n # Initialize C matrices\n C1 = -np.eye(2,3)\n C2 = -np.eye(2,3)\n \n for i in range(len(u1)):\n # Derivation of matrices A and b:\n # for each camera following equations hold in case of perfect point matches:\n # u.x * (P[2,:] * x) = P[0,:] * x\n # u.y * (P[2,:] * x) = P[1,:] * x\n # and imposing the constraint:\n # x = [x.x, x.y, x.z, 1]^T\n # yields:\n # (u.x * P[2, 0:3] - P[0, 0:3]) * [x.x, x.y, x.z]^T + (u.x * P[2, 3] - P[0, 3]) * 1 = 0\n # (u.y * P[2, 0:3] - P[1, 0:3]) * [x.x, x.y, x.z]^T + (u.y * P[2, 3] - P[1, 3]) * 1 = 0\n # and since we have to do this for 2 cameras, and since we imposed the constraint,\n # we have to solve 4 equations in 3 unknowns (in LS sense).\n\n # Build C matrices, to construct A and b in a concise way\n C1[:, 2] = u1[i, :]\n C2[:, 2] = u2[i, :]\n \n # Build A matrix:\n # [\n # [ u1.x * P1[2,0] - P1[0,0], u1.x * P1[2,1] - P1[0,1], u1.x * P1[2,2] - P1[0,2] ],\n # [ u1.y * P1[2,0] - P1[1,0], u1.y * P1[2,1] - P1[1,1], u1.y * P1[2,2] - P1[1,2] ],\n # [ u2.x * P2[2,0] - P2[0,0], u2.x * P2[2,1] - P2[0,1], u2.x * P2[2,2] - P2[0,2] ],\n # [ u2.y * P2[2,0] - P2[1,0], u2.y * P2[2,1] - P2[1,1], u2.y * P2[2,2] - P2[1,2] ]\n # ]\n A[0:2, :] = C1.dot(P1[0:3, 0:3]) # C1 * R1\n A[2:4, :] = C2.dot(P2[0:3, 0:3]) # C2 * R2\n \n # Build b vector:\n # [\n # [ -(u1.x * P1[2,3] - P1[0,3]) ],\n # [ -(u1.y * P1[2,3] - P1[1,3]) ],\n # [ -(u2.x * P2[2,3] - P2[0,3]) ],\n # [ -(u2.y * P2[2,3] - P2[1,3]) ]\n # ]\n b[0:2, :] = C1.dot(P1[0:3, 3:4]) # C1 * t1\n b[2:4, :] = C2.dot(P2[0:3, 3:4]) # C2 * t2\n b *= -1\n \n # Solve for x vector\n cv2.solve(A, b, x[:, i:i+1], cv2.DECOMP_SVD)\n \n return x.T.astype(np.float32)#, np.ones(len(u1), dtype=bool)\n\ndef triangulatePoints(P1, P2, p1, p2):\n \"\"\"\n Custom impl., as cv2.triangulatePoints resulted in memory Error.\n \"\"\"\n\n pt = [p1, p2]\n P = [P1,P2]\n\n n = len(p1)\n\n pt_4dh = np.empty((n, 4), dtype=np.float32)\n for i in range(n):\n A = np.empty((4,4), dtype=np.float32)\n for j in range(2):\n x, y = pt[j][i]\n A[j*2+0] = x * P[j][2] - P[j][0]\n A[j*2+1] = y * P[j][2] - P[j][1]\n U,s,V = np.linalg.svd(A) # U.diag(S).V == A\n pt_4dh[i] = V[3,:]\n return pt_4dh[:,:3] / pt_4dh[:, 3:]\n\ndef recoverPoseWithPoints(E, p1, p2,\n fx, fy, cx, cy):\n # mostly based on cv2.recoverPose() implementation\n c = np.reshape( (cx,cy), (1,2) ).astype(np.float32)\n f = np.reshape( (fx,fy), (1,2) ).astype(np.float32)\n\n # normalize points\n p1_n = (p1 - c) / f\n p2_n = (p2 - c) / f\n\n R1, R2, t = cv2.decomposeEssentialMat(E)\n # choose from argmin(R(z))\n\n cand = (\n (R1, t),\n (R2, t),\n (R1, -t),\n (R2, -t))\n\n P0 = np.eye(3, 4, dtype=np.float32)\n max_det = None\n R_res = None\n t_res = None\n\n # candidates filtering by chirality test\n for (cR,ct) in cand:\n cP = np.concatenate((cR, ct), axis=1)\n Q = triangulatePoints(P0, cP, p1, p2)\n\n msk = np.logical_and(\n np.greater(Q[:,:2], 0.0),\n np.less(Q[:,:2], 50.0))\n det = np.sum(msk)\n if (max_det is None) or (det >= max_det):\n max_det = det\n R_res = cR\n t_res = ct\n\n return max_det, R_res, t_res\n\ndef normalize_points(pts, cMat):\n pc = np.reshape([cMat[2,0], cMat[2,1]], [1,2])\n pf = np.reshape([cMat[0,0], cMat[1,1]], [1,2])\n return (pts - pc) / pf\n\n","sub_path":"robot_learning/scripts/classical/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":4653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"304336614","text":"from __future__ import print_function\nimport os\nimport re\nimport tempfile\nimport requests\n\nfrom django.core.files import File\nfrom django.core.management.base import BaseCommand, CommandError\n\nimport httplib2\nfrom lxml import etree\nfrom bs4 import BeautifulSoup\n\nfrom parlhand import models\nfrom parlhand.management.commands import utils as import_utils\n\nclass Command(BaseCommand):\n args = 'xml_file_name'\n help = 'Import a single ParlBibXML file and create a new record or merge it with an existing record'\n\n def handle(self, *args, **options):\n if 'pm' in args or 'all' in args:\n self.mine_prime_ministers()\n if 'dpm' in args or 'all' in args:\n self.mine_deputy_prime_ministers()\n if 'opp' in args or 'all' in args:\n self.mine_opposition_leaders()\n\n def process_xml_file(self,file):\n print(\"importing - \",file)\n with open(file, 'r') as imported_xml:\n self.parl_xml = etree.parse(imported_xml).xpath('/biog.entry')[0]\n\n ph_id = self.parl_xml.xpath('name.id')[0].text\n if ph_id is None or ph_id == \"\":\n raise Exception\n self.person,created = models.Person.objects.get_or_create(phid=ph_id)\n print(self.person)\n\n print(\"Done\")\n\n def mine_prime_ministers(self):\n print(\"Mining Prime Ministers\")\n http = httplib2.Http()\n status, response = http.request('https://en.wikipedia.org/wiki/List_of_Prime_Ministers_of_Australia')\n soup = BeautifulSoup(response,\"lxml\")\n tables = soup.findAll('table',class_=\"wikitable\")\n\n rows = [row for row in tables[0].findAll('tr',{'style':\"background:#EEEEEE\"})]\n\n for row in rows:\n cols = row.findAll('td')\n \n # Damn you Billy Hughes\n if row.findAll('th') and (row.findAll('th')[0].text or \"Billy\" in cols[0].text):\n #This row is a new starter row, find the PM and do stuff\n pm = cols[0].text.replace('Sir ','').split('(')[0]\n first_name,last_name = pm.split(' ')\n print(first_name,last_name)\n first_name = first_name.strip()\n last_name = last_name.strip()\n qs = models.Person.objects.filter(surname=last_name,first_names__icontains=first_name)\n if qs.count() == 0:\n qs = models.Person.objects.filter(surname=last_name,preferred_name__icontains=first_name)\n if qs.count() != 1:\n if qs.count() < 1:\n print(\"ERROR - no PM for -\",pm)\n if qs.count() > 1:\n print(\"ERROR - too many PM's for -\",pm)\n print(\" found these: \",qs.all())\n continue\n pm = qs.first()\n \n if not pm.picture:\n picture = cols[1].findAll('img')[0].get('src')\n self.scrape_image_for_person(picture,pm)\n start = import_utils.str_to_date(cols[3].text)\n end = import_utils.str_to_date(cols[4].text)\n #self.make_pm(pm,start,end)\n else:\n # Continuation row use prior pm\n if not pm:\n print(\" -- No PM?\")\n continue\n # This is a different prime ministership\n if len(cols) > 2:\n print(cols)\n start = import_utils.str_to_date(cols[1].text)\n end = import_utils.str_to_date(cols[2].text)\n self.make_pm(pm,start,end)\n \n def mine_deputy_prime_ministers(self):\n print(\"Mining Deputy Prime Ministers\")\n http = httplib2.Http()\n status, response = http.request('https://en.wikipedia.org/wiki/Deputy_Prime_Minister_of_Australia')\n soup = BeautifulSoup(response,\"lxml\")\n tables = soup.findAll('table',class_=\"wikitable\")\n\n rows = [row for row in tables[0].findAll('tr')]\n\n for row in rows[1:]:\n cols = row.findAll('td')\n \n #This row is a new starter row, find the Deputy PM and do stuff\n dpm = cols[1].text.replace('Sir ','').split('(')[0]\n\n first,last = dpm.split(' ')\n qs = qs.filter(surname__icontains=last,first_names__icontains=first)\n if qs.count() == 0:\n qs = models.Person.objects.filter(surname__icontains=last,preferred_name__icontains=first)\n if qs.count() != 1:\n if qs.count() < 1:\n print(\"ERROR - no Deputy PM for -\",dpm)\n if qs.count() > 1:\n print(\"ERROR - too many Deputy PM's for -\",dpm)\n print(\" found these: \",qs.all())\n continue\n dpm = qs.first()\n \n if not dpm.picture:\n picture = cols[2].findAll('img')\n if picture:\n self.scrape_image_for_person(picture[0].get('src'),dpm)\n\n start,end = None,None\n start_loc = row.findAll('span',class_='dtstart')\n end_loc = row.findAll('span',class_='dtend')\n if start_loc:\n start = import_utils.str_to_date(start_loc[0].text)\n if end_loc:\n end = import_utils.str_to_date(end_loc[0].text)\n print(\"-----\",dpm,start_loc,end_loc,start,end)\n #self.make_deputy_pm(dpm,start,end)\n\n def scrape_image_for_person(self,url,person):\n print(\"Downloading \",url)\n if url.startswith(\"//\"):\n url = \"http:\"+url\n request = requests.get(url, stream=True)\n ext = url.rsplit('.',1)[-1]\n filename = \"%s.%s\"%(person.phid,ext)\n\n # Was the request OK?\n if request.status_code != requests.codes.ok:\n # Nope, error handling, skip file etc etc etc\n return\n\n lf = tempfile.NamedTemporaryFile()\n \n # Read the streamed image in sections\n for block in request.iter_content(1024 * 8):\n # If no more file then stop\n if not block:\n break\n \n # Write image block to temporary file\n lf.write(block)\n \n # Save the temporary image to the model#\n # This saves the model so be sure that is it valid\n person.picture.save(filename, File(lf))\n person.save()\n\n\n def make_party_membership(self,person,data):\n party,created = models.Party.objects.get_or_create(code=data[1],defaults={'name':data[0]})\n mem,c = models.PartyMembership.objects.get_or_create(\n person = person,\n party = party,\n start_date = import_utils.str_to_date(data[3]),\n end_date = import_utils.str_to_date(data[4]),\n )\n\n def make_pm(self,person,start=None,end=None):\n self.make_ministerial(person,\"Prime Minister\",start,end)\n\n def make_deputy_pm(self,person,start=None,end=None):\n self.make_ministerial(person,\"Deputy Prime Minister\",start,end)\n\n def make_opposition_leader(self,person,start=None,end=None):\n self.make_ministerial(person,\"Leader of the Opposition\",start,end)\n\n def make_ministerial(self,person,ministerialposition,start=None,end=None):\n ministerial_position,c = models.MinisterialPosition.objects.get_or_create(name=ministerialposition)\n models.MinisterialAppointment.objects.get_or_create(\n person=person,\n position=ministerial_position,\n start_date=start,\n end_date=end)","sub_path":"parlhand/management/commands/mine_wiki.py","file_name":"mine_wiki.py","file_ext":"py","file_size_in_byte":7623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"407255366","text":"\"\"\"Flight class view.\"\"\"\n\nfrom flask import jsonify, request\nfrom flask.views import MethodView\n\nfrom api.auth.auth_utils import is_admin\nfrom api.flight.models import Flight, Seats\n\n\nclass FlightView(MethodView):\n \"\"\" View to handle Flight functionality.\"\"\"\n decorators = [is_admin]\n\n def post(self, current_user):\n data = request.get_json()\n dept_time = data.get(\"departure_time\")\n dept_from = data.get(\"departure_from\")\n destination = data.get(\"destination\")\n no_of_seats = data.get(\"number_of_seats\")\n\n try:\n flight = Flight(\n departure_time=dept_time,\n departure_from=dept_from,\n destination=destination\n )\n flight.save(flight)\n seats = Seats(\n number_of_seats=no_of_seats,\n flight_id=flight.id\n )\n seats.save(seats)\n resp = {\n \"status\": \"Success\",\n \"message\": \"New Flight created successfully.\",\n \"details\": \"{}\".format(flight)\n }\n return jsonify(resp), 200\n\n except AssertionError as error:\n error_data = {\n \"status\": \"Failure\",\n \"error\": \"{}\".format(error)\n }\n return jsonify(error_data), 400\n","sub_path":"api/flight/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"296306510","text":"import pandas as pd\nfrom typing import Any, List, Dict\nfrom utils import load_templates, merge\n#from src.utils import load_templates, merge\n\n\ndef build_settings(snapshots: List[Dict] = [], playerSettings: Dict[str, Any] = {}) -> Dict[str, Any]:\n # load template - settings.yaml\n settings = load_templates(\"settings\")\n\n # inject the snapshots\n if len(snapshots) > 0:\n print(f\"\\t- {len(snapshots)} snapshot found. adding to map.\")\n settings = merge(settings, {\"snapshots\": snapshots})\n else:\n # if no snapshot defined load the default template and fix the size by and color by\n print(f\"\\t- no snapshot found. injecting default\")\n default_snap = load_templates(\"snapshot\")\n default_snap = merge(\n default_snap,\n {\n \"layout\": {\n \"plotType\": \"network\",\n \"settings\": {\"nodeSizeStrat\": \"fixed\", \"nodeColorStrat\": \"fixed\"},\n }\n },\n )\n settings = merge(settings, {\"snapshots\": [default_snap]})\n\n settings = merge(settings, {\"player\": {\"settings\": playerSettings}})\n\n return settings\n\n\nif __name__ == \"__main__\":\n x = build_settings()\n print(x)\n","sub_path":"src/build_settings.py","file_name":"build_settings.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"305312434","text":"import os\nimport tensorflow as tf\nimport numpy as np\nimport json\n\nfrom models_clevr.model import LCGNnet\nfrom models_clevr.config import build_cfg_from_argparse\nfrom models_clevr.vis import vis_batch_vqa\nfrom util.clevr_train.data_reader import DataReader\n\n# Load config\ncfg = build_cfg_from_argparse()\n\n# Start session\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = cfg.GPUS\nsess = tf.Session(config=tf.ConfigProto(\n allow_soft_placement=True, gpu_options=tf.GPUOptions(allow_growth=True)))\n\n\ndef load_train_data(max_num=0):\n imdb_file = cfg.IMDB_FILE % cfg.TRAIN.SPLIT_VQA\n data_reader = DataReader(\n imdb_file, shuffle=True, max_num=max_num,\n batch_size=cfg.TRAIN.BATCH_SIZE,\n vocab_question_file=cfg.VOCAB_QUESTION_FILE,\n T_encoder=cfg.T_ENCODER,\n vocab_answer_file=cfg.VOCAB_ANSWER_FILE,\n load_spatial_feature=True,\n spatial_feature_dir=cfg.SPATIAL_FEATURE_DIR,\n add_pos_enc=cfg.ADD_POS_ENC, img_H=cfg.IMG_H, img_W=cfg.IMG_W,\n pos_enc_dim=cfg.PE_DIM, pos_enc_scale=cfg.PE_SCALE)\n num_vocab = data_reader.batch_loader.vocab_dict.num_vocab\n num_choices = data_reader.batch_loader.answer_dict.num_vocab\n return data_reader, num_vocab, num_choices\n\n\ndef run_train_on_data(model, data_reader_train, saver_train, lr_start,\n run_eval=False, data_reader_eval=None, saver_eval=None):\n lr = lr_start\n correct, total, loss_sum, batch_num = 0, 0, 0., 0\n prev_loss = None\n for batch, n_sample, e in data_reader_train.batches(one_pass=False):\n n_epoch = cfg.TRAIN.START_EPOCH + e\n if n_sample == 0 and n_epoch > cfg.TRAIN.START_EPOCH:\n print('')\n # save snapshot\n snapshot_file = cfg.SNAPSHOT_FILE % (cfg.EXP_NAME, n_epoch)\n saver_train.save(sess, snapshot_file, write_meta_graph=False)\n states_file = snapshot_file + '_states.npy'\n np.save(states_file, {'lr': lr})\n # run evaluation\n if run_eval:\n saver_eval.restore(sess, snapshot_file)\n run_eval_on_data(model, data_reader_eval)\n saver_train.restore(sess, snapshot_file)\n # adjust lr:\n curr_loss = loss_sum/batch_num\n if prev_loss is not None:\n lr = adjust_lr_clevr(curr_loss, prev_loss, lr)\n # clear stats\n correct, total, loss_sum, batch_num = 0, 0, 0., 0\n prev_loss = curr_loss\n if n_epoch >= cfg.TRAIN.MAX_EPOCH:\n break\n batch_res = model.run_batch(\n sess, batch, train=True, run_vqa=True, run_ref=False, lr=lr)\n correct += batch_res['num_correct']\n total += batch_res['batch_size']\n loss_sum += batch_res['loss']\n batch_num += 1\n print('\\rTrain E %d S %d: avgL=%.4f, avgA=%.4f, lr=%.1e' % (\n n_epoch+1, total, loss_sum/batch_num, correct/total, lr),\n end='')\n\n\ndef adjust_lr_clevr(curr_los, prev_loss, curr_lr):\n loss_diff = prev_loss - curr_los\n not_improve = (\n (loss_diff < 0.015 and prev_loss < 0.5 and curr_lr > 0.00002) or\n (loss_diff < 0.008 and prev_loss < 0.15 and curr_lr > 0.00001) or\n (loss_diff < 0.003 and prev_loss < 0.10 and curr_lr > 0.000005))\n\n next_lr = curr_lr * cfg.TRAIN.SOLVER.LR_DECAY if not_improve else curr_lr\n return next_lr\n\n\ndef load_eval_data(max_num=0):\n imdb_file = cfg.IMDB_FILE % cfg.TEST.SPLIT_VQA\n data_reader = DataReader(\n imdb_file, shuffle=False, max_num=max_num,\n batch_size=cfg.TEST.BATCH_SIZE,\n vocab_question_file=cfg.VOCAB_QUESTION_FILE,\n T_encoder=cfg.T_ENCODER,\n vocab_answer_file=cfg.VOCAB_ANSWER_FILE,\n load_spatial_feature=True,\n spatial_feature_dir=cfg.SPATIAL_FEATURE_DIR,\n add_pos_enc=cfg.ADD_POS_ENC, img_H=cfg.IMG_H, img_W=cfg.IMG_W,\n pos_enc_dim=cfg.PE_DIM, pos_enc_scale=cfg.PE_SCALE)\n num_vocab = data_reader.batch_loader.vocab_dict.num_vocab\n num_choices = data_reader.batch_loader.answer_dict.num_vocab\n return data_reader, num_vocab, num_choices\n\n\ndef run_eval_on_data(model, data_reader_eval, pred=False, vis=False,\n vis_dir=None):\n predictions = []\n answer_tokens = data_reader_eval.batch_loader.answer_dict.word_list\n correct, total, loss_sum, batch_num = 0, 0, 0., 0\n for batch, _, _ in data_reader_eval.batches(one_pass=True):\n batch_res = model.run_batch(\n sess, batch, train=False, run_vqa=True, run_ref=False, vis=vis)\n if vis and total < cfg.TEST.NUM_VIS:\n vis_batch_vqa(data_reader_eval, batch, batch_res, total, vis_dir)\n if pred:\n predictions.extend([\n {'questionId': q, 'prediction': answer_tokens[p]}\n for q, p in zip(batch['qid_list'], batch_res['predictions'])])\n correct += batch_res['num_correct']\n total += batch_res['batch_size']\n loss_sum += batch_res['loss']\n batch_num += 1\n print('\\rEval S %d: avgL=%.4f, avgA=%.4f' % (\n total, loss_sum/batch_num, correct/total), end='')\n print('')\n eval_res = {\n 'correct': correct,\n 'total': total,\n 'accuracy': correct/total,\n 'loss': loss_sum/batch_num,\n 'predictions': predictions}\n return eval_res\n\n\ndef dump_prediction_to_file(predictions, res_dir):\n pred_file = os.path.join(res_dir, 'pred_%s_%04d_%s.txt' % (\n cfg.EXP_NAME, cfg.TEST.EPOCH, cfg.TEST.SPLIT_VQA))\n with open(pred_file, 'w') as f:\n for p in predictions:\n f.write(p['prediction'] + '\\n')\n print('predictions written to %s' % pred_file)\n\n\ndef train():\n data_reader_train, num_vocab, num_choices = load_train_data()\n data_reader_eval, _, _ = load_eval_data(max_num=cfg.TRAIN.EVAL_MAX_NUM)\n\n # Load model\n model = LCGNnet(num_vocab, num_choices, gpusNum=len(cfg.GPUS.split(',')))\n\n # Save snapshot\n saver_train = tf.train.Saver(max_to_keep=None) # keep all snapshots\n saver_eval = tf.train.Saver(model.emaDict if cfg.USE_EMA else None)\n snapshot_dir = os.path.dirname(cfg.SNAPSHOT_FILE % (cfg.EXP_NAME, 0))\n os.makedirs(snapshot_dir, exist_ok=True)\n with open(os.path.join(snapshot_dir, 'cfg.json'), 'w') as f:\n json.dump(cfg, f, indent=2)\n lr_start = cfg.TRAIN.SOLVER.LR\n if cfg.TRAIN.START_EPOCH > 0:\n print('resuming from epoch %d' % cfg.TRAIN.START_EPOCH)\n snapshot_file = cfg.SNAPSHOT_FILE % (\n cfg.EXP_NAME, cfg.TRAIN.START_EPOCH)\n saver_train.restore(sess, snapshot_file)\n states_file = snapshot_file + '_states.npy'\n if os.path.exists(states_file):\n lr_start = np.load(states_file, allow_pickle=True)[()]['lr']\n print('recovered previous lr %.1e' % lr_start)\n else:\n print('could not recover previous lr')\n else:\n sess.run(tf.global_variables_initializer())\n sess.graph.finalize()\n\n print('%s - train for %d epochs' % (cfg.EXP_NAME, cfg.TRAIN.MAX_EPOCH))\n run_train_on_data(\n model, data_reader_train, saver_train, lr_start,\n run_eval=cfg.TRAIN.RUN_EVAL, data_reader_eval=data_reader_eval,\n saver_eval=saver_eval)\n print('%s - train (done)' % cfg.EXP_NAME)\n\n\ndef test():\n data_reader_eval, num_vocab, num_choices = load_eval_data()\n\n # Load model\n model = LCGNnet(num_vocab, num_choices, gpusNum=len(cfg.GPUS.split(',')))\n\n # Load test snapshot\n saver_eval = tf.train.Saver(model.emaDict if cfg.USE_EMA else None)\n snapshot_file = cfg.SNAPSHOT_FILE % (cfg.EXP_NAME, cfg.TEST.EPOCH)\n saver_eval.restore(sess, snapshot_file)\n sess.graph.finalize()\n\n res_dir = cfg.TEST.RESULT_DIR % (cfg.EXP_NAME, cfg.TEST.EPOCH)\n vis_dir = os.path.join(\n res_dir, '%s_%s' % (cfg.TEST.VIS_DIR_PREFIX, cfg.TEST.SPLIT_VQA))\n os.makedirs(res_dir, exist_ok=True)\n os.makedirs(vis_dir, exist_ok=True)\n pred = cfg.TEST.DUMP_PRED\n if not pred:\n print('NOT writing predictions (set TEST.DUMP_PRED True to write)')\n\n print('%s - test epoch %d' % (cfg.EXP_NAME, cfg.TEST.EPOCH))\n eval_res = run_eval_on_data(\n model, data_reader_eval, pred=pred, vis=True, vis_dir=vis_dir)\n print('%s - test epoch %d: accuracy = %.4f' % (\n cfg.EXP_NAME, cfg.TEST.EPOCH, eval_res['accuracy']))\n\n # write results\n if pred:\n dump_prediction_to_file(eval_res['predictions'], res_dir)\n eval_res.pop('predictions')\n res_file = os.path.join(res_dir, 'res_%s_%04d_%s.json' % (\n cfg.EXP_NAME, cfg.TEST.EPOCH, cfg.TEST.SPLIT_VQA))\n with open(res_file, 'w') as f:\n json.dump(eval_res, f)\n\n\nif __name__ == '__main__':\n if cfg.train:\n train()\n else:\n test()\n","sub_path":"exp_clevr/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"76904147","text":"import time, os, datetime, re, json\n\nstart_time = time.time() # 初始时间戳\n\n# documentId = \"1bB3RGo1tHFTnYgQ9M56d42AtYq9x3fLYVBR4qHdmrzY\"\n# documentName = \"sample.docx\"\n\n\na = \"\"\"\n var documentId = \"\n\"\"\"\nb = \"\"\"\n\"\n var forDriveScope = DriveApp.getStorageUsed(); //needed to get Drive Scope requested\n var url = \"https://docs.google.com/feeds/download/documents/export/Export?id=\" + documentId + \"&exportFormat=docx\";\n var param = {\n method: \"get\",\n headers: {\"Authorization\": \"Bearer \" + ScriptApp.getOAuthToken()},\n muteHttpExceptions: true,\n };\n var html = UrlFetchApp.fetch(url, param).getContentText();\n var file = DriveApp.createFile(\"\n\"\"\"\n\nc = \"\"\"\n\", docx);\n file.getUrl();\n\"\"\"\n\nscript_list = [\"function runThis() {\"]\n# ================读取剪贴板================\nfrom tkinter import Tk\n\nr = Tk()\nread_text = r.clipboard_get()\ntext_readline = read_text.splitlines() # 对数据分行\nprint(text_readline)\nfor i in range(len(text_readline)):\n if \"\\t\" in text_readline[i]: # 接受key value格式\n split_line = text_readline[i].split(\"\\t\")\n documentName = split_line[0].zfill(2)\n documentId = split_line[1]\n script_entry = a.strip(\"\\n\") + documentId + b.strip(\"\\n\") + documentName + c.strip(\"\\n\")\n script_list.append(script_entry)\nscript_list.append(\"}\")\ntext = \"\\r\\n\".join(script_list)\nprint(text)\n# ================写入剪贴板================\nimport pyperclip\n\npyperclip.copy(text)\nspam = pyperclip.paste()\n# ================运行时间计时================\nrun_time = time.time() - start_time\nif run_time < 60: # 秒(两位小数)\n print(\"耗时:{:.2f}秒\".format(run_time))\nelif run_time < 3600: # 分+秒(取整)\n print(\"耗时:{:.0f}分{:.0f}秒\".format(run_time // 60, run_time % 60))\nelse: # 时分秒取整\n print(\"耗时:{:.0f}时{:.0f}分{:.0f}秒\".format(run_time // 3600, run_time % 3600 // 60, run_time % 60))\n","sub_path":"~Google硬盘-生成下载脚本.py","file_name":"~Google硬盘-生成下载脚本.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"360693877","text":"import os\nimport re\nimport random\nimport hashlib\nimport hmac\nfrom string import letters\nimport webapp2\nimport jinja2\nfrom google.appengine.ext import db\nfrom models import *\n\ntemplate_dir = os.path.join(os.path.dirname(__file__), 'templates')\njinja_env = jinja2.Environment(loader=jinja2.FileSystemLoader(template_dir),\n autoescape=True)\n\n\n# parent class for all the handlers\nclass BlogHandler(webapp2.RequestHandler):\n def write(self, *a, **kw):\n self.response.out.write(*a, **kw)\n\n def render_str(self, template, **params):\n params['user'] = self.user\n return render_str(template, **params)\n\n def render(self, template, **kw):\n self.write(self.render_str(template, **kw))\n\n\n# sets a cookie with name as name and the secured val as value\n# the expiring time is not specified so by default this cookie will\n# expire when the browser is closed\n def set_secure_cookie(self, name, val):\n cookie_val = make_secure_val(val)\n self.response.headers.add_header(\n 'Set-Cookie',\n '%s=%s; Path=/' % (name, cookie_val))\n\n\n# if the cookie exists and passes the check-secure-val return\n# cookie val\n def read_secure_cookie(self, name):\n cookie_val = self.request.cookies.get(name)\n# if cookie_val and check_secure_val return cookie_val\n return cookie_val and check_secure_val(cookie_val)\n\n\n# sets a secure cookie, with name user_id and val user's ID\n def login(self, user):\n self.set_secure_cookie('user_id', str(user.key().id()))\n\n\n# substitudes the cookie user-id with nothing, keep the same path\n# so that overwrites the same cookie\n def logout(self):\n self.response.headers.add_header('Set-Cookie', 'user_id=; Path=/')\n\n\n# checks for the user cookie (user_id); if it exists,\n# store in self.user the actual user object.\n# it's run in every request and just checks if user is\n# logged in or not. And it is called by the App Engine framework\n def initialize(self, *a, **kw):\n webapp2.RequestHandler.initialize(self, *a, **kw)\n uid = self.read_secure_cookie('user_id')\n self.user = uid and User.by_id(int(uid))\n\n\nclass MainPage(BlogHandler):\n def get(self):\n self.render(\"mainpage.html\")\n\n\n# this handler class manages the blog page, it renders to front.html\n# which renders to post.html which contains the button edit\n# in the post form to edit a post\nclass BlogFront(BlogHandler):\n def get(self):\n posts = greetings = Post.all().order('-created')\n comments = db.GqlQuery('SELECT * FROM Comment ORDER BY created DESC')\n\n ##########################\n # UTILITIES\n\n # delete all comments\n # for comment in comments:\n # db.delete(comment)\n\n # delete all posts\n # for post in posts:\n # db.delete(post)\n\n ##########################\n\n self.render('front.html', posts=posts, comments=comments)\n\n def post(self):\n # Logged out users are redirected to the login page when attempting to\n # create, edit, delete, or like a blog post.\n if not self.user:\n self.redirect(\"/login\")\n\n else:\n # post stuff\n post_id = self.request.get('post_id')\n key = db.Key.from_path('Post', int(post_id), parent=blog_key())\n post = db.get(key)\n authorPost = post.author\n posts = greetings = Post.all().order('-created')\n delete_post_flag = self.request.get('delete_post_flag')\n edit_post_flag = self.request.get('edit_post_flag')\n edit_comment_flag = self.request.get('edit_comment_flag')\n like_flag = self.request.get('like_flag')\n # user stuff\n username = self.user.name\n # comment stuff\n comment_flag = self.request.get('comment_flag')\n commentContent = self.request.get('commentContent')\n deletecomment = self.request.get('delete_comment_flag')\n comment_id = self.request.get('comment_id')\n\n # COMMENT STUFF\n # Permissions:\n # Only signed in users can post comments. Users can only\n # edit and delete comments they themselves have made.\n\n # edit comment\n if edit_comment_flag == \"zero\":\n return self.redirect('/editcomment/%s' % str(comment_id))\n\n # new comment: the author of the post cannot comment it\n if comment_flag == \"zero\":\n if(authorPost == username):\n error = \"Sorry %s you cannot comment your own post!\" % username # noqa\n return self.render('front.html', posts=posts, error=error)\n # yes add comment:if you are not the author of the post\n else:\n if commentContent:\n commentAuthor = self.user.name\n # create an istance of coment\n c = Comment(id_post=int(post_id),\n content=commentContent,\n author=commentAuthor)\n c.put()\n return self.redirect('/blog/?')\n # no add comment:no content\n else:\n error = \"Sorry %s there is no content to add!\" % username # noqa\n return self.render('front.html',\n posts=posts,\n error=error)\n\n # delete comment: Only the author can delete a comment\n if(deletecomment == \"zero\"):\n return self.redirect('/deletecomment/%s' % str(comment_id))\n\n # POST STUFF\n # Logged in users can create, edit, or delete blog posts they\n # themselves have created.\n\n # delete post: the author of the post can delete it\n elif(authorPost == username and delete_post_flag == \"zero\"):\n db.delete(post)\n comments = Comment.all()\n self.redirect('/blog/?')\n # no delete post: only the author of the post can delete it\n elif(authorPost != username and delete_post_flag == \"zero\"):\n error = \"Sorry %s you can only delete your own posts!\" % username # noqa\n comments = Comment.all()\n self.render('front.html',\n posts=posts,\n error=error,\n comments=comments)\n\n # edit post: the author of the post can edit it\n if(edit_post_flag == \"zero\"):\n return self.redirect('/editpost/%s' % str(post.key().id()))\n\n # like post: not for post author\n if(like_flag == \"zero\"):\n return self.redirect('/likepost/%s' % str(post.key().id()))\n\n\nclass EditPost(BlogHandler):\n def get(self, post_id):\n if not self.user:\n return self.redirect('/login')\n posts = greetings = Post.all().order('-created')\n key = db.Key.from_path('Post', int(post_id), parent=blog_key())\n post = db.get(key)\n if not post:\n self.error(404)\n return self.render(\"error404.html\")\n subject=post.subject\n content=post.content\n authorPost = post.author\n username = self.user.name\n if(authorPost == username):\n self.render(\"editpost.html\",\n p=post,\n subject=post.subject,\n content=post.content,\n username=username,\n author=authorPost)\n else:\n error = \"Sorry %s you can only edit your own posts!\" % username # noqa\n return self.render(\"editposterror.html\",\n p=post,\n error=error)\n\n\n def post(self, par):\n post_id = self.request.get('post_id')\n key = db.Key.from_path('Post', int(post_id), parent=blog_key())\n post = db.get(key)\n subject = self.request.get('subject')\n content = self.request.get('content')\n username = self.user.name\n authorPost = post.author\n # check for content and subject\n if subject and content:\n post.subject = self.request.get('subject')\n post.content = self.request.get('content')\n post.put()\n return self.redirect('/blog/%s' % str(post.key().id()))\n # button \"done\" to edit the post with no content or subject\n else:\n error = \"subject or content, please!\"\n return self.render(\"editpost.html\",\n p=post,\n subject=subject,\n content=content,\n username=username,\n author=authorPost,\n error=error)\n\n\nclass LikePost(BlogHandler):\n\n def get(self, post_id):\n if not self.user:\n return self.redirect('/login')\n posts = greetings = Post.all().order('-created')\n key = db.Key.from_path('Post', int(post_id), parent=blog_key())\n post = db.get(key)\n if not post:\n self.error(404)\n return self.render(\"error404.html\")\n authorPost = post.author\n username = self.user.name\n # YES ADD LIKE POST: not the post author\n if(authorPost != username):\n likes = Likes.all()\n if likes.get():\n idlike = post_id+username\n likes_new = db.GqlQuery('SELECT * FROM Likes WHERE idlike = :1', # noqa\n idlike)\n likes_new_average = post.likes_average\n # if (likes_new.get() and likes_new_average!=0):\n if (likes_new.get() and likes_new_average != 0):\n error = \"Sorry %s you can't add more than one like per posts!\" % username # noqa\n self.render('likerror.html',\n error=error,\n p=post)\n else:\n return self.render('likePost.html', p=post)\n\n # If there is no like, first like for the post\n else:\n return self.render('likePost.html', p=post)\n else:\n error = \"Sorry %s you can't add like to your own posts!\" % username\n self.render('likerror.html',\n error=error,\n p=post)\n\n def post(self, par):\n post_id = self.request.get('post_id')\n like_confirm = self.request.get('like_confirm')\n posts = greetings = Post.all().order('-created')\n username = self.user.name\n if not self.user:\n return self.redirect('/login')\n if(like_confirm == \"like_confirm\"):\n key = db.Key.from_path('Post', int(post_id), parent=blog_key())\n post = db.get(key)\n if not post:\n self.error(404)\n return self.render(\"error404.html\")\n authorPost = post.author\n username = self.user.name\n # YES ADD LIKE POST: not the post author\n likes = Likes.all()\n if likes.get():\n idlike = post_id+username\n likes_new = db.GqlQuery('SELECT * FROM Likes WHERE idlike = :1', # noqa\n idlike)\n likes_new_average = post.likes_average\n # if (likes_new.get() and likes_new_average!=0):\n if (likes_new.get() and likes_new_average != 0):\n error = \"Sorry %s you can't add more than one like per posts!\" % username # noqa\n self.render('likerror.html',\n error=error,\n p=post)\n else:\n author_like = self.user.name\n likes_average = 1\n l = Likes(idlike=post_id+username,\n author_like=author_like,\n post_id=post_id,\n likes_average=likes_average)\n l.put()\n\n post.likes += 1\n post.likes_average += 1\n post.put()\n return self.render('likepostdone.html')\n # If there is no like, first like for the post\n else:\n author_like = self.user.name\n likes_average = 1\n l = Likes(idlike=post_id+username, author_like=author_like,\n post_id=post_id,\n likes_average=likes_average)\n l.put()\n post.likes += 1\n post.likes_average += 1\n post.put()\n return self.render('likepostdone.html')\n\n\n# this handler class manages the single post page, it renders to\n# permalink.html which renders to post.html that contains the button\n# edit to edit a post\nclass PostPage(BlogHandler):\n # the post_id parameter is taken from the url because it is a get!\n # instead the post parameters can be taken with:\n # self.request.get('')\n def get(self, post_id):\n key = db.Key.from_path('Post', int(post_id), parent=blog_key())\n post = db.get(key)\n if not post:\n self.error(404)\n return self.render(\"error404.html\")\n self.render(\"permalink.html\", post=post)\n\n\nclass NewPost(BlogHandler):\n def get(self):\n if self.user:\n self.render(\"newpost.html\", username=self.user.name)\n else:\n return self.redirect(\"/login\")\n\n def post(self):\n if not self.user:\n return self.redirect('/login')\n\n subject = self.request.get('subject')\n content = self.request.get('content')\n # author = self.request.get('author')\n author = self.user.name\n\n if subject and content:\n p = Post(parent=blog_key(), subject=subject,\n content=content, author=author)\n p.put()\n self.redirect('/blog/%s' % str(p.key().id()))\n else:\n error = \"subject and content, please!\"\n self.render(\"newpost.html\",\n subject=subject,\n content=content,\n error=error)\n\n\nclass EditComment(BlogHandler):\n\n def get(self, comment_id):\n if not self.user:\n return self.redirect('/login')\n\n key = db.Key.from_path('Comment', int(comment_id))\n c = db.get(key)\n id_post = c.id_post\n key = db.Key.from_path('Post', int(id_post), parent=blog_key())\n p = db.get(key)\n comments = Comment.all()\n authorComment = c.author\n username = self.user.name\n # YES EDIT COMMENT: only the author can\n if(authorComment == username):\n self.render('editcomment.html',\n c=c)\n # NO EDIT COMMENT: only the author can\n else:\n error = \"Sorry %s you can only edit your own comments!\" % username\n self.render('editcommenterror.html',\n p=p,\n error=error)\n\n def post(self, par):\n username = self.user.name\n comment_edited = self.request.get('comment_edited')\n content_new_comment = self.request.get('content_new_comment')\n comment_id_new_comment = self.request.get('comment_id_new_comment')\n comment_id = self.request.get('comment_id')\n posts = greetings = Post.all().order('-created')\n comments = Comment.all()\n if not self.user:\n return self.redirect('/login')\n if(comment_edited == \"zero\"):\n key = db.Key.from_path('Comment', int(comment_id_new_comment))\n c = db.get(key)\n if c:\n authorComment = c.author\n if(content_new_comment):\n key = db.Key.from_path('Comment',\n int(comment_id_new_comment))\n c = db.get(key)\n c.content = content_new_comment\n c.put()\n self.redirect('/blog/?')\n else:\n error = \"comment content please!\"\n comments = Comment.all()\n self.render('editcomment.html', c=c, error=error)\n else:\n self.error(404)\n return self.render(\"error404.html\")\n\n\nclass DeleteComment(BlogHandler):\n\n def get(self, comment_id):\n if not self.user:\n return self.redirect('/login')\n key = db.Key.from_path('Comment', int(comment_id))\n c = db.get(key)\n comments = Comment.all()\n id_post = c.id_post\n key = db.Key.from_path('Post', int(id_post), parent=blog_key())\n p = db.get(key)\n authorComment = c.author\n username = self.user.name\n # YES DEL COMMENT: only the author can\n if(authorComment == username):\n self.render('deletecomment.html',\n c=c)\n # NO DEL COMMENT: only the author can\n else:\n error = \"Sorry %s you can only delete your own comments!\" % username # noqa\n self.render('deletecommenterror.html',\n error=error,\n p=p)\n\n def post(self, par):\n username = self.user.name\n delete_confirm = self.request.get('delete_confirm')\n comment_id_del = self.request.get('comment_id_del')\n if not self.user:\n return self.redirect('/login')\n if(delete_confirm == \"delete_confirm\"):\n key = db.Key.from_path('Comment', int(comment_id_del))\n c = db.get(key)\n if c:\n authorComment = c.author\n db.delete(c)\n return self.render('deletedone.html')\n else:\n self.error(404)\n return self.render(\"error404.html\")\n\n\n# Accounts and Security\n# - Users are able to create accounts, login, and logout\n# - Existing users can revisit the site and log back in without\n# having to recreate their accounts each time.\n# -Usernames are unique. Attempting to create a duplicate user\n# results in an error message.\n# -Stored passwords are hashed. Passwords are appropriately\n# checked during login. User cookie is set securely.\n\nclass Signup(BlogHandler):\n def get(self):\n self.render(\"signup-form.html\")\n\n def post(self):\n have_error = False\n self.username = self.request.get('username')\n self.password = self.request.get('password')\n self.verify = self.request.get('verify')\n self.email = self.request.get('email')\n\n params = dict(username=self.username,\n email=self.email)\n\n if not valid_username(self.username):\n params['error_username'] = \"That's not a valid username.\"\n have_error = True\n\n if not valid_password(self.password):\n params['error_password'] = \"That wasn't a valid password.\"\n have_error = True\n elif self.password != self.verify:\n params['error_verify'] = \"Your passwords didn't match.\"\n have_error = True\n\n if not valid_email(self.email):\n params['error_email'] = \"That's not a valid email.\"\n have_error = True\n\n if have_error:\n self.render('signup-form.html', **params)\n else:\n self.done()\n\n def done(self, *a, **kw):\n raise NotImplementedError\n\n\n# overwrites the function done - used if the username is passed in\n# the url (get)\n# class Unit2Signup(Signup):\n# def done(self):\n# self.redirect('/unit2/welcome?username=' + self.username)\n# inherits from the class Signup\n# overwrites the function done\nclass Register(Signup):\n def done(self):\n # make sure the user doesn't already exist\n u = User.by_name(self.username)\n if u:\n msg = 'That user already exists.'\n self.render('signup-form.html', error_username=msg)\n else:\n u = User.register(self.username, self.password, self.email)\n u.put()\n # sets cookie\n self.login(u)\n self.redirect('/welcome')\n\n\nclass Login(BlogHandler):\n def get(self):\n self.render('login-form.html')\n\n def post(self):\n # the user inserts his username and password in the login-form\n username = self.request.get('username')\n password = self.request.get('password')\n # User login (checks val password)\n u = User.login(username, password)\n if u:\n # Blog Handler login (sets cookie)\n self.login(u)\n self.redirect('/welcome')\n else:\n msg = 'Invalid login'\n self.render('login-form.html', error=msg)\n\n\nclass Logout(BlogHandler):\n def get(self):\n self.logout()\n self.redirect('/blog')\n\n\n# unlike the class Welcome, the username is in a cookie\nclass Welcome(BlogHandler):\n def get(self):\n # self user is set in the initialize function for eah request,\n # that reads the cookie, checks it is valid and sets the user in the\n # blogHandler that here is inherited\n if self.user:\n self.render('welcome.html', username=self.user.name)\n else:\n self.redirect('/signup')\n\n\napp = webapp2.WSGIApplication([('/', MainPage),\n ('/blog/?', BlogFront),\n ('/blog/([0-9]+)', PostPage),\n ('/blog/newpost', NewPost),\n ('/likepost/([0-9]+)', LikePost),\n ('/editpost/([0-9]+)', EditPost),\n ('/editcomment/([0-9]+)', EditComment),\n ('/deletecomment/([0-9]+)', DeleteComment),\n ('/signup', Register),\n ('/login', Login),\n ('/logout', Logout),\n ('/welcome', Welcome),\n ],\n debug=True)\n","sub_path":"blog.py","file_name":"blog.py","file_ext":"py","file_size_in_byte":22395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"124535316","text":"#!/usr/bin/env python3\n\"\"\"\nMujoco multi-agent network, include two actors and one centralized critic\n\"\"\"\nfrom torch import nn\nimport torch\nfrom typing import List, Tuple, Union, Dict\nimport torch.nn.functional as F\n\n\nclass MujocoNet(nn.Module):\n def __init__(self, observation_shape,\n action_space,\n use_lstm=False,\n policy_internal_dims: List[int]=[128, 128],\n value_internal_dims: List[int] =[256, 256]\n ):\n super(MujocoNet, self).__init__()\n if use_lstm:\n raise Exception(\"currently does not support lstm\")\n self._use_lstm = use_lstm\n self._observation_shape = observation_shape\n self._action_shape = action_space.shape\n self._check_space_dimension()\n self._obs_dim = self._observation_shape[-1]\n self._act_dim = self._action_shape[-1]\n (self._policy0,\n self._policy1,\n self._critic_net) = self._build_policy_value_nets(\n policy_internal_dims,\n value_internal_dims)\n\n def initial_state(self, batch_size):\n if not self._use_lstm:\n return tuple()\n raise Exception(\"currently does not support lstm\")\n return tuple(\n torch.zeros(self.core.num_layers, batch_size, self.core.hidden_size)\n for _ in range(2)\n )\n\n def forward(self, inputs, core_state=()\n ) -> Tuple[Dict[str,torch.Tensor], tuple]:\n # [T, B, num_agents, obs_dim] for mojoco\n # core_state is the lstm state, which is not used in mojoco\n # return dim: [T, B, num_agents, *]\n x = inputs[\"frame\"]\n T, B, num_agents, obs_dim = x.shape\n x = torch.flatten(x, 0, 1) # Merge time and batch.\n critic_obs_input = torch.flatten(x, start_dim=-2, end_dim=-1)\n policy_inputs = [x[:, i, :] for i in range(num_agents)]\n # flatten the T and B dimension\n if T * B > 1:\n joint_action_last = torch.flatten(inputs[\"last_action\"], 0, 1)\n else:\n joint_action_last = inputs[\"last_action\"]\n #if len(joint_action_last.shape) == 1:\n # joint_action_last = joint_action_last.view(1, num_agents, 1)\n joint_action_one_hot = torch.cat([F.one_hot(\n joint_action_last[:, i], self._act_dim\n ).float() for i in range(num_agents)], dim=-1)\n\n # joint_reward = torch.flatten(inputs['reward'], 0, 1)\n critic_input = torch.cat([critic_obs_input, joint_action_one_hot], dim=-1)\n\n policy_logits = [getattr(self, f'_policy{i}')(policy_inputs[i])\n for i in range(num_agents)]\n\n baseline = self._critic_net(critic_input)\n\n if self.training:\n actions = [torch.multinomial(\n F.softmax(policy_logits[i], dim=1), num_samples=1\n )\n for i in range(num_agents)]\n else:\n # Don't sample when testing.\n actions = [torch.argmax(policy_logits[i], dim=1)\n for i in range(num_agents)]\n\n policy_logits = torch.cat([policy_logits[i].view(T, B, self._act_dim).unsqueeze(-2)\n for i in range(num_agents)], dim=-2)\n baseline = baseline.view(T, B, 2)\n actions = torch.cat([actions[i].view(T, B) for i in range(num_agents)],\n dim=-1)\n\n return (\n dict(policy_logits=policy_logits, baseline=baseline, action=actions),\n core_state,\n )\n\n def _check_space_dimension(self) -> None:\n \"\"\"\n check if the dimension of the spaces are valid\n \"\"\"\n if (not self._observation_shape[0] == 2 or\n not self._action_shape[0] == 2):\n raise ValueError(\"Have to be two agents\")\n\n def _build_policy_value_nets(self,\n policy_internal_dims: List[int],\n value_internal_dims: List[int]\n ) -> Tuple[nn.Module, nn.Module, nn.Module]:\n \"\"\"\n Build policy and value network based on the observation and action dim\n :return:\n \"\"\"\n return (self._construct_policy_net(policy_internal_dims),\n self._construct_policy_net(policy_internal_dims),\n self._construct_value_net(value_internal_dims)\n )\n\n def _construct_value_net(self,\n value_internal_dims: List[int]\n ) -> nn.Module:\n # value net take joint action and joint obs, (MADDPG)\n value_stack = [nn.Linear(2 * (self._obs_dim + self._act_dim), value_internal_dims[0])]\n value_stack.append(nn.ReLU())\n for i, dim in enumerate(value_internal_dims[:-1]):\n value_stack.append(\n nn.Linear(value_internal_dims[i], value_internal_dims[i + 1])\n )\n value_stack.append(nn.ReLU())\n value_stack.append(nn.Linear(value_internal_dims[-1], 2))\n value_net = nn.Sequential(*value_stack)\n return value_net\n\n def _construct_policy_net(self,\n policy_internal_dims: List[int]\n ) -> nn.Module:\n policy_stack = [nn.Linear(self._obs_dim, policy_internal_dims[0])]\n policy_stack.append(nn.ReLU())\n for i, dim in enumerate(policy_internal_dims[:-1]):\n policy_stack.append(\n nn.Linear(policy_internal_dims[i], policy_internal_dims[i + 1])\n )\n policy_stack.append(nn.ReLU())\n policy_stack.append(nn.Linear(policy_internal_dims[-1], self._act_dim))\n policy_stack.append(nn.Softmax())\n policy_net = nn.Sequential(*policy_stack)\n return policy_net\n\n\n\n\n","sub_path":"mujoco_Net.py","file_name":"mujoco_Net.py","file_ext":"py","file_size_in_byte":5956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"362927405","text":"\n#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom thrift.Thrift import TType\nimport json\nimport types\n\nclass ConvertException(Exception):\n pass\n\n\nclass UnsupportFmtException(Exception):\n pass\n\n\ndef thrift2dict(obj):\n '''\n Takes a Thrift Message obj and convertes it to a dict.\n\n @param obj: Thrift object\n @type obj: C{object}\n\n @rtype: C{dict}\n @return: an instance of L{dict}\n '''\n dict_obj = obj.__dict__\n for k,v in dict_obj.items():\n if v and not (isinstance(v,str) or isinstance(v,long) or isinstance(v,int) or isinstance(v,float) or isinstance(v,bool)):\n dict_obj[k] = thrift2dict(v)\n return dict_obj\n\n\ndef dict2thrift(cls, adict, strict = False):\n \"\"\"\n Takes a class representing the thrift Message and fills it with data from\n the dict.\n\n @param cls: Thrift object class\n @type cls: C{classobj}\n\n @param adict: a dict data\n @type adict: C{dict}\n\n @return: an instance of L{cls}\n @rtype: L{cls}\n \"\"\"\n \n def _dict2thrift(cls, adict, strict = False):\n \n def _is_primitive(T_type):\n if T_type == TType.BOOL or \\\n T_type == TType.I08 or T_type == TType.I16 or \\\n T_type == TType.DOUBLE or \\\n T_type == TType.BYTE or \\\n T_type == TType.I32 or T_type == TType.I64 or \\\n T_type == TType.STRING or \\\n T_type == TType.UTF7 or T_type == TType.UTF8 or T_type == TType.UTF16:\n return True\n return False\n\n obj = cls()\n for index, spec in enumerate(cls.thrift_spec):\n if not spec:\n continue\n idx, T_type, T, attr_spec, reserved_1 = spec\n \n if T in adict:\n if _is_primitive(T_type):\n setattr(obj, T, adict[T])\n elif T_type == TType.STRUCT:\n value = dict2thrift(attr_spec[0], adict[T])\n setattr(obj, T, value)\n elif T_type == TType.LIST:\n if not attr_spec[1] and _is_primitive(attr_spec[0]):\n setattr(obj, T, adict[T])\n else:\n setattr(obj, T, list())\n for item in adict[T]:\n map(getattr(obj, T).append, adict[T])\n elif T_type == TType.SET:\n raise UnsupportFmtException(\n 'TType.SET not supported! field: %s, value%s' % \\\n (T_Type, adict[T])) \n elif T_type == TType.STOP:\n raise UnsupportFmtException(\n 'TType.STP not supported! field: %s, value%s' % \\\n (T_Type, adict[T]))\n elif T_Type == TType.MAP:\n raise UnsupportFmtException(\n 'TType.MAP not supported! field: %s, value%s' % \\\n (T_Type, adict[T]))\n else:\n raise UnsupportFmtException(\n 'TType not supported! field: %s, value%s' % \\\n (T_Type, adict[T]))\n\n return obj\n obj = _dict2thrift(cls, adict, strict)\n obj.validate()\n\n return obj\n\n\ndef json2thrift(cls, json_str, strict = False):\n \"\"\"\n Takes a class representing the Thrift Message and fills it with data from\n the json string.\n \n @param cls: Thrift object class\n @type cls: C{classobj}\n\n @param json: a json string\n @type json: L{json}\n\n @return: an instance of L{cls}\n @rtype: L{cls}\n \"\"\"\n return dict2thrift(cls, json.loads(json_str), strict)\n\n\ndef thrift2json(obj):\n \"\"\"\n Takes a Thrift Message obj and convertes it to a json string. (Not recommend)\n\n @param obj: Thrift object\n @type obj: C{object}\n\n @rtype: C{str}\n @return: a json string\n \"\"\"\n return json.dumps(thrift2dict(obj), sort_keys = True, indent = 4)\n\n","sub_path":"lib/thrift_helper/thriftjson.py","file_name":"thriftjson.py","file_ext":"py","file_size_in_byte":4067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"197701658","text":"t = int(input())\nfor x in range(t):\n\tflag=0\n\tl = set([])\n\tn = int(input())\n\tif n != 0:\n\t\tfor j in range(n,10000,n):\n\t\t\tfor k in str(j):\n\t\t\t\tl.add(k)\n\t\t\t\tif len(l) == 10:\n\t\t\t\t\tprint(\"Case #\",x+1,\": \",j,sep=\"\")\n\t\t\t\t\tflag = 1\n\t\t\t\t\tbreak\n\t\t\tif flag == 1:\n\t\t\t\tbreak\n\tif len(l) != 10:\n\t\tprint(\"Case #\",x+1,\": INSOMNIA\",sep=\"\")\n","sub_path":"codes/CodeJamCrawler/16_0_1_neat/16_0_1_siddharths2710_bella.py","file_name":"16_0_1_siddharths2710_bella.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"447314824","text":"\n\nclass Event(object):\n\tdef __init__(self, name=None, start_date=None, end_date=None, country=None, \n\t\t\t\t city=None, state=None, url=None, dance_styles=None,\n\t\t\t\t details=None, teachers=None, bands=None, status=None):\n\t\tself.name = name\n\t\tself.start_date = start_date\n\t\tself.end_date = end_date\n\t\tself.country = country\n\t\tself.city = city\n\t\tself.url = url\n\t\tself.dance_styles = dance_styles\n\t\tself.details = details\n\t\tself.teachers = teachers\n\t\tself.state = state\n\t\tself.bands = bands\n\t\tself.status = 'upcoming'\n\n\n\t\tdef __str__(self):\n\t\t\treturn 'Event: {}, dates: {}, location: {}, {}, dance styles: {}'.format(self.name, self.dates, self.city, self.country, self.dance_styles) \n","sub_path":"event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"312004124","text":"#coding=utf-8\n__author__ = 'TANUKI'\n\nimport pytest\nimport allure\nimport time, re, os, sys\n\nsys.path.append(\"..\")\nfrom appium import webdriver\n\n\nsys.path.append(\"../ReleasePage\")\n# from 文件名 import Class名\nfrom Case001_Login import Login\nfrom Case002_IntoMyself_tv import IntoMyself_tv\nfrom Case041_StaffProve import StaffProve\nfrom public import *\n\n\nclass Test_StaffProve:\n\n\n #@allure.feature('用例名称:登录恒天财富账号')\n def test_StaffProve_001Login(self):\n \"\"\"\n 前置条件：\n 安装恒天财富APP\n 测试步骤：\n 1.Appium启动恒天财富APP\n 2.如未直接进入登陆页面，则在首页点击\"登录\"按钮\n 3.输入自然人的11位手机号(账号)\n 4.输入登陆密码\n 5.点击登录按钮\n 期望结果：\n 登录成功，页面跳转到首页\n 检查点：\n 无\n 测试后续步骤：\n 无\n \"\"\"\n userid = '13263160105'\n psw = 'aa1234'\n driver = Driver.driver\n TestResult = Login.Login(driver, userid, psw)\n #登录恒天财富账号\n assert TestResult == True\n\n #@pytest.mark.skip(reason=\"不执行这条用例\")\n @allure.feature('用例名称:打开恒天财富APP→进入我的页面')\n def test_StaffProve_002IntoMyself_tv(self):\n \"\"\"\n 前置条件：\n 处于已登录状态，页面处于首页\n 测试步骤：\n 1.点击\"我的\"按钮\n 期望结果：\n 进入我的页面\n 检查点：\n 验证标题是否为我的\n 测试后续步骤：\n 无\n \"\"\"\n driver = Driver.driver\n IntoMyself_tv.IntoMyself_tv(driver)\n #进入我的页面\n TestResult = IntoMyself_tv.IntoMyself_tv_CheckPoint(driver)\n #设置检查点验证进入我的成功\n IntoMyself_tv.ShowPage2(driver)\n assert TestResult == True\n\n\n\n #@pytest.mark.skip(reason=\"不执行这条用例\")\n @allure.feature('用例名称:我的页面→进入员工验证')\n def test_StaffProve_003Into_StaffProve(self):\n \"\"\"\n 前置条件：\n 页面处于我的页面\n 测试步骤：\n 1.点击\"员工验证\"按钮\n 期望结果：\n 进入员工验证页面\n 检查点：\n 无\n 测试后续步骤：\n 无\n \"\"\"\n driver = Driver.driver\n TestResult = StaffProve.Into_StaffProve(driver)\n assert TestResult == True\n\n\n\n #@pytest.mark.skip(reason=\"不执行这条用例\")\n @allure.feature('用例名称:输入正确工号进行员工验证')\n def test_StaffProve_004TrueStaffNO(self):\n \"\"\"\n 前置条件：\n 页面处于员工验证\n 测试步骤：\n 1.输入正确工号\n 2.点击查询员工信息\n 期望结果：\n 查询到员工信息\n 检查点：\n 查看员工姓名\n 测试后续步骤：\n 返回员工验证页面\n \"\"\"\n driver = Driver.driver\n TestResult = StaffProve.StaffNo(driver,\"H017639\",\"赵晶晶\")\n assert TestResult == True\n\n\n\n #@pytest.mark.skip(reason=\"不执行这条用例\")\n @allure.feature('用例名称:输入错误工号进行员工验证')\n def test_StaffProve_005FalseStaffNO(self):\n \"\"\"\n 前置条件：\n 页面处于员工验证\n 测试步骤：\n 1.输入错误工号\n 2.点击查询员工信息\n 期望结果：\n 无法查询到员工信息\n 检查点：\n 查看员工姓名\n 测试后续步骤：\n 返回员工验证页面\n \"\"\"\n driver = Driver.driver\n TestResult = StaffProve.StaffNo(driver,\"H111111\",\"赵晶晶\")\n assert TestResult == False\n\n\n #@pytest.mark.skip(reason=\"不执行这条用例\")\n @allure.feature('用例名称:输入正确员工姓名进行员工验证')\n def test_StaffProve_006TrueStaffName(self):\n \"\"\"\n 前置条件：\n 页面处于员工验证\n 测试步骤：\n 1.输入正确员工姓名\n 2.点击查询员工信息\n 期望结果：\n 查询到员工信息\n 检查点：\n 查看员工姓名是否正确\n 测试后续步骤：\n 返回员工验证页面\n \"\"\"\n driver = Driver.driver\n TestResult = StaffProve.StaffName(driver,\"刘涛\")\n assert TestResult == True\n\n\n\n #@pytest.mark.skip(reason=\"不执行这条用例\")\n @allure.feature('用例名称:输入错误员工姓名进行员工验证')\n def test_StaffProve_007FalseStaffName(self):\n \"\"\"\n 前置条件：\n 页面处于员工验证\n 测试步骤：\n 1.输入错误员工姓名\n 2.点击查询员工信息\n 期望结果：\n 无法查询到员工信息\n 检查点：\n 查看员工姓名是否正确\n 测试后续步骤：\n 返回员工验证页面\n \"\"\"\n driver = Driver.driver\n TestResult = StaffProve.StaffName(driver,\"周杰伦\")\n assert TestResult == False\n #输入错误员工姓名进行员工验证 ==False就对了\n\n\n\nif __name__ == \"__main__\":\n ENV = environment()\n if int(ENV) == 1:\n pytest.main()\n elif int(ENV)== 2:\n pytest.main(['Prod_014_StaffProve.py', \"-s\", \"-v\"])\n","sub_path":"Appium/HengTianCaiFu_Android/PROD/Prod_014_StaffProve.py","file_name":"Prod_014_StaffProve.py","file_ext":"py","file_size_in_byte":5395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"513816060","text":"\"\"\"\nShock and Detonation Toolbox Demo Program\n\nExplosion computation simulating constant temperature and pressure reaction.\n\nThis demo still uses an old, slow method of solving ODE systems in Python, with \na constant time-step. It is sufficiently fast for the purposes of the demo, but\nfor development of new ODE systems, please refer to the method used in, e.g.\nzndsolve, cvsolve, stgsolve, where scipy.integrate.solve_ivp is called instead,\nand the ODE system is defined as a callable class method rather than a function.\n \n################################################################################\nTheory, numerical methods and applications are described in the following report:\n\nSDToolbox - Numerical Tools for Shock and Detonation Wave Modeling,\nExplosion Dynamics Laboratory, Contributors: S. Browne, J. Ziegler,\nN. Bitter, B. Schmidt, J. Lawson and J. E. Shepherd, GALCIT\nTechnical Report FM2018.001 Revised January 2021.\n\nPlease cite this report and the website if you use these routines. \n\nPlease refer to LICENCE.txt or the above report for copyright and disclaimers.\n\nhttp://shepherd.caltech.edu/EDL/PublicResources/sdt/\n\n################################################################################ \nUpdated September 2018\nTested with: \n Python 3.5 and 3.6, Cantera 2.3 and 2.4\nUnder these operating systems:\n Windows 8.1, Windows 10, Linux (Debian 9)\n\"\"\"\nimport cantera as ct\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import ode\nfrom cycler import cycler\n\ndef tpsys(t,y,gas,mw,T,P):\n \"\"\"\n Evaluates the system of ordinary differential equations for an adiabatic, \n constant-temperature and pressure, zero-dimensional reactor. \n It assumes that the 'gas' object represents a reacting ideal gas mixture.\n \n INPUT:\n t = time\n y = solution array [species mass fraction 1, 2, ...]\n gas = working gas object\n mw = array of species molar masses\n T = temperature\n P = pressure\n \n OUTPUT:\n An array containing time derivatives of:\n species mass fractions, \n formatted in a way that the integrator in this demo can recognize.\n \"\"\"\n\n # Set the state of the gas, based on the current solution vector.\n gas.TPY = T,P,y\n nsp = gas.n_species\n # species molar production rates\n wdot = gas.net_production_rates\n # set up column vector for dydt\n dYdt = []\n # species time evolution equations\n rrho = 1.0/gas.density\n for i in range(nsp):\n dYdt.append(rrho*mw[i]*wdot[i])\n \n return dYdt\n\n## initial gas state\nP1 = ct.one_atm; T1 = 3000\nmech = 'Mevel2015.cti'\ngas = ct.Solution(mech)\nq = 'H2:10 O2:1' \ngas.TPX = T1,P1,q\n## set up for integration routine\nmw = gas.molecular_weights\nnsp = gas.n_species\ny0 = gas.Y\nt_end = 1e-5;\ntel = [0, t_end]\n\ntime = [tel[0]]\nspecies = [gas.Y]\n\n# Set up ODE system for integration\nextraParams = (gas,mw,T1,P1)\nr = ode(tpsys)\ndt = 1e-8\nr.set_integrator('lsoda', method='bdf', rtol=1e-5, atol=1e-12)\nr.set_initial_value(y0,tel[0]).set_f_params(*extraParams)\n\n# Call the integrator to march in time - the equation set is defined in tpsys() \nwhile r.successful() and r.t < tel[1]:\n r.integrate(r.t + dt) \n #############################################################################################\n # Extract TIME, DISTANCE, VELOCITY, TEMPERAURE and SPECIES arrays from integrator output\n ############################################################################################# \n time.append(r.t)\n species.append(r.y)\ndel r\n\nspecies = np.array(species)\n\n## Plotting species\nplt.figure(num='TP Reactor')\nmasterFontSize = 12\ndefaultColors = ['#1f77b4',\n '#ff7f0e',\n '#2ca02c',\n '#d62728',\n '#9467bd',\n '#8c564b',\n '#e377c2',\n '#7f7f7f',\n '#bcbd22',\n '#17becf']\nplt.rc('font',size=masterFontSize)\n# Change linestyle once all colors cycled through\nplt.rc('axes', prop_cycle=(cycler('marker',['None','o','v','^'])*\n cycler('linestyle', ['-', '--', ':', '-.'])*\n cycler('color',defaultColors)))\n\nplt.plot(time,species,linewidth=2,markevery=50);\nplt.ticklabel_format(style='sci',axis='x',scilimits=(0,0))\nplt.xlabel('Time (s)')\nplt.ylabel('species mass fraction')\nplt.title('TP Reactor')\nplt.xlim([min(time),max(time)])\nplt.ylim((0,None))\n#plt.tight_layout()\nplt.legend(gas.species_names,loc='center left', bbox_to_anchor=(1, 0.5),ncol=4)\n#plt.tight_layout()\nplt.subplots_adjust(right=0.5)\nplt.show()\n\n\n","sub_path":"demo/demo_TP.py","file_name":"demo_TP.py","file_ext":"py","file_size_in_byte":4714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"122411811","text":"VarFloorNameList = [\"SS2\",\"SS1\",\"RC\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"]\nVarNumberOfFloor = len(VarFloorNameList)\nVarElevatorList = [\"Elevator 1\", \"Elevator 2\"]\nVarNumberOfElevator = 2\nElevators=[]\nFloors=[]\nElevatorDirection = [\"Up\", \"Down\", \"Nowhere\"]\nStatus = [\"Idle\", \"Stop\", \"Moving\"]\n\n\nclass ClOperator:\n def __init__(self,VarNumberOfFloor,VarNumberOfElevator):\n self.Column = []\n self.Column.append(ClColumn(VarNumberOfFloor,VarNumberOfElevator))\n\n\n def CallElevator(self,CurrentFloor,Direction):\n print(\"I want to go \" + str(Direction) +\n \". I am on floor \"+str(CurrentFloor))\n\n Elevator = self.FindElevator(CurrentFloor,Direction)\n print(str(Elevator.Id))\n self.OperateElevator(Elevator)\n return Elevator\n self.CallElevator = []\n\n def OperateElevator(self, Elevator):\n if Elevator.DestinationList == []:\n Elevator.Status =\"Idle\"\n else:\n NextFloor = Elevator.DestinationList[0]\n if NestFloor == Elevator.Floor:\n Elevator.Status = \"Stop\"\n Elevator.OpenDoor()\n elif NextFloor > Elevator.Floor:\n Elevator.Status = \"Moving\"\n Elevator.LevelUp()\n else:\n Elevator.status = \"Moving\"\n Elevator.LevelDown()\n\n\n def FindElevator(self,CurrentFloor,Direction):\n for Elevator in self.Columns[0].Elevators:\n print(\"Elevator \" + str(Elevator.Id) + \", Direction \" + str(Elevator.Direction) + \", Status \" + str(Elevator.Status))\n if Elevator.Floor == self.CurrentFloor and Elevator.Status == \"Stop\":\n return Elevator\n elif Elevator.Status == \"Idle\":\n return Elevator\n elif Elevator.Status == \"Moving\" and Elevator.Floor < self.CurrentFloor and Elevator.Direction == \"Up\":\n return Elevator\n elif Elevator.Status == \"Moving\" and Elevator.Floor > self.CurrentFloor and Elevator.Direction == \"Down\":\n return Elevator\n else:\n return self.IsShortestList()\n\n def IsShortestList(self):\n ShortestListElevator = None\n for Elevator in self.Columns[0].Elevators:\n\n if len(Elevator.DestinationList) < len(ShortestListElevator.DestinationList):\n ShortestListElevator = Elevator\n\n print(\"Elevator with the shortest list is \" + str(shortestListElevator))\n return ShortestListElevator\n\n\n def RequestFloor(self,FloorsNumber,Elevator):\n self.Elevator.ActivateButton(FloorNumber)\n\n\n\nclass ClColumn:\n def __init__(self,VarNumberofFloor,VarNumberOfElevator):\n self.ElevatorList=[]\n self.ButtonList=[]\n\n\n# Elevator\n for x in range(VarNumberOfElevator):\n self.ElevatorList.append(ClElevator(x+1, VarNumberofFloor))\n\n\n# FloorButton\n for y in range(VarNumberofFloor):\n FloorButton=[]\n if y is 0:\n self.ButtonList.append(ClFloorButton(y,VarFloorNameList[y],\"Up\"))\n elif y is -1:\n self.ButtonList.append(ClFloorButton(y,VarFloorNameList[y],\"Down\"))\n else:\n self.ButtonList.append(ClFloorButton(y,VarFloorNameList[y],\"Up\"))\n self.ButtonList.append(ClFloorButton(y,VarFloorNameList[y],\"Down\"))\n\n self.ButtonList.append(ClFloor(y,VarFloorNameList[y],FloorButton))\n\n\nclass ClElevator:\n def __init__(self,Id,Floor):\n self.Id=Id\n self.Status=\"Idle\"\n self.Floor=0\n self.Direction=\"NoWhere\"\n self.DestinationList=[]\n self.ButtonList=[]\n self.CloseDoorButton=CloseDoorsButton\n self.OpenDoorButton=OpenDoorsButton\n self.Door=Door()\n self.ElevatorNextFloor = []\n\n\n\n# loop that created ElevatorButton\n for x in range(VarNumberOfElevator):\n for y in range(VarNumberOfFloor):\n self.ButtonList.append(ClElevatorButton(\"Elevator \"+str(x+1)+\" Button call floor \"+VarFloorNameList[y]))\n\ndef LevelDown(self):\n self.Status=\"GoigDown\"\n NextFloor = self.DestinationList[0]\n while NextFloor != self.Floor:\n self.Floor = self.Floor -1\n self.DestinationList.remove(NextFloor)\n self.OpenDoor()\n\n\ndef LevelUp(self):\n self.Status=\"GoigUp\"\n NextFloor = self.DestinationList[0]\n while NextFloor != self.Floor:\n self.Floor = self.Floor +1\n self.DestinationList.remove(NextFloor)\n self.OpenDoor()\n\ndef OpenDoor(self):\n NextFloor = self.DestinationList[0]\n if self.Status is \"Moving\" or \"Idle\":\n self.Door.Status is \"Closed\"\n elif self.Floor is NextFloor:\n self.Door.Status is \"Opened\" and self.Door.Timer is \"Active\"\n\n\ndef CloseDoor(self):\n if self.Door.Timer > 0:\n self.Door.Status=\"Opened\"\n else:\n self.door.status = \"Closed\"\n\ndef ActivateButton(self, FloorNumber):\n print(str(FloorNumber)+ \" is selected\")\n GoActivateButton = 0\n for x in self.ButtonList:\n if x.FloorNumber == FloorNumber:\n GoActivateButton = x\n\n GoActivateButton.Status = \"Active\"\n\nclass ClFloor:\n def __init__(self,Id,Number,FloorButton):\n self.Id=Id\n self.Number = Number\n self.FloorButton = FloorButton\n\nclass ClFloorButton:\n def __init__(self,Id,FloorNumber,VarNumberOfElevator):\n self.Id=Id\n self.FloorNumber=FloorNumber\n self.VarNumberOfElevator=VarNumberOfElevator\n self.status =[]\n\n def setButton(self,Floor,Direction):\n x = FindButton(Floor,Direction)\n x.self.Status = \"Active\"\n\n def FindButton(self,Floor,Direcion):\n return Button()\n\nclass Door:\n def __init__(self):\n self.Status= \"Closed\",\"Opened\"\n self.Timer = 3\n\n def SetTimer(self):\n self.Status = \"Inactive\"\n\n def Open(self):\n self.Status = \"Opened\"\n print(\"Door Status = \" + str(self.Status))\n\n def Close(self):\n self.Status = \"Closed\"\n print(\"Door Status = \" + str(self.Status))\n\n\nclass ClElevatorButton:\n def __init__(self,Id):\n self.Id=Id\n self.Active = False\n\nclass OpenDoorsButton:\n\n def __init__(self):\n self.Status = \"Inactive\"\n\nclass CloseDoorsButton:\n def __init__(self):\n self.Status = \"Inactive\"\n\nController = ClOperator(10,2)\nElevator = Controller.RequestFloor(2, \"Up\")\nFloor = Controller.RequestFloor(4,Elevator)\n","sub_path":"Residential_Controller.py","file_name":"Residential_Controller.py","file_ext":"py","file_size_in_byte":6435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"548324168","text":"#coding=utf-8\r\n\r\ndef func1(p):\r\n dn=0 #数字个数\r\n an=0 #字母个数\r\n en=0 #其他个数\r\n for i in p:\r\n if i.isdigit():\r\n dn+=1\r\n elif i.isalpha():\r\n an+=1\r\n else:\r\n en+=1\r\n return (dn,an,en)\r\nr=func1('qwe 123')\r\nprint(r)\r\n","sub_path":"0604-作业参考.py","file_name":"0604-作业参考.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"361006106","text":"from getinput import get_input\nimport math\nimport itertools\nimport operator\n\n\ndef divisor_sum(n):\n return sum(d for d in range(1, n+1) if n % d == 0)\n\n\ndef divisor_sum_sp(n):\n return sum(d for d in range(1, n+1) if n % d == 0 and n / d <= 50)\n\n\ndef divisor_sum_p(prime_powers):\n \"\"\"prime_powers should have the form [(p_i, a_i)], and we are finding the divisor sum of the product of p_i**a_i\"\"\"\n prod = 1\n for prime, power in prime_powers:\n prod *= (prime**(power+1) - 1) // (prime - 1)\n return prod\n\n\ndef part_1(inputstr):\n divisor_sum_lb = int(inputstr) // 10\n\n # Handwavy primes/bounds; these depend on the particular input.\n # Not sure how to get a good list here algorithmically at the moment\n primes = [2, 3, 5, 7, 11, 13, 17, 19]\n pwr_bds = [21, 9, 5, 4, 4, 1, 1, 1]\n\n prods = []\n for prs in itertools.product(*list(map(range, pwr_bds))):\n prod = 1\n for p, pwr in zip(primes, prs):\n prod *= p**pwr\n prods.append(prod)\n\n prods = sorted(prods)\n for prod in prods:\n if divisor_sum(prod) >= divisor_sum_lb:\n return prod\n\n return None\n\n\ndef part_2(inputstr):\n \"\"\"Elves deliver 1 present per house to their first 50 multiples. Find the first house that receives at least\n present_lb presents.\"\"\"\n present_lb = -(-int(inputstr) // 11) # ceil(i/11).\n\n # Handwavy primes/bounds; these depend on the particular input.\n # Not sure how to get a good list here algorithmically at the moment\n primes = [2, 3, 5, 7, 11, 13, 17, 19, 23]\n pwr_bds = [16, 6, 4, 3, 3, 2, 2, 2, 2]\n\n prods = []\n for prs in itertools.product(*list(map(range, pwr_bds))):\n prod = 1\n for p, pwr in zip(primes, prs):\n prod *= p**pwr\n prods.append(prod)\n\n prods = sorted(prods)\n for prod in prods:\n if divisor_sum_sp(prod) >= present_lb:\n return prod\n\n return None\n\n\nif __name__ == \"__main__\":\n the_input_str = get_input(20)\n\n # print('Part 1:', part_1(the_input_str))\n print('Part 2:', part_2(the_input_str))\n","sub_path":"2015/day20.py","file_name":"day20.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"322511452","text":"##########################\n# Name: Cherie Liu\n# UNI: cl3945\n#\n# Represents the Student class, a subclass of the Person class. Each Student instance has a name (from Person), as well as a skill number and their grades.\n###########################\nfrom engi1006_simulator.person import Person\n\n\nclass Student(Person):\n def __init__(self, name, skill):\n super(Student, self).__init__(name)\n self.grades = []\n self.skill = skill\n","sub_path":"student.py","file_name":"student.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"68890109","text":"import math\nimport sys\nfrom os import rename\n\nimport requests\n\n\nprint(sys.version)\nprint(sys.executable)\n\n\ndef greet(who_to_great):\n greeting = \"Hello, {}\".format(who_to_great)\n return greeting\n\n\nprint(greet(\"Boris\"))\nr = requests.get(\"https://elysee.fr\")\nprint(r.status_code)\nprint(r.ok)\nname = input('what is your name ?')\n# print(\"Hello, {}\".format(name))\n# print('Hello', name)\n","sub_path":"HelloWorld.py","file_name":"HelloWorld.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"647563449","text":"# Exercise 11.3. Dictionaries have a method called keys that returns \r\n# the keys of the dictionary, in no particular order, as a list. \r\n# Modify print_hist to print the keys and their values in alphabetical order.\r\n#\r\n# I included two ways of doing this. The first takes dictionary view objects\r\n# unpacks them and converts them to a list of tuples. The second uses a comprehension\r\n# to iterate through the dictionary, appending each key value pair to a list.\r\n\r\n\r\nh = {'M': 1, 'i': 4, 's': 4, 'p': 1}\r\n\r\ndef print_hist(d):\r\n '''\r\n Takes a dictionary and unpacks the key-value pairs\r\n with d.keys() and d.values(). The list method is then\r\n applied \r\n '''\r\n keys = d.keys()\r\n values = d.values()\r\n \r\n x = list(keys)\r\n y = list(values)\r\n\r\n pairs = list(zip(x, y))\r\n return pairs\r\n\r\ndef print_hist_comprehension(d):\r\n '''\r\n Uses a comprehension with the d.items() method\r\n to iterate through the dictionary, unpacking each k and v\r\n and appending each pair to a list. \r\n '''\r\n pairs = [(k, v) for (k, v) in d.items()]\r\n return pairs\r\n \r\n\r\nif __name__ == '__main__':\r\n print (print_hist(h))\r\n print (print_hist_comprehension(h))\r\n","sub_path":"exercises/ex11_3.py","file_name":"ex11_3.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"387351851","text":"\"\"\"Collections of polygons and related utilities\n\"\"\"\n\nimport sys\n\nif sys.version_info[0] < 3:\n range = xrange\n\nfrom ctypes import c_void_p, cast\n\nfrom shapely.geos import lgeos\nfrom shapely.geometry.base import BaseMultipartGeometry, geos_geom_from_py\nfrom shapely.geometry import polygon\nfrom shapely.geometry.proxy import CachingGeometryProxy\n\n__all__ = ['MultiPolygon', 'asMultiPolygon']\n\n\nclass MultiPolygon(BaseMultipartGeometry):\n\n \"\"\"A collection of one or more polygons\n \n If component polygons overlap the collection is `invalid` and some\n operations on it may fail.\n \n Attributes\n ----------\n geoms : sequence\n A sequence of `Polygon` instances\n \"\"\"\n\n def __init__(self, polygons=None, context_type='polygons'):\n \"\"\"\n Parameters\n ----------\n polygons : sequence\n A sequence of (shell, holes) tuples where shell is the sequence\n representation of a linear ring (see linearring.py) and holes is\n a sequence of such linear rings\n\n Example\n -------\n Construct a collection from a sequence of coordinate tuples\n\n >>> ob = MultiPolygon( [\n ... (\n ... ((0.0, 0.0), (0.0, 1.0), (1.0, 1.0), (1.0, 0.0)), \n ... [((0.1,0.1), (0.1,0.2), (0.2,0.2), (0.2,0.1))]\n ... )\n ... ] )\n >>> len(ob.geoms)\n 1\n >>> type(ob.geoms[0]) == Polygon\n True\n \"\"\"\n super(MultiPolygon, self).__init__()\n\n if not polygons:\n # allow creation of empty multipolygons, to support unpickling\n pass\n elif context_type == 'polygons':\n self._geom, self._ndim = geos_multipolygon_from_polygons(polygons)\n elif context_type == 'geojson':\n self._geom, self._ndim = geos_multipolygon_from_py(polygons)\n\n def shape_factory(self, *args):\n return polygon.Polygon(*args)\n\n @property\n def __geo_interface__(self):\n allcoords = []\n for geom in self.geoms:\n coords = []\n coords.append(tuple(geom.exterior.coords))\n for hole in geom.interiors:\n coords.append(tuple(hole.coords))\n allcoords.append(tuple(coords))\n return {\n 'type': 'MultiPolygon',\n 'coordinates': allcoords\n }\n\n def svg(self, scale_factor=1., fill_color=None):\n \"\"\"Returns group of SVG path elements for the MultiPolygon geometry.\n\n Parameters\n ==========\n scale_factor : float\n Multiplication factor for the SVG stroke-width. Default is 1.\n fill_color : str, optional\n Hex string for fill color. Default is to use \"#66cc99\" if\n geometry is valid, and \"#ff3333\" if invalid.\n \"\"\"\n if self.is_empty:\n return ''\n if fill_color is None:\n fill_color = \"#66cc99\" if self.is_valid else \"#ff3333\"\n return '' + \\\n ''.join(p.svg(scale_factor, fill_color) for p in self) + \\\n ''\n\n\nclass MultiPolygonAdapter(CachingGeometryProxy, MultiPolygon):\n \n context = None\n _other_owned = False\n\n def __init__(self, context, context_type='polygons'):\n self.context = context\n if context_type == 'geojson':\n self.factory = geos_multipolygon_from_py\n elif context_type == 'polygons':\n self.factory = geos_multipolygon_from_polygons\n\n @property\n def _ndim(self):\n try:\n # From array protocol\n array = self.context[0][0].__array_interface__\n n = array['shape'][1]\n assert n == 2 or n == 3\n return n\n except AttributeError:\n # Fall back on list\n return len(self.context[0][0][0])\n\n\ndef asMultiPolygon(context):\n \"\"\"Adapts a sequence of objects to the MultiPolygon interface\"\"\"\n return MultiPolygonAdapter(context)\n\n\ndef geos_multipolygon_from_py(ob):\n \"\"\"ob must provide Python geo interface coordinates.\"\"\"\n L = len(ob)\n assert L >= 1\n \n N = len(ob[0][0][0])\n assert N == 2 or N == 3\n\n subs = (c_void_p * L)()\n for l in range(L):\n geom, ndims = polygon.geos_polygon_from_py(ob[l][0], ob[l][1:])\n subs[l] = cast(geom, c_void_p)\n \n return (lgeos.GEOSGeom_createCollection(6, subs, L), N)\n\n\ndef geos_multipolygon_from_polygons(arg):\n \"\"\"\n ob must be either a MultiPolygon, sequence or array of sequences \n or arrays.\n \n \"\"\"\n if isinstance(arg, MultiPolygon):\n return geos_geom_from_py(arg)\n\n obs = getattr(arg, 'geoms', arg)\n obs = [ob for ob in obs\n if ob and not (isinstance(ob, polygon.Polygon) and ob.is_empty)]\n L = len(obs)\n\n # Bail immediately if we have no input points.\n if L <= 0:\n return (lgeos.GEOSGeom_createEmptyCollection(6), 3)\n\n exemplar = obs[0]\n try:\n N = len(exemplar[0][0])\n except TypeError:\n N = exemplar._ndim\n \n assert N == 2 or N == 3\n\n subs = (c_void_p * L)()\n\n for i, ob in enumerate(obs):\n if isinstance(ob, polygon.Polygon):\n shell = ob.exterior\n holes = ob.interiors\n else:\n shell = ob[0]\n holes = ob[1]\n\n geom, ndims = polygon.geos_polygon_from_py(shell, holes)\n subs[i] = cast(geom, c_void_p)\n\n return (lgeos.GEOSGeom_createCollection(6, subs, L), N)\n\n# Test runner\ndef _test():\n import doctest\n doctest.testmod()\n\nif __name__ == \"__main__\":\n _test()\n","sub_path":"Rasterio_osgeo_shapely_PIL_pyproj_numpy/source/shapely/geometry/multipolygon.py","file_name":"multipolygon.py","file_ext":"py","file_size_in_byte":5554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"371412456","text":"from urllib.request import urlopen\nfrom multiprocessing import Process, Queue\nfrom get_db import get_db\nimport time\n\ndb = get_db()\n\n\ndef save_image(q, try_times=5,\n folder_name=\"/Users/age_prediction/all_picture/\"):\n while not q.empty():\n url, actor_info = q.get()\n response = None\n for i in range(try_times):\n try:\n response = urlopen(url, timeout=60)\n break\n except Exception as e:\n print(e)\n time.sleep(5)\n if response:\n file_name = \"_\".join(str(x) for x in actor_info)\n file_location = folder_name + file_name + \".jpg\"\n with open(file_location, 'wb') as f:\n f.write(response.read())\n sql = \"update info set image_downloaded = 1 where image_link = %s\"\n db = get_db()\n with db.cursor() as cursor:\n cursor.execute(sql, (url, ))\n db.commit()\n\n\ndef update_actor_info(process_count=50):\n download_pic = True\n while download_pic:\n db = get_db()\n sql = \"\"\"select image_link, info.id, birth_year, is_male from all_url, \\\n info where info.id = all_url.id and image_downloaded = 0 \\\n limit 500\"\"\"\n results = None\n with db.cursor() as cursor:\n cursor.execute(sql)\n results = cursor.fetchall()\n url_q = Queue()\n if results:\n for item in results:\n url_q.put((item[0], item[1:]))\n task = []\n for i in range(process_count):\n task.append(Process(target=save_image,\n args=(url_q,)))\n for p in task:\n p.start()\n for p in task:\n p.join()\n else:\n download_pic = False\n db.close()\n\n\nif __name__ == \"__main__\":\n update_actor_info(process_count=10)\n","sub_path":"download_image.py","file_name":"download_image.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"575281967","text":"from optopsy.enums import FilterType\n\n\nclass Filter(object):\n\n def __init__(self, name=None):\n self._name = name\n self.type = None\n\n @property\n def name(self):\n \"\"\"\n Filter name.\n \"\"\"\n if self._name is None:\n self._name = self.__class__.__name__\n return self._name\n\n def __call__(self, quotes):\n raise NotImplementedError(\"%s not implemented!\" % self.name)\n\n\nclass FilterStack(object):\n \"\"\"\n A FilterStack runs multiple Filters until a failure is encountered.\n\n The purpose of an FilterStack is to group a logic set of Filters together. Each\n Filter in the stack is run. Execution stops if one Filter returns False.\n\n Args:\n * filters (list): List of filters.\n\n \"\"\"\n\n def __init__(self, target, *filters):\n super(FilterStack, self).__init__()\n self.filters = filters\n self.check_run_always = any(hasattr(x, 'run_always')\n for x in self.filters)\n\n # check if our data source has columns needed for certain filters\n for f in self.filters:\n if hasattr(f, 'required_fields'):\n if not all(\n fld in target.spread_data.columns for fld in f.required_fields):\n raise ValueError(\n \"Required fields not in data source: \".join(\n f.required_fields))\n\n def __call__(self, target, quotes):\n # normal running mode\n if not self.check_run_always:\n for f in self.filters:\n if not f(target, quotes):\n return False\n return True\n # run mode when at least one filter has a run_always attribute\n else:\n # store result in res\n # allows continuation to check for and run\n # filters that have run_always set to True\n res = True\n for f in self.filters:\n if res:\n res = f(target, quotes)\n elif hasattr(f, 'run_always'):\n if f.run_always:\n f(target, quotes)\n return res\n\n\nclass EntryAbsDelta(Filter):\n\n def __init__(self, ideal, lower, upper):\n Filter.__init__(self, 'Entry - Absolute Delta')\n self.type = FilterType.ENTRY\n self.required_fields = ['delta']\n self.ideal = ideal\n self.lower = lower\n self.upper = upper\n\n def __call__(self, quotes):\n return quotes.nearest(\n 'delta',\n self.ideal).between(\n 'delta',\n self.lower,\n self.upper)\n\n\nclass EntrySpreadPrice(Filter):\n\n def __init__(self, ideal, lower, upper):\n Filter.__init__(self, 'Entry - Spread Price')\n self.type = FilterType.ENTRY\n self.ideal = ideal\n self.lower = lower\n self.upper = upper\n\n def __call__(self, quotes):\n return quotes.nearest(\n 'mark',\n self.ideal).between(\n 'mark',\n self.lower,\n self.upper)\n\n\nclass EntryDaysToExpiration(Filter):\n\n def __init__(self, ideal, lower, upper):\n super(EntryDaysToExpiration).__init__()\n self.type = FilterType.ENTRY\n self.ideal = ideal\n self.lower = lower\n self.upper = upper\n\n def __call__(self, quotes):\n pass\n\n\nclass EntryDayOfWeek(Filter):\n\n def __init__(self, ideal):\n super(EntryDayOfWeek).__init__()\n self.type = FilterType.ENTRY\n self.ideal = ideal\n\n def __call__(self, quotes):\n pass\n\n\nclass ExitDaysToExpiration(Filter):\n\n def __init__(self, ideal):\n super(ExitDaysToExpiration).__init__()\n self.type = FilterType.EXIT\n self.ideal = ideal\n\n def __call__(self, quotes):\n pass\n","sub_path":"optopsy/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":3833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"151761272","text":"# if not auth.has_membership(\"admin\"):\n# redirect(URL('default', 'acessonegado'))\n\ndef product_posted(form):\n redirect(URL('list'))\n\n\ndef show():\n pid = request.args(0)\n produto = Product[pid] or redirect(URL('list'))\n return dict(produto=produto)\n\n\n@auth.requires_membership(\"admin\")\ndef edit():\n pid = request.args(0)\n produto = Product[pid] or redirect(URL('list'))\n form = SQLFORM(Product, produto.id).process()\n return dict(form=form)\n\n\n@auth.requires_membership(\"admin\")\ndef delete():\n pid = request.args(0)\n produto = Product[pid] # db(db.product.id == pid).select()\n if produto:\n produto.delete_record() # db(db.product.id == pid).delete()\n db.commit()\n return \"\"\"\n jQuery('#%s').hide();\n \"\"\" % pid\n\n@auth.requires_membership(\"admin\")\n# @auth.requires(auth.has_membership(\"admin\") or \\\n# auth.has_membership(\"admin\"))\ndef new():\n form = SQLFORM(db.product).process(onsuccess=product_posted)\n # if form.validate():\n # imagem = form.vars.picture.file # binario\n # filename = form.vars.picture.filename\n # # store\n # Product.insert(**form.vars)\n submit_button = form.elements(_type=\"submit\")[0]\n submit_button[\"_class\"] = \"btn btn-primary\"\n\n return locals()\n\n\n@auth.requires_membership(\"admin\")\ndef list():\n products = db(Product).select()\n #products = SQLFORM.grid(db.product.id>0)\n return dict(products=products)\n","sub_path":"controllers/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":1453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"176108730","text":"#!/usr/bin/env python3\n\n# Task 1\n\nvalues = (2, 123.4567, 10000, 12345.67)\nformatter = 'file_{:03d}: {:05.2f}, {:.2E}, {:.2E}'\noutput = formatter.format(*values)\n\nprint(output)\n\n# Task 2\n\nformatter2 = 'file_{a[0]:03d}: {a[1]:05.2f}, {a[2]:.2E}, {a[3]:.2E}'\noutput2 = formatter2.format(a=values)\n\noutput3 = f'file_{values[0]:03d}: {values[1]:05.2f}, {values[2]:.2E}, {values[3]:.2E}'\n\nprint(output2)\nprint(output3)\n\n# Task 3\n\n\ndef dynamic_format(seq):\n l = len(seq)\n return (('The {} numbers are: ' + \", \".join(['{}']*l)).format(l, *seq))\n\nprint(dynamic_format(values))\n\n# Task 4\n\ntup = (4, 30, 2017, 2, 27)\n\nprint(f\"{tup[3]:02d} {tup[4]:02d} {tup[2]:02d} {tup[0]:02d} {tup[1]:02d}\")\n\n# Task 5\n\ngiven = ['oranges', 1.3, 'lemons', 1.1]\ndisplay = f\"The weight of an {given[0][:-1]} is {given[1]} and the weight of a {given[2][:-1]} is {given[3]}\"\ndisplay2 = f\"The weight of an {given[0][:-1].upper()} is {given[1]*1.2} and the weight of a {given[2][:-1].upper()} is {given[3]*1.2}\"\n\nprint(display)\nprint(display2)\n\n# Task 6\n\ncars = [(\"Ferrari\", 90000, 2), (\"Mercedes\", 17000, 6), (\"Unknown\", 100, 17), (\"Pinto\", 5, 30), (\"BMW\", 9000, 10)]\nfor car in cars:\n print(\"{:10} ${:7,} {:6} years old\".format(*car))\n\nconsec = (3, 4, 5, 6, 7, 8, 9, 10, 11, 12)\nconsec2 = []\nfor x in consec:\n consec2.append(x*500)\n\nprint((\"{:5}\" * 10).format(*consec))\nprint((\"{:5}\" * 10).format(*consec2))\n","sub_path":"students/alexLaws/lesson03/strformat_lab.py","file_name":"strformat_lab.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"5879522","text":"import re\n\nfilename='Q1FY22.txt'\n\ndef domain_count(domdic):\n for k, v in sorted(domdic.items(), key=lambda x: -x[1]):\n print(k+';'+str(v))\n\n print('\\n\\n\\n')\n\nquestionNo = {}\nuserNo = {}\nuserNoFinal = {}\nnecpattern = 'jp.nec.com'\n\nwith open(filename, encoding='utf-8') as f:\n for row in f:\n columns = row.rstrip().split('@')\n domain = columns[1]\n\n matchNEC = re.search(necpattern, domain)\n if matchNEC:\n domain = necpattern\n\n if domain in questionNo.keys():\n questionNo[domain] += 1\n else:\n questionNo[domain] = 1\n \n domain_count(questionNo)\n\n\nwith open(filename, encoding='utf-8') as f2:\n for row in f2:\n address = row.rstrip()\n\n matchNEC = re.search(necpattern, address)\n if matchNEC:\n emailsplit = address.split('@')\n emailname = emailsplit[0]\n address = emailname+'@'+necpattern\n\n if address in userNo.keys():\n userNo[address] += 1\n else:\n userNo[address] = 1\n\n for k, v in userNo.items():\n columns = k.split('@')\n userdomain = columns[1]\n if userdomain in userNoFinal.keys():\n userNoFinal[userdomain] += 1\n else:\n userNoFinal[userdomain] = 1\n\n domain_count(userNoFinal) \n","sub_path":"emailstats/quarterlystats.py","file_name":"quarterlystats.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"96390560","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.animation as animation\n\ndef trapezoidal(primitive, result):\n for i in range(len(primitive) - 1):\n trapezoid_height = (primitive[i] + primitive[i + 1]) / 2\n area = trapezoid_height * trapezoid_width\n result.append(area)\n\ndef colorGradient(val):\n NSAMPLES = val\n\n # Synthesize R, G, B and A channels with dummy data\n # The thing to note is that the samples are REAL and in range [0..1]\n r = np.linspace(0,1,NSAMPLES).astype(np.float)\n g = 1.0 - r\n b = np.full(NSAMPLES,0.5,np.float)\n a = np.full(NSAMPLES,1,np.float)\n\n # Merge into a single array, 4 deep\n RGBA = np.dstack((r,g,b,a))\n return RGBA\n\nport = 'COM5'\nbaudrate = 115200\ndirectory = 'C:\\\\Users\\\\ycpig\\\\Documents\\\\data.csv'\nencoding = ['utf-8', 'euc-kr', 'cp949']\nindex = ['aX', 'aY', 'aZ', 'aSqrt', 'gX', 'gY', 'gZ', 'mX', 'mY', 'mZ', 'mDirection']\ndf = pd.read_csv(directory, sep=',', names=index, encoding=encoding[0])\n\naccel_x = []\naccel_y = []\naccel_z = []\naccel_sqrt = []\naccel = [accel_x, accel_y, accel_z, accel_sqrt]\ngyro_x = []\ngyro_y = []\ngyro_z = []\ngyro = [gyro_x, gyro_y, gyro_z]\nmag_x = []\nmag_y = []\nmag_z = []\nmag_direction = []\nmag = [mag_x, mag_y, mag_z, mag_direction]\npressure = []\ntemperature = []\n\nvel_x = []\nvel_y = []\nvel_z = []\nvel = [vel_x, vel_y, vel_z]\ndis_x = []\ndis_y = []\ndis_z = []\ndis = [dis_x, dis_y, dis_z]\n\nrows = len(df)\n\nfor i in range(rows):\n # for j in range():\n # df.iloc[i][j]\n accel_x.append(df.iloc[i][0] * 1000)\n accel_y.append(df.iloc[i][1] * 1000)\n accel_z.append(df.iloc[i][2] * 1000)\n accel_sqrt.append(df.iloc[i][3])\n gyro_x.append(df.iloc[i][4])\n gyro_y.append(df.iloc[i][5])\n gyro_z.append(df.iloc[i][6])\n mag_x.append(df.iloc[i][7])\n mag_y.append(df.iloc[i][8])\n mag_z.append(df.iloc[i][9])\n mag_direction.append(df.iloc[i][10])\n\nfrequency = 100 # Hz\ntrapezoid_width = 1 / frequency\n\nfor i in range(len(accel) - 1):\n trapezoidal(accel[i], vel[i]) # acceleration to velocity\n trapezoidal(vel[i], dis[i]) # velocity to displacement\n\nsample = len(dis_x)\n\nposition = list(map(list, zip(*dis)))\nposition = np.array(position)\n\nnfr = 30 # Number of frames\nfps = 10 # Frame per sec\n\nx = []\ny = []\nz = []\n# s = np.arange(1,nfr,0.5)\n# for i in s:\n# x.append(normal(50,i,200))\n# y.append(normal(50,i,200))\n# z.append(normal(50,i,200))\n \nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nscatter, = ax.plot([], [], [], \"o\", markersize=5)\n\ndef update(frame, xa, ya, za):\n text.set_text(\"Sample: {:d} \\nPosition: [{:.4f},{:.4f},{:.4f}]\".format(frame, dis_x[frame], dis_y[frame], dis_z[frame])) # for debugging\n # x.append[xa[frame]]\n # y.append[ya[frame]]\n # z.append[za[frame]]\n # scatter.set_data_3d(x[:frame], y[:frame], z[:frame])\n scatter.set_data_3d(xa[frame], ya[frame], za[frame])\n # scatter.set_3d_properties(za[frame])\n \n# graph = ax.scatter(x, y, z, color='red', s=10)\ntext = fig.text(0, 1, \"TEXT\", va='top') # for debugging\n\n# Setting the axes properties\nax.set(xlabel='X')\nax.set(ylabel='Y')\nax.set(zlabel='Z')\n\nv = 0.05\n\nax.set_xlim(-v,v)\nax.set_ylim(-v,v)\nax.set_zlim(-v,v)\n\n# Creating the Animation object\nani = animation.FuncAnimation(fig=fig, func=update, frames=sample, fargs=(dis_x, dis_y, dis_z), interval=1)\n\n# ani.save('animation.gif', writer='imagemagick', fps=30, dpi=100)\nf = \"C:\\\\Users\\\\ycpig\\\\Documents\\\\animation.gif\"\n# writergif = animation.PillowWriter(fps=30)\n# ani.save(f, writer=writergif)\n\nplt.show()","sub_path":"programming/python/IMU/plotting3.py","file_name":"plotting3.py","file_ext":"py","file_size_in_byte":3616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"259481773","text":"import string\nfrom boyer_moore import boyer_moore\nfrom preprocessing import BoyerMoore\n\ndef approximate_match(p, t, n, missed):\n segment_length = int(round(len(p) / (n+1)))\n all_matches = set()\n for i in range(n+1):\n start = i*segment_length\n end = min((i+1)*segment_length, len(p))\n p_bm = BoyerMoore(p[start:end], alphabet='abcdefghijklmnopqrstuvwxyz ')\n matches = boyer_moore(p[start:end], p_bm, t)\n # Extend matching segments to see if whole p matches\n for m in matches:\n missed[m-start] = []\n if m < start or m-start+len(p) > len(t):\n continue\n mismatches = 0\n for j in range(0, start):\n if not p[j] == t[m-start+j]:\n mismatches += 1\n missed[m-start].append(j)\n if mismatches > n:\n break\n for j in range(end, len(p)):\n if not p[j] == t[m-start+j]:\n mismatches += 1\n missed[m-start].append(j)\n if mismatches > n:\n break\n if mismatches <= n:\n all_matches.add(m - start)\n else:\n del missed[m-start]\n return list(all_matches)","sub_path":"venv/approximate.py","file_name":"approximate.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"416325163","text":"\"\"\"\nExports post routes.\n\"\"\"\n\nfrom datetime import datetime\n\nfrom flask import (Blueprint,\n flash,\n jsonify,\n redirect,\n render_template,\n request,\n session,\n url_for)\n\nfrom tiny.forms import CommentForm, PostForm\nfrom tiny.helpers import (author_required,\n get_comments,\n get_posts,\n markdown_to_html,\n post_required,\n serialize,\n sign_in_required)\nfrom tiny.models import Comment, Post\n\npost = Blueprint('post', __name__, url_prefix='/post')\n\n@post.route('/create', methods=['GET', 'POST'])\n@sign_in_required\ndef create(current_user):\n \"\"\"\n Create post route.\n \"\"\"\n\n # parse the form\n form = PostForm(request.form, obj=Post())\n\n # render create post form if GET request\n if request.method == 'GET':\n return render_template('post/create.html', form=form)\n\n # render form again if submitted form is invalid\n if not form.validate_on_submit():\n return render_template('post/create.html', form=form), 400\n\n # create new post\n new_post = Post(author=current_user,\n title=form.title.data,\n lead_paragraph=form.lead_paragraph.data,\n image_url=form.image_url.data,\n content=form.content.data).save()\n\n # notify user\n flash('Post successfully created.', 'success')\n\n # redirect to post page\n return redirect(url_for('post.show', post_id=str(new_post.id)))\n\n@post.route('//show', methods=['GET'])\n@post_required\ndef show(post_id, selected_post):\n \"\"\"\n Show post route.\n \"\"\"\n\n return render_template('post/show.html',\n form=CommentForm(),\n post=selected_post,\n is_author=str(selected_post.author.id) == session.get('user_id'))\n\n@post.route('//settings', methods=['GET'])\n@sign_in_required\n@post_required\n@author_required\ndef settings(current_user, post_id, selected_post):\n \"\"\"\n Post settings route.\n \"\"\"\n\n return render_template('post/settings.html', post=selected_post)\n\n@post.route('//update', methods=['GET', 'POST'])\n@sign_in_required\n@post_required\n@author_required\ndef update(current_user, post_id, selected_post):\n \"\"\"\n Update post route.\n \"\"\"\n\n # parse the form\n form = PostForm(request.form, obj=selected_post)\n\n # render update post form if GET request\n if request.method == 'GET':\n return render_template('post/update.html', form=form, post=selected_post)\n\n # render form again if submitted form is invalid\n if not form.validate_on_submit():\n return render_template('post/update.html', form=form, post=selected_post), 400\n\n # update the post information\n form.populate_obj(selected_post)\n selected_post.last_updated = datetime.now()\n selected_post.save()\n\n # notify the user\n flash('Post successfully updated.', 'success')\n\n # redirect back to post page\n return redirect(url_for('post.show', post_id=post_id))\n\n@post.route('//delete', methods=['GET', 'POST'])\n@sign_in_required\n@post_required\n@author_required\ndef delete(current_user, post_id, selected_post):\n \"\"\"\n Delete post route.\n \"\"\"\n\n # render delete page if GET request\n if request.method == 'GET':\n return render_template('post/delete.html', post=selected_post)\n\n selected_post.delete()\n\n # notify user\n flash('Successfully deleted post.', 'success')\n\n # redirect back to homepage\n return redirect(url_for('home.index'))\n\n@post.route('/latest', methods=['GET'])\ndef latest():\n \"\"\"\n Latest post route.\n \"\"\"\n\n # get query parameters\n skip = request.args.get('skip', 0, type=int)\n limit = request.args.get('limit', 12, type=int)\n\n # query for latest posts (making sure to exclude the actual content)\n results = get_posts(exclude=['content'],\n order_by=['-created'],\n skip=skip,\n limit=limit)\n\n return jsonify(serialize(results))\n\n@post.route('//comments', methods=['GET'])\n@post_required\ndef comments(post_id, selected_post):\n \"\"\"\n Post comments route.\n \"\"\"\n\n # get query parameters\n skip = request.args.get('skip', 0, type=int)\n limit = request.args.get('limit', 12, type=int)\n\n # query for post's comments (making sure to exclude the post itself)\n results = get_comments(post_id=post_id,\n exclude=['post'],\n order_by=['created'],\n skip=skip,\n limit=limit)\n\n return jsonify(serialize(results))\n\n@post.route('//comment', methods=['POST'])\n@sign_in_required\n@post_required\ndef comment(current_user, post_id, selected_post):\n \"\"\"\n Post comment route.\n \"\"\"\n\n # parse the form\n form = CommentForm(request.form)\n\n # form is not valid so return error\n if not form.validate_on_submit():\n errors = []\n for field_name, field_errors in form.errors.items():\n for error in field_errors:\n errors.append(error)\n return jsonify({'errors': errors, 'success': False}), 400\n\n # create the comment\n Comment(author=current_user,\n post=selected_post,\n text=form.text.data).save()\n\n return jsonify({'success': True}), 200\n\n@post.route('/preview', methods=['POST'])\ndef preview():\n \"\"\"\n Post preview route.\n \"\"\"\n\n return jsonify({'html': markdown_to_html(request.form.get('content', ''))}), 200\n","sub_path":"tiny/blueprints/post.py","file_name":"post.py","file_ext":"py","file_size_in_byte":5737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"266121474","text":"from time import sleep\nimport boto3, json, os, logging, datetime\n\nconnect = boto3.client('connect')\npinpoint_client = boto3.client('pinpoint')\n\n\n# This function can be used within an Amazon Pinpoint Campaign or Amazon Pinpoint Journey.\n\ndef lambda_handler(event, context):\n\n logging.getLogger().setLevel('INFO')\n # print the payload the Lambda was invoked with from SQS\n logging.info(event)\n\n for record in event['Records']:\n payload=record[\"body\"]\n pinpoint_event = json.loads(payload)\n\n endpoint_id = pinpoint_event['endpoint_id']\n application_id = pinpoint_event['application_id']\n campaign_id = pinpoint_event['campaign_id']\n address = pinpoint_event['address']\n contact_flow_id = pinpoint_event['contact_flow_id']\n instance_id = pinpoint_event['instance_id']\n queue_id = pinpoint_event['queue_id']\n message = pinpoint_event['message']\n\n custom_events_batch = {}\n # Gather events to emit back to Pinpoint for reporting\n\n logging.info(endpoint_id)\n logging.info(message)\n logging.info(contact_flow_id)\n logging.info(instance_id)\n logging.info(queue_id)\n\n try:\n\n response = connect.start_outbound_voice_contact(\n DestinationPhoneNumber=address,\n ContactFlowId=contact_flow_id,\n InstanceId=instance_id,\n QueueId=queue_id,\n Attributes={\n 'Message': message\n }\n )\n logging.info(response)\n\n custom_events_batch[endpoint_id] = create_success_custom_event(endpoint_id, campaign_id, message)\n\n except Exception as e:\n logging.error(e)\n logging.error(\"Error trying to send a Pinpoint Connect message\")\n\n custom_events_batch[endpoint_id] = create_failure_custom_event(endpoint_id, campaign_id, e)\n\n try:\n # submit events back to Pinpoint for reporting\n put_events_result = pinpoint_client.put_events(\n ApplicationId=application_id,\n EventsRequest={\n 'BatchItem': custom_events_batch\n }\n )\n logging.info(put_events_result)\n except Exception as e:\n logging.error(e)\n logging.error(\"Error trying to send custom events to Pinpoint\")\n\n sleep(3)\n # Sleep 3 seconds between calls to avoid rate limiting\n\n\n logging.info(\"Complete\")\n return \"Complete\"\n\ndef create_success_custom_event(endpoint_id, campaign_id, message):\n custom_event = {\n 'Endpoint': {},\n 'Events': {}\n }\n custom_event['Events']['voice_%s_%s' % (endpoint_id, campaign_id)] = {\n 'EventType': 'connect.success',\n 'Timestamp': datetime.datetime.now().isoformat(),\n 'Attributes': {\n 'campaign_id': campaign_id,\n 'message': (message[:195] + '...') if len(message) > 195 else message\n }\n }\n return custom_event\n\ndef create_failure_custom_event(endpoint_id, campaign_id, e):\n error = repr(e)\n custom_event = {\n 'Endpoint': {},\n 'Events': {}\n }\n custom_event['Events']['voice_%s_%s' % (endpoint_id, campaign_id)] = {\n 'EventType': 'connect.failure',\n 'Timestamp': datetime.datetime.now().isoformat(),\n 'Attributes': {\n 'campaign_id': campaign_id,\n 'error': (error[:195] + '...') if len(error) > 195 else error\n }\n }\n return custom_event\n","sub_path":"pinpoint_connect/pinpointconnect/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"440829371","text":"# Copyright 2018 Adrien CHEVRIER, Florian HEPP, Xavier HERMAND,\n# Gauthier LEONARD, Audrey LY, Elliot MAINCOURT\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 json\nimport os\nimport datetime\nfrom dateutil import parser\n\nfrom adapt.intent import IntentBuilder\nfrom mycroft.skills.core import MycroftSkill\nfrom mycroft.skills.core import intent_handler\nfrom mycroft.skills.context import adds_context, removes_context\nfrom mycroft.util.log import getLogger\nfrom mycroft.messagebus.message import Message\nfrom mycroft.skills.settings import SkillSettings\n\n__author__ = 'Nuttymoon'\n\n# Logger: used for debug lines, like \"LOGGER.debug(xyz)\". These\n# statements will show up in the command line when running Mycroft.\nLOGGER = getLogger(__name__)\n\nWEEKDAYS = [\n \"mondays\",\n \"tuesdays\",\n \"wednesdays\",\n \"thursdays\",\n \"fridays\",\n \"saturdays\",\n \"sundays\"\n]\n\nSKILLS_FOLDERS = {\n \"/opt/mycroft/skills/PFE1718-skill-listener\": \"skill listener\",\n \"/opt/mycroft/skills/PFE1718-habit-miner\": \"habit miner\",\n \"/opt/mycroft/skills/PFE1718-automation-handler\": \"automation handler\"\n}\n\n\nclass HabitsManager(object):\n \"\"\"\n This class manages the reading and writting in the file habits.json\n\n Attributes:\n habits_file_path (str): path to the file habits.json\n habits (json): the json datastore corresponding to habits.json\n \"\"\"\n\n def __init__(self):\n self.habits_file_path = os.path.expanduser(\n \"~/.mycroft/skills/ListenerSkill/habits/habits.json\")\n self.triggers_file_path = os.path.expanduser(\n \"~/.mycroft/skills/ListenerSkill/habits/triggers.json\")\n\n def load_files(self):\n self.habits = json.load(open(self.habits_file_path))\n self.triggers = json.load(open(self.triggers_file_path))\n\n def get_all_habits(self):\n \"\"\"Return all the existing habits of the user\"\"\"\n return self.habits\n\n def get_habit_by_id(self, habit_id):\n \"\"\"Return one particular habit of the user\"\"\"\n return self.habits[habit_id]\n\n def register_habit(self, trigger_type, intents, time=None, days=None):\n \"\"\"\n Register a new habit in habits.json\n\n Args:\n trigger_type (str): the habit trigger type (\"time\" or \"skill\")\n intents (datastore): the intents that are part of the habit\n time (str): the time of the habit (if time based)\n days (int[]): the days of the habit (if time based)\n \"\"\"\n if trigger_type == \"skill\":\n self.habits += [\n {\n \"intents\": intents,\n \"trigger_type\": trigger_type,\n \"automatized\": 0,\n \"user_choice\": False,\n \"triggers\": []\n }\n ]\n else:\n self.habits += [\n {\n \"intents\": intents,\n \"trigger_type\": trigger_type,\n \"automatized\": 0,\n \"user_choice\": False,\n \"time\": time,\n \"days\": days\n }\n ]\n with open(self.habits_file_path, 'w') as habits_file:\n json.dump(self.habits, habits_file)\n\n def save_habits(self):\n with open(self.habits_file_path, 'w') as habits_file:\n json.dump(self.habits, habits_file)\n\n def automate_habit(self, habit_id, auto, new_triggers=None):\n \"\"\"\n Register the automation of a habit in the habits.json\n\n Args:\n habit_id (int): the id of the habit to automate\n triggers (str[]): the intents to register as triggers of the habit\n auto (int): 1 for full automation, 2 for habit offer when triggered\n \"\"\"\n habit = self.habits[habit_id]\n habit[\"user_choice\"] = True\n habit[\"automatized\"] = auto\n\n if habit[\"trigger_type\"] == \"skill\":\n if not self.triggers:\n for i in new_triggers:\n self.triggers += [\n {\n \"intent\": habit[\"intents\"][i][\"name\"],\n \"parameters\": habit[\"intents\"][i][\"parameters\"],\n \"habit_id\": habit_id\n }\n ]\n else:\n if not self.check_triggers(habit_id, habit, new_triggers):\n return False\n\n habit[\"triggers\"] = new_triggers\n with open(self.triggers_file_path, 'w') as triggers_file:\n json.dump(self.triggers, triggers_file)\n\n self.habits[habit_id] = habit\n with open(self.habits_file_path, 'w') as habits_file:\n json.dump(self.habits, habits_file)\n\n return True\n\n def check_triggers(self, habit_id, habit, new_triggers):\n \"\"\"\n Check if any trigger of new_triggers is already a trigger of a habit\n\n Args:\n habit_id (int): the id of the habit to check\n habit (datastore): the habit to check\n new_triggers (datastore): the new triggers to check\n \"\"\"\n to_add = []\n for known_trig in self.triggers:\n for i in new_triggers:\n LOGGER.info(\"Testing trigger\" + str(habit[\"intents\"][int(i)]))\n if habit[\"intents\"][i][\"name\"] == known_trig[\"intent\"] and \\\n habit[\"intents\"][i][\"parameters\"] \\\n == known_trig[\"parameters\"]:\n return False\n to_add += [\n {\n \"intent\": habit[\"intents\"][i][\"name\"],\n \"parameters\": habit[\"intents\"][i][\"parameters\"],\n \"habit_id\": habit_id\n }\n ]\n self.triggers += to_add\n\n return True\n\n def not_automate_habit(self, habit_id):\n \"\"\"\n Register the user choice of not automatizing a habit\n\n Args:\n habit_id (int): the id of the habit to not automate\n \"\"\"\n self.habits[habit_id][\"user_choice\"] = True\n self.habits[habit_id][\"automatized\"] = 0\n with open(self.habits_file_path, 'w') as habits_file:\n json.dump(self.habits, habits_file)\n\n def get_trigger_by_id(self, trigger_id):\n \"\"\"Return one particular habit trigger\"\"\"\n return self.triggers[trigger_id]\n\n\nclass AutomationHandlerSkill(MycroftSkill):\n \"\"\"\n This class implements the automation handler skill\n\n Attributes:\n habit (datastore): the current habit being handled\n habit_id (str): the id of the habit being handled\n trigger (datastore): the current trigger being handled\n to_execute (datastore): the intents to execute in the automation\n auto (bool): True if the user choose to automate the habit\n manager (HabitsManager): used to interact with habits.json\n \"\"\"\n\n def __init__(self):\n super(AutomationHandlerSkill, self).__init__(\n name=\"AutomationHandlerSkill\")\n self.habit = None\n self.habit_id = None\n self.trigger = None\n self.to_execute = []\n self.to_install = []\n self.auto = False\n self.manager = HabitsManager()\n self.first_automation = True\n\n def initialize(self):\n habit_detected = IntentBuilder(\"HabitDetectedIntent\").require(\n \"HabitDetectedKeyword\").require(\"Number\").build()\n self.register_intent(habit_detected, self.handle_habit_detected)\n\n trigger_detected = IntentBuilder(\"TriggerDetectedIntent\").require(\n \"TriggerDetectedKeyword\").require(\"Number\").build()\n self.register_intent(trigger_detected, self.handle_trigger_detected)\n\n# region Mycroft first dialog\n\n def handle_habit_detected(self, message):\n if not self.check_skills_intallation():\n return\n\n LOGGER.info(\"Loading habit number \" + message.data.get(\"Number\"))\n LOGGER.info(\"multiple_triggers = \" + str(\n self.settings.get(\"multiple_triggers\")))\n self.manager.load_files()\n self.habit_id = int(message.data.get(\"Number\"))\n self.habit = self.manager.get_habit_by_id(self.habit_id)\n\n if self.habit[\"user_choice\"]:\n LOGGER.info(\"User choice already made for this habit\")\n return\n\n self.set_context(\"AutomationChoiceContext\")\n dialog = \"I have noticed that you often use \"\n if self.habit[\"trigger_type\"] == \"skill\":\n dialog += self.generate_skill_trigger_dialog(self.habit[\"intents\"])\n else:\n dialog += self.generate_time_trigger_dialog(\n self.habit[\"time\"], self.habit[\"days\"], self.habit[\"intents\"])\n event_name = \"habit_automation_nb_{}\".format(self.habit_id)\n self.schedule_repeating_event(self.handle_scheduled_habit,\n parser.parse(self.habit[\"time\"]),\n 86400, {\"habit_id\": self.habit_id,\n \"event_name\": event_name},\n event_name)\n\n self.speak(dialog, expect_response=True)\n\n @intent_handler(IntentBuilder(\"AutomationChoiceIntent\")\n .require(\"YesKeyword\")\n .require(\"AutomationChoiceContext\").build())\n def handle_automation_choice_intent(self):\n self.auto = True\n self.remove_context(\"AutomationChoiceContext\")\n if self.habit[\"trigger_type\"] == \"skill\":\n if self.settings.get(\"multiple_triggers\"):\n self.set_context(\"TriggerChoiceContext\")\n self.speak(\"The habit automation can be triggered either by \"\n \"only one of the previous commands or by any of \"\n \"them. Do you want to pick one \"\n \"particular command as the trigger?\",\n expect_response=True)\n else:\n self.set_context(\"TriggerCommandContext\")\n self.ask_trigger_command()\n\n else:\n self.manager.automate_habit(self.habit_id, 1 if self.auto else 2)\n self.habit_automatized()\n\n @intent_handler(IntentBuilder(\"NoAutomationIntent\")\n .require(\"NoKeyword\")\n .require(\"AutomationChoiceContext\").build())\n @adds_context(\"OfferChoiceContext\")\n @removes_context(\"AutomationChoiceContext\")\n def handle_no_automation_intent(self):\n if self.habit[\"trigger_type\"] == \"time\":\n dial = (\"Should I offer you to launch the entire habit\"\n \" at {}?\").format(self.habit[\"time\"])\n else:\n dial = (\"Should I offer you to launch the entire habit when you \"\n \"launch one of the previous commands?\")\n self.speak(dial, expect_response=True)\n\n @intent_handler(IntentBuilder(\"TriggerChoiceIntent\")\n .require(\"YesKeyword\")\n .require(\"TriggerChoiceContext\").build())\n @adds_context(\"TriggerCommandContext\")\n @removes_context(\"TriggerChoiceContext\")\n def handle_trigger_choice_intent(self):\n self.ask_trigger_command()\n\n @intent_handler(IntentBuilder(\"NoTriggerChoiceIntent\")\n .require(\"NoKeyword\")\n .require(\"TriggerChoiceContext\").build())\n def handle_no_trigger_choice_intent(self):\n self.remove_context(\"TriggerChoiceContext\")\n if self.auto:\n if self.manager.automate_habit(\n self.habit_id, 1,\n range(0, len(self.habit[\"intents\"]))):\n self.habit_automatized()\n else:\n self.set_context(\"TriggerCommandContext\")\n self.speak(\"One of these command is already a trigger for \"\n \"another habit. Please select one command.\")\n self.ask_trigger_command()\n else:\n self.manager.not_automate_habit(self.habit_id)\n self.habit_not_automatized()\n\n @intent_handler(IntentBuilder(\"OfferChoiceIntent\")\n .require(\"YesKeyword\")\n .require(\"OfferChoiceContext\").build())\n def handle_offer_choice_intent(self):\n self.remove_context(\"OfferChoiceContext\")\n if self.habit[\"trigger_type\"] == \"skill\":\n self.set_context(\"TriggerCommandContext\")\n self.ask_trigger_command()\n else:\n self.manager.automate_habit(self.habit_id, 1 if self.auto else 2)\n self.habit_offer()\n\n @intent_handler(IntentBuilder(\"NoOfferChoiceIntent\")\n .require(\"NoKeyword\")\n .require(\"OfferChoiceContext\").build())\n @removes_context(\"OfferChoiceContext\")\n def handle_no_offer_choice_intent(self):\n self.manager.not_automate_habit(self.habit_id)\n self.habit_not_automatized()\n\n @intent_handler(IntentBuilder(\"TriggerCommandIntent\")\n .require(\"IndexKeyword\")\n .require(\"TriggerCommandContext\").build())\n def handle_trigger_command_intent(self, message):\n intent_id = message.data.get(\"IndexKeyword\")\n if intent_id == \"cancel\":\n self.remove_context(\"TriggerCommandContext\")\n self.manager.not_automate_habit(self.habit_id)\n self.habit_not_automatized()\n else:\n intent_id = int(intent_id) - 1\n if self.manager.automate_habit(\n self.habit_id, 1 if self.auto else 2, [intent_id]):\n self.remove_context(\"TriggerCommandContext\")\n if self.auto:\n self.habit_automatized()\n else:\n self.habit_offer(intent_id)\n else:\n self.speak(\"This command is already a trigger for another \"\n \"habit. Please choose an other one.\")\n self.ask_trigger_command()\n\n def generate_skill_trigger_dialog(self, intents):\n dial = \"\"\n for i in range(0, len(intents) - 1):\n dial += \"the command {}, \".format(intents[i][\"last_utterance\"])\n dial += \"and the command {} together. \".format(\n intents[len(intents) - 1][\"last_utterance\"])\n dial += (\"Do you want me to automate your habit of launching these \"\n \"{} commands?\".format(len(intents)))\n return dial\n\n def generate_time_trigger_dialog(self, time, days, intents):\n dial = \"\"\n if len(intents) > 1:\n for i in range(0, len(intents) - 1):\n dial += \"the command {}, \".format(intents[i][\"last_utterance\"])\n dial += \"and the command {} together \".format(\n intents[len(intents) - 1][\"last_utterance\"])\n com = \"these {} commands\".format(len(intents))\n else:\n dial += \"the command {} \".format(intents[0][\"last_utterance\"])\n com = \"this command\"\n at = \"at {} on \".format(time)\n for d in days[:-1]:\n at += \"{}, \".format(WEEKDAYS[d])\n if len(days) > 1:\n at += \"and \"\n at += \"{}\".format(WEEKDAYS[days[-1]])\n dial += (\"{}. Do you want me to automate your habit of launching {} \"\n \"{}?\".format(at, com, at))\n return dial\n\n def ask_trigger_command(self):\n dialog = \"The habit trigger can be \"\n num = \"\"\n for i in range(0, len(self.habit[\"intents\"])):\n dialog += \"{}, {}. \".format(i + 1, self.habit[\"intents\"]\n [i][\"last_utterance\"])\n num += \"{}, \".format(i + 1)\n dialog += \"Please answer {}or cancel.\".format(num)\n self.speak(dialog, expect_response=True)\n\n def habit_automatized(self):\n dial = \"The habit has been successfully automatized.\"\n if self.first_automation:\n dial += (\" You can change your preferences by saying \"\n \"'list my habits'\")\n self.first_automation = False\n self.speak(dial)\n\n def habit_not_automatized(self):\n dial = \"The habit will not be automatized.\"\n if self.first_automation:\n dial += (\" You can change your preferences by saying \"\n \"'list my habits'\")\n self.first_automation = False\n self.speak(dial)\n\n def habit_offer(self, intent_id=None):\n if self.habit[\"trigger_type\"] == \"time\":\n dial = \"Every day at {}, \".format(self.habit[\"time\"])\n else:\n dial = \"Every time you will launch the command {}, \".format(\n self.habit[\"intents\"][intent_id][\"last_utterance\"])\n dial += (\"I will ask you if you want to launch the habit.\")\n if self.first_automation:\n dial += (\" You can change your preferences by saying \"\n \"'list my habits'\")\n self.first_automation = False\n self.speak(dial)\n\n# endregion\n\n# region Habit Automation\n\n def handle_trigger_detected(self, message):\n if not self.check_skills_intallation():\n return\n\n self.manager.load_files()\n LOGGER.info(\"Loading trigger number \" + message.data.get(\"Number\"))\n self.trigger = self.manager.get_trigger_by_id(int(\n message.data.get(\"Number\")))\n self.habit = self.manager.get_habit_by_id(self.trigger[\"habit_id\"])\n LOGGER.info(\"Habit number \" + str(self.trigger[\"habit_id\"]))\n\n if self.habit[\"automatized\"] == 1:\n for intent in self.habit[\"intents\"]:\n if intent[\"name\"] != self.trigger[\"intent\"] or \\\n intent[\"parameters\"] != self.trigger[\"parameters\"]:\n self.to_execute.append(intent)\n self.exec_automation()\n elif self.habit[\"automatized\"] == 2:\n self.set_context(\"OfferContext\")\n self.offer_habit_exec()\n\n @intent_handler(IntentBuilder(\"CompleteAutomationIntent\")\n .require(\"YesKeyword\")\n .require(\"OfferContext\").build())\n @removes_context(\"OfferContext\")\n def handle_complete_automation(self):\n self.exec_automation()\n\n @intent_handler(IntentBuilder(\"NotCompleteAutomationIntent\")\n .require(\"NoKeyword\")\n .require(\"OfferContext\").build())\n @removes_context(\"OfferContext\")\n def handle_not_complete_automation(self):\n self.to_execute = []\n\n def handle_scheduled_habit(self, message):\n self.manager.load_files()\n self.habit_id = message.data.get(\"habit_id\")\n self.habit = self.manager.get_habit_by_id(self.habit_id)\n if self.habit[\"automatized\"] and \\\n datetime.datetime.today().weekday() in self.habit[\"days\"]:\n if self.habit[\"automatized\"] == 1:\n self.to_execute = self.habit[\"intents\"]\n self.exec_automation()\n else:\n self.set_context(\"OfferContext\")\n self.offer_habit_exec()\n\n def offer_habit_exec(self):\n if self.habit[\"trigger_type\"] == \"skill\":\n dialog = \"Do you also want to run\"\n n_commands = len(self.habit[\"intents\"]) - 1\n for intent in self.habit[\"intents\"]:\n if intent[\"name\"] != self.trigger[\"intent\"] or \\\n intent[\"parameters\"] != self.trigger[\"parameters\"]:\n n_commands -= 1\n if not n_commands and len(self.habit[\"intents\"]) != 2:\n dialog += \" and\"\n dialog += \" the command {}\".format(\n intent[\"last_utterance\"])\n self.to_execute.append(intent)\n else:\n dialog = \"It is {}. Do you want to run\".format(self.habit[\"time\"])\n self.to_execute = self.habit[\"intents\"]\n n_commands = len(self.habit[\"intents\"])\n for intent in self.habit[\"intents\"]:\n n_commands -= 1\n if not n_commands and len(self.habit[\"intents\"]) != 1:\n dialog += \" and\"\n dialog += \" the command {}\".format(intent[\"last_utterance\"])\n\n self.speak(dialog + \"?\", expect_response=True)\n\n def exec_automation(self):\n LOGGER.info(\"Launching habit...\")\n for intent in self.to_execute:\n self.emitter.emit(\n Message(\"recognizer_loop:utterance\",\n {\"utterances\": [intent[\"last_utterance\"]],\n \"lang\": 'en-us'}))\n self.to_execute = []\n\n @intent_handler(IntentBuilder(\"CancelHabitIntent\")\n .require(\"CancelHabitKeyword\")\n .require(\"Number\").build())\n def handle_cancel_habit(self, message):\n self.habit_id = message.data.get(\"Number\")\n self.cancel_scheduled_event(\n \"habit_automation_nb_{}\".format(self.habit_id))\n\n# endregion\n\n# region Habit modification\n\n @intent_handler(IntentBuilder(\"ListHabitsIntent\")\n .require(\"ListHabitsKeyword\"))\n @adds_context(\"ListContext\")\n def handle_list_habits(self):\n self.habits_list = []\n self.list_index = -1\n self.manager.load_files()\n\n i = 0\n for habit in self.manager.habits:\n if habit[\"user_choice\"]:\n self.habits_list += [(i, habit)]\n i += 1\n\n self.speak(\"Listing habits one by one. After each habit, \"\n \"you can modify it by saying modify, move to the next habit\"\n \" by saying next habit, or stop the listing by saying \"\n \"exit.\")\n\n self.speak_next_habit()\n\n @intent_handler(IntentBuilder(\"NextHabitIntent\")\n .require(\"NextHabitKeyword\")\n .require(\"ListContext\").build())\n def handle_next_habit(self):\n self.speak_next_habit()\n\n @intent_handler(IntentBuilder(\"ModifyHabitIntent\")\n .require(\"ModifyKeyword\")\n .require(\"ListContext\").build())\n @adds_context(\"ModifyContext\")\n @removes_context(\"ListContext\")\n def handle_modify_habit(self):\n self.speak(\"Modifying habit {}. Say 0 to not automate, 1 to automate \"\n \"entirely and 2 to automate the offer.\".format(\n self.list_index), expect_response=True)\n\n @intent_handler(IntentBuilder(\"ExitListIntent\")\n .require(\"ExitKeyword\")\n .require(\"ListContext\").build())\n @removes_context(\"ListContext\")\n def handle_exit_list(self):\n self.speak(\"Stopping habits' list.\")\n\n @intent_handler(IntentBuilder(\"ModifChoiceIntent\")\n .require(\"IndexAutoKeyword\")\n .require(\"ModifyContext\").build())\n @adds_context(\"ListContext\")\n @removes_context(\"ModifyContext\")\n def handle_modif_choice(self, message):\n auto = int(message.data.get(\"IndexAutoKeyword\"))\n index, _ = self.habits_list[self.list_index]\n self.manager.habits[index][\"automatized\"] = auto\n self.manager.save_habits()\n\n self.speak(\"Modification saved.\")\n self.speak_next_habit()\n\n def speak_next_habit(self):\n self.list_index += 1\n\n if self.list_index == len(self.habits_list):\n self.remove_context(\"ListContext\")\n self.speak(\"Habits' list finished.\")\n return\n\n commands = \"\"\n _, hab = self.habits_list[self.list_index]\n if len(hab[\"intents\"]) > 1:\n for intent in hab[\"intents\"][:-1]:\n commands += \"{}, \".format(intent[\"last_utterance\"])\n commands += \"and {}\".format(hab[\"intents\"][-1][\"last_utterance\"])\n else:\n commands += hab[\"intents\"][0][\"last_utterance\"]\n stat = \"automatized\"\n if not hab[\"automatized\"]:\n stat = \"not \" + stat\n elif hab[\"automatized\"] == 2:\n stat = \"offer \" + stat\n\n optional = \"\"\n if hab[\"trigger_type\"] == \"time\":\n optional += \"Time: {} on \".format(hab[\"time\"])\n for day in hab[\"days\"]:\n optional += WEEKDAYS[day]\n else:\n trig = hab[\"intents\"][hab[\"triggers\"][0]][\"last_utterance\"]\n optional += \"Trigger: {}\".format(trig)\n optional += \".\"\n\n dial = \"Habit {}. Commands: {}. {} Status: {}.\".format(\n self.list_index, commands, optional, stat)\n self.speak(dial, expect_response=True)\n\n# endregion\n\n# region Dependent skills installation\n\n def check_skills_intallation(self):\n LOGGER.info(\"Checking for skills install...\")\n ret = True\n self.to_install = []\n\n for folder, skill in SKILLS_FOLDERS.iteritems():\n if not os.path.isdir(folder):\n ret = False\n self.to_install += [skill]\n\n if not ret:\n self.set_context(\"InstallMissingContext\")\n dial = (\"To use the skill automation handler, you also have to \"\n \"install the skill\")\n num_skill = \"this skill\"\n skills_list = \"\"\n for skill in self.to_install[:-1]:\n skills_list += skill + \", \"\n if len(self.to_install) > 1:\n num_skill = \"these {} skills\".format(len(self.to_install))\n skills_list += \"and \"\n dial += \"s\"\n skills_list += self.to_install[-1]\n self.speak(dial + \" \" + skills_list +\n \". Should I install {} for you?\".format(num_skill),\n expect_response=True)\n return ret\n\n @intent_handler(IntentBuilder(\"InstallMissingIntent\")\n .require(\"YesKeyword\")\n .require(\"InstallMissingContext\").build())\n @removes_context(\"InstallMissingContext\")\n def handle_install_missing(self):\n for skill in self.to_install:\n LOGGER.info(\"Installing \" + skill)\n self.emitter.emit(\n Message(\"recognizer_loop:utterance\",\n {\"utterances\": [\"install \" + skill],\n \"lang\": 'en-us'}))\n\n @intent_handler(IntentBuilder(\"NotInstallMissingIntent\")\n .require(\"NoKeyword\")\n .require(\"InstallMissingContext\").build())\n @removes_context(\"InstallMissingContext\")\n def handle_not_install_missing(self):\n pass\n\n# endregion\n\n def stop(self):\n pass\n\n\ndef create_skill():\n return AutomationHandlerSkill()\n","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":27015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"542328744","text":"# -*- coding: utf-8 -*-\r\n\r\nimport numpy as np\r\nimport chainer.functions as F\r\nimport chainer.links as L\r\nimport chainer\r\nfrom scipy.fftpack import fft\r\nimport pickle\r\nimport math\r\nimport random\r\nfrom chainer import optimizers\r\n\r\nwith open('train_dataset_tda_normal.pkl', 'rb') as f:\r\n dataset_normal = pickle.load(f)\r\nwith open('train_dataset_tda_inner.pkl', 'rb') as f:\r\n dataset_inner = pickle.load(f)\r\nwith open('train_dataset_tda_mis.pkl', 'rb') as f:\r\n dataset_mis = pickle.load(f)\r\n \r\ntrain_dataset_normal = []\r\ntrain_dataset_inner = []\r\ntrain_dataset_mis = []\r\n\r\ntest_dataset_normal = []\r\ntest_dataset_inner = []\r\ntest_dataset_mis = []\r\n\r\nfor i in range(2160):\r\n if i%30 == 0:\r\n test_dataset_normal.append(dataset_normal[i])\r\n test_dataset_inner.append(dataset_inner[i])\r\n test_dataset_mis.append(dataset_mis[i])\r\n else:\r\n train_dataset_normal.append(dataset_normal[i])\r\n train_dataset_inner.append(dataset_inner[i])\r\n train_dataset_mis.append(dataset_mis[i])\r\n\r\ndel(dataset_normal)\r\ndel(dataset_inner)\r\ndel(dataset_mis)\r\n\r\nxp = np\r\n\r\nin_units = 2000\r\nhidden_units1 = 512\r\nhidden_units2 = 128\r\nhidden_units3 = 32\r\nout_units = 3\r\ntraining_epochs = 15000\r\nbatch_size = 216\r\ndropout_ratio = 0.01\r\ndisplay_epoch = 100\r\n\r\n# バッチ作成\r\ndef make_batch():\r\n batch = xp.zeros((batch_size, 2000))\r\n batch = xp.array(batch, dtype=xp.float32)\r\n output = xp.zeros((batch_size))\r\n output = xp.array(output, dtype=xp.int32)\r\n for i in range(batch_size):\r\n index = random.randint(0, 2087)\r\n if i/72 == 0:\r\n sample = train_dataset_normal[index]\r\n sample = xp.reshape(sample, (2000))\r\n batch[i, :] = sample\r\n output[i] = 0\r\n elif i/72 == 1:\r\n sample = train_dataset_inner[index]\r\n sample = xp.reshape(sample, (2000))\r\n batch[i, :] = sample\r\n output[i] = 1\r\n elif i/72 == 2:\r\n sample = train_dataset_mis[index]\r\n sample = xp.reshape(sample, (2000))\r\n batch[i, :] = sample\r\n output[i] = 2\r\n return batch, output\r\n\r\n# バッチ作成(test用)\r\ndef make_batch_test():\r\n batch = xp.zeros((batch_size, 2000))\r\n batch = xp.array(batch, dtype=xp.float32)\r\n output = xp.zeros((batch_size))\r\n output = xp.array(output, dtype=xp.int32)\r\n for i in range(batch_size):\r\n index = i%72\r\n if i/72 == 0:\r\n sample = test_dataset_normal[index]\r\n sample = xp.reshape(sample, (2000))\r\n batch[i, :] = sample\r\n output[i] = 0\r\n elif i/72 == 1:\r\n sample = test_dataset_inner[index]\r\n sample = xp.reshape(sample, (2000))\r\n batch[i, :] = sample\r\n output[i] = 1\r\n elif i/72 == 2:\r\n sample = test_dataset_mis[index]\r\n sample = xp.reshape(sample, (2000))\r\n batch[i, :] = sample\r\n output[i] = 2\r\n return batch, output\r\n\r\n# DNNモデルの作成\r\nmodel = chainer.FunctionSet(l1=L.Linear(in_units, hidden_units),\r\n l2=L.Linear(hidden_units, out_units))\r\n\r\n# 重みの初期化\r\nfor param in model.params():\r\n data = param.data\r\n data[:] = np.random.uniform(-0.1, 0.1, data.shape)\r\n\r\n# model = pickle.load(open('dnn_0713_model_3kind_all_row_row_row_fre.pkl', 'rb'))\r\n# optimizer\r\noptimizer = optimizers.Adam()\r\noptimizer.setup(model)\r\n\r\n## テスト用データをランダム生成しておく\r\ntest_batch, test_output = make_batch_test()\r\n\r\naccMatrix = np.zeros((training_epochs))\r\nsumsMatrix = np.zeros((training_epochs))\r\n\r\n# fine-chooning\r\nfor epoch in range(training_epochs):\r\n batch, output = make_batch()\r\n optimizer.zero_grads()\r\n x = chainer.Variable(batch)\r\n t = chainer.Variable(output)\r\n x = F.relu(model.l1(x))\r\n y = model.l2(F.dropout(x, ratio=dropout_ratio, train=True))\r\n loss = F.softmax_cross_entropy(y, t)\r\n loss.backward()\r\n optimizer.update()\r\n accMatrix[epoch] = F.accuracy(y, t).data\r\n if (epoch%100 == 0):\r\n print(\r\n \"Fin[{j}]training loss:\\t{i}\\t acc:\\t{k}\".format(\r\n j=epoch, \r\n i=loss.data/(in_units - 1),\r\n k=F.accuracy(y, t).data\r\n )\r\n )\r\n x = chainer.Variable(test_batch)\r\n t = chainer.Variable(test_output)\r\n x = F.relu(model.l1(x))\r\n y = model.l2(F.dropout(x, ratio=dropout_ratio, train=False))\r\n sumsMatrix[epoch] = F.accuracy(y, t).data\r\npickle.dump(sumsMatrix, open('sumsMatrix_nn_tda.pkl', 'wb'))\r\npickle.dump(accMatrix, open('accMatrix_nn_tda.pkl', 'wb')) \r\npickle.dump(model, open('model_nn_tda.pkl', 'wb'))\r\n \r\n# 以下、再構築誤差取得用スクリプト\r\n# テストデータの読み込み\r\n\"\"\"\r\nwith open('test_dataset_0713.pkl', 'rb') as f:\r\n test_dataset = pickle.load(f)\r\n \r\nbatch, output = make_batch_test(test_dataset)\r\nx = chainer.Variable(batch)\r\nt = chainer.Variable(output)\r\nx = F.relu(model.l1(x))\r\ny = model.l2(F.dropout(x, ratio=dropout_ratio, train=False))\r\ny = F.softmax(y).data\r\n\r\n\"\"\"","sub_path":"train_tda_dnn2.py","file_name":"train_tda_dnn2.py","file_ext":"py","file_size_in_byte":5111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"308599309","text":"from .unit_base import UnitBase\nimport os\n\nfrom .raw_unit_s import RawUnitS1, RawUnitS2\n\n\ndef s1_func_by_is_symphony(file_path):\n return True\n\n\ndef s1_func_by_has_symphony_1(file_path):\n file_path = file_path.replace('\\\\', '/')\n if 'symphony' in file_path:\n return True\n return None\n\n\ns1_funcs = {\n 'is_symphony': s1_func_by_is_symphony,\n 'has_symphony_1': s1_func_by_has_symphony_1\n}\n\n\nclass UnitS1(UnitBase):\n \"\"\"\n 是否是交响乐\n \"\"\"\n\n @property\n def version(self) -> str:\n return 'v1.0'\n\n @classmethod\n def extract(\n cls, encoder, midi_dir, midi_path,\n pos_info, bars_positions, bars_chords, bars_insts, bar_begin, bar_end, **kwargs\n ):\n \"\"\"\n :return:\n - bool，表示是否是交响乐\n 可能为None，表示不知道是否为交响乐\n \"\"\"\n if 's1_func' not in kwargs:\n return None\n judge_func = kwargs['s1_func']\n if judge_func is None:\n return None\n\n judge_func = s1_funcs[kwargs['s1_func']]\n file_name = os.path.basename(midi_path)\n is_symphony = judge_func(file_name)\n return is_symphony\n\n def get_vector(self, use=True, use_info=None):\n value = self.value\n vector = [0] * self.vector_dim\n if value is None or not use:\n vector[-1] = 1\n return vector\n if value:\n vector[0] = 1\n else:\n vector[1] = 1\n return vector\n\n @property\n def vector_dim(self) -> int:\n return 3\n\n\ndir_name_to_artist_name = {\n 'beethoven': 'Beethoven',\n 'mozart': 'Mozart',\n 'chopin': 'Chopin',\n 'schubert': 'Schubert',\n 'schumann': 'Schumann',\n}\n\nartist_name_to_id = {\n 'Beethoven': 0,\n 'Mozart': 1,\n 'Chopin': 2,\n 'Schubert': 3,\n 'Schumann': 4,\n}\n\n\ndef s2_func_by_file_path_1(file_path):\n file_path = file_path.replace('\\\\', '/')\n file_path_split = file_path.split('/')\n first_dir = file_path_split[0]\n if first_dir in dir_name_to_artist_name:\n return dir_name_to_artist_name[first_dir]\n return None\n\n\ns2_funcs = {\n 'file_path_1': s2_func_by_file_path_1,\n}\n\n\nclass UnitS2(UnitBase):\n \"\"\"\n 是否是某艺术家的作品\n \"\"\"\n\n @property\n def version(self) -> str:\n return 'v1.0'\n\n @classmethod\n def extract(\n cls, encoder, midi_dir, midi_path,\n pos_info, bars_positions, bars_chords, bars_insts, bar_begin, bar_end, **kwargs\n ):\n \"\"\"\n :return:\n - str，艺术家名字\n 可能为None，表示不知道艺术家是谁\n \"\"\"\n if 's2_func' not in kwargs:\n return None\n judge_func = kwargs['s2_func']\n if judge_func is None:\n return None\n\n judge_func = s2_funcs[kwargs['s2_func']]\n artist_name = judge_func(midi_path)\n return artist_name\n\n def get_vector(self, use=True, use_info=None):\n value = self.value\n vector = [0] * self.vector_dim\n if value is None or not use:\n vector[-1] = 1\n return vector\n value_id = artist_name_to_id[value]\n assert 0 <= value_id < self.vector_dim - 1\n vector[value_id] = 1\n return vector\n\n @property\n def vector_dim(self) -> int:\n return len(artist_name_to_id) + 1\n\n\nclass UnitS2s1(UnitBase):\n \"\"\"\n 艺术家\n \"\"\"\n\n artist_label_to_artist_id = {\n 'beethoven': 0,\n 'mozart': 1,\n 'chopin': 2,\n 'schubert': 3,\n 'schumann': 4,\n 'bach-js': 5,\n 'haydn': 6,\n 'brahms': 7,\n 'Handel': 8,\n 'tchaikovsky': 9,\n 'mendelssohn': 10,\n 'dvorak': 11,\n 'liszt': 12,\n 'stravinsky': 13,\n 'mahler': 14,\n 'prokofiev': 15,\n 'shostakovich': 16,\n }\n\n @classmethod\n def get_raw_unit_class(cls):\n return RawUnitS1\n\n @classmethod\n def convert_raw_to_value(cls, raw_data, encoder, midi_dir, midi_path,\n pos_info, bars_positions, bars_chords, bars_insts, bar_begin, bar_end, **kwargs):\n \"\"\"\n :return:\n - str，艺术家label。可能为None，表示不知道艺术家是谁\n \"\"\"\n raw_s1 = raw_data['S1']\n raw_s1 = raw_s1['artist']\n return raw_s1\n\n @classmethod\n def convert_label_to_id(cls, label):\n return cls.artist_label_to_artist_id[label]\n\n def get_vector(self, use=True, use_info=None) -> list:\n # 顺序：artist 0, artist 1, ..., NA\n vector = [0] * (len(self.artist_label_to_artist_id) + 1)\n if not use or self.value is None:\n vector[-1] = 1\n else:\n label_id = self.convert_label_to_id(self.value)\n vector[label_id] = 1\n return vector\n\n @property\n def vector_dim(self):\n return len(self.artist_label_to_artist_id) + 1\n\n\ndef s3_func_by_is_classical(file_name):\n return True\n\n\ndef s3_func_by_has_classical_1(file_path):\n file_path = file_path.replace('\\\\', '/')\n if 'classical' in file_path:\n return True\n return None\n\n\ns3_funcs = {\n 'is_classical': s3_func_by_is_classical,\n 'has_classical_1': s3_func_by_has_classical_1,\n}\n\n\nclass UnitS3(UnitBase):\n \"\"\"\n 是否是古典乐\n \"\"\"\n\n @property\n def version(self) -> str:\n return 'v1.0'\n\n @classmethod\n def extract(\n cls, encoder, midi_dir, midi_path,\n pos_info, bars_positions, bars_chords, bars_insts, bar_begin, bar_end, **kwargs\n ):\n \"\"\"\n :return:\n - bool，表示是否是古典乐\n 可能为None，表示不知道是否为古典乐\n \"\"\"\n if 's3_func' not in kwargs:\n return None\n judge_func = kwargs['s3_func']\n if judge_func is None:\n return None\n\n judge_func = s3_funcs[kwargs['s3_func']]\n is_classical = judge_func(midi_path)\n return is_classical\n\n def get_vector(self, use=True, use_info=None):\n value = self.value\n vector = [0] * self.vector_dim\n if value is None or not use:\n vector[-1] = 1\n return vector\n if value:\n vector[0] = 1\n else:\n vector[1] = 1\n return vector\n\n @property\n def vector_dim(self) -> int:\n return 3\n\n\nclass UnitS4(UnitBase):\n \"\"\"\n Genre\n \"\"\"\n genre_label_to_genre_id = {\n 'New Age': 0,\n 'Electronic': 1,\n 'Rap': 2,\n 'Religious': 3,\n 'International': 4,\n 'Easy_Listening': 5,\n 'Avant_Garde': 6,\n 'RnB': 7,\n 'Latin': 8,\n 'Children': 9,\n 'Jazz': 10,\n 'Classical': 11,\n 'Comedy_Spoken': 12,\n 'Pop_Rock': 13,\n 'Reggae': 14,\n 'Stage': 15,\n 'Folk': 16,\n 'Blues': 17,\n 'Vocal': 18,\n 'Holiday': 19,\n 'Country': 20,\n 'Symphony': 21,\n }\n\n @classmethod\n def convert_label_to_id(cls, label):\n return cls.genre_label_to_genre_id[label]\n\n @classmethod\n def get_raw_unit_class(cls):\n return RawUnitS2\n\n @classmethod\n def convert_raw_to_value(cls, raw_data, encoder, midi_dir, midi_path,\n pos_info, bars_positions, bars_chords, bars_insts, bar_begin, bar_end, **kwargs):\n \"\"\"\n :return:\n - tuple of str: 所有适用的genre label，已去重。若不知道则为None。\n \"\"\"\n raw_s2 = raw_data['S2']\n raw_s2 = raw_s2['genre']\n raw_s2 = tuple(set(raw_s2)) if raw_s2 is not None else None\n return raw_s2\n\n def get_vector(self, use=True, use_info=None):\n # 返回genre种数个列表，每个列表顺序：是, 否, NA\n vector = [[0, 0, 0] for _ in range(len(self.genre_label_to_genre_id))]\n if not use:\n for item in vector:\n item[2] = 1\n return vector\n if use_info is not None:\n used_genres, unused_genres = use_info\n usedNone = True\n unusedNone = True\n if used_genres != None:\n used_genres = set(used_genres)\n usedNone = False\n else:\n used_genres = set()\n if unused_genres != None:\n unused_genres = set(unused_genres)\n unusedNone = False\n else:\n unused_genres = set()\n if usedNone == False and unusedNone == False:\n assert len(used_genres & unused_genres) == 0\n if usedNone == False:\n for genre in used_genres:\n genre_id = self.convert_label_to_id(genre)\n vector[genre_id][0] = 1\n if unusedNone == False:\n for genre in unused_genres:\n genre_id = self.convert_label_to_id(genre)\n vector[genre_id][1] = 1\n na_insts = set(self.genre_label_to_genre_id.keys()) - used_genres - unused_genres\n for genre in na_insts:\n genre_id = self.convert_label_to_id(genre)\n vector[genre_id][2] = 1\n else:\n value = self.value\n if value is None:\n value = tuple()\n for genre in value:\n genre_id = self.convert_label_to_id(genre)\n vector[genre_id][0] = 1\n na_insts = set(self.genre_label_to_genre_id.keys()) - set(value)\n for genre in na_insts:\n genre_id = self.convert_label_to_id(genre)\n vector[genre_id][2] = 1\n return vector\n\n @property\n def vector_dim(self):\n return len(self.genre_label_to_genre_id), 3\n","sub_path":"musecoco/1-text2attribute_dataprepare/midi_data_extractor/attribute_unit/unit_s.py","file_name":"unit_s.py","file_ext":"py","file_size_in_byte":9664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"531426210","text":"#! /usr/bin/env python\n\n# Downloads and analyzes a fill scheme\n\nimport argparse\nfrom argparse import ArgumentDefaultsHelpFormatter\nfrom python.getFillSchemes import *\nfrom python.analyzeFillSchemes import *\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser(description=\"Downloads and analyzes a fill scheme\", formatter_class=ArgumentDefaultsHelpFormatter)\n parser.add_argument(\"fills\", help=\"Fill scheme number(s). Accepts comma-separated list, e.g. 5501,5502,etc\")\n parser.add_argument(\"-i\", \"--indir\", default=\"fillschemes/\", help=\"Directory to save the fill schemes to\")\n parser.add_argument(\"-o\", \"--outdir\", default=\"bunchpatterns/\", help=\"Directory to save the analyzed output to\")\n args = parser.parse_args()\n if args.indir[-1]!='/': args.indir+='/'\n if args.outdir[-1]!='/': args.outdir+='/'\n\n #Process fill schemes\n for fill in args.fills.split(','):\n getOneFillScheme(fill, args.indir)\n filename = 'fillscheme'+fill+'.txt'\n analyzeOneFillScheme(args.indir, filename, args.outdir)\n print('')\n \n","sub_path":"lhcFillScheme.py","file_name":"lhcFillScheme.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"179433727","text":"#!usr/bin/python3\n##BROKEN\ndef fetch_message():\n message = input(\"Enter the message to encrypt here :\")\n return message\n\ndef fetch_key():\n key = input(\"Enter your encryption key :\")\n return key\n\ndef print_msg(message):\n print(\"Message : %s\" %(message))\n return\n\ndef encrypt(message, key):\n i = 0\n alphabet='abcdefghijklmnopqrstuvwxyz'\n key = int(key)\n encrypted_message = ''\n while i < len(message):\n if message[i] in alphabet:\n num = ord(message[i])\n num = num + key\n print(\"shifted = \", alphabet[(num%26)])\n encrypted_message = encrypted_message + alphabet[(num%26)]\n else:\n encrypted_message = encrypted_message + message[i]\n i += 1\n return encrypted_message\n\ndef decrypt(message, key):\n i = 0\n alphabet='abcdefghijklmnopqrstuvwxyz'\n key = int(key)\n decrypted_message = ''\n while i < len(message):\n if message[i] in alphabet:\n num = ord(message[i])\n num = num - key\n print(\"shifted = \", alphabet[(num%26)])\n decrypted_message = decrypted_message + alphabet[(num%26)]\n else:\n decrypted_message = decrypted_message + message[i]\n i += 1\n return decrypted_message\n\ndef run():\n message = fetch_message()\n print_msg(message)\n key = fetch_key()\n print_msg(encrypt(message,key))\n message = fetch_message()\n print_msg(message)\n key = fetch_key()\n print_msg(decrypt(message,key))\n return\n\nrun()\n","sub_path":"src/secretmessages.py","file_name":"secretmessages.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"319265761","text":"# ve_tablespaces2.py\n\nimport cx_Oracle\n\nconnection = cx_Oracle.connect(\"lilian\", \"lilian123\", \"localhost/XE\")\n\ncursor = connection.cursor()\ncursor.execute(\"\"\"\n\tselect tablespace_name, tablespace_size, used_percent \n\tfrom DBA_TABLESPACE_USAGE_METRICS\n\twhere used_percent >= :vthreshold\n \"\"\",\n vthreshold = 90)\nfor tbsname, tbssize,tbsusedpercent in cursor:\n print(\"Tablespace:\", tbsname, \", Size:\", tbssize, \", Used Percent:\",tbsusedpercent) \n \n","sub_path":"ve_tablespaces2.py","file_name":"ve_tablespaces2.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"520739736","text":"import tensorflow as tf\r\nimport cv2\r\nimport matplotlib.pyplot as plt\r\nimport os\r\nimport numpy as np\r\nimport tools\r\nimport glob\r\nfrom network import Vgg16\r\nfrom network import Timer\r\n\r\n\r\nclasses = ['__background__', 'have_mask', 'no_mask']\r\nmodel_path = \"D:/program/mask_detect/model/model1200.ckpt\"\r\nfile_path = \"D:/program/mask_detect/test_image\"\r\nimage_list = os.listdir(file_path)\r\nn_classes = len(classes)\r\n\r\ndef _get_image_blob(im, pixel_means=np.array([[[102.9801, 115.9465, 122.7717]]]), target_size=600, max_size=1000):\r\n\tim_orig = im.astype(np.float32, copy=True)\r\n\tim_orig -= pixel_means\r\n\tim_shape = np.shape(im_orig)\r\n\tim_size_min = np.min(im_shape[0:2])\r\n\tim_size_max = np.max(im_shape[0:2])\r\n\tim_scale = float(target_size) / float(im_size_min)\r\n\tif np.round(im_size_max * im_scale) > max_size:\r\n\t\tim_scale = float(max_size) / float(im_size_max)\r\n\tim = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)\r\n\tim = im[np.newaxis, :, :, :]\r\n\treturn im, im_scale\r\n\r\ndef _get_blobs(im):\r\n\tblobs = {}\r\n\tblobs[\"data\"], im_scales = _get_image_blob(im)\r\n\treturn blobs, im_scales\r\n\r\ndef im_detect(sess, net, im):\r\n\tblobs, im_scales = _get_blobs(im)\r\n\tim_blob = blobs[\"data\"]\r\n\tblobs[\"im_info\"] = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales]], dtype=np.float32)\r\n\t_, scores, bbox_pred, rois = net.test_image(sess, blobs[\"data\"], blobs[\"im_info\"])\r\n\tboxes = rois[:, 1:5] / im_scales\r\n\tscores = np.reshape(scores, [scores.shape[0], -1])\r\n\tbbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1])\r\n\tpred_boxes = tools.bbox_transform_inv(boxes, bbox_pred)\r\n\tpred_boxes = tools.clip_boxes(pred_boxes, im.shape)\r\n\treturn scores, pred_boxes\r\n\r\ndef vis_detections(im, class_name, dets, thresh=0.5):\r\n\tinds = np.where(dets[:, -1] >= thresh)[0]\r\n\tif len(inds) == 0:\r\n\t\treturn\r\n\tim = im[:, :, (2, 1, 0)]\r\n\tfig, ax = plt.subplots(figsize=(6, 6))\r\n\tax.imshow(im, aspect=\"equal\")\r\n\tfor i in inds:\r\n\t\tbbox = dets[i, :4]\r\n\t\tscore = dets[i, -1]\r\n\t\tax.add_patch(plt.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], fill=False, edgecolor=\"red\", linewidth=3))\r\n\t\tax.text(bbox[0], bbox[1] - 2, \"{:s}{:.3f}\".format(class_name, score), bbox=dict(facecolor=\"blue\", alpha=0.5), fontsize=14, color=\"white\")\r\n\tax.set_title(\"{} detections with p({} | box) >= {:.1f}\".format(class_name, class_name, thresh))\r\n\tplt.axis(\"off\")\r\n\tplt.tight_layout()\r\n\tplt.draw()\r\n\r\ndef demo(sess, net, image_name):\r\n\tim = cv2.imread(image_name)\r\n\ttimer = Timer()\r\n\ttimer.tic()\r\n\tscores, boxes = im_detect(sess, net, im)\r\n\ttimer.toc()\r\n\tprint(\"Detection took {:.3f}s for {:d} object proposals\".format(timer.total_time, boxes.shape[0]))\r\n\tCONF_THRESH = 0.1\r\n\tNMS_THRESH = 0.1\r\n\tfor cls_ind, cls in enumerate(classes[1:]):\r\n\t\tcls_ind += 1\r\n\t\tcls_boxes = boxes[:, 4 * cls_ind : 4 * (cls_ind + 1)]\r\n\t\tcls_scores = scores[:, cls_ind]\r\n\t\tdets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)\r\n\t\tkeep = tools.nms(dets, NMS_THRESH)\r\n\t\tdets = dets[keep, :]\r\n\t\tvis_detections(im, cls, dets, thresh=CONF_THRESH)\r\n\t\t\r\n\r\nif __name__ == \"__main__\":\r\n\tsess = tf.Session()\r\n\tnet = Vgg16()\r\n\tnet.create_architecture(sess, \"TEST\", n_classes, tag=\"default\")\r\n\tsaver = tf.train.Saver()\r\n\tsaver.restore(sess, model_path)\r\n\tprint(\"Load Network \" + model_path)\r\n\tfor image_name in image_list:\r\n\t\tthe_file_path = os.path.join(file_path, image_name)\r\n\t\tdemo(sess, net, the_file_path)\r\n\tplt.show()","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"365451627","text":"from xlwt import Workbook\nwb=Workbook()\nsheet1=wb.add_sheet(\"Sheet1\")\nsheet2=wb.add_sheet(\"Sheet2\")\nsheet1.write(1,0,'a')\nsheet2.write(0,1,'b')\nwb.save('example1.xlxs')\n\n\n\n\"\"\"import xlrd\nwb=xlrd.open_workbook(\"example1.xlxs\")\nsheet=wb.sheet_by_index(0)\nfor i in range(sheet.nrows):\n print(sheet.row.values(i))\n \nfor i in range(sheet.nrows):\n for j in range(sheet.ncols):\n print(sheet.cell_value(i,j),end=\" \")\n print()\"\"\"\n","sub_path":"excel1.py","file_name":"excel1.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"474332754","text":"\nimport os\nimport sys\nimport time\nimport atexit\nimport signal\nimport logging\nimport json\nimport Queue\nimport argparse\nimport paho.mqtt.client as paho\n\nlogger = logging.getLogger('mqtt')\n\npidfile = None\n\ndef goodbye( signum, frame ):\n if pidfile:\n os.unlink(pidfile)\n logger.info( 'pid file removed' )\n logger.info( 'exiting ...' )\n sys.exit()\n\ndef on_connect(client, userdata, flags, rc):\n logger.info( 'CONNACK received with code ' + str(rc) )\n \n \ndef getSleep():\n return 300\n\ndef getParser():\n return argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter )\n \n\ndef addOptions( parser ):\n parser.add_argument('-t','--topic', action='store', default='ha', help=' first part of mqtt topic')\n parser.add_argument('-m','--mqtt', action='store', default='localhost', help='mqtt host')\n parser.add_argument('-w', '--wait', action='store',type=float, default=60.0, help='time between polling')\n parser.add_argument('-l','--log', action='store', default=None, help='logfile')\n parser.add_argument('-p','--pid', action='store', default=None, help='pid file')\n parser.add_argument('-v','--verbose', action='store_true', default=False, help='log level = DEBUG')\n\n\nclass MQTT():\n args = None\n q = None\n \n def __init__(self, args ):\n global pidfile\n \n self.args = args\n\n logLevel = logging.INFO\n if self.args.verbose:\n logLevel = logging.DEBUG\n \n if not self.args.log: \n logging.basicConfig( format='%(asctime)-8s %(levelname)s:%(name)s: %(message)s', datefmt=\"%H:%M:%S\", level=logLevel )\n else:\n logging.basicConfig( format='%(asctime)-8s %(levelname)s:%(name)s: %(message)s', datefmt=\"%H:%M:%S\", level=logLevel, filename=self.args.log )\n\n pid = str(os.getpid())\n if self.args.pid:\n pidfile = self.args.pid\n if os.path.isfile( self.args.pid ):\n logger.error( \"pid file {} already exists, exiting\".format( self.args.pid ) )\n sys.exit()\n file( self.args.pid, 'w').write(pid)\n atexit.register( goodbye, -1, -1 )\n #, signal.SIGKILL, signal.SIGINT, signal.SIGSEGV, signal.SIGTERM ):\n for sig in ( signal.SIGILL, signal.SIGTERM ):\n signal.signal( sig, goodbye )\n\n\n self.q = Queue.Queue()\n\n\n def run(self):\n mqtt = None\n while True:\n if not mqtt:\n mqtt = paho.Client()\n mqtt.on_connect = on_connect\n mqtt.connect( self.args.mqtt )\n mqtt.loop_start() \n \n logger.debug( 'wait for message' )\n msg = None\n while not msg:\n try:\n msg = self.q.get( timeout=10)\n except Queue.Empty:\n pass \n \n topic = self.args.topic + '/' + str(msg['id'])\n if 'type' in msg:\n topic += '/' + msg['type']\n (rc,mid) = mqtt.publish( topic, json.dumps(msg), qos=1 )\n if rc != paho.MQTT_ERR_SUCCESS:\n logger.error( 'MQTT error, return code = {}'.format( rc ) )\n mqtt = None\n time.sleep(10)\n logger.debug( json.dumps(msg) )\n\n\n\n\ndef mqtt_main():\n parser = getParser()\n addOptions(parser)\n\n args = parser.parse_args(sys.argv[1:])\n mqtt = MQTT( args )\n\n mqtt.run()\n\n\nif __name__ == \"__main__\":\n mqtt_main()\n","sub_path":"mqtt.py","file_name":"mqtt.py","file_ext":"py","file_size_in_byte":3506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"378891767","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime # noqa: F401\n\nfrom typing import List, Dict # noqa: F401\n\nfrom mist_api_v2.models.base_model_ import Model\nfrom mist_api_v2.models.member import Member\nfrom mist_api_v2 import util\n\nfrom mist_api_v2.models.member import Member # noqa: E501\n\nclass Team(Model):\n \"\"\"NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n\n Do not edit the class manually.\n \"\"\"\n\n def __init__(self, id=None, name=None, members=None, members_count=None, description=None, policy=None): # noqa: E501\n \"\"\"Team - a model defined in OpenAPI\n\n :param id: The id of this Team. # noqa: E501\n :type id: str\n :param name: The name of this Team. # noqa: E501\n :type name: str\n :param members: The members of this Team. # noqa: E501\n :type members: List[Member]\n :param members_count: The members_count of this Team. # noqa: E501\n :type members_count: str\n :param description: The description of this Team. # noqa: E501\n :type description: str\n :param policy: The policy of this Team. # noqa: E501\n :type policy: object\n \"\"\"\n self.openapi_types = {\n 'id': str,\n 'name': str,\n 'members': List[Member],\n 'members_count': str,\n 'description': str,\n 'policy': object\n }\n\n self.attribute_map = {\n 'id': 'id',\n 'name': 'name',\n 'members': 'members',\n 'members_count': 'members_count',\n 'description': 'description',\n 'policy': 'policy'\n }\n\n self._id = id\n self._name = name\n self._members = members\n self._members_count = members_count\n self._description = description\n self._policy = policy\n\n @classmethod\n def from_dict(cls, dikt) -> 'Team':\n \"\"\"Returns the dict as a model\n\n :param dikt: A dict.\n :type: dict\n :return: The Team of this Team. # noqa: E501\n :rtype: Team\n \"\"\"\n return util.deserialize_model(dikt, cls)\n\n @property\n def id(self):\n \"\"\"Gets the id of this Team.\n\n\n :return: The id of this Team.\n :rtype: str\n \"\"\"\n return self._id\n\n @id.setter\n def id(self, id):\n \"\"\"Sets the id of this Team.\n\n\n :param id: The id of this Team.\n :type id: str\n \"\"\"\n\n self._id = id\n\n @property\n def name(self):\n \"\"\"Gets the name of this Team.\n\n\n :return: The name of this Team.\n :rtype: str\n \"\"\"\n return self._name\n\n @name.setter\n def name(self, name):\n \"\"\"Sets the name of this Team.\n\n\n :param name: The name of this Team.\n :type name: str\n \"\"\"\n\n self._name = name\n\n @property\n def members(self):\n \"\"\"Gets the members of this Team.\n\n\n :return: The members of this Team.\n :rtype: List[Member]\n \"\"\"\n return self._members\n\n @members.setter\n def members(self, members):\n \"\"\"Sets the members of this Team.\n\n\n :param members: The members of this Team.\n :type members: List[Member]\n \"\"\"\n\n self._members = members\n\n @property\n def members_count(self):\n \"\"\"Gets the members_count of this Team.\n\n\n :return: The members_count of this Team.\n :rtype: str\n \"\"\"\n return self._members_count\n\n @members_count.setter\n def members_count(self, members_count):\n \"\"\"Sets the members_count of this Team.\n\n\n :param members_count: The members_count of this Team.\n :type members_count: str\n \"\"\"\n\n self._members_count = members_count\n\n @property\n def description(self):\n \"\"\"Gets the description of this Team.\n\n\n :return: The description of this Team.\n :rtype: str\n \"\"\"\n return self._description\n\n @description.setter\n def description(self, description):\n \"\"\"Sets the description of this Team.\n\n\n :param description: The description of this Team.\n :type description: str\n \"\"\"\n\n self._description = description\n\n @property\n def policy(self):\n \"\"\"Gets the policy of this Team.\n\n\n :return: The policy of this Team.\n :rtype: object\n \"\"\"\n return self._policy\n\n @policy.setter\n def policy(self, policy):\n \"\"\"Sets the policy of this Team.\n\n\n :param policy: The policy of this Team.\n :type policy: object\n \"\"\"\n\n self._policy = policy\n","sub_path":"mist_api_v2/models/team.py","file_name":"team.py","file_ext":"py","file_size_in_byte":4653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"352570337","text":"import modules\n\nsize = (9, 9)\nenvironment = modules.environment.Environment(size)\nagent = modules.agent.DepthFirstAgent(environment)\n\nenvironment.maze.display_cui()\n\nf = open('depth_first.log', 'w')\n\nf.write(\",\".join(str(i) for i in size))\nf.write(\"\\n\")\nf.write(environment.maze.dump_params())\nf.write(\"\\n\")\nf.write(\"0,0\")\nf.write(\"\\n\")\n\nwhile not environment.exit():\n agent.choose_action()\n f.write(\",\".join(str(i) for i in environment.current_coordinate))\n f.write('\\n')\n\nf.close()\n","sub_path":"depth_first.py","file_name":"depth_first.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"59213381","text":"# -*- coding: utf-8 -*-\n\n\nimport argparse\nimport asyncio\nimport logging\nimport logging.handlers\nimport os\nimport shutil\nimport sys\nimport traceback\nimport urllib.parse\n\nimport tornado.ioloop\n\nfrom . import client, server, utils\n\n\nasync def connect_server(url, workspace, token=None, auto_reconnect=False):\n print(\"Connecting to remote terminal %s\" % url)\n if workspace:\n workspace = os.path.abspath(workspace)\n cli = client.WSTerminalClient(url, token=token, auto_reconnect=auto_reconnect)\n while True:\n if not await cli.connect():\n print(\"Connect websocket server failed\", file=sys.stderr)\n return False\n\n if workspace:\n print(\"Sync workspace to remote host...\")\n await cli.sync_workspace(workspace)\n print(\"Sync workspace complete\")\n terminal_size = shutil.get_terminal_size((120, 30))\n await cli.create_shell((terminal_size.columns, terminal_size.lines))\n if not cli.auto_reconnect:\n break\n print(\"Try to reconnect websocket server\")\n return True\n\n\ndef main():\n parser = argparse.ArgumentParser(\n prog=\"wsterm\", description=\"Websocket terminal tool.\"\n )\n parser.add_argument(\"--url\", help=\"Websocket url\", required=True)\n parser.add_argument(\n \"--server\", help=\"Run as websocket server\", action=\"store_true\", default=False\n )\n parser.add_argument(\"--token\", help=\"Authorization token\")\n parser.add_argument(\"--workspace\", help=\"Workspace path\")\n parser.add_argument(\n \"--log-level\",\n help=\"log level, default is info\",\n choices=(\"debug\", \"info\", \"warn\", \"error\"),\n default=\"info\",\n )\n parser.add_argument(\"--log-file\", help=\"Path to save log\")\n parser.add_argument(\n \"-d\", \"--daemon\", help=\"Run as daemon\", action=\"store_true\", default=False\n )\n parser.add_argument(\n \"--auto-reconnect\",\n help=\"Auto reconnect server when connection closed\",\n action=\"store_true\",\n default=False,\n )\n\n args = sys.argv[1:]\n if not args:\n parser.print_help()\n return 0\n\n args = parser.parse_args(args)\n\n url = urllib.parse.urlparse(args.url)\n if url.scheme != \"ws\":\n print(\"Error: Invalid websocket url %s\" % args.url, file=sys.stderr)\n return -1\n\n log_file = None\n if args.log_file:\n log_file = os.path.abspath(args.log_file)\n\n if args.daemon:\n if not args.server:\n print(\"Error: -d/--daemon only supported on server\", file=sys.stderr)\n return -1\n\n if sys.platform != \"win32\":\n import daemon\n\n daemon.DaemonContext(stderr=open(\"error.txt\", \"w\")).open()\n else:\n utils.win32_daemon()\n return 0\n\n handler = logging.StreamHandler()\n formatter = logging.Formatter(\"[%(asctime)s][%(levelname)s]%(message)s\")\n handler.setFormatter(formatter)\n\n if args.log_level == \"debug\":\n utils.logger.setLevel(logging.DEBUG)\n elif args.log_level == \"info\":\n utils.logger.setLevel(logging.INFO)\n elif args.log_level == \"warn\":\n utils.logger.setLevel(logging.WARN)\n elif args.log_level == \"error\":\n utils.logger.setLevel(logging.ERROR)\n\n utils.logger.propagate = 0\n if args.server:\n utils.logger.addHandler(handler)\n else:\n log_file = log_file or \"wsterm.log\"\n\n if log_file:\n handler = logging.handlers.RotatingFileHandler(\n log_file, maxBytes=10 * 1024 * 1024, backupCount=4\n )\n formatter = logging.Formatter(\n \"[%(asctime)s][%(levelname)s][%(filename)s][%(lineno)d]%(message)s\"\n )\n handler.setFormatter(formatter)\n utils.logger.addHandler(handler)\n\n if args.server:\n host = url.hostname\n port = url.port or 80\n if sys.platform == \"win32\":\n loop = asyncio.ProactorEventLoop()\n asyncio.set_event_loop(loop)\n server.start_server((host, port), url.path, args.token)\n else:\n if sys.platform == \"win32\":\n utils.enable_native_ansi()\n\n if not asyncio.get_event_loop().run_until_complete(\n connect_server(args.url, args.workspace, args.token, args.auto_reconnect)\n ):\n return -1\n\n def handle_exception(loop, context):\n print(\"Exception caught:\\n\", file=sys.stderr)\n message = context[\"message\"]\n exp = context.get(\"exception\")\n if exp:\n message = \"\".join(\n traceback.format_exception(\n etype=type(exp), value=exp, tb=exp.__traceback__\n )\n )\n print(message, file=sys.stderr)\n if not args.server:\n loop.stop()\n\n loop = asyncio.get_event_loop()\n loop.set_exception_handler(handle_exception)\n\n try:\n tornado.ioloop.IOLoop.current().start()\n except KeyboardInterrupt:\n print(\"Process exit warmly.\")\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","sub_path":"wsterm/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":4997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"200690369","text":"lexicon = {\n\t'direction': ['north', 'south', 'east', 'west', 'down', 'up', 'left', 'right', 'back'],\n\t'verb': ['go', 'stop', 'kill', 'eat'],\n\t'stop' : ['the', 'in', 'of', 'from', 'at', 'it'],\n\t'noun': ['door', 'bear', 'princess', 'cabinet']\n\t# numbers - check for numbers in code later on\n}\n\ndef scan(words_to_scan):\n\twords = words_to_scan.split()\n\tscan_result = []\n\n\tfor w in words:\n\t\t# does word exist in lexicon\n\t\tword_type = get_key(w.lower())\n\t\tif word_type is not None:\n\t\t\tresult = (word_type, w)\n\t\telse:\n\t\t\t# check if word is a number first\n\t\t\tnumber = convert_number(w)\n\t\t\tif number is None:\n\t\t\t\tresult = ('error', w)\n\t\t\telse:\n\t\t\t\tresult = ('number', number)\n\t\t\t\n\t\tscan_result.append(result)\n\n\treturn scan_result\n\ndef get_key(s):\n\tfor key in lexicon:\n\t\tif s in lexicon[key]:\n\t\t\treturn key\n\t\telse:\n\t\t\tcontinue\n\treturn None\n\ndef convert_number(s):\n\ttry:\n\t\treturn int(s)\n\texcept ValueError:\n\t\treturn None\n","sub_path":"ex48/skeleton/ex48/lexicon.py","file_name":"lexicon.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"336212318","text":"\"\"\"\nhttps://leetcode.com/problems/split-two-strings-to-make-palindrome/\n\nWe are supposed to split at the same index, so we know the mid position and potential pattern.\n\nWe can build a helper function to check if a[:mid] can be used to build the palindrome. The function would check for any i < len(a) - mid, if a[mid:mid+i] == a[mid:mid-i][::-1], if so we keep traverse until it does not satisfy. Then we would check if we can fill the rest part from b.\n\nWe can call the above function 4 times to check palindrome formation.\n\nTime complexity: O(4N)\n\"\"\"\nclass Solution:\n def checkPalindromeFormation(self, a: str, b: str) -> bool:\n if a == a[::-1] or b == b[::-1]:\n return True\n\n def helper(a, b):\n mid = len(a) // 2\n if len(a) % 2 == 1:\n # skip mid\n pattern = a[:mid]\n start = mid + 1\n else:\n pattern = a[:mid]\n start = mid\n p1, p2 = mid - 1, start\n while a[p1] == a[p2] and p1 >= 0 and p2 < len(a):\n p1 -= 1\n p2 += 1\n return a[:p1+1] == b[p2:][::-1]\n\n if helper(a, b) or helper(b, a) or helper(a[::-1], b[::-1]) or helper(b[::-1], a[::-1]):\n return True\n return False\n","sub_path":"1616_SplitTwoStringsToMakePalindrome.py","file_name":"1616_SplitTwoStringsToMakePalindrome.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"470304637","text":"#!/usr/bin/env python\n\n#*- coding: utf-8 -*-\n\nfrom time import time\n\ndef measure_time(function):\n def inner(*args, **kwargs):\n start = time()\n return_value = function(*args, **kwargs)\n end = time()\n result = end - start\n\n if __debug__:\n print (\"%.4f -> %s\" % (result, function.__name__))\n\n return return_value\n\n return inner\n","sub_path":"measure_time.py","file_name":"measure_time.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"639917429","text":"# -*- coding: utf-8 -*-\n'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.\nCopyright (C) 2019, 2020 Caleb Bell \n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\nThis module contains a class :obj:`Wilson` for performing activity coefficient\ncalculations with the Wilson model. An older, functional calculation for\nactivity coefficients only is also present, :obj:`Wilson_gammas`.\n\nFor reporting bugs, adding feature requests, or submitting pull requests,\nplease use the `GitHub issue tracker `_.\n\n.. contents:: :local:\n\nWilson Class\n============\n\n.. autoclass:: Wilson\n :members: to_T_xs, GE, dGE_dT, d2GE_dT2, d3GE_dT3, d2GE_dTdxs, dGE_dxs, d2GE_dxixjs, d3GE_dxixjxks, lambdas, dlambdas_dT, d2lambdas_dT2, d3lambdas_dT3, from_DDBST, from_DDBST_as_matrix\n :undoc-members:\n :show-inheritance:\n :exclude-members: gammas\n\nWilson Functional Calculations\n==============================\n.. autofunction:: Wilson_gammas\n\n\n'''\n\nfrom __future__ import division\nfrom math import log, exp\nfrom fluids.constants import R\nfrom fluids.numerics import numpy as np\nfrom thermo.activity import GibbsExcess, interaction_exp, dinteraction_exp_dT, d2interaction_exp_dT2, d3interaction_exp_dT3\n\ntry:\n array, zeros, npsum, nplog = np.array, np.zeros, np.sum, np.log\nexcept (ImportError, AttributeError):\n pass\n\n__all__ = ['Wilson', 'Wilson_gammas']\n\n\ndef wilson_xj_Lambda_ijs(xs, lambdas, N, xj_Lambda_ijs=None):\n if xj_Lambda_ijs is None:\n xj_Lambda_ijs = [0.0]*N\n for i in range(N):\n tot = 0.0\n lambdasi = lambdas[i]\n for j in range(N):\n tot += xs[j]*lambdasi[j]\n xj_Lambda_ijs[i] = tot\n return xj_Lambda_ijs\n\ndef wilson_dGE_dT(xs, T, GE, N, xj_Lambda_ijs_inv, xj_dLambda_dTijs):\n tot = GE/T\n\n sum1 = 0.0\n for i in range(N):\n sum1 += xs[i]*xj_dLambda_dTijs[i]*xj_Lambda_ijs_inv[i]\n tot -= T*R*sum1\n return tot\n\ndef wilson_d2GE_dT2(xs, T, N, xj_Lambda_ijs_inv, xj_dLambda_dTijs, xj_d2Lambda_dT2ijs):\n sum0, sum1 = 0.0, 0.0\n for i in range(N):\n t = xs[i]*xj_Lambda_ijs_inv[i]\n t2 = xj_dLambda_dTijs[i]*t\n sum1 += t2\n\n sum0 += t*(xj_d2Lambda_dT2ijs[i] - xj_dLambda_dTijs[i]*xj_dLambda_dTijs[i]*xj_Lambda_ijs_inv[i])\n\n d2GE_dT2 = -R*(T*sum0 + 2.0*sum1)\n return d2GE_dT2\n\ndef wilson_d3GE_dT3(xs, T, N, xj_Lambda_ijs_inv, xj_dLambda_dTijs, xj_d2Lambda_dT2ijs, xj_d3Lambda_dT3ijs):\n #Term is directly from the one above it\n sum0 = 0.0\n for i in range(N):\n sum0 += xj_Lambda_ijs_inv[i]*xs[i]*(xj_d2Lambda_dT2ijs[i]\n - xj_dLambda_dTijs[i]*xj_dLambda_dTijs[i]*xj_Lambda_ijs_inv[i])\n\n sum_d3 = 0.0\n for i in range(N):\n sum_d3 += xs[i]*xj_d3Lambda_dT3ijs[i]*xj_Lambda_ijs_inv[i]\n\n sum_comb = 0.0\n for i in range(N):\n sum_comb += xs[i]*xj_d2Lambda_dT2ijs[i]*xj_dLambda_dTijs[i]*xj_Lambda_ijs_inv[i]*xj_Lambda_ijs_inv[i]\n sum_comb *= 3.0\n\n sum_last = 0.0\n for i in range(N):\n v = xj_dLambda_dTijs[i]*xj_Lambda_ijs_inv[i]\n sum_last += xs[i]*v*v*v\n sum_last *= 2.0\n\n d3GE_dT3 = -R*(3.0*sum0 + T*(sum_d3 - sum_comb + sum_last))\n return d3GE_dT3\n\ndef wilson_d2GE_dTdxs(xs, T, N, log_xj_Lambda_ijs, lambdas, dlambdas_dT,\n xj_Lambda_ijs_inv, xj_dLambda_dTijs, d2GE_dTdxs=None):\n if d2GE_dTdxs is None:\n d2GE_dTdxs = [0.0]*N\n\n for i in range(N):\n tot1 = xj_dLambda_dTijs[i]*xj_Lambda_ijs_inv[i]\n tot2 = 0.0\n for j in range(N):\n t1 = lambdas[j][i]*xj_Lambda_ijs_inv[j]\n tot1 += xs[j]*xj_Lambda_ijs_inv[j]*(dlambdas_dT[j][i] - xj_dLambda_dTijs[j]*t1)\n tot2 += xs[j]*t1\n\n dG = -R*(T*tot1 + log_xj_Lambda_ijs[i] + tot2)\n\n d2GE_dTdxs[i] = dG\n\n return d2GE_dTdxs\n\ndef wilson_dGE_dxs(xs, T, N, log_xj_Lambda_ijs, lambdas, xj_Lambda_ijs_inv, dGE_dxs=None):\n if dGE_dxs is None:\n dGE_dxs = [0.0]*N\n mRT = -T*R\n for k in range(N):\n tot = log_xj_Lambda_ijs[k]\n for i in range(N):\n tot += xs[i]*lambdas[i][k]*xj_Lambda_ijs_inv[i]\n dGE_dxs[k] = mRT*tot\n return dGE_dxs\n\ndef wilson_d2GE_dxixjs(xs, T, N, lambdas, xj_Lambda_ijs_inv, d2GE_dxixjs=None):\n if d2GE_dxixjs is None:\n d2GE_dxixjs = [[0.0]*N for i in range(N)] # numba: delete\n# d2GE_dxixjs = zeros((N, N)) # numba: uncomment\n\n RT = R*T\n for k in range(N):\n dG_row = d2GE_dxixjs[k]\n for m in range(N):\n tot = 0.0\n for i in range(N):\n tot += xs[i]*lambdas[i][k]*lambdas[i][m]*(xj_Lambda_ijs_inv[i]*xj_Lambda_ijs_inv[i])\n tot -= lambdas[k][m]*xj_Lambda_ijs_inv[k]\n tot -= lambdas[m][k]*xj_Lambda_ijs_inv[m]\n dG_row[m] = RT*tot\n\n return d2GE_dxixjs\n\ndef wilson_d3GE_dxixjxks(xs, T, N, lambdas, xj_Lambda_ijs_inv, d3GE_dxixjxks=None):\n if d3GE_dxixjxks is None:\n d3GE_dxixjxks = [[[0.0]*N for i in range(N)] for _ in range(N)]# numba: delete\n# d3GE_dxixjxks = zeros((N, N, N)) # numba: uncomment\n\n nRT = -R*T\n for k in range(N):\n dG_matrix = d3GE_dxixjxks[k]\n for m in range(N):\n dG_row = dG_matrix[m]\n for n in range(N):\n tot = 0.0\n for i in range(N):\n num = xs[i]*lambdas[i][k]*lambdas[i][m]*lambdas[i][n]\n den = xj_Lambda_ijs_inv[i]*xj_Lambda_ijs_inv[i]*xj_Lambda_ijs_inv[i]\n tot += num*den\n tot *= 2.0\n\n tot -= lambdas[k][m]*lambdas[k][n]*xj_Lambda_ijs_inv[k]*xj_Lambda_ijs_inv[k]\n tot -= lambdas[m][k]*lambdas[m][n]*xj_Lambda_ijs_inv[m]*xj_Lambda_ijs_inv[m]\n tot -= lambdas[n][m]*lambdas[n][k]*xj_Lambda_ijs_inv[n]*xj_Lambda_ijs_inv[n]\n dG_row[n] = nRT*tot\n\n return d3GE_dxixjxks\n\ndef wilson_gammas(xs, N, lambdas, xj_Lambda_ijs_inv, gammas=None, vec0=None):\n if gammas is None:\n gammas = [0.0]*N\n if vec0 is None:\n vec0 = [0.0]*N\n\n for i in range(N):\n vec0[i] = xs[i]*xj_Lambda_ijs_inv[i]\n\n for i in range(N):\n tot2 = 1.0\n for j in range(N):\n tot2 -= lambdas[j][i]*vec0[j]\n gammas[i] = exp(tot2)*xj_Lambda_ijs_inv[i]\n\n return gammas\n\nclass Wilson(GibbsExcess):\n r'''Class for representing an a liquid with excess gibbs energy represented\n by the Wilson equation. This model is capable of representing most\n nonideal liquids for vapor-liquid equilibria, but is not recommended for\n liquid-liquid equilibria.\n\n Parameters\n ----------\n T : float\n Temperature, [K]\n xs : list[float]\n Mole fractions, [-]\n lambda_coeffs : list[list[list[float]]], optional\n Wilson parameters, indexed by [i][j] and then each value is a 6\n element list with parameters [`a`, `b`, `c`, `d`, `e`, `f`];\n either `lambda_coeffs` or `ABCDEF` are required, [-]\n ABCDEF : tuple[list[list[float]], 6], optional\n Contains the following. One of `lambda_coeffs` or `ABCDEF` are\n required, [-]\n\n a : list[list[float]]\n `a` parameters used in calculating :obj:`Wilson.lambdas`, [-]\n b : list[list[float]]\n `b` parameters used in calculating :obj:`Wilson.lambdas`, [K]\n c : list[list[float]]\n `c` parameters used in calculating :obj:`Wilson.lambdas`, [-]\n d : list[list[float]]\n `d` paraemeters used in calculating :obj:`Wilson.lambdas`, [1/K]\n e : list[list[float]]\n `e` parameters used in calculating :obj:`Wilson.lambdas`, [K^2]\n f : list[list[float]]\n `f` parameters used in calculating :obj:`Wilson.lambdas`, [1/K^2]\n\n Attributes\n ----------\n T : float\n Temperature, [K]\n xs : list[float]\n Mole fractions, [-]\n\n Notes\n -----\n In addition to the methods presented here, the methods of its base class\n :obj:`thermo.activity.GibbsExcess` are available as well.\n\n Examples\n --------\n The DDBST has published some sample problems which are fun to work with.\n Because the DDBST uses a different equation form for the coefficients than\n this model implements, we must initialize the :obj:`Wilson` object with\n a different method.\n\n >>> T = 331.42\n >>> N = 3\n >>> Vs_ddbst = [74.04, 80.67, 40.73]\n >>> as_ddbst = [[0, 375.2835, 31.1208], [-1722.58, 0, -1140.79], [747.217, 3596.17, 0.0]]\n >>> bs_ddbst = [[0, -3.78434, -0.67704], [6.405502, 0, 2.59359], [-0.256645, -6.2234, 0]]\n >>> cs_ddbst = [[0.0, 7.91073e-3, 8.68371e-4], [-7.47788e-3, 0.0, 3.1e-5], [-1.24796e-3, 3e-5, 0.0]]\n >>> dis = eis = fis = [[0.0]*N for _ in range(N)]\n >>> params = Wilson.from_DDBST_as_matrix(Vs=Vs_ddbst, ais=as_ddbst, bis=bs_ddbst, cis=cs_ddbst, dis=dis, eis=eis, fis=fis, unit_conversion=False)\n >>> xs = [0.229, 0.175, 0.596]\n >>> GE = Wilson(T=T, xs=xs, ABCDEF=params)\n >>> GE\n Wilson(T=331.42, xs=[0.229, 0.175, 0.596], ABCDEF=([[0.0, 3.870101271243586, 0.07939943395502425], [-6.491263271243587, 0.0, -3.276991837288562], [0.8542855660449756, 6.906801837288562, 0.0]], [[0.0, -375.2835, -31.1208], [1722.58, 0.0, 1140.79], [-747.217, -3596.17, -0.0]], [[-0.0, -0.0, -0.0], [-0.0, -0.0, -0.0], [-0.0, -0.0, -0.0]], [[-0.0, -0.00791073, -0.000868371], [0.00747788, -0.0, -3.1e-05], [0.00124796, -3e-05, -0.0]], [[-0.0, -0.0, -0.0], [-0.0, -0.0, -0.0], [-0.0, -0.0, -0.0]], [[-0.0, -0.0, -0.0], [-0.0, -0.0, -0.0], [-0.0, -0.0, -0.0]]))\n >>> GE.GE(), GE.dGE_dT(), GE.d2GE_dT2()\n (480.2639266306882, 4.355962766232997, -0.029130384525017247)\n >>> GE.HE(), GE.SE(), GE.dHE_dT(), GE.dSE_dT()\n (-963.3892533542517, -4.355962766232997, 9.654392039281216, 0.029130384525017247)\n >>> GE.gammas()\n [1.2233934334, 1.100945902470, 1.205289928117]\n\n\n The solution given by the DDBST has the same values [1.223, 1.101, 1.205],\n and can be found here:\n http://chemthermo.ddbst.com/Problems_Solutions/Mathcad_Files/05.09%20Compare%20Experimental%20VLE%20to%20Wilson%20Equation%20Results.xps\n\n\n A simple example is given in [1]_; other textbooks sample problems are\n normally in the same form as this - with only volumes and the `a` term\n specified. The system is 2-propanol/water at 353.15 K, and the mole\n fraction of 2-propanol is 0.25.\n\n >>> T = 353.15\n >>> N = 2\n >>> Vs = [76.92, 18.07] # cm^3/mol\n >>> ais = [[0.0, 437.98],[1238.0, 0.0]] # cal/mol\n >>> bis = cis = dis = eis = fis = [[0.0]*N for _ in range(N)]\n >>> params = Wilson.from_DDBST_as_matrix(Vs=Vs, ais=ais, bis=bis, cis=cis, dis=dis, eis=eis, fis=fis, unit_conversion=True)\n >>> xs = [0.25, 0.75]\n >>> GE = Wilson(T=T, xs=xs, ABCDEF=params)\n >>> GE.gammas()\n [2.124064516, 1.1903745834]\n\n The activity coefficients given in [1]_ are [2.1244, 1.1904]; matching (\n with a slight error from their use of 1.987 as a gas constant).\n\n References\n ----------x\n .. [1] Smith, H. C. Van Ness Joseph M. Introduction to Chemical Engineering\n Thermodynamics 4th Edition, Joseph M. Smith, H. C. Van\n Ness, 1987.\n '''\n model_id = 200\n @staticmethod\n def from_DDBST(Vi, Vj, a, b, c, d=0.0, e=0.0, f=0.0, unit_conversion=True):\n r'''Converts parameters for the wilson equation in the DDBST to the\n basis used in this implementation.\n\n .. math::\n \\Lambda_{ij} = \\frac{V_j}{V_i}\\exp\\left(\\frac{-\\Delta \\lambda_{ij}}{RT}\n \\right)\n\n .. math::\n \\Delta \\lambda_{ij} = a_{ij} + b_{ij}T + c T^2 + d_{ij}T\\ln T\n + e_{ij}T^3 + f_{ij}/T\n\n Parameters\n ----------\n Vi : float\n Molar volume of component i; needs only to be in the same units as\n `Vj`, [cm^3/mol]\n Vj : float\n Molar volume of component j; needs only to be in the same units as\n `Vi`, [cm^3/mol]\n a : float\n `a` parameter in DDBST form, [K]\n b : float\n `b` parameter in DDBST form, [-]\n c : float\n `c` parameter in DDBST form, [1/K]\n d : float, optional\n `d` parameter in DDBST form, [-]\n e : float, optional\n `e` parameter in DDBST form, [1/K^2]\n f : float, optional\n `f` parameter in DDBST form, [K^2]\n unit_conversion : bool\n If True, the input coefficients are in units of cal/K/mol, and a\n `R` gas constant of 1.9872042... is used for the conversion;\n the DDBST uses this generally, [-]\n\n Returns\n -------\n a : float\n `a` parameter in :obj:`Wilson` form, [-]\n b : float\n `b` parameter in :obj:`Wilson` form, [K]\n c : float\n `c` parameter in :obj:`Wilson` form, [-]\n d : float\n `d` parameter in :obj:`Wilson` form, [1/K]\n e : float\n `e` parameter in :obj:`Wilson` form, [K^2]\n f : float\n `f` parameter in :obj:`Wilson` form, [1/K^2]\n\n Notes\n -----\n The units show how the different variables are related to each other.\n\n Examples\n --------\n >>> Wilson.from_DDBST(Vi=74.04, Vj=80.67, a=375.2835, b=-3.78434, c=0.00791073, d=0.0, e=0.0, f=0.0, unit_conversion=False)\n (3.8701012712, -375.2835, -0.0, -0.00791073, -0.0, -0.0)\n '''\n if unit_conversion:\n Rg = 1.9872042586408316 # DDBST document suggests 1.9858775\n else:\n Rg = 1.0 # Not used in some cases - be very careful\n a, b = log(Vj/Vi) - b/Rg, -a/Rg\n c, d = -d/Rg, -c/Rg\n e = -e/Rg\n f = -f/Rg\n return (a, b, c, d, e, f)\n\n @staticmethod\n def from_DDBST_as_matrix(Vs, ais, bis, cis, dis, eis, fis,\n unit_conversion=True):\n r'''Converts parameters for the wilson equation in the DDBST to the\n basis used in this implementation. Matrix wrapper around\n :obj:`Wilson.from_DDBST`.\n\n Parameters\n ----------\n Vs : list[float]\n Molar volume of component; needs only to be in consistent units,\n [cm^3/mol]\n a : list[list[float]]\n `a` parameters in DDBST form, [K]\n b : list[list[float]]\n `b` parameters in DDBST form, [-]\n c : list[list[float]]\n `c` parameters in DDBST form, [1/K]\n d : list[list[float]], optional\n `d` parameters in DDBST form, [-]\n e : list[list[float]], optional\n `e` parameters in DDBST form, [1/K^2]\n f : list[list[float]], optional\n `f` parameters in DDBST form, [K^2]\n unit_conversion : bool\n If True, the input coefficients are in units of cal/K/mol, and a\n `R` gas constant of 1.9872042... is used for the conversion;\n the DDBST uses this generally, [-]\n\n Returns\n -------\n a : list[list[float]]\n `a` parameters in :obj:`Wilson` form, [-]\n b : list[list[float]]\n `b` parameters in :obj:`Wilson` form, [K]\n c : list[list[float]]\n `c` parameters in :obj:`Wilson` form, [-]\n d : list[list[float]]\n `d` paraemeters in :obj:`Wilson` form, [1/K]\n e : list[list[float]]\n `e` parameters in :obj:`Wilson` form, [K^2]\n f : list[list[float]]\n `f` parameters in :obj:`Wilson` form, [1/K^2]\n '''\n cmps = range(len(Vs))\n a_mat, b_mat, c_mat, d_mat, e_mat, f_mat = [], [], [], [], [], []\n for i in cmps:\n a_row, b_row, c_row, d_row, e_row, f_row = [], [], [], [], [], []\n for j in cmps:\n a, b, c, d, e, f = Wilson.from_DDBST(Vs[i], Vs[j], ais[i][j],\n bis[i][j], cis[i][j], dis[i][j],\n eis[i][j], fis[i][j],\n unit_conversion=unit_conversion)\n a_row.append(a)\n b_row.append(b)\n c_row.append(c)\n d_row.append(d)\n e_row.append(e)\n f_row.append(f)\n a_mat.append(a_row)\n b_mat.append(b_row)\n c_mat.append(c_row)\n d_mat.append(d_row)\n e_mat.append(e_row)\n f_mat.append(f_row)\n return (a_mat, b_mat, c_mat, d_mat, e_mat, f_mat)\n\n def __init__(self, T, xs, lambda_coeffs=None, ABCDEF=None):\n self.T = T\n self.xs = xs\n self.scalar = scalar = type(xs) is list\n if ABCDEF is not None:\n (self.lambda_coeffs_A, self.lambda_coeffs_B, self.lambda_coeffs_C,\n self.lambda_coeffs_D, self.lambda_coeffs_E, self.lambda_coeffs_F) = ABCDEF\n self.N = N = len(self.lambda_coeffs_A)\n else:\n if lambda_coeffs is not None:\n if scalar:\n self.lambda_coeffs_A = [[i[0] for i in l] for l in lambda_coeffs]\n self.lambda_coeffs_B = [[i[1] for i in l] for l in lambda_coeffs]\n self.lambda_coeffs_C = [[i[2] for i in l] for l in lambda_coeffs]\n self.lambda_coeffs_D = [[i[3] for i in l] for l in lambda_coeffs]\n self.lambda_coeffs_E = [[i[4] for i in l] for l in lambda_coeffs]\n self.lambda_coeffs_F = [[i[5] for i in l] for l in lambda_coeffs]\n else:\n self.lambda_coeffs_A = array(lambda_coeffs[:,:,0], order='C', copy=True)\n self.lambda_coeffs_B = array(lambda_coeffs[:,:,1], order='C', copy=True)\n self.lambda_coeffs_C = array(lambda_coeffs[:,:,2], order='C', copy=True)\n self.lambda_coeffs_D = array(lambda_coeffs[:,:,3], order='C', copy=True)\n self.lambda_coeffs_E = array(lambda_coeffs[:,:,4], order='C', copy=True)\n self.lambda_coeffs_F = array(lambda_coeffs[:,:,5], order='C', copy=True)\n\n else:\n raise ValueError(\"`lambda_coeffs` or `ABCDEF` is required required\")\n self.N = N = len(lambda_coeffs)\n\n model_attriubtes = ('lambda_coeffs_A', 'lambda_coeffs_B', 'lambda_coeffs_C',\n 'lambda_coeffs_D', 'lambda_coeffs_E', 'lambda_coeffs_F')\n\n def __repr__(self):\n s = '%s(T=%s, xs=%s, ABCDEF=%s)' %(self.__class__.__name__, repr(self.T), repr(self.xs),\n (self.lambda_coeffs_A, self.lambda_coeffs_B, self.lambda_coeffs_C,\n self.lambda_coeffs_D, self.lambda_coeffs_E, self.lambda_coeffs_F))\n return s\n\n def to_T_xs(self, T, xs):\n r'''Method to construct a new :obj:`Wilson` instance at\n temperature `T`, and mole fractions `xs`\n with the same parameters as the existing object.\n\n Parameters\n ----------\n T : float\n Temperature, [K]\n xs : list[float]\n Mole fractions of each component, [-]\n\n Returns\n -------\n obj : Wilson\n New :obj:`Wilson` object at the specified conditions [-]\n\n Notes\n -----\n If the new temperature is the same temperature as the existing\n temperature, if the `lambda` terms or their derivatives have been\n calculated, they will be set to the new object as well.\n '''\n new = self.__class__.__new__(self.__class__)\n new.T = T\n new.xs = xs\n new.scalar = self.scalar\n new.N = self.N\n (new.lambda_coeffs_A, new.lambda_coeffs_B, new.lambda_coeffs_C,\n new.lambda_coeffs_D, new.lambda_coeffs_E, new.lambda_coeffs_F) = (\n self.lambda_coeffs_A, self.lambda_coeffs_B, self.lambda_coeffs_C,\n self.lambda_coeffs_D, self.lambda_coeffs_E, self.lambda_coeffs_F)\n\n if T == self.T:\n try:\n new._lambdas = self._lambdas\n except AttributeError:\n pass\n\n try:\n new._dlambdas_dT = self._dlambdas_dT\n except AttributeError:\n pass\n\n try:\n new._d2lambdas_dT2 = self._d2lambdas_dT2\n except AttributeError:\n pass\n\n try:\n new._d3lambdas_dT3 = self._d3lambdas_dT3\n except AttributeError:\n pass\n return new\n\n def lambdas(self):\n r'''Calculate and return the `lambda` terms for the Wilson model for\n at system temperature.\n\n .. math::\n \\Lambda_{ij} = \\exp\\left[a_{ij}+\\frac{b_{ij}}{T}+c_{ij}\\ln T\n + d_{ij}T + \\frac{e_{ij}}{T^2} + f_{ij}{T^2}\\right]\n\n Returns\n -------\n lambdas : list[list[float]]\n Lambda terms, asymmetric matrix [-]\n\n Notes\n -----\n These `Lambda ij` values (and the coefficients) are NOT symmetric.\n '''\n try:\n return self._lambdas\n except AttributeError:\n pass\n\n N = self.N\n if self.scalar:\n lambdas = [[0.0]*N for _ in range(N)]\n else:\n lambdas = zeros((N, N))\n\n lambdas = interaction_exp(self.T, N, self.lambda_coeffs_A, self.lambda_coeffs_B,\n self.lambda_coeffs_C, self.lambda_coeffs_D,\n self.lambda_coeffs_E, self.lambda_coeffs_F, lambdas)\n self._lambdas = lambdas\n return lambdas\n\n def dlambdas_dT(self):\n r'''Calculate and return the temperature derivative of the `lambda`\n terms for the Wilson model at the system temperature.\n\n .. math::\n \\frac{\\partial \\Lambda_{ij}}{\\partial T} =\n \\left(2 T h_{ij} + d_{ij} + \\frac{c_{ij}}{T} - \\frac{b_{ij}}{T^{2}}\n - \\frac{2 e_{ij}}{T^{3}}\\right) e^{T^{2} h_{ij} + T d_{ij} + a_{ij}\n + c_{ij} \\ln{\\left(T \\right)} + \\frac{b_{ij}}{T}\n + \\frac{e_{ij}}{T^{2}}}\n\n Returns\n -------\n dlambdas_dT : list[list[float]]\n Temperature deriavtives of Lambda terms, asymmetric matrix [1/K]\n\n Notes\n -----\n These `Lambda ij` values (and the coefficients) are NOT symmetric.\n '''\n try:\n return self._dlambdas_dT\n except AttributeError:\n pass\n\n B = self.lambda_coeffs_B\n C = self.lambda_coeffs_C\n D = self.lambda_coeffs_D\n E = self.lambda_coeffs_E\n F = self.lambda_coeffs_F\n\n T, N = self.T, self.N\n try:\n lambdas = self._lambdas\n except AttributeError:\n lambdas = self.lambdas()\n if self.scalar:\n dlambdas_dT = [[0.0]*N for _ in range(N)]\n else:\n dlambdas_dT = zeros((N, N))\n\n self._dlambdas_dT = dinteraction_exp_dT(T, N, B, C, D, E, F, lambdas, dlambdas_dT)\n return dlambdas_dT\n\n def d2lambdas_dT2(self):\n r'''Calculate and return the second temperature derivative of the\n `lambda` termsfor the Wilson model at the system temperature.\n\n .. math::\n \\frac{\\partial^2 \\Lambda_{ij}}{\\partial^2 T} =\n \\left(2 f_{ij} + \\left(2 T f_{ij} + d_{ij} + \\frac{c_{ij}}{T}\n - \\frac{b_{ij}}{T^{2}} - \\frac{2 e_{ij}}{T^{3}}\\right)^{2}\n - \\frac{c_{ij}}{T^{2}} + \\frac{2 b_{ij}}{T^{3}}\n + \\frac{6 e_{ij}}{T^{4}}\\right) e^{T^{2} f_{ij} + T d_{ij}\n + a_{ij} + c_{ij} \\ln{\\left(T \\right)} + \\frac{b_{ij}}{T}\n + \\frac{e_{ij}}{T^{2}}}\n\n Returns\n -------\n d2lambdas_dT2 : list[list[float]]\n Second temperature deriavtives of Lambda terms, asymmetric matrix,\n [1/K^2]\n\n Notes\n -----\n These `Lambda ij` values (and the coefficients) are NOT symmetric.\n '''\n try:\n return self._d2lambdas_dT2\n except AttributeError:\n pass\n T, N = self.T, self.N\n\n try:\n lambdas = self._lambdas\n except AttributeError:\n lambdas = self.lambdas()\n try:\n dlambdas_dT = self._dlambdas_dT\n except AttributeError:\n dlambdas_dT = self.dlambdas_dT()\n\n if self.scalar:\n d2lambdas_dT2 = [[0.0]*N for _ in range(N)]\n else:\n d2lambdas_dT2 = zeros((N, N))\n\n self._d2lambdas_dT2 = d2interaction_exp_dT2(T, N, self.lambda_coeffs_B,\n self.lambda_coeffs_C,\n self.lambda_coeffs_E,\n self.lambda_coeffs_F,\n lambdas, dlambdas_dT, d2lambdas_dT2)\n return d2lambdas_dT2\n\n def d3lambdas_dT3(self):\n r'''Calculate and return the third temperature derivative of the\n `lambda` terms for the Wilson model at the system temperature.\n\n .. math::\n \\frac{\\partial^3 \\Lambda_{ij}}{\\partial^3 T} =\n \\left(3 \\left(2 f_{ij} - \\frac{c_{ij}}{T^{2}} + \\frac{2 b_{ij}}{T^{3}}\n + \\frac{6 e_{ij}}{T^{4}}\\right) \\left(2 T f_{ij} + d_{ij}\n + \\frac{c_{ij}}{T} - \\frac{b_{ij}}{T^{2}} - \\frac{2 e_{ij}}{T^{3}}\\right)\n + \\left(2 T f_{ij} + d_{ij} + \\frac{c_{ij}}{T} - \\frac{b_{ij}}{T^{2}}\n - \\frac{2 e_{ij}}{T^{3}}\\right)^{3} - \\frac{2 \\left(- c_{ij}\n + \\frac{3 b_{ij}}{T} + \\frac{12 e_{ij}}{T^{2}}\\right)}{T^{3}}\\right)\n e^{T^{2} f_{ij} + T d_{ij} + a_{ij} + c_{ij} \\ln{\\left(T \\right)}\n + \\frac{b_{ij}}{T} + \\frac{e_{ij}}{T^{2}}}\n\n Returns\n -------\n d3lambdas_dT3 : list[list[float]]\n Third temperature deriavtives of Lambda terms, asymmetric matrix,\n [1/K^3]\n\n Notes\n -----\n These `Lambda ij` values (and the coefficients) are NOT symmetric.\n '''\n try:\n return self._d3lambdas_dT3\n except AttributeError:\n pass\n\n T, N = self.T, self.N\n lambda_coeffs_B = self.lambda_coeffs_B\n lambda_coeffs_C = self.lambda_coeffs_C\n lambda_coeffs_E = self.lambda_coeffs_E\n lambda_coeffs_F = self.lambda_coeffs_F\n\n try:\n lambdas = self._lambdas\n except AttributeError:\n lambdas = self.lambdas()\n try:\n dlambdas_dT = self._dlambdas_dT\n except AttributeError:\n dlambdas_dT = self.dlambdas_dT()\n\n if self.scalar:\n d3lambdas_dT3s = [[0.0]*N for _ in range(N)]\n else:\n d3lambdas_dT3s = zeros((N, N))\n\n self._d3lambdas_dT3 = d3interaction_exp_dT3(T, N, lambda_coeffs_B, lambda_coeffs_C, lambda_coeffs_E,\n lambda_coeffs_F, lambdas, dlambdas_dT, d3lambdas_dT3s)\n return d3lambdas_dT3s\n\n def xj_Lambda_ijs(self):\n '''\n '''\n try:\n return self._xj_Lambda_ijs\n except AttributeError:\n pass\n try:\n lambdas = self._lambdas\n except AttributeError:\n lambdas = self.lambdas()\n\n if self.scalar:\n xj_Lambda_ijs = [0.0]*self.N\n else:\n xj_Lambda_ijs = zeros(self.N)\n\n self._xj_Lambda_ijs = wilson_xj_Lambda_ijs(self.xs, lambdas, self.N, xj_Lambda_ijs)\n return xj_Lambda_ijs\n\n def xj_Lambda_ijs_inv(self):\n '''\n '''\n try:\n return self._xj_Lambda_ijs_inv\n except AttributeError:\n pass\n\n try:\n xj_Lambda_ijs = self._xj_Lambda_ijs\n except AttributeError:\n xj_Lambda_ijs = self.xj_Lambda_ijs()\n if self.scalar:\n self._xj_Lambda_ijs_inv = [1.0/x for x in xj_Lambda_ijs]\n else:\n self._xj_Lambda_ijs_inv = 1.0/xj_Lambda_ijs\n return self._xj_Lambda_ijs_inv\n\n def log_xj_Lambda_ijs(self):\n '''\n '''\n try:\n return self._log_xj_Lambda_ijs\n except AttributeError:\n pass\n try:\n xj_Lambda_ijs = self._xj_Lambda_ijs\n except AttributeError:\n xj_Lambda_ijs = self.xj_Lambda_ijs()\n if self.scalar:\n self._log_xj_Lambda_ijs = [log(i) for i in xj_Lambda_ijs]\n else:\n self._log_xj_Lambda_ijs = nplog(xj_Lambda_ijs)\n return self._log_xj_Lambda_ijs\n\n\n def xj_dLambda_dTijs(self):\n '''\n '''\n try:\n return self._xj_dLambda_dTijs\n except AttributeError:\n pass\n try:\n dlambdas_dT = self._dlambdas_dT\n except AttributeError:\n dlambdas_dT = self.dlambdas_dT()\n\n if self.scalar:\n xj_dLambda_dTijs = [0.0]*self.N\n else:\n xj_dLambda_dTijs = zeros(self.N)\n\n self._xj_dLambda_dTijs = wilson_xj_Lambda_ijs(self.xs, dlambdas_dT, self.N, xj_dLambda_dTijs)\n return xj_dLambda_dTijs\n\n\n def xj_d2Lambda_dT2ijs(self):\n '''\n '''\n try:\n return self._xj_d2Lambda_dT2ijs\n except AttributeError:\n pass\n try:\n d2lambdas_dT2 = self._d2lambdas_dT2\n except AttributeError:\n d2lambdas_dT2 = self.d2lambdas_dT2()\n\n if self.scalar:\n xj_d2Lambda_dT2ijs = [0.0]*self.N\n else:\n xj_d2Lambda_dT2ijs = zeros(self.N)\n\n self._xj_d2Lambda_dT2ijs = wilson_xj_Lambda_ijs(self.xs, d2lambdas_dT2, self.N, xj_d2Lambda_dT2ijs)\n return xj_d2Lambda_dT2ijs\n\n def xj_d3Lambda_dT3ijs(self):\n '''\n '''\n try:\n return self._xj_d3Lambda_dT3ijs\n except AttributeError:\n pass\n try:\n d3lambdas_dT3 = self._d3lambdas_dT3\n except AttributeError:\n d3lambdas_dT3 = self.d3lambdas_dT3()\n\n if self.scalar:\n xj_d3Lambda_dT3ijs = [0.0]*self.N\n else:\n xj_d3Lambda_dT3ijs = zeros(self.N)\n\n self._xj_d3Lambda_dT3ijs = wilson_xj_Lambda_ijs(self.xs, d3lambdas_dT3, self.N, xj_d3Lambda_dT3ijs)\n return xj_d3Lambda_dT3ijs\n\n\n def GE(self):\n r'''Calculate and return the excess Gibbs energy of a liquid phase\n represented with the Wilson model.\n\n .. math::\n g^E = -RT\\sum_i x_i \\ln\\left(\\sum_j x_j \\lambda_{i,j} \\right)\n\n Returns\n -------\n GE : float\n Excess Gibbs energy of an ideal liquid, [J/mol]\n\n Notes\n -----\n '''\n try:\n return self._GE\n except AttributeError:\n pass\n\n try:\n log_xj_Lambda_ijs = self._log_xj_Lambda_ijs\n except AttributeError:\n log_xj_Lambda_ijs = self.log_xj_Lambda_ijs()\n\n if self.scalar:\n xs, N = self.xs, self.N\n GE = 0.0\n for i in range(N):\n GE += xs[i]*log_xj_Lambda_ijs[i]\n else:\n GE = float((self.xs*log_xj_Lambda_ijs).sum())\n self._GE = GE = -GE*R*self.T\n return GE\n\n def dGE_dT(self):\n r'''Calculate and return the temperature derivative of excess Gibbs\n energy of a liquid phase represented by the Wilson model.\n\n .. math::\n \\frac{\\partial G^E}{\\partial T} = -R\\sum_i x_i \\ln\\left(\\sum_j x_i \\Lambda_{ij}\\right)\n -RT\\sum_i \\frac{x_i \\sum_j x_j \\frac{\\Lambda _{ij}}{\\partial T}}{\\sum_j x_j \\Lambda_{ij}}\n\n Returns\n -------\n dGE_dT : float\n First temperature derivative of excess Gibbs energy of a\n liquid phase represented by the Wilson model, [J/(mol*K)]\n\n Notes\n -----\n '''# Derived with:\n '''from sympy import *\n N = 4\n R, T = symbols('R, T')\n x1, x2, x3, x4 = symbols('x1, x2, x3, x4')\n xs = [x1, x2, x3, x4]\n\n Lambda11, Lambda12, Lambda13, Lambda14, Lambda21, Lambda22, Lambda23, Lambda24, Lambda31, Lambda32, Lambda33, Lambda34, Lambda41, Lambda42, Lambda43, Lambda44 = symbols(\n 'Lambda11, Lambda12, Lambda13, Lambda14, Lambda21, Lambda22, Lambda23, Lambda24, Lambda31, Lambda32, Lambda33, Lambda34, Lambda41, Lambda42, Lambda43, Lambda44', cls=Function)\n Lambda_ijs = [[Lambda11(T), Lambda12(T), Lambda13(T), Lambda14(T)],\n [Lambda21(T), Lambda22(T), Lambda23(T), Lambda24(T)],\n [Lambda31(T), Lambda32(T), Lambda33(T), Lambda34(T)],\n [Lambda41(T), Lambda42(T), Lambda43(T), Lambda44(T)]]\n ge = 0\n for i in range(N):\n num = 0\n for j in range(N):\n num += Lambda_ijs[i][j]*xs[j]\n ge -= xs[i]*log(num)\n ge = ge*R*T\n\n diff(ge, T)\n '''\n try:\n return self._dGE_dT\n except AttributeError:\n pass\n try:\n xj_Lambda_ijs_inv = self._xj_Lambda_ijs_inv\n except AttributeError:\n xj_Lambda_ijs_inv = self.xj_Lambda_ijs_inv()\n\n try:\n xj_dLambda_dTijs = self._xj_dLambda_dTijs\n except AttributeError:\n xj_dLambda_dTijs = self.xj_dLambda_dTijs()\n\n try:\n GE = self._GE\n except AttributeError:\n GE = self.GE()\n self._dGE_dT = dGE_dT = wilson_dGE_dT(self.xs, self.T, GE, self.N, xj_Lambda_ijs_inv, xj_dLambda_dTijs)\n return dGE_dT\n\n def d2GE_dT2(self):\n r'''Calculate and return the second temperature derivative of excess\n Gibbs energy of a liquid phase using the Wilson activity coefficient model.\n\n .. math::\n \\frac{\\partial^2 G^E}{\\partial T^2} = -R\\left[T\\sum_i \\left(\\frac{x_i \\sum_j (x_j \\frac{\\partial^2 \\Lambda_{ij}}{\\partial T^2} )}{\\sum_j x_j \\Lambda_{ij}}\n - \\frac{x_i (\\sum_j x_j \\frac{\\partial \\Lambda_{ij}}{\\partial T} )^2}{(\\sum_j x_j \\Lambda_{ij})^2}\n \\right)\n + 2\\sum_i \\left(\\frac{x_i \\sum_j x_j \\frac{\\partial \\Lambda_{ij}}{\\partial T}}{\\sum_j x_j \\Lambda_{ij}}\n \\right)\n \\right]\n\n Returns\n -------\n d2GE_dT2 : float\n Second temperature derivative of excess Gibbs energy, [J/(mol*K^2)]\n\n Notes\n -----\n '''\n try:\n return self._d2GE_dT2\n except AttributeError:\n pass\n\n try:\n xj_Lambda_ijs_inv = self._xj_Lambda_ijs_inv\n except AttributeError:\n xj_Lambda_ijs_inv = self.xj_Lambda_ijs_inv()\n\n try:\n xj_dLambda_dTijs = self._xj_dLambda_dTijs\n except AttributeError:\n xj_dLambda_dTijs = self.xj_dLambda_dTijs()\n try:\n xj_d2Lambda_dT2ijs = self._xj_d2Lambda_dT2ijs\n except AttributeError:\n xj_d2Lambda_dT2ijs = self.xj_d2Lambda_dT2ijs()\n\n self._d2GE_dT2 = wilson_d2GE_dT2(self.xs, self.T, self.N, xj_Lambda_ijs_inv, xj_dLambda_dTijs, xj_d2Lambda_dT2ijs)\n return self._d2GE_dT2\n\n def d3GE_dT3(self):\n r'''Calculate and return the third temperature derivative of excess\n Gibbs energy of a liquid phase using the Wilson activity coefficient\n model.\n\n .. math::\n \\frac{\\partial^3 G^E}{\\partial T^3} = -R\\left[3\\left(\\frac{x_i \\sum_j (x_j \\frac{\\partial^2 \\Lambda_{ij}}{\\partial T^2} )}{\\sum_j x_j \\Lambda_{ij}}\n - \\frac{x_i (\\sum_j x_j \\frac{\\partial \\Lambda_{ij}}{\\partial T} )^2}{(\\sum_j x_j \\Lambda_{ij})^2}\n \\right)\n +T\\left(\n \\sum_i \\frac{x_i (\\sum_j x_j \\frac{\\partial^3 \\Lambda _{ij}}{\\partial T^3})}{\\sum_j x_j \\Lambda_{ij}}\n - \\frac{3x_i (\\sum_j x_j \\frac{\\partial \\Lambda_{ij}^2}{\\partial T^2}) (\\sum_j x_j \\frac{\\partial \\Lambda_{ij}}{\\partial T})}{(\\sum_j x_j \\Lambda_{ij})^2}\n + 2\\frac{x_i(\\sum_j x_j \\frac{\\partial \\Lambda_{ij}}{\\partial T})^3}{(\\sum_j x_j \\Lambda_{ij})^3}\n \\right)\\right]\n\n Returns\n -------\n d3GE_dT3 : float\n Third temperature derivative of excess Gibbs energy, [J/(mol*K^3)]\n\n Notes\n -----\n '''\n try:\n return self._d3GE_dT3\n except AttributeError:\n pass\n\n xj_Lambda_ijs_inv = self.xj_Lambda_ijs_inv()\n xj_dLambda_dTijs = self.xj_dLambda_dTijs()\n xj_d2Lambda_dT2ijs = self.xj_d2Lambda_dT2ijs()\n xj_d3Lambda_dT3ijs = self.xj_d3Lambda_dT3ijs()\n\n self._d3GE_dT3 = wilson_d3GE_dT3(self.xs, self.T, self.N, xj_Lambda_ijs_inv, xj_dLambda_dTijs,\n xj_d2Lambda_dT2ijs, xj_d3Lambda_dT3ijs)\n return self._d3GE_dT3\n\n\n def d2GE_dTdxs(self):\n r'''Calculate and return the temperature derivative of mole fraction\n derivatives of excess Gibbs energy of a liquid represented by the\n Wilson model.\n\n .. math::\n \\frac{\\partial^2 G^E}{\\partial x_k \\partial T} = -R\\left[T\\left(\n \\sum_i \\left(\\frac{x_i \\frac{\\partial n_{ik}}{\\partial T}}{\\sum_j x_j \\Lambda_{ij}}\n - \\frac{x_i \\Lambda_{ik} (\\sum_j x_j \\frac{\\partial \\Lambda_{ij}}{\\partial T} )}{(\\partial_j x_j \\Lambda_{ij})^2}\n \\right) + \\frac{\\sum_i x_i \\frac{\\partial \\Lambda_{ki}}{\\partial T}}{\\sum_j x_j \\Lambda_{kj}}\n \\right)\n + \\ln\\left(\\sum_i x_i \\Lambda_{ki}\\right)\n + \\sum_i \\frac{x_i \\Lambda_{ik}}{\\sum_j x_j \\Lambda_{ij}}\n \\right]\n\n Returns\n -------\n d2GE_dTdxs : list[float]\n Temperature derivative of mole fraction derivatives of excess Gibbs\n energy, [J/mol/K]\n\n Notes\n -----\n '''\n try:\n return self._d2GE_dTdxs\n except AttributeError:\n pass\n\n try:\n log_xj_Lambda_ijs = self._log_xj_Lambda_ijs\n except AttributeError:\n log_xj_Lambda_ijs = self.log_xj_Lambda_ijs()\n try:\n lambdas = self._lambdas\n except AttributeError:\n lambdas = self.lambdas()\n try:\n dlambdas_dT = self._dlambdas_dT\n except AttributeError:\n dlambdas_dT = self.dlambdas_dT()\n try:\n xj_Lambda_ijs_inv = self._xj_Lambda_ijs_inv\n except AttributeError:\n xj_Lambda_ijs_inv = self.xj_Lambda_ijs_inv()\n try:\n xj_dLambda_dTijs = self._xj_dLambda_dTijs\n except AttributeError:\n xj_dLambda_dTijs = self.xj_dLambda_dTijs()\n if self.scalar:\n d2GE_dTdxs = [0.0]*self.N\n else:\n d2GE_dTdxs = zeros(self.N)\n\n wilson_d2GE_dTdxs(self.xs, self.T, self.N, log_xj_Lambda_ijs,\n lambdas, dlambdas_dT,\n xj_Lambda_ijs_inv, xj_dLambda_dTijs, d2GE_dTdxs)\n self._d2GE_dTdxs = d2GE_dTdxs\n return d2GE_dTdxs\n\n\n def dGE_dxs(self):\n r'''Calculate and return the mole fraction derivatives of excess Gibbs\n energy for the Wilson model.\n\n .. math::\n \\frac{\\partial G^E}{\\partial x_k} = -RT\\left[\n \\sum_i \\frac{x_i \\Lambda_{ik}}{\\sum_j \\Lambda_{ij}x_j }\n + \\ln\\left(\\sum_j x_j \\Lambda_{kj}\\right)\n \\right]\n\n Returns\n -------\n dGE_dxs : list[float]\n Mole fraction derivatives of excess Gibbs energy, [J/mol]\n\n Notes\n -----\n '''\n '''\n from sympy import *\n N = 4\n R, T = symbols('R, T')\n x1, x2, x3, x4 = symbols('x1, x2, x3, x4')\n xs = [x1, x2, x3, x4]\n\n Lambda11, Lambda12, Lambda13, Lambda14, Lambda21, Lambda22, Lambda23, Lambda24, Lambda31, Lambda32, Lambda33, Lambda34, Lambda41, Lambda42, Lambda43, Lambda44 = symbols(\n 'Lambda11, Lambda12, Lambda13, Lambda14, Lambda21, Lambda22, Lambda23, Lambda24, Lambda31, Lambda32, Lambda33, Lambda34, Lambda41, Lambda42, Lambda43, Lambda44', cls=Function)\n Lambda_ijs = [[Lambda11(T), Lambda12(T), Lambda13(T), Lambda14(T)],\n [Lambda21(T), Lambda22(T), Lambda23(T), Lambda24(T)],\n [Lambda31(T), Lambda32(T), Lambda33(T), Lambda34(T)],\n [Lambda41(T), Lambda42(T), Lambda43(T), Lambda44(T)]]\n ge = 0\n for i in range(N):\n num = 0\n for j in range(N):\n num += Lambda_ijs[i][j]*xs[j]\n ge -= xs[i]*log(num)\n ge = ge*R*T\n\n\n diff(ge, x1)#, diff(ge, x1, x2), diff(ge, x1, x2, x3)\n '''\n try:\n return self._dGE_dxs\n except AttributeError:\n pass\n try:\n lambdas = self._lambdas\n except AttributeError:\n lambdas = self.lambdas()\n try:\n log_xj_Lambda_ijs = self._log_xj_Lambda_ijs\n except AttributeError:\n log_xj_Lambda_ijs = self.log_xj_Lambda_ijs()\n try:\n xj_Lambda_ijs_inv = self._xj_Lambda_ijs_inv\n except AttributeError:\n xj_Lambda_ijs_inv = self.xj_Lambda_ijs_inv()\n\n if self.scalar:\n dGE_dxs = [0.0]*self.N\n else:\n dGE_dxs = zeros(self.N)\n\n dGE_dxs = wilson_dGE_dxs(self.xs, self.T, self.N, log_xj_Lambda_ijs, lambdas, xj_Lambda_ijs_inv, dGE_dxs)\n self._dGE_dxs = dGE_dxs\n return dGE_dxs\n\n def d2GE_dxixjs(self):\n r'''Calculate and return the second mole fraction derivatives of excess\n Gibbs energy for the Wilson model.\n\n .. math::\n \\frac{\\partial^2 G^E}{\\partial x_k \\partial x_m} = RT\\left(\n \\sum_i \\frac{x_i \\Lambda_{ik} \\Lambda_{im}}{(\\sum_j x_j \\Lambda_{ij})^2}\n -\\frac{\\Lambda_{km}}{\\sum_j x_j \\Lambda_{kj}}\n -\\frac{\\Lambda_{mk}}{\\sum_j x_j \\Lambda_{mj}}\n \\right)\n\n Returns\n -------\n d2GE_dxixjs : list[list[float]]\n Second mole fraction derivatives of excess Gibbs energy, [J/mol]\n\n Notes\n -----\n '''\n try:\n return self._d2GE_dxixjs\n except AttributeError:\n pass\n # Correct, tested with hessian\n try:\n lambdas = self._lambdas\n except AttributeError:\n lambdas = self.lambdas()\n try:\n xj_Lambda_ijs_inv = self._xj_Lambda_ijs_inv\n except AttributeError:\n xj_Lambda_ijs_inv = self.xj_Lambda_ijs_inv()\n N = self.N\n if self.scalar:\n d2GE_dxixjs = [[0.0]*N for _ in range(N)]\n else:\n d2GE_dxixjs = zeros((N, N))\n\n d2GE_dxixjs = wilson_d2GE_dxixjs(self.xs, self.T, N, lambdas, xj_Lambda_ijs_inv, d2GE_dxixjs)\n self._d2GE_dxixjs = d2GE_dxixjs\n return d2GE_dxixjs\n\n def d3GE_dxixjxks(self):\n r'''Calculate and return the third mole fraction derivatives of excess\n Gibbs energy using the Wilson model.\n\n .. math::\n \\frac{\\partial^3 G^E}{\\partial x_k \\partial x_m \\partial x_n}\n = -RT\\left[\n \\sum_i \\left(\\frac{2x_i \\Lambda_{ik}\\Lambda_{im}\\Lambda_{in}} {(\\sum x_j \\Lambda_{ij})^3}\\right)\n - \\frac{\\Lambda_{km} \\Lambda_{kn}}{(\\sum_j x_j \\Lambda_{kj})^2}\n - \\frac{\\Lambda_{mk} \\Lambda_{mn}}{(\\sum_j x_j \\Lambda_{mj})^2}\n - \\frac{\\Lambda_{nk} \\Lambda_{nm}}{(\\sum_j x_j \\Lambda_{nj})^2}\n \\right]\n\n Returns\n -------\n d3GE_dxixjxks : list[list[list[float]]]\n Third mole fraction derivatives of excess Gibbs energy, [J/mol]\n\n Notes\n -----\n '''\n try:\n return self._d3GE_dxixjxks\n except AttributeError:\n pass\n # Correct, tested with sympy expanding\n lambdas = self.lambdas()\n try:\n xj_Lambda_ijs_inv = self._xj_Lambda_ijs_inv\n except AttributeError:\n xj_Lambda_ijs_inv = self.xj_Lambda_ijs_inv()\n\n N = self.N\n if self.scalar:\n d3GE_dxixjxks = [[[0.0]*N for _ in range(N)] for _ in range(N)]\n else:\n d3GE_dxixjxks = zeros((N, N, N))\n\n # all the same: analytical[i][j][k] = analytical[i][k][j] = analytical[j][i][k] = analytical[j][k][i] = analytical[k][i][j] = analytical[k][j][i] = float(v)\n d3GE_dxixjxks = wilson_d3GE_dxixjxks(self.xs, self.T, self.N, lambdas, xj_Lambda_ijs_inv, d3GE_dxixjxks)\n self._d3GE_dxixjxks = d3GE_dxixjxks\n return d3GE_dxixjxks\n\n\n def gammas(self):\n # Don't bother documenting or exposing; implemented only for a bit more\n # speed and precision.\n try:\n return self._gammas\n except AttributeError:\n pass\n# xs, cmps = self.xs, self.cmps\n try:\n lambdas = self._lambdas\n except AttributeError:\n lambdas = self.lambdas()\n try:\n xj_Lambda_ijs_inv = self._xj_Lambda_ijs_inv\n except AttributeError:\n xj_Lambda_ijs_inv = self.xj_Lambda_ijs_inv()\n\n if self.scalar:\n gammas = [0.0]*self.N\n else:\n gammas = zeros(self.N)\n\n wilson_gammas(self.xs, self.N, lambdas, xj_Lambda_ijs_inv, gammas)\n self._gammas = gammas\n return gammas\n\n\ndef Wilson_gammas(xs, params):\n r'''Calculates the activity coefficients of each species in a mixture\n using the Wilson method, given their mole fractions, and\n dimensionless interaction parameters. Those are normally correlated with\n temperature, and need to be calculated separately.\n\n .. math::\n \\ln \\gamma_i = 1 - \\ln \\left(\\sum_j^N \\Lambda_{ij} x_j\\right)\n -\\sum_j^N \\frac{\\Lambda_{ji}x_j}{\\displaystyle\\sum_k^N \\Lambda_{jk}x_k}\n\n Parameters\n ----------\n xs : list[float]\n Liquid mole fractions of each species, [-]\n params : list[list[float]]\n Dimensionless interaction parameters of each compound with each other,\n [-]\n\n Returns\n -------\n gammas : list[float]\n Activity coefficient for each species in the liquid mixture, [-]\n\n Notes\n -----\n This model needs N^2 parameters.\n\n The original model correlated the interaction parameters using the standard\n pure-component molar volumes of each species at 25°C, in the following form:\n\n .. math::\n \\Lambda_{ij} = \\frac{V_j}{V_i} \\exp\\left(\\frac{-\\lambda_{i,j}}{RT}\\right)\n\n If a compound is not liquid at that temperature, the liquid volume is taken\n at the saturated pressure; and if the component is supercritical, its\n liquid molar volume should be extrapolated to 25°C.\n\n However, that form has less flexibility and offered no advantage over\n using only regressed parameters.\n\n Most correlations for the interaction parameters include some of the terms\n shown in the following form:\n\n .. math::\n \\ln \\Lambda_{ij} =a_{ij}+\\frac{b_{ij}}{T}+c_{ij}\\ln T + d_{ij}T\n + \\frac{e_{ij}}{T^2} + h_{ij}{T^2}\n\n The Wilson model is not applicable to liquid-liquid systems.\n\n For this model to produce ideal acitivty coefficients (gammas = 1),\n all interaction parameters should be 1.\n\n The specific process simulator implementations are as follows:\n\n Examples\n --------\n Ethanol-water example, at 343.15 K and 1 MPa:\n\n >>> Wilson_gammas([0.252, 0.748], [[1, 0.154], [0.888, 1]])\n [1.8814926087178843, 1.1655774931125487]\n\n References\n ----------\n .. [1] Wilson, Grant M. \"Vapor-Liquid Equilibrium. XI. A New Expression for\n the Excess Free Energy of Mixing.\" Journal of the American Chemical\n Society 86, no. 2 (January 1, 1964): 127-130. doi:10.1021/ja01056a002.\n .. [2] Gmehling, Jurgen, Barbel Kolbe, Michael Kleiber, and Jurgen Rarey.\n Chemical Thermodynamics for Process Simulation. 1st edition. Weinheim:\n Wiley-VCH, 2012.\n '''\n gammas = []\n cmps = range(len(xs))\n\n sums0 = []\n for j in cmps:\n tot = 0.0\n paramsj = params[j]\n for k in cmps:\n tot += paramsj[k]*xs[k]\n sums0.append(tot)\n\n for i in cmps:\n tot2 = 0.\n for j in cmps:\n tot2 += params[j][i]*xs[j]/sums0[j]\n\n gamma = exp(1. - log(sums0[i]) - tot2)\n gammas.append(gamma)\n return gammas\n","sub_path":"thermo/wilson.py","file_name":"wilson.py","file_ext":"py","file_size_in_byte":49136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"178215717","text":"\"\"\"Base utilities for scrappers\"\"\"\n\nimport cookielib\nimport urllib\nimport urllib2\nimport urlparse\n\nfrom django.core.files.temp import NamedTemporaryFile\n\n\nclass Connection(object):\n \"\"\"Connect to a website by login in and keeping the session open\"\"\"\n\n opener = None\n enable_session = False\n\n @property\n def root_url(self):\n \"\"\"Root URL to use\"\"\"\n\n raise NotImplementedError\n\n @property\n def login_data(self):\n \"\"\"\n A dictionary containing the required login data.\n Must be implemented in the children.\n \"\"\"\n\n raise NotImplementedError\n\n @property\n def login_page(self):\n \"\"\"\n Url to the login page.\n Must be implemented in the children.\n \"\"\"\n\n raise NotImplementedError\n\n def _login(self):\n \"\"\"Log in into the website\"\"\"\n\n if not self.opener:\n cookie_jar = cookielib.CookieJar()\n self.opener = urllib2.build_opener(\n urllib2.HTTPCookieProcessor(cookie_jar)\n )\n self.opener.addheaders = [\n ('User-agent', 'Mozilla/5.0'),\n ('Content-Type', 'application/x-www-form-urlencoded'),\n ]\n self.opener.open(self.login_page, urllib.urlencode(self.login_data))\n\n def get(self, url):\n \"\"\"Get the response from the given url\"\"\"\n\n full_url = urlparse.urljoin(self.root_url, url)\n\n if self.enable_session:\n self._login()\n return self.opener.open(urlparse.urljoin(full_url)).read()\n else:\n return urllib2.urlopen(full_url)\n\n def get_image(self, url):\n \"\"\"Download an image an store it in a temporary file\"\"\"\n\n img_temp = NamedTemporaryFile()\n opener = urllib2.build_opener()\n opener.addheaders = [(\n 'User-agent',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:15.0) Gecko/20120427 '\n 'Firefox/15.0a1'\n )]\n img_temp.write(\n opener.open(urlparse.urljoin(self.root_url, url)).read()\n )\n img_temp.flush()\n\n return img_temp\n","sub_path":"room/scrappers/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"578557454","text":"def sum_of_digits(n):\n a = [int(x) for x in str(n) if x != '-']\n return sum(a)\n\n\ndef to_digits(n):\n return [int(x) for x in str(n)]\n\n\ndef to_number(digits):\n result = 0\n for x in digits:\n power = len(str(x))\n result = (result*(10 ** power)) + x\n return result\n\n\ndef fact_digits(n):\n numbers = to_digits(n)\n fact = 1\n sum_digit = 0\n for digit in numbers:\n while digit > 0:\n fact = fact * digit\n digit -= 1\n sum_digit += fact\n fact = 1\n return sum_digit\n\n\ndef fibonacci(n):\n result = []\n a = 1\n b = 1\n for i in range(0, n):\n result.append(a)\n a, b = b, a + b\n print(a)\n return result\n\n\ndef fib_number(n):\n return to_number(fibonacci(n))\n\n\ndef palindrome(obj):\n return str(obj) == str(obj)[::-1]\n\n\ndef count_vowels(str):\n result = 0\n\n for ch in str.lower():\n if ch in \"aeiouy\":\n result += 1\n\n return result\n\n\ndef count_consonants(str):\n result = 0\n\n for ch in str.lower():\n if ch in \"bcdfghjklmnpqrstvwxz\":\n result += 1\n\n return result\n\n\ndef char_histogram(string):\n h = {}\n for x in list(string):\n h[x] = list(string).count(x)\n return h\n\n\ndef main():\n # print(sum_of_digits(-10))\n # print(to_number([1, 2, 1, 2, 3]))\n # print(fact_digits(123))\n print(fib_number(10))\n # print(palindrome(\"kapak\"))\n # print(count_consonants(\"Theistareykjarbunga\"))\n # print(char_histogram(\"AAAAaaa!!!\"))\nif __name__ == '__main__':\n main()\n","sub_path":"week01/first_day.py","file_name":"first_day.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"191328189","text":"from miniworldmaker import *\nimport random\n\n\nclass MyBoard(TiledBoard):\n\n def __init__(self):\n super().__init__(tile_size=100, columns=6, rows=6, tile_margin=1)\n self.add_to_board(Ship(), (1, 1))\n self.add_image(\"images/galaxy.jpg\")\n event_console = EventConsole()\n self.window.add_container(event_console, dock=\"right\", size=400)\n self.window.add_container(ActionBar(self), dock=\"bottom\")\n actor_toolbar = ActiveActorToolbar()\n self.window.add_container(actor_toolbar, dock=\"right\", size=400)\n\n def get_event(self, event, data):\n pass\n\n\nclass Ship(Actor):\n\n def __init__(self):\n super().__init__()\n self.spinning = 0\n self.add_image(\"images/ship.png\")\n self.costume.orientation = 270\n\n def get_event(self, event, data):\n if event == \"key_down\":\n if \"W\" in data:\n self.direction = \"up\"\n elif \"S\" in data:\n self.direction = \"down\"\n elif \"A\" in data:\n self.direction = \"left\"\n elif \"D\" in data:\n self.direction = \"right\"\n\n def act(self):\n self.move()\n\n\nboard = MyBoard()\n\nboard.show()\n","sub_path":"examples/gui/debugging.py","file_name":"debugging.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"570405331","text":"# !/usr/bin/env python\nfrom tkinter import *\nimport serial\nimport time\nimport serial.tools.list_ports\n\n# ***** Window *****\nroot = Tk()\nroot.title(\"ARC Triggerscope Regulator\")\nroot.iconbitmap(r'ARC.ico')\nroot.maxsize(height= 600, width= 725)\n# ***** Background *****\ncanvas = Canvas(root, width = 725, height = 600, bg=\"black\")\ncanvas.pack(fill=BOTH, expand=YES)\n\ncanvas.create_line(35, 50, 285, 50, width = 1, fill = \"white\")\ncanvas.create_line(35, 50, 35, 285, width = 1, fill = \"white\")\ncanvas.create_line(35, 285, 285, 285, width = 1, fill = \"white\")\ncanvas.create_line(285, 285, 285, 50, width = 1, fill = \"white\")\n\ncanvas.create_line(290, 50, 665, 50, width = 1, fill = \"white\")\ncanvas.create_line(665, 50, 665, 285, width = 1, fill = \"white\")\ncanvas.create_line(665, 285, 290, 285, width = 1, fill =\"white\")\ncanvas.create_line(290, 285, 290, 50, width = 1, fill =\"white\")\n\ncanvas.create_line(290, 300, 665, 300, width = 1, fill =\"white\")\ncanvas.create_line(665, 300, 665, 525, width = 1, fill =\"white\")\ncanvas.create_line(665, 525, 290, 525, width = 1, fill =\"white\")\ncanvas.create_line(290, 525, 290, 300, width = 1, fill =\"white\")\n\ncanvas.create_line(350, 300, 350, 285, width = 2, fill = \"white\")\ncanvas.create_line(475, 300, 475, 285, width = 2, fill = \"white\")\ncanvas.create_line(600, 300, 600, 285, width = 2, fill = \"white\")\n\ncanvas.create_line(285,110,290, 110, width = 2, fill = \"white\")\ncanvas.create_line(285,230,290, 230, width = 2, fill = \"white\")\n\n# ***** Background Logo *****\n\nARC_BG = PhotoImage(file='TB.png')\ncanvas.create_image(275, 375, image=ARC_BG, anchor=NE)\n\n\n# ***** Functions *****\ndef Port1():\n print(\"YESSS\")\ndef Port2():\n print(\"Great Job\")\n\n\n# ***** Drop-down menu*****\nmenu = Menu(root)\nroot.config(menu=menu)\n\n\nsubMenu = Menu(menu)\n\ndef on_select(selection):\n #open the port and command it to start the LED blinking here\n print(selection)\n\nports = serial.tools.list_ports.comports()\ndefault = StringVar(root, \"Please Select Port\")\nCOM_Menu = OptionMenu(root, default, *ports, command=on_select)\nCOM_Menu_window = canvas.create_window(350, 30, window = COM_Menu)\n\n\n\n# ***** Quit Button *****\n\nbutton1 = Button(text = \"Quit\", command = quit, anchor = W, bg= \"gold\") #Quit Button\nbutton1.configure(width = 10, activebackground = \"#33B5E5\", relief = FLAT)\nbutton1_window = canvas.create_window(10, 10, anchor=NW, window=button1)\n\n# ***** TTL Buttons *****\ndef labelsensor(): # Turns the Triggerscope TTL on.\n if Toff1['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL1,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL1,0\\n\"))\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL1.config('relief')[-1] == 'sunken':\n TTL1.config(relief=\"raised\")\n Toff1.configure(bg = \"red\")\n labelsensor()\n\n else:\n TTL1.config(relief=\"sunken\")\n Toff1.configure(bg= \"green2\")\n labelsensor()\n\n\n\nTTL1 = Button(text =\"TTL 1\", bg = \"gold\", command = toggle)\nTTL1.configure(relief = FLAT, width= 5)\nTTL1_window = canvas.create_window (100, 80, window= TTL1)\n\nToff1 = Label(bg = \"red\")\nToff1.configure(width = 1, height = 1)\nToff1_window = canvas.create_window(140, 80, window= Toff1)\n\n\n\ndef labelsensor2(): # Turns the Triggerscope TTL on.\n if Toff2['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL2,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL2,0\\n\"))\n\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL2.config('relief')[-1] == 'sunken':\n TTL2.config(relief=\"raised\")\n Toff2.configure(bg = \"red\")\n labelsensor2()\n else:\n TTL2.config(relief=\"sunken\")\n Toff2.configure(bg= \"green2\")\n labelsensor2()\n\n\nTTL2 = Button(text =\"TTL 2\", bg = \"gold\", command = toggle)\nTTL2.configure(relief = FLAT, width= 5)\nTTL2_window = canvas.create_window (225, 80, window= TTL2)\n\nToff2 = Label( bg = \"red\")\nToff2.configure(width = 1, height = 1)\nToff2_window = canvas.create_window(265, 80, window= Toff2)\n\n\ndef labelsensor3(): # Turns the Triggerscope TTL on.\n if Toff3['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL3,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL3,0\\n\"))\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL3.config('relief')[-1] == 'sunken':\n TTL3.config(relief=\"raised\")\n Toff3.configure(bg = \"red\")\n labelsensor3()\n else:\n TTL3.config(relief=\"sunken\")\n Toff3.configure(bg= \"green2\")\n labelsensor3()\n\n\nTTL3 = Button(text =\"TTL 3\", bg = \"gold\", command = toggle)\nTTL3.configure(relief = FLAT, width= 5)\nTTL3_window = canvas.create_window (350, 80, window= TTL3)\n\nToff3 = Label( bg = \"red\")\nToff3.configure(width = 1, height = 1)\nToff3_window = canvas.create_window(390, 80, window= Toff3)\n\ndef labelsensor4(): # Turns the Triggerscope TTL on.\n if Toff4['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL4,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL4,0\\n\"))\n\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL4.config('relief')[-1] == 'sunken':\n TTL4.config(relief=\"raised\")\n Toff4.configure(bg = \"red\")\n labelsensor4()\n else:\n TTL4.config(relief=\"sunken\")\n Toff4.configure(bg= \"green2\")\n labelsensor4()\n\n\nTTL4 = Button(text =\"TTL 4\", bg = \"gold\", command = toggle)\nTTL4.configure(relief = FLAT, width= 5)\nTTL4_window = canvas.create_window (475, 80, window= TTL4)\n\nToff4 = Label( bg = \"red\")\nToff4.configure(width = 1, height = 1)\nToff4_window = canvas.create_window(515, 80, window= Toff4)\n\n\n\ndef labelsensor5(): # Turns the Triggerscope TTL on.\n if Toff5['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL5,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL5,0\\n\"))\n\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL5.config('relief')[-1] == 'sunken':\n TTL5.config(relief=\"raised\")\n Toff5.configure(bg = \"red\")\n labelsensor5()\n else:\n TTL5.config(relief=\"sunken\")\n Toff5.configure(bg= \"green2\")\n labelsensor5()\n\n\n\nTTL5 = Button(text =\"TTL 5\", bg = \"gold\", command = toggle)\nTTL5.configure(relief = FLAT, width= 5)\nTTL5_window = canvas.create_window (600, 80, window= TTL5)\n\nToff5 = Label( bg = \"red\")\nToff5.configure(width = 1, height = 1)\nToff5_window = canvas.create_window(640, 80, window= Toff5)\n\n\n\ndef labelsensor6(): # Turns the Triggerscope TTL on.\n if Toff6['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL6,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL6,0\\n\"))\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL6.config('relief')[-1] == 'sunken':\n TTL6.config(relief=\"raised\")\n Toff6.configure(bg = \"red\")\n labelsensor6()\n else:\n TTL6.config(relief=\"sunken\")\n Toff6.configure(bg= \"green2\")\n labelsensor6()\n\n\n\nTTL6 = Button(text =\"TTL 6\", bg = \"gold\", command = toggle)\nTTL6.configure(relief = FLAT, width= 5)\nTTL6_window = canvas.create_window (100, 200, window= TTL6)\n\nToff6 = Label( bg = \"red\")\nToff6.configure(width = 1, height = 1)\nToff6_window = canvas.create_window(140, 200, window= Toff6)\n\ndef labelsensor7(): # Turns the Triggerscope TTL on.\n if Toff7['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL7,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL7,0\\n\"))\n\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL7.config('relief')[-1] == 'sunken':\n TTL7.config(relief=\"raised\")\n Toff7.configure(bg = \"red\")\n labelsensor7()\n else:\n TTL7.config(relief=\"sunken\")\n Toff7.configure(bg= \"green2\")\n labelsensor7()\n\n\n\nTTL7 = Button(text =\"TTL 7\", bg = \"gold\", command = toggle)\nTTL7.configure(relief = FLAT, width= 5)\nTTL7_window = canvas.create_window (225, 200, window= TTL7)\n\nToff7 = Label( bg = \"red\")\nToff7.configure(width = 1, height = 1)\nToff7_window = canvas.create_window(265, 200, window= Toff7)\n\ndef labelsensor8(): # Turns the Triggerscope TTL on.\n if Toff8['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL8,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL8,0\\n\"))\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL8.config('relief')[-1] == 'sunken':\n TTL8.config(relief=\"raised\")\n Toff8.configure(bg = \"red\")\n labelsensor8()\n else:\n TTL8.config(relief=\"sunken\")\n Toff8.configure(bg= \"green2\")\n labelsensor8()\n\n\nTTL8 = Button(text =\"TTL 8\", bg = \"gold\", command = toggle)\nTTL8.configure(relief = FLAT, width= 5)\nTTL8_window = canvas.create_window (350, 200, window= TTL8)\n\nToff8 = Label( bg = \"red\")\nToff8.configure(width = 1, height = 1)\nToff8_window = canvas.create_window(390, 200, window= Toff8)\n\ndef labelsensor9(): # Turns the Triggerscope TTL on.\n if Toff9['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL9,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL9,0\\n\"))\n\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL9.config('relief')[-1] == 'sunken':\n TTL9.config(relief=\"raised\")\n Toff9.configure(bg = \"red\")\n labelsensor9()\n else:\n TTL9.config(relief=\"sunken\")\n Toff9.configure(bg= \"green2\")\n labelsensor9()\n\nTTL9 = Button(text =\"TTL 9\", bg = \"gold\", command = toggle)\nTTL9.configure(relief = FLAT, width= 5)\nTTL9_window = canvas.create_window (475, 200, window= TTL9)\n\nToff9 = Label( bg = \"red\")\nToff9.configure(width = 1, height = 1)\nToff9_window = canvas.create_window(515, 200, window= Toff9)\n\n\ndef labelsensor10(): # Turns the Triggerscope TTL on.\n if Toff10['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL10,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL10,0\\n\"))\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL10.config('relief')[-1] == 'sunken':\n TTL10.config(relief=\"raised\")\n Toff10.configure(bg = \"red\")\n labelsensor10()\n else:\n TTL10.config(relief=\"sunken\")\n Toff10.configure(bg= \"green2\")\n labelsensor10()\n\n\n\nTTL10 = Button(text =\"TTL 10\", bg = \"gold\", command = toggle)\nTTL10.configure(relief = FLAT, width= 5)\nTTL10_window = canvas.create_window (600, 200, window= TTL10)\n\nToff10 = Label( bg = \"red\")\nToff10.configure(width = 1, height = 1)\nToff10_window = canvas.create_window(640, 200, window= Toff10)\n\n\ndef labelsensor11(): # Turns the Triggerscope TTL on.\n if Toff11['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL11,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL11,0\\n\"))\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL11.config('relief')[-1] == 'sunken':\n TTL11.config(relief=\"raised\")\n Toff11.configure(bg = \"red\")\n labelsensor11()\n else:\n TTL11.config(relief=\"sunken\")\n Toff11.configure(bg= \"green2\")\n labelsensor11()\n\n\n\nTTL11 = Button(text =\"TTL 11\", bg = \"gold\", command = toggle)\nTTL11.configure(relief = FLAT, width= 5)\nTTL11_window = canvas.create_window (350, 320, window= TTL11)\n\nToff11 = Label( bg = \"red\")\nToff11.configure(width = 1, height = 1)\nToff11_window = canvas.create_window(390, 320, window= Toff11)\n\ndef labelsensor12(): # Turns the Triggerscope TTL on.\n if Toff12['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL12,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL12,0\\n\"))\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL12.config('relief')[-1] == 'sunken':\n TTL12.config(relief=\"raised\")\n Toff12.configure(bg = \"red\")\n labelsensor12()\n else:\n TTL12.config(relief=\"sunken\")\n Toff12.configure(bg= \"green2\")\n labelsensor12()\n\nTTL12 = Button(text =\"TTL 12\", bg = \"gold\", command = toggle)\nTTL12.configure(relief = FLAT, width= 5)\nTTL12_window = canvas.create_window (475, 320, window= TTL12)\n\nToff12 = Label( bg = \"red\")\nToff12.configure(width = 1, height = 1)\nToff12_window = canvas.create_window(515, 320, window= Toff12)\n\ndef labelsensor13(): # Turns the Triggerscope TTL on.\n if Toff13['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL13,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL13,0\\n\"))\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL13.config('relief')[-1] == 'sunken':\n TTL13.config(relief=\"raised\")\n Toff13.configure(bg = \"red\")\n labelsensor13()\n else:\n TTL13.config(relief=\"sunken\")\n Toff13.configure(bg= \"green2\")\n labelsensor13()\n\n\nTTL13 = Button(text =\"TTL 13\", bg = \"gold\", command = toggle)\nTTL13.configure(relief = FLAT, width= 5)\nTTL13_window = canvas.create_window (600, 320, window= TTL13)\n\nToff13 = Label( bg = \"red\")\nToff13.configure(width = 1, height = 1)\nToff13_window = canvas.create_window(640, 320, window= Toff13)\n\ndef labelsensor14(): # Turns the Triggerscope TTL on.\n if Toff14['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL14,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL14,0\\n\"))\n\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL14.config('relief')[-1] == 'sunken':\n TTL14.config(relief=\"raised\")\n Toff14.configure(bg = \"red\")\n labelsensor14()\n else:\n TTL14.config(relief=\"sunken\")\n Toff14.configure(bg= \"green2\")\n labelsensor14()\n\n\nTTL14 = Button(text =\"TTL 14\", bg = \"gold\", command = toggle)\nTTL14.configure(relief = FLAT, width= 5)\nTTL14_window = canvas.create_window (350, 440, window= TTL14)\n\nToff14 = Label( bg = \"red\")\nToff14.configure(width = 1, height = 1)\nToff14_window = canvas.create_window(390, 440, window= Toff14)\n\ndef labelsensor15(): # Turns the Triggerscope TTL on.\n if Toff15['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL15,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL15,0\\n\"))\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL15.config('relief')[-1] == 'sunken':\n TTL15.config(relief=\"raised\")\n Toff15.configure(bg = \"red\")\n labelsensor15()\n else:\n TTL15.config(relief=\"sunken\")\n Toff15.configure(bg= \"green2\")\n labelsensor15()\n\nTTL15 = Button(text =\"TTL 15\", bg = \"gold\", command = toggle)\nTTL15.configure(relief = FLAT, width= 5)\nTTL15_window = canvas.create_window (475, 440, window= TTL15)\n\nToff15 = Label( bg = \"red\")\nToff15.configure(width = 1, height = 1)\nToff15_window = canvas.create_window(515, 440, window= Toff15)\n\ndef labelsensor16(): # Turns the Triggerscope TTL on.\n if Toff16['background'] == 'green2':\n print(\"ON\")\n print(writetgs(\"TTL16,1\\n\"))\n else:\n print(\"OFF\")\n print(writetgs(\"TTL16,0\\n\"))\n\n\ndef toggle(): #Toggle animation for TTL Buttons\n\n if TTL16.config('relief')[-1] == 'sunken':\n TTL16.config(relief=\"raised\")\n Toff16.configure(bg = \"red\")\n labelsensor16()\n else:\n TTL16.config(relief=\"sunken\")\n Toff16.configure(bg= \"green2\")\n labelsensor16()\n\n\n\nTTL16 = Button(text =\"TTL 16\", bg = \"gold\", command = toggle)\nTTL16.configure(relief = FLAT, width= 5)\nTTL16_window = canvas.create_window (600, 440, window= TTL16)\n\nToff16 = Label( bg = \"red\")\nToff16.configure(width = 1, height = 1)\nToff16_window = canvas.create_window(640, 440, window= Toff16)\n\n\n\n\n# ***** DAC Sliders *****\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC1.get()\n out = \"DAC1,\" + str(DAC1.get()) + \"\\n\"\n print(writetgs(out))\n\n\nDAC1 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC1.configure(relief = FLAT)\nDAC1.bind(\"\", dacPos)\nDAC1_window = canvas.create_window (100, 110, window = DAC1)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC2.get()\n out = \"DAC2,\" + str(DAC2.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC2 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC2.configure(relief = FLAT)\nDAC2.bind(\"\", dacPos)\nDAC2_window = canvas.create_window (225, 110, window = DAC2)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC3.get()\n out = \"DAC3,\" + str(DAC3.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC3 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC3.configure(relief = FLAT)\nDAC3.bind(\"\", dacPos)\nDAC3_window = canvas.create_window (350, 110, window = DAC3)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC4.get()\n out = \"DAC4,\" + str(DAC4.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC4 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC4.configure(relief = FLAT)\nDAC4.bind(\"\", dacPos)\nDAC4_window = canvas.create_window (475, 110, window = DAC4)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC5.get()\n out = \"DAC5,\" + str(DAC5.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC5 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC5.configure(relief = FLAT)\nDAC5.bind(\"\", dacPos)\nDAC5_window = canvas.create_window (600, 110, window = DAC5)\n\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC6.get()\n out = \"DAC6,\" + str(DAC6.get()) + \"\\n\"\n print(writetgs(out))\n\n\nDAC6 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC6.configure(relief = FLAT)\nDAC6.bind(\"\", dacPos)\nDAC6_window = canvas.create_window (100, 230, window = DAC6)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC7.get()\n out = \"DAC7,\" + str(DAC7.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC7 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC7.configure(relief = FLAT)\nDAC7.bind(\"\", dacPos)\nDAC7_window = canvas.create_window (225, 230, window = DAC7)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC8.get()\n out = \"DAC8,\" + str(DAC8.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC8 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC8.configure(relief = FLAT)\nDAC8.bind(\"\", dacPos)\nDAC8_window = canvas.create_window (350, 230, window = DAC8)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC9.get()\n out = \"DAC9,\" + str(DAC9.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC9 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC9.configure(relief = FLAT)\nDAC9.bind(\"\", dacPos)\nDAC9_window = canvas.create_window (475, 230, window = DAC9)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC10.get()\n out = \"DAC10,\" + str(DAC10.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC10 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC10.configure(relief = FLAT)\nDAC10.bind(\"\", dacPos)\nDAC10_window = canvas.create_window (600, 230, window = DAC10)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC11.get()\n out = \"DAC11,\" + str(DAC11.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC11 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC11.configure(relief = FLAT)\nDAC11.bind(\"\", dacPos)\nDAC11_window = canvas.create_window (350, 350, window = DAC11)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC12.get()\n out = \"DAC12,\" + str(DAC12.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC12 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC12.configure(relief = FLAT)\nDAC12.bind(\"\", dacPos)\nDAC12_window = canvas.create_window (475, 350, window = DAC12)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC13.get()\n out = \"DAC13,\" + str(DAC13.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC13 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC13.configure(relief = FLAT)\nDAC13.bind(\"\", dacPos)\nDAC13_window = canvas.create_window (600, 350, window = DAC13)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC14.get()\n out = \"DAC14,\" + str(DAC14.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC14 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC14.configure(relief = FLAT)\nDAC14.bind(\"\", dacPos)\nDAC14_window = canvas.create_window (350, 470, window = DAC14)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC15.get()\n out = \"DAC15,\" + str(DAC15.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC15 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC15.configure(relief = FLAT)\nDAC15.bind(\"\", dacPos)\nDAC15_window = canvas.create_window (475, 470, window = DAC15)\n\ndef dacPos(event): # Event that grabs the value and sends it to the Triggerscope\n DAC16.get()\n out = \"DAC16,\" + str(DAC16.get()) + \"\\n\"\n print(writetgs(out))\n\nDAC16 = Scale(width= 10, from_=0, to= 65535, orient=HORIZONTAL, bg= \"black\", fg= \"Green\")\nDAC16.configure(relief = FLAT)\nDAC16.bind(\"\", dacPos)\nDAC16_window = canvas.create_window (600, 470, window = DAC16)\n\n\n\n# ***** Voltage Range Cascade *****\nRange1 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange1.configure(width= 6)\nRange1_window = canvas.create_window(65, 140, window = Range1)\n\nvariable1 = StringVar(root)\nvariable1.set(\"Volts\") # default value\n\nRange1V= OptionMenu(root, variable1, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange1V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange1V_window = canvas.create_window(120, 140, window = Range1V)\n\nRange2 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange2.configure(width= 6)\nRange2_window = canvas.create_window(190, 140, window = Range2)\n\nvariable2 = StringVar(root)\nvariable2.set(\"Volts\") # default value\n\nRange2V= OptionMenu(root, variable2, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange2V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange2V_window = canvas.create_window(245, 140, window = Range2V)\n\nRange3 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange3.configure(width= 6)\nRange3_window = canvas.create_window(315, 140, window = Range3)\n\nvariable3 = StringVar(root)\nvariable3.set(\"Volts\") # default value\n\nRange3V= OptionMenu(root, variable3, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange3V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange3V_window = canvas.create_window(370, 140, window = Range3V)\n\nRange4 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange4.configure(width= 6)\nRange4_window = canvas.create_window(440, 140, window = Range4)\n\nvariable4 = StringVar(root)\nvariable4.set(\"Volts\") # default value\n\nRange4V= OptionMenu(root, variable4, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange4V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange4V_window = canvas.create_window(495, 140, window = Range4V)\n\nRange5 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange5.configure(width= 6)\nRange5_window = canvas.create_window(565, 140, window = Range5)\n\nvariable5 = StringVar(root)\nvariable5.set(\"Volts\") # default value\n\nRange5V= OptionMenu(root, variable5, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange5V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange5V_window = canvas.create_window(620, 140, window = Range5V)\n\nRange6 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange6.configure(width= 6)\nRange6_window = canvas.create_window(65, 260, window = Range6)\n\nvariable6 = StringVar(root)\nvariable6.set(\"Volts\") # default value\n\nRange6V= OptionMenu(root, variable6, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange6V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange6V_window = canvas.create_window(120, 260, window = Range6V)\n\nRange7 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange7.configure(width= 6)\nRange7_window = canvas.create_window(190, 260, window = Range7)\n\nvariable7 = StringVar(root)\nvariable7.set(\"Volts\") # default value\n\nRange7V= OptionMenu(root, variable7, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange7V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange7V_window = canvas.create_window(245, 260, window = Range7V)\n\nRange8 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange8.configure(width= 6)\nRange8_window = canvas.create_window(315, 260, window = Range8)\n\nvariable8 = StringVar(root)\nvariable8.set(\"Volts\") # default value\n\nRange8V= OptionMenu(root, variable8, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange8V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange8V_window = canvas.create_window(370, 260, window = Range8V)\n\nRange9 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange9.configure(width= 6)\nRange9_window = canvas.create_window(440, 260, window = Range9)\n\nvariable9 = StringVar(root)\nvariable9.set(\"Volts\") # default value\n\nRange9V= OptionMenu(root, variable9, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange9V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange9V_window = canvas.create_window(495, 260, window = Range9V)\n\nRange10 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange10.configure(width= 6)\nRange10_window = canvas.create_window(565, 260, window = Range10)\n\nvariable10 = StringVar(root)\nvariable10.set(\"Volts\") # default value\n\nRange10V= OptionMenu(root, variable10, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange10V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange10V_window = canvas.create_window(620, 260, window = Range10V)\n\nRange11 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange11.configure(width= 6)\nRange11_window = canvas.create_window(315, 380, window = Range11)\n\nvariable11 = StringVar(root)\nvariable11.set(\"Volts\") # default value\n\nRange11V= OptionMenu(root, variable11, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange11V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange11V_window = canvas.create_window(370, 380, window = Range11V)\n\nRange12 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange12.configure(width= 6)\nRange12_window = canvas.create_window(440, 380, window = Range12)\n\nvariable12 = StringVar(root)\nvariable12.set(\"Volts\") # default value\n\nRange12V= OptionMenu(root, variable12, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange12V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange12V_window = canvas.create_window(495, 380, window = Range12V)\n\nRange13 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange13.configure(width= 6)\nRange13_window = canvas.create_window(565, 380, window = Range13)\n\nvariable13 = StringVar(root)\nvariable13.set(\"Volts\") # default value\n\nRange13V= OptionMenu(root, variable13, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange13V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange13V_window = canvas.create_window(620, 380, window = Range13V)\n\nRange14 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange14.configure(width= 6)\nRange14_window = canvas.create_window(315, 500, window = Range14)\n\nvariable14 = StringVar(root)\nvariable14.set(\"Volts\") # default value\n\nRange14V= OptionMenu(root, variable14, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange14V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange14V_window = canvas.create_window(370, 500, window = Range14V)\n\nRange15 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange15.configure(width= 6)\nRange15_window = canvas.create_window(440, 500, window = Range15)\n\nvariable15 = StringVar(root)\nvariable15.set(\"Volts\") # default value\n\nRange15V= OptionMenu(root, variable15, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange15V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange15V_window = canvas.create_window(495, 500, window = Range15V)\n\nRange16 = Label(text=\"Range:\", bg = \"black\", fg = \"gold\")\nRange16.configure(width= 6)\nRange16_window = canvas.create_window(565, 500, window = Range16)\n\nvariable16 = StringVar(root)\nvariable16.set(\"Volts\") # default value\n\nRange16V= OptionMenu(root, variable16, \"0V-5V\", \"5V-10V\", \"10V-15V\")\nRange16V.configure(width= 4, height= 0, bg = \"black\", fg=\"gold\", relief = FLAT, borderwidth= 0)\nRange16V_window = canvas.create_window(620, 500, window = Range16V)\n\n\n\n# ***** Triggerscope Connection testing *****\n\ntgsCom = \"COM7\" ##### MODIFY THIS LINE FOR YOUR SERIAL PORT NAME OR NUMBER\ntgS = serial.Serial()\ntgS.baudrate = 57600\ntgS.port = tgsCom\ntgS.bytesize = serial.EIGHTBITS #number of bits per bytes\ntgS.parity = serial.PARITY_NONE #set parity check: no parity\ntgS.stopbits = serial.STOPBITS_ONE #number of stop bits\n#tgS.timeout = None #block read\ntgS.timeout = 0.5 #non-block read\ntgS.xonxoff = False #disable software flow control\ntgS.rtscts = False #disable hardware (RTS/CTS) flow control\ntgS.dsrdtr = False #disable hardware (DSR/DTR) flow control\ntgS.writeTimeout = 0 #timeout for write\n\n\ntry:\n print(\"Activating Triggerscope...\")\n tgS.open()\nexcept Exception as e:\n print (\"ERROR: Triggerscope Com port NOT OPEN: \" + str(e))\n exit()\nif tgS.isOpen():\n try:\n tgS.flushInput() #flush input buffer, discarding all its contents\n tgS.flushOutput()#flush output buffer, aborting current output\n tgS.write(\"*\\n\".encode() ) #send an ack to tgs to make sure it's up\n time.sleep(0.3) #give the serial port sometime to receive the data\n print(\"Rx: \" + tgS.readline().decode())\n except Exception as e1:\n print (\"triggerscope serial communication error...: \" + str(e1))\n\nelse:\n print (\"cannot open triggerscope port \")\n\n\n\ndef writetgs(tgin):\n '''send a serial command to the triggerscope...\n Args:\n tgin: input string to send. Note the command terminator should be included in the string.\n Returns:\n char string of whatever comes back on the serial line.\n Raises:\n none.\n '''\n scomp = '!'+tgin\n tgS.flushInput() #flush input buffer, discarding all its contents\n tgS.flushOutput()#flush output buffer, aborting current output\n tgS.write(tgin.encode()) #send an ack to tgs to make sure it's up\n time.sleep(0.1) #give the serial port sometime to receive the data 50ms works well...\n bufa = \"\"\n bufa = tgS.readline().decode()\n return bufa\n\n# ****** TEST ZONE *********\n\n\n\n\n\n\n\n\n\n# ***** Input Buttons *****\n#input1 = Button(text= \"Input 1\", bg= \"gold\", anchor= W) #ADD COMMAND\n#input1.configure(width =5, relief = FLAT)\n#input1_window = canvas.create_window(300, 150, anchor=SW, window= input1)\n\n#input2 = Button(text= \"Input 2\", bg= \"gold\", anchor= W) #ADD COMMAND\n#input2.configure(width =5, relief = FLAT)\n#input2_window = canvas.create_window(370, 150, anchor=SW, window= input2)\n\n#input3 = Button(text= \"Input 3\", bg= \"gold\", anchor= W) #ADD COMMAND\n#input3.configure(width =5, relief = FLAT)\n#input3_window = canvas.create_window(440, 150, anchor=SW, window= input3)\n\n#input4 = Button(text= \"Input 4\", bg= \"gold\", anchor= W) #ADD COMMAND\n#input4.configure(width =5, relief = FLAT)\n#input4_window = canvas.create_window(510, 150, anchor=SW, window= input4)\n\n\n\n\n\n# ***** Status Bar *****\n#status = Label(root, text = \"Running...\", bd =1, relief = SUNKEN, anchor= W, bg=\"grey75\")\n#status.pack(side= BOTTOM, fill=BOTH, expand= YES)\n\nroot.mainloop()\n","sub_path":"ARC Triggerscope.py","file_name":"ARC Triggerscope.py","file_ext":"py","file_size_in_byte":32691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"31294135","text":"#!/usr/bin/env python3\n# coding=utf-8\n\nimport math\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom functools import lru_cache\n\n# common setup\nN, M = 25, 25\nX = np.linspace(-1, 1, N)\nxy_range = np.linspace(-1, 1, 200)\n\n@lru_cache(maxsize=None)\ndef logistic_sigmoid_function(a):\n return 1 / (1 + math.exp(-a))\n\n@lru_cache(maxsize=None)\ndef basis_function(j, x):\n return math.exp(-(x - X[j])**2 / (2 * 0.1**2)) if j != 0 else 1\n # return logistic_sigmoid_function((x - X[j]) / 0.1) if j != 0 else 1\n # return x**j\n\ndef design_matrix(f, M, X):\n X = np.asarray(X)\n\n assert(len(X.shape) == 1)\n\n Phi = [[f(j, x) for j in range(M)] for x in X]\n\n return np.array(Phi)\n\nif __name__ == '__main__':\n alphas = [10**i for i in range(6)]\n betas = [10**i for i in range(6)]\n\n Phi = design_matrix(basis_function, M, X)\n\n fig, ax = plt.subplots(len(alphas), len(betas), figsize=(len(betas) * 2, len(alphas) * 2))\n plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0.1, hspace=0.1)\n\n for i, alpha in enumerate(alphas):\n for j, beta in enumerate(betas):\n S0 = alpha**-1 * np.eye(N)\n\n Sn = np.linalg.inv(alpha * np.eye(N) + beta * np.dot(Phi.T, Phi))\n\n kernel = lambda x, y: beta * np.dot(np.dot(np.array([basis_function(j, x) for j in range(M)]).T, Sn), np.array([basis_function(j, y) for j in range(M)]))\n\n image = [[kernel(x, y) for x in xy_range] for y in xy_range]\n ax[i][j].imshow(image, origin='lower')\n ax[i][j].set_xticks([])\n ax[i][j].set_yticks([])\n\n xmin, xmax = ax[i][j].get_xlim()\n ymin, ymax = ax[i][j].get_ylim()\n\n ax[i][j].text((xmin + xmax) / 2, (ymin + ymax) / 2, '{:d}, {:d}'.format(alpha, beta), verticalalignment='center', horizontalalignment='center', color='white', weight='bold', fontsize='small')\n fig.savefig('fig3-10.png')\n","sub_path":"c3/fig3-10.py","file_name":"fig3-10.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"210968535","text":"import numpy as np\r\nimport random\r\n\r\nclass QTable:\r\n '''QTable for Q-Learning in reinforcement learning.\r\n \r\n Note that this class supports for solving problems that provide\r\n gym.Environment interface.\r\n '''\r\n\r\n def __init__(self,\r\n state_size,\r\n action_size,\r\n alpha=0.8,\r\n gamma=0.95,\r\n init_epsilon=0.0,\r\n epsilon_decay=0.995,\r\n min_epsilon=0.0,\r\n ):\r\n '''Initialize the approximator.\r\n\r\n Args:\r\n state_size (int): the number of states for this environment. \r\n action_size (int): the number of actions for this environment.\r\n alpha (float): the learning rate for updating qtable.\r\n gamma (float): the gamma factor for reward decay.\r\n init_epsilon (float): the initial epsilon probability for exploration.\r\n epsilon_decay (float): the decay factor each step for epsilon.\r\n min_epsilon (float): the minimum epsilon in training.\r\n '''\r\n\r\n self.state_size = state_size\r\n self.action_size = action_size\r\n self.alpha = alpha\r\n self.gamma = gamma\r\n self.epsilon = init_epsilon\r\n self.epsilon_decay = epsilon_decay\r\n self.min_epsilon = min_epsilon\r\n \r\n self.qtable = np.zeros((self.state_size, self.action_size))\r\n \r\n def bellman_equation_update(self, state, action, reward, new_state):\r\n \"\"\"Update the qtable according to the bellman equation.\r\n\r\n Args:\r\n state (int): the current state.\r\n action (int): the action to take.\r\n reward (int): the reward corresponding to state and action.\r\n new_state(int): the next state after taking action.\r\n \"\"\"\r\n # begin answer\r\n self.qtable[state,action]+=self.alpha*(reward+self.gamma*np.max(self.qtable[new_state,:])-self.qtable[state, action])\r\n # end answer\r\n pass\r\n \r\n def take_action(self, state):\r\n \"\"\"Determine the action for state according to Q-value and epsilon-greedy strategy.\r\n \r\n Args:\r\n state (int): the current state.\r\n\r\n Returns:\r\n action (int): the action to take.\r\n \"\"\"\r\n action = 0\r\n # begin answer\r\n if random.random()self.min_epsilon:\r\n self.epsilon*=self.epsilon_decay\r\n # end answer\r\n return action\r\n \r\n def set_epsilon(self, epsilon):\r\n \"\"\"Set self.epsilon with epsilon\"\"\"\r\n self.epsilon = epsilon\r\n\r\n def train(self, env, total_episode, max_steps=100):\r\n \"\"\"Train the QTable.\r\n Args:\r\n env (gym.Environment): the environment that provides gym.Environment interface.\r\n total_episode (int): the number of episodes to train.\r\n max_steps (int): max step to take for each episode.\r\n \"\"\"\r\n # save the rewards for each training episode in self.reward_list.\r\n self.reward_list = []\r\n all_rewards = 0\r\n all_steps = 0\r\n \r\n for episode in range(total_episode):\r\n total_reward = 0\r\n state = env.reset()\r\n # print(f\"episode={episode}\")\r\n for step in range(max_steps):\r\n # begin answer\r\n action=self.take_action(state)\r\n new_state, reward, done, info = env.step(action)#observe\r\n # print(f\"step = {step},reward = {reward}\")\r\n self.bellman_equation_update(state, action, reward, new_state)#update\r\n total_reward+=reward\r\n state=new_state\r\n if done:\r\n break\r\n # end answer\r\n all_rewards += total_reward\r\n all_steps += step + 1\r\n self.reward_list.append(total_reward)\r\n print('Average reward is {}, average step is {}'.\r\n format(all_rewards / total_episode, all_steps / total_episode))\r\n\r\n def eval(self, env, total_episode, max_steps=100):\r\n \"\"\"Evaluate the QTable.\r\n\r\n Args:\r\n env (gym.Environment): the environment that provides gym.Environment interface.\r\n total_episode (int): the number of episodes to evaluate.\r\n \"\"\"\r\n # Training has ended; thus agent does not need to explore.\r\n # However, you can leave it unchanged and it may not make much difference here.\r\n self.epsilon = 0.0\r\n all_rewards = 0\r\n all_steps = 0\r\n \r\n for episode in range(total_episode):\r\n total_reward = 0\r\n # reset the environment\r\n state = env.reset()\r\n for step in range(max_steps):\r\n # begin answer\r\n action=self.take_action(state)\r\n new_state, reward, done, info = env.step(action)\r\n state = new_state\r\n total_reward+=reward\r\n if done:break\r\n # end answer\r\n all_rewards += total_reward\r\n all_steps += step + 1\r\n \r\n print('Average reward is {}, average step is {}'.\r\n format(all_rewards / total_episode, all_steps / total_episode))\r\n # change epsilon back for training\r\n self.epsilon = self.min_epsilon\r\n ","sub_path":"assignment6/hw6_code/ml2020fall_hw6/reinforcement_learning/qTable.py","file_name":"qTable.py","file_ext":"py","file_size_in_byte":5600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"172945305","text":"class Dog:\r\n name = \"Woofers\"\r\n color = \"black\"\r\n\r\n def Bark(self):\r\n print(\"Woof my name is \" + self.name + \"!\")\r\n\r\n\r\nmydog = Dog()\r\nmydog.name = \"Glock\"\r\nprint(mydog.name + \" is \" + mydog.color)\r\n\r\nneighbordog = Dog()\r\nneighbordog.name = \"Lisa\"\r\nprint(mydog.name + \" and \" + neighbordog.name + \" hate each other! \")\r\n\r\nmydog.Bark()\r\nneighbordog.Bark()\r\n\r\n\r\n","sub_path":"dog.py","file_name":"dog.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"425283462","text":"from rest_framework import viewsets\nfrom django_filters import rest_framework as filters\nfrom billcatcher.models import Bill, Lawmaker, Vote, Rollcall, Party\nfrom billcatcher.serializers import BillSerializer, LawmakerSerializer, VoteSerializer, RollcallSerializer, PartySerializer\n\nclass BillViewSet(viewsets.ModelViewSet):\n queryset = Bill.objects.all()\n serializer_class = BillSerializer\n #setup for filters\n #NOTE: True/False must be in sentence case\n filter_backends = (filters.DjangoFilterBackend,)\n filterset_fields = ('watch','bill_id','bill_number','sponsors')\n\nclass LawmakerViewSet(viewsets.ModelViewSet):\n queryset = Lawmaker.objects.all()\n serializer_class = LawmakerSerializer\n\nclass VoteViewSet(viewsets.ModelViewSet):\n queryset = Vote.objects.all()\n serializer_class = VoteSerializer\n\nclass RollcallViewSet(viewsets.ModelViewSet):\n queryset = Rollcall.objects.all()\n serializer_class = RollcallSerializer\n filter_backends = (filters.DjangoFilterBackend,)\n filterset_fields = ('bill_identifier','rollcall_id','desc','passed')\n\nclass PartyViewSet(viewsets.ModelViewSet):\n queryset = Party.objects.all()\n serializer_class = PartySerializer\n","sub_path":"leg_tracker/billcatcher/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"469793544","text":"import argparse\nimport os\nfrom pathlib import Path, PosixPath\nfrom typing import Dict\nimport numpy as np\nfrom numpy.core.shape_base import block\n\nimport pytorch_lightning\nimport torch\nimport yaml\nfrom albumentations.core.serialization import from_dict\nfrom iglovikov_helper_functions.config_parsing.utils import object_from_dict\nfrom pytorch_lightning.loggers import NeptuneLogger\nfrom pytorch_toolbelt.losses import JaccardLoss, BinaryFocalLoss\nfrom torch.utils.data import DataLoader\nfrom src.custom import find_mean_std\n\nfrom src.dataloaders import SegmentationDataset\nfrom src.metrics import binary_mean_iou\nfrom src.utils import get_samples, load_checkpoint\nfrom dotenv import load_dotenv\nimport inspect\nimport pydoc\n\nimport matplotlib.pyplot as plt\nfrom pytorch_toolbelt.inference import tta\nimport cv2\n\n\ndef filter_dict(dict_to_filter):\n fn = pydoc.locate(dict_to_filter[\"type\"])\n\n sig = inspect.signature(fn)\n filter_keys = [\n param.name\n for param in sig.parameters.values()\n if param.kind == param.POSITIONAL_OR_KEYWORD\n ]\n\n filtered_dict = {\n filter_key: dict_to_filter[filter_key]\n for filter_key in filter_keys\n if filter_key in dict_to_filter\n }\n\n filtered_dict[\"type\"] = dict_to_filter[\"type\"]\n\n return filtered_dict\n\n\ndef plot_image(*image_tensors):\n f = plt.figure()\n for ind, image_tensor in enumerate(image_tensors):\n f.add_subplot(1, len(image_tensors), ind + 1)\n plt.imshow(image_tensor.detach().numpy())\n\n plt.show(block=True)\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n arg = parser.add_argument\n arg(\"-c\", \"--config_path\", type=Path, help=\"Path to the config.\", required=True)\n arg(\n \"-i\",\n \"--data_path\",\n type=Path,\n help=\"Path to the masks and images.\",\n required=True,\n )\n return parser.parse_args()\n\n\nclass SegmentDocs(pytorch_lightning.LightningModule):\n def __init__(self, hparams):\n super().__init__()\n self.hparams.update(hparams)\n # self.hparams = hparams\n\n self.model = object_from_dict(self.hparams[\"model\"])\n if \"resume_from_checkpoint\" in self.hparams:\n corrections: Dict[str, str] = {\"model.\": \"\"}\n\n checkpoint = load_checkpoint(\n file_path=self.hparams[\"resume_from_checkpoint\"],\n rename_in_layers=corrections,\n )\n self.model.load_state_dict(checkpoint[\"state_dict\"])\n\n self.losses = [\n (\"jaccard\", 0.1, JaccardLoss(mode=\"binary\", from_logits=True)),\n (\"focal\", 0.9, BinaryFocalLoss()),\n ]\n\n def forward(self, batch):\n return self.model(batch)\n\n def prepare_data(self):\n self.train_samples = get_samples(\n Path(self.hparams[\"data_path\"]) / \"train/images\",\n Path(self.hparams[\"data_path\"]) / \"train/masks\",\n )\n\n self.test_samples = get_samples(\n Path(self.hparams[\"data_path\"]) / \"test/images\",\n Path(self.hparams[\"data_path\"]) / \"test/masks\",\n )\n\n self.test_samples = [\n # (\n # PosixPath(\"data/test/images/511d8f9625834804a89117853b565d6c.jpg\"),\n # PosixPath(\"data/test/images/511d8f9625834804a89117853b565d6c.jpg\"),\n # ),\n # (\n # PosixPath(\"data/test/images/ID-CARD-ITALY.jpg\"),\n # PosixPath(\"data/test/images/ID-CARD-ITALY.jpg\"),\n # ),\n (\n PosixPath(\"data/test/images/01.jpg\"),\n PosixPath(\"data/test/images/01.jpg\"),\n ),\n ]\n\n def train_dataloader(self):\n train_aug = from_dict(self.hparams[\"train_aug\"])\n\n result = DataLoader(\n SegmentationDataset(self.train_samples, train_aug),\n batch_size=self.hparams[\"train_parameters\"][\"batch_size\"],\n num_workers=self.hparams[\"num_workers\"],\n shuffle=True,\n pin_memory=False,\n drop_last=False,\n )\n return result\n\n def test_dataloader(self):\n test_aug = from_dict(self.hparams[\"test_aug\"])\n\n result = DataLoader(\n SegmentationDataset(self.test_samples, test_aug),\n batch_size=self.hparams[\"test_parameters\"][\"batch_size\"],\n num_workers=self.hparams[\"num_workers\"],\n shuffle=False,\n pin_memory=False,\n drop_last=False,\n )\n\n return result\n\n def val_dataloader(self):\n print(\"val\")\n val_aug = from_dict(self.hparams[\"val_aug\"])\n\n result = DataLoader(\n SegmentationDataset(self.train_samples, val_aug),\n batch_size=self.hparams[\"val_parameters\"][\"batch_size\"],\n num_workers=self.hparams[\"num_workers\"],\n shuffle=False,\n pin_memory=True,\n drop_last=False,\n )\n return result\n\n def configure_optimizers(self):\n optimizer = object_from_dict(\n self.hparams[\"optimizer\"],\n params=filter(lambda x: x.requires_grad, self.model.parameters()),\n )\n\n scheduler = object_from_dict(self.hparams[\"scheduler\"], optimizer=optimizer)\n self.optimizers = [optimizer] # skipcq: PYL-W0201\n\n return self.optimizers, [scheduler]\n\n def training_step(self, batch, batch_idx):\n features = batch[\"features\"]\n masks = batch[\"masks\"]\n\n logits = self.forward(features)\n\n total_loss = 0\n\n for loss_name, weight, loss in self.losses:\n ls_mask = loss(logits, masks)\n total_loss += weight * ls_mask\n self.log(\n f\"train_mask_{loss_name}\",\n ls_mask,\n on_epoch=True,\n on_step=True,\n logger=True,\n prog_bar=True,\n )\n\n self.log(\n \"total_loss\",\n total_loss,\n on_epoch=True,\n on_step=True,\n logger=True,\n prog_bar=True,\n )\n self.log(\n \"lr\",\n self._get_current_lr(),\n on_epoch=True,\n on_step=True,\n logger=True,\n prog_bar=True,\n )\n\n return total_loss\n\n def _get_current_lr(self) -> torch.Tensor:\n lr = [x[\"lr\"] for x in self.optimizers[0].param_groups][0]\n return torch.Tensor([lr])[0]\n # return torch.Tensor([lr])[0].cuda()\n\n def validation_step(self, batch, batch_idx):\n features = batch[\"features\"]\n masks = batch[\"masks\"]\n\n logits = self.forward(features)\n\n for loss_name, _, loss in self.losses:\n self.log(\n f\"val_mask_{loss_name}\",\n loss(logits, masks),\n on_step=False,\n on_epoch=True,\n prog_bar=False,\n logger=True,\n )\n\n self.log(\n \"val_iou\",\n binary_mean_iou(logits, masks),\n on_step=False,\n on_epoch=True,\n prog_bar=False,\n logger=True,\n )\n\n def test_step(self, batch, batch_idx: int):\n features = batch[\"features\"]\n # masks = batch[\"masks\"]\n\n # padded_image, pads = pad(features[0], factor=32, border=cv2.BORDER_CONSTANT)\n # plot_image(padded_image)\n\n # plot_image(masks[0].squeeze())\n\n print(features.shape)\n logits = self.forward(features)\n\n mask = ((logits[0] > 0).numpy().astype(np.uint8)).transpose(1, 2, 0)\n\n # plot_image(logits[0].squeeze())\n\n original_img = cv2.imread(str(self.test_samples[batch_idx][0]))\n plot_image(\n torch.from_numpy(\n cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB),\n ),\n torch.from_numpy(cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) * 255.0),\n )\n\n # for loss_name, _, loss in self.losses:\n # self.log(\n # f\"prediction_mask_{loss_name}\",\n # loss(logits, masks),\n # on_step=False,\n # on_epoch=True,\n # prog_bar=False,\n # logger=True,\n # )\n\n # self.log(\n # \"prediction_iou\",\n # binary_mean_iou(logits, masks),\n # on_step=False,\n # on_epoch=True,\n # prog_bar=False,\n # logger=True,\n # )\n\n\ndef main():\n args = get_args()\n\n with open(args.config_path) as f:\n hparams = yaml.load(f, Loader=yaml.SafeLoader)\n\n hparams[\"data_path\"] = args.data_path\n\n pipeline = SegmentDocs(hparams)\n\n logger = NeptuneLogger(\n api_key=os.environ[\"NEPTUNE_API_TOKEN\"],\n project_name=\"seakmengc/midv500models\",\n experiment_name=f\"{hparams['experiment_name']}\", # Optional,\n tags=[\"pytorch-lightning\", \"mlp\"], # Optional,\n upload_source_files=[],\n offline_mode=True,\n )\n\n Path(hparams[\"checkpoint_callback\"][\"filepath\"]).mkdir(exist_ok=True, parents=True)\n\n trainer = object_from_dict(\n filter_dict(hparams[\"trainer\"]),\n # checkpoint_callback=object_from_dict(\n # filter_dict(hparams[\"checkpoint_callback\"])\n # ),\n checkpoint_callback=False,\n logger=logger,\n # overfit_batches=1,\n )\n\n # trainer.fit(pipeline)\n\n trainer.test(pipeline)\n\n\nif __name__ == \"__main__\":\n load_dotenv()\n main()\n","sub_path":"src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":9402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"552663278","text":"#!/usr/bin/env python3\nimport attack\nimport sys \nseed = 0\nattempts = 0\nfor i in range(1,10000):\n _, rc = attack.attack(\"wotsp_faulty_sigmas.txt\", 20, 100, seed)\n if rc == -1:\n print(\"ERROR: forgery failed!\")\n else: \n attempts += rc\n print(f\"{rc};{attempts};{attempts/i};\")\n sys.stdout.flush()\n seed += 1\n","sub_path":"find_avg_number_of_attempts.py","file_name":"find_avg_number_of_attempts.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"637537244","text":"#!/usr/bin/env python \n# -*- coding: utf-8 -*-\n# 2019-05-24, aaron\n\n\nimport psycopg2 #使用的是PostgreSQL数据库\nimport tushare as ts\nfrom Stocks import *\nfrom HData import *\nfrom HData_day import *\nfrom HData_select import *\nfrom HData_60m import *\nimport datetime\n\nfrom zig import *\nfrom test_plot import *\n\n\n\n\n# basic\nimport numpy as np\nimport pandas as pd\n\n# get data\nimport pandas_datareader as pdr\n\n# visual\nimport matplotlib.pyplot as plt\nimport mpl_finance as mpf\n#%matplotlib inline\nimport seaborn as sns\n\n#time\nimport datetime as datetime\nimport time\nimport os\nimport sys\n\n#talib\nimport talib\n\n\nfrom funcat import *\n\nfrom Algorithm import *\n\n#delete runtimer warning\nimport warnings\nwarnings.simplefilter(action = \"ignore\", category = RuntimeWarning)\n\n#log\nfrom common import Log\nlog = Log(__name__).getlog()\n\n#funcat\nfrom funcat import *\nfrom funcat.data.aaron_backend import AaronDataBackend\nset_data_backend(AaronDataBackend())\n\nfrom sys import argv\n\n################################################################\nstocks=Stocks(\"usr\",\"usr\")\nhdata=HData_day(\"usr\",\"usr\")\nsdata=HData_select(\"usr\",\"usr\")\n\n# stocks.db_stocks_create()#如果还没有表则需要创建\n#print(stocks.db_stocks_update())#根据todayall的情况更新stocks表\n\n#hdata.db_hdata_date_create()\n\n#print(\"line number: \" + str(sys._getframe().f_lineno) )\n#sdata.db_hdata_date_create()\n#print(\"line number: \" + str(sys._getframe().f_lineno) )\n######################################################################\n\n#debug switch\ndebug = 0\nwithin_days = 8\n\n\n#return the day(j) and cross_flag(true or false) if P is true during with_days, P is cross(5, 30), etc\ndef get_cross_info(P):\n \n for j in range(0, within_days):\n cross = REF(P, j)\n if debug:\n print('P%d=%s type(cross)=%s' % (j, cross, type(cross)))\n if cross:\n if debug:\n print('j=%d: condition is OK'% j)\n return j, cross\n else:\n if debug:\n print('j=%d: condition is NG'% j)\n\n return j, cross\n\n\ndef check_input_parameter():\n # 如果执行的方式错误输出使用方法\n USAGE = '''\n 用法错误，正确方式如下：\n python demo.py 1\n '''\n if len(argv) > 2:\n print(USAGE) # 如果传入的参数不足，输出正确用法\n exit(1) # 异常退出(下面的代码将不会被执行)\n\n script_name, para1 = argv # 将传入的参数赋值进行使用\n print(\"%s, %d\"%(script_name, int(para1)))\n\n return script_name, para1\n\ndef quadrilateral_algorythm(codestock_local, nowdate, para1):\n stock_len=len(codestock_local)\n for i in range(0,stock_len):\n #for i in range(0,2):\n #if (True):\n #i = 0\n draw_flag = False\n nowcode=codestock_local[i][0]\n nowname=codestock_local[i][1]\n\n if debug:\n print(\"code:%s, name:%s\" % (nowcode, nowname ))\n\n '''\n if '002307' in nowcode:\n pass\n else:\n continue\n '''\n\n\n #skip ST\n #if ('ST' in nowname or '300' in nowcode):\n if ('ST' in nowname or '68' in nowcode):\n #log.debug(\"ST: code:%s, name:%s\" % (nowcode, nowname ))\n if debug:\n print(\"skip code: code:%s, name:%s\" % (nowcode, nowname ))\n continue\n\n\n detail_info = hdata.get_limit_hdata_of_stock(nowcode, nowdate.strftime(\"%Y-%m-%d\"), 600)\n #detail_info = hdata.get_limit_hdata_of_stock('000029',100) # test 'Exception: inputs are all NaN'\n #detail_info = all_info[all_info['stock_code'].isin([nowcode])] #get date if nowcode == all_info['stock_code']\n #detail_info = detail_info.tail(100)\n if debug:\n print(detail_info)\n \n #fix NaN bug\n # if len(detail_info) == 0 or (detail_info is None):\n if len(detail_info) < (int(para1) + 60) or (detail_info is None):\n if debug:\n print('NaN: code:%s, name:%s' % (nowcode, nowname ))\n continue\n \n #funcat call\n T(str(nowdate))\n S(nowcode)\n if debug:\n print(str(nowdate), nowcode, nowname, O, H, L, C)\n\n\n \n ##############################################################################\n #cross\n MA5=MA(C,5)\n MA10=MA(C,10)\n MA30=MA(C,30)\n MA60=MA(C,60)\n P1=CROSS(MA5,MA30)\n P2=CROSS(MA5,MA60)\n P3=CROSS(MA10,MA30)\n P4=CROSS(MA10,MA60)\n if debug:\n print('P1=%s, P2=%s, P3=%s, P4=%s'% (P1, P2, P3, P4))\n \n p1_pos, p1_cross = get_cross_info(P1)\n p2_pos, p2_cross = get_cross_info(P2)\n p3_pos, p3_cross = get_cross_info(P3)\n p4_pos, p4_cross = get_cross_info(P4)\n\n if debug:\n print('p1_pos:%s, p1_cross:%s' %(p1_pos, p1_cross))\n print('p2_pos:%s, p2_cross:%s' %(p2_pos, p2_cross))\n print('p3_pos:%s, p3_cross:%s' %(p3_pos, p3_cross))\n print('p4_pos:%s, p4_cross:%s' %(p4_pos, p4_cross))\n\n # P1 P2 P3 P4 all are true during withdays\n if p1_cross and p2_cross and p3_cross and p4_cross :\n if debug:\n print('!!! %s, %s, %s' %(str(nowdate), nowcode, nowname))\n\n #cond-1\n c_less_ma5 = False\n s_day = min(p1_pos, p2_pos)\n e_day = max(p1_pos, p2_pos)\n if s_day == e_day:\n if REF(L, s_day) >= REF(MA5, s_day):\n c_less_ma5 = True\n if debug:\n print(\"ma5: s_day(%d) is equal e_day(%d)\" %( s_day, e_day))\n else:\n for ps in range(s_day, e_day + 1):\n if REF(L, ps) >= REF(MA5, ps):\n #if REF(C, ps) >= REF(MA5, ps):\n c_less_ma5 = True\n if debug:\n print('MA5 condition ok')\n else:\n c_less_ma5 = False\n if debug:\n print('MA5 condition not ok')\n break\n\n \n #cond-2\n c_less_ma60 = False\n s_day = min(p3_pos, p4_pos)\n e_day = max(p1_pos, p2_pos)\n if s_day == e_day:\n if REF(L, s_day) >= REF(MA60, s_day):\n c_less_ma60 = True\n if debug:\n print(\"ma60: s_day(%d) is equal e_day(%d)\" %( s_day, e_day))\n else:\n for ps in range(s_day, e_day + 1):\n if REF(L, ps) >= max(REF(MA60, ps), REF(MA30, ps)): #L can not be allowed to enter the quadrilateral\n #if REF(C, ps) >= REF(MA60, ps):\n c_less_ma60 = True\n if debug:\n print('MA60 condition ok')\n else:\n c_less_ma60 = False\n if debug:\n print('MA60 condition not ok')\n break\n \n if c_less_ma5 and c_less_ma60:\n print('### %s, %s, %s' %(str(nowdate), nowcode, nowname))\n draw_flag = True\n\n\n\n\n '''\n cond_1 = CROSS(MA, MA)\n cond_2 = CROSS(MA(C,13), MA(C, 21))\n cond_3 = C > MA(C, 5)\n cond_4 = V > MA(V, 50)\n cond_5 = ((C - REF(C, 1))/REF(C, 1)) > 0.03\n \n if cond_1 and cond_2 and cond_3 and cond_4 and cond_5:\n draw_flag = True\n print(\"cross: code:%s, name:%s\" % (nowcode, nowname ))\n '''\n ##############################################################################\n\n\n\n \n\n ################################################################\n continue\n \n #check need to generate png \n if draw_flag == False:\n continue\n \n\n save_dir = 'stock_data'\n sub_name = '-quad'\n plot_picture(nowdate, nowcode, nowname, detail_info, save_dir, fig, sub_name) \n ################################################################\n\n shell_cmd='cp -rf stock_data/' + nowdate.strftime(\"%Y-%m-%d\") +'*' + ' /var/www/html/stock_data' +'/'\n if debug:\n print('shell_cmd: %s' % shell_cmd)\n os.system(shell_cmd)\n\n\n\nif __name__ == '__main__':\n\n script_name, para1 = check_input_parameter()\n\n nowdate=datetime.datetime.now().date()\n nowdate=nowdate-datetime.timedelta(int(para1))\n print(\"nowdate is %s\"%(nowdate.strftime(\"%Y-%m-%d\"))) \n\n codestock_local=stocks.get_codestock_local()\n if debug:\n print(codestock_local)\n\n hdata.db_connect()#由于每次连接数据库都要耗时0.0几秒，故获取历史数据时统一连接\n sdata.db_connect()#由于每次连接数据库都要耗时0.0几秒，故获取历史数据时统一连接\n sdata.delete_data_of_day_stock(nowdate.strftime(\"%Y-%m-%d\")) #delete first\n\n\n start_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n #all_info = hdata.my2_get_all_hdata_of_stock()\n end_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n print(\"start_time: %s, end_time: %s\" % (start_time, end_time))\n\n #define canvas out of loop\n plt.style.use('bmh')\n fig = plt.figure(figsize=(24, 30),dpi=80)\n\n\n quadrilateral_algorythm(codestock_local, nowdate, para1)\n\n plt.close('all')\n\n hdata.db_disconnect()\n sdata.db_disconnect()\n last_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n print(\"start_time: %s, last_time: %s\" % (start_time, last_time))\n","sub_path":"test_read_db_quadrilateral.py","file_name":"test_read_db_quadrilateral.py","file_ext":"py","file_size_in_byte":9592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"412059772","text":"import pandas as pd\n\n############# Read Data ########################################\n\nFullMx = pd.read_csv('/Users/jtamerius/Dropbox/Documents/Projects/QuidelData/Data/RawData/New2015-2017/Qxls/Qflu.csv',dtype={'TestDate': str, 'Zip Code':str})\nZip2LatLon = pd.read_csv('/Users/jtamerius/Dropbox/Documents/Projects/QuidelData/Data/RawData/Seas16-17/ZipCode2LatLon.csv')\nZip2LatLon.columns = ['Zip Code', 'Lat', 'Lon']\nFullMx['Zip Code'] = FullMx['Zip Code'].str[:5]\n\n############# REMOVE DATA WITH POSITVE FLU_A AND FLU_B ####################\n\nins = ~ ((FullMx['FluA']=='positive') & (FullMx['FluB']=='positive'))\nFullMx = FullMx[ins]\n\n############# GROUP POSITIVE COUNTS ########################################\n\ndef _ct_id_pos(grp):\n return grp['Zip Code'].iloc[0], grp[grp.FluA == 'positive'].shape[0], grp[grp.FluB == 'positive'].shape[0], grp.shape[0]\n\nFullMx_prime = FullMx.groupby(['TestDate', 'SofiaSN']).apply(_ct_id_pos).reset_index()\nFullMx_prime[['Zip Code','Pos_A', 'Pos_B', 'Total']] = FullMx_prime[0].apply(pd.Series)\nFullMx_prime.drop([0], axis=1, inplace=True)\nFullMx_prime.to_pickle('/Users/jtamerius/Dropbox/Documents/Projects/QuidelData/Data/RawData/New2015-2017/Qxls/FullMx_prime.pkl')\n\n############# ZIPCODE -- LAT/LON LOOKUP ##########################################\nFullMx_prime['Zip Code'] = FullMx_prime['Zip Code'].astype(float)\nFullMx_prime['Zip Code'] = FullMx_prime['Zip Code'].astype(int)\n\nQMx = pd.DataFrame()\nfor name, group in FullMx_prime.groupby(['TestDate']):\n tmpMx = pd.merge(group, Zip2LatLon, on='Zip Code', how='inner')\n print(tmpMx)\n QMx = QMx.append(tmpMx)\n\n\nQMx.to_csv('/Users/jtamerius/Dropbox/Documents/Projects/QuidelData/Data/RawData/New2015-2017/Qxls/Qflu2.csv',index=False)\n\n\n# ins = np.nonzero(np.in1d(Zip2LatLon.ZIP, group['Zip Code']))\n# lat_lon = Zip2LatLon.ix[ins][['LAT', 'LON']]\n# plt.plot(FullMx_prime['Pos_A'])# plt.plot(FullMx_prime['Pos_B'])# plt.show()","sub_path":"FluTS.py","file_name":"FluTS.py","file_ext":"py","file_size_in_byte":1929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"171445729","text":"def Chiffrement_vigenere(msg, clef):\r\n\td={'a':0,'b':1,'c':2,'d':3,'e':4,'f':5,'g':6,'h':7,'i':8,'j' :9,'k':10,'l':11,'m':12,'n':13,'o':14,'p':15,'q':16,'r':17,'s':18,'t':19,'u':20,'v':21,'w':22,'x':23,'y':24,'z':25}\r\n\tt=tuple(d)\r\n\tmsg=msg.lower()\r\n\tclef=clef.lower()\r\n\ttaille1=len(msg)\r\n\ttaille2=len(clef)\r\n\tnew_msg=\"\"\r\n\ti=0\t#message counter\r\n\tj=0\t#key counter\r\n\tz=0\t#dictionary key value\r\n\t\r\n\twhile i < taille1:\r\n\t\t#test if the counter reaches the end of the key\r\n\t\tif j==taille2:\r\n\t\t\tj=0\t\r\n\t\t#test if the character is not an alphabet\r\n\t\tif not msg[i].isalpha() or not clef[j].isalpha():\r\n\t\t\tnew_msg=new_msg+msg[i]\r\n\t\t\tj+=1\r\n\t\t\ti+=1\r\n\t\t\tcontinue\t\t\r\n\t\t#put the key value returned by the dictionary in x and y\r\n\t\tx=d[msg[i]]\r\n\t\ty=d[clef[j]]\r\n\t\t#test if the value exceeds the interval [0.25]\t\t\r\n\t\tif x+y > 26:\r\n\t\t\tz=(x+y)%26\t#z=x+y-26\r\n\t\telse:\r\n\t\t\tz=x+y\r\n\t\t#put the character returned by the tuple in the new string, whose z is the value of this character\r\n\t\tnew_msg=new_msg+t[z]\r\n\t\tj+=1\r\n\t\ti+=1\r\n\t\t\r\n\treturn new_msg","sub_path":"Vigenere_encryption.py","file_name":"Vigenere_encryption.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"31903784","text":"d_value = \"Not Assigned\"\nclass Employee:\n def __init__(self,idno=d_value,name=d_value,salary=d_value):\n self.idno = idno\n self.name = name\n self.salary = salary\n def display(self):\n print(\"IDNO = \",self.idno)\n print(\"NAME = \",self.name)\n print(\"SALARY = \",self.salary)\n\ne1 = Employee()\ne1.display()\nprint(\"-----------------\")\ne2 = Employee(salary=125000.00)\ne2.display()\n\n","sub_path":"OOPS/Demo9.py","file_name":"Demo9.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"482013699","text":"\"\"\"\nFile: gridswap.py\n-----------------\nThis program shows an example of swapping items\nin a list of lists (grid).\n\"\"\"\n\n\ndef swap(grid, row1, col1, row2, col2):\n \"\"\"\n This function swaps the elements at locations (row1, col1)\n and (row2, col2) in the grid passed in.\n \"\"\"\n temp = grid[row1][col1]\n grid[row1][col1] = grid[row2][col2]\n grid[row2][col2] = temp\n\n\ndef main():\n my_grid = [[10, 20, 30], [40, 50, 60]]\n print(\"Original grid:\")\n print(my_grid)\n\n swap(my_grid, 0, 1, 1, 2)\n print(\"Grid after swapping two elements:\")\n print(my_grid)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"discord_extension/Lecture13/gridswap.py","file_name":"gridswap.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"400407492","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Friday Jan 24 13:34:32 2020\r\n\r\n@author: simoca\r\n\"\"\"\r\n\r\nfrom scipy.integrate import odeint\r\n#Package for plotting\r\nimport math\r\n#Package for the use of vectors and matrix\r\nimport numpy as np\r\nimport pandas as pd\r\nimport array as arr\r\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\r\nfrom matplotlib.figure import Figure\r\nimport sys\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.ticker import FormatStrFormatter\r\nimport glob\r\nfrom random import sample\r\nimport random\r\nimport time\r\nimport plotly\r\nimport plotly.graph_objs as go\r\nimport json\r\n\r\n\r\nclass Ecoli_Aero:\r\n def __init__(self, Control = False):\r\n self.Kap=0.5088 #g/L\r\n self.Ksa=0.0128\r\n self.Kia=1.2602 #g/L\r\n self.Ks= 0.0381 #g/L\r\n self.Kis = 1.8383 #g/L affinity constant\r\n self.Ko = 0.0001\r\n self.qAcmax= 0.1148 #g/gh\r\n self.qm=0.0133 #g/gh\r\n self.qOmax= 13.4*31.9988/1000 #g/gh\r\n self.qSmax= 0.635 #g/gh\r\n self.Yas= 0.8938 #g/g\r\n self.Yoa= 0.5221 #g/g\r\n self.Yos= 1.5722 #g/g\r\n self.Yxa= 0.5794 #g/g\r\n self.Yem = 0.5321 #g/g\r\n self.Yxsof = 0.229 # g/g\r\n self.pAmax= 0.2286 #gA/gXh\r\n self.kla= 220\r\n self.H= 1400 #Henry constant\r\n\r\n self.G0 = 4.94\r\n self.A0 = 0.0129\r\n self.O0 = 98\r\n self.X0 = 0.17\r\n self.tau= 35 #response time\r\n self.t_start = 0\r\n self.V0 = 2\r\n self.F0 = 0\r\n self.SFR = 1.5\r\n self.t_expfb_start = 0\r\n self.t_constfb_start = 1.5\r\n self.t_end = 15\r\n \r\n #parameters for control, default every 1/24 hours:\r\n self.Control = Control\r\n self.coolingOn = True\r\n self.Contamination=False\r\n self.steps = (self.t_end - self.t_start)*24\r\n self.T0 = 35\r\n self.K_p = 2.31e+01\r\n self.K_i = 1\r\n self.K_d = 0\r\n self.Tset = 30\r\n self.u_max = 150\r\n self.u_min = 0\r\n\r\n def rxn(self, C,t , u, fc):\r\n #when there is no control, k has no effect\r\n k=1\r\n #when cooling is off than u = 0\r\n if self.coolingOn == False:\r\n u = 0\r\n if self.Contamination == True:\r\n fc=np.random.randint(0,10)\r\n fc=fc/17\r\n\r\n if self.Control == True :\r\n #Cardinal temperature model with inflection: Salvado et al 2011 \"Temperature Adaptation Markedly Determines Evolution within the Genus Saccharomyces\"\r\n #Strain E.coli W310\r\n Topt = 35\r\n Tmax = 45.48\r\n Tmin = 10\r\n T = C[5]\r\n if T < Tmin or T > Tmax:\r\n k = 0\r\n else:\r\n D = (T-Tmax)*(T-Tmin)**2\r\n E = (Topt-Tmin)*((Topt-Tmin)*(T-Topt)-(Topt-Tmax)*(Topt+Tmin-2*T))\r\n k = D/E\r\n # Volume balance\r\n if (t >= self.t_expfb_start):\r\n if (t < self.t_constfb_start):\r\n Fin = self.F0 * math.exp(self.SFR * (t - self.t_expfb_start))\r\n Fout = 0\r\n else:\r\n Fin = self.F0 * math.exp(self.SFR * (self.t_constfb_start - self.t_expfb_start))\r\n Fout = 0\r\n else:\r\n Fin = 0\r\n Fout = 0\r\n\r\n F = Fin - Fout\r\n\r\n qS = (self.qSmax/(1+C[1]/self.Kia))*(C[0]/(C[0]+self.Ks))\r\n qSof = self.pAmax*(qS/(qS+self.Kap))\r\n pA = qSof*self.Yas\r\n qSox = (qS-qSof)*(C[2]/(C[2]+self.Ko))\r\n qSan = (qSox-self.qm)*self.Yem*(0.488/0.391)\r\n qsA = (self.qAcmax/(1+(qS/self.Kis)))*(C[1]/(C[1]+self.Ksa))\r\n qA = pA - qsA\r\n mu = (qSox - self.qm)*self.Yem+ qsA*self.Yxa + qSof*self.Yxsof\r\n qO = self.Yos*(qSox-qSan)+qsA*self.Yoa\r\n\r\n #Solving the mass balances\r\n dGdt = (F/C[4])*(self.G0-C[0])-(qS*C[3])\r\n dAdt = qA*C[3]-((F/C[4])*C[1])\r\n dOdt = self.kla*(self.O0-C[2])-qO*C[3]*self.H\r\n dXdt = (mu - (F/C[4]))*C[3]\r\n dVdt = F\r\n if self.Control == True :\r\n '''\r\n dHrxn heat produced by cells estimated by yeast heat combustion coeficcient dhc0 = -21.2 kJ/g\r\n dHrxn = dGdt*V*dhc0(G)-dEdt*V*dhc0(E)-dXdt*V*dhc0(X)\r\n (when cooling is working) Q = - dHrxn -W ,\r\n dT = V[L] * 1000 g/L / 4.1868 [J/gK]*dE [kJ]*1000 J/KJ\r\n dhc0(EtOH) = -1366.8 kJ/gmol/46 g/gmol [KJ/g]\r\n dhc0(Glc) = -2805 kJ/gmol/180g/gmol [KJ/g]\r\n \r\n ''' \r\n #Metabolic heat: [W]=[J/s], dhc0 from book \"Bioprocess Engineering Principles\" (Pauline M. Doran) : Appendix Table C.8 \r\n dHrxndt = dXdt*C[4]*(-21200) #[J/s] + dGdt*C[4]*(15580)- dEdt*C[4]*(29710) \r\n #Shaft work 1 W/L1\r\n W = 1*C[4] #[J/S] negative because exothermic \r\n #Cooling just an initial value (constant cooling to see what happens)\r\n #dQdt = -0.03*C[4]*(-21200) #[J/S] \r\n #velocity of cooling water: u [m3/h] -->controlled by PID \r\n \r\n #Mass flow cooling water\r\n M=u/3600*1000 #[kg/s]\r\n #Define Tin = 5 C, Tout=TReactor\r\n #heat capacity water = 4190 J/kgK\r\n Tin = 5\r\n #Estimate water at outlet same as Temp in reactor\r\n Tout = C[5]\r\n cpc = 4190\r\n #Calculate Q from Eq 9.47\r\n Q=-M*cpc*(Tout-Tin) # J/s \r\n #Calculate Temperature change\r\n dTdt = -1*(dHrxndt - Q + W)/(C[4]*1000*4.1868) #[K/s]\r\n else: \r\n dTdt = 0\r\n return [dGdt,dAdt,dOdt,dXdt,dVdt, dTdt]\r\n \r\n def solve(self):\r\n #solve normal:\r\n t = np.linspace(self.t_start, self.t_end, self.steps)\r\n if self.Control == False :\r\n u = 0\r\n fc= 1\r\n C0 = [self.G0, self.A0, self.O0, self.X0,self.V0,self.T0]\r\n C = odeint(self.rxn, C0, t, rtol = 1e-7, mxstep= 500000, args=(u,fc,))\r\n \r\n #solve for Control\r\n else:\r\n fc=0\r\n \"\"\"\r\n PID Temperature Control:\r\n \"\"\"\r\n # storage for recording values\r\n C = np.ones([len(t), 6]) \r\n C0 = [self.G0, self.A0, self.O0, self.X0,self.V0,self.T0]\r\n C[0] = C0\r\n self.ctrl_output = np.zeros(len(t)) # controller output\r\n e = np.zeros(len(t)) # error\r\n ie = np.zeros(len(t)) # integral of the error\r\n dpv = np.zeros(len(t)) # derivative of the pv\r\n P = np.zeros(len(t)) # proportional\r\n I = np.zeros(len(t)) # integral\r\n D = np.zeros(len(t)) # derivative\r\n \r\n for i in range(len(t)-1):\r\n #print(t[i])\r\n #PID control of cooling water\r\n dt = t[i+1]-t[i]\r\n #Error\r\n e[i] = C[i,5] - self.Tset \r\n #print(e[i])\r\n if i >= 1:\r\n dpv[i] = (C[i,5]-C[i-1,5])/dt\r\n ie[i] = ie[i-1] + e[i]*dt\r\n P[i]=self.K_p*e[i]\r\n I[i]=self.K_i*ie[i]\r\n D[i]=self.K_d*dpv[i]\r\n \r\n self.ctrl_output[i]=P[i]+I[i]+D[i]\r\n u=self.ctrl_output[i]\r\n if u>self.u_max:\r\n u=self.u_max\r\n ie[i] = ie[i] - e[i]*dt # anti-reset windup\r\n if u < self.u_min:\r\n u =self.u_min\r\n ie[i] = ie[i] - e[i]*dt # anti-reset windup\r\n #time for solving ODE \r\n ts = [t[i],t[i+1]]\r\n #disturbance\r\n #if self.t[i] > 5 and self.t[i] < 10:\r\n # u = 0 \r\n #solve ODE from last timepoint to new timepoint with old values \r\n \r\n y = odeint(self.rxn, C0, ts, rtol = 1e-7, mxstep= 500000, args=(u,fc,))\r\n #update C0\r\n C0 = y[-1]\r\n #merge y to C\r\n C[i+1]=y[-1]\r\n return t, C\r\n def create_plot(self, t, C):\r\n S = C[:, 0]\r\n A = C[:, 1]\r\n B = C[:, 3]\r\n df = pd.DataFrame({'t': t, 'Substrate': S, 'Biomass': B, 'Acetate': A})\r\n fig = go.Figure()\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Substrate'], name='Glucose'))\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Biomass'], name='Biomass'))\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Acetate'], name='Acetate'))\r\n fig.update_layout(title=('Simulation of aerobic batch growth of Escherichia coli by acetate cycling'),\r\n xaxis_title='time (h)',\r\n yaxis_title='Concentration (g/L)')\r\n print('print')\r\n graphJson = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)\r\n return graphJson\r\n\r\n def add_noise(self,t,C):\r\n PV = []\r\n PV2 = []\r\n PV3 = []\r\n PV4 = []\r\n import numpy as np\r\n C_noise = np.zeros((C.shape))\r\n import numpy as np\r\n for i in range(len(t)):\r\n PV.append((((math.sin(t[i] * 33)) / 17) + (math.cos(t[i] * 17)) / 83))\r\n PV2.append(0.67 * (((math.sin(t[i] * 31)) / 11) + (math.cos(t[i] * 23)) / 73))\r\n PV3.append(0.37 * (((math.sin(t[i] * 23)) / 7) + (math.cos(t[i] * 37)) / 53))\r\n PV4.append(0.19 * (((math.sin(t[i] * 19)) / 3) + (math.cos(t[i] * 53)) / 37))\r\n for i in range(len(C[0])):\r\n C_noise[:, i] = (C[:, i] + PV * C[:, i] + PV2 * C[:, i] - PV3 * C[:, i] - PV4 * C[:, i])\r\n # C_noise[:, i] = C[:, i]*(math.exp(PV)+math.exp(PV2)+math.exp(PV3)+math.exp(PV4))\r\n S = C[:, 0]\r\n S_noise =C_noise[:,0]\r\n A = C[:, 1]\r\n A_noise =C_noise[:,1]\r\n B = C[:, 3]\r\n B_noise=C_noise[:, 3]\r\n df = pd.DataFrame({'t': t, 'Substrate': S, 'Biomass': B, 'Acetate': A,'Substrate noise': S_noise, 'Biomass noise': B_noise, 'Acetate noise': A_noise,})\r\n fig = go.Figure()\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Substrate'], name='Glucose'))\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Biomass'], name='Biomass'))\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Acetate'], name='Acetate'))\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Substrate noise'], name='Glucose noise'))\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Biomass noise'], name='Biomass noise'))\r\n fig.add_trace(go.Scatter(x=df['t'], y=df['Acetate noise'], name='Acetate noise'))\r\n fig.update_layout(title=('Simulation of aerobic batch growth of Escherichia coli by acetate cycling'),\r\n xaxis_title='time (h)',\r\n yaxis_title='Concentration (g/L)')\r\n print('print')\r\n graphJson = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)\r\n return graphJson\r\n\r\n\r\nf= Ecoli_Aero()\r\nf.solve()\r\nC= f.solve()[1]\r\nprint(C)\r\n\r\n# plt.plot(f.solve()[0], f.solve()[1][:,0])\r\n# plt.plot(f.solve()[0], f.solve()[1][:,1])\r\n# # plt.plot(f.solve()[0], f.solve()[1][:,2])\r\n# plt.plot(f.solve()[0], f.solve()[1][:,3])\r\n# plt.show()","sub_path":"static/models/Rx_Fermentation_Ecoli_Glucose_Aerobic_Batch.py","file_name":"Rx_Fermentation_Ecoli_Glucose_Aerobic_Batch.py","file_ext":"py","file_size_in_byte":11181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"24291506","text":"# -*- coding: utf-8 -*-\n\nfrom django.conf.urls import include, url\n\nfrom menu.views import menu_list\nfrom .views import ProfileEdit\n\nurlpatterns = [\n url(r'^$', menu_list, name='index'),\n url(r'^profile/', ProfileEdit.as_view(), name='profile'),\n url(r'^categories/', include('categories.urls', namespace='categories')),\n url(r'^menu/', include('menu.urls', namespace='menu')),\n url(r'^clients/', include('clients.urls', namespace='clients')),\n url(r'^mailing_list/', include('mailing_list.urls', namespace='mailing_list')),\n]\n","sub_path":"manager/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"194911798","text":"import json\nimport os\nimport requests\n\nfrom flask import Flask\nfrom flask import request\nfrom flask import make_response\n\n# Flask app should start in global layout\napp = Flask(__name__)\n\n@app.route('/webhook', methods=['POST'])\ndef webhook():\n req = request.get_json(silent=True, force=True)\n print(json.dumps(req, indent=4))\n \n res = processRequest(req)\n \n res = json.dumps(res, indent=4)\n # print(res)\n r = make_response(res)\n r.headers['Content-Type'] = 'application/json'\n return r\n\ndef makeResponse(req):\n if req.get(\"result\").get(\"action\") != \"fetchWeatherForecast\":\n return {}\n result = req.get(\"result\")\n parameters = result.get(\"parameters\")\n city = parameters.get(\"geo-city\")\n date = parameters.get(\"date\")\n if city is None:\n return None\n r=requests.get('http://api.openweathermap.org/data/2.5/forecast?q='+city+',us&appid=06f070197b1f60e55231f8c46658d077')\n\t\n\t\n\t\n\t\n json_object = r.json()\n weather=json_object['list']\n for i in range(0,30):\n if date in weather[i]['dt_txt']:\n condition= weather[i]['weather'][0]['description']\n break\n speech = \"The forecast for\"+city+ \"for \"+date+\" is \"+condition\n return {\n \"speech\": speech,\n \"displayText\": speech,\n \"source\": \"apiai-weather-webhook\"\n }\n\nif __name__ == '__main__':\n port = int(os.getenv('PORT', 5000))\n print(\"Starting app on port %d\" % port)\n app.run(debug=False, port=port, host='0.0.0.0')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"webhook.py","file_name":"webhook.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"490609946","text":"class Solution:\n def partition(self, head, x):\n l1 = ListNode(None)\n dummy_l1 = l1\n l2 = ListNode(None)\n dummy_l2 = l2\n\n while head:\n if head.val < x:\n l1.next = head\n l1 = l1.next\n else:\n l2.next = head\n l2 = l2.next\n head = head.next\n\n l2.next = None #!!! elsewise TLE\n l1.next = dummy_l2.next\n\n return dummy_l1.next\n\n\n\n\n\nclass Solution:\n def partition(self, head, x):\n dummy = ListNode(None)\n dummy.next = head\n cur = dummy\n\n less_x_dummy = ListNode(None)\n cur_less_x = less_x_dummy\n\n while cur and cur.next:\n if cur.next.val < x:\n cur_less_x.next = cur.next\n cur_less_x = cur_less_x.next\n cur.next = cur.next.next\n else:\n cur = cur.next\n\n cur_less_x.next = dummy.next\n\n return less_x_dummy.next","sub_path":"leetcode/py/86.py","file_name":"86.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"381723969","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 28 10:38:18 2014\n\n@author: Rylan\n\"\"\"\n\n\n#import numpy as np\nimport cv2, serial\n#time.sleep(3)\narduino = serial.Serial(8)\ncap = cv2.VideoCapture(0)\nfont = cv2.FONT_HERSHEY_SIMPLEX\nbgs_mog=cv2.BackgroundSubtractorMOG()\nwhile True:\n ret, frame = cap.read()\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n fgmask = bgs_mog.apply(gray)\n cv2.putText(gray, arduino.name, (10, 500), font, 4, (255, 255, 255), 2, cv2.LINE_AA)\n cv2.imshow('frame', gray&fgmask)\n c =cv2.waitKey(1)\n if c==27:\n cap.release()\n cv2.destroyAllWindows()\n break","sub_path":"bgsmog movement subtraction.py","file_name":"bgsmog movement subtraction.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"466587162","text":"from turtle import *\nfrom turtle import Turtle\n\nn = 19\nfor i in range(n):\n forward(30)\n left(180 - ((n - 2) * 180) / n)\ncolors = ['red', 'purple', 'blue', 'green', 'yellow', 'orange']\nfor i in range(500):\n pencolor(colors[i % 6])\n width(i / 100 + 1)\n forward(i)\n left(59)\n\n# maidou = Turtle()\n# maidou.screen.bgcolor('black')\n# colors = ['red', 'purple', 'blue', 'green', 'yellow', 'orange']\n# maidou.screen.tracer(0, 0)\n#\n# for i in range(300):\n# maidou.color(colors[i % 6])\n# maidou.pensize(int(i / 30) + 1)\n# # maidou.circle(i)\n# maidou.fd(i)\n# maidou.left(59)\n#\n# maidou.screen.exitonclick()\n# maidou.screen.mainloop()\ncolors = ['red', 'purple', 'blue', 'green', 'yellow', 'orange']\nfor angle in range(0, 360, 15):\n setheading(angle)\n pencolor(colors[int(angle / 15) % 6])\n forward(100)\n write(str(angle) + 'D')\n backward(100)\nexitonclick()","sub_path":"MAIDOU/JUNIOR L2.py","file_name":"JUNIOR L2.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"276397232","text":"\n\nfrom xai.brain.wordbase.nouns._detonation import _DETONATION\n\n#calss header\nclass _DETONATIONS(_DETONATION, ):\n\tdef __init__(self,): \n\t\t_DETONATION.__init__(self)\n\t\tself.name = \"DETONATIONS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"detonation\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_detonations.py","file_name":"_detonations.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"73994056","text":"#!/usr/bin/env python\nimport numpy as np\nfrom math import sqrt\nimport matplotlib.pyplot as plt\nimport time\nimport rosbag\nimport rospy\nimport os\nfrom cv_bridge import CvBridge\nimport cv2 as cv\nimport csv\n\nimport rospy\nfrom sensor_msgs.msg import Image\nfrom sensor_msgs.msg import CameraInfo\nfrom sensor_msgs.msg import Imu\nfrom nav_msgs.msg import Odometry\nimport tf\n\n\nremoveLP = 1\nLK_flag = 1\nremoveOutlierByBestBuddies = 1\nbestBuddiesSqrErrTh = 4\nremoveOutliersRansac = 1\nransacErrTh = 2\nuseZ = False\n\nfigMask = False\nfigViz = False\nfigVizFeat = False\nfigDepth = False\nfigDisp = False\nfigDiff = False\nfigTraj = False\n\nMIN_FEATURES_ALLOWED = 10\n# Create some random colors\ncolor = np.random.randint(0, 255, (500, 3))\n\nclass OdomCalculator():\n\n def __init__(self):\n # params for ShiTomasi corner detection\n self.feature_params = dict( maxCorners = 500,\n qualityLevel = 0.05,\n minDistance = 10,\n blockSize = 25 )\n\n # Parameters for lucas kanade optical flow\n self.lk_params = dict( winSize = (15,15),\n maxLevel = 5,\n criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))\n\n #params for masking\n self.MASK_FRAME_X = 0.025 #pct\n self.MASK_FRAME_Y = 0.025 #pct\n self.MASK_FRAME_W = 0.95 #pct\n self.MASK_FRAME_H = 0.95 #pct\n self.MASK_THRESH = 250\n self.MASK_ERODE_SIZE = 25\n\n\n\n # params for pico\n self.camProps = {}\n self.camProps['fovw'] = 62 #deg\n self.camProps['fovh'] = 45 #deg\n self.camProps['pixw'] = 224 #pixles\n self.camProps['pixh'] = 171 #pixles\n self.camProps['depthScale'] = 1\n self.camProps['fx'] = 0\n self.camProps['fy'] = 0\n self.camProps['cx'] = 0\n self.camProps['cy'] = 0\n\n self.flagsGrayDepth = [False, False]\n self.cnt = 0\n self.bridge = CvBridge()\n self.calcOdomBusyFlag = 0\n self.dZ = 0\n\n def pico_makeMask(self,frame):\n y = int(self.MASK_FRAME_Y * frame.shape[0])\n x = int(self.MASK_FRAME_X * frame.shape[1])\n h = int(self.MASK_FRAME_H * frame.shape[0])\n w = int(self.MASK_FRAME_W * frame.shape[1])\n mask = np.zeros(frame.shape, np.uint8)\n mask[y:y + h, x:x + w] = 255\n mask[np.where((frame > self.MASK_THRESH))] = 0\n return cv.erode(mask,np.ones((self.MASK_ERODE_SIZE,self.MASK_ERODE_SIZE)))\n\n\n\n\n def rigid_transform_3D(self,A, B, fullCalc=True): #fullCalc = False --> optimize X,Y,YAW only\n assert len(A) == len(B)\n\n N = A.shape[0]; # total points\n\n if fullCalc:\n centroid_A = np.mean(A, axis=0)\n centroid_B = np.mean(B, axis=0)\n\n # centre the points\n AA = A - np.tile(centroid_A, (N, 1))\n BB = B - np.tile(centroid_B, (N, 1))\n\n # dot is matrix multiplication for array\n #H = np.transpose(AA) * BB\n H = np.dot(np.transpose(BB), AA)\n\n U, S, Vt = np.linalg.svd(H)\n\n M = np.diag([1, 1, np.linalg.det(Vt) * np.linalg.det(U)])\n\n R = np.dot(np.dot(U,M),Vt)\n\n # special reflection case\n if np.linalg.det(R) < 0:\n print(\"Reflection detected\")\n Vt[2, :] *= -1\n R = np.dot(np.dot(U,M),Vt)\n\n t = np.reshape( ( np.dot(-R , centroid_A.T) + centroid_B.T) , (-1,3))\n\n #print(t)\n else:\n A_2D = A[:,:2]\n B_2D = B[:,:2]\n centroid_A = np.mean(A_2D, axis=0)\n centroid_B = np.mean(B_2D, axis=0)\n\n # centre the points\n AA = A_2D - np.tile(centroid_A, (N, 1))\n BB = B_2D - np.tile(centroid_B, (N, 1))\n\n # dot is matrix multiplication for array\n H = np.dot(np.transpose(BB), AA)\n\n U, S, Vt = np.linalg.svd(H)\n\n R = np.dot(U, Vt)\n\n # special reflection case\n if np.linalg.det(R) < 0:\n print(\"!!!!!!!!!!!!!!!!! CHECK !!!!!!!!!!! \")\n Vt[2, :] *= -1\n R = np.dot(np.dot(U, M), Vt)\n\n t = np.reshape((np.dot(-R, centroid_A.T) + centroid_B.T), (-1, 2))\n\n ### Make 2D to 3D, TODO: add pitch,roll from Z plane\n A_meanZ = np.mean(A[:,2], axis=0)\n B_meanZ = np.mean(B[:,2], axis=0)\n dZ = B_meanZ - A_meanZ\n R = np.pad(R,(0,1),'constant')\n R[2,2] = 1.0\n t = np.pad(t,((0,0),(0,1)),'constant')\n if np.abs(dZ) > 0.005:\n t[0,2] = dZ\n self.dZ += dZ\n print(self.dZ,dZ)\n\n\n return(R, t)\n\n\n def getDepth(self,x,y,frame):\n if x == int(x) and y == int(y):\n if frame[int(y),int(x)] > 0:\n return frame[int(y),int(x)]\n else:\n return np.nan\n else:\n #if x >\n cx = int(np.ceil(x))\n fx = int(np.floor(x))\n cy = int(np.ceil(y))\n fy = int(np.floor(y))\n if cx > camProps['pixw']-1 or cy > camProps['pixh']-1 :\n return frame[np.min((fy,camProps['pixh']-1)), np.min((fx,camProps['pixw']-1))] # todo: better interpolation\n wcx = cx - x\n wfx = x - fx\n wcy = cy - y\n wfy = y - fy\n dcxcy = frame[cy,cx]\n dfxcy = frame[fy, cx]\n dcxfy = frame[cy,fx]\n dfxfy = frame[fy, fx]\n allDepth = np.array( (dcxcy, dfxcy, dcxfy, dfxfy ))\n if (np.max(allDepth) - np.min(allDepth)) > 0.5:\n #print(\"!!!!!!!!!!!!==========> CHECK\")\n return np.median(allDepth[allDepth!=0]) # todo: ignore \"0\"\n if np.all(allDepth > 0):\n return (dcxcy * wcx * wcy + dfxcy * wfx * wcy + dcxfy * wcx * wfy + dfxfy * wfx * wfy)\n else:\n 1\n return np.nan\n\n def PixlesToMeters(self,xx,yy,depthFrame,defaultDepth=-1):\n Z = self.getDepth(xx, yy, depthFrame)\n if np.isnan(Z):\n if defaultDepth > 0:\n Z = defaultDepth\n else:\n return np.nan,np.nan,np.nan\n X = (xx - self.camProps['cx']) * Z / (self.camProps['fx'])\n Y = (yy - self.camProps['cy']) * Z / (self.camProps['fy'])\n return np.hstack((-X,-Y,np.array(Z)))\n\n\n def AddDepth(self,pnts,frame,camProps):\n pnts_3D = np.nan*np.zeros((pnts.shape[0],3))\n if camProps['depthScale'] is not 1:\n frame *= camProps.depthScale\n\n pnt_rnd = np.round(pnts).astype('int')\n pnt_rnd[np.where(pnt_rnd[:, 0] > 223), 0] = 223\n pnt_rnd[np.where(pnt_rnd[:, 1] > 170), 1] = 170\n Z = frame[pnt_rnd[:, 1], pnt_rnd[:, 0]]\n pnts_3D[:, 0] = (pnts[:, 0] - camProps['cx']) * Z / (camProps['fx'])\n pnts_3D[:, 1] = (pnts[:, 1] - camProps['cy']) * Z / (camProps['fy'])\n pnts_3D[:, 2] = Z\n\n pnts_3D[:, 0:2] = - pnts_3D[:, 0:2]\n return pnts_3D\n\n\n def ShowFigs(self,good_new,good_old):\n frame = self.frame_gray\n for i, (new, old) in enumerate(zip(good_new, good_old)):\n a, b = new.ravel()\n c, d = old.ravel()\n # mask = cv.line(mask, (a,b),(c,d), color[i].tolist(), 2)\n frame = cv.circle(frame, (a, b), 5, color[i].tolist(), -1)\n mask = np.zeros_like(frame)\n #for i, pos_i in enumerate(np.flip(pos, axis=0)):\n # pos_i_rnd = pos_i.astype('uint8')\n # cv.circle(mask, (pos_i_rnd[0], pos_i_rnd[1]), 2, (0, 255, 0), -1)\n # if i > 15:\n # break\n img = cv.add(frame, mask)\n\n if figViz:\n cv.imshow('frame', self.frame_gray)\n cv.moveWindow('frame', 20, 0)\n if figVizFeat:\n cv.imshow('frameFeat', img)\n cv.moveWindow('frameFeat', 20, 300)\n if figDepth:\n cv.imshow('frameDepth', self.frameD)\n cv.moveWindow('frameDepth', 20, 600)\n\n if figTraj:\n plt.ion()\n fig = plt.figure('position', figsize=(10, 10))\n # fig.canvas.manager.window.move(20,900)\n plt.scatter(self.posFromStart.T[-1][0], self.posFromStart.T[-1][1])\n # plt.clf()\n # plt.scatter(pos[0], pos[1])\n fig.canvas.draw()\n\n if figMask:\n cv.imshow('mask1', self.old_gray_mask)\n cv.moveWindow('mask1', 20, 900)\n k = cv.waitKey(1) & 0xff\n if k == 27:\n return\n\n def publishOdom(self,rotMtx,transVec,badOdom=False):\n if badOdom:\n return False\n\n matrix4 = np.pad(self.currToStartMtx_rot,(0,1),'constant')\n matrix4[3,3] = 1\n matrix4[0:3,3] = self.currToStartMtx_tran.T\n\n '''\n matrix4 = np.pad(self.startToCurrMtx_rot,(0,1),'constant')\n matrix4[3,3] = 1\n matrix4[0:3,3] = self.startToCurrMtx_tran.T\n '''\n odom = Odometry()\n odom.header = self.msgD.header\n #odom.header.stamp = rospy.Time.now()\n odom.header.frame_id = 'map'\n odom.child_frame_id = 'royale_camera_optical_frame'\n '''\n odom.pose.pose.position.x = self.startToCurrMtx_tran[0]\n odom.pose.pose.position.y = self.startToCurrMtx_tran[1]\n odom.pose.pose.position.z = self.startToCurrMtx_tran[2]\n '''\n odom.pose.pose.position.x = self.currToStartMtx_tran[0]#self.posFromStart[0]\n odom.pose.pose.position.y = self.currToStartMtx_tran[1]#self.posFromStart[1]\n odom.pose.pose.position.z = self.currToStartMtx_tran[2]#self.posFromStart[2]\n\n q = tf.transformations.quaternion_from_matrix(matrix4)\n odom.pose.pose.orientation.x = q[0]\n odom.pose.pose.orientation.y = q[1]\n odom.pose.pose.orientation.z = q[2]\n odom.pose.pose.orientation.w = q[3]\n odom.twist.twist.linear.x = transVec[0][0]\n odom.twist.twist.linear.y = transVec[0][1]\n odom.twist.twist.linear.z = transVec[0][2]\n x,y,z = tf.transformations.euler_from_matrix(rotMtx, axes='sxyz')\n odom.twist.twist.angular.x = x\n odom.twist.twist.angular.y = y\n odom.twist.twist.angular.z = z\n\n #odom.pose.covariance =\n #odom.twist.covariance =\n\n OdomPub.publish(odom)\n return True\n\n def calcOdom(self,img,imgD):\n\n self.cnt += 1\n if removeLP:\n imgB = cv.blur(img,(31,31))\n img = np.int16(img) - np.int16(imgB) + 127\n img[img<0]=0\n img[img>255] = 255\n img = np.uint8(img)\n\n\n if self.cnt == 1:\n # Take first frame and find corners in it\n self.old_frameD = imgD.copy()\n self.old_gray = img.copy() #cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)\n old_frameD = self.old_frameD\n old_gray = self.old_gray\n\n self.p0 = cv.goodFeaturesToTrack(old_gray, mask=None, **self.feature_params)\n # Create a mask image for drawing purposes\n self.old_gray_mask = self.pico_makeMask(old_gray)\n self.height = old_gray.shape[0]\n self.width = old_gray.shape[1]\n\n self.centerPoint = (np.zeros((1, 3)) + np.array((np.round(self.height / 2.0), np.round(self.width / 2.0), np.array(0.0)))).T\n self.currToStartMtx_rot = np.eye(3)\n self.currToStartMtx_tran = np.zeros((3,1))\n\n self.startToCurrMtx_rot = np.eye(3)\n self.startToCurrMtx_tran = np.zeros((3,1))\n\n self.lastDepth = 1.0\n\n self.posFromStart = np.zeros((3,1))\n else:\n ticAll = time.time()\n\n self.frameD = imgD.copy()\n self.frame_gray = img#cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n frameD = self.frameD\n frame_gray = self.frame_gray\n old_frameD = self.old_frameD\n old_gray = self.old_gray\n p0 = self.p0\n\n # calculate optical flow\n if self.p0 is None or len(self.p0) < MIN_FEATURES_ALLOWED:\n self.old_gray = frame_gray.copy()\n self.old_frameD = frameD.copy()\n self.p0 = cv.goodFeaturesToTrack(old_gray, mask=None, **self.feature_params)\n self.publishOdom(0,0,badOdom = True)\n return\n\n\n if LK_flag:\n ticOF = time.time()\n p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **self.lk_params)\n tocOF = time.time()\n affMtx = np.eye(3)\n\n # Select good points\n good_new = p1[st == 1]\n good_old = p0[st == 1]\n\n if removeOutlierByBestBuddies:\n backToOld, stBack, err = cv.calcOpticalFlowPyrLK(frame_gray, old_gray, good_new, None, **self.lk_params)\n stBack = stBack.squeeze()\n good_new = good_new[stBack==1,:]\n backToOld = backToOld[stBack==1,:]\n good_old = good_old[stBack==1,:]\n\n bestBuddiesSqrErr = np.sum((backToOld - good_old)**2,axis=1)\n goodBestBuddies = bestBuddiesSqrErr < bestBuddiesSqrErrTh\n good_new = good_new[goodBestBuddies==1,:]\n good_old = 0.5*(good_old[goodBestBuddies==1,:]+backToOld[goodBestBuddies==1,:])\n\n\n\n if removeOutliersRansac:\n H, ransacMask = cv.findHomography(good_old, good_new, cv.RANSAC, ransacReprojThreshold=ransacErrTh)\n good_new = good_new[ransacMask.squeeze()==1]\n good_old = good_old[ransacMask.squeeze()==1]\n\n #old2New = cv.warpPerspective(old_gray, H, (self.width,self.height))\n\n #dif = 5*(np.int16(old2New) - np.int16(frame_gray)) + 127\n #dif[dif<0]=0\n #dif[dif > 255] = 255\n #if figDiff:\n # cv.imshow('dif' ,np.uint8(dif))\n #print(cnt)\n #cv.waitKey()\n\n #if cnt==314:\n # kkk=0\n\n '''\n displayPairs=0\n if displayPairs:\n height,width = old_gray.shape\n dispImg = np.concatenate((old_gray,frame_gray),1)\n for k in range(0,good_new.shape[0],4):\n cv.line(dispImg,tuple(np.int32(good_old[k])),tuple(np.int32(good_new[k]+[width,0])),(0,255,0),lineType=cv.LINE_AA)\n\n if figDisp:\n cv.imshow('',dispImg)\n cv.waitKey()\n '''\n\n good_old_3D = self.AddDepth(good_old, old_frameD, self.camProps)\n good_new_3D = self.AddDepth(good_new, frameD, self.camProps)\n\n ### FILTER POINTS WITH NANS\n\n validVec = (good_new_3D[:, 2] != 0.0) & (good_old_3D[:, 2] != 0.0)\n good_old_3D_filt = good_old_3D[validVec, :]\n good_new_3D_filt = good_new_3D[validVec, :]\n '''\n validVec = np.ones((good_old_3D.shape[0], 1))\n cntTmp = 0\n for ooo, nnn in zip(good_old_3D, good_new_3D):\n if np.isnan(ooo[0]) or np.isnan(nnn[0]):\n validVec[cntTmp] = 0\n cntTmp += 1\n \n good_old_3D_filt = good_old_3D[np.where(validVec == 1), :][0]\n good_new_3D_filt = good_new_3D[np.where(validVec == 1), :][0]\n '''\n ### CALC R AND T\n rotMtx, tranVec = self.rigid_transform_3D(good_old_3D_filt, good_new_3D_filt, fullCalc=useZ)\n\n if len(good_old_3D_filt) < MIN_FEATURES_ALLOWED:\n print(len(good_old_3D_filt))\n # Now update the previous frame and previous points\n self.old_gray = frame_gray.copy()\n self.old_frameD = frameD.copy()\n self.p0 = cv.goodFeaturesToTrack(old_gray, mask=None, **self.feature_params)\n self.publishOdom(0,0,badOdom=True)\n return\n\n else:\n #p1_extracted = cv.goodFeaturesToTrack(frame_gray, mask=None, **self.feature_params)\n p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **self.lk_params)\n len_0=len(p0)\n len_1=len(p1)\n H, mask = cv.findHomography(p0[st==1],p1[st==1] , cv.RANSAC , 5)\n rotMtx = H.copy()\n rotMtx[0][2] = 0\n rotMtx[1][2] = 0\n rotMtx[2][2] = 1\n rotMtx[2][1] = 0\n rotMtx[2][0] = 0\n tranVec = np.array([H[0][2], H[1][2], 0])\n\n '''\n pos = (np.dot(rotMtx,pos) + tranVec.T)\n pos = np.append(pos, centerPoint,axis=1)\n \n pos = pos.T\n pos[-1] = PixlesToMeters(pos[-1][0], pos[-1][1], old_frameD, defaultDepth=lastDepth)\n pos = pos.T\n '''\n\n posLocal = self.PixlesToMeters(self.centerPoint[0], self.centerPoint[1], imgD, defaultDepth=self.lastDepth)\n\n self.startToCurrMtx_rot = np.dot(rotMtx,self.startToCurrMtx_rot)\n self.startToCurrMtx_tran = np.dot(rotMtx,self.startToCurrMtx_tran) + tranVec.T\n\n\n self.currToStartMtx_rot = np.dot(self.currToStartMtx_rot,np.linalg.inv(rotMtx))\n self.currToStartMtx_tran -= np.dot(self.currToStartMtx_rot, tranVec.T)\n self.posFromStart = np.dot(self.currToStartMtx_rot, np.reshape(posLocal.T,(-1,3)).T) + self.currToStartMtx_tran\n\n tocAll = time.time()\n #print('Time(OF) - ', str((tocOF - ticOF)*1000.0) ,'ms , Time(All) - ', str((tocAll - ticAll)*1000.0) , 'ms')\n\n #print(self.posFromStart)\n #print(self.currToStartMtx_tran)\n # draw the stracks\n\n self.publishOdom(rotMtx,tranVec)\n\n #self.ShowFigs(good_new,good_old)\n\n # Now update the previous frame and previous points\n self.old_gray = frame_gray.copy()\n self.old_frameD = frameD.copy()\n self.old_gray_mask = self.pico_makeMask(old_gray)\n self.p0 = cv.goodFeaturesToTrack(old_gray, mask=self.old_gray_mask, **self.feature_params)\n\n #tocAllBag = time.time()\n #print('ALL WITH BAG --> '+ str((tocAllBag - ticAllBag)*1000.0) )\n\n\n def CheckDataForOdom(self):\n if not all(self.flagsGrayDepth):\n return\n deltaT = self.tG_org - self.tD_org\n if np.abs(deltaT > 0.01):\n print('ERROR: SYNC PROBLEM ==> FIXING', deltaT)\n if deltaT > 0:\n self.flagsGrayDepth[1] = False\n else:\n self.flagsGrayDepth[0] = False\n return\n self.flagsGrayDepth = [False,False]\n img = np.asarray(self.bridge.imgmsg_to_cv2(self.msgG, 'mono16')).astype('uint8')\n imgD = self.bridge.imgmsg_to_cv2(self.msgD)\n #print(\"Start Calc\")\n if self.calcOdomBusyFlag == 0:\n self.calcOdomBusyFlag = 1\n self.calcOdom(img,imgD)\n self.calcOdomBusyFlag = 0\n else:\n print('*'*40 + 'error' + '*'*40 )\n\n #print(\"Good\")\n\n def GrayCallBack(self,msg):\n #print(\"recieve Gray\")\n self.flagsGrayDepth[0] = True\n self.msgG = msg\n self.tG_org = msg.header.stamp.to_sec()\n self.CheckDataForOdom()\n\n def DepthCallBack(self,msg):\n #print(\"recieve Depth\")\n self.flagsGrayDepth[1] = True\n self.msgD = msg\n self.tD_org = msg.header.stamp.to_sec()\n self.CheckDataForOdom()\n\n def CamInfoCallBack(self,msg):\n if self.camProps['fx'] == 0:\n self.camProps['fx'] = msg.K[0]\n self.camProps['fy'] = msg.K[4]\n self.camProps['cx'] = msg.K[2]\n self.camProps['cy'] = msg.K[5]\n print('UPDATED CAMERA PROPERTIES')\n else:\n return\n\n def IMUCallBack(self,msg):\n return\n\n\nif __name__ == '__main__':\n OC = OdomCalculator()\n rospy.init_node('Calc_Odom')\n rospy.Subscriber(\"/royale_camera_driver/gray_image\", Image, OC.GrayCallBack)\n rospy.Subscriber(\"/royale_camera_driver/depth_image\", Image, OC.DepthCallBack)\n rospy.Subscriber(\"/royale_camera_driver/camera_info\", CameraInfo, OC.CamInfoCallBack)\n rospy.Subscriber(\"/imu/data\", Imu, OC.IMUCallBack)\n OdomPub = rospy.Publisher('odom', Odometry, queue_size=10)\n print(\"======= Calculate Odom Node Initialized =======\")\n rospy.spin()\n cv.destroyAllWindows()\n","sub_path":"CartographerFiles/PythonScripts/src/opticalFlowD_oper.py","file_name":"opticalFlowD_oper.py","file_ext":"py","file_size_in_byte":20749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"413368053","text":"import sys\nimport os\n\nfrom enum import Enum\nimport torch\nfrom typing import Any, NamedTuple\n\nimport data.data_cost as dt\n#import models.graph_models as md\n\ndef load_data(data_file):\n # type: (BaseParameters) -> dt.DataCost\n data = dt.load_dataset(data_file)\n\n# def filter_data(filt):\n # type: (Callable[[dt.DataItem], bool]) -> None\n# data.data = [d for d in data.data if filt(d)]\n# data.train = [d for d in data.train if filt(d)]\n# data.test = [d for d in data.test if filt(d)]\n\n# if params.no_mem:\n# filter_data(lambda d: not d.block.has_mem())\n\n# ablate_data(data, params.edge_ablation_types, params.random_edge_freq)\n\n# if params.linear_dependencies:\n# filter_data(lambda d: d.block.has_linear_dependencies())\n\n# if params.flat_dependencies:\n# filter_data(lambda d: d.block.has_no_dependencies())\n\n return data\n\n\"\"\"\ndef load_model(params, data):\n # type: (BaseParameters, dt.DataCost) -> md.AbstractGraphModule\n\n if params.use_rnn:\n rnn_params = md.RnnParameters(\n embedding_size=params.embed_size,\n hidden_size=params.hidden_size,\n num_classes=1,\n connect_tokens=params.rnn_connect_tokens,\n skip_connections=params.rnn_skip_connections,\n hierarchy_type=params.rnn_hierarchy_type,\n rnn_type=params.rnn_type,\n learn_init=params.rnn_learn_init,\n )\n model = md.RNN(rnn_params)\n else:\n model = md.GraphNN(embedding_size=params.embed_size, hidden_size=params.hidden_size, num_classes=1,\n use_residual=not params.no_residual, linear_embed=params.linear_embeddings,\n use_dag_rnn=not params.no_dag_rnn, reduction=params.dag_reduction,\n nonlinear_type=params.dag_nonlinearity, nonlinear_width=params.dag_nonlinearity_width,\n nonlinear_before_max=params.dag_nonlinear_before_max,\n )\n\n model.set_learnable_embedding(mode=params.embed_mode, dictsize=628 or max(data.hot_idx_to_token) + 1)\n\n return model\n\"\"\"\nPredictorDump = NamedTuple('PredictorDump', [\n ('model', Any),\n ('dataset_params', Any),\n])\n\ndef dump_model_and_data(model, data, fname):\n # type: (md.AbstractGraphMode, dt.DataCost, str) -> None\n try:\n os.makedirs(os.path.dirname(fname))\n except OSError:\n pass\n torch.save(PredictorDump(\n model=model,\n dataset_params=data.dump_dataset_params(),\n ), fname)\n\ndef load_model_and_data(fname):\n # type: (str) -> (md.AbstractGraphMode, dt.DataCost)\n dump = torch.load(fname)\n data = dt.DataInstructionEmbedding()\n data.read_meta_data()\n data.load_dataset_params(dump.dataset_params)\n return (dump.model, data)\n\n","sub_path":"ithemal_new/ithemal_utils.py","file_name":"ithemal_utils.py","file_ext":"py","file_size_in_byte":2789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"187756122","text":"# coding:utf-8\r\n\"\"\"Module of Table Account Query Function.\"\"\"\r\n\r\nimport time\r\nfrom hashlib import md5\r\nfrom uuid import uuid1 as uuid\r\n\r\nfrom models import User\r\nfrom workers.manager import exc_handler\r\n\r\n\r\n@exc_handler\r\ndef query_user(**kwargs):\r\n \"\"\"Query User Info.\"\"\"\r\n sess = kwargs.get('sess')\r\n\r\n email = kwargs.get('email')\r\n user_id = kwargs.get('user_id')\r\n username = kwargs.get('username')\r\n\r\n user = sess.query(User)\r\n\r\n if user_id:\r\n user = user.filter(User.user_id == user_id)\r\n elif email:\r\n user = user.filter(User.email == email)\r\n elif username:\r\n user = user.filter(User.username == username)\r\n else:\r\n return dict(\r\n result=0,\r\n status=1,\r\n msg=('Missing Argument, '\r\n 'either \"user_id\" or \"email\" should in arguments.'),\r\n data=None)\r\n\r\n user = user.first()\r\n\r\n if user:\r\n result = user.to_dict()\r\n else:\r\n result = None\r\n\r\n return result\r\n\r\n\r\n@exc_handler\r\ndef query_username_exists(username, **kwargs):\r\n \"\"\"Query username if exists.\"\"\"\r\n sess = kwargs.get('sess')\r\n\r\n username_exists = sess.query(User).filter(\r\n User.username == username).first()\r\n\r\n return username_exists or False\r\n\r\n\r\n@exc_handler\r\ndef query_email_exists(email, **kwargs):\r\n \"\"\"Query email if exists.\"\"\"\r\n sess = kwargs.get('sess')\r\n\r\n email_exists = sess.query(User).filter(User.email == email).first()\r\n\r\n return email_exists or False\r\n\r\n\r\n@exc_handler\r\ndef query_email_or_username_exists(email, username, **kwargs):\r\n \"\"\"Query email if exists.\"\"\"\r\n sess = kwargs.get('sess')\r\n\r\n email_exists = sess.query(User).filter(User.email == email).first()\r\n username_exists = sess.query(User).filter(\r\n User.username == username).first()\r\n\r\n return email_exists or username_exists or False\r\n\r\n\r\n@exc_handler\r\ndef insert_user(email, pswd, username, **kwargs):\r\n \"\"\"Insert a user.\"\"\"\r\n sess = kwargs.get('sess')\r\n\r\n new_user = User(\r\n user_id=str(uuid()),\r\n username=username,\r\n email=email,\r\n pswd=pswd,\r\n active_status=0,\r\n create_time=int(time.time()))\r\n\r\n result = new_user.to_dict()\r\n\r\n sess.add(new_user)\r\n sess.commit()\r\n\r\n return dict(result=1, status=0, msg='Successfully.', data=result)\r\n\r\n\r\n@exc_handler\r\ndef update_user_name(user_id, username, **kwargs):\r\n \"\"\"Insert a user.\"\"\"\r\n sess = kwargs.get('sess')\r\n\r\n update_result = sess.query(User).filter(User.user_id == user_id).update({\r\n User.username:\r\n username\r\n })\r\n\r\n sess.commit()\r\n\r\n return dict(result=1, status=0, msg='Successfully.', data=update_result)\r\n\r\n\r\n@exc_handler\r\ndef update_user_pass(user_id, pswd, **kwargs):\r\n \"\"\"Insert a user.\"\"\"\r\n sess = kwargs.get('sess')\r\n print(pswd)\r\n\r\n update_result = sess.query(User).filter(User.user_id == user_id).update({\r\n User.pswd: pswd\r\n })\r\n\r\n sess.commit()\r\n\r\n return dict(result=1, status=0, msg='Successfully.', data=update_result)\r\n\r\n\r\n\r\n\r\n# @exc_handler\r\n# def update_user_info(user_id, **kwargs):\r\n# \"\"\"Update information of a user.\"\"\"\r\n# sess = kwargs.get('sess')\r\n\r\n# email = kwargs.get('email')\r\n# user_id = kwargs.get('user_id')\r\n\r\n# user = sess.query(User)\r\n\r\n# if user_id:\r\n# user = user.filter(User.user_id == user_id)\r\n# elif email:\r\n# user = user.filter(User.email == email)\r\n# else:\r\n# return dict(\r\n# result=0,\r\n# status=1,\r\n# msg=('Missing Argument, '\r\n# 'either \"user_id\" or \"email\" should in arguments.'),\r\n# data=None)\r\n\r\n# invert_dict = dict(\r\n\r\n# )\r\n# pass\r\n\r\nTASK_DICT = dict(\r\n insert_user=insert_user,\r\n query_user=query_user,\r\n query_username_exists=query_username_exists,\r\n query_email_exists=query_email_exists,\r\n query_email_or_username_exists=query_email_or_username_exists,\r\n update_user_name=update_user_name,\r\n update_user_pass=update_user_pass,\r\n)\r\n","sub_path":"workers/task_database/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":4062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"396607486","text":"import os\nimport pdb\nimport sys\nimport tempfile\nsys.path.append(\"/opt/tosca\")\nfrom translator.toscalib.tosca_template import ToscaTemplate\n\nfrom core.models import Node, NodeLabel, Site, Deployment, SiteDeployment\n\nfrom xosresource import XOSResource\n\nclass XOSNode(XOSResource):\n provides = \"tosca.nodes.Node\"\n xos_model = Node\n\n def get_xos_args(self):\n args = {\"name\": self.obj_name}\n\n site = None\n siteName = self.get_requirement(\"tosca.relationships.MemberOfSite\", throw_exception=False)\n if siteName:\n site = self.get_xos_object(Site, login_base=siteName)\n args[\"site\"] = site\n\n deploymentName = self.get_requirement(\"tosca.relationships.MemberOfDeployment\", throw_exception=False)\n if deploymentName:\n deployment = self.get_xos_object(Deployment, name=deploymentName)\n\n if site:\n siteDeployment = self.get_xos_object(SiteDeployment, site=site, deployment=deployment, throw_exception=True)\n args[\"site_deployment\"] = siteDeployment\n\n return args\n\n def postprocess(self, obj):\n # We can't set the labels when we create a Node, because they're\n # ManyToMany related, and the node doesn't exist yet.\n labels=[]\n for label_name in self.get_requirements(\"tosca.relationships.HasLabel\"):\n labels.append(self.get_xos_object(NodeLabel, name=label_name))\n if labels:\n self.info(\"Updated labels for node '%s'\" % obj)\n obj.labels = labels\n obj.save()\n\n def create(self):\n xos_args = self.get_xos_args()\n\n if not xos_args.get(\"site\", None):\n raise Exception(\"Site is a required field of Node\")\n if not xos_args.get(\"site_deployment\", None):\n raise Exception(\"Deployment is a required field of Node\")\n\n node = Node(**xos_args)\n node.caller = self.user\n node.save()\n\n self.postprocess(node)\n\n self.info(\"Created Node '%s' on Site '%s' Deployment '%s'\" % (str(node), str(node.site), str(node.site_deployment.deployment)))\n\n def delete(self, obj):\n super(XOSNode, self).delete(obj)\n\n\n\n","sub_path":"xos/tosca/resources/node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":2180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"21019721","text":"# -*- coding: utf-8 -*-\nimport logging, logging.handlers\n\nclass GmailHandler(logging.handlers.SMTPHandler):\n def __init__(self, mailhost, fromaddr, toaddrs, subject,\n credentials=None, secure=None, timeout=5.0):\n logging.handlers.SMTPHandler.__init__(self, mailhost, fromaddr, toaddrs, subject,\n credentials=None, secure=None, timeout=5.0)\n\n if isinstance(mailhost, (list, tuple)):\n self.mailhost, self.mailport = mailhost\n else:\n self.mailhost, self.mailport = mailhost, None\n if isinstance(credentials, (list, tuple)):\n self.username, self.password = credentials\n else:\n self.username = None\n\n def emit(self, record):\n try:\n import smtplib\n # from email.message import EmailMessage\n # import email.utils\n from email.mime.text import MIMEText\n from email.mime.multipart import MIMEBase\n from email.mime.multipart import MIMEMultipart\n from email.mime.image import MIMEImage\n from email.mime.audio import MIMEAudio\n from email.header import Header\n from base64 import encodebytes\n import email\n import mimetypes\n import os\n\n mail_from = self.fromaddr # отправитель\n mail_to = ','.join(self.toaddrs) # получатель\n mail_text = self.format(record)\n mail_subj = self.getSubject(record) # заголовок письма\n mail_coding = 'windows-1251'\n attach_file = 'C:\\\\13012017.zip' # прикрепляемый файл\n\n smtp_user = self.username # пользователь smtp\n smtp_pwd = self.password # пароль smtp\n\n multi_msg = MIMEMultipart()\n multi_msg['From'] = Header(mail_from, mail_coding)\n multi_msg['To'] = Header(mail_to, mail_coding)\n multi_msg['Subject'] = Header(mail_subj, mail_coding)\n\n msg = MIMEText(mail_text.encode('cp1251'), 'plain', mail_coding)\n msg.set_charset(mail_coding)\n multi_msg.attach(msg)\n\n # присоединяем атач-файл\n if (os.path.exists(attach_file) and os.path.isfile(attach_file)):\n file = open(attach_file, 'rb')\n attachment = MIMEBase('application', \"octet-stream\")\n attachment.set_payload(file.read())\n email.encoders.encode_base64(attachment)\n file.close()\n only_name_attach = Header(os.path.basename(attach_file), mail_coding);\n attachment.add_header('Content-Disposition', 'attachment; filename=\"%s\"' % only_name_attach)\n multi_msg.attach(attachment)\n else:\n if (attach_file.lstrip() != \"\"):\n print(\"Файл для атача не найден - \" + attach_file)\n\n port = self.mailport\n smtp = smtplib.SMTP_SSL(self.mailhost+':'+port)\n smtp.set_debuglevel(1)\n smtp.ehlo()\n smtp.login(smtp_user, smtp_pwd)\n smtp.sendmail(mail_from, mail_to, multi_msg.as_string())\n smtp.quit()\n except Exception:\n self.handleError(record)","sub_path":"gmail_logging_handler.py","file_name":"gmail_logging_handler.py","file_ext":"py","file_size_in_byte":3337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"587696122","text":"import os\nimport textwrap\nimport unittest\n\nfrom conans.client.importer import IMPORTS_MANIFESTS\nfrom conans.model.manifest import FileTreeManifest\nfrom conans.test.utils.test_files import temp_folder\nfrom conans.test.utils.tools import TestClient\nfrom conans.util.files import mkdir\n\nconanfile = \"\"\"\nfrom conans import ConanFile\nfrom conans.util.files import save\n\nclass HelloConan(ConanFile):\n name = \"Hello\"\n version = \"0.1\"\n build_policy = \"missing\"\n\n def build(self):\n save(\"file1.txt\", \"Hello\")\n save(\"file2.txt\", \"World\")\n\n def package(self):\n self.copy(\"file1.txt\")\n self.copy(\"file2.txt\")\n\"\"\"\n\ntest1 = \"\"\"[requires]\nHello/0.1@lasote/stable\n\n[imports]\n., file* -> .\n\"\"\"\n\ntest2 = \"\"\"\nfrom conans import ConanFile\nfrom conans.util.files import save\n\nclass HelloReuseConan(ConanFile):\n requires = \"Hello/0.1@lasote/stable\"\n\n def imports(self):\n self.copy(\"*1.txt\")\n\"\"\"\n\ntest3 = \"\"\"\nfrom conans import ConanFile\nfrom conans.util.files import save\n\nclass HelloReuseConan(ConanFile):\n requires = \"Hello/0.1@lasote/stable\"\n\n def imports(self):\n self.copy(\"*2.txt\")\n\"\"\"\n\n\nclass ImportsTest(unittest.TestCase):\n\n def setUp(self):\n self.client = TestClient()\n self.client.save({\"conanfile.py\": conanfile})\n self.client.run(\"export . lasote/stable\")\n\n def imports_global_path_removed_test(self):\n \"\"\" Ensure that when importing files in a global path, outside the package build,\n they are removed too\n \"\"\"\n dst_global_folder = temp_folder().replace(\"\\\\\", \"/\")\n conanfile2 = '''\nfrom conans import ConanFile\n\nclass ConanLib(ConanFile):\n name = \"Say\"\n version = \"0.1\"\n requires = \"Hello/0.1@lasote/stable\"\n\n def imports(self):\n self.copy(\"file*.txt\", dst=\"%s\")\n''' % dst_global_folder\n\n self.client.save({\"conanfile.py\": conanfile2}, clean_first=True)\n self.client.run(\"export . lasote/stable\")\n\n self.client.current_folder = temp_folder()\n self.client.run(\"install Say/0.1@lasote/stable --build=missing\")\n for filename in [\"file1.txt\", \"file2.txt\"]:\n self.assertFalse(os.path.exists(os.path.join(dst_global_folder, filename)))\n\n def imports_env_var_test(self):\n conanfile2 = '''\nfrom conans import ConanFile\nimport os\n\nclass ConanLib(ConanFile):\n requires = \"Hello/0.1@lasote/stable\"\n\n def imports(self):\n self.copy(\"file*.txt\", dst=os.environ[\"MY_IMPORT_PATH\"])\n'''\n for folder in (\"folder1\", \"folder2\"):\n self.client.save({\"conanfile.py\": conanfile2}, clean_first=True)\n self.client.run(\"install conanfile.py -e MY_IMPORT_PATH=%s\" % folder)\n self.assertEqual(\"Hello\",\n self.client.load(os.path.join(folder, \"file1.txt\")))\n\n def imports_error_test(self):\n self.client.save({\"conanfile.txt\": test1}, clean_first=True)\n self.client.run(\"install . --no-imports\")\n self.assertNotIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertNotIn(\"file2.txt\", os.listdir(self.client.current_folder))\n\n self.client.run(\"imports .\") # Automatic conanbuildinfo.txt\n self.assertNotIn(\"conanbuildinfo.txt file not found\", self.client.out)\n\n def install_manifest_test(self):\n self.client.save({\"conanfile.txt\": test1}, clean_first=True)\n self.client.run(\"install ./conanfile.txt\")\n self.assertIn(\"imports(): Copied 2 '.txt' files\", self.client.out)\n self.assertIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertIn(\"file2.txt\", os.listdir(self.client.current_folder))\n self._check_manifest()\n\n def install_manifest_without_install_test(self):\n self.client.save({\"conanfile.txt\": test1}, clean_first=True)\n self.client.run('imports . ', assert_error=True)\n self.assertIn(\"You can generate it using 'conan install'\", self.client.out)\n\n def install_dest_test(self):\n self.client.save({\"conanfile.txt\": test1}, clean_first=True)\n self.client.run(\"install ./ --no-imports\")\n self.assertNotIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertNotIn(\"file2.txt\", os.listdir(self.client.current_folder))\n\n self.client.run(\"imports . -imf myfolder\")\n files = os.listdir(os.path.join(self.client.current_folder, \"myfolder\"))\n self.assertIn(\"file1.txt\", files)\n self.assertIn(\"file2.txt\", files)\n\n def imports_build_folder_test(self):\n self.client.save({\"conanfile.txt\": test1}, clean_first=True)\n tmp = self.client.current_folder\n self.client.current_folder = os.path.join(self.client.current_folder, \"build\")\n mkdir(self.client.current_folder)\n self.client.run(\"install .. --no-imports\")\n self.client.current_folder = tmp\n self.client.run(\"imports . --install-folder=build --import-folder=.\")\n files = os.listdir(self.client.current_folder)\n self.assertIn(\"file1.txt\", files)\n self.assertIn(\"file2.txt\", files)\n\n def install_abs_dest_test(self):\n self.client.save({\"conanfile.txt\": test1}, clean_first=True)\n self.client.run(\"install . --no-imports\")\n self.assertNotIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertNotIn(\"file2.txt\", os.listdir(self.client.current_folder))\n\n tmp_folder = temp_folder()\n self.client.run('imports . -imf \"%s\"' % tmp_folder)\n files = os.listdir(tmp_folder)\n self.assertIn(\"file1.txt\", files)\n self.assertIn(\"file2.txt\", files)\n\n def undo_install_manifest_test(self):\n self.client.save({\"conanfile.txt\": test1}, clean_first=True)\n self.client.run(\"install conanfile.txt\")\n self.client.run(\"imports . --undo\")\n self.assertNotIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertNotIn(\"file2.txt\", os.listdir(self.client.current_folder))\n self.assertNotIn(IMPORTS_MANIFESTS, os.listdir(self.client.current_folder))\n self.assertIn(\"Removed 2 imported files\", self.client.out)\n self.assertIn(\"Removed imports manifest file\", self.client.out)\n\n def _check_manifest(self):\n manifest_content = self.client.load(IMPORTS_MANIFESTS)\n manifest = FileTreeManifest.loads(manifest_content)\n self.assertEqual(manifest.file_sums,\n {os.path.join(self.client.current_folder, \"file1.txt\"):\n \"8b1a9953c4611296a827abf8c47804d7\",\n os.path.join(self.client.current_folder, \"file2.txt\"):\n \"f5a7924e621e84c9280a9a27e1bcb7f6\"})\n\n def imports_test(self):\n self.client.save({\"conanfile.txt\": test1}, clean_first=True)\n self.client.run(\"install . --no-imports -g txt\")\n self.assertNotIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertNotIn(\"file2.txt\", os.listdir(self.client.current_folder))\n self.client.run(\"imports .\")\n self.assertIn(\"imports(): Copied 2 '.txt' files\", self.client.out)\n self.assertIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertIn(\"file2.txt\", os.listdir(self.client.current_folder))\n self._check_manifest()\n\n def imports_filename_test(self):\n self.client.save({\"conanfile.txt\": test1,\n \"conanfile.py\": test2,\n \"conanfile2.py\": test3}, clean_first=True)\n self.client.run(\"install . --no-imports\")\n self.assertNotIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertNotIn(\"file2.txt\", os.listdir(self.client.current_folder))\n\n self.client.run(\"imports conanfile2.py\")\n self.assertNotIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertIn(\"file2.txt\", os.listdir(self.client.current_folder))\n\n os.unlink(os.path.join(self.client.current_folder, \"file2.txt\"))\n self.client.run(\"imports .\")\n self.assertIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertNotIn(\"file2.txt\", os.listdir(self.client.current_folder))\n\n os.unlink(os.path.join(self.client.current_folder, \"file1.txt\"))\n self.client.run(\"imports ./conanfile.txt\")\n self.assertIn(\"file1.txt\", os.listdir(self.client.current_folder))\n self.assertIn(\"file2.txt\", os.listdir(self.client.current_folder))\n\n\nclass SymbolicImportsTest(unittest.TestCase):\n \"\"\" Tests to cover the functionality of importing from @bindirs, @libdirs, etc\n \"\"\"\n def setUp(self):\n pkg = textwrap.dedent(\"\"\"\n from conans import ConanFile\n class Pkg(ConanFile):\n exports = \"*\"\n def package(self):\n self.copy(\"*.bin\", \"mybin\") # USE DIFFERENT FOLDERS\n self.copy(\"*.lib\", \"mylib\")\n self.copy(\"*.a\", \"myotherlib\")\n def package_info(self):\n self.cpp_info.bindirs = [\"mybin\"]\n self.cpp_info.libdirs = [\"mylib\", \"myotherlib\"]\n \"\"\")\n self.client = TestClient()\n self.client.save({\"conanfile.py\": pkg,\n \"myfile.bin\": \"hello world\",\n \"myfile.lib\": \"bye world\",\n \"myfile.a\": \"bye moon\"})\n consumer = textwrap.dedent(\"\"\"\n from conans import ConanFile, load\n class Pkg(ConanFile):\n requires = \"pkg/0.1\"\n def build(self):\n self.output.info(\"MSG: %s\" % load(\"myfile.txt\"))\n def imports(self):\n self.copy(\"*\", src=\"@bindirs\", dst=\"bin\")\n self.copy(\"*\", src=\"@libdirs\", dst=\"lib\")\n \"\"\")\n self.consumer = TestClient(cache_folder=self.client.cache_folder)\n self.consumer.save({\"conanfile.py\": consumer}, clean_first=True)\n\n def imports_symbolic_names_test(self):\n self.client.run(\"create . pkg/0.1@\")\n self.consumer.run(\"install .\")\n self.assertEqual(\"hello world\", self.consumer.load(\"bin/myfile.bin\"))\n self.assertEqual(\"bye world\", self.consumer.load(\"lib/myfile.lib\"))\n self.assertEqual(\"bye moon\", self.consumer.load(\"lib/myfile.a\"))\n\n def error_unknown_test(self):\n self.client.run(\"create . pkg/0.1@\")\n consumer = textwrap.dedent(\"\"\"\n from conans import ConanFile\n class Pkg(ConanFile):\n requires = \"pkg/0.1\"\n def imports(self):\n self.copy(\"*\", src=\"@unknown_unexisting_dir\", dst=\"bin\")\n \"\"\")\n self.consumer.save({\"conanfile.py\": consumer}, clean_first=True)\n self.consumer.run(\"install .\", assert_error=True)\n self.assertIn(\"Import from unknown package folder '@unknown_unexisting_dir'\",\n self.consumer.out)\n\n def imports_symbolic_from_editable_test(self):\n layout = textwrap.dedent(\"\"\"\n [libdirs]\n .\n [bindirs]\n .\n \"\"\")\n self.client.save({\"layout\": layout})\n self.client.run(\"editable add . pkg/0.1@ --layout=layout\")\n self.consumer.run(\"install .\")\n self.assertEqual(\"hello world\", self.consumer.load(\"bin/myfile.bin\"))\n self.assertEqual(\"bye world\", self.consumer.load(\"lib/myfile.lib\"))\n self.assertEqual(\"bye moon\", self.consumer.load(\"lib/myfile.a\"))\n","sub_path":"conans/test/functional/command/imports_test.py","file_name":"imports_test.py","file_ext":"py","file_size_in_byte":11458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"627091559","text":"import os\nfrom tkinter.filedialog import askdirectory\nimport pygame\nfrom mutagen.id3 import ID3\nfrom tkinter import *\n \nroot = Tk()\nroot.minsize(300,300)\nroot.title(\"Music Player\")\n \nlistofsongs = []\nrealnames = []\n \nv = StringVar()\nsonglabel = Label(root,textvariable=v,width=35)\n \nindex = 0\n \ndef directorychooser():\n \n directory = askdirectory()\n os.chdir(directory)\n \n for files in os.listdir(directory):\n if files.endswith(\".mp3\"):\n \n realdir = os.path.realpath(files)\n audio = ID3(realdir)\n realnames.append(audio['TIT2'].text[0])\n \n \n listofsongs.append(files)\n \n pygame.mixer.pre_init(44100, -16, 2, 2048)\n pygame.init()\n pygame.mixer.init()\n pygame.mixer.music.load(listofsongs[index])\n pygame.mixer.music.play()\n pygame.mixer.music.queue(listofsongs[index + 1])\n updatelabel()\n \ndirectorychooser()\n\nvolm = DoubleVar()\nvol = Scale(root, variable = volm, activebackground = \"Black\", troughcolor = \"Blue\").grid(row = 13, column = 2)\nvolm.set(100)\n\n#print(volm.get()/100.0)\ndef updatelabel():\n global index\n global songname\n v.set(realnames[index])\n #return songname\n \n \n \ndef nextsong():\n global index\n if index == len(realnames) - 1:\n index = 0\n else:\n \tindex += 1\n resumesong()\n pygame.mixer.music.load(listofsongs[index])\n pygame.mixer.music.play()\n if index == len(realnames) - 1:\n pygame.mixer.music.queue(listofsongs[0])\n else:\n pygame.mixer.music.queue(listofsongs[index + 1])\n updatelabel()\n \ndef prevsong():\n global index\n if index == 0:\n index = len(realnames) - 1\n else:\n index -= 1\n resumesong()\n pygame.mixer.music.load(listofsongs[index])\n pygame.mixer.music.play()\n if index == len(realnames) - 1:\n pygame.mixer.music.queue(listofsongs[0])\n else:\n pygame.mixer.music.queue(listofsongs[index + 1])\n updatelabel()\n \n \ndef pausesong():\n pygame.mixer.music.pause()\n resumebutton = Button(root, text = \"Resume\", command = resumesong)\n resumebutton.grid(row = 41, column = 2)\n v.set(\"\")\n updatelabel()\n #return songname\n \ndef resumesong():\n pygame.mixer.music.unpause()\n pausebutton = Button(root,text='Pause', command = pausesong)\n pausebutton.grid(row = 41, column = 2)\n updatelabel()\n \ndef changevol():\n pygame.mixer.music.set_volume(volm.get() / 100.0)\n \nlistbox = Listbox(root, background = \"Blue\")\nlistbox.grid(row = 1, columnspan = 30, rowspan = 40)\n \n#listofsongs.reverse()\nrealnames.reverse()\n \nfor items in realnames:\n listbox.insert(0,items)\n \nrealnames.reverse()\n#listofsongs.reverse()\n \n \nnextbutton = Button(root,text = 'Next Song', command = nextsong)\nnextbutton.grid(row = 41, column = 0)\n \npreviousbutton = Button(root,text = 'Previous Song', command = prevsong)\npreviousbutton.grid(row = 41, column = 1, sticky = W)\n \npausebutton = Button(root,text='Pause', command = pausesong)\npausebutton.grid(row = 41, column = 2)\n\nvolbutton = Button(root, text = \"Change Vol\", command = changevol)\nvolbutton.grid(row = 32, column =2)\n\n\n\"\"\" \nnextbutton.bind(\"\",nextsong)\npreviousbutton.bind(\"\",prevsong)\nstopbutton.bind(\"\",stopsong)\n\"\"\"\nsonglabel.grid(row = 42)\n\nroot.mainloop()\n","sub_path":"music.py","file_name":"music.py","file_ext":"py","file_size_in_byte":3265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"296600845","text":"'''\nInput: a List of integers\nReturns: a List of integers\n'''\n\n\ndef moving_zeroes(arr):\n length = len(arr)\n count = 0 \n for number in range(length):\n if arr[number] != 0:\n arr[count] = arr[number]\n count += 1\n while count < length:\n arr[count] = 0\n count += 1\n return arr\n\n\nif __name__ == '__main__':\n # Use the main function here to test out your implementation\n arr = [0, 3, 1, 0, -2]\n\n print(f\"The resulting of moving_zeroes is: {moving_zeroes(arr)}\")\n print(moving_zeroes([0, 0, 0, 0, 3, 2, 1]))\n\n\n\n#TakeAway:\n#Original idea was to have arrays popping and appending to each other, not only is that not very time effiecient it also created several issues with mutation that were complicating the solve\n#went with 2 loops as a simple save ","sub_path":"moving_zeroes/moving_zeroes.py","file_name":"moving_zeroes.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"275527009","text":"import random\n\nimport numpy as np\n\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import adjusted_rand_score\nfrom scipy.linalg import fractional_matrix_power\n\n\ndef load(verbose=False):\n with np.load('dimredux-challenge-01-data.npz') as fh:\n data_x = fh['data_x']\n validation_x = fh['validation_x']\n validation_y = fh['validation_y']\n\n if verbose:\n print('data_x: ', data_x.shape, data_x.dtype)\n print('validation_x: ', validation_x.shape, validation_x.dtype)\n print('validation_y: ', validation_y.shape, validation_y.dtype)\n\n return data_x, validation_x, validation_y\n\n\ndef save(timeseries_y):\n assert timeseries_y.ndim == 1\n np.save('prediction.npy', timeseries_y)\n\n\ndef lag_data(data, lag=0):\n \"\"\"Lags data on axis=0.\"\"\"\n assert data.shape[0] > lag, 'you need more samples than lag'\n assert lag >= 0, 'you need a non-negative lagtime'\n\n if lag == 0:\n not_lagged = data\n lagged = data\n else:\n not_lagged = data[:-lag, ...]\n lagged = data[lag:, ...]\n return not_lagged, lagged\n\n\ndef pca(x, remove_mean=True, whiten=True, axis=0):\n if remove_mean:\n x = x - x.mean(axis=axis)\n return PCA(whiten=whiten).fit_transform(x)\n\n\ndef whiten(x, axis=0):\n x = x - x.mean(axis=axis)\n n = np.take(x.shape, axis)\n cxx = fractional_matrix_power(np.matmul(x.T, x) / n, -1/2)\n return np.tensordot(x, cxx, axes=(1, 0))\n\n\ndef cluster(timeseries):\n kmeans = KMeans(n_clusters=4, random_state=0)\n clustered = kmeans.fit(timeseries)\n return clustered\n\n\ndef cluster_compare(true_states, predicted_states):\n clustered = cluster(predicted_states)\n return adjusted_rand_score(true_states, clustered.labels_)\n\n\ndef shuffle(x):\n y = x.copy()\n random.shuffle(y)\n return y\n","sub_path":"varitae/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"368674267","text":"import os\nimport glob\n\ndef rename(tar_dir):\n rename_list = glob.glob(tar_dir + '/*')\n for i, f in enumerate(rename_list):\n os.rename(f, os.path.join(tar_dir, '{0:05d}.jpg'.format(i)))\n\ndef main():\n path = '../JPEGImages/2018-04-03'\n rename(path)\n\nif __name__ == '__main__':\n main()","sub_path":"rename.py","file_name":"rename.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"245462475","text":"import numpy as np\nimport networkx as nx\nfrom corrupted_msgs import sample, msg\n\n\ndef parse_msg(data: str):\n rules, candidates = data.split(\"\\n\\n\")\n \n parsed_rules = {}\n for row in rules.split(\"\\n\"):\n num, rule = row.split(\": \")\n rule = np.array([[num_or_char(x) for x in single_rule.split(\" \")] for single_rule in rule.split(\" | \")])\n if isinstance(rule[0][0], str):\n rule = rule[0][0]\n parsed_rules[int(num)] = rule\n\n candidates = candidates.split(\"\\n\")\n return parsed_rules, candidates\n\n\ndef num_or_char(x: str):\n try:\n return int(x)\n except ValueError:\n return x.strip('\"')\n \n \ndef check(rules):\n ends = {k: v for k, v in rules.items() if isinstance(v, str)}\n middles = {k: v for k, v in rules.items() if any([end in v.ravel() for end in endpoints])}\n starts = {k: v for k, v in rules.items() if all([v not in endpoints.values() for v in rules.values()])}\n return starts, middles, ends\n\n\nrules, candidates = parse_msg(sample)\n","sub_path":"day19/day19.py","file_name":"day19.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"582712024","text":"# module to pull data\nimport gc\nimport pandas as pd\nfrom tqdm import tqdm\nimport sys\n\n\nclass dsn_connection:\n\n def __init__(self, dsn, uid, pwd):\n \"\"\"\n setting up connection with dsn (if available)\"\"\"\n import pyodbc as p\n self.connection = p.connect('DSN=%s;UID=%s;PWD=%s' % (dsn, uid, pwd))\n\n\nclass tablesize:\n \"\"\"\n from query of an n x m table:\n get estimate of n: row, and one_row: data frame with one row\n \"\"\"\n\n def __init__(self, type,\n path=None, sql=None,\n connection=None, tweakingfunction=None):\n if type == 'csv':\n self.n, self.one_row = self.csv(path)\n elif type == 'csv.gz':\n self.n, self.one_row = self.csvgz(path)\n elif type == 'sql':\n self.n, self.one_row = self.sql(sql, connection)\n else:\n raise ValueError('Method not exist, please assign arbitrarily')\n\n def csv(self, path):\n f = open(path, 'r')\n n = sum(1 for row in f)\n one_row = pd.DataFrame(str(bytes(f.readline())).split(','))\n return n, one_row\n\n def sql(self, sql, connection, tweakingfunction):\n n_query = \"SELECT COUNT(1) FROM (\" + sql + \")\"\n n = int(\n connection.cursor().execute(n_query).fetchone()[0])\n m_query = \"SELECT * FROM (\" + sql + \") WHERE ROWNUM <= 1\"\n one_row = pd.read_sql(m_query, connection)\n if tweakingfunction is not None:\n one_row = tweakingfunction(one_row)\n return n, one_row\n\n def csvgz(self, path):\n import gzip\n f = gzip.open(path, 'r')\n n = sum(1 for row in f)\n one_row = pd.DataFrame(str(bytes(f.readline())).split(','))\n return n, one_row\n\n\ndef read_sql(path):\n \"\"\"\n pass path of sql syntax, return as string\"\"\"\n with open(path) as scr:\n sql = scr.read()\n return sql\n\n\ndef new_itemsize(hdfobjects, df_name):\n \"\"\"\n given list hdf objects, get max itemsize\"\"\"\n\n infos = dict()\n itemsizes = dict()\n n_hdf = len(hdfobjects)\n\n for hi in range(n_hdf):\n h = hdfobjects[hi]\n info = h.get_storer(df_name).table.description\n names = info._v_nested_names\n infos[hi] = dict()\n infos[hi]['info'] = info\n infos[hi]['names'] = names\n\n for x in infos[hi]['names']:\n hdf_itemsizes = [\n getattr(infos[y]['info'], x).itemsize for y in range(n_hdf)]\n if n_hdf > 1:\n if not(all([x == hdf_itemsizes[0] for x in hdf_itemsizes])):\n itemsizes[x] = max(hdf_itemsizes)\n else:\n itemsizes[x] = max(hdf_itemsizes)\n\n return itemsizes\n\n\ndef chunk_copy_hdf(oldhdf, newhdfpath, df_name, chunksize=None,\n itemsize=None, tweakingfunction=None):\n \"\"\"\n chunk copy from newhdf to oldhdf\"\"\"\n # delete df_name from oldhdf\n # oldhdf.remove(df_name)\n\n # chunk read from newhdfpath\n for z in pd.read_hdf(newhdfpath, df_name, chunksize=chunksize):\n if tweakingfunction is not None:\n z = tweakingfunction(z)\n oldhdf.append(df_name, z, index=False,\n data_columns=True, min_itemsize=itemsize)\n\n\ndef calculate_chunksize(one_row, total_count, factor,\n max_local_ram_usage=8):\n \"\"\"\n Input:\n one_row: df of one_row\n total_count: total rows\n factor: default at 100000, the estimate of one_row is by data type and is\n typically wrong, inflate by factor of `factor`.\n max_local_ram_usage: maximum local ram\n \"\"\"\n one_row_memory_usage = sys.getsizeof(one_row) * factor\n max_local_ram_usage_bytes = max_local_ram_usage * 1073741824\n total_size = total_count * one_row_memory_usage\n chunksize = int(total_size / max_local_ram_usage_bytes)\n chunks = int(total_count / chunksize)\n print('query pulls %i rows, break it by every \\\n %i rows and %i chunks.' % (total_count, chunksize, chunks))\n return chunksize\n\n\ndef read_sql_to_hdf(hdf, sql, connection, df_name,\n chunksize=None, max_local_ram_usage=8,\n tweakingfunction=None, returndf=False,\n factor=500000):\n \"\"\"\n Input:\n * hdf: hdf path\n * sql: sql to pull data\n * connection: database connection to pull data from\n * df_name: name of df in hdf\n Warning: if df_name already exists, it'll overwrite\n * chunksize: save memory if pulling large data\n default to None to calculate\n * max_local_ram_usage: max local ram size in gb want to use to work on\n extracting data\n default to 8.\n * tweakfunction: if tweaking is needed, pass in tweaking function to\n tweak x\n * returndf: default to True, if ram not permitted, do not read\n * kwargs**\n \"\"\"\n # init\n pd.set_option('io.hdf.default_format', 'table')\n itemsize = None\n\n # remove table if exist -> update\n if isinstance(hdf, str):\n hdf_path = hdf\n hdf = pd.HDFStore(hdf_path, mode='a')\n if '/%s' % df_name in hdf:\n hdf.remove(df_name)\n\n # getting how many rows are there to `chunk`\n total_count_query = \"SELECT COUNT(1) FROM (\" + sql + \")\"\n total_count = int(\n connection.cursor().execute(total_count_query).fetchone()[0])\n if chunksize is None:\n\n # if query is too huge, save ram and monitor the progress, chunk it\n if total_count > 1e6:\n # to be work on: calculate chunksize based on table size\n # it's not accurate though, 100000 is the magic number I found\n # works better\n\n size_of_one_row = \"SELECT * FROM (\" + sql + \") WHERE ROWNUM <= 1\"\n one_row = pd.read_sql(size_of_one_row, connection)\n if tweakingfunction is not None:\n one_row = tweakingfunction(one_row)\n\n # debugging\n # print('query pulls %i rows, break it by every \\\n # %i rows and %i chunks.' % (total_count, chunksize, chunks))\n\n chunksize = calculate_chunksize(one_row, total_count,\n factor=factor)\n else:\n # moderate size, no need to chunk\n chunksize = total_count\n\n with tqdm(total=total_count) as pbar:\n for df in pd.read_sql(sql, connection, chunksize=chunksize):\n\n if df.shape[0] == 0:\n raise ValueError('Query returns no records.')\n\n if tweakingfunction is not None:\n df = tweakingfunction(df)\n try:\n hdf.append(df_name, df, index=False,\n data_columns=True)\n except ValueError:\n # min_itemsize do not fit, create a new one using the new slice\n # copy the original ones and attach them to the new\n # create a new one\n hdf_expand = pd.HDFStore(\n 'temp_expand.h', mode='w')\n\n # append to new hdf\n hdf_expand.append(df_name, df, index=False, data_columns=True)\n # to calculate new itemsize\n itemsize = new_itemsize([hdf_expand, hdf], df_name)\n\n # delete the original copy with wrong itemsize\n hdf_expand.remove('/%s' % df_name)\n # append again with new itemsize\n # update itemsize\n\n hdf_expand.append(df_name, df, index=False,\n data_columns=True, min_itemsize=itemsize)\n\n # append copy from the original one\n chunk_copy_hdf(hdf_expand, hdf_path, df_name,\n chunksize=chunksize, itemsize=itemsize,\n tweakingfunction=tweakingfunction)\n\n hdf_expand.close()\n\n # copy newhdf back to oldhdf to continue appending\n hdf.remove(df_name)\n chunk_copy_hdf(hdf, 'temp_expand.h', df_name,\n chunksize=chunksize, itemsize=itemsize,\n tweakingfunction=tweakingfunction)\n # clean up\n del df\n gc.collect()\n pbar.update(chunksize)\n\n hdf.create_table_index(df_name, optlevel=9, kind='full')\n\n if returndf:\n df = hdf[df_name]\n hdf.close()\n if returndf:\n return df\n\n\ndef read_func_to_hdf(hdf, read_func, df_name,\n path=None, sql=None, type='csv',\n connection=None, chunksize=None,\n max_local_ram_usage=8, tweakingfunction=None,\n returndf=False, factor=500000):\n \"\"\"\n Generalized version of read_sql_to_hdf\n Input:\n * hdf: hdf path\n * read_func: pandas read function\n * tablesize: (n (rows), m (size per row)) to estimate chunksize\n * connection: database connection to pull data from\n * df_name: name of df in hdf\n Warning: if df_name already exists, it'll overwrite\n * chunksize: save memory if pulling large data\n default to None to calculate\n * max_local_ram_usage: max local ram size in gb want to use to work on\n extracting data\n default to 8.\n * tweakfunction: if tweaking is needed, pass in tweaking function to\n tweak x\n * returndf: default to True, if ram not permitted, do not read\n * kwargs**\n \"\"\"\n # init\n pd.set_option('io.hdf.default_format', 'table')\n itemsize = None\n\n # sanity check\n if ((type == 'csv') or (type == 'csvtz')) and (path is None):\n raise ValueError('Please specify path of text file')\n elif (type == 'sql') and ((sql is None) or (connection is None)):\n raise ValueError('Please specify sql and connection to query')\n elif (type is None):\n raise ValueError('Please specify file type')\n\n # remove table if exist -> update\n if isinstance(hdf, str):\n hdf_path = hdf\n hdf = pd.HDFStore(hdf_path, mode='a')\n if '/%s' % df_name in hdf:\n hdf.remove(df_name)\n\n # getting how many rows are there to `chunk`\n if chunksize is None:\n # not specified:\n # determine how many rows per chunk\n\n tsz = tablesize(type,\n path=path, sql=sql,\n connection=connection,\n tweakingfunction=tweakingfunction)\n total_count = tsz.n\n one_row = tsz.one_row\n\n # if query is too huge, save ram and monitor the progress, chunk it\n if total_count > 1e6:\n # to be work on: calculate chunksize based on table size\n # it's not accurate though, 100000 is the magic number I found\n # works better\n\n # debugging\n # print('query pulls %i rows, break it by every \\\n # %i rows and %i chunks.' % (total_count, chunksize, chunks))\n\n chunksize = calculate_chunksize(one_row, total_count,\n factor=factor)\n else:\n # moderate size, no need to chunk\n chunksize = total_count\n\n if type == 'sql':\n reader = read_func(sql, connection, chunksize=chunksize)\n elif (type == 'csv') or (type == 'csv.gz'):\n reader = read_func(path, chunksize=chunksize)\n else:\n raise ValueError('type not specified')\n\n with tqdm(total=total_count) as pbar:\n for df in reader:\n if tweakingfunction is not None:\n df = tweakingfunction(df)\n try:\n hdf.append(df_name, df, index=False,\n data_columns=True)\n except ValueError:\n # min_itemsize do not fit, create a new one using the new slice\n # copy the original ones and attach them to the new\n\n # create a new one\n hdf_expand = pd.HDFStore(\n 'temp_expand.h', mode='w')\n\n # append to new hdf\n hdf_expand.append(df_name, df, index=False, data_columns=True)\n # to calculate new itemsize\n itemsize = new_itemsize([hdf_expand, hdf], df_name)\n\n # delete the original copy with wrong itemsize\n hdf_expand.remove('/%s' % df_name)\n # append again with new itemsize\n # update itemsize\n\n hdf_expand.append(df_name, df, index=False,\n data_columns=True, min_itemsize=itemsize)\n\n # append copy from the original one\n chunk_copy_hdf(hdf_expand, hdf_path, df_name,\n chunksize=chunksize, itemsize=itemsize,\n tweakingfunction=tweakingfunction)\n\n hdf_expand.close()\n\n # copy newhdf back to oldhdf to continue appending\n hdf.remove(df_name)\n chunk_copy_hdf(hdf, 'temp_expand.h', df_name,\n chunksize=chunksize, itemsize=itemsize,\n tweakingfunction=tweakingfunction)\n\n # clean up\n del df\n gc.collect()\n pbar.update(chunksize)\n\n hdf.create_table_index(df_name, optlevel=9, kind='full')\n\n if returndf:\n df = hdf[df_name]\n hdf.close()\n if returndf:\n return df\n","sub_path":"data/pull.py","file_name":"pull.py","file_ext":"py","file_size_in_byte":13463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"351806022","text":"import RPi.GPIO as GPIO\nfrom Lcd import LCD_LIB\nimport time\n\nclass KeyPad():\n\n\tdef __init__(self):\n\t\tself.lcd = LCD_LIB()\n\t\tself.rows = [17, 25, 24, 23]\n\t\tself.cols = [27, 18, 22]\n\t\tself.keys = [\n\t\t ['1', '2', '3'],\n\t\t ['4', '5', '6'],\n\t\t ['7', '8', '9'],\n\t\t ['*', '0', '#']]\n\n\n\t\tfor row_pin in self.rows:\n\t\t GPIO.setup(row_pin, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\n\n\t\tfor col_pin in self.cols:\n\t\t GPIO.setup(col_pin, GPIO.OUT)\n\n\tdef get_key(self):\n\t key = 0\n\t for col_num, col_pin in enumerate(self.cols):\n\t GPIO.output(col_pin, 1)\n\t for row_num, row_pin in enumerate(self.rows):\n\t if GPIO.input(row_pin):\n\t key = self.keys[row_num][col_num]\n\t GPIO.output(col_pin, 0)\n\t return key\n\n\tdef get_key_combo(self, color):\n\t\tkeys_pressed = []\n\t\twhile len(keys_pressed) < 4:\n\t\t\tkey = self.get_key()\n\t\t\tif key:\n\t\t\t\tkeys_pressed.append(key)\n\t\t\t\tself.lcd.lcd_message(color,\"\".join(keys_pressed))\n\t\t\t\ttime.sleep(0.2)\n\t\tkey = \"\".join(keys_pressed)\n\t\treturn key","sub_path":"picurity_system/keypad.py","file_name":"keypad.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"463995956","text":"import MySQLdb\nimport requests\nfrom lxml import etree\nconn = MySQLdb.Connect(host='127.0.0.1', user='root', password='123456', db='crawler', port=3306,charset='utf8')\ncursor = conn.cursor()\nurl = 'https://www.zhipin.com/c101010100/'\nparams = {'query': '爬虫', 'page': '1', 'ka': 'page-next'}\nheaders = {\n'user-agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36',\n'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',\n'accept-encoding': 'gzip, deflate, br',\n'accept-language': 'zh-CN,zh;q=0.9',\n'cookie': 'sid=sem; toUrl=/; __g=sem; lastCity=101010100; Hm_lvt_194df3105ad7148dcf2b98a91b5e727a=1561082535,1561082567; __c=1561082567; __l=r=https%3A%2F%2Fwww.baidu.com%2Flink%3Furl%3D5u_-dqcCl1A5qJ1A834PR6FH-CLc-cEqKnioVC8bM8d3E6hlwgLcAg3iNEiuSltR%26wd%3D%26eqid%3Dfca5a35400002d35000000035d0c3a9e&l=%2Fwww.zhipin.com%2F&g=%2Fwww.zhipin.com%2Fuser%2Fsem7.html%3Fsid%3Dsem%26utm_source%3Dbaidu%26utm_medium%3Dcpc%26utm_campaign%3DPC-yixian-pinpaici-2C%26utm_content%3DBOSSzhipin-hexin%26utm_term%3DBOSSzhipinwangz; Hm_lpvt_194df3105ad7148dcf2b98a91b5e727a=1561083258; __a=73534768.1561082533.1561082533.1561082567.14.2.13.14',\n}\n#proxy ={'http': '47.107.190.212:8118'}\nfor q in ['AI', '大数据', '爬虫']:\n params['query'] = q\n for p in range(9):\n params['page'] = str(p)\n text = requests.get(url=url, params=params, headers=headers).text\n html = etree.HTML(text)\n #print(text)\n nodes = html.xpath('//ul/li/div/div/h3/a/@href')\n for n in nodes:\n url = 'https://www.zhipin.com'+n\n text1 = requests.get(url=url, headers=headers).text\n html1 = etree.HTML(text1)\n try:\n title = html1.xpath('//title/text()')[0]\n print(22, title)\n salary = html1.xpath('//div[@class=\"name\"]/span[@class=\"salary\"]/text()')[0]\n print(24, salary)\n company = html1.xpath('//div[@class=\"company-info\"]/a/@title')[0]\n print(27, company)\n position = html1.xpath('//div[@class=\"name\"]/h1/text()')[0]\n print(28, position)\n sql = \"insert into t_boss (title,salary,company,position) values(%s, %s, %s, %s)\"\n cursor.execute(sql, [title, salary, company, position])\n conn.commit()\n except:\n print('该条采集失败！')\n","sub_path":"boss_spiders/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":2517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"47246495","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nTime-sharing chart for huobi trading.\n\nCopyright (C) 2018 by Zhang Shengfa(shengfazhang@126.com)\n\"\"\"\n\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtChart import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\n\n\nclass TimeSharingChart(QWidget):\n \"\"\"\n Implement Time-Sharing chart of the past 5 hours.\n \"\"\"\n\n def __init__(self, max_count=300, parent=None):\n \"\"\"\n Initialize.\n :param parent: The parent widget\n \"\"\"\n QWidget.__init__(self, parent)\n\n self.max_count = max_count\n self.series = QLineSeries(self)\n pen = QPen(Qt.green)\n pen.setWidth(3)\n self.series.setPen(pen)\n self.xaxis = QDateTimeAxis()\n self.xaxis.setFormat(\"MM-dd HH:mm\")\n self.chartview = QChartView()\n self.chart = QChart()\n\n self.chart.addSeries(self.series)\n self.chart.setAnimationOptions(QChart.SeriesAnimations)\n self.chart.createDefaultAxes()\n self.chart.setTitle(\"Time-Sharing Chart(BTC/USDT)\")\n self.chart.setAxisX(self.xaxis, self.series)\n self.chart.legend().hide()\n\n self.chartview.setChart(self.chart)\n self.layout = QHBoxLayout()\n self.layout.addWidget(self.chartview)\n self.setLayout(self.layout)\n\n def _shrink(self):\n \"\"\"\n Shrink the data to max_count\n \"\"\"\n while self.series.count() > self.max_count:\n self.series.remove(0)\n\n def _update_range(self):\n \"\"\"\n Update axis range\n \"\"\"\n min_y = self.series.at(0).y()\n max_y = min_y\n i = 1\n while i < self.series.count():\n y = self.series.at(i).y()\n if min_y > y:\n min_y = y\n if max_y < y:\n max_y = y\n i += 1\n\n self.chartview.chart().axisX().setRange(QDateTime.fromMSecsSinceEpoch(self.series.at(0).x()),\n QDateTime.fromMSecsSinceEpoch(self.series.at(i - 1).x() + 600000))\n self.chartview.chart().axisY().setRange(min_y, max_y)\n\n def clear(self):\n \"\"\"\n Clear chart.\n \"\"\"\n self.series.clear()\n\n def append_data(self, x, y):\n \"\"\"\n Append new data with (x, y)\n :param x: Epoch time with second\n :param y: price\n \"\"\"\n x *= 1000\n self.series.append(x, y)\n self._shrink()\n self._update_range()\n\n def update_data(self, x, y):\n \"\"\"\n Update the latest data or append\n :param x:\n :param y:\n :return:\n \"\"\"\n x *= 1000\n count = self.series.count()\n if count > 0 and self.series.at(count - 1).x() == x:\n self.series.replace(count - 1, x, y)\n self._update_range()\n elif count == 0 or self.series.at(count - 1).x() < x:\n self.append_data(x / 1000, y)\n","sub_path":"hbtrade/sbin/tschart.py","file_name":"tschart.py","file_ext":"py","file_size_in_byte":2906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"85048481","text":"#!/usr/bin/python3 -i\n#\n# Copyright (c) 2020 LunarG, Inc.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to\n# deal in the Software without restriction, including without limitation the\n# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n# sell copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n# IN THE SOFTWARE.\n\nimport os,re,sys\nfrom base_generator import *\n\nclass VulkanReferencedResourceBodyGeneratorOptions(BaseGeneratorOptions):\n \"\"\"Options for generating a C++ class for detecting unreferenced resource handles in a capture file\"\"\"\n def __init__(self,\n blacklists = None, # Path to JSON file listing apicalls and structs to ignore.\n platformTypes = None, # Path to JSON file listing platform (WIN32, X11, etc.) defined types.\n filename = None,\n directory = '.',\n prefixText = '',\n protectFile = False,\n protectFeature = True):\n BaseGeneratorOptions.__init__(self, blacklists, platformTypes,\n filename, directory, prefixText,\n protectFile, protectFeature)\n\n# VulkanReferencedResourceBodyGenerator - subclass of BaseGenerator.\n# Generates C++ member definitions for the VulkanReferencedResource class responsible for\n# determining which resource handles are used or unused in a capture file.\nclass VulkanReferencedResourceBodyGenerator(BaseGenerator):\n \"\"\"Generate a C++ class for detecting unreferenced resource handles in a capture file\"\"\"\n # All resource and resource associated handle types to be processed.\n RESOURCE_HANDLE_TYPES = ['VkBuffer', 'VkImage', 'VkBufferView', 'VkImageView', 'VkFramebuffer', 'VkDescriptorSet', 'VkCommandBuffer']\n\n # Handle types that contain resource and child resource handle types.\n CONTAINER_HANDLE_TYPES = ['VkDescriptorSet']\n\n # Handle types that use resource and child resource handle types.\n USER_HANDLE_TYPES = ['VkCommandBuffer']\n\n def __init__(self,\n errFile = sys.stderr,\n warnFile = sys.stderr,\n diagFile = sys.stdout):\n BaseGenerator.__init__(self,\n processCmds=True, processStructs=True, featureBreak=False,\n errFile=errFile, warnFile=warnFile, diagFile=diagFile)\n # Map of Vulkan structs containing handles to a list values for handle members or struct members\n # that contain handles (eg. VkGraphicsPipelineCreateInfo contains a VkPipelineShaderStageCreateInfo\n # member that contains handles).\n self.structsWithHandles = dict()\n self.pNextStructs = dict() # Map of Vulkan structure types to sType value for structs that can be part of a pNext chain.\n self.commandInfo = dict() # Map of Vulkan commands to parameter info\n self.restrictHandles = True # Determines if the 'isHandle' override limits the handle test to only the values conained by RESOURCE_HANDLE_TYPES.\n\n # Method override\n def beginFile(self, genOpts):\n BaseGenerator.beginFile(self, genOpts)\n\n write('#include \"generated/generated_vulkan_referenced_resource_consumer.h\"', file=self.outFile)\n self.newline()\n write('#include ', file=self.outFile)\n self.newline()\n write('GFXRECON_BEGIN_NAMESPACE(gfxrecon)', file=self.outFile)\n write('GFXRECON_BEGIN_NAMESPACE(decode)', file=self.outFile)\n\n # Method override\n def endFile(self):\n for cmd, info in self.commandInfo.items():\n returnType = info[0]\n params = info[2]\n if params and params[0].baseType == 'VkCommandBuffer':\n # Check for parameters with resource handle types.\n handles = self.getParamListHandles(params[1:])\n\n if (handles):\n # Generate a function to add handles to the command buffer's referenced handle list.\n cmddef = '\\n'\n\n # Temporarily remove resource only matching restriction from isHandle() when generating the function signature.\n self.restrictHandles = False\n cmddef += self.makeConsumerFuncDecl(returnType, 'VulkanReferencedResourceConsumer::Process_' + cmd, params) + '\\n'\n self.restrictHandles = True\n\n cmddef += '{\\n'\n indent = self.INDENT_SIZE * ' '\n\n # Add unreferenced parameter macros.\n unrefCount = 0\n for param in params[1:]:\n if not param in handles:\n cmddef += indent + 'GFXRECON_UNREFERENCED_PARAMETER({});\\n'.format(param.name)\n unrefCount += 1\n if unrefCount > 0:\n cmddef += '\\n'\n\n for index, handle in enumerate(handles):\n cmddef += self.trackCommandHandle(index, params[0].name, handle, indent=indent)\n cmddef += '}'\n\n write(cmddef, file=self.outFile)\n\n self.newline()\n write('GFXRECON_END_NAMESPACE(decode)', file=self.outFile)\n write('GFXRECON_END_NAMESPACE(gfxrecon)', file=self.outFile)\n\n # Finish processing in superclass\n BaseGenerator.endFile(self)\n\n #\n # Method override\n def genStruct(self, typeinfo, typename, alias):\n BaseGenerator.genStruct(self, typeinfo, typename, alias)\n\n if not alias:\n self.checkStructMemberHandles(typename, self.structsWithHandles)\n\n # Track this struct if it can be present in a pNext chain.\n parentStructs = typeinfo.elem.get('structextends')\n if parentStructs:\n sType = self.makeStructureTypeEnum(typeinfo, typename)\n if sType:\n self.pNextStructs[typename] = sType\n\n #\n # Indicates that the current feature has C++ code to generate.\n def needFeatureGeneration(self):\n if self.featureCmdParams:\n return True\n return False\n\n #\n # Performs C++ code generation for the feature.\n def generateFeature(self):\n for cmd in self.getFilteredCmdNames():\n self.commandInfo[cmd] = self.featureCmdParams[cmd]\n\n #\n # Override method to check for handle type, only matching resource handle types.\n def isHandle(self, baseType):\n if self.restrictHandles:\n if baseType in self.RESOURCE_HANDLE_TYPES:\n return True\n return False\n else:\n return BaseGenerator.isHandle(self, baseType)\n\n #\n # Create list of parameters that have handle types or are structs that contain handles.\n def getParamListHandles(self, values):\n handles = []\n for value in values:\n if self.isHandle(value.baseType):\n handles.append(value)\n elif self.isStruct(value.baseType) and (value.baseType in self.structsWithHandles):\n handles.append(value)\n return handles\n\n #\n #\n def trackCommandHandle(self, index, commandParamName, value, valuePrefix='', indent=''):\n body = ''\n tail = ''\n indexName = None\n countName = None\n valueName = valuePrefix + value.name\n isHandle = self.isHandle(value.baseType)\n\n if (value.isPointer or value.isArray) and value.name != 'pnext_value':\n if index > 0:\n body += '\\n'\n\n accessOperator = '->'\n if not valuePrefix:\n # If there is no prefix, this is the pointer parameter received by the function, which should never be null.\n body += indent + 'assert({} != nullptr);\\n'.format(value.name)\n body += '\\n'\n else:\n # If there is a prefix, this is a struct member. We need to determine the type of access operator to use\n # for the member of a 'decoded' struct type, where handle member types will be HandlePointerDecoder, but\n # struct member types will be unique_ptr.\n if isHandle:\n accessOperator = '.'\n\n # Add IsNull and HasData checks for the pointer decoder, before accessing its data.\n # Note that this does not handle the decoded struct member cases for static arrays, which would need to use '.' instead of '->'.\n body += indent + 'if (!{prefix}{name}{op}IsNull() && ({prefix}{name}{op}HasData()))\\n'.format(prefix=valuePrefix, name=value.name, op=accessOperator)\n body += indent + '{\\n'\n tail = indent + '}\\n' + tail\n indent += ' ' * self.INDENT_SIZE\n\n # Get the pointer from the pointer decoder object.\n valueName = '{}_ptr'.format(value.name)\n if isHandle:\n body += indent + 'auto {} = {}{}{}GetPointer();\\n'.format(valueName, valuePrefix, value.name, accessOperator)\n else:\n body += indent + 'auto {} = {}{}{}GetMetaStructPointer();\\n'.format(valueName, valuePrefix, value.name, accessOperator)\n\n # Add a for loop for an array of values.\n if value.isArray:\n indexName = '{}_index'.format(value.name)\n countName = '{}_count'.format(value.name)\n body += indent + 'size_t {} = {}{}{}GetLength();\\n'.format(countName, valuePrefix, value.name, accessOperator)\n body += indent + 'for (size_t {i} = 0; {i} < {}; ++{i})\\n'.format(countName, i=indexName)\n body += indent + '{\\n'\n tail = indent + '}\\n' + tail\n indent += ' ' * self.INDENT_SIZE\n\n # Insert commands to add handles to a container, or to process struct members that contain handles.\n if isHandle:\n if value.isArray:\n valueName = '{}[{}]'.format(valueName, indexName)\n elif value.isPointer:\n valueName = '(*{})'.format(valueName)\n\n if value.baseType in self.CONTAINER_HANDLE_TYPES:\n body += indent + 'GetTable().AddContainerToUser({}, {});\\n'.format(commandParamName, valueName)\n elif value.baseType in self.USER_HANDLE_TYPES:\n body += indent + 'GetTable().AddUserToUser({}, {});\\n'.format(commandParamName, valueName)\n else:\n body += indent + 'GetTable().AddResourceToUser({}, {});\\n'.format(commandParamName, valueName)\n\n elif self.isStruct(value.baseType) and (value.baseType in self.structsWithHandles):\n if value.isArray:\n accessOperator = '[{}].'.format(indexName)\n else:\n accessOperator = '->'\n\n for index, entry in enumerate(self.structsWithHandles[value.baseType]):\n if entry.name == 'pNext':\n extStructsWithHandles = [extStruct for extStruct in self.registry.validextensionstructs[value.baseType] if extStruct in self.structsWithHandles]\n if extStructsWithHandles:\n body += indent + 'const VkBaseInStructure* pnext_header = nullptr;\\n'\n body += indent + 'if ({name}->pNext != nullptr)\\n'.format(name=valueName)\n body += indent + '{\\n'\n indent += ' ' * self.INDENT_SIZE\n body += indent + 'pnext_header = reinterpret_cast({}->pNext->GetPointer());\\n'.format(valueName)\n indent = indent[:-self.INDENT_SIZE]\n body += indent + '}\\n'\n body += indent + 'while (pnext_header)\\n'\n body += indent + '{\\n'\n indent += ' ' * self.INDENT_SIZE\n body += indent + 'switch (pnext_header->sType)\\n'\n body += indent + '{\\n'\n indent += ' ' * self.INDENT_SIZE\n body += indent + 'default:\\n'\n indent += ' ' * self.INDENT_SIZE\n body += indent + 'break;\\n'\n indent = indent[:-self.INDENT_SIZE]\n for extStruct in extStructsWithHandles:\n body += indent + 'case {}:\\n'.format(self.pNextStructs[extStruct])\n body += indent + '{\\n'\n indent += ' ' * self.INDENT_SIZE\n body += indent + 'auto pnext_value = reinterpret_cast({}->pNext->GetPointer());\\n'.format(extStruct, valueName)\n body += self.trackCommandHandle(index, commandParamName, ValueInfo('pnext_value', extStruct, 'const {} *'.format(extStruct), 1), '', indent=indent)\n body += indent + 'break;\\n'\n indent = indent[:-self.INDENT_SIZE]\n body += indent + '}\\n'\n indent = indent[:-self.INDENT_SIZE]\n body += indent + '}\\n'\n body += indent + 'pnext_header = pnext_header->pNext;\\n'\n indent = indent[:-self.INDENT_SIZE]\n body += indent + '}\\n'\n else:\n body += self.trackCommandHandle(index, commandParamName, entry, valueName + accessOperator, indent)\n\n return body + tail\n","sub_path":"framework/generated/vulkan_generators/vulkan_referenced_resource_consumer_body_generator.py","file_name":"vulkan_referenced_resource_consumer_body_generator.py","file_ext":"py","file_size_in_byte":14263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"268792975","text":"\"\"\"Configs for the library.\"\"\"\n\nimport os\nimport logging\nimport logging.config\n\nLIBRARY_NAME = os.path.basename(os.path.dirname(__file__))\n\n# Formats for logging\nLOGGING_DATE_FORMAT = \"%m/%d/%Y %I:%M:%S %p\"\nNORMAL_LOGGING_FORMAT = \"%(asctime)s %(levelname)-8s [%(name)s] %(message)s\"\nVERBOSE_LOGGING_FORMAT = (\n \"%(asctime)s %(levelname)-8s [%(name)s.%(funcName)s:%(lineno)d] %(message)s\"\n)\n\n# Configurable logging settings\nLOGGING_LEVEL = os.environ.get(f\"{LIBRARY_NAME.upper()}_LOGGING_LEVEL\", logging.DEBUG)\nLOG_ALL_THE_THINGS = os.environ.get(f\"{LIBRARY_NAME.upper()}_LOG_ALL_THE_THINGS\")\nLOGGING_FORMATTER = os.environ.get(\n f\"{LIBRARY_NAME.upper()}_LOGGING_VERBOSITY\", \"verbose\"\n)\n\n\nERROR_FILENAME = \"error.log\"\nINFO_FILENAME = \"info.log\"\n_BASE_FOLDER = f\".{LIBRARY_NAME}\"\n_BASE_PATH = os.path.expanduser(\"~\")\n\nLIBRARY_PATH = os.path.join(_BASE_PATH, _BASE_FOLDER)\nERROR_LOG_PATH = os.path.join(LIBRARY_PATH, ERROR_FILENAME)\nINFO_LOG_PATH = os.path.join(LIBRARY_PATH, INFO_FILENAME)\n\n\nif not os.path.exists(LIBRARY_PATH):\n os.makedirs(LIBRARY_PATH)\n\n\nclass ErrorFilter(logging.Filter):\n \"\"\"Filter class for error only logs.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def filter(self, record: logging.LogRecord) -> bool:\n \"\"\"Filter down to only error level logs.\"\"\"\n return record.levelno == logging.ERROR\n\n\n# General logging config here.\nLOGGING_CONFIG = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"filters\": {\"errorfilter\": {\"()\": ErrorFilter}},\n \"formatters\": {\n \"verbose\": {\"datefmt\": LOGGING_DATE_FORMAT, \"format\": VERBOSE_LOGGING_FORMAT},\n \"normal\": {\"datefmt\": LOGGING_DATE_FORMAT, \"format\": NORMAL_LOGGING_FORMAT},\n },\n \"handlers\": {\n \"console\": {\n \"level\": LOGGING_LEVEL,\n \"class\": \"logging.StreamHandler\",\n \"formatter\": LOGGING_FORMATTER,\n },\n \"info\": {\n \"level\": LOGGING_LEVEL,\n \"class\": \"logging.handlers.RotatingFileHandler\",\n \"filename\": INFO_LOG_PATH,\n \"formatter\": LOGGING_FORMATTER,\n },\n \"null\": {\n \"level\": LOGGING_LEVEL,\n \"class\": \"logging.NullHandler\",\n \"formatter\": LOGGING_FORMATTER,\n },\n \"error\": {\n \"level\": logging.ERROR,\n \"class\": \"logging.handlers.RotatingFileHandler\",\n \"filename\": ERROR_LOG_PATH,\n \"filters\": [\"errorfilter\"],\n \"formatter\": LOGGING_FORMATTER,\n },\n },\n \"loggers\": {\n LIBRARY_NAME: {\"handlers\": [\"console\", \"info\", \"error\"], \"level\": LOGGING_LEVEL}\n },\n}\n\nif LOG_ALL_THE_THINGS:\n # This will log everything from all libraries (altering the root logger). Use when\n # troubleshooting third party libraries rather than your own.\n LOGGING_CONFIG[\"loggers\"] = {\n \"\": {\"handlers\": [\"console\", \"info\", \"error\"], \"level\": 0}\n }\n\n\n# Configure the logging, and then log a message after import of the library.\nlogging.config.dictConfig(LOGGING_CONFIG)\nLOGGER = logging.getLogger(LIBRARY_NAME)\nif LOG_ALL_THE_THINGS:\n LOGGER.warning(\n \"Warning! You've enabled logging at the root logger level. This may result in a lot of logging!\"\n )\nLOGGER.info(\"Loaded %s library with %s log level.\", LIBRARY_NAME, LOGGING_LEVEL)\nLOGGER.info(\"Using %s log formatter.\", LOGGING_FORMATTER)\n","sub_path":"src/rom24/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"48390580","text":"import sys, os\nfilename = os.path.join(os.path.dirname(__file__), '..')\nsys.path.insert(1, filename)\nfrom zoomapi import OAuthZoomClient\n\nimport json\nfrom configparser import ConfigParser\nfrom pyngrok import ngrok\n\nparser = ConfigParser()\nparser.read(\"bots/bot.ini\")\nclient_id = parser.get(\"OAuth\", \"client_id\")\nclient_secret = parser.get(\"OAuth\", \"client_secret\")\nbrowser_path = parser.get(\"OAuth\", \"browser_path\")\nprint(f'id: {client_id} secret: {client_secret} browser: {browser_path}')\n\nredirect_url = ngrok.connect(4000, \"http\")\nprint(\"Redirect URL is\", redirect_url)\n\nclient = OAuthZoomClient(client_id, client_secret, redirect_url, browser_path)\n\nuser_response = client.user.get(id='me')\nuser = json.loads(user_response.content)\nprint(user)\nprint ('---')\n\nprint(json.loads(client.meeting.list(user_id=\"me\").content))\nclient.chat_channels.list()\nchannels = json.loads(client.chat_channels.list().content)[\"channels\"]\nprint(channels)\nfor c in channels:\n print(c)\n if \"Chat Bot Test\" in c.values():\n print(\"Found channel test\", c[\"id\"])\n cid = to_channel=c[\"id\"]\nstop = False\nwhile not stop:\n print(\" \")\n message = input(\"Enter message: \")\n print(\" \")\n if message == \"stop\":\n stop = True \n elif message == \"help\":\n print(\"stop -- exit\")\n print(\"list -- list all the channels\")\n print(\"create -- create a new channel\")\n print(\"get -- get a channel with id\")\n print(\"update -- update a channel's name with id\")\n print(\"delete -- delete a channel with id\")\n print(\"members -- list all the members of a channel\")\n print(\"invite -- invite new members to channel by emails\")\n print(\"join -- join a channel by id\")\n print(\"leave -- leave a channel by id\")\n print(\"remove -- remove a channel by id\")\n print(\"send-message -- send a message to an individual or a channel\")\n print(\"list-message -- list previous messages with an individual or within a channel\")\n print(\"update-message -- edit a chat message sent previously\")\n print(\"delete-message -- delete a message sent previously\") \n elif message == \"list\":\n print(json.loads(client.chat_channels.list().content))\n elif message == \"create\":\n channel_name = input(\"Enter channel name: \")\n channel_type = 1\n email_string = input(\"Enter members' emails, separated by commas: \")\n email_list = []\n if email_string != \"\":\n if \",\" in email_string:\n email_list = email_string.split(\",\")\n else:\n email_list.append(email_string)\n email_object_list = []\n for email in email_list:\n email_object_list.append({'email':email})\n print(json.loads(client.chat_channels.create(name=channel_name, type=channel_type, members=email_object_list).content))\n elif message == \"get\":\n channel_id = input(\"Enter channel id: \")\n print(json.loads(client.chat_channels.get(channelId=channel_id).content))\n elif message == \"update\":\n channel_id = input(\"Enter channel id: \")\n channel_name = input(\"Enter new channel name: \")\n print(client.chat_channels.update(channelId=channel_id, name=channel_name))\n elif message == \"delete\":\n channel_id = input(\"Enter channel id: \")\n print(client.chat_channels.delete(channelId=channel_id))\n elif message == \"members\":\n channel_id = input(\"Enter channel id: \")\n print(json.loads(client.chat_channels.members(channelId=channel_id).content))\n elif message == \"invite\":\n channel_id = input(\"Enter channel id: \")\n email_string = input(\"Enter members' emails, separated by commas: \")\n email_list = []\n if email_string != \"\":\n email_list = email_string.split(\",\")\n email_object_list = []\n for email in email_list:\n email_object_list.append({'email':email})\n print(email_object_list) \n print(json.loads(client.chat_channels.invite(channelId=channel_id, members=email_object_list).content))\n elif message == \"join\":\n channel_id = input(\"Enter channel id: \")\n print(json.loads(client.chat_channels.join(channelId=channel_id).content))\n elif message == \"leave\":\n channel_id = input(\"Enter channel id: \")\n print(client.chat_channels.leave(channelId=channel_id))\n elif message == \"remove\":\n channel_id = input(\"Enter channel id: \")\n member_id = input(\"Enter member id: \")\n print(client.chat_channels.remove(channelId=channel_id,memberId=member_id))\n elif message == \"send-message\":\n text = input(\"Enter your message:\")\n receiver = input(\"To a contact or a channel? (Enter CONTACT or CHANNEL)\")\n if receiver == \"CONTACT\":\n member_email_address = input(\"Enter the contact's email address:\")\n print(client.chat_messages.post(to_contact=member_email_address, message=text))\n elif receiver == \"CHANNEL\":\n channel_id = input(\"Enter channel id:\")\n print(client.chat_messages.post(to_channel=channel_id, message=text))\n else:\n print(\"Wrong input.\")\n elif message == \"update-message\":\n messageId = input(\"Enter message id:\")\n text = input(\"Enter new message:\")\n receiver = input(\"To a contact or a channel? (Enter CONTACT or CHANNEL)\")\n if receiver == \"CONTACT\":\n member_email_address = input(\"Enter the contact's email address:\")\n print(client.chat_messages.update(to_contact=member_email_address, message=text, messageId=messageId))\n elif receiver == \"CHANNEL\":\n channel_id = input(\"Enter channel id:\")\n print(client.chat_messages.update(to_channel=channel_id, message=text, messageId=messageId))\n else:\n print(\"Wrong input.\")\n elif message == \"delete-message\":\n messageId = input(\"Enter message id:\")\n receiver = input(\"To a contact or a channel? (Enter CONTACT or CHANNEL)\")\n if receiver == \"CONTACT\":\n member_email_address = input(\"Enter the contact's email address:\")\n print(client.chat_messages.delete(to_contact=member_email_address, messageId=messageId))\n elif receiver == \"CHANNEL\":\n channel_id = input(\"Enter channel id:\")\n print(client.chat_messages.delete(to_channel=channel_id, messageId=messageId))\n else:\n print(\"Wrong input.\")\n elif message == \"list-message\":\n receiver = input(\"With a contact or within a channel? (Enter CONTACT or CHANNEL)\")\n if receiver == \"CONTACT\":\n member_email_address = input(\"Enter the contact's email address:\")\n print(json.loads(client.chat_messages.list(to_contact=member_email_address, user_id=\"me\").content))\n elif receiver == \"CHANNEL\":\n channel_id = input(\"Enter channel id:\")\n print(json.loads(client.chat_messages.list(to_channel=channel_id, user_id=\"me\").content))\n else:\n print(\"Wrong input.\")\n else:\n print(\"Wrong input.\")\n","sub_path":"bots/oauthbot.py","file_name":"oauthbot.py","file_ext":"py","file_size_in_byte":7098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"561348602","text":"# Copyright (c) 2014, The MITRE Corporation. All rights reserved.\r\n# See LICENSE.txt for complete terms.\r\n\r\nimport stix\r\nimport stix.utils\r\nimport stix.indicator.test_mechanism\r\nfrom stix.common import EncodedCDATA\r\nfrom stix.indicator.test_mechanism import _BaseTestMechanism\r\nimport stix.bindings.extensions.test_mechanism.snort as snort_tm_binding\r\n\r\nclass SnortTestMechanism(_BaseTestMechanism):\r\n _namespace = \"http://stix.mitre.org/extensions/TestMechanism#Snort-1\"\r\n _binding = snort_tm_binding\r\n _binding_class = _binding.SnortTestMechanismType\r\n _XSI_TYPE = \"snortTM:SnortTestMechanismType\"\r\n \r\n def __init__(self, id_=None, idref=None):\r\n super(SnortTestMechanism, self).__init__(id_=id_, idref=idref)\r\n self.product_name = None\r\n self.version = None\r\n self.rules = None\r\n self.event_filters = None\r\n self.rate_filters = None\r\n self.event_suppressions = None\r\n \r\n @property\r\n def rules(self):\r\n return self._rules\r\n \r\n @rules.setter\r\n def rules(self, value):\r\n self._rules = []\r\n if not value:\r\n return\r\n elif isinstance(value, list):\r\n for v in value:\r\n self.add_rule(v)\r\n else:\r\n self.add_rule(v)\r\n \r\n def add_rule(self, rule):\r\n if not rule:\r\n return\r\n elif isinstance(rule, EncodedCDATA):\r\n self.rules.append(rule)\r\n else:\r\n self.rules.append(EncodedCDATA(value=rule))\r\n \r\n @property\r\n def event_filters(self):\r\n return self._event_filters\r\n \r\n @event_filters.setter\r\n def event_filters(self, value):\r\n self._event_filters = []\r\n if not value:\r\n return\r\n elif isinstance(value, list):\r\n for v in value:\r\n self.add_event_filter(v)\r\n else:\r\n self.add_event_filter(v)\r\n \r\n def add_event_filter(self, item):\r\n if not item:\r\n return\r\n elif isinstance(item, EncodedCDATA):\r\n self.event_filters.append(item)\r\n else:\r\n self.rules.append(EncodedCDATA(value=item)) \r\n \r\n @property\r\n def rate_filters(self):\r\n return self._rate_filters\r\n \r\n @rate_filters.setter\r\n def rate_filters(self, value):\r\n self._rate_filters = []\r\n if not value:\r\n return\r\n elif isinstance(value, list):\r\n for v in value:\r\n self.add_rate_filter(v)\r\n else:\r\n self.add_rate_filter(v)\r\n \r\n def add_rate_filter(self, item):\r\n if not item:\r\n return\r\n elif isinstance(item, EncodedCDATA):\r\n self.rate_filters.append(item)\r\n else:\r\n self.rules.append(EncodedCDATA(value=item)) \r\n \r\n @property\r\n def event_suppressions(self):\r\n return self._event_suppressions\r\n \r\n @event_suppressions.setter\r\n def event_suppressions(self, value):\r\n self._event_suppressions = []\r\n if not value:\r\n return\r\n elif isinstance(value, list):\r\n for v in value:\r\n self.add_event_suppression(v)\r\n else:\r\n self.add_event_suppression(v)\r\n \r\n def add_event_suppression(self, item):\r\n if not item:\r\n return\r\n elif isinstance(item, EncodedCDATA):\r\n self.event_suppressions.append(item)\r\n else:\r\n self.rules.append(EncodedCDATA(value=item)) \r\n \r\n @classmethod\r\n def from_obj(cls, obj, return_obj=None):\r\n if not obj:\r\n return None\r\n if not return_obj:\r\n return_obj = cls()\r\n \r\n super(SnortTestMechanism, cls).from_obj(obj, return_obj)\r\n return_obj.product_name = obj.get_Product_Name()\r\n return_obj.version = obj.get_Version()\r\n \r\n if obj.get_Rule():\r\n return_obj.rules = [EncodedCDATA.from_obj(x) for x in obj.get_Rule()]\r\n if obj.get_Event_Filter():\r\n return_obj.event_filters = [EncodedCDATA.from_obj(x) for x in obj.get_Event_Filter()]\r\n if obj.get_Rate_Filter():\r\n return_obj.rate_filters = [EncodedCDATA.from_obj(x) for x in obj.get_Rate_Filter()]\r\n if obj.get_Event_Suppression():\r\n return_obj.event_suppressions = [EncodedCDATA.from_obj(x) for x in obj.get_Event_Suppression()]\r\n \r\n return return_obj\r\n \r\n def to_obj(self, return_obj=None):\r\n if not return_obj:\r\n return_obj = self._binding_class()\r\n \r\n super(SnortTestMechanism, self).to_obj(return_obj)\r\n \r\n return_obj.set_Product_Name(self.product_name)\r\n return_obj.set_Version(self.version)\r\n \r\n if self.rules:\r\n return_obj.set_Rule([x.to_obj() for x in self.rules])\r\n if self.event_filters:\r\n return_obj.set_Event_Filter([x.to_obj() for x in self.event_filters])\r\n if self.rate_filters:\r\n return_obj.set_Rate_Filter([x.to_obj() for x in self.rate_filters])\r\n if self.event_suppressions:\r\n return_obj.set_Event_Suppression([x.to_obj() for x in self.event_suppressions]) \r\n \r\n return return_obj\r\n \r\n @classmethod\r\n def from_dict(cls, d, return_obj=None):\r\n if not d:\r\n return None\r\n if not return_obj:\r\n return_obj = cls()\r\n \r\n super(SnortTestMechanism, cls).from_dict(d, return_obj)\r\n \r\n return_obj.product_name = d.get('product_name')\r\n return_obj.version = d.get('version')\r\n return_obj.rules = [EncodedCDATA.from_dict(x) for x in d.get('rules', [])]\r\n return_obj.event_filters = [EncodedCDATA.from_dict(x) for x in d.get('event_filters', [])]\r\n return_obj.rate_filters = [EncodedCDATA.from_dict(x) for x in d.get('rate_filters', [])]\r\n return_obj.event_suppressions = [EncodedCDATA.from_dict(x) for x in d.get('event_suppressions', [])]\r\n \r\n return return_obj\r\n \r\nstix.indicator.test_mechanism.add_extension(SnortTestMechanism)\r\n","sub_path":"stix-1.1.1.0/stix/extensions/test_mechanism/snort_test_mechanism.py","file_name":"snort_test_mechanism.py","file_ext":"py","file_size_in_byte":6101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"461387914","text":"from Crypto.Cipher import AES\nfrom Crypto.Random import get_random_bytes\nimport os, random, struct\n\n# https://eli.thegreenplace.net/2010/06/25/aes-encryption-of-files-in-python-with-pycrypto\ndef encrypt_file(key, in_filename, out_filename=None, chunksize=64*1024):\n if not out_filename:\n out_filename = in_filename + '.bin'\n\n buffer_size = 65536 # 64kb\n\n # Open the input and output files\n input_file = open(in_filename, 'rb')\n output_file = open(out_filename + '.encrypted', 'wb')\n\n # Create the cipher object and encrypt the data\n cipher_encrypt = AES.new(key, AES.MODE_CFB)\n\n # Initially write the iv to the output file\n output_file.write(cipher_encrypt.iv)\n\n # Keep reading the file into the buffer, encrypting then writing to the new file\n buffer = input_file.read(buffer_size)\n while len(buffer) > 0:\n ciphered_bytes = cipher_encrypt.encrypt(buffer)\n output_file.write(ciphered_bytes)\n buffer = input_file.read(buffer_size)\n\n # Close the input and output files\n input_file.close()\n output_file.close()\n\ndef decrypt_file(key, in_filename, out_filename=None, chunksize=24*1024):\n \"\"\" Decrypts a file using AES (CBC mode) with the\n given key. Parameters are similar to encrypt_file,\n with one difference: out_filename, if not supplied\n will be in_filename without its last extension\n (i.e. if in_filename is 'aaa.zip.enc' then\n out_filename will be 'aaa.zip')\n \"\"\"\n if not out_filename:\n out_filename = os.path.splitext(in_filename)[0]\n\n buffer_size = 65536 # 64kb\n\n input_file = open(in_filename, 'rb')\n output_file = open(out_filename, 'wb')\n\n # Read in the iv\n iv = input_file.read(16)\n\n # Create the cipher object and encrypt the data\n cipher_encrypt = AES.new(key, AES.MODE_CFB, iv=iv)\n\n # Keep reading the file into the buffer, decrypting then writing to the new file\n buffer = input_file.read(buffer_size)\n while len(buffer) > 0:\n decrypted_bytes = cipher_encrypt.decrypt(buffer)\n output_file.write(decrypted_bytes)\n buffer = input_file.read(buffer_size)\n\n # Close the input and output files\n input_file.close()\n output_file.close()\n\nif __name__ == \"__main__\":\n key = \"0123456789abcdef\".encode() # Use a stored / generated key\n input_file = r\"storage_accounts.json\"\n encrypt_file(key, input_file, \"encrypted_json\")\n decrypt_file(key, 'encrypted_json.encrypted', 'decrypted_json.json')\n\n","sub_path":"encrypt.py","file_name":"encrypt.py","file_ext":"py","file_size_in_byte":2496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"56746560","text":"from tkinter import Button, Tk, Label, Frame, X, PhotoImage, Grid, LEFT, ANCHOR, RIGHT\nfrom database.Models import People, Block, Apartment, Compartment\nimport Auth\n\nclass Empty():\n\n def __init__(self, root,msg,user):\n\n self.root = root\n self.port = 0\n self.msg = msg\n self.user = user \n\n if msg == 1:\n text = \"Não há Compartimento Disponível no momento\"\n if msg == 2:\n text = \"Não tem encomendas disponíveis no momento\"\n if msg == 3:\n text = \"Não existem encomendas para este endereço\"\n\n self.parentFrame = Frame(root, bg=\"yellow\",width=1024,height=600)\n self.parentFrame.pack()\n\n self.emptyFrame = Frame(self.root)\n self.emptyFrame.place(x=70,y=150)\n self.emptyLabel = Label(self.emptyFrame,text=text,font=(\"Helvetica\", 40),bg='yellow',fg='blue')\n self.emptyLabel.pack()\n\n self.backFrame = Frame(self.root)\n self.backFrame.place(x=320,y=500)\n self.backButton = Button(self.backFrame,command=self.back,text=\"Ok\",width=20,font=(\"Helvetica\", 40),bg='blue',fg='white')\n self.backButton.pack()\n \n def back(self):\n self.parentFrame.destroy()\n self.emptyFrame.destroy()\n self.backFrame.destroy()\n if self.msg == 1:\n Auth.Login.doDoliver(self.root)\n elif self.msg == 2:\n code = People.update(codigo='').where(People.id == self.user)\n code.execute()\n Auth.Login.LoginScreen(self.root)\n else:\n Auth.Login.doRegisterScreen(self.root)","sub_path":"Entrega/Empty.py","file_name":"Empty.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"560556896","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nExtract from all the UE for all the years and semesters\nthe number of student registration in the UE and some statistics.\n\nThese informations are stored in LOGINS/XXX/XXXXXX/resume\nas a Python fragment.\n\n{\n 'UE-CODE': [\n [year,'semester',#prst,#abinj,#abjus,average_value_in_0_1)],\n ...\n ],\n ...\n}\n\"\"\"\n\nimport os\nimport sys\nimport re\nimport collections\nimport math\nimport time\nimport html\nimport tomuss_init\nfrom .. import configuration\nfrom .. import tablestat\nfrom .. import utilities\nfrom .. import document\nfrom .. import inscrits\nprint(configuration.suivi)\n\n# teacher -> { (year, semester, ue): number_of_cell }\nteachers_tables = collections.defaultdict(\n lambda: collections.defaultdict(int))\n\n# teacher -> [ (year, semester, ue, column, date), ... ]\nspecial_dates = collections.defaultdict(list)\n\nday_month_year = list(time.localtime())[2::-1]\n\nclass UE:\n def __init__(self):\n self.infos = {}\n\n def add(self, table, line, result):\n prst = 0\n abinj = 0\n abjus = 0\n summation = 0\n nr = 0\n weight = 0.\n key = (table.year, table.semester, table.ue)\n for cell, column in list(zip(line, table.columns))[6:]:\n if len(cell.author) > 1:\n teachers_tables[cell.author][key] += 1\n value = cell.value\n if not column.is_computed() and value == '' and column.empty_is:\n value = column.empty_is\n if value == configuration.pre:\n prst += 1\n elif value == configuration.abi:\n abinj += 1\n elif value == configuration.abj:\n abjus += 1\n if column.type.name == 'Note':\n try:\n if column.weight[0] in '+-':\n continue\n cell_weight = float(column.weight)\n min, max = column.min_max()\n if value == configuration.abi:\n value = min\n else:\n value = float(value)\n if value >= min and value <= max:\n summation += (value - min) / (max - min) * cell_weight\n weight += cell_weight\n nr += 1\n prst += 1\n except ValueError:\n pass\n\n summ = \"-1\"\n if result:\n min, max = result.min_max()\n try:\n summ = (float(line[result.data_col].value) - min) / max\n except ValueError:\n pass\n if summ == \"-1\" or math.isnan(summ):\n if nr == 0 or weight == 0.:\n if nr != 0 and weight == 0:\n print('Null column weight in', table)\n summ = \"-1\"\n else:\n summ = '%.3f' % (summation/weight)\n \n self.infos[table.year, table.semester] = (prst, abinj, abjus, summ, nr)\n\n def __repr__(self):\n keys = list(self.infos.keys())\n keys.sort(key=lambda x: utilities.semester_key(x[0], x[1]))\n s = []\n for k in keys:\n v = self.infos[k]\n s.append('[' + str(k[0]) + ',' +\n repr(k[1]) + ',' +\n str(v[0]) + ',' + str(v[1]) + ',' + str(v[2]) +\n ',%s' % v[3] + ',' + str(v[4]) \n + ']')\n return '[' + ',\\n'.join(s) + ']'\n\nstudents = {}\nstudents_index = collections.defaultdict(list)\n\nfor syear in os.listdir(configuration.db):\n try:\n year = int(syear[1:])\n except ValueError:\n continue\n for semester in os.listdir(os.path.join(configuration.db, syear)):\n if (semester[1:] not in configuration.semesters\n or not os.path.isdir(os.path.join(configuration.db, syear,\n semester))\n ):\n continue\n semester = semester[1:]\n for ue in tablestat.les_ues(year, semester,\n true_file=False, all_files=True):\n if not ue.official_ue:\n ue.unload()\n continue\n result = ue.columns.result_column()\n if result:\n ue.compute_columns()\n name = ue.ue\n for i in ue.the_keys():\n students_index[i].append((year, semester, ue.ue))\n if (year, semester) != (ue.year, ue.semester):\n # ue.unload()\n continue\n\n sys.stderr.write(name + ' ')\n sys.stderr.flush()\n\n for column in ue.columns:\n dates = re.findall(\n r\"\\b[0-9]?[0-9]/[0-9]?[0-9]/[0-9][0-9][0-9][0-9]\\b\",\n column.cell_writable)\n for d in dates:\n if [int(i) for i in d.split('/')] == day_month_year:\n special_dates[column.author].append(\n (year, semester, name, column.title,\n re.sub(d, '' + d + '',\n html.escape(column.cell_writable))))\n break\n\n for i in ue.logins_valid():\n i = utilities.the_login(str(i))\n if not i in students:\n students[i] = {}\n s = students[i]\n if name not in s:\n s[name] = UE()\n lines = tuple(ue.get_lines(i))\n s[name].add(ue, lines[0], result)\n\n ue.unload()\n\n# Update all the student indexes\n# It is done only to be sure there is no bad index file (initialisation or bug)\n# But if a student list is modified while this script run\n# then the index will be bad for one day.\n# The index are computed on 'suivi' semesters, not the others\nfor login, value in students_index.items():\n if len(login) >= 3:\n document.update_index(login, lambda x: value)\nutilities.write_file(os.path.join('TMP', 'index_are_computed'),\n 'done')\n\ndef safe(x):\n return re.sub('[^a-zA-Z]', '_', x)\n\n\nfor i, ues in students.items():\n print(i)\n try:\n utilities.manage_key('LOGINS', os.path.join(i, 'resume'),\n content=utilities.stable_repr(ues))\n except IOError:\n # Non existent student\n print('Non existent student:', i)\n\n\nfor teacher, tables in teachers_tables.items():\n print(teacher)\n if utilities.manage_key('LOGINS', os.path.join(teacher, 'tables')):\n # Only update the key if it exists\n utilities.manage_key('LOGINS', os.path.join(teacher, 'tables'),\n content=utilities.stable_repr(tables))\n\nfor teacher, dates in special_dates.items():\n mail = inscrits.L_slow.mail(teacher) or configuration.maintainer\n message = [\"\",\n utilities._(\"MSG_hello\").format(teacher, mail),\n \"

\",\n utilities._(\"MSG_special_date\"),\n '

']\n    for year, semester, name, title, cell_writable in dates:\n        message.append('{}/{}/{} {} {}'.format(\n                        configuration.server_url, year, semester, name,\n                        year, html.escape(semester),\n                        html.escape(name), html.escape(title),\n                        cell_writable))\n    message.append(\"
\")\n message.append(\"\")\n print('\\n'.join(message))\n utilities.send_mail(mail,\n utilities._(\"MSG_special_date_subject\"),\n '\\n'.join(message))\n utilities.send_mail(configuration.maintainer,\n utilities._(\"MSG_special_date_subject\"),\n '\\n'.join(message))\n\n\n","sub_path":"SCRIPTS/bilan.py","file_name":"bilan.py","file_ext":"py","file_size_in_byte":7782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"420436219","text":"import argparse\nimport asyncio\nimport logging\nimport os\nimport random\nimport resource\nimport socket\nimport sys\nimport traceback\nimport signal\nimport json\nfrom datetime import datetime\nfrom multiprocessing import Process\nfrom os.path import exists, join, isabs\n\nfrom qcg.pilotjob.logger import top_logger\nimport qcg.pilotjob.version\nimport qcg.pilotjob.profile\nfrom qcg.pilotjob.config import Config\nfrom qcg.pilotjob.errors import InvalidArgument\nfrom qcg.pilotjob.fileinterface import FileInterface\nfrom qcg.pilotjob.manager import DirectManager\nfrom qcg.pilotjob.partitions import GovernorManager\nfrom qcg.pilotjob.receiver import Receiver\nfrom qcg.pilotjob.reports import get_reporter\nfrom qcg.pilotjob.zmqinterface import ZMQInterface\nfrom qcg.pilotjob.resume import StateTracker\nfrom qcg.pilotjob.utils.auxdir import find_latest_aux_dir, is_aux_dir\n\n\n# when qcg.pilotjob.service is launch as partition manager, the '__name__' is set to '__main__'\nmodule_name = 'qcg.pilotjob.service'\n_logger = logging.getLogger(module_name)\n\n\nclass QCGPMService:\n \"\"\"QCG Pilot Job manager instance.\n\n Attributes:\n exitCode (int): the result exit code\n _args: parsed arguments by argparse\n _conf (dict(str,str)): configuration created based on arguments\n _wd (path): path to the working directory\n _aux_dir (path): path to the auxiliary directory where all logs and temporary QCG-PilotJob files will be stored\n _logHandler (logging.FileHandler): file logging handler\n _ifaces (list(Interface)): list of active input interfaces\n _job_reporter (JobReport): job reporter instance\n _receiver (Receiver): the receiver instance\n _manager (Manager): the manager instance\n \"\"\"\n\n def _parse_args(self, args):\n \"\"\"Return parsed for command line arguments.\n\n Validate arguments and initialize _args attribute\n\n Args:\n args (str[]) - command line arguments, if None the command line arguments are parsed\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument('--net',\n help='enable network interface',\n action='store_true')\n parser.add_argument(Config.ZMQ_PORT.value['cmd_opt'],\n help='port to listen for network interface (implies --net)',\n type=int, default=None)\n parser.add_argument(Config.ZMQ_PORT_MIN_RANGE.value['cmd_opt'],\n help='minimum port range to listen for network interface if exact port number is not '\n 'defined (implies --net)',\n type=int, default=None)\n parser.add_argument(Config.ZMQ_PORT_MAX_RANGE.value['cmd_opt'],\n help='maximum port range to listen for network interface if exact port number is not '\n 'defined (implies --net)',\n type=int, default=None)\n parser.add_argument('--file',\n help='enable file interface',\n action='store_true')\n parser.add_argument(Config.FILE_PATH.value['cmd_opt'],\n help='path to the request file (implies --file)',\n default=None)\n parser.add_argument(Config.EXECUTOR_WD.value['cmd_opt'],\n help='working directory for the service',\n default=Config.EXECUTOR_WD.value['default'])\n parser.add_argument(Config.ENVIRONMENT_SCHEMA.value['cmd_opt'],\n help='job environment schema [auto|slurm]',\n default='auto')\n parser.add_argument(Config.RESOURCES.value['cmd_opt'],\n help='source of information about available resources [auto|slurm|local] as well as a '\n 'method of job execution (through local processes or as a Slurm sub jobs)',\n default=Config.RESOURCES.value['default'])\n parser.add_argument(Config.REPORT_FORMAT.value['cmd_opt'],\n help='format of job report file [text|json]',\n default=Config.REPORT_FORMAT.value['default'])\n parser.add_argument(Config.REPORT_FILE.value['cmd_opt'],\n help='name of the job report file',\n default=Config.REPORT_FILE.value['default'])\n parser.add_argument(Config.EXECUTION_NODES.value['cmd_opt'],\n help='configuration of available resources (implies --resources local)',\n )\n parser.add_argument(Config.LOG_LEVEL.value['cmd_opt'],\n help='log level',\n choices=['critical', 'error', 'warning', 'info', 'debug', 'notset'],\n default=Config.LOG_LEVEL.value['default'])\n parser.add_argument(Config.SYSTEM_CORE.value['cmd_opt'],\n help='reserve one of the core for the QCG-PJM',\n default=False, action='store_true')\n parser.add_argument(Config.DISABLE_NL.value['cmd_opt'],\n help='disable custom launching method',\n default=Config.DISABLE_NL.value['default'], action='store_true')\n parser.add_argument(Config.PROGRESS.value['cmd_opt'],\n help='print information about executing tasks',\n default=Config.PROGRESS.value['default'], action='store_true')\n parser.add_argument(Config.GOVERNOR.value['cmd_opt'],\n help='run manager in the governor mode, where jobs will be scheduled to execute to the '\n 'dependant managers',\n default=Config.GOVERNOR.value['default'], action='store_true')\n parser.add_argument(Config.PARENT_MANAGER.value['cmd_opt'],\n help='address of the parent manager, current instance will receive jobs from the parent '\n 'manaqger',\n default=Config.PARENT_MANAGER.value['default'])\n parser.add_argument(Config.MANAGER_ID.value['cmd_opt'],\n help='optional manager instance identifier - will be generated automatically when not '\n 'defined',\n default=Config.MANAGER_ID.value['default'])\n parser.add_argument(Config.MANAGER_TAGS.value['cmd_opt'],\n help='optional manager instance tags separated by commas',\n default=Config.MANAGER_TAGS.value['default'])\n parser.add_argument(Config.SLURM_PARTITION_NODES.value['cmd_opt'],\n help='split Slurm allocation by given number of nodes, where each group will be '\n 'controlled by separate manager (implies --governor)',\n type=int, default=None)\n parser.add_argument(Config.SLURM_LIMIT_NODES_RANGE_BEGIN.value['cmd_opt'],\n help='limit Slurm allocation to specified range of nodes (starting node)',\n type=int, default=None)\n parser.add_argument(Config.SLURM_LIMIT_NODES_RANGE_END.value['cmd_opt'],\n help='limit Slurm allocation to specified range of nodes (ending node)',\n type=int, default=None)\n parser.add_argument(Config.RESUME.value['cmd_opt'],\n help='path to the QCG-PilotJob working directory to resume',\n default=None)\n parser.add_argument(Config.ENABLE_PROC_STATS.value['cmd_opt'],\n help='gather information about launched processes from system',\n default=Config.ENABLE_PROC_STATS.value['default'],\n action='store_true')\n parser.add_argument(Config.ENABLE_RT_STATS.value['cmd_opt'],\n help='gather exact start & stop information of launched processes',\n default=Config.ENABLE_RT_STATS.value['default'],\n action='store_true')\n parser.add_argument(Config.WRAPPER_RT_STATS.value['cmd_opt'],\n help='exact start & stop information wrapper path',\n default=Config.WRAPPER_RT_STATS.value['default'])\n parser.add_argument(Config.NL_INIT_TIMEOUT.value['cmd_opt'],\n help='node launcher init timeout (s)',\n type=int, default=Config.NL_INIT_TIMEOUT.value['default'])\n parser.add_argument(Config.NL_READY_TRESHOLD.value['cmd_opt'],\n help='percent (0.0-1.0) of node launchers registered when computations should start',\n type=float, default=Config.NL_READY_TRESHOLD.value['default'])\n self._args = parser.parse_args(args)\n\n if self._args.slurm_partition_nodes:\n # imply '--governor'\n self._args.governor = True\n\n if self._args.governor or self._args.parent:\n # imply '--net' in case of hierarchy scheduling - required for inter-manager communication\n self._args.net = True\n\n if self._args.net_port or self._args.net_port_min or self._args.net_port_max:\n # imply '--net' if port or one of the range has been defined\n self._args.net = True\n\n if not self._args.net_port_min:\n self._args.net_port_min = int(Config.ZMQ_PORT_MIN_RANGE.value['default'])\n\n if not self._args.net_port_max:\n self._args.net_port_max = int(Config.ZMQ_PORT_MAX_RANGE.value['default'])\n\n if self._args.net:\n # set default values for port min & max if '--net' has been defined\n if not self._args.net_port_min:\n self._args.net_port_min = int(Config.ZMQ_PORT_MIN_RANGE.value['default'])\n\n if not self._args.net_port_max:\n self._args.net_port_max = int(Config.ZMQ_PORT_MAX_RANGE.value['default'])\n\n if self._args.file and not self._args.file_path:\n # set default file path if interface has been enabled but path not defined\n self._args.file_path = Config.FILE_PATH.value['default']\n\n if self._args.file_path:\n # enable file interface if path has been defined\n self._args.file = True\n\n def _create_config(self):\n \"\"\"Based on arguments create QCG-PilotJob configuration.\n\n Initialized ``_conf`` attribute.\n \"\"\"\n manager_id = self._args.id\n if not manager_id:\n manager_id = '{}.{}'.format(socket.gethostname(), random.randrange(10000))\n\n manager_tags = [manager_id]\n if self._args.tags:\n manager_tags.extend(self._args.tags.split(','))\n\n self._conf = {\n Config.EXECUTOR_WD: self._args.wd,\n Config.EXECUTION_NODES: self._args.nodes,\n Config.ENVIRONMENT_SCHEMA: self._args.envschema,\n Config.FILE_PATH: self._args.file_path,\n Config.ZMQ_PORT: self._args.net_port,\n Config.ZMQ_PORT_MIN_RANGE: self._args.net_port_min,\n Config.ZMQ_PORT_MAX_RANGE: self._args.net_port_max,\n Config.REPORT_FORMAT: self._args.report_format,\n Config.REPORT_FILE: self._args.report_file,\n Config.LOG_LEVEL: self._args.log,\n Config.SYSTEM_CORE: self._args.system_core,\n Config.DISABLE_NL: self._args.disable_nl,\n Config.PROGRESS: self._args.show_progress,\n Config.GOVERNOR: self._args.governor,\n Config.PARENT_MANAGER: self._args.parent,\n Config.MANAGER_ID: manager_id,\n Config.MANAGER_TAGS: manager_tags,\n Config.SLURM_PARTITION_NODES: self._args.slurm_partition_nodes,\n Config.SLURM_LIMIT_NODES_RANGE_BEGIN: self._args.slurm_limit_nodes_range_begin,\n Config.SLURM_LIMIT_NODES_RANGE_END: self._args.slurm_limit_nodes_range_end,\n Config.RESUME: self._args.resume,\n Config.ENABLE_PROC_STATS: self._args.enable_proc_stats,\n Config.ENABLE_RT_STATS: self._args.enable_rt_stats,\n Config.WRAPPER_RT_STATS: self._args.wrapper_rt_stats,\n Config.NL_INIT_TIMEOUT: self._args.nl_init_timeout,\n Config.NL_READY_TRESHOLD: self._args.nl_ready_treshold,\n }\n\n def __init__(self, args=None):\n \"\"\"Initialize QCG Pilot Job manager instance.\n\n Parse arguments, create configuration, create working & auxiliary directories, setup logging and interfaces.\n\n Args:\n args (str[]) - command line arguments, if None the command line arguments are parsed\n \"\"\"\n self.exit_code = 1\n\n self._parse_args(args)\n\n if not self._args.net and not self._args.file and self._args.resume is None:\n raise InvalidArgument(\"no interface enabled - finishing\")\n\n self._create_config()\n\n self._wd = Config.EXECUTOR_WD.get(self._conf)\n\n self._log_handler = None\n self._job_reporter = None\n\n self._setup_aux_dir()\n self._setup_logging()\n\n self._manager = None\n self._receiver = None\n\n self._tasks_to_resume = None\n\n try:\n self._setup_reports()\n\n self._setup_signals()\n\n QCGPMService._setup_event_loop()\n\n self._setup_tracker()\n\n self._ifaces = []\n if self._args.file:\n iface = FileInterface()\n iface.setup(self._conf)\n self._ifaces.append(iface)\n\n if self._args.net:\n iface = ZMQInterface()\n iface.setup(self._conf)\n self._ifaces.append(iface)\n\n if self._args.governor:\n self._setup_governor_manager(self._args.parent)\n else:\n self._setup_direct_manager(self._args.parent)\n\n self._setup_address_file()\n\n if Config.RESUME.get(self._conf):\n # in case of resume, the aux dir is set to given resume path\n self._tasks_to_resume = StateTracker.resume(self._aux_dir, self._manager, Config.PROGRESS.get(self._conf))\n except Exception:\n if self._log_handler:\n logging.getLogger('qcg.pilotjob').removeHandler(self._log_handler)\n self._log_handler = None\n\n raise\n\n def _setup_governor_manager(self, parent_manager):\n \"\"\"Setup QCG-PilotJob manager and governor manager.\n\n Args:\n parent_manager (str): address of parent manager - currently not supported.\n \"\"\"\n _logger.info('starting governor manager ...')\n self._manager = GovernorManager(self._conf, parent_manager)\n self._manager.register_notifier(self._job_status_change_notify, self._manager)\n\n self._receiver = Receiver(self._manager.get_handler(), self._ifaces)\n\n def _setup_direct_manager(self, parent_manager):\n \"\"\"Setup QCG-PilotJob manager as a single instance or partition manager.\n\n Args:\n parent_manager (str): if defined the partition manager instance will be created controlled by the\n governor manager with this address\n \"\"\"\n _logger.info('starting direct manager (with parent manager address %s)...', parent_manager)\n self._manager = DirectManager(self._tracer, self._conf, parent_manager)\n self._manager.register_notifier(self._job_status_change_notify, self._manager)\n\n self._receiver = Receiver(self._manager.get_handler(), self._ifaces)\n\n def _setup_address_file(self):\n \"\"\"Write address of ZMQ interface address to the file.\"\"\"\n if self._receiver.zmq_address:\n address_file = Config.ADDRESS_FILE.get(self._conf)\n address_file = address_file if isabs(address_file) else join(self._aux_dir, address_file)\n\n if exists(address_file):\n os.remove(address_file)\n\n with open(address_file, 'w') as address_f:\n address_f.write(self._receiver.zmq_address)\n\n _logger.debug('address interface written to the %s file...', address_file)\n\n def _setup_reports(self):\n \"\"\"Setup job report file and proper reporter according to configuration.\"\"\"\n report_file = Config.REPORT_FILE.get(self._conf)\n job_report_file = report_file if isabs(report_file) else join(self._aux_dir, report_file)\n\n# if exists(job_report_file):\n# os.remove(job_report_file)\n\n self._job_reporter = get_reporter(Config.REPORT_FORMAT.get(self._conf), job_report_file)\n\n def _setup_signals(self):\n \"\"\"Register SIGINT handler.\"\"\"\n signal.signal(signal.SIGINT, self._handle_sig_int)\n\n def _setup_tracker(self):\n \"\"\"Setup tracker which records status of computation to allow resuming.\"\"\"\n\n # just setup the path to the aux directory, the StateTracker is singleton but first initialization with\n # proper path is crucial.\n _logger.debug('initializing tracer ...')\n self._tracer = StateTracker(self._aux_dir)\n\n def _setup_aux_dir(self):\n \"\"\"This method should be called before all other '_setup' methods, as it sets the destination for the\n auxiliary files directory.\n \"\"\"\n wdir = Config.EXECUTOR_WD.get(self._conf)\n\n self._aux_dir = Config.RESUME.get(self._conf)\n if self._aux_dir:\n try:\n self._aux_dir = is_aux_dir(self._aux_dir) or find_latest_aux_dir(self._aux_dir)\n except Exception:\n raise InvalidArgument(\n f'Resume directory {self._aux_dir} not exists or is not valid QCG-PilotJob auxiliary directory')\n else:\n self._aux_dir = join(wdir, '.qcgpjm-service-{}'.format(Config.MANAGER_ID.get(self._conf)))\n\n if not os.path.exists(self._aux_dir):\n os.makedirs(self._aux_dir)\n\n self._conf[Config.AUX_DIR] = self._aux_dir\n\n def _setup_logging(self):\n \"\"\"Setup logging handlers according to the configuration.\"\"\"\n log_file = join(self._aux_dir, 'service.log')\n\n# if exists(log_file):\n# os.remove(log_file)\n\n self._log_handler = logging.FileHandler(filename=log_file, mode='a', delay=False)\n self._log_handler.setFormatter(logging.Formatter('%(asctime)-15s: %(message)s'))\n top_logger.addHandler(self._log_handler)\n top_logger.setLevel(logging._nameToLevel.get(Config.LOG_LEVEL.get(self._conf).upper()))\n\n _logger = logging.getLogger(module_name)\n\n _logger.info('service %s version %s started %s @ %s (with tags %s)', Config.MANAGER_ID.get(self._conf),\n qcg.pilotjob.version.__version__, str(datetime.now()), socket.gethostname(),\n ','.join(Config.MANAGER_TAGS.get(self._conf)))\n _logger.info('log level set to: %s', Config.LOG_LEVEL.get(self._conf).upper())\n _logger.info(f'service arguments {str(self._args)}')\n\n env_file_path = join(self._aux_dir, 'env.log')\n with open(env_file_path, \"wt\") as env_file:\n for name, value in os.environ.items():\n env_file.write(f'{name}={value}\\n')\n\n @staticmethod\n def _setup_event_loop():\n \"\"\"Setup event loop.\"\"\"\n _logger.debug('checking event loop')\n if asyncio.get_event_loop() and asyncio.get_event_loop().is_closed():\n _logger.debug('setting new event loop')\n asyncio.set_event_loop(asyncio.new_event_loop())\n\n def _handle_sig_int(self, sig, frame):\n _logger.info(\"signal interrupt\")\n print(f\"{datetime.now()} signal interrupt - stopping service\")\n self._manager.stop_processing = True\n self._manager.call_scheduler()\n self._receiver.finished = True\n\n async def _stop_interfaces(self, receiver):\n \"\"\"Asynchronous task working in background waiting for receiver finish flag to finish receiver.\n Before receiver will be stopped, the final status of QCG-PilotJob will be written to the file.\n\n Args:\n receiver (Receiver): receiver to watch for finish flag and to stop\n \"\"\"\n while not receiver.is_finished:\n await asyncio.sleep(0.5)\n\n _logger.info('receiver stopped')\n\n\n _logger.info('stopping receiver ...')\n await receiver.stop()\n\n def _job_status_change_notify(self, job_id, iteration, state, manager):\n \"\"\"Callback function called when any job's iteration change it's state.\n The job reporter is called for finished jobs.\n\n Args:\n job_id (str): job identifier\n iteration (int): iteration index\n state (JobState): new state\n manager (Manager): the manager instance\n \"\"\"\n if self._job_reporter:\n if state.is_finished():\n job = manager.job_list.get(job_id)\n self._job_reporter.report_job(job, iteration)\n self._tracer.job_finished(job, iteration)\n\n def get_interfaces(self, iface_class=None):\n \"\"\"Return list of available interfaces.\n\n Args:\n iface_class (Class) - class of interface, if not defined all configured interfaces are returned.\n\n Returns:\n list of all or specific input interfaces\n \"\"\"\n if iface_class:\n return [iface for iface in self._ifaces if isinstance(iface, iface_class)]\n\n return self._ifaces\n\n async def _write_final_status(self):\n try:\n _logger.info('writing final status')\n\n response = await self._receiver.generate_status_response()\n\n status_file = Config.FINAL_STATUS_FILE.get(self._conf)\n status_file = status_file if isabs(status_file) else join(self._aux_dir, status_file)\n\n if exists(status_file):\n os.remove(status_file)\n\n with open(status_file, 'a') as status_f:\n status_f.write(json.dumps(response.data, indent=2))\n except Exception as exc:\n _logger.warning('failed to write final status: %s', str(exc))\n\n async def _run_service(self):\n \"\"\"Asynchronous background task that starts receiver, waits for it's finish (signaled by the\n ``_stop_interfaces`` task and clean ups all resources.\n\n This task can be treatd as the main processing task.\n \"\"\"\n if self._tasks_to_resume:\n if Config.PROGRESS.get(self._conf):\n print(f'enqueing {len(self._tasks_to_resume)} jobs to scheduler')\n\n await self._manager.enqueue(self._tasks_to_resume)\n\n _logger.debug('starting receiver ...')\n\n if Config.PROGRESS.get(self._conf):\n print(f'{datetime.now()} starting interfaces ...')\n\n self._receiver.run()\n\n _logger.debug('finishing intialization of managers ...')\n\n try:\n await self._manager.setup_interfaces()\n\n _logger.debug('waiting for stopped interfaces ...')\n await self._stop_interfaces(self._receiver)\n\n if Config.PROGRESS.get(self._conf):\n print(f'{datetime.now()} all interfaces closed')\n\n _logger.debug('waiting for stopped manager ...')\n while not self._manager.stop_processing and not self._manager.is_all_jobs_finished:\n await asyncio.sleep(0.2)\n\n if Config.PROGRESS.get(self._conf):\n print(f'{datetime.now()} all iterations in manager stopped/finished')\n\n _logger.debug('finishing run_service')\n\n if Config.PROGRESS.get(self._conf):\n print(f'{datetime.now()} finishing QCG-PilotJob')\n\n self.exit_code = 0\n except Exception:\n _logger.error('Service failed: %s', sys.exc_info())\n _logger.error(traceback.format_exc())\n finally:\n if self._job_reporter:\n self._job_reporter.flush()\n\n if self._receiver:\n await self._receiver.stop()\n\n _logger.info('receiver stopped')\n\n if self._manager:\n await self._manager.stop()\n\n _logger.info('manager stopped')\n\n await self._write_final_status()\n\n usage = QCGPMService.get_rusage()\n _logger.info('service resource usage: %s', str(usage.get('service', {})))\n _logger.info('jobs resource usage: %s', str(usage.get('jobs', {})))\n\n def start(self):\n \"\"\"Start QCG-JobManager service.\n\n The asynchronous task ``_run_service`` is started.\n \"\"\"\n try:\n asyncio.get_event_loop().run_until_complete(asyncio.ensure_future(self._run_service()))\n finally:\n _logger.info('closing event loop')\n\n asyncio.get_event_loop().run_until_complete(asyncio.sleep(1))\n asyncio.get_event_loop().close()\n _logger.info('event loop closed')\n\n# remove custom log handler\n if self._log_handler:\n top_logger.removeHandler(self._log_handler)\n\n @staticmethod\n def get_rusage():\n \"\"\"Return resource usage statistics.\"\"\"\n service_ru = resource.getrusage(resource.RUSAGE_SELF)\n jobs_ru = resource.getrusage(resource.RUSAGE_CHILDREN)\n\n return {'service': service_ru, 'jobs': jobs_ru}\n\n\nclass QCGPMServiceProcess(Process):\n \"\"\"This class is used to start QCG-PilotJob manager in separate, background thread.\n\n Attributes:\n args (list(str)): command line arguments for QCG-PilotJob manager\n queue (multiprocess.Queue): the queue where address of ZMQ interface of QCG-PilotJob manager should be sent\n service (QCGPMService): the service instance\n \"\"\"\n\n def __init__(self, args=None, queue=None):\n \"\"\"Start QCGPM Service as a separate process.\n\n Args:\n args (str[]) - command line arguments\n queue (Queue) - the communication queue\n \"\"\"\n try:\n super(QCGPMServiceProcess, self).__init__()\n\n self.args = args or []\n self.queue = queue\n self.service = None\n except Exception as exc:\n print(f'init error: {str(exc)}')\n _logger.exception('init error')\n\n def run(self):\n \"\"\"The main thread function.\n The QCG-PilotJob manager service is started, and through multiprocess queue the address of ZMQ interfaces of\n this service is sent to calling thread.\n \"\"\"\n try:\n self.service = QCGPMService(self.args)\n\n if self.queue:\n _logger.info('communication queue defined ...')\n zmq_ifaces = self.service.get_interfaces(ZMQInterface)\n _logger.info('sending configuration through communication queue ...')\n self.queue.put({'zmq_addresses': [str(iface.real_address) for iface in zmq_ifaces]})\n else:\n _logger.info('communication queue not defined')\n\n _logger.info('starting qcgpm service inside process ....')\n self.service.start()\n except Exception as exc:\n _logger.error(f'Error: {str(exc)}')\n\n if self.queue:\n try:\n self.queue.put({'error': traceback.format_exc()})\n except Exception:\n pass\n\n traceback.print_exc()\n# sys.exit(1)\n\n\nif __name__ == \"__main__\":\n try:\n service = QCGPMService()\n service.start()\n sys.exit(service.exit_code)\n except Exception as exc:\n sys.stderr.write('Error: {}\\n'.format(str(exc)))\n traceback.print_exc()\n sys.exit(1)\n","sub_path":"components/core/qcg/pilotjob/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":27916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"557742952","text":"import matplotlib # must import first\nmatplotlib.use('Agg') # allows you to not have an x-server running\nimport os, sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pylab\n\n###################################\n# written by Reed Stein\n# 10/2017 - 4/2018\n#\n#\n####################################\n\n\ndef boxplot(data, namelist, system):\n\n\t#data = [es_list1, ld_list1, vdw_list1, es_list3, ld_list3, vdw_list3]\n\t#lbl = [\"ES_up\", \"LD_up\", \"vdW_up\", \"ES_down\", \"LD_down\", \"vdW_down\"]\n\t#plt.xticks(list('123456'), [\"ES_up\", \"LD_up\", \"vdW_up\", \"ES_down\", \"LD_down\", \"vdW_down\"])\n\t\n\t\n\tposition_list = []\n\txtick_list = []\n\tfor i in range(1, len(namelist)+1):\n\t\txtick_list.append(i)\n\t\tposition_list.append(float(i))\n\n\tplt.xticks(xtick_list, namelist, rotation='vertical', fontsize=8)\n\tfig = plt.figure(1, figsize=(25,10))\n\tax = fig.add_subplot(111)\n\tbp = ax.boxplot(data, patch_artist=True)\n\t#bp = ax.boxplot(data, showfliers=False)\n\t\n\tfor box in bp['boxes']:\n\t\tbox.set(color='#ffa07a', linewidth=2)\n\n\tfor flier in bp['fliers']:\n\t\t#flier.set(marker='-', color='#e7298a', alpha=0.2)\n\t\tflier.set(marker='o', color='w',alpha=0.2)\n\n\tfor cap in bp['caps']:\n\t\tcap.set(color='#7570b3', linewidth=4)\n\n\n\t#pylab.legend([bp[\"boxes\"][0], bp[\"boxes\"][1]], [\"ES\", \"LD\"], loc='upper right', prop={'size':6})\n\tpylab.ylabel(\"Maximum Matched Decoy Tc to All Ligands\",fontsize=18)\n\tpylab.xlabel(\"Ligand\",fontsize=18)\n\tpylab.title(system+\" Matched Decoy Similarity Comparison\" ,fontsize=15)\n\t#pylab.figure(figsize=(20,20))\n\tpylab.subplots_adjust(bottom=0.15)\n\t\n\t#plt.figtext(0.80, 0.08, \"vdW\", backgroundcolor='#ffe4e1')\n\t\n\t#plt.show()\n\tpwd = os.getcwd()+\"/\"\n\tpylab.savefig(pwd+\"/\"+system+\"_tanimoto_similarity.png\", dpi=600)\n\n\ndef collect_IDs(smiles_dir):\n\n\tdecoy_file_list = [name for name in os.listdir(smiles_dir) if (os.path.isfile(smiles_dir+name) and name.endswith(\"_final_property_matched_decoys.txt\"))]\n\t\n\tlig_dict = {}\n\tdecoy_dict = {}\n\tname_list = []\n\ttanimoto_dict = {}\n\tfor decoy_file in decoy_file_list:\n\t\tprint(\"COLLECTING DECOYS OF \"+decoy_file.split(\"_final\")[0])\n\t\topen_decoy = open(decoy_file, 'r')\n\t\tread_decoy = open_decoy.readlines()\n\t\topen_decoy.close()\n\t\t\n\t\tfor line in read_decoy:\n\t\t\tsplitline = line.strip().split()\n\t\t\tif len(splitline) > 0:\n\t\t\t\tif splitline[0] == \"LIGAND:\":\n\t\t\t\t\tlig_ID = splitline[2]\n\t\t\t\t\tif lig_ID not in lig_dict:\t\n\t\t\t\t\t\tlig_dict[lig_ID] = []\n\t\t\t\t\t\tname_list.append(lig_ID)\n\t\t\t\t\tif lig_ID not in tanimoto_dict:\n\t\t\t\t\t\ttanimoto_dict[lig_ID] = []\n\n\t\t\t\tif splitline[0] == \"DECOY\":\n\t\t\t\t\tdec_ID = splitline[3]\n\t\t\t\t\ttc_to_any_lig = float(splitline[11]) \n\t\t\t\t\tif dec_ID not in decoy_dict:\n\t\t\t\t\t\tdecoy_dict[dec_ID] = lig_ID\n\t\t\t\t\tif dec_ID not in lig_dict[lig_ID]:\n\t\t\t\t\t\tlig_dict[lig_ID].append(dec_ID)\n\t\t\t\t\t\ttanimoto_dict[lig_ID].append(tc_to_any_lig)\n\n\tname_list = sorted(name_list)\n\tdata = []\n\tfor name in name_list:\n\t\tprint(name, tanimoto_dict[name])\n\t\tprint(np.mean(tanimoto_dict[name]))\n\t\tdata.append(tanimoto_dict[name])\n\n\treturn(name_list, data)\n\n\ndef main():\n\n\tpwd = os.getcwd()+\"/\"\n\t\n\tsmiles_dir = pwd+sys.argv[1]+\"/\"\n\tos.chdir(smiles_dir)\n\t\n\tname_list, data = collect_IDs(smiles_dir)\n\t\n\tif sys.argv[1].endswith(\"/\"):\n\t\tplot_name = sys.argv[1].split(\"/\")[0]\n\tboxplot(data, name_list, plot_name)\n\nmain()\n","sub_path":"KCNQ/docking/scripts/dude_scripts/0005_plot_tanimoto_to_lig.py","file_name":"0005_plot_tanimoto_to_lig.py","file_ext":"py","file_size_in_byte":3231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"340368174","text":"from __future__ import print_function\nimport tensorflow as tf\nfrom tensorflow.contrib import rnn\nimport numpy as np\n\n\ndef string_gen(k):\n return [a] * k + [N] + [b] * k\n\n\ndef test_string_gen(k):\n return [a] * k + [N]\n\n\na = [1.0, 0.0, 0.0, 0.0]\nN = [0.0, 1.0, 0.0, 0.0]\nb = [0.0, 0.0, 1.0, 0.0]\ne = [0.0, 0.0, 0.0, 1.0]\nvocabulary = [a, N, b, e]\ndictionary = ['a', 'N', 'b', 'e']\n\n# Training Parameters\nlearning_rate = 0.001\ntraining_steps = 10\ndisplay_step = 100\ntest = 15\ntraining_iter = 200\n\nvocab = sorted(set('aNbe'))\n\n# Network Parameters\nnum_input = len(vocab)\n\nneuron_num = 10 # Number of LSTM cell neurons.\n# char2idx = {u: i for i, u in enumerate(vocab)}\n# idx2char = np.array(vocab)\n\n# tf Graph input\nX = tf.placeholder(\"float\", [None, None, len(vocab)])\nY = tf.placeholder(\"float\", [None, len(vocab)])\n\n# Define weights\nweights = {\n 'out': tf.Variable(tf.random_normal([neuron_num, len(vocab)]))\n}\nbiases = {\n 'out': tf.Variable(tf.random_normal([len(vocab)]))\n}\n\n\n# noinspection PyPep8Naming,PyShadowingNames\ndef RNN(x, weights, biases):\n\n lstm_cell = tf.nn.rnn_cell.LSTMCell(neuron_num, use_peepholes=True, forget_bias=1.0)\n cell_states = lstm_cell.variables\n\n # Get lstm cell output\n outputs, states = tf.nn.dynamic_rnn(lstm_cell, x, dtype=tf.float32)\n\n # Linear activation, using rnn inner loop last output\n return tf.matmul(outputs[-1], weights['out']) + biases['out'], states, cell_states\n\n\nlogits, states, cell_state = RNN(X, weights, biases)\nprediction = tf.nn.softmax(logits)\n\n# Define loss and optimizer\nloss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n logits=logits, labels=Y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\ntrain_op = optimizer.minimize(loss_op)\n\n# Evaluate model (with test logits, for dropout to be disabled)\ncorrect_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n\n# Initialize the variables (i.e. assign their default value)\ninit = tf.global_variables_initializer()\n\ncell_1 = tf.summary.scalar('cell_states_neuron_1', states[0][0][0])\ncell_2 = tf.summary.scalar('cell_states_neuron_2', states[0][0][1])\ncell_3 = tf.summary.scalar('cell_states_neuron_3', states[0][0][2])\ncell_4 = tf.summary.scalar('cell_states_neuron_4', states[0][0][3])\ncell_5 = tf.summary.scalar('cell_states_neuron_5', states[0][0][4])\ncell_6 = tf.summary.scalar('cell_states_neuron_6', states[0][0][5])\ncell_7 = tf.summary.scalar('cell_states_neuron_7', states[0][0][6])\ncell_8 = tf.summary.scalar('cell_states_neuron_8', states[0][0][7])\ncell_9 = tf.summary.scalar('cell_states_neuron_9', states[0][0][8])\ncell_10 = tf.summary.scalar('cell_states_neuron_10', states[0][0][9])\n\n\nmerge = tf.summary.merge_all()\n\nwith tf.Session() as sess:\n # Run the initializer\n sess.run(init)\n\n file_writer = tf.summary.FileWriter('cell_states_summary')\n\n # ####################### Training ####################### #\n for iteration in range(training_iter):\n\n for step in range(1, training_steps + 1):\n\n # batch_x = np.repeat([string_gen(step)], batch_size, axis=0)\n batch_x = np.repeat([string_gen(step)], 1, axis=0)\n batch_y = string_gen(step)[1:] + [e]\n # print(\"Batch X is: \", batch_x)\n # print(\"Batch Y is: \", batch_y)\n # print(type(batch_x))\n # print(type(batch_y))\n # print(len(batch_x))\n # print(len(batch_y))\n\n # Run optimization op (backprop)\n sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n if iteration % display_step == 0:\n # Calculate batch loss and accuracy\n loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n Y: batch_y})\n print(\"Iteration: \" + str(iteration) + \"\\tStep \" + str(step) + \", Minibatch Loss= \" + \"{:.4f}\".format(\n loss) + \", Training Accuracy= \" +\n \"{:.3f}\".format(acc))\n outputs, Y_res = sess.run((prediction, Y), feed_dict={X: batch_x,\n Y: batch_y})\n\n outputs = np.argmax(outputs, 1)\n # print(outputs)\n result = ''\n Y_result = ''\n for i in range(len(outputs)):\n result += dictionary[outputs[i]]\n # Y_result += dictionary[int(Y_res[i])]\n # print(result)\n # print(Y_result)\n print(\"Optimization Finished!\")\n\n # ####################### Testing ####################### #\n for test_num in range(1, test + 1):\n\n # states_output = sess.run(states, feed_dict={X: np.repeat([test_string_gen(test_num)], 1, axis=0)})\n # states_output = np.array(states_output)\n # print(states_output.shape)\n\n counter = 0\n input = ''\n result = ''\n for i in range(len(test_string_gen(test_num))):\n # Gets the four element array and returns the corresponding character for printing input (for debugging)\n char = np.argmax(np.array(test_string_gen(test_num)[i]))\n input += dictionary[char]\n print(\"INPUT IS: \", input)\n\n outputs = sess.run(prediction, feed_dict={X: np.repeat([test_string_gen(test_num)], 1, axis=0)})\n outputs = np.argmax(outputs, 1)\n\n for i in range(len(outputs)):\n result += dictionary[outputs[i]]\n # print(result)\n next_input = test_string_gen(test_num)\n while (result[-1] != 'e' or counter == 0) and len(result) <= 2 * test_num + 6:\n\n counter += 1\n outputs = sess.run(prediction, feed_dict={X: np.repeat([next_input], 1, axis=0)})\n outputs = np.argmax(outputs, 1)\n # print(outputs)\n result = ''\n for i in range(len(outputs)):\n result += dictionary[outputs[i]]\n\n next_input = next_input + [vocabulary[outputs[-1]]]\n print(\"K is: \" + str(test_num) + \"\\tResult is:\" + result)\n\n # ####################### Testing for 15 ####################### #\n\n counter = 0\n input = ''\n result = ''\n for i in range(len(test_string_gen(test))):\n char = np.argmax(np.array(test_string_gen(test)[i]))\n input += dictionary[char]\n # print(input)\n outputs = sess.run(prediction, feed_dict={X: np.repeat([test_string_gen(test)], 1, axis=0)})\n outputs = np.argmax(outputs, 1)\n\n for i in range(len(outputs)):\n result += dictionary[outputs[i]]\n # print(result)\n next_input = test_string_gen(test)\n while (result[-1] != 'e' or counter == 0) and len(result) <= 2 * test + 6:\n\n summary = sess.run(merge, feed_dict={X: np.repeat([next_input], 1, axis=0)})\n file_writer.add_summary(summary, counter)\n\n counter += 1\n outputs = sess.run(prediction, feed_dict={X: np.repeat([next_input], 1, axis=0)})\n outputs = np.argmax(outputs, 1)\n # print(outputs)\n result = ''\n for i in range(len(outputs)):\n result += dictionary[outputs[i]]\n\n next_input = next_input + [vocabulary[outputs[-1]]]\n print(\"K is: \" + str(test) + \"\\tResult is:\" + result)\n\n# tensorboard --logdir=1:./css1,2:./css2,3:./css3,4:./css4,5:./css5,6:./css6,7:./css7,8:./css8,9:./css9,10:./css10\n","sub_path":"Project_6/HW6_Pattern_Part1.py","file_name":"HW6_Pattern_Part1.py","file_ext":"py","file_size_in_byte":7420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"114191502","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom views.overview import Overview\nfrom views.set_slots import SetSlots\nfrom views.all_liquids import AllLiquids\nfrom lib.context import Context\n\nfrom PyQt4 import QtCore\n# from Qt import QtCore\nfrom PyQt4 import QtGui\n#from Qt import QtGui\nimport sys\n\n\nclass MainWindow(QtGui.QMainWindow):\n close_dialog = QtCore.pyqtSignal()\n\n def __init__(self):\n super(MainWindow, self).__init__()\n self.context = Context()\n # self.showMaximized()\n self.setFixedSize(730, 400)\n\n self.mainStack = QtGui.QStackedLayout()\n window = QtGui.QWidget(self)\n window.setLayout(self.mainStack)\n self.setCentralWidget(window)\n\n self.context.add_object('mainWindow', self)\n self.context.add_object('window', window)\n self.context.add_object('mainStack', self.mainStack)\n\n overView = Overview(self.context, self)\n self.mainStack.addWidget(overView)\n self.context.add_object('overView', overView)\n\n allLiquids = AllLiquids(self.context, self)\n self.mainStack.addWidget(allLiquids)\n self.context.add_object('allLiquids', allLiquids)\n\n setSlots = SetSlots(self.context, self)\n self.mainStack.addWidget(setSlots)\n self.context.add_object('setSlots', setSlots)\n\n # actionOverview = QtGui.QAction(QtGui.QIcon(resource_path(os.path.join('assets', 'home-300px.png'))), '&Home', window)\n # actionOverview = QtGui.QAction(QtGui.QIcon('assets/home-300px.png'), '&Home', window)\n # actionOverview.setStatusTip('Switch to home')\n # actionOverview.triggered.connect(lambda: self.mainStack.setCurrentWidget(overView))\n # self.context.add_object('actionOverview', actionOverview)\n\n self.mainStack.setCurrentWidget(overView)\n\n #### Color... ###\n p = QtGui.QPalette()\n gradient = QtGui.QLinearGradient(0, 0, 0, 400)\n gradient.setColorAt(0.0, QtGui.QColor(240, 240, 240))\n gradient.setColorAt(1.0, QtGui.QColor(128, 128, 128))\n p.setBrush(QtGui.QPalette.Window, QtGui.QBrush(gradient))\n self.setPalette(p)\n\n\nif __name__ == \"__main__\":\n app = QtGui.QApplication(sys.argv)\n # app = QtGui.QApplication()\n # app_icon = QtGui.QIcon()\n # app_icon.addFile(resource_path(os.path.join('assets', 'appIcon-300px.png')), QtCore.QSize(300, 300))\n # app.setWindowIcon(app_icon)\n app.setApplicationName('IDPA')\n\n main = MainWindow()\n main.show()\n result = app.exec_()\n\n\n#\n# import sys\n# from PyQt4.QtGui import *\n#\n# if __name__ == \"__main__\":\n# app = QApplication([])\n# w = QWidget()\n# p = QPalette()\n# gradient = QLinearGradient(0, 0, 0, 400)\n# gradient.setColorAt(0.0, QColor(240, 240, 240))\n# gradient.setColorAt(1.0, QColor(240, 160, 160))\n# p.setBrush(QPalette.Window, QBrush(gradient))\n# w.setPalette(p)\n# w.show()\n# app.exec_()","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":2925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"315014912","text":"from Connect import *\nfrom ConnectDB import *\nfrom bs4 import BeautifulSoup\n\nclass Parsing:\n\n # 맛집 리스트 URL 수집\n def getLink(self):\n html_doc = driver.page_source\n soup = BeautifulSoup(html_doc, 'html.parser')\n links = []\n\n # list들 중에서 a 태그를 찾아야 함.\n for link in soup.find_all('div', {'class': 'info'}):\n ref = link.find('a', href=True)\n links.append(ref.get('href'))\n # print(links)\n\n # 각각의 링크들 순회하면서 정보 받아오기\n return links\n\n # 맛집 리스트 내용 수집\n def getData(self):\n html_doc = driver.page_source\n soup = BeautifulSoup(html_doc, 'html.parser')\n\n # Default Values\n title = ''\n point = ''\n addr = ''\n phone = ''\n category = ''\n price_range = ''\n\n # title \n title = soup.find('h1', {'class': 'restaurant_name'}, text=True).text\n point = soup.find('strong', {'class': 'rate-point'}).find('span', text=True).text\n addr = soup.find('span', {'class': 'Restaurant__InfoAddress--Text'}).text\n tables = soup.findAll('tr')\n\n # table 안에 labled 되지 않은 여러 내용들이 들어가 있어서 일일히 필터링 해주어야 함\n\n for row in tables:\n temp = row.find('th').text.strip()\n if(temp == '전화번호'):\n phone = row.find('td').text.strip()\n if(temp == '음식 종류'):\n category = row.find('td').text.strip()\n if(temp == '가격대'):\n price_range = row.find('td').text.strip()\n\n connect = ConnectDB()\n restaurantInfo = {\n 'name': title,\n 'point': point,\n 'address': addr,\n 'business_hour': '',\n 'pricerange': price_range,\n 'category': category,\n 'parking_space': False,\n 'location': '서울대입구',\n }\n connect.addRestaurant(restaurantInfo)\n\n # print(title)\n # print(point)\n # print(addr)\n # print(phone)\n # print(category)\n # print(price_range)\n\n\n\n","sub_path":"Mangoplate_Crawler/Parsing.py","file_name":"Parsing.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"291692504","text":"from util.splunklib.searchcommands import Configuration, GeneratingCommand, Option, dispatch\nimport sys\nimport json\nimport subprocess\nfrom util import ConnectionManager\n\n\n@Configuration(type='reporting')\nclass Ouroboros(GeneratingCommand):\n connection = Option(require=True)\n query = Option(require=True)\n\n def generate(self):\n connections = ConnectionManager(self.service)\n connection_string = connections.get(self.connection)\n\n if connection_string is None:\n raise KeyError('No such connection - {}'.format(self.connection))\n\n ds_proc = subprocess.Popen(['datasnake', connection_string.clear_password, self.query,\n '--output-format=json'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n ds_out, ds_err = ds_proc.communicate()\n for line in ds_out.split('\\n'):\n if line.startswith('ROW'):\n _, _, db_row = line.split('\\t')\n yield json.loads(db_row)\n if len(ds_err) > 0:\n raise RuntimeError(ds_err)\n\n\ndispatch(Ouroboros, sys.argv, sys.stdin, sys.stdout, __name__)\n","sub_path":"bin/ouroboros.py","file_name":"ouroboros.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"472440713","text":"import sys\n\n__author__ = 'gregortaube'\n\nnumNames = int(input())\nnames = []\ndecreasing = False;\n\nfor x in range(numNames):\n names.append(input())\n\n# check first two names for decreasing or increasing order\nif names[0] < names[1]:\n decreasing = False\nelse:\n decreasing = True\n\nfor x in range(len(names) - 1):\n #if first was decreasing. ALL others must decrease x names[x + 1]:\n print(\"NEITHER\")\n sys.exit()\n\nif decreasing:\n print(\"DECREASING\")\nelse:\n print('INCREASING')\n","sub_path":"intermediate/lineup.py","file_name":"lineup.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"311094613","text":"# import flask modules and functions\nfrom flask import render_template, flash, session, redirect, url_for, current_app\n\nfrom . import main # import the blueprint\n\n# import the instances registered by the app through blueprint\nfrom .. import db, mail, moment, admin, logger_all, logger_warning\n\n# import the function from the email module\nfrom ..email import send_email\n\n# import the forms\nfrom .forms import UserInfo, BookInfo\n\n# import the database models\nfrom ..models import User, Book, Linklist\n\nfrom flask_login import login_required, current_user\nimport time # Use to get timestamp\nfrom datetime import datetime # Use to get current date\n\n# Using blueprint to define the route\n@main.route(\"/\", methods=['GET'])\ndef index():\n # Get all the book belong to the admin\n admin_user = User.query.filter_by(roles=True).all()\n books = []\n for i in admin_user:\n a = set(books)\n b = set(i.createdBooks)\n books = list(a.union(b))\n # Display the Top 5 books\n while len(books) > 5:\n books.pop()\n return render_template(\"welcome.html\", books=books)\n\n\n\n@main.route(\"/user/\")\n@login_required\ndef user(username):\n user = User.query.filter_by(userName=username).first_or_404()\n gender = \"Unknow\"\n if user.userGender==1:\n gender = \"Male\"\n elif user.userGender==2:\n gender = \"Female\"\n return render_template(\"user.html\", user=user, gender=gender)\n\n# For user to modify the user profile\n@main.route('/edit-profile', methods=['GET', 'POST'])\n@login_required\ndef edit_profile():\n form = UserInfo()\n if form.validate_on_submit():\n a = current_user._get_current_object()\n if a.roles:\n logger_all.logger.warning(\n \"Admin \"+str(a)+\" is modifying the user profile\")\n logger_warning.logger.warning(\n \"Admin \"+str(a)+\" is modifying the user profile\")\n else:\n logger_all.logger.info(\"User \"+str(a)+\" is modifying the user profile\")\n a.userName = form.username.data\n a.userAge = form.userage.data\n a.userGender = form.gender.data\n a.userInfo = form.userInfo.data\n db.session.add(a)\n db.session.commit()\n flash('Your profile has been updated.')\n return redirect(url_for('main.user', username=current_user.userName))\n form.username.data = current_user.userName\n form.userage.data = current_user.userAge\n form.gender.data = current_user.userGender\n form.userInfo.data = current_user.userInfo\n return render_template('edit_profile.html', form=form)\n\n\n\n@main.route('/create-book', methods=['GET', 'POST'])\n@login_required\ndef create_book():\n # Get data from session (store the data before redirect)\n status = session.get('con')\n if status:\n session.pop('con') # Clear the session parts\n form = BookInfo() # Create the form instance\n # if submit valid, store it into database\n if form.validate_on_submit():\n session['con'] = True # Get the data from session\n\n # Admin cannot create the book with the same ISBN\n if current_user.roles and Book.query.filter_by(bookISBN=form.isbn.data).first():\n logger_all.logger.info(\"Admin \"+str(current_user)+\" creating a repeat book\")\n flash(\"The book has been registered!\")\n return redirect(url_for('main.book_list'))\n\n # User create the book with the same booke name will be prompted\n if Book.query.filter_by(bookName=form.name.data).first():\n logger_all.logger.info(\"User \"+str(current_user)+\" creating a repeat book\")\n flash(\"Seem there is the book in the market which has the same name!\")\n\n # Create the book instance\n a_book = Book(\n bookName = form.name.data,\n bookISBN = form.isbn.data,\n bookAuthor = form.author.data,\n bookKind = form.kind.data,\n bookAbstract = form.abstract.data\n )\n logger_all.logger.info(\"User \"+str(current_user)+\" is creating a book named: \"+form.name.data)\n book_creator = current_user._get_current_object()\n a_book.creator = book_creator # Add the foreign\n db.session.add(a_book)\n db.session.commit()\n flash(\"The book has been added to the list successfully!\")\n return redirect(url_for('main.create_book'))\n return render_template(\"create_book.html\", form=form)\n\n@main.route('/book-list', methods=['GET'])\ndef book_list():\n # Flash message once to hint admin manage book\n if session.get(\"fhint_admin_manage_book\", True):\n flash(\"Detect you are the admin, you can manage all the books\")\n session[\"fhint_admin_manage_book\"] = False\n\n # For user can view the books belong to admin and themself\n vaild_user = User.query.filter_by(roles=True).all()\n if current_user.is_authenticated and not current_user.roles:\n vaild_user.append(current_user._get_current_object())\n books = []\n for i in vaild_user:\n a = set(books)\n b = set(i.createdBooks)\n books = list(a.union(b))\n \n # For admin they can manage all the books\n if current_user.is_authenticated and current_user.roles:\n books = Book.query.all()\n return render_template(\"book_list.html\", books=books)\n\n@main.route('/book-list/', methods=['GET'])\ndef book_list_kind(kind_s):\n # Dict to map the input (From GET method) and datebase title\n kind_dict = {'name': Book.bookName,\n 'category': Book.bookKind,\n 'author': Book.bookAuthor,}\n kind = kind_dict.get(kind_s, None)\n # With invalid input from GET\n if not kind:\n logger_all.logger.warning(\"User \"+str(current_user)+\" Trying to access nonexist route\")\n logger_warning.logger.warning(\"User \"+str(current_user)+\" Trying to access nonexist route\")\n return render_template(\"404.html\")\n # Get row(objects) query from the database\n books = Book.query.order_by(kind).all()\n return render_template(\"book_list.html\", books=books)\n\n# View the book information\n# The login user can see the delete option\n# But the normal only can delete the book created by themself\n@main.route('/view-book/', methods=['GET', 'POST'])\ndef view_book(bookid):\n book = Book.query.filter_by(bookId=bookid).first()\n if not book:\n logger_all.logger.warning(\"User \"+str(current_user)+\" Query a nonexist book!\")\n logger_warning.logger.warning(\"User \"+str(current_user)+\" Query a nonexist book!\")\n return render_template(\"404.html\")\n return render_template(\"view_book.html\", book=book)\n\n@main.route('/delete_book/', methods=['GET'])\ndef delete_task(bid):\n book = Book.query.get(bid)\n\n # Admin can delete all the books\n if not current_user.roles:\n\n # User only can delete the books they created\n if book.bookCreator == current_user.id:\n logger_all.logger.info(\"User \"+str(current_user)+\"Deleting book :\"+str(book.bookName))\n db.session.delete(book)\n db.session.commit()\n else :\n logger_all.logger.info(\n \"User \"+str(current_user)+\"try to delet book :\" +str(book.bookName)+\"without authorization\")\n flash(\"You don't have the authorization!\")\n return redirect(\"/view-book/\"+str(bid))\n\n logger_all.logger.warning(\"Admin \"+str(current_user)+\" Deleting book :\" +str(book.bookName))\n logger_warning.logger.warning(\"Admin \"+str(current_user)+\" Deleting book :\" +str(book.bookName))\n db.session.delete(book)\n db.session.commit()\n flash(\"Success Delete the Book!\")\n return redirect(url_for('main.book_list'))\n","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"267918568","text":"\"\"\"\nCopyright (c) 2004-Present Pivotal Software, Inc.\n\nThis program and the accompanying materials are made available under\nthe terms of the under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\n\nimport os\nimport glob\nfrom time import sleep\nimport tinctest\nfrom tinctest.lib import local_path\nfrom gppylib.commands.base import Command\nfrom mpp.lib.PSQL import PSQL\n\nfrom mpp.lib.filerep_util import Filerepe2e_Util\nfrom mpp.lib.gprecoverseg import GpRecover\nfrom mpp.lib.gpstart import GpStart\nfrom mpp.lib.gpstop import GpStop\nfrom mpp.lib.config import GPDBConfig\nfrom mpp.lib.gpfilespace import Gpfilespace\nfrom mpp.lib.gpdbverify import GpdbVerify\nfrom mpp.models import MPPTestCase\nfrom mpp.gpdb.tests.storage.lib.dbstate import DbStateClass\nfrom mpp.gpdb.tests.storage.lib.common_utils import *\n\nclass PgtwoPhaseClass(MPPTestCase):\n '''Helper class for pg_twophase supporting functions '''\n\n def __init__(self,methodName):\n self.filereputil = Filerepe2e_Util()\n self.config = GPDBConfig()\n self.gprecover = GpRecover(self.config)\n self.gpstop = GpStop()\n self.gpstart = GpStart()\n self.gpfile = Gpfilespace(self.config)\n self.gpverify = GpdbVerify(config=self.config)\n self.dbstate = DbStateClass('run_validation',self.config)\n self.port = os.getenv('PGPORT')\n super(PgtwoPhaseClass,self).__init__(methodName)\n\n def invoke_fault(self, fault_name, type, role='mirror', port=None, occurence=None, sleeptime=None, seg_id=None):\n ''' Reset the fault and then issue the fault with the given type'''\n self.filereputil.inject_fault(f=fault_name, y='reset', r=role, p=port , o=occurence, sleeptime=sleeptime, seg_id=seg_id)\n self.filereputil.inject_fault(f=fault_name, y=type, r=role, p=port , o=occurence, sleeptime=sleeptime, seg_id=seg_id)\n tinctest.logger.info('Successfully injected fault_name : %s fault_type : %s' % (fault_name, type))\n\n def inject_fault(self, fault_type):\n '''\n @param fault_type : type of fault to ne suspended\n '''\n if fault_type == 'end_prepare_two_phase_sleep':\n self.filereputil.inject_fault(f='end_prepare_two_phase_sleep', sleeptime='1000', y='sleep', r='primary', p=self.port)\n tinctest.logger.info('Injected fault to sleep in end_prepare_two_phase')\n\n elif fault_type == 'abort':\n # In case of abort fault we need to include this error type fault also, to fake a situation where one of the segment is not responding back, which can make the master to trigger an abort transaction\n self.invoke_fault('transaction_abort_after_distributed_prepared', 'error', port=self.port, occurence='0', seg_id='1')\n\n self.invoke_fault('twophase_transaction_abort_prepared', 'suspend', role='primary', port=self.port, occurence='0')\n\n elif fault_type == 'commit':\n self.invoke_fault('twophase_transaction_commit_prepared', 'suspend', role='primary', port=self.port, occurence='0')\n\n elif fault_type == 'dtm_broadcast_prepare':\n self.invoke_fault('dtm_broadcast_prepare', 'suspend', seg_id = '1', port=self.port, occurence='0')\n\n elif fault_type == 'dtm_broadcast_commit_prepared':\n self.invoke_fault('dtm_broadcast_commit_prepared', 'suspend', seg_id = '1', port=self.port, occurence='0')\n\n elif fault_type == 'dtm_xlog_distributed_commit':\n self.invoke_fault('dtm_xlog_distributed_commit', 'suspend', seg_id = '1', port=self.port, occurence='0')\n\n def resume_faults(self, fault_type, cluster_state='sync'):\n '''\n @param fault_type : commit/abort/end_prepare_two_phase_sleep/dtm_broadcast_prepare/dtm_broadcast_commit_prepared/dtm_xlog_distributed_commit\n @description : Resume the suspended faults \n '''\n tinctest.logger.info('coming to resume faults with xact %s' % fault_type) \n if fault_type == 'abort':\n self.filereputil.inject_fault(f='twophase_transaction_abort_prepared', y='resume', r='primary', p=self.port , o='0')\n if cluster_state !='resync':\n self.filereputil.inject_fault(f='transaction_abort_after_distributed_prepared', y='reset', p=self.port , o='0', seg_id='1')\n elif fault_type == 'commit':\n self.filereputil.inject_fault(f='twophase_transaction_commit_prepared', y='resume', r='primary', p=self.port , o='0')\n\n elif fault_type == 'dtm_broadcast_prepare':\n self.filereputil.inject_fault(f='dtm_broadcast_prepare', y='resume', seg_id = '1', p=self.port, o='0')\n\n elif fault_type == 'dtm_broadcast_commit_prepared':\n tinctest.logger.info('coming to if dtm_broadcast_commit_prepared')\n self.filereputil.inject_fault(f='dtm_broadcast_commit_prepared', y='resume', seg_id = '1', p=self.port, o='0')\n\n elif fault_type == 'dtm_xlog_distributed_commit':\n self.filereputil.inject_fault(f='dtm_xlog_distributed_commit', y='resume', seg_id = '1', p=self.port, o='0')\n\n else:\n tinctest.logger.info('No faults to resume')\n tinctest.logger.info('Resumed the suspended transaction fault')\n \n #Wait till all the trigger_sqls are complete before returning\n sql_count = PSQL.run_sql_command('select count(*) from pg_stat_activity;', flags ='-q -t', dbname='postgres')\n while(sql_count.strip() != '1'):\n sleep(5)\n sql_count = PSQL.run_sql_command('select count(*) from pg_stat_activity;', flags ='-q -t', dbname='postgres')\n tinctest.logger.info('stat_activity count %s ' % sql_count)\n return\n\n def start_db(self):\n '''Gpstart '''\n rc = self.gpstart.run_gpstart_cmd()\n if not rc:\n raise Exception('Failed to start the cluster')\n tinctest.logger.info('Started the cluster successfully')\n\n def stop_db(self):\n ''' Gpstop and dont check for rc '''\n cmd = Command('Gpstop_a', 'gpstop -a')\n tinctest.logger.info('Executing command: gpstop -a')\n cmd.run()\n\n def crash_and_recover(self, crash_type, fault_type, checkpoint='noskip', cluster_state='sync'):\n '''\n @param crash_type : gpstop_i/gpstop_a/failover_to_primary/failover_to_mirror\n @note: when skip checkpoint is enabled, gpstop -a returns a non-rc return code and fails in the library. To workaround, using a local function\n '''\n if crash_type == 'gpstop_i' :\n rc = self.gpstop.run_gpstop_cmd(immediate = True)\n if not rc:\n raise Exception('Failed to stop the cluster')\n tinctest.logger.info('Stopped cluster immediately')\n self.start_db()\n elif crash_type == 'gpstop_a':\n self.resume_faults(fault_type, cluster_state)\n if checkpoint == 'skip' :\n self.stop_db()\n else:\n rc = self.gpstop.run_gpstop_cmd()\n if not rc:\n raise Exception('Failed to stop the cluster')\n tinctest.logger.info('Smart stop completed')\n self.start_db() \n elif crash_type == 'failover_to_primary':\n self.invoke_fault('filerep_consumer', 'fault')\n self.resume_faults(fault_type, cluster_state)\n (rc, num) =self.filereputil.wait_till_change_tracking_transition()\n tinctest.logger.info('Value of rc and num_down %s, %s, %s' % (rc, num, fault_type))\n\n elif crash_type == 'failover_to_mirror':\n self.invoke_fault('postmaster', 'panic', role='primary')\n if fault_type in ('dtm_broadcast_prepare', 'dtm_broadcast_commit_prepared', 'dtm_xlog_distributed_commit') :\n self.resume_faults(fault_type, cluster_state)\n PSQL.wait_for_database_up()\n (rc, num) = self.filereputil.wait_till_change_tracking_transition()\n tinctest.logger.info('Value of rc and num_down %s, %s' % (rc, num))\n if fault_type == 'abort' :\n self.filereputil.inject_fault(f='transaction_abort_after_distributed_prepared', y='reset',p=self.port , o='0', seg_id='1')\n\n if cluster_state == 'resync':\n if not self.gprecover.wait_till_insync_transition():\n raise Exception('Segments not in sync') \n\n def get_trigger_status_old(self, trigger_count):\n '''Compare the pg_stat_activity count with the total number of trigger_sqls executed '''\n for i in range(1,50):\n psql_count = PSQL.run_sql_command('select count(*) from pg_stat_activity;', flags='-q -t', dbname='postgres')\n tinctest.logger.info('Count of trigger sqls %s' % psql_count)\n if int(psql_count.strip()) < trigger_count :\n tinctest.logger.info('coming to the if loop in get_trigger_status')\n return False\n return True\n\n def get_trigger_status(self, trigger_count, fault_type):\n if fault_type == None:\n return self.get_trigger_status_old(trigger_count);\n\n return self.filereputil.check_fault_status(fault_name=fault_type, status=\"triggered\", seg_id='1', num_times_hit=trigger_count);\n\n def check_trigger_sql_hang(self, test_dir, fault_type = None):\n '''\n @description : Return the status of the trigger sqls: whether they are waiting on the fault \n Since gpfaultinjector has no way to check if all the sqls are triggered, we are using \n a count(*) on pg_stat_activity and compare the total number of trigger_sqls\n '''\n trigger_count=0\n for dir in test_dir.split(\",\"):\n trigger_dir = local_path('%s/trigger_sql/sql/' % (dir))\n trigger_count += len(glob.glob1(trigger_dir,\"*.sql\"))\n tinctest.logger.info('Total number of sqls to trigger %d in %s' % (trigger_count,test_dir));\n return self.get_trigger_status(trigger_count, fault_type)\n\n\n def run_faults_before_pre(self, cluster_state):\n '''\n @param cluster_state : sync/change_tracking/resync\n @description: 1. Cluster into change_tracking in case of resync/ change_tracking. \n '''\n if cluster_state == 'resync':\n self.invoke_fault('filerep_consumer', 'fault')\n self.filereputil.wait_till_change_tracking_transition()\n tinctest.logger.info('Change_tracking transition complete')\n\n def run_faults_before_trigger(self, checkpoint, cluster_state, fault_type):\n '''\n @param checkpoint : skip/noskip\n @param cluster_state : sync/change_tracking/resync\n @param fault_type : commit/abort\n @param end_prepare_two_phase_sleep : True/False\n @description : 1. Suspend resync faults. 2. Issue Checkpoint before the skip checkpoint, so that the bufferpool is cleared. 3. If skip issue 'skip checkpoint'. 4. Suspend transaction_faults based on test_type.\n '''\n if cluster_state == 'change_tracking':\n self.invoke_fault('filerep_consumer', 'fault')\n self.filereputil.wait_till_change_tracking_transition()\n tinctest.logger.info('Change_tracking transition complete')\n\n if cluster_state == 'resync':\n self.invoke_fault('filerep_resync', 'suspend', role='primary')\n\n if checkpoint == 'skip':\n self.invoke_fault('filerep_transition_to_sync_before_checkpoint', 'suspend', role='primary', port=self.port, occurence='0')\n rc = self.gprecover.incremental()\n if not rc:\n raise Exception('Gprecvoerseg failed')\n tinctest.logger.info('Cluster in resync state')\n\n PSQL.run_sql_command('CHECKPOINT;', dbname='postgres')\n if checkpoint == 'skip':\n self.invoke_fault('checkpoint', 'skip', role='primary', port= self.port, occurence='0')\n self.inject_fault(fault_type)\n\n # Can't do it after filerep_resync resume as gets stuck due to\n # filerep_transition_to_sync_before_checkpoint suspend above for\n # MirroedLock\n PSQL.wait_for_database_up()\n\n if cluster_state == 'resync':\n self.filereputil.inject_fault(f='filerep_resync', y='resume', r='primary')\n\n def run_crash_and_recover(self, crash_type, fault_type, test_dir, cluster_state='sync', checkpoint='noskip'):\n '''\n @param crash_type : gpstop_i/gpstop_a/failover_to_mirror/failover_to_primary\n @param fault_type : commit/abort/end_prepare_two_phase_sleep\n @param test_dir : dir of the trigger sqls\n @description : Execute the specified crash type before/after resuming the suspended fault and recover\n '''\n trigger_status = self.check_trigger_sql_hang(test_dir)\n tinctest.logger.info('trigger_status %s' % trigger_status)\n sleep(50) # This sleep is needed till we get a way to find the state of all suspended sqls\n if trigger_status == True:\n if cluster_state == 'resync':\n self.filereputil.inject_fault(f='filerep_transition_to_sync_before_checkpoint', y='resume', r='primary')\n sleep(15) # wait little before crash\n self.crash_and_recover(crash_type, fault_type, checkpoint, cluster_state)\n else:\n tinctest.logger.info('The fault_status is not triggered')\n \n def gprecover_rebalance(self):\n '''\n @description: Run rebalance through gpstop -air is much faster than gprecoverseg -r for test purpose.\n '''\n rc = self.gpstop.run_gpstop_cmd(immediate = True)\n if not rc:\n raise Exception('Failed to stop the cluster')\n tinctest.logger.info('Stopped cluster immediately')\n self.start_db()\n\n def run_gprecover(self, crash_type, cluster_state='sync'):\n '''Recover the cluster if required. '''\n if crash_type in ('failover_to_primary', 'failover_to_mirror') or cluster_state == 'change_tracking' :\n rc = self.gprecover.incremental()\n if not rc:\n raise Exception('Gprecvoerseg failed')\n if not self.gprecover.wait_till_insync_transition():\n raise Exception('Segments not in sync') \n tinctest.logger.info('Cluster in sync state')\n if crash_type == 'failover_to_mirror' :\n self.gprecover_rebalance()\n tinctest.logger.info('Successfully Rebalanced the cluster')\n else:\n tinctest.logger.info('No need to run gprecoverseg. The cluster should be already in sync')\n\n\n def switch_ckpt_faults_before_trigger(self, cluster_state, fault_type):\n '''\n @param cluster_state : sync/change_tracking/resync\n @param fault_type : dtm_broadcast_prepare/dtm_broadcast_commit_prepared/dtm_xlog_distributed_commit\n '''\n if cluster_state in ('change_tracking', 'resync'):\n self.invoke_fault('filerep_consumer', 'fault')\n self.filereputil.wait_till_change_tracking_transition()\n tinctest.logger.info('Change_tracking transition complete') \n\n if cluster_state == 'resync':\n self.invoke_fault('filerep_resync', 'suspend', role='primary')\n rc = self.gprecover.incremental()\n if not rc:\n raise Exception('Gprecvoerseg failed')\n tinctest.logger.info('Cluster in resync state')\n self.inject_fault(fault_type)\n\n def switch_ckpt_switch_xlog(self):\n '''\n @description: pg_switch_xlog on segments\n '''\n sql_cmd = 'select * from pg_switch_xlog();'\n num_primary = self.config.get_countprimarysegments()\n for i in range(num_primary):\n (host, port) = self.config.get_hostandport_of_segment(psegmentNumber=i)\n PSQL.run_sql_command_utility_mode(sql_cmd, host = host, port = port)\n\n def switch_ckpt_crash_and_recover(self, crash_type, fault_type, test_dir, cluster_state='sync', checkpoint='noskip'):\n '''\n @param crash_type : gpstop_i/gpstop_a/failover_to_mirror/failover_to_primary\n @param fault_type : dtm_broadcast_prepare/dtm_broadcast_commit_prepared/dtm_xlog_distributed_commit\n @param test_dir : dir of the trigger_sqls\n '''\n trigger_status = self.check_trigger_sql_hang(test_dir, fault_type)\n tinctest.logger.info('trigger_status %s' % trigger_status)\n\n if trigger_status == True:\n if cluster_state == 'resync':\n self.filereputil.inject_fault(f='filerep_resync', y='resume', r='primary')\n sleep(30) #Give a little time before crash.\n self.crash_and_recover(crash_type, fault_type, checkpoint, cluster_state)\n else:\n tinctest.logger.info('The fault_status is not triggered')\n \n \n def cleanup_dangling_processes(self):\n '''\n @description: Since the test suspend transactions at different stages and does immediate shutdown, \n few processes will not be cleaned up and eventually will eat up on the system resources\n This methods takes care of killing them at the end of each test, if such processes exists\n '''\n\n num_primary = self.config.get_countprimarysegments()\n for i in range(num_primary):\n (host, port) = self.config.get_hostandport_of_segment(psegmentNumber=i)\n grep_cmd = \"ps -ef|grep %s|grep 'Distributed'\" % port\n cmd = Command('Check for dangling process', cmdStr = 'gpssh -h %s -e \"%s\" ' % (host, grep_cmd))\n cmd.run()\n result = cmd.get_results()\n if len(result.stdout.splitlines()) > 2 :\n grep_and_kill_cmd = \"ps -ef|grep %s|grep 'Distributed'|awk '{print \\$2}'|xargs kill -9\" % port\n cmd = Command('Kill dangling processes', cmdStr='gpssh -h %s -e \"%s\" ' % (host, grep_and_kill_cmd ))\n cmd.run()\n tinctest.logger.info('Killing the dangling processes') \n","sub_path":"src/test/tinc/tincrepo/mpp/gpdb/tests/storage/pg_twophase/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":18396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"618767273","text":"import requests\nfrom bs4 import BeautifulSoup\nimport random\nimport csv\nimport pymysql\n\nrequests.packages.urllib3.disable_warnings()\nurl='http://www.xicidaili.com/nn/1'\n# url='http://www.baidu.com'\n# url='https://sclub.jd.com/comment/productPageComments.action?&productId=5487565&score=3&sortType=5&page=0&pageSize=10'\n# xici_headers={\n# 'Referer': 'http://www.xicidaili.com/nn',\n# 'User-Agent': 'Mozilla/5.0 (Windows NT 6.2; WOW64; rv:21.0) Gecko/20100101 Firefox/21.0',\n# }\n# headers={\n# 'Referer': 'http://www.baidu.com/',\n# 'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3534.4 Safari/537.36',\n# }\n#\nsession=requests.Session()\n# session.headers=xici_headers\n# session.proxies={'https': 'http://58.241.186.90:43043', 'http': 'http://58.241.186.90:43043'}\n\n# res=session.get(url,headers=headers,verify=False)\n# print(res)\n# conn = pymysql.connect('localhost', 'root', '326..dd', 'IP_Proxies')\n# cursor = conn.cursor()\n\n\ndef get_dict_proxies(proxies):\n return {'https':'http://'+proxies,\n 'http':'http://'+proxies}\n\n\n\n#获取代理写入文件\n\ndef get_proxy_write_in_file(url):\n session.proxies={'https': 'http://218.7.221.166:31485', 'http': 'http://218.7.221.166:31485'}\n res=session.get(url,verify=False,timeout=5)\n soup=BeautifulSoup(res.text,'lxml').select('table#ip_list')[0]\n trs = soup.find_all('tr')\n proxys_list = []\n for tr in trs:\n temp_list=[td.text for td in tr.select('td')][1:]\n temp_list2=temp_list[:5]\n temp_list2.extend(temp_list[-2:])\n proxys_list.append(temp_list2)\n proxys_list=proxys_list[2:]\n # print(proxys_list)\n #清除空格\n for pro in range(len(proxys_list)):\n for e in range(len(proxys_list[pro])):\n proxys_list[pro][e]=proxys_list[pro][e].replace('\\n','')\n\n\n with open('ipproxies/3.txt','w',newline='') as fw:\n # session.headers = headers\n for pro in proxys_list:\n proxies = get_dict_proxies(pro[0] + ':' + pro[1])\n # session.proxies = proxies\n print(proxies)\n try:\n # baidu_res = session.get('http://www.baidu.com', verify=False, timeout=2)\n baidu_res=session.get(url,verify=False)\n baidu_res.encoding = 'utf-8'\n if (baidu_res.status_code)==200:\n fw.write(str(proxies)+'\\n')\n fw.flush()\n except:\n print('failed')\n # print(baidu_res.text)\n # print(baidu_res.status_code)\n # if (baidu_res.status_code) == 200:\n # fw.write(str(proxies) + '\\n')\n # fw.flush()\n\npage=1\nwhile page<100:\n get_proxy_write_in_file('http://www.xicidaili.com/nn/{}'.format(page))\n\n# get_proxy_write_in_file(url)\n\ndef write_csv_file_in_database(filename,datas):\n conn = pymysql.connect('localhost', 'root', '326..dd', 'IP_Proxies')\n cursor = conn.cursor()\n with open(filename, 'r') as fr:\n for line in fr.readlines():\n datas = line.split(',')\n for i in range(len(datas)):\n datas[i] = datas[i].replace('\\n', '')\n # sql='insert into xici (ipaddr,port,address,type,http_type,available_days,last_verify_time)values (%s,%d,%s,%s,%s,%s,%s)'%(datas[0],int(datas[1]),datas[2],datas[3],datas[4],datas[5],datas[6].replace('\\n',''))\n cursor.execute(\n 'insert into xici (ipaddr,port,address,type,http_type,available_days,last_verify_time)values (%s,%s,%s,%s,%s,%s,%s)',\n datas)\n conn.commit()\n\n\n\n\n","sub_path":"myspiders/tidy/Proxys.py","file_name":"Proxys.py","file_ext":"py","file_size_in_byte":3634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"411122374","text":"#!/usr/bin/env python\n\n\"\"\"parse puppet fact data from a file and report useful information\"\"\"\n\nimport json\nimport logging\nimport os\nimport yaml\n\nlogging.basicConfig(level=logging.INFO)\n\nconfig_file = os.getenv(\n 'PUPPETDB_PARSING_CONFIG_FILE',\n os.path.join(\n os.environ['HOME'], '.local', 'puppetdb_parsing.yaml',\n )\n)\n\nlogging.debug('config file: {}'.format(config_file))\n\nwith open(config_file) as cf:\n config = yaml.safe_load(cf)\n\nlogging.debug('config: {}'.format(config))\n\ntry:\n fact_file = config['fact_file']\nexcept KeyError:\n fact_file = os.path.join(\n os.environ['HOME'], 'data', 'puppet', 'facts'\n )\n\nwith open(fact_file) as ff:\n facts = json.load(ff)\n\n\ndef disk_devices(node):\n \"\"\"given a nodename, return a list of its disk devices.\"\"\"\n disks = []\n for fact in facts:\n if (\n fact['name'] == 'blockdevices' and\n fact['certname'].startswith(node)\n ):\n logging.debug('evaluating {}'.format(fact['certname']))\n logging.debug('blockdevices fact: {}'.format(fact['value']))\n blockdevices = fact['value'].split(',')\n logging.debug('block devices: {}'.format(blockdevices))\n disks.extend([x for x in blockdevices if x.startswith('sd')])\n return disks\n\n\ndef disk_device_size(node, disks):\n \"\"\"given a nodename and a list of disks, return the sum of the capacities\n in GB\"\"\"\n BILLION = 1.00E+09\n capacity_bytes = 0\n for fact in facts:\n if fact['certname'].startswith(node):\n for disk in disks:\n disk_fact_name = 'blockdevice_' + disk + '_size'\n if fact['name'] == disk_fact_name:\n logging.debug(\n 'adding capacity {} from disk {} node {}'.format(\n fact['value'],\n fact['name'],\n fact['certname']\n )\n )\n capacity_bytes += int(fact['value'])\n\n return round(capacity_bytes / BILLION)\n\n\nif __name__ == \"__main__\":\n n = (\n config['nodes']['test'] +\n config['nodes']['shared'] +\n config['nodes']['production']\n )\n for host in n:\n print('{}: disk devices: {}'.format(host, disk_devices(host)))\n print(\n '{}: total disk capacity: {} GB'.format(\n host,\n disk_device_size(host, disk_devices(host))\n )\n )\n","sub_path":"tools/puppetdb_parsing.py","file_name":"puppetdb_parsing.py","file_ext":"py","file_size_in_byte":2492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"241681014","text":"# Returns an array mapping index -> # of ways\n\"\"\"\nThe algorithm below gives not only coin sums but how many\nof each way to make the coins. However, it requires n^2 memory\nand n^2 processing power when compared to the n memory and n processing\npower of quickCoinSums\n\ndef coinSums(n, coins):\n rows = n+1\n columns = n+1\n\n coinCounts = [ [0 for _ in range(0, columns)] for _ in range(0, rows)]\n\n # 1 way to make 0c change with 0 coins\n coinCounts[0][0] = 1\n\n for coin in sorted(coins):\n for i in range(1, rows):\n for j in range(i, columns):\n if( coin <= j ):\n coinCounts[i][j] += coinCounts[ i - 1 ][ j - coin ]\n\n return [ sum([coinCounts[i][j] for i in range(0, rows) ]) for j in range(0, columns)]\n\"\"\"\n\ndef quickCoinSums(n, coins):\n columns = n+1\n coinCounts = [0 for _ in range(0, columns)]\n modLimit = 10**9 + 7\n\n coinCounts[0] = 1\n\n for coin in sorted(coins):\n for i in range(1, columns):\n if( coin <= i ):\n coinCounts[i] += coinCounts[ i - coin ]\n coinCounts[i] %= modLimit\n\n return coinCounts\n\nif __name__ == '__main__':\n coins = [1, 2, 5, 10, 20, 50, 100, 200]\n inputs = [int(input()) for _ in range(0, int(input())) ]\n precomputed = quickCoinSums(max(inputs), coins)\n\n for query in inputs:\n print(precomputed[query])\n","sub_path":"Problem_30_39/Problem_31/coinSums.py","file_name":"coinSums.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"376772892","text":"#!/usr/bin/python\n\nfrom preprocessor import DataPreprocessor\nfrom sklearn.linear_model import Perceptron\n\n\"\"\"\nUses a perceptron in order to determine if a movie gets nominated.\n\"\"\"\n\n# Author: Omar Elazhary \n# License: MIT\n\n# Preprocess the data:\n\npreprocessor = DataPreprocessor(['Nominated Best Picture',\n 'Won Best Picture', 'Num of Awards'],\n ['genres', 'plot_keywords', 'movie_imdb_link',\n 'director_name', 'actor_3_facebook_likes',\n 'actor_2_name', 'actor_1_facebook_likes',\n 'actor_1_name', 'movie_title',\n 'cast_total_facebook_likes',\n 'actor_3_name', 'facenumber_in_poster',\n 'language', 'country', 'content_rating',\n 'budget', 'actor_2_facebook_likes',\n 'aspect_ratio'],\n 'movies_original.csv')\npreprocessor.preprocess()\n\npreprocessor.numerify()\n\n# Create the test set:\n\npreprocessor.create_test_set(0.3, 1, True)\n\n# Perform cross-validation:\n\nclf = Perceptron()\nclf = clf.fit(preprocessor.features_numerical,\n preprocessor.labels_numerical[1])\n\n\"\"\"\nscores = cross_validation.cross_val_score(clf, preprocessor.features_numerical,\n preprocessor.labels_numerical[2], cv=10)\n\nprint(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n\"\"\"\n\nscore = clf.score(preprocessor.test_features, preprocessor.test_labels)\n\nprint(\"Accuracy after testing (no CV): %3.2f%%\") % (score * 100)\n","sub_path":"scripts/perceptron_win.py","file_name":"perceptron_win.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"369074428","text":"#!/usr/bin/env python3\n# -*-coding : utf-8 -*-\n# author : Chris\n# date : '15-9-9'\n# description: some description here...\nimport time\n\n__author__ = 'Chris'\n\nfrom multiprocessing import Process\nimport os\n\n\ndef run_proc(name):\n print('Run child process %s(%s)...', (name, os.getpid()))\n time.sleep(1)\n\ndef main():\n print('Parent process %s' % os.getpid())\n p = Process(target=run_proc, args=('test',))\n print('Child process will start...')\n p.start()\n p.join()\n print('Child process end.')\n\n\nif __name__ == '__main__':\n main()\n \n\n\n","sub_path":"10.进程和线程/Process_1.py","file_name":"Process_1.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"373720347","text":"from tornado import gen\nimport tornado.auth\nimport tornado.escape\nimport tornado.web\nfrom handlers.BaseHandler import BaseHandler\n\n__author__ = 'shyal.beardsley'\n\n\nclass AuthHandler(BaseHandler, tornado.auth.GoogleMixin):\n @tornado.web.asynchronous\n @gen.coroutine\n def get(self):\n if self.get_argument(\"openid.mode\", None):\n user = yield self.get_authenticated_user()\n self.set_secure_cookie(\"user\",\n tornado.escape.json_encode(user))\n self.redirect(\"/\")\n return\n self.authenticate_redirect()","sub_path":"handlers/AuthHandler.py","file_name":"AuthHandler.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"520647206","text":"from django.http import HttpResponse\nfrom django.views.generic import View\nfrom django.core import serializers\nfrom django.utils import timezone\nfrom django.shortcuts import get_object_or_404\n\nimport datetime\nimport json\n\nfrom users.models import Comment\nfrom users.models import Reservation\nfrom api.models import FinanceReport\nfrom api.models import StandReport\nfrom locations.models import Event\nfrom locations.models import Location\nfrom users.models import User\n\n\n# /comment/id?id_type=\nclass CommentView(View):\n\n\tdef get(self, request, id):\n\n\t\tid_type = request.GET.get('id_type')\n\t\tif id_type == \"user\":\n\t\t\tcomments = Comment.objects.filter(target_user=id)\n\t\telif id_type == \"event\":\n\t\t\tcomments = Comment.objects.filter(target_event=id)\n\n\t\tcomment_list = []\n\t\tfor comment in comments:\n\t\t\tcomment_dict = dict()\n\t\t\tcomment_dict['text'] = comment.text\n\t\t\tcomment_dict['owner_id'] = comment.owner_id\n\t\t\tcomment_dict['owner'] = str(comment.owner)\n\t\t\tcomment_list.append(comment_dict)\n\t\treturn HttpResponse(json.dumps(comment_list), content_type='application/json')\n\n\tdef post(self, request, id):\n\n\t\ttext = request.POST.get('text', '')\n\t\towner_id = request.session['id']\n\t\ttarget_id = id\n\t\tid_type = request.POST.get('id_type', '')\n\n\t\tif id_type == \"event\":\n\t\t\tcomment = Comment(text=text, owner_id=owner_id, target_event_id=target_id)\n\t\t\tcomment.save()\n\t\telif id_type == \"user\":\n\t\t\tcomment = Comment(text=text, owner_id=owner_id, target_user_id=target_id)\n\t\t\tcomment.save()\n\n\t\tdata = dict()\n\t\tdata['text'] = comment.text\n\t\tdata['owner_id'] = comment.owner_id\n\t\tdata['owner'] = str(comment.owner)\n\t\treturn HttpResponse(json.dumps(data), content_type='application/json')\n\n\n# /stand\nclass StandView(View):\n\tdef get(self):\n\n\t\tall_targeted_events = Event.objects.filter(date_end__gt=timezone.now())\n\n\t\tlist_of_events = []\n\n\t\tfor event in all_targeted_events:\n\t\t\tsmall_stand = 0\n\t\t\tmedium_stand = 0\n\t\t\tlarge_stand = 0\n\n\t\t\tfor reservation in event.reservation_set.all():\n\t\t\t\tsmall_stand += reservation.small_stand_count\n\t\t\t\tmedium_stand += reservation.medium_stand_count\n\t\t\t\tlarge_stand += reservation.large_stand_count\n\n\t\t\tmyclass = StandReport(event.location, reservation.event, small_stand, medium_stand, large_stand)\n\t\t\tlist_of_events.append(myclass)\n\n\t\tdata = serializers.serialize(\"json\", list_of_events)\n\t\treturn HttpResponse(json.dumps(data), content_type='application/json;charset=utf8')\n\n\n# /finance\nclass FinanceView(View):\n\tdef get(self):\n\t\tdata = serializers.serialize(\"json\", FinanceReport.objects.filter(date__gt=(timezone.now() - datetime.timedelta(hours=24))))\n\t\treturn HttpResponse(json.dumps(data), content_type='application/json;charset=utf8')\n\nclass RatingView(View):\n\tdef post(self, request):\n\t\tif request.POST.get('id_type') == 'event':\n\t\t\tklass = Event\n\t\telse:\n\t\t\tklass = User\n\n\t\trating = int(request.POST.get('rating', 0))\n\t\to = get_object_or_404(klass, id=request.POST['id'])\n\t\to.grade_received_count += 1\n\t\to.grade_received_sum += rating\n\t\to.save()\n\n\t\tuser = get_object_or_404(User, id=request.POST['user_id'])\n\t\tuser.grade_given_sum += rating\n\t\tuser.grade_given_count += 1\n\t\tuser.save()\n\t\treturn HttpResponse('{\"grade_avg\": ' + str(o.grade_received_avg()) + ', \"grade_count\": ' + str(o.grade_received_count) + \" }\", content_type='application/json')\n\n\n","sub_path":"stand_off/api/json.py","file_name":"json.py","file_ext":"py","file_size_in_byte":3275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"87013230","text":"import socket\r\nfrom settings import Settings\r\nimport threading\r\nimport datetime\r\nimport os\r\n\r\n\r\n# Функция обрабатывающая запрос\r\ndef request(conn, addr, data, directory):\r\n # Получение инфы от клиента\r\n msg = data.decode()\r\n print(msg)\r\n\r\n # Получаем имя файла, который отправим клиенту\r\n name = msg.split()[1][1:]\r\n\r\n if name == \"\":\r\n name = \"index.html\"\r\n name = directory + \"\\\\\" + name\r\n\r\n # Вычисление текущего времени и приведение его к необходимому формату\r\n now = datetime.datetime.now()\r\n date = now.strftime(\"%a, %d %b %Y %H:%M:%S GTM\")\r\n\r\n # Вывод в лог файл основной инфы\r\n with open(\"log.txt\", \"a\") as log:\r\n print(f\"Date: {date}\\nAddr: {addr}\\nFile: {name}\", file=log)\r\n\r\n # Формирование ответа на запрос\r\n try:\r\n size = os.path.getsize(name) # Размер ответа\r\n\r\n # Если файла с таким именем нет\r\n except FileNotFoundError:\r\n resp = f\"\"\"HTTP/1.1 404 Not Found\r\n Server:SelfMadeServer v0.0.1\r\n Date: {date}\r\n Connection: keep-alive\r\n \"\"\"\r\n with open(\"log.txt\", \"a\") as log:\r\n print(\"Error: 404\", file=log)\r\n\r\n resp = resp.encode()\r\n else:\r\n decr = name.split(\".\")[-1] # Получение расширения\r\n if decr not in settings.dict.keys():\r\n resp = f\"\"\"HTTP/1.1 403 Forbidden\r\n Server:SelfMadeServer v0.0.1\r\n Date: {date}\r\n Connection: keep-alive\r\n \"\"\"\r\n with open(\"log.txt\", \"a\") as log:\r\n print(\"Error: 403\", file=log)\r\n else:\r\n try:\r\n # Если файл текстовый\r\n with open(name, \"r\", encoding=\"utf-8\") as file:\r\n resp = f\"\"\"HTTP/1.1 200 OK\r\n Server: SelfMadeServer v0.0.1\r\n Date: {date}\r\n Content-Type: text/{decr};charset=utf-8\r\n Content-Length: {size}\r\n Connection: keep-alive\r\n\r\n \"\"\"\r\n resp += file.read()\r\n resp = resp.encode()\r\n # Если файл бинарный (картинка)\r\n except UnicodeDecodeError:\r\n resp = f\"\"\"HTTP/1.1 200 OK\r\n Server: SelfMadeServer v0.0.1\r\n Date: {date}\r\n Content-Type: image/{decr}\r\n Content-Length: {size}\r\n Connection: keep-alive\r\n\r\n \"\"\"\r\n resp = resp.encode()\r\n with open(name, \"rb\") as file:\r\n resp += file.read()\r\n conn.send(resp)\r\n print(resp)\r\n\r\n\r\n# Функция для работы с клиентами\r\ndef connection(conn, addr, directory):\r\n data = conn.recv(settings.request_size)\r\n if not data:\r\n return\r\n request(conn, addr, data, directory)\r\n conn.close()\r\n\r\n\r\nsettings = Settings() # Создаём переменную в которой хранятся настройки\r\nsock = socket.socket()\r\n\r\n# Подключаемся и начинаем прослушивать порт, указанный в настройках\r\ntry:\r\n sock.bind(('', settings.port))\r\n print(f\"Using port {settings.port}\")\r\nexcept OSError:\r\n sock.bind(('', settings.port2))\r\n print(f\"Using port {settings.port2}\")\r\nsock.listen(5)\r\n\r\ndirectory = settings.directory\r\n\r\n# На каждое подключение - свой поток\r\nconn, addr = sock.accept()\r\nwhile True:\r\n print(\"Connected\", addr, \"\\n\")\r\n tr = threading.Thread(target=connection, args=(conn, addr, directory))\r\n tr.start()\r\n conn, addr = sock.accept()","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"72469581","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\n\nclass Solution(object):\n def flatten(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: void Do not return anything, modify root in-place instead.\n \"\"\"\n if not root:\n return\n self.helper(root)\n\n def helper(self, root):\n if not root:\n return\n self.helper(root.left)\n self.helper(root.right)\n curr = root\n if not curr.left:\n return\n curr = curr.left\n while curr.right:\n curr = curr.right\n curr.right = root.right\n root.right = root.left\n root.left = None\n\nclass Solution(object):\n def flatten(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: void Do not return anything, modify root in-place instead.\n \"\"\"\n if root is None:\n return\n stack = [root]\n while stack:\n node = stack.pop()\n if node.right:\n stack.append(node.right)\n if node.left:\n stack.append(node.left)\n node.left = None\n if stack:\n node.right = stack[-1]\n else:\n node.right = None\n","sub_path":"python/114. Flatten Binary Tree to Linked List.py","file_name":"114. Flatten Binary Tree to Linked List.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"347329040","text":"# -*- coding:utf-8 -*-\n# ! /bin/bash/python3\n\nimport json\nimport re\nimport requests\nfrom scrapy.spiders import Spider\nfrom scrapy.http import Request\nfrom urllib.parse import urljoin\nfrom picScrapy.items import AfscrapyItem\n\n\nclass PicSpider(Spider):\n name = \"zlwm\" # 定义爬虫名，中粮我买\n headers = {\n 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) '\n 'Chrome/61.0.3163.79 Safari/537.36',\n }\n\n def start_requests(self):\n start_url = 'http://www.womai.com/index-31000-0.htm'\n yield Request(start_url, callback=self.parse, headers=self.headers)\n\n # 一级页面的处理函数\n def parse(self, response):\n datas = response.xpath('//li[@class=\"kinds\"]')\n for url in datas:\n category_name = url.xpath('./h3/a[1]/text()').extract()[0]\n next_urls = url.xpath('.//li[@class=\"sub_kind\"]/a/@href').extract()\n for next_url in next_urls:\n next_url = urljoin(response.url, next_url)\n yield Request(next_url, callback=self.parse_data, meta={\"cat\": category_name}, headers=self.headers)\n\n # 二级页面的处理函数\n @staticmethod\n def parse_data(response):\n item = AfscrapyItem()\n # 截取jsonp字符里面的json数据部分\n datas = response.xpath('//div[@class=\"product-list\"]/ul/li')\n for data in datas:\n item['goods_id'] = data.xpath('./@data-productid').extract()[0]\n title = data.xpath('.//div[@class=\"list-title\"]/p/a/text()').extract()[0]\n shop_name = re.search('【(.+)】', title)\n if shop_name:\n item['shop_name'] = shop_name.group(1)\n else:\n item['shop_name'] = ''\n item['category_name'] = response.meta[\"cat\"]\n item['title'] = re.search('\\S+\\s*\\S+', title).group()\n item['sales_num'] = data.xpath('.//span[@class=\"evaluated\"]/a/em/text()').extract()[0]\n item['unit'] = re.search('\\d+.*', title).group()\n # item['price'] = data.xpath('.//div[@class=\"price\"]/b/text()')\n product_url = 'http://price.womai.com/PriceServer/open/productlist.do?' \\\n 'usergroupid=100&ids='+item['goods_id']+'&mid=0&cityid=31000&picType=0&sellable=true' \\\n '&properties=title&pics=pic60&prices=buyPrice&callback=jsonp'\n response_new = requests.get(product_url)\n if (response_new.status_code == 200) and response_new.text:\n res = re.match('jsonp\\((.+)\\)', response_new.text).group(1)\n result = json.loads(res, encoding='utf-8')\n if len(result['result']):\n item['price'] = result['result'][0]['price']['buyPrice']['priceValue']\n else:\n item['price'] = 0.0\n else:\n item['price'] = 0.0\n item['location'] = \"\"\n yield item\n","sub_path":"picScrapy/spiders/zlwm.py","file_name":"zlwm.py","file_ext":"py","file_size_in_byte":2991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"289479186","text":"\"\"\"Integration tests for training.pytorch.model\"\"\"\n\nimport tempfile\nfrom unittest.mock import patch\n\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torchvision.transforms.functional import to_tensor\n\nfrom datasets.imagenet_dataset import ImageNetDataSet\nfrom datasets.ops import per_image_standardization\nfrom networks.pytorch.object_classification.alexnet import AlexNet\nfrom training.pytorch.dataset_transformer import PyTorchDataSetTransformer\nfrom training.pytorch.model import Model\nfrom utils.generic_utils import cycle\nfrom utils.test_utils import df_images\n\n\nclass TestModel(object):\n \"\"\"Tests for Model\"\"\"\n\n def test_load_weights(self):\n \"\"\"Test load_weights method\n\n This tests that a model saved using the `Model.save_weights` method can\n be loaded using the `Model.load_weights` method.\n \"\"\"\n\n alexnet = AlexNet(config={'n_channels': 3, 'n_classes': 1000})\n model1 = Model(network=alexnet)\n model2 = Model(network=alexnet)\n\n fpath_weights = tempfile.mktemp()\n model1.save_weights(fpath_weights)\n model2.load_weights(fpath_weights)\n\n def test_fit_generator(self, df_images):\n \"\"\"Test fit_generator method\"\"\"\n\n dataset_config = {'height': 227, 'width': 227}\n dataset = ImageNetDataSet(df_images, dataset_config)\n transformations = [\n (per_image_standardization, {'sample_keys': ['image']}),\n (to_tensor, {'sample_keys': ['image']}),\n (torch.tensor, {'sample_keys': ['label'], 'dtype': torch.long})\n ]\n dataset = PyTorchDataSetTransformer(\n numpy_dataset=dataset, transformations=transformations\n )\n data_loader = DataLoader(\n dataset=dataset, batch_size=2,\n shuffle=True, num_workers=4\n )\n\n alexnet = AlexNet(config={'n_channels': 3, 'n_classes': 1000})\n model = Model(network=alexnet)\n\n assert not model._compiled\n assert not model.optimizer\n assert not model.loss\n model.compile(optimizer='Adam', loss='CrossEntropyLoss')\n assert model._compiled\n assert model.optimizer\n assert model.loss\n\n with patch.object(model, 'loss', wraps=model.loss) as patched_loss:\n model.fit_generator(\n generator=cycle(data_loader),\n n_steps_per_epoch=2, n_epochs=2,\n validation_data=cycle(data_loader), n_validation_steps=3\n )\n\n assert patched_loss.call_count == 10\n","sub_path":"tests/integration_tests/training/pytorch/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"7889242","text":"from tkinter import *\n\nclass gui(object):\n def __init__(self, master):\n frame = Frame(master)\n frame.pack()\n\n self.runButton = Button(frame, text=\"Run the Model\", command=self.model)\n self.runButton.pack()\n\n def model(self):\n run(Model)\n\n\nclass Model(object):\n def __init__(self,Juv,Adl,Sen,BR,Jrate,Arate,Srate,Gen):\n self.Juv = Juv\n self.Adl = Adl\n self.Sen = Sen\n self.BR = BR\n self.Jrate = Jrate\n self.Arate = Arate\n self.Srate = Srate\n self.Gen = Gen\n \n def gen_0(self):\n print(\"Juveniles:\", self.Juv)\n print(\"Adults:\", self.Adl)\n print(\"Seniles:\", self.Sen)\n print(\"Birth Rate:\", self.BR)\n print(\"Juvenile Survival Rate:\", self.Jrate)\n print(\"Adult Survival Rate:\", self.Arate)\n print(\"Senile Survival Rate:\", self.Srate)\n print(\"Number of Generations that should run:\", self.Gen)\n\n def maths(self):\n self.gen_data = []\n for x in range(self.Gen):\n adults = self.Adl\n\n self.Juv = self.Juv * self.Jrate\n self.Sen = self.Sen * self.Srate\n self.Adl = self.Adl * self.Adl\n\n self.Sen = self.Sen + self.Adl\n self.Adl = self.Juv\n self.Juv = adults * self.BR\n\n self.gen_data.append(x+1)\n self.gen_data.append(self.Juv)\n self.gen_data.append(self.Adl)\n self.gen_data.append(self.Sen)\n \n def display(self):\n print(self.gen_data)\n\ndef run(Model):\n a = Model(int(10), int(10), int(10), float(5), float(1), float(1), float(0.5), int(5))\n b = Model(int(20), int(40), int(10), float(0.5), float(1), float(1), float(1), int(5))\n c = Model(int(40), int(50), int(20), float(2), float(0.75), float(0.75), float(0.75), int(10))\n\n print(\"Generation 0 Data\")\n a.gen_0()\n b.gen_0()\n c.gen_0()\n a.maths()\n b.maths()\n c.maths()\n print(\"Output\")\n a.display()\n b.display()\n c.display()\n\nroot = Tk()\nb = gui(root)\nroot.mainloop()\n","sub_path":"OOP.py","file_name":"OOP.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"217960636","text":"import random\r\n\r\n\r\ndef game(user_choice):\r\n list1=[\"stone\",\"paper\",\"scissors\"]\r\n user_count=0\r\n comp_count=0\r\n computer_choice=random.choice(list1)\r\n print(\"\\n Your choice:\",user_choice,\"\\nComputer's choice:\",computer_choice)\r\n if (user_choice == computer_choice):\r\n print(\"Both players selected\",user_choice,\". It's a tie!\")\r\n elif user_choice == \"rock\":\r\n if (computer_choice == \"scissors\"):\r\n print(\"Rock smashes scissors! You win!\")\r\n user_count+=1\r\n else:\r\n print(\"Paper covers rock! You lose.\")\r\n comp_count+=1\r\n elif (user_choice == \"paper\"):\r\n if computer_choice == \"rock\":\r\n print(\"Paper covers rock! You win!\")\r\n user_count+=1\r\n else:\r\n print(\"Scissors cuts paper! You lose.\")\r\n comp_count+=1\r\n elif user_choice == \"scissors\":\r\n if computer_choice == \"paper\":\r\n print(\"Scissors cuts paper! You win!\")\r\n user_count+=1\r\n else:\r\n print(\"Rock smashes scissors! You lose.\")\r\n comp_count+=1\r\n \r\n return comp_count,user_count\r\ndef play(count):\r\n c_count=0\r\n u_count=0\r\n while(count>0):\r\n user_choice = input(\"Enter a choice (rock, paper, scissors): \")\r\n if user_choice==\"rock\" or user_choice==\"paper\" or user_choice==\"scissors\":\r\n comp_count,user_count=game(user_choice)\r\n c_count=c_count+comp_count\r\n u_count=u_count+user_count\r\n else:\r\n print(\"Choose from the available options.\\n\")\r\n count=count-1\r\n return c_count,u_count\r\n\r\nclass rock_paper_scissors:\r\n\r\n count=int(input(\"How many times do you want to play:\")) \r\n comp_count,user_count=play(count)\r\n\r\n if comp_count>user_count:\r\n\t print(\"COMPUTER WINS!!!\\n Comp_Score:\",comp_count,\"\\n Your_Score\",user_count)\r\n elif comp_count==user_count:\r\n print(\"IT'S A TIE!!!\\n Comp_Score:\",comp_count,\"\\n Your_Score\",user_count)\r\n else:\r\n\t print(\"YOU WIN!!!\\n Comp_Score:\",comp_count,\"\\n Your_Score\",user_count)","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"215767638","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom sklearn.linear_model import LogisticRegression\n\nimport wisconsin_cancer\nimport mnist\n\n\nnp.random.seed(10)\ncancer_data = wisconsin_cancer.get_data(0.7, 0.2)\nmnistData = mnist.get_data(0.7, 0.2)\n\n\ndatasets = [[mnistData, \"MNIST\"], [cancer_data, \"Wisconsin cancer data\"]]\n\nfor dataset, name in datasets:\n x_train = dataset['x_train']\n y_train = dataset['y_train']\n x_test = dataset['x_test']\n y_test = dataset['y_test']\n\n model = Sequential()\n model.add(Dense(x_train.shape[1], activation='relu'))\n model.add(Dense(32, activation='relu'))\n model.add(Dense(y_train.shape[1], activation='softmax'))\n\n model.compile(optimizer='adam',\n loss='categorical_crossentropy',\n metrics=['accuracy'])\n\n hist = model.fit(x_train, y_train, validation_split=0.2, epochs=20, batch_size=10, verbose=0)\n\n pred_train = model.predict(x_train)\n scores = model.evaluate(x_train, y_train, verbose=0)\n\n print('Model: Neural network, Data: ', name)\n print(f' Accuracy on training data: {scores[1] * 100:.2f}%')\n\n pred_test = model.predict(x_test)\n scores2 = model.evaluate(x_test, y_test, verbose=0)\n print(f' Accuracy on test data: {scores2[1] * 100:.2f}% \\n ')\n\n\n # Logistic regression\n y_train = np.where(dataset['y_train'] == 1)[1]\n y_test = np.where(dataset['y_test'] == 1)[1]\n\n clf = LogisticRegression(random_state=0, max_iter=3000).fit(x_train, y_train)\n score = clf.score(x_test, y_test)\n score2 = clf.score(x_train, y_train)\n\n print(\"Model: Logistic regression, Data: \", name)\n print(f\" Accuracy on training data: {score2 * 100:.2f}%\")\n print(f\" Accuracy on test data: {score * 100:.2f}% \\n \")\n\n plt.style.use('ggplot')\n plt.plot(hist.history['accuracy'])\n plt.plot(hist.history['val_accuracy'])\n plt.title('keras model accuracy')\n plt.ylabel('accuracy')\n plt.xlabel('epoch')\n plt.legend(['train', 'validation'], loc='upper left')\n plt.savefig('../plots/benchmarks/keras_accuracy_'+name)\n\n plt.close()\n\n plt.plot(hist.history['loss'])\n plt.plot(hist.history['val_loss'])\n plt.title('keras model loss')\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.legend(['train', 'validation'], loc='upper left')\n plt.savefig('../plots/benchmarks/keras_loss_'+name)\n\n plt.close()\n","sub_path":"python/benchmarking_classification.py","file_name":"benchmarking_classification.py","file_ext":"py","file_size_in_byte":2437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"86344446","text":"import sys, os\nimport copy\nimport numpy as np\nimport waterlib as wl\nimport parmed as pmd\nimport mdtraj as md\nimport time as timeit\nfrom scipy.interpolate import UnivariateSpline as UVS\nfrom scipy.optimize import curve_fit\nfrom scipy.special import iv\nfrom scipy.special import kv\nfrom scipy.special import spherical_in\nfrom scipy.special import spherical_kn\nfrom scipy import integrate\nimport matplotlib\ntry:\n os.environ[\"DISPLAY\"]\nexcept KeyError:\n showPlots = False\n matplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\n\ndef HBsurvival(topFile,trajFile):\n \"\"\"Runs the dynamics code for a trajectory in chunks.\n Inputs:\n args - (optional) list of arguments, first is topology file, next is trajectory\n Outputs:\n Only files holding the autocorrelation functions.\n \"\"\"\n \n #try:\n # topFile = args[0]\n #except IndexError:\n # topFile = 'EO59wTEMPO_24296.pdb'\n\n #try:\n # trajFile = args[1]\n #except IndexError:\n # trajFile = 'nve_production_output_wrapped0.dcd'\n\n #Load in the files of interest\n #I generally find parmed does a better job loading different topologies, then pytraj is ok with parmed objects (the above statement is not true a.t.m.)\n pi = 3.14159265358979323846\n \n #identify centroid and water nucleus locations\n top = md.load(topFile).topology\n OrInd = top.select('name Or') #np.append(top.select(\"name O4\"),[top.select(\"name O1\")])\n OInds = top.select(\"name O\")\n nucInds = top.select(\"name HW\") \n\n don = []\n for i,ind in enumerate(OInds):\n don.append(ind)\n don.append(ind)\n don = np.array([don])\n donh = nucInds\n acc = OInds\n\n cutoff = 0.8\n query = OrInd\n\n dt = 0.01 # time interval in picosecons\n chunkSpace = 100\n chunkSize = 300\n nChunks = 0\n number = np.zeros(chunkSize)\n\n timeOrigin = 0\n maxSize = 0\n\n for i,chunk in enumerate(md.iterload(trajFile,top=topFile,chunk=chunkSize)):\n maxSize= maxSize+len(chunk)\n\n j = 0\n \n while timeOrigin<=int(maxSize-chunkSize):\n for i,chunk in enumerate(md.iterload(trajFile,top=topFile,\n skip=timeOrigin,\n chunk=chunkSize)):\n print('the index of the for loop is {}'.format(i))\n if i!=0:\n break\n else:\n print(chunk)\n time = chunk.time\n timevals = (time-time[0])*dt\n timeOrigin = timeOrigin+chunkSpace\n print(timeOrigin)\n don = []\n for i,ind in enumerate(OInds):\n don.append(ind)\n don.append(ind)\n don = np.array([don])\n donh = nucInds\n acc = OInds\n boxL = chunk.unitcell_lengths\n BoxL = boxL[0,:]\n boxV = boxL[0,0]**3\n\n #get density of the box\n n_waters = chunk.n_residues-1\n\n BulkDens = n_waters/boxV\n\n init_traj = chunk[0]\n trajLength = len(chunk.xyz)\n\n thisnumber = np.zeros(trajLength)\n\n acc = md.compute_neighbors(init_traj,cutoff,query,\n haystack_indices=acc)[0]\n\n donh = []\n for i,ind in enumerate(acc):\n donh.append(ind+1)\n donh.append(ind+2)\n donh = np.array([donh])\n\n don = []\n for i,ind in enumerate(acc):\n don.append(ind)\n don.append(ind)\n don = np.array([don])\n \n accpos = init_traj.xyz[0,acc,:]\n donpos = init_traj.xyz[0,don,:]\n donhpos = init_traj.xyz[0,donh,:]\n init_hb = wl.generalhbonds(accpos,donpos,donhpos,BoxL,0.35,120.0)\n init_hb = np.array([init_hb])[0]\n \n thisnumber[0] = np.sum(init_hb)\n print('printing thisnumber variable {}'.format(thisnumber[0]))\n\n start = timeit.time()\n for t,frame in enumerate(chunk[1:]):\n t=t+1\n\n # calculate position of acceptor O, donor O and donor H\n accpos = frame.xyz[0,acc,:]\n donpos = frame.xyz[0,don,:]\n donhpos = frame.xyz[0,donh,:]\n\n # find the hydrogen bond matrix accposxdonhpos dimensional\n hb = wl.generalhbonds(accpos,donpos,donhpos,BoxL,0.35,120.0)\n hb = np.array([hb])[0]\n hb = [ [init_hb[i,j] if hb[i,j]==init_hb[i,j] \n else 0 for j in range(hb.shape[1])] \n for i in range(hb.shape[0])]\n hb = np.array([hb])[0,:,:]\n \n thisnumber[t] = np.sum(hb)\n \n number += thisnumber\n nChunks += 1\n np.savetxt('HBCorr.txt',(timevals,number/number[0]))\n plt.plot(timevals,number/number[0])\n plt.savefig('HBCorr.png')\n plt.close()\n print('printing the number variable {}'.format(number))\n print(timeit.time()-start)\n number /= float(nChunks)\n number /= number[0]\n\n print(\"the number of chunks is {}\".format(nChunks))\n\n def fitfunc(t,tau):\n return np.exp(-t/tau)\n\n popt,pcov = curve_fit(fitfunc,timevals,number,p0=[0.05])\n number_fit = fitfunc(timevals,popt[0])\n\n print(\"{}\".format(popt[0]))\n\n np.savetxt('HBCorr.txt',(timevals,number))\n np.savetxt('HBCorr_fit.txt',(timevals,number_fit))\n plt.plot(timevals,number)\n plt.xlabel('time (ps)')\n plt.ylabel('fraction of HBs')\n plt.savefig('HBCorr.png')\n plt.close()\n\n plt.plot(timevals,number,label='data')\n plt.plot(timevals,number_fit,label='fit')\n plt.xlabel('time (ps)')\n plt.ylabel('fraction of HBs')\n plt.legend()\n plt.savefig('HBCorr_fit.png')\n","sub_path":"PEO12/OPC4/T290/nve0/hbonds_int_new.py","file_name":"hbonds_int_new.py","file_ext":"py","file_size_in_byte":5338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"109495452","text":"\"\"\"\nUnit and regression test for the geometry_analysis package.\n\"\"\"\n\n# Import package, test suite, and other packages as needed\nimport source\nimport pytest\nimport sys\nimport numpy as np\n\n# A means to test a system that use multiple functions.\n# Multiple asserts tested.\n@pytest.fixture()\ndef water_molecule():\n name = \"water\"\n symbols = [\"H\", \"O\", \"H\"]\n coordinates = np.array([[2, 0, 0], [0, 0, 0], [-2, 0, 0]])\n\n water = source.Molecule(name, symbols, coordinates)\n\n return water\n\n# Checks for failure and pushes the pytest forward to completion\ndef test_create_failure():\n\n name = 25\n symbols = [\"H\", \"O\", \"H\"]\n coordinates = np.zeros([3,3])\n\n with pytest.raises(TypeError):\n water = source.Molecule(name, symbols, coordinates)\n\ndef test_molecule_set_coordinates(water_molecule):\n \"\"\"Test that bond list is rebuilt when we reset coordinates.\"\"\"\n\n num_bonds = len(water_molecule.bonds)\n\n assert num_bonds == 2\n\n new_coordinates = np.array([[5, 0, 0], [0, 0, 0], [-2, 0, 0]])\n water_molecule.coordinates = new_coordinates\n\n new_bonds = len(water_molecule.bonds)\n\n assert new_bonds == 1\n assert np.array_equal(new_coordinates, water_molecule.coordinates)\n\ndef test_geometry_analysis_imported():\n \"\"\"Sample test, will always pass so long as import statement worked\"\"\"\n assert \"source\" in sys.modules\n\ndef test_calculate_distance():\n \"\"\"Test the calculate_distance function\"\"\"\n\n r1 = np.array([0, 0, -1])\n r2 = np.array([0, 1, 0])\n\n expected_distance = np.sqrt(2.)\n\n calculated_distance = source.calculate_distance(r1, r2)\n\n assert expected_distance == calculated_distance\n\ndef test_calculate_angle_90():\n \"\"\"Test the calculate_angle function\"\"\"\n\n r1 = np.array([1,0,0])\n r2 = np.array([0,0,0])\n r3 = np.array([0,1,0])\n\n expected_value = 90\n calculated_value = source.calculate_angle(r1, r2, r3, degrees=True)\n\n assert expected_value == calculated_value\n\ndef test_calculate_angle_60():\n \"\"\"Test another value of the calculate_angle function\"\"\"\n\n r1 = np.array([0, 0, -1])\n r2 = np.array([0, 1, 0])\n r3 = np.array([1, 0, 0])\n\n expected_value = 60\n calculated_value = source.calculate_angle(r1, r2, r3, degrees=True)\n\n assert np.isclose(expected_value, calculated_value)\n \n # Using this will result in a fail due to a slight difference in precision\n # expected_value == calculated_value\n\n# Template decorator for the pytest case is decided, utilize variables given\n@pytest.mark.parametrize(\"p1, p2, p3, expected_angle\", [\n (np.array([1, 0, 0]), np.array([0, 0, 0]), np.array([0, 1, 0]), 90),\n (np.array([0, 0, -1]), np.array([0, 1, 0]), np.array([1, 0, 0]), 60),\n])\ndef test_calculate_angle(p1, p2, p3, expected_angle):\n\n calculated_angle = source.calculate_angle(p1, p2, p3, degrees=True)\n assert np.isclose(expected_angle, calculated_angle)\n","sub_path":"tutorial/day4/geometry_analysis/source/tests/test_geometry_analysis.py","file_name":"test_geometry_analysis.py","file_ext":"py","file_size_in_byte":2884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"135975496","text":"import sublime, sublime_plugin\nimport os\nfrom . import history, file_listener\nfrom . import codir_client as client\n\nclass CodirListener(sublime_plugin.EventListener):\n\tdef on_modified_async(self, view):\n\t\tis_delta = history.is_delta(view)\n\t\tif view.window() and view.window().id() in client.sockets and not is_delta and view.file_name():\n\t\t\thistory.buffer_history[view.id()].append(view.substr(sublime.Region(0, view.size())))\n\n\t\t\tsocket = client.sockets[view.window().id()]\n\t\t\tdeltas = history.get_deltas(view)\n\n\t\t\tpath = 'codirSublime/projects/'\n\t\t\tfile = view.file_name()\n\t\t\tif file.index(path) > 0 and (deltas['additions'] != {} or deltas['removals'] != {}):\n\t\t\t\thistory.delta_history[view.id()].append(deltas)\n\t\t\t\thistory.history_pointer[view.id()] = len(history.delta_history[view.id()]) - 1\n\t\t\t\tprint (history.history_pointer[view.id()])\n\n\t\t\t\tpath_start = file.index(path) + len(path + socket['shareid'] + '/')\n\t\t\t\tsocket['socket'].emit('workspace-file-edit-update', {'path': file[path_start:], 'deltas': deltas, 'shareid': socket['shareid']})\n\n\tdef on_text_command(self, view, command_name, args):\n\t\tif command_name == 'undo' and view.window() and view.window().id() in client.sockets:\n\t\t\treturn ('codir_undo')\n\n\tdef on_load(self, view):\n\t\tprint('loaded')\n\t\tid = view.window().id()\n\t\tif id in client.sockets:\n\t\t\thistory.init_view(view)\n\t\t\tprint('test')\n\t\t\tsocket = client.sockets[view.window().id()]\n\t\t\tdir = os.path.dirname(os.path.abspath(__file__)) + '/projects/' + socket['shareid'] + '/'\n\t\t\tfilename = view.file_name()\n\t\t\tif dir in filename:\n\t\t\t\tsub_path = filename[filename.index(dir) + len(dir):]\n\t\t\t\tevent = {'shareid': socket['shareid'], 'path': sub_path}\n\t\t\t\tsocket['socket'].emit('workspace-open-file-update', event)\n\n\tdef on_new(self, view):\n\t\tid = view.window().id()\n\t\tif id in client.sockets:\n\t\t\tprint ('new view')\n\t\t\thistory.init_view(view)\n\t\t\n\n\tdef on_clone(self, view):\n\t\tid = view.window().id()\n\t\tif id in client.sockets:\n\t\t\tprint ('cloned view')\n\t\t\thistory.init_view(view)","sub_path":"listener.py","file_name":"listener.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"593128163","text":"\nimport cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimg = cv2.imread('res/real1.png', 0)\nedges = cv2.Canny(img, 100, 200)\n\nplt.subplot(121), plt.imshow(img, cmap='gray')\nplt.title('Original Image'), plt.xticks([]), plt.yticks([])\nplt.subplot(122), plt.imshow(edges, cmap='gray') # 1 row, 2 columns, position 2\nplt.title('Edge Image'), plt.xticks([]), plt.yticks([])\n\n# img2 = cv2.imread('res/real1.png')\n# result = cv2.pencilSketch(img2)\n# plt.imshow(img2)\n# plt.title(\"Pencil Sketch\")\n\nplt.show()\n","sub_path":"opencv.py","file_name":"opencv.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"323381002","text":"##############################################################################\n#\n# Copyright (c) 2003 Zope Corporation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.0 (ZPL). A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Bootstrap tests\n\n$Id$\n\"\"\"\nimport unittest\nfrom transaction import get_transaction\nfrom ZODB.tests.util import DB\nfrom zope.exceptions import NotFoundError\n\nfrom zope.app.folder import rootFolder\nfrom zope.app.folder.interfaces import IRootFolder\nfrom zope.app.errorservice.interfaces import IErrorReportingService\nfrom zope.app.principalannotation.interfaces import IPrincipalAnnotationService\nfrom zope.app.event.interfaces import IEventService\nfrom zope.app.hub.interfaces import IObjectHub\nfrom zope.app.publication.zopepublication import ZopePublication\nfrom zope.app.site.tests.placefulsetup import PlacefulSetup\nfrom zope.app.errorservice import ErrorReportingService\nfrom zope.app.servicenames import ErrorLogging\nfrom zope.app.traversing.api import traverse\nfrom zope.app.site.service import ServiceManager\n\nclass EventStub(object):\n\n def __init__(self, db):\n self.database = db\n\n\nclass TestBootstrapSubscriberBase(PlacefulSetup, unittest.TestCase):\n\n def setUp(self):\n PlacefulSetup.setUp(self)\n self.db = DB()\n\n def tearDown(self):\n PlacefulSetup.tearDown(self)\n self.db.close()\n\n def createRootFolder(self):\n cx = self.db.open()\n root = cx.root()\n self.root_folder = rootFolder()\n root[ZopePublication.root_name] = self.root_folder\n get_transaction().commit()\n cx.close()\n\n def createRFAndSM(self):\n cx = self.db.open()\n root = cx.root()\n self.root_folder = rootFolder()\n root[ZopePublication.root_name] = self.root_folder\n self.service_manager = ServiceManager(self.root_folder)\n self.root_folder.setSiteManager(self.service_manager)\n get_transaction().commit()\n cx.close()\n\n\n def test_notify(self):\n from zope.app.appsetup.bootstrap import BootstrapSubscriberBase\n\n for setup in (lambda: None), self.createRootFolder, self.createRFAndSM:\n\n setup()\n\n BootstrapSubscriberBase().notify(EventStub(self.db))\n\n cx = self.db.open()\n root = cx.root()\n root_folder = root.get(ZopePublication.root_name, None)\n self.assert_(IRootFolder.providedBy(root_folder))\n\n package_name = '/++etc++site/default'\n package = traverse(root_folder, package_name)\n\n cx.close()\n\n def test_ensureService(self):\n from zope.app.appsetup.bootstrap import BootstrapSubscriberBase\n\n self.createRFAndSM()\n bs = BootstrapSubscriberBase()\n bs.notify(EventStub(self.db))\n for i in range(2):\n cx = self.db.open()\n name = bs.ensureService(ErrorLogging, ErrorReportingService)\n\n if i == 0:\n self.assertEqual(name, 'ErrorLogging')\n else:\n self.assertEqual(name, None)\n\n root = cx.root()\n root_folder = root[ZopePublication.root_name]\n\n package_name = '/++etc++site/default'\n package = traverse(root_folder, package_name)\n\n self.assert_(IErrorReportingService.providedBy(\n traverse(package, 'ErrorLogging')))\n get_transaction().commit()\n cx.close()\n\nclass TestBootstrapInstance(TestBootstrapSubscriberBase):\n\n def test_bootstrapInstance(self):\n from zope.app.appsetup.bootstrap import bootstrapInstance\n\n bootstrapInstance.notify(EventStub(self.db))\n\n cx = self.db.open()\n root = cx.root()\n root_folder = root[ZopePublication.root_name]\n\n package_name = '/++etc++site/default'\n package = traverse(root_folder, package_name)\n\n self.assert_(IEventService.providedBy(\n traverse(package, 'EventPublication')))\n\n self.assert_(IObjectHub.providedBy(\n traverse(package, 'HubIds')))\n\n self.assert_(IErrorReportingService.providedBy(\n traverse(package, 'ErrorLogging')))\n\n self.assert_(IPrincipalAnnotationService.providedBy(\n traverse(package, 'PrincipalAnnotation')))\n\n cx.close()\n\n def test_bootstrapInstance_withServices(self):\n from zope.app.appsetup.bootstrap import bootstrapInstance\n from zope.app.appsetup.bootstrap import addService, configureService\n\n self.createRFAndSM()\n\n name = addService(self.root_folder, 'Errors',\n ErrorReportingService, copy_to_zlog=True)\n configureService(self.root_folder, ErrorLogging, name)\n\n bootstrapInstance.notify(EventStub(self.db))\n\n cx = self.db.open()\n root = cx.root()\n root_folder = root[ZopePublication.root_name]\n\n package_name = '/++etc++site/default'\n package = traverse(root_folder, package_name)\n\n self.assert_(IEventService.providedBy(\n traverse(package, 'EventPublication')))\n\n self.assert_(IObjectHub.providedBy(\n traverse(package, 'HubIds')))\n\n self.assertRaises(NotFoundError, traverse, root_folder,\n '/++etc++site/default/ErrorLogging')\n\n self.assert_(IErrorReportingService.providedBy(\n traverse(package, 'Errors')))\n\n self.assert_(IEventService.providedBy(\n traverse(package, 'EventPublication')))\n\n self.assert_(IPrincipalAnnotationService.providedBy(\n traverse(package, 'PrincipalAnnotation')))\n\n cx.close()\n\n\ndef test_suite():\n suite = unittest.TestSuite()\n suite.addTest(unittest.makeSuite(TestBootstrapSubscriberBase))\n suite.addTest(unittest.makeSuite(TestBootstrapInstance))\n return suite\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"Zope3/tags/ZopeX3-3.0.0a2/src/zope/app/appsetup/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":6256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"443476136","text":"import os,sys\r\nimport main\r\nBASE_DIR=os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\r\nsys.path.append(BASE_DIR)\r\nfrom Renzheng import Log\r\nfrom Bin import Manager\r\ndef run_run():\r\n while True:\r\n a='''\r\n 1 普通用户\r\n 2 管理员\r\n '''\r\n print(a)\r\n choose=input('登录的身份：')\r\n if choose=='1':\r\n Log.log() # 用户登录和验证\r\n main.list()\r\n if choose=='2':\r\n Manager.manage()\r\n","sub_path":"Main/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"} +{"seq_id":"185129634","text":"\n\nfrom xai.brain.wordbase.nouns._codicil import _CODICIL\n\n#calss header\nclass _CODICILS(_CODICIL, ):\n\tdef __init__(self,): \n\t\t_CODICIL.__init__(self)\n\t\tself.name = \"CODICILS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"codicil\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_codicils.py","file_name":"_codicils.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"69"}